From 5657166e8a228c78e55ca135ea70458c135804ce Mon Sep 17 00:00:00 2001
From: Jean-Luc Parouty <Jean-Luc.Parouty@simap.grenoble-inp.fr>
Date: Sun, 31 Oct 2021 23:56:51 +0100
Subject: [PATCH] Update for CI
---
AE/01-Prepare-MNIST-dataset.ipynb | 39 +-
AE/02-AE-with-MNIST.ipynb | 18 +-
AE/03-AE-with-MNIST-post.ipynb | 2 +-
AE/04-ExtAE-with-MNIST.ipynb | 13 +-
AE/05-ExtAE-with-MNIST.ipynb | 10 +-
AE/modules/MNIST.py | 14 +-
GTSRB/01-Preparation-of-data.ipynb | 32 +-
GTSRB/01-Preparation-of-data==done==.ipynb | 5230 ----
GTSRB/02-First-convolutions.ipynb | 310 +-
GTSRB/02-First-convolutions==done==.ipynb | 2104 --
GTSRB/03-Tracking-and-visualizing.ipynb | 28 +-
.../03-Tracking-and-visualizing==done==.ipynb | 2523 --
GTSRB/04-Data-augmentation.ipynb | 17 +-
GTSRB/04-Data-augmentation==done==.ipynb | 7062 ------
GTSRB/05-Full-convolutions.ipynb | 58 +-
GTSRB/05-Full-convolutions=1==done==.ipynb | 1007 -
GTSRB/05-Full-convolutions=2==done==.ipynb | 1007 -
GTSRB/05-Full-convolutions=3==done==.ipynb | 799 -
GTSRB/06-Notebook-as-a-batch.ipynb | 4 +-
GTSRB/06-Notebook-as-a-batch==done==.ipynb | 232 -
GTSRB/07-Show-report==done==.ipynb | 640 -
IMDB/01-One-hot-encoding.ipynb | 16 +-
IMDB/01-One-hot-encoding==done==.ipynb | 2041 --
IMDB/02-Keras-embedding.ipynb | 10 +-
IMDB/02-Keras-embedding==done==.ipynb | 2883 ---
IMDB/03-Prediction==done==.ipynb | 525 -
IMDB/04-Show-vectors==done==.ipynb | 487 -
IMDB/05-LSTM-Keras.ipynb | 34 +-
IMDB/05-LSTM-Keras==done==.ipynb | 5169 ----
MNIST/01-DNN-MNIST.ipynb | 37 +-
MNIST/01-DNN-MNIST==done==.ipynb | 1500 --
MNIST/02-CNN-MNIST.ipynb | 37 +-
MNIST/02-CNN-MNIST==done==.ipynb | 1697 --
README.ipynb | 31 +-
README.md | 21 +-
SYNOP/LADYB1-Ladybug.ipynb | 25 +-
SYNOP/LADYB1-Ladybug==done==.ipynb | 21176 ----------------
SYNOP/SYNOP1-Preparation-of-data.ipynb | 8 +-
.../SYNOP1-Preparation-of-data==done==.ipynb | 3192 ---
SYNOP/SYNOP2-First-predictions.ipynb | 17 +-
SYNOP/SYNOP2-First-predictions==done==.ipynb | 16422 ------------
SYNOP/SYNOP3-12h-predictions.ipynb | 8 +-
SYNOP/SYNOP3-12h-predictions==done==.ipynb | 553 -
VAE/01-VAE-with-MNIST.ipynb | 22 +-
VAE/02-VAE-with-MNIST.ipynb | 20 +-
environments/environment-cpu.yml | 1 +
environments/environment-gpu.yml | 1 +
fidle/01-update-index.ipynb | 271 +-
fidle/02-running-ci-tests.ipynb | 23 +-
fidle/ci/basic_example.yml | 34 -
fidle/ci/default.yml | 28 +-
fidle/ci/fidle-ad_s04.yml | 51 -
fidle/ci/fidle-ad_s05.yml | 68 -
fidle/ci/fidle-ad_s06.yml | 144 -
fidle/ci/small_cpu.yml | 250 +-
fidle/ci/smart_cpu.yml | 122 -
fidle/config.py | 3 +-
fidle/cookci.py | 70 +-
fidle/cookindex.py | 100 +-
fidle/img/00-Fidle-header-01.svg | 2 +-
fidle/img/00-fidle-header-01.jpg | Bin 0 -> 64802 bytes
fidle/img/00-fidle-header-01.png | Bin 0 -> 10344 bytes
fidle/logs/catalog.json | 54 +-
fidle/pwk.py | 19 +-
64 files changed, 959 insertions(+), 77362 deletions(-)
delete mode 100644 GTSRB/01-Preparation-of-data==done==.ipynb
delete mode 100644 GTSRB/02-First-convolutions==done==.ipynb
delete mode 100644 GTSRB/03-Tracking-and-visualizing==done==.ipynb
delete mode 100644 GTSRB/04-Data-augmentation==done==.ipynb
delete mode 100644 GTSRB/05-Full-convolutions=1==done==.ipynb
delete mode 100644 GTSRB/05-Full-convolutions=2==done==.ipynb
delete mode 100644 GTSRB/05-Full-convolutions=3==done==.ipynb
delete mode 100644 GTSRB/06-Notebook-as-a-batch==done==.ipynb
delete mode 100644 GTSRB/07-Show-report==done==.ipynb
delete mode 100644 IMDB/01-One-hot-encoding==done==.ipynb
delete mode 100644 IMDB/02-Keras-embedding==done==.ipynb
delete mode 100644 IMDB/03-Prediction==done==.ipynb
delete mode 100644 IMDB/04-Show-vectors==done==.ipynb
delete mode 100644 IMDB/05-LSTM-Keras==done==.ipynb
delete mode 100644 MNIST/01-DNN-MNIST==done==.ipynb
delete mode 100644 MNIST/02-CNN-MNIST==done==.ipynb
delete mode 100644 SYNOP/LADYB1-Ladybug==done==.ipynb
delete mode 100644 SYNOP/SYNOP1-Preparation-of-data==done==.ipynb
delete mode 100644 SYNOP/SYNOP2-First-predictions==done==.ipynb
delete mode 100644 SYNOP/SYNOP3-12h-predictions==done==.ipynb
delete mode 100644 fidle/ci/basic_example.yml
delete mode 100644 fidle/ci/fidle-ad_s04.yml
delete mode 100644 fidle/ci/fidle-ad_s05.yml
delete mode 100644 fidle/ci/fidle-ad_s06.yml
delete mode 100644 fidle/ci/smart_cpu.yml
create mode 100644 fidle/img/00-fidle-header-01.jpg
create mode 100644 fidle/img/00-fidle-header-01.png
diff --git a/AE/01-Prepare-MNIST-dataset.ipynb b/AE/01-Prepare-MNIST-dataset.ipynb
index bf499e6..eb901bb 100644
--- a/AE/01-Prepare-MNIST-dataset.ipynb
+++ b/AE/01-Prepare-MNIST-dataset.ipynb
@@ -35,9 +35,6 @@
"metadata": {},
"outputs": [],
"source": [
- "# import os\n",
- "# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
- "\n",
"import numpy as np\n",
"import sys\n",
"\n",
@@ -59,7 +56,27 @@
"metadata": {},
"source": [
"### 1.2 - Parameters\n",
- "`prepared_dataset` : Filename of the future prepared dataset (Can be in ./data) "
+ "`prepared_dataset` : Filename of the future prepared dataset (example : ./data/mnist-noisy.h5)\\\n",
+ "`scale` : Dataset scale. 1 mean 100% of the dataset - set 0.1 for tests\\\n",
+ "`progress_verbosity`: Verbosity of progress bar: 0=no progress, 1: progress bar, 2: One line"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prepared_dataset = './data/mnist-noisy.h5'\n",
+ "scale = .1\n",
+ "progress_verbosity = 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Override parameters (batch mode) - Just forget this cell"
]
},
{
@@ -68,7 +85,7 @@
"metadata": {},
"outputs": [],
"source": [
- "prepared_dataset = './data/mnist-noisy.h5'"
+ "pwk.override('prepared_dataset', 'scale', 'progress_verbosity')"
]
},
{
@@ -79,7 +96,7 @@
"We load : \n",
"`clean_data` : Original and clean images - This is what we will want to ontain at the **output** of the AE \n",
"`class_data` : Image classes - Useless, because the training will be unsupervised \n",
- "We build : \n",
+ "We'll build : \n",
"`noisy_data` : Noisy images - These are the images that we will give as **input** to our AE\n"
]
},
@@ -89,7 +106,7 @@
"metadata": {},
"outputs": [],
"source": [
- "clean_data, class_data = MNIST.get_origine()"
+ "clean_data, class_data = MNIST.get_origine(scale=scale)"
]
},
{
@@ -110,7 +127,7 @@
"def noise_it(data):\n",
" new_data = np.copy(data)\n",
" for i,image in enumerate(new_data):\n",
- " pwk.update_progress('Add noise : ',i+1,len(new_data))\n",
+ " pwk.update_progress('Add noise : ',i+1,len(new_data),verbosity=progress_verbosity)\n",
" image=random_noise(image, mode='gaussian', mean=0, var=0.3)\n",
" image=random_noise(image, mode='s&p', amount=0.2, salt_vs_pepper=0.5)\n",
" image=random_noise(image, mode='poisson') \n",
@@ -199,9 +216,11 @@
}
],
"metadata": {
+ "interpreter": {
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
+ },
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
diff --git a/AE/02-AE-with-MNIST.ipynb b/AE/02-AE-with-MNIST.ipynb
index a96d725..4ec01cd 100644
--- a/AE/02-AE-with-MNIST.ipynb
+++ b/AE/02-AE-with-MNIST.ipynb
@@ -79,7 +79,8 @@
"`latent_dim` : Dimension of the latent space \n",
"`train_prop` : Percentage for train (the rest being for the test)\n",
"`batch_size` : Batch size \n",
- "`epochs` : Nb of epochs for training\n"
+ "`epochs` : Nb of epochs for training\\\n",
+ "`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch\n"
]
},
{
@@ -91,13 +92,14 @@
"prepared_dataset = './data/mnist-noisy.h5'\n",
"dataset_seed = 123\n",
"\n",
- "scale = .1\n",
+ "scale = 1\n",
"\n",
"latent_dim = 10\n",
"\n",
"train_prop = .8\n",
"batch_size = 128\n",
- "epochs = 30"
+ "epochs = 30\n",
+ "fit_verbosity = 1"
]
},
{
@@ -263,7 +265,7 @@
"history = ae.fit(noisy_train, clean_train,\n",
" batch_size = batch_size,\n",
" epochs = epochs,\n",
- " verbose = 1,\n",
+ " verbose = fit_verbosity,\n",
" validation_data = (noisy_test, clean_test),\n",
" callbacks = callbacks_list )\n",
"\n",
@@ -407,9 +409,11 @@
}
],
"metadata": {
+ "interpreter": {
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
+ },
"kernelspec": {
- "display_name": "Python 3",
- "language": "python",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
@@ -422,7 +426,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.7"
}
},
"nbformat": 4,
diff --git a/AE/03-AE-with-MNIST-post.ipynb b/AE/03-AE-with-MNIST-post.ipynb
index 69edb03..3cef862 100644
--- a/AE/03-AE-with-MNIST-post.ipynb
+++ b/AE/03-AE-with-MNIST-post.ipynb
@@ -77,7 +77,7 @@
"source": [
"prepared_dataset = './data/mnist-noisy.h5'\n",
"dataset_seed = 123\n",
- "scale = .1\n",
+ "scale = 1\n",
"train_prop = .8"
]
},
diff --git a/AE/04-ExtAE-with-MNIST.ipynb b/AE/04-ExtAE-with-MNIST.ipynb
index c93538a..3b88bab 100644
--- a/AE/04-ExtAE-with-MNIST.ipynb
+++ b/AE/04-ExtAE-with-MNIST.ipynb
@@ -82,7 +82,8 @@
"`latent_dim` : Dimension of the latent space \n",
"`train_prop` : Percentage for train (the rest being for the test)\n",
"`batch_size` : Batch size \n",
- "`epochs` : Nb of epochs for training\n"
+ "`epochs` : Nb of epochs for training\\\n",
+ "`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch\n"
]
},
{
@@ -94,13 +95,14 @@
"prepared_dataset = './data/mnist-noisy.h5'\n",
"dataset_seed = None\n",
"\n",
- "scale = .1\n",
+ "scale = 1\n",
"\n",
"latent_dim = 10\n",
"\n",
"train_prop = .8\n",
"batch_size = 128\n",
- "epochs = 30"
+ "epochs = 30\n",
+ "fit_verbosity = 1"
]
},
{
@@ -134,7 +136,8 @@
"metadata": {},
"outputs": [],
"source": [
- "clean_train,clean_test, noisy_train,noisy_test, class_train,class_test = MNIST.reload_prepared_dataset(scale = scale, \n",
+ "clean_train,clean_test, noisy_train,noisy_test, class_train,class_test = MNIST.reload_prepared_dataset(\n",
+ " scale = scale, \n",
" train_prop = train_prop,\n",
" seed = dataset_seed,\n",
" shuffle = True,\n",
@@ -325,7 +328,7 @@
"history = model.fit(noisy_train, [clean_train, class_train],\n",
" batch_size = batch_size,\n",
" epochs = epochs,\n",
- " verbose = 1,\n",
+ " verbose = fit_verbosity,\n",
" validation_data = (noisy_test, [clean_test, class_test]),\n",
" callbacks = callbacks_list )\n",
"\n",
diff --git a/AE/05-ExtAE-with-MNIST.ipynb b/AE/05-ExtAE-with-MNIST.ipynb
index 1fba6a9..effa77c 100644
--- a/AE/05-ExtAE-with-MNIST.ipynb
+++ b/AE/05-ExtAE-with-MNIST.ipynb
@@ -82,7 +82,8 @@
"`latent_dim` : Dimension of the latent space \n",
"`train_prop` : Percentage for train (the rest being for the test)\n",
"`batch_size` : Batch size \n",
- "`epochs` : Nb of epochs for training\n"
+ "`epochs` : Nb of epochs for training\\\n",
+ "`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch\n"
]
},
{
@@ -94,13 +95,14 @@
"prepared_dataset = './data/mnist-noisy.h5'\n",
"dataset_seed = None\n",
"\n",
- "scale = .1\n",
+ "scale = 1\n",
"\n",
"latent_dim = 10\n",
"\n",
"train_prop = .8\n",
"batch_size = 128\n",
- "epochs = 20"
+ "epochs = 20\n",
+ "fit_verbosity = 1"
]
},
{
@@ -357,7 +359,7 @@
"history = model.fit(noisy_train, [clean_train, class_train],\n",
" batch_size = batch_size,\n",
" epochs = epochs,\n",
- " verbose = 1,\n",
+ " verbose = fit_verbosity,\n",
" validation_data = (noisy_test, [clean_test, class_test]),\n",
" callbacks = callbacks_list )\n",
"\n",
diff --git a/AE/modules/MNIST.py b/AE/modules/MNIST.py
index 63dfa0d..6e040a6 100644
--- a/AE/modules/MNIST.py
+++ b/AE/modules/MNIST.py
@@ -33,10 +33,11 @@ class MNIST():
pass
@classmethod
- def get_origine(cls, normalize=True, expand=True, concatenate=True):
+ def get_origine(cls, scale=1, normalize=True, expand=True, concatenate=True):
"""
Return original MNIST dataset
args:
+ scale : Proportion of the requested dataset
normalize : Normalize dataset or not (True)
expand : Reshape images as (28,28,1) instead (28,28) (True)
concatenate : Concatenate train and test sets (True)
@@ -49,7 +50,7 @@ class MNIST():
#
(x_train, y_train), (x_test, y_test) = mnist.load_data()
print('Dataset loaded.')
-
+
# ---- Normalization
#
if normalize:
@@ -64,6 +65,15 @@ class MNIST():
x_test = np.expand_dims(x_test, axis=-1)
print('Reshaped.')
+ # ---- scale
+ #
+ n1 = int(len(x_train)*scale)
+ n2 = int(len(x_test)*scale)
+ x_train = x_train[:n1]
+ y_train = y_train[:n1]
+ x_test = x_test[:n2]
+ y_test = y_test[:n2]
+
# ---- Concatenate
#
if concatenate:
diff --git a/GTSRB/01-Preparation-of-data.ipynb b/GTSRB/01-Preparation-of-data.ipynb
index 8e7b713..ba42110 100644
--- a/GTSRB/01-Preparation-of-data.ipynb
+++ b/GTSRB/01-Preparation-of-data.ipynb
@@ -92,7 +92,11 @@
"# ---- For a Full dataset generation :\n",
"#\n",
"# scale = 1\n",
- "# output_dir = f'{datasets_dir}/GTSRB/enhanced'"
+ "# output_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
+ "\n",
+ "# ---- Verbosity - 0 = silent, 1 = progress bar, 2 = one line\n",
+ "#\n",
+ "progress_verbosity = 1"
]
},
{
@@ -108,7 +112,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.override('scale', 'output_dir')"
+ "pwk.override('scale', 'output_dir', 'progress_verbosity')"
]
},
{
@@ -178,7 +182,7 @@
" for filename in filenames:\n",
" image=io.imread(f'{path}/{filename}')\n",
" x.append(image)\n",
- " pwk.update_progress(name,len(x),len(filenames))\n",
+ " pwk.update_progress(name,len(x),len(filenames), verbosity=progress_verbosity)\n",
" \n",
" # ---- Return\n",
" #\n",
@@ -583,20 +587,6 @@
"pwk.chrono_show()"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class='todo'>\n",
- " Adapt the code below to read :\n",
- " <ul>\n",
- " <li>the different h5 datasets you saved in ./data,</li>\n",
- " <li>The h5 datasets available in the Fidle project datasets directory.</li>\n",
- " </ul>\n",
- " \n",
- "</div>"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -646,9 +636,11 @@
}
],
"metadata": {
+ "interpreter": {
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
+ },
"kernelspec": {
- "display_name": "Python 3",
- "language": "python",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
@@ -661,7 +653,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.7"
}
},
"nbformat": 4,
diff --git a/GTSRB/01-Preparation-of-data==done==.ipynb b/GTSRB/01-Preparation-of-data==done==.ipynb
deleted file mode 100644
index 6640893..0000000
--- a/GTSRB/01-Preparation-of-data==done==.ipynb
+++ /dev/null
@@ -1,5230 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [GTSRB1] - Dataset analysis and preparation\n",
- "<!-- DESC --> Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Understand the **complexity associated with data**, even when it is only images\n",
- " - Learn how to build up a simple and **usable image dataset**\n",
- "\n",
- "The German Traffic Sign Recognition Benchmark (GTSRB) is a dataset with more than 50,000 photos of road signs from about 40 classes. \n",
- "The final aim is to recognise them ! \n",
- "\n",
- "Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset\n",
- "\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Understanding the dataset\n",
- " - Preparing and formatting enhanced data\n",
- " - Save enhanced datasets in h5 file format\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Import and init"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:35.418263Z",
- "iopub.status.busy": "2021-03-01T17:43:35.417737Z",
- "iopub.status.idle": "2021-03-01T17:43:42.001437Z",
- "shell.execute_reply": "2021-03-01T17:43:42.001931Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { 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- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
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- "text": [
- "Version : 2.0.17\n",
- "Notebook id : GTSRB1\n",
- "Run time : Monday 01 March 2021, 18:43:41\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import os, time, sys\n",
- "import csv\n",
- "import math, random\n",
- "\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "import h5py\n",
- "\n",
- "from skimage.morphology import disk\n",
- "from skimage.util import img_as_ubyte\n",
- "from skimage.filters import rank\n",
- "from skimage import io, color, exposure, transform\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('GTSRB1')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Parameters\n",
- "The generation of datasets may require some time and space : **10' and 10 GB**. \n",
- "\n",
- "You can choose to perform tests or generate the whole enhanced dataset by setting the following parameters: \n",
- "`scale` : 1 mean 100% of the dataset - set 0.1 for tests \n",
- "`output_dir` : where to write enhanced dataset, could be :\n",
- " - `./data`, for tests purpose\n",
- " - `<datasets_dir>/GTSRB/enhanced` to add clusters in your datasets dir. \n",
- " \n",
- "Uncomment the right lines according to what you want :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:42.005298Z",
- "iopub.status.busy": "2021-03-01T17:43:42.004821Z",
- "iopub.status.idle": "2021-03-01T17:43:42.006466Z",
- "shell.execute_reply": "2021-03-01T17:43:42.006939Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- For smart tests :\n",
- "#\n",
- "scale = 0.2\n",
- "output_dir = './data' \n",
- "\n",
- "# ---- For a Full dataset generation :\n",
- "#\n",
- "# scale = 1\n",
- "# output_dir = f'{datasets_dir}/GTSRB/enhanced'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:42.010350Z",
- "iopub.status.busy": "2021-03-01T17:43:42.009887Z",
- "iopub.status.idle": "2021-03-01T17:43:42.012655Z",
- "shell.execute_reply": "2021-03-01T17:43:42.013141Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "scale : 0.05\n",
- "output_dir : ./data\n"
- ]
- }
- ],
- "source": [
- "pwk.override('scale', 'output_dir')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Read the dataset\n",
- "Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset\n",
- " - Each directory contains one CSV file with annotations : `GT-<ClassID>.csv` and the training images\n",
- " - First line is fieldnames: `Filename ; Width ; Height ; Roi.X1 ; Roi.Y1 ; Roi.X2 ; Roi.Y2 ; ClassId`\n",
- " \n",
- "### 3.1 - Understanding the dataset\n",
- "The original dataset is in : **\\<dataset_dir\\>/GTSRB/origine.** \n",
- "There is 3 subsets : **Train**, **Test** and **Meta.** \n",
- "Each subset have an **csv file** and a **subdir** with **images**.\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:42.017731Z",
- "iopub.status.busy": "2021-03-01T17:43:42.017267Z",
- "iopub.status.idle": "2021-03-01T17:43:42.065688Z",
- "shell.execute_reply": "2021-03-01T17:43:42.066179Z"
- }
- },
- "outputs": [
- {
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- " <td>40</td>\n",
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- " Width Height Roi.X1 Roi.Y1 Roi.X2 Roi.Y2 ClassId Path\n",
- "0 53 54 6 5 48 49 16 Test/00000.png\n",
- "1 42 45 5 5 36 40 1 Test/00001.png\n",
- "2 48 52 6 6 43 47 38 Test/00002.png\n",
- "3 27 29 5 5 22 24 33 Test/00003.png\n",
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- "source": [
- "df = pd.read_csv(f'{datasets_dir}/GTSRB/origine/Test.csv', header=0)\n",
- "display(df.head(10))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.2 - Usefull functions\n",
- "A nice function to read a subset :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:42.071992Z",
- "iopub.status.busy": "2021-03-01T17:43:42.071511Z",
- "iopub.status.idle": "2021-03-01T17:43:42.073164Z",
- "shell.execute_reply": "2021-03-01T17:43:42.073635Z"
- }
- },
- "outputs": [],
- "source": [
- "def read_csv_dataset(csv_file): \n",
- " '''\n",
- " Reads traffic sign data from German Traffic Sign Recognition Benchmark dataset.\n",
- " Arguments: \n",
- " csv filename : Description file, Example /data/GTSRB/Train.csv\n",
- " Returns:\n",
- " x,y : np array of images, np array of corresponding labels\n",
- " '''\n",
- "\n",
- " path = os.path.dirname(csv_file)\n",
- " name = os.path.basename(csv_file)\n",
- "\n",
- " # ---- Read csv file\n",
- " #\n",
- " df = pd.read_csv(csv_file, header=0)\n",
- " \n",
- " # ---- Get filenames and ClassIds\n",
- " #\n",
- " filenames = df['Path'].to_list()\n",
- " y = df['ClassId'].to_list()\n",
- " x = []\n",
- " \n",
- " # ---- Read images\n",
- " #\n",
- " for filename in filenames:\n",
- " image=io.imread(f'{path}/{filename}')\n",
- " x.append(image)\n",
- " pwk.update_progress(name,len(x),len(filenames))\n",
- " \n",
- " # ---- Return\n",
- " #\n",
- " return np.array(x,dtype=object),np.array(y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.2 - Read the data\n",
- "We will read the following datasets:\n",
- " - **Train** subset, for learning data as : `x_train, y_train`\n",
- " - **Test** subset, for validation data as : `x_test, y_test`\n",
- " - **Meta** subset, for visualisation as : `x_meta, y_meta`\n",
- " \n",
- "The learning data will be randomly mixted and the illustration data (Meta) sorted. \n",
- "Will take about 1'30s on HPC."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
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- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:42.091360Z",
- "iopub.status.busy": "2021-03-01T17:43:42.090885Z",
- "iopub.status.idle": "2021-03-01T17:46:54.629637Z",
- "shell.execute_reply": "2021-03-01T17:46:54.630136Z"
- }
- },
- "outputs": [
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- "\n",
- "Duration : 00:03:13 550ms\n"
- ]
- }
- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "# ---- Read datasets\n",
- "\n",
- "(x_train,y_train) = read_csv_dataset(f'{datasets_dir}/GTSRB/origine/Train.csv')\n",
- "(x_test ,y_test) = read_csv_dataset(f'{datasets_dir}/GTSRB/origine/Test.csv')\n",
- "(x_meta ,y_meta) = read_csv_dataset(f'{datasets_dir}/GTSRB/origine/Meta.csv')\n",
- " \n",
- "# ---- Shuffle train set\n",
- "\n",
- "x_train, y_train = pwk.shuffle_np_dataset(x_train, y_train)\n",
- "\n",
- "# ---- Sort Meta\n",
- "\n",
- "combined = list(zip(x_meta,y_meta))\n",
- "combined.sort(key=lambda x: x[1])\n",
- "x_meta,y_meta = zip(*combined)\n",
- "\n",
- "pwk.chrono_show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Few statistics about train dataset\n",
- "We want to know if our images are homogeneous in terms of size, ratio, width or height.\n",
- "\n",
- "### 4.1 - Do statistics "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:46:54.652473Z",
- "iopub.status.busy": "2021-03-01T17:46:54.637257Z",
- "iopub.status.idle": "2021-03-01T17:46:54.681001Z",
- "shell.execute_reply": "2021-03-01T17:46:54.681475Z"
- }
- },
- "outputs": [],
- "source": [
- "train_size = []\n",
- "train_ratio = []\n",
- "train_lx = []\n",
- "train_ly = []\n",
- "\n",
- "test_size = []\n",
- "test_ratio = []\n",
- "test_lx = []\n",
- "test_ly = []\n",
- "\n",
- "for image in x_train:\n",
- " (lx,ly,lz) = image.shape\n",
- " train_size.append(lx*ly/1024)\n",
- " train_ratio.append(lx/ly)\n",
- " train_lx.append(lx)\n",
- " train_ly.append(ly)\n",
- "\n",
- "for image in x_test:\n",
- " (lx,ly,lz) = image.shape\n",
- " test_size.append(lx*ly/1024)\n",
- " test_ratio.append(lx/ly)\n",
- " test_lx.append(lx)\n",
- " test_ly.append(ly)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.2 - Show statistics"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:46:54.707883Z",
- "iopub.status.busy": "2021-03-01T17:46:54.701481Z",
- "iopub.status.idle": "2021-03-01T17:47:01.683695Z",
- "shell.execute_reply": "2021-03-01T17:47:01.684196Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_train shape : (39209,)\n",
- "y_train shape : (39209,)\n",
- "x_test shape : (12630,)\n",
- "y_test shape : (12630,)\n"
- ]
- },
- {
- "data": {
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\n",
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\n",
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\n",
- "text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
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\n",
- "text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# ------ Global stuff\n",
- "print(\"x_train shape : \",x_train.shape)\n",
- "print(\"y_train shape : \",y_train.shape)\n",
- "print(\"x_test shape : \",x_test.shape)\n",
- "print(\"y_test shape : \",y_test.shape)\n",
- "\n",
- "# ------ Statistics / sizes\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist([train_size,test_size], bins=100)\n",
- "plt.gca().set(title='Sizes in Kpixels - Train=[{:5.2f}, {:5.2f}]'.format(min(train_size),max(train_size)), \n",
- " ylabel='Population', xlim=[0,30])\n",
- "plt.legend(['Train','Test'])\n",
- "pwk.save_fig('01-stats-sizes')\n",
- "plt.show()\n",
- "\n",
- "# ------ Statistics / ratio lx/ly\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist([train_ratio,test_ratio], bins=100)\n",
- "plt.gca().set(title='Ratio lx/ly - Train=[{:5.2f}, {:5.2f}]'.format(min(train_ratio),max(train_ratio)), \n",
- " ylabel='Population', xlim=[0.8,1.2])\n",
- "plt.legend(['Train','Test'])\n",
- "pwk.save_fig('02-stats-ratios')\n",
- "plt.show()\n",
- "\n",
- "# ------ Statistics / lx\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist([train_lx,test_lx], bins=100)\n",
- "plt.gca().set(title='Images lx - Train=[{:5.2f}, {:5.2f}]'.format(min(train_lx),max(train_lx)), \n",
- " ylabel='Population', xlim=[20,150])\n",
- "plt.legend(['Train','Test'])\n",
- "pwk.save_fig('03-stats-lx')\n",
- "plt.show()\n",
- "\n",
- "# ------ Statistics / ly\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist([train_ly,test_ly], bins=100)\n",
- "plt.gca().set(title='Images ly - Train=[{:5.2f}, {:5.2f}]'.format(min(train_ly),max(train_ly)), \n",
- " ylabel='Population', xlim=[20,150])\n",
- "plt.legend(['Train','Test'])\n",
- "pwk.save_fig('04-stats-ly')\n",
- "plt.show()\n",
- "\n",
- "# ------ Statistics / classId\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist([y_train,y_test], bins=43)\n",
- "plt.gca().set(title='ClassesId', ylabel='Population', xlim=[0,43])\n",
- "plt.legend(['Train','Test'])\n",
- "pwk.save_fig('05-stats-classes')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - List of classes\n",
- "What are the 43 classes of our images..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:47:01.699164Z",
- "iopub.status.busy": "2021-03-01T17:47:01.695621Z",
- "iopub.status.idle": "2021-03-01T17:47:13.376558Z",
- "shell.execute_reply": "2021-03-01T17:47:13.377073Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GTSRB1-06-meta-signs</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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aNWrE6Pfe48UXnmf9unXcdd8D9OlzImvXrEZRFOrUqfOXVx4teUyOPCZVcRQvdkVFBQsXzKdZkyY0a9mKytmzKPz3swSXLzPOP3E4TLesJEmGVi1JSDYbut8PkoSzSzdSzzwLT7+TsbdshZKaWlVPLFFio6qi7C+vUVz7dFUFPbo5VZKRvV4crdrgaNUG7+DTCedtxT9/LuVTv8E/fz56KIDs9qLrhuYvXjQ9EkGSFbDZCfz2K3t27yLrjntpe/GlbFy/ngXz59GtW3fS0tP/coOqxaPFo8XjsQOLR4tHi8djBxaPxwePor2BQIBlS5dwYr/+7M3fw0svPM/0776leatWPPLcvzhz2Fm4vV7C4QiKIptKoy68VQnexVjl8WhC13VkWSYvL4+X//MidXNyuP7Gm8jMymLhgvm8/cbrdOzUmeFXXkVKSgpzf5vDB+PH0apVa+646x6aNG2KrCi88p//MHHip+zYsYOHH32ctm3bsWjhfLp260G9evX+csmPLHlMvjwm7W0Q5PkqK8nfs4fcerk0a9mKfZM+p+jlf5suYtQIuqaZAqZrGpJiQ1cj6H4/zk5dSLvkMrwDB2Fv0tR0A5vxv7JsuJMPA2IjK1RZVIQrGF1Hdrlwtm2Hs207vENOx//7XMq++ATf3N+MwcHpNF4AYl48TUPyeolsy6Pon/8gkr+HVrffRXpGBosWLqBb9x5kZGb+ZQZVi0eLR4vHYwcWjxaPFo/HDiwejw8eY8NTZ07/kc5du7Fh/Tr+76knWbxwAacOHsJDjzxKz969ASNDp91exYdOjGKYoDQeLYWxpoQ5oXCYUe+8xYb167jvgYc4ecBAiouKGDN6FPn5e3nw4b/TsWNHiooKGT92DPl7dvPk08/QqHFjfJU+1q5dA5JOn74nMn/e79x752088fT/MfDUU5kzezZdunWnfv36fxnPoyWPf4w8Jk1xlCSJ0tJSlixaSP36DWjXoQPFH4yl+LX/oBUXI3k8hruVKrc7koQky+gBH7YGDUm//GpSzz0fe7PmRlpcXTPifWWlRtJ0QNKNc1GMDLgHFtiahNpMrxutD9XQ7u31G2C/4CLcJ/ajYvoP7Bs/htD6tUhON6DHDRR6OIzkdKKVl1H65qtoFeXk3Pc3unXvweLFi+jZs9dfxiJn8WjxaPF47MDi0eLR4vHYgcXjX59H4bkrKSlhw/p1dOrSlfXr1/H4ww+xYcN6Lhl+OQ8+/Heat2iBqqpIkmR64GoKRY3t76PpZYxVGsX/f5o+nU8//oj+J53MiGtGAjDth+/56ovPufTyKzj9zDMB+GbKZL6e/BXDL7+SgQNPRZZlJn35OVO++orTzjiTh//+GOPHvc/4MWN47JG/8ffHnuDsc89jzq+zsSkKdXNy/hLKoyWPf4w8Jk1xDAYDbFi/jvbt25PboCHFY0YZ5FVUIDmc1ciTZNmID5YkUs48h8ybbsPVuSuSTaly7SoKkq3mzcdQRdqhiurBhNpImStX1S9J2HPrk3nVSLx9+1E6/n32TfoCPeBHstkNqwXGi6RHIkiKgh4JUzZ2FHooRL1HHqNjp84sW7aUbt27k5KSelQtUsmAxaPFo8XjsQOLR4tHi8djBxaPf30ehYfq97lz6dWrF0sWL+KxRx8hb/Mmrr3uRu578CHq5eYSiURQFCVOOUz0AP6Rz7ln9y5eeflF3G43t9x2O3Vzcli9ehWvv/IyLVq1YsQ1I8nMzGLZ0qWMfX80jRs34bobbiQ9I4N1a9cybuz71KlThzvvvodWrVtz/gUX8d0337Bzxw6efuoJAE4780yWLFpM1+7dyfwLeJAtefxj5PGoK46aZhxeuXrVKlJTUqnXoCGlH46n+LWX0CorjfhhsREVqsgLh1Gy65J1062kXTocJT3D+F5Va3QLJ1NAE8uuci0b7ZFkGUerNtR59B+4uvei6PVXCG/ZhORwGDHLUYuQrqoGiZpG2YfjwW6n3sOPUVFezvq1a2nTrj0pKSnH5KBq8WjxaPF47MDi0eLR4vHYgcXj8cGjrutoqsry5cvo1asXK1eu4KknHiNvyxauv/EW7nvob2RnZ6PG7PNLVBKT5WGsqa2xeybff+9dVi5fxi2338mpg4dQUVHO2NGjyMvbypNP/x89e/WmrGwfn3w0gTWrV/Pvl16hbfsOBINBxr4/mtWrVvHEU/+gY6fO+P1+Pv34I0pLSuh/0sksWbyI/3v6KdweD31OPJEVy5bRtXs30tLSj0keLXn8Y+XxqJoOIpEIsiyzdu0aZB3adehA+eRJFL3yIlpFufEw0dhcMDpGkmX0UAjnCZ3I/c+rZN5wM0p6BrqmGQ8WtfAI6Lqe9Bc31ookhNT8TlEMzV7TkF0u0i64mPqvv41nwCnRrEdRi4Fob/QFRNco/2AMhW+8SouWLfF6U1i/bi2RmLNajhVYPFo8WjweO7B4tHi0eDx2YPF4fPAowk6XLFlMnTp12bp1C8/939OsX7eOEddcW6U0qirKISiNyYSoR3j8fvn5Jz76cALde/TkuutvRNd1ZkyfzqeffMTpZw7jggsvAmDmjBl88tGHnHve+Zxx5jBsNhvTvv+O/076gpMHDOSSS4ejKAozp//IFxM/o3efvrz25tvcdMttFBQU8M9n/sHa1atp3aYNK5cvPyZ5tOTxj5fHo6Y4isxLO3bsoKSgkE49elD522wKX3oBtbjIcKkmkIckoUfCeE4ZRO7Lr+MdcIrROVFrQKx1hRji/ihrh6grjkjRBlk2P3ed0Incf79C2sWXQdRpHHuWiyBRC4XY9/477Pv8M9q2b48syWzauBFJko4ZgbR4tHi0eLR4TCYsHi0eLR4tHv9MqNGw0w0b1mOz2ZBliZf/828WLpjPRRdfwv0PPVSlNJr7+v5YpTGWj9j6CvYW8MYrL6OqGrfdeTf1GzQgb+tWXnv5Jerl5DLyuhuoU7cumzZuYNQ7b5ORkcF1N9xETr165OVtZcz7o1EUmbvvvpfs7Drs3LmDN994jdTUVG678y4aNmrELbffznXX38iGDev513P/pKiokJx69Vi+bBmSJKHGvON/Jix5/HPk8agojpqmYbPZ2LNnD/m7dnJC9+7oW7dQ+K/niOTlIblccW5igwAJVJXUs84j94WXcLZqY2QHMr8n/u8/kLhExL00NbRN13VsdXPIeeIZMq65HhTFfAkhSqmqItntqGX7KHrjZXxz59C5e3d279zB3r17sdlsf7owWjxaPFo8Wjz+UbB4tHgEi8e4vy0ekw5N01BsNrZu3YoaDpOZmcXo997j++++Y+Apg3jw4UfIza1vKpdAdAEfo1D8QYh9F7RoFtBxY0czf/48Lrv8coYMPY1gMMiY0aNYt3Y1V159NScPGICvspKJn33K/Hm/c8NNt9CjVy9UVeWTjz5k7pw5jLzuRrr36oWmqYwdNYoVy5ZxxYirOXnAQMLhMBkZmdxxzz1ccNElzJkzm9dfeRm3x4OqRti+bRuKohwTPFry+OfI41FRHGVZJhQKsWfXTurm5pLhcLDnpX8RXLYEye02N6SaDy1JoKmknnsBOU89iy2nXpy2/1eCaR0AJLeb7Pv/Rub1NxsHiArLVPRaPRJGcjiJbN1C4SsvIu3aScfu3cnbvJni4mIURTFjtf8MWDxaPFo8WjweC7B4tHi0eDx2cLzxGA6FKCzYS3pGJjOn/8iE8WNp3boNDz7yCC1btTaUxoT9bVqMgpG43/BoI7F8TdNQFIXffp3Nhx+Mp32HDtx48y0oisKsX37mow8/oP/JA7n8yqsAmD/vd8aMeo9Bg4dw3oUX4nA4+HXWL3zy4QR69OjJ8CuuxOVyMeuXn/n8s0/o3LkzI6+9Hl3XsdvtRCIR6tdvwD33PUCfvicy6csv+Ozjj2nStBl79uympKTkmODRksc/Rx6PWHEU2urOHTsIBUM0adKUwrGj8E+bimR3QMx5I9E/0MNhUoadTd3HnkLOzIwTkprie49ZxLRXvJiS3U7WnfcaFoCohaPqpZSMrEduN8H5v5P/9uvUycwiJT2Ngvw9xhV/0gts8WjxaPFo8XhMwOLR4tHi8djBccjj+vXrSU1NZeuWzbz79ptIksQ99z9A3779jH2EMcdOVD17daUxGc8h3ovY8iVJoqSkhDffeJ3ysjLuuuc+Gjdpyu7du3n1pf/gcbu58aZbyM2tz65du3jrzTeQZZnrbriRJtHrxo55n9LSfdx17300btKEwsJC3n7rDSr9fu665z5yc3PRonXbbDYikQgndOzI3fc9QN2cHN57922WLllM/foN2LRxQ9JDdQ8ESx7/XHk8IsVRxH/v2LGD4oK9nNCrF8H5v1P24Xi0UBDkBCGTZfRwCM/JA6j796dQsrLNRicKyV8CCe00SXQ4yLrrXtIuvMRwldf0XDYbFZMnsW/S57Rv34Fd23dQWFiAJP3x8eMWjxaPFo8Wj8cMLB4tHi0ejx0cZzzu2rUTVVVxOByMHzuG1atXcfW113HOuefFcROrUEiAJMnVlMZkKRvCowTGPj5Jkvhownh++Wkmlw6/nKGnnY6maYwbM5olixdx6fDLGTxkCKFQiP9O+pKfZ0znqquvYeCpg9B1nSn//Yrvv/uWESNH0v/kAQB88uEEfvv1Vy659DIGDRmKFolEzy00nkmJhj4OGTKUG2++hYK9e3nv7TcJhYLIio2dO3dY8vhn4BiQxyNSHIWLs6K8jMx6uXhDIQref5dI3lYkp8uIr4UY8sI42p1A3b8/hS03t0Yh/MuQF4Vob5wFAFDcXrLvewjPwFPQQ0Hj+aMx6rqqgs2GVlpC6YSxqHlb6NK3L5s3bKSiouIPDwGweLR4tHi0eDyWYPFo8WjxeOzgeOJxz+7dZGZl8eusWXz5xeecdPJArhpxDd6UFDNDZyJXUg2fJYPHRIVUVVVsNhsL5s9j3JgxNG/RgptuuQ2ny8WcX2czYdxYunbrxrXX34gky6xYvoy333ydnr37cNnlRjjqogULGDPqPdq0bcuIq68hJSWF+b//zoQPxtGiRUtuuvV27A6HkYUzhg9JMrK42h0OLr3scoaddQ7Tp03jv5Mm0ahhQ3bt3Inf77Pk8U9AreURjqo8HrbiKLLx7M3PZ++ePbRo1oySSRPx//ITuN3mC2jaY1QVJSuLug8+jLNtu6rB5S9MnkCixUKSJHSMjat1Hvg7jlZt0MNhJEWuCkOIRJC8XoKLF1H0yUdkpaUh2+2UlpT8oW23eKyCxaPFo8XjsQOLR4tHi8djB39lHoU3Zdu2POx2ByXFRYwZPQqXy8W1199Au/btzQydic9YE3/J5lGULcsyZWVlvPf2W+zZvYt77nuAFi1bUlJczBuvvkIkEuGW2++gUePGlBQX8/Ybr1NRVsa1119Pm7ZtKS4uYsIH49i2LY877r6Htu3aU15Wxvuj32P3rl3cec+9NG/e3FSyJFkCTTffZ0VRCIVCNGzUiBHXXEOjxk0YO2Y0W7dsIS01jV27dietD2qCJY9VODR5DBlhumAoj5EIkjfliOXxsBRHXdex2WwEg0F2bt9Oh+490PK2Uvb5p+iVlUiyYhKE0PrRybzuZjwDBxuDiiBL4i9NnkBN7dd1HVeHE8i6635kjwdiwxp03Xi7FZmKKZPwz/+d9l27sm3rZkpKSpBlOelWHIvH6rB4tHi0eDx2YPFo8WjxeOzgr8qjUIBKS0rIyMzk++++Y/6837ngoosZevoZgKGkxd5Tk3KRTGUjsdxIJIwkSXz+6SdM/fYbLrl0OKdF2/rB+LHM+XU2F1x0CWeeeRaaqvHtN1/zzddTOP+iSxh21jkATJ82jc8/+5SLL72MIUNPB2DSF5/z/bffcs5553PmWWfH1S3JiqFhRM8zBHDY7WiaxoBTTuWSyy5j65bNTPzsE1LT09lXUmLJ45+IA8vjfcgpqch2O1Ikgh7tF3T9iOXxsBRHUXBlRQU+v4866ekUT55EcOlSJK/XTIGra5pxcGUggHfgINKGX2lYNGIfWP8LxRYfIswXMip4KUNPJ+3i4eiBoJn1iGgoh+RyEd6WR+nnn+KVZTzpGfgrK4Dk94vF44Fh8Xh8wOLx+IDF4/EBi8fjA381Hgv27kWSZPbm72HCB+No2boVF158MRkZGYTD4TjFUbQrVrkQilQylcb4EFU7y5Yu4f1R79GocWNuuvU2UtPSmD9vHuPHjqVVq9bcfNtt2Ox2Nm5Yz+uvvkzrNm245tqReFNSWLtmNe+8+QYNGjTkmmuvJysrixUrljFu7Ptk16nD7XfehdfrNZIByXKVwi9VZeQU/1dVFZfLxXnnXUCPbj2Y+MnHbNq4gdS0NEqKi5PWL7Gw5PHAiHtXgZTTh5F67gWoa7dgLw8gl5YjhcKG19F5ZPJYa8VRWG/C4TDr167lhO49iGxYR8W3U4iyEfcCoqooOfXIvOk2bFlZ5pkpAscSeQfb6GyGX+gHzsBkDjjR+GLZ5SLj6pE4O3ZCDwSqBlVAVzVklxvfjB/xzZ1Du46d2bhxI2VlZYgY82TA4tHi8VB4jO2rA32WDFg8WvIYC4vHo9vO2N/7+6ymew6lTItHSx6PJR4jkQjbt28nJyeHqd99w6aNGznn3PPpe2J/dN04giL2npqUxj+CQ1GHoihUVlTw/qj32Lx5E3fdez/t2renoqKct994neKiQm676y5atGiJz+fjzTdfZ/euXVw98lq6dutBRUUFH3/4IStXLufmW2+jR48eBAJ+Jowfz+pVq7jjrrs44YROcXUa73RMO2K4t9vthEMhunXvzjnnnkd5eTmfffIxaWlp5O/ZTWVl5V9CHg+2YjnctU1t7jtU+arpPvP6Gm6LC13VdWS7nay77iXzhuuRikpxVAawlfuRVBW0GHn8rfbyWGvF0dT6KytQNY2s1FRKp31PaM1qJI8HPRIxSZQUBT0UJP3Sy3F371ntAQ+G2nZsbe9LvOZg7Yr9Xgws+60nRhh1XcfRohUZV18HNgUjFiAKTQO7nUj+Hsq+noxL10jNyKCyvPyg7T8S/JE8Qu0F8nAE8VDbZfF4+DzWdH1i/+3v78TPEr+zePxjeYR47g57Eqvhu1hYPB45j7XlKfHaxPsT98ccqIwDtWt/31s81l4ej7YcJpZ7qO06HnksKizE43FTUFDAxE8+pW27dpx+5jBcLhdqpOqQ+P3JRrLWOjV5MSPR8wenTP6KSV9M5IKLL+aMM4chSRKffPwRM2ZM4+xzz+Psc84FHWb8OI3vvp7C4KFDuWz4FQD8OnsWH4wfy5nDzubsc89DkmWmfvctX078jKGnn855F14MkuHZjPV06sR7rXRdNxUySZZBlhl6xpl07d6Dryf/l82bNpKVnc3e/Pxa91NtcLTkUSKe41AohN/vx+/3Ew6HqxkMDoZY/mLv0zSNYDBolh0MBs19trHXxvb9geqIvd4o5MDtkqKefntOLjnvjcYz/DIkfwhbZRBpXwW6plbJ4ze1l8daK44i8872rVtp0roV7NpJxbSp6OJJoi+apCjowQCOtu1JveBiJLvdCFmQ5cPrqCjC4TDBYJBAIEAgECAYDBKOOegTiCPkQIubml6OSCRCOBwmEomgqiqqqhIOhwmHwzUeACvKqqGCqmui93kHDcF70kC0ygoku73KGhe11vl+mUFgxTJatmvPpk0bagyfOFr4I3g0iql50tI0jVAoZPIYCAQIhUJxwnWgcmI/t3hMDo+JltdIJILf76/GUaKc1vS3kNWarLmxdSXC4vHoyqNA7BgrIP6ORCI1jrE1yXJNRgOLx6PPY+ycJkkS4XAYn89HKBQiEolUsxAnLn5jZTRxDPb7/WbK//3Jp4DF49GXx/3NkaqqHrYcxpZt8aixe/cu0jPS+XX2L2zcuIFBg4fQq3cfNE3DFuNtPBIcDo+JfatGItjsdtasXsV7b79FTr1cbrrlNrKys1m+bCnjx7xP/dz63HHXPXg8XvYW7OWV/7xIKBTixptuIS09nfXr1vLe22/icbu59vobqN+gAZs2bmTc+6OxOxzccefdZGdnm8daxLZVGACIU1KM3zabjVAwSOcuXRgyeAj79pXy3bdfk5GZRX7+HnMMSQaOhjyKfo6d5xwOB263G7fbjd1ur6ZIHwixY6X4W9M0AoEAsizjdDrNsp1OJ4qiEAgEzDoS27O/OkB4Eas+N+Q1FCe/hkyHzARCks2GHggAkPHoY8gNG6CHQzhKK5FLy9D1qDz+XHt5PCxpLSsro7LSR4Pc+pTN/Y3wyhXIHneV1g8gGal90y68FEeLltHa5KpOOEBHJX4vyNA0DbvdjtPpxOVy4XK5cDqd2O12dF03rxHYn0ZfUx2qqqLrxuZbu92OzWZD0zR0jDAGu92OLMtEIpEaJ9f9TfaA8TKrKraceqRddBlyihdd1ar6StPA4SCyYwcVP80gzeXC5nBSXl52ECaODMnmsaZ+CoVChEIhZFnG4XCYPLpcLhwOhxlWEgwGzToSBTS2DovH5PEY2zeapmGz2XC73eYAGAwGDxrSEAqF0DTNlNVEPsHiUSCZ8hiLRKOc6DsxqdlsthrHWEmS8Pv9++1vi0cDyeAxcXFit9vxeDw4HA5sNpt5hEAoFMLn8+Hz+Uxrt9/vjzPQ+Xw+gsGgOQa73W6Tl/0pjRaPR18e9zdHCsVCUZQDymFNfFnzY3WUl5URUVXUiMZXX35Bw4YNGTR4CG63O+7sutp4mhJxuDz6fL44HhWbDb/fx/gx77Nq5Upuv/NuOnfpSsDvZ/R777Bl8yZuv/teWrVuTWVlJU8/+Thbtmzmv99OpV2HEwAjc+y3X3/HTbfcxon9TyISifD5Z5/y269zuPmW2+jZuw+6rpvKgXgPdaocWfvrBeF1PHXQIFq3bsOU//6X0pISXC6XudcxWThSeQRMA7iY5z799FOeeuop/vGPfzBnzhwzHPZQZDeWb2HMk2UZl8vFunXr+OCDD/jXv/7F888/z9ixY1m5ciUul8tM1CTaczDDvXmNZOhCQuG3242xX9d0c30mPhPXSS6XEbbbsSOem29Gxgg7tpVWQHml4XXcWXt5tB2cruqorCjHnZ6GEvBTPvNHtGAQ2eGocmkrCnrAj6Nte7yDhxrWt0gEyXbg6moSvnA4jN1ux+VyUVRUxOzZs8nLy6OoqAhZlsnMzKRZs2b069ePunXrxt1TU5mJg6AkSSYRRUVF/DZnNiuWL2fP7l34/QFkRSYzI5MWLVrSp18/unTpCmBuKK7J6gDVrXUC7j59cfftT+X0H5BT09CFt1TXQVGo+GkG2ZdfRYPGTdi5fQdZ0cNKk4Fk8gjVFQ9N03A4HAAsWrSIJUuWsHfvXiorK/F6vWRnZ9OpUyd69+6N0+k077HZbDUqjxaPBpItj36/H7fbzdixY/npp5+477776Nq1q3mdqqqEQqG4PhKLE4fDQXFxMS+99BI5OTkMHz6cnJycav0eW5/F49HlMRb7M+YII47P5+O3335j3bp1FBQUEAqFyMjIoEmTJpx88sk0bNgQwExdb42r1ZEMHhPnw//+979MmzaNvn37UqdOHZo0aUJubi516tQxx9iDYd26dcybN4+FCxcyfPhw+vXrZ74LYPH4R8hjTXOkmPt++eUXVq1aRVFREaFQiPT0dJo2bcqJJ55Io0aNgKr3oSblzpJHA8XFRXg9XtavX8eihQs5Y9hZ9OrTB8A8fuNIkDgGHoxHMZ7279+fBg0aAESVDglFsTHt++/5+MMJnHPe+Zx1zjkossxnk77k6ylTOP3Mszj7nHOx2Wzs3bsXt8dDx86d+fq/X7F58yYyMjKprKzknPPOoVOXLjidTqb/OI0Pxo+l/8knc8nwy7HZbOa7ENf2Gp4lkW+7zUYkFKJr9x707tOHjz6cwPzf53Lq4CHs2bObujk5R9yf+0Nt5DFl6GmGkqtrSJKhOAovu9vtBuDFF1/k8ccfJxAIIEkSdrud/v37E4lE4va81oTE917cs27dOp5++mmWLFnCtm3bqKysBMDtdtOkSRO6devGgw8+SPfu3YlEIiiKUqMMHWg+jUQi/D73NxbMn0de3lbKy8rRdA2v10vDho3o1r0HJ57YD29KCqquoWgasqLgOfdcQmPHom7ejJ7qBjnqxTTlccQhy2OtpSYcDrN7x06adziB0Ib1BBbOR3a54jeDywp6JEzKaWfiaN7C+CzqEk/slMTPYr9TVRW73c7ixYt56aWXWLp0Kbt372bfvn2mpchut5Oenk5ubi7dunXjrrvuomfPnvtVHhPrM6w98N9Jk/hownhWr17FvtLSaPirDrqRptnt8ZDboAGDhwzlxptuoUnTpmiaiiwrNZafCMMaF8GWU4/U04fhm/2LYYETL4eqIrnchNaupnLZEnKHnsGOLZtNz9vRRrJ4NJ83hkvBRTAY5PXXX2fSpEls27aNgoIC/H6/eY/b7aZOnTrUr1+fM844g9tvv52cnBxzMWPxWB3J5DFWDgEWL17MhAkTWLBgAfXr1+eUU05h4MCB9OzZE6/XW+3+8vJy3n33XUaPHs2yZcuw2+2kpaUxcuRIwuEwTqezWhssHpMjj7Hf16Q0rl27ljfffJPffvuNXbt2UVxcHGcVzcjIoF69evTs2ZNHH32Udu3aVZPL2DosHpPHI8C3337L6NGjmThxIgDp6el4vV48Hg8ulwuv10taWprpTRQLFFVVKSoqYvfu3RQUFLB7t5HYon///kDVXiKLx+TymCiLwhATCAR46aWXmDRpEtu3b6ewsNCUQ1mWzbVO165duffee+nVq1eNcphYz/8qj6qqUrC3gIaNGzPjxx+QJDixX39ycurFrRHh8PbnHQmP9evXp1u3btx33310794dgB07dvDGa6+QmZXF9TfdTG5ufdauWc2490eTlprG7XfeTVZWFpO/msRHEyawZ88uigoL+eWnn3C73UQiETweDw0aNuThB+/nyhFXs3PHTsr37ePW2++gUaNG1c6rrGle2C+i75InJYX+Jw3g6ylTmPrdd5xx5jAqysv/XHlUFPRwmJTTz0Ru1IRgMAS68e6JyAybzcaKFSt47rnn+OqrrwgEArjdbvx+/0GVxVgkyoLNZuOrr77i7rvvZvv27eZ1Qkn1+/2sW7eOdevW8csvv/DII49w5513AtWNMzXVo2kaiqKwcsUKRr37Dr/NmU3B3r0EAgF0vSq03OFwkpmVRddu3bnu+hsZeOqpaHYZORjE2bkzzttvZe/9DyI3yIFwGF2LyuO61VQuW3zI8lhrxdHv9xHRVLLSUildMA919y6klFQIRUMLZRk9FMRWvyHek08xYozDYSPWnZoH05osNrIsEwqFePrpp3n//fcpKCgwrxchFbpuhOYUFhZSWFjIypUr+fHHH7n++ut5+umnq9IMRxFbr6hHVVXeev1VRr37LoWFBWYIh8PhMNpkNJBIJMLWTZsYs3kza1au5Imn/4/OXbpUixM/oDUuOt66evXB2b4DgeVLkT1ew9UORnyA30flnF9JGTzUcBuXJSeMIxk8xn4ufgeDQZxOJ7Nnz+bBBx9k0aJFZgy2CJWSZRlVVfH7/Wzfvp3t27czf/58Jk2axNNPP80FF1xglpNYr8VjcniMhegXl8sFwPr161m7di2//PILdevWJTMzk8zMTHJycvB6vaiqSmFhITt37mTLli3mIJSWlmYqmLF9K35bPCaPx5oWByIC4JlnnmHUqFFxE56iKLhcLtPwUxI9r2vt2rXMnj2b5557jssvv7yaXFo8Jo9HEaJaXl5OeTSJQVlZGeFwmKKiosNuc3p6urnAseTRQDJ5TJRFofgtWbKEe+65h1mzZpnXirBxofQLOVyzZg0zZszg3nvv5eGHH45Tgqz5sQqVlZVIskzA7+fnmTNp1qwFvXob3sbEcxsPR3Gsicdly5Zx5513Mnv2bPO6/fG4evVqfvzxRx548EHuvP12Xn7xX6xauYJHHnuC3n36EgwGmTB+HMuXLeWxJ//BCR078urL/+G9t9+iqKjIdK7UrZuDpqmm8rp50ybQdV75z4u0bNWK5198iQGnDkLXdZPHmtZSh7QeiL5nvXr3oXWbNsz/fS578vfgcrspLS2pdR8eCg5JHoNB7A0a4u4/EJvLFafc5OXl8cMPPzBt2jTmzZvHjh07AEw9A6qMZoeCRAPK+PHjuemmmwiFQubWAbF/XEDI5+7du7n33nsJBoM88MAD1Tz6onzxW8jezz/N5P/+8SSrVq5EliRs0e0KICFJBp+aplFUUMC0qd+xetVK7r3vAa4YcTWRcAib04nUoD5hGzhdbgiFDG+tBFTUTh5rvcexvKwMd2oqkq8S37zfjQeNtUDZbGh+P+6evXG0aWN8phy4mpqUxuLiYm688Uaef/55CgoK4qwB4XA4LtlGbLz+nj17ePbZZxk5cqS5abQmqKqKLMtMGD+WN19/jdLSElJSUkzLnSBB14yDUGVFweV243S5mDXrF575xxNs3bwZRVHi6jig0EVDAxzNmuHpdxKoalz8NboONjv+hfPRCgqp16AB+Xt2H7DvDhfJ4BHiX3ixqJwyZQqXXHIJ8+bNIxKJmAvS2AQ5Iq7ciN029lmsXLmS66+/no8++gin02mGQ8YKrMVjcniMheiLigrjnB+v12tuVs/Pz2ft2rXMnTuXyZMn8/HHH/PZZ58xY8YM1q5dSzAYJDU1FYA+ffpwySWXxIXDCVg8JpfHmhYFlZWVXHHFFTzxxBNs377d3NAvJqtAIGAmTpFl2Rxn8/LyuOOOO5g6dSpOp5NgMBjnnbZ4TA6PYtwrLS1lz549AKayHqsIOJ1OHA6HOScqihL3IwyvQlls3bo17du3jzbPWG5ZPCaPx1hZDIfDOBwO5s2bx4gRI5g1axaSJJnRGLGJqkQSQKfTaYYqPvLIIzzxxBPY7XZzARyL/3UeS4qL8Xo9bNywgc2bN3FCp0506NDB9OAc0vMcADXxeOWVVzJ79mxzPIX982i329m7dy8PPfgg740eTatWrWnRqjVXjrgGm83GD1On8sXnEzl5wEBuuOlmJn76Ca++9B/KyspISUkhLS3NyAyrRkwuZVkmJSWF1LQ0ANatXcv3331LUUGBObYntj+2H2oyMsauucQ+wJatW9GpcxeKigtZsnhRXHbVo41DkUeCQZSu3fD26MmHn3zC2cOGMWjwYDp06EC/fv249957+fLLL9mxYwc2m81cg9YGsf0g9qb++uuv3HjjjYRCITOUNBQKcdFFFzFnzhy+++47MwpSKIGqqvLQQw8xdepUs9xY46uACGddsngRzzz1BCuWL8fj8eCMrqGN+4zQaNE2h9OJNyWFnTt28K8XnuObb77G5vYAINvtKKluUGP2heo62Gsnj7VWHAv2FlCvfgPCu3YSXLXSiDHWYpQzTUN2OPD0ORElMwtdjSDJNVs4YhGrEITDYZ544gk++ugjc4GpKAo333wzP/zwA6tXr2b16tVMnjyZ4cOHx2UIUxQFm83GhAkTeOCBB1AUpZrbVQwaixYuZNS771BZWYnT5TKJFal0qzKNhUxrnKqqpKWl8ctPPzF+3BiCgYARKx4T4rO/55IkCSIRJJsdd88+KNl10EPhqoxPqorkcBDeupnAxvVk161LeXlFbSk6JCSLx1gOnU4nS5Ys4cEHHyQ/P99UCAOBAKeccgoTJkxg8eLFrFmzhnHjxtG4cWOzz8UegZKSEp555hmWLl2Kw+GIy0hl8Zg8HmMhJlhhPRPZb2MV/cRFqvi/w+GgvLyc+vXr8/e//x2osvSK/rR4/GN4jFXuQqEQt956K5988onJGxgJHXr06MGYMWNYsWIFGzdu5KmnnjL37YTDYTweD8XFxbzzzjv4fL64PXEWj8njUSxySktLKSwsBDCTmIg5MBQKEQwGCYVC1bJmCs+TyJ4pFpDDhg2jdevW+P1+0yBk8ZhceRSLVrvdTn5+Po899hirVq3C5XKZxvB7772X6dOns2bNGtauXcu3337LFVdcYfa9UPL/85//MHHiRBwOR9xax+IRSkqKSUtLZ9783wGJzl26kJaeXk15Olwk8vjoo4+aPIr8DAfiUSicAC+//DLLV6zkvvseICsry8iEOmY0uq5x/0N/Y/26dbz5+qumQV7It1BKY3kUYzWAw+7gm2+m8P7o96p4rEFJiX2mmpDYT4pio1v37ng8XubMnk2KN8WMhDjaOBR5VJxOXD37gN3OsgUL+HbqVH6aOZM1a9awa9cufD6fOdeJcfNAz1sTYudQRVEoKCjgyiuvrJaJ9MYbb+SLL76gX79+nHnmmfz4448MGDDAHMOFvNx6661UVFSYXkeIXxfZbDZKS0oY9e47LF+2jLS0NJNzEXEZDodRIxFCIcORJt4Dj8fDnt27eeuN19i8YT0Act0cbA0aoolzVjk8eay14uj3VZKZmUlg/TrUPbsgmu4WovHtwSC2ho1wdups3KBVvWwHI0hkevvyyy958803TS9Tq1at+Pnnn3nnnXc47bTTaN++Pe3bt+fcc89l/PjxvPfee8bDREMeRcePHz+eyZMnm+UICPI+/vADtm/bhsPpRI1JR56ZmcnNt97GO6NG8/yLL9Gv/0nmi6JpGpGIisvl4qtJX7J48SIA1JgX4oCQje8dbdriaN0GPeCv2heh6yDLaBXl+Fcsx+twoB30yNLDQ7J4TAxBHDVqFOvXr8fpdJqT1dtvv820adO4/PLL6datG+3ateOaa67hxx9/JCsry7w3GAyaGareffddgLijVywekyuPxqPoZhYwn89X7TsxSInFhrB6isVJKBQiMzOTN998k/79+xMOh+OSqoDFIySfR4FAdOHwf//3f6ZhTkxCmqbx8MMPM3PmTEaOHEnHjh1p0aIF27Zti29r1ICwYMECfv/9d9PiLsZdi8fk8CjkpKSkxNy6UZtFb+y1NpuNUChEx44dueSSSwDMBawlj3+MPAolb9y4cUyfPh2n00kgEKBRo0bMnDmTl156icGDB9OuXTvatm3LsGHDGDduHGPHjjWNBU6nE5/Px4QJE0zDuYDFIwQCQRRFYeG838nOrkPHjp0Prf2HACFPgeiRBx9//DEzZszA5XIdMo9gGGKdTidbt2xha14epw87i1AobGRCnfMr199wE31P7McH48awfds2nE5n3PE5B+JRVVV0dNJS0/hy4kTWr1tnjtcHeq5Er2NifwklqWOnzuTm1mfp0iX4/f6kHaty6PLYpap90UgMEToKmOuUI4GuGxlpNU3jnXfeYfv27XF7yLt3786TTz5p1icSIj355JPk5uaaRndZlsnLy2PChAlxZccqpwCzfvmZ6dN+wOv1mvOsLMvIksTpZ57Jy6+9zhvvjuKKq642o0hE3W63m1UrVvDFF18YfdWwIa527dH9RyaPtWdZUXDKMoE1q9CCgfgQhCiBzrbtsDdpGq3h0KoQKftLSkr4/PPPgaqwmRdeeIE+ffqYoalC4xbnsFxzzTVceumlputYVVXcbjf79u3j008/BaqEXFjzVixfztzffjPuibZB13XcbjcPPvx3nnz6/zj9zLO4asTV/PulVznp5JNN63okYnjTdu7YwU8zZpguarFJNRFxQhfdZG5v0BBXh46G1ST2+2g7g2tWIgWDeLwph9R/tUaSeATMOO+FCxcybdo0AJM7gKlTp1JWVmYqJOIcsjZt2nD77bebggFVwrNw4UK2bNliWmQBi0dIKo9QNUj6/X5TcaxpsRoX9hQz2QwcOJCffvqJCy64wLTMxkJY1Swek8djbBSAy+Vi9uzZ5qJFLEA0TeO2227jySefJDU11VxY5Ofn8+WXX8aF9Yh3oqCggL1795ply7Js8fgH8FhaWkpBQYHpOTCKrvLyJ/7Efi72KQsF47TTTqNjx474fD5TYbDkkaSPq2KNsmvXLr7//nsAM6zx+eefp3fv3qbxTax1xD7Gyy67jGuvvRaoUg6XL1/OnDlzsNlsZg4Bi0ew2RT2lZWyft06GjVuROs2rQHiFOzDVSLFWtPj8bBr1y6mTJkCVBlgasOjkOMVK1eyYsUKli5exJjR79Gn74mMvO4G1qxZza+zZ8W19VB5FF7N3bt38c3XkwmFQmaOkP09V2KfVPc2GmNFi5YtadK0Cbt27mDnzp1/qjw62rbD3qQJAHLUsyjWkWKrRSzvhwuxvtm9ezf/+te/TIeVwDnnnEPDhg2prKw0o658Ph+DBg0ys9GLIzPAyO6aeASIWCuVlZUx/cdpFBcXYbfbzeytgUCA8y68iBdffpVLLrucs885l3++8G9uuOlmoMqjKSsKwUCAWbN/Ycf2bXiaNsferj2Srh2RPNZacfR6U8DvI7h+rVFxYoywJOFs1wGlTh0jrOEQB1SxUFmzZg0///wzsixTWVlJ69atzZTFsfs5Yl3ODoeDq6++GqgaEER5K1asYPny5XHHOwD8Nmc2e3bvMr0fYCyiuvfoyYUXXWwujsLhME2bNePS4Vfg9XqMGHJJMq17v82ZTf6ePdFB5ODx0mYYh8OBo217JI/X2AMgSNQ0JJud0IYNaOVlpGdmHFL/1RbJ4hGqlL3NmzezadOmuFA2m83GlClTePbZZwkGg2ZyHMHB4MGDAeIsRKKsFStWmJ+JOiwek8djLHw+n5laWngqrr76agYMGECbNm3Izc0lKyuLZs2aceqpp/Lggw8yZ84cfv75Z7p06RJnSavJqmbxmDweExcB77//Pjt27DDHRF3Xadq0KQ8//DAul8s8Z1Vg3759NR7x4HQ6zcWuNa4aSCaPsR5HXddNTho3bszChQvNqAARiip+gsGgGRb34osvAoYhr1mzZlx66aVA1WLXkkcDyR5Xxby2ZMkSFi9eDBhjrCRJtInumRReo1gDQCgUwu12c8EFFwBV8+TevXvNhB/WOqcKHo+H7du2UVRURNOmzWjQsFHcdpcjxdHmsaSkhF9//ZUPxo/F5/dzy+13UC83l59mTGdvfv4R8/jrbCMbZ+xex8MN1dU0jaysbJq3aInf52fDhnWkR/dVHm0cijzaW7fF07Ahuqry3PPPE4mOfeGY30uWLAHiEyPVBrFrly+++IKKigozKktVVZo0acLQoUOBqmSCUOUEGzRokMm/KGfr1q1Mnz49LtxbcLNl82YWLpiPw+E0jbPhcJh69epx+ZVXkZWVbeo6drudy68cQYuWLc37tWhI++YtW1g4dy7YbCitWiN7U45IHmvde6kZGajl5YS35hnnFYkQDUkCVUX2enG0bIUkyVALARUDXVFREcXFxaYCuGHDBk477TT+9a9/VXVGTLiEKF8IaeKCdPv27WzYsAEgmuRBQtN1lkVd67GCKMsy/U8+GY/Xa3ovBTp37Uabtu3x+33Y7HZU1UjysmH9BrZs2VLbbgTA3qJlNP4/VBVvrOtIikIkfw/h/D2kpWccVtkHQ7J4FM8AmPHusVYeMdB+/fXXZibA2A3K4nyqRCWjrKyM4ugBs2IDuMVjcnmEKm4qKiri9i+ceOKJjBo1il9++YV169axe/duioqK2LJlCzNnzuSFF16gb9++phfSaFL1BC2SZMkjJIfH2AVBIBDAbrczefJkPv/8cyTJSF4lPMAPPfSQucdYHE4NxuTXvn17gsGgaawT+3c6duxoppF3OBzoYPGYRHkU14pxUCxMzjjjDNq1a2cuYKAqAkBE6MiyzJYtW3jiiSfMcKaTTjqJPn36mHyJOix5TP64KtYnu3btoqyszOx/wZf4e38QZ1aLegOBgGnY06JjrMWjkcht08aNqKpK02bNcLvdtU6IciAcLR6FIuNyuZnx4zS+/+5brr3+BgaecgoAK1esMPcgHw6PIinh+nXryM/fE9euA+1pTJyz45/F+Lt5i5YoNhsb162r8Viuo4FayaNmBFrGjoGi3Y0bNz7gMx8KpKgiHhvNKPjLycmhY8eO1ZIvib+7dOlCeno6UMW5pmmmtzq2DoD169ayY/u2aAIkFZvdjt/vp3PXbrRs2cooJ6oH6bpOg4YNObHfSahqxAyndTgclJaWsmzZUgDszVuiZGUfkTzWWnFMSUtFLSxELdiLZHeYYQuSLKOFQihZ2dgaNaltsSaxIhwuNqQtFArxt7/9jbffftu8NnEjaWIInCCzoqKCffv2AUQtTTL5u3eTtzUvTth0XcflctGhwwlAFamC8Lp169KmTVvCoXCUEGOSLi0tYfOmDeY9BxokzG+i8f+23FxsubnG4bixFhBZQasoI7xrZzTd7tFHsniMhbBiJ1p3xDlHNcWa1+TZEIIqBmLBm8Vj8nkU/VBZWWlmVQXIzs5GkiQz82bs72AwaHIlYvljrWmJYeMWj8nhMVFBB/jll1/w+Xymp1CEWg0aNAioki2xQMnIyOC5554zzwgT2VbT0tK4/fbbadq0KYFAAIfDwZ5duywekyiPYlEam1EVoEePHua+qsS+0nXdDHF6/fXXKS4uxu/3k5WVZe5tjF3sWvJo4I8aV8WYGqsQ/Pjjj4BhGAgGg3GKjuj32OPJwPCsiezVRBf6Fo/gdLnYvGkTTofTVBqOJo42j1nZWezNz6dp06aMvO56PB4v27dvY1vekfEojEpl5ftYtnRptbbWhIMpV5Jk1Nm0aVO8Xi9bNm82x5KjjdrK49HxJ1eH6K8dO3bw+++/m5+L/k9LSyM9Pb1ahmPxffPmzeOOJBN9HJtdFTCjKTdt2ojP5zc5lCVjy0jbtm1JS09HB2RFNsuRJCMBlKJUjenCS7llyxZ84TDORo1Q6tWDyOHLY60VR4/bS7ggH62iPD5GVpYhHEbJycFWpw7Rp4jrjANBPHhiyAxgkjB+/HhzI6r4Xty3e/fuanUl1isEd9fOnRQVFsYpNLqu4/F6qZdbv1q7NE0jJSWF3Pq5cc8sSRJqJMLWLVsI1jBpV3tG8V1U4GxZdbDl1o9u8pVEQ0ACPRQmvGsXjiQJYrJ4hColvlu3brRp04bKykpcLpd5llEkEqFPnz5kZGREi6+qXyj5sdYwTdNIT08nMzMzrh6Lx+TyGHttRUUFFRUV5sSUlZWF3W7H5XLhdrvjfot08SI8JBaJcfxg8QjJ4TF2b6PT6WTlypVMnz4dqEpEpmkaZ599Nrm5ueZ9Ijun+Dn77LOZOnUqF154Ib179+bcc89l8uTJjBgxwkzQARaPkDx5FEpdIBAwF5siJK5FixbR4qoWD+JHlL169Wpefvllc0HQrVs3zj33XHO/E1jyGItkj6sCsZ5ecXTGs88+y8cff4wkGcdyyLJshhuL/VTCQyE4a9SoEa1atTLLAotHMBbg27Ztw5vipX6DhsDhhykeCEfKo+jPli1aMnjIEC669DJatmxFYUEBWzdvoaSkJG7uPGwewxHytm4lGDw4jwd7n0V76jdoQEpqCrt27SSiRmrRa4eOw5FH0caaDKhHirlz59b4eUqKsTcw0Ski3ot69erFhbAKb25BQQGbNm0ydRtJkqisqGBbXl41JVhRFHLr18fhcKDFyJTgq0HDhubZyrH15+/NJ3/HDlz16iPn1IuG+x6ePNZaWp12G5V7C9BDQSS3x9xUSTRkw1anLrJwc0YF9FDIinX11qlTxwzHMZ6pqnNFBqtYd72qqixYsCDuczFpZmRkkJ2dHVdXfv4eKirKqwmO1+s13cgCgnCbzUZ6Rkac+1nXdSRZYvfuXQSCQZwxiVsSQ/LAsMRJ0X7RNRU5NRVbnZzodQlCqmlE8vegHMZkdChIFo+AGcPdsWNH/v73v3PXXXdRFj1QNBKJ0KBBAx588EFSU1NNl76uGxk6N23aFFeWsLw0b96czp07R5tjtMfiMXk8xoapgOFxLC8vx+Fw4Pf7aRLdhL5582ZUVTUzBDqdTtxuN3Xr1jUVithDcePCXjQNFMXikeTwKL4XG+oXL17MihUrzLMXhZz27duX9PR0fD7ffi2NAwcOZODAgXGfCR5EsgeLx+TJoxgnS0pK4vayde3alYYNjQVxYuIHcU8oFOLxxx8HjIy4DoeDs88+G0gwtFryaCKZ86Nxi3FPnehiVxjYREbqK6+8knnz5nHttdfSsmXLKm8ihidr/PjxceV07tyZXr16EYlETCXG4tHY41WQvwevN4U6dXPMZz1aOFo8CgOAqoa59NLLSI8a1X/4fiqbNm1EIn5LDxwJjzsJBII4na649yIRNfVT7PpafJ9dpw6pqWmUFheb6/OjjZrlUa+1PB6OcacmLFu2LO7/ghvBR011a5pGWlqaaaiL9R6rqsrKlStp2bKlOW5XVlayNz/fNPBKknGtw+EgMzPLvDexroyMDFxuN+XRNbeu6yiyTOm+fRTm76F58+bI2XWOSB5rrTjKsoxaUgSJG6QlAB0lKxvZ6zUIiumYgwmr6MyuXbsyaNAgJk6cSHp6OmVlZXi9XioqKmjdunVcfLAoNzbeWGj6QuFo0qSJuf9RCFFRcZGZlj52AHS5XDhjrAEmotd4Yg4+F4tqWVYoKiwkFIo/K7Km5zUtwhiTtGSzo2RnIdmUqMYvVU1QsoxaXFR9E/BRQrJ4FNeINP/XXHMNrVq1YvTo0ZSUlJCbm8utt97KCSecgM/nM4VCZJ/66quvgCoeRZa4Pn360Lp1a1MJAYtHo/jk8Ji4p6GsrIyKigozImDmzJksXryYn3/+mfz8fEpLSwHDE1m/fn169uzJiSeeSO/evU2PiFAwEpVSi8fk8QhVY+vSaIiSuEcsepo1a4YkSXg8HsrKyti9ezcVFRWoqpGOPzU1lYYNG+JwOMxzAlNTU6spKhaPyR1XwdjLJiJwQqEQjRs3plmzZohQtMTrwchIPWXKFNNg0KZNG0aMGIGmaaZxJ/Yei8fkyiNUeajatGlD+/btWbNmjTkXCu/ya6+9xujRo7nssss466yz6NKlC3l5eVx33XXmsUaBQID09HRuuOGGanVYPEIwGKK0tBRvirdahFNt+NofjgaPQi5btGhJ/3792LNnNz1792bXrp18NGE8Kamp6OhxMn20eTwcSJKErumkpqaRmpJK3t691Y7sOlqoWR6loyaPtcXy5cvj/i+4EYbXmt4xoVzWlNk1HA6zatUqzjvvPPO6QCBAcXERirk/OXpmqM2Gx+MVFVcry+X24HQ4KItZZxnJRn2URh1yUmYmkij3MOSx9oqjrqPu24eUqKnqhvYvp6YhOZ1GxdEOOpRJUZZlQqEQXq+Xu+++m19++YX8/HygKn78tttuIycnxwwFEOXOmzePuXPnmgILVYpjx44dad++PeEYS1z5vjIikfj0t7qu43S4akxTLP7ncrqw2cQzVbW7vKycUDBklnNIiF4np6aDYosnKvqdVl6WvIkxCTzGXiMGzUgkQv/+/enfv3+1axPj4b///ns+/fRT82X3eDz4fD5at27NXXfdBRiLYKFUWjwmj8eqYoxyKyoqMM72Mo5viD071eFwmIuTrVu3smXLFn777Tdee+01unTpwo033siIESNIS0sjFApV7WONNsPiMXk8Cuvzzp0747ISG0UbdbVt25Z9+/bx4YcfMnfuXJYuXcquXbsIhUKkp6fToEEDTjzxRAYMGMDQoUNJT0+P82yIciwek8ejUPLr1q3LQw89xPnnn8+cOXO44IIL8Hg8plEm8R5VVXn00UeBKmPcSSedRHZ2NoFAID5sKvrb4jH546rdbiccDtOjRw969erFmjVrcLlc5sJbRAkEg0HGjh3L2LFjadeuHYWFhRRGw0+FHN93332cfvrpMQfDG8dmWDxCKBjE5/OTkZWFNyU+ccvRUCpieezdu3eteVQUxUw8dvXVI7j1lptNb+OnH3/E0iVLGDDwFHy+ShQ5/pD4w+FRkWXKysqJhA+euOdQoOsabrcbb0oKoVCYQPSM36ONZMtjbbFz506zjtg+rClHh0Ci4pi4bUdEkujR60KhIBXl5chyVYIiXdex2+w4nNGTCmqox+FwYI9ph5DfYNAoD0BOSQOb/bDlsfaB5ZqG5g+gI1XFsgvIMrLHgyTL6JHIIW9QFR0izpzp168fkyZNMjfz22w2LrzwQi666CLzeuFpVFWVBx98MNq0quyNwip+/vnnG5XoOlJ08g0EAtHMnPGWOJv9wOe8xIbbQZRMWSYQDBzwQNUaIRR8jxfJZjfaF/sSShJaZaX5Eh11JIFHqLKwiGex2WzmQfBiEbtmzRq2b98ebYahjKxfv56XX37ZDHmUJAmfz0d2djYvvvgiHTp0MJWOWIuMxWNyeBQQz1FSUgJgJtrwer1UVlaiaVq18BS3222eHbVs2TLuuOMO5syZw/PPP0+TJk1MpUM01+KRpPEoUsHv3bvXDAMXCkQwGCQzM5OJEyfy66+/MmPGjGr3V1ZWsmvXLhYuXMjrr7/OWWedxd/+9jdOPvnkOOURLB6BpPAoJn5d10lJSWHYsGEMGzaMe+65J1qlFreAjF2QiCNxhCG1bt26jBw5Etj/IsfikaSOq2KOFPPYddddx5QpUygtLTUVEagKe7Tb7SiKwtq1a4H4xeqDDz7IE088gd/vN7PlWuNqFUKhEOFwCJfTZYRmaka4Jhy5R6omHidPnlwrHsX4fPWIEfTs3pW8vG30qpfLr7NnMfq9dznp5AE0btKUBfPnYXc4jgqPwf0kJjzQM+6vr3TAYbfjdrmJRMIEkxSqWqM8SpLxI0uHLI9HS5mMzTAPVeukxP2zB/LIx/5f13XTQSZGG1VVCYVDVdGV0XpkRcZuizcSxsKmyNgUJVpSFe+RSISQWF97PIbHMcYbHW3MIclj7RVHXUcPBUx3sKjM0PwlpANo3PsvMkabjgpcv3796NevH8XFxWRlVcXziqMYhHXtn//8J/Pnz48rT+zdGTZsGBdffLGpcITDYWMCVSPRwbUqxleSJBRFRj7AixXr5TQfHYNgVTs0QUyE5HSATTFe+ISy9VDIODg3GUgCj0YRVS+heB6Rwl+SJKZMmcL999/Pxo0bD1iOoij07NmTRx55hHPPPdfkG0xHlcUjJI1HqMq4uXv3bjPrl7ByV1ZW0qJFC3r16kXdunVRFIXi4mIWLVrE6tWrTcVfHEz+ySefUFRUxMcff2zuORYcWTySNB5jz/7buXNnXFQGGAurf/zjH4Ch8Mce56Cqqnl4tdPpRJIkvv32W2bPns348eM5//zzowuf6JmrFo9J4TF24abrumkwVVXVsC7HhH8nQngbhfexbdu29OnTJ97znwCLR5I6rorncDqdhEIhBg4cyOOPP879999vHgQe68UQ53GKz2PD4vLy8pg3bx59+vSpZsixeISIqppeP0VR0A3Vw2jTESoRsTwGg0EGDBjAY489xgMPPFArHrOzs1myZDHNmjbh8SfPY/fuXUz64nPyd+/mvffHsmjhQiorK3G73XFrq8PnMXLInsaD9pFuZPW02YyQ2MRsokcNpjxSJZNCcURCcjgPdHdMMUfmYRUQhvRE1CYqLxFlMXsSATRNJxKJSSgVvU9WZGRl/wmeJEmOKpsxXYXhHTaTijocoCjoNRwldCjyeHiprHS9xthawHQT1+xEPTASlUdVVcnKyiIUChmu+ejiRSgRX375Jc8880zcYkgojW3atOHZZ58FYkIiBSGqFp1oqx5DaOUcgHhJktCR4p7diOPXa/1C6tF7JVk2yqwJmn443ViLRiSPx0RUVlaSkpLCO++8w8aNG6lXr565x1FcL/bJgRE+984779ClSxcqKipISUkxjQYCFo+iEcnhURxSPGvWLKZPn45I66woCvfccw8jR46kXbt2cYuVFStWMGrUKF5//XUzQ5jYKzdt2jSefvppXn311ehhttHYf4vHaCOOPo9mGGl5OaFQyFzkCFRWVpqHU/v3E2YkMuSCoVyWlZUxYsQIJk+ezKBBg6JJdWwWj2Yjji6PiVZ/u90eF5aaqDSK///444/MmTMHwIwAOOecc4CqMTr2XlGExaNoRHLkMZZLh8NBIBDgvvvuo6KigieffLKawhF7byx8Ph8TJ05kxowZvPfee1x44YWmBwsw99n9L/MY296jGbqYyKPT6cTv93P//fcTDAZ59NFHTWPNwdq0d+9e46egkBP7n0RpcREvv/YGD91/LyedPID5v/8ed1afWX8tedSjn2majn6IHX5Qr6wEki4hKTI6HLZh4RAbYzyEphk/kmTseZQkMBWpZA4IVdifx/ZQzgjdnwwlevN1XTM9grH3Skim17wmSFGFOrYa4a3UoseYcKB35xDk8fDyEpuafg0wO/TQhTTRSyWUR1c0e5fD4YjL0Oh0Opk1axa33norkUjEJEtk+MvJyWHs2LG0bNmSUChUzX2sKHK1wVmSJHRVR5wPUxM0TTdDLQR0arbsHPSZxf2aVj1uW0CWatONtcdR5jEWsQMcVIVFiWx++fn5lJeXs2/fPkpLSyktLTV5kiSJ0tJS7r//fj744ANSUlKiiobFY82NSA6PQq6EJUx4/ps3b869995Lx44dCYVC5uHTwWCQTp068dprr3Hrrbeae5EBU/H49NNPmTlzJna7nUDA+MziUTTi6PMo+lT0f02hTZqmEQwGSUtL48orr+TZZ5/lxRdf5IEHHqBjx45xngwREldRUcE//vEPSkpKTNm22RSLR0jK/Ch+J46rArH/F3L7+OOPm3MpGGeMXXzxxXGfxZdh/LbkUTTi6PKYqGyI/nW5XHzzzTfMnj0bj8djGkgdDgcul+uA+6Y8Hg9FRUVcccUVfPPNNyiKYm4dUGSLR9lcYNde6T0QauLR7Xbzww8/MGvWLNxut3mswoF4jH0Hdu7cyaWXXcaOnTt5+snHGTL0dAAikXBMgpSY+mvJo1AeZFk2va6H8pwJDa7xGUQCHlnaf+jsESN6vAs2G5InJrMqHPF6tbZIS0uLb1q0nw5FcdwfxFEe4gkk01gQX4+ug56YtCsGBh963NAljAay6ENN279R7BDksfYeR0lCcrriKxU+UV1HP8xsTTUpj6LjBITVdMmSJVx33XUUFBSY14pELI0aNeLzzz+nb9++5vXC6mU+tN1R3T2r66iaut++BNA01WhjggVAsR041vxA0INBiFR3F4NuhHfISRLEJPGYyJv4LQbNa6+9llatWrFr1y4zdjscDrN06VLefvttk8+dO3eyc+dOfv75ZyoqKrjtttuqhVdZPJI0HqEqG2fnzp1JT0+nsLAQgPPOO486deoQCASqHd8gogGeeOIJvvzyS/PMOV3Xcbvd7N271/RUadHB3m7xmFQeYf8WUiFv3bp149lnn+XUU0+NS5hyzTXX8PTTT/P555+bkR3iSIdZs2bx0UcfcccddwDgcDgtHpPMY+K4mvi3SOX+ww8/sGTJkrh7W7VqRYsWLeLG0bhyor+tcZWkz4/idzgc5qWXXuLxxx83PQ5iLZMY9udyucxILAGfz4fb7cbv9/PYY4/RtWtXGjVqBFjyCEZfGlFoYSLh6mGyh4Oa1jjhcJiXX36ZRx991NzOURseA4EAKSkplBQX88233/H++++TkZFBJGK0226zocZ4n46IR7vNzPdxqM+a8GFcmaqqEQ6FTSU5KZAkI/mNiAIQDy5JoGtHPK7WFmlpaWaCnFgI7gXiIzoOrFyKrL+ib2VZNtZguoYkK8a+Q8nYU6tp+ydeS/RUSpLxf1lGEQntQiGoIUz1UOWx9h5HWUZ2uwzrUWKlqorm8xkPKMu1dhonKopQZZERceorVqzg6quvZtOmTabiIRJ2dOzYke+++46+ffuaBxtrMZq1Fm2Rx+0yM1TFkhpJEOZECGEXlhsxAbhdLhz2WgpM9DE1nw89EgYp4TBWXTc2lB+igNcaSeAxtj9jeQsEAuZvh8PBaaedxsiRI7n66qu55ppruOGGG3j11VcZN26caRED45wiVVX5+9//zsyZM819qqJPLB5JGo+AmYiod+/efPHFF9x+++0899xz3HnnnTidzjgFQ0BEBmRmZnLFFVcYE1V0sSEG1WXLlrFr1y48XiPLncfjsXhM0rgq+lMYAWqyuqempvLKK69w5plnYrfb8fv9+Hw+AoEAHTt25IUXXqBx48ZxE54o99dffzU90g6HA1s0u67F49GfH/eH2OcT/fzPf/6TUMjIrin2VQ0YMACIVxZj7xV/WeMqSeExdn0j+vHll1/m4YcfNo3cQtlo1aoVY8eOZcqUKdxxxx00bdqUQDSpSaK3WEQBLFu2jA8++MD83OPxmNmu/1d5dDic2B0OAgE/gaC/mgf2cJDozNB1nZdffpm//e1vRCKRw+axoqICr9fLjBkzeOedd0hPT2fcmDFs3bqF9IyMuOi5w+VR03U8Lre5L/1QntUIjzTGh8SekySJUCiI3+/HZrfjctdwPMjRgJBHSQJNQ49NwqPqSRlXDwRxbqeAeCeEkaCmdyxRcYwLO5akamdAiiPqNHFd9DtNVQlH9p+kKhKOEIn5XvBvRHEa6zPd50MPH7481lpaNUlCScuoHq8uGa+WVlaGHgwaDx/TMbVBolVOZONcv349I0eOZOXKleb5NCI8ddiwYXzzzTd06tQpzqIqwis0VTXduympadiEUmk2XyIYChIJx6evFt8B+P0+VE2NG4RVVSUtPd1Mj3vIFq2oy1grLwU1YriH4/pSQk5NMw8zPdpIBo+Cs0TeXC4XkUgEl8uFoij4fL64H7HPasSIEXHn2IgN4fv27WNU9PgHsWcOLB4hufIYq8QPGTKEN954g4cffpimTZvGlRO3+NR1M5mKOIJFlCF427RpE+vWrTMVytS0VIvHJI+rQsmPXWiIpCrXXnst/fv3JxKJmJ5hj8eD0+kkEonQuHFjrrnmGqBqv7jwjixZsoSVK1cadbjdFo9/wPyYiFhjnd1uZ8GCBeYB1eI7t9ttyuP+wpU1a1w1kUwexfELP/zwA3//+98BzOPIIpEIQ4YM4csvv2TkyJGcc845/Pvf/2bq1KlMnDiR888/v8bMpsIoN2fOHHbv3gUYnP+v8+h0OUnxevFV+syslQKHK3fiPr/fj91u55dffuGRRx4BjpxH8dmKFStYtmwZn378EaWlpaSkpsZxdqQ8Jh5Cf1DEvOexhhRJkvD5jTWc0+nA5fbsp4AjQ5U8RutXFONHlgEtKePqgdChQ4e4/4t+rKys3O89iUajWCiKQtu2bYGqpIF2uwNvSoq55ceMUoiEq7Kj1kBfMBggGAqZ30mS4aV0OZ2kRENs9Yoy9COQx1pLq6ppKFlZMZvDAfSoKUJCLS5Gq6yIe9FqEx6QqDSK0JsdO3Zw3XXXsXjxYvOIB3GW0f3338+HH35I06ZNzcWPELyFCxeyd+9eZEUxNffMzEycLpcZl220EfyBQLWjBWLbX1lZiRoNt4htX92cujgPIauTudAGwzISiaAWFaNH1HjBBNA0lOw6SRtQk8FjbAiH4O3DDz+kZ8+e9O7dm169evHJJ5/g8XjifoRnUVVVLrvsMqBqcSMsOKtWr2b16tWmEgoWj5AcHmP7RXgrFi1axOWXX07fvn0577zzmDdvnjkRxZYXa9Ft3LhxXH1iAVNaWkpx9CBagMzMbIvHJI2r4hqn01ktWYNQAk877bQ4Q1zsvZIkYbPZ6NOnD1All4LL/Px8M6lVekYGLpfb4jGJ82MihKEOqhadr7/+Ovv27YtLGudyuejcuXOcMSixHLHSsMbV5PGo67qZEOeTTz4xE4cJnnJzc3nuuefo3Lkzfr/fPGuzffv2XHLJJYwbN45vvvmGli1bxpUr5sTVq1ezYYORsTw1LQ33/7g8OhxOMjKzqKyspDSaCVM/QrkT/eJyuQgGg3zwwQfGwvwo8Cju37Mnn1de+g/r1q6madNmhuIQ4/0+Eh7rHCKP1ZCgiIky9+3bR3l5GSmpaXg9yVEcq8mjrsfs0zv64+rB0KVLl2gzjLrEmCqO6ajpHVMUhbKyMnOcjlVsHQ4HXbt2NcqKPqPL5SIjPSPO8CPLhuJYGT0nlETDFkb4eigYRJKqlE1VVUlJSSEr2/CUaiUl6JHIYctjraU1FA5jy6lrpL81JxsJdOPgzUhhAdq+UrMRUN29fSDEvuSxL+ZNN93EnDlzzLBUMMgaM2YM//znP8nMzKSyshKbzcbKlSvp27cvF154IYMHD+brr78GqjT9nHq5ZpbOKmIlfBUVcZk9Iep1kYxsksXFxWhioSzaCDRo2Mi06B/oZTW/i8YbaxXlRAr2Rl+ghPtkGVu9XNQkvfzJ4DHWXS/LMqNHj2bEiBEsWrSIFStWsHDhQq644go++eQToHo8uKIotGvXLq4sITR5eXnm+UdC4CwekyePkmRkL7bb7fz444+cffbZfPrpp8ybN48pU6YwaNAgfvjhBzMULhaCH7GBPLYvZVnG5/OZGTw1TaNebj2LxyTxKCa0OnXq0LRp0zhFX8hf7HFHiRBjZt26dYGauRSJd3JyckhJtXhM5vyYCKHcgzF+lpSU8Ouvv5rfCdjtdpo1a1ZtzI0tRxgFrHE1eTwKQ9yKFSuYOXMmgLmFA+DGG2+kZ8+ehMNhXC6XqYyEw2FCoRDp6emcddZZjBs3jpSUFFO+hewWFBaai9e6dS15lGWJern1qKgoZ+/evdFmxYcKHg4Ej8uXL2f69OnA0eXRH/Dz+9zf6NGzF6cMGhQ1+CgxvXf4PDY6RB5rghRzj2hrYUEB5WXlZGdl17h95WigmjxCVXbVJIyrB0OvXr3i/i/6whdV6GINsOJ7RVEoLCyMy2ou+tJut9O5c+e4kwM8Xi/1cnMTPI4y4VCIkqjhPdZQL1BcXEwgEIgxFkloukZmRiZ1c+tDeRlaUeERyWOtFUefz4+9bi5KaqpJEGD8bbej7s0nEk2iYWr+tSg/Nh5fCNqtt97K1KlT45TG+vXr8+OPP3Lttddit9vN8DiANWvW4PP5yMvLo6yszCxPkNmwYUOys7PjBhBJkqisrGTnzh3V2yNLlJeXsyu6GVZYZ3Vdx2l30LxZC+wORzXvy/4f0qg3UlREZM9uJEWJDwPQdSSHA3uDBoTCNU/0R4pk8Sj2aqxfv57XX38dMEJmJEkyB5VXX32VPXv2mPsAjCqMOsQiNnZwkmWZ8vJy8+wcscCxeEwej0Kedu3axfPPP8+ePXtMr5XD4cDn8/Hiiy9SWFhoyl/1tvmi1VYNbJqm4Xa7SU1NBSAcCdOggcVjsngUspKbm2uGwsR6DZ1OZ40ZNhNR0yJL0zRSUlJMLhs2bGTxmOT5cX+IRCIoisIXX3xBfn5+tMqq+jMzM+OuT+RTlmVrfoxBsngUfVpQUMD27dtN2RMy2bNnTwCzr8T8Z7fbzT3+kUiE/v37c8stt8RFCciysagUddRv0JDs7Dr/0zxGVJVmzZrj8/nYtSvmuTgyj1SyeBTzaCAQIDu7Dtdcez19+/XH7XYjy/FbgQ6Xx6bNmteORwFJMj2egHlI/K6dO6moKKNBo8Zm8pWjjWryKNqS5HE1EaK/OnXqRLNmzczPYzPQFxcXV0sSJL7ftm2baTSPjRYZMGCA+Q7JUa5SUlJo0qRpTCnGtaqqsXPHdgKBgLmHObZt27dti1NOoy2nfm4uOQ0bEtizBy0/39jHeJjyWGvFsaK8DCW7DnLduuiRMFI0hl3XNGSHA7WkmPD2bbUt1igjRmkUE+Hdd9/NJ598Yu5l1HWdrl27MmPGDE455RRCMQOlLMsEAgGWL18OGEKcmZlpbmS12Wzouka93FyaNG1qCqKw2gaDQZYsWgRUEa1GFZu9+fls3LDBFDgRJptdJ5uWrVpWa/8BEc2IFNmzm8iePUh2e7XJSU5Nxd6gIT6/bz+FHBmSxaNQBNetW8fy5ctxuVz4/X50XScQCGCz2di8eXO1w04FDiWdsbC+WDwmj0fhRVy2bBkzZ840z/8Th/za7XaWL19uhpzGLk6ExSw2oypUeb/S09NNb6SmWvIIyeNRURQikQj169c392WIxY0sywSDwRr32giI/tu3b5/RngQu69SpYxp76jdoQOMmFo/Jmh8PBDHuTpkyBZ/PF53rqsbWRMUxMbJHfBaJRCx5JHk8in4T++0S9wwLI4xAYn+JfcmSJNG7d28g3hAUO7bWb1CfJk2a/E/zGPT7adGqFeFwmO15eUet3GTyCIbCefmVVzJg4ECys7Np3rwFsaGHh8tjZlYWnbt0Ne45iOJ4MI+s+D5v6xZ8Pj8tWraMS8pyNHGk8hjrfTtSiHDzCy64wPxM9H9RURHr169HhIcmfr9y5cpqc6kkSeYWregHZn6Qlq1b43a7TW+jMDCsWLHCDL3WVNVUQlVVZfmypaZSL9rhcDpp1bIVDkkisHMH6t58sB2+PNZacSwvLUVJTcXRrLkRIys2V+o6KApaRQWhTRuNhsdowwdD7GAkNo8/++yzvPPOO+ZB4gBDhw5l6tSptG/f3ugQhwNFUZBlGVmWqaysZOHChUDVAeZCiI0Yb2Nh27lLV1xuT5zVRdd1Zv3yEwUFe01NXsQbL164kE0bN+B2u81zzQKBAO07nECTqOWhtq9kePMm1KJCJIcDPXpwqiRJ6KqKrV597Dm5lCWEIhwtJJNHqPI2xe6nEYvYRo0a4Y1m1EwUaHHkQ6xQaZpGamqqma5YlmVjwLB4TBqPiWc4xk5qIjw1MzPT9PLHQkxS4jgAUZYoo3nz5rRo0cK8FrB4TBKPULWY6du3L1CdD5FWPHFSFeE1uq6zcePGuHvFQqlTp060adOGSCSC0+mkU+cuFo9J4vFAcDqd5Ofnm+H8iUgMIUvcDhL7uTWuJo9H0Zdi3EyM1DgUb5iQwcQsjGBEFghDucvlotP/OI8VFZW0aNkKh91O3tYtlJeXHfaRIrFINo8Z6en0PbEf2XXq8sPU78jIzCQ7O5ugSADD4fF4QscTqrYdJITH1vSMNRmXYi5A03Q2b96Mpmm0a9eOyor9J4c5EtRWHjVNM/NmxCZUrCkySjyb+B17raqq1UKbRZ9ceeWVAKZ+IkkS27Zt47fffgOIO4ZF/P3LL78QCASqTnzA4P+cc86JG4tFXW3atqVxkyam0yUSieB2u1m+bCnLly8FqniUZZm8rVuZ//vvKDYbIs9IKBQiOyubrt27ARDZcuTyWGvFsbKyAtxuHG3axcT+xyO4djVqYYHxQIfgQUpUGp1OJx9++CFPPfVU3DVDhgzh/fffp27dupSVlREKhczMnGVlZaiqyqpVq/jpp5/MiTItLc1UOGL3cfQ/aQD1c+sTiVRlpbLZbKxds4Yxo0fh8/kIh8MoisLiRYuY8MFYM65dTK46cPLAgeTk1DNikw8wIAnZ1HUdbDa0YJDg2tXofp+x4VdcoCjokTCO1q2RU1NNq8LRRjJ4hKqBpaYsjuLvW2+9lYYNG1ZLxqHruqn0J3o2GjRoYIYGyLJs9rXFY3J5FCEXsZ5gwePVV19N48aN0XXd5FHspwkEAkybNi3uXnFNx44dadq0qSnrYPGYLB6hisM+ffowYMAAsx9FOMvkyZOpqKjAEbVOC4j9FpWVlfz0009AFfexIVkpKSlmWf1POtniMUk87g+RiHE+3axZs0zDW2LkRuJiOdZgF7twtOZHA8niUcxnGRkZpKSkxJ35B1VRGvs7ZiF2rbRlyxag6qgygO7du9OhQwez3P4nnfQ/zaPPV0mjRo3IqVePbXnb2LF9e1zSqMNFsnns2rUrnTt3xufz88Zrr1K/QQOat2hJMBAw6649jzoDTjmVujk5plIBh++FUxSFvfl72LJ5E6mpqbRu04aysn2HVdbBUFt5lGUZRVHMH9GvNe3BTNzKJq4R98YegRLbV506deLSSy81ORZe5KlTp5r/V1WVcDiM1+tl9erVLF68GKiaWwHuv/9+0tLSahyHW7RsRc9evU1nmrivorycse+PZtPGDaahvri4mHfeeoPt27fFcauqKm3atKFH3xPRQ0HUjRvQfJVHJI+1D0jWNAKahqvDCchOd/x3qorkdBJat5bQtjxsOfXMzatw8BCHUCiE0+nk999/Nw9SFZkAJUkiFApx8803U15ejq7rhEIhwuGw+VNZWcmuXbuiTYmmFk9JqWbRiUQidO7Shf4nDyAvb6tZv3D1jnr3HVavWkX3Hj0oLipi5ozpbN282Ty41eFw4Pf5aNO6DaecOsgkLnFDbGy55nOrKpLNRnjXToKrVxkHbdZg8XG174judBGorKj23VFBkngUwpCdnU1mZqa5LxGMQfb5559n5MiRpgVGeBSdTic+n48JEyZEm2cM7EL4TjjhBHr06BF31IrFI0nnMTc3l5ycHDOxABhhb3/729+44447TIucCB8W9y1fvpyff/7ZtJJJkoTf7yclJYWhQ4fG1WXxSFLHVUVRCAQCNGnShHPPPZdZs2Zhs9nMyejDDz/k6quvZujQoUQiEXO8VVUVRVHYuHEjkyZNMrm02Wz4/X7at2/PWWedZfRPNPmDxWPyeNwfRFjTokWLKC0tNcfUQ0VsnaLPLR6Tw6PogzZt2tCvXz+mTZtmHuEA8NVXXzFs2DBSU1MJBoNxETuiXIfDwe7du825Mlbh6NevnxlyrCgKnTr/b/Ooqka0UvsTOrJg3u+sW7eW9h1OiFu4H47c1cRjrDFu0qRJR8Rjnz59sNvtjBvzPnN+ncXlV11Fl65dWTB/Xpzn61B5DPj9tG7VmtNOP9PYCxsO73dv+6F4S8XcsGHDBrZv306Tpk3JbdCALZs21aofDxmHKo95hjyWl5QQ0nXzDENN07Db7eRFw5VjlTTxnNu2bUPXdfLz8/F6vWiahqZpeDwe3O6qOoVO4nA4ePjhh5k4caLp2ZMkiZkzZ/Kvf/2Lhx56CKhSAp955hk2b95sZjDXdZ2GDRtyxx13VGuL4CgtLY2hp53BD1OnEggYx7+Ew2GcTie/zprFrTffxCmnnoLL6WLunDksXLggbi2mRiJ4U1IYOmQo2dl1qNywntDqVaDqUINR41DlsdaKo8fjoaS4mLqt26DUb4C6aweSzYauquiqiuR0Edm5g+CKZXh69o5L6ypJUjVrgRAiQUR+fj6PP/4427ZtMztJXDdr1qyDti/2vBOoWXEURF5z7XUsnD+PtWvX4PV6zaM8wqEQP/7wPd9/9y2SJGG321GiG5dtNhsSEroEl11xJSd07BRnvTnoIBR9YYPr1xLauB7J7a4iMNo/cmoark6dqQwGkKXkpKk+2jwKiMGoU6dODBo0iC+//JL09HT27dvHbbfdxjXXXGNag2Lh8/l47bXXmDt3rmkRFHtWHQ4HF154IRAv8BaPyeexS5cunHbaaXz44YdkZGRQWlrKfffdx4MPPljNAiewZcsWbrvtNqBq75XY63ruuedy0UUXmUYisHiE5PEYu0gBGDlyJD/++CM//PCDuW8V4J577uHjjz8204yD8Q7s3LmTBx98EF3XTS5FyPmVV15J586dzVTzQjYtHo8+jwdC7PmoYPAmFjGCk8RkDTVB9LElj8njUSgX9evX58wzz2TatGm4XC5zX+rEiRPp1asXt912W9xiNRa7d+/m//7v/5gzZw5gcBsKhTjttNO4+OKLAeK2EPwv8+h2G8d39enTh2lTv2PFsuWcf8FFceuI2iBWRgSPw4YNMw0AQqGaOHEivXv3PmQexRgdDAYZNGgQV111FSuWL+eDcWNo06Yt3bp1p0ePXvzy809s2rgRj8dz6DxKEqquM/zKEbRq1bpapFciauRV9Ff0O/EurFi+lIK9exk69HTsDgeadnRC7xNxKPKo5+9m34J5eHr15r4HHuDzL78kMzPTNMqEQqEaIzKEjvHGG28watQosrOzzWNyCgoKeOqpp7jvvvuqGdh0Xadz584899xzPPLII6ZCqGkazzzzDJIkMXz4cAKBAM8++yyffvpptAuNcVlRFN555x0yMjLMSC2jq+PH4VMGDeLsc89lwvhxpmdS9P/qlStYvnQJoKMoNjMEVqyxK/x+Th0wkAvON/Zj6ju2E164ELsvhCZVoHnc6HLt5bHW0lqnbg579+zGVr8hzo6d0EKh+DM/ZAktEsY/by5qcRGSoqDHaLaJr1VsHHUkEuHZZ59l+vTpZuap2iIxHtnr9ZKRkWF6PUSdqqrSsVMn7nvoIRo0aEBlZYWZWU6WZZxOJ16vF4/HY34mXNEVlRVccMFFXH7FlXGKak2DabW4ZZsNLRzGv2AealERkt1etZFVUdCDQezNW+Bq1YbCwgJS0uI3WR8tHG0ezduiCTfS09MZOXIkkmQcp+JyufjnP//JySefzJgxY1i4cCFr165lzZo1/PTTT9xyyy3mIbrixRfKy4gRI7jiiivilA2weITk8CisVcFgEI/Hw0033URqaiqlpaU4nU4ef/xxBg8ezEcffcTixYtZu3Yta9euZeHChbz22msMGTLEPIBccOD3++ncuTMPP/wwQLUFiMVjcuRRPKfgMzs7mwceeIA6derEee9Xr17Neeedx7hx41i1ahVr167lhx9+4MorrzTTzQOmsnnhhRdy9913I84vA0seIbk8HgyxB5yLfhPzYOx2jf3Bmh+rkKxxFaq8VZdffjmnnXYaPp/PVAQAHnjgAS6//HK+++47Vq5cyfr161mzZg0LFy7kzTff5PTTT+ett94yQ/BCoRApKSnceeedZGVlmfvgLB4hMzOLfaWl9OjVB7vTwfJlyygoKMAekzzqcEM1BY8XXXQhZ599tundE8lo9sfjggULePPNNznttNN46623DEUvqoimpKRw7733kpqayrvvvMXGDRu48557adOuHW3btePRx58kt359KisOzqMtymN5eTkXXXwJV119DbISz2NNSNzLZ44jCYpTIBBg6eLFhEIh+p18EhVl5aSkphxWXx4MhyKPejiMb56xv7AiGGTfvn2UlJRQXFxMSUkJlZWVeL1e3G43LpfL/C1+3G43iqKY15eWllJZWWkmd4yFiOpQFIV77rnHDFnVdR273Y7P5+Ohhx6iadOmtG3blg8++MDMqCsiKV944QXOPvtss7xE2RKGiNTUVG6/8y4GDR5MebmRc8Jut5uyapyH7sXhcJiGA3ESQZu2bbn73vuom1vP4DR/D9qOvdhDKlKFHzT1sOSx1h7H1LQ0tm3LQ/d48PTpS+V3U4jbLh2JILs9+BfMJ7h+HZ6+/eLCOGoS0tgQ1TFjxpCRkYHf74+z1MS+6KJzYz+L7XRVVfF6vRQXF5OVlWUKpRB0oe2rqso5556Prum88tKLrF2zxnRpG5uNAQyhCYfDaKqK2+vl0uGX87eHH6VO3bqmhelAg6qJaPhGaMtm/L/NQbLZ4vdGSBKEw7h79kauU4e9ixeZSUSONpLBo4DT6SQSiXD22Wfz4osvcv/99xMIBPB4PCxevJibbroJwEw/LTgUE5Zw+weDQc4//3xeeOEFoLrV3OIxOTyKz4SScPLJJ/Pqq69y3XXXmcrkzz//zM8//wxgWjWFoUdYT0V4eTAYpFOnTrz22mt06tTJVFhi+9riMXnyKPrB6XQSCAQYMmQI7733Htdffz0lJSXY7XYkSSIvL49rr70WIC7EWExEwWCQYDDIkCFDeO6550hJSTG9XUKGLR6Tx+NB645JAhc7L9psNtq0aXPQ8i15rEKyxtXY+a1evXo8/fTTbN68mY0bN5qyJkkSkydPZvLkyYBh/Pb7/XFeCa/Xi8/nQ1VVPB4Pr732GmeffXY17///Oo9ZWVnszc+nRYsWtG/fgTWrV7Jy+XJOHTwYNSbs9lDCVROfXVzvcjpp37Y1v/2WZWYZVxQFh8MRx2Nqaip+vz8ucsPj8ZiJBNPT03nxxRc5++yz+fijCXw7ZTLDzj6bYWefiyIba9gzhp2Fruv8+4XnqvEoII6xEzxedvkVPPmPZ8jMzDRDjQ/0vDV+HvNZlbdxOStWrKBB/QZ06dqNoqJC6jdocCi01BqHKo+V8+fB9m0UlpQCVdnAqz9OzYl/YhVEEY2TeFYmxK89XS4X77//Ph6Phw8++MBcB7ndbtMDGA6HzfKysrJ49NFHue+++4AqR0ni+xVbR4uWrXj62ed54Z//x4zpP1JZWYGi2My1l4CqqkTCYWRZoVuPHvzt4b/Tt09fIn4/NrcNbes2ZEBXFHSbjC5JRi9GaiePhxWqapMVisvKSO3Zm+IGDdEKCkyLm65pSA4HkT27qZz1E+4ePZHsjrgXNfGlFWQFAgFTiGqLxJdAkCTc1CIuGKhGyrnnX0CLli35aMIHzJ0zh927d+Hz+QzLgCRhdzioW7cuLVq25OxzzuPS4ZeTmpZmCtD+BtNqwhn9279gHsG1q5E8HsNKKUlEg7GRvF68/U8mKEmooTCpKcmxxCWLR/F/MTjdd999OJ1OnnvuOXP/qd1uN7kQ2XCDwSCBQABdN+L209LSGD58OM888wzZ2dk1WsksHpPPozACXHvttWiaxmOPPUZ+fj6SJFU7A1BYzX0+nxkql5qaytChQ3nhhRdo2bJljUqjxWNyeDQeMb6PXS4XwWCQCy64AKfTyRNPPGFu2JckCa/Xa/YhYPa7rhspyC+55BJefPFFcnNza1yEWDwmh8cDQRjULrjgAiZNmmR6oDRNM88MO/fcc4H4LNeJsOSxCskcV4UsBYNB+vTpw8SJE3nwwQeZNWuWuehUFMXMPK7rOh6Px/TyiHwOAD169OCJJ57g3HPPJRQKmUqjJY8G3B4Pmq7hcDgYPPQ0Xvr3C8ybN5dTBw+utpY4VCRe+9WkSYx+520uvOQyyv0Bvvz8czPvhlAOI5EIlZWV2Gw2UlJSTEXC5/NRv34DGjUy9rpdffXVrF61io8++ABvipebbrmNzMxMc/0aDoc586yzadK0KR9+MN7ksaKygkg4Ys7LBo+tOOvsc7jy6mvisqseivKf6G2MVZSFR3Xe77+xZfMmzr/gQrKzsinI30tmZtZhsHRwHIo84nJB/h7UH75lQM8eRHyVpKakEEnYy1eTdy/2uaHqjOOSkhK6deu233lVyFZKSgpjx46lY8eOjBs3jq1bt5reSnFtbm4u7dq147HHHmPw4MFmPyZur6tpHNZUlTZt2/Lvl1/ls08+Zuq337Bly2bKyvaZOo7NZsPr9dKgYUP6n3QyI64ZSZu27YyQZZeLSHk52oaN2ABN0o0QVZH4x1M7eay14miz2WjYuBHbt26ha9u2uHr2oWLS58hpaVUx7KqK5LBTOe170s67EGfb9sZ3UetOIgnCWtKgQQMuueQSU0BqK8yxHkeXy0VhYSHDhg0z2w3xG39jie/YqTPP/etF1qxezcoVy9mxYzuVFRXYbHYyszJp0aIlXbt3p27dHLOcRCvBgaCrhks4kr+Hiu+/RQ8FkZxOM3ZcUmzoAT/Orj3wdu7Gtj27SU1LxVlDFqijgWTwaD5rzEATDoe5/fbbOfnkk3n99ddZsGABmzdvpry8vNp9KSkpNG3alA4dOjBixAjOOeecuPIS+7qmRY7F49HjEYz+Ed6n66+/ni5duvDyyy+zaNEitm7dWiOPaWlpNG3alDZt2nDllVea5x1FIpFqSqOow+Ix+TzGeh5DoRDDhg2jV69evPDCC8ycOZNNmzaZx68ISJJEs2bNaNeuHVdddRVXXHFFtClqtUWIuN7iMXk87q9OVVUZPnw4s2bN4r333jPDVt1uNzfddBPdunWr5jXaHyx5TA6PiXOYkMNu3brx/fff88477/Dtt9+aCUcSZREMI0Hz5s1p1aoVgwYN4tprryUnJ6dGg1xsnf+rPCqKQk69epSWlnLKqYN46/XXmDvnV7blbaVJ02aHnCQncQ0ilLDFixbx7ltv0KVbN/7+2OO0aNmSu+rWZf2GDRQUFLBs2bK4OTIUChEKhZBlmW7dupGTk0NJcSEvv/IyPbr3IBgM8NWkL5j3+1yeevr/6NW7j+lVBMPoHolEOKFjpyiPq1i1ciV79uzG7/Nhs9tJTU2labPmdO3Wjbp1c9B13dzXeChKYyJilUYwZKOkpIQ5s2cTDAY486yzqKysxOP1HNJe6sPBochjJBzGlZrK1o8m8PgXU3j8H/9ISltqki1dN3IA3H///YwcOZKvvvqK1atXU1hYiM1mo3HjxvTs2dNc08ZuE9kfF7FcydH9k1lZWdx6+x1ceNHFLFmymC2bN1FSXIyuQ2pqCg0bNaZT5860adsOqEpipPr9+L/5htDcuYaHUdfB5QCbDd3vx9mtdvJY+6yqgNebQnBrHhGni9RBQ/FN/YZoTxoPrKpITjeh9euomD4NR+u2xkbW/Wj4wp3brl07Pvvss8Np0gERiUTiLHGxVhQgblNr+w4daB89KHt/ZQn3cE2DdOxz1fQy+H6fi3/+XCRvinEejbhGkkDV8J46GKlePXbP/Y227dodxV6ojqPJo3Fb9T4RezA6d+7MqFGjyMvLY+HChWzevJnCwkJ8Ph8ul4v09HSaNm1Kt27d6NixI1AlXInlxlpTLR6TyyNUKY+hUIiePXvy0Ucfmaml8/LyKC4uJhwOk5KSQlpaGs2aNaNr1660iz53KBTCbrcfUNGweDz6PAok9rHweITDYerUqcOLL77Ijh07+PXXX9m8eTN79+5FVVUyMjLIycmhe/fuZrZGIZM1eSAsHg0ki8eaIO4RxtfXX3+dk046iYULF5qL06uuuuqQlUaLxyoki8dEORTbNe644w7uuOMO5s+fz9q1a9m2bRulpaXmtp3U1FQaNWpkZhgXWzvE3rqalDuLR8jKymb9+nW0bNmKkwYMYN7c35j72280adoMVY0gy45qzxWLxHFOixrN9pWW8t47b7F7924efPjvNGveDFVVyc5Ip1vnjpw6eChb8/LIz8/nl59/4ve5c+nWrTtDTz+devXqkZ2VxZJFC5j9y89kZ2fjdLmYMf1Hxr4/mlMHDeaiSy4197nFtkUcy2DweALtO5yw32ffH4+H+n4mXifKmz/vdxYumE+HDifQrXtPCgoKaNiwYa3Hr9rgYPKIqoLDibZ5E7vGjSHtxluQlGg238Nsl5DR2MiqmuZTSZLMtW5WVhY33HBDjeUJo8HBlMaa6jAMHDqRiEq93FzOOHPYftsdiUSQbTYUXUcHIps343/kEdS8PBRJIqJG0GXZSIClqXgH1U4eD0txTEtPx+P1smv3Lpqc2I+STp0JLFqI7PWii4Q2uhHvXzbpc1JOOwNn67ZxewASO0gIiAitOVJIkrF51eFw4HQ6D7rIES5jcXBo1Xc6ug6yLJkxxVA9NPaAFoMYK1zZl5+h+XzIKalm+IYkyxAKYWvcmJRTB7PP70cNR0hJSc5GY4GjyaNATQOOmBxDoRBNmzaladOmB2xXIBAw9wjA/gcyi0cDfwSPYgCNRCJEIhE6dOhAhwMsPAAzSUNNPIo6YuuyeDz6PArUNBGJhaff76dRo0YMHz58v/eLYzpEiHlNMmnxaCCZPCYisa8kyTiYWhxODdQqRC22TIvH5I2rsf0jzosT817v3r3p3bv3AdsWiURMhVLIcU39bfFo7C2024yF//kXXMT3U6cy48cfOe2MM8nMzKw2z+1PRszPox7Kb7+Zwn8nfcHwK67i9DOHIcsKU7/9hgkTxpObm8vNt97GaaefDsCOrZvZvX0rzz33T04aMBCAyy6+gGnff88nn0+ibdt2bN++nXffehMJnZtvvY36DRpUOzYjdr0seIxEIuiaZiZkkiTJTJxTE48HU+4OZCyQJIlwKMTMH39k+/btXHvDjXi8XvzbtpGVnZwwVYFDk0cdXZKI/PAt7gsvQmne8qi3oyYdQvwWa51gMIg4qiyWj8S10IE42V8dYiwXW0gS311FUeJ5B0LLlhHIy8Ph9RKqrCSUlgJOZ1Qem5JySu3ksdZZVcUZX02aN2Pbho2QWx/vaWdWPbgkgRhEnE7CG9ZR9uVEtHDIiEdOOF8qcQBNSUk5Kj9er5fU1NQalUZBSk1kiUxFwspgtztwOBzYbPb9Di41CmLMSyHiiCumT8M3ZxayN8V40cV9soIWCOA5dQiujp3ZvG4tLVu3wm53HPFBtfvD0eYx/tGrXngBRVFwu91omkYwGMTv91NZWUlFRQWVlZX4fD5T2FwuV7Uzhg4kXBaPyedR/G2z2XC5XKiqau7RqKioMHn0+/0mj06nc7/Kf02TssVjcnis6oKa+ytWLmP59Pl8BAIBU/FIzGi8v8WVxWNyeYxFIgciEZyQx9ikcOL6Q4HFY3LHVaiulIl5LxKJmPkeEufIWHkUyQMPpNxZPBo85tavT2FhIX1PPJGuXbvy808zmPvbr0DVkVHG4+zPuVC1902SJNasXsVrL79Eq1atufraa8nIyGBb3lbGvD8KdJ2XX3uTZs1b4PP7ePrJJ/h84mdcMvwKep/YD5/Px0sv/ptZP//MPfc9wJChQwmFQkz+6ktmzpjO1SOv45RTB6Fpetw6qCZ+hffKGZMdVOQeOBRluCbsj3dxNuj8+fOYOXM6jRs35vQzhlFYUEC93Fzs0T2+yUCt5NHlIrB2NcWffYwWNvb/1XZcPRASdYia+tXpdOLxeExdRBh4oGbF80B17U+Rj5ffqr/Nfew6YLNROe0HKv71AjoQjIQI1stATfWC3Y4WDOA5dXCt5bHWiqNolMfjxW5TKCwrI3PoGTg6nIBeWWlk0NL1KhIdTso+/4zAwgVmGbEvVywJyULt6tj/i5+oDB2wPHFdlPDgxvXs+2CsYY2MFS5ZhnAIW/36pJ99Hn4dKkv34fEm1wp3tHlMROwLH9tvInW02+3G6/WaguXxeHA6nXHHNIjf+520DgiLx6PNY+zApShK3OAYO0DWxGNsebWDxePR4FEgls/E+hP59Hg8uFyuavvDD+R92D8sHo8mjwI1yabT6TTlMVHZj/19eLB4PNrjauL1wjjn8XiqzZE1yaP4u3bz5P8Wj9nZ2fj9PtIzMhh+xZXs2rmT7775mtLSUtNjG/9YegJHVccvVFZW8u7bb7J92zauv+lmunfvSSgUYtIXn/PTjBlcf9MtdOzUCYDlS5cyZfJX9O7ThxtvvgWH3c6mjRv5aMJ4TjihI8OvuAKbzcaypUt556036NmrF1dcdXX0KI34o+USkTge7++z2sr9/q6TZZlQKMT3333L2tWrueiSy2jYqBEFBXvJrV//gG09UvzR42ptUNvxNFHxrG0dhnwe+Hpd00CC0KYNFD7yEMFly8FpJ5TuRU9LQVaOTB5rrThKkpF8xm6307pdO1YvXoTSqjUpZ51rDA56TDyxroOioBYWUPzOm0SKCqNu/uov+rGDg2v/ByPbnMg1DUmW0fx+SsePIbhmlXFQqcgwBsb3AT+eIafj7tuPtSuW06J1K9Ki2cwOlAXvSPBH8XiofVbTfbG/aw+Lx6PNY+zv2rTvcO6LKeGAZVs8Ht64+sfLpMVjsubH2vBy5AZai8dkzI/i79q2MbGMWtx9wHKPNx5tNhtNmjRl546dDD3tdLp1785333zDrJ9/Aqp7HRM9QrGeoR++/47PJ37GsHPP5bwLLgRg/ry5jHr3Xfr07csVV12F2+2muLiId956g4K9+dxx1z00bNiIisoKXnnpRYqLirj2hhtp2649RYWFvPPWG5SXlXPjzbfSslUrMyOraM/+ojtif+/vs8NBYn3hcBhZlvltzq98PWUyjZs04cKLL6aosIAGDRqQkpKCrut/eXk81lElm9W/M40GsfI47n1CO7ej59YhkuaBtBTQdLApRySPh8WyKDQlJZUUbwp7S4rJPu9CnN17VGn/0YcUrmPfrz+z76MJ6KoG1GxpOx4QZ/2Nslvxw3eUfzkRyRUzmOp69ODNAPZmzcm8eDgV4TDBinLcHu8f0laLx/3D4vH4gMXj8QGLx+MDFo/HB/6KPNbNyQF0MjIyufra6ykqKmTip5+wa+dO7HZ7tTNpa3reLZs38cp//k2DBg257oYbycrKZs+ePYx69x0qKsq5/a67adq0GQBffv4506dN4+JLhzNoyFAA/vvll0yf9gOnnzmMCy++BE1VmfrdN3z79RQuvvQyhp19DnoNIarJjMg7GHTdyOpaWlrKl59PZMP69Vx19TU0bNSI8vJysrKzzXYmE5Y87h81y+O3lH/1BaR5CbtsaGmGci/ZbOiBqDxecnjyeFiKoyRJZmr9Bo0bsXbJUqTGTUi/eDhSipFFS5JlQ783LVIKpeNGUTlzmiEECa70vzqJ+9tfEFi5nOLXX0ILBOJvkCTD6KdppJx3Ea6evVizdAmNmzUjMzMzqVa42PZZPMbD4tHi0eLx2IHFo8WjxeOxg78qj8JblZmVRf7efAYPGcrgIafx/Xff8s3Xkw8YmivaGAwGefftt9i0cRMjr72eE0/sTzgc5pspk/num28Ycc21DDxlEADLli5h3JjRNG7ShJtvvR2Px8P69et49+03yc3N5dbb78DtdrN27Vpee+UlWrdpw8jrbzD2mx8kRDXZSFRUVdVozw9Tv2XyV5Po0aMnF1x4MUVFRaSlp5OZmWXJ45+EA8vjy2h+PyAZx25IUnV57HF48njYTIs4+3r1cqlXP5eNmzeTceEluE8dDIGAuVG6qiYZtbSUwhdfILh6lUGwrsW54f+qJCYKmvh/OH8Phf/6J6HNm5DsdnQtJjmPzYZeWYmzR2/qDL+SotJS0HQyogeo/lEWJovHKlg8WjxaPB47sHi0eLR4PHbwV+ZRhH02adKUSDiM2+1h5PU3kJKaypjRo1kwfx6KosSFrIpnFJgxfRqfffIxpw4azCXDLwdgxfJlvPn6q3Tq3IUR14zE6/VSUV7OuPffZ/PGTdx59720bNWKUCjEu2+9Sd7WLVx7w4106dqNiooKRr37Nrt27eLa62+gU6fORA4hRDXZiH1HRSKmNatXM27M+/j9fq6/6Wbq5eZStm8fjRo1/kPbZsljFf5MeTwiE4HIdJSSls6+ggIqZJm619+MvXkL9EDA3LBqxsHb7YQ2rKXg2acI79yJJMUfLPtnuuOPBLEvn/itVpRT9OJz+ObMRnI4o9YPyXhpZRkiYZTMbDKuvha5cROWL5hPi1at8Hq9Zr/+UbB4NGDxaPFo8XjswOLR4tHi8djB8cJjgwYNyc/fZexHvHIEq1etZMyo9yjYuxebzWaGrJqLbUli5/ZtvPKfF0lPT+emW28jJyeHkpISRr/7DgUFBdxy2+20bdsWgKlTv2XSl59zznnnc+aws1AUhe++/ZqvJ/+Xk04eyPArrkLXdWZM/5EvP5/I6WcM4+JLLkPXNGwxmTf/bI+jpmnYbDbKy8qYMH4sv835lfMvvIgzhg1j85bNNGrUGLfbjaqqSfc2xsKSRwO1k0c9Ko+RoyKPR8S2oiioqkrDhg3Jzslh1aJFOHv0Im3ESGSXC6Ln+sRtorY78M2dQ8GzTxIp3FuNvL+a9h/rKtZ1HXQdPRCg6OUXKZ/8VVW2J/PaqLs4ouK94GLSz7uQVStX8v/snXWcJNXVhp+S9nGfdXdf1nDXkOCQAIEYkIYQIPlIcLcElwkkuDuBIIvDAuvLsqy77477tJZ8f5R098ys7yj1/mh6tqX6Vr117z3vOeeeW9yrN7l5eei6bnuc2gsOjw6PDo8Oj50JDo8Ojw6PnQfdicei4mK8Xj/VVdWc/5vfcNBBB/HWm2/wyssv2Ru0W9sRWGmu//n3EyxbupRzz/81hxx6GIqi8PFHH/L2m29y5plncdwJJ4IgsHr1ap584gmysrO55NJLyc7JYdPGTfz7X//C7fZw2Z+vMLfu2MhD999Lfn4BF138RzIyM9GSrl+yaG0vWGLV4ljXdTRd45233+KFZ59l+PARXHRJkFgshqBDz169nP7YQdjz/ojZH5X90h/32U0gmQT16t0bj8fNhg0byL3wd/iPPwnicTt0bDde1xFcLpo+/oiKW29CqaywyU25EF0Ard14ejRK1UP3UffSc9aHUs5HcLnQw2E80w6i6I+XUVZZSTQcorCoMOU47Q2HR4dHh0eHx84Ah0eHR4fHzoPuxiPAwEGDaGpsJCcrh+Cf/kxeXh4ljzzEx9M/srmx9mz8+ssveOWlF5kydSrnX/gbBFMgPvTA/QwcPIgLf/d7MjMzCYfDvPzCcyz8fj7By/7EqNGj0TSNZ595ih8X/cAFv/kNU6dOIx6P8/ST/2bVypWcd8EFTJk2zd4jETomRTVZiIAR1ZMkiW9nzODhB+5HlEQuvfwKBg4aSOm2bQwaMjjle+0Npz92bH/cL/FlK6Rd1KMn1RXlVDWFKLrqatwTDkAPh+1qR3YDdR1kmcYP3qP8xmuJb99m5h7rrV+UzkContiPsDXPjCAIaI0NVP7zLmqffdLYx6jZzWhUMwrjGjCQ/Cv/D6WwiOWLFtG7bz+ys3PaPeTfHA6PDo8Ojw6P7QqHR4dHEw6PDo/tAU0zqoQWFhdTVVXB5CnTuPiPl1JfX88/7rqD+XPn2sKkvKyMhx98AFmW+WPwTxQVFdPY2MiT/36cTRvX87s/XMyYseMA+Pbrr3nx+ec46uhj+cUpp+F2e/ji889547VXGDdhIudfcCGiJDHjqy955aUXmXrggZz36wvRdb3VvTnbC81FY/K6xn/ecxcbN67nvF9fyMm/OIXVq1ZR3LMXmZlZnYJHpz92TH/cL6zbKQBFRRT26MnyH39A792H/KuvNfKOo9Edkzj9A8quvpLoiuUJ4pp5AVII7aCOpZOa897cWxHfvo3ym6+j7oVnE+SZ7wNG6DweR8rKIefyK/FNmsLi+fPo2ac3+QUFKR6njoLDo8Ojw6PDY3vB4dHh0YLDo8Nje0EURVRVpXfv3vj8AbZs2cR5F1zIWef8khXLl3PHrTezfNkyJEniyX8/wYIF8znnV+dxxNFHoWkan3/6Ca+//BInnnQyp5x6GoIgsGXLZp54vARBFLk4eClFxcWUlpby1L8fJ9TUxBVX/oWi4h62EHW5XPzx0j+Rn5+fIlTaO0V1R6Jx86aN3HPn7Xz37TeceNLJBP/0J0q3b8fr9dKrVy87ItmRcPpjx/XH/eYusCpS9ezZk7z8An5cMB//lGnk/fXvSHn56PE4gtSSRMHlIvTtDEqvuozGLz4zXhNFdE0zcpOTOlB7E5ms6pPJ03Xd3mRTEAQiixZS9tc/0/Dft0BMvQnBIA9NQ/R4ybwoSOapZ7J86VJ0QWDAgIG256QzwOHR4dHh0eGxLeHw6PDo8Ojw2JGwREf/AQMQBIGa6mouv+Iqjj3uBGZ8/RX33Hk7r7z0Aq++/BIjRozgt7//PZIks2nTRh564D4Kior43UUXkZObSzwW4+03Xufrr77g93+4mMlTpgLwxquvMOPrr/jVuedzyGGHAfD8c8+yYME8zjj7HI446uiUaE97pqYmo7loLN2+nXvuvJP3//ce06YdxN+vvR5V1Wior2fU6DHoevuva9wRnP7YMf1xv8aZZVlG0zSGDhuOIEosW7qU9JN+Tt5f/oaYkQFKSxJ1TUNwu4muWE7Z/11BzeOPoVRXGxdHFNFVNTVkbKnuZuHY/Qn75koizn5PVREAQRTRQk3Uvf4K2y//I6GZ3yLILpt0u7kmeYIkkfGb35N3yaWsXrWKcCTEsOEjkCSpQ8P9rcHh0eHR4bHzwOHR4dHhsfPA4bF78Gilo46fMJHa2hrS09O57sabOPjQQ/l4+kfcctONqKrCVX/9Gz169iYaifD0f/7NyuXL+fWFv2HylGkALFgwn38/8ThTpx3IGWedhd/vZ/asmbz4/HMMHjKE3/z+D3g8HmbP/I4XnnuGESNH8fuLLkEgsan9vnDcWmrljtItk3lLjqolROM27rrjdl579WXGjB3LTbfcRn5BAWvXrGbM2LFIktRhAndHcPpj+/fH/d6TRVMNDx8xgnA4xLo1a8g8+1fkXXU1QnoGuhJPCR9boVfB5UKrr6Pyvrspu/oKQnNnm94Cw7OhK0rqhaFtPDR2Z0o6tg7oqmK0U5LQgeiKZVTceiPlN11LfMtmBLfbeH9H5F3wOwqu+j/KSkspLdvO0GEj8Pv9bXYT7iscHh0eHR47DxweHR4dHjsPHB67B4+WkT523Hi+//578gsKuPWOuxk/YQK1NTXk5RdQVFyEIMCMr7/ixeef49DDj+CXvzoPURSprKjg3yWP0dTYwB8uvoQBAwdRXl7O00/+m61bt3D5lX+h/4AB1NXVUfLowzQ21HPpZZfTu3dvm4P9dW3sqFTScZPvneS0VJ3EZyzRuHHjBm675SZefvF5ho8YwS2338nQ4cNZsmQxB0yajMfr7bQ8Ov2xfftjm+UNeDweBg0ewprVq1m3Zg0DzrsQZBdV99+DWlmJ4PWix+OJm9zK25VdNH0ynciPP5B59rlk/OI0XAMHGQs8dR09HgdJMvYkaQYdg1hd1xEghYTW0LxjGV9JIk7T7Lxhy2MR37qFxk8+ovaFZ4mvWY3g8xmkairW/jDGecjo8Tii10vGb/5AwRX/R3VNDUuXLmHSpMkEAgE0Tet0XrjmcHh0eHR47DxweHR4dHjsPHB47B48ut1uDj7kEFYuX05+QQH3Pvgwf//rVcya+R233ngDf/hjkMcefgiv18fvLr6YgsJCNE3j/ffe5f3/vccfLrqIIw85FOIKs7/7ls8/+4xTTz+Do485FkEQeO3ll/jqyy857fQzOeHEn9kVWyEh+PYEzcVhsihs/r6FlPRLBHvLEVmWWbpkMXffeTsfffA+Y8aM49Y77mLipAP49psZHDBpMunp6V2CR6c/tk9/bNOE88zMTAYNHszaNSaJ55yLGAhQdd89xDesR/D5QVVSbnJdVRD8ftTqaqofuo+mzz4m/fSzCBx+FO7+AxCsDVIVxcg3TiLTuvS76xFo7XM2abqRB22V9Y1v2Uxo5rfUv/Ua4bmzARHB70dXFUAwyLPINkvfSrl5ZF0UJPeiP1JWWsqSpUs4YOIBpGdkdIlOaMHh0eHR4bHzwOHR4dHhsfPA4bF78BgIBBgzdixff/UVQ4YO4Z/3P8jNN97IF599wtJlS4nH4/z2d3/g6KOPRdd1Vq5YTsmjDzNi1CjO/tV5pOXmseDLL/jqvXcZNXgwF1zwGzIyMvhh4UKefvLf9OzVi+CfLsftcbcQii1SE1sRF83RWmQxJaq4k2NYUUZVVfni88+49+67mDN7FgcdfAg33XYHQ4YM4asvv2TSpMlkZ+d0KR6d/tj2/bFNhaOu62RmZjJw0CDWrF7NiqVLGHbyKcj5hVT88w6iC+aDx2PnFAPGhVAU40L4A0SXLyV62400vPUGacedgP+gQ3APGYaUmZn4HUWxFTqiCGbYenfaZ5BlEIYgGuFs88JqoRCxdWsJz5lJw/QPiXw/Hz0eQ/QHjEW0ZjutYwmSBIKA3tSEa9AQci6/ksxfnMbK5cspKy9j8uQpXcZzkwyHR4dHh8fOA4dHh0eHx84Dh8fuw6PscnHYEUfww/ffU1BUxD/vu5/77/sHb772mlGIRVOprKzE7/dR8ugjbN68mX/c/wBjxo2noaGetz76kFfeeJ0H7nuA8aNGEm5o4MVnn6K8vJxb77iLIUOHpojGHUUadxWF3FlkcWfQNM0uilJTU8Pbb7zOww89yPatWznt9DO4+trrCQQCzJz5HZMnTyE3L69TVFDdEzj9se37Y5sKR+siZmZmMWr0GJYvW8r38+Yxbuo0etz7EBUP3UfT9A/RY1EEjzexINW6KEocweMFUSS6bAmRJT/gev1lfJOm4psyDe+o0bh690XKzm7x28kK3nwF2zdg5RJLkpnLnOgUWkMD8a2biS5fRnjeHEKzZxJfuwZd0xD9AUPVW8QJgnF80Qwpx+OgafgOPYK8K/6Kb+IBLFu8mJgSZ9LkKfh8vi43mILDo8Ojw2NngsOjwyM4PHYWODx2Hx513dhTccy4ccydM5uePXtx3Q030btXH/79eAmPPvgAleXl9OvXn/+991/OOOtsjjv+RAC+/uILXn7pRU4440yO+MUpSIE03n32GT7+4ANOOOFEfnHKqVhrEHd1bXYVcdyZ4Gztb4B4PI7L5UIURRYtXMjzzz3Dyy88T1ogwOVXXMXFwT8SjyusWbOaSZOnkJOT0+VEIzj9sT36Y7vVRvZ4PIwdN55VK1fy3VdfMvngg+lxz/1UDR9B3YvPo1ghZEkywrAmkbqqgqoaOb2igFJaSv0br9Dw/ru4+vTFM2w47qEjcA8ciKtXH+S8PMSsLONi76TUrK5r6E1NKHV1qFUVKNu2Elu3lujKFURXLCO+fh1aQz2C243g9xs5zPG48WVBwLohBJcLdB29sQGpoJC008+i8KJLUfLyWDB3DoIkM2z4cHw+324NGJ0dDo8Ojw6PnQcOjw6PDo+dBw6PXZtHS3S43W4OOvgQvl8wn5rqai76Y5BhI0bw4H338vKLL+DxeCgoKOSII4+ioKCA9evW8a/HHiUQCHDRRRdTWFTEqpUreeaF59Alid/94SLS09NRNc2w+61I1U7asauIo/V+81TV1gSjLMu4XC7Ky8v48P33ee7pp/h+/lwmTZrCZX++gmNOOJENG9azbs1aph44rcuKxuZw+mPb9Md23VRH13WGDhtGeno6X33yKcNHjaLPxZfhGzeR6mf+Q/jLz9BDTQj+AKDbYWQEwVDbgOByIXi9oGnE1q8lunI5gvQeYnoGUn4+roJCpPwCpOwcpMxMxEAAXG6QRMMTEFfQQk2o9fVoNdUolRUo5WWoFeWotbVm9SUXoteLmJUNmpZKnJVDLkogiujhMIIAvoMPI/P8C8k48WRKy8tZ+cXn9OrTl4GDBtnn3tnKGO8tHB4dHh0eOw8cHh0eHR47DxweuwePABMPmMTatWtYMH8ekydP5ZGSx3n+uad5/ZVXqKqq4PVXX0ESJebPn8fChd/z92uvZ8y48SiKwosvPMviH3/k+htvZvwBk9AFQ/AJSde3OfakSE5r1zlZQFrrGF0uF42NjXzz9Ve8+cbrfPjeu8iyxO8vuoTfXxKkZ+9ezJs9G9nt4rjjj8ft8XQL0WjB6Y/7vz+2q3AURRFVVenRsyeZWVmsXLaMyrIyxk6ZRq8RI6l+500a3niN6KKFRr6wz2eo6mQiTU8AgoDo9YE/zcgVjseIb1hPfM0qdM28WNYCVlEyo8U6aHriGFZ4WpYR3G6DbFEEVTO8D7GY/bs2cbJs/DsaRY/FcA0eQtqpp5N31q8QiopZvGgR8XiUkaPHkJefb4eIu9Ng6vDYPeDw2D3g8Ng94PDYPeDw2D0gCAKqqjJw4CBycnJZumQxBQWF/N/V1zBlyjSe/PfjfPj+/5g7ZzaSLDNk2DAOO+II/H4/H7z/Hi+/+CJTpk7jzLPPscWiXZWzlevU3Mjf02up6zqqmXbpcrlswTjru2/58IP3mf7hB2zZsplDDjmUP1x8CYcfdTQN9Q188/XXDBo0mKHDhgHGOsjuIhrB6Y9tgXYVjmBsuqooCoFAgFFjx7Jl82a++vhjhowaRe9f/5bMgw+j+r13aHr/XWIrVxhE+v2gg66pCU+NrpveANMjIIqIPh+IgcQFtx7m563Oau+Xkvw5a9Fpax4fQbDDz3o0CrEocu+++I8/kZzTzsQ9eixllVUs/+xTevTuTd/+/cnIyLA9Pt0RDo/dAw6P3QMOj90DDo/dAw6P3QMWj9nZ2UyeMpVVK1fyww8LOejgQxg/YQKfTP+Y5597msU//oggCDx4370cevjhvPXmG3g9Hq65/kbyCwqIxaKIorHp+o7SBvfGyNd1DUUxBI4oSjYPW7dsZtZ3M/ni88/4+qsv2LhhPSNGjOTuf9zHSSf/nOycbJYvW0YsGmPS5MkUFBSiqupO29eV4fTH/YsO6e1WGWC3282AgQPJy89n7epVbFy9iuETJpJ/xV/JOv5E6j6ZTuP0D4mtWGruTeIDtzvhDdA0+5j2otRk7Kwj7iolQBQNr4EggKaiNTUiIOAaMBD/MceRefzP8E48gPpImO9nfofL5WL0+PHk5uYBoKpqtx1MLTg8dg84PHYPODx2Dzg8dg84PHYPJPM4avRoqquqWL58GRnpGZxy2mkccthhfPrJR7zy4ku89eYbTJ/+AbLkYtjw4ZSXlVJZWUFeXr59PKuyqRVhTI4MtbpVg55Yy6jrurn3oIAoCYiihMtlCL2qygqW/Pgjs2fPYvbsWfzw/fdUVlYyfPhwbrn9Lk782cn06NGDrVu3smb1anr36cPgIYMRRalbi38LTn/cf+iwO8UKhauqSkZGBmPHT6Cmuoo1SxazGhg6dhx5l19F1i9Op3HWtzR9/gmRBfNRK8vRERA9HnC5QAd0oxOmqPzk551BMP9n5Z5bpMXjaJEwgqIiZGfjO2AyaUccQ9rBh+IeNpzGeJz58+ejRqP0HdCf7OwcvF6vvbFrdwr17wwOj90DDo/dAw6P3QMOj90DDo/dA5Ikoes6mqaRk5vLtGkHsnHTRlasWE5WVjZn//I8Tjr5F3z3zTe8+87bzJ83l8U/LuLqv1zJ0GHDGT1mLGPGjGHoiBH06dOXzKRtHZJhiUML9ppIkkSlec0bGhrYsmkTq1au4MdFi1i8+EdWLF/Gli2bcbncTJ4yhVNOO53DjzyKvNw8ysvL+OGHheTl53PgwQfj9/sBfhKi0YLTH/cPOvxukSTJXoibm5dPIC2dhvp6Viz8HlGS6DdsGIXnnEvWST8nsnolTXNmEZ4zi+jypagV5RCLo0sSoiyDlQeclBsMpP6d4g0QMO4AQFHQFAVBVUCSkLJz8AwZhm/yFAJTD8I/YiRk51DT1MiiuXOJRSMMGDiIQFoaGRkZ5iF+Oh2wORweuwccHrsHHB67BxweuwccHrs+LMPc4rF//wEUF/dg+7ZtLF2ymPSMTI49/niOPe54li9byheff87XX33BnFkz+erLL8jMyqK4uJhevXvTt28/+vTtQ4+evSkoKCQ7J5v0tHS8Xi+y7EIQBXRdQ1VUIpEIjY0N1NbUUF5ezratW9m0aSObNm5g86bNbN++jdqaGmSXi8GDhxC87FSOPvY4ho8YgcvlZsuWLWzZvJCioiLGT5hARoYhWi0ef4pcOv1x39Ap7phkL4DX68Xr9ZKRmUlDfT0bVq1iZThMXlExvUaMInfCAejn/Zr4li2EVywnsmQxsVUrUDZvRK2qQguF0OMKAprBW2thYwHQzNxjWUL0+ZFy8/D06o1n8FA8I0biHTEKT5++CNlZhBSV1Zs3U75kKZIoMHDQYALp6bbHxsoN/yl2wGQ4PHYPODx2Dzg8dg84PHYPODx2DyRHH71eL/0HDKBHz55UVlaydMkSRFGiuGdPLr/iSi4JXsq6dWuYN2cuc+bMYvnSZcz89ju++OwzZFnC6/Xi9wfwBwL4/T7cLg8ul4wgiOjoKHGFaCxKOBwi1BQmFG4iHAqbKZcu8vILGD/xACZPmcLkyVMZMmwoPp+f+vo6Nm3cSCwWo0/fvgwZPBivzwcYqbKAw6PTH/canerOSe6QHo8HT34+Obm5RKNRtmzaxIofFhKPx8kpKCC/uCdZQ4eTefIp6JEwam0NSlkZ8dLtKGVlKNVV6HV1qKEm9GgEXdMQBNHeH0XKzETKzkUuKMBVVIyrsAgpJwfB50eVZeobm9hUXkrVyhUImkZWdg5jxo3F7w/YN5zVAX8q6Rq7C4fH7gGHx+4Bh8fuAYfH7gGHx64PK/popZZ6PB569uxJjx49DOGxYQNbN2/B5XaRl5fPL889j19f+BsaGxrYsnUr69asYu3a9WzeuJ6K8lJqa2ppbGigPlaDEo/b6x8ll4xbduMPBCgqLKagoIDevfvQb+AABg4aTJ8+fcjKykbTNGpqati6ZQuKopKRmcGgQYPJzMqyi91YPHbH4jf7Aqc/7jk6lXCElh1SFEV8Ph+Dhw5FVVUi4TBlpaVsXruGlaEQsttNekYGGVnZpA8djmfsePyShJRUtcgojURKXjGiiCaKKKpKJBqhtrGJurIyGurqiIUjeL0e0tMzGDF8BP5AAJfLZbfR6YC7hsNj94DDY/eAw2P3gMNj94DDY/eAtQYxmceMzEzGjB2LqqrU1dWybetWVq9ehRJX8Pq8ZGVnc/hRR3Piz/xIsoyua8RjcWLxGPFYHFVRjLVzgoAsScguFy63G7fbhShKqKpCJBKhoaGBqqpqtm3bjixLZGZmMnjIUAIOj3sMpz/uGfZYOAaDwbZoh4N2hsNj94DDY/eAw2P3gMNj94DDY/eAw2P3gMNj50LHS1cHDhw4cODAgQMHDhw4cNCpISSX/nXgwIEDBw4cOHDgwIEDBw6aw4k4OnDgwIEDBw4cOHDgwIGDncIRjg4cOHDgwIEDBw4cOHDgYKdwhKMDBw4cOHDgwIEDBw4cONgpOt12HPsTwWCwF3ArcDyQC2wH/gvcUlJSUtOBTXOwmwgGg2cAhwHjgLFAOvBSSUnJeR3ZLge7j2AwmAucCpwEjAZ6AjFgMfAM8ExJSYnWcS10sLsIBoP3AAcAQ4A8IAxsxBhXHy0pKanquNY52BcEg8HzgefNf/6hpKTkyY5sj4NdIxgMbgD67uDtspKSkqJ2bI6DfUQwGDwEuAI4EMgBqjHmyQdLSko+7MCmOdgFgsHghRj2zM6glZSUdPkNUbutcAwGgwOBmUAB8C6wApgM/Bk4PhgMHuQYOV0C12MIxkZgCzCsY5vjYC9wJvAvDMfNl8AmoBA4DXgSOCEYDJ5ZUlLiVOrq/LgS+B74FCgHAsBU4GbgomAwOLWkpGRzxzXPwd4gGAz2Bh7BGGfTOrg5DvYMdcCDrbze2M7tcLAPCAaD1wO3AZXA+xjzZR4wHjgccIRj58YPwC07eO8Q4Ejgo3ZrTRui2wpHoARDNF5eUlLyiPViMBi8H8P4uQO4pIPa5mD3cSWGYFyDEXn8smOb42AvsAr4OfBBcmQxGAxeC8wFTscQkW91TPMc7AEySkpKIs1fDAaDdwDXAtcAzqZbXQjBYFDA8JRXAW8Df+3YFjnYQ9SWlJTc3NGNcLD3CAaDZ2KIxs+A00pKShqave9q9YsOOg1KSkp+wBCPLRAMBmeZf/67vdrTluiWaxyDweAA4FhgA/BYs7dvApqA84PBYKCdm+ZgD1FSUvJlSUnJaica1XVRUlLyRUlJyf+ap6OWlJSUAo+b/zy83RvmYI/Rmmg08br5PLi92uJgv+FyDG/4bzDmRgcOHLQTgsGgCNwDhIBfNReNACUlJfF2b5iD/YJgMDgKIytnK/BBBzdnv6C7RhyPNJ8/acVYbQgGg99hCMupwOft3TgHDhzYsCZEpUNb4WBfcbL5/GOHtsLBHiEYDA4H7gYeKikpmREMBo/c1XccdDp4gsHgeUAfDOH/IzCjpKRE7dhmOdhNHAj0B94EaoLB4EnAKCACzC0pKZm1sy876PS42Hx+qrv0ye4qHIeaz6t28P5qDOE4BEc4OnDQIQgGgzLwa/Of0zuyLQ72DMFg8K8Ya+EyMYrlHIxhsN7dke1ysPsw+98LGGuOr+3g5jjYexRh8JiM9cFg8DclJSVfd0SDHOwRJpnPZRjrx0cnvxkMBmcAZ5SUlFS0d8Mc7BuCwaAPOA/QMOo5dAt0y1RVDGMGjEXjrcF6Pavtm+LAgYMd4G4Mz+qHJSUlH3d0YxzsEf6KkfZ/BYZonA4c6xg3XQo3YhTeuLCkpCTc0Y1xsFd4BjgKQzwGMETHE0A/4KNgMDi245rmYDdRYD5fAviAozGqx48CPgYOBd7omKY52EechaEzPupOReO6a8RxVxDMZ2fdnAMHHYBgMHg58BeMasfnd3BzHOwhrDL/wWCwECPV6m5gYTAY/FlJScn3Hdo4B7tEMBicjBFlvM9Jheu6KCkpaV7FcQlwSTAYbMQYX2/G2ArJQeeFtT2DgBFZXGT+e2kwGDwVI3PusGAwOM3pq10OF5nPT3RoK/YzumvE0YooZu7g/Yxmn3PgwEE7IRgMXgo8BCwDjigpKanu4CY52EuUlJSUlZSUvIOR+p9LYh9AB50USSmqq4AbOrg5DtoGVtGxQzu0FQ52B9ae4uuSRCMAZiaAlY0zuV1b5WCfEAwGR2A4VbfQzbZS6a7CcaX5PGQH71uV/3a0BtKBAwdtgGAweAXwKIZn/AizsqqDLo6SkpKNGI6AkcFgMK+j2+Ngp0jDmBuHA5FgMKhbD4z0Y4D/mK892FGNdLBPKDefncrxnR+WvVq7g/ctYelr+6Y42I/odkVxLHRX4Wjt9XesWerYRjAYTAcOAsLA7PZumAMHP1UEg8G/AQ9g7HV0RElJSfnOv+Ggi6GH+dytJsluiCjw1A4eC83PfGv+20mN65qYZj6v69BWONgdzMCoKj44GAy6W3l/lPm8od1a5GCfEAwGvRhLcDSMcbRboVuucSwpKVkbDAY/wUifuhR4JOntWzC8cE+UlJQ4e1Y5cNAOCAaDNwC3Agswiqg46aldDMFgcBjGZuOlzV4XMTavLgBmlpSU1LT2fQedA2b62+9bey8YDN6MUTDnuZKSkm5TBbA7IhgMjgS2Nx9Lg8FgX4ysDoAX271hDvYIJSUllcFg8DXgXIyCVddb7wWDwWOA4zCWVTmVx7sOzgSygfe7U1EcC91SOJoIAjOBh4PB4FHAcmAKcARGiup1Hdg2B7uJYDB4CnCK+c8i83laMBh81vy7sqSk5K/t3CwHe4BgMHgBhmhUgW+Ay4PBYPOPbSgpKXm2nZvmYM9wPPBPszz8WqAKKAQOAwYApcAfOq55Dhz8pHAm8PdgMPglsB5oAAYCJwFejHVV93Zc8xzsAa7CsE+vCwaDhwJzgb4YhY1U4A8lJSW1Hdc8B3sIqyjOvzu0FW2EbisczajjARgG6/HAicB24GHgFifi0WUwDrig2WsDzAfARoytARx0XvQ3nyWM7Rtaw9fAs+3RGAd7jc8wJsKDgLEYZcabMBxxLwAPO+OqAwfthi8x9qwej5GaGsBYJ/ctRn98oaSkxKkc3wVQUlJSHgwGp2BEG08FpmI4Aj4A7iopKXGWVXURBIPB4RhbVHW7ojgWBF13xhUHDhw4cODAgQMHDhw4cLBjdNfiOA4cOHDgwIEDBw4cOHDgYD/BEY4OHDhw4MCBAwcOHDhw4GCncISjAwcOHDhw4MCBAwcOHDjYKRzh6MCBAwcOHDhw4MCBAwcOdgpHODpw4MCBAwcOHDhw4MCBg53CEY4OHDhw4MCBAwcOHDhw4GCncISjAwcOHDhw4MCBAwcOHDjYKRzh6MCBAwcOHDhw4MCBAwcOdgpHODpw4MCBAwcOHDhw4MCBg53CEY4OHDhw4MCBAwcOHDhw4GCncISjAwcOHDhw4MCBAwcOHDjYKRzh6MCBAwcOHDhw4MCBAwcOdgpHODpw4MCBAwcOHDhw4MCBg53CEY4OHDhw4MCBAwcOHDhw4GCncISjAwcOHDhw4MCBAwcOHDjYKeTd/WAwGNTbsiEOdo2SkhJhX4/h8NjxcHjsHtgfPDroHnD6Y8fD6Y8OLDj9sePh2DndA63x6EQcHThw4MCBAwcOHDhw4MDBTrHbEUcLJSUlbdGOFtB1HU3TkCTJfk1RFMLhEGXbS2morycUCeH2eEnPzCIzK4tAWhperxeXKCJoGlgPPclpIQggCiCK6KKEomlEY1GaGkPU1tXQUFtLLBzB43ETSEunsKiIQCCA2+22D6FpGgCi2D66OxgM7vdjdiSPmqrS0NhARVk59XV1hKMR3G4P6Vl7waMgokupPNbV1VBfW0ssHEYSRTKzsiksLiIQSHN43EvsNo8ek8fMfemPMZqamqitraGhppZYpPvz6KB7oL36495C13UEQaCxoQFBFFi1YgUffvA+27ZtIz09jcOPPJoDDzoYXdfJyMhAEAT7O50VTn90sCO0VX9UVRVJkqiursLr9eFxu5HkPTan2wWaqtpzal5eHpqmtelc2e3tnEjY0B22vZqO1+tJtXN0DbTmdo5o2jliwl5tClFXW0N9F7NzOt2dnkycJEmoqko0GmXzxo3U1tagqCq5hYX0GTyErIx0JFVBb2pCqalGWbeGSOl2GstKUWuqUWtr0UJN6NEouqYhCCKC24XgDyBlZCLl5CAXFOIqKiansIj83DzE3n3QXC7qQyEqK8pZuWIFaCqZWVn07NUbfyCAbA4Q7U1kV0JzHnVdp7GxkW1bNlNVUQmiQG5REf2GDiUzvXUeG8pKUaur0epq0Zqa0GMWjwKC223wmJ6JlJNt8Fhs8ZiP2Ls3mstDUzRCeVkpK1euRFcUMjIzKSruQVZ2tj04ODzuGDvlsbISRJHcwsK95FE0efQhZWQhZecgFxTgKu6R1B9b8qgpCpkOjw4c7BEsAVhfX4/b7eazTz7m3//6F8NHjOCww49g86ZNPPLA/axetZLf/u4P1NXVkZmZ2SXEowMH7YWEaKzG6/XhdrmQZBmlqhJl+zYEQURHB8z+ktJtmr8mGA+h+ScE+61W3gSEZm8JiX8IOggCuqYhZWYh5xfgcbsRgIqKcvLzC9pcPHY17MzOqayoRGhhr6roTY2oNTXE1681dUcZanUlal2drTvQNBBEcLsR/f6WuqOoiPycXEN3uN00RZrbq1kUFRd3OjunUwlHRVGQZRlJkohEIjTU17F+7TrC0QiFPXsycuIBpLnd6LU1xDeup2HFMiJLlxBbuRxlyybU6hr0cAhdURHQ0QUB0A3iLFjRDl0DBJAkRK8XMSsLuUcvPEOG4hk+At/I0Qzs259BU6YQ0XW2btnCksU/IgADBg0mkJZGWloaYAwkoig6E6uJZB6j0SjV1VWsX7MGRdMo6tWLcQceiF+W0WtriG2weFxMbOUKlM2bUGssHhUE2AMefSaPPfEMNnj0jhhJ//4DGTjZ4LFseylr1qwmHovRr/8A8vLz8Xq9gMNjc+yIR1XTKOzVm3FTD8Tv2gGP+9wfs00eh+AZPrIVHrc7PDpwsJtIEY0eN59On87jJY9x4W9/x5lnn2N/7tjjj+f6a/5OPB7nkj9e6ohHBw6SYInGGks0yhKyy0V8y2Yq77mdphlfI3q96KpifkMwok2tCkmhmfAzX9IxviPooAup4rG171qfEcy/0RFkGS0axTNkKHl//TveMeNwyTLp6RlUVFSQn59vn8tPHTu3c3ox3rZXa4ltXE/jiuW27ohv2YxaXZWwV3XLzqGZnWPaN5pmcCvLreuOEaPo329Ap7dzOoVwtG5gWZYJhUI0NtSzZtVqZJeLASNGkJeZCXV1hBcuoGLOLCJzZhlisbISQYmDJCO4XCCKCB4Pgk8ETUfAMkqt/6V6dhBFM6wMWmUl0dJSonNngygiZmXjHjIY78QpBKYdxMBRoxl48CHUhcOsWrKEeCRMvwEDCKSlk5mZCSRuwJ8qknmMRCKUbt/Oti1bcHs9DBs/gZy0NKirJTxvDpXz5hBO4VEBSWrGoy+VRzCFRrMBVxTs9ACtsoJo6fZUHgcPxnvAFAJTD6TPqNH0nTqNhliU1UuWsH7Naop69qS4R0/bEeDwuJs8zt9dHsXd4JFm/XE3eYxGWb10CevXrKGoZw+HRwcOmmFHovG3f/gDp59xFtFoFLfbTSwWY/SYsdx2593ceN01AFwSvJT6ujoyfiLi0SnG0fHorEWOkkWjx+fFLUnIbjfxbVupuPMWGt5/D9HnQw2HUpdj7DZa+c7ODrOT39ABQZJo+vJztMZGCm69E++oMYCRgl5RXk5+QcFPWjzu1M6ZMIGcQBrU1RFaMI/KubMIz51DbMUy1IoKdFN3iLJs2DtuD4LXB7qOkMxLkr/AQJK9qmloFRVEt28nOmeW4TTPysI9eAjeSVMJTJlGn5Gd087pcKvKIk9VVaqrqli7ZjWCKDLigAPI8vlQ1q2h5t23afryM6ILF6BUVYIgIno8iF4fiH5QVXRdNzqSouz6R5vDzD8WPB7w+0HX0SNhIvPnEZk9i7oXn8UzajSBw44i/dDDmTRpEk2KwsrFPxLbsIHe/fqRnZ2D3+/vFGHkjoDFo6ZpbNq4kfLt2/EG/IycNIlMr5f4mtXUzPx2JzwKLXnck8HXMmZa43HBPCJzZlH3wjN4Ro8hcPhRpB18GBPGTyACrFu+nMU/LCSvsJC+ffvhdrsdHjs1j8/iGT3a4PGQrs2jY6h2PDqrobo/sCPR+Jvf/57TzziLeDyOx+MBwOPxoCgKY8aO5dY77uLGay3xeBl1dbVkZmZ1a9HowMGOYIvGmho83iTRuH07FXfeQuMH/0MMBNBVdS9FI6Skm1rYh2lL13XE9HQi8+dSftO1FNxiisd4jIzMTDtt9acoHpPtnI0bN1JRuh2vP8nOWbuGmlnf0vSFaedUVoAgIHq8CF4fQmu6Y3/YOeEwkXlzCc+eRd3zT+MZNYbAEZadM5EIeqewczpMOFrESZJEXW0ta9esJhwKMWLiRLIDacQWL6L84w8JffoxsZUr0FXFSEVMywB0o4Mq8Z3/SPP88GTozaIfupGVjqoar4mi4UEQRFAUwrO+IzLzO+pfeQHfkUeTecLPmDD1QBpiMZZ/v4DSrVvp038A+fn5wE8n2pHMY3VVFStXLMfn8TJk3Fiy/IEEj598TGzVTnhs3ukEAUGSzJSNlusAgARvVufVdaMzg9GRLYeAxaOqEp410+CxX398Rx5NxvEnMWLyVJo0jfXLljF/zhx69ulD3759MQ7j8Nh5ePQbf6tJ/dHm8WeMmDzlJ8+jAwcWkkWjx+Phk+kfpUQa4/E4Lpcr5fOyLNvi8ba77uKGa68BHf542WXU1tWS9RMRj/feey/xeIy0QFqnLXrSHRCLxQiFQng8Hv7yl790dHNahTU31tbW4vF4cMsystuNUlZKxR230Pi/dxF8PvS9CVq0gL7Tf+7x0aJRhECAyLy5lN94DQW33mWKx7gRefyJiceWds4K/F4PQ8aMJSsQILbkR8o//ojQJ9OJrVqOruy+nYMkGWOjnaqaNE5aWY/NbZ3W7By/31jBqiiEZ31r2Dn9++M78hgyjj+REZOm0KTrrF+2tMPsnA4ZEa2Ti0ajbN60kU3r1zN09Bh6FhejrF5J+Ttv0fTR+8TXrDLI8PmN9G1VhXis1WMKZsUiRNEgINkIBePZEpJC0sP6jKahJ1d8tP4NRqpcIA0QULZuof7JJwhN/xDfMceRc9pZTJ46jYrqGpYtXEBRjx4UFvcgKyur2xurVidUFIXly5dRW1nJkFGjKczPR1m1kvL/vmnyuLp1Hq1rbXW6ZP40DTQVXVHQNdWoUKWbfFjLx0URJBFBMtIFEEXjPV0zfkPTjI5pOQMkCTEQIJnHpukf4j/mOLJPO5NREw6gpr6e9SuWU1VVydAhQwmkpf0keRw6ajQFNo+76I/7wqMAgiAa35OkPeCxtf54LNmnncWoCQdQXV/Phi7EY2euxpksQGpralA1lf79BxhvKgrIMrF4jC2bNqFpOgMHDQLo9AKjO1fj3LFo/H0L0aijI+iCnYpqicfRY8Zy2x13ccN116Cjc+mfLqe2toasrGyg8/O7L4jFYnZV2fi2rURXrUQLhcylZ8mFS5KxO8VQdvF6q8durThK0gtW1n+rdOzsuzt5Y0ft3d33U15qcZKIfj+ufgNx5eWRIUnU19fvqHEdimTRKLtkPC4XksuFUl5GxZ230vj+O4ZoNOem5HTuFlmK7QDrNy1ZI0gSeixmiMf581Iij4IC6enpP5k1j83tnBrTzinMz0dZvcq0c/5HfLVlr3oREFq3c0QJQWpu55j2iqomaYlE5xRE63uSYbOK5t2h6aZtlGTnCICYZOds2UT9f/5F6KP38R97PFmnncmo8RM7zF5tdytKVVVkWaaxsZGVy5YhSSKHH3scYl0tVU8+QeNbrxNdutgQBGkZhvFohYGTe6EVyRAlwxCNx9GiEYgriXUYljFqkYueIEdR0XWjsiOyhODy2OuyjBtASRGdejxu/KbbjeDxolaW0/DsU0RmfEXg56eS/8vzOOyoo1m6ZDHrVq+mV58+FBQWdtvqVcmVxZYuXkx+Xh7TjjgSuaaa6iefoOGt14ku/dFYf5qWYVxDRTG4Mhd/20JBMLwrWjgM8Tg6OqLsQvB4ENPSEH0+cHsRXDJIIqg6qHG0aAw9HEYPh4zvxmLGvCu7ED0esEoZK4rRkVXV6KBWNU+PB60Zj7nnnMu4yVPYsGYNPyz8nr79+tOrd++fEI+5Jo81Jo+vmf2xjXhUFLRYFD0SRg/tLY9Wf3yayIyvf5I8thWssVTTNKP0vMdHUXERkRXLCH31JUpFBe4hQwgccRQDBg5iw/r1bN26hV69nGvdUbA4q6urw+vz8slHO4406uiGcYRu1K1qLh7NtNWbrr8OdJ3LLr+CmpoasrOzu/V6x0AggCiKhGbNpPKe24kuW2I6kk1DsNW0tN0RYBYsEz/ZzN/Vd3bxgRTFYP29i4PtFn07Pq+W77RyPhbEJJEryXjHTSD3L3/DN34igbTA7jSkXWHNjXW1tUiShM/jNaqnVlRQcdetNLz7NoK3ddFIB/UNARK/bRZqSRGP8+ZSfuO1FN56J55RY3ADaWlp3V48tmbnHHjEkUi1NVQ/9W/Dzlmy2Ij4paWbukPdsZ2jKmiRMMTiBucuGcHjRfAHEH0+w26RTTtHM2wmPWraOeEQeqjJ1BTAbtk5HgSPD7WinPpnniQ84ysCPz+NvHN+2SF2TrsKR6vc7ZbNm1m6eDEjx4yhV69eNH07g+qn/0Pkm6/QY0Zo3TBQzZCw1QF1EuSpGlo4hB6LI7hcSBmZuPv0w1VUhFRQiJSTg5SRhZgWQHC50EUJQdfQ43G0UAitvg61uhqlogKldBtKeTlqTbXRwWTZMHJdLkNEJqUg6GZIWZBdCG4P8U0bqX30QSKzZ5J53oWMPOU0yisrWfHjj9TV1dne9+40yVo8rl+3ji2bNjFs5Ejy8/MJfTuD7Sk8pqXyCIBgCAdRRI/H0RsaQNMQ09Jx9+2Hq28/XL16I/foiZxfgJSTg5iegeD1IsjG9yzxokejaA0NqLU1qJUVKFu3Ed+6idimjSjbtqHV1xqDqM+H4PYkPEKW+GnGY92jDxKePZOs8y9kwM9PJacgn9XLllFTU8Oo0aOBnxqPX6PHIu3MYznK1u27yaOGrug77o/JPC7tvjy2JazrVF5eTjwSpV+//jTNnknljX8nsmyZ4XXVIe2kk8m74Vb69O/Pjwvm06NHT0RRdMRjO6OlaJzO4yWP8ts/XNRqeqpAUoTETKFqLh7HjB3LLbffwc03XIeuw5+uuIKammqys3O6bT9yuVwopdup/OedhOfPQUzPRBBUdq60OsFyZWEHf7cbdnINrH3tdNBRafryc/RYnOJHn8BVUNA+zdtN2KKxrg5BFAn4fYiSjFpVaYjG/76F4PXuUDTuIPzbPkhqg9CaeFwwl7Ibr7UL5ngFEdLSqKysIC+v+4nHHdo5331DlWWvRiItdQfQup2jGnZOn364+vTF1asPUs8eyHmGnSPZdo4ZiDJFqB6NoDU2oNbUoFRWoG7bSmzLFuKbNqJs34pWV2f8os+7Y3vVZQQ84ps2UPvo/URmf0fmeRcy4BenkpOfz+rl7WPntHvEcfmyZVRXVnDEccfhjkWpfOQB6l95EWXzJiMFzhdIijBagtEoL4wgGKq9oR7Bn2ZsuTBiJO5hI3EPHIirRy+k3FykzEzjWDsxWHSMhahafT1qdRXK9u3E1q8lumI50WVLiK1bg1ZbYxDl9xuDXdKaSt0MTQs+o5JSZM4s4mtXE/7he4r++CdyjjySxQvm8f33Cxg+fASBQKBbTbLfL5iPpipMOvAgvPEoFY88QIPJI815NGFzGAqhx+OIOTl4x47HO3YcnhGj8AwYiFTcwxAZbs8et0lXFNTaGpTSUmIb1xNbtpTIj4uILPsRtawcBAHRHwBRsNtmpUJaPEbnzKJy7WoiC7+n4JJLGTtlKmuXLOabGV8z8YBJ3ZDHBWhq3OYxuT92Lh5/ILJscTMe5R3yaPVHm8ep3ZvHtoCu64iiSDwWo666mj6DBqFUlFP77xIiS5ciZWejxWKIskzj++/hHjqCvD9fRY9evVmzehVDhg5zRGM7Ilk0+nxePv5oOo+XPGKLRiU50pgkEK0+sGPxGGfM2LHcfNsd3HLD9ejo/PnKq6iuriYnp/uKx+jKFUSXLUFMz0C3lsi0FlBLXvLiwEDyWq/mMNd2iRkZhBd9T3T5EuSCI9u3fTuBJZyM9FmdtLQ0BFFCra6i4u7baHjnTQSP14gG0clEo4Udicd4HMEfILJgnhF5vO0uPCNH43W70fRAtxWP3y+Yjxa37NUYlY8+RP0rz6Ns3gheP0Jgd+2ccXjHWHbOIKTiYuScXKPAzR5CVxTUmmrDztmwntjypYQX/0B06RLU8jIQxCR7NW7aOQpogl3BNTJ7JvE1q4gs+p6CixP26owZX3NAG9o57SYco9EoS5csRhIEDjz0MOKrV7H1wX8S+uwT42ZOSzPzg83onhXulyQQRCO0q2u4+/bHN/UgfFOn4R0xErlnbyRzO4xk6Kra4kawYQ5qgteH7PMjFxbhGT6SAEejNTWhbN9KdOVywnPnEJr5HbHVK9FVFdFvFOew1D9ghJtFESEtDa2xgYbnnya2cjl5V/4f4ydPZeWyZaxauYKhw4bbVVe7ojFl3XyxWJQZM2bQv08f+g0Zirp6JVsfvDeFRyNPOxGltYqjaKEmEEQ8Q4fhP+QwfFMPxDN0OK7iHkaacNJvNU+HNA6UvNhYT302U5PlvHzkvHy8o0ajn3gyalkp0dWrCM+bQ+ibr4guXoTWZETRBCFJQKbw2Ej9808TNXkcPnkqaRs2sHzZUoYNH0FaWlq34PGbGTPo16c3/YYM22MeBZNHX1fjcWP34LE9sb10O/6AH5/bTe0n0wnN+BoxPR0tEgFdRwNwu2l481UChx5O3oSJ1NfUUFtbS1ZWVrcVFp0JqaLRx8cffcgTJY8lRKOiIO9ENFpoXTy6UOKGeLzx1tu49aYbQIcrrvoL1VVV5OTmdk+OdWN9NqJpQNvjV8uP2plQ+wxLmVo/ojd7j2av7yQ1tKNgFf3YkZC2hbYGqoIeibZv+3YCSzA11NejqCrZmZkgiGg11VTcdRv1b72B4PGYa9g6qWi00Jp4FARTPPqJLJhH2Q3X2OIx4PWi63q3SFtNtXO+Me2coahrVhu649OPjesQSDeXru3EXh0yFP8hh+ObMg3P8OG4invuws7B+J/1bHwo8Wy9LknI+QXI+QV4R49B/9nJhohMsXN+RDMLHO3QzmlooP7Zp4iuWE7+Ff/H8MlT2txebVPhaJHX2NjI/Hlz6denD/0GDqLpmxlU/vMOoj8uMvZ5c7tt4gRBMDqlICDIMno4DIKAZ+x40k84Cf+Bh+AaOAgpPT3xO8kC0SrMIUm7zNTQrUiFbi5kFUTEQAD3oCG4Bw0hcNRxxDduIDx3Fg0fvU947lz0WATRF0DXtcRmnuaNI4gSyC4iM7+ldPs2ci67kqFnnMWaVauYN3cO48dPICMzs8sZqxaP8XicRT8sYsigwfTp14+mb76m8p93EfvxB/RmPKLrIAoIkowWCSMIIv7J00g76ef4px2Ea8BARDOnW9eMFGLA5k/Yi8W9uq6bkWDV6MCiiFxUjFxUjP+gg8k47UzC8+fS+OH/CH33DVqoyfDoJBVGMngUQZZNHreSe9lV9D7jLHRgwfz5jB03jqysrC7P4+BmPEZ//KFFf9wRj+kn/RzfgQfh6jcQ0dPFeNTh+/nzGdNFeWwPJK9trK2qYtT4CUTXrqH+1RfRlTiCbBoUkmQ4+NxuYhvXU/faSxQOHUZejx6Ubd1KRkaGc23bGC0jjcaaxt9ddBGnnX5mSrGE3RV4zYWl7DLE49hx47jp1tu45cYbePB+uOIvf6G6uoqcnO4nHl39B+IaMIjYqpXGchfLLiFhH9oCW1HQGxuxN/pugealUvSkv8wjJi/JSf5qc224M03ZIhra+ut6601J+eGUNuyw0ouQ+gFdN2wvr7fVQjFGcUnjM3pcwdWrF+6Bg1o7cLvDFo0N9cSVONnZOQBo9bWGaHznzcR90Nz50tlEo4XktlniESPgIfh8RBYuoOz6v1N42924R40mzdwWorKigrwuKh5b2qsD6d2vP03fzjDsnEULW7dzLM0RiYAo4J881bRXD8bVf4CxDhHQ9bazc1zFPXAV98B/0CFknH4W4XlzDDtn5rdoTU1Gccfmdo5k6o7vvmH79m3kXnYlvU8/E522s3PaTDha5IWamigrLaWosIh+AwdR9/YbVD3wTzs1FdVYCGp7OjUNQZLRVQU9HMYzeiwZZ55N4LAjcfXpa6ef2oTvJWlgTI6GlzDRMawUVHQd0evFM3QYnqHDCBx9HOHZs6h/8xVCs2YaA4fHk5rjbqWvBgIomzZSdectKGWlDLr0cjKzslgwfx7jJ0wkKzu7yxiryZ3wm6+/pn//fvTp14/at96g+sEEj4LJo/mlBIfRML5xE8g4+1cEDj0cV6/eiWPH48aCeVFK8eC0aAMkPGa6npiEzEHQgl0K2bquSSmMSBLufv1x9+tP4LAjCc+dRd1rrxD67mtjc/okLpvzWHnnLcTLS+lzyWWkZaTz/YL5jB03ntzc3C7J44yvv2bADnikm/OoNONx3Ljx5HQhHtsLlgBYsXwZxb16IaoaDe+9Q2TRDwh+v1n5LWFE66qK4PPT+MF7pB19HFnHHEfFtu1UVJRTWFjUoefSnZEiGv0+Pv7wwxai0TL8koVd8t+aptlpyYIVlUgyjMG4H2SXi3g8zpix47jxFiPy+OB93TPyqKoKrj59yTz7XCpuvcEYi6xxhVRhrWsaYkYGviOONMr2W9WfzYJDyX/bwinxDzMIkfi38V+zTAz7vSSVZ6bH2pou6ftmr0z8vvk5+zeTmmW3xz655HYntzdZwSb/jnE/oesIoogWDhNft9aIlIhii3vJ+pyuqmScdhau/gPQOjjN1xJIjQ0NxGJxcnIs0VhP5d23Uf/2G0aqp3WeXUE0WrCy9ywb2xoDVBXB4yHyw/eUXf83Cu+4B/fI0aQFAmiaZq9j7kpzY2t2Tu9+/al7502qHvwnysaNO7dzwmE848aTeZZp5/Tukzj2Xts5ZvmxXdg5diCruZ1z+JGE58yi7rWXCX33jfFdtzvVzsG0czaup/KOm1vYOfvbXm0z4SgIArW1tSxcMJ/i4h4MGzGC6uefofrh+9Cqqw3jw1Tt9qAiCMaAEgkh9+hJ5i9/TfrPT8HVr7/p3dOMXF9RalUs2oRhRYN33plbTdWxIpbm76EaXkZXcQ9cp56Ob9qBNH72MXXPPU1s1QoEjw9j8E/qmPG4UbGzoZ7axx5Ca2yg4Kq/MX7CRL7/fgEHHDCpy0QeBUGgoaGBNatX0a9/P/oPHETV809T+/D9qM14NL9gchhG6tGDrHMvIOPkU3GZ+8zYwlzaeedLHowtLlOerdd33ng7hchOJxAE5Px8I2I2aQqNn31MzbNPEV+5AsHrNSZYcxIzePSiNtRT++iDaA31FFx5NZMmTzYjj+PJ7iJOgGQe++8WjyCIUrfiUWuoo+bRB1EbGii48v+6JI/tibraWtyyi7z8AsILF1D/+ivgciUMQItX03gWZBdqfT21zz+Nd+w4+gwcwJrly8nLy+9yXuuugOai8ZMPE9VTk0VjitGe9D0rJTX5nrf6QPOIo/W3yxSPY8clicduGHlsqK0jKzeXtBNOounT6YS+nWGMkWYmg70Pmyyjh0Kkn3E2ef93LWJ6ul0JMSHy9JSnlNf1Vt4nVeS1+pkksWdpvdaEavL3kuRi68dOft38DT053Nnis6lC1s5SCDVR85/HqXv1JaM+hKKYBV4Fw5FnZpJ5Ro0m/ZTTESSJhtoaOgrJojEai5Gbm2tkzzQ2UnnP7dS/+bphF3ZF0WihmXi0I5CWeFy00Ig83n437pGjychIp76hkaqqyi7Vp1uzc6pffJaah+5DrapqaedgpKbqkTBScQ+yzv016b84DXef/W3nCLu0c+xAVgs7p4D0n/0C36SpNH423bBzVq1s3c5xe9Hqk+ycK/7aJnZOmwnHaDTC6lUrGT58OEU9elL99H8M0djYiOD2tBCNlgdKFwTSTjiZ7IuCeMeMQ5ClhBKXJKNS0Q7Q3CDdFXbVEYytOsTE7wsCrqJiss+7kMDUA6l97inq3n4TPRJGkI0UBtsDb4aQdSVO/TP/QY/FKLzmekaNHsOiRT8wfsIE0tLSO32HjEajzJk9m5HDh1PcqxdVTz1BzSMPtOARTNFthtEDx59EziUmh5Kc2NemlQhxq9dgf1yTpPQaQRCM8shge2rkgkKyfvVrfJOnUvP0kzS886ZRDVaWbW9UgkeF+qefRI8rFF59DWPGjGXJ4sWMHTeOjIyMTi86DB5nMXL4iD3gUTV4vDiId6zJo64Z1fG6JI+yyeN/0ONxm8elSxYzZmzX4LG9oOs627dto7hXTwiFqHvlBeJbNiOmpdlbE6FptkEoCAK6qiD6/YRmfkPj9A/J+vVvyMzOZuOG9QzoJOlo3QUt1jQ2izSqzURjcwFo3eexWJQF8xeyfVsZY8ePYtCgQSlRRuvzyX+3Jh4fuh8u70YFc9w+H5FQCG9RMZnnXUh44ffGem9LNNowDXKziJ7gdht7m5rVhlvF7qQKg21wdkx11H2De8BA4zokj6X2dTMM5KzzL8Tdtx+apiXWkbYzmovGnJwcQzSGmqi45w7q33zN4MtyjnVF0WghSTxaaautikczbTUjECAky1RWVpCfX9Al+nQLe/Xp/1D7yP2oDQ07tnNUlcBxJ5p2znjDbtB1e31z+9k5SeKzNTunsJCscy9I2Dn/fdPeBcK2c1TF+Hc8Tv3T/06xc/anvbrfLSRN09A0jWVLl5Kelk5hj57Uvvgc1Q/fj9bUZJxUUsENWzTG40jZueRffR2F99yHb8JEQzRaqaCynELWDhde7wc0P7ZgRjwscYuu4x40hLzrbqHw9ntw9R1gEGjvF2keR1WNiUbTqH/xOcruvYeCggJ69uzFqhUraGxsbOEN7izQdR1N0/hx0SJGDB9GUa9e1LzwLLWPPNiSRyvKGI8jZmaS95e/U3TPffjGH2CmAKh21Kh5h2vTwUjA9qqnvGzu76mbC/g9g4ZQcMMtFN5+N67efWwubWPL4lHXqH/hWcof+Cc5WdkM6N+fpYsX09TUiNjCoOgcsHn8cREjhg/fQx7/ZvA4IYlHuhuPWfTvP8DksanT8tjeqCgvR5REMjMyafruGxo+fD+x0bU1ublciUk1SZigQ+0LzxBdu4YevXsTDoVpaGjoqFPpdmhZCOejFqJRbBZpTBaDqqoiiiLlZaVc9/druOeOX/LJh5dyefBMXnrhJUAzgyutb9MBpIjHm269jVkzZ/LQA/eTnpZGTU11R12a/QZd19F0DQ3wH3wo6SeciB4yxkvb+BaMaoeCz0fDR/8jsniR8WVBQBBEY+xp9rA3/d6dNmBECa3rv+8Pw6nbZg/TKI+tWU3D++8ZIjppKY8hsF1oTQ0EDj2CwJHHAFBRVtbmG5a3Bks01tbWIAgCmWaRRS0UouKeO6l/4xXMxtuiMVl4dSnRaCG57ckOJU1DcLtt8Rgz9zT0ulykpaVTXV3VaW1VaGavDjPt1ZeeT4hG2ZWkO5LsnIxM8v5yNUX/uB/fxEmmPWTes5LcznZOYqxNebm5nTN4KAU33krhbXfj6t17x3aOptHwwjOGnZO9f+3V/SocFUVBFEVWrFiOqMOwESNoePdtqh68F62xwT55C4JlqMZieEaOpui+h8j+/cVImVnGQKTrLYzU5EmsrZDsnW2NRB2jo4leLxmnnkHxI//Cf+jhZmjZjFRa7TVJRNdoeP5pKh99iAEDBxIIpLFq5QqUpGpOnQWqqiIIAot+WEi6309Rr940/Pctqh+6F7UZj7Y3OhbDM2QYRf94kOxLgkhZ2TvkMDm83tYerGRveWtcgsmlz0fG6WdR9FAJ/gMPsqMqzTsjukb9c09T+a+H6dm7N8XFRSxdsgQ16b7uLEjmMc23BzwONXm8+NKfAI+P0rNXL4qLizstj+0JXddRVZWy0lL6DhiIUlVJ7fNPozc2mulahmdTALSGBrRwKNULbxog0dWrqH/9ZURFobhXTyorKuzjO9h7JItGr89rVk99NGVNY3PRCIn+qSiGsVxZUcZNN9xMY90TPHTfRh66bxuXX7qIl1+4lueffRlR1G1jzPhuy3WPrmZrHmfPnMnDDz5AIBCgtramhWjtSggEAkRjcaKNTUgZGWSefS6unr3Ro1F7rZstJGQZpayM+tdeRmtqNN631k81g4Bgj2Xt/2hdzO63h5nG3jD9QyKLf0hxNOlmtpGuKIhZOWScez5yfgGhxgbyCwvw+/3tym/KPo2CiNfnQ5IktFCIqn/eRf1rL5mEJYo1Wv9Oee6KSI5qWc+6bo/dkR8XUnbD34gs+RFRlvG43Xi9PmqqDYdQZ+vPlp2zcOH3ZKQFKOzdm4Z336H6gX+2EI2G5jDsHPfgoRTecz/ZF1+2UztH76x2zhlnU/RgCf5pB+7QztE1jfpnn6by8UdNe7WYpUuW7rOds9+Eo1W5bcuWLdRUVDJ64kSaZn5D5f33oFZXmeSlikbLY+c//EiKHniEwKGHm2u1dVtB2xcvSTC2V7i8VRKtNpiKXdd1vCNHU/TPB8k442wwk1WT95C0SNRiMeqeepy6N15j6PDhiILI2jVrEASh0whIK8Vp7do1aKrCoFGjCM/8hsoH/mGshUvi0eYwHsc37WAKH3iEwJFHYxsZlhckyYCw8uvbO+WhNS71ZC7NQcM3djyF9z5E+s9PNXLbIVGQyeqM8Ri1Tz1B7Ruv0W/AQDICaSxZstj25ncGNOdx8J7weL/Jo/BT4PFxk8cBZAQCnY7H9oYgCGzauJHs3Fw8LhcNH75PaPYsu+iQYK3dEkVjS51xE4yoS5IHU9c0BJeL+rffJLRwAdnZOTQ21FNdXd3u90t3gnWf19fX4/V5+eSj6Txe8ii/2cWaxmTRKMuGaLzx+pvQlRe44/YY/QaD1wvH/xyu/ss2Xnv5ep575iVE0TiGIR5hZ+LRSlv97ttvefShh/D5fNTW1rb4fFdCZmYmsXgMdPCMn0jGqWegx2J2JWFrDNRVFdHnp+HjDwnNnmV/vyue897Cmkuiy5ZS/8YrxpKipOwES2Dr4RAZJ51M2rSDiSkKbo+X2tq6dm1rsmiUJIn0tDQjAhMOUXXf3dS9+qLxQUs0mu2nO/LZPJvAjjz+QPkN1xjiUZLwejx4vF5qzbWoneXetrhct24tuqoycMRIorO+oypFdyREo23nTD2IovsfIe3oY007R9uhndOemsPCbts54yZQeO/DpJ98yg7tHC0eo+7Jx6l74/X9ZufsF+GoaRqyLFNaWkrZtq2MnDABfcN6Kv9xl1HFyOtN3Q/O9GCiqqSf9AuK7rkfz6AhdidNJsn+uwPIS25DcjuSXweDUDm/gIIbbyPrgt/Zi1stAgUMEgWXC7W+jqpHHyA06zvGTJjA9q1bKC8vR5blDjdWNU1DkmU2btxIPBKh36AhCBvWU/mPO1vwaHdCVSHtmOMo+ucDeIePbJXDZA9XRxuOyW1Iuc+szqZpuIp7UnDrnWT+6tdmdEpP7YyyjFZfR7XJ46BRo4g0NbFl82YkSXJ4bAf8FHhsb+i6TiQcprammh49exJdv566l54DTU0tox+L4Zs4iaJ77ifv2ptxDxpibNUiybaBJYgiakU5tc8+hd7UxKChQ9m6ZbP9Ow72DMmi0ePx8MlHH/F4ySP87qKL7X0ad0s0lpdyw3U3osVf4M47ouTmqKhh47NKg85hRyn87f+28dpL1/PcMy+3EI/J0eVk8Wht1XHL7Xfw7Tff8NgjD+P1eqir65ri0V4DJIqEGhsQ3W7SfnEa3lGjjRR/l6tZ2qIRga996TnUmppEMZWfAIxK+Ma4UP/268TXr0NwuROROt2sWhmJ4O7bn4wzz0EIBIiHw4RCIXtNbHuguWj0uN2GIR4OU3nfPdS99LydypkcNTb8Jt3Q6SUkFamzhIqqGuLxh+8pv/7vtnj0eTy4PR5qaxLisSP7tKZpSJLExo0biYXD9B88BGHjBir+cQfxjRt2bOccfaxh54wclWTnJEmhrmbn9OhJwa13kfHL8xP9LcnOESQZtbbG0B2zZ+0XO2e/CEdjkX2M0m1byS8qIsvtpvT+fxj7pfh8qQtSTQLRVNJ/fioFN9+BXFCYGtnoQrA9A4Dg85H7l7+R/buLjbCyNWGan9WVOILbg7JhPZUP3ouwbSujJkxg47p1VFdXG6kSO0hxaQ+Iokg8HqemupK0zCxyfV5K77uH6A8/pPBocxhXSDvuJApuuxtXz17GBNI8UtxVoCcNLrqOmJ5B3t+uI+v8C833k9YDKGoKj+LWLQweMZKy7duora3tNDxWVVSQnpVFrtdr9scd8KjESTvuRIfHTsZje8JKSdyyZQt9+vVD0HXqX3/Z3sMOyziMRpFzcsn81fm4+vTFP2UqGWf/CtHrA1VJmdAEt5umLz+j8fOP8Xl9uN1uyspKu+Z91YFoIRqnW9VTU/dpTBZ1sINI4w03oysvcucdUXKyVTQlsW+9LIMShsOOVPjb1dt4tRXxmIxkHmWXC0WJM2bsWG65/Xa++fpr/vXoo3g8XVM8WtcuPT0dRdNRYjG8w0eQceYvjXWOpiiyo46aiuj1Ef52Bk1ffm4dpEun6+4Oks8rsmghDf9928xOUGwjFjCKBSkK6aediWfseHRdI64opJn7cbfH9bFEY319HbJsiEaX240eiVD14L3Uvfhcimi0U3yhe4pGC0k8Jac6WgVzym+8luiSHxEkCb/Hi8vjsSOP0HH3dkt71WfYOT8s3IG9Gift2BMpuP1uXL16d207h2Z2TkYG+X+/nszW7BxVQfB4UTaso/LBfyJu22raOdv32s7ZZ+FoqdWtW7YQi8bo06cvlc/8h/AnHyG4mi2ONv5Aj8dJO/Fn5F9/M2J2dsqk0tFejD1CUnstEgWXi5w/XWlEHs3IakIMGxsFCz4f0bmzKfvXI+Rl55CWmUFFWanxiQ4aoCwe16xejYhIr169qHzmKcKfTkfwuJP2pgIEY2Gx/6ijKbjxVlP4aynpu10PLdMSBJ+P3CuvJvOX59kGgv3pJB7LH3+UnMwMsnPzWLd2rfHdjuZxzWokSaJnz15UPvuU0R/dO+DxiGMouPG2nzaP81J5XN/BPLY3RFGkrq6OcDhMTm4ekR8WUv/W63ZaDACCCKqC/7DDCRx9nL2XbsYpp+M78GC0SCShQsAY6yMRap59GrWynP79B1BRXo6m/bQiufuCZNHo9riTROMfjEhjPG4XFtmd9FQr0piTraIrdiAea2eGZPF4zdXbePWl63i+ReSx5fYeALLsQlEUxowdx6133MWMr7/i8ccexe12U1dX16XEo5XGJYoigmgsxdGBtONPxH/gwcZaRivqaHwDMLbiqn3pWRTTQdLcIO8uSFm6I4poTU3UvvwCSmWFncprnb/gdqM1NuIdO470k09BlGXCTSFESUQUxXapZG2Lxro6ZNmFS3bhcrvRwmGqHrqX2uefTqQoJhXCAbq3aLRgnWOK4DDF4/fzKbvxWiKLDfEY8Hhxud0dKh6T7VXBtFernjPtHJerdTvnSMteLbJFY5e1c1pJoxV8PvKuvJrMc85NXZdLkp0zZxbljz9GTmYm2bm5e23n7FNvtTrjli1bqK4oZ+SkSUTnzqb+xefQYlEQU9NaBHNdkf+QQ8m/9maknFy70a15Sjs9mrXTFo9uNzmXX0nGaWcmPG80Oy9ZpvHdt6l7+w2GDx/Bts1bqKyssCes9oTF47Zt24iGmxg4ejThubOof9nkMWmytzj0HTCF/BtuQS40O6Egdk0OLSR3suTz8PnIvepq0k/6hbFPTvMJzuLxnbcYMHAg8WiErVu2dDyPoRCDRo8mPHf2nvNIIpK+v3lscw/83vIopfIY60Ae2xuWt3Hjhg0MHDQIPRqh9rmnUJONQHOvK7lnLzLPPAfR6zXWiaoqcm4eWb86H7moCD0SsRfxoxtVFGOLF1H3xmu4PR5yc3NZuXwFgiD8ZKK5e4vmovHT6dNN0fh7Tj/jLOLxOLK5t1iyEWH1reT01BaiMWmrQUHA3vFG1wzxqIbh0CMVrvnbdl5pFnlMmdOb9TdZlk3xOJbb7ryLr7/8isdLHsPtcnU58WjtPZqWlk40HiceCuHq0ZPMM89Bysm1qxla42WiMuUPNLz/rnGQLnKue4rmHIZmz6Tp0+mI5lro5Ggsqoro8ZB+xtl4hgxFjcWIqyppaen2sdoSiUhjPbLLhSSKuD0eQzQ+cj+1zzyZEkEWktv+U0Ozc7cER2TBPMpvusYQj3LHisdUe9WwcyLz5lD34vNokWjKljkJO2cy+TfcilxUnCIau5296vebds7Pd2yv/vct6v6bZOds3XM7Z5+EoxXibGyoJ7uwiEAsRsVTT6Bs3IDg8RoeC+vEzPK37mEjyb/2ZsPIaDYBNZ+IugKS1zkmt1/yBci96mr8hx1u7Clneu6t9Y7IMlptDbUvPIO6cT1jp05l3eo1NDY2tnuKnPV7ZaXbyS3qQSAapeqpf6NsSPBonasej+MaMIC862/G3adfi07YFTm00KoDQ9eRMrPI+79r8E050KiqZ7rpm/OorF/HkNFj2Lp5M+FwuIN5LDJ5fGLXPPZN8Ai06bZhxrVNzTBoi9/oyjy2J3Td2AC+orwcr8eD3++n6YvPaPzi05R0e3SjIE7gmOPxH3yYvRG6VZk3cNSxpB9/kqk+UsWBrmnUvfYy0RXLKSzuAUBTY6OzX+ZOkCoaPXz60fSkSOPZxONxXEmikaR5SBCElEjjDa2JRiFJNCaCR8a/NZAs8XiEwt/NyOOzZsEcTdd3OHcni8fRY8Zy6513MeOrr3j8X4/hdnc98WhVbExPT6cxHALAd/hRBI46Fj0WNS6Unlo8D12n7tWXia1f10JYdyuYdp1aW2vYMU1NCSe59b4so4Wa8B90COnHnYgORONxBEFAFEX7+rYVUkSjLCOJIh6vFy0SofrRB6l98gmDv6T0VD15zPupIfncLUdUPI7g9xNZMJ/ym64huvhHBFk2xWP7p61a83F56XZyC4sIxKJUPvUEyoZ1CF5PSzun3wDyrku1c7q1vZqVTd7V1+KbPHUHdk41dc8/g7JhvWHnbNpzO2evZ26rCmh5WRnlpaUM6NePmrdfJ/z1l+Dz2RV+7NtIVZFycsj/v7/jGTqsRSft6gQ2f9YxCubk/fVa3IOGoMdjhjcH07ZSFIRAGtHvF1D1ykvkZGQgulz2wuP2guVl2LxpE1o8Tu+ePal9540WPAKg64jp6eRd9Td8Y8a2TPvrwhxaaN5+S/C7evYk7+prcPXtm/Dk6HoSj/Opfu1lstPT8fg8lG7f3q7ttnncbPHYa+95VBT0eKzZI570iCU9x1p5vflnkp51owck3yttJR5T/r0XPLq97c9je0PXje03tm3bSt/+/dFqqql5zihoY3tuRckobDFwEJln/8r4ophaTEAQRTLPuwDP0BHoySmrpvEY37yJmuefRtB1evTsybp16wC6fTR3b9BCNE7/iMf/9Ri/+X0i0pgiGiHhoDU9x6nVU1/cqWg0E2XM46WKRytt9e9Xb+f1l27guWdeRhJB03ZPPI4ZO9ZIW/3yKx5/7DE8HnfKmsfODqt4hCiK6AgokQhyRgYZZ5yNu09f9Eg4ZW9H3bzfY+vWUPfGq7aQ7G6iMXnOaPzsY8KzvkN0uxN2HRhOpXgcKTeP9DPORi4qJhYKoWgaGRkZQCKq21aQJImGhgYkWUJOEo01JQ9T8+TjxrmYUdEWorEL3J/7Hea5W9dAby4e58+j7KZriS5eZIpHD263p93s1mQ7R43F6d2rF7Vvv0n4qy9at3PS0sj7y9X4xo5D17WfkL3ai7yrr8PVuy96PLEmwbBzAkQW7puds1fC0ZoYotEoWzdvZsSEiWgbN1D/xqvoTU0IopS6YaoooqOT/duL8R92VKqhKnRPAsE4L++IkeRcfhViWjqiy4WgKMZAZU4oSCKN771NeO5sho8bx6YN66ipqbFz/9sSuq4jSRKxWIzqqir6DB2GtmE9dc14tFPVFIWs839D2rEnJAyWpMG2q3PYHPZkb56jb/xEci69wljzabyRxKNk8DhnNgNHjqayvKxjeKzcBY/iznnUNQ1BlhFc7mYPV9LDnfTsbuX15p9Jem7Ns9vG2FseB41qXx7bG5ZBvGLFcnr16GlUzHvzdaO4gF01UjTS7b1e0n92Ct4Ro+ztNywIooiuxPEMHU7GWecgBgK2KLevvSjS9PFHNM34isysLNwumarKym4dzd0bJItGjyUazUjjGWemRhohkVKeLBqNfRqN6ql6szWNrYnGxLGsNiTEY/Kax7//zdiq45mnXkSSDPFotWFX4vG2u4w1j/969FE8bg915lYdXQHWGJWVlUV9KISuqvinHUTaCScnfyg16igINPz3LSJLlyQyObrZ/CgIAvHyMmpffNYoRGJlGFhjqWhUWk076hgyjjqWhsZGPH4/jQ0NQPs4jRoaGhAEAZck4fZ60aJRav71CNVPPGbMd5ZoTE5d/KmKRgtJ4lGwxKO596EQCBCZP5eyGxPi0e/x4PG0vXhsYecMG4a2cQN1b5p2jrQDO+e4E22HtT1G0f36Yws7Z8JEci77M4LblThX3VzMLkg0mbpjb+wceW8aaJXBbWpsJBQOkZeZSeVzTxnVNwMBuwSuXaY5EiFw9LFknHOucQMmG4x69yTQNsSBtONOJDxnFrV3/gNPThaKGEH3ug3+PF7imzZS+8arFB8wGX9mFuGmRrKzs9v8ulg8VlZUEIvFyM/KovL5p4xquIE0g0dLNIZD+A8+jMzzLjT2Y2olT7y7IcUQMtdApJ/0cyIL5lP38vPGNVLi5iJyk8c3X6X4gAPwZ2ZSW1PdgTw+3TqPkRD+g3bMoy5AdOVy4lu22MVP9hmCcS2lnDw8w0cg+v3tev90FR7bG5Ik0dTUhCyKZObkEF+3hrrXXjYMBHMzb0ESIargHTeBjLPONr+YKMZiZ1nILnRNI+P0swjN+Iqmr74wVIfxQSOlzYxm+idNoU+//qxauZKc3NyOOPVOieaiMVEIp5VIo1nNpjXRWFVZxvXX3gTqi9x5e6J6qijuWDRaSPZpNhePhx6hoGvbuOfeGxCAC393PoqiIcsiuxKPo8eM5bY77uKG665BR+fSyy6ntraGrKxs83c7b9+yUiqt7U5URUH2eIx7fea3RJb+iOgPGP3G6g+ShFK6ndqXnqPw9ntSw7p0PWd5i8wwSxy/8xbRJYsT44XlGHS50CMh3H37kn762eDz4Y1EqKmpoUePHvb1bA+4XS5jTWMsSs3jj1H9+GOJG9wRja2jmYdJFwR7yxUhECCyYB5lN11H4S134Bk9Fp95yZLTVvc3bDunMtnOecZwdAYChr1i2jlaJIz/wEN+8vZq2s9+Tvj7edS98qIxRimKYed4PcQ3bKD2zdconjgJf8ae2Tl7LBwt1R+Px1m1YgUjJ0xEWb2Sxg/ew5zJUvNfVBWpoJDsi4LIOTn2CVkpmzv7nT2JSiRHTvbkXJLXWuzud1M+38qJpKSs6jqiy0XO5VcilFcR/s9TuP1e1LiKkiWCJCF6fYQ+/5TQzO8YduDBzJ3xFWkZmWRkZLSZN97iUVEUNm1Yz9Bx41FWraDxg/8lzknTDGtDVZGyc8i+KIirqCh1PVw37YQW7HvQLAQiBgJkXvBbwgvmElu7GsFtlB7XNc3k8ROaZs1k4EGHMO/rrygoLCIQCLQTjxsYOm6syeN7O+bx4lQerX6gxWLUvf4KdS8+i7Jtq71WYN9WPVpeMBEpPZ20408i+6I/4irukbiP2mGy3hce57cDj+0Ny3Bbv34dvXr0QJZlyl98nviGdYnUO1FEVxTEQICMM8/BVdzTSHWR5ZRxOdnokjIyybrw90SWLUGtrDQMSGsdkyQRnTebhvfewffL8xg0cCDfL1jAxAMOaFdDsjPCuoZ1dXV4fV5zn8ZE9dTmolFASMnWUVXDqKqqKOO6a29C0F7k9tuj5OSqaDEQpV2LRgu2eMRaqwayBEoIDjtaBbZyz703ogO/2RPxaKat3nT9daDrXHb5FdTU1JCdVFm9s8K6NzMzM6mtqyVbFPEMG076L04jtnY1xOPGXoWamkj1E0WaPvmY0M9+QeCgQ5LGu645b9pFgMyxIbZ+LfVvvJLwNJiO8kSpXoG043+Gf9pBoKnUNTaSk5MDtN/5ez0eo3pqLEbNEyVU/+thdKsylCMad46kyGOr4nH+XMpuuo6CW+7AO3osPr+UWJq2n5Fi56y37NWVNH74XoI3y87RNKTsbMPOKS7+SdurUiCNrAt+T3j+POLr1iK43badI/hMO2f2TAYeeDDzZ+y+nbPHqarWAZuaGlE1jZz0dGo/mU5s+TIEv99Q/dYNJ0nosSiZZ/0S34QDUk4ueU0GQCwWIxwOEw6HiZuLp3dnMtmZ6FNVlVgsRiQSIRwOE4lEiMVi9jkkfydZQO7st1qkZe7iPhQEAV1VcRUUUfDvJ/GfczZCOIbcFEWoazQmGpcLpayU+vffxatrpGdl0WSmc7QVrGtQVVWJ7JLJDgSo/fTjFjwKkowWjZJ+2pn4pkxLOUZbpRrZRTV28/h7+vk9bEyCa3OS8Y4YSea5F2KG0oxn1eKxjIb338OrqmTl5VFRXga03YDVkse0PebRSjEKfTuDyrtvI7psCVoohB6LmY/oPjxi6NEYejSCUradmn8/Ru0Lz9gpJebFaZNrk3qK+8Jjfpvz2J5IeG4r8Xk8ZOTkEpo9k4aP/mdPvnZfUlW8EyeRfvIpdhoztBw7wVzTpCr4DzuC9BNPtidzOwohSWhNIWpefgFxyyY8Pj+ySyYWizmiMUU0TufxkkdbFY3GF1K/Z4hGkarKMq695kYE7QVuvy1Kbq6KGgNR3n3RaEEQsJWjdSvIMihNOocdrfG3v27hjddu5JmnXkCWRRQlMafuKm31ltvv4Ltvv+WRhx4kEPBTU1Ntn09nhqIoiKKIKIhEVQVN18k45XS8EyehxWIgJkUVTftHqami7oVnja1q7PHO+kjnPl8Lts1jvWDOOXWvvkR8w3q7irLhZEhEfDwjRpJx+lkIgkAsZtzDoija17E94HK70eJxap99kurHHkLXVIRWRKPFxC4Z0fWdP+8p9vL7yfeOTst26+Yxrefk30t5b1ew7VzTZocWkcfym64jsvhHEAT8Pt8encfuwrJzqquqkFwuw1797GNiy5e2tHMiYTJOOQP/1ANTjtFV+tu+YEd2TtZ5Fxivpdg5bpTSUho+MO2c3HwqynbPztnj3mutSdm8YQN9Bg+CbVtp/OQj9MRomBCN0QjuocNJP/WMhOfZ2iA66QQVRcHtduPz+fD5fLhcLrva1q7IbuH1BqLRKNFoFEmScLvdeL1efD4fXq8Xt9uNKIq2iLSOsSuR2iLKaEJVVeLxGIqioKoqqqqiKIr9GmB46CMRALKuux6xZw/0eAx3bRNibT26riF6vYS++pzI4kUMHDactWtXE4/H22yAtXgs3baN4n4D0LduMXk0YW/4bRTFyDjtLESv166m2JZobnTsDHsamd6LxqRybw5gacceh2/KtJT9vIxolZfQ158TWbKIvoMHs33rVhRFaTPBkcpj/yQezd/bBY+2d0qJ0/jpdLT6esT0DNs5sl8eomF9iv40kCRC33xtGxyJqGb7wPKa7zaPi3+kz6BBbGtjHjsCmzdtokePXoixGDXPPY1aWmqM01YkWlWRsrLIOu8CJHOjbmh9Ak6sZZQQgKwLfmcUBVMS2xFhblcQW7GM6ldeRJYlevfoycqVK4GfZqGcZNHos0XjI/z2DxfZ+zS2KISTNN6lisabEPUXue3WhGiUZMByHu9hQMUSj8IOxOPVV23hjVdv5Oknn98D8RhnzNix3HzbHcz67jsefvAB0tLSqK7u/OJRlmVUVSUrK4vGphAoCnJBgbk9R45hTJsiyuZKFAnN+pamzz4212Cp9vtC8oXtItBN2y68aCEN77+HVa/CWpYjyDIoCpLXR/rPT8UzbDixUIi6hgbSzTHE2ne0vaDV1dLw/ntokbARFU6e+5Ky33TTS2Kt9U95mK+TJDKBhH1gXYM9fZjX1H62Irc7EKbWZ+2/Nc2Yy5q11fIQCWYb7WMIiXWLtHaeu3hYgtMWj2nphGZ+S/3774LSyvYP+wmWnbN921Z69OuPbusOi+QkO2fAIDLOaGbn0LkzGtoCqXbOCfgmT21m56iGnfPlZ0SW/EifwYPYvn337Jy96sH19fU0NYUYUVRM/VtvEF+yGNHvS4k2Ihgh44zTzsI9YKDxxeSCCoJgN1CWZV599VVWrDD29zr66KM56KCDWnpaW0HyZKqqKrqu4/F4AFiwYAHLly+noqKCSCRCWloahYWFjBkzhmHDhgGGyHS73TsVH62lZWlmB5UkyfaWK3EFHT2lzfbnvF4jVXDUKPwXX0zTDTcguVxotY3E3G5IT0PZuoXGLz8nb9wEZLeHhoZ6cnLabg1QY2MjkXCYooICGt56g/iSJcb6M0UxRlJzC5X0X5yGZ9hw40tJE6N9TVQ1qZpVaxPhHlor5lqSXYnCZCPDel9VVcM7ZQ1wSS2yj9Dyjx1CluWUycUSO66evcg47UzCC+YZA6qZCorbjbJlC41ffkHe2AlILhc1NdXk5xfs/jXYQzQ2NhJO4TG5P7JTHu0QhKqhVlUZ5xGL7X8DTteNysKA1tSE1lBvvK5pqZvGtxGa30fNeYzsiMevPidv3Hhkl4uamhry8/PbvK1tCSsltKamBp/HjS/gp/btNwjP/CYxPuvm3a5p+A8/Ev/hRxlOPyuysJPQlRFdVHH360/W+RdSecfNaMkZJKII8ThN/3uP8JHHkDlxEnX1dVRVVZGbm9sum4F3FqSKRh8ff/QhT5Q8lhCNipKyT2Pz7yVEYznX/v0mRP0Fbrs1Sl6+ihoByQVYKad7azMlAg425bIM8Sadw4/RgS388/4bEQT4ze9+vRtpqy6UuCEeb7z1Nm696QbQ4Yqr/kJ1dRU5Oblt5wTcD7DalZWZSZU5rqcdfxJNn39q7N2o63a6nB19q6+n7pUX8R9yOGJ66t6Fu1qy09FowYWmoQsCdS88i7p9m53WDtjLk7RYDP+BB5N5+pnEFQW334/HjDJ2REq6ruuGsWxGGq1UWnteT47Mmc5ORDHJ4aWj61pC0Jnn2Zw729ZI/G/v2gv2b9u/0dyZbrVVlhNRJMzXNRVd01NTi602J4lHy9babSSLR/N4qAqC2w3hELqqIbShTyDFznnbtHN8zexVxbJzRhhfsqPhXXvnht3FTu2c088k/P38lKVLhp2zmcavviBv3Hgkeffs1b2iuamxAV9mBlIkTMMXn6JFo4hutz2AWJtFu4cOJ+2YY828fmNzccCMyMXxmWHte++9lxtuuIFIJIIgCLhcLg466CAURdmpcGwetbQqvT788MN8+OGHrF+/nu3btxMOhwFjkXt6ejo9evRgyJAhnHHGGZx33nkAtsHS/OZqTTRag5+iKMyeNZN5c+ewceMGGuob0HSNQCBAz569GD9hItOmHUggLQ1V15A0DVGS8P/858SeeQZ13Tr0dJ+R4qJjVHT88nNyf3k+PXr3YevmLW0qHKurqkjLykYKh0weI4judNBUo9hFJIx70CDSjjneiJpa65tImvhMT8/+Nv5TjI6dfC65o1gpeG0xMSVlbtnwH3QIvklTCM34CjE93djXS9dtHnPOOY/iPn3Zvm1bmwrH6qoq0lN4NPtjCo+DW/CYCjPhJcnw2Z+DrG5Nxgiga4kL2Y7j+I6cEP6DDsG7Mx5/afBYum1rlxeOVlW6DevXMXrMWNSKchpefQmtthbB50tUvNVU5MIisn79W0Sz8IWFXU3AVhXfjF+cRtNXn9P0+afgchkGmq4bBYg2b6DmpefoMW4C+fkFrF23jtyfUKGcFNHo9/Hxh8aaxt9ddBGnnX6mPZ9Zn7WiB62LxhuReIFbTdGoREC2RKOl7/dFoZjfTRaPLhniTRqHHyMCW/jHfTehA7/dhXgEkF2GeBw7bhw33Xobt9x4Aw/eD1f8pfOLR0v8yLKMJLtQ4jFkr5fMc39NZMF84tu3Ini9duRHMI3z8MIFNLz3Nlnn/6bFPNpZz7U5rLm+6asvaPry88RWDdbSH1FEj8WQsrPJPOscyCvABWzYsIF+/frZjvb2hn1tzfoadjo+2GJQEEXDI6JpRgERRbE/L8gSgiQbnUrAeN+q1WFdg6QARkrKcqIVrfe/HfGuqinVx61j23+7XKDE0ZqaDAFg/gSShOD2GO9bx0haemA5/XTzOuhWCnWyh731i2hUW3cblchT6h+oKu0xkSfsnDANX3yGFgkjpmck7JxwGPeAQaQduzM7p/tjZ3aO74DJhL75upmdI9P05efknnMuxX36ULob9uoeX9V4PM72LVvpP2IksdWriMyfa4SEkxdTmvv3pB13AmKvPkSjMdNYFHC73ciyjCzLLF68mLvuuot33nmHSCSCz+cjHA7vMsoIqcLCSoP58ssvueaaa/j++++Jx+PGCcqyLVDj8Th1dXXU1dWxfPlyvvzyS1577TUeeeQR+vXrl+Lt3pGAtMTJksWL+c8TjzPzu2+oKC8nEokYXikM4txuD9k5OYwbP4Hf/u4PHHbEEWguETEaxTNmDJ5L/0j5X/4PsUcBxONG7r3XR2zlMpoWfU/RMcezZf06otHonlK0W1BVlbLS7QwZM5boquY8GpOdHosROOo43EOGGl9KWstgX39VpenT6USXLzMMFU0FTTf4Tkr7EHQt4bnTNHTN8IzZn0UHTUfu3YfMs36JXFDYwsPWvCPY2sP8jKZqfPrxxyxZshh0DU3T0TTVvkc0zXxNNzxn9vvma8b75v6jmk6PXj355XnnU1xU3NKLoyi4evYi7djjCc/6zo6yGxWrTB5/XEjhMcezac0aOy26rXgcOnpXPB7bgscU6Pq+OEl3jB1EpnZ/UUnbYbd4XLGMpkVtz2N7wHJ4rV27hn59+iDLMlVvvEpk0ULbILD50nXST/4FvvETUooL7MjIbTFZCQJiWhrZv7+E6KKFKDU1idQZWQZBJPzVl9R99D6ZP/sFXlmmtraWrKysFNHUHdFSNH7YQjRaBvbOHJdVlWVc+/ebkPQ2FI2Q4jVLEY9SsnjczD/uuxGBnRfMAeNekV0u4vE4Y8aO48ZbjMjjg/d1DfFocZOVmUlFRQUFubn4px5I4PgTqXv+6cT6OTNCI8gyeihE/VuvEzjqWFw9egKdO9IIrfR1XUcLh6l7+QWU6qqUYIFdEEdT8R94CBkn/pxIKITo99t7NnYUn0a0rZXX7bRL2aiqHQohpqUh9+iBlJ2LmJaGHo2g1tai1VSh1tSix+OIPh/IsiGekiKQKWmhlo2DZto4pkDVMV7Tk7IIdMtpa/4TEN3uRKXa5EJYomhEFBubkAry8U4YglxcjODyoCtx1LJSomtWoZZuN5x1ZrVrK7qanCItejy4R41G8HqNNtrXyRrDsdMVdEVBLS8nvnUTxBXDOaKqxgdMR2NbIsXOWb2SyPw5iF5fqp0TN+2cwS3tnM46lrQHEnZOb9KOOZ7w7OZ2jpfY8mU0LfqBwmOPY9Pq1cR2oTv2eHYOh0MomkpORjq18+YY6Qpp6RAzfkgQRfRoFFePnvgOOgzZ6035kY0bN/Lxxx/zySefMGfOHLZs2QIYnjzLKNudyoXJE5EoirzxxhtcdNFF1NbWIooiLnNiUhTFXmsIxqAvisaEVl9fz/vvv8/KlSt59913GT58OJrZyVqEfJMm7K++/ILbb7mJpUuWIJqToN/vBwRzYjVESlVFBZ989CHLli7hyqv+yq/O/7XhofR4EHoUE5fB4/VBLJbw+jSGaPruW9KOOsZIV62v31OKdguhUAhd18kKBKhdMDfBYzRi56/LhUUEDj3c2Ng3Hje8WCQGPF3TqH3mSWr+9QhKdRWCKCHIUmIyMfJAWqZvJIuF5EiGuQ2AWllJ/o232lGfXaUPW4L/qSf/zWMPP0R5eZnhEZaMtuhJv2UnsOqQvDxctwdu49lazF9RVs6td96F3KySpAX/lGl4hgwlsnyZWZI9ahjGTSFC331L+pHH4PZ522yPI4vHzLQ957El2kDFtTpYJ60TaeexfIfeuB3xGG4fHtsalsOrrq4Oj8tNICeX2Ipl1P/3bWMTc3/Arn6qqyru/gPI/OX57NBTviMkpXvpuo5v8hTSTzuLmn//ywhT6bq57YkHtbKchtdfIX3awQwYNowlixYxasyY3XIcdlU0F42ffJionposGpPnt+TvJURjOdde0w6i0YKVCcfOxOMWQzwKAhf+9jw7Ktq8z1l/W3P02HHNxGMXSFu15hyfz4eiachA1nkXEJr5DbGVKxA8nsSF0nVwuYguW0r9W6+T+6crjb1QZeM+75RRx+aOczNVveGj9wnN/s5Yt07CFhBEET0SQS4qJvPcX4PHgysep6ammpycVf2HhAABAABJREFUnA6umizY2W4WUqJ40Qhy336kH3cCvikH4urVGzE93XCmqSpaUxNqTTXxDetp+m4GoRlfo1SUI3o8idTVeBwxN5ecSy7DM2IUWn2dmTqpmqmjKqhmqqdq/ltRDfFl/ltQVKPipa7R9NmnhBfON8SRqhr6TBTR4zGknFwyLjiDtBNOwtW7D4LPZzoqdPRwGKV0G00zvqT+jdeIb9iA4PWkBHZ0QNA0xOxsCm66HVfffsY+nFZGELQcMzQNra6O6KoV1L32MuGZ35qfS0qRbUOk2jnzULdvRwikpdo5BUUEDjvC4KWZndOJela7YEdjim/qgbiHDEsUT4zFDCdIqJGmWd+SdtTRePx+auvqdnr8PRaODfX1+NLTEUJNhObMNr0oSTeNLEMohDRuPIGJB/DiK6/w6gsvEIpGKd2+nbq6OmprawmFQubHZXtd2p4MLFYUSRRFZsyYwQUXXEA4HMbtdhOLxRgzZgyXXHIJI0eOJD09nYqKCr744gtKSkqoq6uzI4sul4vVq1dz6qmn8s0335Cfn58iHi1YXvCF3y/gtptvZMnixaSlpdkiUTe9RsmXwu3xIIoiW7ds4R/33EVGdjY/O+FEAESXCyndB2pS8QhzggnPn4tWUUlhjx6UlW7fU4p2CzXVVQQyMxGamvGo6yDLaLW1BA47As+IkcYXmq07EgSB2JpV1D7zJGpdDRmnnE76yb8wJsPWJsBkT1ZrEASU7duo/tejNEz/gMwzz8YzcvROIxyAfQ+sXrWK5595mrKyUk4/8yx+/otT8Hi8Sb9tPTU7lpD8p7VuQae0rJR/Pfoo7//vXX51/q8ZPWZMiiGHJKHrOu6Bg/FNOZDIkh8TKSqaBrLJY2UlBcXFdlXO/Y195bGjYF/2dog4pqTpNIfJo2vgYLy7xWN52ze4DbFt2zYK8nJxiyJlr75EfPVKBK/PSDeyRZ9Ixtnn4u4/oIV4Sf67tYyM5qnlgiAaBvU3XxFdsczw7uu6kQrt8RKZO5vad94k5/cX07tXTzZu2MCgwYPRVBWxm1VabbGmsVmkUW0mGncWabzumpsQNWNNY+4OROOOgv17DQGEVo6fSFsVMMTjDQBc+NtzUVUNUUx1xCb/3Zp4fOgBuPzKq6ipriY7J6fziSqwl7VkZGSwdetWevbsiWvAQDJOP5vq++5Gs9JRrXRBSUIPh2n437ukHXsCnqHDUiL5nQ3N/Q2CJKFWV1H3xqtodXVGWruZriiIibWDgWOOJ3DQIaBpVFRXk5+XB9CBohFjPG9+nQVznZ8oknnu+WRd+AdcffoimvUxWmIgvomTCBx3IrFVK6h76TkaPnwfolFjDtGMPYB9B0zBO3bcbt+zVros1rOqggDK9u2E5swCf8DY6kWW0WNRPGPGk//Xv+OdPLX1tqanIxcU4Bk5msDhR1H90H00fv6pXfQs9RKISPkFSLu7HKqgEPfgIfgmT6Pyn3dS/+argAtdENBVjbaczFvYOZqWaufU1RI49Ag8w821jUl8d8bxo91h2auDBuObMpXokh+NfmvZOS4XkXlz0SqryC8upry0dKeH22PhWFFeQVGfPsS3bSW6dIm9lsqGpiF5PHgPmAIuF4vmzeODjz5q+cNmBCc5Grin5IqiSDgc5pJLLiEcDiNJEqqqct1113HDDTcgSVJKytMRRxzB6aefzq9+9StWrVqFLMt2AZ6VK1dyyy238Oijj7ZIq9E0DVmWqa2p4T9PPM6PixaRlZVFzCwiIgiCvYWIKAiomoosu+wqq36/n9Lt2yl59GFGDBrEgGHDEfMLkHv0RIuYHhNNM7x6bjfxDeuIrFlF7uSpbN+0aQ8Z2j1UV1VR3K8/8e1biS5dbESjknaJFl0ufJOnIucXtMwVNy2G8A8LUUq34Z00hfzrbkIuKNzndol+P6V/u4qmLz7HM3K0MZDuJG3NcjgsmD+Pbdu2MmnyZG64+VaKi4v3uS3paelc8adL+WT6R7ZwTI5A6/E4gtuNb9Jk6t5+HT0atRcjWzyG16wib/JUtq5fv8/taQ3VVVUU991LHpujI9JGm3X55gaz+UerX92d5qbw1YqAtHgU3W78kyZTvwMe7f64ceOenV8ngOX0qqiowCvL5OYX0PjVFzR9/KFxbc3iSYL57J1wABmnnN7qsXbkyTTSn1q+r+s6rl69yfrdRZRfd7VZNAQ7hU9tbKL+nTdIO+QwMoYMo66unvr6OjIyMrtVoZwWovGjj1qIRrFZpDFZaCmKiixLVFaUcf11N6VuudEeotFCUoChuXhUmnQOP0YDYQv/uPcGQDcjj7pZa6Tleq3m4tFa86gDf77iKmpqqsnOzmmDE9l/yMjIoKG+nrT0NDLPOIumTz8mPG92SpE3w1HiIb5mFXWvv0LBDbckilTQuaKOLZxC5rxR/9+3ifywEMyqjDYEET1mVLPMNMv+xxWFgM9nV8Ps2H7cUjhaZ5d1we/J/fOViP4AgL2JvJV+aryo2+OklJaGb8IBeIYOxzdxMpUP3YdWUw1WRVZziZQeiUByKi+kHtM6bvJ79meSWqhbOxRE8U48gMLb7sYzYpQROLFsZ0sAmPcZAJKEd9QYCu78J+Ldt1P/zpumeFQTXgFNM7aJAaNwnSiBmY3XHHrSseX8fPL+7xrU8jKavvkaQZQwlibtHht7g6qqKnr07Y+yfVuSnZModCRKsmHnFBS2sHM6vkd1DHZo5xwwhfq330y1czweYusNOydv0hS2rd+w02PvcW8Oh5rIzs4msmolauk2MLfZAOwbXO7ZC8/oscYPmDe1ta7RGkCs7Sv2FtYE9Pbbb7N8+XLcbjeqqnLsscdy++2343K57L2j4vG4vX5t4sSJ/PWvf8Xv99tVXS3R9+STT7J9+/YUYxMSZeJnfP0Vn33yMYFAgHg8bke7REHguBNO4IGHH+HRJ/7Dr877tb2u0jpXn8/H0sWLefPNN41j9+yJd9hw9HA4tcKlKKI1NhBe/CMBtxutjXpjKBQiKzPT5NHIh0dRzcJGEeTiYrxjxyfa1fz6A5H5c0AQCBxxlNFh43FbAO/xwxxw/dMOwtWrN01ff4FaX294C3cC6z74YeH3hMNhjj/xRIqLi03RntgiZU8fmqZx0MGH0H/AAD7/7BNqa2sQmg9BZts8w0fiGTAwwaWu2TxGFi/Cb1btbQuEQiGysvacR70VTs0FGJ0CyQamIIqtPsTdeDQfPJsfH9gtHhP9setBlmU0TWPL5k0U9+2LHmqi/o1XULZsMSIHVjVsQUDwesm64LfI+fmtGvnNU8StcdD4YFLkkaSsACDtmBMIHHmMMVmJImCkrIoBP7GlS6h981XcAmRlZ7N169b2v0htiGTR6PV5zeqpj6asaWwuGiFx/VJF440IygvckbRPo9ReotFul3Veid+DxFYdhx+t8be/buX1V27guWdeQpISa8yNc2q57tHVbM3j7JkzefjBBwgEAtTW1qSI6c4Cy6mcnp6OqqmoioqUnUPm+RcaxSeSiobo1tyuqjR+Op3wvNl2RLIzQ9d1BFkmvnEDDf99Ez3UaK+bA4xCWKqRFph+6ul4hwxDjcWorKokLT3dtpE6FIIZFTUFoCDLaJEIgWOOJ+eSoLE0QTFSRAVZNiJYjY3Et28jvm0rWlMjyHLCwR+PIwYCZJxxNuknnWwb4AiinR4puN0IkmQUlLEekpQQj4JgvG89zDkNUTSEmIDRVjM91T1gIPnX3mSIRrNwj1XRVtm6heiSH4mtW2O0xSqqpSjI+QXk/vVv+A893AxSJASVjp6IeMsu9GiExg/fp+7Vl6h75UVqX3qe2peeo/b5Z2j8xNiqy04LzS8g+9I/4+7XD90satmW6aph084Jr07oDhTFtnOk4mI8Y8eaJ9bMzukEzpiOQsp4KSTZOf0HJNk5xjXSGhsIL/kRv8ezS7W95xUIJAmPKFK/fKlZhTMj5T09GsU9dBiuPn0AEM21ZqIo2p4nK81jf+zb9dprrxlpk7EYLpeLww47DEjsDdm8yIKqqpx22mk88sgjLF682I466rpOLBbjv//9L3/84x/tiU3TNFwuF/X19Xz26SdUV1eRnZ1DOBzG4/HQ1NTEOb86lxtvuZWcHKOc/AknnkRhYSGPPPSgPTlKsky4sZEZ33zNrzZfQK++/WkYNhxh1reteqGiy5cgRKP4A2n7fI1agyjJJo/LEjzqullMJYp78FDc/fqbH04N+yMIqFWVhH9chJiRiW/SFONNWUIQxD2e3AXBqE4GIGZk4h03gYYP3iOyYD6BI46011dYaR3JAkAURSorKlmyeDF+f4DJk6fZx2y+rmFP2qNpGplZWYwdN56333yD+XPncfSxx6Z6T83qkXLvPriHjSA8f57JpZDE41KEaBRfWufisVUh28E2WXNxEo1EWL9hPeGmkFm8yCiupJmFB+wiR7r575TXDINl+IgR9OjRs0W0MUUE7RaPbdsf2wpWtLG+vp40nx+v203Nf98i9PVX4PEkjClJQotGSD/uRAJHHGV82TJcdiAarbHc+LeKKDbbRsdqhK4jpaeT/buLCc+bi1pdlUiRkQwjqenD/9F06BHkHnIYtdXV9hq3jl0bte+wrkV9fT1ee5/GR/ntHy7a6ZrG1kTjjdffhKC8yB23R8lJ3qex2ZrG9rCTrN9qLlbtfR6PUoFt3P3P6wG44De/QtOEpOrlLZ0RzdNWb77hegRB4NI/XW4XTupMkTnAvv8DgTSqa2ooKCgg7djjafr4Qxo+fM+O4Oi66Sjx+YhvXE/dm6/jHX9AimO0M5xbi99XFHC5qHvzNaLLliK4PXaGmWBFx1QVz7ixZJ1zruEEcbnIyMhscU93GCyxpusgyWjhEHJhIRlnnIWUnZOyHi48ZzZ1b79OdPlStPp6I5qVlY1n+AjSjjoG37SDkdLS0DWNhukf0vjh+3ZkUauvo+rRBxJRL8ncKkMUEFwusn79W3suVmpqaHjnTbRwCEGSESTRjhwKkkxs1UoEt1nYxucn+09X4ps4yXA0mCIzNOs7ap9/muiypWhNTQguF64BA8k670LSjzsBXZbR43FcxT3JufhS4uvWEt+00ShsE9cSkVQTanU1lffeRXzzpoTQBjuql3b8ieRfdzNyYRFaLIp/0hQCxxxPdMXy1P162wAJO2cpWjiCmJFq53gGD8XTb0CivTb1nWOc6CikjCmShK5ryH364B4+gvCC+aleQHSiy5YgRCK7tFf3WDgGAmkQDhFdtSIx+ae2FNfgofh79kRXVe66+27uuvtu+21r4li8eDFjxoyxBeXeYvny5Snpotdeey0LFizgH//4h10C2rp5rNTW3NxcMjMz7deMZhsXeM6cObZwBENoiqLI+nXrmD9vLm63h3g8jiiKxONxCgsL+eW555GTk0s8HkeSJFwuF78893w+eP9/rFm92jhHs4T3uvXrmT9rFr3OOhtp0GDEQJoxGCSlGgiyi9jq1WgN9WRmZ+31tdkZfGkBCIWIrlye4NHuZALuIcOQCgpT9m+zIAgC0VUrUTZvwjVwEJ7BQ8zrnLiWewzrBne58E2eQt0rL9I040sCRxxpD2AC2AITEvfSihXL2LhhPQMHDWDgoEEp7dxbWN+dPGUqr7z0Ip9+Mp2jjz22hXGnKwqix4Nn6HAEny+xT5TJY9TmMXuv27Iz7AuPydDpWN3YPALR1NTIvffczZuvv2bwbEYAdd0oAGdZrZpmtNxyKiSvGYlGo4wdN56HHvsX/fv3t8eZ5hkFe8ZjVjtelX2DlWIfDodZv3YtoydONPaKffsNtNoahPT0xISv6UiZ2WSe/1uk9IwUT21zg9YSo7O++46Z333LmWefQ5++fe20/5TPJ3HqHTOOzF+eT9VD99qbM+uKguD1Ed+0kfo3XyNwwGSyi4qoqSgnPT0Dl1VZsAsaAMmi0ePx8MlHH/F4yaP87qKL90w0lpdyw/U3oSsvctedUbKzVLS4mahiZohZQq5d87KERJJCC/EY1jnsKAWEbdzzj9bFY/PIoyUera06brn9Dm66/joQIHjpn6irrSWzE4pHy7ls1VdwezxkXvh7QvPmoFZVGtGkJI4FUSL05Wc0ffk5acccl5pa10nOCTDmDJeLyOIfafzoffR4zCiilbS8yBI2WedeiJ6Xh4zAurVrGTBwYCfiSLA7iCCJhkN10GC8I0alfKrx808pv+k64ps2GIVxrD65ZTPRH3+g8d138B96BDl/vor4+rVU3HYDalWVvbWKHg7R9PknRjXVpAicIAgIXg/pJ/wMTOGoVVdR88SjKGVliYq0ogC6YPdh0R9Aq68n/dQzSDviaONF08nX+MlHRlu3b0vsryuKKNu3EVu+FK2xgcwzz0E3UxH90w4i7aSTqXmixHYINheOmEUDLdh/mmtxG959B9fAQeT9+a+2rZd21HHUvfqykfLahpFlX1ogoTvE5nYOuIcMRSosNNaadmFHY1sgdXmVgujx4hkyrFU7J7Z61W7Zq3ssHNOzslAbGohv2GgMdlrSzKGqiIEA7oGDjMiTFkeXpJSb0xo8e/funXJSe4vyZsUqJEnijTfeYOvWrXzyySd4vd4Uj7X1+zvyYpeVpRYxsdq3auUKtmzehNfrNQwkt5uG+noOO+JIBg40xIooJBb+9+jZk2kHHszKFcuRZS+apuF2u6mtrWXRoh845ayzcfUfiJSTS7ysFMHjMdMljIFBKSslXlZKRu+++3R9doSMzCzUxgbiGzeYPFqbgiqIfp/BobnmKWX7BtM6iCyYh9bYgHfUGMTMLKMy2L4OHOZ6Ru+oscg5eYTnzia2aSPuPn3tScjI4EhEg0VRZNHChVRWVnLCiSeRkZlpcKzr++TttL47fsJE8vLymD3zOzZv2kiv3n1a/bx74CCk7BzDUHC7baGmdlYeW0XHe4et1KbPP/2UZ59+ir79+vHLc8839zM1Jwu92ZghpP5p3R9Llyzhxeef5f13/8tlf76i1bGmua3dUTy2Bazz3b5tG31690YGqt57h8i8OQiBgF1OXpBltFATWWf/Et8BBxhftowLUkW9rhtbH03/4APuuesOVq5cwYIF8/nb369l7PjxdoQw2bgXBMFe05h55jmEvv6CyA/fG2Oeqhr3rNdH6PNPqP/4QzJPOR09EmbVqlWMHDlyjwundQa0EI3Treqpre/T2FKcJ0Uab7gZXXmRO+8wRWNiCTPQQaLRQtJvp4hHCZQwHHakAmzj7h2KR+scEucvu1woSpwxY8dyy+23c+N11yIg8MfLLqOurpbMzM4lHpML5VRUVFCQn49vwkTSf34qtU89kVjHCKajxEt8+zbq334D39RptqOm+fzW3mjtd3Vdp+6t14mtWY3g9dui0f5cPI7/sCNIO/EkBFWjrrGR4h49Uj/TGWBlkACCriPnFxjRRl0HSUSPK9S//TrxjesQ0zKMooXGpwHdSF9V4jR+Np3IssWgKKiVFQger12NGkCQXYnzTuqggsfbUtBIshHpbG1eMoMIYiBA2jHHI2Vl2Q6G2Pp1VN7/T5TS7Yhery0AdUDw+1GrKql54jE8Q4biHTveqJwpSaQddxKN0z8ktm6tsaWI1lw4Gqdru53NdumaBpKMriqEZnxF7NQzDZssHsc9eDByYRGoSpsOPxmZWWiNDSgbku0cQ3MI/gDugYONtOl43Fir6SAFLe2cwa3aOUppGfHyMjJ6tW7nWthj4ZiWkY66dStqRTmCy52y+asWi+EqLEJO+lFrXmkrpKWlUZ+0ZYVVbGfRokW89tpr/Pa3vyUajeIxK1BZpd7D4TCw6zUTkiShKApr164hFAqTlWWUYBZN4TJ06FBDrABis9LjY8aORZISxoEVpVy/fj2heBxPr15IhYXEt26GpDWRiBJaYz3xbVvxDx2+vy5VCtIyMlC3bkGtqDB4tNJBYzGk/Hw71TiZPV3XjfLUqkJ44QKQZHwTJ9uRwH1KTbHSSQBXz154Jx5A09dfEJ432xikYjHD0GyWLheLx1n0w0IURWHKtGmJbTP2UcRav9Grd2/GjhvPl59/xrffzOCX555vR1YA2yiQe/RALihAKd1u7IukKEZaTBvzmG7yqO0BjztER+hGe241Jynz/vn2mxmIksTfrr2eE048aa8OfcZZZ1NTU8XH0z/ivAsuJDs7u8X9adXR7Wge9zcsAVdbW4umxMksKCCyeBENb7+JZqaT2WtE4nFcvfuSefa55t5YqrHOhpZOGk3TeO7pp3jogfso3b4dj9fLF599Slnpdv52zXUce/wJdmTXMqiTI4+uXr3I+u1FlF19xf+zd9ZxdlTn/3+PXLf1uHsIBAgQgjsJtDjFCsWhWAtVHIJVaPttf9/2S0u9pbgW91IkIcHi7rJu1+8d+f1xZubOvXs32UA22UCe1yvZ3SszZ+aZc87n86gTrWJaDZz1jg46Hv0XoQMOJFBbRzSdJpPJ4Pf7+0CBjZ6LmzR6fV4XabyM08/4Blo+j1rS2gi6D0818v/gvnuyVFbqmC7SKLk3152J0e1zux0tjudRkEeJTdz3s5uRJLjgwtKw1a4VZFXVg6Zp7DV5b2becx+33XwjkgRXfPtqOjs6iMb6UCikJbIsEwqFxDMbDFJx3gWk3nqd3OpVhVQLKypE8vlIvfsfkq+9QvS0M52QUPERFwPfgeI+m01SUu+9Q/K1lwtebcxCrr9hoFRWUnHhJciBoNX7zSQQCPQZUg+IC3NNFtNe7x1Pn4LR2Ybe0gqSDLKEmdfF704LMet6VBV98yZxWHu/LTFiOM+kK1JK3EBXyo9hFNY/9/Gt92xjnn+fKQT23sd6XUdCpfPxh8mtWCZIp1PR3zpuPo8UDJFbvpTOp58U9Q1U4XUMTJ5MYO99ya1cYX8azJJoP7fH0a1DXUPyeMlv2ojeUA9Dh2HqGlIwjBwJWz26zV5bhsLRKPqmDWjlcE5NjQvn7JZyItnriYNzBqHWlsE58U7ymzYRHDt+i8fb5p04GAiRb2rASMSLFzZZhnwepa4O1SrB7H7ftjy7/94eMmnSJGfzgcLDnkqlqLdKyrrf83g8LFy4kJaWFqC4Z6QkSU41ThssSZJEMpFg3dq1XSaFoij0HzAAr9eLUZoIDwwcNAif3evHdY6GxgYaNmzA328Acl0/awFxWakkMHN58ps24e2lRtjBQIB8o0uPpmn1Hcqj1tSi1loVUqViq7Dk8aCtXUtu6WKU6hp8e0wq9CLSNOE1/Dz/rIRvM59H8vkIHnIYRjxO6r/vOGGEJsXPjdfrZe3qVSxZsoTaulomWC0n7MJLX/Sf7RU49PDDyeZyvPH662havrjPnD0Ra+tQ+vUXG4J9z3aAHgOWHvUe6rFboLWzAGjJ+WRZpq21lU8/+ZhRo0Yx7aCDRLEiq2BRT/6ZZqFa8zHHHc+a1av4eM4cAMdrVvjnnBjYmh439poet7coVvnt+s2bqaqpRc3n6Hz6cbJLrT6Vdni8ZaWNfuMcfONtUlwM4m2An0iI8OF7755JQ0MD/kAAwzAIBoMsXrSIH//g+/z5wT+IVhoWySwCVdbNDh1xFOETvoaRSlkFHiygGgqRnv0Bbc89TdDjwTBhw/r1YkR9BYRuRUpJ42svv2yRxks5/YxvkC8hjY5H1ro37vBUhzRa4ammhWXNEoLWZ8Q1JgmBSVUV9DQcdpTGjT/azMP/vIW//fVfyHLxfl1KNGzPtiCPk7nr3vv4z1tv88D//VbUHOjo6FPkxL6GcDhMPN6JhIln6HBi532rCLCBFQLq9aG3tdH59BNoDfVd2iXsaDpcXERE5I4Z6TSdTz1Oft1aZKf9huQQYFPXCZ94EsGpB4Fp0tbeTjgc6VN6sUWEZgJW0y0zl3MqSaNpKBWVgnzoVuEZr69gfDatdAjTFDqySGfps+veQrtcvSxhKi64bRqYNmnrEpFnWuuyhn/SXihWtXpJUdHb20m9/57VtqM41MZtcDB1g/QnHwmjhaxganmQZHyT9hL9+6wUhW2zu0ii96QdqmyCpCqiBdsXSDfriQQDQXKNjWXwqoZSU1uo6F9K3ncL4FpPHJxT68I51nMkgZnP9QjnbDMK8nlUko1NmLksUiDoTCok0ctHrakVoYuuQZYNEdtOij3//PN59dVX8Xq9zmaTyWQIBAKMGTPGGoYYRzabxe/389JLL7FmzRqg4KG0J//+++/vjNkmlclkksaGBhcYEp/3er1OqfByi2VFRQX+QIC45RE1TRNFlmnv6KC5oZ4RI0YgV9dY3yu5H4aB1lCP0ksTwOfxkGxqFHoMBrEuGnQDpboG2Y5xtsPVALJZUrPeJ/7vZ9Cbm1EqKok/8yT4fdsPxVihc/nVq5BDYdKz3qf1978jeuppeAYOdu5zNpvlw9mzeOqJx9m8aSOhcJinnnicUOgldFfoyBcaCgKAr1uzGr/fz4ezZ/Hb//cbTjvtDIYMK4TPmoaOHImi1tQ6VllH+pget3hfdqLHEQpzaOnSJaxbu5bTTj+Diootx9p3J3YY4KQ99yIYDPHKKy9x9HHHFcf7lxh0TH1remzsNT1uT7GJXmtrKx5Foaq6msTbb5D497NIPr8obmFZtc1MGv+ek4mefJoAr65cWDdp3LRxIz//6X08/eQT6LqO1+t1qlLruo7f76exsYGf3Hs369ev4+rrvkNNTW2Rp1CSZUxdR4lGqfjmRaRnz0LbvMliF7oIpTYMEo8/QujgQxm652SWL15EZ2cn0Wh0l/A6lieNlzmk0VNCGt2/63rB0+jkNN6TpTKmYxoFvLSTHFI9EntstofKFFFugjweqWGam/jJz27GNE0uvOg8B28WAXDXT5s87rnXZGbeKzyPAFd++2qnbUtfEVuf0WiMttY2KquqiJx0ColXXiQ9+wMREmYBNTOvIQeDpGe9T+KlF6i48BIxL0tqLuwoAlZE2vMaksdD4q03SL75BpK3hBhIIkRQHTKU2HkXIKkq6XSKYDDgGKz6lriJu8CqemMjemszcrDgqYqdez65lSvIfDQH0zRE0Rqvt+CZ1EVPTptklxrFtqQrSZKKo6DMrmGirt0ITAPZ68U7brwg7VYBn+yyJeQ3rHdCb23yWVTx3TCQPB7yGzeQX7sa74iRzrm8o0ahRKPojY2Yru87I7CKy9kT2Q6ZRVUxDQ2lqrrQ99EKFTV1rUuPyO0tPo9KsqlJVI21o/Ps57C6BqXCatnTE5zzFRR3+LFp6MjRGGpNTVfEbvYMr24zcZRlGb2tBXT3g2IHRpsoVdXIoVCRBas3FkD7eGeffTb//Oc/eeWVVwDIW20djjnmGE488UQntxAKYafPPfecA35yuZxzTJ/Px/Tp053jm1bVv0wmQ2trC4oVBglWtVVVJWj1/ylnuvEHgvi8XjpdG6EsyySTKdpbW8V5KisFgCsyIwOyLKoP9tKElO3j63b/HZv4mSiVlSi25VCWnUbFiXfeouFH30NrqEcOh9GaG2n5zS+3faPoLn7ZHfKkKEiBAFprC0333I62eSP97rxXgCuvl3ffeYfvfedaNmzcQCQSoaW5mV//6heOfrafmCiKSiAQoKO9nbvvvIM1q1dz/y//ByRJ5F4ZguwqVdUCJZWgu76gx63PRdP13Z0j9tg+mjOHdDrNlANEtd5CGf9tE8MwGDx4COMnTuTd/77Dxo0bGDRosPN+l1A3a8PtXo/NvW5Z/aJirzG5XI62lmZqBgzEjMfpfOwR8vX1yOGI6NllhY8iy8TO+Sbe4SMK1nRLbKKWSqX4n1/+gocf+icBqzebXTTMPqemaXg8HrLZLL//3W+prq7myquvRVHU4mfOstT7J+9N7NzzafnZvQ6gNjUNKRAku2wp8acexz92PLX9B7Bpw3oiEyZ2CWvsi5KIx/F6C6Txoksv7Uoa6Uoa7TxOOzzVyWmstDyNuwBptMUhj/Z4LfJYyHnczE9/disSEt+66Dxny3PPx3Lkca/Jk5l5z33cepMgj1dceRXxeGc3o9g5Ypom/kCATDZLLpvBW11DxUWXkp33aVFRGeGO9WAkE3Q++xTBw47AO3KUEyJaGl2zw8ZvrYF6S4vwhjY1IkejIlXE8qpJsoSpGVScdS6+MeMwdZ14Z5zaurq+OT+dUFUwdQPJ7yO3fBmZefPwDB7qhGsG9t6Xfj/9BR2P/Iv0nFnk163BaG0rGNN8PkEk7RZiYHntXBEV5a7dFGRVcrcWK0McHVhkESIpHMbTf6A1blGoKLd8mfC6yYUw2qJzS1KB6CU60RqsRu5W9Iw6YBBKNIZWX18IoXWNQFI9xWTZfkdVoTNPYL/98YwYKa7Z40VvacFo7xB5ob0oBd7h6sRgXbdSWYUcCfc659ilxX0vdAvnVFcXteOwnT89wavbThxNE72jA6kU+VuTQ45EkXy+Qql1emcBtDcVRVF4+OGHuf3225k/fz7ZbJZJkyY5vRrtlh+pVIpgMMgvf/lL3nvvPQdcueXrX/86I0aM6BJ6kMtlScTjyHKxNdSjevD6BCktx4O8Xi8ei7SKWyRAXTYrjgeICVfq6rcmrBHv7D3CYZronSKkpuAtFD/lSBTJ79KhBSjTH89F7+wQpZB1kQclxXrJ4muKZrqyz4epKOSWLUVvacasFmHQn372CS2tLVRUVDjVCWO2p3u7D6UAjD0eD0sWL6a5pZl+/fo7YwWQo1FRgrvI/S+Jst47WY/S1uai2eWXHSOu4Wj5PIqqMnfOh1RWVjJ58t6f/7CWRVhRFA497HDeefst3vvvO3zj7HOdEOSum8tW9NiL83F7y6ZNm/B5fVREInQ89RjJN19DDgYxddHgWvJ6MRIJQkcdQ/jY48WXbEMROEU6AHK5HGtWrwJw1k23Z8G+15qm4fP5SaVSbNq4yfKgqRhGiSdJF6XqoyefRuqdt0m9/y5yOCzCxwzRsiD+7NMEDz+KyiOOormhnvb2didHtS8DAkmWeP3VV/l9N55GsILlXPfO3heaGhq4/bbbMUtyGkvDU/vw5TtSjjyW5jz+5Oe3YJomF1x4HqYpFfVdtUPJy5HHu+8T5FFRVC669NKdfKXFYo8/FArR2dFBtc9P6NDDCR03g/jTTxT1TRX9AMNkPvmIxIv/puqa7zrKFaB+JyjbqrKYeP0VUu/+R6wZ9nit6zNzefx7TSZy6plIikIqkSAcCffteSnJFqnSkTxetLYWOp94lMD+U0XPWouw+0aPpe6WO8hv3ED647lkPp5LdukStPXryDfUY3R2iEI3Xq/4jmlibok8OtuxhCm5ijQaZqGwpFtMURzR1PIosQoU26NurRda/eZCJWw38XThVcfgn82ht7WJty3SqlRWFlpZlJJX08BIJsQ+V1R9S6S+BPbZj+iZ5yB7vY4HNLdkEVrDZjxDhpZ1nmwvsXkHuMZs3Ws5ErEiaXqAc3YLNs5RItFCgVMH5/SMd2x7wo5hYKQzIkW6YCq0rNcScjAoHlpty1WWvqhi3QCisrKS3/zmNyQSCQzDIBoV1g+7QEQ2myUYDDJv3jx+85vfOGXq7VBURVHw+Xzcdtttxce2rk/XdXL5XMHibX1GVmQ8qqfr4CxRFRlVUbCBvD2hNU2zEslBCgaFhdGVD2TdIIxksvdCANx6tF8zAVm2dKgUW0hBFMywCJ3d341czrm+nkvp57v/2zRNTE0v8oQAKLLoSZPP5537lsvlLN2x3fiPvVna59CsVizlRA6GkFSly8JmpPqWHrcsO2/B9fp8rFq5kkULFzJ23Dhqa2vJZDKf2+NoG5b22XcKoXCEl198kdPO+EYRaSwiIrazP9SNHntzPm4Hsa8lk8mgZbMMGD2a/Pp1dPzrHxiZjKjAZ5fftkJGY2edi9qvf3GIaslxZUnC5xXFxdy6KPWaCc+ZjmwR9tLjON9TVExNwzN4CBXnf4vsovmYmawwUuk6eDzoLc10PvwPAnvvw6DhI1i1dCmRSBhV7dvtOZYtW8ofHvg/vnXxxeU9jUhdxi+Itc4v7r8fPVcgjUZJIZxdhTTasiXyeNjROoa5kZ/dfwf9B9Qxfcb04rBmqVDFt2zY6j338aPv38C48eN28lV2FbuWgurxkE4mCYRCVJx/Ien33xXWfFnChfIxgc5nniR4+JH495xcaM+xg5RdZNDxeMhvWE/nU49jJOLI4Qjk80UGNFSV2AUXo/QXOVLxRIJ+/fv34XkpFTx0ktV6yR8g9c5btPy/X1L7w5tEJIamYWMPz6DBeAYNJvr1U9Cam8itWkl2wTxSs94n8+EstNZWEUJqGIU2YWUnqAVG5JJQVcOV49hluCJHUQ4GkcKhwmEAM5V00gzKXGVRLpup6xhWEUi70I8UCIhiKIUHsPB9jwff+AnIFRWusYp75xk2jIoLLnYK9diSePN19OZmJI/q9PfsFTEMjHS6bO6oHAxaZHtbcM5XWGyKEQxZNQbcubYWztmKEWDbiaNpYuYyRe5/hzgiiQaxPTrMF0P2pbkQmUyGoJXjlclk8Hg8KIpCPp/H5/PR0dHBtddey9q1a4tCVO3f77vvPiZNmlS0+NkjNAwTTSsUv7GvW1ZkZKX7nBtJki2yWUToMU3D8YRKXq9ozFkmL8/M5XpvMjp6dF+TGGh3OnQWOvcCKElWuvm2UI5yi2uBLjq1uVxWeVE6uvANUQSlULnR/pzkvtHbQbqMVJYxTFdRFfd7Pq9oP1Oiy52vxx7OtV60GHYVyflpIkKkXn3lZR5/9GGamhrxB/zMvOO27bJOZDMZME0++OB97r3rTi665FKGuFq8dPmOZyfocTvKpo0b8QeD+CSJln8/Q2buHAFyNJe3sbOT6JlnEzz4MPGlrQA+E7Ps/Sp9TZIkDNNEM7QtP3XWGhI64hgiJ55M+z//hhyJYho5K2Q1QPKt10m89jKxM88hGotRv7mewVYbp74qr770EmPHjeXMs84pqua9pSIwiiKzeNEyFi54gZ/dl6KqDoy0qCq/q5JGWxzyiO1FE606jLTJEccYLFm6nqeeeFIQR718i45S8pjL5dhr8mSOPf543njttZ11ad2Krd9wOExnRwe+YBDfnpOJnnEWrb/7NVIgKNoG2CQmECC7ZAnx557BO3a88GZ187z05njtNTnx8guk58wWPaZd3i1JVTHTaULHHk/oqGOQZZn2lhYqq6pKjtPHRKILWQdB9joeeQgznabqiqvxjh5jvW060Q+SoooiczW1BA84kOjpZ5H871u0//XPZOZ+KCq920Z/tuAp7hKqalieHspvz6YJHo8oPON+We/ecNnlMKZpFTQqHFKigNWLPI6miVJVQ+3td4tCOkiFS5AV1IEDkf0it9DM5ZC8XpL//Q+J114u7NG9ih9MzFwWU5JxfPKmiHKkh5xjtxRLWbwqST3COZ+vRKC9E9glhSVJ5FhJEjhEqvdBqHthDbjaWfj9fgAnvDCfz3PllVfyzjvvOBsPiNYcuVyO733ve1x33XVFZeSLL9dwFofCa8J6XLQYlBkfUnHlKttbabhJWHcLbglZ2u5iGF0nuyQV+v2VvFdulI4XYTsMx9m+7IXd/UbJPRK3tvS13tm4SsmLZCOgEpGsRa3LOztdj1s/xPbTYs/EHVYjA2vWrOEn99zFZ598QmVVFcuXLWPBvHnb5TyyJBEKhzAMk9/+v98QjcX47g3f77Y/oGjYvRP0+AXEXgcTiQSZZJIRo0aRXjCPzkf/VQg3N62CONks6qDBxM74BkosVtyA3CXO02B5EMtJKbh1wtm2cp/sqBQ5HCZ69nmkPpxFftVKJL/fIbhGXqP9H38jOHUaA4YNY8n8edTU1jrre1+UTRs3sf9UkZvrdaUpdEcCbIyZSCbx+zQqKwErPPXLIk7wjosASxJgmPTvb5L5IIXTo7XLd4vJIxTu6+AhQ1i2dCmyp+8BRzvaQfV4SCcShCIRoqedQfLN18gsXSJSMDRNTDJdR/J56XzuGULHHEdw6jTxnqr2OhlzjLXWGpBdupjOpx4v5JLZCrOahCsVFVScfxFqRSWZdBoUxSlM2GeJY1FxHCxMVogq63z8EXLLlhA99QwCU6fhHTkayecrlFzI5QT+U2TkWIzo107BP24iTffNJPnma04vR/v5NSWpeCc1TYETpWKPo2TFIJTdVCQJNE2kFrgPZeiYUjdYrMyRinRiR7SZpvikaRaiBk0TyesRhXTKiKlpGNkskioKBmVXLKflf+5H27wRyefD6G1vn0kx0XXfAJtz7FDD964vkrwlvLq9PY7ijNa3RQNTM5MpnN6xcOyYRaS7DbmQX2Nw/fXX88gjjyBbYaKA4438wQ9+wM9+9rOiMJlyVnQbDBWIufh7ixYg07TCkFyvIYCsbN/DcqDfFrmbFWJ7STnSapoFHW5pI3DQQC+IfV5nUbNfL3zE3IEYvnhyubygJWIaRvkx7XQ9bv0Qpv2dHSTOpmyds7m5iWQySSQaQZIkfD4foVBou51P0zSrirpM/ebiNj2lYlre7C7S23rcDrJh/Tr6DxkMmTSdj/6L3OpVolhZXliRkWXMXI7I104msN9UAcqtatGlF20YBrKibDVUWCpd4ADTMDEMHdMstEgp+ZIIjdV1AnvvS+zMs2m+/yc28xQVbv1+svM+pePpJ6i++rvU1fVj44b1jBo9ps8C1VhFBWutit25XM5px1Qa1muLLIvCOOPHj0H1TuCRR1fy3evB0AwUm2Dt4l7HUq+poYPsg/pNCo8+GuLQIw9GdAbrauUup+dcLovX6+OjuXMZPHgImxoad8yFbIPYYw4Gg8Q7O8nncnhHjSF27gVkZ95ifwgQ643k86FtXE/nk4/hmzgJJRJxitL16jix1kGrn2v838+SWTC/sGa4vI1GKkX0G+cSsIqWpdMpIlYOXl+ci7YIu3PxfXSnBUk+H5mPPyKzaCHeUWPwT9wD/55745s0Ce+YsSgVlYX9yjAwtDzeMWOp+eFNaA31ZBcsQAoIg5cpSYWeeaWjcPdx1A2R51gOupsipclIpzDTKes16yiqgtTNNl30snUMyaMW/gaBNzXDihIzC+uyVH6uOaNXVauIIyTffYfW3/ySzMdzhcc1nyoQ0N4S23ngYEPXezYG78PPYF8UU98Sztnyvdx24ihJoviNzf6dB08C08DMZbf5kNtLSivUGYbBjTfeyG9/+1vndXvB0HWde++9lxtvvNFp3+AOeXSLLMsiT8U0RM6Y5WUVfeO6nzBGqadSkpzNQLEs/GYu1yUkzroaJJ9XxCv1hkgSktdfFKpgm4S716HkeJrtmP5eLb1tmqKxrHVq91MuW+e3vcRmb4+FQnisyFMqM9xcDsmyPLoX4Z2vx60vqCU+3s8n5TbMHqJdRRHW9Vwuj6p6nF6a20tso5AojLNlXZi57I7X4xcUSZJobWlBAqoqq0j+5y3izz2NbHvwEJu/mUrhmzCR6Cmni01f05C76dlkexl9Pp+TF7r1gdjjAY/HiyTJqGr34Nc+YuTk00m9/y7Jt99EjkREWJKuI6kqnY/+i9ARR1M3eR+aFzQRj3cSifRuFb/PK0cefRQ//+lPmD3rA6YeOM3JcXSHHnYxTCIRiUT57g03cc/MZvy+OVz5bdCyBqqCgy13RfLYhTQaIHugoV7lppt9DBxyMeedf64VhqpY3ykXpinWtXw+j9fr4/FHH2HxwgXccde9/PFPf9pp17clsb2OgWCQTDqNx+sldPwMQq+/QuKtN4oqHJv5PHIgQOLlFwgffwLho4/dcYY8a56lF8yj85knrYIZrv7WVqN179BhRL9xDnIgSHtrK4rq6abIWN8SUwJTlou3Qfd8NAykcBhT18kunE92/md0Pv8snv4D8I4chW/iJAJT9iNw4EHIoTCSV6ybvvETiZ11Ho2LbhbHtDFRqccRhMGuyMBmG+rKRAqZpvh8JouRShe9rcQqCj0k3QVs7OsBx5OKqoocVet9ACOVxEgnnTXFdPCrmJz5xgZBVnVDVI61+loaqRT5dWvIfPIxqf+8SX7jRvB4SnBHb4rAOQWCai8sBuxEzrEri5nLIRklOMc0RYrTVnDOthNHWUYO+MVJDEMQH9sqppsYqZRT1t22p/S2uDcZ23NomiYzZ87kZz/7mfO+XUa+srKSX/3qV3zrW99yysp3CUOVCvlyqqri9XoxrLA622to6Dp5Ld/tuLS8huZ637bueTwe/H4r9yWVEpY91YPpjis2TVFspbcsjrYe3QuXhLNIuEMvHFEUUGVRJKc3PY7OqibCJyRJQgmHUSIRQSYUBWTJCQXq1YIlruv0eDxkMmlCoRDRSKRLXzkjmcDUdNH3yiwsbL2ux+AW9GiU0WMZ6fYTpunKPS18ttSy6gZ7PTl26afscHBZlorm3nYXSeTgdfceIJLDd7Qev4CYlgFl/fp1jB0/Ab29nfZ//hWjs1PkUDt9uQBFJnLyafj33AvDIo3xeNyp8mx7nk1A1zQUVSEe7ySVSgljzdYHIyqENjUxf948PB4PRqlhTALDNKmtqWXgoEEYuRyegQOJnX0emYXzMTo6BMnVdVBVtI0baX/o79SNGceIkaNYungx+0yZ0icB6wFTp3HKqadx9513cNsdd7L/1APJa3k8annyaFphbIZhMPXAqdx48/3cc9f3QZrDlVeaaFlzlyWPZUmjCg2NKjfe5CNaeSm33H4LsViFs250n9snOST8icce5a9//hNXXXMtEyZO3DkX1wNxh9amkklymTTefv2JnnM+qY/nisJyNvg3TVEUqq2NjkcfIrDPvihV1U7Rqu39rDtrulVoxUgm6XzqCfJrVolcY3dBHKuYVvSsc/HvMQktrxEMBVG2UBiwL4lIKZKLN52Svcou+CeFw+I9TSO3dg25lStIvvMWSmUVoWOOp/aHN6FYld0B/Pvtj2fkSHIrlyP7RR55sVHIPl3JnmYYgIF7Z3UokSkqqxqpJHpLs3jRbtw+YKCI8ssXcKVDFt3XpmlIwSBqXT/rmKKIj97cJKq8Y5Eus+CtM5MJmn9yN9lFCy2di/6Mkmli5HLo7W2YnZ2gKoWUAvu7jge1l0SWkYMBp8CYI4bZPV7dLeXFuk1GKimeE68Pp+cTpigOuL09joYkoUQrCg+romDFgAEGRmcnZjYrLN3WjtHbG3y5zeZ//ud/uPPOO5337VzHSZMm8b//+78cfvjhThuHTZs20d7eDgjr+ujRo4HCpPd4vITCYaf3o+2hzGv5QnXUMpeXzWbI5nLOezax9ft8hK3Kr2aiU1h1vF6xjjgHE+0USquJbi8xJEnkN+EuKGN58eKWDu28UWsMgSn7I4cjaPWbxcO23Qmb6V49hfEh3olaXUPohK9DMIRs3e999tmXqqoq1q1bRyAQcJqRFx2rB6cDyjKbYm5hIkkyWj5PVXU1p55xBsFQqOARsydiXOhSkn2uHBF6X4/RMnrEFHMxI/S4tWILTqhqCalyEwnnNcui6hiGttWAUMbSaiftiyH0rhe725VoJ+rx84qtz82bNlFVXUPA76fjuWdI/eetQsNxhLfRSCQITp1G9OunACKcX1ZVHn7onzzy0D+RFVHIyw5bFaGmojBYa1sLXp+v27BVt+HO5/Pxn7feYvasD5yCKIUHRrI8y1kuvPhSfnzzLeiShAyEj51O6r3/0v7Q3xzCa+o6ks9H8sXnSR97POFjpxOJRmhtbaHKbkTdhySfy/GtCy9Gy2vcdcft3HL7HRxw4LStkkeRRqEz7eBp3HLbL7j7zhvAnMuV39YEebR36l2EPJaSRtNFGn98k49Y5aXccddtVFRUOdXPt7RGuUnjX/70IFdedTXHHj+ddDrdzQj6htjGxWAoRD6XxesPEDzkMCLTT6Tj0X919ToGgyTffpPkO28TPeX0XsNPztGstTbz8Vzizz+DFAiCuyCOx4ORShHYZ18iXzsZFAUjl6YzmaCmprZPGm/KiSSXNJAr2WOcKvEWIZNkGSkYdCweWksLnY8/gnfYCKquutb5vlpTi3foMLKLF0FwC6kVUnE9DNPoWhzH+dUUocNGIk5u3VrxnrUA+MZPRIlViP6MSiEMFcvb6OCmXA7vqNF4Ro4qGkZ+/Tr0jnZR3rgkPcHUdNHfcsFnSB5v8SIjSSJcNRwWeKA0Gqic93Q7io1zQHKzcRdezSAHglvFObuFgoG8s0MQR78fdNtzDHIkslWcs83EUTcMlKqq4gIqzgMoobe2YiQTgnRYlu4dRRrtENW///3v3HDDDc5nVFUln89z0kkn8etf/5rhw4dbYaaiLcfFF1/MggUL8Hg8TJ48mWeffVYAJOvm+f1+KmIVTlgkiNyUTDZHMmXFoJeBo6lUilw2iyTJDuDSdZ1wOEyVZbUy2toKVir3YmYYwrLVS0BV6NFqAOpEA4mFQm9tRY/HRQ8n6z6Ypkng4EOpu/0eEi89L3q92F4M++uS2y/1OUSSnJYA9jKqVFQQPs4K3QFUnw8TOPiQQ7nz7nv597PP0hnvQJZKihA5Y+nu2StCsyWviR+KrFiEBkxMKmKVHDd9OtNPOAEohPJhFfrQW1tBd/VYsn7uMD06l2bNxbaCHrc2FyUQnmTXOJ3Ft0QnWDlrkus6bW96sUeu8PkikZUu4yj18m83KZpXdq+4bnSxE/X4RSSXy9Hc3MQee00mv3Ej7f/8S6HgjaUTU9OQIxEip56BZ+gwQYgtvTY2NLB40SIUVSnjNRbV9WRZdiI5ym3Mpd/L53Pk88V9cu3PSZJELpsT1W7Fi2CIaq+xc75JZu6HZJcuQQpa1SdlGSOZoO0vfySw3wEMGTqM+fM+Ixar6BItsrOlorKS1tZWLr38CkxM7p55B7fe3r3nEQreAlVV0DSdAw86kJtv+wX3zPweSHO58spdizyWI42SCo1NgjRWVF7K7TNv3SJpdD9fpaTxiquu5vgTvkYqmaSiomLnXuxWxJ4zPp+PZDKJ3tpKtKqKyBlnkXr/XbT6TU7ooyPZLB3/+juBAw/C03+AAOndFKf6IiKqhioiQuHRhzCam5DCkYK30SqII/t8xM45H+/wEaSTSTRDp7KyaruPp9dEolCN046QwZp31oNqOzncRh03iZSDQcxMhvSc2WBcXTi014ccClmeO7HflUUdsixyx2wxi0mbPSZ3So5kmOSXL0PvaEeJVYjw2HET8I4bT76hHgmXEQpKFgQT/+S98Y0Z53gwAbJLFotIFI9H1Oco8cJKoZDo/2ztHUViGN23vOiutch2ki68Awp4ta3NwjmhHcI5dklxPxuyyGfWW1oFDijaTEzUHuCcbSaOuXweta7Wcm9aD5ZdXVVR0JqbMDraoabWaRzfWyGr5Ujj888/zyWXXAIUNmRN07juuuv46U9/it/vdxqAq6pKMplkzpw5tFmNUm1vo/v7wVCIfv37O2Gt4rwy+VyOttZW57OlYKq1tZVMJuO8JkkShmlQWVFJbf8BEO/EaGnGrtBaJLKM2q8/ei9NgFw+j1pbZ3kOXXHqioLW0oLR3gr9+jk6BJA9HqInnULoiKMwsyVx5bIEivqFgIyZz6Nt3mRZswTikIMhPMNHFMpeW6E9qqpy0imncsRRRwsA6uJ+kiSher5YGI2Wz7Fp02b0vCZCyUwDvz/AyFGj8Pn8hWfPEHmvensLWlNjV+ebLKP269e7eqyrK56PW9Bjd3PRxMTMZlzFraxnWtfF69jluSVRJtxNFOyNzunfapNtxGLUxSiiu0CzeCkai+HxeCxvvjjm9lj87SpyIkIgh0f1MnGPPZzrc39uZ+rx84j9DK5Zs5qBgwbhkSSaH3+E7IL5AhjYhF1RMROdhA47ksiMr1mvKcKzgDCAeLwexxDSbdGgLVhzywH/LY4dlxfbNEEW4VX+PScTOf0b5H75M9A08UwZBpLHQ2bObDqffZrKCy9h8OAhrFu7lhEjR/Yp67JpmlRVVdHa2spll1+JhMRdd94hPI9TDyzq61guPUJVlILn8fZfCs8jLvJoha269vne2Vw/r3QTntpoeRorqi7l9jtvpaKyGl3TUdQtk0bNul9PPv6YRRqvYfoJJzqkcXvmQfeW2NcTjUZRVZVsNkvogAOJnno6Lf/vV6J5uU1odB0p4Cc990MSL79IxbcuKuS0bcdn3DQLD07q/XdJvvGqaBNS6m3s7CQy/QRCRwnDra7r+Px+p4ZEdz2N+5aIUFXTSn+xDZ2FSp1W43izUCdBgkJOommKCqemiRQKOWGcgNjL7MJjW9hduxQcMbrWZLBQoqi0quvg85FZNB9t00aR22gVUYrM+BrpD2cVhcVKdgSDqmJmMngGDSFy4sliH9A0JNWD3tFO5rNPMXM50dPXcIWq2pLXMPM553jdLTJd1vgtFXncDuLgVV85nNOM0dYG/fq7cE4ZTP1VFnt9MQWG1tvb0Jqburp5JBmlB7xjm4ljKpUmVtsfJRIpeJ1AKMzjQW9sQGtuxjtqTAFQbutJeij2pLErqL777rucffbZTgiqHVp677338r3vfQ/DMBzSaC96H330kWMFV1WV4cOHFx3fNE3C4TBDhw5zndm0zmuwccN6MpkMfr/f8Sra41q/bh3ZbLaoLDtIDOjfn7pBg8isWI7R0FCc5Gwv2l4vnoEDyeV7p8xxOpUmWtuvWI9WvoPe3ITW2Ihv3ISCDiVJWChlhezSxbT9/ndigVFVzGwOz/Dh1Pz4FpQvULQiv2kjjTNvQ29pRlJFtV6luoqaH91CcNrBRWOx9bhk8SL+8MDvSCaTeD1e8vk8/QcO4I677iFmVXz7PLJ58ybuuuM2Nm3aiM/rI5fLEopEuO2OmRxy6GHFHjdAa2pCb6jvmrDu9eIZOKjv6LH0ANYzLvt8xL75LfSOdrKLFyL5/BipFL6Jk4iddQ6Sx4Pk8aI3N9H+0N/RNqwXoTzuMuS5LJFTTicy4yQR6un1kJ49i7Y/PiDCJnNZlFgFsbPOE2MCZI8XwzAYPXo0553/Lf725z+RTCaRggXbrVR+5D0Qs2BVRkQOfP3kU/j6SSdj5zw7Yhu+dpIeP49Ikmi/kU6lGDt2HNmFC+h84mELJFmfUVTMbAa1rj/R089Eqaoq8kYCjuFNcjaX8iC1O3Bvv7c12RqZtMOxomecReq9d0j+523kQNABaaZh0P7Q3wgfcRS1Q4cx/7NPyWTS+P2BLR12h4p9b2zy6Hge77ydW26/swt5dHva7d8VRXY8j7fc/kvuntmVPHbxOO5sAmmfvxvS+CPb03jnbVRUVqFpepciVeVIo2p7Gv/4IFdecw3HTz+hiDR21yamL4ksyw5GWb16NSNGjEADoqecTvI/b5GZ9xlyOCzqRVjed5DoePgfhI48Gu+w4U5BPuh+fvZEnMfEEGRJa2qi46G/YabSImTNPo9VEEepqSF61rmotbXk0mly+byTZrNrkEZAEkZNSbgZBcXL54me800Ce0+h9YH/R27lCuRAoGgKOb/bmExR8I4ZC4BpiJQHI5lEa2tDUuRu91gxH2RK23F0G5xlGWzlQIDcqlWkPpyNb8IeQiemQXj6icRfep7kf95C8lsFY+xoH1OY5MInfp3gYUcUrbnpWe+TXTgfrGJdbqLsPjdSoUq7JElFFKxomXE7UM0tXM92kHQqTayuTuTfdnYUcI5HRW9qQmusxzfejXN2k8ayYhigyGiNjeiN5XGOd+BAcltpr7LNxDER76Squga5tha9rQVJ9WKauihs4fWit7WSX78Opk4r+3239217iL0gr1y5kjPPPJNkMomqqmiaRkVFBb/5zW84//zzHW+halcztW7WnDlziMfjmKZJPp+nqqoQguEmKKPGjCHg5IrJTpjr/PnzaW9ro/+AAU4RCBuIzfvs00KOkSTCVL0+H6NHjcYrSbRv3IDe2ACqpziczzCQIxE8AweRsMsxb2eJxzuprHHrUXgoJK8Ho71N6LCLWER98yaS/3kTM5sVVsl0Gv/EPTATCUw7zr/Uy7QlsSxHZjJJfvUqtMYGYSlLp1ES/TFs72aZ423etJm333xTNFr2+8lms4wcOYpEZ5xwKMznRVSpZJLVq1exbu1afD4f2WyWyspKMnZ4nS024di0Aa2pEcnjKWy+O0qP1duqRyFFXlNFJXTkMcRffJ7MvE9F2Ec+j2fwIGLfOEfkvpimSFKPVtDyPz9Ha24qbKqqSujIY6n53o/xDB5SCHHOZgUxVRSMtI7k9+PbY5LIgc7nkDxeBzhfdvkVzJhxAp3xeNHjYxfM6fG+ZM3RbDZDMpm0XoNwJMK48eMJhULO/HcDqZ2px20V2/BVv3kzQwYPAV2n7e9/Qtu4sTB2EEBe1wgecRTho493imF0kZKb28UavpX1uqwXcpuvCkxNQ62uoeK8b5FdtAi9tUUYHawiIflVK2l76G/U3Xw7QwYPYe2atYwbP75PeT9KyaPtebxn5p3cfOvtHHBggTyWkqWy5PG2X3D3nd+jKOexlDzuTO+jdV7TtLijizQ2dPE0FpPGctcOVvVjj4cnHnuMP//xQb5tk8ZUapcijbbYz+bgwYPJpNPomkZozDiip3+D7NIlwgBnA39TeNizS5fQ+fTjVF97vTAAbQevowjmMJ0oosTrr5Ca9b4oduIKW5MUBSOZJHLm2QQPPgwAzTAIhcMAfWq+bV0k4T10HIgmsfMvovqGH6JWVuIdP4H2Pz1A8u03MeJxcQ9cLa5EcZgsvrHjiUwXaSp2vmJ+0ybya1cLr/GWaj5IxSkSYn3tukLaIbROpIxhkHj+WSLHHIc6aDBmPo9SUUHND2/C6Ggn8+knRWM1dZ3I8SdQddm3C9Eaqore0U7nU0+gNTaI0Fo7/LkHBW3KkunSv3rZ4xi3eIdaW0uu1XIsGLowZre3k1/XPc7ZLS4xDFAgv3ED+caG4uJ5LpwTT20Z52wzcYy3t6OMHo13+AhyixZYoY444N9obye3coUgTFY4gxuE2AtOuRATd2U1wPmM/VOSpKI8Gzs8taOjg3POOYf6+nqnCE5VVRX/+7//yznnnEM2m3XKRtuW9VwuRyAQ4Pnnnyefzzseyn79+pUd09hx4xgydChr16zB6/WiaRqBQIB5n33KvHmf0n/AACe/S5ZlVq1cyYezZqFY3k1FUchms9TU1rH3vvsAoK1eKbxrXq9TUVWSRMiWp98APHX96bSK9mxvcfQ4bHhBj/k8eHwY8XZyK5aJMZXbnFWPSESWZfB4kAHJH3AKJW3r5ubOpZODQZEf6/EI66A/sMVKlh6PSjAYdMq0y7JMIBBAUZSi3JltFUVRCQQCBINBvF6fKKseCHa7WWZXrkBva3MKkthhm31Cj9uSI2OW/GFSaAhrWWtj55yHb9w4Eq++TH79OuRgiMD++xM6djpqTa04nw1O7M3UsvZimq4NtmBEMgwDj9dLOpPh73/9E+l0xmmDc8WVVzH+c1RPfPON1/n1L+8nb3kJw5Ewd91zH+PGT3A+U/pk7Cw9bqvIssymjRvRdY2qmhqSb71O4uWXXDnCiGqMmQyeocMF+fd5uxY1ABRVwevzFoeqSi4mYIo12N3OqHROlXoj7ZxI96dsUGR/phzwt6upho6dTvidt+l4+J/CsuwyOMaffYrIsdOpOuBAmltaaGluprqmpsuxdqZ053m85647ueW220XOY4/J4zQn51GS5nLFFTp63nCi33YqeXSTRmsMugaKFxoaVG68yU+s8hInPHVrpNE0TccY/OTjj/HnP/6Bq669luOOn0F6FyWNUDAc2+H4doXi8Iyvk3r7TRJvvIYcjbq8jgLwdz7+KOHjTsA/UYTXb5fiH5Z3Kr9xAx0P/c0K1yzMecnjwUyl8I4aReTUM5GDQTpbW8kZBjU1Nc783pXEwRCmSey8b1F7463OGh+YvDe+n/6KzMdzSbz6Mum5s9E2bxbtrGQFORQktO/+VFx0Gb5xEyxsK9ap9Af/Rdu0SXiM3ZVoi8Ts2mu5G6JlT2GkQmXU9Edzib/8AhUXXSYM7LqOf4896f+r39Lxj7+S+mQu5PLI0SihQw8nesZZqHX9xDpuhW52PvU4yf++jWyn/Fj3ohDF9UVurmS1TetF4tjejjx6NJ7hw8kunG/hHB08CmYiTm7l8l7LBf5yiVB0btUKjLY2JI/XiiS0cE7/Aah1/ejYCs7ZZuKYTCYgEMA7djy88FzZJy67ZBF6cxNqXb8ue5i94Pv9/q6XZB3L9granyndJNzx1bqu853vfIc5c+YgyzJ5K6G5oqKC5557jr/+9a+YpkkulyOXy6FpmvNz06ZNTm6jXSm1rq6u7HhHjhrNfvsfwLKlSx2ioqoqiXicv/zpj4waNZqRo0ajaRrxeJwHfve/rF+/roi86LrO2LFjmXLgNMxcFn3FcoxUEilWUcgtUxTMTBrvmDHIkQjtGzf2RC3bLClbj+MmwIv/LvYQSpBbthStoQHPgIGOtb8gpujiTGGhE57Vz7+R2VY+U9fFpmkf03qot/Blp2iR/Sjquv4FhiLugSzLGA5YxgE0XQCLqmJkMuSWLMFMp5AqKkXumNfbd/TYWF6PW79FUhcgalurA/tPJbD/VKEvl7HAsWa7ra/OMVzh2Lj+dsnGjRt49umnicfjyLKMPxDgayedzPiJE4tyjLsdsTXPVFWlubmJzz77zKl8XFFRUfBAusQ0zZ2ux20R+/obmxqZtMcktM4O2v7yJ4zODgH6bFBpGe8ix80gOHVaIUS1cCAAUskUrS0thd6ydvgkBbLn9/uLeraVAtfS1/P5PPlcaXEc8SDIskw2l3N570v0aT1PFRdcTHrObHLLljoeEUlR0Jubaf3LH+m/196MGD6ceQvmU20B2r6S6wjdeB4libtn3slNt95W1Odxa+SxkPP4PUxzDldeiSCPFHP8HUkei+wLJaSxvl7lppv9xCov5fa7rEI420ga//Tg77n6uu9w9LHHkU6nie2ipNEWGwv4/X6am5vxqCqe/v2JnH4WmU8/QU/EraIlulChrKBt3EDHw//Ad+tM8Hix58rnVa9pkUbTMOh85kkyCxcgu6ovO+1BgMhJpxI8YCqZdJpoVZVjwO9r86xHYo/XNDHiHRidnagW1rMrNwcPPpTAQYeitzajbdyAHu9EUj2oNbV4hgwRRYwMQ/S99HhIz/2QjiefQPIU+l462NR9f0yLuLqL49jEsdx9dL0uWttJtP3x93hHjCJ01DGYVpEaz/AR1N5yR6ENmN/vVFA3rfxFSVVJ/udN2v/8IGY6LXIENU0QLLOA40zbOPw5xTTMHnkvP684OGfsBHj+uS4RbQKv1uMZOKgMXv1qS1HdAVXFSGfILV2CmcmI8HQt78I545AjETo2bBnnbHsfR8MgYxj4J+6B7CvJLbEmYG7pEnJr16LW9SPe1kbONJ0QCcMw8Hg8rF271rkoW+zFaN26dZimSUNDA6FQyKmAGgwGCQQCRVbtBx54gL/97W+O18KWVatWsWrVqm26NEmSqK2tLXrNJqPRaJRjj5vOKy+9RCaTdjybPp+Pd995h29fcTlHHHkEfp+fD957j7lz5zggV5ZlEZoSDnPsMcdSXV1DcvkycosWijK4ZbwA/gmTMH1+MsnENl1DT8XQdbKGgX+CS4+SJHTo9ZFdvpT8mtV4Bgx0AKi9mKmDhyKFQhgNDQCY+Rxq//6iXLJpgPT5Jq1aXYNv4h4k1q0V60ImjXfI0EJJaddiYFs8Bw0eSjgcoampyfHqDho8mEgkukWC0Z3Yz1FtXR3jJ0xk1cqVSJJENpth8JBJjBk9pnB+a4HKrV9Hdski0TS1JATF1mN6Z+lxWRk9soXN3zF5UoxGJakI6Dn31tJDodhNycZphQiJ0Bv3Sdw/CyIrMn6/3yGJPp/f0bU7f7g7cX9GlmV8Xi+y9ZrX60UuV1HVaoC9M/XYU7EjNlatWsWAAQOQPR46H3uG1JxZ4CaFsoyZTuOdMJHo2ec6r7lFVsTfB0ydyiWXX4GiKEiyhGRXn0VCURW0vMY777zNmtWrRV9GlwfQFveanMvl2HOvvTj6mONQFLnL5yVJQtN19tv/AGtYZQyDmoZv3HhiZ51L8/0/EX3WXFWMU//9D8mXXyB8yukMHTyEtWvXMmzYsD4HaovIY0sLl152BaYJ9941k5tvu93JeVRVtYvX1imYU1Rt9X7umfl9kD7kyiss8miKJdc0CtO1O0y6vaQcadTyoPqgvl7hppv9RCsu4Y67biVmV0/dJtL4B66+7jscdcxxZDNZYrHYLk0abbGjj8LhMJppogDhY48j9fYbdDz+iAD2NkFDeP/iL/6byIknEzxwmuUk2javYxFwtCS3chmdjz3cNcfJ48GId+LfZz9ip5wh9pZAgCVLlzB+3PhdUwcSFiEWP+NPP4m2eTPV3/kegSn7i9B+cCJllIpKlMoqF9kUrTNMTRNGLY+H7LKlNN//E/Lr14q0C7uQl/vZdo9BllyEzjpWNxaeLrnkioLWUE/jzFupzecIHXO8iMwwRO9jKRAUQzURx1UUy9guk3zvHZrvu5v8+nWF3otFz4wVyVHqEd1W6eWqqkU4x1+Cc3w+ssuXkV+zCs/AQT3DOV8RKbp+B+esFTjHlZcrRMI/0cI5iS3jnG0mjsFgkLbWVmrHjEUZMBB90wYnvEhYbvyYDZvpmDOb4P4HcMP3v8/jTz5JZWUlOcsCLcrHi8ambrJnewv/93//lwcffJDq6mp0Xcfv99PU1MQdd9zBDTfc4ACUDz74gOuvv965Qd1J6UR0v+b+3TRNx+PoftjshfKIo47iayedxD/+9lei0ahDhBVFYdGC+cz79BPARFFUB2DJsoyiKCTSaY487HBOPeVUMd4N68nPnYsnlcOQEhjBAKYsdmI5EsW/514ks5nyQHc7SDAYoq29neqxY1EGDEDftBEsPUp+P3p9PZl5n4qiNK4wMdMw8O+xJ7FvXkjHw//ASCTwjx5LxYWXIPl9juVymyasDTojEaqvvR40jdyqlXgGDaLyiqvLWpFsj+Bek/fmvG9ZRVUSCUaMHMnlV367OI9tGzdYSZIIBAJc/73voxs6S5cuZeCAAXz7mmsZOHiwo1fTKjCQXbSQ3KqVSIGAVSxGLtZjLttrDoBiPYr52EWPnxXrEdjy/bAJhiu8xy2lIGSLx7TvqSwXrK7OJtP1GMFgEMMU+cZ2qGrA6ifa0/xo+/0qq2R8JpMGJKpqaohEyxRvsq4lu2ghudU7R489FVmWyWazdHa0M2TIEIyNG2h/6O9g5Rs74bW66M8UPek0UZJdyyOVNOy2CdvxM07gmOOOtx4PyZkDhmHg9Xrp7Oigvr6e5cuW4vV6u51Pbm/jxD324IYf/HCrINMOWXWvxWCFrJom0TPPJvnO26TeecsBA5KiYCYTtP/jL4QOOYxoRQWblixm4MCBTtGZviQOeayuprWlhcsuvwJJwsp5vE30eSxDHqFwT1VVQcsLz6NDHimQR0m37B1WukoRqdvO0i1p9EP9JoWbbw0QrbiEO+++rUAay+i4LGl84nGHNB559NHksl8e0gg41Uj9fj8NDfXEDAN/KETkjLNIz/mQ/Lo1og2NpomiJ5KE0dZG+z/+jH/vfQSxtKSn5LFL7rGm0/HwQ+TXrS3u9aookM8jB0NETzsTz6jRZFNJ4rruGEx3TR1IxXufqpKe9T6bly0lcuoZRE86Fc+IkSiRSDdfV8DeCvM5UnM+ouX+n5CeM1uEftqkkS3sTaqC5PMXoqds0N7Nx4swq2EgqR7y69fScOMPqFyzhugpp6FU15TNVzcNA62pifgLz9H2xwfQNm0UVeldhezEB01MLS/IpPU7oqbrtotZpkLrdpRgMER7ezvVNu/YaOMcDcnnR2/YTOazzwgedGhXnNPbVrQ+LFKJvsHCOatWifQyV16zHIngn7QXqWx2q7drm4ljTW0djfWbGTBqNL5Je5JYswrZ6y14zWTRyDY1+3246loS2SwdHR1iwNmscyG2J7GcNczuzdXa2ophGAQCAZLJpNPs1wbuN998M6ZpCrC5pcRktr7I2sVuSkNV7fHouk4kEuHqa69j44b1vPXmGwQCQTxWaJjH43Gqp9rXYr8Xj8cZN34837n+Bmr7ixxKs6EeY0Mjfp+PnJkGvxfJ48PMZvHuMQn/6LGsa24iHO1mMfuCUlVdTdPmTfQfORLfpL1IrFktQlasOH0jnyc1+wOip56JWldXIG6yDJJJ9dXfIXTwoWj1m/HtsSfe4SOKej72lDSWWtj9k/dhwO8eRNu0CaW2DiUWcwhaqaVVtl67+prrOPjgQ9i4YQOT9tyLkaNGdQEpPZFSA8Oek/fm/37/JzZt2khNTQ0VlZUF0mi7/XNZ0nNmY7S3IVdUiIJBqurS4xjWNzUSLUdYtoMU67EwHx09anlSH35A9LQzRN6Di4B30ZMsIfu8lmVVce47Hk9x+Ix9jyjse0WRA66/5UBQvK8bBYttMOj6pHVq655OnLAHBx9yKK+/9ioScMhhhzN+woRteqbsY+13wAGc+PWTeO3VV/B4PJx19rkMs3q42l7MLnps2zl67InY4Hn16lUMHTIEj8dD00N/J79saXFojiRBPo9/v32JnnGWeMlFGsW9LHzczgfGLJ+lYtI9wCknEsI4mEqlCIVCaJpWNidKhGIW5yEX6dk0UaIxKi++jMzCBRhW8SeQkFSF7IL5tD/8T6qvvZ7BAweyds0aRo8Z0ydJRil5vPSyK8CEe+6aWUQePSXk0f276lHIF5HHHwCzHfJolCGP2ztsdUukcfMmhVtuDRCOXsKd99xGLNa1T2PhON2Hp17znes54sij0HL5LxVptMW+DzU1tWi6Ri6dJnTQIYRmnEj7gw+IVA3bMIkgdKl33ib5n7eIHD+jYBDtwblK77skSaQ/+4T4v58VEQoubyNWQZzQ0ccSPuHrAGi6gdcncvztQoG7ohQVpjFE8SG9o522B39H/NknCUydRuigQ/FOmIhSXYMcDCHZHnJdx+jsRNuwnuR77xB/5km0+vqiFmHOfbYmRpEfR5Iw4glSb7+OXFGFpChk5n3atWGEm+CUYB3TFAXsjHgnzT+/h8SrLxE68mh8E/dAraoGq1iMmUiQXbqExEvPk/l4rvi+bVB042xT5D/Gn3qM9IezwdQxM1m0hvriXs70cPnYguNme0hVdTWNmzfRb+QogVdX2zjHMq5omsA5p5/ZFeewXZfAXUZK91VUFSObJT1nVjFe9Xgws1l8k/bCP3oM63qAc7Z5FYhEo6xbtxYzGCQ49UCSLz5HkVo0DTkQJPnhbFi/jua2dgCHPJbK1ryBIAgnQLuVsGm/t379eue9csfZ0jnKvZ/NZqmtre3yGdsqrus6I0eNZuY9P+Gn997NG6+/RjKZQFFUx1psi67raPk8sqywz5Qp/OjHN3Hg1APR0mnUgIqxZh0yYCoKpipjStYiouUJ7HcAck0NjR9/xMiRI7ekjs8tVVVVbNy4ATMYKtajJAkdBoNkPvmY7KIFqHVHOe5/4ZQQVcoCU/Yv3KOSBbSnQL+c3iV/AO/oMYVQjG6O6wZXU/bbnyn7ifGUEoPPQ2Ltn/6An9Fjxjjh0g4Attz+2eXLSc9+X1j+7LwFWYa8rcdamj75hGHDh/VMMdsoPdPjR2QXLUSt61cUxuHcF/uavT6ChxxG/JWXMDo7UCorCUw9yLGq2huK4xVyjaPoHrt+907cg+BhR5J6/79IPh+hQ49wWnHYoZVuHcUqKrjz7ns46OBDME2DGSd8jYqKyh57j93vR6Mx7rz7Xk465VR8Ph/77X+Ak6fnSB/R49bEXoPq6+sJBYOEqqrJfvYJ8eeesYCQF7tHk6nlkSNRUUbfbfSxpHD/StZCqQBk3PNSlhXr7x6OFUFePKrqeI2709kW12Ur5zl02JFETvw6Hf/8W4H0KoLUdz7zJKGjj6Nm4h6sWbOGdDrtqn7dt+BCF/J4+RUgCfLYXc6j/T1bPEXk8eeCPJqzufJKCT2vdyGPpoTVhmA7XEAPSeNd995ONFaJrhtFpLHcz3Kk8fAjjkTTNKJfQtIIQp82CVuxfBnjxk8go2lUnnUO6ff+S2a+aM9RqIwsYSSTdPzzLwSnHSzes+fqlvZa15yyfzNyWTr+8Rf05iaroqL1jFntr9TaOqJnnI2nrg49myWZTlMXiTiG9V1S7FBV5+9COoUcCKK3txN/9ikS/34WuaoatbYOpbIKORTERMJMp9AaG9A2bcRIJJB9vgJpLL3/9t5omqKSq2liKgraxo3U//AGoTf3Z62KlpIzqQpj7EoeLV0pKumP5pD+8AOkQAglHAJF7FtGOo2RiIuUA59fnL8b7IRp0vHk41a7I4EZJJ/PavtRTIS7FWttEfUoeo88FnCOm3dQwDkBC68uXFAe53wlvY6u63VwzjLSH85y5eWaLkPzAcjV1TR98nFRW8Jy8rlCVVVZobWzk8h+B9A6cBBGU5PwCOmiLQd+PzTUo7/yAoftNwUtlSQSDqOV5PKVWlTdYk8SwzDw+Xy0tbWxzz77ON8DOOGEE1i2bJnTl7E7AGJ/Z0vnMU2TUCiEz+cres39XUVRMHSdsePG8fNf/ZpHH/4XL73wPKtXr6Kzs8MJxVVVlVAoxMBBgzj4kEM5/1sXMtbKD1D9frR4HGP5ClTAkEwRomqD8mCI0MGHkpUk9FyeSLh3PI6BYBBZkmhPJAhO2R/F0iOqKkIvfD70xnqS77xF4KCDkb2+ovtnW0RtgN2F3G3DWLp4G+xjlJC/UvJgf0+WZcd7bVdz7O452No4ShdZ+/fuqsilZ71HbvkyJ7zITjqXQiFCBx1KTpbJZjLEYhXbPJ6eSBc9DhiE0Vyqx4Zu9ehct0XQwzO+JqrFzfkQ/157Ez35NPGhbcwZsJ8Pz4CB1N15N4mXXkAOBgkfO6PgRS5jEDBNk4EDB3HRJZc6x9oWY0TpscLhMEcceRRA+agE63DpD3qix3Sv6XFrYofEr1i+nGlTp4Ku0fi3P6Nt2mDl6BTmnKkbBKYeSOTEkwpeY/s917zY2j115rq0bbxDksAwDVeqbM9JXJcxWbk3lRdeSvqD98mtWOYYHCSvl/yaVbT9868MuPfnjBg+gk2bNjFq1CjnfvU16c7zWJrz6CaPxYDPJo8a0w6exk23/ox77/ohkjSbK65QypLH7VIwxwaIWyCNocglzCwijeXndxfS+MTjRaRR13Wi0eiXkjTaYhuwxo4bT2trK9FgEHXUGCKnnE5+1QqMXM5JAZIkCUlRSM+dQ/yFZ6k453xn/dySFIVOWvc9+d93SLz1htPH1YkxkABNI3jk0YSPPR7TNMloGrFYrFfvw44RewHruvaZmibub0iQcTMRJ9feahXps/P4JSRVRfJ4RfVUu4Bf6VrlIicOaXSRQsnJ5Rb5/jbxF3qgx+TRNAxknw/kAJimaCFir+uKItptGIZVqV/q8n37+gER/eM+p10R3UWut7psSFj3qveIYyAYRMbCOftaOKepsRjnNDWQ/I+Fc3z+4nWTr5bXscuea/2envUeuRXLkAJBJ7JM4JwwoYMOISfL5DLZreKcbSaOqqoyaMhg1q9Zzd7jxuHfbyqJpx5HjkadcFUtn8cfibDmoX9w6xPPceudd27rabYoNjH49a9/vV2PC11vuHvjNk2rH51hUFVVxbevvobTTj+DTz75mNWrVtLW2oppQiQSZtDgIey5116MHTceKISZ6ek06eefJ/fBB4WSyH6vmADpNL59phDaax/W1W8mEo3gK1N9dnuIoij0HzCAjevWMmnMWAL7TSVu6dEUA0by+ki+8RrR087EP2kvYZlyhbyVblxFxP1zWHdKyUy54xZZzFzvlxK7L+pt6G4sIEJXJFUlv34diVdfdjYfUcXMi5nJ4Nt7H0KT92Z9fT2xWNQJY97eYutxw9q17DlmLIH9u9Pjq93q0Sbppmki+wPEzjib2BlnF7/fQ+JWpBfrmN5hI6i68prCZ7ZAGh1QqWmYULZoSE88ju7P2RWTnWfEfoZ0HUmx9PhaOT2m8e09xdLjZmKxWK/pcUtirx2bN29m1KiRSF4vqZdfJPX2mw6KN00LyGgaalU1Fd+8EDkYLOQcg9Vjr/jebe1eip9s465bIDxb/aRDTruewNGloeMdMZLYBRfRfPcdoGtW1UkZkEi9/iqJY46n+qhjaGlpZuPGDQwaNLjPEg9JEvmjbs+jJIk+j+U8j+A2ltnkUSWf1znokIMcz6NpzubKK13kUXYwYK+TxnD0Uu669zaisUqMrZBGgNJCOF8l0miLbdwIBoOk83kifj+RU88g+Z83SP/3HZEiAI5x1sxm6XzkX4SPOhalrtA2rNy62GWdNE0MKy/YiHcKY5O9p6oezHQKz9BhRM88G9nvJ5/JEI/H6d+//66vC0l46Si6Py4sAaLSKAii5PV1XY8M06moSjmHRwnpc0igWfA8OuewiayLGHY/dtdn3MfU9UJ6mCQVJrs9Rvv1knF2wU+ljd5Lr62cp670NTvEpBejVRVFof9AG6+OETjnyccsnGM6OCfxpoVz9pos7oNtZOwhfvkyitMDef064q++jJnXkPwlOGef/QhNnmzhnK3j1c8VexAKhcmuWYvm8xM56lhSLz0v3rCVouvg9WGsWsmmv/6Z6GVXCqt3uYewh2KaJl6v1yl+IEkS6XR6q7mN2yruoir2ebqQR1kGTDRNp1///kyfcUK3x9M0DVlVUSyrkrZqFekbb0RfuxZFktB0DdOqYGgaOqGjjkbq14/NH7zPuPHjt+u1lUplVTX1Gzeh+QOEjzqGpK1Hq6eL5PeTW7mCxCsv4Rs/UWwwRdbvrhOxtybmzjoPdO8xSb33X9IfzUEOhURiuWRVJtN1Qkceg9SvH/WzZzNy1KheGastlVXVbN60NT2u7FaPReAOxGZiz1VFoTvw152UPh+maQqkiQSq2oU0ln5WkiRUT2lO3ufzOEqS1LWdT8l3utejQejIo3eYHrsTO8e6oaGBMWPGQEcbbQ/9TfSADQS69OUKHXUswUOPKApRldg2z18XMbfte+YXWJeLdQiSLEKnYqecSuqt10m++Xohb8fjQW+op/1f/yCw/wEMGzGSlatW0r//gD7da86OkrDJ4yWXXY5pmsLz6M55dLVXKSaPZlHYqu15RJrNlVfI6HnDjTPFtvt5ze498DQ6pNEwkMuRRgrrgh2m+cTjj/FnqxDOEUceJcJTvyKkEXDSX/x+P+3t7YQDATw1NcTOOo/cwoXone0iBF3XBRFRFDJLFtHxxKNUX/2dkhD0LRlehKGu88UXyMyZ7URTOOuBLiIswsefQGjawRi5HKls1qn3sMvrwsQh345nzppDjkfNvReVqXLfpbp4KbEqXVddpFQycXozlh6yR1zLRUDtENgiIz0UhSU75M8ZdlcC6OwD3ewH3b7vXlRAkA9ZwVkkelG64tV/ixsoKw7Oya9aReLVl/BN7IpzvirSLV599x0yH8/tHufU9qf+w1k9wjmfa2eNxmIEQyE2bd5EdNpBePfcCyOZdMIfrNFjShLaKy8QaGkiFAoRCofFz8/xLxwO4/V6rY1TTJpAIPC5j9fdP3eIYjkrTWHCSk64ST6fJ5fLid5lrt/tvADZ/i6Q++wzMmvXIodCaKZJLhoGnw9yOdQhwwgfcTQd6TR6XiMcDn8e9fRYIpEI/mCA+oYGIgcehHeSS48gwjE8HuLPPkV28ULxmmtR/SpMyC6WXNt6s2E9nU89jpnNWn0LTbGJ53KoQ4YQPuIoOjIZDE2joqKyV8cYiUQIBD6/HsEF1sEKy/GIn5+DNHYJh5EkJI9XHLPkeM77ZV6H4vvf0+et3OdLvV891uORO06P5cT2lq5etYqB/eoIhUK0P/k4mY/mgtfrkEUkCVPX8QwcSMUFFxYKWblkW+er5Pq5rVNd18s3uN7WcxeglYkcjlJ52ZUo1dVF+wxeL5lZ7xN/7hl8fj+VkShr1qxBkkQuWV8VO3KmqrqaRCLBpZdfwfEzZnDPXXfy4axZTsun7uZMgTxqjufx+Rem8sADMopHxjBLcN7nwXb2MRDf1zWr5YY7p/G+2wuksZt8dFufmlU99snHH+PPD/6eq6+7jqOOPhpNy3+lSKMttnGjqqqKzlQKU9eJHH8CwUMPx1agcy9lsS7Fn3mS7IrlTsE4KMdb3Ll0oDU10fHIPzHSaacCqBiAgpnP4Rs3nopzzyedzSJ7vWzcsMExWO3SYor1XA6HIZcvFAQqIUDOsyoVKoAX/bNAdheyt7X90PpPrKFS0b8i8rk1cRFcZ10uOV6XY1vX35Nx9kjsfcZ97xRFFOXRS9t8bH8pwqtTp+GdNFn0QS/COV7izz1FdtFuvArF3kYH50gC5+DgnKFEjjiKzkwaXcv3COdsM3G0gczQEcNZt3wF9B9A6LgZxRYKC8RIfj+ZJYtoffRfGHmR//dFLNFu2ZEeJ/v17jZwu6Kqx+Mp+t2xeIuYO5KvvkLiZz/FBLJajmy/CvRICDwejGyG4JFH45+0F6uWLmHUmNF4PN7t7lG1xdbjgEGD2LxmNdKAgYSPP6Fw/a6S/rlVK+l84lGMTNrpHwRdwfiXXUzLammaJvGXXyA1Z7aw3lgVTJEVzEyG4JHH4p+0F+uWr2DAoMFdC7JsRynocfAX1mMXKybb5u2zpdST2O3xKJ5v5Qhfb8xzR49sTY/H4N9jx+ixnNihbKlUilw2Q2X/AeRWriD+9BMYyYTTssJt+Y6ccjr+PSdjGvoW9dCbIkmi79YXOmOXtVZ4TgL7H0j0tG84YcWiVL2KHu+k48lHya9ZTc2gwWRSKTKZjJP/3lfFvj6HPF52BdNnnGiRxw96SB7VooI5z784lQd+XyCPpUVtevoo2GRTnMxyTHlFn8abXYVwYlZ4anek0RYtn0f1eHjiseI+jblcnmj0y1kIZ2siSRK6ruP1ekmnUuJFr5eK8y9EHTRIeAYcHCEMgLnVq+l89CHx2laInalrSJJM51OPkV20oNAL2TSdtj2yP0DklNPxjBqNT5ZpampiwsSJfTZPeFvEsNaH8AlfJ3jUMZidnVZRIKOICBWRrW6kiLj1dF/akmdvK16/LyTb+9gl98o0DNFSxDBEesupZyBXVGyfc5URB+cMLMGrsoTdn1O05vCRW71K4Jz0brxqe9njL79YwDla3jKQKIJ3HHUsvj32ZO2KFQwcNKRHOGebiaNNhoLBEB5Vobmzk8pjp+OduAem2+tok0evj87HHyUzd07xBe2iUurF2NqlmIboypxbuZzmG39I9rN54POQi4Uwo2HRhDufQx0wgNjXTiZtQrK9g2Cod72NBUtnNbqm0RqPEzv2ODwTJ7r0CKamIQcCdD7zFOkP3i86xlfBglNEpqyE+OzC+XQ89HeXRc+y3uRzKAMGED3x66QliY6WFmrrahEf651n3m2x3h56LNXptnr7tva9otd34PNTVo8LtqbHk3aYHsuJTXjWrllD/3798UgS7Y/8i9yihciBQAH8ICyL3lFjiJ51nnXBhQq42yNcp9z37RD+rvdEQjf0Lxy6VNaQoChUfPMCfBP2EL0prX1G9vvJzfuMtscfxiNL1NXVsW7tWmecfVlKyeMll13OcdOnc/fMO/hwtuV51LZEHrtWW33+hQN44PcKikdGKxTnLCKRWxKzhGwahiCNDfUqN98iSOPMe25zCuGUC091S94hjY/y5z/+gW9fcw3HHHvcl65P4+cRu/JsXV0djS0tSLpOYP+pRGZ8DSSlqBCLiIrQSLzyIpmP524RGJumiaSo5FavIv7Mk6L0vqIUjPeSBJqGb/I+RE47E4DOZJJoNPql2dtz+TyGruMbO466O+8hcMhhYl8sQx63yaryVZJypFGWHcJWddV1xC68FNnrJWd1Odje4uCc6mp0TRc455jj8E6chJlOFXCOZQjpfPZJ0h+8V3SML8szvSUph3MyC+fR8a+/ubzbBZyj9h9I9MSvk5Ek2ltaqK3tGc7ZZuJoW8g8Hg9jxo9n0ccfoYweQ/jEk0qy8XFc2XpzE60P/BatpdkqaFAcirYrSiEsoOt7jpXfyi0w0mna//onchvXY/avQYsGIRoW7mJVwcikCR5zPIEDD2LJ/HmMHDOaaDRa3P6hF8ZvFygYOmIEyxfMRx09lsiJJxf06PpptLfR+offkt+8qVBRlV1Xfz2VojwRRUFPxGn/65/Ir1juaqorIykyZiZN6OjjCR54ECsWL2LwsKEEg6EdpsdhI0bu1mM3UlaPf/kT+ZXd6PEYS4+LdoweS8XWaWNDA36vl1htLakPZ5F85QVLZ1IhF0eSQFGoOPd8vEOGlvUkfyGRnP8ccYf0u89l/3RXVd0eYhsaTMNAHTKMyosvEz0dbVZjEcjkC/8m/dEcKvr1I+D1snnzJieXrC9LEXlMJrns8iuZPuNE7r7zdkEe1S2RR8qQx/t5/oX9eeABBdUno9mX3wPyWI40yio0NKjceLOfSEyQxlhFVbfVU60jATi5mk8+/hh/+eODfPuaazh++glkMrtJoy2mKWonRCMROhJxAGLnfgvvqNHFjdutcLz8+vW0P/KQqJxpNV7vMs+tZ77jkYfIrlguiu1Ya4Nk5b/L0Sixc8/HU1OLqWtks1l8Ph+6rvfpHOGeSjweJ6fl0fN5fGMs8njwobvJY0+lO9KIeN6qvn0tlZdfhezzkc2kyefzvTKMrnh1HqrDO+ywWTfO6aD197txjh6P0/aXP5JbsaIY58gyZtrCOVOnsXzRIoYMHUow1DOc87lWBvug4XCEcChMY1sr1Sefhm/fKQUvBzjWYMnvJ/Xu23Q89A9M3QI95q5PHstJ0aZuLeSJV14g/vQTEA2R96sY0bD4nKpiZjJ4ho+g8syzSeTzZBNxAsHQDhmrrcfa2jp8Ph8Nra1UnXwqvn1cerR1GAiQnvUBHX//C2Y+jyR1LXDyZZNyuow/+wzx554ptG1AeEHMdAZ1+AgqzjybzlyeXDJBReWOyYmz9VhTW4vP592txxLpVo/PP1MoS02JHs84m858nlwqucP06Ba7eEpHRweV1dUomQztDz9Efs1qJL/fySmRZBkznyew736Ev3aS+LJUnKP9uUUq/FLOc2z/k2XZ9b5tNOx6uC/yfJXmuoaOn07o6GMxMxmxjubzVnGElbQ/+i882SzhWIxkIrFDCf8XEYc8VlWRSCa59PIrtok8Qgl5vP1+nn/JIo9eqUfksVvS2Khy400+YpWXcMfdNmnUt0AaxYls0mh7Gq+85lqOn34C6VRqN2l0iT3fA4EAmWwO0zDwjhhB7KxzRAVUt9fRMqKk/vMWqf+8haSozhpWZLRWVdKffkzi5Rchny9uVA9gGAQPPpTw8Sdg6jqt7R1UVVUBX4KCOJbU1tYS7+hEMwz0fE6Qx5n3FnseTXM3eSwn3ZBGO8Wk6tvXUnnF1cg+H/lsDt0wezUtoIBXawt49SQXzlFKcM6Hs2j/258snFO+hsKXScrjnKdI/Ps5UWHdjXMyGTwjRwm8ms+TSyWoqOo5zvlcu6kkiaIDXq+XgUMGs+STT5GGDCV2xtlI4bDIP7ETsG32Kyu0//VBkm++6nKZFo63qyuznHVfkiQyC+bR+v9+hZFOg1VV0rTD9CTAMAiffDr+Kfuz+NNPGDJ8OJWVlTsE7Li9x1XVNWxYvgxpyDBiZxbr0ZmMqkr7P/9G4uUXCvjQ9bDu6josFUeX1jWm5nxI6//9WoB2+1ptPZoG4ZNOw7/f/qxevIiK6hoqKnaCHmtqWW/r8Ru79Qjl9Dhb6FHrRo8nny70uGghFdXVO0yPtmhW/t7GDRvwezxUVFbS+drLpN9+XRTEcW/OpokU8FPxrYtRq2u2Ww55qbj5gE0YdV0nn887+SeSHQuJ8Di6x2h/b3sMxDR0lEiMyosuQ+3XT3hWZEn89HhIvfYKnW+8SmVVFR5FYfOmTc6e1dfFTR6TpeRx1gdbJI+2eDwKmqYz7aBp3HLbL3j+hf144AEV1WeRR4my5LGUNJou0vjjm3xEKy/l9rtuo8IhjQrdk0aKSONf/vQgV1x1DdOnzxCksaJiN2ksEfv+xaJROjo7wTSJnHoG/n2mFHkdRQqQF61+Mx2PPoyRShZ5gcSHDEzDoOORh8ivWyMau7tCVE1dQ62to+KCi8V7EsiyqNfQ173z2yKGYVBbV0dHRwe6aRbI450WeUylCq1JdpPHgpQjjYri2BIrr7iayiuvESkC2SyZXBYtnycSjfbakIrxqo1zhhI785xucU7HQ38n/tLzhbnzVcI5H86m9f/+H6auFa7VwTkmoZNPwz9lP1Z9Drz6uZGQankV+/XrT78B/VmxahUVp51J4MijIZPpUtEPWUZvb6f5/p+SXbTQsn4ZW9wAdxXpUsnI+jvfUE/zz+4lt2qlVT7eZTVXVcxkEt+UA6g5+zxa2tvBMKmoFBa/HRWPbW/cQ4YORfZ4WL9hAxWnnk6wnB4lCSOVoPmXPyfz6cdfSgOALaXhxrn162j5+T1oGzcU8kokqUiP1WefS2tHB7lshkGDBgM7QY9DhqJ4PKzfsJ6KU3brsase19Ly83u3rMezbD1md7geTdNEVVWy2SzpVJLqgQPQGurpfPxRtKYmpzw/iHAzI5cjdOQxhA49wjnG9hur2wgmOxuSLMuk02lGjhzJaWecyYABA8mk0yhW6xYQgM00tx+JLSYn4hz+ffYlevY3MfJ5UXpd05C8PrSmRjoffwStoZ7aQYNJdHaSy+X6fKEcW+xrrXR5Ho+fcQJ3z7yDOVvxPNrEXVUFeTzwoAO5+bZf8PyLLvKoUZY8lpJGSYXGJkEaKyov5Y6Zt26RNLrXjK6k8WpmnHAiyd2ksVuRJNHf0+/3o2l5dENHqayi4sKLkcORYpxhmqCqpD54j8QrLzmRByDy2SVFFR7Jt96wD174vmkCEuEZJ+I/YBoSsGHjRqLRGKZpfqn0Ynty6+rqaG/vQDNMK2x1rAhbPeiQLuSx1GvzlZNS0miKlDPJNMGEysu/TdVV1yL7/Wi5HJlsBkmSiFoRBL0pbrxahHOOOqY8zkmmaPnlz0h/8tFXC+esW0PLz+5B27RRRJy5cU4iiX+/As7JZ7Ydr34hE7ptaQ5HY3Q0NZGQZWovuQLPiJFOCJEzEa2WALnlS2i65w7yGzdSGia3qyavuh9A+6eeiNNy/32k3vsvktdnKdQuqy2DpqFUVlNxwUXIQ4Yyb86HjBw9mlAo5NzXHSWFilUDad68iYTqofqSK/CMtPSoCD2K8FoP+bWraLr7DnKrVzrx47u6Dt1SZGWTJLS2Vpp/eg/puXOQvP4C2XDpsfKCi1GGDmf5ggUMGjoUv9+/c/VYv9nRo7pbj0KPra00//TeHuhxGMt2kh5tYrNx40Yi4QhBn5/O558j/cG7oiKau+y5aaJWV1Nx/kXI4bBzPdtbLFutVZZeIpPJsO+UKdx930/5zW//j7vuvY/J++xDJpMBrP50ml7wZG2vcdhGN1m2LMoeoqefRWDvfUUFPY8HU8sjh0Kk332Hzhf/TdDnIxKNsGH9+u00ih0jpWGrds7jXXfeUQhbtUKw7M9b33T2IJs8Tjt4Grfc/stiz6OrF3m58FRJhcZGlR/f6KOi6lJun3krFZWiMEV3pNEeg+bOabQ8jTNO+BrJZJKK3aRxi2Jb+2OxCjo6OpGA0OFHEzrqGNFf1/acGAaS14ve2kLn00+gNTUK8oMwShupFB2PP4JWv7mQ2wROBJhn2HAqvnkhhqFb51UcvX7ZxG4rUldXR2dHB3ndKOQ8zrxPkEerAieUhPx9FaXMvJas8syVl15B1dXfRfYH0PI5kukUiqwQDod32LzuinNUqi++As/IUUW8wzRNJI9Kft1agXNWrfqK4JwWmn9yD+mP5zjcw8E5+TxyteAdypChnxuvfiHiaBcdGDRoENV1dSz86CN8U/Ynev6FyH4/WD1EihI2PV5SH7xH0z23ozU3dlHgrjZZ3SGqpmWxMjMZWn51P/Fnny7qbVnkLtY0QqeeQezk01i4YAEDBg+huqZmp1j8bD327z+AUDTGygXz8U/Zn+h5lh6NQqNhW4fpj+fQdNftRcnHu6oObbFH7Z6EZjJJy/33kXj5Bctr7LKoWXoMn3oG0ZNPZeXKFXgDAQYOHLTT9RiOFPQY++ZuPfZEjyFbjytW4NsJerQ33mw2S7Kzg/6DB5NZvIj4U49h5HKiEppVzEFSFMxslshJpxHYZ4rrUnoH8KhWM+V8Ps+xxx3Hr37zvxxy2OHkcjmOmz6D3/z2/zjhxK9hmqYICbXvfW+J9ax6hw2j4sJLxPMNVlKe8MTGn3iUzOJFDBw8hHh7m1PwY1fwOkL5nMfjZ8wo5DxarTps6bIXAYoiO57HW27/ZbHn0VVbyU0aZYs0/ugmH7HKS7n9ztuoqKxG03QUVSk7RjdpdKqnPvgHrrz6GmbMOGE3adwGMQwDj8eDYRhkMxkkv5/Kb12CWlsnMJUN7nQDye8nPWc28Rf+Lb6bSgKQeO1l0u//F1TFWRBtj4skSUS/cQ7e0WNQrPYbgwYNwtxFcoE/j9h7Y21dHfF4JzlNx9A04Xm86z4CBx1sGVet/bHUu/tVEdPEpOu8RpaouOhyqq67Xnga83mSqTSKohLagaQRSvCqg3P2I3b+haLauN4V52Q++Yimu24lv+nLjnMSNP/8PhKvviiik0pxjq4TOfVMoiedysoVyz83Xv3Cq4RtpRo8ZAg+n5c1a9ZQfeElBKefCPlCDyJHOaaJ5PGQfOUlmmbejtbcVLhweg/49IaUI7xmNkvLr39Bx0N/sz9UdD2Sx4OZTuObdjD9v30NDc3NZNMp+vXvV3ScHS32QzNy5Eh0w2D9+vVUf+sigtNPKNKjNUgkj5fk22/SdMct5Os3O5NxV7XWmaZphX6bRWSj6f776HzsEbEQdaPH2iuuprm1lbaWVkaOGuUcb2eIrccRbj1ecFGX+WgN0tFj45dZjwlLj49vWY91V1xNS2sr7a07R4+27lavWsnAIUOQTJPOZ58iO3+eSG7P50HCCUvzjBgh8li9InxVKs1z2k4iSRJ5LY8sy5z3zQv4+S9/zbjxE5z+c7quM3bceO7/1a+58upriEajpJLJXr137nzK0FHHEp7xddEM2uvFzOWQg0Gy8z4j/uyTYOgMGTGCJYsXYZrmLgWOu/M83jPzTj6cVUweS+erPYeLyKOd8/h/BfJok0ZdL+Q0/ugmt6exCk3TUdWuoLqINGqa06fxL398kCut6qmpVGo3adwGsfthVldXk0wkwDTxTd6byKlnFFW1NA2Rx2XE48T//Qy51auQgyG0xgbiTz+B1txseRwK4NHUNHx77kXstG8A0NbeTiQSAXDWjy+rOOSxtpZkIk7WbtUxeqzwPB54kNOypCiKDL4S5NE0TUxJcvZP53mQJCouuIjq73wPyR9A13USySSKsmM9jW5x41XDMFi/fh1VF1zcDc4R0Y7J/7xF4x03FRnJv1w4J0HTz+8j/sSjTnSZLQWccwi1V1xFc2sr7a1tnxvnbJeVwjAMVFWl/8BBtDY10pJM0f+GH+Ldd7+iEABngFZ8fuKF52i8zaVI271cSsb6glLNQs8y9zjdvxuJOM0/v4/2v/6xUCLerTxVxcyk8YwcRe31P0Dr15/Fn33GkGHDqays2uklsG1LZ12//iQ722lOpuh/w4+66NHWoeTxkHj9ZRpv+RH5dWvFQrMFHfYJPUKxLikJTbEWTKOjncb7ZtL5r384JnnHol+iR2PAAFYuWcyAgQOdSoF9RY8JR49d56Nbj8nXX6bxlh9+OfX4kzt7pEd9wABWLl5M/0E7Xo+2F6yhoYGAz0dFdQ2pWe+TfPYpsKMWTJFjgqyAphE76zy84yaIA/TGOF3Aqaa6muu+ez133HUP/fr3d5qD25ZK0zSJVVTwoxtv5o677mH06NEY9n3e/iMTx5UVTE1DiUapOO98PEOHF3rVWYVy4k8/SerDWVRWVRPw+2ltbQXYZbyOUN7zeNz06dxz150i57EMebSLFXUlj9OcnMffP6Ci+mQnXFXx2dVT/Zan8VbH07gl0mh7mVVV5cnHH+taPXU3adxmsfXnC/jp7GhHkmVi55yHb8xYUdTLKgKCpiGHQmQ++0RUbgfi/36G9OwPHO9L0XF9Piq+dQlqbS2mYTg5lX1mTe9lscljTW0tyUSCTDYreuCOHkvd3T8hMHUaZi4nqoe6vW4l+XFfNnHWDPt3u4UXUPHNb1H93R8gBQKYhkE8kdippNEWG+fU9utPsrOD5kRC4JwpW8A5b7xq4dU1uyTOEX+WwTntbTTedyedD/+zPM5Jp/GMGk3tDT/A6D+AVV8Q52wXtFFwHfen38BBLJ73KeaQodT+8CaR75jNdk8eX36Bhh9eT3bJ4oLySryPRUrdCcq0gak7n6TUS5rfvInGO26m4x9/LZBGFziRFAUzn0epqKLquusJ7D+V+XPnMGjoEGrr6tA0badvrHYuwODBg/EFw6xZvgyjGz06D63qIfnm69T/4Ltk5s9zrFTue0MJkNmZUqxLySkt7YxXlsmvX0fDzT+k89F/IVw8rrL3igIlelw27zMCoTCDBg/uc3r0W3rUe6THN3ZNPUpb0+PD2NVAyumx+robCOw/lRXz5+OPhBk0aMfr0dZZMh4nVlOLkkjQ8djD5NavLcpRssmuf599CZ94UiHfrzdyG61nQFVVLrjwYr7/oxvx+rxO5bUueTCSqHp33vkX8L0f/ohgMFh0nO0lRc+eRVr9++5H7KxzwdBFSK+uI/l85DduoOORf0FnJ9X9+9Pc2LDTDTufR8p5Ho+fMYO7Z97J7FkfbJPn0c55/PeL+/PA72R0U8JUYdMGWbTcqLqUO+66zclp3BpptPurPfn4Y/zpwd9z9XXXcdzxx5NOp3eTxi8gpmkSDIbQdUHwvMNGEPvmhUBhPTYBJBGB0Pnc0zTedRsdjz2MnkyCohbWO1nGzOUIHXEUoaOPBaC+oZ6oVQXzy5Dr1VNxk8d0Ok0qk0EyDEEe7/kpgQMO7EIegS9tsZwuThCHNErEzruA6u/9SLQfM006OjtRVYVIJLLT53UXvLpsGfrgIdT98OYt45y33qD++98lM++zXQvnUOAfRThn3VqBcx57xHpGi3GOmc+jVFdTde31BPY7gKXzPsMf/mI4Z7vtnoqioGkagwYNoqa2jnkfzSU4dRo13/8xSk2t6KWidCWPksdD6t13qL/hGhJvvu4waNMwiuKQgaJNa0cQSLc30U0abfewUyzis09o+P53iD/zpCgLD11II4aB7PMTu/wqYqeeyeKFCzEliZEjRzke274g9qI6YsQIZI+H5QsW4Js6jZrv/wilpqZIj24dpj+cRf0N15J47WURGuAKBaA7He5AKa9Luugy/dEc6r93HYkXn7fi5M3CJLX0KPl8VFz+bWKnnsmq5ctJZ3NM2nNPTNPss3pc6eix63zc5fVID/VoltHjFVcRPfUMVq9cSTyVYtKknafHpsZGdE2jIhql8+03Sb7yIlIwJEijff8tPcW+eSHeocMK19MLIkkSJuDz+Rg7fjwerxcoFPDoWpjFeu4MgyFDh+G38g57I3S26Hdrz4ieegaBAw/GSCREfkc+jxQKknjlJeJvv0EsHMHj8dLQUL9dx7OjpIg8JpJcetkVHDd9BvfeNbOQ86jluxB6KIBDd7XVG2+5n5dfn8aNN/r52198fOf6GFW1l3HbnbcUqqduA2n884N/4OrrvsPRxxxHJpPd3afxC4q9xgWCQdosT3l4xtcITDuoUAQEMHVN9NJbt4623/+W3PJlyHavV3EgTMNErqqi4luXIIcjZDNZKiur8Hq9Ox0c7wyx98fq6moymTTxVBLJNPGOGkPd3Vsgj6U/d3Up3dddnsboOedR870fI/kDAHR0tOPxqITDO5802lKEc7weViyYj/eAA6n5wY0otRbOKeN5TM+ZTf33riHxyosCC+wKOKccXp37ocA5L73gpOGU4hzZ56fi8quInXoGK7cTXt2uZle73Pm48ROQZIVFCxcSOfEkar73I+RoFLSu5NGuDpZdspiGH3yXtgd+i9baKm6MZU0vUpptCehFa4DzgLmU5ryn68J3IcsYqSQdjz3M5uu+Ter9d0VJ+BIXtwNSFYXoRZdSc+XVLF+2jHQmxfgJE1EUpc9Zv+3Qs3322ZdkOkX9+vWETzyZ6ht+hBKNYWrFpMPWYW7lcup/eAOtv/sNWmuL83B30SE7zgjgWF660SUIXerJJB0PP8Tm71xNes5skSvldvfbhUlkhejFl1N9+VVs2riB9Zs2MmmvvUqaoPcNcesxkU5Rv34d4RNPovqGH25djz/66uhx/bq11DfUM2nPPXfaXMzn87S2NDNs7Fi0TRvpfPgf6PF4YbzW2I1UiuChhxM65DBx7bmc0Ium9eyfvm3/sI6t5/NIui7+1jVxzm7+KYCRz1vn6/5z2+sfgJHL4hkylNhZ56HEYk71WUmSMZNJOh76G7mNGxg8ciQdra1FRWV2JXHIY3UViUSCy6yCOffMvJMPZ4s+j5qmlfUG23NAVQXYOviQafzk/j8Qq76Fj+ddyUmn/YbbZ95BZWV1USjyVknjE4/zpwf/wFXXfYejjj6GXHY3adyeEggE8Hi95HJZ1JoaKi+4GCkUwjRKvMqqghyNiXwmW2e2IT6XJXrSafj32RcJaG9vQ3FFDXwVpUAea8hnc3QmEoI8jh7TvefR9XOXJo9mIQ2hXHhq9Kxzqf3BjRAMIskybW1teDyePkUabSnGq2mBc074usCrsVgX8ljAOSup//H3af3dr9FamncpnGMkE7Q//A82f+cq0nM/7BbnSIpC7JLLqb7s22zauIENGzew53bAq9vdrG4DrwkTJ7Jg/jxWrVjByLPOxcznaP7FzzDinVbZdGENs92ukseD0dlB8y9+QvrjOVRefhWBfaY4i6CpaaKXjMvL0BuhA+UWUhNA10CSnSqxuSWLaP/rn+h89inMXE4ozjDs1lhijG7S+K1LqLvhBzTU11PfsJl9992PoOX+74sLtz2mvSbvzX/efgsJGHj2eRi5LG3/cz96Z7EebR2aiTgtvxL9ASsvv4bAvlOcfnmmrhfp0H2e7S2maTrhi13esyegpcvM4kW0/+VB4s8+ZfWC85b1GDt6vO4GWlpbWblyFVOm7IfP59uF9ChZeszR9j8/716P8Tgtv/w5mU8+pvKKvq9HbD3++UHiz/VQj9+5geaWFtasXcs+++y7U+fjxg3ricYq8MoybS+/SOp9q/2GXYLf3hBUD9HTvoHaf4D42/IC9ra4YUJP7s6OJuCS1wdA+IQTSf7nTTof+xdyRaUIVwoGSH84m8QL/6bq8m8Traxi/bq1O3R821MK5LGa1pYWLr3sCjDhnpkzufnW2zjgwGmij6KqdiGPwvMuwJZhGIwfP55bbr/JObYNPuQypGJL4anXfOd6jjjyKPK5vNPPrS+By11dfF4v6VQar9dHcNohRGZ8jY5H/yXa8OTzTo0IcrmCcV0oGjOfxzt8BLFzzkP2+UkmEvj8PieC4KssNnm051JnPE40GnE8j423/Ij0h7PEOmsRiiJvfomHapcQFwF2rsUmjaZJ9BvnUPujmyEYQpZlWlpa8Hm9fZI02lKEc/7ztsA5Z50rcM6vfo7e2dE9Xv2f+y28ejX+ffdD9nj6Ns5ZtJD2P/+B+L+fgW5xjo6kqEQvupTa626gpaVZ4NX99t8ueLXX4rF8Ph+jx4xlxfLlgjx+80JQPbT88qfozc1Ifj9m3hVWY1vVVQ/JV18mM+9TYmedR/Tk0/CMGi1Aq2mKyoKKUjZvpsh6Alud0OVuXtFGaRiFxpmWZya/cQOJV1+i/R9/Jb9iOVIgIAiIoWP3pRTXoWLm88h+P9GLLqPuuz+gta2NhQsXsP/+BxAKhZxcob4sXq+Xgw4+hOVLl5DKpBl9wcWgegR5bG5y9AiuOHGPl8Srr5KZN4+Kc75J5JTT8I4Y5ZTQLzchu8gXXJC7kH/TFIUCJMkJ7ctv3iR0+fe/kF++DCkQEButexK69Bi76DJqr7uBznSKefM+Y999pxAOh3cxPS619HgReFTafrUFPXq9JF9/lcz8ecTO+SbRvqzHV16i/e9/dubkFvV48WXUXnsDnakU8+d/xr777Hw9JuNxJuw1mczSJXQ8/A/xomnirGo2SFEkMp/MxTNkiPU6dKFy3d5uaQvvlR7AXk0p+X1Lxy/5QJEVrethyh6s3LNS9iXXAV3E2sxmMbMZUUDINMEK70GW6Hj4HwQPPZwBEyaybNHCrV1En5Yu5PHyK0CCe+6ayU233sZUmzx6PEXeRkEgKSKHui56bsqy5IDCUoC8VdJ4xJFomrabNPaS+Px+Uuk0qWSCYDhM7NzzSb37DlpTI1gGPfda4ERNWVFbsXO+iXfMONHiI5ulsqqqzxo7d7QUkcfWVjo740QjEcfz2HjLj0nP/sCpXu32PO5y5LEcaZQkrOpyRM88m5of3eKQxlabNPaBnMaeiNfr5aCDDi7gnPMvRPJ4aP3Vz9CbuserDs4528I5I0f3PZyzaaPAOf/4i4NzTFdEErhxToDYJZdTe+31dCaTzJs/n3332Xe74ZxeTeSJxWKMHjOGlSss8nj2ecihEC2/+Cn5NauRAkHQi8NqTF1DCgbRW1tp/fUvSL7+CpHTv0HoiKPxjhjpNLk1Nc0CUgUSad/6ni6GZdm9TRatWGi7WmF+w3pS779L55OPkv5wFiCLhGFdA6yQKJtkWqVvleoaKi6/iurLv01DfT0LFi5gvyn7EYlGdwmyYUsoFGKPPffi3f++A6bJ6HPPRwmHaf3lz8ivWYUUCDl6BKEbORhAb22h+Vc/J/HGa0TPPIfQ4UfiHT68UE48nxeTrdykdBN4QDKtUtGmWTAQSNJWMayp60KfiuKEK2j1m0l98B6djz9C6oP3BPYMBgveHXsIjh6rqbz8Kqouv4rm5iY++/RT9t13CrGKil1Qj3sW9HjO+SihLetRCgg9tv7q5yTfeI3omWdbehzRZ/TY8fgjpD94T8y9bdHjZ31Hj/0GDULWNOJPPEpu6RJrbdERa0uhghpIdDz8TxKvvyrWJ2vN6Zn0lDhu5ftuPmm97PzufLT0ROWYYxkmKbk/W+b8ZQ9vk2oZM5sVTc+tPsJIsvAye7zkV6+k8+F/UnfLHdQNGLiVa+370p3n8d67ZnLzbbdzwNQDu5BHN4Es5D0Ww4AekcYnHi8mjbpONBrdJcDlrib2/a+oqKC9rY1ACHx77kX0zLNp/Z/7HULjrBUgnnlVxUyn8O+7H5Gvn4Iky3S0teIPBHYTxhIRHnidqqqqgucxYnke7/oJjbf+yCKPvqKc812KPHZHGq3wzOgZZ1Hzo1uQI2EkSaa1tRWP17PLkEZbuuCcs89DCYVp+dVPya9aVcQ7wMY5Fuf4n5+TfNPGOUdtJ5wjIWH2AOeYmLrFP1S1gHM2byb1wbsC58x6f+s4p6aGyiuupurSK2lpbuLTXsCrvUocTdMkFosxavRoVixfzpKFCxj/9VNQa/vR9PN7yH40F3w+xyoGCAJm3RApGCK7eCHZu24j/uTjhI+fQfDgQ/GOHY8SixXOo2kFACXLTkx6T8YnSKJhTSpZKMu6sUYqRW7VStKz3yf+8otkPp6Lmc8hB0PCEuFSnGmKptxIov+fZ/RYqq67ntjJp7F08WIaGhs44ICpRCKRnQ5St1VM08Tj8XDoYYfz2aefsmblSoafdCqe2jqa7r+vqx4lybk3cihEZv5nZBfOIz55XyInfI3AwYfiHTXGadrt3Esoqz/be1z0ky6QswCu7RLksggtRlEwdY3cipWkZn9A4uUXSM36ADOTRg6FoESXklXqXOhxDNXX3UD05NNYs3Il6zdsYP8DphLdxcg/bEmP/Wi6/94t6lHamXpUXHrUNHKr3Hp8HzOT6YEex1J93fXFety/7+ixurqG1NwP6Xz6CVFF1bYilgkpMvN5cmtWgWF2z7G+FFKOaG7l47KM5PU6oXsOYbLIY/zfTxOafgKVBx3Si+PecSJJEoZhFHkeJUninpl3lvU8QiEctbSAjv1eOdIIlHga/7CbNO4gKRTHkPB4vSQ6OojEYkROOZ3km6+RXTBfeB80DdPlfQeQPD4qzr8Iz6DB6Pk8SDKBQGBnXUqfFllWCnOptYXORJxoKIxv9Bjq7vopjbf9mPT774l7bedI7yrksQxpBJw9PnrGN6j98a3I0SiSJNHW1orH4+kT1VO3VcrinK+fjFpXR9PP7iP70YdbwDlhMgvmk10wj/je+xKe8TXBOUaP/YI4pytp7IpzlCKck129irQb52SzW8c5Y8YJnHPSqaxZtZL163sHr/YqcbRvZixWwaQ992LxooV8PGcOex84jYH3/5qmX/+C5MsvYuaySD5/URgAIIp3+Pwgy2QXLSCz4FM8j/2LwP4HEpg6Df+kPfEMGYZSWdnl3G7PofUKhRAqqWA5UBTcGTxGPE5+43qyixeRnjOb1Kz3ya9cgWkYyMFQIU7aPoYVBiUpqmg8ahgEDjuSmu9+n8CU/Vg0fz45Lc/+B0wlEAj0CZC6rWLrUVVV9po8mXf/+w5aPsfwaQcz8Oe/puk3XfVoix0eiCSR/ngOmY/n4h0zlsDBhxKcehC+iXugDh6M7CnkWzg5rQ54tv5zh+XZenUtgI732VViOb9xA9kli0nPmU363XfILFogGoSHQqLAgL0A2KdQhKtfKtHj2lWraG5rYd/99ttlwoxLpVSP/3X0eBADfv5rmneaHk2X96obPW7YQHZpqR6zyKHw59Bja5/TowSk58xGa6xHDoYKYSpQ1kosB0M7aZQ9lS4uSLqGv27rMbfwlUKegthYrYrcJpZ3zTDA40FraiL1nzcJTZ227eftoyLLchF5vOSyyzFNU3ge3TmPrqIppUVvbCkijRTWDHefxj9Z1VOPOPIoEZ66mzT2utg6CQaDxDs70TUN36jRxM75Jo233+SaViai4bkXIxEncuJJBA8/EoCOjg6C4XBZg8FuEeLMpSpBHuPJJOFQSJDHmffReNtNpN//L1IguOuQx3KkUbK805pG9NQzBGmsiIEJ7W1tKIq6S5JGKI9z8vkcI6ZOY8D9/yNwzksviNokPp/IXbW+a+bzyD4fyBLpj+eS/vgjfKPHEDj4UAIHHoR/wh6og4cge7cF57jwzRbxqk5+w3qyS5eQ/vADUu++Q3bxIjHOUKhQ86BwoU5bMQyTwOFHUvudH+CfMkXgnNZWpuy3H8FewDk7rOa8z+dj8t77sGzpUt57+y0OOOQQBv70l7RMmEjHP/+OZoeuWt4h52G3KvlJgQCSLKHV19P5+MPEn38Wz9Bh+MZPwDtuIt5Ro/AMHopaU4NcUSFI3hZKzZqmgZlMonV0oLc0oW3aSG7VSrJLl5Bdsoj86lWikI/XixQMCvW7FgobqUgeD5gmZiKOUteP8OnfoN/lV6PV1PDRh7ORFJXxEyYQCAScvJJdWTweD0cceRSfffopH896n0n7TGHgT35By4Q96PjnXy09hgp6BIeAyMEQSBK5FcvJLl5I5xOP4hs3Ad+ee+GbOAnfqNGoAwehVNc41p1tETOXQ2trJb95E7nVq8gtXii8ZIsXoTc1gSKL58LnK4Q6g5iAVpN1M5FAqaslfPpZ1F32bfI1tSya/xkt7R1MmVIoaPRl0OORRXrcj4H33d9Vj6pSSCjfKXpcSW7RIjLz55Fdsgi9qfEL6HEeLe3tfVOPhiE2ABDri90L1vUR087ZM8wujb13i0vs8CvTAFz30ApnNeKdRfmvXwax8xW75jzeyc233sEBBx7YI/Lo/tt+VcvnUT0eV5/G73DU0UeTz+eJRnfnNO4IsT3LsiyjejykEgkiFRWEj51O8vVXSbzxGnI06jQ+N3UdpaaO2Dnno1ZVk04m8QUCvdYi58skxeSxlWQqRSgQwDd6LHUz76Xx9ptIv/tfES6YzyEhO1X++xx5LEcareg4M5slevo3qLnpNuTKKkzDoDMeR1aUL40xqCvOmcLAewXO6fzn38ivXgmBoJgzbpyju3DOyhVklyyi88nH8I0dh2/PyS6cMxilphrZv+1efDOXI9/airZ5owuvzhN4tbkJSZGRHJyTL/jAuuCcfoTPOIu6S68kX1OzQ/DqDm1WZpom48aPJxKJ8ParrzFh0iSGXnENgb2n0PqXB0m/9TpmKokUDCHifS1wZLuSEXG8kt8PhkFu9UqySxcjKc8hR6IotbV46vqh1NahVFahxGLCtevxgiJb4EzDSCXROzsx2lrRmpvQGhvQmxrR29uFl1P1IPv9yBWVwnrtBnR2joisiOTZdBpJgsAhhxM7/0KiJ3yd+sZGlr75BoOHDmPU6NHOtX9ZFmvTNNl7n31Yu3YNH33wPqPGjmPgFVcT2HtfS49vdNWjbZUBYQQIhTDzeeFBmv0BciiE0q8/nsFD8AwajDpwEGptHUpVFXIkKr6jegp61DSMbAajM45u6VHftIn8pg3k169Ha9iM0dlpnc+PHIsJXdr5CSAmoGXxsfXoP+RQKs6/mOgJX6O1vZ2l779LJFbBoYce5lz7l1GPcz94j3HjJ9LPrcc338BMJpFCIaG/7alHWRYh4pqGkclgxMvpcZ2lx7hzPqFHs2Bcgq3rsU3oMRqr7Lt6lGV8+0xBDkecirBAUdjZbvmCYlnZfZP2cnLlv0xSLudRQrLI49Y9j+XmhP35Jx57jD//UXgajzrmOPLZ7O5CODtYbPAXDAZJJBLk0mm8/foTPeebpOfOwUglnXXD6Oyk4twLCBxwIJoVvWCvJX1u7euDUiCPVbS1tpKSJAJ+nyCPd9xL4x03kX73HdFnN5cTYY99jTx2RxpVFTOTIXL6mdT8+FaUqmpMXaejsxNZlr80pNEWB+esWcPcD95n3PgJ9Lv8KoJ770vLXx4k/ebrAuf0BK/OnUP6w1nIoTBKXT88g4egDhqMOnAgam0/1Koq0RIn4Bc4RxI4x9Q0zEwGI2HhnKYm9M0byW/cSH7DOrT6eox4XDgq/T3DOUgS/kMOp+KCi4jOOFHgnPfeJVLR+3h1hxJHWZbRdZ2BgwYRq6hg6aJFNDc0MHnqNAZP3IPWp58g/vijZD/7RMQNBwIFwApFHkgkSbD8YFgA0HyO/JrV5FcswzSsm+W4ghXLfGpayrCOYU9uVUXyegXJlGXQDaGwXM45r0MYVVX8nc1i5nJ4xowlfOrp1HzjXKT+A5j/2Wfk81n22HMvamprHSvhl2mhtvU4bNhwYrEKlixaSFtzE+OmTmPw+Im0Pvtk93o0DSfJWJJlpEhE3E9NI79hPfnVK4WFRFWRfD4kf0D883mt/FPFIoAaZi6HkclAKo2RzYCWByTwqMhen5h8AJr4LJbHxm6rUqTH0WMJn3YG1WeehTlgECuWLaW+oYHRY8bSv3//r4QeF8yfT3Njg9DjhIm0PrMD9Ki59Jgup0f/tulxzFjCpxb0uHzZUhobGhk1Zkyf1mNHRweR/adSedW1tD34AEZHh7XOwecK5yyR7gJFkUCyAWXJZ4t/l4r+Lj3e5xOz6McXkrIDcYUJyRKoHsInn074+BPI7aJ9HLcmpeTxkssuxzAN7p55J7fcfocomKPl8ahbD1stkMZH+fMfH+Tb11zD0cceRzazu0/jzhJ7/TKs4jf5fJ7wkUej//AmWv/wO1E50uuh4uzzqLz0CuRAgEwySV7TiMViu0njNoh9nyst8ggmAZ8f35ix1N1xD4133Nx3yeOWSGM6TcQKT1VrajB0jc54AlXddcNTtyQOzhk+nFhFAeeMPeBABo+fSNuzT9H5+MNkPy2Hc8zucc7GDeTXrBL3WFWRvALnyH6/CIFVVSvX3gBNx8zmMLJpC+dkXTjHs+14dew4wqeeQc0ZZ2EMGMjypUtpbGxg1A7CqzuUOIKoXqVpGqFQiEmTJ7Nh/XrefuUVxk6axJALLiZ2yOG0Pvc0yeefJbd0iVBkMCj2f8PlLXIsApZVQJaRAwGQQwWiZ/+zPu8kk9r5ie7P2Qmn5Sz8tmsYMLNZyGVRhwwjOP0Eqk47E++ek2lobmHx668xcMgQho0YQTQadfJCvoxi67GiooIp+x/AiuXLee+N1xm7554MuOBiooccTtuzT5N84Vlyy5aIh9+tR9v7Z1tDZVmENQaDhVA9Q8dIpyGREKFlbt24rC+SooiwAtmaJIYpzmFPPuvzhZzWUj3OoPKUM/FN3puWjnbWzJqF6vex775TCIZCXxk9HjhtGsuWLuX9N99kzB4Td0E9nkjlqafj22tvWtoLetxn3337vB43rF/HmHHjqb7sKoIHTCO7cIHYXOy8PUuE/atA9KSi9cqVSwGYmEjWS4Lk2e/bhKpkrSu39pnOfyV/m2BKmGXf28rfNuvscn6Kr9f5nPjdLL1W97jdbNa5TtfrsoJv/ARChx+JXFXN6mXLul7rl0SKyGNrK5ddfiUSEnffeTu33H5nt+RRiLhhNml88vHH+ItFGo+ffgLpdHo3adyJYpOZWCxGR0cHXq8HTyBI7NzzCR50CLn1a1GiMXxjxyNHIuiaRiabpbJMHYjdsnUpIo9trZhA0PThGzOOujvupen2m0i9V0we7RQDJ094B4/ZOXd3pPGU06i96TbU2joMTaMjnkBR5C8labSlHM75wMY5519I9JDDaH3uKZLPP2fxDgnJHxD7jlXpdOs4x8DMpNGTFs5xG0ZtnKPISHIpzhE5+VvCOWQzmNkc6rDhhKafQMUpBZyzetYsPH4f++xAvLpTUJSqqui6jtfrZeSoUdTU1rJy+TLWLl/GhH2nUPvd71Mx/QQ6Xn2ZxMsvkluy0OlNgtdbsAa48lScYjhu2RLb3loImCw7iwCGjpFMICHhGTmK4LHHE5v+NfxT9qMzk+bj99/D4/Gw5z77UF1dAxQq0H2Zxdajx+NhwsSJDBg4gCWLF7N5zVqGT5hA7fXfJzZD6DG5NT3qepFn2Q4rkxRFWG7s193iMg6YugZaCRh2e50lCXQdI5lEksAzQugxOv0EAvvuRyKfZ+knH5NKpRgybDiDBg8G6NNkY3uJrUdVVZm4xx60DRzIsiVL2Lx2naPHihkn0N4benQA/rbqsXg+RqefSGCf/Ujkc7ukHvv178+yJYsZPGQosQMOJHjA1C/giSsla31JXL5MwXy7vmW9Xe4rX+h6FIVsNsvapUvw+nxf4EB9XxzyWFVFa2uryHmELZJH65tFnsa//OlBrrzmWo6fPoN0KkWsouJLCy53FbFDVqPRKJ2dnRhGkkAggHf0GLyjxzifS6dS6LpOMBjcXRDnC4hDHisFeUyZEkHAN2YstXeWeB7zOcAqdmJ9f2cswc65bdLo8WCmkkROOo3aG29HreuHoWnEU0k8qkI4EsE0zS/1vC7FOe0DB7J0yWI2r1vH8PHjqf3O94lNP5GO114m+dKL5BYvFF0UeoRzKHgGtzfOkSQ8I0cLnHP8iQT2nUIiJ3BOMpVm6LBhOxzn7DQkZT+gulXKe/I++9LW2sKKBfNZDoybvDc1191Axcmnk/jgXZJvvErmo7nozY2YSKLykcdjWaQNZ4IAXX9uSZwKSALgYistn8fIpJE0HamyksB+BxA+8ljChxyGd/wEEvk8c+fORc9mGTZyBJWVVfj9fhFCIklf6gnoFrceKyoqmXrgNNavX8+KBQvw+LyMGD+B2utuoPLkU0l88D7JN14lbekRtx5BWF7cZLAnenSH8ln6616PFUKPRx1j6XEiaV1nwcKFxNva6TdwIBMm7YnH48GwjBB9mWxsT3HrsbKykgMOPJANLj0OL6PHsvMRdowe9y+ej2ldY8GiBcTb2+k3YNfT42233b6zh7BbvmTiJo9tpeTxtjuKch5tKSWNV1x1DdOnzyCZSlHxFSKNV1111c4ewm7pQ1JMHttIYVrkcRz9rJzH1LvvIIXChX5/21PcRrZtYaImSB4PRipJ5GsnC09j//6CNCaTyLLskMavglGhCK9WVnLAgdMsnLMQj9fLiAkTqL3meiq/fiqJ2e+TfOM1MnPnoDU1sDW8imlY/PDz4BwrnS6fx0inkQwDqaKCwP5TCR91LOFDDsU7bgIpXWf+gvkk2jt2Kl7d6WhKURRnM6quqSUUjhDv7GTJJx8jKwrDx4+n39nnUXHiSWSWLyU5+wPSsz8gu3ihqLCYy2MqCrKqgp1/6MpJBLqy+sIfOLNQ0zA0DUnXQFFQKqvwjR1P4ICphA48mODEPaCyirZkgs8+/JBcNsPIUaMJhcNEo1HrEH3bq9Gb4tbj0KFD6d+/Pw319Sz++CMUj4cR48dTa+kxvWwJqQ9nkZr9AbnFC9GbmkRMdxc9WgVUbPkietx/KqEDDyIwcRJSVRUdqRTz584llUgwaMgQRo4cRTAYtA6xW4+KojBk6FD6WXpc8vHHqKrK8AkFPWaWLSH54SxSsz4gt0TMRzOXL6PH7Twfpxb02J5KMW/uXNKOHkcStFpVfJX1uFt2CxTIY6XL82hicvfMO7j19pnsP3UquVwWr9dHLpvF6/O5SOPVzDjhRJLJ5FeKNO6W3VJOCuSxkva2NlJAUJLwjrEK5tx+E8n//gc5ECj0Jd/Wk3Sbq01x5IUdsdHdd+zULEXGSCaJnHgStTffgTpgIEY+TzydQpYkIl8h0uiW7nDO4o8/QlE9jJgwntpvnEvFjK8L3vHhLNKzbN7R0A3O2Qa8aucvahqGlkfSdFBVlKoqfGPGEZg6jdDUaQQm7FGCc+IMGjKUUaNG7VSc0ydQldsK4Pf78fv9RGMx4p2drFm2jKXpNDX9BzB44iSq990P85sXkN+wgfSSxWQWzCe3bAna+rXoLS0YqRRmXkPCEHorNyEkrObZEqgKciCIUl2Db/AQfGPG4Zu4B/6Jk/ANHYZUWUFK01m+fj2NCxaiyBKjRo8hFIk4REPXdVEm+ysOUt169Hq9zoRsb2tj1cJFLMllqRk4kCGT9qJ63/2osvW4eBGZhQtK9Ji09Gh21aO7MkdZPQ629DgJ/8Q98A0djlRRQVrXWb1pE41LFmMaJsNGjKC6pgafFba2W49CtqjHRYtYku1Gj9s6Hz+PHisrSGtuPRoMGzFyl9Xj7373u6/Wjr1bdriUhq3aOY8z77iNW28XnkcAr8/H448+wl///EeuuOoaZpxwwleONO6ej7tlS2K3valwk0e/D++YsdTefjfyL35CZuECZJ8XU7Py4Ypyz91eQ/t3l8HUJhQmhfzxst+1fylnlLXOpyqYmQyh42ZQ+6Ob8QwchJ7Pk0ynkZCIRKNfSdJoy9ZwzuJsltqBAxm8x56Cd5z3LfIb15Nx4dX8urXorS68ahhCneXYvI1zZEmQRH8QuaZW8I6xgncEJuyBd+hwpIpYNzhnzz6Bc/oUslIUBdM0MQwDn8+Hr7aWqupqstksG9atY8mnn5DP56mqq6N2wCAqxk0g9vVTREJqextaQwP5+s1oDQ1orS2YHR3oqSRmNmM1hJadvoxKLIZSWY1aV4en/wA8/fqjVFUhBYLoqkpnIsm6xnpali5BMgwqKqvYa+/JBIMh54Gz3cNfhQ11W8StR6/XS12/ftTW1ZFKJtm0aSML5s5FN41iPZ58Kma6RI+NDWgtLRgdHRhJS4/m1vTYD6Wq2tFjPJViXb3Qo6nrVFZVMW7CRGKxmJMrsluP5eVz6fGkU3qoR1MY6bxepGDI0mMVal2/7vW4uZ6WJUswjd163C27ZVukNGz1kssuR1EV7rjtFg457HCGDx/BJx9/xNIli7nq2us49rjppJJfrfDU3bJbeiL2XKqorKS9vY1k2iQUlPGNG0+/n/4KvbNdVNM0yhTzKopiNIuKfxUTQJs4uqp+bZFAlv5qIiFhGgZqv34olVUYmkYylQIJotHdFXZt2RrOWTh3Lrph4Zz+A6kYO4HoSadiplPo7R1ojfXk6+tFW7+WZv4/e2cdJsd15uu3qpp5GKWRRiyzY4idmO04sWNmTrJJ9m4v3IUsBXY3ezcb2DB0sgGjLMksM5NktiWLmTWMzVxV949T1dMDki1ZI81I9T5PPz3TUH26fn1One98cNT40DxH7MdszFe9XpRAAKWyCltN2Xy1YsjuSKbT7Orqon/Dhgk9z5lQhiNQyg80S4TLsozb7WbWnDmoqko2k6G7q4s927ayKZ3G5nDgDwQIhCrwz5mH84ST8CgKSlm11GHV9Yx8RmQZTZYpqirZXJZoMkWsu5tELEY+k8XlcuL3B5g/bz4er3dYHogp3ITZPHwCMpaOXp+PWbPnMGPmLFKpJL09vezeuoVNmSw2ux1/sEzHE0/GI8ujdRz6hL3qGO8SOuayGex2O6FQyNLxAPl4OvYM6eiwD++PJ56ERx6jPw59QpmOEkVV26uOwVCI+fMtHS0sDoTysNXBwUFuu+PLzJ49l1defpG33nyDpqZm/vN732fO3HmieqplNFpYjEnJeAxVEItFSaXTeD0elIoKlAlYwVZXVVKZNDoQDARL2zVYCPZ7nlM+X509F+cJJ+GVZWRjy7+hQp1l8cSyCGf96PlqBfOPmY/HM3HnOfttOFpJ40cGlo5HBpaOFhYTh0nbHyWFPR2d/P4PfzzcLbGwOGhM2v5oMQxLx4nFhPM4WlhYWFgcXsLh8LXAOcCJwAmAH7g/EoncOsZr7UDYeO1JwHzADnwtEolYloiFxSdgP/viFOBfgU8BLUAF0A9sA+4EFkQikcKhabmFhcWRyMc2HK2k8SMDS8cjA0tHi3Hm24hJahJoA+bu47Ve4OfG391AFzBlPBs30bD6o8U4sj99cQZwC/AusAQYAKqALyAMx9vD4fBFkUikOJ4NPtxY/fHIwNJxYmJ5HC0sLCwsRvJ3iEnqVoS349V9vDYNXAKsjEQineFw+D8Aa2NKC4uDw/70xbeAikgkopU/aEQFvACcC1wNPDguLbWwsDjisQxHCwsLC4thRCKR0uT0o/JLIpFIHnh2vNtkYXE0cgB9cazHC+FweAnCcJx1EJtnYWFxlHH4y/NYWFhYWFhYWFiMC+FwWEFEBQCsPpxtsbCwmNxYHkcLCwsLCwsLiyOEcDhcDfwVYhOyGuAiYCawEHjqMDbNwsJiknNEG47hcPhS4P8iqvxVAZ3AcuCnkUjk7cPZNouPRzgcloAvA18HjgEUYBNwF/CbSCSiHsbmWYxgfyoAlr3nTEQBiE8DLkQuz53Aryx9LSwOnP2syDkLkf92MSKcsQ4YBN4Bfl4eMmkx4almeJ6xDvwY+GYkEtHHfovFeHIg18YR7/8T8BXj31mRSGTreLTTYt/s55g6Ddixj8M9EIlEbhyHZo4rR2yoajgc/iFiZe1k4DngF8AK4ArgzXA4/LE6q8Vh5x7gT8B04AHgD4ADoecDhmFpMXH4NmKl+0Sg/aNeHA6HrwCWAmcDjwG/Qej7M2DxuLXSwuLoYH/64/8DfoAwGJ8BfgK8CVwKvBIOh/9m/JppcTCJRCIbjYqUNsS2HH+HWHxdGg6HKw9r445e9uvaWE44HL4MYTQmD36zLPaTA9FxFfDdMW4Pj0P7xp0j0uMYDofrgW8gSsMfH4lEesqeOw94BfhPYMHhaaHFxyEcDl8J3IZYsTktEon0GY/bEVXhrgHuAO4+TE20GM3HrgAYDocDiIUAFTg3Eol8YDz+HUQfvTYcDt8YiUQsA9LC4sDYn4qczwE/jEQiH5Y/GA6HzwFeBP4nHA4/FIlEOsersRYHFyNiYzfwi3A43A0sQsx9/uqwNuzoZH/6YolwOFyDuE4+ANQb77U4fByIjisjkch/jGejDiVHqsexBfHd3i03GqFUoSyBiPu3mNhcbdz/xDQaQVSIA75j/PvXh7xVFnslEom8GolEtnzMcKhrEf1wsWk0GsfIIlb1AP5iHJppYXFUsD/9MRKJ3D3SaDQefx14DREJcObBb6XFIcKsfHzu4WzE0cp+XhvL+b1x/5cHu00W+88n0PGI4Ug1HLcAeeA0I0m8RDgcPhsRk/zS4WiYxX5Rb9xvH+M587GTw+Fw6NA0x+Igc75x/9wYzy1F7A94Zjgcdh66JllYWIxBwbg/ojeOP8JpMu4tDScJ4XD4S8CVwP+JRCL9h7c1Fp+AxnA4/OfhcPibxv3xh7tBn4QjMlQ1EokMhMPhfwZ+Cqw39i/qB2YAlyPCbv788LXQ4mNiehmnj/Fca9nfcxEFHCwmF3OM+80jn4hEIsVwOLwDURCpFdhwKBtmYWEhCIfDLcAFiIWcpYe5ORb7IBwOnw6siUQi6RGP+xB1AQCePuQNs9hvjH73C2BBJBJZcpibY/HJuMi4lQiHw68Bd0Qikd2HpUWfgCPScASIRCI/D4fDOxHVGb9W9tRW4O6RIawWE5KngJuAvw+Hw4sjkcgAQDgctiESi00qDkfjLD4xQeM+tpfnzcdD498Ui3KM/OIrjX9Nz/8Z4XD4buPvvkgk8o2y1/8LYgEHRNEAgC+Hw+HPGn+/EYlE/jhe7bUYHwxv//2AE/inSCQyeJibdNSxn33xX4Fzw+Hw64jcxjQwBfgCYhx9C/j+uDfa4hMRDodlRGHAJGAVpZq8pBFFx5YwFCV3PPAfwHnAy+Fw+MRIJJI6LK07QI5YwzEcDv8T8N/AL4FfA12Iic33gfsNsf7pMDbR4qNZDNyKuOitD4fDTyA64oUI7/EWRNl4a8uGIxOzYu5Rm0twGDkRUXiqnFaGPP27EAXITD7P6KINZzI8J84yHCcRxqbx9wGfQRTm+PHhbdFRy4l8/L74ByAFnIrIZfQgtlRZjigod2ckErFCVSc+f4cYTy+1FmsmL4aD6t9GPLw0HA5/DngDOB34KkPRAJOCI9JwDIfD5wI/BB6LRCJ/X/bUinA4fBUiNO4fwuHw7yKRyFj5cxYTgEgkooXD4csRe3HeZtwKiFXTOxALArMAy3s8OTE9isG9PB8Y8TqLQ4RRAe4/9uP1545XWywOPYbRuAC4DmFw3Ho0F4M4nOxPX4xEIk9jhaJOaoz9VL8H3BWJRJ453O2xOPgYqTh/RBiOZ2MZjhOCLxr3o8rkRiKRdDgcfg+4CjiJsQuvWEwQjNXRnxi3EuFw2I1Yic0A6w59yywOApuAU4DZiBXxEkY48nREIQerj1pYHCKMvrcQYTQuBG43tnWwsLAYf45BhIZ/ORwOf3kvr9kSDocBrrLyHyctvca997C24gA4Ug1Hswrj3rbcMB/PH4K2WIwPtwEu4B5jew6LyccrwC2IMMdFI547GxFmtTQSieQOdcMsLI5GwuGwA+FhvAK4F/hyJBLRDm+rLCyOKnYCf9rLc5ci8lwfAuLGay0mJ5827ifdwviRajguQ2xw+/VwOPy/kUik3XwiHA5/AZGzkUWEPFpMYMLhcCASicRHPHYq8ANE4vh/HpaGWRwMHkaElN8YDod/Ze7lGA6HXcB/Ga/57eFqnIXF0YRRCOdR4BLExPXrltFoYXFoiUQiKxF5b6MwKnHWA9+MRCJbD2GzLA4Ao8rxh5FIJD/i8fMReawgUgImFZKuH3lpC0ZFqucRRVQSwGOI4jjzEGGsEvC3kUhkUsUVH42Ew+F3EeGoaxFaHoOY2OSAqyORyPOHsXkWIxijAuDFiBW1ZcZjI6txXokwILOIYkgDiC1z5hiPX2/lVllYHBj70x/D4fBdwJcQ2yBFGLso1WuRSOS1cWuwhcURyv5eG/dyjNcQRXNmWYbj4WE/x9TXEHPW14A24/njGdrD+juRSMRcJJ80HJEeR6OoyiXAXwI3IvIZPYhJ6TPALyORyAuHsYkWH5+HERreCriBDkR1xh9EIpGdh7FdFmNzIvtRjTMSiSwJh8PnAN8CrkGEH28F/h7RTy2j0cLiwDmRj98fzf1yqxldCbCc1w5S2ywsjiZOZP8qVVtMTE7k4+t4H8L+OBWxO4Ad6EakA/w6EoksYxJyRHocLSwsLCwsLCwsLCwsLA4e8uFugIWFhYWFhYWFhYWFhcXExjIcLSwsLCwsLCwsLCwsLPaJZThaWFhYWFhYWFhYWFhY7BPLcLSwsLCwsLCwsLCwsLDYJ5bhaGFhYWFhYWFhYWFhYbFPLMPRwsLCwsLCwsLCwsLCYp9YhqOFhYWFhYWFhYWFhYXFPrEMRwsLCwsLCwsLCwsLC4t9YhmOFhYWFhYWFhYWFhYWFvvEMhwtLCwsLCwsLCwsLCws9ollOFpYWFhYWFhYWFhYWFjsE8twtLCwsLCwsLCwsLCwsNgnluFoYWFhYWFhYWFhYWFhsU8sw9HCwsLCwsLCwsLCwsJin1iGo4WFhYWFhYWFhYWFhcU+sX3cF4bDYX08G2Lx0UQiEemTHsPS8fBzMHS0sLCwsLCwsLCwOJRYHkcLCwsLCwsLCwsLCwuLffKxPY4mkUhkPNoxCl3X0TQNRVFKj2maSiKRoLe7h3gsRiafw2F34q8IEgyG8Pp8uJxObLKMrGmgaaBroJU52SQJZAlkBV2WKeo6uXyeVDJJNBYlEY2Rz2SQZYlQRQV1dfX4/H7sdntZOzQAZPnQ2N3hcPigH/Nw6qiqKslkgt6eXuLxGJlsFrvNht/nI+AP4PP5cLnd2B12ZOWjf6K6plHM58lmsyRTSeKxGIl0mlw2h82mEAwFqatrwOv14nA4Su87EnS0sLCwsLCwsLCwOBTst+E43pQbGoqioOs6yWSSjrY2+vv7QJapqq1j2pw5BH0+lGIRLZVE7e+juGkDma4Oil1dFAcG0WKDqOk05HLomoYkS0gOB5LbjRKqQAlVYKupxdHYSGVDIzXVtchNzWhOJ6lslp6ebjZt3oRWKBIMhahvbCQUCpWMoENteEwm9qpje7vQUYfKygqmNDUTqqjA7nSOPgagqSqoKrqmga4bj0ogSUiyDIqCpCjYXS7sLhf+UIiGpmYAtEKBRCJOb28vmzZuQNd0AsEA9fUNhCoqLB0tLCwsLCwsLCwsPiYTynAsFovYbDYURSGXyzE4MMD2bVspqir1U6Zy4umfxqMo6NFBctu2EF+3luz6teQ3baDY3oYWi6JlsqCq4oCyJOyMcoNA00BiyAspy8hOJ3IghNLQgHPWbJzzjsF9zLFMnzGTGaeeRhbo7upiy+bNqIUCLdOnU11Tg8vlAoQHTZZlJMlKXYPROg4MDLBjx3bUokptTQ3HH3MsvmBQeH8NtGIRPZ1GSyVQBwYo9vWh9vehDvSjx2No6TRaPo+kqSApYLMhez3I/gBKRSW2qirk6hqUyioUfwDJ40FyOAhWVhGsrGImkI7H6entZeu2rRQLRaa2tFBTXY3L7QYsHS0sLCwsLCwsLCz2xoQwHFVVRVEUbDYb2WyWrs5OOtr24HS5mXfSyVR4vTAwQPqdt+h9920y771DfvMmtMEB0FRQbEg2mzAm3C6QZGEgmujl9WAkYTiCMCh1HXRdeCf7e8itXIGEhBTw45gxE9cpp+E987NMOfZ4Ws44g0Q+z5Z1a9m+dQsNTU00NjXh9fqAIYPpaGVMHTs7cDoczJ45k+qqajC8fDqgJRKoPd3kd+4gv2kDuU0bKezYRrG7CzUaQ8/n0AsFEW5cEk0v+1v8KdlsSA4nst+PrbYOe8s0HLPn4pw7F0frTJT6BuRQBZ5AgGmBANOmTSMWi7F1x3Z27thOfX0DDY2N+HyTR0eryNHhxypWdWRg6XhkYOl4ZGDpeGRg6XhkMJaOh312bBobmqaxe/cuejo7cXm9HHvqaQRcLvKbNjLwxlJSr7xIbvVK1MFBUBRkhxPJ4xFeK1VF13XhadQP4HdmeJgkhxPJZXgnCwVya1aRW/EB8YX34ph3LN7zzsd39nmcfPwJZGWZ7Rs3snLFcmrrGmiZNg2Hw3HUhj0O03HXLnp6u3G5PMyfM5dQVSUgCWMxnaa4eyfZNavIvv8emZUfUti1Ey2dFNopNmS7XYSgulxIbve+NZUkw/jX0GIxsv39ZNasBh5HdnmwNzbiOv4E3KedgeuEE7FPa0UOBglWVvKpykrSiQQ79+xh7ZrVVFVVH/U6WlhYWFhYWFhYWIzFYTMcTUNDURQGBgbYtGE9bpeb2SecQMjtIbdyBT3PPk3qpecpbNuCruvILjeyPwDo6KoKhcK+P6Q85FAywlYxjJByY8T4Wzfy6QCQZSSnC1we0FRyKz4g+8G7xBctwH3OBQQuvYz5p59JatYsdm5YzwfvvUvTlKm0tLSI71csokxwr9XBYJiO/f1s2rQJr8/LzBkzqayoBFlGB4o93eRWrSS99FVSb75Bfsc2yOfB6UC2O4Suug6aJhYBNG1Ii4+LJCE7neB2C701jUJ7G/ntW4k/sQR78xQ8p5+J95xzcZ18CsrUFjx+P/PnzycRi7G7bQ8ffPAeTY3NtEybBkx87+OhKnJ0IOi6jiRJxONxBgf60ZGYZpxXNA1kmXyhSNuunSDBtOmtk8JQn8zFqg4EU8dMJkNHezsOh4PmqVORzD5qt5MrFGjbtRNJlpk+vRVgwod8H7U6ptN0dHRgt9uZ2tIyNNba7eQLBfZYOk4KHZPJJP29vejAtOnTxfWzWByuoyQzvdXScSJi6phIqgx0/gT0/6GlpU88qQE2iXxGoq29Fd12N60zzwQsHScapo75fJ5tW7cQDIVoaGwS18diERwOcoUCe3buQLHZmDZtOjC5dTwsM2LT2CgWi2xYv57Bvj7mHn88tdXVFNavp/vRB0k9/wyFHduRbTYkjxdJB11ToZAf44gSkiyJMEhJKvNCjbjJkghjxaisahqTuqjAOlSAhaH/QRiRHg+SJKF2dxO/7y7SLz6H54KLqLj2Bo457dMMxhPs2Lie/r4+5syZg9fnm/BGxydlpI7RaJRZc2ZTV1WFZLOjA2pHO+k3lpJ4/hky776DOtAHThey0yUMPLPwTbmRaHYo0xMM6JJUHqAqAlZ1nZG+yGHGPyApCpI/ADoUu7uIPbiQxFOP4zr5ZHyfuwTvOedhmzkLfzDIMQE/A4OD7Ny5i/7+PubMnYvXe+TrOB6Yg6mmafT29OAPBKitrSW3YzuZN5dS7O3BOWsO7jPPonXmTHZs30ZbWxtTp05F07RJYUAeLUiShK7rdHa04/V6qG9oJLN6JamXX6TY14tz1mx8n/sCrTNnsXP7Njra22lqbrZ0nGAIHTU6OzvxeDw0NFo6TkbM/tjb04Pb46a+voHM2jWkX3lRjKuz5+C98GJDx+20t7fR3DzF0nECMXR9VOnveQaX/TvUN+hk1ztIve5C7bfhmJHDe16O1unb2LnzdjraX6Kpebql4wTD7I+7d+2iorKS+voG0h8uJ/XCsxT7+nDOnoPvC5cyc9Zstm/bSmdHB41NTZNax0M+GzaNjcHBAdauXkNNdTWfOf985L5e+n/7a+KPPkRh43qRr+gPgK6hF4vCoBvhQZQURRiLmoZeKKBns1AsousakqyUKm6iKML4ALG6WiyiGwaLJCGedziRjBBJNE0YIKbhqOsi106SkOx2ZJcLLRYlcf+9ZN5YiveyK6i+6VZOPO10dm7bysoPl9MyrZXmKUfuYF3ScWCAtWvXUltXx6dPPx270yk8jP39pF95gfgTS8i88zZqJo3s8SCHKoSxWCzzFpvGvmEcmlmMpnGIJJXuS28pe1wvf5/5GsOo1M1tWTCMyGAQNI30W2+Sfu8dXE+cROCLl+P7/KXYpkylsrKKgNfHno4OPlzxIa2trZO+kx8OzAtjd3c3qBq1tbWkV3xA379/i+yqD41tcWT8V11Dzb/+Gw3NU+jcs4eBgX4qK6us8z1BMHXo6elG0zTqGxpJLX2dnu/8M/ntW8U4q6qkXn+Vmv/8Pi2tM1i1/AMaGhuRZdnScYIwpGMPmqbS0DhaR11TSS19lZrvCh1XWzpOOEwdent7KBby1Ne3knpjKb3f+RdyWzcbOmr4XnuVmu9+j6mtraxZvpzGxiZLxwmEeX3s7Y1RSH+TaXOLpN6w0/efFWS3OJFkDXQf3ovS1HwrydRp21m9+tc0NH4TWbaujxOF8v4o6Rr19Q0kX3uF3m//M/mdO4Q9oaqkl75G7fd+xNTWmaxf+SG1dXXYbLZJq+MhNRzN7Rl2bN9O2+5dzD32WGqqa0i++hIDd/2B3JtvohcLSD6fmPgXC8MNRl03DEEbqAW0dBoKBSSHAzlUga21DltDA7aaOpTKSpRgSHgrHXaQFdB09EIePZNGjcfRBvsp9vZS7Oyg0N2NNtCPlssiKQqyyw0OhzAii8XSd9A1w6BUFKRAgGJnB7Hf/YbMu28TuuVLtF59LZU1tWxZt5bBwUGOPe44o+n6hHdNf1yGdBReornz51NTWQWyjKaqZJa+SmzxQlJLX0VNJFB8PhS/XxjrecNjbHoQjXMijESG1S4qf56R567s8WGvN9DLji8ilHVhRObzIqTVKIST/fADcmtXkXzlJULX3YDn4ktRvF6mT5tGRUWIzZs20dfXy3HHnyCOewTpOF7ouo4syxQKBaL9/bTOmUNxoJ/o7yNkVryPEqoQfUqRSTz8II6Zc6gK/zVuj4dEIkllZdWkHEyPNMp1HOwfYGrrDIrdXQz+IUJ+2xaUYAVaIY9st5N+8Xni84+l5h/+mcbmZrZu2czsOXMtHScAH1dHxW4n/cLzxOcJHRssHScUpo7FYpH+3j5aZs6k2N1N9PcRcps3oYSGdEw9/wzOecdQ/ff/SH1TE1u3bmH27DmWjhOAIR3TDPT8iWktayl2O4ne5Se72Y4SVNEKMrJNI/WCG+fcPNV/VaSh/n/ZuvlaZs89w9JxAjCsP/b1iXG1s4Po7yPkd+5ACQaN66OT1GsvE1t0HzXf+FdqG+rZuWM7M2fNnrRzyUPucVyx/AO0YpFTP3sWrnSK3p/+kPjihaidHSIc1G4b7WHUdVE1VZLQs1m0nDBGnPOPwTX/WJzz5mNvnYG9sQmlsgo5EEQ2tljYF3ouh5qIi+0fujoo7NhObuNGcuvXkN+6BXVwAMlmLxXhEe3C2M5DhLJKTidIkF/xAX3btpFZuZy6v/hrTvz0mWxdt4alr7/OKaeeitfrPaKMjhXLP0DXdE459TTcbhc6EoU9u4kvuJv4kkcotLUhe73CYCzXU5JK52Asz+LBovx4JQPSDGEG4UEGZK9YpEgve53c2jX4336L4B1fwXns8YRCFZx40kls37GDZUtf51OnHHk6jiedHR34/H6cdjuxV14k9epLyL4AWj4HqoaEHWw2Eg8uxHPW2dQfdwLr16xmYGCAyspK6zxPELq6OnF73HhcTgYfeJrMW28g+/xouayoSA3gdJJ49CG8555P9amnk4zGiA4OEqqosHScIHwsHV2WjhOdzo52vD4vHqeT6PPPkH5zGbJ/dH+MP/oA3nPOo/bU00jFokSjUUKhkKXjBKG9vROf6+e43QrRR2yk33Ih+3S0nGzoKINDJ/GYF+9nc9SekiG+6V+IxV4iGLRbOk4Qujo7cbs9eF0uBp59isw7bxnjas7QMY/kdBF/9CG851xAzafPIB6NEY/HCQQCk1LHcTcchxJHcyxbuoxpU6cwbfYcihvW0/azH5F+9WXQNCSfH10tDoWHGpN8ychb1NJpAOytM/Cc8Rk8p58hDMamZmSff9Rn6qoqchfLC+KIA4s7WQaHA1t1DbbqGpyz58DZ56FlMhS7Osht2kTmg3fJvLmM3MYN6IUCstc7ZECaxo/xt+T1oWczJBfeR2HTRqr+9hvM++zZ+HbtZMOG9cybOw+vzzdpXdMlHXM5li1byvTprUydOlW425HIvPISA7+PkH73bSRJQg4GxbkxQ3wNo9FUYlgYKhxUo5GRxysPZy19npH6arRP9vvRshliDywks3Y1lV/+Kr4rr8XucDJ75ix8Ph/r1q5h/jHH4PP5J62O4435O+nv7yefyzB15iwKu3cRX7gALZsTCzq62EJFV4tITie5rVuIP7gY18zZTG2dQfvuXYRCIev8HkbKc1QH+vo47qSTyW7eRHzR/WLBzOjIkqKI19rtFPbsJrZoAfXz5lNRX0dvdzfBUGjSXRSPJEbqeOxH6WgbS8ceS8fDzDAd+/tFf9yymdjiBWIctduB4f2xuHs30Qfup27efCrrGujp6iQQCFjj6mFkSMcosd4fcOzxHeS2OIk97EMvyEiKDuig6KBLSA4o7LYTe9iLa26cmtr36Wy7E7//a5aOhxFTR1VVGejv45gTTyK7aSPxxQuNcXUoPUo3HF/F9nZii+6j/phjqK6vp6uzA7/fPynH1XH95Zknt1AosGrVKmbPnMmM2XPIvPISHf/wN6RfeFbkndntpbBUyfQKSZKYXGaz6Lkc7lNOpeY736XxV/9L7be/S+DKa3DOmYfsEx4tvVBALxaEwYiRz2azI9ntSHZH2c14zNxP0AhFNd8vud04ps/A//lLqP6nb1H/y99R+9//g/f8CwHQUikkxYZk7gFZbkDKMrhcZN9/l+5/+QcGFy1gSss0aqtr+OCD94lGo6U8g8nEKB1nz2H69OkoNhtqJkP0d7+m+1v/RPqtZchOJ5LDIUJSjfdJprdv6IDjYyzujVI4qz5UVFfs1il0RBiQkiwj+Xzk1q+j53vfpe8H/w+1pxtJUWhubKKxsYkPly8nOjg4KXU8FJiDYDaTJhCswCErxJ9cQmbF+8hut+ifpT4uo6sqkttN8onHSL/7Nj6vF5ui0N3ddZi/ydGNqeOWTZuob2xCLqokHnuY7Po1SC4nuqaKfm28Xtc0ZI+H1LNPk1z6GpXBEJqq0tfbe/i+hEVJx82GjspH6qhaOk5ATB03bdxAfWMzslokseRhcmtWI7mGxtXy/ih5PKSeeYrUstepCAWR0Onr6zt8X8KipOPG9euor3sAuaiQeNJJbo0DyaWhq2aujojF0lUJyaORfM5D8g07ocosivZ7+notHQ8npo7btm6lpr4BpVgk8ciD5DauR3K7jfopZf1RFeNq8oVnSbz+KpWhEOg6A/39h+07fBLGzXAsNzaWvf4a1ZWVTJk2jcEHFtL97X+msH4tksdbKkRTCl/UNBGWqqpo2Qyukz9F7fd+SMMvfkvFV/8C13HHI3u9wkgsFkuvl+x2YSgqyse24CVJGA7l7xe5lcKQlO12nLNmE7rpVup//Avqf/JLvOdfiK4W0fN50c6yY5lGqOT1UuzsoP8H/0Xvz3/M1OYmjj/+BJZ/8D79/f2TyugYpWNNNVOmTAFZptjTQ99/fof+X/6UYneXMOJVVZwDs+CNgSQOdmgNxlFIQx+t68LrCEPGrVFkSfZ40FIpovfeRfc3/5H8po3okkTzlCkca+jY19c3qXQ8lAwODtLX20dtXR2ZNauJL74fjJU3YHjIstF/1cF+ovfdRbGvjymtM+jv6UXd3+1YLA4qsZhY6KqpqyP94QfEH3kAyeFEV4eiQkr92dheRU2LflPo6qRl5ix6urstHQ8z+6ejPlzHzk5aZs6kt8fS8XATi8WwKTZq6mrJfLiCxEMPIDmdQ9XfDR2l8v6YShK9906KXZ1MmTGTnq4uS8fDTDSaxyF/i5q6BJlVCvHHfGAHXZcR4Thi12tJ0o3+KKEmJWILfRS7FKZMW0lP509Q1dzh/ipHNYlEAjSVuvp60svfJ/7oQ8JpYvav8nFVF+Oqnk4Tu+dOCh0dTJkxg86OjknZH8fNcJQkiUQiwdo1q5k2fTrTZ8yk74//S/8P/gu1uxPJ4xFePsMw0U0vo6KgpdPYGhqp+efv0PDL3xG6+XbsU6aWKqwKY9EuDL4yd705MS1NUD+CsV4nGZ5OyS62k9CNCqy22jr8X7yC+h//grr/+hHOufPRM5kho6MsTlk3Cvbo6RSx3/2G7u//FyGPm9NO/zSrV69kcBJ5rIZ0XMX0GTOYPr0VTZLIb99Kz7f/idhDi0sFisy8QcnYu3FYpzH/niCYnu2SHSkeFH8XCmIBQlFIvfQCXf/y92TefRsNqAiFOO2MM1izehWDAwOTRsdDhaZpdLa3M2vuXLRMhvgD95PftQPJ4RzaJsXIDzb7vF4sIrk9pJe+Ruql53HYbISqqtixfdvh/TJHMbqu093VRU19PVIySez+eyl2doqQOHOcLd/CSJLEqqrbTea9d0g89QQuux1/IMCePbsP87c5ejF1rD1QHZ8ROnr9lo6Hm66ODqoNHaP330ehswPKdBw1rpo6vvs2iWeewmW3EwiF2LN71+H9Ikcxuq7T3f6/VFe9BymF6AM+Ch02JAdi70bEva5J6LrwPuqqhOzWybznIvGcC5cDgr5F7N5lXR8PJ+1te6hpbIREnNiCeyj2dAln0j7GVcntJvPBuySeXILb7sAfDNK2Z89h/ib7z7gZjrlcjnffeYe6ujpaZ8yk/7e/IvqLn6AlE0j2MiPDNLpkWayS6TqBK6+mIfJHKv48jH3K1JInzwxflfYS220abvvjcfyo5yWbTcQpq6owIGtqCd54Mw2R3xP6s68Lg6lYFMZSmSEqqkaKEufx++6i+7//Hz6bjRNOOIk1q1eRSMSRR7xnIiJ0fJv6hkamT5uOBhQ2rKfnm/9I6sXnRUeRjZBDGGZE6xPQYBxGefGlstUh0/uIriO5XOQ+XEH3t/+ZzOuvoEsSfq+P4084kfXr1xGLxSzjsYze3h6cDgcet5v0e2+TeGLJsFAqoLToA8P7oF4sEr33TvK7d9HY3Ewhnydt5DZbHFoG+vvRNJ1QMEhy2WskX3gW2eMZWk0FI0pjuI7mpDV+/z3ktmyiqaWFRCxGNps9HF/jqEfoqH1iHZunthCPWjoeLnp6epCAimCQ5LLXST7/NJL74+sYu/8ecls20zRlKvF4nHQqdTi+xlFPX283aIsIVWZJvekk+YJnKETVCGyU7DqSzfhbEjmPui6j6xBb5CO3zUbTlD0kB79LKlXc+4dZjBt9vb3IkkxFMETy9VdJvvgcstsjdl0w2Gt/lBViC+8ju3EDzVOnEh0cnHTznINuOOq6jqZprFm9ivnz5tLY1Ez/nb9n8Le/Qs9mSkZY+eslWUYvFLBV11LzrX+n7ns/wn38CeJxY8Ip2WzDqnEyjgaXruuY+wCamB4oXVXRdR3H9BlU//O3qf3BT3HMnCVCV2V5+ETYnCxLEonFC+j58Q+o9PmYPXsOH65YQTabGW5gTSBMHVevWsn8Y46lrrYOXZIorF9H97f/icy77yC5XKVtLsrjuUvevIlqMI6kzCtavh+kbn43l5vC1i30/Me3Sb/6MpokUVVZSeuMGaxds5pUKjkpFgHGE13XUVWV7s4upkyfjjo4SOzeu9Bi0aFFIUBCQkslS9760iKDpiE5HOTWryP+8APImkp9UxM7d2wvHd9i/DH7fXtbG9NnzqDY20P0njvRM+nh0QOAlkigpdPDIy6MaJDc9m3EFt2PUixQ19BIR3tb6fgW489wHWcaOt61/zpuM3RUC9Q3NtLZ3l46vsX4Y+rY3SVChou9vUTvuws9nUaSJVEAEEACLRHfh45biC1eiKIWaWhspqenp3R8i/FH13U0tUhn20+YNu0dir0y0YU+9LRk6GjOP3S0hIyWkZAkHV2XjJBVYVDmttmJP+xFLko01L9Ab9ejpeNbjD9mf+zq7GTq9OkUurvE9TGXNcZV8ToJ0OJxtHRqzP6Y37md+OIFyIUCDc3NpRzyyaLjQTUcVSNX8cMPV+D3eKlvnkLsocUM/urnaJmM8MCVr5AZOYZ6Po/rhJOo+9kvCd7xZ8j+QCnkYmTOomnUjacXq5Sfp+ujhCwvqiM7nQS+eDkNv/od3vMvKoXiDQufLcs/iN9/D72/+QX1DQ1MaW5m7Zo1EzK+uaTjihUEgiFqa2qQFYX8ls30fPdbZFcsF9uQmBqVd4yJ7mXcG+X5mOWea10XVetcLgo7d9DzX/9O5s2laJJEQ309U6a2sG7t2gmp46FEkiR2bN9GdW01Drud5IvPkl72OpLLJbzRZp+QwH3q6TjnH1sqSFQeYi7ZbMQeWkxmzSpCoQpkRWFgYGDyLEJMciRJYs/u3VRWV+G0O0g89TjZD94zcuLMXHQxLnsvuBD3qaePirjQdbFNUfzxR0m//x41tbWkkini8bil4yGipGOVqeMSsh+8O1xH4/rqPf/j65hMJi0dDyGSJLFr504qK6tw2O0knn6C7HvviNxGVUWSZGORVsZ73oW4TzkVvTCGjnYH8ccfJv3+e1TXVBONRYlFo5aOhwhJkti5cyvVFQ/jdEDiWS/p95xITqMAjmQU7pPB89kM7hNy6EUJSRbGo+l1lOw68Se8pD90Ul0TJTqwgKil4yFDkiS6OjvxeDy4nE4STzxGdsUHo8ZVbDa8n/s8ntPP3Ou4mnj8MdLvvUNNTQ0D/f3EY7FJo+NBMxxVVUVRFLZv24auFplxzDFkXnuFvp//D1o8OsrTKEmymKwXi3gvupj6n/4K7xmfLRlrJe9d2aTSNE4O1cktz18cNrk1DV7jceecedT/6KcEbrilZHCOMh4lCVSV2N1/ZHDhvUyfMROPy83atWswy/pOBMp1BGidPh2b3U6+s4O+//4uGeOiVW40QlnI4ST54X8kZb83EGGUkstFYftW+r7//8ivWYUuyUxpbCQQDLF2zeoJpeOhJp1Ok8/lqG9oJN+2h+i9d4twbRB9WFHQczlcJ55M/Y9/Ts23/gPH9Bno2axYjDEXhBQFtauD6N13omczTJs2nT27dg3rgxbjg67r5HJZ+nt7aWxqJrd1C9H770VHH4ryMHR0n3o6dd//CTXf/Hecs+eIaBIzv8MsOT/Qx+Ddf0RPJGidOdPyHh8ihunY3Exu2xai9983Wsd8Xuj4g/3VcUfpcyzGD13XyWQy9PcLHfPbthC7/x4xFooXGDoa/fEHP6Xmm/+OY/YctGwGSSnTUZbQ+voYvOePaMkEs+bMZc/u3aXPsRg/dF0nnS4w0PPfNDTuJLfDTmyxF1Hk3ajOr4CeB/fJOeq/P0DNN2M4ZhTQspLYokPH6I+g9ktEF/hQkzJzZj5P265fA6Bplo7jiRlVNdDfx5RpLeS3bCa2aMFQNePycfW0T1P//R9T/c1/xzln3pjjqhodIHrXH9ETcWbPncuuXbtKnzPROSiGo6ZpKIrCrl27yGUzTJs9F2nLZnp//H3U9nYkp2u00YgOmob/qmup++//wTG9tXTChhmGZYbJ4bLGyz+7vA3lcctyZRU13/4PKr72F2CzDfOySEZiLIoNLZlk8De/JLX0NWYfdxzZVIo9e/agKMphNzrKdSzk87RMm4bd4aAYjzPw4x+QWvqaKHIywmgcz7Dhw4aZ8wgjjEc3uTWr6fvBf1HcsxvJZmPG9Onk8gX27Nk9IXQ8lJh9dueO7TQ2NyMjEX9oMbn1a0X+r6aJRaN8HiVUQfDmW3G0zsTz2bMIXH8jmK8xt0XRxOp48sXnSL/2KnaXi/r6erZt2zphw7qPBMz+3LanjZbW6UiaTmzxAvLbtopq00b1Wz2Xw1ZdQ/Dm27E3NuH+1CkEbrxV5FsZK6swpGP69VdJvPAcXq8Xl9tNb2/PpFlVnYyMqePCvelYbek4QTF17O7qYvr0GWLRefFCctu2iMJGmoakCB2VKqM/NjXhPuU0gjfdjOz2gFquo47kEDqmXngOr9uNw2Gnt8fScTzRjDSe3u6nmT71KVBl4o94yW21IdkRIaiKjp6TsFVpBG9IYZ+u4j4lR/DaFLJLBxWQTceFhGTXSb3uIvWyF7c/j016jt6ejciypeN4YfbHjRvWU11bi11WGFx4L/kd24VBWD6u1tQSvPk2bPUNuE86mcBNt+x9XH1zKYnnnhEeTJeL7u7uSdEfD4rhKMsyhUKB/t5eAqEKqm0KnT/5Ifm1a0rVU01EGCiiCM4111Pznf9Eqao+5N7Eg8Uwo9LppOpv/p7KP/8rJIejFEJS8lYWRfXRYtse+n7+Y6RdO5l5zLF0d3YSHRxEUZTDWmTF1HFwYACP30d1dTWqphG76w8knn5i6Ec/0micZJp9bMpzH03j0aiMlX5rGf2/+hlqIoHd6WTmjBl0d3cTjUYPu46HEkmSGOjvR5EVAsEQ2XVriD+0WHjkzXMgy1Ao4D3rbHyfu6TkiQxcfR2eT38G3QhjLzsoejrN4F1/QOsX23r09faRy1nlx8cLSZKIx2OkUykqKqvILH+PxJJHxcWwfCFELeI993x8F1xY0tF/xVV4Pns2ZLOjdcznid7zJ4rdXbS0TKOzowPNrPxocdAZU8fH96bjBfguuOgAdGyxdBxnRDXzOPFYjIqqKrIfLie+5GEkuUxHCVCL+M49H9+FQzoGLr8Gz5lnoY2lYy7H4D13UuzuYtr0Vrq6uiwdxxFZlonHBoj2L6KiapDsaifxJW7hRVQBY/sNXRUhqr7z0ugJIA/+y5O4z8ii5WSkMhmFjhKD93spdtmZPu1tujt+jqYVjpp5x6FGkiSSiQSKolBX30D63bdJPPFYaevAEsUi3vMuwHf+hWX98Wq8Z52Lvo9xtdDRwdRp0+jqnBzj6ic2HE3vytatW1AUhaamJvru/AOZV14SIY3GySsZjEZ4qv+yK6n+1+8gBwKl581wtIl+0kqUtbeUF2mzURn+Kyq++ueGQVVmZEnG1gMeD7kVH9AT+SVVXi+V1VXsMMK4DpfhXNJxyxZkm8KUxiZ0IPPc00QX3C2Mf0UZKoQzWXMZ95cyr3IpdFrXkWx2Ek88RnzRAjSgsqqKyqpqthtbSEy2BZADwbxItbXtYWrLVCSjKmqxq1MMkEYOlZ7NYmtoJHDdjcgeT6kKr622jtDNt2KrrUXP5Ur5w+g6kt1O9sPlJB9/DGSZ2bNn0d7WJkLnrIvjQcU8nzt37KR15kzIZone/SfUgT4jOgTh3chmsU9pIXDdTSLywBgPbBWVBG++HaWxaSj0GEo65tevJbb4fpxOJ7W1tWzevMnScRzYPx2nErjuRrHAaek4oTDP5/Zt25gxaxZkswze/Ue0/r6hKCZzXC3p6CxVcVcqKwnecju2hsaxdVy3htiDi3A4ndTW1LBp40ZLx3Gg1B+3382s1icgJxNd4EXtU5Bkowif4W20N6kEr0kh+QFZ1Dyy1emEbkhiqy+i54yQVTB0hPxaG7FHvDidGlXBJ9i4YZVV4X0cMM/njh07aGxqRk+niN7zRzSj9oLQ0RhXW6aJ/mgfGleVUIjgLbdjb2oeuz9u3EDsgftxOBw0NjZOiv74iQxHMx+uo6ODXDrNzOOPI/3mG8QWLUAvFoShNKx4iiiE4zn3AsNoDALDt3Aw/58UjGhnyXhUbFSG/4bgDTeLapIjv5dRQTL11ONEH3mQ1tYZ5DNZ2tvbDkueXEnH9nYKhQLTp7aALJPfspmBP/yOYk+PSMYvFg1n8SSqmHqQGFYBVFVLseyD991J9s2l6MC05maKRZW2tj1HfL6jpmnIskxvj9h+w+3xklr6KskXnisZjeUeW++FF+E557xSCLeZ8+z93Ofxfu4LQzmOZYsSuqoSW3gPhe1bCYYqSCYTJBMJ5L1sx2Ox/+i6LnTs7cXpcOD1ekm++Byp119FstnRzYUvXQNFxve5z+M54zNiLDBzvVUV73kX4v/CpUM53uU66jrxhx8gu3YNNXX1aKpGOp2ydDyI7L+OXzggHWPDdFQtHQ8ypo7dXV34vD6h48svkHptpI5iQ3HfRZ/Hc+Zny3Q0xtXzP6I/PriY7Lo11NTXg66XKoNbHBxMHXu61+J2vIDHnyX1mo/k6y4ke9lWG8aG197zM7g/nUNPgSSDpICeAu+5WfwXZYyDivcMFcuRiD/iJrvBQ11DJ5L6v6RScUvHg8jQuCrmOYFAgMTzz5JatrS01/vQuKrgu/gLeE4/Y8S4WsRz7vn4LvmiiL4aa1x95AGyq1ZSWV2DphVJpyb2uPqJWmaG5HV3dVLV0IA3laL/T79Dbd9TVvVraJ9GvVDAddwJ1Hzz37DV1AIjJuST0CApz3M0fxBIEpLTReXffgPP+ReVtuooees08SPTkgli991NcetmZh9/PO2795BJpw95qKP5eT09PYRCIfzBIGo2S+yeP5Fd8YHY96tQKCUBD6ueerQw4jdaKpazYweDf/o9xe4uZLudmTNa6WhvJ5PJHPEhq6qq0tnZyfTpM9DiMQbv+hNaIl4qHCXJMnouh711BoEbbqFUEMtEEit1oVvvwDFrNtrI1TibjfzOnQzeezeKojBjeivbDI/ukWyUH0rM8uId7W1Ma21F7esdvf2GrKDnsjhmzSFw/c3ijeUXNUlCkiB465dwzjtmeEiO4Z0vdLQzePefkDWN5qYmtm21dDyYlG+/MZ46qsN0bLZ0PMiYOnZ2djClZSpqXx/Ru/+Enk6V6SijZ7M4Z84muFcdJaHj3Pl76Y9tDN57F5Km0dTUxI7touCRpePBQdc10R/33MvU5hdQe20MLnCjp0Tkna5LwrOYk3C0FghelxThqOXTXwkkGwRvSuKcXUDPSUMzdl08V2hXiC50I6kyTbWL2L51CWDpeLAwIwrb29qYOn06ak8P0XvvEuk15eNqNotzr+OqjASEbrkD5/xjhoeQm+NqZyfRe/6EpKpMaZ7Kjh0Tuz8esOFofqG2PXvQCgWaG5sYfPgBsm+8juTylOJ+zQ1odVVFqamh6h//FceMmei6NumNRpORFUXN/22VVVR/419wzJ2Hnh+x9UCxiOTxklu9kv5FC6jw+3H7vHR0tB/Stps6tre1oWoazY0NAGReeoHE00+AkYhvfr+SQpNUqwOmbOUW46YXCsg+H6mlr5Fc8ogRslqN0+mks6PjcLd43FBVFVmW2bRpI/W1tThcTmJLHiG7/P1SJb/SAonDgf/Sy3Aff6LI8S0bUCVZQS8WcR1zHMFrb0DxeEaVrkaSSD7zJKm33sAbCOD1eOju7j7ijfJDgek1XrNmNS1TW1CcTqIPLSa3dpUowGEa/2oRnG4Cl1+Fc+68YUn+ILYf0osFnDNnEbjhZmS/f8RWK+IakHr5BVKvvYwvFMLtctHb24uiKJMnNWGCUtJx9WqmtYyvjrql47hh6rhy5UpmtM5AsjuJPfIAuTUjdVTB5cZ/+VU4580fY1yV0YtFnLNmE7jxZmTfGP1Rlkm9+Byp117BHwrhtNvp6+uzdDwICB0VtmxZS2P1QhweidhjPnKr7aIgjo7YZkMFnDr+S9O4ji2gZ4SnEXPIVEDPgHNegcC1SWSvNmqLDiSJ5EsuUm/68FencCpP0te3wdLxIGD2xw0bNjCluRm700n0wYXk160Z1h9Ri0huN74rrhaVqffSHx0zZhK44RaUvV0fX3mR9Ksv4Q2FcEzw/nhAhqOu6yiKQj6fp7+vj6lz5qJt20Ls4QfRszlQ5GE5YeZku/Jrf4HnrHOME2EYWRwZoY9jtV/XdZyz51D9f7+B7PeP8NKJqrLY7SSffJz0m8tonTefvp4e4vH4IYlVL9exr7+PKU1NyHYH+fY2oosXUOztEZUxVRVjmWxY+OFRx1j5johFgNjDD5BfvRKA6S3TGBgYYHBw8IjMOVAUhUQigU2xUVlTQ2H3LmKL7kfPZ4dCTWUZCnmcc+cRvO4m4402wFjFM44l2Wzouk7guptwnXo6GJ5t44VIsiy8J3f9ET2bpbl5Cp1GArnFJ0OWZbLZLFqxSKAihLppI7GHFpWS+s1FP4pFXMceR+Ca6zH32iw9byDZ7OiaRuDqa3F/+jNi4dBcZNFFZV0tFmXwnjshEaepuZmOtrZRx7HYf0o6qkUCFRWGjosPmY7tlo4HBVNHNA1vIIC+dTOxBxeJtB9G6ngsgWuvL+VXlZ43kGy2IR3POHOEjmJc1aJRBu/5E3oiQXPLVPbssbbnOBjIskwqFYPiH6msaaew1UXsURd6cSjEFAkoSrjmFQhenUQvAIbRaNoSpldRz0HgihTuU3OioI5k3HSRI6lFZQYXuNBjdqY0PULbrscNT5l1jfwkyLJMLpcjm8kQqqqmuHE9sYcfGDWu6sUCzuNOIHjNdca4ah963sDsj8GrrsVz5mdL/dFcYJcUBS0eZ+CeO5FiMZqmTOxx9YAMR3PS1tfXS76QpyYUov+xR8ivXY3k9Q6dWLNkdDaL7/yLCFx74+gtDhjb6JrMDBnCRn7X+RcSvPFmtHyutGedhPGDcTgptrcRfWgxXk3DV1FJb083MP7nZUjHPgqFArX19WhA+vlnSL/zJpLXXwpRlcpzK44wvfaL8vh0I2RV9njIbVhP4vFHUXNZAhUVuN0uBgcHgCPr9216qHfs2E5dbS02m43o/feS37p5yNtorLDJHg+Ba27APmVqKeYfxPkwF4wA0DSUigpCd3wFpabGWI0rW2lTFDLvvEnsqcdxud3U19axbetWZFmesKEcEx3zvG3bupXp06YjSzLRBfdQ3L17tI4+H8EbbsJWVz8skmRYmL74A8XnJ/TlP0Opry9bVTU+1GYju/x94o89guLxMHPGdFauXGnp+AkYruM0ZEkydNx1yHScZen4iSkvTtfa2oosSQwuuJvCrp2jdfT6CF5/E7b6BlCLw2pJwEgdA4S+9GcodXUjvByAzUbug/eIL3kYu9tDS/MU1q9bZ+n4CVBVMffdtvVt6iv/iN0mM7jQTWGXrbQfoyTr6EUJ2asRuCaJrVmDgshtLE2vzHVpCVBBqdAJ3ZZAqVXRC1JZriOgSOSW24k/5cHmkZnR9FtWrdmGLB9dW4MdTMzztmXLltK4Onjf3aUt2IaPq36CN9yMrbYOtH2Mq4Ds9RL60tew1TeIuXW5Z9JmI7diOfFHH0Jxe5jV2sqHKz+ckM6H/TYcTS9VsVhk944dzD7ueArr1pJ65smhk1AKM5PR1QK2hkYqvv4XKKFQKc/P5EiaVMPQD6ZUDMgohBO8+XZcJ5xcqqpU+jGpospq5tWXSL25jBlz5tDZ0U4qlRrXykrDdNy9i9Zp0wAobN1C/IklRl6msToJ6CPCcY9ayi/S5rlQVSSnk8SzT5H7cAUArS3T6GjvGHcdDyXmPp+9PT34PV78FRWkl78vQppVVeTelH7XKs4TP0XgiqtFHzC8G+WUDEmzUM75F+H7/KVicUnXhq/GJRLEF95HsauTiuoqJAmSySRKeXlri4+FqePgwAAOm41AVRWpN14n+cIzQzlUZUa9+9TT8V9y+TAdxxrDhY5FvJ85G/+lV4j+UZbXLckyeiZDdPECpF07cXh8yBLk83lLxwNguI4KgarqferosnSckAzT0W4jUFlJ6o2lJJ97RhTdGKXjafi/eAW6ppa8jfvU8bPn4P/i2DpqmQzRxfej79pJoLKCQiFv6XiAaJqOotgYHNiKS76XQHWG9DteUi84QSvLbQTQwHVyHv8X0ujZoRDVYSmOplfRJkJWPZ/J4f98GoyiOpKko2sSkqKhpWWiD7qR2py4/LuRir8hn89YOh4A5tx4cHAQp11cH9NLXyP14nPiBSOvj6efif+Sy0RNl331R7NQzpmfwX/5lcJeKq97Isvo2QzRBxYi79iO3eNBRqdQKEy4Qjn73RpzAjzQ349it1Ph9RJ94TkKWzcjud3C22iGQyhi/7bgjbfgOuEkcYCDbHdMNDfuqHxN43/HtOmE7vgK2B3DXm+GDBX7+4g/tQRXsUiwsnrcvY6mjv39/SiKjerqGuFtfO0VsqtWIHt9Q5VxzdzGCXauDxelgUD8IwYMp4vCzp0kn30KNZPB7fcTDPgPmff4UKHrOl3dXdTW1SKrKtF770LtaB/alNrIwZGDQUK33oESCn28A8sykiQR+tJXsc+YOTSOiA9FcjjIrVtDdPH9OB1Oqisq2bFdbGFTNCIcLPaP3Xt209jcjJTNMnjPXag9PaWQGlNHpaKC0G1fQvb5Su8ba8wtPSaLiUro9i/jnDN3uI7GIlp+yyYGF96L3WajqbGRzZs3ARO3EMBER+g4ZZ86yuOtY1MjmzdvBiwdD5Rdu3fR1DwFKZdl8L67UHu6S6HDJR1Dho5eH+Zk6uPq6Ji9Fx03b2Jw0X3YFBvNDY1s3boFsHTcf8ztcN6kuXERUtrB4P1Oij0Kkl14CEVuo4Qc1AjdlEAJ6mZg2pCHsYxS2KosngrdmsQxsyjCXqWhj5UckN9sY+ABJ3aHwpTa37N582pgyAtq8fEw+87uXbtoaGxCzqRFf+ztQXLYS5XhxfWxktCtd4htxsbwMI48piQr6EDoti/jmruX/rh189C42tA0IfvjfhuOZlGKzvZ2Gqe3ou3eRerFZ4WfHcQvXR/av805/zj8V1xd2oBYkuSDZuyVe/cKhQKFQoFisXhYboVCodRBy0vtSsYqH4D3nPPwnnseWipVcneDEdLrcpN+/TUyq1fSMmsWne3tFMvC+w42po5dXZ3U19SALFPctZPk88+gF9WhGHtK4fSWt7GcsuItgJEg7SL18gvkN64HYEpzM12dXeOq46HCLIjzwQcfMKu1FZvPT/L5Z8gsfQ1dloZyG0Hk6Jx1rthc3BhkTXRdR1VVisUiarE4rJ/oqopzxkyCt9yO5HKNXo3L5Ug+8RjZ1SvxV1fj93np7e3FZrMdER7dQ4GpY09PD16XG6/PR+yJR8m+8+bwiBFd5GB7z78Iz9nnDtNxb1WVzbQDXdNwTG0hdPtXkN3uIR3NOKxCkeTTT5J5521CNbW4HA6i0ahV8Gg/GK6jS+j4+N519J1/Ed7x1LG6FpfDTjQ6aOm4H5g69vb24nOb/XEJ2bf3ouN5F+I9+7wD0HEaFbd/GdntHr4XsyRBoUDyqSdIv/cOobo67IpCdNDScX8QOioM9C3Fa78XTxVEn/WSecc5rBIqOqCB75wM3rOy6LmhqbNkvoay1w/VwEEvgGNakdAtCWSX4W00Q1YlHQoSyWddpD/wEKpL45J+x+DgNhTFuj5+XMz+2NnZQdDnw+f3E1vyKNl33hI1GrQyK1/XxTZjZ53z8fqj2d80DXvzFIJ3/NnY/VEtknzmKVJvvUFFXR02WZ5w18cD8n8mk0my2Sx1NTXE336D/Mb1w72NYPQGncA112OfNn2oYAYHx/syshqr3W7Hbrdjs9kOy81ut6MoNszyvcOMRyMUz1ZdQ+Ca65EDATEZLl9psNtRuzpJvvwiAbsdxe4o5ciNF8lkkkwmQ319PQDZD94js3IFstcrKvCVhWNKlrdxOOW/YdPr6HKT37mD9NLX0AoFQlXVKLI87joeCkQRpRx+nxdsdhyxKIML70Ud6B9aFTfOg62mltDtXxKbUpcZjaqqlsKybDYbihEuZ3oMJWPD3MBV1+E+/QxRlKm8IJHTSX7HdgbvvwcFqKysosfw6Fp8PER4eoGO9namzpiB2tlJ7IFFaPG42JfKHLtUFVtDI6HbviTCb0YY/2MtIpXy142th/yXXYnns+eUdJQwF8lcFNv2CB0LBerq69m9a+ch+f5HCoqiUCwU6GhvG9LxwYmg465DcwKOEBRFoVAo0N7expTWGajdXcQeWIgWi42hY4MYV22fRMezhwpzMFzH6IJ7UIoF6hsa2bNnz6E5AUcIpo67dy1j+vRX0Pa4iT9kQ4vJhrdRLoWW2uqKhG5OIDkYEsl0PA6LVWWYISkpoOchcGka95mZUqEcCeP4Tp3iHhvRhQ7knJP6mrvZvWvtIToDRwZm+lZfTy91U6eidnSI/phIlAr5lfpjYyOhW78s6pZ8nP5YHrKqafgvuRzPOeeJPGWMxQFNQ3K6RN2T++9Byeepq6+nzShcNVEYnXj0MRjo78dXEcKWSpJ86UWx1YTTVbKyhbcxjXP+sXjPv4DSvndmntNBKrBiitjf18eDixexdeuW4d7MER1vGGUddthry+9HfeCI9+tDf9rtDs49/3wuuvjikgE5loHsPuU0PJ85i+SzTyH7g+iFvPllwG4j9forVN5yBw1Tp9LV0UmNsd/leDAwMIDP7cbuclGMDpJ67WX0VAopGAJdeMlKp2eSe8zGhbJcR13XxQXZZiP16kv4rrwGZ8s06mpr6ersGlcdxxuzLPX2bdsJBYK4nE7677mT3PIPxHYtMNSndR3fpZfhPuW0UoiVeQwz36K/v59sJoPNbqOysgq73Y5mGJ6SJKEEAlR+9f+QW7MKNRYb8jrabCBB+pWXSb34PM4LL6bVprBixQpOPvlkisUitjFyKS0EqqqiKArbt++guaEeh81G30MLya9ZheRwDHk2JBEG5b/iapzHnzhMx72Na2OF6MseDxVf+wuyqz6k2N9XthIrgSKTXvY6saefJHjl1Xjdbvr6eqmurrF0/AhKOu7YQXNDIw6bnb6HFh0CHVdQ7O8foaNCetlrxJ5+guCV11g67gemjtu2bWVqY5PYEuPBReRXrxQ6wrC5kv/yq3GecNKB6+j1UvG1/0N25YcUB8p0lCSQZdJLXxP98Yqrcdlt9Pf3U1VVZen4EWia8DZu3rKdlvofYXPbGbjTTm6tUxiHpYr0gC6233AeX0AvLyDO2FPOkkRlL5B9OhVfSpBb46Q4IItjaDrYJJAh84aL2AtFglfncHf/hsH+GiqqzqRYVLHZrJzHvWH2x40bN9Dc2ITT4WDwgfvF9htOp5jvlcZVicAV1+A87vhh/dFkWMQh+xhXv/p/yH64QuxgIMtDnkqbjcyypUSffJzQNdfhsnfS19dHdXV1qZ2Hk/0eDVRVpburk9nHH09200ayH35QcreWjClZQS+q+C6+BHuL4W0s+6J76yQHQqFQ4Je/+Bl3/+kP5POF4Z68Q4D5A5FlmeeefRqn08l5F1w4+oejiD3rbNU1+D53CelXXxYVmEyPiuGxKmzeRGrlcuouuYzdW7eSz+fHpd2qqtLd3cWs6dMBURQn8+7bSB6P4W1keG6jZTjuG2NxRHZ7yK5fR371Shwt06hrqGfP8uXkcrnD3cIDwjQaBwcHcTrs1DQ1kd+ymeTjj4iQa59PVOszVuHsU1sI3nw7YtNbIxRb15Flma6uTp556knefOMNenq68Xp9HH/CCXzxsis4/oQTMLfpkHQd9xln4r/iGgbv+kNpzyQzl1Tt7mJw0QKaTz+DtN1OwO8jn8/jcDj2OpE62jEN91gshl2R8dfWkVu3hsTjS9BzOaPfqyUdHTNmErzxluGFsT4CMS/SSwsAuq7j+tQp+K+9gcHf/koY/rouino4nGj9fcQfXIjvM5+ladp0Nq1fRyhUYU1S98FoHWsNHR87BDreOIaODrT+fuIPLsL3mbMsHT8m5To67Xa81dXk1q8l8cRj6Lksksc7XMfWmQRvvPUg6Hia6I+/+/WQjkZxN22gj/iDi/CfeRZTZs5ky/oNBAIB7ObioMUozD0bY7EuvPaf46mIU1jvIf6kBz0rIXl0dFVGkjR0VcIxrUDw+tRwn0a582JflIWsuj+Vx39FisE/BcAm3JW6KiE5NdR+mcTDdvynu2iZ9iIbNl+CN3AqDkvHvWIWxEkkEvg8XjyVlRTWrCL+5BJRKNLtEeOdmVIza3ZpXGWE0bi3+cfIgjm6ruM66RQC193IwG9+UbKfzIKa2uAAiYcWETjrbBqnt7Jj82ZCodCEGFf3O1Q1nU6j6zohr4/0e++gdfeIgi+qyIuTZBk9n8PWPAXPZ88Wblx1KMfrYE7sJEmir6+Xl154HlXV8Hg8eLxecfOMuI18rPx1+3r9Rzzn9fnweDz4fD7a2tp4/bVXKBaL+6yC5D7lFJzHHo+WTg/lOpr5jtks6TeX4SgWcbhdRAcHD8q5Gkk6nUZXVUJV1WiqSnb5BxS6OsVKp6YBQ1sm6MaP3LoNv5mexpELIXoqRfqtN9BSSZxuD06Hg1gsNi46Hira29qorKhEAaIPLiK3QYSnm2X9RTKsROD6m3DOnFU6P6Yncfu2bXzzn/6Rb/3LP/P4Y4/y7ttv8/KLL/DT//khfx3+c1584fmhMQKQZEUU5pg1Z2grD7OfOBxk33mTgccewuN243E62bFjh3hvecSBxSg6OtoJBII4ZYnowgUUtm0Z0tFc7JJlgjfdhmNqy7Dz+ZFj9148H6Fbbsc5/9jhOhohctkP3iP6yIO4HA4a6urYZYSsTpRcjolKR0c7wWAQpywTXXjfodHxZkvHg01XVyd+vx+nLDO4aIEoMujyjKHjrThaDmZ/PGa0jk5Dx0cfwmV30FBfVypAZum4N4QenR278bn+F6diZ+ABB4VtdiSXMBqHdNQJXJ/C0VJEV4fe/lGOFEkaHcYqSRC6MYlzbh69KHIdhY4SkhMyK1xEH7fh9NiZUvNv7NrZDgjvqMVozH7V0d6Gx+vBKcsM3H8f+e3bRBqeVtYfFZnATbdhb55CoVAgn8+Ry4lbPp836p2ItJx9zUfEgg6Ebr4N17HHoRdG9Ee3m+zy94k+/AAup5PGhno2bdoIHP7+uN+G4+BAP95QCCmZIP3eO+iSDuUbjdps6JkMntM+jWPmLONTxs+tmsvlUItFFEXsWaOqKmqxiKqOuI18rFj22L5e/1HPFYulz7Xb7aTT6ZKoo340Rg6Xfeo03GeeJc5b+eBurDRklr+P2ttDXUMDvT0943LeBgcG8LjdyIqCOjhI+r23jeTtsmIlZvvLVi2t29CtdOEu98hqGpLTSWb5+xS7Rf5ddVU1vd2TLxfPXADp7+/H7XQQrKwk9eYyUs89NbT9hrkqXijgPPY4gldfB4hrnGYkmg8ODvDjH/2Ax5c8itfrJRAM4vP58Pv9VFRUsnHDBv7jO99izepVSIaxia7jaJlGxVe+JhZXyj3zDgdqIkHy0YfJb9tC3dQWbLJENBq19iAbAzPhf2BgAIesUFVTQ+LlF0m99Fwp7KaUo1oo4DrpU6Jc+AEwbNxAjIH2hkYqv/rnQ+F3UAo91rJZEo89Qm79eoJ19Ug6xOPxCbl31eFmpI6V1TUkXn6B1EvP70PHqw7os8bUsdHS8WBgjqs9PT04FRtVtXUkXnmJ9AvPAhLIw3V0nnjyQdaxiYo/24uOmQzxxx4iv2E9vppanA67peNeMAvi9PWuxaH/P6qn6CSXOkm/7BYvkCXQdCRJRy9IOI8vELg0ZRZfFUgj7vfCyKwrXQV7s0rFHQkkh156v65JYNPR0hLxJ7zkN9jx1SSwqT8gHu9FlidOgZWJgjmu9vf3Y5cVauvqSbz0HOmXXxDzO0kaKgxWKOA6+VQCl10h+pLdjsPhxOkUN4fDYdQ7UUp9RhiX+ZJxOczALBax1TdQ+bWwKAhooGuasBdyOWJLHqa4fi2uUAUBv59kInHY++N+G479/f3U1tVTaNtDbv06ZIdTfEkYOsFOF+7Tz0AJhsSq1kfE45c8OAeAebzD3Rl0zNUcedQYMCxsVVWRFAXPKaegVNca+yUa50fTwG6nuGsX2c0bqayqJpGIj0t7+wf6qampAaDYvoecGcddVpCkPD4bTUMvFq2bcTON6ZFx66ZhU9i1k/wWsc1AdU0N8XHScbzQdR2bzUahUKCvt5fqhgbI5Yg/tJjCzp2lkDhzwirZHVTc/mWxubjhhTV79BvLlvLMk08SDIXEgJnPl6oRZzJpAoEAmzdt4oFFC8nlcsOqh/m+cCnec88XYXhGDoBeLCJ7PGRXryT26EOiUE5FJZ2dHQATbs+jw41i7Bu7a8d2GqdNQ0/EiT+4iEJHB5LLNVTUTALJ5SZ0x1ewVVUPG7cOJFKk3IPsvfBz+C76/HAdVRXZ4yG/aT3RhxbilGV8Ph/d3V3D3m8hKNexoUzH4j51rLJ0nGDYbDZUVSUeixGsrkFKp8buj4DkclFxx59hqz64/dF30cX4Lrx4bB03rmfwoUW4ZBmX210aVy2GI65TKm27d9DQ9BRav4f4w06K7TbhbTS3zJBAculU3BLHVqOV/Cwj15z3SZnlaL5eV8F3QQbfeRn0nNjqA4SXU/bo5Dc6iD7swCk78Pl+T3u7VfBoLMxxdc/uXdS3tKDFY8QfWIja1Tm6P3rEuKpUitzfZa+/xv333csjDz3Iyy+9yPIP3mfzpk10dHQQj8XQNM0wLh0l43KYgWkU3HGdcx6+i78wqj9KHi/5TZsYWHw/bpuCzWano6P9MJ+xA8hxzKTThIJBMm+8LvYZcjjArIooiy04bFNbcB17nHhDmUG4r4GuPJT1oxh5nJFvOdQXKl3XDWNRGt2YkRiTWsesOTjnzCX9xlKkQBC0vHivLKMlk2RWraTq3PP3Wtvnk5JJp6morEIH8ps3UezuQnK6h/ZuNC9SgK5r2OoasFVVHeW5jiKoRNc0it1dqP19InfV0L80tksSWjJJbu1qvOddiNfvHyrjPEkwJybr169j+pSpePwBEo8/JnJzzQIcmia+fy6L94LP4b3g4mHvVxSFTCbD22++RTKVJBAMlB4vx8xPfOuNN+jp7mLK1BZxHnUdJRCk4qt/Tmb5B6ixaGlxCrsddI3kk4/j+ew5VJzxGfr7+hgY6KeysmpCJJBPBMzCFvF4HJ/Hg9vpZOCRB8m8sRTJ5RTnUtdLOvouuRjvOeeJN+u6SD3Yz0W9UeOvriN7fVT82dfJvP8uam/PkI6yDLJM6tmnSJ5zPrXnnk8sOsjAwACVlZWWjgYjdfSU6ciE0FExdHza0nEfmDquW7eOac1NeEJBYg8sIrPsNbFwO0rHy/CeO346pt9/F62vd0hHRQHJ0PHsC6g791zSyWSpUI6lo2CoP76Dx/ZN3EEYXKCQftMFTkCTDB11tJyE/3MZvGdnhbdRB0n+6KniSCRzgmHKqYHs1wndHie93Inab2z2qOmgCKM1+Zwbz9lFas7Lk9z4Lfr7f0VV1UxLRwNTx2g0itcl0l4GHl5M5s1lpS3BSv0xm8X/uc/j+ew5ALzy4ov88z/9A9HoINVVwgkjKzIOhwO3243X6ycQ8BMKVVBRWUFFZRWVlVVUVlYQqqggGAzi8/mprq7GHwgQ+tJXybz7DsWe7mHXR0lRSD/3DMlzL6D+/AvJbUsf9nF1vw1HSbHhlGVi69cJb1mZexVFRsvncM6dj615ivHYvr+UOUEd2+s4dvS3+doJ+cMfw64aNnAbpXhtDY045x9LetnrIxLkxBJUbsM6pGwOt9c36ngHp5kSLo8HNZMht2GdKHDi8gxrh7h45fFffS1V4b9GqaqxCuUYxn1+x1b6f/JDUm8sQzZWpUpVsUwN165BS6ewhSpwlfeTCY5ZECeVSlFXU4Ps8aD09hB7aLEwlv3+oVU4XUf2Bwnd/hWUYHCoPxsew2wmw57du4QXUB89iTH/t9ls9PR209/fz5SpLcBQ73ed+CkCN9zMwK9/gex2lby+kttDfsd24o8+hPfkU6htbqazbQ9+vyjocDDzqScjptc4l82ybctmjjvlVAq7d5F89CHUeAzZN6SjrmliM+PbvyQeH9HPD/Q8lofKOY89nuDNt9H/kx8iOZ3oqmoU5nBTbGsj/tAiPJ86habprWzZsJ6KiorSarCl43Adix9bR+0Q6Vg0dNwzpGNrK1vWWzqaaJqGzWYjm81SGQoie73I7W3EHl6MFo8hjdBRrqgkdNuXS/1RGqHjgXodh3Q8gdDNt9H/0/Qb6d8AAQAASURBVB8N6VgsIrncFPfsJv7QQnynnkqoto7ezg6CwSA2294rxh8tDOmYZ9vG1zjhpHUUt7lJPuES22/4dPSiDJKOroES1AjdnEAO6Oh5RuUqHgimt5IiuI4rELo+Sf+vgiVPp16UkFwahXYb8Ucc+E50EKx8kYHedwkGW7HZrP5YGldzWXbt2MG8E0+isHMHyUceQk0mkX2+4eNqdTXB276M4veTSae5684/kkom+fa/f5e5c+YxONjP4OAg/X39DAwMMDA4QHRwgB07trN6dYJMOk0un0Mz9rO32W34fH7mzp3Ln//FX3LyKacQvPl2+n/yAySHE10tlgoCFjvaiT24EN9pn8ZbWUV0oJ9gMHjYbKD9Nhy9Ph+k0+Q3bRSrxSNCRCVZxjlvHkpVtQhl28cXKzcaYf9DzCZjIQxJMrYmsdtxzp2L7POKfVzMc2ns6ZjfthUtFiVYERqXdnjcIg5fSyWFlpI8PFfVaI8cDBC85nocM2cP31LlKEZXVdwnnYL/8qtJv/mGeFBiaLJt5Krmd2xD7e/HFhKrS5MF82LS2dmBQ7Hhc7nov+9uMu+8heQV1f7MhQUtkyF41bW4Tz1dvHmkwSFLpXCM8ovUqP8BWVaGDYSmASrZ7YRuvJn00lfJrV0j9jcz+4rTSfqFZ4mfez7+Sy8n7Q+wc+dOZs2aNb4naRLR1t5OS0sLNqD/8UfJrngf2eMb0tFmQ0ul8d98O+6TPiXeZHo3+GQRHOULCZKiELjmBtKvvUJm+ftDk1VNRXJ7SL/6MvFnnyJ0/c14nE7i8TjBYHDYVi5HM+2Gjnag72PrKPogMDofez/5eDq6LR33gtmPdu/ahcvpxOd00ffoQ+SWv4/kHaFjOkXgptuG9cfyfbDL50770z+H9UebQuDaG0i//gqZ5R+MqWPs2aeouPYGsrEYu3fvprW11dLRON9dnXtoafouNtlB/1M2ssudpSqqQkeRa+i/LoXrpJzYd5Gh7ndA3dAMUzXer2sg2SFwVYrUMhfZVU4kh46ugq7JyC6d9GtuYi9mCF2jomX/kS3bz2fe7IajXkeT9vZ2mpqacMoSfUseIfvhcmMv8/L+mMZ/+dW4TjwJgG1btrB69SrOPvdcvvq1Px/zuJqmkc1mSadTpFJpkokE8ViUweggA/0D9HR3s3Tpazzy0IPEYzF+f899BK65TvTH999BcrqGFuU8HjKvv0rs6SeouuFmyGbYunUrc+bMOSxex/22AvzBEGo8Rn7PLlEm3zQcZRkKRWSfH0frzKHNZffxhcqNRkmS6Ovro6OtrbScIsnGHjWSJDIHZQkJCVmWqKuvJxSqOJDvfNgY6T+1T2tFqa6l0NEuvFZGFSZJUVB7esh3dhJonTEubfH7hSdTGxggv2f3UDVVM5YboZ9sdyA5jST6vRX9OdrQxIKI5HSWSpqXK2tqWOzpodjZgXPGTAKhyWE4moNQLBZDLxaZOnMW2fXrSD72EFo+h+z1gpGXqxdV7E1NBG+6FdnjGbafkXlxdTgc1NTUjNomZ+TquVositANv394g4wxwj6lhYqvfJ3uf/6HoYunqiLZ7RT7+4k/sBDvKacRrK0l39ZGKpXC6/WWvKdHG6aO8Xgc1CKBmlpyK1eQfPRhEcokIQxvWUYvFHBMn0bw+ptKFytJUQ7KtknD8n91DXtjI6GvfJ3shrVQVMUHqCrY7ajJJPGHHsB7+mdomTePdcuXM++YY3G5XJaO8Ti6oWN2P3WEsmvPJ/UwjNRx/VojL56xdZw7j3UrLB3LdVQkieZp08iu/JDkkkeETjBcx5bpBG+4WeRYGTqaed8jz9/+GI+j+2OToeO6MXRMEHtwEd5Pn0GooZHU9u1ks1lLR0UhFu2jkPomwVlFsqucxJ/wCi+fXQdVF+vwBbBPLRK8JonsBj0v1ucPBqV9HSXjc5pUQnck6P5XhwiHlRD3Nh01oRB/2Ifv1BjumgE8Hf9NNvtjXC7nUauj+b3j8Th6USVYV0d2xXISSx4ZiiHWRaions/jmN4q+qNThK++//57JJNJTv/0mQCl2gzl9owsy2KnB4+H6uqx23Hr7XfwN3/1F6xds4b2XTuZO/8YQl/+Grl1a0tpY+ga2OyoqRSxBxbi+/Rn8DQ0kM5myedyOJyHXsf9/iRfwE+xtwetrw/J8CSA8DRqhTxKVTX2pinD3jOWmWG+zyzX37ZnD//yjX/gmisu49orL+Paqy4X91dczrVXXMbVV17G1ZddyjVXfpHrr7mKv/+bv2bnjh3IBxDzf7goDe2GwLa6Bmz1DVAsDN8LxshzLHS04fF4Rh3nYGBO0Iu9PWgD/aVcveENHpGzWVYs52i+feTky8xVTacptrcB4B2nkOODjTn4tbXtoba+AYpFEo8/Qm7tGrEKZ4aoKjb0Qo7AtTfiOua4vR7PbheGI5JU2hO0PCzdnPSoqkpFRcWo81TykgDe8y/E9/kviP0jbTZEIYAistdH5s1lxJ5+ArfdjiRLdHV1Dr3/KMRcgezq7CBYWYU9nyf6yIPkt24We1IZ2ydh7C8buOEWHHPmijebxr9xLF3XReVoTRuqXP2RN1H8aFhhMGMo8Zx9Lv4vXoGWSSMpYu2yVPDo/XeJPfkYDqCxqZmuTktH+GQ6DuXgi7+LZZXA9+c2VEVaHOtj6ShZOsKQjp0d7QQrK5HzeWKPPkhu86bSno3GC9ELBQI33FzSsTyvUVO1UhTAgYYajqnjpZeL7cHG0DH6+GO4bTacTifdXUd3waOh/riJquqHsRccxJa4yG+xD3kbAWQRrhq8JoVzbgGKQ8f4xNNVw2gcFjygg/esLP7Pp9EyMpKiG4XkZGSPRma5i+hTTrwuFaR76OzYBRy9Oprfu7urE39FBY5cjujDi8lv2zJU+E9HzItVlcCNt2CfOUssgMsy77//Hl6PhxNPOkmELhtVVBVFKVVUBWHfmONnsVikUChQKBQoGve1dXXMnTefoqqSSqUA8Jx1Dv7Lr0QvbddX1h9XfMDgkofxOhxoOnQepnF1vw1Hj9tNoacHPZUcblhIEhQL2GrrUEzz2jQ0RvQUc+DSNA3ZKKBx5x9/z+OPPYLX5+WMMz/D5VddxSWXXc6ll1/GJV+8jEsuvZSLLv48lZVVRAcHeeLxJdx9559Qje0AJgMlw8z4USlVlcJwVMu8MeaEulig0N6GY5xCQz1eLwBqTzdaKiVCiieJAT6hKY9DKRYpdLSjF4s4JkGOo7miPTAwgMNmIxgKkXznLZKPP2YUoykviJPBNf9Y/FdeI0JHVbXkbQRK/dtut1NdUyMWeMQTpeeHvVZVqaqqweV0jmqXZGz7oQRDhG77MrYpU0VOrqyIviNLaGqRxIOLyG5Yz5SWaaj5ArFYrNSOo4lyHWVJorq6muTSV0k//SQ4ncJjbuqYSeM+4SQCX7xCLASOGE/NhT1Jkshm0qKceDY3rLT46FsWkLDZbCVtoUxHv5/QLXfgmD5jqKp0WWXuxMMPkF25kqr6evLZDIlE3NLxo3RMj61juXFhXivNPLWP1tG4ZbOl34BmesVMHW/dHx0TR72ONkPHxNLXSD79pFGAo7w/ZoSOl11Z0lE3zv3TTz7Br375MySdYfOe8sitj0PpfSUdA4RuuwPH9NbROgLJhx8gs+pDmlpayKSSJOJHd38cHNiNXPwGlXUaibdkks95kJySKIijiYI4elbCdVwO/yVpJLuogCrJQyGmn6jqofH+0lTD8G4qQZ3gTUnsLQX0vASybhTlE4sEice8ZFYpTG1NkE/+M/H4wFGroyRJDA4OomsatTU1xF9/mdQzTyG73MJoLB9XT/oU/ksvR7bZ6O/p4emnnuT9995jemsrs2bNEQajzYYsy3u9mR5IRRHGpWwYlzrgdDgp5AtkczkAZL+f4C23Y58xEz2bHaqdAWJcfeRBMiuW09LaSjaVJJk89OPqflslDpuNVE83WqGAXLYPkNjTTcNWU4Ni5nOZK56mt4HReY0S8OorL3Pv3XdR39jI333jn7j19jv2GrP71hvL+Mbf/S3bt21lx47t5LK5SWM4mq2UJLFtg+zzY6utHe3ZA0Cn2NODMk4/BqcRylTs7RG5i5RfhMxYB4v9pXRNMPRUuzvRi0WUCW446rqOLMsU8nm6OztomTkLLZEg8fBi8u1tokBDIS9+q5KIkQncdCuO1hmiL48RJmH28crKKhxmJdYxMAe9mtpabHZ76THzGJIkiePrOq6TTiZ44y0M/PRHwpjVNPRCAdntIbtuHYklD+Oc8S/UNzWxa+dOjj3uuFJUwmQZJz4JJR0LBQYH+qmqb0CPRYk9uIhibw+Sz4deKAzpaLMTvOlW7C3TSqupJmZY1vp1a/nNr35Bd1d32bncVxsg4A8wY/YsrrzqauYfc+xQKI3xfudxJxC8+Tb6fvBfpXxqszBHftsWYg8vpnbuXKrq6uls78A/N3BUhVQN07Hf0DEaJfbAIop9PSInrlxH+9g6mtdas5jcqpUf8viSx9ixbTvJZBzpI2PnJDRNZcqUKfzl3/wtM2fNEr+L/dJxnqFjO/65c49KHYvFIoN9fVQ2NqHHoiQeWiyq0o/qjwqBch2NudPGjRv4nx9+n77eXmbPnsull11W6p8Hku9YPq7quo7z2BMI3nw7fT8cqaOL3NYtxB95kNo586hpbKKzswOf339U6pjP6/T3PE1N/XL0XgeJhz2oPTYkr4ZeEAVxkAAFgtclcbQURUEc41SVQkwPWruMy7EsQmFdx+UJXp+k/6ehUtkKs1BObqud+BI3rtk6tbVP09m+HL//wqNSR13X2bNrJzPnzUft7yPx4GLUgX4krxfK+qPkcBC46RYcU1tYu3oV3/3377B+/XpSySSFfJ6//eu/pKKigorKSiqrqqiqMiunVpYqp3p9Ptxu915tGrfbjaapJBIJ0UZVxXXM8YRuuYPe7//nqP5Y2LGd2MOLqZs3n6r6Bro6Opk5+9D2x/02HBVZRh3oL5WpHfV8ZRWyxzusKp9U/nfZhFCWZXbs2M6vfvEzkokkt9x2O7d/6cvDQpzMAU44NIuc9ukzmDV7Nhs3bjD6nz55bJzyQd1Y0VAqq8BuM/JVyj24Mmp/36jiQwcLxW5HL+SHtBy2BDZZTugER5ZR+/tF8aNJQnd3N36fH6/LRfyFZ0m98Byy242uGntX2h1o6SSes87F97kvlBZBRuYyly8OVVRW4na7KRaLwyY2w8IYJaiprcFuGI4jjyNJRlEpm53gVdeSWfYa6ffeFWNNIW8UynGQeOxh3GedS+CzZ+NyOIhFo4QqKo4qw1GSJDo6OrArNioCAaIPLCT92itGYSNDR4cDLZnEe+HFeC/4nPnmUlicaWi88Nxz/Oj732PDhnVo6ohCaOZnjtEOSZawvWjnhWef4f/85V9z7fU3GI/Lpb1s/ZdfRWrpa6SXvS4q2OXzIvTY7RHbrJx7AVUXfo6+ri6i0UFCoYqjJidnSMd27LYyHV9/xQht/GgdzeMAFAoFFi+8n9//NsLu3btK11iZofzHMXUEkMVq+aZNG/mXb32Hs885V6zaG7ncH1fH/qNYx/a2NhwOOxV+P7EHF5F67aW96Pg5fBcO6YgsMxgd5If//T12bN8OwC9+9mNmz5nNrNlzDrhIzrBxdVh/fJX0G0vLdFSR3W6STyzBe875VF1wEX1dnUSjUSoqjj4dO9p347Z9j1BII/awk9TrbiS3NlQQx6GjJWW854n9Fc0OJo3saJ84eZyS19GcXusqSE4IXJom/aaL9NtuZK+Gnkfs7ejWST7twXN2jooLMvT0/CvR6DwqKpqPOh0HBgbwerx4nE4GH3mA9OuvihDVYnl/TBC4+FLc55wPwEvPP8+bb7yBJEk4HA4GBvp54flnhVdRklFsCjabzdiz0YXL7cLr8eIP+AkEQ1SEKqisqqSqqpqqqmoqKiupb6gnHouhqirRgUGzkaBI+C67QkSYvP6KWLg3+qPk8ZB86nE8555P9ee+QH93N9FolFAodMh03G/DUdZ11HhsVPgphiCyPyDCaMonkyO8B+Z9Op3mj//7O957+23OOuccvvK1r5VCacYaBCVJbGVRcvNO6tBKo5KsP4BksxvlsYxRwMxnSMTHzXCUjbwyLRYbl+Mf1UiURnM1HhcryhMYsz9m0mkSsRgz5x9DsaOd+KIFIp/Q7RZ7tcoyaCqy10fwhluwNzSOaTSOJBQM4fP56e/v22u/lWWF6upqbDZbKZTEpBRaZYRu2ae2ELz1S+TWrxcJ5EYegmS3U+jqIrH4ftzHn8CU6a1sWrcGr+/4o2J7DnMxLpfLkc9mmDpzFoVdO4gvvE9EiDidYoFKlqFYFKG/N96Mrba2VDHZPEf5XI677/oTv/3Nr+nr7cXhcO73udM0ja1btvAf3/kW27Zu5e/+4Ru43G5kIx/P3thE6LYvkVu7Bj2TFuHymlEIIDpA/P578Zz8KZqmT2fX1i34ff4xK/QeaQzXMTuk46IROiqGjqHROprHAUjE4/zoh9/ngYULyaRTKDY7LtfY19h9tWnVypX8w//9G/76b/+Wm269HbvZHxubCN16B7m1q9EzmdE6LhQ6Nk6fzs6jVMdCLkvzjFkUdu0ktug+tFwe2eUCtbw/BgndcDO22rpSiCq6zu9/G+HVl18qzYvWr1vLz3/yY/7nZz/H5XKXwogPxHgEI4+rWMTe1ETo1i+RW7dmtI6D/cQW3ov7pJNpnt7Kzi2b8fv9R8X2HEM6qhQzP6J+ajeFXQqxB31oORnZpYmKqTJQBCWgEbo2ia1BQ8+CZF4epRH3B4lSyKoCeg7sU1VCNybJrXegZyVQGCqUM2Aj/oAXz3FFmqesZufuh/H7/y8225EflVPeH9t372LO8SeK7bwW349WLCLLRrqWohjjaiWBG28R6WRA38AANpsNm92OpqrIsozb7S4ttOq6Ti6XI5vNAvFRoaOyJCGV8iBtOBx2nC4XhXwBSZL49a9+wc5dO/n6//kLKoJB7A2NYlxds8rIP1ZEFILNhhaLEV94H95PnUpjyzTadm7H7/cfsm2P9t801TS0TLY0qJXQxYVM8nhKRTBGUh42I0kSLzz3LAsX3EdjUzNf+erXaWmZvs8vLUkSsqKUlkZ1Plmo+Hjxsdpkpjt6PMJw1LTh44kkoaVSQ0nz44GqoaXTo7WckGd1MmEoKcvo2Qx6YXJ4HDs6OwhVVWKXIP7MU6TfeausAIeOZLejpdP4LroY79nniDeVUnP3/pvxB/wEQ8GSx3HkazVdx263U1VVvdd8ndJjxtjgPf8ifJd+sTSggpFA7vaQfOl5kq+8hMvpoLqmtlSY42ihs7MDp9uNE4gteZTsqhWiarOZa2iUF/d94RLcZ35WvKls642urk7+49++zf/88Af09/eVjO79KaRiXjQdTqdYIPz97/j1r36BputiYcDQzHv2efgvuwItkxkqzKGKPTpTy14j8dzT+NxufH4/3T3dh/hMHl5G6bhyBbKzTEfF0PHzw3WEof6Sz+X42U/+h3vvupN8Pofd4TDWJ/dPT13XcTqddHV18r3//C7f/8/v0t/fX/Jses89H/9lV46t49IhHf1Ho44d7TjdHlwSxB9/TJT7d7uH98dMGt/nL8V95lnA0CT3lRdfYME9d6MZFddFJICN5559hrv/9CdkWUYtiuvLgRbKAUq/G++55xsFj8bQ8fXXSDz/LF6XS+jY3fVJT82koqPtA5z2RbgUnfhTTrIfOpFdhrcRHcmuo2VkfBelcZ+ZFcbk8NIVB3dqVRYmUDq+BBTBe04W/xcyaFmjUA6icI/k1kgtdZF42YHXq+Jz/5Suzk0HsVETn67OTqpr63DqGvElD5NbtdLY1cAcVxXj+vhFPGecWeqn2WxGXNuM/3XjWla+naDpcXQ4HLhcrtLN6XRidzhKhl2hkCeZTNLX20s8LmoxbN+2lT/94X95c9kysbOCquI753z8l1+Fns1CqT+K7XIyy14n/sxT+L0e3G5PqXDVoWD/DUddF19CLhukSkseklgNNV43+q16aUDcsnkzv/rFz8hmM5x3wQV8/guXkskIYfL5/KhboVAgm82Sz+eR5bLeOAFtnP0ZviWHAxR59NeQEAaHNo6Go66h53Jj5CsduatOhwzD46gXCuPmNT4YmAs18XicfCZDfUMjuU0biC9eMJTFr2nCG5XLYWtoIHDN9SihCuHdkIUBsK9Ji8fjpbKycsxCVqJ4iobL7aKisnLY4yNfB0ZBh2IRJRAgeMMtOGbMRM9mjOpj4gKpZbPE7rubQvse6pqbiQ4OkMmkj/jVVEmSSCaTpBIJmqe2kFmzmsRDi8UEUNNANwsb5bBNbSFw9fUo/oCho4jmKBTy/PwnP+aeu+6kkM/jsDuGXRz3pz2mcWKz2VBVlTv/8HsW378AWZbRAFQV2eslcMPNOOfOQ8+kh3TUddBUogvuJb9jGw1TW+jv7SWXmzw57QfCR+qo70vHwqgQ1fvuuZt7776rVKSh3ADZ33apqorD4SCbzfL7//0tv/nVL9ElCTWXQ/Z4CVo6ljB1TCWTpBJJmqZOJbN2NfEHF43dH5tbCFx9HUogAKqKbui4adMm4kYxGhjafD6VSvLSiy+IAmCfIDRt5Lgqe70Err8Z59y56JnUMB11tUhswd3kd2ynfmoLA/39R42OyWSSdPy/aW4ZJLNOIf6IT3gSdbHobhbEsTUVCVyRQqnQh22/USqIc7BP1chCOQroBZCDOoFrkzhn5dEzEpJNL3la9KJEbLGP/E4bjc1txPp+RjZbOCp0TKVSJBMJ6pqayKxZJcZVw3Fj9kctl8PeMp3ANdcieX0i5xFIJpOjHFvl3kZN04ZVUS1fRC0fc83qq6aRqSgKGJGWiiyjY2x7Vywiud2iP86bP7o/6jqx++8hv20rjS0tDA70k8/nD4mOBzji6AzbLN5AQhqzSMaw1xghqpFf/YKNGzZgt9vxer04nA7cbvcwi738ZrfbcbvdOJ1O8oW82YrJj5HfJSa9pkFs/KmPnUd60NAxjJojd8A4nAhZ9fHV8CCg6zrte/bQMGUq5HLEHnqA3JZNYkNoc/sNWUxw/Jdcjvv0M8X3+ogQVXMAc7pcVFVV791w1FR8Xj9Bo6jWR01qzZBV9ymnEbj2hmFXZ11VkV0ush8uJ/74YyiaRn1DI3v2tH2sY0922tv2UN/UDJk08QfuJ797FzgcxqqpUQyjWCBw2ZW4P3Uquq6VdJQkCbvdwS233c5Fn7sYCZEbZ1aFGwszTG7kY+X3pvGYSCT4XeTXrF+3DlmSxORYVXEffyKB628a2hPVzLtyusivW0vskYewayo1tXW0t1k67l3HoRBVWZb5cMUKfhv5dWkyYXqBywuhlTOWluXPKYpS+j1c8sXLuOGmm5BALBarKq596bje0FE9unRsa9tDw5RmyGSIP7CI/O6do3UsFAhcdgXuU04bNa7KxqRyJLqO2B5nRN74J0GMq0XcJ55E4LrROsouF7l1a4k/+iB2TaOmppa2PXuM9hzZOrbvfprG+hfRMzbij3jJ77KBQ0c31/WNyqb+S9K4T8mh5xAhonDoJqqGYSjZQE+D+4Q8gatTIiFNByQdXZWQXTq5tQ7ij7uxIVFV+Sjte14VhzjSddyzh7qmRqR0mtiiBeTb2sBhH9YfKRbxX34l7pNPBXQkmw1VU8lmM6Xj7M95Kjcuyw1M08jUdd3YjlfD6XJRXVUDIIqPqiru4443+qNjxLjqJLdxPbGHH8CuaVRUVR2ycfXADEdJGpqslaGji/L4+3yrxIb163juuWdwOBxoms4rL7/Eb3/9K558fAlLHnuUx5c8xpJHH2HJY4+y5NFHefyxR3nskYd5Yslj/PqXP2fjhg3C5TuqqMskxPSalpakKEuglsc8zwcNCeMiNbqiq8UnR8gqj3nhnyhIkkR/Xx+KTSEUDJJe/j6JJQ8jO1zCaMSYUGTSOGbPxX/VNUZuzkdPWEqGo9NJdU3NsBxnGFoFVFWVQCiIPxD4yPaOHBADV12L+/QzRC6msWWIbmwZEF+0gNymDVTX1lLM50vbcxyJlJcXr6qsJP32mySeelyE4JR0tKOnU2IblSuuFtEOqjbM0NN1neOOP4Gf/+o3fOVrf47H46FQKJQMh5E305Nl/j+s6EbZ6qyu69jsdvbs3s1zzzwNiImvudDov+IqPJ85S4QeGwWS9GIRyWEn/tAisqtXUdfQQDqVIplMWjruQ0fdMBCffuoJuru6Snlo5vF1huuzNy1LN6PUfD6fIxAIEP6rv+EnP/sFc8x9Bhm6YuxVR7tN6LjmKNJxYABd16msqCT9zlsknloyQkcbejqNa/4x+K80dTQskTK99rbwqBl7qx4MhsZVoYf/iqvxnFmuo45eVA0dF5d0zKTTR7yOAwM96MWfUVGVJf2encTTHmSXjl40c+919LSEc26BwBfTSB6gOA4FcfbayKHjl/sfkMD/xRSe07NoaVl4HREeR8muE3/ES3atnbr6PjLJX5I0tss5EpEkiejgIKqmUl1ZRfrNZSSeeXJ4f7Tb0VMpXMcci/+Kq8TvXtVAkcnn8qRTqVI4qs1mGzZm7mvR7eNipgOEKkKlNptzx8AVV+E56+xR46psdxA3tstpaGwSEQ6p1LjruP+bBEqSsffQiPxG417L50qvG4VhLff39ZPP5cVkwmajo72dH37/e6VE09LLR7xXkiQymUxpRVWfICGAn0QiPZ8XhYRGPSF+yCjjaTjKSE7HGNelI3PwOKQYF3zJbhtfDT8B5upXR3sbc+Yfg5qIE1twD2p/v+jjprcRxEXoiqtwn3iyWO2y2fd9cIYMEafDQXV1tXGoIUPCfI2qqlRWVuLxeBh6xd6PCSIPQS8UsDdPIXjTreTXr0NNJkreSGw28rt3EVt4HzXf/i7TWlvZsmkjJ5x08hFXBMA8l7t37mD2vPkUBweI3ncXWjKB5HCKqAJJEhEMNju+K6/BdcyxwwqpmMcxPVOhigq++Z1/Y/acOfziZz9h186dw8bmcmPBRJZlHHaHMYEZvUigyDLZbJZVq1ZSKBaRjQujXixgr2sgeNOtZNeuQYsODumo2FC7u4kuuJv6+cfS1NxMR3sbs+fMPfJ1HDgwHTGielZ9uGLUQstYRn0ulxtVyGFkrrEsycycPYu/+4d/5IorrxLODfNYlPXH/dKxndlz5hyROuq6zq5dO5g7/1iKg4NE770LLZEQxmFJR+FdFDoeN1pHjIIajL2Uq6rFksdx5CLNgRTLgTId6w0d161Bi0aHjavFrk6iC+6hft4xNE6ZckT3R12Htt33MGva+6iDNmIL/WgJBcmhiYIzpjg2YaS5js2jp4XXryTaeBqNJmbIqvF5kg30LNinagSvT5LdYEeLKcLIVSWwQbFLIbrIT/2cPE31r9LRdi+z5/3lEamjJEns3LGDOfPnU+zvJ3rfXeip1PD+qGlgt+O/8lpc844Z6o9mpIYkl9LpzGPajEU1SZbF9YyhOYouPnzYNbD8fmQbNU3D6/ESCIbEcaAUkWCrrSN04y1k16xGG+gv7Z2NoqD2dBO97y7cxxxHY1MTHW1tzBrncXX/DUdZRna7h22xAYi/zWIr5v97O4RiLsUYjTBWRBPJ5Ef2L3MVtFAoiPByxAqrWYb2QPI3Pim6riNpGvr+5AIZX1RLp4ZVNCw7KLLXW8ohGxcUGdnjHa3lIRnpjmQMHTUNye0RxuMEwxxUOtrbqayswulwEH/mKVIvvyhCVMsLNySTIiz08qtGHQNGh7yVo2kais1GVXWNqKapj66YqqpFqqqrcTpd5oMf2W4AjKItvs99gfQby0RepnEh0FUV2ekk8eRj+C68GO+55xOqrKSzo4OGxsYj6uIoSRKdHR1i2xOXi+ijD5F+YymSw1laXJNsdrRkHM+ZZxH44hXmG4HhOpYW5XQdWZK48eZbaJ0xg1/9/Gd0dnQgGxsYi/QBR6nSYz6fZ8+e3XR1diBJ8ijPo26G2Ggavd3dxAYHqaquLrVN1zR8F3yO9JvLiN53l1g0kyR0TUVyOkg+/xzJCz9PxSVfpLu7m96eHmpqaw/9yR5HPqmOMNQ/BgcG6O7uGWZMmJ9RvkAAMGXqVJqbm40iSFAoFijmi+QLOYqqiqZqtEybxt/+3d9z/IknlfYPLP88MFbsTR3f2IeOF32eii8cmTqa56Ozs4PKqmpcTgexRx8i/cbrSA5HmY5iXPWc8ZkxdTQpRayMmFeY+qkjaiDszdtffjM/yuynI70lw3R8cxnR++4e0lFVkRxOUs89TfKii6n8/KX0dnfT09ND7RGoY3vbCip8d+J2a8Se8pB+04Hk0NC1IW+jlpTxnJ7Ff0l6KDxVGsrmOmSXGeNnIpmBawroGfCdnyH9lovoYh+S3XidKiE5dFIvuEhd4KHi4hTdvXfR3X0FdXXNh6jBhwZJkuju6hTjqtPJ4MOLSb/1xoj+aIyrnzkb/xcvN98o7hB7Ld7+5S9js9no6uokk8mQSadJpVLkcjmKhULJoDQ/07yZXslyz+TIPElZlrHbbfj9fvw+37BtNcz+6D3/IgKXLCN6711mBxbRVS4XqZeeJ/HSC1RedgU9Pd309fZSXVMzbud0v2e0miSJRHxJGm5amBOEeAxyORHHbw5epmFipvAZKzmlYxrl9+0jVttMi52yzzIt86EXgM1ux26373WDzfFG18WeZ7b9aoP4RpqxXYNkd4BmVN80flRywP+ROaMHiqaLPbjkQHCvbbM4QMzQY11HMbdbmYDksln6+/s47vgTKXR1Eb3vTrR8DtkI+ZSMeH/Z6yNw1bU4prcav1XxffZWAdWk/PmKykqcDueoxRExAdKpqa7B4XCU3revY5b+hlLuTfDmW8mueJ/81s2iEmyhALKMFk8wePcfcZ90MvUNDaxfu46a2trDNlYcbHRdp1gs0tXVxTHHH09h926iC+4x9oXVDR0VKOZRAkECV12LvXnKXnUsn3RKioKmaZx2+qf53z/dSXQwahiOIg/dZrdhUxRAolAosHnTRn7x85/y+iuvjDouDC3O5/MFckbeXfk1QrLbCd54K5n33yW/Yb3YV6tQAF1CS6eEjqedzrTWVjZv3EhVdfVBCRGaCJg6dnd3Mf+4A9PRxDTki0YtgPLHyw0KWZH5/Ocv4S//5v/SOmMGNsWGjvBkFYsqhVyeoir2ewxVhHC7PWIhqKzvlDQ2r9GmjjfdSuaDfeh4qqnjhiNKRxB5wd2dnRx7wokU9uxhcMHdosid+TuXFSgWUAIBoeOUqWPqCOZUYGyfo67pwyO/GK6xWdDIDEceCzPPamSYObrYy070x/fIbyzTUZLR0mmid/8J96mnM721lQ3r1xlRJUeOjvm8Sn/PQubP3Uhhj53BRT4R5mkUmpFkHYoSsl/Df3kKxwwVPYkwzvS9qTa+DPu5GNuDSB6E1/FDJ7lNdiSPjl4Q83EtLTG4wIv75DzTW5azaftd1NR8G0nSxaLFJMccV9vb2jn2hBNEFNKCe4wUMWNcVRQo5FGCIQJXX4e9qXlYfzSLil36xcupqallx/btqKpKLptF13Xi8Rh9vX0M9PczGB0kFo+RTCTIpNNkMhmxHY9R5LPcuDTtHtMZls1m8fp9eHy+0fOq0rh6G5n33yW7fh1yaVzV0dMZovfeiefTn2b6jJlsWLeWyqqqUvsPNvttOKqahlJVLQya8gHC+J7qwABaKoXiNFZJzVAWhuzHsShfDSuvPmQOZlrZ86XVTnR0TSeVTJJIJIblchwKzK+i6SLkNpVMih/T3t5QfgJkGV3XKA70G3vkSVAsn1RrKJXV42Y4FgsF7IoNpbLS+IySVY/lcTxIaBpKVVWpaMVEwZwc7Nq1iynNU1BkiehjD4ly/w5nqSw1ig0tGcd7wcX4LrnMeEwZdZyP+iyAiooKPB4vuVx26D1ld9WG4fhxj1nycths6MUi7hNPxn/VdQz84sci9NsIZZfsdtJvvUn86ScJ3XwbTU2N7Nq5gxkzZ016r6PZ/t27dtLU2IhDlul9cCH5jeuEl8rMl1JktGQa/3kX4fv8peKxMUJUTconkKb30eVy09jkHT5G63ppCw9FUTj5U6fwb//+n/zZjh1s3rQJl8uNqo6xFY2kjxpdxF5yBVzHHEvw2hvo+/EPoFgs7V0l2+1kV3xA/LFHqPjqn1NVVcXuXbuYNn3fWzhNBsp1bGw4cB3LGet0mNdYm81GMpnklFNP41v/9u9MmdpCoVDAZlPQNL0UbiwFhjqorg1VRDePNezzzPuPo+Pych2r2b1rJ9Omtx5ZOjY3Y5dleh9cRH7DOuHdMOs/GDr6zrsQ3xc+SkepzOE43BTR0NFGFCkseZNVYajajOOm0ynSqbSI1NJ1FJuCx+3B7/cjG/Mm04DUjcm0XijgOvY4AtdeT/9Pfih0lI2oEbudzPL3iC95hIo/+zo1NTVHXn/c+RiNdQ9gk6F/iY/8ejuSg5K3EQW0pITvrAz+z2WggDDWjClUqXTFoaTMT1NeKMd1YgH/lSnyvwhCUUKSdXRd5DpmlzuJP+2h4vYYVcH72bnjdFpnfO6I0XHPnt00NTeLcXXx/eQ3bRwWVYUsFkL8F16M7+JLxGMjnVi6TqFQIPKrX/LB++9RXVNLy7Rp/PcP/4empqbS67KZDIlkgmg0ymD/AP0D/Qz09dHX30d/fz8D/f3EooPEYjEymazRL1MUCgUCwSAXf/4S7EYV8nLM/uicN5/AtTeS//H3h42rknl9fPRhKr4epra2lj17dtPSMm1cdNzvGW2+WMRWU4tsdwzfZsAoZVvs7UGNRYVBYhQ/KE3y9nHc8rAKc/AyLXThxhXePE3TxGKKJMr4+3w+br3jS0QHB8VF7WDZjZLI69B1XRT90Yfczzp66XGJofCufD7P6aefMeRiHilW2XeUFAU1EUft7dmLRS1hq6tDHSfDMZ/L4vAHsNXWItmUUiiMbn55iwOi9PMzPOxKXb2oyjWe+3HuJ5IkEY1G0VSVipoacps2EV+0gPJsGlEmPoutupbANddhq64etgqn66Mn//siGAzi9/tIpZLYbDYjakAy+ruNqurq0uMfNciNyt8xjNngdTeSfnMp6TeXInu8YkEGQC0SvfdOvGefS3VTMx2dnaTT6VJO5WRFkqRSkZHWmbPIrl5J/JEHhEfD9CCa26jU1xO45jqUUEhs22Ar03HMELkyz2OZF2HkvYksyxQKBZqnTKF5yhQ2bdzIvgbjsZ4x2xS45nrSbywl+erLyG4PGOXJJV0nuug+fOdfQN30GaxdtZJ8rhG74amerHx8HbPY6s3tcELDcuJG6ri3M1+6hmka06dPo76hkUKhgN3o13tzxEty+bHHDoccWsz5aB1jpo7TZrB29UpyuVwp4mCyIklGuf9kytBxVZmOxmtKOtYTLG1rtI/+OKybDVdVU1VUdXRuqqoWURRhDK5YvpwPl3/A1q1b6WhvI5FMoGk6bpeT+vpGZs+ZzWmfPoMTTzoZxdg7TlEUYfTYTR1vIP3GMlKvGToanylpOrH778N33oXUTZvGmlWryGYbcJpbsk1ShI4JUvHHmdHSRm6dm/ijHmM/xKEQVT0nodQWCVyVQqnX0OND3sbDufReCvIzH5CBAgSuTJF+y0VqmQvZjdhnErEoFF3sxXtWlvqpm1i78VlyubOHUkcmKZIkcr2T8TjTW2eQ+XAF8cceEh7G8nE1mzW2GbsOJRgcM9dYkiRyuSzxeJx4PE40GiWZiJNJi8UY8zV2h4Pq6hpqamph1tjtyqQzJJJxdu/azeDgANl0GoApLS0cd/wJJRsIRqcCAASuuY70m0tJvvyCMa4abQSiixbgO/8iamfMZO2qlTQ0NJbG9oPJfhuO6XSaYF0dks+Lnk4P/Up1XSRO93Sj9vfB9NZhxshHGY3lk5RcLkcwGORTp5xKKBQiHo+xfds2urq6RCWjsqPV1dfzjX/6l4814fy4mPkDhUIBRZZFSWyEiKpRMMRut5eMSDOQdqzvOebETBMl8NX+foqdnaJ4irmCK0mYhXEcjc3ki+OzeXw6lcLnD2CrrUf2eI0QFMtg/MSYOuo6KDbszc1I/5+9846Po7r+9jMz23fVe3PvxhhM79UQQqihhk5IW9J7CKGkh/Tk9yqdYsAGTC+m996bsXGVbcmyLMmStveZ9487s00rWbLVDPN8Puu1dmdn7sx37p177jn3XIuFuN44jDdG7PxHH63k4AMPQo3F6LvtJhKtW9Kx9ICIFEgmcR11DJ7Fn0mPMucwhPvFuPc9RUWUlJSyta1Nz6asr1WkadjsVioqxBqOmqoiDzD6XgjD46Ulk1iqqym94GJiH68WiTn0tPeSopBYt5bepbdS/cOraKpvYOvWrcycOTNnLsGehFHutrY2GpsmISUS9N78P1IdHbk6IsJ5Pcccj/vYxWJ0cgih07uSYMPIMmfJ8rAPFM4sDWDaaMkkSlk5JRdcQnTVR6S6u3J0TG5qoffWm6m59pc0NjaxsWUjc+bM/YTo2ISUSA6io4rnmONwH3u8ruOuRTJo+iCorFhQ0uufDp1Cy3n0e+7tRMdEno6bWjYy+xOiY9OkJqRkkt5b/kdq27bCOh6drePA9XGwcMFUSk179A0thOFnwefzccfS27n5xv+yYf16VD0KQ9JDilVNZJ+WFYVp06dz/gUX8YULLqSqujpHAy2ZxFJeTumFFxNbtZLUjq60F1zouJG+W2+m+mfXM2nSJDa1tDBn7p6vY3vrf2mqfwgpCb23u0luk5GsWs4qdFoKPEdG8RwVyU2IY1Sn8e5OGV5HfakQS51K6flBYh9bSfUoSDaRKEdSNBKbFPru8FD94xiNVcto2TCPOfO+tMfruHnzJiZNmgzxOL03/5fU9u3966Oq4jl2Me5jjtcT//VvV2U9sVtn53ai0SiyLFNTV0d9Q0NBw0xEd6ioam7OFWFcWikpKeWRh/+PbVvbqaquYvqMmZz42ZP7hXrnP3u1ZAqltIzSCy4hunIlqe7tOfUxuWUTvUtupOb6XzOpaRIbN2wYlfo47CdPMBCgvKoapbKKRMtG4ZkwGiGbjdSObhJtrTj3PzD9m/T8RArXpexOiqJYOP2MMzn5lFOIxeL09uzAbndQXlnJB++/x7Lbb6Nnxw4sVistLRv5xXXXYrVZM0aqltlnThpkTZ9XmXaqaJk5lMZm+veJRIJ58/firLPP5c133uCD998jGo0wadJkjjrmGDZv2sTdd91FPBZFVhQxX1CfDe32ePB+/ZvU1tUNfBF1wzHZsY1Eh3iw5HtvZY8HS0MjodDoGBwBf4Dq2jpkQ8u21rSWmXJQcMRzrJMPTSyknXu1JQlUFdnlxlIvJpqHAoHRL9pOMDzjO7q7aWxoQLZaibz0AoFHHhSjcDqSoqBFo1ibmig553yRsjqVSk/8H244KYDb5aKsopykPufGKIuqqjidLkrLdMNxiOeSb9QYWf88nzmZ0AvP479rmZgXYHhEZJnAfcspOv5EKhbtR2d3F53bt1NdU7NHhuTIskzn9u2oqkpFRQWBJx4j9NTj/XRUoxGsU6ZQfM75mUxsOoOd90CexXxyvE5GdrqcDfL2q3+maQMcVw899iw+kdCLz+O7/RaRZMoojywTfOhBik44ibJDD6e3r5cd3d3pRDt7GhkdU1RUVI64jgWR9BBjES9DKttQ2NW6UKg+7kzHhx+k6MSTKDvkcHr7+vZ4Hbd3dICmUV5eQeDJJwg9WUjHKNbJho624emYiVkV2yPmbxnzFCU9NHXdurX87U9/4oH77xVLHenLHGlaps+T3UFta23lt7/6BR+v+oirfnYNTZMmpzuaRrvqPv5EPC88j2/pkn46Bh66H88Jn6HskMPYvn07vT09lJWXj9zFHUNkWaajo4NU/EHKq3wEn/YQetKuexsFkqKhRiWsk5KUnBVC8oAWArLGYMb7aZL2OmaHrIbAfWwEz0sOfHd5QAVJ0tLbBx6xU3Sci/KDttPV9xA9PWdRXl42vieyi8iyTHdXFzISpeXlBB9bQfjpJ/PqowU1GsY6dRrFZ5+fyRysk10fk8kkDoeT445fTDKZRJJkjl98Ik6no6BRJuqXUnimmQY9PTt48vHHeP/dd9HQWLRof049/QzKyjLXO9/gBJAsCloyifu4xRR99nP03XpTXn1UCK54iKITTqL0iKPY3tlJX18vpaUjq+OwDcdAXx/K9GlYmyYTX7smY/SoKthsqH4fsQ3rxUnLco7tkV2ZskNcZFkmlUphs9n56pVXMn/+XvznX//ko5UriUTCWK1WGhoaueiSS7nmup/z+9/9hrbWLXRu385N//vPiBkyIswjhcfj4aveKq69+iruv+8eQqEQqVQKu93O3Lnz+Pq3vk1FZQV//8uf02ucGfNHKququOCii6mtq9vpgyCxaSNqeiRWTZdBSyWxVNdgq6tlm69vRM4tn2BQGDJKWTnWpknEN25Acrv7LcGApmY9qzKdSc0wltFyNDY8QMb8p3xyrkfWNnvEvox71thW09KDAPn70lIprPWNWGrrAfD5fAWOOvaoqsqGjRvYe68FaOEwPTf9J5Nu3bhf9brrWfwZXIceXjB0Y2fkGx4Op5OKigox8p3VyUylUniKiihOJ2kael1Oe7P0UXQkCUmWKbvkcqJvv0F8/Tok3eiVJInU9u303vxfHAv2ZtqUqazbsJ7qmpphnddEIJO5cRtz5s4h2ddH383/E+n+9Uyzkj54IVksFH3ms2Jx8V3QcWfkh9X1D3XX+v9Pyv3cOKe0jnp9Kr34MiJvvCYScxg6Kgqp3h303vRfqvfeh2lTpvL6669z6OGH73EDADk6zplDaqx01Iz60v+r3bl+/erjznTs2UHvjYaOU/Z4Hdvb29lrr71I+frou+W/qAF/fx0VhaLPnIzzgIOGpGN+1KpRa2RZJhIKpdeWMzqu77zzNr+87lpeevEF3G43iqIQi8X0/eQP6ojnmtVmxS7Zuefu5dgdDn7x699SVFSUKbfuqSy9+DIib75G/OPVQkc1S8eb/4Njn0VMmTyFdevXU1Zenl5Pd09B08T0iY72O5k/4zlSvTb6bnOhBnRvoyYJQ0sFSYGi4yM4D4ihhTLeRkkPUx13y5EcH0mmS2eB0gsCRN62E19nRbIbXkdI7VDoWeKibkGCKQ0PsarlPsrLLxfRRntQopz0HNUtW9hrr/mkenvovfm/qKFgXn0Uy4oVnfRZnPvtP2DIeDKZxGKx4PF4+Pb3fsApp56GbLEwe9Yc/bpo/X4zaMSOBAG/n2QiQU1tLbIsM2XaNEpKSvptn72//u3qpURef5XY6o/yno899Nz8P+oW7ceUKVNYvXo1i/bbb0Tb1WHfDaFQEJwubLPnZNY/yUZViX+8SqwFpyiZBW3p78HPTlMbj8c55bTTWbhwH6756VW89OIL2Gw2Zs+eQ1l5OS0tG7numqvp7e3hK1+9EpdbZB5yOBzDfDnFyylezqyX3eHAbrez/wEHsnbNGpbcchOSJLFo0f4cc+xx1NbW8u677/DbX/+SAw88iHPPvwANjaKiIlwuFy6XC4fDMag4mqaBnkAg9vFq1FBIJE8xYiAUBS2RxDZ9BnJJKX29vcOVaEhEIhGhgduNffZcPetb5nbQVA3JopDy+Yi++w5aLCZGlVMpMSqjqmhqSngZjKVIjP+nMp/nv4zf52+zZ+wrJRaLlSVSfh+Rd94WIb7plXdJG11aIoFt+nSRHAfw+/2jouNQMUK5N6xfz7QpU3G4XAQefoDIa6/kJmCSZbRoFNuMGZSce0H6M4OhNj7GYE7acHQ4qaiozDHAJUkimUxSUlySHhXflSduOrBAltGSSezz5lN89nliLcpUUnRe9A5Q6LlnCD7xGDaXi4ryCjZu2JAOTd8TMK5/S8tGamuqsdkdBO67m8g7b+ZO6JdltFgM+6w5lJyj65hOKrYbnqWhlXKXfpXWUQ89ts+YScl5XxDZ45KJTCdUlgm/8iKRxx4Bq5WZs2axdWvbgGGxE5G0jht1HR0O/GOpY3pEbeTvgYF0lAbS8VGh46w9XMf6ulosNhv+++8l+tYbBXW0zZ5D8Tnni8+GpGP/z1KpFFarla6uLm65+Sbee/cd3n/vXe5ctpSf/OB7vPLySxQVFaU9kSLJYKYNlyQps/acLJNMJPXIDycPP/QgKx5+EBAdZmN7LZnEPnMWJeca9TGZ2acsE37pJYIrHsLhdlNZUc7mTZtEe7xH6SizedOL1Jb/G4sT/I84Cb9ly3WvyKDFJGwzEhSfFRTyZI1Oj+fcxn4Y3kYRDCeW54iCfa7uKXVoIkusrGskQ/hVB8EnHNiKoL7yH2ze9CaStKfpKLFpUwv1dXVY7Q589y4n8u7bhdvV2XMoOfsL4jMld764qorl9SwWC1vb2rj91iV8vHo1+x94EIsW7Yfb4x7QQNxZm+xye6hvbCQcCRMMBpk7d16/zMf5+8hvV23TZlB8/gVIbr0+Gu2qYiHy6ssEH34Qu8tFXU01bW0j264Oe9hSS6WIqSqO+XuJRYmz0df4iX28mmTbFiyVlTkeGeNClJWXY7XZCAYCOBwOQqEQTZMmccSRR/LgA/ez6qOVnHjSZ/nmt7/Dvvvtx/aO7fzfX//M/ffdyz/+7//4+z/+yT777ssLzz2XDnfTjKGVgoUu7GXK/h69E9vQ2Mi8+fNZcvNNlFdUcOU3vsnFl16G0+nijdde4w83/IZHVzzKs888w5x5c3G73PT19WG320kmk/piyoMcTR91jG9tJbZqpcimmr+5pmGftxea3UE0FBys5LuMhkQ0FMLudmObOw/J5iCnIBJ6qm+J3n/8jdgH72GdMmWCtYzjgHjKE/t4FeHXXhHZuYxU6wb66LJjr73FQ1bTiEQj41Zk0EeoIxEi0QhTy6eR3N5B3223oIXD6fWMJFkWo1Z2B0WnnIF97rycUbjhkG0cqqqK3W6nQjeiVVWsu4oGyWSCsvLyfolqhtohLhiyqmkUn/sFwi8+T+jlF5FtevujKGgBP71LbsR96GFUVFaydu1a4rEYtj0koYMkScRjMYL+AA2NTSS3bKbvjtsgHk/P3TAMaMnppOj0z2ObPiPHu5HtuRiDEg/paAV1VFVKPn8uoeefI/zc0+lQaUlR0CJhem+9GfeRR+PyFNG2ZjU1NbV7zDIrxlz+QCBAQ5PQ0TeCOkrj1M8bTMfw888RfO5ppAF0dHqKaP14z9MxHo8TCASob2oi2boF39Jb0QbQsfi0M7HPmDms+pjf2TOubzKZ5N577uaN11/DZrPR1tpKJCKSfhmRUEb/SJIkFEW4xYwOcfaUAcMY9fX18cYbr3POeV9IL2gOmXa15KxzCb3wHOHnnsn067J1POIoyiqr2Lh+Hclkco/SMRaL4Ot9mfpZq0htceK70wlxSZ/bqGciTUpIDo3iU0LY5ySFt1FPiJMzNYq8z/odkN3vSw1h/+mQVX17SQEtBsVnBAm9ZCf8olO4jySQFA0tLNG71Inn0AQlJW+xruV1Eg0LsVr3nMRViUSCYCBA3ewGUpta8N1xO+iJ/XLqo8sllhmbNj1nLXUjEsrw1t9/7z3cftsSAn4/f/2/ZlRVJZlMZvKcFDAeswfOsz8XSRI16urq+NNf/sZrr75KKpXkmOOOT29v/D7fEC3Yrp55DuHnnyP0zJOZ+ijLaJEwfbfdgueoYyitqGTdurXU1dWN2DzHYRuOLpebXp+PilmzRdbPru3CUtc9NZLDQbKtleiHH+DYZ1E/j6SmacydN59TTj2Ne+9ejqqqRCIRpkyZSmlZGS+9+AL1DY1cctnlHH3sccTjcWbNns03v/M91q1bxysvv0h7+1YW7bc/r778MuksrKQDKdNhGZIEkixmcuSEsyGEMESQJfG9pmmccebnUTWNHd3dnHr6GVx+xZdxOp2kUikOO+IItmzZzIvPP897777NtOnTmTtvPptaWigqKiKeiFNeXjF4FiPdkE6sXUNszRokhxNSWes3ahqyx41z74WEYrFR8wo4XU56e3qoc7uxzZyFpaaGZHdXepQwO2RRDQbxP/yAGBgYzkFGsoc6UfZlOBctFrHeYdZ9lX7Aq2KOqn2vvZFtNoI+37iG7BiN4KaWFpoaGrFYLHQvvVWEjmWPwkkSxOPY99ufkrPPEx9lGY274t0wGmGLxUJlVRUupxOLRawDaLPbUKIWqmuqsTvs6e2z34d0DEQwsZijLASylJZRetmXiK76CNXXJ85TkkBRiL3/Lr13LaPiK1dSV1vLho0bmDt3Xs7C5hORtI6bNlFXX4fNaqXztptJbFzfX8dkEsd+B1B85jnio0GW3xgxtH7N/bCQyAvJAeSiIsou/xKxjz4g1d2dWXtSsRBf/RG9S5dQ+e0fMKmhiZaWFmbOnLnH6Lh50ybqDR1vvZnEhpHVcVBjZHdPYhAG0rH08i8R3YmOTY1NbGppYcYepGPLxo3U19djN3TcuK6wjov2p/jzQ9PR+NtutwuvYFaSPKMTaVyblo0bUVUVh8OB3e7Q52BJ6UE7Y7toNIIE2B2O9NSabC+E8f/Oju309faKcNPssmkaclExZZd9idjKD0ntyNZRIb5qJb3LbqXyW9+nqqJyj6uPm1ruprH6/7A5oOtOJ4kNFrFmY3avJwmOhXGKTw9BnJyEOFmPn/RPpAGMQ8OYSxt1w2k3jX0OZnxqOW9okl4WGUiBUqZRdkmA6Cobaq+SPk9JgfgqGz3LHVR+K0pdxR/Y1NLAzFln7DE6btywgdqaGuw2G5233iRysRSqj/sfSPEZZ4tLaLGkvXmqvp/Ozk7+/tc/c/ddd9LW2so3vvVt5u+1AFmW0xmgBwtJLWQ0KopCIh7n9ddewmF3cN4XLkhvv7OEdAXbVY9HPB8/fF/03436aLEQX7OK3tuXUPm9H1FTVcWmlhamz5gxIjoO23Asr6igq2MbtZOnYJs/n9ATrcguaybRjO4Cjrz+KkWnnCbSTefFurtcLq6+5joW7bc/69etxe8PsGj//bAoFnbs2EFT0ySRCUm/UPF4nPrGBmrr6lBVle7OLk466WRKiktAAovFkh5dy7wUFFlBVkRWVMX4XFGQJRlJlvTPFBRFhLJZrVbmzJnL72/4DYqiUFVdjcvlIhaLpV3WVVXVFJeU0NnZxZw5c/h///w33d3dOBwOVDWFYrHQ0CjWdZEkKadSp8NUU0nCb71BqrsLuaQELS4WapZkGS0exzZzNs5Zc2jt7qYoHcI3slRUVtLZ1UVdUxOWxiYcey0g8NgKpKIi0DOsanorqEkSst2+e73CTxBi9E4khMoOH0ATy6yoiQT2WbOxzhT5mLu6urLm8I0tRgejq6sLi0WhpKKC6MoPCTxwr/Am2uw5o3ByURElZ5+PpbYOLZVEGoE1KI2OSVFxCclkEr/PRyKZJBFP4Ovz4SkqxqGn/h5oxG1Q0sOqutGpe07dxx5P0UknixFHo3OkL0/hv2c5RcedQNG06fgDATo7O6murp6w86sMHSPhMIGAn1mzZxN56w2Cjzwo0uNb9fXX9DBpubiYknO/IJZR0ec+GIza2RXqIBUyUQZ0k+l1iKzQ41QK9xFHUfS50+i75caMPoqCFosRuP9ePMd/hvK9FtDaJjwuTufEXWYlW8dgto4rHhT1UBkbHdMeydG4GQbRsfhzp9E7iI4Vey2gbQ/SMRwOEwoGmD1nDpG33yT4yINoKRXJahFzwwwdiwwdq9I6Zvf/B6Kurh6bzZZef9Eg2+AzojVSqVTaaAQR2WGxWIjH4zgcDubOm4eqqmzZtJmUmkqv0ZrZl/hdMBggEolQRlZnliwdjzyaos+dSt+SmwroeA+e40+kev4C2t56i2g0ikM3VCdyuxoOtxPyvcHs2e1E3igm8IhdzP0z5jbKKlpKQi5SKTk7iKVOzWRSFVMjhfGo5T2Osr2PBpL+XfacyKEOuuV7GQsdI8uoNAxXLet3Rsiq+9AYRSdF8C316PM3AUUsMxJ40IHnmCKq9trMlrfeIhI5FKdzYieSUxSFUChENBqhsnoO4TdeI7jiId1Zk1cfi0soOe8ClIoKPfGfIoLs9EzDHdu28atfXM/yO+/A5RLrnZaWloqBcD0PiKIoOdciewCmkCdSURSefvJJ7r37Ll599RW+/NWvMX/BgrTNkl5DdSDjMadd1dL10XXYEXhOPZ2+m/6bWx/jMQIP3odn8YnU7L0P7W+/RSQSwel07va1Hr7hWF5O+9atqB4PrgMPIfz447l3vB6SEX7jNWLr1uE64EAxfy5rPUdN0yguKeH8Cy7M2ff7771LRUUFnZ3b2bBhHfvut196TaANq9bRvnUrNpuNktJSFu67Lwv33Xc3Tr0wqqrSUN+ABqxft5bWLVtomjQp/f2HH7yPr6+PBXsvpLq2lslTpjBp8iTyJw8XFF5P9RvfsJHwyy+JuWPZYa2yDPE4jv0PQK6qpvO9d5k8ecqInyNAWVk5ra2tYnSlrAznQYcQePzRtJaafoManjRRKfKHtwaKkcj+Pn9YTBtk2z1hX3ke9KzRWEBoGIvi2v9ALFUi8Up3dxdTps8oUKaxo6urk0mNTciaRt+SG9PLbxgNEYgwdOf+B1J0yml6J3ZkvFTG6NYBBx7EH//6d1Q1lQ6fCoVCLNpvf2x2e24oxi54NnPKqLc3ZZd9icjrr4rkT0ZomNVKcuN6em+9merrf0VNVRVr16+nurp6l85vLDA8B1u3bmXq5CmgqvTcciPJbe1IehhRWkdVxXnwoXg++7l0AguDsX7wDzivYgDjsV9IjpEI4JIvEn71ZeJrPk7PVZFsNhJbNtO75Ebqbvgzk5om0drayqxZsyfs6Hi2jlPGWcfBHBa7y0A6llzyRUKfIB3bt25l6tSpoKr0LrmRVPtWJJsN0PJ0PATPZ09JDw5AxoNQSEfDqNt30X7svfdCXn75JaxWa0HPY/Y6ctn1zWq1EgqFmD5jBt/5/g/Zd9EiNE3jsUce4S9/+j2JRDKvfuqdUlVDzauzRgkz9fEKwq++Qnxtno6bN9O75Cbqfvcnmpoa2bq1jenTZ+R4PicSqiqWL9naupypTf+BqEzvbTaS2yxIVt0Lp68/qqngPCCG54QwWlQYYGnPnv6eN4ZJKik2kWWEt0/Vu8MSyEbIdpZxN1iVLmSUaqoesCaRTlGhJbP2L+X+DrI8nBKUXRgg8rqd+HorGF5Hm0Zyi5XepTbqfiUzpeEvtLdNYvrMr6Sv10TDaCdat2xh8qTJkEzSe8uNJPXlN/rVx0MPw3PSyf0G41R9QGX5ncu4Z/ldOBz6gDbw2IoVHHbEkRx2+BGAmANsGH35Iar5BmAwEODG//6HW266ka1b2ygqKuLAgw7GXqDfM9g8xIHa1bKLLyfy8kvE1qzO1EerjUSr3q7+/m96u7plRNrVYcfPOV0uZAl8oSCuAw5GqakRmTgVRb+RVSSbnWT7VsIvPi+SiVis/S6oEcJmzAsEmDR5MgceeBDbt7Wz9LbbePKJx2ndsoUP33+ffzX/Pz5a+SHTps9g7332ASAWi5FMJgd8pZJJUqnUTl/GnCvjRjj8yKOoq6/j3Xfe4Q83/JbXX3uVlSs/5D//+id33rGUlKpy2OGH09jYRCqVQtNIG1cZI4sBb4DIW28SX7VSzH9L6VlMJUmMBjmduA89gpgsk4jGKC0t3QVZh6Cj04lFUejt7kaWFRz77o+1vh7icb2V0zJGkWQsAi6n/4/+d/+XlPWS897zf5O/7cTdF+n3rAXR9ZCB9ACg6AkgeYpwHno4ittNNBQikUpRUjL2HkcjRXvLxo2UlZTgLi4m+MyThJ95StdVToc+aMkkltIySi68BNlTlN5HehBzmJ3U7HpgDKo0NTVxwUUXc+HFl3LRJZdx0SWX8rUrv8FBBx+SbsSMhnF3JnGnG+1UCtuMmZRceGnaSNaMqAhVJfTECiIvvYC9qIj6ulpaW1vT7dJEIv1QbG0FNUV5VRX+Rx4k+uLz6d5Hjo4VlZRedCmyw0m+WTCeo8VGPdEAbQiT8NLtqJrCNnkKZRdfLuYUa1lD+5JE+JmnCDzxGOVVVaBqbNu2Lb2Y+URiIB0jY65j9sSO0Q1bhWHq+KTQUdImvo5btmxB0lTKKirxr3iYyAvPoymZZU366Zg90p82NAq66dOUlpXy3R/+iKZJk4hGoznTYIyEXtnTbkAM1Bnel0MPO5w//uXvnHX2OUyfPoMZM2Zy/gUXMHny1IGvqyQVHEzI0XHKFMouvqygjpFnniT45ONU1dSiJRK0b906gXW00NraDvG7KKuP4X/cQ+Qlm7C8AE2TRL82KWEpTVF6fhC5iKwYUNL5pbKNOk0VPgHFBRYrRCLg65OIRMBiA9mRlTcyz7grVB8Nj2G2cammhMfT4hHd71gMEnGwOEF2ikCUgsam8VkSbFOTlF0QFNlVRcMstkEi/Kyd4NMuKhrCaPE7aWvrQlEsE1RH0a7abVaKy8sJPPIQkZdfSCf2y6mPlVWUXXQpst3Rz1KXJDEneP36dSQTCaxWK/F4HKvNxrp1a/n+d77FNT/9CW+9+SYWiyU9P1jLqovZ78bAzv/97S/84Ybf0tXVidVqZdF++zFjhohIy0/MZ0Qq7qwflKmPKtZJkym99IsiMjC7Psoy4WefIfj4I5RXV2ORJLa1t+92fRy24agoCjV19WzdsgXH7Nk49jsANRIRoajGOarCsxZ8fAXxTS2iEdILmW1RK4qCxWLBbrejqiplZeVcdOll7LX3Qp5+6km8X/kSX7r8Ei6+4HyW3HIzFouVL3/1a8yZPQcQ8f8Wi2XAl2KxpBvRwV7GqIFFt9QX7rMvX/3a11EUhZtv/B8XnHc2XzjnLH76ox+y9uOP+dypp3H2uefnhMjmGhv9vSbGwqLJrk6Cj69AjUbETa2LLNbOi2CdNQf3vovYvn07xcXF6VjqkUZRFGpqamnv2AaAdcYMnAceghoOC0+TBpLWP9BMynv/tFDovKXsSq1pYl2gSATnXnvjWLAQgO0dHRQXl4yajoNhxNNHImFKy8rRAgH6bl+SjoU3Qm2Ne9B1zHG4jz5O3Kt6g2uMig+X3HAN8ZkxSJOpM8JwFYMvuSN0u9IxzgkPgXTYX8kZZ+E8+DDUZFIfUVeRbDaS27bRe9vNyJEILrcHv69vQiZ0MB5OAb+fxslTSHZ307fsNlK9PZkJ/ZDW0bP4RFyHHZmjo/h6tE0EhubC2kkx+oX5yAqaqlJ06um4jzhKDFSi62i1kurqxLd0Caqvj8ZJk+jr7Z2QC1cPpKM6bjqObiu+SzreLnRsaJr4Ovr9PhqnTCHV3Y1/2a2kenYg6YPkOToefwKuw4/K1VEaREcpM/ilqioHHXwIv7nhD8xfsIBQKISmaciy6DuJ/kumLwMiY7qmaVx0yaX86a9/5+BDDiGVSpFIJEgmkzjdbmrr69NtcSHyn/yFdTwD1+FH9tMx2dlJ39IlpHw+GiZPmeA6Jgj6H6Vp8iukOtz47rKT6lWQLCqaJgtvoz7a5T42iuvQKFos491D0p13hkEm64sN2ISxuOw2B1+7soRLv1jGJZeXcsnlZXzvB8W8/IINxSKiXPsZd1rGOwgZo1HKNhpVkO2waZPC3/7k5ktfK+HSy0u59Itl/OiHxTzzpE38xpJZ/EDTMvvQEGXVklB0cgjXITFISoCKpspIVpVUp4LvDgepHW4aG5/F1/sEqVRyQuqoqiq9vT3UNzahdnXSe8etqL29hdvVEz6D89DD+7WrYhORLOrwI46irLycaDSKzWZLe8y3bNrEzf/7L1d+9Uv88Lvf4Y3XX0OWFSTDgMwSznBEvf/uu9x/373E43Hd1kmxrX0bKz/8ACBtxBlJq0D4ubP7QbkD8fntqhgIL/rcabiOPEY469Dro8VGqruL3qW3ovb2UNfYRPeO7vR57vI135UflZWXE+rtJely4zl+sR6aQXrESUulkBxOYh+vIvTMk+KEdQGNE8/HuAiHH3Ekv/rNDZz8uVNwOBy8/957BIMBDjr4YK6+9jouvPgSFIslbaWP1EPU2I+qqlitVr745a9wzfU/5/jFi/F4ikkmk+y1YAFfvfLrXPfzXzJ12rR0quvhEHnzdcKvvojkcguBDW+jJEEyifvoY5Fr6+hobaW2oX5Ezm0gysvLCUcixCMRLKVluI85DsnjEcNlRudbfzFC1/kThX6N0vegrICq4j72eJQ6od32rk5q6+rGvGjGaNLmLVuoKCvD6XLRd//dRN54LZOW2hiFU1NYamspvfgyEc5ZYDHbXSF3/gwoisz6deu4/dYl/P0vf+Lmm/7HB++/lz5G/va7Qrqkxj40DbmkhLIvfxVLWXlmNE7TRNrqV17C99D9eIqKKC8rY8uWzQA5IWHjieE13rSphYrSUhwOB7577iL+7ttIxugipNtda0Oj8LDK8ojpuDsUzme9c30lo6eT+QDZ7aH0iq+hVFaJ3hCIARubjegbr+O7/x5cLhclxUVs3ryJ9HyUCcBY6zgEf+4uncdw2S0di4rEsg4TUceWFqrKK7Db7PjuW070nbcG1vGi4elYKNztmGOP45///h9f+/o3qKquJpmIEw6HCYfDRCJhwqEQ0WiU4uISPnvy5/i/f/yLa67/BVOmTk33U6xWKxaLBatiwTZYAr8CSIZFk/lAJOa44msoFZWZKTdpHV/D/+C9OJ1OykpL2aTrOFHaVWMu6MYNb1Hu+iUOD/jutxB91w42KSvUBrSUhLUuSen5ARGemo55zLwbhlgqKbyJH69S+NZ3S7jm2mIeetjBy6/YePNNG6+8auPOO51841sl/O4GN4GASEijZRt3Uq4jTNKNRsNbqGogKXD/vXa+9JVS/vRnNytWOHjxJTvPv2jj9qVOvv3dEr77/WLWfKwg24X3McfjmFV2uUij7DIfSoUKata52STCb9rxPyLjKIbKouvYvOn9CaVjpl3dRE1lJTa7Hf/ddxF/952C9dHS1ETJhZeIKKgCg3EW3U457Ywz+dZ3vofL5SIej6c/tzscyIpCW2sry5behvfLV/C9b3+D1159RRjqkpRlm4j3vr5egoEAiqLo846tbN68iW9/4+tc9eMf8tYbb+TYEkYEZP6c5uz2ol8/SZKQ3W5Kv/RVLFXVWe2qimS3E3vzDfz334vD6aSyvDz9fNxVHXcpWLmoqAiH00lHZyf1hxxG77z5xD78QIRe6vH2aGI0y3/PcjzHnSBSiRtLc2T728ltIDVN45jjjmOvvRfw3jvv0Nvbi9PpZPbcucyaNTu9ffbE7pHAOL6xX4fTyaWXX8Hxi09gY0sLyXicqupq5sydh9VqTY9ADDqZ1eiY63HUya5O/PfciRoIIns8acPRmLCr1NdTdOxifPE4ajJBaWnZiJzbQHiKinA6XWzf3kHTlKk49j8A5z77EX71JVE+XUstKyzTRCf7Hs7yGNumTsN1+FHIFgu93V2omjZq4cYDYdybkUiEUDDAtBkziG9qIXjPcrRAQKynpje4RmxM0aln4txnUSZxw2D39RDIHjGXZRlfXx///c+/WH7nHXR3dRFPxLEoVkrLSjn5lFO58uvfTCe/MhrQXTp2vgEqSWiqiuvgwyg+4/P03vTfjLfVZkX19RG4+w5chxxGbdMk3nv7LWKxWDoKYjxHV43EDZFwmEQsRunUacTXryXwwD0iMsDQUWwMkkTRmWfjmL+XmBMn776Ou4uENLwEOXmbZD8gNVXFuf8BFJ99Lr3//H/iPlVVJIsFNRTEf89y3IcfRcWUqbR8vHrCJObI0TEao3TqVOLr1hK4/x7USBjJOQ466uFQY8Gwdbx3Oe4jjqJi8lQ2TEQdIxFi0QhN00R99N9398D18YyzcMxfMOx2NX8bVVWZPmMGP77qas4+9zzWrF5Ny8aNbNu2jWQyQUVFBdOmz2D+XguYNn0aRUXF6d/JsqIbJXo0R1Z01fDOfxAd/9WMpFgzOgaD+O++C/dhR1IxZSrrPlo5oXS0WCyEQp2o8Rcoa9xEfI0H/wMOtIiE5FTRUlneRgmKTg3jmJ9AS+qeuuwurF6VUilQnPDWGxauu76Yd96xYrWC1ZqJuhHHh64umeZ/ulm/wcJVPw4ydXoKNSbGnvO6x2nD1AiLTSbhP/928c9/uunplbFYNOz2zDFUFXp7Ze6+x8nq1RZ+dnWAI45KkIrqM8oy3RbxngTnojjFZ4To/V8RkqJ7HS0qalDGf68L92FQVr+eHRueJxKZgtNZMSF0VBSFaDRKJBSkYcoU4mtW43vgHhEFWaA+lpx5Do658/u1q/n1zWazccVXvkbT5En89U9/ZPWqVVittrRRaPQPOjo6WH7nMp59+ikOO+IozvvCBRx8yCF6shtRx/Y/8CDOPe8L3H7bEoKBgEjQKcu0t2/ltltu5rEVj7Bgwd4ce/zxHH3McUyeMiXnHLPJv9796uOi/Sk+5zx6mv+e266GQ/juvQv3EUdTPlXUR6Ofsys6DrtXZFj49Q2NbGtpQW6chOeEk5CMBexzvI4O4h9/hP/+u0UGR/1EBjIaDQFTqRRVVdUsPvEznHPe+Zxy2unMmjWbZDKZ8xDaXe9EPjnlQIxKNTZN4sgjj+LY4xezYO+Fabf4zgxXYw6cMacKIPTs04Sefx7ZneVtBDHnKhLBddSxOPZeyOZ166jTl00YrfAyQ8eamhq2d3aBqmKZNIWiz5yUmSxseh0HJr/1VSxosSiexSdinSNCqVvb2qirrx9VHQthHGvTphaREAfwLb+D2EcfIDmd4sliNDapFLZp0yk5T08LbYSo7mYnNdtoDIVC/PmPf+DPv7+BrW1tYk6zBolEnK7OTv77r3/yi+uuYUd3t16/MmHtu3rs/N9LikLJhZdimzVbX7w600ZF330H/913IQGTmprYvHmTuDbjfL9nvMabqaqswi7L9C69lfjHq8VDMa2jhJZKYp81mxJjcXFpZHTcXQZeQXeIXkfydJRlSs+/GPv8vdD0terSES4ffUjfXcuwKwrl5RW0btkCMO5zcnJ0rKrCLiv0LruV+JrVwmgcFx21rIZ9dBm2jis/pO/OpdgVhcqKiafjls2bqa6uwSbL9C27jUQ/HfV2deasjI7DaFezr1d2v0hNpbDb7cybN58zPn8W3/ru9/j1727ghj/8iR//9Gecf8GFLNxnH4qKitNeCzEloH9bJu8kHL/QMPFA7WrpFy7GPm9+Px3jKz/Ad9cybLJMdVX1hNOxdfNbVJb+EptVoe8uC4m1ViQHoEpZOkrYpicoPisEZB772Y9/wwuoWOHxFXa++/1S3n3XisOhIcti7mAqJaVfmiaMSVmGR1Y4+N4Pi1n5oQXZkXEUqWrWSy+3pEA4Ajf83sOf/+Khzydjt2vpGU/G/gEsFg2bTWPlR1Z+8tNinn7ShmLPzHs0ziF9PgqUnhPAPieOlpCQJGE8yw6N+CorvuUyNrtMden1bNn8ds51HC/S7ermTdTU1Ijn47LbSa5doxuNee3q7LkU68uM5ber+XaIpmnYbFZOOfV0/v2/mzj/gguxWi3pfrNhQNpsNhTFQnd3N/ffezdf+9LlfPdb3+D9995L2wilpaX88KqfsmTpHVx8+RdpaGjMGZTu6uzk6aee5OfXXsNF55/LD7/7HR5+6EHa29vTZcweQMqP3MxvV0vOuwj7Xgty66PLReKjlfTeeRt2RaG6poYtmzfnXMfhMGzD0TjZsvJyUskEPcEgJYs/g3XWHLRIWIS6ZXnasNrw3bmMyDvvFNxfISPQcOkmEnHicfEy1oIz5lyJ7Yd9voOSXw6LHhKbSCSIx+Pp+U/5RmOhB0G6oda/i2/cQN8tN0Iy0f/8kwksVdUUf+50IrJMoHcHVVVVufsZYQwdKyorSakpujo7kUEYr/vuhxoK6nMdRcuoicKOSln2NNIjPOKPLG/jdNyfORnF7iDk9xMMhaiqqs75zWhj3KPt7VtxOxwUV1YSefstgiseQkumMvUShJ6yTMl5F2KbMjU9wXuwkPKhkh2T/9wzT7Hklpuw6KFSxnwc4/9Op5P77r2HB++/T593I+eEa+0O6QEcVcU6ZSpll14hhnUlSU9vp6AlEgQfeYDo++9SUl2D02anra0NRVHGLSTHaO/i8Tghn4+K6mqCL71A6PFHxQaip6CfJGCxUvKFi7E2NI6ojsPCGGXaydfaIObkQGTraGlspOzyL4spEpKkT+IBNJXQow8Rfet13NXVlJcUs3nzZiyW8UvosEfqOIoMXceHib71Bu6qasomkI6bN22iorQUV2Ul0ddfIfT4o3o/JFtHCSwWSi+4GGtj027raPQvZL1fpKoqmh5VYrVasdpsaQ+i8b0kSTnREvl9FXknZdhZDc3VsYmyy7/ST0dNVQmueIjoW28KHYsnjo4tLUGqS/+GszRC9HUHoSedaJqeFDBtqQEKlJwTxFKfRE0IAzHbqEMTNkhShVtucfKjnxSzYb2CzaaRSkmoqoSm5fcNMwak3a7x+us2vveDEp571oakCCNOtojHlPEuWaGlReEnVxXzv5tcJJMSFkvhY2ia+ExVJRwOjZYWCz+5upiHH7KjWMR5ZZ+DpoGaAKUhRenFQSSrfu6qGFzSUjLBx51E3nBQWhfCqv2Vru7QhNAxHo8T7PNRWV1D8IXnCD/xaGZpQC3TrkoWKyUXXIy1vmHA+ljIeNQ0jWnTZ/DLX/+W3/7+j8yYNSudxdgoB5DOq+Lz+bj37uV86bKLueWmG0kkEqiqit1u58CDDubnv/w1t91xJ9f/4lccfcyxVFZWpZf1iMdjtLRs5I5lt/Odb1zJBeecxdU/+RHPP/ssAb8/p05nG5Bp+8Oojw2Noj7as+qj3ocPP/oIoVdepLqmFl9PD4lEYpd0HLbhaHgELRYLk6dOY92HH2CdOw/Pyaemw96yh2MkRSG1vYPefzeLZA6yjJaumYXFAvSYfJtYKNxmS0/8zt1u5EfSC5fDii2rcTauQ/ZIRcF9qPq6MbEYfbffQnTl+0gORyZMEMTcz3AE57GLcR96GBvWfExdQyMut5vdncA6GNk6Nk2aTOvWNtA0LNNnUHzqGWJ9v7y5jvrJjUp59hiy74+0t1EYH57PnoJ94b4AtGzeRE1tHS6Xa0xDHo1GIBQMUlpejhKP47tzqVhc3OHIhEdLIjzasc8iik49XfxYyp2QvStkN2aKohAMBnnyiScI+H2ibMkkmqalOzjGyJ2qqjy64hF6e3tzRvR2u35ruSNznpNOxnPMsWixKJJiEaNyTifxtWvou3MZlkSC4tJSIuFQun6MB8bg1JbNm5k2cxZEwvjvWkaydYvIZJgSgwCSLKMmEmIZlc+egn6iu63jiDFCzUV2OytJEu7jT8Sz+DNCR4uuo91JYlMLvUtvwx2PI1mtJOLxUW1Hd8ZwdXTpOhoDdRNGxxFi6DpuonfprbjjceQJpGM8HsNit+OMRelZehuJLZsL18f9DsDz2VPRT3SXdcwOozPKkR3NlD8fyuhc7uxYO/c4Dk4/HRefiGfxibk6OvT6uOxWXIkYss1GPB5H08ZvCoAR0ZKMP4YsP44rZaXnLhuJVguSXazVCCDJGmpCwrlPjOKTwzlGnCxnjDrJBl3dEr/9rYdf/KqInh4Jm01DVY1rkz1PLfsl1ofUNLH9Rx9Z+O53S/jjn9ysWaPg90E8BqGAxJbNMrfc4uCKL5dy//1O3S+Te4xs72H2cVMpsf9t7Qo//VkxN97kJBwB2Zp1PkrmfIoWh3EdE0GLSUiKJryPDpXEJit9dzuwhC2Ula2gb8dj46qjUSfWr1vHjDlzIBTCf9dSkm2ten0Ua5SI+hjHeeDBFJ30OePH/epj9t/5UQHG3MbPn30O/73xZk49/fR+2xr9FcNe6Ojo4OfX/Yz//usfOX0di8XC9BkzueyKL/Gv/93MLbcv5Yc/+SmHHn4EFRWVejujEovF2LhxPUtuvpEvf/FSvnLF5Tx4/3306f0joz0qFPouSeA+/gQ8i09Ci8Wy2lUH8S2b8N21DMJhps6cyeZNm9LlHg67pLpxkMqqKuw2O9t7e6k4/fPY9t4HLRzKrJemL1ApORyEnnsa351L9QYuN8R0UI9dHvnhGyNJoX3mlzP7faDt0w29/l3wycfx37kMOesBo6U9VVGsTZMoO/tcgkC4r2/MvFSGjlVVVaiaxvaODuF1PPEk3IcejhYK5SxjkD6nT7PxmF1JNQ3JYkUNhbDP34ui085Asdnw9/YQDkfSXuOxwvCObd3ahsNqo7ikFP/TTxB++nGw2XJCVEFDstspvfhyLFXVOYM5u0OmsRX7CwWDbFi/DovFKka18hpow0i0Wq1s2byJnh07RqQcBumRc30gRykppfTyL6NUVufGBVmthJ94lMCzT1FSWordZqN961Zg7BPlGIkbtm7dik1RKCsvx/fYCsLPPQN2e6bcAJqG4nRSdvHlKOXl6dHUiciItNaSSOakFBVReukVKLX1kBTrBJNKgs1O6Okn8T/5GOXlFVgVmW3btiFJY5/QYVd0LNV1ZLd11AY32scuUrUw+TrW1AuPXVpH28TTsU20q6VlZQQef5Twc08PXB8vuRzLCNbH/IFq45W9jtxQ+0SSJGFRLAx+gwzxzsjT0VJTV0DHJwg8+QTl5eXYFYX29vZx1bG9vRe79FNKaxR8TymEX3CCVcrEhAJoIDs0yi4O0IfGlhYFn08iFoeEBrEEdGyTue9eO1/7ein/vdFFIqEvza1KOcahfpkyu9aEV1B8L7a3WjW6d0j85W8ezr+wnEuvKONr3yjh8i+Xcu4FZVxzbTFr1lqwWIVmuYZptgFkHENKG5SqKqEoGr29Er/4dRHf+0EJL79kpa9XIpaAhAqRGLS1yHRFoOJSP0pVSlwPGUgBFo3QMw78z1goq5axqj9j27Yd46KjkUimvb0dt8NOSUmJaFeffy6rPmacV4rLLdrVsrIh18dCIeVinvFMfveHP3PZF6/I6ceIQ2np7SwWC9FIlH//85+8/OKL6UzMxnaapuFw2Fmw90K8X/8GN91yK/+9+Va83/gms2bP0TOtqiiK8Ko+/9yzfPsbV/KVL13Ogw/cj8/n62c86oUR9dHjEfWxrl5MitXro2R3EH7mKfxPPEpFRQVWWWLbtuHXx10yHA1vldVqpbyqkta1a5CmTqPkrHORnBmPmmFoaJqGhETv//5F+Pln053ufJGyRRisARyLEdiC8cODlK1QGIokSUQ/XsWOv/0RNRwipzHWh4ikVArPqafjPOQwNq76iIrqGoqKi8fES5WtY1PTJDZt3oyWTGKtb6DkvAuwVFehxWM5cx7JMog/dWRpnBkgEFmrSs85H9v8BQBsam2lsrKS4jHSEbIaq2iUcDBIVUMDya5OAnffSWL7diSbLXdUPB7HffRxuI8+Vj+30alXqqqSKtBYZx/LuJapLA/kSFHoAeDc7wBKzj0fVfdKaYkEks1OoqMd//I7SXZ3UV3fkM5UaISsjwVG4oZ4PE4w4KeyoYFk+1b8d9+pp/u36KHGWkbH408UafGzznk8kchtA9PRIoa1siv7zNFR7MSxcB9Kz78QNRkX1yWZFMs67OjGv/wOktvaqaxrIOjz5WTGGwsmmo7ZOmSQGWvTcUAdv3ABamJi6xgKBqioqyO5rR3f3XeS2qEvv2EsNWboeJxYfiP7nEeCkRwsVyzKbo3/FtZxX4rPv7C/jt3d+JYvI7ltGxX19QT9/nHUMUjQdxtVtWtJttoI3OsktcOCZM3zNsYl3EdHsBwY5YYbPJz7hTKu+EopP/xxMb+/wcM11xVx8RdL+c73Snn1VRuKbFwTw5jLGHT5kvX/W3gPZVnMfezulnj9dRsrHnXw0ss22rZakGQxb1HVso3CwnMDMvsXEwMMA1WWJTQVHnrYwRe/XMoVXynlmmuK+NWvPPzgR8Wcf3EZ111bTGpGnJJzgsLbaNHQkhKSDVLdCv57nSS32qluWI1vx1Li8cCY66goColEglDAT2VDI8mtbfjvvoNUX09eu6qI+rj4M7gOOyLr+gy9DuUP1KiqSlFREV/52pXpjMWF9ptMJnG5XLS1tfLSi8/3S6aZvX9VVXE4nex/wAH86Cc/5eZbl/L9H/2Y+Xst0PeVwmazkUqleOmFF/jWlV6+961v8M7bb+UYj5kyiChBx94LKfnCRWIZsqz6qPb24lt+B8mtbVQ2NOLv86VDVoeq4y73aI2HUFPTJBSrlda2NsrOPAfHEcdANCLSN6HfxJoGikxqxw66f/8bYmvX6CGrhcNU9zQK3QwSEsnuLnb8/jfE160VHVQts26SZLGghULY9tmX8vMuoMfvJxaJ0NDYKL4fo86foWN1dTVWq5W2be1IgPO4Eyg65fTMaIU4MZFh9dOKcY8aL6sVLRTCc/SxuE89Axno7uwiFo1S39Cg/2Rsr1dbaytlpWU4bDYCjz5M+KUXkFwuMQqcGY7EUlZG6cWXoRQV9UtYtTtk6rDYn9vjoalxEqlkEllvOLNfhmGdTCapq6sflQy06TpnREBYrRSffT6OvfdBi0RE5y+ZQHa5ibzwLIHHHsFhs+HxeGhv3zri5RkMw0Bt37qVIrcHl92O76H7ib7+qkiqldZRDOJYKqvE4uJu94jquHvkliEei4nBk91s3vvpaLFQ/PmzcRprCVutaMkksttN5JWX8T38IG6nA5fbndZxrJ4xE01HSRJzaMZ7UMEoC+TreM6QdNw2jjp6PB5cDofQ8bVXdB2zktxp6gStj7lISMhyZhAh22OZiUcZRvKqfB0X7V9Ax5fwP/IgbocDj8fD1q3jo2Nb21ZKHL/G6ZbxPaIQecOB7BKJYLKX2VDKVSovCfDyexYefMBBa5vCy6/YuONOJ3/9u5ubb3ax6iMrkiSS0KiaYaRl5hsONsYuPje2M7yPkm5AivBSh0PsW5E1NH0eY7qI2uDxAtm+GbF/w2Mt9h2NSrzyqo2bl7ho/oebe+5zsnGThaeetPPQ4w4qzw1hWxBDi0pIVg0tKSO7NSKvOvCvsOByK5Q4f0H71nb9GGOr49a2NlxuN26HA9+D9xF94zVkV167qqawVFVTeuGlYsWH3ayPhoffMPQ8nqKBz9sw5rSBa1J+1ICmiTWtG5uauPIb3+Lm227nii9/leLiIhJJkehGTJ3ReOThh/jal6/g7rvuyPFkgqiPGPXxjLNw7n+gyPpstaLF40guF9HXXsH38AO4HQ7cLle6XR3qAPluuUKMDEO1dfX0bO8g6HBQecVXsTRNFrG1ioKmZs33s1qJrVpJ96+vJ9HR0c9onAgPtV0h2+hNu6sjYbr/dAOh554R3p6spB+SvuCPXFxCyYWXoUybwdoPP6Rx0mQcDkf6uo4VmUy5DXR1dhH0+1DsdkouvhzHogNEyKolK1HOHmrg7y4596rFihoOY5sxk7IvfhlLZRWpeJwNLRtpaGwcUx2N0I1kMknI76emvp7o2jUE7lkuHuDGKJwRHh2L4jnlNByL9k/vIz8KYFcxztdoYIuLizn2uOOw2W2gaVjz1g8zJpUnk0mOO34xFZWVaJqWSd4w0veavliubcpUSi/9oljrSUKEtygKqWiEwN13Et+wjsr6eiySTGfn9vT5jCb5y6jUTppE9KMPCd53N2o8nl4nNFvHotM/j0OfVwu5bdFYkx7/lsRfxkOxpraWRCKhd1alzMa7g66jtXESpZdegex0ic9VVXyXShK8504iH31I/aRJ+Ht79TJ8+nQ0Rsqrq2tzQsVHbALq7rBLOsbHXMdwIEBt0ySiq1YKHTNxiVk6xik67Uwc+0yM+jggkvA+gShfIpFIdz71qlswq+qgGO1qk6GjU3xu6JhMErjnTiKrPqKuaRIhny89oDRWOkYjHUR8/6BmUheRlQ6CD7pR4xLIkkgEoyHm9cUkik8JEZuS4OYb3fj9MlarWPLC7c68LBYtnYRGkO3hk4Zso+Rvb4SvGq9MuKuxnTakfYtHumGUZi/tIvZpnI/Ho+G0a9htGuGoxC3/c7FV06i8LIhk18SdoGogS2hJmcD9LiIr7dRP2YGv+28kE2P7fAyHw8QiEWqaJhFd+QGB++4WuRvy6qMaj1F8xlk4Fu6TdU2GVx/z+/aJuGh73n/3HTa1bEwbfMb36cFwPUqhuraW/fY7IB2qOljUohEVYoSN1tbWcfW113HlN7+FzWpL7z+ZTOJwONja1sZPfvRDbrnpfyQS8XQZAL0+prA2NlF22RUobjf6ouNZ9fEuIis/oHbyZPw+H7FYLJ2YdGfsluEo4nBT1NXV4XB72LhyJc6DD6H4gouRrKKj2M+zaLMReuF5un99PanenvTJ5scJ7ylku4jT3qhEnJ6//5nA3XdmDC7yRE0kcZ92BqWfP4uN69djdzqoq69Pu+LHEkPH2tpa3G43LZs3QyqFbfpMyr/sRamtTU+yNc5VP6ExLee4kR+iqiiQSqI4XZRdcjn2gw5FQoSo2qxW6usbxlRHIwRizerVTJo6FYDgQ/cTfe8dMfKdSIiniL5eqGXSFErOvSA951YyPMojHBpn3O+LT/wMp595FoFAgHg8LhpIi5I2dn0+H8ccexxnnnV2Oix0NMpklMvQ03P8CXg+81kxGmezocXjyC430XffwXffPdgVBafbTTQSYSwScxj1qq21laqaWhRVxX/f3cQ+Wqkv4aNnc9N1tE6fScnZ54qRxCwdx3cAToRvy7JCLBbjuMUn8Ps//5W9F+5DLBZJ36u7Y7SI/GuZc/QccxxFnzsFNRRK6yg5ncQ+Wkng3ruRUynqG5vYuGEDMPxEAMMlW8fqcdUxs87ZAQcdxJ/++jcOP+JIotEoikVBVce3/d4VHesam9i4YSMwtjpW1tahaFk6ulyisyoKInScNoOSs89DstomUH3sj9GWiQ5qikMOPZTFJ5yo3xciXG24eY+zz9F9zPEUnXwKajhPx5UfErhvOXIqRW1jIy0tY6Ojsf8tm9dQU/Uv5ISFwINWYh/ZkF0iFBMJ0RtOSiiTk1SeH2TFMzZee8WGkVxeVSWSycwre/5ibnjqcIxGcrYX+yr8ymw39Psp8zvDqDXKmnc+KQlVBYtV46NVVm5Z4sJzbATX4ohY29KmocXFGpexlTYCD1ohbqWx/n+sW7c65zqPFsY91rGtneLyMqyqiv+e5cRXrxLLb+j1UayLHsc2YxbFZ52bHjgfbn3MdmgZCaisNhttbW389c9/oq+vT6z0oHsMjTplsViwKAqxaJTTTz+TY447DiCdaG8oNk52ptNTTzuDBXsvJBqNYrfb08aqzWYjmUjw+9/+hsdWrMixobJDyN1HH4Pnc6ehRrLqo8tFfPVHBO65C0sySU1dfXp5jqHouNtKGx2BGTNmkEglaWtro/LSL+JcfAJaLAZK1sXSX5LVQnDFQ3T98jrUvt6cTuaEHKEbgIIGbzxBz9//Qu/N/zU2yjkfyWpFi4Rx7H8g1V/7Ot0+P709PUybPiN3P2OMYeRMmz6dRCJO27ZtSIDrhJMovegykWpbr3zpERZR4HEp75iRFWqQo3cqRfGZZ+M59wJkoKuri77eXqZNn57ediwwRoe2b++g2OOhtLyc8JuvE7jvbtILPBn1Th9pKjn3C9jnzBM7GIVOjXHuxoBEeUUFP77qai68+BLKystJJBIEA0Fi0RhFRUWcdvoZXP/LX9M0aVJ6VDF7PyONpChoySRKSalY+qCxSbRVesgVikLwvrsJv/k6VdXVhPwBdnR3A0MP5RguRgr9zs5OrIpMRXU1oVdeIvjQ/WCz5iQ2kmQFUimxjMrM2WIH45TdLhtjzq+iWIjFYuyz77788EdXcexxx3P9L3/F3HnzM3NGd0Pb9Pi+fj/LniJKzr8I69RpmUiXVApsNoIP3kf4tVcoq6rCbrWyvaMDGCMdZYnycdJRg7TRuN9++3Pt9b/g6GOP4wc/+jFz588nEolitY7d3N1C7Ak6btvWjs2iUFFdRfiVlwk+eD9YLXk6itCwkvMvwDZr4tTHgZAkSXQ6kwlmzZ7NL39zAz//1W84/IgjiYTDWKy7NmfN0FEp0nWcMrW/jvffS+j1V6morMQiy/T0iGRoo6kjQGdXFKv2fcprEoTfUAg94gYLoOkDiRrIioaWhLJzgnS7VG692U0oLInPc4zE3Memps87FC8m8EtKv6c1yzofTZOQJbHW433LHby5RqH2Mj9SQxIpLoGih/RaIfiQm/CbMuU1SRzS9+jqio26jpIk0dbWht1qpaK2lvDLLxJ8+P7+if9kBS2lUnrehdhmzBT9t12oj9kOL8MwXPnBB1x79VW88fpr6f6NZmRNTaXSBqbP5+OwI47ki1/+ck702WCRlQPVuVAoRDAYIJVKkUgmSaVSqKlUepnCgN/P//v73+jo2JZrSxntqruIkvMvxDZ1el59tBN88H7Cr75MRVUVMgy5nzMirZsx8bimtg5/Xy9dsTh13/0R9oX7ZNZ2zL44mgaKQuCBe+m87mqSnZmw1WyxNL3TPjEMyey5WfQrpyRJaOEw3X+5gZ7//jO97EGO0WixoMViWCZPofLb30dtnMT6Vauoa2igpKQkHXI4XhjZLaurawn4/XR1dSHLEiWXXUHRqadnkgBkV4ARCnGckOj3X85ghixCy1xHHk3Zld/E4nIRj0Vp2biB2rraMdfR8NoFA0GKKyshHMJ/9x3EN20Ui1IbmlksaNEojgULKfrcaenGQxrFchpeV1VVaWxq4te/u4E//OmvfP2b3+KyL17B177+DX71u9/zx7/+nTlz56bnO45Wfc/Zr26cOvc/kOKzz0vP0UHPAh3fvAn/XXegBgNMnjmTrq5OMU9zlK6XLMuoqRRBv4+Syipkv18so7K1TSyNYyxPYrGIgadF+1N00ucy84smgFdD00RCgkQiQV19PVf97FpmzhbrXh1w4EH84te/Zfr0GUQiERR5173x/XTUNBz7LqLkvAtAEyHHpFJIVhuJbVvpW3Ybsq+PksoqQqHgqNZPI91/wO+jpKp63HSUJYlIJMq8efP5xa9/y4K9FxKPx1m4z778+CdXU1VZmQ67GrfQ5l3S0TemOsajMYoqKlACAfruXEqirbWAjhEc++434erjQAgPikZ1dS1XXX0Ns+fMobGpiWt//kvmL1hAOBzSs64OjXwdNU3Dse9+lJxbSMd2fMtuQ/P1UVlXT3dn56jrmEolCfTcTFnFB0h+Gd+9buJtCpJdyyTEsWhoUQnrXnHKTwtz90N2PvrQitWWNcAxoCdwQk5l3SVUVcJq09jarvC/f7mJT0tQenZILM2pACmQbBqJdgXfcjf0SZRXrqRvx79JpRKjXB9VJE3DXlSMzR+g947bSGxrT08Fg0y76tz/ADwnnZyZ77cLAmWHlfr9fu66Yxnf/uaVPP7oCmw2G7IsY1EULIqCIot1qa0WC4pi4djjj+ean/+C6TNmkkqlchLjDNTe5ofFyrJMKpniuWeepn3rViorK7HbbLhcLhxOJy6XC7vdTlFxMVvb2tiiL61h/B7I1Md9FlFy3oUi5DhdH60kt3fQt+xWpL5eymtq6evrHVJCxxFZpMyI321sbKQlkWDzmjWUHXwwlT+4io6ffJ+U8dBMJdMnJeknFXjoftSAj8qrrsM+Y6YwxLJC1fK9euPRIOd7Qw33vzHZVpIkkp3b6f7j74SnB93az7LaJUWBZBK5qIiyK7+F67AjWPXhB7g8bhoaGkgmk+O2ZpyBYYQ0NjayccMGtmzZTElxMTZPERXf/RGpnh5CTz+BZHcg6SMrkiR9clrNbAoYjenO3j6LqPzRT7HUN6CmUqzfsBGHw0lDQ+O46Ni5fTsSGiVFRQQeX0FwxcPCaEwmhFEvifPBolB60aXYpohw1nS23BEmP45flmU0wO5wcuJJJ3HiSSflbK+hZ/7OCyUZ6bqevb/0vGtFoeTz5xB+9WUiRsILPZQj+OiDuI49juKTPofT4WTbtm00NjWNaJmy6d6xg2QyRVlJCf777yH01ONIxoR/TRP1TNXAbqf0okuxNjWRDp2eCEgSGhpuj4fv/+gnHHrY4WiA1WpF1VQOOvgQrv/Vr7n91ltI6M+CXWk7CuooyxSf/nnCL79I+MXnkT0ekS3X7SH05GMEjj2esjPPpmtbO12dndTW1Y3QSfdnR/cOUuOlo35tkimV2XPnceU3vsk+ixahaiK0SQOOW7yYb37vB2ze1CJ+Mk6Dlbuk43HHU3bG2XRt2zrqOnZ2dhKNRphcMhX/g/cRevIxJHcBHW2GjpPExxOlPuYjSfosPCgtL+Ob3/0uxx5/vMh6LUnMmTuXa67/BQ898ABxY87UkHa7Ex1feiFLRzehJx8jeOzxlJxxFt2Khe6uLmpqa0fllAG6u/2oib9SWpnAf5+D4FNOJKcmvGd6pmcJSFk0ai7z0xLTuPsOFwlVw24XHjhZGfhKZM9wNP4Y1R6RNOifu4Xu0wFJ46UXbTz2jJ0zLgoSfMVB4k07kktFS8jILpXgUy4CR0cpOSNK1/b/o6vzHGrrakawNLl0d3XR5+tjflMTvnuXE376SWSXJ51FNdOuOii98FIRSbSb7aoRfBwKhUlpKl+48GIcLhcpfWmX9Ha6oaeqKjabjUMPP5ymxiZSWRFUQ+nXpG0dRH8oEA4yfdYsfv6b32GzWnMT4RhGpgQSMpOmThP7kOWswY5MfSw6/UxCL79I+IVnc+vjU0/gP3YxZWedS1dHO11dndTUDF4fR6yHa7htp06dSm9vD+tXrmTm4UdS9d0f0vXLa0n5fFmpcjPGo2S1Enr2GZJd3VT+4Ce4jjgqY3RJ/RfpzDbiRptCx8qey2g8cGMffUjXDb8h/NLz6TmNOUajngxBslgovfzLlJ37BTasW0c4FmPRov3SHtuJgBFbPW36dN595x02bNjAzJkzsdTWUfWTn6EGg0RefxXJbs8xHtNGpPFA3dMxHrLZRmM0gm32XCqvuhbb3PlImkbL5s2EQ0EW7bf/uOgYj8fp6+1h5vy9SGzvwL/0NlJ9vcieIjG3EcRC9+EwrmOOS6eJV2OxMe3gSLpRkUgk8+4ZsFisyGMdoq4/5dVYDOvkKZSc+wViH30I+vwzSZZR/X78t92Cc59FNE6dyscffjBqHZxkIkH39u1MnzuPRFsrvmW3iqRUxhxVRIdUDYXwfOazuA47XPwwkUCbIB1VBYjHolx04YXsrSck0JJJ8SDTNJBUFh+/mLraGjwOZ873u6y9JHp9WjyOtaGRkvMvIPbh+6iRSPr5oUYi+G5fgvOAg5gyayYbVq+morJyZE46j2QiQde46qiBJFNS5OHHP/4JC/fZB9XwlGsaaiqFYrVy/nnns2HDOuKRCFZjqZ7xZEg6hvHdtgTn/gcxZdasUdUxkUjQu6OHaXPmkNjahm/prWihIJLbk6tjOITnhJMy6f4T8QlTHwsigaZqnHzSyUyePFmElurRDlgsHHP0MUxumoTL4RDb52VsHNoxJKFjYxMl511A7MMPUGOF62PTtGmsX/UR5RUVo3CykEhE2NHxO6bP+JjEFhv+5R60gIzkAS1hPNs11IiE8+gIjoNiLPmni3WrLSgWjWQsK7RTy3nr94fx35zuT6HfDMRA22r5x5RA0gp/N9jvgPTIwUA/0cRGFovG1h6Zf/zZw75791J9QZC2VTbkJGiSBrKEGpHw3eHBuV+SSVPXsmH976io+MUQTnT4JJNJdnR3MWPuPBKtW/AvuxUtHEZyu8GojxYLaiiA56RTcB16hPFDUR93o2+hAXW1NZx//gXD+l0qkRBzhlOpzHUftGucEUfSHVNFHg/HH7946GXV7Y6cszXqY30DpedfQOyD91Cj4fSNqsVi+G5fguvAg5k8YyYbV6+momLwdnVEe7mGO3bffRfx9ltv0tHaSv0ZZ5EM+On54w1o4VAmvpaM1SzZbMQ++pDt3/sWZV/5GsVnn4dSUiq20ReTLWStj5YHMm3UFth3ujyyjBqJEHxsBT1//zPxlg1iHqCWN6fRuGklieKLL6PS+022bd3K1vatHHTwIeMamjoQho777Lsvb735Bp1dXdTW1GCdPpPq639F59U/JvLWGwMbj7DnGZBZ5c0JKZAkcc9Go9imz6TqZ9fjOPBgZE1jW0cHra1bOHgcdWxrbaWktBSrJNH7xGOEXnoe2e1Jh0qnz0uSKD7tTKz6MiGy3T7mZZUA6yAhUONxtxjGc9HJpxB69mkC992NXFoq5gK43IRffYng4ysou/QKKqur2aQndBhptm7dSnFpCXaLQs+Kh4i8/lpmwr+U6bjIdjvFnz8XS7UwYCWbbVTKs6vYnC4OPeKo9N+ykVDL+FuWWbjvfgN+v+sHFtfBc+JnCT3/HP6lt6Z1lJ1OIm+/if/B+6m48pu4i4tp39q2u0csiNCxVNfx4XHTsaqmlqqa2vTItYGi/7+iqoqKqqoRPeaIYOh4QiEdXULHh+6nwjvKOra14ikuwq4Y9fFV4TVO6yie8bLNRvHnz8FSMzHrYyFkGebutVfeZ+K+sDudOd8pehbsYddP/ToUfeZkwi88h2/Zbbn18a038D/0ABVf+zrFZeVsbWvd5fMZjK1tWyj23IzdqtDzmI3wGw4kt1heIm18AVig7MwQaqPKwYfFmTE3iWLR0NSMMZXu1ukGmQbkG46aJnabnkaYZ68UMgpzDMZsY8+Ym4g4fvq7jLNJbJZdLmPm0EDHzTdOs42ZrONJEqgpsCgQS4Hr5DDuZ5yE7nMjFatoMRnZqRJ5247/ETsVV6Qo8txOW9sl+Wc4IrRv3Yq7SNTHHY88SOTNN5Dc+e2qiuR0UXL2uViqqwHhmBoJNCChauIYQ+jrWSQpU3d20+OZNKbD7eyYul1SkHS7ehLhF56j7/ZbkEv0fo7TSfTdt/E/eC8VX/8OnpJS2loHr48j7h4xDIe9F+7D888/iyRB/cWXo8Xi9P7fX1BDQX3R3GR6e2OpjmTvDrp++0sib79F2Ze+imPhvvqSHmo6aUW+92/EGcAYTc8V02+C2Lo1+JbchP+eu1Cj0cySG9nXQtHTrksSxRdeQvV3f0R3Xx/rN6xn0X77Y7fbxy38dmcYZVq4z748/+wzSNLe1NXWYZ09l+pf/paun/+M8MsvCeNRktDUlEjhnRfiORHPrR+GcaWJdSoNT6MkSSJhSjiMbd5eVF99PY7DDkfWNLp7eli3dg37j7OOqXicuunTiW1Yh3/pEnG/ZZ+TJGHMMwm/8RqWuno9YVUhw17K/d9gp1PwXAt8NsA+hAey0KZD30c6xmhApNwR1n6nK4nlgmQJNRhEDQbTc62A9FPUd9sSXIcdQfXM2fj6+gY74C4TCviZvWBvoqtX4b/jdpEivt8oqRjpja78AOvkKUJXScqcWL9rIZF7laWC/x3S58bxB4yTynyeniORNcoqGb0RyVg+Rinw04FuFuOfAYQ06i6I0edkEhQ9MZSkj8IqCv47b8N11DE0zF/AulUfFT7WbpKr423jpqOGhqZqBRZ6NwbFyJ3LMhQN+jUZO78XCn83BB2TCUgNoOMdt+E+6hga5o2ejuFgUOj48WpRH6V8HY1BOZnoh+9jnTQ50+POP8V+1aaQfjtv96SBvhhsPwWPq6GqRpborMLqX6tqZtHz/vvS78Wccyqgo76BFo8LD62cpaMeD+lfdhvuI4+mbt581q1e1f+cRoBw39XMmddJdI0N//Ii8aGWLVC6qEQ/sGFtSnLiFBWm5ycHGYLHKm+3OZ8PtH2hXff7vMBzrmAVGqZXbQjNOxokEzLRVQpSUt9E/0fTZJA1/MvduA+PUT+3k49XXwVMHl45hkAw4GfWXguIfrQS/7Lb9aU38q1jcd9GV32Edcq0dN970GeaBIYHVxr0ngZJkgv38Qp0loSxpxY+3oD1t/99KUlSTuhpZvv++0iqejZkKfsGytpONPqi/slyur3KtKtLcR11LPULFvLxBx/0L2MWoxZXZ7PZOPTQw1m35mPC4QgzvvRVsFrp+/ufSfX2IDkcaIlExrujr/cjSRKBRx4k8t47lJ5/EUWnnIZt6rT0OkFaMikMyAKWdUHP1yAU3C7Po2ksnG4YjImObYSefJy+W28i/vFqJIdDD8FVkeTc+XAkk2Kx8Ysuo+q7P8Qfi/HhB++zaNF+eDyeIU1CHW9EvPYRrFm9mlAozIwZM3Tj8fd0//aXBJ96HGRJhEPqoz97jPcxy7gyvIuGxzgdchyN4jjwYKqu+hn2ffdH1jT8wSAfvv8eixbth3ucdSyvrkZWUwTuWU5s1cpMQpwsHcQaTDKBe+8m/NwzYvQpu1EboLGUBnoS7tSqzNpwiLKnm7gB7pMBPx3WbVXoAayBrKDGoqS6u5AcDlHn9fZGstuJr/sY/51Lqfzx1dTUjs6cqsraOpREgh13LSO+fp3wUuUlo0LT0FIqvlv+R+jRR/TsjkbdGsQM7/fgHKinkr9tAdL6DPAbLXubwobm4Jmzd96BLviFpgkjIxYn0d6GZNd1lESHVbJaiW/ZjG/ZEmqv/RWVoxRyPJF0HPQ6pweVCv+8wA+G9NGAXw5126HqePut1Fz3y7HRcd3agXXUNHxLbib06AqwZZbe6n/CBepEgQGCgTv/Q13iYYh1udCgYbpYYlpBmkzjPOCh+qEBiowWi5Fs35ppVyWx1p5ktRLftBHf0lupufYXVI5Su1pV9RhyQsF/j5P4OguSy5jbqKYHUIxT9d3hIfiUi5SkpW2OQeXK/7CQ4TUYu7HtoE3BoLoPrQzppkEDi0VDS0gktlmQHBqk9N+pIFk14pts+O5yUfPjJLU1LwAXDeGEhkdFdQ2WRIKeO5eSaFmfmTOu14t0Uplkgr4b/03wkQeFt3FIfc+d3NeDGZQDfDT4wAqFm24jqnIoB9jpzZh/PNHP0eIxkeDL7hADc3qfV7JaibduwXf7Emqv/zU1jQ2F96UzqhOy3G438xfszYsvPg/AjEu/iOIpouevvyfZ2orkcokY5CyDT1NVZJeLVOd2uv/wG4JPPkrx58/FfdQxWKdMTbuexdp0gJwxIoebVKOgZ9EwFg3jQQ+lSrRvJfL6K/juvovIqy+L77MfJtlGo9WKFo2ilJRQ8sWvUPm1b7Cjr5f33n2XRfvtR0lp6R5hNBq43W4WLFzIC88/hyRJTJ8+DevUqVT/8rdYqirx37ccNRxBcjrFYICeOlkMTGVV3IliQBqeRaOMGHUrS794HEmW8Zz0OSq//2Os02cgATt6ennvnbdZtP/E0LGiooLI++/hv3d5TrY/Y2Q3bcTLIhFAsmObPhJWiIFazEJPpfzRvt08kV1mgGHSgTphhRpsDZBlZJstZ9AjnajLZsd/3z24T/wspQccNArnAFVVVYRfe4XAA/cg2bN1zC2PpCiokQixdWuyjP+Beiw7EWWQyzQohS53oe9H/Z4o4AGSZSS7DUlWCuoYfPhBQieeTOWRR49KiT5ROo4ZA+hos4mIo/z+gc1O4JEH8Hzms6Or4+uvEbh/JzrKMmo0QmzDuiHoOFQGatOG8LPhkm/4GJ+NyL0gPLKS3d5vPW9NVZEdjrSOFVnh7SNJZVWQ8Js2Ag+5RRZV1UiIk5ljr2kSkqyhxWTiLXJ2Hz7/bAr+PaLN3W7tZJijtEO8TZO6h1GyIa6TJiFJ+uoCqoxsUwms8OA5PkL54cFdL/4gVFVXE37lJQIP3ofkcGbVR6M8RrtqQQ0GifWsSnu4x69dY+gHH2hwZriFH6h/YyBLSDZ7wXZVstsJrniI0GdOpuLoYwc9zKgajpqmYbVaOfLIo3n/vffYtGEDU846B2tNNV2//y3xD94VGTqN9dPQR/N0r6JstRH74H06P/wAx94L8Zz4WVyHHI5t5iyUoqLMcZLJjFtalnPmRO6sfIb7Nh0uZLGmlw9Ro1ESLRsIv/EawcdWEH3rDdRoFNntFsZHVnx1unMuy2ihEJYpUyn/+ncoPftcNm3cwObWVg448CCKi4vH3dgYLoaORx19DO+99y4bN8LUKVNRqqqo+Om1WKdOo/em/5DYshnZ5Ra/MRZH1iuCcEQUMCbH9kTSBmPab2KERumdORQFNRjEUllJyblfoPRLX0Mpr9AT4WyiddMmDjhoYukYfe8dEu1t4tob9UhvLPK98JLDsWeED48HqVTmeoG4R1UVrFaSnR1EXn4R134HjMqhJU0j/NorJDs7kT2eTKSDXo58o0NyuxlyR+HTJre+tlb2QKIRRZDq2UHo6SdxH3r4qBza1HEEGUBHxkrH118h2bl9F3XMt8LyzYtdEHNP1F8D1EF03NFN6JmncB962KgcXkIi8raTZJcV2ZVKe8uktPGIbgRJSIqG7B7Ovgf/excKu5sMbGTs7jCG0BE0VRiNwtMnjEcsEqkdEHrRifuQ2G4caWBkVSX88kskuzuRPUV59dHwSWTaeclm3zPry1hQqF0FJFkh1dtL8MnHcB9+5KC7GFXD0SiYxWJh74ULeenFF0gkEkw94mjqa+ro+ssfCD/1uEgLa3egpZK5IYPJhN7RlYm+9y7Rd9/BOmUqzoMOwXXgwTjm74WlaRJKcUnugTVNT5mtZt316VKJN8PA1N8N1HCI+NatxD9eRfjN14m89grxtWv0hTTdIl1/MplxLevlNUJTicdxHHoYld/+Aa6DDmFzy0Z29PZywAEH4nK5JoyxMRyydVy4cB/eeP01NE2jqbEJm8NJyRe/gm3GLHr/00z45ZfE9XA40JJJfWQo88DVdzi2BmSWwZjzmf6OJAkvYyIBkQiOhftQ/sWv4P7cacgWC2oqRWtbGz07drD/gQdNPB01vRGQZTHHyzB8jK/Tc4S0tGFpUgAjbDnLu4A+uCTJMmo4PIi3djdRVbRoFEmWMgarkW3UcNjr4YxaKiXaGpPCZIefGxgDhLJCytebk/V6RDF1HDkmgI7pkF5VBTkrQQpZ7aqp4+DsVMc+tNQo6YiGFjfiKiV92gZkpxrRVEnXUYJPqIwj4nTTQ7PSWWaRxI41QAbVrzBaj0dUFTUSEf317HY168wMh79ZH3fCQPVRkkARxuPOsmyP2doBVquVo485lvffe5d3Xn2FBfvtT8MNf6Lrpv8SWHoryTY9dNXwPmpZD1dSyC4XSBLJtlZ8G9YRfOBerFOmYp8zD/ucuVinz8Da0IhSUYlSWorkdCFJg2czUiMRUgE/qe5uEtvaSWxcT2zNx8RWryKxcT0pXx+S1Ybkcolxw2SyX7ilZLGImzUURCkrx33GWVR/5UqSNXV89MH79PT52G///XG5XBhrvezJWK1WDj3scN595218Ph+zZ8/B7XbjOuoYbDNm4l96K7577yKxZYvwzFqtwoDMvkn1kaJ0IhpGwYjM3l/WcfoZjBYroKEGAiiVlRSffColF12Gbc5cJCAaibJh4wZ6urvZ74ADJqSOjoX7olRVi7nDNj1b6oBzyEyGhT7XFatNT9Y1Sk2mouDYZ1+wO9JzDtKGq8nQGexy6YlW7HP3GrFse/2PYeo4IkwEHfddhOx06WHOerZUU8fdx3DA6tMnHPMXjF42WgkcC+LILg0tBZJNKuBMMNklRBiZCGNFwTEvhmQZJUeAxYJj4ULRX/s0tKvDDRsfckjuIBspCqRS2OfM3Wl9HNNF58QSD4vYvGkTb778MrPnzaPmG9/Bte9+9Nz0H6LPP4eWEGnw015DEAakPoIg2WwoThekksTWriG28kOwWZGLS7FUVWGtqUWpqkYpK0cuKUF2u8VNpiiQ0tCScbRwmFTAj9rTQ7K7m+T2bSS7OlF7e1HjMSTFgux0IpeWiZFHfZ2Y9OgjpL2VWjQKmor9wIMpvegySk49nR6fn/WvvYK7uIQjjjwyfe6fpBDBRfvtz8YNG3jv3XeZNn06dTU1WBsaKfvuD3EedAi+u5YReuZJUn4fsrtIjNbqhnd6mQvINSJ3d2pIFoZRChmDMd3IGAajBGowiGS34z5uMSVnn4/7+MXIDidoGjt6etiwYT1ul4sjjhJzMCaajr19vZQu2JuK7/yQnr/9kWRnZ848x4IXdKQb2yxvyuAMMLFilOYiZB8tewqBCFFHD1kq/Ast616RnU5KL7oU15FHE4uPTiiOPxDAc8TRlF56Bb7bbkYNh/Xwe1Gu3b/jtN2/vgMWImv0fjcPMXyyFc4j+2NJAknGc9xiik49nfgoed4H03H3VdRy3naZQXXUcrwxY8/E0NHn91N02BGUfvHL9N3yP9RQKJ2xOtP8Z5LIGNoOFKC664xAvWWwguSWdGzqr/78lxWKTjgJz+dOJW70sUYYX5+VooPjlF3up29JMamg7nXs3/j3e14w0P+1zN+Qq3N+bNsIdmlyd2wwCoINZ5cSgEXGc1IQzwlR4vGRLw+APxjEfezxFF9wMf47l6LFovoqBrtwdQf9yXg9ywZ5jkFWsfLvyIF/MvRDa2nbxnX4kRSf/nkSE8XjCGKtoFQqxeQpUygpLWXlhx+wo3M7sw4/kqb5e9Fz73L8y+8k/tGHYLEgOZygqbkGZCqVjm+WnU7weIRxFwkTX7+O+JrVejiCBIoFSVZAkTOXW1VFjG8qhabpN55FEZ5FhwPF7Rb7S6VI14KskQ1JloURGouhRaNYp03HfdqZVJz7BWiaxLq1a+nc3sHU6TOor69PhzROJGNjd5EkiVQqxbTp0ymvKGf16lX09PQwdfIUXB43ziOPxrZgIe7jFuN/4F4ir75MKhBAdulGvHH9IcczmOOBNN6zQpdzPJLZIc15v0P/XXr/xmeyLJai0FR9WRgLzoMOofjU03Ev/gyWunokIBGLsbm1le3bO5g6dSr19Q0TVsf21jZcs2ZRcu4XcOyzL7HVq9BiMdJXQ9Ny2xXjeuSPuhb6W9IyoSj6/0Umcy1XpGzDMT/8YbD9G50uvYxp/XIM2/yOU97fefs11jvSMh/k/TZd6KzfZ39m1HV9W6sF+5x5uA46BKmoiM1r1zAatG7exKw5c6n89vdxHXo4sVUrIR7LXOZCKeT7GRJ5f2fr1++3eZ/lXAIt7zJrAxyjwOfQf2Ai+x7Usm6c9HbZfxv/z/19TuHTRcg7fk6vLe/cLRbsM2fjPv5ErHV1rF+3ltFgIB3FaQyiodhgkO/0czL+v1MNxXda/nYFr5fW/3j5ZSjYjmTVlX4a5h8j//yy766BdNRyz2kMdWzbsoVZc+ZQ8c3v4jrkMGKrVopB5YJt0eB1s1+G0p3UFbJ3WbCe5+ysXzucvp45Ugx0nw2muVH2QvWz0O8KlDW9XaZN1RBJTGyzZ+M+7gSs1TWsX7cu/+RGhLatn2PmjPsp/0oI5wExYmvsEMvKGqtluhfpLkbWaRY8x+zv0//PHwzN+28/7Xay/0IM9r3Wf3mr3Lo3yHEH2bcou3GRjPqun6sklrKyzYzjPjqGpSLJ+g0LBij87tG6eROz58yh6oc/xXX4kXq7WmCwIf85BgO0gTv5f8G/tayPC9XbAdpA/b4v3OYW2l/WdgX6YNnte782Ir2HvLY3v01I9+H0d4sF2/SZFJ30WayNk3ZaH8fUcASxuHwymaS0tJSDDzmUtWs+5pVnnmHW/PnUfvErFB9xND3330NoxUPEN6xDUixITuEB0lQ1c4E1PTmNsQSELIsJ6oZX0GgJ0i+9UdC/l/K3M4zFQrHRxhxGgFhMJE+pb8B1wkmUff4cHIv2Y0efj02vvYbFYWff/URoajKZxGIZ80s8JmR0LGP//Q9k/fp1rFr9EXX19dTX1GIpK6PojLNwHnI4kVdfIvjYCsKvvkRqxw6w2cQC9HqselpXo3OSNUdP3NtajgcR0D2UGeMw85zKrUBpQ1/TRMbXUAi5qAj3UcdQ9JnP4jz8KCxTpiIDqCrdO3awecsWLLLMvvsumvA61tbVsW7tGhoaGimdvwDH/JFsuAfoGE00RjtcRZaJRKO0frwau9M5Koeorq5h7erVNE6aTPExx+E5+tgRPq/0kwj6PVQmCgMUKq/ou7pnyWIhnoizbs0arKMUGpfWcfJo6pg3gDZh2ElZdkPH7L76mOhYY+g4iZIjj8Z9xFFjdK3zL1LWxZpIUg9Kfud7ACwWYvE469auwTJKIcc1jX9h7QaJprpHKD4khvswI2LE8NZM0Is6EsXK38dIjnlrGeMRWSIWkdi4fiE2VzNw2wgeSFBVWcXa1atpmDyZ4hM/S9Hiz2QXJq9sI374YTK88uxKcXcm5c73mdkit11Nsn7dWpSdTMkZl96wxWIhlUphsViYN38v+hp6WbN6Ne1btjBlzhyqf3gV8c+egu+xRwg+8SiJtWvQ1BSyw6GvQaeHsWYtdq4Zk62zSXuHpMzVyV4gfTCMxDmGcRMOg6ZimTSFomMXU/zZU3DtfwBBVeWD994jHArSNGUKDQ2NAOnz+yRj6Gi1Wpk7dx6dnZ1s2byZjo7tTJk0mYqKcqy1tVjOOEuE+X34AeEXniP00vPEN25Ai0aEEWmxgj7XML1AaVZoab5xaJDztxgVQFLkdDgTmggz1sJhUBRskyfjOvQI3Ecdi32ffVHq6jFmKvr7etm0ZQvhUJimpiYaGvcMHX92zTXjXQSTEeDa664b7yKYjACmjp8Mrr322vEugskIcM21vwVqgMvHuyifEkbeaAS47vrrR2W/JrvGuPWIFUUkrkmlUpSWlnHgwYfQ1trK+pUfYrU7mDpnDlXf/zGlZ5xF8JWXCD39BNF33ybV0wOyhGyzCyNS91blGxtA1v93MsJupKU1MqxKklhfMhqBVBKpqAT7gQfjOfZ4PEcejX3ufMKqxso1q/Ht6KGuoYG58+dhtdpQ9TS3xvl90snWsbq6msrKStrbt7J+wzq2trtoamykrKICS0UlytHH4jz4UIq/cCGxj1YSff1VIu++TXzLZlS/T4QYWyzIVqvQQVHywlP19/zhFkP3VAo1lkRLJsXSEy4XtslTcCzcF8eBB+NYuBDrpCnInqL0LoI+H5u2bCEYDFBdXcPcufOwWq2fOh1NTExMTExMTExMBkPKD+0bCK/XqwE0NzePeCFSqVS6gx6Px9ne0UHbls1YrDamzplLZUkx+HxEPl5F6PVXxRIZaz4WYY8pseajZLGIDIiQ9jYBhT2LOfPUdFdkKiW8U6kUkiQjl5ZgmzETxwEH4z7kUFzz90aqqsIXibB+1UeEAwEamiZRW1eHy+UCGLWQRq/XC0Bzc/NuBxuMlY6xWIzt2zvYsmULVquVqZOnUFVZmQ751QA1FCTV1U1icwvxNR8TW7Oa+MYNJLd3kOrrRYvG0BLx3AGANEaohKSHMztQikuwVFVjnToN2+w5ItvulGlYqmuQi4sz9mYqRU9PLxs3tRCJRmiob6C2thaXWyzitCfoaGJiYmJiYmJiYjKWTIgYvGyvlc1mo2nSJGpqa+nr7WXDRytZFY9RVd9A48JFVB5wENollxPfspno6lVEPvqQ+JqPSbZtIdXbgxqJQDKznAdQIKkK6QmskiIj2Z3I5ZVYGhqwz5yFfd58nPMWYJs2Dbm0jIiq0tLezvaXXwINJk+bSsVeC7Db7elyy7I8oUMax4JsHe12O5MmTaaurp6+vj42bFjP2nXrqCgvo6G+nuKSUhS3B8XtwTZlCs7Dj0SLRtBCQVI9vSR3dJPasUNo6utDDYfQ4nGRGElWkKxWJJcLpaQEpaxcLMNSUSGy6XqKkBwO4bk00DRCAT/bOjro6u4GJCZPmUxFRaWpo4mJiYmJiYmJiclOmFA9ZEVR0DQNVVWx2WxU19RQVV1NOByifetWPnr7TVKaRnl1NVVNkymdv4CSM88Sy2v09JDs2Ea8YxvJ7R0ke3ag9vWJRbtjMZG6V5aRbDbhnSopQykrw1Jdg62+HmtNHXJlJbLLTcpiIRiJsGV7Bz3r1qElk5SWlTN33nyKS0rSa/ip+nxJM5wxl2wdrVYrVVVVVFZW6jq2s+rjj0mpKuUlJVRVVVNcUoLN4QC3B9weLNW12NH9i6qam0DHQF/w3ggvLuTCS8bjBPx+Oru66O3tJaWpVJSXM3fePIqLTR1NTExMTExMTExMhsqEMhyB9LwyTZ+3KMsybreHmbNmM33GTEKhIF2dnWxZv441kSgWq5WikhKKS0sp2mtv7PsfiFtRUIxkOfnzHiVEKKssoUoySVUlGovRFwzi6+oi2LeeWDSKxWqhrKyMOXPm4vF4crxQhqExkRaBn2gMrOMspqszCAWDdHV1sWnLZqKxGFbFgsfjobioCE+RB6fThdVmE8acxTJoFik1lSQWTxCLRAgFg/gDfgKhEPFYHKvNSmlZGXPmzsVTVGTqaGJiYmJiYmJiYrILDNtwNOZpmezZmDqajAZer/cs4ChgH2AhUATc3tzcfOEA29uBK4BLgGmAA2gFngT+2NzcvHkMim2Sxy7o6AF+BJwFTAWiwNsIDVeMRZlNcvF6vRXAGcDJwAKgAYgDHwI3ATc1NzerBX53KHA1cDCiPq4HbgT+3tzcPPjK0CYjznB19Hq9VsCLqLv7AvMAK/Cl5ubm/45p4U3S7IKOM4EzgROBmYj0sL3Aa8Bfmpubnx3TEzABdknHJuAnwH7AZKAM2AFsQLSrtzU3NxdYlHLiYrpaTExMRpKrga8jOi1bB9vQ6/VagKeB/0MYJsuAfwKdwDeA971e77zRLKzJgAxHx1LgVf03KeBfwN2Ih+ojXq/3m6NZUJMBORv4D3AQ8DrwF+AeYC/gv8BdXq83J5jD6/WeBrwAHAncB/w/wAb8GbhjrApuksNwdXTr21wK1AIdY1dUk0EYro6/AIz1RFYAfwReRhgsz5jt6rgxXB2nAxcAPuB+hI4PIYzIG4En9L7QHsOQC2tmgvxkYOpoMsp8B2hDeCmOAgYbFT0DOAxhPJ6QN0p3PXAN8H3MRbjGg+HoeB3ioXkvcG5zc3MSwOv1VgFvAH/wer2PNjc3rxvVEpvksxY4FXgkr25dhdDl8wiPxj3658WIDlEKOLq5ufkt/fOfAc8AZ3m93vOam5tNA3JsGZaOQBj4LPBec3PzNq/Xex1gLkw5/gxXx8eA3zU3N7+bvROv13sUIiLn916vd3lzc/O2sSi8SZrh6vgKUJYf3aFHBjwBHK1vf9eol3yE2KOsXBMTk4lNdvjMEMKhp+nvjxQImXsAYThWjVzpTIbKMHU8U3+/xjAa9X10eb3ePwJ/B74KfG+ky2kyMM3Nzc8M8HmH1+v9J/ArRKfF6OCchahvSwyjUd8+6vV6r0YM8HwN0/M4pgxXx+bm5jjw6JgV0GRI7IKONw+w/fNer/c5YDFwKJn6azIG7GJ9LLR9wuv13q9vO3M0yjpamKGqJiYm48VH+vtJXq83vy36nP7+1BiWx2TXqNXfNxb4zvjsuDEqi8nQMObUJLM+O1Z/f6zA9i8gPFmH6vOSTSYGhXQ02fMYro6m7hOTIevi9XoVRGQAwAejVqJRwPQ4mpiYjBePIMIbzwQ+9Hq9TyEmme8HHI7wVP3f+BXPZIh0A3WIpDir8r4zvMpzxrREJgOiz6e5WP8z20icrb+vzf9Nc3Nz0uv1tgDzEZquHtVCmuyUQXQ02YMYro5er3cyYiAujBjQMZkA7ExHr9dbicgbICEiOxYDM4ClwMNjVMwRYY83HIeb/U//jYQQ+DJgb8CJmED+JnB1c3NzvwenyegyHB29Xu/NiCycg/FMc3Oz6eWYwDQ3N2u67tcAP0Nk/zN4GlhqZnHcI3gY+BJwndfrPd/QTM8+9119G7vX63U2NzdHxquQJml+i5iTuqK5ufnxrM9L9HffAL8zPi8dpXKZDI+BdDTZsxiyjrq3/3bADvywubm5dwzKZzI0dqZjJblzjTXgD8BVzc3NWoHtJyx7vOGIyOS3EAgikjkMOrLt9XodwHJEKNwahLUfAOqBI4BZFBhxNRl1hqPj/cCmAb67CDEibs7xmODodXEJcBJwJWJeYxiRMOdvwAter/fs5ubmB8avlCZD4BrgBES2ubler/dpwAWchmhbw/rf5iDAOKNnYvwe8DGirRwORmK1PaqT80lkN3U0mSAMR0c9tPFWxPPxToTRYTIBGIqOzc3NHwOSrmMDIjngz4HDvV7vyc3NzT1jVd7d5ZNgOA4n+x+IVLifA36D8C4WynRkMvYMWcfm5ub7EcZjDvqyAD9EhDvePPJFNBlhfowwNr7V3Nz8r6zPH9U9ke8Bf0UYlCYTFD0pwAGIwZ9TEGvI9SI8kb9AzHP0DZQkwGRs8Hq9VyLq0yrguAIdFcOjWEJhivO2MxkHhqCjyR7AcHTUjY3bEM/Lu4AL9zQv1SeV4dZHPSJnC/BXr9e7HbEM2c8RYax7BHu84Tic7H9er3c6Irvfm8BPC1W8PW0hzk8Kw8ziOBAXIcKO72hubu4eiXKZjCpGApx+gwTNzc3ve73eHmCy1+utaG5u3jG2RTMZDs3NzV3At/RXGq/XewzCU/XmeJTLROD1er+NWItxJaJz01lgszXA/oiom7fzfm9BzGFNUjgJkskYMEQdTSY4w9FRr3tLEUbjUuBicwrHxGAE6qMRGXf0CBZr1NnjDcdhcj4ik+wtQLHX6z0FaAJ2IObErR/PwpnsNl/S3/89rqUwGSpGdsZ+S27oczkMD4fpqdpzMerk7eNaik8xXq/3R4j5N+8BiwcZVHsGsVD1ZxCj4NkciQg3fqG5uTk2SkU1GYRh6GgygRmOjl6v14bwMJ6GmNZxWYGlq0zGgRGqjw36+x6VHffTthzHAfp7CbABES/+a+BfwFqv1/v/9JAAkz0Mr9d7CLAAWJvtvTSZ0Lyov19VIMX/dYiBrTebm5sDY1oqk2Hh9Xplr9frKfD5FYjBuvcwDcdxwev1/gzRuXkbMSI+WOfmbkSG3PO8Xu/+WftwAL/U//zHaJXVZGCGqaPJBGU4OurPxPsQRuP/MI3GCcMwdTzI6/W6CnzuQYS4gsgwv8fwafM4VuvvP0esD/d9RJKVAxHGoxfoQnRaTfYsvqy//2dcS/Epx+v1ng6crv9prO93iJ4JF6C7ubn5+/r/f4WYE3cc8LHX630MiCAm/x+o/z8n9NFkbBimji5gu9frfRIxRxlEorEDEQN0Z5hTAMYer9d7CeJZl0IM0nyzwDSATcZC483NzX6v1/slhAH5nNfrvQPoAU5FLNVxNyIph8kYMlwd9d/8mEyCuX3098u8Xu/h+v9fam5u/u9oldmkP7ug4z8R6/x1A1uBawps/1xzc/Nzo1RkkwLsgo4/AY72er3PI+Y2hhGRjichMlS/gsi5ssfwaTMcDW/iNkRnxkgN/4yejOMd4Lter/fXZiKHPQev11sCnIOZFGcisA/9l0qZRmY9v82IARuam5u3er3eRcCPgJMRy+PIiPp5M/A7PROZydizD0PUEYgBdyDW3lysf7YBkXr8T83NzcFRLanJQEzV3xXg2wNs8zxZbWZzc/P9Xq/3KOCnwOcBB2Iw4LvA38yEHOPCsHVEhBsflbfNofrLwDQcx5bh6mhsX4nIXD0Qz+1muUyGx3B1/A8QQkQ8Ho0YaO1FeCvvAm5sbm7eo0JVJU375DwHvF7v0YhEGwOt//c4Im38f5ubm79U4Pv1wHRgn+bm5vdHubgmA7AzHQtsfyViofg7mpubzx/l4pmYmJiYmJiYmJh86vi0zXFco7/3DfC9sZiqc/SLYjKCGIMA/xp0KxMTExMTExMTExOTXeLTZjg+rb/vlf+FPhF5pv7nprEqkMnu4fV6DwIWIpLiPDfOxTExMTExMTExMTH5RPJpMxwfRaxBdaLX612c993PENlWn29ubu4Y85KZ7CpGUhxzCQ4TExMTExMTExOTUWKPn+NYIPvfiQjj0Ej1n539Dz2r2BOADZHqeDNi0uqRiIyqhzc3N68di7KbZBiujvpvioF2wAo0mCnKTUxMTExMTExMTEaHT0JW1X0YevY/mpubX9LXqLoWOAaRDnc7wmP1i+bm5rZRLq9JYfZhGDrqXAC4EUlxTKPRxMTExMTExMTEZJTY4z2OJiYmJiYmJiYmJiYmJqPLp22Oo4mJiYmJiYmJiYmJickwMQ1HExMTExMTExMTExMTk0ExDUcTExMTExMTExMTExOTQTENRxMTExMTExMTExMTE5NBMQ1HExMTExMTExMTExMTk0ExDUcTExMTExMTExMTExOTQTENRxMTExMTExMTExMTE5NBMQ1HExMTExMTExMTExMTk0ExDUcTExMTExMTExMTExOTQTENRxMTExMTExMTExMTE5NBMQ1HExMTExMTExMTExMTk0ExDUcTExMTExMTExMTExOTQTENRxMTExMTExMTExMTE5NBMQ1HExMTExMTExMTExMTk0ExDUcTExMTExMTExMTExOTQTENRxMTExMTExMTExMTE5NBMQ1HExMTExMTExMTExMTk0ExDUcTExMTExMTExMTExOTQTENRxMTExMTExMTExMTE5NBMQ1HExMTExMTExMTExMTk0ExDUcTExMTExMTExMTExOTQbEMdUOv16uNZkFMdk5zc7O0u/swdRx/TB0/GZg6fjIwdfxkYOr4ycDU8ZOBqeMng0I6mh5HExMTExMTExMTExMTk0EZssfRoLm5eTTKMWJomkrLho043W7q6uqIvP8uoaceJ9ndjX3WbDwnnISloZFNGzdgsztoaGhAVVVkeeLa0F6vd8T3OVF11DQNSZJQUwlaNt5Lsf0Cqmpl4ptUIm84SO6wYJsWx3lgHEuZyqaWRmTno0yaNN/UcQKR1lFV2bJ5Mw6nk9raWqJrVhN+/lmSXZ3YZ83GfdRxWKqraWlpwWq10tjYaOo4wTC0DAaD7OjuBiQmT5kMqgqpFFitxBNJ2rZsRtM0pk2fDoAk7faA86jyadIxpz5u2YLDbqe2ro7o6o8IP/sMya4ubLNm4T72eKw1tWxqacFi1scJR7aOm1o24ikqorq6htjG9URefIFkVye2mbNwHX4UlooKNm3cgGKx0jRpkqnjBETTVFo2tuB0OqmrryfywXuEnn6SZHcX9pmzRH+1vkH0V212Gsz6OKHo189xOamt+eT3c4ZtOE5UDBG6OrvQNJW6ujqCLzxL19U/Jr5xPZLFAskUoeefpernv2XytOl88PZb1NXVIcvyhBfx04JRETu2d0H8aqqmaoTfldjx6wrC7zgBDckCJWcEqPhukPqGbWzZch19vmWUllhMHScIho5dXV0kYlGmTJ1K6PVX6P7ZVURXr0SSZTQkik4+hcqrr2fy1Kl88PZb1NfXm/VxgiFJEpqmsb2jA7fHTW1tHZH33iX09BN6B2c2nhNPYtr06bRs3MC2be3U10/8AblPE0Z97OzsJBGNMmXKFEKvvkz3z35M9OPVSIoCmob7pJOpuuYXTJo6lQ/eedusjxOM9POxowMtlaK6uobw22/Sde1Pib7/LpIsgyxRdNqZVF11LXWNk2jb1EJfXx+lpaWmjhMEQ4fOzk5UNUVdfT2hF56n82c/Ev1VWUFTU4ReeJaq63+T6a+a9XFCkW5Xt28nGYtRO3UqoddfpfuanxBd9cnt5+x5JS6ApmnIskwymWRHdxd1kyaT7NhG37//QXzjBpSSUrDZkUtKCD/1BP47bkdOpahrbGTD+nUAe6R4nzQMHROJAL2d/6Jp8nqSOyz03egh/IYTxZNCdoBkU/Hd7cF/vwObDYo9K+jrXpH+vcn4ktExQW93N03TppPs6qLv3/8guupDUR/tDmS3m8BDD+BffieyplHf2GTWxwmGqqoAdG7fjqaq1NbWEXjuGTq+8RV2/PkG/Etvpevan7D9qh+QbGulur6BRCzGjh070g9Gk/Elpz7u6KZx2jSSnZ30/buZ6OpVKKWlYLcje4oIrXgY//Jloj42NLB+3VrArI8TgWwdfb29NEydSmpHt9Dx3bdRiouRHA4kp4vAvXfjW34ndpsVd3ERvr5ewNRxIpBbH3uob5pMcnsHvf9pJr5hHUpxKdjtKMUlhJ94XPRXVZW6xkazPk4gsnXs6+0V7WpXl6iPKz8YtJ+zfg/v5+yZpR6Abe3tuNweXHY7/sceIfLqy8geD2osBqkUaiIBDjuBe+8i/PabVNbUImnQ19cHiBvBZPzZ2raJEs8/sDkVQs9ZCD3rQvakUOMyWkIDVQYZ/Pe4ia6UqK4PE/HfQG9vGDB1nChsa2/HXeTBYbMRfOoxwi88h+wuQo1GIZVCS6WQbDYCdy8j8t47VNXUIAF9vaKTY+o4vqQH5BIJerq7aZw6lUR7O75/NxPf1IJSUiYMjuISQk8/Sc9tt+C2WrE5nETCoi7uqQ/GTyLb2tvxeDw4bTYCTzxK+MXnkYsy9VFNJsBmI7D8TiLvvk1lTS2yBj7z+Tih2NbejtvjxmG1EXz6SULPPo3sKUKNx9CSCRE+brEQWL6MyAfvUVtbRygQoLe3BzB1nCh0dGzD6XLictgJPPYIkVdeEjrGonn91eXp/qqCZD4fJxjb2ttxu92in/Ok3s/xFA/az5GB3j1Yxz3+qZ4dY9y7o5vGKVOIrl2D/47b0VKp9HZGKI5ksZJoa8W37DbkQIDymhq6OjvT+zEZH4zr7/MnSYR+Rm3jDuKbJPzLPahhGSRJ1w+0lIZkh9haK/773GghC5MmvUZn+39IpRKmjuNIbn3cQcOkycQ2rse/9Da0RBx0aSRFAb1Bjbe04LtzKYRDVNbWsr2jA1VVTR0nCNu2bRMDAFYrgRUPEXntFWS3J6eDIzkcBO5dTuj116irq6Ovt8fs4EwAsutjX88OGiZPIbphHf47bkNLJjMbGs9Hm43E5k347rgdKRSksr6ejm3bzPo4zhg69vb2Eo1EqG2aRHzzJnzLbhMD4xKggaRY0FJJJLud2Pp1+O9ahhYJM2nqNLNdnQBk18ee7m4ap0wV/dVlt6OpKuhNZU5/tXWL6K8GA5TV1tDV1WX2V8eZ/u2q3s9ZNvR+Tuf2Pbc+7vGGo3HR161ZQ019A0oySeC+5cRWrURyOoS1L0lGfURTVWSXi9DjKwi88BzlpaWoqRTd3V3jdxImaR17Ou+htOQZLEgEH3USecuO7FLRUlK6MiKBlpKQHBqBh12E3lRwl2pYpX/RuX37+J2ESVrHj1evor6pEVlNEXjwPjH/xuEUgzlGQylJon66XAQffpDQKy9RWlyCJMt0dXaO41mYZHdU41HRUY19vJrAHbejoYEmQlDTHRyrlWT7VnzLlqD29TFlxkzT4JgAZOrjamobGpFTKYIP3Evsg/eRHI7c+giZ+vjIQwRffpHS4mJkWaa7y3w+jieGjsFAgNKyMmyyTOCh+4i+8xayM6td1TSQZKGj00ngofsJvfYqHo8Hi6KYz8dxxtBx7Zo11NY3oCRTBO67m+iqD5EcdjQ1v7+aEv3VRx8h+MJzlJfo/VWzPo4r6XZ11SrRrqrq8Ps50p7bz9njDUcAv9+HLEtU1dQQfvct/HffhWSzo6X0+TWSJGwOSRKZAGWZVCiEb8mNJDq2MXn6DDo7OkhleShNxp7e3hBh/z+pqgsQXSXjX+4BGTRNRgynAmiiPqoakkUitUPBf0cRyS6JSVM+pnv7r1FT4fE9kU85fX192KxWKquqibz/Hv47l4HVmvE86Q2qpNdHSZFJ+X30LbmJZFcnk6dPo6tzu1kfxxHjwRgKBfGUlmFLpfDdtZTomtVIdocYHZckYUQCWjKJ7HYTeuIxgs8+jcflwmq1sH17x3iehgnQ19eLzWqlqrqayPvv4r/rDlEf1dzno5T9fAz68S25ieT27UyaNo1Osz6OO319ffTu2EF1TQ2RD9/Hf8dSUJTcdlWSMu2qxYLa10vfrTeR7O5i0rTpdHV2mjqOMz5fH7IsZ/qr99w5SH9VE/UxHKJvyU0ktm1j8owZ5vNxAtDX14fNprerHwy1n+PX+znbRT+na8/UcY83HDVNY9vWdipr6yAYwHfbEpLbtyFZrWL0DeFlFGEAWtr6l51OIm++RuDhB3DYrHiKi2lrbR3ns/n0oqop2rf8lSlTXkQLK/jvdRNrsSDZgZReEVXQVEnIKoGWlJCcGqHnHYSesWO1yVSW3ceatSvH81Q+1WiaRkd7O1W1dRAO4Vt2G4m2ViSbTcy9AdDro+HV0pJiVDX88osEH1uB3WKltKyczS0t43syn3J6e3vY0dVNTXU1oddfJXD/PcJozH7QafqD0WhbY1H6ltxIvK2VpqnT2dHVvUc+GD8paJpGZ0cHlTU1oj4uvZXE1jZRHw3DMas+Zp6PLiKvvEzgsYexW62UlJaxZfOmcT2XTzOaprG1rY3pc2ajRsL4l91GfPMmJLu9YLuKJKElk0gOF+EXniP05GPYLBYqKito2bhxfE/mU4yRnbq6thYpGMR3+xKS24bYX33jNQIrHsRhteIuKqa1dcs4n82nF03T2Jbu54RFuzqkfo4zt59Tumf2c/Z4w7Fnxw4AykpKCL3wPMEnHkV2unLnN1qtYjkOMiPpmh7S4bt9CbG1a2iaNBlfXx/RaHTsT8KEzu1bcdjuxF2sEn7Lgv9hN5JdywlRlSwakiIaV0nSEBMCJLSkhO/2IuJbFGrqOlCSvyQYjI3buXya6ersRFZkSoqLCb3yMsEVDyE5c0M3susj2fVR0+i79SbiGzdQ39hILBolHDa9x+OBGADYxsy5c0n19eHTvRaSRckYHLpmmqaJtOOpFJLDSfTdtwncfw82Raa0ooKWjRvG92Q+xXR1dQISpSUlhF5+kcCjjwxaH7OfjxoavltvJrZhPQ1NTQT9AYLB4Hidyqea7R0d2O023E4X4ddfI/DQA0PSETRQU/QuuYn45k3U1jeQSMTTyatMxpaeHTtQVZXSkhKCLz4n+quuofZXJfy330Js3RoaJ03G3+cz+6vjRFdnJ0q6n/PSLvRzbt6j+zl7rOGoaRqqqrK1rY3J06eT7NxO35Ib0SLRTKy/jhoIoIbD6fXIskM54i0bxMTjZJLa+nra29rS+zcZfTRNI5WK0dn+K5oaPyTVI+NbVozaKyMpGR0lSUMLS2gxSfxf099VCcmmEf3IRuB+F7IqUVX1HG2bb0rv32T0ETqm2N7RweRp00nu2EHfkhtRg0GxtpgxJw69PkbCSJKcWx9tNmJr1+C7aymyqlLTUM+WzZvT+zcZOzo6tmG1WXE5HASfelxkbnS60NSsB6PNJkbKIROyqqpIioJv6a1EV35IfUMDiXjcNDjGGOP52LFtm3g+dnfRd8tNaKEB6mOh56PNJhKs3LkUOZmkrrGRHd3d6f2bjD5Gu9rVuZ2mKVNJ9fbiW3Ijqt8ndFSzdAwG0SKRHB01Xcf46lX4774DWVWpraunpWVjev8mo092f3XqjBkkuzpFfYyEh9FftRLbsAHfsttRUglq6+vZtnVrev8mo8/o9HMa9rh+zh5rOEqSRGvrFkrLy3DYbAQeeoDIW28g2W3phDhoGpKi4D5+Mc4DDxKhG7KcFkfTNCSbHf+D9xJ+83WqqqsJhUME/P7/z95Zh0lxZm3/V9Ku484MkoS4u7sLITjxzUrWv9V3867Lu5p1ieMeiEFkkxA3orgzjA9j7V5V3x8l3T0McQEy57qaGXq6qqvr9P089/Hhhg6fkgiCwPZt66gsW4HdAbGnXcSfcyA49WijIGhWTxzHkWnsB2TRsgKCqBuPoKFpIoKsEV7qJbXOQUlpFJRl9PZ0DuvxUxJBENjZ3ExJWRkOm43Yo4+QePlFBIfDwKOx1Igi7tNOx3Xk0WjZTDEeVRXBZiOydAmJt96kpKQUTdMY6O8f1uOnJObG2NfTy4iRo8i2txKafR9aNmu8wPSCa6jhMKppEGoFUQ5ZJtfeRmjODIRkktqGBlpbWqzzD8snL4Ig0LxjB+XlFThsNqIrHiHxykvFeNT0+in3GWfhOubY3fdHE4/LFpN483XKyssJhQYYGBgYxuOnJKYeS8rKdT0aY1TMxkaCOe5GEHCfcBKOQw9Hy2UH6VFDkGXCixeSXP0OwZISRFGif3hd/dREEARaW1ooLSvDYbMTfeQBUq+/atQ2DuKr55yH6/ih+KqK4HAQeXApiVWvUVFZSSwWIzLMVz81MXlO6cfGc96gpKRkn+M5+6ThqGkamUyG/r5+6uobSG/dQmj+LNCMbsaapheNZzK4TjiJqv/7ExU/+imOA8fqHjlZttKsBEFA7e9j4L670KJRRo4eM+yN+xQlFkuRif+eyqo2Mu0yoXletKyAXsSogQRqBhyHZaj+fT8VPwphG5HTI49G2qr5ulyHSGi+BzUp0jTiaTpa/66nXGnDQ8g/SdE0jVQySTgUoq6unnTzDkJzZug5/voLdDymUriOOY7q3/+F8tt+hn3MAaipFIJUgEdRROnpJjTjbrREnMamJlqN2uNhPH7yIggCWzZvpqyiArssEV6yiPSa1Qh2u77hGSmpot2Jf9JUvBdchGB0TtU08qmrDgfRFY8Qf+E5Av4AggC9vT37zMa4L4umaSQSCQb6+6itqyO9fTvhuTNBG4THTBrXscdT/bvbqbjtpwYek0PgsYfQjHvQ4jEOHHswba3DToBPS5KJBKlUkpraWjKtLYRn3ZdPazT1mE7hOPpYqv74Fypu+xm2kWOMdVWy9IgkoXR1Epp5D1oyQWNTk1WzOqzHT1Y0TSOdTtHX06OnJm7bQmjubDQzT6OQrx5/IlW/NfnqQWipofhqLwMz7kaLRhk1ZgzNRo3csB4/WdE0jWQiQSg8QE1dPenm5qF5Tnr/5zn7nOFohnx3NjdT39CAiEZ4/mwy27Yh2GQrBVVLp5HLywlMuRZbTS2uY47DP3kagttteXLAtP7tJJ5/lujjj+L1eHC5XPT07BomOZ+gmOBo3TmXhtrHEAWB6ANu0mttCHa9CY4gaWgZAcmvEpwYw3FwDs/pKfxXx8GmZwWYKtJUAcGmEX3CRfxFFw53jjL/Enbs2GylCgzLxy/muIX29jZGNDYCKpFF88ls3qSnMRqpi1omjVxWTmDqtdhGNOI+8WT8k6YiOpyg7I7H+NP/Jfbkf3G73Tiddrq7u63UnWH55CQSiaAqCtU1NSRXv0Nk0by8t9SIUJHL4j7jLCp//mvK/+fHOI46Gi2V0usfzdcBWjzGwL13kuvrpWn0GNrb2w1HzrAOPykx8biru5um0aPRVJXIormkB+MxbeBx2nXYGkbgOv4kApOmITpdoOR0PYOV6hh/5iliTz6By+nEZrPT0zPsBPgkxcTI9u3bqauvRxIEIovmk96wLu/EMYwNqaSU4NTp2EeOxn3qaQQnTka0O3RdD9Jj7MnHiT/zNE6Xi4rKSpp37BheVz9BMflqW2sbjaNGIqga4XlzyGzbiiDbduerU6/DVluH69jj8E+ejuDaA199diXRJx7TB88P89VPXMx1tbOzg8bGJkRUIovmDc1zSj8Iz3mS2JNP7HM8Z58zHAVBIBKJkE6nKauoILlqFdEHlupemcLufUoOz1nn4j33fGvQsf/KcXhOOwNSKX3gcf6kaJkMoVn3kOvqYkRjE53tHahmh7Jh+dhFEAT6+zpAfQRvIEx6o4PwEreegqqCOX5Dy4L7lDTe85NoUSALgXExXMel0FICSAX6EUCLC4Rme1H67dTUbmGg589ks7k9XMWwfFQRRZFIOEwymaCkrIzU228TuX+RbmxY7f5FyOZwn3EWnvMuzOPxqvG4TzkVdTAeAS2VIjTzHpTeHhqb9LbV6nB3zk9MzHWuo72N+hEj0NJpwrPuI9vRAYbHW5BltFQSua6ewLTrkHx+HGMOJDjtBsRAEC2T0aMcxvkEm43U668RfWgZDrudivIKtm3duk9sjPuqiKJIOBwmHA5RUlJK6q03iSxdbA2iBnRvWy6H+4yz8Zx7gYVH31XjcZ1yej5aVSBaKsXAjHvI9exi5KhRdHfpMzpVdTib45MQc4aqJIoEAkFSa1cTWbKweF0VRchm8Zx2Jt7zL87rcdwEXCedgppM7r6uJhIM3HcPSm8vNdU19Pb0kDXT0IflYxedr4ZJxOOUlJaRfOM1og/qfFV7D77qu3Ic7nfjqzPvIdfdRWNjI50dw3z1kxST58RNPb4Xzzm/gOeMey+ec2+e5+wjY1b2KcPR3KR2Nu9g5KhRkEwQmnk3ipEbrAGCJKOlUtgaGvFPmKK3x5UkNFVFKinFP+06pNpa3UNuKtEgOZn16wgvnIvD4aCyspItWzbrqQHDm+PHKub9bGu5m6a6RxAUkYF5HrKdEkjoNqOkoaUF5GoF/9UxxBLDkFRAblAJToojl6mDUlZBsEHqTRvR5U5Em0Bj7UJ2bHtiWI+fgJj3c8f27YwaPQYyGUIz7kbp2aUvkEbNhpZOItfXE5gwGdHlAiPdUS6vwD/1OuSqarR0MVkVbDbSq98mvGQhDoeDirIKNm7cMKzHT0gEQaCnpwc08Pn9xJ97hujjKxBstuLh4gh4L7gY92mn6xujpuG95FK8518AuWzRMHkzbTU0ZyaZrVuorqkhlUySSiU/s8+5P0vh/njAgQdCOm04X3p1HIL+M5VCrm8gMHEyotMJkoimKshlZQSnXYtcUzMkHjNrVhNetACHw0F5WTkbN6xHFMVhPH7MYu2Pra16Fkc2ZxkJRetqKoVcW4t/4mREtzu/rlZWEph2LXJlFVo6vfu6+vYbRB5YgihJNI4cyc6dzcPr6icg5v1s3tHMqDFjIJUiNOMelP5eqxYuz1dH4J8wuYivyiWlBKZeh1Rbtwe+upbwgjxf3bx507AePwEp5Dmjxxyg85yZ78FznAU8p6wC/7TrkKuri/XIEDynvIJN+wDP2WcMR03TEEWRnp4eHHY7Ho+H2BOPEX92JYJN1jdFQdDzFyVJJzcnn6KH+QXBqs3xnHUevksut4hQofdb0zTCSxaSWrOaiupqlFyORCKBKO4zt2mvF1XV9bhr1yoc0pO4gjniL7qJP+FEMIxGBOMn4Dkzhee0FFoCBEl/aHHwnpvAc04S1PwxVrOcnEB4gZvMdgcl5SGy6UVEIruG9fgxionHrq4uvF4vbreH+Monia180qqtyc/3E/GcdyHu08+00m4ESUJTFLznXYDvokt0HQ7Go6oSXjCP9KaNlFdVIUkSsWh0WI8fs5ie6va2VkaOHo060E/ovrvQ4nEr/UmQJNRkEvsBB+oOOQRr+LjodBGYfgO2UWP0GnJJts4tSBKZHTsIzb4PAahvGMGO7dv3+o1xXxMTj7t2deNyunQ8Pm3g0YwYm3gURTwXXITr1DOM/VFEEHU8es49H99Fl2LWmBfvj3p6VmrDOiprahAQiMdjw3j8GEVVVV2P3d047DbcHg/x554m9t8n8iS1oAun59wLcJ9xNpoyaF09/yI8F16UTx0v1KOiEJ4/h/TWzZSWlpJKpYfX1Y9ZhuSr/zX4qmxDQyvgq6LBV0/dna+efR6+iy99d766dg0VVdWoikIiER/W48coph67u7rwej243W6d5zz9AXnOuRfgu/AS86R74Dkb9hmes/de2SAxm5y0t7bSOHIUSu8uQrPuQ0sm895wUffC2Q84EP+kqfqBhTdfEBAECEy7Hschh6IVho6NVKxcRzsDM+9BVFXq6urYtnUrwD4RPt43RENRMnS1zWBk4wuo/TZCc9woURG90Z/RMTUtYGvMEZgQQ3BgGZLGKRCcEJwawzY6q0cdxYKoowyZHTKh+W4kQaSxZj47ti0ChvX4cYneXlyhq7OT+hEjUPr79YL9WEz3tBkEVUunsI8eQ2BPeBRFAtNvwDF27JB4zLbuZGDWvYhAbU0N243h1cN6/HjEJKob1q9jRMMIJIeDyANLSb2xSo82mg1xcjkElwvfFeNwHnoYWjabJzi5nF5DfvUEBKfDquUw6yIFSSS2/CGSLz6PvySIKAgM9Pfv1RvjviZWu//2dhqbmlD6+xiYeTdaPJ43GkRRj26MPoDApKl6t+rd9kdR3x/HHqzjURyEx7ZWQrPuQ1A16urr2L5tGI8ftyiKQldXJ00jR6GGQwzcdw9qNJrHlLmujhpNYNI0nbgKg/QoSQSn34D9wIP01OPBetzZTGj2fUiSSOOIEWwzZq0O6/HjkcLxG02jRqH09hCaeW/x+A1Rb6RiP+Ag/BPfha9OvwHHwUPxVRtKRzsDM3S+Wl9Xz7atw3r8OMXkOZ2dndSPaNwDz5EKeM40/cAPxXPus3jOtr2c5+wTO7dJblavXs3IkSOR7HZCixaQXvuOXpiqmQOoc+By4b/iahwHjbXaUpuik5wsjjEH4J80DdHn0wlQQatcBIH4U/8lvvIpfMESnA4Hvb29SIZ3fVg+vCiKgiiKbNr4NtVlC7F7BMKPuEi+bjdmNhrzGlXApuG7KIHr6AxaUjcG9aiU/ruWBOdRGQLjEghODRSKRnSASHSFk8QqN96yDE7bCnp73kSSJFR1WI8fRUw9rl+/nhEN9dgcDsJLF5N++62C6IbuMcXhxHfZlTgL2sSbYuFx7MH4J0xB9Hh2x6MoEn9sBfHnn8UXCOKw2+gz8DgcsfroIggCqVQKRVEIlJSgbNtKaN5Mqz5Df5EI2QzOI44kMH6i/pwx2FgDaxMMTJqK69gT0DKZopRVQZJQBvrpv+9uhHichhGNtBjjOYZ1+NHF3B83bdpEXU2Njscli0i//fag/VEBpxP/FVfhPPjQoqYbUIDHg8binzgF0evZbbQDgkDsiUeJP7cSfyCI3W638Di8P340ye+PG6mprsbucBB+4H7diSPnoxuaooLdge+yK3AeceQe11XnIYcRuGYyktttRSSL9LhiOfEXn8fn9+NyOOnt6RleVz8GMfG4ZvVqmhobkRwOQov3wFcdLvxXjNMdNXvC45gD9AYru/FVDU0QiD/1BPFnnsIbDOJyOukx9DiMx48mJh43rN/AiHqD5ywbiufkCnjOYe/Bc6a+L57j3Mt5zj5hOIqiSDqVQs3l8AUDKJs3EV6yAM1oeqIZqRjkcjgPPQL/+Il6cwbZlv+7IYKse9H948bjOvlUvWGAIOgPozOSGg4xMPMeiESoa2igrW3faZO7N4skSUSjPUjaXZRV9JFtcRBZ5NKb3AiqbvSJQFbAcWAW//iYnooqAlpBw0bNSFlNQ2B8FNcxaX2Eh8lVNRAkFaVXZGCOEy1up7HuUdpalqIoOYrDl8PyQUWSJOLxGLIk4Q+WkNu+jfCiuZbBoHvhBMhlcR5yKP4Jk40DDWNjKDyOn4TrhJP0jo5Wq1yjbfVAPwMz70VLJGhsGklbe9swFj8GUYz5YTu2b2dEfQOiJBGeM4Psjh35jdHY9ERfgMCEycg1tbpRaaaaGmuvms0iVVYRuPYGpPIKfWM0yYumIUgyyVdfIvTgUtweD3abTH9fH6Io7rVe1X1FRFEkHo8jAiXlFWS3bSW8aB5aNgMU7o9ZnIceRmD8pCKDf0g8Xj0R14mnFO+PRi2POjDAwMx7UGMxRjQ20traOtwt92MQM0VNliVKysrJ7GwmPH8OWiZdFDUml8E59hD81+y+rpoaEGQbmqbinzAZ5wknQja7+7ra10vovnvQkklGNDbS3t62V5LUfU1EUSSVSqEqOfwlJSibNhJevMByxhXx1cMOL+Cr77I/Xn0NrpMG8VWtkK/eC9EIdfX1tLe17XaeYfngYvIcSRLxlxg8Z+G8j8hzJuI64eS8HvUX7nM8Z683HE1SsXXbVpoamxAFkdCcGeRaWgaRmxyi10tg0hTkqiqrg5zZDtn83fgFyeuj5IYvIFXX5K1/801lmdSbrxNZtgTZ7eaAkSN5++23hknORxDzvu3YtpKa8plINpHQQgeZLTYEWdONPVFDUwREp4b/qjj2UQpa2sjC0Yr4i/7NVUCq0AhOiyGVq2hZoSDqCEgiyZcdRB5z4PBJ1FfexebNm4f1+BHE0uP27VRXVSPLMqG5s8hu3747Hj1e/NdMxlZbZ9Vu7BGPgQDBG76AVFlVbHQASBLp114m/NAyRJeLA0aN4o033hjW40cQTdOQJMkY5g7e0lKSL79IdMXDVv2UtR4qCu6TTsZ36ZV6BzmD4IiiqKerAqLNhqCp+C68BM/5F5lvoutcVfW0nmSS8NxZ5NpaGTn6AHp6ekin08Mpqx9BzO//tm1bqa6qwibLhObMJNs82Pg38DhhMnJtLbwXHv3+YjwWesclifSqV4k8tBTJ5eKA0aN5663h/fGjiLWuNu+gqqICWZYJz5ttjG0YpEe3G981k7A1jMivq2Bh0dKTqiEFgwSvvxmpotLQY/G6mnz1JSKPPIjd5aKhvp7169cN6/EjiIXHrVsZ2dSEKAiE5swk17LTmuO3O1+tfl98NXjjzUjV1QV4NN5Ulkm9sYrIsvuR3G4OGD2St99+e1iPH0H2zHO2fSCeY813NH7qPOdmpMrKd+E5S/d6nrNX79iF5MYmSvjLy4k//wyxxx+1ivzzi6SK6/gT8V16BZqiWN6bwtk2hY0etFwO96mn47vsCt0yMYdYmzU9ySShBXMRmndg93gRBYFMJoM0qJ3usLy3qKqKJEns6n4Hr2MBvtIsybfcxFa40BQ9ymgZezlwHJnBf3lcNxrNFNUCEYxsVDNl1XN2Eu95CQS0gnRXAUFSUaMi4QUulE47/uAuZOEuYrG+4VSODyGmHnt7e3E5nPhLS0m8+hKxFQ+DaRyY91RRcB57PL7Lr9Ix9V54VHK4zzwH38WX5aP/Jh4lCTUeJzRvFmJ7K5LdQTDgJ5lIDOPxQ4qpp507m2loGIGYStFvdIoTZDm/8eVySOX6vD/R49E3TEDN5diyeTMPLFvGvDmzWbH8ETZt3ERWVSm96QvYR43RI16mUWh8BzKbN9I/dxZOp4PysjK2bNmCIAhGJsCwfBApxKPH5cZXWkri5ReJPvbI7nhUFZzHnYDvsg+AxzPOwnfp5UPjMZEgNH8OYmsLDrcbWRKH98cPKdb+uGsXXrcbX2kZyddfI7r8Qd2gGLyuHn0c/iuvLtZjwfmK9ag3WPFefKnVjKVIj9Eo4XmzUTs78fj8OJ0uI8oyrMcPKqYeB/r7scsS/rJy4i88S+yJFUPyVefxJ+K75Ir3jUfPqWfgu/TK3fFo8dU5CDubsbu9iALDePyQ8nHynII+j2iiiJrNWjxH2CPPmb3X85y93nAEaG1pobahHjGZYmDmfSg93cWNGxQFqaSE4LU36uRmsMdmiHOaaTrB624y6iELUuSMYbmZLZsZmDcbmyxTV1PLli2bgb23YHVvFk3T6Gx/lprKZQg5GwPzHGTbJQSbBmpBtNGnEpwURSrX6xbJTwEYdD7MUkYECYLTY9iaFLTcoJRVG6TX2Qndb8fulij3/ovmHW8Bw/VVH1Zadu6ktr4eMZNhYOZ95Lo6d8OjGAwSnHY9kt9vHfeueBQlBAGC19+MfcwBu+ERu53Mxg0MzJuDw+GgNBC0CshzuWGj44OIWbvR19eH227H4/USfmgZqRdftNJsBLCMRO95F+I54xzUXA5Bkkglk9x7z91MmTieb9z6Jb777W/y1S99gQnjxzHjzjsQDxxLcNp1CA4HKEo+WiVJkMsRe/gBEq+/RrCiAoddJhQKIUnysCPnQ0praws1tbWImTQDM+9F6eraHY+BEoLTr0fy+azj3hOPGHg88KCh98eNG+ifPwdZlqmvqWXz5k3A8P74QcW8511dnVRWViHmFAZm3YvS0ZEfLm7pMUBg+vVIgcBuxw91ToysgOANN2MbPbpYj5qGYLeTXreGgQXmuhpg+/YdwPC6+mGlpbWF2voGhJTBV3cZzrhCPZaUELz2BkSv1zruvfAIELzuxnfhq5sYmDdL56t1tWzePMxXP4q0tHxAnmNm6hT8tBywmoYoCHpmjoCOx/fDc4J7J8/Zaw1Hk9x0d3fhcbnwen2EH1pK6tWXdBCZBW+aBqqK55zzcZ9xlh5tNLzchW2rC8VSpqpibxhB8Pqb9PlypvVfUBMSW/EQiVdfIlBZicNmIxQa2GsLVvdGUZQcoiiybuM2Git/iz0gE33aTvI5hx6NsMZoABq4T03jPTepRxvNmY4MUqNpSBpq0jLgOChLYFIMwWEYokbUEaNDa/RhF6l3nAQqM3hsd9Oza82wHj+AmHgMhUK4HXZcbjeh5Q+RfPE5I6okFOPxzHNwn3Pu+8Kj+TdNVbGPHKVvqE6nvkAXptNlMkQfeZDkm68TqKzE5bDT39+HLMvDevwAIkkS2WyW1pYWmg44kFxXJ+H5c1CjEd27rap6DUcuh1zfQHD6DSAKqIbunn76Sf7w29/Q1dmJLMvYbDYEQaSvt4e77r6LzevWEZwyHftxJ1pDrvWmHgqC3U6uvY3QzHuQsllqqmtp2dkMDNfkfBAx8TgwMIDbZsft8RB6+AGSL7+Qj/IW4NF79jl4zvwQeGxsIjj9BkSnqwiPCAJks8QfeZDk668RrKzEabPrQ+uH19X3LYqiIEkSb7zxOgeMGo3s9RJ9/BGSzz+LZtRmFEapPGechfec8z+YHhUFx6gxBKbdgOB0FkU5EEW0TIb4Qw+QeuctAhUV+Nxuenp6htfVDyAmHnft2oXH6dSdcQ8uJfXKi0Pj8Zzz8bxfvkoBHkc0ErxuD3w1myO2/GGSr7xMsLwSp32Yr35QKeI5dkcBz3n+XXmOmsuhDUr9FwShiL8kEgkSiQSaomBveh88543XCVRU4nI69jqes9cajpIkkcvl2NXVTV1TE0pXB+FF81AjEd3qN40/RUGuqSV43Y36XJWCehkLUIPEUqwooqkqvsuu1GevGAWrAujKdDrJtbUyMGcmcjZLZXW11Q1wWN6fSJJMOhVHzPwDl78bsU8ivMCN0i8hyCqaKupGniIglaoEp0YRnBTE982Fs+CkhfF/jEY5WfBfFcN1bFpPf7WijgKCQyO7Qya0wI6o2qgoXUhX5+tFIB+WdxdJkshkMmzfto0xYw9G6ekhunAuaihk4DFPRuSqat0ZI9ssPFrqGnS/i1JzDDz6rxyP65TTrAJys25HcDjJtTQzMHcmkqJQXV3Dzp07P50bsJ+I6X3esH49IxsbkW02wgvmklm3Rh8+rRbMpZJkfFeNx3HoYai5HLLNRiwW44nHH6e/rw+Xy0U2mwV0b6jdbqe3p4eujg5wOCi95ctI5eWW8WimayFJJJ5/ltAjD+L1+7FLMqHQwF5Zy7G3ionHHdu3M+rgsSi7uokunF+AR9M5qiBXVxO89iYEm/y+9kdT8ngch+vU0wfhUd8fsy07GZgzEzGXo7q2lp3NzZ/8h9+PxNSjx+NBkGVsoQEG5s5G6e/PRzdMnlNZqevRbv9gejScQf5x1+A68WSL55jHCg4HmebtDMyZhaRBSVkZPT27PvHPvj+JJEnkslk62tsYMXo0Smcn4UXz98xXr71Br3l8P3zV/Gny1cuvwn3au/DVuTORslmqqqtpGd4fP5AU85yxKD27iC6YixoaeFeeI8qyVfM/uFGYqqrkcjn+evufuPM//yZjRA69V4zDfcrgddXgOTv3bp6zVxqOJnlYu2YNjY0jsNnthBbOJ7P6HX3RNC1+AEHAf+XVOA4/0lpkgT0aBYWDN83jRbebki98BbmySm91bXl+BJBkUs8/S3j5Q/j9AbwuFz09+jD5vSl0vDeKqcctW3dQVXonDp/AwAN2Um/aQYZ8Hiqgge+iOO5j02hZrLFUu2WpGqq3bMcCA1MKapTcGEUqUdGsqCN65ytE4itdxJ614SqTqSv5XzZubUMQBHK57Cd9K/ZpsRpUbdnCqKYmJFkmtGQ+qbff1PEIRQXgvsuvxHn0MUV4tOx4VUXL5fSHouweZRIERK9Xx2N5uRX90msL9HllyWeeJvzYcjw+H05ZJhKJDOPxfYhZuxEOh/H7vLiCQbKr3yby4FK9U5woYvZl1BQFx9iDrfmbpp8zGonQ1rLTqEtUrE1UFEVUVSUYDFBRVQWA6+TT8V85HhOkVtRRtqH29xNZMBelp5uRBx3EzuZmstnsXlfLsTdKYeOGphEjsEkyoUULSL391hB4BN/l43AMwuMH2h89Xkpu+TJyRWUBHg2SK4okn11J+NHleH0+XHY7oVBoGI/vQ0w9bty4gbKSEpxOJwOLF5B56w2w6R3hC9dV76VX4Dru+A+tR8nno/SWryCVleXPYUQdQSCx8kliTz6GMxikqaGBN998c1iP70NMPW7fsYP6mlrsso2BxfPJrBmKr4LvyqtxHHHUR+Ort3wFubIAjxZflUg8/wzh5Q/h8wfwuFz09vYM6/F9iMVzthbwnMULSb2zZ57jOPJoADo7O5g3Zw7NO3YgCEJRZFDTNBKJBCuffpLnnllJJqN3uxZ8fn1dHYrniCLJZw2e4/XudTxnrzMcTXITiUQoKy3F5vWhrFtL5KFlOrkpbNygKNhHjSEweZpOTN9n9Kiw85gJTOexx+GbMAnD4gBAU/XUKqW/j8jCeSg9u6htbKKzo4NcLodsFMIOy+6SLxRvxi3/jmBlkswmB9GH3ChxEcEOmoIVbbTV5QhMihd/I831Ml+SkY8+Gh1WEfO/a1lwn5TCd2m8KOqoKQKCXUXplggvciMOiLgDuxBSfyWdTiDLtuE0uT1IobHhdDhwl5SQ3biByANLUZNJHY8FXnF700gCU65FsBSki24w6t3gBFnWH5Kecl44M9DEo+uEE/FdPUFfSE08KgqCw6F7ARfNR+3vY8whh7J182YymcwwHt+ntLbsJBAIYhegf/YMss3b9XpEo6YRQLDZCEy/DlttneGp0f+iahqqqiEWjHIQJQm73U48FueMs85i1OhRAIiSSMn1N+E46ODiqKOmIjgcpN96g/CiBdhsNkY2NrJxwwZguCbn3aQQj3abDU9ZGZkN64k8tBQtPQQeR40mMGX6B9ofAYsgWXg8/l3w2NtDdOE81L5eRh54EDu2bRvG43tIIc/xuNyUVlWT2bKJ2AP3o8bjCHZ7sR4bGwlMvU6/9x8wS8ZKddRU3Cedgu/K8UXjADRFQXA6UHZ1E5o3B0ckDLINv89n6XF4fxxaCvFok0R8lZWk160h+uAytHT6k+Wr10wuxqPBV9W+PiKL5qP07KKuaSTtbW3DfPU9pJjn2PM858H735XnmLB4buVKvvedb/Laq68AWMahqqrIskxXVyfRaBS7XY9aApDL4TrhpHfhOT2DeM6mvWZd3esMR1Pa29pwOJ24ZJn+ebPIbtuK4HLpBMQEkSgSmDIdeURj0cL2nimIe/DsBKdeh8Mc4Gl6cVQVweEk9cYqQksW4rTbqSqvsFIA9pac471VOjp2UBqYgyTZCd1vI73RhujQQDF0oHfhwH91HMeYLJrJGU0j0fxvPsBs/a6qkM1ALoNV7yhIEJwWwzE6i5YTdFWb3jg7JF9xEHrYhjOgEfT+g507241zDevx3aS9vY1gMIBdFBlYOJfs5k2ITpdOQAo2Nf/kadhHjirGo1FMLsg2lFiUTMtOMtu3ke3s1I0VY1EumnskigSn36AbHUPgMfnqK4SW3Y9NkmgaMcJKkRs2OoaWfM14Nz6Ph5KyMqKPP0Zi5ZN6eN9IgwK9ttF14sn4Lr7cSp8ycejxeCgrLyeTyWCz2ay6i3A4zPEnnMi1N9yEx+PVh2ALgl4jedOXEBx261o0TdM34nSKyAP3k127BkcgiM/rIRKJDNfkvA/paG/H7/fjEEUGFswhu2UzgnPQ/igI+CdNw9408gPtj1Yb+UHPBadfj+PgQ6yW8xYenU6Sq14htGwJdptMY0MDzc16g5VhPb677GxupqysFAkILZxHeuMGBFfBuiqgZ1VNmIpj9JgPrscCZxDokYzgdTfiMBoe5fWogd1O6tWX6F+6GLfLhctus/Q4bDi+u3R0tBMIBHCIIqF5s8lu21KsR0y+ei32j5OvHnLYkHhMvf4aofsX4bTbqamqYqdRRz6Mx3eXjvZ2goHge/KcwORpyCNHIYoCSi7Hlq1bcNgdBINBMuk0d/7n3yxdshhRFGltaeE3v/g5O7Zv58STT8HhcOrnEUUQhHfhOQ6Sr75MaNkSnec0NNK8l/CcvcpwNMlNf38/DkmisqqK6H8fJ/7fx/OeNtPqz2ZxHnMcvivG5b3kBUWphTLUcOKi2irjNbaaWkpv/jKC3Zk/VtXnlqmpFNEH7iezfh3BmhrQVCt0PAzGYsnl9IY4vbvewCn8gmANxF+yEX/MDTkBJAFN0fRoY1bAcUiWwFWx4pMIBT8HGY1WRF8GWwBkv55Kp6qACvamHCXXRREkDQQj9S4Hgh2UiET0QQ+ZzXYqatNIyi8ZGGhBkqTPHIx7m5h67OnZhdvhoLS8gtgzK4k/vgLQLGPDHNvgOPJo/FeNt463iIsoooRDRJYspOcnP6LrW1+l8+tfpvu736T3d78i+drLunFSUCCuaRq2+gaCN38RQbbno8cmHhMxIssWk9m8CW9FBQLasNHxLmJ+v/t6eyirrkULhwgvmIOyqxvB4SiICKqIXp8+a6qgI655fDAY5JJLL6e6uppIOEQiHsfhdDJ5ylTunjmLY489DtU8lwFY78WX6A09Mhl9s9Q03avqchudq2fhEAS8Pj8dxvDqYdldzP2xt7cXhyxTVlFB7JmniD/xKCYeC/dHx1HH4L/q6nc9Z+HeWOS4GWIPtdXVU3LzF/NpWxTgMZkgsnQJmU0b8ZSXYxPF4f1xD2Ltj729eN0ugqVlxJ9/jvhjK0A1xm+YaeDZLI7Dj9CjEh9CBqc6apqmN1i56Yv66IDB2VWxKLFlS8hs3UxNYxOSIFipx8P7Y7EU8lW7KFFaXkH0qSeIP5nnq1ohXz36WHxXjPtQ7zWUHm21tZR+4UtD4zGVIrrsftLr1xOoqkbQGMbjHqSQ57gc9vfkOc4jjyZw9QQ92U3Qm8bFolEikTCpVIq29jb+/Mff8cDS+9nZ3MxtP/w+zz6zkh/e9r/cfMuXcDgcumPV6Db+7jwnbvCcjXgrKxD3Ep6zVxmO5my9nTt2UNPUhBqNEFm8AKWzA8HpzLeuFUBwOQledxNyWRmw+0b3gcQkrYDnvAvwnn8RWiZdRHJEt1tvk7toHg5RwOv10d3d/fF88P1INE1DlmUy2Rw93auorn4Wog4iS+1kd8oILi2fRiqAIGuUTI0h16hWtLHQQNSfGMJotEMoLLDqBRtr35DRBBBt+r6LCt4LE7hPT6GlBQTRNB4FRLdKarWd8DIbkuSgPDibjvZmgOFB5INElmVyuRx9vX2UVlZBMklkyXxyra16dMPCo4BgtxO87ka9DsokosZim2nZSc/Pf0z3bT8gPH8OyVdfJvXOW8RW/pe+f/yVzv/3DUKz7kNNp62aOdD9Bd4LL8Zz7vloaQOPmHj0kFm7htCSBdgFgZKSUtoNo2O44VGxmBvMli2bqSovx+12EVq6mNQrL4FD77JotfzPZvFeeDHuU07bzaAw7+tFl1zCH//yV7781a/ztW9+i3/+505+8otf0tOzi9defQVFU/O1GIKA5PZQ8oUvI9fUWoOuMTc9UST+38eI/vcxyisqcNjtwzXkexCTLLS27KS2sREtHiO8aD65trbd8eh0UnLdjcjlFbuluRXWVQ31fOHfzL+b4j3fwONu+6OHzPq1hBYvwCGK+AIB2lpbP43bsk+JuT9ms1n6+/oorayEdJrwkgVkdzYjuNz55jWCgGCzU3Ltjdiqqt5VX+9HLL0D3osvxXP2ufl11SgZEF1uUqvfJnz/YkSgpKSE7q4uYHh/HCx5vrqdmqYmtGiEyKL55DqG4KtOJ8Hrdb76cerR4qvpIfjqpvWEFs/DIYp4vV66u7uKjh8WXfI8p5fSqvfmOYHrbkQNlpBJp8kaqaMXXnQJ9Q0N/Puff+feu+9CEERisRg//P53WL36HX71f7/jG9/6Dn6/v8hohPfDc9YSWrxwr+I5e81KYJKESCSC1+XE5XDo7cVffBbB6dCJhhG90FJpPGefh+fMs1E1zVJgxniYv2ezWVSDvJrNHDIFryt8KLkcaBqix0PJzV9Erq3L1wFYJEci/uhyYs+spKK6GknT6O/vG/bGFYgJhubmDsr9P8RdKhF5UiDxjBtsAHpjBcEYk+E6OY3nnITVfaMwFTV/UsNoFA2j0QE7tkn89Kd+bv1agK9/K8Bv/s9LV5eIaAc1B1JAo+T6KFKZ3ihHn1qu6YXHikB8hZv4qxKBGnAKt9Hftxa9Uc4wWYU8Hrdt20p5aSk+n4/wiodJPrsSjHo4E49qOoXnjLPwnH1e/gTG33J9vfT+7teEF87Vo1klJYh+P6LPhxQIIgWD5Npa6f3tL4ksW2KlRprnkLw+Sm7+IlJltREVE/L1AALElz9M/IXnKKuowCbp3l8T68OCtUnFYzFcDifusnKUbVuI3L8YNZGw0oQR8h3/AtfeiOjIZ11Yc6gMb7XL5eLSy67gp7/4FT/+2S8oL6/g5z/5MV+88Qa+cetX+MP//R+7urv1Ddnouuo84igCU6/VIxtmp12zlqOrk8jCeWihASrq6+nZtcuqyRlOkdOleH904XQ4CD3yEMnnntkDHs/Gc/a5+sEFUXxThiKvmqaRy2UtR0Mul9vtGNHrpeTmLyFV1eQNHAuPArEVDxF77hnKK/RxAMP7Y7GY92HNmtXUVlfh9QeIPLqcxDNPgdlIxXTipFO4Tz8Tz7kX6AcPocf3I7sRTE1D8vl1PVZUDGrMoRsfsUceJPbSC5SWVyAL0NfXN7yuFkgRHt1u3A4HAw8/SPKF52AovnrO+XjOPFs/+GPUo+jx7oGv6mnJOl99msrqalCU4f1xkJh63L59G2Ulpfi878VzzsZz9nnIkoTd4cBmtyMIAmeefTb/+9Of07JzJ//++98RBIEXnnuWjvZ2fv+nPzPt2usArchoFAbznC98ac88Z8VDxF94lrKKCuyy9Jnrca8wHE0vXDqdZtuWzTQdcijZlmZiS5egRKIg2/LkRlWRSksJXnsToteLKAiWAu3Gw/zdZrNZStI0Dclo4jDUQyooOHUcdrhOcnI5BGPwql5A7iTX3kZk0XyEeIxAZRUDff1WYe3nXUxQxGIRHPwGX2kMrV0kutRHrldEsBdEGzUQvRolU6NIQaP7aYEYpY9FaapooAmgpOHue9zMmeeio0Ni/QaZf/3bw09+6qOtTUSU9dRU1zFp/ONjaBlBT1tFjzoKbo3MdhvRZQ4I26mueYmOtufJZpVhskq+oDuZTCKLEp7SUtTODiKLF6IMGG2pzU1K05ACQQLX3YTk9+fvnbExRpc/TGz5g4iBoI7DdBotm80/0mm9djmdYuCOf5LZshmr258hziOPJjBluo5HyTA6cjkEp4tsSzORJQshlaSsuob+vl6rO+fnXY+QJxudnZ3YbDZcsszAovmk165GcLutmjhBlNByOfwTJuM89LCi44uGGJupTppGf18ft//pD9x43TTmz5lNe3sbO3c288+//5Uffu87rHnnbWSbTTceRZHgpKk4jz1e17nZGElVEVxuki8+T+ihZXidToL+gOVVHZZiPG7bspmRBx9Crq2V2LLFqOFQER41A4/B629E9PmLHTEMKtEo0K3pYJVlG+lUilg0iizLQ0ZFnIcfRXDKdCjcH3M5YzxHC9ElCyGZJFhRxUB/P7lcbhiPFOuxoqwM0eVG2NVNZPF8lL4+vSFOwboq+vwEr7sJKRB4Vz1+EBHMjRRwHnUM/klT9RRyqUCPLjeZHduJ3b8IUimqGhrY1d01rEdDLL6aSul89eBDyLXsJLZ0MUokjGDyVSPFUSov1/Ho9eWNdEM+mh51cRx2RJ6vmnpUcggOF7m2ViKL56NGI9SNGkVry06LC3/e9ViIR1EQ8JaV6TxnyZ55TvD6m8Dn47lnVrJ0yWLefON1enbtQhAErpkwiXtnz+XY448nHotx9rnn8Y9/38kFF15kpcMONhqL1tUjjnoXnrOTyJJFkEpSWlVNf+9ny3M++/Y8BdLR3s6IESOwAb0PLCX1xipEjydPbmQZNZHAN/U6nEcdDUBfby8vv/QiSk4BATQ0REH3cLpcLo457jgqK/X28GtWv8O6tWuQJbOLpq7AnKIw9uBDOPKoo3SPuCThHz+J+DMrSb3+KoLDqY8OUBUEl4vEM08RXvEIJRMmoyYTbNmyhYMOOsga5vt5FRME3d3tuIS7cHol+ufZSbzqQHRraIruzRQkDTUpELgsjuuEtB5t1PTGNvp5hjq5EUl0wvYtEqtet2GzaXi9GpmMfsxDDzvx+1V++fMoThtgg+A1cRIvuEhvsCHYDANV1RvlxJ50ETkjif9ykdKBH9LRcQWNjXWf0t3ae8XUY3tbGy6HA5fNRt8D95Na9QrCbniME5wwGdexx+sHmx46SULp7SW24mE0BARF0T3pkmQ1PdIAQdN04uJwkt22lcTzz2Afc0B+/qOqIsgygQmTSTz7tD5ywKzJU1VwuEg8+TiRx88lcOXV5JJJtm7dysEHH/xZ3Lq9Ssz1KBwOgapQW19P/MXniT/yUB5kVopqBufBhxC4ZqI1900oSE0rNDB051CMf/79b9z5n38BECwpIWc0aVBVleUPP0Rrawtf/9b/44orr9LPV1lF2RdvpXP9OrRs1sgC0Gs5lHicyOKFeE4+jdLRB9C+bQuxWAyv12u95+dVTDx2dXXS1NiIDPQ9tIzUa68huAfhMZ7AN3EqzmOO0w82vOWF5yl0BJhea0mS6OvrZdGC+bz+2msoisLI0aO56eZbaBgxIu8pV1UEWcI/YTLxZ58m9dYbBfujiuhyEX/qCcKPLSc47hqUdJLNmzdxyCGHfu4drOb9b21tweVw4Lbb6XvwfpKvvlzMcyQZNRknePUEXMedoB88hB4/jOg6N/RosxGcNI3kc8+QWrM6b7gaDVbi/32MyFnn4L/8KgI+H62tLYwcOeoj34f9Rdrb22lsbNT56oNLSb25CtHtLdCjpPPVcRNwHXWMddzg5lMfJF11MHY1Y0/1j59E4rlnSK561UqTtfjqyqeIPPoIwYlTcTscRCIRAoHAMB6LeI4zz3NeG5rnBAye09y8gx99/7v09fdTVlZOXX09jSNHMmrUKCorqymvqEBVVE486WSCJUG6OjuprqkBhtBfoR4/EM9JfKY85zM3HM0NKRKJoGYzBCpHkXrrTWLL7s/nLBaQG3vTSAKTpiA4naBptLTu5Ce3/YhEIoEgGoM5ZZlkMkVNTQ2/v/3PluH4xGOP8be/3I7b7UFRcmgaSJJIKpXiS7d+lSOOPNJaVG01tZTc/EW61q8FM8dZUcBmQ4nHCC+ch/ekU3BVVeNOp0in00VFr583MclHaKAdNfldag5WSa21EX3Ig5oSED1ARkMQQVNArlEITIwhejS0jG40aoYBOVg0jAwaFZAhHBZIJgU0DbJZyOUEJAmcTo0n/uvg5hsTHHyIgpoFW2OO4LVRdv20pKDw2BjP0ScTXeLFc3SYsqo4ba0/Ixa7Ha/XN6zHUAgRjZoRI0itWU102RLUXA7RZtMjDZKEls1iG9FIYPI0RJfLMjbMRiu5Xd1ktm21BllT4G2zOuYK+aZXmqaRWvMOWjptnc+MotjqGwje9EW6v/9tKxqpqQqCzYYSDhFeOBfPCSfhLi0nkM2STCZxuVyfWz1CvganpXknIw84AC0eI7J4AZmdzYg+X34MiqB3dwtMvx5bQ9O7RjfMhhDr1q5hyeKFaKqK0+UmmUxaNXiyLOPz+1n9zjv8+Ec/ZPvWrXzhli/i9fuxn3QK/qvGMzDrPp0sGzM9RY+H9Oq3Cd+/iIof3IbH56O7qwvvmDGf65qcQjzmMhkCI0eRWv0O0WVL9KiCfRAeGxsJTJ6K6HQZacG7E8PiSGPeaPzNL3/BogULSKUSxutE+vv6+NVvfovPrM0x8VhXr+Pxe9/KZweY+2M0SmThPLwnnIS7sgpfKkkqlcLpdH5u8Vi0rmrQ0NhEat1aokuXoGYziDYPZDIIkoiWy2Krb8A/ZTqi272bE+ejiIUlU48NIwjeeAvdP/yO9RpN0dfV3MAAkUXz8Rx/IiU1tXTtbCaRSOB2uz/3eoxEImhKDn9FJam33yS2dIkx2xRQ8vXi9oPG6nzVrhsBZkTQdJyaOMxms2Sz2Xdd6xwOx+5pjoI+ZsVWW0vwhi+QWrdG56tiAV+NxXSn3Imn0jj2YNa9+QYHH3rYMB4tnqO+N89paCQ4aRqC04nH5aa2voGNGzcSDofZvGkjCAIupwuHU09vdXncLJw/j4Xz5zFy1Ch+9dvfMWbMASiKYmVywBB4NHnO976NmYZn4lEJhwgvyPMcfzb3mfGcz/wbY37Y7q5O/KVl2DJpwvcvJLN1Uz6VCkCU0HIK/klTsR801uqUouZUYrGo/ohGiVqPCLF4rCgHOGsQymQyMehncrdOgACe08/Ed9mVqMkEgiQDBQXkb7zGwAP347XbUVSNjvZ24/DPH8nJpz6otLetpapyBULGQfRhJ+m1dt04zBkpqpKGlhHwXxXHcWgGCmxyQQLBsftDdAIOPVCIAoVOMtPu0EtDBLIZkUhELFiZwXtOEs+5SdSEgCBrRgG5iOhWSbzkJPK4HYdLxO2YSXf3NuO8nz89Qt7Y6O7qIlBahpjLElm2mPTG9YhmdEMQdDxms/gnTMYx9hD94EG3TEnE0VIJBIqNEE3T9Ejj4DcXBNRw2OhwlH+9hcezzsF78WVWbR6akcrh8ZJ85WUGHlqGx2Enk83Q2dmRP/5zKOa6NzAwgNvpwOt2E37iMeJPPp43yg0vp5pM4D7lNLwXXGxFAXXjfncvjpkWY66ZoiSRzWaM7w3Y7TZyxobm9/vp6+3lr3/+E//zw++zbctmbD4/3inTcYw5sKDBip76iiwRfXAp8Refp7q6BjWXJRQKWVHMz6OYeNzV1aXXm2WzhJcu0sc2DIHHwMQpOMaaXujiJjimFKceSySTSf7+178wf+4cJEkkEAji9wfweD0sf/hh1q5dkz+2EI9nnoPv0sv1/VEu2B/dbpKvvqLj0WZDUT7f+yMUrqudlFdWgpIj+uBSMuvW6uuq6ZwWZX1dHT8J5yGHFp3j40pJswwPU4/nnIf3okv0+ZGFevR4SL70AuFHHsIly9htdro6O4HPtx4Bujo7CJSWYctkCN2/iMzWzfnGRvoL0TIZAlOvxT7mQF13JrE3SqfM3huapmGz2XC73bhcrj0+zDIB85i8HvXTus88G9+lV6CmU0Up5KLbTWrVq4QfXoZdgNq6+mE9FqyrgdLyd+c5OYPnHHwImqZRWVXF7/94O7d8+St4PB48Xi+BQBAELHtCkiS2b9+Gy+Pm9DPOxO12AyCKu9/v3fB41jl4Ly3gOeR5TurVlwkZPCebzdD1GfGcz9RwNMlAf38/oqZRUVFB9NlniK14GIz0Fyu9LZnAddQx+C+/CkGSLYAKgoDNZit+FNQ4Ft5QURKRZXnIh81mtDQ2AK4pit6Wftr12EeNRjPBaC3eArH7F5J8+00aR40ik0oRjUY/lyTH3NAG+rbiFL+HvwJirwnEHnGDhF6YqOopqlpawHFQFv8VcUSHHn3UBJBs0N4hsmm9xMb1EhvX6Y/162RWvyWz5m2ZaEQACZxuDbdbQ9OEIltFEDQcTo2AX9NTX0XQsiCVqQSnxpBrFLSsgCACit4oR80KRO73klovUNOYJRf7HuFw9+dSj4V4lAQoKysj+sJzxB55EMFm1w06E4+pJM7Dj8B/xdVWLYAgFC8notNljLbJ30eN/CJXtNQZC6fg9eU3WPNPBh4lf4DgtTdga2xCy2Ss8QPGxRNbvIDk2tU0jhpNLpUmEol8LvVoOnLS6TTtrS3UjRlDrr2d2KL5qJEIyLLuqTHun+T1Ebz2Br0jrtkgAxAQLK+4KabuRFFPOTaJjP6+Kn5/kFu/8Q0OPuRQBgYGcLpcaJrG/YsX8+1vfp1nVz6N+7Aj8E+/Xv++iCIYsyMFu5NsWwuRxfMhEae0upa+nh4ryvl5q8kpwqMoUlZWRuyFZ4k/8qCecTMYj0ccpY+nkg08imLB8Pd8h1xTX4oRcX7s0RXMnT3L6gmQTqfJZDKIgkgqmaS1ZSeQd+Lk8egnMP0GbI0jDTxKeTxqKrElC0mtfpvGUaPIZT6/eLT02NeHhECwpITYSy8Se2gp2OR8QxxJQksncRx6GP6rxiPY7JYe4eMjh9b6a+oxECR47Y3YGkagZbM6zzGyPVRFIbJ4Aan166gbMYJsOkU0+jnXY38/oiBQXl5O7LmVJJY/rDdSKcRjIoHrmOPwnXehVbNvpagWGIyKMXblkUce4eKLL2bSpElcccUVjBs3znpcffXVTJkyhba2NqvbtGl0QoEevV6C06/H3jQqP/rI1JEgEF2ykNTbb1NWXU0mlfzc8tXBenxPnnPYEfiv0nkOqooGNI0axY9+/FN+9stfU1NTQyyqjzqRZRlRkpAkCUEQOPOsc/jqN75JTU2t5agbPB5wNz36AwSnD81ztEE8J5NKfSbr6mdmOJqNFrLZLP29vZTW1KGFBogsmofS26srqaAtNTYbgSnTsI1oLErdMMFnPlRVRS34f1EuuaqhqPprrNcav+/mCTCIiuPwIwhMvc4yKCFfsJretpXIYr0xR3l1NZ0d7cahn3kg91MTU4/pjEpH+wPU1G1E7bcRW+Yi02ZHcBREG41bHJgQwz4qB1ndaNSA+2a4uP7GEm6+pYSbvpB/3PyFIF/8UpBbvhjkli8HefYJO/W1CpWVKgiadU5J0shkBM4+O01TUw7MoJUIWgZcR6cJTIih5QBJP04fz6GRXu8g+rALLemgtm4lrc0voWnq54qsFuJxV3cX1SMaUSNhoosXkO3qKm7cIAggSQSmTMc+clSRsVEoclUVtsYm1GzOahVutWYoILMY/wdwHnyIntYzKF3SxKPzyKMITr0WTEIlCFZDh/SmjUSXLoZ0msq6WtpaW63P9XnRY6G0t7dRUVmFU5KJPPwAyVde0rM4DINBkPSace8ll+E6+VTruKKOf3uovxEEEAaFmM3N65JLL+Pfd9zF5VdeRSqZBMDlcrJq1Sq+961vcP+y+ykZdw3O089ESyT0tV7T0JQcottD/InHiDzxKGXBAA673Rrr8HnSofm9zWQyhPr7KK2pRouGiSyaT7a72zIOd8Nj08hBxr8uQ47iMH7fuH49of4B7HY72WwWURTzREQER8FcY1NMPJvdcs1ykkI8ZjZvIrx0CaRTlNfU0tHeZn2uz4sUrqvdnZ3UNjWhRqNElywg096ur3UF7f4RBIKTp+vOam3odfXjui4gv64edQyBKdMhpzeyQtBnD4ouN+kNa/WO1+k01fUN7NzR/LlbVwv1ONDXR1l1DVooRHjhfHK9u4obqQgCyLKR+j9iSDyCjkmzq6ckSTz22GOsXLmSFStWsGLFCpYvX86jjz7KQw89xAsvvEA6nbaupfC6gGK+OmV6vkOuiUeni8y2LYSXLEBKpSirqqaz/fPLV02eUzVixPvjOU15nmPWhft8PqZdex3/vOMuzrvwQrKZDJqmoRrOgFwuR39vD7FotOgaCo3+wusC3hfPSRXwnKq6OtpaWz51PH6mhiNAV2cndruNEr+P8GMrSDz7tBHy18djCDYbajyG+4yz8Jx7vnnwbjU4Rcoo+H9xVMMkO+bf88eKklTEUy0vkSjiu/wqXKeepqdyFJIcl1tvW/3Cc5SVlqIpKqHQAMDnzovT3bWVoOu3uH0asedlYv/1IDqVfEMcWUNNCLhOSOE9L4kg681uRAc89riD22/3sm6dzPZmieZmiead+mPnTomdLRKtbRIvvmTnR//rZ9t2mcsuS+F26Y1xZFkjkRA49dQM3/xaDKeryNEGip7y6r8ygevoDGrcSFlVNVAFBJtG9EEP8TfBV6riFH9EKLTjM72fn5V0d3fhcXvwulxEn3yC+FP/1VMbzUJxmw01Hsd96ul4z79IP6igbhGwFj+ptEx/TcHGqWlaUaqOZkYwsnpdj/v0M/U6kKJIlkF4VQVBkvFdeTWuE08uxqOqILpceqrjKy8RDASRRZFQKJR/38+BmBHAWCxGLpOhvKaG1JrVRBbNy6cZWvc8g1zfgH/SVCSfX6+ZGxTdGNw23pyxaa6f1vOmoZPNkE5nOOCgg/jdH2/nB/9zGz6vj0wmg9fjobmlhf/8+c90ZLOU3/wlhLJyq5bEjHIoySShObPItbVSVltLLpslnU5/roiqKZ0dHdhtNkp8fiKPP0b86af0urfB++NpZ+A9r3hsQ6EU4m1wMw6X24UoiUWt4vURGjkaG5s47MjDgfyepmkamhGNEiQJ/5VX4zrpFNRE8f4ouFzEHlpK7OUXKQ0GEQTBwuPnbX/ctasbr8+L2+kk9vR/iT/xWPG6KttQE3Hcp5yO54KLjLrvDz7j7/1KkSNBURBkGf+4a3Adf5KRImczIqEKosNJ7IElxFe9QsDvx2G36w23PkdirjsdHe3YZJkSv5/w4wZfdXt2x+NZ5+I5q3gczlDnM6WqqorRo0dTVlZGVVUV5eXllJeXU1lZSSAQ4NBDD8Xj8QDFkeciPaoqgijhu3wcrpNP2w2PostN7OE8X0X9/PLVXd3duF0efB+C50B+pm4ul+PoY47lpz//JUccdbSeqWH8TRRFOjs7CYXfu9yiWI/vj+fELJ4jfeo85zMxHK0oVTpNKpGgor6B7M4dRObPQc3msGbuSRLkckjBEoKTpiFXVBW1HP7oF6L/EMBIsxvkQZf0FvW22jqC029AKinRC49NkiOKKKEBInNnofb3UdvUREd7u9V6d38nOeYXPZFIkRj4FdX1YXKtEFnkRY0K+rdLRf+pgejWCFwTxzZCQUvpRmPzdom//t1L/4CIx6PhdulpqObD5dIfdpuGz6exZavEb3/r5YgjckyflkSSwOXSmDgxye//L0xTk4pa4MAFvXZSS4G9KUdwShTJq1nXpSkC2DSyXTLRxR6UPon6xq20bJ9DJqPuRpz3RzH1mEoliUUiVDU06CmD82ajplIGiTGMP0VB9PvxT5qGXFW9W8E/FDhdJAnflVfjOeMs1FjMytcf7OTRL0IleMMXcBx8aHE9CAVGjCTreKxvIHDtDYiBQB6Pil78qvT2Epk/GyU0QP3IUbS17LSiKPu7HgHrc3a0tVFaXoGUShG+fyHpLZut7pdAvgZn/ARcRx6lZ+AP8jwXGhnmppfN5axUKYYgtXq2h+5FLy8v59avf4Pf3/4XqquryWazOBwOevv7aNm2Ddepp+Mfdw1kMlbhsqYoiE4XqddfI/zgUlyShM1up7Oj45O7aXuZ5PGYIptOUdkwgmxrC+H5c1DTu+NR8gcJmHjM5SzHzeB0KPO8kUiENavfsQa7j2hsxO/zoZh6RSdGmXSGq64eT1PTKOu5QoesKOslI7a6en1/DASL90dJQunrIzpvNsrAAA1NI2lvbfnc7Y/JZJLIQIjqEY1kO9p0npNI5FPQRFE30Dxe/JOnYqupLVpXzXTxPT0+rFjej1XhAAEAAElEQVTrqiyjKTlsDSP0ddXnM9ZTo9GZLJPr7iY6fw5KOEz9yJG07Gi2mrl8HvRopXCnUlSNGGHw1dmoxpxafUa0CDkj9XfyVOSKCh2Pg0YmFDpuCvHmdruJx+NkDUdZOp0mm80SjUYpLS3F6XQWHTP4XEgSajaLra6O4M1fRPL5rX1R/55JKKF+IvNmofT3UTtyJG2trSifMzymUimi4TDVI/I8R/sAPMcUMzND0zTq6xs47PAjyKTTSJKEoijYbDJdXZ0M9PcDgxxvQ9zrD8pzop8hz/lMY9SdHR04XC5cgkB42VJSb7+J6MyTG7OdsfeiS3CdYqRSWeTmI9wck6sWPrUnx57xfp4zzsZ3xTjUVNJolKOTHMHlIv78s0QeXYHP7cbj9tDV1fnhr20fEvOL3tn+MiWBpciSQPQJJ4lXHAiF4zdsGmpcxHN2Es8ZSVD0dTaThb//w8P69TJ2u0YuB8mkQDotkEkLpFL674oioKoCmYyA36vx1NMOFsx38c2vx5k/d4B77wrx219HGDWq2Gg0givGxQIqeM5J4j0/gZoQ9agjGlpORHSqxJ50E3/ehtOlUVHyTzo73vmsbu1nIh3tHfgDARyCQOThB0m+/truXrhEAu/5F+E57XT9oMLUtwIxRzrYamup+N+f4znrHNRMBhQFTdFbhWu5nN4W1+Gg9GvfJjj9OqPpzZ4Hlpt49J51Lv5Lr0BNFuAxp0c54k8/SezJJ3A7HQSDQbo/J3g071E4FAJNo7y8nPjLLxJ7cKm+ruayRnRDRksmcB52OL4rr0awO0DJUVijWti8wawxzGaz3H3Hf1iyaCEerxclp+SdAMbrc7kcWYNMmQbCxZdeypdv/VpxhDmn124EJk7BceRResqqbNZ76WQ1smAeqbWraWhsJBmPE4vFPhdE1ZTOjg6cLreOx4eW6eOpLDyCYNPbxHsvvBj3qadbKeAMIpaAlT7Vs6ubn/zoh1x95WX89Mc/YqC/j+OOO4GxhxxKIpnUa3REkUQiwRXjrmb8hAls2LCBN1at4q033+CN11fx+qrXWPXaq7zyysvs2KFnZrjOPMtoJDcEHlc+RfSJx3A7nfj9gc8NHk3p7GinpLQUOxB95GESr76cb6RStK5eiOe0M/WDTI5SkKHxbvJhMWEdZ2Dfc855+C4xGh5JNv01uRyC203sySeIPf1fXA4HpeVlVgOyz4t0dup81QGEHzD4aoEzzowaey+5HNdJBl8tyHoz73XhGmamifr9fqqrq8lkMkUjMsyaxvr6elwul3W8eT7zXKqq6s4cmw01m8V33gWUfOmrKKEQot1ekAXgJv7cM0QfW47X5cLn89G9q/uTvXF7mXS0t+MPFvMcYUieczGe087QD9oTz0HXRzKVpL+vF9F09mgaNpudvt5eqxERBfrfUybBbjzn7HPxX/bePCfwKfOcT91wNL/s8ViMRCxGfWMjyTXvEF0yXycOqgqaWSieRm5sxH/1RD2VKpctaEv9EVI4tN3/uydF6iRH7zDmnzgFx9hD0JIFJEfTC9tDc2eQ2b6NmsYRhEMhMun0J5ZmsjeI+QWPREJk4r+mqjZOerNEeLEXq6BRyzfEkatyBMbFkSo1cgmQPHD/UiePLHdavgBJgssvS/Hb34T59a8i/PY3Eb75jThVVYoxEUUjkxVwuTRmz3XxxJN2Tjo1ywknZnE7QVOKjUYYFHXMgFSm4Z8Qwz4yi5YqSFkF1KRAeJ6PbJtIVU0P0f7fkUgk93s96qmNUVKJBDV19STXryOycF7eU6np3Te1dBq5rp7A+ElIgWARHgd7QcHAjqriGHsw1bf/ncr/+TGu447DVl2DVFqKrakR36WXUvu3f1H21W8ieryWYTE4vaeogDyXQ/T58E+ehv2AA3fDo5rNEpo9g2zLTqpHNBIaGCCVSu3XegQsI6+rs5OaEQ0oA/2E581E6e/XdVkYyRX1WXyOA8fqDjCzexvFhCQ/C3KA3/7mV/z+d/9HJBJBNFNVC5xwetabQsaoxTHTeQDGHnIIdodeu6qpGhiboH3swQSnXodg5pcXzJjL7NxBaP4ctGRCr+VoafkU7+ZnI3k8xkjGYtSNGEFy3doh8CihpdLY6hvwjZ+IFAggmNk4BUY/GOnLhuE/Z/Zs7l+ymEQ8wWuvvMKq115jRGMj1994E7W1tSQSCXK5HGefcw4/+fkvWP7Qw0wefxU3XDuN66ZO5vppU7h+2hRuuv5apk+awI/+5wd0tLQg+/z4J07FedDY3fGYyxGeM4PMzmZqRoxgoK9vv8ejqcdoNEI6kaS6tpbUpg2EF8wxNqhB62ptLf5rJiMFS4x1NW88CIJgRTeGehTi1Xzv9yvF62oWyecnMHka9tFjdtdjOk149gyyba1U1zcQDYVJJj8v+2OMeDRK/YhGkmtWE128QCfyWgFfzaSwjWjEf80kJJ8PJZ1GkCTi8bhVQqAaY6cGRxx9Ph9VVVX6WLmC+2lGkJqamnA4HFYzHfNY83ymgamoqmW8BG+4Gf+48eR6exBdxvpqOOZCc2aR2bGNmhGN9PX0kP4c8NUPznP0dXVPPMc8L0AykaS9rc3q1qpp+liqSCRCu1FLOpRDb7DsxnO8PvyT3ovnNFPzKfOczyzi2N7WRlVdLSQThBfMJdPSAmZhKoKebpPL4r98HK7jTtBra6RPbuykOMS8K1MEWQ8du444Cv+EyXpXQk2vs9QUBcHhILNuLeH7F2JTVEpKSmlr2/8bOmiaRtvOxdTWvoCWlog84CKzxWE0xDFeJIGWEfBdmMR9Uho1AbIb1q+WuPNOD/G4gCxr5HIC14xPcvufIkydlmL6tUmm35Cktlahp0fEbtO7qJqSTgvccaeHde/ICJIeuNqT0WiO5RBk0BLgPjaD/6p43vdgpKyKTo3km06iK5xIiFRXPkrbzhXWZ92fpa21ler6ekgliSyaR2b7tnyhOILeljqTwXfZFbhOOFG/H3vAY1FUSBD1RbSinJJbvkLdvYuom/0A9bOWUT/3QapvvxPP2eeDTSrYUPUaqj2JYKTIuY4+lsCEySCJ5hsbqY4O0u+8RXjZEmRFoaKyar9vsGLVjHd14nQ68Xm8RB9/lMQzK3Vvai4HmEPiY/r4jYsuHXySomivOSC6tbWF237wA+6+4z8oSq4oZbHYCacbrulMJv/MEJ5aDTX/f03De/GleM+/UK/JKWg/LjqdxB5+kPgLz1FaUoIoCvT39X0uoo5trS3UNDSgpZJEFs0l07xjEB71umDfZVfhPPZ4ALZs384///E3fvHT/+XuO++gt6enyLB46403mHnv3djsegdxp9OJ1+cD4PKrruJ3f7ydyy6/gi995av89Z//YfOGjdx1538YGBggGo0QCoWIRCJEIxFi0Si5XJZXX36RhYsXAmA/8ij8E6cYxisFeHSSXvMOkWWLkVWViupq2j8v+2Nrq67HdIrI4oX6XFtzsLe1rqbxXXoFrhNOKlpXTb11dXXy0APLuO/uu7j37ju59+67uOuO/zDzvvvYvn27ZUDsidS+W5prUTqz0XDJeezx+CdMyjuZCvSYevN1Ig8uNfRYRavhzNmf9QjQ3tZKdV09JBNEFs4l07KzmK8agQ7/hMm4TzgJNZdDcjho3rmT22+/nSeeeKIoRdt0qJkjNkpLSxkzZow1ymFwLfLIkSMBigxHs7mjKIq0trZy1FFH8e1vfQtEkXg4pHfnvOmL2JtGWnVyWi6H4HCSWb+W8P2LsRn7Y3tbG7D/67GttZWaD8lzTL0V72W6RMJhent7kWXZ0J2u21QqVdSkz9T7+9nD3j/Pub+A53w6ePzUDUdBEBgY6EdRFMpKy0i8/AKx5Q8ZqVRGtz+bDS0ex3nIYfiuHGe1wS1eGD/4jdkTFRUEoSg9oFDyBNjwDF1xNe7TztALyG0FqRx2O5HFC0mtfoeaujoS8QRxI7VqfxRBEOjpacXG3wiUZEi+bSPyoBfBpuhdVEFviJMUsI/J4r8yjuDVQIFEAv72dy+bt+gpqqmUwDHHZPjm1+O4XRo5I9387ddl/vVvN5mMYAYwjaiGHnXctMnG8y/YUXM6ti1VMchoNI1Jwfi/DP4r47iOTaPGjZRVTXceCoJKeKGX1GaZ8soISuavhEK9+7Ue+/r6EBAoLSkh8dqrRB9aVoxHWUZLxHGMPVhvE+9w6A0V3s24s/RlkhcRTUsieFqwj9yI46B12Ko3gNyGpuYASR/+oGloCAh7WPgGL4i+q8bjPrmgcRUamqJ7DiML55Fav47K6moy6TRRo231/iiCIJBOpxno76e2qZHMzmbCc2ZY6TeAHsXI5ZACJfgnTMZWW1dUM25mXpikRZIk1q9dy3e+9Q0eWLYUWbYhCqI+v9Ho9Db4GlRVIVtgOA4lmobVZEfLZBADQQJTp2NvHImWSuYzTwQBNR4jPOMelN5eGkaOoq2ttWg27/4mgiDQ39+HAJSUlJB49RWiDz04BB4TOA85FM8V45CcTt5atYpvf/Pr/OInP+auO+7gx7f9kL//9S9W6lskEuHuO++gq6sLWZLJ5bIcfcxxHHnU0Wiait1m58KLL+E/d9/Lj3/2c5KJBLf/6Q/09vRYtVWSJFl6z2QygEAsFmfhgvmseuVlJFHEfdmVxv5YiEclj8d1a6isqtbrjIxxAPujCIJAX28vkigRDAZJvLGK6ANLEB2OYj0m4zgOHIv/yvGITmfRuqppGrFYjB//zw/55te+yt/+8mf+9ufb+cff/sLf/vwn7r7j32zbuhkobm4yOAq5J2NxjwYk4B83QW94VNSYw1hX588htXE9lZVV5LJZIuHwfq3HgYEBNFWlrLSUxMsvEn3kwSH4agzn4UfiveQyACvr4l///Cdvv/02jz/+OHfeeaeerSGKRWM1MpkMoihywAEH7Fb/WxiRhLyezdfJssxzzz3Hqaeeys6dO1m+fDlPP/00nkCQTCyG+8ST8Y2fVLA/GnzVJhNZPJ/UmneoqqkhUVAKsD9KIc8p+ZA8xzT+AGvuu/m3vr4+QqEBy+g3Xy/LMqtee5VNGzdaNsbgqPNQRt6H5zmZT4XnfKqGo7lAtba06LOd+vsJzZqBGosWMk2dNNhs+K4aj/OQw/Qvujw4uvFhboyZbz44V1WzZlrtdoQZOpb0Acu26moCk6chVVahGakIuntBQtnVzcCcGZBKUltfb4Wo9zcvjp7CptHVdjcjRqxDCUuEF3hReiVjbiOWegQBfJfEcR6dIRcF0Q1z5rr575MOK0OgvFzlW9+MU1uvomTB5oDwgMDd93jYutWG06mhKAKiWFjzpl/HG2/aiEQERDnfSVV/gfHTMBYF4zlBBi0JtlEKgYkx5BJjtqOsgSqADTItMuHFXtSUxIj6F2jZMX+3zXV/EDMVsa21hREjR6GEw4TmzEANh4rxCCDqjW6chx+5BzwWnRjzxmuaBoKIoK2D7HcheT5a4mK0+FVoiQsgfTHkfo9Apx6dRB/ZURQ6LpDd8FhbR2DydOTycgOPxhdBlsm1txGeOxM1mWDkqFFs2bLF+tz7k5ifp72tlfLyCmyqRnjJQtJrVxd4UwFZRk0m8Zx3Pp6zz9OfG9SEqJCwPP/ss3zja1/hheeew+l0omoqSi6HbBCQoVZgVVHJZLK7/X1wKrNq6sBwCrpPPh3f1RPyBxgzrQSHk8QrLxF5+AFcNjulJaV0tLftl1FH06Pd1trKiJEjUUIhwrPvQ42Ei/Go6STHc/lVuI84knXvvM3//u+PePP11/B6vdhsNuw2O8sfeZDmHdsBWPnUk/z3icfwer0kEjHq60cweeo0PB4PipJ3yuaM+tSl9y9m7ZrV2Gw2i+iYj8MOP5zxEydy6eVXMGXqdI45+hjajDoeW1293qinonJ3PHZ06OtLIsHIUaPZunmz9bn3JzHX1Y6Odl2PkQjh2UbKuJGGn19XBXxXXo3jqKOLU8YNkrp+7RpefOF5pkybxsKly5i/eCnzF9/P4mUPMmf+Qs46W+/cWej4LjQai9Nci+uriv7GoHW1rp7A5GnIZWW6HuW8HrNtLYSMxmmNI0exY/t26333JzG/7y3NO/TZwP39hGbftztfNfY43+RpOMceQioaxenxcN999/Hkk08SDodpaWnhhRde4Dvf+Q4vvPACmUwGWZatenDQjcNCvZmlB3a7Hdn4Xpi15rIsk0gk+POf/8xll11GKpUiEAjQ0tLCnXfeCYBqfCdKbrgZ95nnkNvVjWiWBIgySnc3oTkzIJWirr6ejv2ar340nmPi0dSXaDTAMe/Vrl1dJBIJRMNwNB0DLpeLN994ne9++5s8/uiKou7g7xZ9/MA8Z85MY139dHjOp2o4CoJAZ2cHwUAQt8tF7NFHSLz4nD7LyLyJNr3A2HXcCfguu9I8EMgD+cOK1+ezQslF1r4gsH379ny3wAIp8sTZbGiqivfcC/BdcpnegtnIj9RUBcFhJ/7EY8SeepLS0lJUVaVn1679yotj3rvWlucpC8zB4dKIP+cg/pQTwaHqXUrBGr/hPDxD4PIECCDbYftWiSVLXMTjAk6nhijCV2+Nc/bZGbJJ4z2Ahx5x8PAjDtxulWxWMPBtbniWb4HNW2T6+wQ9JbbQWCwUM6POSFnF6LLqPT+J57wkZAUdCYKRsmrTiD7iIrnKgcenEfDeTUf7O/sVWTX12NHRQWlZOS6ng9h/HyXxzNPFeJRl1Hgc17HH4bv8Kv3g98KjsRgLaAiCCMpzaOlJCNl/IdCKIOg1V4KQRdDWQ/Y2yNyCpm7XX6+pRe8z+LqttzHxeP6FeC+61JiHJhalkMeWP0Tyhedxud34/T4r1XF/ErNmPB6LU1VTQ2rNO0QXzddTzwr1mExia2jAP34ikt9vDPseegt46MEH+PY3vsqG9etxuVzkcjly2Sxnnn0O55x7HplMBpvdjqblvTWaBhoamUzavLCCM2qIglhAVvPXbpZE+ydMxnncCbpX1XRMqAoCEJo7k/T2rVQ3NNDf108qlfp4b+JnLBYe29spKSnB5XQRe3wFieee0Y1/S482tGQCxzHH4b9iHPF0mr/8+XZWrXoNl8tNNpu1CEk8FicWixMOh5k1417rb5IkccVVV3H2ueeSy+UsUgpY9VGjR4/B4/GgqkYKFxgjOhRqa+v4/R//zD//cyd/+9e/+deddzNu3NX6Z1BV3Oedj/eiS4bEY/yxFSRfeBa3243X593v8Gjqsb29jZKSUpwOB/GnniC+8r8FKar5ddV51LH4Lr9qty3LXOdCoRDJZJK6+noOPPAgDj7kEA46aCxjDz6ExpEjLd0NVQ8OkMlkWPn008yfO5tF8+czb85s5sycwZxZM1k4by7r1qzR36/QmW6zoWka3gsuxnPhJcWzHRUFwe4g9vCDJF5+EY/HTbAkSGdnx36lR9DvY1dnJyWlpbicTmKPLSfxwhB8NRbDddIp+M67EACn10v/wACzZ8+mq6uLgYEB2tvb6ezsZOvWrXzve9/jt7/9LeFwGJvNZmVQeL1eAoEAmUzGMhxzuRwejweHwwFAOp3GZrPR09PDrbfeym233Ybb7UbTNDKZDGVlZTz22GP84Q9/wOlyEevvR66oxHPueXpXbaNrqMlXY48/Rmzlk5Ts53z1o/IcQRDYtnUrt//hd8yZNZP+vj4kSUKWZTKZNK2trWQNvZli8kVJknjrzTf4+q1f4de/+Blbt2wpijoX2iOF122d5/3wnBUPkXzx0+M5n5rhqGkauWyWXV1d1NTXk23ZSWjuTL0xiWbU1ogSZDNI/gD+cddgq2/QyY3Z7v+D3ohBnHbMAQcQLCm1UgPM6xJFkY0b1hGNRPZ4GutUmt51KTBlOo6xh6IlkwUFq6Am4gzMvIdcbw9No0bS1dW5W170vi6pVJpI/xyqqneQ67YTmutDTQvWjRJEPSVVdGv4rohjG5sjGxXABevXy7S1i9jtel1iNisQj8FAv4DNDZIL1q+TueMuD4oioGmmp9SMhGgIgoaiCtjtGh0dEm1t5ua552sWzH905yDkQPRrBCbGsY/OoiUFBEnXISKoYZGBOV7UkI2aqtX0di8im92/UuTS6TR9PT3U1NaSbW8nNOs+fVGy8JhvROMbNx57Y9MHwKPe6AR1K0L2/yGo64xwr4l31cKMINggtxwh+zPQwvpxmjrkWXd7T01DcDjwT7kWx+DGHIKAGo8zMONulIF+GkY0sn37tiJP4b4u+WhjG3UNDZBKEZp1H7muznzRv6BbZoKq4r34ctynnGY1oBl8LkEQ2L5tG7/99a/o6enB7fZYRGbCpMn8/k+309jURMYYdoyWr7eRZd3bmkqldru/oiihKLpjTskpZLN6hodmNsTJZrGPaCQ4ZTpyaRlaJmN15kWWyW7dQnjuLCQlR119PS07d+5XjhzQSX5fXy/VdXVk29sYmDNjCDxmkfx+PFeMwz6ikR0b1rNtx/bdoruqqhIMllBVVcWjKx5h1Wuv4XA4SaVSjD34EKZMuxYojlQpioIsy2zcsJ5DDj2MM846C0VRgeL5ZU8/9STz587Wrzmdtmqt9JpXFdHuIDD1WpxjD94dj7EYA/fdg9Lfx4jGJnbs2L5f4RH0dbW/t4+aujqyXZ0MzL4PLZPdfV31evFfdQ32kaOK1lXIr3MjR42irr6eGffew+OPrrBwNziVcbCY+lj9ztt8/Stf4pYbb+DWL93C12/9Et//zrf5zre/yc03XM///foXKIpSnOoK+jgCp5PAlGuxH3DQ7nqMRgjNuBslHKK6ppbuzk6LBO8Pomka2WyW7u4uquvqyba0EJozmK+KkM0i+X166n9dPYmwnh3wwx/8gJdeegmHw0F/fz8DAwN0d3eza9cukskkd9xxB1OmTKGjowN7Qc1xVVVVET/N5XIEg0ELpz6fj5aWFsaNG8eiRYsIBoOW4WnWO2azWRYvXszmLVvwlpaSy2Qou+ELeM46B2WgD8FuN/Zfg6/OuNvgq6Po7OzY7/jqR+U55v297+67+MPvfsuPb/sffvTD7/H0U08Sj8cRRYmWnTuN4EQel4WRf5vNRiaT5u477uDWL32B+5csIhqNIssyqqoWlV8UHlfw5F7Fcz4Vw9G8ETt37qS2rh5ZFAgtnEdmw3rdmjY/nCSiptK4Tz0j37ihwBs6lGX+ft4bdFAdceSRHHTwWHLGvBNTZFlmx/YdvPP2W7sdZ/wnP6POCB07DzmMwDWTEFxua4C1pmmINpteQL5sCU6Hk9KS0v2G5Jj3v3nbfOprliHLEH7YTfptG4I9HxFEAjUp4jo+je+iBGRBlDVQoaFOoaJcJRoT8Pk0zjwjzYxZHr58a5DXXrbRt0vgjrvcbNtqw27XDN6rGQakZtkdaLoTNBIR2bJNQjOcMEW3uPD3QXWOgg20OLiOS+O7PI5g0wxbR0NTBQSbRuIlO5H/urA7oaZiDs07Htiv9Niycyf19fXIgkB4yQI9tbEIjzJaKoX75FPxXazXbrwrHs3jNMNo1BTIzQflDcNoLEgHLwpGZfW/K/NBXWk+OWSqqvm+1mkkCS2XxXXEkfivnojgdOp4FPPdOZOvvUrkoWU4HA7q6xtoadk/8Aj6BrWruxsNjdLSUmJPP0n8iUcRnM48uZT1sQ32sQfjHz8h3w3QkMH3oa+vl/6+XmOGWASfz8etX/sGP/vlr2kaOYr+/n6WP/wwfb29yDYbuVwOu91OOp3G4XBSU1u72/2tqa1jRGMTqVQKt8eD3x+wrh+wvnfeSy7Hc/4FGG2UjWwOXY+RB+4n8eorlFdUkMvlGBjo3y+849a62ryDmppabIJIeNECMuvWDoHHJK6TT8N/yeUA+IMlesTBIP6mwZDL5Tju+ONRNY3ZM+7THbe5LB6Pl5u/+GUOPOgg670H17zd/sc/cNed/2Hy1OnUNzRY6VVmtDKTyTB/zhyad+zA7nBY3nMN9Blk2SzOw47AN36i3i13EB5Tq14l8qCOx9raOnbubN4v8GjxnOZm6kc0IIsikfsXk37nrUF6lNBSKVwnnYL3Ul2PyMU9Fsz7ccCBB/HTX/yKsvJyvv/d/8err7ysp97lciiKomcCDHqYz+vXBAeOPYijjz2Wo489lgMOOBC3x0ttTQ0nn3oKhx95VBEXst5flvV19aij8V09wRjZoxTpMfHyi0QeeRCHw0F1Te1+x3NadjZTW1OLXRQJLZpHZuO63fSoplK4TzsL//kXkc5kcAcCPPbYY6xcuRKv10vcqB2MRqNEIhEikQi9vb3YbDZeeuklTjzxRBYuXEgul+OQQw5h1KhRVoMcE8fl5eWUlpYC8MILL3DWWWexevVqgsGgUWucT0FOpVKUlJSwZs0a/vmPfwCgZDPgdOK94CKkYAlkMjpOTb76xutElt2Pw26nrKyclv0Mjy07m98Xz3GdfCreIXiOKIpEwmHWrFmNy+VCU1UWL1jA177yJX79i5/x1H+fYMvmzZYRWPjehT81TcPtcbNu7Vq+9+1v8bMf/y9r16xGkiQkSSKXM0o8ClLJTXl/POeVT43nfCqGoyAIJBIJYrEoFVVVpNesJnL/onx9IMZClUohV9fgv2YiUjCo5xgPChd/UDFvWTqdorq6hlNPPwO7w1FUhCwIApFImPnz5pBOp4s20qEKWE1PvX/8BNwnnazPpyuYgSZoGqH5s8ls20JVXZ01nmNfF71xQwhBW0SwpJf0VieRha6iNBdB0tAyAnKZgv/KOHK9ipYEyQ5qEg4/LMf3vxvj2mkJvv/dGP/5d5jf/y5MJCLw9W8G+MlPfTz6uBO7XbXqGlVVwGbTSCQExoxROPHEbN6IROOtt+zEYwKiNMjWEAZff97oNCOLKBAYH8d5TEaPmhYiIicQnuMm2+akorKVRGQ5ifi+39BBEPT24olEgrLKStIb1xNZNB9BlPKloZKMlk4hV1bqqY2lZXpb6kI87n5i4xfDQtd6QX3UeG4Io7HoBIpuWCqPg5YGwSyWpeBn/vqLFkOj65n/mkm4TjgJNZOGwi7JRvvx7M4dVFZVER4IkdoP2sibdRKdnR2MHDUapa+X0Iy79eHi+gt0r7iiIDoc+K4Yh/PQw3U97qEjrqZpjBlzIGeceTbl5eUcceRR/OjHP+Xb3/kefr+flp07+cVPfsx999xlvL+CzWYjm81it9u58eYvcPIppwB5MqNpGo2Njfz4pz/j1q99ne987/scfvjhVraHJaqqRzmmXY9t9Bi0VMrw+hpe1dAAA/fdRS4aYcyYMWzbutW65n1ZzFTjZCJBZVUVqQ3riCyZr5MC8zWyicdqfFdPwFZWRi6dpqGpiUMOOdQy3syUtkAgwKQpU3nisUdZs3o1siyjKArnnn8+J554Eq++8jKdnZ1WjVvSmOH46PLlvPrKS8ybPYt0KsVV48ZbKapmJFOWZTZs3MDdd94B5D3yFpoM0uUfPxHXiSfp+6NYWIOnEpo7k0zzdiqrq/WRFfvBeA5BEAiHQmiaQmlZOenNGwkvnIOgt/3SX2OOGauoIDB+AnJZubGu5jOgzJ/m/T7/ggv5/Z/+bGRGbdCbothsVqrc4IckSVYU6/gTTuDOu+9lzvyF3H3fLE446STi8RiXXXEld983k69941tW+njh+wPWuhqYMBnn8SeiplJ6FoMpiqKP52htobK6mnA4THLQOIl9UQRBsJrFVFRXk1q7msj9C/WMuEK+mk4hV9Xgv2YSgpH6D3DXXXexdetWvdbYbrfWx1QqRSqVIpPJEI1GcTqdpNNpbr75Zn7wgx/Q3d1NeXm5NcjdjB6Wl5frza3uvpuLL76YSCSCx+OxRmiYNXyCIOB2uwHdgXTHHXewaPFiHB4v6WSS4DWT9cZV6VRxbbumEZ4/m+z2rVTV1BIKhUjvJ3zVXFffD88JjJ+oZ7sM4jmiKNLV3UVfbw/ZbJZDDzuMAw46iN6eXdx1x3/4/nf+H5s2bbDWWPO4wcYjYDlZNU1jzswZfPXLX2T2zPsY6O9HlvW05cHRR0vek+eoBs9p/sR5zic338IQM3ze1tpCQ8MIhGyWgZn3oHR3Wbm7pmiqivfs8/CcfZ7Vwesji3HjzXEbF19yKY88+CDr167B7fGQy2atUPJzzz7Ls888zQUXXrybobpbDYKSQyopJTD9elLr16H09liNKARJItfczMCs+6j62a+pq69nx47tHDT2YOt+7GuiqgqiKLFpw2KOP+JR1JxEeIGLbKuMIGv5AIag83/3aSm85ybREnowCRVEw3C79Ko0l16e1lNCM3DhxRmOPCLE3//h4d77PDgcKmBGGAUkSSOXA7dH42tfjSPL8NZbNtJpcDo03nrbRne3iDegoGXz11HYpKdIzPoqUbdR5AaF4OQY6c0yalhCsBlRR0kjs0UmtMRFxbdT1FUuo7PjUEYf8B3rfuxrYn7/Ojs7aaivR1AUBmbeS66j3epeDBj1njncZ52L99wLDDwWpza+a24wgNYDWnNeF8Z53/0CNwBJwPG+DxIEAS2XQy4vJzjtOtIb1qH09xfhMbt9CwNzZlF5209paGigpaWFAw86aJ/FY2HUuLy8ArvdzsCyJaTefN3qgggaSDJqLIb75FPxj7tGP1jMd1FlCM9oaVkpv/j1b9i4cYNVWwXw1ptv8Nvf/IpnV67E4XQgIFhGR31DA1/+yle56Qu3IBiGhpleZW5+Z559Dmeefc5unwMMI1OS9LFHRx+L/+oJ9P/9z0ZXOwlzxlbiheeIP7oc38Qp1NbV0dbWRn19/Yd2LH7WUojH+rp6UBS91KGjoxiPgKYoeM4+F995FwAg2e0IwLRrr+P1Va/x9ltv4XK5SKVSfOWrX6Oyupof3/Y/gJ4GW9/QwBlnnsUvf/YTXnn5JY46+himTJvOOeeeh9vjIRIOs3D+XHZ170KWZf7xt7/y3e//gGdWPs3aNatxOBxW1DGXzfHwgw9wxplncsFFFxfp28JjWTnB6QYe+/qK8bhjO6HZM6j88S+oq61jR/MOxu7T+6N+3WvXrOHEk05EzWQYmH0fudbWQeuqoK+rZ56N97yL0LTd19WhohWyqHe0ffmlF7HZbWQKmmwULq9FjekMg1WWZURRJJNJ8+brr1NRWck1EybRNHKUlc42ZJTD1GNFpb6ublyPOjBQpMfMls0MzJtF5Q/+l4b6Oto72hkz5oB9Xo9tbW3UNzQgZHMMzLgHpWsIvqqoeM85F+8556Lmcjg9Hnb19HDuueciCAJvvPEGzc3NuFwuSkpKEEWRdDptBS3MJjclJSX8+9//5plnnkHTNDwejxUxlmWZzs5OfvSjH/H000/jcDiMurqM5VgwDdR0Ok1XVxeiKHLyySdzzDHHMKKhAQBREMDpJDB5OqnVb6P09hbjsXkHA7NnUPXTX1Ff30DzPs9X9evu6Oygvr5B5zmz7vvAPMdc71qadxIOR7DbbHztG9/C4/Uyd9ZMXnzxBTo7O3C73bjdbivNVzWyAszrMDEmCPmRKl6fl61btvC/P/ohr7z0Ejfe/AWOO+FEAKv2fHCzpGKesx6lv28InjODytt+9onynE/ccBRFkV27dqGqGqVlZcSeeJT4k48X5/NLEmoqia1pJP5JU3SADpHz+1HE9AaMHXswU6ZN47e/+TWZdNoaUi2KItFIhHvuvJMTTjwZv99ftJhqZj60+Zykz3b0nHchvueeNeo1893pBFEk9shD+C64hNJTT6Ovt5f+/n4r5WBfEt3rItHZ+TZN9Xcg2iH9ioPoCrdez2iIIGmoKQG5Nod/fAwxqKHF0L9lmpWOjZIAFRA0PUMnl4CqapUDD8zh8aiYDW4L5zZmMgLTpiQZNy7FurUyNdUKmzbL+P0ara0iW7bIjD5AsaKKe/q6FP3dTFlNgPf8BPEXnESWeYzXGZ5fQSS6zIXvrBTlR4foWfcIXZ1Tqa6p2SfJqiiKdHZ0oORylJaXE1v5FLHHV+yGRy2lDzMOTJhS1NSh0Nh4b8nqj4IU4d1kt+czwHvXkg725JkzjzwXXIz3+WcIz58LaiEhEok+uBTfBRdRdvyJ7Ortobenh/KKivfxOfY+MdOS+vv7OOLoY8hs3UJo3uw8kdT0mnEtk0EKBvGPn6iP38hmrYyJInt+EFGtqa2lprYW0De6/z7+GL//7W9Yt24tLqcLNM1ohJPhuOOO5zvf/yFnnXMOr7++ii2bNnHFlVfh8XqLNixFUVCNdLdCI6MwTcB0FgYmTyP58ovEX3wO0elCU4zXZjIMzLoX92mnU15eyZo1q6murkL6BGf8fpIiiiJdnZ3kclnKKiqIPf0kiccfHWJ/NPA4cTKqzUYumURyOiCncORRR/OD/7mNv//1L/T29nD44Ufw5a9+jTmzZrJ1yxYEQcDhcHDVuKvZsX0by+5fQiAY5LEVy3nzjde55LLL+eKXb+WlF1/gpRdesKJWba2t1NTWMv266/np//7I+n4oioLdYWfXrm5mz5zBqaedjsuIdFjXbOLx/IvxPvcs4fmzB+FRIPrQA/guuJiyE0+mr7ePvr4+ysrKPtX7/3GIGZXo6+uloaEByWYn+fyzxJY/MvS6Wj9CX1edTn1dLfA/DtVcA+D+JYvp7urmkYce4OEHluVfK2A5cDDIpb635ZtOWV3FjWs9+JBDqR/RYJHidxNTj94LLyH+3DNEFs3bXY/LluA7/yLKjzmOnt5ednV3U1lV9VFv62cioiiyq7sbVVUoKysn+sRj78JXm/BPnAKyDcHYHysrKrj11lu59dZbefDBB3nrrbd48sknWbVqFdlslrKyMnw+n2VAmg0Zg8EgO3bsQFVVvYO1Ydg4HA527tzJ5s2b8fv9KIpCNqtHxEyDMRKJWNHKK6+8klNPPZWrrrqKUaNGAaBqGjZj7IT3vAuIP3cZ4T3x1QsvpuTk0xgIhejr7aWsvPxT1sDHI3meo+R5zmPL3zfPMcXEX/OO7cRiUUpKSxk5ajQHH3IIRx9zDI+tWM79ixfz0ovP09HRhywbWQCSjMtoWlQ4e1PTsAxJM0sHYPGihbz55htcd8NNjJ8wkcrKSssZV2Q8FvGcZwnPn1OsR4vnXPyJ8pxPdLc1P2xnZwdjDxqLEhpgYMbdqNGYlWMsCIL+wSUbvosuxXXs8e/d7v9DXovZUXXSlGm89OILPLp8eZFX3Jy5cv/iRdx8yxfz3gKK85QRjObVkoQABK+7keSqV0hvWG9tBoIkoQz00z/jLqqPOopRo0fz5ltvcOKJJ++DBoc+fqO95RkOPfANhISN/lkelAFzBqIeHTQbz3jPTuI+OY0WN6KNBi80P7Ek5/dKTQXZCdu3SPznDg/xuICBJQBkWSMeFzjzzDTf+U4MJKiqUhg9SmH9BhuiqJFMCbz1jsx556eRxYJ+IHsQKxhZWPPogOC0KKm37GR22BAcGqgCgqCS2yUxMNdN1cEKY0Y+w4YdD1BV/WXYY0hz7xRzEezr6+PAAw9EMRocqJGw3q2xAI9IEr4LL8Z90slFeNyT/TekCEGgFOgtzjotqm8c9ItQjR5thPe6txaxMvFopN0Fr7uJ5GuvkdmyycCjquOxt4f+++6m5vCjGNU0kg2bNlJeUbHP4dFcl3Y2NzNiRCOSBr0z7yW7s7loY8TwULpOPhXfpVfon7OgIc7gVPzCDUpVVVRNJZvJMn/uHP72l9vp7urE7dI3w5zRSOXqaybw/f/5ESNGNLJo4QLu/Pc/ad6xA7/Pz6VXXFF0TrOWo/D9odivIKBH1uTyCoLX3kh64waUgX4riirYbGQ3bSQ0dxYV3/sRTU1NbN+2fZ+MHpufv6uzk8MOO4xcOExoxl0okcgQ+6OE78JLcJ1wMgB2l0s/iazXHl548SVG+lQPYw8+hC1bNrNk0ULr/tvtdsYefAhvvfUmOSWHzWanrLyceDzOjHvv4Y03XiebyRCPx3B7PMRiMaZOn86o0WOorq7hwWX389KLL+IsqJ2VJJG33nyTlSuf4rLLr9ytQ2sRHle9SmbzRr2zo4nHvl4dj0ccRVNjE2s3rKOsrGyfwyPomNy+bTuHHXYYJBL033cXajikEz3z82h6R0vv+RfpDaoMnmBKYfTdFEEQ2LB+PcuWLsHj8fCVr36NxqYmo9GUiCgKxk8RQRQQBQlRNEOOhiGJ3tRo1oz7WPXaK7S3t/H0U08ycdIU69pyRnSk8H3NuZ3mNZZcfxOp11eR2bq5mOfs2sXAfXfjOPRwRo8aw7oN66isqtrn9Jjnq52MHTsWJRQiNOMe1Gh0dzzKMr6LLsF13AlF+6OqqqTTaWRZ5sorr+TKK69k8uTJPPXUU7zyyis8+eSTtLS04PP5CAaD5HI5stms3qXaZrMcM4Xrss1mw+FwWKMgHA4HkiTR399PIpHgwAMP5LrrruPEE0/k8ssvt2Y+xuNxK0KpgZVmHLzuRpKvvUJm4yC+2t/HwL13U3nEUYxqauLVV1/llNNO2yf1CB+N5ww+186dzSSTScYeciiVVZWoqorH42Xi5KmcetqZrHz6SVa9+grxRIKqqirWrl3Dyy++iMvlslKVYXeHt6lrswzkN7/8Oa+9+jK3fPlWTjnlVOs6io6z1tUbjXV1MM8x1tXDj9R5zsaPn+d8YoajeZE7dmynqqISu9NJaP5skm++UVR4iiiiJZM4Dj2cwMQp+nPS7qlUH8f1gA5sn8/HF774ZdauXk17ezsOhwPFGGqdyWSYM2sGZ59zLqNGjwawZhyZYpEcg5jZxxyAf9I0en//a6sboPk5ki+9QGLFI3gnTKZpRJPe/XAfSq2ySOqORxhR81dcAQgvcpF4xWFZfxpG2mdSwH5AlsDEOIJEvheKZt4vI9pXcH7z/127JLq7RdxuvSGOouieUwPbJBIiGzfLlJVlKK/TqB+hINs0FEWPWq563U40nKCkTG9yA+zZ7ihiqXo5nZYC5xFZ/OPi9P0zAIoAovEiARLPOIk/m8Z7YYbK4L/Yvm00o8dcsM+QVfM6t27ZQnlZGTaHg/CShSRfewXE3fFoP/gQ/BOnWc/B+8CjFco1iUsFSCeCuhmrU+oeLU8ZvdXtqYDbiEK9P3xYJZMGHh0HHYx/0hT6/vS74m6Fokji+WeIPb4c35VXU1VVRXPzDpqaRu4zeIR8FodNlgmUl5N46XliKx62/q5pWr6WqqqawKRpiF6vEd3IO8r29HnNCEosEuPvf7md++65m2QyidOpGyvpdJry8gpuuuWL3PLFLwECv/nlz5lx370k4nEA/vn3v3LEUUfRMGJEvuPmoPcYHOXMZ3NIepTjgouIP7eS8MJ5+aikoDfLiSzVoxy+I44mPDCwz2VzmHhsbm6muqYGyW4nvGgeiVWvDbk/2g85FJ+xP65+522eeWYlo0aO4tzzL8DlcqEoCqNGjWbUqNGoiso9d9xBV2cnNsNREItGeWbl00yZNp03X1/FO2+/RTKZwu8PUFZezsb16wABl8tFPB7n8COO4OoJE5EkCZfbzTe+9f/YunUrA/39Vs2jLNvo7+vluWee4bLLr9ztM1p4PPAgAhOn0vvH36JlM8V4fOE5Yo8vx3vVNdTX1rFz504aGxv3GTyaetyyZQsjm5pwud2EFs4l+crLRXVklh4PGktgsr6uCqJITlHQFAXRiCwAVvqoafAvXbKYzo4Opl93Pbf95Kcf6jpbWlr40x9+hyTZSKVT3PGvf3LscScwatQoVFUpNvgLJJvNIskyoqbhOPhQ/BMn03f773WSXaDH+LNPE3/ycTyXXkFdTS07tm9n5KhR+8z+aPHV7duprjL56hySb67aA189jIC5PxYY/6Io4jKcOplMhkwmw9ixYxk7diw333wzTz75JJs2bWLBggW8/vrrOJ1Oy9AzjYjCSLPpxDP/JooiAwMDpNNpTj/9dCZOnMihhx7KWWedZX2ORCKB0+nE4/EUfTbTkegYcwCByVPp/f1v0DLpfL27KJJ46XmSjy7Hc80kDjzwQNrb26ir2/f46tatQ/Ac6f3zHMF6mUg2k6GttZVMJs3o0aPxeLxWszAlp1BdU83xJ55IeUUFFRUVHH3MMWzdspWZ993L0iWL6e/vw+v1Wt2LC++jGX3MZDLY7XYEQWD5ww+zcf0Gvv8/tzFu/HjrMxVlQJo8Z+K78JzHVuC76pPhOZ8YogVBIJNJE4vGKKmoILezmdCCuXpHJzPn12iDK7jc+K4aj330mKKGOJ/E11TvXpTjlFNP45oJk6wUVquWQJbZumUL995zF0BRoWqRd958TpbRVJXA+Im4TzoVCl8vimiJJAOzZ6B1d+ELBOnt7bHy1/cFEUWRRCJEJPwCZeXN5NpdhOa70BKC3oFUM34qAoJDw3dxAudhWbQk+f4mVp5MPkWUPA8EBQ4YneOcs9MkEgKKAjab3vxGUQRsNnjjDRvf/a6f3/3eS3iXwHHHZPH7NNJpfR7kpk0yLS2S9Y22st/2EB6zMuSMhyCBlgX/NXFcR2VQC3u5SBpKRGRgthu130lp6Vri0TdIpeL7xKYIuh5TqRTJVJKyigpyHW2E5842hskW4FHJITid+K8Yh+PAg/RCceMzvicerQVJQM8t9II4GQR3wfNDHWd0XBUPBuky3cjkPcLG1lsWN8oxPfyBCVNwHX8S5ArwKElosZg+xLm3l9KSUmKRqJX6sy+I+Vm3bt1M08gmtGiUgXvu0qNyhemfRvTAe+75eM4+V9fru6SlDf784UiY3/zy5/z7X/+0UmpUVSWZTHLwIYfyf7//I9/89v/D6/ORzqRYv34dof5+nE4nsizz9ltvcu/dd1mt5QubfgxVU2VldJgXIIogigRv/AL2MWP0xhMGaAVJQunuov/euxAzaZqamlizejVQvF7vzWLWO4VCA5SWl6O0tRGaOxvS6d32R9HlwnPplTgPPIjmrVv41S9+zi9/8mO+/93/x4PLllrny2YyVrfZzZs35WeUCQKaAM+uXImiKNx5zwxu+/HPOPmUU0kmE4T6+/H5/LjdbgRBQJZlrr3+RkYaNXCyLONwOHEVRBtVVcVut6OoquWN31N3TjQN/4TJuE48qXh/lCS0eIzQrBlovT34gyWEQ6F9Co+iKJJMJsmkUwRKS1G6OgnPmaW3yzc6revrqpJfV8ceTC6dRkVAliRsdju5XI5YVJ/FKpBPQ2zesZ0nHnsUWZbp7e1h08YNlnGQzWbJGsbJUI9sNksymUBRFO6+4z+sX7eOy6+4kiuuHMe6tWu545//QBAEEvEka955h8dWLGfRwgU8uGwpL734Ah3t7dhsNkRBQDH0Hpg0VY+yDdZjNMrAzHthoJ9AMEg0GrHqMPcFEQSBdDpNNBqlpLyCXMtOwgvm7IGvuobkq4VrHIDdbsdrpOunUimcTieXXXYZ3/nOd/j73//O7NmzOeWUU6ygxeDsj8HXZ0aGTz/9dObOnctf/vIXvvrVr3LWWWeRy+WshjZut7tozS06j8VXJ+E+8RQ0pSDKLEloyQQDs2fArm5cXi+7urv3Ob6aTqeJxWKUV1YW8xzxg/EcTdNnMPb09tDd3YWqqhxwwIHYrI6sGpIs8c7bb3Pzddfy09t+xDtvv4UoShx40EH87Je/4v/+8EdOOPEk4vG41VhsqMY5pjNOURRKSkrYvGkjM+69m/6+/t04DnxAnlNaSvRj5jmfCKrNzbt5RzM1NdU4bDYGZs8gu21rcShYECCXw3nkUfjHT9Cf+ojjN96PmN6162/+Akcfe6xxQ8UiZS5/+EGefWalVQNZKGYNUaEuRZ+P4E23IJdXWN219M8jkdm4noG5s3G7XdTV1rJjxw4rJWFvFvP6du5YRGPNnUgOCC1xktlg01NQ81X4kAXH2CyBq2OQyxuNWkFtY1E3zYKon6ZCRaXKH34X4Qffj1FerhKLC8iyhiTpUUWHQ6O7W+Kf//Lw1a8F2LhJIhhUyWZ1h2D/gMg7a2x6UKvgK/NeaZWaaUAaBqxcqRK8LoIUVNFyZkhSAAnS79gJP+DA7oX6ir+wY/uyovu0t4r5/d22bSsNdXXYbDZCc2eT2bJxkDEhQDaH4/Aj8V8zSX9GHjq1cc9SoGwNNOlcNNsPQFPRBKOoVJALHjY0LQdCAM32MzTxIEMh739pMvFoAVLTkAIBSm68BbFc75JmvlKQJNJrVhNaMAeH00llZSU7duwA9g09CoJAa2srBx1wEJokk3z0YRKvvKh7vc0NRhTRMhlsTSMJTL1O3xALuj4P1uNum5IgsGDuHObOnoVspKuZm9qFF13MX/7+Dy674gqrwUNZWTlf+spXaWwaSSwWQxAEbHY7C+fP5ZmVK/f4vkOJUPA6TVFwHHAQgSnXGimOinmBIOlRjtSTj4NsY8yY0fTs2jXker23ibWuNjfTUFeP3WZjYO4Msls3IciD8JjL4TziKLxX642NIvE427dtxeVyEQ6F+Ovtf2TVa69a97zXGJFSUVlpdbtWVRW7zU5vbw8L5s2ltq6OL3/1a/z1H//i57/6DcedcCLhcJhUKkUkEuH0M87knHPPs8j0nFkz+Z8ffI/u7m7L0+5wOIjFYrjdbs6/4CK9Tf2g75H1f0FA8vspuekWxLLyQfujTHrtGgbmzcblclJbXU1zc3PRfdpbxby+bdu2UV9Xj02WGZg3m8zGDbvznEwGx2GH458wGVXTkB0OUqkEK59+ij/+/rd846tf4ZabrueLN93At7/5Nf7xt7/w9ptvsmDePLZv34aqKqx4+GG+/51v88Lzz+F2u5FlGZvdjn0PD5vNhsvlZvu2/8/eeYdJUWV//1NVnePkTAYBExgw54xpzTmBmHrXddPv3airm7Nr6g3urllERcScEBVzRCUjaYYZZhgmdJjOXfX+UaHDBGaAAdat7/NAT3dX37p1zz3nnnTPXcNbby6krKyMGbNmce11N1BdXc2z8+dx809+xLcC13L5pRdyzcyr+HbgOq6/5mquuvxSrps1kz/+/nesWrECScsCkPwllMy8Bqm0TD0LT3s+QZJIfrGYrjmPYnc4aKivZ83aNQXjtLsinx/r6mpVfhxQX90P3zkXqB/1kdpYLONEUcRqtRrGPsAhhxzC3nvv3Sff9JuJob263W4OO+ww9ttvPwCi0ajBk/3tkS1wypGnr1YW6auShdTypXQ9+iBul4sRDSNY/1+mr65ft44R9fVYLJZt0nNQCh0AzRub2bJlCzarjXHjx+fVRZHIZrO89srLfPH5YlKpJGPHqlmKeurxWWefw+133cMN37wRt9tNLBYzskDyo496lo++99Fqs+FyuZEshWdm6war3s8B9ZwlOT2npqqK9TtQz9nhhqNupcfjcaLRCBWVVcQ++oDIC89qm8/yrP50GtHnw3/RpWpZ6qIHGg6foz4Zstks1dXVzJx1rZZrnjYWRavVyua2zdz3r3vp0VKvjD4JeaW1dbtC8yi6jjgKz5ln5RQ4UM9sSqeJPPMUiSVfUlZVTbynh1gsttWN6bsS+sb51rYO7NLT+Mq7SH7pIjLfgZIVIT/amBEQ3Qq+c6NYRspqZVNtZvWrJ+anigqQjAusXScxa0aM/9wb4ozTEqTTAqmUgM2mkM2qhqTDofDaAjv3P+Ciu1vEalWd2IoMH35kJZXQoofk7t+H462gH0YKrQVIgOeYBN4T47l+KqiVY5MC4blOUitd+Cs2Y5WfYcuW7t1aWdUF0qZNmyj1+XD4S0h+8TmRZ+ap/FbghUsjer34L7gYS1X1tvGjms+KIogoyAiCHSzfAdsfEYRRqMVyMrl/pBGkA8D2L5DOQdAWOGWrhMvvmFDwqvOj+5hj8Z56BiDkBK7FgpJKEZ73JInly9TjAMIh4+ysvry0uwsEQSCdTtOxpR2nxwObWujSoxsFdMwiWK14zzgLxz77ql7xQXj+9Wfv7u7imXnzjLmje8QvvvRy/vCXv7LvlKnG4mPVznE88qijOeucc7DZrEZqTSgU4p47bmfTppZe3vQ+veF9pazKMv7zLsJ1qJbNoadwiRJKKETng/+Bjg5Ky8vZ0Lihz7TY3Qn6+hiLxYhGQpRXVhL/7BMiz85Xvf/562NG5UffhRfjqKsHOcuEiZM46eRTiMfjeLxe1qz5ijtu/wtfrV7F0089xcwrLuO9d97mzDO/gdPpMqIF+ti+9+7bLHrzTQDGjhvHtdffwF3Bv/PL3/yO/Q84gDFjxnL5lTNoGDGC1atWcsvPfsIvb72FFcuXGfPAYrEQCoXYc8+9+Msdd3Hsccf3uZ7pdMhks8iZDO6jjsV72pl9rI8pIk/PJbFsKRXV1YRDXbs9P+p0bGluprS0BKffT3LJF0TmP4WSTavPVUzH8y/CUl2DqCi8//57fP+mbxO47hp++8tf8spLL7JyxXKWLVvKs/Of5rabf8asGVfwyEMP4vX6uOW2X/Ktm77Lp598zPdv+jaPPPgAmUzu4He9T/nQaT/3ySdYsXw5J50ynYmTJrP3vvty0imnENX2t370wQfss8++3PCtG/nZLbfx3R/8P4455ljWrVvD73/zKwLXX8O8uU8ia5kMrmOOx3fq6bl7KppcTSYJz32c1OpVeMsrsFmtdGzZ8l9Bx3gsRjQSpqKyivjHHxJ94Rk1Yq/LpHx99cJLsFTk9NWC48jyZE82myWRSBhFTlwuF+vWreOPf/wjl1xyCWeffTbvv/9+wfl/xRkY+bJS18nefvttTjnlFGbNmsXjjz+Ox+MxiqzE4/Fee1XzX9VtRdr6eOTR+E7/Rt/66tOqvlpeVUU0EiYe3731VZ2OrZs24XI68ZRXkPhi8TbpOTo19XFsatxAJBKhvKKCESNHGvcDaGlu5tWXX8TtdnPQIYdwxJFHq8XDNHpkMhnGjx/Pj376M/58x10ccuhhxnaOfL7Q6SxJEul0Go/bw0knn4zf7yfbK+I7dD2nsqaG8A7Uc3a44agP9saNTYwZNRpktQxutlUrL04uzVORZZyHqIdu9rlRfBgUAJ2BJEn1Fkw/9TSOOPIoFKVQ8VdLX7/LM/OeQhTFXuH6XGEO7b1RKGcmtj0mqgdYa0JVsNlIN26g68H/IKIwoqGBxsYNwO7rjdOHvm3Ti9RVvoio2Ol6xE6mSUKQtOMydM9MFpz7JfGeGis8gi8/2tjrBoVvRVHh3n+5ufE7fkDh9j+H+f1vQowflyEaVc9z1FNX3W6FaFQkkdDPeQSrFZavsNLZKapZjlq7OonyA1L65/pzGtdkIauAIkLJFRFsozIoWe0CRUCwKqTXWuh6zIYk2qipfIK21tcLxmt3RVNTExUVlVhlmc4H/k2meWNBdU0AJSvjPOgQtZCKVpBDx6D5URBQtDCuGsWXEQQfWL+HbJtD2vIbstLlyJZzyFpmkbbcjWx7BCznIQiSsXcYRVGNx0GilzEiiiAIlM6YhW3c+N78uH4tXQ/dB8DokSNpbm4G2G0dAHo6/bp16xjZ0IDL6aTz4QdILV+a26OiP38mjX3Snrm9VIPM4tC/++zTT1m7dq2xwMiyzLHHHc/NP7+V6upqMplMgRKh/z3j6ms44MCDjFLxVquVTz/5mP/c+0+jTHn+ffo0Hsmls+oQPR5Kr7kOqaJSXeT1xdZqJfX5YrpmP4zL6WJkfQNr1ny1W3vH9fnV3LyRMWPGgqIUHocDhevjwYfiOfUMBEUhi4DT4eDKGVdz0CGH0N3Vhcfj451Fb3HjDddz2y0/5d13FnH3XXewz5SpHHTIIYYhraeWbmrZxDNaVc5EIoEsy4wbP57rbgjw17uCPPDIbI474QReeO5ZbvrWN5n98EPE43GjyEMmnSabzXLljJncGfw7yUSC73/nRl564XkAI6ICubXNIkmI2hwsnTEL2/g9evFjZv26PH4cRUtLS8F47a5obm6moqwcq6LQ+cB/SG9sRLDa1OfSL5JlHAcehO+Ms8lmMsx9ai7fDlzPk4/PYcKEPfjLHXfyxLz5zH3mOebOf5Y5c+fxh7/8FZ/PR2fnFhwOB1Om7sePf3Yzv/zN74lEwvz85p/yh9/9ms6OjoJzNvN5zGKxsHrVKl556UWcTgcXXnwJLpeLjz74gM8+/cxIIz/+xJMJ/vPf/N8Pf8y3bvoO//fDH/HnO+7i8blPc9N3v8faNWv40f/7Po8+9CAZzSnkv/JqbGPHFdLRaiWzdg1dD92HVRCoqaqiuaV5l9FmMMjxYzOjR40GWabzgf+Q2dTSNz8echieU09HkXP6qkBhUbFUKkVSq9jvcDhIpVLce++9zJw5k0suuYRf/epXzJkzh66uLiPKlC+Xi42JvmRlc3MzDzzwAN/73vc444wz+MUvfkFTUxNOp7Pg2I8CmZv3LHrf/VdePaC+KgAjR4ykqakJ2H31VR2NTU1UV1dhyWbpevA/26TnFNOhpaWZTq3CbHG14Pffe5fly5fh8Xo55JDDsFgtBbSyWCxkMhnsdjunnnY6f707yI3f+S5ut7tgG0c+zTOZDAdMm8Y3zjkXAEmTnX3OhaHoOSN2nJ6zQw1H3bPSvHEjSlamtKKC8PPPEF/0Zu7sME0BNc4jufwqRIejl1I6XF7jgv1QmnJz7AknYrc7DI+qHnUMh0M8/thsWjdtMnKT++qbwZyyjG3UaEqvuFot7atbK4K6MTn2+mtEX3kJf0UFDpud5o0bDQN2d4K6CImsX/cFNSW/w14i0POWhdgbThREEFGNAy3aaPHL+C+OIpUq2jkbufTPwVBRy65DluHZ5xzMuq6Ev/3dxTFHp/jn37q56ooYVqt6JIckKaTTavRR1IrX6Gc9btwosX6DBZy54ISeNSnQO/poGI3aljDBBrIC6QTY9kjjvyiqVY3VnxcUWaTnVSexdy24K0RKXb9kw4blCIK4m9JRYOPGJsaOHoXkdBJ//TViCxcYc7KAH8vKKLnsKkRtUz3kR26Hwo/5aTbqfsWsrCBKB2O1/ZiUdAfdyh2kpb9gtX0TUZxIVulddWxbJYAhYOUstjHj8F8+A8GmKnNK3qSIL3iF6MIFlFdVo2QyNDc375b8qHsiE4kE4e4uyiqriC/+lMizT6vpYkV7qUSnC99Fl6rHb+he8X48jH0Zkq+9/DKRSNjYD+7xejn3/Avw+f2kUqmCQhr67zOZDHX19cycdQ0VFRXGoihKEo89+iiL3nzT2O804Fwq9rjr2RyHHoH3rPMK7oskIaeShJ9+ksSypVTV1hIJhw2lbXeLcujr48amJgRFobS8gsiLzxF/a6ExJwv5sZySy2cgapVsJc2BOXHSJL5103epqakhlVIPAf/0k49pb2+nurqG9999h48++oBLL7sCp8NpzGd9TN98YyHvvZurkKooiuoZnzCB+oYG/vi73/DDH3yfxZ99is1mM8YyFosxYuQofvv7P3H1tdfx2KMP84tbb+GN11/nwfvvY8P69cb+H/1Zt2xp5w+//TXvvvM2KAqW0WMouWJGr/VREQRiC14luuBVyquqyaaStOym/KjL1cbGRsaMHo3F6ST+5uvEFr6GutgU0lH0l+K/7EpEt5sXnnuWn/34h3R3dXHLbb/kX/c9wJUzr+bAaQcxbtx4xk+YwEEHH8LlV17F2PHjsVgsdHZ2cvNPf8zKFcu5csZMbr/zbqqqq7nrr7fz4//3A1ZqqaRqpVWVf7JaNPKZ+U+zbOkSTjjpZPbb/wCam5u59ec/Y/26tVxw4cXY7XY++vB9vvh8cUH1R6/Xy5577c2Pfnozf/jL7djtDn516628sfB1kGWsE/bAf9mVhpFspMij0PPqS8QWvYnD56eqopLGxsbd0pmjz9GmpiaQs5RVVg6sr5br+qqTfM0mX6EXRRGbzYbdbufVV1/l+uuv58QTT+SWW27hkUceYfny5TidTurr6400VUEQDFkLOT7NN0atVquhmwqCgMfjoaamhlgsxssvv8ztt9/O2WefzcyZM1mwYIFRTVXfG2k8S/6rnNX01Zn96quRV1+irLISQVHYtGnTbsmP+phsbGpizKhRSA5Nz3l9QT9ydXB6jr5eHXzwoRx86GGcf8FFVFZWFazHb7y+gHg8wZix4zj2+OOBnCNVH2fddshms4wePZrv/78fcVfw70yZul/BvkNRizaWlpZy4cWXUFZWZhzX0tc63VvPmdmnnhN7TdNzqnecnrNDDUfd8xUOhxg5ejSZLe10P/oIcldnriw1GBax54STcR1+pBptFAv34OwMCFqu6QEHHKjuCcm7r6KoBF++fBmL3lJTe/qLOhYoObKM98yzcB91DPqBhIosI1htZNs30/3IQyjdXVTX1tLV2WkIm90JkiSRSsWI97yD178UJeSg+zEnmS0SglVGkUUEZCOq6DoqgfuoOEoCdStVXvrnYLV/QQS3W01FbW+X+PNfPfz0Fh81dTK33Brl9j+H2H//FImE2qAsqwajft4iQDwu8Ic/eljwkg2LFRBB1nkjL5tRyeufoBmsog2WL7HwzRv9PDRbPTfOf1YUx4FJtfAP2nPbZDItFrpnOxGidkr8i4lHPyCdju92qRzqQd1purq6sDtdiKEQXY88QLZzi3GYcT4/uo87EddRxxTwo7F/cAjo7SlVN/6/ufFjvrXwdk579lecMv93nP7sbXz3rTv4oPULJMGKovTvYR36fbV0RkXBf9Y5avn7TEZ9HllWveOtrYQefZBsJMLI0aPp7uw0nEe7E3TvYGNjo7qHQpHVw4ybGo2FIt8r7jjwILxnnlXgTe1v8SlOIW3euFFV8rXvstksY8aM4aCDDzaiVjry29OLjJ12xplMP11Nm9FTq7q6OrnrztvZ0t7e696DSlnVvapXzMA+aU+DjqpX1U567Rq6Hs5Fjxs37J7ZHLn1McyIUaPIdnUSmv0Q2Y4Ordx/ET8efyKuI48q4Ed9nE+ZfipXXDXTUCa9Ph+SJJFMJJlx9TUcddTRnHjyKRx34onGOOj7oFpamnnumWeMfukFcb5avZpv3XAdf7vnbkKhbhwOB6Cue5lsljPO/Ab3/P2fXHL55YS7Qzzy4INEIhEqKiv56MMPeOThB1UFSaP7G68v4JvXXcudt/+FO//yZza3b0YEPKd/A9fhRxWtj1ayba10P/ogcjjMyDFjCIVCyLshP0qSRDqVIhwO4XA6ESMRuh56QD1UvUiuCoqC6+hj8Z5wMsuXLuH3v/st6XSan/38Nr51401UVVcjy7IRydULnLzx+uu8/dZbHHr4Edz0ve+xeuVK/vDb3xAOhTl5+qnccXeQgw4+hLlPPs73v/NtFi5YgCRZUBQ1sm+xqvR89aUXsVqsXHDhJXg8Hu79x9/57JNPuPb6AL/67e8457zzWL5sKcG77mDz5s1YrLnohp5ZcN75F/Kd7/2AZCrBHX/5M5vaWlU6fuMcXIcdXiRXbWQ2tdD18P2IsRhen5dIONQrS2F3gM6PkXCYhlGjyWzZQvfsh/vXV088GdfhR/XSV3U5KQgCq1ev5kc/+hHHH3881157LQ899BCffPIJ6XSayspK/H4/siwTi8VIpVKGAi/LMmPGjCkwDvV2vV4vmUzGcNopilqFMx6PY7PZqKqqwmazsXz5cmbPns21117L9OnTufPOO+nq6iroZ6G+KuX01SOP7s2P7ZsJPfIgcqib+hEj6e7q2i2r5Orj1NXVhdPlQgh1F+o5xXJ1K3qO/qofxXfAgQfyj3/fx6zrrjPGXy9oY9HWw4b6BhoaRpDNL+5ZtLbqtLbb7Rx/4kn8+nd/YOzYcTmdQzMuDz38cKafdjrZvGrH+WvlUPWcbFsroUfy9Jyu7ddzdtgMMFKp1q6l3F+Cw+Eg/NQTpD77BMFuL8gNVLJZrA0j8F9+lUq4ogcYzj0qxQSQZYXRY8Ya54DpUBS1AlIkEmHZ0qUDtlccxhLdbkpmXY9UUWlUN0SREew2Eh+9T3j+PJxOJ1UV5UahnN2lclU2q/Zjw/p3KXffiqsUQs9IJD7Sj9/I5XYqsoBUlaHkkghC3tmLxec29olifVFQz2zMZsHpUBAE+GqNhc2bRdxehdNPS/L3e0LcdGOU0lJZM/7UfZY6JEnho4+tfOtGPzf/3ENTk4joUKOIOokMR4xmNGYzIDrg448sfO8HfuY/4+Def7lZtsSCWK5QMiOM6JdzP0YACWLv2+l+QcRVDiXOn9K44SOgt3NhV8HY8N+4gcryCrxeL11z55D4+CPQvMSGtz+bxVpbp0YCJGm7+dHwlCrqAhhOxbjtg/s5/8VfEvziKRZueJuPmj9iwfpF3Ll4Luc8fyt/+Hg2iUyy4HfbZDzmemG8il4fpdfcgFReVjgRbDbi771D5Nl52B0OyktLWb9+/W7Gj1oWR/NGREWhrKKC8AvPEX/jdSNNRcmjo+T3U3LlTCSvz2ijv0yJ/O90mi1a9CZNTY25AgCSxLSDDqGquqYXPfoqAiGKItdefwOT9pxsRC/083Hv+/e/ekUe+ptbBV5yQT2CwzpiBKVXX6euJ3nfIQjEX32F6GsvU1ZZBbLMJi1LZHcxHvNTjctKS7DbHYSeepL4Jx+DZvwDOX6sq6fk8hkIYo4f8+koCAJXXX01J558MolEgkQySVV1Nb/63e+45bZfMm78BJYvW0Z3V1eBB1yyWEgk4qxd85Wxb0mn07KlS/jg3XeNYh56pcbq6mpuvvU2fv/n29nvgANQFIVJkydz/oUXGR5xFIUn58zhrTcWkojH+c0vb+P73/k2i956E6vNxjvvvM2D999HJp3G4vdTcvW1SOXlqnBWOwc2G4n33yP8zDwcDiclXs9ux496P9Zv2EBVeTker5eueU8Q//gDtUpbftRGziJWV1M+4xpS6TRzZs9mxbKlXHzJpVxy2eUoyLl9o1YrkqQeHp5MJnl8zmzC4TCXXXEV3/7O9zjr7HN5842FvPjCcwiCwLSDDub2u+7mnHPP58MP3uP/ff+7PHDfv5FlxZjzL77wHIs//4zjTzyRgw4+mNWrVvHMvLlMmbofV86Yib+khKuvuZ4Dpx3EW2++ycMP3I9AzgjSlVZZlrnwoos5efp03n/3Hd54/XUUWcZSUkLJ1dcjlZblnltRwGIl/u47dD/7NG6Pl7LSUpqaGgvGb1dDf8b169dRXqLqq6G5j/evr9Y34L+sb30VcrzZ0dHB/PnzWbhwIZFIBK/XS1lZmRGhSqVShqy12+0kEgnsdjuPP/44t912G7FYTK1WrMn9eDzOlClTWLBgAQcffDAdHR1G9FGXpYlEQjUc/H5KSkro6Ohg0aJF3HzzzTz7rHpMk+6Q6Ftf9VAy64YifVVNdUx8+AGhp+ficrnwe71s0Pix9767XYN8uVpZofJj99zHVT3HYt0mPafARkBAEEVqa2txuz0FhrfVauWwww5DEkW6Q91EIpFeAbBiZ62evZFOp9lv//059vgTjGsymQwVFRVcdPFlOJ1OhCL1J7+dvvUcb/96zvs7Vs/ZIYZjLnQbJxGPU1pXR3L1SsLznkSOx3KpVPqDCALes8/Dsefeaq54HqF2dnqRoijYbFbGj9+jt6dbU1hisZ6B26BwsimyjPOAafjOv1C1+rV2kCzIPT2E584hveYrfFXVJGIxEolEQSrsroIaIbAQ72kiFn2dqvrNpL7yEHnahRyREKwKiqxF+bRoo++0OM4pKZS0aojpttWA0Ub9u3z5hVoYUlHUysICYLWoBipZkNPQ0CDzox/38LOfRCgpkclkBC1dVY1AJhIC1dVZjjkmxdPPOLniqlLmzHaQSoNgLeQlvZqrZIHOzQJ33uXh40+t+HwKjY0S/77PRTwk4D00ife0GEo679gRm4IcshCZ5yK11kPNiBZCXQtIJVsKNrrvKuiRnlgsRqInRkVtHak1XxGZ9yRyT7ToUGqNH886B8c+U7abH/ONRlEQSWZT/PHTR/nFh/fTHutEESREqxvJ5kG0upERaIm289P3/sHdXz5FWs4gCiLytkYee0U7Vd5zHnQIvnMuNMqnK9ohztlImNCTj5PesJ7yujoSPT3E4/Hdgo6Q84qHursZMXoUmS1bCM1+mGxHO4LNlotuAMgynuNOxH3Userneamh+iJW/C/fQMtmsyx49VVjA302m8Hv83H0scca7ejory21GMAEZl59DW63m2wmo3rRszKzH32Yd95+q6CYVP486+tfgWGsKHimn4rn+JPUc6s0ZUCw2ci0tarj0t1Jw6jRdHV07DbR4xw/9pBOJSmvqSW15ivCTz2B3NPTO7ohCHjPPlctbJS3dy0/HS6TyVBeXsGNN32XPffai8MPO5x//ecBLr7kclwuF48+9CA3XHM1H7z/njEGoigS6+lh6tT9uea6G3A6XQX9lCQJm91m9FkQBI47/gT+du+/uXrWtZSWlqrRkWwWf0kJV8yYybjx49UzPp0O2lo38Yff/ZaZV1zG3+65h82bN+NwOBAEtajT54s/IxwJA+CYdjD+cy/MyRudH6NhQk/OIb1+HeX1DSQT8d2GH/Vy+vFYjERMk6vr1xF96gmU4kPiFXWN9JxxNvYpU2lat45nnpnPxEmTOO+Ci7TnUQqicJl0GkmSeOuNhby5cCHHHX8CRx51NJIkccWMGXg8HuY/PY9EIkE6nWbcuPH8+nd/4Lobvklr6yZ+/YvbuO3nNxPr6aGxcQMvPq8ameecfwH+khJee+UV2tvbOf2MM6mqriaVSjFu/Hhu+OaNoCjMmf0Ib76x0NiXBTn54/Z4OGX66Xh9Xl5+8QUSiQQCAo6DD8F3znlG9EaPcsjhbqJPziHd1EhVfQPh7pCh5+xqOur6ajwWI51IUlJbQ2r1KiJPz+1fXz3nfBx79a+v6gb/IYccwkUXXYTP58PhcBhR5Pwoom6Uh0Ihpk6dyuuvv86xxx6L1WolGo32GiNZlqmtreWll15ixowZRuVqsUi+p9NpUqkUpaWlZDIZzjvvPK644grS6bSRPQD96KsH9qGvWizIPVE1ALT2K8rr6ojHVX1V2m30VVXPScZjVNTVF+o5xfw4SD2nr8yc4jMY9Wum7ncA1TW1rF+/jlWrVhgRY8g5+Iy1LO93Ot8fOG0aNpsdPSW5tLSMyXtORs9gLO6bge3Rc2rrSMS2T8/ZIauqEd3YsIGa6mqsgkD37EdIrVyB4HSpk1B/uGwG+x6T8GuHGetl4vtLpRoOFEcdATwezwD3HrhPxaFpUNOrSi65AvteexcqOU4nySVL6JrzKA6LhYqKcsMbt6u94/qYrF//LqOq/wSShdBTIsmlNgSHAnLOW6VkBayjM/jPj5K/61oN+WtRvT5vQoFhqSggWGDpcguL3rah7WNGAdJpgUxa3VsoiJBOAjYYNz6Lx62gbe8im4XRozIcNC3Fj38Y5Y7bQ/w92E1tbZabf+7lxptKWPyZRT39QZ/xAqTTgBPeetvOhx9ZcTrUiKfNqvDcCw5eX2gDO/gviWAbn0bJqEazkhUR7DKJxXbCTwtgsTCq+nesX/dBwTjuKuSnNtbW1mIRoPvxR0ktW4rodKpeRYOOWWzjxuO7UDsEdzv5MSco1fcLmz7jjs+e0Dx3FhBAlrNk5QyydnaKKFnIKDJ/+ng2H7YuV/s1QJRsMH0o/q0gSZRcdgW2yXvmhGo2i+hwkvpiMd1PzMYqitTUVBuFq3Y1HfVojuoVL8VudxCe9yTJjz9EsDty3mFUOlqqayi5cqZaDKDIyOsL+YugJEksX7aUzxd/Zix42azM2HHj2f+AAwtSlPR5UfwPMBaic849n+NOOJGsVhTHarXS1trK3XfeSVdnV68+5LdRgPw5qCiIThcls67DUlunbUwWIKuekRd/7x0tm8NBic9Hk7a3aldHOXR+3LBhA1WVVVhFke7HHiG9Ylnf/Dh+Ar4LLlF/XKQc6tDHed8pU7kr+HfuuCfIlP32Q7JIPPzAA/zqF7fS1LjBGFe9ONF5F1zIPf+4lylTp/Zab2z2nAIjyzK1dXX85JafM+2gg43nkCQJUTP8J02ezMxZ16KnTFmsVr784nPeeXsRkiQae+ZEUWTm1bP41W9/T0lJKYosI1ks+C+9EvvkvQrWR9HhJLXkC7ofn41NFKmsqDRSj3cHgwNUOjbU1yMCoccfJbnkS4QiOpLNYh07jpJLLgNg6bKlbGxqZL8DDmTvvfchnU732itssVpp3riRf9/7T1LpFDOuuUatqpjNstfe+zB1v/1Z+uWXfLV6NVarlWw2S2VVFT/66c386Kc3Y7FY+Mc9d/HjH/4ff7vnbpZ8+SUnnngyhx9xJACff/4ZLpeLw444AkEQjMjV9NNO58qZM/lq9Wru+/e/aGtrK3Bk6/3cd+oU6keMZOmSJXR0dgKqXPVfdhW2PSYWyFXB4SSx+BNCT85BAurr63YbuVqQjVNZiV2U6Jr9EKmVy/vRVycOqK/qz2O325FlmZ///OcceeSRtLe3Y9eyI3ToBkM0GuWCCy7gjTfeYNSoUQB0d3cbjiHIpZZv3LiRTm2877nnHv70pz8ZmQKSJBXIcZvNRmdnJ6NGjWLGjBnG5/mv/eqrF/ehrzqcJJd8SfecR7FLEhXl5TQ17h76ar6eU1NTgwXofnw2qWX9yNVB6jnFtNUNvb726DeMGMF+BxxAW2srn33yKdB7XIqNzfx7HHDgNPaYOJF4PK6ukW2tPDV3rhEhzr+2GNul51Rtn56z3YajfkBwKpUi0t1NRXU10bffouflF7QrlNyhvwJgseK/9Aqs9Q3GAcXboyRuK4rvpfRz66EOqSAIRmqVpb6B0pnXqfuQBCFP0ZPpefE5Eh99gLuiihKvj8bGxl2aWqXvQdjQGMLn+CueSoXEJ1Z6XnahZEQQFJT8/YIilJwXxTY6o56qkGcM9kvFvEhjnhOIaATuvNvN+vUWJEk1OgSgujpLTXXWiGaK2v7JdDqXgSCKatGc885N8MiD3Zx9VgKrCIcfkebvwRA/+mGUpcsszJxVwh13uunoEhDsarTR5oCOFoGn59sJhUSsVoVMRn2AeFzgn/e6ad0o4hiXoeSyKEgKCIqaXiWCkhKJvuAm/omF0joZq/JnWjZFCja672zo/Ni6aRMumw1fZSWxDz+g56XnUSsHC0Xn4Un4L7kc28hR282PxgKFWsgjlk7w5Jo3iMS7tf03uYqY2g9AUZDlLBbJyuboZuateYtUNo0kqlHH7UUBP44YRemMa9Uqozo/CmrxlejzzxD/5GNKqqqxihJtbW27tBCAEd2Ix4n3RCmvrye5YrmWxRFXveL5FaAUBd8552ve1PyU+5zSkE6njX+pVMp4Nv114esLaNeURlmWkUSRww4/nNLS0l4e00QiQVtrK62bNtHauolNLS1EtWgSgMPpIPCtGxk5cqTBCxaLhffffYcH7/9Pr0rVmUxGTeVKJkklk4aXPv+eupfVsc8USi69IhflyGZBkshGo4SffJzkV6uoGjmScHcXyWRyl0Y5dH5ct24d1RUVOMvKSXz4vsqPct/8WHLJFdhGjOyTH4udnoIgsNfe+1BdUwvApx9/zD/+dg/d3d3YHU4j2ldeUcFPbv45P7/tl7z4/HMErruGr75abfQRtOqneREwq8WK2+U2+lAc4RBFkTO+8Q2OO+EEenp6sFqt6Cl4oKbHjRkzlt/+4Y/c8otfMWrU6NwzyDKWESMonXGNWmDF4Ed1PKIvPEP8k48oqa7GYbUYxsyuXh9bmptxORz4KiqIf/IR0RefV+knFNJRESV8F12GbfRYspkMa7SxnjBhD+NsNh363Hzj9QXcGLieNxa+zqWXXcFhhx1hXGO329lz770Jh0OsXbNGu41KB5fLxfWBb/LL3/yOkaPHMPeJx3l89qNkMmm8Xi8LXn2FRx9+iJUrloMgMPeJJ/jzH3/Pn//wO/74u9/w9+DdhEJh/CUlvLnwdaPqbn6xFlmWqaiopKKigu7uLkLd3YAarbKOGk3pjGuMs2SRs6pczWSIPjefxOJPKamqxm6x0NLSslusj6lUip5QSD1u4u236Hn5RfUCpQ999ZKB9dV8ntSfa+bMmVRVVRGLxQzDW5Ikkskkoijys5/9jPvuuw/IGeZrNLrq80FfA9rb20mlUsYzXHvttbz88suMGjWKeDxuRB9zDr8sF198MUcccYSR+tqfE7hgfWxooHTmtYX6qrpZTtVXP/4Qd2UVpX4fGzZs2KX8WKzn+CurND3nOVXP2AF6Tl9jlv8bRVFwOp0cedRRxONxPv/8M9KpdL/jXGycAtTV13Pbr37DtIMOJpvN0tPTwx1/+RM/+eH/sbmtraAf+ZHLvtoflJ7znKrn+KursW2HnrPdhqM+AKtXrWLi5D0hFiP8+KNq4Qa73RCqgigip9O4DjwI76mnqwZZkbf664Bir4L7xJPwnHgKSjKppiSl0wh2J+kN6+ia/RDudBLJZiOZiBcszjsbanpKilRsHj7v+1hSEt1POkh9ZUWwyyiaQSUICkpawLF3Cu/pPYYxKJAX5NiKEa6/yrIabXzscScLFtixWhVEUY00lpTIXHB+nNIqhax2LqRm9yDLkJVz91MAu13B7VOMiKecAp9PYeaMOA/c18Wxx6b4xz9czLymhNdetpHMCCgC3He/i4UL7djtarRR1cEFrFaFTz+3Mnu2EwDPSTHcRyZQkgKCpI6B4JRJrbYSfsqJmLRQXv4O0dB8MplUgcd2Z0JPlQiHw/jLSrEkEnQ/9jDp9eu0Q9QzBj8q6TTO/Q/Ee/o31B9vJz/me9YAupJRPm1bCaKVjKwKc4NoBa8CWVkGUeKjtuVE0+oZmsqQ3Ta9UcyPnlNOwX3ciSiJBIJkQUmnEBxO0mvX0P34o0jJJKXl5UTCoV1aCEAfw8YNG6iprdO8qY+qx284nWo1VV0Jz2SwTZiI78JL1PEsMjAymQyPPfool19yEdfNmsm1M6/i6isv5603Fxr3ikYiLHrzTZJ5BRt8JSUcfexxQK7CnL63ZeGCBZx39je44JyzuOSC8zjtlBP5173/NBSjTCbDvlOmcuWMq3OGqCSRSqWY/cjDfPD+e4YCB/Dqyy9x5aUXM/PKy5k140ouueA8/vm3IIlE3FBc9ZQjQZLwnXshzgMPQs6Tq6LLRWrJF4QefwwL6qK8QTtMfldFOXR+zKTTiBYrrnSSztkPk96wHsFRyI9yOo3zgGl4TjtT/XE//JjvCde/y2QyZGWZhx58gMbGDTgcDjWyn81ywIHTuOfv/+SUU6bzsx//kL/+5U8seutN5jz6CIChSEkWC5JWaEE3OPX9j8V90JX/ysoqZlw9i6qqKoP2+nEsp55+Bv/41384/8KLsdltOeM9f308+RTcJ2j8qK+PDifptWvpfuwRLMkEvtIyIpHwLuVHnSd6eqL4S0uRUilCcx5VD4l3OHL8qMvV/fbDe8ZZRtGNUHcISZLwl5QUtKs/09q1a/j9b3/Da6++winTT+W6QACn01mQbl1eXkE2m6W7W43YFyu2555/AX/+651MmTqVVCqF3e7g2Wfm872bbuT/vvcd1nz1FclEgvv/fS9/+eMfuP3Pf+Kvf/kzf/r973j6qbmAOo8i4TC9oWh7mCWystxrn7LnlNPwHHt8nlxV6Zj6ahXdj8/GkkrjLy0j1tNjKP27AvrxB40bNjB2wh4QjxF+fPZW9NUztqqv6jxps9mIx+OcffbZnHXWWUYBHFEUicVi1NTU8Mgjj/DDH/4QUMfbarUSCoVYuXKlMc/0furfJRIJQDWYFEVhn3324eOPP+bYY481jt5wuVx0dnZy+OGH8+Mf/5hsNovL5TL61xd66asnnKzpq4lCfXX9eroefQh3KoVotZLW9mruKn7U5WokHMan6zlzdpyeMxCddei8e+CBB1FaVsbK5StYv35tgaNya+uOoihMO+ggfv+nv3D9N79FWXm5usf5sdnc959/Abk1d6A+D07PcZBet4buOY9gSSYpKa/YZj1nu7g3k8lgsVhoamrC63bj8fkIz3uC2BsLwW4vSKVCUZCcLrVwQ2mZcbjqtmOgTXRDxw43XQW1TK7k8VIyYxaxjz5A7mhXQ2TZDNjs9Cx4lfDRL1H2jXOIdHXS2tpKbW2tMa47C/r9Nm5swSndirdKIPKcROx1J1hy+zWMdFS7QsmlESxVshptzEc/ZMkLjBhOEMkOn31q4V//dpNMCVgtihaEUjjj9ATnnJMgG1drDqDkDM5sRqCXg0TRqqOCYZ8osur8HD8uy29/FeaE4+z87R9uvv0dP5ddFqemSubRR51kMoJmOGqpqIpWrQqFR+c4OfbYJFOnZSi5PEJisR05rD2gDEjQ86qDyJEJfNMFQl0309JyJCNHjjKU5Z0Fgx8bG3E57PhLSgk/+zTxha+pBXFkmfxcYcHhoOTKmVjKK3YAP/ZGNB2nIxkFsajdYuMR3UgU6EiEiWeS+oc7jjE1b5zk9VM68xoSn3xItqsrx48WKz2vvkT46GMpPfUMwqFuWlqaaWgYsdP5US+MsGbNGspLS3CXVxJ/ZxE9Lz2f5y3RGEkQQVLU9JQRIwsWKl1B+3zxZ/z1z39k3bq16rliySTllZXcYFX3s1mtVt55exErVyzPS5PKMmnSJPaZMqVgYdH3XaxauZxlS5fg8XoQEAiFQnz5xRfEenqwaymPiqJw4SWXsmjRm7zx+utYrVbsdjtNTY387Z67mTx5TxxOp9GHLz5fTGdnp1GYZdXKlew7dSqHH3FkoadXlrHU1FA66zqSy5ag6GcHKqAIAtHnn8FzzLGUHHYk0XCEzZvbqKqqNsZ1Z0GfN80bN+KwWigpLSX8zDyVH222Xuuj6HRq/Fi+VX7MNx515fPVV15m4YJXje9TqSTHHnciv/3jn2hoaGDhggW8/NKLhsH+7DPzOf7Ek4xURqfTlefwEsjK2T4VH/2++pw49LAjuOjSy7jr9r8gCALVNTXMuvY6Lr9S3ZdnGP2C1m+1gxo/+iidcQ2Jjz8k29mZ40erldhrLxM+5jhKTzuTcFcXra2bqKur34Xr40acNhv+khLCLzxLz2uv5OiYt8AJDjslV1yNpbLSGCurzYosy4YBoENX9srLKzj3/As4+uhjuPjyKxgxYmQvhS4WixltFbehGxxHHX0M1ff8nffff5d4LEYykSSeiJNKJnn1lZdpaW7mqqtnMWLECGQ5p2yKohqp8vn8HHHUUQAFvCIIIuFwiHCoG5fThUc/ykCno99PycxriX/6MXJ3tyq3ZVmVqy+/SOToYyk5+VTC3V1sammhYcTOl6v5dLRJEqVlZYTmPUnsjdf70VedlFwxE6lscPqqPpZOpxNZlrn11lv58MMP+eKLL3C5XBx00EHMnz8ft9vdi7adnZ2sW7fO2Bep87e+hy9TZDzosuzpp5/m5ptv5j//+Q/t7e2UlZVx9dVXY7fbjVTWQW050fVVr5eSq2YR+/AD5M4tefqqTdVXj3mJsjPPJtLdzaZNm6irq9tldGxqbMTpsFGi6zmv7zw9J388R48Zw7777Mvizxfz+eefM2GPiYbxv7VxFwSBaDTKg/f/h0MOO4z99z+AO/96Ox9/9CHvvfMOmza1UFtbN/htQwPqOVmwWIm9qsnVU88gHOraJj1nm90FiqIYC1A8GqW8tpZsSzOhJx4n29mheiuyWUDd5CmnUrhPOEktw2084/YQcPeMUhbmTKt9dOw7lZKLL0NOZ9RxyWTUDeQdHYSeeIxMSwuVDQ2EujqNvQ87y0Oup0MkEh3Eww9TWbuBzCYH4XlO0m0Sgk1GyWqLvqggpwRcRyRwHx1XDaf8/Yz9Kfr6NfnXChAJCdxxp4eNGyWsFrWSajotsO8+ab4Z6MEi5dkcecORzW1BUJsSQBSVXvJA1k4MERSwWWH6WUkefqCLk09Kcvfdbn79Ww8dnRJWa6HRqEOSYNMmiXv/5SYZBcf+Sfzn9iCnBbVQUEZAsCukN1mJPOUiu8lJZe164pFHiMc7CoqADDd0fkwmk8RjMcprasi0tRJ+8nEy7e0IVpvGj0KOH487AfeRxxht7OjIv12y4pJsucqJg4DTYscq7rhFqK89DI6p++O78DLkdDrHjzY72c1tRJ6YQ7atjcq6eqLhiFECfWemOurzJp1KYnO5cfZE6Hr0Ic0rXhjdkFNJXNMOwTv9jJziqikWFouF7q4u7vv3vWxYvw6f12ukLe2zz76MGz/BuOebCxfS2dFhpB+JosSRRx2D1+M1rtEdIT09PTQ1NWkFrNT0RIfDwaaWZqLRKJDztJaUlPDNG2+ioqLC8JZLksSbC1/nwQfuN4732GPSJPaYNAlFUbDb7bhcLrZsaef+//yb7u7uXDpNHh1dRx6D9xvnoMTjmndc86puWE/3Y48iRSKUVFYSDoV3uhMnf32MRiJU1NWTad1E+Mk5ZDq25PEjRfx4tNHGQN7l/O9FSSISCfPIgw/Q1taGzWbTiudUcuN3v0t9fT0A0w4+mOmnnk4kEsHtdtO0YQP3/etePvzgPTZubCKVTOaltaGlRvWe9zljQzTKy1896xpu1Kp/3vP3f3LDN2/E5XL1meJKX/x4UTE/2si0txN+4jEyra1UjRhBqKur4EiCnYHc+pggEeuhvK6ObPtmInPnkNnclqOjkCdXjzke99HHGkqcKEnU1zeQzWRo3LC+11gqikJJSQmzrr2OH/3sZkaNGtUrmiPLMmu+Wo3dbqempsb4Leh0Ugy5MXHSJK68aibXB77FTd/7Pj/6yc+45bZfcsKJJ5FOpZgyZSpXX3Md11x3PbOuvY5Z117HzFnXcs11N3DhxZdQX99QIDN1Q2bVihU0NzczcuRISsvL9Qcw+ujc/0D8F1yCnEqphUnSaQS7XV2HnphDdstmKmvrifVEd3qhnEJ+DFNRX0+mpZnwk3MG0FdPxnXE0PRVfV5ms1nKy8u57rrrqKys5Nxzz+W1115TC4blRZH1549EIjQ3N2uZArlolc5j+amqkKvOmc1m+eUvf8n9999PTU0N5557LhdccAGxWMw4U3VrkbVe+uqUqZRccilyOlWgr2Y7tqj8uKmFitp6oqHQTufH3npO7U7Xc/RnFUV17Hx+P0cfdzyh7m4jJZ0hjIcoCHz6ySd8/6Zv4/Z4+M3v/0BlZRVr137F4s8+A3KnHeTfPx9D0XMy7W2En5hDpq2Nqrp6ouHwkPWcbTYc9RtsamnB5XbhdjgIPTOPxAfvIrrdhjBVcwsVLJWV6mHGbvewRDd2JxghY23/jWCx4Dv3fJwHTEOOx1ShmkkjuN0k3n+H0HPzcdkdeNxeWpqbgZ2fWtXYuIFy72+wuy1EXxaJveNAcCogizljUAGpRKb0kgiST9vzuDWjkbwUVW06ZLIgWuHBh528uchmbI3IZATKymS+fWMPo8bKyPmVWvNgOHiNGyi5mawZqNksSDY1qpmSIRwR2Lha5PWFNtaslbBIuXb18yDzz4XUo46iCK+9buO55xxIXvCcFcW5dwolrqWsZkREl0xskZPwKwIOrwWv/fe0blq7XfQYKnR+3LixCZ/Pi9PuIPzcM8TfXaTxY6YgWmUpL6fkihmIHs+O40eDTmpbpQ4Pk0pHgJJFzCekQdDce0kQQc6wV9lovDY9vUb/evt4oRc/Wq34zr8Ax9T9keNxgx9Ft4fYO28SevFZXHY7fp+PjU1N23XvoUJPAWveuBGnw4HP6yX00gvE3nwdnC5t/1AuR1tyuSi5ahaSFqUqXkCef/5Znn/mWVxuN6l0CrTzFffeZx8qq6rIZDLEYzFWLl+OrKVGIQj4S0s56phjCsZPp0Mo1K0WLBHU6rm6kdrW1kpnZ4fxLHpfDj70MC657EqjX3oZ+g/ee5dYLEY2k6G2to499piIrCmp6XQap9PJqy+/xKsvv1TQnk5H0emk5LIrsY6fgJJKgb44ulzEFrxC+JUXKfH7kUSBTS0tBeM73DD4samJEp8Pp91O6Ln5xN97B9HVFz9WaOvjwPxYrByk02lEQWD+vHm89+47xqHfmXSac8+/gAMOOABQn9vj8XDhJZdQVlZOKpXCarOx4LVXufDcc7jqskv54ovP8Xg9RkEbOSurKeT9QDdWFEWhpraOn97yc+4M/o1DDj3MiKgUz8f8dKvC9fECnPvl82MG0e0m/s4iwi88q0b6/H6amzca996Z2LixCb/Ph9NmJ/zCs8TeXoTgKuZHBUtpqUpHrxdk2ahguefee+Pxevl88WKaGjcYSn3+WMpaCmhfyn7zxo188vHH1NTWsscek3oZlvn7TvW29H96tOqQQw/DZrcz98kniPX0qOnN2v3yry9Og5a1Bff1Ba/R1trKkUcfjdfrUe9LsVy9CMeU/VRnjmY8ii438UULCb/4PE6HHbfLw6ZNLcNHrD6g82NLczNetweX3U7o2adJfPBeb31VUbBUVFJy+VVD1ld1OuiFoWbOnMkjjzzC3//+d6MfOs9Abh5Ho1G2bNmCzWbrVb0T1AIwUBwFFoyU8RNPPJE1a9bwf//3fwBGFdX8LQsD9Rn64McDpvXmx3ffIfTcM7idDlxuN5tadq6+ujvoOTlZJhoy8Iwzz+K73/sBRxypOhrEITgpM9qal0wmaWluZs+99ubY449nY1MTn3z0of4ove7fb78GoefE33lTlat2Oz6f3yjQOVhsk+GoT/54PE5PJELNyFEklnxJdN6TyJm0qm1rwkaQJJRUAt9Z5+KYMqXgIXfVvpO+MGw90UpUWxtGUnLV1YhOLcVDVq0SJZMlOvdx4ku/pG7USELdqlc1v6zvcEH3fGUyG4h330XV6DiJ5VbCT7tR4hYEi4CSVXNEBUlBSQh4p8dw7J9Uo43kIokDHb0h5KWoZjNgccD771l54EEX6ZSAJClaXQSFSy+JcfJJKbIJEC2A3LtpRS5kJLTb632RFZAcsLlNYP48O3/8g4f/90MfM68t5bvf9/PhhzYEUSuEAwVGo/qaa1eSFKI9Iv+538WmdSKO8RlKLo0i2LWbygpIAtm4RGSem/RKgbLaGDZ+SkvLzqOjvn+sJxyhtr6BxPJlRJ56AjmVUkOn+fyYTOI982ycUw/Ijd+O4Edt3ETNoPDbPJwx9ggQJSRBUMPHegRDT39WZBBFBBQkq4PTxxyO02JXz3Ikp2zuMGj8aBs5mtKrrkbUy5TLaj/kZIrIk3OIr1hGbUMD4e5ug0+Gm465fYBJQ66mG9cTfuIx5FBIpZ1WEEelYxz3SdNxHZ4rooEgGEr7yhUr+Nc//k4qrZ8dJpLNZvCV+Nl7n32NKJ6iKCSScZKJBNlsllhPD/vusy+TJk0umBP683d2dNLcvFGNTsmK4QXu6uxic9vm3kMuCFxx1VXsv/8B9Gh7nBKJBBarFbvNRlpLtdxrr71xezxk8vb4pNNp/vOvf7Kxqcn4TG1U/ds2aU9KrphpRHzIqlkdcjhM+PHZpBo3UDtyNNFIhGQysVOyAHQ6JhIJYtEoVQ0NxJctJfrUk8jJZN/8+I2zcU7dP4+M/VfS0z+XZRmr1crGxiaemDOb7u5ubHY7yWSSPSZN4rIrrkSScgdIAxx44DROPe10kskkTqcTi1VNoezW5rlaxEqPWmfIZvo2tPMLNuiv2WyWbCZTYNRstbCDwY+jKLlqFqLDqQ+i+l1K48flS6lrGEF3ZycZ/YiXnbY+ZoiGQtTUNxBfuYLw3CeQE/FclMqgYwLPGWfh3P9AoHDdmjBhDw455FA+X/wZLz7/PEDB/lE99be4SqYe7Zv31FwaN6zn8COOpL6hodezF9NDP1NVFEUjInTo4Uew/wEH8s6it3ju2fmGk0GXF/q/fIMxnU5jtVp57dVXeeG5Z6mpqeHk6adhsVgLj7vR6Th6DCVXXW2ctaruSRHJJpKEn5hDcvUqKkc0oGSydHZ07FS5Go/H6YlGqBk5ksRSTV9NpUAs5scE3rPOxTFlP6ONwa6P+XTQU+6PPDKXaq/LsGLDMJ1OE4lECqI+Om0A1q5da6zzxUan/ptMJsPIkSON3+b3Z9Ao0FdnIepH9uj8mM0QnTtH1VdHjiTc1UU6vZP1nGSS2K7Uc8hF+XV6jhs/jptv+4WxrUKn02DnTDajOgAtFgt2u50jjzoaQYtENjU2YtXkdP69B8Rg9Zzly6hraCAyRD1nmwxHfSI2b2yioroaiyyrZeKXL1W9qXrFLG1jqnXcBHznXWSEkfPPF9sR0AfS+CfL6r+hTJAir5ssy8iKPGji99kkhc/oOfZ4vKefiRzrUc9gS6XU4zmWLSHy1BOI2Qw19Q2sX6dGq4Z747E+uZcv/ZLRIx+BpI3oC1YSi+0IzixKWk1RRQQlLWAZkcV/XhTRCYpaOE3FQGTMMxpR1HMTI90Cwb+51RRVq7qvMZ0ROOSQFNfMiqMoIGkVVBF6G/WynLcdQdFTVbXvtHu897aVH/yfnxtv8vPXOz3MnedgyRILggAul9pwLr1VjzTmjMb8vY4WCb5cauGBB10ggePoOL4TYsgxLWU1JSC6siQ+sxN61onDCjbrW8jpucaiPJxOEt0LuWbNV9SNGAEoRJ55iuSXnyO6XOoeMIEcP44eg//8i9U5OAz8aOxjAs4YezhnjzuadCKKiIhFtCKJEqIoIYkSFtGKAGRScS6bdBInjjywoJ0dDcHwdID7uBPwTD8dORYz+FF0uUh+8TnRp58CWWbkmNGsWL68l4d/ONHY2Ii/tBS7JBGa/zSJj95HMLypGB4YqbqWksuu1CJYKh31+RaNRvn3vf9g6ZIlBftdYrEYI0eMYuLEScb9XG43p53xDcaMHYeAwOTJezLj6lk4+yms0NLSzOa8Kpd6pDMajdLcokaE8ue8GpGq5dvf+z6jx4xBFEUmTZrMKadMR8rbUzFpzz2prq4mEY8bCrvVamXxZ5/x2KOPkEqljfYEQQDtvt7TzsR9zHHqmYhWm1qIzOMh/sH7hOfPw2G14PF42NSyqc/n2dHQ58mG9eupqa9DVBQi8+eSXPK56hUv5scxY4fEj4Zir0VPn3j8MRZ/+il2u11NjRRFZsy6hrHjxhUorJlMBpfbzXkXXkh5eQUdHVuIhMOEuruJhEOgKFjzznjNylmycv8R2mJjRZIkJM0Yye9rcQSr97Po/Hgi3lNPL1wfXS6SX35O9Om5IGcZOXoMq1etLBjn4YK+Pq5auYJRY8YC0PPsPJKff6ryXDptLD5KOo111BhKLrw0V2RFM8RkWaa0rIxLLr8CRYGHHryftxctMqJLmUzaOCdOjzrqRqXFYuHNNxZy/7/vpaa2losuubTg2YvHNn+M83UXRVHw+Xxce/0NOBwO/vSH37NwwQKDbvo90ym1IJKuRFqtVj79+GPu+MufWL9+PVddPYt99t23170E3TsMeI4/Cc8ppxXQUXS5SC7+lPD8udgE8Jb4CYfDxnwdTuj93NjURFV1DZKury5dovJjRtsjnaev+s+/UI3QDHF9LHam5BcGg/4NuUgkYtCrOFqs73fXz2/MNyz1NnVaFesZQ5F1feurZ6hyNV9fXbqEyFNPImaz1DaMYO2anaevAqxZu2bwes4Fw6Pn6DyXT+tMJmNEiweSeX1BlCQsViuiqD7jvlOmsMceE1ny5Rd89unHQC5bZrDzcOt6zmKi83U9ZwwrVgxezxkypfWB2dLejgCUV1XR884ios/OA6u14OwUQVQP2fNffBm2CXtod9yxk8tYsLR/FosFi8WiVoizSIMmnCRJ2Gx2bHa7+mqzGa/F5bMH3Tf9VVRLU4serzoWY8aqyo0eQbDZiM6fR+y9dymvrMRqUcuPw/CdXaW3u3lzHJ/j+/gqFWKfQfQZt9ZxwYgKCZICGQH/uVHsk9IwlErautGopahigyfnOnj3PVvO2JOhokzmxm/2UFYuG9FMPShVTMJsFmRZU540OSno7Yvw3At2bvqenxdfdiBJ4PXKeDwKVqu6hzKdVg1CWRYMI7Twb/W9fo0gKGTSAk/Oc/D+Iiu2Ohn3+VFs9apxLYgKiiyCBJH5bmIfi1SOSBLa8gc6OmLaswyP4ajTsXXTJtxOJ6UVFcQ+eI/I0/PUqkKKkqOjKKFkMvgvuhTbpMlqA8Mk7CVBJKvIVDpL+PVh13Lq+KNR5CyZZJRsOo6cSZJNx8kko4gInDfpJG49aAZ+u4esIquprcMEfRwknx//pZdjGzlS5UctxQOLhcjTTxL76ANKy8pxOuxGCuZw8qOu3MciEWrr6oh99gnRpx7PRddljY4WC0oyhe/cC3DsO1V7qMJjGt55exHPPTMfv9+PKEnY7XacTvV4hsl77UXDiBEAxh7DGVfP4o677+GnP7+VO+75GyecdHK/SkhbayuJRAKn04nVasVmt2O32xFFkbbWVsMDXawkHXvc8fz1zrv52c9v467g3zn/oosL+jBm7FjGjZ+AIAg4nE7sdjsWqxWXy8XcJx5n9aqVRrsKaPIzo26DuGImlsoq1WmpZ7wICtEn55D4YjGVDQ0IiszmtrYC5WtHQ2+3ra0Nu81KWUUlsffepWf+02Cx9uJHdH7UDflB8mMmk8FitfLh++8z98nHSWj7E2OxGEcefTSnna5WZs2nga4QTJm6HzOuvpr99j+QI446isOPPIqDDzmU6pqagkJWsuaA7QvFEY3+5NtgIh4GP3q9+C+5HOuoMYXro9VK5Om5xD54n9Lycpw2O5s3bzb6OBzQ221v34zH4aSkrIzYh+8TeXquGqEy6KgY/fddcHGeXO1tyB13wglccdUMli5Zwq9/cStvvP66luZtNSpv6lFHq9VKKpViwauvcPNPfsSW9nauvf4Gpu63f0Eq49b2r6kGXa4K5XEnnMA3b7yJzW1tfO+mG3n4wQdo3bTJMDysNitWq9qfjvYtPPP0PP7v+9/h3Xfe5tzzz+eKq2YYZ0gWO0QFSaOj368eudYwEiWZ0iJVqlyNPqXK1crKKlLxGFu2bCkY7x0Nfaw2b96MVRQoq6qi5923iT77tFr4oC999aLLsE2YqDawDetjsUHRV/Rdf2/VDq5ev349VqsVp9OJ2+3G4/Hg8XhwOp2UlpYSCoUKzvbTUdxm8b2H1G/9tUBfvRxrX/rqM/OIvf8upZWV2LUzemH4+bF10ybcDiclFRXE3h9Az8lm8F94KbaJw6fnFDvOLBZLwbo3lKBTKpUmoxu+QH1DA4cfeRTNzc18+MEHxpnOOgZlPPan5xTI1Tw9x243zgvdGh2HXIXCKPcfClFaVY0UDqtlqZubEX0+I9ooWCwo8RiOA6bhnX56Lu92BxcokGWZVCpllALXPXbJZJJUMjXoiZxIJAiFugtyyEVRJBGPE4/Ht6lvBYum5r107HcA/gsvZcsff6uG1lMpBKuNdOsmumc/TO0+UyipqKRrSzvZiophK+ggiiKZdJJwx93UVK2FiEB4npvkWguiDxRtL7aeomrfK4n31BiCDZQECHq3BiubFDVCGQ/Dyy87iMUEvF71CIxUSuCii3o47LA0clrLaNT2MBp8l3NoImfVyKDVqv4TRXUorS544xUbv/i1l82bRcrKZPSCi4Kgpp1aLAMHSPvotnYvhfZ2kQcfdnLgtDSuaUl8Z0fp+JsfbAokQXBAar2VyDw3jkkKY8d/zroNf6W09AdYLI4h3HXw0DfPx2I9lFVWIkWjhObMJt20HtHnNypOChYLSiKOY+r+eE49c9j4MX/OS4J6FuPkslH864Qf8s8lz/JK44c0RdpIZlI4LA5G+2s5bfShXL3nqZQ7/ciKut9xyCk2Q+ybzo/O/afhu+ASOm7/o1pZL5VCsNtJb2wi9NgjOPbci4qaWlqamvD7S4atepy+4CxbupQRY8ZCIkHkyTmkVq9C8HpzclWSUBIJbJP3xHfO+TmvuJaCKYoiq1ev4re//AXNzc15e2agp6eHZDLB5D33xOf3G2loOg474kgO0ypsFlf9073myWSS5cuW0r55C2XlpYYyY7PZCIdCLF+6lFhPDH+JrUCZ0V8PPfwIDtVSa3XZrBvM1dU1jB03nmefmY+iedsV1CIE7e3t/OkPv+Wvd92D31+iti0IRtqD67DD8Z13AZ1/v0eNIqTUY1aSa1bT/dgj1OwxCbe/hB7Nsz9c3nGdH3siEUqrqhCiEUKPP0pq4wZEb9H6mIjh2P8APKeeMSR+1FOhenp6ePCB+1ixfDk+n49UKk1lZSXXXv9NysvLCwwMvW+Kop77F7jx21x82RWaEZDB6XTSsWULDz1wPxaLVOA57w+DUWQG4uE+18f9D8R/4SV0/Pn3ufXRbifdvJHQY4/g3GtvSqur2bxpE+Xl5cO7PmYyhLq6qKqrh54ewk8+Rmr9elXPKZar+07Fe/o3DKVMKNIjsnIWj8fLjTd9h+7uLp6Y8xg3fvN6rpwxk+OOP4H6hgY8bg8K0NMTpXFDIwtee4X7/3UvsVica2/4JldcOSOnlKKvjwPLyWIDRlEUrrnuehQU/vG3IN/99rc45thjOf6EExk3fgJuj4dEPMGGDet4c+EbvPrKSwiCwMWXX87//fDHVFfX5CJoSqGhotMRwHmAJlf/+kdEu0vjRwepDesJzZmNY9Je1I8ZS9O6dZSWlQ2rXJXlLJFwiLLKKkRDX93Ymx93oL5aTJN8YzE/FViSJLq7u3njjTdIp9OsX7/eiDbn47333iMcDlNVVVUQwezLgTAYp0Jf6EVHRcGx3/74L7qUjj8V6aubmlV9de998VdU0rVl87BWrTb0nJ4eyqqqVD3n8QH0nCn74Tlt+PScwWIwNHA4HIwYOZKuji3U1KqFr/z+Eg49/Aju+9e9fPzRR6xZ8xUTJ04qSFfur+2t6jn5crWXntOI3+/fKj9uE7dubmsjm81Q5vcTfvopel57BcHjNqpSIQiqd9xup+TyGVgbRqgPsyOJpw2M1WajvKISl9ujGRcKFslCIhGnrKICm03NtRf6UxS0dkaMHMWhhx+uhYpVj7YoiKRSScaNHz9wG/12sShlR5YRRBHvWefS8+7bxN5aiOjxqhvI3W56XnuZ8HEnUnru+bRv2kR7+2ZqtIOdhwNtbe1YhHvwlslEXrQTfcmF4FALvqh01C4UwX9RFNu4DGRAsIBehGZQVph+TRYcDhg9LsNrb9gJhdUo4TFHp5g5I17gFDJSRvUPRDUFFWDk6Cz+EpmmZomsouDxKkyclGHppxI//ImPr9ZI2GwQjWmO3+21P9TtqKQz8NxLDg54MM3VN8RxfSNG/CMHPR/aEV0KSkpEcMpEXnThPiqO51QZj+OftDRfwshRY7ezE/1jc1sbciZDic9P+Pln6HnpBQSnS10UdQVbURCsVkouuxLbqFEAwyJMi4WZKAjIQK27nJ8ffBXX7HMGX3U3E80k8FmdTCgZQbWzBFADzeI2LnpD7Vs+P/rOOZ/Yu28Te/dttThJOo3gchN96QXcx52A74yz2GK1sKW9nZra4ePH9vbNlPh8lJSUEF3wKpHn5mtn/WVzXhNBAEHAf+kV2LUsDkGSVFYURRRg+fLllJSVcvL0U5G18zPVcvsyTpeLQw47TP1dHsOpyq2MnM0iaZ7TosFDEARS6TQuj4dDDj8Uv89vnNknSRLxRILS8opcQZUiGqrpj+o9RElC6oPhjz3+eDZubOrtnQesVhutrW34/SXG9blCADZ8F1xM/P33SCz+DMHlVOnodBJ9bj6RY46n4qRT2LyphS3t7VRWVW0vufrFlvZ2stkMpT4f4efmE335RQQtnTifH7HaVH4cOTR+zMoyFkli+fJlfPLJx9idTuwOB7GuLq6YeTWHH3mkMTbFXKTzgMvlxuVyF3yXTKVUOosidoeTVDpFNts3LXcE+uXHs88j9s4iYu8sUotapNMILg/RV14kctwJ+L9xDu2tm9jc1kZtXd0O75eOzZs3I4kifq+XyEvPE33hudwZqvmeTYtFpePoMeqz9EFHSZSQUc8WvfVXv6G2voEn58zm1ltu5j//upe99t2XmuoaFEWhpaWFJV98QWvbJvbdZwpXzpzJpZdfiVM7uiYfW43mFslTQRCwORx889vfYcKkyTw++1HeemMhr7zyCnYtcyCdThPr6cHj9XLAgdM486yzueCiiynx+zVHjsa3QuE99L8VWUaQJPznnk/svUXE33s358xxu+l58Tmix52A79QzsDvstLZuoqFhxNCIMwR0bOkgm8lS6vcTfnouPa+93JsfDX31Kqwjdpy+OpARp8tfyWLhlOnTGTtuHHaHw0hBz3e8SZKEz+8HhlZ4ZVv6qv9t8ONZ56r8uOjNHD+6PfS8+hKR40+g9Ozzad/UTPvmzcO6Pup2R4nPNwg956ph1XOgf9oO1nDXv3c4HHz///2I5uaN7Lf/gWSyWSySxF5778Oe++zDqtWr+PSTT5g4cZLqWN9K29un51gHpecM2XDMZDJ0d3UyesJE1Vp99EGUWI+6B8ew+iXknh48p5yaK9yQSaOIOzi6AYwaOYLf/f73uQVOUavuZbNZHA4He0xQS86Lat5h7/QrAEHg1Omnctihh0LeUqt79Lw+tUKaBGrqztbCz33SVFX4lFQKa30D/osuJfnFYuREwlAGlXic0CMP4DzoYEZPmMCa5cspL6/YprHZGlLJCN1bfs3EPRpJt1gJP+Em2ykhehWUtCawJAU5JuA+KoH7iASkQU5q0cYhZl4KAggyCDa45vIY8bDIki8tjBqV5aYbo1SXySj6EVf98IQEZEMwaVyGGZfEefBhNfX0vHPj7DE6w9/+6cZlUZg2JcMOKqCVg6JmKCWSAl8uttK1IUnpmAzes3tILLehpNWLBEkgGxLonuPBPrWbuhFNrFjxS9J1d+zAzuSQTqfp7upk3OQ9Sbe0EJ79MNlIyHBIgCo45VgPnhNOxn3E0erj6JvJhyOFNn/cFb1YjkJGyVJnL6Wurjz3fVYmlU5iFSREQTDOc9wpEATkVBLriJEqPy75Qt1kL2gGSSxK6OEHcO4/jRFjx7Pyyy8orxgefsxkMnR1dFI3ahTylnZCjzxItrMD0eOFfK94rAfn4UfhOe4kAJXGeQujoigcesihTDvgAND27CpGZEBGslgoLSkFbQHSUxEFQBIFJFFNpyxOUdQp4rTZmDnjai668GKk/FQ17T42mxWv2w2KgpjXvg5JFA2DUVFkQ45IWkePPvoYph04DVlRehfFUhTsDofRpgIqk2upVfbxe+C75DKSK5ahCwBBksh2dRF6+H6cU/dj7MRJrF+9itKysm0j1FaQTqfpaG9n/F57km5pJvzIQyjRCIKmcEEeP554inE8lcGPWxGsgiAiZLMgSZT5/fjdHrrbt5CIRjll+qlcdsllWESRbDqFKEoDtlYQ/chmqSgt4+Bp0/j43Xdpa97IAQdOo6K0VL04k0EZbq+9ICCnUlgbRuT4MZnUjFkBpaeH0CMP4jhwGiPHjmfNimVUVFYOS1dSqRTdnR1M2HMv0q2thB99mGwoZCjOgHqwdqwH13En4tKOUZG1VLC+IArqMTnVFRX86P/9kEMPOogFr77Khx+8xyfvvEO0J4ogiPj8fvbYYw+uvOIKTj7lVA486CBkOZurHbE9EFSPr5KVOeXkU5i233588tFHfPnlF2xYv55wJILDbqeuvp5JkydzwAHTGDdxImSzZNNpJElEGeh4JX0+JZNYR47Cf+GlJJcuAe04CUEUkSMRwg8/gHO/AxgxZiwrv/yC6uqa7X+2PpBJp2lva1PXx41NhGY/hNJTpK9Kfeir6fQOne96tdqCqqiArCh4PR5mXX314NrJZFS5O9zVoQVBlem6vnrxpSS//Bw5HjeciHI8RujhB3EeeDCj99hD1VeHaX009JxJe5LetAv1nB2imhQ1oijsu+++7LvvvqBlTCIINNTVcvCB0/jL3UE+fu9dzj37HDXamM0acZvB3W6Ies6Sres5QzYcW5o34nJ7cVgtdD7/DPEP30dwuYyzxdSBAMHhwH/ehViqqtW+a4dN72iUlJVzzPEnbvU6UQu99kf30vLy3NlE/WCHzBltP4/n5On0vPUG4UceRCwpUXOPnU4Sn35E5Nl5lAduwuP3Gcdz7Gg0Ni6nzP8YFqtE9wIbsbediB5ZjTYK+uZC9Vrv9BjWsVnVCHBt+z1VRRLGTcny69tDtLSKVFbIlPrJ7YXcCiStX1d+M8ZhJydJpWDPyWrfzr8yxvlXxba9g1uBKujVyKe9TAY7eE6L0fOWk8hzLkS/jJIUEd0ysXedRF9JUHpZnMqyp1nz1cxh6dPGpiZ8Pj82UaTrpedz3iSdH3WvpWTBd855WDRPkmAbHn7sDyJg08RNBnV6iYAkidgku3HdTjIZc/fTMhI800+n542FhJ+YncePLuIfvk/0hWcpveZ6yioqh+14jrbWVmx2O16Xi+55T6jZCDod8/trteK/6FKsDQ3G+4LvYdDRtH49sUL/m/otNht12r232v5WPa5iL4JbbTb82zI3NfnuPfUM4m+/RXjeXC2FKYnochN75y0iLz1P6RUzcbrcxp6cHY2NTU34SkuwiRJdLz5P7P13VCW1mB8tVnznnI+lZuj8KNlsKMDYCXvwvZ/fytQF6vlwF1xwEbUabaRBrLf5Q68rtNfe9D1qxoylbXMbxx13AqO0bBtpJ8kLfRw800+j582FhOc8muNHl4v4R+8Tfe4Zyq77Jj5/CRuHWEZ+sGjeuBFfSQlWUaTrlRfpeectrZBKPh3VVGnfWedirVPPyhTt9gHb1cfZ6nRy4vRTOWb6qWxobKStrY1wJIwoSpSU+KmrrWNEtao7yez4wiOSBFmgvKqak047nZNOO52kopBMprBIEi5rTjXMav020iMH0RVdtnhPPYOehQvU4n95dIy99w7Rl56ndMY1lFdWsWH9+h36fDqam5vxlZRgt0h0vvAc8Q/61ldFux3fuRdiqdLOyNzB810fj14ZAOrtSWazaqQxX2bmbdMBcFqtW9Vjdzh0fjzpVHrefIPwow8ZdBSdLuKffET42acpD3wbt89Hi3Zczo7Gxo1NeH1+bJKoytXdVM/ZVsjaP4vmWJUBr7+EC66aSVwQOfa007A5nQVu9aHMgSHpOeVb13OGbDhGQiEm7TuFxPKlhB97RM3h62XNq1ZAYsmXWEeNzoWR+3pUoZ83vT5Xil61OykKmWyGXj/U7mmxSGpKgC7nC36e1w75G1nzP9ed2mIf/e8nOiL0eoDcZ3rqVSajRhL08dNCykgWwrMfwXXUsdTttS+rli3t3f4OQDb+U2omdZNaYyU0x4uSAcGihycwhlqwQPwjO5aajJqi2he2NoP177VpkFUfk/F2hdRmgWharaKq9DOc+e3o2UEIMNEpIwoQ+0BCFGC8U0FWCiPGg+7bYJB3baYd4grIURE5LGhWsfaQWh9Cczy4DklSNb6b8OqfAnsP4WaDQywaZfS++5JctYLw7IcKijbokWw9Ch//5BMs9SMQBBFF0BT7IQf4hK1c3//3WgKHYVBkgQyqk0LpfeHg+lEsFvp8nv76pP5AEASUeAIlGS+QZ4qigCjQ/eiDOI88ippJe7Jq6fDwY3dXJxP33pfkmq8IPfwAinasRDEdFdFCuqmJdFOjKi/6yMVW8lJFhV5jI+QMNkMuDzh4GgTjRd2b2P+1hqzsiw59N5n3gdKHJzVPgFBskOYLBVWuCk5XgQxTtAkWeuh+XIceTsP4PVj+xRf9POf2IdYTZfLYfUmsXE5o9kN5fdMXIE3GiiKJxZ9iHTESRYsMb91zVkgrQRA4vq6OIy+6GIskIaSSJJZ+2cdv+mii+CpBLRhUarUy44gj1HkCpJYvLbq8cI0uoF2v+dRfNwZY5/W1MJlESSbz1kedH0VCsx/GddQx1E7ei5VLl/R+mB2AVDLJyLFjSX61mvCjD+bKeBfwowySRPzD97HU1uUcMf3SUaebiIJCJp1BlETGOJyM93uhxKcOnaKQ3txKtHEDABarZeDwQr+324rjRhRJyblzHiWLBZugFoWLymq1ZFErOJjW1xT9hv02ncePgojcE0XuiWoLvHqFvj859PADuA4/kqo9JhEaJj2nJxJm4j77kli+jPBjD+cKKBRAlfOJJV9o+qqertRLQBnPKBQQZCC9VftLU0Yy/fCGJIpY8s867kNHTWezWpZFrtuCIKjP1FcUuI82+v6ut95rdFrR5HEmrZ6lJuXxo6zO//BjD+M++ljq99yH1cNEx1gkyp5D1XO0rRvGejFYXWdAvtma/qNd0+tmRWtsURuiIIIgkJRlgxqiKDDF6eAP18zCZrWSWr4st/e8v/lnvC2ak/rWj8Tg9JyVW9Fzhmw4VtbWIaXTdMx5lNSa1blcccjtSdHSQrvv/zfRF59TveKysnWFs48/+760kKn7q8CoLte6wBtgIVNAEIV+vkRvpbDhgrmxlQ7rXwuCariKIkoySbp5I4LDoZYJzcvPTjVuIPTIQ9Tc9isqa4YnhaOy8l2EtEh4vpPkUiuCU0HJigiCAtpRFKqrEyLPu+h524FgUwbPfDr6klmCVrk0KyBKSqHvYVCMDSiqHFMwgg3kOxEHuj+w9efYSj9EUSuul4Rsp4TgUFRLSARFFhBsMqmVVsJPuaj8bpjKyo8YDsOxpqEeIZMh/OTjJFeuUL2p2nzK50dFEAg99jDRBa8gWq29F88+hOWQbUq1oW35EUPif+hHuPdlBPUtqI3rFQVBUvkx09aa40etMqdgtZFe8xXh2Y9Q9dOfUz1Me6rKK6uwINM193GSy5aqe6m0M9xA90koIGfp/s8/ib78AoLVkhf50J8fDIORvM/yofRlnOWhYGyLZoGgqk19tq8taMpWPUCFPxWKPug3UtmXzqf3VzMqlGSS9Lo1CA5nzjEnywg2G6nVqwg99ihVP76Z6vrhoWNVbR1iJkPkiTmkVq00+FHQjEYlTwEPPfoQ0VdeVtdHZdty62VZrUCsaMqcKImG/6oX+lyrclqobjzKeUrowBXFdQV6iLy7NcNR58fWTXnrY44fU+vWEp79MFU330ZV7TDxY1UlUjab48d+5CqiSHju4/S8sUD16m9Vz9EfNddOJpNFVrIGzQRBUPcAa2c6KvkKcn/N9XOPrXdD70eh81wQ9PMch3ocWZ7hKKpnImbaN6v8mO2DH+c8StWPblYLEA0DKmpqVX318dmkvlqdoyOF+qqSlQk98G96XnwerBaNjvlGVd7z0Rcr9XGtIObRrWgTRm4PQRFtB8tLQl7/9Db6aLe/xgari2v8SDJFumUjgj2PH4v01epbf0XFMOmrNfU7Qs/pteAMHTtk/5PGI31Nr+L7KLpcVoxnLVyTt+ak0z9QjeqB9JzUmq8IzX6E6p/eSs1W9JyhG46VlcTef4fI/HkIdkeed5uCggaCKCLHYyRXrRhYmObrd4NFb7uu8LuhtLXdGOrNdCVHQLDZ1cIWeeOmyDKC3U70hWeITj+V8qOOHZZel1XESHxuI/y0G8GKepwEihosU9QjKBRFPWoCINsh7dDtcLqMywKZ/HYHeQ/dySRov+8rqG0oUPk8toOQUb0SCBJasSAlb9zU8RRsMuFnPHhOiFNyYO9S2jsCZWXlxD75iPC8J9Tzw/KiTcX8SCZDesN6VUktxmDHZqfz13Aib9EVRQSbTRWuffBj5Jl5eE45ldJDDx+WnlTV1BD/5GMic+eoZy0V7eNTdTG1YEO2u4vM5rZ+rKjhQrGgLp4Ig1lQi71DmsdzCJOq2Nev/04gN88Fez9yVTvWwXPiyVQcctig7jdUVFRUEPv4Q8JPP1nAj4ruGMxfH1Mp5HVrhqiY56Arr7qh3ktX3JY2hUKFJDOoBgd508Fcpk+FrfBj+JmncZ9yKuWHHTm4ew8RFeUVxBd/SvipYrnaW89BUci0tKjX9Gls9P0RaOMtQHEZI1lRkFF2AIv3p2QWNiz0yb/be3/1qBJsttx4FcvVp5/Cc/KplB50yPbcqF+o+uq7RObPHXh9lCTkeJzk6pXa+lisoBc+14DYFp2WPm61y9bZIrmevz72IVdFm53I8/PxnHIqFUcdMyw9KivfBj1HlreJfLsbtr/C/OD1nOgz8/CeMp3SQ48YsMUhG46CohB7710y7ZsRPR4jWpa/iOUzo+D2aN4Pts/S/zoi7/BswAhBC5KFbGcHPa+9imeYFkaAxFIb6RYrolNWN57pnkJQjUbdeBQUBHvvYhU7AkW23uB/pBS+7cuZPpzTzWhbQY3MKgJGpFZAdZZYBTKtArEPnTj3Tw1bP+Iff0imdROi26t6dfX+FQtVQUB0bccm1WHFYKm1NU9A/oI/yDC2Pu+z2dx4ofGjLIPVSmbLZnreXIhrmBQcEYXYu4vUY408Gh2FnDpXQEerVfUYmugb+XJVyYssSBKZ9jZ6XnsZ17SDh+XWAhD/4H0ym9sQXe6i9bEPJcft3j45JQjGYcx60GFg9BVBKeITIY+LBlKy+vIj7Cihq2Ut9VofNQdAdks7PW8swH3w8DgAABKffUp6U3OOjuTGo1iuCg7Hdip3hegj/rRLsH331ybFQHJ1cyuxdxbhOvCgHdLfXj1QFGLvv0tmc1/6am/jUXC7GfRT72ri7Gz0pa8qClg0fXXBq7gPG9jg2FZ8ffQcGJKuozuNIZdd1DvlcfC3HUDPEQw95w1cBx06YFNDP44jm0WJx9VFT885FMWCZE5FC6sq2axREdBEH9DCZgUeZ0UBQQZRQu7u7PcA5h0BNZqhpzXk9Un/XlY1CEUWNOOIvvXy/2XkrfD6eGk50irfi6AkhOEbK0VGPaxSVPdZptV9b/mV7/Q0NkVWDAXIRB/Q+VHzVBquEi3tSo5Gho8fs1nknh41ZV5Ak6sChaJBo6MpVwdGsVxVILcfRiSzZcvwVSWUZZR0SnMkCTuFjsMhWnYL0W7QUY8AadDS5+TwMPIjgKyliouiKmOFwmi3KVcHib7kah4/ytGe4aOjLKMkEppczdNX87wspr46SPSnr8qqvpoNdQ0fHXU9R9+fL//v6Dk7XBYX8GOeTNP1nEh4q3QcuuFoseCYup9abUnzfu+QHJn/BQx2iCQRFBn7nnv3qpq4I+HcO4W1NkumVUJw6P0z6bjd0PelyQo4RBz7JEEaJvekIOKYuj+i36dWyLLZTDruaAiCOs77TBk+fpQsOPadCja7KVe3Bf1l0uYHp7XCDvbJew4fHUWNH93q3n+jqp9Jxx0HQUBAwLH3vsO6Pjqm7I9UXk42HNaqEiomHXcU9CqjViuOKVPUQmDDdB/H1P3A7jDl6vZgoOEy5Oow6qu6nuMz9Zxhg67n7Lt1PWfI3BqORPAcfSwlV86k++EHIJFQw55DzlNRtp/mA94uL3K2nbfZNvS3qSHv64KNrop+AjoIIp5jT8B7xtmkhslz0rnFTtnkDOU3ddNxdwmZFglkLelT98QNpqGtXrSd+1+2N7V7R2GobYkgugVKLg3hOiRJomd4DMfuUAj/tIMo++ZNdP3jHuTu7oL8fxU7gNf6QHHQ2XgvKMaJLvnfaT3Rt2r32972jZRS8LLdEAWwWPGdfR6eE08hpZ0ZtaMRjkTwHHUMJVdeTeiRB9TzshQ1bN33/qOhYBvHZIDbFja1K3O2+nio4qwIBTVqJAh4jjsB75lnDZtcDYVCeA85lNIbvkn3vf8gGw1rFTkHN0Z92b56ilExD0FhdT19nuzY7NEdxE9bXauVPmVCYVe0vT4WC54zz8J7yqnDxo9d3d2UTJlK2Xf+j867byfbvgVF1ufMds73weUU039aWh6Fd6AB1OfcM94ofd29aC723fNCaAuDKCDYHfgvuRz3sceTTA3PVo5wJILnyGMouWoWoYfvR47FjL1vu0yuFqPfbgySL4YVA8hXMIwNz/EnDqtcNfScgKbnhPrQc3S+2oG6Tl8JdgJotUB6784XDJqpf/fVlW2Xy320ti3P2tdDFeg507cqV4dsOG7csJ4JkyZT8b3/h+uwI0kuW6Iespnfi16eAKX3Qq5rlr0eXCl8OO3aXJNK0aVFYfOtXZf3d682jb+1mxfkFOc3qRT+nf9RgSAvVtiV3D16jYV2L8mCbcIEPCdNx1pbx1erVzEc2NR2Hm7no/jPTODYq53kKhtyQn9u+mfA4s+2+r6QPfpd55R+/t7ae0Oz2so9htxm7/YKGF67b/57Q7AogBUcE5M49k+DQ2bD6gP76NT2o7mpEdfEiZRdfS3OAw8iuWypWsZeyHVcQCvYk/cM+YpAr3laPIB571XZXFxluJhnitstmOx5fFb8Pr8rffBcn++L5UVx3wt+1E/X8ganoF0FQbRgmzQZ95HHIJaVsXbV8PDjxg0b2GPyJFWuHn4kyaVfQiqFIuSvh0pBF3vLuL4+62fc8r7PF8d9/75YHuq/y1dclQKxVtA2FNKlQAbmT8zc3/oRGpB79n5laX63+5O/Fgu2CRPxnHgy1to61nz1FcOBjY0bmDBpMmXXfQvXQYeSXLZEPRQ+r4sohWq2UqyA9HqGfP6h6L1Og4HXKaWv74wXpZBF+1yf8n9b8CAFXdEnq1LAU3ntDvR38bPnzSFDlkkWbBMn4T7mOMSKStYN0/qoytVJlFx8Oc6pB5BcsQw5mSjkiQHkpD72ffJUwZgqeWM0UHuF79WhzvFuv2PYT7/66mdOvuQRVckjYK+FNX9u9n0/pVff1P8EiwXb5L1wHXoYotfHhlUrGQ40bVjPHpMmU/GdH+A67AiSy5ZAKqn1rajvg5iLBe+NDa/5X+VdU8wb5Bcb6p9Xe8m2vvqg9LMGKwXMSLHMLvhRccqpcVVffdH+LpYTFgv2CRNxn3Ay1traYdNXmxsbcU2aSNmsa3FO0/WcRK5b2qPrc1h/hn51nAHe5+s4OdL1JWOVvN/1p+NQdH3+S38ysPi99ne+jpM/dQdwHuW6nS+P8x5Ub1qSivScgflxyIZjRVU1q5YvZ8SoUXiPPxHPsccPtYk85D9wXwJoO5rebvRx86K5M1x3FSwWUuk0X61apZ7hNAyobvgDX20Qqa14nNIJSeyT0nkMoPdkKxguR9gupfv2Im+CCALxqEDj6kNwldwD/GuH3626ppbVy1dQP3Ik/mkHq0U/dngaTv5CtIOb3iHoS44UT87tkC2SRCKZpGnlSqy24UnFqaisZOXyFapcPe4EPMcctw2t7E7ysxjFC+MugCZXV69ahWWYUuOqa2pZtWIFI0aMxHfo4bj6rd66u/ISFNJKKFSQdwdIEslUirUrV2DTDrbe0aitrWP1ypXU19dTsu8UHPtOGVoDu00qZLEs3I3mnSiSSCZoXLEC+zAV+6rS9NWGUaPwHXu8Kld3KG2Kxne3oTtsldD619ug0+arwqq+mmL1ypVY9dT8HYzq2uHWc/IGY7eiIfQ5x4y3WyHeMOk5Q149b7311qH+xMRuiFtu+RXgAWbu6q78j2DHG40At9xyy7C0a2Ln4tbbbtvVXTCxA3DLz3++q7tgYgfgZlOufi3wc1Nf/VrA1HN2L4hbv8SECRMmTJgwYcKECRMmTPwvQ9jWw4dNmDBhwoQJEyZMmDBhwsT/BsyIowkTJkyYMGHChAkTJkyYGBCm4WjChAkTJkyYMGHChAkTJgaEaTiaMGHChAkTJkyYMGHChIkBMTw1yU2YMGHCxH8lAoFAOXA2cBqwD1APpIAvgfuA+4LBoJx3/f3AlVtp9vVgMLg9ZzeZMGHChAkTJnYx/qsNx6EqOP208W9yZ1JMCAaDw3MytIl+sQ2K6mhg3QBNzgkGgxcNW4dN9Ilt5cdAICAAVwAzgH0BJ9AKfAT8LBgMDs+pwib6w/nA34BNwEKgEagGzkE9V2Z6IBA4PxgM6pXVngbW99PW5cBY4MVh7K+JPrAt/BgIBOzALFRHwFjAATQBrwJ/DgaDG3baA5gAtpmOHuCHwHnAGCABfIJKwxd2Xu9N5CMQCPweOBDYA6gA4sAGVBl6dzAY7OjjN4cBPwMOQeXHr4D/AHcFg8Hszum5iXwMhY6BQMAKBICpwH7AnoAVuCYYDA7POW3DjP9qw5GhKzgFCAQCZ6AajVHUQw1N7BpsKx0/R2XUYiwZvq6aGABDpmMgEHAATwCnAyuBR4EIUAcciSqYTcNx52IVcCbwfJHD5ifAh8C5qDSdCxAMBp+mDz4MBAIlwP9DVXLvH94um+gDQ+LHQCBgARYAhwMrgNlAEpgG3AhcEQgEDgsGg8t29oP8j2OodCwBFgF7A0uBfwBuNJ4OBAI3BYPBO3f2Q5gA4LvAp6iOmM2odDkEuBW4NhAIHBIMBpv0iwOBwDdQ5WwCmAN0AmcAt6Py6fk7s/MmDAyFjm7gr9rfbahO8RE7s7M7Gv/thuOQFJx8BAKBSuBeVGasAY7eGR020Se2lY6Lg8HgrTurkya2im2h459RjcbfokYXiz3n1uHutIlCBIPB1/v5vDUQCPwd+DVwDH3I1SJcjho9fiwYDG7ZoZ00MRgMlR/PRlVGFwAnFf3mNuAW4AfkMnRM7BwMlY63ohqNTwEXBoPBjHZ9pXb9nwKBwIvBYHD1TnsCEzp8wWAwUfxhIBD4NfAT4Meo0SkCgYAPVUfNAscEg8GPtc9vBl4HzgsEAhcFg8HHdlbnTRgYNB2BGHAqqr66KRAI3Ar8fGd1dDjwX204bqeC80/t9Zv9fG9iJ2EHKqomdiGGSsdAIDAOuB41JfWnfWUGBIPB9LB12MS2QKdHZhDXXqO9/nPAq0wMC7ZBro7VXp8vduAA81ENx8ph6KqJAbANdDxHe71FNxq169sDgcCfgbtQ5e73h63TJvpEX8aGhsdRDY4JeZ+dh8pvD+pGo95GIBD4GaqD5wbANBx3MoZCx2AwmOJrtlXjv9pw3Ar6VXACgcBVwFnA2cFgsCMQCBRfYmL3wUCKal0gELgOKAc6gPeCweAXO61nJoaCvuh4MWpl5wcAn5Y6PgKVlq+b+413L2ipjFdob1/ayrWHou7HWhUMBhcOd99MDBl98eNS7XV6IBC4o8h4PF17fW3Ye2ZiKOiLjjXa69o+rtc/MwtV7V44Q3vN11+O0177krVvoUayDgsEAvZgMJgczs6ZGDT6ouPXDl9Lw3EgBScQCIwC7gAe1vbmmNhNMQhF9UTtX/5v3gCuDAaDjcPbOxODxQB0nKa9+oE1qA4AHUogEPgb8G2zAMBug9+hpsC9EAwGX97Ktddqr/cOb5dMDBUD8OPzqOmN5wBfBgKB11D3px4AHIEaqbp7J3bVxAAYgI5bgFrUojjF+1H1qPKk4e2diYEQCAR+gFpXw49aZOUIVGPjd3mXTdRee+3xDwaDmUAgsA7YC5Wmy4e1wyb6xCDp+LXD1/Ucxz4VnEAgoEc3osC3d1HfTAwe/SmqMeCXqApNqfbvaNTCAccACwKBgHvndtXEAOiPjlXa6y+Aj1EjVF5Ub/ga1D0CN+/EfproB4FA4NuoqW0rUPcuDnStH7gAsyjO7oo++VFLFT8PdY/cRNQ18gfAsagRjkdNJ85uhf7k6nPa662BQEDSP9Sqs35Pe2sPBALOndNNE33gB6j73L6Damy8hLqvuD3vGr/2GuqnDf3zkmHon4nBYTB0/Nrhaxdx3IqC811UA+O0YDDYtbP7ZmLwGIiOwWBwM+p+m3y8FQgETgLeBg5GLSl/x07oqokBsBV+1JWaTahp43Ht/euBQOA81Kpl3wsEAr/R9gmY2AUIBALfROWlZcDxwWCwcys/uQxwYRbF2e0wED9qFY4fBKaj7v2fj+qkOxy4E1XGnh8MBufv1E6b6IWtyNVbgJNQK25ODgQCC1D58RuoFatj2nvTCbCLEAwGawACgUA1cBiqE+CzQCBwejAY/HSQzQjaa5+nBpgYfuwgOv7X4WsVcSxScI7NV3ACgcAE1E3k95nnGO3eGIiOA0ErBKCfi3PUMHXPxCAxCDrqzpuX8oxGAILB4OeoZ3V6gcnD3VcTfSMQCHwHNT1xCSoNWwfxM70ozj+Gq18mho5B8OOPUI2NnwaDwX8Eg8HWYDAYDgaDL6JGIq2Yzrhdjq3RUePRaajGvhs1c+MbqJHIE1ArHYdMZ9yuRzAYbAsGg/NQDf1yVMeNDj2i6O/1QxW+outM7CJshY5fO3xtDMdBKDh7AXZgRiAQUPL/kTuKY7X22Vk7q98mCrGNimo+9BQBM1V1F2KQdFypvXb304xuWJopVbsAgUDgh6jnhS1GpeHmQfzmYGAKalGcN4a1gyYGjUHyo14Ap1cxI82R0wmM0lIeTewCDHZ9DAaD7cFg8KZgMDg2GAzagsFgdTAYvBp136OAWsnaxG6CYDC4AdURsFcgEKjQPtbXxz2Kr9f2t45BLYrUVxEkE7sA/dDxa4evRaqqpuD8DlXBObGf9Kj1wL/7aeI01EpkTwBh7VoTOxmDpOPWcIj2agrTXYQh0HEB6sHie/fRhp1cSev1O76XJgaCdlbYL4BPUPdsDCrqT64ojnkEx26CIfCjXXvtdeSGxo96hMOMVO0C7KD1Uc8GeGRH9cvEDkOd9qqnEL8OXAqcAswuuvYo1HTjt8yKqrsdiun4tcN/veE4WAUnGAwuRt331lcbb6Aajj8xjwDYNRiKoqpFNT4rTrUJBALHoe5jBXh4uPpqon8M0eB4EdXAPzkQCJwYDAZfzfvuZtQUnTe3IepsYjsQCASuRKVhFlgEfLuPI4vWB4PB+4t+5wMuRDUsHhj+nprYGobIj4tQnTg/CQQC7xQppLei6gsfBYPByHD110TfGOL6KAKuYDAYLfp8FuoRSIsxDcedjkAgMAnoLl7PNHr9ErVY3Lt59TeeBH4PXBQIBO7Sz3LU9iL/Srvmbzul8yYMbAMdv3YQFOW/d1+tpuDcj6rg3EXfud69FJw+2nkDNV11gmk47nwMlY4avfYC3gA2at/vS+7co5uDweCvMLFTsS38GAgEjgBeAWzAPGAD6v6co1DTjo8IBoO9ypGbGD4EAoFbUSvFDYQ3g8HgMUW/uwEIohbFuXh4emdisNgGuVoPvA80oEb5XwLiqMVxDtL+Pj4YDL43zF03kYdtoKMHaANeBXR95khUGq4BTggGg+uHtdMmekFLM/4jaoXiNajnFVej6p5jgVZU/lqW95uzUA3IBPAYarr4mahVj58ELtCqIZvYSdhGOv6I3BE4U1G3c7wLrNY+ezsYDOr1OXZ7/LdHHMdorxJqOdy+8CZmSfjdHUOl40PA2agGxnTUog1twOPA3cFgcNFwddTEgBgyPwaDwbcDgcCBqIbKsailxdtQUx1/GQwGN/bRholhRDAYvBU1wjTU3/0N0wO+O2FI/BgMBpsDgcD+wA9Rt2/MQK2DsEm75vfBYHDF8HXXRD8YqlxNohoZR5A753gNqoz9S3Ek0sROw2uo69rhqIZDCdCDek7jQ8CdfRQ6ejoQCBwN/BQ4F3CgOgO+p11vGo07H0OmI2q68dFFnx2m/dPxX2M4/ldHHE2YMGHChAkTJkyYMGHCxPDja1NV1YQJEyZMmDBhwoQJEyZMDA9Mw9GECRMmTJgwYcKECRMmTAwI03A0YcKECRMmTJgwYcKECRMDwjQcTZgwYcKECRMmTJgwYcLEgDANRxMmTJgwYcKECRMmTJgwMSBMw9GECRMmTJgwYcKECRMmTAwI03A0YcKECRMmTJgwYcKECRMDwjQcTZgwYcKECRMmTJgwYcLEgDANRxMmTJgwYcKECRMmTJgwMSBMw9GECRMmTJgwYcKECRMmTAwI03A0YcKECRMmTJgwYcKECRMDwjQcTZgwYcKECRMmTJgwYcLEgDANRxMmTJgwYcKECRMmTJgwMSBMw9GECRMmTJgwYcKECRMmTAwI03A0YcKECRMmTJgwYcKECRMDwjQcTZgwYcKECRMmTJgwYcLEgDANRxMmTJgwYcKECRMmTJgwMSBMw9GECRMmTJgwYcKECRMmTAwI03A0YcKECRMmTJgwYcKECRMDwjQcTZgwYcKECRMmTJgwYcLEgLAM9sJAIKAMZ0dMbB3BYFDY3jZMOu56mHT8esCk49cDJh2/HjDp+PWAScevB0w6fj3QFx3NiKMJEyZMmDBhwoQJEyZMmBgQg4446ggGg8PRj15QFAVZlpEkyfgsm80SjURob99MJBIllUxis1lxe7y4XC4sFgtZOUs8niAWjRKLxUgk4iSSSWRZRhRFbDYbTqcTl9OFy+PB5XQiWSSyWZl4PEZPNEoikUAQBHw+P9XV1bg9bmw2u9EPWZYBEMWdY3cHAoEd3uaupGMmkyEejxONRujp6UFRFGxWG3aHE7vTicNuw2q1IokigrB1p5UCZLMymXSaZDJJPBEnmUiQSacQRQm73Y7H68XpdGK1Wo3fmXQcPPoaq1QqRSIeJxqNkkwlkLMyFpsNh8OBw+HCbrNhsVqRJJHBuB5lba6k02kSiSSJRJxUIkEqnUIUBNxuDx6vB4fDicWSE13ZbBZxkHNlR+C/nY6KovTJjz3RKNFoFEVRsNqs2O1OXC4XdocDm9UyJD6RZZl0RqVjPB4nEY+TTqexSBIOhwO3x43D4cRmsxX8Bkx+HAy2TscIABabDbvdgdPpwr5NcjVLOp0hmUySiGtyNaPJVYcdj8eLw+EooGM2m0UQBJOOg8Bg10er1Ybd4cDhdJnr4xCwu/BjT08UWVawWG3YHXZcLjcOhx2rRV0fB4tMViaTSZNMaHSM5/Gj3Y7H68HpdJl03Eb0T8cY0WiUnmgUoIAf7XY7NuvQ6JiVZU2uJohrcjWdSiFZJOy23Z8fh2w4DjfyBakkSSiKQjQapbl5I91dXUiShN9fQmVVJZl0hrbWTSz98H1WrljOmtVraG5porOjk55olGQygSzL2mQAARBEEVEUsdvtuNxuSktLqatvYOzYsUycPJk9JuxBbX0DLqeTWCzG2rVryWQyeDweqmtqKC0tNSbVzibkfxPyGVCSJGRZJhqJEA6HSSQSiKKA0+2htq4Bu92OpQ+mUxSFdFYmm5XJKgqKgkpHQf0nCQKSKGCRRO2fHYfDjt/vA1TmVAV3D1u2tCNnZWw2Gz6/H6/Xa9BxZys6/00oFqTZbJZwOEw4HNIEnQWX20NlSSlOpwOpjzGUFUVd8GRFNQ4VIJ+OooAkilhEAYskYZEknA4H4AfUxTKRTBIJhWhra0ORFRwOBz6/H4/HU9C3nWlA/jehmI6yLBOJRIiEw8QTcURBVPmxYQROhx2xaAxVGmTIZGXSsqzSM6sY/CiJAlZJwiIJWCUBm7YA2m12/D6VHxVFoScW15x/HaDIWK1Wkx+HgAHpGI8jSRod60fsILmq86Md+pSrW5DlrKq0erz4fD6TjoNA/+tjSFsfRVzm+rjbYyB+1AMQLreHmroROB22XmOoKArpTJZ0ViEry2RlUMhlZ4oaDVX5KmKRBJWO9v7ouAU5m8Vms5t0HAL6omM4HKanJ0os2oPFasHhclOnydW+jER1TZRJywqypusoCiCoqZ2iTkdRxGIRcdhtOOw2Y32UFYVYTHUYtbe3o8gydrsNr8+H1+vbreyO3cpwzGQyWCwWJEkimUzS2dHB+vXrEASRiooKRo0aRceWDj768APeffdtPv34E5o2bCAW68Fqs+HxePD6fJSVlTN69Gg8Xi8OhxO7zYYoSiiyTDKdIplIEImqzB0Ohfj0k495642FpFMpbA4H9XV1TN1vfw474gj2O+BAGmpHkM1kaGpsZPXKFYwYOYqKygocDidgKqzFyGazxiTPZDJ0dnQQiUQQJRGvx0dZZRVOh70gCpVMZ4km0nT2pNgcSrCpO05rKM6WaIJQNEUkmSGZzpKVFSRRwGYR8dgt+N12Krx2avwOakpcVPsdlLnteB0WHDYLHrcbj9sNVJFIpuiJRgl1d7GlfTMer5fy8grDq5PJZJAkyaQj6oKWzWaNyF46naZjyxai0QhWqyrMXG4vDru14HfxVIZoIkNHNElbOE5rV5zWUIItkQShWIqeZIZURjboaLdKeB0qHSu9Dmr8DmpLnFT5nZS6rXjtVmxWCY/LicflRAHi8SQ90TAdHe20b96Mz+ejrLwMSVL7mj///tdRTMdMJkNHRwfRSARRFHF7vZRr/KgjncnSnciwJZKguStG45YYjR1RmrtibAkn6Y4liSQzpDPqYisAVouk8qPTRqXfTl2pmxHlLkaWu2koc1PhteNzWvG4XXjcLgDiyRTRSITuri7a2zfj8/ooKy83+bEP9EXHzo4OotEIgijh8Xr7kasqP3b2pGgLJWjV5Gq7JlejW5OrJU5q/U6qBpCr8WSKnmiESChEV2cHbo+HsjKTjv2h1/rY2UEknL8+Vg+8Poa19bF7eNdHr8mP/aI4UlwgVyUJt6e3XJVlmXAsRXcsTVs4TnNnjOauGJu64myJJOjW1sd0JousgEUQsFpFfE4bJW4bVX4HdSUu6ktd1JU6qfQ58DmtuO3WAejYjsfroaKi0pAdpr6aQzEdU6kk4XCY7u5uLJKEy+OjYVQVToet4HexZIZwPE17JEFLd5zmzh5aumJsDiXojKaIJFIk01kyMogCWCQBt92Cz2WnwmOnttRJQ6mLulIXNaVOSl02vI689bG6mkQyRURbHzvat+DyuKmsrNot6LhbGI66ILVYLCQSCTa1tNDW1orb7WH0mLFEw2E++vADXnrxBd59exHtmzfjcrupq6vj4EMPYezY8YwZN5aRI0dRW1dHeUUlPj3Ua7MZg6tPEjUVLk40HKGjo4PW1k00NW5g7bq1rPtqDevWr+OlF1/gicfnUFpaykEHH8KJp5zCwYccytjxE9iypZ3Gxg1UVFRQV9+Ax+MBcobv/yp0OkqSRCqVoruri87ODjxuN9W1tTgcLiySNskVhY5okqbOHpY3h/i8sYslG7tZ3RqmNRQnHEshZGWUrKLm2gioblQdSs4rJ4gCiiTidtqo8TkYV+1h7xGl7DuylD3rSxlV7qbCa9M8PGWUlJaRSMTp7upg9eqVlJaWUV5Wjs2uCvn/dTrqz2+xWAwHTldXJyUlpdTVj8DhdCJqpJBlmfZIkg0dPSzd2M2XjV0saepizeYobaE4PYm0Skc5b497MR01+gqSSkefy0ZtiYsJ1V72HlHKlJGlTK7z01Duptxjw+W043JWUlpWQTweo7tjC6tXraKsvILS0lJD0flfNyDz6ZhOpejs6qK7sxOn201VTR1ut9OILPYk0rR0x1nREuLT9R18tr6TZc0hNnbFSCTSKOksggAKQiEv6qRUMGgpoHlaJRGH00pdiZNJtX72G13O/mPL2bPOT0OZC4/DhtNeTmVFOT2xOF2dHaxevYqy0jLKyspMftSQT8dUKkWXRkeXRkeHw5GLSGlytbGjh+Utqlxd2tTF6rYwraHENspVKzU+J+Orvew1ooQpI0vZs76EkeUeKrw2nHaVjtnSMuKxOKHuTr5avYqS0lJTruah//XRQ3VtnZqCb66Puz3yAxypVIquzk66u7twudxU1dTidrsMuZpIZWgNxVndGuHzxi4+39DJsuZuNnT00NmTREnKCMhaZErASMXRoa+P2teKIGJ1WKjy2hlbpa6P+40qZ+8RJYypdFPpc+ToWFZKMpGgq6ODVStXUFpWRnl5hZFWbtKxkI5tra2kUimsNis1tfUF/Khoes76LT0s2djFZ+s7WdLUxVdtUdqjcZLxDIKW2Qj0wY/6q+poVQQBrBJ+l42GMheT60uYOqqUqSPL2KPOS53fhdthw5G/PnZsYfWqlapc3cV03OWzRhemsiyzYf16tmzZgt/vZ8SIkTQ3b+Tf//wH8+c9xcoVy/F4vUycNIlTTz+Dqfvtz9777MPYceMoL6/o1a4eetaNxXzYbDbsdjulpWWMGDWq4Lvu7m7Wr1vL0iVfsnjxZ3y+eDEfffA+by58nYYRIzl5+nROO+MMxo4bT6wnxtIlSygtLWX0mDHYbLbdIoy8s5Hvtclms2xqaSEei+FwOhgzbgI2m9XQL7t6kqzYFOb9r9pZtKKNj9d20LSlByGdUZlJEhAkUd0XZ1GVfoPn8u4p5L8KoMgQT2ZY0xpmTXM3r37ShGKRqC1zsf/oco6cVM1hEyqZXO+nwuvA7XLidjVQWVVDR3sba9euwe12U1Nbh9Vq/Z/0yun8YrFYSKfTbGppIRaLUVLiZ/zEydgsOSOsPRxnWXOId1erdPx0fSdtXTGETFalo0VElEQki4Rgk0AZBB21PkTiacLRLlZu6OT5D9aj2CyMqnBz4NgKjppUzcHjK5lY66PEZcPrceP2uEmlUmze1ML67i6cLhd1dfXGfPxfMx51frRYLGSzWZqbN5JMJLDbHYwaOw6H3WZct25zhE/Wd/LGslbeXtnGspYQ6VhKVTZFjYZWCaxSnm2oaL/P3TNnRwpqqhyAAqmMzNq2COtaQrzw4QZEp5VJtT6OmFjFMXvWMm1MOWMqPQY/JtP5/Oihprb2f5ofe9MxicPhYPS48X3LVY0fP163I+VqVpOrXbzySaMmV93sP6aMIycWylWPx4XH46IyXUPHls0qHT0eampMOu7q9TGWTLOmNWWuj9uIYn7ctEmlo93hZNSYQrm6fkuUzzd08ebyNt5Z2caS5m5i0aTqVBNFBIuIKAoITlUF13ZvaOtkntGvOeoMO0SBrKLQ3BWnuT3K21+2oIgilaUu9htdxtGTazh8j0r2qi+hwufA5XTibGigqqaWLW2trF2zBqfLSV1dPVarFVmWEQThf5uOLS0kU0l8Xh/lVVU4HQ6DfzqjSZa3hHhn1WbeXNHKJ+s6aOuII2SzKGooEVESsNgtKp8pA9ARcmsj6rXheIqljQmWrdvCk4sEbG4rk2v9HLZHFUdPruaAMeWMqdDXxxGkMlk2t7awbu0aHA4HdfUNBh1h59kdu8xwzPe+dXRsYeWKlZSVlVJXX8/ar9bw7DPzePKJOWxu28z4CRO49IorOOzwI5l20EGMn7CHYWErikImk1FTprT87cHmceuKcr5x6fP5mLrf/kzdb38uvvRyGjds4LHZj3Lv34J0dnVwz1138sB9/+HMb5zFOedfwF57702sp4ePP/qQ+voGRo0eDfzveHPy6dje3k441I3f56Oisgp7XrrN2s0R3l65mRc/b+bN5a1s2hJFUBSwSlgsIthsyArIskqPrKwUaqZbg34ji4hok9SImAytoTgvfLye5z9aT0WpmyMmVTN9aj1HTaxmUp0Pm9VCTV095ZVVhLu72LBhHQ6Hk4aGEcD/Dh315xQEgabGRlKpJP6SEmrq67FaLKqXTFFY3hLirRWbeXHxRt5etZnOzh4EUfWeWWwS2CVkWSt0o2+6yQ6hIwYdJUSbgCiAIis0bonSuCnE3HfXUlfp5ZjJNUyfWsfhe1QxptKLw2ZjxKjRJBIJouEQa9esobS0lPIK1an0v2JA5vPj5rY2enp6cHs8VFbVGGlTqUyWxRu6ePXLFp7/bCMfrdtCJpZGsIpINgmL26aSTd+Tminiwz50DJ1VlWKeFQBJRLBKSOokYvnGLpat3cK9C79i/9FlTJ9az8n71nPA6DKcNgt1dfWUV1QR7u6kccMG7A77/xw/9kVHj8dNVXUtdrutQK4uWrmZFz/fyFvL24rkqtRbrmYNFXVw6FeuxnjhwzDPf7ieijI3R06qZvoUVa5O1OVqbR3l5ZWENLnqdLiob2gA/jfpuKW9ndCuWh9BpeV2r4/rcTgc/9P82NbaSjQawePxUD5ytCFXk+ksnzd28dqSTbyweCMfrd1CqiepGok2CxaXFUVR10ZFHoCGBQHHfr4XBQS7BUkz+LZEE7z6aROvfNJIeZmbIydWc+rUBo6eXMUetX6sFona+nrKq6qJhLpYv24tPr+f6uqaguf7uiOfjq2trUQjEbxeLxWVVVjtdvSA/9rNERat2Mzzizfy5vJWNnf0qGSxSVjsEiCp/Kjk9jP2QkHAUektdvXvrRKizYIgqGvuF+s7+Hx1O/94fRX7jSrjlCn1nLRPHfuPLsPjsNLQMIJEKk001M2G9ev+P3tvHR7HleV/f4oaxWTJKDPEcRI7hjhxHMYJMydO4nAyyfDuzs4O7M7szkwyYbLjOOQw84RjtmNmWQwWM3V3wftHVbWqWy1bstWyMr/3PI8suaHq1j33wPecc88lISGR7JyciOeLNx0WibcfTlVVdmzfTltbG+PGjaO0pIRXXn6Jl194geqqSo465hhuuHEBJ596KtNnzIwoCQ2FQoiiGC5xPRhyRlqck22Xs8qyTNaQLGprqzGAWxfeQWJSIq+/uow3X3+dTz/5mIsuvoTLrriKCRMmUlVZyQ/r1zFp0mT8CQn/8krVfj5VVSkuLsLrdjMkOwe3x4cim8B9V0UjH20q5521JazaW4MRCIFLQvEq6JZzquoGaHY9huMG4bDpfqJh0QJrObq6/X1BQPK4EIG6tgDvrSzgvfXFTB+dzoXHjuK86SM4amQKLkUhJT0Tnz+BttYW9u7NY+TIkbhc7v9n+BgIBCgtKSElJYn0jAxcHi+yaIY5NxQ18MGGUt77oZiNhfUIIQ3cMorf1cVHc2e/Sf3NR1FE8kqIwL6GNpZ9s4fXVhdy/PgsLpo1knOOGsaEnGS8Hg+yrOD1+mhqbKCgIJ9Ro3LD+ub/BT7a8uj3+sjIzMDrS0SWBFRNZ/XeGt5aV8L760soqGgym2i4ZeQEl8VDhzMTnQ6O5l/slGP396J4KcgSsmLyYcPeajbsqeb5b/dy3vQRXDIrlxMmZOJ2KeZeL38CHW2tpjyOGInL/f+OPHbx0WvuqfcloFglqTvLG/loc5deJaZe1buCNoJp74xwXaNx6HrV60IUoK41wLsrCnh3fTHTczO4aOZIfnLMcI4amYbLpZCakYnfn0BbWyt78/LIzc1FVpT/5/jocbkZMiQHt3eA7aNgpkLM7Tqgq/r/bx/7QBF8LCokwW+WpPp8fiRRIKhqrMyr5Z11xXywoZRCS6+KbhklwY2qmw1sDoqHzpejdKqhg2obXFFA8iqIQH1bgPdWFfDu+hJmjk3nopmj+Mn0ERw5PAWXIpOUmo7Pn0BDXR15e/aQm5uL4nL9v8XHwkL81nY3l9dn+Tmwe18TH20s4621xazaW4vRGUJwSyg+hzyqjgrGA9lIJ/XES91Ac9hcQZHMDKYBG/Kq+WF3Fc99m8e5R4/g4pkjOXFSFn63gisjE4/PT0tzI3l7djN6zNjw88WbjwO+SmzQWF9fz7atmxk5YhQpKcm8/eYbLFm8iK1btzBt2jRuWXgbZ559NkcfMx1RFMNgbn9A0QgrR6N3bap7+JzddRXg/Xff4YUlS7j40sv42S9/idvloay0lJLiYlJSUln87DN8/NGHLLjlVi645BJy0lLZuHEjo3JHMWLEyPAxIP9qZDdq2FdRQUNDA9nZQ0hITMblMveXFdW08ua6Yl5bWcj6/BoEzUDyyOB3oWoGoZAlfE7Bi+WYWkYvpkCGfSABBPs/9mvhFAiaapj+kygg+8xo/Ya9NfyQV8Orqwq5fE4uV8zJZWJOMrLPhyjLKIrC3rw80tIzyM7O/pflo12ysa+igsbGBoYOHYrbl4DH4uOO8kZeW13EG6uL2FlSjyCA4lbQFQlN7w0fjS7+OFgUQT3xuUc+ish+CUM3+H5bBd/trGTZ+EyunDuaS2eOYkS6HyUxEcXtoa25kZ07d5CZkcmQ7Ox/2ciqLY+1NTVUV1eRnZ2NLzE5zMeNRXW8vLKQN1YXUbKvCUERcfkUVMPsBodmHJiHEMkrbDBCDB72LJOGbqBaba5Fl4IsQnldG09+vI131hdzycxcrjthDLPHZZLg9yPLCoqikJ+/l7S0dIb8C8ujzce6ulqqKivJzs7Gn5iE29rPUlTTyptri3l1VSE/9FavWmRoBkhCd15Fy91B6lUwAwE/5NXw6spCLj9uNFfMHsWEnGRkv61XZXbs2E7O0KFkZmb9y/OxoqKCxrB9TMHlMn2XuNrHMP+csiqY/BcwWzwaVvDgkOzjXtLS0/+l7aPNx+rqamqqq8nJycaXkBQuS12zt4aXVxby9tpiyiubEVxdelXTdCvDj8U/+u7jgMVPi6nR8mhnsnqQx/V7qlm3p4bXVhVyxdzRXDE7lzFZiSheL3J2DgltrezavYuM9Axyhg791+djVRU1NTXk5GTjT0zGbdnHsvo23lxbzLKVhazNq0HQdFMeE2LIY0yQaDhkSiCmn2N9rEde2vZRswK4AogeBUWAyoZ2Fn22g3fXF3PBjJFcc/wYTpo8hAS/D5fLhc/rY9vWLQwdOoysIUPizscBBY52nX9BQT4V5RVMPeJItmzZwnOLn+WDd98lLT2N++5/gAsvvoRjZ84KZwls5/ZAE2GDwN7Wa0d/zgaSuqYhShJ5e/bw6MP/YNz48dxy60Lcbg9ff/Uln33yMUdOm8Y9993Ppo0bWfr8Ev7+1/9jzepV3HrbHcyecxxFhQU01Ndz5LSjIq79r0B22+KiokL8Pj9jx45FdrmRRIGOgMqb60pY/PUevt1VhaBq4ehphIMaS4lGI4twFmN/0Tih63e3y0QJpWGgqpZAuhVkAXYU1/G7kno+2ljGTfPHc+WcUaT43ShKGuP8CdTWVFOQn8/oMWMObdIGGdnrURAE8vfuxefzMm7ceETFhSSYtf3LVhXy3Ld72bC3BgEDxaug6RCyM4sx+RijHiOCTzGoJz73BCQNAzVk8lHymvuD1u2uYm1+LR9uKOWWkyZw8bEjcLsUpLQM/AmJNDU1UVxcxKhRuf9Ssghd8lhSXIwoCowdOw7Z7UESoKa5k5dWFLD4mz1sL6xHkARcfhch3SAYbQy78dHptdrGLhJwGJpu/i3GAiJOmYx2jMx1oms6QQ2QRVxuN1UN7Tz+0TY+31rODSeO48Z5YxmW5kdxpeHz/evKIzj0amEhkiQwdtw4ZMWDJJpd/N5aV8zir/P6rlc1ndFZiRw1KpV31xYjKrJj338Mh7Uf9Or24jr+s7iejzaUctNJ47lydi7JfheKK40jEpMoLyujoPlfnI+Hyz6GHVPMTKMoYAQ1zpo+gpLaVnaUN4EYw2n9/+1jBDn1qiSJjBs/3uSjAGV1bSxdns8L3+Wzp7QBQRZwJfRGr0bRgXwc+zr7k0dhP3z0mHzcUlDL5uJ6Pt5Yxs0nTeDy2SPxuRXExCQmT55MTU0N+fl7GTNm7EHN1WCmCPsoiUyYMAFRVsLZ4nd/KOWZL/fw5Y59CCFbHqUDyONB+DnW2zF56dTDDhCpqzpBAFHElSBR19LJ4s938vnWcq45fiw3zx/HuOwk5OQUjpx2FBXlZezN28PYcePDzx4PX2fAM47r161DURQmTpzIG2+8xtNPPkH+3r2cdvrp3LTgFk4+9TT8CQlomrbflKtd+91fkxIGjbqOKEm0d3Tw1BOPU1xYyL//7r84dtZsKisreeH556ivr+eP//MXzjz7HCZMnMg3X39FS0sT3337DVu3buXWhbdx9bU3EAh08u03XzNz1mz8fv+/jMOq6zp78/JITkrCn5CA4jbPfdtc0sDDn+7gzTXFtDR3IvlkkBVC6v6ABrEdzUMhWyhjRvO6BFK3DSSwblcVm0sa+HrHPu47cwpzJ2QiKC4ys4ZQX1fHrp07GDd+Aoqi/Oj5aEejgsEge/P2kJmRSUpaGrKsIAjw/a4q/vHZDj7cUEaoM9QFGHsyiL2Jmh4MRV+f7nzUVN0EkB4FMPh6Uzk/FNTx9Y5K7j1zMlNHpCC4zDPmdE1jz+7djBs//kfNv2jSdZ28PXtITk4iLT0Tl8uFIMC3uyp58OMdfLSpDC2govgUVNux2R9YjJnBiOKFbuCSRYZl+KhrDZhdHiUxck+O8zqC0BVtNYTuRlI3CGoGgizhcsnsrWjiP17fyHe7qrj/7CmcNW0YottFxpAhNNTVsnPHDsZP+NeQR5t0XScvL4+U5CTS0jOQFbMUdFNxPQ9/upO31hbR0hw4sF4Nlz11yczNJ4/n4hkj2VBYT0ldWxfQJ1rG9kN91KsSsNbSq19t38dPz5rCceMz0SWJocNHUFdbzc6dOxj/L6JXbTqs9jEsq0aXb2tAapKH/7rkKLYUN3DHktVmaZwtexH37I19nMzcCVkO+/ivKY+aprE3bw/JycmkpmVYVWgGH20s5+FPd/DPbftA1Q5erx4q9UUePQoSBiu27WNjUT3f7tzHfWdN4ehRaWjIZGRkUV9X8y/l59ikaZppH1OSycjIDAfHd+9r4uFPd7JsZQGNjR1IXgU8fZHH/iSHfbQDtNbLdjAgGDTCALKsppW/vLuZ73ZWcs+ZU7hs1kgkUWRIzjAa6mvZvWsnY8eOQ3G54sLHuANHe9DBYIBvv/mGSZMnEwwE+cv//IllL7+Ex+3hF7/6Dddcd304aqWGQsiKEvNaQMTexP6i6BLXLz77lDdeW8ZpZ57FFVddgwF8+P67fPTBByy8/U5OPvU0dF3n/XffYdvWzVx17XUMGz6C5555hgf/+n/s2b2b+x74OUccMZXtW7cyZepUEhISfrSlAPbcqKpKSVERGRnppKSlI4kimqazdEUBD36ynS0FtQiKiOK3BdAuj7GMGQ7FZqf046Wcwtd13DtKIG3FqvgUgprG69/ns7GojnvOnMItJ43D61LIyMrC7/exZ/duRo8Zg8/n+9Hy0R53W1sbRUWF5I4ahceXgCQKtAdCPP1VHo99vpOC8iYEt4TkjVakMfgYjlzHmY/O0rkoPtoAUvEpNHcGefaLXawvrOVn5xzBVceNxuf34fZ4kBWFosJCRo0ahfQj3s8RIY/FRWRmZZKSmoYkinQEVZ75Ko+HP9tBYXkTgkdG9iom8I8whvsBeU4SHLKKeSaVGlAZPzyFP1x6NIu+3sMna4pREj0EVUcnpP1lsmLez2waEdQMJLcMusHnP5SyvayBO0+bzN1nTCTJ6yIjKxuf30/enj2MHj0a749YHqP1amZGOimp6UiSiKppvLCikAc/2c7WgjoEReqFXrVIMPv4GZrBtFFpXH/COIalern15PH89rUNSLKEpkI4GNMX2e2jXg1YenVTUR33nDWFW+aPw+OSycjKxuv1UpC/lxEjR+Hz+X60zuqgsY+CEf46mNtu9ECI606byNEj05iYncxLKwr4bmelWbbcU4CoT/YxG7/f/y9hH8N8DIUoKSkmMzOTlLQMJFGgoTXAo//cxVNf7mZfdYsZrFQOQa/2F/VRHtuDKs9/tYcNRXU8cM5Urp07GlmWyMzKxu9PYPeuXYwZOwafz/8vw8esIZmkpWUCBoJg8NbaEv7vw22s3VONIAkoftcg8FfD//RoH20AKSoSkiCwckclu/Y1sbaghl+ecwTZqT4ys4bg8/nYtXsXY8aMxe/vfz7G1XOymRcKhdi0cSPTjjqastIS/u8v/8MXn3/G5ClHcP/PfsE55/4Er88XPmA2GjT2d3Zxf+MVRZHi4mIe+vvfyMzM4uZbbyMtLY31a9fy5GOPMvXIaVx5zbUkJCSwauUKXlj6POPGjefue37KyFGjkESRhx/8O2+/9QYVFeX88jf/waRJE1m/bi1HHzOdlJSUH50w2nxsa2ujvKyU1LQ00jIyEYDa5k7+8sE2Fn+zh8bmALJX6aoJF7D2U9hXckbFuhzRuFNPAgnhyHsoZJ7UqvgU8sqb+PWr69lSUs9/XngUIzL8eP2JjB07lqLiYnJyckhOTv7R8dEeb2NDA1VVVYwdOxbF5UESBYpqWvn925t5dXUBgU4zOxXSDNQwH4WuTMag5CNgmFlRQRSR3QIb99Zy79I1bC1p4JfnHUFagpuMzEw0TaOgoIBhw4f/KJ1Ve7ytra2UlZaQkZFJWnoGAuZejT+8s5mXlhfQGQh156PQAx97en7bmbTBHphncioS500fzsUzR1HfFuS7XdW0BVTzLE69B8fJSd2yydZ4BPOmmtWAQPEplNe28fu3N7G9vIHfX3I044Yk4UtIZPTo0RSXFDM0ZyhJP0J5DOvV1lbKykpJT88I69Wa5k7+8sFWnvsmj6aWAIrX5OOB9arhuD4osshdp01keJoXQRC48rjRvLGmiC1F9aBIjrVA3yPpfdSre8qb+NWy9WwpaeC3F05jRLoff2ISuR4Pe3btZtjw4aSlp/94+Wjbx9RI+/jnD7bx3EDYx+iMkyCgazojhyRx00njcSsSLlnkp2dNZl1BLR0hzaEPYgR0oI/2cQzFxSVk/0jto5OPpSXFpGdkkJaeiSCYzaj+863NvLe+GFXVUXwW0AiXMjqZ1Qu9Gg/qizx6FbYU1PPTF9ewrbSBX593JOmJLnwJCYwbP46S4mKGDMkm+Ufur5pB1SzS0zMxDIO2gMrfP9nOE5/voqahHdmtoBlOeRwEfg4c0D7quoGOgeJTqG8J8PCnO9hW0sB/Xz6dY8ekk5iYxNix46goLyMra0i/28e4rQYnaPz2668YNnwEO3fu4Fc/f4AvPv+cU087g3888jiXXHZ5GDTaxwE4r+HcixWPMUaPNxgK8exTT5C3ZzfXXHc98048kfr6ep5b/CxVVdUsvP0Opk6dSl1dLUuXPEdV5T7u/8UvGT5iBO1t7ezetRMEmD3nONavXcvPf3oPa1avZsaMY/lh/Vrq6+rCzX5+DGTPS2NDA4X5+YwcNYr0dNMo7t7XxC2LVvLQp9tpbA8he2Wz45RzsdtlaRHlU4eRYgmkw6EOqTqSW6Y9qLH46z3c+PRy1hfUIokCssvN6NxcKvfto6mx8UfFR1tpNNTXU11dZXZSU8x9N6v31nDjU8t5/ps8OkPm84ei+QiDm4+OaKBhmB1eZY9MfWuAv328jYWLV5Ff1QJA5pAhDB8xnPy9e2lqagpXG/wYyCmPRYUFjB49hlQLNG4paWDBMytZ9NUeOkIaksvBR/Eg+SgY2JFOAFkS0TpVjh2dzvUnmPthLj52JGccORRCWrg9fO+v7+Rj99dDqo7okglqBsuW53PTUytYlVeDJAi4PB5G545mX0XFj04eI/RqQQG5ublhPu6uaOKWZ1fyj0930NgeQvLEkMdovRomxyTqOidMHMJlc3Kt/hkG47ITufWUCWYDDRu09IdM91avBjQWfbWbBU8v54fCWiRRRJJdTJo8mZaWZpqamn68fLTtY0akffzHQNlHO3BgmGWqkiCAqrPgpPEcMTzFGi+cddQwfnL0cND0rtUSfds+2cc6yz56yLXsY+OPWR7z88kdPZqMjCwEweDbXZXc8NRy3lpVQMgAUZFMeYRIvWpeyPw92Oyj83Wbjx6ZxrYgD32ynYWLV7K3shVREFBcbkaNyqWy8sfLx4aGBgoL8hkzdixp6RmAQUVDB3ctWcN/v7uF6qZOZI+CqulmoDMcbzH6Tx77g2LZR8eYQiENSZHQNYOdFY2omnkup67reL1ehg0fQVlZKQ0NDf3Kx7gBR0EQaGlpYevmzUyaMoXdu3byb7/8OVs2b+aKK6/if//2d46dNQtN08LNb5wUT8DoHKN9L/v/X3/xBa++8jLHnzCP6264EYDPP/2Ed958g0suu4wzzz4HgA/ff48P3nuHK668mvnzT0YURd5+6w3ee+cd5p90Co88/hQL77yTqqoq/vPf/42PP/yAuXNPYPOmTf3OxHiSIAi0trRQVVnJ1COnIkoKogU2Fjy9gvfXF2MgIEpCV5ti24AJAl3dNAdZRkcQCJ/UijVOa4yapiOI5kHLX22r4OZnV/L51gpkSUR2uRg9OpfKysofjZMTBo0NDVRXV5ObO9rc5C+JfLSpjJufXcm3O/YhKiKC2AMfGeR8DHco6+KjqumIsoiOwFuri1nw9ArWF9QiCgKS7OLII6dSUV5OW1vbjybjaOvVqqpKjjjiCARJRhIFvt9dxc3PruCLzWUIUgw+HqxBNKxoq/UdVTNQPDJXHDeaycNSaA+opCW4uX7eWNLTfKghLTKq2VtA3i0Q0PU9XdMRBBBlmeU7K7ll0Qo+3lyGJJrymDtm9I9KHsHBx+oqjph6BIJkNmtYlVfDTc+s4IMf+qhXIwC7gICBzyNz75mTSfWb5wVquvne+TNGMm9yNmqniiiJXRkvw+g9v3p+sP3rVesA+y+2VLDgmZX809KrgiSTmZnJvoryHx0fB4V9NKwAjyWrgiighTSm5qZx5XG5KJJoOZXgdcncfeYk0pI8XfyO1gt9so8rutnHqh+jPDY3U11dzZQjpiArLgTBYEtJA3csXsX6vTWI1hFCmtYPenWgyMlHIdI+apqOKJn28e01xSx4xg6Si2aQfMwYKvdV/Oj42NzURG1NDZOnTEGUZERBYFdFM7c8u5IXl+8lpIEoO8C/c807eTqYyDkex/gESUQLqkwYmszS2+cxa5yZWRUsG+z1ehk/YSLVVZX9GlyNG3AMBAKsXrmSUbm57Ni+jd/88hcU5Odz04Kb+d0f/sToMWNRVRVRFBHFrqYKzizjQFPlvgr+8dDf8Hq93H7nXWRmZbFjx3Ye/cdDjBk3jutuuJHU1FS2bN7Ec4ueZfiIkSy49VaSU1LYvWsXzy9ZTEZGBvfc91PGjR/PhRddQmZWFpWV+/jj73/HB++9y/RjZ7Bty2aam5t/FMIYCoWoqKhgVG4uQVXDpch8vX0fty5awcrdVYiyjIF5MHG3MglnCczhItsZcv7YJFj/GI6xW8rD0A10AySXzJaiOm5/biXvrCuxlKqHoUOH0trcHObjYM1Y2eXXTU1NtLW1MmzYsHCHv9dXF3HnklXsKKlHcsnoBlb0LVZ2KoqP+5vXgSYnH8NA0gQ8um6eXCcpEt/trOTWRStZvrsKlyITCGmMHj2a8rJSVFU9fOPvA4VCISrKS8nNHU1I03EpMl9uq+A2h3OzXz72Va8KjmyjKEBQ5bgJWVx5XC4APreMbhj85JjhnH3UMNANBBuJHNT9HOMMlwiZGWHNMPc+7ihp4K7nV4flUfkRyaNNJh/LyB2VG+bjV9v3sXDRClYdjF51PK8gghHSOX/6CM6YNjQ8F7Ikouk6I9P93HLSeCSXbB4yLxA154dIvdGrbluvruLd9SXIkojXn8Cw4cN/fHwcDPbRlhU722jt0brl5PFMyE5CNwzrNdMmzBmXyTXHj8FQdURn1uz/MftoUygUorysjJGjRpnPJYkYBoxMT+DMI4ehKDK6oUcCRCcfBxvQcFJM+2i+5rSP34ftYzWSaGYehw0bTltrSxg8DnY+BoNBykpLGTVqFJpuoMgym4vrufXZFXy6yQqqCkT63YMd/Ntk+1nWWEVRwAjpHDEyjWduPo5TjsgJJ8DNIgHTbiqKQs7QobS1tfYbH/sdOBqGga7rbN64kWOmT2fr1i384Xf/SXFRITffspBf/tt/WOepdZWmRpy/CAMGGqPB6uJnnmbbls1cec21nHzqabS2trBk0bMUFxdx44JbOHbmLJqbmnjlpRfZtXMnd919H5MmH0EwGGTJ4kXs2L6dhXfcydQjp9HR0cGrr7xCY0MDx58wj/b2dv77j7/nu2++YeLkyWzZvIm2ttZBK4yGYaBpGoWFBWRkZKC4PbgUha+37+PO51ezrbgBySWb3dnCitPhfAyG7JTTGXI6RdFgJyJaT4RwapqO5JIprGzlvhfX8va6YkQBPD4/WUOGkLdnN6FgcNBmrARBIBAIkJe3h+wh2Xh8PkQBXltdxP0vraWkus3ko6Z3N4Sx5id63pw/h3sdO42iM8pqgw6XzKbCOu5Ysprvd1XhdikoLjfp6RkUFhSg6/qglEWIlMesrCHILhcuReGr7fu4c+lqdpbGkMdDNYgOh9HOYCYkuLn++DEMT/MTtCK2hmEgSyK3nDSeEVkJaCENSTqIrKNN0eU5MeSxqLKVn760lnfXl5jy6PeTNSSLvD27CQ5ieQzzsaCArKysCD7e9fxqtpUcpF4N8xvQDTJSvNx1+iR8Lrlb43iAs6YN5Zyjh6EFQsjRWcf+ol7o1YLKlrBeFbD1ahZ5eT8SPobto/vw2sfwHIMoiaidIU6YlM2Fx47CLF3rCsYbBrhkiVtPnkBudhK61oO+sOlf2D7afCwqLCArOxu3222e82s9aorfxf9cOZ1fnTcVRRQtfL6fuRrMFF53BuEzB2Pax1Us312FAHj9fjIzs9i9exehUGjQ87GkuIihw4chKQouRWFzcT23LV7F8p1VZkmnYfn9P4Yso01OHeIAjXpIY/qYdBbdOpf5k7PNslu6ACOOvxMSEknPyGSPwz4eiq/Tr8BR0zQEQWDTxg2kZ2ZQVFTIn//0R3bv2sV1N9zIA7/8Fenp6dYh3GbaPwI09lfEsxcUcfyGKPLtN1/z8ksvMn3GsSy4+VYMw+DLL77g1WUvc+bZ53DRxZcA8NVXX7Ls5Zc4/8ILOfOcc5Akic8++Yh3336TeSfO57LLr0SSJL764p+8+fqrzJo9h0cef5KFt99JdXU1//PHP7Bzxw7GjhvHti1b0TTtACMdeLLnprioiNSUFKs7nMCKPdXc88IadpU1mkZR35+TenifIWI8thBB9/UVC0BG/TaNo0RpTSsPvLSOjzaWocgSgiwzadIkyspKByXosIM45eVlTJ48GUGSkCWJd9eX8POX11FR147kknouvYkGjE4SBGufthGp2A73HAiO304+GgaabhrHbcX13P3CGtbm1yJJIqnp6SSnJFNcXHTICjUeFC2PyVb31NV7a7jvxTXsKWuywH8P8ngwOtUGjdZciIIAmsa8yUO4ZHYuhmEexwEgiSIhTWf+ZNNRRRTNDEvEOA7iwQ8gjyXVrfzs5XV8vrUCRZIQJIVJkyZRXlYaDgYOJorgY2oXH1fsPkS96nhOURAxVJ1rTxjLrLGZYSxpO3w2r7KSvdx44jgSEz3meWXR53D2J/WGj6+s4+NNZV18nPgj4WPYPoqHzz5G3AMECwTcfNJ4RmX40XTdDA5grgPzowaThyZz68njQdfD2cj98r7X9rE0yj6WDVr7aPMxKTmFtLQ0wJ4jIQy4vS6Jf7tgGn+49Gg8stSdp4PB7vWWBOufA9jHe5auYV1BLbIkIcoKR0yZMqj9HJuPiUlJJCWnIokiOyuauPv51azZU93/QdWBIuf4rPGLooge0pgzYQjP3jKXOeMyUTVThsWoRJz1ZQAUl4vJk6eE9eqhUL8BR83qiJq/dy+SJCEg8NDf/sr6dWu45NLL+FkEaJSALobDwGQZnUbICVZrqmt47B8PoWk6d95zHzlDh1JcVMQjDz3IkKxsblxwCxmZmeTvzePZp54kJSWFBTcvZMiQIRQXFbFk8WIMw+De++4nPSOD8vIyHn/sERITE7nznnsZNnw4t991FwtuvpW8vN389S//Q11tLemZGWzdsgVBEAYNgLTBf3FREX6fl9T0DEQBtpc2cv9La9luR8SdRhG6/z6cZI9LN5BlkTFDEknwyBi63uVERY/XMCKdWyPy+WzjWFzdwi+WrWPlnmpkUcTl9eHz+igqLByUfCzIzyfR78ft8SKJIt/urOSXy9ZTVtvaHTRG/3bOh+P1cORa10n0uhgzJNHMMA0W8OikKGOhaaZx3FJYx/0vrmVXRROiIJCWnoHX7aa0pGRQ8rGosBCf30daeiaiILB7XxMPvLS2K7OhG0A/y6P1VdHaL5WW7GXBieNI8blQrc6ptj61Hc9bTxrPxJwkDFWLdEYPVS30II8Flc384pX1rC+sQ5ZEXB4ffp+P/Py9g5OPRYX4fD5SLT5uK2vg/pfWssPWq1pf9KrRlbmyvqNrOqOzk7h5/jhcshhhY8O8sv5/6hE5XHTsKAhoKJIl7/GW3R74WFTZws9fWW/qVUnE5fEOaj4OSvto2M2rQpw1bShnHz0s8m2H76Nbn73quDFMH5OBFtS6fLDe6PD92sf1UfbRM2jtY2GhKY+ZmZkRshIOmIkCmg5uReSBs4/gL1fOINGtgDMwFm33BpP9OxD1YB83FdbxwIvr2LOvCUkUcHv9+L0+CgsLBiUfi4uK8Pm8ZGUNQcCgvL6dn724juW7YoBGGFz+ak8UDRqxjtcJqpw4JYdnbj6O6aPTw6CxW/VmGDyaYFMAvD4fXq+PgkPUq/0CHHVdR5JliouLUUMhUlJSWfTMU3z6ycfMP+kUfvHr35CdnRMGl9ClxAaanABV180ORM8vWcTatWu44qqrOO30MwgEAjy36Fl279rBNdebnVXb29p4/bVXWbtmNbcsvJ0ZM2eiaRrLXnmJlSuWc8vC2zl25kwMw2DJokVs3byZq6+7gXknzidkzcndP/0pF11yGSuWf8+j/3gIr9eHqoYoLS1BkqTDLoy6riNJEsVFRaSlpuL1JyBLEhUN7fx82XrW761BUmIYxUGoKE2f1cAtS/zy3Kksunkuk+zOcrqjJDoi4uR4DkEg7O1GGEeZnSWN/OKV9ezZ14QiSaSmZ+D1eNhXUTEo+GgHZ8rLykhI8JOclo4sSewoa+QXy9aTV2FnqKJAYzc2Ol6wFJGANX8CHDEyjSUL5/LAWVNQrHK3QaeHnQOyeKzpBpIis2p3Fb969QeqmzqQJQmvP5HkpCTKysqQJOmw7z8Oy2NxMelpafj8fiRJpKalg18v+4HVe6rjI489ZJ1PP3IY580YgabrJr/p0qeiIBDSdI4cmcqVx41GUcygRIQjdijj2o88bimq49fL1lNS24oiS6SmZ+L1eKjct29QyKNTr6anmnyUJZHy+jZ+8XKUXqWXfLQzws4umqIZLLvt1AlMHpZsOQ/2x7ucYlEUCKk6yT4X154wluysBEJBDUFyzm+cdPp+9WoDv3xlPXmVzSiyTIqlVysrKwcdHweFfYx2LEUBVdNJTPJw44njGZLsJaTpSGKXrNp+l7lUDHIz/dxx2kSQzGxF+LpC1D2i6QD28ZcR9jFzUNlHp15NS0vBn5BgbRnq+ozzqSVRwDDM82vvPn0SD103k/QEN+hR+i1WJmuw037s4/Kdlfx62Q/UNHciW5U5fp+P8vKyQcXHosJC0lJT8CckIggCrZ0q//76Bj7dUoaoiN1B44+CNz2AxpDKGUcP49lbjuPIkalm93hJjACL5tcEdOt7ugEr9lTzQ2EdkiiSmp6J2+M9JPvYL8BRFEVCoRC1NdUkJSfz1Rf/5MWlzzN+/AR+8ZvfMHbceBM0yl3lqfbDDRRFp2/tRbdy+fe89MJSJk+Zwq233Y4kSXz37Te8/NILHD9vPlddcy0Aa9es5rlnn+GUU0/jgosvxuVysfy7b1n20ovMmHEsV159DW6Ph2+//orXlr3MtKOO4qYFN2NvTlVVlZycofz0gZ8ze85xvP3Wm7z2ysuMHDmKqsoqGhsbD7uzKooiqqqiqiEkWSYxKYlASOVP727hsy3lCLIUWwgHHVqwyRyX3y1zxZxc3rv/FO48fSJpiW4Mq+RCFKNKTZwOrtN/so2jbhrHlbur+K+3NtPYFsDlUkhNT6ettZXOzs7Dykd7XXd0dNDR0U5qWjouRaGupZPfvrmRdXts5yYGaIzlLFj8FUVTdgzdICPJw31nTua9+0/mkpmj8LnjehzsoVN0gMAw93QIssQHG0r58wfbCKkaSclJSLJEoLMDTYvqDnoYyJbHUDCIpCgkJCSh6Tr/+/52PthYClL85VEUBHRVJyfNz8KTx+OWJeyFEh38UyTTwVowfxxHjkwFzYgcyqGOK6Y8mochf7l1H396dyvtAXPvfHpGJq2tLYddHsGhV0NderUzqPKnd7fy2ZYKBOUg+BgF7AVJQAtqzByXweVzRiOJIvp+KnpkyZTnEydlccnMUaDq3Y9Sif67v6gHPkoumRW7LL3aHsClKCYfW5oHFx8Hg310ZpqtwIEiiRBQOX/GCE6bmgN0ZZdtisxGmP8/b/oIzjxyKFrAbFZo8kboxRqkR/u4YpDaR7tZXCgUQldVZEkhMTHRKgEUrCZGkSDbMMxgiz0tC+aP45EbZpGd4sWwOsyGP/hjBY+x7KMi8e4Ppfzl/W2oqo4kyaSlZ9DW2kogMEj4GAyi6xqSouBPSAQM/vHpDpatKsQQLJ79aPxViwyDiGN6BMJ7Gn8yYyRPLpjDhJzkMGg0v2J0+23L98cby7jkH1/zm9c2UF7fjttldrBuamokEAgcFB8P2TOy0Wrenj0kJiZSWFDA008+jiAI/PRnP2fOnLnmPsKo8tSBBo3Oe4M5oQ0NDTz+2KO0NDdz708fYMTIUezbt4+HH/w7Pq+XWxfeTnZ2DhUVFTzx+GOIosiCW25lpPW5Jc8tprGxiXvvf4ARI0dSW1vLk088RmdnJ/f+9AGGZGeH7y3LMqqqcsTUqdz3wM/JzMrimaefZOOGDeTkZJO/Ny88rsNBNh8LCwpITU3D5fUhAM99s5el3+/FioV3V4qDWgjNzKJdMjc2K5HHrpvNC7efwPwpOciyiK6aLcrDyh+ins++lP3chEHHG2uKePTzXYCBy+0hPTODstLSgXzAHqmspITMzCwUlxsMg398upP3fihxOKkc4Dm7SnUEQFd1FFni1COH8tIdJ/DQNTPJzUgAzPkdzKsA6G44DAPDeszF3+xh6fcFCIDbl0BKcgpFhQUAhy2qat+3ID+f9PQ0XG4PogCvrCxk0Td70DQ7QBwHeXQ4P4ZluM6bPpxTjsgJl8WYH4u+l4Bm6IzMSGDBiePwuOWIphy2g3toY6PrWjYfASSBF5fns/ibPAQBFLeHjIzMwy6PYT4W5JOanobLY+rVxd/u5YXle801CH3jY0Rgy5JTzOMR7jhtIqMzE9B1I5xBilXdIwgCqm7gViSuOX4M44anoNrAwb54PJ2sGHzUDAMUiddXF/LY57sJ69X0w69XB519tLsdOwIHoaDKkIwErj1+LCl+84D6iI6pUSSJApquMyTZy22nTsTrUzAMvWtd9SZD8yOzj7aDXFJcTGJyEv6EBDqDKl9s20dhdQuilbUFJ8i2gIpVcaMbcPXcMTx503GMzEzACDnmuRt4PEwP2leKYR/t4T/zzW5eWJGPKApIioshQ7IpLio6nKMN87GouIjU1FTcHrPp3zvrS3ns810ErWOhDH2A5LG/yK4kccigKAjoIZ1L5uTy+E1zGJOV1LVv2YjU73am0SpA4NVVhSx8bhVVTZ18sW0ff/t4O6qm4XK7yc7Jobio8KCGeUjA0S6Jq6goR9M0FMXFC88/x44d27n+pgWcd/4FEUDtcB2zYd/fvreqqgiCwMsvLuXbr7/i8iuv4vQzzkTXdZ5/bhEbN/zA5VdexamnnUYwGOTdt9/imy+/4Nrrb2D+yadgGAbvv/sOn378EdfdeCPHzzsRgGUvvcjK5cu59PIrOOW007s9ryRJGIbBaaedzq233U5NdTXPPPU4gUAAQZQoKyvlcNSP23wMBoNoaojU9HRkqxnOQ5/uoL09hCxHCeFgF0AHiY6x6hice/Rw3rrvJP7r4qMZk5OEoekYmmHu04uOFDojyBFKVUDTdJ78YjcfbypHFMDrSyA5OYmy0lJEUTwsfBRF0WzakJ6G22sq03d/KOWZr/Z0OfGxIqMxsoySJKJrBoauM25oMn+67GjevHc+Z04bFi6DgMj5HfTkBEW6gayItLQEefCTbazNr0UWBVIzMwkGAoRCocNSkhMtjylp6ciSyJaSeh78eDtNzQFkl9jV4r8/5dGxFkQBDE1nVGYCt50yEegKwhh0j3ICyFZjnKuPH8OscRnWXiC6xucAO4dE0XyURQKBEA9/vpPle6qRBLOzY3JSImVlZYdNHm0+qiGV1LR0ZMnUq/84FL0a1lGmZyeJIlogxNnThnLuMSMAMIidbXTyyj7b77jxmVw2O9f8rPMoFefxEfGiKD0kiab9e/KLXXy82dKr/kSSkhIpHwR8HBT2MZo3hiWXqs7Fs0Zx8hQzYC3bpcdR7IvYomFd59Qjcrh01iiMTrV7l92+rMlBbh9tPgY6O0nLyATg293VXPvU99z/0jpK6lqRLbkwH6v7vjETcBtceOxInrn5OCYMS0YPaT2AxwF9xEMnh27RdQNJFmlrDfLgx9v5oaAWSQBvQgJJiUns27fvsPDRrqoKBAKEgiFS09KRRNizr5n/+2ArNQ1tKC5HFu3H4q/a43SCRsyg/dXzxvDoDbMYmW42u5Ls0xgEIvS80y9b+n0+dz+/hsqGdkRZRMDg+e/yeGNNidXBOoHkpCT2VVT0mY+HBBztFGdlZSWpaal8/903vPXmG5wwbz7XXncD/oSE8FmN3Tv9DAxF31fTNGRZZt3aNTz/3HOMHjOGhbffidvjYcXy73nx+SUcfcwx3HTzrQiiyNYtm3ny8Uc5dtZsrrjqGjweDz+sW8dzzz7DhIkTue76G0hISGDt6tW8+MLzjBkzloV33IWiKBHP6uziqrhcXH7FVZxz7nl88fnnvPv22wwfNoyK8nI6OtoHvATAvl/ent2MzB2NANS3Bnjokx3klTaieOSuw4t/dGUYkTpDtIQrze/i388/klfvOpErjx9Lot+FFlQRBLqi7rFKHKzn1jUd2SWxr6aVBz/eQUltKx63C4/Xj6ZZ5YVWoGAgyDAMyygGQDDweHx43C4Kq1t46JMdVNe1ISsxlGlUiQqYzy8IoAVVkhNcXDtvHK/fPZ9fnjuVJK+CbhgRYFEQ+PEYyKjnVlUdxSuzs6iBhz/dSVN7EBEYMWo0eXt2h43UQJINVosKCxgxKhcB6AyqPPLZLjYV1CE75ZE4yaNgNtAQRYErZ49m+uh0tKg9PdFBQecYUv1u7jx1In6vEt5T7Lz2IVMMPspumfzSRh75dAcNrQE8bhdefwKaOvDyCF18LC4sYMTIkWG9+uDH29l7sHo1Wl4F0A0dv9/NwlMnkJXksbLCsU17Tzb4qrmjOWp0OlpADZc/dcs8xYOinltTdWSXTEV1Cw99vIPSujY8bgWvL6GrbPsw8HFQ2Uez1CDMF/P4DZUxw1K4Zu4Y3IqE6jx+Ixb7rPckK8OW5FVYcNJ4hmQkoIaiuuzaz7U/2o99fOjjHZQeZvsI3fWqCFTUt/P3j7ZTVdvG+z+UcNui1eze17xf8Ahd2dozpw1j0S1zmZabjh7sATyG52jAHvXgKcxz21/WkT0y2wvr+cenO2npCOKSZRKTk+nsaCd4GPhol4wXFxWSm5uLAaiawaOf7WT17mokt0IobB+FH4e/GiVrdj8JXddZcNJ4Hr52FjkpPrOkX+ze9MwGjeZ3DJ79Oo+fvriWutYASCK6qiErEo2NnTz86Q52VzThVmR8icl0dnb0mY8HDRxtdFpSUoxLUaivq2fJokV4PB5uuvkWJk2ejKqa+01gcGQcwVx0zc3NPPPkE1Tuq+CnD/ycMWPH0lBfz2MP/wNVVbn9rrsZPmIEDfX1PPnYo7Q2N3PTzTczYeJE6uvrePGF5ykpKebu+37KxEmTaWluZvGiZ9hXUcE9P72f0aNHh581+rklSSIUCjFs+HCuu/FGho8YyZLnFlFUWEhSYhIVFfsGdF5sPlZUlJOTk4OkuAB4ZVUhH2woRXBLhDRnmj/SaA12EuieERMtvqiazswxGTy/cC7/uGYmMydkmQcbq5rpPMUCVo7MSUjVkbzmAexLlxcABgmJCbjdbvbtM/k4UAEAW+ArKirwen34E/xgGCz+di/f7axE8sqxlWkUMJYtJWMYBrMnDuGR62bx3K1zOSY3DVXTEQWh23z+CApVu8jpdFnPEdIMBLfEW+uKeH1NMQCK201mZhaVlQPLR1seq6uqSEtLw+X2AvDOD6W8sabI3MsWq+zmUOXRaTAEs2U/ms4Eu2U/IAkHMBdhHWtGq8+bMYLTjsgBzSondcrToRpy5/qN4KPMez+U8prFR6/fksfDyMfUNDP7D2ap8Qcbyg5erzorBAyzM6bRqXLZ7FGcMiXb+ojtUHBAR0CWREKqzpEjUrniuNEIFuiIqR/iQT3qVYUvtlaw9Pt8bL3qcrsOGx8HjX10Zgqtv63wDZfOHsXxE7NQta7mVZHfcb7kqBixwM7c8ZlcfcIYUDUUUbDLChwgaD/j2o99/GJbBc8fZvto87GqspK0tDQ8XlOvvrCigC+27UNwSYiSyKcbSrh98So2F9f3Ajya78+bZB6LMHN8pgUeowLPYX3N4AeP+7GPb64t5o21pl71+Pz4fD6qqqqAw6NX09PTkV0eBOD9DaW8srIghn2MYSsHG8UAjfbfd5w2ib9dcywZSR4zAyxG4olo0KhqBo//cxe/eGUdTe2hiABQSNWRvQpr9lTz3Hd70TSdxAQ/Xp+PqspKoPd8PCjg2JXdCNLY0EBKaiqffvwRa9es5qJLLuX0M88yL+6Ieh4O0Bh9T1U1DzB949VlfPLRh1x2+ZWcYY31haVLWLH8ey665DLOPvtcdE3now8/4MMP3ufCSy7jnHPPA+CLzz/njdde5dLLr+C0088E4O033+DTjz7ivAsu5Oxzf9Lt3tFRXjsbOf+kk7nsiispKizg9deWkZicTFNDAw0NDWYHpTgLo5OPhqYjKW7cisy20gae+zqPYEeo65iFcEkcPYQw4zLArt+GYZa96QboOmj7+dENs4xPM6xOhbFJlswGErIosmD+OF6980QeOGcKQ9P9qJ1m9lGyy3acYxEIC7phgGAYPP9tHqvyahCA5NR0NE096I3HfSX7LNLOjg7QDZKSUxGAb3dV8eL3+QiG4fA3LGUa5cBLktmuWe1UGZ6ZwC/Pncqrd53I9SeMDZ8NFL0RG8clNYsvhs2jHnlj/1ifM7qPJe5kdClmBAF0s0w50B5i0Ve72WVF4xS3BzUUiqiaiOuwHPIYCARwe3y4FImSujYWfZ1Hc1MHsiJhaM61eACHrrcUFZU1dJBlkZvmjWNsdpJ1LmPXOKN1ebSOEwTwuWTuOXMyqYnuKOepn8YdXQ2gmw0uQp0hFn+zh50VjUgCpKSlo6kDJ4/RfHR5fLhkia2lDSz+Jo9Q50Hq1eiAgSighjSyMxPNMxm9LquLphDxsWiK5pX9+avnjua4cZkQUJGdmZN4Zx1jBEBsvbrk272syqtFAFJSMw4bHweNfQzPVZfe1gIhjsxN45q5Y2I+Q0y/y/Ga3RHZJUtcM3cMk0amEgqoiPYRLbasCgcQ2P3w8XDaRycfg8EgiseLIkusK6hl6Xd7MUKqOY+qjuSW+WZrOQsXr2JNfs0BwaP9/qyxGSy69XhOnJqDHlStpns4dJ1BRAO6wUwx7KMoigTagzz7VR55lc1IAiSnpRMMdB42verx+nEpZlfjZ77aQ31Dew/2cRCj9WjQaAE9EfjpWVP436tmkOp3m1Ve+wGNoiAQVHX+/sl2fvPqBloCaveqAaz9x6LAS8vz+XaXCfqTU9MJhoJ94uNBAUf7wjU11QiCSFVlJS++8Dxjx4/j4ksvJSUlhVAodFi7EsYuUVXYvGkji599huEjRrDwjjtJTEpi7Zo1LF2yhHHjxnPbnXciKwp78/bw6MMPMX7CBG646Ub8CQns2rmDpx5/jKFDh3HDTTeTlpbG1q2beX7JYtIzMrjrnnvx+/1hRz7WmGzSNA23280FF13E9OkzeH3ZK+TvzSMxKYmG+nqA2Aq/n+cIYF9FBS63m4SEBAzDYNmqQjbm16L4lNglcfEcVjSI0PWw8ZJlEZdLwutW8HkV/D5Xjz+JXhcen4skrysy+kokH0TB3LMQ0nTGDEnk71fP5Nlb5nLOzJFIgoAW7+qcoAABAABJREFUVJEVkQhH1+hSRrpmoLhlCsoaeWF5AR1BFa/Hjdfnp3LfwGaP9+3bhy8hAY/bRVsgxNLv8ynZ14TiltGjlSlEzKtdonb+7FwW3zKX/71yBrmZCd3OCILu61KRRJK9Cl6vi8QD8MXnUfC4FVwuCVl2ZHX1KL7HU9lHG3TBKln1KazNq+H1NUWAQWJiIrKisG9fhTWk+ANHgPLyMnx+Px4rS/X2umK+3rEPyWuV4NjT31/yGJVtBEDXOXpUOjecOJZw9+EDkWNd2AZt/qQhXDxzJITXn9B7R7S3Y3fMg6aZUdX1eTVm9tgwcLtd+AZQHsPZ//IyfH4ffr8fQzdYtrKQTQerV8MOfFe2UbH2tV17wmjmTsgC6AJ89D5Yax/PMSojgWuOH4PHGl+4YVi8s472tYVYerWBF5fnW3rVhdfnp6pyYPk4aOyjc/6t9aDpZvfLy+fkMm1kqplVkA7sd5kq0BHMlkQ03WDG6HSumzfOqjpwgB8b9RxoDfTIx8NnH8N6tawUn99Pot+0a8tWFrKruB7Fq4TLczXNQPIorN1dzW2LV/Htzsr9gkcDI/z+tJGpPHPzcZw1fbi55zE6sNXVQmlwUwz7qGk6kk9h9e6qsH10Ky4Sk5LC9jHeZPOxvq4Ot9uFx2fax3fWl/DF1n2IPdrHQYrWY4BGQzcTGb88byp/uuwYEj1mkkkU9g8aO4Iqf35/K797cxOdqhb57HZgCzA0A8UtUVHZwgvLC2jpCOJ1u0hIMPc69pb6jOxs1K+qKmWlZQzJyuKTjz4kf+9ezjv/QuYcdzz2ERTO7xzuElVJkmhrbWXxs89QUJDPvff/jEmTJ9Pa2sKTjz1KfV0td957L2PGjKW9vZ0nnniMivJyrr/xJo4+Zgatra288tJLbNu2hdvuuJMZM2bQ2dnBi0uXsmP7du6+916OOOLIiHvGGodNdpfVY6bP4LwLLqK5pYXXlr1CUlISVZX7aGtrw94TGQ9ytqWWJAmvPwFJFFibX8trq4oQRLqydeGyhTgJYdgZssGi+bcgCLhdMh63DIaB2hEi2NhBR20r7ZXNtFU00VbeGPOnpaKRzvJGKiubCWnd57DLaJq/FSv7qGo65xw1jCULj+fPl09n8shU1I4QAJJsNw1wOtoQtEsd1xazfHc1AMnJqRi6RiAQiGv2OJxt7OxAFCAxKRmAb3ZW8d4PZilVUItyZizHU5ZFwEDtDHHE6DT+78rpPLdwLmccORRV0yOyjNBlNKMBVFDVqNnXTEd5Iy374UlbRRPtlc101rYSbOww59Uw8LgV3C4pNoh0rov+pOiSHMNc7wLwyopCNhTWI4kC/oQkgLhnHZ3yqMgKXq8PRRbJq2zmtVWFENLC4+zXElX7GhFOqYFLkbjj1AkMSfZ2G2dPujzaGRUwI/J3njaJnDSfIyPTB0e0t2OPwcdXVxaysbgeAUhKScUw9LjLo5OPsqLg8fiRRIE1BbW8vvoQ9Kpg4Mz+iJJIKKAyJTeNq+eOQZFEQo5zMw+0TrtVwcimnF8xZzQnTBoCapTjOxBZRycfbb3qMkvklu8x9WpSciqaPrB8POz20SZn4AArSBBQmT02g2vmjgZAkrrufyC/K3oN2H9fPnsUx03MQgtnVIG+llJH8/Ew2ccIvepy4fZ6EUWBVXtreGttMYIsWKXZ1hdEIZx53FxQx22LV/HJprIewaNAVzWOqulMzEnmyZuO46JZueghc2tHpBPP4M6A2RTDPuoWJntlRQGbihsQBEhMTgHdGFA+tre3kZyajiRAflUzr6woQA+aDS/Da9UJHgcbRfPfARrdksi/XziN3110FD63HBGk6Ak0tgVUfv/2Zv773S2ENAMjGjTaASBbHlUDwSPx3voSvttt69UU6IN97PMBbHaziLraWrxeL9U1Nbz+6qtMnDSJM88+B4/HLO+SLeA4KEpUrfG8/947vP3m61x06aWcdfY5CILAslde5ssvP+f8Cy7iJ+ddAMAXn3/KO2+8zmlnnMlll18JwIrl37N0yWLOPucn/OT8CxBEkU8+/oi3Xn+N0888kwsuvtSKzGjhTaY9lXNFd7s78+yz+eijD/jgvXe59rrrSUvPoLqqitFjxsRt7px8NDBITDSjcO+sLyW/vBGXTyEYimrNbTgEsr/IcBhfyyhKsoQiiXQGVAL17SBAWnYS44YkMTzdx5AkL8k+Bb9LDqfvo0mworE+l8SM0WmAmbm33zNvbYRvbb4vIEpm9jErycPPzz2C+ZOH8PRXe3htTRGtzZ3IHsU0NE5joxsoLomamlbeWF3E3AmZ+L1uZLeburpahg4dFjc+2tetralFdrvxety0dAR5Y3UR9XVtuBJdBIOOKJxhToQsCaidIZKSvVx5Si63nTyR6bnmPIWi98hEzFXXc9jzOWtsBv93y1wCVme5/fGjPaDS1BGksqmTsro28qpaaKxsNseX4MbjVghpOpodNQuvC+Kw9uyIunltXTNweWR2l9Tz3oZSjh6VSlJSIg11tdTX15GVNSRuwNGWx9raGhDA5/cD8Mnmclbn1ZjrTouWx35wVMPXsCcC0HVOmJLDJbNG9fl5o9e5YRgclZvG9fPG8r/vbUWQ7b6d/ehoRwDpLj7uKqnn/Q1lHD0yDa/HjcvtpramhmHDh8ddr9bW1oABSUmmXn33UPSqM2NhrVUBQBS4bt5YjslNR9ONbjJ7oGeMfj+k6aQnurlx3ljW5dfS1BZElK3uvQNhw8NA1fq/U6+uKeL48Zn4vG4Ul4va2hqGDYs/Hw+7fbQpStYFUUBVddw+hWuOH8PorESCqo5LPvgqL9kKPozLTuKGeWNZX1Bn7t+0S96cnVz3N+/R7x1G+2jzsabGlMfkxESCqsa760so2deEy+/q4qOtU0WzI6zkktld1sidz6/hb1drXDJrVMwD1+2/ZUlE1XVyMxN47MbZ+N0yLy3PR5DE7s78YM6E2RRez4alV3VcHpkdxfW8v6GUo0am4nW7cXnc1NXWMHQA5LGhvh6Pxxveo/rx5nJW7qlG9jrso+AISg62OY623TYw1Exf9bcXH8UDZx+BS45sKNoNNFrbMprag/zurU08+tlO89zKWP5B9DzoBrJLorGhnTdWF3HixCwSvS5cHm+v7WOfgaNdA1tVWUlKWiofvv8e+XvzuP2uu5k5aza6rodB4+Gg6MnWVBVZUdi5YzvPPPkEWUOyWXj7naSlp7Nl8yaWPreY7Owc7rrnPnw+H2WlJTzy0EOkZWRwy623kZKaSmlJMY898g8SEhO5ccHN5AwdSn7+Xp5fvAiXy8Xd99xHenp6GDTCgY02EM7cHjntKE477XQ2rF/Hxx99wN33PUBRYT4jRo4MNxfqb7LBbXt7OxlZQxCAbWUN5ll/kkAouo1+PITQubB1HVEScckyna0BtECI4bnpnH7SeGaNSWdcdhKj0v1kp3hJ9PR9fcUSQvP/3QMNdvZR0w1mjslgyrAUTpmSwxNf7GLFzkoQRSTZ3BOB5XMHVTOq+sHGMq4/cRwnTMwiKTmV+trqiHXR3yQIgnmYsa6RkmoCv7UFdXy8qdzMNqoOgGsQHreq6syfNow7Tp3IedOH43PJhDQdWRR6AI1CBPud8zluSBK/OPeIPo27uSNEZWMHxXVt5FU2sTa/js+3VbCvuB68Ch6/m4CqYWh6V8Ssv9dgxDXNdRjSDQRJ4O21xVw+O5cjhqeQkpZBQ31NOOoZD7LlsaO9g8wh2UiiQEltK++sK0FQdQxF6gLP/QUasa8FhOGcmQG++4xJJPtc3TISvdFrEYYO8xDyBfPH8/a6EvIqm/vuiPaGIrKOBqq1bN5ZV8wVc3KZNDSZxKRk6utq4yqPXXxsJyPLbFaztbSR99Yfgl61QaO1RmVZRO0IcfwROVw2K7eHr0Q6Gs6/e+KjrfcunjmKt9aV8M7qIqvxle3oEl9wZA4yIpBh69UPN5Rxw7xxHD8hi6TkFBoGiI+H1T46yZZ5e3yCgKpqHH9ENlceNxqgG2jsDd+j/2/vd73o2JF88EMpH60rRvG7CIXsKgF695xOeXTwcaDto83Hzo4OMrKGALC1tIEPfihDkMXufHSCR93MPBZVt3DvC2toD6hcN29s+DiEmOBRFNF0naGpPh68diYel8Sir/cgiDHAo3OeBiM57aP1t92t17aPpl5NoW6A+Njc3MSQnGFIAqZ9XBvDPg7WBo49ADpDN0j0yPzh0mO4+4xJEQ1w9gca61sD/NtrG3j66z3mZ/dzj2h5DFlZx483l3NTUT3zJ2eTkJhMQ10NmqoiHQB3HJQX1NrSQkhVUVWVd99+i6HDhnHKqafh9XojzgI53CWqhmEgyTIdHe0sfW4x27dt46577mPaUUfT2dHBomeeoqAgn7vuvS/cBfbZp59i584dXHPtdRw/bx7BQIA3XnuNNStXcsONNzFv/kmEQiHeePVVVq5YwcLb7+TYWbMjnMpY5XzR43L+H+DkU05j/PgJvP/uuzQ2NOB2e8N7HeNFnZ2daLpGQkICYPDF9kp2lTQgu+XIDcbmQPv35lEK1O2S0YManQ3tzDtyKE/cM5+37juJp2+aw+2nTuS0I3IYn51EosesY+8IqrQFQrR27v+nLaCGjxGIbTi7eBK999Eu//K5JK6eO5qlt53Aby85hiGpXrTOkFm6al9PN1AUicqaFj7eXEZI1UhK8AMCDQ0N/Tt3UdRQXw+CSFKCn0BI4+NN5dTUtqK4pK6yT8E8l1HrDDE03cd/XXYMSxYezxVzcvEqUrgTn3N+7DkxDWPkPZ3zqWo6bQG1V7zoCKqEVLP9+4ScJE6fmsOdp03i6QVzeOe+k3j0nvkcNyWHzoY2jJCG2yV3lfdEOU/9QlHZKkMzkN0y24rr+XpnJWCQlJRIKBQiEAj0772jqLOzE13X8PnMbOOKvBpW7KlC9MhomiMqHj3ug6WwbBO+pqHqnHv0cM44cmjMLHNvyBmYsX2PcdmJ3HbqBDCMcKa6X3VLFB91TUf2KGwuqg83AfD5/BgGNDbGVx47OzvRNJ2ExAQwDL7cvo9dZQ0oB6tXwyDbdmgNPD4XN544jrFDElH1roY4h0wGeF0yt540nqxUH5rq7BJpA9g4UqxslSKxr7qFjzfZejUBXTdobGyM61BMPh4m+xhNDoAhiiZoTEpyc+OJ48hI9KD2oURwf/6JaOnzrGQvN84fT0qqj1BIQ5Aceqc3OrgHPsa2jwPg52gaiYkJGIbOl9sryStvjJTHiO0nXYEaTdMRFYmK+nYeeHkdT3+5G0kQw2XK0Xv/DcM8LkHTDTKTPPzfVTP46VlTEAwDIWIfbAwQORipm141kD0ym4vq+Gan2YnT5/MhCCKNcfZzOjs7zfv5zb2NK/bU8H1P9nGgGjj2lnoCcrpBik/hr1cdyz1nTg73EugJNGoWaKxu7uBnL63n6S93m/sfo+9hU0/+giWPNbWtfLKlnKCqkZToB0GgvhfyeFDAsb6+Hr/fx55du1m/bh0zZ89h5uzZAHHLkPWGooGBDWI/+/RTXnn5RX5ywYWce955SKLI22+/xYfvv89ZZ53DhRddgiAIfPftN7z6ysvMnj2H6264CYAffljP008+ztx587j08iuRZZnvvv2GpUueY+7xJ3D5lVchyzL6fsDJ/sg+W+iYGTOYNWcORUWFrF29ivSMjPBRAPGiutoaEpOSEQUoq2/n4w1lZkdSe905Ivj9SlHCI0kigaYOZk8awgs/P5WX7pzHHadNYtaYDCTJ3PgbCGmomo5uGCiyiNcl43crJHj2/+N3yxHn3uwP0Dvft3/b2beQpjN2SCL/efE0XrlzHhfNHWN1EjUQ7c/oBoIs8vHGMgpqWgHwJyQSCHT27/xFUTAUwudPAGBvVQufbi5DUMRwm3hJEsNHIlx6wlheuXMe/3HhNEZnJoT3f0Z3THVGUmORcz5lScTvlnvFC69LRpG79pMGQhodQRWXLDJ7XCZ3nz6JV+6cx/MPnMqM8ZkEGjsim+jEaz1CWLlqhvnPhz+UUtnYiShAQmISdbW1/XvfKKqtqSEpOQVJhOaOIJ9uLifUHrQ6rUUMuH9u6MyWAIIBKYke7jp9En531zFKXR/vMmL7+x15CwEDcy/GlXNGM2tsBrq9XzPikfrhmaKAsG6AoGl8vLGM2uZOJFEgMTGJzo74ymNtTQ2JySmIQFlDOx9tLDU7PB+sXnV8VpEEjI4gJ0/J5ifHDDNBgG7ut1N1Pfyj60Y3vkQ39tB0UwZVzfyOZm1iCqgapx85lDOmDbWSjTZoGaAovlMeBYde3VROYU0bYMpjZ0dHXIdRW1tDYvJhsI+xyKn/ADSDEydlc+GMkYQ0HRGhaw1YPNV0I4LfXQEdwNqbpxuOdWN9B0DVdM6cNpQzpw2FoIZ0MJnVnvjYzT7GNyBXW1NNYnIKAlDe0MEnm8pBj5JHu7QxjCO7Muu6piPKErXNnfzq1R948JMd5utRcuX8W7ICPKl+N3+87Bh+ff6RSIKAsL+M0GClKPto6lWDD34opbq5w+wF4E+gM85+Tk11NUnJqUiCQHN7kI83laH2ZB8HE27siefW1oAHr53JradOCCd29wcaJVGgvL6Ne5eu5fnv8sytBM572BRLRqPlUTPl8ZNN5RRWm/LoS0ggFAwe8JH6DBw1TaOmppqkpGS+/OIzBAGOm3s8WVlDCIVCMR2NgaBu+xqtMyTz8/N58tFHSE1N45aFt5GdncOunTt4fvEiEhISufu++0lOTqamuprHHv4Hgihy+113MyQ7m9qaGh59+CF0Xeemm29lzNixlJeXs2Txs3R2dnDXPfcybPhwVFWNKFHt66Z0Xdfxer0cf8KJJCUl88nHH+NxuWhrbYubUjXLcDpITk4BYFNxA6vza5C8juhN14D788bhSIsoimA1svjzguN44575XHf8GEamm5mzoKohYEbA3YqELIndzhDcn9PqpOh9pb39nM1Lxdr8LgkCp0zJ4ZkFc3j8htmMH5qEHjRbHxuageyS2VxSz7p8E2QkJyfT2tpKKBTq60z1ioLBIO3tbSQlmU1c1uytYUdpI7LLiqYKZmfYScOTeeqm2Tx10xzmT8pGEIjIMsZyLvsyTz1RT/wRBfPMSLci4XWZICWoagRCGrmZCdwwbyxv3XsSf7xxtnkuk2F19tTjYGhjZKtEr8SKvGq2lJpR1OSUVNo72g+4zg6WDMOgo7Mj3Nwor7KFb3dWIrhlVLu5ke2891c01ZlhF0UMVePKObkcNz6zf65vX9ua32FpPu46YzKiLBHu4WE7a/2adTQNo6bp4Jb5fncV28ubAPAnJtLe3h43ebT5mJxs8nFTUT1r8mv7Ra8KokAoqJGekcCtJ48nO8UX7qJpGAZYJ91omhHh1EZdBcPKpoRZABi6eQZYSNPRNFP+F8wfx/AMP4amd88Sx5Oidbxu6tVNxfWsza8BICkpibZ487G9w2wawQDax54HBJhV3rqqk57i5cYTxpDokQmEuoCfoRsOH9oIJ6ojLyWY2S+ErvWiW6JoZ8wMg0Svwm2nTGRkTjJqwDyyok/UAx8H0j6a8tgZlsfNxQ2s3R8fHSrE6Yzrugkem9pD/OebG/nz+1vRrAZy5ld7Bo8JHoX/uHAav7/4aNyy9OMDj1H2UdN0BLfE8j3VbC1tBCAhKYn2tvjKYyDQSaLFx137mvhmR5ztY3+QbdscPBZEEzRmp3h59PpZ3HjiOPNt9gcazaqSopoW7l66htdWFpig0SBy/ewvsNODPG4paWB9YR0AyUnJtLS0HJCPfU4Ptre3IwgiHR3tfPPVV+TmjmHmLDPbeDjPbRQczDEMA0mSCXR28vILz7Nhww/87g9/ZNbsOQQCAV5c+jxbNm3kd3/8b46cNg2Al5Y+z7q1a7hxwS2cevoZqKrKe++8xVdffMGNC27mzLPORtc0PnzvXT775BPuvPte5s0/ybqXdEjPbH9/5qzZjJ8wkbWrV1FZVYnH66GpqbHf5shJnZ2diJKI1+OmM6jy7a5KOlo7UfxuNGcXzr5EGA9EDtAoyyJqZ4hJo9J44sY5nGwdXh1QzeimW5G6fbcrQNg9OBHP5jM2X00HzQRc6Qlu7jxtIidOyuJnL6/n803lIItmFDOg8eW2fVwwY4S56djloa2tLS7ja2ttxeXy4FJkGtsCfLV9H4Q0NEUyvQxN59xjR/LXq45l8tBkDCvTJ0uieU4X3ddtf85lT/wJG1nzTQRBwCWbPFc1Hc0wGJWRwH9cMI3ZYzO4Y8lq8ssazSYxqh7zjKJDIue1DFOXtTZ38u3OKk6aNAS/1zxoOF6BnM7OTiRRwuNxAwZr82sprmpBdsvm8wrWwJwd4w6VHDpT13Sy0/0smD8et1W6LFgR8rDcYSdZYu0V7spsxDo6VdfNSoEzpw3l1Kk5/HNDKaJb7jofsr94GcVHSRZpaGhn+e4qjp+Qicel4HK7aWluPvR7xaDOzk5EUXLo1ap+16uaYfDUV3t4aUWBVVVgZk903UAQoC2gcs3cMdx80nikbrcwEBAIqBqLvs7jjbXFJHoVAqpmDcvAMAR0Q0fVDJo7zUOk93McbnwoPD/mGtUBAipfbq/kghkjSfQquFyu+PJREvENpH3sBemWI9oR0nnw0508/qW5v0kULHk0zL2ObQGV06cO5Z4zJ5kt/XXnMA3rYAiDd9YX89g/d+H3KOYB44KAKJphBck6pqUjpFlrwOj7M8fk48DZR6c8BkIq3++upr0lgJLgOjAfoxx+XdcRJYG2oMp/v7uFts4Qv+2h+6UTPOq6gdcl8/OfTMXnkfmP1zfSFlR7Bo8DuJ56TdH2URZpburgu51VnDgxy9SrrvjrVY/bBYbB6r21lNW0InviaB/7g5y2zQKNRkhneLqfR26YxUUzR5nlpzHsqnMrkCyZHdbve2Etn2wsQ1SkKNBo3+8AD9+DXv16RyUXzBhBgkfB7fHS1tq638v0GTg21Nfh9/vYm7eXgoJ8zj3vfKZMmRLuehSer8Ox+K17mmc2ynz7zdcsWbyIM846iwsuuhhZlvn4ww9547VXOfnU07j08itQFIU1q1bx4gtLmTBxErcsvA1Jkti6ZQuPPfoIU6ceyXU33ITH42Hd2jU8/dQTHHX00Vx7/Q14vd5wZvNAjQe6D7XrM6IooqoqY8eN48hp09i4YT0bf/iBk049leqq6rhMVUN9fbi8saq5k+93ViFIIuGTK+zoTb84c3RdSzdQFJFQa5CL543l71cfS25mApquoxvglrtvrg4r40MfyUFRxL4/uo6o2FPZzCsri9hR0RgGMjqY0bi8asoa2pnsdeFPSKC1JT4KtbW1FX9iIgDFdW2szKtBcEumkTcMEAW2ljawbGUhN8wby9ghiUgi4VbO0c83UBS9lzIaoMuY5cGiIHD61KF89svTuf/ldXywshAlwUUopEc2WTnUR3Cud8OMwCMKfLezktrTJzI01YfH54/bvuN6Sx4FoLEtyPe7qxCcx8iEjXY/g2XMjKCuaVwzdwwThybR1qmih/VYF2IQBAGvIu339oZhRqQtroRJB9SgSkaim4Unjee7XVXmmVv9/UzRfDRAEOG7XZXcesoEspI8eP3+uJVVNdTXh/eoVjV38v2uftCrdkBUN0ASaWwL8vnGsu7zZpjv0xHi2Nx07KZWTl7Yf2u6wcaiOr5dWwRJHrCqJiI+ZACK45icgdQTtjNk3VLTzeYqK/ZUUdbQxmRvCr44lsd1s4/9wcdDIcH6x9Lp7YEQK3dWEk5V2KQDigitAZI9MsFTJwDdko5hKqxp5bsfSsGrgKbHfh5ZBEk0119fM2M98XGA7KPJR1Meq1sCLN9diSAJvedjN/BoOv+dqsbfPtpOe1Djj5ceTZLPFbap0eBRtMCjIgncffokfC6ZXy9bT0N7KDIAOlhBI8S0j4Ik8M3OSm4/dQI5qT68fl/c5LG+vg5fgmkf61qDfLezCsHQu9Z1POxjf1A30KiROySJR2+YzU+OGd4jaAQiQOP2skbuXbqGr7ZWILrkLv/OKY+9ee5Y8uiSWLG7moqGdibkJOP3J9Da38Cxvr6ezMws1qxZDQhMO+pokpKTCYVCEc1hBrpMFayJtoBcWWkpjz78D3w+PwtuWciIESMpyM9nyeJnEUSBu++7n8zMTJoaG3nqicdobKjn33/3X4zKzaWlpYXHH32YxoYGHvj5Lzli6lRqa2t5/rnFVFdV8Zt//y3jJ0wI38u+d1+fOzq6IMsyx0yfzpuvv8aK5d9z7nnnU1Fe3v8ThlnimJZhlqTtqWxmW1kjoksO73MxqZ8yAOAAjRKh1gC3nTuFv1wxgxSfyyq3EsINHmJF7+zX+3TrqCfoDW8MC+R2289lvS8KApVNHSz9Pp8l3+5l974mMwwsdikuSZbIr25hW2kjk4emkJSYSElDXZ/G3lvq6GhnSHYOYHZvLKxpRZGliLMrS6pb+eO7W3hrXTEL5o/juhPGkpXk6Spnsz4XHenqDR1q6WasfZTO8mDDMAip5v7S5xcez88T3Sz5bCdKgoeQFQnvv3JHIfxL13VEt8SW0gb2VjUzNNVHYkISDQ3x2ecYDAZIzzAPcS9v6GB9fi24pMgzxvrlGbuTbhggi3y0uYxVe2usrAbh0hdJEmkPqEzITuKvVx/LiHR/t2vYNmx7WQN/eGczVc0B3LJZ8maP3Xai2gLmGYF63Eq0IvkoKDIbi+spqmklK8lDYkIipcXx4WMgGCA90+Tj7n39pFe7ZSRAdEkIVtmpHVIzMMsMQ1Z290B3k2URrH3HHYJg7mNzftggXCYek0/xtvGCQ4NbejWvqkuvJibGj4/d7GNpnOzjgShiziMBhuiSu7FA1w1cskjAreOSpQOOUBbNNeB2ywRVPRxMtNeVgdG9HK6vfI/Bx70DZB+d8ri3soWtpY0ILvNkgF7zMUr+DAs8agY8+vlO2oIqf7liOhmJnvA+NPNRu/wYGzxKosAtJ43H55b42UvrqW7u7A4e7XsOOoqyj4rE5pJ69la3kJPqIyExibLiwrjcORgIkp6VCkBpfSvrCmpBkcw9uZFOXlzu3ydy8tDiqSgK6CGdCcNSeOyG2Zx+5FA03ZS36N4atj9kg8aNRfXcvXQ1K3dWm6DRboTlsAd90kXR8qiI5FU3s6O8iQk5ySQmJdJwAHnsM3Ds7DQPiFy/djXp6RlMnTrNeobDwzAnaLSznsFgkGUvv8ja1at44Be/4sQT5xMMBnnj1VdZsfw7fvPv/8mMGTMAePON1/j6qy+54KKLOOfcn2AYBp98+AEff/gB5/7kfC66+BJ0Xeefn3/K22+8zhVXX8NZ55zbLyWqzu/Y15p65DSys3PYtGkjHR0d3fb09RepqorP50XXdTYX19PWGkD2KWZnkLCjeojRm5igsZObz5rC3646lgSPeWaf7eT0tM/OLJ8inFjq7VxHf6o3XSIFSwidgFHABIxNHUHeXFvMk1/uZnNxA6qqgSAiSNbnrecEMDpV1hfUcv704Xg8bgQhPsc4iJKMy6XQETTvR0DFkF3Y5zViGOY5UobBjrIGfv3aRl5fW8Sdp07i4mPNki/DMLq6ch1gfmw6UPOc3nzfMOzrdAHIWKUaimyerZmW4Oaha2ai6gYv/nNXDPDYD+s1HLkEURBpaQ2wqbiBEyYOwe/3UV0Vnz0cmqrh8XoA2L2vifyaVkSpq3sfEKnw+4OiorS7yhpjgzgri9XaodIZ1Lq/T1dgpaE9xLc7q6ipawOXbGUxut3YvKYVxO73aLvzWrrZHKqmsYOtpQ3MGpuO2+1ClOLTxE1TNXxeU69u6i+92o0ndumo0fW+DfQxrAYuBwbjumGAZoTPpdWEGN9xyEM3RzveQNIQrDVP1wILqKwvqOP86SPwetyIcWr/PyD2MRbtb05t/loDiHlIt0G4aZtmOPc7xrqXtQZ0cw0Y4TVgBxB6WA99Xr/d+ThQ9tGWR8Mw2FrSQHNLANkro4f0vvEx6rnt7KsoCCz+ag9tgRB/u2omw9J8YfAYXcIvOsp9r5k7Bp9L5r4X1lJW14YgWxndGPcaNBRtHyWRpuZONhc3cMKELDzx1Kuahtdj2scd5U2U1LYhy1JkN+HBMGdGFA8xKwr1kMqRo9J47MY5nDhpiNkvI8YaiQaNa/JruHvJGtbn1/QAGg/ieZ1HKhkAAlpHiA1F9Zxz9DA87gPLY5+5LMsSTc2N7Nm9m+EjhjN+wniAw1amGl3yJooia1at5Nmnn2LeifO5/MorUVwuvvzinyxd+hxzj5/HZVdcicfrZevmzdY5jtncfuc9eL1e8vfu5dFHHmbo0KHcvHAhCYmJ7Nq5gycefYTcMWO4ccEtJCYmditRPdTnFkURXdcZM3YsI0eNZP26tZSXl+NPSDjkOYp5P0lCliSa2oNsKKo3Y4u2nbCfoz/4aIEpWREJtQc5b+4Y/nLF9C7QKEVmqe1W4c79UqJAuE2x3Q2wC+wc6P4gi7H548yWOd/TrQyWvdejPajyxbZ9PPr5Lr7bXUUwqJlRf8ncnGw4S10EK0ovCmworKe1U8WtyLhcrkOaxp7I5XID5rmIm4rqEeSo/UjOzK0ooGoaa/Nq2VyyimWrCrnnjEmcOiUHj8uUX90ML3eTq+h5is7IqrrRK17YwD8WT6NLNpx/20ejJPtc/N+VM6hu6uSz9SUoPiWybPVQsJVz3Vvry9B0NhTV0RZQSfQocXNUZVnGJcuENI2tpQ0YgRCizxXlqAoH/2w9kSPSLUhiN5E3DFBkkaCmoygHvr8ogKxIoEgoiogqCuHqGOjCN3YZpZmKpH8NvkMWw+tBNYFcR1DD65JxKfGRR6de3dRfejUK4McE99Ev9SKJa/RGViKcE/veln5wRMojJzv6ewdJAl1r3jDCenVjUR2tnSHcioQSLz6KXXzcGE/72G2e7HnsrmO77tsLXdvb28e8UIzrOx3VPq9fYvJxQ1H87aMkm3xs6QixuaQewdAdykjom+6JngPDQBcERFnk1eWFdAQ1HrpmJqOzzCNy5KizHs3HFtCtLO7FM0fhdUnc8/wa8itbEBQLPB5Kdjee5Jwv2//SjbB9TPAoKHE6w12SZRRZIhBS2VTcgBBSweUCja4gx+Geq/2AxmPGpPPEjXOYMz5rv6DRsORDlkSW767iriVr2FJUj2hnyZ3XP9jntY2wvYYttb3J4qNLlsJ+ZU/UZ+Do8/koLSmhrq6O4+Yez9Bhw+N68GdvyR5DdXUVDz/0d0RJ4qZbFjJ6zFhKS0t5btEzdHZ2cOe99zJ8xAja2tp4bvEzFBTk8z//+zcmTppEMBjk6SefoLiokF/95t85duYsmpubWbrkOfbm5fHH//4zRx9zDKFQKCwg/QmSdV0nLS2d0WPG8v1337Jnzy7mzz+5367vJLcVvWloD7KjrBFDFK3SsX66gSMDJ0oiakBjUm4af71qBhmJHoKqFm6GEiviYjfNkKwoXXN7kB+K6vl4UxkbiurIq2qhtqWTzpAe05GyzywcNiSJbX85n0SPEm4U0VPm0QaqkmgauUBIY01BLY99vouPNpXTHjCzTYJo7hvSIxCaIxppALLE7somKps6SU/04PH5+mliI8nr8wKwr7GDPVXNGNZBuRHRVLoyfKbDB4Ggxmdbylmxp5qfHDOcu0+fxMwxGeFDpJ1ALhaIFBzPWt8WZMqv3qOmvi0yKuYkQcCjiGQkepiQncSM0emce/RwjhmVSoJHCTcRsCOz9r2iwWNA1chO9vLXq2aQX93C3oomRFlC16L2PB4qCVYAQRLZXtpIc0eIRI/ZWCUe5PKY123tVNlW2hhOoJpjsZzJeGU3LFk1iCFKhrmvxz6G4MCJLMHCFvbnzaK38OXCH+sBAPUrmZkO21HdVtpAa0DF65LxWHLT3xShV8sb+levHmgdOOazNzMb8zM9XpvwvQXDDKggClYWhcigTURm4hDXbTRmlSV272uOu1518nF7PO2j0wl0OJ6iQFjuBEnsqggRBOx1HXMwfVwDMSkmv+LBx/jbR5fb5GNzR5Dt5Y0YgmDqA8d4+kSxwKMBkiLy3tpiOjpVHrp+NlOGJYezRt0yj4JZAqzpBmcfNZxFC2Xufn4N20vqzaYnPYHHfjJth0SOebPt47bSBpo7QiR4FLzeePHRtI8tnSrbShswBEeA/HADRidFg8agypyJWTx242xmjM4Irwno7t+Y/qeBLIr8c2sFdy9dw57yRgs0dgelhzxO6OKjLLKroonalgCpfnfYr+yJ+lwf4Pf7yd+7F03TGJWbi9cqyxlosjNT9t/mobgqr77yMsu//57Lr7yK0888g1AoxAfvvsPnn33KTTffyty5JwDwyUcf8P6773L6GWdy/oUXIYoiX3z+GW+/+TrHnzCPK6+5FgOD5d99y8svvsBZ55zLxZdehr0X0b5vPz8VAKPHjEWSZPJ27cbv776fqD/I4zEXRmVjByXRpRL9RWEFbSC7JP7vqhlMzEmmM7R/0GgfcioKAqV1bfzvh9uZ/Ov3OPX3H/P3j7bz9fZKyuraTNAIXUrc8SPYr2O2mY+I6cYAQ5q1hiVRQNUMNhXX87NX1nPhg1/zxuoi2oNqV5Tdfrb9PLcoCVQ2dlJaZ3aL83nj46jairqkro2qpk4kaf+KJZypteagNaDy6spCLnjoa3756g9sLW1A07r2aWi6HrHOu5WnCnSVi9jB6Bj8AOgM6ZTVtfHVtn389YNtnPS7jzji1+/z4Cc7KK9vN3lutTC37xVdBu6WJTpDGkeOSOXPl09HlEVMp4Z+ByKGde5YUV0rlU3mmXHxMowet7k+mjtC5FU2gxTtqMbBONpOqE02WnX+9PZSzktGZyxiXdd57TgbfttRLahppbbZbN5g67/+prBebeigpK49Dnq173NlxPiPESur1OMFutahgMHpR+ZwzjEjkEUB3WpwZAd7IoIRYae3D/c6wDhESWBfUwel9fHVqx5vnOyjA4BHyEHY2TRtl66aZzOedEQOF84Y0XUcSvizvVgHBr2cd+eHerpuP8roANpHWx6rmwMU17aBLB26mXDyzuGzSIrE55vLuW3xSjYV1yNbR3jFAgh21Y2m65w0OZtFt85l+hjzjNsIWQrfi7jYuEMh2z4W1nbZR0+8/ByLj/WtgS77GL2/8bBRNNDvAo3zp2bz7C1zY4JG+7ezuk4SRT7eVMZtz61iT0UTgtIDaOxPm2mYyZ3yxg4qGtoB8B6Aj30Gjm6Ph4L8fNxuNyNGjjy4gfYTRZY2imz4YT1PP/E4M46dyfU33IjL5eaH9et45qknmDHjWK68+hoSEhLI27ObxYuexevzcfd995OSkkJFeTmPP/oIXp+PO+6+h/T0dEqLS3j4oQdJS0vnloW3kZaejqZpMR3aQ6GuLI7JjlGjRuH3+ykoyI9bJtdjRVTL6ttobA92Kav+INuwGQYel4zeEuCBs6dw3jEjCGk6HiWyrDkaNEqiQEdQ5blv8zj2Pz/i315cQ2VjB4JbRpDMksv9Ck6Uct3fRzW9S2AB8iqb+dN7W/jJ37/i8c930mgfMAtdgNEZcXT+hJ0AsyQlFFApqm0Fw8Adp0yV2+3CMAyKalrRAxa4tR9/f+M0uoqUBFGgvjXAw59s5yd//4o/f7CVgqoWBEFAEs1yXG0/TlMYLNi0P+MmmPwTJAHBLVNe384vlqxm5u8+4sXl+XSGNOv8K936eOSZpwAexWz+c+msUdx9+iT0lgAelxyx7vqLRFGgvi1IueXg2HLT32RHVGuaO6lobId4BHJikQ30ISbYj6CDHU6sNShYNx+IaLEVWKxu7mBfo+ngxEsew3q1IQ56tZ+pz+wUBNAM5ozN5K375vO/V85gyvBUMxsd0k3Q43ze/nJ6HXpVEARCnSrFNa0YcdSrcbGPNgA3ojJJYOlaE4gbms7YIUn81yVH8d79J3P61Jzwmbx9ut3BDfKgvtW7Sw+8fbT5uK+pg7rWwAEDq70mO+gWDR5dMst3VHLrsytZlVeNbO1T7wk8SqIJLueMy2TxwrkcPyk7NniMBieDhERRoK41SJllH93uOG3JsfhY2dRBZXMHoiT27zxEBXB6fC3W9wyHXyWYCQ89pHH60cN4+ua5TB2RGjPTCKbc6zZoFOCddSXctngVhVUtXcEqIUqn9pvNjEwItHeGKK0zgeOB5LHPwFGSJEpKSvD5/eTkDDMv4ixLGWCy9zU2Njbw8IMPEgwGWXDLrYwbP4HqqioWP/s0dXV13Hbn3YwfP4FAIMArL77Ixh9+4I677mbaUUdjGAZLnlvE5k0buerqa5l34nw6Ozt56YWlbN60kWuvu57jT5gXs4tqf1D0vrGcoUNJTEygoqIibgequl3muU2lde0YQa074DhUMszuaZ2tAaZNzubGeeOi3o6MuEAXaNzX2M7tS1Zz86PfUtcaQPAo5sHG9k+szEXUmI3wPzh2jXSRrhvhWnNJNDObj/9zNxf942t+/8ZGyhvaEa2mMkb0vXoSXIdQ2yCzuLaNkGagyPHZNK7IZje8ktq27lmc/Y3TJuv5DCvqVFLXxm9f28BFD3/Nk1/spryhPTxHqqZHledalyMqcxWLHHNoGJG8FL0KVU2dXP/wN9y9dA1VTR3mIdRRmUdnlYFNN88fz+QJWXS2BZBkiS4v9RAoylHVAxql9R2mo+qKzx4Oe49PdXOA+rZgOOP7Y6Pej1o4ZDYdmCINY2enRoUFHOU4yWPc9Wqvaf/3MkUxOiXVC4YI5lm7HpfMA+ccwTv3n8Sdp08kK9WLHtQwNLMLb7fouBMo9XUeonWZpVfVOOpVm49l/cHHbvMQ+bpk2RktqJGW4OaGE8fx5k9P4rcXHUWSz0VA1Q/SVzQcexh7GHO3l+MolIfBPrpcCmCwr6GNUGdUYPWQQbLRbZ1ruo7oklm/t4ZbF63kq+379gsegXBm8uhR6Sy6dS6nTxtmgkdnoNcpO4cbPEbZRyOoUVLXjmEYcdvj6LKuW97QQWt7KDJA1S/BDiFybiMyiELsW9iBajsoZmWR9ZDGT2aM4KkFxzExJxlN160zwCP9GRs0moEUWLaqkDueW2WC8Ojjb+LBc8NeU+ZjoOqU1beh6foB5bHPwFHXNGqqKknw+8nINNtVC/YxHAMQPY6efDD3Br62bBlff/UFF158CeedfwGapvHJxx/x3jvvcPW113HKqacB8M3XX7Fs2cucMG8el1x6OYqi8O3XX7PspZeYdtTR3HTLLQiCwNo1a1iy+FmOP2Ee19xwIxDZACg+GUfzeukZGSQmJtNQX0d7R3u/3COaZMnM2lQ0tiP0FnD0hhzXkkUBdIMb5o1l8rBkOoJquBmOEyybLb9N0FhS18aVj33PC5/vwp3oQVP17pmXWJmRHsYsCGZgo8tWG+b5gKJ5XmBNcycvrSjgqie+557nV7O9tBHJJYFhdAdJB5qbsAHp+rOioY2QrvfbWolFQVWnoqENBIGIovEDZP6igbC9t0JySWwpbuDOJau55onvWbaqkNqWTmRJRBSFiKM+oOskkv3q7/1ks3TdPFzZleBm8Sc7uPbJ5VRYgDUaLNrzqEgiHUGVaSNTueb40aAZ5nqzIwaHomSjHBzBMOdX1Y24VQBIllxUN3Wgh6Ic1XhmAfpA9rHh+yXBDiQMAnIYRkEAdJ19De3htvjxoLjp1T5SLzhl8bOPDpgD9wRUjQk5yTx+4xyW3nYC58wYidcloXWGzE7Ull8Q0/HtCx0Gvdp/fHQ6fZEOYLg0vzOEIgqcfORQnr11Ls8tPJ6jR6URtMqAD/YJe6MCjYEU1cPER1UzzIolI6rD86E+fASo6/pb13REl8T2kgYWLl7JRxtLka2tB7G25hiGEQaPk4Ym8/TNczn/2FFWuXL0fegZSPSUIetN5qyvz+0MAOg65fXtqFrX2dD9TbJkNofZ19iOoOnhJKw5iP7wV439z7NA5PzZ70WDRlXn0jmjeeKmOYzJSkTTdSRR7MbvMGi06Pnv9nL30rVUNXeaHce7AdeD0JsHIiH6mgYVDSYfDySPfQaOgUCQxsZG/AkJpKSkmPc/DBlHI6x8RbZu2cxTTzzGpMmTufnW23C53ezcsZ3HHvkHkyZP5trrbyA5OZmy0hKeW/QMoWCQe376ANk5OdTV1fH0k4/R0dHO3ffdR07OUOpqa3nEarBzy223k5OTEy5RjQdFgCjdIDExkcTERFpbW2k7wEGch3BTVE2ntrmzu5NxqE63AYoiEmgJMPOIbC6YMQIgvK/RvIVTCE35q2vpZMEzK/huSzlKoodAQI1U9H0w2g61Fi77UDXTQCmSSEtHiA82lnLL4pXc8PQKVuyoNLtBigKa5tg72Zdd9I6xGdZD1TQHULX47gEOaTo1zQHLdjk8uwPNldPgOT6vWY0vJFnk260VXPfUcm57bjUfbyqjtTMUPl/Rfi5R7N6N84AUg5fBoIqS6OGLDaXcvGglje3ByHxIVJbfXk+Xzszl6ElZBFoDKIpInzrlxSLHPQzAMARqmjtRNb3LGe5nMjOsOrWtnWbjkYjxDBIg1gsaNKARIgyjneGoaemMbOHe7/eMk17tK/XmVt3WVe94Zzs8dhBQ1XTOOmoYy+6ax/9dNYOjx2ZiaDp6SDXLswQzgNgtot9biqFXq+OtVy0+Vh8qH51lqQbmPAhm0FIP6ehBnckj0/jjpcfw+j3zuXjmqDDAUKxubnGtWB/ImFRM+9gZVz4KFh9rWgJh7B4eS6zjZ/p+g66Mjb22RQFdMxAUifx9Ldz23GreWFMU3vYRnXG0/zYzkzqjsxJ44qbZXHH8aHRN7+rXsL8ATCygETNz1g/P7AwAWGOrauqIq14VRQFVN6hp6oxKJBwiH2PMjQBdGc1YwC3G3zZovPr4MTx6wyxGpPutCrqeQaMZyzR49us9PPDiOupbA93P9IwXaIQu/R9+dIHaloDZIf8A1GcvKBgI0NHegdfnw58Q2bgl3sdwxIrWtLW28vBDD9LY0MDNty5k0uTJNDY28OzTT1FRXs7C2+/gyGlHoYZU3nrzDb768ktuWXg7s2bPAeCVl17k+++/49LLr+C0085A1zReeelFVqz4nssuv4IzzzobVVXjlm3s/ow6Xq8Xn99PKBSivT0+GUcwj1BoaA2CIETqk35wukVBAE3j1CNyGJuVSEdQ63Ywrv23feufLfuBLzeUIvsUQiHVjLzY4+njmJyPo+q62R7bOtLhqx37uPfFtVz1+Pe8v7rIvLwioqpRHVptEGZHlvpKgkBje5CQGl/gGFR1mjpDB8e38PMJ3Z5dU3UE65iOt1cWcOXj3/HTl9bxzc5KNN2OkhpdQPtgyMlbUSQU0pC9Cp+uLeaXy34wO9CFA6aRBtfcC6sxMSeJ+ZOGgOMA60MOfoAjmggNbcGYZbr9SZpu0Ngegujp7A8Hp5/ogNPqjLUcbnIAI9slaGwL7ne/bn9QXPTqIVBPd+3WIEcwX7XfPVB1o7PUTtMNEr0Kd58xmdfuPpH7zz2CYRkJqB0hMAxk2ZF9hIMDkOFxDoxeVXWDxoPlY9hpJ0K3SlYzL7UjRFaKl9vOmMgrd5/Ir84/kowkT1QnTvNSusNGdl3fcZ+IFyL5c6DZPayaRRBobA/FnY+abtDcbm75iQwA9JM8OrNfDvBoaAaCIlJe18bdz69h6Xd7HUcOxwaP9haNYWl+Hr1+NgtOHo+h69YycoAIp73uCRw6P9efACQasAINbYH461XNoL4taN42PBb6KeNoX8+cp4g9xbHm1fG6COiazoKTx/Pw9bPITvGFq1p6Ao2iIBDSDB79fCc/f2U9jR2hgQWNEGEY7EBOY3vv/Jw+F5YHg0GCoSAetwe324Ouxy8K76Ro0Gjf9803XuOfn33K+RdcyMWXXo5hGHz5z89549VXueiSSzjnJ+cDsG7dGpYsepZjjpnOFVdfjdfrZd2aNby4dAmjR4/htjvvwuV2s+GH9Sx65mmmHjGVm2+9DVEUI54vnqARTIFQFBdejxdVVeno6IjbvTTDoCOoWpEjg0N2+GzQKAkEAipDhqVw0qRsoAsDdvsKJshctqqQpV/tQfa5UEO6eYjiIQiN81sJbhlFklhfUMsrqwp5ZVUhVdWt4JaRPAqaqnd3eA+Rx4YVhWwPqAS1+CrUoKrTEVC7Ozh9pWjQJWAeCm0YSB6Flk6VxZ/v4uPN5Vx7/BiunJ3L9NHp+Nz9sD/FYXBVTUfyuXj2i92cOW0ol1hReFHovq/YXlenTsnhlZWF1DSZG+cjWpofAlmBONoDanwj/5jRx7bOUFckFyIj2oeZjKi/Yw1JOKQF2M8kOH5bw2rpjD8fNcOgPRjqP70aBzJw+p2WrOjOd63fUQDFPnM1muygYEjTmTA0mQevmclZ04bx9Je7+WRTOR3tQSSvbGZcNCv1E8sxO4C8DqRe7bN9tIUiXPVhowQQJAFJEFA7Q8humdOPHcnCUyZw/vQRZvm/qqPIYriBRsQ4dGu7hhB1s1jg0eg6b3IQSWI3Mvko0B7QBoSPbbHsY3/KZbTv4ASPskh1Uwf3v7SO1oDK7adOwF4bsc4tlqzO4plJHv5+9Ux8iszjX+wye7CIQtfet1gAIzow4/xbsBRhP54HbNvHloAad+CoGQatnSFAiFzbB/ss0ZlGwZzbS2aNorq5k++2VCD5FDMw3gOgM8Xd4I7TJvHfl08n1e9Ct04F2B9oDKoaf/toO398ZwudqjbwoDEWCQJtQbVXGcc+e3yqpqGqKoqiHJazG8Nn4YgiO3ds56knHmfEiBHccfc9eL1e8vfu5R8P/p0RI4Zzw4KbSUtLo6amhucWPUttbS1/+J8/M2bMWFpamlm86Bkqyst58JFHGTNmLG2trTzyj4doaWni3//zd4weMyZ8PmS8AaOTJElEVhR0XScYCMTtPppu0KFq/XdBQQDdMM/ba+7kmGOGc/KUIWi6EVGmGiFMmEcQ/Pq1DQguySxbEQ9daJxR1y2ljXy4sYxXVxeyu7geZBHZq6BqOppqdFf6/UWCQFDTwx1C40WarhPU9P4zhDEApA2uZa/Cvvo2/vrOFj7cWMbVc0dzxpFDI+r1D+m+Ft81TQdZ5NevbeDso4bhkSXMJpzOTsoCLllC0w1OnzqUqcNT+HpfM0qKl4CmQT/uY+tUNfP8rziSYUAgFGOtDBLgIfTwd+SHBslge6BgSOsWfOhv0nWDQJyzKH2hnkC+vWdVEgV0WYoK7kV+I5yE14SYHUadZ63qhrk//IwjhzJ7bAavrS5i8Td5rM2rBgQUt7l/EBsM9dVZGjC92gf7GMvpswCkrIioQQ1V0zlqTAY3njiOa44fQ2aSx+zqrRsocveOizaJgojbJWFYjThiL1/DGjNIknXIvA6DGj0KENS0uPPRMMz9uAM2GRYoDoNH69iKhrYgv172Ax1BjXvPnIwide+y6gSPumGQ4nfx5yun43PLPPjJdlTdkamMBR4x7y0Yph9mCJgZ3YhsZT9HIgWBzqDWK8BxKKTrBh1BLSIQeEjkkDNBAEPVSUtwc/tpE8lJ8XLZw9+wo7TROluzO3gUDLNk896zp/D7i48m0auYwPAAoLEjqPK/H2zjz+9vtfy2QQAaLQqqeq+OV+wzcIzVqCLeFK1MDcMgEAjw2CP/oLysjP/6438z9chptLe38ezTT7J3bx7/+bvfM2v2HHRd59OPPuT9d9/hxgU3M/+kUwB45803+fijDznvggs4+5yfAPDmG6/x5Refc8EFF3HBRReHQaP9rAMHHs225oZhoBnxU6qGYRmX/nokp0UTBCYPTcYlS7QH1HBWKrI0w6xdf/67vZTUtoa/1x9Co5tnbFDfFuTWxSvZlF8HGChehZBmmGWpzixjnPiqOyLA8aKesgCHTBEZAfNPVTWzwYpXYGdJPb8tbeDtdSW0dIRAEvtnHLYx1Q3yq1tY+n0+d5w6Mdx1N5oCIQ2fW2bS0GS+jsgy9ZPyFUAzInVfPMjMAhmD29n7kZKdMYo3+Acs4MRhBfwHekpzmQkgiYRUHb0tgC7YCnE/39Z0mjtC4WvYd3PaRVEQECWziVayz8XCUyZw0uRsln6/lxe+z6essgXRIyPIghmQcmYfo7OQPdBA6dUD8tHpwIMDNIIoiQgYqO1BMtMSuOb40dw0fzzTRqYCZnZWicowRnTdtF5r7QwRaOo8QCDMAgQ66F4FJNEqVjjgSjjAA8aXBoKPA05O22P9NnQDQRJoDar89o2NtHaG+NV5U/G65B7Bo2iBjQSPwu8vPRq/W+bP72+hM6QjSEL3oxqwOrR2hJicm87lc3J5eXk+eeWNZmWVndntz4yjdUm7a2y8Se/PaIgjNS+LAqGQxlGj0hiblUhuZgJP3DSHO5asZmdpgwUejTA/BdO95OfnTuW3F07D55YjqqJ6Ao2tnSH+8PZmHvpkh2mLBglo7CpaOLDGgIMAjl3RxoFZKN1LVDVEUeKdt97k4w8+4NTTz+DyK68E4NtvvmbZyy9z2mlncOnl5ms7tm/jiccfZfz48eEmOdu3bWXpkufISE/nrnvuIzExkd27dvLMU08xbOhw7rz7Xtxud8TzDWTGEcNA181mPLIYv6yuKJjHLPSnoyqIgunIp/k4elQaQMy9jQA6BiICT3612wQLUoxzaw5qEFZEThLp6FTZlFeL5DEVdCg0MIDRJlEQ4q4D7DPA4ngD87cNIA2DUMhAVGQEATbm1YAi9kum2Fac5vEg5rlmT321mztOnWh23kXoppTtZ5+em44nxUdnUO2KyvYHGeYZS/GWf0HAOqM0rrc5aDJwZPKJPUxhkA5fwAxUSQOgw0VBQDC3sg1aMotDDAiojB6eyshJQ/AokuW42ljIbPogCkKXHjPguHFmN/WuDoqRe7Vs6mqiZTAhJ4k/XXYMp07NYdFXeby9voRARxDZo5iZiojsIwcEkAOhV/drHw3oar5kSYMd9BEFZElADaiIisgFc0Zz6ykTOGPqUBTZ3Gcvi0I30Bg9h/bzHTUqlUtPHo89z3r4x/yOARi6+XdQ1Sirb2f33hqzmVdvJukwCuxA8BEsH2Qgm4zFAAKGlS3sVDX+570ttAdVfnfRUfg9SjfQ4QSPhmHgUSR+c/6R+NwS//XWZtoCaiR4NAwEUUANaWSl+/nT5Udz9rThbC9rIK+kAVEQMHGjbcP7R1F3iasxQMuoHxFvmDcWcBIFZoxJJzczgc6QxvzJ2Txy/SxuX7yK/MoWBEU0P2cYuCSR31xwJL/8iQn+9wsardLVpvYg//nmJh77fKcps7GyxXBYMo02HyVR7FWDuz4DR1mWURSFYDCEGlIHDEx1lahK7M3L4+knnyAlLY1777ufhIREystKeehvfyUlNYVbbrudzKwsmpubWfrcYgoL8vnrgw9z5LSj6Ozo4KWlS9m2bSv/87//x+QpUwgGAzz52GOUFBfxX3/4E1OmTg3voRxQwGiRpumEgiFEUYzbwbhgBgE8cv8+myQKqJ0qw3OSOWJ4ivma1R3OuSANw0AWRX4oqqO4tq1/nSynMIoCkkd2dErF8TvOfLUUjBTnPcCSKOKSxPg7qraTZPlJdkmD5JHNTGM8ImaGQWFNG1tKG5g2IjWmPMrW+jpyRArZKV6KypsQPXJXhLUfyCuLcQcdggAeOcZaObxJgS7qxXQ6YzKDkdwWOIoniaKAVx74bRy9IZuFmmbgUyQuP30SvzpvKhmJHtyy2NWEg64ggA0msf52y2L4fbDFvctZIuIaAoosoOo6AgKnTMnhmFFpnHXUMJ76cjerdlWBJCDJUtdeInugNoKNXv8GuCQp7nq1R/toj8cQnP8BzOY3mqajdqocNSaD20+byMUzR5GV5EE3zE7UBwKMNtn/O/uo4Zw8JcdSvYZVBWtE7lO19HJA1WloD/DY57tobguhWjowVgwt/P3DFeAYIPsoigJeq8nbgCqnWODRsl+qDn//aDvtAY0/XXY0KX53j+DR/q3IAvefPQWfS+bfX99AY3vIBI+aYf3WSUvw8PdrZnLB9JGIosDxE7J4Y3WRaavDz25lp/txMlyyFPdeJ4Jg6u9+Q7z2nxbgTk3yhoNislXxd9rUoTxyw2zuWLyKkro2JEnEJUv87uKj+OnZU3Ar0n5Bo10lVd8a4DevbuCZr/eY1YTmhyL9pMMAGCPIMPC6pLA/tT/qM3B0udwoLhednR10BjriWsLZLUOlm/W3Tz/5OHl7dvHb//oDR0+fTigUYvGiZ9m+dSv3/eznnHjifAzD4JuvvuCVl1/igosu4ayzzwHgs08/5Y3XX+X0M8/k/AsvQpJk3nn7bT784D3mn3QKl195FfYeylhdXONNgiAQDAbo6GhHVhQ8Pl/c7iUJAl63YgWh7Lr5Q7umKAig6qQnuBmV7g/fB0zjZoNH3TCQBIHPtpSbNfjiITZ2cVKUwu4CEJaBHwBe2kPwuWUT1MWRXLJolgIbRrfgVf+T0FWKY81nxPEl/XhzwwyBEQhqfL61gmkjUq1urpH8s6PquRkJpPpcFFmR9v7YvWs/qtet9OeWyZgkCQJ+j0JEiCUsk4MEjh2QvftxtAeanD69df8Ej0Iv7OIhUTz0an+Rrfrcisjdp09iWJqPjERPn6/Tk22Mdpxski2nMqTqpPrdXD9vLHPHZ/LKqkKe/WoPZVXNiG7FCkjh0DFErH9TxRj43BKuODOyRz4KRpccOLI9IqB1hMhM83HT/PHcMG8sU6zgaUjVkSWhW/Mbw9Kj0SbJOb8eRcKj9D4QMSLdz9+vnkleVTMJHtPFi7Uv1bxR1P8HaJ0OpH2UBIEEtwJEZcUGQi9FM9YJBhF4/J+7aAuo/OXK6QxJ9oaBRk/gURQEbj9tIn63zC+WraemqRNJkdBUjUSvi/+5cjrXnjCGQEjDLUrMGpvB6MwECqtaEN0yum2r+7FcFcPA75LNc5TjSJIg4Hf4OV33p+/P4ghQSRZwnDgsmem56YCdCTeB3zlHD+eh62dx55LVNLWH+N+rZnD7qRORJaFXoLGyqYNfvbKeF77PN6vq7Ps7/aXDDBrtipwEt9yr6rU+A0e3x02C3097WzutUWcM9ifAir6Wvd/w/Xff4Z233uSkk0/liquuQRAEln//HS+9sJTZc47j2utuQBBFCgsKeOShh8jOzubGm24mMyuLgoJ8lix+FllRuOue+8jMzKK4qIhFTz2J1+fl3vsfIDEpCbv5xuEgQRBo72inra0dt8uF3+8/8JcOkmRRINXvjiGIBwmu7K/pBkk+hYxET7hbHERGVHUDJGBzSQOqatXsx3PK+zsT1hsyDFJ9Llyxskj9SC5ZJNmrxBsxRlJPZRb2e/1ABmY0MBjS2FrSYL4WUdHRldUIqTpDkr0kemXQdRPkHaxjEH4uwr/TEtzxLQfGNGApfpdVAuccTz8a+ThTTNYfrrE7ouoCgAEpfqVnJ7qfyNSrrv7TqwdBMTNMRtcB3V5F5ihrK0FQ1Xtc2zHZKRDl0Mbyj3soX5XNjse6YTAuO4l/O/9ITjsih6e+3M3ra4oJdITMsvcw62JlHG29Gt+sbs/20ZGxsYCfEdAwXBIXzx3NXadP4vgJWbgVqevsYLl3WUabogFDTxnDWGQfjWI7wc6SxwiQH/4n6qIDJa8DZB9lSSAtwazc6grI0e9Zt/2S017asiGYjaae/zaPtoDKg9ccy/Dw+X89g0cMuGHeOHxumQdeWkdZTQs+n4vfX3wUC08ej2EY4TmdPCyFY0anU1jWhOQRrI7Sh1iu6pg2G3Ak+13xt4+SQLJfse7roL7yMapM1S4vP3a0Wabq3HssWV3eL545ipBq0KlqXH3caFOP7Rc06kiiSFl9Ow+8tJY3VhUiypLZuHqwgEan4rZ+9dbPOaiMY0pqGvsqKmhssJ25rgUeDzIMA0mSKC4q5JmnnsTlcnPvAz8jNTWVmupqHn7w70iiyMI772LosGF0dHTw0osvsHXLZv7jd//F7OPmEAoGefP111ix/Hv+7T9+yzHTZ6DrOs8tfpatW7fws1/8imOPPRZdNxW9qVQHJtsYHb1tamqipaWZxMREEuIFHA3zHL7MJE/38rJDeF5BEEA3I1BgnqGoxDgu1JaZguoW09GX5P5tBBQWCKH7a/EihyDaCjUzydOr1P+hkCKbfMRwOGx2xD6ezxyvObaVqpX51zSN/JoWgB67t9rrzKvIoB+izEY7V5it0SVJ7FXHsYMh29BkJHgQosuOB9LB6Q86nM5oxH1tr6DLSc5K8oSzX3G5paVXs/pZr/YHRTZeMRu/CHBQjntvqnF6Kl8VRQER69gdUWDuhCymDE/h7KOG8+SXu1m9t4aQ2Y2KsJNLd70qxVOv7tc+2hlHc2GLIkyfkMntp07kghkjyEj0YO7t1Lsfr2GVmx5IP3VvmNL7odsdOQ3D3HbeE5/CjxDxbL2/T5/pMNhHXTe3xGQle7oOdTcHM/ABuSjwiGGgCwKiLPLGqkI6gioPXTeTcUOSzHOnHVVvkTw0j8i6bHYuHkXi317bwGWzR3HPWVOsFWmul6Cqk+Jzcdz4TN5eV2x2rw0/r3DwdiUcNLHZaZCZGN/Aqr21KSPRY95TCL9h/qcvt3badyvbmJzgYe6EzPC97N92DwXDMLh8zijsRjexgjHhMmRL7otqWvnpi2t5b13J4AONznuH49QGQ5K9vbKPB9UcZ0j2EPL27Ka6uhogLvsBo7ONgiCweNGzbNzwA7/59/9g+vQZGIbBi0uXsH7dWhbcciunnnY6hmGwZtUqlix6llNOP50LL7kMUZT4dvk3LH1uMXOPP4FLLr8Cj8fDZ59+wluvv8bMWbO49oYbEWMcuzEQJarRC6+2poaW5haGDRuG1xufUtWQpqHIEkNTveYmXZsOcUELFmCxnZGeu2yarze0hQ6fY9nf5BRE689hab6wAYgXuSSRoak+E2jhOI7tMDuq/UECgG7Q2Bba7+fsZeaS+2Gvp4NXAoAoMjTVa3ZeU9VDvHhs0jQTOGYle5AUOaq8cfDw8UA91wbPSOly8G07LYvkpPgQRSFuRwColl7NSfX1q17tD4q2bbEcvd7acGe2v7efjb6HbDXPsR3cq+aOxqOI3LO0hfL6NrNtoeEwDg69OjTNhxJHvbpf+2g577YDmOJ1cddpk7hx/jgAgqqGLMU+k9HEKv07v+awusB5OLMsdH89JngcKDoM9lHVNFyKTE6KF0MSI5bTYQnIhUGDAzwiICoiH/5QQntQ5dHrZzFleGq3wIOTh6LVnPInxwxnYk4SOSmmfdJ1w2zMRdcczxmXSW5GAkVVLUhuR7+Hg844dgXkrDsxNNWHLPVjQ7ooMvWqTHayF0M4xMCqY4ySKKCqOmNzkpg5JgPoKqvvlvUHq8FR9/dscG/zbM++Zu59YQ2fbSpDVMzmOTFB40BXwTkp2s8RRFMeJXMd7Y/6HGpUNY3c3NG0t7dTUVFu3X//ZReHQqqqIssyn3/6Ca8ve4UT5p3Ilddci6IorF61kqXPL+GIqVNZcOtCFEWhqqqKRx76O16vh5sW3MqIESOoqKhgyeJFtLe3c8fd9zBy5Cj27dvHc88+TWdnJ/c98DOysrIisgmHo1TVsO5fUV5Oa2szQ4cNR5L74XD1GBQMhhAFgRFpPlCkSEf1UPkYQxZ6ms+AqkXXHx7avQ8n2crBJkFgVLofRYof4AipKi5ZNPeTRjs4h2EN9ztZjxSwzlOLfiJndBC6IqCHdk87k2qVebklRqT5MPcfBw/t2j2Qfd0hSW7S/IoZcPkxiYIj+Tx4RLhrIIZh4HHLDE3xAqD25/m1DurSq97+16t9oP0B/P+PvbOOj+M69/czsLwrWDGzzMxxmJmZOY3SpnT53l9vb297y2nStBtO0zRNmqQNNNg02DDHjiGxY5BMksXS8g78/pid1Wot2ZJjgZ15Ph955dXszNn5znnPec/7nnNGEiXc4/n10U3nyDzeXB1Uxxjs2dYV4nt//ph/euhDWvsiAys0D3zAKB+AOPZ2dbftY0aZesIJvvfYJ3z3j++zoa0PuywhQCoyMSiCMYoyjOb+pmd8ZQ587+4841pXJ6B9NO1qaY4br8tmrEI6UIAxueYeEZLRvjRHQtN1JJvMyyu3c/Xdb/Hhpk5jaw11cJ80cwBA16GxJBufyz5oD0EgNedwVkUus6v8oGgD8/R1Pelw7U35TccHo6mVRcrzPEiiSCIxNjrGTB1z3cjOLzmwKqS3C8bH51f7qS/KIqFqg6YyDHIMMdu3we2K+X/TaVy1tZvr7nmLv32cdBoz94A0yzCRTqNZBkj1cwS7RLnfgzH1Z/c6jtpxjEUi1NbXk0gk2NLcvFfl3RPmg69pGrIss3XrFu68/TZUVeWb3/kuRUXFdHd38dtf30Kwv5/rb/gG1dU1JOJxHnnoQd56603Ou+BCjjjqSBRF4em/PsHzzz7DZVdcyfKDDwHgoT8+wBuvv87Fl17O8oMP3ePI3HhglqF58ybC4Qh19fWo6thUxGg0CkC534PfY9/jCMOXZdj7mWaD9nsyKqLNJVNV4AVBIBqNjcklo9EYgiBQVeBFcmUY1MnTg//SDPd0pjpLY3RdVdPJ8zgoyzVSxs16s6+JxYznI9/nMq6laBNig748wogiKuOKYIygFmU5KU46jub93tek7GqeB7937O3qkAxz+9Mdi6GcuJH+7E37mJ5uZ15ZFIyNwx94YwOn/+pVfvTESjbu6DUWM0vZ0rTrCIYzZnPIVCftamzM7Ooe2se0cmm6zpad/dz07GpO/9Ur3PHS5/RFEqn5pDojj+QOxd7ok/7Z3UUvx3VscSLax2Q9L8p2UprjMhaHmRR2NRntSzkUxnQF0S7z9mc7ufquN3nz852DnMf0epuewqwm5wxnpk4KgkBcUcl221lWX4DgkAfXrb2204MdJ4/LRoU/2T6OkV0163lJjosCnyPje+wdgigYiwp57CxvLAQyx6oGbF3mdLzMYJmqGU7jx5s7ufbut3l19Q5EuzwQjEp3FCeD05iBqunkex3GQBkQ34OOo3Ycg8EQtXX12G02mjdvor+/D0nad5PU0x98cxXVP95/P2+98TrXfO16Fi5aDMCfHvwj//jHa5x2xpkcf+JJAKxY8Ql33B5gwcJFXHjxpdhsdlZ88jF33n4bs+fM4bwLL8Ln8/HmG6/z0AP3M2XqVK646mrsdnsq3Xa8GTQamOzgbNy4EU3TmDp1KqFgaEyuG4lEACjOcVGZ70VXtIw5APuWPY2e7v+xsQwdVZ3SbBcVyZVlI5HwmFzVPG9lvodin2sIg7qf3tkMozrck5lqSMeiCKIAikZ1voeipMNh1pt9TTR53iyXTENxFph7sJlfbD+JHg/dFk5s2QUBUFRqCn2pFUTHrj4m7Wq2i8q8sberw7K7W572NzPqZ+7VOJKfXRbryDi12eHaxTlNOl9isp174/M2Lr7tda65520+2NCOJoBokwbvB5vZPqo6JTkDdjU81jrmuKjM9+yqY3rHTxCS5RZZ1dLNjfe/x7m/fo2/rdxuRDBS/Zld78lQ98r826DUxFH8gDkXfGiNBjNedXNi2sdYUke/10G9aVfFyWJXzdTdgWdJSzqPn2zq5Jq73+LFVdtTzmOmU5g+B08cpk6axx/UWEBlvgctoQ5OUf8y318Q0FSNslw3pblm+zhG9TFq6FiY5aAq3wOKund+V1rUWxKM9r2ywMfSuoFtOIzjho/imxhzxY16LYki725o5+q73+Ltz3ci2qShncaBD+9F4ceGofo54T30c0btKYXDIcrLyyksKqKluYWtW7Ygil9+0YhM46YkEsiyzGuvvMz9v/8dSw86iAsuuhiXy8WHH37AfffeQ2VlJdff8A2cTic9PT3cevOviMdiXHHV1dQ3NNDZ0cHv7r2b1h07uPZrTUybNp3Ojg7uu/ceWlt38K3v/BNV1dVoqppyGsc72jhozokksbOtlU0bN+Dz+ahvbKSvr3dMrhuPGSOqOW47M8qyd+2o7uMGZU/3dPJUo71EHzA4ogCCqjKlJJuibKMiRiNjE6kyz1uS7aKxOAtBUTMcjv3+zu6WfV9X9dSLKAgImsbM8lxj1Vr2PBK3t5j10eu0MasiF0hrW9JGpSc7k+Zp0wdeJUFA0HVmVeSktieIjVHkeJBdLR97u7o36GkOhelsBKMJukMxukIxuoIxOoMxOvujdPRH2dkXpa0vQltvhB09EfoiicHOI2np4mkj8wMDwMmFWpKdsi9a+/i3hz/k9Jtf4fH3mokqmrEgFMm5NWZfwMwjMwqdYVeNAYCxsquxNB2nl+XsqmO6U5vmFAuSSFzTeWHlNs679VVuvP89Vm3pMZxzcaCjaZxi4D6ltvtInX7g/gajCXb0RmjtjdDWF6G9z9Clsz9KZ9DQqysUozsUoz9qzAU3HIkhyjpRTFD7aOroc9qYXZELmp6RrjnBFsu8fqbzaJNZu7Wbq+96iyc/3JKa6ziS/mn63839COdU+plZkQtqxvcfLXpG+6hqTC3JJi+5au1Y6ZhI2ussl50ZZYaO0qB+zghPlHZvdAAB5lb5mVqajZKeprqHxyKVPZFMD351bStX3fkmH23sRLCJAxkKk6HuDUXafUv1cyoG+jmx6O4dx1FPoFNVDZ/Px7QZM3n/3Xf4/PPPmDZ9xqCI3Widr8zRNU3TkG02Wnfs4K47biMUCvGNb36biopK+vv7ufv229ixYzs//flN1Dc0oGkqTzz2Z1588e+ce94FnHTKqWiaxot/f4G/PPII511wIUcfexwAf33ycZ575mnOOf98jjrmWKMAQuaqVeND+vXM7UbWr1/Pli1bqKyqoqS0lM2bNo3JtTVVRVFVfE4b86rzeFD6YoiO6iQwrPsDOqTv7SUKAoqqM786D6/DqGKJ+Ng4HOZ5fS4bc6v9vPLJViSBtBW8YH/azmFv2SdfLyPKIQCIIvOr/XgcMglFQdPGZm6cqqnEEwp2m8zMihxsLpvR+KR/sQleXXUSNn9DM0S0SpdE5lX5cdklNE0fswGAAbsqG3ZVnBi7OpxWmW1tfyTBY+8388g7zXQGo6nUNzNyaPhxOlqy+Iqqcfmh9fzLyTONLZUEEHRhyJVCzRRLc4P3jv4of363mVteWMtnW3tAFFL7mumwawdLEIy/ZNjVBdX+5L58YzeQo2e0jw8N1T5mdgp1PbWQjiCJ9EYU7vj75/xt5XZuOGYqFx1UQ0muG5L3WBAG9qA1MhcHb2FiXubhdzZz07NrkCQjNVHA3Gcu+crAebJddk5bWMH5B9XgT9tUnmH6N+NSpyewfdRUlYSi4rBJzK7yIzhlY57joKScCe7nmM9Rmn3QNA3BJtG8M0jT794mllA5Y1Flck/soXcxGC4qFlc0I121oYDnPtmKou6lHdIZ9MwnA1XMq/andByr+qiqKoqi4nXIzKn0GwNNo7arA+2nkaaq4XbaWD6lEIRdzc+eMDM1YorK859sZfWmLmSXbWBeanqZJpPzOEQ/RxdEFtTk4XEYC/nsaSrHqB1Hl8uJoigsWbKEF557lk9XrOT0M87aYyri8N9h17C66ck/8vBD/P2FF7jxW99m2UHLAXj8L3/m+Wef5cSTTuGU005DEATWrllL4De/oa6ujquuuQaXy8Xnn33Gb2+9heqaGi6+9DL8fj8ff/QR9917D+Xl5Vxz7fV4PJ7U4jvp158INE1DkiQ+XfkJ7Tt3cvQxx2G32/f6vu4JWZYJhyNk+bzMqcrF43EQSSgZHdUD3NvYV6Q69AMNkui0sbA2D4dNIhKNoe3LrUbSUJQE0Vgcl8POwtp8SBrwfbLs9n7EPqklg1JJQNM1fD6H0eEQBMLhMDabbV9caRdkSSYajWC3+WgsyaKmwMf6Hb1pK0uyXwwACLtuXjABhUi7vmgMdhbmuIwRdwRisejY2tVQmKwsH3OrcvF4J49dTQ3MJp2JUEzhR0+u5Kd//XTgGRuubKYTp+ps7zLT0ZJ2RTB1H0BJbi8jCQLhuMJra1q5+fm1vLBqO+g6oiymnNLB1yCtAzicXc3HYZOIRmPojI1dTW8f5w7XPpplTu+wpqfOCwKCTWRzR5B//uP7PPFhC98+fhrHzCojKzmybyyoMVBnhuqD7OyNsLalC31PKc/JW/nCJ1tZ0dzNTy6YT67HgaYZUZEJ69+MpH0co1WOZVkmEg5jy/IxvSybqjwvzR39IIp7fubHk0znQhCMFTxtItu7I1x/79vkeR0cNbPE2LppBGUeSHM2/n9wYyFluW62tAeTq6uO0gam9yMEAB3ZLTO/2o9NNnQcq6EIWZYIR4z6OKsyhyyfw4iuj8qupju9AqqqUFaSxcENxvzG0W7vY6bcO2SJb50wg43tQR59ezOiGXHMdGgneoDCJLOfo2nkZjuZW2n0c6KR6B4d3VGnqubm+unt6WHBoiXYHHZWrlhBe3s7NlneZcLoyL/HwEiboiSQJIl3336Lu++4nfkLF3LhRZfg8XpZ9elKfnfPXfjzcvn6jd/C6/URCUe47be30ta6g8uvuIqZs2bT39/P/ffdy/rP13HFlVezcNFi+vp6efCB+/ls7Vpu+Oa3mDZ9espZmyjS75MoikSjUT756CPi8TjLDzmYYH8Qn883Jte22W0E+4298RqLs5hVnoMWT6bsTqK0qv2C9HRQwZhw3VDkM1LVgL7+frzesdHR58uiL6njrIps6gq9qIo62Dgc4Omq+460NBxRRI+rzKnIpaHI0C7YH8ThcIzJle12W2o+c1mum8V1+ZBQjTkXZkhGYHKNXA7FpPBtB+tIQmFelZ/qfC8AwWAQl9s1Jle2220Eg0EAGoqzmVWRO2ns6sBCDsb1/7ZiG7/521oEUUCwycYgxTA/giggiCKCtKvA6c6fqumoyb3zdF3no82dfOcP73POra/xwoptxn0QMzpWRuHSC5o88e7tam9/Px732OxzPKr2caiyJzuKRnRLQJQl3lzbxoWB12n63Tu8vX5nahVGTcvYtipzvqMAyEkddqMRsvEjOyTuemUdT3ywBRh+79txYwTt41j1c+x2G8GQUR8r/B4W1uWBOc8vPc1xwu/REE4Gyaqmacyr9lOeXLhkpIuPmfVdlozN6udV+ZlWnmPsc5x+3RGXMUPHhEZjYRbTynIAQ8exsqs2m51g0KiP9UU+Zpfnou+tXU3dX4HZFblML0+mqaYFrkaKsbWTTnGOi5suWsSpCyvREmkpr8PoOrEM0c+pzKW2KK19dO1ex1E7jn6/n1AwRG1tLdOmTWftmlWsWrnSeJDUgTSukdz8XVJUVRVZttHR3s6dtwXo7unm69/4JnUNDYRDIX7/u3v5bO1amr7xTabPmAHAs888xdN/fZIjjz6Gc867AIA3X/8Hf7j/Po4/6UROO/NMAF584QUe+dNDnHzqqZx8ymmD52joo1tafF+Qfj3Tgf1s7Vo+/fRTSktKmTN3Hh2dHRQWFo3J9fP8eYSSBrUoy8kh04rRVY3UtkHm/JJJ8aBPYlLpoMaPJAroMZXlU4ooT640Fgn14xwrh8NhJxI2dKzM83JQYxF6bD90OCaajOddFEFXdQ6bXkyez9AuFAri9+eNyeX9eXmEQ/3oQLbbziFTi9BlKS27ZhKNju+BCS1ipo7JXw+dVozfYwcMHcdqIMefl084ZVcdho4TYFeHO7um6dgkkVA0wXMrthLqjyHJErq5PYmZMpfxY5oSXR9wcsxrmG1oQtWQRGOxjs3tQX7x9CrOvPkV7nhhLeG4mpz7k7Y0PWkdql2+wDB2tbGQ8uQKx+FQEI93bBzHL9U+pjqKA79rmoYgiyiqzh9fW88ZN7/KDx5fwbrWPsTkPUskF0Ax74d5ZsPJJqXDcBolxUEXBPSYwlMfbaGzP4YsicPupzzmzcII28exGpDz5+URDgbRdfB77Rw2tQhdFAc7TqZOE8VQzkWyU6/FVQ6eWsxvL1/KlNJs1GT02PjY7sVL/7ui6mS57RzUUIhol1C0UUa/UjoaZZNFAT1u6FhiLqgS7Mc3ZnbV0BGgwOfg4KlF6Ko+crs6KLMRVFXH4ZA5ZEohkiju9VCerutIyX2By/M83HLpYo6dW4qWUAf24JxMEceh+jkaHDa9hHzvQD/Hu4eBnFE7ji63G03XsNvtHHXMsWzduoV33307WaZ0cUZ+g1LHJl+ffPwxnn76KS659HIOO+JIAJ579hke+/OjHH3MsZx59jmIosjmTRsJ/PbX5Obm0XTD1/Fl+di6dQu3/OomcnP8XHb5lRQVFbPu88+5+47bycrK4tqvNZGbm4uiKKlo40Qve2863O++8xabNm7g4EMPJc+fRywSITsnZ0yu6XA6QdeIRGM47DKHTS3C43MZm8NOgrSq/QYhrVVMDsYLLpmjZhTjddqIxRPE43F8WVljcnmfL4tELEYsniDbbefIGcUIDim5oStpZbN03C2Z6RuqRla2i8OmFGGXJULhCKIgYLfbx+TyDocTTdNSy9Ivqs2jptiHqqgZq3LqkzoRIN3HnZgCDNZRVTXy/G4OnlKEJInE4gli0Sgez9g4HA6HA03XCEdjOGwyh00txu11omrja1eHO7sZfeoOx1nf2o+5NUJqzt5QP8Ocw+x+m3N6bJJIdyjOg29t5OLb/sG/PvgBze1BJKfNGJzdZcGI3diloeyqU+aoGSV4XUm7Govi8Xj37gbtgS/dPgrmwN2Ak2zO+RQdNtp6wvzvox9zwa2vcc+r69nZF8UmiQiC4UACqRRDLTUAmNZPGu5HNFKRkQXWt/bRHY4nzzFBlXKC20eHw4mua0RiMUBgaX0BlYWGXWUy2FVT1wynUZJEtLjCgvoCfnP5EqYkF2+R0pzGkSySY/bJza966NQiY0siZZSrq2bYVTB0PGK6oWM8YdRH9xjZVafTia7rRKJxHDaZQ6YU4clyjLI+mvdCAFWjKNfNoVONwMxo7usAA8dKohHVrS7wcsulSzh4WjFKKG3P58kScRyin5OT6+SwqYXYZCnZPkb22D6O2nGUJInCoiJ6eno4/IgjcTpdvP3mG7Q0b8Zmsw3KVd/diEjm0sGKoiCKIh9/9CG/vfXXzJw5k4svvYysrCzWr1vHPXfdgcPh4Jvf+S65uX4SiQR33XkHn6/9jEsuv4JFS5YSi8V44Pf38fFHH3LhJZdw+JFHEYlEePThP/Hee+9y3fU3sGjxktT+kJllGS8yryfLMt3d3bz5+uvEYlFOOPEkwuEIXp9vzDqqgiDgdLno7e0BYE6Vn6X1BWhRFUkSBxvRiX7YJyup+2J0EgRRQI0rzKv0s6g2H4C+vl48Hu+YzY1zOBw4XS76k2lVS+oKmFnhR4krhsOhJ8s3qLwWg0i/L2ajHTWiG7MqcwHo7e3G5XaPmZ0QBAGXy0Vfby8ADcVZHDGtGD2uDKRVmaOFe7tx8z5gj1dOBpImhCF0JKayvLGI6WXZAPT19eHz+casPgqCgNvlpq+nG4A5VbksayhEi4yvXd3TqRVNJ5owBiwHHTqiTCHj1UhLNdMtdV5Zs4Ov3/cOl9/xJm+ubkW2yyCKqEraYhHmCYZxSgeXYbBdnVvpZ1Fd0q729uLzjq2O+6R9NB3INCdZUzUQRWSnjY82dHDN3W9z7d1v8cwnW4kmVGzJCKFiOtqmk7276+3ynrFoR1wdm7mDI2IStI+mjv19PQBMKcniiOnF6DHFmFs60XbVjOKlORWyTURNqMyuzuM3ly9hTpXfSP+WBrrre95mZeA4GEhXXVDtZ1pZdnI0IsmeImEZ1zF1nFORy8IaIwOnt6cXny9rTHV0OBz0Jevj7MpcltQVZtTH9Poy7IlS921WeTYzy3ON/TN38/2NvWeHOufAZ7TkXpqRuMLUkmzuueYgjl1YmSp7qlwTHdlO/W60j3pM4eDGImaa6cZ9vfiy9qzjXm1c6Pfn0R/sp66unoMPPZSPPvyAt996C2DQhvXDdbKGTlGV6e3p4Y7bArS1tXJd0w1MnzGTaDTKQw8+wAfvv8f1X/8Gc+fNB+DlF//Onx95mMVLlnDpZZcD8MF773HvPXexbPlyLrjoYgDeevMNfnfPXRxx5FGcdc65Rk5v8gaOd3rqUCiKgiAIvPfuO3zw/ntMnzGDeQsW0r6zjeLikjG9dkF+Af29PWg6lOW4OHFuOYIs7LpcsxWtGhphsENmEwV0ReOkeeXUFBgj4aFgP06nc0yL4bA7CIfM/H8vx88pRU9o2KQMh9HScWgydBQFQBY4eV45RdlOVF0n2NdHfn7+mBYjP7+A/r4eFE3H67Rx/JxynD4jEjnYGROYiNHxkZjLkc6/GROG0FGXRE6eV0G+z4mq6YRDYzdP1SQvP5/+/j7Drua6OGneeNrVZNs24vk+mf/fc5lUs4MrCUiiyOqtPfzXox9z0W9f58FX1xvpdA4bipJMSxWFwR1kMwg1bJl2b1d1jG3BxlrHL90+mk6jkJ6qRip/WlE0BLuEKMKTb2/iksAb/OtDH/Dhpk4kUcAmGX2V4dJMd7nWrm/u8WNjakZG0j72j337WJBfQH9PD6oGPpedE+aW4/A6dl1ddSLsanrEEcMpU0Jxqgu93HTJIpbWF6TSITM/N5JBzEHzj5PpqssbihBtUjJaNwIblK6jDjbJ0PGEueVUFyTTxsehPhYWFdHX242OYVdPmFuGIAmk1rRJjVMM/50EDCdPtkscOqUYmyzu0q4ZWd8DdtTcLmfwMTqKqhFXVGOvVlFAFkVcdploQiXbbefoWSV4HPKg9PMJZaj2UZY4eX4FBdmuZPsYwm7bc7BqrxxHn8+HTTY80tPPOIvunl5e+vvf6e7uxmYbvBLo7pyzlPOYXK77maf/yhOP/Zlzzjuf4044CYBXX36JB35/H4cefgTnnncBsizTumMHtwd+A7rON771HfLy8+ns7OSWX/0SQRC54qprqKysYsuWLdwR+C2CIHDd9U2UlJaSSCQGpahOdLRREAQS8Tgv//3vbNmyhdPPPBuP10ssHiMnN3dMy+JwOpFlmf7+IAgCR80oZlqFHyWqIEoZRnQSONmTisz7IQok4iqlRVmcMKcMWRLpCwYRBIGcMUo3Nsn1+xGAvmAQmyxx4txyigq9JOKZ6ThDlPurTsYonCgJKFGF2VV5HDG9GBDo7+vHbrePWfTfxOF0IkkS4ZCxSM6y+nwObihEjyoDo6qpzuoE6binjKbUP+PMLjqKKFGFeTV5HDrVWDUvFA6j69qY21Wn04ksSYZdReDICbCrYyGBAIPKu6M3wm///hmX3vY6P3l8BTu6Q8huO5quG1E1gbROsZDx+zAXGYFd7Q8Gx0XHfdI+mhUiLWU13YHW1eRiQm473cEYv356FZcEXufnz6xic7vRfpgbjY8KQ6w9HzdWZmSk7aM49u2jw+lEkuXUnNWDGgo4uLEQLZqYeLtqDqhgOI26ojOjNo/fX38IR80oIa6oKJoxf3hQuvEQ9WeotToGpasm+wKHTy+iMMtl7Om4p3TVYXQsLvBx4pwybLJEX38QxqM+OhwIgpiqj0fOKKaxPIdETDEW7Rq23PrAPRYEUHUKspwcPq3Y+EoZc0aNZAjjvXSHUdN04orhLIIRxbXLEjZJpL0vyjvr27nn1fX884Mfcu4tr3Lzs2uIK5kR/4lqt4dqHxPMq87j8GS6bjgcBl0n1+/f4+lG7TiqqoogCBSXlNDR0cHSZcuYO3cur77yEm+/9QZgRNFMhgqppztOmqZhbKmxml//6ibq6xu49IoryMnJoaW5mbvvugNVVbnxW9+hqLgYTdf4w/2/57133+XCSy7lkEMPQ9M0Hn7oj7zx+mucedbZnHTyKSQSCZ58/C+8/NKLXHr5FRx+xJHGwgBpIdjxdhozMedZvvfeu7z88otUVFRw3PEn0rFzJ8UlJchpK9Xua0wdXS43fT1d6MDM8hxOX1iJrunG4ipgDr+kpdxYALuMwtllY7L4qQsqmFtlVLy+nh5ssowkSWOmo67rRtq1IBDs7wNgUU0eJ80rR4+p2OW0dBxzdPMAZK++Vfr9SOojJTdMPnNxFVNKstF16O3uwu3xYO4xOxak6qPbqI+qplOe5+GMRVXoNhkhbSl7zBVzxnUQQEhdeVKSoaMsgq4ZOjYWZ6ED/b3d2O32Ma2PAzp6JtSujuSUozUFevJDwUiCZz/ZytV3vsW3/vA+H61vR3baQJKMKCMMDFhlRFT2eNHh7Oq8Cual2VWH3TE+Ou6r9nG4dLXkeRRFA0lAdtlZu6WLf3noQ664403+8l4z3aG92BtPNzq9E2btR9w+2sZFR6fLRX9vN6qmU5Hn4czFk8WuDmAOzJTneYkmFDbu7EcWRWyS8SMm57/GFRXFXEgp/fPJQQYzbdN0JAfm4RnbRyyoyWNKaRboGaurDlU3M6ONsrG40UnzygfqY18PtnGyq1nZWal01VnlOZwyvwJd0bANms6R+V2EtO9ilG96WQ6zq3KN7AjTSRQGb1mTHlVUklFFu2w4i+GYwormLh58axP/+qcPuez2N7jot//gunve5jdPfco/Pt3O9q6QMV950BzWCaiRQ/ZzjF/PWlxFY0myfezrQbaNrL86asdRTEYH8/LyiETCZOfkcP6FF7F92zaeffopenp6sNlsu72w+TdzNdFQKMQdt/2WLS0tXHXtdcyfv5B4PM5jf36EV19+mauv+xpLli4D4J233uTBP9zP9BkzuOqaa5FlmZUrV3DX7bcxbdoMrrj6GiRJ4pOPP+b2wG9YuGgRF158KbLNhp7cS2+iUlQzHVVRFInH4zz/7DN8tmYNZ51zLuUVFcZqqgWFY1rWlI75+QiCQF9fP5IkctqCchoqjFGc1KiqWeQJnFs1qRhmFK6o0MvZi6tw2WXCkShKPE7+GOtonregoJBELEY4EsPjtHHOkmry8z2Do46ZKVYHEHv1jVLPsz4o2jit0s+p88sRRaNeiJKYWk11rAaaBuxqPiCkoo7Hzy5heWMhaiQ96phsACdkEGDPIcdxL9UQo6mJqML0aj8nzytHEASi0RjxWCx5f8fBrublJe1qEEkUOX1BBQ0VuSSi42NXx+TbJdNOX1nbxpV3vslz7zWj6TqSKy0tNRlUSz2f5sbvI3lWd2dXl1bhchh2NRGP48/LS35kP2sfMx1IPe39ZPqqaLchiQKvfrKVa+9+i8c/2DKwPPA+JdnJ3teMtH1MxMkvKEh+ZGx1zM8vQNchlLKrZRw8ZRLY1TT9NU0HWeTl1Tu4JPAGFwVe5+q73+KW59fy8podbO8OY0tGucz5jglFI65og9OZk1HuVLQ6PV1V18ly2VneWIQoi6iqNnybNoSOSlwlL8/N2Yur8DhthKMxEuNoV/1+P/FYlP5gGJsscfqCSqpKM6OOg50k8yabaaqiJHDolCKctl234lOTUcVE8r6YUUVJFNi4s5+/frSF/31iJZff+SYXB17nurvf4mePr+C59zazsbUPHbB57cgeO6JNznAa9YnpQ6dfMy3aOLM6j1PmV6Tax1gsRn7+yOqjvNu/DlUGwdh2Q5ZlKiur2LZ1G8ccexwPz/8jzz79NMcedwKnnn4GiqKkonuZKaGZ///b88/y6CMPc+Kpp3LaGcb2Ge+9+zZ33XEHS5Yu5cKLL8HhcNDV1cmdt99GV1cH//t/P6a8vIJgKMitN/+Knp4e/ulf/53Gxil0dnRwe+A39Pf1c81111NXX08ikRi2PONB5vXM8rz15hs89dcnqais5MyzzqGjvZ3S0jLcHg+apqUqzL7GjJ7YbDZUVSEWCaN6PSyuzee8pTX88C+fIAsCWqrhTxuNO0CjViMi3QhAajQ1HlM5Z0k1yxuMitfX040oSdjt9jHVURSNJe6dTic6EOzrwe0q4rApRZyxqIq7nl+D3SMRT6R1EoQDT8dRf5NMHQWQBIEEcOHyWuZV+1E1jXC4H3RjAavxq48q4XAQp9tNbVEWFxxUwztftCfnSsCg3uok1HFcSzOkjpAALjiolrlVuegY9VGSJGPV0/HSUVOJhPvxej0sqsvj/GXV/O+fP0FOjvyPpV0V9qVPkD4/D4EdnSEQQPY6UFQVFNNhHCJ905zTuCeGs6tRlbOXVLO8ccCuSpI4vjqORfuYPv8xNenTeDXTfGWPna5gjK7+mBFCh33+nOzzbuxo2kdx7NvHdB0TSoJ4JEzC5aK20MdFy2t554t2IwV0ouyqYP4zcN8Sqs7Ongg7O4K8s3oHksNGcY6LmkIv08uymVOVx5zKXKaWZJHnG5gfqiUXVBIFI7qY3t82kZK/HzWjmLteWUd7bwRBFpKBuvT+QdprcmDVrI+nL6zi4CnGgLip43jVR1m24fF46e3uxOd1s6Q+j7OXVPHLJ1diEwUS5v6w5vdIlj11r1WdvGwnR88sHnhL09A0UtvimPNJd/ZFWb21h4+bO1nR3M3abT1sbg/S3heFRHKvbJuxyJUgCKi6biySk9AGrpe6n2kDRePZQA7RPooCKILAhctrmV2Zi6ZDf18vojByu7pXKpsnLSgsBHRycnK59Iqr6Ozs4JE/PcT2bdtSHaDhv4/hSG3auIGbf/lzSkvLuPLqa/D782htbeWuO24nGOznhhu/SVVVNQB/efRRXnzhBc4+93yOPPoYAJ74y1948YW/cdwJJ3Lm2eegqSrPPfs0zzz1V84+9zxOPPkU9EmWoqrrhiHr6enhL48+wvp167j40ssor6ygP9hP7hjnipuY96C0rJxwOEwwGAJB4IJlNSyoLyARSSDLaaNxVsrqLkZAlEQSMYWGCj+XHFyH0y4TjsSIRMIUFRePa9GKi0sIh0JEonFcDpnLDq6jpswcHRd3Lf9XlVSjaHb2QJZFEpEES6cWce6SakCgvz9INBqlpLQUGLtoo8lAfSwlFo0SSY6On7GwgqNmlKBFEthS9VHPaNzHhz1datyfqoz6KCVHUxc3FnLOkipAIBaNEQ6FxnyxsYEiJXUsLSMeixMMGnMdL1hWw8L6AhLh/cyuppdP1xFtorEnYUI13hNTYUbS52yNysYMY1frK3K5ZHktTptMOGrY1XHXcazaR9NpMO2QSVr6qiCLiLZkXtlkf05gRO1jJGLqOD7to6mjGWiIRiMAnL6wkuNmlaJHM+1qRmd7LEld06w/AggCok1C9tixeexoAmzrCPLGp9u584XP+O4f3uOi3/6Ds255la/f9y73vLqeDzd1EI4p2GURObmti6oZkTNVG0hrFZNTMRbU5DGlJGtgdVVByHg12xfjv6IkkIgrVJRkc+khdXidNiKxGKFgP8Ul41sfc/1+VFWht68fWZK4YFkN06vyUKJp9VHIqFepSD/UF2cxrTSHuKIlnUURmywSjSt8tKmTu19dx/X3vsNZN7/Cpbe9zr899CH3v7yO9z/bSXtvBFEWsXkcyC4biCKKqpFIqMaAjzkIIQopLdMHBcbXacy4D7oxL1ONJFg+tTjZz4FEPE5/Xx8lo9Bx1BFHGIg62mw2cv1+2na2cdTRx3DU0cfy/LPPcOjhh3PNddfv8pnUKqpJjzYWi3HHbQE2fLGB//iv77Fs2XISiQRP//VJnn36aa67/gYOO9zYx3HFJx9z3713U1FZyXXX34Db7Wbdus+547bfUlxczPU3fB2Xy8Wa1av59c030dDYyOVXXY3L5UJVVSSkCXMYM6+rqhqyLPK3557hyccfY8GChZxx5tl0dnSSk51NTm7umI7emKTr6HA6iEfDxN1uppfncNURDaza0kNcVQfSZHYZJf2KkRpBHzACgqCjiSJXHFbP4rp8NB16uzuRZRsOh8N49qRdUyL2JaIooqoqTqcTQRTp7+3C4Sxm+ZQCLj2kjv/5yyeIZrrCoLS+A8eBHFUTLyQbalNHUUBVNVxuO9cc0UhjSRbxhEosEsLtdo95tDFVrFR9tCNKMol4lGjcRWmuh2uOaOS9jR30hmIIkjiwIuA4VEV92P8MxTg+T5n1UTTaFrvTzlVHNDClJBtVg57uTiSbjH2c6mO6XZUkCSUWJZZwMa3MsKufbu02tkkYQ7s6kvow4jozaMQ6GS2FwVFG02FMdZZGU9jh7eqVhzewpL7AsKtdhl2dCB3HtH0cKn012eHUNT01t3TQMfuKfekbjbB97OmeOB3tdgexSBiHw0lxtotrjpzCuxs66OyLIsimXR3HwdVUxJFBDquxwBSpOiY55NRjFlU0mtv6ad7Rx2urduBx2SjKcVFT6GNWZS4Lq/3Mq8qjrtiHQx64t6qmAQKqppPttnPo1CLeXtdupLmmf9eUjgPROlEQUHS49JA6Dp5iRI27O9pxOJzj1s8xdbTb7dhsduLRMAm3mwU1eVx+aB3/9nCPYZtSad2D9dN1EESB42eXket1kFBUvmjr48NNXby/sYMVLd1saOtjZ0+EUCRBMgwJsmg4iYLxlqbraEpGUMy0hYMulvEcjXdfa7h+jsfBNUc0UlfkQ0m2j3aHfVT1ca97QubJKyurUBIJXC43l191NV6fj3vvvpv333vXaDSHWCjHdKJeevEFHn7oQY448ijOOf8CAD5duYLf3noLs2bP4ZLLLsfj8RDs7+e+e+5h4xcb+MY3v01dfT3xeJw7Ar+lefMmrrj6GubMnUcwGOSuO25j+/btXHHV1cyaNRtlgldRTb8uGAviyLLM2jVruO/ee4hEIlx17XUUlZTQ29tLWXlF6jPjgXlvCguNvTljkTAAFyyt5rSF5ehxdfC2Dl9VpxEGjbobE8WN0ZvjZpdx6cG1gLH9RjweS43CjbWzYWJep6S0lEgkQjgZ5bjysHqOmFGCGlGMUVWTA8hphNE+kWajaNRJm2RM+D9rSRXnLDL2XopGgvT19VOQnKM6Xjqa9bG0tJTW1lZ0xdhE+NT55Zy3tAZd1VPpRuM1Z8K8tyO70jiGQ9LrI0kd4yqnL6zg3MVVAETCQaLRGCUlRtR4vHUsKS2lta0VJW7oeP7SGk5fUIUem1i7mhYjHMWHBjuQAwMXadHvvSpMho6yiBpROG526SC7GpsAuzqu7WO642meJr2ujwH79KyTuH00dSwqLqarq5tELIoOnDyvjIuW15n9aoOJaBszHY30ARhdR1U1EoqGomrGFhQOGdltQ7RLhOIKG7f38dLHW7n5mdU03fsOp/3qZU676RX+/eEPeez9Zja29QNGGqY92Q84bFoxPrfNyCJI/7pmOXQhpaOSjFJdfkgdoiDS199PQkmksqomQsfe3h6iyfp4ycG1HDerDC2qDNTH9DRVAF3H6ZDRNJ2fPLWKs3/9Gqf+8mWuu/stfvn0Kl78eAubtvcRiqtIdgnZbUdyGPMUFVVDUTRjcbxBNjBjoCwz22JC+1imjR7cPp6zpIqzFhv9nFgkRCg8EP0fqY5fSm1zpaPS0jLa2rYb8xEvuoQ1q1dx71130r5zJ7Isp1JW01d52rZ1Czf/8hdkZ2dz7fVNFBYW0t3dzd133E57eztfa7qBKVOmAPDcc8/w2F8e5ZTTTueEE09CkiSefeYpnnryCQ4+5DDOv/BidF3npRf/zl8efYTjjj+Rs885D13TkJMpqkMtVTxepEdaZVmmv6+PP/z+d7z15hucfuZZHH/iSWzauIGKigqcTmfqvo4X5ihDdU0tW5qb0YAcj4NvnzCDKZW5JMwJ5DCQkvNVJC3VQJSNFJzyIh/fOXE6ZX4PkVicSCiEzW5PpWqPl47po3GiJBMJh4jE4lTme/nOidMpKfCQiKkDhmG8UnHGidF9k4HUIEkSSUQUZtb4+dbx0/G57ag6bG1poaa2NhXNHU9U1dCpoXEKmzZtRAfsNpEbj53KwoYClKgysBl0Wod7IjGf8nFtJ9Pqo5RcEKexIodvHj+dHI+DaCxOOBzCZrONe32EwTq2bN6YtKt2vn3CNKZW7Wd2Nb0jlLqHwkDE5Mvc1nS7mkxtLCvy8e0TDLsajcaJRMLY5InTcdzax0H3c4h7PpkH+0bUPgYnpH2EdB1r2LxpEwLGXMBvHDvVWChnkF3VR9uofDnS9U09P0M4kYCuGY6komhoOgiSiOSUkV02BEmkL5Lgi229/O2jLfzsr6u44o43Of7nL3LGr17hB4+t4PkV29jaFWJpfQEzK3JAY/C1U46znkxRVfH7XXzz+GnUF2cRicWJRSJIojyhOlZW1bClpRlNh+IcN986YToVxT4SMXWgPmYUK6qo/Or5Nfy/Rz7kr+81s7a5m95wHEEy5ipKThlBElB1I1VcVbXB9TmlR0ahJmX9HHiG5WQ/Z1ZdHt88fjpep41YQqG/rxeXy43NZh+Vjl/KcZQkCVVVKS4pwely09XVxSWXX8Hy5Qfzlz8/ykMP/pF4PJ5awMNc5UlRVe6643bWrF7NRcktNRRF4W/PPctjf/4z55xzLsedcCIIAuvXr+fuO+4gJzeXr91wA7l+Py3NLdx5223Y7Q6+/s1vpbbuuOWmX1BQUMi1111PVnZ2at+bzIV5xot0R9n8XdM1Hn/sL/zhvvuYNm06136tiUQibjjgZWXouj7mIf9MTB1tNiN9pLerE0XVWFpfwLdPmIHXZ0dVNAQxOcQ8xqOgk45Bk8aN729MuJe54ZhpHDurBE2HaKifvmA/paVlqRWDxxNJktA0jYqKCnp6eolHQmg6nDyvnK8dPRVZFpMLAWSkUBwAOo6oVqe+Z7JRFAVURSM7y8F3TpzJgpo8EqpGT2c7DqcrNeg1UfXR2DvSQU9XJwlVZ3p5Dt89cQb+XCdKQh3Yg2scdZwUT0pGfRTMFByXjRuPnc6yhoJkfQzS19tHWXn5hNXHVGqV3UFvp2FXl9QX8J0TZuD1jp1dnahB0lExpF3VsdkkbjhmKsfNLkXTIRLunxQ6Wu3jMIyqfQxOaPto1ken20VXZweKBnVFPv7l1FkU53sNu5rpdIy3jplOyJCOy8DfUo6kqhlTjmURySEj2WU0QaAvHGf91h6e+qCZHzy+ggt+8xpH//jvXHvP2/SE4yAOTHVMXSMjbfrqwxs5c2ElOhALh+jt7aG8omLCdXQ4nfR0daBqOsfOKuH6o6dit4uoarLPn9Hf0TWdvlAcRQfRLiE5ZURZQgcU1XAU9cwFdtKdxdR9mkwOYgZD9HMURSUry8F3T5yZXPgPYmEjq6qkpGTUOn7p+LJ5sbq6ekLBEH6/n6Zv3Eh+fj6BW2/hb88/l3KcTOfxtZdf4qE/PsCSpUu55PIrEJIO4i2/uom6hnouv+pqsrOziUQiPPiH3/PxRx/Q9PVvMHPWLDRN477f3cPKFZ9w2RVXsHTpMhKJBPfefSfrPv+ciy+7jCXLlqX2SISJSVEd2EzUuKbZAX3jH//g17+6CVESueEb36K+vp4d27dTX98w6HPjjXmvampr6ejoIB6LoOlwxSF1XH5I/cAgS2ak6kBvHIeYEygJIKg6FxxUQ9MxU9B1gVg0QmdnJxUVlRNX1jQqKitpb28nHouiI3DjsVM5a3EVgqohmSlQg77bhBV1n7DH4g+ho5medPXhjVy8vAZNh3gkTFd3N9U1NRMyiGOSWR8TsSiaDucuqeK6I6Ygy2Lya4xnfdzzufcqBXJURdhVRwEQdJ1LD6njysPr0XSdeCxKe0c7FZUTWx8H6djZQTxq2NXLDq3jisPqjTUUzDTPr7xdFRBUlfOX1XDD0VPQEYhHo3R0dEwuHa32cYCRtI9MnvbRzLiprKyiu7uHeCSMpsMp88u58bhpOOwSmpocBJgsOg4XzTLb8YxUSS3pSKq6sTqvKItIdhnRJqHqOj3hOJ9v7ebhtzayekuvMY/PjLCmPb+yKKArCifPK+fbx09DkkSi0Sjt7TupqKwal68+HOk6dnV3Ew2HAYEbjp7C2YuqETTV2J9xCOdRlI3FgzTdWG9kUPop6Y5i2jNgzpmcxP4iMGw/RxIFvnZkIxcfVIOq6ahKnB07dlBdU71Xl9knicnmcsfFJSV0dnaweOkyrrv+Bvr6+vjZj3/EB++9l9pUcmdbG7+++VfIssz1N3yD4uISgsEgd995Oy3Nm7jqmuuYPWcuAG+89hoP3P97jjr6WE47/Uzsdgcvv/QSjz78EHPnL+CSyy5HlCT+8eorPPTHB1h60EFcfOnlpDZFZ2IcsUynMX1e489/+mOamzdx8aWXc+oZZ7Bu3TpKS8vIys5OpTZNFOaog9PlRknECfb3YbdJ/OdpszhpbjlaXDXmV2Xm4u+LEfJ9UP59TuZIKkYFVOMqh04r5vtnziHLZSehJOjq7CArKzs1UXyidExfKMfr9RmjqopCjsfBD86ex7LGQtS4mlpyekBHJqkI+4AhRsQlQQBF5bQFlfzrKTOwyRL9fb0oShy325OK3k4kg+pjPEawvw9RFPmnk2ZwxsIqdEVLLmi57+vjUOgjSYsVxtB1HKY+anGVo2eW8Z+nzcZll1EUha6ODny+rAmvj5Cho5Kgv78Puyzxn6fO5sR55WjK2NjVSVudh9BRFgXUuMIhU4v57zPnkOW2E48bdnVS6hgf3/Zx3yMkd3H8EmXbQ/v432b7mEjQ2dE+KdrH9O057A4H8XicUNCY//fN46ZxyfI60DIGASatjmYbTkZkLO1905HUkmmtgoAoiYg2yVhkzUxnTPUBkvVRElFiCrOr8/nB2fMoznETi8Xp6ezA6/NNEh0HFjxSlTi9fX1kue18/6w5LJtShJpQdu3nJO+HbqbkQsZ9S3cUmaQpqMMwTD9HV1TOWFjFv5wyM7mqrsbOtjZycnKNe7cXOu4T1c3Oanl5OS6Xm61bWrj4sss59/wL+GztWn70g++zds0aJEni7jvv4MMPP+D8Cy/miKOOQtM0Xvr7Czzy4B858aRTOP2MMxEEga1bt3DH7QEEUeS6phsoLimhtbWVe+68nXAoxLe+/V2KS0pTjqjNZuP6G75BQUFBynGbiBTV4ZzGLS3N/PT/fsibb7zOiSedwg1f/watO7bjdDopKy8fFCGdKEwdKyoq6O7uJRwOoqgqxTlufnbhApZOLUyrjMKuletLMOmq5VCNoiSixhRmVefxswsXUlPoQ9N0eru6iESjFBYVTUhqYybpKeTBUIi+7i40TaexJIufX7iQqZW5qPH0eTnmd524Mo8ZQxlTUUBNKBwyrZifXjCfgiwXiqoSCvXT1xekrKxsUug4qD729hEJh1BVFb/XwY/Pm8eh04sHBgGEfVsf95YxW+NlN/Vxbl0eP71gPhV5HhKKSldHO9HkAhyTTseeXqIhw64W5bj4+QULWTYlza7uQx0nZXUeRkclpjCr2s/PLlxIXVEWCUWhu3Ny67jb9nGS1MdhMfvIe+sL7a59rDHax9pCH4mEQndnB7F4fNK0j+k69vT0EAqFjFR3u8T/njOPU+eXoyW0wYMAMDl1hF0rummDh0prTTpO2qDVegc7S5JkbLVTWeTj/86bz7xqP3FFpbu7k0hyoTF1UvRXjX5OZWVl0q6GUFSVhpJsfnb+QqaW5xjtY3o/J9PBzhww358cxXR20885bHoJPz5/PnleJ6qm0d7WRiwep6i4eK/r4z4bLjA7qzW1tQiCQHdXFzd+6zsce9wJ/OO1V/np//2Qh/74B/704B+ZPn06V159NZIk09LSzC2/+iWFxcVcde21+PPySMTjPPboI7z26itcdc11LF6yFIBH//QQ/3jtVS686BIOOewwAO7//X18+OH7nH3e+Rxx1NGDvOeJ2qsx02ls3bGDn/7f//H0U39l2bLl/Nt//BeqrtHX18es2bMHRUgnGjMyXFVdTTgUoauzA03XmVaaw80XL2Z2dV7S6RgqlJ/O4Huf7szvF2Q0GpIkosZV6sqy+MVFC1lcl48OdHbspD/YT319w4SmNmZi6tjQ0EhPTy/dXR3owEGNhfziwoVUF/l2NapDvU5SRvw8ZUQApGRkY35dPjddspiGYmMudGf7TmKxOBWVlZNSx6qqKsLhcLI+Ql1RFjddvIh5dflGfcwczJkoHYUxanOHqY/1Zdn84oJFzKvOQ1E14tEw0WiUmprayatjJEJ3Useppdn86uLFzK7JG9BxKLs6Sh2/jE8wpgxnV0uy+PmFi1hSX5DUMTK5dUy2j93DtY/7SMdJy+7axwuN9lFRNeKxCJFIlLq6+kmpY3VNDZFwmI72nSAIFOe4+MXFi4yVyBPKrs7j/qCjkPonSYbDlHpbH5zGSVLHhEpBtpMfnjuPE+eWGyuKxiJEIxFqapP1cTL2VyNhOtt3omo6y6cU8tMLFlJZ6N1DP4dJOsI2Sobp5yyoy+emixdRX5SFqmnEohFisSi1tV/Oru7TOLMp4rz5C+jp6cbn8/Gf3/tvDj70UP72/HP8z39/D1VV+M4//SulZRXEolHuvetOPl+7lksvv4LFS5YB8OGHH3DnHbezdNkyzjn3XNxuN++89SYP3P97GhobueLqa3A4HLzz1pv84fe/Y/qMmVx97deMfTeTTuN4OSjpq7WmRzgHnMbt/PhHP+ThPz3E7Dlz+O//+V8KiorYsP4LZs+ZiyiKE+bgDoc5J7S6pobenh66uzpRNWNRh1svW8IMM2IlDn5YgVRFNBcmMlMFMiO/5qup0qQww8M0DmajWFPs41cXLebYWaWomk40HKa9vZ3qGmOp8cmooyAIVNfW0tbWRiwSQdV0Tppbzk0XLaJiKKOaOSdgkmAWRRzmOTKfM+M4YdcPpxnTOTV53HrpEhbUGM5GV0cH/f39VFVVD6rDkwWzPlZV19DT00N3ZweqprGgJo9fX7qYGVX+XQdz9qGO+jC/76bEX+p6AxfbfX2sLvJx00WLOGpmCQlFRVcTrFmzhrLyitSzP5nI1LGrsz1lV39z6RJmVvqHtquZ9nUk6JPou49Ex0sWc9zs0qSOCmvWrJ30OlbX1BiDcp0jbB/3Rsex+g7J11EVYxTto1kf165ZS1l5uXHNSapjVXU1fX19KR0birP4zeVLOHhq8a7O4y46Tp42cniGuO/pkbUMpzEvy8kPz5nPJcvrUvVx1arVk74+VlXX0NffT19PN4qqcer8Cn5+wQLK8tz7TT9nxOipfwbXx7R+ztyaPG69bCnza/JQVBVVSbBq1WpKy8q/tI77PEHZLNCcufP46OOPKCgs5Ac/+gnz5s+np7ub/IJCikuKEQT4x2uv8sD9v+fQw4/gggsvRhRFOjraueO2AMH+fq752vXU1tXT3dXFfb+7l63btvDNb3+Xmtpaent7Cfzm1wT7+7jh6zdSUVGR6vSNp9Nofuf0a5tOY3PzZv73f/6bBx+4n2nTp/E/P/w/pk6fxupVq1i4eDEOh2PSRuAEQUAURWpq6+joaCcRjxGLJzh0ahGBy5cx1xwh3yWdA9B1bMlK2h9VUFSNSFxJ/cQUlVBMSV5n4GMTRmYqSvrITTL9pqEsi1svXcIp8ytQVA0RnQ0bNtDQOAVRFCetjuaoUl19A198sR5BN1ZfO2NRFb++ZDG1Jb6BtNVJ3DiaRQnFEsQVddDzpKga/VHjeZKTixqknidTR8Ewpgvr8rnt8qUc1FhILB5HScTo7OykprZuUg7imKTXx87ODhIxoz4ePKWI2y5bOhCxmuBOzuDhoL1kUNoNQ9bHumIft1y6iFPmVxgrNkZCtLa1MW36DGw226Stj+k6dnd2kYhFicUTHDK1iMAVS4exq7va1z0zCb7/SOxqaRa3XrqYU00dwyFa21qZNn36fqJjLR2dGe3jFcO1j3uj4yRglO2jjqFjW9tOpu5HOrYl91uNJxJML8/h9quWccTMUtSEaixilb5gjp6M6e9PjofpbAzhNJnOf2G2k/87dz7XHtmQXNE4SPvOnftPfaypZcf27WhKglg8wblLa/jlRYuGjjxOwn7OiND1pP0QSEVqMvo5C+ryCVy5LLXCeCQUon3nTqZPn47dbv/SOo7ZzFa73c7BBx/CluZm/H4/v7j51yxesoQ1qz/lB9/7fzz37DPc8qubcDpdXHXddRQWFaFpKk//9Ume+etfufDiSzjiyKMBePop472zzzmPI48+BkEQ+NODD/DqK69wyqlncMKJJ6dWbIWxH9lK32Yj/ZqapmGmna5a9Sn/9W//ysMPPcSs2XP40U9+zqLFi/ng/feYO3cuXq93UJknK7IsU1ZWwebNm7HLIvGEwqHTirjr6oM4ZFoxmqIkI73Gw6vrOkgivWFjw+vCLCeyJOKyy6kfhyyR47YDoGnGgz8hVTbTkEIqfUMQBUQB1JjCgvp87rjyIE6aV05CVYlFwqxdu5bKykpsyX1CJ6uOZrkcDgcVlVV88cUXxCJhEqrK6Qsruf3KZcypyUONKrs2jpBmWPWJ6Ysmr6kl1wvP9Tiwy9Kg50mWRAqznAD0RRWQktuOJJeiFgA1oXLEzFLuvHo5yxoLiccTOGSZTZs2UVFRMWlSqPaELMuUlpWzefNmHDaJeELhkGlF3HnlMg6aUoSW2IOOX7px3MNzLoDwZYaBdimnPrhRTM4x/u0Vyzh1fiWqppGIR9m2dRvZ2Tl4vd5JGTXORJZlSsrK0nQ0nMe7rjmIwzLtKqOvjxPaBRqhXZ1fn8/tVx3EyfMrDB1jUbZt2/903KV9nDpM+7gXOo4ZIx57GF37qKga4WA/27ZtJTs7e7/SsbyikubNm7FJIglFYUZ5DndevYzTFlaiq8a2F6mtOoD0DjswuR2P3TgbctL5ry7y8suLF3HtkY0oqo6SiLFt2zZ8WVn4fL79RseKyko2J3WMJxKct6yGWy9bwtTybLS4giSKQz7Tk34QYBfHf+A9URST/RyFI2aUcvfVy1lWX4Cq6WhKIqljNj6fb5/4HWOaqOzxeJg9Zw6vvfoqjVMa+flNN/P9732Pl198gdVrVpNIJLjyqms4+uhj0XWdzz/7jMCtv2b6zBlccNHF+Hw+Vq5Ywb1330VxSTFXX3sd2dnZrPzkE+679x7Kystp+saN2B2DPeixfMCHO7cZZVRVlZdfepFf/OTHvPvO2xx08CF8/39/SOOUKbzx+j9YsHAR2Tk5aJo2oSvEjQav10tJSSmffrqKKVMaUSWJhbX53Hvtwfzbwx/yxActqIqObJeIJjRw23ntszb++y+fcPi0YvqjCWzJzrz5zEuiwCfN3cY+Qub+guNJyuDvOhosSSKqogE6J8wr58cXLGBOpZ+EqoKq0NFpLA/v8/nGt8xfkuzsbHRdo6u7myKbjYSuc8zMUu65Zjn//qcPeXHVdnQEZJuIomiD74sgDIySj2Pjoek6yCLt/VF+8exqZpbnoGoDz5EoCCRUDZ/TxourdvDuF+3gshFTNCRZQk2oyLLIWctr+PG5C6gp9BrGVE2wcu1qamrrcHs84/Z99gVer5eS0lJWrlxJ45QpqJLEkvoC7rn2IP75oQ955uMt6HqGjkONru5Rx/TOrp72++4+sZfPRuoZ23U0XJRENFVHU1UOm1nCzy5YwOI6Yy6cGo+xqXkzpaWlZGVl7RedGxNDx7I0HWUW1uRzz3WGXX38/WZURcdml0hk1kcmpj7ukRHa1ePmlfOT8xcwt8pvzKGKx9i8eT/WcQTto80uDugoTKyOe8xk3ov2UVE1dDVBe0cHZWXl+LKy9qt+js/nQysp4dNPV9HY2IgkSdQX+bjj6mUUPOriD298QSyuZtRHkn5Y+kCAMMEpVGkMaVeN90XJ+F2NK8yty+fH587n+Dllhl1V4mzauInS0rL9T8esLDRdZ/XqVUyZMhVV0zl1fgV+j51/eehD3vm8DWRp4DkW2Mv2cRzZTX2Uk4sZSZLIWctq+PF5C6gt8iUH42Js2LCB8vLylF3dFzqOqeOo6zqyzcZhRxzBJx99RGFxET//5U3c9Iuf8edHHkaSJBRNpaOjA7fbReA3t7J1yxZ+etOvmDV7DsH+fh564H5Wr1rFz2+6mekzZhKNRLjrztvY0rKFn/zs5zROmTJkyuhYfBfzGulomrEPjCzLdHd389ijj3DrLTezbdtWTjvzLP713/8TX1YWH33wIYuXLCNrP6uEYHz37Oxs6hsa2LxpM7l5fgoKi6gv9nHnVctoKM7izpc/p6sniuySURDpC8X4wR/e4wdRxdgsz3zYYaChtMvgc4BobMw6LhU13ZBmvi8K2CSBRFTB67Fz+aH1/MepsyjJdaNoGmoizvp16ygrr9hvRuAyycnJRVVU1q1bx7Rp01BUlQU1edx73XJ++MSn/OGNL4hEEtgcMglVN3YGNr/ieBtWwdgQHEmkP5zgn+94E+LK4E6XOQKn6eCygduGIItIooASUSjIdXHd0VP47gnTyfHY0YH2tlb6+nppaJyCy+Xa73Q062NDQyMtzZvJyfFTUFTE1NIc7r76IH705Kf87rX1BENxbM4hdIQR6pjpKAr7fkB2UMeGgTImy2uTRRIxBYdD5vxl9XzvjDnUFvpQNZ1ELEpLS4uxndF+bFcbGhppbt5MblLHuiIfd165jIYiw6529kSwuWyDddyDs/GltlnYG0ZhVy89pJ7/PG0WpbnupI4RmltaKC3bv3UcSftoc8m713Ec7KpOWqpZ5mOyt+2jarSPn3/+GRUVlfudswFp9bGxgc2bN5OXl0dBYSGFWU5+fekiGop93Pz8ana0h5CdNlRdR1d1w2nUhbRXJt7x2J2OgM0mkoiriJLAKQur+MHZc5lT5U+rj82pbeL2Vx3N6TmFhYX48ws5eEoRv79uOd9/bAWPvreZRFwx+jmKtpft4ziwi45pUVKzPkYU/DlOrjtqCv984gxyvXZ0HWKRCNu2bqWyqioVadxXOo6p42h2xmRZZs68ebz7ztuUlZfzX9/7PhUVldx5e4Df3PwrOnbupLq6hqf++gRnnXsexx1/IgCvvPwSf3roQU465RROOOlkJEnikcf+zDNPPcWxxx3PaWeeNeha+zr/Ov18Q3UsE4kENpsNURRZ8fHH3P/73/HQAw/gdDn5+o3f4mtNTWiaztatW1i0eDFuj2e/q4Qw8N3dbjf1DQ00NzcjCCL+vDz8Xgc/Omcusypy+OWzq/lofTuCLCLbJbC5kSUzBpGWIoHRUVdUjdhwI7AMOvzLjeCZI4KZSz7qeurcNpuIomokwgrTq/188/jpXH5IHXabhKrphPp6aW1to76hcb90NtLJy8/H5XazZvVqqqqrcHuzKPd7uPniRcyuzOHXz6/l85ZuBIeEZEathjOs+2KEdSidM9MyRAGH340smfNJhUF/1wEl2YgrcRUlprFoSiH/dNJMzllcCYiomkZXx04ikQh19Q2p9NT9TUezvC63m7q6ZH0URfx+P0XZLn5+wQKml2Vz83NrBuuYMKIEyZMMfh2ygUyPOJrOSPoo+1BlG+GjMGTHRk8FNiWbCJpOIhynsiSLG46ZRtNRU/C6bKiqRjgUpLm5mbr6elwu135tV11uN/X1DTRvNnXMI9fr4IfnzGNmRS6/fHYVH63vQLAJyDZp6I7Ormcf+y8wSrs6rcrPN0+YzuWH1OKwyaiakda4ZcsWamrr9nsdR9U+DqfjvrSrI2BQj8mMvKT+OIr2sd9oH9MH4/ZHHXVdx+UydGxpbgZBID8/H4dN5l9Onsm0shx+9teVvLG2DUESsNkyoo8CpNolkr+PZwRyKLuaFhmVZBEBnUQ4QUGem6uPaORbx02jMNuVSjPeunX/r4+6ruN2u2mcMpUtzc0A5OYV0FCSTeDypcwoz+W2Fz9ja1sfktMGooA66vZxjBipXVU0EhGVufV5/PNJMzl/aY0xhq5BKNhHc3MztXV1uN3ufa7juK2pa7PZWH7wIXz80Yf0dHVz3fU3MHX6dG7+5S948IE/4HA4KCws4ogjj6KwsJBNGzdy+29/g9vt5rqvNVFYWMi6zz/nd/fcg8fr5cZvfzsV9UlnX3QCh4sumiQSCWRZxmazsXNnG88+/TS//929fPjBe8ydN5+v3/gtTjjpZLZv38bO1lYWLlqMy+3eL41pJpIsU1VdzYYNX6AqCfx5ecg2BxceVMvsilxufeEzHn57E729EQSnTEIB1GRvMPN+mg3VLmk7aX//Ms5j+jwS0gwAA9cQZRERUMIJnB47Zx1Uy43HTWNRbT6abix01NHeTndPDw0NDciyvF87jSZut5vpM2fyxfr15OZEyfHnYZMlmo6eyvzqPH79t7U89n4L8XAc2W1D00FLOfnJk5ipExlzJkZFpr7p2qefU9eJxRViQ42QI4CcHKCIKuTkuLlgeQ1fP2Yq08qM1FZFidHV0U4wGKauvn6/r4cmZn3cuOEL1EQCX3Y2LreHrx01hblVfn713Boe/6CFRCSBzWVD0ZKj5EM2kEPraA786AP/2W1dHDYNzmwQ9eE7NoIkYBMFEtEEyBInLqriWydM55iZpQDE4nG6Ozvo6ell6rRpqfq4v+spSek6xsnKzsHpdnPhQTXMqTTs6p/e3khvTwTJbQMEVDXd8ciMLIxhYUdlV+M4PQ7OPKiWG4+dxuK6fDQd4okEXR3tdPf00jhl6oGj4+7ax6SOD79ltI9D68iAXRW+hF3d2+bJHLAbSfuoHXjtY3qwo7qmhvXr15GIx8gvKECyOThlXjnTy7L5zQuf8Yc3NtDZGUJy2UDI1FHfNzqOhPT6OIxdRRSwywKJmIIuCBw+p4wbj5vGafMrEEWBaDxOb1cXPT3dNDROSS2Es7/Wx3QdK6urWb9+HfGEQn5BAdkeO/9x2iwW1ORx8/Nr+NuKbegxZa/bx33GiO2qjhJO4PU5OO/wGm48bhqzKnJRNR1d0+hob6enp5spU6eOmY7jvhnL/AUL2bRxIx9++AFLlizlN7fdzu9/dy+P/ulPdHa288ifHkISJT744H0+/vgj/u0//ovZc+ehKAoP/OH3rPp0Jf/5vf9m9ty5qXPuq0VxMhe8yfybOY/RZrMRDAZ5/bVX+fOjj/DsU08hiHD5lVdz9TXXUV1Xy6pVn+LzejnksMMHnftAQBRFGhun0NLczI4dOygqKsJmdzGzIpdbLl3EUTOKueuV9by4egdCQkV22tDAMKzpDmD6/ciMMqb/Plqn0eygplf6QfMRjAooCaBEEiiiyMEzS7jy8AbOXlSFz2VD1UFXFLZt3YouwLRp0waKeoDoKEkSjVOmsGnjRhQlQVFxCQkdltYXMK00myNnlHDvq+t5+7M2BHRsThuqZqRnD1oOeijjOlKGGxwwf09/VobQUZIkY1J4NAF2ieMWVnL1EY2cMr8cuyylUm/a2toQRYmGxsZ9npkw0YiiSEPjFFpamgkFg4iiiN3hYml9AbdfuZTDphdzz8vr+OiLDgQR7A6ZhNlA7jIQwC46Gg6jcc9St264iOPQb6c1iubg0OAGEVHAbjM6NnFFZ2qVnysPa+CSg2spznEZOsZjdCU3E596ANbHdB2DSR1tDiczynO45eJFHDm9mLteWZe0qwqy04aOaVeT+oxlAffCri6fUcpVRzRw9qJKfC47qg5qPEZ7+04SCeWAtKvDto/ludxy8SKOml4ySEdbZvu4L+zqiB8EPeNXfeTto3pgt4+CIDBlylS2bGmhtbWVosIisDuoK/Tx8wvmc/i0Yu58eR3Pr9yGHo0ju+xp9RH2iY57JN2JEYa3q3GVeFSltjyXSw+t47KD66gu8KLpRiCkK7mPceOUqfusPz1ZEEWRqVOn0dLSzLYtxtQGm8PBcbNLmVWRwx/f3MjvXvuCtZu7EGwidrs0gvZxH5Lq64zQrkoih88u45ojGzljYQUuu4yi6QiaytatW1FUJaXjWPkd4+o4CoKAqqrU1NaS6/ezevUqCgsK+ed/+w+WLF3G3XfezrNPP8V7776DJMs0Tp3KYUccgdvt5pmn/8qDD/yBJUuXce55FyAK4pA3JX1bjN3dsKGOG+p4VVXRNA2bzZZyGN9+8w2efeZpnnv2GbZt3cJByw/m6mu/xpFHHU04HGbFxx/T0NhIcXFJKkR8oFRCGNCxsqqKrq4uNm7YQGFhId7sHJx2O+curWFZQyGPf7CFB9/awLvr2xEUDdkpgyCgaLoxMpdeKWGg15mKOpnRCfM1oyB6xjGD5lAycKI0IyolU2fVaIKEIDC3Lp8LDqrlrMVV1BUai91EY3EioX62bNlKWVkZefn5+2XKxkgQBIHaujpaW3ewZs1qqqurUexOst0Orj68gUOnFvGX95p56K2NfLqpC0HQkR3JDuvudEwxSg11YfA5htFRloy6q0US6LLIQdOLufCgOs5YUEGp3w1ANBajv6+X9p07KS0tIyc3F1VV95sVVEdKqj5WVtHa2sqmTRspLCjAm5VDrsdB01FTOHRKIX96ezMPvbWRjVt7EGQRm0NG1UHL7OgYJ03VIzPiOMLC7HpkerqPidkgSgKyKJCIK8SjGqVFPs5ZWsPFB9WwsDYfwNh+JdjPjh3byc31U1NTfkDWx3Qd21pb2ZjU0Zedi8Nu49yl1SxrKOCJD1t48M2NvJNpV1XNuMU6X26O476wq7UFnHdQDecsrqKuKGlX4/Gkjq3k5+dTVl5xYOs4bPto6Pj4By08+NbG0bWP6aRP8djL/oUOoOnGImSCsfDNiNrHYD9bth7Y7aOpY0VFJZ0dHWzY8AWFhUX4snNw2G2ctqCCJXV5PPHhFv74xkbe+LwNQTEGykeu4x7ax9Rhw2k9hLMoGAuKySKGXY1oFBd6OXNxNRceVMvyhnwQBOIJlXCwly0tLRQUFVNTW75frPQ/WtLtak93N198sZ7i4mLc3mxKc93888kzOWJ6MQ+9vZlH39nMltbepAMpo6S3j0M5kSYjrYtDHSek/Q12a1fn1xdw4fJazlpURXWBF4CEohIO9rF582bKysrJLyhI6ThWWo57xFGSJBRFIScnhyWLl7Bu3TpWfPIJyw85lHnzF/DC357n/vvu5dOVKxEEgZt/+QsOPfxw/vLnR3E6HPz7f32PgsJC4vEYoighiuIggzXS0ZLhjtM1DTVtWw1JkpAkiW1bt/L2m2/y8ksv8tqrL7N58yamTZ/Bj3/6C0485RTy8vLYuGEDILBgwULcHk8qQnkgYuro9/vJzs6mpbmZRDyOx+fD5fFRkefhxuOmcsKcUp5fuZ2/vLuZN9btRIvEEewSkiwNOB9m7namgQVSPcyh5EzvyAzVoUmO4qQqn6qihVV0h8zSqUWcubiak+aWMb0sBwBF04mGQ4RDQfqDQabPmIEsywe0jibFxSUUFBSyfv068vLy0DUfNruDxuIs/v3UWZw8r5xnPtnGY+9v5v0NnQgJBcEuI8niCHTUhx9oFcx/0kNZyZfhdFRU1KiK5LJzxJwyzlpSzXGzSlMd1ISqEQn109/XRyKhMGXqtNTzeqDqaH6/4uJiCgoKaGluJh6L4fFl4fH6mFmeyw/OyubU+RU8/kELj73XzLqt3QiA5JCNxlU3lpzP1DBVtUbiiwx1jPlcgFGVRWNrDQA1liCuQlVJFqctrOTMxZUsqytIzZ2KREL0dnURjkSoravHbrd/JXQsKi4mP6ljNBolOzsHp9tLRZ6Hbxw7jRNml/H8ym385d0WXl+/Ey0cR7BJ2BwScfVLDlTurV21G3b1jEVVnDS3nBnlOYBhV2PhMMFgP6FQkPqGhq+MjuntYzwex+vLwuXxJtvHaZwwx9SxmTfWtY++ffzSaXMCyCIOWSQWU9DC8WT7WMyZiys5aW55RvsYJhzs/8q0j6aOefn55OTm0tzcTCwWJSs7G6fbS3GOm68dNYVjZ5Xy3Aqjn/P6up2o4dHouJs+jokgpDWRGQNwJD8uGZu/o+mGXdWhsjiLUxdUcMaiSpbVF+Cyy2iaTjgUoqe7k0g0yrQZM78yOubk5uLL8tG82WgfE1nZeLxeFtbmM6cyl9MXVvLEBy08+WELG7b2IAjJ9lEUkqmgu+ursue6OCiauOtAKoKAKAmIGJFrLZJAt8ksmVrE2YurOWluOdPKsgGjPsajYbo7OwlHo8yYOWvcdJyQp8TctkK22Zg+Ywbd3d18vnYNXl8WZ5x5Foccdhh//9vzPPTAA/zlz4/y/PPPIEs2pk6bxs62Vjo62snPL0idz1zZ1Iwepkf4hks7NV91XTMeBgSjQyNJyElHtKuzk08/XcG7b7/N22+/xScffUhHRyfTpk3jf374Y048+WTKSsvYsWM7q1etorKqivLyCoADuhKamDpKkkRNbS3t7e10dnbiDoXJysnF6XLSUJxFQ3EWp80v560v2nn2k628urqVLe1BBE1DsEmIkmRM6tWT2y+YFUpLXmjQxHPzvYzCpI0Eick9pnQ9+WxEFXQdivK9HD69iBPnlLO8sTDlaOhAPBanp7uTYDCI2+2hrq4eMDpFB7qOJpIkMXXqNLZv28bWrhZy/blkZeciyzKzKnKZVZHD2YsqeWPdTp75ZCv/WNvGzs6QEZyyScl6N4SOmQ3ecDqmO5eZOqoqWkxFF0UqC30cObOYE+aUs6y+gIo8YzsNVdOJRaP09/YQjkTIys6irNyIWn0VdMysj50dHXR3dRGNhPFl5eJ2O1lcl8/86lzOW1rNS6t38NSHW3lnQzuxUMxYjdYmIQgCGsm9M/Xk3qy6Psj/G45U/8b8XPJNszEEHTWhocRVJJeNxY1FnDy/gmNnlTK7MgenzdAoGovT19NFb28fubm5lFVUAl9NHbs6O2lv34nHEzLmPrpc1Bdn8fXiLE6dX8HbX3Tw7CdbeWn1DrZ1hBAicWOUfDeYfR90UvoOOTiwJ7sKFOV5OWxaESfOLefgTLsaj9PT1Ul/MIjH7aH2K2RXh2wfOzpwh0JDtI8Vae1jG1va+xA0ffft41B2VdfRdUMkTR9B3FnVEKJxogIU5rg5YkZxWvvoheTc5l3ax/qvpo61SR072jtwu0Nk5/hxOB3UFvq44ZgpnDq/nLfW7+Tpj7fyyupWtrX3G9nd9j20j0O1jemkC2kGqkQBURAMm6vpaHEVRdGweewsm1bCifPKOHpmKbMrcnDZDY0isTj9Pd309vaSm5tL+VfSrsrU1tWxc+dO2tvaCPb1kZ3rx+N2cciUQpbU5nHR8hpeWr2DZz7ayrsbO4gGY4ZjbpcQBGOwXDMHWmFkGprHmSQdRUEwthUTdIygVTSBogvk+90cPq2Kk+aVc8iUolR9BIjFE/R2d9LX109WdjYN46zjhD0pZrqYqqrk5uayZNlBbGlpYf26dfiyfJx/4cWcdMqpvPn6P3jy8cf54P33+HTlCv7lu99mytRpzJo9h9mzZzNl+nQqK6vIzs4e8jp6snU0BgiMUZ3BTqUIycy1YH+QrVtb+Pyzz1i54hM+XbmSz9auYevWLdhsdhYvWcLpZ57F4UceRUF+AW1tbaxZs5rCoiIWLV6CzWYz5n/BAV8JTSRJMlIGNY2CggLy8vLYsX07W7e24LDZKSwpxe6wU5nvpTLfywlzyviitZ93N7Tz2po23tvYQXNnCD2SMHqdkoAgicbG7UmdhhpoGyrKr2kauqKhqZqRqmWXKPd7WVCTx2FTi1jaUMCUkixyPY7UeePxBB3tbQT7+/H6fFRV16QMjCiKB1xa40goLStD0zS2tDTT3dVNTm4OWdm52O126ouzqC/O4tT55Xy+o5+31+/ktc/a+HBTJ9s6Q5BQkzqKCJKQaiwhrV+6Wx11NE1HV1Q0VUdHR3DYqCn0sbgun8OmFbO4roD6Ii9ZLjuQHHmLxejt7iIcDuFyuamqrkYUxa+cjun10Rwlb92xg21bW7DZbRSVlOJyOJhb5WduZS7nLqlmRUsXr6xp5bW1baze1kMsHDfqWlJDmyQSk8W0FZKHRxBAlgz9bZJIXFfRVQ09rqHqILtszKzM5ZCpRRw5vYT51X4q8z3GpsxANJagfWcrof5+vFk+amprv5L1MV1Hf14eObm57Nixg61btmC32ygqKRtkV4+fU8r61n4+2NjJ859spczv3u3gtwDIoqGTLIkkdFKd0PRjBttV1ai6domyXC8La/I4dFoRy4azqzvb6A/24/P6qP6K2tXdtY92m52iYdvHDl5b0zq69hGSm9QLqLJR/4ZN8khSkO3i2KU1nDS/goU1eUwtzbbaxyEYSsft27axpaUZh8NOYbGhY0Weh/Pyajh+dhnrWvt4e307r65t5f2NHWzrDKHHk+2jTUppaA62pQbc0q5rThMYVJfNNjJuTKNCEHB57Myo8nPIlCIOnVbM3KpcKvzuVDZeNJagY2croVAQt8ebsqtmSuNXUcfCwkLy8/Np3bGD1h3b8Lg9+LJz8LhdzK/OY16VnwuW1rBiSzf/WNvGPz5r5dOtvYSDMXRNA0mE5NZf6QIZY3CDh2xSewuk6ajrutHHUdTkftUShdkuo786vZjljYVMK83C73UaxwOJhMLOtu2Eg2FcHjfVNTUTouOEezeSJKVGcyqrqiguKaG1dQeff7YWt9vNcSecyLHHncCaNat55aWXeO3Vl3n37bd47ZWXyc7JobikhPKKCqqqqqmsqqS0rILCwiJy/bn4vD4cTic22YYgCui6hqooRKMxgsF+erq72blzJ9u3baOlpZmW5ma2bGlhx/bt9HR3I9tsNDQ00vT1Mzj62OOYPn0GNrudrVu2sHL7CkpKSpkzdy5utxHx+CpEGYfCfGBNHcvKy0kkEvR0d9O8aQMul5scfz5ut4tsl50FNXksqPFz4bIatnaH+Wx7Lytbevh0SxfrWvtp7QnTFYojqGpygjIwaBI4aR6IYOwTaRPJdtspznJRX+RlVmUucyr9TCvLpjzXg99rTzW0ajJVo7enm/7+fvx+PzW1ddjthiNyIM6DGy2iKFJVXYOqKGzbtpXenh58ybRHl8eN3+tkWYOTpfX5XHZIPVu6QqzZ1sOKlm5WtXTzRVs/bX0ReiMJBEVDT0VAMlJyzFVRBQwdZRG/105JtpvGkixmlecyuyqXqaXZlOW4yfEYKxFCMrc/HCbY10skHMLj9VFZVY3NZgO+ujoOWx97emjZuAGX20O2Pw+vx0NFnvFz1IwS2nqjfL6jj482d/LRpk5Wb+thS2eYUFyBcJxQVDFSrnaDruuEowqEE8RFAaddorzIw/TSbOZV57GgJo9ppVkU57jwOGypz/WHQvR0dREK9pPr91PcUI/N9tWuj7voWFaW0rF54wZcbje5efm4XIZdXViTx8IaP+csqULTdMMxZOisG02HcEyBSJwEgKqhpdKoksfvYld9zKrMYXaln+nD2NVIJExPdxf9fX34/XnUWnb1S7SP1QPtY3OyfWzrY0dPhO7h2kddRxVFiCuEogmjQ7prgQCQRIGLltdy/rIaCnyO1PtG+ximt6fLah/TyNSxvKKCRCJBd3dXSsdcfz4ut4tst51FtfksrMnjouW1bOk02sePN3exoqWbL1r7aOuLEI4qCKo2eB/rQc5F2i8CIIiIDok8r4PKPC/TyrOZV+VnbqWf+mIfhVnOVHRRJ8Ou5lo6wq46lpaVkYjH2blzJ23btyFKEv6CArweD+V5xs9RM4pp75vC+rYgK1u6+HhTF6u39dDSEaQzHEeLJRA0PWk+R6CjJOJw2ijMcVJX6GNWRS7zavzMKs+hMt9DvteRcvpVHSLhMN1dnWk6FmObQB0nhZeTHn202+1UVlZRXFxCd3cX69etR9c0ysrK+eZ3vsPXmm5gw4YveP/dd3n33Xf4bM0a3nrjTV5+8UVkWcbpdOJxe3B53LhdLhwOJ7JNRhRENHRURSEeixMKhwiHwoQjISLhSPLaNvILCpi3YCGLlyxh8eKlTJk6BZfLTV9/Hy0tzSTiCSqrqmicMgWHw5EqtyiKX0mnMZ300RybzUZBYSH+PD/dXd10tLeCDl6fz5jn4XKS43GQ43EwszyX0+arhGIKPeEEO/uitPZEaO2N0NEfpTsUJxRTiCZUVE1HFAQcNhGvQybb46DA56A420lRjpuiLCe5Hjtuu4zDNlCZdCAcjRHs7yfc34eu63h8PhpKSlK6fdVGUXeHmfYtyTKVVdUoikJbWyudHe0InQIenw+P14fTYSfP5yDPZ0Sxzlpk6NgdjrOzN5LUMUpHf4zecIxgTCGW0NB0HUkUcNokvA6ZHK+DAp+T4mwnxdkuCrOdZLvteOwSNnlAD02HaDRKsL+PUDCIIAq4XW6K6+oH2RFzbvJXmV3qY0EBfr+f7q4uOne20Qm4vT6ysrJwu5xUF3ipLvBy1IxiQjGFrmCMbd1htvVE+GJHH5quGx1MGDYdJ8tl5+ojGlA1nYbiLMpz3ZT5PeR57XgcNmzywHz0aCxOX28fwWAfuqbi82VR0tCALA84/lZ9HNw+ZurY3tYKAng9PjxJHfO8yYhRWrqw0Z8xUtsActx2Lj+sgQW1eWhAQtGQRDFlV3M8DvJ9DoqzXRTluHZrVyPROMH+PoL9fWiaii8ri4bGKZZdzWB37WNneysdOnh8Pnyjah9jdIdiqfZRUTVkSUQWBWoKfBT6jGiFqXv68wDg9w5EF6PRGP1W+7hHMnUsLCwiz59HV3c3HTtb0RmsY57XQZ7XaB/PXFRFMKrQFYyyvSfC9u4w27rDtPVE6QrF6I8kiCVUFE03xm1kEa9dJttjJ9/npDjHRVmum9JcN0XZTrJddjwOadA6H5FojL7+PsL9fWiajs/no6Sh0dIxg0E62u2pAZ2+vj7aW1vpEATcXh/Z2dm4nA4q8rxU5Hk5fGoRoZhCXzTBzt4I27ojbOsOsaM7QntflJ7kIGtcNfo5sihglyWyXTZyvHYKs1yU5roo83sozXaRn+XA57SlHH6TSCxpV/v60HUNj8c7aXScVJ5OupB2u52iomIKC4sIhULs2L6Nzz/7DHQoLCzkwosv4bIrrqS/r48tW7ey4Yt1bNiwkZbNm9m5s5Xu7m76+vpJJDpJJBR0NAQEZJuMTbbj9XopqC6kqLCIyqoqaupqqatvoLKykpycXHRdp7u7m23btqEkFLJzsqmrbyA7OztVSc201K96BUzHHM0xdZQkmfyCAvx5eYRCIfr6etm+tQVBFHG7PfiysnE6HdhkiRxZIsfjSK0Wha6j6jqqpqfNC8AIMkJyzo0xIXyoUXVF1YhGIwT7g4RCQXRdw+Vy4c/Lw+vzDYzoWIZ0F4S0joYgCMiyTFmZsepaX18voWCI7s4ObHYHbrcHr8+Lw+HAYZNw2CT8XkdqFT49paGRYqNBWrDYmDclicmJ/UN4JAlFJRqLEervIxIOoygKHo+H/IJ8vF7fIO11Xbd0TGPX+iil6mM4FKLXrI+CiMvtISs7G7fLcNqz3XZqkhqqmoaq6chicjVrBjqhJrquk+9z8i8nz0QUBGRJ3OXvwXCY/r5+wqEguqbidLrIz8/D6/VZdnUPDKVjXl4eoXCI3t5ediR1dHsMHV1O56AOpVGXjdcct43rj55izKlhINAoIiCKjNiuhsNBNE3D6XCSl6GjZVd3ZXft46D6uI/aR5tk1FdRGLDlJrquEw6H6UvWR81qH0fMLjrKciqFNRQK0dc7tI5Om4TTJpHvc9BYYkyvMm2r+ZMmo7F+QLIuyuLQdVLTNILBEP3BIOFQEHQdh8NBXn4BXq/Xsqu7wdTRXCPFZrORl5dHbm5ucjGvEFubNyPKMi6X27CrLic+lw2fy0ZZrpt51QN9HEUb6OvoyUwAgYF5jFJyRXhJ3HUFYlXTiESiBINBwqF+VEXF5XKRl58/6XScVI4jDK6Q5saVXq+XhsYpaJpGKBikvX0nW7ZsIR6LYrPbycvLo6bmOE451YUoS2iqSjyWIBqLkojFSCgJNE1HEAVssozN7sDpcOJw2pEkGU1ViUaj9Af76erqZvuOVmRJJDs7h4aGRtweTyr9DQaEO9CWn96XDKWjz+fD5/OhqirRaIT+/n7admxH0zVkmw2nw4nT5cLhcGCz2ZFFEUkUkfZwm3VdJ6GoKIkEsViMSCRCNBZBVRREQcTlclFUWIjT5RoUFf6q5ffvDZmNlCiK5OTkkpOTi6oqyc5jmG1btyQHZmw4HE5cSR1lmw1Zkox5b3tA03RUTSGRSBCNxohGI8SiEZREAkEQ8fq85BcU4PF4dql7u9uD1WIYu+rz4d2lPm4z0hxtNpxOJ06XG6fTgd1mwy6L7H7mv9HRsYtGGlA0FiUajRGOhIlFoqhKAlGUcLlcFBYW4hqmPlp2dXiGbh99eL1JHSMR+oP9tG437KotqaPb7cHhsCPLttTicTZZwMbw93oXuxqNEI0OtqsFBcPraNnV4RlVfdT1L9U+guH8qYpC1GwfIxESSiKlY6HVPu4Ve+znRCIEg8EBHWUbDqcTl8uNw2HYVUkWh3QkhkJRVRLxRErHmFkfxT33cyy7OjzmvUnXMSsrm6ysbNRClUgkQjA4fPtoS/Vz9nwtHVAUlYSiEItFjfoYjaAmFCRJxOl0UVgwtI7pZZ1IRu04NjU1jUU5LMYZS8cDA0vHAwNLxwMDS8cDA0vHAwNLxwMDS8fJxcS7rhYWFhYWFhYWFhYWFhaTGiFznoqFhYWFhYWFhYWFhYWFRTpWxNHCwsLCwsLCwsLCwsJit1iOo4WFhYWFhYWFhYWFhcVusRxHCwsLCwsLCwsLCwsLi91iOY4WFhYWFhYWFhYWFhYWu2XS7eM4Wpqamn4KLAQagXwgAjQDTwC/CQQCnWnHNgBnAscBDUAR0A28A9wcCAReGdfCW6QYpY4VwL8DC4AqIBfoBDYA9wIPBAKBxHiW38JgNDoO8/l7gCuT/20IBAJfjF1pLYZjlPWxGti0m9M9HAgEzh+zwloMy97Ux6amJgG4FLgCmA24gFbgfeC/AoHAunEpvEWKUdbH+4DL9nDKlwOBwFFjUliLYRltfWxqanIAV2PoWQs4gS3A34FfBgKB5nErvEWKvdDRC/wrcDZQA0SBDzE0fHb8Sr5vOBAijt8GPBgV6Rbgj4ACfB9YmXQyTP4X+AmGw/gs8EvgTeAk4OWmpqYbx6/YFhmMRsc64CKgF6Oi/hJ4CsOJvBd4oampab8fFNlPGY2Og2hqajoFw2kMjn0xLfbA3ui4AvifIX7+PA7ltRiaUenY1NTkBP4K3AcUAw8CNwP/YKCjZDH+jEbHJxi6Hv4PsDF5zHPjUWiLXRixjsk+zEvAbwAf8BBwO7AT+Aawoqmpafp4Ft4ixWh0zAHeBv4LUIE7MNrEWcAz+6PfcSB0rrMCgUA0882mpqYfAf+BEZkydw99HvhpIBD4OOPYwzAegJ83NTU9GggEdoxxmS12ZTQ6vgXkBgIBLeNYG/ACcDhGZPmRsSywxZCMRsf0vxcAdwEPY3RYDxvjclrsnr3R8ZNAIPD9cSibxcgZrY6/BE4GfowRXRzKxlqMPyPWMRAIPIHhPGYemwP8CxDHGBiwGH9GUx/PAJZjOI/HptfFpqam/wG+B/wTAxk6FuPHaHT8PjATeAw4LxAIKMljC4D3gF80NTU9FwgE1o9HwfcF+33EcSjxkphOQ0PasfdlOo3J918DXgXswEH7uowWe2aUOsYzOzTJ9xMMNJgNmX+3GHtGo2MGdyZfb9i3JbLYG76EjhaTiNHo2NTUVAd8DSMl9T93Y2Mtxpl9VB8vwUg7fiwQCHTsk4JZjIpR6libfH1miLr4ZPK1YF+VzWLkjFLHM5Ov3zOdxuQ52jEG6mwYdne/4UCIOA7HKcnXlSM83mwQld0eZTHejFjHpqYmCThxpMdbjCvD6tjU1HQ5cDpwRiAQ6Gxq2iUgaTF52F19LG1qaroOyMOYc/x2IBCw6uHkZCgdL8AYTP49kJVMHa/A0PJla77xpGQ0/Zxrkq937vYoi4lgKB1XJ19PaGpquiXDeTw5+frimJfMYjQMpWNx8nUju2K+t1/NNz5gHMempqZ/ArxANsZcjIMxxPvJCD5bhSFcGGMuh8UEMRodm5qa8oGvAwLGyNsxQD3GvJynx6nIFkMwUh2Tde8WjAWNnhjnYlrsgVHa1WOSP+mffxW4LBAItIxtSS12xwh1XJR8zcZYaCwv7W96U1PTbcCNgUBAHfsSWwzF3vZzmpqalmHMqVpnLQI48YxQx2cw0hvPBD5tamp6ESPNeEHy+Fsx5j9aTBAj1LEDKMFYFGdNxinMqPLUsS3pvuWAcRwxcr2L0v7/PHB5Mhw8LMlVq/4IOIB/CQQC3WNXRIsRMBod84H/Tvu/DvwC+I9AIKCPXREtRsAedWxqajKjG0Fgv5sg/hVhJPUxjLHw2BMMjKDOxpjbcQTwUlNT09xAIBAa89JaDMdIdCxMvv4AI5LxT8BmYDHGgg5NQDuGrhYTw171c4Brk693jUmpLEbLHnUMBAJ6U1PT2RhzGf8fkL4QzkvAg9YgzoQzkvr4NEa0//tNTU0XmJo1NTXlAd9JHuNoampyBQKByHgU+ssi6PqB1b9uamoqwpin+BOMlahODgQCHw1zrISxUtU5GItyXGA5HJODvdCxDGMy+Q8wRnVOCgQCXeNUXIth2J2OTU1N38Vw9E9KX5I6GaU6DGs7jknDaOpj2mdk4A1gCfCtQCBwy5gX1GK37KE+vocRddwKNKZ3YpqamuYAHwEhID8QCMTHu+wWA4yyfcwGtmMECsqs+Y2Thz3URydwP3AChoPyJMYg3XLg1xiryJ8TCASeHOLUFuPIHnQsxtjyrwpYheH0u4HTgH6MaKQbcOwvdnW/Xxwnk0Ag0BYIBB4HjsVItbl/qOOSzsYDGE7jI8DFltM4eRipjslj1UAg0JLsmF4HLMVwIC0mmOF0TO6p+iPgd/vjPkZfNUZTH9M+owB3J/976BgWz2KE7EFHM9vm+cyR70AgsAJjr04fMG08ymoxPKOsjxdjdEytRXEmGXvQ8d8w+qf/GQgE7ggEAq2BQKAvEAg8h7EfoA1jmofFBLM7HQOBQCvGgNyvMbbwaMJwGp8GjsZYsKp3f3Ea4QB0HE2SG6OuAWYk58KlSI6EPwScjzEf7sL01Y4sJg+703EYzP2pDh+zQlmMmiF0nIGRHn5FU1OTnv7DwFYc65PvnT4xpbbIZC/qo5my4xm7UlmMlmF0/Dz52jPMx0zH0jWGRbMYBSOsj+aiOHeMT6ksRsswOpoL4OwyJzU5kNMFVCVTHi0mAcPVx0Ag0B4IBL4ZCARqA4GAPRAIFAUCgasw5j0KGCtZ7zccSHMch6I0+ZrKA29qarJjRBhPwxgVuGKoZcctJhW76LgbypKv1kDA5CNdx83APcMcdxLGSmSPAn3JYy0mD6Opj0uTr0OtKGcxsWTq+BLGxuIzMw9MrgVgLjG/ecxLZjEahq2PTU1NS4A5GIvivDqehbIYNZk6OpKvu2y5kayPWcn/7jeRqq8Io2kfzUGdP45RWcaE/dpxbGpqmgr0JEPB6e+LGIs1FAJvmQveJCvbYxhbNtwDXGs5jRPPXui4BPg0EAiEM473MpC68cyYF9xiEKPUsRu4epjzvIrhOP6HNcdx/NnL+vhxZqpNU1PTkcC3k/99YMwLbjGI0eqIka2xETiuqanpmEAg8Pe0j/0/jJUDX8s8n8XYshc6pmMuimNtwTHB7IWOr2MM4vxHU1PTm4FAIJb2se9j9N/fDwQC/WNeeIsUe9E+ioA7EAgEM46/GmMLpE+wHMdx5Xjg501NTf/AWD68E2OFo8MwlrltZcCjB7gdw2nsALYB3xtiz7hXrZG5cWe0Ov47cHhTU9NrQAvGhPEKjEnkOcBbwI/Hq/AWKUaro8XkZLQ6/hQjNedVjIVVwFhV9cjk7/8vEAi8NQ7lthjMqHQMBALxpqamy4AXgOeampoeB5ox5uccipF2fC0W481e2dWmpqYs4DyMiNTvx620FsMxWh1/hLEv4FHAZ01NTc8DEYzFcRYnf//muJXewmS0OrqBtqampr8D5kD4IRgabsDYvzrBfsT+7ji+iDGSthwjHSMHY9W3dcAfgF9nrKxZk3zNx1jieDhe3dcFtdgto9XxruTfF2HMZXRjRLA+xEhDvteaszohjFZHi8nJaHX8A8aKxoswBm9sQBtGXfxNIBB4fdxKbpHOqOtjIBB4o6mpaSHGNkdHJD/TljzP/wYCga1YjDd7a1cvwphb/CdrUZxJwah0DAQC25qamuYD/4oxfeMKjHVJdgD3AT8NBAKfjWP5LQxGWx9jwJ8w9ng09znegGFjb8qMRO4PHHDbcVhYWFhYWFhYWFhYWFjsWw7YVVUtLCwsLCwsLCwsLCws9g2W42hhYWFhYWFhYWFhYWGxWyzH0cLCwsLCwsLCwsLCwmK3WI6jhYWFhYWFhYWFhYWFxW6xHEcLCwsLCwsLCwsLCwuL3WI5jhYWFhYWFhYWFhYWFha7xXIcLSwsLCwsLCwsLCwsLHaL5ThaWFhYWFhYWFhYWFhY7BbLcbSwsLCwsLCwsLCwsLDYLZbjaGFhYWFhYWFhYWFhYbFbLMfRwsLCwsLCwsLCwsLCYrdYjqOFhYWFhYWFhYWFhYXFbrEcRwsLCwsLCwsLCwsLC4vdYjmOFhYWFhYWFhYWFhYWFrvFchwtLCwsLCwsLCwsLCwsdos80gObmpr0sSyIxZ4JBALClz2HpePEY+l4YLAvdLSwsLCwsLCw2F+wIo4WFhYWFhYWFhYWFhYWu2XEEUeTQCAwFuXYBV3X0TQNSZJS7ymKQiQSIRQMEgwFQQebzYbd6cTlcuNwOLDZZCRRRBD2HAzQAVXVSCgJ4tEYkWiEeDRKIh5HkiTsDgdenxeXy43NZkt9TtM0AERxfPzupqamfX7OidRRVVWC/f20t++kvz9IPBbDbrfh8fpwu93IsoyqqUQiUcLBIOFwmEg0SjwWQ9VURFHEbrfjcrlwu924PV7cLieSbENVVSKRMKFgiGg0giCIZGVlUVRUhMfrwW53pMph6Thy9rWOmqYiiAJ2uwOXy43b5cLt/erqaGFhYWFhYWEx2Rm14zjWaJqGrutIkoQkSWiaRn9/P/19vUSiUSRRxOXxUlJajsPhQJZ27Szquk5C1VBVDVXX0XQML1EAUQBJEJBEAUkSkSURWXLgcjjIzs4CQNU0wpEo4VCQjo5ONHUndrudrOxsfD5fqvOsqiqCIIxbh3V/It3RkCQJXdcJBoNs27aVnu5uJEkiOzuHgsIClIRCW+sOVr/3Dp9/tpYN6zewbfsWujq7CAWDxGJRNE1LPhvG+UVRRBRFHE4HbreH3NxcSsvKqa2tZcq0aTQ2NFJSVo7b5SIcDrNx4wYURcXr9VJUXExubm5Kx/F2PPYnRqtj6x511NE0dVcdHQ7cnj3ruGHjBjRFxWPpaGFhYWFhYWExrkwax1HXdVRVRZaNIimKQldnJ/39/YiSiNebhb+gCLfTMehzsYRKMJqgKxSnrTdKa0+E1t4I7cEYvcEYwViCWEJD0XQkUcAhi3gdMtkeO/k+J0XZTkqyXRTluPB7HPicMk67jM/jxudxQ2EhkViccDBIb083He078fp85OXlp6KQiqIgSdKIopxfBRRFQZZlJEkiFovR1dnJ5s2bEASR/Px8qqqq6Ozo5P333uWtt97g4w8+pKWlmXAohM1ux+v14svKwu/Po7q6Gq8vC6fTicNuRxAldE0jnogTi0bpD/bT39dPX28PH334Af949RUS8TgOp5PS0lLmzp/PQcsPZt6ChZSXlKIqClu2tLD+888or6yioCAfp9MFGAMB4gij1V8F9krH5mbC4T3rKIrGoFAiESea0rGPvt7eQTranU7K0nScv2AhBaaOLYaOFZVV5Fs6WlhYWFhYWFiMKZPCcTQ7qLIsE4/H6e7uprurE6/HR1FJKU6nC1lKdgJ1nc5gjC1dIdZs62VlSw+rtnSxvq2P1t4ofeE4gqqhq3oqykh6B1IfWFNEEAV0ScTjslGc5aS2yMfM8lzmVOUyvSyHqjwP+T4nLocdl8NPjt9PNBqhp6uTL9avIzfXjz/Pn0qbM7/HVxVVVZEkCVmWiUaj7Ni+nba2NjweD9U1tQT7+nj/vXd5/rlneeuN12nfuRO3x0NpaSlLli6ltraemrpaKquqKCkuJa8gnyxfFi6XC5vdnnIGzCiY4XTECPb109nZQWvrDlpamtm0aSObvtjAps2beP7ZZ3n04YfJzc1l8ZKlHHP88SxZuoza+gY6OtrZ0tJMfn4BpWVleL1ewNJxaB1b8Xi8Q+j4Bu072wZ0XJamY2UVJaWl5OXvSccE0WgkqWMnra072NLSzMZBOj63Wx2bW5opyM+ntKzc0tHCwsLCwsLCYgyY0F6V2XGUZRlVVdm2bRuxSASny0lNXQN2uw3T5esOxfhsRx/vrm/nH5+38cHGTrZ0hBASCroggCQgSCKSJCLIRuqa6SKmLz8ppL8KoGsQialsaO1nw7ZeXvxwC7osUex3M7/azyFTizmooYDpZdnk+5x4XC48ZeUUFBbTsbOVjRs24PZ4KCkpxWazfWWjHaazoWkazZs309HRQXZ2NhUVFWzbtpV77ryDJx9/jM8/W4vX52PK1KmcePIpzJ03nxkzZ1FXX0deXv4u5zVTl81nJR273YHD4SQ3N5eKqspBf+vp6WHzpo2sXvUpn3zyMSs++YT3332HV199hYryCo494QROPuUUauvqCYfCrFq1Cn9uLtU1Ndjt9q9s2uOuOraTnZ1DRUXlbnQ8+UvqaMfhcJCb66eiqmrQ3wbp+PEnrPjkY95/9x1ee+VlyisqOe6EEzhpkI6f4s/1f+V1tLCwsLCwsLDY10yY42h2UCVJoq2tjXAohNfrobCoCKdjIB11485+3vh8J8+t2MZra1vZ0RFE0HWwSciyBHY7mg6aZnRKVU0fFFXcI6Z/J4uIdglRADRo643w3ActPPt+M3l+D4dMKeKEOWUcOrWIqaVZ2G0ypWXl5Bcq9PV00bx5E06Xi/LyikHf70AnXcfOzg4+/+xz/P5cSsvK2PjFBp766+P8+dGH2dm2k/qGBi669FIOWn4IixYvpr6hMRUR0nUdRVHQdT01b3Sk80dNhyTdKcnKymLuvPnMnTefCy66hJbmzTz04B+58/bb6OrqJHDrr7n/d/dyymmnc9bZ5zJj1kxCwRAfvP8eZWXlVFVXA1+dqNWuOn6G3++ntKx8Dzouob6hYRx1bOZPD/2Ru267ja7uLn5766/5/e/u5dTTTufMc85lxsyZhELhr6yOFhYWFhYWFhZjxYT0pMxOnKIotDQ343Y5yc/Px+nxYksudvPZ9h6e+WQbj7/XwttftKPHEmCXkF02dB1UTUfRNFCTJ00P8KXCiruJ+mU6l7qOpuho5ucFAclpQwS6gjGefGsjT37QwvxqP6cvquLkeeXMrcrFbpPJySvA7fESCgb5Yv16KiorcTgcB3xn1XQ2FEVhzepVBENh6uvr2dLSwoN/fIAH77+ftrZW5sybx2WXX8URRx3J/AWLBqUSJhIJRFFMpUbuDYIgpCK86c66mQYpyzL5BYXs3LkTXde56pprycnJ4ZE/PcRfHnmEvz33LGeceRbnnH8BjY1TaGtt5cMP3mfq1Gl4vN6voI4h6usb2NLSwkN/fIAH7r+fnSkdr+SIo46aMB0Liwrp6OhAR+eaa7+GL8vHI396iD8/8gjPP/csp595Fuee99XU0cLCwsLCwsJiLBn3XpS5AM727dvp6e6iqKgIry8bh8MOwOb2IH9+v5mH39rEBxvaEVQdySmDx46i6iiJZJrboJxT8/cMR1HXh3YezbmPugCC+R/zPT31WVXRDb9UFJDddgTgoy/a+XB9O396exPnLqnm3KXVTC3NRna7EWUbdpuNjV98QY4/l5KSUjRNOyDT5Exno6uri1WfrqCioorsnBwe+/Oj/O6eu/n005XMmj2bq669juNOOIG58+alLYiS2K2DYUarzNc9Mdxx5mqdAE889hceuO93nH7W2Xz3n/8Nt9vF1pYttDQ3k5OTyz133cmzzzzNlVdfw2lnnUWJP5ePP/6YquoqKioqvyI6rqSionKQjqs+XcnM2bO5+trrOO74E5g7fz6iKE6Yjn994nHu/929nHn2OXz3X/4Fh93J1i0DOt5715089xXU0cLCwsLCwsJirBlXx9HcZmPz5k143B7q6huQ7Q4kAcIxhb+838I9r6zjtc/aEBQVm8uGpoOiaqDqyUggA85g0jEUBSNVNeUophzGYTqr5gRHgcHH6Ax+z3QkdR1FMa4vOm3IAqxp7uS/m7t4+pOtXHlYPecvrSbH48Bmy6XO7aazs4ONGzZQU1s7BndyYjG3Z9i4cQPbt21n5oxZrPx0JffefRdPPfkEfr+fb377O5x+5lksXLQ4Fc3SNCPis6eOu+k8jHSeaOZxpgNiOgmffbaW3956M3UNDVx19bW43S5eefkl/vb8s8yaPZtvfPPbfPLxx9x/3+/45c9/xrvvvM01113PkqXL2LRpI91dXcyaPWfQuQ8EhtRx5QruvecunnriCfx5fm4cUkdtfHVUVURJYv26ddx6y83UNzRw9TXX4nA4DR2f27OOmw9gHS0sLCwsLCwsxoNxdRw1TeOL9evJzsrC4/UaKywKsKKlm1ueX8Of322mvy+K5JZBtpFQtLSVUdnFYTR/1zQG/L9BK+GkRRNHQmrFnEwHdODEmqKhAaLDhgR88FkbK5u7eHl1K988fhrLGwsR7Q7yCgrp6uxg7Zo1NDQ2YrPZDpjOqgB88P772Gw2pkyZwqOPPsIdt/2WDV98wVHHHMMVV13NkUcejcfrRVXV3aYI6skI7766L5lOYzgU4vbf/obmzc38+3/9P5YedBBtra3cf9+9dHV18b//9xOOO+FEGqdM4dVXXqavv5d/vPYqn376Kddcex0XXnwZsViU1159hUWLl+DxeA4YHSFTxz9xx22BAR2vvJojj5oEOkoS4UiE2wO/pXnTJv7zv7/PwsVLaLV0tLCwsLCwsLAYN8bccTQ7Z4qi0Lx5M/n5eeTk5iFJIqqq8fs3N3LTc6tZubEDwSZi85gOYzLCKAqkhQIHz03UdMryPFx0UC2BFz8jGFNIeY5DOn8jJHV88tq6MPBeMgqpKRqaADa3jbiq8egbG/i4uZMbj5vO1YfX47LbKCgsxuvx8vnnn1FbW4fb7d5vU+VMHePxGK+9+ipTp00jHovzk//7IQ89+ABOh5N//td/58JLLqU2GWVVEgnk5F6XmeeCwXPa9hWZqZF/e/5Z/vLIwxx1zLFccNElAPz1ySd45qmnuPZrTRxx1NFomsZfn3icVStXcMHFl1BWUcG9d93JTT//Ges+X8c3v/NdZsyYyepPP2X6zJl4vd4DU8c/Tl4dX/zb8zz68EMcfdzxnHfBRejA038dQscnH2fVp0kdy4fSccYBoaOFhYWFhYWFxXgzpo6j2fELhUJs3bIFf54ff34BAtDRF+XHT63i3lfX0dMXQ3bZUFSdREJLOoykRQ/THEDTGcT4+8XLa/neGXPojcS544XPEG0SWrqzmIpMwqBw5JBzHzOczPSU1sy/iUY5EgkNRAGb28YX23r5tz99wIqWLr53+hwq8z24PF7q6+ppbm6muKSE7Ozs/a6zauqYSCT45OOPmT1nLlu3tPCzn/wfL77wN6ZNn8G3/+mfOfHEk3G53SiKYsx9y3A29nVUanflFUWRTRs3cvMvf0lBYSFXXXMt+fn5fPDee9z+298wc9Zszr/oYrxeL2+/9Sb3//4+6hsb+Pr/Z++8w6Mq0/99n3OmpfeQkIQSeu8dpIOiqIBY6SBgFNu69bu77q7b1xVr6B1BRUEFQaWI0ov03klo6SE9M3PO+f1xZiaTkECCBOL+3vu6xsnMnPKe8xm83s88z/s8L75Mnbp1kYD3pk9n5WefcOXKJX7129/TtGlT9u7ZTdt27QkODv7f1PEXv+SBBx/CtwbpePHiRab/900iIiKZ+OwUQkND2bt7N4nvv0fLVq15ylvHBQtp2LARL0z739VRIBAIBAKB4F5QbbMl9yQ1OzuL82fPUrdeXcLCDNN48up1Js3dzttfHyW7wIHJx4TTE2V0Gz0vk+h53+tvTaNFnRCe7dsIX6uJV4e0oHX9MDSnVno/96NUDis3fu6OcHp/7v1cXpqsO31W13E4NRSriQK7yvzvTjF+1lb2nMtAkSVMFit169Xl2tWrZGdne4qL/BzwNhvff7eJmNg4jh8/xq9fe5UN335L/wGDePvdDxjx2OMe02gymUqZCneLheqITLmPX3a8drudWTMSOXPmNE8+M4o+ffuRmZnJ/HlzSElJYfLU52jZsiUZGeksWjCflGvXeOW1XxFXpw55eXmcOH4MHZ3OXbqwd/cefvHyi+zauZMOHTry497dZGRk/G/qOPJxj2msETo6HMyZmcjpUyd5ZvQYet13n0fHtJRUJk99jhbeOqZUoGPnLuzZvYfXXn6R3T9jHQUCgUAgEAjuFdVmHCVJIi83l6tXr9GyVUtkxYwsS+w8k8aEWdv4cu9FdCRkRTJMo7ET4I4s6q4MVcnLNLo2w6i0OG1QU+IjA1A1jcZRgfz24VbYrKaSi5IkJFlytQko/ZBlGUmWvI5vnKK0UXWd3x2t1Mu8xjVO17aqqiHJEpIis+nIFSbO2co3hy5jUmTMFiv169cn5do1rv+MzKMkSeTm5nL44EGaNm/OyRPH+d2vXuPQwYM88eTT/PPN/9Kxc2dUVfUUTfGmOo2G9xjd53K/3vDtN3zy0TK6de/O2HETAPj263Ws+nQFI0aOZPADQwBY8+UXrP5iFU88+TS9e/dFkiQ+XfExq7/4nF739eHdD2YyJSGB1JRr/PH/fsvaNavp1r0nhw4cICsr639Ex6dqrI7fbdjAR8s+pEfPnoweOw4o0XH4DTp+bujYpxwdE2cyNSGBlJRr/OFnrKNAIBAIBALBvaLajKPT6eDKlSvUr1cPu1PFYjbx3dFrPDt3G9tPpiCbTOi4qqFSJpqnu00ZJe+7XkiyhO5UGdAimsc613VNNo3PhraPY0LvRmh2J4okgUNFL3aiFTlRixyoxQ7UYuNvrciBXuwEV6RTliVk2TUxLhux9JhG13i8i+jgZTAlCV3T0XRQLCYOX8jkuQU7WLUnCUWWMVms1I6pTW5uLjk5OciyXCrKUhMpLi5m5/bt1K1Xj2NHj/C7X/2Sc2fPMn7CRF7/yxvExzfA6XQiy3Kp6/GOTt1tLl++xDtv/RdfX1+mJLxAragojh07yntvTye+YUNGjx1HSEgIhw4eYP7cOcTG1WH8pEkEBQdz7OhRFi+YT1hYONNefplGjZsw9JFHCQ8P59q1q7zx59dZ88XntO/YgSOHDnl0rOmmo5SOR47w21I6/rVG6njt6hXefutNfHx8mJrwAhGRkbfQMe5GHUNvpuMXLh0P/mx0FAgEAoFAILhX3PE1jrquo2ka586eIzw8HLPVhiJLfHf0KgkLd3HiUhaKxYRabvsMSqJ6JQcsta5R0sHPx8JL9zcjLMCGpusosoSq6fhZTbw4uCkHkjJIzsinXrg/tUN8qRfhT1iADV+zjMUsU1Csci27kAvpeZxPzeNiRj5pucWodtXwgUpJZEX3HkdZk1c2fdXrWVV1FIuJ89fyeGnJLnR0hneqi83HD5vVxsGDB2nTpg1mi+VOS3BHcBuGg/v30659ew4fPsRfXv8jFy6cZ+Kkybz6q18TFhaGqpZU2izVtw+qXpToJ4zVfV5dh7mzZnLkyGGmPJfAwEGDycvLZcHcOVy8eIHX//JXOnbqTM716yxbuoSTx4/z7/++TbPmLSguLmbhvLkcP3aM/3v9T7Rp25bCwkKWfbiU7OvX6da9BwcP7Odvb/wZHz9funTtxqGDB2jXvj1+fv41skpnuTr+6Y9cvHCeCZMm84tb6AjVv5bRe6ze5503ezZHDh9k6vPT6Nt/wO3p+Meb6fgnfPx8fhY6CgQCgUAgENxr7qhxdE+4Ll64QEhIMMGhYcgSbDuVyrTFtzCNnuI3FR9fUWTUIgdP3deQPs2jXCfF6K/o2q9hVCAfTeuN3aERHmDFapYxybIRTaTkVKqm49R08oucpOYUci41l8PJ2Xx/IoW95zNIzyk0ju1KZ5UkyZWh6rXmsez6x1LprTqqqqFYFJLT8nl16R4sJpmH2sXhVCWaNWvKpUuXqFuvXrWnAFYVd1P4fft+JCwinAsXzvOPv77ByRMnGDdhopdpVFGUckzjXbyWsu03Nqz/lo+WfUi7du2Z+OwUdF1n44YNfLT8QwY/MIRhw0cAsGnTRpZ/uJShjz7K4CFDUBSFLz9fxeerPqNHr1488eRTKIqJ9d+uZtWnK+jcpStvvv0uy5Ys5p3p/+Xvf/kL/3zzLRo3aczhQ4fo1LlLjSuw4tZxfzk6jp8w0cs01jwdv9u0kQ+XLqZ9+45MmPjsHdXxv2+/y4eldPwvjZs04cihw3Ts3LnG6SgQCAQCgUBQE7hjMyRVVT2m0dfXh5CwcGQJjiZn88rS3RxNKsc0wo3P3pQxlapTI65WIJMHNMbXdSzZ5Rjdk11ZgrhQPxrUCiDI14LNbMKkyB4/qrm8nkmRsZkVwgKsNIsJ5sF2cfx6aEtWvNibb389kLee6cR9zaLwtZhA1dBdKWyedZGloqR66TF7feY2jxdTc/nV8r1sO5mKSZGx2Hzx9fXh/PnzSJKEqqp3SoqfhOqqpHn2zBlMioKExPQ3/8PePbsY8djIMmZDAShlMu6G2XBH0dznc58/JSWF99+Zjqo6SZj2IjGxsVy8cIF3p79FrchajJswifCICM6eOcOcmTMIDg5mwsTJ1KpVi3Nnz7Jw/jx0Haa99CoREZEkJyXxwXvv4e8fwHMvTKNOnTpMTXieCROf5fTpk/znn38jIz2diIgIDh86VEN1PI2imG7Q8dWaqCPG+dPS0nj/7emoqkbCiy8RXbu2l45RXjqeZs7MRIKDQ8rV8cWXXiUi8kYd427Q8e9kpKcTHhFe43QUCAQCgUAgqCncEeOoaRqKonDxwgVCQ0Lw9fPHpChcySrgteV72XsmDcVcjmmsaH3fDUYMTLJRSXVKv8a0rRPqmuS6Nym9DkvTdTTN9fCanIL3skn9hu0A/G1m2tUL5ZUhLVj9i/6serkPE/o2JjrE1zCQqlaShumJkpaNPpY2w4Z5NHE8KZtfLd/LqavXMZsUQkLD8bXZuHLlCoqi3PPJqqZpKCYTFy9exOlwEBwSwtw5s/h63Vp69+nHL3/zW6Kioj2mBEpXwrybeBsbTdOQJIkFc2ezZ88eHnv8SQbd/wDFxcXMnzuHkyeO8cyYsfS67z4K8vP55OPl7N61k0mTp9KhUyecTifLP1zKzh3bmfDss3Tp2hVN05g3ZxZHDx/iyWeeoU/ffjhc9+SFl19m2IiRbNu6lffeno7Nxxen00FyclIN1NFJcHAwc2fPrPk6qoaOC+fPZffuXTzx1FMMGDjI0HHeHE4eP+aprFqQn8+Kjz9iz65dTJo8hY7l6Ni5Sjq+jc3Hp0bpKBAIBAKBQFCTuCPGUZZlnE4nTocDxWQiIDCQYoeTv35+iG8OXUYyKahlq5V6/12WUimsICkSzmInnRtF8lT3+pgUGVXXkcuJjkiShCxJnmI3sisNtLyHezvvDFm3odR1nQAfE4NaxzBnYjdWv9qfhEFNiQzyQXeqrrTXMu1CPOaRksWRZczj9hMpvP7ZQbLzi7FYzISEhVGQl0dRURGKotzT4hyyLONwOEhPSyUwKIhN69ezZOECGjVqzC9/+1saNGxkmA2vtXDGJd79lEb3ud0Rsx++38yypUto2rQpk6cmYDKZ+OH7zXy4dDE9evbmqWdGAbB7107mz5lNv/4DeGT4cCwWC99/t4mPl39Iu/YdePqZUdhsNjZtWM+Kjz+mVavWTJj0LABmsxmn00l0dG1efvU1unTtysrPPuXjZR9Sp05dUq6lkJ2dXfN03LCeJYsW1mgd3T8+bd+6haWLF9GsWXOenTLVo+2HSxbT477ePDWqRMd5c2bTt/8AHhk2HPNP1nEFHy9bVqN0FAgEAoFAIKhJ/GTj6P5V/vy5s4SEhmLx8UUC5m8+w6ItZ/B0ULzBXFUwSS27htBl6hSzQsIAd/sN3aia6kVFk169TMSx7Gnc+5Z6lNkfoEN8GB+M68qiqT3p0jgSXdVKrwMrzzx6vw+ouo5kVlix6wLvfnsC0LFabYRHhJOcnFz+/bhLuHU8feoUAf4BnD93jpkzPkCSJF7+xWt07drdWH9WJq3xrpsNSq+/k2WZjIx0Et9/j5ycHKa9/Cr16tfn6tWrRmVVHx+enTKVqKhorly5QuIH7yPLMhMmPUudOnW5fPkSixbMJzsrm2kvvULdevVJS01l5oxEiooKeeHlV6hdO8bzPTCZTDidTlq0bMlLr75GRGQks2fNYP++fURFR3H2zGng7powb0rr6M/5c+eYNSOxxunofW4w7ldWVhYfvP8euTk5vPjKq8TVqVtax8kV6FjXreM8srOy/id0FAgEAoFAIKhp/CTj6I722O12VKeTkLAwTLLEtlOpTP/6GAUFDkwmGV0rp3pqeXj6JOqe1yZFRit08miHOIa2i/FsWtkJXUWT4pvtfkNk0rWO0qlq3N86ho+n9WZ8n0ZYFOPa3J/fYB7dJ/J6X5IkNFVjxoaTfHXgMpIENl9/QoICSU5OQpblu54i59bxypXLqKqK2WJh0cL5HD92lDHjJzD04UdKTfDvadVJqUR7p9OJJEksWbSQLT9s5rHHn2Dw/Q+gaRoL589l/74fefzJp+g/YAB2u53PV37G5o0beGb0WHr37Yem6XyxaiXffL2OZ8aO4b4+fQBYungRO7dvY/hjIxk4cPAN16soCrquM2DAQJ6dMpW01FRmz/yA4uJiJFnh0qXke7JO7gYdrVYWL5zPsWNHbqmjt5m7G3j/u3Tr+OGSRXz/3SYef/IpBg4aXAUdNZeOX/PM2LG3qeNzNUZHgUAgEAgEgprITzKO7lSu0ydPUqdefSQgM6+Y6euOcTo5G7PNhNPpSvUqz1SVRdJLm0dZwunUCA31ZXL/xoQG2HCqGopcOdPiHW2s1ITYa5vytjcpMqqmUzfcnxnju/B/j7YmwGZCc2pG0ZzyrrPMa03VMFkUrqXl8dbaYySl52GzWrD6+KGpKg673TOhvVu4dbx27RohoSFs+WEzKz9dQc9evRk1eix+/v6eHn9lUwzvFuWlqJpMJnZs38aSRQupV68+UxJewMfHh23btrJk4QLatmvH+InPIskyhw8dZMYH79GxcxeefPoZbDYbe3btZOG8uTRu3JjRY8YREBDA9m1b+XDpEurVr8/UhBewWC2lrtW7+qfZYuHxJ59iyEND2fDtt3yxaiWxMTFcuXyZwsKCu57q6D5filvHzZv5rIo63s3WG1Ci457du1g4fz714+OZPDUBq83Gtq1bKqnjrp+u4xNPMuRBbx1j75mOAoFAIBAIBDWR2zaO7l/hr1y5THTtaBSz0Y9w2Y7zrN6XjGRVcKhlC8bcJOKo6yXbuArimGUJHCqjesTTu6nRfkP2Mo23Mi/uqIZ7kqhqOg6nhkM1Hk5VQ9W8UlnLjK1smquuGz0jdV3HYlL4/SOt+edTHY11j2oF5rFspFUCh1ND8TGx6cgVFm05C+j4B/hjtdq4cvUKwF2bqLp1TEq6iMVsJjMjkwVz52Kz2Rg/cRJNmzXD6Szp8XevI47u8yqyQnZ2FnNmzuDa1au89MovaNSoEVmZmbz/9nScTidTnn+B2Lg4sjIzmeFKgRw3cSKNmzQhPT2dJYsXcvHiRRJeeJHmLVpyPfs68+fO5uqVy7zw4is0aNiwVCqn93UrioLD4SAmJpbRY8cRW6cOC+bO5cL58wQGBHLlytW7el+8dTS5dZxXOR3LfscdDgcOux37HXq4j+V0Oj3n9k41zsnJYfaMRK5dvcLLr75GfIMGho7vvF2ujnk5uYz36JjGksULytfxahV0jI1l9LhxxMa5dTxHYEAgV++yjgKBQCAQCAQ1ldsyjrque1JUdU1DMVuxmk0cSc5i/nensRc6UBS5xAy61iqi3zQ/FI+x1EFWJBxFTprVDWFMrwZYzQoOVfMUxKnsOL0nx4osYTbJmBXjYVJkwwgCdpehLFsopKSxfOn+du6+jgkDmvDeuC5Eh/iiqzqyIrtPXn56ric1UELSdRZ8f4btp9KQgKCQMDRVpbi4+K5EObx1zM7KIjgkhK/XfsXuXTsZNuIxBg6+H6BUX7t7YRrLntPpdIIEHy9fxtfr1jJ8xEgGPzAEgMWLFrBt6xaGjXiMBx54EE3V+GrNatas/pJhI0by4ENDAVj/9dd89sknPPb4Ewy8/wEAVnzyEd9+/TUPPfwwDz089IZzlzVZZrMZXdfp3acPI594kgvnz/HJR8sJCArielYWWVlZyLJ813UMqYKO3t93d3Vas9mM2WLBcoce7mOZTCZUVfWc0+lwIEkSKz5azrqv1jDy8ScZ5BpriY4jb9Dx0RGPMaQyOg6too69+96gY/Zd1FEgEAgEAoGgJmO6nZ3ck7CrVy5js/ng7++Pruss33Ge/WfTMfuacbhTVKnAQJU+oMcwGtu73lckxvVuRIf6YaiajlmpmoHxbs8hSxIZuUVczipwFTyV8LEoBPmYCfWzYjGVHNuhapgV+ZbmUZElHKrG413qkVfo4KXFu8krdCCZXes6b7iu0imrFquJ85ezWbL1LO3qheJjs+Dj68fVq1epV69e5QW5TdyVLNPSUpElmZRr11iyeCENGjVk+GOPERwcjMPhwGw2V/tYKqLsPVdVI2r24969LJg7l9oxsUx5/nmCgoLYvWsXixYsoGHDRkxJeAGz2cypEyd4753pNGrcmLHjx+Hn58/RI4eZOeN9omNqM2bceMLDwzmwfx9LFy8kNCyU56e9hL9/gKcZfUVjAiPSZ7XaeOSRYXy3YT2ffLSMh4cNIzq6NlmZmYSEhFS70fbWUfLWseGtdfT+bsuyTEF+PkeOHCErK9N9te4tK3jtjV7m/ZLXZrOJ+PiG1KtfH3ClqJrNHDywn3lzZhMbF8fk5xIICAwso+Pzt9AxkeiYmPJ1fKGKOtqsPDJsGN9tvDc6CgQCgUAgENRkqmwc3RNMh8OBopjw8fNHkSV2nUnj4x0XkGSMfo3gZRRvURTHY66MTU0mGWeBg75tYniia72SDygp5FGVSZym6ciKRFJ6Pr9e/iNn03LxtZiwmRUiAm3UjfCjQ71w2tQNoWl0IAE+Fs81uNdTehcR8TYyZkVG03VG92zA2dRcpq87RqFDKzGJ7mgrZc2zjl3VkawKn+6+yLBOdRnUqjZBwSEUXblEcXExVqu12qIc7iiV0+nkUvIl6tapw9y5szh75gyv/OI1unbrga7rpczGvUpRLRU1Vkzk5OYyf+5szp87y5tvv0vz5i3Iy8sj8f33yMxI5zf/93/Ex8dTUFBAYuL7XLl8mT/++S+0bdeBnJwcli1dwtEjR3jj7/+kU+cuFBQUsGTRQo4dPcpf//FPWrVqDZS/3q9stMpdnbNdhw4MfWQYf/3Ln/h4+TL++Ke/cO7sGWpFReHn51djdfSOOF6+fIl3p7/Fxg0byM257pXC7d6xnL/LekX3Z95Ixn2Kj2/AlOde4KGHH0aWZfLz85k/Zzbnzp3lrXfep2mzZuTl5TLjg6roeLiUjku9dWxzGzq2vzc6CgQCgUAgENR0qmwc3dGNjPR0dCAgwB+nqrFqbzJnL2dj8TVjd2iuSaU7yiaVH6DAaxv3JFSWcDpVfAMsTOjTiLrh/jicGmZT1bNq3RNkd1Gb1nVDqV8rgPU/JoFZAVUDzZj5mnzNxEcG0LZeKE92rc/DHeJQZAmnqmFyRTorMk6apmM2yUwb2JTVBy5z+HwGsllGc5tF98WX3VfTMVsU0tPzWbHrAj0aReBns2KyWslIT6d2TEy1GTVvHX18fEhNS+WT5R/RpGlTBj8wBJvNhtPhwOQyHDUhRdWINpr5YuVnfL7yMx4dMYIHhjyILMssX7aUTRu/5eFHhvHQw48AsOHbb1j16ScMGDSYkY8/CcDWH75n8cIF3P/AEIY+8giyLLN2zWpWfvYp/QcMZPiIkUiuyrbuIkVlr7vsOjn38+AHHuCrr1az+ovPGTVmDKFh4aSmpFA/Pr7adUz36JhWZR0lSaKoqIgP3n2XeXNmY7PZkGX5jo3Z/WPTju3bSEtLJyg4mPt692bVpytY+dmnDHtsJPc/8ACSJLH8ww/ZuP5bHn50GA8NNXRc/+3XrFrxCQMroeNn3jpKd0DH0XdHR4FAIBAIBIKaTpXdmHsSVlCQT1BwCBJw5FIWX/yYhKRIODRXhE2SSvViLBfvCqquX/9NsgR2jYfbx/FIhzjjPaV0QZyqtOIoiSoYaxxfGNSUlg0jQAZrgBVTgBVzgBUNOHU5m09+OMOU+Tt4ZcluTl69jkmRcapaqeN5j8FtLE9fzeFPKw+RnJ4HioRWNupScgHuwYEEdqcRdVy9L5n9F430wMCgEOwOO6qqVttE1bsCZ3BICFu3/MDZM6fp138AnTp3QdM0j9moiLvRwsGzHs5pmMbDhw8zZ9YMIiIieHZqAuERERw6eIBF8+cRFRXN89NewtfHl0vJybz79nTCwsKZ9OwUgkNCuHDhPB+89y6BgYGMmzCR2Ng4Tp86xaIF87GYzUx76WXCIyI8ZgxKR5orulZ3xK9V6zYMGDCQ69ezWbtmNcEhIaSkXvO0m6gOSusYzNYt31daR+/iT8lJSaxbu4aAgAAsFovHON6Jh7ugVUREJGdOn2LHtq2cO3uG2TMTiYysxZSpCYSGhXHo4AEWL5hHdLRLR19fkpOTeXf6dELDw5noreO77xAQEMBYj44nWbhgHmazmRdeeonwiAiPaYTKVYstV8ev7o6OAoFAIBAIBDWd2yqOU1RUhKpq+Pv7Azobjl7jRFIWJqsJ3V1JtYJKpaWQvNYBArIi47Q7iY0KYGKfxgT4mHGqWqnJWlUnbiVVOGUcqkaruBAe61IXRZEpLlZxOjUcDhUNUKwmzH5W0q4X8t66Y4ydsZV1By5hUuRSKX1GIRGX0VVkPv8xmbGztjJr/XGyc4vLpKmWaUFSXtTRrJCSlsfaA5dxOFUC/f0AieysrCpda1XJy83F4XTidDj4fOVn1I6JoV//Afj4+JTqXVeRWa/uCqveVTBNJhMF+fksnj+PY0ePkjDtJdq1bUdRYSFzZ8/k3LmzPP/iS57qoXNnz+TE8WM8M3oMPXr1oqioiBUff8TuXTsZNcbo/2e321nx8XJ2bN/Gs1Om0qlLV895b3at5VUjde/Tt98AGjVqzJeff052VhY2qw9ZmZk3HO9Okpebi9PpwOlweunYv9I6AmRlZ1FYWIimaTidTnRd8xjLn/rQNB1N07Db7UiSxPnz5/jgvXc5ceIEz73wIq3btKHQS8eEaV46zpph6DhqdImOH33Erl07GTVmHH1cOn7y0XJ2bjN07FxGx7LVkb2pUMf+d19HgUAgEAgEgprMbRnH9PQ0AoKCkCW4lFnA2n2XQNNLomze0caboZfeTnI9P9alLgNaRpekid5qYnqzh2Y8dFeBHIAxvRrSrl4YOFWj+qsrrVZVDRMpm2QUq4ldp1KZNHc77397Aoeqo7sWbzlVDVmWyMwr5o+f7mfK3O3sOJGCyWpCNste6bcVFAUqE3V0aDqSSWbtgUucS80DwM8/gKKiotuRp9JkZmbi5+fLqZMn2btnD526dKVTly4AnrYNN8PbiLgNh6qqqE4nqur62/XQVBVNU9E0rcKHt6be1Tfd5mfd2rUs/3CpUS1z6MMoJhMrP/uUNV9+yf33D+HRYSOQJIkfvv+eZUuX0LFTJ556ZhQAe/fsZlbiB3Tt3p0RI5/AbDaz+btNLFown67du/P4k09jNptxOByea/MeizflmS9FUVBVlXYdOtC5a1cuXDjP7p07CAsP59q16m3pYOjox6lTLh07d/GY4MroCO5/At4Vhe9MtLEk6mhUJXX3z1z56QqGPDTUs95x1UpDx0H3D+HR4W4dN/PRsg/p0qUro8eOBwwdZ8+cQfeePRn5xJOGjps2smTRQrp278ETTz2D2WxBVdVS/Sory73UUSAQCAQCgaAmU2XjqOs6hYWFBAUFA3DgYhY7z6ah+JhQVa10WuqtJmzuiCNG5E4tVmkbH86zfRobg3P3RbzVpPRmD9l4uKugAtSP8Of5QU3xD7ChOrVS50Ey1iyqqobJauJKRgG/Wf4jH6w/joRRRdWkyOw+m8bYmVv52+eHSb1eiGI14VQ1IxJZNuJ4w3WXfk/XdEwWEweTsth9Lh2AoKBA8vPycDjslRenCqiqSlpaKoGBQWzc8C2SBN269yAyshYOV5uEkuHeeA3eUTf3GjaTyYTD4cCpqTgcTux2O8XFRRQVFZJXkE9ebh45OTlcv36d69ezyc7OJjs7y2h5kJlJenoaKddSSE9P86RKunsPnj51khkfvEdIaAiTpkyldkwMJ44fY+H8efj7B/DCS68QFBREWmoa778zHUVReHZqArVjYklKusj777yNDjz1zCgaNGzIiRPHWThvDgWFhYweO47gkBDS09LIzc0hIyOD9LQ0UlJSyMhIL5Xu6J2yWjZapWkaPj4+9Oh5H4GBQaxbuxabxUJ+Xj7FxcXVr+P6b5GAbj16VlrHEj1vvc3t4H2fNE3DbDZzKTmZsPBwJk2eQlRUlKHjPEPHaR4dU3n/7beRZJmpz79AragoUlNSeO/t6Wi6yrjxE2nUuDHJyUksmD+PwsIiEqa9SJ06dVBV5w2a3ey6ytXRdnd1FAgEAoFAIKjpVLk4TlFREbKk4GOzUmR38v2JFArzijD7WVFVr/WMFZmmUu+7KuJIEk5VR7EoDGgZTYiflVNXczApEqor0md0t9DRdVzRxJKokKbj2cYdZSzZTkfTQANP2pyOjk1RqB3iw6l8O7jXUHpHAnUdp6qhWBTy7Q7eWHWIiAAbT3ePZ8aGk0xfd5TTV66DoiCbZMM0VwXPfTDOpQEUO9l49CqPdogjwMeCxWolPy+/igpVjoKCAiRJorCwgO82baRevXg6dTaijbfq21heD8A9u3exeOFCsrMyUV2RRV3X0V1piqqmomsamus9Y1+3CXNrqRlfB1miZ6/ePPfCNEJCQigqLOTDJYs4sH8fv3/9z3Tp2o3iYjuLFy3k0IH9vP7G32jV2qiguXjRAvbs3kVAQABLFy5k2dLF5OXmcXD/fmw+NpYuWsSqTz8lOzubs2dOY7VYmDdrFh8uXmREFiXJGLNrLCaTiQGDBjHlueex2Wyee1FR1BGgU+cuNGrchN07d3At5Ro2HxvXr2ffUf3cGDrKFBYWsHnTRurVr7yON1ANS1XLGld3hdfRY8bSrXsPiouLWVKOjksXLWTPnl2MmzCJAYMG43Q6WfXZp2z+biNjxk3ggQcfwul08uXnq1j/zTqmJLxAn779XNd582JWt6K0jo3vio4CgUAgEAgENZ0qG8eszEx8/fwASMkpYsvxa0iKjMc3SSVmsELKTWHVQZJZvuMCq/dfchnGErPo/ht0TzDPbRbdaMZGN+6Hu5Wi8YZu/Id8u1pSyMaTVkqptYmqqqGYFLJyinj9s4N8tPMCW06kkJNXjGI1obpSYY3UPq/zuK+/vHYF7vvk9ZmqGUVytp1M5VJmAc1iLPj5B5Cfn3cLRW6PzMwM/Pz8OHP6DOfPneXBoQ/TvHnzUoVhjGFWvN7PHWk8fvwYv/3Vaxw+eBCT2UxIaKhhWlzXZzzpRmryLdA0jfT0NI4dPUZYeDhTnktg/fpvWLxgAQMGDWLY8BGYzWa++HwVn378EX37D+Cxx43U053bt7Ns6WIAcnNz2brtB2w2G7k5uQQFB+MfEMDly5cpyM8nJ+c6ISEhBAQEci3lmmEyygjlVJ1kpKVx8sRx6tWrz6PDR3h6ArpNiff9kWUZp9NJg4YNadW6Nfv37WX/jz/Sp39/UlNSb0+oW5DlSjc+c/oM56qoozd6yb+SO4p3hNZisZCTk0PvPn15+NHhKIrCmtVfsuLjj0vpuGvHDpYsXkTjJk2ZNGUKsixz6OBBZrz/Ls1btGL02HH4+Piwc/t25s6aSavWbRgzZhy+vr6eCHXZa65M1NFNaR3bsn/fvmrXUSAQCAQCgaCmU2XjaLcXExoRCcCpqzkcuZSNbDGhat5pquWs6fNa7+cJSpb5XNU0LqfnGQ7wtjPmvI9ewUG8Wn8ge63F9DZzZcwjZoVzabmcu5oDioRsUYxej14mUXJ7I++1jTe7DslrjLqOYlI4m5bHkUvZNIsJJiAggKSsjNu9ETclKzOTiIhIdu3aCUi0btOWwKAgHA6HJ1JVmcm2qqp8/dVX7N+3jxYtW/Lqr35DmzZtSiI/elktKziernsM/a5dO/jDb3/D5k0b6NGrFzM/eB+rjw8TJ02hbr16nDt7loXz5iLJEi+89AoRERFcz85mZuL7XLt6BVlWaNK0GdNefoWlixdycP9+Xnn1lzw8bBhXrlzmzX/9g907d/Lr3/4fAwYNpqi4GLPZXCpd0R0B3bj+W/79j7/zxapVPDj0Yc+9qciUuAv5tGvfnk8/+ZhtW7fw4NCHuXL58m3pdCsyMzN+so7GRtUScLwhMq1pGrVjYqlTty4Xzp9nwdw5yOXomJWVye/++Dp169YjJyeHD957h6zsLF589TVat2lLWmoqC+bP5dq1a/zqt7+jSbNmOBwOTCaT50eNiiLlFd6CCnX8qNp1FAgEAoFAIKjpVNk4Op0qvj4+aJrGwaRM8vOKMfmaoaI01XKii7qX4ZJko4iMp66OSTaK2FTk+zzGrvyPbo3nTGje6xDLrkn0Nn+uzyRJQrJIRhqsV6hTxmgj8tyAJnz5YzLnU3KRTLLHCFUYfdUll3ksGZZe5GDPuXQebh+LzWb1pN3daYqKi5Flmb27dxIWFk7LlhU3Sy8Pd7SxsLCQXTt34OPjw/DHRvLoo8N+8thi4+I4sG8/n6/8lNf/73ccPHCAKc89T59+JRU0t235gd/+/o+079gBgBUff8SWLT8gywomk4kJk54FYM/Ondz/4EOMHjsWP39/tm3dwpbvv+exkU8w8smnXJWBKyYurg67d+5k965dHDlymHbt2t9gQLz/dkf5WrZqTVRUNAcO7qewqNBTmOlOU1T003QsoTpsY3lnMQy5qqos/3Ap27Zu4Xd/+CMdXDp+uuJjNm3cyCPDhvHQ0IcBWPPF53yzbi1DH36UJ558Cl3X+ebrtXz5+SqefmYUQx8xvnNmr7YjZduolNdKpyzl69jK0PHAfgoLq09HgUAgEAgEgppOlY2joiiYFIXrBXb2XchEcqWOAqVNV3lIEr5mhWmDmnLyag4bjl4lr8BuGEFZRpINU6ZqVVwveFuUXs9Y7rjLmEdXJmzJe5qOrMhodif92sfy+vA2BPlY+Muqg66jV+BwPcfHZR6N8ai6DrLE/vMZ5BY5sZpNWCyWO3rVbkyKwvWcbE6dPElsXCyNGjcCqFR6o3dk7vLlSxw7dpSo6Gj69O0PGD0X3emcVUXTNCwWC3369WOFK9LTrXsPnnxmFGazmY3r17N40QK69+zFyCeexMfmw6GDB92rps0AAFqwSURBVFiyZBGFhYWYFIXYuDhiY+P4w+9+Q0RkLSZNnoKfvz/Hjh5h+pv/JjYujvGTJuHv74/dbsdsNqOV851zp1f26T+Ab75ex6YN62nXrj2aqqK40iHLGhF39dD4Bg2oU7cOe/fs5vKly/jdwqDeLiZTWR2NwlJVSVMF9/e6WoZYahyqw4mPjy8/bP6OJYsW0L1HTx5/4ilsNh8OHTzIwvnziYqOImHai+Tl5bF1yw/MSPwAPz8/WrRuxenTpzl/7iyzZ80kICCA+AaNOHL4MPn5eZgUE06nE03XUJ0qkbUiadmyFRartcrrHcvV8XL16SgQCAQCgUBQ06mycbTYbABkFdg5dikbXZaNyF250UG9tDFTNdo0COdXD7XE12piy8kUlmw9x7eHLpNyvcgwGq4qqO6CKaX2rxJSSe6o27+5DFqpdNabRQThxshjqfeMMQb4WZg2qBkhflbG9W7Il/uSOHAxE0mW0W9mTKHUfdN1wKRw8moOKdeLCA+wYfP1vY1rvzW+vr4kJyWTkZFBt+49qB0TW6ph+q1wT8IPHzxIamoqvXv3oVHjxqiqWukWEDcjNjYOf38/LBYLTzz1NI0aNSIpOYn58+ZQVFRIwosvEhsXR0FBAXNnzyQ5KYl69eqTdPEijRo15ovPV3Hu3Fl+87vf07FTZ3JycliyaCHnz53jjb/9k7bt2uNwODzGvLzrdpvJNm3a4Ofvzw+bv2fipMkEBgXdcB/K7hcaGkb9+AZs+eF7Tp06Se/efX7yPSmPG3WMqZKOJVSvc3RXyLXYrCQnXWTOzBkUFRfx/IsvERMbS35+Pgvmzebc2dP8+79vk5WZycsvJHDu7DmKigpRFIV33/ov7771X5yqir2oCMVs5u23/mP8qOP+ccddMEvTsFqt/N8fXueZMWNvqzXHDTqePEnvPn2q7R4JBAKBQCAQ1GSqPMO32XwAuJZVSFJGvpGSqVUw4fQyTbIRoOOR9nEE+xmT9YEta9OveRT7LmSydOs51uy/xMW0PFSHCrJc2rTdFl5rDMtdf3mLNYhlr8PbBOpgMsk4Cx080bshvZtFAUarjxcGNiVh0S7saklabEku6k2uR9eRFYlr1wtJzsinRWwwvj4+lb3YKuHn58fZM6dRVZW69erh4+ODw+Go0HCUl56p6zq7du4AXadVmzb4+fnd0POwKrjTXx0OB99v3kRGegaBQUEeA7fyk0/Y8O03vPDSy3Tv3hOAdV+tZs2XX9LzvvsoyC8kOeki169fZ8f27fTo1YunnhmFDmzd8j3LlizhgQcfYvjIkUDp1MbycF9vvXr1aNKkKSeOH2P37l0MGny/p0iOe9ze27u1rh/fAEUxcfrECYYMefC278vN8PPz4+zpyutYEdVlGb3vjaqq2Kw2fty7h9zcXCY/l0CPnr0AQ8cvP/+cQYMfoE+/frwy7QUO7NuH1Wajbdv2PPjIw+i6kXr81Zov6dGrF4PvH4KmqciyQtnc9rzcXFau/JT58+YyYNBgasfEoGlaFVN4y+h48gRDHqweHQUCgUAgEAhqOrdhHI2I46WsfLIL7MiyZBSJKYv3PE6S0DSdiBAfBrWOQXa91jEqWXaKD6dTfDgJA5rw0c7zrNqbxImrORQ7XCbkZhG7u4Unaukyj7KE06FSJyqAib0b4efq46jIEiO71mfl3iTW7r+EpLjWOt7MBHuZUVmScBQ5uZCeh67rWK3Warkcq9XGubNnsVqtxNWpc9NtPe03KKk8qmkamZkZ7N/3I75+frRu3cbzvnufyuK+te5WDXv37Gb2jERsNhvpqans2rmDuLp1mD93Nu3at+epp0fh7+/PmdOnmDt7Fn5+/jzy6HDefXs6mqZxYP8+fP38mDwlgdCwMJKSLvL2f98kICiQsRMmEhoaSrGrIE55KareYwKw+fjSpVt3tm3dwvebNjFo8P2eBvNw4/o597rUunXr4ufnx7lzZ28jAlg5qqLjTXGlYt9pvNcaugvOZGRk0KVrV55+ZhR+fn6cPnWSBXPnYvPx4eVf/JLUa6kcP3YUs8VCWHg4r/3mN9zXuw8p166xdvWXREVF8+ovf02PHj1veu7mLVrw6ksvsm3rVkY+8YRnHLesMFuRjmfPosjVo6NAIBAIBAJBTafKlVesFjOappOcUYBuV41JmHvG6W0WpJK/JQBVp0XtIOIjS9YISZI7LVVH1XSa1A7i9eFtWfOL/gxpEwMOFZMi3626HRWje1XkcbX0MMkSqBrjezeiY3wYuq6jyMaENNDHzMv3NyfIz2oMvWy6bVlTJUkeQ6m7Pk9Kz8ep6pjvQNpneSgmhaSkJHz9/IiOjgEoKfxRZnyeibbrbc1lms6fO8+F8+eJjY3z9A40m83GOliTqdIPxfVsNpvJuX6dt//7XyRJZtyEicTWiWPL95t5449/JC8vn0lTnqNho0YUFxezbMkS9uzezau//BUms5mrV6+4zJzO2LHj6TdgAA6HgyWLFnL0yBEmT5nqSRm1Wq3IsnzLMbnH1a//APz8/Ni1ayfJSUmYzWZPdLWiYjnRtWsTEBDAlSuXcTgc1aCiS8fkyul4M3TPf+4s3j8gKLKC0+kkLDSM8ROfpXGTph4d9+37kSnPJdCmbVt279pJXp7RhmbkyCfoeV9vCgoKmDNrBjt3bGfU6DH06NETu93uSUtVVRVVNfqHapqGw+GgS7du1K5dm3Xr1pSqMlt2XGUp26vTo+PVKzic1aOjQCAQCAQCQU2nyq7EpCgUOzWuZBcglTKKZX7F9xR9cfkmVaNZTDC+FtcpJe9dJRTJ6GWoyBKKIlPs0EDV72mQ0YN3tNBVEMdZ5KRdwwie7FYfkyLjVDVMiuyJrPRpHsVT3eoxc/1JJLPs8p4VRB29Wne4b+nlrHwcmlZtxlFTVdJSruHv50d4RIRxme7US6lsR0OvqKOuo5hM5OXl8f3mTeTn53vSAK9cuYKiyLexJLVkrenKT1ewefNGHhv5OI8OH8G+vXvZuXMHycnJjJswgfvvfwCA77/bxEfLP6R3334MGDSIGe+/R0F+PrIkUbd+PP0HDSItNZV9+/axaP58WrVuQ6/efUlNTUXTVCSpcsV7dE3DZDbh5+9Hnbp1OX/uLN9++w0TJz17Q8SxrOEICw8nICCQrMxMCgoLqnpTKkVVdayQaqqO4x2NVSwmcjNzGDh4ML379gNg86ZNLF/+IT1cxY4A9u7ZRWFhIZ27dGHcxEnIksS+vXtZsmgh9/Xuw6hx4wA861MriiCGhITSqnUbNm1cz+nTJ2nevKXrUvUbNPOmbI9OQ8cAsjIzqk1HgUAgEAgEgppO1V2JJOFUNdJyim4sp1GqnYVeUrgUCWSJFrHBmBTXhK2cQ2su43gwKZOdp1PBouBwt/m4Vw6ybGsRd4Edk8zEPo1oWjsIVTNMo2cXwKzIJAxsyteHrnAhLddYs3nDsW6sQmuktUJqThFOtfqqyxYX28nOzsbP35/g4GDXMMoZG3g+c0+oD+zfz8L5c9ny/feYzWbOnD7NS9Oed60hq/pYPMtHNZ1TJ09gs1o5dvQof/7jH7h48SIWiwWz2UxBQSF2u52UlGvMmpmIqql06NiR//zzH2zasN7o4adp5OXk8Lc3/gK6zqVLl8jPzyMrK5N//O0NnA6Hy1hVziQZY5PQNJWcnBwcDgezE98nNyeHseMnEBIScoMRcf8dEBBAQGAAKSnXyHdF0O40VdXxZlT7vzDXeAICgwgLC+PK5cvMmzMLp8PBtJdeJjq6NufOnuXkyRP4+/szNeEFomvXJiMjg7ffehNd15nw7GSioqJxOp2YTKZyU43d/SJlWaZz166s/GwF323c5DGO3r0lyzOP3j8A6JpLx4BAUlJSqk1HgUAgEAgEgprObYWznJpOdr4dJKl0dKnUBKxklZiu61isZppEB7omdeVHEt2ZZHvOppOZXYDZ14LDod2FGe1N8KqgiuYqiFNg5/6OdRjW0b2mrEyaouumtIwN5rkBTfj1sr2e4kClK7NS/rVJEtkFduzO6jOO9uJiCgsKCQ4Nxc/fr8zpy7/h7kn6e+9MZ+WnK/D390dRFK5dvUzSxQs/XSZJwmq1IkkSJ44fR1NVTGYzZrMZSZL4bMUndOrUmWJ7MZs3bWLy1OfYs3sX323YQFBwsDFuWeby5ctcuGCMx+wynZeSkzl/7lyJwavCsHTXPTGbzVgsFi5evMjb//0PcXFxjBj5OE6n0zNGzz6aho+PD75+fjgcDgoKqidSdTs6lodeTWscvXGvk1VVJwCffLyc7zZt5Be//DWdunQFMKrhnj3Lo8MfY8DAQaDrLFuymO3btjJpyhQeeGCI8b1wReK900+9cX/euWs3IiMj+fabdYybMBGr1YqiKJW+N7p+d3QUCAQCgUAgqOnclnFUdZ2CIoeRWkkFrTg8SGi6Toi/mdohRmsJnZJ1WO5f/DVdR5FlcoscnE7JAbWsq6rIZVUjnhRSr4I4To2AQBsTejeidogvDqeG2VR68uo2x7Is8Uz3eFbtucjOU2lgKhN1lG68JndKa2GxilOtvqm83W7H7rBjs9qwWm2lqoRWhCRJ5OcXkJKSQkBAACaTCVVVMZstWK222+rbWBZNU9F18PHx8USO3GOzWiysWf0lF86fo1Pnzgx5aCgHDxzA5uODxWLBbrcDRgqjzWbzrH9z92N0v3d7GFFHzWUIAVKuXQPKXy+nA2azBR+rD06nk8LCwts87825HR0r4m4YR6fDib+fPzu2bWX+3Dm0a9+Bx596Cl9fX3bu2M4nHy1DVVV69OyJqmns3LmDuXNm0ax5c5548mkKi4rQNBX3D1Neq49LzuP6r6ZphIWH07R5c3Zu387ePXvo3adPqWije1wV4dHRVr06CgQCgUAgENR0bs84ajpFzkpOMyVA1QnxtRJgu7H9gXfVRSSJ3EIHV7MLjWgmXtG5u433mkTXa7NiVDwd1jOeIe2MQiQmxSvKVGqdm/E6JtSXl+5vxt7zmWjo5UQdy0knlMCuatWaqupUVU+krCoVP2VZQpEVT8sHt7n7KW04vHGneqqqWir1013FdO+e3SiKwut/+SuNGjehqKgQ3WUu3XiPx73/nRijUcxJRlMNc3ur+6YoMiZX5VZ7cfFPOndF3K6ON6CXqmd1x3FraLVauXz5MnNmzSQrM4M3/vYP4uMbkHThAu+9PZ1Lycn4+vry+aqV7NyxnRPHj5GelkZAQACzZiTidDo9PRlveUm6jqIoJCclU1RUxPQ3/01+fh73PzDEM6bKYOhoqlYdBQKBQCAQCGo6t2UcdV1H86z3uznutWuRATb8yjGOJRFHUIDrBQ5SsgpBMd7z2vDuGkhJdxX4kVz9FWUcxU7iogIYe19D/KzmcqONnt0lo02JIsH9rWMZ1jGOFdvOIVvdLSBccZKK0kKrOXXQe+Jdpd52ug76jf3wbjfKVf4pShcncY9VlmVycnIYO34iAwcNJiMzA1lWkGT5hqqmpdJGyxzvp4xL13VjjaSmGf8GboqEJEvogKpXz48At61j2eNUk3O8oY+jzejjeD07m6eeHkXffkaRnEUL5/P95u88UezvNqzH6XRitVqx+fhw7uxZjh054kl3rXz2gY7N5oPNZmPblh/Izs6mXfsOREVFVeEqJCSXWdWqSUeBQCAQCASCms5tGUcj8kLlc9t0HX8fE2blxsleiTkwXhfYneQWOVzrJ73Madk1gd45anfaT3pMqneBH4ORXevRt3kUqqaXMo3lFdlQXD0ug/0sPD+wKZuOXiUjz15SYOeG85UgS1K1+mRZLrmRVU/frF4D720Y3WsSTSYThYWFtG7bllFjxmDz8SHp4kXy8/KQvSqkuiO95bXHuF1u0Lay90vXXRVcwVRN/f9+mo5e6FXq3lFpyuvjmHrtGi1ateKZMWMJDArmwP79bNq4wZNmq6oqgUFBSJKMqjo9htPf3/+2rtEdvfbx9SU/P4/srCyio6Mr1dPRNXhX4ScJWfRxFAgEAoFA8P8pt2UcFVnCVkGkrSIKip2olViz51A1nFpF0cwSI1dhmuedQPJ2pDqKIqMWOmgVH8aYng1cZsYdNSzfNJZEuYzXXRtGML53I9784hAmmxmnd+Gbstegg0VRUO5gFK8s7v6EdrsDp8NZeXPluTe3T2Un7N7VL2VZxuFw0LtPX1q3aUtRURGfrfiEpKSLWG1WrzTUO2+4S43Va0y3Oo2qajjsDmRJxmq13tlBubhtHctw67XKPx23KfQPCGDs+Im0bdeOosJClixayOmTp7BaS3S02+2ef9/u/ZxOp2e0BjcbsOvfrytCLMuysb9ktPupCh4d5erTUSAQCAQCgaCmc3vGUZLwtZqNTEvKRAbLoOuALJNT6KDYees1ZqrmWttXtmLr3cJjRktSSVVVR7GaeKZHPG3qht40RdWNewIvS0b7EqtZYdx9DfjqwCWOJ2chmRX0cgyyO2Lma1XKjdDeKSwWKxaLhaKiQoqKC0u1k7gZupdZdxef8bxfSYx0Q6PyqPd7rgN5/Tbg9Z6uYzabsVltKIrCpys+5qvVXxrbSJLnWOW1Z7hT6LqOW3UjPbbibSVJwm4vprCwAJPZjM3Xt1rGZLFYMd+GjuWi37iPd9QXyo8Gl33P/XfZ45jNZq5fv86g+x9g0OD7AVj95ResXbOasonZbl3LjxxX5trcBahK76+7iiVVlrI6+lSTjgKBQCAQCAQ1ndsyjiZZIsTPavyaX6rwaQURQNloL5Ff7LzxMxfu3QJ9LIT6WbmWWYBsMqqTGh7OOLbxp45nfeCdjjp6mRUwrtVZ6KBn69qM6tHAeE8pbXJuNUlXZKNqbIvYEKb2b8JLi3YhA6r7PDdEHHVCfM1YqhjVrQpWmxVfPz8K8gvIK9Ob7maN0c1mC2azCXd7Cl3XS6ULV2ZS7q6UajKZPOfxVE9VFE+RF03TjMIvrkbvZrOZJk2bkJ6Wxvw5s3E6nQQGBpGXl4vsWucou/Y12ojeiV8eJK+vhJFqWVCQT0BAEPXije+DLN2okyRJFBQWkJ9fgNVixc/P74Zt7gRWmxX/KupYHrqOK4p+IxX1SqzIKLrTUt3bld5HIyIigpjYWM6fO8f8ubMxmU00aNiIixfOo6OXSj2+E5V6y467KqHVEh3zqlVHgUAgEAgEgppO1Y2jrmNSZCICbEhSmSlYeZNUXUeSIT23mPTcYuIjA1xvl45YuNtz1A72pUntII6dS0exmdEMdwVIyOAppFMS5bzD5tHrWJIs4XSq+AdaGd+7ITGhvtidKhaTQinjetPD6a7CIBqyIjG8Yx2+/DGJjQcuo/iYUZ2l+1QaEVyICPTBXMWUuqpgsVgJCQnh6pWrZGdllRprRWZDkiSsVguPDn+Mw4cOkZGR4VWsxOsKKtLCdXx3L73CggKcmgo6nveKi4uxFxejY7TVsFqtFBcX43A6eHrUaO7r3Yc5s2exe+dOfv+nPyNJEjM+eB97cTGq00lhbo4nonnTsVSSUhEq1w8XNh8fHn/ySe7rbbR2MJlNpe6f+/n69evk5uYQEBCAfzUZDovFSnBIKFevXKm0juUhSUaVWO/Ks7quI7vSPD1FrLwq2Fb044IkSaXMf2nzZ6xVttvtLF/2ITu3b+MPf36Ds2fOcOb0KSyuHwmqA+OeyF7rQsunfB1zq1VHgUAgEAgEgppOlY2jU1UxmxSiQ30o1Ubdu8VEGSRJIrfIwdVso3m2VOYzAEUy2nyEBVhoERvMKtldnEZHkRXUQgfdWkTRrXEkczad4nqeHdksuaq73kHz6GUGZYw01YEtavNYp3pouu4yjcZVVCai4/7cpMg4VY3YMD8m92vCzjPpFNidIFMydq9LqB3qi6mSbQduB1mWqBUVxelTp0hNTQXwFCcpe11ljchjTzxJragoDh88eEOLC1XXUEulJOuGGca4tuLiYr79eh0XL1zgoUceJb5BAzRV5et16zh39gx9+/WnfYeOIEn8uHcPG779hp697mPshIn0HzCQvbt3s2DeXHredx8jH3+CuLr1yMzIYOH8ebRq3Ya+/fp7zKymuZvN3+73woiwmhTFk7lsNptp2rw5Xbp2xcfHp1RkrGzKZnpaGrk5ucTExODjUz0pjoaOtTh96uQtdbwZTqdKYWFhqZYekiThcDhwOBweI+VeUwk3RgPd26iaht3hQAZkRSk1lmJ7MWaLhc3ffcfSxQvp0rU7o8eMY/asGYZRVZRSKcx3EofDQVxcHFHRtW/a7/Je6CgQCAQCgUBQ06mycSy2O/DzNREX6gtm5cbKp27KtAnQnE4upOWhlUlv9UxuXVE5RZZpUycEX38rBQ4VZAld0zFZzUzs05inutXnaHI26/ZfKjnnnTKNXseRZRnVoRIV6seUfo3xs5lwuqKGVUsBLEnZc0dVB7euzWOd67Fo40nMvkZbDzwxMuOa6ob5YVYk7M6K03t/Ck5VpV69+nz79ddcuXL5hrF64x190XUdm9XKwEGDadCwIWmpqUiSsd5PUzVCw8KoV79+qXskyzJ2ux0fHx++WLWKj5cto0vXbvzxz38hNjaO7du2suKTj2nRsiV/+9e/ybl+ne83byYn5zpWq4WAwEBCQkPIyEhnzuwZFBYUMPX5adSpW48rly9z9uxZdF2n/8BB9OnbF1XTQNepVSuKuLg4VO3G9iG3wh1t27ljO8eOHkFWFKMlqapiNpkJDAxCVVWP0SpltDUNZJkrly+Tl5dDTEwXFNNtZYXfkqroWB5uTRs2bMDTo0bz6YpPUF3fOVVT6dW7DwMGDqKoqIigoCC+27CBb75Zh8lkKmUI3ed0OJ00btKE5194kaLiIubPncPJ48fx9fPj+vXrdO3anc5durBw3hwK8vN5/qWXiIiMpHOXrkRFRZOWmkJgYJAR1ZSoVCp4+ejIyCBLRpcRWaJ123a8+stfExAQUKkfZO6mjgKBQCAQCAQ1nSrPgoqKivDz9SEuxI8QPwvZBfbyNyxT2ARd4nByNk5Vw2JSDANJ6UmhOzOzU4NwOsWH8/3BS/gE+lCYW8RjvRryULtYbBaFFwc3Y+/5DNJyipAUVxGdn2oey0QudV0HWeKRjnEMbhODU9UwuQZYtRTAkm1lWcKhagT5WpjQuyHfHbtCUlo+sllGUw0Druk6Zh8T9SL8QZIoKqqehuPFhYXEN2yIw+Eg+eLFm25bNnVPU1UkWWb+3Dl89snHmC0WJEmiqLCQhx8dxn/eehvV6fRMsnVdx8fVPmPBvNk4HHZeeuUXxMXVISs7i5kfvE/O9ev8+Y2/Ua9efWa8/x5/+/Pr+Pj6YrFYWbv6S+zFxTRv2ZKN69cz8dkp3Ne7N2D0/9u25QfMZjOL5s9l8cL56IDmdDJq7Dh++39/AC+DV1nc1/rN11+zYM4sLFYrsixTUJDPkIceplfv3hWaD/f7Fy+ep6DAuM9G5PPOUxUdy+LWVNd14urUZcTIJ/h85Uo0V/sKXdPp2LkTE5+dTFFRETabjft698HhdLBpwwZkRfZUOrXabKhOFX8/P6a9+DIjRj5OVlYmX65axTFVRXH9eBAdXZvz58+xacN6Jk6ZSp++Rh/HQwcOkJaWysgnn+LxJ58CQNeMCKSxFpZSa2mNDcq89npf1TQyM9JxOB1IGGtfGzVqTKPGjcst7FMeHh0vVL+OAoFAIBAIBDWdKhvHwsJCIIRawT7UDfcj61wRksVVIdQbLyOn6YAicfRSFrlFTsL8lXInfYpspHPWDffngbaxfH/8GsV2lcgIf6b0b0xEoI0ih8r9bWozsnNdZmw4aZjGSqw1vCVex1AkCdWu0jAmkKn9mwAl/fKq0nC9xIuWtCxXXOaxe+MInuoez79WHUSSXGsmAU3ViQvxJS7MWEtVWFjw066rAvLy8olv0BCL2czFC+fJzc3Bz8+/3G3d1+l5dhWiKcjPJzMzE4vFgixJFBYWkJeXa1yHruG2ak6n0SZiyaIFbN3yA7/45a/p3LUrAMuXLmHzd5t4bOTjPPDgQzgcDpyq0xUllD3FcU4cP8aBA/to0bIlTz0zioCAQLZ8v5kVHy2nfnwDFEXm5IkTnpRap9NJQX4+UFKspSppv7quoygKmqbiVFUUpxNNlnE6jb6MsiyVai9TyoRIRlGnc2fPoWkaTZs2JT8vv9Lnrgp5efk0qKSOt6K8tX+Sq9Kqe/1jnbp1adCwIeu//YY27dsRFBRMTEwsyUlJbN3yPQ8OfZgHhz4MwPHjxzl54gQ2m43i4mJ8fX05evQwe/fspkXLVjz1tKHjD5s38+GSRdSPb4Ddbmdm4vtISKiqSotWrfnlr39TZeOfnZXFL175oNT5h40Ywauv/eqma0DL1fFc9esoEAgEAoFAUNOpcvUVe3ERACF+FprHBIOqGSmYnjl0OWsdXaliZ1JzuZjuqvwolV8x0b3b8E516NUsCi2viMl9G9OjcSS6rrsqjUpMG9yMxtGBoGqe9E5XaciqXhLetV0kSULVNBSzzNPd42lbN5Rih2qkKWo6mq57TnEzM6K7UnI9fQhdE1VZkjDJEiZFZlKfRnRuUgu1yIGsuNoPqCqNo4OoFeQDQFFhUdWvpxIUFuQTGxtLRK1aJF1M4lJyMrIsV6qdhfuaFEXBYrFgNpsxmc1YrDYUxVj/JrkqjTqdTsxmM5s3beTDJYvp2q07T48eg6+vLz/+uJfFC+ZTt149Eqa9hMViITc3lz2797j65Rn312q1kpmVicPuYNyESbRs1Yq0tDQWLpjP1avXmPbiy9SrHw8YRXbMZjMWiwVFMbnG4rr3rkIvlXu40mwlGbPZjGIyedb3SZJ8w9esVORcUUhNucb5c2cJCAigYePG5ORc/0l6VURhQQExt6ljWcr7LrvfcTjsKIrCpo0b+OSjj+jUuQvvJ85k2ccreHrUaJKSL1KvfjwJL7yIzWYjMzOTOTMSSUlJcRlwDbPZTFpqKsXFRYybONGj46KF87l69SovvfIqubm5rPniCzas/5Zvvl7L3t27sNvtqKrqKcxT0cPdHkbTVIqLi7l44QInTxzn1MkTnDxxnNSUlFveg/J1PEeAf/XqKBAIBAKBQFDTqbJxVFUjAhNgM9OuXhiS4tVwXS9pul3yGkBHliXSrxex7USqYaoqOL4iyxQ7VBpFBfJ4l3r0bBfHU93qYzUrOFUdWZJQNZ2mtYOY2r8Jikkuv+lCVfyjd7sPwyHSuUE4CQOMaKPVrBiVImUJxWUqvKNwN7YioJRp1AGHU6PYoVJod1JgV0nPLSIi0Mag1rXBpKBpOiZZQld1OtQLw99qmB67vXpSVZ2qRmBAAM1btOTy5UucPHkCKN16obxefN7IshGFkmUZyWVW3AVHvIupXL50iTmzZpCfn8+0l16hbt165ObmMnfmDK5cvcrz016iYaNGqKrKFys/Y+P6bzwVPI3zyBQVFnH/kAcZ8tBQAL5Y+RnfrFvLiJEjuf/BBz0ptO62HO5x3S7uS/X18/OkY+oY33+rxeo69o1fMlVVkSSJ06dPk5ycTJ26dYmuXZu8/OqJVDlVtco6VkR5ETj3PbRYrKSmpDB31kxDx5dfoX58A3JzcpiV+AHJSUk8P+3FUjpuWL8em83qKaCkKAp2ezFDHhrKkIeMqKRbx8dGPsGgwfejKAo2Hx98fH2x2Xw8Rt3b+Lt/CCjvYXxuPBSTgs1mw+bjg81mq0SGQMl9Kq1jEnXqVa+OAoFAIBAIBDWdKqeqmk0mCgoKCQzwp23dEHz9rBQ5nGVLpZZ+dkfpdPjm8BUm9m2Er9VUrjGRJMmzlnBEpzr0aVaLBrWMFh7u/omyy5SN7hXPl/uS+e7o1ZIFku7op1TRAqibo2k6skmhcVQAxy5d58CFTMwmo/iLSTEiTaH+VhrVCsDqKg50YzEZtw81TO53x66ybv9lCp0q2fnF5BY6uV5op9DuJC3PjqLIqKrLJNlMdIwPw2pWKCoqRtfU8ob5k/HxseFwOunSpQvfrlvL4YOHeHTYiJuaDI9Zdr3u1LkzKz5eTmFhAU6nSmBgIJ26dHZ96kq91TQ++Xg5Gzds4PlpL9G9Zy8AVn36KV+vW8uQBx9i6COPIkkSx48dZfasmYRHRODv78+Z06fx9/enoKCARo2b8PSoMYSFhbF3z24WLZxP7doxPDt5Kv7+/rRr34GNG9Z7ehkGh4TQqXPnUuOuSlEj93YDBgxg1WcruHzpEpqmUqtWLQYMGuS6DzceS9M0FEXh8KEDpKWmMmDgYKPFRDVVx70dHSvCHSV3f4FlWfZUOHXruOHbb5j28it0794DgFWffco3677iwYeGltJxzuyZhIWHYbVYuXQpGZvNRlFREQ0bNuaJJ5/20nEBtWvHMHHKFAKDgpBd6cmq04ndXozNx4bZXPp/U5UxgL7+vvj7+1NUVIQkSdjtdkJCQ8u53vKPdaOOg6pVR4FAIBAIBIKaTpWNo8ViJi83l8AAfxpFBdIqNphdJ1OQLWY01d2T0Cvy6J60AygSO8+mc+xyNh3jw433vKsyugyhIhuvo0N8iQ4pXf7eu7BFmL+NXwxpwZ5zGeTbneieOeCN578pZSaDGvDhjvMs3X7eFckwDmkzKxTYnbx8fzP+8XgHT4Efb0pMinHq7AI7b6w8xA8HksFsMhZ8Gg0pjXslycazLKE6VBrXDqJFbBAA13NyCAgIrIQqVSckJJTr2dl06NQFs9XCoYMHSUtLIyI8/KZrwFwXia7rDBn6MJcuJbP2qzXoms6QBx9ixIiRXtcvsW3rFubPmU279h14evRoAgICOHL4EAvmzyE0NIQXXnwZf39/CgsKmZX4AVcuX+L1v7xB6zbtmJn4Ptu3bsGpOnniySfp3qMH17OzWbZ0CSeOn+A/06fTqm1bNE1l9JixZGdnsXnjRswWC8NGPMaAQYM81VG9taksuq7TsUtX/vHvN/lsxSc4nQ7D6D78iGEsyqmwKcsyRUVFHNi3D7vdTo9ePcnLzSMgIKBqAlWSn6RjGWxWV1TOdc9UVUVypezu2rGdubNm0r5jR55+ZjR+/v4uHecSGhZaomNhITPef49rV6/y6i9/xf59+zh18gSBgYFkZWbSrHlzOnTqRG5uDh8uXszJE8f5z1tv07JlKwAeevgRDhzYR1pqKlFRtXlo6COeVFeopPnXdfz9/Hn6mdFcu3qVzIwMOnbuzMOPPArcuGbXTelCVmV17EVebh7+1aSjQCAQCAQCQU2nysYxNDSMS5eMVhi1Am30alqLnceuYZJBU7mhOqn3syRJZOQUsvbAZTrGh98Yr9FvbP0A3hM6qZQP1HWdAS2jebxLXeZ/dxrJLBtFeqQqmEbjBF7m0fhb1Yz1iJquo6s6JpNMbn4xTWOCebJrPGaTjEPVMCvulEWvdgx6SYuSH89nsPdCBuZAG84yBYQ8p9QMs6wWqvRoUovYUFdhnPx8AgOrZ6IaGhpKakoK8fHxNGvWnOPHjnDk0CH69u+P6nRiMpVuau99bW5tfGw2Xn71NZ546hkkICo6GjDS/EwmEykpKcyemUhWZhZv/P2fNGnSlIL8fBYumM+J48f56z/+SfMWLQBY+9Vq1qz+gn4DBjDyiacICAhg5ONP8s26dfTr35/HnngSgG+/WceKjz/iwaEPMfThR12pyxAUHMzv//gnxk94FrPZRGStWjdUz6xsxLHU90/X6T9gIP0HDPSoXFGfxJIo1SEOHz5M7ejatGnbjvSMDGJjYu6McGW4XR3deP87i6tbl1739ear1V/iVFUaNW7Mfb37kpeXx9xZs8jKMnRs0KgRBfn5LCpPxzWr+WrNavr268+ESZP58vNVbN+6hfz8fOLq1KHfgIH4+Pjw8fKVfPbpJwx56CEeevgRTCYTTqeTR4cPJyg4iIP799OyVWv6DRhQauyV0U9WFDRdY8TIx2nUuDGXkpNp264dtWNiK/x/i/c5KtYxndiY2J+omEAgEAgEAsHPkyovArPabOi6RmFRMTaLid7NovD1txq9826WrkrJirDPf0wiNafI9bERzis7gSsbLSlbbMZ9PKvZaM9RJ9IfXfU2je6NqpBaputGiqtU0shcU42KqE7VaM8xuldDOsaHeZlGKC8lVpYk7E6VL35MoiC/GIdqGFBdK3l4ivm4g48+Jvq3iMLfZqbY7sDusBMQWD0RRx9fXzRdw2Kx0H/gIC5dusSuXTtct6HiyrFltVFVlejoaKKioz1r2dzbfL7yM9Z+tYZnxoyhb//+AKxbu4ZVn65gwMBBDH9sJLIsc+H8ORI/eJeQkDCee34aAQEBXLxwgX//8+8EBQcxetx4oqNrc/zYMebOnk1gYCCTpyYQFhaG0+n0VNxUVZWY2BgiIiM9a9Sqajq8x+/+W9M0T/qke+1kWQPiPj/Arp3bOX/uLD3uu4+w0DCKCwsICg6uqkSV4nZ19P7cfR3BwcH89g9/ZOoL0xg9djx/fuPvdOjYkeUfLuHrr9fyzOix9Ha1z1i39itWVqBjaKiho6+vL506dyE0LIygoGB+/bvf89Qzozh18iTz5swhICCglI4mk5G+3q//AF557ZcMHDz4hojpzVJwPWuKdR1ZMtbctm7TliEPDaV2TCyaq5/nrSKxFetYWG06CgQCgUAgENR0qmwcJUnCZvPh+nWjumDbOiF0bRiBVqiiKHLpeiHlVTmVZQ4nZ7F6X7IxKXcV1CkbEakojcz72b1F6zohPD+giZFi572bJ9pZqQtz+b8y0Ur32ka7k+Gd6jKlX2MATF7tObyrq3pPbM+m5LFm/yUwKZ5jGefxegCSLKHanbSrE0qn+HAAcnKu4+fni9lsrsTgq46iKETWqkV2djZ9+vbDZrOxY9s2Ll68gNlsvmlxFW/D5N0mQVEUnE4nsizz4949zHj/PVq2aMmoMWMJCgrm9KmTzJszG6vVykuv/oKQkFAcDgdzZs/i5PETjBk3ns5dulJUVMTSJYs4duQITz8zmgEDB5Gfn88nHy9n757dTJ76HF26dkPXNE9EzX1+9/fIMy7p5tVvK6Js2qLJVVXVfdzyIpgmk4msrCy2bdlCcXERDwx5kIKCQvwDAoz1cdWAt459PTpuJakKOrpNlK7r1K8fzx9e/zNvTn+b/gMHsu/Hvcz44H2at2jBM2PGEBgYyOlTp5g3Z9bNdexq6Lj8w6WcPXOGp0aN4ulRo8nPz2f5sqXs27unXB3dKbIOh6Nck16Z9Y3ubd3VZVXV6XldnuG/mY724uK7oqNAIBAIBAJBTee2yk5GRESQez0bTYeYUF8ebBeHZJJKTJt7UuY2R6XaXYDTqTFjw0kuZeZ7zF9lowreGNEgY9L3TI8GdGtcC82uehXIcZu/ShxPL1NMx5VWalJknEUOujStxZ9GtCUswOrqMXjzaJam6SzbfpaktDwkRSo/8qnroINZltCdGg+2i6V+hD86kJ+Xi81mq9R9uF1CQ8PIzculQYOG9LzvPvb9uJed27cDlGp0fqsURzeaK0U1KzOTWYmJpKZeY/LUBFq1bmOYiGUfsnfPbp57YRpt27UHYNOG9Xz6ycd07tKF0WPHAbB7104Wzp1L565deXr0aAC2bd3CogXz6dO3L489/oSx7s1j1ktHz9wY1XurFm281TV6H9sbd6/K3bt2snfPbpq3aEG7Dh1JS0shKiq6yueuCm4d4xs0pGev+9i3dy87qqijt6EqLi5GVVUyMzKYM2sGKdeuMTXheVq0aOnScWmldVy0YB5dunbj6VGGjlu3/MDihfPpXZ6OrjEpimK0QPEy6ZWhvLRTWZZRFFOlo86lddxDsxbNDR1Tq19HgUAgEAgEgprMbRlHq82GyaSQm5sHSPRrEUWz2BCcxU6jH2HZqKPbuOm6q0iOzI/nM3jn6+NIkoRTrULhizK401djQn159YFmWKymkovyRDy9jJvu+o/ueeF9IMMw6iArkss0OunWLIr3xnahVVwIqqajVBC5cB0ISZI4cfU6i7acc6W9ep2jLLKEw64SUyuQB9rEYFJkcvPykCSJ4OCQKt2LqhIQEIDZZEQ0Hx02guzsLDauX09WVhZms+WGqMzN0HUdyVWEZvWXX/DF5ysZMfIJ7n/wIQA2b9rI0sULua9PXx5/4ilMJhPXrl5lZuL7oMO0l18lLDyc9LQ03n7rTSRZYtz4idSrV5+LFy4wa8YHSMDkqQnExMbhdDi8jEX5Y6pqiurNjnGrzyRJwmG3s2n9epKTk3h0+GP4+/lTbC8mOOQu6jh8BNnZ16uso/e9slqtKIrCN1+vY9VnnzHyiScZ/MCDgEvHRRXpqJfS8Z233kSWZcZNMHRMuniR2TNngF6BjuWM71bptuVdR2W3cx//ljr63x0dBQKBQCAQCGoyt9XHUZIkfHx8ybmeia5Dq7hgHulU1ygi4w47etbvSaCXpGWil1QiXfD9Gb74MckwaGrpKF5Voo7uTQe1rs3jXeqhFRvpkoYvdJVE9Uo9NcbjMo/ucboMo6RImM0ymkPDWehgQNsYZk3sRqf4cJdpLL94j/tvTdexO1TeWnuM5LQ8PGHYspNZV7TRYpLQ7SpDO8TRtq7RLiAnO9uTFnk7bRUqg1vHqOho0tPT6dqtG23atmPzdxvZsX0rgKd/Ydlr9H7PjXv92NHDh3nv7ek0bNiQMeMmEBoaStLFC8ydMwvVqfLiy69SKyoKTddYsngRu3ft4unRo+l1X280TWPZh0vZsW0bjwwbwcOPDsNut/P5qs/YvHEjo8eOo29/o1iKySuF93Z+cLiTuNdZ7t69i02bNhAXV5fB9w8hPS2VqKhoz9q96qBcHdu1vW0d3ev7jh8/xjtvvWnoOH48wcHBJF28aOioltZxaQU6bt+2jUceHc7QR4bhsNtZtfJTNm/cwOgxldfxbup6o45xDL5/iFHhNbp6dRQIBAKBQCCo6VTZOLpbG4SFhyMhkZObiyLLPNohjkZxwTjcUUeoMFVUx1jXl5FbxB8+3c+R5CxMioyqVa36ZcmYjH6JgT4WEgY2ISrc/8bWIJ6T6x4v6UaSJRSTjMkko9tVHAUO6tcK4HePtWXGhK60igvBqWqlTKN7f+/xqpqxzaKtZ1m+/RwoZUz0DQM3oo21Iv0Z2bkuPhYTBYVFOO12IiIiXbtWz0TVo2NYGIWuoh9PPv0MVy5dZu2a1WRnZ2M2mytVjMRdhTI3N5eZMz7g8uVkxk+aTKfOnbHb7az8dAWbN21i0pSpdOnaDYCd27axbMlimrdowcRnJ2MymTiwfx/zZs+kadNmTJz0LIpJYf++H5mZ+AEdOnXi6VFjsFgspVoz3AvKfj9lWcZut/P12q84cewYI0Y+TmxcHOkZ6UTeKx0v376O+fn5zPrgfZKTkpg4eQrt23d06fjJDTru2L6NDz06TrlBx/GTnsVkUtjnreOYn4uOTxAbF0vGXdBRIBAIBAKBoKZzW8VxNE3DbDbjdDopLipA1TQ6x4fxeNf66Doo3lFH756KxgE8KauSSebwhSx++/E+0nOLyo3m3YqSqJ/xumN8OJP7NQKnisldrEf3ii66U1IlCUWWkSUJ3aGiFthxFjtpGhvMK4+04sPne/HGY+1oWCsQp6YZBXK8Jpo6ehnTaKSwbjp6lTdWHaTA7iwdZZTKmEgdLIoRbRzZpR49GkUAkJOdhawonom12xjcadwVUU0mE3Xq1OHypcsMHDSYdh3as3bNGn7Y/B1w82hVWZP/9dqv+GzFJzzw4EMMGzECgN07dzBn9iy6dOnK08+Mxmq1kpmRweyZM8jMTOelV35BbGwcubm5vPv2dK5fv864iZNo2rw5qampzPjgffLz8pg0eSqNmzTBUSpFteo/MvxUyp7P4XAgyzLbt21l9ZdfEFenLsNHPEZ6Whq1a8fg6+d3l3W8ZOjY/vZ1/Obrtaz45GOGPPwwjwwbDsDuXTuYM6uMjpkZzJk5g4yb6NjMpeNMbx0b/xx0rHNXdRQIBAKBQCCo6dzWLMg94YqJjaUgP5+8vHyQJJ7qVo/2DcJxFDgwmdymzStV1Mu0oevGnyaZr/Zf4s2vjgKgajcWObnFaIyKia61kmZFZnSvBnRsFImzyOkqTENJRVOv86sOFYtJplF0EEO71uO9Cd34+MU+/OPx9nRrFIkOqJqGqUzPvrJVHjVXCuuxS9n8ctlektPykRS5dLqusaOncI9sknEUOWlUJ5TRPRtgtZgoKCqmsKCAWlFRtyNLlXFPgiMiI9HRCQ4OYcyEiWRkpPPJR8u5cvkyZrPZk75YHu57cOb0ad5+601q145h/MRnCQ+P4Nq1q8yZPZO83Fyef+kl6tarB8Bnn65gw/pveezxJ+k3YKDx3opP2Lj+WwYOHszIJ55EVVXWrlnN2q9WM/yxkTw09GF0XS9VZfZep6jquvEDSnZ2Np+t+ITTp04xaswYYuvEkZeXS8hdWhNXWkcMHcffno7nz53l7f/+h9oxMUyY9CyhoWFcu3aNObNmkpdXRscVK9jw7beM9NJxZTk6rvtqNWvX1GQd9XJ0HOvSMe+u6SgQCAQCgUBQk7lt46iqKmazGavVhr2oALtTpUVsCBP7NsbiY0ZVNdf6Pq9UUe+IH+63DNNlck1+q5oIZvhR4/judNeGtQJ5flATFIuC7D69V7qohI7VrPC7Ya356rX+fPZqP+Y924MXBjWldZ0QLCYZh6ohS6C4xlVe9Um3aZRliaSMPF5espt959KRzbLRp9E9QO/BulJlJXR0RWZC74Z0bhCOpsP1zAxMZjNWq9XTiqA68dYxNDSUlNQU+vUfSP8Bg/h63VesWf1FuevhvFMbZVmmqKiQWYnvc/7ceUaPHUfPXvfhcDhY8+WXrF2zhtFjx9O7j9H/7+CB/SycP5e4OnWY8tzz+Pr6cuL4MWbPmkGtqGieSzD6/x0/dpT3351Oo4aNGTdhoifiAzUntVF1FXX6Zt1XfLFqJR06dGTY8MfIyMggMCiI4JCQuxKlKk/H/gNcOq6tvI7FxcXMmpHI2TNnGTd+It269XDp+EWVdZzqpeN7b0+nYePGjK+xOhqG+l7rKBAIBAKBQFCTue2ZkDvNzN1DrrgwH4CnutXjkQ5x6MUqZu81fpRE2zzHkCVwajSKCmTsffFA6f6IVaHs9g+1i+PRDnGoRV7RTxeSa5HjgJa16dcimlaxwUQE2lA1HYfLDJgV+cbWDpJUrmlMyS7kF0v3sv7wFWSz4u7kUdoke6XMmk0yaqGDwa1jGNPTuO78vFzs9mKioo2S/3drkurWsU6dujgdDnx9fBk3cRL+/gHMnzeXPbt3efozuimbWrj+22/55JOP6dO3D088/QwAhw8d5IP33qFV6zaMHjsOPz8/8nJzWThvHufOnGXaS6/QoGFDiouLmJH4PskXLzJu/ATad+xIbm4Os2cmcvXKVcaOn0jbdu1xOpz3NLXR+7yAp2H98WPHWDh/HoWFhUycPIVa0dFcz75ObGycZ5+7QVkdfdw6BgQwf27ldNy4YT0fL19G3379GfnkU0DVdUxy6djBreOMRK5evcrYcRNp8zPQsege6ygQCAQCgUBQU/lJ7kRVVRRFoV79eJIvJqEBIX5WXnmgBY3rhOAocqIo7lOURNvAHSXRUcwKwzrVoVFUEA5XZVX351XBvb0iSzhUjfAAG5P7NSEy3B+nQyupburattiukppThK7rFDtUT2Gb8gxjeWmqqqYhyxJnU3J4dt4OPt11AdkkG6axbGTV67plk4yj2ElsrQBeHdKc2qF+FBbbKSzIx2y2eFIK7+ZE1X2+2rVjSEm5Speuxjq240eOMH/ObNJSUzGZTJ7IjPc9Sbp4kXfeepPgoCCenfIcUVFRZGVlMXfWLNLS0pia8DxNmjQBYN26r1j52QqGPvIoDwx5EEVRWP3ll3z15Zd069GTZ0aPQdd11n/zDSs//ZSBg+9n5FNPuqpvmkqd+15Q8oOB0bA+NzeHJYsWsH3rVh4dPoL7hzzI+XNniYuLw2az1Rgdjx29tY6XLyXz9n/fJCgoiMnPJRAZGenScWY5Oq6tUMfuXjp++803rPzsZ6BjjqHjtm1beaQG6CgQCAQCgUBQE/lJxlFRFE+KnNlq4XpmJk5Vo1ujCF59oDn+fkbKqiRLN5gpSQJUlQa1AhjV48Zo4+0+wDCPmq7Tu1ktRvWMB4dqHNsr8IlTJzu/GEmSsJhkT0qqN95RRjfehXD2nc9g/OztrN57sWLTWGZtp1FYyMTzA5sxqFU0mg5F+bnk5OZSOybGU9nybuLWMSo6GpuPD1mZmYweN55uPXry2acr+GjZh9jtdmRZ9rTdkCQJp9PJ7JmJHD92jCefHkXvvv1wOp184zIWI0c+zuAHhoAkcfr0aebOmkVwSAhTE54nJDSUC+fPM3fWDCwWC9NeeomQ0FDOnz/Hu9P/S0RkJM9OmUpIcEip1MY70ZuxqpRNUdZ1HU3XWPXZZyxZtJBmLZozeWoCDrvdMG4xMUYKdg3RsYdLx+UV6aiqzJk1i2NHj/DM6DH0uq93iY6fflpKxzOnTzNv1swKdXzBS8f3fi46rvyMJQsX0ryZS0fHvdVRIBAIBAKBoCbyk/Mh3ZOq+PgGpKenYS8uQtNh/H0NGXtfI6MmDdxgpjQNZEVmeKc6NIsJLtXH8afgLpSjasY6xnG9GtIqPhxnkRPFJJdE/zSNjLxi1Just/KOMpZMNHVkCT7dfYGnP9jClmPXStJTy1nD6TqAUcVVAknVeap7fRIGNkFHwl5USEZGBnFxdX7Sdf9U3Do2aNCQvPx8QkNDeX7ai4SHh/PBe+/wzdfrPPq4TcfGDev5+KPldOrcmbHjJyDLMqdPn+ad6W/RoGFDxk2cRFBQEIWFhSxbsoj9+/aS8MI0WrZujaqqLJg3l8OHDjF63Hi69+iF3W5n/uxZnD51iqdHjaFHz16e3npw76pvus8NJVH2rT/8wLvT30KWZZ6f9jINGzbk6tUrNGzYqNR+d5tSOuYZOia4dEysQMfvN21k+YdL6NK1K6PHjvcUO3p3+ls0aFRaxw+XLGJfRTqO/RnrqHjpeOXe6ygQCAQCgUBQ07gjC+ncUTKbjy9ORzF5uTlYzAq/f6QVQ9rGotlVFHfkDdeTphFfK4AxPRsYZk/+6abRja7rmBUZh1OjVZ0QJvVrhGJR0DTDwLmXXKblFONQXRGICo6j6brnM0mSyC108LcvDjN57g5OXslGNsvGccszjV6vFVlCtavc1yyK14e3IdDHgsPhJCMjncDAoLtWEOdmuNusREVFk5GRTueu3Zjy3PPk5OTwr3/8jb27d6MoCrquc+3qVd57ezomk4mpL0yjdkwMeXl5zJ09k6SL55n47BRat2kLwNbvv2fp4kX0HzCIhx8djsViYcP6b1n56Se0aduWMeMmoCgKmzdt5KPly+jStTtjxo43UhtNJamNd5uyZsN7Pdx//vUPLl48z6gx43j40WGcOnWK2rVjCAwKqjk6Rt+o47/L6JiaksK7Lh2fe34aUdHR5OXlMWf2TC7eRMdHytNx/M9cx2E1S0eBQCAQCASCmoTpThxElmVUVSUuLo6LFy5gs1nx9fMnKtiX/zzVgYzcInaeSkWxmFA1l5eSQNXhg/UnPVG8O4076njteiGBPmay8opBljznT88rwqlqYC5JRdNdRlHXdSQkZNfAVE1j79l0/vr5IdYevIym66C4TCPc3DQqMmqxk1b1w/j30x2JjwzA4XCSlZFGUVExMbFxqKoTRbkjctw2bh1jY2M5b7dzKTmJUWPHceHCeT5cspi//eVP/P3fb9KseXNmz5zB/n0/MmnKVAYMGIimaWxc/y2fLPuQIQ8O5dFhw5EkiUuXkpk1MxFZlpmS8DzR0dFcuXKZeXNmkZeXx4uv/IKYmBiuXb3Ku2+/hdliYerzL1ArqpYnKnQvUhsrMhvJSUn86+9/ZfvWLTw49BESXpjGtatXsPnYiImN9Wx3L6mKjnNnz+LHH/cyZWoCffv3r7SOUS4d5/9sdbzIv/7+V7Zt3cLQoY/wvFtHW83RUSAQCAQCgaAmccdmRu6JYd169Th39iySlEZ4ZCTNYoKZProTU+bu4NDFTMM8qkZ07nxqLh98fYxSLTvuGK5juiOBJhkUGbyMXnpOEcVODX/AqRrFbiQko9qra6KZX+zk+JVsVuy8yNLt57iSnodkUm5cx+j9N5Q2jXaVBjGBvPl0Rzo3CMepatiLCyksLKJBw4audVQ1Y5LqXidXPz6erKxMsjIzefHlV0lLTePrtWv41z/+Sv+Bg/nk4+U0a9acic9OwWQyc+HCed6Z/l8io6KYOHkKoWFhOOx2Vq74hO83f8drv/4tnbt0BeDj5cvY+sMPjB0/kd59+gKweOF89u/bx/iJkxgwcKDHbMC9q2hZ1mxcu3qVf/39b6xZ/SVdu/fgt//3ezRdIycnhw4dO5WKrN1rbqnj3//G4Ace4KNlH9K8eXMmTJqEopjK19FRsY5bKtBxXI3X8e+sWf0l3br14De/+z1qDdVRIBAIBAKBoKZwR2dH7rVL9erX5+yZMygmheCQULo2jOTdsV1IWLCTY8lZnsgjuo5kuQuFJ3SvbhwS6OggS2TkFWN3GBUmLaaScRQ5VFKuF7LvQiZf/JjEV/svkZ5daJhPk2GQPUVvSi6+dBVVSkxj/agApj/TmUGtauNwqqA5OX7sOG3atXPtWrMqNrp/BGjXvgN79+ymXr36/N/rr5OXl8M369axc/t2ZFnm5ddeo06dOhQXFTF/zmxOHD/Or3/3ezp36QbAjz/uZfasmXTt1o2Rjz+Or68vW7f8wLKlS2jYsCETnn0WHx8ftm75gSWLF9GseXOenfIckix7fka4W2vhvCNT3ucsMRtX+Mff/sbHHy2ndZs2vP6nN4iIrMWRQ4fo1qNHjUxpLKtj3Xr1+f0fXTp+vZadO7ejyDKvvvZrasfEVazj3qrrOPnnouOf3yCiVs3WUSAQCAQCgaAmcMdnSZIkIcsy9ePjSU9Nw2kvptjuoHezKGZM6Ebb+mGodqdnzaOu6dX/0PVSaaS6DsgSWQV2rhc5KHaoXMkq4OTV63zxYxK/+ehHhv5nEyPe+Y5Fm06TkVeMbPGKMrqfy67X8vrcnZ7aKCaQ98Z0YWj7OKOCakE+KSkpNG3eHLPZXGOLb7grbrZp2459+/cRERHJX/72T9q178D17GzCIyKIiYkF4IfvN7N08SJ69+nLU08/gyxLpKenMWtGInm5uTw79TniGzQkPT2dRQvmczk5mWmv/IIGDRuRlZVF4vvvkZ+bR8ILL1K3Xr1SlS/vBuWZDV3XPWbj4sULvPHn11m2dDHNmjfjz3/9O02bN+fokcN07NwZq9X6s9Bx//59hEeW6JidlUVERCRR0VFIEvzgWsN4+zq++zPV8UiN11EgEAgEAoHgXlNt+Vgmk4mY2FjOX7hA08aNsTuc3Ne0FnMmdefVpXvYeuIakqIgyVLpdYJ3C1kiNaeI6WuP4VQ1zlzL5VJWPhcz8lELHWBSUMwyutWEpmnoZdcyeqekercZkSUkQC120r5hOG8+3Ym+zaNQNR2nvZjLly9TOyYGf3//u15Z8nawWCz07NmLk8ePE1ErgjfffoffvPYqO3Zs543X/8izzyXwwbvvYLP5MHHKFCJr1ULTVNZ8+QVfrf6SSc9OoW+/AQCs/uJz1q5ZzfCRjzNw4CBkSWLZ0iVs+X4zjw4bwYNDh3oqfQLVbjrKrn9z/+1uG2EymThy5DD/+vtfWffVV7Ru05Y///XvdOrcie3bttKhQyf8/f3RNK3GR6pK6RgZWaLj9m385Y9/qFDH1V9+wVdffsnEKZXR8fsareO///ZX1q79eesoEAgEAoFAcK+o1lmSf0AA0dG1OXT4MJrTgarpdIwPZ97kHgzvUh8ZHc2pYza5hqG7ckrdz+737hQew2esf8wpdDD76+PM//YEPxy9wrmUXAAUXwuYZFRN90w+S6WmllrHWLoIjq7q6KrGA+1imT+5B32bR+FUNRzFRZw/f57aMbUJDAz8WZhGN35+frRu04ZTJ0/h7+/Pf956mwEDB7Nt21ZenvY8x44d5elRoxkwYBC6rnPyxAkS33uX5i1a8OSoUQQEBHBg3z4Wzp9LrahaTJo8leCQEPbv+5ElC+dTu3ZtEqa9iNVqK3VPqvMeldejE4yURlmW0XWdTRs38KtXX2btmtV0696D/0yfTtt2bdm65Qfate9AUHDwz8pslOh4skTHQQ/cVMcZ771L85YteOqZyugYU3N1fOVlvlqzmm7du//sdRQIBAKBQCC4F1TrTEnXdYKCgmjUuAkXL14kMz0NHWgUFcjsid147cGWhPhbcBQ6DfNYqsgMN0b2fiqe40sgGcc2+Zox+VmQrWZQDLOoOjWvcbgMY0Wnd43RbDJSU/1tJp4f1Ix5k3vQpk4omqZjLy7kwoXzxMTEEBgYVCoa83NA13VMZjO9+/Ql9VoKPr6+/Oe/03lmzBgKCwrQNQ2nppKenk5hYSGJ77/HpeRkJk6eQuvWbci5fp0Ply7h2NGjvPDiK7Rq3ZqCggJmz5xBcnIyz70wjWbNm9+Qalhd11KekdE0zZPSmJWVxcJ5c3n1xWns3b2bR4aN4M3p7xAdFcO+vT/SuUs3QkJCf3Zmw6NjX0NHX19f/vPft3hmdImOqqaSkZ5OYWFBJXXMZ/bMGVyq6Tru2c0jw0fw5vR3f/Y6CgQCgUAgENwLqrV0oHvi6OPjQ4OGDUm6eBEkCA0LJ9Tfyt8fb0eruBDeWneUfafTkEwyJrOCw6kZ1U+lUgdz/aGDLt1eEdYyEUd0HafTu4WG67lUbxC30SxzHNfbZrNsRBQLnDSvF8pL9zdnXK8GWMwKqqaRn5vLpUuXqB8fj4+Pz89ykuqenJtMJtq0a8eunTuIiY3l93/4E3GxdZg9M5H3355Oemoq9erVZ/WXnzPi8ScYfP8QADZu3MAnHy3jgSEPMuShh1AUhc9WfMzXa79iwMBBDBsxstS5yks9/Cl4m5fyjulwODCbzciyzMED+1m8cAHLly7B5uPD8y++zNSEBDRN59KlZDp17oyvn9//lo5//BNxcYaO77l0rFuRjh+7dHzQreMnfL32K/oPHMSwEY+VOtc91XH/fhYv+t/UUSAQCAQCgeBeUO01570nq/Xq1+fM6dOoTichYWGYzVae6RFPmzohvPfNCT7aeZ6c60UoviZAQlXLGEhJwhMt9G61UfnBGM+61zGkco7hvW6xbMTT9SSbZGTAWeDA5mdhRPd4XhzcjE7x4Wi6MYnNSE8nKzubxk2aYDKZ0HX9Zz9JNZvN9OjZi/37fiQ7M4spzyXQtHkz3n7rvyxbuhir1UZkZC369utPZGQkZ06fZlbi+/j6+jLluQSioqI5fuw4ixbMx8/XjxdfeZWgoKAbIlN3wmzcyrg4HA5MJhNms5nU1BTWrlnDogXz+XHvbtq2a88LL77MAw89xJXLl0m5do1OnTrj4+v7v6tjM0PHD5cuwWq1lq+jj0vH6J+Zjg8+xJUrho4dO3XG939ER4FAIBAIBIK7xV1tViZJEo2bNCHp4kVSrl4lMrIWZqsPLeNCeGdMJ/q2jGLuptNsPHoVyenEbDOjgWEgdfC4tlJrDW+jB6Tk+o/keWGgU9pIlmqxYWwqm2QUCZyFDpyyTM+W0Uzo04jHOtUlwMeMqoOzuIj0tFQcqkqzZs1KXf//Cu07dOT8uXP8+OOPdOnSjfcTZ7JowQJWfLSMjPQ0Vny8HFmW2bNrFwf27+fXv/0d7dp3wOFwsHTxAo4ePsxvf/9H2rVv7znmnWoOX9G6N/dn7lRGs9lMXl4eW77fzKcrPmHt6tVIMoybMIlJz06hfoN4jhw+TIC/P/f17lPq2P8rlNKxazfenzGTRQvms+Kjj37eOkpldDzyv62jQCAQCAQCQXVz142jqqrUqVuXzMxMzp0/R2REBP5BwdgsFp7sWp8ejSJZuTeZ5dvOsut0GpKqYbKZQJJwaroRgdTLprGWOVF5EcNSn2MYRO/Io/s43gbVHTyRJRTFVS21yIFDkmgbH85T3eMZ0aUuDSIDACgqtlOYl8O1lFTCwsOIiaz1P5kK59axfnw8IaGhHD1yhMjISH75m9/SpWtX5s6eyVdrVrNzxw5MZjNNmjalT9/++Pr58cWqlXy07EM6denKE08/jSwr5U7ivdsp3GyCX9525W2vqiqapmE2mz1GY/u2raz7ag3r1n7F5UvJdO/Rk0mTp9Kv/wAKCgo4sH8/jRo3Jioq2qPj/5LZKF/HCH75m9/RpWs3oaNAIBAIBAKBwMNdNY5gNCV3Op2EhoYSFBRE0sWL2O3F+AcE4ePnT1yYHy8NbsqQNrVZd/AKK3dfYOvpVLQCO5JFQTEp6IDqNpDuKqwVmcfy8DaI5RlFCVcvRpdZVFW0AhXdYqJr01oM71yPB9vE0Dw2GACnplNcUEB+Xi55+fk0aNgQi8XiiYj8L+LWMTg4mC5dunDq1CkOHjxAj1730a5DB779eh2LFy7g8KFDSJLEu+9Mp2evXqz8dAVWq43f/P4P1KoVhd1uR5Zlz8NNZaNWFW1nFHrRjIIwJhOKoqAoCpcvXWLHtm1s2riB7zdv4sKF8zRr3oK//+tNHhw6lLCwMM6dPQtIdOjQEV8/P6GjR0d495236Nmrt9BRIBAIBAKB4P8z7sksymQyoaoqiqJQPz6etLQ0MjLS8S3IJzA4BJvNRqOoQBpFBfJoh1i2n0lj7f5LfHfsGpfScpE0HcmsICsKkmQEITVdv2EdYqkKrW7KFnn0Wj8pyxKyK1CpaRpakRNdh1rh/vRpVosH2sbSs3EEDWoFeg5lt9vJzsggNz8PP18/4hs0AAyz+b8+SXXraDKbad6iBVlZWZw8fhz/AH8eHf4YvXr3Yf03X7P8w6WsXPEJX69dg8lsoXHjJmSkpZGenkZ4eITneJqmGT0zXVEn78hQRemK7mddd/falJBkCUVRMLkMTGZGBocPH2TXjh3s2LGdA/v2kZ6eTrNmzfjzX//Bgw89RO3aMVy9epWjR45Qp25dYmPjAP6/MBs36pjJyRMn8Pf3Z9gNOq7g67VrMZvNNGrc+N7puH07B/b/SHp6Rjk6Xvn/UkeBQCAQCASC6uSezaQUxUht0zSNiIgIwsLCuHLlCpeSkrBYLNSKqo3FZqFOuD91wv15oHUMp1Ny2XUmje+Pp7DnXDoXM/LRC52G+VNkJMUwf+7JabmtIL0KpLo7fmiahu7U0FQNHR0sCrGh/nSoH8Z9TWvRrVEETaIDCfGzeo5rtztIT0shNyeXgIAA6tWr75mAy7KMoih350beY9zXqaoqISEhdOnWjeSkJM6cOkVAYCBPPj2KB4c+zLYtP/DFqlXs3bObo0cO8evXXqVJ02a0at2GVq1b07R5c+rUqUtQUFC55zHMhe4KLhsiljYjMrhueV5uHpcuJXHixAkOHzzA4UOHOHH8GJcvJWMym+nUpQvDho+kT7/+RISHk5KSwrFjR4msVYtOnbtgNptLNY///4HSOobSpauh4+mb6nj4nuh46VIyZrOFzl268OjwEfTpN0DoKBAIBAKBQFDN3NPZlCQZEQV39DE2NhaHw0F2VhYXL57Fx+ZLcGgYvr6+BPla6Fg/jI71Qnm6e30uZxVw4sp1Dl7M4nByFqev5XD1eiFZ+XYk1YmuUjr11I0nNVUy2m6YZYJ8LUQF+tCwlj+t6oTQpk4ozWKCiA3xI9Tf4pnYqppOYUEB17OzyM3JISQ0lPgGDbBYLMbnruv4/xFvHevUrUtUdDTXrl3l5Inj+Pr6MfiBIQwa/ADHjh3lu40b+X7zJnbt2M73320iKDiYqOhoYuPiqFu3HnXq1qV2TCyRkbUICQ0hwD8Aq82G2WRGkiV0XUN1OikqKiYvL5fsrCxSU1O5fPkyyUkXSbp4keTkJK5cucL1rCxMZjONGjXi4RdeZMCgwTRr3hyLxcal5CQOXblMdHRt2rRti6+vH/D/d3Tq5jr6MviBB6tJxzyyszIrpWOCS8fmzVtgtlhLdKxdmzZt2uLrJ3QUCAQCgUAguNPUiFmVd/TRbDYTERlJaFgYmZmZpKemAODvH4B/YCA+PjZC/KyE+FlpGRvCw+3jKCh2kl3gIDWniGvZhVy7XkBajp2s/GLyix0UOTRUXUeWJGxmGX+riWA/K+EBVqKCfKgV7EOtQBshfhZ8LSas5hLzpwMFRcXk5eaSn5eDpqoEBAbSsHFjz6T0/7coY0V4R60sFgt16tQlKiqarKxMTp86ja5rxMTG8tIrrzIl4XnOnT3Dnl272LVrJyeOHWP71m1s2rABk8mEzWbDz9cPHz9ffH18sFptmMwmZElGQ0d1OrEX28kvyKcgv4CCwnwKCwpd5zYTHhFJhw4d6dSlC507d6VJ0yb4+PiSk5tDclISDruDOnXr0rhJE6xWq2fcsiz/f282bq7jKXRNIyYmlpdevfM6FhbmU1BKx4gKdUxKuojd7qCu0FEgEAgEAoGg2qkxMyt39NFtIBVF8aSw5ufnk5NznSuXkpBkGV8/PwICgrBarVhMChaTQrCflXoR/sbBdB1V01F1HU3T0aAkyAiutYwSilx+qX+nqlFUVEhebh75+Xmg61itVsLCwvH39/cU/xCGsXy8dbRYLNSqFUVkZC3y8/O5euUyJ0+eAB0iIiN5etRoxoyfQF5ODsmXLnH2zCnOnj1H0oXzpKamkJWVTU5OLg5HBg6HAx0dCQmT2YTZZMHf35+IepHUiqxFnbp1qd8gngYNG1GnTh2Cg0PQdZ2srCwuX76M0+EkKDiIBg0bERQU5NHRnc4odCzNLXU8YegYGRnJ06NdOubmkJx8ibOnT3H23DmSLlwgNeUaWdlZXjo60dGEjgKBQCAQCAQ/I2qMcXTjbSDdDboDAgIICAhAVVWKCgvJy8sj5eplNF3HbDZjtdqwuaIZZrMJk6KgKDK3mj7quo7DqeJ0OCguLqawsJCi4kJUpxNZkvHx8SEyMhIfH59S0QtN0zzjFJRPeTr6+/vTqHETNE0jPy+PtLRUkpOTKS4uwmKxEB4eRv36gxn6sA+ySUFTVezFDoqKi3AUF+NwOtA0HUmWMJtMWCxWrFYbVpsFRTGhqSpFRUXk5uaSmZnJlavXMCkyQUFBNGrUGF8/P8xms2eMbqPxv9Yu5U5SOR3TSE4q0TEszKWjjw+yoqBpKna7g6KiKuqYl0tmZpaXjsE0bNQYP6GjQCAQCAQCwV2nysYxISGhOsYhuMsIHf83EDoKBAKBQCAQCO4G4id6gUAgEAgEAoFAIBDcFEnXyzY2FAgEAoFAIBAIBAKBoAQRcRQIBAKBQCAQCAQCwU0RxlEgEAgEAoFAIBAIBDdFGEeBQCAQCAQCgUAgENyUGteO46eSkJAwGljsevlsYmLi3HK26Q78HugK2IAzwHzgvcTERPVujVVQMTfTMSEhwQwkAG2BdkBzwFx2O8G95xY6NgKGA4OBRkAtIAvYCbydmJj43V0erkAgEAgEAoGgAv6nIo4JCQlxwHtA3k22eQT4AbgPWAV8AFiA6cBHd2GYgltQCR39gLeBcUAUcO2uDExQJSqh4xvAPzEM41rgv8A24EFgU0JCwot3Y5wCgUAgEAgEglvzP2McExISJGABkAHMrGCbQGAOoAJ9EhMTJyYmJv4SI3K1A3gsISHhybszYkF5VEZHoAAYAtROTEyMwogWC2oQldTxa6B9YmJii8TExCmJiYm/TUxMHA70BxzAfxISEqLvzogFAoFAIBAIBDfjfylV9UWgH9DH9VwejwERwOLExMS97jcTExOLEhISfg9sBJ5DRB7vJbfUMTEx0Q6su4tjElSdyui4sIL3v09ISNgMDAS6A59VywgFAoFAIBAIBJXmfyLimJCQ0Awj5e2dxMTEH26yqXsC+3U5n/2AEcnqnpCQYL3DQxRUgiroKKjB3CEdHa5n550ZlUAgEAgEAoHgp/CzN44JCQkmYAmQBPzuFps3cT2fKvtBYmKiEziPEYWNv5NjFNyaKuooqKHcCR0TEhLqYqSrFmD8oCMQCAQCgUAguMf8L6Sq/hGjsmbPxMTEwltsG+R6vl7B5+73g+/AuARVoyo6CmouP0lHV7T/Q8AK/CoxMTHrDo9PIBAIBAKBQHAb/KwjjgkJCZ0xohr/TUxM3HEHDim5nvU7cCxBJakGHQX3gJ+qY0JCgoIRrewBfAy8eWdHKBAIBAKBQCC4XX62xtErJe4U8IdK7uaOKAZV8Hlgme0E1cxt6iioYfxUHV2mcSkwEvgEGJWYmCh+wBEIBAKBQCCoIfycU1X9gcauv4sSEhLK22ZOQkLCHIwiHS8DJ4GOrv1+9N7QNfGtj1GM41w1jVlwI7ejo6Dmcds6uv7tLcMwjcuAMYmJiWr1DlcgEAgEAoFAUBV+zsaxGJhXwWftMdZZbcUwi+60uU3AM8D9wPIy+9wH+AI/JCYmFt/x0Qoq4nZ0FNQ8bkvHhIQEC0aE8RFgMTA+MTFRq96hCgQCgUAgEAiqys/WOLoKb0wq77OEhIQ/YUxUFyUmJs71+uhT4F/AkwkJCe+5ezkmJCTYgL+6tplRbYMW3MBt6iioYdyOjq5COCuBIRimc7IwjQKBQCAQCAQ1k5+tcbwdEhMTcxISEp7FMJCbExISPgIygYcxWnV8ilGUQ1DDSUhI+A3Q1PWyret5fEJCQk/X31uF2azxzMQwjenAZeCP5aS4bk5MTNx8l8clEAgEAoFAICjD/1fGESAxMfHzhISE3sD/ASMAG3AGeBV4VxTk+NlwP9C7zHvdXQ83wjjWbOq7nsMx2nhUxObqH4pAIBAIBAKB4GZIui58kkAgEAgEAoFAIBAIKuZn245DIBAIBAKBQCAQCAR3B2EcBQKBQCAQCASC/9d+HQgAAAAACPK33mCCsghY4ggAAMASRwAAAJY4AgAAsMQRAACAJY4AAAAscQQAAGCJIwAAAEscAQAAWAGdqLPr6qyBBQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 1152x1015.2 with 43 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_images(x_meta,y_meta, range(43), columns=8, x_size=2, y_size=2, \n",
- " colorbar=False, y_pred=None, cm='binary', save_as='06-meta-signs')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - What does it really look like"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:47:13.393169Z",
- "iopub.status.busy": "2021-03-01T17:47:13.391572Z",
- "iopub.status.idle": "2021-03-01T17:47:23.572561Z",
- "shell.execute_reply": "2021-03-01T17:47:23.573082Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GTSRB1-07-real-signs</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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4XLVHorkqIjMlG7dqFkGpconZtrqa+brtdlNgBYWNh69gc0WdVR/fr273+ACc4IrjiZXmcs7jgpX5oCk/vMPHyDDu2O+PHG9uOdy9YX93x3B7iz8c0WFH9gOLBIo6vo3+XgDHLdrWZ+9/uzn3b/aMj0UbWX1DTTzyNs0pwdOF5e2Jpy8vXE6eGL/Lm92PcPyAyJ7bUfn0Nn1bcqNfmfL421yNptQt2V18pW3RLjNaGv56oLQDp1oimoWmac2sx9rjqXottcpAe2CyVNtTLTfQNkHBDvYVGK1ZqrSsbLfVkxPRvuGaNqqVIFiZ1apJXbMywupi0DS4rQ/PV9sKtiyrFV17tSVV02IVVYpdBCLszn/+t56357RaXjbvXbW5jqt+4Jqq5WxmRe2u4GyEmIY6tILH6kfvpNcV7ELMxkJ0JXzUg1lQA2vtgO8acsF5IJdav09JpZByJrd6g7S1U0gpGcB0a5kG+jObS0YAcVUwKigZqaUdtCgl556lU3DVjRMszZpD8Pg2ty3xSuNYL0JrmYw2qlfYq1+1Qr7t9LRPv4kaAFzTzbSA/yaMtjFs9oo25toWy7ofqzDmnAccpRiQ16JsRMHt09fWfQDgdvf9tp426wpsvUnZOtC2Jq6MusV49Kfp+v3nSPElZvDVk+PXTx/Tk8NqgNq5ZaAxkVMDjYmSbQ86MUF0iKECRxAyzQOAesJpqXuirPt7o22hs15Zd1+XJ2UrcEJb0+te2PCgelCZS5q55AOUlEgJQy2VJ0gFNaZQegZWPzQom/Ooc8wNkF1JaIK6a2V5isVZSVmf3T1lXpA0V2ttk0nK2u6mF2hnu4gDVZwXfHD4Idocu8jh9o67u0+MW90/mlVrzhV0P5dBN8BYNuMAXfGKeJBaPqKAplKt21W+qZkwnfcEF8hecK70eDzXXC9d5VOiFMTkjmLWVZWaKRTQ4lc5Dqyf1RunaAbNCIrzzs72ek80gyYo1UqsGSfF+G5lFYrdKxRleCEFgNaavisnWcfX1d3SxryNg5QC2ayOZvG1MBWtYMpTFZY0/ye7b8bhSCiuewkYHzLAqHUPrzxqw/G2YKWOc7tEdd0vxkely9Vls4NWRRArv2x7ri+za8WTCIivVv7gcT4gvtZ3dA4fAnHcMR4OHG5vOd7dcXxzx3hjcY3sdpQwoi7g8Li/18BxY7pri93khWfHqGIHe8mggnPBYpiaDNSFMJvV5y4W3xZX9Ddrb32tB9EW7DZ3W1teGS5neHfPdH9Bl4D3d/jhCOHHHPf/Gd+5+x1+/MMDv/s7C//Nv/6/f5z2bcisPqv2s2kK+wbo/mXvd3AbudRjqrpk/WzvaFmFXFmvVW1btR0AdiA2wLgyx00jZHMf3X5S3YDaM6/P8LXk2zNhWK/usX5Bthy7fatOaBfZO7PeOhRs2t6YY1sMLwQ4StVWrtaWtS2mEatj+AHw2Og5618VH2qKuGrV0woOmnBP15zbTZxWUOjWIHbZXGtZyyyN9LV23NXvWluLanXjKgZIlD5+qlBycy0BnIHOphRAzEXHLAZNA7nGO2oxVxZypjjTrFqB+npQigHHCq2grxVbD859+4H6q9IKjleG8FyjvzKSVYnSBmWdj/c+retvXR/Szz2gpjI3y0Q7H+lA8yrJzHbXS63liMNhsSKlZaVr11wxw83rZi80K0ePrNwqI+qze60q2jnTmKtJfeuafA8jPlvy2g+mF5ZXX+nvOZlysVrJmitizuSUzGpoZiNTojizOsbg8b7Q3FItO7Lt14KY5bL9a/sdNgyq7gVZz9VmEbRPV4l0y4H7mt6yypo10QePAMlQS7dwbF39QHBVqfQevbehrp/dXezZ7L92brQ3qvJYq3UIGnDk6rsvQWVJNW691LJB0jqxGQOTh5x3VXYUQgz4GPFxZDzc8Mmbz7m7fWNKRzzzksjpZG7EqCnRBLSUWpd25YEOWbNvaiFpBesOUs4sOUPOBrfdumaWJXE5n/A+V5fmTNG0ntcVZGaam7F9X7HcA0Wpbsa1HqCmbl0TTQjN+mzjYoDR48TAgy3AgmjG4QkUvNh3U544T56Mw/sdh3Eg+pHDHD+waP72dG2hb+vH4Zz2Uk5u1e6alK0gpZByIblC8pafIWMZVyWAZ4Mj+vcdGQcstk60hdMUGzOoCqDGI1srV/m07YPVkKI0kC1bBtRkWWnnxabPjdc3UVSbQcXOkYL2MldOBAm+AsdgJUicB2d/x2FkPBzZ39xwuLvj8OaO3e0Ncb9HxpESIniPuICTgOfvM3CstDkiP0i2YVy/6rmrxa+LrprQTdX1YMgLPJ4oTxdcdiA7Jt2h4TPuvvdP+f3f/9/wv/xPv8s/+v3LiwBHW7DSt2O3E3SA9F4Paj+2mFI2+TlWIGV/bXUyShPG+y6oQmu9y4oJtQUMV4bUXtv1fW+ultDtQdXbUSuKNxC7QY9Vi/Rth1tzw2t8b43Ha2C287yVD3Whdx26Do9ejEqxuIcVql8LGnb4KKt1Zr20Nf+5MC80hoPV/akasW5JVtai7XU0HGu8BSI9GLs9tVBwFTyaVa+gFsxTawsJV4PXtLV9DutTxJu21QvBVwu4M+Bobhl+A1irBbOY8EeNsWluqcVp1ZoKxZlwZ8OkvbDv6sbSivG+HG1rNz37gHWk10QvqzACZhkBcW61QEpfhpu9Kd2yAQ2IVY1lW60by98WjT33HmhWd6S5Nznanmvwtseaw4YVNbBoVCpzbGqcttZWxtzAY+uF1Ju1TtT1JK3Nbdw2u0LXdmzk749O/1j+nHk+kdKFzIwbHPvbO0IcGYqyF880Jf7sy7f88RcP/OJd5uEh8Xh/j+qFzz674ZO7T0B3LLPnuNvx6ZuBRZ/46vTIaS7McyFPM04yt8fI4IWHd0/Mp4UbFziqgPOchpHHKMio3N1E7o4H9uOOW+/5bPDcjR40cXp64HI6meAQPalkEqAx4PeR4+jZ5UR5mjidFh6XwuOl8DQpD+fM/flCUbg9HDgOO0ouvH068bP7e7LCpze37MShi/Lm8IY3h8Cbw8wPvxf53icHWISv7ifeTgs5DLg4sN/tOR5Hpumex4d3+DDgZGA+z2iND3w6T5zOE7t/+r//6PMoTRFTFM0Fzbm6ma7FuF0DBTRRtSrEnOuWqGa12Bgg2wPMw0OMH230tuCkWzUsIVc23Z27VsasLtuN327O8ppJ0QVfLylrSn4aT6x7n2/YEhveqs82VYe9UtVU2kCu3amUREkm6Dr1q3Dbj/nrp72UvOYkVLFhlTWaXLJaBKvni3hCCITQrDYRPxwY93fsjm/YHd4gWliWzPl84nJ5YllmUx4Ej1dPyhmtsa822G10q/eTKlmEjCcXSGkh51StZlj8owi5FOZpIqeM96GvqaLmplqyI6kjl6qscw7xrjFvUHcF3ktPnFc9bzRVQFihjHi883gXcRJBPaXW5xXAqykJ0ZmcYZpakryRw2HHfrzhsEvktAN+8fEnsoG6ztBWpUd/u3GwLpxZlwtUL6bqyeSUIGbN9ZWneZFNWQvbZa0kh2jGqYA3pbJlLi0VtF+Jls+MUN1H7grD6LqBehvbh1esf7MpFaonnK2h5keHoybBcR04ijdZCB9wYcAPA8P+wHhzazGN3dK4R4aIhog6s8TiPM4FxP09TI6jDYE3QaEJTtu/S4Gy2KEaPM7tnlkDZnIRRIMdDK4fl+9ZGjeGzV+xvfWX9v0P3c9mvgpxHsIAxTPO4GcoOqLjp9x87wf8+J/8mH/2z7/LP/sn8Oknu1+tUb+szf2ftaufb7JtuF4v5P5lrXv3GoysX5cucK7spo6/rG52ZnnanHVXYGaVfmWzeSrHu+7JVRtXhzXd/F8RYLslbZe2e1591J/z3q92mwY+NmCnx+PB1cRLHc+XAhyt1t0Kop+11Vqxnm6rrFHBnVwvWzGlcEtYo2quhO32fT6VdR0AUYRQrY1dIKkAUtpYazEXGpfRytgKBVWLJ5Rq0XTiCD7UVNmtjwYOvQ81q6qBxx6HAXTfGeiJbJq7q8VEGoPwImhTfhftCSZUnAlzFdiKtrOnZ7h4f5A/EvUA9/fur43bsLU4vLcqFboatCZluAKNIlwx37out/GKzc66jWls28ZAu/a9aXi6jb1sjo22t9dnX4FSthLys33Tx2IVQjtwxKJqmzu8PaNaPLYdbWOo1/fbnGjrmy9ASRNJF8QLx/FA3EdcHBAVgoJbMuWyUBbLHHyaFqZLQpfMYQx89+6OTz99wzzB2WUOe+F4CGS3J/nM8nDm/uGJ8/nMYR+629VudIzJ8Z2c+WFwDKPjF2Phr4dA3u24Pe4Zgud8OXO+nDh54fPbA28OY82ijFXN3pnguA+BooV5WWoijsglL2QNZKEmwkhkdRz3R8Zhx+1uT8iZp+mRkBbexIgLkUPwljk5em73gbvjjtv9wH4IOI1cLhce7h+5n2f88ZZ9GFhS5v7xxNPTE+fzzBANol2eJtJlIqWFS0osufASHFJNSjcLUnPFrjzNsJ3Q1WJFyTlTkp1/zcXQvB3MTfPKte2DCK3f2BRh9SBWGnBURCswoFUtp26WtkeooR/YAe+o/vf1SGugsQFW+pahHef9vOg8Y5W59L2dSj96ViVTvaqGiGQUgiLOd5C2VSu/NO33R5alMC2JXKbOS5r8Yq75q8cCPcbdQxHynFkuifmSSKMS40Ac98TdgI8OmTKCq5bdiKRCFnNVtkQrNVFX5X8tE3XWRMY8hkDxTogxMITB1sy8kHIi50uNW3M1GZxQBFKRHhfegCPeo056ds1mpGiKxTbBWsFQszEbW/A4F3FuBAm0/ABaLEZQHDhRlAxlwmti8AOqjlJ2pHwgeMtkWl4UOEoP/engUTdi64YH9eux8chqsY/BFXxTGHhPC1/yrOFZomvSHFc8xZu10Syxulr+GjivjzNe877harvP2u9b0g9e2PCKzWlRC9vIjTeKyUTiaz98VR54h3iPCxE/7Bj2e8bjLbu7O4a7O+LNDe64h3GgBE/x3hTnNSbShQDuI5fj+LuilmXqgyQAGcqJnB7QBMPuM3DXQblmbPmQ9fFDU/dyJFAPJEfTReEGYMewDDBHQnjDZ9/9XX77n/0j/sl/8Rm/95/Ap5+8XJt63TXWkejwpjKPVhOmbcxWVLsJiM2i0CxtGz7zjKTfvdVf215oz7Dd0gRMJysj25r+V/vTdqPSrTVozfXR0WiDkVSAuFqkLfNm6YOwVWh1UNm+vOlXu1ehuT3btWt4dWv2xmXhhagzwk7Sx+X5+9YUG5NmNXK1nUb1AEQtPYx0O/Haj+cCuZp7TC/v0O7nHOrEspzW+5eq8CnakgBgbkSKufaA1V3CtKrObRJNbJiDaxo2Z23u67SYFl819yx0xnwNlDlxFFcPSRVEWmRcUwQ4NoEb9afWCrya3Y9P3Vq90WJ1INYGujNErS7I19E1XfnR44XrvpPN8q31nVbQ6GrCoiZjSlUYbAHkasVfA/fXNSHS1pBcteO9jdN396rV/ZCF9Xn21L5HZX1ZLRer0NutlM8Ooav1W7/xUmJrShPeF25vbhjHkfN84eH+iZKVgwsogcslcbosPJ0WzpeFlApOPN4DRRm9Y390OJ3Y7RZu726ZUuKr+zPp/IAvMzsPgUQUzzgEypxwo/D5/pbfvduz2yuHqAxhx8UdKUlYpjM6X3C6IOJRMrkkohRCBHwm6YyIEIMjL4nHx0fyKcB4pCRPLsJ5LjxcCo9TJohnjAFRzzIZ0JzmhBPHp3d3xGFHXhY0LezHgcNN4PbNnttDpPjCL+5P3L994N3pQg6B6MwGcjqfOF8uTPOM4rgsBs40WVbK5CaSnhl3L5M8TospuUrWHgpAFQqdk5oUQyzxTC7kJZMclphicFXBYWn/bRts1/AH1p7Y0dOyYYo6SjE3/JIL5LKu7Q367NxNCzTlj5OaWbMeZ6xCdds3bT+Uuju60qolt6mNkg/ulK3E0PZv3VOiG54iBh3VWYKYLt1vWEk7X75B1PvbUogjhYzLIJJQFqC6hLraRqUrCiQXxBVrf1Z0yrDAICO7cISjuWcSAn6MhDSguQJjPFI84sTKYxXt4K55rBTNFpBT144rdh4PITDEgRAGC9NwBVdqRvCa8VOLdOBd1NZoO4+1CFqc8duqvGjumxUK0SW8ugbXpWhzruJRPEVDl8dsbRRSWayfZQEHgyjj/g1OPCU7zrkgLuF9Zv8SW7IDxzVL6Opq3Tjdh66XLj8WVZIqS1GcK6YcEEfw0sFj6ErU1T01t8zsbN25LXFOqaU61gBluW7CZp8+X+JbqUu270mTKBuPLL3eZPd5EqmA0UCiBG8xjTW+0YWBMO6J+yPj4Ybd7R1jA42HAzqO5KEpGkwuUueru6rd89voNww41iFsB9dWCof1tBHAQ2ZmvvyC09dfcH5wlPI94v5H7D/5nOPdwDjadUqhFDtBXZ/b1T9fNkJZs3b+TUi27du8oZsVIX5dVCmBf7vAg/Iw78j+O3z6+Q3f/yf/Gb//z3+ff/pPP+E7nyyAlQd4CSpNGGsosf6KNHZQBdDGODZ90QrArqQ3muXvOZuxa7RvIK1gr7q5tfOpFzbfHAZ9A8Gq5VkbssZGtue3g67eh43L3aaf0p4hatqcDpjWYbiyTIjwLPVoF3a7dfzqM+nS6iq08iLUsk1ec95rxt57tBXQdXX/a+VhmvDgxSxzUVytrViQZ25a2+c57CAJdhTb0wSKcyRxJCwRjmoVwiRjiW6UXAUzUa0av948i8nRZEyzykalCCnbelqkuoz0VNk29oZPa5xkzdLaElmUkskNkPb1vQKtDsU636ygUZ7DkY9LV3PTQHjfIPQ2tfhQGlOq6881EOhWRglNGKzyoFqSGTNIbsB+e5Y0D9C2Xkpf92aJFFqhi8aorzNarwkKtv1SWN/vAuU1QNx00fb6FjRurIobJTId9KtZhroV9Btmqj1TmqD9AhScMgyBN3c3qDi+erjn7f0jy5I5+IE57jlfFt7en3l8urAkLBlDCCxkLtOEL5nPb3eMsiBj4HCIcLrg04UxTdzcHBh8oKQTbw4D43Hki5LRwTN+5zvw6YEUzoie2ePRRTidFvR8YdTC7WHPp8c9xzEimimpmGeAA/JMWTJFLb7HZbO2PC2OKAPzAvdPEw/nxGVRIok8Z846maCK4rwy7Pccjkec9zyVTMGRg3AuM4/pgs7K9Hjh7buvOV9OeAdvxpE4jKRl4f7xkafzDBIgROZkLroh7rm7OxIksz/fc7fzFQp8XLLapLpJ/ETdf6tQ3lCZlkJOieQV7zFPCCc9k6aqucWvQk2FgFXqb3jKOcF7hw9VdafOtKDFIqss5rLub4WuEWpCvoI4RzBcY3VrzUBCqaVFisMSndBYunalyyr4tvOFzryuPQDqGF3x15aRkzWzY8uqWksDtHNpCxo7d9IP79m/LV2mC0tSUk41u/bqntrGsZ01VnKlkF2me9wU0OLQy0SeZqYYOC2JSQUZdgxFKUsiJyEni6fLOaE5m7Wunl9N+aj1x85aZ66S3hFDqB5zNrjOmfKmpXBZawzSAaFNqLWbOg8qpZaUskF2YvF83jWnajYoyya5raVs+MjKVNV17mvZK3WeVGriHRSVgXFeGJcZuJAVsk4gDy8CHFv8YdtX4pwBa+dqmRVdeeJ2HVee3qITcwOORREpPSeD91aeqynMvVpZjqSW+CdroeVTb5bcInZXlWtvBIDNaF/JSlsZed2Dz2iDT0oVfLphZDsG3iOhhuh4A47OW1xuHPcMhyPD4ZbdzR3jbQONe8o4kmKoLq01v4Pz1MyXV/LDN9FvGHBcybTp2yGV6p4KRIcSyL4wXb7gp//+/80f/ps/5y9+fku8/V/xe7//X/Kf/C++xw9/B8YKHLM6RD9U1/IaNH7sXnTAUd0M9AKXnz3y8KdvOX155qdyi//uP+Yf/uiH/NZ//p/yD//p7/C9T26IJFLJkF8mGYdsDvF1CHTz+fr+Cqg2gKR+ue+DDc7q1N6TBh4bwyibUd+A7M2TNvxrtcZsQVAFhquQ2lDt+xtRNi3vU9Jx6naVbYVgVuFSla2L3LW2tLaqCba0dkv/jK1w/JGpWeafb/UVpq+gsbkatXFrx2qva1THxWPMJjiHB1wGqk+/Maf3R8zAoxLF1UQIwlLx9nUBiyaoVDeYmiJeqlbfUQUdxWIocgV+igmfKXUlULPzap/MTc91BcJNKDHFg2lrYT3Pm+LoCuD39dwOeb3aCx+btgqrJhz0v5pw2NbVBk82ANhcmbaJv67savVvQfBaLSa6zj1s7t2E0AYsN4JiB5JXGl+5Wt9tXE1fs45tE0YUerxpm6irY0P1qv3ygZ+1PVoVDLp6Y3Gt/LveGauHxEvQPg4Mo63jyzKz5EQCpqzM5zNPeeLxaeLnbx84TwV0ABESEF0gDgd2LrDHMd7cskTPMs+QE9+/veXz8cAQduy8x+sbwt4zD5ESjjydMl9lmO9POHdm0omLLizLTD5l/DIz+ExYFL3YHvIhgBsoNQRuEIWyoEvBIdyOB0r0oJ55hvN54XxJTBmmgtXHU1M0WUKOwjgGDuPIOA6WvdjbqTNp4fL4yP3TI06Ep2niMS+4MXK79+wEbihM08Tj0xOpgAQhJZiLMKmgeaboyHEXOIQDhwjvXmAeS1NytcRj2sT2yk/EdQGSqrCwBDqQs9REKy0lPqhTirC6mW2ERxMGTbgPzhFCTbpDQDTgdCFhNR8NiNS4SdiAL2udEwOLgxfGIARvsXHiheJddUdr+7v1dmX2XakKdGZax6DFfNkn203eauUKLhgICjHifajZrX1NUEM9y7cA9OXCOADevXuLqqtnviJOK/Bozd8or6RaUIsBBa9qFnXnGJxHVLlMC4/TxGkqKCNu8MDMki5My8QyL2gyZSellj8qzStDuw66KQt8zfbppMaFFrveOwwIYHauXOtEalHEY4BOBM258teauM05qxNaLC7XgKmVn7gKV+ngoIIQKsiX1UztnMMHU0KISPXgsVhNJHM+XfD+nnE3oLJjKRNzesdvffrxZdbGP2C1DqpzVnNxC3Q2DKJnyK9j3sEjylITRRUBxROduXsGWZU9HsWrsmhZE8pRY/JphonSvRO09IOiz+/Kx5u8ea0kWeW0+rfI5j27P2oKCNMZeVxwljU1BHNPFod6Z66pcWDY7RkPRwOMN3eMx1vi8Ra/38MwkH2guJoLotVSrYl0EPOK5D1Ptmv6DQOOdoBJNfu28W3DbHV0CwGHkBmmR85/8Wf8yX/3r/jX/89/yx/95RF3M/NXP/sOp3zk4o/81m8v7F0x//Bsh5dzUg1JK9jQZ8LG34RWI8x1FBIYQ3F+Xc35aeLh5/e8/eKJxxS4fO8f8L27O9783m/zg3/y23znszsCsBApORDDy4iqzcL0gd7Q5L0GCNZ1vn5hy0if3/C9bKVNgN/cpcUJSv+udEDWgU2/5QaZtu8DzbF1C9o6W3sG5K76V7at6R3Y3Ls97xoFr5omrkGjVqbb1+sWfL6ckArrGtZN9dme5vxKKF8PvjXJSTvctJaisAs8m2xjG4Eh199bEPmqfKjPqBYv753pO9RiGAst9pEOcs1ClKtFsR6MpViR6GIh+VnpsUX2Y+44yLUb9RWgquPf3TE3glC3vtX52hiGrz+n4x2ur3gp2HgNxLowuX381VnY/mhxf7JxF5YOQtf+aN2TpQoMFq+45o9te0e7Qmh10V0/tzH+AHCsgkh7v+2FBjSbSNq9DhrO3wDH1q9rANkGh3Wxwgqa2czIFeqXq//Xed/M9QttyqUooRSW5WKJLVxhCJ7FO6Y88+7dibfvTtxfMpmwKWHjGIaRcTgyTcrb+YnxZkdGOJ1PuDJzczywexORUgha2IUdSYQvU0ZkoJB597jwdZpQLmRNzNlcsm7DwM0QGJxQloWnXKAIu70lwViWTAiwGyKD91VILXgxELAsyjxPTHMiFSEVRy4tthmiFxKF87wwJbO6OC04BzEIsypzzuQl8TTP5CUxFWUOnhgKexXzRMiJklumSnsva0ZdQKKnsDDnEzsG4gjBvcye1LK1eq9KDt2sva0lwfIumFup5oxmj/q6x5zDSyFvhMLtWd1jrJwQfc346NRS4hdv9yoZVSXndt5e86Z2P4fixIBHrPdDTSgu3lO8q4qWdQ+s6pnnL9vYzI2i50pOajeqwNWBj544DsRhxPmIqrAshTKnrnxkoyjTq737cekynQADROKkn5emfNzGrtbDQS17KdQ4Vu+IPuCdI6fMOV14PJ05TQtOYZBgVspMDYuYegkQS1ra5uUZf0JqOQ1bEy1rb3tu8IEYA8EHVAvzMjPPpmQtWSyxmzgQ30Fwt4B3xRub2Ss9vKGrGar1OvrAEHfEuAeJKKZc8L4CWGmWZVO65lJggctlwod749kSWPLMZXkEvv/R53EFhpUPOGflNmpekxZe0a5dZcTNj5iaOau5oLbE7Ca7WHIgPLUMlxXOc6W5Cq9qd6lhZ45Sz6yaNbmCyKsd1beKdNni+VpvMb/Sv9l2XjUBk0Gqtdw7XLDyGi5E1AdKtRi6Chp3xyOHmzv2t28Yb+4Ihxvc7lBjGq20S5PVLNGgN+BY62tbLOXfK+D4DYdHnQV1QhJHoMD8BH/6U+7/uz/iz/77P+SnP/kZ56fvkKef8If/4/+Dsz/zNf8x/6x8l//oxyOfeyuYWrInJcH7dWldRwrVRz4/H3/F1l8dygppSTwtiXsXSJ9/l9u7ke/84HM+/9F3uP38pn9PMuaD/3Ky6soIq+CtzXLVtCvNKiXrOF25lm2oewZUIbKBqSvLh7KJt1qF/j4P7exG1gyMsjnTeTae7e9Nc2SzW7cuyP0QUWCNNO1fsuQ80vHWh6jVmTNvH3kPIPc2b27QgNVLMcZWe+rDa2/zm1ZFQLMiwSaGUXseheYc41UhF3LTvKtlEEvUNNebe+capxGdQ+tP1sJUMheUVA8l19y1GzKTUhd6ArIltEk1GUWNMlgTCzQQ9J7o9Qy00BdjA40NVK6JVOplG6CyxvOtl3TAtNXAvxC1emfb9bvOWwPBK7Dt7ZO19MiWR7WLutDX7qXN4qwWB9VBto1B0VK13rWuVz0XXNXENlDYYNvWdXwL5Zo1oZG52m33yzPl0uYIbqVPrB9yNS6rcokOrp04VEwKKFucf/W0Jvy+5CzCz59OfIYyRI9TcCXjS2HwA2kIXFziQWeWGk/UtNQmnAYeThPp6QlXZoZ9ZLzbE8fAGJUhQ/RWY2wpCZ0Tp0X58vHMl6fEnANRBjyxYv8FVxZi8Nzs97zZD0TJJBJJhLMEzpeEzhPkiSEKHsd+HHAinPPMNM8UTeQE5zkxp0JKpl334tnFgYMThExeLuSSKUmZpwscRnZjZFHPQxYuOSPiCD7i8EQXSRRcnvkkRO7GgCcTvXA87JmKuYoNIgZgiyVYGQMcSUQUJy8jwjSLY0tIYXvjeu20+O+qRquJvDeWyqKVKaxoU7ha6qsc7C0rdXAGxNsXHA3stIyZ155C1xhOTRmXnZV3KJnQAJpzZO/I3qOlkLLdoGwsqjw/QzZNb+9d7drO51aht+8v73GDxeyVIiRd0CV3hV8vl6R2xpfyMrvSedeVqjandr44p10xvVUCWFxi/dPZuKsIS8os5wvnrEynM9M0WQIVJ1ASabFspU4KRWqJrHZui/REdPZWA41W39H2qsUxShs7DzF6YgwWXqEZSYLmarVNLeRCaPWKcym1fnSpIN7VGo+g2qLxqgSjZpkUHENw7HZ7drtblGBWRS2IyyiJVL0KchFUfQ2fcqSUmS6PLMtkdS3TQioTLwEcu9Aiz/jyVqHItXxo67oBvuYobK9JWWt8i41VAGJ11/RxsHwL1UJetFksnckmOVFTP9U1b/HCTQnSyqAZz1vDobqypgnEbLdY4/ettZlCNt7m1NZQFFwQJDrEB/AR7wISB+K466DxeHvH7vYNw+EGt9ujw0gOgeIc6gxLUV1edWttLKvC/tvoNww4AkjNDLY5xErpWgaHwLzAn/6M9N/+AX/5r/6In/77r5kvI7f7G2Z3z9sv/xVv/38/4x1fMPl/TnA/5vDjA7cC3lNdPlaQ0eLktpbDvwlo3CrC3/va9o0ESR3zMJJv7/DccvzOgU9/9Cm3n+8hgC7mBe/D1mXk49M1oKsHWhfK9OpAbXUNO2Nom2O7YVXrFK3gq6fG12cbpFuCNm3pm3zd9LI1Oev63at4Q22HxVbw743fPMMu3lphYDPGDTRuJe4rCfzaQtoKD7Tru0Ht6hh43wL9sam1cG2pbPrXQGttTVMEsK5xsfPDYhRFakxjBcmVEeU6PwVI9d6iuub266DSUg8kLUw5c0oLF1Wyj3jxBCK+aQhR0ITmQtFEKYmcEykXSqFB2Z4h7lqA6U5iXRhrwKrzkc18PHMW6Vax9k7fCz3OtiWQacBKr65/CVpjAOn7h82KtKev/agy5DecU89WReOc9S3d/C39qvqsorVOXemKEqprl4qvCt7VRa89roN0udqsvUVtnJ83cxVU1zmRdj91tLI6/Sabu7ezy8aiZn21g2NzxTpm22F4Kfrp04x3wk2wmmllKvgMO+eZd4FyTMxPC5dlglKqK5oBk8t0YZlORCl4ybhL4Y5bvv/97zDuR6tZBlhiG+Xpkvj6ceLd44XzbBb8GB37/cBuHPHBgHQMntsYOGgheKXsBh5E+Po0sTyeGQocwoHdGInO41L1KlDPlCw7seCr5WhmnhaCDwzRsXPCcQiUDKc5A8Vq2qngcmFEmaNHSmTKhXS+MGblECK7GAii7EfP92+OvBkGltOFABwPB5zClDIerHxBdkhSxlTwUyYvhUvIcPj487iCxfraueV2J9Z92OqfUq0RLc46KzVhf60j29apQzYr0VkiRCsvJHb2llLIqZAXJWWL7VZ8QzRXPLD/prZ3c8rkeam12TLeG/iJzrN4b/VxawKxlac3da08UwKzHjQizzafkZM1fj2lAqngslrMuw/GYJZMq0/ZzlfvDDhafckXAo4x1Nh4LIaxNI8ZXctFbQCzqiJFa6Z+i/taSqFcLC73UpQ0XdBpNqCGIiS0zGjNfmteWs0rp7oCdgBZAV0FjU3xW5oSnxbr6nsBd5WC1Hp8ouY1l5FamsPmpcXi5lqzcRtT3hT+Xf6RpiA0gJOrBdq+461sloJSYz5Ly0wu5GxjqarM89w9E3JOlJLpdQ4/Ml3tuivmt3o+bTlPv3zLHxvAq7XFzerqSKr4UqxUh1jWdRc8TgdcMXdVry1ucZUqGlDsyiVXk02Z/2s9G9pz19fOB5/LxXVvWDI4szLiCuJNAeJqAhzxHry5p0qIuDgSdwfGww2H21uOt3ccbm4Zb27xuz3Esbqn1nXolJ4Nr7qelfr4UkFy/iXb8TcLOGpFL88E7lLN7KHadMoXE1//Dz/hj/7bP+AP/ujn3D8FkD3g0PI1eXnL0xdf86d/oOzGW27dd7nRW37vH8AQwPuMFkfO0sHO1fTpCorWVLsfkNBWpGkvK9rqlwhiCc8UcgKVgbC7JRwcaZmYcuBpcgwXGEbrrThF5NuzGv1tqR9UuuVB6yrvQmPtRWMaWyeZBkDWOLAKRDA3QGOc30BtnHTrDlR5VXvWRlB8Jvv29rbVsia90LW0SH1Qt5jarmWbsfc5rFizVa5OA1RgsZ3a1R20nU9b+1V7vl4z4Zem2oEPP+rZ+t2sa4+5nA0iRDV3J1UloVWDWdNA1x+whDmUUmv3GahcikJKFFGmlDilxKWOXCixarVN02pJeTKahCUX0rKYu061YLWEC23+Wga8Dn0ruLgCU12j2/5ewSO67uF1HuuaE3oc7naY2vfbtWxePjZdu81vXt+Dve0LV1/ur30VSgPETbxl0xnpwmIpavO9uWStnrnpuhVkq0CtjfPGqttBYzsT2lkhaya4b4Bt18mlNkJ4rc/YzoLn326KmTYE0rLN6gZuN6UgrT317xdSys06cJoKb/OMlMwlg0oEcWaNEyBItZAqrlVSKxnmQqbgRyvcrqKE6Lm53bPfe3Q6s2Qh4yjJcToX7p8y56lqiouSJDGORz777IAPpvgJPnBwjjDPiGazOiEkFdR5hvHIzf7AzW5PRJF0YcpnUk4s2dz6nPMUZ3E2Pij7QXBe2bmF424AjZxmz6UEQhiIYQR15JyQmPFRwZl3AmoupoOH427ks7uBN8cBVxbLwKpigVWlZkLGMU2JaUpEdcgCshQIivqXEVShrpeNUN8VpnVddfkVViUnBhxLwer5FbrgbmFDm3VNtfyL4p3W4uoWJ5lSYp4z01RYFkcqwVauc7UCzrqWm6srrb2pkH1GU4ZccF7Nkukd0TuSdxX0sPLffnLYvrGjovHK+l7trG74Yd+nCqlkA6Rzwo2ZWAysgpigK80lb03M1/etvMw8FvFsE9J0hbWuALa7kTaBqHpXiPOowJISc3oiM5FU0ZSQlKACKtUMLMCCaLEM4dXQIbUuY4snazGNzW3W1QRKxTnSPIMq3gUD3AhLzgZ2nbOkJ67gigHuNlulx+vV83kDRhGtiYk2y7PLWmYnW5aZ0+nJwrj8SLNgKgnVZB4HpZCymAKjCILVRp6mjJYEtRalfyljx/a+ugFfladt9RzvXV+/1Pcz1WsMetkNy7Za8E6oxcHw3sMQu6dMCwVpYLxIje4V45+WSEkMuLfzosvZm7wgz4Aj0nh+A7UNgyjeO8Ig+Ohx0SMu9Oy36j0SI2F3YKxWxuPdGw43t+yOR+L+gESLadRa/9ryTzTgqE28rWNTKDUe+Jk/0Hv0mwUct4fW+mcPcPUKnOHhrx/4gz/9a/77v/g5f/KUOMuRVJTz6YkZE2YHySxf/Ql//Yc/4t+5f8iNfo8d8Du/Uw8qB+R17r5Nuv9Wy98HPlsXcF3QVRuUihCC5+ZwYJkKT08zl2nh63cJH0feeBj3rh+izxX0H5euhUntWlPWYdggxMY4LWvlBkRtzRe989cWkX7Jdqj0Gsw1abdbkvrhoP31GcSrYKA2UTbjpduHNU3eClC7VC4VVLRr9f0xv/pzs9FXa3Udt8Zz6r27+3Mbjxc6T9dD6L1P6pBuxqwJ1A0s1X47MZeWCAz13ZbMt0hNVEMFkVSrThGQ0oFDUVhKJs25uqkmLqUwi0O9rRvLImfpor33phDyk90/m3ZztYQ2zZtBGSeeVegy2u5Y+eDrup5Xa+tWSNhYpOv7DbTZ0D2btO2HH5u207h9xPM2WcPsWtEVAPX3+MA6bn1ekbBSNc3UzLy13qajuq8IltRBVxaiWp0/3MpEt1kmr+Iza8bi0udAuwB9fcBI/81eN8IwoOpM8KsKoevlXoHxM0Sp29+6QK9X6+WlaBcOnE4PfKGTxafHiPiBKRfO5xN6PrPXgsbAWa1UzM4JN+LZeQ/R4/YDfhCcK3z3zRveHHbk9MS7p3tScuTkmC6J6ZR4OhXOSWuCDGU3CMfjgcNx5HG653Ge8C5S4shN8LgC52kiZ2UPuN2BfRhwccciQ80wGJkuhVM+c0mFJZngXbTgdwO3YyAJOFFuoudm5xEcl3xgQUkaUBc5I5SyoCxETdw64bM3b7h1nr2DMUb2+5HD3lPKwnQ5c1kWVBzpcuZ8XlAfWVzki7cPPJ0yt7sjh8ExhsJh9BysstpHp640go2wV9dT84hqoFE219e9Z1Z7aIW6u5KrFVSEK8uQdwWpLo4plwoaM/Mi5Aoae9y/01rQvfQzrWEDoSbLquKvF4evCcuCb8BljXE33tT4dS2D1Oo8VmXsFjw+30S9TQKazWpTRIkJEIcPEeccJWfm4EjOrKntXOki/wtZHC0uzyZF2BgJNmf8RmfHNpbVYkoLpUyUMpPrvbIawHdVuVmq0G3fMx7lqitqix2TChgtYZDr/fbe5khxSHVDDS7gsNjjnGuWAOcIIeB8A42uxhtmUs6UkjAPnZo7IOcKBKyFzU22i1obaJBz4nx+ZJlnnB+qpVNqIqHSeVBRRy6+ZhhWRDNCoqqy2gp/EVoBWwU5bb7Y8PQPfGfr2Ub/blOuSk+sk1VZVHG5mHdNdRt3IdLyOVh24JYp2COLh2VGsrfQDt1knlftMa616tcz4Liuwdb2oqZALCWh6hBfkMHhR08cfU1k5inqyRrAjVZy43Bkd3PL/u4T9nd37I43DPsDfhzRChpLVS71aCEHSC0Rg9ZwJKllP/wv5ZO/WcDxmaBmB5eldhaEMmfOXz7ysy/+kp+d73kbHU/jyHk6c5kvTMtMFkWiZ+cm/HzP8ouf8Jfy/2Gnnig/xPmRH/0Dhxc1jUIGirMF2APtV63AdQD3syZuhXJ74/2/60QVTOAad3AnEMLI4bgnpcIwSq+5ZM9rQc52mL8IybrNt7xgC6Tbp00jUfTqgmdan1WLVbbfXhEm68Vrwor1fs2SsTl+dKNVfa+tq9C5Wmu0m9yb361ufmc9Mvr3n583TUhfn7EZrs0AaWc4Uq8vrVdVo/nsxi8KHJ8djts53DDFNkc0S1SzstFmpf6uq5CtYp72Sc0aAlRtqvRB6Zq8ZAw0azH3HkBDBXvSUu4ERIIVEw6FGAZmH6p728rcZdNWO4Gvgcnaw+vf5Oqzde1c9e+DjKautLbv6zh0YQldX1+E1vV8Tc/W6DpJ/Y/mzttaVvnUFXiUKjy20iurMGAZbUXdVYIdcKhblS2lPRKtAlMT+6S/L8/PTFoSo9amld33Ie17ZbXx905ea4NohZD7iNXzBl09KL4Jkm6tzS+V4RigzDNPpxNEz83uBr8bmUU4L2fyMjMsM3ei+CEwlwwifL478AMvHKTAYSAdRjQ6htFxd9zBvPB0mnh3zjxeFh5PC6fTQlpgnoWSYYieIVhdLlCWZeHhcuFdTuQ08TZ9zW/d3vHdTz7liBCnBBoQNzIl5f48kfK5CqeFlByTDmSUc5o4LxMFCMETQmRwwjh47sbIiCMthSE4xuApS2HKCc0wKuxxHHxgtw8c/ZG7EBirNVVjIOvM+TIxnS0BiDhBpBBSYUkLF02cponTYo6BC4GYCnMW/O3+JTxV8V2YY91MnYc05Vbl2a7tGeOVuYJGFRPcVVyNEXarRbxZ+rSVQjH3tJQT81y4TMq8QM4BpSZRcSCYm6lxHONnTiyhhxOPF08MjiEOxDAQfMA7T1GzBvl2BmCeBr3motQEW7WU7VbZCpuTqYLEVWrvqt7u/mhaR4/3kWEYCcEjmpkvgXkSNBWyUktPCtcP+Mjz6KykiVZvhE036jlYAUm3DNlZknKuZ0jun1nlCwPtvaSGkxp+IagGGsbu7qje47zvGWZNlq15CTTTq7QgNbuqRbWWrGgrgYW5ETsnePNbtNmTCmRzIuVAdpZYKmehODrAa+fwKq40mUG6gs6spxeWNBsEdHU9VDdJHyJSrVYpA8UsmTEEgnembDBp/WUm8gPUz/ouY64frPrUrVdMvU7WHxXpKWiSVh5WFQBZzJ1aYkSdQ0LAh0iMEYYBN0+4tKB5gWJ7UragsNV/bdm++zlSuGJWG16cSyGXhaIJ5wphEIbRMwwBH72BwOLIGhC/I4w37A63jLe3DDc3xOMRfzjgdjskDuCCnT1ItwaLqJ1N0GOxm4tqy3Pw98ziCFcsX82o3A6XaXnk51//KT+7/2Mm/8D4ZsB/OXB5mzjPM2AHYp4WggZGvxDnn/H0i/8Xf8LPcfH3cfEfg/stfvTDvQ2kB81rVqOVtgLbNwl1z1q+kTHtFvV7bq0PQ4A4wv4m8Em+tQB6cfighGcZVF8MNPb7QxPYnrt7tlNmm6mqg4z2uV4LaA1cmnKrbky7zbPRfb9fXd7vQEf7blvHQfqGaxairurVan3Zvm4FVHqXnj1wFXi1d3J99tVX6ve0joFWqbe1VbXF/W1HhO23Pz5thPTtk3QdyMrcP0wFC4ZeMBdVah8KloZ6LoW5ZEtfrdXS5NdCsW2NWIxLWcddTJMqIeLCSAgj0Q94CXgN5prlCzKMsN8TUJZ5Ai20xC2C9lqPJTd1xFoWZe20rH3cdLS7VPbZb2D3fS0kus7hFrhcyTUd5Hx8aolnoIIj3cCoDuZgCxG74IPUUiOtD7VISdufzYp7xUC19bwyWkvz37ZV+1Q3Y2K8tu1rE1rWvfM+cGx9KZv7rdYN0z3o9np9bpFkMy9r77f/a79m7T8837PrLa/P9I9P59M9kif8zR3xMLKgPJxPLIslqSm13EUJkd1gLqaf3R740Rg5ykzeOZ52gTwY0CIl3n1xz9fnxLtZ+PIp8fA0M83Z4o2y8RZ1Qojm3jbPM09PymWZIVh2z2k6U/wNu9tbdmGkTAVS4LJ4Hk73fPnVW5asuBjBFUKAwd3ihpFyuedpmphyYgie/RA4jJEgAdGWJMMypdaIKlJJ1r4khBw5hsAQPXtxHB0cgsNFYSZzWgo5GVA6TyZ5j8ER3UhKCykv7HYRQoRcUF0MpOCJQ3yReWxWzBbb2JQU/SQVvbK4N75gyWYsZq+texHtmXQsdrwmpamMyTygTJjLWphzYcmQiwcZ8K5aqaSAJru+W/pt/j2uuqMKQwyMMRJDqPX7qMKqnc9SMyY7wNdNaHF4ZhlDrN9NAP8g72g8uJ+pLUdzBTniCS4whMgwBCiJYYxMl0BKycpLaEbU1bPgZQBHdII4oWzqNW+3fu+btvOkxq2lTMql83xgI2C3WF4zavhqiWpxwI0sU67DO8uOKs5iFLVZohRUzUOnBvZbmEA28AAY4BUMtTpHtxBLgGo9DiESnCOJkKTVK3a2DrVZHnPrHdKi9ZyztSOCr3OZMQCVtZAytb6yR3wg+DbPlhzLOUvW5YN5g+A8+PFF5lE3r9ufzve3bLsu3NV7qe3RNd70qmahSHfbbhqCglodVKkWeh+QEAlxwI0jYZ7Jy0JJc82iW/rZB6yxjRU4Nhm03V+0eRXa9QUD97kmQiqaEMlY/hshRIe3Gi1YvteIhB1hODLsbxgOB/xuhBjRGCjB2osPSPU66COoprhpZfG64l8NvFZj6bfSbw5w3GCD67fWwzqXex4vf8bbhz/k8ekvmKYvWZYT8zIxpxkRVwe/oGUh+ie8+ymqX3L/5V/wJ//TLwhxYvCRUX6H7//ADsns1RIVFG/YRKqrVW+E0BNkbK1s7RzaarPt6itAZSdjk/GMMXoc8JzpqQlPTdR9IaxhT9LNRmO1bvbahRsgvCGB1R2OZplqG2ZlslIDz7cHqbabtr49B9qbK21Rl2vGVTVBWyjQ2rGRp5/fySxoDWfWXrS1pY2hbFCHNsinHX51ELK1OHbLXVmTiTxPENLX0EtO5nO6Ajjy7PnrrmrRJk3TpljWVIcdYotqBY6lxjYak5GePdWvh6Gpj02QEY/3njFGZLfHjwfcsMe5XReCvCjiInHcMTplCo55CuS0gBSqtww5mdvWshRyqW5RrSfS1u06fc+HuZeH2H7YAFAbjwo6OvBqu7hr6LZY9WXmscV9Qos/rHWhah9anEqzUmyXegNPWwG3G9X62dRKdlwLg/WJ9e+yArQ2JvUeq5tOsNJGbef34sT1fnUMWxZIVdfToLf71uNwLYfyngVwdRVbNbOytou2g9vp0/6/pq1Q2Oayxbbw3jM/Dokmhl0k7EeSKF/fv+PLr96yH0c+Ox4prnBZzuwOnrvRsyRl8LA/jtyNkYubWSSRPUhOTE8nnt6euD/B20vhq6czczLBsTQh0DuSwpIzS85c5hnnFZcTA5mb0XP8/qd89/PPCbuBaSoWp6QDj6fE268uPH49gRfC6MzcVgLjbiCEEfELU3rgcUoEX7p2elkWJi/snQc1t7mCIt7c3ErOzDNcnCM6hw/e6hG6QnFisUVVgMUFluw4zTbvOcIQhCLCOEY+CREVh8wzsWTG6Hhzc+C7n77hixeYR5et9IGrfe3r5gPUZUOtwLCvSanyBKwurquLOCI4LzXphWWRXi0gUq0FkeCjATxy5TMG8pzU7JnUEkpigm6ogqKoklOuWVQzaVnIaUFLTeIila/V7KFNmFbWzOFOHDhbL4V2RrLZfZXHi4FPp5gbX+WPFGtj8J5hGIm7nXmjLItl426Y+oXYo6/jnKW5vNcPOuBY/SCg8ZSVFzTG0rlF5XXSZIGi4Cz7tJU1CCb/NZAuirM6LZCVXEu2dHmpuSYW7RnPt5nrV2XdZj6a9061ZrrgCCJWqkEC2bnqbmilRVJOZiVsFs7KA7xIr9XsEbyLSBzJzjPlzJQmi1+sWg9LwJOrgjhDgbwkUl5ITnHjgRiHF5nHbxDv+mfdmtgW5saq2EFkzSJqChJ7TzdWR6EmhZHVCFKEtaa11HwQpVhW1ZQMNFbgCBs5s+EGBVQ6kBRWq2YrIaLQc0gULfUnoVLLcLjSPRvMTTbi3IALIy7uCcOesBshWrmdLDXTune4UK3crVRLydUroPH35n3WwOPfy3Icz+hK4J0ReYT8JdPjT/jir/6An/3kSx7enm0h18Wec9UEaWE6vwMeGd0A+ZHHt8pP/v2ON8P3+DT8NjvvOH6/CiEtD0OhqxubH/oHRZIPCR66AVb9vc0ffwOl2rVw9AIkBbnS8pVV2985wvr4dnRuweLaWO17tuHkawF026l6INZ7doF8c5ExI2Of5mLRT3vMAr2JE6kAb80aVu8islpA1k73ZwitEPymv72Rz7omrd8bELxhOKUyENcgx7MENfr+bT8ebQDsOl99ULnu3wp6GwArWlNTA1ms6K1AjcktLKpkzKJhQsaaW0zV3L29OMRrPUgz3gvDbmeuE/sjxBGVAcUKibc4GucUGCgB9sExD5GUFyB3haBZMxamKbMkyDV4u1QA1bUHlcF+kwCi9brn67iixnYVbfRWq9z6SQNZL0E98x4tkYb0veKkxuassljfZz3GqDGh5+dSBXHtuuYGLL2fLfB/fa+dWd4JlszI40MkhAHvo7kVC2gxJtdqzK2KljZSraSKabBL1X43JssH6qm2vmxEuf7SdnhbznbWriBz61Z3BWbrYl/dVfXZIf3x6LAb8KOQvGOeF07nmZwyYec4DgPDzQ1DVnIc0GlhFjhETxgCMhRiUIaSuOSFZZrQy8SgSiyCzokyJYoK3lXBo9YHzKLonAiPmV0sDMORTw4HJC+Qk/FA5zklZboU0rkwnxfefnnm/u07dJrBewqOMEQrY1C8lSIowlyESR1zLqTzzJQS+yjIEBgH84ublpk5JzIBip0D3lkillM2C2mSwuAdo/MsecFC4XaoOOYCi0LwgTDuGQbPLsJhcMxVcDuqciuF4+A5HHcMw8AX+QUmMiWkFFxRywnFlo9cnwYt8U1XLm8UVO3c1ZJrFFhd31ILs8dWk63GMZaCiw5XBCkW6+adM9fZumzbPtbKdB3mPu6y7asiFpM+5UzyiyngSmZJiaVa+yjN8v8MNDbGIODEo86UE8XSb9bsp7n3w2KPK9+rWe+diCkNponz+Yy5dSohRnb7g9WdmyYr81Db8iEp62NQD3cSd83vN5/buG6CWFQNZPbL19a1GHDXeY25wuKrq7BvwLEmjVFFSyKnRMlqLqhFu8cFWsi5FXivYAatSsPKrzbLydVae4oltnLB42Ow+DdfSyZ5wYpLiGVZdbaPNS/d0kWVvZxUpYM6hrBj2L+BYc85L8jlkZQuCOaNkpKV5ii5WLmuopS0sDCRghDGA8O4f5F5hM0a2YLCtr/aZQ3oN8tE4/jdArkCyKY80Mp3S2f8m73dgGd1O5YKtFupDtTK3nTguI4uUhPJSd2nwpqxvidy1E3yQaq7N2YpLtXyWDQZ33RY4qQQCWGswHFAwoDECMFTPBQHxVkNUgneaqmKs72QoSmIG5ftPBSTqXLLQvwt9JsDHH8pH18gncmXB05vf87bv/pz3v7Vl1zOEfEHnA99AkIIiEDJF9KsDMOALEo+/4yHX/wJPx3+J34y/jafDL/LCOy+l0zL4FzVxDZhW9fF1ZLHyLo01qZXgfzZJ1fyT2cidWHqNfhqFpEmyNGf+TJkzykbYK7rAbkFcdWVZG3nVui2a7ZfuXKKvMKW+v6b/SBo15Su1m1Zu6yorjEq7wPex1rfsgqKq7S7aXe7ZbX9yhZkss7t1aJbYwBWlcHz9q6t7gJDfWxdHfQEIE0AFrkekxeg5yPaYs56Jz4w9qrNDYsrbZtr4rkqWamgEdphLGIaLRRwzuKd4ogHyjyR5wveO46HPTdvbhmONxQJzElJycak6i/7WlfvCTIQg6PoYKPoqstbygw+E0NmmmHJypKbS0cy0NLX0TrXbaq3I7PRG28+bwy6uYvWMZT1e1rH8PmK+ZjUl/Hm7+ft76v6yhK66dPVtZu/apa3thJbIg2r3WnwzkNNqlGt+S2ZQ4jEOBDjjhh3+DBYoWQEc1nKJiR1MGp31ppZ0rLzN623uR0u1f0wl9Lrum1BcN+HumXF6/5VqpuXrPP4IdC8dbNfnQDqmnuhiTweBha38DSdSckylv7weMftbuAYPE49N/HAu/PEw2lmQNgNA857kha01lhbpoW8zIzi+fRmT5LC2yUTJ2GZCiUtKEoqSkmCRCFLxiW4HRS+e8Pd4RbOD9y/u+fddOasA/vbA67suVzOfP3VO+6/uiddLpAXShZySTj2SIzkXFjywmXOZDzqB1KeyfNM9Oa2FcdICJ60ZJSMhSqrxenkwjBEQhRUFqZcTIAfzY1KsMyUSLEssx7iLjCOI/vDjjEGQnQwRLJYfcNPBD4Lys0g+CgsJTUc81GpLEs/H5pCsKswKwPQngCKKjM0i/7qJteub9p9wLCl8/ggHTQ2t38XCj4qQaHkasGrWfzEquhafPgKc+y8bKUmMpRFSE5qEhxpO3Wt89d2q0hN3lKF2CYsAy1EqMnRUusMFs1Y8j67S48BrfyhdoOcEufzGVczuMYhIi4yHjwy7AjzzDxPLNNEnmd0WT7+JEJN+lGHfXO+9zlpmuHG+SpoBMU7aszhxvuk8cF62lkB9WD7wQeE6iKaK3CsVuKSCmlpJTCkZre0Q6lU0FGQqqTQrSjD1vhTc+HSHCPJDp8NPLpQ4x/Fg5h3iHeBliW2iFByqq7KbSSyfe4DPuzx4QbCkcEnMp5lCZQ8k9NCWhaWlJFSzC06eIbgiCFSYiDcHNkd37zMPD6TFduvLb5xlfCkyzsbUWD9xsYCScvUvpXpRVBxlA3QzFITTtUfrUpe3/Zei1nswYzWsPVfy9NaV41W2buuP+k/1Py/imomlwQlocW8BBDM9TREXBzwYcCFwTJQB48E8z6h/rTSHRI8Ila/VbCsv1LEvCC1yYs1UY6aciP/xtdxXHf1KvRuJnxdJ448FR7fnXh4+0R6mgkJosJcFHzAjx6HMjjPIOCzw7sEqZAvF5yeKPIVj2//PT//yS1f7u/5Pp9DOcL3RvAZdabtY2N5XGkV0nTrcvkeYvxQP9vnCtU9UrsgIx90C31Jcq0trJvsuaBqGcK2AvP74ux209rG214rlSdtXVoNaLe+d5cRgOr21jZXWRbmy5nLPKHAMO4Zd+bvbYLjL4Fk9WSpUGh9e23qe4MudWxUakr1Lik8E803Vj4Bmmsu0uJDtmDtmy1hf1vaCsudIUo7DNq7enVtK3/QPXHoMICeRQ+lNFeOei9XD1TnzEUmxoHd7sBhtycAi/fMaqnlh+jZDYEYnbnQlYQFPdWB11bgtgohWqol0uG84KvFMTi1RFYOgoc5YeU7ykIuMzlPJqSWXAFeZQTvuUq/Dxqlnz32/gpY2niVK2DykvuzPWN99nZeqwZ6Y8nexm9cgcrtd55Z8Ve42PknXiAIDLK6uQXnCd4TgyW3GIYdw7Anhh3eDzWJg5g9UdYEDiY4O0SsSHTKMC2ZeVmY08ycZi7LhYvAGeWiyqy5pv9ek3U4MW1t4wkNLDataM91tR0h3fR0M2et191jQOj9fwm6GR2P88LTOaFlz6effpcffu+77AKc7r/macpcJPOUlKmWK5pSsn0jwjQtPEwnztMFJ8p+NxL9gVguyLtMAw5bK3IqmbIUfFAO+x23N0fGYSAvieU8kZYFxJPOM7PMRLcnTTBfJpb5hKYZyQZaVZXsAykacJryghYh+pGIQM4MfuBmt+cwBiCzaMJHx+3tnj2epI7LZeZ8OcE8czjeEIdAWgqjt3IdMnhiGJCcQB1D9BwPYy1DAk/LmYcFQggc9MD+sCc4WNLM4zTjBHbBvXd+fywqNTlKPdxpvzZ9Y9uqfed1y529ul4+qLqDNfdUYM26adkZTdFiliQRwXslhFoTLmekKGg24bEY2BZyhRoG6Cz+zkIm1izkjc/XNlPBSm2jeF9d6BRcbafUWMXaH4spB+8MZKiqWTRqLKd5BVVXuKZkR8klscwzl4uBYkUYxpFx3DEePIeSmeYL56cnzg/3XFJ6kXl8v5yKsPXQWc+I1WVQUDsXnTA4ZwmGvMUUZtVaa1gtjjNEYoi1fIaz+pvLQk4T1AQnljgu25oqxptajHhtpY2d6sYzpq04rjy4Nj2zkc6FXK2bkpzFGbqAuAH1mOLPe0Iwj6JcrcGoKXqKKjhn2UNdZE4GoIvzBD+CZpaqQMopU1ItOeIDQxw4HkbCHnQccIc3hMPdi8zjVfxg5Xtd/uuDszEUrEKl/f9McdP3QAOSbJQCbS/Lmk/BMtiuAT7NYukFWjYp42Ol12xtsr09rkHIhhksHthioa1tVv5RuzK1OG9WSIQi2brkPCLBgKCYJ4I4c3mXWuvRR48PvidmEnGr5XRrdVWx6hLNwtW8h0qh/JJCjr9+4Ljh4M8F+W1iBhhZFs/9u4X7txN59gxhz5xrsWHv2e13eOeIAkNRgiYoMylPLJeELxNZvuby+Ed8/cWZXww/4wv9j9npPyTIb8FvmZldHcZIKyq3Zlo7VuH7GtZe/96kmAY46oLWKui5rfXjGm12QPrBZ3xEujKHcg3g2ifVhN2AxiqiXrsGdCe3qjVp7/cso+15V13Rvpm3TxbMDUBzIk9nTg/veDg9UUTYH2/tVZxZHre32wj8rf3tcGlvrLOxHhK16xUA6vq+rkBive12DVSQ3O+xMs3t+Hbb5YtZj9uscCUYX39s13TAuP18s8Q2GBmQrs3rMa3VbXGIA/tx5Ljbc9wfGeOAlIIviuSEsBjDmc5kXZiWwmWaWRazbTqoUle1eW4AkXPgg4Po8N7jcARqPGWwJANWT3IhlzNLgmUpLIslM1AVUN+R0bam4PXy028Aaytwbp+rrsPyUrPYhYgmyG2YJKqWkr2jng1wbCtsM4fbvaqsXhKlWh5bIauWvGF0ws47ds4zOs8YLKnFOETGYccYR2LYEdyAk2BlVZxZgFy1DDcB24lpN4s65qRMLnEWxyQwiXKRwtnBY3A8LB5Ni7lAFUvp7qtw1/pklvHmmtwY/zpWa2mg60Reupnf9aRdz5pvLbH0t6AbX+PsnbC/ueW3v/ddvvvJG6bpia9L5hfThXenMw/nE+diFrj76cKbtCMOnsTqNeO8wDCw4JmKJTTYDSPjYC6MDYzM88z58sjRR77/6ad8580bpChPj0/onIjDgd0YydnB04T6BeYCc4ZUY3RakJ4FL5Ini0FLWHmA/bAjL+DSwsE5bmPkGCPIQhabqxg8+xCIYYfeCefTI6ozbw57xhjJASSMxCEwYzGQZZpNoZQdow8Up9xfJt4tmYvzjOOOz8NAGDI5L7y9f4tLJz5/E/leOLKPL5Mcp9TAxu5B0klWZZS2M6bG/3p3BeihnSmF5i6N1H3XrPsFy0ZdLGd1qSkeu+cH9pm5dKYKHutFbb60WC2/Yq+ltLIAXAvSTaHmLLTAFa3xpc1V1RlwFN/74KTF1ZnyDp8N5BbrV5c5Uaiu6Y13CrZVS4a8KCUK3o/sjgdC9Cxp5iG8pcwz58fHl5lHVvD4XD6wo0Y281SvESvF0M/EEBh2ZoWfcuHpcmFeMt4FhsEy16IWVpGXRJonNM+I5Oraq5ZDw5bPRlawlrSxgiY/XZ9NplBY393KGi3mUqu8avFwBkCzM5/DEAMhCN4HS34jYi6zVWlXRCjiyEBeZnJS8IL4RMmZnC1bK6VUa2WubpiWIKdnWe8j+xKkdeyuAV6HB/3Z0kZs/aauXkOrrMQKoGRrHWyWc7da7DeW4ZQViqDOrK5ND24ZUXPNimrunk1RYcmiTAmzDbKigdnGq1pbMaWMNvkIAbztR3XkAikVvGSLv9RCECu344KV7fDBSp41hc6V3Nf2ds2e7KTJFza/FHMf/zb6NQJH3fwPvYDuds+U6kTnARyJwHTKXO4Xpkth0UCqh12IkXg4EodIVMXnhKQZzR6SUFIizTNn/QWnOHE/3vNX8YGBMxc38N34ObcDxM/qoz1QxHyChQogt6t0OxUfktbZyvTA38zq9IKQ0e6/dWJuKq2NyX6Lw4wlWIN6YdMOkISNeYtmGZDrHf0cM3YtwZqAozJUQEomLTPT6ZGnh6+5f3ggizDnbAkDXGQcvR3YWEO72+rasgoe2+/NelEBH5sI0j6Nndv3Q2YFE20A3u/QNsOdbub7esBf5kD9sDby+UW1vx+6sM0D5kKzTk0/3sx7uFoaQxw47A+8OR65PRzZxRFRIc0zxQc0DpaMYZl5fDeRUZYls6RMzgagGxh6DlptvpQYHeMYGYaIcxHViJSRgMPLAMGBRArCkjKTT1wkMc1KujrzrvdhW7faeqzXP83irTRL4wY0vhQ/fI9Wxva+oqVNy1aL2f5awdKazbEKtvVeZgnIFv+gFjPmfGAInmMIHL1n7wP7ENjVzIxDiETn8aq96LWoMY7ghODMzXQFs24FjkWIKRPSQkwLQ07snHDYjexkIOREmSbmy4VlnnsKfLRmF6xycblKwLNB8VseciVw9Rnu47Il2zMvc7oOjOz9SDx4Pv3sDd+7G/HpibfvvuDh9I775cy75cJpmVjSTHCBKWWmnCnelDJ3Q00cUzIFx2XKnKaMuMDt7Z4QBsiWmEIVJufQ+cwxjtzuj2iGh/tH9gHeHO4Y48CcEro4cyXMM8v5zHQ+k6fF4vgQSgUkmhJlXmzfB1PgjAwkzfghMpQZnWcYXM0cW5jShNdCdJ7DMHLY3+A+/ZSSLjgWooAMwiKOWZTzkjg9XChPJyIRlchchHlOXKaFx6ych8hSIrtJiGUiXx64PD1wGOGN27EouPllXBzL5vyzn+fRzd3+gTiPC1b2Ylu+Sym1VI5pn2uYcs9i6VUhl27dtFhgevgDuaCpoClbMo6cN8CxKkQqr9JqASyl/d5aX69py93VpC2llQFZraRmzfB1L9dcAk4ttspXbwLnzRVOHaSWKKlaSNQUd85b0pUhRMZowEpU0AwOz37cs785UMqClMzT/bsX449NXdSE8xVjPJM/N4lzTKHmGbxn5zz7MLAf9vj9nqeUmZKypIuVqXBmOVqWxDIvlMUSpliNw0JLtCLaxNjG/9q51eSRtb1bHy7YgJyNQNn4QAcbFRz1qHIx0JdKghJwWDkHF0zJsYhlMjaLsbP6m0xkLWS8yb4lU/JEzmY99VKqhUrQUljmmdNTQqdCPgXcITCkA3zvBx97ErsIXhqIbuPR5Z/3DRDX1KzOdVQ3Hjtd2YP0GN1Wh3MFjpUf1T1KqeAc2+e5FJZiycnmYvVMDTx2FUp3V20YewNt+0sDku3UacBcNontnCpOM14hIgzekpNFJ8i2/EuNybS45Y1s3/v9zHylPdiqKq6+mX6NwHHVDCibg227u4WrFSCXRHmamJ4mTueFy2IB+8U5xAfiMLA/HNgFK8yZLifSDN4r0Qn5fGY6PfLoTrwNF5zLPBXh6/A5/3D3Y35n9ylvHIRPALKd9OX9JbilLTh5D0c2UFX7dAVk1vV79aVrcPly0LFk7cLeRlHTWlGXbT2ktF0LbUG3oHL7nnTBtEvaCqvfsbw/v+07VeDrH6tSUmI6n7g83nN6+JrT4z0LQtKCCwMh7vB+tBo7IlBjQFZhuY5eba9lq4OrBdUFg1VQb5unAwbdjsJK2+V5JX++txDary+HOrbueI0b6dXnfWqvv9MPjU1v6nJd08u3OEfTTPshst/vub058uZww3EYkWLZFfNslgPvzO1qWS7M85llmSm5VLff1e11/aUtLMtEJ6IQBDdGZBxMSBZL8S3icd4yDbog4DyjGxjcjiCKZ+YyF+aEad3RbnFuGQG3FsY+16V9Vt2+WprqrTCjL5UWp96+CYJ1OKRZidnOnW2ebdxur8Ol1OxwG611H9om9jZ3uRrkHwaGOLIfBo4hcOMdB+c4eFfLITiCKrIskGbKYoKscUTWrV1fW5KBZrnIeFt/KeHSQiQTBsdu3DHuRhywXCIX55nVk0lo1g4UqRnmerKK2u1v3I/ts5bBstZ9fH8Hv9zJOuuAhIOVl1me+OqrmQCcL0/ociaWmb1XGAL382R7R4vFpgSHhSc5nAqShHkpnOaFy6Is2VzlVLKB/uDRbPsi1sRFpylxnicoZ777yZHfuvucXTxyefuAeCEONzw+Tty/e8f5dCanbAKN2KlfipKXRJGlxj2Zpjs7GL1n2A34VJinM1+nmWEe8dEhkgi+1gorME9LXcc2j+KEGEDLRFYhZ2WaMjk5NERSdpwuC/OSyEXwKnBauJxOfO0WUoTAxO3+wI9/+7v84Lt7dPma88MjvEAix+K2Nr+Vpb0vlMiacdjXetOdh1gmzwbEHOYa3n5cKSaAasumuPFyKJhFORtwLB042vn0XqWu3sBqRdkoTprQ3F+bpaMAkmnFzZu1yvZz5ZkCeFPkOK8EJwge1ETHVCwvQW5ZGlWhJvQZY2QXB3zNiOvUbC7BR3bDDiVwHkeC/+UFx39VskR62s8FGyI7SFp7pV7R6xpWK6tztRZlHBniDvE7fEl4P+F9sng3LeSUmaeZNC+0Ugu9+EEb6826WZXcUmtzruBk+7N1zaxfvFJs9pOwHnztm45aMqQopSwsxTL4D0R8DAQnECKCNwu1CiknRM8gM92VNlt1AnTBSbZkrs5ZAnW1WrEpFRYyyTtkcoxLAF4AODag04Fe86C6Boyw7s51TPng51uvJKlxoAYca93U5sq6YXJSwV1pyppilsalZOacueTMVDJztvjz1JQ4SlcJuG1ftu3tANjWpHeC91aGwztLiGWurubBEApE5ygpI7kQtNZpqG62XflTV6PJwc+Y9mZ8Wv+9c2sYzzfQr9/iWA+7Dxkc1Xu0VeK9PBB+9gvKF19zeTxzmhNziSTnSDiomYBiHLm5OeJK5unBW9HyueBLJnqHLoXp9MQXUjgV4eu85zz8GXL4I+Iu4viUT37kkE/FRseFq0On1RS80lg3d4QNe+mT02Kurjqnm9dvRJIvSqrtaGrPhw5uWfFfQx7tnFr1rB1CXgGQ9h3DjRsraweX9T4NqHap02r1pXlmOZ04Pdzz9PCOy+meeXpkrnzO+x1DODL4A8GNuFgF1Qpae6xoc2VoYBabs7ZVdSOYW/M3rnC6MkH9pnlp4Gtr6ezM2Uamlbh4frB9XNIP/7UitNqGa5fLPq+6GS/ENKK048vSBBQHMUaGw47DzYGbw45ddEiZSeeZ8+nCPM0opWd7JCs6J3RekKyEWueruX6tmTM3+8PYFU4x0JAzOVg9I5ViaeF9ghSRQSyJRHD4OFbrW0CZKWVhWqw+VksC0V2W6mF/dc5cWRxNm//cPe1axfDxqbmlN5FBNivH4nn7iWnvq64aw7bmac4wVyKjuReJacCdc4ToGIeBw3jkdjhwEwcOzrMXZYcyUIgKPhekJMqSSdNCuszk2WJVXVFC9RCANdGS9UWwAlSB4hxLKaSSUAdeBjxmuc4hcnbCOQs5B6aaPU5KQkgoC1X0Ztv77ZLfupz23dwOpzYu721hufrex6SvlWomXZieJuZZOAwjaOFYefgn48CjHyjLwrvlUmvaKUvKpGVi1oz4AUXICFmFuSjnlLnogl5OeIXBRyjV2q+F02Vi+uotJTp2IxzwpLCjxAOLz8yYd+pXD088PDxSltRFCpRaekFrMo8FlkiIlmBjcB6Jg2nA1VGiQzQxesfgHV4CMQijDwjK5XJmSjOQGIKgMZBKYc4z2QmiEYdnCo7kB+asnJeZvFhh8aMWmGYEIQ6eOS8Me88Pvv8Dfu/HP+Jmv/DFz98xL8uLAMdcGdSqQKyLSVZ3tiZ4XXF+6f9V0FkVOJjFINB+tIPqXJNr9PjEBiizJVkpOVdXxFJLIWy2wOZ5Tfht7mnbc6uXzVFdf69x09ZYrYkyNhYWodYdLaiak514IUiospE3q2guJmfVLC4GXszCFgSCWAZQy8BdeX2xrJE5l02s38cnE4VLnadVVrnm71prMq5goRgLQ4MV3s4uUrKwVEVN8BHI5GUmLYlUFaTbUW/8Ytu7ZiRpNTOlGj26W6G6ZkQ261KXLSz7ainJas1vwaPa2SdSXfpLdZOrNyoKCwk0MeiAr+61IpGUSrVMFoTJxsBZHU6T1wre1zYXk2eLk1qL1CzclgJIYFoo8u5F5tGJqzUtV9DV9uJG4u6/9W1xhcxYv7cFTltAWjMDt+vWJFHmbtqVtNWFN6fCnDNzSlxy5lwyl1y41DJmKbfygNbKbtFsJUFwncebpa9muhUheEcMgi9qINLV7OdirwE1q3FSXFZiKgZWFUKV7RtwtKOrSYLCRiymJ/IRV2OZxfb9t9CvP8ZxIyD1ppZiqNlXsWS6wF/8lPLHf87ysy+ZTjNTFjKOIgHcABJAAj6O7A63RMthb4HDNbB8HEZ8yczLxP3Dhcd8z1m/xO3+jMPNjjc3Zz4Z/yM+CT+AeAefbAy2GwF7K7mscCljVfCaC6jf/FyJqZvf/25A4ofomrlA10ZSD7dNM7UJ4A0M9135POFEiyfb3vXqqV2o0yr4WnprQXNhXhLT05nz/QOn+3ecnh5I8wlNF7Qoizou/oFzfGQfbxmCWR6ddyA1sY4qyOpioNqyVMFqNWrieW3j+9k2rmfp2WFz5QInrO67/Zdqudr0+WXB45VYXX+rT+8uOev8boGuKSu3B62B6qb3Ei+E4BgPI4fjnuNxZIgC+cJ0unB+OHE6XUgpI84Rg8dRjImqI2CxWr4FdNOsjlfiVm1Wweyb5o5V5glNBhyRhLoFlQsSAn7xxNETd4EQIxIDEFF15FxIeba6ZUUtFTWuhgPVzHZbZNEZ8Ptt6qOpH17RH5faztFr0HOFh9YTR7ffaBpRqTHCUC2olkjDk4gexsGz3w0cdkeO4x3HcOAgkb1CzDOuzCiZJJmCnZt5NtC4TDN5XqzkioKvjNQSB6wCoHOC1GyReFf3XzFrWki4WWCGIDsO4nkTBxgCl2wxdSUtqE4UdTXuq5BKS8hR+611X15Zz9koRzagul1Hu3Z77n1cejef8PnELjgOux27MdQ4tszee/bDjqKeMcP9fs95MuH54fFEyZ4iMxodw96yWav3uEHJnLjkxEXVhNR5NouweIJarbjzlJhywt0ekOOeC4778wXVPdkHHs8XHh++5unrr2GZK/MXAwZia8dXS1MuibJcyBNEd2CIwWrFOUcMA+ObG272nptRkJQ4n87My1ILhhf8LkDypFLMs1Ey05JYcuoIXoBzzjxczpTFIVkgw0Bh7+DuMHC8ucPtdrw7P3HYj3z26adE57k8vmO6LKQXcjnObI6DSk0RKnVh9dinWt8OweqtbkBm66jQBEJL6BfAkvAhFExNIoLNRcnmnpoMMLaYozUDot3Ujq0q9LqN4Ot8T95h1zSwuMZCtlI6rZOi2ksMWNbUhHGNjDpPxuPE42oxeO8i6hR1hSKJQkIx90jX+pESJSXwkeCpHkJKSjOnEyx55nR6YlnmFxaF1v2+VUK1g0TqGeFajT+1mnqzwiyeOUQywjInpiWhxawzOVlITV4WS2KkdHloXTZu6yUIrma+dJbx0oVICFZiQcTTslGzAY6CWbVKSpCWtW7gJq4VXZMKNqViXyWqaMqk7j4rhCEQnEe9Nz1XKWhJlmHbOYLE6upY11VqPNOsWUmppV0akDbeXubLy8xgtTI6UYqYIkCqrPJcB9iHu85rB+nPQWffzPW1zv+VNU4ExJJYOecthKIoJVtSsjkpl7lwSQYWz0U5F7gUS9o551pupfufr3HDIt6sh2qWRKf2mYWAQFQxJZP2RKnmqSAGIovYeekyhKzMWRmy5Xex/DwtYZVbF1SdxVK9v0ppRhRn+9bbHlXHt9KvDzj2rKTXIMzk79IZvBTgi0f447/gqz/+CY9ffM08F5ZilkYnkTgeGW5uubn9lHF3g/gRFzyHG7CgbmUWJWmBPJJqTNbyNJH4BT4KtzcT776TmKeBfPqU8PXnnNWjBxgGy+4I0AvQbxaraa0SIjOw1PUREQYDwP3C9UBp399muvy7JEv/DbAme2lm+LVdrW0rSGrfWYEU1/dpHFJ1NYs3ELK9ugrrJuhCyollOnN+euDp8Z7z0wPT5UReZshmtUInyvnEMj4y7Z+Yxx0+WLHjrZRtPLsdLHRpsicoYm27rbfqWlTbc92r+roBouv4bAXVrRXreuxeNGXuB7S1V7YxadfousF0+yH9jNR+sFiBBnGeIUaGfeB4HLg9RMaoUCw+anp45Px4Zqq15cR7UvZ2TJUManWmgg9EP+BcpGUrszPbGqO1vp+WRCkLOVu21JQTmhYQy96JZFQmNHtc8uQcKbpnYI8bImMMqFahN0+moc3ZitpiTFlrEpA1Zm6ldUVs3uvo4289U7+E6qQ0NL95fse1z79R+1A21mJXreClaTmLJTOIXjkGz91u4M1hz83+yCEeGWSPTw6/LLhF0ZRI5UJiBk2Ukskpk1OyYuKaQbQWTgaoyWAwN0rQqrEGwVK3m2BarKzBnMlPyYDheGAMBz5hzxB3nLLjooVZFpJ6c2lUK14OhUXNJb3HcPbjph2m1wqSnvTg+dw1MPkCdDk/8Mm+8PndkeNuT14sm2wpxQTEw0BaCsvJtPuH/R7B89XDE28flThGDnc3lOgRmYlhYNiPhPFMzoWcklmuvO8xpjdD5BB3nC6JL6eZ4CPjMFCK8vB0RnTPsgjTZeL08MByORNqrGpVc/WYd+e9AX9VcppIs+B8xDtPiB6k4J1jt/Pc3AqHmCkTTJMjXWDOCzgsodIAUsCpaedTSuRc6nlY0KzM08S7x4yWHYNEQ2xSuNuNfH7cczgMLK7gx5HdeDSwebqQ5jOXOdHiBz82XaWj1zUuqR2WV7GMWsibkiC9Rtwz4GjF4AUvSkSMb6kpXmTjsprLJqaxCp221ldB0CyZ9ne3Em1qzbU2UBWdzcW1g8aWPbHk7mXRObW2EkfVUlc8JXkyA0LAS0T8QHDVQi2LnctcEBYTPDWT0kxeJkp1eUYt9v309ICehXm58Pjwjmm6vNzx2jI+2zTSQxW2QnxzAxTp572qWdImwJeCI5NSJqWFkmv84JLIiynSRDdJuJpSa+MySK2r52JAYkB8uAKOzlndRVsLdHYtoqAFr1Z0nhSr1bkgxc6DvCyWKbXUjJ+1WEddetZ3VTRnltkyLEOw+o/Okb0n6ULOpgAxq6irOQWq+ybViuwcEgeS2lOW0uQcb8/Kv6QA4K9IznlT+FY3zSsQ2Hu6KgT61pMNYHSb79VN2cHjBkBqddWF+rtYplrzPhSKZhbNTAkus3KeCpesXApM6phUmQtMRVlKS+xWrsQwo8Ia+Vjjnhtw9MKCGHgsUoGj1HAA8Ih5AajDq7D00leQVQwYVotpDUyl7WcqaFzjL+1zEasPTJNZv4V+AyyOK9Pvv9b6bgGQE8xfPPLln/+Mv/zpz7l/PJOKkNQxIwwusD/ccPvJZ9zcfUKMA/NikzSEkePtHcLCY55Z5onsPC6ODOLJ88zp4Su+9k+8u5t5utzyqD/m7XKifFE4fQ3jZ/D59yGM1kitPmNyVaqjTcqCHTXtvcDLWih+deoAlrp5tI77eqaygfKscKR+sgUeyrUgXkHIqtmXjeBbhcu2V1UpJZGWC8v0xDw9slweSdOZPE+UZYGUcAVEEy7N6HQmnR+57EYkwiADPggdP66nJfRtqV0m730GLDi8tqkPzFbN3EDD1tpxPRbN9acJsWs2XH1miXwBqlO0OTY3gHHTB9ik+35+jzXZdRNInERiHNnvdxyOA8eDYx8TPp2Zz08sj0/MT2fmKdWQt5rlsWRMC+5qfMiAG3a4OBL8gKsF5L23JApg8Ts5LaQ0kdNEWc7oIugykVOqjDLVFnpLDFIcucSaSMIz4HHRM8aBsh8pZSKXyYrOp1IL2rYqhW18mmXu2XiyWUKbVfw84vBj0hpTcD1B24Lc9un6x8oKpAqQrtfk3MZzeifsYuTNbuA7hyOfH47cjjsGCbBYTJvOlvBE0gR17HKZzZJYn+FCwI8j3tUscRvGZCILVieuu/yacCNacCUhmijzTMoXynyG3cx+B7s4cHSeUww8FuVJsDgRTSwasXLmrXBLMTdorH+uKXNk3W+2F2UV2Fn/614VL7Qfd5r53s2BH765Q9Tx1eXC6TxxWRIZBz6SinBWRZywi4FFPffzwmWeGTJ8Oio6WkISqvUfsWQqoSR2Q2R/2LEfIrvouNuPHOKeh4eJcP+IGwbufGTnArkIp2khz4rMC7GY65T0hCw1yW6L0wvB5jUvFbhYUjkVTxCPBDOALTnzeJ64XE6UqfB0Up7OpuQpLpN8xtVyGUK9T1rsbChCy/rpckGnxJwTi3M2v0Fww8BhPyJlYl4mxt0NQ3Ccnp7Y+R1OhJT1lxaq/lUpl6ZEXF33r4XUzWtXThooFsWsFy2bBeak73QFj07MhRMVy15bXVRLKxaf0goaMQHYNRBS6/SJ+FqY3OKgpIL+K8tJtbZrc4UtTUlnv5eSqgvkahXRWk2u977yt1wd05WIlz3iB7yD6BYT1LCdKiVVq0wmpURIC7I4sgBlplwcSTNzmpjOJ5bp8kuTcfyqVEpalfNcn/et/JP3VXZodYXrfivAUgqXnHBFrKRGWih5piTzaNG0BY01uKNbfgMqZqF1IRCGgB8GXIyo99X6aEmVqEruVs/PRI7qqYFZEUOIuGjWRQcGHJeFNFt8ZUkJKRnRbPyyKQTEQEJRKLkYeGRGcbhgc+i9JTTL2Ur7lJIowWLoBMWr4rGSTeLMuuh8rRupiqhDVPC8zIYU8TinZCkYz1mtjlfU5C14D2Be7Ql5/rlrYqopc6pVUMWjLphnIxbfmRJMi3Kei4HGWZkyLOpYqC7OrOPhxFHThdPCZXKxs0ur14Gv5TW0xpBmNQyei+ALBG+AMqvVZw1g66cIsQhZnSnHpTqJi6+KpgaCWWW8BhhV+u9Sizoam2yn3jfTrw84tljB2sCtQGSTBpKA08z01dd8+fNf8Iuv3vLwdCYl03AnsVpjfhgJcQCxLIuLJlISyuDwHiQMyDCiPpJlpjghBIg5kZeJ6eHM2184/vyvP+P46Q95W75DJpDlO3z+wwO7m8A4ri6numnrVrvRY9s2/OQabT3DNGwAzPrGRxvib6MmXBlIrxtpG8epct3PjYa1CWEbSNYFuatn2GUbIbwBqG2werZDeH4iLY/k5YGcHyn5bJanZDFWIDgKLic0XVimB+ZLxI0g4QAu1kPNrYxyw+C3rN7OiLaRa0urteL5fukQUHUzNS2p0+aizVCZX/zG9Vd7jz86yRYJbvZUT2i0ma9tE20em2BT41nUckE554lx5LA/8uZ44OYQGMOC5Avp/Mj8eM/ydCJPGdRcLtQJBWd2Vx+IcSSMB+JuTxj3hDgSwogPQ3XNsYBvKJS8kJYJt1xY5jM6n5HljJvPpJp0IOeaHIUWG2SFzxedUDVGNuwH3CDs4oDud6imutzMC6DUAVgP0MZg2hzR97MNzwrQ3hMWX4iu7n71/OcKj9rmtr/ENIxOtdZish8vwhBHbnYDnxwOfL4/8Pmw54DDLQvpPDFfFvK8IM1VVRPkhZwTilo23WFgtz+w2+2Jw9BBKjh8iPgYwXmWnLjMM/N0IS8LkhZctuQ4ulyY04UyJ1hmfPKMZWLYz2S/MIr0AvLmLink4nEumhuPJpxmE9CzacZLBY2r3Un7a9P8X5+5ba+/DH12PPDJsCPOBfED++GOn6eJv/rF19wvmTyM7G9uuBt2DIOyzGcyhWXwnIvnNM3o4yN+DBwPI6dZeXp8x9PpgTEayB5jZD8M3B13fHJ75GYcKNNCucB3DiNuGDjGPePuAGHHlJQ8zZRpQuYZSdky84lpz8V5xsOR3bjHO8+yTKTTglk1MpoWi0sMZnlUAlnNurhkZbkkHs8LT+eZYYwMEixjaCo1uYPVeyzFXGtFIc0ZweE14rWQ58yFmewyMQZSFpbLgpcJJwmnibJMTClzHiH4mTkZ6HkJKrVUijio/3Xep0qPg3LmvNkzpa7p9mkb1tZgAwBQdS1Vy1ld53PJLGlmnmeWJZFS7jz6qu5jDckRZ1aqlsDIB7uGljCu/lR9ShVY1+RfrWZbzhafl1Kt05ebBbIdP00hI/0MVjzqBoLf47wjusWuKgnyTCnNzdUgaCqZMs+UZSahNRihxYjNaJpfDHA0pW4jVwGhnfOuxjY20LjyA6ng3qzJyZQsuQLHZSHnhVKThNkwrbKfc4L3HlxEXMSFgTjaj7nvm4BfWjbbrvG22LU26ibHuLVdWjOzVj7oihLCSAiJxS+kaaKkGfLcZaBW00iqkkILaCoszChCUHAhEINHZGDR6v211PjT4MBVry7nrbZzLiyYVcv5YBlY2zn7QvKrOIeUVSGyepI8f94mJKeO5AoOW/M27ski3Y11u8dNJrIyYIojq1j8YCpM08LlsnC6JC5TYlosg2rBksI5gYjWMmKWidZ5Y9S5WCz7tGRzYy2CZZ8KphBS89wpFFMoFQg1f0sJNubeV/82D74a0dqzpbvVtsQ4G7l9a7/ogLGCyFqLsq39Xybn/NotjvVcM+q/VJCWM1y+Jj/+gsv9F5we3nE5Xyy1PwLearupCNM8kR4L4j0heJJ3TEvBkaAsFIm4YY9bMmWegMIQPU53lDLx1dtH/sc/+gnvlhs+/a4Qh6/YH3/I7w0/5ntPP+Kzz6zGY7OSXQ+sYObebU2pWN/7uwGCf2OqwplsylN0LKzrrFwLslwvvo0rTP1mf7tr9upQrZB7vYFpQRN5uZCWE3m+Jy32syxPpOVicWrJtEuOTEkTeX5imSLTFPCTIwyCD6AumGdKd+l2K3rt1DJC9rxnV31fD54PiJYNPDYLXu1ocVLdRFiZ9WawGpx7CTL32i2oWZ8m6LWXrGzmVZoYY69Fa30ncQwxMOxHbm723B1GDkFxOTOdzswPj0wnqw8nVagXGUyz6jw+RIbdgf3xlv3NLcP+gIujubv5SKjA0dyrFMtqlQl5wc8X/HxmWM5onijzxHI+MT2dmE5n0rygxRKzSLX8a0mk+YwdtTtGIi4KY9xRdlCKQ/UCmliy1lIdlcm3MavrX7sG4MMj/XcBGlcXm/VZW6PjtgZYB7vNLQosIVAtFO5QhhA47vbcHW94czhwN44ccIxzopxPpPME04LLGY9ayvWqJRUfrHj0uGN/vOXmzRtubu8YdzsQKxmBcwzDjnG/R0JgWhIPT0+cnp5YzheYZ9wyI/OFfDlzPj9yns4sOSGLJ14SUS6E6CguMYswucxMYkHJ6lA3UPAUElkTUsyytSYy4nqdb5HhZjrXGlnPPviIdBhH5jnx5eMDmYXHLHzxduYXX808pILuPW9CYR9ctzSgGafgRckU0pKYpoSTwHQ+c//uLct85uYQGGqNTe9dBdlmxXNScK4QIozHA7vjHRJGcMEsP/PEfD6RpqnWf1RwSowefzhyuP2UcTwYkDmBW85ITqhmNC9IEkhVkAoWu2+ZDRezQDsFlyzrongri0PGN21UUnIxYYysTLNyyoUpO5RIRpnmxCKJyxB4uhTe5hOHcWHYC5JntDgk7lnmC8lZUqH0QoXjrxRFur4oJmxShc7gXS0Sb5YAcTWTb+MBtQaHqODamSVW/y2LZUGeS+ayzJzniWmeWVK2zJUilmHXewOJzjJKizNXUR9i/bHEM01OXNmzrLy4gNvENIKdEy4vFnM3O5S5Gk7Tet2GmatWa4qAOkGcJ3pTAjqfKD6QpcYlV5dy80IopLSQciblZF4gzapZMk6zZZh9ATLXS/p82axUJVsFeSYst/5W0Ois1IYXkFJQXSiLJcMpaameNesZYgo26nwFxEfEj4gbcMNonhoxIs7Vnrd8EFItltK9Jfvq68C/M4UKKGtTzeCH8yPiEuIiaTpTFtBczMOoYGNdWs8r38iZPE+gSmQkDpEQIh64zLCkxZQyWizcJNh4FISUC4saL3VisZrWrNUt+GPTCrAb0Kn92YxX54cbGacBxKvBZf3OttxGo2ZxVnFW4xKre5lS4TIvnC8T5/PE+TIzzbm6qTu8eLx4qm0S56z2qY8OHz14IalymheeWHBlYS6KqkewkikGXq1mL5rJTms8oik8ijfjZUEhG6jM2tPqYJ4IzbHYPKy0Kg/61tfW/wYcHS36Vzb/fxv92mMc2xpogm7fHCi63DM//CX3X/0pD2//iqf7r7mczyypUMRBCEgI4IRUMjkZ87W8SLXAcE6mqZGR8fAG7wOX8wPT+RFRhxt2LMnzdJp5/Iu3fPX0x9x8unC4/Su+94Pf5ZMfZC7LZ2T2eCx+ylwFej66OvihLuJQz5OmHbyehOeGxfcsj39HJH2JtE2zHk6tUW0e2jeuzgR9ZnGjHci6ea90zcYqq9WtXetOWSzHTFnOpOWReXrHdLG4h3m2mJj2nFIyS5pgfkInj5siwzJQ8gA5oE56zZr30gl35ldVhNKS87SObtzYNh3dQrI14Jx6qJum0GnNPnclpG7H+mUO0/6sTTsNDq0pAK6a1Q7b2v7mBtciI1QEiYGwG9gdI4dDYBchpJl0emJ+eGR+OpPnjFNPcDvE7cluQN2AH0bi4cDNmztu33zC/vaOsNtTvGnExFm8o3c1YYDl9bbYt1KIaSKlCfKMK4uV1DmdOD/c83R/z/nxkXS5mOuyWt9yUVKeWSZLzoDuGPYjLgyMMZDHQM6BUs6oWq3ArKtW0sZt1fjq80X9nF5om5oCx/bKh7S37Tcn61/r/02jagJhqyE2Bs9x3HG3v+Vuf8fNsGfEIctMPk/k0yNlOkNejM04AzOmgR2IIRL2e3a3b7j99FPe/P+Z+/MmSZIkuxP8sRyqauZHRB5VWQ1gMEO7s6AF0dLQ7vf/CkO0NJjdwbFodFdXd1VXVkVGhLub6SEH7x8soqYeWV09BKRnQZI8I8IPc1MRVRF+zI/fe/81dw+PxHGkYv2H4hzDNDGdz/gQWUvhcrny0qjMdVlhXWGZSdcL18szL5cXrtfZKJAZyryYUmtcid4zOmHCrGDURYQIKFUzzq/GUqr1x8/Vq031tg/tGPJAMX8rqmrJmT9cnlnnymV2fLpmnq4Ly+ZAIl4jed74lBIDFRccY1V03fCl4IbIMI7UtfD58sR8eSHnhbtz5P5snqnasNhSE/m6UMvAu+keiY5rztTzhJwf0KUwIbii5tt4vaK5K6kagInniemrd4wP73EyUrbNKs9pxNeMbu0MdYLmYGIoWc1snED0E2P0yMl6gDZR5lIJtZgoDoVcC1IVqqdqZdsqS1JetsqlCJs6ci32bFLZUuXTmqll4+tQ+IWLnEwKD5FKKVu7Z91+LT/18P52JtrdZLusnRcmkx+9Z/LelGWdAUdTIjX7kuoa8BILzuwMsf7gTO/fzawpMW8by5ZIOZuwmNwsPpwPzZct4tyA8/bh47gDR3EdJPaEio1+FepA1QOdOaMGCopvYi23vsySaFXD29llR7e9dskZZUNcNPFkr3hhV1nOGKjIWsmt0tpCWJw2mnTra0etf/mtAEet9bVS5ivQcaw27jusMSx87z30UCslV0qjg1LKDWi2SRbnzCMxBCREA3N+xIXJVL9joHrLaJuDSW+d6THvLeZ61ad9mJmmwrDHIh3gIOCGRl+kkskUes+jNLHJzg9pcUFT8K1qCYzghBAjLkS6PcuWk5neV0hVEWdtD+pcoyfVPY6jJV5V34YBcGRHHdevBz+vkobHQ3qvvMvrT3MDTq7FPre+RvuzYjGSVQkNNF7njeuycm2gMdshRWz3zOA8owiDKINTYhCGMeDHAMGzqvISEoHFKqi1UKprT8iN2VCqeaLKnoCxSn1Ws7EuLfAMhb2/sagYvbVZveohzjleM7CLbL0KZ9rN9n/mbPyLVxz30YJ68a3JlsK8fOKHP/4tv/vHv+b3f/wHPr18Zk6JjFhp19ti+BiZzmfCOCC+bwhCkIjqSE3G+x5Od/jHO5brwNMnZXmplFXsd5ZISsLzpwvL8ncMTx+RsPB8+Y61/BsSX1OBXKpJSrdxqy55bo9lH69LxT8zNvyz41jmfx2eHt7oIYjuKqj7d91q3jegucdtB/CoN8Lr7fP2M1orlIymjbJd2eYXlvmJeX5mXROlCEhAxB4qFTF1sW2hLh43Dwwnq3bUYYQQ94zKraogtyQFnU5b903jBqJ0f2v7tbTXOHZrHmeqf2N/IPUV5PxiA3vL8eWv2ivGt6zMYQYOfnt9bgwEhxAI48jdeeA8eaLPaF7Z5gvby2e264W8ZVCH9xPe3+H8PbgzEk7Euzvu3j/y+PU77t89Mt2dkThQxIIJYKeNHKnNXdDFl4lQEqIZXwu+Zupp4TQ9MMYzz/4jV/dEXhZcKdbPIa1HsmTS2gFzYJhGnA+McSCPgVpuEuImHd8OYXFtivq90O9T+WIDFXZfjJ9hCV9Xrvd3sH+ui0v1w663U/XiYxTHXYy8O018dbrjMZ4ZZERTYVsKcl3Q9UqtC+ILzjvEBaO7hNAExx65//pr3n3zC9598y33j+8ZTiecN6pixYRwwjAwjFYBKQj3KfN4XdmuC3leqfNMXRbS9cLl+Zn4+Qn36SPPnz+xzS9s24rWFTQgY2T0geICQiRIZHWBtUKVRFJBXAXJqNTWx/Pl03mb0z+VCFD2ifzJxzpv/PB85XmuvFzh5ZrItYsHVeq6cs2F1VWG4Hk4n7gfB+6BQoY4UAbHZTXF4m1buTsPfP3+kftpYLle+fj0zKow3J8IMTSAUlEPs1RetHJXlRMOvxXq9cpyuVDTRlCrFItz+Djg4h063FOne0KYIKxIq/6TM1o3Uipo3tDkcD6iWdnWzDhaZV+yIhEYBj5sM8/LhaCFs/d4lJIzwQViCCwpc0mFtcKswlyUNZniaq0VHzylKh+XlXXIDAR+FUbe+YBT2CSxVUtQeomtT/qnHybm0vqQtHuIitlKBFOPnkLg7AOTF4ameFhRsvQPSyj2liJR19J6tveVJlaybBvzltlyMX9lxBRMfcB7C+b73+3fAz4Mtn4tcc4OGqHTE+F41/eKlvR/WtDsLBCVVoHzImSxPq7XtFVLL1Y14bGKo2xKCQPBO5zfWj+XHb5ZTVVVcsFFCDEwxIiUaJTVrfWzt75KeaMMgPWGhn2f79fSwcRt5+jA3nwmYzBQjkIprY8wJSgVjyLS1NppoiI+4BtoNOA44OJooDHE3WP3tcigrdCNdnm7116vnS1Yj2n6Wtn+1uZPwEePMNAVcWuW9ndT1u5CSK6tvapCydRNyGI9fSEOyBDARyRZu4KtJ5b88RCiJ6pSVqMb1wrWU/d2wHGH6f0ePgDBfQ0Fbi1W3IAmtxTjLXF4/NctwtNWravirMrXQOO6ZS5L4jKvXJaNOWW2UlEc0QdCsPNvcoF7gbMoJwdTFIYp4qeIxsAMxLCBOrQIook1KaUeKvXto7QLLsW0A3JVQlWyF4IARXc11S0rW6qkVIzqngrFN6Ek51/Hu19MRo8pbnPyJd33x+MvCxy/2PPbfdlG4nL5yO++/3t+/dtf89sPv+eH6wuXXEh4aqOp+uYvd/f4yHg+UTFahBOxXhxxJiOfEmN0jENlmDyKKQYuZQallemNalLXC0ue+fgh8vsPv+EPT//IL7fvuB8myygdKQoNUH3R9fbjS90bdl9f+F8KTEpHsq8wot4eTjh+Zgd+Pci2L97EX/ZNmR//rGX9dRehQdU2X7VqY9lmtuuV+XJhvsys60YuakbvPu5qnFpvqnN1XeB6wY8TcToxjmdiVCS4PRO1h9W3t7tfct9OBIyepQeiYt+Q9o3pBipf7/ll35x2quOhwoHQxDtuM/w249bPuePdI9h9tVvYX3cqVQOP3nviYGI499PAGBTNF5Z5pr68kK8X8raBYgFMmHDhjPOPDPEd7vyO0+M77r964P79HdN5xEezYzAZbehUFj28RwNBFk657h9VC04K3hWiDAx+JLqIdxHnAot7oqwLmjek1JaesYb+tCZMpArCaBn6MQplLOSSyK13r96gIl/evXIAjz+atzdbx8NNSr9Pb1Szw3fsa9q7X4ROTLEYMnrHyTkex4Gvxomv4shZIiFDXgtlTrBuuJpxvuIHwcXGkHADbrpneviax29/yVff/RVf/fI7Hr7+hul8h/i46/bhGpWuCYGI86a+iWNIlbQ078dlpW4baV0Ynl9wHz9Sv/8DKfyO7cPvWS8bOW84tyE+Ehi4cyeiGxhdZGHiopCSt567stnBfjgMj/Da9uO+ZMe9+oj732bjLcWRsmPNlaSWnS85k/JGViBDjOAHo6laT7Hj4XzCjSbpfqkJqYlAYToNfPXVAw+nAapV4agJFwLTFDlPnkkr3lWCL/iorMXojlONbMvC9vxMWld8rbeEkfPgTxAfkfE97vSI92YJ4POKTzPkZObSOZuA2WaURHxlWyrbIFTn8eqsTw8zQl/XTAFKWSm5EH3g8W5EfKSQWVJizo6tOlKp1hObs/VajlbxmLeNSmXOA8rANA5EClU3NoSqHtWAk7cJYWL0je1S0CbG1FkmzgsxeqYYOIXAyVuFwZmBDYl+ZigZ3YPRPUlVrdK4bYl13VjXZP2arQnbi/WFeteqjN6Ao/PRBKqCx0dvoNn1s7XRj7X33enrI0f6GdX2tJZARRTxjoDHEdseYkAkY9UL7a9HRdjspGv3Yk0DNQSECrrRHHNNjTklJCYGVUKM3A0jUStlXVhVWUom12Lv6Y1EjoY4oer26zY/2zYlQA+SO53RuYD3o9l8ESg1U7IJiBl7TVsSoPVESlMi7lXGMFgvcBxwoQH7lii1RBu3SpkekoEteb3TLfdM4gH47GDRAEZnVohYj63zzpIpcsLhSRKpNKV/NlvvejtfUDGQUjI5ebwvti2EgcELEsxKgpLRkqlUawlw/QQvTQGdRqWWV9Xun3Lo4aNvYT1Ae8UuexVft4TKqxj3VXB7A0+0jOtu3WUJtlQq61a4LtbD/bJsXLbMWipFxIT+YsTFgSFGzj7wIMKjKPceTlGIY8RNkTJERgDnKRVyNRoqZNbcxavsWe7yVNoStKWAq4WsglchiphVWlGGVC35tlnP5RgyMSRC388bM+7Y67nvEbTUxe5Pr/vHnxs/P3DsWOKQLeiffhWU1cz1+Znv//iBf/zhBz5eLlxTYsnK5hxVPMM4MpxPjKdTkyyP9nD61n8QowWizuNjwImioeKnM6fH9+Rc0eopLwtSM0FhcJBL4bKufPzwB/761/+Fb/7Lf+Luu1/yP/+r/5F3TXwlbRtA89/p8d6xAfeW7T5ajtx8Z74MB3/e0TfNtgse+OHt68IOhL74yu3fx/SN3IBle0kDTfu3WDZKm4SK/XCi5Jl1vjC/PDO/XFnnjZwUkYAPEzGe8H5AcKgWUkomppITdb4gcSCOZ6bpgXG8p0sU7B5V7YGxDdbenGsb7Q00Gq3D+n4sDJdOH3mV/jORgP0T+/5029z7PL6SfeaffxD/m8d+0HwBMPrbQ15R9aAfYBbQBBc4xYGHaeIcPaGu5Osz5eWZepkhJaN8u4DzA4QBwoQb7xnOXzM9/oLz+/ecH0+M54gLLZjJLTgRew5sjrFEgDh2Z3uVlpH39Ip0rYqTgI8w3j3sPoFehPlFSHNFNZt6mYqpbZZCXjMiCRhxQyC4yBgHUoxsyZGa3YC+PlH2uTou1ZEp8JYr2AO9Ha7qj2Dr7Q1yA5X7noOZbUfvmHzgwTu+Hke+GiKPToiloClR5hVdV6QUU8yLETc63BBxccIPd5zvv+Hh2+/46rt/wVe/+ivuv/6G8f4BiWOjNHcFTqPSqVhbQNEWJItHB0FGxZ8rkptQT86464y+f085nUhBSJJZZaVcN3LZCFu1fThEzh5SCMw+ggqLKE4Dmr0BR+mV19uc3JIo7SA8xglHA+Q32ncrofU2OQvAqdTmxzeFQAiOGIUwRsJ5hOCYU+I8Doynkfl6JaeV4BxfPZx5dz7x/uFE0Y3necZp4f3jPTKN+NOAl8KgloGeouNddgRRhlyIm6NcF/K8GEWQJg3vHNV7NJ4Y7r/m/PgLwuncvPcqbpwIpzO6rZR1Rb3tHaVm0pZaP5WQFli8EsWxKSzLDMvCmE0l9MPLlWVNvH945P7eEoDOBYoqy7qxZseSlC1lnBPGcSCGgNZCLpWcK1tSliJkH3CuUkqiVMdWHFvr5XqLEYPrxDEy5XZUehO6CMERB88YW8URRchG326UTpPhtz4paHtLNRpaSpl1XVnWjbRlSq80dnVbF1rCNNAFcTqdlJaEK2p2HVadqO3O152WecuRSBO+k2YQ3zwc6f3MzcPRgQ8OrWE3d7eCYI8Dcnt6MrCBrmgdqKUp8WpGKFaNU9sTUrJeOecc4zgyiVBEkC1Rts1md/eJ/unHeLojbXYvqZZWbewiQT1w7lVD6090LgKRWoWaoebafDT1tk83U01xpmYrPoIfwI2tImwgv895PcQLtyvt+9Et+Nr3rgPFUvcz0r7D1GqLVei9IwZHp5yIE2KccM0ObtOFwkzVYveh9D3yFnOrgqtq92BW02mJkSE6ixlypmybVZqbzUqt2f4sCWq7L9Ujb7SO+zuW19iwg/fDHz1F8E9u8T2k1dsksFN1mtVNUdiyAbJ5yVyWjcuSuK6ZpYkDqfPEGJAh4seBIQ6cgufBOd4JPHo4RdsndIhs0cpimwhrtUphrkLV1fr3c2n7xg04Vvr/7M5wqngVs+DyQkiVIVSWrTKvhTEkhpCIPhF8NpuXbs/z41mwOfxi/l4h6n9i/PdDVeV1wIbCumx8/vzM5+cL1y2zVTHVJxVcHJjOZ053J+IUwSlFzVzYBdfUsxrFxAvORaMc5IJUhx/uuHtUtDpy+WgmtNUEP4IqopWn52f+89/8Df5/+3ecv/qGd/f3vHv/C6OqemuYPTA/Dvn//7PjLwMaX7+DTtl7faPs1bVuJSHsm4LRHPr36R5s79WRnsHrr99pyNYZZXLRNZkX1/yZy8snXp6fma8LKVXAsn4xTsThRPAjIkIt1neR1ZT+0royv1yI8YVpnBnHbNn86FoQXg2svKLb3PyazJ5C25/1tn6vdqbDpt4poNqyNPSHzl6n9wxYMN/8efZMzk+8cLeV6ity+9R+T95A7jGTuZOHWwZdxEQORh+YnCei6LaRrlfy9YqsyXzHfATfAmM/4oaJ4Xzm7t0Dd189Mj3eE04Rgj2LJSfrYdXaihzN/Di4/eA1KW/bEOXVXButdCsJO72VME7cPb4zE1yUl5opdTOlsSaDX6taYmFdUByRER+V4KwnKTi/N8TvpJY9w9P/rod56l9+Y+B/GGppRqochY+w9Wrv85aUsK9bnhSCOCYn3MfAY4w8OsdZC+SFtGR0W9G8WWWhVTEkBNw4Ec93nO+/4vGb73j/3V/x7pe/4vz113C6Ywm+0e6syhiiUbNc8BbEFsjJ+tlqqWYsjBgPLlgghlbq6Iihci8bWWeqXi2wkcQ6P1FSJpRCHDLjWFBXiRE2hGdxeHWQHTUZcNw1B/vWu8cLr2H3vpb7395mXJbMmozetG2JvK1EJzycT9ydTwxe2NLKtRRqEyBa1ytLdgzRU6oJ0txNI+/P93w9jZy8slXHcHdmmSaK8yZE4sVo3VSkOkY38dV4IlSPzjMhJ/K2IDnhteLFPDerCBIGhvsHzu+/Yri7o3hPpUCMSB3x44SeTtSUkLQhxfqKtSTqNuN9oG6e2UPyirjA/elMRHh+ufLHeeF5zWwFpiJsqTIENWEXN5DzlXUrbMWEQoZh5DSOoJUtFbSABkdWYc5wybCJcilKcp5Nhad5Zlk23v3qp1/HwQklGH3PCWQ1yqlzpvzrguCDIwye4B2hg5EquGpqtK6BDaszuj0ZVpMB8G3dSFtqAWOjp8oNOOJCo48bhbyotH2xIFlBsonPNCCoaDMHd3jf97l+FoIphJpKaKkm8CJSzRdO6PbLJvbmAxJAVMzXsfZ+xNJibIdzCSRRa0SKib6AUSEdBmZrKc1Pt9mv2OTtFdXd9P6nX0IAQhzIJUPJ9Grsl32gt9HPQwNMNRezKiqtT7MlxPu+YuJF/iCGE1uVcUC8rVnVagk1dPdfRLX5R/p2/W1/2oHd6zjLQHxTtFVLOmybeTfG4NAYqA07BueJTTgpjr5BgErRRFUPYkJcPfKr2hO62D1UCpILIaoJLimUos3PUCilUHJL3qeyq7bvkMvJn5jX//ZxE975E+BR2YsfO3h8nTXff0D31zsO2T86aMwdNK6Zy5q4Lol5tcpgVmNaOG+VxjAMxDEyDJEpBE7ec++Fh+A4BWPzlAbuN5QBGEflVJRNm/UGUETJND/w2p/Z2wc0PY02B84pwRtwXLfCEgtzLAxrJoZMiAnX7kMfWs27BYJfaqoc0wlfNl39qfEzAseWXdnlhuXHXz5SQMWzbImXp2eePz9zWTaWCgmPCwPD6czd/T2n88mMibtCo7sR0LqConOG9CtWHpYqeDfghzumu8KWVnKaSfN685rCUXPmh99/z3/4d/+Oh9OJX5xGvv5//r/4xd3Xrd/gMNntxuxNqbv/Lh1w2LgBiRuv/S8z+ix9uR79Abz1Jt4e1NfA+AYeX33aXktaxbHNh6B7bwBqlcZl/szL8w+8PH3kenkxfyE1FTkfRkKcCGEihKGtiVBKU/ETGjVxYX65cBkvnKaZId4xiEea/HFv3L7Nde+vs2vdE039cJV+nftVth+TwzX3kr+03owbOL71x92+95ba+ulH+238iSfq9lW9bQWv3olaAOBdJLhIkGDJ2K2Q10ReN3QreIUgES8T6npW9UwcJ073A+fHwHQPfkxkEnnL1JrQJl2uOSMihGFkmE6mLCcmdgJq1V8BtBgFqiZTS00r2zKT1hWHMsXINJ3MeFqrUeHKQtVEV6a0gzqZnUuqOG+bp4iJdQTv8E5wrSB9uMtv98DPCBJfj8P+1x+qVzQcsapDDyzkdl9LtWcrOGH0nnOI3HnPSZSxJEoy823JCUchBk+IcafZhOmO6eEd919/y7vvvuPxl7/g9NU75DyxOVhrJpdsPV7ajFdqQkpfOwsUSynkYhQ31KH4ZjdgwaqkBScznAp3Xw+Q3yE6A4WnH4R0ucBWcFpN5n9YqeKZfGByhREhFs+WPKW4m7rhIVHfZ+y4p/X/9zV/KwbA0/PMnFeu88K8JDzC+/d3vH93x900MAbP56cL10+fyakQpwFxJg6zpYRKZRoDD+eBx7uBUxBiLYh31DCiSZlTYV0zSdX8vcSTicDEGCIpFXLNSJohvUDZ2v0hzfcvMtzdc37/nvHuDkKg0HqnhoEgJ7Ss6LYSJwOfNSdjZdREzYpLAzWPpM1TgnI6eR5OA+cYyZuizyu1mk1PrkIughKbxLynYEI5iCMOjmGICJipejb7JWnWH1uBl6XiVVkJ+NOIIqzJ/B/fYgyiVCd4jOrl1Gio4qXRsi1BLdEjoR32tUDBqqBaYPdFbvIWFTQbhbNsm4mtJPM8dNJAarfd6KBCAogzD1DFzN1q3besqgYqqnYrCEcMzT86+F2PoValZFOhTWnbq0ZCwTnMNsVZEhEc6s2GwFIz2WjLmoB2Xd7h/C0xW6pviUjdX68WNRGWnMzyY0vk2K6hef3ifBPSeZvnsXY6rusHsr7eU78Y0n+qND/NkhAt3SIdE3NrgXej54s3/1PbSyPim20FNH6VrVEuGyktaM1EH5nGEyFaUuFmESJ7UG+AsVByNuGo0l6nJR5qqWQvJG/G7V6E6APTeGIIJ3zwRAYqiaLBRFiqJfpsy277oHZQUdGakOqpGhD1lKKktFHSism12vlcc27httE7ezL2rcSqpFGrdzm/Hqe9SureYvEOHo8wfB/78h/ZJ02URpVc1KqNW2ZeE8uaWbbClrXpbrSYKUSGMDAMkTFGhiHsfx+DZ4qeMZilz+ZAsGfXKcRBGatyUiuEFTGRIkvNZDJm5mjn1a1QUwGKUeCdq6SkbKGyJmXdKmusLLEQYybGjPiEihDUVLBLvYlN7mqzrx6HG3j8c+Nnrjj+M5vDFxyxMi8snz/z8vmJ53lh1gENA3E8cT7dcXc6MQ5hl8J2oU9E90Cyx92oGU27S7w9yKWaKtswcrq/Q3VldoXtciUns/WYVEjLzPNvfs1fR88vx8D7EIj/y//C+9NX7YrMC8u37m7hNu0HiHW4yC8A2l8KPfaHqz94HRz2jeRAO90zbLd0fgMjLYPxCjTe/tehqTs80EIl5420vHB9/sTL00cuL09s60Ktah40vRLiYvP9G0Eh1ebn2DKkRqNIlHlmfXnhOr0Q4wmcEEeP8we4pBhCFDUlMGgGwJYlcx3sQaseHgCEHDYXuxKT9vbuRunth/khMH0lIPRG40/eY3r7yo/2BHoioz0r4vHe5thLhIxVC9eEptq8owLejXh3Rv0ZhhNhOjHdnZnuIsMpgX9i3p64NnXA2sRryAXNFe8i0/kRXCT6SHH2e+z0wYBg2tiWmZwsUE3bwvXlhXVecDjeP7zDv3vPNJ45PWRSXkgNOGbNzbew0j21rGxRoA4mjS2mCmeJh9r6HLVlK3uu8svDqE/02z6nN9/PG+x5xZNtoydh9uXD5k5UW8bZMTrHFDyTd4xAqNksFcqKaMF7R4iRYTzhTyfkdCLePTA+vGd49zXh/h0yncjBoVS2qqwKSU0oom66q2BWFO8d4xAZglkG0IyIq1ZSSlyuV54vT8zzE6QLZ0ncu8opFB7eRVz+Cgq4Engpn9DrFSkVTRukC8RM8CNT9Nw7x6YDlJG1ZGopbb/nlm7+Ytlu09p2A/3nm///a8e8JNac2OYVj+Pr94/84pt3nCdH9MrddMK7wPOSyM5z9p4aB2pNLFrwY+Q+Bs7TgLrMVUGkct1WnpeNOSkpC+tWSTnjnSOdTqTxhHcTKhPjoDh9Zr58Im0vaPM6VARxgeF0x+nde4aHRzSYCnhXOnYihGlCygldrtRhIEwTNW+UuXl8Kmhx5C3iPOA9uRbWlExgYzozjBvDahWbvBVSFbbqeZkzL9eNXAUVb9WxJkSzratR8LCe6+A8oo6SYVkrgxNcnIhuglqYQmDw/s8vyH/lGKRSkWbXID2jiHhnICs6XAy46BuDAmpxFJqiofQzoJjJt0It3UQ+o12ds1ZjW7QzxSqcfgck4jzqPFWacEYtt8RHo1t2W5pOt4R2pqnbT25TJM9s60raVnK2tRSp9r3eFEGd78kF30CM39lbok3xte2ZSFfktsBexO/vIdTb++pUx21diNVUVfcKGsZDeivgaNep7ToPFNW+X4jsPXK35HjFMgAZ1IAj7VpegbumeutbAi7EgATzZzxAHFSbd986sy4vBr6G0ZKY3rdSr+zvB7kJuRnIt4JGzubvmZOpvGpViijJKU4q3kH0gVoyOlWG8YyPjkik6ECpycSIar+eVmeqZs1S69bouA4lUNWRc2Hb5mbdkXG0n1WrHpvwUNOg6GfuG4xXEO/guXmLRw8NCtpjzc4Ku3FzbI13mZhbNK72fOWDGM6yZZYtsWyFlM2rEdzeezz4gSlYlXEIZgPoY8CPRl11Q2wFjNbzW7NRxRtrJ8bAWCFrqziqiQgmVVOxRenePnsSVEFp3q9ZSa6SggnjbNkUqddUGFImbgnx3tSzteKcQI/Z++yI3GLdQ4Hjn4t2fkbguK8wILcexxYcqZiEdWxPtDw94f/4geUPP/D58xPPa6GGCYkTcZjMRFyEII7oHc67tpG1A3KvEJm5bmkSyEVskbQoroCXQJzO3Il5l4GnXBakwlCVB62U6wvPv/4b/g8veIVryvzf/u3/g6/efUNQQbISpTIM5mfVdWE7G+JWvj+ACZH9Rv9LQMceKO8Vxy9A+95c3yg60Ob1VfClO8WkHavshGlueYC950JBcyavS7NY+MT15ZltWdqBJPsa0OwbvI/EYYSq1LK1Q1zb1mY3uy+JslyZnz8ThgG8MsmJOHicY5dQb4WRRo9sgHd3YuhA69b7djzK9PCB0FTSuN3W/RByx5+5gZC3g46H3/cKw996vV73j7RspoKoI0jA+4HoJ6s4FqPmaGqN9NoOEUacv8cP7wjnB4b7iekhEE6OIheW+RPP88zH5yuXeaMWJWDuRE49QzxTqsPFMxpGqheTDMcUUTVvLJcXXp4/s8wv1LySt5X1emVdEtENlKx4P8LDPTKeGB4fmepCIaHaDOtLwUm1DHNgr04b2FeiN0XEVIqJUdTbZrnf168SBj/XeI169gPvxrl59Wc/BB23bKyvJok/eGEKntF7gtKoZsk+qKg0eupwwk2PuPMD/vwI50fKeM/sAiUl/HxFaqaGQBFPUmVdNy6z2W1c5iu5ZIYYeLy/5/27Rx7vH5nGM947Uq1clyuf/vh7fveP/8DHj7+jbp95HCvfPUx8d/fAQ7jj4fGM5gB5xNeRRX9A1heriswrm0TUnRjDmcdhRHXA5cxlSywpWRAKt73qVdLrNm6sgNve9FOPZUmkkiip8O7xjl98/TUPp4iThcEr1A2pmYcx4IbIaQwsvvK0JBaBr+/veD+NuJpZSuIpZ64p8zLPvFwXltUAiq8O36rM81YZzwP353vOY8RpZls3lsuVuqVm4WGJrjidiA/vGe/fwzhRGxtHuJ1TisPHieF0j6ZETitDzpQqJjRXTcm45kxKHvXelFVbNaaIZxhG7scNaqGkhQ+fP/K0zFzmmed5peJBghlq5ErO1utGq7pJoyE7FUpSkmSGyXOKE0E8ooXHYUCB9Q3WccJUUVvq2fqNBMQLPhpVO4wBNwTwJqRRVcjimj9jS6xWEx/RolaQTLWBxoproLEngwSr4Djvm/2GiYsVZxTjpLUJapS2N7efO7RidMqjKxUXtFG5lVwtibNtG2lrvWnU5j0vZkbfAlQRj3ehsTOaBFWtUDMmNGNV/lorKoWuYu8w4/EgnXnVdrRaqTlTmiK+r9Vom9i+bKfA24xakoE9Kir1x4klxearVZHtCTC7DZGMSG4JyK522oVqWj9qiIQ4EIbY+r2PRh2mzFlyIa2JbVnIacGJJRNq2aglmn3cwdieBjZzqSbolhdK3ig5NdAIWm4xSteOqLWSGotHGjslDhMueqKOVqGsoKklgA4RXa0ZKYrziuBxapXH3l6kWqAWKgUvgg+xWWsNOGcdvqXk1mr004+etpcmxLjn8r/Il8vx73o7J/fzQG6Aqc95B/elmmvCmsoOGtetsOVKLpiPrHjDDM5ae04+MDpPdM6KR85ZG0GM5GHABYdqZSsbWypsGDhULFkTY2CoylgM+MVcCL7g3NGixhiQh8oEWlt7SFZ73a2wxsISMzEV4pbxIYNLjc4eCEFa3/0hvtlrGy3RU5VOp/5z4y9UcTws8T43gsZof58v8He/RX79O5Y/fOLlujFX80lxwwjRqItarXcqhIA432TPmympSMui2ANVVE0lrN0gaGO3qsO7kTg6IAIDyDP1ciWUlVEczgl1Wfj93/4N11L5/WXlNx+u/Jv/+d/y3ftfMjIQUB7uHe/fefZp1VdEuH6hezjfA/q/BHjU41+OVEppn+yApxdhpL9r9sBrD7KbB53uge5NXUu0ZVJRtEJOifVyZX55YX65sM2LVaewHo/aICFi1YsQAzHG5qVkHoCCNB+/lmnTAnkmLU9cXwIygERF3NTujRu97/Ds7c+I9ApV3132xTgK3vQqhSHD7sNkhcaW7GgVzdeb2c8BGW/3kP6Jzx5px4d9AgART/ADQxiIzuNLMTGA1pNSMc8xJwEfzsTz10zvvmJ8nIinTPUvLOtnni8f+eHzZz5+vjCvBVFP9JHRBaKbGIeCuBNuuFL8gBvNE05J1DyzXl/4/OkHPn/8wHx9RrN5tdWUqUkJbgIGXDiRnON0isjpTNB3DLpSykpNm1FotLQselOKa0a+wQkxeIbg2ZIj09V0b4i7K9n/JZI5yOGwk96L26vah2SGdPDYbX9N3GIQYXKeU8+Ceo+vrcVeC2Yvb/d6BrLz4Ac0nCCewI8UFa7rhuiG3xx+MvNqfCTVyuXlwsePP/Dhhx94enoipY1xiHzz/pH0i18g3/wC//AewkReCtcfPvLD7/6Bf/y7v+YP3/8def2B9yfg23eMv/gVw7tfcRq+4uHdCcoJVyIvRVg/F9K6sK5XViD7RIiO8xjQGGAYIA6o31jLTVCAnhRqM3QD3rr/8ZYVx2XZKLUieIY44p1jWxeGsBHHAGWlbCtTgLu7gdN55PNSeVqNfuTjyGk64etigfaSeckbL1tmzrCppxYhZGG0XYxUV5LfGE7KBGzXhZfPL2xzItadbIj4yDA9MNy9J0wPEEfwljmraqrGtYlkBDcQpwdqyuQtIangilrwua2NkhmoxZMzuOKpwVFKYctW1b4/jXiXeJmfeXmZyc+BgiepVaRdq+5oMRl58Y5hGvAOoBKsLZCcKzlI8xtUtm0hbStOtIm8/PRjCo6syqo3eXx1DmliF3EMhCGYsbdYNbFJw1DFo2KsJ612fTUbcKylQi2Imhq0h1fJV9crdsHEp6rvVUzdqxG1kSo6kVSco9e37OyVhisbgG0gZMuZbdso2SwWvG+0WG/U1OoCtVlXaKt6OrFoRu3NgxZKMdBoNVlBtBKC4rztW6YKa+bkVduJ2apaNGr5EV4pNOuBtxitv7D3yB0zSqoY9dQS1kbVpZ2Xja2izXOyv1yPc5xVaP1gPW4mhOPYLTeaQbRWs6PJyYCfqFUGzbbERMNw9RajtPOnqvWu5tJaBKppcHi1M1uCGb2LaxT0ZsFBNUbXKkuzBvH4EPBxIE72wqVVM6GyK7xWY+FJUSgeqQMeSwoQPGRP0YLg8F4YYiD6gRBHvJ9QhHVbSTW9ySoaM/h2lwOvEvc9utnjmuPP0pKw/b9jnIpdf1FIxfqr183sgdZUTB27KKX17PekyuAMMA7iGHD4dq5ktUTWLEJwjtySPmsRZlXWWkkNj9Q9BmvnvLuB2f199is4JBVs2H1W2t65+KakGgIhbPi2f+CsPbBqJaojOKM1S9f2UIyBVxszs5oJ5H9Hqqo9uP7y89VUob03K45U4e/+keV/+/d8/x/+C88fX1irZyOAeIYwEMaJOIz4MBg1qmcO2mZZtIGzal5EVfNNpUhdU8KKlomtmaLm9eNiYDwHSnGsKVPSRgDG4Mla+fjyzA//5b/wh6vy4Un4+MfK//VfJR7HR6IIX78fKf/ygW++bVfseQ0iVOiNyPKlrOzPPuR2cxxQpBz+1LZeO3js3ys7hDxE2f0Wbz/Q/XS0HREVypZZ55XlemW+XlmXmZS21jjvAFOQcz4S49Q2pdgMycVA5DCQcqBWy+Q4rUjd0DyTVo8sHj97wmh+WwZanVEG9gevva8d5N0ENqRdy05Bbd+i7eE9zlUPQHfKYP+/9OBU9w3qzSjJeuuv3WHq7ZRroLgvYs8LtLSF2qbVpeUnJ8afzyZo4FQb595RXMCNJ8aH99x99S3D40j1F67LC59envnhh+/5+Okz1zmhRIZ4wgstgdMoi5crxBeKHxjcCTeYSuIyX3j+/JGPHz/w/PSZtM54lEEUL47gbXO7zjP18ydmJ9zLPeezx9/d4es7hrShW4JcKakLIfS+DfMyCt4xEi2ISoWUSoNSr1M3epjbn2vo3phh/xY5gkdpiZvWGN8XucUnASF6zzkG7seR+3HiPIwM4ghlM8n7JrpiQXCGWtCcCT1JkCuSErLYISJLwQUIowUd6gJbyrw8PfPDDx/4+OEDz89P5LQyRc/6/ECdnygvn5kf3jP4iXUpfPjhE3/87T/w6R//gecPv6dun/Fnxycqj+HEOTwSH94zTGfefXVHqIFYMk/lynP+TN4yG5kSBOJIkIEJq+zkEMlxoJbCVjJ1b1G49SEdU5V9SJ/gNxhpSbhggX/JhefnJ8ZYCQ+NChgcw+RxER7uJ1NSzTOjNOuGlFBOjKeB4mcyHu/OhOKNcjyO5M2xvizk9YpzyjgMjMNIwJHnlfn5hW1Z6X1kqqDO5k+mO/z0SDg94OMEXTxMzLPMlHE9pTrEjYTpnrht1PahweGKWDBdElIHo1oWqFnIxUBOCI57PzKOhdNg1N2UAnOJvFRhLfb7OtAxpV5LEg7BtEyDN/PtpSirOK6lcHle2PJqtHQvDWT+9OPuFNlqP1+sJ8814DgMnhgd3kISoMUaIs0mxpKfN7qcZfCt+lgavbolffp9KA7vvFF0Q7CEZ6PA0sRvSu1CKy057loguIPwdlI582S1N9fWtVh/Yy6mEBu8+Sr6GFHvyGJefkUFqrTf0WX/Iz5WXAOOVdVEkgporfjW6+g9jZpnlcpiTee7yqzWJrLT32c7ODvd9a1GT5je+vm++LoYNdo34bRaq/U3ZmOwHLKsbamcVdziSIwDEoK1QCm7hAuAqgXitTFhpFpc5aWvWRPQo/1Qi0e6MFyplVSKifvkjJRKkN6bbn66VYtVLlUsMYG1ZaWS8XnDl2gtNd7j4wBDNX/Wkiwe1R4jsFeGa0poSjg/mt2ND2go5EbXHZonZwwDQ5yI4wkVbwb3ZXujNTwwbeQA/vR10lxe/f8Ylcrrn2vxnOqxt7HRPXNp1E9toNFq0FZTN93+IEJEiAq+If1aKlspzKUQWqN/FEtabLWwFPvYSiEVJZeWFMvW9pFLaUJX/R7qgaVw89vmcGXafCYL67Y1UNjVgY1SL66/hmImnNJaE9pzWW8xkpbm9Xnwb/2nxs8EHA/BzhcHdvf2c3gGoF6VD7/+Pf/5//uf+Pe//gc+zRn1E7Wa75N3gVPr0Tg9nHFDILcgGB+gCDnZwyoUkII28QwR+y0iHrwzukCFWhQtCsVRNaJ+xA8jflusmV3NvyYIsG18/v0f+Nv617jrAy+/rXz18DWn8cwvvvmW9XpH2uD9L2CMXQzGd4Yu0B+Cnm07Qo6fcTiB6prSF/05ur2vFrhaOvUGHm/VDoMp/X1b5lH2/a8DMqO0KCUZXWOdV9ZltV6LtJFzbknIluEMI3E8MZ3uGKcJ8b5l8BXxnjiNjHpCNZO0ULJtnCSTSZctkLeRnCZKGigu4i2Va9kcXsHiw8bSKbmHbOTxGnduBP3cY//0AUz2n7mBcv2CcvhTj9egp/9+aJlTffUG25umAf6K85UYlHGAUStVk1GCc258VhMA8EMkniamhzOnd/eEu8hcM/MVPl1XPj4vXJeMuIH78zse7t9ziidchbxW0mY9TPrygo4jOpmMeCqJl+uFT8+feb68sJWCHyamGDnHyIhArqxzYk6Vz5dnXpzyEirvwj3308BwfmTYMrJmZK2s+UquBiAFwQexQC9GBmfB7eIzXpLliFvmWQ9z9yOg8Rccsucs9q5tduAoQnTCKQTuh5GH6cz9dOI8RIZSEd0gZ2pO1JpbJcKZ8XNOSFqp60KaA8VlavEwVnwo+KDIbNWLrVSWZeP6cuHy9Nwo5le0bpRN8PWKbM+sn//Ih+FMkEjeKi+Xmc9Pz5T5mVGAOBLFU3NgviqXS+Y0FIaz53Q+EavHrTN6/ch6HZFN0FQpS0bDiupqFJ/iGF1gDAMpFLIazUjbRmWUsS9go94mdE/c/dRDleADMQjUTFpnxhAYx4lxHKlVGU4wucj5PKAUpGyMUhmcw9fMlla8t73pMQbOBIaT40Qh68Dq4HktLMnsPU7DHZ5AnRdS3ajbasFMS5opan1yw4g73xPvHvHjyWw5avOma20HBUDMI1AV8ANhOptpe06QV6QkXAuMyrYiDtQ7ijhKBZHINEScqzjxDI8DYc2sF+WPC/x2TnxYUtdIaT0/RnPcq8VisUGqyjVXPswrP1wXtu2C8zCNA9PgmQbP6Q2W8e7+RNRCqAVfK14rGw71gTgI3isipaUoaOenYPV/D6133ocujGNm6aotOKsF0BurwJu4ShgGQhzw0YSFaNRUa7cpZraO0VlDCMQQiN5EcLQlyhCsytT8j+kCOh2wOs84jEynEz5EigDFembNOqKCL1QVNASrxoWIVHvv5u1X935LtOKc9V31OKf3bO5OEqUJZzkTSjO9CUGdQ+Xt7DgEYwH1RPC+d/Y1g73a6FrVz3wNk/lQ1mr+3n0/EWmq0oMVL+KASrg9O+1Fu/ptraZAuot4NRDgxFRlfesl3XvMdssga7HKxQzdpVQiEEPgNJ0YxnskBHLOrJsjJ0Grw/zJrVVjFyTTgN/vsYEaNmryBh7pFcum4FoqecuEmNFY8R4cjuAieEuexBgZhrEJwhhwrM6RBeL2FsRxm9edYtr+c9xa3nbcvVc6boWCVzGqtJ9sVmBK62gtSs5K6h8N2BWFokLVLvzT6n6KJQJqhSJUUbIo62ZJCPGe6oWhGqcglczWQWO2nsmc1aqaa2ZdN7Ytk/IRPB6fiUNEcohTqyq5FLbNNA6cN7Xn/mFVTKzCKBVwXSDE+lW7Ym+1hFbNydoQ6n8XwHEvyfxo9ExQTxzqUvnwPPPXTy/8ep75VJQkgSrO5KmHkXB3x/jwSHyYwFeyNk46JqW7bkYN8FIIoSBScJQGFwqWwNGm4NbUs1LPMCVqrqjzyDBQ68qSM4jivXAnjjUV0h9/4Hfbf2H9fuP9V9/w/v23rC+OQb/mYZq4P8H01fEab9WsHtjcIMzPP6yZ+bZh9+W5gQ7aQ+qsGNjSIDuY7LLBHCtuuquo9s+bgE2x5vhlZV0WE0FIG7lkUsnU0iqKLhKHM6fTHdPpTIiB0ig2SsU5xcVIZCLr1jZGA58UcMnh0kBKM3lbKOmMhgJB++POMZzslUBxveKo7VZ9HWgerQ+O4OLVtqS33s9+RO1zeASdP/F4Ndf9GuiZ1VbhPtxgelhppOJcIfhM9BuxVrKuSF4hJwO9zuFjIE4D0519jOeIjoG6OpJ6sg7gzkzTwOn0wFfvf8n7x68Zw0heE9enF57LhZQKui2EbWYoI656Ul6Z1wuX+YU1b/gQOJ0eeDw/8DCeOHkP28bL8xP56ZnLurJeKtdByJPHxXtivGM8V8I5o9dEWjNlXcilIFUI1eHcQBhM5j4GJfqN4DyJQj0kTNjX1zbX4/P51gByr1DsC7Zna/avHxMbfc8L4hhD4DyM3I8n7oaz9TeW1awxkoFELal5jgkSXRNygEUz1+3KlYWUwSVlnGAIljVft8RlXrleF7Z5pWxGm3LR5N9HVxG3sq4rn7bPXNThqjMxkEare3c/8P7+WzwQnWeKI3DHvHqui3IaldMYGO/PyLsH0tMDL59PhOtgPpAZ6lpBMsRCwBtdKAysobCWAsXt+cmdQs/+x/6QHKfxpx5DEMYQmE6e8xlOp8D5PHI6n/FxYL1cUeB0GhkGz7rORJc5BevBiq6ybTM5V8YAUQWfCmcn1Oh5WTO5Vk6DY3APOD/iw0jJSi5XqxbXbadA2t7jwQfC6Y7p8ZHhbqJ6R97vc1OrztV6cHCR0Y/mI+gH/DASz2f7nrRBLri6oaWwLTOqinfWolHFBEOGaM/ZGEceYmFcE1dWkib+uB2qOMpONxXRBq5AXKNSCiwpMy8b23IhlY3pFHnUiDpTs3wL4Djcn3BacJqbtYb5i5bOXpEOCrQpoNLUVj0i3X5IzerCVUSTCZrsgKLsjBTnAq6DxmHAD8aysaC27FWrWrpth1WqvDO64BCthcMqEFYVcs2Sw7Y22eloghBCYBgnpumMD0ZBz3VDakFTY2e5Hrw6CKbw6n2k+ox4E/bR5vWIKjVXqi9UX/ezEum6ArqrBntxVG8JgiKy2zx0RsVPPbwLLfEMu8dBT5LrrXrlxGKQTv/L2aibHg57hW0erqmoumACfipuF8Ox0foG1axPctkoZbOWHMFYVS7ivfmM92t/lQJuVUdTwc24Whli3NctjhOIR7wx7kQCVVfIK1U3pPlTV80oBSQ2waVgwkdd9r9vmP1910rJJryipd6S6e25di4QwsQwnjlNE+No1iOpFLxLDOP5TdbRQF+3OLuxcPbotcduKrvOUP/J9nC2n+92BzeWmYEv2dXAc8H+XaHUVoXnRpNVtbmppVBSs9BQ1wSiBHEbOAOTQ/Q4Zz2kuRRSrWwlkzoldjXLj2VNbFvzcizH+JK9Lex2RV1VtmEJNbXUlDPrlkykJ/hGRRda62Xrb2xem6rW89sqjDWbfVpJqdmt/Pmu45+v4vhPndQ9KFc1ikR9QU6Ce39HujtzwcRoCCPjNDKcJ9w0UqMne9eaec1ktaTMuirrmilbJjjbdGNQUw1TsQzNPHOdV9Yt7b57VKxMn1cLsFQgDEjsZrYJzZXBeaJkWC+kD781k+OXb6jpyvv7B/L6LTLf4S8eTgqjvbx6WkAoh+mQL5meP+s45N6oIo3uQqN79erG7c3trJpX4PJQuWv0zNoFR4R2U27krfUrrTPbuliTfkpWJcARvN/tGobpRGj9rkYrNBqa8+CDWhA0TE0UQ6ikZhi9oeuMX66My0yeNnQs+/vaG9z3KiA/nvcvqCxHwHmbNN2/1j9/M0k+ZDX3NVa+9Mn8qYZlmW8VzVcZ1VaV+hI4akvkSAOOIpvd77Wg5UotiwUqsFNchtPIeB4YJocLShGjt4U4cXf3lSmv4rg7P/D48C1353dIdSxc2AI4n5GygvTcbEZrJeeFbbuSkpkUD/HMeHpgOn/N6XTPQ4y4suF9ZC6ZT2lh3WbS1TNczzyc7ng3TcQBmDbyeGXxM5AsOEsFsKoqtWXqXW/st43VVT1QpQ55vv1gevshx0P8yyDqVeNGyyCq3XtOrU8qimPwgcFHoosEMWGRmiuaEjVvQMWHQDyPDPf3+Lt35PER9QMvWrmmhWvZcKVyh0cnh0fJTeE2b0YRDEEY4sg4Ok4DjL4QdSOUhM8VlwuuVASjH47DiXG8Z4hnAiPUQG0CD7VGlhW2rcJkJvDcnTnf3TFOZ3wYkZyRKkgxQOoJODdQPAyeJiaQENleVdj1+My2FEBPSL8VC/kdFbzj/HDm4Z3ndBKmaUSGgaXRoG6VhcIQ4fHO43Fsqz0j3ql5+lXXMuFGD2xN4khVpmHAne6Ae1Qjqom8vZDWF3SzSolo6wPynuH8wP27rzk93JuNhBjg2bKaEMQ6c50vbKUQ4pnTeA/jyCmOCFaZ9sPIcDqTUjKPu2Lgp5ZC3op54o5W5SrqjAp5Kmz1haSVqwiXWlibtytqVgVdq0BVWi8RhNZLJArbtlFTsmq4Tqxr5VmyBd8B3r3BOuoQAWeJJ3UmY191Z9WAiWv1mqM06qF3juo8amazOMzvuVaQNe1BXq0VqTT7jWY8H6KBER+sN2lXUG1zVFt7gdPXrJ4dCUmzlupVJBsmoNNFRQTvfKuKR6sklkqSQlDr89JifXcdMOAa0PgCeFjc1loCatmD6d6Ltj+Jar16azZwVmqwPxWysFtXvMXooFDpgPYLUEEHj3Y9tfbK6es5VK23RIw4EN8U+ndi/KvXpK1bqXlXRFWtRB8IYSSEodGJfxyE9M+YlUlBs1mguGBxjx8GELPwcC4QRw8uUqonJUXb2Vdbe0Lf9wTrXUU87IDdbqQO8PtQbfoNlban+tbXOeLjHcP4wOl8ZhgCpRa2soB4s057g2HrUw9J+BtfrH1Do1c6WkPHcSZfJTN63N37G6u5ZBgltQq1yuHPfl40sKqt37gUO7OqUKujVEetof2KrsYMVT3eA1ptv6iVlAvLmliWxLyY1ceaLVmhVQCHU5qRS+vZP+aQW5WtsxXcQeE418qaEn59DRx9/xBtiSdLS2kDjqW0+zStrR/3vwvg+CfCr06lEQHn0DpT52eu19+j8ol4Bhk9ay0sJZs3zXlkPI+4wZPFetuiN4GUUirzUllmyGs7MT14L6j6PYOzLSvz5YXnlwtrymhTlXQuIs2mgdZvYaa4ppa0LUraZoRK9NaPULWQlwvL8wvLQ6WkR7y8x5dAfTqDC/BuQO5a5m8vox+3lz8Lq99s3Opi/RPH4PmWWXkle9yLWeirTab/fMdUJppj2YxazLMxpQtpu7CtF9ZlblTVQlXwPhCHlhgYIs5Jy9QVtpRJOZPVDk6v9gC4MDBMRvlAZsq6mj3AOuOuF8uInR6pp3KrQtQesN0qkLpXJ3T/s19vr/AI7XzsGUt9ddmv5rNDsz5l7Rfzz1T+/6uHNpD+Y+ZdC56F3ZZyz5j163IKUlA281fLiZIXSkkWFLZAwQ8DYRpNRTAKUMx7DDiNJ/xXv0QevyY4zxBOxHCHMDSjYkcqDiTgQzW6aHAEUUQzWja0bFRNFhA5D25A3QjuhIsj4zhRauY0PxOuT+iyUdJG3jZqViAYtS+eCOGECyPOrxY4q/Ue2IfigqVE7Lk2ilcVbmroers7/hRwfLMAp/1PtAOcW0Bxe0pl/0Zrbpc9bHFqz4ZTMWyuWBUgG0VMS0Gi9Sye391x/803jO++o46PDNWxXC98etlI24LWjeg9d3FkiJ4QwU+BqQX3IQSGKTJNnmmEMSSGuhLKRsiVUMBXm+PgI9NwZhweGcIDnjtKiqxz5XpZyVuiJMjJQEgYAnqaiKcTbpwQP4Bbb4yEMOLiRPETNcM1K95tdP+5WxZ9n9V9vvrzfpzRn3q8c5XZO3QYmF1hTSuTF07O4YqZwg9eqDVbhjld2eYrMWeb7+lEGCdcCFQRLsvGvF2NdaEQfIscJFg10J8RGcnpymV5oqwLsiULEjEqYJxOTI9fcXp4hxsmqw6qUdLW65VP88KnyxOfPv2RmjMP919T7ipyd4e7Gxh9xA8nwrZRxxU5bWytgk2piFqAW33Gx2BBWIHoI4tzPH2eefn8mW2Fj/PKy9b6oFTRWix50wGU6wlMy7yrYObtDQhrVTsPcjIaZBC+e4N1vJZCUWtTyVrJqqa12e6v2ipThYqXsivBipjgjHaFUtOZtRiHJqq2q4reVODZq28OtfKA3aWtEmZzJGjt55H5Mm5iYNT7do+rVdesAumMzlcP4KHFINrAawC8OAbvGZ0Boa0qomYET+h9KvbecLK/J2BnR0jtEbi2c5b9WatqCWQloQopNCEmha33bb5RAKQtucEey7ST3zLd9u9DxXEf0mmNPTF82FZci1l7DAE7yNSWWChVySXtnpmqFe8DwzgwjCM+xttrHCoHPRYUbWC/GjA3vOqQ4FHXBJNUofWzevX4UBC3tPuzgYLmIdoBjX2/bxRhE09yqj/aEXufpdRqCsius3YmA6+H97+LBXkT4nmT0fs12rPSK27H2NlAvXK7mfoZ6va/96Sh3ZotBaQGErW6pgFg+hlVLem/+7CrXWtpfYXahMRyEUoJxpxsPYYhmV+0UCGYiJFVNi15OG8bl3llWTNbUnJrm0MF30SWitravE7+d22K3iNtYkXOibUXIOSqrDnhNvuc82Jq8q0n3BKTIC1pV6qxLlNKbC2uyvnPixy90SofAp32z32B+6fahJiaVKVw5eXp1/z+7/89f/c3/5F/+Pv/zKePfyDlZOpVY8SPAT8EQjTVLqFQykauibQVlhnm2VFTIDjHEAbrRXCFnBPburJcDbyoLojUpjjkoPVPxmh+dlIHJG9I3uzhqIVSzMy8lgUP+JAQN6N5ZnkpvDxNPD2PfPi8IbzjsT5yF7/hdBrNt9su/FBivxUXbpTDnwdCHvt/blWq/h5uANCUt9z+CPYHTlv1bq+U9Ae20u7ypuRYVkq+kLbPrMsnlusTy3xhXTZKUcQZaBymkRADSCWl1RTFcjGlXG1S6NjLiji8H8xPB1MCTKWQivVNyvXCMJxZTlfOp41xNONyaYdxvw/7nO/00p0ydchQ0TePY9+iHT9fiqrcgnpezafSDtM3GMdr+PGtowfe/+09WhxwQ0qK9bPUmi1LWUpTqfQ45yEOSIz24btybMULnIaJcwxEJwQXkOrIybEsmetl5nK5MK8ruVacD8QhEmM0jzYSUky4BW16ny1zVtSqFuqCKcONI2EcCNHjt3Zo1l5xM2EDwoiE2PzIAq5l06RRS6hqWX5sc/YtKVRc3b3IboGF7MFAP6D2JX6DsefCZYett3OP/ojpfnjaGXkEjYqrirO0qbWn5kJt/BsRxQfPcBo4P97z+M1X3H/7HXL3NWMW1k8f+aQrT+mTiY8ks3y484EQB94No5lC+wE3DMjgkKg4vxFZGKowFM9YlEHNcDiIJ7hADBPRjwSZcHpGw4lBQPLMNb+YunW1YDfEAOOAGweIkepNeEJEiC4whYE4NODolCGVXdTiy9ncc16H5/JL6vZPPb6OwveivKSN9WkmbxemaeT9u0fejQNn5wjBIVJZ143nT89sLy88xoH33z7w7qtvwHvzUvVQKPzwnEkl4SUwxUDxjqzWAzeOHlFhuSwslwuaCrFn0lEkBMbzI+eHr3DDidrWpKaV+fKR55dPfLi88PvPn3n69ImgQFWiC+Z1qgPvzyPn8c6sevKKpBXSRMkba072jPne5uFQIlUGEp41Fb5/SXx8WshZWLdM0rZGamvhxfqmnHeIFNsXtBhDIATC4MkU5nlhSwnxgopnXhP68jY9VZ/mtXmvNQEt4542oTa3iy+VLorTqotWPWynjPZ1sE6Pqrp7CEPPYXaqmTZwrPv5grjd19hXCFoaKIBSzKzdTOA3UzTt9S8H0XuiBrzzqJZ9TwMTyklpI28roVFax+CoQ4DskWJ+f66/n6om9LeD29vJdqy32fncvv/wCPZqecV6KH0tBl561VFvpuQ/9Sg9AG7vq9P8nNxg5I3+2Hrfvhiyx0gNvDjXANit0tgki/bztZTCtm1s20KpGe+d9eVOI3Fo1UY6rbK9bpvYPRa0V2w9lh3kmGCdyK1ZSLCmo16I6ZWxDv66crgI1qbg7b3r4Vxjv5JD8r8pbWpT/HTB/Et9UGrdmJfE5gQXvFHLA0h+m5X8Ef35cFbe8oSvotTD9x8OUmgOXLfkRst3NKDodrCoVQ7OFLIrru/WOKXgcyUVoahZ24TgiNk32ra1eVXRlsSu5GqtV/O2mef1WszqA491pDs8xsKzXcTaSfSYWZFuXYf51zu3A0QcFJStFCQl85z1jiHYR/SO4sFXDP9USyynktlyYkuW7PjvpOJo4xhm3x4OW+haLnz+8Gv+9j/+r/zv/+v/m3////kNv/v9lZI90zQxTJNJEAfHEANj9OAyW1pYlhe2rbJtA3kbyEmsAXSw3oNaK+uaWOaFdV2oNRMCJndNU5DDTLFP08AQHZpX8jJTFrtbglYGqeR1oW6JVGcCBecy6MZ6yXz4PvLrB0/2C9+m7/jV+C/5H8oDp8NeVDkEhn/B8fod6E6ZfQ0+2oq1gLWDxvoq/XYLzGgPpNu/VlBdqemFbf7E/PID14sBx5wzgiPGkXE6EeOAirJuZgJdctvwGo/fhWC87SCYyrAitSCumsJZCPi8UbIJNqyXC/P0wjxeGAZTIAstALXrPV5AB439cOCwCdGh5m1KfgwZ26fbBi+vtil76N8oWr3lKXm1X75aID1ssK9SdByus330YLsFLrUZUKsPqA8WPIn1Hnlx0IQZBm+KYyVVUplJ68yyvLAsT+3wrHtlP/jBmu2pzW/Z6M1VW/XPOTvcgmXfq6uoE9Q3o2pnm7jl1K3XxwsQQqN5NVqNCFXEMnT9A2uq92IZQS9Gx6z9/qet3wE0fvnnW4x2V+7315/+Pa/vwx60dQOgiAFIaSBZk4FGhxK84IfAcBqJ54n4cM/pq3eMj78gErgOkT9uL/xw/UBZX5BaCaqcnOM8jER/Ig73hPMDOo4sFF7ShWX7RNqupJQoeTO6WjXgGMVTXKEIZCcECQxuwLuJoEapjRLwVKITO9zGiGQzTyaYR6C2tQwiDCKMzlFDIAuMMRB8f65vsyRyu6v3mWvBYeNfvcEqwr+MwqqVy7wgoRAzaFmpYSGMlhxzoeK9okUY4pl4PnE3RJw/syXM05AN9ZktXSl1Q1CC9J2nmlDFEPCusl4vrJdnyro1m6IWkHqPG0f8dEeYHgjjHbiBJRW2eebl+onn59/xxw/f88cfnilJGcPA82dPkNgAzWRJ0vOZOJ4JeaGkGZcGfBpxeSOnAk3Qh9xURV1FM5hX40Twd/Z1NqgrSStFaovBrTqNQikbMSQeToH785nomxVTcsxBWTdPHEZciFxzZlnexnD86bpRShOkofd6CyFYhn9XjG4qqtJUTh2CqxhVsIPCJgzTRS92YNhGD9ItsaW71YZZYpi3ni9QeuKjYmIzVEpuLRyuIlJ2I3iJ0Tz2JL4CSwY8LZGe0sYQA8ELMQgaHTV6NHtyLpZbvOWXD+egdHTRtqpmC8Lh6Onxe/v5qmZLUmq1c7t73jXK5VtVHKmlhyUtOanmHdr3c+17gn0ojZXQE8U9tmnvsfexGl23i/9oq0bR6NaFlBPbtpLSCrUQhoFhGIhxsGQsvSey9d69AnHS7pFOR7S/1y5y1M3cm2Jy/xwdHLa53xPeTTkT1+6DA3g0iuerXdKuvdjvcmoHp63fRkogZEpyjS0Ase1ruWZyXt5kGXuuyf5+ANptbrqp/f7142gHQqemWvHG7t8WfjTQaOtneY5DdbKyE1m6DZu0wohQCJZ1NtX2GKw3tHY1Zmf2FmD3RbF+/KVW+2iVTUHpd0UXY3LeEQV20y1pKYxeRXbaKLFtPhp7qtdupFZ8zsScWJJnTJ4hOoYC1dk5qLWSS22CPbePkjMR/0+ux1vVldufLRDqtIbDdxzWGdgonz/y4e//jr/9T/+Bv/mP/4Hvf/OB6zzA+B5/HnHDhBssWzOOI0OM5JrY5pnL8zMp268qJZBSpmwFJ0bvcZLIebPPtwxM0SYMIxC8x3ujEMRxwAdBHbtEbdGCaMGr8d9r0d20PEglENFF+fiH37JJ5fua+JUTtu++5V3MfEMl4FoGCJzTPdDpdI5j4LMXFn62IXvAfFurV9vYl+fAF8Bpf6LboWfacqqFmhfq8sJ6+cj88onleiFtGyjmLTROZlIbjOK0pY20JnIugODCQHDB5MOHSIge15t6u4qbWrUiOmf+WbVQ1pXl5cJ1eGEYTvgQcG66bdp7gH671v1W7Tx49sSUzZF09bMvrvuQ5VKRbuHEfv//CGb+dOMGam7X8BoUvw5SaN/f0o8ojqpup2oI3qTeWwbVgkCP+miqxd7AmRfFt1yzqFp/cMnmvTdf2dYLJb9Q6gu5ZEptst4aEEa8jKiYmnJXNy4tOBHvmsm2wwVQZw6EFWMI3JTCMBDrPcE5aoivG//pYPEGMnfQKF0S3XoB3Q5Eu6iITZ5wO5z2DfothnYhnr6O/c+bGEA//Lq8fU8IBIHBOQZxRKxnk9IqjWoCD4P36BAt2z1EiAE3Tgz3d7hw4p0qj08fOH86s+UnHBmn1jt5DiP3p0fuHr5lfPyWFEf+uF74/Lny+fqB5fmCnz8zpYVTrsQCoXqChAYOB6JbGEPlPARO04iIQ5NCUUv+iBKDMAweisdb8yJ4U1vufVquFHwteFFG7xiDIwRpB7vuD+wRPNLXrn3OOet5eovxfw8wV+UlF9LgOZ3u8Fo4h8j7YUSDQ0JlnDxTUCZ/T2AgaGVNC09PzyyaKZIQn1m3Da3WGxVbMBjCgE53ZDcwXxYuTy+U+YpHbxUTAYkDbjrjTnf48UwcJhLwcr3yfH3m+eUzf/j4PZ//8I/ky0YIEwIsl2c+qdkLFH2kP0GP9xF3usOlGVLC5YQricLaVIwTgw84PEhAt0zwnsfTPaEKuWSuOCRXNG8UEQtgvIGHkhKOzMPjwF/94j2PpzNS1D5fBur9tCcct2wKrdv8Ntbx26bkYlS0CjiPVWN7uU2wBFanCobB2AuYzyFN3bK2AHxXHH01ZKe03tJWfRdy7VO7zBFmKG+9lruVyZ4RK4iYkrwl9TB7D+ep2oAuh8isH5xSEen0VgjO6NBmdu72s69XbqR5Hd72wWNUIDcEuPcaiwG2VvWq5ltiQM0ZBRLv/2Sl76cYXR249iqo0io5bTbkdjKg0vo0Wz8rt7Ozg0NTwHWvzhhbB/vubn1SmthIrQWTzjHKqQkc2bJ6b0nLrqR6vA/kOJeHGd5JpYcE9qtIrAOpti7a515t7l37fa5XHXucs/9+9qqa6u0UynkjrRnElF0Hbx6fIuBXS+zXph76NuNwrQfKar/Fbiyv23zJqzllB4rWcmTPjjYg2PI2vWV3jyt3gbV235R+TjcKsRmggMuVoRRye957T7FTSwT1ql4qmU0rSZTsheJNtdyoyRXfntXgBfGhJaRsP0XEKKxdYVlzj4qMYlu5eamLUMSRMTr4lgtbzuTsycEszpDmy1vK4cOqolWV+GdW4y9QcewbnhhdDoW8Uj585uNv/sAffvsD80ticCOnOHF1wShzcSROZ4bTiWEYzFh5q6xLYp5XSnV4X3fD1S0ValHSVgmhtoycxzvrvVq3lVwqPnqmSZsvk1C0kLYKxVQl1VngXCWARHyYcINYj1XZyFsmEnCiduD+Y+H3fuTl/V/xuMD/iGdFbxOttwcU+kP+s6JEext7he0QXL1OsPVvvD2gh9EBi32L7oGvVUAqUjNlm8nXF9bnz8zPTyzXF/K2IQreRUIYCGHANTBipfdKJrXeBGv+Ds442sF7EzRpv7eKUXe0Kbt5EQOPCq5kynJleXniOgz44BEPUcYGHm/7fgdb1lPyGsD3zcY1I3ZUb7TG42Qd1Lz6fd5Bxz5jPwoc/tvH7tPYQK009HsDj4eOy34QqDVI2zx4coG1VHyqSBGEYNTqloUVb6p/Lgw4H3flQHNe9WhpGez5heXymWX9TEoXVK+ILIBSygnSiVI86IjI1KqzEVVnm157y+LBB8EFUyY0Srn1a2hzZO3Bi2WAjdLlfSBEqz47d7g3hdYP2OqNYj5oTswCwOkuGXQLFuT2lO5Jnb4pv8H4Euy/zjUcD79DJlktg+xF7BlxjiBimct2EDm1fgbvxTz4gjNqUYy4YSBOE244c0qZ8/0j4/kev44otSmiClOYeHf3nsd33zE8/IIX8cgmzPP3fPy08PLDE275zF3ZuMcxaiBU7BBUszEavedu2JBzwjWmxrImlnUjuEzKG0rBR4j1aLAerGesmgWA5gQ54Wom+Ej0EJzgXAuU6HS01wSl25ra1717m3X8n4Ly95vwe3EsKO+c534cOd0/4sLIi1YUT4xnhgCJhBYgK7lWlpx4KYlNM9IEpKhCDI5YTT1xPH9Nmd7x/cvM0/MLab4iJRHa8229TAEJJ+L5gfHhgXgaqJjVx7w+8fHlB77/8Ec+fP+R7ZKYJKAFqmYkJpb52ToPpFVpvOCne85xwE8PhK2QU8LnjBdPXk3dOq0rUT3iAnlZcC4yhQjDwOVqoj0BCCLkFnmKc+0MKQyD55v37/jVt18xqFK3zdpUasURUI3kZePzZeXqEpfhDTZVQMRsKKq4Ril1UL0JOrU9xXmjcDoJhGAxCWr9m6XUJmvfz9ofH6t23Nip6ZwpVvouRCOOTuXt4h21K5vT+qfEEbwQglU7unJiaBZLQ1DzU1RIfX9re1gIgRj9/nMWCBulvfvH3gBM+5u0XsAOHr8ce06176bsQYX087UF4Kq392NHzds8j8H7JoBoVbnbNnsAw9Iop73K2JRpj/tHy6OysxacXeMxiurVv9IqNmamXkArJSe21ZFzaYmGSBwsdnGlWrW60ZJfTWmrgMnhfuksvV4pNJzQz6bbGXVrr2nwT2uzW2k/1J6/ql1v5QCE97jKXqdkE9DKJRGcYxziDh4R2W11eBv8fwDO/S2+PhP3a+z/19t7+TJhYnhQWzzUQJfqTieveitI3F7hBh45xIVCt+yw/sVSiiUe9pjC2ARFi/W8FtPrqE7Q4JvQr0I2f2XziXRGNfdtX4kD4iMmiGQKxSlvbMk+1mafUQC8t9gu+MbYaUk5reRm72KJLEAM5FqfZrVEmWpLc/z55/HNexx30Ynjl9pNJog1vr8s5D8+c/n+iZcfNkqyzPggIwsDGkbi6c6CmjhaMNuyciUbOKzamsdhlzH2AiVgj7e3TEkIDp+tPyDljaqVEAq5bNSttkWxZvxO/bLrCCa2ox7BMgFlUWrJlFzxWBn6OSfy99+jf/893/6LZ/71r+BXD55paMavvu7ZEMNkXdHquAn0uXoNZH7KUZt/o8AhE2cL9Po3Koey26tg9kaJuL2Wc+BrQdNCvr6wfH7i+vTEerlStoyoEHwwAZMwIhJRLKsSYyT4aHzwnI0ugxKaIhS1UHM1fyzXfImc3zd+14Aj2hS48kZanrm+BGRwEIWTE2IcrCdOhM7F6YdZ5yeo9H+3/MYR9fW5OmDqfWPrOK6Bxtcpgp9+3DbK479vv097cLJnEO0EFGc9veBIW2UuJoYwlHZC7mIjDudNlCSEeMgMS+sti+Ag1cxcEtfliWX+AHJBZCX4hIhQzSwMrQ4YEBlRyYBVPbXTh3pioPkOiVSjX0l/VlqOtJ97+0ULznlCiIQQ9+RAv+YdPLf3L+2+6dTFJky25yj/JHB883FYxOPBePiyHkyi+/4g0Ki37ibS0a9k30+ULrIQYmCazMctjhOEARcHwmSiNP5lpObFFBbFM57ueXz8Be8ev4PxPZclsS0fmZ8L89PGfNmIuXKOA/F0x91wT9DJaIub4rLg3UgYHgnDAxJO5BqZ08LTvOFk5TQPXNNCIeECDKNnHAdijHjvWyDeqwHZgly6Oqju90bfrwTZ17NXA/p9chS9+KnHGJQxB05OEMmMXjiNI94HljXznFYkOEY38H6IOBG2upJrJjnzAhMneI1mQr1lQHGumphTGMHdU8rENl9Iy0JNK65aH1tVs4zAjQynd9y9/5bz4wMuCqmupPTCMv/A06ff8/HDB+ZLImjE+2CqgqWCJAuy5wreEg+nKVLqGZUBH+8IJ6NA1ZwppSLO3mfNGwWzb5DgEVdw4bA2FMwewGhWNLofWonRc3dnrSKBzMMUCVMEzSZRr2qUyNPIMAnbc2V9eRuqagwDTipOWmtGA1OiWFUKE3UpQQ+ea7f4w/wOD+diD+55fV5Y3G37kPfN8kD8nvzr1Q8LZBvo897AqvfEIMSgBJ92USEDj0qQilCoVQzANIEh7x0xBnu2gjeMWhsdUW+JF7gB3A5aXJ8HDtfSj016uvS2d73qITyAx74niejeq/UWw7u2ZiqmzNyre9L3AXfYH/taNb7NMWiVvlq0ovMNNNa+7rSewpKNkVYKqPnj5VrRXC2h4wMhZkop+OB265QYBwYRRG7VvFt193Y2HKAgPbo+JgWgJxbb+h2BTwdLHej1Lx8Sl8edUdVaD+xlLGbNJePWatoD3ftS2Km8bzH2+Ky9jx649rXTI72kXdM+dT86Sm+A2taNVpFtlena5+l2juoh5tPDvJs6re4Vy1JvrVx7IUYrqk25tGZLxnmQKM3Go+zZIcEsZMYYOA2BYTBBIh8G8J4K5FJYU2ReHTIreS2k0hkNav23IbTWPgEvjWLbEgdNib+qNhGnBhrrYS9rrQP/1PhZK442bg8uYBq4l5X6eaY+beS5sG7CWjyZgISJcbpjOt+bBYNihqituRicyV0rzc7DFt85z3QaOZ8dsJmdBuC9ZxjOjBlUFlM6yon5atLXuZjMuChk7xm8JzqPj6Mp/HlTm3RAqJWaNqNYbokaTKhjuzzx8e9+w2/ufsOvT/+WfzXA+38NdwOIq6B+V9m8xaT9ZvyZVqFJF3dxGz1sRzug3G8c3TeOV1Sw/hU9ZugULea5dX1+4vL5M9fnC9uSqMWys86POH8CP4KYga74SIgj4is1FTa/UHJp23OjBLRmCMHjxe+Z3hAiJUe0ZOOjY9UWqRtlu7LMHrl4XAxG0UBwgzUiH65unwM7POv+IGqjNdxMw4+ZvBuQOkzXYY7edk3/6We7b2+36mPPqu1NqG1XLaWSknHbfTkoztpM4cTvQc3RNsKJ4EKwJE4tiPcUrRTd8G4jBstuxeYVaNLPoNWjasD0tbRCO8ydHAILmqKgJSVuxJpDgqNv1NIz976ZOdvXdJ+lQ4b5diofqlOyX3UfBzbS7Xe9yTg8f/Jl0kFf3YuHO/Xw/faxZ8/bqdmBvgWzmUglBs/pNHE+n4jDQNLmQ+YDfhghRnIWUzwUT5zuOD9+zenhG1Y9kZ4+s70U8iUjqzJo5Dzd8f79A7/89jveP3xL4ES6VNbnGebMJAPvpnfcn77GhwcuS2F1C88VVDOnbeUpLcxlY8JEBsYhMkYDNdLaDnpG2FH3CnSXibiBxz4tHTy3wGkPsNgTCD/1mINRfSa1PmCnMKfM9nzhuRSeakGdkOeNcp44DY5EYlFtvaBGuQ8VtmVjWc3fESfUGPEusm5KLitlSUhKaN7MwFmFYjQNxumO87tvuHv8Cj+e7LmshVxW1vkz69NHdJ6JLYlk55Hd/zVbVbhm2GZPGk8mgAMIHnGRMJxhWtF1Zl0WHOZZSLEzUSRQg6d6UzMu9SbsklTJriV6Ol3OWWI3Do5aMrrMPE6BMTouqbBQLFnkHVMcCfcTW3gr+G90Ztv72PdPaQDXgnIDjjlntpQI60Z2npzMo62UTiW3+6ynum1b6896Dz45bDSvhxWGLFFKEAIOkWgKw863irv1NpqgR8W8blqFkkJJQt2KVevbe5KGZqX3SB1jMto1S0eE+mrf60DwmNw6guFXr9UvU4/xez+b9ECrfZtzsrbm9V6V26e8f/QIv717h50ztcU6RybKkZLV7oibf6M08N1phCUfElzNtqahK/u+Si6pqXAKMQRET02saLgBwX3O5PZ7v7jpd1ZNA47aPteX7Lgu/VzfP9cTpIeA79U6tfNTxBHiYJNWssV5jXLsXEDcDZy8xdgrhfv13AoXty3+dt/td5PcznekA1Bb2xtVtekrqB4qjrL/PN3Hsc9nT3w0FpolIxy3/sjDntHuL9FWx2vJHGl1PSe2v4srBFeJOEavTFE4DY5xDMQxmIqtj6gTSq0MyWPFafORzFsTSHLOdCC8s6SEt9jp1sFgM1ibUJT1X9uHgUdLslhc9k/vrm/b49joCEdzV+lIv3+uFnh5IXz8hPv8Qr1sLGtmTpC9IHEgTpYZd8FTVdlSAikmyJCs4qcUcs7UYkpj3gfiODCeB7QG0nq1vjgF5yPTdALnWNNq/O1tMd+3aopFwYd2eAjiPMFHggtQCnkRqhaCZuuF3EwYgioMCmW5UL//LZ/93/Db8e/5+9P/wNfBM/xLiNFUSr+MP/fMScsQ2Q33dqDjZrVx2DjamtGxAfojPPvqLekxkDdZ9lozab0yX5+5PD9xuVxY10QpiuIRiYgMiB8Rf0LCiPMDIrGp29pD3Dfj2kr8vph3m2BBTsaaiZ0LDOMJCx4LSSuaCqqFWlbImELkElln65HtPn4GMCz5IK4ite1EWtuDw6vr1/2/Havspq+HMvHh/8eD+CdYtD8xqv4TROf90ND9MOn/Vm0y29WCoJ7x9ChOK028b7/a2oKknoW7XZPdr/bzgRBH4jhR9Nx6ZTaoiegLQVxTFTR/qaplB3U9c9vvJNuQW2+b27sO96z28eT/UQ9DvfV19LFjP8ECpeNa9Jv/FlO8Ao7Hn9fjP37i8aUqboeHguy+qDcVvNdh2v7eDiFGP0T3liPtFgBmrD5EY3V476kZMpgQUohoCCRgLcqmUENExhPEibJ60qrka0KWwlQ943Dm4fGBb//ql/zyX/5r3r3/FVoGXn64UPwn1M+MMnA+f8Xd+RtUTlzZKNNCOd+bBcA0sThhrrnpyVW8k52evu80PYDYEwKvw4nb9X85bkHVLTP/0w8Zgbow1YirwrImntfCc1FeamUdInghPV8o0fN4HiiDZ/YmBDR5zxmoKZG3SspQvcPVADTbqFKp80xdFtjMX1haQFpUGeLA3eM77t69R4aRAntrSC1qDJmqTC2gytrP433Xs6qaFDRq0/qTnbrmQ0R0QONIjBNuWHFpQ1OC3suVV+oGGia0ekpR0lbIWVExWx8R33rvpInBWF+hEJkYkZeNjZVFElfBKKvRsZRKSpUpRr57eGB+g3WstWAUUsEYLLRzoVEem+BFSol5nk38S5zRFFNGCzj8zp7IX5wP7P9qFYraEtalIKHg1IT9vHNosIRdbUlT51rfv3g8pe3ZRocseaXW1eCBeIRIzo6c1JSlFfOny8l820pnULU9l1Zp2AFj3T+6yma3/LjhXTlssv2jg83XoPM42i2591a+xci1tF4+C547E0HRpnQuiFgSyvaHW0/gTcV6X6zbe+9pAL0BaG3iNSUlak4mHiOKD47gLOmj+GYqX00BXor1s2m0nrccreJMf9kOXXubye3E29OhezL39TnQK4z9TLOfsb99aT8i++scTph96ez7HB7vDER6wQoqLiAuNNuvSql/3sbhv230++mQdjGA0eKcfi2Hfb7/2c/C3rPYr6/fn/tL36IAXoGnI3g8nLNiKvN7MvoWLO74x4kQnCMGx1CFKEqg4KtRTB0mljY44SSOk/ecomOMwhBaq38wVdtedQzRQCEC1QHB42uhesE1wTjv3M7W8y1BIa7nCOxZLs0/MmejrHbfSnl1h/14/PwVx2pqQCrB3lZd4dMH/O9+h/zhB8p1JWcl49AQTPo9+OZrUnFYRud6zczLi23aObfy67WVYx2ilS2vbEkIwQ67mhu1tBnrirupWdWmVCZVicFzniZOpzPOG1DxLjLEEatfKrVsoHZQqioVszBwW2L0DqdXwtM/8vz3/wf/8P7E+/FfMciJb38ViaNaT45yuOlvN2MPg94ONh6D4z9xc/wosO6f08bf6BuMPXR2/ypaM3lbWOYXLpdnLvOVdduM+41Rxqo08OhGfJjwccKHCCJsqZC3lW3dWoMulrnbFjQ4phDwPlhmPRU8SvSOYZhsQ8PoIVu2e0S1QsFU4tJMWq/k5UyOJ+pQIRyyr0jrW+iX1ibhQIG4bcp6m4I/NXcdf97wzJsBxy/9NPcjRm+HxK2ievuZ3sCvMRKGgdMQOCWPUyXlhBkHG+AoNZt8ezGwZ3Lf0mgdFbwJRIzjmbuHrwkRhAtSF+o2E/3G4D1ZBSGhdaHWCC61qlHL8u1VM+v1cC4iEqFlBLV93OCdHQiu9UrXZplTcm4JoB1fmgqvdHqrZQH3GZEvwOKrw0H39bfb5I0Axxf/bvmblsG9ZZq1v/8/iWLbNcnxtpX9zxv4otFUWrKnPZvqTa20OE9CWKoyl8o1V66pElNlTYW0ZXQrxKycJOCHB94/3vHum3/B+Rf/Cv/wDfNcefmc+FwqbBvemb9UxfaAEgbk7oEhVJx/YHwf0POZFXBbMtP3fr/t19YnQQ9XdFjK/r2HOLZf715s2O+xt1nH8C4yvQiuFtakPCcx0FiURRwpC+oqJW8MXtCtkKLn6h0aHPeDR0JEspKSsDaV0kBk8BPOD+RUzAv3OlM2Ayk78AuB8f6O6fERP55MtbJWA4QtIPDiiT4SfWDDrHD66L1tRY2y5ZpeZhfysx4pwfuIxAk3nJCTJWxTXm3f7cyCJNQtQLT2ASeB6EcmP5BFrB8oZ3oPJQq5KCkF5jTwOS1AYgmFxdnLOFFqruQNvATGcXwT4JhyaqwW3wyzsQlogEJQqErJVi3eUrbgsTZ7nEY9DU2IyUl6ha1uj6/tWzVn1CcoAake8Y0SGkxuv/qu3hpxEvESTcC/gGajr5VtI6eVkhdUm3IpwYB7dSZKg6BVSNmzbSY658aAN9kwBNfwokLrb+3JRqol/rqmwP4s7dvogXWEXduR77E/yfuj12mx2mi2b7COaFO3PAbEXaG0cWqc399n3xdrA8e3OOyGzPbEUwONnafSewFrMts2p5XgPUP0Fjv6ETSQirKmlTWZ/ZWThs1Lsr5Yb0mL49Db1v7qA2ig6DXIfVVV3G82aXBI9o8vd8GOQVUtXevoa2xJBFFwzre2r4B3AcUs0ajZhAvfauxH9uuAqleTjyJyxy9KO/j3E/A4Ma9w4+vsxzEtvcPvQ/JWO9OlxyFy4BUcQHtw1tYjPgJWjS5UVDNBCyJCjI7JBU4hMvnA6ALBm+2g+S4aZhArz+PV40ITaoqBsKzMJZNoNNgglrx3BjzNe97sx8DemwnjGGhM7e+12QX9c8fjzwYcb3uFPcSdJsj1I/zuN7z83a+Zv/8j6bpRqkO9N0GOGMEJKW9Icigep4V1WbheX7her+RsIKGUGZECBEoNXJeCysbd6cQYPLhgAXDayGpADzEDekcPSitDiNydLIuOc6xbhiqEaEIt6EgpI6om6OCwPSRt5iPoa2EKian8ke3pf+cffzNzd/o3jPH/gve/4uvvBkK0SEa/WKjbNmt/fyuq4x5Y/QlEY5vlESZpfxZsjg6bvIPm9VQoaWVdLszXF67zhWVdTMlJa1PMNNqj8wEXIj6OhGFEvKNoMR+Z1eYwq8mN11JIi0n64z0DFoRYn4kpx0XncDFSy2RqZk36vFTrZdW0kraZvM7kbaHmZOpmTe1uL+P3jNsB8Nl1S6t5tWt3rpsB2Uy9opTsn4ZWoetVtbcYX66f7m+8b4Z2Dx2/T1vlr5QEOhCCMIbA4CzYS5QGHIV9o8uJ0rKotIPVlG8VxBHDwOn8DnGOdRzJ2wvb/IzyjHMLMSqiAe8LyIpqBN1QtcBVBDMQ9tEEeIigATMQCzjCIYg70lvZq081F3LKTZigCVLI8QmyYKifwseghj3bejs8bvSX2/y+IQng8Lvbu9UbpcvOfvenv7ud9EeRqsPsABaEalMQLLWSkhn+UgqKN/AfLL1ZnCfjSVpYM8xbYd4Sw7axbZGaC1IqUR0nNzGMI/d3X3G6/w6ZvmHxd3zOL3xYVj4+P+OePoObmGTCyR3EyEqA6cx0DsRT4XQP/s6TpbKkxLysbNtqLQZH0Q7XFHDFaLj7k3fIGu/Bw4HeJC2w6knhtyI5hocRGR3zUniu8FIdzxVmzJd024xWGGJgCAGnjroViiaqU65eGE4TQSJrMsAuLjK5iRhOBIks68L88kJZ5uZN10J27wmnE+H+ATndoSEizoSuXHtugypDU9sUjr5vt+SlTU2r8h4ob9KiyRqcqYjqmZISuRbIKy7N1Gy9PBVw1YJowayUTnEyL1etrLXwki8saUNFiDQvWBxP18Tf1AsfRmWKStBC8CBSuWZrDanFH2hiP/2opTRtsGNCqVfbansmu09etv61BrO9WFDnxBKdSLUexF2RtCWu9PYaBrYzUjK+BgKu0e11L2odq0VdDXoHb9yeeaum1Wb91c4A8XjfOGuuoJpNzCgXvK8gLSGr1j1UOzhue4rdB2W3fuizLvv/jjtqfxbr4ee/mOBdDba2XtK36VWtIVLaPucxnQmQZv9k66utz7b7He6AggMYu2VdDUDrMflo11dLsXMyJSiZ6IVpHBjHAR8GxBmzKlRgddbXnFajCjrXGG83Jo7N0yFp2Lw+9z3+QEuFDp52QvQOXva12rmd7Otyu7tlB1U3n072vdJhImfVNUssb4rXReTQH/ua6fNm40/8Cjn838I06YiynYCy36zS/R+PHjB9Oenfc3uaOoikJcy/TCTYT1jF3vUzSWk+y0r0Hh8dI0J05mPqU2J0QvbmgznGyHkYmcJI9AHf+sRNKdBZIqnZqBC8JaZi2J0JxtPGnDbWkkiaqRScq0SnRCdE34WwWuKkWoIkZdNmyUXJlhs6JEb+6aj1bYBjn9O+joeSfxUxPi8L6AV+/3d8+tv/H3/zm7/j+09PrBmqeDKO4DwhRsR7ci2U5WKKpzWR141lXaklH7LHzTxYKkq2QHKDHAJRRqPzKFaVqAlEDNU7T5VCrgKSzTOwU7lCwPtMTkZhrSL4ITLenRDJlKXuO7aq4rvsdlnI6x+YL4mPP1z5h39YGM+Y+MTwwPtvTI1aHUeEsj/c+2bxJgtkL/7nQOOOfdrnu9KUuv0x3DNWqpWaN9I6sy4X1uXCti2kvJFLtopjBZxYFnaIhDESBo/zQnVqFJ+aKJpQKS3Qs8pYLpWEUkQYciW4gMMRgSTNNNUpPkSG6dQOBbUMbOtDlWVhiAs5rdS8oc3s3ur8duV75fGwadM+9QpgIIjrB8yfgvbtsOz3PPz44PyJhu73SDsEXu1n7f/HXy63NdNqAYTQ/NQ8FDGYq41yZr2iBakZSkbLDZjnYgGEK0KMkRgH2zxd4FqjBSJVqTjElxZQKeISsKHaK5vaqBSeGCJeAqijFkGLPZ/BRYLYuttp2fOHTQJdM5qz2YIcJFpFek+HXVdVC6i0BWQIByp9C8EPlBNpYEOOk/sGYxejYF9J+5vePieH+/J4z/WKbW3N7zcRgUO8s29SoKXZp+Qm4ODNF8x5b6DdB1R8a5RvhxfeqsylGnCsEGRgivdMY+Q0fUuI36Duga0G5iIstZKwe2cuK8/zTAgLnnuSG5rFC0jI4E2wIdcKKVkSbrPnVYvt6SF4QvD4EEyiHGmshGZPcADNtr8ZfUz7ga96mLG3GWmB7z9d+Owi9eGMDAGKkrdGEXXK/Sny9WngvReCmr2IlEa/d1Bz4Zo3Xq4XrhWG8xk33OPcmTRnri8vbPMFXzJO7WkVcfg4ouM9TPcwnnHDZP34ogQPaSnUdUFLMqGxYsG6k1ti6RYq2Z+12vMgKqQtUceC8yNFKuoD7nSGvFLXATeeiEWhzmwpUbOAyyAJGmVvDAFRJa+JkjZSzRAHALzaE/iyzPxwfSaMcIqZd77w3d3IwzmS6L04haymdXB+g3X0WnHqcAdaph60+qWdF7aHWGjdTcOriPWzSbDKrK+ELkTTuWJtsrW9blfgNKaEAVPXgEltv7PWm7CeBxCPV8A5Y+1gLSAuV0rxLZFkySJtCrGephRrGd8WPJrdUd4qW2o2JNr6Do8KZI3e9loCuzFBuF0LzerstaVD2133vHPvH7QWEyfHHe0nXMfpRCqFmjd6tR01+6eK4MX2kkP6xdR0nbQC8+u9wrxN7dkxESMj1pdqrVIlJ7SYldHgI9NwIo5jS9oFnDcqahVhyyu5LI39Y8Jfx19mZ9Rtz9rnX29smh4j9qqXtPU6vu+bdZOtAqqmWl9vDCLZ16qdNtKTrgpUS9Z5o/yaoFUlNd2Q7kFKtX7ONxmHvfsW69xwxZfx2eug7HUyWJyYUBIdVPZX+DNlmgMY3RldPbnQQKXjmMy0lxRMsHGKFhedg7Ui3FVlRsi54r1nHCamcWqOERb/lOqoOFSinZVhaJZozVtbHHGEYaqMOXPaNua0suaVVCy+ClIZfQeO/uDl2mw4am2gUVtLg+0NzokJl/4T42eoOL66uy1jIYrUK/zwj1z//m/4m7//W/7jH/7Ab+eVFU919mFiDQN+HCgOUt5IdcVVC3icwNDEE3pDq+wNq7ZyQSvkTBFv6kXFMnslbwZkwoDiKSpktVsyiCOLKUVN3hNwrDWxbplaFBcc4XTGecgeqnNU2ag4BkxyOeWF6zVDWHGT4j8E4v0953e/4P7hO6Zx5PQAzulNKZFb5uQI3t5ivKps7nHmDW4cQVQHJ5W2SbjWk9J+Vkshp420zWzrhW29kNJsvaMlk4tRDb3zZoQ7RYbRN4++3ALehErGuYr6FvxWC/ZLyWw1k2ph2DbGYWTwwaivGfLmiEMgBk8cTnYwqJK1kraNkjNpW1mXK2mZd/BYq4d6tOY4VHReJaOOAfstk7WrxO0bRQeT3G570cMG89OPV5XE/f+HLGl7Dg577GGjtCBAO71st7vom7P1PPpaca3/Rks2lTgg1Z5xN7H3MVqF0DHi5ASsVF1RKeAz0gytkWIfu7BJryY5gnME8Th1+CoEFSKO0qwdaB5bhh0NOJjMdIVsNi4WFdyClh7gmRJisTOOXg3uAPS2Zj0T1A/io7reWzX/39bv+MQfUzc/5h/Y82d9bVkrWW3zryKN9tsCRmj+sxVt1UJfFV8rvloFzKFGZfEB76KtlUKQwBQmTnEkimfeMut1ZVsyVE/094zxRPTvcDxAOeG8YwwnHu4ekPfvqKXgV9ic41KUkJXkYSmFuVyZ55mwKe90QAea7Uampo2yrWhNOCohOMJglivqPEWx/owmMd6fhRtVu90nlea2xe1seKON9a//YeEP18r1viXInMCSKUsGhIe7ia8eBx4HuHNWafMloQlSE88o2dowis4MQwsm/D05Ra4vF+bLhZpWfL3di1U8LpyIp3eMd1/hxzuyerYlAYVAZbvMXF4uLPOVbVvIOTc61YG62zb1vnV459FqldJl3ohhpVZlzRvUwugAF9BhwuWEFEVTtqCyZBPacaHZEbZeuibg4siEAESPaAM6IhQql20h5cLkQaPjruMtJ7sRdtbabFh++jEcvWLb/rlX9dvcmEm3UdBEgvWuZbX2iI4xW5+k81Z97P554qptf7R9qiU+wDzgXKNv6q7IWEjZFC3Rgqc0ujFEJ7gYkTDhasWXJjDUk70domlXAjDmloijVGXdTLgsbWZjllqywK7N+swNOBtw0aqHZ+34PLUnrtPfVQ+JLDkkvHSn3Pfr9G9UOR6nM3lbyNmSHyJdxVt70a0lyNTeout2Iw7ZaZdHENbnoOwg2WxzjcFjIlLaPIIDIsae6dUjJwGcJ4TKMEyktDaaoFJy9+fUljhvv3Q/Buxr9vvr3ja002btkDPA0kHfq4qxfU2rGuOqHAHl7fqggStvKF+bEJl9wfwCS6VZN7SE3e5d+EbnY1+sH32+z8phSPt2sZXr9XELaVt13jlcS/TccOYt7nn9qv3+pYHBL34fh/1Auurw7d/ReaYQGaOVO7IPrCGyTVtzCbC4OAxjEyDyVP3/8/ZnT5IkSXon+GM5VNXMj4jMrKquo4FqALNYAtG8zcP8/4/7tOfs0CxAABpdV15xuZuZqsrB+8AiquqRWdXAIBya5Onh5uZmanKw8PHx9zlKFbKaFGCRQHWeIp7qhComExjFMagw1MowJoZ1Zl4DS/KU4hAKg1OCa33qiiHxSiH1/sZqiaLaklrSILCU+a9Ox+sEjvLCfbV/t0PBOUG0wu2Z/P23fPvnP/BP33/PH56f+TFlripUZzpjYRoYThNuHFBfcVpwOhDUI6W+MIq5ie2CNZQ73bPMklZjOaOQ00xeF3JeQIwsAAmsqbLkDN6IcWoI4IxAhVKQas3nSYxcZxxHhjEwxED2gZULVWd879laEuuSqM/K6h1rGOD8lsevf8c3X/8db+5/y+AEd85Nt4nDaPVkymuFjbzYhPvbbNHO4deyOeDan9yMlOGkFc0G0UjrzLpcWJZnluWZdV0ajBijoB4mhmkkjgM+OvBNvlQLkBDJON8CGK2m4eeBYk5vqTeDrpUFDZEkglRzeE/TifP5jnGM+DgRhkRIKz4ng0+lhXW+sdwuLNdnxmnCRatkiTj7TFJbRqn3z9kqPhqJ/rv+0/b9YNg3yMfB4bBB+OKz+FegIdo9Hl5Mbf/WJlz6vXWnRcGYSRsUCrVqYzH2RlKipkKJSnWOrMqcVur1QniGKQTGIRgFOmLSGMOIj+ZQVu0U5j2gs2DRNcekc1p7UcbgOA2ByTuiJpaU0GWlptIYIJ1l9sU0kkpJSF6tmlIag2vPuopstP+9V7r0yqN8Nh7bmLW+hT7bvfT3Sntyq3p2J+swVz+XPNX23CrWj5ar0nxW1AmmHWQC25VW+c0FTRmXM6EUhlqJWq26gvWuuBBMV1W8OaHqiGosnzon5qcLl49PzNcFLWJU4XJC80hdPKyOMQ68Pd0zfv0N92Vl9oH0cWZdBz5V8Esm68zT7cLT9UecPBPfOhZ5hMfJgtlaICfIK5Rk69GZbce1xFA2yG3JZevNOIDN2G2XWo4E/fmE9Be8/m9/uvFp+oo5xBaUg0szsiR8mDiNA9MUcC7ho5EUrYtB9SQ7ajGHzEfH2/OZh+EOjRMhO0qqpGsjodEGxQeyCOKMpOj08A3nh68pEvj44ROX+UqpiZpmyvzMcn3P5fqJ5/nG2phYu1xJd54VQWvFtyTqelv4oB/Ja+J6fSbXwloLwTvuTyP3U2QYT7ha7N5ixGWPromaVnANuqmKq5iWo8L9GBkcJAIp2ZxlACdEL3j13A0jp2lkUSVfEs5DHExWq0ptjvaXv4aeIKQ7n93WdsfemE5DME0+cZFShLWR3NSqpFyIzadQzFn1rTcsN1RM69yxdbtBEetmk3tlpVZjvl5X6/sXEtFHxsFTI8RgTPLigpHNtYRoT4YWNbp9s/eO0pjdLeBZyVnbXrKe0+AdMZikijGR7yiUPXjkRdJhS9s0u1t7gNlN2mGqNvkjscpmeCVW1fMwIdOJldIkXTxarO+0VnvMl4J6t1dXO1SvVWf2ENm+S0fi7JFYS75aItR8XAtQc2mIHB9bVdhRFEQ8IRi5lJEpZYMIbzBYuw9DS7D5FN2XqMWYc81W27na2TG777iRh3f/oweRLflq8g/7pGyru52XvXpZGx9C1cYO2/z72hI40pId/es1rhcQ6M8fV15US4EtBqnH+9oy592HcxuiaGfMrZ99tb/Tw2u319+iSDZ3a1vT1lLRbIT3DD5wjoHJC24Y0fFESQktDZnmPYQIPlDFU9VT1FMwdYmMoTBXNRvZRJpQZ1Vvr8bj4l0nxRHrp68Jbwwj1KqtBczWT0qZNWdSaa1k4mzOnSWM/tb1KoHjzy0dm0C28v68zPzw/kf+8OMPfH954iklrqUwV08dPHEaGU8jcfJotAbmwZ+ZgmcAy0anbIvAORO4zNa47bCAQrvulxplea3ZmsfTapACMIeWTK6tVy0MuOmEH+/ww2SyHGkhL1a5Kt5RpWlCxolhGKkuMONa9sm0CqU4clGWeeWqn7jKgJz/xDe//I+8//iG23Pl7ekNhAlG/9LDf6XN99dnaT8kjyr2+tkz7eFW4lds0RdjdKs5W3C2XJnnZ+b5wpoytbpG3z4xTCfiNOGHgHhpQaMF9ELGuYK6agynjkYp7ImqIJlcCloSeS1oMaHlWhXvIkU8fphMLLVpD/oY8TkY+UDvwbw+cx2NpdfHYNVjZ8xYW5DXmLL6WPSA4sXYbVa2B4v7GL3MAfTD83UCx34dmVO3x/q99MCIPRmxB7TNgBIse9+SJd6Z2KyUTF1XyrKQ55WcCk4N11pFuK1Xnj+9p8xXRue4P525vz8zxIjzSoyCCxa0ZVPKNaIGMQIIH5rcAkLOiXW5UdKCl8IYlEGMmn99fmK5XMlrBjUNTwneqtMkap0h3yjJ/r6z2iEgXjaJD4OrGnmSVR/3ILaP0ZZg3H5g25Ovlcv5OZIjtvvSwxrcb6DSHEJVMvZVBNQ1uEn0VO8PbJqFsiZ0XpF5IawrMWeiV4Kz/gnxHgm2BiSv1GVlfXri+YcfERbef3/h6d2PrNerQSDFQxXynFiebwx3d5xPI+fxxOn+DXGd+bgW1vTEJVfyvFDzR/Ki3C7PrLd3TOFG9hO8icjkcCkhLcB1tRC0GtSrFnJeYVkoxZEKP6k27uMJ3d0wRv7Omgwi+mrz+P+9LBSXSQHuUuUxBO6CIzklVUt6CQ4XBKKjOAM9uMF60FyW5rSfGU4DWSLXZbDz7mZQU3JBqtkcm3vHME5Mb95yfvOGMEaeLxd+fP8dny5PLOvM7fkTZb1BXSj5yrqure/cBktbNN39fAQb7+VGWSvz9cLlyVOlkrTip4lpOpHKHX58S5xOeC3ouuLXBZcWyEY2V9NKEXM2JAq+wug9w3hH8cJlVS45kVRZWhJSc+UUPW+mE+fzREkL19uC1kyYs8HqW+/81//my89j3MZFt+rZNjjNMfQN/uV9g6C2IlBBKSWTVphbpbxmqzj7YLJTWgU0t+BRG0LC/JWSDQqp3XnHEptabQ+vyaQeVreSsyeNwhAr3qdWJdN2Bjat3pZgpWY7q7VVjGol5UROuUFULTDwXvDBIdHjojGPS20BUikb+zy0AKUlG8XJtp621paevNv2o0FvrdXD4cRakmKIX34SgcdhYLq/Yx6E25yYl7ohoBTZ+xXhheev7EHjlkikBWHWZIvThmPYKnqH2lZLwNStb8yISWrzE1GDxLqmJ7y9X1tjdiwLhhqRJjPXYMK1jV8va9dKzY3NtcHPTQ/52Lfc71u31+iB5EYAd4zLWtBaMRhuyqn1LtctEdv9JD0E0q/FydETFftENZvVK99b4Lj9epuXWu2eXRuXLVgEjq0oG3RaDC6+BY7a2VV1e+njG21nSauaW9Wxk2MZgVB0waDLrdfRR2O270zYRYQizr7wmDJ8oEiktMBxVbEYqpo/YKIe1rdsb+3Q2H0JSwqUgyin+AABAABJREFUTCu8ZJJWMrZ2Synmb+VEKpmiZry8OETbOv0b1xcKHI+ete56d4c1pFoxZL5lw69p4S/vfuAP3/2FHz594HlduJVCEo8MkeE8EaeAeKVKQVzAh4E4jAw4qixkVhA1w+0dWpWcjCCjlmxwkGASETkXak2IFNPNwWhtEcGJ9VJOw0Q4nzk/3HM63ePFk9LCOq/G8qdqxrRl9sQPpiN4L0TBssYkFk1IEWq1LPE1z1zrO/zwj3z31cgPf+f59MvKVw//imH6Jeo8NVjV0TflV3lhcF/h2hIwPVUC+iJofOkw74D6Zig6o2bNaLF+lXW5Md8u3G7PzPNMyYKIld+H8cQwWmCH84fKU4MrNmeuExJ0cdkxBEKshJTILeBHjTq4d4N0p2SplVBMLF7FGrhDsAxj0YqUTF5m5ucnfBxwQxNJbX2uchgc0YMxPQRbG4S4GaZj35TsQ8n2TGUrVr3G9XlgaHfSDCLHgKiz4O3JM60NqqEBkaYd5RLORZxPVkGohbIurLeF5boy3lXCnSDRhKNLmbk8f8/zhx+QnHk43/HV26+4v3/AeWd6c7VBlqvHmfItIgPeFxM9HkaC96zzyu36zOX5PfM4skphViU9feTT+x+5PV3QDDGOhPHEOESCq1Bv5PSMLp9I6zMl31A1J8p7jw8OH8TYnLVQaqY0KmzrI2l77jCG27gdMgKvGPe34Ka9z5YdPbgszanRbRvaQboHj+wVVG9OiS8ejU1/MzvzMZZCut7IzxfK8wV5WIjjidDpvtuXCx5WZb0+8+4vf0aeClpPfPiYePp0o5RKmCIOoaRKuSQ0KuNdhPtIHEZUjNhoVc9FHR9K4nJ9Zn3+SLkkZJ2Z3Mp59DwOkZNzuFQot4U6L5ASQdVIGbRSU2K53ViLow7KSkCL5V73uXk5S3sAKZuTZFv3dXbkexzzdSbGkVE8EhznaSCdK9e1EF1lisI4BPBC1ow6NQiui0bCRmCII94PzEnNsS8z63ojLxdKTk2KtZF2xYHT4yP3b78ins6kmpiXD1zn7/j09IHL9cJ8uSIlt6JKBbWgpMMOj73A0BJRtbDOzyhXM8xYL5zGwOTeMIzGP+CGAT+OOC3IaYW8ENaZmjLUTCmJulqgECTgvWfyIzLAKpVlXfCirDWz5ERaVgIwnAdi8FSqQeNxpKSUqxHk5KqkUvh3rzCPvhaT2mp7z6lSuvMou7aioTNoEh1KE6qllspaCrWkplFoOsTORUIwyaNawDpb20lYrbKTUya35I2nkRg5a2uxgKH1mReo1ZErrEnxPiNifo/3jhg9sfkrKhbkVC2GTqiQS2XNJvmSmgC4OEybtweNoa2XLqPUIMja+uxwxtbovBGD9DVpI7VXabY8ZXfw2/p14nFNquI1rrsQOMV7hjFQ5Mo13VirsXk7geAtcey6VMeh0qa8bHcQsAA6Wz89wSD+L/rT22fsu8k3JmCtiZzV/MbmWJTaCOca+VfXKu4BiHQhd++pJZmznzI6ZJwPgDO+jpJMOzQnk7kS2poxwhZLPLR9Xm19SiPL2hhx5VAoOASNaCX387LBULUfPM6Cz06YJ8LG2vkql25eKXt02IM3aT2Ie85/m79aTZezPdv1yWTLExgyxNkcmC9aD1Dlur+f9N5I3XyvbYX3eRda1c+SSt4Z0U3nsrVilzTGWj0kqPbX6ftEpZ3pGMzaglJL9dcKoibTYwz3CuoRCca6LImqySDQyfx0PfBU5JKNO6ahJMW7DcX5zzmsX7Di2CfxeHDL9iutClv1M7Ckhfc/fsd33/6R7z+848M6c1NHCQ4/joTTCEFIdaUWy6KrOnJqGYLqwUWLuZ1nGka8D8y3mY/pmbWqMddFh6sJXdWYWqsQamhwSGtqFxnw8UQ8nYmnE8M0Eb2npMxyXVmXRK42sDEaFjn6SId0+jAS7x9wkqj1RikzaW04fnFQKvn6zPP3K98/Jv70C+XXvxq5/+YeKQ+EPFo4bQRK+9C9auR42Dn0ZKrN4RaMdCO/xZOWybDAw7JMVNOwXJcb8/XK7Xpjvi3kVBAiIUbiOJkOZw8a5eUaMce4a/cpEsQMrpk/qEpNiXVeSOtihszSvq2sPlBj157LxA7RaKL13gWcK2YkS2KdL8hzRIZI02phHCeC83smqjucwvbJocNz9rnpVamfW/ndGascyAS+8CXuM0N9gDa+CBwPWba+rExSw1GKo9TQJFM6aYm3O9dCSYn1tjBfFsa7RLyr+KB4rQRJOC7k9R3L9cpyDczzBx4e3hDHgZxhngtL8uDu8GEixjMhTASfGePEeTpxmiaWZWW9PfPx/XeEkqjTmZMq+XLl6eMnliUT/cTj+Y7x7oGHaWCSDGlmvX0g396xzJ8oZcH0sSxrHqJVN5UmLVKSrSEth15HtgOXvua34PswoK8UPtZat/fb5/CQkJPDfbQH+mFTFOvn1UqmmKCwBwm0oDngczCW4lRYn29cP3zk9vED8c3XcLojhNC2ghCiYxg8YYF1vvB+/TPLD09onbgtjqUILkQ0DOTVN3kER3Yz471nfRwI/p51XZnnwnVVrkW4qPIp37hdL3CZeXDweH/mt798y2++eeTNacKVlfW6sFxu1GXFVZPdcWIVu7zOaHXU6kh+tOqHHufus/npXsRn12slARIT4gPDMDBEE2zOAdwUeBgjv3o88fVpAEkIBXWK8zAEjwsDqOA14GQgJc+aDPaYy8wyP7EuF2qpiHgyYrbr/p7Tm7fEu3sQT8kzwsoYE0FmPCsPJ08kYHILan34KbHWdKCz2FNPu3NUcd50kb33hDjghoFwGnk4n3j78MDD6cwUHVJH6jih6URYb9aqsKZNp9BQKdU07byjkikpk1NqjnEiryuqyjBNjKczOFjTYkGWBAqFtUDOtHXwSj1Vtdh66hW/nkzstt61YE7Mca61wQRN18sSVKVasEDre1JQPOIGnNctEBQ1eTDTVcvkNZFcwEmEQaAhQGIQSjT0gPbgsWY0WfXQOXN2nTPUGy2gw4XmREuTNaqNTGs/90XUtOK8Iw6eOHhCFEOh1EKpKzUvlgQo2Zzk1mpgvZvWC9nBj7o5v/t7HB8TFavkqDdnV1+pa6pm4ugpbiIOBfFLs/k74YvzezuCVZId1YsR+pV+bjc/RQ3yX1OixoRowLlg0MRWPawKRQvW31pxrhipWM0GI/aBWhMpzaR0o9bE4Eyw3fsetNrZHoL1dOds+uRpXViDaYzTCE7WlJsOn6HrLBHfWT5DCx5l5zMoTa5KOyxWd6egnzMCXdOy1mTjZSW7LWFJm1tDVxvzqvvbCMf/09eLyrV2d6q1E2kPHrufsyf0u59PaclZxWC4rUphzKeutUFI+zqglnsi7YAY617ysf54dO/2feE3vXAVk6O6pUJSRUrLHLWWmQqt7UQo6sjVUaqnECkyUP1A8QPFB4oPaKtEl6rNzhjiMndFgazUQjubC2lZKevcqtJWma61Ia4smjZJpNbAqt79zejwC+3W/cCBvULzOWfRfi2kd+94/8c/8pc//ZnvP37kU4Ib3gxmHJBxRKPf4IzdWc+tn8Vh+oHeWbVo8CZ6uQBrylxzMRrk02hU/gFCbmpF3Ym2ghmqDudGC3KcQ1Ii32aWeWadb9S84oMwjCPjaSJMo/U+qhnsjBJErC9zHJEYKECqlapCUEXKSnm68OMfZv7DGeKbB8rbX/Kv7n/F1/GBM4JrAbY4M/IWu72Oi7NVG7sD2ioYZjTcgfilp6S0GZre9W+HY81GiDNfn4244bqwLhWtLciOE3EYCWFAnKciW3+DbXy3pxxck2lxttmlza1XQXNi8JE1RMNlK2TnKN634NEZM5xmvFqPnjhjsvK+QBXEWEIoaWG+PcMl4oYBFyacG/FDg45Qsabwbho6TKKyccm2ZJGNYI+sD5WPLQiwQ/K1IBw/6YM9/CyH5/TMGrBDShRqFXIR1mx6Q4IzeKP3dgiVSi2ZvHSHfmE4L4wefMicvPJw8qSzo66ZZZlJnxK39UocRnP26gDcMY0j0/mB6fTQKioLJQ7cnc483t2zLiuX643L03t0vnLzgVGBtVCzght5uHtkfPvI9HBmGhyxLujtI/PTj6TLB9JyodSEOCFEjxsCLnjE22GeSyFlgz2XZrD3VW6HTu/5QuTF2HXn5zWuqnvyYZMdls77Zle/x/5NW89o0spSCnNemdPMnB0nCQQpxuYdA6EM5FqoRVmuM5cPH3l69yP+4SvcdEbOZwYKZw8Pg8dPgbg4Yl2p+UaqGBNxiaCOWpMxJ5dKkYJGB0NiuU7MlzPOwZKVnAQYCPHEOMF0Sujpilfhm2nk73/1Fb//7S/5zVf33EmlfFq4XmaulxvzvJDzipLtsPcBvKM6SFTWmlmLwY77Onfu53bajgYAy9jKK2XGE4EYIsEFYhjwQ2BJCX/yfDWO/Oo8cecgibBih/8QIsUFE3bSBjKqQqmOVCq3OfP8ZFXDmotlmQWDQJ3ODPdv8ed7iIPZTBXOw8A3b94SfSQnZfQelzJpXVlT5tPzMx8/fiRL501sy+pwlCuAc9yd73h4eOA0TZzv75ju7sANDMOZ+/s3THEgOvDjRCqZmhfCekaXlbJmSAlpBFvGzGx04sbua7qMJVu1DaE5y9a7Vaqx0Rq6Tkz/2VnlTEtl8K8zj7X1qymyVy+6pRAaDLDV1ZomYG1w0y41UVvwaKpfrsHAhC4xJK7gfLVzv0kZ1FIt6SoZEQsofbAgIAQxe4hVsnJujKsomwyDNOiaGIoEMbKNXpUxeGQxojFvFsU5bX13BlGNQ2AcHMEpqAX0Jc2m05nTJsfhnDMG+hY4btDBQ8XkpeN3/KH5HirUIqS/TuD433U9XZ6IdaJ60+SMIRL8Ss65BVJsLaWiBvH30YMGHE3vu+6VLpsjIwOUFPAxIM4RnWng1hCpbqVmJZdELoJriR4a4k61GCnOOlPqinOVED0hupYIli34iDEwDiNSMqwrS0pwu5JqxflgxHA5N7+M5ivX9vcB74aNhV5rayk6cAD0aemUeM4iqE3vWCktGaE0Kt4ted4RTFRDIthLvY6fsyHFj1VHpVUINlYK08SkFzqgI+TAEBT9DDB4dTtRxWRavAPvm+RTSwrt4aFuL3z0ufZ/7kmljkbw3qr9iJFvzrkya6Gklbyuh2ozrdJtkHQj2RJycWSNqBvROMFwgvEEg0O9IyusRVnXQloSKdlerZoayjJb0Lhm0mxKFHnd23hUW7K6ESFJda2iXixyfvjr8/FF0zxHVOOWHRBLCFTv2XRk3v9I+sc/8eN/+TPffv+Rj3NlkZHkB1ZnUTots6kiqAsNm2xvUjHImTgTtgzBIVrI88pytf6NVaxnJ4RIdNZDGOtIdI4YAlHEmIWWzDobZCDPmTJf0FpZU2ZezcD44DhPJ8bzwHQawDeq6a6DkhY0XdE8kwrUjknOySAkwOg8lMTl3RP//j/8I8/jI8/nv2O9+y3/dnpLHEYi1sMgYnCRlwvzS1/dcJvT2sklLMGq+1NQup6L1WcLSkFrsp7G2xPXpw88f/rA9XI1QqBqAZj3pl/kw2DQChfaAafb5t4JSBzivBmplkRyzuHF4wUj13HBoMGpMBfdKqJVGpZ9O+htE4oaUYC62mDqLdgtSkkOuQ2E4USMd0R/JroJL74Zb+u91C291arTB/ddDhWq4zTthqWbr5+phHyhyzn3k2DmRV/DAZZx/H2vzmgjcZjXjAtG716do7pWtqXJceREXWbW5yfmcURkwp9WTgJf3Z1x9Wu8d3z4dGVZK3Mp5JRaFtUzjGfuHh95ePOGu7s7hkEQMkMI3J9O5Edj33QIy22hpIXLcuOWC1KEIUzc35346quvePzqLePZg95Itwvzp48sTx9J12djS4amFWpEL64x8BYtpKKs2bTnTMz84M70SL9VFhRLgrmD7dld7C97dd20ja34UDl+4YBJbXvI+ksqxm4758RlnZkWxxSUiYGAEj34IRDqSMiJokJaVi5Pz3x69w53/z1xGin1DZNU3npIU+B6HvH1xN0YudeBUe5QPTGvjsucuaWVOV+paUV9IfgBZaDkK8vtGe9HVEbGcOLNXSDGlbvTjbfjSL4bGdeVX04j/+Kbr/jN1295M0T0duW2FK6Xhcv1xm2ZWfJKdhmNLQk43AETSwmsq2k+psYO6pyDuu9RxNI8etBb66x37vNK/Re6Vtnz0FWFjKe4SpzgfAoMvuC0NAfFURrL41phWROiwuA8wUdwAyVXLs8LT59u6FyMGM4ZMyY+EqYH4vkNMtyhIRgqxg2IeyTGkTdvBO8CXoVyvXC7fOD5+kRKM09iSYoCDa66B9fdQjsXOZ3f8Itvfs3DwwMPjw+c7h/MVuM2W+kFgvMwFROgXxfrRV1T600vjdV8pRZBckSdsQwGl3GsOIQhBHxwBG+BV8pCyS1h3GVXnEN8xVEZ4uskcsohcKzSHesWFG4zrDtyqeP/5fhlzyqtLFNactjQaa0i5Dzeq0FXm5NrPoAFj1BQtTHxEpBoqKQchFz8VjWgnUXSKl8heGKMBB8Meqna4Ey1QVl3dsxO0COt2mb5GcVT0GzMxnldjB+iBRzivCVmW0/0hnyp1bgP6JXG/drRHL16J5QCKSvOvY5d/fHjR4Zlxg8jRUwaYQwe1k76UimuNpH1Bg/2zlpYamjJgPJC0q6qMb3LukIIxhbvBwgRHQxSv9bEWlaYTWYshAHnhtZbCvO6kNOKQwnBAkQf/AaTBQtmovcwDLhaLFmeVvKycEvZbFjz0Yyhto2zOIK3xFUMJu8gYOzyabWqU60HO7WzuPrW5+6da+eRPWafuzPzNh+is+16h1bXs52vMo96/NryN9rzOPR3NrImjlLjLQlWkRY8K2JtbO1zG0y1BY5ODsFjq15uH6sVxv5KeLwd0T3wF7cVRlJRFs2saeE237jNhsioteAdxibvHR6BIpQEpXiKBtSfkDEjJ3Da4K4BsgrLklnmleW2kNaVWhKQETKqRgzZhFmh1JbAq032p4H2EDz23h6D5cs/Qzr2BXsc9yDnZZLJqjQbs9jzE/yXP3H9j3/k3R/f8/4pcy2R6ieqDqgfIAyEcWI4TQDktljRujE+Co7gIQ52yJR04/p05fJ0Ja0ZxonqAsVZA3sQq/wNITSxczEIqVvwCnO1jWxyA9YE7moleAiDZxiEISjoyrosrGuFCpIL6XY1sfM848isayUXsb6BXPDOMXgPDFyWwrtvn3jn/5Gn8X+D0zechpE3//CvuT/fE8QbrLcP3ytVOHrQqA2yUtv7VNrC2TZKc5ZVG57fDGmuC2l+4vr8nqdP73l++sQyz9SCZbrCgAuDUbFLMGy/81uGaEN0Kw1WYdUV1yqfvd1yw3T7FkT6ERcqumbL3CQTkVepGJOkWIbVdYKcRt5TLFNYmogxxQLfNM+st4UUEzloE0nta7kFjvuIbdmR7j60WToYjfaZeo66jfFrxf9m1NoR0DNie2rsr/6dk+Y1YoQji3d4AlEsIaNuxwsEFK8JyVfK/IHlE8DEVIR4hofTG+tVnN4y3F243FZy1ebAjIThjmn6msfHr3l8eOB0GvHO2FGD85zGE/r4iBNhjBPzzfqjyppIy0xNlTiceXjzwC++fsubx3scifmykp4v5E9P5MuVsma0CoJHiahGjOYimANXyy54W+vW43wYuu3a6OZbosq9SAZ8+ase1ogc37/9f4eRuy1rYcGwsqoiueCWhRhgdJWJzBiCwTx9IMTBkgKNfXqdZ66fPuHf/cg4DpRamE6Rb6iEaWB+vMNFOCPcxztO8Q2VE5dr4d3HK/rpI/maqUXwMTDeDUznkTg4S9DUwjBExvsHHu6EtSSWdKO8vcetX3HWytvo+XoYuQsBWVfm28zl+cLT85XLbWZJiayV6h1MA+7+DKdHkg6ka+W63LimxJKzoVBE7CSslg5wFjnuSUx6r5ghGV7jKlqpouA9a4UyL7gojIMjh8I6Wt9jdpXihYJnXitPa2ZZi5ENDQ7wrKtyfV5YnhdqqobGgF1qxUf8eMd4fstwukdCMKcuBEK4xw/nFkx7NBX8OVCpPC/X5izaPfegp8th0M9bcfgw4vyEG+44PX7DcL5D3EAI3nSIe8VL7bMTBuLpjKaFMt9w44gvmTIvlFKsh7p4qzoScQyMUcmtN6tKxXnrIBCBNZtjVMqOkvDANA44L4TXChxbsKdgzM3t+74jt9DxxSO9W6knf6wB0nrxe8eC1BZENXIUp9XOvdoiuGpaqYVMImGuncHOY9O6rVGsvaBBtbW9nojbgh/n/Sb4bZ60t6Cx9nOpH2etXtOqLEiFahWRss4mZ5HWjb0YsYSBc41My1uV06pBu5hBW1wc0TdbElUscFSFUpX8SvJ/T7cbbp0JMZrUAY6AMRFrm2NLSAhd85JtLD0ixSpa0s91DHlVraeQdTF5jcERXYRxoJaJrIl1TaYlXTJDqHhv+uKlKLlmtCreB2J0hBhbct18JBHBqRLEcMcy2tooYHqqKTfH3wId35AWThoCLwyMcSSG1oees0HHl5mak/U4vgjHaOsm4kJsvpo7zF33odjOn54IcCKoc3vl6BWuLRHR/Kktetx+uz+hB5DAT5Pmh++2PQ1R4GGrNHbIaiu+bv6xHt6zow96RXK7h768+52pkotJZS0lcVkWPt1mnm83bstiPdCiDCKMzhFV8FWQImjxqAwQFWqTd3EBEKo32ZxlSczXmeU2k9cVasaLJYa8GIFTcIIPnjpEK7I4gdokuHwLWL0jtHXkNvK415bj2LzmfZYs7mgBZfN1ai74dx9Y/unPfPzj98yfVnKORsFNxIWRYTwzTWem0x2n0wRU5mVlSdWEoIM0uIEFjNGD1pX5+szzpyfmazIYjQ8MLhBxRMGgQ8IGaTUKaGUUx+g9pyG2Mq5pRPbJroIdUEHR9Mz1lnm+rdzmghSHq5CXmXS7oGXFiUH7ltk0HzsI3HodQGREc+Xj90/8h//P/87kAl9L4dej8ov/6f8K3NNRVKWUn0IRv9hljujRPd1/dagItp8NGmy6frVk1uXGcvnE5ek9l6ePLLcLOWVQy3i6Hig6C+KkNQjb2V/MOeml8pYp7cC83lfZDYGxknXoqSOImkRAqXhWajE4hfWAd+Idg7b5CBRI2USAS6tMSs1oWqnLQr7NpGEhDYnQsqe+NyX3GGw/CenMZ00CaDMc5nNJe842lJ+P7pe9jvcmnz3+MhTaP0vDXkoLHFVXcoHsoxkOL5ZFFGMic1Q8CakXyuJY/Iq6s1Ud/Znx/g3D/TeMd5XT48J1XUg5AeB8NJji+Mjp9JbzNBF7BlwNai5xwLkHoh+5Oz2SUkGzaXDerlfSsjDEga/evuHtmxN3gyNfV27XC+nTE+kyU5aCFnO4lYjWgVrte9CASm0OShe43oHYetxjW2VRD705B/KaV7qOgaK2n/d10/qKG4xWXD+wZPvbWiuaKmFWRqfcOeWsA0NjLTTYkkmXSBU0ZdbblduH9+QY8FoIj3e8HR3nGMmPj7j7E2MYmIZ7huGRyonpVnAPF9yHE/FyIpWZOAp39yfuHx+Zzo/E8cQ0TpxOZ8bpEXGBVFZyviHlzETm3sFZK2FZyc8Xnj595MMP3/Phhx/49PED8202so4w4KdIOd1Tz/ek4cQlO56YeSqZa06sxeBzPdHUHdQOM1Zky6h3ba3Xsquq1qeS1JPUEmd+CPjJUyfPdYykAJVCFUfVwBIquVq/IzjmGrjeMutzYb5ar6evajBDlIyYxvF0Znx4w+nh0Vo7nAU8nemSI3GCgjiPem8VRjq8sTlAh8SIilX2DPVosjcJSM6TXWjrs+K14RYa6kELZu/jiJ/O+POCpIwvlvWutfXH5WTwRhe2HrnzeGIMnlwW1rIYZFMq85J5vtwQ55nGiTF4Qi2UoiwizK+VyKmKyh44VqzyKH53Fze3sTmf2tobjKm52s/OgWc/2xrPhmxnmjQx8h2iLmCMiyVTUtqSqiJhs8/eB4I2WY0tcPStiukaUQ0cQ1vnPKKunZPNN1PZ9wQKar1PuWRqCzTSshi5Rm1nh2stHS246P1WFRqs96UNM7/n5Tzt56mt1Z/06n+hKxWxhH66EsKAd6NVvFEbS1o/XCOJMhlF6/2v1aFqjro0Kr4twdiCx5otAe1cwEdPCIFhGkmaKJrJayVl85lcS0a32NuCxiDE4PFhQPzQAke3BT/engiMVJzpmzvrHaa2Pk1njr9vEMkhDAzDydpBnCUX0mo62zktuJra59kDIcHhxFBizg8WoDRbKi5YX7XmJu1h86d1Pyel9Vy+1nVMy2gf/w2lsyd5u1+xL7fupMkL+29otkaEhKCNfMo3giLr1rEgrid9Nih6cww7kqXlXji8Y7tpO5dzhkzhmlc+LomPa+ZTKlxTMXSBFgZVJlUmFYYqRPU4iSaFIwWCSWnVZTE2ZBxLrizLwnq1AkjNyXgnAoxRDDnglTgEXBhxo4M8QMlILXiaFI43GHzoVVcBh/Jd/h+s47hF25gBxFmWR7wnX6+8//Z7nr7/QJ0rnsEGGE8cJuLdPefTHdMwMg2DbdZayetMyq33zXu8obbRkljmK5fnJ27XC1o9p9PAeHdHnCaTzXBK6HAakUYtbYsteE+cBsbgKIM34enSBHdb/0ytmbzeuD1feb5c+XRZmOeKFmel41rRsqI1g1bL6tdqG997VCtrasxZw8DZC2mtXP/0F/5A5T/4yu/PA7+6O/Pr3/xbOotQF2d+tUv27MyxytHenA7ks75Qc8kphbourNcLt+cn5udn0nyjpNI2rLPTtfVa0Denperaodqyq23DWyC3BzrSFs+WCaf1qPdMiHOEGJhqRDSyklHNBrsqluHBQfQeHz1OBUrLEDZGuC5OXdaZsl7Jy5W8TqQI0hjexJuz6RDTLOrLpleEt4Nxz0j+NLzYwsovOnXbq/cqwU9Sa5/3Vb7MwFnsqDipQMIcRoFgLH5SDa5Dc/pUF7Q+m57TusBtxrlH06IaJsbxLfE0MN5VHsrS2FSzZcCD9bjFOBFEcJqN4VirOSw+GvmDHzlPNLZXg9as80xOC8HBw2liGgTyjeXykdunjyzPV/KcqFlQDSgBrZHKALkjGDyokeHkluGuSoOD/tTib07O5ksrP53XL3t1kezmt2yVzj0pYH3PBh1uQRG2gwqGyqBWrjlzTcJ19azOU7wj4FtfH81uCq4KrIl0eUajx0sl6Mr0cOL+HPF3D7Z34oALJ9TfUWRCzg5//4bT11/z9XIl15U4CKfTwHSaCGHC+RMxPnAaHxnHexBHSoGcMW0+r9xJxc8zy+3C9dN73n33Z374y1/49P13LJ/eQ1rwPuKniXx/Yj6dubgTT0X4MCc+zAtP68JcklVdmjcjm9ewkyX0YHKbzm2vvsI8SqBoZM6RE4+chzf4GJEQKCFyUYGszWGxOS1O0FhRKZbYWGfm5wv1eqOuN9NRrf0ssC8fJ85v3nD/9g3DaSQ5s6PONVhl0zB2zT64YL1kpVcLXJNZEHuewR13SLaYQUYb2ZLJcCSqqwxDQOj9ddbZICg4MxdIwI1nwrkQ1hb8pGQkDqVS0ooTkx1xEsyBCREJjrQaHG/NmVKEec7MSzFkUXRMg4elcL3MfCwgD9PrTCQvg5+uXQe9KmUQ49631mUwLPjoOrHtb5zBy0SweSw9yUgv7W7QNmALFFGTnSpJEYpBKcUIWJy3De1k74tF3AaPo1XUeuJyg/C1x/teMKhaa1HpeoSpyS8tBn8rOUNVOuGKtGQwDZXSj8OmKrKD+Tc72hKV5vGzEbJg+8AHY7V/jasSEWaqrqRUyFSEaPuj9X5a4q1QazAUGa6FI+5lPUn2cRNa4FgSkj3VJUNWeUcMkdPpjAgk76kp0aNqbfJX3nviEBgGb58/BLN3vrVl0eUjfEsEdSb/iA8DZTCGTEevEhl3R/BWbRzCiPPRiLWSBY45rWhdUS3Nd+lVMmnQ4wgSgcGqW961GKk2eL1DtVWd23fT4O5FkX4ivfbV+Cd0T9QcuQDk+K9u7xpKwzlpMjUGy+4yZq6yn5Gt0hi8kKtQD69aa22JGhuX7hvo4e22u2zENUpl1cI1J645cymViwpXHEkd5ELMhaVU1qqc1HESGEJo9thainLOLPPMsq7Mxdo01mU1iatkLPjRtfXbenrH4E1f23kCA75WXC2mqduSJ8GJtfw5IbTA0Qt8992Pf3UGvkzgKHvm235sw6emH+JC6ElgtGZu794x//Ajel2QarhpXGA83XG+f2QcRgtCEMYYEVWDoeUVzSt5UWoCJwU0kW9Xrs/PrOvKOD3y5s0Dd1+9wY2DSXlowXf9nGrCybUUcoOIUC2IqCm1gMJoj3PO5JwoZSGtN27Lhcv1xjxn1hW0Ohy+Za7AskmtcVwajAOMfVSMpc27yMl5BGWtBf/hmR/+w3/h35//X7zVM/I/O7757d/jz2d8eB2mMWjVPtchEdIyOG06u3HvOdW+8UptTLM35ucL8+XCuixG2EAjthGPSsuWuGiMjt42rFVv6uZwHCsmaN0c+Z9z0XsW1/xCk1g4yUD0SgpCymsL8rEEgzOGtOA8LgqM1hhea6Gsc2twXwj+Rlmv1HQhp4GcMHZDH4xmvB3EO/GYbtmutkh5aSz3o2Vb83z+nC9/2Xu1wPtQPfi5yoruv8Ya3wviC+Krpdqk9YfmaIkCzRRN5jzUjOSMJGVdBsI146Pg4sAY7ziNjmnI5DqTS6KK9cO4MNhYNhgjmlvg2hINNEY5PFSrlogCp4xok7/VhK4zz08fefrwI5enTyzLarT0GHNZrYGiA6oTIhMicQuwslpDea82tnTqT0N9YZPoYFuTuqfSX+E6hqbHSqf2+zn8zrIstuOkP4ZCrdyyckuOOWSWWMnOESWiYvTt1p+saBGDA89X9FmpviCyMvDIFN9wvrtnuL9DxoksAysjRSbiaeBOIl+L9Yxqg8VYxtI1aPqIdydiOBP8RCmV1RWyD/hamKTgcyHPF54//MAP3/6Rv/zxv/D+u2+5ffgA840JIYwn/OkOme65+oHnDD+sKz9ebjw/X1iWmVobi2C3KVsO5YX7YHt4G8PXIzkq6qFGip6Z1zvc9czEiayBS0vg9j603kNTamHOiZTA5cJQI+RAWRfSfDXJEWVnPw0D08Mj9199zXB3tmy5WIVwJ7zoBku2MdHaxLvdIfveNABqa3YLzhGDBQWm89g4/0RpvRlWndjqFT1IN9tuy9IjfsQPmXA6UdaZtC6QjI1TSyGvC3iHyGhC8yKoOopav41WJZVMShVHIDpvAlq6kktiLZVcHFFfB3LsfKAVGuikG/XggBpcI0DX4EPorG/azk8LOq0XWbydR9vWbSyPtKr4ljTdoIFqLRjVeh4tQPWoBmq16pZBRGVHTOheBenn+a5vJ3uyRNtctdRwNyJas3E9rDPrMhsTYypogwnvSKKAOv+TwOrYM77lu0S2L7oNZU8Ki1gbSoivM4/iBoY4IGRKrqRkOsCOtuaqUGrr/aoFKtvnxFdLttRWQdY+V81X0mLqK8VkSsgBUcH5wBRHovekODQmy0bw1Po/QwgM40CIDVbsTLPaNXKcfeysA01EDWbsAkOI6FC2PrX+ZYFjIPqIw1MK5JTIq7EXUzKiBWnyC7uJ6Gvao2pfSMSJ6baipb2+/a9ok3CourEa94LxKxWO25n8+aNHqOhP/mJHVokYa6gze9crtL2P0UjmxPp6+5cXQoXY3lT2t9yC1dr80f1e2A+gtvdqMQbjtWbWXKxKWJVVhUUtcNTiSEkoWdteU0x+WRAvuAqlVJZG+nmplWtKzI1kR1PC10p0EMWSccEZU/c4COfBcwqO0e+NO+ZxqVWpxYLkHjgGscoj3/31+Xi9yIRmHD5rsiy3K+u7H0nv31FvV+sjEIfzgWE6MU1nayRuh9M4jIwh4lSIcmVdrVqRk1WZRE3UupaKF+E8Rd7enznfjSSUeTF4TMaCxZISJRslcU0rdV1Jy0xeF8q6oqWg2Yg9TA/SmI+0JHJN5Gz6SVLF0pC0Q6QJjDvRJjehaGlBJLRsoUOLR9RzP07EcWQaT9TrxLf/6SP/Kf5n7uUN0U+8/Zd/b1pqrzY3tenetPum95FuT9gOHNsIxm433xYuz1cuzxeW2428GlS0B3ROIuKtn2AYRmKMbZOaOLls2c5uhPv78RMjsANpZXtOO6LwYrpzgx/J3rEmz5ISa61UddQCRaB4sd69OCFTwWjME0tazDETTx4m8jpRUqRkoZSKK6MdHg2GtHvv+6iwj87usInS0whbwKgH+/KlrwPsTj779+eBY8+uqeomM2IVGvuqYgzBNLY8Nw4bNKXkRK0LrmRcUSRHymrYetwN5YpqYLqPhMkg4d43mFc7TUw/bndErRJtFc7OfAY9UHcEhMFFghQ035gvFy6f3vPp3Y9cPnxgvl2tAtoy7hVHFd/kRCYkniAEimv9QqrkeiDEkc1d22AvfYyERrK0OTyvm03dqOv7V8/cN3/UAlndt8CmMdVXpNmbBbgmuIbALVeW6AhuQJ0jSyTXFdGEpIpbjNjG+UT1GSQRXKEODn8ame4E7wdKmBhkpLrJAoJoeqwSfCPaa86FdmrzASf2pdWRtOBdJTuDiMs6s16eePrhW77/8x/49o//me//8k88fXhPXWZGBIY73HiG4ZHs7rllx6d14f0t8+Eyc7vNaFqQmvEdIvV5MNgCyD5m2xTqYb9+4cuS8cqalOucWOqNIZnUQa2lJQJsjXtvDItrmplTQsTxEAe+io5YKmVeSMuMK2pBFeBCZDjfc3ps8hsxtrXR1iwdC9k+bFvDVS3wUWeQS+dMIN4SKoqEyHk6cR4insJaVuZSGkyO1tvWzrVaQIyhvKoN7IvqhW94oJjw40SYTmgjytFSNoRJSashDUQQF4zZU62vK7jALZlzHJ1j8g6vlZwyqVTq1h/4827jf+8VhrGFzGoawLTgTho5hXpUnTnZ9CE/wD4b6+oxoWFEcGxJKHXsNtBBD/jsy5rJVO3MMikNT62eUiOhBFyM5iNs6B5LHm1Vlv5hWi+hvXxtXyYfbpJLtn9NLiWRU6s0JuvDs8qmbwGV3+CUdHF69jzFvq/kxZh0u9p16/ar2a5Xsq3j6cRpqjjEZMKKUpLBkPspLWqVIckFpLSqVMCJ7Y1ck1XSaairNoda7Rzt2ngqyRKegpEyxsjgPWUwdt0unaBKk7axtinjVOgV57bHdIeP92qxx7VkuIMQXiSJDNYqhLY+azb917QsRhhXsq1FNdKnl5c07Ua3txU1AKglGW2MPJ3wxT533RIk/TO5v5L2/++/WuxnLvf2mGxJnW0d6f7ptK+/XiAR12Cqsn9vgaPHTF2oQqhCrFB6UUv3goYAlDY/W0vL3t+5PUfZAmtVbey3jWMhd/1UZc1QsxCyINkRihLFeh2dYO0LrpDJzKlyUeW5ZK5pYU0rtdj558TQjUMQxgjT4JgGYRqEcRCmKEzeMXqzt0G6HqSNa4eomoyzBZN/6/qiPY6bEKr2JIZDYkAxRrH5hz/x7j/9e7778x/58PED87yg1TaR8xGRgHORcZgYp5HzNDDFSHAwes95iMzLbHpvq1CKQDWJhzApGgvTEHC6kq6fuK4rT9cLOZlofM7ZGotbH2NJidwgGTmt1JTtd7lQGkkO2nHktAxxsI3boy3tUB2rMqpgYsW59QmUghdn7KIugIs4GRnjVzw8fMP57mtO0yPT9BUh/sLICNzrOqk2ZYXaZSJaZsY+TjOIbRP2O8lFyUvmep25XK7cblfWtek61UKtBuHxYcDHiWEcLaMWQvs8xpbaDxP0ZfD1wt5sFaHNhacHmmboazMY7VDaKrMecm7EBlCTVZeLcwwSGIYRrRM5zaRk917WmbRcSOtEWkdiDtTi7ZDWpvOze+vb/e4OAlgPWs+gui1o2+z6Kx6MR3bI4yG9B479jvfAEToZkpnY2oS0NWeyOAYCgzPouFXqrbuj5pWcE14czi1UmUlcqOU9KVVSvlHyibvHkXgy3bHqhNwOGbYx2vsCOqeuqLMxapVGr1hvMhXJK+v1mcv7d3z48XueP7xnvV0pKW2HZ21ZQ3xr8B+MlZlgMi0pV9ba+pbbm29BI4flpzv99nYefbY2X+PqxBKd1Bz46ft3B11oDkC/y1bJl8qilZtULsFzKYWrCkEGxEeKK2TxliVPM7hEDeDDSvAJJLFQuDlh8CZxNPiR6f6ExAENI+pM9F2CNyRJC360Suu5MdFjh7FWl5yRtCJ5IaSZslzJ109c3n3Puz/9gR/++I+8+/YPPH/8nrReDb0xnJBxpAwnkkw8rZGPSXlalOtcWWYTSadkE2rfVrVs68FGxmyHQkN99ATU55P6BS+p5DpznT9QpOLzjeeZfd5Et/MRrZSyksoKPnB/9wY/voGqLJcnlssVzSbSXbEEaxhGTvdvGM5vIBoBHOKsmtFdJjGot7Y9Lp1kwDXHS9qBJqBUwhC5f/yab958w/0wUpYLH57fU5eFGkacD9Sj9t8Ly60/CRZQMRmYYcTnE2Fc4LQa2+S60F9BakXTCmIQOxGTEjHX122tH8EJ0Qkeo6qvOHAm42Gv9+WvME2oVooawURpEDU2e2VebB9Pq0ZZMB7UWU4/t27SWrYzqwMZ1OlGwe98HxFLbpsdbFUhaUmkYjbONHczpUaiVrwaqUovVNnr7/IcVsk1G19Kb6MpWP26IJotaCyp9etZ1dEScpaEcOKaTqNJC+yB6pFMhpcui/SAZ4fV89lTujh7KYbseo3r/DAxDWq5LRVSMu4JrYdWALGEj7W4FLzPRlbjLDngiqMUaX7Rto1tfkqlSqa6tZ39QDGmd4dvvaAO8cZU6WvzZVxn65aNA0TbGXkkZut2Slt03o9zaZXm/hTX5klVqTm3nsZESatxg1Sb553gRfZceIt2xEtjQQfEGGerlkbaVLc59E6pzmDvJkVT9t7yVyId82KJL1rwuCdp2o7c7qU25tQOn22VO7cHkH0Puval0uQ4VKzyVoVQhOj3wNF1f7ON3RZUN7Tclpk5XH3faScWalqvOTU1hzWzpoKmimbBF0+qsGLsy5WK1wx1JSdlFuFaK7eSmPNqsmPNPobBM8bAaQzcnSLnU2AaPUMUou9VVMU3Asi4kYzC5pO1sc2On5M+fnG9TsWxZSCkwWBUV57+/Ee+/X//P/jT//P/zh//9Ee+m2duqqaB46Pht6vg8ZzHM/f3Z4ZJCFKN7mKITMGxRMc6eNIarPqXC2VZWRFKWpGaeH7/A+uPytN843K9NfFUi/hzzmipfVahUYVrNSOmxfShdMsU9iZ4w7xvfXtiWYqgSlQr/W5EAqqNUMShXhAfcPGMH+8ZxjeM0yP3b77h/vGXPD7+lq/e/Jbf//43/Nv/+Rf8i3/3lsdfvdmywa8XP/ZsUd+EDtWdaXXvLxQrt6fEuiwsy8qyrqwpkXIiZ4OAUI1EyDX9shjHFjS29dCEdF0PrPSlwy7wmcHc79P+TvZnt7mpPXhDmkCyLedcaxOlNaNmsln2nBA8MXiid01TLJPX2SQExogfg2n/xX4gt3GSlztp04yT5qLWdvh3eIR14O7VvVeaxx44bgfaIaA+ViO3++7Pkh5AqbE150rSjCvWP80wEP1AHAMOb8kQlJRXNGecWyhyA/1IShlZL6R8Rss9cM8dJ8Jp3OgRO6jEtYPyEEfu/nzvmdB2n6Va9vvyxNOHH/nw7js+vX/Hcr2gDR4tnSHQBsP6RcZAGD0SrSl9zWujR887myq70dw22hbotwy69ANWkH/GkP73XooF8w6ldnjM0dtqUaSKWE8argXjPddqjyxkrsDT4vg0Js5TxQ+OIYyUAbQ4as2sZYaU0KUQRUCsYX7JBVcqrn0PVRiq5/Q44GVoup/GUNybQQSzpZY8Kzixnu+aKmXN1HVF0oykK/X6kdvHH/n47Z9494d/4uNf/sjtw4/U+YJ3ShwH3DRS4kiSwDXDh5T5uFQuayKtGU2N075a5aTS7UMPwI/Z34qxJdr4bY7XK82j81ClkNOVSsGtT619wWj3Q0Om1EZCAokxOKbzI48TTJLI1yvL8xMlmSYtGAzVx0CYToTTA/H0iB/uwFvloVYjcIEuNfICL7J9fuekad4qSSvjaeSXX3/DN7/4HdPwSFRB8xUJnvLpI0mFIUbrt+n7tYV2eyDU93R9kSJzcWA4ndF1QdeFeFoZ0sxSM5qtn44CNXurbvnWL8iuOScUhhAZgiUjTBg7t8+ilLS+yjzKOELNxmFQshFKVbUECdIwrO3TurZFWwDsZGcDTylTam1BR69+7/tbvPEkKBakog3+t61j3dZ07UinVjmSVvVwwTJt0nopO/RQenBXsQAnGXdD1RY0kkEzta4WNOZE6f5Qyxp3eJ/JofXXli1wlFZ1/PyA60k5158PW9JNDobNyEOatMVrzKPLBghzgThMTFMCLeRUGmzUdklteVTjt0im++gjPihxsMpyWZv245acakF5KdTWt4trSDMtqEYjmvEteS5mil6cyXvGcvO9+sMtJXqAGO9/orpT20jzS1WNgDKvM2mZW9CYofavur1PP3g3sqPg8MEIVcQ1Xo/SkgdbuXq/Z9OK7NXLjhaSV0us7lXFNn6u+zqu58DomueVJifxM8mLHt/ur8XmjwbvKKrEKpRgxGJaLaAuIrjitkSkJdJa20sbz67r3R0IsxcGHzfYqlpRas3kJTXtxWqVJrVWmqSwYNO1loLkFU2V4hKrCItWlpopmkFM9WEIntPguTtF7s4Dd+eB8xSZBsfgLdAXVw/OdouYmxnr59OW2K+WfPpb1xfqcXz5Yw88fHdq08qnP/+BP/7v/xv/+d////jLjz/wQ07coFW9LOK3iHlgHCIeqOvKUhakFssMVBNeLeuC5gq5mCzDauyYZV1Z08KSVm7LwnVZmFcLGmspVn0qtvGN8KWJQbf7FdSqg94hEg+rzLZxVW0CuJblCM5xdo5H7zg16EzxHg0RN0SGuzP+/ow736PDPX58YLz7itPdW6a7R4bpgfu7X/OLr/+ef/jXv+Nf/bt73n69j2NJ1ZrgX+XaTVNf+D2QfBGxqlVOS1pY00wuq/W81UouPRBvEDX2BmPbOA0Lr4eqgLMG+/6++uJOuuP3WfJyg2xsd9wybUZaIy0oEdWtxF7aG7jWTFIzgPWMOGn6SN6a4TUZK692NlEfkDDi/YD643B0yy3b2tiqp3K8uz3ooN/v/9lp+meuruO424TPqo6fRzw9AFc6vVRb15YVoyhFTZpjHGPTivNWjdZKqraPUloQfbYstlypa2TNI7XcUeoDOT0y3t/hxpHqPFUc4kws2m+ObQE19U2txXoisx1yWavpLV4v3D594On9O54+fmC+XqkpbbAZI2QQwODufvCEUfBDpopls1OZWfNMKq0aTVszh7Ta8XDe+j/6vMo+tq917ZXTls+0FGozP/uBrC3q2eBu2qHUjaxEK1etfJKV07wwjCsyFO4HIUwBL4qwwOKt0rykLWsqqSJLYVkKzCt6W+C6wmVFvl6Y7t9CnFDxlEa8I943qRxph7dpVq1LEx2ejWhD00xenrk9v+fj++94991f+PiXP3N79w653QyeGiJuOKFh5CaeuVSelpWPS+FpLaZ9VlJzhiwT3qsiu9/ysrpv27Az4bXH6DnjV5lJRMypEjWWu7FRno8O6zNxVjVTVbwfmU4j4+mMd6DLlfX6jC4zUnXLposIhIAbJ+L5nuF0b9p0VHLPsG92yWzRjohg+9k1hz+L4KaJN/d3/PY3v+OrN79G62jsmTlQyKyqzCkzDgNjjNbDituqC6otEXhIoPWzpHR27WEinu8gJ7QkhrygOZPqTGnnei3FqiO+VfXEY31oRhBxHgdG722uqxGgqRqxHeV1KlXZNQKcWlsisDUgNHtvgVxtdkI2Blrv7ZnFt4y+KmupJpDeg0fdIgh7bW9huLbXNXyHAr0FBDaDpEIlNwit4mrFxYr4Ykl6emJ7D/RqteRvaYGjagGpbFpvZTX0TbHzsdu9zj7Zv7ot6nIwveoF3ed7EVvsdrMP6s8kMlWVYs14rzKP1+s7qJ4pmrbhNHmEwuoSaU3kXF/In4Far2dZcNKk1EaPMLCqETvV0jQQpVeeKiWvFkhJxjEgOrRPHkB0qy62o3d7ry0huA3RSzvVUV/H+zsGkNtZr9gcr8nartKyETZqKWYvD+/c4ZzihBADcYiEwYPYGim1EUNugVHzk9vd1kak5MQSJ5vf90pnZG9x6vIZbEFjS060M7tqxWlfl3s71MtbexmJu/bLIFj6VRuJY2N6cgi5wVxFpO096wGvf+V1ezVdGxJEWyW0ZkuIlTVR1kxJFaohrjJCal7uWhUhmy2QQnWOLJBRCqbFGoIwBc/dOHB/mrg/29fdKXIaglUbnRJcJUjrP21Jrg7Drwq5kXlZE0Gb2/8RgeMLY8FxY7Qrr8y3Z94/feDHyyc+rDPXqqxgzKWsDKfCeQzcTQHNMx/ffSBlY5SjZmpK5LSSmnBqbdp8JRlsqeZMLda/tubEmjMp9zK7LTxfa8Ml7yxUIhWt1jfpBIY4MsTJRMNbQNJZDkvJduAqROd58JFfDgO/HkfeRiOCqXFgfHzg61/+im/+xd9z+t1v8b/4JYz3SDjhxxPhdCKMBh8bwh135ze8/erM49cvh23Td32NSzpq22ZsM/wdRK42NlUzpcykdCWlC7ncLCNXMjkVcqrm4PRep5ooeTYDVFvATdmCmy1zeVwtyvZzp/NuN/li4x9ZVvff77BMaORltUFQGtW4VpoI9UppVO9eHOIDlUItmTRfyaJk56g+4oIxRJq00l7V2yCM7X7qMZjcbPrn2bnXCzjsrffA+qWlPD6Jnz7QYt0+tqVqI65JzARGiUxTZPADflKCFoIW1mUm1wzpiqsZFU8VTy6BUkZSumO+3jPe3REmgznS2OBiHIjBZD+k6XlaQihZYmKdqXmBYlWKfL0wPz1ze35mmeeN4Q9xzfgZGZNJr0R89OAKpd5IWpjXxLImUjJI9b7WDkHjz4zplvc9BuGveG1Z4HZ1XcejJIw9Q/fmR22BZNdqa48tWXmmMNwWwnDFDVdcHLmPwuitB1H9iK6LJdXWYkyzqVDDSp4r9bqQnm+kpyvLhyduX3/k/u3XhPMDOkxkHyjeem1iDNaHLkLNhfm2cL3cWC438m2hLjN1uZFuT1yf3vHxww98+vADl48f0XllVGGMZ3ScSGFkJnLN8JQSz2vheYVbqqSyULIR4pgzZEnAKq3P5HDYaduDL4ohfYw/Cy6/5FUx+Ys4Os6ngdMYmGKwjHBwnJyRE5idsx7SECZSgXleyXMi5JnaKgQbBM15JIwM5ztO9/fE02D2qpTNBhkCwqpX0Jlb+5JvOmC41tpx5s3pnl+8/YrHhzeMcWAczlCVy3PhlCfePr5hSQUJkSHEJoPVSEXocU+DA+sO1+/7JlNx4gjTGcmJmhbiOqOnBKUasVop5hhhSR31rqFWRk6joVhOQ8DXlrFvkENz9BxeXoeNcy7aEEmKq0qoasyLW2BsGXxzrPf+Ke99E4T3JuZdrQJFraYK3ATIW3YRpTFXOqz6KA6RQO2Fnmrop45gQa1VQ0tL+NUCOVuy0/n2QrLNAWKBaik7VFW3KpHxRNRqLTWd6bwnHEzvtCcGoJ8ZnWykB07d0a49uIXmX/S4SPfczh5FtkCqnQGvJOVwffqIlAE5TcQwWB/Y4Iy1uxTI3a/Y+0BrLeSkiBZctDXvxghaWdXaHTgE2GjTiJaCvWDFB6ATzai105i0gq3f2sZTO2LpMG6b3WrJoGNS0xKe3ba14Eit0lhSJi9rY49PUM131rr7X/Rgt/3b+4AfBtwQwTtyUxQopWxzu1dCbX6r9t49m7Mua7Gx57/GVetm3o+Jcdt3bTvpMWnu9rX6E99nH+qWA2k6Bo5Ify26M7kVRPoxW1TI1b5vdtDtc9bZiWsVSnYGVW0BvKEszGfeUDPFWlQsdSJkwG3KDxXEGdO8mD8gzkhtpuC4GwceziOP9ycezxN3p5HTEBgHaxWKjvalxpiKWrIOtqAxqbJWW9upli2Q/FvXFwkcf26tHI/lqtZsfy0zz3nmWlaWmslqAtYWiKxQF9LyzHxd+fD0gWW5WjNyLaRlNjKW1Ju2m/FsDKmobhPbo2VBCPQMmRi0in7Y0bLOBZw1RQsGCbIzQxE/4EPAhcAQrdk5ThPDOHGeTrwdRn41jPz2dOKrYcDHiA4Dd199xa9/9zt+/W/+Ded/+D384lcwnoC2Qn+WeepQ8WtWtgcsr3G96Dt4mYChmae2ARKlXFnTJ+blE7fbM7f5mXmdTRtRG7ejGMwwlwXWAtkOHG1se92h2GEDP101u7O+f9/2vXaHb/sA2532n6U5Y70aablfsYO2M+c2Q9p1tCxbVSlqVdV6u6BhxMUzYbgnDhBiC3S3z8FmVE0OQ1/c0+GTtJ9+kjb8Ylc/vHYn7jCEL4aqO5E75dB2W9LAZ1q3ACKRWFxgblW8GAIyjXhNeFpms+ZGKGCixVRHTVfKcmG9fCJOJ2PGHEb8MBLDSIwjQ4yNfrwFrDmxppV1ubGuV/J6paQbdZ3Jy0yeVyPDaox0aBMb74FdCPhhxA0BPBS1Ho8lL8xrZsmFXJrD2R2WNnj70u/rSJtjCi/W65ZZf515fJmS2O9NenRvN7fDpLT/FS/yFKiQqnDLlU/zig8XfIyEKER/zzgEBg/iB8rtTJpdY5RWShG8V5KsVLdQn65cPj7z9O4TH99+4P7tN5we3+LOd5QYKd5bUDEOTEPEI6zLyvVy5fnTcwscZwsc5yvr9Ynr83uul4/cbhdqzkQXGYYzMpzJw4nVeeaifFoLn9bEZVXmrCy1kutKrgslm9QLjRnS9vKhqiiH9FIboA2Y3Kqrr0WOg/M4UcbB8+becx7A6cI5Br66mzgHByVZsOcnRO4oZaSWhKsLmm/UPCPVoNhdJsPHidObr3nz9TdMdxNI3R19sJ4pgO6E90Td5r4bqYUTRwwDb99+zel0z/3pnjFEkwIIHi+C6hlcJYwD85qpBOLpzBgHgnMvsv9uY/HsQy89f0FP0Ch2/+P5Dk0Luq7UobU5JINICp3nwFNxDMHh4gCuIDUbuUQ1cittbMJehCGOrzKNl3U1pFMphIPjLYj1N2K9SypCVeu5dBoQkRZsOZzS/JTaWJNyq9T0is0eQCKCOG08CQrFW5CM2VUx4bxW9WlQ1pzIJaMuU53fqslwqATS2084+Be6BY8GqWzJ9bZGOtvu1hv2edKsnTW9qqPHwHF/ir2/6ou/d4fAZXPc5RhQftlLVUlrYgZ0NJTYTuiyn9tmFrTJqJnPiRas7gxOPEMcWjsF5JysIqWNW1YUbfrA1ixm/a7VgZSKrwEfQ2PCtfVb6cEOrV9RW5LgmAPrEMuO6Oijp71jxzRP12JEhesCddmCRhqLqrTb2qrCzuFDtHMzRFScVcaLJdJ7Fdb8HNDW49wraX1RdZ/5ryKcvtQ81tZve0zm0gJxtgW+BdeujeleVNhP0zbl2/deKfXNTqJAUGgFCIfii7S1q2SFoEJulU2htS05oFUXaymUDKJGWJZraaScvaWDpsYgLYjryShp7Nl62Bu96NL+zkMMjtMQuTuNPNydeLg7cX+erNIYjCQnBscQHWPwDN4Ib5waFL00xE5RNWSJWrJsLpWlFlIt/K3rlXoceRE5ZjLzeuHy/J7L9SPX9cZcKmsjtkEgpZkP77/j+fbENS08Xy/kvOIFpFZyMmhBbXCKLqZSj4ekc4dkm+C8GdN+OxsVr+sMZBkRYZoGxrsT6uD5tvJ8LYiD03livH/kdD7z9du3/OqXv+LXv/ktX//yVzy+ecvdMHIfPG9C5BQCPgTUe6bzmTdv3xJ/+Uv45pv/ys3UoFdFWsP86wWN0I/0vc9QhC0LZg/2QLZXHJ+53T7w9PzE9fnKcluotTYIi/U5lNp6NEraK4cCnztpL7KXsD1R919uJ8+LH3/i7P30QDtGTRu+vcmwaIdraHO2aqPxd02zxyu5JK7zFX+5MJ1mzneVUS0IbhHp9vodWtTDjh4Q79O9wzx+BnPyRa6f9N+1tW+/28fBbOfnAZO2ikxTrjq8UCmFZU1cnEMlmqCsb1C59lxjSax2yIogTWajppU0z5TrFT8MhDgS4kgJAzkM5BBM9Ni7rZK/ppW0zqQ0k9OVnEysuDYotK1Jvx0EG5zKe3w0aLgGZ+ypObGkmWVdWHO2oLHR2R+dGJV9Tvs/jyvsxXN3z/hVLtWXIaK91QFQedBQOS6pF0zIGNSmAGtVLikjtwvOK94lglwJ7kzwA8MU8HJPlQmdDSKcey+MriRdSZqpzzfC843p05Xzhyemh0fC+UyNA9X7VnG0HjiPsK4Lt8uN2/XKelsoi1Ucy3wjzReW2xNrulG1EGIkDiPu9ADDA8WNrAUuaeFpyTwtK9ecSKWw1mJQ42JyDLV2VshGgrM14JnheQlZ/SxNpT2Z9QqX8wSnnAbP4ylwPyi6LIyucDdEorfzsFApEs3pri38E0jZ9oFNrPUWe+eZ7h55+OoXTPcP4L0d/NJZDtv21tp6HY8JtqONtV6vu9Mdd3eOabrDuwEvRnaUVMmquHHgFO4hBtxajCV7PG2yWj0Y6L2Vbnv/F4OMdwduABc2tE1eF3xK+Nwrx+aMuqqE5hhqcAxeSLpwyyupmsbZmmuDcwuDCI9xeJVpvC4rrlajua+9K3p3PytQRMjS2htcwIVqFSYaJDjAUHcHH4REboQjevBdmsPaJspiyV6NtICyh+BbElTtdax/sbDrN268rS1nckhIvTgoDj2UgGxQPKvyNoJNqyzy07Pu5evJC/v0M4thO5r3I7QnO183cByHs9mzVOy7s+MqJdN/3d7c4oRGiGifW1ASCVGIQRAXiEPrfcOYxntvtzS/SQsmU1TWxh1UwCd8DsQS8ENEgqdKt16uBfx7AN5DnX3cemuF7uQvm09jEml5NfgjJSFqElbGF1A7h2Mb+yaP1WyvjyM4x1qsJakHhb19q/9dpfVR9+BM2ap90taJtITJa1xaawtM/c7hYL8xn7O+TATIyxHcL9l9pl5J33yoti5pfkmPK5woyYGURh6DktRiS6oFsN6311DTBM05kSitmlhJtTayTFtfDotDvGcj2SkNvSDSCX1oknBmT4xSwAh8xsFzmiJ3p4G708jdaeI8jYzRE531aw7RMw2RaYiM0VsbVzHyq5TWRoBlfvtalbkWLhlujUjw8W/Mx6sEjsrx0AaXFpYPRojw4d33XJaZVSO5aa+Jc6xp4YcfvjUx69pKpVv2RTdnwOZetgXq2mts0c/h3VVc015qWH/BGoFjJHiH1hVH5fRm4Be/+4rwcObdLRGeK3F4yzff/Ia3X/+C+4d7fvn11/z+t7/l3/zDv+K3f/8vefP1L/AhUq2UsWXprKm8LezOvvpfdRlr2SvHi9vVxXj3u5MX2UBzZBUo1JpIeTYdy9sT19tCTrXRQweg07Jb/8SRHXJzcg///8m9tLt4UXF58c9j/rq9nv7c67X5/5mXOf5t//zGdFdbU7s31kEMNnlbVuY1kVKhlJ7jbmuPzmXZD2f7pE7ZmV459oz+/Of+EpccvMIjC9tPgp4WYfTgSNvzj7d2DBwFJZfMbYVCYVXPFIQhRAJCEE+RdZPC0dqCcdVNG1VqhZyoYaH4CC5QXaA4R3J7Bq9Wg1xZn81KLdafQUktuN8dIvAm3+OcaYQOA24MEBxJlJQLS0osaybliiXO9krsi7FT3bLmfaTQ4/P2cRVe0bs5vNvPOWWfu2B7DkBaENTvuzug5mSkWrkuC14SXm44nhF9QM9vuI/3JpMgDqRS15WSZrTesF7WxFqhlAx15pYqz/NK+PSEG0c0BJNZcdbbHZzdV8mZvCYjBUmtyr8mSloo6UbKC1UyLgZkmsh3Z9bpnuLvuZbI05p5TpnLqlxTZs4zua6kYlnQnPOmjdVnzslhHUtf44fenG1Ij+D8V7qaDtjoPXfjia/vBk539wy+MpwCVSE1Hd9rDqRs95dzZr7NrMva9lLrdBOPn+44PXxFmO5ZK/hSWyvFYW1iwXBFaVye7fe90mi73ofA+XwPwma7S9OSNBdIkWri3hIiYxhxbjD6/8PI1UN1pIcqus3D7oyb7EiglEJSQcNEmO7IS8ItCz4WfMFYS9cZJ9WSCT6QpJKKBdWrCnOppGIyFYP3DM4xvdKeXJeE7458SzSiuvU09cCxOLE5qoIrQi4mLt5F3L0PxLiPi0JjOM10aYxe6bPf1ca+aAGnNFtnLo6CZrqmpjYfqVeCevKkV5Y4zFefj25X9sRCkz/aehlpahtWSerEHtqCBlUjHjGpkZa8E5qUQ291OAxk9836iSmyMZnuptc+z2tcIYyNSyixprT1nfWKmvltng5tt/mmBXFiSc0mhRKCR1wkxH7WGoPpJm3WA/ZicFeR1QLHmqnVUYvHl4CLAby1WNBbLTrp4otpa/6THKp+pTTUVPt3yZScKalAKVYlb5Ib9pl2n5jm//ohEoeJEEfUeVIx1vFSrfrsWjCGdhZl882OCUsnRjQYvLN1gm69r69yHRK8W6Jhu03dvrYBk23FvbjaEDTSJwseXTdiCM5Z8krUtbPF9B1dVsRZW0RWGNTaEqRpTnvXbGAPGrWaPRCbiUUbq3sxUiag9YuDc0bmuOV3GuzXe+NbCc41jUX7it5Z4DhGpv41BMboGWIgutZTHyPDMDCOg6G8BCODdCuKJ7Nab3U1BFcqwpKVOSlL/h8QOO553f6zHMxAZfzwifTtd7z/9jvef/xEzh7cSFXX4BjVNE5SMkpkoRleMKa2Y0Zsd+BEmpVz3oSNZV8UWmt7LSFEg5wapbbiholhmpC6osUxPUx8/bvf8PW//pf8ZnpkkQce3/6W3/z6X/LNN79gnALnCF+fBn77zTc8DL/ePp0bXtIPy/apDbNci/Ug7BWnPRtiWTfXDgc54KTbOB4CgS9+1R7atTt54VHtsM8eFOZSmwaeAx+aBEnr/bMTzwxoeyF3vHfZ3+dFSuFzx46fbvRuHfoZ058jLejZiES259r3vabWX7ynlNxuTFs/Z6UaVE8hR48EgyDkqqzJDpwB32iz++vaf/UAHTE4eu2l2xcGTZUOpP+i19b32YahVwPEuS05sA/jnkHckixHyleRF4FvpbDmSiaTNVCHgERP8BE/eDyRIiYwrMWqt14VJ7Xt2w7TyhRWI8jZQvCXIZH2MW2ZUqEStKe97XBVQnNQPM57wmAZXIIjSyEXY1BdUiEVzL5IMwyyr549yDgGEvse2AOxl4H1zwWfr3J15+4wNe3Y5MVu6R5Yq67JNq9CFctgLjnBnFG9UuuFUhdSVeZz4BxO+DAioyBEe62ioBXnwatHG3x5zZklP6PXi5FwiLSg89Bzc9irO0SuU6RnWw/RQwwwDZTTHbfTmWsYWYrjOVU+LoUPS+Z5zdxSYskGT80lkZtNb23rdO+zHtZ+H6u+1tuzdrvxIlv95a9xDIxe8d6Rk4JG7u7uGHwm64r3HgkTORlTpzhPSYXb9cL1+UJei617NTRNGE/c3b9lOr3FuxNCxGOyFUKvULTgRitOjkkuLGikB3gKEiB2WJzZOYPM1caTqlZFqRBoQujijE5+YxpnS1hYb5Yl4DjIQFgLj52/zvkmX+CpGoCIl2Dn37ZuzKGqzjTwnDcuAY8jSDTnTYz2/wiZS+l1SFVqNqiWqDmKtCAy06nrpWliOqhildNsY+NQkwVqJAXOe3yo+OqNmdG1hFlnnxd7n1qh1EIulZKBVslAPOKtcmGxQMVC2dYf2SQejmfnAbV9CM7Yeuq662pVogDOI94hvkFmvTnKyg6J1irdbbAkKS3wQsApUpsy5Lb3mq/T7Wq3VZ8FjcrrIQBEvGlyVmFdevBoVXxLGFsWoCdgjSlUt3srrZpVs1GHBG+JshgjRlOEkZ3owdpo80+kND/FEpglg8sOFz0+RMQNqATANRkjC953N0abed9bskrNxhTfeuZKI340XodWkd58FHlJ0ueMmTmOA3EYQSK5VFJTJ+h6noVDxbFPlvDCboo4QgiMQ4SWeLIg83UCR7Nl7MiS7fzrg8X2mftjn0tK7HnVXcOxB4+yrUXsdb19Rt8CR+8UydXOVQxOWhGkAGrEOq61a5UMa232AAscV7WvrNarTbVCQ2g+qXq77U1f0lvVMARH9J7oDWYd/B44TmNkjIEY7PHgncFUvSf6wBAHhjDgvUkBWiufEY9VNfkRVwGpqGZKhpKUvFoi4W9dX7biqNbI2R0LtMLze+of/8j852+5fHziljIQ8M5vkW7NZkSdE6L4w7GnWwZjN0Ata9WayNXZV1fKCNETozNDvi4IwnSeGMbBBOLXShjvmO4e8ZopOXC6H3j45ht+9Q+/J/zd3+Mefsc3v/xX/OZXf899OKFcgXdMPCF8bB/2K+Cv91dIG1wVMUmCn33GbthfilO0z/maXuohaNuz01tip21SB+KBCDIQ4pnTnScOlZJMR4tklSXXHZj+0kca52Z4thCrHyx6qDHuER4/fXB3SPuNboaVw0huUUD7RAfj0Q/arj3lEMSr9XBqMt1NFIlG2z1MBs3KpZLSipNIjG5LqOnx7pRN16k2KuOfAA9fqepYa90+6+ZOi1ilrp3OL4LXdr3o2zu8Xnc2zTm0w6BmaY5lJRCbzErARQ9YH1JNBll15KYNZoRTtVHRN+rPZtO1p1D3A6lpdGhzzMQ1qLnziAsogUpsRDje9KaiQwJUsWrUmhNLSqScMcUd1z/s35gG2QLIl7/rwXjfENtTX+XqDqS9c7+zPj+6n48v2Hp/ElkarGi7xyb8nptshSaywlwCz3nkYRo4Oc9YI6MzVusQBoIoIoHIRK6ZNWXWNbGuq1X8DiRDCi2LbzvCi8M7myN8Owf6XIaIjA6ZAjoN5OHE1Q/civK8LjxdC8+XlcvtypJm1gZLLTW175WupNShnZsjWrdj/7AVDj/z0vF5rYmM0TMEC8Suy8qn20IcHD4vpHzldLonxIGktTH4CdfrlevzM7lVG1Vcc6SFMAwM4wRVKHNhdB50IV2vm/ddms5al4c4koX1/yvdFvYUzcGGteBNNzvbNH3VHLFa92AN2Uk2bPgVwSQejvqBtRhxD6pUaRlzrazXG+Vyod5mdE3UpsW6wV+rkXwgJow+iEedUl1FQmFFWXKmVOWmmVJeJ+DoJDa1BY6dwbdD+o0Y3G3nWa0VzWyjrbVaPyjSqoi9YtfIZNh/ttDd2BxzNVibJUNsLMV1ptS2312wJKZ3SCOJoh+3zRHdLXs/k83A6PFRadqMLmz7VZydi+LUvot9luoUstJP7F6JFW1Q5X7WbAPIZjP3x/eAkraG+pNfq+c4jhMxKLVESrmi62z7hbpVhUXEFHtau4CTSs83dj/D5jeDgvdiep1EEFNTyLkFb8hWZLX9UekyGLWqaehmh4aM8+YLow4Vt81BP7NtDA8V31raV27kK3WDlro2nF0CvPtYnUfNOYcfAmEcCcOAOiPCWVPTNq8ZWjhET2iw+wnSk4W1WoFGjnDQ178+77XVo3/c1t+2xnRzE7eYYW/f2WGqx+Dx6ABYvsd6Ar2zaqJrchaV2hhVbYa9gFZLnhgfi5FgGVLCXs8STlZIKt3tqYpXUGm6mRskVaz30TdNyeCILXgMjZ07BDEY6hgtaHTgxchvYq80xoHorXfdNHFNU1toIVowTVFXCg5vMj+13WBWSH97P35ZcpyqIL3vDVhW+POf+ct/+k98/P57ain4EMlFTMqhmuiqNhw9zu3Z0j7RDnZRddomMJFvJJKrI2nbyINjHD3BG/68J0uGKRLH2DKEgo/3hOEN0SuOwN2bwHR3QqXyfHlinr/jOg88fVxxkljXP3M3feC3X1XSOPDuNsD6FafxX/D48GuGYUJLRmuGJsvRxX35b8R871XG3rfzSpf2jA1bINbfu2d3wOFcxIcT4/RIrYFpMhhTWSt5SZR1RappbfYD0xqDrQ9JtTXZbr0S+qIa8FNPXg6P6Xa2dNax3pC+OzwcYkY5+NIHyJq0Q1JszYh4vPiWgcnkuiBlMVmB6Z7T3SPn8z3DMCLSDn6t6CGNe3ztbmC7/dnPS2ni3fBaNjbn3EZq/z/sB3mHKPXgoxMCHUlfPg+Yulh1H0/TIFPyqizaRIx9JLqARDs8DW9WKJoQXY0ooBrzsWXF2/rqBtrpZtntkO6fwO7Jewfe2FJxlriAiLhoBAMeqq+oJFITxJ3XTEqF3Oj6D2lt+nr4/NrWUVtIW+WqryM5HJ5fYsL+mesYFh62wT6/G/To+BfKBtGUVoCgj6VH8cy1UhYl6cqtXHhOH3k8eR5i5TFMPIjDSWXwGHtmCOCUXAthXXFuxqEklJq1sdz1gHanSPfOERoKRJ2neo96j0SPHz3uFNEpkMbALIGnrLxbbrx/Ljw/rdyuK2lZKHWh6mrJh16N0r6nZRsU3cbnp/P70jb0MX3hun7xa1lWNCt+GkCE7ISPy0Ipz6iuzBI4MVIYSaVwud64PD+R1qWNZ0uiIq3PXVmWKyn/AE8fCMGabWoj+LJAxKr7/fPu31oFUftj5kra/w97o1UNt0ScSBNX6nu//Ur3oJPD60vrpWo5+BZsGmOfVusuDs5gsCVnclrJ6UZNC3QShm5HVY0RMgs+mNSLeI8Wb7q6GPvzXDJrVdIrQRyd2EaqXYutGgx4dyZbcqTroalasrqdg9RqUG5t5CXFEFUp5a1qvlGttf91vlPtgYzIJrRepfUdisNh7LjOqEFbH7i9YEcxtYmlJ3C73qIT84G2ALMl5sT7RtwCaqVe4zxrGQR1Vm2pYpVHxdad1P050pPFx/P982vLednZtElavFLgOE0nvFdKCcQCISmlLJRmv7zzHEMg6y3z7dhswZfaGpfmk1mrlJEXhsbqq+x61oJBw4XG+rzZnB7RGOlglYqFnZaqySIvmZAPUXgPYC3pavutB+5m4+TF5zAMgAX64sTgqeOAb0FjKoV1bUFjMYjqESe4wZCdw3nzZ6tCFYc6C6xLKaxr3YNbDsi1L3xtCMQ2L1v+tL+hEw5QFDoirPs82v52D4b757Mvm50tpdJ6XC17UHtAjqGYzBfsBF2WDBAVgpq2O410pvdJViwkb8rThqRQ2+cmY2UJcku69l5GS1CEYFXH4JtGdTD4qhHfeEIwgjInltSKDiPDCYHgg9mCZoO0yu7nuID4YpJJYnGXrxCqEKoQ69+eyC9acex9Jx2Rl1Piww8/8Idvv+U6z9ydTjyez1yeE2vKlt1xjiK2yLsxoUk2SCvdi/cNUmEfJoRAjBOqkbJUaqq44IhTJIwCdbWDNUZcxyXXjDqxZmBvB3fwjjgJ450FBz/8+Q/8l/f/B//0fabyFVO8Z5k/sK7/yD/8y8r/+r/8jt//7u9YLyfy+mt++3cD/5c3v2FscJKaciuHu5Ys6if2f+1uOrqNr33Jy3/q/s/dpRC8j4zjHagwxLvtIK3ZmrI1Z4MnomjJpJRY5hvLbOQmpeQ9KGgHbPNzX2R5+rubA6j741t2y567Zae7Jeh/uTlMh6yU7plb12jvnZ9wbsA7y9Z4X6kk1rqQBGQ6MZzfMJ7uGeJICGHrp9XNgO/jJv31W+DYn7m590JrTn+ded0qjj+5WtDXnHojkrFqUHCWvfLS95Rs0K+XTeZtPMXWci2FVa3HpQQYghk+mlyAffQKallsrQpe6HpI3fnsTJj2FnsHaz+2xAmWRvNUFzASaU+VBqly1pentVJqZi0rS1pZszEv1kN184VvzCExI9DlWo53si2rFoRJH7/2+Cv5Nz8xEXugeHhM9fA7PtvCsskGtVCyZb4dVSKqdtCXFZaauKZnLrNwHRPrcKLGgASHj3ZgGeuew+EJbmAU8N4xDBFK2aFqvbenvWFv5FfnKeLI4shO0CC4wcHgqYOneOGaM+/nxHdPK+8+LlwvibyYtBJq1PZqfPmHqvW+Jo9j8vm/j8y5fXCPDI+v5ajergs1CKdhAMFIn1LCyUyIwporsioiA+s6c7ncmOcblITrneGqm8xfzgtPn96hfNgqW0apYZO8wTZ7KPjZmuh2c4syt0pEx7i4LaB8kXhqjIVHCnwOFbNjAC6HAGVz3LYKAFt/oGjdYWEvgt0dn1G14DTj1OF7Rc9ZFl1FWXsiEtn6vl7j8s5bwqJVjGq1oNH8EWNaF+9Rh8lstDHsEhmpBclalZKLIVpyITWZsNLs1DGNoQjaHGTTb/bgGjNnR4E4cxy9gKdCLlRxFOlMmK2Khm6kG92vct43+yl0fJBzDdXRZFY2UI6nwVbbueYUb00dhmCoNvYWGDVfR/fkQ78+s1j0E7qzUe4r6HXOR98kMJxAjMo4WTKjrOu2H3pg1itrfpPf2ivFIpWKVfmyAji8eIOei+l+Z1es17AnzNvHc0eHAUClMYRn9mTLnvDbzt1tyD63d23V9L159KU6eqftKe+cwVOHgRCNPTVl08RNKW2KBEfEm2gPrNweWIlYoHo4CEvJlKLbvXUJjFe5ZN8r3VZsJ/fm/JnB0rqfg/3g3jVuOdxv/x3bK+v+Jya9IztpZK1QvFCqoE1nNolSnT3JKfjazsW62+XeJ0pb816M2CsgB46A0HwzCxydKN4baaNvwaIPDhca2srLi2qj04qn0Bp6iNKCWmpDjLT3t0O7wcnbCSAQ2j2NYvB7/88UvF6HVbVdCi377xmGMw93b3hzWpHlwoJQ/MiMMKuyajXjFSPqnGU2RBoBg0c7jEKg+kDxA7X4Zogtq5lV0dSY99KKaMUpFFakCLgBEaGIsBTrRYvOs9bKp09P3D7c+MMfv+Mf//DMbTnjGVnmd5T8Tyx/ER79P3D59Huy/IpxOnP3K6WMBlcVZ/ThHf7w+Tj0q/9mO3iPDg7/DTHmf+e12UzZDSewZf+kNesKjhhG3MkzxA6PULT0vhpboKKVvC7c5hs4R9FqGWfMAHc//lhx3IKS7eBswVmjxYYmnNsXcWcT614x8uK+D27l9jqqiriAixN+uCPGO3yYiGFkjIEhgvPFevVQivMQJtMEbH0qrvWqFLXP/aI6RqsMK9v7Sv+c7J/11QIO9vexIdmr85s4vHO4EIghMMbIGIIZKOlyJAZFzY3YJOd8WBGHuammYaWq5AprqXhnFdzt+HcGV3REBN/2oDbmRG0OZCfUsNevzQnsBrsbSXEOVTPUpbPVVftctRSqrpTe/1YKudYWNB6uQyB1TEb87THlJ+tqTxi80kQermPlf3/bn7mjl3Hl5oQfH1dMFsAChYIWbUiPhZLFBN+HKzpEcvCsUbhlz1SbKHSjkJfTaL0xteJViM4RnVXuez+jJQ7NcSkqJIWlKrVkVs0kMftRsnIplU/LwvuPN959uPL+00paKhRa35tVsDpphmxBI4fez0PQePjs23D1wP/oZbRfvlafY67KgPUtpZQgzwySuT87zsNk1PfFkYuSFku+1ZLQmraKES0BQK3oulB0adN/DNh2tIA91HoM2ffVFvztKa99bNpz+ri5bkfa+upD5jarBvu60238Xzj87WbkcH+yvZ8eoLIWkAjuxZ6URvRSq+A04FqfroozEiyn1HVuf/L5p/qyV4yBpGrBY+sf887jgunFWuDoGpyxszo4IyESNchtCxpzKoaEyK1/UVty67he+8g4t7HRbkRXyIb0Nz5+c+a9WBKtEwDWXnFRWqW4bPPmXMB5u2+8323iAXlCr3g1co5jsl6oiK/GnO2wILEqFdOkE3pS+LM9+WLztX3X46ANEXLw5r/wtc4LzplclBPPEEZkrCQRq9T2qrI2eYQWSCuKFsHI/hqaSssWBGgL+kOTtXDe4XwlSTJZjGJrwjXEXJfx2APlFue92Jc2K3syZRuobcvtCa+9erQl2qXJjzkLZo0tORCGiI8BFSHlbO0cKVFKAtWmUdj8VnWH+enJHKUn7fuNd/Pbq3p21Vf2X7sv0mxfDwjhYBOMnWHbT58Fi8d1pofX7c9zKhuSRtoAdLKkIFbRq14wJnpHdFAKVs1rvbLS92nVJrlj7+TbO1qc2febaygd2/ehQWNb7rbBogUXBAn23XnBB8H79vsWaIpWpGb7Kql90g59tgC42+KqRjSnNSOqBIHBO6r3SKj8c+q4XzRw7AaoEwTFEPjFV78g//r3fPz+R7794Ykp3NBReJg8eRh5KoU6m7RDOJ0YTncU55iLVQ9UIB+yBU6EgrAWa+hNWijSDpxlRaQdxFrMxio2od5Zg3pQnGQKC0k9t1xwz4lVC4WVIUf+7v5ryvmMlxHKhNbA3fDM93+auaQfCW8f+M3fR1Y/kA4hS/Oe9rr25+Pzk5//B0WJP3PlUmyDmfXabXg3BmxDbi6I8+AFkUoXLPZi2jdedCO/cHlFgmvBv0FtEP8Cqlp7aq31YnTNqe5ldLppgQY4b/1vpTfq74/3EO7oLm3BqetZWo/6gAYPMRKGE8MwMQyBEBTvC9ENRpGvUNTZ++RkMiMOEG9Vt6Z7JSJIk3uR5uQZZHcPinotcAscX4E53nvZKpo9Y7ozkJr1kWBBwDgMTMPAFCLRWdbaRq1ScmZdhFmNc7rUnzplff6M9dhkEsRlw8i317MsGYbJd75VpSqUeggcOwPrwWlQCwist9FtfT21GlwyqWkPmdyEUrRQaqJqanOidJ3G4+m1x1s/52K+dGpgDxJfBI+HGPq1dqzIT29RX/zjeGeHG+snPAd7It2Hb7tDHL1zTbEKQSqKYJIDlIWSPLMXnrxwGjzTHBhPI6dpYhwn65mIsTG8BfwmCB9QhFLZ1ozSmNqykUvd5huXJbMuKzklylq5Ufhwm/n08cLzxyvztaDVGWlK3+8bzO5w8m8f/+X63GbxMHH6+fT+zBB+6auqJZhStZ6/8xCYnPAwGMtqdZHbrTDPM2Vd0Gz06FKqVX5aMCXtA4j26vweou1BMZtzt635DnPtDrrsfW122SDsHBOHtdPO7hbatQR+W0MtEdad2pfEbe01OhP3lhi0l6/t7q3KtK9Z+73dT59ND2b/c7KkcTBHuGCtN717XPo9vVLkOA0DFLVKHjTUipGaWKDgt2DOi7Qjzvo9RY3dUrcg275qa3frhCNbkgB2yKG0w7V934hsPjdt7bk++EbVXyilGuNwMcHxio1PJ8AJ0aSRJBg6pGhvwWi2zygeD/bXbYHDHvj0y6oZWxW7xxQ/M5Z7krzvWnfY071V4nUm8na54v1A8NE4MMSKE05N+DzV0s6PRvXXxqC3Z+wFPvu5tL1TVaxfTRwMJsUWnNDDg5LbuDpL2Wmv1PY9150rPSRUDra8B/Qb87i2eZB9D+x/slcIO1mkcwF8xEWrjquw9zSm1GQhtMEce/uKo2Gkt/1eq4KW3bwcbKs9dkj+KLwWAqC2/tO+b/agse+P7gkezoU+ntte2/db98n6813zRUVavNDt2BYwN/I/qrGTdhZxsS+tYsa/5zpVoPWGS93tqKAbA7E4tUq+U7yreIHgOhyerf/SebXni1W+xUmDy8sm0WGI+QrF5NAKapBm5+myPqYVaXqRpWZKyeSSQAte1JCT0eHUkwVuf2M+viyranf2VKkiDMNI+Pu/57eXK3/67gP+P34PfGQcA8MpUMZAXmaetcCarZ+pldNLVRLFDK1YZguxHhrXBkVVoUXegk2OYBTY3sWWQXKtEdaMfogTPgaL2APghbUI9eaIw5mv3nzNr35xjw/3pnfmFeonbvl7Pqw/8u23EFa4+8qxJiEX3bC5LzJGh6v7Ky+O2u0E6EHUZw7h54994WtNK9oYuKQ57JbB7jpB5rA517PGPaBrRqOdZoqStVLLypoW1rKQaiKraZVVoWXj/ObUaWOBk57lOjjk/RDtwEXfYKDUiooRAViAZMFozwC2/Lrda+3Pa+PsjFcw1YpoITo15lRnVaxSmlhud40ajKqQKTXhdcBrRKXB/WrGOUdoVUkthbqukLMxZfWxZAeAKcDDP5fH+W+/hiE2ZErPmtmBLxuJTGwEGwPjYNXGMQQGb7o+qoWcVpbZ+hGDMziG6g5n2/phZc9uVi0UClLKBmkpYkxdvbdug64cjS/HoEy39bT9RrEMdjuASjWSjVSMGKWoQcNKLVaBfOnqtrX0uTOyV2f26whTPTy1/W5/7OXi1Nfakwcdx7/mSO1Vt0M9eDMqPWyULR7ojnXdnNKmc9uOy1wrNzVihDXBs8DglMELw+CZ5oHT+czdOXGezkzDyBACQxBO6hlVjQAM6+HIIi3xYuiPuRSua+Z5Xnm+3pjTM6nOFJdZKXyaF56ebyzXlbqCiPWvShvsLUm0fcSX83pEbej+rBY49+ewT+GLQPJ1HNXamF9zzjgPD/cTj0EYgvXm96pULStpvZHTummUaa8gslf6aEkWRDZoWrcoosf1wh6kAGreD10CVdHNcdrSSgfdhs2ZOji1G7oV3c4De7+D99hfW7fYZq+AAM2dYdNM3Tc/Xdi6P4/uKAvk2uQFvGXnc7XeRnuegxeiI1/+mmKgrIkkdnY4cTgfWtAYNluogPeeEI3kQjQ3ApTauAKtfKBFqM5Iqqi6yQBtZ+LnDu8hmap9x/dFrFbJ0BbkBG/QcFcqOWVLprGPlxeHa77PMI6mN41ujqMlke3d3db3aOe1VmNUL+2rlrr5Df06zsA2670SdPy/stnoF9dxuXzhK6+F6grqrT/MJLx3jFMXtt/mQF+SF9lnoc3DIZAotn47Uco4OLyP+DEwuJGUrGIJXRsR23i1B379vG7zySFu/MxZ3HIP/XxHWvJdm5C8+VfO+80HRyzRj0BWSyrknMktaBSUY6+ftHNTnNt9oLrLHm23089E9nOq/05+Zmq/1KV6ONubt/fi3XSvgu7fetDYfaIjC8XRXNpjvd1IHOZv9oVZjfhPWvDosOBORa3XlV4k0H29q6JSLTHb2mrc5te3VjypeKk4qQZ7FkeQVnWU3n/pmu/VnW6/EyE1+Rwve8WxlmwkjzVTQza/u7eNYImS3DQ7SzU5l1oLTirBgXpBoiP8jwgct6sbu059FwO8/Qr3+39A/48/kOO/J8v3MAX0LNRQzABGoWQh58Jym8kIc06k2lla26HiTO9Iev9NE7r0zrem0sZK1NmHvLfm5wa7cM4bXMNH61No+HdcNf2WEJimiXE84/2IbzS4qMflyMqZ58szT99l3t994PoPFyTxMnD8GUKbvWfscGq+cE73H48b8zWvdV0sO9QNR4MBW+BYGqtc+93h77YMXDsERCvUTMkrKV1Z1iupaa9V8g6x8U3oFmnos76R28ffIupuY/tB1hymWhDx7Z6b9tJnMAW7H8UYF0sTpzVDaAdfQvJCqSuVSNFCKolaFyC3kr8D8duBULPD1ZFQR3AGjc654JxjVAihmjbOMqMp4RWKs4xqT0D1IeOfBQD8t1/n80ToJBIirerWMszDyDCOjNPEMJh2aXAGMxyCx4tQy8rtWkirYpnF2pKu0rLjvDjR7P/7QWuPWeN12daHqdCh3vp/SmnEEZ1LwpzIWqxKuImEH9ZAXwf9oK7V5FKqHvH5ffMcJD66fyp22PQtp4ff9ecdr/2c/qnDs62tV92W7Q5e+tY/85x+Ry/+t/99O1Y/r8ftM9V7bmnjUinFIKWtk9QynkkY1sRpLZyWzPmUmIbBAkffkw+R4KIdTDiyinUlViXlypIy85y4zjdu84U1Xcl1ppIoZG4pc51Nl7HTonWwkXaHpTsCwl79+mwsNjKjfWT2QAWFg9NBf81XmssOjzfB7Ix3hbvTmSBiBEUC6oSihSUtpJrBB+vfaxVbS6jpNqVbQCb9eNirdXqwMT054JrD1O/j6ERZ4KjNFlu1YJfVaI6W7ImvfdFr64bcK0+K25KlLxfs0aGTnRzssIHkOD+H99z+1DmKN3aWUq3b1foCj8HJHqB86Su0L4/pNTrXSe/8xnrb5UY81hMZvJ1VtZY9EdYp/4PD+na7Bd13qEr7Ojq9eqgwHwIXVI14TBXX+qyceDYJpobs6ZUFMCIfHyPDODayGE+thSQYO6cUirax7KR+Ik1v0gKO0qQfVMuGHIFeLTvc93Hu23T25MP2sO57cnP4X+ka4h2leGpp55NTVK3aUqtJP23BhjbJmd4jt1Vj22dFNxkwxRJbJaV2JlXGYSSEiB8EXKDkFjxKxUnAielzdvmgXm2v/aDty7+Pi7IlJyxf0lhwRVoAb9XCEFtCw3Vos/1NFWsXyqnrB3bEVoP/H3w7SwyZ/7rZycNBtKc1+7z1e+snixUC/Cvtxx1n0OzY9u82Vp+/7zEBdrAvO9FmN6zS34BemZRmk+j2bmOabezVx37u3oJT7d97krwnJeqGUuvrSRFEXfvyiAZUG6uxGurE7LhjY85Qk9HoQakhUaQlk6vZ2VrIWpHqqCXhy7pB06tzVmEvxdCaJTc+i04oVK3NyDe/6Z85IL8sq2q/epTeruXNW253j9zCHXO8M921uLJo4qKFHDw6CFkFLZWe//I9i9K+e+8J3hODJ4SwMQ71nw061Z7n7LvvTd+wLZwWrrfSvjnawLYFlrUCt60CJ1qIY+Ru/IaHW+D67iPP4XvS9x8Yym72aq3tQP5sfF4s6s8X+M8M5mta0naltO6ZJietp1G3BW6aQQVVy2x457fDvrUfbnkftFI121e1vjPVRAN2UHGWrRELHsVb+bxvfjOWujlHx+9CO2ikZV424yWbc1i3KGPPzG4GuDtQanTWpfz/2fvzWPuWbb8L+4yqOefa+9ecc++779kPGycCBDIWjUGB0DhgRB8MAWMLoiQGlEDCRrIxWJAQmicICYjY2ARtIELgNI4QSmwrgA042KFXQDiYgAXYwPPze363Oef8mt2sNeesqpE/xqiatdZvn99p7m+f5t41ztm/tfdac81Zc1bVGOM72pWUFlKyPKSiyfKLaj6IN+e2gnWWvh7Md4qEkTUVUiq+tpItn5KpQV5BKgC39ZBLjbh/HPrG++8xjgNxiE2RVA2EODJNF+wuLtjtdoyjVZGt5dPHaEnY66KssxDIrtjosULmQvKIkWg3N40/e6GEbNUUDUzmVlSnZKspVptZm1LilnM2a2AVRBXoVSXZL1vLh7hO1iuuvdZV9zsdnjoGUtDlR4Az4FNgJtvv9f1HEozq+69d7VTZ6rxA29COx9LZO/sBt08K7qXy79rf4nX96rmtSpushZhXxqxMS2J3f2AazVNt+Y2BUawtC2GgyEASsUqXubAkD4tKmbR6O408U3RFsfDiXAy04lXlLEyTZunf7rNToI7ubbvzYy3nhN+eKLfSgZ9HId/7qtlb2Kjlj0hkTsp+ObBfDmTNXs1yaApILVVTgXOVJkUtv7imgURwj6OnhrhCZH0ZbX2XDny2jQpWGRPa2UtKkDMxDgiQ1qWFuBcxhTKgLbSxPldFXDHSZhy1kC/3WnSGvVaQuk1HDa1vqpqDUsvVbDjXe4ylnFnT6nnM2mT4Y02kriuSM0GVWHmFeE1a702a3VMctebJSVcEDFfsbKgqULzQjapXXTzarxso7xXy4z56ONCyoi0pWH6hhuK8cDOsWci0XzsIcRzZXVxweXlJECGtCyUtrZKvLRGXq/h5inupkimax0CKTVcR6bbfttk2zrN9pYlqoLPwPZrO8/y9H+OwLyxzIqcDRWaUbHJfNxBVzWnqniZVWspMndQqF0N9RsXk2LIuBg7I7JiIIVgdhCAEvCepP4Pqua3eOnNL1+tsc1ypgUYR14UHQCx8Uc3baIbjYTP2YEWdSjHP0pott/a4XUg3j9UgJ25AktOCe3XHmnG3Aufaw7rKyqZfPwI1vZ0u7PRohJte2OvQ9Vj5uJ+je+wjV2Qz1LHxJst5Lh55Vr3xbswu2P70dWMe29pCpYJIj9pDPOQ0EqPVn0AHwjC6kcHMuBCdn3rRwaIQvM6Ih76bnlpY1PTtLGb4jyESsrfFCu5xzIU1m+ytMmZ7Bj6fzvPfRu/I49hxhYc+LcKswn0MHC52yAjzAPfznlsCZbpkNw3swtj6xSBioXbRN0UMDTgOg73Wik+WQOpAsXofxSuy9qCRTfFrORchImJWHC0eAlfDYMGtbIU4DTyZnvDeuHCXbri4X7mYVy67mO6HZFhVVOqz2Z7UyTNrrpDuMT6iclNythBSCQ783JLipZVzSaQ1oWqe2Bhi54UyK0UV/MH/Kx6+IbIJf4oBBxAY3GorsikXzsgqKNx0fTla0EEsfL8eYOxcqYXjddvhbD4VbYV21E3VWszzuKYFa9FRtqqZVEHndiZNphBU6R8MGGn2MM6U0VoBKwhhHCzEgOieB6FIaWGXj0Hf+pFvMoy2H6yYDKBCiCPjODGOO8ZxYIg1Tqa4lRpErRVO9HCLIP3yOw4jOwIx9bUxXhf8rvCUAilp6zFVvFprMwLg8fbucaxX2Dj+thf61iHYrW3GBR/Dx+qO3ftVORXZdOn+s03YKP0Yqnd0s0Q8Dm0FcTpfqgvo5p054vIbqO15yjF+ruez7+fqxdAqX+XofK1nJYpYUg0hZ+KciXFm9ObDUwyMYvmIQUazrktkUcs9n1OyCrfZi2kpSNuxNR/V79c9/JZfI13p+jomGi8+IuEYLDbFgW6++idFfRKb0vYIJA6mUlpRHVv4YkCJIZDSwn4/k1Iyo2aIZM+JaYW42ICjeThM0Nste1QN29yrh4fjsqwWK4pN9lWAWUPtnIdXDhwjlMIwThbqlJOlc1xcUNtdW9RbncMKTtzL4OM14GIl67WNkG3/emVQKwFgpTtN1uQGSE35rWFiyXatisnl2kO0nhMeCvB5J5T2B6uSrsXHa4pjygnR0NawBCHnzJpMjgoZSWqJTwXfRzbOiIWVFedBFsGo7V5Utny22rtP0U0fclurYB4u+7y0SsZQvcwbn7R14c3adxO7i8nWVF7Ao4vQ4l5GG092cJ6Th7Q5aGyI2BY6voWdv/DGntOj+dEqIqDb/73seAx6/o0fA1nI5Z51zpRifM50QdoeE/GMUi2ImgGrRj8YP3FDTqs4GSgCiUzytbm4kWOwxnymm7hxbYjW9iMWrxFQQ341WzhjOQblzRHme8d01RriWPUm94SLG3GwfW1ryFJqSl6tVUvWE36/zUsFZFD5gmzzUQVGLbQHR8aiasBsUvORDKvSmi3a9fp7cYl1Iht7QLs9R4usC28AyCprlZq/baRuFLE2h2Y8SyWxZjeK5mzAsbbtqcDRe25qy6E1Hbvurbr+QzCnVx5HdJoQJkQmJIwGJLUg7m20yJCC5oiIkqWQKEiJECM5BFbBw1w9RzKF1k+5iEci1Hxl1wHa/Rf1wl3lE50d7yjHcVNiAGzfdWrnzZ4yH1iHTH46MEwWc5xLghKYholpd2k5kd7+oP70cdvV8xVbr5uOkbE9iIq6azWoyiRCfUjS2VtyprrnKwOPIbSNG4IpM9ELfYxEnsUL3ouXXDIRcs8tjzfNMS/d/npwa/U68+Ppp91oatq9PSPNtsAbaHKBgSshFqozAMXmt1jIsMWFF6/sVnmQbVCqIKwV3tTCiGtIBbI956opb3JGWzsEO+U2PzWhX7vNfgRunBFunsbS1gHqwHGNIAMh4iqVl7l3K7IBm0xWhWSfSlBKCZRioCsHJUv0OPdanWxTAkLLx5VmUXzX9CPffN/bbITGXG0tmyUruDJalbX2PNVyGsXzDu2Y0JQkRx7b42xrZiNpFuN+HuzrtVKdT2WbI/z3oq7IyrYnanheDeHpC22ZxVcaw6vnpQNLbcSdsnL0Pr0Jh01mtrur/KI/YQVem6LxGFSryrbHrtvoa0n9iuuOTSo9SXeS7T1LyLf3t+dZr7PdVO9XroUvsoe/hFRIEfIYgAHxCtKESCGwZjhkCz+d15U1pRaxIL4v6ngUA4nGk3tv8/HQt7l5wHDRtNKPmRHtD+5uV+udPg6pC+elFNayUshIMMCxn1fWQyZq4GKMpCCsGQa1io/DYLItOl/M3vuvZGUYhIsYvbejy0IJZFUOKbOsmewAspRqJPCqmFoVSS92UYzniRsKqwJ6KMUqf4qwi54/489tN0YudjtElHmdWVKiqLohd0CB+2Xl7rCy1hx2V74tOmNo1f9GMVmyJrjfz8zLAShe5VpAColMCZGaEzjGiI6BNVdlj6OopndN835P6iJrCmogCm31AESESLSQ1TVRsjUBDwVCEWLxImtdaKfJMjfM0FVBlW6hQmuNZGH5m2dWXKZWsFXU+/lV8FgLxjUPjfHTMETG3cg0jWhOzC7jS7aidpV3mDe05jNWI3puQKIa3to466+dEl8/2IImT5RQB8YVkNj3HmdHjhcXjIswrolSRkqOoBaIbIWMXP5RTRm0nOIQQ8u6shSOmgdn4MP0mAjZaiFkL6hXPK1GiFhnD2ltPiREgqr1IC5WiC5osVQOb41BMKdJaK2nat6hAxNoAAgxz3IptcVLAWzOSrFwRCmFoepb1TBVCyNWudwcKth1oMmSolU2V8NwBY1Narfv9tEi75JaqKrUgdX3esF5+qX6rQ4gNmtB97eP3vSSTmkRKME9y2KRGlmzAce0bq11au5vc1LkBhxrL/MtbNWj0Iq7PUJgHAZKmVASiIWMtn5X9W4dy2ixNKCcMqrB9PQQWLvKrDHU/EjXpWpSZOUJ/nuoRghnT+p55LX/9tvo3YaqVsVGgrnSARLMr1+Tl3uGCS6eXbDbBSQ8QcaJaZ0ZxsnC6aaRwUPutvLQNW67sxxAAwXagQj6caCNOVWDSrMS4onDWNJ9yclaBIgBm+iKtuLtINQ8I+uayakgMhDkkmW54PVN5Eff9+cwbA/DmI367xtXfcPTWIVAXdA9n31EALkZcGoxHF/wNVTV41Gl89z2Vhsbnlk8c8meaF/bIQRUInhRo6JYHlPJqK7ECCGoWcfdEl21uiZqHECEuuCpc14aGMx0RVW6HClF21wX78FVrUhFCzkn8ziGghQBjMniTX5r+42qgBHsviQUtFiLCCSwakRKRL2foVl1qg80tDVrazHzGPTs2ZNji5oXHGohIwqtzI1sXkXKJtSb5TJsi7cZHKXmOnEEZhzatXH0axz/bNuTsVkrq2UUwXqFOejst4Ff+AT/SPt+v783JaYC9jYiVDd1ZBtetwMdeNR2Kt2paaHTR9jk8TZkFdhvaGbtAO16n9lYjkAWJ3+cfChaQXN3r/U+O+WtWpANpOvR1UQ8bH0cmS4vuZgsFzyVCEtmnRdihhAKMegb57DbcDWt8kK/9in4rz/b/Ww31eavm5v22N5C9dsfp2e8Cyq+nldVklc5LCLkJbEuMzELT8fBch5VmYaRyyfPGONAzuaZHFzGrTlzWGBNxi9Hz+evbaqUwO3hwLzuOSQ4LMphTp57VwtCVIWzWH6rWJjxDiEGV45iYFVhWVfWnCy0LgQuKTwZhedPnvDs2QVDEPOYrrCUEcTaWUWFu/3M62Xhfk2sJRJKIfrcxxgZtTCN1ndsFCVk6wVMOkBe2I0Dz8fALlrSwEGV5OF+MVo/UVFYQiZJZuvm+Dg0H2arruk5ZeoeOM0J8fDAYXD1yeWbZgOAEUF0wMopFhCTVHZoaTlx1bi25e9WQ2s9ZgsHtObdYTPOOu4s7uYVUQ9Pa0pYk5viz9BqP4h7uCuwSP4QLSKlqHgIefbqrOaRrM+68nntfyqoPHmGp6BRjj/s+PCn2Lyfk1RWQswMk6Ilklf3qqfSmE4gb32YxWvKBLE2MN7wHfc8C2IeSSISBiJiil8JqK4GvEuTcv4s7TmP40SIA1blUinB+L2UTafVbCA+xIFhNxGHHRDJqbT2CbRcuezAznNRPTqOFl5rRouq08Q4WI4u7uUvb87L6TTU+a1G1ZYH2nSMGg3kvQIfaSIrXuzXjL0nTVeUDgAfOXGa7tEPT/qPjgFzdwvFdbrqrUtqVd5Tqd7G1PJHbb+YEcBAY2o6dfGCi/3erg4ZJTfAaEVxxYrEqRdT62+golgBSUISYfbnEULc+j164ZyGk2uB0BiJcbTovShtExe1mhOtCFZ5+zy+4z6OVRHzSVSFeWY/36EkLncD7z3ZMe1GhjFy+eQJS0pWOCUehyzWKa1Ka1OTtAt5qA/syBJRv7extlp+oynEPrQskLDNraUwIAhDi2FvPfgwZlty4bAqawns88CHr+Fnv5eZfiSwewJEYxdZHQsdu9P62zp+ZF8CWbVUNzUcldWuAHJLeAbPnzgqyV0FWkFLTZ5Xe07q9rsaAuzXqY10RayCqYTcvIY9bflM3gbEe3o2BUhLV/nYDQwSW34hDh7r5qw/Irgl1zY8yWSraian1Xra4B7n2j4EINQchwxqBWgUMRCZrYhD9RTUsCERL6SgbNd7BJqGrr1wu3+o1uxqWdzUkpNjWh9P3RQarX2QajjxsWJ/6llvi1hAWr6wdIdLA2xmyJMGYnuB1LzC3Tl7g9GGdrrxnIzL2IBSq7BtftBuPMf/VOPi8a2ZPnZykYc28Luh7VJvMoT+cZ/slA7YbhDv9Du9kqZbGcftVXoQ5s8bL0TmpzRvulqBsWFg2O0YL58yDJfELJSwshQhpsLgGknLifI93trkdM+1hvY8dMO9Vdu3f1sv/T03gF/P2Z+m3fspXHwcxls0W9GDEAlhpDCQ1YobBBkZREEzkcwQYffkkm/9yHPGcWCZ92hOljOXMqkEpt1I1mjf8TDzpIUVZc6Jw3zPzd0thwT3c+H2frEqoCG2u6wKVozBimSFkehFjkqwvNTDmphTJoswxIESImEIvPd04Of+2BOeP51Y55m7fUGiMIm1pRgQDjd79nd7bm5m7opVVw6lMGFhU9mQSmuJQCloth7Lu0kZp5HL3Y73dhMjmcNaWFMhixLHyC4OkANpLZtM1eoff5x5XHMiMGwhnGqW+KxqFSxFINI8ULWvX9Hq+bNNZakQAUgeAubGy5rnqDU0zu6lGTyr7DpeXK37wbbya+5/5au1rYC0Z2XFAauxWB28mhysvF60+FgsjaXmZG0gpN9HJwzplGc1Hlr3+hbS+AYzq0ahN2TKuyEtB0RWhpiQEbJMrIuSU3cfslBN0K1KaQioV20vLvNRy0EOYQQmTGMMBkxD1ZFWl7cGFCgGPsyErgxazJikBt5qaSuxuGaKg8FBRna7iYsnzyllYL+fyflgD01qqLLnY3oRo9zNWQ2ztfF6vptHJRW1NhA2RaXNYTUl10mUxli1VdpvuqLY2mtex2pQeCRLTjUut1x/uumrakEnxKVbk6ZabIbmuiY52V+9SPQT2X4Vr+autZXFZtSp1Xdz3U95C1GtUQObx3FzarRx47nEOSNpJayWmhcGz3dsu05plV7ZeIQ5UVy/i4EwROJghT9DqFE9NRd2YBwnJqwHs6jxiHqO7Hnb5YvyOJ7SMTsvEGG3G3h2uWOZJ4YhMu4mdrJjLoVMIZWMak3A7idxY6b2sCswrKxTKo+29z3G25RGsQlNyXrseTjemlcOKZEQNA6GHpJVoDKBZAzatr4pz2VJlDWzXxKzwv1a+M6LW37yZz9k+OaP8yM/F55cBga3DxylCD/IFLVZSoxxb8p32wOPCSrrc2qM3UItUTdodBuxFCWt1szWxm1WyOIMiqYoWqKuJQsLVvQiOOAsLjeqgEyUvMXLt5unk0USCJIpFRTSeZjZgKOKFXrZ4uA7pvHGeesGd8EeBNXcFXBRStj6oDnadAHqZUTUWX0WNERKtfRIDfesXlrztJnX83GAo4i2svxtTXWAaAuRNQFTpMb5yxbNJBUw5sYM+7Rp9UdYFZa281yhwmFhCxnuLcg1WoAKADZgIw20HkfU9+DCwixDu6f6efcE2r9H4U5N6B2Hpyqn27Eip/5M9RrH67Lyo8eiN2Hp6TpmA//9SPTjBLY++OsbRzVFQJvBq4INEbOw1+JPuXjV1AKjWERIIVjJe/e61+9tYbDHQrrT/beh6ZbrtZUo6IDh0S91auva34whR0/saLLffJaPQVbpUJkGCz85zIllGBnixLom7pa9eQRjZLqYmCZQFlSUcQeUguRClkxaEnGA3WhiOidXFlOipIwWCJJQXbm7nzksIGLezOIFVIrvgRiiVeinIJKR2sDejUUBGONo3r1o/QEvLiaePouM40LJd2hekbKa7MiQdSQpvLy75+XdgTnhFVAzUbFm0lJ7xLksELPWBynIIOws/IQhAEOxegZkyLXKtYIULwziuY5ezTQcWwzeKWXfjMFZmXbKczViBpFWS0HVDU01XFiCN/kurYdacU9phQu13dFmEOn1HY61Y7Z9Ut+r+d/gGXUV8KjS8rjqcd1ZWsXQKk/B5rRIM+RY7qPzwGZUtvG0aLC2IXWbB5EWwdCN+sgjpA5r7OPqaHicoiqRhUESGhMygOhAiR41JYp5gxO4D7s+rxCk5beZN9HuU0IkDhMiF6QcLdNJAjFUvSiTNR2BAxBKSaQsDdSYkr7iceOWjyzagIRqYTeNPH/6jFIG8hpYxPaNgTdvR1WqkcHmYANM23wEz41UlQYyWs9jBDg28NEM0L2M3wBateE2Y0D90U0Pf9dU8yn7bdGwwMk1ew2w7d267r1mxVaA6k150Fo5YXu06OYQOoo8755RvVoPDKHq9xuwBo7CzuuArbCgpTQkL15Tcw23yBwPh9Xq/KitNTJJtRU0C4MVS7QaJhYNN8TIrgASCVGtvz1YJJKacaT2md3kxsfTowDHpnKJwDjw9HLHsycXXF6MjNEyBkQ9Dlwq48QXbGjha3VuTNn3Z9wWS1UvLF/GhNlqseZqltNCYPEWH5oKl7sd0ziyP8y8vLtjRpiePmW32xFLIeaEpATZPEgqSiaYEFEhJ4ttzghLWXh19xHf/uCP8PSDHVw+R2PkyVgLjWwMflvI23tHStPJszv667EE47rCpur7mBwgqTowFJRMySs51IqhDtnUKokGtHny6uLPHrZjyeEWZqXqOTNBEGeydfFnt3Zu3KhusuBhAtlzobaxuj3Pcg9rlcDK6BvosDF4P4+OeWSv5lg8B6iWJdb22sIpxbWHGvrVFFc7e5FADrUoU2zGiT7+/7Tv1bskOWLqx0pyZbCtPDRb+OUW2lqB2mYRa/f8oCPoRJHpPjzCLr1Qkaoo1DDa+hVfS1uh/yPA1gCNbk+vMWVHsUdjkm50DhpOxGiDwva1t8yJbOFDXxQ9CBpPAGF7JN2X1LXPapQ5/eKb4vFjwJTLJysX34ep2hPLWSmS0GVFlhWWlUkSWiJzLqzZehjWPIlqEa88rBfWm8a8FTF4Y2htsboAPuGZm/DW49urzwW2PNwviASrgzeKUNbMzf2eUZUxBPbLyn613qPTIFxOEaJyO98y6sQwCpoOhFwsf5HU7jFrYk8iB0ghIyEzBGGKEN2LKVgPsCDmVcjB+GMMkd04MsVAlIyWlUMWVlF3eKi1V5GRBYWo7HYjF08vkDEzr3fk+Z6QCrEIMWfWtbDqzCHDy8PC65yZs7g3IzCOgymrqBW2Gy0XfM2JOa9WuTdYjuAYA0WUgxaT2UEonjISo1U+LwVWLazu9ROEQWAcHgdwaNiMIFXZFzdeVmBR84kkhK0whlrbINXiBkhAISOsYqFlWUxuaFD3ZtWt0O3UbskeK8JuXGn8++TvYElZNR/Pvt9FgTUOyHZdcNlXAUjZQEjz0nTAouXKu5zpZEsbetPyfQxNJnK0V+uxj1WteizJDCajMBdBs+shcSKEBDKbmHMG2ER+sBDFKjdbr0eRraBiiWxZ4/ZzXKlzUyEMEFrkkXiRlJwTQiEHwI0sJQg5mQwcY+Tp7oJSRg5j4iCR1DS2bZ4o6qlWbJ/5DAUfQM2hrSGIbQqqQaCuP5HubmjnaXJW6hqoU+vXavLncagW7Xm4ausmV94cQR/pchz1Un+Oj95ea4ucUv9uBg/TBy2Ny+peBDmp84F41WdLVwRxb3DNke7qe6BNjmmpulgtolOfcP3dC+/k1PDIkrIVXBKBaBFBta9nDNaWcDeMiETioIzFIyOqDkvFU762RB56kEf0jorjGPVhJKoKMcI0cfH0KRdPLpimyBDcA+PlbLOqtSfBH7YPemNCxqgMfGy5Abb+rVxtVhCHyyXPpDKTFBKRw6zc3y1oUkoKpEm4P6zc3M7Mqlz4OaYAmjKUTF7UwA3KWpSU3dIkATKUEkh55rC85ubuIz569U2m9weGi0vGENkNpwpsd3/tWX3MM+yVZi2PVgBgPuzpgWMVJy2BV7VVyKPGWvtGsdYLFTh6CIwW1mVhXRNpzW6xqJXFBA1eeTSASA3TqeEyegwcm0nLPQqq1ky1MbBOmLGxjS3kpwqk4BXbawsRCxmwimbWQ3Qr2lG9mZVNlA77dGMCtrysygAs9yjESJR4JCBraMFjUaBWO5NjQFGfglbQ3G6zLb4GpKleP/WeYf08HCsucvL68J1t87DVXumsmPWYXt+X7f0OO7Y5ruK5iT3ZznSKOfpB92N/Y8Tds/hYOprLx8MgcmRo6kd8xECakgen9+b5hO1hPACm6peqmNWjN4H6nLWlHXstXnABSlY0ZeKaiGuGaEWvUjZPZFZ7LbVQQ28BP5pYmlJKP+5+err337DinqDIBkiP1kL1dvvl+yf2aEqOKVolKTkV9ofER+udFXSLARkmC4+PggQLhUwlURKsBdb9PSPKxbgzwJQz8zKz5JX7ksnBqphejIMBl/1CjMLl5Q4E5tlaB+GFy2KM7HYTl+PEiMndpIk5FQKFKUYup5EpDGSiFUwJym4XGcfAsh6QXHgSohWCy7XwizKvhftU2OfIopklryCBi2lkN01WTGbNmNfD1kNKyYri+awNpSAhMo4jWeBuXck5m/U8RII/o8NSmJdkxUqwYmOjCM8up8eZxuCxnR0gCwqi4u2+vBaCh4MVAXTLT6zFn+xHW/GS7Pn/GipIo4ob0zBVfe9ucr+t9ROQ+Abgqp6iriYB9XhqzqtXMPaCK4pHwFY523mvej6zARLaX00iuphp0mKzDG7XPzIIc8RPa2G5xyBdZ6ZhIk5W6XxdMyLqlfhN8WyFyVxe2zP2ua3hoHjONliIqVj/RjM+Wn2EOmfBDSbVw1VaFVdFye2c9aEVXwNBLHpKxTxUAeu5rDIwDQNDFFLSFgppZXlLY2nBdaY6B1WUV9lgodOhXbtG91V9va6tvrIzqh59VXln2Pg2bADWF4GWxwOP9YpGdTxyLJc7vcXuwbeTbvNLd4aHRrv1ALYjarpWqyHhKUlFArVoUhHTbbWYUam1hlOh1GdSsJYnndFnCzN3/8TRYGukm/p8Vpm+6c4WEmupYgkoOUBavSPFwBAHdBiJBPKgzWNqS8eflUI13iMeZSFvxx2P4nE8hfLy9JJwMREjDFKsvYFaXLApJxZPbjH2nl8lW2EFAxyBlBLrYtWMkEgIk4VXFPVy54pKpqiVO095IC8DFMtHW1dFSyKtEGVi0IIuhTwsMFryfUqJZbV+Y4XMkjJrUsZxx8XukigDqRRCWkl5Yckzd/M9r+5ueLKHZ5eX4NmS6Kb8vKnA1mfFA6vXJYnW3nrvnvb72343bYC1Je97bH7NV0Q8HMZFiI+tlrLGlYJ1XSyxXmkbQ6jhkX53ncWH2uy6jaDbrC7wmlu/WkIaA9iE58YvNg/GJtTsS1a0zN392ULKqjWtAryt2puH1tYH1qGyDWZXa1wghS1M9fjYelcPQ6zvlzZLsu2Zk1aATq7+t0fWJgJECVLsWXT5jq2AiZfhroK1vvcGHOs1iJPVvins4Y190IdwbH+7/1GP9fuqYzWbbmfd7cHjpmfpptBskqXz0nYCRto/23k4vu+Hxvtu6biQTB1v9/Ebf/aHvfFdv4mjZ366DDvjQH8GPXqtC8AVkKMGV70nQ7qB1L+PZcG2d08G45Ndr1d3mPpnvbrQj/sY/B6vuTaMo90nPPyc3xG5Qp6LkpI9q2mYmIbBZFwUQoQxCtM4Mg4BSYWUF/LqhRUEUjZvY8mJsibLnUSJan2LxzEQYmDcDVw+2bFKsSyqsjaNXCUw7kYuLiYG79Ubg/HUIQjDCE93A0/HichAKtHGooVJMlIWcl6ZSdYcuggpF+aszFm4T4GbfeH+YJUFpxjYDZEpCpDImkCyeVI0GUCJYs3KJTQjnRmSE+KhkjW5WBWWZEUmDnNhTsWMEur5WzHw5MnuceaxFuWryiJm+FRohWZCE0kmL2oUTfLiEgqddmr7wHKmgiM1qMhNApsC39Z7Z3jxbXXaRqAa/gRXkLEorpqOUAchDTT6D7V9x8ZrcRDRxlVlWOM7pz7LbY9Jz4C2jbcZCztPkdLrHBXQPI58XPZ3XD4VhjgxRIix1lmwkG2kFi3qwoZdpxEvjNhI7LM1rT7u2gHXiv6ZMR3TadS6WKs7OhAvCNi1k6knbfqQ59MixdecFUaRKIzRvIdavB6D9i1SigOV7byNgzag4aBEXL+u+ecdj+VkXVXSTj/0wTdZsLXvqO+/s6k7prrA2x8tfop20U4H2KRB1efqa3doc1I9pJ+7nGjPZDO6bD+l+9yBf7DMUhw0nkYstZBnH5u0tcZWEVU857VFwnludydepQ7ff4JgBolSLA0rZUosMCgDwQoJe1/ZKr83m241MnhR0MAntlV5N1VVT6W66tGGy+NIGYKV4sYKjLRKTCipZ6ohdPx0S/BFlXVdub+/Z14WQhyJsZBzpBQYB2G3szGsJbEsB9IayGmH5ImgQl4zmi2U5unFE6ZivW5kWd06KyTFSu2ue7J6r5ZkxQoEYYw7SokkMnNauNnfsrt5QbwVns+BtUxoe6ybqntKevLMNkWpKrX+/UcqqnLY33vp4C0sxRiKx8yLWF+w2uJBt80hoq0ojjarl26lhtU2tUoFX1sbiJYn0gRa9DwSaTKLbrPWsM9WAEnfZHS1lcQGoTYhSxOutQelVdHVYopY3YDtyXcekk1AdtT1M6pb+MiCdDwCB9X65nneEfXW6G3onV2trbNN6G/hoAaeY4RhEIZoIJJqMa/fE6/eV3V16ff8Ccs9MpI06MXxHniIKW1r4o3PK3esQpAaLNUL4O2Kb+CVI3C0jfHN8XTjlP5Z9eN6NMhxOoLtr2N52R2wfXC0JOn+qOuvWVE7CNUpLw/9fXqNTQG1fqxRLK9Xte5T//HrdsVqT+7oWPDb0Ow+RY7vqiqZRxaEaiChqT1vDLtXn05k9+lDeqf0y/+6X/Gpj139p6eI3e78wPtPur8904lnPwrPPvswPzP1Y61jeQL86Bdw7S+PpCnklZdvMrDqnTUWwgypWpJHU5ksPJZntoeOd6EerW1T2r1yokq3F073rTQ+rrU4hiihGap9gAi10YRQgeOwKcF1LzpqOno9eRanuqRIC5zt7Diy4Yj6DGWLNmlVlruK1u73/Ewz82np9v6GEmCcLrxolIFGLdYTVBvTqSHDFUBWj1yNysF11A6siTWrsQJ0HZBTQALB9ahmlPf8ug209DLFeWewMVYJ19ppecxzcZ1V1UBvDaNVLd1cbAa+xj/BHDZU/eUUhLmsK1t0Ust39XuXdnivGW3eStONHoe3HtsJP06XenON2uEO1rTuWdjy6U9kYP2KcFRtvBY9rPMhYqC+PnXz0nluLNtga2X3uvNLLTJU14rYWKK4Ma+CRzBQqd5ux8eswX4k1Eq5ASuh4/9lB4/VEy0BKdo4gBCabrSpdNUBwrG39C30OB7HUxJaVa+gmVCUoAGNA2uxHlRJM67BokKzLAbUiwEk7u/vubu7Y15WqzwUVnK2ydtd7GC0KleHBQ77TFmtT471/TMLjhAZhwviMDBmYV0SkrPlOA4jGmv1KSEUGEMgDADKMs+UEYbhCRoyt4dX6Ac/w3xZ4HnhW8slq76/bbxP92iOlfy2O7182vo4wPHP+e/+4kc575m+WKpJzNWyuP17/NuGP6ogqdZCL5c/DkzTwDAnwuqluuta7PCHUgFBJxCRngvVy/lyPlIhaIoEx3jmYeW+GgfCZnGr4TXGSY83UOXXPeOTY8DYBODRderH0iyzqv24euHySKBDj2dtu+L2FNsMnjyr3ttajzq6ywYaH6JNqTsej/2jJx9teVVV2ByL36pk98rxKXhs131gNL2XuVd6jr6pyuksftyZj/ZFXRJvexxnOhNgIGdbJ+YI9eiaGlKq5ukRxJU77wXMVt1SK6+sxWqknnernGoX6F7cUNIqIHd8uA6mjwLRtr5bUGUz0NRr106FqrH9Lq174bY/jnhx2yOnu/sNO87JcW/Sqddqgyt23sfqc3x7f8eqhd3FQhx2qJqnO+WVnFeTBz1wxH6yg6bSjctCkLM/Tz2pBt2BxsZnAlHE27mZR7qCGJOZFVQ7+Ah+3rpWKkhpLenwarzFgeIWWtwK2PTnRo/5nGYH9tIAS7XuNbBcSgNY0L3fPHYuxU9k/fbL4zBWPbroFklUw4fVHRpVv6g3vvU+PgaNJyNvf2k1Knf6zOZxlK34oRe/svBiA3OVF9RcSIsesKSnVjJTq26xSekADAGGGBijAcgoXsUcN+4LSLCwV/UweY2182gndb0/bFElFGVQGEQYxHs8So01oBOIFRwHlIKHP7x1Ph4JOB7BIYRig47WLF7SQghKkJGSEvM8c0iJMO0I045VYPGS0BGFnFnmmf3dnvv9gZQSIivIYps5DIRRWfMlWoR5HljXgVDDEXWxTS6RIDuCjFhnqNKyv8Qkg/W5GkdEds5klZKFJVmJcC2FYQwMg7KfX3DzvQN3w4HpmyN/9OFHSX3/E+2sMZ+Keg1YIRWYT23SZzrTRqZo9CL4eO+92SRdNmHjFYBiHJimHbtd4mKGlGzN5eIVhU+YiOs123l7bZ/64cev+2bl6hT46rjsLV3aH+0CouYOdR85KD71dHZCgE5YdB7SBn11AxsVNPYjOL33x6GH4VX97CGY1b5xwjaAI89df0ilvrrtm9fdnuKRUti0ie74qlBs+simyB7NYHee9mtVRE5Hp9u9yZsQs41OT949WYb9Mf1q7MHku6Lr6+tHsiic6csh45E1zNBydi0/TiRafpB6YT6EWiguioG6+oN6PQYtaPMM0M6tRwywW6P1ly7SoqLFalBpvP9k3be0C+o+N0W2dF61ChxFLIQZZUu2awPol3TdkXr89gP843j30/Zlp6cecbuPMxC9C9ovszknUmK3y4hMXvl9JXuPvf6JNQOBN2i3vqwd2Ec92kGad7YCkw1cOS90YBFUWh2HTbbINn8tIqYCka13tkMdrDd2BY+KGUB9HWjllzZ+6/deK6maR7oOSkIv07e1J0G2dMlqmd2EZ52o7vMKqDZD4mNKxwqc6+WP+HxbXFVG1TBtv+c3/ntTpviJtrVZvbbdJawols1NDAbgVB00Etx4pF5sRtsTDhJQr9EooauCi3rfULEiNiEwhOivZnSoP6FVwrc5DTESdJvDBgSLXaeIGSKmaD8GSG3cDxVwwm0Im5719ul4N8Vxqk52OpJ2QPFKVhaumsuCLom1DMzryv1+5n5ZYT4QxolVYc5W0jigkK3wyjKv3gRVsbh4CzWIQRGSu2kDlJ1VnBtgiIVcZtY12YKIGWW1foPFa3MqpFwgZBD1JpkTiPXhy8lKaSOF3W7kyW4gjIV1uWN/d0t5Ca9efoP7+1/glbI25vhxVRmbwlaZqr9pT81Lrs8L+XAatPT90VnB+cGiByFazxgathN/xQRAES+/HhGZGAflciekJwOlLAjJjCV5E+ofJxrefP9NINDnsxwf5/ktCq2n5BHnakEYNIHQnf8hcFRZ6VEBhwfG3F47ofRxZ+t29ANnewd0rElt1ID/iapVlReOv/dQqM4pD9owtBy/93H6W1OMOqu80ilBevJzehN19L5apVuLvUJaL/ZxGuZ2Qw+8eQKC9eRwfzgPg9kznemYtFaM8b1W+5uJ54OXIt6ywlMkdAsHI3hqhxu5aqG55oHUrQ2V9gqv9CGfJ1VKq0lGt99PoxTqvjwCbSIgNZ+x7mEfW/dfTefYzrF9X7TboycK+9F1egNmiwroTJm6Kew1nF3bsY/DV9espFL1PSHGQkrWHkM1sTWfqlDKDQa68TgFL9xf5ytvSm9jVRvPkm7GDHDg4DG451HaV9v9V94rWG6lmFGiqNoyrN7qNo8VUj7EzwyIxGEw3TZZSlEcBsJgPSSXlChly+FrBSi7cwl4qtI2ztJCV+2fWhiwyuXySMUc27p8g3FvKTUVd9hS3NZZD8BPizQdybBeb9KjF7/UFs4ZqtdPgxU9s53fxlS0bIYYAQgtj7kW3qzrYwhWVDGGaIW34kAM0d7vUkJaD05VQgnuoY7bw4lY8S6x9kAhDIzjaBWzY2T0Kqux9neU7rG1HVj5ytv347vJcfzEI9SA41AR98q6Fu5XuFsT9/PK3bySvCTsWpTVm9B7z1pj5HVv+gRa+KswjjAFtcbJJTKEC+LugqcXgWlMrPmW/VxYVktUzprNgpi3PlearflqCIUYYRwHQojWUyUnC+cbdjy9fMLlbgRRUlw55ERZXrG/+YjD3Q2aM7Hd9dsefqfttQ3hSlPOMM+kw4HDYfmcs3KmHw6SI8W+BwVmkJBNmdCwMYmmDEVgYhgCu91ALhMlH8h6IOtMKltPKrtaVb7fVBo+ZnTd772i46CxMda6vzcAUq1o1UPaeH6PTTqj1TGTP36jx2R9TvYbmIReiTmFpY8HHI8E2pH0ku63+nfvrZWjQ3vFrS6GY+vpA9xamsg4eu2BmHaz1yznVWBJDc7BdeC3872P+7Qqk3Ud2C1saozd81bdcFPR3jgLx3e03dip4eFMZ3qI/pw/+8/+sofwSHRguICL977Jj/23vvllD+bR6Vf8tb/yyx7CO6Ef+aOf88fz877sYXxppL1wegAICrpVhK/AXWje0OMaFL1XTZtM6SSN54Nux9B91vp8xkD09LfcDvZjCxTZ0mrEx93Ckeu5Al0rt+1nqFVRuzZvNYTUusMZgDSAqKA1gzFQzNJg358mpmlkHAaGGLzQaO2t20Vvdc/CXt9xVdWrq6vP+pUTGmHsUup38PwZPP8+z/o2irzPxSNUEPjW5Xt8q/7xU3+E3/dTv5Xf93//re/+Qmc608dQufjWg++fQpxjO1ttZHOmrw7JyWsPdk+BzgNo+Qjk1S924JEj2//H0HayChrVhe+b5t7tXNa6wzzHvZe3v5MjuPjAr234ejLCZiysFnlp1zz+7gmEbKC683D2Y/9ka+eZznSmM53pq0APhNKcgkGRrUDQZnQ1D9PWR/REOp0Im2ZYbPJFu890C0EOAWqruhZ+YxqVBURq81DauXqjr3kfDThaW5gQ4snP0DyQ5tHdgKN4W5XWR1KjF4EO3j/efo9xYBhHxnFkGKJFfMYN+IbuUW2BtVtRqLfRF1Mc50xnOtOZzvQWOgGMFbj1YWKdJbVWa+uDi7bfO5D3RkLIA28duWHbVdpfFbBpf26XMz3cFbrwF+lHcuq5lTaO44tqjxDN2yz9IXJy/ENw+1TgP/Rcf3jo6urqVwB/PvCLgT8Vs9H+luvr6//xA8eOwJUf+6cBvwgYgb/5+vr6n/2Chvyl0jmV4weDzvP4g0adVBJatf2+4JTl7HaAsX6jCqTWi/Eh86zTSZTS8Y9dq1ZQbb0rauEpLG85SPECR+AZj5gUrJKpFtsxLCgnoDHGwdPlLOpRQqSGm1f3pYgiwcJja26ihatKA7YxRoZhZBgGA45d79nmcYTmXa19O7c2Kx9Pnxo4njfimR6Trq6u/lHgvwP8CViV9z3wh4DfDvyT19fXH3bHnhWc8378AaPjsNQ3HYMdkOvQlJxaYvuYnpYwdYLQpDumwk9tnz5wzS1UteZDtQHqZoXdiiR8uqX5sP+zl9ynnkIHj9LdUx9m1IXkvvFcHr6tHwb6ezHAeAv8NPAL33LsU+A3+u/fAb4N/ILHHNyZznSmM30ShZN8/L7C6VZZ1UrBb57FKgMr4HoTCoIDwvZXTYKw39uR/vUg1t88OGjUoNaHXr1oTSjtd/X2JOqgseaxNlklQAgGAGNsAFLc43gMGsMWRCMBCVt0j7UrLO051bYhMUbiMNhPjObZlODHdMbjzuN4/PqW+fg8k3imMz0C/VpMcfldwG8CfguQgJ8A/pOrq6tegakKzt8I/Dim4JzpTF9zOg0w/j5z8Vp1h756IyfgbhOUcvJb/UJX2mgTKFrPX49ygS1HfsE37uFIZMv2+9FRzWX55vlq8Qr1+7NhbH+/OfpjwX9sbf6hoF+LGePeA/7WTzj2HvjvAz/v+vr6x4F/7pHHdqYznelMn0gGdrqfLlev/+nthS0vv3kct9f230lBnP532GRHbV4jsHnrQq12uoXCbp7QzSNaA3SKF12qPXi1hp+G4AAyOGgMSKxtWLwVS/3x+0LEjw+EGJE4WIvCwX7iOBLG0X6P0aqw1jaDtbJqzf/kWCZ/GjqHqp7pq0LvXV9fH07fvLq6+oeBvwf4X2FeRtgUnP/4+vr6Z6+urn4C+Ae+qIGe6Uzvno4BVsN5R4zc7aC65e2ZhVU/5vjuzP7Px3riWojoBiDfmg15XFYVOkFcx99bLXuYemzpPBlnP5QagsOxB/G4WFN7s534tDT8sTJx8r0fcLq+vv499fdPqk9wfX29AL/zscd0pjOd6UyfhU4rgAfZwGTP998sl7bJye1XaS9yclQf7tN/thkfO/NqFyrbg8Q33kPeMHIe9X+sMasO7FpV3ZbXaOfYKu9Wv6XlSmqpeY9by44GQOtPD2Y7wNyClzpB/GkqHJ+B45m+EvQQaHT6FzHg+Md3x54VnDP9QNGv+hveSDk705nOdKaPpaurq28Bfw3wVwB/MvDzgQX4/wH/PPDPX19fP05/hDO9U/osqTo/jDQeIxvICrnQL+4A7ALsppp32FOGks3VlyyULX3OsQzQuSIHGAZg9znP9vGkKZHT8SiFT1/YsHo2P+99vo2+1sDx8zDOq6urZ8DfDfwK4I8BDsB/BPz66+vr3/HFjf5Mn5L+Sn/9T77UUZzp+6LPUqTjTGc605nO9In0K4F/CvhZ4PcAPwX8XOCXA/8s8JdfXV39yuvr6+8z5v1MXwD9WuD3Yqk638XScf4sLFXnb7m6uvqzrq+v//CXN7wznWmjrzVw5DMyzqurq28A/zbwJwH/GfDPYBv0rwL+laurq19zfX39T3zRN3Gmja6urn4d8Ax4H7PA/RIMNP4jX+a4zvR902cp0vFDQ+ciR2c605k+J/2XuO7SG8ivrq7+HuA/AP5aTBf6f3w5wzvTZ6DPkqrzQ0Nn+fjVpK87cPysjPMnMND4W4G/7vr6OvnxP+bH/++vrq5+5/X19R/4wu7gTKf06zDwX+lfBf7G6+vr731J4znTu6FfiwHGP4h5Hn/P2w8/05nOdKYzfRxdX1//7o95/9tXV1f/NPAPA7+UM3D8ytNnSdU505m+bPpaV1W9vr7+3dfX1//SaTjq9fX1t4F/2v/8pd1Hv9xf//4KGv347wG/Hmvp8L94vBGf6ZPo+vr6x93K9OPYfP2xwP/36urqT/9yR3am74eur69/z/X19R84h02d6UxnOtOj0+qvj5HidKYvjs6pOmf6ytHX3eP4NnqIcf64v/7XDxxf3/sLH21EZ/rUdH19/R3gt11dXf1ezLP8f8a8xWc605nOdKYznekBurq6GoBf5X/+q1/mWM702eicqnOmrwP9QALHtzDOD4A/CiuK8/tPvvbH+us59+orRNfX13/o6urq9wO/+Orq6kevr68/+LLHdKYz/TDT1dXVXwH8GuAXAd/Ccsz/I+A3XF9f//tf5tjOdKYz8Y9gRtbfcX19/a992YM502eic6rO14A+TxXcq6srwXDJ3wT8KcAl1oP8PwT+3uvr6//yCxn8O6CvdajqW+jjGOe/7K8/cXV11SraenXWv8P/3F1dXV1+McM806ekn+ev+UsdxZnO9ENOLjD/ZeBPx5Sa34RVA/wfAP/u1dXVuUrumc70JdHV1dWvBv5O4D8H/idf8nDO9BnpnKrztaFfixXW/F2YDPwtWHTjTwD/ydXV1S/oD766uroA/p/Ab8bm9v8G/Ebg32IDoF8b+oHzOH4C4/z7gb8Eq8b6J15dXf0bwBNM6bnBGss/4QxQvlC6urr6hcBLz03t3w/APwT8HODfu76+fvFljO9MZzoTXF1d/ThmEf8O8KdcX19/t/vsLwB+N/APAv/XL2eEZzqlq6urvxr4q/3PmqrxZ19dXf1m//2D6+vrX9cd/79ki7r5xf76N11dXf0S//3fub6+/mcfa7xn+vx0dXX1t2FK7O8H/sLr6+uPvuQhnelz0jlV5ytPn7UK7q8Hfhnwv8O8i6dtAsdHHOs7px8o4PhJjNOrjf0ZWGuAvxKb2BeYBf0fwvIcX3mD+TN9cfSXAf/Y1dXVvwX8V8CHWLjGn49Z3L4N/M39F84KzpnO9IXTfxuLUvn/9KARrPjR1dXVDfBjX8rIzvRx9IuBv+HkvT+WLTXjD2HGgEp/GcZ3e/pz/KfSma9+xejq6upvB/5x4D/FdJ/vvv0bZ/o60DlV56tJn6UK7tXV1R+HFd38D4H/9UMFAq+vr9fT977K9AMDHD8t4/RY8V/jP/33/wJAsMk90xdL/y/g/wj8uVivv28Ad5il7f8C/BMPWE/PCs6ZzvTF0h8AFuDPPFVirq6u/jzgOZbjcaavCF1fX/8EFj71aY//pY81ljM9Dl1dXf3dWHrOfwz8xWdw8QNH51Sdrw89VAX3f4gZXP9PwHtXV1d/JfALMAfJ776+vv6DX+wQv3/6gQCO74hxVo/Wb3lX4zrTp6Pr6+v/FPjbPuN3funjjOZMZzrTQ3R9ff2R89rfAPz+q6ur344Jvz8O66f7u4D/+Zc3wjOd6YeLrq6u/j4sPPw/Av6Sc3jq14/OqTpfX/qUVXD/DH99H4uo+1b3mV5dXf1TwK++vr7+2hgGvvbA8bMwTt+IT66vr29P3v+fYVaB/5gzcDzTmc50pgfp+vr6N15dXf0k8M9xHD7+B4HffA6RO9OZvhi6urr6GzDdJwP/NvCrr66uTg/7yevr69/8BQ/tTJ+NPnOqzpm+MvRpquD+HH/9B7Houl8H/CTwZwL/DJYy9z0+Q2TIl02i+vXtx+2M8zdjjPP/ALx64LDGOK+urp5hhR1+F6boAPz3sAn8r4C/6Pr6+icfddBnOtMPIT1QpOMvxXKK/21/76hIx5m+mnR1dfV3Af9b4J8A/klMqfmFWNL/XwL8Y9fX13/XlzfCM53ph4Ourq5+AvgHPuGwf/McnfPVpqurqz8J+FuxVJ0/muNUnX+Fh1N1zvQVoqurq5+LpUn9I1jKxi+7vr7+vf7Zf4B5HX8a+BOur6/33ff+VKwq+R3wo1+X+ipfd4/jH+OvEfjbP+aYfxMDlwAz8C9g7uS/2N/7rzDm+xtOPZFnOtOZ3hn9Yj5bkY4zfcXo6urqlwL/KPDbrq+v/47uo997dXX112CKzt95dXX1T19fX//XX8YYz3SmHxb6rPmrZ/pq0udJ1TnTV4s+oQpuDTH+V3vQ6N/7fVdXV/8Nlu7xJwK/7wsa8vdFX2vg+DkS/1fgf/pY4znTmc70MJ2VnB8I+mX++ntOP7i+vr53y+pfA/xpmDf5TGc605nOdKYfCvqYKrj/BRaN8/JjvlaB5demf3z4sgdwpjOd6Uxn+lrQzl8/ruVGff9rEW5zpjOd6UxnOtM7ptMquP+Gv77Rh/Pq6mrH1rrjJx93WO+OzsDxTGc605nO9Gmo5qP+LVdXVz+//+Dq6uovx3J0DsC/90UP7ExnOtOZznSmx6arq6tfeHV19eMPvB+urq7+Yd6sgvs7sQicv/Tq6uovPvna34dVW/03T6vqfpXpa10c50xnOtOZzvTFkFel/teAvwi4AX4bVhznT8TCWAX426+vr3/TlzbIM53pTGc605keibxn/D8GvK0K7l94fX39+7vv/BLgXwcmTG7+Iaxgzp+HVVT9JdfX1//lF3cX3x+dgeOZznSmM53pU9HV1dWIFXL464FfBDwBPgL+A6z637/+JQ7vTGc605nOdKZHo89bBffq6uoXYYU4/wL/zneA3wH8Q9fX1z/9RYz9XdEZOJ7pTGc605nOdKYznelMZzrTmd5K5xzHM53pTGc605nOdKYznelMZzrTW+kMHM90pjOd6UxnOtOZznSmM53pTG+lM3A805nOdKYznelMZzrTmc50pjO9lc7A8UxnOtOZznSmM53pTGc605nO9FY6A8cznelMZzrTmc50pjOd6UxnOtNbafiyB3CmM53pTGc605nOdKYzvQu6uro6twv4kun6+lq+7DGc6XHo7HE805nOdKYznelMZzrTmc50pjO9lc4exzOd6WtIZ4vql09ni+qZznSmM3116e/59f8oYwxMUZiCMApEICBIk6CCigL2Iyiogijqr0IBCqrFfu+ON7LfVWE7sbSPa790rcdJQTQDBUHNgyMCEvxUSskFlhVdVvJ+4XB34OZ2z8u7ez66u+fV/p67+cC87MnrjKSVWBKjFkZgFCGEgIhAEFQEQkCCEEMgBiGKMAhEEQSQNk6/AxFEhBAECfa7BsGeSCCrkLNQspKToln5rf/+f/0YU3mmrxCdgeMPGZ0Bx5dPZ8BxpjOd6UxnOtPjUhADTgaLAN4meuWNX0UM9KnBKuSNrwsGs8SP1zc+VlU/qoJGA6SqBh4NiAaQCAiqSkqFvKykeaUsK+u8cneYeXl7z4cvX/G9l694eXvL3eGeZZ2hrEQtjCgDyoiFEwZAHCwSAgRBJBCCEBwwBoGIvUp/4+CgMRimFdD2E1AZKBrIWUhJWZdMXvNnmJ1PprO++uXTQ/rqGTie6UxfY/oX/oV/EVWzhJq5MyASCSGCBNT/A0DMahj8NYZAHEbiMKEaSEshrYmAEqMiktGS3Mpqp88lk3OmlEI1zMJmoYQqaAFkE8BHfwcTXhJ8PC43UbQUSs7krGYhjZEwjMgwEiQSJDBIYBgCQQo5razrTE4JtPj18bt2MSgBQdx66uNTG7xSXyuZhFQNqNrvIkKI4mOEX/nL/6rHmcwznelMZzrTO6MNNH4cYHy7Ddfkm8G+Y9B46m3s3zvGOtL9IyJA2Y5QRVQpmkEha2FeMvNhZt7PLIeZdVmZDwu3N/e8ePGK733wgg8+esHLm9fsD3tSWhAyo3tUB/dgBhfOJu8DEt376HIw+Gv0Y0J9ViLbWP396mEtFAqgEtEwUBgMOK7KsiTSkjhnwP3g0xk4/pDS/+Yf+OvJKZPWxLos3N3c8cEHH/G9Dz7k9vaWopkYIQZBRAkhEMPAOAwMMRIB1IAFJROCMA6RGCMopJwNcIRADMGYDx6NAZRi4EMcvIgE1pTJyd4TAvOSuN8fyEW4fPKc997/Fk+ff5OLJ8/ZXTxjnC4oDCQCxB1hmEgq3Nze8/LVDXf7A6kUAwpFG2BQVSjKGIQnu5H3nl7w3rMLnl6MTFEgZwKFGCNDjKgW1nUmrQu5FDYLYwUZQsqFUoQwXBJ3T1iK8NHr13zw0Ufc3e8pCv/u//tff5S5VLSFwpikUxunqA9R2o9JrIIUFx4ogUJRm0/R0n0nQIigDsD8uUHpZGO1o/YIUZod1uZcfN6rAN8EeZPFWihayDmTc0ERggRCiMRgQNiAY2SIgTGIX93GpcXBs68z7UJuRBVtF9qkfxP3hibt2PqccDTbrMxiIUzvmM4W1S+fzhEAZ6p03o9fPr3L/djLvs900k1gdF7EKmfrT2nvV1Ml7XOock7EDKWbVDSdyq4haFHWXFjSyn5J3N4duL295/7unv39nnleORwW02tevubFhy958eIFt7e3LMuBkhNBlCEIQ3AvowNS1MRYC1lt8o3mRawjC/VzwpHH0e6wUEohayajFImoDKgM5BJIa2FdTJ9875s/77NN0qeg6+vrd37OH0xyvUfd2OHvtHnUNrXH3/Lw6OoJV1V+9a/5NR97lTNw/GElMVZXtFCKUtwDs6n1SvDwBdPRFbSgRZ0zdafyGPhQmbNbsxR30Ti4EDYLoIFDbZYuEIKr5sEPjhjzk3ptLeZVcqYoalZAUZrXTVSIQRiGwBADRdWwDuK5ClA0t2PHYeDy4pLLiwuGQaAktsSEYgDMryuiBNFj+CCyea20h0t2TZFg96qPo4+oC4fukp0AOxqmCRH7EipKRoGVks1TqXkTeqVUgVkZEZTqofMTagXl9ahu/vsxbdZNX10qzflo4zGhVNSMCWX7UmN4qPrzd3tmKahmSs5mFNhuvn86/m9xIR2697T9C1VPqB7RbqwOHOWhGzvTmc50pjN9hakzVB5pzKfewo63b5GnHmnT5T9KBYzlBER2P82IKxZ+GgKmaot/N7cXipLWwv3hwKu7PS9v7vjo5Wtevbrh9c0td3d79vPC4bBye7fn9uaW29e33L2+YTkcKGkFLWYA9qgdAQeNTSpaqCobgLDf6z32oHrzOLYn5fI5lew/hSxi4JGBQqBkyKlQUuZPfgTg+JWkNs31OcvJxw+/33/3s1kzPg3VebZFXIFiNaQ338GbEdWmb1H1pLfTGTj+kJICuaiBxuJhjg4Sg+vPQczjGHpmg5ojS5rtzL2RHXDEvlftceKopSnfDcRUIGEjElVjgKpQClIKQQuqDryKgaSgNX7fPEWhAVN/1eJATRtYNdxoSfAVPlRAE2Mwr5bQAINUoFqgCgoRCNE2laqxaH9sDWyj1dG3hXqIGCh+nHks7T4b7JftVeo9dR5CcDBdlJxXhHwElFRtXfSWVrtn89z6LG6i0j160j7ZrtUEVWfpbPgMbYC/Acd6rE2G2XFL7gKABFUXXnkll4yqIBLbs6hD3oS+X4vSWdw60Hgy7ro26v1YUQCOjn7X9N0XAykMDE9Gnr038I2dsMuZvBfSekEpsMwfcnv/h7lfPyIPF2j4Edb0BBh59uwJ7z97ypMYkfWOw+33uHn1PQ53r1n3t9zdv+b1/Q1rTsQhsBsiUgrrUliyMudCUmW32/F0t2MIMITCNAihFJZD4rAq+6wcciHEyJNpYgwgmgihEIJQVNjPC/OyYmUkImtWkioSYAiwGwZ2w0iMoRlctAhrFtaiNrelkFUx85EFX9U9LlKcB6gbv5QigmDh2RarXDbWIvjfSogWIq2q/BW/7BxyfKaHaT/9MaRlJq17ctqzHF5zf/eSu9cv2N/esr+/Z5ln1rTaWs1Kzl7QRDejVOM/HtVwrLUd85JO13e+3UWS2LvtOBOpzsPUkY64p6HJ2S0kX+TYCKcuuKQbWzUCHvFx7SSr0H0qGw9vgzq6m5MnapuxN7tqZ4gU4Ff9qv/Rx87H5yXxHEe7oJyMsafe0tm/3c9VOfqphtUt5qZOnvq1xPhRyzbELLLrCusMydbPzXzgw5sbvvvyNd/94CO+9+ELXrx8zevbW24dOO7nlf1+Zt4fWPYz6TCj62q6Tr228z1lMya/LUpmm+fNOKxtvu3toqYf5pJIKbM6cEwKWQKFgEpA3EN7muL5A0WfABQ//mt6tFu+OCDZ6V8d9eZvdefLg27It9AZOP6QUlJtwLEKl4ADKTEF0CpuuRLvoqLGxVPfCYEY1KpuNeB4nE/WL2DVzbtZhUlJnlBdlKAG/DQrrJnoWnyUQCAQNBAkEiUS3PUZnHWGENCilJws7y2v4Ll0xQWjoA2YBIGSM+u8sE6RQQaiBPM++bGhw1yWl2cCoRQD3o2qJ/X0QdeQz0fiqLoNDhooqkKrhohyNDeW3e4iryiQ/BTdnPVCXToRUz/Tk3s/vXGp4LmCMK84hwnV+m0TTNlyG9sNiIWXakELFNE2H1ktP6TmQhYvXCASEQn2HSwn0063XauWOFA3YlSwfVw44fhG+mpyqNjafQSadhNjDBTJpP3MIRXz6C4DOStrhvvDHXf7W+7XGxY9IHHHbnrO08vnXMSRdFi5yXuizqxLZj+vzPNCzgbCeqUte9W+JWVSMetxQVlSQcuBSGE3ClxEQlHmlFkzZA0UhJwLt4eFSGEIyjQK0xARlCkGhosRCQNFI/OamVMCUYYoTDEyuQEmuzGiqMPAtrbE9reYoaCQzesNWxhWN1v22Uo1QOFrQoOZbCQEYgyM42hGrZweZR7P9INBoYbHxwHVAYnRPDrBAF0IJm+CyNGaPFLUHOFt4K7Tz5pub4aTY66zKfL2egznpEa51KOrYVCrjK3MTU6uecK7fHxHZ3N+2g9IGmjsT6H0TF+7aBNpnLbHydv9t/vyn8cxqfrYe4NoG8UpnT6X/gR0d2IyyV2FnIaqInosf1siiEXnlHWl7PeU+zvy4Z552XO/zLzY3/Hdl6/49gcf8e3vfcgHH77g1esbbvd77vcz+2VlXhPzvJKWhKaMpExQdT9mlclmgNUT3mgjbIvp+BPXA6oeWF9NxlrKSymFlBMpF1LOJFXWotSasBKEECKDpyX9wFLVe2sYc6cH0+8Luve746o+cvTeybnfDW1zvHkaN6NTr4+fvr/9+3b61MDxHPv/5dO7jP3PuXgYZ2ed6kBGlGAVt6hgqRYYqQGlNTw1EGXzUlJFRvXySWchpQcetgENSJqib2GpVia75Gxho6UgMdJgmQZEI4KBR5FALg5qqQBGUc2g5gWxsMotB8FuxyqFlVJY14V1GdgNAQbp7sMUBFWhiLhnq2oPCh7sWVlEDzvUn4E48OhzJt4tPSwQtrFsSoDWkQqdF9Luw0Jv8qa4d9i+ekxVpPvc5/CIER7fY/Xo2vCq4N2+q5waEuqpLGdRFLSmWwTQgln3scdf7bwdB/c14sraCY/uVJlmQDh6Lm+M373FnqPymJTyHjSQ14zGzJyUvCZ0BomZEkaWvHA3z9wdZrIWxt3C5ZgJZSHvZ5a8kNNKSXvW5TWH/T1pmdF1Zk2pGVdQcQVBUAmWswJIjBQtzOtK9Ep/YS0MEihixqak2UKvFFLJFFEkRMuFlWDKzGgRCIJV24sIY4ioQIyRMQQi0kB/qcaCIAS1Cn9DGAkhoiWT8kqhkN0gNYRIVEVKpqhVBkx4gYlqCpCIaqConTfGwDAMjDGa4m9Z2mc608eQIA4ciw5IiGg1VMaRYSjkXEjJqmLamtPGl8DZSweSjv/oYYztg83jsx37RtRHVUe76/hw3wQFDb69TS99C3R7Q+Pbbqjy7oc8GqobUOljYU7wSjun8vB53gVZSow0Xn80gt6Y2ynO3W2ejFZpgNFbabTJcFBpcqdmDULSwposYmO+vePw+jXz69ccbl9zv7/l9rDnxd0t33v5iu9+ZEVvXrx6zf39gXk1wLismSVl1jVTckGKmkEdk9xSDW7FdKnea93uoHmnt/upYreCzeIgsaYulVIo2da5FcVTstpPUotQUUCCMAyFOI6PNo9fJvXrvN/HR+YeeeOXN+mB9b/pi37OXk/0cNPPYl3pQ1I/1UCcSlGWZWFNq9Ue+YTrnD2OP6TUPI1AdQtVZV6glW2mAabOi+igqnkggzSrVyshUs/lAFK94qUB0+CCpxeU/iVX6CUIKgbNcimEoqwFlqxMSbnQQAiDeQGdmamah3CcIhcXEwUlqXkHpY1FgOJFXozphRiJg1X43IBR8VC4cMQUFFO87XY96FXqM6x36HcjNTE+8unsOJ+dTu1bBuY2W2719Km/0sYkbdgQmsWpz5ls1dikegA3ZcdOtQVeVBXlNIei9zT33koDjJvNtl2nv6lmxbfr1kqu9TqmEIQ3jH39kz4aTzf6tkbbQzjxPIrn94Y+P/Px6PXr7yBhQkJkGgNhCehhT7qfkeGOME3cz7fs55VlNhA/jJmS7zjsF0qa0ZxQlJQOpPWOss6oZnLJLuQNgasquUCMI6PAclhAYNqNaMnkJTHEyDhF27siyBAgW27ONA4gkNaVYRjZTZM/WyVGK9BgIe2KDAWJMBFRLzcvVWcrYs9XrYqtqBkowjASwgUxjiCZUmayJgpqxZFCJFQLviolBAYKWRdqKL0ZGQJKhBhaOLqqeaofo9DRmX5wqMqLECIxDoQwOmDcUcaCek7XGhJsvhfwcPnagqH35zVrf73IA8ZEacY5569d3nw1wDaPUPvM5XI7VDuw5L8fRYnIZmQ7Ok+9816UyTEg7a5xZKj0a21e0uN7OwaHxzLiU2vFn4eawa+Tyw/8th2j3f09oJtgzEXxQnIAakarXDKZYh5GGSmaWBZhf79yf3PPzUevuH3xEXcvX3J385Lbuxtu7+94fXfLi9c3vHjteY33B9aUKEVJHhVGLkhWQtGNf6ob/Jp81+Pn3oyrbiTsblnVaxYUpRQ8Zan70ULJG3gsBYpuxlUpQlD1fpTCgFU7n8IPtkHuDZvCY17o837Vv/sw3jze5wqkNXOYZ/b7A/OyoKqf6Dn+zMDxt//2f/kY1UoHKpATxXG7ePUsNIYsodn4GyMUb1LqzHML7zAG3phqsTCuxqAFJAohDsQYUYSSMyklNOf2mGrYgin0EQkDwX9MmVrRkpBQGKJ50Uoxq2JKBXCX/DAyjiPDMJlyEwJFgpUqLsWqcmphRJjiwDBMhDiiEslsOTxmIYLq7YEMYvZ/1WrpKfylv/TP/azT9MnkcxFEmvzSZjVT995JYzjGJKoQ8SXpTH/LVdDGrEoTaFWgWN6hVckMaAFTBbfw1yowLRkKNA8Wuoe4VyGwqrAUIRcBiUgwhbY4CJUQmKaRy8sdBeWwFJaygdEKrgoFFSEMA9NuxzDtIAhZEzXUsaCg2YS/G36sOmtdmtv5to0oDogqwrHiOBWovvt59Ev71Ag9w9A2N6UWkBGxKmv1uQPoBiZRL7itEEMkDkML6bV8wtJEaPUUViNAU1QaP2hbuNrTqAWKjkImQrCwL8TyT+sDli0EtvdU4vcRJLRz1PVXFba6PGukUlWHRKEUX7N+YDWQUI/h2FAivqKPweW7pfnuBTE+YRgm1tXyAufDnvlwj2pyz3cmpRXNxpzKunDYv+ZQIK0GmsZxYAgWEpp8n2iMaNpCtcyQogyDVaiNSwKB3TgAgSSZi3HgYjdQ3OuvEpBBGVUYd4PtZwK7aWQcRpZ5AS3sxh1JM+u6MERhHISAurEomNW8FIYwUoKQU7F9LIGSCiqROOwoTGRGdhcDF9Ml5APkjBDQIuS02myVqpCuDCEwxYAuK4f7BXQH04gOA4jvy1zQYlUI3yWdI3K+fHrX1ThDjS4JkXEY2U0X5OkCXTM5ZNdJBkQStqNqjvVmXNuAyKbENf7cMesa1q9vXUWb0rd9/+O1xA2gHoepVzB5bNR768M4Gmf1hByBy3bFk4E4v2+PpTJkbdKzU2HfPWkFV7ivUL3GgXQ5ie0Wji2PdifHANJy8jNobi00tGSWdWVeZ+a0csiFtQSWHNjvM3ev99y8uOHVBy+4efGCu9cvubt5zd39Dff7e+7u77i7u+d+P3OYF9Y1+XyENrRYjNdV9asWv9Fietu2xk4WgcvnopveUsAB41bjonod1f+uYautNqCKecNEiOAF/0x/iyKMEpgkMMnXFDge4ynftpsu056xwJERuXpyT3bYAzNxdPoa+fXG0fLA0d0Wtz+rIcm1mhrm0Bm4W7D4UcTXdpZczIu8zCv7/YH7/cy8rqgqYxwI4zsGjk0L8wdWAcZmvepmQE+23ra+7cZCp9X1zLVOTnswPSCV9mNerApYBMs26MqQVAXfNepqsbMGqIEYByQMWOhWIadMKYkYDVRo9UB5UZYqTFq/m7ZS/BmohXFWy74BmxEJI8hAViHlzJrtOkUziLuFjxQZoSq0ITyOolp7/JROMS+q9MurGRrVcY+6p1Fdnvr7UkoLN7VqqmyVMdm8kUHVCt5gHoZQwVc3v0UUJZKBJEoKSpGREi/QuKOEiayBlCGlggRnih4rq1geXNZM8fURglDCJpW1uHFCzG9aRChCyxVRB4pJlVjwPMdg7zfmvQGUxjpkG0Nz5gbz2h17Vt8h+XNroZsOdKwCbZ3L0jx8EqLtXSmbdVIrg6xlw+2LMQ6Mw4gIFElIXsklNaFSmavqsf5Sixb1G7sCxU7y9ViN2mg4+nebd7Mx7frcpSkyha0wUhX89ZyVI0lVCKRevgpQrxtc71l6Ri/OhNWFo4LWUsKPE7L6redPyXmgZGXZ77nbL9ytC6uuBFYmCqMqlOwVZYU0H0hLsp6XKOMwECIMHkqaszInN3wVJas9ldqAOmerxBdj9Duz/Rrd4JdTcSOXrafa2lmTVzAuUFJhLSvLnGzdyIpqZl1WhkEYsqAkkGzvrxYKFYKtlFwECTY/ySs2iwTmpMxkcrzk+eUFYw6MaWEogTUVDs6vQrTFlYtVSH7/yQVhOPDq/pZ9WsmDFXKo1nWyraWs77ZR9Zl+sKhG09SWPCbzq2mr6iWBGK1dUBbLmd1aPuF/1/NtvLLisKr7PKxgbgrgJjq2KomwyVo7brtYNXgdKVZ69FfTouje0zfeYTuqvnSgsSqm1WjrB1S2bWesaLjJ1nqYdKc9VbrfHRW/qMH6Htr3CucxOPSnfvL+9rd54RIlm2NiXVfuDwdu7+95vd9zM8/cLZn7uXB7u3Dz6o6bj15z+9Er7m9eM9/fMe/vORzumec982FmmWfS6q2oiktRF2C1EOBRRmWn27Yc2G4tqW46XVHLcW8hqFoLI5YGIJuxtsnHPvrI57sVrBMvYmhrJgYYJTAiP3whjM3acwIUH9hKX2QYb91dCt24fOV727N5nrm7v+f27p75sKASGKcdcRyYpumt5//s8+wPqj2qFjIm3ULbhsnR0OXoXe3f6g/BH7I4njryBrgy2U5V3Msg1LYJNRyr7QXwSpcbsAzu5RAvnFJyoSSr7CgUNPRAoTLkWn0z1BZ5oFiVPvd+aM90JKLBQrQKgVzUKgzmTM4LSkLEq3XW0NBW+StQ2w88Bm0VRAHdrEz1OddE61qgAsXBgmd6aA3OKV7cJBOA4iGHilACjixx0KhYPyAceDX13n6qwqvBKiyqkImUsKOES5JMFIZWqXGZV2QIVlFxGIjDQNbCsi7s93sO8wIymlc5miJdPWMSLAwvl8JhWZiWSAwjg1uYNSfLj/J5r+CisDHZZuToBEwDOfh1KigJjyUaORbIdRTVyqybkAzewT60MW+KTduM5o40JcnXbhAxBR2bv5ST51J0ykK7d8tdq02DBTpBZJECtbiEBDcyqFoua21dIlD3taJdD87O4ycd66iKimzi9Qg0ghUnqBZaFSyTzsKIt3XYMy/31FK1vE6YPwI9f/qU+9vEfJhJ+z37+wN3JSO7gWcXT3gWIa4Lh/0da16tD2oupCwWdjoOxCjknDikQl4X5jmxpmpRNg+rVJ5YYFkTQYrzMCHNVlymFMhrZq+rKRxSeROIZtJS2nyv6+KPyx7Mmg60FZe6QhJqALJoDX1PNl8SiSGDKFldOVFlLYUUB1IcyOPEKIVYhAGF6HmufmwumbwGhjByuXvCTnaUKVHSwn1eSAKJSNBAxHIcH8uQ89v+pX8HwcIXa85wNcxYxerq0Y7EOBGHCUIg5cSaFlL28vqyib3Ni95HZ5jyKqJcXD7hm9/4Ub75I38UT59+ixAuSCuWs7IupNVel2Vhng8s84E1zbYIglAopLyQ8kKQzBgKgyZCWgiauXgy8d6PfZP3f/6P8+yP+vnE5z9Kkkvu7wr3NwfKvLqXOxNkBl15cnHBt37kx/nRb/0Cnjz5Oag8IZVIxrx4OWfm/YHD3T15mSlpJaeZNR1IJTGMI5fP3+Pp0/cZxktSwcKp5luW9YaSbwllz6Azl3fffufzKGJGSdzosK6JeZ6ZvSF7ybYHggjRi4J4cNMxmLMJ7KJSOxl0qtY1wPnQ2uwZz/a9xo/09FuyvakbJJSezekbI6UJtZNrbV87ZoDVvvfg2PxbLVJMKsDsjHr1fh9JPFp18O3vBmjbrRwDxg009t7j7TmZgyGxLjPLfGA+WIjfze0dL25ueHF7x4e3d7y6n3m9X3j1es/tqzvuX98yv74jeQuNklbyupDTQlpXyuq8onQQpD1ce1ChPirtDGFN51bnsTUCqFZDNd6f1QyA7bXzLLZoqNM58PNKx4uq0aOuYVBi9UKyOSS+NlSdCc0A3/+9LZwjD2GnU9Hr0Wyft2mT47c3D2X9ev/s+4X65q+b7nO89604YH+R/kM9/hwPq84z63pgXWe0JGKEMER2u4lp8kjKt9DnNBBIr5n5Q94sFMfs8IhT+V8GJjaceRwqURlNO4tuz+E0nsPKSleQaCAGr8a45QL4NVxhljZWLwhSqoemdCEAbqFypTVIdOti6EL82PoQilXvKFUJdY9WVgEvzJI80Th5sjGaEMnuVQxe6VO257sN/p1ToIZTdqEK1Yvk91/hQBWg2qpx+vC8YInNlYeddoyuT8jeKrNtwMv2SnAFngYiUlaWJMxpYE6wpshBC3NZWNMBzQMjkREhlkCJQpwGhmjhjjEIaDbvTIiGhcS9qwK1p1/wNVyZrFqlDZvLVqHTnpZV7azjz+51rYLGhUwL09A2hxLEwqhPBO67oq2Cms+WQi2o0Aw5fp+18i2oO7i1Kfz2HGo+n91/QSDbvgmd9NgqX1IR2zafipWrVoNm9NZvVx6aoSRIs5pWZlDZStuzuv3Uczdvv2B7GAuj2TzXXckc8WiCkt3wIdTQSAn22phRe54bTxFwoKP+3B5nP/7Uz/wM6yyEImQPswdlDMJuGJgGM9LEVWBJ5DVTSiQXoUggyEQS2K8rWor11CpCCDumXaSosCz3LMtKTrVJdEHIW3PoGvbk95xzsNzhBugLUqQz5oXWQ7MPdd7OAaqW37tVpbXZscJW/ndWEA8dVwOu47hjd/mcZ0+fczlZMRykoJoQgWmcGMaBUszir24w3B8UzYEwXjDsgJLJaSbrBLJjGMxD9FhU0KOdfuTZqBEJzmNFqzLohjbd1t+mpNe/7YvFH6yB0uzfr17xrecs1YM/DBZ5kbO1R6l6ohcO0gxFC8nznCRaTrG0RKpM0ZVcZnJZ0LISpHj7JctLtRJqhei8vBmT/edIcfen0ufEhzAQJ8tDVSCvC0UjWtzX0kVBHNPjearQ7DzBooNSWpkPC/v9zDovlJSOjGB1D23pFlCt200GCt0xuvHGKnP9Z+NgPY+l8b1evzL46bxJ+k82HvqGV6Re059hPaZehfb+9vdWqRXaepSqY233s813p+B19+1SHqt25ufS/nrvlkopLmusYFfE+0PX+27X3t7bvI3l5NUqgC/LzN3tDTevX/H61Q2vX9/y4uUNL27uHDje8+Juz8vbPa9v7rm/vWe5nymH2aqh4i3H1KNHsrX98vKDm3Gv6R8daGDjH1vaTMdvqfmK3tKoB46qDh430FjlqnTX6PmXsazT9/vn1b1f1HtB/4CT4GG73X7rl0ylrguLf22rPtzWnmwfvnmZ4w8+jQrZw61G2yoyg+NKSgs5r4QAF7vR5cVAiBFRWNPbI3I+M3DUxqi6IdWF6/9WxvnmfXbMSGsYQVMzGvM6isd1pS04uOiRf93i4psnZwufMk/hNi67XMeO1RhARoDiCmW28CnP93Is5Iq4Ccmhy8Wqm61aHFXEM+M2z2FRsRLGxSzQuUBWz2H08VdQacqUmvLkvcgaQ34EakCti3EvZau0Wi1RVdyIBt8w3kZdMA0kqlvHDVCHYLanorp556rS7Z6+IJGcpVkDY7S5zTmxpsSaI2uJLGvg9V3mZplJsjJOC8+fHPix5yvxm3ARA7EWyCmFUZQ4DpQnl6zLExCxFgKlICLEaPNjbSsLQYRxHLnYXViBjxAs5FZxQBEsn1Y6xZjsnsiar+sLpfZuQqt+SLUn1Mqsj0baqZpH+sa2Vx3zUmNa9ejrptRboRkHU0ILcxGUKLZHsq8R/JmgtaiSi5Hg68VBXC1Zj4CWaKCb2r4Fz3u29b9VYD0VjseGJep3OtBbinTzseVSo5vHrdRcVx9fnaga2mvGI20KkZ2nKsBeSVd7yfDu6A9/8AFPhudchIFxGPjG++/xzWEAyURxY5ioF60CdcU1hpExBCiZ9XDPwYsYiAwMccfFNDKNloOzR5ixCqlW6RSPHDBDQjVaFcUWbhQ0bwUYjOeJVyr2/JtQ2+EYpwji+RO5WO6qz2nwSoMlWV5QGAZCCB7yVdpT1pKRULicJp49fcqTcSJqIavla66laeBE906rFkR25Jy5vZ+5z5aXnMYIayEkJRQraFGaR/txQo6rL9YtavZUpBrqcAVjk0bqlpRabqw0Zd+K+W+6bScT/fyuvvuZarXHQgVkEgMQIbt80prrbGHa6rI6l0Bt5WuTuvEuioHUnFdynillBUpr3SR4USMV8+GL3VOQvso14DlltcB0U54EQowMYaDESMoKa82tMv4imDGw8ozKzB5TRVVNltedakiiRSvkrBZSmBJaktUqqMrA9m369bUB58qc5fjQ02sDPVDuFcgaadV7MI/0qqYcbYq99gpqf+gbY2i78OhcDUi1i9Zexr1hWLZTbf+0sTfDX7tWz0cfbyZLysg4WGsxAmMbve+XVkiu/zGHghx9ZuPMeeVw2PPy1Uu+++1v893vfsAHH7zkxctbXt8duDksvNrPvLo/8Pp+z+3dnnk/o2tCkrUXG7BK0KKbgTNoB92cR2xG+X6+afKwB481h7Foafw3565CKuo5nv3O2eBok5dw8p693xum+/kynS60FK5PBW6+AtQqpYr7/dtz7sKwT3BN07OagRoD/qmQ10JeUyu8FuJAGAYkiqdfuD5U9Y4AHwOSjsfZ/Xv6q5/hjVO0taL9TJqOk3MmrStpNf0hiBAG9xerUFbvkf0Jas5nBo61wM3RQCs36DwR6hLzqER1s0ptf2v3/a2WjrNO1yCDCBpqEYROgFZG1N72nnBalWF7aNJ2Yx2mhfloMf9gPUUMFTAY2KkeBwtRDU0JlrrJN0zkJFvYqiufqbhX04ewlRbBlW6v+ikVXBwvhccCjs2i1X66EsxtHsWKEBWvtl9bcuBMTXCLaw39rQVLBLQQqzwVDxCOAeKAykARKGu2Z+m3WLJ5ZdccWbNwe0h87+We793OHIA43fF8d8vd8wNDVp6Nkd3wlDgYcGS1IiK7GHiy27GuiXzIZAcAQ7CxJS3eSEMYYmSaJsZxRLBKYkHVej86wCzioZqyhVZW4FibkINawQ3ntoI0D3IIXo31kebRfqEZlo9zgrt13yHG+rH9aYVLrPqrtVVQURRTjiheK07L1vLA57mWqAdreVC9u60naF0X3k+vgrOanxxUqcVpe4XE9rYLMr+fbU9XqlXt/LfsCpdoq4Rahao5Gz10uPWOqSGUaqGsXnCr95AgdMfXuX739P7P+fl86+mPoPuZKSg/9+f8HC4uJm5ff8TN7UvmtDBrsRQ9vE1OGJmmZ8RhJKWVw7JnzZlMIA7RDFElk5aFw7ww54BcPGMKAzEXJoFBxLwn2cxoKScLg1UD5qUqJZVv+TNs+72ijDojWlq1XNWtpFBde1UepVLMud+8LvZcrbjYSmRl0JlybyGUpaTNyxOCh71bQbEasaGlsCosNXcHQWJkQpBVWfNq4bmlRka8e1IRV9gs5aH1+nOAm4v6vrCCPa0YHNKKw6liCmGnV1Rjyva4bc81kaG4IurrV7aCXhlrC5BKIavz925/GPjzRt5iESaCFx5Ty4m18KZE9rwudbkeqoxW8+SYPnTc77eNtn+jicHNwIQbFutclmxyqa6z0Ys5rcm8Jk1APwLlklkXCyUsabVQesVbSOFh4slrFZSj+gB1wmrkUVUwtiiqTXFvelF9p3okm5ewB2VSv1Yv0ulPlUfW4+36VeWqivJRDQqOf91GdvxQ2/4VmjJawWjTniprPtL1/Jg37o+j+5ZHmkNg66/ZSaoHjzsBji0F6sQgkNLK3f0tH374AT/9Mz/DT//0z/Lt73zIy1f37OfMkmG/ZvbLyv3BQptzSlYNtdqXsT0ZtGII6a4sLh+dfzb5vkGIPo+21huorxtozBaO6hE5Nl22RmLlG92EVZZS1071fPeFL+swVLeey1JlfAxWnf5r1Mex7omPXX6+B+uj2qJHnOmWQjmsLPsDh/2Bw2EmrSsBYZgmxmkiDhGJlgs9xsg4OF+N/hPCw+ivDsH/PYpcqfv947+wzS3S9n9JmbQm1iW3iKRqK7f1kyhZSAXWTxCPnytUtbK/fqyn12lgTbt7ODmJLegttImq9Lb7rprwdp3twW1q3NGidgvLNgg5ecSuwGhGvDBN2xy1508/UThg8vy7mk/ZirkURXOxQjqYpZ9QlV8DjS0sABxplsbaG8d1y2pl9r1QfQyqlWm1a3FQ72+zA2zVOAMwxG0R1hC1ow1lCNG8iOJ9EtnCEyUEigwUiSQvWx0UcrKz5CwUHckMHFZ4eTvz7Rev+c7tzDKOxKnw6m5luVt4osKPXO54drnjcjcSCqT9gfVwYM4r67xsOY0Er4DngbSlbLNc3PucI4HN7F6Zc8m2SqOUZtGr4Gm7bfECOv58mlfLQ/xCfLRcVTnaHK6cVYEtm6DeVIJaQbRTSsQ9EwyoB/IE8QxWKRRNrggrNTRLvYUCmAd5GEbiGImD2L4q5qkUaGFcxhKC74dOiakgVisodx9k9fz7s91C4asoFKB01V2rh9t5lIR2XgtNriGXno/cKXKtWh4GmHrwLWzGp8cy5Dx/70d57/m3iE9nLmLmR3/sG+zGiOg9yyIc1pll3rOuC0ED47BD9IIhPEUYCbInhsQUAiVEJA6gmXmZ0byyFpgunnHx/jeZph0hJyZMACyHA4vn3rAcWLwoTu2zaKHkrvCptPXdgKGI5X4DJSfMki9tXkJbg3V91pCZ4h7Jbd4MtO6ZDy8JLBbdULI3mR5AImQ3yoRACNmaU6+rezmFOI22NhOQLLeaqKiuJECLtJyWd04hkBPknJEAowgSB7f2GgAZwmAtReJgPBHnGc4rSvbqxep7x/dDrVTd2uRoXZfe5qT+J5s3fy2JOa8saSGVlUK2EvrBzmWej4CGSNCBEAUJLuOCt5kQUz5L0RZGLZLQYipv0GI/JROiFXyLYt9zCdDpRp2W1MBlB5hxua5b7rxQCDEyDkKsudZqhqyP1/q+P8pe9CQtCyUtpGWm5BW0tnIxuZFSagbXNzhDZbMNNOoGmqi6hP9ej2eDY0dG9o5H9qemO85+dX3nROnqedmxzO6BKd0nnTfK5cjmreqM8b0y2IORRhtf7sTl9r2qbzzSfgxB0JxIZWUOlq4yeNjqdska81XHvhk2bZAb5FzXlZubG773wYf8kW9/m5/+Iz/Ld77zEa9vDqwJr2UhrKWgayauBhprgZsWoq1VR6zGtB70b4YCbXmLFSxWWbXp1qanceRprCGqla+2Ptxd0Z0tzYWjVzNc+DhPgGM9clsbm+EnBAOPXymqlpMTfa2vKNzr2aLHe8OWZuWr3XmLku9nDq9uuXv9mru7e/aHvRkJsCIz426yqvQxMg4D0zAyDZZSFYcIwwDjYOlUMVgPcTdSH+t1G590wNLGdkrS3cORna5YbZWUstUYYSCItkJ5YI4PGWhY5G302YHjieJkFn5plpTjy2n30t9s91B0Y2X9d7Yio8fgr0f/pyOhvt9tiMrguqWOIyQ7viq0tT2IbB4UO7xeW7oiHtKAQCkFTdly47wJdn1O2p5IHXUFJt2PL+x6l0ePDGnK77smLeZlqYyltSqpDK17nsXvWYNbr/yYgnrzbmggXwshBg9f6IwArjAmr2BbSrWIduFTOvr9DqScub1bePnqnleHBM8spynNK+Fu4cO44/Zb3yJ/qyAlQrYGpkta2a8L+5JZVVENBBkIMvgcGiAKwZQmxKxza0oMwcPD/FbKkTXGBYlmFMt/KSpbiJ90AEN0W3uhNl1/lGls89b2RxXo1KVV16psDLNatbVs606CA0Fb/xJo3mDzPvo68VA/i+4WRCLDcME47Zh2gyt2NbwrbXuxrjXdBGB9Jr2+ERpzdNDnf2/Ase4q209FcY/oxgA3ger+LnHQWMONmxzpvsMWxt44mX+nDgm0Z0zvlG5e7bmQA9967wlPnkaSZJbDnllXNCpogjQTcvaWQ5GSJ0oeKCWiDMRhYoiFMAwUhXmeOcwHVJXx4pJnz9/n2XvfZBpHAzACQQv74Z4ggXQ4eG7l0kBibT8Uw0DyvmItd8OfVet3J1sojnmqtwkWlwHBw5psAiy0shbOqGptKYnD4YaUDgxx8JDUyBALIhOlWMXfiwsL6T8c9hwOM6rCOE3sdhPjOKFlJM8H8rwQl0wQSCpk8MqF757iOBKy5eiJWE/KOFh/TLtmsf6Z48gwjCDRQ/aDtZOq0s1Dr2vRNK0MKZj1WqxHjj1DBtAI6iFj7inMmlnSyrwcWNJM1hXFQoFRiwwgeGueYTCgWkGfBvc8gsSCyOBtUAppTQQSmgNS20hpRssKsiAhIQyIv4+25ky+p7p14Fb9LfIldz8JLStaFisypwuqq73vVbMfy5CTsxkNc8rkZWFdDuR0QHVFas6b4l6evCnqR2fZDK3QqU6dYbtipq2ifg3cd/4NWw0JO+UbeK27XNUz/TpHH/lbmyeyXvsI5mmbElc+39ToqkGwjsnVuI2Pn9A27k5Jbx7VTS48BgUK67znsNxDWhkDXIwjl7sLKwIyDJu23Y14A47mAa963Lombm/v+OjFSz588YoXr2+52R+4nxfW1fZtFSFBsUrYbEBgk3XafhTXJaht6gQkNp3J9kemYOus1uWwcdp5DTjWqqnaLbFN7rfojjqn0i8UafKvGZv92N5RQlud9rvQHePg8StDR2tq20PSwCTdM9gOazmmYQsYPzoqQ7nZs//oJTcffcTNq1fc7+9Zl9XDPwPLMBpoDNFC8YeRcRgYYzTDxRgJ40iYJutbPA6EIdpr9MKd/hOahzLaUvTI0gf3m689ORn0tsWC9aMNZliMXsE3eAcHceNKLnJ65iP6zMCxlVeuo9KqxJ3eQJ0caYq43Yu081RUvH1WlUntj9yUtg5YbYtZ2gLvGXK/aKSOsYGcLsRDtbWkcN2aFhbXWZoa8wesvUHorEbavXpPQQdmDzFE7ZgGbnWU/r3jJ/7wRHyfVMHbduPBAcUW3mKWltC8iD54tl5+npPTmhxKl9jdTkI1DZSCh0rZ86lhTYoVQmh5LXmEBGWBtE+wKNOTgakMlHllPazMlwvpUMiLsuwXyiwWXlSyVa1VD8cKEaucGTaGLoE4wDRE70NnlUPrAS2/TZyB1zlz63sMHsrhydFHVmQXOkUsxEtELC/tkRTVaona9kMNYSlt0qR/reDxdJ11QqwyHglC0GAVhquBwQvsWJHYaOGS48S0mxiGgEh2fdaLDRVTCGsObQOO9rCquKLXehoYbiBvE3it+llVpDznTfU4j2zzh4d271sVYftHjkBoHUIXbdA9oqpEPc5uBE0WHprDwBJGSiqkBRYdiLtnvPdcmUrkcL8n5cKshTXXqqVY/1HNDFII/vzTYn3FZBjYjSNDHGBd/XtCckNIUXWPPB1vN+vxEAemYSCIFbnJLXQ7MgQxe5ls3LIXxKV56et+8fDlaNeyHDLzbFoOcvA847pGLCRRfVLXdTGgIiNDzN5b9J55vWfN2Vor5QIpQww8f/KUJ8+fsd7ecfP6hrt5rRox+ZEmchwnhExOZmAZBu8tXAqDRESVMY5M444QRi9gVCjqlQnF8vm0DA04ApuxJRjARCx/i5xQBqwIUaQWflLUvWYzy3pgTYuBeSmYbrdF25jCEAky+BrIkFZfCkqIBQkDSnAwlVBWSo52rBSUZH2QORBIVtxGF1uf6jF6DhglRNuv0fNe6T0ptTJ6trnWlZz2pAyH9cC83pHKQsE8kbk8TluVyh4tr9HCdNWL2SEemYDLwLKleFSw2BTVXh5W6vZI40myGbcre66Oko1PnVI1evcyV7v0j95gru16HUxo52mfg/Hb9o6DE6nn0+5sfm2EWrJZjjwI2gHGGrr7wBXl5Pm8Q0rrnrvbj9jffEQ63DECzy8vyc/f59nz99hdPEGG4QgYNX2u3eVmUF3XzN39zM2dtUxaiyJxJI6QSyKlRE7Zw1ADQ1NpHcxVfQ/1fWNGHBEIuLFJBvDoCstLLFDEIttQj8qokWIe5q4NM7R9pt0zFzZdrM7J8ROv64Nu/rb1WKnKQNPVqyym8favFHCsnjnVdjsNTWi33jkBYLLdZy1epF4DoqTM/PKGu+9+j5vvfo+7Vy+4v71lnmdKStT+2NWJJO6FDcHkwBAiQxTiMDBMI3Ga7HWciKP9HcfR3xtNfgwDYRyQYYA4wBg8UbbjCXWSTiBH9TvZHFmxtOjHFs+PDWFgHAfGIbQTfhJX/XxVVUVa7HjPf47BewV1Hax0ZWDz0DyAkxp4KrS7qJ6PXhkODmq2k7dNs3HdNgr73RXKHjRWT4q1EHB3rdgi2QTAxjgtdACzIIvlGtkwahWrrccdelwopYKMo/ts1khTsvFr23tVGL17qtbR9sycAdUKz+aVCy6IKkOpwMKUx1xDeMMGSEroctF0C+utz1wr0PQZKxIIYSBrZE2JebHcERZlTMJlDjxPhcsVdlFZ5kJcCsMKZc7cv75nng9kUcIYieNooA2sP2O7SW1CTkJgjJHdbuDyYsdumkxZy8mqiGr1Km+guakJQYm9THGAuj1LqL1aQhCzHmXL2XksOlpj9V7ZmKPSKQxukayCw5a1AxBDYtS82xC85LyHnyg04Cg1jy6OjEMgBoWykmvFrrS64aS0/MHSXLhtt1If2bbHzMPSPOAV7NZw1y5/uO6dOldW5KVQqhXbmZN4UZvirWGarka7ZGOsm9LgVP+uoT6PJBifPnnCtNsxZ6HshScXz4nDM6I+YRruGab3yPE1t/FDXt28ZC4zlEwYBJWArjNLOpDWwhA8rDBlW4fR9vE8H1iXBYgWEunzPooSc7JCIL5OY4yEOBLjSF8RMQ6BMI6M48QujkxREJLngpmXUd3ggAZUjUfKACFY/8bgbg4tllurbMWrbK1ZRpJ4sSa8Q1gQ2lpalgPLvJDLnqwrxfOJU1aW+wNLWnn+9JLnz9+jhEieF+Y1saRSK0Q8Ck3DwBgu0WmkhuwKYg6EYbQiHRI9AqKGzgsBrypbK2zrFurdAAjqYU0RLVjoabaoCvU8ZVfpqAXgUk7ktHqYZfKWIH7zHrIfCExxYBxHL1iUWQlkLRbWGjMSh21/5IxKalYaxaJPkiakWN/RMQ+o9qGdOAvuwtpqqyJoMqiekWqYU6uKu+SV+2XvebxLy7mmpMeZyAYCN9VS3CJv2ok2mQeytUaA7T6227ZzdYuu8uRj3UDb9zb7vDSwUU+9Gdo3qser8/DN2Ibz2yOT2HZv3TXxMdGwn3RgtAm8oxcXjtu4RKqi9YY+2Nntmzyt43uslJwXH36H29ff4+7V90iHGyZR0pNnhHxgkEIMMMRLrAOhpzBQPY6VqpvHWq4sa2GZCzlDCBO7C0EZQGfKWii6mmFcfB5gK3TTQExxLld1xBoZY1E8EkZUBneEWkHFWpFDxELDlepZ9CghLLpIvAjg9kS33xqIZVtTenTElgRS12g9ajM6WJRCaWPX6iN4tL7jn4W2tdygc1vPfoR9bgfZ7zWdrOr+YBEd+wO633NYVlLOHO4PvPr293jxMz/L/Ycfkg978jKzLguasndZcD5c9ZYQzfMYAkOMDENkGAdroTVNDOPIME6EcSROO8bdjvHygt3FpeVJTqMV2hlGM3KMERkDcRRkiJ4naT9Kfa16vBevDFbPQ2NsYHIDjoFhONZrPing+LN7HNsvGzhobv7GAHzhSuiaqvdWs5pwumng21K399S1cJEKONR8qFvTNr9W6Bi2n7tZRbrxViwHzZqND78OrFDcSxQsz0M24dC767VkG4oIGszqWtSqEaWcWximJfV7MqxXn6uKeW/QspzBQpG8XUPU8zUfC3BsQk6hq7jnjL3KA3/kFZC0+Pq2soz9WaGLcAQUWyUMgdpI2cCphfdUQ/Ssyrok7m9nDvcLeRXmvSKHA8+KEkvm8rBnypllXiAl5HDPzQcf8EfKgRIKJcD05ILd06cwjqwhkIZIGUYkjqb4BCuMEgcDdHUt5JyRgik5DmykrVFfqQ2Q+Ub0PxXMK4LlCdm1JmvfUoQYzRjxCZ7/z03bOleOFpXUHXYCgtgApVkfTe0D80aIZLey48IPJJilrLFe338hBFP2JVPySk6JtCyktDaQWMfVekZ1e7XP8dnGfMxDeuBYmW+7t3YftXqn51PV/Emp5+r2kbOPDZzSFroB0+05qrqCqAUtYmGC8jg5HEOIzPsD8wLDGCll4unTJ0yXz5H0GimviEMmDjdean2hIGjO1imxFLQkkiql1PkRphAtQV+VvCYSBtBGGQlALitrWclp9UqR2kJDwzAiYfBKuoUwRC4vLhgvnhDjxCCRMQhBVtblnuWwpySlAiIzEI2M4wWXFzuGUSnpnnU+kFNBJbBSi4kpKpY/bG0YDETG4YIQnjAOlzy5vCRI5uWLD7i/v2E3joyi3B1WSvaQyWKFTUQD+/t77sYI60oSIAbEq949UsSxFeCS6Epdq/oEGECLbmysOnsrnqWgUtsUWPkjoWyF5cSeq7iSYOHjVkG3ttioRYlsCducacleMK56/qQZSmp7qigwhsAuRmIcyBLNAJBXMhENSrOeI96T14Gbb6iCtD59IVvUR8nGA1paB2zAM/f59VtUUfM6UoGzvZdLslxWLWTxc5VCXtdHmceck4M6N2R5brSEAQlrU9Rq6O1WaIim0DeRUQHSiXpezw/twO21A1M9BKih+5s2I9vnPTKT7VOCtHH1IL5eqwKElj4g2/Ft6CfAl+5S9ZdqXK4X0O4aVVY++NkjYo0/9N/8AZbDC9L8Akn3XERhyM+5iMpuioy7gTBFN1jVZ3oaKVblkXse1eoBSBgZhompBDQHdFTKsKJr8P3m5/N0rFpIStzoEYMVFozViEJAaq0BmVAGK+JXQIswuHODAMVz03Jx3l8t3KHTfJvRwGiL5PBZOPK6deuw6tcnT6C9yvZ7/dvW8qNO5aembRtIx+c2HNJwxdF3aoSTfzkluLklv3zB7c1Lbu5uubvf8/rlDR99+7u8/M73yLe3jKqEksmrGV5jA440EFrDTg04DoxjZBjH5uh4AzheXLK7fEK6fMJ4cUEcDTBaSKsdH8eA7gbiNBCmEcKAhgHCCNEdOAGkeLEzEeIQm7lABGo6atUJpYBmq2+wfkIkx+dox9Epq0dWom0arNCMx4a3yaIBzOMwgOOFexTKqn5DwsZovMpb+0YFKZ334Q3gWIGr0m0cv04I3aZoSM6cMKF0ZwkV0hr7VUsytbC8TEJZPdcBTPlCB88TcbHoVVSNGW/Wm3ZNLwhQa69u1U3fPfWFSVS9t0/HWGoBHBzAhsZQtoqJWhTCZhKo2Rmhgsd2vwMSJvNyFKtumNat/cayrOzvF/Y3e+bbg1VCnQvzzcyUDsSU2d2v5ubPFuq6vk787E/d8+F3B3Io6BiZLi/ZPXuK7C7QaUQvLggXl4wXTxh3O4ZxIo6RYRpQRguzK0qKK9MgjCEw1kJXbb11OViV8atWp2KrGmntCwYk7gjDBVEioaRmDEIeyTJus3m0BzZY6DbmTljU44+SUsSsmLhVU1UtAk2sEu00WIuIECKoh2apF78oK0kLJaVW5jnn0pSo5ovVbXxH496W1TZ+7X0tPs7iObY19r/zUFTQF5CtCn7Z3seVopbXJgKhdtzrvQpVSXSwi811a02l7kl7BFr399zf7QnjE6aLSCkHNE48fRbRBLpfCIeFZUmsabXoBsXb11iPPsQUIPV7jCEwhuiFVNxAEiJDHHly+QShsMyZZT5Q8koLhcSK3SCBVJRVBR0GdrsdF0+ecHHxlDHuGCR6m5aZIRQiyrospJRabzIJIxcXT3jy9JJxLOQEKUCaE/d5ZcEiFzSohdBqRjUaoCEi0ULLwzAyOXCUG4vRGaaBGGBZB3KaEQoxBsYQCEXZv77hO/MBsHDD4i0dQurt7e+YSqGUlVJW44bB96EbTwpQxABakNEUCjckZvcS1krGtVz/ZsRwRWColYINaJcSvHpiaYDxWEnc9orlprtEk63N1CBWbTFisjvFwTybWkHh5hGJzjjaLjU3B4XRc24Taw7kLLZnHPCaQSpRkrIma2mBg+cKGg3kpgZKQxCGYWASYQSGIKQsVgGwKJrSJ5vHP880Zm/I3qIdIsgAMiBxtPDdVKhF9WK0QPmcj03kjcce/f4mYKrG8dNVWRVyOwY3bG2+w6ZFdMbtNwPqqxyoeXsVHPlX6byddCGl2q8cH8ODT6verz5wlLxxX6c6zeMlAMAf+C/+M4awZzfMXA6ZYTewxsK83zEfnrEuz9iVJ8CO6nFsz+eB5xjCYGBx3DENO8a4UETJoTDGSBpHckpbHrV6DjguV4oZqYMoQ4RxDIzDQAwjMKFlh+qOojtKGcwZIdZHXD3tBvek5VxIqbBS08ldD6nOB2jeXdXqJaRbO00B2GbspG1Yk4xVke7AaAWKxzaRx9FXPxXVNdwZY6pmau9D9QCD30rVd0W2xLRFWT98zf33vs3tBz/LzasPeHXzktevbnj14jW3L18z3+4J68qEVZauqWkWqlq5o1PYCpoNMTLEaN7GChyHkWGa3Nt4QVoOpOXAetgTp53lQg4DcXRv5DQxTAPjbmC4GInTCOME8QIZIjICoxvBO69rNQjVch19GOuyZOa9FclbUyIdtct5kz47cNSNKW55QzU0zBhtcO+AlXN3AaZVwXb41ZjpBhurnWPLMfTZ7q1Y9bttcnqufLzQ21qW7jwtTLKOp/t2dx1kY4f1yi2HwU9Xy9ZnXUl4Pg5ueatNxtkS3bUC0jYsA6R1s5mHMXvVv42FPQ7ZvPRlxLU9X1dUpIZ1lRZOUblEa1ZdMtb+IFCw5qG1P6OxqhGRS+CCkgLLsjDPmXUprGvifn/g9es7bl7fcbjdk+5n9LBQlkSeM2UxAZ19bgrW8Pz2Dm4/AiJoFBgH4m7HcHFJuLwkXFwQnjxhePqUy+fv8eTZcy6ePGW82DHtJuTigiFfMExKGIO3MDDUaAqVydnNK97lffRtItqzCxSNlBIgBRKBlCK5jO7NfTzhuIU4bcnqNkDcy61V6zgW747uPNK4zX1WpRZGsRDCyOQFUkpRYyq5bJ6vVD1WmZLsu/X6hkvfXMV1t262p6q4nD4n9eFXw0YgaGwVUmtEQ83VatfsuGK/vpvipg/tLW1tCqw/oVbbgTPf2rLk3dPh7hVLUWScWQusqqRhYdZIWF4hdy8Z7l+zHO5bDm/K4v1QrZBC9HyIXCshx4EYjO9pyZYHmQPTdEmMgagWXhhJhIj1SiQSs613awyvpDAQpwvixSVx2BGD5QZfTBNSLE8xygWDBOYQLd+DFRUlxkCISsoLSmYQ4WK3I2VhJVkQWBwgYmCrZM8Rseuv6eC8M5N1DyTm9R6VzDwnhqjEEJmidWjbDZO3GJlZ9zP7w0z2NTyK4M6sR6tynNaFlPbkvBhfjJ5zWIqH8hbLGx13jJMB6yBiXt28+vcXqP3AfD80b2JMhDJCEHJJBhwxQ+Zpe6VqCKkVpQtbpdsK+AYJBIlWoTVvYXO1mNSWP+XSqvZro+5r5ytxRATvIyfkvCPn0XM1zdNaMOPqmuwnZ69ObeqMydOyei6mRfyEMDCNl4RJyOOOtIyss7Cm2YB2zo8CHK3qby2KJpY/KwMipuTHkAli3s4QAqKWx5pryH8Vle0Z+Yml430dQKsscvModmNp56Fqv/6/GyYaurS5DBKt76UbzHL1PmtdJy3eyni1bByy5irS8fD6otQIsG1smzzpdLruBo4iu968q41PPxJ4/G/+4H/OsyfKN54L8nzgMjxBpwHNB0qaKSWh9IC607re6PFowHGadlxcXHIxXbAPexKrt/AyPjMMo+3XbHn+0fdTRAlFLBIpBHajsNsN7HYTMU7ARMkXlHxBLjtyGcgpk1Ige86NRU0lCtlTezJhKVh7XIveaKquNo2uu6uu4FE3l5vPv67bblaa2rdZE6Q/uJ3ucTXWT0sbHD4Ozn6Q9Bgr6JJYvvOS13/4Z3jx7T/Miw9/htubD7h7/ZKbVzfc3dyT50Tweh41PY2mH9dUgU2fsrcNOGYR1mD5/HEw4BiHiWGaGKYdaV0Yk1VznscDYZiQYSAOI8Pugml3wXRxwbgbGC9GhnUk7ibCdEGYAuNuYohieexRzAtSUT5eZ8SDNIbRwOO6Zu7vZm5v75gP9+SSrPjaW+hzAke2cMTt+ZuygmO0bvGWWgFKumphbaZ6VVHaBBg46y1rnWVK6Awjx5yseib7cYoEJNZyxGZ1qRWorKx3Le5QmXDomDFNOfUz97zbPI84vmqMRlAsTKjGHduzcCUOD5WS48nZgJxfo7MevWtSPMm69soSWoEYC5uFUEK7vPqmMLCkrYxv84qqEohH57Bk3IkgT8hpYt0n9reZ/f3BQtvmA69fv+bDDz/i1UevWPYzrAnWFV1WixlX8wpGz8cB8dxFt7gLMAQrbTyMhHEiXlwSLi8IF6bsPn3/G6RvfJP8/vvsLi8pT54wPH3GZYEwDIwhMsZAFMuRC2IWeOvJ5jZd5y6lSAt1pAlmZc1W7jgvK0mFVSNzgXnNLKuSl8diqNXb6Eu0WZ7tWflCoxd+0izWbXJtPaDkYt3xCC4ER6sGZmX7Sy3f5knjqSm7xcMcRTuFyOm0qMyJvGnFH7rhsJVw2HIzax6wKhY+B7TSj34CwbxljWn7nZamhNeN3IsUA6TWdsDWulU2i963FbS2HQmPAxzX5TVrUXI6sJZE0QTDgVyEsN4Q7l4gt6/Jy50XgFLWBBIuLKR1N5LLymGdTeGOXtBGlaIr1pc2k3IFN4lCImtiGCEOQhaBEphL7fkHEiKTG2Sm6ZJh3DFNI9M0MAzBMuvC6FF7HpYvXhgqFbNqamJdE2lNlKgMWkhpJZWMeM4HUSg5oJKxs4p7rlYSByTeInvLh8RbMSxrJiXcmjtswF4MctUiQklBQmEKgZBLy8N8DJrnPTnPFJ0BRbKnbBRFczKwVSIBA9Vj9FBdTaQ0My8HcloR3Ur4q7qHEZAyWBGdaGHKkKhhrejWxqN54aXm10T3huataFsIFnXnhiO3ARsVWsRIy1mHoz1aeTBiIfohjBQNlBQpZaSUC1RHWhlA3EtZlSxqeKcbK93bWnT1QkFWXGicLghhYI2jeR5LZlnuQUJX4O0dk+dEW/qGhajGODLEEWJBYyKFsBV4a1EQD5yqPbbK0/xRdjrSyYH25wMGtw6mvYG16vof4sAUDTwWtfSZXPJxcboT5a0aGo5AKhzNt/3SQUX3prVw6u4rlZ+bviccH1Hvf4PQjyUdP/jOz7I8E1gHJr3kaQQunjKI9wU9mrdtFH0USg8mY4xM48Tl7oLLacf9MLKGhSRCEgMEYRzMGJQKmq2HMGIF5aYwMImyG+ByF7i8nNhdTAzDjiPgmHeUEh04mvcRRggzyEoqicOyMoSVQGJelVRct63yvNMnq4zn6JlvxZOqDnG6hvunUMFjBUNNIzsS918CcKzbpzNo1bH23vQe/KoWyJ0erpl8v+f2Ox/w8qd+lhd/+Ge4+eg73N18wP72BfvbVyz3e2TOTBqI0fJPW0s7xxntQk0pq3qKmO6CJ9QEIYdAGBZCXAxAjjPDPDMeZvM0DpPx1cG8isN0wTDt2F1csLucmC53DPNEvLwgXhYmHYnDpUtPOZ68OpZc0GRVo9NaKCUxzyv39wcO89xSUsbh7XrO5yuO0+Ccdn9v7xSH3EcLF+0Y5vFG3WSSQmcBO7pvfxjVy6DS5R/5SYwRH3vSLCnUAIyVXo7mvfI+TZbXk6kAPYYak+yCrQOhp0qn1YUJDA40cxWkKBRLZ8543qJ7cWp1TvUcjgqU67iblch36ich/89LWWuz2ExJxUPLSmd81OMNxzbr9b3mwfHUHLQCB29hICPoQFqV/d3M7as9ty9fcbh7yXK4Yd7fcfPyJbcffsT9q9fkxRQryRlNqzclNwUq6jELbyPx4jwaoitCAzrtkGlCvNyxvHpFeP0avvENyvPn6LPnDO8d2OXCZQzoaAU/RNVCwTLWZ0cCEre+oM1rHjz3zo0QqRSWnJnTypxgyYklR5YCS0rMy8o6P04ujk9MA429PeJNcdwJie63un8sT8qUoDiMjLuJaRwtbytnF2KWy2vFbpKFdLllteoyNSl9u+LJKNogO41j00yOj63Kh7OFtn9KdzKpxVRqnmKFiKXtx4A0Y5PtsU7UKbTqdqoUgvUh9ciJ2pYjiL3/GFTyYqHbZaZkZQgDurM5Sctryv0r8v4WTQupJJZcSDkwDSNPn73P06c77u5v2M8HisIUzeqds4cDRm/tkAvrunC3v0dDQYLwjcsLnlJYXu0RrPDV4gaaabezPIvJQrMud5dcXJpV08KVA2GcakYeg8AOK1AkIXml5OSR0Zk5LezXhfWQWLNAtLLlErzIj3irG8+r1JzIpSA5ODCVloesKFrE84dt3pfsMqEoia3YV0mFmUJ0I9ljteNYlpUQsocrWa/K2pt0EK9CS4G8QB4IjG6YTKxpYVln0MIYqhGOBgYFv/fiXi3NRC0M4oU4XDkQvHptDS2VQAmDh8o5eMgZKbbWNdBCasUL3rROHxXgdYpo9c8kLSQHD0MczVhRLKWjlIFSdqATViTJw+VCYIwDhOJGjOLtnryQlmZr4xMt6mOIA0OcULFKvEGSWd+HkTXEhxnMO6CcV2oPvYJCgHEcCLojUyAtLLJ59U7Tb05pC9d0dNh0lU2etarXdBrWg+DxOLy+6T/B9vgQbB9G3NgQzOOVncdZ5dq3jxWc9zZMe4w0j77eDAH1/npZ3UeVbJ8fQ41P9At9bkrrzP4+cyPCRUi8N10g7wUupgueXjzhYndhYflHYw8bCDihEIRxGNhNI5e7icvdRJ4Xymp51qkMDAoljjAKOSshW9bToLAL8HQIPN0FnlxGLi5Gpl0tQuZhqvkCLROlROMfeUfJI6oThZmsC0teGOJKkAWrT5CRpGZ89ErmNee4mk7rs94QPb4O7d5afY16SH2fE3DoxgKRB+btS8CND5Hfwgl172SFtRbrS+Sb19x8+zt89yd/iu/+9E9z88EHpP0d5XDPur+l3B8YU+aiFp8p2u3fWrPEOOMGWvsBbelNQkEzZDJlzUhM5GVF4ozsDwzTnmHamVwdveLqtGMYZ4ZxoiyX6HoBeaXkC6IqI5EYF/QiWcGwNALZFl1d3qkgq1opgKykJbGsM/Mys64WdbQbL5l2o7Xsegt9TuDYMTS3Pmx6n3pSvuOBSYQAAQAASURBVAtscJDUT17/SHuGW60BvfJbLZXdAhaheKUvUwGVVs46Bvd82FmDhwVNFxeM4w6JkVyw/lbzwjLPZLHGzCaYQmtYXtXquvGk3q+/LxIIQ7QKhIAUDxkoDv6KJfWL55BJe1C+kaUXJurXKk2YmNn3kRTVKrCKktaFdZ4pqxUEiBJoE+gKWqnAcLv7ZtER91SUJGgMDMOOYZjIJXC/z9y8esHNiwP3L+/Yv3rNcveK+e4V890t+5vXrLd3xMOBkP38Xp1FS97m3jfi1tzUQYLatVWyrRBZYVkgWoPtMI6U+3v2t7fozSvye++T33ufcnNDurtjnfcs64/w/jee8/RiJA7GrHOxfCkzJOhRU91qzQchYeF8cyocVuWwKktW5pQ4rCt3+z1393fs7+8fZR5tOXoIUf9BNVY4OBN/eNJKq3P0BfUwgVBB4zSxGwfL3cmZnFbLYUyWW5Rri41cQVx40/L4Bi7sw6o2a2UVVKac+tFVtp0Iok24eRi1uMak2tqvhBp76+uz8qhtPJWp17CSDUC33paiXuTYwlab1e4TYv8/L6XVQveyFoSFsu7Je4G1sM43pP0eXZPxlGx5lxKjhZimmcM+scwHey4SSCmRspKz5dmOUYhhQAezNr6+35MHeP584vk3nvPNvHJ3s5LKyhIGxIt6jdMl07RjGEZ2k/3EGDy6QsiuNMowEjEQN6BciBDDwrIspJS9Gqq1NUhrJqlVPd4KEihbOwYHQLL1hcQLxUixhV5nwaIdPA9NIeWVrN64HlsPA86T7VAD1I/UxoEgVrwgBkpZWdcVzcoQAxfDyChYu5BSCDkRNYOvWWnVmK1s+hD+/8T92ZLkWpK1iX26B8DM3CPiZOY/tJDCJvuGQgrf/2H6prtryqrMPCcmH8yAPSgvVDcA8ziZ1ZV1/G+EWLi7TQD2qEt16VKjuEoLJu/vuYI2LwzZRTHChQFS2wuD2Boeg4vxCPYzRCSq51nZa3cPQ3zWTn0I21j7N1XW1lhbs5weoGKU6qCQJSAxgXS6Jlo32r66su6YYyGa6BIB1rWjmJBWYNSns3qQKsGpqqO2nan0Gu01kYLX9zSJp9+8G2/XF2Kc3JlmlOAQIKSAFAP/wfd17R7Re7tYvfl7GLPqAH9bLQ+00PH6DkZ3G3+8abBJRtTWnJlWhy1FiKFBr+40E9/DLC6tHZqrTP8q6h7L5fDMjfm32S1jvfw1xP72/vfvPJaKOt7sv4O3/9PHec6INtZb5fZSKB+VIBOX0wc+PHxiOj1AzHv0DDZHoY9Yjj0gQAxCjtHqQU4Tbc60mt3x12kiiCQmMr1HpEKqytzhIcCHKfLhHLicAtMcSCm6JoiBQ40J1EvsdEU7TptWag8UjaSaEArCjFCt/mpphNqQ1qi9ErTSgudbbw4Du4thv23qu4c+k8OAGzt1YN97+2E7fKPU8Vej7u96jEDTNo88nnq4h+FUt5CsQAGpzdTIX77z/V//yC//8A/8+Z/+gS+//IXl5RnWAmulrav1IYnkpYRqsxJKQcaeYvb+QAt3Qjt+FceUnS1I1BVp3fQbJKChUJbVoo/zRJpn4jQTa0Eny3vvMaAx0FOgh4ikiZ4qWgp9XVivEdYbPVRk6qST7QVUodeAaCYGUyxPmlCUlC2nPWcrr5bS34aGf0cdR72b6FvU6W7w7QAI90wMgHFUIBsNuNmSdyvIMCzfnM8jDvRBMzXAF8UWSPHNRN1oiCkzTTPTfLIaWyHRFGqrLFNhnRbqulqifj9Ih7u3Dj1KJptnV3SEgkfNFtnA7qA0jkE88k0MOHo+o7IP9kP0dVM1G9cwNvB3OCw6aopcrVTKslCLyemHzYs5JoKawTZA4vBOj0V1yMH3COFEih/J8cS6rHz55Wf+9K9/4umXr6xPL7TXV8rLM7en76wvL7Tbldg6pwFSdWyqLop0MPJ9v+RuoepqkvEbBG/0WqxvYiKWhK4L6+sL9fWJ8vzE+vzE9fEDT9+/8v3lG08v37j99//Gf/ndT4SHByRGSB2dIjHHrX91UJd8LKiY46phQKo0Za2wFOW6dJ5en/n69IXvT595eX56l37crA3YWanWEIdp44uZg8bDFrlFTvFcp5gSeZqYpokU2SLzvRR6rfReDyC6HyI3I8NhB2GbXbTN5UNJjf0y90+q7g4LDtev+zuPc8N+9g08qhyMqQO43+g1b9vtLYI+AMze1ETQeycEo1wGgf5ODIDbUq0ItDuhermxvDYIlbq+UG9Xowl3i7JJmEgxEWLn9fqNl+e61S5VlKWsXqoBLC/TSqeEGCk1sKigEonzAyE/IP0KGm1Wu/obYSLmCzlfmOaJ05yJyQ0Qf5/6OhJCIqawtT+uEKgKvd9oxdgNrQsarCyFdLEIt6/pvdsY82w6o2rGaIWYh+DLQfn52JcmpzNKsXSnzgbfW9yJ4lTr5vXQ3uOYzyem1Amh0ZvRdlNXppQ550xWtahBqSQ1QSGJgWlKTHWm+WzJMRIJtn9EfLX1Odua961adCmIU8nNYWriRrKpP4vvYQSIBCLZHHwSOKXEKQYSivRKU3cqajPg5uye1jtrrdxG+kCItAAVA47Nvt6ZLCYW15vTYseaIGwiO+YoCtu6qnRXW630Vk2Z0IXixn4aPOq/KVC6jsB7HE9PX5mniwu6FVpbCL1CWVmXV9b1Znm7vblAUfX0jJ3uqd72dsi+9R+WkN24HfvXr6xTclipdOyJsgGc4OI8MQopmE2kzQSqjBFg0WCzuwLNxZSGZsMAo+O6j1vs/T3s9ppR38d6bE4PHfv38eoP136HJEd7jL3+V4Hof/743cdHyiIELQSZifHCPH/gfPnE9PAR5guEzCizFmTMtLe5l/foKoiSkjBPiTplWsmW59+bseLiiZAvCCdCjcQCU+tcUB4zPMzKKSspmMpqb0rrFW3qc6baqqa4o6cYE8vZB6q2LoZsAaUclDkpt9JYmzkAqy7UdrM89WblQ4bdN9r/TTjY7m7st4P66b+PcTFW+Dv77PD8/1XHsBrk7ZUc7lELsEKooLVye/rK1z/9kb/8wz/w87/8I08//5n1+oSuN/piOhs0CE63N0eSk3wFcyqr9aHe2RnDALtvEWG072Fc+RpIMGpz7VafVgd70W3iGBK07IybPihilpveOroW1pdXylqo0umhkGbldInkaULIqJ6QOJHTRIrKLJPtrd3ns7CtsX/r+LsijnLQMpcffjt4lvT4GXZjTg/vsV3B7bu9ww/Nv/0mYFQal8wP2hFRogg5BXK0ItcxJkJIroBlNcdSmggxGapH0DzRpk6dTpS1UNaF6pL0zdVRzRBTl0n3CeSTahjJnW6h3+E96Oq1eGxAdEanNAOPY1LaKrUZ1T+63nxR/huUkv/M0ZorYdbqBp0N2LU21mrFlUMwg24j9unYtMVrWhlw7N0MgpAmcnwg8IH1Gvn68yt//ufP/Olf/oWXr7+gt1e4LZSXK+vLK21dCbW6qp9/l3vFLGgUthZ5uyDpoS/2zcsV3JxCIN08r1qFIkJfXlmur9xenpk+fCC/fOD1+szt9YV2W+FWkd8r/XJiniNZO0n7VrpjLI2bQmc4OD4AtFPWzsvLjW9PV74+febL97/w9PQXXl+/8dM7qDjsfodhqOzYekTzdFv0dyMBZVMnNu/+qMmYnZ5qOVS1rLSymnphb05RbWbk6TAexkN3RbMB4sTFI4ZIx11OyQHxqm45lJu4x/C690MUXrfbYNsqFDa+tBuho97a2wjnzmbwBhrQVY+sCDt3czpf24Q6eDfq+LpY5CkFi56W9UopVyQ0elto641eFSFY9C9P5BRQrbZulWrk9xDpBBdjiltNKYv+RGKafQ2MpIvRT7893Xh9fqZVZYmJGgVCIqUzOT9wOj1yOiditA1yB2OHPlTr45T3NV5VyPMosdG26BMOHsQTcoIOmbO9ILZ11HAEuqhLH36FA7nt4DEf+XNWX/TwvO/WA0gHMTr2exzn8wmLBJjXe3K4N6XMHIPVy/T5GLEqlSEIOWRO5xMqQu9tG96iQ+jE8+n6KHpvbRVHKSSGAW6RsIivoaPR6JbDnYKJJ0hmipFTjEyqSFnoZaX16jnOWG1WgRas/UtTixSHREgTVNx5JpZH2rsZrH0lEKyenVb2fS241ppSm6VKj9JUvXd6ba7uW1ENdK2HyGcgBssz3h2W1orvcXz/9oXTvJKSUb5avdHKQlturLcr63KzPN1mdP2t3BC7e+vXRMHwd4z3MN57eHU3Pu+dpnpY70YN6RgSOSZyEmLoiFZoK1qLOVVDQrsQ1BSySQM4hr2u7jjHYHPc//eDVYIcwMXbnXnsQwf0ue/VY/29t/HeE2784dMH1iUh2vn4cOHDxz9wefw988MnmB8guprqpjjrbS3hrm0ON7iB6yhCjoF5yrSSaDWh2plUkHwmzx/I4ZHYZ2IR0tqYWuEkKzMrqa2EbnnmrVbW0igFczipj3UFRlkdrahYybgQApNEkiTmlDhLYE3CkpW1F2pfqP3K2gJrVZaq3Kqy1kbpBiDHPNrhoR76Ffb+PTjt5fC0DgApm837f0XEcYjxjTG3CdK4/bGxEgvICixKW2/cnr/w9c9/5M//9H/wy7/8M8+ff6Zcn9F1QdfVGD5VETUHYEcPec9+33fzZMxPPcyMQ2Nxn0Ms/vtmwvVhs3W0dKr2HSAqVAms4AwSL5sVjFmnMVIR6lqoQagBSJ00Q1sD8+lEmh6sbmQUwiRIEnYZejtV6wPr/O1+/I9HHA+IeqNMvB0sx8F36MDjy8c1VQZ6f2PgDXC5bzueu8Fe/yoGyDEwZSsZkKKpP8YBHEOyDUcVrVZIdSgciQRayhSEFaz2X4wGHFW9nonVj9ry3Aheb8xLEjQQ/9vU+sTzo6INoObg63gjW1vuwGjPb9yeOLonfvNDum7COISIpERHWIrl5MVotb2CgyNEDjX1zPALwzpXkJBI6UQMZ9Zr4NvnK3/6p8/88s8/8/SXXyivn2F9RW8L9bq6MpUtvoC3706lOB5HL+zduuaA9jhJxcebLYLdKAWuUttaZS2rOwlWTr2g2qA2YhNiFaQK7Q8/8cCJkyiZTnbKZpBA9wj4oMgFbEPOwUAmpbK+3Hj++o2vX3/h29OfeHn9mbJ8h4f/6Tfvx+3exVVA71rvaFS74+bu0ztlJUQrSmsFzMXoLnU1Y7JWE+LQEWUc+YC7AaSwF8+VsI0RCaYellI6FHfnAOZ2uvkQwFEHpr0bkLRct7afm83n/mYh9si+inn/PeRi60uw8hqyJUf66cc1DDQ9rtDzgfz9Oqir74M36M3USXvvVsbAHTYhOB2uVXob65/1W1OlVSvPYSVSTMnPFJ1dkESN1tpbo7VOmoCQkShMMaCt8cv3b7TnJzIKeWIFiIlpPnG6XJjPJ3IWoDDkzMemZ10ZNvNCJFpU0pu205gG/XdZKVSq071tTimibZ/3zkY58iy6z+PmQiwhxIPfQDYJ9LEJ73uJs0bwiIgbNjGGdytULQETxHBnhwzKaLA6cUNpcXPw+JwNwSK4EsUINVsdVNnqXNq6Y5ROhlEhhxk4nufgzPLnRRsSrM6kleCIzAPM1kJbG73eaM3KC5CCzdeg5o8JBlJinMh5JuUTpSr4vGhNLcraC6qrT5QCjHp2BvSGx/4OOHahNxNM6q2gvSCaMLGfowroYCsEBuzWdwKOL0/fKTcDjlGU1hbKaqCxlJXe6paHu4NGXz+G0wu2ObJdv3//tmb6Z0bw7mhKHdfurSvVtR2cxhtjJKfEFBTB80Nb9TqbWNS2BathGsw2STFRfAwOlV1kp9LtI2pDiD9e1N1dyB593KL7w0G5G9P3d2Pvf2+g8YdPH2j1TAyBD48f+MN/+e88fPo9+fxgspIbeNrl2O7v+Neuz1tHTBMjp8hpykifydHU1WO+ME0PpPBI6icTW5NCWK7E0oilISx0vdHawrqu3G6mOF+LYtyAsLElbPOxPS2kaAAgTQRXgj+FiSqRGoXaG41M1cDa4NY6L0V5XuFlLfSitLZLU2zj0oUE9Hib3gQ6Flaw94z1awBGxs//K47dAvzVp1TR0pG1Q+n0deX2/Stf/vJH/vzP/8gvf/wjr18+028LsjbaUmlLRVonbXhkMCiHPQ8gVnP4sPcM2+Z+f3xzXQdHyr5Oj2u2urqKXXNxyriNg05dF2q5oa3gYSks5a3Ta6GKBUl0SoQ5uViZ0FvkEmA6R0IOv+pvkwDJbfx/7/i7xXGON7q7mH484X2b2Qp5pDOMiMBegJNtETLbfBh14rkgpoI5ieV2TBFyMgGc5DXIBMUKhnXA8iVMstxNTpEtCiJgAgNe0HiKAZkSGgOld67LjddbZ6lt85bb5mcbWtDm9AbcgDEjwXIpFdmopsrbpfPYDtvmODaRrZnfZyqGji9MEQnQJVI63GpjqZUscbsn2OXZrei4RZ1CCA7MEznOBDK3W+Xbz7/wp3/+zJ//6V94/vln9OWZsFzpDhzD2gjdFP/Gwth0BwQe/d83VrYRt220o2X2NvNNe1CFBDemPQoWhKA7IKkoq3ZTbC2daw98rhaJvfXC7/gdH+XCRTLuttqUeYPsAlAShCyROXSKNE4ClxQ5pUik0deFdnullxs8/Pb9uC3eeiD13nl0ZZfP3xpsa0m31U1FNMZEjBG00epKKwvdRYoGaNuLduP9dACf7v0yEZNoZXmSAdLswFFxY+Vwjap7bhaqTsWrHtXcI5ytVlq1nCjRwza/GVgGPpGG5URtO547OnSLEo9zb86vwzXZM0NMQEZz2WL+XqlxQViXlVI7sWBGguCljcLm1UWE1htrKYTaqXW1sgbqNbpUidHjT71tIkJdFcJCrMWEwqaJuHRaC9yuL9RajK0RMhoSeT5xupw4nTIp4mDlML9UD1FH3QbXiPamHOlEomZbY7F6hSj0WnwMOKuhNSur0AUhbpHyjudkuRNPMUP5CBwHZT6g9F43KurIYVQRm7fuQAieC/lex+32asa7Wj6KpTJYtK7TTTG6uGFvt7AB41JWyrrSyq6qKoo7rCziaE6ValFJGSUWPD/wmCM6FL51RHEbouZ0jWIGQnBlwV4rva60eqO3BQ3dnIndna4hINkcCZfThfN8IeQTZe1EDNzRQGIj0EihkwQX7HEGQXemNf5oQLdIuJXv7LRWaa2AVnfu2j201qk0SrX8Xosym3NEeR9PzrostLU66AftlVIW1nWhtb2tR33Off/WfU74cfebHCM749l977LjCN32aOO+J441W0g5kCOE3qAWtFak6V6TDs8dbs0dEJ6SEIxu2azEvNFbd/rKD1d+vFq2axrXuuFFxh//vtWiB5Cm/yfe//cdf/jwaIJM84kPHz7xh//633n89Ik4zyasNw5xKvjYg364Irn7TQ5/WGmNTBbhMs1AJMUzMc6EHi0Ft3V0rXBbkfUK5QXtz7R2pdYbZVkot5WyFGpRVIPblWP/MaAaUrAc6nkmTmcr6RMFDdCZaBJ924t0ydR84qqdU7UC8D0sFCqrNg9mqTMTDtHtN4bqr8AyaybZnYijjnKQ9+rJv368jWAfR7GAtf/a0GVFl4Xr8xNff/4Tf/mXf+SXf/0jz59/ob28ImVFvQxcWztJ+UH8ajAGx/4zGDLDTg2H/XF3xviVHO39gx073q94m8p4e9/YJ6KdVhIahFpuJoCDsaK0G1MjTIkqUENA6kzsZ3qf0GlimiJRJlKcLah12LrH5e7Lzs6e+GvH3wUc93Pci224D+muIWAsKmOJsPyYO9N2A42O3rdP9t2TEUxdbYoTp5CYgzCFzhSVnJQYzNPXa/OacsYXpjvsHB4CtxaDP6wuoD0kBuJkyaEhR4oavWtdhaKejyNWyFW6oFSG/I9hk5FrYAt0F49ujjGzWar3C/O+1/iC5Q18Z+f/xkfQSJRAlUDtC7e1cV0La6msraHBom/RhevC6Du3AIRg9OD5wpQfSXFmWTrfPn/m3/7xz/zln/5soPH6nVRu9FKoS0Fqsxo4o5wF3i+HxWtfmO9Bo71TtrbZJpu/abTzHgk3g9LR1Xa+3pV+WyhdiR1Sh0Lge+ssvXClsIaGpv9q0bIIFAOZIbohitGYo/dvD8IpBn66zMz5QsqJUp94eZ5ZXq3+17scEvab582Gfbfi73nF29w8gMbgipWqFhVp1XKNDHwMcRM9Wgf2VeLtHI2+Fl1dUULyaKMl/8fo3w8W/d/G907rGAskMdJb3M6rrgIZ4ooEaKX488PLMna4kRMQNvNGNWyb3BgTGwNga5o9X+/wlIEgP8WYl+9l4qQUiWU4AAwI9K5oD3Rx4z1GkG7VUGrzca4O0iNBPVLlbbFROneEjKqp+4qqKZsK9F4gKg1BJDPPj1wuF86niZQU4yRi7azCWPOO81JVvdzSAC2YZ5xMkCEQrgx6/1qr96E7BZrSNThwVHfOuEdVIYXkYkCWNzkcGEa4ElfANRDaWvVbdqrrsA+VO6fkexwvz8/WJ2p5hUFcpt/rgkpzhoPvP0mA3kx9+Xbldr3RWyOy5y3CHhfxm0e1W0kPRo0+f/iKuK+Mun+H2DVF7UhVFBMpoqz0daHXFdqChgYSCXGyvLCUSfPM6Xzh8XTmlCc0ZBKVSHQHqs3rZF4CJhdqERxYtWb7J8IQ6bJbUev72qjN8gWVDk4LV6C2xrWuXEtjUS9NLcGMY32f+dhqoWlxlhNApzVTztwBo4975X6uHefcFoG7t4NgX0qPtvZxfN7rH9hPwbQcYorkHL2NK9pM2ZPWvIxLuBPuoJkDQaPNMUsFMQr+5l06Mp7YGWX7tfpi+Cuel62Q+pv7sDV3OJmO7zjs4xye/o2PP3z4QJ5nTpcHHj5+4uPvfsfl8YGQEiN65IXY/KreZpnewUQGe0VGyo7n3qY8mfqlu7FEM1qhrJaaU58r/eUK1xfC+kxoz9BeN+DY1xXWSiiNWPfz7oBO9726VkJthNKQpSKpgMxImIgSkSiEpMjU0RQ4hRM5R3pKLCFz1RuiK1qasR8Odji4Q5j7AI/uDcDYvI8RagOPxzH7P+B4O4GOhsTYry1dFGqnLzduT9/4/svPfP63f+HbX/7E67ev1NvV23+lrw1tIB6S2+fd/bncv3A3WuTuUoYxOl7hLiA0HLDqQG1c/qDZDhwkmKO1aCN0q9OsAdYlEF689JRgYkhrhBzoMYJWajfadE5npnhmSmckZHPQNrYqSSOg3boFTmpdKGX9m03/d9dx3AwzXx8O7XNoYPXN+giUBi1ADgumQZM90d38kmPBsaKqszVAOJElkOkkij+MtKLqqn1LMZnv5kI2jsx2hO3UJvDSHqZSZFS6TIsNERNpiDRyEGpINI2uay+QGl0X1PM4Aqa2FT3aspOFdth1aJx9gL0xYPdhyZvfftujFKF24boo359ufPn6zLenZ15vN0pZWUthLVbPxbz1ChjlKQXIcSbKhcCZKB9pa+DrL5/54z/+C//2j//A01/+gr68klshVpuUUhqhmbdb3Mjtft/bhByLke7rwF1LbJvNASluE/iecDKipYMmLF4+pSuspVJrY1VjY1loY+GqC1dZaaGTUuQUE5MkJNmYVTH67nAImMPADKY5R3KceJATkhPfXz/yy5cHrq8XyjtZqmO82KJ1pG9u/921zzg2wRovORG8UGzf8gxder83f+z0jMM32+dTto0zzcQ4OWj0iGMUL7XA7tWS3dcsbliM5lEX0IliYkvdKXkhNiQOmjSW09dGKZ6jMaYwBANDcOVlQTY1XDvrMZI9gOX97DsY4Nt9v9+mmFLkdJpBEyKV2r3WoiUQo92U3AYYYoy9KCSxNWfbzLqxITbDLzjwSmmPUmRBe6PWukU2VSJpOvNw+cDD5URKBmJBLQLl7Sje5opuOaujnukQZFHBHQcZJG6tPbNTk8taXY2yW+bkMMbAnHPN5y27MIqq0W5r9QQ7Rg7gEC8w4SZzDtq1eqreFh26N5B/2+O2rFYTFwd7A0CGYNfpzo4kgUSgItCtBuyodaldvT/YovyuGb2tO2Msdw6CDfc2OdssFS/TIEIWIapCq/RqDhlqgbIgrSLaCb0ZLborcwhMOZNnq9+ZYyCoUt2pFFzgJ2knq+dfpcDkYi1gezKUg2aAjWf1CKOxCGwcqI8DvD9bV3pZeV0KL2ulBkVT9xFkuZXvcWi3fuhjHqnZF6OdBwYairH7EqS8HVybHTui9OP5O/tn//wbc+nOFRDEIkc5JesLOtoKWle0mbgdQ1Xav1rQbV23dSN6nlSjYUrId3bIHWh9cyeuwParM+hgZx3M61/5CT800jsdv//4kel84vT4yOXDR84fH5hPmRAFPBI/FtP9mh103Bu022MEOjYF/hjJMTOnyOS5223pLGXl9nzj+uXK8v1Gf74S1xu53Uj9huiN1le0rlArqVvpjg1QAJsKhje99IYUJfQOtaJhRcONLpkuCY0JSZEwBWKPxGCCdxojS0y8knjtgZsKXRd6rYM+xbDNj7etb4bzfi27nur9438gcNwijbstYX+7vWq5EkhTtBbW1xeefvmZz//2r3z785+5fvtKv77SF8tdHrXDUTwN61Beb7TLQQF5O7OOFImR88gWpTzOlmFn2FqwMwnG+7db8gEQgrtaXYALz1/XJrQ1UII56FWE2ipxjsSekSnt7ZFPuEa6sQ6WFaqJo8mkaBBKhWVR1lJZypXb8srt36kC8J8Lg8j9Tz0MHTfX3PDSNx8YO7k/ty3CB6gl1nkpRdu40okpnokym4pQWei9UsUSjAlm5JalmEFSzeAaRg56vyD3bQZAF8uzkCq0FqjlSsjRIh8aSBo5xUzt1lEhR2LqdCKlvlKqDRIJxjdXcEEcn4weldl8cmPsycEA/2EKjtfeZ4H9ei3clsr3pxd++fKFX375hW/fvnC9vpi3QdXafp4QEaMR1RspND48nPj0aDmkMFOWiev3hb/8y1f+9E//ytc//5H28o1UVhM8KM0l6J0eu+sD+r3fjYx/x7B7+8pxU/IxuP15VNg60gw8StGVelu4aadrJfYVpNLE6MfnmHnwCHe4nEiDo9o7IY6EcKMCSYBpSnQypQshdmIKzPOJy+kD5T851f7asU+jnaK7vyabgb8H52Q3CmR4TwegGlG8tuXutNY913CnFnkFUos0pkyaZgeOBhqD5K3mnjpl7sci1/fb8v1m6dcTgkcmxIHAAMeCsNIotDaijGw/t0pziq8BugsEHe0j1TdiPm8M7i0sqls78k4RjhiFEJLnZyqxZwJWB0wP0RnFStSo7zBNzbgOoW2OAwadWKz9RS3nOsWERItYpK1un1HUNUzk+YHzwwPny4mcolENha2NxAHIMKIt8hWARFdXUHURoeB5eeasiZvxC+bfT8BNlcXbe1QAGoUXBXPEabOE/a4j95NNYMU+6TRMFKV61Ko7jXFs4M27cajrjJzq3/4QSdueM1SCB8fG6GO+7sdAC4mmRqlWdYGqYPLoOwvnMAbBQNUm/DMgePB+CNt89jf7dBv0RCEHIbojqNbVpeYLoRcHtdC7O280kCQyxUQOgSiKtkJZr1QJtAqiDSMlQ6Ah0pDYSTEQpNK10PoNIaI0VBqKqcK2utLajdYXVKuvXxHChIq1TakmDnW7NW6l0pM7jtRIlu2d9sfe2ma76Ma00K1ft/XMmQxj+Xm7H73drzbw6G87vtNe9/e4nXC3bEqwnMaYmGI0UaRWTQjHI40j/ca+exhV7qrv3RwyAXNaBIuE0w4cgl9d33bAt93VFknd70+3RVy2MczdJ+/vVkb7vVf4H/jdT78jnybmhzOnxzPTeSLl6DTondYtP/TE8XoH8Ve2/40REJhiIkhkDpE5Wumfdltp68L69Mzr5288//LM+vQK14VcK2hFqASKOyjMARPYo5lHJ+3eVoADCXqH2uhSUG50iTQiGiKSIjJnQp8IYSaGmSlmzinyeIrcNFDVyvfUxUCVLaqyjef98Wv2ll3H25Hi5sX7H94t+5XJ3Y8N5w3U2yptuXF9+s73X37h+88/c/v+Db3eYFnpy0JfVqR3F2rbKdR3/48o2TAtYBu7x1Gyr/26jXE5vP+ozCHHS5c3N3A8D2psmtWf0EANK4QrXYTcK0knMhbECCERlc1JuF6vAMTbQsgzkq00V6WzrJXXW+V1XbiuN243E5H8W8d/0pqVN9PpAITcC33XFIfQ7f1i6bL3qpaLFiCFSI7CnDOnPDOliUiyRa52eqlQV1RuRk0Knd4qda20ddRYiRs626gWDhaPYeat7iLQaqcsauH+mAn5xBTORAnOH48ksUicBmUNlZt0Su90gglReHSme12XEZraIl/g6oxHOsig0e0ASLZB9tsf//j5O8/fX/j6+Stfv3zm+7fPXF+/s65X6rrQeydGA46EQGmFWhfmrHT9wHn+iBKoVbi9XPnyb1/5yz//mZe//EJ/eSbUK7RCXSu6en5NiBvAGaSqvUQLDB75Fj3cw7LeJrpRB+/AphvRxyi2jTcY9eZgKDPqBp6SCK01yrXQqWSpxGDg6UbgezrxNZ84xYwKnMNs9cpUsJpiI7/IktajK9gt68L19kqpKzFFTuczKUbK7bev5TioDVs0T9nac8dTe36MjcW9LMaQtHeTHLRtm9mmFuglWvANbUQFZIBGr/NnkTAXxvGQvqppZcrdSD5sRj/YDbr13ZiruOyPhGTeci8qYEQyz/EbwkoywIKNh4CYob2tPeP0+9jSQQk8enIYINXut2+8//cxclSbgbKmG6ixnENxhVQz8lSVtVXP+cPEg5oBvC2qxfAcu1ErVi4HF1uw1J4hrJJBJvL0wOXxAw+PZ6Zk+aCWUxn3tQsDX9Ir2hdaL14hZELCGYnJgkUi25zEWSMhDS9o8H/msSdEpBQDh63TaS7aNfrAFu/emz83ANYAhnZNzcsz9bcucu1ej9PNe9GDU++3P+b50ctKWM58BKIDaQkjmirEFAgxoxL9nhI5neiT1ZEN6nlqwY3bQUPd1Kx3WKhkINtcdrLcvsP6zFNBNI4iKfZKdwXOXvxaBUgIHbqgVm/Dauq2Qi836vKKhIkaoJeIaHPKLEStiBRCaEahlJXWr9R2tSxUmVCtNA0mBNVW0BWRguW7BqPHqiIkWhPWUugIpZj6d5dA6ELv1aOU7zUfnT6mnievR0X0MTgP8EIPoipv7O2xq+3ffTROtxXPQLvq3Vo1nH6I19iNpikgiPeLiXgENQeNRd5x1khl5LqaUe/lyzABjhyCp+kwJpJd/g/W/4/gYb/FsSbKHbjZN6O/1sL7Dq5vG+w3PD7+7vekKZBPmXzKxBwINuV8Gxjtcw8cdzgwHhuHYCt5M4WA5EwiMIVAUtBlZXm9cv36ledfvvD8y1euX59p15VQKrF3OkpzVeThzBJc8NFTeOAevIwr6z7+bH/b8/A7wbQ8gjkDq0ZgpovVEVcR8pR5jJl2sh11FuUm3QV5qmtCCMe84bEl/vVI4qHd5G909295jNP5nr2NnLGHi9vPotArfbmyPD/x+vUrz1+/8vrtO+Xl6oBxod0Weilksdq2qrtjcdz7qC+tepgfOl7f22AI/I0a8ww7fvTpWENEGA6Lzc4Yttuwxnw/s/MFq+PZCr2p2Z9i4jetWwmk7JHJpspEJAar67i8PNNqI7y+mLbBNCNTpgmsrXJdF16WGy/rwnVdWdaFsv7mVNWBgw6h/fux4xv6vhBYoXaGNbi9b3hNh2HbBaIbBVNOnLPVvpqjFZemVtpa0LUSyg1pK1EK0gsqJoutpSNNzLi8c4EcjamxIOtG7w+H61A6VIUEkUiME0Gqb1hqyf9NzJuekm2ItbF0pbRGawxk6ufal0U9PLZBMlZc2a/RQM8PVvVvdvyvf/w3vv78hadfPnP9/p1ye7YSAOuNVlfoFiqXFNFgCxICpyzEULjMhfPcaLzw9Jdf+Pmf/43vf/kj+vrMVAu9VLSsFvrvuFFj9z2Mu23YbI2jv36/b/agYx3NfYmVbfnn8Nue6G3G01ZX04tji0DrCstK9c0kNuiSWefPfDudiSmyauGT/sSHDw+knBEJW1RuGMegNG2UurLWhU4n5ch8nplS4Nu7AMehjXZo0DFJ376XMe92wDhoqhIU0Wae/148x7G5t91ze12W2nItDDTmPJu6W/BkWBk0yt1U2tYJDp446w7vtw3VHq51jxYPwAAe5Y0RyWG7fx3ggH34mCN5X8R3C2+c4dA+x3Zjf9399AzKvPBXxudvcPReHR8Y1VJ10But/lqMifM8Wc24lxdqay6cY2qdY+5I39t19BsOHAcNGOk0rdQW6DKT5088fPiJh8eZKUOgb4XXrf2w72jNxZIWWnullFeWtdP1RMqQTxfSFLfcr+BrRlOTDo/ZCqrTIc0CQaxWJoqW4lFEM4hG8BGBEANIYjfg4haNGzRqG3pGzxqOpTBM9q4QAykF6N1Ad3+ffjyfP1pNXK1EgTmZkxHVrTZXjMFon5PdR3eDIeeAkmnFhKEs/cEe4GVNhM2w0z7mTfb2STb/DqBEfBz0Lt6mwzGjlk9syNABaUQ0I80MllIUua1obEyykmIl5UgIGUIg1ETs9l0mvloJUiBWUuzAQm+vtPZqBc5FUY0ebeyglRQqIXY0KJWIxtlFfCJahbZWV7JuJm5lSm5or6D9naRxjDE0KGY7FXXMf1sTR3BGEFx+fLMh8K17h0ZDFGfsFWY42lf6engHvvx8/re4UnsIycs0DLUh3QzowIHtQUd1RXV1p4vTMrWZkyBYWRYrzWHX0A5nle0e3oK/45rM1i6bCbPf6cHuEd+/fw1ZvB9oBHj49IkQlZgh5GglCKIcceDh0LdPcA8aB3A0R8kUEzmr0c4V+rJwfXnh+fNnvv/lF55+/sz16xP1dSE0E1uJo9u1Ow3SziGHFcuGxbBFRwuO9vQxdnfV6ja5Q3Bt1Brot0bFa5Orfe95ykjKTOczF+k8S+OZxqt2Fhef6uOcPk6Pc+zYW29/wth73vewAMIAbwf69xGU+fv6cmV9/sb12xdev39neX6lXG+U2412sxruphh/YGZtZxoBDf/ejcForwFbapXK4bMbFhL2vj3YJuyObBFcUGi39XdkFXwecwBWxoYoy4pqIHY1On/vtFpM5G6t1NJY18ayNtJtJc1X0jQR8gTZKM1NoPTKtV55XW+8lsJSq7M8/rYK4N8ZcZS9dfymxngZg3t3yh2Hmtx9eutedcluN1ZySpznE+dp5hQiqWOgcVksebVUpBUiK1EraKFrBVUDnjGBZCQkIGwy7CPvakQDWxuGrPWqdVI1T50vo1I79BEfrgYYCDQCUSFkyCFSk7B6gzdtG7Vv310ObXZsx0FHGq9utDBrt/dKNv5f//Ef+f7zF65fvtJvV4LnSZRiE2nkB3WUFgNhOpHnM0rm+TXx/QnO08LUv/D1l5/5/su/UJ5/JtVXtFfWUulrI6pR48QVVI9F6o9j4Lj6bN7YbWzJmxHkv+uAF0fAsY+ybcEc55MdUo5oeBRTpauqlFuh9VcmjWi+UF+eefn6GXJklYamwDTPnM9nN8ZMvEM8iUq1ec5WQ4IVCJ7mRO8ZEnz7bbvQ7ndrqP0J0y+QO+/VaDt7zvojuJMmBaOZqTZ6XWjFKKCj0Pu+dQlINIn6PJGnHTSOot6jBMYwgyxKJofNTvf+2PrLqU13W9BxM9jdA0ZbDcZooxPV+f9VD2DgTXuMHI4R8fEI47ag/7AFyuEvH00hmPjMttn/tseW46W7Y2WzIjwibKVIzOiL0ShNU84WlTzUZO3dfdEhQkxIykhURsyvNysv3SSS5jOnxw88PDwwJwgU62k3RFCge1mEVpFa6OWVsnzjtjxTq1itSRI1KshESIc8772ShEd/E5JBuhBFmYbcuCpLazTBDF+xHLsBtKKL4oClFfiHQPboNqhR7zxXflPE8/6b8mQgp69U3qcfp3ymFRjK3XmamHO2CNli6RUiiZQmYozufLIal1a6w/KBLfPKAHdKBvZ7Dwb+tsi/je1ORDVu0Uvfzrb2GgrDvVlplojnMTOEaDoaE8RMlEyoILXRWuF2vVF7p/bIKRRCSqQwE1JG22zeXjFhHNFKkIaE5jWAV1pf6H3F9s7kEXLbJ0U7KSgkU1mVIGjwqDWCNNBSiQkmV6mpUajSoTdC7+QfkchvdmyrwaANq3s9jnuYwnD4KocokO7zZ1cfHsdgxox1zw10358Ynx172HDWOXCMEp1WvIOIrkKTgMSZPJ+tnioLpbywLi/G0OoCYmJnBKOC52TlI5qOetPc39/RzPMF+61hfXf8Gg4c2HOAbN6srf+BPvmPHqfT2TzBsVtJkhQhBqvjujFKdHTY4cqOVzUaxYUBuxKxiCPRKIFaVtZnA43f/vwXvv/8C7ev3+kvN2LrpssRIxm7FGPJjnPs7KrBoBhnvKMCHwMvh4YPAnoAwV09L7p0qm0ods2qTFyY5swlJS6nE7M2orM5qq6sqrgg9SYMtbXFEcC8PcbY/R9+HG2/MWfsaLWyvrxw/fKF169fWV+eqcuNtq7m3PNarKONd/bCHtDZHCN6sFfFbKHuk0J9wVXZuANb/xjzUg7zZgNGfsU/kqRVDmJoONunKzgLSBW0VqouqEL0Pbq3Qisr67IQloUw3YjzlXy6MJ3PTKczkidaCDQRWoAWGqUXlraytkZ1gkv4dxzk//E6jsMYPSTHHtp726jfUoF2BaYdZdp7zAsWpJNi4jQFLqeJ83zmFDOxg9ZCu63UdfUFsBG0ARXFpM8JEFNmSmdiPBPiCQkGHkNMXirAvO9WT8rU3FrVbSPTXuh9pbcbtS2motaV1lbLzZAEQdACRc1bFzQjOboHD5KLWRiloKPs1fVkeCX/yszTu//10Ea//fFP//CPXL8/U19eibWSsM241QHATfSlaaeFgHSr85jkQm8fKMuJl+8rt/qd6/O/0ZY/Qf1GL6slGRejOwUJVtdS9nvZp/Zm3dxt0octc2+dsfOMUae4d+vNOBu//JV2E39N/WeII6dAzdOilRpurOmZ2zQjOdNzQqfE+eGR2+MH6ulCmP3cTqXT3tzzAyKNFJU5C6c5ID0i8X0M1W0B8tvdPZT2GAuQKezZs6MGZwyePC8KmIqqKRIXVyTeXTzdPxdjIjlojCmbqA77IimbdaHbPiywi2iF8Z0HL/7hZmRfHhj1Q/cbHYt0t0hajJZjqY2GKY1aqQ65GwdblHP7vvtIta1hB1r53rAMb66xAKyQ9nscg15roNHbXEFbJ4jlOl7rygihp2iGT0qBKUYaTljqLpYCRpt3YCUetWpdaJroMRHnM6eHj5wfZvKkZvRrNyqre2BNxMqoqbQFbQt1fWa9fqWsr4QwkVNG1IzTwkzUMymf6BI3z3DHIl4heJ3HBqKdPDmNE6x+brd6g2E4Zrp6uUKnELWdQoi31Va+x73PcXjuD6aE0VuzefwnIfw7HtW/9xAHb+NhKrEBNOBlSS01YjMedS8bhLhotaKt+5zZFjr3oRiA6AQrodCtTNQuxoZPP18HHMASoqu8mhG5qSXT7GMpEuYTMZ5tvVpXluWVWgvlZo5TCZ0UM1lOpGm2dUSjjbEhNKJD1E59mTaDChlTq6O9er3GjnTZRD+0NWLvW3nKUBuxNXKOTFNmSpGbKNeyQjMD/r2A427S7K7JnXB/T3Uee9WglA1z8O3ydVh+xlM//HIXK97Am2knjL00SiDqTqnrqjYeYkLmM+nygfmUUG7oVal9tXnj5RfoDeleziMEegzU7gyaw3J8F23ZVswd4Ayf9z739m136Dtstyfs7TgcyP7Vwxh/j2OUbSIkSMEcJMPRubf0fjE/dA4MsUatFa3NgaOTurulTS1Pzzx9/sLXP//M97/8zO3rd/R6I9dGJJDES+Gw781DSZ5jTO9gm2xP+f/7eDrutz7ufN80h6j6GtDoZdCffZoDk5xhSkwxE+YzrSlLF65NoBdX5r5n6L1tmuPof6+++9VDxzn3NK67eXQ0LV+vLN++8frtC8vTd+rrC+12pd6u1MUc5Op1dY91Rk0ncB/Mqrgyx+5s7ttpBORNX70JBBnVddCL3+T+6l5nwoKQus2RYysfk9ZG/9KbsfoAVxujtUovxURw8opMC2m+Ml/P5NOZMM30GKkh0AJowtL8HKwmF9+zusN//fiPA8dtkMs+yMcddd0MxO2GN2AwInADbJp3TugOGuE8By6nzMM8M6dsm1Kp1Fuh3hZTn9KG0OnSzXuLqS2meWI6XZgvH5nmj6T8QIgnUh7CHXlH/8MY7lBLZ10K67pQ1yulvFLWZ/ryTFleKetiNcFUCaF7fpvCqkgPros3E7IVUxayF+GsVrR65NQBbDqfu3jHPh+Vfazur/06yPzPH9//9GcD4aVuKpqbm9qvQQOmmqhCXzsNJU4nHqY/MMcH2u1GuX6nLd8IvND7M2VZ0BXQsOVibB6942r8ZpxwGCXHu37zqe2D+ldegV8H2z84MoDNW4QtvkkiqKCreapCSpAz4XKm3j5QbjeW1xvX0xURo+fZItG3DdwmfycHOOXAZYqkHtF3Ao6bXu9h037bLsal9wVebOEJMmqfmtHXW6OXSq+Wn+Vyt9s4VDHKYEgmCW2A0dkCvvRpFzfS2YzXAf429U1GX+zAcTe63Egenvq7ft7Ff+w77O4lJiIzotBc4VhkyPf4oWpgU3eDRfCI1B3ofgNW9e6Ht+F7keMc3I57HII4qqhYjbXg99IxYzJEo9F2tTVwiifSNJGnCSSw1kZpzVQTsehd7RaVmKYPXB4euTycnJ5a9xybUQbJG1q0gxZ6e2VdvnO7PlPWF4RGCkLiiqrSWsGiWBGVyWh1e8blzqII2zMe+TbHzclz1qw21R65Gf2tA21xANmMCLqbOt4+g5JuXWbOjdK6jZc8QXgf4Ni6i6rgYm1lBbV8vNaM/t0ClLXYNYpua0fTTqvNi8tbuZVWnQmD5UaRvPTKiDq25q/cGxn4OIoxkCdT051UydoIRWlq4K33auWFciKfz8zzR2BiXgrXa+Z2tXto60J5ubGGF7LOpMtEzBEJM03iAbOaWJJIRsKExMkduCMHs/t0VKOINfU6dw1qtXJNzWuYNkhq+2qaJ0pOaGsstZhAWVcrWP0ex9EeHHYOY93SbQ2Tw7pk43k4og7xFx1rnAMuf258bPtu3dci/7YNNA6xsRCUJN3UVFX3KGGMxGlmulyYHh4IOVCb0uuEpJmQOlq8RE+3mqk2juxnDJ3afJUdfXnn5D7yMn69zd9u8dvafQCNdl96R2n8cSf/DY+1GPNCIpoybCrPR9qh3qlqwxGgKQxBslodNAYi0dSQl5Xl+YXnX77w5U9/4euff+b1yze43khNPW/Oc47vylnt1uB2zjFOxprnD+dk3F3bYQUdQ4dRU1DGe3zN1FLMyeDfJwJJT4ScaWHmNgkvLZCrEJqA18NlP4MBNd0B77bEHFHbr7Lq3us4DiDbszcBvN7htqJPz6zfn1ienylXA43t+moKqrU4Xd8/P/Lgt+19APhxtlEbVRmWya/z4A42/OYgUQd2o3PYGnIDk8f78I8MCrmdQdw+sPeGoSHRG7Ri6Xkx0D0Q1gaNvTd6W+l1oaw3wnxC8ok+ZTR54Z5s6RQpGmtFYiT/1sBxb6T7fLJfNVp/8J7sC60eLLgQA9MUuMwTj6eJU05EFWpp1NtKWaxGkVBt4xGlquWWxZSYTpn58YHzh0+cHn5iOn0iTR+I+Uyezh4dSdYp3TrMokxCWTvLbeV2fWW5PROWZ1hm+nWivmYbcMuN3iqC1cAK3oFamwt/mFGUcyamSA1scUbF6q4dNxUZA2asEFvL7WB8Kyj8TsCxfX+2TcMnzqDHDdJN89OmECy/tENS4SGd+enyiQ/ziXZdWF5fKa8vtPWVut5cpS9a8duNYqfbpAgjpLTdlk8MGcah7kqXb47xsf2je9tsH9Hja29A6MELvwHH7XmvmyZi0efrlUUETYlwuXD6+JH1euX68sLrPFt9y5CNarUtDnaeEGBKgYc5QZtZpNHq+yyoG8bZbvcwzra79zdtU9eiB0ZH9hprpbnR2vfF8rAumnDFAI2jxpHnzvj3jg1uUJK7lxIY4ighxkP/7fQcH/bO9deNkjkWb+sjy9Pb5oj3ZZBoACrZR9TpjSO/0WienvjnKqCj70eTHGeYvvV8yW5OvCcVZ9BDxw1v+RZqjgiRiERbswxUGBg3kKRG8c8T0/nMfDoTU+Lk9ThLLdzWlWupNImc5gsPD594vFyYMwQxxodRjgW6mIiGrw1BOk0LZXni9fUrZbkhNHKEKXQCK613glasXzMqVm/K8u6iTQoZdxqMwurRsC6Q6JzFcitv1ytlXVGUvq0XuvW7RVj2nB8T/LC+6WrjZ5NRF4totq6sxShZMYS7Pv8tj9pWGPT13inVc94duEtwAG+SpMgou6EWTR35aCJjX1VatZSBLqaWN9pNwph7fXvoBmwsRSBP2YSOWmPqlVQ6lO4iCwvaCyFn8pw4PVy4XD4R5ExZCjklkjRer1bfty2VVa5kfSH2mXxOhJMQ5AQxOg0wEUInpomUPpDSIzE9EMKMCfh0YlBiGNSutu2VSYCgG00qipJEySIkL3OVUC/NYj0e32tO6qHUz2G9sd/3OrAbOFK2KNa2t2x7+Oh/d4Dd2UPjEztNdIAYZdCXrX5pToEYOqIV0Wp5nr1bu0wz8+WB+XIhzTMaQSVDnCHOSGgghc2J1r1eXQiuB6EeKXYNADkCZLb1crP53oLLzZaR/f2qHKMxuKNuRL/G2v6utf96BxIajIqNZMzsPXgg5QfT//BzQDfL402upJpDRKtyfb7y9PMXvv75L3z78y+8fPlOe12YmzIHK88RMG2M5rnVd5G8DTR4Ex0vw9+zF3PznVN3YLhfso9TRj5+OLyklpu+QBNjoSiBcE7kPDHHxJwCU4a0Qow3Os2jl+NLvMcO3o8dCbD//Z59OZzQNknenMrbtQNrg5cb5fmFen2l3G6U65X15YVyu9LWBW3FVIYVF+7C8/pls0e29nTHwrCkjrMaGdB6t0vuQOWhkWSkVvgTRxN4+07dzuhdKgegyV3bi98uOsoqVXoMdAl0Z5KEoGhollMulsoRQiSQiCkT54k4J4LnPXYXOAz/Tj/+HRFH3f8fHvsfXt8H0U4POy60480m+pBzYJ4Sp+nEHCeyQi+FdivUxSKNEfNCKuaZVYGUZ+bLiYdPH3n46RPzh4+k0wdCfiSkCymfCdMJUkaT1yEz1Mqg24a1E3Ih5kQ8JfI6EcpMXi+k1wfi83d4euL2+korK9obE2r0PjXDrIIXcxYXTom0qNSOUcO2gegdfwduDpvPYRCpvmmr3/iYWmNIt+9q4ztSEFWPflvR+xwTH05nfnq48PE8c0mR19dOuy28fn+hfHtFbpWkRmcMw3Wjh7EwTn6H/tgWzmMT3S3mh+YZb7wbc2NNG3/rjy33doMa4hrHc28EM7E8gbrcuH3/DpcL04cPPHz8ievlkfPpzDwl8iSWaxbH4uF5fQJTipznGdFGFqWWv+3B+bsPGUaL/+kNtwPHw6azbeTD5G5GjWvFoyGd3o2SebQDJFiN05iM9j1UHXWI6WAUw9qcqnsH8Bw4RqN5W15AIKVESnkDoWDec/uMfS80BwqWwyOSNsN53C1E8+gGJcROSFZrdPPw7RNv31x9sd9p0f7/XXH4Xe1sMzKEuw35tzyCb8idYawdBCiApp3hzbGXhxiUA/AQ6cCyVkq/Et2DGDHap3YTXknzifPjmcvDzJytTq0ZjVbiIGKFw+meQkBH+41ye+L6+kRZrghKToEcIAUh4PLwogRJaIseBVvQfibE2XIexWryDqddcIMYzCFnZUIykcCrA8Q9sjIihMPZczTvZHMYHPt6kIBMwMqj4mUA9PdZW0U6KUEgmhfbVSyDiNXRdOdUGFGkGDxvpbvsj9NJ1UbauNbNWAymmmpRfCFGW28kgNJprVFbA0mkmLjMMz0FKCuyNNBKqwutLmhfkVBJ88zp4cTl40cul58QzqRUsLzIhdavqK7UdqOtnVVuBF5QzeQQyHNmyifiaUK8XEZIE/P0gTx9IqYHJJxAIzEoUw4EEi1m0yvoXjvS83Rrrah2G7/R0jt6rbRg61IOli/WYrQ6mO9w9N72veGwjoy14Z7BMsBj39bjzS46TuL7jWx/evte7iJxJl6WjB6cAykowSPFWi0qjQgxZ+bzhdPlYhTiUXZHTsRppaYbGgp9lEJBkd6NYizB11UI4oTybePYoN0bg/m4jw5bdrdmj+vn/sb984OCfTSc3wtw6DRDykgcAlKDSi53S8ARFo/es/4/GhbiIGsiEmlL4eXrdz7/+We+/OkvvH75Rn21SGMUi9okgu9pMKp/7Lwq2c62/fUWDP61+9r6ZQ804P2wwUbf3yJmq2ot9JtQJIJkAjPCTAqZKQin2DmlaiW4dHXnoe59dXC2Dov+DjQivE8v/pW2GNNOdtAoFfTaqa8L5XqjrxZ0Wm8Ly/VKXVfb24ZTG1zt31krw1nij43qDKMsJJbfeB81x5+/x0N7P4vbUUdwvf/uI8LvcdDFdXyDHsann3Mo1Y90rdqr3bsmNOJG7P6REJKDRtnE2ebLmeliNcd7jPQU6SEa+6W+hzjOG6N+W0424/VwjMbaFqHRTGY4pJzsJqZMjhOhJ7R26lKo14W+FgKdHG0hre5NDVPm/PDIx9/9ng+//z2XT5+I5wd6muhhgjChcaanTJOwGVibQIt6fkCEPhvlJk1C7Bn6GemfuNxeOT09Eecv8PkL1+dndLkBnSgdsJw4rYW2WP6O4OpnRHOyBxciUC9rcDRkcS/EmABjwBy9m+80FeMwtkafHDe4sRr0TtNOkMD5cuKnjx/46fHCKQW0LJTXV67fX3n59kp7Wpk7zDKRJdnk6uMeffG82xzGa2NP3ZeibcPdDHfFcmr1btXacwQO36f3tKDjVrc7zY4LoIDsi0UMXnxe1ZKrr6+0b19JHtF+uDzycHmgXE7UkojJVQ+3fXOngc4pwjQRtFPC+/TjWNBHs9xt1gfP9sjNGV1rQM3VxJqpOA6VxjtbZ6j5RYsYGr0pMnbBrpXaFkqtlNIsuq6WETAiiAbS100pU0JimmYgkLI3nqrnflmupXajnXZ1mfBuBk5MRjsPcVfWNKPahWB6QpoLQRwBI30DhqJyv+hL2NuGAUAOL2+NdoTjv+0RRLzm4u5GGsDW7sGKdW8AtytarWQO0RRvW4faK1orMZmKaFChFmjqxuXjA4+PZ+YJYjBguAf1dvBl4Huht4Xl9sTL8xfWm9FTp5RIUUx9lW41d33DFbV1o7PQ9UTvnZSFkGYkRsPzwQG4N2eI2Uyg3giSLFqtXrGyFEaGZDOOrvf3XQeC6EbNQke+lTBKPSlW2HmUHngvA2eess2j3DePvdUkNqAXNmXbwCi/ZPmondRtvKvXpRyRN1W2vFVCQGLwdguWqySBmKxGZO1G9YwJppQQCfQmlFZYW3GP+5XWFiQ08hw4PUycP1w4fXxkOn2AdqL3lbCshHwm5jOhXgku3lRLY7nd0PCC5MR8OXNOkfP5gThNdIIJ7UwP5OmRFM+ITEDEhmtmyia2RKuE3pDe0VYtp7KaqjM+B0qr9OVmNTpT2BQta4iUd+rH3trdbN/L0bzZLxlz5wAAxuazHbpb2j+AxwMF8A6+GKhLMTPlzBSNTm556CvSGkLwqMGZPJ+JeXL1WvG6rRPazvR5oS+FVsoW0baMAF+nQ7Sab6HS+jEfa9uG7/DMvs8MwHuI/mxBAmHkHI/I5T2QPLTRAVT+5sfDA+SAxl1uZLubO5rSm37R3SjfnnfaaUCgNtbXK89fvvHt5888ff5KebkSazcqq+c6D2foUDO2bxxKyseN9h523VuJb/DktocNu4jtGsf/9+DFR1bv9FopS0FDIQRXLM6BrJlTmDinGW2Vog1tZh/2bWzfG/hD6PAIjt4t4ijHthm8ReHuRwcK6FJot4W23Girl9xYFtdIKWiz9QaPOA4l+BExHE7ne3vKy9b4FSj3IHk4SzZbbLSLjPSdQWE/vF/Y74Njn8s4zY6pNrGsEX+W+7GhztQKHTRugsGCZ9r7HpFyIk+2ppxyIsZo+6x2wBy5qtDr306t+juAY2ews3UbR5tFvjXBPV98WCZsm7qEQIqJKc3M+USOM0EjWi2not4WWrmZMTHCq8E225wyp8dHPv7uv/DTH/4bDz/9nnR5pOfMipVWxRXICCY3bRL3gyrqC72LLkiOZmT1BDoTtBPp6KUwnz6ax1QuBD6z8oSUG1CABaFZkqqXChESZJPMti3OBHz6IQKyxQoGYNk2lfuBwN3w+G2P4ava61TtlxB8EdjFKCOX04VPHz7ycD4jvXH9/o1vP//C0+evLC8rqQW/313Vb8yOXTn5bvm5v2lGbtLu3Rp5IuHw3nv6j9y1lT/l7xaXrj+cx8P+4xoGrWDftqwdeu/+WbHisbcrt2/fePnyhdeHjywfP1FKpbRGaGw0BxCnUXVPpDZppBgCEt8n4jhoTUHGrLRjbFRjPdPDKjTuWF3Rke45ZWPh9LYSL+UwihJbno3n1qnQOiYKVCulrJRqyo92Peobm247zPDWiYqVk2jNRB9iNHphrfZoxYWqqj+6KWlKIqZOyJ2YMzEla1tvdxVXywsGWrf6ZAwPslNWt6iruw7E54OweW6HobML/rwvcMTrL46xbEn3Nr579zLnanNr9G3Q4ygzIm/wshMSoNTKUjpdM+fLhQ8ffsfl4cw8BVK0yNaese4b5bY+dXq7cbt+4/X5K8vtBaEapSmZcNa2WwuuognoStdO6JMbjIlGNo9nivZc6wZ+sEBl9Ch2WysaIJ4uTNrMeAGEQkNsbKmVKRngckRnB8Wo9T3jb8QczQFmaQoxiDtj32ddfXi4GB1trF1hqErv9HjxWm1KwALJ6oq61QHuyBv2vYohhDKcyF47Fovc5RBIeULcCOhqRaCTU69aU9ZaWG9XlusruiwEVVN8fZy4fPzI6cMH0vmBMJ3pdYYS0DybIzZOaJygFUZdzFaqKRQuGSkP5N54iJFpOtNjpodMTydCnBCP9AjRS7R0RIPVJmgB6cYS0BYIKRBqpNTiKsFKrYVaGz01EypDEZfQ7+80H3XU9znMyb/63i0qs210+2vH9/l/d749DvP9uFsJSAzkHJlTJGIR2V6qzR+EGCfS/ECcH5DogN0/Z9ERscjv+RGpBtRbq5YTrWPeCkSjAMcYbD/b9kX8YsX3kB00DrGqbTdR7sDgkY57t2a+Zf1s3/FOx+Xia9S4zh2O2Zq/39+Pij3sf7cO68p6vbE8v3B9euL163eu376zPD3TrguhunpqCJvzuTsTwgD4DkLu7vruXLtNo3fP8qt/6fGZ+4HF6EWRQXf1fqyNtlZaXOmyoj0QAsxEHqKV61i6gcfWBkw6nGiYrLJfydufv/1xd6dvDu/DrlAruq7outKX1QHkYg7IWi2XujVoI+XMLeHhYOxDTf7gIJGxI+5x3g0Ybvv1GDP3wPGYNGTP7+wC7ubI3odmOt9buYNxsq1Jfk6z0T2VZXxObX01t9BgkPluEYyJkoLn6K4LK50aBOFCiAmrGvG3e/Lvq+PIIUx7Bx73Rnh73NMEIYqQQ2QOE1M4kWVGOrTS6Us170Azb7OOiGHMTPOJ0+MjH373Bz79/r/x4af/Sr58pMWJddQdC3hBdjequ4O0vgOksdBJEFI0GqtqGrJ3JnEbGyHMiJwQzgTOvMgv1JdvtPJkxu0YL9ptcQ5eAy0m72gTr+gqd9GEQ8v4IvGOi+evHF33XG3t9702InPdjZ8UE+fTmcvpTArC8vLMt19+5vvPP7M+vxCbMseJCQjNPR+jyPw2QH59A96mzHFv4c31/FrbHKivRw/vr8US7pec+0V7z9Gwa7ComKnL9RDMJK+F/vpK+fad28cnlpdX1nWl1JlQ7TtSDB6Rcwlr7WYQ9U5U5aji9pseIi5q4tQ2dwRsdF093r1sXP2Ni7/VA3Mvm+KgUbwPTVgAL6cSnO4g6mzGrl7MGyyikAmSPb/VC7f7khucUilepLq15vU0MeO+DUXX6qBW6d1Ed2ziNGq1HLJAJ6FMTnfdis77nJYQCEP0CTfsxsTXfSHeyCQiPkR34Hh8/m/uW79JNxqN0/KhuzsjgoNEpfaRwxZct8DucwDJUeU+YrVwuypLgVsPpNOZ+eNHHj98MHpq6KQY2GiQ4AI4BlhEO7Qb6/WZ1+dv3DzSmHNgipaLFlB0lK5DnTJpLIamdfPkdm5ojabgRkXCZAqHogyqvDp1tQe7VxUlzidOvREIrHJjXUHVyjxYnqeNrVZ1B9YOqLobbHuW7JEmb0ZCe6eyKo8Pl22ciRhoDAe2ge2bDhxVvBSf7REmNDV+ujjQZtrItiZr398bUVIQcpqQlJAckGRAPtCgrrTbK+X6zHp9pixXQqtMOXF5uHD59IHTp9+TLh/peaaGaN8zKWGeIGV6SHTJdDJR20bpkl7RstCXV/T2iiw38nxCcqalQIk7KLYqKcHmVuvUbuWftFUHjoOV06m9UXqndjWBOYUm5nBofaX0xrreKGsx8bn3OAYQfDtODhG0Hz5ygH/HJeNoAjKcWbKPzbfgy6j95gBKySL7WovR7mo1myZkYj6T8qNTgSckJOu7KDRVajfqeJ4vhNZN4KjcqC7MBO7E97UmhUANwVU1j8BxbCOyXd8PmHpgZnQM8m3L3y1D8a32frP/d3D5f+qQ5BLOzjjYDW+/l7EvAnK42DuF8Fpp1xvXL1/4/Oc/8Zd//SO//Nu/8fz5F8rLC6k0zhIIaTLgKEJSCC7mNYCGbmD7sI3szbX9baBMNmGVv7Xl6MF2uhtzY5/z1yIO9PxNWit9WalcaaqEHDkFRV2pPPaVWwssNFwWhO0svk8OHHMPlN7p2KNUhzm2uTJ8Wo15cqMvNweNK3Ut1LVYqaq2O8mP8/AwvLdj3JfKnhZyRytmXMshBirs82PM6WOrHJhzP7LA3DbbHuO1w3wbX/PmYXNZ/ft0bxW1vbV1IXS3+zvmiFsWeoUaOi0lpK2EmiFOVtLwbxz/8YjjWPD2WOvotv2OYOcdHz8o1jQm+yrMITCHyCyR2APSlV46vZg0t/Htrau6ADkxP1x4/OknPv3+D3z46ffMDx/QeKJqZO1QMaM/uKJQAiZgUpvCTSxVqG730emtuKHrw6DboisKEjOnywfQTJCZJJkXEa7PhbpcGRTY6AA1tG6CMyERVUEiKoHuxv3upRttcoz2bb/si9Y7Ha13P93utR+XhDqA6uo5f5EpT0w5Utcrz18+8/T5z/TbMyeBmDNZO1PvnidhoEM3t4wfd/f+ph0OgG4Yo+rt8XZBOk6mfT2Tu6/+dSC+U2qOEdbj0jzOablmHWmNUCtxXeH6Sn1+4vr8xPX1I/nR8kk0AVjCfPDcpIiSHKgFEdo7UVVDjB5tVJPZ10Ez2m9QfccYRui4T1PYG1Tq3bFi7xmgMUEwmqpEE6IJQY3Gog7sNHpieSLE+Q44oo3Wi1EoUQOOwQRTzGmhW2mA1gqtl31tIdmVBHxRNOp0a8UiRiJeh3LQs8LhgYMSy4/cO/3QLjYkNkbEMGiGyue+LO8bxntNyRFbD+LR7t22cfqvMSb2KwrbJ/dJYkZ6rY2qQuuZNJ95+PQTDx8emU6JxMgljK4X1Amb0MCI/Cys1yeuL98P9FSrdxqkE9G9bQ9zeNBtB10UxCi0rdPWQusXYn4gJo+IiCAx0AfdauS7ihJ0Yp6V5LVGLaq6mFdYO605VVo8BufP92GJycHoH84Et2T7Jp7z2x/ny/lujOyUpbcbvQFHHWBQw/1jM2d2A3fbn1TNa+PCMkaFjYQUCUmwdGKLMNXrK7fnb9xevlGWVxPDkcA0TZwvH7k8/p58+QnyB6pkECElIZ0yU5nJtzPxdkZuV5AGrAhWxy6oIq3Sb1fKy3fWOTNnIUclJLvR3l1lNU4+xpRSK6uLqRl4tBIHFkHEhUQaTW1eSEjEmOkitFapS2FdVupaD7XXfttjcyDZH+xm3L5r6Jt+hh2bvAWNR2to/84358PWnjAoZUkQtZwzXVdzqqlanneakHiGcEbChZRPTFMi54QGsXz1UdZHkhmD84m4ToS2mHJvq/Rq4yFgDvQco9WU7cMUhs3xIjvI2u977MJHm8Z/bu0ibMupvAWT73zUCnRcBXC7rh0hDFLf7gywOTs0NBtlXXj59pUv//ZH/vQP/zv/+n/87/z5n/+B77/8Bb29cgowTxPSQLxmoqircMpGFLW5/9Yekvs/d2023ZgwHNqLwzjb/tO71revHeNrNLZ/l+U+Kr1XWrlilagLgRPTbE4HIUJLtBAYFc37hqoGE2zv371m4X+0c/5zxw4bx61aLnRZV9bbQl0XWq202jyVpmyq14rpf0vXbf/H9yODN/dBJr27Qcckeh+J3efIbn0Ou16PHS/7GLTP2Ls6Q/htP5/Zj+wO/8POAM6SOjrNo1FSJSXEf5IyuPBNUdDWaKUYvlKQyXQ6hoM6Bsh5/ptt/3eI4xw3P/97436O29kN1KNJbt5tqxGYBeYApwATSugVaoNaoFfL7xAgyCYXHqdMOk3k00ycTLWv9EbtK9ceuSGsojRMojbVygnlHCNzjEhM1CCsCK13Sms2mKoFZlNKliir5t1E1QzTOHF6OBNDIokQKDS9UvsrWovREYGkEH3BqAJrEEIPlgTXxKUa3Egcta229txG577M/AC03ucI7v0e9BNVbEHpugGfKQWSNPryyvLyJ9rtF06ykueIlogupvAWR+SGAb7YJsrYRN7SMMaxTUNh87a9jcVu42lz3cn9i2Oys0+0t6D47pzDo3p4YV+wFQZwLAVZVzPEXp55fXkhX8+EdAIiMYBGsWCCGnUyx0jomaJY3t07HDlGBie/671E+6A72t6zo5GxGWkfVFUHjocG2CKNISExE2I2YZNgbdU9srMZdlgeW4gTQkKwUh8xKrXduC1X1rIaUBGj70aXgzchpoqO/C6JiCSGkqgVvFdUC6UuHpFs0Jx6EhKSnA6sltM1qKf33a6bJ3aLrh43bh+r5iH2sgFHi+cd56JtQt33L4tIDWwYozigwLyH6pFjdxIEMVqiBKFpZ60VlZk8P3D68JHHh0fmnIlbu9v3BjD5bVWC56T2Vrndnnh9+cpyewYaUxJyEhMoQ318jDnjm2hvmxeaodYLxhILldA7tIjIDNL9Hn12e/667WKBELLl02UTLpCwnZlaFlozOqdF6XzjDyYAJN5eUUx9tjrlMAzxAzBa8DvV45yn6VcAx95Og150HHpWk8+pbcNgGUXAfc0cKqKbk9ZflG1fBTPxTNkWFdq6sF6/c3v5ynJ9otcrQdScgPMj0/QTMf8E4RMqDyAz6oI9KQD9RK0faHVFm3LrEV1eoa/W9r2jrVDXV5bXxC0HcgZyh1jRdLEcKibLvcGNu948l3Gh12KeEVV6U1rrJrKlSoim+hjTTEjZ94dCS51aK2soBzj22x6979GanYr6f37+H/2Y98uMbHv9WydmEBeuyJmcTNiKke/ppSBs3kQIGY0TEk+kfOY0n8lTMKDTKq2rFfluVtIFFTRlwjQRSkRbgdboFMBq4kax/aT1SBv7waghKnt44I5GNwzlEWW8awTuQM3eGL8SiXmvY10t3IYYNXoDurCH4IaAjQmCjHqcNo8Ky+3Kty8/86c//hP//I//G//2j/8bX//1j5Sv3whrZRIIydIIah9RLWf0MJKCfmy7cR16ePArr2844+7GBpqUu+/8IaLpT6o6C0GMidAUWxtrRWIlqpLCmZRs368xsIiwjgg4YgrXw474oSIAd9f6mx9HifftvrgLUGnv1FKpy0pdF2qxudNbo/nD8t31MDZ3ToqViQLUgli77biPmUGvtlSz7dn7KxPZ1+/xiuzfIwOkju87WKzD4aAj4i3O8NrsuGObyOaA1VHrNWVSSuScTXxwmpBpgnmGaUbyhMaIur0gUczRpLYvBTGxrdN0+pvd8feJ4xya6bhBwtEYG5SGHaW7Xj6CkIMyR+EUIYtFHHpbkb4QtDJq0ihO28iJNJlHpGrhdXlhRYhrpccza8jcJPBaK9d1od1upHXlop1POfHhfGK+nJHZvKprqTy9vvL8/MztdkUU5nnidJrI0aIlZoRMzPMjc3rk9DAT+UhvV2p9orVn1tcVaZ3QjTqXtBOoEKoZaQhRA60FKzbqhcaH91UPw+vQdNvxa5SY3+Q44NJdJtwGUBseeVXE1RlTgCQFwpUcXznlK6lXYoVSOqtf58jn6cq++fg9yWHyjc1zm1j+2FviAHw4eLgOF78vVvfTd+Q27i3L/S7tP4YXZ+sHd1b4vGYrHtEbrayU5cq63FhuV16vr+TXKzFbxEuzq7X1sbBYHUsSHlX4T/fYrx4xBHrr1md9L2arWxRpAMbRBmPxN9AobQeOd/0g4jmNcS/DkSLiEaXhGTPxnISESMwTIU7QI4HAlKPl0lWjlnXPP0OSRTVCJAVh1Llzvqs5K2KyCH9IVm8yev6kGP2ndRNkGUnux3sUjzBtoPmt5XIMN48R5ZvIlr+w7YD75q3vBDbsqsxYEYEQOgSL2Hesj0PCi8MLdIMHiNXpM7GaSFMoqqwIeZo5P37kw+MHTlM240bYqMZsJQWUEISkirTCenvi+vKN2+0Z1cKUYEpClJ2sMzaszUjxeW6gXxyo2d+qK6GbYARkFAMAnYkomWE/DgeOUcSCqSCC5ayKcPaeuImyrt1pzorEAaKC51mG7Rr6mMjje70jTSzgfXKOd0+//6e+7w3YOBwWbESkbeUbdY3tN3eaHdHHOIbjw/tDx9yXQuuVrtmA9nqjLs+U2zOtvEKvpJTJ80yeL0i80PqZ3k4EPRHDyajmVi2Fec7Ih49et24iMrPwlbZ8p/RXE7epzfaANXJbAukqMCshNPrUre6kVEb0V31whBSJZCtrpPZyLY1yWymtoCrMOZGnEymfIJjgWsoz0zyTpow+K2td36UfjzVoN0fn0UD3n3J4//h7S6MGmyvbJ45G6N63I6NKQiSmTI7moBZt9FqQ1k09ICS6CF1M/TDkzHyZOT+cyDnQe+FWrtx6pcZID5OxNGsjdggpI3myWqYSiIrR+WuxnomBKC6k4Xv48T63+3gLBn/l2N5/tzcf2myvMbHNg3c5um4OuONd3PdFsBn3thQToL2yXl94/vaZL7/8iS8//yvfvvyJ1+fP6PrK1AQ0ompCOLVZWaugigZLHYCRArPbQ8f95QgaB0A/JOLsY+y4jR8/PdYTPbbzoVV9nRePXgXpaFALBIRuSshpIqRGz7bvrNEc3zHYOOkDyCgOjGyQb5BKeM9evFsr/8obzA4qK60s9GJCOL2YYjxe4miswWPfG+JCP4D2IAS9VwC4t2UdbG7n1w0MgrVX3+xWYdeKkO2xgciBAkQ3m3ek20gY+hJiwmjuJB2OcdMiDEiKJjIXjaGTB3icZ2Se0WlGk6mnahBjqsVAk86tF9bSkTxzniZO5zOnfP6b/fF3AccjL3x44rai9ocGEXZ6kPrgFjoxCMkE4ZiSkoPJ+TcWhAWlWtQQRcUbL1lyTZfKbX3h1lYkPZPmD+TLJ5gfuCl8fXnl67cn1pcraS089MZLCvzuw5lPf/iJ06cPlBh4eX3h589f+PL1K7frlYByOmXO58z5lDhNwRqfC7F1ppSIOREeZh7LI+vyibU8o32hXzvSV4JCxNQI6UJME1OMzJLQZjW9TKnq12lSd4vJ8HK+E+CwrtOtyKgZfkZhHcAxCu71zaQIKVRSbny4KOliBb97xVQSpwkQqk8eK2i9bzJvQd4wqu5LD8j290hmvzfftwv3J3yz9jYKw2LlABpHRNG98uP898BSNgNW3GgbdfJEDAC3siK3hbSunErhtqzk28J8OXmpCFO2NPqZLzQiqEQ0KEbzeodjJHT3vouFHJwCewfcA+8NKI8lcQDqzdj2e3JqqbhktYo7dAS2wtQIo1bfAJpJgguR1IPgih0iJlKSYiCJ4b7QD9ctAiGScmZKk0XTKEizvB/ViFRTrQxOC+qto3hdMzEKaxNX0zwex8Hoi7Q5Gvz8B5eteSBHzcqDMfgOh8igHLfNuO4ejQ2i27huqlS6RycS8zQxxezRH0VDJM4X5scPnB8vBhqDkIMYSN/aIIJWX48htIX19p3byxfK8h10NXDvoNGqaI75o4f60Lsi7U6327fUjted6wsQbC71FdUTyAMpC705s2Hkqmp3L2tCq6JRmeezl8sZmz9IaEbLrc3zsmQvjKzmnEuHmo37hs2/W6fq7z3W63W7vr2x/ZpHaZENOILtkjs6NLA43jf+PkxoPRg+6iuXRzkERXIiz7Pdd1nRcoO6IG0F1ARVUiKmGcJM14nQJ4SZGCZrFxfLSgLn85kkmchE0MRTV167OdFqrfSghCxMNbOWhdvyCtdAjEIgInJG4shhNPsg5EwKM1IVNBG8fM+6VJYutNXy0ULMTNOZPJ3oauJXMQVC7MQpsNaFp5fnd+nHLXrOAUrIwVty3Ji3aXV87QAQ33z3PvQOjBx3lsSQCRJNZXbkyKNWxxITGdJgRmKeI9NZSHml1srr7YXremMV0Pls1l0LVu4kBEQyIWdCTLZOYGtnGzUdxdw70dV/gxfVObbB0Vl0fP5ezZuNcqkMKuD++gAwo537Nr7f4UgR4m4vHE8zUjJ2181xLgIYA64tV9brM8v1ibK+AisxNXrs9NZZWoUSaNUE4zrO4sJSo0Y/d5Efz39Ykkf1+R3SHjU03wDG7Xd/x2FNeQvSFfUAoXqEylgYEo3FmHImTJ2QoSWo6ntzMMVdW2eOLJP7b99uQ0DeaZM8TJmDMXn4Q7EIeltpdaHXlV5XWlvpbTWRPW10T3/bMz0OWAY28DeirHut6ZHDKONP3ur0bmBSnG0nA3DHDTSG4J9yJddtTxodPPQZogkRStzTcMZzIYTtekxaIhKnyQTSnMWVY2TKmTTNaM6UGKlBjQHiGjBdlVsxNknHSu7NpzOXyyPn/G4Rx72/dvPb18B9FLENLqdiBS9COYySYZgQGxoqSkHV1Nt0hGKDCSdUGtIX1mWhNKVLZr6sPMZATkJdK09fPvPLX76wvq7kDi+t8Y3K95eZ/x5WfjcpLQe+Pn/lL5//xNev36ilkKJwXeDlqjxcAh8fZx4fLuT8EyITygXVGQmJdJo4f3jksnyklitrKRa5oVk5gWqCIUww5TM9ZLR2kwm+G2bH4wgavW0HmnuHo7uanz1cKKeL1QHzmmxzysSUjdoUEwlIokwRigtZxJSZpguaTqhEmjpVQ/XucR/lGX87RXL8Oe53/4MjeOTuO8f32M9RVPhwhv1Uv7JdIDtg/qE/dP/qEfXpCktr5LLyuiyk2428rpQ2KHo+1scCDaYEKbJTCd7haLXSetvoqXb5u/H844Z8dOKMp3YIPRbRrdSFL1RDoGpLu3bKoyZs4Rnvjw4KRRDttFqopVgpGi8uG+MAlS6yMiwNHbmFTp3KiZQiURRtbiyLCY6o7B5WVbz2lBmo0aOlITS6O6DkcG++Sx8eRxNvb4ddMGe03I+z9rc6RldZLsnQER6bljMgROnB8stEgrVNTChQutKI5OnC9PiJ88Mj8zSRkikzpuiAzge/dKNzBunQFm7X71xfPrMs30EXcjLRlSggThQda7uREXRvo2BgD8bGOs6DG5998/MIDe3mDW5iwDzkCQjuxQ6E6PrAXb20kWXGW9qFb34IoRaCWK5GqbIBMmQkS7ghdkAAhl2GwfjbH09fP49Tbf8f17KN8PgDmhhzddDGdXMEHdfQ4fBRNTBvIjXGGohBCPNE7ZWeMlILUgtRG8kNmbH2EQIxTeTpTJjOhJjBqV6trUTtIJEUMjknLg8PtNppbaG0V9b2TCmvtFIJa2CaClMpxHWhX42FkWUmh0JK1cCodjRGRgZ475He1CnrEc2CxuDGtjmiUzLqZpdoLIooIJWhGRjeaV3duuhNlGiUl7gvB6OH33Trys0O4n7t0OMA8Ah7iIkYMlGylVrofZc1l1GRNEI8kaYT03kmzZGmr7y8fOd2u3K7Ximq6DQRkjNBMPGwmIQUgT7Tpom+LkipvtWasyKoGaRRLD2gulNuC7yOe7rbyvWHNhlOuCPD5W0N5bdRs/fCjeSM8eUbxxrG94df53a9YOPPxKX6eqOtN2iVnIWHhxO5PbAKlKeFUipNG0qClFwxWuhymL++7x7tkl87xm40rmTzz7GPpM1SHNsDcndXb+2do6iLqtfcpaPRgjIxq5UVy1CDkLuQYyDGaOkpOvbp4RPZO+8oknlsvf8xx+FsB62E3gq1Lq6ZUKm9sraVtRWadEjm5Mcd/CMaGDb1a9mcG1uEcEQAZbcLTEF/1GIbnxu2k0WcN4e6BN9/w7ZH4hHFEA2gW1TRa/xGZ3sd3iNOR93WIXHFmJRIeSJN5nQy4GiBL0mZivDaKku70UMjZUCUZS1IVaPGzyemhw9cTmdO84nL/PA3W/7vAo5vfQ7DUBgNfTTGxruGyTkKUKZk3kNjDCl4dEBdVc4iGhFipIXArTdKWYb/36T/JUPMXJp5VHW5Ub9/Zfn2lVqUEGdu2rnWhZus6PNE/zBDjnx7+cb31+9cy9WuG6GVlaXcqK0jMpHzT5zmCZGCSKNptcEWI/F8Znp8ZLo+0643l/pVWi+UUlhDo0+JNJ855UTPnRoXLxHw4/R6a6AeAch7HK13au+b+lpTnGphEccQA1mMM53zbDU2NVKXzu21UheYokv8f/yvhPNHigi3slJqcZVGbCIPKuSgUbpy7ZA+3tRuRxFvQ7EOLPcyEeP17tE1aywzpILqXqy2e5LxWLBHvoHuVMSxYdp7RgRYN2VSVa8jp+KiEzMhZ0rrvF6vpNuNSzV1OtsoXZVyX83d4+Td/U72Ta1W6sXucT+GcbIFZ9HDJYxtpu/GqB9Hj+FGG/TFa1gN9hxIsrI3Eq2tJEQvMqugvoCXheqiDhLGZjTEa/qwizbgaKA0+TnFSyzpJhZz3CtGf6OYp07CBj5HRrEt1P1grLmH8ZDvuXeZbvbOPkaGYaV3UYjf/GidgjIYtsNgHevpFq0JkRwdGEfLx166UlRI04nLwwculwfmeSKnSM6JlKPTri0aKx2LZEhHdOV2/c7z82eW63e0L6TYTDnV18V9TOhh7VLHaLtJLI4O1Z1R4m5Z66fKEBeJdKPdrplKMmZDnmw4yuhHr3UWzFs7XDNpPnHy+RoXMYq8l+04Rh7Hd9wZ7OxOJ30nz/i3z7/sRhq8ccrcOyaQ3egb7cZYjw7gER1AfbsLM9jdURLUdAPmNDEDGhLaxWr9teZUYZtDwSnHIQbmeeb08EB4uFCisNYbpb4ibSVpoyAkSUSZERLzOfFQL5T6QKknan+h1MJtqeRcyVNFssn857iisRByoedGSM2cBhLoQFG4ro2yFkJo5JTprbFqo/QVWqf2FdVGTOYNbwq1FpblxvV6ZV0L8E6UY7nPHds7dDiZ7t4N2whlR+fDicLe8wdo5cbpcM5FohiADr4mDsDR1IU8QiTlC9PDB+aHTEiVZX3h+vqd28srtTYkZVIUpBVCrETJ5JSZciBFIfYTejqxrgu9Npp2uuzXJWK55zEoMVhKwLjXwP0Ivm+Be2A49ue9BQ7rrAiHtxtQfi9LJ0a2ltc7zHN/CIcOGnfZoBQv71AQVU7TTPz4iZYzSz7zGl545UbxXN4UTyZehDgbqJqd4/O7HwXsxnz3vem4zG7rAfgertvzFoQ50ER1G1E/jNlNF47N42d2FWZniVoNzyBCcsAyqbPtUiLV6mwgHRdy315v2/CdmByHO+JX4al2c061as5qf/TesExepQiQo0X1MXvD0iKSPULwXPi9fJJ4akcYqvleP1rElIiDxG2P2ioJuDCfAcH99Tua6nh/jJ4GdHj9TuTPnDhb3eqxrox9cksnitv1x+AlYVx47lYbYXmFtZCiMp885QildTjlmenDR/LjJ9L5gdN84nz6jamqP4wVwdG6bvQfG99uTBw+MEQccoqkbAqNioGV2upW+FdUiTER54k2z6wxsPRGuy3IasYVEkgpmzmsYiImpTK1zoMEWk5M5wc0RtZ2o4TKdwS53pACz6WgOZIfTiQRonRa6ZSiXJcb4Xklp8Rp/sTD2b1qeJROBPJMPD0QT4/E6YquFiU14FhtISmFrEoKgRwSKSTqXW7N/TJ8Bx7FJvt7pVU1B4i1d4tWdC9B4WDSvCWBEBIpzeR4gha4PneevlVSO/GH3/83/uf/+//C4+//J/p85gpc60ptxSiDqCk4ufd0o1S6Z8h+94WsO8DbQOXxPV6o3nN5Wm87LdMVJ0eu23jPAJf9DqD2rYHHwt23qKf1RXDBCaOgmGhSnGfS6Qx5ZlW43m5MtxutjVzchrpTwaUqsU3Y54d7q96nH5tjJ99YHNzte4wOXAUMqHQA2dtmcjButgVuLKJxExcZ5oMQTIEtQFAjUW220og6t7r3FYzGGIjEj7EhCjBKfiRCsA2/D/n/YAt121ze9p2WwiKWTzrk/utBgGQs1Fv7jHPdI1Hx+baL3wqjttPIHf11k+k3OtQp4iFsKrKjD2AvPhx8I4nBcmpLVyoBnSbS5YHT2empKTJNJudveaGy9T3SERqUK2X5zu31C7frE01XUrBSSUkGNZUN8O1ghw2U7WB6tKVuRuNGWfWPW+Sy+Xgp1PZKXZVGJXEhphNooLQhsmPCPSJKSNYGIiNLUogK2sZcxxxGvdqasl33AN/7XBh5ge9xPH3/ssPEzVlxaKejwbyNzaNz4j4f8v67jsCjEbRZWRoR5mR0crHkcovK1o6W7uIz3UpURSHPE6fLidPDhdPlbFSmvlLKQilX6Ddz9pQKTUjpxHl+JOeJ88NMbQ+s5YG1PrO0lVKV262S04rERI8JjY2YG1pcxKq7oBVWKqbUzuvSuL2uhCBMk0V4lrpQ+4L0SmuZrishdBvHXSmlsa4rt9tCLR3R9wOOY88fND/vCe5h0v7c/nNDSJtj4Age784xIgqyl1SKMqjfpgDfEDRE8jQzPTwwPzyS5oDKDbo7Vrpa9JeAtmZ166SQpjNzzuTsrIU8MZ8ekLWwrI3aFkbtN+0BK+sYyZh4SpDDnikG8gZFcNDB7WbuDfq7tBvZ66zK1raHtpBgdPz3OFp/A2h2aup+/Nq5lVGf23LlGtKFnM5MD5l4/ol6XrieXnk9Xym3jshMSmdCTKDOBqqrq9T2g/1iFNfu9s1m99w5zo9MLX+MGoPDZnrjPFcnFt9hxy2HT9lprZZqoA2k2iM1QXo0YZUIU+rkWIghEYKzGg7rmXXm2BN2IPNeKQCHs/6VN+hWU7q3hksym2K3JOJ8ZpYANIIE0+1w0T+JyR3g0cHgsHt22y2kQRU1BXlxOrdINFvF7Y/N4e3AUkY6zxbVtM8RAhoixJEO5PNjtKU7wkcd4DAoqlt723WO9I7eTWl8i/yLuc5bMxtfQyRNMylPnM7JtBGmyuXSCdNMfnwknR+R/EDMEyn/bXv1Px5xPHgdtpBuGOjbXjDlIn/P7hLxRXFQrPYbrqWyLgt1WdFSyRJIOZPOF+rJ/MvXsnBdrUhyisHpkxdiPKEa0QpZI59Oj6SfJlqcCedH2pS5amHpV4o0vq0dSqcRmR8+cPKacNIbrSTCTVlvnefXhSALp6ny4cFCwuKd0SWgKSP5TMgPhPwKyemqbaV5or+URqzu5Se6QZy9mLl7fX5w3Ry9Om83qN/u6G1fxMYDvxqjEA6Vt0Ty3Ive4HaDZUl8OH/i//n/+v/y//l///+YHn/H96Y8a6NF3JtVN++WYUHdwNsoPD+A5LZ4HqKK9EbvdQOPcASF7c5YNN6tA83WNtA4DLHem4lGeB2fDQzoPR3MCqcaEGqtc1sLa++kPJHnibUL30phrUONt26y+GjDCtsF+x4HjpaA7cq673BslMGDk2Gj8m7gKez+yWG9HDam4wiU4R+WQdcYFI5g0SWFTUFPZKNG2Hm7sQVG32nbNu0tudwaZQePW3TZzm0evLAZbV09UrpRGgYA0G2DVHBFseAbcPX7GYb5X5lFG2AdUdcDuByOinaImB89rr/1sSniGtsief0/EUH7cM6Z4NIAk9YOQswT+fzA+fLANM8WYUiBnEykxNrJNrRRM1Pbwnp7Zr1+Zbl9R/uVIJZKIA4a44h2omxiCEep2sMhbkgcI7sbbWezKS1KZoDUVGJbLTQtqDsjQKAfNmRRRHw+Ed29EAhY6ZshwrXDrb7Tx3UYNLsnXtB37cbb7fXgnGCbWPsKf5hpcvi5OX/sTtxUP9hqcvi8Ao0ojRCszu6UIlNO5JCsBEpRc4TWTm2N3hshJ6bLzMNPH3j46RPT5UwHlnVhaQutrSgV0UKpV5bXF8qtMeUzfFTS9InpnDj1M+flgdv6YM6zslLWxvV1QUJEYzbQWLvlIreGtIpoRTX5vBZaE0odzoyGSKW2ldZuBG0oBZOB8qIAbsiFGIlpIsQVeafc8Y1yDa4az+gkH6vH/jh+xvpqjDZ+eJc9vzvm4rbGmvO9OgW5ukAdECNpOjE/PHB6uJDnGY0KzEwnIWqkr0IrT/Qe0CYGCvpw1JuNZsH4RJ4uyKlavexqeWHaLbdaQnLDWkixU5sBelv/Nk4G+P5mRAi9dy4d7vPOljn8Pmwb1e7M/HeakbUMY8b2HcbDD1WO0Y2733q3qGxptLXTa0D0zDQlLnkmfRDax8r6h5VWFJFsucNiJYhqLdSy0pqJv23qntUAzqgrOB7ahi3UNjD5w2PUIXQH+w4iRw3bfa/a98dDVBNAO00bBROcqzXSaoAeLUIdhCl2csrEWAi1ccioO/TVG8thc3T+jzkO24rdb+ue6y3kPFHTiRBuxHzhIZ98f7LUlTlnTqczKU9oCKYuHozebdTRsM9Rr+zA+N3B4QCYBhoNBDIYWjLUUMP+cNA5AKWG4KlMh+aUsZcO+0s2sDk+t7e1p+2oOY20FFqpHgSycVF7paLEyfQipjkxT5mUgrsROj0oPQX7qSu1KGu5/c22/49HHGVfMPepPrw4eyqvuqfY3modppgMewyG+AWhVfMgLkuhrY3YFMkWTZxPJ/LlAWKyPKnFahlFhJwyOT8Qwhntmd5MxOX3P534+EFoaabmmVsQQl2gTKzlldJWXywSMUVi6ERpZlDpTMyZ1uD69J2nLny8QFkD4AIhcaTEzoR8IeYPxHRDYyWESmMxufLWkar04vQzjQQmz7sSRBpKdU/R2IYO24s4xzq8j0d1UCbopkwZemfUOvNKjCQJTDEzReNOG3ALhHjm4eMf+C//0/+D//Z/+59hfqTeFhOVmT2SrK5kheVs7iI8I8LnEcDe3NPmTrUtCuZFrp2yCgOE7sBRByDt6gvqAC0H3652eq/UVqi+WG+yzNrN4HTAikckgzkbWUullWYKkb3z/eWV56dnu85mINXA5m62btcrR8eKGcPvcWzKxTIcj8PYHMbn8GQdPnT0Zo7v2Vps961v3H5hV9HUw2KNRxzHnw62VPcarOEAHG3cR6dXbNnp4LByi9Cyg8auRvcd9GEdNtzBdOMwxhhe/BjQKl7m4mDW+H9bOvwh2owOSvMA3zuwBl/77i3A3+4QbDPxa4nJHGza7d5CiF6aItHUCnyrCJIy8/nCdH7gNJ1sc8jJUntGMXlJ27hOAbo2buXGur5Syo2uC4GCoESM9jiihdsoOtiFg97bh3dzvE+HMyF4u3kNqu0mdZsrAUFFSVpNIbdmSz3QgKTJFPK7UYQQaL2a7yAmulq0O52EGaUZsvZrtnHUmmx17AaoFXc6bA3+DofSOCb+yOFcO/bQu2dHFOeIN900354b6yjqgkOYKmDOgdOcucwz52lmyhkI1NppazV5+m50xJQT08OZ86eP5MdHigivLy+8lkaVSsiNlABVSltYl1eW60qvjfP5AvKBmDPpNJMvF+bbI60WikIvhXUphLgQ80Q/VaQ0QmvEVom+lu+RjwCSEMlYXqeBiNYKvVuqQ5BGCJbC0rXSCYScmC8XCsK1dOT2PmWO9uMeIL5lAN1TNN9+5m1fy/YwYzIZoydGUhJygECxfaUXn7uuJHu5cLpcmOaZkCLd86eCJjRCiisxGjMnhJk5n5nnM1NOhFFbFbCamCeYKnleKXWl3+pWGF27jUeRQIqJFC03tY09+w0wHIEBceeSooc9aTeAt3aRYztYwOF9UwAargaChy7Y7VW79m2FepsDqcY8arVTi1LXaArE+YHT+SceTxeyR/ltCbL1uStWb7QWo016zUwDjXWrLfgWPGprJvJ2dG47oOz98Ppwprv9sds9fStRdHwMx6etx7aylFq5lsKiHWJCOCPMRCZT0A+NFDIpJEIYeZG7E3Ao4+805IF63qcnj711xFm+EZltqrY/TOeLlVtqwmuFUzwTc2SaI8a+gdNp5ny5kHJ2opjnBA/K6igV5rkjKiMfcthFA8gN0UAXrPGfO911tMk9VVWCWBkMcR3tX4m4m+nk+9oA5TIcxrt7KsAmpFNCoa4rzQMb2hshCnM+cTqdyNOJGJOD0YZIoerCa73y/PrE87WwNNuh/9bxnxLHMePfdCjRgAaL2LwtytvVPAFIRzXskvCoTabS6HXU5HK+f0yklIluFJ1jZm2dslTaWqB2ogSkZ7RFZDoxnU7ExwlCokritXXW6yvl9kxdivG44wm0sJYra70SQuV8iuTLiZTOSJy4Xhv9uVGqUEqm98SWfwWE6KIBk5CmQsoLLa4gN5AMRMcQ5v2ji3HJQyaFalF0wQFR36wyPRoVbkiG94pUuaFnS6k4N9oGefP+S8Ek0aeciSJoNyGDeT4R08RSGi/LyjwL8fxACtCyeS5MCMlPJlY3bQiyb9E9p6Duc0a2IqhDHGGLVQ2PWh9RLR22vre1v+dARx2GatdmC3mrh2iky96rg+juRm03WTRxB5u0DsvK69M31v5nwsvrtljTrXC1LQu6Kc0Ju1iPcAAm73AMCtA9PUWHE273nMu+yR9B4xEwjmTxAd4GkMMB0/YJPZhLx7E88jx15CofqMBDVSzufPwNcG/gblCYBk3vsDzqXtRgM9fGwoyLCOgm3bMtsvciFgfwuH1z38ajjvn4pqt2UYwfDcff6ggxEkOyPLRopRBiCLRibSpqjhxFKKoUxGlnZ06nM9M0G0U1mzfRIjGj0zrDrUdr1OWF2+07y/pC1xUrJN/IQBIheb8f8zr3DVC2MbOnJrhh6qDxfkzhtGj7e5/PYnmXGqxUR73SeqD2juiZmM5O/RE4GD4iRtuzMZ6J85mTWn3KNQjidb2qdFrjUFJgbObwQwf/lsdhvI2Nf4MN2/g5gg05vG9c249gY1cn33ObYlRyMvW805Q55WRlFGqlrJVaLJJECMQ8k85nwulMy5nX1ni9PfP1+8ptLcQ58PDBaWla6d0AnFG71OjTQSBFJFu7T+cHWimEqrRmVLFeKm1Z6bcFvd3gdiOcFlK3Qux7mS3MmA8RwdSae7cC1LU3wrGPFJp2mgQr7xEiqSkx38zb/y7H0d00nA73QH5/Jz8YzL6j7xH6w2HFus3oTHGysiMJAqupeDYr8YVrDKR5Zj6dydO0UVtTSihKXTrrotQqFvEKkOLM6XThfHkgpbzlwEsQAsmcFGEi5tlKKJUF7RZV0t7ptUP0dk7JGTuucn9/1/7bPuOPTpJh8I637o677S+OY/tdjs0bOVbA8fgV4Hh3V7qpFRtwhFYjvSWEj+Tpv/L46Q88XC5MORo7w2/DaIMeZXSm05HxNMBiq21jtWwldRwYDkZYvwOO/QDwPadvewyRPBehcufncJRrNyf+YJEsa+HpeuPltrA0YyOkdCJIItAtzzVGBxnFI4n30f3hsN72hV/Zb9/j2JXPR9TabEaJkXQ6E1QI+YSGiYtMcLuRcuB0TqRs2irzyYBUSNHuIZhAlcQMW5RwHzuquxKGOTAHxdSBo5tUw2rFxbsGc8que8wgfyYc2k9gqGrdzbKx1ngb9+4qyH3YVcYMiimRYrJ9EBNy7CJINJ2DeT4xz2dimFASrTZKrygrjSsvt+/8/PkLP3/5zq100jz/zT74D6+6uxd692iMHABbmHav02Bdd+20UUfFQWMMYp6au0FtRWhjGItWZj6dSR9/gtOFRjLJ7ucry9MLdVmQIkgLxHxievhEnE40ArV02vMLr9+eefnlM6Veefx44dOnR1Qzn1+fefr6FZWF9NMDjw8zeZqomojTAyEtSFMCE9KjeeGG1yYYCNQpMc2VMt0gvdLCM+qS2aPDg0LoQiSSJNFCZqP0iYnc2+Dv24AZxA/ZAPZvfxyplEP0OYQ9bwnMiJxSZErRNnJtlng7ZWqvfP76mT///DO/my7w8SckRVatNJfTNyl8k/odCrnNjQB1B5bR4naPlZNSUdRrDrG1z1gUt1m6wwQGUpINTbrBSic4qJEDGBnWV+u7Ry4oDGszImSJTB36ywtfYuTz12/m4PLFekSjgl/Dth8eNsctuqrvVI5j80C5INBBXXW3WQe100GaHBYvcIOUrW2OVAl5g0rlDRhla/vjGthNzaw2au1U04w2ynPKxBg8Cj+uQ7cvH+NvgMXjVr4/eTQy9jGwmQJbFHF/fbTB+LlJkuhonyHENGj2w2zfDR85/P9bHykmYs6upiakZLRMDc3mqI68JxOCkTQR5zN5fiBPJ2dgJHL2e+1ep8oBoIgZQmV95vb6hXX5Rqk36CtBG0mEHK1kRwiDAaDD++B3vsWW749jVHYAG5+P6uNvGNA7brP5aLTTQO9Xy7nWAmpqjylDrwKiG21XECRYdKMrEBLTfDHnl1+zBVca4IaYj4XgVLXuRvB7HKMO2/Y3A4Ic89XHgLobWGwmw6GenGxIZcw5vxcRQjAGz/4P6JXqeYC9rASBaZrJl4l8eaDHmW/XlfX1C0/PnaenldaV8yWjYbJ2xxQJYxDiPHE+nZinmZCsjIPGDPlEmB7IU0NmtSLz9UboGGXqdqW+vtCmZzh9ID4URDotKNp2NW/FI6kdWlNqg9atKE0f5Y0ObVlVqQqeXf5e05EQTcV1X2/u151tKZK/fgmyrTv7caTsB6+1NudIEgMGFp1qlrcdMzGfSNOZmEzR3ROgGXnbBlQOZbV07JV6d25jF/la6KwPDTu1LoRA6GJlQJpR/UN05eVwP6aHcTtA4T5mj43yFljft2LQu7e835Git9kRMNpj26bZnTHboRbRq9XmU6tKbxE0E8IH8vR7Tg//ncfffeR8noiWA2Bt79Rwa6ihr3AAdcPmeCMaOBxDmxZEu8+F7KMO+FELQl3JfwOqzZ21oL17XcmKtualnQJ0uC2Vp5cr359feH69UuqK0q0MXi8IwexxB49S62HX3Pt0B4z/A/rycNwBVLcFQ5qYLg82T1JhCpnHNJNuN1BTxJ3Omek0k0+zldcbojIOBHHtAN0iqLu5uQkSCXtQRyL4GqZbbqqvaeN7ZB/8ujmoh/3NNpdBPPK4t+cGXsc6KRad3JTFHfSKK68Hv66YIqoRQT0tEHqtVFVKWVnLSikvNH02Zeb1meenJ8qyEELmlKe/2f7/aXGcLZ+KbupYh0Wte6fq8AZkIU8TOWdfqLwWm6ppowXxBUVIMZos7OXBvaQzpZu8rCRQWdG2ElRJEpmmE/F8oabMdSk83658//6Vl8+/UL9+YYrwu58+8l/Oj5S28tpAX16peqWfXYQhBgsfhwQhE1QthwvZVDutH8UUJHNims/U+YLmE10SqhYVGGAiKiTM291iosVE75WG2GIr+0JmdKRhRDNGzn+0i/7PHbKfleiGaRjUPnvRwtliiesejRuD3vZ6o4k1n1mjvEdFzYBwCpKF4j1ZV9mK1YvukZAjBuliS3kQXKwGRm1EQyi6GVSyTecBMI9m/wCSNuk31V8ZFAQr8dR9wgdlE9mJBCYJTF3RkFleXphPJ0IMaLEI5qD/7Kvo1mmHZh7L7fsYqiNH00DjUA9l95iNtj1EPRXlOKqOG7qMD/g9bRL+vtGLiOWByn6vG2VFdlOp1cpaVmpVVBPBS7uYV0wIh6y0vW0OeZCHn/vswI0Wv9bRl/4IQZzaqa4YN75GNvCrh6/fz+s7w4iObi0jbEPOd4z32h+3Uhne7q3ubRJChD6KwnerkTdn8unMPD0w55lpCuQYEI73EHYTqTdKubFen1iWb/T2hHSLSI0SSTHGO5GKISt+mF1uJOr2uozdj3F9o60PjhTYG3FY3DLobXvEV6QivdKL0iVSsejiiLCkFHenh2BGrxi1KE34GtRpXbGyTnWfEy5tbJer7+fI0bczfQyeg8kle/uMsbgb+ONLxmx00K/+2f2Tttd0MbZ/6zQxEZpSCrWuaO9GTz2dmR8eCacLVTMvzze+3154fqmUxWr8daKVUyowp0bWxjxnpnjicv7A+XIhpkyThAZF0pk4NdKkSFZCduebrNAqdbmyvjyxphPl9IH5w5XUrXQKMvJmd5XJrkpr0Fqg92T7sMYNPIqNbFpvrLWzlJXa67vNx5jyoQt3Q2/YO29ZVdZrug3Nrbd8Euw0e5svEi3ykbOQgiKt0kqhV6M6h5DJ+UycHgnpApKdTidIMOp2VzMUpylTorD0Ru0FXUBeIyrC+fxo9d2wAvUiYjUhQzLHtUc/bZ8H6FueNSG43oHZAX0LEvz6cVw3/1q/uO27OZTsyffZGwGMe71ZGW8e4xreegDUbJ1qUftaVqf9QRCLzOX5A9PDT8yPPzE9TB74HsJ+bieNVA12oLfd69ufh1OP1JlN5M9zHve1zAHKdr5DdHOk8zgAba3RHThqa4hfxrpUHq8LH55e+f79O8/fv/H/5+3flyM5kjRf8Kd2cfeIAJBMktVd1TPTp/fIvv8T7KOsyMocmV05093FIjOBCHe3i+4fambuSLKqT/UhxkkkgECEX+yiqp9ePn28feG+vlpdqJo+CL6Bq1PtojnB5LAJ2qsfthl/8zhgPy3NNESzE2sVVK0i/honwjazrw/QDGFC5gt1itYPNQR8MIdMJ6Qc0b0+1E3vdYA8yo9G/U63NGVksHW7rDOoGiv98T6Ro9JW6qEHaxvUzrbc99LQDw66HgM3ovpWQ9sYZdv+tdKXQjq1REtJ2fbKnhK5bNR6J+mDXHe8m/jxx++4Pn3i6eU74P/1V0f/P5/nMYyHQ9npEAgAMtIGBSHEyLJElstCnCa8GImI01YjNTSoGSoxRm7XC9fLhd053h4b9/VB2YEtU/eKK0p0whI80xQgOlYt/GV75edf/p3Xn/6N/e0nLpr4fHnmj0/PfHd54r7d+YsLXFplT2wTpYg1BBHLPW4rwqIs2qJffSGpYsyugThPlBjJruUrt2fwYKBYKyIQnZCdNYUdrI/YwmojNvZDt3WOmtLf9xjL8QxQB9A4flZtqQ4tqpVLJudMnCKff/iBH/7wB+LlwlsuZK244Ig+0CODQ0R3crMO0LBIp3RPTl87TVGpk1NKIYPBaqy7/iDa77MZtS1VbYCNs2HW0gZOWN3m3fU51pF66lWMhroWnJuY5gvzcsF5bxH07tk73c35Z+mKuO2NWj9mHnvbkUHgwgGk5PygbYzepZm2Q7ox2xwZB5x6B6FOS+U4wwCh0pSZGkNvLsW8nZXWgsdqeZxzHR6eznkWjacL2sX+6rMPU3w8a+VX5+E9ND2f2n7+xtt7Bo79ts6A54O0o6qScyOUaul7h6lj+0HVhJD3ARcnphBZppnLtBADeHc4dfoI+wbOStrZtztpv1PyipSVoMnWuniC72k1OtYR7/agnPaY0J0hpuCGLdMMGhkGxYj+Sk/3PSK+pqgtQqhi6d5RhVoidV/J1UOccDEOj66cgp4irskMAReQOBF1aQ24jWG3aq/74Z2h82FVVSdnx/m72VVyfG8Gym/dhZzOc6zxQ9faeoVz3VYtSqoJrcVo6LUgXnDR46eFMN8gXskIj7Rz33b2Ulp7qMy6FnJKpFV5unhergvL9ZmnywuXyyfidEV9NBIJL4RJmJYAu4dN0M0YJJVM1UxOG9v6yt1PxPmGf3phfnpGpxkvszGZ4xAVamnrtgqiASdzc0xNVHUmQ2q19kZqGR9Ua0XyUQ3HfbBIgunkoxGFyTdzdp8mdkQkoOmSMy5BRwTQiaXrhxiYosO3+rGaVmprW+Sdx8cFH2+E6QkJVyRMiI+NpEOHTjbGf2mZMGYMlr1Q7xaxFYlc3GxpeHT5oIgPhGkmzjM1r9SyD8ewcS8w+vf5TuhRT2M9FKsMQf6eZPPQOXJa+9/ujEPbfNAxWOzbTX4jwg3IfmtTmN7POZH2jbRtlLRDzXgnxCkwXSLxMiFzRGOgeoZT6/xv6aZUy3j6q1LnNDY9W0NqbV8mw90I0ggHnOnEOFbPbqCxNvBSCS3VlWIMsZoKNVd8VuKuXD5vXH7+hfnf/pWfULb9AbWRo3lLVe5Elt+mUJ7vXY5l8CHHsdtOtswpOifBoRqtPCwomituEqZYEQ+VYnjYBaqzPrLgcOpRorERqw4iLAOBHQAOiT10cg822Ht7RFsaX5GO8bexsut0XgdxR4aUfbX0/RM3xyj/6YBdOm9GD7TYJx2ClEbitDcnR6tvrK1LRSmZvO/kPbGnYq0M6becqSjOT1xvz3z6/Ac+ff6R6/Onvzkf/wngeNLAp+jUu6PLlGZNSBM8PkSrW/TBPFiUtkn7JHQwKsQQmOeZaZ7I4tj3ja+vG3lV/F6RLeGLEmfHFBw+QCLzyDu/vP2FP3/5n+xf/52pPHi5Lvzj9y/84dN3LMuVnBKzm7hOC7tmJu9bEpa2qJg2g+OU3seh8Af7JxalDMFbT0onDAuFFqFs5CleWkPtRjZyGD/S7NLuiegpFA1YfRBLlQzQ2MPqJ5TWjTx6jrx5sqQpwFJN8SzXG5fbExoDeS8UBResDuOcMmlEGDTt2gq0m/eyA8d+VOdaWqutq6MqoY+bHkZ8B9nKQXLTgRPCqGkaSqzNcV9zHErOQK2MNTi8R6p4OeWyywms0cH92WGiTZBKe1ZQ6oelHB9MtG3ymqfqXWH2bxbmnUClHEXuAzSehvkdaORQW9/cCTTQeDCRmtD03hsR1ehF1Fb7O03TdthwWvyWYa3vfrKp7c6BDniaoeKA1qvo8Op2gNnn3TyCta2LM3Ace/T8/Zt7+D0PrZWiRujigrNm3W1xj7q4Ft3zccJ769O4TJ45Cq7Vool3lNYH0Hr2VWrZyPsbeX+j5JVaElIrQSH2HlBtDI9ISncIdOP52CdjLLuJVOromdq9oGPgunLte5c++2MGLVLcAEFwGaWwp0Spm1Gky2TgtxiNvO2lJiedxzm1SE3TMfNsRlbtzgvpZQBdnvfMid//cO8X9RE871MIRxrUN8fh6JKTjPt2nxzvtsbuE95HQCglU0oil2RkOCFCDGiIVBdRF63a1UOYYHbGfFr2jbzd2cuduleiW5Cnifk6c3l+Yp5uIBON7xNxkWmakDIje0BXJT1WsnuFKqiawet2wbtX/OsvyJefqbdnwnTBTZHJeSbn2FXQ0urLESIB52ei93g/oXiTKTlbzQ49iweC0PqT/v5HX+lDhnbrYIDCs7xqHxoq9CSnmv3Q079xroHGQHQKZSenBCmZk1LECDrC1Fjbn6x0ZgotqqWNsdrAQcmFvG3seSer1dKRCyTPHjbWdcf5xLwEi6JWcwR7F5gvV0R2tKxsaW1yu2eKVUSPdgDeObJTKPX0dCdDBoa8PsbuGJcTvhzy3XRA180fsx/fMaiOuTxvpo52z/drqaUlJ1LayWmlpg2pOz5mpqkyzYoLRoa4pmR8CI2gBOcGV4Oiw8bpQZJ3Ko/3v3Txa7aFozqo0tIfT+81dd8h6kEeqN1GaymtNMDpakX3RN0Tda9IdSy3wCUrMV6pqXD/+gtefNP5Yg6MECzF/yTOhyOgjd9hG/wvgI7nSEC3a3rKWhXLWvQTEivqoJaMeCPV8oD4gBUieQtEEDDU39totOgf3Zlw2FJGBt/1RzM6+xN3c9rOPHCEDVhn61dbIxx7wQ1ngpEHGuAsw54zndAxl13QdbygWNp/yWhOaE7UfaPsG/u+kffNnIGpRc73ZN0TvCdMk7HmxgnCjMTIfH3isjwRwhWR5W/Oxt9f4ygH0BhG2DGtJ1DU/25Aq5RKyplUaqtNscVfWjF8rdUAQmsS3slzELEm9IsjbJ607ezZ+jnO7X6cA9FMKSv7duex/sz6+Aslf+Hi4fb0wvPLE5fbDfEzqgGRgPeRoGGApA6meoDhHTvVGAD750ScdzJe4bSf2uKy1AWjlH+/tXq9WFdE0hSRsam2fm0fBBydnIgj+vW74O/POAC0GVoOCMFbWkza+emXv/CvP/0b03Nmk0DyjroVyIdx3kz57m7oQzOEk2sC1fBN89gI1AYwj0j0eyDfwbzNk31v/p0BPqRLuXYPJsyhN9iGw3vTSpnpbUGcOCYJOD+B7OSWHpWyeWhovXwOw6Fv7gaMW/S5vh/k3/2o9UijQJpn+HSpb2wae+0bMNkhQMMpx/v1PUA8zqXjX9H+ShO4pVKbgW9z0Xsf9ca57/fAWGPV5kia967TYR93c3YY6Hi1n0hO9zA862PvfmNcnoz5sd7Pz/3uOP3ho3Qi1ry+iKCuwS+h9dA6gK24gA8zYboSl4V5ngkBRDJoofd9qm1iRAQtiZzv5PyFkt8o5UGtyYzuVkwvffROoHGYBtIVqBkvSIvmtfkYza07F3OXn8OB1sZNz5HQA5H39dDPoZqtXbPm8W4Zqel0+8gyEBzNOWdRGpxRqtSeotU8AM65Uf9j93Um0Pq9jzNoPH7urHk9WqjfLKi+BL8FHaNm1J3fbamOIU7EaSY05kySomWn0KK5wcEU0ThT/IQSUefwc2D2EZ8SaV2pm0Wky/4go2gJ5uyNATdPEKOljTamWhEzKGWe0IsjrQmmL+RtolahVBtj0YzfVtzbK/L1L+jtxjJfcBKJdWECJlXIFi0JVAKe6mdi6MBRyKXXp9tAuKLG1lpbicuHHAeS6CmonTXU5BocOvOMivqnj/3Ud5NrbPJTCAQnLS17g1RwWnHiqM4akuMjEmb8dCHMF1x0IJmqeyOrqeRc2B8b+/3OtqdWKlLRolAyLiUeDyOdKrWyXBYjwKNFKkMgTAtxuVDSTqpi+68q0tpLObyxvqrVlpZOHHeCDofxdwZfOoDk+J339uNhOH7UXmw39beOd9tQT18WxaslUbK1iKn1gbDg/Iq4O6V+tX6i1ep1JURCnJEQKdLbBJn1Y/YF+LFUuhL6pjxj3Im8u5uhxc6OivGIZ4erAc2qainltH68irGGSkRcxdXA5CZ8dZCFt8svxHDBSRhgabSJaYRnhzU3TMZ3It5u5WOU5Ig0ci6dOL+h2XotI4cYKZqtHZEaWPKtD2NndfeDRbW34hCQZrto02ctOuhaDeFwltZ6mpxOidPusukkp+19ABQG02q3F7se6OdozLnG+6Iti4HmEGgkSBxZbrWYc1Sz9cqteSftK9v6YFtX0r6x73vrJmBZBCFG5suFZZ6YgyfOM36ZkHmBuCDq2PdMdfvfnI//VKrqsfn7/B31ROfDNW1XayGlzOqUfQ6Uy4x6AwVZjUlNqwlOy6s2dZBKIdaKnyae4kx2Feqd+/qFqjulkShAa/ycoex3yn5H64p3mRgj8RLxlxkNE1UCWT3lVHjfC1aFXmtkxtSYUw5wgjIAg0oDV43G+lg8zRgaoW5thpOMDSdwSkNtBhfmmTQyCGM59R+2Eb+VVQ0IqRlq/VnrKdc+tDSNtDvuj1f+x//4PygVnn/8I3J7Zg+BHRNcxhLp3gnGs3ADA1geY4WylFFHFWvyroK1bWm9bqTVDtRaTwa/jHEcr3bQeALcPYLZ0w/Mrm41K+9QRBNOCtDomUWo6thTYd8SpVhD+hD8iDT32hCljjWvoqPWwaI4H4g62li4U11jj3x3IdMfcniD5RsgP56bd58YTrP++QEK+mvNuD3VwHawNuoFWt9G5/pYvd8fgyije6LPVNZwYqg99qHy3pd8PEkDsLWRo3xjlIyZOC3FrshlQKg+CAfA+fXI/L5H0Q4qKqpmBPpo9OLHlY0wI8aFebkyzRHEHG9+GGltdBoarlopZafWnaobpWyg2eqaveOI/jUDo5vG3SmiR1aJtpxS8TZStRRLeZE2fm3vDYO5LZxj3vQbQ83u2UoVlIKStJC1gHf4KZilhckCj2uRie5kY8hW3+TLmhP3x4NtM4VZax8LodSMoq0s4aOM1a4ovrlGMyLHOACdVusYIz327VA4di5Xj/5f3htB2TRPxDkSQyC2tV7q3sCjjZlMM265QLxQ/EQVhxU6CSVlUsqkfUdzxmMR6NCyK6o4ct8VIlbCoW0diOK8I8zRGMm3K5Iu5DpR0gpFca4SpZD2lcfbF/jyE3W5MPuIOiVUJdRErVab6aQ2wBsGcUVVIddGXKYKNVOyRU9o9/whhxw7/52c7BH4w3w+vaOvyTbH5z+L8TZMMTI5QWqh5gQpN46HVoohHnWzgcZ5IsyRED3qlKw9DdH6CG/rzvpYSetqWUF9v9dC3TeqvrHvlXV9sKU7t3zjelmYgqeo9RnUqvh4Yb5UqI5S76CJWiu+FuNy8J4YHLlCrljqYxsjzsv0tN/7r/33w1Y+tfRo4/SRrONHmvGYor/2xl99SetpC5laN0p9kEsgpV/Y1j9z/1ooeNYtkzK4MBOXG26aUWcET32lOBHCqCU9Wiwc9sq3312rjerfOZzUZyOyO2Pd+bNGPtStmyjGnC/iqVLRIEgNeI2QKsIMRPuSCOJB/ftxG1ry9Kse14fDKfyhh3LwLsDgERuiUzCdUW2XplJIqtB6MyJCFW3tfnqpRcMBtVBbjW/pBEMtqFD7s1Vzch29NRkRGNEDpMNJno+b69+UM8mjOdx7u5U8CN1GW7BqDoxa0rAnLSZVoailIOdEThv7emfbVosw5sy+W9QchGWemZ9emOaFWSFWJaoSxRhlNXiKO+yGv3X8p2sce71K9z6dhaf92xYxNCP+GwOub5pTauJJ/LbF0MSM93g/E7MQZnBxsyJ9TfSQLWqEHHnfLa1ACz46/BxhDuTgSc6h6iiY53SEe1XoTEjHXTbSFT2Bi/ZM/T2dLtmol60+6fCOHwbA8LVrB5fHSI3N1gxlL53AoeUvf5BnXHpEbhiZh2A4RwFKtXQvozt2+CAIlfX+yp9zoabC7csr7vZMDoGkFnuw9MRwCLWePtnZEcUZjXDr6+mdUbNX562+tKXIBO8II0plY2Fsq71BujuAYxdgnS3uJJhBrMcOh6HruletC3TXgH/LSWdX1m0nv65sW/PEIkTvWabAZbJG65a6bvnxox+PHlGvJoE+ZB5tPE4NZzmpvlMEaaQEwbv114+D5Ko9C2O5vot8mK7ojqMu4Hobk1aPUS2CWPp7XWfuO+o+z5F8bc0guz9vALmuLJt18qudcNpnfX91VrohlOW957Yr3mMMlB69PkmuPgwGf6WP28exHIdgXs9BrFAypSEuA3huGIXGWJsRL2jwBHEtQu8ag3GrIxWTvc5HnJ/wboagmKgqZgQ6ORHU67tUyy6X4KgmkH5Pp711LJu2xjoIHocbnztA0xGtkda+qCpkjRS/WJRlmlEfWqsgh0caY6qdpTZKeqcVSiJvK2nb2FJiL+Zl1VIP5dzWR3Xf3t/vf+i3Fs43JpXpRP/O2dMdmOalbmlNMmIVIC0Vd5m4LBPL5Jm84EXxVCPGGUzWCsERloV4fUbiE7kGcq2kmtjyzn3dWe8PyrrhqzKHicsUiGEG8aSqbKWiwfaAkZYplQqaceIJEebrRM43Unmm1Dsp76S8EqtQXKWkjX39Cm8TvC0wzbgoaHL4moiacFSToy09j0bIURFKbbZEVWqqVsOTEpTayh4+ag6/+VnO0uGYz25/dNBor3UyNttjIlYvFp21O6jZIgWdqd0yLWJrk3ElLjfishAmj3htZDhG6FRVSTmxbRZRqLXXUtm1Synkulqk1u2IC2zpKylfqeWZp+sV78ScoBWCvxAmR9kVFxKU3FpRVLwzB4F3gg8G9gdTON2aafrh9Px2Q7/We2e26w8HjQCDCfqvXOcE7g3s9wi/4BuBkQRrNZfKiq7K69d/Zf5JWO8/k7LyuGf2BC5cmJYnwnxBQhglBpbuaw4ZyyJrTtShFy2DwEn73jN0eusq32yd9jUAI1hErDedbz8PMNp6HFhCpuBcwE3W0kazJ2/Cuq2s98z6KKQCKkYMqdXaxpVylC+ItOmU87zJ+Nv7Af2Yo2ODfpmxG/vU9ladVZrD+hiPTlLSo+olY/u2GlN5qbmVZZVWitS1PicQV1uNdT3AY2lZhc3B1W12a/HW7JBGWDTayXVZ39uz5Dx6eUrfz4ZmW9/P3UBltQ4VPVhlbeMKNW2k7cG+P1obJst2y6VQSrXSAufwCr4KUgQpIEUbYE1QPOJbS5/4EX0cT0bc2RA9Jrf/0SwNo4j1LEtgmqbR1Ln3TnEhUmtCS4/elcOIESHVwr3svG29P0mjzh3FZY1mvVTSnti3nVqqkXFMExonivfkVsBaT2BCxBSUGWaWZlNq3yx6PMc7RWG1XPbeZABWM2g2I3qErnthePt+ajDbmfK686h7mfr67mxc1sD2A45ukI+f7T5sytr4VBvPbd/ItSDRvFB530j3O37b+ZIqr798JbtI8UbxLW1+fCNDcQ04ju8iTTi6ARxdp0JuNQL9/b619egF+sf3Q/Bqn1NkUG8PgTwYrVr0s0ez+rW9N4p5EeqIeE8oifWRWV8f5MdX1vsKCtF7JvFM3jN7R3St710Dj5VDoB5K9OxY+b2nsQFg1/vnKXRvGXZPR+xDT3dypHycyWqaQ2wo0q7czdPVmffsTT3tT7U08HikWdTmQHBtTt1owdGigZ08yTlcMNNY6kEu1YFqZ9btxtnZu3OM6OGUGfWe2kW+jhHQbvg1z+2ZBkjG9U5A8xQ5O3t0P+KIk29U6ULJLc2p7X0nAcRq2Oq+oS5QBFJduFyuhHnCtRwoE1WOnjXgfCTGJ6gOqRNeHhR5Je139pwITgldv560sZwcXO+WMaYQ6ynFZNTfqI4xHebhGK9m8AxZ2hG8oFXINVA1IP5KmJ9bS6Sl6Qh/cnS1Pj4YmyCNTTCtD9b7K9u2D4eE9Q9O0GR6b2Dee8B+xOGdfweO2yC8iyRII/oSF421uzm0tBH5NHTcmlBHIFANTTFNkct15nqNRF+RvEHebK20miYRxQdhWiaWpycuTy8UeWbbhbLtpFzZduWxJrZth5yJzjFPkWk29uOqkEsll4qvlglThzxoW8gJfnJ4iYg8AZ+hJu65UspXcrFryf6grIX6AHefCfMFPzvKPqFZ8QJT8IRotWFJK6U7+4yexVJkK6BWQWTRgtqbkPzuR5c/bfra2h2us/fz+401dAZDrjnHnXi88yZtK6MkAlq2QWMI9vHKdHlhuT0zLQvihapGOCRoAwoMlk0R6+FmgxNw1ePUNXKyDacZqZ5ahVrvUB7U8mw93XwkuIg2djh10XSwk04aYGmvDnBCEE9xHpVC7iL3r44DBxgbc3SWDdIcSfbKx8ENxyhG+41Nb/ZXM+6pjN7CTvDBE6dInAIuegqFff2K+6mS9wdTmCkJtnsmJ4fz1h4pzBd8iKNlg3d+9EUMzhsgbXLNtRpz5+111xzu9poxfvrgLWLW7GbpilHOoNEdjqbeJ7nZeJY4Y+DV+cnGJBW2e+Lt51d++csvfP36xrZnKq1OUx216OgBCXCQ0B0OQGm263AiftBEHrlTXc99w68i8n5bOgPIMUS0GNGRtl7yVvYEVR1ZPNAID0safB69PnWUFpxapLxrh1J7tLA5zlXtEqr23taapQPHHq2snQm35BNwPM5B13WdM6L9vdaWUdAczHa9Ss07JW8tu6hlYmrrFRsnJr8wR2O115ZxGVyEEK1NXk6NMMfhuRD8357Ivx84/soNxwnu940oJ+MTS62JgXmyXmM9QoPQeo4ENFv/oFIKKWdDylrJtbLuia+p8nqHLXWqY4sSWuTQepaUrOyPzPZIpL2YMeZnfFxwYQYf0NLqYLSb1A5V+3z3Tp2S5Dgqcw6hZwuq9xRMoAkhAxbGPui7pdHc974wjLMOwHjyatCiRq4btbWzsX3AIeOq9lwnb2EXEFUL+/5gfbyR8gt1jmitpG1jv78RJLBtmVz+wiMVizSG0OrZTIg5J0cPthZx7Lnz0vPnh9fsyD8/3kdLYT4ik84fHjukR6ztu7TIpZ3fD/A4ejm1388C3Xk37teHQAwTqp7HI7M+VmreKPmVlBLeWe1jVJBScVUH8FXnOudOK2DmiDh+lKV6mq8j9aFtvG7xjHV2pCD39XbGQec1OUh3uoeZ9zJ6gEYr6cbqQaypcS0K2hRiiPjgWzRUQVuuvlq+vjlvpLvXADjSOBjG2tk0OaD5b42qnsbhWNNHSnJfi/0EvzVmnD6v730s/4n5+b9ymN6zqIt4N8hy+hoyxjwDaZIC6sygDr4yR4ebIiKVWnLbc97kkAR89KABiFQXzCekStqVPe/gdaROCa1gv68DDrOrt2LpKac97buPzFjvch5GHYPWV9E5rbVWax6fNVDDDT89I9MTLiwtUuobk6o2gi6g9YAMolTNpP3Bur5ZXUeydB5ba01Bl8qvMko+KOIYQni/UZCjXt2H9hVxLuKdfR9RgrayhWqgzMXWsDla83F1xBC4XALzLEh9sL9ZP85t2yCvlLLjnDItE7fnK7fnZ5brC3u9IdkyAXLZyaXrznZfAXy0Pas0koYWZu5ReTrhVXf+iRJQRBxBFrx+Z/KwONYaqPcvbPmNzErwD5a1Mq1XyvYCzNR9oRZH8DPTZWKeJ1LN5HU7ZE4D2Vps7XsfmMKEy4mqQsrlY0TruD6gRwfTc0Tx/fFeMshJaIzURDBg3w1MGiGGCCqeEGf85cZ0fSYuVyQGI9lotbm9FYdr6cohWD0atYIH7wrOKz56tn0ndzZ0zWiBvSS07OSyc7m9cLm9sEwz0mJSOmq+GkDp2RvF9p2lMgvVCaPEqz3mme9i6Jpjs70fEz3GtxU0fJx2PAQ9f116d2O1WsZQq41wwRPniflyYbrM4IX1653t/sbrTz8RCUgWdIdaGhtwtJ6bwYfWA7FzdnirWR92THOmDtB4BpANOIYmL4Jr7zV7iua06UCx2zrD4d6uZyyeNr7eCdFHprjgZWbfhS9fNv780xf+/O8/8eXnv7Btq82Hd2hhtAKpozvCr0dQOcqAPs7COTs19Z2FPqZVFGrfY/Yn74TJm9Oi5kTaTT7SMlEaOfPR+qT1wtTB63H026y9r2YvS6snmdj6eveoo1ZzuHRG0w6+DTe08qXee7P1bT3AaNsV9QCZTfGjzQFaxpzUIU+oBToGaW3wEM80X5kuFy6XhWmeEefN6VPMIe+bzW11sRVfu2792/Px95Pj9Lk6KeEDcIy5pVsPQlPYjbCglEypQhVvDyiMXkKKRfz2nHmsG/F+J93vJDdT6oKKH+/rzLWpCqkIkoS9eVH2+07ZipHqhJl5vjI1D9uelVyUVNVy9oFUYKkQ1DE5YQ6B1Xtc66VW1SIAbgAA7SIfoSAUaBHHAShpbQhCwPlgyiHX0cJhpL91A02gU4t3YDnaPH7UcYoGHPadIK3/kNZETrCnu224lppYciY9VnaEEBI5VfK22zrwDfw1gV04AMkBVo50VbrHqlubo4D4SGvtRq3rAvN0DlqkUDs4PHvfOoDt6QqnCORoevxNZLKniSC+NaMGpFLrzmN/RUtlipEgHsmKFIsIeT9ZU+pm3L6P7Z0QzO8/ie8A6hkAHUCQtmHNgD6Ye/sbmrp/V6jc0vrqUejdxbWe9kGnA68lk3Nqac1GOe+isXbFxtqpPSo59oh5/krVJhvN821boglzaOyg7a706JPEkB0ynlc6oBmC9YTbT2sG6UBGBwB67/c+OVXgkGMfNI+1Gegtac3GuhkHvTeX1es40r5arZqL7PvOGoK1SAg9tZGxv7QDPT/hQwPumltNHOQdsiZcxbzaKEgdc6DdtOuOCXTI/gOIHaAc6nBAj/FTjh5ZGFGEx1GqNXxP6lE/I+GGxAsSJlyIBB+baGgRb4arDy9qlPn7yva4s64re07WQL1a2qY2dt9OIKXNa/KR8Y1pnum1SOYRd4PwLYbIFCdimAhhxvvJgKMLp0yMLsYcIUw4PwMztVjU0YngXUFYSWshKZQ9UR4blBVxhfkSuDzdeH5+5np7IiwX8rZg1OuJqhZRcOKN6VwqPoAES3kuZg9BNf7BcHLbaKu5OmcPeG0tG5YL/hlCcXwtwr0U9seddV/xktFZWdZn6vaK44rxH01Wz7dM+MtCTjs1mU6RFhnufiQRj4uBGDySd/ac0fvH7Mf3MEPffxsy4K+4r9pWkRZp06qolLFXhIrrzjnFepVOE/FyZb5eCfOMeo+1kG/OmeEDtDKQKc7UGYrr9VaCRAeyUGrmsa481pV9t0hLr5VKeyLXV/YKSTyVmVsM1q8vRFwrL7FLKq5F9kU83llEqYiVIdQhC84j9Vu7y8bpLDs7aPzWKfn7H3L60m/+cnZqtUjjuaWTc0Ymcr2wXG/EeSLlxOOXL7DuhAyTeqJGnE4IE0gEDAAG50bW1QB4nXCmtzg52SojZdU1QrmWqdMjl86fbKIOHLudMxzkPb3VDzZUEat3js0e9m5iz8LrW+KXrw9eX99Ytzs5P1BJiFfEKZUySMW6XD9j8PM2OCzXjzr65pNhOx/z+O7PFjUuGUk7kjbYV+rjzvr2he3+Rt5XyEYiVXOltLTxhpapauRTegKNZYDGg1HVIoPNpmkArzvba2mf6RHHZvdzAo7ax/bbTKrmJO02TPepq3ZG//rN322filRwTWKIlZVFgeickXHRiOOy9fqte0JT5LJEa5HoA35amJqz4m8d//k+jqf5PABQ+136Vm2Rq1rZ9ozqjneZyS/4OSK1p3y2JvKYwZMV7vtG/fqV+XrDxxv+8sSyLOiWecjGroaYJxXWpOT7zrqt5LcVHomgyiyea5y5TgtzmHDiKDWzlcpaYUUsrzsV5j0zFWXGsTjPw/X0IW3tOfRdNMlY0wrUbMQTNZ0WSBNITpC28bWBz97w2ADaeRwPY1x7nn0HPR9wjI3XduEA/eN2Cp2ZUVxGXaY6ozhGjKVtSwnZ91ag2+o1cnMCfBu5OW31kW4wvrf3HehyGPjybpz0AKDtTN0greLQkabaI5h2L+dUCjpgdZ057ACpFgpvdZCus9sFcGK1ZQ7cFLjeXrgsT0xxMcPPyOHpaXQ9nUsPScARt/n9j9Fstl+vK/5vJ7X/OObbxsMitofg6n2kaJ44V6oxNB6uotM6USsmL5lUcmu+7i2a4ieC90Zc0tM6TmmqBjgrJVdyFmqr4+rRIq2NzKjXbiqUYlkItaeot/ntu6SqQhPyUo0E5FsCJDmvOTWToZsL7xTSWVPq8f6POHKxRuz2EI2oy7WmxE0xWbefREnF6h9bShKrkilcLheWGO1Wa23rX4yCW1qtuE6gF0CIav0iS76TawIRgm/Rleb8GkbrN0dzCR7v6a87S5Pt6+wwesegDyWp6ijqwF9w8QbxgroZfMQFqwfqNX/94+ZIEKS2NgSPxiCXs7VtqNXSbor1K+tsve+2Ivpe9v6Ox7RcDscWh5HnnbcWUzEyxZkYZkKYTX60CIP1Teu1VeaM8nHB+wtOFqiemjNp+8r97cGWjHq9ph3NO04rUwxcLhduL99xe3ohzov1LhOxfdSMTZzVs7gwmT4LQHBGYY9Qq0eLgwxSFPEt5dthREZqfAKkhK+VCYcXz2WKyNMzpETND/b8C/tD0XWDN2H5+spl/oqrt5F2iVuovpI95OIo4qiYozmnPBwmzjvwHvWuyYZqBDMfwJBzUl/t+6C2G7+3n07vb045VVRbJoz2fqyKaDHnsCrmZDZ94UNkWi7MV2NLluhbC4bmNGsOVbuaIuKJ0ww4sreIhWi0Sg9nIGhaduL9wdvbg33boBaETK4bqSTeHm8UP+H9ldkvTCEg80xOtvdKxrymWhBte10sjbU4ITlGX0c5/XsAxOY+bQNpmRstg6vnM/4KiH/08c7g+ub7SQuMSEivKV643K7MywIirOuD/ecv+DVzIXBxM5EJIVKqo1bb+96dCOtO9kzXQYc9YvM75vlUq9hleK9v7F8DdJ4c4b1Ep+uF4z2td6j3zRnnySpsqbLlSq4KJhIQVxBnKZ3aiJh0sFGfMuUGwzCn7/BhgvU8dwoHcR3vsL7dW4btjr59JT3uPN7euL9+4fXnn3n78jPb2x3N2aKDudcbZ6uVb07anl6qzfFYRvTR1kfv+00DftJtmhZtHM7t7tAfjqIO+koDfS0N+GTYjrHWo0ayK9JBeqQHiHX0emqrP1fAB8cUHZNzeNT0xr7h3IRKBC2kWok4JCwslyeWacKFgFsWJPxtofp3A0cdobJjAo+H7p5W3kWEzOCzEOq0K2tyRG9McQWblFQNWXvnUe9JKGVb2e9vXJ43pidHdJE1VDJKQvHek8Rx3xNOC4/7K/XtzlwKMXhepolbjCzeEWipsDWTRcnBsfnAVoSyJ6b7yjw/zNhNGV/q4GS09hBubHynjfY6beT9zr6+kXajuRepJuNbVKc073eukEumVGMsG2lbwnuq9r7ARsTo43xx56PPWfd2GH2wEmIgzA6CkqWiwePmGZmmxsa34xsQMeKTtimGhGGAx2/h43ujVA/7vCvh9r73Q/BeyVjst8sOOT57EsrvBNoIhxwb9T2JDiMtyMWIekdRSFUJy5WXH/7A9frEp0/f8/T8HXG6AN50bBP+FlE7ATE49cf7nQ/t66U7azgUU19jPZwsh7F/jMVRDzeefRg3pdWQFaPi76XJQ++b86TUTK65tQGQI3LbolRaS5MbJ+FKHQXcx1i1wvFqhqFiEQ6DxC0VthRSqdaI3HpWjPTu0eh4FK+fF1SPl45ROvmXW0TqtNCGIXgyMHoE9iOOlAvimqGlhyruqdoqas+EjV3NK3mVFgk2ZSTq8Ferx3Ge5inWA8h4h8iME4+Iecd7dKyku9Uxo+bZPruUpY9Eu6uxdY5/j1H5Nv7Qx7OxDVexvYJQmJAw4adnCE/gr2hY8CFa1J9Dsar2/pCgJbFuK9vdIo0pGdtxd2Dkkg+Pb5XjrpSx7c+S6Pc8wrQ0Y9GN3rM9g8M7q/N1VDwFp7mXj9EwkRl3UQhBEK+EqEyz4zJPBDeR1pVffsp83b6yvv3Mvn5Fy4YXZQ6BZZm5PX3ievueaXkBN5G1kjWhZMRbdNEHq6ESH+3GvVhtlG86u06U5MkJSrbUJRv+1gZiX0nrnbo+8LmwuMASL8RwIQbP5XZj359Y9xuPNLPvK9wzy9c7l+krnituFtwkIDOZnaqRXdXYWxFr4ZWMFt77AAhZE6WIUc3n3foWfhS1ascQp33wm+CjR80GaOypewHnAsGBl4wjozW11DQDjS4EwjwzXS5GjR/C0edTbN0OJ99YwIr3DpkcwcfBNO4NoaKq+FgJ8RnvH6z3B9QEbOzpFd1e2Woh79bjrSrmvJCJME/4PaLJW9ReC2ize1qNZq4OX8zBL93hy/ke+6+HrLVhOsvS97b+xx/9Kud9f3YVNln57iOWMRanmeVyZblemZeZ4B1bLZR9JVUhkIAJCNQi5GIP1/c2pzO/k47nv8lhQx/v7aqr2dUi5isdzu6enXUuxWngs/998EwcLa5EaOnRAjHa+rtejBTLO1QzVTJFc0uLbCCn3coAjSd9+dccjL/38R+tGaFC2anrG9vXX3h7e+X19Stfv/zC689/4e2XL6xvr+R1szZAva6w1PfM8D09tAc+9OiQ0FuQSa8p1xbo6ctJeybru5k8IoktDXaMaf+Sk008AgF6quIxHTZKB7Rn4Bg/91gwAtZKx3g8nLR095RwIRFnZVoWLp+/4/b9D1yfn7ksF+IcYHYwB/j9I47yzawdhr18+6fxlp7LbQohlcpeCpNYDxV1pthQiMHh5gk3RapT9rLj8oqrO4WJXDNZMyUIDk8JsOYV3RLr21fc9saTq1wvkc+XiWsQSCvb/QvqCzk7NChcInqZSTVQy87b24MoX4g+kt5WNO3mVfBuNHU1a8wM4Jp29scb99efebz9TFlf0bIhYmk7Vcxwy3mnsFLUW6+2slM1o1KbIDjS/wa4ak45WxsfFKnqkUbsmjKE/tCWZiqKUrSwlsxUs9XrXC7E642UMmV7UIulK51N8qEjTlGvbqwNw23IcEuPfb94mpI8TkRP5T08F3Z0qnYFAwtd0fbIG9/Azb5Bz+eR4z3ajO2anDkpqlIkEGLkMk18evmO7z5/z+35hTBNFomuerAaqdi86enaH+1V1e6AOIGdAdZ7BOT9+7sS0Q6wtTswLAXbgF4xhrVCAzaWPtPXibYC7874R6sXNZBgEUXRlmbTR7ctsR41dCI4f+T11wKIGvGFWNucXAs5JXLufWCdXctLc8bbfWpn0jwBvCGMT86sY2l9AyGGfXF23OgwID9qFnMDjrijjoXmTZymgJdAzcVqHFRItVK2laLSyFUcG6sR6TCb0YMOBmJxINXkGRJwhGZvF0SKOerSSi4J1DILOlDritL+7YbNQGDHX/r6GYqyGYstIiziKKItwd+Dn5FwpYYrhAsuXIhxwXsHLU3KxILNhcPW6LZvPO5GO55yJnV261ZTYrUoOpT32VjrAPijtmMIM47A0bNL216y/VRLoaBkLWhNSPW4GqjVU2sADaARrQG8p9Qd7xW/OJap4vIDyhfWx595vP2ZvL3iNDNFxxwj1+szl8tn5uUzPj5T3UQuSq4bVTPOJUKAaQrUPCM528g6oUqgYrWUpQZSmtg2IUYFsTWxl8Tr+sb9/oX0eIX9QSiV3UXS9MRl/kSMV0KMLLcnLtsLj+0rKVsz6sfrxlv4gpeJWCvOFUQ9VWe0BKp6dBDedelZgUxRY1StquTNiCCC+yiHHGP99mgZNOP9tB96f8mhw2kEX1gdWwiRqUfxS7KWMKVF8XzETzNxuRCmBYmxZc30SBPvZI6lnusItvS6ONHTexsxmvOC94J3F5Zpo6YHJb/iZCdlTy5lEHkItAwcj4sTYZph32wflZ4OZ+m1Ii0i7h1OGwFIH58OfHr21DtW4WZIC21+GTrx433j315Av/mqY9aOj0izWwM+RuI8sVwWrrcrt6cb+vZGSQlZjfFSa0HUiEdqVagmB3sT+KNrQNOC3V6h66fDiB5A5XxH5zXRf282zqHT3MEiLtAjkINcTDCdLtUA6OSJ14WLe8brJ4J7Aj+Z3arFbO2aW41tK0HoDsRBFNXv5bdH+kOOkw+F8axiQK5ms823lcf6xv3tC6+//MzXn3/m7Zdf2O5v1vf0fietG5qL9djsUcPao87t9E2/HY6OE2jr67dhaJNY1mJp6L7m2B3zqbYulG/O0R+tq1bptn+LbuKGJOztOPpgaHMEwDH3Sivf8pbe72KwrLHgicvCy/ef+fSnf+L2wx+IYbYyvAhcBP4DRlX4T7OqnoTZ6YGF9wungyG7/0iI4P2RAyzRESdPzsK2JuvPJIIEh48W5aqyk8uDPb1R8eZNJqOuUqWSqBZu3h+k9Qu6vzG5xDUElqBQVt6+ZsqXr2h8okxX1FXixRNvC74u6FrY1p2v+QsBYd8Tdd+ZppkYW0+pEBBpplZO7OvK4/5lAEe2r/iyAqUxAJsRnMuGiFA0mFAnmXGs1mRe1I9+kO+oqrVF/+rHAEcZ/7yfV+gGo6C1sm2JL28r0+sD/BMLM26+sTwnogrl7smPOzXt1KoEGEp2PE8zvo8YIu8xm5w+AyfBdHpP38Hf3Hiv1uvRrTNQpZ2rA4TjM8e1xuvaDd5DwOZkbKEiwjxNPF+ufP70ic+fPvH89MQ0Ww1W1YpUa/B6MMj1k+q7a//ux6j/64rknCbYBKC0IvlWWD4UWQNH1tKkMiKSaiLKvM0ZrdIJviwa6DowaO8ZRdlmFPT5lOGLsLTT7qyw9e0aqLB6CktOM5r/WjtVvRlRVaGWQsqJWhXnAhKCsSZ30FjeU1brkKAnlSaHx7XPzxDipynri+dYx3KM3QfN5TCPVfFqYA9L1WBaItdlRnO1vntFjRlTlZI30ure+curJqpaawUn0pShmUd9TzrvQSdElgYSA0k8aXtlzwX1EL2hzcM2t/VVmyPiuPnz3uqjdOwxObFfq1pKsroLuCtFrlSZcT4SohEpiTRjtM2NYGzKlEzeHvaVN1LJpE5nXjKlM8/1qPYpcjuMWuqHbkcnEScBkV4rVkGtPryqsRSioFVw5ZR61kBACJEYjRQIsdc135ndTtALeVvZ1z+z3//Mvv5CLbvVUoWJMC+E6UaYnnHxBfFPqEwGVMtuPTxrYXKKnyJRr2wK2YVWNuJIJSAukmsklZk9ebZNsWToytfHK3/58mfe3v5CTW9MmpmALBNpy+QkXC5WnxnizHJ55nr7ZPO13sm78nh7w4ljKhuRjcvi8HrDy5VqubIWPXeBKUbiZCnLqVjtY1VFS8E5ZblM8Ld7Vf+njsGmOFbQN85V+pLqjrrD4VtpcxocITi8KFKq1X9nS21zzprF+/lKmC5IPDGadpCh75fqcOzBYMo8opG0hI4jGuSC53INLDGSVmG9r+xi4xpqa4UlTd+3+8YH/DQj84WcjTW+oEgrKerEd84HvPHmnFggGeOiJ/3+flwZY/Xewfm/4nhnnXJI3fN3Oaz3VisoPrTI8MRyXbg93+DxxJ4zpT6oOZk9S5dZDnEmg+DILjuX4vymC1IOHQ7W2mFE9jo462viHXBo9pow7sDWokUdS2Nctw8XkAxe8dUj8UIulv1R3Iw6T6Kwa2Zv5SfmFG4uwzNo+9aGg/d64fc8zqbkeO08X9gGyNkYSGsh50zaNtL6oKwPdFvRfUN3q6Ou+27vVUZaqPRWSO1ilnlzDLf26+ixdgfx1XAUumH6ySDaOTIYhwTp++YUie9cHnb0exGrNcZIqeoAn/aZ2uCqfcJ2lWuOBGlcE2FZcMuN6fkTL99/z3fff8/L58+E56tNXIYaFMI3AYa/cvzdwNGdwrV6WkgN9I8BfBcpwhGCEEXwg1XME70j+hlXA3nNrKlR1ZbU6MUFqQ4pd0h3xEU84FxFNbGlB7VW5gBOE1U3Unols7NslW33ZN24r5n7psh0I758hsuVECq32wz1ZuDzYSkd0nqpWPuQiet1YZpmY81EKLmwbzvr4431/pXt8YW0vSL5DnVDSKgUxAnqKrgKrtj41IpIRslN+bUcm3fAUZtHzjb/x7GqHpHOkR7XwpyutQWpqqxb4ZcvD9x0x4WEzhcmfyFelFCEVJWaEmXfx0Z2cpBOob+uJzIB+F7RdE/W+W9dPXehd/ztSCDsf7Oel/bCMFzbhj8A7DfOjdM6PVj8aARd9reK4HxkuVz57uUTn7/7zKeXTyxLj4xUlHLcT7/UMJal/fyfm6b/6OjCWsZTn5iAtT+XCcXD8GijJv3nDqra3rYBYaQdV9fytX3bnzZI2qj/nVNESks7BWkpkN75Fkm2xVC7t7xdb1yj3a+p1kZmUnNTaFj/ttqIfZxnCgE/RULwJlAb3XbJLWXxV1F6Uy5upPfAua+fjcnxr313TS8ehsQHqUS7wyZAzfBs+6FadEq1mDLxjlK8RfeDrb01V/b9QSeustThnVoXnN5wUziUVAdiYkzXXiLOXaku4tyEE0sPTVulqEX7XDNknLaNobR5txQpaY6WIy2nK4QW5W4gzhIBLEmzugjuirpn1F3BL7g4G2h0tlbk3VxY6lDZ11YWcCfnlZz3Y95TMkbftgYr3fjXUwryia33g2ZTqjRSLkutVM3DCdjJFAbplLTaU2HUZQcfCCFafasqzgl1eyPWFW5Xyrazv/1E2b5CfphO8ZNlxsQIcUHDFfwF9QvqPEV3UtlJ+x3NhegiS4hc3IVNAqvfWi0h1prIzeBmqnj24nF7YSuZPa/8/OUn/vyX/8n9/hNeV65RKMGTWdgd5BzJdeKyGAaK08zl8kTeV9YKUjP7ugO/sJU7MxvhOuHzZ2a+o6riq1JVCD4wzxNxElLa2LMRcFU1eR+CYwoz6aOA45jUk0E8HIEHeNTukOl1ia3fcQiK9xkphZqbsapqLVj8RIhX42+IV8TPoxWV2Z0nR/I3CvRwX3V5e6TUMcoOzEESGpNvIFJTJPgJ7ya8y7Z3W1mKduopF3HhgsQKsRpDdiecygX1lgXiJLRsBjXin/N4wbsMlr86tiPi+JGS9WwpnBxJtJKMDvqbpW+iwZxcLXf8XYuwECPTPJOvF3hs7Fsmb9bP2mynfl4aGzTDDpGmb81Odu/u8F251/j5dDQ0fl4XZ2zegYSivR+9vb+lU9rRyBylWvaQKmimaiJrQtRA4qaFrRpwHGRjzY5zSusvfNxr/8+J8B/Hqn6f43j2IyBh9YWtn6I05loRvMIkBqBLVVItBNRqeVXNmVNbRH2Mr7ba3rP1qce4niKFFnFsg1JBKQ319E/23fUbZmCf8w6CO+A8ExV2h8AJwLYbGSu6ytn2ax0JfGhMvpEwX1ieP3H7/kdefvwDt6cXgjhjBp08LLQyhXcX+KvH3w0cJ+MDQ6UOoNEfrodsTfEfXjPRis+tiNMXvAiTwuwcS5yIVSlzRrYMeSWtli7lcPhJ8fmZWB44d0NCJEfHhrWKqKUwhZk4e3LyJCls2xv6yNRXWxyvbxuv94SEC7e8cvn8PWFZ+DRHFp7YBLYKe6kUzXjvWK4LLy9PPD3dmOYJxFGyWp/I1Zj89vWNkh5oXaFuaN1RMjg1L9XkcZO3QtM2DpItQqOVg8VySJhmJrX+TCAfJlR7pO4Q8Md17NIO1FOqsu7K6z0xL4lJhCgXXBCq21AMUONcE0TvHSndqDyue/x7di4c6W3H5nwnQGXslf7P4eU5v9Y3cQeCpytIe97DS6stJbgv3A4P7Dk6jb7MC/P1xvXpmdvTM5fbjXlZjL7eWSra8VxuCNLTVVvU7/c/DE9YU2ETaJ2KWVqzDNq4VtMf4z6OtIZ3xfvN2Ldt3HqqVml1h1Z/aH1QD6/bABFS2px3gg/fBHEeEaADqB91AINQqvnLpPVRKpwiRyp4H5lCYwAL3pzzrQZDc3oXbTz6PB3j78Q172BPG6mnqJS8W3cmuN1YF31luA/aj17OkVoZ9RTUypZ23AqqjpIZvf+C0xbNSJStGshWY0E0oCJouTBPfjyGb04hBatxcx6VCSXgJ2WWjDgl7Q+yJktnbR8edlCLYHpHq0W0Qzl2MpjyRppZ2oCjSqTKgvoL4p/w8Rk/L4QIrvXYkmaA08Forc17vJL2lbTf2/dETpaimlOmGAWy3YUe3t3jBrvC/00V/rscUmuz/Z21UlClFIvMu85FqZXSWPBEWqRDbIVl5wlpt36itZjxsa9MNcF2gVxI969I3nC9pY2RAyBxws0LMs1omFEXyAh7zaz7yra+IrkSp4VLCLhpZvEzc1zYkjX8duKJYSKGgHhIJPK6UsqDx/rKly8/8eXLX9jWX/CyUSPkGJl8IXpP0TeqTFSUKViEP04L83KjpkzdV2pNbI8Ha3ojsTPfrtRPf8DPK7F4fK2UKoiGURtavLX0seXQI+hQP4g8roMyYNSS9fUzokXvwKRtqi73QvRMriI1W0Qq781Ja6DRhQUXLvh4I8QbPhopjTTnUY8o9vMfmrK7PrvjjiHv7CPNfFQbIaWACGEKLNcrqWZ2hfJ4GB98KaS8E0uwNldhgqLUUCk+gUvmLNQKucl87xBava4UVOog5dAGKPr9nnVrH9fx/SPx4q8OPX39lT+/+5sAHkKAYK22VJzx4Yo356iL+DChoULIaJFxqu5Qk5ZZc4pnDj3b5aN0x0MbO2mlXe/WXecaqNWyg053OXS5tvhmXzYcDghtAMY3oi7xUJ31MCzVsRehFKVQeOTMmjNbyeTaHbFnO184Uc4xzAc+0AnwG6cdgLv/roBaKUZwscmxiSnO6DSjcSJ5R6uebzXnjtwc/25cpjvQlcOOa/tLj+9j9IWT7fveaS1tbx4Op1+vsvPznJ0qJzOVkYXX1+nJgVXbe6W3pBOxSGOLlouf8HEmXm4sT8/Mtyf8PNm9lg2VgMT4zvb9j46/Gzi+zHNr7t0NuvYAbQT6uulG+hjMmpFsNPBLDCzOja8YHXVZkN1aadR9Za8bHoebPW57ENNG9IUYF/QykS4TNRsJxHSJLJeJ6gtuu5FZeYgQasYXZa2FvWbIG2F7Y9pmrtFznWfUX9lw3HE8vKeknRAct6er1bE9PRNCtJYBOZNSJrdU1pp2tGzUsuFqAqoRdcSAmyfqPKPzhIrVN2YtlObV0RaRqmea47Z89LB/+M0d87sdZpALnVTmvLAFa1LtQDx7qjy2TFpAloUQYMNbenHrZegBKXos7rESDsV3NIjt2/KvKJCzAJJfvy6nr3O6ve2tbxJBTuCz7TB770nhfqtWDFSJpauEiJ8sFSwuV6blyrRccBPgkjGRtc3s3oHwdsEW7fqIw6IUxlxKLVZKBY2ox4BHb0cwmF1PjorfMqK782ekq1ao1RlorK3ViuswwYNYb9YQAor1ofKNsGYYW42hjO4iUFp0qEVgOuBVRtpFbyyv0kB8nIjzzBSiebqr1TVScwNLlXfglL6Fjj6dtsx1MMaOeT4/f/v3kGXH7x+lF4PvqY3nFWgztu6JVCvgcRKYvaW/eRSv1bI4AN1Xq31UqwPV1sD5qov10B1M0dbmpIpFx9S3CJ8sODJBlYojpwe5muHYa+WlGaK99pBhQLxzCdHXvtDHfkJlQWWmyEz1C2Fa8NOFKQZErAZQtfVS61HHWig5kbadvD9I+2Z9ZPfd2GVzsdrxts772FU6E7b+6r5+ZSP+jodUY+dzYlKo1kwqO1oTXhpbYXeStGjjINBQi7SrFAPsDSAVVVYnuJSRWijrhuRC0Epu1GAuOMI8ES+LMXMGo+xYS+Jte3BfX9m2V2Ix43HSK5MPlDARJpiqUqW1AfEBL6Bltz6+6yuP+y/29fhqRHClsYmnzB48ywTLFI2xb3fgdlQnorM1FsJCjJlaHeSHkRjlHRXl8csX1pdfmMMXEHBVEHVGNJIDLs4m58JMrFDrbsy5tVBUmT5mJjk7KPtvtUcjug7hJEmdI/pADIHoBakZzRs15zGX1m8vgI8QZny8EOKCCx6LFvY07SZs9CTHxhUPg3W89o1ccmLrvNZMEXOATtcbN2estYr1etSS2bcHPnpmv+D9hERBQ4awoj6YjG21jFp6hNyIN4z5+H005K9vrf/4HR9zHNq9jQr6buba/Or5ve1V75EQcHHC+QmRiGqkakCJiJvxQZHQHLVV21w3A76dp0eyDp1k13Lf2Eeo6XSHx0tzJHRYrgXqYd0cQO3Isvg2CXaQv6nVu0fMXsAFigsUZmqd2XKAXcheedsLa25pqvVoLyffyvjBqXCYVf8rfQEHaGz34Tz4CXyylGsfiHFimmfKNuGjlUOEGCxDJenxbH2Dt2COOz/J2abrnpp3c9sBl57ef7It2/faHAiIcDoj3UY8Ain9qc62tPRXzhj+8FlVRZ1aTWsIg5fFMkgC6iLVGZtuqhARJHrcEgaDaj/3Ef3+68ffDRx/eH46CjQ5eqCdp1AbJPDOWdpf3knbg1oqkwhXP3H1E5M6XK5IFa4xIsvCo+xs60YpoKnCVmDd4b7i/UaMV9x1RvSFZQGVzHKbmKYAk+dJMnqbcBTcHHDOsfiMTEYCME8TEZipLFgdQLpeuDjPtlxQrcQpcLleuT09M1+fwAVyqc27nYzaWgtSU2sOv0HZzbsaJtyyUC4L+zyzO89WKvueSPtuxf1VETw9GsJp7GhgRlr49sMiVSIjIgjvPTfjZlpqn4qw5cx9s7oiHzwzM4TA7oTSgJFzDt9Awq9WON2bcvYwfKMEtQnCJlm7U6Lbg9KuYf5OYESMeuSQv+4w6Zd9t5kPYV6RlkYkg0yERm/sPPg5Ml0vLE9XlutCnCL42tKSdSgJebfZdQgZf4rM/J5HT1N1bfxsrI6IZz3dh8oxSD3qNAZHjm92XqWnuNSqkIXS2SJdj6l6+m53PhIdJ4CmaLVm7KX10zsa3DIiasc8HPciYhFUbcQ5Kg4XPFOIrYemQM2U1g5HT0yqR6uPtqaaZd57efZVd1z7eGhbje193ZsjchqTHon8/Y/gA90AsAhsZxM1lr6iiniYfFdSZcihYD4oshZSWls/UWuTchBx3fDTZCuitedQXGsVIZamKrMpUi/46FEcJd0pNUFVgjPCItezCzhtug72B5hsddqN8w03AxdULI1S4gU3RXxUEOtpBb1hOaPPXc2JvD/IaSPtD9K2su+FkqDWVvPRcrN69kRp5+g6ivPaGgr+Y0wcLQkXC86VNgeJlFa0bgbQWzrQu9rsIfzavbUx9QIeh8ehSUk1QSmUPQ8WQEfFeyVOnstt4fp8I14v5OjJtXBf77w9Xnmsr5R0ZxJPdJXFC9EHipuorYaXACE4YnCIVra1sm4rj+0LX15/Yr1/IaU7WnZUrXl1Khs5WUaC9foD7xIpX4j+incXi7K5CyEItQRbFzVB2chb5vH1ja8//Yx3f8ZPFXRCCGiFPZlh4/1ECIEJI/DRCjlZS5DpA1Rkk0rNODtS0+xvxz/D3nFmkEfviSK4XKhlR9MOuVqURjyKN2POm5ffRTPyDOQ1R12TW9Loq95l7bQaxl5z7ZqYOmzWXhqiI9pRqCZDvSdcLjxhDoLH/c6eE2nfYLW0zMu84OME04JMC+wPNG8NzLZUR62tebgQ1NpP1NPY2NCc5OQ7g/gA3eMTH7gf399V12ndUfbN2+SQYqNgwEurbbxxe3rhev3EOq/sYcO5XpbUUj9dPlg5sXTrEYniBCSkExw1EjLk0Jv9S4Waj/FRxZjGu0mhndXoAHPDKXp6LictIiW0mlYrUcDNqJspLBS9UfJC2T27U+67lY39KtrIcUmLsjGA1qEkf4/5+iuH8i31ha39tk/xgkwB9mi9UaXVjoeIBJMjBhwnst8p7G3PHTX1naNxnPM0d11q270cxrO2ORrsqnSZUcdbFTmIi/qLp2c4m4t9kI/MgXYjyIEP0Na2b9zp6XkDLkYkRAgRYqSGQEJ4lILLyZybU8BfpsP+6cSSZ3nzV46/Gzh+frphIoTh1X0nFvQInQbncUDZNzZR8q5EL1zDwuwmXBFqY/ELOC4xIvOC1I1UbGDInvKopPAguDfm28Icr3x6uTDfHIWEeMAJUxBewh+Ynp+oOVtqLAZkrqVFxhCrkargcrY2fc4xXa88PT3jfCDGmWlerEjcR1SDMdOlTNp3ctoo+9oMmgclr0jNSJyI00JYXsjLleQcW8m8Pu5sj42y77hScNpSIBuFsp5WTvd6HQvog1hVxzX7z7zb9IeispYimq2mdC8r4jKXxTM9L/C48LpHSt2R3L3oYspqAL7TeU9XPMBW25I9XfL03i64bJ+aMPDiWq2tRWyPRzo8OeccdE7XaQ933Em/XtuQKtZ3zfCD9cILk+fydOH5uyeenq/My4QPjtIJZVoKZE8t7Iq7fwm0HpEfc1StlMJQMP0RR5n36fn1PLjN+DCs3oTg6DYr7edq/0umFDf6Q3kXW4/R1rdPtJFCAdpaZ9RTA/YWXaQDo2+8cX0VONci3eKhffkQ8NETfOuJ1vtKVW19lnqz96PR7pH22VlebRwGW/FpGFq+7rv0MEVG6r209M4zmP69DzMIpDGpu8PTK4KqR7S5S8TmO2sDj2LRhYo2ZjilJCVrRbTgqGwP8Ng5psm1fpDKgYFbCl2YQT21RhRvZms/X21y2vM+k4S+dr7xdFeTAyqeQqBopDKj/pkYn3HLhI+CozASgYaBI2hVSjZHnYHGN9b1zrbt5FQpFWOYLb1Figwjgm/W1JHxp6f7/RgLp5YElJZ2a/WmKa3UuoEKQcxA76lqnX3VZJwxHgqKFyE6R3SByU04CZSs1L2Q9967r+CDMk2Oy23i9nLl9nzDLQt39eRtZXu8GeDbXwm6s8wXni4Tt+uCCzOP4qkpk2rCFSU4RxCLAGdWan4jbV/J+ys5vTV9t6N1RzQPY0vLRtkhy4b6C06fWlQ8gESCu1BdpIhHpVBZEV2pObHdN778/BeQhflWkekG08WAU82UbOvM+UAM1ofO49iqsu8fpB+1g0boIOI4hKOWsPmmnGuZFh4prZdoTlBqc3R6VAL4CQlmW8Q5Eiar6y1qdcWNWudww5x04qijZJiR6Hj3+0jUua5fgawVismKaV4IzjGFwOvbK4+0s+0rLk3EOBFcwE+W3ZH2CLtFvUQbQzOZ7ldTheJay8c6oPY3pJvd4n9vjL5TRb/LpP3W0YV9s69OdY1yvp+mAw/gWPqLzPPMy8sn8g879a0iq0NW4csDtrVSXUGdETaKx+RP6ylLk03WK7nrWNBGluUK4B3BO2KMeDehGigJSqqUakpInG8M4tKcpTbHg3v4PLzDqdGeU2i9H6URMEVwC+JvSHgCdyXrzJ6FB4nHXoz4p2d/SB/DDlK/ARVHvwi+nePf+5DzT91s7Zd0oBMQ5R1HofGyOZyP+DgxTRM6zZAyNTVStRaRBYZtMUhvum3Yb0CP94310oyKk2v6BBDl5Azine0jMJwL75/wlKWHvhveDj+lOUZ79wfr29lI1lqblTDPuBjBB6r3ZCfsqmxVrc5TOgjs9Z3NdvwP7Jy/Gzheo6f7nox1+NcGQzcoevpi1UDIkzEGOpjchBRnVM61CUsxQ2XyAY0XvHNUp1ADuipJVpx+QYvDXRI1BpxUKp1Vz/g7YpzBRXIq1GT0/CKKC0a57FrDzrTvaMom7ONEmBbmOBPnKyFc8GFGXGyGj0JNRif+eOPx5WfWrz/xePuFnB+tbpOG9Bf89IzGG1qVPb3y2Cr7XnBFrTWB881o8EaZ28dxeDZtRLXqB27Dw6A6dgIDfPXFX9U890UT2/7gsX1lS09we+L26YKUT9T8xltZKTkhtNQr9x4A2iXl3e89f/+4dDf+OjMc0KIiZ8bS2gx95wSPGylpo06geYO+pTo+9t8Qqe/UrTiLAhv0qEgQltvEy/fP/OGPP/LDP3zP7eVKnB34rtT9KSWlAa2zUmrP8FGqUbHm8RXG80JPHWqAGxmNhPvwnBWqdibWJtzQ92ILgCoGBiXb+vUBnDWQ9z2N0ZlHug6yhVPtS1c+40L9Rk4JMC3CLS5YY3QJBB8JIRCCAVRLkTPFbPTZrR3IeL0PgTvO2SP3YvXZo4fVOezatcKwcWwEnNo6c+4Mcn//Q2v3DEuLJnQw1Zo9t76A1ErpHkFtz9J+FqylndV62t4tCqLCXR21KovOTJMntMj9ULoiVrMTI5VgDcilNBehkjcld6ryluncp9R34NPmcXjFxaNEkgY2DaifmMKVeb4RokNcYfQ8bfvddrm2tgWJmi2lcdsebNvDoo1FrHawNtnfG5HLr+dotFHoBhRtxX3QRNZirNlmeFrLjZKN0dThLDUU1wh0fLuzQ2G7Nr7OOYuw+UhwEVFHyZmUCvtupBUiEKbA5TpzezbQuNyuqJ9wu5GW5fVB3e/4urNM8HSbeX65crveqCxsdyWvDx6Pr6jbqbsnPs9MQSDf0f0N8oMgmSk052myOsUlOq7LQhSQktH9K4gnLInFexZ/IbqKakDCBReVlD173oCA4BEKJWUeb2+I/wupCtMtMwXBuyvemVyrqUJRfPQscWESRygVl4qROv/ORzfWhmxiqKqhN7sIEbH0/OCtz2Et1lS85tbjTWy+vV+Q6UK43JhvTyyXBR+cpf0PI9S18zdYeEIE3dikrxc53UxbSWfP4bHydZQrKCZj4zQ3ErtMeiskra2dTaFKIPrAtMyUNJN3T0pqZShWGQlYRhniKOrI1Voa2D02mX4Il9MzfXs0na+/9bf/+4f2cfnNL9793OXDkYNkn51i5NPzC+FHIe6emCb87mFVfnlk8mND3W5kiO0zmjuhWzt377MogvEDtCs5QWIgXqwOOIQLVM++Kvua0VxMHgTTr7UmatrQlKDkdw6CDuR7JtVYwN5ZGybnwXmqC4ifcfGJMH+iTFfEe1Ld2crOmoyturao5vtxOS737XQqykeFOY6L/sahp785DoBN0weixsHRoo8+TMxzwZWK5jIyoo59ftilZiKc18vpsm29ix4Ac2SN9N/fQ93jLMNe7dc5MgAH1jy28gB1/TxnX0wnVrOaRktV7WU9fpogeKoX1DvUB6rzFOlZX+089PKTFoiof1uo/t3AUVLCSv8P46+JsnZhbTTEB2L3tTK3onsn4KtDd/Oy2QKviBjBiKswuYgXZwYSDs2O+tjY6i+kPcH8lRwC2UFuPWlwDvWegidXR0pqNTApWf8qKQSpeFFcbTzSar2QpnlhuShGoT5bJKOIEXBEa+Bbxdgb1/udX37+mbevP1HTF6JLLBdPJOJkovgJkZYCUI2ePCczaI3Qwo2+a10RGElIPZRCe70LgQ85fu0AHK+dAurNWwhCpeSN+9sv/PJl4lOEaZlZPr9wSW9s+50tWUNhxzdKrXlGztHEDk5sr3UNrSOSbek/QoiBaZrwYQKcMSdmq7Py3ghSzKDaR0PXoWZPqYnfPugB8ZoydtZEtwC7ZqAyLzMvP37iT//8J/70z3/i8x8+M1+nVt9n9awqfghVHYCxsSUCqBsK+0OOBhwq3Yix2tGzkdObBZ/fSxsjw45dybR+f2hLuelgrz1TA2k59dCi4MPRt9HO2Ma/9bMUcZaO084zQE5f3+0aBi7bOJkLvxljgehblKxWKK1fYy2th6Sth1p6erS8+xqpP21+K43Uq41LT4mE39gOHYhobSVKH9ceR1vtitU3K72XnWvgXJDG2KaUTh+Ms2yADjjV9lLAhH9Nidzqj0pVci3sZG5y4TrPOMQig86yHko1I9fHYNFBEjYjDtSR9zer11NaI3sZXk+lgTRxBloRnAYqC1knclhw8xVZAi6oEcWoRVdKG3trYlwopZGJ1N3ajWzGXphSpZRqqbu1jrkZ89fW0Tv7uQMAJyZ7tcvcj5rH/lxNFmht1PAJJ45SHDWERobUpEXpztZTCrlgDgtvurYWa8WytfrOWiph8izXC9fnZ5bbDTfPSAioczaqpUDa8DkRgvBymXl5vrFcF8IcqdUTveJrJd/f2NNXXPJc3RPTJVqa4v7AlZ0lCNFPKDN72ZCiXG4Ln28XYi1sr7+QHhvBe27+wvMsLAGoJqvnySKOK4LuX9hbhIpeh936IosLlio9T6BPuFb7WnJqDtyKiw6n1Wp8P2QWGfrJ3AxdnnS9KL1rRutBa+1iTC3o6CM70t5MIOJdJE5XlssT03LBe48xV1czxOR9hZoZpnUIJruDgw26g61BiHG6/TNQ6/u164ValeoEFyPTvDCXBC1DS7OR3YgTaw0zT0xTRPcAmrBIWWljYIMQvbcaY6Xt/XEXx2D2348BGaP7zkr+nY/3WvdbAHCASDl/QNXIH6EROjqmeSY+gf9OkbtSXhP71zvp6yv72ytpe9AzQACk9nPo8VrrwygYkHNeiFNgXhbmy415eSJOV2Bmzo6coRQbbyVTykra7qRH04XaI5p66CrhYFVtNphFGh3qHLVFHb2fcNMFt9yoy5XVKSVntlzYkjGqlmaX9YyIEXhs5z5Y7E0DWNeuD7JXx4w1u/Q0s71d6ABvzYZQJ6hrtqgTS1f10QigYkHnQkyJkLPpk97Ga+z5DtoO8AyWiTUYu7vCQc5Le9xlX+kjathvW6TXFR17t81hG2569gyn81omWD+PgNOjD7eIgcYp4ucJmSbwFpwa9q72nAbXSBXttmo2AOwao3Mu6W/Ow9/fx3HfQE8RlI6Ye9i1DxBHvq9rFxJv7KJa3gtEuoCkGz4miKsIVUCrUFOhljtptV5EmwjJQfWCBEPZ6jxZA7l6SvWUDCknct5QdqJTYqARD2gjdfFM00baMyUpyyJMUfBBmBbHhPViiph/FFVyyaScEJQwR+Z5IeCoeWJTz56FIrBmSEktV11dMwQEp63+ZwzVaUeev+vHbsLTTrNl33bJATpsjgdLb8487q/8OXguk8PH77kuM/7Td0zraiQVr3dySgYOnFVpGAnY4Urp9QXm7ThFf5w5C7JY9NhHT5gj4XpludyIYTYvfcWMs1ooeSetD/R+p65r2/yHsB4XOqexwgGgmlRQZ3O2q5IFa/j7+TM//tf/wh//+Z/58U9/4unzJ8ISLSVi1KC2WnjVofwcpqi7V6jWDxSoDRR26mdgGCw2wHWAR4umNqA+QO5JGIulVIlrzotOwz0UqzF25tz3qz2z95HenFo7BHDR6uZqy/1v69kIQJrQ5fSatrq8Bj4stcT2DFqt31JpzKnFyHBKzuZIKBa1MiIWkzgGQF0Dw9ZXb6QKdXA67JXDWdMG9eTzsDTgrpg/LnXc3A5WvdvBrT2DjS0jzVY49owZg0e6mo21/VZbFEHBmnVTqU4tmiUBN9la7pFqc2cdaYFBr1Q8XfqBkLZXck14OZwj2r3zrT1HFajqqBqoXKjuiTg9Ea8XYgxA651Ffz5swwO1VIvQ5Tt5e7A+HjweD6O9T9VIxkppwLEb198cIoMB9lhn9nR9PX2UbO2Ase+ZbhSoWmZAzpUazehX58b9H9m2/dNtDStULRZp3Fa2baOkZGQn88Lt6TsuT98h05WkAinjg0U7vdrX5IQ5Xvj0dONyuYAIKe84mZmnwG3xvPlKvj/QB7B7JFYk70jZcbUQQjDW1ilADNRa+O524fNlxm0rr48HDsfVR17mK8/zFdSx7ztedi4XR7ws3GUjvQmvJVNTQr1F5Zx2BtIHefPs20Tcr4T5ivPRUtFzQveV7CtaE/t6Z3u8cfkAdpweBbZJPYCFqZIOHluLH1WoZXxGtAyHyjulA8d7004uqTkwDHyKd4cp0HVi2+unwOKQ23Y0p9Jh2bb3MC46QKPIyMiofY1GzzzPqDQm51KRyHBqIGLRi2m2tPBcLU16ZIe00gXvWxuVVtfVH7pHZYZAPfTvu4f6BuL9bkePOJ10YweLA1ydrz5A7ZEAbHNmGS6zEy5T5Om68PR04/H8xPZ2pex39rWi2cSLtABf1xfanQItgyTGyOV25fp043J7IswXfLjgwpUwPxMvz8Sl7dVt5f71Z15//ndef/43ck6Q9sa6XN819jg4H1qWjTOOAB3O05bKOEfCMsFlos4RV60X5V4ze0nktp77OjwiXK20pQMcGGtPsTyL/2XHOyCGDXUGTZZpU4QGlHtrlYCbJlzOUAuuFPw8E7O1HMnJyIAMiB/rF+m/dxtSDr3CGJh3JHtm6h5z3m2HgYq6PGhypNsXtSv6AR67fpbj9w6Me9q6GJgU740AaF7w82xMqTFaJqTzQyY4Wn2ydf4hF9g2u2SIZtuV/8BB/ncDx5BPPX/0bEYdHrZhaHaDRFq6FQenVW3e8z64Nj/aPHl2ltIBt9h7UzbK9aJKBrITqgd8M/xxZPUUJqOYV08qhT2tlLKyuUJwELzrsgMnQtw3SspoVjSBzkqcavNqJtwUEVXmGHi+3cjffUf0O6qRecrMEbQIeXOkNFEK5GrMVDlpM+y8jVvtue7vBaVtxva3s9fiowwcOZ+6+VLev3i8FxntHtK+8cvrF0L0aAh8//zCvDyzfFakOB76Z7bXr9R9x1PpZCqj9lWNpr/H5+y6zmpJw2SL3DvEQ5gccZmYbjeutxdePv3Idy8/clmu7NvK11/+nddf/p3H68+IF/Psp/Ruc/Un+EZ/jxRr6+1n6ampZnZRZJlZvv+e7/7pv/L9n/6Flz/8E8vzd/h5sUJrf9SaGemLCVkRxbcaMPPGarNRzxLu9z0s6nqqcxmRlA7WjtQn7VGpQ0ty+LgNtMgJSJh91FXBsDhGQ9uRwhHVUlelGwlGne/A0s31+BoRxm/CQ7W14Kitaa333spC1dKntOyWBtiijbWW1vC9tAa+fb5b3aVzOBdxPkJLOe8PLpRDyHfwU4caaGPpxrwdgPKDwP8YXdf6xUJR66tna61HE/sjnAFuA8iYQSsdpaiOulstuRkToM6RXGCVgEOY59C1k+3TavOAOGMR1BbXEZhaC5a8v1FqMcXlwEp9rcbSt0yKrEKRgMqCi98Rl2em2eMktf0C4Jtjou3NUtBc0ZxJ24PH/QuP+8q2FXKuBrxOtbOlHmMwFP3Ye/ZY9UTUNSItLUr0EUfPJRFaVLZH+7HortVmDlhJn82+B7uDSxvpiNWmZfZtZ9vupLQhCjEuXK8v3G4/MC+fwV3YMpR9ZyIgAsFXJi9onLheIrfrE8HPpD2R9JUpOqZ44+XJsb156gZeCqEmXPW4WnC1EsSyO+KyEOVCWK4olZdl4skbq2b2M85fuMYL1+mFSS5sO6THivo3wmXlMs3oVAjO2MhL3m0UqgFHpwnKSkmQVs/6WJC4ECYPZW6O4EwpGzk9SPsbaVu5/Pj7z+M5C6GVGtMlQ2dEpzlTtFrJjGs5s64WtCGIbvipFkre2DahaMJvwfZjteieb83mx852Jgt6JoyduAHIIbtatodz75jYzb7v7znAp6oM3aS1p6NVQnRE9eSs9Gg5WAaCqsP7CeICqVLz1no6ZttrzWlofR1PwYN36/rdyI49r03utBv9vz9pv3n8dZl9tgcGQETNmTbgmNqzriv5fmd/vFHTAyeFeQ5cny5sz8+UfQOFXVcoqbFbOrwrTbc1Y7ylLc/zE58+/ch3P/6B2/MnJCyU6iFcWD79wKc//olPf/gDYYo8vvzCn//H/8H/+d//3+zbyvp4s36aTTVL8yjK6cmc6+mp9l2cQ3wghJn5sjBdZ+ItUi+OHCqkTCaR6k4qFoHrzNm9hdlIdFFGhktfb33WPzRVlb4DO+zv9kx7rYKmSknJ9EQDjtqAo58mRMEjlktTKj4l4mylHcJmfR2LItqdoW0VjGDEGfR1O5PuDWl/7vqn7QM57r6dbEQsgWOc23t7tiEchDtdhx16wk5VW5TZe0+cJublQlwuhPY13W7Mz0/42w2ZJ2L0zFGIXtBS2dedlGHb7RK+Ks4d/CR/7fi7geMsp7aWct6WlqrRTK5fiYFBAIAJNQtrdyXZAYuMajADGc0b1wgzAhbnSLUYc1mLUKVGHZyqUmQCb+jbIl5GNFAQi4hRSdKLvG25lyRILvgKQYUAeArZJRwz1JkQJpbo8d99xzJ71u+u7PsXVB9oTaRk/SmzBGqZyFWsF5WLBL+0YmYrOIbGhtZW3Lf5yza+jIX0EcexIX5bZB/MZ2bMCL1WovDYN376+pXqJ7JO/HB55nb9kdvngKsCtfDQZH27UHw3bMfZD3+epW1EXLxCvOLmK9Nlxl8888VzvU48XW+8fPqBf/jxn/nHf/wXnp++Y11f+fd//e/8+V//P3z98/+PXzzWZPpxR0sTKtK9NA0A9JvQI6nUJGKlaDKipWni9vmJH/7pv/DjP/3vvPz435hvf8DFJ/DzYOfSloLWWbnOXikLSaqle/Y+hB+kGI2N8zBBu8ABjtx7Drl2RIkOOG1N3nsNqWmIDiClg0Q9qQY1oVtRkipUxcfJIoTNs2lAznoN2i21KE8Tkodg6kZES1AWsb2L1UCX1EgmageNRsOvxUB799Adhrg0r2rAh8nqlK0M3K7dasu0p3z0fXe2Itr9vDctPvaotNREaalkpVB6tIpmjFowEqVaKw2xeMPRSsdkpQLU7pBr/TtRNAvVObLzrCIohaILl2nCezdSJrVlCzgx46dKRWQmiLGq7ghph6r7Afy0INVqKBRPxpPdBTddiMtCmGJrjm3ypGXkGsxUS0G2QvVkZGqPRwONmVxO9Rg6ltFpbnqqzyGrbBmrGdV9jWht6VcdzP3+xxABzSCWVstOYyAGW3/25RiEUfTYUe+zKdQCqWbqntjWlX1b0VqYYuCyPHG9fmZZvieEFyoTpfrGG2C15tFV01k6c4kT0U1QIe/WzsKpMHtl8YWnRSkXh2rBa4a0QUkElKmVV0Rab9Ymc+ZgKaOqgSlc8LMyxwtSF/bVsT12tkem+C9s8094SWz7V1L+SqkbPcZeayUlqxELsuNDQrKQthm3zlT1eG7U4khpY99e2fdX0n6n5g24fMhc/vrQMcGKQ9UDDicVIWOMowdo7LLN1kKhlJW8ZVJ+NLZy0w0iDt+M++HEczKAo/ZoYo9wdoZo18sE3LBnz9klprr7eU0e+9abGNVGwGWx7TgyMKy8p6jHIQQ/IX4BVyguW11ythY9ZCsswQXj/m0kaaIWtTH5c6CNwwJkGCC/Cjx+xJydZfjJMf8uksxhc3Uzvt9vLYWyr+yNaGpb3yh1xUdL187fvVjWC5ZlkeuKkHGSwWVEkwETAXD4MDNfXrh9+iOff/xnnr/7EfEzKQuEheuP/8CP//Iv/PBf/wvTMvP253/DycTj9Y2vP//E6+tf0IdrgSkd2UbjSV1j12w8BNrq+3ycmJYLy9MTl+cr7imS5gqslPwg1zu5bmTNVK3dBdvWWK/fPMZpWIhjvclHT+Y3R5cgfS1ZmUMpG7lmcnNEdwbj0GzN6oJl4KSM33cLhhXLfEjV2GTP5Iojr+ZsCow6XhhRSc7A9qydGGMz2Fa17R1Aa3cA836PnO6h0VMNG8x4ZqxPq7hAiJ75MrNcL4TrhXC5MN+u3F6euH76hL9eqT4is2eKiujOelfKw1FVbJxEyCqEYPX1f+v4u4Hj1Dzih6fmGBx3/i6nQTylbWhjcrTUiJNB27dsswxK8xSLQBBLhXAVkgibCMV1FqFKLrmxOCr42oSrnVJda8hLIKVsqRY0ohzplYawtToDL9o8hyvIA+GCcMPpjRAvXJaFZY6kdGHbn9n2O3vaCLkSFyHuji0Jbq+QdnyZ2NNE3u/UZN6oY9GYMV3pqVNd2LYxFfmwGo7hkDyJgHOhdW/z0HHIuf6ylMxj2+DrHScPfH0izjduF8/tuwK6UVnZ74mSS7uCQ72jU4R3BlNxFtnw4YpMn/DXT1y+/46n7688fzfz9DTx8vLC589/4rvP/xvPtz8xTU/cyoPLd9/x8sONf///RqgPXn/5yaKHysGQ1ddX1xf9lxZxKFIpNVFkxy1w/Xzhh3/6A//wX/4r3/3hv7E8/wm/fMbPC34KSDAhcRSf25gZgYob/RqLtmhjq/fSD6rh8O7kGVWgG6MM24FO4X70B+MQSm2fGsg+C34ZdYqWLz5G0+rEELRkqgpJ7TlDDGZUVm3KigZA7L4OJ3gvPLBzCsd49uhRrZWUDUTUbEyVaEtXreYh1J5r3LzpSq9pNOPWGtwHSpVBoOKV5rk/hHVf/51t1MZKxv3048OaGwNdHth6bekyzp0Y+LJ5Qnv6C4ITNaKilpjZ969rc9xp2BGhqrWwONqsFnLZSTkjtyfcPLcepM3pUgEHvvXhE50AT5givVRhz3dqi1o4gYKDGshcqe6CTFfCcsVP3ogdtLa1IGP9mROioGVD607aN9bHG+t9Z9+VXKQRrrVsDOeaE+KcvndS8t1R1Kxo62d3xNVVrbm8/yDgeDaczElKM/KtHrtHwW1tykhD6p5z1yjzHaBFLdK676R9p9ZC8I55XqxOLj7h9AJ5snM6Y5uWtFG14OrO7AUfI148da9Aoe4ZLZWshTWvUCu+PrgEi4pIWkllQ/NOUGV2LUNnT2gpw7jJG+y14LZMcBNhEoTI417ZtpV9S+xbwjfSkK9vP7GtD74+/kImW/8w78i1krcHriiRwDJZxDOXC26/AgvqPRCoJCo7lQ1kA/cxwFHGv8LZiTpgkAqqnfTGWzmHZsgZHWw9hzzV4YAraO1y5zB9i+vmobz7aHdw6nitA8Zm0Pc3So9GHoZ8B43H99Yyq9tO/dlEcLhWbpNJ6Y6EwBI9wQV2CRR1CB7nAs7VwU9h0dju8FCcFKocTsLz8S1oFBjBB/n2zb/TYa0vjnOfTM12Dyd9NxwDjFGXVktoEqSQNbHXlaQb6ivhOnGpnyjqqUxUXVj1lbo90LI1vNXTsbG+j/MT/vKZcPkD8fZPLC9/ZJ5vIAGZFi7f/8jL5//K5ekTLsJ081ye/8Ll5Ufm5+/wv/wr9c2TN7MzehcDFYs+iTN+Du8j6gJVAsSIny/EpxvLp2em5yssjt3t5H1jL2+k8kquRnz1nu28OSC6DSGH1djXkJOzJfkhM9mcDO+vIN+8R6SAlJFdIz60NlQOfEFyoeLQbP3Yc2uP12tGrR+0BZgodbQ3G3bkcIR050Ir22kRmBH9bO/V05rqJxmR7bHbu71xPNFgbJVjZ8j4VHP6iCJBiEu0bhPXK/PtRrw9E5+fWD49cfn0zOXTE365kDGCHGRn276wr46Ex4WJaZkJ02S8Lt5bmdHfOP7+Gsd3GPzsdTg/9mlDiryfXDkGgJNgHAi+gQrX39vSZFxr7XHMgVBUyBVyYxC09Kpg7ELOtcobGkVtwNVoEaJsRkyljuJQ0cpDK6oJrStFr8ATTp7xAqV5jX01Iez9wjQ5xC2EWNqIBEpxrLvy2BJb2tnzxp7ubOsbab2TN/OSat1sg1JaelgdRo/ZGi094IMMHCfvvSI29Gfj3XXNxdE+oY7PlVrY9sTXt42ZnaVcmObIcn3mpt9ReYAU0n01RjYsRdW1OX7HxiueEC7Mt89cvv8jL3/8A5/+4ZmX7yeenic+f/89n3/4b8zLn0jpiUcOxPmJT98tTC9KKn/h3/7P/44L7gQG2qlPBrWtG21/d1RpHibN+AVu3134/k8/8OM//SPf/+Efub38yHT5nri8EC4TflZU+7xZipFTM9rPwFFrtehNNWFX+3U/5OiC/ACEA+NxHgoZQLLXlxyCv71DxgidPtOJb8wQeGdCtTS62h09NaKhDE+5FHMWjFrFs0fSDcx99HfszglVas6UbafsO7WYNx8tRhTQnUpD7PZz+1HDYffQxLr22gFFxNpUdCPqYDHu1YVy3Md59PoYfJBqtP2hFnXTSgwevMc5tcg1JiN6yxBbV4pXS9Pv81rRljpn4+GawWgB8ALJmsZrTZBncusxp1WYJyMlc3KMrQHunkanqAZchIiiuyOlB7VsVLFqm60GcDfc9B1hvhDiZMyYvRanrSEnFvXQYrVtWnf27Sv3+xv3+862Z0qxmiyLhHaH2rfGfJ+XZsyf9nuv8eG87psyr/Vj9mPPYjy3fVEacHQOP+pNpOm8XuNissObed6csOVdKxtBCd4xhUj0EamOvBacJnyBMDtrBUEiFatZCs3QI1dy2mx9tb6n2+ONXW3Nac5IybiSyZsBg1ILrhZiLZTcmHXVWr3UxgJcqYSqBAUngZyUL+luMr5lXKAbb/c3qiq5Gj9AcYAL5jMvVqsuOROdR6cKS0DSFZdWxG8gCRGoUlCvENp+/jC52g3mw7c00uylr0La/ClH6aw2dpJuz3QZ1dO/es3bac1w1sOnRM9mD+g7ndbk8FlHw6H3ur1lxsN4nzs5y4pzhAZ4uxNfwoQLE5qFfctGbrVMRC/UXspTWxV2KzVBsWd1NAehDFBxtiXg8BP99ijzUWKVY2S/tXbk3WsGut14TVu/SkoxoqA5Ei4zbgnUAEmy1drOgcCNWWcuLCRdqBrZ8dRNqWQUqy00Vs8rbnlGLy+k8IldPlH8D8Snz1xvV+bLlXB7IfgX1q/m6Hu8CqkuyPyMuz7BPJOdY1UDthFz8JmsZ8gZ7yMqEQkzMl/wTzf8yzPu5Qq3QPLmOLqXr6zplZTvaNmA0iLHJ6CmbWWeQH9fZ31dORH8B83jb62ffqmulZ0TywaLARdbvd80A1B9pu6ZojtVMuojbpoJy8WInaqVQEjJeC0NDpqDzZZ6WxtHVKddW8d3G48eAz3sGbruaoaZ+8aMOJyd7wMM55rJLoQUKK1ESgLEKTJfZ+JlwS0zflkI1yvx6Ua83Vr0cSbME04Ec/MaieWuQpZIdA7nZpY5MM0TiKeUvz2RfzdwzKNo8hiw/lwCjd3xMErPC42xBntY/Vt/QXtTA0zNJwadFah5nVUcuSp7NpbAVIsRRHuPa4jZiAfkuI6zKISUTCU3FkbzTAQnZAWq9d0qZaXoCqT2HAE0IkkQl41so1H+ercQgscH8ySrevYM254HcEzpwb5ZIf/+eGVfX0n7GzmvpOqgkX5Y2qM0w4oB3D7iOMP5oaj6JL1LO2hRjkGp1QwvtUjvum28yhsXPLMG3Azx+sST/IA4x5t8YX19UHPFq6XE2NwqBatBKqUQ48QPP/4jP/5v/09uf/gB/ySES0YuDl0u5HmBOLPVSNZAnR0slfUx81rgbU+kUofX3h6pK7peldsMTicUMY97FoVp4vLdhc//+D0//um/8PnHf+T26Tsuz89cnp+YrjdkEqpL9JKpscbFVr01UndjfJQGGEfE42MObcyUZjAcNYTv0GOb8fPP0j2Hw+FzACjO+1Nc6x3VyHBG3U4/VW1R1bafim9p4q2+okf8gx+1XsrhoVStrQdjZ4kzZV1yIbcaRu09DakjmnpQi/U59cNB1NuqGEDO7blqizbVg7xIHOKU2tZNH6UDeBx7oKdUfdRMeu8IXugkP94K5A7l4wQtvdi+p22aevMN/He2UG1MK+IsEtsjfK6lrapWci3G/KeOO0ZaVvTKPJki6eczrNbXQVNyPuPkytRKCPZdSHUlqaO6mWm+EZcnS0913aho0r6ndqOWUl4LlETZVu5vr3x9fWVLFS2NKKRaC5bSiDe64+dIJfvm0OOHo46kG686HBTlgzbkmbSoy1Nx0pyNFv2p1Uh+vLM961rqoZXrG9Nvz97xzhwDQSzw7zDHh+ad9LizbxUJK3GJxFlAMntd2WtGfcDFGecitShpz0bjXwtaEiXtlLShudg1hVb7ZgREZ6PInGGlpcLWtoZqA8kOvMO19PKcM1prx+zmZEyZpNVS5iZryF2rsicjiOlyO+WKPlZKuKNhxU+Z2NK7qloaWdLWPqIxK3/U8c6RdlpXJnsKtCRBc2xkq4tSh0hojrFWotFOIKdzHbHLw/3V7Z+TJEIb4Ze1FzjSS6U7wX5lXNoPg2L/HAmSnugmZOeMfFCVguDmhThfrK523UkpU+7OaqG01ZO3aEy33Oyr14O19lhOGhlcHzOlR0O7Y+fdrfYx/TjkyG9IiW9e+0ZPNp2kJRsDv3OwzMSnjLwtlNmzeeUhauSMy4wXxyQLlzpRayMSk0LeE6oZ9UCcYL6gy4283Fj9wluduOrMS3zm8vIdT8/PSJhZd+H1Kzy2yrY/eHtUdhfIIbA5x6NWHrXgVCneMwVPdI7gTIYE7609TJio0xW5PuNfXpCXG/lpIs2Vtez8Uh582V+5b6+ktKFaWtCmlbC0MZGzAc9h758Zfb301ky//zEcEG2tvLudpqfECRI9boqENFHnCaHivaOETGKjpGLr3jlLX12WVgbReRtKq9oxB0vZC1r0IPgbKrnrxhPQ42RL9+9j79tn3HgWSzI+B1CGScVhhxz6t/tpGttv8IQ5MF0WpsuFsCz4eUHmC26+4KYrMl9x84UwL0yXhdp0TG4XERUmFwlxZllmlmViipEKbP+Bfvy7gWMp5/4ep2HSHilrSv0kuPrCMxzUjAY9D5F8I06OdE0VzJiUBhzb2xRtSiSTuzFr7haj4R0Gux4/9+9qaVyNVYOCjHOaIZ4RsZRXpx6Kp0wO8QXxs4W/fSDEQIjRvDst/UPFMzeWsTh5UgmUEil5odyu7I8r6+PCti5s6c6eNx5p47Hv7NnC5tLD2U2Jf8TRBflY7tqB/hl36FjNTtyYbbu1Si0J7x5s6Qv3LfOFiFPH8xKZbj/i/BXhivIT6+srNSeqWs2pa3UWW0po2vDe8cOPP/LP//L/IH5+4Uv6mXv+iX3d2L9+4VV+YrleiNNMvF1JUXnbN/7nTz/z3//1z/zrX75y38y7551rQESH8qel3lnkq5BqplBx88Ttu0/88Md/4Ic//SOffvgHLs8/Ml2fmG8Ly9NEWByFTM7JBNHZGXKK3g0B0Zf1COv+pon7uxy1GW8jbaJH79odDjl+ahw99srJRLHv2gRj96q5wzwXM5YUMU/s6ZntRkoDLRlqr7ux/eiyI3uLuHRA2o1KVFtrjVYPWpvSHtGKnuZunzTZfbQDMQnYoow+DtDIuLfe6qf1l6yKkeecDLQWkTvXWp/H5de//P5HaClkne1XVenoZvQwdeYZVep4/r4Xe21jj8yJtAhjk53tqYHG8psLKhncTsnCY6sUKVSuyDRbjWpjYDWZaUaqeAECyIx3ZiQrnn0LFCfI/IS/XPFTwObLCLJGPV/z7rZQIpRM2VfW+xv3twfrlii1rbmq1quskaJVlD7150g5tLkTRqq41pb229ZObfK0p63VvxYC+b95iHiQ3qNRB8mJpQAZIK9ayTmhzo15d0IDjo0jQK32DFGCA/VCKQo1k/cHqzp2l1EiiMdHh/dQsV5sxYFfLsy3Z+J0sXrJdSNvm9Wo5Z2cNvK+oSXjm1w2cFKHgdLXvSpINecftVpacyOQUOfMyEJafW6XveZwLLmSk5FVxCkS5gt4b/dTE+I8y3IhzkLSxL4nyr0SLnBTj3cB54S9VPaSWVO2fp57hfRBdBxy2CBdxh3BlxP80U76VnE4nJvMIXCKOI4EVD3gIf3z4/TvzjqcIt1wlM4uLSbHWmL+uz3QnSW16XPtdhHvxVc3vUXV+qEC0hx14Ch7oqadosruztdokf8Gng9HmqUvBycQPKrO7BbtXAZnuhw5DP3+ygeBDcCIidrpRySo3wq2hoczqt9G10HZnsGJswbqU6RMgT04VlHuFGqL3IpMOJ2YivXLdVLY3c722NlLIUumxIibJ2RZSPPMFiJ3ER6q7CjVO1w00FkSbG+Jt8fKmh/c9523nHjNia9p42ve2Wth8s569U1Tc8jbHg3OM4eAn2a43dCXF/TTC+Vp4esEGytvaefr+uCX9cF9X8klW8lqE7K2RrC90BwhY10OJ1wbWzH98p9IYfz7jsPzwq/cDyLgvQHCaYI841Bym9iyJ9P1jTBHQsDPExNWMShYjW5qtoml52YjXEzWD74DutJB3Qku2nLS4aDpiaz9Tp10DpcmZzkqFseePD2ofqOjRhaI98Q4My8LYVpw8YKPV/x0w083ZLpBvCLhip9vTFfrGavSHEYxoCGYw0+NdT6ESOx1jf8XVOPfH3HM6b2wGkKPMZ9dsfcwdo+GDOn5K2x9GEFj0OhCBSqdkbXFIB1WR1UtpaYzC9kaN9pklTqQOg1wVtWRYqPaUX1j3muCo6pQKqRUeNxXpARqCiyLJ0yKiwUXIjV0tsGISqLU3XpQEUFCu1cleCMwcbNHrhPlOrOvM+vjwpoerGlj2lf8unLfLCXEPMKNfjx/QHdjzPgWORb8t4axcihOaZMq6tqmqa2f3U5yb+y18kgrkQlhQdwzz9dPTLdPPMnNGG7lX9lfv1BSMgpp72nBOqtxUUXCxHy9Md2e2LaNdQ0UyWx5p6x3Slh5mgsxCtlVvqwr//aXL/zPn1756cva0rbEIsut/5fhtmaVOUGlWqpCyMTJc/3+E9//8b/x45/+hU8//iPXT98zPz2z3D5ZCsAsSMiUslNqMiKElprUwcxBl9wFgJHLOITaFqZ+kH1T1dy7I3W0z1lzAgxnQLPDu8PmDI++FVbDnywyntEyNbqycAc4PQu3LkxLY10WA4kiQnZHemHf1wIHPboeKXnjOv3exjI9GyFCb7dh6ekRFzyIO/pY2V00z3xuxBW0COlRjzocTMeljjEZzodv//D7HqrWn1DhlOLHqRbpqFOqOdGjwc1GpAPyc7rmiEA2I97OdaSg19bnr0gBsdosh+IVJAYMwhxgrR/eB3OK1AAaiHPkEp4I3uOmKzFejRCrFoQeqTaPba1qPXQpaN3I+4PtzUDjvhdqMZCqtfXn7KCxtlLb2rNP9TRZ7zNcBKxUoY8B7brtHn7DLfC7Hc4HOvmN9YAz5yJSCc6IB/xIG2y33/pneRG8aGthVC3CUxKlJJSMOIv27amS9wzcqWolFJZkUSg1kbSgwROvNy5ZmRZz5uQtUTarGdZi0caadrT1bOttlwbg7mPbnBRm+Dii88fwyckGx9pYhWjRCsM6ivMZ4kz1wVqyXBaqQlZHVCHGyPPzjWmJrGlD14el2HHFuwvOGfNvj9TmouQMOQuaP6hWFU6j0AFQf+TufLBx6205cHGkJLvudNNuNJ7P201H4Zw+3fWV6RQYLYo64JQ69v0B4BROe0E56t7OFuAZPGoHUA1kVgXNO0VL9zghzZztfdHf25I9E6zdVy3gLMrlnKNWT8559Gfl3V3oSa8co/xBFADjqu/2+9BjnaSkrfgeGa2WDVFbqUlnv33UyoPKQwsPKnepFIxIMQTFzQ7/NHOpN2aX2ENifc28rmpZbN4ihkyBsATKIpSpkGTlkV95WyMhQvALpTpz3MVM1Z1NV77ub/z8eOWXxxtvaUedMM0L4fbMNC9MCCEnYk5MCJcYmS4z8nyhfLryeF54TJ4v7HzZHrze7zweD9ZtZ8u1AV7HyJgY49UDP4f+rmppskcGiK3x8EFOgHdZQJzW1TevI2Jtg5ZekmL8ALpbew4NDpkigQrBoclTY8AHhw/WW1NaCw9xHmFHMZvXCNyg1bTAePazBXXgGP1GZow92OykjrsHUV97p7S12J+zk99VqRAEFwP+MuOWKzIvuOmJcPuO+eVH5uc/EJ8+E1+eWD5fWL67EZ+vuHkCrOyPeQL/13oYtY4X/8F+/E9EHFN/viEYbSzeCymGYjxHPRgY/HipG4P9exedx/tMTDlKq6Oxomyjk66NAco8IobIpZrnrBdFGnbUUYtVLIewzYh5VxExqnbM8MwZ9kYw4PQBGolV8LXgSsAXZ70iq8dlI+FQCSATTiLOxUaBbClKMXiC8xA9Swws88K6bzzSjt93/LISHw/WbaXsO5oTxG+TeX+/w5gvG5oYZieHt7LNQrfy+5yakSCt7mpvqUk7m0zc5WJz7G+oDzxdLsTnmZv3aIA3D+nrF0racR4bG3FoCTxq5c+/fOXTv//M9/PMtEx8fvlEdTNMETdfiVNAaiWtK5VMXndKqkbfvkPKGDOuGDGLZT8WIzl1HnVYjZyrxGvk9v0L3//TP/H9H/+FTz/+71ye/4H59qlFGmfiFI2IoaTmoCiomrc3yBE1606L88oVMHYzldE8/iOOkTbRHCamlxvok1MUijNwPGrbXl40AAEAAElEQVRPzCfQhJSePMo9PtWipoN8puf6d3ZVmiH1jV4+FlETkplf2erDZTRc+aex6+fiDFrOSsOEu3PBmt629NhDaDeg0Jj+LMAtbX03+cHhPNJ35+7PxUjDeYdgP+DYWzT+zM3mxRnIbfLNt4htaXd4Th/SZlzW/z97/x5s27bvdWGfX2ut9z7GnHM99uucffa593IFUSCKV8ooIsrFGwQUH0iQkPAwVYJVQwshUkStQCgoEygTg4SaYABBEQtiAvxhCCkM70hixYtJBJT3fXAf57n3WmvOMXrvrbVf/vi11nofc6+9z97nrnnWPmv3395zzTnG6KP31nt7/b6/x/fXNqUSflO9tlKerVjfoookq/en2BqKKqM6nAqwo5euFFn3Fjpb7t+5gOBJhfDKu5698+xDQIuS3xiqikfZjH/lumoW3nkeOd4+5eb2lmmKpFzuvNVytDqdVr7CLZ7Y2hflebmaw6X182r4WMa0PS+3zNl78jh6V72N5l30riOEDieZ4GEIns4HG/MZq2lcC0H7EmLmFI3KNEfG8ZY4nXAkukI2FefEPM/k6EC64pE2D3PWRELREAjimQl0Y7KyKlGRUmLDurKMNVn/LOBxMfwu7J2V5t+VMPTqrc/lubeafrX8VgkPjwrZeaS3umJRMxp6hn1k6HsuLy/p+o4+JcI4od6zv3xMF/ZIdiSy5cMCTjze9ajLVqftXqQaIe6Gki2v7N6wtJhKVIONVV+BY9GT3gccZTVv1dZT1SXHri6f2gzdhdnaQ2PoraBTqye9rYwIFTzeWWNpp7dnCYgu1yAbEK5ecOqekOs8qvekZrAUC+V0WZFsRg8jGZTCZr9aNcuzqIasOpdtvN3X2lrWu7O3tCoHrZSWshgHrNxTKZuTjdAopsRt0dcmTcwuM4llMAoTQYXOBbo+M1wGvLsgdTN9H9EbZT4FJoTcD+iuQy487hLcfiaHZxwjvPt0JKYbhuHKorV2HaGLiJuYbm+4mZ7wbHzKMY0kD323Y/fgEVcPXuNyuCBkcKcT3Xhi0Myu79ld7nBXA+NVx2knHHXmK+MtX755xrObW+JxQudilbOkhxJdtniJrdfLf1IYsUu0XtUizON410DyAmXZfpaXHzRkvINhwGsk64xOQhK1vOou0MnegFSK6DyRx5G5C8YYXVNEBOsvVXwxLqTJ9MeMeWAryGtKyqo9dT+u87RkNheVqETtITR/QplaZ3WJa4pewSjOC74PhN2ADAPsdriLBwyPXufijc9w9do77B68Tf/gdYY3Lti/Gdg/7IwhVcyTbfHEHwL7ytSIXyPQ8evIcbQzNlBYr6fnHVk3oPqPnJmUlgG3/jk/4i5wzEQRZoRZlTlnYo5W06tWJ61WpGwWdDuhWUdbviRmlV9fvy7eKoWaNpt1Ikn5O2VjX+KIS9HyKIPgZ8EFKbUHO5AecUMJV+nxwcJYBU+WQFbzcoTQgwRwA+pmNERCH+m6I93NM05yi4bI0PVc7O6JarwoWoCFpWEbQ13EW0pj64/apwt0kEKqMIvjiIUBJNkRTxMjM5MbeHAxMDx8xEWIiFdO3hFvngHJIhpDwOmeCeULX/0q4fu/n9wJb37uiquHPXRijGC7Pa7rmeeZ6ekTqzd0+xQdR1xWvHiSs/w2IxJZvFfiHBI82Vm/drueR29d8da3vs0bn/92rl7/VnZXb9Pv3mS4eMhw0dPvAF9IJhpNq3laY7WwulpQXJt3rT6ruhEt68k9AUfqOietH2vINiLNu55ZhV5VxbsqiuV1AxtU3bt6+utNCBaGp5BL+OKZslLaVIG03rlzXWb5EsIhLd/S3peljWc7g7S2KMXw4RwueLMUCpQgLVhdIxePgEjA+8qUlgsT3cq7unqirW1Vma7t1TuHvkCZ5tkiU50jhI7eBwIY+2VOhUSkbCNaivjWvlZtZWGM9KusbiWHs3kl6rMr+aheyphJoBLJOGY9FXCa2euOodS+EpVSakWaN0OdPVdVrP5k9SQoFBTXNsqcEgu7YCTPJ8ab2xKeOpUwTIUckVLPMcVkRcexvq2bbHGC1BlWxqe2z5dRs/xVyxCY1zOxHlkvUpzzxdtkhhdXiEgyns4rQ/D0XcDhSHNe5fFW4OjoglCrJY9xZo4TfScMQ08QYXaRlGbmDCJqudXl0eea0+o9CeE0z4xJ8AQ8jiBiTKCmJ5qFXbuS41hrYL7fq4+U3GVvIbeVgK4RtNS54jy+AMuax5zVckqjlrx2lE4hDHsrhh46+s5q+AaE3ZWgPhD6geB6q92cZ/M2I/TOCJq8Cknvi3fcNU+rIfzKhL4YwnPRGyxHU0nZohucxPbd9m9bNwpAaWkAdSTbZ2WRpHrP209bf91izKKQCNbccFUL1Xe+GMpzOeYOaNRlj28GmFKPmLzOX6R5RB2FIK/qDNTl2dJ5RB06K6oW6RPEcsTyHcC5akbZZJa19j5E2zXW+mEuBFGx5YyuDZbGu1BqxiZzRszzzPF0YkqTlScKIJ0WUhXTi7MaU3LfZzrn6f0e8Q+JHUynHSkrqb8gXF3QX3V0lxm3O5G8cDsfSc+ecJwu2e0fMlw8xA0XBlB5j1N6j1N8RmTCD5794ysuhx2PH71hta27C3wEbm5xtzcMaaLvHN3FAHsHXWLiyE2cePf4lK/cPOV4c4tOkZAglDDUZozUVT9VWXXR4oE0o6wrhvT78v9LSUV4noOq6S6tTIPtceodhIDremSI+Ay+M14tnzMSS0j2ODL1Pb4LVvPS1zrQViPbOcfsvHGryISmEoati6dctWkmi/dcy8wukyVDqRqxmoMrp0y9qfpapfr9bY11QyDserr9Jf3ukt2D17h4/S0evPU2V29+lotHbzNcfob+6hG7N3qG12BF99H6rekyLJ+1/hQha+Y4fXik48cGjusQxmURXJa9s9ZQn8WidK1jzNu3pALHZRFdqW+tQ6LCrDBnZdZEzJlELsQnvhxdWlkApLqiQEkJ+5LKMrgOg1tCWSWr1U9zQt08muU0TsQckSiIF3wB8j54XBjwYYdzFhYX3UxIPZ32CMFyeZzDu4B39kUp4R07p4SQ8a43d0KyAXZ1ecVrj18DvvJxu+lrS8kxfF6IY10fKkCsshgHjMABtXbOMaJAlIkTI8d85FafcJLE6AYeXQj7qwdceWHf9Zze/Srj8YYxRVQC3l/guo4x3fDVd3+Q/ZeEfnhMzgHpM24Y6LWny3s0ZuIxcXt8ynvv/QBPvvxDjDdPcJLZ7TrEJeJ4Yp6t7EkIRjeswQgZdvvAo9cv+ey3vsVb3/YOD976HOHiTfxwRdfvCy2xB2c1N1OxMJlFLpNTIX4o4QW1qHLTl6uhpLLp5dWidk8iUi190uonSll46qJqx8nKALB8r3KwVO+c9XNu4BGqXaZE6AuIU0QKOUUJh3xfu1bPoy2MKxDTRFdexgI+ljYv5iM7yjdvh/OCD7Xu3QIIKykWda2SEuopDucsTDIXkq8zD6zwvnrUFZg1FeeePFU5aQF8lVil5pPkpsBZm/Oqfdo+bztmhVMtxNXm6GLHrDtTrtDGiE/i4hKOqFlWK+lFB5oth64m9Iuz8VENJgZci9JbwbZaaGVTTslITsRpZL695Xhzy1TqNFpuq4Xm55SLwmmEPKKV9IezvWIhL1qHB5Wx04bYYimvis59SjVttDlGnW8W2kXOSMklI2Y0RlNEnJgBDPM0E4wV0A0D3mV85wgXA5330CWSj8gM4gLede3eFDNiZUrOoXiEgEhXirSDVTUWvDPPnVQyEM2ldqcRTWU1Y4XikNDhQo8EC1PO5cacMxDpfWj1Ai2gx8ixvC9GnZSsVEU0EjiR4l0t0QKIee2cDwTfWVkf54yQJ55IKla7tOxPDiMOwt8XcDTfWSU/U7EUEkhtDxSRErBjc1KkVlpbFLN1GNvy5uouCkhsGLHoQ+YdX1iRK2slZc6BNk+7QwtxkZpe4QNIIVIqY7Eqq0u7KDqYtpzatsGbMlS+tCiay9zS1b15VK1EGyX/us23s+dZJ2RRrhsIOAeUL1rOwgjNslJAci7rTKLVri3NTLmUwUmpgMdIjFb7VDTTBRh6x9A7YoqkaSSlmZid/TihD4rfOTq3Z9854nyJVyF2A35/UYzTGcKRiZGUHMcYeDbv6ONT+viE0O+Iqjw7PuHZzZdI6YZh53j8xkMkX3C5v+Dxwzd4cPE6vb9EJtDhiOye0c23ODejA0SfOeVbbsfMzenE7e0zTrc3TNOESxmfbd66tkuskA/L1k39XGm6Yx2Xbj1+76cjS1tMF18aZrr+PEfmaSbNFhHmvBCcpQ6E/QU7Cfg+llrtio8RLbm8cdhZHepC4FfXoCn0jKFnPB5x4baQ/nniPFkKWS5RXsl0aC3eeouseb6+0NCNQGVeXvZLGk5BzNAGahwqfY/fD4TdwO7yAQ8fvsZrb7/Dw8+9w9Xbn2N4+AZh/wi/uyJc9ITLBTTW57eCbSUX016aIXopl5Zy4jidPrQ7vg7gWDurNkZpDIdthC2LxDoEwY4q/8r6qLrF2lEVnGqz9ts5sxa+hPo3lRKcYsmuC1sJQ6irbO2g1fVh1ae6nN/IJMxe3EL31EoB2I5ccgJyJiXFeUgpEDLkXD2rM+Dpuh7yAKknebNEe98RQl88lIL3toE4dWhOxH6AmBCBi/0lFxeX3AdwNKZMCluqPY+m4Nxd8s8Gv/3dCsOqWejGWZk04vOJU3jKKUdGvWXUgUn3vHk58PjyIZdhYN9d8PTJe+TjkVnBdXv6YaAbMjF/lXffncn6RS7e6+guOvrLK3bHif1lxPtL5ilz8+RLfOmHv4cvfeF7ePb0S6iOdL31bEpmNPCdZ7e7oOsvyGGHv7jgweuP+Mw7b/KZb3mLh595A3/1kOwvoOsIPfgugkSizhYGXbxYuYa1FKCUcWgJoTOD0DJ+qUpB02aVpvC/6H6ss6OsA5UqXtf91jbxRfmoFPIlgLDBDmngqYK33HQJLWoGuFJiwSMuFjCdVuNkZfLR1bJAm2pNqV9mNw00Vo+WczQmVosN8YjrSo1GywuzvJ98TujRrmN9UWgrbJNzheRF0jkQW2lUsnp5XyGNd0XqmgMW/q2xrUMibrWpt2bWBrbNRovCp1SgvKzJS9ZKg9TnYyQnXCzjSS2kf1I4FXjZhx4r/6E4V2qSrcZeLS1hIWy5KWNaCFTMYxGZxyOn2xvG2xNT8TRKYU7NaSalWJwnpQA6GLAp+a813LoNn7KP1JIFVTm0z+3+pZRliXkJr743UQMXUgKK0WT3Nc8IiTnNZhQwpy+V+0yCL0qtNdA5z7Dbo05IaU8I0HXeohx6cDslJUHEam6ty+YYRFdiFrI6RHqCHwh4iObtJSuhcwxDQFCm05HpdEJFCX1H6DrLJZyMrMiHDhl2SAkzjRpRrJxV8B7tjCgpltqTqpnQCb23MRE1M8WZFCOqJexahByThTmCjXOfcD4WkqsaqlVClZ03RkTEDC2psqp299CRJehSPE5qwW1F0wpkwGog1jWnGqRs4avKYw1bW3yQBWDmoji2qVq9EEpJczPwjRQPvx2TsoWf+0o6ltWirypBWQG8RuhR53rRhZohrSYklHJSBfCh50aP0iqadlsNSuLJ2TFORn4kLhXSu8nyk1dPc61EN8/MPQLG90kz/hUQ3HS75acsfWVsZXLMaDR2VUkRnzOdKEMQ9r1j6p2V5Y5GLKYKSR0RYRKHBgHX0/U9Vyr04kmhgy5YnprPJI5WfzyDZgcyIPGIOz1B8OScmMZbTsd3UR25vBq4uHiD4IX97pLL/SOG/iFO9+gkaLiAboBpIOYbJndk1IknY+RpnDidTqTjiMwTPmW8FuCnJY1hvYdLe3Srx7gy6mLjo4YlWzDk/e6X5wZlgMw8T5xOI+NpJKWEE6EPAdd1+K6j2/e4PjPEjMsZnzPMMzpN5HmmmyO+H3ClMkLoBlI3EIc9p35H6J9ZVGE4Ebqe6XQkjiN5npGcWlSXNkIt24PPwk6B5opZK2p2eKnBaYtALrpXzInknJXcuHpIf/mQ7uKKh6+9zltvv81nftS38PDzn6N//Q2kv0R9B52zFLd1IXN4n31GXHV4VKbX1TO23fJD++FjA0dhnci5qIjV8ntGm7uydNdntSix5ftaP21HlQdZztMUYVlQuharerGKNS9ocR+rsljZ2yLB2YBv6pMu1BhaMFRVonNOTaFRXyyjzlTNpKVWnwIURsI8mRKrYpM+TpBnNA/4Ul8yJQMkQXvEBVtoAU0ZT6TzArseMGvtPE8wfNxe+triasiZCKWiu3nPmqK/wHoaiF5P3CX0FxWM8C8hejIConwipcAce2K6xOVH9A8e0A9X9I8HLror0u0tpxjB2+QIvZLlKV958i5ffarsLy949MbrPMyJUeE0zQz9npSVm5sv8ezpD3I6foXMEQ2RFGfwEXfhuLy6Yr+74OLiEf3uEX73kIvHr/H6Z97izc+9ycM3HhIudqTgmcTqLIUQEX9s3p1cxkXKkEr9QNHU9s9U+ZdauFSdrA5dBdRV5fW+pAE8qKq7WVUraFjNTau3ZV5GY3CkzRlymce17EJV1BFUi/26MRsXkg8AUaR6jSrKXAGxtpiuNiUrgl2VqeU+6k8FjS5Y3kVWB2q5xK4ARyQVhankOTdAWkUwi7haKYMWfbDkPtbCuw1jl/VoUfjK+3LPgGMF6sn23HPNIZMF9K0jPACct1BIJ1hYZ6bkuhYFW8z76F1ldLN+jbXWo7P8RY8gOcFcWTWViHAqynvaKX3Xoyh1tNcSs1rW4/OcqJI7rlaWwWkmzRPz8cjp9pZxnEjRAKGU2oI5pkaEYyHRZfzmqmqvwoCotTlL51TCGRbgr6ptbYPiRBHbNO8rAiCnCUvotY035Yl5HonTiaypgFjLI1U1VjtT/DFCjphJPuM6RxgG9n1nhktJraSJ7xxu8GZM0cqAWIdFDezVwkIecG5HH/Z4POk0Mh6tlnE3eIZdD5qY5xNJE04z4hTfOdRlJJX1RFzJE3eknJlTIhOtbi1gsXvKlJXTZOVTejVGQ+cy83ximk7klPHOWYhpSq1vLCSsqhF1rbJwMSl1YdV5sjgSjuK8LfU4Xzxw1GoMV5sjlIzoomastsYK+myhW/9uq5uUCJ2yF9S6nSJV1V7WxUXxrpEd0owCIqYT2Po5NY+dgWwt5Urs+9b/Vl6gRhTVHHVX1gtxS15a/b2E5y+eJFuDVh571UIGFNAkpBitWqGxo5iHDiPpaXsQ9XktmsVyq/e3slbvVMt/rvrg6vK1U6vPoXglkKy4QpRDyriU8Nlqo3YCgxPm4Mg+MzsteW+ZOVvf5VIeSl0guA7nghk/HKgYR0fMkZgTUSGpJ+WZnE9odGjM6BSRNEI+4ZnZ73q6fiD0HV2/w/sLkvTE6MnBoT1o3oNEchyZk3KcRp6kI0/mkdM0wZzoWlkqV0zBxessa8OIrJ9SwTxF/6uAg8IGTdUp7kfPWesOUow1qpF5njjeHjmeTqSU8CXVI4RSM9f3RljVWx9LtsgPY82NuHmGccZ1Oytd1A90w57Y74jD0TyRvf2E45H5dCL0BTyeTqR5MtLFhO1hxRatZb7lptPQ2l1rMde5ryX6SUpuZcoW6aZOcEOHXF4SHr7G5eO3efjaZ3n985/jzW97m8ff+hbh0QMY9mW/pMQKmzFdC5fL+7z/qz5qe+zqveAdl/sPBx0f3+PoSmL2yoZ2FhVfF5emNK/XWDlvuC6flw9sAosN4rw6ti1kCF4xdksonZTa6azszrlHdA14yoXbv1LBYy1XoHUdMSUsxpkozphRvW9IvRpnVIWcBaIirhQOrRpKnhEiaCSEYPkhLhCjWS5C1xlDodSw0UTnBSedKeg5c7y94dHVx+2lry2+gIgahrk8EVYDe7Gg3JUKUoDiESmLdJrIJFJ2TM6T5oDGGRczkpR49YDLfoc82BF2D+jmmZgntMsQMimN3Dx7xniauMyP2D26MiV2PpHSzBQCijIe3yXnG7pe4Wpg1p4pj+A8F/s9jx495MGDx+x3jxh2r7N/8CYP33iLR2++ztXjK8LgKy2IjQEHMBUiG2lIIedCqFSU4OpzM++3tFy5utkuuaHFA1CLQ98T7FgXsLfXVW1cQKujAkYLG6v1tkIBE5QabZTwbLIWZr6yyVZjithrq4mbjX/KOZzrjCAiG7lCi/dvm/Q6VLcqVAXkOKv/1YwTqIGAQk3uvS9tKop2UdQo3qyUzXOYNHNnJalPqPyck0+sLadtbWh702o21BX/flHjMp+0hjmuPGzokj9Y77+st94Za6dUwC2pAbdYcrWdd0VhNKNb9ZrgjcgkOEcomnjKJfhfhCyOebKch1iKIkvfF14kZyRUgmlbklYEhcoq0RUw0Dgdj0zHE/MciSV3iFIvzSz7SmHxL9VcZCnLAsUra/euRYnJ5OahFGekJHWcZ63g1VQjEV8MDlbc+T4kpgnVGcuxV3KemOPINJ1IqFnAQzCm0OzNKJUVSZlYflzObayrlFpfNS+3rjNIfUjtOYtmnMs4p1Yaod6zC/S+wxPICl5nNELXqRV4T2psrh5EMhARiQTv6LtCOlW8WjmaOc0KhKdiOPKWS2txxWR1pKSkXMkfMpZUEskkUE/GvK2p1IU0LaIAnQJCvPftB1/aIIGMRwkFeAtw+cL7UTFSFIHGvlwxxeKFK8/d+RUR1LIO1TVGKkqrSoMI3gd86PHewjyTWsjkkgOlOOcJvit1idVI9rrQtK2cUzFIB8SDC0qPQ0JPRJnmkRTXBrsK/sp+n1lpbxUsFCUWlrEmNI9+tW1ZBEcmZ2FOmSjWf1LzAlUWwqC6dpWnVqMalDVAuR9JKSI1DK+t4as5tN43qwFVa65eCfdXJaYEs4U36jSjs9XBDYixDAcj7yKZ2j6VUPFqAGlzSEw/TGr8CWOKjDkzKkzqrExYFHQGGRNumunSRC8zQ5cJweFCQEJgFseYEjGdiHMkzw6dMxonNFv0xjTPHOPI7XziWRyZ54RE6BXyKhfb9G4zOFbj85ke356dW8aK5qVGqRq1jj+Lj7wPKf1FYppGbm+fcXt7S4wR5xx9NzAMgaHbEXxvxg2EFpCx0pVEFTdFtJvoQo/0A67vmbuBGHpS11sofdfh+55ud8t0PNINPd1xYOxvmE8n4mQEmY3MLeaSSquF3Ko0XeuubWt2wqJc89IkWwsE8IHd1Z6Lx4+5fPwGl48/y+tvfBtvvv3tvP6jPs/ltzwmvL6nWWJ0tTDVqyzDu0lbsyr4ru9XoxfQBc/Dyw/nVvn4rKpiQVMtGrosDlofgJaGF+uVeTcWa/cZ9q2W9GVdaxY7ZEk8rcWBVSxkw1WPY1MKKoqrD2FtYdLlAqsObAeu32pKmhZQYMh/zhEXJ7IrhBTeU/OQNFfmsFp3a8npydlCONBIjKUAeqHVD8HT9RbG4H0HeBylaCvOlL4YSfl+FJxKgmDKyeoDKcCxWjMbxl930urxifWpL2+oJiMgyeX5SOK2WNKnOfFsijy8esBud4GEQPKOnIUcZggRSQ6dbXN0Q4/6zlhu55mcj4xl7E3jESdweXlJHgLzfsfp8hKniUePr3jzjdd5cPWYvntAv3vM5YM3uXr0OrvLC3wvJLW8hayVcVMbY5aFGBlRkrHwVgXUQKNFLGuzXNbnUcN8pTyjNrq0gqN7kGJ8aS9ZwFpbKqURDBcAaax5wbuF6Ei1hC5nczw2eFcWvuIdrMaYVE4cKHlOzrUQxbYQlIVTcya7qvzbxpJKTT0pOVIIpfamFAKpUvuu5E1Z5FWBCmVea04kTaRViOpapI1VAy85Vy/jktuyPLhF2asbaf1mBeBw9qhfqNh9Vm/dAtbv7AVFmau7YM3Vdnhfw+SVHJPlR5QxmiWTXeHPqMDeecud8CujT2EYFMXAHFLKAdgzFs04vaDrupZR7st4qqGpja1ajf0SEnG2YvXj8cg4TcSYjFQiRXK0MM5cUwAK821TLldeX4vOqHXSpERNJKYUmzehmV3rGM6FZr4w8HrvcV7beHzREjWSiAQsbDBlW2emOKHOkf2Av9gz9BdoEk7Hmfk0klLGJfNmeGwuxJSZUySmGcj2rIuRxZIM7XnUuSAkglf6zgCfkw5Rh8szmmcQCIiVB/EJIUKayfOIy4muzpU0ojN437MLHV48UxLiFMnO4YIyeEGdR7wSgtAFA6k5OfubTPCuEIhl8EIOtldaREomZTGQkSKaI1o8tVbuSPC+1Lj0pnzjHeI6cAMiHeZpvB9F1XtpxjEpwE2K0U2oYV6YIafWcK4bYq4GqVzWS9sLG8OzD/iueI58QIVmAJO6JqmFK3ehawYU56zvXNEIc4oWQuzMINL1DudNyY2qyORx81SIqcD280rKU/f0Jb8x17VnpR/VkMC6o7S9hKpjlXHoyj2kaLdfuBy06IZa9wYtv4uFdaUz35MUZbDoqUIu/A4GmWogfVVgtfyILiNLs6JzIp5GxtsTp9uR8Tgzj4kcQdQXD2wlgjLj6kyJbLCDUOZm1EsamVPilBNHzdwonLIwZkeOgotCNyf6OTKkCSWWNCpHEoWUmWVmSo5pdsyzI0eHRoU4k+ORFG+J8cQUZ8YYmaIZE8lF2xR7DouGvHYCsQASbcHFDehLyTlvhusSFeLd/eQcGxl4hX6ZcRq5uXnKzbOnTNMEQAgdseT6usGIcSj5hqXZzVQBBWvszCMp3tN1nRmrQ0furG5n7Dq6vicMA/1ux7g7Mu12zPsjw3HHdDwyj0fb46aJOEXiXABkMiZoX/agapAQaHplEiHXfG6H1fbtAlcPrnj9s2/x2mc/w9Xrb7J/8BYP3niHR5/5PFefexP3yJ5zZjWPwTgu9P2gcfUI7HUF/BUHtPMsoccfJh8bOJ5YWbCKC7SCxpyrr8M8HF7MChGK3uVWQ+9MmpumDEsn1PA701VtoXbOgKMUBfDM4tqsCCvQWPIAqpK1tta2S9ZbsUe3nitkbKObNaFxtiLJKeE6K7dBARckxeXC1hTqHVpOXMLYuyxEqWp/lrcQuo5+6K0WT9gjMtASa2ucfb6f3Li2EYpYxFMuuoiWZHsECkHKmQK7embrMG3B+rtieNQK0yeShWvFzO0ceW8euTjdcnFxydDv6LqA99CT2YvQ9z27Rx0X2bMfrhDfM04JN6WiXGTzFCYl+IH+clfyRO15hyA8eLDn0cMrdsMF3u8J3RX98AB2A6Nk0jST4khOM47CSuiELGpU9uJQsdDHWhNrMYossejN+4w0Rbc+06xVma4/99SNZUlf9vpz36bUT6vSUhTutROtWkUXb9fys1ylhncudjvbDM2r4OxERbOwkxuBjpLFLcYe522rLs8V53C+2Hc1FcuXecmQGh7bICzVFmQgq5LhrJaAks9cx2yb12VxtQU8tzzKgsbOnpeWZ7KoSwWfP8+E94JEatmTu+8/5/WyjdPm27qkAs4hXkuoVfFi5GyWTFUsR7XWmSu5iBU0lk1D1JSianxLOTOWcw37PV3o8U7pvCOUPLZqTBF1FrqkmRQj0+nI8XjLPE5M82w13lIixdkiOlIqURqurb8tz7b0B67W3azsf4J3FYQYC2ndjMz4x0ohWhQjC83KzYDxoiVjpAYup7J2lPWqhAsm55DdjnBxRY6CplvmcYaU8FnpFVwGYmZKM6dpZI4TImrGFFeMtFnLWLd5mwtw7DsQOjrX2/PKmJdQI0hPJ4KXhGgkpxPzfGKeRquvmyOqiZyjWfD7bAAqC/M0kSKod/R7T98JrvOoV2M29g4VTxc8fedwki30VGsYs8e70PJ3DDCuFKgUyXkGUsu/jrHkYjsxJtcQ8EERXwC0CF+HCvORZOjDkqNXrIUueDOqNWOdAcJKuqfVVV6GVuVcqGuTc2XfDx1dGBros/FsocGVtKqxOrqShaiFeKMo5j5Y6CMlV1Q8ePGN7dYBXW8pMuehh1VJXOZ2W0Sa8Ubb0XXtzG3NtPeqjuVYjIGSgJQXw5aUnO1cdJliILBzLs96tZq9cPG+MP8Wld1CUbMZqGwrMLbTZGuVtbNEzqRMnmfi6cTp9sjtzZFnT295enPk5nTiNEdihqyepGYAT6mU+yuREimXCJ2sBWjlVmJp1syYlRuFZ1m5STBm0CR0WdgluEgJzTOqxr48ZUHiRPLOctAjjLPNlZykuLASmiZyGq1+ZJqJtR0q7cfGgd754X0/TfepY962E1KygZTIZK9olkYK+MJFWwOYZgONz569x+l0a+PQdcQYQYXgOoYh07dShdrGWN3fWd9S54EOceCdmu7RebTvyH1PN+wJuwu6/ZHheMt0vGUaj8xHC1edTiem0dbReZqYx5k4z6S5EJ/lXMZDMaqrEUrOKaLO4bseQkd2njAMPHr8mM++81k++63v8PpnP8PF4zfoLh4RHjzCP3yAu1zAuZR9b5k/2gDyfRpkPvaq+2SyiV8378pQmkt4W7XAeXF4haCWKzYUL8da+bLxuF5BtIFRKA+j7A+VPKIpvvocQNUWQ/Nc2Hs1YWt13PO0sVUbqpXMQKMtIFFnXLEK+5RxPiN4ai6E9w7NxjBme1pVOIXsHKIr5j9VshNyHNHUoymSu4zzphzH5C1fqVBG34dV1XJTBHWFrbEk4ten6oDcQsMKqFxL9ci057qyXhRQULYNU5g0k9LMs3gijDfsb/fshoFhGBj6wH7nmXaBB0PPZT/Q+x3O7RkjxGejAY/C9JhLqE/XXzLsBrphYNgNDPue3T6wGzxd5/DOl5qaAzjPqDNxmi2cLM84TQQRcrZir1kcuXhgTfEsG2ZRio1HyO7Rueppq329fnJLP6eW93c/yHEN6M+GeQURa3TRFpXiMU2pgb66NzTrVZlz7auoLayVil/kTPHIbYeplyngWqqhcBW2QyWC0RW6U1Rd81wpLCGx1RCxAhNnVp87Uv2+i+W0KnDagAUr5W8JzTkHjRXJLUNcz573ixSB8myqelfmnpbwRKEpgC0qo/QTqpZnFGfmWg/SrEAtLw4tZS3qbafU8mB99aKYe8XCPRGznKdUnqcSUY5YeZBhd8GuG6w+oBrDpWJ7gNV+hDyfmE43TCdjOTbvWSrWWFPSUjZV2aI86sNopjd73UCjtd1Cb4FC/V8/07LeZM2Wc9JsAjWs0lkobPGM3IdoCCRxRMWUSnPzG5W7YiFpIvTOk70wO8dc7isBUUGKV3hKmTlr8x7nlPB5DRwrVjYjihPwak9f1EqPMM/kcSbHE1mNXCaI4tSMZ9M0Ms+ThQ2XMaUC6jP0QvAQdSbOJae780joCX0p6eEsB9fYRqMBJA+uaOUxGqDIOZDzYJE8uRgOUirAMRLL2g7Z8iy15CNhBDC+EyslIuBVUZ8MENxTJEcXPC0CoIws56phQ5uxxpUoomoQpoE0qCaenC3nyHkhhEDX9Vamq5DL1XXU9IalnIlUL1kb+7Jc3/kl3EwVXEAkt+tlLH/Nd5YjvuhdxSC2Wtdt4GptfrlmVUZrznOZV2uguVoctaQ7pORKP9peqRjZTMLyWe0c7zfUnZs7X5yYZ7quD9VIkSyaa57JybxDcZ6I82zeojkWNt9kedmnE+PtDTfPnvL02Q1Pb2+5PZ0Y50hUy79XHDm7Enqey9pm14ppAQ5a8iezqq0HCLdZeZoyN0k5JVu7exWiSgPticgkCTcreYRZYFRljJkpqoUk11SBbNEjOUc0z6W+68p4Xed5A4fLvmjqczXILgBfddkpVSEmA9t13MSkxGw/92PKAU2JcR65uX3Cs2fvMo43qM5YFIrl4VrefUuWXn15eVnDwUHRwmBKZ2XGxAnSd8YzcrHDX14hpxl3muhPR+Lplvl0w3Q6MheSnOl4Yh5HpvG08jzaeMqFNTulaMRgpfySSxHJiRQCbtgThku64Yqr117jzc+/zWe/7R1e/+ybXD56iNtdQr+DoYfgStOz5f4XB8YHPLE21yo7/ZqjZP16ea+Ohw/vi48PHMepGpPM9ltmvnkbQXwgdJaH6FWJtXyBk6Z0LuGR5yCytHq5mFT1ryxhoiXUwhbFpXB4/aeAxlxyvGreQRv99WrnD1raYlrAkJjHLbfN3xhbnTMyepej5XRQY8TN6q7ZkVOhyK5hRa54WyozWmmjRshZiGqhEXFWnEsgA5lg1iutrHEXH7ebvqa40geWE+rKAmX3aU5ObQq0YCE6DZOXcxScfzb4locqq7lqCp2qkuaZlEbSfGQ6dYTgCcGzH3rmqz1cXZL7xOgyXjJOeiNIMPYIcs74ELi87BguL+l3V/TDnv3FjqsHey4vO3woFnYA8SiOpImYzMqTkjEcBlFSMRKQxHIQKiFJU0PNaph18SbWnIWazMwd+GUYSo1ASauX6342RifSrqfQmIhbN5Tnb+GZ9p46LVQPtCR3KAp1VQioBhvKHLKFpe97+mFAxJFSMuOG1tzGmo+2LDyLylOB5DKjbU5b+/KKMdM8YNCgQzNC1Jtae0wXcFhxaAOpwqoURctYb/dnLvPV5sgCxGupFTubtsF+T91Y2GOtFUUfX3FGlBAzBzUn2yIqMploYWlkYpqJaS55kWWDKbMw56oAW4CWKq1kwtIbIKWO7TKOreahEzNqWfilfd+pQNcV41hZ8wTQyBwn4vGW6XjDOI3McWYuilmMaVVyQ1breKUYXfaF2o+rJ7FA/TImrT5jjQzIDSQvDJLFEJHqRqqr8L0XK873iAQUq6UnrpSx8BOqFkJ8ShmXIiTHpJlEiVIQR1KhpBqRcOBDUegj57VYa2iy9ZkBDm9588HaICmj84ROM3FKkKye4kwhiMip1apTZQWAhJQcJIgyW1h/rqtcIE+Z2Hnb00wRICdfSGty6ZPSVnUoPTBYxIOfkHwEd7T1hkiWaOHU2F6YNONyqVXqAtAh2kHuIAWyWrqKunRnJ39xopXutm56lJDOKmdGibyMWWFpUwFfIjXcXgDXaNNc0QVySmhKFooaQrts6eZ2sZWKV9Z6Wn07qB6tVOZ6VSgXY11tT+vnphvV9R0sL1aaMabqRcvcKtwSq1DTYskoxishxljIt4oG4BzOW9u0MNM34FnByL31pAdjXWCaZqZxYlop9mk271CcJqaxeoxG+yxGUnk9nYzU6+b2htvTkeM4Ms6ROdW0JuO6yGpAOWsu4dhlravM0MXgk9XKy00ZbrNymzK3KTMm68eorkQ+Wc7bTMa7BGLZwjOZOWWmrAWYls6obtS2H69B4HpvvuNpZPmoHXv2vjQSvazawLB9wzNmZYqZcU73AhxTSszzyM2teRrH6RnOJbrOiLLmmBACXejohsFK0pyZcKjot72hZQ7UDUaLtx7FQJpmuFT8CHJKdOOJPN0Qx1vzMJ5OxGlqv+M0FgPE1LyPbYxFG2NxHInTiCPTewd9jxsu2V8+5vHjz/LG597h8bd9jqu3X6e/3BVmcQvTZ6UjAEvEX1PE1zf74bLC0c+Xr3GOj93Ht7FWGzuHfZaeJDgyneUKG/VttEVRHHh1QDZUX9nCWtSvtVbL6NVzbeFMMTSPo6yUvDLIa/x8zX08mywF4daHXU1d0K4JDX5WVbMc5lA8WY3Jri5+VrepBOsVhStFzoCjK/mM3ldGNBrZQzZGlpLDlxEXETeBdEaKUHKx7gU4OlfAUBlCIgU05mVDLA/qzCMly9/LhnNuuWiPVwv0apZi2yw1R2TOpDS1WjVzH2C6QMcjN2FAco+THT7sCd0AArkU7N31A58JPQ9cR7e/YthdmMex6+m8A4ktxLSoiaRsBB+JbEMDSp5eDYeTxQpbx4KaByXmTNKSd1OSBZdQtwaj2zDNLS+yhP6sjnrR4sS9jxjmTFabQc4JnLaSFA5aPquwWA8B7mQ72ELjPF2/Y7+/RJyU4uWxKfG5WJ1rKE4toVCBp52nevzWT45irY6L56MqOWt42N47/y7tCBvLS02p+gBWF6v9xDLvFxOHNE/zWT3HFr3wwY/5Ryq1PpXmSj5SLq6CedGKdwN7XS3EqZRCcZaY2J51tfrDEva/5L6uDR9mJV/AuZa1TQoZSHlSYiGoFNAYCYw1v64LeBH64AhByHlmPN0ynm6ZpxPzPJnHca6eplxCTFmQOlaXrjFFFgOId8vn2u6n3Jnz1LJJJYFzsSavWVPV1gCz+Jdr3hOrau97gu/xfgBVvE+tBFPWhPhAUphSskiWbHnVC2lJCXOvwLlEPpo9xrLpYRnfjSHZmdI0hAEvAU3KPEXyOBPHEznOhRXaAL8UwiBcXyIIXAmDNO9JHUOaE1FNWVUxBuNximRn1SADASm1DbXUFOxDsHxEOpABkQtwe7J6xjgyzu/B+FV0UvIcyYWE1gh1MpIzQRxdNzB0O3uW2L6bErgsFopdCJvuQ+I8t2fdwFXb9WhGpGZwqQaxsj9aVEBFl8v7KWPhhJLsHkjkODHP0cqq4BAXytS3sLk6F5vZRGqbrB2LelSiCuxQO0a0gXnTn1xJK1itxe0vM0bUtja9qOoBUnKqWzQVZR/xbc1OOVqevEYzMFXjetHbtPzkeoLVPdyHzExMceZ0mjgeTxyPJ06nkWm0n1hA5DyO9jONTKe10j9ZlFI55jSOjJOxk07zzBxz8TKCERpyBshqPdy2B6odZ04JMRbilDmV6ILixEPE2N5dyX2bCvmQajRDToqFtE+LAa7qwLoA87os6qpNZ4Cx8Mavp1A99uz9OiAKMM6ZWNLSzAGhhJQ5xoR38R6oqrByG2Op/Xs6IiSLLOu9rXOz5RObY8ozJwWJltO+KANlbra7On+zRa6spAMG8Dtg3kHcE+KJMM8WqVFCUnOK5GkmTyPzeGKabJxM02KomE5HpttbGE+IQLfv8fsLut0VDx6+yVuf+TxvvPMOu8++Dr21ZclhLBpKXXPu3BOr+6J8R1jmqh20pBq1Y+68tvN87dn48clxXGexulo8MOWOqjLuysaesuKzhT+qKpIMaCUSPggeXzoVBG+Dsj2ec3VwdWssCgSrh2ffWVtKTEMoSe2U/CxWh3Nn4KykAQiqJdeKJ5s1rrD21bDbNmFLQdlUVgeBmhPmS6Hy4AuzYbXqOY8j4FWtoHZWcBmR2IBjZYx90bIG/g1T19AbsoVvSslxVEvyVmrIat1Nyu8yAJfFpkKxlXIuQmhGEbH+qSUcspFA3GgijRNOApo7kB0SdoR+B97bppQil7sd/cWet1D63cDl1QW7LhgpQ+2HnEml3IJiuYpRIVIqrGVFciIg+Er9L5azmtOyBSfE8hfUck9chq7yv6xAjK6eqHllSuh2Oey+NkYnruSmltlTx2QREQvhqGAONeIiVzyr6/lVDQZUj3NRUmrYUp0LIezwwcqX5AJaLF/fGMVytjBIK2dTAGEyb/H5plovaiGRMc4tp7fm9tR2tfBTWTIV6ghr+Yh17JV8TtsEM6vLFIWvLqariS/nK48WK56sDE5rJtYXLbnmQWimUvnXsCCFVmLDFYNbjbwQKDUVS605Z+QxUh9cvW2crcXFSlw9lwbUpQFHqQuCLPRIZIySXut1BWVkKuAiac/gAwFXcoXGYn2diTG1n7o+GmisecPna31d0w0U1TzOWtsTaIzBdpBZv0vNuOLREXIjo6pwWOqp6xi/pxDH3nn23UDo90yamWKk8x1914F0JZ/dwufqc1gqP9gaJOLbwlyZddEKG2qP2o8T6L2nD57edwQXkCRMcyIfZ9JpJM8jqrMBTO9BOrz0OD/gwg7f7QjdgPMdqp6UhBgTKU6kdELyhOQZ8kzMpjBNOTJlGDTQ6d6MgsHZPhcMxAZ/gQ+P8N0bSHhMlJ7jfOTp6Yv4Y8AdM3JM6MmMennOzDEiqoShZ3f5gMuLRzjpGU+R6WSFt4MrRettYN5LP6ZUQXoFi2Wfh3O9Q5c/70Y7LG8a2ZrVobWcUdMZZnKcmecT4xgtusb3uCD44JtxXct16t7ZWlXa04BeAas2zotxcwVcKTN3mXeruVfmgxROiMWAz3JOSjh6mkm5RCK4gPiuzdU2v8gtRNJJSRtxYh4FHCTXvGSrrfSFyw985UucTjPH2xM3t0dubizf+nQ8cjpZeYX5ZKCxeR+LlyjNsYQbzhZeP1vURI2emGJhhy5lkGqdzNo3i06aFxDedAQxAp2sTDkzl0guLwZ8QtEdk8AETKpWDzZOpDiWnOBVFI1WcqO8GMXKwlL12Vz3Qy2G3VVaSJUKMtuLZRemAtGqS6lY7nF2QkiZLkbuhxrHgGOcIyh0vqMLjv3OE4IgWZhd4cfPyjRHkk7EXtkN0Pu1F1/vIKzyT7mf54oDemzs6oDTQMiWtrbUKwbijB5HKzl1OhGmETeecKN5I93xiNze4k4nxMFwuWd/9YD95SMePH6DR2+9xe711xpoPNc0aj/X/fyOUql37utMzjHVnbOeneajTsOPX8ex3xWvQglZqOFJ2IBLQrNUZzDmJuepYYazZLoEQ1Hs3MKT+8GJtauHouU6bbiXRVzrAr5a7NcelDPtXdePcr2ACs3VUL4bnKcrwFELcPQ+0HXBwotyTaqORVG1cL+cSi0WTSVERwjF6+h9wDmP98FY9LwSglnkKqdzm+TcH3BcnlN5T5awMdXcQlgFRZ2FYDhpaufyuwFoe366er6CnIVPLhlltqB5cVRWnWmMxCkhEkA6IJI4QTcgXbCFMUbmOPPG6UiSRLfz7C87dsHjNNk4UyOmSFrIbrA6k1GFOWPhJYX2vsPT+bJR02HByGWhr6PMgXlszMIXk5K85WYZI2W995KAX7zPCo2/6SMYcb4uMSu1tqdaOtIU78p2ipDJNd2UoqVSiR28K6GIOZYQrVTwV1F3nCB4pBR8ztk8DV0IFFJp28SS2JW0WKBL/tI8mbdYC/lNfbJLDkXxTFJBDaYUlUGk66nuzIbdahPJstGtx24u513P/+oZf75F7c5CXD0G9bMCqu4LOKZU2QbrDJEF8BaxaxfjlaPUWStMzznZuMylBmY9R7nfBsKSvael7psRP9Yy4HV0SCMPszGsaC654m7JlUw5MeeJlAbcbofPjilOpHlsOR0pmRGlgaSiPFn/rYijimJqN0pb0yuD7gIcbdyqmCaUUiLmVPJbatitW3ChSPuuKwRQ9f37kB7H3vd0ww6XM8dxxJc9xHeOYbej73tUPOm5JUHMQGOFOSO5EG2A4isXXNkbDI4InYO99/Q+IOqIMTOdZqbjSBxHyBPeJ7rg8b3HhQHvL/DdFV1/Sbe/ot9dErodOXvmOTOeZubpFom3kI5oPBLHW+I4M00TOp/ocyZLsCiGYlQa3MCuF3a7gd1wxW73Jv3+c7j+M0xuz5PpBnc7wNOIPj2i7kTSiXGeSNmAtiDsfWB39YAHj96AHEj5luOtKe8ScvFEZ4yR5R5EChAAlg2yvKpROuWzGqFQPXB6d+IiKB7EGfGUOIu8SZF5OjKOR6apkABNEd9lK14efOvvur+2WVDnAJDbmuYshaboVdW4fU6U8f7ooIr4pNpjtBjlKskZUvzQNueneWScJxQhdLtS/sbXgVkIkcp5y1osWczA6IxkCMml9q821eE+5K/9re/jdJq4uTlxc3Pk5vaW49F+TrfmvZpOJ9I0kaNFAdhPanmONZQ4FwNobL9TMYRZ6OnZfnEXOFYDJjUVRs5AWC56pfOe0PX40Bv5kQg5J+Y0Ws7zNDJPJ7JtqiUP3q5XDeYtagPXxgXU9TQ3htuFGX4VEaQr/brq1Ku90LhMIIml9nhsrepcppN0b8AREZzvGHph13WEEOl7C9pFq4G1EP9Ug03xkFcXUtP4q6OjwYpiNPmQvb0GaFCYrNvLuzLOdMcTcjyhxxM6nnDFiNpNM0MZa+Icw8UFFw8e2M/DBwwPLqGvUYmmrC0kYBUkrdqkK86DpvtUToS17sLz97u7nv5msP7a8vHrOHZWsy2nEgpVYmUUNa9G8RvW4tPqy7jOEFFmzQxZbSFxlkjr6g3WzqQoMWsLXznmTPFco2i5e8trqwllQbNz1MGiZ4eWxVcX8Chqyllwlkej6sF5ui4YJb235xCdkCJlk7fzZSi0y5QQISWp4NVb/oZTXFJCUrpgi4/3ikiyUKXm3aghZy9WRNbhvucZBuZ5rLlOi9h750C7KTLlida/c/2MJZTmDKCzKPp2nFkoUzYuW3EOZSYZzzM18b+ytYoXnAffKb5LOK+lAGtJBEfJQiHfYMlDUEHVQXZlw/JAQLTDSY+4YDXQynPJSMl5TeQ8I2kyAJZtrKiKMbCyBHcui5fdm3Mr9POC5XyRWN4z70IpoC1CSkvgZ/UaObE6fl3n8Q4r5jyrFcclL5uOeFIJo0oxM0/RaowFC0EyUFBza9qe2YismvenWOfO+l1riGxRnKriBW2e1/lZS+/kBh65q9MtgJT1krAs8Q081gaspG637Yyr89cjPkoYx9cvpqgptJBwEMtDdAYoqq9lUfRshvlC5uE0G5W2FI9WnWvUfU/aHAXMG62rcCco+U1tUtO23VKrU4tySM5omiFHOjLqHHGyosiquZACpOJtXnkZC/A14Fc7uxqjtFy6jOvSpgZiTeUyYFzWohbmWckCat+d7bV3N9D76ccf/w/8+LPX384V8Pl7udaLl6kV895B+Xf3dZ1pBub8jKe3z+D2r599tgf2Dni0g0ff9tFO+E4PPP662vL1SCWqsolSNsICsNoOWBa7lh5TxpTCnX2yaDjO3qsB5zFGxnFkmmdydiiJcZ5w04TrA16CzfuSn9xYre8wAksDg7YeN+DWDHM16qKAVrfsBdX7VCfMWpVs31Et5HQRnSfi6cQ4nVBnREyd76E4yWtpj3MiL/OcJ+r6GqwclKsRMIl4T2XH/vrf+B5ujyM3Nyee3Ry5vT1yGgtYPxlwjOOJNM+QtLHy2x6vBiIbsU3RL0rpFANeix6weBspa9jCWF2JvmrOq/VKXesd4gM+dPQh0A97un6P84GkymkeSXlmypkxRuZ5RucZcmrV31okR63RWyLj1tEp2sZEzS+voaoLm24z1hX9+2w/tK9bSR0R1DurE+yEyQmzQFwS81+o7C8uLOolZ0QnnJxAjkzTiTHOzKnwiLiOYdgx7AYL81ZnbNCupj1U8PgRLlo6sxqwl6nxIXvHYN73gKdTT1QHEui6DHtFHuTC+eLpd3uGiwt2Fzu6fYcrzpFqdK/h5U3uVff4ePKxgeMskEttNbAOsc26WrHtKZdMnWIdLgQz2VFV7KBCVydcXQx1eTZLIfX1k1t6b52w3ViAPmjQrpSPMyWCVb7M+nNdQvhES/0yFPFYmZHioRFRq5+Gx2GhkTlXT5TiciDlSNbCRCsGsnNhgSJDjIl5hi5kgkvNQmjH3+NYKSGzVM/xyosrVKuFPbVqSa37VfOmiT2rc3BblNqV8l6PNaPAamOqJp8G6N1Z/7SwvM4hwTW23qvLCy72O7reYyy0k523LHgJY4CMGZJKY0XNGUSFgBVJ9kAQT5CO4Du8M0tf6MKKMCiXcW2J/ylnnKaSG7gsptXiBWIbQSt/Uf2y92MAsAasnn4BkjW/1vvC3Cc0soRKLFU9UU4qO2ywmkPevEAZVux+RraSUmaaZlStTJK4ak3N7VnkWtS7eIBjjCw1E7U9r2rMqXPQiStjZckHWcJQ1YwGRSHLDTDXUbsYP+oG2ZQhWcadVssU67GovH+LXJ1CizZRwM39yDp3qVrhpTXECtxz5hUwMGjrbp2j3nuK04HstOTA8D5rqtbFVpfRWYFZzrUmLZwViE7ZQmGd4NTWQA9ImhlvEyNYHm1ZM0mFaTGXvMamyC5PefESW6sqqq1KbXm3/RZKCGt5ToKVD2k5YDWEVxZrev12zTeW5fKbbPJckRX5nmoBZGU/qiVFyoclZHDJQswoUgwldUwuHoECQJKWvT8V44npUDHNjPMJPwd8F+hcaHtxwQM4V3NeFyO4Ge3MtyLFG19TBura61ypE+09YIAmFSCkyKoEiF+F7dXw9UyOs4WgFxKQJB5xPanPqC/7SjF+CZaKIzVfNpcwSswA5sQVUqFCu3tP8/GHfuCHuHl24unTW54+u+V4GhnjREwTKZ5I80ieRjTO1gxTVqm2gBYOSgXElj60ELhB5b1YeBK05fcv3saa+lH7rKgPIjjvSx1e8MHT9z3Dbo8LvZFXqTKOx3ZPTrCaf7mQ8MVMjoXELxW9i9z2C3NaVT2rrK/VS9f06DV4XID/uf5NOXPxOAIqiejEnCfOCILuQy4udpbCH9WcNHkyp4wIEgKuF8T1FjbtfOETESNEy1Zxw9cauB9wX+vIuKKS3jGlsDzD5WvLnlb1jM4Tdj19AfJVvPdWl1Yczge6fqAfenzvcEseV7mnc+PQGreuDd+mY1cQ834nwofJ3SM/Ts99bOA4ldj2OvqLqtxupD7IBZybh8dhIXWo0bVHdSsPUAkja1fRdm7W762A5VmctvmFKpI5+/7Sz4uGKrK0n/X00LrQL5Y2s6onxCXLhXNm8baaUzYQKngUMslZAU28o8NYCVNjnyoTPSkx2oTXlHFkZmeJxa7WBhTBeVmRQ7xYMYtqsX6WTWfx5tZnUBEHbeQa6ZSWQqPl8eXlGVYl0xVlbtG9q0d3FRbRnnkZOoU5so4q76x2l+t7XOhw3tP5wNXlJfuLPSEEFGVKibSa4BksNxHzcms1BIiawusdnXf03hPE4/A4OgspC7a5OifmMc52vypKBCZ7xWI6sbbWQMsFrBQiqHan9wM4dDW2qiylQurik20jKXm+rlmu7bOUImA5qMF7CKEYQCieX2flobIpSTEak+ocMyLZvOcljk6zWp2+2fJRLRwxtdxFw3/lb9Ze/xWIa+tgXbYLmCpBxDmnhqCaoamcooHEtglUpFVfrYDJak1om0Qbo9quXJrFGpy+aNGcF1RYgWNpVw0/ErFc6bMtRWghRwJ455tCIIJZeaRS4NcspTU4WwH3WpajAPq2DlLz1s0o5vtdyc9Weu/wLhvZRJwR5wje49WU5FpyYbHQw6JxlRsAKnmPLGj9bPbc/b3eSM0YQuvztTmgPd+2tpWN+SOZnD+6XF9fb1D0VRKpZTbKmrFeV7TsgfYh3pU1uBzjsGmXG1HKYiCpHjZQ7FdJNWiAohCgpJmYou1FYkZTzal4rzIUA4lxiemiNJZ1ug7GVEqfiFhIc0ZaOZxYchUp5zOFeyAEhw+rNVSTlUyZJ6Z5su+lyKwZdRN9F+lcj1+l8qizsj5ajOQpJsznWGdhYip57aoR5+9n+sQnN0xPj5ye3HB6dsvtODLlSJaE6ozkuJR/wSLhcjKSw5QqozogdbdaABeFFbqyxdYUlVx4FirAbGOjrk9Ft5JSn7RGj5hRoKZC9YQwEFwpCSKeKIJ4Z8XpnZLEMWskoYv3k9W+V3Mcl0G9ejKmn2ilPqyGWa3OlLL7PQcoVVEt4bEuoRbi9OI7cCXigWyhu9McwSm+G9i5ATpHTD0J5fZ4IiYjmwLLrw1+TcJ5dlbb798XsXhHzhWBBSy2jZpSxk1tDvSeXnvzTmMeXh8CLphhxodg4chdKauz2o/ugr/70Rx/ZPKxgeM/+0/+o/fRjo8sD4A3X2oLXh2pZDi6WgyrgpV1UTPXw7iGkIlqDa2neg3tuNzmny1NZ3MCU/YXi221tdaz10Wr1vzzPtjmGWyy7fqB/d7yhMRZ4d2kvli6FotFFssOzdXyVjZ0yQkv0AfHrvd0ElrNM0fGuxmXZyTZBu+yhW2ae3gmp8m856W4ylp5bbfZLD+rxfeekjhqaQSK13/lK29eOjCAVwKIaYsfUqyqkZwF8ea9cc4jAci6lPso4VJV+W/7iZRQp8oUnLWQ4cxmcCnmgza2miW+NHP1BJdxWLY+WVloFWtjG6N1oV8bfmjlAVwFlu1CKxjYsOgyDpvha9kRVgCW5Rr3JLkAx7N8hdYS66uap6gUkGkJLqaIZutbKTU2gZVXsnrB6/upRFEUVUiXZyble+Qlk9l6xpPVIdLT9xdmlIiRPghdUDQbcExJ0OSszFDKliNUQVtBqe/ru9UCUY1JbWYJLefq3OBXRGqYXlUAzj5cFLd2uJz93mST54rUcjYrg6SKeT3ujCdjo61GKzOA4s37kso+UrkCBDMSFR8gPnQrQ48QgiME13LOcwr4YHM/oQVoqZUpESHGTExWoir4YGtWztRsi1yI57z3+DJX5nlinEZinFAsX9R5h9MOlYBT8BQCtcIKmmJkSpG5sOzGbCygOs2M3UTvO2NMFSu51hXTquJQEcuFLWVNMmrn8AENHs0T+tx83x+5PMxGfudCj+szLgu3aWLKE0oqKRdKEMFnA3MJYcaYTKekzJW9lOrBNVBWy2tUz58ZC7TUcUzNQICUtbWECCPGFeFLrWkfPKELltPqveU5+sDggxnPXEC9x/lA7HtyJ6QUGP3MbYY46zL2XB2xdeerHu+FbG4xoUnZAVZGZZbxXdt6lhMrK5Z9VfMal3255lzfixRbY4wTt8cjp9MzxEd870kSSCrMyfJvpzkyTR1dZ5Fkrq939gEi9TkoqxoXzWB6rqHU76z+KEaBqhiJAE4Iu0CXejO2p2wlNYK3lA8vaFCylH0ba6IuXXfmSTxvrtxpwjd+L7uvWp2bfMKl5uBZuEvG0t+1xOIv+QmLgrUsNLV2lOb1orJSzNEWQmPGlCU3rHo77EV9v4COlfXG1TpgIlYHyhlb37Dr2e0Hut5qlWUNZIZS76uEhpZ49iAWLiICXhSXs1kYSXQeep8JEpEsli/AbDXUUiKn2QBVy8EDNFuuh/M4qT7GZUGqoaornbfk2UmzXr9oqUp585a1dc82NWMYzVauQer7C9hsarWqgfDqdXIWzmRFjDNZS5i5msfGhY7QCSKlyHApC5GzbSa+lHqwuoCp5Ppq8Z61p9PGSWnC+8BZMySAhdrm9aYGFZhXKGL3shDL6HoMspxrIcE6v2YDLmdgZg1A70e0sN2Kd1YfsWxSVv2lkBiJ1YRFM1r6iVaCqFjGya1WbvNENpwmVC+Cq4QKmVa3tlrEq7cyayXewPrSBVxh7vTeyvk4B8FnumAkU+OUSu3e0i+Z5ZoC66Dgpb8XcFf/tf6ra8by/KX0+WKw0gYea9814qQmNVyZlVFnk00+WFQKkVLTHKWsFboaqdJ+i9x9q0QNSYGLWksglXBqKQy3GiBnYox4J/RdR9f1Vk8uWzhpyQey0ijTSEwJ74xsZ55tfQ5dh3a9gYSsJR08MUf7wTlyGfdTVsZkpDWu5k87bzmLIkt+dVk/UsrMOTGnzJwzc7IQeIu9sgiTcZrI3lmVyrLPGElCxrvAEEIpq+KZNZB8z4OrgbDLTNNXePKl77uXfvy2/SXT7iE3j+G9MfKV21u+/OwJ7968y+n4BGKkU2HnPIM4OucheKJ6Tlm4TZmbeeY4G4FTLPmOxbXYDKFZ+UDgKAU0upoqK+CDWJpM1zPsLuiGPb7f47se5wPee/rQ4cTRdT1d33ORd8QEiZkxztzgmKfE6MxEbmtcJQhb9BGhpptUo5mWPUNwxYhRgeN6tT0f0GXfrTrDynjs8pIbep8ra0qJcbzl5vYZz26eknRCPMwqTNEDPUN/weXFJV3n6buucTw8V9bv17SjVW1cm8aLkX1tRHr+6dY6MIDgO0+3G3BZLT3Mu8Wz7Fb72dpIzvqJL8/045TLuG/5yMBxC8V5xaToXCsNvMlZqGAbpCu1W6qHacljrOtLs1Y18KRNv1uUwvKfwJphtX7apl9VdKsVs+/Y73dcXlyw2+1wrgPtEB0IbiB4wXtjH3Teig4bC2eCHCHNFlabwRERnSwPK1OsZuXmUyoMoxEtDKNaeOWcK1ZBh3nxFJB1GMSdxaOFEN3P9LFFv3ijznBrzanAwqldtUArWdSS/5uC3eAVzS4uFsgSW82mkqtcQVoIhCEgzpNns45rAdYAtbA8Wupnas35KCE1VblgzfK3IMAaCm3joILeqnTp0uZqJFxbRbOSXbW7Lv6t+mxEWFhDpXqKbZCuWmLvVvBy5o28B8kLc6iWOSNajSaV2baMN81oNooYy18pDIuVAKo4JGsoekaNqEYzhXLVgFz5ScUrp+qKfryGc9WO7Jo3M+eEc2qeCpQ0G/ugzSuLFKiMjNpUGS2K6N2SLC3RoIG+MnLP9/WqlLQuuGMtaFbj1ff0zmZePap8LRVgk0+9iKNU+l3Wj2asWXw2wGKcae/UyA47VrSyRUsLRXS+LieeGG2ceucbSNACLmnMwyWSI85WpL7MYAunNKU6p2weFufQbGUzxmkmabbc/gpAJeC6wcp/OMFLZeD2C5OnWp6m1jDzkroQM1aCIksBOIFMZppHNDuCL6k8hVVZEPzQ0/eeXd+DXDGmS9zlm3z2x3yeNz7f8/S9v8pf/u4v3Es3/p2P30CuHhEvrniijh988oTv+aG/zfd+//fwxdORabqFnAjOcRECl/2ePuwg7BjxvDdHvnx7S7p5xikau2mKCclLzVhd1V9swLGGuJaxI3lhOXAOPAEfBobhiv3FFd1uj3QDodStDqFjCIHeezQPXOoFk8/EHJjyzM10IiXlxk+ITCxXqmu/NL1MGkGda2t7ziUezDkkmy7TIlDWkKUsvLoq82KfWNoPFBI8qLvEvfRjJjOOJ26PR47jyGmKzGm0nOApEZOj7x+we+2Cy/2ORw+vCKEzYpwlCK5IeTBtYi8iH+Hvu1L16BrSXu1Nxsfg6DqrdivOIV5K7ukaoN9pF+/TIst7n5w9a/M4fkplsRy1Ub9Y7dv4lHZc8yDmVT6GFmVNlgXGlHtdKWnCumBw3XB1aUi5UgWh0qwvDTQ6V2iqO/phoN8NdF3X6PedeHrX0QdHF5TgwHshBCE4RYjkqGRiAwpGrT2SYyzhJqsAD1XQhBhfeFljHCKdUYl7abmete2LLErxAuLuHvPiRNcWTTnrOlNGxHJwgy99WRPo813bmJoCYW66UnYkEQuw1mZO1BYGnKTk8wrt2mdPooQrNgutLlZNrV41XWDKAuDserJ6erA8z1orFli8S3dw3VkodAUPZxbRauGrobcLI57WMW0X+Lr65ePKP/8L/oVvyHU2+fTI4XB4A/i5wD8F/L0YvesE/P+A3wP8nuvr67w6/tuBv/khp/yD19fX/6N7a/AnSH7ez/nOl92ET4H8ILwHj+n51p/0s+/lCt/+1tvs334H98ZneBZ6Xv/KV2AYePrsGV/94he4PUV0nq3Ew0XgqrvgwdUjuv0VJxcIp5HJB57NkXx7y5QycY5IzqV+LlDs6A0wNMNC2Tt05S0CjGWxw4c9/XDFsHtIv79AQo+E3oCjN4bVCzEdhqCkwTOmntv5hArcnKZWbqtqUNWYymr/rOQ4a2+hVX6TZqxrzNS6GPpL01lZ58orWQywshDsLek5L17m05Hbm2fcHm+JOROGHUE6co70UyJnxzBc8ejBFVeXF+yHHrDo0BRpOihn97P8as2uBn65+8Ei7/f8VSPm2tJpP8458zLWaLAWePb853Q3LFjKewLfMF3ko8gGHD+l0hRuVxY6LV6VuwDHiYVyrr1BdcFwtHy0GvOPWyxTKhVorp0DFWSuVfjlX5Xl73YxZ3TV4gPIwtIGipNM5xKdi/TiCJrxOeFV6dQWDke00NM0lbBKK6SrcUJzpDkOiyfMyhYs8Nb+LXR2Rql7Fove/lAtHkoL0Vly8fTevFXrDekcpC45YVJsgU7Eop7Qyme05N7IwlaZ1WLyDTQWtrW1RQ2rARgzqBYWTWp48pIvm7PVvsoptcWvjYHSxgbw6kLdhuBqkVwbB1fhic2iWg6qi+zaE7A2aogu4LSNUa3gcbUJ1Ge3XL59sskm30Ty84HfDvwg8CeB7wU+C/zzwO8CfvbhcPj519fXd4f2/wf4I885339zf03dZJMXL69/9nPw+W+Dz36OoR/IVw/44tNnXH3v9yJ+YJqBU2KXA3kfGIZLHjx4jeHqIYN4Tv6G/jQhvkMRYlIjhcvJiPaqobPsWRWYNeBV1Kb6j9Xw9GaEdgPO7aymarjAlTDV4HvLSfWeIXguekcYPDl2nOYOOQq381RYa6sptl5LW0W5spMBVWezNsJimK3suUtdw2qUXZaEFeQ1Pa/suQsHQWFpv0ef2OnpU26ePuU4jhA8V1dXXF7s6Ltg95EFESMV6vu+fc8JVirmAxv2HLfj15CPEioqTpdrytmvD//enZZ9UnWODTh+WkXMQ9VSEbQq77Je6criYshhCUMtxbZbXlEBClh+RfWACayYLu1AA6vSZsWilNcV1r6gqx9x3nLuxBETjFOpZaQR5yLezQSxWHtyJOeJUm0I4/2o4T5WHoI8Q5pxOa0AjzTwYonsbhW24RDxqFgeSM0VUaUA5fq6huFVCnQLxZPCanpfoqVMRalu12i+pYL5spG5Upts2dgqMJbGFFhrQcWUiGqexUbNXixmxvxbciUlFWq/zBLCVcufVO8r7bMzY9/KSresxWduwWUrUs4W0boHVsvnYjlcILSWi6w30nrhsm3aseuw2AYqzx7wvQH/LQVgk3uUvwL8M8D/5Y5n8d8C/kvg52Eg8v9853v/9fX19a//RjXykyTbfHzF5LPvwBtvQT8AcHn1kMurh+wuH+KHS5IMxDQxZk/2O/z+If2Dx/SXD4lJkTGirlYb99TSGzVrIJcw5UbeBStPkb1stYobyDLgCB1ZAyl5cg447RAJ9nn5r/OefdezdzvIAzcnzylHvL+pfr8WAdb2QdUlDWVl6K/tAkoOreXdVh6GygTfjPxamfBZPJhSdcK74FEaeLwP+Td/w2+4l/Nu8vXJKw0cD4fDPwX8a8BPAN7ALK//FfDvXl9f//mX2baXLd/+Y/7+l92Er1/yxBe++IN84Ys/+LJb8tJFsQT9RLF+3gFeIESXcKXEyFIrs358ZrMs5WcyKS+gUWthY7A6pt4RSm6MsOSqLblr9dTFW1wS8Vl9piug56Qiu3zHQksDfctbzZ/Iyt1YbxULn75rt7v7b7GY1FDrO+1Z53HocjP3Bh432eQ+5Pr6+k98wPs/dDgcfgfwbwPfyfuB4yabvBry5mfg4rK9TEkRHwi7C8L+ATpcEI8Tsx9I/QVcPsRdPob9nniamNSM1VbSy+FdIDiPZkVaWSmoMT+upsKgUOs9FoupRf14vOtwEtDsSLMwTeA6EG/cDBa9ZSd2zjMMA5eDR7RHXaY/3eLcUvpCVqCPVVvuuryax1FArNZWCegqxGzF6FpZU8sO2CJ66l20XVA4A4yu7uWbvPLyygLHw+Hwm4FfA3wZC7v5EvB3Av8s8PMOh8Mvub6+/o9fXgs32eRHLr/wF/7Cl92ETTbZ5JtP5vL7eXUQ3jkcDv8yZmz9MvDnr6+v/7/fsJZtsskLkvzw0cJ7nuB4eyRGhdAjuwvo98RwJLqe1O3Q4ZK8uyCGnpPOnGJmjFbP2eEIzpNdMII5oZEEQgFRbsXGWz8qPBKVvdaYPj2ahXnOuDHiuoQLig8teKelWoSuY9gNODrGNOKDERgtZszF4/d+ucOuUI2rDVsuddTt+65ES608mAILG3Yxy67RYwOpH9SGr1+2CIBPprySwPFwOLwN/Grgh4GfeH19/YXVZz8d+BPAbwA+dcBxm4ibbLLJJp9eORwOAfgl5eUfe84hP6P8rL/zp4Bfen19/b3327pNNnlx8q8eDh/42SXw93zL2/Atb7f3/uaXf4C/+eUfODvu8YPA4wefAz53T62sMttPhjzCV0b4yhP4737gOYfu3+Sdb3+Td779npu0ySbPkVcSOAI/CgtK/3+tQSPA9fX1nzwcDk+Bt15KyzbZ5AXIZgDY5D7lcDj8D4GfBnwH8PcBD4Dff319/Yuec+yPxXLlfibwYzEClq8C/0/gt1xfX//Jb1CzN/lo8puAvwf4o9fX1/+31fu3wG/EInT+RnnvJwK/HvjpwP/9cDh8x/X19c03rqmbQIug+geAvwt4EzgC34P11W+7vr7+8urYDjhgc/fvx1J1OuCXXV9f/65vaMM32WSTV05eVeD4VzHa8X/wcDi8eX19/aX6weFw+McwJeiPvKS2bbLJJpt80uV/gQHGZ8D3Az/uQ479jcAvAP4S8EeBrwB/N0bM8s8cDod/7fr6+rfeb3M3+ShyOBx+BfCvA/8t8IvXnxUj66+785U/czgc/gngzwH/EPAvAf/eN6Cpm5zLrwK+G/jjwBcwh9lPxkD9Lz8cDj/5+vr6+8qxl8BvKX//MPBDwLd+Ixv7smUzrG6yyf3JKwkcr6+vv3I4HP7nwL8L/KXD4fBHsFyNH4MpM38c+JdfXgs32WSTTT7R8qswwPjXMM/jh3kN/xjwm6+vr//C+s3D4fDTsLX23zkcDv/p9fX1xmb1EuVwOPwrGOj7S8B3XV9ff+WjfO/6+joeDoffhQHHf4wNOL4MeXh9fX26++bhcPi3gX8L+DcxLyOY5/ifxNhxf/BwOPx64H/5jWroJpu8yvIxo3F+L/BLv8Yp/8T19fV3veBm3qu8ksAR4Pr6+rccDoe/BfwHwC9bffTXgN97N4R1k5cr22TcZJNPjqzDSw8fkidUjv29H/D+ny65cT8D+Cls7J0vTQ6Hw68E/ndYLcbv+jr2vy+W35cfetQm9yLPA41F/o8YcPyxq2Mn4P/6jWjXJpt8CuXjROP8EeBvfcBnvxj40XwTztVXFjgeDodfA/yvgN8K/DYsXOPHAf9r4PeXXI1f8xKbuMm5fOon46dNDofDLwb+o/Jyy795NeXD2Ds3+QZIib75TcB/DfyMderGx5CfXH7/jQ89apNvtPzT5ffGervJJt8Y+cjRONfX13+E56TFHQ6Hx1jVhwn4vS++ifcrryRwPBwO3wn8ZuAPX19f/89WH3334XD4uVhh5H/9cDj8juvr620j/GTIp34yfprkcDh8K/C/xwwFVy+5OZvcgxwOhx8FfBcWOvdnXnJzPpVyOBx+LcYg/l8B/8SHhaceDod/CPgLxWO1fv8fx9Zn+BQykX+S5HA4/GpsvXyEkeX8VAw0/qaX2a5NNvm0yMeJxvkQ+cXAHvgDX6ch76XKKwkcgZ9Tfr8PfFxfX98eDof/Evi5GOPYBhw/AbJNxk+PHA4HAX4Plnf8h7DSOZu8QnI4HAbg9wMD8Guur6+/+pKb9KmTw+HwSzHQmIA/C/yK56ytf2sVavybgf9eCS/+/vLeTwT+8fL3r72+vv4v7rPNm3xN+dUYa3GVPwb8i9fX11/8gOM3+YTIlo6zyUpq+tz/4aW24uuUVxU4DuX3B5XcqO9PH/D5Jt+c8k09GT9F8iswZfQ7WZTSTV4RORwOHvh9wD8C/EHgf/NyW/Splb+j/PbAr/yAY/40S3TG78MMqv994GdjJRx+GMuj+23X19d/9r4auslHk+vr67cBDofDZ7G84d8E/IXD4fBzrq+vv/ulNm6TryVbOs4mHA6Hfxj4e4G/8s1aqupVBY5/FvhXMZrqf//6+vpv1w8Oh8PPxhSaE7BZT18ReRUm46dBDofDj8eUnX/v+vr6z5QwuE1eESmg8T8Gfj4GOH7R9fW1vtxWfTrl+vr612PlGj7q8b8b+N331Z5NXpxcX1//MPCHD4fDd2OpN/8RVptzk0+ubOk4mwD88vL7d77UVvwI5FUFjv8n4D8H/gfAXz4cDn8YI8f58VgYqwD/xrpo7ibf9PJNPxlfdTkcDgHzanwvxgS4ySskpX//Eww0/ifAL7m+vk4vt1WbbPLqyvX19fccDoe/BHzH3ZrVm3yyZEvH2eRwODwC/gW+yYG/e9kNuA+5vr7OWB2jX4XVrPq5WNHjn4wVqP6Z19fXWy2qV0Relcn4KZBfh+UV/4vX19fHl92YTV6cHA6HHjPY/XzM+/GLN9C4ySbfEHmn/N7m26svWzrON7f8IuAC+EPfzMD/VfU4cn19PQO/pfxs8mpLnYybFe4TKofD4R/EvIz/2+vr6z//stuzyYuTQoTzhzBj3e8Gfnkx3m2yySY/QjkcDj8OePf6+vqH7rzvgN8IfAb4LzYCqldbtnScV0Iq8P/3X2orfoTyygLHTT5V8kpMxldVViGqfwX4tS+5OZt8BDkcDv8c8M+Vl2+X3/9wYfsD+NL19XVlw/0dGGj8EvC3gV/3nFCsP3V9ff2n7qm5m2zyKsvPAv6dw+HwZ4C/jrFRfxbLk/vRWBrOL1t/4XA4/Bss5CvfUX7/Tw+Hw08tf/+5rW7uN51s6TjfxFLKHf19GPD/Uy+5OT8i2YDjJt/U8ipNxldYroC/q/x9+oD8jt95OBx+J0aa8yu/UQ3b5APlO3g/HfyPLj8A38NSRqWyd76JhSN/kPypF9S2TTb5NMl/joUm/iPYXvcYuMEMcb8P+K3Pqc/5szBguZafUn6qbMDxm0S2dJxXQirw/6YPM96A4ybf7PLKTMZXWEY+mK3xJ2F5j38O+O+ALYz1EyAfh5Hz+vr6O++zLZts8mmW6+vr/wb4Vz7md77zflqzyUuSLR3nEyIfMxqnfuch8Asw4P8f3n8r71c24LjJJ0K2yfjqSiHC+Zee99nhcPj1GHD8D7fQqU022WSTTTZ5n2zpOJ8c+Q4+ejROlf8JcMkrAvw34LjJJ0W+g0/5ZNxkk0022WSTTTapsqXjfLLk49bHLd/57cBvv4/2vAzZgOMmnwjZJuMmm2yyySabbLLJmWzpOJt8okRU9WW3YZNNNtlkk0022WSTTV5JeU46zs8E/gbwZ8t7H5SO8wNAB3x+i6za5JMgm8dxk0022WSTTTbZZJNN7k++gy0dZ5NXQDaP4yabbLLJJptssskmm2yyySYfKu5lN2CTTTbZZJNNNtlkk0022WSTT7ZswHGTTTbZZJNNNtlkk0022WSTD5UNOG6yySabbLLJJptssskmm2zyobIBx0022WSTTTbZZJNNNtlkk00+VDbguMkmm2yyySabbLLJJptsssmHygYcN9lkk0022WSTTTbZZJNNNvlQ2YDjJptssskmm2yyySabbLLJJh8q4aMeeDgctoKPL1mur6/lZbdhk0022WSTTTbZZJNNNvn0yUcGjptssskmm7xY2QxyL182g9wmm2yyySabfDT52MDx+/7630BEQLAfBc2ZNM3EGNHyWpzQdR0hdIjziBMQRTE9SRScCKpK1lzezYgKKQvznEk5AYKi5JzRnEEzToCcEQFxwpwyOWPnVsUJOHFQfrSoBd7Z9VKq1xMgg2LfQcA5+y5a2miHKYqIELzHO48i5PKeE2ffd4LzHucEQRCxa3ahA/XEORGc4AN0IeB9IAEJu07nHcHbsxXnAAeamYb9j6iTN3n1ZAMcL182wLFJlW0+vnzZ5uMmm7xasq2rL1+et65+bOD43u0tQ98RvCOp0nU9WZWUk4FDNXAVuo6u65ACDp13OA8UoOVRnCqII4mQNBkoVEHVMQ+QckazopoRFClwz0EDiIgwp0TOtYX2vuAAYXlbyfMEIqQgTHEuAFJQMjkpSUFJDdyiCjkXYFugpggiDgWSKiD2nhO8E3y5PymP2gl0oUNch2bBewheCMETQoeKNwDqoHMO70BEyzkcgrDfgOMmm7zS8qf/+B+1P4qxqixt5bfgRNprLcau+hlIseOVdUvqbzNg2W9QsSNUsXXRli/7brGzqQhZzVCXstrfqmS1w+sl3Kpdvvx25TqUfxXK9xaDYVsYS9ucCL6swk50aU89zIm1CYgZYlJiKm3KkLOSs5qxUsFjBkkz3pX1upxPxe4vqZIy/PTv+lkvsAc32WSTT4psgOPly2bIeXXlYwPHcZ4BJTohi+JCIKZETIlQQKI4T86J05RRjXQOLtwOIowpAtAFzxA62/xVEVE0R9NlxBO8w4cCMqXDiXkHNZtS4wDvXFFIZPVjCoipC46o9rcXJZ4C+ED2gdM8knNCVVGcKVOa7bXWNUfJMZFyBtSUD0zx0gJKl+9gv7OSsv2uys9pPCEyIRJAMs4pwXmc9/as6jXVPKqmiJnX1AfPt7355o+kjzd5heWv/MX/NzlHsmZSgjna+JPg6YeergsE5/BSPPxkVM1Dn1JmjokYk3nHveAdhODpQo93XdP+HTaPVMxDLl4YhsC+DwSBOM4cj7ecxpE5JlISsnoynuzABWHXeXpRGEc673j0+mvsHz3kZk589b1nnE4zZEVTJqVIjrHMUQMIc0rMOZO1AIxixLGQAJtDbSUoQKtN5RId4J0ZejR4GHp2Dx/y+mc+w9uf/SzvfOYtHj244HR6wg/80Pfx1Sfv4fsLLi8e4aIyPrnh+OQZFxePX3g/1nWlBHG0taM2P7NmMnMlemM5SEvExNlOraBikRPWjbZa2k8FmwayXEGrSjWSFTBXESpqyxN2HSfrn/P7WIsU0LiAwbpelygPWY5KuhxXgWkFnVkrLLa9gqztaiIrgEgF02eNgLPnKbh7ooX7y/+P/5Ss0SJXuoHeDww4LrLS50icZ57OE09RUhfo9j0Xu46ddziFlJQYUzFiOnAOnCcjxJyZ5sg4jZymyGnKTDPE2bYOjx2OgyxCLs9bnOCDJ3SBYQgMvedi13GxGwjBkdNMTBGH4EOPSAfOg3dkTcR4QjTx4GLP66895urhQ6IXvnJzy5e++i5PnzwljxOdQhDBWcvpfEcfduTkGefENM+oRIJXnGRSnJnGSMqCCx3SBVxw9J1n1wWG4AmuRPiIY46J2+PIs5uR0ziTFHwI/KTv+M776cxNNtnkEyF/4Pf/gba3aNsJYNnw6t6nbRPS1QFS9pl2vLav2XeFpvfbbz0/jjUuOLvs+evVK2V1znapxbRa96y7out2tjYsMZJnb69frO4BaJGW7cnc2Zyl/XPng/IIf9Ev+h8/p3UmHxs4duJJKZOzMgwdXiAVf+CsSo6RWSNMMxnFu8RbFz1v9Y6U4ctTZEyRRAdXV8TTjJxGHl/uOGpmqtpTjmZ994Gu7wG1UNhsN5lUySkRBDoXUCBqBk04zQQVIo5crOwisO97ZoRbUeg7XK7G+QDOIU4QFI8rTy8X76KUwWqgrr1XFRtDv6SkpJyZ50ieE8F7AGLOi3VcE8FB3wWc8/YekFNijhHNdo2q6PsXTHy7WeJevrxIS5xqIudo80EbFCA4T+8NNDoxI4tzkDLMMTLNMzFlBIf3juAsTDp0nm4Y6PsdIh1pysR5RlNCi5Ek5wQxkYgk6cjAOE6MUyJFIUchp0wJKMCLx2VBZ0giODooimB8dsPT08yzm5GcIIgHFXJScsrknJAMoooY2iWnZADGO7wPiPML0sq5LNjZwCS2nLgyvytssnD3ien2Pd794kyXb7lyRx6Et3i0C7i3HhC6mXdvZ2K64UF/wXDZE5+kF9V1Z7KOjGixFVLbWz5afIrNqVhF1p+V8xhirICwnt6MXBWhVY9h8Uk2gClariYGSEQhl/M5MYBQPY21/bm0toJVKhBUPfP81VsTJ8WYga2PlFSDCixZg8CVy7Nu7Fqhc4n6OAPOev48yv2t23If8pnX38C5SNd7+m6H0BGnmfnmlvE4krwS+oEHIZCCQzyEThBv+4rLio/g0hJtE3MmKpyiFgCmTJOSxgwRggZLl6D0U4YsEFXJAh4PqewjGhEyXXDM2QCeOBhU6RGCKiklZjL0geAzwWe8ZC462HmBGDndzjx98oRnT54Rx9nAcRfoBVAzRAUfCL4jiWeeIaeRpBNOMl0vOFFyUog2RkUVjZmoymTbLckLgqWpxGlmPk0wJbxiETmbT2OTD5H/7A/+gbJU1LViBTpWYgbI1evVMbJW9lmtJ3UdkbqmlnG8UvKbea4sfrr6vqV8LZERUJ0SC0BYAwwtjpkzg9naKobNqWXtq9ayc9DR9po70SrLtWQ1r9arqD0jV8DGEodXdHbvQDw/5Wf8TO5HdNVvcvej9/Vp+2ANHutzXv0LLP3XrLe1b1efr2UFGPXsvbUV9flj7YNkvdOfWY/PP3n/3lUPX3+o62a+/1sf0NSPLB+fHCcnVGyTn8aZIAEQswjOM3GOZcP3ZIGgkMeZPkyI9xzLZLmdIuPNLWmceBxnLnJgHCdOKYPzaMqmOGpeJq4IXdfTDQNJlRgnBlWC7xDfWR6kRnqBHkFEic4zK8Q5suscznnmnLmJE3k80olD8Kj3uOBxqvQFQKpmnPO2s4pQUh9xZBBHLm3yweNwxSqspG5GstK54lG0xlvYFYp3QnBCKJ+nZF7NmDIpFeArdfxuO+MmHywWUpgsXI8A4nBO8CpIMnU+C4h3aIaYI3OcmXMC5wghMPhA7xyi2jYTFY/4AN5AoooZUTzgBciKT4k827KUMjjXMwyOIWgBd4YcVczIYp5/sbWBzLPbkXg8cYyZaVZQD2VuDP2A9B0aZ+I4Mc8Rr0oonsWsZQMr+33b7MTygtv2WjdNAS8CUjxpqqgm0pQYdeapRN71mYt0Yr4cOOnEeHvkNGacv2R/8YCLQfC3kXfne+jH0lZpv8uWtNqZ1tuHslgS2xJRDZUrjNXg4PqgpnjUgH7Ms4gZsZwIuXzWVBAniC1keIQAdGKPPwMGRwp4rKBR7TnXMNsaylr3b2eJ4aQCZhcn4uruVM420BoRolXD0kW5Kmn0q/tc5P0qx/2sq1e7C/qQ2fWO3vfkLDyLiaNmbnNCguPi4QMeXOyYVTlNJ2KaiSmZc9F5JNgckxiJMZLmmUmF6Hpc3zG4jk4SSWbyrGgWRAVXQoxHVWa1vSOLA3WQikdesMiElJlSwqljJ45LH7hQxc+R22kiZkA7+ouA74Tee646R58z080tNze33D59RjyNtmf6wF6EfXB45xHvEAlkdUypTNOc0ZxwAkMf0OxINUJCTLnPKZGiMk8zp8ID4ARcSsgUISZCBsQRXTE2bLLJB8ju6or3af/t5WJ4auFktDiOZQ9ZoRKn56+bAl7TABpku4tkKqi8o8CvgePq3HLn86XtZ1BlpfCXHaOco0aXtNVOawScrO5rAbmGl8q+U/cR9AxQCK48pvpUyl91UddcH9D9yOrUKsszOgM9zwVcd4GTYQPVEiHZEFd9YnXvXcbNAuyXSML34zph3Tvr5/wht1LG0HKts77RCumX69phywt93lVKQ+pzOu9HPvDhLcPsw/fHjw0cLd9PDCQlmOcMQvEEOoRQdc+S+whjFJ6eJlwQknNI6InjxLP33qMTxe0HUpk3+XhiTiC+wwcHqsRUSXJgipkuJsR7hMyUEylmnCt5P5LInSd7C2MNXnDiLU9SzPOQY2IaR3SaSFkBDyHgvWcoyqUPMKPcjsnC57I91BACfdcZb01V6LINQin5ik6Uzns0OZCAlPxOM9hbQJmqkFJC1Lwl3gshBFPLCgmQiOC84z58HH/5L/5F+mGP7wZUhHGeuT3dchqPaIp0Ar1zdOLoXKDzA6Hb4UKPOkdMkSneEtMtMNH1wuXVFQ8fvsnVg7cZ9m+Be8isA1E9SQS8opIttDJHNCeblE6KNwhijNweb3jvyVf46le/yHvvfoHj7bsQRwbvudxfcrl7QNddIBqIUc17FidyntE8ozna80MNRGgmp3rNjHNC5ztCCDgfUO+h63HDDtdfgNsRo2c6JubTSJ5HRE94GfFuxLuZIJneO/qup+8GQuiR0JGdZ8IzRuE0KtMpE0+JNGa+66f9HS+8H1OyEFVFEOdw4pFskylPM+oyKjA7ADEDBRl1nq7v2Q09++DpMqQYmWJiHCcinmDD3Mau82bsKKFokmZynokxkcXh/Y6h3zOEHbsQCJJJeWKcR+YUyyJtcyblyDSPHE8nxhTJzoMEVCMpJYIEdvs9+2GAGLl58oTb+MyWR+fMq1L+btu8iIVqurLYqjabqIFGt1hu6+asYp5WAszKs68+4wdvRpwkjjrzzAd48CYPH7/Bxf4dHqnQv+Z49ws//ML7UauHryz2WSsmXjbyCoTXG4urVmOpn51vV9pAKEv4bv27PLf15lHfdxTyr+UDA3pqHshQfpxYxEkq17LwfbB89aKMSA1vXf0ucapmhLTbrK3IFXi6oodo3bSLN+3OjtzAaH0O7wOJ52Khu/ek4KRs18yZlE/MMTGOM7eniZuU2e0Hdg8fcHV1yXEcGaeZ0zgy5gw+03XCzjt2ZJxGSM/I+UQWh3QP6PuBwV3QRYGTeeDmKaIxQ8oWthoTI+C7HgmBlBIxzmX/C7YHqjKmhEuefefYe8fDGCFNpPHEKWWyG/DDnv0QyjEemSdOtxO3z26I04x30Imjl8xAYu88u6HHdR0xw/Fk+6dowomW6IdAF3agMAWLgKjRDCla5E3KasYLH+icYxDYZSUkNU+3M30gaf7w/viYskXkvHx5kRE53X5ve6LUmAxtIO+u7tyAZAEKWt5rtixokS/1+7a+1Tzs5VhTI7V9X8/W4Oo5XK7Fqj1n75X1eTkP7dgzQElZ/4q+s6KhXB1vrbZIOblznhVY1QqBdfWtxUAnq8/qSXK9aec/ct98HDkLwWywlrN9YI3YhOes8PL+Q8/eqV1Sn1wzpK9OsXrdjAqr8aRnn3HW13q3n+txZ5h2QaO66uzF4HAWqLveodu510/K/q/As/V2Mxis22pgtBoSPnwZ/PjAsQw6KYrYPEfMwmEP2juzSljeXiajHFPmi+OET8LceWY8Y0ykOOODWWTnm1sYRy5V6YYL9OKCWZSUIjElU2jn2fKcUuai70GUmziTXcBrZq/KrnNMXpjLAA9JcGRyTIgEnDfLLClbSFSuig3lHPBYlN51PHFwO03oHAkpG0OsCKd5Is6TETIUpSll6xTLvVSCN0+mhB5xjqQzc5qJGYI4roaBXReImk0RwkgijD9I6LsO500x3l09+rjd9LX7ESGpWaoVR86CVq1vtRnbopLspwSjCRQnbA0zy0ikkA15nB8I4Qp1D0ixR7MU4B5xbsZJJksiy4xqsstpYeegxA87MbN7cIg3q7n4QnikQk5GohRTBU/OwEnWAkiTtVUqgEhkTaDR4rhEEPWmhPgO8T3iB8T1qFgoZRZnQJcE6haFWYzgyHubaOYxntCYyD6QXEemwzmP886cYLxYBaeKEZgYaBTxCL5Z/2oYdSaR5mqFFNSXsGypY7WClkxKmZiUxIiq0gfPMAR2XU/wDldDRqNnmoQ4jWQ8obugHx5ztX/Emw8f8ODCMZ6+yrvvfoFxHHHiTZGcJo5TJibXQnXaiBQLz0spkVNH8Hu8H0j9wBxOBgLLXElZUbHcJ1agsG60OWcMUBWilBqmWsxtuYREDt2O3X5P5z1phmenkWm85dl8goePePPxQx7uP4/nLcbxxBx399KPixVw2Rb0bLugKQltUZcl76+Gl9ZNc7Eal++tNry2+a2s17o6tG5mzXsna6WhTM92nnOLeCXTkaajLXcgWNSG5VRanmZaqzhlk9UyTtdM2QnL/8taNrh6/vYslr49p+ihRpC1nI+WU34PMs+JPMEUIzpNzClxUmVMmYRDvUeCQ0WZ58hxjJxGGDNESfRpxg0dPmfidIJ8y26fkABPY2SKmf0+8GD/AL/zTDfKeMrkKJAc8wxxstDW0AeS90yjRSTsOs/F0NF15tlXtdSKlM0QDELwQt8JvYPZmUnA4XECmhPzHJnGiRwj3sHgHZ1TerV1pAvgg/WzpkzWaHNRsnlU8aCelIOFSftMCBBTRGOytTxm5qREVXCZ5D3e+6JjZJxzKMZzoOl+1tVNXg2ZJuOWcKtok7bYroxmyvJ3Wzeoyv6i2wGoc6u1xc5bM7mXoJ2F/KsCviVEfg1rdHW9QoAmlQjtHBSchbGKnLWz/m6QSrS1oX55ubaUtXD1jt5dN+uaTFtAWzhr+XQNZhvAvhPyex/SnLPv+4Bl/3uetIdXN7nnHbd8psiyv6zOv5Bn1r5YAPbz23v+2fOOOw9iPj/uedCwneMj7WNrM8m5XqDrQ1pLvvY5PzZwlBK2CbIoZ9QNv9hc1AaSF9tAosJRlYAwTpFTnMjZzpVm+Oo0Ipq4RHlnf8HDB1ccu573xpOReMTMNM5MKZLF4SqASJnTbB7GXpVBM6iQFPNcONBpJE4ZMnjMgumdTYCcoPeBSrARRPA50Wmmx5fcn4wXuBw6ht2eyXveuzkSY6JDcL6S+9iTCEVpjzHhPUiOzHNmTiMZJRMYZ8WlSLh0TGliThFyolMt4WGC7i8QH5hT4j5U1ew8WZzlp+YVXtSqJC5gAgQ026YtahgvF29BNo+wAnMsIFgC4nuQHdCZx1aNVMkAXQSdkDxbJ2DeWpy3sAGnBTR6JARc14EYSEccOcOs5gWeZ4jRPBFoAUo5ohrtHkrYcRZFMUCpCojDaU/nvHkLux34nownZUFyyS+VQBaP4MyfLpjnzRuZjCBGTJPtVjQLGgK5eFSMqXJRWl+0pMxiyHGWH6jFuiTeA0pOkZiTecVruZmsaJzRpGQX0KTEVC2pUvIJZ8RDF3qGXYd3jjhZWF3KmTlbLlLCgeuR/orh8nUevPY6bzz0pKlj3yWmm2doVG5GG+dzMtblXkDnmTEaOQdlvI1zhKMjhI596DCPfMBptvBbi3ssgNFRgyopoAUqOCgbu6u5UIsPrYKb0HXsdhd0PhBq+K1aiH3ndzwKl1zkHeNT4avvnrh599mapeaFiZPVwr7sS2ebxvlyXow2FTDWDW+1FtUwVJU1wCqbUwGNuShQLVIGbWtAvXhlT3UsQLF6fQUKkUsBtKqrNcQscgvOK8pMyTfNaCMXMyMPzSqr5fyazfCTtRpJ6oOolu+q0NT7Wl2bRZmqykYu95fy/QBHFccULf8wnSIpJ6J3qFiOMaqcxlvm+cS7T255djsRNdg6EydmjczeQZyJt0euLjPvfMsDuHD8zR96whe+8BWCBq5CX1KKImEA7T1kQVLgMnaQYRJPRNi5Hj8E9sGzHwIESytMAuTMNGdunNA7YdcHJOzpcrQ1UxNTnBDxzERSggj4PtDlTBLbE5wHgoWPpjgTp4k5KXOCaD1onE4IMSm3x4ngPYrDeQ85krQSX3m8l0UZVSkcASV1pSi6ThVJ95Nz/Kf/+H/GnCxk33U9w85+uq6j7zu6vqPvArvOs+8CO+8gJ063t7z3nhEGjeOIqhEOua639bgY7FwBH5WgbJ4TCfMSD/s9+/2OIdial+eZOM2kQixYc4OROqcoCru0cF/LMa9zy9ZAHyz6yZ6flv3S8kxjSsypAPdc105bg0NnObSmAyjBObpiEDWNrxgpyzx25f4MNJlxoq7HNo9zAxk5mwHlNE781H/0u154P97c3ADF4yha9BppOuyiL5/DtAUYOuuzsvqZwax+VqIwyjrWtOEatSF3jKN3rmGyrMvraA/X4BnUNfcc1tbTyvs8llXvcfXazeC4XHENV5dWSPuvrse4mosJNTuyefx0CYldk8Dcq6w9gHc3yLsv7xhM6+G1pXrndCvzafkzF91qfcT5HVb8KXc7VvVODz7v2dR+WYD5XVT8vvPePYN8sL6wljYE77ZC73xLKM6kD+/Jjw0cQ+jIKqSc0ALEcgbLBxRjQURLKIlZ9X3n8X1HUmOVq4mnphQ4khrLXQ6eGx84HY8cb24YlbKYJcuDqQtjthwN74QQOuZsnINJhFNK+CiELuCDN6tujEbQESO9phYugDh8Z8yRZhV1TN7xFc0MOTELli/ZBy5DR9bEPM6ICBf9wGvDgPfCk9ORU7TzdmUzTJpRcYxx5vY0kjVyebnncv+I01FI88xxnFCdgEzv4Mp7BueZshKjKej5niw46i2s0oBPqZOp1aIkVHXcNphcgJttClo3rQIa55hK6LJR5VcafwpUVs1ojuQ8g4zACc0TRtRQNlHvyqC1jRHvka7D9QMu7uyz4l2LMSOayFmI0YwEau5SNCdyiqjO5t3AtdxSm+R1UzTvbu+8eXe7nuwDU4YUE8yKy8bqi/OgFTiW8EYP4ow/MGWjMlRx5gWrpWC0PQHSvQQcW+j4Wb+W69lmZq9TVcCcw4cO7w2gewGnptmnrKW8jMebi9QIcARGETSpKY9zZJpm5nkipomoGdd78ILb7+gvLyw0Ns48unrI248umd97l6/88Jf4SnqG7h10O9w0IfNsv6eRKc5kTaUED2QXmBVC6XOcR1yHiLaw9KVEhOVDm0JyvmA7VxSqs03WDCFVkRHn6PueQRw+J3wBOw5HfvKEG36AzDOe3TzjybN3efj4xfdj4+2RD9hiGvArPwWgLZu2Ns3A5q82RaX92xSMpSxH3acqkGx6DKWsUFEgrFbt8j2V2oQSorVqXg1JrZbzquDmVd9UoofKJi3tu8vWlpESnppX4JT2h6g2cMz696KntXUMWbVZFL3LLvSCxHLalSSB5DvzMHohiJKJpBh5+uQZicyz25kxenwX8OKRGNEcSTExnU4cb088fGPHj/3xfzef+bY3efRXf5Dv/gvfx/FJ5ni6BfGIZKQrURgqiHTsxeOTcjsZ8+jF1QUPh4EeJceJSRMngRED0FNSnjrQzrHvAi4E5uSIaUZjJBKZk0U0aPYkPOq8hTNn68MZ4ZQyU5qt/XMiqQCerK5Eo5hBK6VEPCWcqwAx2R6EmiLkHA5HKPu9qKIplwifjPcOV4weuzWl7wvtSBu/ZrwwQ7CbLPJEijXEwhYTkhN0AY8SNZMEsoPsCuDVjEtlr8PZmlQNXGCMweLaPDUwl8mWVGx9i/WvlhDwEsPd/qvAsQJs2xNrXqsr+b+Kq/mkVIOKQ9ySj6xlbqXmyVgmdv0sayZmLcajMiGlrhOlLJmzXPuWV6tlHVct3u5lHals1/chMSYsOXtRziuT83pfaKBuBbTq6zXcWxuhaKBiCd1cmL7rMr2sM2synCpr4OrcAh5rruFynnr0arVb9eOCMLR+ZH2+igpRPQcwS9vq2l/3idUlmqER6+N632UPOQvPvXO/L1Ka104X8La+7rrfzu+t3t/731+TGNWbXgDwEhB7F1tVPbKexS59F7ZVr/HynfeP8DtzjPN7WIfFrsdDu1S7d1rnnj39plOsnkLRsfXsoOV6Wo95X1vP5WMDR+9cYzaFaj2yK9nij5XS8I4UZzRPZDxjzhZKKKWeY7EYagF8iGdywrs5mwvJOULXkVIsTKti74nViUyobUaq+DKCRlWmlAkpc+UcIVwwq2PUEZ8TIQQ65w08UMLXZFncAG4VnrrAThwXCA92nYXjJOU4niwv0QV6J1zkzEU/0O0v+PLNLSAExDxULlh4UlbUB2JMnI4jl11kCD1jKStiW6sjZGUQ2DtP3wXGnBk13dtEpNWQ5Aw4ogvdvq2JtmGrGEtlUwbVanfOMTFHGwOxWk/jxBxPOHds+YbkCdWRnE+kPKJ5xomFNvvO/HltsonHhQ7f7/C7S0JKBsbmiKojJSkAUUgJY/nEGHW15E9mLay8GHERTUkt4LSAwM5bWRjfdcziiVkhJSQrXn1xbAmabOMNWL3N4AW8J2kBjMVroL4j+87IQsozyhot3PU+pPSFefuTKRAl5DMm8xiYXcXjvYHG4L3leQZH8KFsK4nqeVIEzQbEcopmLGEka0dWX/T2AozFSjZkp/hB8d3Is5unjE8nLt/5DJ95522S23P6whPemzNBHCLKHOE0ZsZJidGRk4WyxixGyuMGsnpSwtYKFyzUTbPlK1rMoo3bUsevsq22Dbrs8NW7WDd1EXBlk4wxMs8TDD0heCTa8xQgjyPPvvTDpJuI9I84aeYUn/CQyxfejR5sLapbVgVK5Y+7yeoVMLZtbrVMuKIECLrkdlbLMefbla60CYGSl7haA6TWlVyUj1TWeWXx7BpR2OKMbQrWqtk1nSGq4nO1VNMQXVOpVgD1jK11pdhWj0kl+KnXbDcmSxtqWG0Wbe38Wsn/X69kdaUURyVGmwEzdLnsiHFmvk1EzUzZIb5DnF82cjXugNMYOUXo9g/4lm//cfyE7/gJ7F/7YZ7e/AX++n/7/czHROdgCB5VmOdIVMGHwNAPuKzEFBGBt1+/4u3Hj3HTxLP3nnAzz3gVUoyM82zLs3dEFZ4mY1BFFUlWNsNltXVRiseIojCKINj1xykxajQCrtnSSVAauZxoLTFl/ZhSYo5z208MVJpXtnqam2eolOhBMyE4ehdIhXRL7imnSs7Wj9zWGUHMIp/VjOAZREOZa4VZXgRCwGXIhfxnjgmXIajHB6ombgYRWV7XPZeUyG5JBUkVzJU1j1jW9srd4EN51nYexFFThdBsgT4AnpamAAv0hGWO17mjaNMN7GA7LuXcgIwW4CROLJXBeby38k81hMlwstFvSRYkl6vWPRktBr576cg7CrUZA9ZemvavrEIT1560lVWsAqezNXdx59gzLIB/MYxpO866egVkyhetT1xp7gLvWj+Vda89d1brdz1n2T9sLS1jVZbrnqOKcwC0gLLnPD8wL2Zta0Mrcra+38+Keleeow+vvHR3Qd3qoA8+z9lHZ9B6dfiaVGjdhoIlPqipa29wO/UCtu1DZR0p9ry2r4FnI1KSpf+f14A12Fy36ezaq6PXesbX2h4/NnBsYUVI82i0Qe6keGng4eWed97+Nh5cBt57+mWePD2Sk5G/aLaQ0moJzs42AXJe8nScw3tP13fMldo/2yJkCkwujJI1lLKAjhJOoSqcTjNTSnjvTTFzjnGamXK2mpPO2TVZzota2OM0Wy6b86asqipZfOusnDOnlFBNjMXakdGWLxJRUrQQ2SACrkNz4vbmmZUQkGp1swETgShSQiU9c0qWm/ZxO+ijioRVQfCqnpUNolo/1by7VmqkbJ7lWAONkTlGqwPovNUgS5F5PjFPN/jgUe1MEZXYiGtyylZuwZpBLiGddUCI84RuoN9fMaSMJiUlgTgWIiLzAFaF0wwZuYBc6xPVBFKU3GyMoiQ1tlEx5r/gOwtRrNZRhM5BDlbKBXFItPAozQZKcQmHjU3xAbR4GbWz/EgXDEiW0itZI0rEufvpSV+cg+LsGYgovlqji4HHuYAL3sI93RIOI3g0WWgualbimudooU4RRE05cz3ogLDjYhi42AvOnTilW0aBLiiiN6QxkY5HxjHxlb7jS/0DujERk2eeM0/HI1+6HfnizYmbYyTO9nxzSep3ztH1JYHfgescu2EPXcdpmrgdJ05xphHDeCVLIoq1fyk4X0CS2jx0HmM/rvl1ZdMz786RuXPM9OQpchxv//+8/dmTJEmS5on9WERU1czcPcIjMvKs++yu7unpGSyWgN0HvIMIb/hf8TgLIhAWRIsFYQYz0zPT3dN1V+UZhx9mpqoiwnhgFlX1yKwje9JXqzzD3U5VEVFh/pg//phxnKxeu5tRvSXPZ8ZSGMczPAJwDIstcPC4jaA258xB/YqMfP0jnkGza7Ywjn+aX2/YBMl8YPC7ZnFugn9elBbAkQ1otH25gbcWJFyyvst6dIei/bf1X2znvnhtLVMuq0PWWofoyhBo2RRxR021Nl76yopo4MPHaWtkA551XiL5/vpHclSrdEg3ECKUfDIa3jwZAyUEVDprQ0OAkAipMxvoGSJEqGqAsus7YrpgnAZO54GqF3T9Fanbkccz4krgtWZqrhS1Pqwh2lz3nfUvvtjD1UUkDgOhHgiTqQm/me6ZzsX6rabAOCm1zgQp7JJw6BNdSsTkddJVXf3bAr7RVU8rkblmA6+5UopQihnUEFodtQm92anV5T5d4JJYMC4EsT2hGlejhSLMBzPGhAnGWbDosY4QTYRLSgtOGVz224jGcsje4zYAMQbmbGULiGWaRa2lUC4VqdXuR88OLwESfO26R2cCJ3mRGrD9sSztavA6/lKKtSsKQkcgBi+7CXbPIHhr6LqAP4u/251joNSur2WOG0BZPdPqgVl3an1b0ro2YwBPgIZACMn2TW09sk2zwL4Lspnp9XtL9b3nkSZyo5jZ3Ps1yPYw4EZLUCx/6rpfLu9fKfKwOtoL8BI269L3TNa9Z+vIm18kPm5h8bseOu26JNOWcW8U5C0o3Xzmw9/kQeumLaRq59Nih76wvzQmq+IrC8hZPn672f5hCPWNH1+Fe9qDiyDNV2HF5uSvWP7h2+WtMdzM/1d/ubw1BtuX/eHR2MJTe79bTN2Cx/bKL/++wY4Pk0sbe7z9mAdz+KWLkOUNbU38qXn82sDRKKq+f9VqTewxJyFKMGMmlYth4P3ra5496TnEmR2RkhNodPAhfpHuJInXTLqzm1WpVen6RkZyp6hmpyJ6VsDBzVrc7E5TrYzniSrQp84dGgOOVZVdjBAjVO9CKQYchhDptCLVsMm5WC1XDAFJJngSi2V2ziinUpjBqJVYRhWsH1eXTPCmqCKdCetoc3o8wlQ8M0QIHAnkXMlkppopOpEfycHRlpyShwtsmVs1qljrCyZi/cSCZ3VynsnzTMmZUiqxRANLuZDniTwfEYmI9N503T7Div4SuTbasWUN1aN0JVitXj/s2YdodYNTYTyVJbsVQyKEhEokUxzY4huqC/gEUDEwX4uJQ4grHkqMpK4ndR7t97Us0RRzU4qUEskTzHX0xtsT1BmC1bZKCAbGNFFLpNYOlWQ/Km74K0IhhoI8FnB0im/b9O1Xr+UIAljtaIhxieybYI7VDmsxinX0Og5aQMaDIhIgpo5u2NP3z7nsnvHi8pJnF4LILa9PL3k1nZkDlPvX3N4V+rnQlcTnZ6W8PHK925MkcXn1lFdAnCq7IXhgweluWDBnCMHUaqMwpMhln7joE2jh9nTiZb1FSmHW6pzIQI2BpHERZint3+IKvlrdoAtotMjucl8pZZ44HaHOIzpnTqeRPBcO+wPdfqDbRebpyPl8ZBwfoRcHWwfEnBfZGmd7Yok2N8xoBjLQak387nUbpMu8NsEyM4wez5YVMDYT0moWmziNLo+toLEuP1DwmkM/59CyNLJ1uhqE9EwNstQjLx6NGChohlN976/tmhePa3WUlodW08DWGxCnzokHpRY3Shud7r95yr7yGGOPyM4o9VTO5cgUhRg7un5ACeTTSJ0nhIio2b2crcYvON192F1wGExE5u///td8/PKO3376in/6+e85Hkd2MRG6SJFK1gJBXakWshZElS4FYihM+cjdmDh0B7onF3TnCjf35KLMU6VqMM0wLZQy00XYX+7ZDXsuDgFkYpxPRuH3MEKQQJJIlERRoUhEJIKUJRDU5ihEA7OhzYUEOgK9BFKwdl5ZldIAFdnWs2c5RQMhJghCSYk5CFOZ3UZ+yYX8Ro4YzTZvmQ21FErJRpUVAZoSrJJFvL1IXvpcL9n0DaFa/T56iA2a6Jf6n4WmQm3gsq62TdWDXxZ8ztk2Awkt47jeEtGNu8E+vxdXz9DsdfVA4YN7e91j7F6uiyZFkFY75ywe92fsfAW0idTZ56sUW9+1Mk6FObOGfLSB1bpc+zd9iK7Zmq+s25JmNR86yw8EyDbBti36e7D1bN73lSwx3xeXQJy3zQrBanxN3G5lXfmn+X/Vwba11rLYff2S3N4W8MiyDjYIT9a/1mzU9nw9mSFvfxr+Xl3fiywttrbg65GIHJvSiIdz8/Y52oubP795rv3+lUCwBV5XQNdE9tpnLl/5B0DiQ1Dnq011+xEPkOLDS/hD4HC7Hvny4/rlFa1f+u3h+T8Aze0ENhj7wfr4I8fXV1VdGmybW6HqBsERR4qRoesJBH73q9/y2/kepKAaqDWaEfAVVhbQ15x+mxUJtiiL34T2cqPzlGKS/Q3Pr9EYYbmV1MBCiEYbKqWSAXEqnzlFTrv0zb9F3tuGK0v2wmoMxmIRUAGoRreqGL1GAeoaZRfn1AexcWn948AoQe0cW1aMAFUCGathKBgFpAD6SKE4ydWATzKVwxLDMjZZLVOqmOFsdY6SM+rN4KdpMmenFMtstZYlpaB5hjJC7ZDoQE+iU3Ijc0zEuVqbFXUaTlXrNRaMdtPHhKbOhJHuz8xyBmZX8uvpwoBqZJ4yojDV0aK6WpGoEIUarIYnO4iQasENglFhJSYUYS4FmTNROoahI3Z7ak2cj5kyZWtI7eo3Qb0OJwZCSlS1rGPVQNFg9Flatk+91kyJ8XEMY1hS5a0exxydgNHKY0iWMRRxB8AdA49iq4sFqG92BrI9LOxGfS4Z0crTw4GPXnzAty8uuOSenI8cUs++CjdT5Xh35u72nqsMB9mBvuF4c+TZuy/49rc/4Pvf/4jvifC6VMYaKMGovSIRKoRa6eaZfjwRxhNlGtGSCaVyGic+He7YpYHb0z3H6cxYZjIW4JhLYcrZai7FVVOluBKnGwYVSrF9K6bgYNnqN8+nkel8dgqa7WtTqRznmUzlNJ6YpjOaH8nBCW9v8JvNuzksulTSeDbQ5r7U9TkzAj7/Yn0zU6O8LXtpc5Y2lCfWz1BY2mtsDXU7t1aPtjJOZM0YbJyI5XPfOtTfvzpiYTH0hhcadd6eg7c+bHF+14zNQ1U6dwI2mdNGw4uLg/A4++pNVYpmVCJpv2d3PXC9E/Z9h0jg7uae8bMv0LlY25yAZ6SKCZV5P+ChG9jFjmlU/vN//ifGOvPFzZHzuXLYXXJxsYcuUuqMdp23cgpAZCozoaqJswncHu+oUnl23dMPB+5PJ14dz5zHjEiCEkyQSgsxRIZdz364YL/b03e2hwqTg5ZkginBBFNq8RrzYjba9qNsmE8sA63YXq8KiUAUoesSuxjpU4eqMJbCOVuQtoRA8jrstjZijKgIcwgGlsUE0kp9LCZHIEh1FpPtg8ammV0oKmJaNwb6tBQDUaV67aP671j9aoRWB2hJJne+qea3OEjEVdZrNUZXaMJfou4QQ/Jyg1ohRNOUsNZSFkSi5sVBXp15D3a6MBWst1RjDrQf9Te27KrIxt9r9lOsXKSoLq3KTCjPssSLYBeVUmfry3nOzBkE788ZrGUai+/3zR9vw4QFRG4d+rZn+V6x0j9Z5mXdAx+65lvRseU73gI2y5yLLKJB0Rl1IYaFEYG4cN3yGca7aPuthXbDug02MSldAxLLXthaGLFSkdvFLvXs2021gbIt0HzwrnW8littwT5Z19gj4f/NOeny28KqYQuyVlslD963QKgHj60LQjbrYAWND+1xo2Zvj4cPSAPjm+9+EG/Ygr3NAnx4Cm9nnfGhdV9tc1Irhmxz+tYYPPgcfTA/y/NvfVfbmf7Y8fWpqnUlEVkWr1JdRKQFkfe9qVWex5HzcUTqbNREb1nRNgqLyokvQC+O96uRsIaJ22DmRhvBN14fq+W6/T1Gsys2SE5BrLq8fI3c4/fr9gYS6/m2OEyeiVz+1uWvjcKW+Jw4enfHbHHD2sD44zGmBTiGLhG7gU4CV8OOy93AaZ44no+mOrd7HPn/kAsxBjqJVIEco4FscBqp1UeI04cLQs2ZPE0gwjQbcNTaNi6rQ9FSoBYHWYVINRplwIVjIjEJc1JitghuUWswr8YrswhclyhFnNbrFA4CKQR2/cCu2yMkxjgjdUazjXfFs7kpUIKQPaqafb6KWFPspig7m9ISopkhWsTfnJlESU5ZCsEUXumRvlr2OHWQklE9xVqb5GLCAa0GSHytxC0N+Bs+WiH+UhfnoN+c+WDZJncUqrbgiG8gtS4Ux+JZpAZ4hUAI1QIatTDlEQmVq0NiHzP59SdQP+bZtbIbdshNB+ceGa55cdHzzq5jN0QO+4Fvf/A+P/rRD3nnh98jvHiB7g4QE9pF6BJSBJ3d0RpPxDevyS+/4O7mDbdvbjje3vP69T3Pbo48v7vlzfkNb45fcHt8zd145jwrp8nvt9Lq7cTl/qNnylzxVz1Lt+glCHgNkmpT+LU5H+eZ+dVrRFw9TsRA7uNM5MaIud1vIdbNa5qRbNsf0FzL5WMEIYoDR/Ba8k0kc3mh1UHpIma1OpJoo6Lq6kwu714Bprj4VKuhXDKb+Nps7129i83f64kvFNhavZRBl9c0cphZj9UoWO+09uc6Ng+Mpbb9176uZUUfKY7Dm3zmdD4h3cCLZy947/sf8d2P3uPQJ25evuQX//RzePlquZ7QTASCpGQZ9CVrIUxj5vZ45H46M1VI3YHUeumqgTWJBsZUcarkTFQleu3kmCs6VWP9kHh1nnl9d0YlcnV5xTxXjvdngkQuDzueXB24utzT9wGto9FO6YgpEcJA3+/pYrLsfD6RR1MGLyFSrfkVWS2gJzUaXb9MBFH6vuMwdPTJlEh7t68QTaU5WyDSHGxfJ7bKUIRJXHk8RELStXbvGz5MMdPurdLATa0OEK2FlFGebQ6D6rK2rOVKImoxBVrE7CdWMxrbvux7z9s1vBbcq6jand1GKCz3tovPyLqvx7Aqh6LBslM0J9D+IwsSDDQ6//rjtPUgxszAicK6bkoLOHEwtbbPsrVXqprKrVova/O/q/cRzZSSqcXA0+JLOSJ5tEzVZl9bHuPB1rM8sPUL7b3re5Y96K0sT/us9ZcGEGVRMF+ZHxZYaXMX48p8qS1p4vt+S4Iseym4LQ8+B8VVhrcMjVZGUBe3s12XepJnCwCbtM0CuXTd29t3NpvT/OYGru31dePz2gg9Vu34lw/DDiLtpFacsD6/XskG1jcDZH8tBmtbY79+zjbx2MZv+Zyv+G2zDB6c6/rPdnT91cvXfvn72b5aHTz+ySF+sLr9u9+a1z9g//7c6fvawLEBJ8GchSXi4YMQxehm4pL9lUCei9MEnU7WuPnaLsOdfvcVlpoUlxS2zbRtWmZpa10jJ9pAm2ysSPNLzBtGaI4zy2c0N6bpUDZ55aIusrEx4C3SsOyjKEVXZN4qx5rDpazu1boOFCRQc/XgoonLCJHhsOMgwqUIVxeXHLvIm/Mdt/lxLGMqhQ5rrtzAgYRABWanlmRMac3i2ErNlcyMgXirVWz92hYBg2KF/SZioiYZH8QMkghW+2GRtthVci2UOlOx7JemQE3BlOnUailrNdXUIOqCEMJ+iBb9DhV1Wmmdq9O9IKSEJBNcES1Qmyi8iUDMRZmyZd0kWOuNmDNlnilhMsNaMyFAN3RouCCEyG5X6fYR6RMarD6wgAu7NHlq/98mE8sjzWMM0ZpgL6EwpyuJrBSYdp96QEZcVEpdEIclI9PABBhNNdL1gZQidB1BKuP5ji+me6Yv/ivPr2/43osX5MuOMfRcxg/59pOf8O0PX3DxAvonkf3hguunz3n+3rt0L57/GVd0Ad96QZp/wO7mlqu7G463tzx/debDLya++OINn9x9zMv7X3O8+4RXb17yxc2RN/eZaVKO48Rxmii1LMIZ1engFaG1SLGUesuo4eqHfhcH2yNyzeQ8o1pNVKg3AZ3HOFaM6PV9be/xDb6BouUvdwbaBTTwaGIxBhwDxngQd9AWP9wPy5xYZmXb23BxInWTXdwYUmgATVZHqLU8aZ8TVmdjbauw/Ge5VsCT27oB+M2JamvTHPjKQ9XV9XPa0eCjPaPqQYRal8x8a2PwVeb9mzhynTlOJ9CRa645PL3m3Q+/Q6fKm1d3HE8j0zyv9Fk1W5pSInXJnPucTbRJoQRlLoEQduyHnq7rCSJM49nrem1OKi1LW4kofRCGGAiSSCS64ZLYHZC4ox8uefqk0IeOw/6CaSoc709IEK4uDlxdXtB3Ea0TebpHAqRuZ/3rpCOEnqBKnk+EolBmap4pIaAhYrXW2efW1UGrsUmsNtnrsNUABa0XK4VaZmqpLoYVF+e9ubTFrWrwerqWofmmjwa4luWidj/FYCA92MUh6ll9ETpXB+9iIIfIHAvTVJiz2QHx87bPcOAX1ESbZKW2Pfjxm1awe038fipuTxb/hAq4ME3s0GIZUhPQWYFpURcKC5uAj6u1LhlR7O9VfGsLFjZCegvIEFpWrhZjTBFs36mq5KxoFaKYTZYQnT6/EZZ4pKMFxERWVk0Df+KoSptzuKRfNvDA788HYLOtyWU79uBGC0pFWUTo2t4YHEQ2qm8L96rvdwGf72r+dWi2CSP2NhvdvqMpEG/36VINQBYN637agFJdQaE60FvKu97mX+p6gWY7Wu2d77CbtbCUnfkYP0Db3+CxsF/YTMTb5/0VeMnOSzcv+aqsYUPY7TPeAm0NNH7lWl0BqfrgLme1xYHb02YLMbUZbv+u5ofx8HO38PTB+nsIcNdz3l5Ds5n+X/3q6kvZjOefYgB8beBoLTd8g6ubXXWD6ZsiZ84z02wN13tJy0kRV+pQA25tVbQxXJWDHDyKLpLSy+JZ0Hf0bILVdi3bgp9HoxEEv8GWSLYCWmix7IU+InY17f26TF4DkNsFqzy8GvvPOm8e5QlbZ8kdOklUtT5Gpcyk/kAadriwI6qYctsjHEkKnZhwT42BUFwFUILVjXg0K2IGs2KZyNBAuHOMRWUBjtQmma5uTMNSd7OUi4tJ+0tMZoi1UjRRNVPE2j+oYPSWeWSez9Q6ATNBsjkdMpPCREoQKOQpM3aFPDaAKUbR7AZ6CZSoEGbqaE2mcxXOUyFIpu8CKZoS6TzNnDha30/pKMUyO8N+oN8LMQ50Q6XrlZCsP2n1AEJpQ7AAR3eIq6AFtDyOoxpooMeyhIsaYAiLKp/WNTCyROfM2ixg0hzrTc2hmINnINwcomk68+nnn/D6eMuQb/nW9/d89KMPkWfvo88u6PNf8y+++z/yvR9fIy+gdJOJ6tewym3+uUcX4fk13TtPeILybIrwCt7/7I4nr37Bq+M7lOMn3H76CR//5lM+++LI3bny+njk9f0dZ8+MK1ibmArFgaOKtbuZ58lqW8XMMwCC729NfMJWbgt4FXeIv+mjBb+ag7fUAdJsmfj+Jotx2lqK1aiztsPw12hlo6S4mq3lu9vrtBFY3zYeTcjMHRhZs7kxBlKIGyn5FshbhXdqczC00dq2Zqxdf10c58VRe8tQLwb1LX+BzStANhHidh62N7V6x6W/2iMcu6CcQ2bOhfH2NW8++5zPDlfU88gv/uGf+Pi3v2M8n+hDIGI17zFYv7zYdyDW0qHOmVyUoILS27wS0AI5T6ioqyMHy9DWTKLSJ2E3RIbOFMRFOkgD3cUThrQndXvef2fPi8MLOhVSiFbKMRsQ6bpE6jpUhFxG5q5DdYeEag5rDeSpMJ9HZKp0FXYhUKKBlxmraez92oRCREh9Rx+FvgsMwUpExjxTpkwIEel63zCz6xwIGmVp2+KEnbf6F4q1cXqUY6WVtjUYxBTjU4xOzVZaLXFKgS5Z3WGs1X2ggFax/otN/XRzr9hnBmLwXodLHEjddjRXpQVRALzEoLaMlPkk1YUDg3R0MSJJyTNELYxamP3zazHGTZRoNZEpohTrfSzqttnr6Zxt1NQ+8XMrXhcPLGAmBFnGpGXABO9vXAWIpGQ2Zq3le8vpf4yjgRzWrcPUU1d/bE1gsOwd22OBcAsI3Xx8A4QtoBCElKLtjWLlEEvtt7KI2zR7kktGxdSQa4XiVGijIheKVsQzldWVqO0e2HiT7fNFQOoCko1G7GJiAQvetL13A3DWTJoNygJ0NvZm6/Y20b0GGps9eKwp/KrjYf3farcaz89e9Pa7Htq95pv/4cW3Qr7l6zaU0DX8oMsj26jC2lrlK9aU+46LidUVND7QO3iAhvWtz5AHoNi+dvt62TwOK531D1zv9qk/YR+/NnDsY2LCjJpDIFuEjh9KsWLrXZ8WoxYkLpuJS5d4C4ONmAltsVfLXLU6QW11Es2hq5uAgN/9weBNcGe3aFMR22QlfSClIcZNRL85KeZL1xW0YgIjCz2DtrHX5drtxrO7bYnC6VsbkDQf7y3SlUCjc40lM3WR+bDj5vUbjqczRSvdI0VwkgvNrHbRo74e/VWpi+OtmGBDUaF6Wwq0UVr8R/0G9ujZElnzyNYypLDcVEjw3lFWWN+A/KLYOp0o+QQ6EUMmMEOdKHOmpEwKPQElhRMpnBFGqDNa7Rq6biD0O0IRQpyZOKFlXGpeS1TUayEpiupMzmrfFXokdhAiXZdM3CFFYiqEZGIU6gCLwBKwWBxlAA1YQ6/EIpH3DR/i9GC1VL3TUv1eQiwCXAoqXhvTDFypNNnwtlBrbSIILq0eM7mcKGMgqjCGA29y5Fno+Pa7P+Q7P/gWH/z0r9h/9BHP75+Q9Ht88OKasPc1dncHn7+G17dw94bzzRum+3s6+zJe3h85Dzue//AHHJ4/54tPP+Pu8894fnnJiw8/hKfXhKsLePoE+gjvw8V7l3zr5i94Oj6F6Q188hl3T3/Pb3/5Cb94c8Onp1uene65P95xHE/MxQIFY66cczWnHFN+tCh521DtXvR83+I4tYCSUfGVeXqcQM6WKqqbmsWtEW/Pb+cMPMi1gMbVYDXwtn3N4gapeqBjrfH2F9K+qNVQtlYh2yxjiHF1ope+bZZNAVkUOIuujtLa4sXaFLW915R7g0fb13qnbaR1+/tXgb62H7ejgd8GXhVnT2hY6nof4+hr4SLAORfmmzd8/Iufk1/fMp/OfPzbX3Lz6gu6BGEYlrPsovWSjSFQa2EEZrEMuN3XVtpQZkUS7Icdh/3Arh8Yuo59TPQoqYxEJrpk9ihP1cVTEjF0iApSlIFEjEKYMmEewe0xQJkm5vPILIJ2wZWjE5pHBKVDCBqsrUI2wTFCQNJA1Wo1btVosoDbfxhCZJ8iuwgpCJXCuRTOeSYEZfCewgnzAWrxYgm/R5eenQJl9ZsfLQBgmdFGD23QwZVEBYhhTVAFMRZN1yEoZarL2m9tNOx9LTPffAr3QfyDjE5owcZGEY+6Bmuq3xviQcJ2f9oeZiJHItCnSCdCFZgoRAqnXJhq8UBmWATTovdULLmi0nprhgUgLpkmbfTZFuQRtgIvKVqbpW1tvLFurFhDNr7F4oi37Apvu8Tf4Dx+CWBs66K//DrlYQ3Z+t71Ne2spWWPPYCT/PeFRuxzBd6Xs6nIVlPYraWYfkAIaN97K5vZstHJ2FalVkJUCOLKy3UNfDVEElyU0gNrVocaFhtirAtBw6qMXx9c4urftiXtg7KAsuXvaveBv4sHiLIN1CMe2xrU7aH+3R5ystd++RXbT3rr37deJm8/vzGMjiEW3/+ttzcw3/zfdUzbe5uvvGaM23c0G7uC+rfPewNY3r6sBmLauQrLekd1ffoPHH8EUn7p+Pp9HL2VQdHiNQjNYa5UNWGQcRwZ+kSXOrrUUXOmSagbOPMF3aLcD6I85ty0OiXFZMYFp686Kavd/BaJr0bZ8Uxo6wfVovB1eRxvSuvgT9pgOc2iRfEasAHvDeevapudGCCWNkkLUGjX4YZi2Wy2kQRdJhEsmtf1HSElXp/OnOtLap7pBqshiY8kxmHCRsWcOG2g3K4txgQiS7ZREEQDq2JaG/3AmkparytIWCX9fX0IVi+oYiIHpnqZKTWT60QpE1lnshZmzRaJm0ZCmejIBCkoE2UaOc9KqD3UwYMTJ9AjWs+UMiG5Iyqk1DPsLxmkI8aR+1kYTyZ1Li5fHkMiEJ3OWEAschuTEBGnJfkcVwtqBNTFDszwpgQ5V2TOi4KrLWN3hQKPJnJkdSNOaAlrEENCJKYOChSdl0iG1bVClIw02XCvqyrtvKUiIaNyJMiR2PfsLp5xeXHNs/0HfPfZC/7ye+/x03/9Y9793o8ZLq9559nsdRn3cPMG/ulXnP/pV4y/+4Th9St2t6/RV19Q7m7ZSUBL5vjmhjfXT7n4P/6P7L7zXW7/4R/5/O//nt2TK/jJj+G9F5Srp/D8BfH9j9D33kN2ey6fdhz4Dsp3iM9HuPiYZxe/pPvtx1zcvuKcb7m5/4yXbz7j9v6ecYbjWGGcOc+FyUWZ2ga/qvo1QLH9Yfm3FmsE/hhHUydVbUDHfdK3z2ITkdr2HRNZ78bljhTxDH67Kl32oZUa2lQUHxqbBkBDcw797+igcaFixbAAR/vbsi7F76fsALyoOUE5F+ZQUFeexK/JTqGayJjbihaMsl/M4IYtOGxOTXPovhSp9Vpjf6zt+w/k5L/hQ89numqB1DKdef3px5xfvUZzYTze0gXrA0u1pvcxRFMQjiA1M+eZWE0oJ/vOGZzzUYoSu56nT97l3Xfe4fJwyZOLK9598oxnfYfcv+Z89xnn8TX3xztej7fcnifyNFI5o/XEVGem+zN6e6KbZoZmz3zySy0c58wYI+nJBekwWFuR+xuSKpe7Cw67C7ogTAJ4UEpTzxSEcSqU2ZquB4momLCW5oygpJDoCMwKWQNjtT67SQNJhQ7r0Wwlz2o9ngGpK206PMi2PBJVVVZ/IYQWCClMeSZ2re2FEagzVjuvXn+WVZlrZS5e8lHNxgb/UGPUOLiqK0vFFpADSq+rWRxIWmCveLZ/nbem+2CthSND17ED0MIUo58vSK7M1UG4qtXXun0TDIybqFZYS5A94KnqQnmyBnJsLzF/MEbLOJr6Z1nipOpzFNCl/dfqdckCmv+UGMd/+7FCCtszeOtc1r+/yoFutdNrYM5taeo2NYsOGrGAibrIXNXiirx18RtbGZRRnpU65yVAghZK1hYRQMmWtVVvNecBlbZI2+eFJlrkgc/k522aDlamQ1DvjayLnXkAHGVtcdRszRbEyOLkriDNvs/s4iNtqw/aU61+9PqNXwJYwmZOt7/I2y9bXrt85OZZ8Re0wOWyZjYvbAmD5S72eQkxuv+oPEDqbqwXerE23/uhrf/qO0IemC/dvHqxhctYyIMrXla64zZ7cLX92yDLnwrIfW3gmKsbsxCg1QI6gla1XoSnnInjZCn7rqPMc6sitAvcRA1EomVzEKdIuvPuG2wDYC2juZVIEKeJGKBUlh4zDh4V7yUkYXESoTnWwYFO42+vFKv2PdocyU20vU1ORUwIBrWIz3IL4QXzm8hCu8W2YR0UgpD6RNoNSIycz2fydGLXmyJfke7RqKoFq0UkzxSJLg5iogQpJUSt92VEbEyrmNJbWTNrtIi4L81tltHAZps/hehF8SH451TynJnmM9N8Ys5nSp0pFIpYtFlKptOMhEqWQq4j+XxLLRM6RTQPdENHydnAIzMmcGKGPXU9u/0BjQOqHdPdxCxngipd6BjSwNANVMQK+HUNGiQxpzOXbFF1ZkRmQiykDlKfCCl4naUBMZSlB+ISjZVk9STp65cT/1nzWJziFFZqIiJ0XcfFxROEnnGayXkmhEIflOSKEnWejQaTYvPCIATGOnE/3ZPSmRcvdrz34Udcv/NT3rv4a7539RO+/dH7vPODa158+zsMuwRfwPjq1wg/pwuvOP7693z2b3/F/c9v2J2U6zyh5zvy/Wvq8Z5TzuicYZwI+cTdP/wd88vPOH/8KfLp7zh9Efji7iX58oLXfc95tye++w4X3/sWz771bZ4++4hw+X242sFhgB9/j+f7C356+QFPP/+Cl9PHvDp2XH5R+fyzyqvXJ6RA0IjUTJkL0e9bU91ljaz7fhlEqCJWc+e1I61NyWMcDdg0m7SEmzz4skBZCVYv7HO9vtYcx0UzTGRx1LYgdAGii11Y3rD8ugR/Ghjc1GW1CHtMaaFKxriqBDaRjqq402yU39mVQyVkKwgOZWmbUl25WasxTQxA1qU+fdmzF0POEpST7cU8sHVrcBGaa7q19o/jqM7jhDrY0FqYz/eU8z29BHZJiP3OMnPZ2sRYPKl4YM57/dGCrLo4eLXi/RE7AgeG7hmX++c8u3qXd5+9zzv9gHafc6+R26pMOqKjMt5NnGuhjkK5L5xy5f7VDeXNLftSufA2UY1po8Axz5xjJE1P6C725PnMfHdrgPjiTHhS2MWE5BnJxQRfemGIA/vBamZ1LggzyAz1DMXrxmNAYzQ2hCYrvSKSaySoUVibCExbsWu9r3kRVuvq98QjzWO7B0IwMGZ0zmr1qSlCClaKAUipTKUSiys6u/haZc045lqJVShqdRvCSulcnccWSG9BEzsXBe+fafX+MTpACBbwLXjWt0v0u55d37PL2eirKoQ0EGIPISNzWamarh1QS9PzxUpU8uzfu/pENiatrc8agDJ7j2WzHVQE33tqA5dgWdmNf2UfyOK/PRZubMF9s+sPvP3NSTwEHg8c7a3vJ6syagyBLkbSUq+51i7WUlaVXXUxpVJM/I11vw5igWmFtfdjC/JV11FtQOABaPNxVPdPXUxMqlonAUzksJ1TFSGHgGjFFAhBxHs109bZJnCxAY3NeGzLC9bxWQN++tZz3/TRTNTb8wbrfbJ5aMEMPAB8XwaNilq5VQNZm/Fdr2gbUmiP61uftD214PRiA46iZtdaoNNEqhomUQ8iwRJEcfC/MV4bkPwWW6aBPn3Lvj0YmK8YQ5/jBv63nykNn/yR42t7s3MtS6J62SzW86ACoyohZ/Z2p5lR0saX3lyXAwxVVy5bPoj1RmGtK9T2383qaJtsE+kRHwZQ1wW1yQkS7bGmNuabAXXb2B4fxgYALbq+1t9sfzYD3hRivIt3bZMsgkhdInwtetNuypQScbenxMA8zwyi1kR4nnidZ+aiHPPjUBxnd15Qa3Y/q42N4H3fUKJ69EndrCgmYNOIbY3ihxuPBhhxh2+JkLUxs3EuC3CcmE5nzuM903ykVqvd0aDUAKKFmCekZkQLtWTqdCaPR3RSRAe0Dr6OTCW27zuk7+k7E5Loup4aemIsBLHodpRAFxK71LHrenNiQrG2HWICD0WA4pnykhG1GsuUlK5Y5jVphFCtbqRUSs7UnI1eEiLBKTpBVmXTb/oouqkfpRkLq7fZ9QMp7uk7ZZ5HhIkomej1LK2pTUyJ2BkYyDFwHOGoHdfvPOeHf/s9/uVf/RUvnv+Ed4e/5ntPfsaT9w/wFJiAX8DxH37D7af/b3b8O5L8jvH3H/Pm798wvj7Q79+jDjumPqHsIcF4OqN9x8XTJ/S7HdOrL7h/84ahwmG/Q6Yzn3/yMeOnldtauS8z8dDx/MN32X/3hxw++hn9O3fw/KeUj57A+5X47Re82L9g9/Etuzc7Lu8rL66Ep1H4zfwpL/OZO4IZ5JKJwCSVcZN5bIZfF0fD7ufikf5VnOubPxbg6EcDcIuT4QZFnEphQeO1CfeSIWlv9vWg0jCh7aXNvKzX3LJ4Zjoipt6XQiDFtDhHKVgtY3JwGBbQuKFnBVmEHBShA7LTzkMx1gASCbESanWn2TMy3k9OSzWF5eVnred6cAfJeg2r4d6a9q03Yc9X1szK4+yqTYETmo9nwlzm2KfYk4Kph6p4xFkLU54IQelCQrqIZAXNzedxSrG16Rhn4c3NxD6dSWUmzTN6d89rORLGN9TziXE8c3c3cjxOnE+Zc6nk+Z7pbmQshXweidUygKXqAgaCr5IgRm8sp1tqPqO1IPMMVRnv77nJhTEmQi3UnC2bMc2kNPCk25NDpIRCzSPomdafWQRmIM+FUb1/MRHV4AIqiharigSxjCVGXS3Fhbxi8B6cay39YxzSBGkkEoNNYa6Vec4QZyRZz97oAecwzYhYJr5avyZzFr19QgXwjDuyyc7pRhHTvhlYsMLCQHj4Gpuv6rJVBCUkoR8Sw9CzS5E0T8zTTMiVvh/QlJiZqXU0MTUULVZy1Gjjtt9Vo61qI+Y2P6bRbCOh7SFtj6TtRc33tcyW1OYhuZ8UVh9R233rQEkfax43v+sf+P3B65frWZMSLXHQShZiDHQxGTWVTUBO1ex/znbPqBpocGpq1A3tcwMCtrhtBT/rebQzbsyttpsvehtqQSqlQKMh+3lrCMt1ZGxdxhhNaV5Wm9ZKCZpS7wIqtqB6AZP6AJRtz/2R8P86IvIW0N3O8LIA23k1aPjVx9tAdwsa23ex2OUtaNx+s7Q3r4GTELyW2TLR4vRe8fMLBK+NFYr33G5Zx/WzNvZbWTDN9oz1wS/rKx4+33aV1Vf/yhGR7TX/6ePr93FUL4z2mp8WRRJ3jqtH8Kdc6GKkC4JEWaPK+EaIDUiIkd2wIyWhiRAbhaMAwWsvHJxtFnWzzja3G0+p3TAN7LUb39+yxgp8KKv6TdciPjjiN5XRGBM1t3onf4naOODXvXwtIARqhTlbwbmtJX2Q+m0tI1LqCbFjLnbT7/ueXR85jierJ5TwaBvqVIrVA5ZKIXg/PIfmDtybWmfrJbUU+0obS12c1UZnwyOYzdFu9qFqIefZRHZqpGa1ZsnzRB4t06o6W+1gcPqDFjRPMI0wz0jOSy9Jo7y5IEDqCCGw2+3oiEi/o9sfSKlDwShzTiEVcOEe6II1mddkwk1tAx2LOQhlymg2oJVE6aJRi0OBea7Wrw1TjZvOM3maKHlejD1gzq/39HqMowquFmw0mJQ6oxpXZT6f0WQuYZTiqrSBrusIXaR2HWOeIQh91yNpYAKmLtI9ecoHf/ktfvY//A3/3c9+wvP+BZ18xOXVwSb8OKL/8Z/45N/+lvz733NVf8Fu/jm3L/8Tx8+/4PK440rfZaj3kDNFMupKvEggpsRuGCAkpuNIzkc792BKuOM409fMvhTmMqHHO4b7O+KrI/zuyHx9w/nJL5GPXnDx43fhxz+A9/bsLq9475P3eX6TyU+veDdecjUP/EI/4+P7k7EPREjnkdtxJIeASrdQiIKb54qwhkFaPF4J8XHuRzvWnWkRmmEFR61JtAoPspCL7aLtXS3T2AJc7TUbwyNiQlObaxSxe6ML1l+vi2kBkV2IC3CURfrfKVq42AYsQhNercgiMd2c8GhiVda71evAimVkstfj1gYgtVCL1f0ZHa85Kb73i6zXpqtDsdBbfVOW0KCxLo7rY1Hjip9MG/cudSac4vZy8ro3DYboi1amkgklQIyuhOy0Q7Fa0FwLuQaK9IgkpiycjxN33FFulM/nV+icSfWWPrwEfck03nB3nAyQFTWWhk5EVYYQGA57OsHbLVTYBB16OgZgUiXPEwLWtzGAlMJ0f08GuhiIAlkK5aSE0NHHAyF1VOmoKaAaqARK7S2IUKvXHRt9k2p1tuNcHCGz3H82V2IKs6VAtL2LaB5qa4HxGEdz1hpgiIElM2hBwkKJgcaUnSlYn2EDydWZICjLHWbi2ibGFxdB0TWThVRaS7JGVy3FuU/OBgJBYkCCOZwxKikYaLwYEruoxDqbzRytTRUIoU90CH2ozHX2oEVdXSlk2WfMDhq13J6KLhy4gsV2zi3wr6pUcwv9nrT9KqgrMuu6u9l/g2cw/e9HC8j5Lqnr3rcwpnzPW796Awraebk/aOU3YaHopxiW/a7RTmspC2hstZ7twoUmvtiAiO/Rflot2/8w0yTLOa6ntgHrtI9X3/+USnHH2utJ/XcJa/kQ4C3G5IEI0rY1xTIizY95ENV08PjAtqzX9BjHCprNn16z8VswtYHl4uUYYMB681kPM3YP32fvfRsAN6yhy0vdE7b174Pa+tNLq/lvWcX2Re3esdUAyz5Qm+zKg5NoWGkVmNMF0AMrnlqgoD488bfvqbe3Snn40Lb2/5tXVW0GW1xVcgGO9mO1a8Wa88bAbkj0fWIs2SNZfs5u9GNM7A8X7A8DKtYDT0W9ENgKq81JqO4ZefivVmgi8a7U1Qa6ff4q2mOARpe+kWJX4g736mp51CxYprBFLVpdn92DwSOQ5UGUQLDPChilSE4T43gyg704cix1EMRo0tSqRFX6GLnoIn0Q6tCThz1zhnh3/LpT9Gcd0+ztL6Q4tcaK6Wu1omzNGUr1Or/oGdvge6F1Klyyqa0GCkx+3TNvVkdmt06planM5FpAk9UylYzWDMuP1eKpq43NeabMEzpP6HgyamW12gxRK+jPUyUJhNgx7DokdoRhh+wGNMA0TZzyxOl0JucZUHPK1BRiuwBx1xO7DhDyPHM8nTieJ8bzEbwO6KJPxL4nJmvknQvUXKxGcy5M08w8m5R882RbL6bqLUoe5xDAAxtS3RGJaFXG6Ugpo6tfei+pFEhBiBqoIUAyA5O6RA17ohx4/vwpz374IX/73/0Ff/0v/4JvP3vBgDncM3d0b27h3/8DL//nf8vH/+Wf2JWJd59APJ+4+83IdKtc7w8MfcdpvOd0/4apzGjNRNQpXoH5NHv9loliKLM3e4GdBIJ0pK6n6w8UMlOeCS/vycdfUT674273j8ivrkm/+za742v4V39NuLri6p0npsp6+T7X3btchGtS/w/wu98iN7eeUQ9UFULMTJ4JqDmjpWz6WWK1osSlZvqx2nFsAdDGLVh+U2lMCmhsjAe1XrBkF5d9cMVO62vwGgl/PC0RdQOBBhrTChw9E5vE1VMlLs4iWglVkKCEog+U9RYl01oJtdCp3XediAmjCd7/NFCCOdVzVXIu5FwosVBqpIRMKYFc8+LkNFehOXU2RsrbjcRFWKTw27g16us29v9NHqV6VliBIMTYsdvtiSLkaWSeRldHbFltYzhkVTRnU4qOkT5GmAvncSRPmVmF0A/sLi+4fnLN1XBBypnx9nPGY6bMGdETSW4QvaWWEzUXD/xBV3XpydeJBQMszmdCJ80wCyxOcawuYkRT6rXLqi1zUqq1kvBa+RDOhHCk29l1EwLKQJWeicopT0zziaJnA0jFeqfm2YMFtQWi21qXJfA314pKQjWiJFRl02Pwmz/W0XDRvRCthUj1zIG3WrKXecB8LmQrKl99FFcZN/01Xep6tZpIUBTL1hfUalpbKUhVSqlAWZz9sGmhYbX4ShfFahqHjn0UUp6p04Scjx5sjWSaOid07sBmFxlModlnv2qx8ouaq7OxGqhx99Szbs0HbOCneqBrFS1i3VdY9yMnucKi5G2+Wf0Tjuo//3gIGrZtHQSj+q6KtayZRtY9MogJNMYY6VJa6hgX4Rv3WYyi2vyeLTPNx1AwWqS085DNOa5MN93saws8kXbOstiJLUtl2XdVV+2ClmxRa4XXeodWT/jElmTZBF/afK42aGuHGvFY/P+b+0/+t6tS1bcf9EMe/PYwUPDgVW5st3ZxRdTbT/J3ynamGlwUjG5q/y4AyOfOb9ElgCAPvpEHoE6QFgtbzmPRWKGVcngCorXtaTbsq4C9j8vbO+PDzOlX7Jxq2Ourn3x4/DP6OGIbCm8tYo8gCnj/PVN0086auIeQyJq9ua8s6BxVpmkkRKU/7LxwvELq3HnzEJbExWghzgp1BN4iAYptzrBGTxcCaog2KA3lIg4KdTGEXsZqr5dgG8NmQeBRmYIaAPTRbXTZIIGam8RyXTZbxRJORpFVp8B6zy6UIZgQTR1npA+kPnKblbt7iGX42lP05xzTnJdNQ6MV+lc3gDUX8mytK0zV1oRkpNFVVRFcYKT1fhJbHFqNspmdsiG1WgaxWj1ebg5CEeuZOM/UeUbzjNYZpZDrzDRNjNOZPI0mrDBPME0GtFsXpBIoMxCUJLLUXoUYqaLkeeY8FY5T5XyayPO89KUMqPeahL5LxP1ACYHTKDCfmYr1Y5NxMrW80NP1O6P9+PjN8+xZkkyenaLiTeQrJhAQVL2W9HHaOIhnLsQj1E3kSEMgpEDsoEuVFCNdtLqMoEqZJrTO9H0k7gaIA4SnPH3yYz740U/56b/+Ln/xFx/wrWfP6QhUJkL5lPDZL5j/w9/x8n/5Oz77u18yv7lnCIE39xEZj+TxkhT2iOzJEjlR+DyfuZ1GBGGfOjqJUIzWI0Dw/aFl0WK0DFcXrCcs0RvCSKaUyvn+jjKPTPefc36duHv1c56Mv+VZ/h3dT38GFx+iwzUige7dd3h3/5S836FRCL/5DXJzB2pRwV02MZATE+cCkytDFnWiu0Ri8nqFFOm6xwGOsO6ny569zabIZlPXjWu9sWh+V65v8fFca8Zk2RNN+Th4M3FrTt3FtAGNkeRZROsLaWJR1qan7QGbc1WW1gH2t9MT1ShaUVgi3wRzoswtxgJXQShBmENiCpm5ZKacyeJrvFjv2OY8GNNhA1S/IrrahB428l3mzK5plm/8UCJo8OywMI+VcrqhExhi4JAiOQYm9ZIPnyBrh5gpWhnCQOo6OmDOSgiZPnTsr3a8eP6Ud66eclkj9eaGeXxDnUdjzdSJsZzQOkItyxw3ZfCEZyvEWDZNfbxqAzfeuNzQwiIEog6U7FRNFKSNo2oxN6oqOk5kuSVqptsNhNih9FTZo6GjdJkityDGyEGzO+62EtSljMx53bqBxbIMIkvdoLSa+8fRxiFEm0eq7a9RdaH9SVtDHiRc9XlavZTSeKZmZ3RZc6Yg3tam9X8Uz/7j4MLwZnV74mtdsEzjMii2fw1d5LLvbV9FkfFMuT8STyMpu4qwZ8JSEu+NHRZFdUGsXZQrupp6qzeaxx5n0xfQ6HbNWbVTaV3ZxB9s1FTzp33M3PWy9jJCy77Y0lod4G/6WMFVc7i3z21+f2tPaJmhZe9z5dilhyfrXlyL2/7i9FT3OpvKaVsLbU0bm6qFrxoIYQHa4v2XTYyv+Ji2bFX76jbGDipdTGlhttW6rNW6sSMtQ6+LKyze6qMu4wQsJL4V7Oh6yV8FPtaM0J8zLf/M44+tkQa0H5IyN9O0NawP/92ccvPp3/6mNfO8MpFa1rB1I2hz0YIGrT0d7nMu1srbfS33gX/BAsvFg4+Yjaw0HQulldRVB3lO0ljO+WHgdAOA/1iQTTbX+2dO49cGjkVNdUskLnWFOLoOskIlE0coVjQeIsTkNSvrptMUDXOZUU0mPyxWVB387FWL19jFVYVVxGg92PtRmlC2OSXVOfoe4XqwPvxGFtbITnO4FyMJUPHUvrknRlFr6WLb6CxyXDcG1ihIFSXniZYRfRBtU89adp2Jq+BAWoVznsk6UaUjz0C+ou93X3eK/qxjnosZvID/J1omoKm8VajFBUEohFA89b7eHGEDjsFBeLV2Gnm2DBzz7HRj490HxahH3gtsOt0znY/k+UjVkVozc54Yp5FxPJOnCUom1EJ0oRq7vZwSPNv5lToTC8SqSCnUeWKKiXMNTDPkk4G3oJahtAbpTgkLIMlAVVcTKRkVSOtMrTOqXtfQDaS0t2bydaLUFp1THx+rg1FtkVoItSKlmOjTYxwSCKFtCrYuS1VqjIRdR+wgSCVFYegSQ99b1mdKUDNxl6j9jjlccbj6Dj/64d/wt3/7L/nJX7zL0wMwVXScqPoGXv0C/u5/Zv5f/xdOf/9rwpvCc92zm2C+ndCc6WMixZ55qhzPd7wOcNv1nHcHM2K1siPQxQ5CIofoLiumhOsAWBc5e+sxF3GasABaCWWmJzPmyml6ifzd5+zGT+lub+Av/w/o5fdA90ja0b3zPu+lggSj8g6/+i2fyg03KXFfCt1pROfCWXHCmYuAabUaPhFCTMQYH61W1eaShm7WAOiSUWu7+woaw/Imj+e5M4E7eK1m01TyfNtqUR6n0qQg9LJmGYeup4uRyPreAKbAWItnIopHNJ1l4DVoC92Jlb7WQJ74d1rGJLjelgUQq7hoVquhbNkMd+a2tUYmM28CKsEspQX+2kuag7ox8tts2nZMH2UKQ+fA0dZKPt1Tb94waOXd6yuu+kuOonxelCJKli3VvLjTLotz3ichSke/P/D02ROun1zQ18r59o75zSsTnCojSxsEl+wXfRgFX3wWB2ZFDTCWpUHWmlWkjfuqt7k4Jct8LgPq9hFbA/P5SJCZwECMA1UP1LiDbsfQCbFXujByljMjxo4IQchRvMShTdeyuryfsFJ807baMYO6j1WrmlIyYCoG4qT6ADVCiTv1dj/Zmu1SIgVjEZELdZ6ZalmrHFVB/b6LkSRWI4fXKgd0yTyoU7hNQMnOQ6og1QKCKSQkdPSxYxd7BomEnKnnkXo6mzhhDUhIhFAJoZJTpMRIFmvzEdS8zqKVUFnahiyZ/ebf0cjDcXHGF7VVkfW+2jq+tOCULAmZdSGu4KUs6vSPc9SNvOtyzg1arA7hV+IeAwNtn3SqJ7qCspZpzNkzjQ4+Gwb1z3LcuGRhceG85fNbG622R3YdEoIxt+aJ1sPUF4b7F86Uc3tUySxi9+2f9sUNhPrv0pRf1QJLLWCB1GUuG9B4CDca9XMzwJu52yqfftOHrGnQ1UziY7rM3XYtPggDwFtXsn7Y5urafMEaZHChTnHBsxY4EMSDRP6Xf/cS23UfRjfru53rg/BFs1EtIBU8XOELxvZlA/9tV1b/1oqxfVoi7ytbd2yBP2/Ti9dM9nJessY6/tjx9YEj6pRMWU5U1jm15/3i5lqZamXfdWiweg7RtcLEHElT3SvaBGScEKM2Ycag8CHbfA8iXoTeIoDrMIUQFkXPit8U6oX0m0i5Vl3UPjc5VBCx2jkagA3rJuNfpNWoH5aJ888S60U2ZZdVb2lkv1Zx0N13PTFZ4X+XrEfeVDKTYoZhLPRa6bqIpMdxckouSPRIiNNnl2g5tqkhAXUaZPXNcpHnD2xAo/fO1OIqlIVSLCOn02Q0184ENoooNc+MxxPn+3vG4y15uqPkI6WcKWVknkemeWL2jCS1GuDz7IUsxkCN7joXJMxINKNKitQuUVNPlo6qkTALZOuLmbBIbwqeeXTnKQqkKHQxkKIQg6JSicHUykJMiKRFYRNVo62k6PLZmVImSs2Lk1VVCVoI+jguTlO6M2HUaPcFlYwyu6HTWk39LSb2ux2Xu4F9HBCEWSp3dPTDe3z4nZ/xV//6h/zVT9/hsAfuJvTmJXJ6Q7z9BP7rv+PNv/3/cP6Hf+TJMfOO7pFSkGxBhBIKWWeidBSNvD5n7kPP8N63eOc73yGXkfGLT7lEePH8XdLTZ0x9bzS3yWpYQ86E8wSn0VRXp5E4j4QyApVAJWFZ67EUdjFQBc6vb3n1d39PqT3PuCR8T2B4Qg6RtEsMT9/h3Z/+S3bdJU+l5+e//iW/vn2DnCfyNHOPghYP/ESy12MqFYlGx5tzYXoksarF+C3/WQHOAnR0CdHQ3DTrHyoLRdXqeGUBlUu2wkFj6/EVg6lC9iEyxMgu9QYaU2cOUrW9ulGypFaqO8O1uBPSQK3v4VTvMbaJyS+UNgcxhmeak+RAUARCJCYInhXRGNAa7H5DSUSy1/vVWhYaoFGzLLgozUldDPnqoOLgsu3hj+WqxpiAyFQKdZp5ur/ih+9/xE+7yIvTPfPplt/pTIlwTsEEuUpBqklV1VrRaaLWQt8l9ruB/XDF5eUVl5dPCRWOb15z/Pw1en9LKEeE2WyyCKJGAzcQbXZ0zed5gNXXgca4zI9IE0ny9dWCndX3ef8sadTVwGoLJBAVi6KXTBkzU50IYbI1GXbEfiDtOw5Dx64/MMTMXYycpoyUQozJ2jO5MMsyf6qoRHqBIsFq/yue7bRA9uPMY1gCpCCmlOtgq3qAtBZFYySERNcndkNPnzp7zTwzU9GC1eLXxowyvyY6ENlmJVZvzYNnYADZ/RG7ySuBREyBID0p7EjSkUqFeaZOBZkzoRQ6IIZClyrdoNReKCkyEYhFrCSESlSoweY4u3J0qWVzSi1g7z2uN07og6gEdp8HXGgs4LWYvv8s3j4LAMIDrY8mOrb9XD/3VWujvWaLH8QDlNGFkSzTGILZ0sbiaKKK1enWSzG3Z5SXz29gMVgwCRdOSV0ips5EVGIgRGcGiCDR7XgpJsyHbe6tJ3Ot2YQCvYh27YXexhVnHuly/U1lewH2m+Bc2y+DrAGi7VFbxvTLg7UZ18c9FjDjy28FhrKAne1rm58Pqx16eFUbqufy/rXudMX+/vlhvWdjwxsu1KIK0pgXootCuPmISyRsAaZb9N1A4/JTxSKiDTi2OaP52w4e3bcT3I42DQAe9kGWja3b/rsGCB4+50uYPzWjXxs4NudEKX4z2eW3EW9OgeK1jjkzDImuj5QhkMdsGz8NrAjzPHM6nZAuEQcDYrVWo3i0z7PZt/fhjaSdyx1dg77dKKvSWnMc3Aj6o2sxMETSGoVX3/ge3FjbjU2WRamwRosMIlBqZZwmxnH0DXHdKasqEiJd39P13do7ESv+H6eJmjok7shzpYtClHum+Q0mYfnNHlqLjVnwKInxaKGpyZkO+dKIuCXEtVGGFmcUNwCQcmbOeRGjyfMMaXLAFS3LkDPT+cTx7objzQ3z6Y46H6nlRCkn5vnMPE/kPFsDYpcKhzUi2uYAWWtsG+1LDZ1D1yHDjtDtSHFHrBFRIanSwQJCI5XoLlXw36MoSZQYbHxio1ar+6stgajeiiAJQTK1BOaA1f74Zh6asqw8FuCwe6LRiU0G3ta8BU1sjXZdz35/wf5wYNgNpNBTcwKNXByecf2tH/LTn/2MH//wQw67AK9OzJ9+it79hv7Nb+HXv+T2P/8XvvinjwlvlKfxwI6Occ6M8wShEPtMSNDtD2h6h/10yeXhBU/+5q9492ffZ8pHbn7/Wy6BF+9/CC9eQNdDLnA+g2eoOY9we4a7CY5HON/C6SV6/xpO98g8k8dMmmf2MdClwKsp8vHtyPhff0UX/lcuj3fwwYeEi6dofgd9+hG7p99l9/0LroqizBx/PXKez5zIDEEZkpCJzizORjf2IFEp5pyXR8ocr7UbD437QhFzw9/2HnUA0By5tw0ebBRWPUUUXKjM6rUinQT6GNmljiH19LEzJWCX6jXWRl2yK3XO3jd021ypRVZdFqZFLJtl8H2igTUtuhhjool/aQgQzMiKKikGBhFCSmS1uvcaXFa+VuZsbAYToqgr0EHXjUDWmtDFOLMa1I2p/GbnMQgxRW5e3XI8nvnRd3/M//X//H/h/3R1xf3/89/w7/9f/4ZpvuHV9RUvU899scyS1LqUX2g20L7re64unvDO9TUX+wOaA3e3t9y/umG6vSPmkSi+P6q1AIqiy365UKbUnY1gQQOJidh1xMEosTG6nmpdHVBRRXPxcoK8ZFW0Zozb6GFD9Yi7Kgnfj6fCPGcT1wwJDbdoqYj2dBLYX+zo+h6NJ6Z6x6xnF++KCNmzKXiWxHyFGJMFJUpjdXj942NFANoYijlf9q/7GbVSizEgOgwEDEPPbj/QpYSUQtGCRshBSFFIGliURzGwWFvWSnVpTdOUklcez0pHbG6W3UhiAFIGOhKpzmhWarbekJacVVsfYbZygKFHu2RK0rPVVGZViprdtPIKr9WrG38Hp9Z524JFkEjc/W57jJ/bIpwVhBDsvcUBcm33abtdXXDosTJVW82LVdXffZnFPVsza+11TTE6xbSAxpZpNB0LC5YtPWdhzTbB0jGgKfNLtBKaEJO1Mup7uq7ztg2BENfsj/o5GRfHP7OapoMU24OlZqSs92UIgpbgwmJqquktKLE5tp5x++zavscTNU0kqSmtLoCxPafrzrod40e7Ff2rHuAu1r9bEHX7mGzOq+38X35/A8OyvujtL3TQ2Hz9haqsDtI24PFB4Ket6eXxZt8fjpW2M5cVELJko2UNsrbzX0Czz2C7qFBBi9cqr0JyD+ImfqkbVPzVxx97zo9/VnO5lev7pSceRC2KKlOZmafARd8TDwN3NTOPxWvNPGKulXmemcaRXedF9bpKm0vwHo+qpBSXtHtTGQMHLx49qdr8BFkGetnrxHQTK8UBIv4+xVSo8PobXeo62gy0GxpX6VPMiDUBiXmcmc+j1fXp2li7LYiYOkLfL8qxgciUC+M0MddKCgE0MRWYQyHOdzCf/jlT9CcPrWppbkddzQE1CqrdKOLt/ZoaWLt8i57ZI6UWxDNwSYRpXp26UkwFtc4zWq192/E0cXdzx/2bN5xubynjCevzdbaM4zxS5tGiba7E2yh3wMbwbCIp6tLhahkiFZAuEyuEGohdXGhrEaVTJRSQMhHqTKISgoFF0WK1ljUj1A3NpnomcXaqanblWKCa8+ZLw6kFLdrXHKxHclT9v1aDlwiY7HYA+hDZ9T2HLnK537E/7JEUeXM+cXd/wzQPXF19hx9+9wf8zV//BT/9ybd5Z9jBF1A+eYm+/AX1i//C7W/+geN//SX3v/mCfKwMXHCcKzOVcwjciDLVE5dXA+9+5326974Dw3d51v+Ei3d+wv4n32L/7R0ljOxuj3QqcHGA3X6hHpKtz5VxpDOcKtxWuBvh9Are/Ab57FfUT37H+PqG6f5MncxwziVTOqGTyPHmnt/853/Ps9OnPP3pD+i/81NqHKgp2xo6vMPuuz/i+fyad0+vuLl9zZ1mDkl4uu8JVdERaugI0Z3GxaluffceaR7f2rC3IHB5SlZNDqN5NvukjRi/4KciblLFaPdhabMR6UKiC4EhJobU0Ydk9UeluKJpRWr2HmQuGuQNrNv+vq5rXWr6VJq7s6H9bwypgve1wi7EhcgkyBKRkZQYYqQPgSLJ6JzBMmSzKuO01n9QC1qMkqYqXvO7Mc/a6D9ubLW+Pczf8FGIIVLLzDQWuv6aDz/6Fzx/ds3+3/1HdgQGKXSpUHVmyjamsY1TrUSpdKnj8nDg+dPnPL18Sihwc3vD7ctXnG/uIc90Wj0mYPay2RWhWG2WZzcqFlTqhx1p2BH6gdAP9Ps9/W6wmkXVB0Iu0rJm48g8juR5pOYJzSPzfGKeR3Oem8H0IyCU6gJwQdEwQbil1jNztR6UMV4TD0/o0yVD15HLG6vLjNEZHYVS3IbkCpKsrj1E64Xn9VwLzesRjlarvuQtpIFIczerWq0v4myUZP1wY4pWrxhNMTEFYeiMthyK9WI00FgosvoRBfN3mnIr4jXIsfUJtPWxOvaWFesk0RPp62wZ0OKBJrVX5zKhsxL7QM9ASgMdkYoyqTK3LKN4bd4SJG/BqjWAVGtZ6qGDBPcW7G5SjyI3kGE+tyzXV4uJXlXPrMMSIlv868c4WqZzkxugBTrEN9TWxq9R+lt/xeRCiOKSl6JrNqmJ4fhqofWLFMJC8QgxklJH6BISExIjIaVVh8FBYxXxvZplY1JaP0wbLg2gndf710CoEamdtcmZZ0eWhZILBFtjoa5t5KABYlnWkvVNXcHgFjwr8mCvf0Dvb69t5/q/BXDUh/fhCv629798RQDCF9eXsqKeKfT7eU2z+cpt97uz6xas0GxZNeXvWnWtL3WQKG99tb/rK85NaP6hv9weKizgcRHEWgqpfbHiOAyjRqoGHyMT92v3mbIGAnxbad/0JZ9UkS8P0x84/nniOOiD6Vpj5e0BX7AYWJhzRRNoNXluCRuE7gOi1foKztmiY2CTNefsUU17ecTqpLRW4tJGod0gm5NQFs73Q2fP6pVsEJ2D7JvwVnGrvT9IW0hqN6d/Rqm6yrqrqQFO4wmcoprbZ6kBrxgjxMBUi9fYmaWfNVBiokrhnDOlHikl0nUBxkw/zl97iv6co9Eu7PR14ZBv4zatD81STdXS3gswAigUa0pmWcYmjFPykq7XeWIaR47jzN3tPXdv7jjd3jEfj5Angs5QJ7SMUCYkZ2KttOynSXevE7tuhrbuVhPmG55FHYhzIYRMZLbMoAYiStQCeaZOQOmJMpNiZYwFdCbPIzlPqNeniBhozPmMiEmk12rXCOoGZKLW2Ryv2NbpeoM+FnDsQrQ6EVq0NJKwGzuUguRs4eeqTPPE7Xnk09sbPjsWhosP+ZsX7/GDn/yEn/3oO1xf7uAV1E/uiZ+/JH7xa06/+nuO//RfGH//BXKbGbQjERgVTjFy7jpu+565v2T4yQeE/+5n8K0fQfddLvd/Ce9+BM9Ae+sReHjfznuN0345ItgOKaD3wOmI3L+PvPqQ+ptfkX/5W+bPXsJppBzv4O4OlchF6pDxyHTzhttf3BI48jT09N17xHCP6gXsIlxc8uTDb/Gtm5eMN/fk44xwJpQOmYvRjlNlUitMVynmICIPDeg3eGyNzfKrbhorLwE5lvqHquvYNbEFWiNgZckQiAR3+Ex0yOipLoQTE12IRlcrmTq3GkYF752qfi+jTfBBjHngJ77W6SwVldDAjK5UvLZXmpBLXZyx6DTAltkkZ1Mw7jpqjBQRsjdVDwG068zRFrHeqT5WbYC0bqh+7UeayXQn6bEyHFRKnuj7xOEw8MXnJ/7N//3f8vrygvDb13x+/Zy5i+Shcr47c8qZSCKEgFYT1+qHyPXVBe8+u+bp4ZKQ4e7NHW8+e8nx9Y31THQjb+DFxIWM5VARqgl5dImYBkIckOHA7uoJ+8snxH5PSB1xMPZL8ObsYaMeaBTKTHHwmKeRWs6U+cj59Ib7uzecjyfmufjNLP6vt9BqzkotwBElkKeOqlD0QFcukeHAISl0hXF2FlMvFM2MJ2XSiVJ8fVcDoo1NsQRD4uOo4xidsTl2LQvIGoVWO6dSTJF5LoWuVnqB1CVCTtQYqDEQQkdMikwzp1Jc1VJc7MeCoRX1Fhle3hAjKfWWwRTsnnHqOGKCVikG+igMovTeU5MmUqgW1KUY4DMVuIHUD8RuoHQ7ZgKTwlRys5weALUAfLvexfnVxgqw/cSEdNZ9qbYMjMcC1fuV6jJw0DLVfhnr8UioI8bwADittsZ3BvE9wQFjcGpq8n6h5lusNFWtKwODlo3zixGJS8skQiR1HanvCMmBowcXWkslJ5ou+9Y6KOZvtv19GRwxqj/te6rXIUu09REKEt3ml2LlBe4rfzkDJi501OrlWtJgBTGN8QJ+z7UzeRtwbIL4j374OTc/dZ3Lt162udTmz2+w53q+S3KHpc+iJcAC4sEf0+dzH7ONZ6mLEvSDL1rmrH3TBpA21ssfGKwHfqKCSKVqIGiw4KprjLQWhStlNdj5eWsf68u6rs1lfJb5fDAC/6zjawPHDS6zr9cl2YpuT1VMfbRg8c9cQUlIHKhlXGu+WjhIra9fnS1fYg00xXtDlqU4VT3KSlgj2o3qsPQ7E8AVQNezUrqUqNULsr0eEbW0/tJTyGsw29i2qITv3ijqWVU1+o3YN8yz9/CrBdMlM6dJHJAiQkqRUqxv3tB14DWFs7ehECrUGYsa9JQqUB5J/l98YVWWzONC2NgYx4UPH3wUpVHkjIpdQiJIJRRdxqI63aXWgpTMnAt348ibuztub2453d2TT0ZNDCWjmhHNaJ1BC1Gw6BwRkWjj5FGwZnh0MUCrWIjxu40mWgUTiMgFZfIAgjUvFpf2z3MxZ6ZcEHUHGqnlRJ5P5HymVstSmcLdxDyfCFK8vCpbVLxkhIIY8QfF1kSI7mAXW3sbE/ONHn2KTMWyraVkYhJrm1AL8+lIPgvnFJh2R1Is3M53fDqP5Cfv896Pv8tP/vuf8dO/+R7XV3u4O1O+eI28+dyEcH77K6Zf/gb55BXPp0yQyt18j8aBw9Vz6uGanHZcHA5cfOsdrv/2+3T/8kfw4kOozyA8hx0rJ+brHhHkEjR0cHgPff8Z4d33GS7fI/zit5TXt8jdG+L+FfX2NXm85zIqMiv305HTrz8jxZ/Tx/dAnqABchjousLl5XO+9e2fwF2BsxK++JxyOjExM6ZESWYcsipVnM/tDtljHFtDCCzre1nhiwFstd62t3pF+ELZtjfbP+aQYvdrsMxFJ5FeDDgO0dRTQ8WFGLK1cPD6xibYYnvamsEycZtG3/EoerT6nSZIJuLA0VWHlzquJVo/U11QJ9ZVWK2UaqnSavV0sevQlAjYPh1CoAuJ0gVzfLGyAAnVNqRanWjSKL74e32E3+rp9U0fqpVxGkkpcHV5yatXb/i//U//D/6/u4FvX8+8851vU65eUOdbwmcvCXqizpb1EYU+Jq4ur3jx7DnPL5/QV7h9feOg8Q2MIx2WDQnuCFRVZrzaICaGPtH1HV3q6bo9cX9F/+Qpw5Nr+sMVMQ0u3hNMqVPUhcLWuqdaTa0gqBJKpssztYyUfCSeLpDbPfLmBu5O5KlYpitXNJeVqaOYPa1rmUOeRuZyT5x6dlcHhn1E0oCWMzOCpGDtlqLT97zWtXpLrHYDOiz12tBv/ggxGF17cVI33+02UYGcrTwlnEfvTduTUoKus4BwzLgFo5TKJIC6YxqiZfuW+5zlnheJdF1nfStVKbMau0dM1bbvI7s+MKRKKoWYzzCfiXlexttq7CuaK/kszOGOFDq6y559PzBJ4lyU8zQuwVcrOxa0rhQ6lutnCQAtzCTfsxYmEBZQtnYikZbFaIJAxpBo52djUbFA+2McK3DEffaHMGPFCl7D5i03krPKUHVVamc0ecZ4YVA0H8n3QdsfowVmOssu0jKNXXKF44fn2FgD2/1/ARjSHH91NoGwyPiKIsn22loKGjJabR/WnNE8L6GPVj5Tq2Uaw+LvrpmoZkvaSamzN1qC/yE81AVALsDskeZw+Q4egjJ9+7l1yJaAhS7P61vvFtj0g7Rx8jSF17bGIJYEEAXMLtaSve2KLqBxzeSx+M0NKMrmZ9lJ9O0zb2vJqPkPRKl8rVlAogUK7FwRC8w0SmsMIBrBlZAXgat2Iy+3wYNVtjzecO1bN/5XHl8fODZjhRsatojfKQpitShVBA2RMWfOIuyHHb3smWulzhNLT0gbTaM0TrP3yrNoXGj9psDBXl0jR5uF2uStlXYz67K5VxrlMVAp5FrW3oMtQi3l4WC1idsuLFkjVIIs4hGotRYo3vvQ+v4EqxXyaUoi7LqOkDp2g3DoOuap8uY4Eqr1OhOX/D/O1eh3GJXkMQ71MbFNHgjukHp7h9VcNodPwev0Wg1Ay24RjRLaq1qDcDDBjJxRGRlz5f7ujps3r7m9uWE6nyyr2OqnagE1wC1ivfJi7EmxQ+is8rBlTxbet3rNhNMbMeC9iC3VQqGSc6Wqi0dEc0CrZrJmpjlznjq6cUcaI3OM5OlEnU+W+WyKvprJeWQOQowFrQIUVCdqWduIWK/RFhX0ga5t8TxSjaNUj05abYMZQaUW8cwoIML5dETkTE5nDs+e8N7Pvs//7n/4V/zrf/VjPnp6gCmTX38Bt78k3vwSfv9zjr/6OedPbwinSl8hMNGnCbnac/XBe+j1T6j9e8j773H9lx/AX74P7z+jcIXpBVYPBojJwjdHEs/kf1X2rm2agmdKMyIZuh4dLgkXV4R4oNu9IH96h7x+Tbz5PfryV0yvPqY7QZginBOn88Tdbz5niP+JQwyE/kQa3gW9RNIVl0+/y7c+qpRTZhK4//wz7mtlnxI5WZNvrQ26eW11ebxaVZ/QB48t4HHj/KBGS22mcw2kbD/BBSpa4+pgfRg77xHYh0QnyUHjTJla4MtrclumEJBGb02d06wSkkzcIXWWGZTgwR1ZK+xQz8pkrwfKhZoLWmaK92iteXaKqkm4iL+n5mytRZpzhjmfKhAl2rUkoVOj9xWP1jYHtv3v7dhqa8n0WC6OBCVPE6KRLlmLjDflDaMcGJ9ccP7gBZfXSsr3PB0uOKZX3L28Ybo/kiQy7C94+vQ9nl68Q6odp5t7bj57yfGVgcbeHXtzxs22FS3WAzB17C+uuLh6wsVuR9JATB3D1VP66+fo7kCJHTVYvWAbDyfYLSq1xWvRqttQ6XpIiVo7qD1pN3B1uCTtb0iv7zjfn8jniTqNlNFLDXI20NlocEV9f5wp5Z5alBjOpNDTJaUPHbkqUy5UtR7DqYtOfY1UjWtmGVYBikeyjylGSlOJV+97q4rIBqiqBRXnuRCniXnuTSSqS4QYLFCcoq9NmzdrLLTJ7Bofzh7dOJfij4dgHW5t37TymqFPXO06LgahkwnmE2W6pY5H63UsWDuXOKAUpjwzl8r5eALp0bhH4t7qm7uZISXmGChFKMEUV5vjLS2OvPw0FOGBNAddEk0xvQafEy9BqrX5CqYHEFpmvGWwHGSKPlbt+JoEWCZNGhBbr68JFnUxLkEZUW+l4m2F2IidLCJRDTTGaMHumAip8x97TFIipOgt3Nj68A/P1RdFG97l/HG7vkB2v6dEloRHiAEtwdRdxfVvFVDrXW1Zbs+QeduCJevqmapFf4lmfsVZJPa+P3SrrRkt+IMv+m88ljp/eIABHwA/0a8+hTZWbcI3Z768hBYAcFvZel+LIhTXObDSmJZp1I3NbjWtLP+21j1hyUKLA7LFUrdgizpKqavdbaV3wNKbcxEwajZO7D5DqmclZdk38GC3lfwLqq0s8MHArCD3K8b7jx3/7Iwj+Mm0qIhHTWzPqEBm2B0IXc90e8cxZ/rO1Mf6uWNahE9w58CprdNEPwxI6kxtzrOBLaJhCyj4kq/LQhBYVeQUa/1BcO69LW4TdmiOwzrx0m4saUK7bVBbcX7TL3J6iTtWrVA2ex+/mtVk7D0jaY4U9ENP3w+EEBj6wMUh8OSwI8/KVCd0tEUSoxVSj7mQa/YI49edoT/vWBwrVar3qVrmV1oPJwPVRTPecQ0RiCkSQ/L6i0SIQtBI0konBhxLLszjiObKaZo53t1wun3DeLylzJNRRmlzaj0hQzBBjNj39P2eLu1BekoNln31qKX11TMet6lf5oXKI9UibWWezSGuM8w2lqSMBki1WJYzF+IY4b4jdso5dkyniZrPBPUWEChUz5Dk4NLqZuiFTFWjqFbNdh8Eo6ysFolWi/8ox1Qm33+MUlWLA/wQQCISjR52zhOxKzx7dsn3/uoH/Oy//xf8i7/+Cd99+owO0OMbOH1CuP0F9eP/xP0v/5H7372knE3U4uU8EmNh994V+48+hPe+i1z/NZfP/5rwnffh+z28iFiL6bZowxKQWGJrbQOH1TPZhr3asQQrBPrO6RkACT54Ad018UmFz17Cm0vkEroDlE8/sRrIPpDSnnHKfP7rn/MsnLnqT4TdX1OHCyo7YkhcPHmfdz685Yvxlk9Od+zHkX2K5NSRU0GzKUPjGbTySAEAXX4MpK7OjdNXlq1uHTtniNOCU9qCXWI1Op337jSg6D/R6KlJwkovHyfKPHrwxj+rOUbRWgd1w0DX9QtQ7IaBbtjR9TtC6sxQhdauZBX1Qb05+pSZJgOoWjIlT+TpzHw+MY9nA5EubNMAYJ3zkkoVEVJMRG1sFkhibV20q2S1gKCtE4vWLE5tuxebnX6UGbRjt0toFeo8EznRX1wQDpfUvfB6OBOr8l46EIcr+vuJ1N1ZDXkMDGnH1cU7XB3eJ3LgeHPH7Wc33L++pY4zCejcKWggquqMSiZ2if7ywJNn73J9/S77/mCti4LQX14SLi85S+RcrBYqtiVW277v9s7pbQVbX6bA6/Q6IshA33f0/SWpe0LX33O8uWO8uyef78ndHdOxMOnZso9qO706GJGQiXKk6kydR8p5Txg6UkgELKKf60ygMnQRjZFaI7mYzoHVFAmo1TqGRzKQXUpQymLHcfGYXKCV4izAx8/LaKuZuSQ61Po8Re+jWMtSu6jqvaox9B+DoBqc+STL59ZiAZGUIlE6gihdgKtdz/Wu4yIqYToxnW6Q0z2arayl63YcLi7Y7zqgcjofubu7Z5xmjvdHajzSpz2yGxhC4tD11HymVrF+0x5s3GZ3wev3nQVm4MecP8t2eOZLvQfuEsVanet2HwYPINp2JYC3j3mEoxRXgl1uelmCUX5WLuJj9d8hNN9yJd1TqvkoW/VbBzLBVdUNNJrOQOx7Quq8ptGyjQRZywk2x3LVbb+Ulvx4q6aONcv9oAYVC+xZDXCwvrd4/0kHzZJbUkWokhe6bctctqlWacyWVvgj3q4DamOorafLAwTXEkCP5bC+ffyxfVzW+HP7WcDjw49Y/m2tMFZRpLAITGlTz81lpaf6e1hstYPG1mIlRlfMNWbHkn18cAZeYrcJSJimQF1Equqm7K/hFmlqlo09U52No9hzwqqg3lZcCwQt1/zw3sbHaM3S/fHh/+cBx5YtqB4PV9yw4BtHIabAYegIseMuRKY8cZonDtG4+bnRv6SBRjvhuVSmOdOnskSt1t5gzvt26XGkcfJ9wQt2M7gaG9uaDTyS7U5IUzPT9jm+CEyV1a6z+g5uN2qrxcRS+KjRaEthOo+U2WSTpU2Wz1LsOtJuQGPinGfG40QNwv6qZ3+44HI+U+eRoYsMfW8Oak28nmaCQt91X3eK/qyjepRDgOD0zqjreLXNttYmAW0UWpMFjxaJEZZecFGj94ayQvhpPJOLogRO08x0f2dCOPOE1Nnrxz1r4uMfUyINPd3uQLe7IHV7oIcakGKRvdQlui45cKyE7NmLkqk1W8/HeSbECZnO1EmtRUbJtiEGq8WoHuGR8Uy+uyGQmbqe86xep1pXhcImEBKDUXhayxgxMK2LBLJdr928lh2R6sGJR9pP51JWQ9G+pJrTEmKCmMilMhMZrp7y7R9+h//93/4r/uVf/ISPnl1zIJqK43Qk3X5C/v0vOP/yn5g//j3hfqLqwFSVKonD9XO6H32X9N0fweUPqc++T/etH8K3EvUJKCYDH4qrZIa1NUOzxG+1T95sZA+PBxt8bAZc0VAh9vDegAyg+x3180zYz4R9ZJbAuQjlfqSvlTCdKMdbTr/5BalX9t2eEJ9RD1fU2BMOV1xeP+fJ9XOuXr/i6pzJWdAyWcZRrWasUsmyLVL/Zg/bOxtNzXGOCE3dtDkz7bWKbmqwWZwLcJAVAr2L3+yiic0MXwKNM2WyzB+lWMChOa8exOmGgW63s59+IA0DvQurdMOO2O8IIRlodOep9cb1TRityjQVuvPEPI6UPFHzxDye4HSk3N9TzkcDr9V6rS7tNkpB59moQ8kEi1BhVrW+nyLWVsId/Vo9OrtxthbDji7/PLQM39zx5NDThx3zaP1nY7gDEY7HL7i/G5nmC4b0Abuh4/bzz7n9/FMYZ54OA1f9Fde7p/R6wXQPx5dnbl8fmc+z99Vc6WwVZa6ZykwYhMunB568eMGTZx+y37+giwMR05PRLjIHa2fSAg2iSnTaU3OEVT1DXKtlxYK39nA6W61WY1OIaAx0+z0xXhLTFV1/y3y8YT4nYjcj8cx8zORcTNuA6OIRM0EyIWREC2WuaLgwsR7pKWK2IYiSREAjpZq9KUVd0VuoRYjNZj7CkaJnh6juqCm5eFWaqourrBQ0VQMpsyuL26kHNAZTl6+mut4c2erFm6Jh8T2DA7BWAF5qJdcKRFJKdBEOUbgaei5joC8z9XTPfLwjjCM9kd3hgsPFFfv9ga4TY3xQrQyonMk5M51O0B8REboEhxSpfeelF0ZnbBtR84NEvPbPMzIr36HdXrK0wgkqTgl3Z3q5DxvY1kV/wpxaq11+jKO0NijNZX8LOdj2YP1jQ3CatLAJbm5AY1MpdTX80ACC7z+x60j9QOh6Y825WJIEY8+p6zZsjy3jbi2/gSUjoyvYaEEK9Yxuu4AG4BExFeMYrA41uj1gUw9XoOJiZ7Uu82Nqhw1ceWdlX4oNILfvflssZ2118bigcYtn9MFf219l88qvhpZbW6nL2vU+jdF/HDRKtQTXFjTaevYyKglLzSGhrYnk7VaS20NXP92ARxEW0LhkGDf1s7Vk82tzE2FsN0uzq0pL5lDXmsc1gx5MUKm2AK4uPup2uB4EA/y/8vYTX3F8beAYxNoiNMVJC08FpyWo1/VZNnI+nogykoKQYzLpbPX6DL8owydrFMiiiibQEIKsEYOmLFbK4oXrAqXt5qntJgjWqDb4TqHa0vbroDUA3G6MZkDN6Wkc/LbxeU9JzzSKhU5NCfZ0YjqfjaIKCyBFradXGgaKBEangUWUVzdntLvnYg93r2/ZSeDycEEgUEpFB8vITiWwe6Ti/zWC4QXtTYWrSY+312lrCGx0UtTq9Yp4q4naUvFmTHO14EEmg4yUCnMuzNMZyaZgqmIwvNXVBKfopn4g7XZ0+wNx2BPiDpXOFMTUqLGxWzdjaiXlDs2zUTQcOKYuw9yh0UQjdIRaJmYX/aji4F8FcmY6HtGambuekUjNRkxsGdFWp7Pc4OsI2rVrZGH4Rsu8qIRlg38sQRUAQodoJYo1hUbMiY+pJ8TArIWpjNQ+cnj2Lh9+96/4/nf+BR9dfsQTTSbBHgpyvIXffszdf/0N4+9fcTUqSRIv50wlcvXOh1z/6Lt0P/4xvP89dP8+4fkL+CjAU7+narN3LlTijtKW37+OHA+ND1+xVz2wFG4st489BTQg+hQNH0FK9NoT5MDpk0+Y795w0EJiz/3pzMuf/5YrLnmi14TvX1Kv3gXZ0Z8uOByecXX1LtfHSp2FPN8yzhNFTelxrlbbGsKf2FH/mUdre6PutCz+vF+uLMZOH2z6K+2tgUZrWG1COIEhBHYxsUuJIZqSqlQo80yeZmou1mdUGhw1BzZ1Pf1+z3C4oDscSLs9oe/pdjv63Y5+tyd0PRI7JJhioMRW49Oo/GawalXCXEhzZh4n5nkkT2fCOCC7AfoO7jv0eE+dz0ZjF1MHnYupMleAmIgx0UVrq2M9WCG4amhNlejAR33NbPexZYX9YZ/iv/m4iMKw68kpkHOl6Mw4vmI6nThNJ3quuD8E5pi4+d3HTF+85LLf8fzwhKfDnoNGuB85j5nT3Yk8WyVrEAVva1ARZiozSuw7DtcHnr33Hk/eeZ/h4hkhXAC91S9GJevMmL1XslguInpT82b3zEZuAoetvQctgKi+Bo3+PlXQFIl9R3eZUPF5TwWJEyEWziFyvp8pk3mggtkYc8yNWl/mQEg7OryfHYHZ5ya4ql3GRDuqKcM07gspBXbD4wRWYzT19QV2FF0AdqupX1vmOMhz5dBcTZxIQ4QY0TC7CqtTgNu9rsXLXcLSXFy9d3IDBsVrjUUiu67n0CUuUqLLFR3PzPcnymmkL8puf8E71+9x+fQZIQam6chcMilGDsMAWTmPlZwn8vlI7IQgiV6EfUrMqWPKlTkXC5p7cBcxKqQBx2hAxM1ic6sskOXqqz4+rR56DbbpkiGR9lfb1h4JOTaBugXabMC+mZ/glH6vMlSj/Vkv8DXDuLRl80yx1QfH5SekiHTJMuQuDhWxdaGlLkFBWNxQOz9a+7f2XPMv2gQsQ7f4bO21zawaOaeVGkULCKn3QPcVXFFqtiQBGpY6TetH7kVe4h+2YWg0+912zyUrugTmGvj/cu3mN3k88KFaBEDXXx8857RMWGKXvswaPXnrd1gw2ACf06mdntpqGi3oUlbQGJxpFzqQ6GJgLdMYSF3yn44Qmz4Hy3RuKaeLeFETi9sCxzmRQ2tN573BFfeDfB3aDWX7dzFF30abDRiLobX4Wc3edqLe8k3dB/lTk/m1gWM05AgYDWYr3NAyUG39TXMhSEFCInSJqpCrsus6ui4yldlAqF+oXXwlzxO5s+LwZUPxBRuCC5d7irGlzEVYU8KqfnM1Rz8sGzweTWliDC0WtdYyuWTxAi/bhmMmK2ACDXWu5DEznScTyamVlgVQ/AbuOmJMBk7Vi6w1ULXjzc3I/bGQJqupEi3mlApGoayZTpQuPRZwbGDaYxXVHDwJlmFBG7mBzdryTbQUspoQQgmBEoTiG2WrCaDhLTeqtRaiVo9c2bgGEVKM9F1H1w90+z1p2CHDDtJADaYPGokIcbnB1W8OoilfhRisKLwmpBZiKcTSE3srSB9jZBpPJi9fM4iLAChohnqerQ6ry5Q4ULHar00f1uXyUaOEqmI923CxEI/WaQxoCBQRB5szIlaf8hhHv9sTtBIRknQgPYSe1HcQMiXfEztld3XB83e/zXsvfsqzq58w9O8AwRqIv3kFv/45x3/8FXe/eQ1vKjL3hDIhtdA9ecrl939K96O/hXe/R75+D3n+hPDOBfXJTKOnSkju3NmxhYtvmcPFEC2R3U3ktf1twhS60GiW11SF4KD0qoLu0fABpCuEJ3ThwtT+dGKg0gUYz8rxdqL84nd06R/Y988IP9zDbk+6uuby6Qe8ezOTj5VyzpzPE6d5pJrHi2ZhlrrUhn3TR6MxqcoSfMPHQ97exJux9vXp6u+0uoqWuWg0VevT2NGFYO1GsmcaJ5Nx91tpGfPYd+wPBy6unjBcPiEdDoTd3pq4O3CM/QCxAQYDja3OsVF+AtjeWAphULpa6ebMNJ6Zxp40WgYz9j2xH4gpMd1HGM/Eki1YV62HZp1n6ni2aHrfWx0KXgYrAiFRzU+3a1Q8yLcBjG4nNoon3/ghc6YTozpqEnIVYlVmekInDDEx3RyZa0VOI5fScR0HnoSOS4U0nUyIa8qQT3QBqooBOczu5ZKZBWTouLh+yrP3XnD94l26wzXEwZpxhKa0amBmKsXGSVzle3NvfckbFV+QnsEF2dQSWt3MpEqmEkIyKuuwI5SZoBOdFGJMiOyp9Y5STpSaV0ZLheLqxakm+qEyBBPjkAJJ/DyCKwRSzQ1wMFVFvHRB0TI9yjwafVR8GCzr5BVDluUPDkLU5qRk610552xjloLXuyVk9hZgWjwIa3PaAqgqunxeC0CK2Fq27caeT8la5yQVmM7k45lymtAMQQZ2wxOunr7P9fP3qKLc37+0z51trmuplDqZeF0+E6ZI7Ab6TqgxMqREHwtTMNqt+TFhYRKkaPYXxftYrqChtfMCp9tJsMDyRhltyVjRavVYjMFjxlYbuKIBB5/DB+0p3rJDwQEjLNjRxZmcvhnjA/BIjBQwurwqUmZ3F5w1oS5w8qWNx/fruG294GBGdLV7bYwXQKfreW32OuMeOYjxFg2idp5UpVZvFxMscFG97tFaIoV1IjagcXX/fKLaILKBIG2sHmtjXYarhUm/Yg9f9rB2YsJaJ9PIty2IaEHWhz07G2j05FUpaH6rd3EThIsdMXaIdGgTRIqRlCKp8wRHjIjXBDSi7xJwApYss+rK0KnqpVHZ3y/ILDCLl2S1iWmEYju0WqcDE8/xGkc81yy6MgBoDM23gWEbu/U++GPH18844kkVN0xntfhSxIsv24CgaIjm5DsnfsyFTmZ2h0i3G5iLNRhuXHLBHMk8mRBJF42ioRjgXL14aYEFtOpKe/SLXQQSPCQmm53J3u3RGf8MNhCy8d1pk8m6DpsAT82ZmivkTPBo0nLNhmDtJnTOfC+CNw7BahkSY1HmkhlCRIsyjiN932NQRLlMkdp79OoRjpatXWo3N2nzujSrt0EyY7aGt6yWzpzCKkYdKhhwjIoXYVutzEK/wx3cGJaoT4xGNdoNe4b9gX6/R/oBTT1ZAoVAdUqA3eStX5muCz+a+i41EDyjKFqRWqhdR586uthxCj2TnK3GSi36nrVY76NayMWvqYuWsdYWsbGKxgU7LlnrpjKZPKJkunRVDEibg2M95ozS+jjz2KXeNzsBOkR2hLgjpghyZhc7uhTZv3jOi3c+5PnlBzzZfUDaD8aL//wX8Pf/keN/+Pe8+c3vKMdMVzrGuTJNlTD0HN59l/idH8AHP4Un34UnT9B3O+qTwDq7K3WpHd+ICVl2xodGq8m+EwWeDiA7kKdQd6CVMN+S5qNF7G8LNQ4kAtOYefXJJ+j1Lzg8fQoffhuurrl6obx/X8m3J+5vb7jtb9hPHQXTyp1rXalkj3AsjsAmJiibAWjPNf/MnJKVsdH2pihCEuiCuPhFt/RplKronCmT1f+qFhwvmo2NQuwTOweNF0+e0V9cEfcH4m5P2u3spx8IXQdOUVWJ4PXkrSYs+L5BVY9y2x4Zcyb0idAnSt9R+p6utxrwLnWcY8d8dwvjGWVEojEdVC2gKGebfOltXatYT7kZRYOQoxI9O2D17E1kQLejyTe0Or90hNaCwPfOKND3gct+T9IdCkznCZkLvfTsLgauuh370LMTIerIVGZymUlkJNjeRFW3r7YiY9exf/qU63c/4snz9+kOT9DQU7DaqhgDWStzqagEpGs9Ax1wLasOC8B60GCJjntUYsk4LTQUWV6TtaIl04VI1/d0cokEyDFRw46+DOQ5kbNS6531YMTo/rlWslZQCwpIKaQUkS7S1URhpnjgN4orBuKCQFXI1QOSZXyUeYxekybVA4XOHw1ej9jqrxVcnp+l72StasqMycpOpsnKcpb2VNUFcrytQohuLzwjiZFWSGaKCFGMORCihejmQjlNlPOEFrV2H90e6S/R9ATdPWc4DMTrJ3THC463n3J6o+g8o1OhZqXUmVgnawUjiRq8FjpGUigEqct6EW+1o25/LUi4YWZ5ADiEQK6KziZw1Lr64OwbrQ6+hbXvKyCiS2bwmz7EfcMGHhd/WWQ5h+DgDZoPyrrPtje5GEpTlraJaUJhERWhKFbWo0b5zaVRtVd1y8C2tZivaQcuMXaklDzzZcAjuX+zqHqoeumQ1cyW6iBn8WlXVdDWbg3WrJr1SI9L0HHx+4DWM7v58Esgow1es7+w2CD7bH+oBaEeZybX/Qe+xOLSt/+S5pWs2VF7pvlKPASNTRAJU5RuoFE907j4/xIJPu8hdkjoCbEjxGSsuRSJXXhYC+w+5ILNaN7Spm61gYygoAZCa4xmQDxRYjpyayurt7OCTQuhJdsaQDVlDnW5jRVuvj1mjdL858zh1waOqlZ70MXI7GnQJaPXpGPFs4ctQkPb5CuTzkxdWIqIS10ljsVpA1QlTzOln63Q2AUPLHNnG01bxI3iIYJvzK0YVOzyluwjSyRvoYIhS0DiQQRqAZ112XRabaRqIM+Tyx1bHQtLCtmoJqYy5tEo76FmqXyL7sxaDAShzNNMjfa542QknBgDF0NPkcR9eZxM1XJbbRqLmqGsmHLcW5zo7XrarCvbmww8WkDYs6/VjYcXwVtPSKObto2x7wf2uz2H/QXD4UDq95A6coi+gcsi+9+Ei2y96GbTsk1VQsBI3UrE1NBI1qsuhZ4gA1FOTDJS5xHVmVwt41LA+3J65C1WB/AeBPD/oRaokCgOfJPR9CShBCqB3JA2CmVGo4Jk4HEi46UUpprNMMRkUTM1bn5KsN/t6S56Lp694PrpM55cXnJxMVhU/zwy/fJXlP/0H5l/+XO6m1tkyki2jGrc7dhfXxGfPyNeXMCTp/DR+6RrYADKyCLD5jRhSlmiZpRMKcXqQFyZEbGMbIvS6pZRwMYgtOxEcy583S1tFXw+TJQFeNJe8xStz5Dz+3TjEeaZOhntZAiBEjpinqmvv6B8+jvCxQU8e5/D83d4MRby/Q3H28+5v3vJ/dQxYZmyKHmRn3+UYzG67fr89wYS0SUYiGzrvbEMn1oWKWFNx/sQGFJk6Dr6aGvZ6C8mHEUtC7hT/0l9Ynd5weHJUy6eXLO7fEraX5B2B9L+QLfbk4beKKmuHEjsgGBtjlwYY7tnhMiyn2qtEMRbLQQDGLGjpo6h672nZOIkkVFumNWoQqj1Cqy1Mk8jSEWlkuLOlF1j4iyVUiFEE8giRqtP8f1+9XSawX6caYz9wJxnxunMnCcD47ueLkamXJgmC7QGNZJtTANdf6DrD/RdINYRyJR8pNQZzdDS+NZepRASDFd7rt95hyfP3yftnlN0MIfQMyBFIKtaEKvV4qjTeM1LdCezhWwbIAhLO4bkKpMaIlltfqs7ZdWpmbWAxs5qFPcXxG5A4sC5dtAJaV/oppGcz+hYFjVC9b265ML5dCL2Rw7dgd1+oMqecZ45z2dbQ66HEMX0EVAhF/EWDo8zke2eMuBhoL3VQOEKy7asVse7tLoxNVX1PghSEmPYVna3AJE7ojFaeRnF6gu1LoGXKEpHpRNlJ8IO6EslzhmdZvAemiEmQr+npIHbEtAcebK7YvfkCcM8cP60ME9HxvsjY4QxV2qdiXUmqDEykMRYK/1cSCGburFWlj6NuKYAnnHV9fEggS4ly5CWSi0zZfa9INt6r00BXUwEMKbkrWDWXpaPMo/S5tDmrTobodFTm5JmCHGh1jcfv/WgtaxQ8/qtjQrB9AOsR6O1GWlTXGsl55k5z+TsWebSWniwOFNhk6gQCcQ006Xe6+MifeeKujRFYWXOmWmel17Zs9MoG9PEaM+mgN2nZKJFrc7Nz13dkbbegE1wp1ovXdwzFGh0SGjUSlYAzool1+OxQGM7vnyvt+zZ6p7q8sq2O4hjha2JbYmmVtZh/qku2T4DjVaiZP0RGxU1bX46zzxaTWtMFiyKEQ/OtYyiY48NeN2ev4BlgJsolZphDp4FbZQiESHPMzW7GCSedWxf0O7IZQ1v8v0itFBQbX6U6oMZW2KDG3D5h46vDRwbjKmetQFb0EutjUco29RpLZRWTIwpo425MMQNEqme3ZNmVIwKMWZrZhq6zjcrd3RQjwC2m9lY3I0GErzOB1zWQovPh2wWzVqXKaLOW14XoQSMOuNRHtRbGzgwniZritz6WFqE2amyMZL6njgMzKUS1NpUzJO1qChA9c6iEmHWCgWyWBuOEFzdTOJS7/HNH03pSU2A08WNigPHlq1Vbc3Am5lYIBtRokW3xOoeGmCxAbT5WYyOzxpiUuepH+j3B4bDBf3+QDcckNRTQ/ssp88tawof61aDuW5yDUDar7q0SZGYbCPVjkBPZCByZuJIzUdrEYAV/5uQDU7Dtu+IDgKlRYaMr4KoLA3VU0xI6FASWV0MJgjiDRNKyF638jgZx/PpzDifISaGXaAjEXK2xtMidN3A5eU1109f8PzZcy6fHdgffKP53e+4+fkvyb/7PZfHey5q4ThPnKYzXZ+4fHoJL64p+0ScbqC8hMsMQ4LPPoW7z2HoLDR+OsH9PUwz5BmmkXy84ziNdLs9++vncPUU3R+ol5fEiyvY7dbo2J+60LeA1fq4Lr2MrOZRIF8QTi8I5zPhlNFzZcqVMB4ZUuIiBZhPnF59QvfkkmF3QA7vsH/xjBe373B69ZQ3b/a8Ot9xXwrJeyCaRPvjOKpLKKQZNl0frU5naRHUtq23epMWuIoIXatp7Dp2qaMPHgAphToXyIXgmYK2F4YodH3H4eKCw/VTDk+f0l08Ie4uiLsDaeegcecKqiFYEMBbcyBOgyoV0VbIrxtLVJ0m16T6A6HrSHimKwgaAqntKX6FubrgFdXEsVxhrs4zGkxMK6TB6tGiMKfKqBFqNLAlLeTTjOQ2Svs481hToqqJoZzGkZjg0ClRI+E4IqOJO9XQUyWSYiLtLhguLkmholNGxdp41PlMnQPIgIj3xAuR3aHjcH3N5fUz+sMVkvaoZxoJwjhnxjIyqqmjNjVPPHPUiTB4ILQFI5qwQkDcLgU6zEGY5pkxZ46lMLmStalWW91vDB273YGLiwuGYUCHS5hB5kIoE2m6I829KWDPZhdCkAV0nU8n2/eHQHe5I3UDmQ7Jk819sBrHXsRBDgZoNXiQ7ps/VpVF8WwQxFodQFQH4e6J1OYfRAtmi1gLHLGAamxOvTQaGQa81FVil9YVBsxbdjeokmqly5lBAkMQBgnsvF4Sbz0lqSMNPdp33NaJm/s3vLwPXB4OhBA59wPHfse56xnDyIhpAnRTQKeeNJiAXw6Rc4Z4nv26TBDMgsGytKdIC/unlRk1eGHjYM5y9S476tRcDyi16ZJKDJ6JVTWxoEeZyO0vsoDCZtODZ52MZVZNjMzvmVUZ2j7BGBXt/ohIa7fhFNOgTvFd3mvbTdOIWJKq3uukooS6+X4Ekbzcf7kaq635KaVWpmnmPI3Mk9e+zbNluN0fCRKJMZFTpvYOHrdzJIEgxkJoNNg2SLqdnvboJvFipkZYabK6rIGWaHiswOoaMF4eAR5SLsWDg7I8v17L4jn49a4lHWGpb202zPo0Omhcgmmbditeyx+ijfXy44q8LYtbtbjfa18u/jxtPjZrszGKlte2hEUwHRA86SISyCLUjCtM6+Z615/2mH3Hw1m1uOH6XevgbAHtH5+PfwZwNIlosi6GphVFW3Rwpbq0PV2rCY0YkFOmUkjVFIcMXGaihGXgWnSleIS0pdCDL8wKS1sMAzrBI5EWubMJZ5mg5mS15IgNWFNH8pS8n6cBE3EwavSDdefQ5eYdJ6ttXCSPv9W9gwABAABJREFUfUKCCJ2DxgnjvEuFUCy9HFOy2h3fnKpY+viscFZhKnhEwaOQjwUcW3Y18ECEyCgjZTFstiGsqfGW7LXor7flkGRjudB3LDK+NH7ewHyRgMaE9D2hH5Cup6aOWQJazYCUtlcLZlTbRtU2bmn02tWptmmq3oeuUtRUFyONEhsRTXiHI8bTTK6BUlhrTKrVZwrFs5yRB6BUW5TZxHMSpuoYJRhlzrUPLbpRUDWqcXUa62McrbgaqZSaCWV07BtJXY9KYn/xlA8+/Bbf+94PeO/9902t8POX5N/+lvHTT5lv77g4j8RxIpQZpFBiRA8Jeboj7GE8fkr+zf+PGo/U2FN+/Vv0zUvYJZOcvz9S707IPJPyhIwnxvnEuc6k/QWX1+8yvPsh4aNvE7/7fbi+NvDRDPPbDuBm51oKwh0AgCzidN7GyCgeAjwRpF5C+QDJEY6K3I3o6WSKu6rIdGY+vmK87ZC7a4bxfTg8Ie47nj6/Znz3HT5+9TG7+xv6OTOkwjkkn9/HjKq2ehv7a9m3lh11Pda+jos3QhSjpw5dz67r6VNn+24DjSXbXuqvVwWJgb4fuLi84PL6mv3Ta9LVFdLvoN9B11sPzRjJLTCEQosPaRMMEu8puHEgtNUoFqtbz7PRgKTVEVnPW4IB/9glhv3e3lMzc56YSqZUi4Yb5dUoWjoXapyJcSaJtRrpUiDWAMVpdRLdWrSShXUMv1Q3+g0dd+NkADcEYtcTgxKJJI0QerqgZE2MNVAkErsdu8MThsOBcr7neJo43Z2MhpiVxJqRURG6/sDh6gkXVy9Iu0s09pA6YjD2zvF45OXdDW9OJ05amdUAuAL9bsfFxSVP9nsD/cXsXRDbs1qJhwUFAzLPjOd7bk5HPj+deTVN3E0T5/OJ+TwiVYliTlUadhyeXnP97B2e7q84DAc6raAT03hD6AYkzVAzrWVf8LtpzjPH4z3cRdIB9kFAOiR0oBOi0HkGL4HbLfMorHXIN3+o+wEhRmJRUhSqtwFBoQazi6U2+9N0ElznAXc2vCYp+DjFoMSg3tmhMpfZ2DGLurwuDiFVCaUSyZb9LUYnHcgULcxlXsQC49AR98JJTry6/z3Hj78gHvfsd4F9Hkmxpw4HtB8p45maM9N4YjomDrs93b5j3+/Y9dCFE4KBvVJlaZsmignoUZfgfHBgmeeZnI2eWbIuLQ/rhgUmXk/Y7Hao6u179NEyjkvwesM6aGI0W+BgGbf2miZe1IJ1D8FmozjSxH9CXDJ0TfwIsTUqQRENXlXRWFeyglJ06RsYgilxdl0iJStump3q2sZo8ozjNJniZmPmND0JiQ7Ea2H22lZSMpvvK1ucxlrrCqK0BZKWUWtBSpZNcwFeEqzdna6BAhYb9Yj2Ub/607cg5w+CxpYx9d+DU3eXFhkO9nTp0dgYGWEBiYvYU/tpwNtPoGo15feSnW1nCZjW7UFaZtPXXAO+TZW/gceGJ1qCS1JH8gYrwecoo5Dr0q7DrrX56K2K+MuuVXOl2vwCiy+14Mc/4/hnUVVpKkzNiWuT5KBhAW1inGBFDM37hjGXSi6VoUsMQ8eUJxvw5fRtEvM8k+dESp0JoLRoURPgkbXHidXwCzEYf1tE0dawHlxS2iLc1SWVg90DNMQv/pqWfdveEAb2IE+Z8+lMmYu3P/Aspe8uISVi1xvoLBm0rvzpONANHVAYp5Hsst1WVA2jKrP7ObEN+CNx/1sEJMa28HVRxa2NbaZtdp22639vo3WtubhDb6BSG0L3UIC29eCy1ZI6NCRTm62Q5wJ59D5S6jd4q+mpC91VgmUqYuoQiUvvrNJUy1SN9lwsq5JErNF57Igh0vcJtEdrTy0dpVq9jXpDPBFFcjW6m1arPZGNGBPKgoBrQGomVMu4Enxj8TYcxbOXizPwOLNooDgMHg6s1DIB1sh5RskE+v0l73/0Lb73gx/yztMLuAP95BXT558Tj5YlzOczp/NIEUH7jnMUYlAudol6SBzvbrn9h39H/sf/QM6V8eaOfBrNztZKnWbPZkEqmVBHJCnSR6Tbcb74jIvjkauLC6J8D4aeFm4xuruv+GUzebhW2wNf3thkpSMKaAzw7IDIDuoOvZuYXr+kXLxBS2Yej9wf78h6RvcRzh/AdETHI0KPHPZcPnvG4ckT+i8+px9HurnYGt9EZ7/xw3d0u4y2k38Fvab97YPRziiAteBIHbuuo0+9ZQaqmjEs1r8rbERWNEDqIvuLA5dPn3F4+pzu4gqGPTV11K6npA6R/z9vf9YlOZJk62KfTgDMzN1jyswaus/Ey8Wn+///CflwuRYvufp015SZEeGDGQAdhA8iCsCjqk5VNtOJWlYZ7m7mDkAVqrK3bNmiEvG2rrt0PHRTnKxtMqzWVwMuWxebUHIh55m8Lgd3OO3POzhICKH0HmkNgiNNI1O5IxetFZIKtAUvBZrDicqI2pqRsJoRmUp0tcm4kT4+4FwnwfYb6XoU/AbH09Mz4xBIMTKkQQNGlMA6x4nRBW7NU3KjOk8MJ8bxjHeJl1vm559fWJ9nUhVGItFHqt1/cRGfLoyn7xjP3xPSA4QRFxKlCo+PX/nzz3/mz59/4vPtykutLLWSiwLH6Xzh46dP/Pbjd/i7e3zSMYvBGQOtgXVAew/eXq48ffmZHx8/8+eXF/5ym/n8cuPp6YUyzyqHHlSiJSGQLg98/O53/Otv/iv/8vF7Hk4PeFnJ6yN5fqYtKzVboIRsQZBDnT6vz8+EURA3kYaIcwOtZaQVnI/EoNlSb6SnUpzujWzH9HBgcsJdVugDW9/jJibN7xumdBfyZvt6lzn2AFQBk+8kS2tbWZKz50fJFlUjJeeZQmAKjsE3a2EyU/ONVgsuqF9EugzEs4M68/z8xP/8euP6Z8/d5cLv7h/4fhgZ7t4RSyHWldwyJa8styvL9UI4PRDGyBDHTTYefaUg2gMZNrOrJlrDGYMGsrVVZLVguVW7Lg6ZN9njQd8zrmzB+fZMvsHxrQnIERBpLNnJSLE5KXt2oQMjMLJrBxGa3VdTMMIhHnYO57QuMaJ18RK1RVHwYTeT2rMq2886EOltHEqp6nBbNShsNrccBnhjMuDbW2WwZcg7OO7EnfO9mU+Pcd0OgNohOpFts2Hf7XYQ0ku3XCfxORJyb5hxPIzfdpr0srhjiHAY7w6o+hjbm7pfRic+usGVtANp04H0BvRUwSIGNqpTsl5cA6cZYY33Kk0KImXLOHYiwXdX8Bghhm0f7s9NV8QImiRxLhC8xpcheFQ/12NrM+ySuj1LfS452uaKvdUR95EUZ8kYtr/fk17HG/yPeJxfDByxhRsDgRsbYROVJhZk+cPJeJqTzSil1MaaM0PwDCnRUiK3vGfNbYzbWigh04ZKCGGTmip8MUBnRjZd593ZnO4ghzTLYMI+vQ3ibH/Q9Uujb2v9Vms20huQVWMJStEckrMJ1689BIjqokqpDE7P7+w9KY1qd+1VapVEGIKaB61VWNbKanKuJKKvlKgbgvx1j858KHPSawzMMvyQAtflYa902L4nZkJj2UjXe3Law6sZY9X3i+m1e5+jkAbEq0HQumTddJqjVpUKKCuoGvyNuXFCiIk0nRjGiRAitaINl4vVh7SmvfdKwdWmm25KnMaRIQ5470lJqKOnlECpkdo8pbgN5BHUVTagD566d+pcksMCI4VtXglqpuC9h6CBzCpaJN+kmEznbY5mC7269VdlHr3HuQFJA366Y7z7wOX9Jy7v70mA3Ary5ZH4+JXxeqXkQmxosB2iRjFjhPMDcvcOLhfCIoTnn6k//UyYZ8aijsBtyRrAt/2ZUZfXQjhFwhhxYSZmIX74Hl8yLmdYFhhtcv81ZfjXx04X/tXXOuXcVhjuvEM+BpCPcP0Cjx+Rl5+QPGvriXmlzjfK1y/Un3+CT19ww3uYPKRIOp8Yz2fGaWK83RjCapnl3UTh1z768rQzprYuyb4Z66awL/r9/c5hLqqBIUSGkBQ0iuN1/zF9Hl2r4EX7NJ4mxvt7hod3+PMdJU1Ul2g+4Zy6C7NmmiwUc472XnsqRnuFmDWIiQkXEuIUGC1rZp5n1vVGyYu2AcgrpWi/xjEIp6DmYUGsV6uRU2EcON89aAeg6sjtCdZZ14JO3lSBUqBkzVh6rSdXU4lIKxXB00SzaXqvhO4++xZHnVeai4Q4MaQJEUddV0puakTiJ1IYGZwGggqOHMtSeX6aeX5ekAViHLRu24gRcWg9cJwIwwPj+IE0PtBCYlkLn7888j//49/4w5/+nafrI3PNzKUw52L12xDSyPz4BNeF+MNvcQ/3yJjUvM3tQWBrjnUtfH164aeffuLz0898Xa48X2eenm48P93U+TslrSdNnoJwm2fyKsQ2cvFnpg/3jOcHzvUjbb3SbjfqnBGT8ff90znwNEqeuT7DMDh8mIBIbVByIThRQJ4iOFX+IEJMkbewx1H2fpcdbnuf24NzDdQbvqlqqIPGUgprKTSv/UZzE1PRWCAaVPIpW/2/Eh4NMz4R2Qx2pnHkbho4R0eUQp5vzLcX6jzTcKTLmendA8PdCTc5WGdq+8Lz17/w47VwvfvI5Xeej9//QLgbGKhUWXFtRR61Pc7t+QbDDScR3xxjHDiPA2sre41ex1PVVD2993IQqJXSNPgFy2CI29Zo5/W6g8Ube1JBV7bQCb83Gcdj1Ne/s4OnLVC3MddMpL5TLHuoWNIfHGbVFAwjzzCjIAV0wfoOO5PiqmmQrk27rDHGuLWbOba46EC3diKbPSOIleCkEIhOgaaqvoxE39ouWKsiu79dmdeVWq7/zAe8bzTvdY9Ato1oA5iy7UTb190sscf8r3oJv1Weo4/TNwTgbhDHbqDK9sbDL3AGBv2rulLXYycjwDlkG48OpQ3MJKdBK7gGpXaism1ZZwVzxeK+onXhZpjmfSQNI7qmawu71qq6hjddF0X0WbPZRAwjQxy1T6j3+AjR/oaI1v/XY6bHyI6e4ehrlq24uv9xeDYN87zeDf8xkfOLgaNzftNsq6OpFvn3IesbsrfBbPaZrk8RtFdjLY1WmgGYAReELWfKbpgoVXX83cq7EyL9CMcLbF0qYKyS6aBEjok70+V7t2WHFAjtQFhBoRWpm2HPWgt51dqAKEIFW/htATLJgtgk86ImLckJgxMmL4hvfL7dqMvC+1EBzUsr5FpZRMxlTqA1koNTdG+2oIbu1uWPbMN+M0WZAZtUzVzWFBzqFGy2sVYQlRl3cxyt1yhqxoEazfhgznvjhA8jgidXVKtf1Wihlt5gVQhO7WYEY3A8xEE3vkYjxETrwDHX7fO1NGo24IijDVGNW8ZiLDbE1EijI9dArvrzVgXqPvbOd1CMLcWd6DEpruhCXZr2wAvRtOhBZdmuZkQyrRXNqL0Ro1qrFkp36QXeKVbwjjidOd1/4nz/HXG4UKqQnSMuX/HzI+nlifDyQlsKg08MKZFF2x2cPn7g8rvf4z78gAyRu1tkvCws1wbthUTG1ZUSHDVqxoLWGVG0NYc4JDeiNEYXiFYf55YFnp/BBZVDHsHg31ux/tFKZrubyqXt/R8d7nbP+OUT1y9/gusjoxTOAertievTlfXPP8KHH+H+ezidLT3kScPINE1qruQgOlE36fY2A3nYD19dboeK3eFukzlZSNQDlBgCKWqmK3plJrvZgxh43PrjismjpoHhciHe3cHpwhpHMpG1eZWolUbL2dwi1YxB0Hl+GgbOpxOncaKlhG9VwYkPVHFcc+H5OvNye1GjmHWmrDN5vpHXG1IXRt84D5FLGhjjqI13RJnV4ALDdFKCJjdu2TJnedmAoxfBt4avldaKMrROM68tBIoPVCMw+7TYW5e8zTiOIkRz5CsuUyrM15liralSCqq4cIEUNKAsS+ZWC+uyqtrEaw0Nzmr0EZp3uJgIw4k43jGdHhhOd1xz5vPPf+H//Lf/yb/9x7/x+fFnWl0JTogiXJrQnMqMl+WF53nlL2sllEatP/D+/TvuTp7JewWPArkWnueZvzw/8+enJ55fnlnyjKyZs3Pcv3vgHEdOzhGpNFe5SuWpCvn5mc9/+pE/hTumGPj0cWB89x7KFbleabcFKWaiZ/PTO5W8NhFqKZQiSAs4r1L73BqUSpKEC4M5m6vE8xyHNxlH78zwrcOOA/Hs2M1VgvM069vkBGqprFlB+xC1d/Uq6spcRUkL70UBl/oKWvDp1KW2qSENwROHxOl04m4aSFTKvLDMN8oy4x2M5wunDx84f/xIfLhQvZql5HKlzI/kp5W1Rdb3C1k8TGdiaAxtNpXFQrg1yjxzfXzCNUcbEpMP3A0Da8maMW2NYrFVV/foVhc2iamWTJj/gNVxhhDMuND1lOp2fzdTOwv74hs9j13K2RfWDqa2APtv/N0uXeyfF3Eb+HW2gPjgNdPo/eZG3GPfDWxZxioGr9nGEIkhqflNd4NnL83qf7LWitQu67fTsJd3jhB7tkx/bwjqPF9EyBZ3uVY3sNXBs9JoFjNbdrNn1Jrs8R5o39CNKNmyVf12WdLG6b06kqnyRkoO3BEY/u25In/jJ8ez2TKth7ZVHUgjYm3kDiZ0Pe6zsdGYD8sydsMswfmqmWjvUP1gNeCojv21VSWZQkPQOlRMNVlrptSFVrOBwWYleg6IpCjIoDJVVfmoYs+LvVoxI0pV5ogRXt0oss9X59Ce686SPn1tk2/v0z/3HP7yjKOYqkjclrYWkU2z24wVqdIZDk81MNdRv1gWsIoQiLg4QKmaNewn79RIp1W1xKU1zTh2xkpEzWza/pBvzNGWGbC6wwOC3t5pveCCM7dTgQ6AtYE1WwBaSts0760oAA7Om6tbzwQ47WUWk7aoQCVZ0GjeUX1jWa8stytOYEgXpnHg5argImy1fI3o1VI/UN+q9t8WG/rdts3Qq1dJq3bvuozQmFdbEXvye9e4t+3mNnOlaqbzxonKM1IkDiNxGHF+pDZHy4WcC0teWXOmFqsDdRA9eNcANYtAQIpAVnAUakEEamkUyzjWIvbqbm6YUU7DUcCpW5nzlZhUphdTUsvsij5wWnSqi78c6suELdfTmqM6rfdyTa3LQ1I5mvdi5XYZxBYD2htZ48AQI61524jCZuYTU2KcLtrU/uG33E33JCphrbjbF3j8TP76hXqdkVIpBSjQ0kB8/wPTf//fcP/jv8H9PW5ZcNfAeJdxc4MWGdoVikfdKoI6qlKp7EKgLCrZTeJJ2w4oSAwqVY26/DjL2m/Zw3/m+LuBhq0xHSB8vMDvvyf8/Inw9JlYC6FWnIvIMtMen5EvX3DXZ3j/QXfVlBhOJ87TiSlGklNTjsE7lrcayD1O2Xb6LamKCsHVNl6vzUqglLE398sUAsmrKYdV2+pm27Tgv5Wi2eDoGcaRdLmQLhfc6UROieI8N4FbbSySWaWS15m8LEjJVj8sJOe4G0daLoR7SLb+V59xLrDWwtNt5ev1yvN847ZeWeYr6/WZ9fpCW2dcywy+cU6R23TiMlamODC4wOAjk1nRD9PIdDrT5kUzjnmGlrVWszV8bfhW8S1Y70i/ybe899aMW19Cs8S0e7Pn8T5p0+VWVm45c1srt+tMq4IPA2FdcOGFOJ45398zJqhlRpYV74TTNFKX3hNYiYEKKgWdTkz395zuLkynER8c89dH/vAf/2/+7d//P3x5udK8J7iRSeB+HDmPI+I815x5ut24rivXL1/4Q2ssrbA6QULQYNYB0ljKzNPywpflyudl5nrLSKlEF/nw/p7ffv8bfnh4j59nrl9+5nl54bFVYm58uQnzyyM//fQnTpfIcPnIx7uJy7sP8PKCXGfI6jr5LRDTjH7EEXGMeN+I6YTLN9a28JKzrk/eQ4PUoJS3UXME58ELLQR8g95Q3BmN2Psye+/wzeClNAOOhbXUrbdfbo21qWzNea9S4N6Wwllv7L6ceaeZiXFkHCfGQa+3rQu3643bvBCc5/7+jvt3Hzl9/MT0/j3hPLLWG9evwnVeKXMmVCFUoebGXIUlRlI649cz6WXCXwdS1bFdX55pTQh3Z2LyTDFySpGlFmq2Mpbas49O/bBQaW0HEBpk65oTQiBEzay5EBS49ISD7Peyt8t5q9rxo1BlBw77PtNjH81Gud2UTHbvhP6evabN/uMd4rdfTs8a9rozTZg43RqDqbKCyZNF/S2c7zXMBlg7MXjIIomdD2JtjrwBRstkxhDMCBAkQ7HWMb02dTu/figa1J1aes/HbgjUnyfZkz8WWLtX98L+YfepG+S81bp6PHWxsdoPef0v6Y+T286xf9nHbuuigEX9ljQS4ZWTsBi/o3Da/tsMIDutufdN6/y19tqSTvtvoDugY/twqQ1X1LCz1N4nMlumUjOU1RIU0gJOMp6IGzTD6bySj6ElpGV1WFWN+0EVZzLpjtM4zt8DMcJ+a6T/4/Ut/ZvHLwaOPXO3N8I2yYVlC2ttiEetrLu1t/SZ5/YBRdk5iYkwJMgLtWSCyPa71aelUNaFIQ24FE1fbyY8FnB2VmA/ZBt1B8beqg68tX2h0gfHgKM9kAd+RcGiWSkrk6foPgAu+M0UKIA2xx0Sq/fkWtQgAZ1MSxOqbSautc2lcWvOiseJZlW9N/c0D6Pz+DcicIJXkCzb/zvbxLw2Y5TuImtjbNnG/emjx+iWbdR7qzWGBYzxCsFpM9QUCSkR0gBuUGxaTFix6cp350jY60adwxboRm2ZNXt8tdqlLqttugDgdOOVoMCutcaaV7wv+BAZXFRiIIjZJydCrJpxNMCsoHh/mPo1NltZpF+4IRRfhdQUQHVpiaOA6DzoEpi3OO7GEbxXW3CvLGh1Hj+eOY9n3l/e88P73/D9w0fexcDw/ARPX1i//szy/KSZpAa30gjV4e7ODN/9Fvev/wN+/99hGOHxKzwvtPSZTERKw+VKLB2sVyQXNb4Aek1sxSlkFvC1EpZFHVdThLs7ersctWb8lTTZfWPvX8cI7+4IH96Tzhd4erLeWmo84mtF5ivudoV1gWnCjSPj6cx5OjPFxOi9vmJgbm8TqB6Pvvjvl7Svmm4L0nZjk+C0dUI00NhVEk7257LVQmtaqzmkgel0Jp0vhEnrGavzrChwfCyVx7LyvN5YblfauuBs3fPA4KAsI1G0NdOJ3flNGsxZeL4tPM5XHm9XXpYXbi+PLM+PtPmGb5XoNPuthhoq+6ljo8UBn7R2LRizPwwD7TTBOtJKotTVWHlt/eOb6Npp59F7rfVaFu2XtwdEYszxWxyX0bNSuNWFdS0sSzGQ5PFUmiy4FgihaMZ2ElgWSp5xFFJ0kEGKNlEHVBYWE6e7ex4+vOfu4Q4fHfPyxNevf+TL53/j+vInGpE4PDAw8S5N/OvHT3x4uGPJmS8vLzwsV56vL/z49IWvX35mpiEpMk0T96eJU4qIFNZy5bY+cl1feFlWrnMlSuLh4YF//f3v+d/+63/lNw8PyMsLn/904sevn/HzwnqbmfPMvC48v/zM568D9x8Sd+cHpumOy/0H6v2VvKzUa0UM9G11O+JwzSM1Im3AR09KmZiuLHllKQXJRaXYEnCtsaxv8zx6r0qlIBB8zyw0jgYAHfB657byjlorpVRKa0QLOhUY7KYyLvRWH94AiO6tEcdgNv+X6cQ0DgQcZVnJLy/cXm7k3JTUenjP3YcPjA/vSHcPuNMIs2NukeebkLMnuKimOOJYaiObiZE/T/jTQJoGYrZsaJ7JDlJyJD+QPIzBMwTPWttm7idNtp7OuwWHwzqmbGoINSyMDMOIjxFB9L7UbK0p7PfpDP/nCcNferiDKcr2Lbebm/D6Z52w6QYpG5PXAacZpMj+y/Rv0I36zA+pBw6mwvFllw62WnYAaz2RxEBmjyu8ye7bFmq5DfyqzFQz1dH6PWqQ1Lb3iGXWdnWK61vjDiJNorr94IAnXo3HMbY+JGWakekix/e8zUhqtrEDoO3EDrjwsF/C5ofyzQcO91LbVwXcFqsjr3o17ORtz8weWrM12QTB6ptiH1Eeweme4xRf+KDtqrC+rWKZe5DddVhXk+1iVUbtkAbF6tSdd1rn6Dt4jPiaCLXhGloaVg2kdqxjbYV20OyOw70Fua/bhbC52P6945dnHOmuYb2+qmdkGiLdVMZtf3h7iLClxgMEXcxKIdTMMAwqIxTdGNQl0nr8NG2YHONCCgH8Lj3YYcYe2m8Pl/fbwy82LH02qD7f6ke649d2o+y3tZ0PrbWxzAs5r1qb0O+0/T4fIU2jBr+tIC1raOc8VRxzEVwpZBG62UoulRgGkESgqQupa9vEw6sz4Jt54/QF9ZUrE9tmiHPHdYxu3GEfVrDbZ6DY+LcOGptlhFHJjYFGlxIuJnBJg7wgRF+poQIe4i7JU8HvasysQAAJWthbWlH3U8OyTqz5ufVUJEZt/VYyUhday5uk1cdG8FEfouA1S+yjBQV6sVvPpm2E9wDg6HKpUgBsAdF3KFRy9t8e3DurV/r1j8k5NRwaR7zZsrsYkTBxf7rw8f6B33/8xG8fJi6APD1Rv/yEuz0TpbKK1j/EEBnPd4TvfoBP3+Mu98h0hsudnvvTV2pIFFCX5KpNcnugJEXJgk7filTL9jhjqxuuFNy6wO2qr9PDYT51UuKvj439bcd7v/3fNwGAaN1JJ7bsAQqD1vOFZEtea0QRhlrx6wrLDckLbtT+hNP5jrvzHedhZPKBKUamGrmVt7HiEDk8S+yL5mZ335lSoDswojEHwRtwdH5rA+DEpKpVyRy1uxeCScbH04l4OiPTRI0D2QdW51kq3NaVry/PPN6eWOcbvhZS8CQfiDZWcxOeUIZ+qZWhFEJruDSyVljXmWW+8nJ94unlkfn2SF1uJKnEFBlDUkfqJqxFuM6LqgOmxhCMu/baHD2NCZlG2jKR1wG/ztSadWxrw9UKtRGikKKC/FahmVTV+aCyd9sLjmver33E0VOrI4gQgzANjmT1iiFqTVgc4HLveHjniF7rvHO+IcsNrMdm38JFoDlPHEZOD/dc3r9juEzkuvL16088P/9ICDcu50ZdtKfbOF347vt/5V//9V95dznx9ekrLn7mvq4s988413j5yx95/PwTw2nku3fvkPfvCT5RXUPqjbw+s87PrPNCWR2n0z2fvvtXfv8v/5X3Hz+SkiocPvpInS68/PgTKX8huZWVhZqvzMsT1+XKnM+cYyCMF9Llgfjygsw3dekFNRkBpDbKUslzo2QlOIIfSXGgNnNgL5VGohFYRffRu7cYSBf2oNt5rWX0+t8uycTt+6YGgZpx7AoYEW11FYLWRNZa6bK44K1hk8NAqRC94xRUmXMZJqYQkLyyXq+sLy/UteB9JA4TYZwI44nhdGa63OFOEzPg/ERtiSYD3gW8GxA8zQVIaqAT5IK8nPHPzzBn6lKoVGrL+LLgqwKTGJzKqZ2VcIgR9gZ6Oq7qIMV52XpaduO6UurW1or+vr0hrb32Uqdf+9hIuB7PuP0vOY5noHv1pqJir3Xb+j326972G9myOB04dt+DJqJGQq1p3OFUjZVDH3OtxXZenXt91H64atLntqyYZ/fjc85vO6QDBTNRlRbVkhwdgkhn3LczZQO/+/ccYtnurebzrwDDa0DWR8rwFH3bfqMw9Zuz+Ft/5a+/Z+Eqhy31r97tnSagAnuZmkivPe7ISuerN5mxCxHxOlOqGHg04yu9fyZfDY4Qutt4NG5c1DTSwGGrpipB9zrntMd1L5vThJvaqoGCR4pDXCC67oQd8XHQPbBpHFxESfFmKkpp7vW4u52E2AaS16DxFYPwd45fXuO4T0uDbK8nozd3qD1uswdQNJbrLRWqDZbLK947hpTwVYuxi/o4K/hDWby8rGrAMCV7mMRkERt2trG2NhNO2Zqd/VEwqtpP3Qya2jFuVyZbQbe5hDZBmtYW1HXdrOi1MN9ksMFDSrSkZgyxFpKH6tTVcimVpSkKrSiz1GrhVgohCaU2QhN8DBTvtKG7NFwTFlFm6v6XDtI/c2yr/v71xrD1jKzba1V3AaL9y/WHoEtaUV1i0yxAcMrauhg1q5eSARrLW3RJTkr0ui2/ZagbtayUosXDqinHmD7dALReC8s2aP+3GDwpaLAh4sjrSl6FWjK1VUoTYtUeSaJ9SLZXpyN6pvG4fG497m3ObnPaAQRbXOKuXRcL3Omg5g1NVeoKweEk4XwgDSfSOEGcuFwuvH+44+O7C3cDuLWxPD0iL5+Z6o3ohdksqGM6cX7/AT79QH14jxtGXPTIeYBwhvOIS0HBdvSbw2F37ToyrwA4TzVGLtCzRw6oyO1Fs5g+IeN5B462wX97bBm3XtD8txY1t//M2RzRr01OEj3pPJBOAzxZZq4JLCu8XGG+QVnN3t4zjicu5zsu48QUI2OI6jQY8q83eN8eG4Mrr+bhzjT3y1SqAlTWrVk2t0mXnO3krTaoFbF1y3mngYa1wvHDRB0m6jCQfWJtjjUX8m2h2T1JNZO8YwyH+kkD8ddlobbGS85MpXBqwnhGKZOaaetMfXmivjzi85XJC5fTxGU6McQJJ56yZsq6sNaMnyvRNa3tJuL9oNLyBG0cCeOIT0qMkAPKDikw1lYrkcE7xBlx5DPVB5q3PqqtB7vWe+8NDjcEQmtMMRGHSCNYaxAQKs5Xhilw9zByOidaFua6kOcX5HYjtKY9bI3Z73VxYRgZzhfS+QwxsF6vXG8vtLrycDci7p7wWFjzwKd3n/jtb/8L73/4LWP0rCgBS76xLnA7n/kpRZbbjeXpieX5mbaueJl0/rVMWV7It2fashAYOZ8eeP/xB+7ff0cLnq/rQnIwvHvH5BPTWhlfruqSWzMSoLZMNoOexTnGkPCnM4wjNXiyaD24Eh0YEbVSblnluoPDE4g+MsWEoIRhWzPXylZv9xbAsWcRqoGIHld47xHfYw9wx0bfqAOpGrSpuigEx5QiMiZWEVoVC0VMSu41SEwBUgqkNKizaYiMteHWlTbfIBclbaISnRVP84E4jpzOF/xp4lYKKZ2IYcK7ZBBCsx8+RMIwMJwTQ7iH+YHy/MTyPLP6QnFQnaiUvVV1a42RFCvBl20d8lirG9wGkKp0stnmuagkta5CrpUQe4mIqZyk+1B0wOK3Wsk3GElsYe3oaRuvQ5i8v5UD2LTzC0FjBny3JpNXQKzPlf6TPg9aa5RcKUXJ9HXp2R7t6x2jN0foxDCOhJDoEbYmEPeeuK/2AgO0uG5dyKvr2PtPbhDvuJEcYps+P9yhp+P2S7bkiI77/rfEenI6nGXQ9OYdw8m3Pl5Jm1+lSXcA1L+0LfPwia4W6DCumz3qm/Q3qFLF+0gIGr/6mBCvXSK8cpbIAX9olwatX9aXjaMBx1I0vGiWHdxjpa6O0XPxhlFEvCbhpLce1NZ+iNaje3P1DXHANzufyubQqh40Wirnxe9jb//3ymiPDrTdN/fqbx//CeDYR6UDyH7jeiC90xCKJdTPR5caLZrufX1EBFcqU2pE59VVs6nkqLW2XZRDpVa1ZEJVMOIOD76OtsMRtF6qqcXtpjLAGWAVk6MaaPVuCyD0cnattsPTama5XcnzDFXDYEEsCLBGvzFBHFibQCtMom6es1O31NXAlEhAXCQmZR6XWhlbQdpK8nVjnlbnqDmziGrUU/pPJYX/4VFb2b84ZHSa9D427fUks5i+O005A0ldtunMgMN1Ftl51dHHiLcXISI+gCgA9CGSEiRzPA0x4pyjVu0fVtuqmQMRxZo4dTMjbE2Tte5KU/fJe6Y0ENME4lm9B8lIszFvsrGC9DlxZFD75R4WXdnm8t6nzplcqbvEqcPkgA8D4pLeByMfNpD9RsCxlKI1nznQfMSvFaInnSbS3T3j3Zk4eKRCXWak3HD1iuQX2vxCK1nbzACFQBzO+IeP8P0n+P4dnEZoS+8poGPqFG/j+r0y/sWAj9sWJrcHWEDPHDNGmEaIcVstNBCx5ruyL2jd+fcV8P5f3Uv3WjZCSnB/R/j4QPh8wf0YIVhdkQjX6xW+fOb0/ETKq55JUAB+Hi+ch7POqc2F+I1sju1OiLjt+uGwgPfNUfpGrv+NTttQ7FJVnXPSDDTWai6ITkF/ivgh4cYBN44wTtRhZJHINVfmeaXdFqZSGJwnjJPWAncpNAa8qja5z6WQ5pniPeK9uQEG6rzg5hspL1ykEobIZUrcny+cpzMpTiCRZVl5fH7i5eWROS+kIOQxIdI0oDYjmVYKbtCekoSECyuuiJJ7lv32tRJqJXmtLavBU0OgVSUJO0/4psFNTAwuMNKMB4mI86ytMeeFnGdKhlZ7n9hGXq6s8xNDrYwhKSnZbF11XhtOpxFJA9UHdelsEEPiNF3AZ2KauJwjPn7i48f/xqdP3xHGRJHKdDlx8lAfhcfrZ07SuIuBlxiIrSHrSs2r+gm0pgY1y0JdFkJTd+/TSQkp8V7XChFWIHsH54nL+3vun858/ex5roVaVKlTWiPXRhaIISDDCMNA9X5ro9FDPW+Epa8qfW+lqn9WiEQ30KqQc+O2zDwvhUXApcR/eYNh7KHxnurvpTdC86IlCKZQ6YeIflBqo2VVTwXnGVPETxPRefJaKUXIxbINDoKHEDyjD4zOkaSR8kqsjaEWgnfUGMnNUXoj8mFgOJ843d1xupwhRWJQ0xX1L9D1TQBLe9JCgGFgGO5J9SP5eqM8r9Rb1dY3rhFcU4filBgcjA3SWgi+mEEem+GGINqkHkfswa4zCWMTKirZ1ecyEq0thXT3TydbeYnbSp9+3cNtm1PfqnqM981xUF1tXx6iauN/96zc8fUt1ex0IpSiSqdma3DJbYurQvD7eJVKbUJKTdty6BlYXLY7+x4zfHquO0jaDXr0510BheZBvjm/7U302Ea2lkX9N+p/e3bKYxlyI3edZeXeDO9/c7jt4u38/upc2eJ9OF6t++Z9Vmt6cB3V+9TBtn5GW9VpaVVMg2aEfdjqH/Wc2PCFugb3R02Bo5ZHGEnZdO611ihV128dG0dXSHaCuIP5V9PU7dzHFi+5gHcRvJo5iv0XVzd1VuvKo8N9+ma27t+XnXD/R67j/ylznP0hdxvjpv/2G5tTNy18f6tN8g4MwfziLGB32pdmzeom1TNcCjAb1YpIaRFpXRKLpofxdGdPUTcIuoyyp2x36+NmqNr6p7DHv0dmQpqQS2M1N0Ft7mrbSW81EtSeWdkBfciL14VztUJVB6ThhLiR26KNofFVk3NSGGLF10ZzgA/4kCjeU/KKD4Fpmn7xEP0zRylZJ2vnHWQHYjsbZy5pNpEVK5gZymbXYQEvbC5528Tsm20I2wsfoAV94LwjJq33VGv/iIiwrjM5az/C1rS20JnbiTOL6z4ezvp/Ou+0piIl0jAgou6/MQRtZt/8/vBtjEQPBoIxyV4Nb9A5tPWjM7KgWb2CdCmZD2atPRDjQAiJ6iLdWauDbM2Yvs3GmFtFCta/NLC2K14il/M73p/OlGlkbo15haHOuLZCW8jLC+V2pZZMw7EIXJtwDgPx3Qf4zW+Qj+90o316UrY1enX5dfsydCwD3yQ+dCDeNyRbB7q77DTC3QXCoHOtyeZQFw5Zy/78N+dUImvf73+vHV7H9wMMrZmMN8GH9zA/4/94gmj26c5RnEp8XC2kZSats8pt/UBMI6dx4jyMDCFZTTD/UPv/nz4cbMjGNuh9gddFSRq44LbNHBzReZKZ46gpDPv8Net8LOjwIeBTwg0DYRjx0wTTRI4D11V4WjPL7YbPK+9CZDqNDJOaW1SEXCq5FkprFHuu8B6JCtTmZbGG1ZCXAuvCXQg8XC6Mg+c8DZynkSFNChzdwC0VShXmZWYtC2vReo5mYDfGAD5ScsYPZqgUotmfVxAzoxJVprAsCBUvjuQcQwi6ntZjsHGcKb/uIS4SYyP6/vc02ChZqFWY56btNgaYzn2cM4HM4B1jVKlrsf4H3gdiGpUMCwNZnLYh8YHL3QM+QLwNTKWQxnvu7n/gfPkeF0dyzYhUhjEw+ZF1CbxIwdWsdfnObYY4YhmS1mRzp6aqpDKmRIwBsabzg0v4IVFKYW6ZEB3nu5H7u5ExOKiNSqNWzUY11HOMGPHjgBsGJASa0xrCPsW1XtAy2qUgtRCiEqq1Bkp1tFJZ5sLLTbOY3r0RsaonpPXCYo6LumkdMMO3uR4DJ01N9KQUXIz6bI4jwXlm1AQj12pyNTOqw+PF40vDy4prCqRHHFMaEOd4XjPiHMMwcnl44OHDB+7e3TNOA6sIQgVp5lKrBm7eWmE1B1kaGXDjidO7j0y3TH1eeb5mbX3WCrFVcM4csCND84xrJa3m0WDEFebG3qwPZXMqu9xj7x3UNBElscSyKRZo9S3RcPmbHL6fk238m3rD79LTfp5HbLL9s+9psn/OWxlRD8GPZjv0iFZUEq+O9Bxksv1vWosgEVxt6tjfhBj2Nh1bCwq7Sf9oxeqA4nj+zn2z0hlI11KqvU5Vb0+/UdAzjr0lSC/TaUBPCkitW9b5mKH9/8uxDZB7vZxv4NF98/Xx2COIPi+2PVZ0j/dO/S/SMBLiAOYWXpsqDo8JCO/NYTkE3Z6C4FxV3NKquv4XIWehFNGsY89UNp2jWmrq7Vlwr3INzmJlrW08av/EEji9Z3EAV7YLPsbkG7453j8LN8TeLN/M///V8YtX3R7YbZOyrxTGcvTCzC02N5DWOfStMtE5mlOGOufCGNRJLIuQb7M65Tkz1hGnAck8k1IgxEBzxpCY7rsH97AzAJpF6xNLz6htTTJ7U2UbsAPFVHG0oqYqpWqfFN/22hgf+iCaRa5zyiQ0YenpFdPICo0sRcGu91RzU6utkdeVUFZG73EhMftA25jmYBvT2zyI2ibCb2lxMBC+jelhGXJdPmEOhR002sr/umeuLYZb2w5zngsKsgiRRtRi+qDudTFGUlKpZy0FyMa4O3otZe876UOg4amOfcFDg+IYEzENhJhMceitmL2nymT7jF0wIQaVy/aC/dLHuZkM2u9rkGgLDpqm/53XZr3KSA3g0pYxUjefQ9bxjai5LJVWtG7TUyjtRsGTP2Y+psicInMH2SJqtPD8wrhmlTsYsPVpYLi/J3z4gNzdQ0j9JsE4wpCgAwV3mDOvzsZIoFcI77A11QrzotLQlys8nIz0MZ1FiNtc60cDnm43Pj8/8/T8wm1ZqNJYS2UpmeY9PkWdb1IZx5F3D+/5/nzHh9OZkykRxEcqgegD4gNFoDrPcHfh8v49Q4rIPMOy4M8T4zhyNplqcm6r02lvtTEecI2uZxttRpfF6/u63En/G81VL5qZgjqpdsDYtnWoZxx9jMRhIE4TfpwgDRQfuLbM87pS14VzbZzHyLvziek8IsGz1sqSM7fVTEpAN4Cgjr54R8lZa+JKo+WKF5hS5DxNnKbINGrLlxRHYjoBA0JhGBfNqK03bXFTFWSBBdXBE8eBMAwmVU3gIo6Ck7qD7FqNaKrgItGppHojDV+FUG8zjmvpTLOuH4Jl50oh50YuAecTpSRadQwBzudEuETcNdNqprVOzClwTMPEeLoQ02h18+B8Yjjda0Z4uqPWQhrPjNM9IXiaZIJrNKd1Zre8Uj20aaSmxNIaay0EaZtc0mE1aqIEobfeZLp9asBUTQmEczSnOozgHSE2DZq8BbzSa8XdTu6EQJwm4jgqicgWv3QeT/+WZT2l6e8WHzTwqqKuzXFiGO3peKPacRGtOwzO/EO2aMZv56kXIChR6DZyp1WhFZOk4rb2B0eg0p/3Js72/ICXQASSVJUst6q1cHitbYowDiN37z7w4bvvef/dJ873F1zytHWlNnVyd04IXl/e3B8b2jqqCIhPxOme9C5Tv1t4eZl5nG+sL1dymSnrSBpPuCEwxYnL1FhzoZRCLVbHbsBxJwv7iiWv1rEOqlQervfJdcmEaMmR31Dbr3+k4K3HoRzIiR6Z7H+3rxBqaPLaJGVTrhnRvNU1ute1jb2MpZm3RvCqqiLYLxEtbfI+qPu8kWO6PpmJGbUjTL2T/fz865KwI6C0oO319znso3IYl47Se7Z8iwHcBk7/eh/S82+yE7U7YGybghAR3qokZxuL4xp+BIyvQI97/SnZ33C8g5uyqa9XRhBoQsHk3UH7E2v5hfUKb3tP+v67gtf5EYInBI37a9F+9cuSyVlN4FrzaO2is1hHE03usK/3FoHeiB8XdM56U17t2UiNx50LCDuR8Tf3tr9Cjq9v3z/z/ePxn+jjKNbsu2Ne+ebnHX7tABE60BSTtOkDGlygmTPaECrDqE3iQ8m0RdP3PUASEUrWXleb0YpzW1D1eu1x+2Lm3WvWRfx2470Zm9gPNuSt2axCrQslL2ZWsLM/Cj7NwjwmbfIpujiVqhKrwXtSNHmjFZ7r4uJwaL3dy5wZW+E0jKgFeVC2sjqTb+oG/hZHa5XeDNSZiYk+MFjKum2LkbP39JeuhIcJ2qUg2520vQU2CUXw3oLASCXS7cK8V9tuF6LqwBvkrGw10j8XtXeR0jkGynY3PecDPkaCgUZn1vutB+O9ntE1ekbcWYFxCBEGrXertSJNTV7U5XW/95b437OxTrPNLmoLFheimRk5y3JbZsgCircCjsUYX9fU1bQIzCWSKLQUaDGqRb/3RBfIS+b58UZcG6cwEEKm5UYcBqZ37+DDO+Q0Irng5wJThGmCcbTicKtV2Z6944LFgbnSTQXpBI7gSqPdFvjyiPvyFYY7ZDprv8ySgQwhIGuhzCvXXPjpeuMPP/3Ev//xT/zpx594fH5mLYVbXrmtC9VBnBLD4BgHz4f3H/jXf/lv1H/9H0zjxOTNyGsuSIFIwvmkc8M5xtOJu/s7iIG6zsgyE88PhCExpkTywVpbdEXFGwHHvlzbJrcbSNhPe9B5WMw6yxk6qdI3lO1BtEjW5p732hsuTRPDaYJxwMVAa1BEZYW6Ke6kjNrte4YxEseRmIu6464rS1UZVpNGNWfATh5tfcGqQ2oHsJrJGKczabhQW8DVVXvzxQEfIiLaO05ry5xJedQEIo4DcZwIcaD4qHPPwLECHq/rl9ul58dQCju3tzxuuVDJRK8mWSKe3BpLLuQm4CMhToQwWv2eMA2RNgTyVWs9sRpptd/3DMPINJ21ebTTDKY6Q2uD6Mt0opai41gbtc54y0RXaVyXG8uyUqXxNAw8xchza8y1MjiVJcaUtIxD9ImOUWV0oD1yS9WaHG0fIFvdbAgq36xStf2S1z1XSVpVHmngDuI14+2HQcsXvFdDJMcrw5XaKqVka8+lc6rUxlobxUWG8x3xMrE2bR3yFkdrjeC7kV5f4TrI6FlHBc5HQKi1TE2laBYcatZhZZ4X5qVQcjuY3u0GGykkTjEwUnFlpS2FZV2pVfAu4scT9x8+8fE3v+Xd998xGWison1MoR3MeMAXMWJFX96uQc0LA2E8cX73jvtPT9xdH1nWK3W5cRMPEomXQEwDl2GkTBMlrzQWMro19jrHY3bErnozyXHmK9HjgddhrcUX33z+1zyCD7qWtq48670Yj+erXyvnv/tb9uiHHve4Q/YSZ0ZBsJHsPZvnAzEkNd1LnQ3Rv51SwvtAKZl1XZSgdq+9QrrMsBug4RyhO7Yf7lv/1xFQKmjtz5PnOEBHjNXrR3o2i6A14M2MQQ6h9OGalQxqRkSL9e7UE9E3tX8IOf5zR4/oN0ANB9C8f7m//zUxoAS22665Ezk9yWUjuN1vLEnSs7L9/d6rWdRmqGN/o2NT3aM13ilVe7oueSVnoTW/kQ/eRyub6+dnCZJDNamuL3CIJO0aDL+g56Oxre8X9s2d2wbP7svfh5ff4vD/1fGLgWO/uc4mtcdt8i3n2lY72Oe83xYHC0Q6i9JZcDwVx2y1KpratzolafQWB5sFcC7UXEhpAKdSxh5Q9TF/7Zx10LSLDYI4gusmGm4DffSJ0xo1a90HVQvDj20/BAhRa4Wqc+QCzXsiMIHKS4LnPGhdQYuJVQJfrgvXUmnOkWujUokxkL32igKz/xZHFGfys186Qv/kODZBnEqFu2zjyG61prUIeg/dNla7g1SfxrpQ9XqNvnASMMeu/rBqEOR6jaLoQuW8R0KgoMZA2Xo7tqIZC+8jPnpC8toj04eNAe6LpvMmGQ7dCEPNl6p01yq9Cp0C8mrh8Wqtpdmu3jB8ZxnYPrrH9Gysg/dgGazmAo5gbFwHGIct8o0C1iYCoRsKLeCFIQjnMXIeE1NKDCFqtwunGe2ahbI2NWqwzOjWk8t7iAGXAtLNaCw9LF4lEeI8vdNnFaH2QBeTtJi7W3MO8Y4sTYHomqFUgvDKDl2kkdcbz5+/8uNPP/GX//gTn3/6ysta+Pk286efv/DHH3/k69MLL/ONOa+stZBbZW0FCZV370/89jcfefnhN7RaeX+641+//w2eqGPXHI4B/AheWcSQV8r1yu3rV6bzBVc/GthtgEmsrRG71vK+Xf+/v8XxHTdvZ7WeupHLtq71RtD74+v23yf7yzmVrIUUSeNAHEeISd3ZLBvdn6XmFFT4ZWV1wsDIEE+kcSAMgjivz+maqWWlOnBDIkwnlcBGwK20ZSXnzEyxXqcRCZF0OjOMdyzZIXMzmZu51tW6uxN6C8wcuNCz+wkfB5yL9N1F+/1Wagoqh4yjyoqy1mE2ZNsT2lGO9QbHXAuFQvJixGhlLSrzK01lt85Hgovq7twKy5y53VYkN4IEC+6xcfMqhU8jzkcliXoWB11zQ9Ba4bau1FqIXg26ypKZ55VbLlyr8JgX/vLywn/cZp4EGCamyx3T6UxMadtDvT/0hRUlSrWRddukp3vDcmsq7bz1YvSbYUatWjtXqnaWa6bZk2CmaSHgq2w10L47nYsampW8UnJAfKVUyM3RfGQ8nxnGC6VWnp7b3xyH/9+PXc/iDTx6UNDVx8C5LRPUa7Cakce5FJacCUsDMsty5TYvrEulNU8Vb/2eVTIYQmQcRi5DZJRKpXGbtZ6T0jhNd9zfP/Dht7/j429+y/ndO9yQqDQF7FK1BW30pKgKhOLqZp6VgmcI5ops5R8SIuF04vRwz/27e+bnJ27LM+12ZakRkchwcUzR01KijANNKnMTmrnCHgPunnXcb2GvY+wZEwus/U4oOyfbfXybUQQswN7AmYGlbf+xhIbWhGHO+TrWPRiX4zNHX2UtLnXWxixp38rW3Z7p4cIhY+TVrTeXqOqpVrasXbMHWnpZ1Cb/9BvA2cDJdm32Rw6qlE08xy6jPM5RrPtAD3d3U8Qe3+6Avse7WzztDvsKh8/3D7zlIdv/fQOCD984fO+4Tir+6CO3w7Djp/pY9etoZvLkRHsuhmjGgBZbVjFljBzGxEjy2po6zptisYFK34P6BSixEOwzui5qhO03h1+z0+RYo9tEJezQkznm1bI75/3t23actW6/cvfqPX91K//u8culqsHqyzoDCZtEtJm5Cm7vI7ZnIPZB6W08uslIRc0DfHXa+Nl7KnvaW/MaNjBWaCytmTWu3UTpZjq6CHWmE0xY2c/B5KZGgCPOWkHYUHkg58z1eqUtC9Ht2cuewYwpMYwnKl4BoINcGkMT7pzjLgQG55kaEBOz6PndjZG1NlYRNYrBU53Wb0W0X6QG1hVXHLU62lsBx87eWKNR3wERtpn1Oy+ya5/9a/a1r0N7f0xUDucSPqgboj/UgCortwcmrdeaAdIKpWZyXs1qv2mmJwRi1D6QEiPFeZs3fYEwV1SnDTyOgEaXyP1B6BKLfnUWm23F/LuDmdsJh74AOXOx8rIBR/FBgawLVCKeuAVVbI5Yolqn9jZtHJq5aWpNWyZNA6fzxKfzmffDwEMK3KVI6unfDvBqo+Si5+c9VYRcC2NrWgc4JUjauLk/p5sNWWdWt5cxX1sm2GaI9zSTR/oYqagMXJvP36nRiQGWUjI//vEP/B//j/87/+f/8f/iD3/4ka/XhS/zwtfrjefbrNnoZhJVB81DboXCDPOJS1oZx8D58o7r7bptaOLAhURIo0pwfSDFRMKxfv3Kcy74u3tGdP5KU7k1XoPc1tviyNs1OH61Rr7+CRtj7PZlXsk0bzU3PYDpO4JsDYHZ+oh6a8qdNOsTtW1NCIHQzNlS/xpZhFnA1UrJjuw9JQQGp6Y7eV1py4JbZkLJupkOiZC0hk0qUBqyZtu0jbH3XgmaYYBh1HXDMvXdqbE/w7XfEyNonPc2//z2zGMkj5YJOIoI1XtaDLTqKK7ZBr87Iu9B0tsEOUW0vyRYf6+m8tVcHThtF6RkTSD4gVaEl5fC02NmqI7LMOAI2udLC9/BRSV98PoYWjDRpJvIOXVCrQsOYUgDUgpfP/+Fzz9/ZfWBdZj48Xrj3/7yZ/78+SuFyLsPH/n+h99x//Ae54O2zHGHLPaB2GmtmbKmbUx73zkjXsGwmX1476AKpWSWRevVtVeyUBHLmPoDeWQGehbLNRqtrZQ8sy6RFiqleMQNhGEiTYlxDISiTpVvcfRgvxPRgUqFgytDX+UO8kdn+0mrzPPMk6+sq8d7bfCds9bzqiTRU5xDQsQFRxoS0zgwBUdYDTSvWioTfWC8nHn/3Sc+/vADlw/vceNAdqKUn9e2H9ErQEzBE4M6LacQmIaB0zgxpETc2mE4qnNICPhx4nS54+HunjRX1pdCXRfKywvBeYbTyOQ9d8Ogzo65knH6jNIBsDNSA3D+UL/YAVMH2thaZoDN1q63kjges0KddNsAZC9nEvPjkNcEbwe3uyQVC9r7G8TajqlbtdDbQDlTLKF3yKmruHMqmy1Vs7/RafWGlufI5tfQ5EAOog6ebHFLlym6foNxFrN1jKEJ1n3f6BmqffuQA3AyZZDV7X1LcDvn6Olxnets0mI1FjQUIH/HdOhXPfZ72i+mx/yGYC0mfA0Lu2v/a8BoLwPr9k6L+TTf3Fql1KKlEsFtzsDNOWrTmB9UndaJD+0B2qjVZP12r0JQk7MYEynqWql9NnsWu8PGnp7R7K6YQ6qOp2ztA7eEjD/GYscEkK6rW6cI2W/bngjaYeK3APIfHb8YODYxELcxbO0b6sMCgMP79gS7BZQmHehAQqRSslBApapDomWPlA7+bJKKFqmXvBLqgHfaWLaPvxN1EWqV3Yoa2cHB9lA4qtQODxBpJqv0tFJZ54WaCwg7iHL64MYYGE8ntcS2BtRNrGeRQHaOuVUIntPpQkFY80pzDWkZL5XkdOJ4L0RE3eU60xOE1APBpo2t3+IQ6bV7u4HJ5ozZDU0EyySBdffFuS7NkY1t6qYKoNKQEB0xAVGs6MWZ9r+p0YADH5zJHtSauLZKrZkqvU5DJb8xRJLJVFsIdAUQfW7Z4t4dT6tJn460il6WHIrN+0OutaZau2Eufk1B9MZ+G7PjncNHrXVrrWog5wKNSHMJcSPNDZZ9dcoktg5IKyJvAxx9GnTLbhCd5zyOvLtceD+dOPvAxcOdJtlAKjVrYNtljs4XZdOS9jlkGnXMerTdZT2+j3nFu8YQPcRIylVNbWwpqbVoXY6PlOCowTNNJ9zdPWUceUZrC4lRAQFAiHjnWa4z89MLrhQGEer1xvz4RM2FUHSO+dYYndVaNDWO8ENk8I3r4xee7y40GmFIr4xsujFMT+EH79U1tFbimrcGzX3NoINh32u0dEOgvVWG47DeAz1F1r9stvY51zs77dKVzij3Rupi0k1EWzt4p9nTEKMZVGmmXK28E1E8wa84JzQa1TWK0/ZAzkEtmfm5wPMzrWTqstDmGV8ykxFpYVS3zBKCNiy2MoNhGLmcR86XgWGacDEqWeYsAxUsgMMoB+lBqInGuprAd+ljM9XKIWNtz34RMcdRoUgfN21p0aQz+nBs4fRrH17Urbo1tMVBhdpUsupDxIn269KGKtGCgoHWOokSeJ090JoYkXAIcGR77bxUppRF5ZXBUdYbn7/+iX/79/9gkQDnO77myteffmZ+vjGMEx8/fs9vfvsv3D08WKasEpw2Fffb8y47Udt6lvFwCNsa6cxW3pv5Ri2ZvMxa7kHdO2FxDOE6CbLXSWlJgTaLX7JmLtfmcHFknE4MY8T5gkgmvJHJ8ZZRBGsq5BSkbZukzS7HBop60kxaY80LvKysqyMlfeT2wE33BJzKfFPyjIMnRaBk8u2F5fmZvCx47zlf7nj/6RMfvvvE3fsH4jRpxk8q0YuaHAV9xehJ0W/teVJMTOPEaToxpsEIASXZq0NJsZAIw4nT6Z5wrizlxm1p5HWm3DzBN0IKTD6wxoHaspEjxm0YiHDGvHundZkbuez6/cQG35QE5hPQya+3OA6YQM9XAxwjAPr3Dtkmq//bYobDZxXUaRzhezxRm2aJOkHe/665dgtuS3oABpR1NklzRmh1LssCeYv1+5/uc6bLTjuk1Cb0XQ0jh/2irw9shCeohHkDnQc1CkdC7QAm+t/3bi8H6+9wnZTtoPE/P0S/4HB087j+9VFN2MHkX53LlqGzMcbuu918ZzEv3ZG840kxUG0S8Bh1/IrtQ2JeGK+uXrSkqTUzv5Luvq9tM2KM+rXra8gOYg9OBuwOrwfcJ/Yu6fJZrDayP0cHMk5xI13y3ImIv7nvCXtyyB3+/v/i+MXAsbS6sdPKUOhf3sKYTrvBxkBtLH3POnXAeEDXrZrhQkkMMdCGkbmtVkCM9VnRCyprJq4r0YwTit32TZduIFMQurlxz3L2ovx+3vR71Rq0QstqBe62TIwC3GC26C54amsECsnOvdj1Vue4oo6qL0W1zSEEzarVSsuZsw+ENBgwaTSn/VmyTUTnYLAasgHh9EaBqkom9gdGugU82CzSu9kLhnUidZKgUwHu1Xs2/bX3XSK+mYkoA2cZkF47IprB0B6LWTcksVoN1xubB2KIuGBBf9PzeMVV2LHJp9zxh+7Vw7M/RDYvmmjv0Fw029uwh29fPp3TViFRQFyBrDNOJakRYQQ3AZMBaaht1YxxNWMa9zbjOJ3OtHXBO2HwyixPaVSzFDQuj/0BLJVmphPeKSPtjaL0weOHCMmej9rwucFozYiC09UiNVosZApBKqtokO6NndEshCMAuYlm5GtlrIVrLdxyxpWszrStgclfu3tZShMPlwfaO7jNleuSwWW8q7Qlm/27EkpZKolIGjzJadAenDCOiXEYtg3bwWYhv71EjZmGNGi7DfFIbrRia4aZSuDss+gm0mr5dgh+laPvfz12eDWvsfWrmylsl3EAjV2uKrtJQzeN0WbTvcbYb/ccqzkOTdnUGAMheihK5uSi2R+VVmVKWWl5xdfC0CqT86Q4aLuBIdFipDhznqtN+6uGwJASQxqJVn+8AXHLQDgviNt7u+lztfda7TW1KiWT7T7t8iC9Sw3MXKepwZl0ggFqtSysvN6gf+1jdFgzblVSqKEMCOYG5lSq3/AU8XgXcGEghAHXjTG2cTdirWn/P9q+D3YQJg6kqSQ0lxWJUJpnrTMv65Wfnj7z9LLghhOViFwXQi7EUXuVTpcLcRiRpp4Cwes+Fywb7ewGty1Qen3vNsNkLPAyh2qH1YqXrOUe0jaTmM5F9aDY0wNTiw28qgkqmrXM6HOfQrTaIKdkHOXNJI7uGL3LPt82K3+7F5tk3PefaeCIdOOKA2hwuoVpzAMETxwDd4NnDIKrC2W+kV+eWOYrIo3xdOHuwwfuP33k9E5BI9EjwbJgzgz+vCNEb89wMBMWjw+JlCbG4cQ4TMTggIUmVbO/XoFjCyMujoThRBwaoa7k2ih5ISyeiNbAJh9IoZHb/ixuA2n3TfFXl/V36eOB7LHwwnWXyLeyVLW/r6e2FajQ68c3GnnL0sixrbc+X7buH3tcb0F9j+Z77INjbzWCmZzYgt7j5K204FADvMXFh3Io6ffsuB70U9tLJzYt/wHabU+TdPxgsa+Y6ZbJYTdEAvt1bRd/OG13yE3J4X7ZWiqHS3x7AGnzrGdS++n/1duONe773OwZYa3r18SM280wtvu6Xbe5EofotBUPahC1roU1N2o1B9ZNPWFZwapEm0OTKTFFYopWGqJ4R+tRLY4UT5er7sC8ryfQ23awEVrOQGp//JzVTvrNebWP1x73YnPaffOSv75//+D4T3hZ20T1Dik2OftiYA+Wpobd9rC1Q+p4Y0+2qbjfLFpTUOgSPgw433Aubw9RD6patfelhPfD4bT8JtlqralDndOBFdh6pfS/KttCoUFZy1kzMq12eH9gtdEC9d6k1emka63g0CBcxKnENnpagFvNjFazWpoAgRQCKUKwJy07r9myqkFD8IlchVZWnG+c3S8d0n/u8D5uhdTbAmQOtT1bJxsrohOvWd+lvt44M49xGGPY9vGprrN1XjMl9EDWLKoRKhqMNKnUVkzvX3CocY9mSnTzcz7RnAcqe2NUtoV+m1/2vHWwuy/y+2LsDu/XnnRi0rAuRdwDUn1otVdj555bBamCSMAR8X7EuzMw7TKFcqVUDaKddCHPr39czhdWp+Y+gx/wPpBr3WSoTTw5Awu0rJIY771lGLS+wtGoNZNvN9LtpoFe9Aoa7UY0J9TJw10inzyfnTpAFpQw6b0/xYijVivFwwq0lxdoDT8OnByMMdii1gxigp8mxssdMY3c1sLT9UaugvMRpFBK1cW+wZILxWqulvnGOhd+d7rjv//29/zwL7/lu/PE1CrjYTmUPilwKjEGMwFQmNvWSrtlKKo+cE0zHjjR2ga/y4ze5pB+q7fDmV1xJ2YQoVqQKrZ5HCV1+zbKFsxsmZO+6Xhti6PtLHZ5S4yRaRrhtJLLyjrPrLdia6ZmN2rNOMmkjbjRIFkU0di24GwTVRlpzpVlWQnRIcHhJ2NoDeVun+9jtbGp3Yirk4DH392fT3kVW+kzrSO6sfht3zSbAa++/r/FcY4DS60sTfeC0pRY9L2Wi73WX5xeva59WTMZru8vnTDoragy0gqOtIMY72mijpfLspKL4HwgV0eRQBwvjOd7nm6FMs+AZ+xrs2Su8wtfX56ZxoE0JJwXmhS8DwxxIIVkyhcFSrWqMuTbZu1d+ud9MKmq2cqLBhgRv2Wgjg3N1d3SnKdNHuadgxgoKbJ6mEtmqasGbAHishCGQAy9l/AbKTksm9TJii1D1vo198AO26t6NkevLabA6TQxTQnvLRaxPpk+aL1USkl7ZAZHqmqGs16vlPmK1IpPieF8Zny4J97fwzjSwh7MghGvgHiB4PHJ46M3gsh64JrjeIqJGDR7vBp5R/BaMpBGWhqpMekrVFXWGFnWqjfQ7zbg4zpwBguQD8qlDXTZvuuUe+yswV4j6rf79hZHN5/R2PGYmdlXgH7+fWS3cbVxVsNCI7zRdaVtpIFJ5lUu0UMnjguTlkrtcaTUrrazTL2mTvYY+nAO+4ZwICAOoLH7tves1xa/bSucjU/PpknfC1//iT1WEkuPu80pVkPE1/Ws2mbMrlFBwLbfvNnhXl/jdq2yQ8T+X2cX1n/iHNs5qtJN+kDipZfC2T7R91Jnsv3YnbnFfDhWliWzZo3pY3DbmtZjyo6FvMXG0WsJnkPNv1oxw7HW1X29UfZRQmrzyWvl42uDysPF2l15vdfvb/jb2UO3AbJ+utuf3e/y3z1+MXDcDam6Vn635xaqZTP2rN72gG5R/s4EiOwPVxOhesfcVF7V6yVa0eAWu6ZqjGu1LFEM5vIIu864qIb8eO27057Qm7AeJ544rdlY1pVayqGWyXjQJlbLM+GTp5VVM0+KoQnOU8RY8+CYTgN3pxO1Nm7zSnWeEhxrbYT5xkOMnMZRz6l1+aMnhpF1zqxV8K0Sg3D6pYP0TxxqfNAXdl2EetpbEeCmtN7CQ81m6Pho3xkAYzkEZYGbgcDa8M3Adkr2cFqTZ1HQqM9Ge/U5qQVnjn090MVHcNE09bp46aKmn6ez8HbWWlvbDkHjgVHZghb9PZuMpANOdgbGYnV6E27nA6WBo1h7C4dDm1MHN9IYaa1Sy0Kp2o+tP7Pev83GOKWB0JpKbNHelfM8c1tmlpxZ1sZtFmrQBc138wr6pmDsaCvUdSbON8gLyKGGGVQmPI5wOVHvziznG2Xx5tyaaUug1WimCUo61DGxpsDVO8o4cfnuB6bf/A738J7qg8mwlWltEjid77l7/5F4/hEZXkh3jocwENKVkGZO4jgjLK2S0sA0JKgzjmf++3//wP/+v/9f+f73/8IwfeRTjH/93PTNQ1CiqOg8rZoCo7VD9qMWcsm69mxr1C7J/rWPHqSAbTx9YT+MQm/A3Bob0azMfu+xarI/TEK2ATQ2u/Vj31Jcd6nQoC+lQEtR1+Gs4NGhvRTVl6dZyaHepYa6sfpqz600kykHs5wXcq3MXvAB3BBJRixqBvigAJAe6vTNb98gm0klt3qpHiwcso/9HoT++0xWrLLd1yTS6zDj1z2SjyxZA80sYgRH1dpvqw5oorb7jaqN1UPWl/RwrY+m6J4qGZEVlKbByQ62pBk4zxUkIG4gV5VpP7z/Db/7F0caLlyfnmhFjcfSmnmWhc+f/0T8j4HBB+6+/0ENRGpW528frcWP7Y9NK/yOx2sarmeG/asgMvig86FLcC3RscUQBJxLOBfwTm3tSRE/RGp0qGOu3olSC9fbjI+J02kCItLym4xjtLnVRTj67FhNXnD4plmLI/pw20sDzpQGpmkkBUctK+tyo+ZM8DBFNS6L3hFLxq2VmjNuXgitMYSADAPxdMafL7jzCRkTLWjmA9d3ZrFnUawDlBl4xEBKkFIkxGB1qwqgaqu0Wqi1aquPYSDd3dHmBVkL65qpWxykIMc7hxhwPsp47dYomYV/lX3S6FC/0c0RXwW2BlD2ZMKvfwRrxyPHtaJX6bADX+zr47oCexTksZjiVQZHthiKPhWO8bZp47ufhko+7Xmy6+2umNput23n+MrxU0yhta2Q7C0krB6/x+Ud6GmLOTU9663MeqzbsFrNjURjIwWagdce+3Rar213q3/vMP/c/hC81Th2QnoHxt+CQ4v7DgAd4AiaNpWrkQj7jntETK+zvZoR9xZ3at2jkmemODga0203TR+Abhy67c8dXIolE2rV1j1NSTMFiWG7hn4d27zqz852bbDPpG1j2+fIq6s6/K7D79sv/xu13j9IWP1y4Gh/qFlw3/++nlinMJptbry6GOjGOLZtGHMliLKnWF2Id1bc7SneI9Xaf7s9U1BqIVVrm2AX3lPy9hRucp6tpP04CjZ5dLJ5ilTmNbPmlWBMgWxBrRBDYBwHiJ65FFytTN5zGhIhBp7mTKEiwVHwlAKtWI+s6NU1VBxLW6Fk7nCMk9b6ZWkED6VW5nw1t8vGIs0krW9wbOOyp7Rp8s2E0QCus2B9sdQicS2MVr+KQM8wSy4q53OFJuqzQqsaVPYHpmfgRMNhzMq9Ns1i+aZBqATrkxm6m2cvqte+NXq6Ot5OGkGEYMtb3ZY5tifosNzo1539/paRsTnZd5mtH+SWAYGug/PitHGz+K2+tjatvxN0wdON6n/N4Pxnj5qzORlaJj6vtOVGmq+83K683G4sS6ZNaoyg56I2+jFFNTeplRQCY4oqG8wr3G64ISDjiAtq3V6nO2R6R3y3MLR7eKiUucGS8XmllJWFhktRJVV3F5bzifV0gnfvGH//Oy7/5b8wfPoNwmjzqCHVUWfhcn7Pf/2//N8YTw/88C8/8fj0zHJbyPPCvC7MTVhjIFzu+PTpEz98eODsBVeeOJ8KHz5NnC93xPSe+4fvSL0A6iA9t+70uLzaOqG1V35KuMuEG3e35lIauVRqLTQrUn+jYdzWyl2EbYTO63fsm4WBoC4NO9Zjdeih77ff09+jHNFm177JWppQi0oea9VebSpV7MYVFmg0sedwN6OqorWnrmnfwNaaNYfWPlTapzURe42l24kfseven8BDENbPf7uOw6Z+uCtddh1EyKXS2kqTAFV24kNkc6Q73p9f++jNntfcyE3dMb0TYnDEoFlYqYK0QpPMGBuXu4S7T/iXFZGCbNuyABXnK96r3N1tM6SvKBrWejfgYyDEEe3PO/Du/cQ43vFw/46Xx6/UZWZZbvz0+IX8+We+fv0RnOfddMcPd++5i2cQb4YdfQ2zdV0awTmGGEjeUWon63QeqSuz2+zrtTzasclzxePEmPOma5W6LwecG/B+MM+jhkvgkmccAuICUSKlrhSpNIF5yUDAozWkb3FE0IAeoXlwrW2gMUh3HfeINFNUmQlK6GGixkgiotm+4KFVltoIwOgjkwv4onLeVjJSCr42tVmLkZpOhGHCTSfcaYQpIskp6VALjUIv+2kOqlPw6IMjJE9KojL+5IkBtJVaNTVWJZdCIDCkwPSg5mADUPPK8+1KXTTj6J3209TMh+yGXNKBhAXovciRPaDt6gHsfYjVhO7BmSa53ijOURMStuzpLvHbXTC2kxDoseEW5MPB+OhbwKhqAIfQe542iz27SuRVht1k4AoeDyfpPLjjIrjfmy3iF5Vwe7d/09Hl4wYoN2Bqe8JWuNZ/4w4UO+HQFWXaWkT3gtZjPdc/daxx1J/VXu8vbH/7CLh/7eMI8PrYbABS5PCe/r7Xn97i0wNx6SUQnBCaqs32BMM+extihLGyDYI+BzH24mqVzjvQZ6uTyw3U7EZPTKqSgM4H1VipkQZSbQzM6C5YORwowSi0VxlEOby2zW2bl20HgP1OHEieLf51+5jtsm1n+7C8wm1/7/hPmePsmF6ZkA3x9wvcBqC/05ByLwa1CdrZcLc1oodSC8s6k8aRmAKlBOraF5fOIDjTGa/4NGx1Pr3xKqJMk0izNhs7CNj0y43NIrkW7Q+Zl8Vs6XsgZvIZD2maSEOCViCvgDBOI2lItNYYgyNE3URjiCTxsBZcElKMiAuUtWlTZrI5BzZqKwSEU/K4KdKa53q7cataJxHi8EuH6J86eqPnIyXRXN0fORvnvdl7rzEyGY9N0N1RU2Uzm7xBKq46nBkIUZvu8q2C0+yqLuJtk8GVslLLQuh1tD4acIyI1+bw4kB8UEddS7O31pBSNetRi2rSrQmy94dFpqf6xRmTXbeXWkGaZM5q/iSoMYfzCpKr2TOr/bmCDkWKhVaUqdeAuYBrmyzG4/Fv5OK4zLOaWeCpOVMcSAks8wsvz09cX66s3d3SrKDTEAjJ41e/u9qB9lPMK6wzUgquij14Hnd5IH76PUMFd/rE6TfA6mhrw1WVF0tdmVsljBPD/T3cP8D5Avd38P69vi53r6kwm17DMDB8+J77+w/85rf/jZfnZ15eXqg5ax1xLVxrJsfI+f1HfvjND3x3sWdDKjz/iWX+rHU403sY32t7ltpwa8aXCmWF+Ua7vdDWRYN0aeCaAse7M0zaZL01yFVYc2HNmdqKzYu3GUfjsbb4YefBjzxz37hhY8ZlzzJ2cxx3eKiPbLjxHvuzS/8mlFK43W5crzdaLtpvMQ2EzlJXlZK7rv4QrN1ON3tBs7hUatFshhMhWXbocjmRTiMphUPQ6LFeOLp+yF51sT2LehkbsNV+hc0CdTbpXDCZfSuFglCtAp0eKB4yV8fp92sfSy7UZhk3QUGjuVvG4M24rSFi8tQQmE4ROQ3UJSO5Kujt7peipmqt5c26f5Oc2fobnLoExzSQ4mDMgJAGlUueppH13TvaOnN9ecQneJyfeb49sj5+5fmnn3n58D0PQyJFzRruruQWhLaGKxmfM6FGGxabm4LOPh+trMDTnOZHi3NUI/loaD1R1Wx/rY0oAeci4pMmwENFXEWcEFPgbkwUN7LkmVteWRsspVLqTETv71sczgBxN3vxZqLhRbSVsPTgTiW5va2ENyJG+1Nn0mLlKcGhljHBWmREonjNOuQKpSC1KrByHsKAH86k0z3pfCGcJsIYITaKFGpplrHW93e5eEiOOARcUIApFEIQ1BdM91oFj2Y05SCGyHCetGWHNNb5xtPLE9f5akRwRqr22A04hhBU+YX6OaxGqpn/I3SywR7zvpI1mwRKVhnA/AYE/NqHHJgpEQ2UNUastEPiopPQ3ua7nreBh9aDdyMee41ra5a4EqoR5W0L3vdckEMJhSi9BhKrt9ZnrNVDvaFN527iorGoZQvpwKeHzj0TquBGmgLAhrP47pAN/AbUHWvo9Dy60YuNn4GkI0nVr3VvWdUTR/vYvc0o9uM1vdgBpLz+0atjX+tlkxeDgvjoIDTDMNsv2ffO3o6j1LrX4jpHiIlEwAeQ1iXATcNb+vMFW79zEQ17EVNVeM0Qi6j7tGGaGAJxiPgQtYRB6kY+uQNhQJcI71B+MzDrxAXseGxvxbLfk/2LI+D/FoD//eOX1zh2yoIOrg7fdg4XglnB60mJ7ItJP+ndldWCBRFtquk8uWbWZaF4xxgHhhipQd1Odw5LH4yyZnJaScFcrTqbbGx6NeC4GUoYgN2YB6+3PedMWVacmYZgG7N34ENAgkdSQoCPAh/PZ/I08LM0ruuKb5V7J+qv3ITkA1NMRDJFNNtWRX0nYxNrJeC51UZr2UCSNRpPEerAMi+kEDintwGOtZrBy8Y07GvXge8ygN+zjm5nNA5MU19s1ezCFuPOglkmU6qypKE28OZOJ2ysXa4ruSy0umhwGpJ6eERl6ZoPtnCpuYfzKh2FilRR0LTMVB/6VeG9OoBqNO7oQaqIBnA1qzxIa0+0WYSSIepQ51JEUqB6p+6rtZHrSqsFL2qmJGWhrFe8i1RGC2pXfGi6URDwNeLfqB3HMi+UoFKw1grNCy17/Hzl+vLC7Taz5sbatOVCPA2MlxMuekrNdr6Q88rt61fGLz/jl9/jhoScJ/0j4uB8R/jdf2O6/4h7ucJSoUWtyRoCpIaTwiSCSwlOEwxncAOEoKY76XDifYLVbVgA8DEyyB0+jnz48B1xjGifDHVXrEDqph3bESCMjKd3an023KE2skZWtQbzjDw90Z4fybcbuWTdFFpjlaquaUOAIUJQSfKaK8tayKXQzFHxrdz/MNC4B1A7A7jfrx4E2LPaibIj+7MF8v3XyLa9bB88bJDSet2GkmfrsuJrY4yRMZkDHJo1904zk60UKBnXqi3jvXbN3IP7Bt3ldEYOqVtyz1gEHJpttmSAPrPOWT2gfk6aNktvtdqrGDnT9oXKuWMNhZJJYmxxM2mvXfcr2fobHM7BMES8OGIL1JZxToge6/KKBp32KhWWtbCqE9Fm5qZxqpKf8+1Ge36Cuzv8edoz6SKw3beee7TPWf269+rufUoDdb7ipXJ/vvDufOH5eWEphfXpkafPn3l/mYj3IzElYkomr1X1j5dGfn7m+vNPnMNHhstJiZmGtc4KDOPEeDqpK/rtqqZnaF2ugO4B60pZFmqxtloowHFS1VSoB94VfHNE5yE4cvNIcWSpZrxUEYe2GXqDo7ca0bGwDJtq/PTRCbq2R1HJqjeScqunRU3vljXjnbbJaLUiTo1rgo9quOcb4iPN6fORWw8iR4bzA6f7D5zv3zGcT7gITVZKK9puyuSLzXoDhgDDmBhPA0THIpm5rYhvxOQYgkrH65Z925MAjQbBkS4Tlw/33L08MM8vrI+ZvN4U0DbBDxNTjGq25R2sK2Ut5Fqom6izK3bYDDt2Uqyp85E32sHi1rcqASilGJiXfc/pBDiytZPY1se+qDglxqQZcMThXCOaaqEb/WlTAQXhxVrWfCthjSHgJNFis9IIBY+tVmpWR3fFJD2+BXCm+jbQ6HqnAgNudMJXlLCr5pZuAFj/hmzEX7+7+y0Q84PQeSrtoKqxFg9bixKc+V3s2dZ+x/p20wmAt6px3AiGb/99uKrtL7vjz/QbHRL1eltvhIt3qoKr3Rxj2ye1hKrWSqiF6h3e2q14p8qDTjhI09rwbgDZk1u9Rza9vt1wifcOxCPe6+81+bc6I1t9MkYodMBo2cG9prhnjF+Dxk4YbNO9g8YOubaI4jVY1H8dwOU/GMZfDBz/+hfahi2CtI5sDRUbO6HGKn1/76WfenM689JlVL0nXSnCGNn0+a1Vm5hqjIKh7FoKSVcBNr21OZWKcMiYWX827wk+6AIm2rNxXVakVgIcHhZlKKILSExUpz0bI/BhGGh3F76+PFPKqpb0TogJhjAQiGoM4APONW65bOnrmmcQLWifS8N3SS9q9OFDAa/ucVof8jY7Yy3VGBRzf+P149fjMY239qnK/lM2Rt8Wts0xzHua7PILqSZdq9XcNAWcFmq13oajzJQ6gyzgtH9jSH6Tt4kPdk5C8NoPR8xAiVIoObNyxdNIddWHshZEVpxrmzQPiUgL2vutZzBqBnNz1cDWa21EDLSo7HlrZXeWbMVqDDKl3Mjrk85xP9JweN9ISR18m0+QI+S3AY6lVnKpJgHUeVvyisw35tuNZVlZSmVpNh+HkTRN1nxYdrfDWqnzlfb8hL++qFatH85BGpFhhOGEnGZkreBH3HhCVOG2zYpeE1Gz0G4Veboh66qUz5Dw5wAe6s3RCvhR8CO01fPydeXp+ZnWVu5OI/f3Z+I5QtLm4h4o60otmkDM64q0GyGsjJMnxYhzgc5o+1bBAm95fkKWZVtcq4EPidqYXgvxNHtWWmPNmaWsFFt7gtfM1lscvQZo+9q4jj2hcqRz9jCnSz77Pfdsj+srcq41Yy9pXeih66do3VvJVpcsEHEMzjE6bxnqgdPpzDhNILDcZm7Pz6zzC02yGn+1iq8KAIJoi4AG5JJ5uQmEzDk0hsuZZGYBBQcVWpENNGkbgb2vW7P63Wbrh9YtapDdweNmn+49LkScJKieUpTskb/hTP1WtTgxeXxSiWVpnlyU8AgOPBpY0hUOTt1er7fM7WVlKELwNnfNyKM24Xa9Mj8+4h/eMb57Rxr0grvCJgSPc9GCy6bZV4eC9aLSyGj3JoWBMU6chwvn4aY1jXlluT2zLDfu7hPDEJmmREx+X95FyMvC7emF9e7McD4TY6RY6yYFqQMpKfDEaU/L0qoF50ItK/l2Jc+zeRdY0COrmQBhbYCC9jxrHoqaB5VSWMvKshaaOAZn/QPlbcYxo9LnTRDsHRbPbfU6IkoAe9+2bITS5LJJxnOrsKwsTj+mTvBJsxbeE1EZZa6ZjLrwtuCJ04np3XvuP37i/PBAHBPNFUpdKM0c6E1e2Opetx9jII4D4TzCbYVpgOTVkMfa7XhT2Ti096MTJY6oOpfS3YXL+wfmlyee5xvtZSEXwdszOobAGCMRgVoprhhg6sVCBkd70IvWiVcj/Z31C/VhsyR5I+s4NnJCVW47SNzJtG/e72AbdVtDNzMuW3toQUtpesbRJP+I9Q+3XuN0dUZVN/PgvWUl7e83oZiCAr/fj77Mb74ctq87TL5YrWa0aRs79YxQgFuLgcimxEptJstEY+cONjrgaLXtGc8DaSn9RDrYaL0UrANF/T/F+/37bwMa6fdkQ7+vvvjmTd9+xN67DXUnT9n2wR1o9v8dsspSaS3Q+8aDkpG1mAmiuV1jgL1nX3SmKUJVk0dVJPqAEqqmXDsSwyJmkomOc7H39OjbdwJrGxYjEWxvb22vwe0X2JNlx1KpLjfu2OxbWuGfGcVfDBy7REEvpR0Bq06k1s/Z6Y03tLwhfgMgtRl7b5u+9vLqF2F9/wAfA8RAy25zp+ryGB3AsmmtcW7b5PQMe2PszjZ3CYWCpVZ2xqe1thul9LNw2uQdr02q11b5c87Mj48MZaaKMKItShbXGLxnOA3UlrjdVqYQuOWFJWvD8pIV4PjgyAi1au89TC4roE3JS6WiZju5vI39vz4U5sfl2HvA9EhB+mLRbHE4Tr4OODt4NFmVFXqLmW6I7Ol+OQR+Zm+oD2PJtLKq2ZAsBJdV6puc1mYkrXEUF1Qm5LQ9xxAHfDKWrQm1FuZlpraVmK1tClCrbmjaOymCRJWLVaFmJR6kFZCKw2pSe52IOdgV2lY/WIu2DFGupFDLzLI8KYsbRlpI+BhIXggSaC5RWzJH0zcYRlF3UXHgQqA2x5JBlqaZxlxZrcdoCwPNj4gfCWEkxoHsM1EKYwycUsTRKC/P+J9+xE8jcnfRpu0itHllud2QUhl8JA2RlrQfmLAJM2gCty9PPH154vnxyvIyU5Z128x89Nr7zw/gPNkCp3UtLHPmtsy0lhkjnMbIOA0M08g0DIxRszk5C1lgPJ94eHfGxZGlrOSlMEVUcgeQs/YffHxC5oXoHC5GZqegJIZEnE6E0xmJg65brdJaJtdVZaq1mjthxLm3MePogSCwF/H3AAMDOj2Akf0zPQCSw3t7oLS97OedkVSZttU50bSVgxFtAcfkPRcfOKfEdJoYzmemyx3jdNKxjTPSVLWwZgVwrjbImeaBohkq0GBLyTlhWBNSMq4UfKj6mdr2Gmin6+HWRN6CIVe6FF3l6FILrepneiuC5p1K2GNCSFSgrCoz6vIdB3spwhsFOT5ibuAQvO412pMWnNNgpEmhWb9H7X2YwCV6nkvHcq83KqXglpmyLkgpYBb+DTXdidazdM2VuVR8g6G7+AkqM5ZG8pHTeGKIkzLeTcm04HsPQg2YgoeUAinqul5q1X0/Jvw4UfAsSya5yDgM1ArzcuPltnLLqgpgM8XbgXIrQp5n8rIgJmXGzH9qK8xFM25hPHM+3+P8RBMdw95rt+aVJo4aVAnyVgRARo1dguyEt5K4nl6ff6RTt0fNAsBO5DTRjKwINGeN4KPa8w/eE1ohi7lQa+pS17p399x/fM/l/T3pNNKcvkezQ7K1QWnV5or3GhOJQEyc3n/g4/TA3d0nhruLBolF/QOCWIsoEX1OBWqxnnNAS4l4PnO+u8DzCzk3ylJpeaXN6jcQGTg5NdYBR/CFtYkmBFzvy2q9ldHWB3pvNLbTnrTNXO/Zg91f+djaMB0D5B5Qbwht+z96QLpFkNv5aTDfalU5dfOqnBK/laSIcxRpW/++7jReJe8JEpuxXRUBPaa1GrdoBLDFs6Bqp20vaNXq8XQt7H4ezpzS11LUyf3Yxsj2E/1tblNy9Eyl9td2289br2nooPGQseyClaNb/VE22drbrKuvkaN+3bfJ/ef9nn37MXe8HLuA1jEeGxFre65+a79HR1MbBedKtOSi5jbO2mjsvgLObot6SfsQ7KV7Q6uZUpUM077EzsZCe8dLcVoWJZrICCEQ+vzYgKMBxGO20cgbjs/SYVrjXoPGZvG54t0DaHT/eHf8xcBRtuBls6Kxwu89eFXjnL6Ytc3KF2wxqaoHTy70fqtqsNAxHko85lqZpok4TSxZG7SbX+s2WVuplJwZotXaiIEg6X3BZLuPfeI0jC2qRRfDnF8txvS/4VU6KlE1/WspXEvmp9I4LTc+pch5GvlC4PN6o11nHufKNFxIPrGUwrJmyloQB6W07dyzPYEN1NCkVAO5lea9tqoojfWNuDh3eM76ghVC3OoVW1P7dal2rw089szitkvajtmL4MWMErTmt+EqeJOZYbWOLpg5TmsaCJUV1zKBSgrCkBzDEIgpbA3L1VhBCE4YfMDFRByaFfrr71nrSi4Fvzq1JPcmayUZcAzQdIFtReeOWN2lQ80nVCpnmQ8tINE51IpJWhVkgoNWqHUhZw32SAXchHPa6FVc0N5pOdhi/AbjKI0QBBe03L+1gEiEmmjZkZfKPGeWBpdxwI0XSBdcOuOGCbcs+FYJCL5W6rqQnx+Jn3/G393BMMApQq2025X1+RmcYzjfQQDXGjI3XHT6EsHdFtz1Rnm6sjxfWUtFvJCrkGeV5UyjoOVYnlIK87JymzNFGi4oaVBl5bquWt+1VmrIlGFktEDZucA0Tty/e0cchOv8AkYubEcpsCy02402z8SsMmMn2kOVGAmnM2664IZJSaJayeuNeXlhybMC76bmH/WNqHHZ5J476aXfP8Y1+4awsej2w74Wb1Fsfzb7+wWTpTZK0U2rlUJxXiVWJrNyIgzOcT8MfLi7cHm4J55O2qNRzNQEcCYfLzi8qIwN2whLhpZVU6IZS6/ujt7TcmG9XWklUGaQuqiLslQcor1bQ6C382hNLOPYaLnQshmJtILQrOZZn/PmPcV5sqCOpmLrw0E+ps8MuD2V+6seRWUlNj4aYBlPiQZh1cieTC6ZISam0wXuHnByo+Vel69DuDkfN62DI2sWkRgso9DUF6A1blUoFUYv3EXHEFQOKdUR8Uwx4oeCx5NzZc1FFRLBev8dgJi67Kqzeakr17yyxgB3F9o0kZsQS2UwMUVeCk8vM4/XGy/LqnPJia3ZlgmvlbIsFAOOfSY3qRQRllrI4plCJI5n4jBRmLUHp5mreelyv8rqnUnM3mAcDRj2/UDHz6RnW/Zlr/U6IEnA7cDA1DMxBJWsxkBKgRgdQRquZuqyUNZMazCcRi7vH3j36T33H+8YzwkJQmnFaus5IFRTG3g1aJPmEQnE8czH3/yeD3HidLrncrrX1i8146kEp3WSrVkRkQtbWN5QgOtSZDydcZc78iosbWYuQlvVIT6IEMeJSxrxcSCNSlD2u9FEyKWx1MP3u6QcDFhh5UJvReNoLLiBHyON/CtQpPlhZxSAVVi9WnP3pUM0NtqIRKtVs+c8AEk1kJRmhS9GClSr6e3heTPAHkLU8iT7u9HiMLy3DFSDqp+VpjLtVgulwIrY91UlJWAZK0ccEsknjWOcJ/R5fMg2imVGO6nYM9h01RgdF5rjqijpqFN9v0lbeCh8E0P/iserjOj2TbYs5wEcH3+2jaMRF4Z6t/mwAeLDr7HNEmw/3DOJlkypRUujctEcCHqPNWHSSQH9hQGHC0IwkKp9fZUMq83AG04JxVLUI0AUJ+G6K7DFoT0x1s9x8+jogLHLZPvxN+KAnWreM44bn9Kpg/7vv3/8cqnqNvX1FILfB0eJiv6A9hOALlXow+q96sud9f46sjB9FtYmrKURWyOmgWGYyPWm7MrhyRYR8rriYyQMw7YwOa92xLQDa+8cYBbttVLKQl613qJvEp0pE8AnlX2sTU17Sqs0rGZS4FohL5XnJlyzZr+omVYXTqNKk3IRcmu0Vqiiuupg/bdKU5cldfoTpCiz61PU9iStsbyRa5y3e+jsfIIPZruv9RIUtsWkZyG3WlHr09Yzyd2Nq2cbm/MUNFB1teFKJdZqhivabsNR9GdVG0R7KXgnWqA/6OYaohnTmCTWi9UAWXuMlqy2oBTWslCy1oz6Jto7LCarI2FrW6C17SbPMOMeZb6bVWg4c4pVF0QXvNY0dJZqez+ASQ5qpna5SlQDBHVh1UwIvV7gDY7oIQxqILQWj0hk8BOjG0nFIbNm8ZYq1OgZL/e4+w/IdM8afqaEQCuOdZlp7QttGOHdO/w6WyaInVn0jjBElQOOg9YtWgmpgmsd85Ai0/sLD24gnSsuCXGE5SYsL400Ok4XT62emh0hNcQ1np8b6wrTGU5nk4lUNRdxLVBnzUaNF73XZXWkYSQMCT/AZC5lzh+WNadyu5ZX8vMz7ukRd7tSihlUpUgdR8IwEdIIUbNiy+2Z5+dHbuvM0hpzbdxyZf0bssdf43gNGu3UjUjrjaQt5NIt0aEF+/Ycb2oN57ZC/iNL7KQHMU2VD2umpUwLcds8pGlgHvCMIXB/mri/O8M4cq2N+Za5zZplzFUoTfvTesxkoJpMq4BKLVUxEFMkpAgukNfC9ekZHyq5eGqekbYgdUVaxoVeT6kn3mwNqblS15WyLtSi2es9WNdsY3OeLMJam4H9ZoZWxh5L79P6mqv+NY/c+obMvk/Z/a1Y9tNp7dXjywsQOaUT93cfyKtjzS+aAfB9TPV3tVI0aF9XWq6kNOFi4Do/8e9/+SN/eXoip5G7+498OF1wpeJXx8l5hhAYQ8LVzPpyZbm+UPOCOM2ISpd8iu4HznlCGDhNZ87nE1+ebrzkK3+5fuW8fMfAHWOIsGbW5auSKnkl15Xn5cbzcmOtmdFPpEGzkikEZJnJ80xZFlptdLhSa9Ug2HvieOb0cM94OeFjoK46X6Xp2pzsXFstrE7eaBQNsJvme+sDiDbqrk7ziVvcY+7iyqGaAcv2EKs89TRq+6ApRc7REaWqQddyo64LUhtxGLjcP/Duu488fHpguhvwqVHImu0yYyS/BVvYtuSpVsM/DGfevYucvMeNJ0IYCNIzFY3gRM9fNGuPNJxL2i6l7+kh4FIiTiNyOsGcKUVr2KU1al6p3qvkNo2cUiR4tP60VTM4rJRurtZjJsuAbVvhIdMhb4Q4Npmp83Dw2uhZxS5z35CzyLY6bE6hsmeYlfyu+tx4r47HTg1pPJCc+nzor1ODkypCFU2OfOuyGoLuado+Rft6xpSUOMOAYHY0tCa/Z3F7O5XaNJ4FzIxLXax7i45oWW4Fjei1tM44tB0Q7eiBXjbxmpKxrJpzliXuzwbbO7vM982OvwKHh7+1fV+2E38dcvUB3n/VK1zgjr9Y9nHvWcdD94YjR+Re0R62Yvdbi9AK1ianaZsj12WlChh7KV0TLTsqRjiJw0wedwPDHTRqbNTPT6SXcLR9LL/d4vo8N+T22qX8oCU8AtP/xfHLgaMcvJZcZxtkH4xatxvfA5xtwGxj8sEuwBbn/nA3ka2fl6A9sNZlZUoqUWs+bAFD1+6CU4e2kvFD0sfX2ImtILWDx+6eZLWReZ5pOVuD+qOeHHxKxHGkOUduXRolev5O24Z8aUJbMkutVKkMIeLEU9aFa81EY20WcdoEWjCnwUh1kPPC2rTvpTJJWh/kRKWFIsLzGxWN94Vxs9Y2Rry7c8nmmmWMuVkL+15/9C0Q6oxscFpUjmYdXW34UrdsgaQMYcUFUdDWMkEqEXAhqN17HPBBM8gbR+eshsPmR/AeiZHaBtyQqTlRQgQKLkCwFgAhJFSi6myxNIlHLbtEjn6tthA47csZQ8THSKzWFmVjnl6zM30JEnTsWpdnOeuZpTnuNxnHyzAwnBIuDiwlsORIZWAQIS0zbrlS15mXdea5jioD+/AD7v2fWH7+C+36qEFOq9TblfXrZ+TLO4bri16DR3s7OY9PI1OMEBM+JWWAmhnj94SqJfviMPDuDHcrxHE/33KDeGiwWGcI3YNHYL3BeP47F9vVvn3VsrYBHSfGOOrfF6upRoM65oX6+Mj65TPu8ZFwu9LyqhLblHDnM5zPMEwaOK0Lt+dHnp4feZlvzCVzzYVrzixvlXLE1lHZ11Ww7Xrj3NzOnBq5oQBRNrLLKaVONzfYZqYIvmqWvRVlTGXNSNJnNNLZcs30ZWnMpZDyig9eTZEtAMpFyKvWoWviSEkD7zxhSISo5lMiQkG4lUJdGhnRTFVuOJ9pzTPf1Fyq1QVqhuhxln10StFS1kpeVvKswElq0cydE3VXNpmY1rxpb8nSWd1qWUcjfKQH9f9gY/zPHuUQkWgyrG/OzYCwhv65NsrzFc/IdHpgGO+QOLPIjW5+IbYHSBOtQ7xeuT29ENId6fRATIn8vPAff/z/8P/893+jTBd+87v/Qfv4W3wYFHDGRDjfI3henm/89JefeH78oqZsWkKIqeiMGFTCK8aJ0/med+/ueXx55vr8zJ8//wH+MJBSZLz/QJtn6vMz1cEaPPP6zPPyxG19QVzldEo83F04nyaCc+RlYbleN6mqBrOO0rTnpaREOp2ZLhfiOFCaOhqva1HCFk9wkUDbTHLe7OjBeG9Z41Q43FtWWetjK21wuzu10/WygxIsmD8NA+/vLtyNkdAy9fmR9faEzFekVc3Mn8/cvX/P/Yd3jPcnfBL0qWk7Eb+hHIeWiGDlQYJzgdN0geHEAmSnLaICmFTaaHEpSMuarQBVw2yZVa21IyUlEceROiRKTLSo64czsO9qVbfZEEnJAwXJml1by8KSC7kIVdxm/teD9B5/uMMcf4tj7xvZiQBdY1vf70U9DNwBDPQ10x0+r2U3Yso6q3UsFi+K4E3x5pwjpESImjXsrS2U0PObbD56BYqDtSmKMTAM+vXWy885Jcid20BnsPNrRqo1I5hoptaIgTENpDQQfVRDPKtxpJcMGRjqWcfe/9D3tZSeLdd7qHi6bftLcF3OKjuJbkD3rQjy/Uz+we8/TCN3+H8259RO0tq3+3zcXv3nO2hUjw5VUGjsGYhB6DWtdmMOv9PWCMvo5dxoeGI1GwWMAPJRw9LmDC+1XQXgLWkRVXXXTSc3XCU2B6WagVzd9rm9bvF4Xd7Ch2MG/Xif9vn/zxy/XKramgXxbk9ld+AofYHdgWLPWO1JmqZBhT2iG/A0Zqo7dDbQYLY5nMTNcahK3WQDnT3Wrgh6A1WeiMll+814zQ3UUlnnhbIWY2Zs8KUXbQfiMOBCZKk9AKmmSfda8C/CbBMAk4q0JuCtVrJVvT8+UF2g0AitkVD7eHwgeDU2WVveZAy60Ji0oylj9RbHZmdv7L66m8oWrPQJSGdT6RvjkV9hD4psE8NrCwuCupeKVGpRB9waV2qc8eaG6gQCleiAoC6pQ4rEOOL9YP0a3Tah1TnQvsCrmaa50NY86piLWsqPYyJFBYytir6k11gWaBnXMl6M6MCy4J1ZsyysD4lII/hMN63QvnjG/Tp1NFVLdLdJU6ToWLaadQzd2wQ5H+7e4UePhEAsjhQduTR8u+LmLzB/oZZHXuZn0uxIfuDy8BH38XvSn/+D6+OPOAdTDITmWPPM8uUnyo8/wtOTgkd0HvuQ8D5pphEOBM3hhA5ru3OvQSO8Bo2wg8b+/r8LGuGvV6sAwX/DjG6rotVALCv89DPtz38hP34l5ZlUClIKLXrCODA8vMPfP0AaoFS4XlmeHnl5eeZlnZlzZimZpVTyG2UctzXTJrszoKjOf26LaLZn4aAW77ITQ530PlXOqeyMBmItEFqpkIvKHovWa/qiboHJO6oP5Fp5XDPt+YlnKaTzCT9MNCINnV/rqi0BvCil5H0gppFhOoOL5CLc5oXrfKWsM/4mjGNkmiaGIRPcilRYl5VlvkLJRBoRZ+0KlLRqpVKWlXxbKYuVFVQ1+en9KJsFMmLMv6o1hNLaBh67q2rbdp63OXafSjhsa3T+tJP9tWjbnjwIMmrbIfED1QUNTN3e0sA5qCVzfXrEx88Q7hnOhWmMOArRzbT1M4/Pn5EipFsm3X/Apwk/nPGSmV3l6fMXfvr8mef5RV0AqYioNPg0nRjHE94nHJUQJk7ThXfv7rkuj9zkhc/rI3/547+Tqqe+e+ZSKzI/I66xBPjLyxPPz59xrLy7nPjtpwd+eP/AJUXKPHP9+sj16Ym8LNDE5ugu4Rdx1GptIqRtwHFeM84biYVHXKCTmm8ljXNNzKzJpGJdNWXk8p5y0GezG1cEv49xBwwpJu6mE+/PZ87RUa4LL/ONfL0ScmbwET+eSHf3nO7uiOczbohaL+z63rqDrD7TlJQwGTDODN8KS85cayUbsh1CJESPq4JrCyUvZh64B5nSHZAxsJQSfprgdKJMM3nO5KIZx06Ai0ntcI4WPLU65lJ4WWZuy8KSG6X57fwDTdU4h6xWEwWhtb6RlwPfSPGELUB33ZtBNGZVY7EOEjfrPwu6jXhqos9nbTRne6PFdKqWsPIYp8FVCJ6S8haw9/rOEAIpRmIwh9qgvQG1O4CtH1ZrGEUYRNs4tBiptVBSIh0yjg4hOc8QNe4JJo3eYqYOFM3gp1UtVehxXrB+2Ye00IH82GWde3unzpCxJxEOseGvfRzbivQQ44gh7Ef81Rm4fe19/eRsaZGNMHHesrJGxEgvdahqhKMBq8aFQ/L4cKhnPZJ83sorbB6J9TYuWEMek5+q421vI9isnafW+vvojVCIWwlZv44ukddMo5ZPSTe/63G7uG197eq3njg7cJts8HqLOSx2+rUzjtreIuwLtnTYoIxz6zulmKQUb0ENhniPuPZQ1+P2IvOd8XDbohZCIMRkC4wBVfZ0a8mZVKsCiS0Ni7FA+l7vPFIVxJR11cHqJ+Z1Q6iIskUhkpuQjb3WhdYZm+AMcO7CJ+8CvhatCxoT6zIzppFFHHVRC20A8cKSi7qchWgTy+6pLcR9gekPydsc+6MjVm9Y4a/6wDjXsx5dImOsh2ixuLONcsv2e6dMSQj4pnbt0tQYKM+z2vu7hkcby0cvjNERGXDek2LSjKMfcFsvNujLRDdg6efXQtBWKdPJHAmFISWGISn7WwqrLLSyIpKNocnQsgJIy27opbnjhYALOBfsAQzGxusZOALeRXtZTZbdv1IyVZQlV2Y3vxlwvH/3kaVcueWFdcnK7opA8axXz8vjH3h8/COnl/fEu4GH0wkZ73F3H/AP73CfT7j1mVAqsYGXTH35wvzHP3D+n3/Av/8N7l/utF1fimzIcJsjRxbQntkjpeXMMEv0365/1JoUo2XQHPtTGbGrH9+YM9tH2Zk9bL7pOuJ28qe70xXgz59p//4Hyo9/pl6fqOtMLQtNKn6aiB/e4z99gssDQsCtK/V6Zb4+83R75nmZuZaVWymstaiBxRscihP7It7VFN8yrLKRdv3O9Bo42EJJ+10oieOtMXtr1Kxyx7auahpUtBifXHHriqsVnCM7x2MtXK+VoayMy8x4uhDjRF7h5XZjWW54aQwpkUbPeBoYz2fG0x2EhFsrM7CWhZcslLIQ88KUM2PKBJ/wDc16rgvRCVMauEyJKQ0E502eWVhuKnFs6wLFpO49YLXWHuI9zQXNiIqw1sJastXT1w049rjgrYjxvnarRTtmrNgl8G7PFIjDeVHQ7aIFmsnqojNbWwrUqIvWWK5XcF+JwyPT5R3ESnLC77574PnlA/zhZ64//omf5sb0cSE8fKSMjcen1STCP3F9+cptvXFdZ0rNxDjwcH/Hhw8fuJwveKfttIJPjKNKlZf1jrm+UD8/8/zyyB/nf2P+8xceUiC5QiPznBcelytLWbg7j/zuh0/8l++/4/vLiZQz85evPH/5wnK9IkV7F3sN3TVTjUqMyzqzzDfSOJClstTCUooaDnnNvDSctXnYnpxf/QhgtWF+J8ltEdJsvgGQg9ekPnJW2lB1L3fOMY0jd9OJU4j4vFBebuTrDVlWosBpGIjnM+F8JowjLXiKs0xmcFt9bJ9f3ZRQ5GgK6FiWmR8/f+bPX7/wkituODGdLpzSxBI8yVc8q74curf1tdVirVLVfMnhcONIvLsjrgW3ZiSv1LwSmoAZ66GxLxXH0irXZeU6a7axit/vTdOyEAXieh/VSVfVYvmNzOMUMBzjqB7Q20rQNL5w214CfU71nuXSA1MbhdYa4irSnEpWq37te7mKzekxBcZh0JgD+gamsTF7JsjbPXnVjL0nW/6/tP3nmy3JceYJ/szdI+KczLyiFAoFAiCbZIuZbT2z8q/f3edZ0T0zPeymbAqQQFWh5JWZec6JcGH7wcwjIi8KBIGtDCArb6pzIlyY2/ua2WtqfuIUBB2s/t5aDKnXy5mdsMhisPpw9ir5RhiqBzu02Zg3FxhrTuB0JtLWlzx43lWIqTUn57fRXEGbP9ejpqruLt3/V/dWwM/M1SXZStW2+2TFDR089rpOUQOPPbrcaqVKNfV+aWarYyAlsRKzuCNQ+lrz9dWa9/7utxR6QCJ4zbPV1hp4L44FLH01pkQaBoY4rC2xbLs5tG8GGm0es/dZrit+sCk0QjeGaH46CtTVf1+nUbbVsh/df+z6HdpxbP1LNiexh7E3dP9g4dGTT7fUPnFj3G94Y4Q3B74pzLkySuZqPDCMg/XRKw54bDcaM50LZV4Yh2RtFPrikT7gtoiaN2s3UZS9OTF2vmEhf4IpZC0ebewrrjnYFGdiJASr1WjVVNJKQ2TmWpTrFFjCwKlWzkt2IGzPpbUiLnevtZE9QgkYq4e3Jnkk6Ghso5jcr+dbN7pB7abLK6s6AeAD1UGtPPhd6OpGIQZiSnawVXdaa2NeLiANlcwgB2QcSXGwmk5JiFhqaYwjofdplN5ElVXdqo+KgKvUDYSDMA0HRIQUbLNZTcAZ2uKKjNbI2BiaTBeqgJ2zHiMqgap4mwFLwzPVs0QMDWJzsJisn6TfozarYyouLa/SQA2kij7OwShX15xe3vLy9WvuT/dWAzgNpDhwmYUXr77g6qtfkH7wHsf3n9OON9QwEaZr8vVT2s017fKGpdyjtRhBkC9cXr/g9he/5PrmR6TpOXx0hXqKubjnrfv9boO4u7EN+Kzffjc62f/eyQlbavZiWxeaPSO22ZTt7Xz1OctGjPauBfjiNfqLb1i+eUG9v0PnM3k50/JMGwPp6Q3xww/h/Q/g6toO9lzI84XL+cTpcuK0zJxy4ZwLc64mfvII174n7lYb18cQdzbYtSv0yMyammLf2xf8G4saPSXNond1XqjzTJtnZDoYw1oKmrMpnIopVma1mu04z6RSGOZMCImalXJeCLVyHCM3VxPTcWQ8HkhXV6TpSJPIQkFKQcoVqo18UeY8cznNDKmSQmJQYWiNA3A1TTw5jNwcD1xPE5FkdY2XTJ7P1OWClgVqRjxLoDvyRFO+bhIptbGUylIKuWZP5+ljspfif5wre5+21oGjOxQGlOxda1OaBouqNpNZH4bEOI3EFLjMlVKzM+Hu3GtDSqFdziz3b7l/84IWr0jHygdPP6R9UpE88sUXr8lvX/MiV9rpxDRe02pgWQolnyjljiXfcckXkMjzZ8/4wUcf8v57zzkcD2v6Ykrm9JbDkafXT5iXmZIDUmfOpzNvzplLigyxUdrMOc8UlKsn1/zgow/5ySef8PGz95gU5ts3nF5+y3z7Bs0zUU0wJypEJ2tFmzm+AaIUar2wNO/v26x3p8RdtC+GncjJY1yyOvd4ZKC7bXia3rsOl5E6BviougLHMQ1Mw0isSr4/M9+dqJdMVCFJNKLzcCAej6RpgpSsd7RHsEIQtrYHPa3Tm4z7e6gqp/tbXrz4is+//JLX55kw3fDk5n2eXD/lZjpwGIRxqIyDMg2eUePgUd3BrdVePyoMaeTw5Kk7t5WyXLic72hLpgFRi3kNHqZp1eobi2dnrWSYE3pdSGhVkvDIV62Z1h4r4mhXj6CskbMe7XGAYC1tHMypeu3+bo6lGRHX7W2tayuO3iKoFVf+T7IC5E46PzirYFWzxEmQPkz0e6MDUFuJUQVisLZRsDr+zQmmNRusGzdVz/ayVky1VKgmeFhLWcWp1vN4BRz7Dgj+HrBT4NT1LILvAGSPZFm7O7F/9e4D7v9tn/t/t8/7QL1VLamrzyqbiyumkqsN8fpcW6M+ftKQoB5Ztojuw1rB7V6UDVCuhVDi2Wzi+9rXW2vqteYBqRUVIaTkEeloNaoYUBWxPqKqbQWNtdjHeua4jQpimiUhRHpf5A00/uo8/TYz9zuI4zx809Ys91f74bZzDtVZk43HcefQubotdYDVOPdkwb6AS1HOWomxmopVDGsUy/a19UlRVcqyUJbEEKf1wN2YEPtcXda7H+jmfGw5wSF630BhTaForfc+kxVrKtCaTWIvf5MQWEomtEY8JH/d5gBFnam0RWZmoa2Mk0hbDyn1+jjs1h7lUtzo9LC8bIeyAePNYbVojtLVx1arpboJx4j1fwtOCsSY/Nti0bdWyHlByagsNFkY5YoYjqRoAIwwImF0Bt5A46quyEaRKBZ2t7PbU4uDpaUKEVUTSWmlYdo7bdc83MBja8WLiXnIuMVIDcGkxUtBERYFrUokMAQTEwlYSkrf8EYIWBp2a6DBaiGgIK1gUvvf//WmVF6ez7y6u2M5nxiGRNORwySoLLx68xJ++Q/oB+9x/PAH/PD6GXUYGW5uaE+fkq+vaW9H6vnMIBBbJeWZcL6HV9/SPvuUdnVNCB/DB1fm6QVZwbSffetagQ3grYD/HUHZd6M9v+nrd68HP19RkkLwL0qjffkGfvYZfPY58c1rwuUeudzTltkO9eMV8el7yPP34OkzuLpBiHCZOb99y/3tW07nM/c5c1cy9zlzKZn8WI6qOymy++gp/N3ZWR0xbD/U1si1UlpcWfTWDaqYPdIYkObq1q1RciZfLiyXs4mJTQcGhWO0NKhZG2V3ABW1lkXz7DVR3u/2ehi4Olxzcz1xvL6yliaHI3E8kDWQdWGsyqRQPB9+OZuISqkNdWA0BuEwTjy9uuLp8YrjOFqtZW2UOVua6uVMWS7UZd4UVUVt/8VIC4kakpE91dpXZI82rvWStkicUX88ZjyXRm3q4JDVTlakNy5az0VTrrbnidPA8XricjMx57fM84KqEHXELI85kZGFurzhdJdow1MOXDGMz3j/yUT7+IpRv+Cbb7/hfHrJl6c3xDihGq2OJiio9QKMMfL82fv86Ec/4uMffMT1zZEUvRbfm7OPw0CdrsjH5zyrAZVrpuHEm7cXzqcZ1UahUsVqmq+vrvjwo4/40Q8/4YPnH3CIiXJ/z+X1K+bbV9TlnqAV6eean4Wt2Xk8TQPXTw5MV5EiF3I+U+qJ2vIqDCNO6sadyuBjXGsKJz3CvyYuuk/jWTgSCKGnp7GS5w8+FKSYL1Huz+icCRqIYbCipzgQp4np+prx5obx6kgcB4s2evFdT2tDeuRArMQGK43Iy8zlck/OZ/Jy4u3r19zPLzkeb/nog0+QDz9ifHa1kp8WcfS+yyI79WH8LLMzcToeTfyGSr3cUu9eUediJURlIZYFqZmUYBQYJTBI2MVirV53rc9qPVIH1ZvOiygpPc5E9rNoi+zA2tNZeMfp76VWtlfEFgIdWYhHg/BWBlZzn1fgoWoEyOg9HU2Aj60+bbupjbzyDKHmxOgKIEXWtWS/a2uwR0U7WdDJwvVePb1HPZqbl5maM61YeUIredXrMDHI4DW5HdD7+dJ0rdVtzYTGusqoXQ7KVjdReVCm9j1fPfTUr5UI8J91yNpToGWNfu5TbGUDoNrBYzNNAB/z/RkMWOYIFaSYPxpM5T/0FPau2tvPE2FFuXvxma0bxcYQdK0QA6/279CM+A4xWg/zEKxGWZ2UUjWyxsUma16sK0NtruHk6ak9ku0ZE/iY0EFjB5Ai6xr7Lrfq112/Q8TR36IPijs86835jTTdFrTqri5upQY2FtgeygZceyRSFbCJySJcmjIGIEakBmdGhf1j9waaoTWb4PV17F1qreScKaXLWveCXvvVkBLDdIAhrpFGq+lcXWTf9D0KBdoqRW1yCrAAh8ME1wfuCdzNiwngxEQg0JpFO4cQSCGwNAOP4zisUvnWxsSYosdp4kD36a24d60Mlz617vBsB4lo2P1hB3G+YRqIuGsk5hyFEFehFBRTF2uNlquDxwyiTCJEGYgyrfnldivisvl+Xnb7jYsP6Ho3tkncLWvFyIZlyeTZN1Uua8Sx1sWdz+Zy4A7co6W7aYwU74mUlwzFlK5qMRXWoLtN5kRH12pbc+69sNoEuevqvD7G9fWbN7y5P5Grem1opJRASZZOcZnPfP3tV7RffsrNxz/mR08/4kfP3uPw3nsMtx/CNze0YSCOA6PCMs/EXDmUzNV8S3v9KfPniTEVYvgE3r9GQzI/yh2lX/dk6xyte36H83Zfr0TT7oW+yyHczznr+yqiJsgEIKXAN7fUX3xG/ezvGF9+ynD/iuF8Z+06aiUdDnD9jPzkfeT6Odw8hesrOGfOd3e8+fYFd2/ecp5nTqVwnzPnkrmUQn1ERxVVTy8NbvxNQa+r+u0lw6squUGsYtG1GK13X288t//wwivFbOCyLMTTiRgTKsLVdCReXXNIjftL5pwzcxOqp3K1UqhtQau1zDmOkSdXkZubgaurielwYDgcGY5XpOlA0QgyUDWYKEAIHKKwxGSRw1otzZXG9ZB4drzi2c1TrocjQcV6Ni5mK+qSKctMns+ugL1YrXIIaBJaimiIZBVyM/vfeosdrdBlf3k8Eu7BPDbQJu4QsoF/owntzNENPOacOV1OXKXI8Wrg6QdPqNzT3szkuVDWmhBLw6fO5PkNehY4BEgTrV4RwzUfvn/FNEwcRvjiy894+/aO83KHBBPciE7IXV1d8ezZcz755Ef83o9+zPPnT4lBUK2EsDlaMUWmww2NARmfcP008977Z168eMmrV6+prZr6tZcmXD95yvsf/IBnN+8xhoHlfGa+u3C5vyPPd7RysbYiDqkMXFeKVoiR8Wri6tkV6Zgo8z2l3lHr2RxW3/C96gVn02N8pBNSd/xoBz5u7PpxGen1tR2INNb4gnS4D1qLRflLRpaFUOwcaRIoIVKHATkcmW5uODx9wnAzIQOoWImMCEhMhCDWcmMn9AEmwLbMM8syI6iJHuWZN6/P3N1WxnjN+8/eJ6aJ6TAwJFM137uLPQ5vvke3F0bKHoYIT6+5PL1mfn3kfJmpS6XlmTifGaeBIQpXIrRxRKcJFObiAIsdKHJA04kvaN7WIvIol6Gw1U/A5fYkGBGgbXU4PLJmtaorb97aCuTWagj3X5tWtBhhF7RBTARVijd+7L/feyr2c0s8vZ7evoEN4G50w25WtB+cTtz7c/U0495QfvXJWqMWI93Ksnhtu4FHrfZ75rt2v8/qhq2V2vaMypYS2zpw3N1dv79+qPea3se49oI2e59+HYzd722qsPrA59jgcU8ndS3cRrfOaxZLL2tesU6tVMmrKEivibRoXlifW9dJ7ve1zevqVj84h8wRCgJDEKKvN+milcZhED3SaKDR5rO3BCnFsoOsZjes682AYwfRO9HLB46U27c+rB1O/YbrtwaOwdM0eg2isBWGb/LVOOhoHk2zm1kFV+iIABOWQVZVIEXpfWPQhnpdWcGaQ6dx8v6CVvOA7mp8PCe515eEIB4RswWVl4U8L6sC4KqUJZYSNAwjwzQyt0rpG1HV6zf7POtaV9TTD9ZNRmCpSg0JHQ/c3p855QIhMo6eyrjYwROaEmnEJFRkdRY7I6G9X85jUardBHhahLo0DM5W4TUJewNld/JQ0L45y9aaGd7QgZ64cVTQ6GkTa+Sx4PmOiEZCHZAxWS1PUDTaewcv2N7qKzvbympQTX3R1lSrlbI05ktmmc3hbHW2tFTP6a8uENIjwOJCRcQE0XrBmaBIg7yg4MZT0S5B2AQNVqfUNBprE8xNiKirXFeL6JrosrPD3//15u0rAwIxWZq2JCNqdDRmty4sp9e8+PYLvvjl53z9/BN+//iMp0+eEX/0I65ef8zy+gtYzpQGWpTQMlzOlLdfUVJGp0acEnG4Bm7guZjlCOr1ML4mVlbNVse2dC1qva2hd7+2a18vuRbhrwziDmD2r/sfBv+7BXjZ4JevqV98xvL1P1C/+Tnx5TfU01u0ZRYRJCSGwxOu3vshw0e/hz57HwmJy90rvv36Gz7/+lu+fXvL3bxwzqaoeimFXIu1V3mEa20G7Tan13PuxxBktT3qB1qulbkIU4xMMTGGYY3KEJqrB4WV/WytUXNmuZxJ0VplHKcjV9PE9RA4xoXby8y5ZApebB8jLQrahJSU6+PAk6srrq+uOB4ODNPEcHAAOR1pRIKYwnWUwDEJeYi0cbQG7tbokRHleog8PV5xc7gmkaizRTg1VzQX6jKzXE7MlzM1z1ZjY3n8tBjJMVBFmJuSS0/JMmDqxmt1Hvrq2q+l7/vqNXHd2Wxq/UJ7+lBD1v0Bwpwzr+/eElG4mUjXE8dyzVxO1HKmLAZGRCxqXGqmzYVyCXC5guEZtcFxGrk6jlwdYZoWprHy9bcvOJ0LIt4bjkRME0+fvMcPfvBDfvDxxzx79pyUEq1ZmrqY/peJCIVIOiSO6cBwBQRYlplxmpgOIwJcX19zmEbSYIqex8MTNEcu9zPz/Uy+zCzZBVnKYiqcdNEzO0NbisTjgeHmingcaalSLhdKOdHqYqSgxJW0tTNZSCGS4uNMZAcW0P0oYRPA6QT3zgvs2UKKC6+ItVkIwYDj5YzUwpAzpSzMruochgEOB4aba8aba+LhQEvRhFeagzm147KTDeprqFTLAKilrER9LoVlMbG9GIIpHGsj12JEUIxINNthDcetNMdaZViJj1azHaqQq7XNagrT4cjN06dwKZzrmZwz7XwmpoEpRo4pIeOEVkU1AIv18O0j6ftAtW2tKSQQoyu5PsLVeoBiF3lEdn5Vr5GHlaQzsbuNeHPMZlFY2ca/k/vq0do42HuVWak5b1lMIubHONiwNFb11mdiIjqhtx7bWNTu4+gO/PoPebjq983qO2icycti0cbqYnCeadf/1rQpAr2+T/G0VLylBLj4TltFV+RX3vsf++p7vFYS7h97H1tp+why9xZ3OSf+XxvXhrdOoj+bpxYHyw1RTwe31mBAxg8QI4dELDq4rhPw1O1d9HrHru8J837HZlssm3Kv+QJWIBZ2oJEOGpfFBCdzsci2n2krYFxVWDvuerc+dRfM+5Vx/s3Xbw0c94i0I3RFXZFzY1KCKLiyE9j+rNUWYg87WyRLttSBftjr+lMEM1o5W0Pj4zSiNTPXstYRWLpooNbKPM+EYWBIw5o2qa2RfaBbtdcNIlSvzVQCKY0wDCzNmtaWtpVAvpvaZCCmA+ctfbU1hSgsufD27S05V6TBIVo646WaupbVAFlvR5owpoBSCSlxSAfuT2frF7eKDT3C5cYCT7XsC795fQ7VDJGFyM3QPXC2BLq8tYX9mwmWBpAuBYgt5BgjMGB1EMZ85LlBW6CeIUeGEUKqSLQaD4mJmKwdRoi93rFHe73lRa8TcMnkkqv1LLws5NkMZqAiGHDUWq2ReOspwuZQSRqQmFxgAxe18eaqnaExdQSLiNBTcoMbEPGazOTsTrbN3JzV3R9U3/NVzmerFQou1hNGxNl4bc0Y/rpQTrfcvviGV1+/4M3zH/HBkxumDz7kyU9+zPL2S/LpxGl5SxltaS+XE/N8C+2OYRL0+BSmj0F/gLaEfIBFlINs6ljr0viuZ92zb7+j466sUbf1Jfv7LqDfgHxZ4OvX8PIryotfcvn6c/TlC6vRCcoiiSUl3nvyHtef/JT4o5/Srp8xv77j5Rdf8OmXX/LzN2/44v7Mm8vMfS5camGp1dWUH4lRBXoWR//oKTPBZdy7KVjbbDhpk7yur8RKiw2CNXMnxU1Cvqu/NbUI4rIg6UI8j4TxwJRGpnRkPE5MKXJfFpZaUHUCiQmhMQxwPAwcDhOH6YpxvGKYrhgPR+upmRKNgEymjjol4bIEahnRcjD2u2akFQZRDskAb5KEZlj7pJWFejmznO6Y7+/IlxOhFqv7TpZWW0KkYoIpi1o6VasGHGl1Oxgt1EE/Wx7Pu7G+ab2vrWiPKqllLYhsNSiYzVzyzNvaaCWz5ANXo5F4MhwJqUGulhnh99xqY9GFdjqRxztquudajkxDIpA4jBPDhx9zOE48f/9jTucFMAl51UAII9fXT3n27D2ePHliqe2tC37gyp0u7ILX8gxCwlQj45j4IHzE1fUVAhymA4dpYkyRJIGWldP5Qr3csZzvmC8n5vnCkjNSi6mPa3OyVEjjyDAMyNWRcDyyxEAuF07LTK7ZMn02v2sVxbHany2p5Xufx5g28Oimq/f2pW5ZUdp0JXlXMlZNn2AcB6YxkWiQZ6RkYpmJZYG6IOPAeHXg+r2n3Lz3jOnmCh0CuTUqzUWDjJDXJms0IdBMXd4j7EGEw2FiWSZUrUfoYZr4+Oo9DsfnXF8/IQ1QtVC1WC2ViAuBWPP4OAxoCORmKDWIEJpSlkxeZnRZCGni+uY9uCh1gXxv4CSeztSYSMcrDmmgjIGqVtULM3knaNhJw06Kh1105DGuXme69Sns/9n5VfKQiK5N1khB1+KwPwuWwtojdbRVbwGvTTPw5ZXwvb1RT2l0sbIoRiqIR6uCt+7ogavNtcfJCOXh+O26D9Rqqd6111kWSnZQ4VHGXtPYNSLsVXrat71pdaDa+ofbgE5I2A29Ux/XCbD9Yf5Y/io8mDK/oV/5wZ7HeXAr+1vstJXquo/F/6a3jBHw7IseqaurVofmiuLZLDRgJGC4Z0Mu/qZhv+QcM3Tyod+37wVZJ9+BOn3OHM63SisLZblQVlKgA9+eotr3VH8fu38TznHfvr+fZ8J8V8bXb7p+pxrH/rl6ON5SDPtkOIPjDMF6aKuuLE5XM9qiD84miA+Av36PSKizYzUv6GB9arRUljrbAMv2N600lnkhjiNDnABlKSaco6WsaYT4YFs9o+n6NxGWWrmU6mpVbX3Y6vfitbQWHfR73wwPVIV5ySx5IUqwHpTOPGsQLiilKSpKFpiaMuZMTCM0mIsJuSAdrzzORgzB+g6tRk1sDkwNSqCJs5ysAHnraWSD0tXdOkNVaYg30Nv4WovGdaHnhlIQtBbKolAXWj4xjEpMBUkz4sAxpLT2RAoO7Iqa+MRapK3qtVvFpf0XlstCLZWAkgRCaA4Em0+pg8Y0koYRSYNFGoGiBhBay6DFGqLLTj7ZVsHGlLoASQgJiSP4GpCSobmR7YvmMeaxVWOMdSvUjr1vk9e1USv1cuby9jW3b17y6vY1r04Tz66Fw4cfM/3+HxPuM+flU2p9CzkziNLmM5cXCxoHrsf3YPghtKeIfoRIgg9kXT/iGT99Le0vi7awOu3r3ndD2j8bkbSn5vyf639YmxirqnuNDSkKLyB8ucCXX8K3n8OXn7J8+Rn66lvS5cwiyjkIchi5fv9DDj/9Z8Sf/iE8/4CQM2+//oLPPv8F//Diaz69fcuXpxPfns7clsy5VhbvC/hoCQB9T3Xg6K2F+oHQx291H9xXDc3q+nJp5NgosTIGU36TGKEprUZE6ka6eKRCS6bNZ8p9IiGM15AcAE41MrtSWxDrBRajkIZIGqwHWUoj42RtHIZxMvJFnSUdAimOTENgztH6RtbiPVRNiiuJkAS0WEpqLRlqIbRMzTPL+Y7z3Rvm852JqggMKSEpkGMkSyCr9W2sKlYz5YqB1LauNyM4+xjv1tEjXEnE+8L1bIhetmHp+hZEcjtSK9blNVPzhdPlnuM4coyR2I5OzFzYalzt71ptXM4zc3iLRgONooqWAU2R4/SU44fPef6eiQTpqgQsgAsmRGtpUXKhFyH3DJwtIoq3vxKQRqtm2K+ub7i5vnYHJDCEwera5gvL+S35/Ia63FKWt8znN1xOJ+pSGFYA7OlYcSBNB9LxCjkeIU3MtXGeM+e50Bou3hIpzimHKAwxWtRGdvbie75iip5dZWMurvq+caJOcGsnUL0ekUYUi+RfH488OU5MKOQLdT4T8pkklZvjSHr6lOcff8CHP3ifp+/dEK8GluhiQdhzDimZX4UTvVhqaiUTkvUyTbGPReH9p085f/Ahz581putnTIcnxDgxDAPHQUne/moQsdYYqib5n5IBSiolNGtFIooGL+VTIcjAOF5Rj5n5mFlm5bIUlsvMEgaijMTDwBQmB4+sKY51nynBDjR6j7rHvfp6Vn9v+95DYKGrTTDtB3Ooe/sx27bi2hnuldX+d+43eURP/UfIrgSmn5PiZ2ZTQvISuVW0Yy2IWn1Mi1jZuur2pHcgoAukuM9Yd6Cxecp+q3XNsOqrWcGjn3ZPzcFxf8bu1xpc7RmAAD1zhe+ITMnuv9//tWJVYFVk373raiHXG5DdlH/XvW1ZO+v6UFa9CvvbnrVi46+KtWHBQJh5s5YlmXSw83b1lW3uwq5P/Z5c1/V/D+93xTs00N6nsVpd/5p+PJPzYpmXHcfs6hrXLELdUnJbays50DML2a3t/Sjux/vXXb99xNEZdwmuJuVv0DeY0NU6m6P5hjduXIuE++Rrrz2R3SrsJMYKK/FoJK7YVcxQRs8R95w1deZMsdYPNRdaGrCUjEqt1oohShct6ZFOTCUpWdFrdVWw2qwpdujFr76o1pvs3mzvhSQ2NKU1pAgpCiImfkAYLBJbm2/KRlEjtcYQGIcDSCA7KxQDVE/9fKxIlYnPWF2gEnwee32Gpa0EByCm2GVpZ5bS4MX0bCkbtbMpQegKrCsJ4BG5AGs9SMHqDEuF1jK5KDHlNfonMVpdqB9qsjbC3QFHbPq1VWv3MS8sy0LJxcRsAp4GIoBvMuk95wbGcSINE6SBolBaoZTMUhZaWwjSGKMQh8iQBu+nk+jOhOfMrWk+0reTVtDgLRDVhXweZx6Ds55m5ypNsx8AyQxBE1oG5kI+3XI+v+Dt6UtevVWEI4zPOH78xwy3FU4L5XKCpXI9RdDRhFS+fkEefsmYnlqUWD8G/RB4H96/snTIoPzTBEc7vfU7PrBgh3p0A5grfHuCX76Cr76Gbz6DL/6e8tnPWb76iulyZpLGrJmqgSfPnvCDP/pjxn/xr+EHPwVN8PIL3nz1Cz598Rm/vP2WF+e3vLqceDOfObXGXD1NtRaL5D3GJax7pV9b7yz7hdWB6ctJQRoUMTIqVxPLya1apF/ECvljsuiVYranVEJeHFyGtTQgxsAwRA7jgTgkDlhv2hisF113MMWBR0wDwzgyjKOlQnqGhGIaSgOBcRiYBqGWaAINtTqPYhGNWgrLcmZZKtVbhdTZIo2XuzdcTrfk5UykEtKAJEsrbyIUAllN0KW2RnFp/1ZM/KqDt9VtlF+TnvM9XnFXPtHFiqoayboK5gDiRcI9ojE3mEul1AjTkQMHc2KC2p7DyVXf7Voa+XTHOQrzUCjpQtFrSrsh8oxpeMJ0NaBiCq2tVXraVGumftmj2Cv5ZXfNWnulCtrF73wNet/HIXgbIhLSIm0pXO4WTm/fMJ++pVzesJzfcrm/Y7nMxNrrFC3FK8VAHAIxGYFHnKgaybmSs/kQvRVSa9EyltT78YZmpJ2ERyNy1vSznX+jTlqtsvcrmcpq36NEhiRcTRNPjwdupsSYZ8hnlvMdlIVxGrl+9pQnH3/M849/wNMPnjEcBxplq+GMgQFhlEAMyYhqsf1YgRIaU0qQwtrTNekV4Yc/5L0nT8i1EdNEiCOq1uM0xkAKyqDNhNBcmbb7cpXG0BpBG0GrKQEPAWSiECnlTBabr+lwxThllnKm5spyWUhDRtKBOEYOQaixUFNCa2Fp1WI0PdggvU9ydLvxOPP4ICP1AXQwQKZeP9sjeAaIesDD/6Rn1fQsqhg3B99v3JaGol09PQgqXnqlVsRia98ARYzCIL1OzpO3tTkoktXnBVMgFvc1V6/YlVK1ZHCbV/OyCuFozzLxNMYHz2WImOYkb8P9eQ/qWEDFRRqrmYPogZK17cQOYdht2t8/pibH6kPJzqbz6yHrgz/ZAbM+d9sU918yXxjwoI08fF3xDEVvtWO+vBNITUlD2lRMwxbFDHQs12fPyeEO1HYHu6rNrbQCrv7fWqaVxev9bY5rqevaW0VwdiJM/bW7Gm5Xd9322cO8MN39dxu9X3/91sBxDdsjfkiyshMidjg33fOjBii6BK5dwdGuH1SwC9PiKlMmetG/VoyYKV7bqC7DLmowpYedbWItZF9zRkSotVKK9ycSkGiFHLbR1XqqDMlEVzxC09Nf+xCvJKOENZVnHV+19IvO0BRryEKIXfzGnv0+L2vqhK1MzyUfBubLQi2F6TCSUGouvtgfZyuKF0Mb+O2LRNZ6khCtPtS2kuXlR2cHFZN9sVoGXYFzE6hqc72qQLmLJII5sA5CJSTv4VRNsatlG/eSNxayF/pGA/UN7+Pl79nnZO23U4qLY/iaUrFeUm6MVSCkwZrvDiPDOJHGCQ3J0o0WLwav1qYjJLW05+NoUZXBxGdEMQemQmsRDw3QNBhP0gKtCb2Hm4qaA/gIV3BPQ723k9CoKEGas2eRGgSdM8vpDbdvP+PlmyPPnjSm8UeMh2dMh48JP1gYTm85ltdUuYPbjJZEiBM6N+6++gJtC2n+EuYfES6/Typ/TNCfwIcDLQgarGWA9D4da8TMF0A/eB/+553v7/6te8PcLIIbgTQ48AC+FfQX31J++d9p3/4M/eLnlM9+yd3X31qUtRYqhTIq1x+9x3v/6o8Z/w//Bn78R9R4Q7i95fbFt7x4+Uu+fvMF395+w5vTG87LhaVk6y1XrTZXnRR7nMsPts4CKtvh0tHiehJubLnVoViz+6U15taI3o9xCJbOF1IkMVJVaCVbbdSiNmfR6m/icmGZB2SMpATTYeKQkjkDIRDTQBxGQhy9+N7SvGPqct92/zFsNdCstiNRA6bwWo3ooEJdfM8ulTqb2ms9n1nu75jv3jKf7qjLBSjWiiEGagzUEKgSaGL1jUWhlMqSM6UrCLa6H9ad09CB3eNcUSweUf2jqFLYaq0eODwCa39XccItjLRwoBHR2NAwo8Ejv36uRrGm4JoX6t1rLmHhpBdS+QApgdYmapsYr4Q0uvPTNqJW+/sFW0/9TJden6nbudzv1rKFeopjg4pFoYE8V863Z+7e3HK+f8t8fsV8fsl895Z8WpBi4MfjZZbyOURr26SN4HvbfNxGBKaUqDpQs6W1KkL1qL/SzNmOYV1v3/slwVVGG0H9nFyjMc7b97Ns5fGFIUWOceDJ4cDTlDgAUgu6zJTlQtSGyMThcODm5pqb62uGlKjzhflUyK2hISIp0mKkpsEIk5CcsLUISKQRKDQtJvIh1vX4veM1T4aJUoq3YrBdaE5kg6UiWQnR0pdDP89b817VBmB6WxYJlkYeYqR5AaxIdHswEGRG1aPapRGa+tlvkbUWgtVHIz3xbK0xNpVg35WPxcftM1d+5U12CKi1dX92ABWgl7PZORaC+aLGtnQj7T6//7WnsWrz9dKwc7Gp9bYONjaxVaIGglqLj1Drugfto/l6w9+neaDGNkpxcZRW6taSIfc2c5ZtIT4XK1jwM7j7Vs2zfNaz+V1wLVva5uYhwsPJkvV78ojz2N/czsY96Hloyd8lXtdfeYgy15+vv7/6J13wUB/Cxt5nvp/JDWq21xQ/n0UHJA0QFVFrgSEtbmI6K8TZBGpkvRcrdbOsOO+72TKtLbQyWyQ5L9RsPrOj/RUw7tVTccLSuskZcamsYSE2tUK/791I9mH6TdfvkKq6gQW2s4d9+qmqHUXB70KcibZGzH2Ogq9VD3/7QS99JpxVNwfSmNEqpiAYUySkgZAKtdV+A1vfLCw3f0gDqsoyL15AusFe9frGlAamw0AWmJeZ1gpRdEXq0rxwvP+teA0cyhpt3B0dPZzfFfTSdGAGTpeFpTS3K5sUzVIaL16/ATUnK5eMiq69XvSRDkYL08NW7rwxJN2BiNoeAkdnCPvCarvek7hjoQ7w1oNiXQM27iHaa4SUDMxLJrtE9JqH746vzZODUrogQWdUWFma/UdPq+jPolRacwMYo4PGwVLthoGYBjQki6y0RqwDUROKkgYYjhPD1ZHhcEVKI4KYMFO2HPc1PVvFQafVIWnvA4SxmiuT/D1fQW20e29NMNGe0iwdWdTblNRGPr3lzYvP+OrLyPXNxNXNBzybrGcmV+/DRx9zWH5IbXfMOTNfGnq4IuXKfPeGJb9mWr5hurxiuLtHzpGwXEP9EfIxpmwawi6vW3ab+He4OogS2WxddLaxAl8Af/+G+unfk7/6M5Zv/5rL579g/uoldVGqRGa1KOyTZ8/48F/8ETf/9t/AH/4RXD8nZOFyd+bF/S0v715z+/Zb7m5fcD7fe32spQJTswH0vqge5dL+/3U/7sHjjr6ygViBiFIb5KYsrXFxJ6SPnUUeAzECyVNVWyWXQp0NPEpKxDwg8xkNMKIcpDGEK+IwQLLIpKWWWT/T3rYoxK0+eu0/CSvo7/vUNShs+KrSciWfL8z3Z5bTiXI+k08n5rtb5ru3lPtb2jLbuAeBKBQ3hUXEwGMIpmRdrdXPsiyU0ns37jfc3rn5px+Ov8sVgezkTcZAY3U7u/bu6ufFLiLaIwEtQhXL5pAoSApotd+yLWV5DZMqURXJC/WuclIlFqEtgWGG+ZIZLgcO1xPDZKnLFvUsnmbltl/cVdK2SwNvmz1xZt58Ku85KUKiIXkhzxdu35x58/qWu7tXLJeX5Pk1+fKWcjmTqp0nSQzwdgc8EIkaiE2JzVQ+a6uIZhONGyIqA4XKUtxP0k2UpqrV7vBIhJwGoTYTsTN/xlUx/VyXrWzNnHy1iO40DDwZR56OiUOrhGWhnc+QC9aaPdBK43K6cHd7TzzecymVeZm5P50otW5npFjt+jCMhBCpLqSSxoE0RErLXJYzpTVrFj5ahlXNmZazE7ubt2ERI+uBKsFaj0U/z0uzHn+tA2NvvRBDJIYBaUJbdqmYrUfbTBEyCQxBGTBQ29yPqwFKEIu6t9XLMOJPWYXtwqPZ1X5ttlO6v+r3I73Mw/diw1NI+05tsope2euYTQqYMJ7W6im5umbUrWQf5gto7ES3jW1tRphoqoi3AAueZdUVfMXfr7VqGQNrv8iyCt/gyv+t9vZiGyDZ27lV+Td63eUKFjs4dZtgR8smXuRXDw7ZGfUwzXeXC/Pg+9/nte/hvM3k9pU8+En/Td75+rvXWB+x1S5/x0+3FOtdcpWawmnvQyutmu8aByN/QlizJtTbB5nbvUtSdTTZXLm2+8GoRxvrQlmBY17bbnQfPQQjz8JaTOn4xDM+O4G5+enbs6Dv4uktWVV+ZfweXr81cOyMjIh4ymDzRsWbcl93YI3g3DZBzym2sTIxFUFIYqky24HWgZm9V2sGFyqBpTZCbhynkcN0sDqcUv2124qgS86c7u5tE7RqheC9gFUVpBJTJB0mNAi5LBSPKqwREu05wh0MW/8+SzdgV3OgXlIjptCpWw1HbWqKjKUD4z65rOkI2oxBDTFiHZD8sE12aDzGpauiSZ+vPrvGt/SjpksBJ7EUpTik1aGtsjlkIURL9xVZ+5c1IPl8BKcaLW1VPO1VVgW/pngEGK+Xs/q82iq1pz179DEETxVplobcDXbsmyhustXaBXTcTsZokdPYHWH3AIIIw6gcQiOOgkomDVhT7sMBxpEqkVqaqdgtGa2s49MBYqted+ANHcVrIB7LU41qaRLB95piyplrbyBjOkwZ7zLz5sVLfpECdbgmHn7AB1cfw9HSdXM6Ep9+RPp4oS2Jpb5C7meGpoxB0flM++ZCmxfibYO3A+fXlXj7knH5iPiT9yCMD3L5m6ltmAPWGVNldRS6PfHJ2j5EIJnSrQAkk8EQgNNb+LtXXP7mLeUXnxJe/AXl5V+wvPwH9PVLpsvC0iIXieQYOTz/kKd/9Mc8+Vf/DvnxP6Ne3ZjDp5klX3h9f8vLt294e/uW0+nEsiy01oiqJFUG8x0sgvtIDs62BZUm7eH3xZ+8O/CdiFHbLw1LU51rJZZqaYAtmLy39vQhrzVOCbRSayPngspsUfg0oJ6d0VojqLqjqwSZUDHH0pjwrV65lwhIl3LXbZ7VhbZaUxPaagpez7icL1zuzsynE8v5TL6cuNzdcr59Q767RZaZ2BoSDRwWgeIArIpFMirC0gpnF/CwflbuGNNJTvrT84ARfyQPJ/gZpKgRKepnheKss9sczDY1j7IZ2VgpeqGo0OLAMCpDS7QaqbXSWe+gSkJJvfQjN+a7M1LfUBchHRbi4ZZ0PpDzDU+fP+dwfY1IXBu8q1gbCSN4neTdSe9vPeJWnRBUC1EsqiZAvj9x+/INL1+85u3tHefLiVLuaMs9LWdCEwZJa6sFwUgHkQRqqyuGyBBBZTE72WageKQ8IGp1lqGpi+J41BPvy/dIhFyMcQVZKysvsjL8MQgalVo9ldejfmMSDmNgDA3mmXw+oZczqTSiJKIoJTcur+84kbjLynA8MM8XTnf31JI9EuhRLz93JUQ7sdPA4erIME3ksnB7uqNoYzwcGafJQMX5DKWSvCyj9+pz5GLz6v6GhORn9i6lbWeH7R4iQ0wMEgnNiPjLPJNzBm3EHgHVQiATvTmWSmUKyhyF2WAWVcWibObBWnQsgjxSO45tmzv5Jh7ZWUmt3TmE+569BGuvVuPnqQRxUSGzxyG4qnotq96HKc3313dH3PtCtx2D1nJAohc6es2huG+DbGdkr1Hr+hzNe4Jq9e898Lu3p32Q5SNimXZxE0Nco4zu37a+53fjt54z2zfogHrNBqK/zKZc8jiXrC7NNmH7u9UHnx78fN/Tzf2LhzBTt/uX7eW2eJxsga7+UuBRYLObBhoLLWZPwTbxI1kjjvvsK7Y5UFc7L9V0AFpFvK6xtYVaFivBWGsardNEWD/66dajy50A2cokNvq5m4HvOP92z/WbTsffHjh2EKjYu6gxcOvPd0zFVhHoNYV+CPUDXbE004FAClBUrPH6bvn1eBj+Vrk1Ql44JHOMlmB9jSx9Y1sKUhslX2hY+tQ66f56IQ7EaYQYmWt11UTblPZwWymxdhpCLYUnutEFNxKo19O4kyBCozHnTMvN0k+QNfUBoGIAK4rVFaUgpGB9V5orBEbg+GiOqqc4yn4fWj1qL/pdx5KwHmApJhORaa685ou/K6BaY19dm8xa/XhbN6YV2UczYqEreEHT4EqunXTwNaQ9BdPAYQqWJiNYT7rijHAQS81Lnora002qs3XdEEtfrvLOWo2BMY6EITAyQihIVCQFiIncAqVav6x8vtCWTFJhjKZIGYJFvq2tTIHO7K5r51GmkaCy1h101gjx9MRhpJFMRrwKWoTzXeGL9pr79ilh/IAfPf+Y37s5IIdESE+o0+8Rnj9hzM+R8vfc1s/QfOLJYSRU4e58guWWcPma+lZ4+823yKuf8cH5D4j1n8Mf/ATC0RdZc5bN1ejEHOl3L/GTck33ccLIQGMfPP+75TX86V+S/9e/5OXffUl99S3X52+Q+19S718x1MwkgdoWCo3j+z/k43/+P/Dsf/gPyO/9K9rVD015lpm5Xnh99zXffPsV3758xdu7C5elUqrVUqkqUQIpRIsaiXQq+lGu1ZnxlPpu/faAZwNCrNH8prYfl1KJoZCiidkUFZKGTWBL3FnUAUUpJZNzIZ4vBAmMrRG6Al81NdVWKvHYYGyQFKlO8LRmKeUxElVNvAo1YanqET8/DGtXaK5KWxaW85nL3clB44Uyn8nnM5f7t1zubmmXM6O6yJNEehfU2uu8JFAkkFvlkguXxdo9aM1A3fwhXzpmomR1Vlfn4zGunUx7DBGRRGtCccG1RjV59RjWNgimjdGAgurFUgqD1V9rGD26hjme7gSsjc3VCMtWGsvpjFYlLRc4DZBGLqenSKsW8RsnhgaRZARnMBts3VsUggmjrSlTO+e3K/OJVmpeuJ8vnF6+5PW33/L2zRsu88xSrE8uNSOts+A9Gu0OtAyIHCEcCEMkHZQwFrLM1JZd9dPOoKqbCIUEJQVZnfmtrvtxDOuUEkGtZCGJOUpWomERfA1qLRWi+i15rZy0VYwtLxfy5Qw5m/UKEVUrt7nMhdPrO05FieNIzpk8X2iukipia0Lwulkn3iVFpsORNAzkkrm/nCmqDNOBlAYTSLlYpH6M1rezuQ9jDqwfR8JWBuL+3FYGArBllgkwxMQ0DESEuszMpzOlZHouZ20Lcz4TlmipeimANFPiVTESSkGaA5e+NtzOPVbAsbVdKdTua12/1w0C7jh3z3lztqtsYo/RI+e4j7qlcpr8nzF5e0nDrVTHoxju1AMS0FyMwOn7pH+sIH+71KPA/f78lldiYwVwfhs2z7KC0l2InC2YY6/be1Oq9vYpYVtzwCqYJXvffHWLYWeTHu/aIQN/UPXv735l9+89kNaeyPjgGda/9/nVtX5SdxMhO0Ap3m5JHfApaKOVZmn0oVCjAbo+1l1UsoPHlSTo8+7AsTlw9Boo8PZ1PZpsKd4WxQwuyLhFwbdnsWOveUuVLt+zv96F97q+xj/1+u3FcXZvLZII0QxH62mb/eHWG+opV233UB55cigXQjMpb5+sDsDU6wM7m9FTLZpiUR1ncJE+NDsmxI1vr7Gzays+ji7uUAgOLrqK+7bRbSOE9Yl7eqflvPtztS4os64re/KmzKXY2Oz+XlT7/lsjd31P9hSAzpe3ar0nH+PSzqaiqwOqmIKT0KN4Dra8d9Xa70ltXPcNR3tRsLHYlZ5+vNafavMF7BsxCBFLYXKNRcjV2qU0T64JDY2V4GIEBoYsxRQMOEbJFiERYUiJlNKaTtvWuoXuQcKmkNU/HHRJsNobCUSJVvcoDRWlVItszEvmcjYnV0phkoSMiRQK4IIJWlE1IaZNq6wb3se4LBXJorYKAYYhcTwcGaZrcglclkIrJqEuEqitsfCSXxx+xt89f59Prgc+efKE+PGP0fABRW9J7z1hqJWx3bFwpuRKXBJSI3UpXN7esZwW7tNXlNtPmd/+nOOrXxA//33CBx8yDNccrp4Tnr0H11dUJxn2Bns96NYn+dVLX77i8uob5uUNJZ/gs89p//WvOP3lzzi9eoPMZygzQ1uoLVI0ca8z57iQnl/z/I9/wvP/4d8gP/7X1JufwvE9wmHgfPeSX37x9/zN3/4Vf/Ozv+bzr1/w5rRwKda4eqltBYtW2+I2If7OJvM3zOK+L5ywKm92km4dNdiLA4AdDkXVFHZrIRVIwRsKi7HY/U+DeMo2iaiN2pQlZzifaa2SxmHXuqYyLplhXgiHIzJOhGGiDYvVXe0ErELo2QZO1PT+ZqVSiws1lEo5n5lP91zu71nOJ/I8ky9n8vnEfL6nzBekFZoDxCbmrNcQaGmgxYESAkutzCWT80wpyyp+tiXlOQElrt6IrA7OYzo5Vcye9jKDnv7UWrO+d9pIMTLJQErGHFs5lKeeSgYaVaBGa6+T2tFw+OXiPTAFDW5Tmj1LFwmr85lWFhs7Avn+nlgqcSlc3TwhJFORDmlAhoRNWwe7tuYUtgyQnsJYjAVvZeF0vuf05hW3L19yevuaZblQvW1L7/1r8SXIXu6QfP9HAhIOyHCDHAbquFDCLZd64dwWsokWmJprLRQXg7F6xmj3597qdyWWfV/XVYoUNaAXo51TVS3lr0mgdeAYdD0b7Shp1FbItVLrQqmVCKhYhFxUUTFRmlYa8+0JDWd6rzw7hf1SXUuBViddoJxmq/lvRjBXVer9YqDao/sCFMkIW30tsDHX3b9xQUFVS2vbt4vpDrV4ivLJe273GkjZzUGpBZ0v1ACTKMNhgmip161BC6B9jHpd7UrkPiJw7KDqnQig1d+zswPumYSw2V68NMZBJGLztxFRrCSnBRJMUEbVxmkV42q6jVUnqv211P2SFTRq34H9XvXBPcK752RPFN2i4h2IdzuEa0SoyC5FtRP2bT13Vv/zQeRx22V9/N6dKtnd42PZ1Qf1iGwAuePx/dy+ewfd99qLo8Hub+QBwmAvZGjvGekztEYOAWfzVqKtYjY4VD9zu6Pv472BR4tC4mrpHR+p96enVY8kV/cn/bn8LA9OSFqUfhfE2+GrNVTj695e4jvmZnUu3oGWv0F2/LePOKq6uElc02xMRdJuzJhWA1ut9e/3DeMpMn7DfQP2Hj6b0lFY/23RzbYDZJZfrq0icWAcR0pt1N540cOatn8sYtHTyyR05sDzvF2TsnlPpKgm4GJMghUrxxC87UjYbSillLJjsXs0xc2PKBqCKzB2wyXeOmSbvh65bWpy+jVY76YuvNPERB8e41KtWL8NFwpCHUh577ZeHixxZUz6Im1dHVA6cHelv942QAIE24CWAtE24Kib8ZKYSBppkmiiXhmkSDBmLwVAGoP3oCGIqax6tFdiRUK0VE3BU1C3aKMCujKc1oi730voP8dNrztK1rDbxkaDiTT1yMZ5nq1eNldiU1q0MaveGVabr6WeWa4NkR1z/whXc3ha1ZOPfT+lFEkdyDfIzdO5Q0S00e4vvPrl5/zlmLiKlX/7b/4tn3z4Y8IINVwoAlHeMMgbWqpcvn1Dqws6RYTMPF+o80IskcvylhdvviB+9TOO//1Djh99zOH5x8hHP+H4e78PP/kx4cP3f8Vu9eP53bFZQdEXX9D+5m84/eJvuf365+RXX7J8/SXLVy+ob05MGjiqQq2UELhI5K5VZmB67ynv//Of8vR//JfI7/8z2nu/R3j6CfJkAKl8/e0X/Nmf/2/8+V/8JZ9+9Q1vbu85nWeb51LNpuAR8V6PLfKIYhy9xqIPUpdmf3hYwhYl76rQ3byW1lhqIRa19PLgioxBvXjerJGEQCSRFGso3JRlWWyveh9ELZXqSsXDvJCOM3E6EIeJ4rXBsuuxuh2GYHU93nDa631rrpQls5xOzKd7lsvJIo3zheV8ZrmcqXkxwkUMGGUsHb6ERE2JmkZKCMy1ccqZeZm9zsfbLK1ZErouqp4ab07dLsPgkRzVTI9QKForuVTmpTBns2EhRrNtYrWKIlv9UU+ha7VwWRYqkTEG4hgZdKRSKZdGy0YWiKorHhqRFxUTmvF6RTGZb84NXl1m5qsrhvGADCMyjIQhEQcTQpM1LOKiH7VtEv9LtvT8ZtL/l9M9pztTTM3LDK4LEOmROTu7LBhoTmsIkSSRAesHqYNQQuNSZ3I5seiZrNmUHoNF7rSZVkBIkRSS7b3WVdI3+/EY14RamxOiRWNpFK8xbDFRtUGw6KKVPwSLfmq/v2blK524keDP07wm0EiFkov3tu7MlL2/sBOXcx9KwUVssovkCEMIJMHmxtDYqgDbHeres62nIBtedHBBB1XdOe02yKxR16lo2owcEC9ZiW4H3R+MwQDGeV5YEA5BSMeJgpCbUmqjeRSrNldhR60tFngU7/u/Nqd52/urOOHud1bgSG+cvtUh29XW0itViwKLC+RIcz8v9uicrEDHiAKrgQv9vfG1K9v89CvIhu0dCb7zQDsg4H/X56/7aGv/XycJ6S1BZFtczessu9DgvleklUq29bX6e3S72u95v2b2nx/j2hPva2eG9RvrrT/85r5bw/o667+2++3gsZ+r+5fbRfRkBwTXp5cAYmUEeLbkpjfBbsx9LDuA7CC+0wSqLgDYybe2Qnb7/bgGaEQSiAsydtZBzUatoFEdBAO/OljfMb7vfP5N1+/Yx9EfCCy020kcn1BrSrmlCGx8yVb/1tXbUrKolYnj9L+v4DUNnUVZNz2W9pPJHIaBcRysT2O2HPMgPc4ja4549XsJ6g1Y49bwtqqpJtKqp8IIpXbGhxUUdmn16D/Q9nCz2oqApnUXrdyMVn+OdRHuFmlPP6rVmF9kt8AfKVJlEUercewHjLEbvaQWT2cRA+o+rq02l5V3jstBo/UN7qB9HwlmrZ9QlCaWI04UQjLVz+ARnbX3mlrqaBpMatxYXAeP0jejmgMcoo+vH5i+VqqaGl8VWdXDRHdFzFJdEMIcsFYt1SCXmaoZghKSoNHk/q3FixMMITLGxOBNops2qAX15taWpurpvp1seCTk2N1lDb1Y2uZpmWdqhlyFmiu1KdZhvZCGxNhGTi8v/PV8y+v5jl+q8O///VP++ftPeTYcqMN7zOljGGYO40gOX3DRb2mnM1GUKR6QJTOUQrwsLCWjt69J33zN+OxL0tMPuT/8HW8/+iHP/t2/4/Av/ghdFt788pcM88LV1XHtnRqORxAh39+T708EbQzzAt98w/L5p8jXv2R89QXy5lvC/VtirmgLTGHgWRo4DInb1rhvmfM4cP3R+3z4L3/Cs3/5R4y/90fU995H339Gen+AAPeffs7f/9mf8qd/9mf8zeef8+a8cLpkLqcTpRRzclS9LshsRiC6I/eI16rQuIlLrQzvzt7vD7/OyzX/nFsjFLvPGBbv15oYg/eBa/6aEpxkwesarfZwLfDPFc2Vlgt1yaTLTHLgKCkRhpE4jMQhwVoCIN6OZwOOVKv7LUshXxaLLs6WnlqWmbxcqPMMeTGhp2gOWXNAVSVQYiKngSUGLk255Mxlmb0WNa9OWbeoe0fqgTOjm/P4WPsxt94L12zGxXsSlqbEYWBMI9M4MIRg1EBVAyYSEDG71fwZ5wbToFwNE9MoRLV+jYsWpHq0C7Nnwccg7colECMr2+mO+8uZi7dTISY0JkiJOCbSmAjRnJDm0ZGg6uSBKdXWYrU3tGo9lYv15Iy6rcG+Hld985CQMBLjgSQDgwhDEIiFLCfr2VhPLHpCybTgNYvep603Rw8hWZkEYjWyze3rjqT+vq9YM8OQDCB57boAGiMFyIqlcwcjk1KM7mcEtAmqkRBHQgpeZyyINIJUrxsWq6HGzyx2Dqv7D0lMtVZgJXcbloLeZAOAYHpkTXq7L/dXVhEN97EEet1k6FEWTwduHinvCrZIT8G0q2qjeJufIVhrKrMbwjiMHI8Hqip3lwvzZUHHicM0kRtccmXO1c5iDPzmar5CDFZ7Fx8Jc0gHEOx8st3PV3X29fs94rQn8dy+OvnaHX5ceEwiK2lgNseFbwBX4LG9CLu9gkcvN+Cw4p+VP3iYiinI1p3ASR77fY8uud+l4jWTPdIYOlm/BXRaczLfS33aSqo99FXW4MiGf1i/2LnJ34ndvtfrnfvq/9m/sf+738/2y3v8sP3yu1HMfnZ0AcbwACj6mczDfUEQRPvp0zbQ3/VadukCO+hLx3t9Xdge1021Vw0LBDH/O8aw1sBCBLWgl+5et8f/zSfshMCDEfs1I/vw8z/l+p2AYwQzpuyOCt84oRf+w8qO9HRG8BxztZo2ET9gfcCrdpBhh6nhEFmZlT6BrSlLg7kUxigM48AyZxcFMXnjVRQl9JpMm8gQAmmcCMPI3Nras7EL1JgBMOYJWBkZ/CCz15GV0Oi5+ntAvfl1fbI6I2MOm71mB9b4+4Q96eF/8avZyd/X1ZXTOlnRD3yLNrJSYj29oWGsIdLFgPqN9tQmNaakA0ORNW/e2ErrR2SRRq+tSMZRK5b6o9HaWQhiPxsCIfl91WJMbk951t1r9TH052pqDXgrpr5oG8wYHG0Va7UUQApIQYmoVmrJlOVCrQuSQKaEjMMKGKJYxHMIgSkEEx9wdrG4lPmmztZ3h/oYPMo0dv06eusSS5mJlFypnGjqgDIJSKXmGWog6UjO8Pa+8PnpLT/P8EIHwv/8H/n3H71PSs+R8ae06yPD9fuM43uM4884ffUpcjtzSIEQBrivJLVm76qNvFTG12/h7YXX57/n9XSk3L3mk5dfcvfqJd/8+V9wdToxPXsKKXKOkfjsGRITpxffMn/9DcN8YWxKmzN1viAtc9VmtGXQAYmDAYumTCiJhupME+XJJx/z43/373n+r/81fPIJ9fiUcPMh8sERJjh/9kv+/k/+v/z1n/wXfv7Z57w6X7hUuF8yp/MM2qyHleARXGxcxQWeHgtx+IHUiY+eZtKPQW9ssh4y7cFJ5NZCTQkySyNWiLkQZDG2cjCSpYs7AGvfpyCBVi2Lo+VKrUrwj1qagcd5IY9nQrRIYxwn0jQRh9H2et3EFSwtwQv9i4mXlSX7x+I9x2aXF1+QWhlUXX0urm02NEQ0DtQ0sEjgrlbuc2aeZ+oyWz3IWvejD2zZWlfizrL6uQIbWfcYVyvFHTRL3yvFW2lI9HTLPldtVUFUB8n97Nt8j0ptF0rNDCGRhkg8jERGWBq4eAJ4SwZ3LIN0cTDzWFrN5DJTFhuXJh4VDVa/bX1yvWXDPq3OI8/NlRvNud0iGqyj6TWo2kxQJjTvTwcqCeQa4pWRAnGhhZnMLZWGpMogJsdmDk/YSBPMEe4ZSaxRZROksZYcj4M4tGYkWcptJwF7BNCURtVVcr01SDDZfVSoLUCIDNNoNZClEwQwBrVe0r0Gnh793mqR+vwHDMBbGvKWuSLSo2J2Dq/njrKetQZwIqoGZEUNZHdyv2cViTRUCk0rRXuWxVae00e3tUjShsTEeDiCJOY5o1JJhyuON0+o2ji1t+RlJlYhNSE3U46fiwHHQiBXddVvL0pyv+Axrm2d/mqZhO7swbpbVrO6gx/dJvuZIFif7RhN2CnK1gsXsHTCVSDMs0I8BN+8XceGd9RSWf07KwHHtg76jbV+T9L3yA4Qh14zGlYRwQ4anYux+29q89zqmmLZI9Ft56Ss5JOD1LX8bBf8ePd6tKMR1kj5+h7iD/XAP95IAt0jyO4rvnuza8h3u/l1XewA3ToW/ncbhPRzJkYz+c2zWtr+JllftxOXvwLUeiRyXW/sXjusgo7B26gYMSRWvsC+MGP7eBc57IOvD36y23ey+9m7VZDvXr8bcNTe1wnyeth1dNtZC11vqrM6nb3Qbayo1STke4rEunjFWOy6vtduEiSQEU61ojUwBGMGtewm2qd9Xea+sYdxJIwDC8rSXBBnxxJb2qOLFrADjrrCP6rPTuqbyB+o5yP3omszJj1G2da7Cr2uwPNtVC3CZs9v/SZjD2f/thP0T7x6KsLWf9KHvW8w/0/TrhhlqmkqYVWZ1f57DviNXetsypZvLX4A9gilxERIA6QBbcFAnghVQJMVzmsU6/3mr2NA0JJAtz5arEaWPspuYJtHGhti/eqsSacr8VXf4NCaEKM7P6XAMhO0WCNnXC3WwWc3HkOMjCGYcE9plKIeaWRLEdDtPh3lPs48uuehasqTMY0cD0fGILa+aCbKIOp90IqpdZXsh0nj/Dbzi7/+UyLKh2Hmw//pP/L7H/6QOPyY+PIZXH0A1+8xPJ+4flKYP71w/+pErcDxQIyjHZS1EmohF6UsdyynM/l0x4u//gvm19+w3L3l/PlnsCy8PhzRINyHgB6vIAbK7S3tzWvGeSG2LQ03eWP6OARSmqxNhkSyNt5qYalnyqC8/4MPefYf/jVP/0//N/jpv6IdnxGmA3J9QHXh21/8jL/73/4Lf/6//C/89d//jDd39yydEZ8zpbmQVgzehkbpDZpXp+ORTsf+sn29N23rN0Nf53vG/B0Mu8oGqBIaLJiUh/3LK5KH0SIUQUykArO7XY3YouqZWhuhFaQJUhuaM8TFD8jowHH0CKQBx1ab9WVdUyWNSNJSaB6x2hpT279rtb5jUVw5MkZK8NpGTwlsaaBI5NIa9zlzN18oeYZaCGstSK9V7nZhs2F7p1odWQo8GpFDraRh690VYuQQB2OKgxFYOS+rA7oCXpx8E3HhnGC99qiIFmorKBOaBphGEKWVGYpliETzS73ORukKgqJY9oT2NkVeA6q9D20mZz9fd84Rar3n7NzzFbTbB72mWj3yV1GyWsQ0RZjGRAgDrQ2oTtR4TR6EFpTcbjmXezQGpmkipJFSDWg3FaoK1ZuPiVrKb1V1gQjbFzacskbcvu+rtUwuaoSuz1JXJxQ6aA1rymwIQguRVg0sjTFxmI5EjdTZs1WGQDwkhqgG+LUSg6/97tjTnde2pq5pr1t08DcOA0NMtNaY58XaWvlaqq16eu+IxJFlaZxPC60p4+HANI4W8csLIo1hAAmV0mYoi7eREixlefMH1p7OaSCNB5REGEz8T45XhOsbi2YWRSRSw8DSLDpaCZQGCyYO1YjWUm1MxCFZqvQjAUdzwneK/+CgYN/CilVkRNbo1Io4DGTvQEv3Z0Or1GqKsylYpC+Ia0FES2O1NWuFnq2YkEZXogZWIbHgZVSrzyDbWRNcsbpWU5mOKa2K8gJb3+vQ/auAxmj7UlePfO3V2VoPluhaW7uawz059C5JtPt37429+vqPfG3LY4ui7b/3EGmv393+/sHnvT+2IaptDKz0R0QIuqtpZLOPZgO8vlmiBRBqQ+mCXo4+fF21HsjaPcN+yYd1rOl/aRkFMRBTXHuoo4KXQq/zapxED1h0H3Tn0yMPnvjdEXgwzt81kN9x/fY1jk08hdGiT6tUMJ2h8Df2kQkhrj1ugLVvKljhd9NA6SqCotuBuh/VFaFsiLhiaUFDgyEZKm9BvLiYlf3pgLZHY9JgzcqXJVOqNXs39tlF6837WgFn073x8Pdu1VOj/Fld6c2DiKxQTGUtpl1ZAjdCUcQKxvG0nOAsgW9kS497TAXHbYGtTyy7xe4CHK0pVRpK9pYaXpC7Ls0NHvqI0VM8ul0W9QNRxOchEdKIRm9qLJ4WGC2X25rTNrJWKAXVbP3HWmVn4tgnEGzOtN83GNumYkqB4odvK3bHDhy1CZqajXVrxqqKGDhMA2EcKTEirRFrAbX6htSjLK2YsUBXB3aLOu637+MY1+bsYkOQNHB8+oyP3nuf6xSplzPLciZrJbdKqeaQWWR1AbU6nmdROd+95PVf/xn/tSnXBZb/6X/ipx//kOm9p9aQ/BBITwvpaaXeDLz52885f3XPOEQOY6WeL8ZeSkC10ASurg5ICNy/ecm3b18i2jgGkGngXi2ytTQh31on3aCNdJzQw0RrdlJHT6VZUGvTos1qfsbAHCNva+MyTDz/0Yd88h//Ndf/l/8b/It/R776mCgHZLL61q9//nf82f/7/8Gf/Of/lb/9+095eXvmXCxl6nLOlFxdar+nybGp8MKDiNVjXJ0hXdfN6qSysWFsRMmeHVSV1bFpzWyj03Ao2ewO5owyjIwOFPt5ay2GlKgW8TdHHbRYTykjGFyNz4F1HAbi5WIEENaaqdRqbSPwJuCoydTX6mRNT0VvvpeblcDFgKZEC8lqoiRQY0RjIovVNF5KZl6sCXIrmdBMOABfH328HnCl3R5sx0//9mNtRwbF22QEhsGImS7L3h234uPbxaKUzQ4HF4mzNPpAGg3YNwJztdp6msv2p4EYITGSakFypi4zuRZ3OqzO2fVNSZhv2XyvmYpmj34Cu/vpADv44RScYBAJlsrdbJ5N6bwyt0oGZBgIY0IOI0MYoR1pOqISuIRKbZmlzCztbPdWvKyhViimTC3qJ5+ItTHQuo6dpWALKe4ikY9w1dZoy+Lg39Z+EyjqdZdioLUT6N3Bblg/1aiQQmQII1UTGhpxTIzXAzIAmo1oFryUwkRxVDafYO/6BTGhqxSjqYd7dpSUwjJnclkcSKgJyE1HkIH5lFnCvZFBV1fEqyvbgqVYZHgUJBSoZ3S5QDFxF1aHecumioCEhMaB1iIMjVgVHSfmaTI/7+qaIAENsKiwKFTPHDB/KDGkkeQ1tiEKQRTRR9JyaB1UbACneyx9531HKdwKHtff2xkQ+1czaY8erfO9Fh24RW+rIV2ERyEm67HblV0t28AI3u73rgEYd5xEZO3D18SUNCWaqBXRat5iSg6GxfC+o466ChLa2VVqtX7meHBD2PQm3JfqgGLL2HBA/c64fLcBfUwA+V0wRx/89Fff/SFA3MDjjiDbB6TeOd/Vgx2bxFn/u0bvKBHx9jx42Yd2bQ9Wu49AaObT9hK3B6BWeCi64+/UbU8ITtj2bIEH3qVu6qn9DNTtafXXjt1+dHYgun/9G3yd30EiUOzAqe7Yd8fK3Rk7YNLqrIsry0UMHPZGD31SesRLOmr2p2mqxN4bcnWQ/F9ij1hqNbXHUZjGwVOg6rqIelSsp8rGlGxxtEYSYdHmh2BP79ne+yEz0QV8dBc93TByv4IbiRDU6kFUHSj6AurRSGeVgqsGdpaAXqvjtZb1HVbi+7y0p9Eqm3jQynpsmdyqxnTZsCmESs+VX1PA8PnfqZHtYSX91V0owNj3BGGw9xwgNAPK2kwAoTSLRqgutLYYQPM6yb1RW8ezsywqqxHtUeTqPXFEK6H3yBMTN0CMsQeTSpJgbPKYEuMwEofJGb4KNa6iSbK+3+6zG/zWWeLd2n0sm9piQlJEhpHDzRPe++gH/OCDD3giwuXNK169zuTzbNEe8LXYCBJRGkGFa4QjSnn7mp//5Z9zPy98en/Hf/wP/0f+1cc/5cPYkJxp8cjwh/+W9OHvc3X8O3T4OfrmlnA6MYQ7FKHkhRgTU7DaoFPOTJcLcynEmBiGgRiNQInAKHgE22tWh0hKiSEaqx6xg3VZrHeY1oo0ZVbhToV2fMr7H/+Q9//D/8j0f/338C//Bdz8gIEELcM88+UXn/Nf/9N/4n/5//xn/vpvf8ar+wtzVZZcWUqzaEA1cFSboEVdUMactV4btGeov++rHyiwAcJuZ9ZdJfLg7c0f2h18/p+m5uBqa9YDURdXTLR00qthZAzRgLJuTkJIQkIs3bKz0p2IUYOjCBADtRTCYmmwVhvX1ogMsNbJBfUo0buOWjC7rCFQQzTBEYlkAtm/VySwVOVUCufFGiBTC9IqaPVoTFtZ+o0Z98NT987AnkuVX3eW/v99pWCqzMTIOAY07JlmO0M2dnibcTtjIqhQi4mzkZRBRkgjBeF+KZzPJ4JmpjhZps1wYIyJEUXmMxlhmU8GQyS6kJyd1203RoKLwbmjamvngTuzfXZiN4iREaLebsjP9qZKbopOI+P1DePx4GmRgchA1Upu9yxtYdZblnahtkxeMvW2kIbopOzmlNp5axGYZqk/1kidRhhcjEmCExXf/6XNs1sqpvIdTVykyraGHqivO2PdhcooBV0WxmS9EmUc0CGgEpmiRRkDFjXKuXDJmaUWGhBTZJhM+C+5ENUwjAzDZIJneWHJmRSCvX7O5Nu3zOczIUWm62sYDywFLu3CUpRcKm0cqcPIOB0tihEEjZXGjNYzoS6EUp1QcnDsTuR6tjkBXmtARmy9SuAkVjZRp8HKjFo1JfIKFUuVH2IkDiPDOFk/aNcH0FotNPYIV1dch90ZLZsf2e3qOu8dXD4IgMB6gEsnqx3mqa5kmTa1TAmNxOjCP57GGsXFapxga2uAwFRPtQvVeFmN7QG8dAFUbT+FGB0wClFGj0RFyzZQ2x/NU8Z7SVTzGl2rUYZenkCUtZa8P14HtaELHtKDOvtRfTfKKA/s7GNce82QvU+8F+/ZR4X39yLfiSo7MfCPwar1xDLfVvpeaKsvr/4GPWgmQVwsSXZiNta+Z8t+2ex+H9tf9fN1myePgqviGYA9RZ0HoHEPHPfPD/JgbB6O4f6Nt4H6Tf2qfwdV1UoVoGdcq20Kcy50darX4nzU64McwAlWYO8T0OsZmoMOe8C2Tk6QLUVwj4sV8ZQLS4MaUqKMI61dzPCt7JLQBS7M0bJGvMdpRFMlB6hLdsdtSxXoTEuQxrZzDFQ9LKh2nqbWdTK0bs9qNqfRc+Bbn21v5txlfAU/oBSr89HODD+OQe2Lfh3Z1Tj8ipVw0Oggqwl7dS4wYRxdx3hrYOtnzQpIO3PiSYAgkTAMjDGZMZyhLErJmaZWs9oc9GkzhnZzsjoTszljJrzk96fiwkeZ2hZomSjFGPqYGNNAiiMpDObodU2bvqukA10TZ4gihLUuyaLstReZt7aKPmkvNleLsKyyxo/EAIQ00EIgjiPHmyuun11xfT0ylsxCoeYL8/nEpVaMMzZwLowI1iKB6j2/auXVq6/5/M9v+dvbl3z29hV3/+4/8O9vnvDs7Vvi4YB+/IdMH9/wfPgDrse/5PT5F8TXr7i6fw1vv2W+fYUsM6NCvlyQOXMd4NnhCiRZ6lJTRJoDWHz+vB1OT58ZnC0VqxlaGCgxMBxGpvFIDQMSR24+/iE/+Tf/msP/+X+Gf/nHLEMgzm+I9SXUWz7/xaf8l//83/hP//m/8Vc/+wVv7i5rC4rLvHitrq351qzBLtUVDYNYRJcdufNI11of2C/ZQGNfO7q3/HsCa93F6q/lkUd39ItCJdPrf4vCMZlAy4i4vTahhzCIReqLOHHTfA/3AxOoXldYLC2uOXlk6pkuMd6lxbVtfel6RolnHhAjLQaKCAUxkCsWcVxUmWthLoVlySzZ0jVN298f0Oux5R0g2PH9NqS7yBDdEXssuxrd6bSaxoQD8NqIwfjrtY61OyHrMwitBXPyPGUwBKEizK3x9n5mXjJDGNCxUcNA0wNRDozTxDReMR0mLufE5XLvUbpi6azF6kiTiyT181kInQfcOdDmQDS3Z53oLZ76XqhUGrWJR4iVeBhJN085Pn2PYTggpZn4UV1oevIa9kaI2dSyi1BrZs6V0iLD0EmlrT9y1WbptAo0aNXSRSU0kt/bktujzGNrdq416d6GGgjvQiNr5GW3T91xK2opiC1nihjoS2mAaM66xsA4GMm81JnbS+bt/cIlZyRGDleR6yFykEQMiRgnjsM14/HaSBY9UfM9U4pcXx+JJVPnhWXJDNOEXD+BYSSfF8pY0SulLpm7psy18SSOPLl5ShwijQVtZ6IciVIZu20Rf2Y18NyqO6zWURwq5iCr0KqVQNQCbQhGShalLZaqWtQIjDQOHI4T48HGIjelZSM1Ao9lW/XBfjeCxPUX6Onb/pvfQZx0YrrvmS2vgW58zRNuSpPqbWkqsQViTEQvdYkhEL1pqkry1OO2uup74hD3f3d4zsTZQlxFjUDRYLbTXEovucJ9Jq3rmaLaywd2UML9HOuQ4EBQsdpI9hBCVvzcx7N/2kZiA5KPFejo77O6nrK3VyumW0Gi8vDf+1d59/oVnOVnxoOWFGu7iw7OGlsWoSXzrz27QrCsCe+1uNpbus1nnev+Hj3LQ3eD3WFHJ62bOqHlARHn02z+/bXb+rd7zLR/6m0f/OpY/NP34G8PHKmsPSVkS5yyiTJj3pnI9cZ6PVy/dod4FFmFVkQ2bTxB3eHwFCl6T0fZik+9FmLJBQmDKbClZHU2beuREoJJehMNtKQYmaaRozSux8Tbuwv359kYG93YTugNzPvh1LmmLfpo37V6lLX9iEQT+MFD2v1GcMAWgjFMdJbJjibvpWvv4wfj4/CpDmzZptLflm05beDQ3VZf9JZW0VmWrUzYHR01oqBHkvuLWK1YRLAayVKVMATiMBHCwdKbQqO2Bc2V6v7h5v01E9zo4j0anXXr60NpxetU3TmsrXrdVUYopEFJQ2KaRg7jgSEeCGG0dZQbZTEJdcFSxUJTS0cVl64udacu2Fx2vReZ4x+9htKjLOtwPo5FHdJgZG1V2jKznG65i4V5Wbi9e8Xpck+pmVY6wE3QlWwFjxQ1EypRe6b59szrvztDvuXq9peEn/4Bf3j9ER+8/4eku6dwBfzeHzAMB65+8M+oL96g918R33xG+vozTi++4dXbt8y6gBwYw8CQrhAsBdUUlBdoC60u1FysF1kUigQK2P0uZ+ZSuZSGxombJ8/58AefcPjwE47XH8HV+6Tf/5Dh3/wU/tnvQ4T8NXz+zWc0/VvK/AV/+d/+nP/n//1P+au/fcX9ktAQqbV6Q3b1ujLd1vmO2Wym1rUd7o8yg/j7bXYP6Vb1ISDqTtAGFf3qDoaykl/7Av8Y1EFcNoDeGmWoHNNg7VlC76frKoESDdyErUXE1vN0GwnBHBbxGvVen4ewRitNVAokWL2xHYZW56wpUkOgqHJpjaxQAyz+9Sln5pypufie2jH1zV6717n1FK8NXOPbT7bxlJ2teqSrqdd7UqnBFXkV1MUTYvTZ83tVtVpTVajqyqZqaU9NA/OinOYLl5w5z9YyJYzKnJVZM1GU2ibScM3hZmS6GeEo5FusSXuuSAyWpuqPvT9XV0rUHTFxb3UVqoN1HTWvZ5RkTuxSG3NpaBoYb26Ynj5juHoCOrKUmXk5k+c7hAvDAabjyDREWpuYs7IsdvaHaDWzw3RgGBJNG3lZTEipNZrVdKyOU64NKRURaz/zGFdpuy68TXZEju9MwWut+5o258+iPZj2WoqEYUTGAUkTw2Hk6jgwTSauU3OmLUpumTkPLDVyOByZrq+ZrhJEZS6VVjJztR6gJSViGyBMBBHmJuRqGRglDKQ0wTARxomokUGt9n3WxnJeyEshZmVsEQmTC/wEhqExDgHrZmVZOK1maqmUAiVb7XSyBYE2i4xXV97VmilFkWzrHgmEFiH3NElFi7US6b5CK5W8FFo2ddnHuOSd193aIHRTv/lmq331uQ475BacGFesVpAOXITN4cejzU2pTayvbkxr9N3I87CeveKZZXRqUrcaS9GtVs7eLLq+hLodVGvB4iBiBRcOFDvoX8GP9ndxsLsLkGy2cQNb/Y96RkHX7viuiN56HjweanRg6OBx994bIHt4W+K3tUHbX3nF7/iO/+YKTuXhr+sK2TFizc6/2sSq1dSIbnOAg5+tkdAVUf2l9hHH7RX9jN1l0EDPwmxrpLGTvwYW3/2s+6lb1/ceVH/XGP7K+L377N9x/U59HDecsCmP9qtJBzs2yuL5vj3da92Y7OoQt/3j0bzmaWL2AxVv96Dbsd+bpyrCohapswJhK1LW2BWslJQSaZqoVs3DEALvXR14ehho9xe+LsqntfHyMm/OBt2eGLtgw/xQwXOP54MLH3RGndYPHYWgLqOMOwRbTegm3GDOUBSvXVAlfMfi/r6utXdmB/19Me+AjhmVXmdlKWatH5T0OWV12iw62sAFBR58rJLgpvzYmqVKpZhI0wHaYHLfc1tFbdTHaBsFA4+ibL3QYgeOULx3YmseIfIdFsQU0MZRmA4Dh8PEOEzEMADRWO3aKGKH7Pp+rbnTqiy1sOSFUmZzYlXt5x4la82FnRw4dths0RVZ7/H7voYQoRTqfGF+tfCmnNHbkVAql/sTOWck2JrPvVK+F+aDKVh6KmjThqTIkxhIy4nl87/n53rP0/Mdyx/8W346/B6Xz97wfHnG1cfK+Hs/ZHjvh6TbBpdv4O7HpK9/QfnsF7z+8iuW04kJc6YvS4BZCFlJeUbKiTLfslyq9fgDSkjkYaDGRE2ROQTuauOuKNPNc4af/AHDH/9Lrn/yh6QPfh+efQQfQHsKnGD5ufIPf/cz/vLb/5237b+R737GX//5X/O//9UXvHozcTh+SAiBJWdLOXaBo74LQhC3ae4wdNXLd1i8x7g6m90N/a9mWeg7BzobyPWfbeDJjyPZvidBV6nwnv7XmqIpmZBCsNKCKLo6OhqCpQf29CVfNV1UAcHIMW8VYGrJ7pi1AOqNk7twAxhoDEbi1WCtDZZWWVStHqo15lo518o5L2QHjdLTUp2U2c4PB0Cq6HeQdH2Y1qgQ76bzfL9XUcjZWrpoSl6j4kSqE6vaPDtHwtp+pChGfNUNZDasj28uhSXnVUytFBMhUlFiHImtct8KQ4vk2KgxUKbRUlOrMEliUiHVii4GxLsTIms0QteIhEX7fJzECT/vBxeHQDp42mVtpt4sETle08aJC8KyLJzvTyynM9TMOKor+po64DBYC5A0Tsy5ohoIwwhposVoDrEo1gsX34O9hsuc56VUH5/HiTgufQ+pIcSg4h+2rnpPvvW49P90By+NkcPVFcfjNSIjMY08efKE957fcDwkapm53J+gBJZjYMkjB4k8ff6U9z+8YZrgstzy5u6OZV6YL0K7ROpBuRkT43CkSuF2XljmC4sCw0gNkVytDnyaRsD8jUteCDlSNbCUxvlSCDExTsFKAqJpRRh4bDRdWIq16aADHo3EkEieUl1ypcyZWhaomZKFeW7M0igo1bKZaVpZ8mzCMCJAJCZhWSrL3NAaCHF4lHns2K8Dt9VVX52YTaaq+znmAe6/F1byxOpPLbr00DHfgwAcBNgeak1WskZc2bbbpC6U2GpXXpfdmaP+pQDF3ks3Iq8LrpiN163UC13t3UPb1zNn+n2/A6n6c/hrPAQ3u3NFt7HdANzuRR7h2iCGnzXr97f7f/dxvsvEv/s9+TU/eTeauf1G95N7lqUL6biqdf8D8ZYo6oJF60T4e3b8sgePWzqs97B1wNgFKttujvs8bN0e98/xwCr96jPvnm1LzdaHP38XSb5z/U6qqurIegUbDibN0TdD0VWXpG0qTb6Ntqhaq36ju60aXfJaLL++tDUpFpNxUJsQtoWcWyVpZYjRIovZ6/JopGFgPBwgDWZQxdITUoDnh5HUIJ9mvnQFT2MqKluBur3vppCJ7+duTIyXVBcLaJiIR2d3kd670t3TnfKqWEfulR1ZwQYdcIQHE/p9X9Jl2/3Aw+dzZZ83CG0gtrP7YashrN2JZGNMEG82r70Qe7f51N+r9fXSGyRHiM02WsD6g25es71OU6Q1AtY8exQhJWPiqipCTx0V37yQkhXgD4NyPAQOk6VESbC/qbVQSiPXZsIHwZRwDVQ0Wi20Wsi1kJfZFBFb2W2snTRyVavBc2IgBCFE8bzHxzGooorUihRTuLsvC/nOHPbWGlGCNREXYx9VBE3W37LX9xaFKi4hnxJTjNxoYwqRuMDLt2d+9s0LbuunvP+m8sH5fT5oT/jok+e8dzUSrwLwMZSnyE8+4eYP/pj24g1lmS09+P5M/faW5du36Nt7hvM96TKynMR6yR0q2uCOAOPE9bNnjD/4iPr+e5ymI7MkppunfPR7P+bDH/+Y9OHHcNjGM7yZ+exP/oa//JOf8+c//zt+cforbst/53z3C7756gVvztCCRWa78qeqRcY7aywdMK4MsBl8dVn1x756pKdboZUJZh8j03dP7Q007g6U1dYAVNDQTIwlWIP6zYyZw1G1kVNkoBFVzJEMCfF9pVgbidTTwr09g52JCtgYrW0eEFqy9HMFA6Cw9hITb7mR1VIwLyosKuRmdfNzrSy1ugqr1Vuu6n+9LcTqEXnWw85p6JGzfmps52RXydkxs9//RJpN0eZCYF08QS1LAfV0NtY2DpISqJDVomiuC23Og2dZhGBRw6aNUiotKGFQ4hDQQbnohZenM1EWhNmedTowDUduhiPXIRKXQjldmC8zeemAvNeLWu3wJmBkCybEhCT7iENkmBLpakTHaL8/Z065MhM4z5kyN073C5fbe6QsHA8D8XAkDMFaxWRrn5WG5OtyMZE6TbQS0WLlELSEyEQMhapW3249QgMqFsGyut3HmcZLbSuZbV1cu51wEKDmp5gG+OaEry0zQmAYJ6bjkSAjKY1cX1/x9NlTro8DNc9c0sAYJwITMS40Ik+fPeG9J9ekqTGclVobkCm1q7MDMSJjpDYlLxcWVXQYkBRZWqPe37GUwmE6EAIMKTIdJqqMNJ2IaTTQXRuxKIK1umqLUgchRaXqwtwWlgYqAyGNjGlijAMpRCsjro02FdqyUPOFvCgpmtLkhZl5nk1joFRyrtbiJLgPFqAUpWYQBpo+jtDRKk7TbYLu3PUO1NgAZnftbTp1Z2d3GFM2v2h10R107ZUx1X3eLrAoYGSz22t3qVC1uWhVV6IEejZFcKKpv4/s7NnD/IlenrX3NPp50EFhMzZxVZHtka4+AL01x752kH5LK4jdzhtk/36P56tu76AbBuEdgLf+V//Jt2KPsEHk9RV87vsy2NZBWMfSNKS8Nymbz999amtlh4udqfv4uwHTfhL5vPn5aFxsXQlG6/rgqfP+N6to54NXWFfZg3W79Zb8rqffg0bd7ut7jzhiy1XeWTQGD+2bFn3r99E3pucMt11otSkGzF26WJRpmojRAIJoJF+sdYLlC7sqnCNywyZKWRYWCuPhyHEcoFXO5xMxBaarAzIMLjRjkacswov7e0qdeX684bXA3eVClA7ufCJDJEahrrU+jvJ8A0VXTOqMfGdruvAPbOAWcNAau74+oKToh2FTtPpCdWaqqboK1mNc7iB7Wq71zdwST4P09AlWqxFjJAzmEDagtEorHl9uxlp3tT3EQPKWsKrrgt8WslCap15ooOKCNUHto/dCUnVpeIssR1GSWBQthUgVvD9V32RqNQHRZL+HcWCahMPBGNXgUc9cCnlp5GK1G039oIkJUrB2IJ4OUkumeU+6rsQrziypz70ZXWvGbTjM1qxG4ZHORdsboqQUCZLQCpdike8QE2MyyXi6qE9Qmlja6pKzRTLAIhdpWFX7DsPAzc0110+eE8ITq6/iS14tCy/0DR/WG+byDD55zrPrG6SOqI7IzSdMTz/ho3/W77DB6S18/ZL21Qvy6zeM5xNyuUfP99RlNkAigXNT5nHk5sOPSD/5CfzoE3j+Hgzj+rylNS7LhTLbXmxfveTV//43/Ml/+q/8v/77X/G3r77k1F4xL19zuryh1kgcnkA8kouTNk4+NTzdsbPRO9AoWPqJrWMXcdiM2vd/rXTpzqi+Y7z3NY4PfrJzNrr9CPRUu40QqurOhRpAaLVQRcgCF5TBG3IPWCP54BkC4qBxSNGUsEM0MCNdwKGtjeutNltYO3p3pt7JRvxwbCrkpiz+UdVaO+VqEbbmBMzaYL6DPemiA525d4d+78StkNtHRBzc9jTpzUP83q8YIwms3ntIxBQ9SmDgtzPH4mrIiqe2GnVN8AhgK9uZo4K1LAhWi71CYlGQQmNm8X7EQq/lDgxpQNJAmCZiHBkGGIZrhitFm6UnR6kEKqLWZ1fo6evuOIVIwyKiBEWiooOSxdJFidCyC00tZ5YK85xprXAYB6bDxHgYiV7KUaoS20iUiRCUEBbvu2y9Z3PJgDAGU2WVsSDlQimzt+to7tjYWojxcQzr0ipramF35PvhD7YHdaszXtN8dw5bQyEKw5AYh4E0eHlFCg7CrJWYaETkzFIaMRRKviAhkOLA9fUT4tioLRBlMKXvJLRQQRo6Wi2dVmGZF06XC2WeGdPAzdUV42ECCUaej4GqIyKjtY+gMC+Z8/lMq9aGagiQotKkkKVR00CcAsejMMQB4rCq6I/jQJpG6hy4nAraIiITgpAXpbYLS17I3i7E6lctUmkkrxA0Gfx+JDen9w/vTjZrNGXPNO2tep9fNvsrD3+02qIdW2VYsftUTpCxRQDXEoKuqqkdaLrvULUnuDgxBsag72ug3d+U7p95Wit4Wxp/Nv9nBxkrMFpRZC8D2kTLcCC4iuPwEJStY7Ij3XrpQocwj2RSefiuu7naPc934pxfE3TZ4bZ/5F2kC6fa2K8inp1N0s0/cNxgANFexYvZUJTq6t/0qVntxBZZNlsjKzHRy616psV6dOpGZO+xxf7h3k3Pfjhy7/oUu5+tP/7NPs7v0I5je9B3fgLAWlTtm2N1rpuJCj+IoLmQQn/YquZE0CptnokSac2cOtG2hWXXMG/HYIqWRsuZGCNXV9fEcbR7CoHskc0kxtItWCrky1e3yKt7TueFS/WDgriyOkLFgks+okHoOcbCFmXcFsBqnnwD72fWQtnBAbKxFbqpVwkQA4IpjhYcrDxSxHFzj4PXpPo90wUnWHtmrUZUAkESpgWtiAoim8VXdx61oyT1EL7TNr1Ppj18cPZLPfVTUIoxktEEEJpYOgbN6g278J6plG1z3w3mJjghHvW3gz9GiMmcjBDdqVTj9U3uweakN1YO0W20ND9fuoMgiMTVLe3EPLhqoelz0/eHDdlmJB7jCphjchgGUhpZ5sbd+WKCAyHQGiytIKWYgxYKJc+cc+Y8WxruMIwc4sgYR8bB0nivpgNX1084Hp5xGJ4yxYkxNKi3vH1zYj43Wp6oy1NunjylLk9J9SOeX33Msw8T02G7Q66ewx88J/z4p0xLxotmkJJJreGFNRyBY4xwfQPj+CvPWpvyl3/+F/zs7/6ab19+Rbuc0Bevuf+Hr/jFL77gH96+5kW9UCiUopR2Y+A9HoGIaYzuWeG+D3YGdGViu7GO4GnkViP6OB7OLuDopn3vrezWTmeA31lPYXV++hG+1TZY+q0JkXUqx5wEy8zIVJIqMQpJTVl10GL9yMSUAYdoCtAxdFAYjahxO6YY+Ok1xs2BeAWKp92sUVFn3Ws1Vevq41pcybXbPDuXXTZjzfaRNd1r79h1m9WZ9D6ouh7QBpS1CtAVV77/K6bEcbC0TokJBXLOzLVQcrWzQywSSfTavVxoxVobjFUMMErXet6eRcCAUkp+jgi1ziyLIgxIClbPimVStDbTqlCXxpImDmFkDBNpOjAME+MQGZOSghIDjMn6IXclarPlwpwrlzkz5zO5nJnnM5dy4X5eOM2ZOTdyg9xsDUQNHMaB4zgypoS0QFU/bwRqjVbi3ARqQhruKAX3L2yttWCgKERAGk0L1aMyUYw0TI8EHHMzUiTQiITtXHdH3866h2nT9mGtbRQTZ1NRQhLiEDANtoqKkoZIiqC1sCyRpUTapZDziTdvT8QhkcYR0sA4GWMeQ7St1zJFMzFVxjFQa2C+v3A/33N/vqfmTM4JaFyJMlwdrQ1ZC2g1QrfqbKmmy5mcL7QyE7UZcAzQglIC1AFiLbRQrd9xq0ittl6GyDQNVKnkxdMwGQjeF7mUSs4mchdjJA0jaWigs/fpFKIkNkGpR7hEVoD30JXq/qrw8AfywLdefbn+Wjtsputr6IN92pP6VbcIp/RMhwcRT1nrlxRZ97hnOa7mrStrPrB37uCb+9XPtL3tw99PQN/xuzHisJ8kdq/bV4L5uB08rjibDqK2rzdgrGvg5DGudzPvZPf+PVtnDV692+7g3TP0nZ9s7+Gf2c5jWevoenbeDsSFnrXUgbdjHjsZUfXyJcVFE3U9w9YVtIsuK0YsPcgekl7m4F7BKqZRV3+3A9b90/So9/ZQK9x/Z3h24yLbxP6m3fg71TiGvqo7KPQBMRlmc8fNYbHDoqkNMG5w17q6/jLO0ogXxbfWTOxDdop8ND90ZX1cE5JQEAOCS64gmeEQGcYDTdXY61b8/aGoMKaB4XDg1e2Z12/eGr7EajZCtDpD6ZLNPughWn1jbZ512EBXxubh1ZVX1zqWNexfUZx8cPvSUwNUlWmcGIcr5gzUAlREHudgtMUY/PXNMAXrFo2Bwm4sFdRUULVZpI5iaUK1daBogNM/0btxrtnXKrCOiSvsrrSYD4g0a6ia7CNEpWTP43cjF9nWnb+jEQraW254ebDX1bZmUtkl92atliYXUyKkyBAGQrI6mVqqNehtSmimjBaiM87RLHmTiEpFgqUQtVYtlbV1qWtrWb+332uQ6pHOxTEGUoBxsBYWWpUUKtoyolBzJdeMNGN5l5w5Z0tBIgwcDkeO44FDGpnSwDhMDNOBMIw0DbQKgwpPYuIQhJoX7k9n7vQty9K4Px8Yphta+5CPrpXjxx+QrhJopsmF1gJVPXprfAE6WhPz3uOop0mv5EUplPOZlrMB/xS5ff2Gv//vf8Of/Jf/wl/81V/w1ZefU8531MuZMi/WBz2Nbo+AMDLE3pzeojrdmEonHURWMgLUZMnpdqs77W5pOrP4aFmr/d36YvkNi8bvuYPgbms3r0I29c7+WmIgUiXSvCF930tFlaRCUUhqYC+JkgIM3syeYKWLQeyAs13uJBiwMilu+4taFLHQKM0AUe/7a+nCBjqbq61WQJ3E6q2Y6NLnsKoTbordW72LyCZW0mFkj0rZ+VkpJaNLT7N/HNkxSz812XwRy6goaiBpaSYK1snVoK7wHAdGrVAs+n8YR5aYuDQlt+rP6M/hn2tTy/hwo9taoWoyIKpq0eRS0LYQ5cwoiSGOjPHAkK4YhwPTmBgHizBFbxERgxMCrafKNvJSucwzl8uJebljySeWfGHOmVyUhpUaqNv1FAemmIja0JzJpawkXgsmaCdzdu/ZAaN4G54p0bRSWmGulmovIaFpQLBej+JObYqB8ZF6OSpbrGYVh7LmJXZk7aLHsJGUIVhKbe/BJl1SPjQjRROEZE6nqRYXJFTS0IilcVlmLpcMEhmmI2k6mIhcEkjFztGWCaFa5HIU5kthnu+4vX9NzpUxDsQhWsAqNO/5B1QjDWuxbVVysdKLWkwvIA2klIjR/J+KlX20uXEOmaQXNA0kaUyD5TSE2GCwKKqEaFoBpVKyZefQTGOAmLxsxKPpzSPHwRJ+Hyte1QMd3Zbq7kD+7lqu/rMdKPLPOOnTo4RCryWU3TvYH2rr9Wj2fjuXwK/dGeNAtAtWbaDRwVw/j7pYjrLakBXwNfUOAJ20Z8XAfQ3399fd99anVreh/QxsnukQwu4PcJ9f1t/vvh/eRuQxS6v6tY+obc+0jaesAZtfv6a2OOn2eu/GwsR/Zgnp3gKo7+3Q58BAnDmc0COwXdBmnQHdxnt//z2LUZuf1aGv0/7enhW4+iTBhJMaXgNpPmvXkGEF0uKugGdQ/prgxcMt8E/fg799H0cXoAHZhGAw/jn093YjqhjQWpuw+r2LdmDht7qnD7yGLcTBnIOm6yLtOcDdZbDX8xYXAhdV5mVhqJXDYQICRX0ziReXdkU/sWhMf//Wga72iGFzFcC49nSDlUsypn0dlA5mt0PE8ZcDGbv/tk7g6vetxmVVWhLv4+OG6VcN2/dzhd22WOl8wOjfviHsZ+obsTRgqaiYImzV6vWsW8qDrd1trDaD2UGkbbDeRjGoNxYW7MBKgZYCxfOm2t5YIjQJ3ogZpFZqyVRY04nFVweKg7tCKdnAYQtUJg4xmIJfmkCjKapeZvLpQlkWpFZSUAaJpMF6zEkTWlI0KpIboVTKsphaZcsOGvWB0W9qjOBeye37vgZnqMplpkqlViE6q22pYZlaM0bMN3KbyW1GhgM3z57y/tMPuE4TYSloqUR3bFQauWVazUheCJezsePauJSZu3biPF94/RbicODqCTx9r3L98YGYgE9/Qfnmc75eCt825dWycFpmLrmYeFZK1h8TI3dKtogotVLnmfn+nuV89qh743J/4tW3L/n6qy958fIFt3dvyctMacXqUtPI4CmvNGUVshLnlnauiYg7+N2mN7W9rk5GiK4HcPMUaRFj/L/D9n4v1z4CutnvdyyMg7xOee8PvDUat2On13T53YOLkyeWRm0Rkq5MKkGQaLWuGgIa/aM7/DZwlqEQwwr8+y1152QlOZs73GIb3uo2ZD3TOzkV1FLVJHaw2F9gG4PeUzOssvibL9PHp0d9NtEx9Wc2OxKWuBJKITzSRKrbzeKZD46IVRMNIbdqafG5ERNcHQ/cHK44xkrLd1bTOR44HA/EplxKXonZ9Xn9LCnerqhniZRmtX/2kLt+kK1ybgJ6wpibZJHkACkaYdeVhS2nfqvh6sBdm6lstpppa2skAeIaZYtdTTOaDZIK2gJVutiSosFq6gTveemNrWMUpuPEdDyw1MKbuzvu5xNaC2loVlc3HIjR+v6FZi0cHqtdla2bLX3MSM9GI+DaRZ6t0v1p8VL2QBAleplEiDtth2iRx5gsA6Arfht4bERvb0zxXokNpFjUcm2FEyFIJQRhGCLDICwzVJ+bGCKHqwPHcWJIiWEciSnZSa8NrdnVx520RokpcRgnrg9HrqbRswuUSy1caiUb7kWL7eU4WAaCCSjb2gmp60tkq4EuJmiVREghot6Wwh7CyWp1AcOg7J3p7/OqbRVTQHYWo/vR6+rZRRW7DdsDtRUorRod/gNZ4R8raIStVpjVJfbIoX2nw0lLN+1Ov9ku6YdWH5Pua+6jljtACp5Btcny+3vuAXB/KT87AhZNWwHXli5pAMZJyRDwgNoW3QN66uaKULEEzccGjj5822qR9T/v/tZ6xvyT7kjWGVw/S69iFvssbq8soOAISPHMA93Vhz88m9bXXb+/OzT9h01szbTgZ7j75WE9x52wcB+zicVbWlMq4pk+rHOyQ1e+RHdR8RU7bY/+YNr+Cb7qby+O44i4KWvj0LAHOOoNT6MLK9RioKz2R9mHXe1RLa/aNlDQHk1q6+ivTTT76Skd8riD5Mi74oPfGkM0Rr2qem+aZKlXQNXKXBZrMi/qG2jn9GvvkWIpkzbpu428IuAtz3yNhvozxRjXHn+i1YCa+BHvzxiDsbOlGmNRSqGWOwOrFMQPlMe4HjLzPjPii1p6yJ1uLX0OtxYizQ9DpB+fbTV2q3RzfzPf6N2uCRZaD+rKquqKlgRkiNQxsszRHEkH9XZ/YY1MCBhYK8XT3up6z2uE1/uYKbONrZrzEsaReDTxghgGYqx+QF+sH2drDDEwBRNyqMMEKpSlUWgIdQWEpkaou/VjkVvFWEeBXVPYR7jUWoTkUnzpdUl/kz5vWmkYeG4shFR4chWJ08jNkyMfvv+MJ+MV+e6ey929o/nCUq13ZhkC81l4m88gkUUieUy0ODDfL7y5u5BG+NGVMj2BdHMPL15x+as/5R/+9m/5+Xzhs5z5+mg46hEAAQAASURBVPaeN/cn7i8XSmsuvGGORKmVUgotV7RkdMmUZWbOM/Myu0qlJ+452BmmAQarH6sqtAZzUXPCurgIvU1EV8HraWVsMvqYg6h9ja3Gth/QbWNrN1v8vV9BNkC1HgC7g2U7BLYb2APhDoaNEXXnfwciu8qbuLqlxOgiFQ4cxeyReEpciKagGCQ6cPRIXwguYBUgGmiwCN4uDUs7SWYZAcFZotB6rWj0WrWeIbCWgGyHGX0+tj0dwt7mAzsb8+7vdFnzHhVozer4TAyrPFrj+NaKOU/NzrxcA7Na1F01oBoptdrXMqJ6ReMJPWm+tbOllxY/X9c04+7wygq0hu5Q4mlLzQBHN8VykNVOqmeHtGpqlz21PmtxMS/1c8l6kNmYNgKWohgHmPqa4Oh2LhLCiIi1+EHi6nTYEJjDg3gdm5/sQRoxypolgUQkDRyvrzlcXXNZMrkK95eZeb6grRIP5njXVqnFHLT4eKVxKOK6Cj7+utWgaQhoiBCMDOmK4wEjLvo+2hywLc3VUq8DvUetCSiZIxqStSyJ04EQJtJwIKQE0lCKRRmDEsJASsFqDJMwjo2rq6fMi22krhqe0kAcrL41SOAwmqxcO9g812Y6BRA4pIGr6cBxHBiTnZNTaxxqpbjGxDSMHFNiGoTDGBhHazVQqz1riIGYEkOyFOIhCCkKA0IN0CNx62Csg60gjzOTzVPSexsvcYC04/BXINbFAnsLhP3t+TS6zdp8o9Y2+7s5Tbu9itskefihK2i1m7AUbIeSsgNpu2ESiTS6QNj63e3c2Dn/wcmOzgetpQzdF+v/dcJj9aulp9nuXk/Y7nf3JnsovikMP84BKbsJkweTt/1OLy/RDuj/EdD44C5lG4uHwNH/10uYJBCdwIwdorliTTV5fdAdPNsRBw+oEd1/2u5Qpa1g3bRQwPLsNkjYHzsEsYwwh4OWHfAQsO6fcYP37Oa63+dDv8Lu5R+fx99dVVVtkQXBC37Z9ZlpZtw7vhZYczM7guhhVgWJzkj6G7jbTf8z6ahbvQ9Wt+BixdZ9sezD8bkU708WDMyaKBkhgrZKFSVESMFYYXVnAzfo+CEQ1qfQjgndOQGkSyl7reMaXbW0t1LMGUj7henPEmMgpUBtzZWzjMmKwQ7xMYLGCOmRZKrdsfMn39LN+nP2a8dymfDF1i+ohw6tzvGh59cXanfotqiP7sCjWkpos4bOBCEMiToMDONAnK2eUmW3QaSnIog3yTanausF6nOmzvL687TayFmRpRByJRS19ieqtFIpS6YtGcmFQeAQhEOy/mJlHMkVtBaqNIqa09VckVC0WXTVaxNgW8P4mfhYrRyWslhkVXthdnPhImuYLqaczt1yocnMjz56yu/98EMiibIkrgJcj4nLNFCX4H+foVWqJnIeuLsI9+o5+NMV0zgypCNlLuTTTFCxxtF33/Dl31349B8+5bM//W98+vNf8qI2XtfGm9PMeSmmnNlMWRLRVTkOhVoqmgvSTPFxKYuJK+S89s4KaoZc1er1moOU6k6xAfgeQZHVnnRgb1kfstqxNSyJp632eXJhpi2V3Of0kQBHbz3UCauH3soeRq1fPgCOKwD0NEn72tWBg9XbSU9PdVZz+xthLxEvrqYZJK6HpkqgBkG9Lr0Gi0T21OB9P1r618GBY7Nm14q62M2W3t6fcI/KN9+r29nNjuyfXLcfsKpd9rlt6tkA1gy7FiGGSgqJECI9d+T7vopWtFqaZ62RUiGrlVIEEkmUSkVCYohXwIHLRagZYkkoibJUCgs1+Vxh+7JHvlMMpBRNrCyYPbR5NFmjVgXRYBGpZCmFumb5hNUeohUoRqhEsXrCkEBN/IhWELKNW4IxDcSY/DUUVSNkVZOBYne6uxKg9YKM9nsaLEqphRiUYYhuRwYHHYlhMrA0LoU5w+lkfSChEMaIauVyOpOXzHFIpHGgPpJadQMkRmIKLqpU3JkOIN4HNypSu/CFxwVkAwo9SqnCCiSrmkhVkECMVhpQB2v1lJIQxonhcM3x+JRpukJipLZMKfMu0ms9V5PP/3QYeO/9xDBeMV9OJiwVbQ/HNCFxIoZISoGbm2jZXCFRgSUXSjFSZRTzSVIyNfAxwFEEDZEUTZhnDIEkjSEq42CR5kwxKxUD0zQg5cBlSgxRSAESahkwPUPJhfQAT1K1tfk414rQHFCwztVqYXWXjspWPgQ8+L31N9RLs9a/346RDk7t39vZsn6sRJ6sEhg9o2B7hx5ckc3urWS+2ex93WIHcN1PW+txt5dzUs1/27+W1Vnp4Hqzrx28dp+3j9s2Stp/lcebu3cuH2il+6//yHvrbn5/HXzcjhyfv23Mwzr2YQXeober0uoBg2p7zYMNezvQ7+EhPbA7w3c/e3CnHfAG1qCz4RFjKP5/7P13uGTXed6J/lbYu8LJp3NAA2ikBohAAiApZiqLpBItyZJthbEtjzQljxxG1/bVjMYeP9dzLT+eGad7ZDlbwZJs2rIkihJFiZRIkSIIEEQiMtA5n1xphxXuH2vtqjqNxIb6oAFyv+TBOV21a9faaa3v/cL7iZhUNSK0I3tGRJVpP7qOk8c9vo8n77WXwStEji+bOCohQpjUhRMnY66tAbCVZ82DdRPmjhiRxXBQbnxYfkxiQsRuHOKXQoTWDyIqIPpYDFx5t6tUAmKYnfHk7ZxDI0lUKPZGBtVOF2ZxnCd6LkVUdXUxl5jRwydliChVIgW+GqsLktOVZ6qKOlUZuSBGaSDVBRMjsh09Xw688ROpmGEfqVJMNRsIYyBpYNNtIo5RrAch8SLWnVaEcMuMMY66+OpqylEmbrVJxDgcPnomxfgBGBPJ4HMV3iKcAWsQTqF0glSKNE1Dik2SInWCMUGaeNRnLM4KPkbxxnZ2NeGGDVWQI8STRMPWURoY5haflRTkSErcIMd1e8j+kIY1JElKS2maSYpIErwOQgNGOApXUpQZvsjB5AhngrrkyDioTt2lS872YJjn+JFTIkZknY1pVaBkiA46BVNzO7n5zhu556brUIOSCyfXyDIJSuPTlLKRYmxIu00EWDzGlHQJ9Z1SpzSFR+FRLvRwc5kFSoqVVc48/jjnzJCVc+c4d3aZYWFQMg33jfRoLYAEfHiWpQztVLQOrR+C+GUw9q0pKU1BacrQCqUsKYqcIs8oizJK+Afj1EaDWMqgJjqeP+RIHGUkqER4Fqun08W7teKMorqhRkJefiwWUqk5bwNUTO2vZLsnYkxcujiK8U02IkxV9FDFXnkq1hRppVFJgtRJSNGXUfinUiathGYqo0rExVJKhAhEUxDVVasVVFRz8hjjflLVwhjmTuVdiK+8SHFolR45rgCKHnoYTy6jeWd89PHDFbccRxpHKT6BOGIczpSYMsdNeHNHkdhtQGFdFKYBH4XHpNBIEhIUwoW2JV7qQMQQ5EVBkZc0BDSaLZpNjWgmoDWoeN5jzb0Q4V5JtAwEMkarlFKxL22Cc2PimCYKETMghBThmkZyXwnJC+lD1EvrQD69jEaQQfgCIQLZU0qFCLQX0VED1slQx+zia5UTx/soxKSB0G4kTNQGKT2plqRJQqKTQMKqNHMkQqUszDmGvQ3y3jLOOpQLIndlHnp7NpMkkKptsll3X3PbS75X3cnxyLZg4dKNLZiBoTfo0Vvrceb42Vf45hzoAxcuY7STGKmSMQRgEH9eYyTz6EPz7Hrtv3kLxranH08iftJSYWzsu5DpNiJzEIx1JoxwL8brBGPnYxUtBD8y5scOrvG2YzEbOXrtBemdcQ6uHEVi0pDyjLoWVAqo1ZhG68Uo7dyPxjoS64v9J0cqrEz0gxy12nix3yNqOjHQyiKslqPtt3e2kteA0fmZGJafWDteCqNPjMj8WIhmgurH2vnKZq3CepU4lo1ZalXm2fi6i/Fpm/g+MbnJaB3fMtJoB/lIFPzI4VDtRUTnbyXGVS3cUcTPT16KsdO7+vSWK/gqL9llE0c5WtUrIhhuHRVvTEusP4tMvWpVUXmzQ22Jj5G2kDLh/YQnv7ILGD+grkon9T5GNysiU3lRBaH6W0TCF9XYokGhlRgpdMqo4uWcx5jw/VKECyHjWJwz8YuryBqMtHl9IE6jBz6u5pVN5YnRE+9H56CaPnz1ehy/tWLU50VHcSBcSavRxmtB6QR5sT0RDjFK43NRFCRWYozkmP2YoE/29oniD1UEtlLeeym/zmQCw/hBrOo3w4MXerUl4SqqQCR0EtpDCJWAKKkiaXJ01WMPr5GfoZpgqwckEn8Z0qesFxhvsE6Qlw6bFWRWBhGm/hB6fRp5TiIlSSpo6oRmkuJ0QiFDiohxNqZPDvF5hjIlSVSNrQzw6nqGyMuk22hbLmNopxHCRCNjOuTBC4RM8aqBbLVYmJvhmhsPcMdb7+Cew4dobfY50z7BydMrLPczjPWYZpusGEBRIrTAOEluSwrn8ElKKhOUsGTFAJc78uGAMitQec7FPGPtpGNYDumWBVZpphdmmWm2aRhL3u3T7Q3p5WYUZU/TYDhWHjMV0ydNWZB7h3EhHQwX5OEDH481et7F6+9H902ik0AKjaFKU63OjZSCRIjoGbQIL6LRKcYCFz7cN1VVUkXiKqErNzkfXGFU7WQs44j6iy2GgrE/edJmHqXSqBCJ0FrTSFPStBFSspM09AuMKfwipp7KS+XqIZLE4HQRYxfniKSpGKGcXHWiP48qIUwgxi19Rk9ENDQuIW1jwhnm4PAYx1TW0cJajTIu5WEqHvG/ygsbem8SUjJLi8nzGP1zCKkRcf6/lIxfKZTOYWzouyVkUDxOVIoSGmfAlgbpLV6oQMakwVHiRIlINdMLc8ztWKQ5MxX7X/qR0Irz40wcTWifoUNJajwPcpSuKny8TpUzqzrPlV8wrFQI6an4thMCF0UgpATpNRKNILZ+8GAr4bG4lllfCfXEurnqNR/uI6IjQ0sVr6ui8p47Efp5VjW3LtbEaqWYaafMtlM2tSTPQTmJ8UFhK6T/KYTUhAKVGjW+Aoy4Y5iHJknbZMRxNNdssSfCJwPfqlLiqz0xniPxEEVUqmdtTOpiqm4MkIyM+Gr9qSJeI7Y3immOxiBENU9XjjJAjJ//agxheo6zqpg47jiH2NjmbRQU2EIcieenGn/lmJ44l5PkeXQO2DbHatXmYnw24hcyee589f8t222toK22Z3ze4rkWsZZRVva8r0odqrXLB+dCVPqnEm6MF7laf7b8nrB/R9+75cxVmWmOcSZONcpI6KlKM0B4iaxUBtWYNKqqFIVIHpkUZ+LFGWJ1c1RncvIZuNIRxyoiJQRRtS9k4baVRglP11kKFN4FARFV3XRU5DF6xpMgFmBNaBDr2eoJCYtDrDOMJNGLUBQ6eXMEB3hc+bxASI/SCUma4q3FVOTOGyAydKpoYdXCN3pJ443vIHyni/34ZPiM9ZWBQ6AvIl5U54PH1G9Neahu7MowGqUWTDgPqolDiKAeiXQMTIZWKblx5EV5uZfoK8I3fPu3bMt+rwxSYBrYc7UHAsBO4NqrPYiXQFFakCIIj8CoBlDqBClTVDKDnt7B/P6DXHPoMHPz1wFzKJkyPTWk3TboAhLrafiQQmpNUJX1zlGWloHzOFtisHiXU+QKbwlNxPMSU5YMVguML8mlh3aD6fkmjak27XaTacAkCiEh3+iR57aaLqlSGytDtkJILTYhGhP7TRoTWgSM1M2iGm9obxBaCeB8qMGrPMMTBCna0Fuew/gATpCx6BAhOLdsbCVBdBBtT4Lj2LCH8RxTLdRbjZpJ0jixese5U0kVSbQiTRJazSbNZgudNJBJgkzTEFlSavzxuPiMKXNcbMXY8TL2WFf32ERaKJFIODeqR5axXUKYn+PCK+ISHhdZR1U3FubckWMuHrTHjhbU0RGLKq9kHImrosPhzITWD6awFJQ46xEupstaB7JknIi0DXAQVKhjxHZkUMaetC6ofAsUUlqkKpG6QAhHo91ian6W+cUdtGZmIPbyraLJjnGGiyT0s1XSjSKKVbuj0Zof/45lj8ETPVKe9tERx2g9NZH4haT0qlRDUwmZjDN9KkdKeE6rlio2PiZBW5oY5bVR1EigCNtb6yg9FMIhjCPRmlT72FcSpBYkCTQSQaJEFNdR0eEoRxFP46pymCuHpaWlbboxalwVTESdJtsheD9eB0ZKqxOeqIq8TSg1jMjJOMNhbNPJ6hFhbOduiXhVLRsQE7Ps+LtG+xlNsnGOG2VcjLeHreR2VJsnqu+fyLyqPlUFXSqSiNs6VzB2Ko0IZjye6lgrlbWK/Igx26AimtuL8TGF8fqRE3J8ZuO//NZPjU/d+ByOrlNVgxrFcMZpqlHwMAZ78H6sYhp/VzsKmXhRSEeo+DPZa3jMgbaiIu5ulMZdEdNKJHNU0hGvjXNjSipHpSdhfiXWZAeJmFcigVtXwa2+4JefBl9FOw5GUf9qQUqkoCFC8+JECZqtFnmW4co89OmJA/Sx7kEpTdpsUlqLsRlShn6G45zrKvw9DgtXD5Lz8UZ2Y0OiEkF0uEjANEqp0DLCGIz3SEJUVEZjo5LTlj6yc+fGIV8qsQYf0h2rJzKm/xlbeZHiDRQfmi193mIUrqoHnIy8ja66H5NlKQjGXipBlBTO42VCI7l84dsaXzswzhI7AgfygKRKh1YypdWYZXp6Nzum99DwLS6c2qA4uUxrOMD2emz2C3InQCco30CbApFnlCYnz0sKY8iNJR84+krQaiS00yapbuKdxpaeoggqeiiB0g3SpEVTaoQtQvuBRpP2/BQ2UQylINvskeWWIno7dazJc9UEq0JtqTIGIUpErOeBJBilwkeRAEtVqycgFqjHRTlO2FUaXEUYPYSWFIwX5PEiH9LSERVptJhKdElIlKji3VceFT2alPffkm4CW/5dETmFGKmmVnOJVpJEKRpJQrvRoN1qkTZaqEbo4afTNCggUtkCI0Y4MqgqAwSq81gRx4pQVgtlTHG3Lqp8hvlQCRXbO1Ty8m5i3IKY0zDq/VjVZlS9zyoKMmp2LIiGwXiODvvyo3pj64LqpbEABmNFmKyjWq8vS7xQ44jqNiCkx4/TfJ2zFNaGhdx4jImZL/gQIFAOoUJrheZMi3SqhZOSonSxLUI4ZhfruSVVRk2VphqEyZy3IRNGEtYp7yey5SMJ9OCUGxuQjOtdBKDF+PxKv8UhPdqGuFbiKlE0i/QmZtgEYlelfFXGgsSTSI+yjrIssMZjkOSA8QalDFNpSisJNYVSOKw34E0gkypmENlwj5SlYZgVQchJ1zyvxktjbDxvfXXy71G0DiJZnHDIwYgYVO9tiUSKS/cTv9VN5qb4LaQrPFeVVTz6kjFZreanKkAyQUhHx3IJGfATG1fRqYqsePyY7Iw+MEFC/aW/xxRsFOTwxF7XYwI92sCPbeHt5o5+fESMaL2/lBxWuPSFasx+wkEwsaYJgfBh7gpkUYyykEL2ix0rhcdTWbW0C/oBKmRPiJANEcS11Ei5fLzm+InfY9LorMFaA9YGkbUQyWJcvjdOO42aXIHYI0f3k4rX3Qs/en9MrSfv/K333oQrYstWL4XLJ45MelIiEcOTuRIpIG21ac62KRuSvs0xuQWhYt5tOLjSONwgBwlaeJqJoqE1uXPk1o8KRJ0PUYLEe7QSWCkYlhbnqrRSQloOLqSgVSudkEitoyCOi9+rELioHsioVUSwMX0UNpFB3Soa3847nA0SKzIaVZURVfUji7NJuPhU4ebJSxCNr/jK2JkUjJ6wqEoaUtI2htkGNGZbrOSOzW5OIq9sjWPtUf3qwr1ve/9XsNUAd+4pzp97ivOvuG0CjR3oRoj5TgO7X83ADAw2YLDhOROrbQKmaE5P0Zx+4Uc8jBLPdKvN7Pwis6/mu9+AGE3qEyk31esB4pKpf+yHjubMaG7SUpImimaqaaUJU80mzfYUaauNbjRQaagljoW5MZ03kLlq1qq8m6P9iirCx8gjXiXwhBpQh7OhDlRAjDjKEXFEVGk/YkT2fSSPozRgP/baV/Xjl6YnTc6n0cWH8xbrbBBeiq0uhPZhDdAabyVGhNTkcenB9kyDt9zzXX/qfZQmozTZFRjNawWBkEls+bP1dQCMITdm/FIiSPGko+1CnXthoZj4dDK3g+vv3LFljy9deVijxgvx577/z13tIdS4EtgSDpuYuycCseMIZCz3mNjuxaJuk6RRirHDS8TA1fh3XItGfYj9yJkcSjeCwngQoBv/SKlRSkcRsyhqNBrL+MdXjk9b4o3EmzIQQ2uJnDWSx/jZGDEWFeUhOnLjmhZKpgROiJEI1djn7Ed9QcM5GCvK+8nz+wrL46uocQxe4RChC4N2QpAJBcKjvadhHK60Qely1LxybDQI77BlAQJmpxrsSBNUaRhqxUoRIhxEoY/EQxtHS2vyRJPZDFvaGGAJaaYjg6oKW4uQ0uqsj41RQ+2UdyG6gXWUw+HopAokOordVOHUsc+7OvlulBLgR2F/Rg1Bgwci1LTJ6P7wwKhdWEy5Hfl0laSRhp6CLi+YAnaWBQdlg7n9+/n86Yv0hqvMtNTlXqIaNWq8wTC5rFXe7cqL7C/dbuRpjBGmyVQYQluNRGsaSUIjSWg1UqbaLRrtKXSjgVCh1U34HerMxgJLgYSOF7eKTo7Dml6EjAzjPcYGZWWEQ4lQvxYcYpUS3XhxG0UvqwhS9NIGB6AbK7N6xuqcfpw6PEoFq0bjq6ikxKJDD9DC4MuYoaJCHYgFSh+IpfVj0bIaNWrUqPH6x5//wdoB8HrCq8iDHBsQVTqKdwKng4BImZVYNyTPMkwJidajSJ2IngBBqNuwzqIEpFqCDZ5yGXPBK+VO6T1aQCLBaUGqJbmtxFoi8Zss6o3NAq1zoR2GY5TOUhlVBj/ue+f8qP2Gt1HyVlSRVbkl/aASOgkeBzlSNAoGTYwm+sozX71epUqEY2qkCVqD0JJWM0UBWZmPvAeBcCrywlIaNzq2GjUmUUeOv7rwTd/0gW3Zrwc2gI1eD3q9bfmO1yOS+DOtgJkEZhaBxW37vvp5rLFd6HQ63wu8D3gzcBcwA/zy0tLSD77E9g3gR4EfAQ4TpFZPAp8A/q+lpaXjr8Gwryrq57FGje3DZRPHt9xz93aMAwiz4SvJNx+8Qt+1Z+++K7SnK4uLwEUDB66Z58A1V3s0NWrUqFGjRo2riP+NQBh7wCngyEtt2Ol0NPAHwLuAJ4FfIfT4eCvwPwM/3Ol03rm0tPT4dg+6Ro0/LWoHwOsTtfJKjTccOp3ODwG/EP/5V5aWlv7N1RxPjRqvFvXCWKNGjVfA3yAQxmcJkcdPvcy2HyaQxj8AvmVpaWmkiNLpdP4P4H8Hfgr4S9s22ho1anxV4ysmjrWBU+P1gE6ncw3wzwne1xeRWKlRo0aNGjW+OrC0tDQiip1O55U2Pxx///YkaYz4DQJxfKXErho1atR4SdQRxxpvGHQ6HQH8e2AF+G8Ez2mNGjVq1KhRA74cf3+g0+n800vI47fH37//Go+pxsug0+nsIESKPwTcARwgCAw/SrB3/v2LOAFq1LhqqIljjTcSfhL4BuD98XeN1xkuV8ghfuadhDqeryMIOTwL/Dvgny8tLdmX+lyNGjVeHV7Nc1rjDYHfJjhV/wzwaKfT+X0CCbkHeDchW+dfXL3h1XgRfB/wc8BZQhryCWAP4Rr+G4IT4PuWlpa2u01ijSuATqfzIeCvEboH7SBc1y8C//fS0tKfXM2xXSnUxLHGGwKdTudW4B8C/3RpaenTnU6nJo6vT3zFQg4AnU7nu4D/CmTArwGrwHcA/w+hVuf7tnOwNS4PnU7nGHDtS7x9fmlpae9rOJwarx6X9ZzWeGNgaWnJR6fA/w78DFtbX/4B8J9qZ9zrDk8D38kl6cWdTuengS8A30Mgkf/16gyvxleKTqfzs8DfImTF/XdgGbgR+C7gezqdzg8vLS390tUb4ZXBG544Xo7nNNbH/b8J3rdrgQXCBX6OEOH4paWlpfK1GXmNrxRRKe4XCZ64n77Kw6nx8viKhRw6nc4s8K8BC7x/aWnpgfj6zwCfBL630+n8wNLS0q9u+6hrXA42gH/yIq9/7fT7eOPjcgRXarxB0Ol0mgThuA8AP0GoaxwQnHD/DPh0jF79xtUbZY1JLC0tffIlXj/X6XT+JfAPCFlWNXF8HaPT6ewllE+dB+5cWlq6MPHe1xNsmr8P1MTxdYDL8ZzeAPwF4D6CN2CVEEr+AIE4/nCn0/nmpaUls50DrnHZ+N+BtwDvXlpaGl7twdR4aVymkMP3EoQafqEijXEfWafT+d8IHvL/CaiJ4+sL60tLS3/vag+ixqvHZT6nNd44+DuELI2/trS09PMTr/9OdLI/BPxTAqGs8fpHFciobdLXP64ltJ+/b5I0QphvO51Ol68SYSp5tQdwBfA3gJuBWYKR+XL4HLCwtLT0LUtLSz++tLT000tLSz9GIJR/SPDq/JltHGuNy0Sn03kbIcr4f3215IfXGKFKN/7dF3nv0wRP+TtjQ+saNWrUqPHyqARwXhBBXlpaepjgLL82CrLUeB0jZlr9cPzni62RNV5feIZQT/y2Tqezc/KNTqfzXkI25FeFMNUbPuJ4OZ7TpaWl4iVeLzudzn8nEMebruDwavwpMJGi+jShXqPGVxduib+fvvSNpaUl0+l0jgJvIkjMP/FaDqzGy6LR6XR+EDgE9IFHgE/XtVM1alx1VE62F0Q2ogNuNv7zRW2hGq8r/EPgduBjS0tLH7/ag6nx8lhaWlrtdDp/G/i/gccjp1ghBKa+E/gE8GNXb4RXDl8NEcc/NTqdjgI+GP/5yNUcS40tmCZEk28Fsk6n46sf4O/Gbf51fO2fXK1B1njVmIu/N17i/er1+e0fSo3LwF6CQ+cfEGodPwk80+l03nc1B1WjRg0+E3//9Itkavw9QrDg/qWlpe5rOqoal4VOp/OTwP8CPAn80FUeTo2vEEtLS/+EkLWogb/COHX8JPAfLk1hfaPiDR9xfDWIYeS/CgiCZ+6bCcpH/wn46FUcWo2tyIF/+xLv3U2oe/xj4CmgTmP96oOIv2sZ8tcP/j3BOP0y0CVEg/8q8D8S6qjeEVPiatSocQXQ6XS+G/ju+M9KtfgdnU7nP8S/l5eWlqqexv+AoEr9jcCTnU7nd4EhQRznbfHvv7b9o67xatHpdH6CUIf6OPCNS0tLq1d5SDW+QnQ6nb8F/J8EIap/AZwj6K78f4Ff7nQ6b15aWvpbV3GIVwRfk8QR2Mk4YgXBMP3HwE/XvXJeP4hCOD/6Yu91Op2/RyCO/3FpaenfvJbjqnHFUEUU517i/dlLtqtxlbG0tPR/XPLSY8CPdzqdHsFD/vcIzaxr1KhxZfBm4Ecuee1w/AE4TlBzZGlp6XSn07kb+NuEhvJ/kZBZdhb4D8DPLi0tPbn9Q67xatDpdP46oRXVYwTS+FURofpaQKfTeT/ws8CvLy0t/c2Jtx7sdDofJpTk/C+dTudfLi0tPX81xnil8DVJHOPEKWKK6gGCofP3gXd3Op0P1R6eGjVeEzwF3EtIR/7i5BuxvvV6gprcG3qS/RrBvyQQx/de7YHUqPHVhKhg/PcuY/uLBCL5U6+0bY3XD2J93D8kKN9+89LS0vLVHVGNy8TLCVMNOp3OFwhc4y28wW2ar0niWCGKOZwA/mmn0zkP/AqBQP7VqzqwGjW+NvBJQnucbyM8e5N4L9AmiK7kr/XAalw2Ks/41FUdRY0aNWq8wRB7F/99ggP1W+rgxRsSLylMdcnrb3hhqq9p4ngJfif+fv/VHESNrwyX64Wt8brERwipHT/Q6XT+edXLMTax/v/EbX7uag2uxmXhHfH3G9qTWqNGjRqvJTqdzo8QSKMl1I//5It0CDi2tLT0H17jodW4PHyGWO/f6XR+fmlp6XT1RqfT+QChzjgjtAV8Q6MmjmMciL/rRqs1arxKXI6Qw9LS0man0/krBAL5h51O51cJfca+k9Cq4yPAr702I6/xSuh0Om8Czl7qDe90OtcShAAAfuk1H1iNy8ZlCq7UqFFj+3B9/K2Av/4S2/wRoUa1xusXHyH0afwm4IlOp/PrBHGcWwlprAL4O0tLSytXb4hXBl9TxLHT6bwdeHRpaWlwyevTBBUrgN9+zQdWo8ZXD97MVyjkALC0tPTfYxuH/xX4HqAJPAv8TeCf1WJVryt8H/B3Op3Op4CjBFXVGwgiHE3gYwSRsRqvf7yZy3hOa9SosT2os6e+OrC0tOQ6nc4HgZ8AfoBQz9gmOMM/RrBnfu8qDvGKQXj/xrbLXsRz+q2EdKmqn9HIcxobcr6f4L05AQyAa4APEHrFfQ741qWlpd5rMfYaNWrUeKMgEvwfJxT37yXUM64TxBx+EfjFmujXqFGjRo0aX734aog4vpmv3HP6r4E+8FYCgWwDa4SC5P8M/LulpaU6VbVGjRo1LsHS0tIfEZxuNWrUqFGjRo2vQbzhI441atSoUaNGjRo1atSoUWN7Ia/2AGrUqFGjRo0aNWrUqFGjxusbNXGsUaNGjRo1atSoUaNGjRovi5o41qhRo0aNGjVq1KhRo0aNl0VNHGvUqFGjRo0aNWrUqFGjxsuiJo41atSoUaNGjRo1atSoUeNlURPHGjVq1KhRo0aNGjVq1KjxsqiJY40aNWrUqFGjRo0aNWrUeFnUxLFGjRo1atSoUaNGjRo1arwsauJYo0aNGjVq1KhRo0aNGjVeFjVxrFGjRo0aNWrUqFGjRo0aL4uaONaoUaNGjRo1atSoUaNGjZeFvtoDqPHaotPp+Ks9hq91LC0tiT/tPurrePVRX8evDlyJ61ijRo0aNWp8LaCOONaoUaNGjRo1atSoUaNGjZdFHXH8GsXS0s/y+Gc+w3/7zx/judOnWOuf58h738VP/a//iJaTtIXHCYF6/jif/shv8Stf+AJn1y+wvHqR3BqkACHBeYsXAqUkWklk4lEzU7zlHd/A3+z8DQ7uWMRai1IKAO89QgjA4z0IIfDeIYzk9LPH+INPfJzP3f95VtbPUdghG/2MfJgjrWHgLOm+vXzd+97Oh7/jg7z5tneSekVLeIrMozUoLfHeAwIhHB5JsQr9lWdp7OyTz+8GdjBLghbgEQg8eEAI4qDwBry2ZLnii7+1zn/56D/nqaN/xNmVi+R5CcLivaGhBMYZ7M4dfPAv/EX+5l/+UQ41m1hjULp6vMJ4Op3OFb+Ov/prv0E4Co8Q4WiAeI4FUgiUEGilSBKN1glCJ3gpKYHcWvKypCwKrDEIaxHeIfEoIZACpBAI7wl79EgpkFoilMJLifES4yTOSbxT4MM1dc7gXYn3JXgbPivC2MIPCCmQSiF1gtIpUiaAxDkw1mKswdoSa0u8s+Bd2A+E8YgwLgloKcNx6gSdpogkxSpF4T2ZseRFiSlKXGnAWqQLY9pybD4ct5ICrSUyUaAUVkiMA+Pg+7/7Q1f8Oi4tLeGcB+8RUsZbMV5PUT0rhFF6H/8Ot2t1vcfP1pWHj+fICYE4eYKP/uIv8C9+8zc4XziETLHOY70F5/HeY8oSrRWb3R77r7+OH/+J/4k/+93fybTWlzwbVw/b8TzWqFGjRo0aX824+qt3jauEKVrTszSams1hnwP7D/AN193M3KnTJAf3YwqHSBNY32Dz/Fm6bsi6t/TKklQrhJR4b7HOgwRnLd5bEiWxRU6vu8Hm5ip+cT4YwVSG/oSBO2H8igSWp1Ie3tjgZK9Po9Gi6GZoL7CAQJBbC60peskOTq0rDpWGHRqaXtJoSLyAigo6PAKJ3Sg5f+IiO2c0rdYiDdFAeImM5HVEGMUEqTVgtCHJS/QnTvOJj3+Kzz7/JP2VNdKypJloLBLrInERnkR5pCgAc8l5ro58u1Dt28f/RiocDX0PIGU8Rhl+4uveByMfAVIpRGQhwgnwNrx/yRGIeI4DYwkkMpEJGo1zCmsExnicNfFTIpIf4mfG58IDeAFeIBEoIZFS4r3ECz9BMAVCSMCOiBJUTCoS0EAB4w847xDOxnsibKOkwCuJdLI6A+DsJedP4ADhPdZ7vHPhnpACIRRKbs+1NMagtSYQQ4f3YvSs4MWISIKPr09ej4l7d8wwtxEChAgEd3QdwrMUbg+PFIHaS6Vw1jEcDuL4qzNdo8brD3Xq+NVHnTpeo8brG18xcawn1KuPKzmhrpw6yX2f+yInly/QLYfsa1/HNTccwZDieorGtMIN4LHHjnN68wQN1mm4jIWpFiAw3lOULtipkYBYQHsB1pJ11+mtL4O4Dil0JChiFGUEIBITKSXnzjzPE/f9Cba7xr4duzB5n2y9hxSBiHkhcM6ye34f+3d+HWV5M3nZQCQ5giQSEI8XItKVAJEIphfbtHcuQjNBQYzKjaM4k1EbLBQ6w5ompz/R5eO/+K955tyD7G7MsJbOkzRnKOyAXtZHCIn3JUp7koaj31vBuwyYwr+G5vELb4pA0PzEv0akUYhwLj24EW+OJCBGhcGDHZ8jH8lBdV5F3EaIcO1kohGigfOaUgDeUDqPwAZCPhrEeGxseS1ckyo66iNhqqKdTgqEC6+HMXm8IEYd44iCVyKSRo9wHuEcTopRVFkKgZISr2S4V5wbfdfkGD3gAJzDu3hfCImQIIViO+C0xjqPtw6l1TjiOEnK4nNzNSKOk/AyXBMEaCkiBa+ePREeRQlKKYQUlN6RGxvOaY0aNWrUqFHjDYs64vg1ivv+80f47U/+HqeGG6xbw427dtF60+2UM4uce/wsTZFz4subPPjIKYbNWQ7vMMy4Aettw4X1DXpFPiIdEiIXcOA9yjlkPiCxAwZrF2nOLKJ0I0RvpNxi+AYL2ZG0Gtw5O8vufQd5XC/z+PNP0yCkUtpo4LfThN0CZvoZG2cvcvZ0yXU3zSKKEp+0QMSomQhRLI9HtTWLB+fxVTVvCOCMA1aMjXNnLVZpUpdw4qOn+X/+6324Azv58L0f4Oh9j3BMtTiRbbK8niG8xxEITJJq5uZmmZ5qx0jm9sYYJyGq/1zCU0Nwb5SIOyJWLjI5JwI5cuEMxIikGEXxwj6q6ySoIrmB4BHSVaUIxFFG4ukUXoFT4KwLqat+kjn68WD9OOKMB+FBekb3kpcC5wXCizBYsXUP49tHRGJX0dlAHKkijha8lKPrLStiGneyhc9SfY8fk0cfU1md3/rFVxirGZx74ix2MOTInTcwNRMGNYo8hqs0MVgbfyte61J1oQRCWyBDSoXzGoFHxQG6GJGWwgE5Qhbohh6d6zqcUOP1ju/+9j/HzFyL2YVpZuZnaU/P0Gg20VrHrIPwRFog9zB0nsx4nIMGgikpaEtoyGpKrZ7gCedOnKPDdPjCdcN4KATk3lHiMSgGBk6vex55/gRf/PITPPf4M2THzjC7vsYBMeTArGPfwRkO3HwN1991O4dvvYv5HQeBsO4665BSIrYpc6I6rAovsjS9JH6iTh2vUeMNgcsmjlPlkAsnv8yFk6dYWc7YyD1dJ8jQlF4hkhbN2VmmZ+doN1roWB+llULrBCnAOYv1IZnQC0koXfJICVIRJ9nxhCNFTKGL6WxaCYQ32DIHY0hFikqmsKpJjqe0NnwnILE4bxHekqqElfUhy6Xg3d/zbfz43/pRbp+bxljHTFky02xsOdY8K2k0EwCOH/0Dfvlf/WM++7FP0F32DO0861bTt5bCGYRQIYLjPUonNJIErTVSSpTWJIlGSDBlgbMOpRKkSBFCIZVAYLC2AO/RUgdvPQKB5W3vf9+f4hK/OB6+/0+4sHkem+TIVLH3wB7aM3OcWX2G3vARTj2jOX56NwfveQvz6hDD4yc52z7KIyeOsUIPbwWgQDi8MyAcyLAwJUKjZcmZM09ihzk3HPk6FnY1In8IC6dzIYMyLJqSubkD7LhzntnuH/LF554l72+Cyyjyfrh+StNKm1w8c5SjU5/l9vd8AzMz0yi7gR1IaDSRrWBOi7hvH8mjFx7p40JZrWQivF/VjAFIpSl9xunf+jS//5sP077xEH/mL/wYb/aCT5z7Z3Sff44TAxsiWgK8N+g03Nft9iy33Hw7STIdKJZ8bYz5yvZ4uffD75iCGV+zo4hjIJPOe7zzYB3CBQeAGO2gqhmtonYCKRVSqpgeCd5ZvFcIIVFK4bTCulCr6CfTGGE8isjgRSR6ODeqN1RCYGMdJMSx+vGnAw++JAoaSa8nRFSJx+RGEcX4PT7szMff489Ww6simHEe8g7hXUhr3aaI3q///H2snDnBwuIsaWsPt795GiEFzvpA1EapqmEIzmZ4b1B6GlBjkn+FM6Mv3Z0AjHc0pzULCylrZ/oomZAmKc5ZnHM4FAoo84y5tmShLcjXl9Fx/M57tiduW6PGlcHa2jKOGXQiaLaa0LIxld+H+TI65AxQOihtqH/2TmAioXRiwlUW16IqOQIYT8aviEs3qmY8iUCGNH4xkWHiHb6a517w2dc2E+ZqpanVGXJXH3XK8VcvXkXEccJUezFLRTCqM/I4vA9pYdY5hLWgZEx7E6NIgyfakATjKBihgVgKIXAxTSwEFnwVPEEKj3OWrBhi+5bSDygI9U1SCrSUSAnWlQg8U01wLhihzvlRZAFefILbaktahHI4Fbx/JQ6Dx8bICCIYRF4QhWMEOo5BSYEWlUkLTopQAxQnexFCd4TYnYtfLKPhvj3P3hNrq2xsrrOwq80Ne/dwy/w8uwCt+izsb1HMX8/+I7dyy10gSsPzv/cQdDd5Vp8llQmJLCiiYW6sRchQQ2acp6kTesWA3/mjj3PDjW9jft9bmJsBrwpk0sRZi1QC7wApEaZE9xIuDhUfO3+R+48fpyhyBqYI0SMBWkmGzjIoM9JijVuahh17d5GyCmurMDPHslTIhmUhpsZWAjGVYMz4XFeRnMoQD/Elax2/+9GP8u8/8tvc9qZv4Cd+9MMc3Jmy8XgXvWMPg+efo3QO5z3GORweqSRCKjILemoOKzQuXr3wVWLC27wd8NH3XTlXwn+rutLquMNPNHi8GEXUqrpA58BaB8YinIsiMUw82mPnjRQSFYkjQmCdw3qLx4BPgrNEKWRQwhkTtGraiHsc7d77mBZqwVpQoaaxit4G8lYdpxhNPWJyJ1UEsYomVsfpwAkfoqvOxR8bJgL/Ik/9BGkcRSR9MMZwjpCQfeXxziM7eZh1Lp6z5AM7GZtldBKqM+cNthxibYkSDVA6HvwWdn758ITa0i1GbryjqnMKJK0GjXaD+ZkWRw4v4t0UwoWvdt7jnCM3BqUsebnB9MwUB2amacjJfU0MV4wj0JOryRXmwNuK2lC9+riShuqw36PZTChzgy89woaMCEFwTho8xgtKB4WFwnoK63HW41wlriVQeJKo5FWVUbwwJncpXiY2P/aahZUtZlsIJEGUjNF8O3KOXbqDbXqoJkf9Skf4Up+tUaPG6x+vLlV1IjUMKnOCEeGpXpk0lqtJLrzut0xuIopjVJvLSsBjQkTFxc+J+F6IFHhwFluUDLMhw8JTVMZdrLGRSmK9QyuJEsEzaJ0KhCRi66QVIhQ4j5DjqhxnJMgE1Wwh0pJiYCmRqGaDsnBkWYZUCY1mE28ceTZAao1MEqSTeKNCjZT3SKHG1LoSwogpLB4ZxxNz67ZpRn1+dQOhF1mYu5733P0u3nvP1yHwLLb2w9w1wI5w3EWOSBvsP7KDsw8kNJM2GokU4K3FOhOUMaXAeYsUCodgozfAKMc+X5JxntNPHaMxlbD7xjdHwYwYXQayBx6jeWAvT/W6fObxp7jQz2kIT2k9Wic4ZymNpVdkJHMLpLOaU2ef4YuPzXL9LTdgH78PeRM811+g0Wozv3uOSohlC/mfiDSK+A9rHFJtAKf52G8+xD/9pYdp3vVNvOcvfQdzO8Pj0VzQrAyCU8IDNgqqhKiiwvkE55s41AsvV3SkbBtt9D4ey3jZHqWXvthg4o/3FWkMz6LzIbrlrUe6iuRVNDQ+tV6MCNylYwiOnvhMVsROXHrcYxI/rsWL+3YOZy1OWqSUI/JWpRPHAOLoKMYkavz+ONtWEuahkCob6jmD88pZh7fj6Ga1p/FcVT19fms410eqvU0Rx7u+8QZarTZffOAMiWq+/MYiRHidLdkSbn6VQxtFhKtjmyCM1aX2I0ebBAkL++a548034cU83rbxJszbxjqKMqMwOUlDcWH5FI1mk+sO7QtOFIgOsslTGUnjRI3m9pDGSTr+RqGkNa4GBApFAy1aKJ8inUL6MH85IHeezPoYafSUHnLrsKWlKB3Og0gEoqGgodBajp1d1TPFhO/9km9/4V/jf4fpvZrsJEJMZH5UhDHaMd5XBQnyxXf4IngZ2vqyEC/y91fiBNpO0vitybN8+VSXBy52aSYt9s832DudMtdQtJRgM8/JygKdztDdtKxuXOCC9CgLNy3M0VSCzBjOZpaVvmGtb1jLDGjJ/NwcU0LRkJZ0boHm1CLnl8/Tt47Zuf3cc9fdNBZ2UOoms+1phNLIRhOdNNANTaM9w2xrhobwKEqM6VIWfYr+Jlm2SoGk3d7J/r3XohpTMWMoRFuElJTWoYRGCyh9qMdPRUI3d/T6ivt/61f53Jc+wn3PPsrtt9zEt7zju9grYXX1BE8/+QjNjYsURYltNTi5scnJlQxTekpjR+tmq9lkYbrNfEuzc1pzYOc0B2Y0e1sNEq1oKEka78XMOjZL+ALXb+MVrfF6wGUTRxcjLpPqhn7CyBCiqnsKMiSeUFeklEQqGYwPV0V6xoZpFU2QMRonvADvQrqWAO8ceDkyPavIpBAOISzexpYCzoC0eGEoS0AoHJI0SSlSTVEIjE9x3kYyeimCSAcKisKQJPHVZBYvGuikSXOmiXagSmhOpcjUY31G0lBMTTVxeYnLLI1E0kpViM6IQI69EDhkNGYtEOqvXEV0IHoKY8R1myQlVNqiaxNmr30b7/jAn2Pf4X1Y20elO5ismZJKY4FmmjDTSOkOSgqlKSPhEAKSJNxGxpogPlKUuKHnxttv4U03XsvZo4/w/Jkz7JyZYnqxiWq+iROPO266V0JZ8NyJ53nm47/NydVNDqx3ae8+yMrgImtZl+Ewp8CTect0q8HcoMfUiae47vB+dvaGPPAH97HyxFM0N5cZ7DnMzPxdbDZhfjZElrcsVlv+4SgKxdmzJXn+IJtrn+aTv/sY773rp/jOH3sH1+w2UHpIYG3DsmoMhVAID4mUWKVweBIZ1ER3Ty3wriNvYkEpXGw/8moX4MvGiMyJGJnfEiqkMjeq/1XP6ziwVj1VInitYfybIHoSaJMnZLPG+kEnY+uI8Lx7JHiJtz5E9EfELHrFJ0jOlsiSD0ZORe5CZK/yK/ktR1CNaxyA8+PIlRfjI5USL1Q4Vh8jjTb84PwW0ogQ48wHXxEYt4WIBhstGGrbgc1TYPuS+flZ+t0CfBqvxwRbHk26QXUWqnzvF7U+vzL46vqM/w5EchxxFQhQosqBoKsVs0eOcO+uKc5eyBB+Bq0SytJSFCXG5hhXYIVjsbyeqfYUrX37MUqgASErxyETt+lkLWcchRdXmKdPksbtMVd/57d/E+1B776eG6+9iUNvuYG/9G0fYufTj/DzT88yKC9gis9y7NlV3vuN7+Kvdv5ftKUCuuQbhlNnzvD0yhc4uO92brnmrTzx3IOsbAq+4e1vwTkHXiCV4Nin/5iPfvw3WPj67+fzv/KLfOnxB7nhrq/n2p0pf/SJ/8ba0COVZ7C2ihc6tLXxllRJSmdAKpqNFu32FIvzcyRagTMURc61997B3/ipn+bhX/gl/t1vfIxBmvMD776VX/3YQ/zdn/s/+eA7vpsv/Mbv8a9/9T/y7MlzLK9dYDhYQykTnhVnGGYGr5oMBqt8z/d/gJvuuIdf+49/QLG6wka2gSkLkkQiU8+fvedG/u4//Gkas2/jYx+9n5//5f8fhz7wQf76X/5xlo+dYIdcY9Zp/sef+Sc8/eQjDIerlEVGYQsS6fmuP/O9V/w6thozNBvzpHoeLadRpIhYKO8IS0PhwfiQxWG9w3pPaQ2UBuEcqZM0ZEqaCDRbieOWu++l7vGJZ6Oa4at5L8xbMs5NMWWVMXn0MS1/TBy/NtEtBT3rsdaxISxNoViUigLBcFiwWRgy1eLCRkZ2fpXrd06x2EoQaLTwGFNSOE/hYKOE88MCbQwLrSZNLG2pmJ9pU2C5cPECm2XO3labPfM7KLwnEQW2dAw3C1SSQN4gSVvoIsHkJf3NPlJ5WokglR7hHRaFaiyyODPP/PweUA0KK1HKIaUGISlLg7eewpcMvCcrS5wVLJ95mtPL5/i9T3yak489xAwb/Pm3vo+73vlWNgeGp84eZ+PoYyxSMD07xZqHxzZ7XOxllMaSFy5IVRDW+2Ge41yJMAl75+bZMd1gcSZFOYuwBcgEKzVCCAZ5wVoJNF7pqnzlqDM5rj5eLJPjsomjdS4ahTCe/sYRDwEkUpEqHQwN74lqGjGCFg0DEUii9yFyFeQL1djoFZHAiWjEULUZGM++riKcWqIaksQrtAiRLONKirLEWHBeh75wJqcoPAWOwlmMiLUIMHZ/TxgzWU+Q92BuF6h0CmLKytTUFEYp/LBEthJaQjE1pRFSkSRN5HSThFlaOiFNEoQMAhbOQVkasiynyMpggFeHWJ1gEYwm52005LbnubGux/wNt9M4sI8Hj54mmVUc3rdzi0MAIRDOIxUUvT6b58+Smz49V2CitIpWOhJd0CpFSYU3JWW/x4UTz/DRjwwwvQWuW1jk7XfP8eR9z3Dx/GkYNJjrLbL7lp0c2nOQA+95N+7J53jsI7/LZ2xGv9hAeclMe5a1LGfn3t184z138tb5eQ7s2IXes5cv3/8Q9x0/QWmH2LPH2HvNJh/88G2BNDqDlAnWhpYR49NbqU8qiqLg1JnnOXb8Sc48+2W+9b3fyju+9c3M7QRrBSpRlIVnbdhjau9e3HPPooSi3WhRmhwlQ8S72RDMypwnP/c5Dn/rt5Ak6ah3ZRVF2a7Zb+x0CddsIqbHhNkfqmFEdMoQWpeETiqOyh0jkTghkF7GSNbYyK6eQEtw4jhjRyqlMhEoleBJcFbijMU5G509W4lsSD9nbDwJRp5U512ofbYWvB+L08Sj2KIYWk0LTAgteR9EdkSVCh5C2jaSFOdCZLOKL251KoRa7EAeo1NE+hhliA4xIbetdnX2OsgGgtMnC0yZAdPVYTL+o5qjDFT9MU0JusmIZH6FRGt8X06Se0Z/j5weE69BqOnaFNNcsLvpSoHeoSgLSWZLSp3jGh6dJCRKkrTazM7MMDs1w8z0HIVUpBCdDBN4QWS6OtbourjiBHL7sG/Ok6qdlIuLzNx4I3e89b1cv/dazj73NDfdeQF16BayRyWnH/u33PfcOb51/QyLosVKu4voO1afeoi17BTXtN+Pc+s88eQjTN/7NgC8z5HSA20Wbngrpf4E5x/+CEVrk+uO3MNtb76TBz/162z2esxcc5D5BUFyLmV5JWdt2GNYgnEhah4yRcL9bLyjoRMoBEJryt4Kv/tLv8K5x47S655D71vgsfMCv3MHx9eaDIAb3vEW9n7uj3jq2FGyLCMrDFI6ptoNLCqUpDjHzNQM933+IWavu5v3f/Av8tlf/ZfYGUPWEzQa0HeOb/8bP0vjunfgjOXtB/fy36f3MHX6GMc+9ykGeZ/nTj7Osaeep6HnufngHRw7/QBrzqJcOXIAXWkkjRmEmgUxg/dtvFejEmknwtoolUDLmPVeKkpvg8CXgEQrdCKROjjz/AvNi60YPXATx+OjQvHEJmOzK85HQiGEinPdOFW1qnP8Ss/P5FbiRV770+KVXDXb9Xif6pWsZ2UowxAW72DTePq2JC8Nq/2cMrPIbpc9801m2g1KV2AsZMbggc3Ccaabs7FRkGQ5M1MtEuUZ5n1m5lsYnbA+zDBFxpRNcHqGgYOdsoThRaYaCyjZZLPfZ4hiqjVNOtRI1QXdIGkKMuFQooESJQpHq7lI6WdZ2cxB5KgkwVlLUVoECm/BWUNRDBkMM7qbm5w9dZ6Tzz3IE09/nt7mMnccPsKRQx/gxgMLPPb0k1xcfgSzfI5Z2YI0Ydk7znQ3uLjcpTfwFIULyt6x7AApUFimlOTA4gzX75zhxvkm7cRjSokjCWUlUrDSH7KeFUylr5AtU+OrApdNHKWUaKVRUo5Nweixl0KgEMjA0hDJWP5/7CvfSpJiQCBEIUWIbkymcow2ZfyadTYKMdiQ6ik9utVgptXCCUtp+tgsR9hg+FnrcLbEuRJnIXOCbpGzaQx9QAlic3EblDWlwQwdg7WSVCYwIyiWV2BYIm2IrkxPT0HDYZTESwGijbEh5S9RmkaSoqUOxjACmTbwSLJun15/QG4NwgkSKdBCInwk5EKENcCDw22J0lxJ7Jq1qOIiR6amuffmm1hYCFJCVaphdcaNDjfJxaPLPHLuNNZnCJPhXYnDY51FK43zAlsUKOlwFBTDFVYHiqnFe9mz+wd5/9sOce+bPLaVcCAvseuKuWsSmGkw8/YFcE3Y02bfnadpPfEo89Oz9GYWKDx803vewZ/5ge/gyA3X0gAaRnLxxHku9hy7m7NokbFarnOhzHn8kd+nwdspzM2UEg5fX92lE4uXEFhn8Rr27V9geuadvOOet3P4uiOQNHHOoITGY0mUYudUG1mUNFVKqznFsOiPiEvpSrwZ4ujSaDZYWVfs3gVKhaiyHBHV7VkaK4XYUf1fxCidiXHyuPA+xJJjlMr7EBkUvoo8hvEKUcUlx6SRSDIcsd2FtQgBWmmShFHkERe3c2Nvd0VkK4972N8EcySQWOs9WBuVX8ffVaWljz7rid1XJqORIWVXeI/0oBAQW3FIXxGQSAgnonjjcyZG+x5nQlyCbSYvjbZH6pLClBOE0VcDnTAuBchgCKEnBvUVjG8yLdXDC8i48w5kdNp5YAhmmLN2YZmNixtsDEoubmyyvHqBze46g2GGKQ1lPiBNPTqRGCOZmdvB3OIu5Nwu1GyJmhmSzDZx7RZTU7Mk80Ay8dWj1PIXc/LwCs/Q5NP9SidBXPL7yuJH/ocP8W//3cfJzz7AUc5x0+4pfnX5OO1WiyQveOzz97PWFVx/95s58fzjLP2Dv8Vb7vhm8sEKB3YtMp+2uOPWr2f/4gxF3uX2hTn2TC8AIKVFUGBpM7cTbtu1m2cvHmWXn2I1aXPyzBOsbTwNwqKTVX7kh76P5mPr/PLHH8ItO8pul9yUWO9otBLSRKOVREtAChIFG2mTb/m27+KajWl+JXsKkyre+t4Pkp7JuWF3zvSh61gHWitr9NY3cdmQhnTkhEyDorQYY4gF7OBhY5AwtfdDlOs9pmfmMKbElQ7PgMW5WTbO98FapFZ87PgzPHH+HD/4XR8k0w1wBTfc+r089+V/ww9/7/fz8c/fz5OnH6PRLLCuQJTltlxHJxsUNmVYaAqjsF6Oon7Bqe1JCE4vWwTyqI1DekjTlKkkYbohaaYglQi9jonz7KXO4peAn/ip4obWh5/qWZEx4hhnwLHjK2ZuCPfieVXjb9h+j8zk1DV5TGLiZ7scqxf7fTJTYm1JQylSobg4cGSlZ3V1mdzmLKbT3DHXZKot6fc2KJ2j8FB6TeYUa8OMi90+DTQHFmfJvY3drQT9ckhvw3KxO2BGKK7fPctZ26UlW7Sbu8gH59k50yTHYswqVs6wue7AliRJikgSdCMlbSRIMUDLYBMOhsuY9VWEjFkzUlIaQ2ltKCVxln5/QK+7SXdjldXzpzh17FlEOeCahQWOvPVudrZm8bnhvvt+l+dWjyJcybWNBKUEy5tDnu8NuLC2STYw5IXDu0qYDgQWrRVzLcU1Cy2u39Xm+p1T7GmE+63rSwrvcVLTyzL61mAVzCd6Wy7m7//ORzE+qLkHB7AP9zeedqtJszlDYUCl4F2B8JLcOEobBNusNVS2iPCOJNGk6QzOahCWouiFYIMCmSTgJXme450JLcKsRSlJo5EwNd2mKAVFDtaUCFkgpAxaJ/G5dNGhnUhBqiXWGqyF0kjyskRJj5ZB06TdatJuL+BFA+MGFMUmWrVwtCgpkaJPUynarUWmp2aYak9jHJy/eA5XFhR5j6nZlEZzgQPXvY3rbzvM4Rtg7fTzfOFPnuJCt+TY6VMwGOAHGSHpyiOUpChLrAnnRgofswcFSii+5QPf+ZLX47KJo04VaTNFJ2riwY+GVvQcu7LAFjmJUgidjIhjmDDD39Y7sFH2PorKVCl3UoYwRDC8JUJUkYJgjFpvsabEe4eLjcp1qkmTlMwU5JmhPywwpUGicN7hhCM0NRc4YxkaSwYUQCog+NILimyFM+fOs3ZhiC5mWGjOcfb8Oiefuh+zmSFkkzwzyCY0Gy0y78lKE4iT81jnKbxhKILARV4WlEBjdhaVpPT6PfrdTYSDhmogJVF5dnz8+CCXHYSFtifN5PDO2/mmA7dxz913sPvwHFDZqFsXEm0E5kKX586d4aTN6GddtClIVUKJpLAWWxQ4K3C2QHhPoixaJuTNOXa86c18/Xvu5p17BWkKNGEOtqxZHih7hnRKkF57DQvnTjDfXWVtbgeHbryVv/yjf56bDl8LxZCuM9CeoX0g4e5rdtBmiP7s5/nMM+s8eHGdJ58+zpc/e4ykeS967zXc+74F3nTtAklZoGemkcMBQgtcYwbdVOy7ZjeNi5rhsMvRC+vs2dmg3QgWrTMepWF+d8oe5dnRSLkoBc6GpvDGWpxwyIagsIZ+0We1t0aJZsdci+m0sa2kcSsiQa6U9MREpK66uNEDXdUvOh+cPEHVNET9K/NjTLfHqCTjK+OEWJforSVRFik1NkYOPRYf9znhK38RO8XH/zuci0QPAyJEP8d1OhWBrD42rmscnQFPOD7vkIyP08QogBDjVhFbhHHExJkaKTgTiOgEuaycW9sFIRQi0ZTmRb7Db/2HEMR50r1iNw7vJ87TpChU3K+zFjQh5iwl5JbTx49hNjPy7oCzx55n5ehxeufXWF/pMej3sEWfLB+SFyUSgSszlCpQWmKdZNiYoj89z9rMTlqz8zSnEtLpNot79jC//xqmDuymvXeatN0iaU8zM9MgxWGcizXu0YEhto7/xZ+nMWm8tMfl1k0n/7E91/GLxwr2HXkH+2SfJ0+d5qO/+q8oPcwt7mMhz8nbYJTi0O6D0Mt49pGn2Bwe5Ns+8F5mcTz/9EUOv+kG0gNzqF7C4vGSxdhf1fs2Qk4HJ2ejwXLW5f4HnseIgmeXz3Lx2Bw6g0GjyZ13vYt33/kN9DafZN++ixy7eCGeD4c3BmvkaP20LmRHYARJ0kRPvZ8vPfIH/PGjn2P+tvfzQz/+D3j6V/4Vf/yZx9l8bI1PPPr7rD74y5w9foysbGPsOolMKJ2kKKPysgolIq4saU41ONRc5fAtu1m99QgPPXY/SbNksLnJwf2L3DBrYLPLZx4/zi//2i8zdBdpLTb40De+k/s//wCnLz5KNp2yd9dp3r0/45HZg5zfeCY4j7bpecydh9KSZJapzDJdCpo++NwkYU4qDJSFx2YOmxdgSlKpaCVhDWk0IVy64ASrxHUqR1AweS5Vdd56bzuCGI8VoT2HcRBLtEfO+kmneuUc8y5kbnhbCY7xovt/uVfHc+3LbHgZ700SYO9jFbp4uVH96bGgEqanBSubfRbTBrLX45gX2M2M/abk0DV7aKgErRw69eykgbeeTSs4Ncx5fnOToXVc02qye2E31hQMsgEWR2EMyJKVjS5lXjC1/wA+lbSygoVmC92cYcfuOdJWwrn1i7hU4de6ZAPJIOtRakWSpug0pdVskaYaLRWJUkhJEIUEnA12ZWktDgelYdhdp9fdZHN9g2F/nby7yt7ZhNmFa7h25w7m0pTB5iqfffgJTm2e480tmE+nOG9hdbNLtyhZ72f0+waTl6NMvhLPTiU4vLiAm2qwo6W4bXGK/ft3MD+V0ncGBBihUc5xYXPAaWvYKwUHkoREa9gGX04jbeKKIpS8yXBLWyzWFJTGossiZLzhUIlA+ARrQvp45Va31kX7OggbCmXCqu8NUqrg7MciRCDpzpSjAJkUMsyfUZhDihDxxRqUCAKJiUpx3lHkWXDhOIcVkOWEoBgaKSSJkjhnMdbSSDVKaoo8Q2of11SD0gVpqlDeUJQFYqqNzbtsdldpNaZxXjLMhxRFQaIl3V5GWXY59uwjOL8GxTyD4Qbr2ZCL58+TOEeWFTRkQpJoDBD8/ALnHSJmY5WlRWn9ihk+lx9xVEHhdBQ+mIgQVgYrQkyaiuOJbXQRg+E2arxNiGRS9Uca5YR4KlM22AVh6hHCh0wYQCgRa69Kyiyn2++zublJNsxizyKDVII0TRAYrAFKSaIV082UGYDS4VIwYpOVC4/w7EOPcPRED8UcMyJhuHyOc2efZ2WtR+lTelkf5TNEUzLIDd1BgfOB6Fkf8umttThvMM4gGhqfpihjGQx6DAY9tNDoRoJXYeIPvaFCdLbyIG5fgiMs7Pwx3v09dzJz7zSFh8Q5hArCPN6Bl46i75HdjHNnj3E2v4haSGGzIB8OyJxmYAuEtCgZ6v1C+xFQQtNoTFGUC7z5nju55WaPGQzRIsU5wHikFmBDPawA1LSE6WnU2hxOOqzUHL75dr7lB76Pw9dey9kvPcHK8qOQNEinb+XAgWuZ3a3wdDm/fIait5sbrr+bs8vL7J1NWNx5gKeXFU8+cR7dPc28chx8062IjfPk2TrLrTkyM83C9A6OHV3mmaNHueveO9mTaJz3Md0xRsMSz+xcAyljm4ro8Q0kR9BstlgfDPjDz3+S0me84953ovXMKOK4/djqs62es8rwFnGy9M6GumCvRlHF4Iyxo4g73iLGT93Wr5gIGY4MFGvBGLyM9a0WnDdhnz6oI49zX14aIzLqA4FEVuIOMXrpJgno1udi5MAazRsuHkesLZbjTPktRhaXEAsRtYxjTWMQ5wnWXsh2rZqabA90IwXV2EIcX3oGeOF5eNGtqpTUUUSPYCRQmbMCqRV4yC7mlN0hq2dO8cDnPsnGykk2Vi9y7tgJRLeLzkuKwRBfGiRx8ZUNrBWkCoQosKZACElBaJGTpdOs6gQrLUZBc36Bqd17UAu7mDtwHdceuY3ZPQfYf3Av+/bMo5MJFuyhSm328YXRsVTOtor9jkhj5UBhRDq3nq/KUt2e53Jx53v5yZ/8EX77H/0kIl/j0dMG1+tinGX+pnvZtDmue5YzzzzDWq8LSjOz/FmeemI/XziesTE4xoE33chxuw+OfRF1WrPjG3ejrEEqHVPALQxWWO9mbGYbbBYZiRuy95pr6Z4oOdSe49ZDd/GpT65SnFUcvbjKMNuMz7wk1ZrSWErraPiw9ipC6Yce9viFn/8ZNi48x2q+wlsOX8eOKfjsw5/H25LuU3/M8YvHGZ4fMjuzn3uvLVge7uLc+lnOra5Q+OA8dM6iEgGlRcy1+YZvvptrW4t87ON7Qh2st/SGA3Zdcysn8jch7v8Uq8+2YRWaDcenfuM/o8o13nX31/HYg0+CKJjecQv3vvUge/7wDzm/4tBCYrep5riINVx5mZHlCUUhcV6F6CyOYVayvJEx6Gb4PEdbQyoV7WYLoTS+kQQdg7i/LZlWE5iki5e+PxltNH4cbbSjubVypkW7KjqeK9+hdx5nLBgLyo9qtrd8wYSTpRqImPj7SmIUNZ34ejyj/q/bAtXACwsqoZt5Nnqr2IZmvtXihoX9KA3elaAVTkpKBAOfcHbdc3b5FM1pR6Mxy+7WDFMNyWBITMMWtHXKmc0hG4NNDs7O0HB9er2CNJ1BCo9VlpnZFsPuBs2iJOtKzjx3ln4hyO0qQgu0bpKmKc00JU0SdJqghKDZ0jghsKVF+BBlc67ElJZ8MGTYXyEvhhhTkgjYvXcX040W2hYMNtbYLAYce/Qh+v0ue3degyJl6IecXV1nLSsZlJYsKyjyEoUgd5Yp7bh9bpq9uxbZt/cALlG0xYBdTY0oSzbWDD0hMKLEihSz3qNXZuj5WYQLgpT94XCbusMrpFDgHVol4BXO5AghKI0BBqSpAXwgl0kbn0eHtDfBMSYcriwRUuOsoygypChGzgvnXMzkEQipwnPsPdZDojVaxjp875iansWYAcMyp7CeVrNJq91mOBxgrAktAwUgJEpKCuMorEHpEGCzzsWIo6A76AOC9tQUSZpinSLLe3iXoZXGGonwikSXFPkmWeaABt4U4A3Wp7hSItyAsjzB88+u0u/vYceuXczNLtCb6bOx1mfoNLrZwktFmiisK4KT3xlMYfEiRBudfWUb5/LFcfDYKjImJmX9GRNBrZBJgot1Q5pK7GDs6RejVb36d2zTESMMwkVjwAVBjrBN8PYpKUBorC3xKBBQFDnDfo9hr48rShIZPZ7eopWg2UhItEQJh1SSBoLEWGaAlpY0pSDvX+D8U1/k+Jcf4JlzOZu9lGIzZ7i5SVFmoATd3DMoHcJn+NLRHRQMMgMiCcqUsd4vpNFapPI0mylNCN4JWyJsyJ332gZvRiQgQqkQ1RkZ57Bdvrg7v/Ud6Lc1sOQkFoSSkRSF2i5QtCSsFSVPnzvPw6fP8+zaJoNhjhCSNGnQlOHCaJ1gJIAnSQRaBGGf1Gl2i4Kmk4hGC6minmIaL7tmZAT6soQkJU88K+WAZM9e7nr3e7jhluu5cCpn7bmjnD32AJkVLO51mM2cfVIyv5jBzCy3f9vX89Yjhzh/fsiBvU3ac4LDT4NQy4j8Ao3+OmrYY7jZ48lHH+FYfwCtg8wtHOLM+fM8e/Y8++66nWulIHE2TlLh3DcagnRmBoPD2hxvQyqWFIJEK7z1nNvskZiSdH6afTvnaEoVhF4qT9V2LY6T1kZVoycI0UMhK5MiRN2dxVqDEwIvHcZ7rDE4G4he5ZQZax+PISb+G/6Mjg5ftfEocbGbjLUW58oReRynq473MfK0V973yP4qFeUqElmRRu/cxGcqEhcOdjJd1zuHExNEOIwylFmL0DfWi+qzY5GvSYf/uCZzUgSs2mZ7LqQnPEdKJ5SFfaEVucWyDIuXc+YVo43jdM/xea8sQ+nCa6bM2Tja5dzjxzjx7OOcevoplk89TnftBN2NZUxW0FLjPrUiphEXSuETaE1N4bC40oQaVVOQ6AQnFN70sIXDeoMTjo31C3TPHsfpJivz+3EnzzG7Zz8b+6+juP129ly7B9GUyLZCpyLeA6EevrrGlYLk+FKEkzOZ2jpJGsfR1om7eptq44qVx/jIf/kEN9771ykf+9s88sQJ0qTBZnmeB5/+BK25gyzOzbB7cZ7s+E4OHvow0/qP+OJ/+Tk2ymla+yT//t90ueNb38ShmTXyCw9xm/12rBWUvQFzc00Ke4akbHDz7r3cPzPNuZPnkXv3cu8Nd/HHz32GQkqeeuhpzjUfZm35NIUdMDOzB9ddxtmUrBiSppL5RiP0VvaA83gUicsZnn+K3vIF9s/O0Rgc59P/6d+yfvE4gyG8bfdhvu+7v5+0GFCgObhvgV/7pU/yC7/+c0i1ii88zhcIKWikLQbdVa654xbK+UVOPnOa7toZNrorOGloT7W577N/QjrzW/zM3/lxPvvH/4xzU8/zPd/+Q9x26D1kfsDp4xlTpmRPo8t613BmLaVnQ2RDFmocsrrCKJ1BuRzjMvKyQVYklEYhHQytY3VtwNnz6/Q3u6gypyUcLZXg2tMIF9NIVQMhBYnc4tZjNAcCVX2v3/rWFlTbjdxmVSqqtwgsUvh4GnycF8KPtxZvQlQE56JY2EtEF8X4u/zE8xLevHQwkx+85L3JeWpsyowet5HFN/l9fvsijquFZZBbNoqCvMzZvWOKG6fb7GhPYcuC1WGBThQLToHVXFhd5blhycXzm9yya4odcy3ywlFQIozCYXBNhbCKYmWV5d4mzYU5Ds20QQnWSoPXlrw4T+YPIpmje3KdtNtn5dmngh2sBWfPHmMaS7PRotApotlGtdvodhuRpuSFw1iLKYNjXiOQ3tB0oEuLFpZ2M6G0ntKUFEWPfneDnXmfM8ryyLGjHHIF7z1yPd2u5YnNHucHPfJhQV4a8rykLA0qztHJdMLdN7+J71Al4sAcp5qztJEU3SEmldhiiHAKkSRsFBmrmxfYIST7dsyTNASDrGAtFwj0thBHYy3g8cbgnUf5kBVmoo1QGAs+RyMxicZJQUmI0IYMKKjuMgnBLrPBoeKRGFPGZ2j8PLpqzbQOGXuU+yIQUeM1uSkxEJTo84Kp6SmSNAnfJzRSp6RJirQWkSqstRhTImwosbMuGMEyScjKEtvvMT01j5SBBxV5gdMOKVNMNmRYlAgPzuaAI00SGq0ZBkVJmZfYrGB+xzTWWY6fOM3KyjrtdgONx+Q5SZog0oRWs4m1hmIYHPta61Ci52yUlPEhsv0yeBXEUYAUsbC+mgCj/DM+pKoJgZUyEKJqZvCVyuKkAIUbTTYhDWwybz9Gd6L338aghRQSKVxI1XBBxRHvkV6RypTpBrR0qDU0PtQJKg2tRkojbTM7LTCuwXClyzMPH+XIe+7kZilhYxP9pac4/8CTnD5+jrVBwuqmYH21R5YXWB+M3zI3ZDYoZzo/pCgcWBGPPnotIlFWiLAgFxY3LMMNUdjRwYRmDhM9+HwV3Ygm9cibfuVx7/s2abGLYihQLcI4BcHrNoRio+DihUd57OEneeThZVYHU+TqANYVHDrYom9z1rMeg0GXoixibVy4Q7w3WFOQigGf/t3f5La9Bzi05yCmLNFRprZanCqD1okUCnD9LqZl2b14kBtvvB1nwKw/jdo8w/IzpykstNwxSDSL192C2H2QPffOwbV7QMHsfILHY2zJLTdLYGf4WV8NdTxTO2hO30A6WEPQYOPCKU6feIKNwSqf/bxg9/x38Ob9O0NOvBQoYGNNsZqn9Io+vf4apQnN171wSDSDQYabm+GOt72Hd73rgzSRWFOidBLv4e2MHY/hYSTwEm69UNPoRXj2rHN4TJgYhMR4j3GhpcqING4xGiZG7ccWQeXs8BXpIygcO8+oaN+7MRmtSGAVuXsB4jMvpYqZB8HAqdSXQ0qdH80jW6OGsU0IwakVAoUhBU94OzJKKvLopQBUjEqGRQVCS6At0aqQxxCjjsGpI4VEqm1xp+KBJElQWlEOcvzohE1sUMEZvLGhrUhpxrWCl1iFL5bWWenOusIhMk/RXeVL932e419+nN7p05x99jnWTp2iZTOUGzLrypCm41zsgetRQlE4jxGSufmdHNx/HRfPneb86WUaMtSXWmOwPqPZEKRSYstwPgUCNxgiZYkYHuPYynmmdu7mzO49HHvqC+y75Vb2HL6R2f3XcuDQAq2WHtWyVoZoOFQ/cTwvdjare6/6rUDoUZR9u0oAnj/W44bpJ3lm5gGe789w193fy5mzn+eJU8/hraDo9xlstPD79zCzax49f4IkOcD87B6mheT6gzdz4uwKjz/wj/myOU7bNrnh4d9Hip0cPnAtm2ceZfeuW2Bhih3752l4R+EkH/xzf5u39yz3936D03M3kpbzdM8eZfXCRXZMzSFmE4qyhfWauR3z7J1fQJSGDVMyHPRJlaCVTtNwgtIO2bl4kMM37uaOe97L/mQnczajPzfHez/4Hg5N7+bzD57hbffso7EoyN1FBr1NnAiRSy81xDkH1WL/7pvZB/zW5+/jgS98msKXSOtItMT2LrL83Ef5xY81+LnP/BLf/r4P82P/80+xKwUx9Hz6Y09x9Pwa505eoPzDX6c9ez1zU/OclhpkgqfYlutITLe3GEpvGFpLL4esD6uDkvPnN7lwboViOKAlLF56DIJyWGKtQCpFoyVpttJK7zTs1lO1LKV6YEdW0YThSvgTASgh0HicAK1ACUVIsQvzu5IyRFls1XYo6D+EfrxuxNQEjLJQwv5f+OwIJuboavqpSolGW4RRTzrSRsfgt26zdc+Tf3mqvKrtDDie3xjQ7XXpFwUHF+bZOzNFK9FktqBnDUMPZlBwfjNHlxZR5EwJ2Ll/kZ3zs/SyAdYXzKgE6x2lN8wnCecGBc8PuuyZbbBvuoFqabrDDCebWGeZmiqZa3pOP32U80cvsDIYoGdn0eUq+bljHJzRTE/PkGpNuzlNu9EmSZsIrZEiilBK0O02SZqG6yvAO0FRGPK8RbfMafc3kGZANyvY2OixPNxkR9nnG5sNdt98Gxf7qxzrrbLazxkMHUXhKEuDNdGpbQ2LU5J33nMjB6d2MsWAqTlBQysK0aAQ0ywmgqSlyQsYGst8qphbnGbfwhypFHgsc802wwyGfnvWR0eJUhLrQ4mQQCCUCCVRJghNKgHGSwQKrQRKlThpwrplbbgfRy3FiDd6qFeWCpRUGGtD1pEMvao9UJYZ1gpwNszhBQyGm4GDRA5UFBl53gu1r1i8VyFzz0qc9SRpA2mGuNIivUMpgYilPipJEYTa8F53AyFFLM9TWOMRicEYSeEk0iXBWSQdXmsEmnarwUa2Rlka8twgvaA/zFhbWWFqOtzvg14XqTReJCAMxmQUxTDUo0eE+8sG3YtxbvuL4vJVVb0fGRDOjdsAVD+OqFTqwU9MCeMpZOSDolLy8z6KYMT+NOHEhdtFyrCXql1FiHD4kHLqVNyxRGlFe6pJu+UxpSUrS4yPkTQBiZBoPcXMjMY6xYWT5/jM793HtfML3HjjPuSjJzj+iYc5+dwyy4WiVyqKwmNQOJmQFya0+zDxgtrgMRBotA4N1EPi/mRamMRYyIcWKXKUEpR5uPmCqIWgkk3z3od6DTfhVd9yBq8smkUD2iCj0WkQaAG+a/n0nxScTk9hjv4Rm7/7eRZn3sw7v+FD5PkGzzz5CMXwIg8//RC2LKORHcbqosfTuxItDd4MWb94Bpf1wz3gLVCRqXAbOO+REpZPW1xWcGL5NKUv2XfgADfsmGHaOY6dfJqjX7oPs9bFy4Tzx57l+r17mW/M4sUc/vBciFB7CPHtoPDqXLiH8B7mFxFAY3aB2/dfy87jQ/pmHZH10Pl5jrRmOb26yfNffI755jQHFpukOIZrnkcfzPnUM8f50uljdPubWGcpnQNZkTHPzsYUR3buRZUWpyUy1iUxEQXZPox80ZfcMT4KzUClTuzixGCJNTPejb1LIiikRTnT8OPHojZigrSJUGTHqD1F7CPlXSCRW0nj2CESIppitA8RU4JlZfxIHVppVKOPyqyT2QqT5kiVQhqkOKpaohBhFd5RNcWGcV1g+GcU7YrPrY+OnDDU6HHzNsxJKhjEYluJo0OmGp0mGNvf8k44WYwtOqlD8ZSVjPoFjQzP6rxMRuCiqRZsBdwAuqcsG+ee5dgTD/H5T/02q2eewg82ydYHaOtwiJgJIWPJQJyNfFBFNWXBVGuK6w9eRz70DHsZSiY4myMRNBLFdKuFkpI8L4KCNGMSL7zFmwHG9Fk7s87m5gnOnHyCc8ee55a738PuwwWidzfX3qrR7ZCqJWRs1DFxbJ4Xiy5avDcI4bC2xyDrYX0DIZvBoBZ21EP2SqPV7rNx7As88en7McluNlpNBt7Tai5g+xnWWZIs58LFZRb2QrlumD10J+0dezh+6hmGJ5dpt+axJ7/AxvmzJHtu5pd+53F+/PvfwczO0zz/zCa79syDL1GNOZSDHTsX0WXGZ46dx83sYCbrkp16iDOnjzG72MD5nPmpXUw3Fzh9/iwHDiyQDYdcGGaopuSOO+7kXfe8k4PTUxx/+lm+9OXPc3owQO17Bzd93bfw4Ef+GUfPDbn+m7+ZC70BTzz6x7i9+2ksCoq+Z/V8TiJDWqSLzgHhQisD1dBkpx7liSc+xi//0j/n+ZWzNDUIpUIkGTj12Je4/48+xbV7dvN3v/PD7E6hyDPSVpNDtx/i458cYuQeZmcOMJVqbjhwkDMnjtPPBnixPRdSErUWlKSQnp61uIGFXLDazbh4scvG+ibC5mgtKEWwhyih2WhjyxKco+ooO3axE/+CS6nVpRCMutEi8WggEUFwQ8koYOarbIpqx1HgBIHyHmmDUCHeRVG0l8alAcbq30GkjFGNe1WbOLndqHYx7kD4cVllIKzj5IjKHy6q/Yx9klccq90B/UGBUlNoOY3NJQMnyb3lQndIP8txJkdi2Zlqrl1cpJk2QGq6WUZpHZ4pZClxwmJdSm8tY3PQxTXaLLRm2d1IWSkKuoUlaaUMC4HzCgroruegSm5+y40024LHH3uUqf2H2bljFtVq0lAp080WzUYDpUKbuOCIhSRpIFQS2th5S3+Qs97rgxkyLLvMNRu0k1nSZoPTFy+QqYydhw/yZgeHtOPRcsCFzQHnh5ZeFiJmxhisdQgPqXfMtxt849vfxM17miRZwfT0NNPTgpZsseIkqZM0bYmPTohUeaZTxWyrjVQ+XkeBRoMOpVnbcS2ds6AESauFdZ5sWIRnQimkC6KWpXNgLLYw6LQk1QJfiqAk7cO2TotwLEpijME5h5KxVV4V2DKGNE2RQpBlORAOScbnR6ugsKyUxAlCtBJHPhwgpEBJDc5jrcG4nGaSIE1JyzuajQZeCrJ8iHGWoizBhDIEJYOIDjassUIIvLMUhUU1mqSNFsoYFIa02aC0UOTD4Ng1BuNhs9tDK0mz3Uan0+SlocjKUI9rDDpJcNaRFzl5UQS7zQu8jWuqCC7zaDy/JC4/4mgtRVlQGIOJKUt+lGdfGQaVMqOYfGXkERcAUo628TakoomoShRKokRUpAyCODETEmNMMBSdQKCCYSM0TkQVR+GxwlC6ILSgZBKa0gtJ6TUojUKweuEcj/zJF3lg5zXsPWvIv/gUjz70PKdKx0bSZn2QM8gKisJgjMMZcFbhHFgXIpojB78njrMixNHs9gTBC2SITHqLKf3oNe9FrF/wI+XH6vz4Uah4e2bU6WaQTRZSB24lDQxWyP7oHI88MyR/+wF+6Ht+kEMLN2GP3II6cjO9PzrKnCp55NENmmm40asaRQiKsNaB9qHhsSsFU0lCU1sgD60D0uaYx5R9NjbOsLJiWVlpsbG+wbNHL7Kw4yZuvuMOpuc9PPgkZx54CDZ7NEtLv8wQaYONs0e5cPJmDh3YG89XjAyNjrBqw1ERmwnSoWDv9U282IcwluvnZjh2+hzz+gJnLl7kDz79IO9+21u4vtflyfse5slhxgMnH+PZC+dolTlaBoqiAGsMaapoCsnKseMkb30nUoQ2IOoVBAmuBEZr7lZn7wSqfICJ4JUPhNJVqa0jD/GI441fu8QgEZ7o2AlZB8ggYjPKIHBjcZ2YuTr6fPiOSETFeB+h72sVdYw9IUWgb1X7gGrcMOGx98Ro6vh7qixGH7f3VTpqTLGsDBY/OrDYO9D7iSfNjT6LCEJIIpKnbeIb4bs0SK1DinP1+pY/xqaaIEjxx54qMHoKKkNtgmC5SnRMMtzY5MKTF7nwzFGee/Q+nrz/c/TPH4NiDVtmpF6TqODcMVU7FUK0oyoZsM6SKIl2lpVTJ+kPDfmwH2pJHUFVOk3RUobaHMDio+8iHJtxFi9Cf19R5ohuQWOQU/Sf5LnNIcOz59k8u8GFtes4fNde9uxaAOPxKhD4F6tnHP8d5xvvWF1ZY7XXozEzy8rqWfrDIcM8C7U424AsO8qymWGgr2OuvcKpU/exZ2E/1+5bwHqDLTbY2LyAyQYsH3uO3XtbnNfPoWdmmJubZWXzOEY47rrnnSwfe5jjx08z355mMLOX7vHn2bWwm6YC0/f0zR7K2d0MVo/y3Bc/yd37jzC7a5GV82coG+vsedN13HbwAG+a2c0wmeWOWw7zn3/xFzm9dpqzKxfJE81b7nkfP/rnv4+Ds5r+2bMcXDjCgV0N/vj+P0a0mmysrLCZbTCwmjsPvZkbphc5uv4F9hy5BijodjNW/Q58o4naWI/1a+Ehs9ahE8nZL36Gn/pLH+fpE4amamONDcaOkFgvMaWg2Kv5np/9Rxy5+y6MNaRR1n/vYsnOtMVd93wzf/aHv4PNJ8+T0uSBR7+M3jiHEdtzHZUPitEiSSikZLUoWVs3WGvp9jbZ2OyTDQdIm6ElaC2Z0ilNrWilCanWCCexhcckIbNoMvI4RpUOP56Dq8m8ctg5Yymtx4owq2oZUhXxMX3OWpwLJQd4T6ISGjpFWkHRyyg2e2iV4tttvJAjYR0IjrQqcjiKIE7M+Z6QshdyUgj6EozjouMwABjBSBE7KHMKtB8f03jGhSpFV8Tv2a4VUjtPKjReaNaykk3jKIRDZwX7Gpr9rQbNRoskkcymkrZO6VsYljnShTpaYx0oTUuBKx3HjWMzzzkyM8tcu8WqCQr9qIRyOKTnNXkpuf6aG3lqeI59R3aSzDZ58ktfZt/sLDOHrkVrj9ZB5TUlVO9YUwahqlBtTInBZwV51qfMCwrjkGVJw3uumZujLUrWjeWc9bTaU7z/prcwPTODWO+yPtjALi/jXRRUsjaI+jmL9Q4tPUemm9x983XcsmeenW1LY6aN0hKnwvFiBeBQzlIgKbFo4Wk1UxwOWxqE0IFsSIFTITqOeYWL8irgvaAsy9ifWeCFi3GX2CLLmmCXF7GUzvdI0xTvJ/QaBCgVAltSKqyM67zz0a43COlD7WPUcbHGxDXUhaVWelTVo14o8JUehqIsQSWaZmOKLMvxrkDp0LJHS8H8bJOk2UY126x3B6ysrmBtRmmLSGA1iU6wLtouOFKdYL3HGEOa5Cjp0ELTak+jTEGRDzClCxmalQPVOZqJQLQarK55itwjZUpZFgyHOVpbSmuDWJALYkCy6sAWe1VX2V4v+Vy9iisIQqAaDZKWxRch/IoYe6FGP6NIxdiYGbeynYyTTPwnGgRVb7nK8KsmKOtcIIU21jE5D75q7m1wLpzkoiix3iOtQGvQEjJTIIShcGCzkvUTx7j/k5+j+/hJNs48wYULqyQtj7WCjYGlKAzCeGxh8MZHYqsJ6X5jg8y50H9uVIvJuKZTidDfsRqXcx4q0ujDTR7kSMK5rfpABSN+axThSkLrEKlYPyPxEnbuH7L2pU/z8JfWef97/gda9ybsTR18+4eoVBjNjfMsP9Jlvd9FyBAlqrypwgfVOOcFCA1a0c8NMk2w5GA2cU6PbqGihIHtcWH9OMeObrB373vYve8A1x++ht37ppmZT/GnLReefo7h6hp5llMWJSYf4roX6K+coHfmIv4iiJ2Mb5At5+sFLzAaQMUwtEQc3Mt0vpuZ3nO082OsHz/Nb3/5eZqrF1m/8BQPrJ7gufMXaYhYX4sLpFS4kFZiHaWQpIs7MDGUsTVF8CXGcUUw8cRd8hXhW6tpM7p240SriFG96Jip7uUY+A8KgEJseZ6rxT94nMXIQwUhIXBkEMT+cJVKavVoj+wSET8/QRqr2kZB9OyJ0GFSOYeUFqJK8QuEGyrCDON0KlGNMvY1cxWBjGTQVWnz1WmbICATs9PofIyk7W0QAdgOeEI2pRRB5fQVth0RR3/J62JMsIWIkbpIGvONnOcfeITjj36Gk088xHNfepre+WXmlEA6sDYJAkcmCgRURDt6WquorIt1EdbknDtzDJU0Q+cFb5FKIlWCdZLB0MRLodFaxvMX77Vq4ncuCAmUnoazOLPOyvPrDLrnWFh7mnTlZjY23snb3vYO5hfbOOmRDR97AI+Pc0wePd4XCDGgHORsrGQMXIpreJ4/+hRffuwJllf69IYl7St6AQNOHFtlajpntm1pL+zh3Yfuobh4kTPDi9z4lq+jlQ458dBnOPrcCg0juHjySTYHs8zsvIGD19xGc+05Tl84xsmVOZwqkMKw+sjv8HmfcWxmL239DOu3rXN49608/shDnDt1gtI5Zm86wu07b+G2PW1+8Tf/K7qdMHNgD9cevps7b7iNL59+ivd96Js5+diX+K0/OIcRKYsH9vKhD36QPbbLmd/972QrPdp7D3DLDe9kWDq+vPY0938mQ+b76Ymcpu6zcfR5lk8+yhOrXY58zwe4Thvuub7NfZ9VCCGDcRoFJrxzOKvY7KYURtJsGEzuSXUTJQW5MTR0ynJvnb/yIx/mZz7w/RSFJU1Db+DCeZrzs+zadQCbKNAwe/sebtu8i0OfeZjVzQux5+mVR9D403iZkqNCxpHJKLIh+aDHMBtQlgW+GCC8odkK4hiLc7Mszs8x1WoFY7KINo0ORuulwTXPeN4aTd/RMyZFiDYKL0LrVhF6VCcStJIoEQRDgtOtivsJpExQIsUbSd4zDNs5zSlDMkVwuggHMftgZKS9xPpU7dWO9s4LjgHGEUczXm1Gtt3knsWl/94ev/gIuXMUUlB6j8gzWmbIXCul1W6zb+c0DVWSCIMEGkKQCAFJEPMaFCWuLElUyXSryWYpWKVkY32Z+Xab/Ytz9F3OoLRY3cD0LakZINImcwsHcDalOTXFrn3rbGye4MZDh7hmeiGo0BdDhpsbUOQIYzBZhnQercJcG2rFY2TQBcVe5SVN77FKonyCHA6ZVgW7U83ewzeway5BaYdJJCtrkrxf4FtDWq2CbJhjiuAAXGzA9QszvPeW67l+9zSz05pWI0Hgsd5Q2qCBoFCUXpD78KxK4WgoibAu5jWOL7QpLR6P8i9+H/3pERyP2WAQMoAE6EoJ1QUHtvceG2shM7KwoFuHive8MWW02wTOuaiM6rBSIXUSAvPe4J2jyPNw/zqLGglwBXs9NyVJ0gi1xPigvOp9SP+VkLZSdOkpzDCsb0Iy22oy02zQnJpmZuduDhxIOHn2LOeXz9LvbTIcZoBF6iSMj1CKIpMELQXKWTBFENPUDbyHmZlp7EyTQW5x3QzX6wZOFM+FKA0JElSKLQvwUOQFZVGGdRowzuO9AaVioGXior4MXlUfx7SR0p6aYjAA0Suw3gSjoqpXq5rPVkOIefZVT7vghQ6WpKsMO1GlhwTRkSheRlXDGMiZxzhPZmxoc1GUOGOi4RvC0T4aJtbHpBlvkNbHxufVxC1oeAFZl6cf/yLHnvkyXgzRqqSNQ+UOZzVSanTi0D5H+iL0XhRhX0H8Jnj4qhlybLdW9QQQZHjtWOkx1lQFwZJKzCQa1y4YtaOIzoQReKXhtECe3WD9/tNcXJxnev8eZq65mbfccYHpdyXIBLx1OK0pCkNTQdeXnOiuYXWCkw1S3aSQBkHoS+Nw4CwoRWE9trBc2CgZmjmQu0Bm4B1iY0g/adDTUyxec4R9e1okej4oV6WLQc75TB9/YZP+6VPk65sh3J4XSOdww4zehfNsnj1K9/wxZqYOQFvHyJEYeV5GAkxRLWO0iMU895FHVXoWrpO0567lTdl+Tj35JA9+4UHO5V0GVlIOFDsaM0HBr5CUZYlxFiFUoCfCMvSO070hRTXHuEFIKfQJQqhtu44VRsdWKYOEm2/L60IqpNQgQwQ+nCgH1mJ91arCEnoxsnWFjxO3iESrihhWG1X/rQiglx4vbHz+J/bB+NmQo5+t+whsTiKlDykbsUdkVfdMtc8J8jh20VfbykBaPWPyGCWnGaU1M64/nfROjRBeCFwzLkpiG9yp1fEosMKRGzsO720dSvxbglIIp5gIa2+974FKTEhISW91kyc//TDPP/gHnH32Dzn95ON0L2a0k5kophEyIdzo/FafDftSEBbQqjbQWRItaLYaodDfRuehB1EaQn1o+G49muujHLoQKCEmot7hmuemRChLKiW9C0cp7Qrzw3VO9QrEZsGRt9/Djt07ST0k7SpddWvEMcY+CK2VeignaTXnyPNNNi6c5rkvPsC50xt4r7n5jjuv7EUEZOko+0Mat9/Owsy1zPmbeOyZP+HcYMjOrIfLhszuvJ5r7CxHn3kWXxo2LqyQG8XM4l6G64IdTU3v5El6vQ2Mhz3XvYVieIJi9zy33vBh5hobTGmDXX6Qld4yNFu87xvfxY6pmzn92EMcPLSbhx9/nvTAIu225djzT9OenYFGg1tvuZE//NxnaQ3gne/5Zt66e4bNz/4GzdWCGS/onz5GMb2T2266jjOfeoK14QaPPX4On8BnP/dxnn3kc3TTTY7cejMHF/bT3ziHF6vYvMRLj7Ue4QjkUYf7pZcXKCVAeOYW9jIjBKuDFXAaa0qK2UXufc9PoYFSju/7VAZVmbPraxx+22EA8mLIrW8/yPWf+mYeP/YQrWT1il9DCOqlhfFB2El4cixZnpH1uuSDTfJhjzIbQpnhpEOrJlNTTebmZpmemUE3WqFsRQRjMtRIh317X+UTiS09rkNvRz9KIIhJvySJDrXDPtzZwiukb5KqFlPNFnaqRSNvkbgGFscwd/QGhv7AMpsLnE2Rvo0UMa1dqi2iWlXW2CirJHrwqrG5iR+BiGqxfkQkx+8LXFx6HKGsqfLPjiKUI39dnGcEjFNIrjxyD92ypHCOHUnCvvlpdk2lSNXAOBtsUjxNCR6Jlw4pLKX3rOYFoijYMz+NKQ0rheWxC8ss6gY37tlJolPKQpKJgs1hn0Y5ZKE1wzVH3sSdd9xBUwv2797JVDrLwuwOcm8ZLl+kXFuh2NhEFznSW0xRhn6ABKE3i0AoSVMlpCoQh5wk1PTh6TlHPsxoCsWsbrMoFKkQpN6Res2mnGatHNJX06i2YW5WoZwkEWv0HbxpdpoP3X0jB3fPIq1Bq5BdYr2L/Y8lxoc6eFNKVoeWRHhmWk3SoM4SL6LARn22IDonSLYpdZxI0JRKgqowZSyhSBCqgbAWXImUitIEARuJQPkQrZQyBmuci+bRONJojCFVCqkk1oRa0kAnXGx3E27q0EPSjtbL0XNgDDK2kQqEM/TiTCQo4UmVpJ2kzDbbaJ0yWFulPZdwcN8Uzu+jLJsMswt4X1KWZdB4iM9OUWQoFdpjOOdBBQG98Pwm6HSOfLCKBxKdUBQeqRpIMYX0mkTmGPKoKh/CCM57TFki8Cgh4/od1OwdsQTxFZzXl00clRQkSuGdpSwKXCRlfouhNhbCgXG6mK8KU+Mk5cR4wlJSoKVHEVMwvMA7gbFQlDYY69ZifFBQykuDMTaqLbpRU+8RMUBGr6eIJHJrOoSWCuEKet1zFM6TNjXtVrjplPBoIUlVglSKJG3g5ZCyGCCcQbpQTOttzOqPqacuTvzVNCnwOGewMUJqnd1imI3S6IgtOOSE8EfFR7eJb+huj8d/6/cw4lpuuuc2HAZ9zR3MLQ4hsXgXUgbxjjSueHNpk3tvPMxZkbO5skaWBXWuTGQIbIj4WoeVItRxGstwA559NuXWg9BuNyk3+rjVDRq79uPTKeblDKo5Hpd1ZTCG24qN489w8dRJ8v6QIiuCtDgeYcH2eqwe/zJrZ/Yxe2Ae5DwugZg9MJGqNz6fQbBhos4uvqe8h9Sh96RQpBwc7CUfHMCyybm1Jrt23gzZedZXj1OaEpSO0W+PER6pPIOsy5lzFzh5ssee/bMosY7zCil24r3athrHcZL4BC0btXMh/hYxHUGF2ksREqeUEIQwV4hceRNEZS6xxKlqyUaKvxPEZHy/R9JGyHlw41j0aD+TqYWj+z/OD1KOt62mjiASJWOPpShoIybE3H3FpfzoiatII4g43xClzMdzzYsqa1YRtol9j88xIUoZi9m3BdX8pR3GlrzAcbslTGEQ3gYibc04f/YS9753HqkkG8ur/MnHPs4Tf/xJslPPsXryeYq1AQt6CmereVMGAh9JcpiWBFXfx3DtgrmolEQpjVQKpQQ6SQk1rgUCFxfkcE2U0ggvMSbDOhPntdjU2rvRd4crHSxO6RxtIRleWKNXHIWeYXX9PKvZMm999zexb98+nHM0p6OIwIt6uR0aTyoDGbGmxGZD5rRkKNi26yilZHmjz/qxDc6YZ3h09fdAKbyUfOkzn6EpU3YstEA5WvP76J87RT7IMP4kTz86y8GdOzlw6yLtss2Dn3+AfpLQ9Zvc+963sah2ovbNsmvvPrJTJzl2bplUa3TS5/E//G2yA8/TaneZXWyCKXj2uVPM7Vrn6+66nbceuZkvfOpJlodt/PQOrt2zkw9/4zchn70fuXKeFIOzGTMIslPPsGv3Ndxx3RGOfe5z+MF53CDDy4LW3jbLm9Mgr+e5hy+ybk6TGYlEER/aUOtuoqKxkthYgSwQ6DTcG1leYoVEFAVTR65n91sOhO1jdNuUjmM9hzp6krMXnufOHe+M94pCKLjrLdM88PkD5Bsnt+U6Fh7yYYHwQ6QBrx2+sJhhTtbtUwx7YIek0tFqJExPt2hPT5G0mqATnJQIDaIhkEmc16qf6DB3k68xdsqNfI/xdSWgrUPP6c0chgMohk0S0WR2qo2fm0HZIcL2yYc5K90hyfImrbk+szthnha2bKEykM0XHOqIHE5mc4Rap3GkcXKclnHdppx4P350RCaNGEcp8ZUYIDHVdes+XyHA8aoh8GjvSZRi9455dNOzLqBJiS49uZU4LdBKBxIhHX0sG5lBlxalU3qFYXmYcWJ9E18KDu7Zh1BwyliGvRxrVtBz89x18Ajv3b+HmQ9+O/10D1OZY+dUgrk4YPXoSfrnT1OsXcDlfWxWhlRBayiNGaVgVsuu1gm5F5RJSpJoVCMNypxpggaU1DSTBtaF9NZmUyCNY5gVXNjMGebQ8A1m0mmYDivkjvkGNzZSbrvmIPsWPcoPQCWx93IgztaDLQ3OWlZzz6nlNVJbsDDVJOuVJNKTquDszb3AOomOhDfQOB3ybq80pMAJBV6NerpbJFiP9YHMBdNE4qXAGMvQDkmURgiFFqEUJmi02CjPEOye0howBWVpwAuSJBiRdmQmWLyXsZ46dnqwDh8jewgR+iFCiDyWBRJHUwummynNRDPVbNJKEvrrm5w9+TxJK2Pv4Rs4tHAzxk6z3F1DmBzpPSKSW/CjzhTOBhV1LwRC5jgpsKJJvtnFlGUUtwzEWgrNcGhRKj7T1pAqiW6mwWa1Fmtj3Wi027y1jEqa/FiH4qVw+RHHQH0pBgOyzR6+tCipcSLcdBXZcZ4gCQ3RlSYqizGYukIg/VjcQAlBIsA7i7Emfl6RF57hMKcsS6wN6afWeUKrERHqBYUc7VMwNg5Hk9mk1R5FQkxUklRS0VJBXaocGtDBoC68pVAS01A00wSRhB5usgy9Y4R3qMThrMfZGK1hkhB6iC0QjA0/LirHVhPEeLGovIwyGEJxP25EvrcBScp0S9O+bpEdhwBr8VJjp6eQNnCJeMKQGvj/8/ZnwZZl530n9lvD3vuMd745z5WVNQJVBQIojAQJQSJBURKlltRStxWWbIcddofDEf1gh1/84PDw0g6/2BEdlh3dkjo63JIlusVBJEiQIEgQ81TzmJmV053vPfMe1uSHtfc5JwtFUIAqvStuZea9556z9157rfX9v+///f/e0zu1widefJrdowccrqxyPD5B1cG6cbXypRC14m2Iio/5jJd//G10a8LnXrzCaRHIy5JOH1pIhC8hN9x9b4dOe4XNC6chATM64b23X2dyfIgrcryp4jkKj/CSzFrc4QMevPZDVs9eYO38U8h2QmipeYXv/fL8NGCxDrDj6cbezFhl83gl0b0W5y5eZlwFxsU6cuwoDj3VdJ/ZbIqvn2HrbFTpdAE/zcknBW+9/h5PtK6wvh3FHxr/rEd3xOB+zmoWUdBkUUkD6v5BJeO98UvbvpQC3ahmSouP9tY1qFsmbi5/xaxzjcqiyvK8+lizCppE0byCuDjjptreRBVCxteJpjIciKbHoRmjuu9Rufm8Wn43qDPXze+L+nPn69BykqaB1MvKaot7twCkCzrrXPkvLGVbP+Sj6SnotwSrziCSegJ+UK+BkAQcIZiHTNAa3NhUWKWSTPZzvvFvvsZ3/vj/Q7n/BtX+ADtxZHp1qegg5r8Yb2PTJC9j0k0IvFZYJWl12vR6XVSaIZIEkWagNUkiEcKCjyyMYA0iSHwFxazEFFN8PsXNykj7r8+1ERxoBI6iLQQgBIlIKUYjrH8bzCHTIicxHdTnv8DWhQ2EgKzbMDOWcx0xsJA1lTz4CLSzRCG9ZWW1x5mrTzIwP10A4Oc5hvkU4wz5m68zIQb80YstVnOnacLoWFLhSFTCpMyjD9nIkup7TDuWl252uNY+YTatKDPDn3/1D3j2489xY+WQt27/Gdvbv8KWmNBfXaPfX2E0Ljgje7xw6RlmScbBxoBza7d59+SYB3d3Obh6jv/6n/5fSM7cYCUTiN6Mv/d3/jHX9T7H976HKks0tbiV8IjJkGLnHS4+/hTX3tvjjXtHXO12WO9l7N97nZnrM+g/wK5cZHyzhakUKonJMa11zcYBb6MuqfMCmcaM/WR8wsh5rLeotEXwJZcun2J7dROMRSW6fiIF5bRgPVnl7NaLlMOIeCrnSYHHb6xSBktpH42q6tRaqnJCKAJZWZBkAmumVOMx1XhMMDMy7el3EtZWuvT7fZJ2C68UFdHqT8UIH4+n9AEdYowTbYEirdOGRcUuXjd1dT7aL0aj8tiUOC0Ue/uSO/csh3tjzKxABYsXDi8DTgqM90zGBfnuAaLTR/dP47JtunmL9LhHd12ysgGtrF7nfFS/V6o+aeJ5mRAwLFUT63Oqw6smcJkzFpu1VIYFELVNxbL+n/IRU7TUIiZb8K8ezaGlQEnFSrdDmqVMquih7aygKwVFZRDWUwWNDoqOksy8I7cCRxSlyXPB7knOtPKc3jiFSFKmxnIyHaNC4NylPZ793K9zavq/4YvPTdg/M2Fv0IeDm9y/+TqHN9/CHh/gp2OkKfC2Ah+Qvrn62NveWLlJKbFSRXcClWB0ikpSSDJClpC0UnTaJrTbJFkUa9HWYaeeWWmiKnfhSL2kLRNEq83GepfzKxkvbLRpa09ZnOBMHa+KmlGDoPSCqjCMJifslQ5TTui3FMLHXuJYiQeCZOYBKUmkhOBqNdjykQDHJhYRwsfqoW/VINAs2h5iqSa2E4XYy2tdQEmHs5G9FJ9XMY+to22Owtio1BoIOOvm8dM8fqy9qJVSGB/7/GVtJBzqmKnZP6WPPo6dVNNrpaz11lntnKIYHHN87z5rHkIuOL65w7kbpzl/dp374y6DnUO0TAhe1w4LdQtbrXngPczyKNgkywqdGrIkjUAzKJwrkSJST4tiGCeeiPFTojWoFkVVxLiVeL+cX2Jrhro4EKjV5v/i42cGjoJYoVE+Ns0qapl7GiVCP88++LDINcKi0sBSJWhebRQC4WI2wFpL6TyVoa5q2Rodx8EOgVo+f+m8HqLO+TneEvMKyVJfTfNH8AihUKIOLm0U4/AiBsaVcBTWklhJmggSqWOlMUikqkV5MJS2wJqq3gQas1Zfx6R+HrhS90HGzECtYDlfif1PLJ/LweuHfZhWyqVf/WVoyTqDmCBEQLsw30AaKqcQcdcIEvSpLqdW2vSyBNHYOFA/o0IjZeSBSyTSQzG8zWj0NW4dwdndU5zOOrSzbr1DWrw/RPqM0U6ADcfmVQhHM2Yvv8HR7i2m4yOqaoa3FSE4GsQdKoOdDBju3ebmy9/m8UTSP3UB5CpkaoEPPwi1iaYnalGBRMZcrwQ4t0FSWU5f7PF08jHGL9+lUp7p0W1KMYRgo92AM4AmOEEwilBJ7t7bZ/DUNdbVFiGULFbRR4cel5+QOT6eR9M18Gno081rapTRiNXIhyiefn66jdRTUxsSy5/T8AR9oPbdqW0bYlXB1ZSsRaKkfp6WQGm04QnzbHeMMaIqqqtBHzTqq7IWwapfW4PlUNNJwhLzoaFfNe8RanrqInPwUA4h/ruh9zZKrE2ARJ25801I9OEfIk1gULFy94jzhSN8fweeP41saeYCOPOBjlQSb2trn7mAr6jtgEAqyejggD/713/Aj/7gK7i925jjfWRu6Yg2gki7aTYWQhS0oE56pUpj8SRphuj08OvrdM5fIGm36fR6rG6fIl1dgayFSrNYuK6Bo60qKAt8XmEmFdUspxgcMrt/Gz0cEfIKVxb4MnqKzYPIUK/jIa6fsuYMuckYEQzSdnjnm1/FhIKPfeEXuXj5IpX0JC0xp6029wfqNdiBqNX/OmmLrso4/dhjPPZLv8LXf/+3P/RxzMuqNpcekmYJlgRrHUoHVnttTF5ijEA4x6Qa44HEC3TaJp8M2Smm6J02M+XIK8fK+kX6XcfLX/9jrj7zFKP0mGk5ZQvFlnKcTCZ43+be4ZRic4VVBTeu32D3Zsney1/h5K0f8ocv/4DVi/DJJx5nbfM/4dd/Y51feuISo2/9O6qDfToiRZMgZMA4S08Fqp23WL90g4898xRvTmZs9M7w1s2XuP70J/i1L3ycBysXuf7MZdb7E1767lfRvS6too0InsoZBB5jqzguUmCDI0EiQlRdlDKKRlgduNSRbAFhSfUzhMBTp/uchBF7o5e4+1t/zvj+M7zwiV/isUtXefnNNxkVRwQh/8Kx+A85RmVBURhCmJHmCWkCzkzIxwNcMaOdwGo7Zb3fYa3fo9VuI1SKkxKvAkIHrBCYKvqxhaqkLQQr7YxOO4tBNlAER+Wok+4x7nAeSmPIiyiZb2xBZSyDE8uDB4adnZzBcEhZnlBN9ilODrAnxzAbEaqCYHMmrsQoxdRK7uyNSLINWr0+W+dWOHOhz8Zai1Yt6NPLMlS3A0lKDIQFZXCUPuptR5XrJlko5sKAUIvDs7RU1qwyJ2LHiaH22rMeZR1dIdBCoedWbvCIOBwAtBJNbjwqBEZ5TpJpkgB5WTBzgdwHUAJdFrQQrJhQAwnPyDlaXjDLcw5Kx2qrRyfAg2KGDp4N4WG9y//gf/G/5tKFv8F3/+Qi93sP0Edvcv+rr1O8+TrTvftQzBDOoOdCj3F9kvXeK6RCWEv0HBRIJ4AKJQRCFIDCIFA6RaaakGhM2sOt9JCZRmcZqt3CeoOxjpZx9GxM5Fsp6K/0uLK1woWtHi3tseWYJF1DFlOqokR4wHrGec5+CFAZ2kmHM2mLrL9FX1gyCamWcy2OECQmRGoyQpAQENZjH5HNkbMOJyK+yGSKku0oqilyQgMem6wpgURrjIk+7jJ4nI9xhVQqVhq9WzCpmr19ufDkZa3jEQU6EZEVJaVAa1XfAxfHDglB1OrHnkwqOmlCO1X0OxnrqxuUI89of4Q0daIrtKAQjPfuQeq4fGGdDkMOd4exnahO5qZpGuMp66KugA8YF5DB4twYbyVZ0sVahbUeIQzOlggRUKppTRKRAYGknbURwpMXJTbUAqRS4NxCWC9O5J++rv5c4jhCRLpqmmh05eoscU05EzEAkXWTYoNiG3/DCOpqZC4aallUVjXWYp3BeEthLLPCUlUOX0vFNsHj/NrmlRHmgCKeYphX8uaVmDob0ZRhZZMN81FdSNQc4OCJALgOc50z5FaQpfFhSIVGIgkhQYmASku8N+RlSVUZPIqAqsVGWArQmZ8FEqRaPJTNZ4aw6G9sruNRAcfEe+z2WqSP+EBowKIS8yoMYtHMP7+9HkInZWorKgFOxOyNEIpGtD+EKGSihMQXE+7df4f1F1/ksLLc2j3gcgbTgeewm7MlKvqyxZWP3ECt1ON3lDO6c5PxwR7jYorzFu8tUslIfQq15Uqe40+OOLp3m82t07R0SpJ1CVk2B0gRorwfIiw2OSGaSlR8xqy1aK05KHNuHY2QK8+QdQpWyxUSarWp0Ch31b8XBImDMBxw2D/mW4cHjNpdLmWa9ZU2NtRqY4/gCEvPff2d+eI57yckgrX5lwjz5z/O28YktwZUYrGpL9M5AzHzTBAPAykhIqgHghc1aPQ0ZxFEAz4XPcZQg7GIG3HE4F74eD4uxGDKh6V8dH0xTWGwqSKL+TcbelUD/Oo+4hAeukUL6Lj0tvX6EpDRFmfpCZqDZO95RNMxHjrgFFgnl2izH3AEECiETBqMFOenX/Q0zo5y/vzf/B7f+4N/SbFzCzGa4SfQlq24CnuHkmKe7BNS44LD4SCVhFSgux22zl9h7fzjdC89ztmnn0WkXdJWQm9jHZ+2qYRApVl8LnC1SIBBWoMyFXYaQWJ+cp/9N3+M3blHfrTP+PCAcjShGk8xeYUMtcBBraIrETjn0ELgnMKMDV4cMOGHvPadITIR9LJfY/vCGs4EpK4z0fPkQFTE8y4aGngHAUVv9TSd/iVyu/ZIhtBYMNajpcK6QCoEaxfOcenxK7jDEeWoZLXXoZyMeLB3yOlTT9BrFbx95xa582gsVZEzVAnTYsYnnzjF+VMf4a3XX+H/8eZvsfXERzj/+BuYaow63eVCJ+PWcMy7acL+YIYujjl6sM/WmS3+xqV/xNuvf4u3br/OwK4zzdtsZe/y2OWP4HZfx9y9zWZvC3xOqCyl9aRSR3+xqsDu3eTipad5YXCDO/cOETjuH+/Bj7/P3Z1vsZE7WrPbCPGAixfXOJkO8NbiiErELkQqV/AeG6JKaRTFCuikVgTWKZd7XXrFGLJ+VFeXEhTsO8cP7h2xnw94bKNF4tZ5+4ff4LXDB7TNea71+nzPmkcyjqN8gikDzoCcRHPxYGb4KqetBa1Wl36rTb+d0G6lJEmKTDJUmiEThZeCWQWjwjGblFDNWMsUmda0O3HaWgSlE+RWYKzAekFZeaazgvF0zHR6zGx2wng8ZDCYsb834WB3xGgww7sKgqGaDpidHGDGQ1JnyKSHYBC5ZVLm7B2d0ErfRug27X6f0+dPc+HSac6cWmGjm7HdaXFmZYXtrQ3k+jp0O3gSvKyTe0FEhoOoE3319hJiRYC66LTYZ8RifY2dxvHL+YC2nkSCD0sJAh5VKq5+f28QShG0Ii8LAoo0S8i0opSKvHSIoPHGoFOJ8JJJbR8kg2RQekZ5jrcuAhbnkYliLevw0W7GL/zn/zNOfeIXaX/jhI3hIQ9uvcLNP/6/sfvKDqtSo2yIVWa52Lw8EVw3YiTBB4RaCEJG8bl6HwoWiYtJWGMwNgJOqQtCMUO2ElTWxfd6yFShpUS7QMs7ggi0tWal3+H0SpdUKUIQqHQF5xStlqRwnoO8guEUFRwbnQ4ySelkPXKhsLMxiXNkiUSJWIkOwqNEXJ8j/o+QiUQi3PvjkQ/n6HS7FNaST2dICrQS8yR0jOep7++iKKUktUAfxMA1Jj1qeLhInEO951C3Z8Wko/ACrRRCJUu01QhKg/dxv4xIOqZbQiBRgm6q6bdb9LI2vfY65axkfHCELicxx1snzjPn8ZMxdtIn6fYh9NCqivCzXgeti0UwXYvX6HouWhv1I6wBZ2Z4H6vj3luUiC2FUgaSRINOyKvajoPYk5nqhHamMaaiKKLAmGysK8KCCfQXHT8zcGz6nBr630I3MQ6YJNIDEgF1foIQZN18ufAcihZJEikk1jqKymBNhfcW4x1lZamqaN7ZgEtoBn5eCGCBk+sTCovMwZySFp+wxUPCogrjaa6lAWy1bH8txCFCwNuACZA7gdcahSb4gJaCdishU4bMjrG2wLowD5ab5llRm6dLGRBKInXsDVIi3ru555xozq252Y8uSg1Sopv3lzHL34CQ5SrdXPa+LnoQApPZBJRHJ5H6uOh4EItqVe0Pk7DOSy/nTB4/YX3rLuZHP2LUXuPsub/BcQH93jb9JKG7aSN4LQLV7i0O336H2XBK0u7hvSNUFYi4yRgXUIlGhIzJCfRngsnBkEnngJWVbWRnoZf4QaBx6YfxFUs/EkqCBZc7Br5AVofodMj05A4y5Fhbq+nVwMi72GAvbMHurR/g1JDf/vo9bl9+jP/k4x9nfW3r58jO/Psfi9pZTMo0gKmZj3OLkhD78ySizqRBEAHjPc47nIueRkGwdD8aaBkPH7NAdYVQNMNMQ9SOgUUM3gORAhPpCA1l9WFgSk3Z8kTASB1w1hpREZwTIedDfXdL1dPFpJH1nKuf5ZqmEauXzUc3qYTF0VgCLXosfU2ZfajEN7+nj0ysKjfQyxhd3+SdNwb80sfP1d+30NYPrwVCorI2wkuo1ZGp77mUksnJmB/89jd46at/SLH3LnZ0SDkwZEk3MiJcoxRXG4gLSeV9pNh1e6iNNdKtdc7duMbZG8+yfv5pNi8+zsrpcyStFJXEKsIsB+l8zJ3LhSebEFH5MdMQHJjKYotL9C6c5vi9dxnv3EHcv8Pg7g7to2PEwTFmWiBcXG+drwXSlKp7V2L7wHR4iGKCkoE73/kWP2pt8NFf/CSnzq3H57ne65rHN5oZQxCSygcmHuTmKSY+ZfDOo+mNw7toMUIE48WsREwmPNgbsmI9STtFtRIwiu7WKfR6n4HxrF47x2WdcXTvkPF0xLioyNIW3/r+dzh7JUXnlt39exyT8MM3X+PxL36Zs9d/BF/7JhaL9ZrR/SHnziouXFulUi/TWXmBtvoI7x28we69Hb7+9T/iiysnfPbyLzN95bu0neOBk7SnU06lUclYEGnJQa+x+9YbbK2e5lOPXWH33ttcvnQeqVZ545t/yrlrX2ZVr/DmzQf8lS/8OlfO3+L27X/OeHSI1Arhogy+F6Luf/SR5S2pE1fxoRVpynMf+yztVj9WYxrqcoAzmeKZrVVWnKewM46nCadkSn84pPBr3B2d4OQHrOsfwjEpYhuOK0qqfIotxshQ0U5lpGoLDb7EuxSCQyeaVqtNmnVBaPICxqVjnBvKIvZ+hbZGaBW934DcwKxUFAaKKvYuDgZweDTi5OQes9kORX7E4PiI3d1j9naOGQ/G2KpAioAIHlPMKCcjQjEjE9BKJErE3rnR0CPZj3u0VKSdLg9217l3d5PT22ucWu1wpt/hzEqfM6dOsXHhLN3z50g2t5FJl0ymD4E6LwRORCVGayPDLDoyCZSUaCVqG4MFIGy+Ytyo5ntus5qFpa9HcQQUQQpmlUUKQSISOqoFwpMXBlGrdq9123SURwZH2sqoCgvGMClKRjPDZr/HWieho0u2Tp/nTPfL/Ge/cZ3yM1v84N4FzNe/x9Hb/y1v5++Rvf0OZ9v9Wi8jCkRFOQyHQEVLlDqR2gjDuTrOikwbX38/InQpY5zqQ1x3U4BqijU5zBQynRCqHJG1EElC2m6j05TUG4QWrKYJ0jhy79GpRCUCqVexlcdpz1gYVlM43eljhawpsp6ZF1Qu1G1osbDhhcC5CEwqGwBJqjVVnXgWPvBBodZ/6CGAVprG/suywroyxg/Nfl5XiKSQdXWwEXKLr4kFxRCVvQW1h7OvVd2jN3x8G1FTWmXdq+/RSpIlbSrr8cHU+2dMqvjGRtA7hAhkKqWVRpXabnsN5fpMB7v44qhm6NUtVCLuTyafwXTG+tolZj3JwWGOoIytOSHM7UCUUiAEaTtD6xRTmdrPPmBdhQ9FFCYLMbZHSJSMgpHBG6QMeGeojKudDwAh6fZXSFsp49EIay3NrfzQgaOHRYTQoPB66ksRKyvCRXSrtI40KJirrjYhZOxpFATnMGVJXpSU1uBDFJEx1s/7m5p28abC8xAWmNPLGpCzyCJoIdAyTkbj3JybP+93an4/1AvbErAkNNmfOuR1AeMdwQuUjA+YBmSQJGmbbn8VhaScmZg9BJwU8x5AQV2N1QqhY7lc1UC36ZlrxCkaT8imz+xRHA1QFCzAYcOrfuh18+pTDDKd94yFZ21jhfW9hIFUzGreNyLibRniA+wqg1GCqtpgWLR478EdRq9+h7ULz/DcekoiUzLvY4XDxQCxONzh7g++x8HuXlwElEIkCUEqSm9wEnS7g2y1Ea02/bV1qplj794D2r1V1N4OrSwlXV1fgN4POD7gUol0PYmZzkhbCesbq9x87x7v3Xub4+Euo2LEzBSYEO+frKt7wQucKdnffYvC3KV/eI39iWH3/FW2V9fIJbT6vQ99DOsRevhC6gx/kM3ciZMlLnBxYWgSAaEGEdbZ2iqmFh9q1pWH7k38Rpxn4qFvN55Dc0Gs+rmKFfXFF/X8a8ZkAWpjtSwqLdcbVCOkJRbry/yK6/kk6vdowHOTwpr3Btdrwk/cr+b/DQBdOqf4k1rBtYmAwtKa84iOxtYkryx7wVEYR5oofMMaZylZFgJKSxTJ/KR8iJvlbDThR3/0x3zva79JtfcWYjDDTSy9tI0PDTug7kOt1xyBw/iK9uYpti4/S3bpOqeefpxrLzxNcuoMrrWC6q4xqgW8vAXnYuXIi6h8KUKTGmiyrwJhBVoLfEvi9Aqta0+ysXmG9fFHqPb22HvjdYa33sWu3+fkwR1mgxOoAq6w9UZfW45E7Ty0TMiHBZohVfoOr3z3K+Sp4fO//MucPturE5MLJWEpmp4Wh0dRBUW2vkY36XI8Pnkk4+jnz6UiENBJSj4Y4cwu7QvbzGYnjEaHZJmmyDrsnrxH8IqPfeGTXCk9PyzfZDgZYn0AZ8kPJe8MvoZWEp1owp3b/PHv//d02+usFQnXLlzj+K3Xsft7vDUccqZv6Z53TI48+e47XHrsGl/Y/zRf/eH32N5o8def+5v417+C23uAnxjUeMLmZq/e0qP6tzCW0f4uRhm4+yZXfuGv8sT5s9z3mu6Zz3IwuMf6xhnE6nnk07/MM194mpdfvU9ponqfCx5B9CXLhCBIML5OKPkINuL4OvrrG6hJm2I/55YwpKnksW6PJPGMRwf87tduMxATtINLL1zkVz/5HMw8/+6rf8r62mmq4eiRjGNe5ghjcPmEcjzAFmPaiSBtdUiTFghDZXKKStPxfYSS6CxFKE1hYDKDcV5RmhIhJa1Wl6ydIVKFAQoD4xlMCsgrmE5hcAx7D6bs3N/l5OgmVXkPVx0zHByyv3PA0cExVRn9SQkRHLqqItgK6R2OQFXUHVdzwcBQ76MSU44pZ0cMD+5yuNplZ6XDvW6H9W6Lrc0NTl++wPkbNzj7+BNsnL1EK41tFsvLnpVQBEHpfIyH6nFOklh21HIBGkNYVBOlkFFU6/3bFYvXP4pj6j3GBjppilAKpRKUTmklEudzKhuvQRJplt55rAcbJMeloZxMOdftcb7fJteO1sXr/JVLz/PUJ/4BOy8krD34FsW/+j3+7Pd/F1H9OZkVrGartQK/nLNAQg0YPDUjr05YexHXjIwAUke2VJpEECTj+heCjcldG5DOgbME61HeEioQtsKYEtHqQpohrCPtd5FKo7ME5QOmqFCpJgRP5SE3FuWg11/lsbRD0rW42YS5Oi6ROVZ4wSGBdQJtEVc3LVUU0vMCoVOqUPdxE5BB/Dw8xr/0cD56AidK4HSkVsYYI6qCel/bhMXSRVT31Qrtah/Lel9qKn5A7b/YtAI28W89d2QU+/JEbZIkBNJEkZcV1lqUilYgqlYUj8mTmIBuJZpWkpAoEGZGUhU4a8DVOsmyictqhf7BkLWtgksXTnFncI/B/oC2TlAyqqkGF9vnEqkQSuKVxgdbt+K4OQgUwtWdQ00wHjVgpNSRBl33f4a6WFBUJU7F+7ooQNR76F8S6/zsFcd6AkTvoPgd6sGQsh44a3EmGqJKqQjEptJERZVOGaKJZnBRVrgsCorKUjpXKyTVIK/xYqs/uQGJ1AHlAvAtBZdC0kDALE1oZwnWVkxzEwNq1FKwK6gj7EVwWB+iXnznyn8iNsBaH5VdpVBRFKfwtDNNN9ukRZdCjBnPpvhaKUvpFCV1vWDGsl2Q9YAuUQKXqzzLAjuPLFZtQCNLl/1T6JRCxvqxamWcu3GNtaNdVhJPT8NJrR8uarAtgojXLxymGDKeTFkxE3zyFM8/81f4zDM3QMLa0md6JVGzGcNXXuH+zbcR5YTgS0bTitnokF6S0u6uk66ssLG9TaI1J4MR4/GIDoa8mrC2voHsrrG6sU26ur6EDpeAzgcBxqXrdyGQ9FK822c43mNWKu49uEk5OmKSF3FhNnGxVmJx92zlaPczugQ2bYE7esAf/N5v8cd/pMm2t3jq+Sd/7qH6ace8JzEsnqWw9Az5Bk6FQBBh3l8RpMJClOG2LqpMNhY6YpFCmU/xOa5a3M8mWdN8cARj8cUNjTQIuQCbYQH25kfNfQoyxKBVQNNjOM8Tieb94/uJuir4vo5LmgRSZCk02cZ6bRBieXovrm/pWuPHNQturZgWxdERtbqreGRy4/HwISaojJ+veg9f4vy7MdxqbDOkFJhJ4Na33+XN73yd4YMfUB7u4aeQ0EYEifBx/OM9DnipsEowtAX985tc/OgvcPaJL7H9xCe58Mwl9GqbXAqsEjhEpBM3S2INqKWPFUepqalVoHTdQ1w/g0KATlJ0krKS9RErgWT7Optnr7F75iVO3n0FtZZyePsm04MhSkmqaQEedG3JEJ8HiRIp1XiKEzdJM8veqxvc3LxBt/cEvdUYUMim51MpXKgQztJLEtKQUJFTzg442d9/JONnbd2bqWKS1Fof++HLIXffnRC8o9dvkZYBseo43b1INbrLpFijVBlF+R1yKVAtxVraIzjDpCgojCMvLCFIdl76Mf9073/PP/xb/1PSjUvsj77D6Mff4PGVNXbbVzm5+zpXHvsrfOY3PouelvTLLr57jqc++Qwr5Qm7r77GqnfoTmBjZZUEmDkDOBKlsVay3feodg8GD3CD9/jEcx8nuX+LQ7fDysoVhu/9Mf/2//U9Lj/7Gb4y3efB/VfoZxlHSLSSeBSuKll/8hnaRjLavcuoqmoLq5qK5ysQiv2bB9x+7TXC009w8/U32dm4QmsjcObdb/B0/xLfbW/x3Eee5tc+9RzgsVng1/7mF5iFLf7p//W/eCTjmBtDmE0JszFUOS3h6aUp3VSTJjLKAAjq6pHEepiVFWWoyKs07ltFgQsVSZYSUkVlFeMCJqVjVnhG08Bo5hjPHIMjy8FOzt57uxztvs1k8Da2vE8wJ+TjY4qTExhP0HXbjncWaw3KRzq3EiB9bftVJ3mFqNldsu4o9w5TTqlGnnKoGbcyjtot2q2UXq/D5s59Lhwd8sSs4DET2D4dUDIFF+XU0jRB64xUCIwQFC4yVhLto09liNphLoAJUBGwIgbuONA+1Oymh9VYm/aCR3GM8xItEzY6LRyRnu+yhNBK0DiScoJUitJajIiUy0FRMikc42HJ9somp9YzjBtz4dIpnnz+7/PpF77M9sUhb54c88bvfpsf/8t/xSkfSEVGkngkJhYoaqXLSJ+M+6KUcR31BAwe1WqRddv0V1bIej1kp4dKUoSMHuDGQggl3hSYKmCrEpdPsbMCkxe4ssBZiy0nYCpE2gEXq0y624nWW1DHZoGyClgJJgjaK33StIeSBT5MEcFiprP4PAsorGdYWmTwpG2N1E1VEawNjF2AYEll3WbvofQ8EuCo1Cq2HKGkQyTgaucEpWJ7WZQ1Ebj6GVMyClhKLQlW1a1yLoLCprgkmv7FhtHoiX7Rch4vESLQsq5EoOd7mnPRfV0g0EqQKkWmFJ0soZ1mdLOUXhow+RFmdoKwrrYEbA4BQiKDgmlBdXiP5AJcONsnpcfkZIIPsdJofdRGcc6gcUipqEwAb3GuiskJEYXJYiuVx/tYLJNek6ZdUgXOSKwriEVwh7MGM3NzC0PqOEc0hcGfcvzswHEuof4+wY2mYifqUq9UCKGJqgkLhK9qCVznHVVRkOcFRWUwLmCDjyo/i4LB/PznwWYDpsJy5aQpgjaIWZKohE67Ta+TYq0ghJy89Lgg8EHW2fdGOam+iDnMDvOgONSZfMSiJzIAqPgaVwVE0GTtLt12Fyk0VXCYYkoQkiRJUDJd9HwS/SUXldIwv3fz66KOy/6SwfsPPeZ44C/5nEAEjngPaYp+9gZb999E/lhgRSzlixBpFCAwzgMWrRzF7IR+0uJsUnG1e4Mbn/8k7SdVrfdRo4Kmr+XgEHPrFtPhMeVsSOVKinyKyiRqY4O1s5c4feEi3V6LyfEBo3xKUnryk0Nk1WJ8coQ+HNCr6n675VxAfXMbYBLVax9OfQpB3PW0YPui5tx0xvBIsb2iuflgyEqnjVaK8bTCAMHWRAmh4sanFBc2+my6nHVpcJQM8hK1a/j21259iCO3dCxX/pdAVhCLSqAIYh7UixpZeVGDgVBbJ9QArkm6NFXH933YHE4uYI2Yg7oFIFw80/G1cun1C6C5sOyJJx+Nw6lPvnk2H/7ceIGy+RcLQvpycmCJUcDiz0UvZFi6vkXVPTRU3Dl4FPP3FbKxBUn+/cfmZzrq+yBiFtHU9KTwvh9DM13rKm59rq7I2XntXd778Tc5ufkmk509xCQnJQrhCO+jABkCYw1ZmuCV4tg71q7e4CO/+FlOPfsJuhc+wqkrT+B6gqK5LyI+P0LEeS7iadYU/Ni/5ut7FUTM9sY+nbr/qc7LRd80hWpD2dKk/atc2eiTbaySbGzQXj3F3ptvcLJzL3qXTUu8NWipMD7MPeK091TDIe7ePUzvNvdeexmx3uL6c+fYamsIGoTG+qjeWU6GTIsJLeB4PGJ3/5DjoxP6q9sf+ih65xBeYkNz7TEBGXxAhBJQjIc5IZS0jWd1ewWtFNN37nJ/NeP+wW22H3+CFy9f4N4rb/Hu4QDvciSaLMsQxuEnJcYe8zv/5l+TFAOKakq5O2b36F2qbuCFJ7/A6fUnKMoRp1ZX0P0ZTz7d5hM3rrD39f8n6XSIFnUrifB4EYGerKlXMlGsddtY4v07uf062x+/wOXqLLdffcD5659m/93vcrT/fe6/LdjIn0TrQGlzgjN1YCJResoLz17h3psrFMk9Mi/Jy0b4SCKCoBye8Ac//kNGnWOeLJ9k9eyzlO3Aa1/5FhdHEhH2UCLQWu9wcn9A0u3QXY2Z+E6WUYQKaH/o41i5QJWXyLyih2S102O1l9LOUhKl0UlK1u7SaveRukVeOcrBGC8C1mWRdWRtrNx4xyRYjJOMZoKApagqxtOC49GUw+MJ+w8GHN47ZrCzR3HyADu7jy32cMUxJh9BUdIydh6XeO9IXKzyKClrbbKwVE1Zmqd1G0CAWngqqrxPywpbVOStlPFsxuFoxN5wzKhwTEvH5atjOq0OOghaSUavt0anv4bMOtF4nQQbYrDphYhqk1UEipYQ2UaCmASrPMI5rBaEJIp/NHFOUyF6FEfpHP1Oh41U4JOEkYWdvCDzHltYNJpOkjKoYtVOJSmHhWM2qTi1usK5jT4hDOhcvcELz/8yX3zxM+QrCa/cfZt7f/Y1Xv7Xv8lZbwFBRaTytqWKa2MIUdBFMG8NsK6ELCNZOcWZK1fI1lforG/Q7vUJrRYuScmUJjgTBQADsZJf5rjKUpQ5opjhZlPMbEY1HlKMxujZDFuUUI1xvqL0fYwxaGNJOh1UITDaE9opSkv6rYzgYTI1JFKidBcZDC0nGE0rjPAM8pKj8YRMgQ4pM9UojEJlAiPnkcLSVYK07icsrYfswx9HKSUtrRBSYxDI5r6q6PPsvIlaKFCz55ooJQpZRmOAxmosJpOjJdOiwtZgF6iTzfWe60P0emzUTWNxBJyN1lSJlLS1pJeldFop/W6PtV6fcjBkfHxEKApkLZ4TdUygYT4KBKn1iNGA4iRjpdOh6KxQjSps3S8qZWwBc56os2BMrYTq58w3pTSxNSBWSK1zcV0XFaDodTu4NMVRV8KdjUkAF4FjvG9yjqHEcsDxAcfPJY5DrTIk5mAtLgKeBdBSKonl3nrC+NoHTelYLp+WFZNZBI3W1SIWkQwfKwtNIFerGc3jKlHL8tYBK/UCKeusmwASpei023S7XbKWILEWfAstoKoCxkeDUxsEzi+CzybonoMIFuI91A9X8PMaBg0N1xiYCpAdjW51SXxBS3iCj/0dcXFsgugFVVaIWoFWirpcHubnEfeBpcD6ERw/61rtQ01XrRyvvrfDifX4ROOdJTiDcY7SVlhnSVuxgVgEuLDW40yR88RawdUn00iRYwGMhfM4KZGjIeLWLarJmFlVYqqcrpT4Xp/2xSucffpjbJ86TbA5UiUUU0MoKoIxTKYFo4ND0rUj7GQcT3je/xLm4xfBQwN4mpGR84mstKSajnnn1fu8+dIt9vfX6KVdzmyepzApJ8Mhrpow8+CJC09jfCzTPk+ceYLT93e5cGqTO6fO8kffexl7dEKnZWn3Tn8Yw/YBx4KeFxMO1POR+UIZczexMhl7BpuM7wKUzTNxsDQfRP1+c/S1NFHEHEg0P6L+zAWBtJktDZBlHuD4pUBHzI2/657gZr5LUQtGUS/471/SFvCvEW+YT+AaSS/OcZ6FeujZn6ur8UGzbfH+sR/i0ag4zq8+BGz9ZP0Efau57XN4r0AkBOMZ39/hwVt/xp3Xv8Lhu29iBpaebKODwvko/hWoPawyhdGSUni2HrvCi7/yd7n28b+KOH0atd6BjmDiXOyFVbXUeGP74esKRoDgxdwk3DaqbLU9TSOmQYj9MIhIP3Yi4FVsH/BSsnbmFBeyj9HqnWe6co12pw16xmzvGI/HTCzeWagZGs7HviHtJbPjMQfvvINaeRm32aezmrJy/Sw6SZE1QHbeRhsabwiuZHfnLnt7++SVeyTAUSe67vORdY9/NJ52LopuyRDwriIgyAcFe50xTz/5D2gPbvL6q39MjuDx85d57PqvUO4NOBjnTJSi07/KY09cZ++9b6OsRmvF0Z23sQQykTKe7fLWdMivrj7L6XNbnO6v0pYa5+DKkzeQ7hTceQ178zZdZxEqitUkSmF87Ed03qFlipKqpvA5EBI32GW6+waXzn+SG4O7vHPwY9JNSdtc5skXPsWNC6f4nX/7MsOyiAlZZwmuotXa4uSVN7h26hItt8Vbtx4ghcI5h/EWrSRtPK/cfI2BmPHq7Tvo83s898J1tmVgRz9OMbzD5atPsn7x85zkkjN9hxApxwPLzutvc6HV+UvH5Oc7opw/PganWW3GHhUUQaJIdIskaYNIKcpAlc+wwRLIUKoVxatIqUpBXljc0CESiZAOa3NG4wG7B3vcf7DD7r37DHf3qQbHiGIE5RCbn2DzIZgcHSAhVrGicEvDuqD2tYOHVq+wWAsDEBxx3ZOR8u2Dx1mPES4yg6zHjGccj2fMTGCSl+zv7rC5vsJKp8dad4PN9bPYLchWJD5pgVcEL7E2UBQx6WZtwHlBUAKnwcyBo0MHF3vo6jW/6dtzoRZHewRHSye0lERrRUWFcxCExlfQkilZS2CrgmA9lZQM85LZJKebdUkTwc7kiIvXtnnh+U/zC5/6e3TWexy98R3e++bv8erv/w5rRC2HQCOAs6wFEHUslIyJpFJqks1VLnz0WVYvfYT1Cxdpb64hWj2sEAilI+U5gHCOssyxVYErDaKscMWMqphhZ2PMbEIxGZOMh7THY/LxiHI8wk7GmKLATCJQ984QHMh2N7LcSo/1AUNBYgUqU/gkQUvQuodKFHvVETvljPFogibQtp52aWhJGVVTg6cjVKwwixBt9YKDoHDVo7HHEWJMuy2ALsbNENLEvdJZpIotcR47LyZZY5GKWkfEI2VsKfLzuG+RsPZxctRMJ1mPo5/HBRFQxufW+8XYSmIiRotIT22ngdVuynp/E5d7jndPsNOSjoyVyrmHe/17DVtLOY+djHHTDv3V0wyUJcgKSRX3Pup+cS8oS0sIk/g9Ed8jTWpmZ3AoFZCZjnPSGMpyRKIcISiSVIJuYU2BlDVmc9GOI2oKyPr6Fnorf9Hxc1Qcw/sqDGL5h4TgsS5yskMNvMDNaRM4iykr8jxnVlWx+TYQlVnfF7nFhbG+kHm1oA4Sm8Cy/l+sQwgyLem0WvT6bbKWRogoY9vvdmkJxUxUTCtH7pa40PVEnw/oPNuwJJDRZNobABnm8BHrPLOqwklHlgl01qMrFKGsMJWNvXl1NO9DHChR84+F8FEKdzmIXsI7jxI4/vseAuqKUCzhj/fHjMcK4TMSITFVGcvoKgJypROk0gQrSFsJCM96X3PqjCYBhPWgVd2DETOQSekpXr/J7s4uvixxvoZlUtNe3+LUE89w6vHnWF2pKQukTMY5+XiMtw6ZT5kcHaP793HjE7C2bhJuKjNNFayZ+DH94IgN+1EkBAYngbd+/IAH7ymuXv88TzxxiruveuyFy3znR3+IrRLyIvYIaFF76YW4OWTdU0z9aQ7skPuv/JD3uu8SfI8bN55mfaXDO7dvPaIRWkpmfOBPF9XVZQp0vP1ND+4CHM4fvw949Ba4bJFsafrbljPeUYyKBYhsKprzz1/8fd5f0DwPYQnMNv8tl0BFM/+a2fEBV92UX8Oil3cZJv/k3Vu+i/V7iubT6wv9mVMtP89RV6iWTw4evvYaNDoCwQXE1HKyt8Pbr36HWz/+JtXBlK7uI0OtfFcbFXs8SikqrRkrxfb1a3z6b/5Nrn3ir6FWb8CKQveiHUBbq5hUa0SGfLRNcj5mTKUUmBCpaKEev4Y2G320Hu5MVbX4kK+TiFJJUIGpg876GhvX11C6R8jGhCTn7o9fpXK74B1uGuk4kbESleaElLSEYLa/w4PXX0Kt9Rmf3WawsU17S9IRAW88xtZ91q7iZDRkf3+Pk+MjDIJHkcaRKkUo5hTdRmABBH5ero3X4b1nb3cXr36fDZmzOzhg48JT9LNn6Vz/FM/3h+z/83/B3nBCFfYZmTP8yt/+33K+nfCjb/4L3r59xNRUnExPkDIwvX+X8UQyHPc43bKQJXi/i/UTtivBg5f+mLZ1yLqyH6m8nhAMSbZCZ/sSoRxQTsYEXyFrG6lO8AzffYuV7nU+/czn6L72Dj+y9yk7Ga9/89+xd+UsO2PHdDalMAU6gFIaZw1XP/oZ+vo6797+7yILCcOiR1linCTLLYfvvE1be85mjuKgS/rYs2wLw1s/kgwevMXxvYtM1x3Dw1PI7ONMneGTn/gsYe+b/HB8+KGPY6ZTZNYGaxC2pCgrtHRkpAitMdZTlY6ycKBj+0200nC4YPBak7baKNXBVDCbGWZVhRceIQ3WThieHLF7/y7377zL4c4dytEhohgjy5xQ5rgyJ9gC6e3cZiiunL7JHdW9hIugNB71WsvSWl7/XdYS/PHhjInzqrJQW6Z4W/Dg3gOKMuf44B7nzmxw7tQZqq2LhCpgbUIrV6iuJMiYoLEeysY+CYFQInpYOmLi33ukj2wzKWN10tY/9uHhbePDPlKdoXUHki5lGZX6VUsgg6edSlSqsXTwbsasmnIyzFlLU3oZODdk49wmTz7zJT7xsV9ne/0sN9/8Dg++9cfc/NrX2DQO6SPTSsmmUkMNJGPyyALGD1g7d5FTVz7DC1/8JVafuk5YXUVlLWSaxnshJEpGNW3pHcEG8qLCVxWhLLH5DJvnVNMR1XRCMZkipmPc+JgwHsFoiOwMce0h1WhIORkzmx7hXS/iAO/RoYU0UEmPaqck3YxgBUZ4ChkQosX0eMhhbnkw8XRo0009p1LBRirIBFg8BIcSCke05JjkOVJqhNSo5NG0cqhQodC0uitUAUozwjuPqxyJCiQi9oAb6yKjoY55glrObC8xiETtrOAD3i1iFu8Fqmkjk3Hfa2igHjBlFe3mRKTCJghaWpMkCZ1uxtbqKqJ0HN3fR+QlqYzxZRABpZP686L6aSx4aQKeqihxgyFbpwPXLl7lcDJhcPwAXc/1QPRH90VFw7hSNSuqUeltYhypFIlUWGOjJaKwWAXdbpc8z7FVPr8lvk4aKiHn1+zcchz0wcfPV3GkCelqK42GEqea/pLIAfY+zKlKQsZScVFVFHlBWZqozhUWgHE+9eoAcrkHsPnM+asaldQYnSKIJpeddot+r02rHX2pIpqOAjaZSFDkeGaYyuCdAClxXkSQ0lxbHZQuA+OHzqG2cGiUmsBjvcGVFouml7bI0hTBjODHVFVUPPLzrEZz7nUfVs1JXj7mxYVHDBybDeUvAh3vPycCSK3YXl3hUCn2qzL2PuoEUdtleGq7CqkIUjKzhpE0TIKkBNpaPvSmwlom33uJez9+meNihreGEDxGCnS/T//iZXoXL9O/epGV9grF4ARbGtKTI7KTE4q8RJUVsizJD/cY3LvN+aeeRK5v1fLucRwbWrAPsWoxmRS8vT9i6+Im66liBY8roMwrrj75OM+9+BgANy7D4Hsz9t7dYDIaooVEKIUkIREyyjJrRdBt/uSdXa5snOHFT1ziyTNnOH3qOpcvX0MS+D/9n/+Pj2D8PI2ymKgrbk0VqEmqxJ8soOVCPXf+ggUlvHnfpSzO/LkQy89JTVWfg8a6+a1JqDS/v/RnBJ3Nv1lYTggimPd+LlK1/DUHbnJ5PjbX1LzF4ukNS9fzQXds8R7N2tPcwTD//vyclt83hKXs/qM5YhDYGAL9BbM/SBAtnCsp8grGlluvv8OPv/ky050R59KVmMWvn/3FMCtkopkFy9qV63z2N/5TbnzurzBureAyh06jFHkrEQQLPrhF1ZnYS2JtrETqWrjHixi9BhfmmyyND2YNkmJPCHWgGym4xkeWiNeQu8DKuqAv1jDio5yWK0i/iam+xrScoUxClZtFTqJ+zxRJWzpOHrzJ/VdTLl29xujKFWy/hdCKogrMZlPuH93j5p332N95QJGXTCYF7hH1qgbnQUZ6VKiz2U2i0NpI65ci9soLAZnxjO78iCpN8EEymh5z5+R7/LVzn+HEbDAUFusKfOHIxw9Y39SYZJ2//Y//D+y88d/w27//Nexul5Opp9o94k+/fZtnnv8yUsJscMgb777C2V6byd23GO/doesNWkKWCAgerTOqEGh99NN0PvJFpt/5bdytWygzQ5iAUpGdUO3tMr79Q05t/RorvQ7p8AFMB4ynOb2DI3befo3x6CTSqjxonWKd589uvwPFLuXsuA5+au9Q5yl9zITLVGMKixvsk6Ut7n79bV75rW/TXTsm7V2lHB3w+je+ztFtxcc/+lnEGUgqjTNTTOsSPArgiCTL2nhr8FXJeDzFVNCnjUw0uiiZqhnIFl60aNFCJZIgFN5H+wkXBEFojBfklWM8MRib4/yUanbC4PABh3fvMHpwh/L4HqEaIu0sJk6LkmDtnHLqvKdJJwXBkraDiIylhx7CuHIss0Ga74sQwC1EBL2N9jlCCqSOAXM5nrKbj8mHexSDdcJ0hCwdmARTZnQmktZKIO30UEkLj6L0kiIErPBI7dESEgVKBjIZSFSk82kt8RLK+bXESFKJD1zp/sMPIZn4wIF1lJWjrCqkMiAUZfDkwFGRsz8aI2cTrnR7dFZ6WCz9zct87Kln+czTn+fK1mV23n2Vuz/8U+59/xvovMDagK7j1GhH7IkO0h6lE5yEEwJP/dJf46kvfIkzz/0yvbOn8WqxuwXmNrwPrfUB6PS74APKB4pZTJIn0xXEdAKTGelsghn1qHojZHdI2j6maHeRWQuVJcjhgNlsjPXRQxPbRWQZOov+4zME2oLWkiMlOMkNfhZAZKxKh6nBb6oFQXisCLUdF5E1IAKViXtMUJoyBCbh0bhyOht9bVvdKDQ1nU1p4n7ha49FFX0Og3dzq79lr+JQPw+Nf3rwMZGqpKCq25uEqAtVTTK9Dnp9iGOqaysOhEfhaKcpnZag08pY759DOc1g9z5uekJLOqyrJ6ESpL0WWkuqssRWJVQWby1SKEBRTApm+zusXuuxdbpLaSRuWuF9QpMtj23M0YIoSImQCmNMncj3WFuBqGJbDZAmaUzYKYEPFUUxo6y9hgmNYCkEHzdoKSVOSJyzP3U8fg5xHOobLBY9hnVWXwqB0gqdaBCR4y8QJEm8qMJUVDVorPxCJj+ERmUqtkz/pN04dWXk4XNp/IMC0eek1WrT7XXJ2glCekJw9Q1VeKLCXdaFNg5LQFYe4wWGmgLyPvgUixbLnkOBxoqgecGcs1w/b9Z4KjRpkpDqDJXl0ZuurHBOEpcJMUf0DVidl8Xnq8cCOj6KownimyDsL1IfbV7beG2iJCsbKSu9GaE8JCGw0u0zKgqstwjn64cwTrpmwu3u7HBwuE9y5gzeeaRStSRyQB4cc/etV7i7+x6mzKlMFcl6maKzvc3auSusnD5DZ3sFlWUkrNIpz9MbjpgdnVCdDNDFDOUrQjFk79YbbL51iTMf/xSotC5YL66xCUCtcczygsqHullfsXkWPta+UlMnAzmOjeue5D1JK0twpkR4T5KkMTA0Du8rZJbhQ4LoXOCTv/5L/OP/6Et0H9noLY1NeB/EEPUFzh+kh8e1GcsPGu8GYMwpTg0obDLU89c0/c31DA0hgr6a29WAznkV/SEqwSKgmSctwkM/ZS5QQwP4mYP/edZQSBpBoGY+LRKLi2tb7rsMoVlTlvNpDcOgOcemD7a+7pqaRt3/9YjzOASiKmWTkZ9/4PJwCgnRTh07m3Hnpdf4/lf+iOF7+6yla3XmPyzGLXiCkORCcGwNW09c5TN/529z47O/guucJekGlG5uZ6xCKB09rZyfD28EhlIRRJOVFIvxq+eQrIUFBA1FFSovmg6rmEAUULpIk0lTSaIFkwCrGy229HX27Tk2fUJe7fLe7IRRMUA5AXVCKQoUxTF3oUJimOze4vbLL7P52FNcOnuZwgXGheV4PObe7ZvcevNNBsfHjAdDXOWZK+h82EdtdRMV0TUi0ejg0XisjzfT1up6c+X6kFAZUEpgp0eYg1f47//Zf8Gdu0NGk5TV1S0moz2mg31+66v/FZ948Vf51N/5Evvur3LtB/fJyx2mswKbF/z4q7/Df9Xvsf+5z3BNv0MiO2yiufPyn9ImkEqPRtRJXUXhc/z1Z+h84W8ReqfJTj6JHxX4wQOcsgRrkAH6iWJ4/03kEx/BuxmrKy0eWzvP7W/8mO/cvMvdB3eA6ElsHGArdNri+NUfMXUl586eZjgocEEig8PWe44QAectUkhu3T2GzkXOyBE7swecv3CK4ckByahEnDnN2KyzeWaDI/8e2+kpXvjUFvm9K7xy8IMPfRiF8Wg0XmQUQUU/aW/QGaTtDKUzpCpRukTpAilbJF5HS5igcThmVYG3ktkMxrOS8WTMLD9hNj1gerLL+OA+o/172JN91HRIsBOwM0RlkNZGrQ4p6zlWr1lSLFqAhIg/n/cmwUPp9RAIdQ8TddLL+0WCL+rVx/YFEZhbaTjvcFXJxOYcuILUBlSloWxhi4z+LNCZFnS6K3RX1sh6a8g0wwXBrCwpiwIdPF0l6aWSLNN0OimZligNNuaa5kJd+hGyOUpjQOXMpiHGowqksJRVhbWOWSgZDge0CkuvvUJvtU27o2h3ely9+hwf++hf5eqVX2AyHnHwztc4+vGfYPfvooqAFrLOZdZCdD5aCDnASI9NE770P/rH/MLf/R8T2j1CIigXdDUaRfDAgtsWiFYtgciAkgqEkrRWO6TdNrbyqPGUZDRCTCcU7Q5FNqBq9yFrIVptdKtNmWV4neDkPtUkR6AwIRAtURIIntyVKBMTfrM0xSFodTdwZkLiplT5CKU8iYoe3dHrOSaGvPfYutIthcTU9iz6Ee2PxlqCh+OTAVpr2mlKJQSFtxHMejDeRiYHjSMDdUneESSEsHBncM4hgkdLSZpmeG8iLXTuPx3i/isWbJrKWqQUpIkGZ2glmk6a0M8SNvo9tEuYjCbRYsNXdSNTQCiJ6qTQa7N59jSFMRzu7OCOTwjGRYzhAtqBGxwzObjPSieh2lxhUFXkNQ28KVb55tmAed+lr89biEbWOOBFwHlJK0kI3rK/e0CZG7xf0iqQMfKNya5Fr6P8S/bHn0v/SNRlzfdDG1/TVFVw4B2+LBDek+oUbx1FWVJYU3vHzQXA6pYzMQ9cFgqRC5GOWBhYfFrD0Y1+kIp2K6PbbZO2M4QKdSOsjzegppdaQCQJ7W4HKSQZJdMyNok2vqUNja6+0vo84v+bj18EufUyHRYBavABYxylAKEFKI3SCYkLdaYmzIMo6s+LgyXmrXfM3//RgUfRBJY04JHltrCHX1ufV9Nd5jstVlZarGaas+ubmHRG6U4IVmKDnQePIUQJYBkcZnCCmE3QRM+i+L6BIAXs7eIe3GUwOKYqclxw0b6l20Vvn2Lj4mXOnDtHojTIQLreJlSbdDZP0V0/RbG/S1bO8HlFlU+YHuxy+MZrdNc26F5/Gm8EKqUGRZFW6l2g3+vysSe6qARaeKhyprNjdNoh62wCgbYRWJHwamr48fEhk9IQpMQHiXWC2XgK5EjtSRF88Yu/wpf++ueZEaimBWvtlMb4/pEcNXD8oIrhT2DDJaC1EJt6/2s+6JvxkA1YbEBdvUCF2ngx1MplDQhb0K8XyLDONT2UcW0+t5lni58CXtRaOfUvLqmtxKSOfPh36gxhs5Q8fF3vv7L6OWzA4zIA5+HEVQOmfXjEFUeiyXJVr1cPHfPeXAEiSuXv3r3HD7/+h0zefYNtxLyHLAaUcTNIlaIKjoGp2LzxNC/+zb/HlU9/iXxlDd2VkNSxZAgEE8gBqQNSCayMSolKAEEseiN8VJ0LIvZNpTr28iHqgFTF1zjvKIJgWoGtKsajIbOqxKUamWYkSUqrndLPUoZK0tWC7uUVvLzG2vTjmEFBKF5jsLeL8gLvZRSmollHogS8KmaM793h9ttvos+32FgBK9s82N/h7dd+RHkyYnx0hC0qUqVx/tFkxiUCnSS1QEz0TrVakHa79LUisZbZLKeoDEIojPNU3hJVewXCJhzsVBztv46zU7a2Po+z+8yGe7iiZPettxhdPMs3v/5f8sVn/jPktR/x52+/QVsnFGZE8d63+er//Rvc/cbn+Yf/6Zf58rPPM/rzPyEdTklxURQnBCQSqRNKmdJ//OPQOw1BoK+/gLr/LqI8BjtDYxAikGQaW5YM3/4RTzz9BQ7v3+HVV18nDRozNYiQoVVci0LNLgpW4LygrTuMDofRvkFInBOoEPfBKFQX1XODSXjv3Tcpz465vr2O6KyR+nOc7R3wD778N/nmd7/OP//Nf8aXPvcJznz8N7g3tPzRay8/knHEgHcabzMILSAlYLDWYKoCm7ZrZVMfqxXTgqoKiCQj6AyrAoWz5HbKLIfppGI0GTAY7HFydIfRwV2Kox3C5BhRjklMEfvZqjKqWCJALtY+oaKis1A6ogkVLb1iX5wC2VRURG0zEBO9zlqctXMAGVxU0A4urtMieJTwSOEJTb+TAiU00jnyUcFhOELbFsK2wSX4ymBnQ2y3j3Jn2Owqur0M7+G4LBhNh8jKgtZkrQRJRtqKypNIiQmAiwbtiVhU3B7FYb2nDaQyoTAFqtWFIKhMhXE5Y1+QypTHV7vQbVGmilZnlfNnLvP8kx/j6pXHaHemvPnD73HnpR/j7u2gpoZE6Lhozin4Ai81pZ+Qra2ycvqzvPh3v8wTf//LDEKCIYp6daSsxRHr7ZPF7kX9d8cCTPoQ9wQpBEELrJR0WyvoLMW1Wqg0QaUtxKSNTxRBa4LWWBm9b9tCoMMxs+kMj8MIaMtYpAmO2N+ctki8oKPiM6SzNsoJ1rKUbjjEuwJfe44H5wn1nIXaWscaCIIsSaL1yyM4Gp/F2WxCpjVZK4U0xdiADS7u397VPo7Rd7JJTCB1rdob73KkicYWDuc9RVEgReO2QEzMyCZ7HMGy89S9kCC8IxWBTqLptTI6aY+UBF+e4PIR2luMD7VftiDNFLKb0D99is0rTzApLbsnFaWd0pa+7pOU0bNxNqUanZCdOYet2lRVUvsRW6SKFn7Ci5oVWSuqN7GcjBZQ8fGS9X2zTGd5ZP95SaITnPMEtwj0g9Agagqti+D4p9SRgJ8LODa9eY3B2DxtinMOVxZ4oVApaJGSKcBH1SNj4w01NYAKYVH9mNNEYY7y3z+lmuBtTiEVgkRH0NjrRnqqlGExwFBnqGVkwtVZzjRpkXYUqYfgHVUwWE/sI2gCqHm1ow5nm56VpWphA2obwZ4QQHiPw0WTehGFV6RKSLRABEfpba3MKliuZs4x8/y83xdAf8hHWAIRzbV8EGgEoqLiQ8F8l1braT75vObVm7c4eu8W3aygclHKOPasRQqldwEdPLPjMfnJLP56iH46QYC0BaPXX+Lo3Xcoyhzjoyx80Bq1ss7apWtsXbxG1ulHIB4ADXpdkR336W5s4bZP48oJ1hRURUV1MuXk/gP0zZtcXD1F2tuANPry0CQjajsZhYMQZbjF4IDB3e9i9Qann/pF/P0DOsMR1XM3+Mad2xy6EpVKhBFY48nzkhAkadbC2ECqJpy/UFJQUpBxqtuOlZdHMoLz0aEBSmJeKmue2YfBDzRP1NK8ej96W/pDLH1FlcymyhgzVpG6WQPGOpP9UAX0oUzLIhnzE4/YT/y8Ecaq34Y6RS3qi5ovbPVr3/+GS9XR+aX9BTmYpdo/jfUOIQKAeSKrvpmPWqxqfkZC1v5eS3drPkGbc5WUQ8/he7d58M6PEMUxbRXIKxOp1NRriBBUwZH7nNVzG3z8r36JG5/864TVc5gWpJ1YIbI+IFWIWVkPzoKpmCuZBgGqBoM1Oqyp+jWQrJMEUsQGe+cCiZaEoBmdeHb2Dzg+2OHue+9SuRLV6SDSPmubZ2j3V9laX2Oz36Ofwlbq6Vw4g80/gTly5CNDXoyoDgbz9cXP+8Rl7JnOK4rjfXbffZP2+VXaj1/GOE3pCmCKFhWdliYAxgnso8GNsRjsQ9ycidVxZwKjwYwsVXQ6LVbW1umXBdNZEe+liF5kwTm8lJT5DKUCPmjK6i6ZCLTaXSZFgbCWl199nXLrafzoN8mnt9g4vUErK5mMHGMTaCUJqT3g8tZT9E922bn1PdpekooYYAUhybRmWkxJPvolWs9/mXn3b7dD+4lnKQYPEGWOFh4vLdZ50kQy2XuX7Nw1nnnuGR7kXY4HN+m2jwgnUTky4KmcRWsVvY+ril7Woao8Sljwgcp7dB1UBd8Io4RoWF1Z9g9LkpWU52TAdVo40ebmzW9w9sw6vfRLPP2JX2Szs8pLJdz4W/+IB//if/ehj2Oqu1gD1kqcywkiI4Qc5wzGFlhbYZ3HWk9ZWozNkcogkwqZGWxSMXOCSRWYzTyzScXo5Ijjg7sc7t9kenQPPz5Cl1MSV4Ip8FVJcDaCv6Y/X4BQOtp66QyhEpBJBJJaI7WOwhg1cIzMr7pfyQecq4GjdTV4dHhrsZXFGRM/D0sIFd47PA6FQGuFROAqy3hUoPwRwkf2jq1mFP01ik4fSU5nLaG3lhDQWDulKGeI3JBJTWE1RjtCVyNCig+S0kZBHIUgUSLKBz6iRE4LaCsVRXpUQukEwlm8CFQCWh7WWl1cOwUVK9+tpMu5y0/TvfAY2fo6O/deYu8Hf8zw5ruYwRgtk9jnVlcOfQgoJRlaQ3ruFBsf/QVe/Fv/OZc/f4OZd0gBXRFbREpi0C3rGff+HUXUP69TsLFDg8Ua6wWUAtRqG5GmqG4Xl7VIshSRJQQla20HVVsoCbQL2HBMPsuRAQohyVb76CTBigjkvXVIERM5eOh2VtDak+QTykmOUBGQRYZAVGUPQkUavpa0pMK6wOQRratKpzG2qBNOVWXQWUqv2yHPizlds5k7Lnhc8KjazkkKgZYy+j8qOadmulpxuElo+7qvO0syCJ7KlCjCAsQHTyYlqQi0EkW71abTWiGRCmcn+HKAySuUUHHsMo1oJXQ2V9i4eAHfXkUmgbPXnuZg6pju3CGRcS4QoMpzmA7ph4tcPv0Yo5OK0WCHllYRN4imR5k5bnI141MrOccUIUQLEh8cGIevLGmSgq4VVz1UxoCpey1VVKquKht1DH4iqHr4+JmBY1NxmKsbLlWiIPKLyyJHedBtiVJAiAtW8NEs07oo3dtkJxoe8bxxtf7mPBhmAdzkUlCYKE2n3WGl16Ld0lGUQDSF/kWz6BwRNdRaIdBJSqvrqbAU2Ghg6gVeiihjO48jHw7cHg5J6wC2CWjrAfTEwMTIgJYSISOglcKjVR1M+FAH2fXi4Zfpok3WgEcWpzaS+c2p/7RjDjFrTq5Umsce+xyidZ6dk5z2zj7dbMqkmKHEol80VhwFzjrCbMqtd/Z580XYXhVsAGDJv/sN3vj+tzgYHIGtcK7CENCtPhsXL7F+8TLt9W3a3XbM4tcPtEwU3fU18vVtys1tWpMjxtMxWrXwheXk/g7pqXtsXTym092ag3vxvosSNeXR5jDSYLcVJ8MpBz+4S/XqXba3DNeff5JffuopNscVr37321S3S7J8xsz6esIJlAwkqqCvD9iQY3okSF9njWHRz/chHws/wzmxs3l8Fl91wmO5VtbQk+McXvzOvI8svvl8Dgohajr5grpBTUteSEN7FmJWsPzwBuD9FNIFg2A5afSwyM/yvYu5lLqvYZm6GqJOb1iePs34Bt4HLJtey+UkyWL7bgSb4trc3NHl63i0FUchIhXNhhg86IfOsf6b8IhSUu2NGd2+zfRwh2o6phXU/B77eq0KUlA4R7axzmOf/SyXP/4pWD+DardQbSiKSI31EvIQNxM7rqhGM4Rs41zUeNWrfURbkGlFW0ajb6Uis8C72L8XvbMiaFJSYSrLe7eOeOPV29y7+xZ3br7N0e59kjSgkhQbWqyeucTWpcsMzp6juHSB7VOnMO2MM5tt2peuk57MSIZ36ZzchUlBMakiayDeLAhRbdJbw/hwF/vuG5y5eh55/hKlE6ysr3H9xlX2bu+iWjnJtCAvLNY+mvm41c6YlSWW6L8ldewd81VFkU8prUOspPTbfToS5KyisJ7SxYDMGoPzDiEVUlkO9m4jgiTVAWsqvBfs3hsRXvF0km+yvnqatJ9zPHwNnbXor61y7cbH+ZUvvcgnr55h77e+gigCUsR+OS81Fph5CFub9D7/S5BkMXCXKq4L5z8CF24jZkP81BFcHpUTvUWVhsHNH3DuxV/huSee4PXXb1OW07inCQ2hIlESrRQCiVaB2WyGbewJjEGl0Tw7uDBfc5wLIAI6KKrZLoOh4/BQ0xMlQ5cy6Tr+/q/+Db7yjXf4F7/5Ff7aM5scmB7rsvdIxrHTWWNqPCEorJ/ifMzKG28wtoqVR2ORlcNhIgNQOqS1SGfwKqHwUJWecurIRzOmR3tMDu+Tn+ziZscIOya4GaaqCKaK66aqGVIy6gUopVBJRpK2UUkbodIaOGqkVjVtVczX96ZnsGGEJSGKM8Uqowcf/zSVoSorbFngXUFwInrphlhjMS4QgkDJBOsE42kO7BNEwLqCjWKDsr+GTALpaosiE0yzDlVZEZzBW0dFoMBRlhJjLa0Qg+/KQlEFhI82CdJ5vC0fyTgqQEjPpJxhvJ2LknhrCM6RigwhNOPKY5yl029z5vI1ts6dZ+PUNpPhMTdfeY3Z3XuYw100tqY8imhxRUAozdQVtC+scvVzf4/nv/z3ufTxS0ycw9UJgCZiDKGxHhFzrkyk7S7vQvEvrqmYEV/vRT3GIaqZ6pZAph0yKXBKIbQisREQKaXjWAlH6RxtBJ49iukML1NEkpGttGK+11QIpfEkSK0ROJRUJEmKyxOmpcdJgfB1wh8iMPVREKlhmE3LioO8hJVHMpQEBFmWxXXEekwV8KEGPlJFZpeMMYpSOtrhOL9IfAOxsubxXuAbLV+hkCyS3tYa2qpFK8kwzoCN1ck0UZgqJ9WCjtJ0W9F+o9fpQFkyOBkRJjmtGjQmicQnis76JqunL0J3DZMqnAykqyusbG2Rn+xjylnNEogsoXyUIw8OOH35CXbPbXE02cd5hxK1eJLSMcnjw0MyDM45mjScUiCVwJtAsJ7gDJ1um7KMoNGEEP2u6+fJ2lq9VdaMpb9ke/y5VFVDjRijTG29SIWAFLFP0HuLCp4s0ZEPHCJlwjtXm40LBMkciDRRXAw6YzA4D7TDIqxsvBpFCGgpaKUpvU6bbjutM7S27jlsIFFzY6JksqizcqZ+4FWSkHU79ERAzErKymN9tOkwHmyIXOlGhj8GnAtqZ3Msn2vMQHmMCAglkFqTyAxpo3qdA5SUEVTV2armvoqwVCIO8xD3kRw+OCSyvl+1kmWtNPqTuYa47InaFkUo6F0XiEKy0hG0UkumBKnSyMBc5SnU72+NpWMtD+6+y49uvcPHXrjOhneIN9/mlT/9M44OD6nKElOVWO+wiWbt9DnWL16jd/Ycvc0+Uou6elwDQBnpX93NTfKTLdKTDTrDMd5YcAZmEw5u32Lz0l22rj2+uMfEACZ48NKhkDAoeGf3x3z17dt87Wt/zvm1S/xP/sln+YVzXfKdPQTw3KlrVOcDs/0pJ8cDTk6O6IhAEWJPnxQSZywrWYt1IcmCR4ml6fWX1f5/zmPRayjmT3wDFkVNI6RpDK+Vg5s5tYBoMUvRVPQffv+YrJF1fjRqQdWgcd4/4xa50/eBweY9lp+qmBBZ/rlYVBjhoedvXihcOjfh67SVbLZd6vNfvpZlUZgPPubTttmxl1gP83spYjAW80LhL11Q/0OP+HmB0oBLlxfo+k4EEEIRnOX43tvc+uH3Ge4c0KnVEl1oFHMDUoD1FtXpcvHGi1z56K+TnH4CVjSyDbmLAbu1kHu4c3zEg917zB7c4+i9e6yvbVNVFhNg+8YTiNV1Tm9ssdXv0m1DrxWV3QIBrRVaiyg4gCJY+PGP3uBPvvo73Hr9JY73Djja2Ue6ChFs9PFTLXKVsnn1MpefeJz9vce4/PjzPHb9aVyrxVa3xeZTj1Hk15ntvkE6nHBYHWLzkqSmGQlioNtOBKPZgOP33mR69wbm6V8gWV+hu7oNW+fQJgWxi1AJnbYlLx6NbPylS9scnxxyMgxUpqKwliB1tOmQsTfoZHhCaVusdTP6OiOZ5agyUPloJG+tiwknF7AYfIDSKAQKax1pNcLu/yaHt77EUT5jNN5HKR/pv7riwrUWv/ipFxm/8ie4w/u0AOENSZLhfLQ2GZqS9Sf/LvrUs4QaqMYjQCslvfE0xf5NhDX4iUEEixKStpRMjnYY373F2e2nWO0WJDLQyqKitncKj6yfq7L2940MG1VTFdtZYGtlHRG6FOWISV5gvcE7F/sjpWB2f8hu65jT6wG1eZVnP/tFvv+91/jat34Pmxlen1q+f/t32eg8/0jGUakEKT1BaFyQGEdUJXYO4yyVNaiqIogS5ROE8lEczgqoJF5ISheYFY7puGQyGDM93KM82SdMTpDVBGyONwXelOCiJ5zUsUHWCxnVybMMnbVQSQupWwiZgUqiiqVqVMObs56nymuTqTpGCwGUn/c6EgKkDpIKdIIpJa7JVHmJc7ES6TwktQ1PqCzejwky4ILBmAJrKnSaoA46jLWk7K4yNQprBLiAcYISRWWSWPkQMdA1NjDLA670JB6kM4RHBByDlBTWUKCQThDMiFwE1tMWp7Xm0MHIWjoyUHjD+fOX2LjyONnqaaTzDO+8w+S9W0x3dqL1l4/rcwTqgqR+//TceS5/6jNc/fwvs/mRq4yJQliOSKmvhEARE4G2/l4DwgJgFsPXtEDOixUgonp1/X1VVyGDFDgCqt+mnyjGxN5lVVc2hXeUPlbFE2fJbIGzI8pqhs8TXJaSqgwFFN7gvcCVYLAIPCFLKW1kBcymY7JaVT84hxICKwTOS6SQTI1hWpnl1PyHO44hilx659AqxcuoGO6DQQhotVqUZYmzBqkEWkevUGsdIbjIkKFWc1cJSgRkcFTeRjG6EOP42N5hKYoZwkU/zSZBLbwglYIsEXSzhHYi2Ox1kdZxtLuPnRSkIvo8Cy0JiSRZ7ZBtbpOsnEG1V0FF64t2r4tdWyXtdSlNAb6uenoBFbjBELO2x/ZKi+ONNUZHRwRiX7pUMaaD2IrSCPbF6ml8kIJ3EZvJGFuVRY5SHiG7hKCjTUsTT9exTRO/iXnV6i8+fmbgGClCdSakpgpJCcLX1QcRkFrRamW0WxlaSkxZUhUVpnKRFj7P6D+cy4//X4Yutcx0EwDXwauWgnaa0GklpJqYRSPMzys2esdpGbyvb2j8ewiRVWcJ0WMnS1mRkkxqptOSaWWjUE4IQI3qkUjqbP77gttlSl1NnItULQtOxH6XTpKiWxplA85NYwldqVgNcXXHoxQ13W+eSH9fVeTDPQ4GilPrMWh3BtIkzMHjTzw19Rg89BMVoO/YOtth/ZZm78BGPEFY9A+FKIBBIhkWM3YP36OY7JNxHX/o2X/5TY53dphMplSViQuB8Kh+l9apM6ydvcyZixdJWskSdZdoaK8CspfQ2ziFOTPCTCaYwQgxG+MLg60KzMkB+zff5tSlq6w8eT0CZUKkbygPXvMnv+d4/dX/ks7Kq3zrjw65sPU/5+/+2qe5fjFF0KKzvRY/81TKqNjgaNxhVlcHkCCCIwSPFgqM4/79PYJM0CJmvFKt5/fvURyyFk8gxDkYwU6j2ivm9A2ItH1fbz4NMPoJHDRHag2Aavoa47wixIy1dx9QaZzP4fe9x0+59ubjxPwv7//p8jqxNDFCiGvOHOU193iRNApLcPohILv0r8ZeqHn/BQCu7+Ny6dI3dNVHdzQfF8VneOh+xEuP1PvR4IhXv/Nn3HnlJXRlSJUm1NnVeRqRQFCgNtbpXfkY29d+idVzW/hWvG8SMEYgneDw7h4//M63efut7zPev8PR/V20SpFoWr0Ntu/ssHL+GpOLj+OvXGBzswd9wWo/rl3COnwZyFoaF+A737vDv/3//hbvvP67TE7uMzqYkZHRUQqCAwcCg7GBgzf3obxHWexTlpAma6Tda8wyOLuxxmPPfxSz8wb3Dk7wJxOkMdT+z/VY+RoQWSgGDO/d5/jBCWubl1ldP4UY7OCnhi1j0FlCWVS0y4riEYyfydY4dTYFv8/xoMSjiMS0Wm21TnoFazgZWdZXe6yur9KaTJhVlsIGjDVUzuFDrKxrFYltjsjUmcxKwt33kNW/Y33zRS5deZr7rx2yN5uwsXWWT37sM5xTexy+8UPSWYH0LvYQhkAiEkxVkZ69wsqLn2dBmWmmX0xqqq0bpJefJ0y+jrAVrrRoYaPYAorJgzfpb1/k+aef5p1336a0ENyEYWUonI3sGhkr0U2/kLUVQiiyRHL9ytPgt3nr5jcQoiT4eI2IKK1vHOzcus3BYMrnrzzP8f3b3K/abPRWGNsj0rNdfvXTv8Lk3W3ee/DtD30ci3wa6cyiBGGiWJv3kUlkI1VOigLPFBUE6Nq3WkahGusDeVkxmeaMhlOmgxH54JgwG6CqGaHM8SYHW8VqiFZRqVvVit06QacZSdZGphlBJjiZ1H2NMmoD1Gv7ssL0PG6g7s6aJ6AbSn+9JyQKJRSJlAQFoYTgFDiNFCUeQ/B2LugUJAjnmc7yGJAi0ELT7vTRgxOKrE1lPFMyikoiy0ASNEG3kTL6KEoVz8S4QF46bO5JnCf1JupiPILDWIu3Cq1TRtOSVmK4sdJBqRZTB9V4hpKKEthYX+Xc1inanVX67TXcyTGHt9/g5N57FEfHYCMQjeywgJKKmYd0a5MLH/8sVz/zG1x84XmMjF6WTniskNiaNtzsO4a4JzaOJY66nSUskpaeCATaMlJ5XYjxZKOP4ZyP00pKKqDSKXZ1HSFqNWfnwXmk82TWEkyFNYbMQnVygsknyEQh1SqilUTxLmuYWYvIMpRQtJIM0eqi1zYhVaTCM5jNSDNNS0fAWJYC4x1CerJEEJKM3Ucwjo1Pu1aSEBzO1jEYUZTHh5gIFI3Pcgg1eKx1RerYItRUVhUrHCSddrTSM6ZWYo3zpSgrRFUhEaAk3lt0cHQSSSdJaGVdzmydRnvJ8c59/HhIl0bwC4RWZL0uaqWPXlsnW9/AKQ3SxrnT0vQ21km6fcxwRIKZF6qUdaT5jDA8RmRtukmbKmlT2Wo+52Wi42t9mPctx8t289ivKXoJobHOMB7n6EQiVQuCnXs2hlCz0Zafv78kzvmZgaOWARMcxhkqa3HzqA+ECKSJIGlndDsttFY4ZymLirKMFNXAgocLdejaREjvA7pzWDlf8EBLVZeHW2SpQmCxxiCVRKq4qEIj9hKRuZSN6lAcWKVrM08CSkCaKRIhkUicyLGlwQWPrmdzqAdUNODxg4BUAx7rhdo5jxOSykhC0ibNMoQHEzzeVjWdNSC8RUmBVDFLECu3dRbgIfrqh3v88PUfsrbWI1MZV0+fYnOtNacvvv8IDWhYjsHrSZi0M9LgcfksVguJ2TQfYpJBScirCrGywekrj3Fp6wydAszBLsO771GeDJnlOZUzsaKsFKK/Qu/CJVbPXKDdW51TDcXyPQ8gEkGy3aI9Xad1tEG2sYUcH+NMhS0qisGY6d4Oe2+/juhqOhfPoYoKfTJlkhd855sT/uvfucms9YB/+Iznf3XuCpf/4Yuc+vgK+Cqqm7Uzkvom3Nm/w87kiGFlKH0s60sfg8FUSnxVsLtznz/97rd5/tlPcq63UtuBPJqGcYjVaxBzvouoV4A5YFvEhfPkRkNdre9q/bOw9O8wv9/ziuYSPdV7G5NEwS/1IjdWDO87wTm1vKkE1uByueL40C8sPWhLk0vUvzM/bx8I0r/vd5r7LOefO084NPfiofOLPwsL5Lj4U7z/G7XwxyMqOc5HQ8RFpHJ1j+MiQ9W8AOsd77z+Ejdf+h7pdEKCQLkote+X1r5E6nhLVk6x8cR1zj2xRdmTFCH2NJaVx5YV77z6Or//O7/Hay//kNl4n+nwiGA93kGiEkha3N+5x/bV6xy+9xaDgyd5+iMfQ106j04DXQUqSBQe4eDmO+/x+7/7r3nzlW8zPtknHw3ptvqsJV1WhWCt30ckmulsggmOaZFzdPcOtwx4sU139TztrR7ts5vkmaa7fo4Lz75IcZizvz8gjEcxWCcGZC7USqVSoExFcbDP6OAANT2NavVYWd2CaYHSgnanzWQypcgL7j8C5FgZjVSSzdUNJJpxPiQvHUYpJAGpxZyXFkKgNBKJJ221WM0cSVFhrWJWVhTOYgMIYkbcVBF4SSSmlJyc7FLJW5h0C18GnK144pln+eJHPsHw2/+MMDimVzNlJHE9lqnAKcv25/46cvUMwVmE0ovcVjNHtEJdvoHbeZM0VFSuRASDdBUKxWD/HuXdN3nhmRd45603GU1/xPF4hvcerRRKxqo4UsZEX3AoDUJq0B0O8yFIi25nbOh18jJnPJngQqy4SiEpjCeZHfFs/x4vfe1H7F65wfU1R35f8Nvfv8nn1p/nP/rU4/y7r3z44zidDnEm4EMBchoNxxF4rzAGCmHxfkriJcoaRNJUAhOEUBhjmUynDIdDRoMhxXiCmY2hHCNMBI6uKpAEklrmHykJSkXZ/6xNmrXRaXxfLyS+pugv1kIPNW0SsZSGbzQJ6naReZUxNByTKMQk04REyUizVQJX6Vj9FBopKrwt8LbEBBdpswKMcUynOVpqMt2m3Rkiun2ytI0Lkly1KQ2I0tNOOiRJl26nR6vVBaExFkoTKI3HVjnBWbQIUTjnERwyEQRSpqMhPlhOnTrNpVQzKg07swlCxB7EymlWV6+w0j9Ht7NBW1nswX1mu3fIjw4JVYUSjT5DFGZzwZGsrnD2mU9x6iOfY+XaM9h2SpJEIOgQnIwNR6My9oyJyHQzQoJISAiY4AlSkojIMKg3SZRyZG3J6X5KqmLC2wcwgdr2Q5KXMCkth5OSvKqwZUVSTvG5Rck2urWK6nsy57BViS4rZFGQzCbYwmDyHJl2EDpBqqhLoZNo/aClI5gpWmla7Xb0uRyNyK3HS0E301DP9cpZOqkiBMXkEYnHNR7cgYBMNJkSlGWFMTFZGhWDXV1oitNCQO1hHCn4wUdlWIKrKa0KQqxOBmfqxPjCSkrUMY3wDoQnTRJaiaKbKbZWN8nEJoPde+TDAS3h5oUjqTWy3UL3u3S3N1k9vQ2Jxgcf47O6ctveWGP7ymPcH80oBwfzuS1DwOdTbH7M+uZjWHGBycQwq45IZLQ0Cj7qETSsMomKgqShWRsisPehERDNIrXYOaybYKyJsZGP1VxBrEz6MBeT+Knj8TMDx1TEal0VHEXwVCFgfRxQrSWtTot2p02aaYJ3FIWJD7WHUJdxl+uMDweNza1fVACamDtWGhXtdpuVfoduOwFcBGE+Ug+dkI1KNTLELyUiRVU2byqi+ERzHj54TAhonZB0oB08NnhEFVAIrIwGty6IpXvZPFxiDrRCA0zrh1wiCF5gKsi1QLdSVKdHKj2+mFJZhwsWJWO/h9b1016X5Ju1/n036EM73nn7d3jyuV9CZNex7qd/iAiRzy9q1UQfAhQCtXWRQfknPNjdoSgXUVizaRGiUJHUsHX+PDce/yRXT1+jGwomD15icP8m+XCGqVykM3kH3Q4r5y6wcfEx1rYvoFM9tzuhuR31cx0AWtBe79JeX6e3tc10cIDLc0Rp8EXBeHeXw7s3kS3LlfaMfGy5+dodXnl5n5de63H1xpN8/q//Ez66WnFK9OGqosKSeI3WxEyOVJQnU/q9IUodE7xDqRTvLEIszs8UBYPRAJcm+CTB0UiNP7pDSkVdxmeOsJtqEyxlnBfgbZ51ZimmaN6woWu+nwIbiJYbtS9dCAv11PlvL82PBfZbVDMWJbQmSl18XvNrH/TAN3XAOXWnfh5rfglBxqz7HBCL+WR/aAmMf2+A7HIvpVgC0s3rm0CrAbq1zcj/HyqOQkT66Pu3YFG/YHp4yL23X2awd5MWliqIxc9hnm0sbECtbHP1+U/y+Cc/SrIuGAcwPlKrrTG89vIP+bf/8r/lh9/5BsV4hKg3X0lNc3El1k6ZzY6YTu4yHZwhz+/gg6LVWUe3OiQtkCEGEft7Q77zzT/hnTe/wuj4NqYoQLRQWZf+2jYffewxtjY38d4ymx3xYOc9jo+GTI8sBw/2kcnrdNdOs3Zug3MbffK2RtKjd+YZ+hce0Np8Fbu/A7bOlMjo4yVEDNZsWTI9OGCwu0s2vUKnJWl3VlCbmyRTQdpKaWUZ4/H4kQDHIh/iV1r0222unLqI9Dn33rvH3eMTKhfwQiBDNFtPtMaWBceTiqydcnqtz0arxXicxzRFGe1+YjI5ZtBDqMUpioqAItE38XJKlmpO3Xic//g//kf0jr/L4M4teloivI2K4c6TpikGyC68QHrthfj818ByKR9aVy088tQl/NMfx33/CFVOcVYhtQbr6QrF7PZrrJ55jE9/4hO8+c5rTELK+ubj7OzvU9kZlXEoWe+dXtDKEqwLaLlKNRnTubDO//BL/0u+/81/wzdeeYtuZ5W8zCmrAukC1gWyIue/+Tf/HbZ/nwufeIPHPvHLnBufY2f/u/z5n/4BT2fPfPiDCFS2wJkSa6Z4P0FIiwgqxgsV4CqMmZAYi0oLRJIhkxZat5EywxhLMZuRT4eUswGmnOCq+qucRYAWXPRbU/V9VSrS6LIWadZBp22kTkGq+XpMs8a9f92eHwvwOF9PxfKe3CQPJUpEddbYbiSp0NgQRW4Cqg4sfSQI+FB70nmEcOR5yXA0RqVHlCqhjSTxAt/uYx0I4whK0G6nrKxukKRt8gJGFcxygbEOYyuCLdFak+r0kYyjB6azGaV3XFrtsO5hYKL6/7oA1+1yOM0J3S1apy+Rbp2n1enhyynF4R7F4QFuMsH4UPeYRYs3Gzyh22XjyuNsPP4c61efYWV7FSkDzgRmueF4OOHtW/e5vz9AZG0kliQISiFxQaGDBxWriLIO9Js2sFPbq5w5s4bvpHhVqzXXe1AiFPfuHfH63X32D0cURY6SgkRJWlrQwpFaQ2orUi9ptXuolXXSosTmU3w5Y1YNMMUMqTukSqHbGQkxOZxbiytKZEujkxQlMw4qy6S0SBdYbSckKI4JDPIZmQx00lYU2LGPTgOg1WqhUg1piyTtoUYTTo73UEKQJZoQFMZ6vFA4EfDOIIm9+DIASuKswRiDkzIWkJwhSRK6/VWmk0m0ipMS72zMyRDr+alWZLUYzkqnT1clTA73sKMBWT3njPfoRKHaCaGtaW1vsXXxCi5rUxqD0joW05B4GSBLaG1v0z9zluPZAGEK0gAqCKR3jAdDsvUJm5tXOZxOOcoHqODRQcQ9YM5QXBTdVJJGxvn7hiGE6HUZiIzA+OM43q5hKtXAUdWJv592/MzAsRpP0DIh664i2xVmNsV4W8uyK5SMi0BSZ92KqqJyPlJ2aqTtfey/aYZlURFZgpRLzX5CRJWvTqtFv9+h02mhdMw2i9B4zIB1tT+UkiRSkcmFvMWcAmYdpaviBq4UUkSesBMCoRStdlTCLCgojKPwzCWumhYBGojY0Gib62Ch8CjrvjJnPbPSgBZkmSKkKcJbZKhiD4X0kVwX6ncQy6JDj+443H2Hgys3OPfUc6yuZUDT57IIp5u/NaqqcYEDVcGduxPuHdziD196hfvDEWQpyjoSKbFCzmX6lVSoLGX1zCUuXrnAlR5Udx9w79UfMjg6wFaxkd56h9OK9VNnOXXtcVbOn2ft1Dpp0oioNOe0ICAGIphNul3WT5/BDweMD/YohyNsWdJyBjs8YfjeLZKkoiUn5Nka5co62x/t8fmnL/Lsxx7jwoV4TwpAB0gDcWbUi02cUIZ2aunrQEvF/pPKxoZsAoSqwlWSw+mEztUrhKzNtKpYTdP6MXk0gENGvkp8LpsEdOOnOB9F5vevMcZtKuY+hJqCGV8b50tDTxXzBYW64hhqekP920vvv/iM5b//RO6jCWaWKmgfZB2y/HtLsHfxq01mMcSkUVxbGkC8XOFdBo/iJ76/ALgxWG5Q8hx6e7/41yMGjdAE7uDt4t8Qr7OpXE8Hxxy+d5PhyT49Y4CkHokFMdcQ8FJz8YmP8tEv/grty49xkAdCVtefXGDn9rv89m/+K77953+CLYdoJfBB0Oqt0Fnpo4QkxYO3jIcDitkh+e6E3QA6ucTK1hWSteskOgUcwUtOhjm7O7cYHN9hfLyL1l2S/hrbVx/nc7/4RV547jnaaYrPJ8xG+6y//QpvvvwaoyCZ7p8wuP0Od/obnL16EfP4DdxqB5I2qruFXNuiu72Jv9fFFeNaTQ5EHRgksRSDGQ05fHCP7mBIe72D1C2y/gpBmdhrGAA87H34fVX7B3dYYxvZWaGYTWgpzTR48jInCBkzzrVwQVUZvLTRIzMP7AtPp9Uma2X0ZABr8NaT+wg4lY5qgN7FVVAiKWZjDpXm1MYav/G3/g4vnj1L+Qf/b7JyiggeKT3KQ1Ca4Cvy/iobX/onsLIV1wkpYxKlaVNoUGR8+lCnr+G2LiHzCaIqMEISfI634KYDpnde4/q1z/LYtcfYKBPuTa6wfxJtPCpTy94TfXunecljFy9y/dwlJsqzeeoaO0PBR1/8Rc5d+Qjf+tY3uLX7AOkC3hukjrTQ/eEaafEWO99+i5vXPk1vfMwzW0M+86m/Rn5z+KGPIcSWi9JMMdWQ4GaIyK3GOzDO4WVAWY9zFuUM0mYo5whppBZ743CmwpkSb3NwOcHOsNUUV+UQXAzQtI57jIz0VJlm6LSFTFpRQfX/R9t/BduWZeeZ2DfNMtsff71LW5lZacplWaAAFEAS9ADRYpNskRJDoZCXIvSkJ73oUXpRRAdDoWi1pGYrKIoUmwS7QYIAiIJhFcqjTGZWupt5/b3Hb7fcNHqYc629b1YVigXlWRW37s1ztllrrjXnHP8Y//h/oRFCRRXw2Arjg0SX9KJbsVvAEfbqFhyKthVqbR0ngsewvkkPSmpEkiO8QvggzGWdQyiLjEk+Z4MqviewoypjmBUF7uSEQkoGUjJWCdmGD/6lxiNp0IlAJAlFA4cLOCqg8AGEGmuxjUF4wRkVHDk8nSGVZWuyQaZzqrqBRJJIGCQZp41DYtneHrO7M2E07OOrkrqZMX30kOXBIXa+iDg87gTOQSoZ7F1k64mXmFz/COOLFxG5xHiYzTz37h3w5ltv8s777/HwYIrxAilCtdKrUA5TQiKEwrhgwKG1IM967J07z2SgEX4T5yXWexIBFklh4I//+E2+9c3f48HxAWUlg7Jnb8Cg32PQ7zPpDxgmUNYVqXU0VtJLh6jRmKxc4KqCulhSnsypijkiS1FphkoECY7GgyelqiWpdghrMUJTosmVovaCReWZlw3eefqDIL3jrI+Mpw//cLF63uunDLfPUVU9irkjzXqYaol1DWmWkeYDykbhXYGNfdNSSLzwpIlkOBzinGZRVDSmREoLTqLSXhDYcTaKvoVAIzB3FL00o58mDHtjcj2hWRTY8pCEZRCyc7HylyaoniTfGbB37RrJYIvKtR8XxLacNwjvcArIUwbbm8z2hzQnFcQQRHiBLS2n9x+xlW2ytTVidDyiOjkOlUai3ZZ3oUXJh95JC7jYvtS2FUKoLQgZ1LtFtGrxvqtVhQJJyCSG8PGnxKs/M3Cs5gXpIEdnY2S+xMkiqj9pIMHZBOlTFAmVrTDGdWpf/icEX22Z9LGftUEhgdfcyzOGwx79XhponbGkqlSC8wZrHbUxLJ1DKMUwz0PfT7QIkMIHL8miZlE2GCFI8ow00YGCgEdJgU40CT1SQBQVtra4WGxcNZG2yy6P0UlX9dJo70M4z8o0CCNwyuONCdkMYgYgdMTi7Vqwuh7gnlGw+ujBKRcWFWnfkCjwLlRrV0x88fi/4iTyxlIdLVmWU7771tvc3i8YTC5grWM2PSE1Ca6pQ5lcSJwQjCYXefYjn+TZZ67CcsHRt7/Pw3dv0xQltinx3mCFIx1vMrl0lZ2rT7F3/RLpYM2mJT4PXSDdjrbwkCb0Nrbpb+3R29ilOD7FFDO8CWbTy4OHLAYJh+MB2y88zVPPfwJkEkeipqklSkIuRNjE15IC3nmQksFkhPCCVCj6aY8862FsSVUFHyFlPdYYFsuSo0VBuQmTx3bts0kDdNkm3wrgrJ7VLkJogwgRRJ6EVCEREDPKlk4juQOLYfGKQUh40Ok8I9ejj/a16+f0gWv1H/ivtnK2Xt1sf9S+fq1o+MErjgFuK6gTLtg7F5I4XnZN7rTztv3mn0CPFe1cjZWWEF/5bizbD1j/94d+tAknGbwYjflADL/2xUd373P3rfcopyUbIgkqiC70vEghgp8untHWHk+9/Fk2rr/AgQOJRZqgjPrwziN+79/+G77+1S/jmgJnDCrN2d47z+aFq7z8qS9QF6DNknr5Pjd/8A2qacJ7B4fMmwekg7d4990/ZfPqkI3BJXpZwmxRc3j4kOnxnKNHC5TvodWE7fNP8qt/6+/xV/7qX0PnmlRYkrrm8N4+23sfoZf9MUfTf830ZIYoKu7ffJ39uy9Rl8EmqUk8w8mIzatXmFw4h3mrT3lSUNSuo47J8CiTCYHylvnhA44ePWDr0hNIPcTpgsFoTKIkOIdSHvjwgaNzlvnxAfX8lNo6bBOsENJMx2DZgfF4G6tHSpBqFdYzlTMmZeE9up8wlgOYFoi6pm6pWlLjTKBdFUVF0yhqe8i1T3yav/Lzv0jznd9leXPKRAXQ6LUK3ykFy7zH8DNfQl++3lHo15MybdUeVlVHMdpF3PgY1fEDRG1IliehL0sq+kKwvPcO8sozfObnvsRQOL7+m1/mOz3F/iJmtZ3DOI/SkCqFFzDc3mRgDfXB+8zPXeM//Tv/E773W/+C17/1B1yY9Lh9aKnqGu0goF6oq4Z6nvDmN/4D5158iusPDvjB96Z84xv/DLIP/TZSmZqiLmiqBcKWaCzSNzhXIWO1OHiaqtDn7hpsUyOoQWtM40LLRF3S1EUEjMsAGl2DkqC0QkbBIKRC6BSZZHidYAhVBeFMUGSXgtbbu41BhJBrFL64Hn4wfADwbXViBRxb711JADBSSJJEIwhJZEew6Wj75Z33QR3duKAeq4KCrKgr3GKBPz1B9/torUiyfvDFsw3Lcs7+0SFOjjhaOBYWyCRCuSgE1Sb8z0YcxzjP9mjC+f4QIR2FM/R1RiIScAZfV/TyIRe2zrGR90hdQ9YsMaeHLI/2aeYzRNOEPHIrQyk1SX+LyeUnGVy+Ru/CReRwwKwquf/wkLt3H3Hz5pu8f/Md3n3/XQ4OZ7jaghBYAYkU6IhNAi53pL0+k8kWly5fIfFbZCrI7xjr8EKynMKjozv8s9/6t/zbf/P7VMsTtA+2GXmeMxiOmWxsMtnYxI5G2H7CQAuMiW0lKPJ8iB5MUMMFzGYki4q6qajqCml68TkMoj3OGmqXIJxG+QSpe6BrKtswt4amqUmlYHPQQwiBtWCsQ/4UG4c/79FYS9nU+NmM3vZ5amOoqpo8Tal9g7WG2hqyNEH4BGHqwPxT0TrQO2pjUT1J2hsgjUPYCueC/YRp5vjYN4p3gd3hPMIbUiXpacUgTegnGanUWGto6iXCVDgX5qFKJCJP6G1vsH3lMqLXpzAOoROUElhn8TYIeEocTipUL6c3GaMHQ6rpSZi6zmE8aCdIqxp/ckCyuc1oPKKZnoaUoYhzOFYclVBIHyqHQZCJLi7yIjCMhI/A3lskMTkUi3KGKMrWstXcnx3o/Ox2HCrjdFZzcLRkvqyiN6JCSI2SOakekqi4cLgq+qKEqpVvxTvWP2+t1ND1Waz9rgWNo2Gffi8lJGtcXMwExrogL2sdZV2zNAapNZlOsEpFJB6Uh0xTU1d1UAoUEhupAalSCB1uhgOSRJGJPgZJQwnGRVn/UMXxbWY2nCVdhCnW4daqSuddlFq3Dlc3uKpC2mj8GbqSoi9emymM48H693y4h6stH3/+OS4NNynrhkGadFezDpxWxeAAHI1zLOol1y/s8p/90pe4juDd177H+3dusT+fI2X0r5GSCihlyqde+Rxf+vRnOZ/1qN++w9Gtm5SzBfPlksYaBB6VJujNTQbnLrN39QabW5troFF0z0ZXGQul0DDWKTDOUJtb9PYu0Ds5oZjtI82SermgqQrKoiCRKVsXL4NMaKo6ZHhlSrLGkgmgxUdZ80ARAY/vS17+2Mvs37nL6dExTV1Sl9MoakSkNzjM6Sn9kxM2Lwda9+Of/OEfj/WkRs+iMFBtKBiBnRBRHVhFhWABruW0P6avGnpN4wIiVhkTxBp4fOwcWM1q3/7/2kuEePwdP3kk2qdvjXnQrQnrslmiy4h1ABkCdRVHUE6RHRjuLqH9vO7b2rAZiEkTF1NwvlOglQSLkHaCni1ylFKRqCQEaKzsNToq7bLk9K13KB8d0pMZzrnYo+A7+rzH00hJuneOwYWnyUcTbGbxJkioVxW89vpNvvHNr1OVU0y9IEk1G+fPcf2JF3n5Uz/HJz77Kzy6VyHqY2heQ4tjbv3wNoPSspgXPHr3DXYvXKA5fAp//jxJX/Fw/yHf+Pf/nje//i2SRoIc0Ovv8urn/wKf+6VfRaQ5WerIU43MEvauDdDyCpeuz9k59w3u3L6FooJmwWz/AYuTGeLqblg7hWLr4gV2r1xiMR5R6FNc08Te29X99oB1DcXJAeb4iKS6jurl+LQPzZJeLw9BeyKAww/9LtomSPBbbOg/1hrjIMkk2jjmiwoXrSdUaKLHWI/H4EQfetukLLB2AVoynCiysmJaNVQmlKGFBGOisqOG/uaEv/4bv8FlV3P4g6+QuQKnbOjRdw6vJUW5oDr/JFsf+RtxiYgzqeOn8iNrfdRRRl2+QfLwBrZYIm2FMzW6akiFxtYzpu9/kxc/+Ruk2Uf45m9/HVuekkhP2bgQfBFYNTWCfKfP8y/+Cm/eekA9+yYb6Sn/5P/zf+G5nT1+7Vf/Mv/5P/3nDPMc4R1Vs6TBorXHCs/xtEK+eZ/LT7/KnVnKW1/5ba5dvMit6Yd+G1lEyqxtaoSpguWGq8BVKOHRKkerjCzV6CzBCx3ZJy74JDaWpq6pq5KqWNIUS6hKhLNoIUNPmYpUa6nDXqSD1YZxYGxN4EGEuKRN1gkZW29kiLmkioI5UfgtrFlhRXbeYZ3FmiYkq9dUVb0NFCoJJEqRJRmJTkhSgfcK6xTOBWE3oYIIh3MO52pq4xDWkSCiZ6DHmZpmOafJchIh0VkPZ2sOj/ZZVA4j+lQuhXxAJocokWCcpjaK2llqW3z4NxHIdBAyyfMca+doJdnMUoraUihB6S2DyS4b5y8H9VrpwBQUsyMW0+Nw39xqP7MenEzY2LnC8NxVervn6G1vYJTn6HCfe+++y4O79zl4/y32b77Jw3ff4uH+Kd6HpO2gNyAbDaJirwchSfKEjbzHxd1NLu9tc2lrgycu7LCxOWaUCE4eGr751bf56p/8P/i9L/8uxweHWKNJkh7TJGV3d4etfkLOGOUbXLMk0RPSfoYrPKZ2NEIiZIrMergsR+R9RL7EzguoS3RTo5IUvIr7uSMBjBGc1h5riV6FFV5DoxXeWIq6xCARXobn/6dQHP+8h3UO7zVFWfHgwX0EOd4t8FjSNA82gNaEap4kuCzIoIQqZYYA6qbgZFqgEwMmVPwb40LFzdVB5C8GQFJIEBYlINOSXiIZ5ikbgxxXlBSLOdWiJrUhqSO1RGaKdDxgfP4qerTHvHFkfWKVT0P0uVUyrK3GA1KSTzYYn7tAcTzFLKYgQg+tcCCKJaI4pL83YXOyxeLRKdYsY1IhVKoBlFJoBDrVqLxPWUNRFAjRgDMxEUhkLYTEoCUo00oZfEi9D3M8UOL/7ATAzwwcZ1XN/YNjHhwccTqfR/NTBVKRJj0GvTFp2sPaBmtDRthFaXzRlRLEj0aPa0CsrS4pJcnznNFwwKCXhY3QW6QPGTLrPJV1NMZS1DWLsg7mwgiquiYVkOu4pDoLxoK1nemwa4JhPYlEqVZZyMXsQULWFwyFQJc10hpa1mqn9BoHeMWk9B2OXIWlIeR1LmyiSsiOK++NCcCW4OmDD0F7G/KurVcf+pGpHl/7D19jo3eRj5y7GEr6KvY1iFaV6fGKlQe0Umyf38Znmvz2CR974ipZPef4aB8dq3VSaZyvMcKyef0GL37i57i+d4WsLDEH7zG/d5vldEZjDMYatJSotE9v5zw7N55msLUbjHJFlBKHtfNYq/ysMDrkkG4PGBzvYU6OqKcPaaoFlDVlWTE7nvPO2++jn7jFE+NzJGm6Fimt8FZbDevyuFHOWCC4euMCn3v1Feanhxwe3icRoJXGigQposFyUfDd3/8dnt/dYffcdaytUeps+jeAbsJDrOb7trKvwqZPCxzXwaNcy1yz1ru3CiBFC5RCLju+xq3+TZvt+jEn1T23j0G9x5/lNVr6j/uIx17fZZTaZ2HVdynFqv+4uw7nQspGisc/fL16/xgypZvLQXlWIJzsfhemQRTzPqvm//i3kpAqi28KBEnwrY2BPN7D8RH13Xuk80WskPoOR7t4D50X1EKRbO8yubRLPoSlCGBJWsXxYUFtS5J+wmxxSp+ErLfJ5Nx1Pv1LX+ILv/Ar5MNN9i5YbLFJs+wxHCi+ov6Qh9OvMV8eUp8ccO+N73Hw7LOIG0+hdlKwBWq5Tzo9ZChgZuHi5Rt85KVPIftDamvpxab+wnt0rti6IDl5NGGysUkvS5ifLpCmoZnPqeZznAGfBt8pkeaM9y6xsXeR6a0DfBEUKR9LPgLOGRbHD5ndvok/fQrd36GibaMQIbOsf+at7z/qqCuLcxKfhpxDC9KcC8pVWjlqG5Ktrq2SS0Ao5vOHFLubDPUOJ0cLpBIoqeknMihkOk9tDUoInJJBDVIWfOpXfp1f//gLzH//v0YcTdHaIqOPm5aKRliKZMDWZ7+E2AiCXSvxgLjS+TY5seqxF5ENQzJGXfk01f3b6HKO1KEfyjYVidSUD+7jDt7gNJ/zbnnAVi+laRqKxmAI/bLGOs7deJGnnnuOsn6LZPs6w/zn2X/nywhmFJ/9z2jcFX79xS/wm298h+mt93DOURsPIne0AAEAAElEQVSCCJuWeOM5ePA93vkDw+7VJ/jos9e4dnWLW1/58FVVg1ejoWkafF0gbYGiQssGlSgkCiUdaSpJ8wSHprYSYxyNa6irhqosqJZLqmKBKQpkU5PSejsH0IhqQWOGVCnWSyoTgmDvXFDh9S4IDREsO5TWKJ0EER2doZMMIXVcqUO607kAGpumpq4rrKlxbfXJGfAW4S0KSLTGpj16eY8kSZCJRzuN81mE/Crm4yz40B8oG0fThP3bOYM1Fc1yTqk1iZKkSmLqgtPTQ05mc5zKUb0RebJLisY6TWMSqsbgjKFuzkLjONBwpfIsqgUZDb3egGXRUFpYWotMM3rDjaCErlOkNWANzWyGWSwxVRNpqiLicUEyHjO6eIFkexc53EQPezjpKYuGVAK2hKZGW4OyNSklKs3Y3NjmietXubC71e1/DonKBmyeu8jlq9e4fPkGly5fYmt7jM4V05Mlf/r1b/Dtb3yZN3/wpzSzBbZ0aFmijWdrc4eXnnuBza0NesMR2XDEoDdgNBphbYPQNnr+OYzVkKSYNEPkKUk/RxYlwlQ0VYHK01AxdCCcQFlHZQuWVYHEk8mK0i+pa0D0mZbgfIM1FoHEeYF1Df2dM7iPQmBMaDVoFkuUbNDS4qxDSY2TPggV+tBX5oSPz6pDWB1UWWWC9xZbVWglkFKhdYL3JlJhA2NERjaBdI5BmjDMU4a9HjsbmyTA4fEB9ckxiQitWCiJylKyUY/ezjZqOMHoHo2S2LIgzXqRYdIm48K+ICEEMP2c8aUrFMdTFu8v8cagI07wpqGcHpOVcy5tXaXcK7h9/11UpDcrJeKq7RE6eLsqJdFKhnFxwc4p2Bi2caAEIVGowCSwDVIKlJKrIs1PuR8/8+751nvvM1tWzMsKG/sJJSELlmcJvTxBCaia8ECtKp7rQen6sQrmWiqiiPLlQkp6vYzhIEfLUL8TQiBcUHZ0ztM4y7ypmS8rqrpBJhqNxBhDLQWpjBJXLmxgSki8ramjtL6PNA0pwyROlKIFfTJJGADaga8cNcHUNtzvLrz+QEDcbsCr33hvaRqH1pJ+3iPXEruYBVVaZ7vKliCK+sR3/mht58M7Hh2ccKl2JJFa4KVgYS0HQrAlBEPa4F2sstL42I8R/31lD3u4z2J6Ql2VSNFWSARITzbKufjU01y5/BxPbGX4+zd5//Vvc7L/EFsFA17jLZaEfGuXzas3GF+4Sm84DvSzn/D4fvCnLUW4tzGm3t6hOthhsHOO+fSYal6ghGN+MsMMTykfHWKXS9R40mXW2+fS/5jPDoMTEUoKy5MDqJYkOrxJ6wTjDN4ZnHOcLgsO6wULPweWQPLjPvFDOwJwhNWztl6JY6WGyprSKu3rV7/rwCAy9I3hIyW0fe86cFqB+fB8tDXLLmfSHW0tU4j2PoWT8x8I9ttzX1UUu694fA50vwqf6bq7H17YZcwI88rH6upK5qZ9v+/OJfxwra8z/tv51a+DR6T7aYm4P/8RK5lSerSqsPUS/DgE8TZ4xHlgdnjI6f172NkimFsLsdoYuidYoYYbXHzxRTavX6ARYJ1ASI9KBEdH93nje1/j6P77DDKJKwXj8VU+9dlf4dNf+CK7FzapnaO/qXBVj3J2nWG6zenDmu9+7zUUB0jXcHzwgOPDh4FuDkg5Z5AtGSaWUxxKZEwmF+gNdymdY9RXhOZ8cELS4BkNYbLboz/qoVTwOnONoVosaMoS54JSrBGAkPQ3dpnsXUT138GdTFExqGufgrD8GOryhGp2iCsLvCP0GEkd+tq1IUnOgN8ICBTeQV0H5b4kUTjrsNKA8ORZjlA2GlMLiBu2lAKL4+GD9zlWOcIb+sMBWS6hqOj1c6SAReFwxqFTRWEsV579CH//7/59em++w6M//R4jF57+VMuuf8UzZ/TKL5C/8CuhOt2pmre9zWFSyWgMYD2o1tNXROGpS5fR156G+TFJWWBME8YT6Nma4uZ32HzpMq9+4hXeuXObpTEsjcVUDi8Ezjg+8XzCucElXn/9kKPlv+bq3hXeuVkzrB/wj77xv+e//z/6Bzw5mfPw5D5OeFKV0fg6qI9j0Upiiin3Dt7lr/y1j1M2+5i9l4APHzjKyJV31tCUBaKZkyYNWQ6pAi0tGkMiPVkakoemAts4TF1TlRX1sqApS2xdxyRx2Ed9TOIJqQKNLc2QOsWLUJ1uGkvdVCHw98G825kavEMnkiRpQWNKkvbw3qKiqitR1Ma54J9c1zV1VWGa4HFnTagYCxqUcCEgVxprSpyrggCJSlBakqYZxmusq/HCgWgQ0gQlY+upyopES7QGnQjqJAStaZKQpAk6ScLaqwxCGAQKJUsEJsxJI2lqhTMC9NnYcXgpWVQVSZIyznMSL3lUNzjnyaVC93NGgyG9LKWXCJQx+KbALqbY5QJXN3E3CdnQNOvT29pDb22jd/ZINnfwOsVbS6YDQLHC0wiDxyCkYLK5yc7OeZ548jkuX7vGuZ1N8KZL1krVZ+fKda5cucS5nT3SUUoj4HBe8sYPXufmu99jdnCbxckRRTnHacfmzkVevPokn/n8L6JHfZZNgc5yelmfJO1jnaNpGpq6QjobAJXwJFmGznPEcEhVLsnmGldU+LLE9/sBfMRoVHhP4hyTPCNVmiZpsG3f8UnBYQNZIthtYwUl8U6fic1RmxAtCousSvoDibcWa4IrgbWBVt00FVImmKbGu6jnEFwyEUKhhcR60fU+KqXwHow3OC/AR2EcL8kTxShPGPUytifn6CVjHt15n2p6Si/GAVIJvBbkoz7DnR2SzR2S8QaVTjldTmmKOf2kR3+wgVAapVRnnaGURFIjUgGDHqOdPZrDhxTTOiTFCZYv86VleXef7WyPvb0tHpw8xCzmCBHYRkpF8SwlSdI8JAFtg1bBj77r21nL9HsXqNNSSbyXkU3gu5hY/hQngJ8ZOD56+AgvNcigvOM8gRetguqQ1uBtyNRZG7tCY7ZG+B9VOWyPTkvVExq844aaJAqtVaDJRfU8Yy2mqWm8p7aWoqxZlg3WQZ5opNKs91C1MXFA4qES0zRNUA+UChVBkfMakQVjXYEjMI0UXquQJWzPu7sRq0A9FsToRHPaYDoG303jSJIE3evTSzTWNFCXoVztYp9OrLDJuJE78WN89j6k4+DohO+//iZ/59eHcHSKeniHwXMv8C0CYB20PY0f+Jv2/sUYtRI1QjZoGR50SRA+StIebjDm3NPP8dSzm/SN585rb7J/882wIFdVAI7OMtjYYnLhEsOLVxhc2CIfqi6J0D0rH0R1oj2FNdSnBeloSL61TX6yRX+8iVssqU2Dd5Z6esL09i2aYkk6ngSlA6W750TELwzP6o8pp0kYZQkDlSKFCrLzSuKsR4UEFydFxf1SMJebwBh8WLTO6kYqpVbP2trgdEVBH6rtLvb6Or+qILqYQW4VgdtqZTu+HcT0PgIvt/p9KKXw+Df/eOj9QfD3waHwP+b/V2CAx76j/cb1Odhm01ro20pvd2eyNo9WsDmiwm6gorR1tAsSMqhfOtdCZtFZXZzNEc5MSYGWHluXCOfiwu6CB6m1HN6/x/H9e7iqDF5OEDaPKHgkfTAvz3fOM3nuecz2Jo0NdLPaQJLDyckj7r3xPcTRAdd2dymKjJdf/jSf/8KvsLN3jsZaklwFJWwpGExSkmrMIB8z7PVRSlCXNco1NNJTuXB/jFmyXB5RNwUIgVYaRIJXKTKTUe3N44xAqJB8qxpQwz5ykMb1ONzhqiho6qq7RR4QKmW8fZ7p7kV8b4BXClwwaPZdpjRUyZyrcL4J6o1NqOw5BE5pssEAIc/mRoYWDRcSGt7TeBMSTEqSJn0UDnQT6CTOYKwLdiKNBSTeFhhdMUgz6sYx2hxhiyWFrxiOg/jF6XRO1TT4kefX/s4/5Ocup5z84/+OrACZBZugEEgojK8xwxsMn/+LIVnjVpnJVpjOuxm2FkyrIZV7h92N60ivVouu9yASsuufprx7F5YFom4QwqFdjdRQHu2zePRDPvryCzz/xmscLGbMSkPVhAqcShzvvH6H4tGfcNR4Fvs34fARlduj9B/lyWcV33/tJv/qtdc5d+1pto6POXhwk6YM88/KkPQVKuHRw0P+yT/9//Kxn/8Cz3zu6pncRyU8wgfVdltXCFMilSNVCZkWpMKRCEeuFf08oxKaeVNTmZqq8tRFGRIfVQXGxKR32Fu8CBRPoXSwQpE66AE4uj5YG4NiF0GjNTV4g7ISazVaJzhtul6kRATxHKSMVQSPMRbTGKwNf0zsuXQmgjcVgFWoSAWjdOctedZH6xydpOF5saHag9QInwTBHGdoypICg1YugEclUVJSphlpnpFmOVmahkpeIlEqJACsrbGmoS4lTeWiKvLZMDmcM2iV0+8PUFkSKKrY6OUnsSIlG/TIMoX0NcImuKrBFgtsVeKsJRHRmxuJSodk43Nk4wlZb4DqDSHVYTxF9GRUiiyBLJNcunYFsi2uXHmSJ598hotXrnNua4s0Matr1j3GW7uMxj3SNCpflo6Ht+9x9OA285NTjucLnLeMM8cnX3yBp5/7JT72kVfY3dvE+pqiKViWNVUVKsDOWow11M7hTYO3NQMhSLOEdDQAO8Eu5gglMXWNUxWqKNFphpLBGq6pTVC8zRKUaLA658QXHJ2ccHI8Zby9zSCXjFFIBNaBceJMgKPznl7WC2rjzlIWVQcWnQ+CNs67GOc0eAGJTkLs4xq00gghgzBnovHIgE+sWxUPJCihwRtS6elnKf0sYXs8YpgOOH4wpT6ZI6NvfHiDRPczxLCP3txisHceBmOaqg5evHge7B8wLAyD3gAhVTgHqUjzlFQbEIL+aAibW5wMhtj5KdZZWp1DJRV6UeNPjshGY4aDESfzoKTbsgMhepYWJYPRGJlJTFUGFmH7mkhFdZFVCSHqCG1WIEQspnkXxubPOH5m4JiJkJe0zmN8G7qHXkQdy6bGGay1BKU22fXvfRAyhvpG+5M1QXwfgKOM6lPOO3RcdI0LIjhV01DbhqI21KXBGRAyQQgdKUmSRIdyL76VKFSxpBcCDtcYjGyoY9beIRDCIVKJTBQei/UOFxfkxyX7W3rPKgDp+rz8enDsCXKXFm9CH5yXEqE1Ok2CYEWU2JUQq6IhGH7MAeRDPuplyfTeff7xf/lfsT2dYqpDso99lNGNZ3np458JIhutB2GLB+L1rgI8Q55W+PoEWyxisCIwEpxKeO65V/jiZz7LueEQ+/4dZrfeZX58QLFc0JgG4xpIE+TGJoO9S5y7eo3J1qQbTVgVOv0akGmHNeC8NZAhPdlwwGj7HMXpQ/L9R5Snp6higWsMtlxwdO99jt98ncHGGJ8NYmwUazVt0sKLrsexPQlPCHo3Nrdwsse8AiPBujqoWhGe9bpskCZlfjqk3IVcqxXt6wwO1QpctNTPFlBFIOtcWymM1YVOEdWv9cdFJoBo/8Q+2w4w+g50rsAWq+eiQ4Y/ATR+8MdxEWuBXjz97vXr68PjhO/2h20wu/ada1/UAl3RalJL2V1ctw5Ff7O2yim9C30orSqhkIEKGtcG64KxouNsZmQ7REqFJFVdFIFSqSTGBxEL6pLDh3eo5sdkOBoXEwFCBHpu4NmikoTx3mXMxg7zVKGjp1UQzVFIUfDyR65xbai5/eiAc5ee45M//8tsbu/hRfC1bUxIZFkPeQbDscW5EikFiVZYU+CMxXqFR4WKlUrQSpNIhRSht0pIg9Se2oETj2MRG9f5Wkq8Dv6HOBcCKmfD57tQnQ5tBJr+5BKDnSuI3jB4vjahItA+3y3wxBqWsxnFosBZT4NDaYlIEhKpCXIAH/6RKrAutFFYQv+MUgJbG2pXBWVFBUIqamtJs5yqDlSpfpYFAKAE1nma+YyH5ZThaMwLLz2Prwvuvneb8bjPwXTGx1/9In/vV38V/+1/T3XrbQY+J1GWNCYUcFBRoT73JcRTL+N9UM5uk4DTxQNKN0QdFrz5xn/gu2nN9avP8Zc2FLZNRrA2vSd76EtP404eBBuswoA0CO/p1w3LW99j5+Wr/NzHXuGdd9/lZFExLwWV6LP91JfY2hsg5kfsPnmByxd3uZBlvDOdwdZl9kaORzfvc/36J6mF5a39GXl2HvSCpimp6xKrA2ARXvLW2/s8Wn6Z0fnLZ3IfbVOGfi7bILxFS0GqFKlSZEKSCclAJ4z7A3rDMd5AM22YlxXV0uDKCltXYJoQaLbrbowlpNIIpXBC4WOvuccGa7M24U2MM+I5eR9U2hsfxkAJi7MGY2poNEoqBDoC0AA+XZTt91F5sQ1O2p6nmOoN/oLGIpo6nJtM0TIq+SYaZxK8C5YHwgVvTpzHNYamrGgKTa0TEpVi8gpTVdimCWyJuDd6L2jqBiNKGjenWCqa0qCosKI+k/uYpynDXsZyXlAtagSWxlqECxTu/iAj7yX00jRcl3D4psTWFd7YgASRofIrFel4RDIaobIBKsnRWYZPCL63eCxB0yJNhuzsXGWS9VDjbc6fv8aF689w7foVzm2PyJIQWxkPJoakQgVld2U8y9MZy+MDmuWCujzFKUk+usJzNz7Gi5/+DDtXnmH34mW2RgNcPWW2OMGYA4qipK4MRblgXpZUtQ1MwLSHFAlSe1TTUItTvJKhSCIVtna4xgf1Te9pyhojFGke5OUbK1k0cGduODix7I73GCUJ2hqsDt6jWMdieTb1Ro+jqkuUSpHCobRApyl1bTHW05gAxLXS6CTBIYPIXF0FZWkpwSuEEkjlcVisa/DOBWuLuMdKAZlUDBJNP80ZD7boJWPmx0csjw5JXd0xfLyAJEtRgxw9HjG6cAm9scPUeHySge5jqgYnEpZFibceKRyjjU2SNMc4g856JInCN5LR9oTR3h4nh/v4uqKNeaSDtCpJ5se4SY/haMTpg0MEliRRVKahrgO9tbE29LO2SswxcG4xiYu9ym1VURAKciGsCvRdZ+1PZVb9zMBxoBSlddTedkGYkyrQjFSgfPp2sVoPJruepsdLpi7+DtrGzbUqm1h7rw8XbZyj8p6lMRRlSV01WCtQBOPStq8rTTSZVkjhAm4TAhezzsTsnPAe2xgqQqNqILfFChESkYTythcBPLr2/GIA6mK/U0tva8/1R0jCcZy88zjrcTFwCB45nsR5klDrQOEDcGzfdkaAwxUV8uiY3/+t36LY3OYv/OrP8xe299g6LVmHbiEeb8FU+G9FQPFeOHY3NxkJiZvPQ++NFNRSsXXhKp/+9Bd57vI18hLqu3cp791mOVuwrCqMNUHVL0+Ru7tMrt1gcu5iUJV7rLrZghh+pOJIO4FZvV6OINse0Dvapre9y+LkCDU7RdmGpqqoj0949MZrjDY2mTz3Al4nsRIe6ZOdJ97aF8bzUVJAv8/towMO6pI6SakbQyJDVcg5cI1leXjA7NFdZnsjVB+SJD2zDECbSQr3Zj2x0fY6Rsqz9xH4Raqpb+doqOwqGSptSrSV9ZgY8b57XadK2gG/gOiFIFpF+MdeJ1o0yipxJOIv2ip2q5zczsn2xgbM51cKqeGmrMBjd51+tWbEMWj7cVvAzNrzRATB69XGrg+zfS0eKXyga4hguWKcBBOUmc/kiKcnZTDarouiM2Nqh900BcfHD5nNT0LWkNUcFSJUD8qmIdM5V248zd75S+GZdRalFYnwCNOwtzdg+dx1hlvnOO8z9s4/ybkrH2E4yOilDtvIDuApJagNFJVEDYfM64rpsgieVErRE4pekpOo8Awppbu+YI/DmgJrKgREimpUfWsfQxsBZ6LDmBOTiW3g6wNVNTiupAiVkfa3yQZjhFarZ7W7u2EwlXDYYoktKqwXlDZWRZIULR3GnI2Iw0c/coN333yPuYEmSrqb2oEMAgxKyWBl4RqElNQ+GnYKSCebXOllzOeHnJQOaQ3SNpDkHB6cYmxF7ROc8WS7m/z9f/C/4snqkIdf/SqiUtg0BP1SBjGG0tXoJz5O9vLnu1yJ9QaPI9EpD7/9Ng9nR/zet9/ljdvf4td/+aN87hN/NTyHq+kS5qv3gXnz3CepDt5F1wu8rcBajAnJgWQ+wzx4gyefeYGPP/Ms+9Mp02VJrXp85tVfoTj8IR978hzP7gz47Xv7HBwecG5L8O6tb/JHD97ixoVP8IXnn+Lb07tsTwb88vMf55tvf58f3H4Hb0NVVklJXddkecrh7bv8wX/9f+XZ53/1Q7+PZTmnqYJBfaIkmUzIUkGqNKlOGGQ9xv0Jk+EWurcRWmXMKfOioi4qVFXjTd3RBFsRMkFQUpVKg0xwQtA4j/MGL4IeRBhyiVQSiQ7xRqS4eWdD7IJGokJvs7UY0+BV6LV3Dqx1WLe25nuLwAfGlcy7+d0aiIuo8O5cqFQqZZAiVK+VVvhE463G2ZAoErhuvmIdpjKYpMGkJlqRhGpn0xjQDiU9CE8jDc4U1FZQFxpbWxQlwp2NOI5QkuPTU1JPVEMPFgWpEFyajLhw/QrntjeZJBptw0pjmqCL4I0N44FDCIVLUuRwRDIaIgZDxGhM1u+R6qDeGoA7gYaucvRwG2M9yqqwduke6BSv4aSsmS4rahcSk3mqyVNFmmgS4HQ2pSyWlGXwdB0PRvR6V9i4eI06P4/sb9Abb9Kb9ClnltTU6CSlMafMlwvqahn2EJmBVCRpTqZ75MJiOEEoDVKis4xaFri6jtcLlXHYXIISFHWFdIKptdybV+zPax4sDcd2Rn/u6SUKBTHOECyN4caVD/8+KiUxpuk8Bx2eJM3ROsM50NrTNGWwBrQOnfTBBdEoBxDZCk1tsE2FUrECF9lJGhBSkEpBHtmTw16PfraBLSX1fI6wc4R1BFK/Q2UptpchBjmjK5cQk02mpaFEsFwUNEVDNTcsp8ugyqtnDMcZ/f4eOs2p6xgDu0B5JdNsXbnM7OSY4s5trKkDFvAC19TMTx6S7464tnuJ6rjk4aP38bbGGhM1DgLmcmVoGwtyWS70Nfsu+qINpIwNydOu0OVDoaH92Z91/MzAMfE2mJ/6NWAhFZmWpInq5GydjYbZxHQ3vgOPreR/V7XzPgaNLRAQUVSnvVgRA91AbaytYdkYijJ4AAp0kKfF470FHFIJtBYo12arW7Dnu++LrY/UjQ2+KW0I6R3OKQQpUiVBEZa297GtcIgAptobQQQfa8Go7yJdQTDYE0BUUVMpKknRAnI8mZChDd2HHGAYqzVA+iEfzjjK0zlVKth75iN8/i/9XT67l8PJrAvC2gA8XItDIKnmDcvjAzYuX0CQcjozHE1LLHQ2Kf2NEZee/ihPXn+VS4Mecv8uj959jaO796mLoMrWuKAqmI4nbFy+yubla/RGI5DiR6uLtNXFD/y8BY8BMcSKIaixor+5QW9rj8HJEfOTI2xTI6uGZjrj8N5tJjffYbh5AXnhPKgV1m+rYy3l2Hsf++Q8+zcf8r2vfJ1ysc9wIKmnCkECvsY7j7ENMpU8ePc13vz+v+GTT2+wMblKVUN2Rvo4rTG9B9b9Lp33XXapzah14jFRHbUFeFIEsQYpVPg3kd5KSNbYNfDV0XmJa0AEjZ3gjnNBsY8IakXLi4vfL6JiGWE2hUx7PIfI1fexEupd/N2PmQdd4bDLyYuubwix8jtr65qP5fDjGLSzlkhRDQMHweRaRns1FRgCVuCcoTmrDEA8pJRoqQK97QNCPGVRcHhwQFEsI6ht50VcLwUYBHk2YGvvKllv3F4OwgaxKWE9e3tXyT+9SV0rUt0PSti6R78HwgjwBq1DFbFyNvqHKe6czrl3ckppTAB73pD5hkFCpKoL0iRDSo33Ah9Vr030FrHOYxRd5VYiqBqLExKdJGipUDJ02DhjQk+W9xhCj5GU0TagNyQbDNC63W/iM+lXSQQpCFYBZYnz0EiFQaJ1QqItvjkb4DjcvcDkeI48mVE1NUUd+uklskuGIESgt8t2zwjzbDlbcGwsx6enzGtHlmSkicIs5py+/hpSKNLRJl5n/NKv/jJfevU5Zv/tf4k9eUQuIQ9UFVzcS2opyJ/+EiTnsC6oCLYMn/e//h7/xT/+txzXb7JzecT/7n/9v+WFJ19GJW2fi+wSeO2ejffQ3yK58hHcyT7CGvyywUqPw5Aby+zWD8lffprPvPoqr737FoeLmv658zx33uF659m8kDObPiTPB9wv7jCfHULZY3tyldHmDdRIcc46XpMVN5v7FLnCSYmXwc8QBNbVNFVNpnu8+8MFzz7/4d/HupxhmgK8iX17GWkqSFJFlvcZDjcZTbZJszGNSyjLkqqw1FVIULq6BhMVDZ0NCTAZE0MyAEcvdVg7fegzCmuaj89ISMRImYRKESIkJo3Fu9CbGNZREdkgJthLWIt3AQB662MSP9BQpfCoNEVJDbEq7rxHSI+UHoSN7IqYtJE2yPxLEQRDdBquSQRhjlWsRFSRbWiqIAzU/lunFqkd6CA4Zo2gcYba1uE7rME0Sxo3+/BvImDKglRrsjxhksMka8jzFJqUJy9dZDDeYdIfMMpzZrMlS1MwagpsbTEmsnUiy071esh+H9nrIfIcqxU+agy06x1C47ygsYYai/GgIk25sZbTpeHuvYfs79+jcg3IBKV7bAxHbI5zJuMxeaJYFMtQ8XYGqQZsbo7waUqjapwyyERRmBJ0gso0fgZ1VVDFiqMxAiFSjBU4UrzOUZlAOELvaZKgkjQq+YIzJd4UgUFFinBV6GO1hsYITkzDdGGwiyU9MaNaCtK0T2kCMKm8o/GxMHMGh4wJftNYtAi+iY21KJUgROgPFzJUtYOtRIUUNlDOUSA1XiSBTuzAmCYky71Hx9hBSkGmBYMsZZgl9NOUVBhcVeKqJdK5mATyZL0UkyvEeMjg0lVOVca9R/sYA8uqplkWLKbHTPcfcWF7i6sXL5Fh2djbRo1GVEbS7+cIEealcOBTDaMRW+cvcXR8TDk9DhfvHalOMMYze3DA5Poe166c53DxiHo5jVTvwAyUiE7EyongHdqGLFIEi59OWRUZcVaoQLq4D4WE/Z99P35m4Jjr0PugfajNWQQqS+j1MvqJwuNw1sQqRduc2lYaVp/zWK9UF8KFv1vo5Xyw2WhcmHjOe+qmpiiW1GWFs+ESBDHDLVbUuiBkEbwJhZMxtliregqgzW/HBdkQjLOJjaIChUgD/cPLmPkLtyBWU2RXQWkvpa2ArWBODJK9Bxd6pjwSqVOETZHOob0jiUbMsgWO3SecEXAkKPPNl3Mu4Ljz3iFfPxrzqY+MVmInInaZrXENTe1YLAs2Igi4+fCY2wYWw0HgV/f6yK1Ndp76CE9cu8gFDbffeoP333qd09NTirKmtA21cGT5gMmFS2xdvsLGzh6jQS/2lohYPPaPVVx/7EjE+7ouISx1Sn9ri8H2lPLkgOHxIctiQWIqfFlx+uAe9+68z+Tq8+xMzuMHweesrVYTRz+oT8EMx8IJjvenqPdv86lz2/Q2x3z7jbcolKZoyih37ukh8CeHvP39r/GVJ67yzDM5F4bbnN8+I5lqa9egjOhApIs01RY8dgCzS2zEd4jgGyhjz7KgzcS5VaLFtRVE2SV4nBOxGhh6qWTr4GxtlzARsm2y9jjhOs9TIWVcqFz3fMtohi0iRdpHKqEgLHhts/Zj61nMGHZVzEgBQ8Y+kSg/72MPwwpCruZn628ppOyuHQdeBnAtRAz4ukfxbIBjNw5SkiZpJ5bVDQJQLJfMZlMaY0hjxTYMQ5s8CNeaZmMGoz1QCdZBpgRRNR98Qp7tkvT2AmWOoB0mPHgbeqOEFGgZ+g+TJMEYeO0H7/LlL3+Vo+NpZG9YLlzZ5fqTl+j1NdYEK5E07aN1BqIAJMZC3TjcKh5GEHo5pfA0cY1UKkEpHYTKvAz9XaYJ+0mkwqrwF+lgSDrsx1xcSMp1qYG4dgVxwxJblQhCBcYjkUmCThS+ORvRqlQNuHTpGvklze07b3FwPKeoShpiTiIq/VkvsI1FOYLlgodqfsK7xyZUpzwUVRBwSDuVPM+iecDuUzf423/jH5Df/BqPXv9TpFUk0nYgVGrFwjckr/wy6fMv4wHlHNPDQ958/w6vffU7/Lt/90/44eKYVz//F/mF3/hPuf6R5/BYnA9zvGN9rINH4thef4nq/l2S5QJZl3EdCPqbcjnFPvg+1268zEeffJZ977nwyse48eQLzOtjFuVF1PwB33/j/8ni6DZbkx5/669+Fu03qDc/xsufusjX/g//G1KpeWM248rVTa43z3N0cguzOKU2NiYnBB5Dlp3RuloXYGsEQd1dJ8GnTaUp6WBAOp4g+iMKJ5lPS05PFlTLKii3O4uzTRC1sYa2PUDGKqKITAYRAaQWYd21Nia+vY8KiAItJalWKJkAoRfJRi9oWKPA+thO4Czexb5358EGJXnhHUqJIFyje3if0DStpL8F0YRkYlR/Z22v8JFeiwoJbyct3vqupyz0tjlq61B1gy4rkqIk7VVkWYPqCVKZQtLDJzmWhMYFAOmbClfMsdXpmdzHgdKMck02yjnfT7kyGjLKUiqj6fdSsixBpwmLpmJeLhng8HWNcJbGGRyORGfIfEC6uUN/NEEPB6jBAKcTnPCBlh7HwPrQMtAy1IyWZPkG1dLw3ns/5Kvf/zZH9+6RCEvey+j3BwwGW9Qbm9jlkHK+IE1TbFVhWjaGTECnLJsapcNeWNc1xjsaL7FS0TQGRUameiy8wTQVZVVTe0jckAaJUSlOStL+AJfkKJWFvVoK8BbfNJimIctSrLGBXi8sx9ZQmJpxknJ+OMT0JIvaBYqotTQ+jEHjQTv302/Kn+OQQpGlCiVNTEyLAAh9SD5YawLrUarwyDc1XtVInWEaSAl2gY0I3u9NjNfwYc8V3pNKQT/VDPs5oyxjmKdksmReHWCqOTomzGWq8Qn0JimDC7sMdi5yv6i48+AuhwcHnByfkCHw9YJxT6Nthndzdi5fJRtOWLpAWQ1J7IBtpAAjPHowpLe1h++PqedT8gjqjDFYJHZeYU8O6E82GY2HnJSLTmhNqRAjBW2ZAKTX0uOh9zmKo8lYBJBCdJR2oAPQrYDPTzp+duCYJmgXuNnWe6wQ6Dyln2dorShMWLysd7RVurZa1B4dbc13JDFawBh/05YyIk0sBEVVXbNYLikWBbZxSELAG3wSPU60guKsKSKKDgCIDky2VUxCBYSQocOEioJTYTH0ToIVJFK0bovh/ER7K3x3QWv4l07fMP667cHDh0yz9SIESUojTBD2aOuRbY9a+21nBRy9CL42SisOH97ih2++w1MvfyyO/9rrfFvNC9c02MoYDK92qpjD0RWuv/B5jhtY3rtHOtxgsXeRFz/1SS6fA3/rIftvvsX86CF1UVBVJcbV2ESRb20xOX+VjQuXGe9uohO51rjrfwJS/AkX073WB6XCfp/h1jaLw20GGzv46QlluQRTU08XPLp9m7277zA+v0M63IuJh1VZ03sRVIHFEiNqrNzghVef4YXs1/jhH3+L9958nZ6WKOk6oKakQjhIrWdxPOdPf/AmpE+x+/yEEJ5/+IdtF9G15MWq2riygWmBxfrNbQ2kg6pwFIWJGezVnBT4WIUUUsXKZACNMr5OquArBgIvVChxQTSzj1Ri55B+pdglhEA6j1CxqiiDwpmIMthCSLxyK+AY53B3+i1AaOkmiNivEea3iwkgvKMlicWrbtFosMERodLaJq3Cp7q4pli8DQGVdUE44qwPpQJdydmgXhjOWIB3lMslVVnEZIHvAEZrFxOuR5BlPSbjHbI0wQlHFsG7se3oKWxtQ+Ct43ooBE4EGr2SKk4pi3aK99++zR/+9r/hre9+J9gKqITBZMy15z7K5ec/ihr1MM6hVUKSZB0AhLAp1cZgXLiOpM2u4gMrJNLwtNLR106ABWdMt3mFjTVmTwWIPENkGVaIoFkc562AYN8jQOGDz6O1IZipa1RfIJIEmToyczYUgJ6YMXniBtVBxdbWCb3hZYrFHfYPjpg7H5XGQ4Yc6/FetL7bAVwkGhE3LyUUWnqccRRVg5SGOkv5h7/+9/n49gazf/FH5EWJFh5sTeMkuUgRWNzOBv1P/iUQA0zd8Pq/+pf8N7/zr/jqD28zPZ0zNRVqNObWu9/kh9+6yDMXn2S03aOdYY/ZMbVJEyHCnOpvkT39Eu7wNtgS6RuEcXhhGeiE+d03SXeu8+pnfp7Nl59n58kJv/97/4aLH/0YVu5z760/ZndjyLXrH6dYnrL59FPMihEns5q33/gBR6cnJJNtPvX8M/ytj3+R772xye9+9x9x+t2vsPQjls2Coiri+nY2varCVghnOm83KYMNhkxTXJJSSoWvLbZaMFtaTk9nNEWJsBZpbXh+oyhbCOLCerhKnEmQKq6pKgpxBXXKVtFS0PZdiWjhkeBkSPQZH5OG3iP9qp2jA30u9nhb11U8lZBorYPoDdGOyhhcNEJvGSlta0IYiPhIiNY+JPhHIg3OBkEUYT1Yj7QOaQyqKlGLBWnao58N0UNHTwUPR5f2sRYWVUNRLLHTGaKYIpvpmeyQQicMhppzG31GIoifFEWNVZa5Kahcg2kqmqZCGMNISVwTBFWEBj3skfUnJINN9PYF8u0d9GRCbzJh0MuDBZkXJFLhjcE1FXiD9B5lDImQ3H3vPj94+KdMF7eYlTX9Xp9+b8Bw0Gdjc4Pd3YqBEiy0RUtwpcRWC5ZFw7IyWCnDs9R4VCbwLmhMpELhrMQ4QSMUhUmYFY77j45wvgFvIU1whHYyL5JwvkqBykI1W6rYeuVir2xD6oMKtastSjoGSnJhcwPtK8g8jw4dTtRIY3BOUVmBaAzUlsaezXwsiyI4K0jVMvvDs2oN3phoueeRUpMmGbapMc7iXRDQlFVF0s/RCrwTQRsi7qMt06+vNeN+Tj/L2BhvMZCC6cP7FNNTlAsVOpVoyBLSQZ/xzjnyrV2ywYixzhkcnXBcG+xswWK5ZLOn2dkcMtaayXiM7fWZu1jrk619FgQ2pA+tcFqRbU7I93Y5OHhA0tQkQtIYg0eEpMxijt6c0B8OONlXCGyse0RKfMu8gq6lBVpcFWygAjuwHchQpCOuc0FR/88Ovn92cRyVkEgFUTjC4FFJSh5pZjWuRWw/9mh/E9QLfQci25+GAJ5QzVMhUG2cp6hrlouCsqgwTTTHjhWS8PpVxs22QK5b9fxq8Y/BbMRxXZVJERZd0zSBXuXCHim8I5Uy+BV1O2gbkLvH+hu7ME8E0Oji97b+KT7GstaBjaqCQkqUdyhEpPvFLMgZA0chBcbWDLIMf3AP92CfyadHrEhk3S3pKKBd1TvR3dheunCNZGpIqlNez3JOCsH5T32GTz/3JOkSyjvvUR3cZXZyTFmVoToiHHrQR21uMrl8nc1LVxF5ivEEukF73W3g8tOupX1pC3ClR/Y12XiDfGub9GgHjvaRiyn1aYUzlub0lAdvvsZ4b5sLu5tByrwFLFikVewfLSm5z7W9IdtujhMj5HPP8oMvf4vvv3mT6WKB9Tb2h0icgLIxyEZzfDLlFy5c5JMvvRg2zTM6nLVxkRBdlda7QFH6ESPQNhYQMZURAZloKU9A27sYMpGhcb5V+Q0VPRVonNAFlm3FDkSgWqtV8NkmAT5oCdIp9bbnFPtsBAKkRyrXzc2wVqzPsdUFuQ44Qts7GU7Xg7DgQzbRsdZPKSV434qOg1ilacIRfPO8Ddl/J0RIkrmzA47tOq0UpNpi6prOM1KEYKFYLCiLMk6LQI1qqYeSWNGSkjTP6A/76ERgpUMJQiUx0oKFJAiZqeCPF4TFwthaQ+hHAhSKh3dP+Pof/wFvvfbvcPVBEApJ+5y7+AIXrr9KvnkuyIlHemuSqBD8ilCFsramNhWNcyACDRUXlU61RkmHk6B1qKq1/aVYS8iJCKSOF0cQkfAKrAzewe0K2VaDRcw5Sh/Ao/IBTArnkDIBpVCpQNuz8XF8/9Yh29kEVy1JBwNEX6PUNk1TYeZLjNPM6hpBoN16QgLWCU8iZcd2Cc+yD3NOQ5Ym2GbG81/4An/nN34N9fZvkuzfCmBAVNgErFIkSrEwiuSJv4EYXorVLonb3ubCpUt8vFbcfrTP0aLkoGj49Kt/k//x3/p7ZMPeiprqV9X48PyFv0KeNfxe7t3Ann8KU56S2ALvA8tIComoFpR33uSp577Aszd+kbfuZXz2xdeZnBvwvTe/wsP6bV565Yt8+/XXqKdzfudrf8Klj/81Nnf3+Nbv/ROabMS5q8/yyZc+xXT/Ne7P4JXP/RqffPUVfvOr3+Ttt/d5dHKbZe1XJ/chH4Hu1aqhxoSUSnBCsTSOcr4EPM6m1LVnuSyjGE4Ntgl/nI3PM3GdXK19UqmggCpCpdF6gfeh/i+IPcbe4QU4EwRUggqnCuuCcGuXLgjl95a6GgON1oaBGGe0artSIIQiiYnDRlhcIzoqtY/JqHb/FbF9JFr8dSqNbSXaOh9EuZxHORdZYXN6aYIdjEm8J9cZPh1QZgNMXTO3S07mx9iTI7J6wUA1ZwIcvVIhmWUsaM2irkmUZlZp/NKhlxXDxRLhBP0mtD15HDJR5KNNmswz2Bjjkh56skFv5zzpxi7ZcMJo0CcWefCmAVshXYOwIZFSz6fcenib73z/HZanc5QEpSWnWjIabdK/8ST55oRRJtHUmKrCGE+mBHWxZDY9YracUXvDcrbAi4yeDO0kWdIHo1icLpjNTzg4PObOw/e5e/tdposFUmfkSYYSCYgUpVOkTsIcl2HtlzoWL0SopFnjwjgBUqX00oR+z+MSzaiXIq2ncprpzIKWZD4LquPeMXQN6nhOk57NuiowOCdQIghxhuS4oY1bwh4Qks9SKIRK8dYEurF31E2Bm4e2COcDk8MRlEuFd6Ra0Us1mXJsjAYMszGLRwfMjmZoE5KpCIFIJKqfkm/ukG1cordxHpFljPMe167dYDQYc+H8EQ/efhs7O8IZz8Zki42NHSqhwp4tWvAfYq9W+0SI4Kuoe5KNS+c4ebSPe3APa6soFCpwdUN9ekSyO+Hi7h7LwwVH+/dxUQkgANJAHw9jEeinnuAQkagUYyzONDHxLFZzPY50iKX/7Nn4M9/lVKUrCqEUeCWQSUqqNFbEjC8tIawNRtuN/YPCNy0Vsn0RrG8EwQ/LYOuGuiwpixprQIgkZLS7RnKBFA4bAZ2zruuP9H614Um1UmpdhaAh69uh8k7hLPjsCGswUuJsAKRdcI7jMSJwm/Fb/4+48bbZQHxbRAgKtMprZBNkxnVUfgwqiXQ0QHk2+yIQaH25TpHFnH55k54+pLLbJGpteLpp+YEjXtNgD540EyZcw/o503LA0y+8yFhCWhYcvPce87v3aIooiANBgWs4YuvKdXauPoEejbE62BB4VsHwOk31px3re2iYJaA3UrKNCfnmJr3tHeazE3RRYqs5djan2H/Iw7ffpL85YvzUk4iTBagx7vQIlXpkqjH0wPbg5l3kU8/ztW9+kz/47teZ2SXgMBFMeCFC76+xiCoob7339rscv3yTJz/ykY4K8GEfK/GXDm7T1caF6DxBW1GnwIL3qyCGqDgas9UrsapQqVdahyx2BI5E8+ofBXIhMloRzlf3ToiVeIlY+5fwsQ67dptFnFPCxyBn7bPju1pYG18Xqo6edl7G9SdSNUS05Wjp12Ltc7rP7nrjIkUsnlLbH+RiEujswtTVoaRAK4dvqk4VVoiwGRTLgqZuOrCLJ1TonO0qj0iFyrJIH4ZGeKwAkYQ+zbCxxKRYDFqdcyHwbMfbQZLC4Sm88/p9br75dY72f8hifoLUPZLekItXX+bjr/wCl85thiQCAqUFSrdermEgrTM0TR19oyRehN5mYcHVDUmaYLRAJ5ok9uBpAOewTaxGq9W+Fir7QUq/oz7H5yCuyhCTiTaOn0TE3q8oTKLDuZ7FMdq8RFpXzGlobIPwOcvaoAcZT+ztUMwH6PEO5ckb7D/c53i+RIlAgTTWd15aATiGTT9cr0NvX+Af/A/+pzzlp5x88+ukTY0SIctuvSRJQ7+1vPYk+ae+GMbbB8/HV37xF3n5c5/h5P3b/OHv/DN+67e/zqk95VBLHvUH7DlHFoFs18/YIca23z4+jN5BPkQ+91Hc9F3cwyUyqRCNwuHpD/vMjm7i5xfJ+i+RpSVwxP2bpxy+e5c7h0ve+u43GWSaT7z8qxwdfJcX87ssqj7/6vtvkA0FR/ff42tfd5y/9BGSS4q/91d+jmc3/yJPfuaH/J//T/+IxfdPKMolZ6VVFeidFiEsQuiwTAgRqjuVoVkuMLYCl4BTmKbBmhpvTZiPbaWxzfivP24RvBGTbQ6JdXIN/MWA2HucddTOBvXgVi0xbnIi1ku8bwVrFEKoCPwC20LG+9n2oSNCSrrttcR7rIvruY8Mk1aAtQ1aBAjZJvSJy3Gb6JN4bwOF1gWLJ2NLmsZhTYb0DVpKtEwxZCAyhAKkwLqKxsxRZhGkI8/iEJJ50TCwnqavqLyhaAr2T5aMNmuu9DfQ2RKpNUMRkqIy7dHb3MGT40uLGA/I+iPUeAs9GjGabKCThEY4lLB447BVTa4gV5BI8KZierzPvffewi2PyUVQc1X5Bjd2L/Dcs8+we/0Gw81tsl6PNAn+l6YuOcVRFHPMfIqtCg5PTigqR2+8wfndi5zbu8A4G7CoLcfHx/jimNOjI1zlOT+c0O/lFChEEYBTsA5LQEickGRJgvOCRXyCtPcIa0Pi3Dmkh0wn2DxHZZZeniJzDS6FSrAzGjArC/brhtoIJqlkgmSyt0kjJMdncBsHeY4xoS1HSkmqExoTxJdkTDhLGfpNjTEoH4NtHxRVvfdUpgLvO2sOAaRKBopqlpJnGePhkEHWZ350xHL/gF4UPZKAkwKdZ+jxELW5Qb61RzqcULkGKWBrawtUglMZ1azg1vEBi9rE3s+YJO/YcSG53xa/CD9CS9CZRo9H7F6+wnw+ozytCAsQJALMYk716CHja1tcu3KV6WJKuZzSS1MGg5y6blguqshaoGPTCQg4I9Zs2/Yf27W8tEU2+RM91NvjZxfHUUkU2gjZMKE0Ok3RWkfV0VUn4Y872iUvuuCGQDFWHsXaRiWcxzYNyyb4/XnbinEka1WMNosXsmKiBa0u+JAYF7N9BAAmIt3CtxUQvwrA4hLZDZp3HusttbDYuJG61u+OKOYjxGPD24aWHc1jDTwSs3NB7lighUITfNfiykyLckNLYXueZxPgSKFIkjScjym4e+tP+P5bVzh/4zdwXpLR7nXhQV+vOnaHEOAtXNxBlBP69xIm567z5LkLjBLH8Xtvc/OHb3B6fELV1KHR2jtkmrJ56QobV28w3D7HaNgnVaEyJFfz6meL0kUEBmuTUKaK4c4G1ck2drrL8uSE09kMYYNc+HR/n+zebfpvZYwHnqOFYn54TH38AJs8Ql2/jBhe4t3v3aK8fZvZt37IwekpV3dHzI4TqsKSpwn1siJRgrqJ/Q3OsViUTIslpalXwdhZHV3GyHfPXltVjOsN3ocNPiwMa0qlhMrVurpxeGxDVtx7CcqvrkEGm4o2sG1xOt1zvIKObdCxShaxmhtdmrC7iLXPCPM5/Of6i9bp7B9YY9os+5pabCcaxNo5tN/RfoJvWQ8xM99+S3x/W6l07dvO7AgfrmJPU9k0HVW1HSpnHU3dVnbWsjvxfgkR7qNXApXqzlesjuMu1xIJ7UQOy2CwAEIIpAyV2MW04eTwmLvvfZt33nido4NTQGGd5dyly/zcL36Kl186H2JfF3qEhdShP7MVSiJ67pqGDkgSsK6WYaxDFl6TZVnov5IieKJaG7zsnMcS1h5royqrtVgbEnudYYTolk+iUwBaBEqSwOONCXYCSiK0QumzYQG4+oT9ewV1f8huNuLk4BG1h3F/j/4opX/pIrvjPR68e4JD8+SNEWY+4/b+I+Z1FQpF3oN0KBRCBHpd7Rb8wl/7df6Tz3ye4vf+c7LjJdKD8xahQibbCIsbTui/+t/Dp4NAU4ziHeCxKmXzmWe4ev8FBn/wHTJXc/jO93l0dMDuzu7jYDE+M6sKPmuANuxxavcJuPoJ7GyJxiLcFGcbEp0xUJblg5sMHvyAiztPsPXiK7z+3bf4nWlC2t9GVDWfevHnuXvimRUJ3/7dP8GKE3rac1If84W//Gt84plXePvoDpd2PsONrRTfS7ly4SK9qxfxb73PyJbU5emZ3EdrDQIbqnwi7P2NcdC44Gvog+m4sz5QNhuDtU1kCayUq8MTGVlYgjBP2jVNCKQM9mFeapwR0Xxc4XygAFhvoxddSA4pFdSMldQdkAzqbjoAx6i/0CbAwr1cIb62VUDKKObSMj1EAJWiTaJ162fLEomCU13AtdZME+kK3jqcqYMFh/cobdGZQiYJximqEqyATGk2+jlMehRFglh48Gdjx2GsYVGkzLxEFzN8UyATwWbm6Y+GzJY1vbJkbzAk0wInUkTaIxt6TJPgM0s+mVALST4coHspjiaoRTdLTKmRNiVNBAhHY4LxuhA1db2MCrwZtV9w9fqEp57/Eh977ue5dnWb0+WUytUYBIlSCGdoyhO8rymWU06nJ5zMjilMTZaPuHblKT758c+BtSzqmqKoSIwicZatjQmmGDCdQ1E/Ym5qbLGkP97oEoRChH7dxgYQ64UMbIfVExtaX3zol2vaDjnh0MJgbU2qJI01TIslPZ3QzxP6iSKTGuFg3pwNVVVpTZJmGGNCy4VX8Xk0AYsAwoX9RhIFo2zYl6QQWEKILQmsw3Y/SpWipyWDTDAZjtkYXKCazpgd3CdxJYkICXWhJDJLUIMeyXjE+Pw5ssmQxluciHPdWZwQlAaMT3Aipaw9h8cnnK9rst4QZ0OizxPWDx8sFpAyWH557xFaoHo9BlvbVIMhy/lRSEI5FwCdhWpWUB+fsDXZZXt7hwd1iUfgnCZJEvJcYmIBw8VafujzbwKwjkwz3zFE20RltGj7KfHqzwwcVUdRECv6l1sFca2oRrtIhexv+H17v9aPrv4Q17aOnegsrrZx4XQxo6A6eo9vKwgdUGspqHIVZNnA5Q29Bav3rAeSbT/iutVAAC4heHQIvIgiG/F/K6of3QLb0uraQHRF42vBaQuUBd5YmmWN9DUqBjcf/O6WsurFBwbsQzpkHBDT1GQqYf/BHf7l7/wW/txVfunlz9KDmBFpz40WWYe/YozhDZDATHrUYMS1py6ytQscLrj3+veYHTykKirKJihvoRQbO+fYvHyD4aXLTHa36SXBK6jLcrRjtwYUfurh/drDHoGBFKj+gPH2NubogN5wTDWeYG2FNwa7mHN6/x6T7TEPb47I955k6yOX8LcsXub4qzc4fLRkdjLj3N4V9s5tcf61d5jdesT7+YhBv6A0lsaE/gCpofEOJRVlY3j/wX2OiikCMKb6UO7bB4/OczGOQbh60T13opsvARj5ruOvfU7b964AYAs4BeIxo3S/HjDEnsN2YnnRjvvq75UC8ZrokGjv6eN3tn3M2zzJ48tW+7D9aEpKrH+vEN012bbHswXTMRnTmsqurpXVivABTNuOQVe57RJHH/7R3kOlZKAbLuoYeNIhX+9aNWtJa9TbqTESex6FQulAw8GBU2G8XVw0XWxmcJE1oaVa9YQSwJkwDlMuuHfrj/jmn/wLbr59k6bJ8N5y7vIFfukv/CKvfuaj9HpQmpBRCjmF0D8lVaSyt/tp4NB2iQZBoMpKKUBClqakWYbWGqU03kZhp3WRNYLKdhA7s0Hd0du4Hsd+edE+cyGBaQggWgDONGjZI9M69oudDaVK6gJvPaK2iOGYjfMnmMMGKXKqogDxPvdP7nF0dBr2pSwl1dts1w32dIqnJpNR0diHxOHSlVx65gX+h3//bzN+9HVO33+DVBiEDteqY0BUOYO/9hJc+kRYD1XLyIGwP4ZzHAx36ff7SCE4vnWLd++8z3MtcGyZBH61v3VV+o46IwihZgrnn8Hf/gG+XuDFEuENtmpI05z5wSMWb3+X7KMXaXqXeOfwh9xbPmTv2YuI92/xu6+/Rf/aDTYvXeJ0bji6f5vB5JQXPv8qn3zmed689R6LC0fcdid85/4hz154hq989Qe88olX2B48xR//3n/J9Hh4JvfReYsSLrI/g6hE1RicF+hUkOg0CEcBlbGhBzoqQLW176A8/aM0fdb2Kak0qcqQNqH2HtOA9RLXlvecx1oT7DacRysDSRL671QSAV+oNIpYMRPtwka8+b5bQsI8jKwRb1eMrHavEG1MRVzvnO2slqQMc9Z31xD739t1MshVIr0gSyVZL0VmCZWHellT1QV1LdB9wYaSZMOcxaRH6WY0y/mZ3EfvGiyOaVVxNRU8e3GTfj/j0Evea2BWWYzrMbQaJRyFrxikGWmeodKURHtU3mPQG5IMJmHd8A6JRTmLNIa8l4a9R+hA1cDhXUU+yLhw/hzndq/yxFNP8+rnPsb2uadIejssF6fIecLp/ASDwZsGa4LHYFEsODk6ZV4ZnNZcfeIFLm9d5dlnn2c2nfHo9JTapexu7TFM+0xyzaw55v3qLsfLKSfzE04LT6/XZ9If4L1AJzn9QYZfnlIbh5WexpYYb6hxiDQBrQO9QwisNyGVKgRWKXpSoaXidLHgtFhi85RzSpMnGUvruvYuc0aFDuchzTKE1tiy7hKrguA/6mNsiQehE4wTwTdYROqmCWw3VNgvFYHdk2lNnikGecq418csaprjGUnTIL3FOPBCkmQahjl+PGJ84SKDrS2sFFjfrhECY4Ny9WAw4lBorFAsqprCWJbLBWmao1UW1JSVQAnVppXCeca4rLEWnWWozU2mmxuYo/uoukKJSNG1DoqKbLGgP95mkqacqoSmrlmYBUqrrg0ueEcGDCYDbzUA5xjHiSiG5mLsFCfNT1XH/Zl3T0GUvJES64OSljUW5VbKlG0EFhbKtUDLP/45XUYrAqy2YtduWOF1ATB2QV7c3EL1aw2Jtln3NWsA6xxWtcqnLXBc72dqv5Mu6Og+sdtD/crKoHuhWH1eG3y3Deo/JrBss7ZtlcZbi3EViQhl9rafkTa1SwStZ1ilErKVJIamcngheXDnLt/5xrf4xRc/AzLSZj1xIwrV4TagaJ8rqUPpe3dnh+Ene4w39kBYzL1bzO7epFrOsI0LzB0pkP0e2fYFti49xfmr1+kN+7R05Xb8WpGaH2fL8ZMvaE0JsE0oKdB5Qj7Zore9y3DvCFvMMWWBqyqcLSlPT5g/PKK5+iTnLl5G9Afw9A2CMV0Cm0t2XxwyykcgUvIHnq2th2zcP2WaVNR9gfGKxfI0gDTXkGiFFoLTg4d85xt/wMtPXOTK9hNncBfp6AYroEO4uSG9HQILQiAYMslt0BeDnB9BYnRzTaz9rKU60FUbw3eEW9VSIlbn0c6h9ggBrHhsHq3m+GrFaCFna63w48Di46e7Rndf/74oDBSqN2EcZIycQ//nagxankQcqC4oCupjMgKv0Dfgz1ggR6rQJ2iaOpznBw7VUtrXUmAtSDJtTO+haUwUoYn7mgzCXC2Q8KJtyI+X6wW1sTgrSZxk//57/OG//+/49jf+A+XS40TOaGOTL/7yX+SLv/BLjMeb1BUIFbPZRIsApUP/VgTb1tTUTYXHIUXcsAn/J50MJ6cUOkmiB2QA99a5GCzbzghJApmC0jtcWQUWilg9bIJQOfU+rF06y1C9Hi56WWWpZpBoEs7uHkoJqllS1JZy7xLbyYhF+TanyrM5hGq+5PDEMtrdJpcKu5xjRA55n41kwJVexuHDd3hwMkfqhEQojKj5S3/97/KF81sc/uv/gnTpkDiS0MgZ7rcrcRdvMPrF/wSfysi+eZwR0wLAnfEGozQnSzJm+/u8/oPv8YUXP4EWkHSvXXtPuxavUVa7quPeOcTV56hPHiFUhvTgmxKBp5/lNPdvM7j2kFzBxTTn1csj3rj9NreXlivpAbe+/A3m232ubV/k4Xvf4cVPXuKZQvEH//yfUu/0eVKd59rTMzK9w/GbhiPX56/+zU9x+1jx3Of2+L//H/+rM7qPoQ8wUDSD16Ktg49c5gVaZaRao4UEbwPDSZquF9pFqupji6lgNWsFMXEShMWElwgZYygh8VJGtmtU4TVR5MwF6rVEoYRGKdGJgoXl0q2AquDxda1bIOPJtA+Hjz9bq4SuQpkV8A3Jcd+FQH7t/cKDcA7lJblOGfQGpHmfGjhaLPHlKVYJ/HKJXHqEqpD1DO3q4Ip3RuvqdqbY7mX0Ms2F0YBkICit5c7BlHeLJRt7E0wj2a9h6Axb/QQyhUVBmkBZk0iN1BlNHTxLAx0/3kMdgvm2TUo5i/SBtqjHY/qDMZeuvMBHP/EZkkwFm5LlAuVzVJ1R5TmVLzHCodOEqlgwnS+orKQ/2eWlZ68x3noer7a4e/qI2XxK2u+zO77ERn9MufCciAF3H7zDdH7KrJjhrWHc32B3a5dMe/o6YWtjg0zXSB/YeMI3CBp0lpCOR8Ff0qXoJA2MDeFQOJTQeCmZNg5pHWVjGGU9+onCmIajumLmQDqPM7Coz6bi2BiLmS+RSgc/YNvEglJrL6Gw1qGiHkLYs32r04eSeiXUh0fLIMyWJ5pRb8Aw76G9x1X7yPoYFS2kvACRSJJ+ghhnbF25zPj8NRqZhKJS7FOOux3OW7xwSC2w3lI0DdNiwcnpMb00IxtkofLnA200tKbFfuVOYyPox+heyvaVy0yP9ikePCARxHmi8EWNOz0i2d1k3EsZ5kOmZopxDaaxkQUQVxsholBhG7+EVUEpiTfBvjDE9hFcttYyf8bxs6dd2yjPt4qHMUiNa5IUa3SvuJm38Kxbtn4ksOzgW/falnq1DipWYWbcEkUIizrAwcpaQEaQ1sl++HbjjFS6HzMwHXjzK8D7gXh0tdb61U/WzcU73NI9pMQFV6yCa61IkxzlPa4po6JRFzKvfdHZVDcAGhO8gITUODxZloY+pONDJm1q2reUOBGrLS14bE+wFVOwDIbbDIZbOHJoGsTdm5QP7lHM5zhjSAArJWI0pn/pCluXrjLe3EHp9d6ZCFD8ipPdxsL/MUcnuCJakOSRqSTZHCCmW/RnF3GLBcvTE6rFDF812LJieXjI7OiY+WLOQOT4NCMqijDp96HfxwHmYUMy3OTyk8/x0bJEupLlgxLZZGjTw3uD9glKhmB30Bje//6f8pt72/zCr/zah3wHwyGl/MAD2ho5rIklhF/QPat+LUnTPqXtdAJWgLINGOLbY29jsK5owWk7Odfpr+0ceHzmhNvcPluPBx5r6JG1mRjO06++57FnIaTFefxdkki87Qp2kqBirJQCL6JkfWzkofWMjFnLeC4iqq2GPjEC20y4jj76YR/tuCglSbTCmHrNjDdkDdMkQSrJ+hV7HzdIQuXBOYKPVxMsk9phCt9BF2CGdIJaCV7FIFNrwcn+kt/77T/mD//911nOYyIkdbzyqU/z+Z/76+zsPYnznlRBZT3ehgESArQWIW+Bx3uDEBVCVPHfaZcYCB6ZgULkfPTdjT7AArDeRSpPoK+HP55Ega8qmuUyBNK0S2W4Shl7uJyDbDgkH/aDwiCGPJFkUiC9xf1HZ6R+tqNazDHGkfYSyvkR+1URBKxsQekSBoMNVJqysHPy7BKbuznv37nF5s4em6MJd2zJePIsV+qag0dH3Jqd8Plf+g3+Z3/711n+6R/Q3JszUdHaoQky6iJJsD5DfewLsHkllI2VWuVA1hZRD4w2d9kcjpBSUc8rvv+1b/DGp36Ozz79FMIGAQmx1lzf7mmI9Q5jQUhfpIhLL6HuvElTLrC2JBUe7yzDNOFkNuXo7W8wefIX2d17hfk77/FwWqCyJxjqDYbyfYoTCyPBxsYGNx8MaJaawZVLnN/e5JWrL/Jw2meZTTgnK/7yL3+So2VFOknYufQyv/Lqa/zw4LUP/T6maYb3BqLsRGhdMZEiDYlOSFVOojMgwTpPbQJV1UQLDFzUYheis/JaV3snxgjGGUz0VES40AMYlxrrgvVRUKsOEZW1Ais8XnpEEuYS0uMIVU/XrYttcnClxxD28fCnY2B0APPxtW31BHhw8XNYa3UQPMa4UihSldLPhvR7G6hkwLL22GaGVxlKW0SRYE9LrFtgzSLQPZviMdbah3lcmAzYzDVGQC3h3tJzfLLgvZMFR/TIyxLbHFPkI0Qj2JIa0gzvHDLNECb07RbLOck4o9fP45hljCZjZLZKvmnd0B/0uXLjWbbKGjncYrJ1kY3tbWrpaUwQY0mTBJnWmEzR9xmJSKh1iTOG6fGSxirGW9tcuXKd7a0dFouK/aNbzApLrzfGiwxjSw4P52idMStOqOoaiWNra4e6yRBSMchCD92gp5AssbZB+hrna1CC3niCNALhNVLNqIpAtw6JO0michCCRVUjNEEsLu9jlxWLWR32Gg8LH/qjhYPano1+vDUB3OBk10/cFQkgtm+ECqBrmpUycIz7g3oxUdAStAiU6SxV9Ho9eukA5Wp8M8fWi9B9IRVIkFmKGuSMzm8z3N3F6jwo3XoX2s1igjuRoYoZ5kZgYTnnef/OHWozp//qhK1+SLRqqcCBahlQIrRvhFMO6sdeWfTmBpNLV7DzBWY+jXMurCfz+ZTqwS32bjxH6VJO33mLuixJVcgmdtwH7xFuBZpDuOewTUWbOJcAgs6+o1UL+EnHzwwcfdyJRARDKvY8tdNeiqB81NlLdG9sod2qd2m9lrCCTaLzMGqBVifGs17qaoNbAa3J+GO0CynRQgbWv28HrA2c4wm1AWoH2lbn2v7HesdmCFA+cN5irZfMh6ws3bmvsn3tmXtAaE3S18jGYU2QFUeG17sW4Pp2rT+bBVUpRZIk4Y8SKJlSFjXzg0ccHD9kZ/McTqooxqHI0rZytcpGE68xNOQHqXmdAEdT7r32NouDY+qywNoKMKgkZXj+AueeeIqN3fMkmUYFlmp3L9fHrxvz/9gjAvCVOmCorameJNsY09vcwx6fkI8eUU2nVKbG1CXL4xPuvfcuYvM7XH/x4/S2d/Bext6h0DMrhSQ9l8AoYWt5kc27d9ic9Ogdg582JDqhMRaQoU+gEdiiZnZ0wDe//TWWnI1v3Do9dXWs5kk7J7q54R97uBFx4fMEfNZVDfHdtYOP9hUBqEpEN77+A8/oKt/RzpL2dy1QXT8eT5aszo/HgGRgJ7Qrx9pn+DYYal/YJq3Emg9iEJ9KpI5N8QJnQ68ebRDlzYpS2Y5mpH75GDQ+vvac3aGkJFES1wQrgPb6hBSoRJOkSagy2LUxb9egsPJjG0dVNkH0gCB2oTSr6iKs3bvATjKVp59oFqcNX/ny1/jy7/wRs9MCRAra8uSz1/joxz7G9rmn6Q36yMQEWqsLNMl2iJJERXVUcK4hSSDPQzbW2GD3gRQIHYCgteAj6AxtBRIhgprtutqbEAJU0ByxRYErihCcE9fodkMlPMfWg9carwTOGRSORAcqmBACoc/GHme+XIa+NeEw00NUlpPubJNN52CHLEpPUx3jvadkH6zl+pWLJPoqN+/egm3Fc08+z8PTfT760su8Yj1/9x/+L7mYHvPozd9lEGBxB9ukTlg2Bc2VV9l4/i+HudSyEATdWtiOoQf0oM94PEKjwCbc+t43+ef/7P9G+nf+57x04xJ9QMbASwiBl+2sgsfmX0wYsbWNvPEK7uQh2pVgLcbU1NMTBJ7ZrR+Sn3uaVMLy6C7nP/48vfcXLIuK8ZWPcnHnPEe3Xue0PEHmNXeWC3YewflzV7DWUhx4HqnX+fgXXqJ8f0Huc+zIMxwN+cKX/jI//H9/+MAxz3sY04TkhbehBceZUCAXIgSvqUVrgdaaRFt0tBMSwuNFDHTX0nbRTHq13vkQWFpM7OeF0NEbqwKRqixlgtZZWNV80H9o3+udRdDKInu8V4+zSiIlPZzH6mePVyB/0r/j3W7PuZuLrPzD3AqEKhkS4lk6IEnGSDnEkNJ4D7bEoXCNp6rnlNUUY5bgDVKGhNVZHJuDIdIb5sZy76TgtKo5mi44WS5I+ynKQy/RCO9jO43ACkma9RB5Q20LSqHIBkMm21tkgyFpf0h/PMTWNbXzpIMeqRBsbp1DZRPqCiZO4UWP2mtOZhWJgmE/JZEuCIYZSHpDxlpyenpCWTVYGyra1gnGw00WDZzevk8/7zNINKnS1M5QlIZFOWWSZWz2gs9jb3ObrTxhVs6ZnSY4U+GFp3KOCktja9L4XKvxJFiieXBFgxWSsqoxtSXNdWB4iGDRUDUNRgjyPGVe1tw7OqI8nSEbi4rsI+88NSEhWzaWwRncR2ssiVJ4ahQelA6PsQhYREkdfUVDP3srLhkyo0HfQQiFVgKcRUtNP9fkiWSQZ/R1Rnl4yuxoStI4pAixgkoEqp8z2r3EYPsiLukjtKbxoT3CReVk4UPSUkUPaRFtTqwHvOTWvSPcn3yTp59ZMNnaYTQcM+738TicFHEeh2qiEArrHFKn2KxHtrnNYLLBbDkLe5z3QaNEaZbzJe7ohMl4k8F4zKKaxQT4qiWoBaPCBdaPVMH6x7oWMEY1c3xHt9fqz94ff3bgSGQXRQUj2waXMfIPgye7ymM4VlHgB/Na66Hgai0T3QWvv0984H0hIPaRQhNe0HrYhQJJALHtL4X3hCpDWJx9JH10S6Z//FtWmP2DV0CHoLrft9F5984Ig1uk1WbEY49A7TyJD2fgxSr4WW0rIWA+q1B12OtFYQuPcZb5wiASzcHDB/w3v/3f8sW/8jfYG24zm03BaLbGA/qZfwykwwp0CxJUksCyYv6Dt7m/f0BZNTR1hXU1CBhsbLJ15Rrjy1fZOLdHlukVXbELcn70XD9473/S0dGpunNagY7haITctpijQ3qb21TTE2y9oCkMy9kc+fABp7fep7hwhXxjM/LlY5AZJ5F3IPqO7MI2hRUsqiWjXkpfKhaNp64arK1w3pI4jfUG3XP0jg44eO3DD26AqIgZ/t32ILUBi2elPOxjZa2bOX71lIXhWo0ZeOg8IG3g3juB1CKqlK7m57rf1weTJd3P8Kwe6lXlsbPHWAuuWvDYTX0R1geB7yqI6/OM9XNo7TxioqYVggki90EsRQhJy5B0HX2Xjjq2qgbEim2LGT+QZDqbI0h0ay2DSEYrjuPDvEuylDRNuub+LrnSVnzxCG+xVYmpqqiY6nEiWjK4kGlUKoyTi4kqjyBRUM0rvvuNb/O1P/pNpge3gg+iztk6d5GrT3yaa0++zM6FHjIF6xRK+JCFjqwFKRR52iPVaRDikZr5vGL/YMqyCqb3xnmyJIx3ZQRC0dkcyfj8hv5rh3N1oP0BXsbqi4d6fko9PUXYFXU+wCnidYXvSfIeSZaDc+RakSU6Br0SmZxNj6OSCrSmWS5JkwScpZaS4dY1lJtzvJjjxYTN0QCVapr6CFfmvHP3mxSZ4hKb/PAbf8Tux/4az3zm49wYDfn0E5uc/v7/C308i76VhESOVjQG6smA4V/8JcTGKPakqccAY3dE4CjSnI2tDZQIIX91XPCV3/ldDg4O+IUv/iW+8MlPcfXSRVKCmmDiPTgbWCBtz3v4wDAv0cirL6HvvI6qCmxtUMZijUUKz6CaUz74Lpe3Xuazlzf4eqXQiWIpH2HSPpNsn3ePb4E6x1OX/wLDpGS+fJ3rlyZcnAxRB+9x+MhyukyojgpU74ALV0dsjbd4ePf9M7mPWZYhpaSpBcawYilIVmykFoPFsZBCopXCaYnXEtew6rF24VkPcUhcZ+LcdERdrwhOvTOxWilQMkErRZqkaKXxNogG+ijEY50JaskWfKQtC6+6ddV1637ohw5rb9xvV+XPVfVmLThrQ5q2at2qvPs139f19VsphdYpSuUE9dQeWW9EoocYn2IaG/31KhpbU9dVbO2QqDOyrHqwrKgrw6P5kqKsWJY1c9uQZoJhliLznKX1mLJmpBKkE3g0aInolShXoHSP3nAjsD2MQ3pBOTtmPhP0N7dIMovXCcb1cELhE4F3CXUTVLmTJGVnZxDjUsnmZko5T1jO58yO74BX1I2grGqq0mAMzOZLZouazZ0J2XCIK2JltGponKY/GLExHrIxHjDIJjS54nB/ybJuQDicIiQ3UBjb0BiLygb0B0OUUhTGsDw6pppNWZyc0JQVziicUCHLqBROK0QKSSrJnKMpC871cxa1RfWCHYYXUDkQjccaj5U//Z78eY5QVAkATenAlKmbALRl255mYszhiYq/MaaUHqVUwALOooQnSzX9PGFjmLM56DPfnzE/PEFYuoSxUArVS8g2hiTjbdL+LqT9sPzGvdMJgRcWi6BuamxtGCoZrJWkwuMxxgOaW3cecHh8xHA0YWvzHE9dv86Fc3vIPCXLFdY2sQgT92sPMs0YbO6yGG1SP7qPMlWgxztHsygCC2e+IM822Oj3mGod/EQFawwyYpwUVmtr407pI2NNgI+JAuFsXDP+bOr4z757thWhdlOK8uHCt1VGgSQaiq/zgTrq4QpQelaBWAsSHweSMXAX6+vZKphaYbJYhP1ApW+9btXK8Qf6SaAuia7jRq7BtRV4WT+Xx6oca3Fwx7eLAHYdNPrVCXbZ8/Z6rTVIa7rvawPsNsg+89qG99jGUtcVWkmUU6RIqsWMr3zjq6TXL/HZj73C1mTA4shxOl2QbOSkWavS93gQ7ZxFKsV8esrN936IwtJUZVTD9eTDERvnLrNx4Robly+Qb+RroDH2u8WqwfrFt3We/xjwKKCjuhKD/fbfMtH0xxPq7R1mB1uY0yNcMaeuKrw1TB/tM9p8QHU0xRmLSh//0u5eIiDxPPXS01TcpXzNUZaSd+89RKAwxuG8gSi/7W3KznCTK9tP8fr97/7/f98+cDRN0117AIGxWoak7XX03Qiu/enu4erv9nXet2CvpXD6tpgVg3i3ynjjaf0gvSAIuMSs1cpOwz82X2VcP9Y9GFt5dyAae0crBdHKRke6tG2TVG1SoJ3/dKIPAfi67sqFt2ANXrY02xD8rSe2hNDdnF31gooO/AYFyzgIZ3CImDSSUpImClpDbujWjTTP6PdTjqWPlBvZ3b8QlHikh6ZcspieMjChR0V4R1v8sDZYb2i16mNSwmMrwa13jvj+t/+Qe7f/A8vFPYTwpNmEnb1P8vwLf5Pnnn+e8TihjoCtvU/tkAuVYFXK3DhqL1A6p5hb6pllrPuIBnwSEmRNE+hHGoVOJHmeohKNEyHYtrYhqFqGKqNIQiGtnC84fXSf6nTa9XC24+QjCPZeIJKMbLwJ2YDaCnSSkmdpVBm1XTLowz76WcZJA3UTEoL1YkFZ1mT9sEbmWY+NrTFVvcTUBdXCcHB0QJI7Rnmfo0cn7L7yaXbzy0xvfpun/97/Annre7h3vk/qFalywZ4kGt+XpiJ7+W+RXPt0WIOl6ubTui9jq8ppvSfVCt3vo3SUY7cgThbc/IOvcOdbP+BfXrnMRz7+UV766Kd54enneO7KDj2lgBScCwye1pKnTYr2+ugrz2IO7pI0FbiGzELlLJvjAYcP75FtPcUnP/VZxLfe5NtZydiPOTh9yIP5LZQsObf5BJ+4sovZGnPnqMe7797ELDKGXGSgau68fQfu3mF+acBH5SUe/HDOwZ3xmdxHrZMQaDmCFZcjJmdif3dUfTY22PWYKOQkCMkDF60vnBCrVsduX/dxnbQBaMQhdM7iop2HFASGhNYkWkfgmIADI2tMLfAuWFz5JtJViSqrcelqw691cNclDdeTbz/yt3/8p6tgJ+zRa3tIuz8HtVYV+5Q1CI3UOVl/hMzH1EayWNZ41wTBLgUGi7NNMHX3yZkEPTcPTjmdz2mMJQU0nkEvpdfvMUxTFuWSg/mCc6MReZaQKoGzFicz0uEWHoU1nuVySm0MxlqqpkbmKb3xBnVRI3RNf6io6yXGC2oHRVWTpgM2ehPSBJrGIZRgMMrxEmQS1riyDgmxyljmy4qiLCiWNY33bG7tIESPZXlKM5vS1KfM5wVZb8IwHdDPFePBgH7SMJ2VOFe3RrxUTUg+WC8xxmMah04yjDM0dRVsHqYzypNT3GKJrSqcSEEr0ME7VmQKh2TcS5iIgtEgZ2EKpj1NbQwSTUPoa0004DSmcZyFDGDIszi00ME1wcUuf+/CHJUiVtpWpZ420SHj38I7tBTkOiFLFMPhFlvjLYqTKdNH90lM1YmsORFU+XujIclkQra5ge73MDGLokVAEbNiycl8Suk9/V4fczKlnDb0+328lFjnI3U3xBbzWcGyaDjaP+Xw/n32drcZbW5w4dIeOzsbCOGROg9q2RKc9CTDjI1L5zjav0NzsE8eHSSEENRFTTY/Zby7wzhTZCqhDgXHECshHxP2C6FcmLWy06RpGWRBTVV6fqy+wvrx50u7tuAx/qeIPogdtTJm9h9bCcJu9jgM64BHGyC1lKsIMDvU4Lsghw5EtpBarFlFrBYz2b4/DpLv/t+BcAjhEH7NmuOxRSu+NwakrRdSF8fRUm5XfXjd53dNQ+twPwa3InyOEkHEof1KSdQVajOAfjVGj3/Kh3e0VdkkSWIQHch9plpilic8fO8N9ncz9q69wGS0g/KQZiLe+hXMXQf5ACznTN9/m8P33qUpiwA+tMIOR/TOX+Hqk8+ws7sbXrtWiW2TCh8EjX+uI1aCRQSi/z/a/itasuy888R+2xwT9sa16U15jwIKAIkC2ARA281ms6d7elyPlszS9GgtaZbe9S696EFvepLW0ox6ptdQ05yeZrNpQRJ0IAigvK/MSp838+Z1ccMes40e9j4RkQWQaIKVp9atezPMiRPnnL339/++//f/Cw8i9ehOgu6vkW9sYkZD5uMhajzC1gXUNSf7h9y/dpO1p5+h3W6F3quHGPvhKuvE8fRPPcmpxzqkQmMnr3NXDnFe4ZFYE+4xqRzHJyNOKs/g/PPw7mcPHE1dLcZDWMAlUlgWnl+RThLG5CpoXAbb8ZQtwIsQbmEoG5r7IlAk9J4hlqDBE6S7faxEND6qEO0f4r3iFtS3QOlogGNYAEAqGURVCPQPGz2blAymvkKIhUWDt+6h0dfUURvguEg0xSoczuNsFTJpSj1cNfEQyggsqv9usfg0YzpgcOl4hIJVYeLWCvJEB1+tWGJ1seLYa+d0Oy0yrbDCPDT7iHgNE+HxVcHJ8JDWZEa/3w/zTbzIjrBbG5sahIC6rJkONbs3bnDz47fY37vNvChAdWl1Bzz7wud49atf4cxpSW0c3oCUHmtDVbuh1xvnMXkLsbXF/NoNSlOzrhPkaEJ5a5f0iYuIRFIULkzDItBjTFnj6iBlXjkXqpdRwbGZ70MuwjAa7jN+cA8/mwavLhr10aCALXwQFZtWFVUr5YQaOZnRX9O0uu0QzHsfaFaPYNsfzzEuQaoODoUxkizrkeEotQSZUE6OqasTqnlOlhrybg8rBK1+TnH/hNHUcFB8wv/pV77Gy+snTL/zp+TFjEyE4NP5cP6sk/i18+hnvgJChwQJy4TKKnhUKiz1zWyWb7RoZaHPZ+YFZS2wSmPGU2bvvs/BB+/xWu9bbF24wOXPPcHnvvQKLz/9Ck+f2aEjMywO4fxiLHmhUE98AXt4F//JW0hXYosaD1SAqCsm197izMu/yplxwZ3DQw6mhoNpAfOK+XTCf/xrF/B7f8HV8hIvPfcU1eQa79++wqtf+wbXpwd0njrFqxd2uCM1V9/7kDuHM+TnXoA/fAQXMp4zrzxWBjqfcwuYhCdcB+tsFHKqMNYE4QsnwpmOVhlR3zdWHt0CNHrncNIG/lNUT3UuJEsSpRA6zGUyMrgg/K2TBLylrmqsq/HG45VESYtQoSfYiYdnhyatH8zGLVI0tDgWx/awavTDjURNgsZZi7ONT2Uz74Skn4xCP1IqpEoQSiNUAjoNa1Ei8LXBICiMZV6Vwa9WJHiXP5LmuKOjYzKl2chzvNaU3pAoiRKKyXSK8Ir19TXyROOFx0gBkcInhKIcT5gNjwGJSrrMWi3a6wPaG+vUlaVlPPgEoRQ6S0ldUCPtDjokSYuqtngvSYRDKYktBfvjGa6aMxufMJ0XTIsZ5WzMcHjM0ckJRVHRl+DKBFlprKmYj0fM5sfMKodHUtU9al/hhEWImtLMMLbGW4OvK5QLbBBTL+0pqmJOKgz1+IRyeEx5MqQcT7BVFSpxiYhCS8Hr1gpHS2e0JSRaMHOW8WxCWRdY52hlKdILEgROSsZFxaSuHklTjsdinUA4EWndLgphsohrQgwjF7Z5izW8AY1K0E4Tcu3ptzMG3S2KieDk/lGwaYnhaO0dOkvR3Raq36O/vUNn0KcO5UisdzSig9Y68izno/ffZ/f2Tc71+iibU7mEQafLcTlbMjKj5Y33gtoZDo4OGY1HiERz5ZOES5fOcmrnAlubZxkMejiqUDlUnny9z+DsWY4nU2xZRCyh8M4zn4zwh7c5f+YSlVNcv3ObyhT4T+mRBPGbpQc3DTtTLoFVSAZ+SjfjR2w/EVV1+XcDdIKHiXcyVB5XKpKrptwPZa4alNtkuCKwXOawGgqoZzEDfsqaYjWEW6WENspfq3RZIWJpNv63MD5vwOSKWE5T/VzsN36Pxd5W8bBYvLT5VvFrLEFRA0xD9YTolRZ/msnXh96JGOouz8EjQo7OBwCrlQpAW0q0UEgnqCZzdq/dYXjpcQ7bM7YGFb1WtqQ/NTREHwJt4Txea5hO4e0PmOzt4WZzbBG4/SQatbHJ9pNPs3X2wiKIWTl1S9XOT23xll7eAiu/f9S2vFVE4L+vgtEWiF5KOtigszVmfPCAJD0gcy2qKhjuHu7eYHLzBu31PiR65cOWR9seCJSCJMvo5glZXZFrjdYpRalQMgl9VUpjjOTBaEaVPRq1MWvtAgeGaqPDRTVQIZrKwFLsYpHxBppKeBgGq/2/DbXJLmhO+JDYaAp+C8VSIfBORuGZ+Hikz/pGuh0fqV6NIl8Aag2t3APOqaDMLMJkHPykPCgVKmYCnI2CKdYukkNhPw3+i1XLZv6J162xDLLCIyNtszHfDvz+RkgoggmxCpo9QZqtWYQeDXAUhPMhJWSpjvNpnD9i9q+3NqDXXwv+q7Jasj0W85gPfYPVjPHxHoPxiDXXQ8Y+YolfqLI2FWOcp5zPGR1f48aV3+ej997l+KhG6zY6zXjuxef5hV/6IpcuS6o6XF+lQiY10O5V9FkO11i1c3qnNiEBa0ukr5g82OX+lU+4fPoMvp1S1YZESrTUmMpT157DwykHh2NKExSepdYonZAkKgoIAN4yOr7HcO8mbjaJmdTlD/igakdFq6fZOtumtTanGh/TX9uk1c7i2fYrScvPdkuyLto7bG0o5wVaOTwWpyQCy7R7HkrDMy9dwBYZxckBn1y/jRUpVWGpvEU8uMlX/+GX+PmvfpPqrf8Ree8TUikRwoHygfaIZqYK8m/+E5JLT+OdQUgdx7VYMHGEEJTllHff+D43jke49S120gGH8wnTXDKajTAxqWNshRMCrSTeS6bHx0yPD7jx7uu88bvf4tTFyzz5xZf53Jdf4adf+CKXOusxSy3Dvaq7JOdfxB3ex9sCVdZobainM3pac3iwCyc3uXx6je/VE+7s7pJLgc+76H6HvWqObnn08R3u3sz56XM7HOz9gG/98XuonbP88heeZH8+5sr9GZ3jKbvv/g7f+davM3gE19HFqqrSGq01tlZ4t1SrDveci6AQln2JEucU3iV4nwAaIWo8sac6+jIGb0aDc6EquWBZxHhAKYlSkRoKca520f4iKrFKibPhvSL+aPWpGGT1J86FztoIOJbzRqjWNKXRVTH+JsiMTJCooi+iV6OIyfCFGKEQKKkXlUfjBN6A8QpHivUVZe2Zzgvm8xnCVaS6Ycd89tvWWot22iZTmrmzjEc1ygs8NUU5p6MTWnh6eR77eoOXZjkbMStGzI/vUh6dMJ/MUUmb9mADOxtTzSYkm7Ng25K2ke0O7bxHt6NZyxRCQVEY0pYkzQSTiaIoBHVVkmQSJ3Lq0uCrMaY4Zni8y+HhPkejKYP+Br1Wi06WIm3FdDRkdDJiXs4wPiFPOriypC6m4CcUZUVlHbXzTIuSuiypiorhZEraaiEpaWmLchVVMaE4OWY2PGR8tI+pokCKFCR5QtLK8UrjhUIoRZJptDbgKkw5o6hKLAJkSObqCISmzlAaFyiaj2BzzfzeMKxiPN8kcYjJRkGwyqlMjTV1aKlzDikVqVIkStFptem3+9hixuTgBG1NCAWcw3iPTBVJJ0X3O/RPn6G3tYORCtvEEgTRSI8LdjpSMmh3GamU+mRKOSuYW42xYxJXhxkijtuw5IZ13Yqg8Is1zGZjpuMRDzaPGfRu89zTj3Pu4tmgHyNAdtqkG9u0+gfYw73QwuGbtizBaDiks7bN9vY2x7MZ9x/s0iSGm6LOp3GZQCBFEz+EeNmKGFl95qqqzRreUC9x4A3OmKD8hQhmndojjQXTTEYrQGqRDft0r1wDmmK8JtwPgYUFbmmqSTFjxqIGGICsEFGNL950gga4KYTQIQMWbwSwcYcrVdJ4o/qY9W9AbkM38YvAdPHixXtXK3Lh3o6PS1DSIwgLB85Fmyi/8h3k4mI/IqGxcMTOI3UAF9Y5rPPhZhCKclJy75O7XDl1m9MbzwbKDNDPBdb7wBWHBTBz0qBtQvH+iCtvvUc9nVGX5SKT2hqss3HpIutnLtFa20AkD9+U4Zt7VsmDTTVzFVz+h4DHxfMCVqFos6fe5hrMSoajEe3NbabDY6wzKFNST8aM929zcu8Gm+efQG4NIIl7FMvEgVJBmCLPU7qpQhRTtnodDosxlUzxUlKYMk4UguFkxmi+93e7YH/t5h86Rz4CPxfPg4gZJdnc22L5woXNTDOJLPoCXAjAV82sCT2GDh9l4ZeUEO+DNU/II/mV3jyx7NuNxrIeEUp3NAH/sk9mWc10C1Ee73yobggRbTSWMveOh5MN3olFAkpEo+1QEZHxe7vFZ4XkUjDRDoISQVQifKMg0LKgrq4EVU3m/zPfBIvzphO9MANenChA99fobWyRtHJmfrSw1FhYAUWvzrqcc7J7k8eGB6y7M+AlVQRXUsroROIDHjYCqiGffPRt/vI7/5bd3bsI1aP28Owzz/Grv/L3ef6ZJwJolAHIeykCXdL6UF0xYIyjleXkSlIMj2lpAa2U8ckhs9mYNGuzfzhHdhLa7STSWz3jUZgv7965z/W7e5RIynicvbV1NrodUmfJ0aTec/XWx9y68RFlOQehVpCjQ2uNMTVC1fzU557lhce32e6WmJak3SEmABzir509/u5bu91nPhvipMdLT20EUtUcjqYYqdjqWlqdbcppjZnc49a9MWmvjTMGt36aV37q8wxSxy9+/e+xPrzC5P0PyZwI1cSolie1pKoM+eXnSZ98KXybKFAnlv+LYzlUr4ob1/jz3/kdPrp3iPSSJIW9w6M45sIYaRI5Nq6JQgTJeOUlo8Mpw4M3+fCtN/mT3/q3fPfnf5Ff+Qf/mC8/8zw9pQNF1nvE2aewe1dh+AClDXVxjFSh/0nWNeNb77D93Nf54pNPUhjN1Ce8e/cTTGuDA3mal57fId+7T6IGvDvuUG5sY9ycM4OzFAdHHI/vcnJQc5hcYNzTPDNWPJKZ1TVVbxlE4qQKiu1xjZaeSAE3SCFIEkHmMrxVlEZhCnC+IpqXEWKJZt1fgsegTt9oGS5jq0UypEmANwwSZFCrblhdQi6uN/F6B5KJaP6g6eN2jbeti4k5RGB8LGj9HklMrEUqv4++rw2mDLTnaEbeJPVjQNpUHIUM1VaPxHqJdxLrFdYKTC2oSktZlNRlQSKCuEiikuUi9hluL14+z8moYliWlHXoC/VSUNYVxjr6As61EvoJ4AWmmjNzhtlsTu5rVFFRH+1Tn4zwac58PkfMKxIcPs3wvQ2qWYGczdGtlPl0QuUMaI3UCc47jkcHDOceV/c5vZYwaHeofEE78bR6PUZ7u9SFoa48G+uneezi41w4dx4pBOPxkLGxSCXpdrq08i7rg23WB5sMOl0ypVCqi0p7WHlIgWQynbO3e5syy9nu9GilGV2tkOUUOxtRT09wxQyFJ1EaozRSJKTtHmm7E0TFdIpQGUkrwypwM0c1q6isQKogphJiAEldG+aVwUvJmlScfPaXEaJ3scPE3jwVHR2CkrgQNlghxYSwkjoU/QnJYuUdiYBOltNrraNcSjUfoswRwpah+u8daIVoJaiuYnBmnf6pM1jVpnYhCd4kv5UMoCtLFFXt2draYTqa0PEJ662LXL/5gHZryNHkBpPCMJvVlEWFECq23vhFMt8jEUpQlIa9+/eYT4a4asRkfsLpS5fJszYiTelunGLe22d2uI+QDulCe5wtK6xIKI5OyLd6rPX7HA4PqAuLUkGFNgjqhEnFxzhmqT3RtBVFr1+hQmvD37D97X0co3pXM8il8AufLGctUmpSpUi1R8smc088QU0Pllhk0cJBNxOdAC+WPVI09DoWv5vKCit7hU+HAZ5Gan81kxUqLzoEjKgYPDY1vkY1tDnGh0FtU2lbVhh9nK+XU75YfvrKxL8Ew1ExP2SOvWGh9uEbelzcddzRKiD4rDeJwttAD3NSR9qgp64MxliUPOGDt94kzdd49vmUQb/HzpbmdL9PQ+FtqFBSJBR7Y45vf0xdTJiNTvCmBgmq1WZw9gKbFx6jc2YL3dcP2TQsz+tKn2Ncxpor1wyvECCvBMo/YmseXllLF5kGL0BmivZgjWJ9ndb6Br2NLabljKSYIpxhfnTIvRvXOPXYC/TX+/jGLoSGpku4R70j6XW5cPYcl7dPUY5L7p0cUWcps7oBPY6qhoOTYNr7KDb5UIZvNXGxcmuySsdeAec+msYvKocrRvcP0T1Ws9K+4Q2GrJSIQLEBjuECxvO0BIWN0HOoZD588URATUtJdh8TNBEMedtka5bzRXjfpwFAM55EzBzIJmMUepJCo8PidUI0PWAha2pdPHa5zGg670KGPk6kj1IcxzmPUpCmOvQ7effwC6SkOxjQG6wz2TumqMxC4bY5PQFglwx3r3H7te/z+KVTrF0+w/15oOr4WNGwxqBVghTw+lsf8Ju/8dt8/OFNlMqogfOPPcvP/eJ/wsULLzGdVHS32hjvcS4oxTnnUZ7gA+XCflOVsd3f4vzmFldSBapiXI25ffsjrlx9n9bGNkf1hMuXd+goGO4fs95b4/13v8+f/9lfsD88QSOZOctj589y7tJFMi3RxpEpGO8/4NoHH3Dw4AHJIuPMgm2i4hqidZeO2uDo4z1Gh8esn90gbw2CfOyKTc2j2Mp5CUaETLH3lFOJqwtq59CJYrR3AzeoaOkd5ieGzbUu46KAzS5f/0ffYHztQx57+klefmyN+V/+DyTjfZQNFFVksFHSSjKWit6L/ym0T4VkilTLhJmPST0R1Pqkznnly9/go3ff47kXf5qymvHg2iecUyn3hiNO5jW1g8o5KueoXZCO86aitpZaGIIioSIVgtn9MX/4G/+aO9evwv/+/8A3X/kS3ufhjOqM5LGXqO/dxpUl2qZYW+FrSwdB9eAW6vQuX/jyl7lyWHHjwRRXCfI0Z3Z4wAcPRogdzQuDW7zz/i6f/8pX+bmNL3H92g/4rT/6No+fPcPofsGfD6/yEif0zLPs8dpnfh2lJ4hQSIGVKphvC4WWgkQoEiFIcCgRJIuVTheexrbWGOXwFBFALZPOC+p/BJA4Gyh3NE5wYWxV3qKsRytBEivvUmq8E9TWRPXDRoW4SWouVr/IvgiK9zYCPrBI6fDCxYpF8PRzvkY2yfbF6ksz7dMIEC6IF01s03xWkJQM40up8Jku1AwUgZ6NS3CuwtQeW1m8MeAMUvlwLoV6JMHO/mzKtHQ8mM7w5ZxcBMGS2hjaacr2Zh8lDcYWpCphPDrCiYQWII2BeegbtNWM2pa4tEev1SbpdZCtFjJto1SC9o5qNsYrTSvLqI1jPpmx92CfyewuhVN4d5ae6tBPC9bbCrvZ5aMrD7h6PGZ/aultXeD02Uu88NxLrPVajIaHjKuKWk9pDdp0u23W++us75ymt3GOtJNhhGdWOkytGB4d89Htu1x/7116qePUhQt0spw8SZlMZ/SUQ5ZTUmGxaUKZZlQ6QagUlWWQ5YgkDa1FKsXrFJdopmbO3mjOQeGYG8hEWHsrPEVpGRVlUKAXDiP0I5lZtVQxmRobzpwL9GihFu0weB8F4SxCCjKdYG1NIgXtRNNJErpZRlslaGOxsxlUoU1JIFFaIjKN6nTond6htX2aMklBykXSRUgf2wWC8qjwIJOUTr9P4QVeKS5ePsUZ4bh4+ixTe4bD8ZxiXrG/t8/hgwNcMQ8iUTawNWz0c5SE/ntjLZW1jOdz1o0jzQOKavf79E+fYrh/CzM5pkXU8DAWWTj0dEpno6aTBc9KFsUh4k+IFWXU0XdNmxByUWhR8BC++2uvx090FX0zabGY8LxzCO+C3IwH5YNJsWyqUsDKN1hk05ugHFj0SDa1p2Xcu4gQWGTvhGzeFf7teWjqbJD1ohKz8ndzDAtz+/ACHj5bS8DwabDbvFSIIEaxAMExXm28jRoo6bzHSY9QEp0Eg29lTGjYbQLRBQhtAqH470fEVU2kpq4N+GA0qoSnNmVUjhN4PeVk/zbf/fM/oJjP+aVf+GU2+hcWIGMRtFmP1wLHHcqD7zI5uIkpS4Q3eO1J1/t0T53l1OXHWDu1vpS6FCwyHp+Wi1+G9gvyYHiDWL6XSMNanJ0FZW/58uZ3cx0WptWtnPb6Nu3NQ8rhMfX4CDU9gaLGlwUPbn7C3Y++T3a6S5qfX+5bLC+TjNdl48xjPPPk89x8/wpZkpFqmFUVAoNzBKWx+ZzR4eizvYBxU6rJaC+/eFO9bzLBDYJctZ1Z/PjmOsTMMiCiiXTjg9QED4vz3JzRRlVvMdGIlf/i0azev6tAnnBdZLwPvF8+/vDYpzmA5S7iJPfw9vDnyFhtlFIupLGX844P2TpCr3PIwHusDfuU3iO8xfsAGMOPYyGM8Yg3KTze1cvvvPJV+xsbtDc2cekuvo49xPH8e0LvXu0KipN7mON9zMhQzsNJEQJwDucgTTTCwYfv3+G3/923efetW2A7SCy9QZsvvfpNzl54lXb3LP21UM1KpGRWRe9GPMKaQH9VEicFlU9J2xtcuPwY585tc3V8lUQaRoc3+d6ff4tW3uPU5Wf4ZGzopQmuGvHe7l/xh7//m1z58Aq18xRmwtqpDZ56/km2drZDPyUKO685uH6L/Zs3mU8nBKKQQyGiAElI6k2cp5wrzqrznD/1ElNxjBFdzlx4iiRpEWTZEx6VytHcVCTSY4oh+JR2N8W7Ni0sMpFkvW2090i1j5EVt+8e01s7x4tPPom/v4szXb70+Z8lvfl9Jh9fJS2D54kTDmEFFRLjoPvFV0mffTZWmJbK4J+2M5Kxp7N1+TxPfOEL2PVTfO5rX0dMD7lz7SNu37jGen+N/Tu7XH37PT6+scfu0ZDRZMrEe2rnsCIETUVtKYwLxveF4t6VaxwO9/GokDiSURFwcBlx/gU42UP5Gjs7QXpL6j2J8dR33qPzufO88uwFDu7/Lmc2BIMzF2i1L/D5z/8Me7de42D/DfZv3OfK+tvkz7XxquD+7Q/Y6SQkpWP/+se8ObrPwNfQ+uyvYyJk8EPzAiOCaIYWkkxpWipUvxPnyKRA5Sm1TPCVoKwsUnmUqvAqwRkV13GIGWLwgebpnQNTB6KTlwutiCAOZTHeYG14TEuF8DLMU7UJfYaEnqXok7SIU4SMatpS4qTEInCx91KIKArog7qic2EOlNItxQ1X7p8mibdIDjb+tzFGEQvfuti/JSQWMM4hbEgGSh+YR8YECrerDcJalHMo6ZDOIiJd8LPe9o8LhsMRUyHpCc2g1+JoPiEXcHYjo5vMmI2P6a5vgMgwXuCdQWmF1J68l1NPEnLbQ+gW/e0d8l4XkabIRGHNHFsXuLJAJRmtThDUmRd7HO7tMR5OsM4wmQxJEs+0dY6J9cwY8vb1j7l2+xaHo2NSlXHp3CWee+FzXL58nlQ5lJZYndHa2KHd6zBY79Jrp+h2C6cTrPTUpWA2E1z96F1e/96/5Xuf3MLNNIPHLqNkFnoaZ8fka9ton6FFSo2imFVUFUidImQZbIt0is6yABylIM1ShIa9vfsc7t9mdHJI4j2zalnBHheGqVdkeDJtmfmazc/+MgbBQU9M8vpYQAhaDAtRJqKXY6OnQMAgiZR08pxultJJE9oJzMcjqskYjCFB4qUCrVAtTXd7i/7OY7i0h/WQRbVifEjUC0LLjBAx4SOg225z+fLj3Lt7m90Hf8WZnRSVbHFq+zE2zkmwhvFoxMH9ParxiNlkzMl4zGxeMJxOKY0JpSzjKUqH1Dlp2kHJBITCeotWnt72gPVzZzm8NsVXFYiw5isH9eiYenKfweY2W5vr3C+KIJgWk+erCfsmR9/0PDZxoIxFhh/nq/oTAEf/0G8p5EKURvqAWIV3IZvkw0FL31SRIlnsU5UDYIWy1uCCh/9NrDg1FbwFllz5f9jPCle/2VeDzZpgmDBB+kUVSywqJY384KriJyxj4yZ7SPPSpv+rOYros7K01ggBDkIgE4VONFoppF12VjXnQcrFgf7QsX/WW4agdiBUqL1aZzEmZLa1UFSz4LE0n1Ucb36C2b1LurmB7gaXnoZKarVH+Tnc+oTDq+8xvr8bbmIclRJ01zfonbnA5pnzZK12KPWv9tutgGUfsyCOZvHzD0XNi9cvqmcrwH+Fu700cm8EY5Z9o0KAaEuSjT7pySat4yPmRw+QwyPqao53FeZkyL0r7zO4dJ4z26fw6EUCY2k/AlZAtrHG5s4OfHQdnaaBpkPoHZXYkJEqSuqDvZ8wS/M3b1LqBZpqzo9YSXYsEzUP3/+Le1k0E0rz3BJECiFjg/USQK6CQr/y/+aRpqdUrDwnHnrNyvjBhwp/89qHBvwyObGEufFxsXyZX3mvgGBTIGVYSLSOmdBQbbYLJgMhEHcOqKNKm1z4ODoXBII8ZtGb6T/12Y9ic9ZDAv2OCvL6i68cP1RpBqfO0Dl9FpNdwUxmxPbF5bwqwZma+fgYN59QjCr8GEzXkwbVDAI1THDr5n2+9Xu/y7tvvw1OIklAKk6dvURv6zSyPaCwnuF4TGkF48kMlbaY1xVVNUd7h6kq0m6HfGMddIpKWwzOnOX8Y48xPT5h994BdXnC0c2P+d7v/xaPP30DJXvkiUaKEa+/8R1u3vyIykyQWpH31tg6c4Hzjz9Nb22ANWFOF0VF8eCA4b37zGYzcpHjfKAMuUgrnFvDsVJ0LzxJ7/kv0Hv2Bez8NmubGWunzwMlzhqUUg/dkZ/pZhy1m5IKsDIhbWm8CRWYJAfJnHkJVz45IpMJSdahvbaJM+eophVf+JmXearXY/bdvySbF0hsWEGcQ6oEJaDeOEP7c/8xniQkeYRc5lQXyaFmLIpwn6uUV77ydf7d7/63vHHlE7afuMTLX3qVX/rSN2iMEOrjQ3Zv3uHDj97lyscfcP32PY7vH3J0POK4rjFOUlczCltTVBVb6QYb517EkqJZtpWgFfqJpzD33sXszZA6wZoq9scq6pP7JAfXeO7y49y4sE11O6dgzqmnX6DbP0My6PM7rx3QLnOOr93h2pkDcr/B6bU1dja2+M0/+CM+uvka//X/+n/Fr37z7/P/+L/9Xz/zy5hIQaY0zoeqBj7EOqnWZEqhnUc7Q64EaZ4yF4pJZahtsI+RSqNUEqxqrFzOv7GvONBcLd6Gce+8phGraQS6ghBNoOc7Y1BCBSpxIz4m43okoaGyShXEaZwXoBxeBosF60MLgPAm2BZ4FoJlqmFCSR+B4GpFPs4rLipTRwEW4e2CKdb0lQfR6cBiMd6CC96EwtRYG/yNm+8T2nQc0jt8XWKKKfoRJADUpOCUVDwAtrpdpK3wznC22+GZdsJaCjpNKSYzZsqTttqsWxN645I2Mk0ZZCnZdIatDSppUdc1sijxkylCpLRafWopqU3GZO44HE8pqyGzyZiiKJkX09CTmFmu3Zxzq/Ic7H/ErYN7zIqKTrfDxqULPP7s8zzx2EW21hSpgrXuKS6abWrvqRKFUaGwCzFuNJ5RVXP1ow/45KN3OL53C/ngHuvbT4VrIKDb63H5zCnObPQZ7R1SlgWz8ZR6ViCMpZ7PwXucF7TbHdpZTp0KZKcd/C3xtJSmLQQyzZnNChyS0jmq2lALybQyOOvpdjP6j2h99E3i1hOLGz60xvmoEh6F8aQQSGcx1uFMTSqCT2eaaNrtLr1WBzObMTk+RNQ1OtJGvQSZp6RrPbKNdWj1EDJbsjZkaG1TMaPtCWJWnuATnUp4/NwZNnLFfHqNbjckh6rakmiNl4L1wRpbGwO8tZTFnOHJiJPRmHsHB1y/dZt6PEaqQPF2XsSedYG1kcouQXRysu0t8oMHyOMjamNCP7ZSiNpwsn+fbK3P2TNnmE/mDE8OQ2LXAkSf+BhYNKJbAU+FcdvM4v7HrJB/e6oqiwIGTd9YUG2KDeUOUqnI04TSWkpvMW4Z+gUCacPpF0u022TklrdKfGxRe1h88KJKEuHfw3fYKqhcAgYPC/n/xRfxTSVmVWVxFVw2YWaz6+WnCbEEwItDoxFkiQciAq9YK4VMFImSGGOYmRpp5mS2JqfhF6+c4HgcCH5sk+pPuukI+JXwOFNR1TVSBA80Zzx1XVOVFWknYXJUsHt7j/MXLrHe6QRFWCHAO5RQ8M4un/zh6zy4OyITCeNqglUC1enSO3uBzfOPk/YGkfb5cHXs4esu0SpkLGuC756FBW2xqUAtRFwWcrrEHjq/6LNotuaea0CGd0EXRfYUSb9Pvr5Je2Ob5PAQqpJqVuOmBZODI46u32T7/NOo0+cW+/oUjIWuor3WI2/ldFodtMpRKo+9gKEi4osZo/tX2MjPf+bXUUROf3MPN/Y1oedkWSn3PgrbOJbVw6Z8FzqBWfTzhh0FYZ0Gz8WT14zbxYj+oQSOX/n34ihXbuumJ9M/9I4l0Pz0GY4f0Pz6NFXZr3IJxFKcQSdIrUGqQP/yoYchDKtmjNpFoqGx3wi5iqa3OVbzmjnoUaJGD16E818WHUzdjuqMLK+t1/TXzzM49zh67Q3s0cFCICB2jTb5Esx8xs0PPuTiS9e5cPFx6C4TYGmimJyMeeuNv+C9d36PYrKLszV1YegPTtPuXGD/8IThyXV2bw4Zndyjv5Zz5+491gYbTMsZs/kIiWd4NOPisy/z0le/Qpq3WW93OP/4U9y9+STl8Qmmqpjff4CdH3Dzw7/izsdvsLa2iRSCk5MDalciRQV2TNJeY+PUM5w+/0Uef/pLrA26CFejhac43OPeBx8y3H2AMiBSScP+8ISel7Ku8N0e5198kc3HL/PJ/XuU/oBLTzyNNxVCe0Tsw/+hdeMz2ra6OeOxQagEIYLQk1CQpilaJkynQ+ZSoPOzXDp9GeYPGNUPuHf8NhtPf42vfOE5xIf/HndwgLCWVHu8VCivg2ealaTP/yxsnAlp42gvs1AVX2GwNM5GzRzQ3drmH/38f8lv/f6/4frrv8sLly+QDgbUVYXUGr2+yaX1TS59/mV+GUdxPORkb4+TwxEP9if86Z/+Lq+/+RZ3hieczCuEFFR16GUPdyEE1UCH6J5FXnwFc7yPrksqUeLxSCdQtaO48z75S2c5e+kFbt/6Nvt7R5THu3SY8ldX3yXPMn7tV5/jD7/zZ3z47W9x4YkLrPWe5P/3r/4t98e7/OKv/Vf8w5/+JQbJ7iO5jqlWKKnw1uNsBGs+9PSGOdSB9QhvEMISGGPBrsEjkCrB6wQndUBlsUerUTUVzoKTi+lOxD6Whq/hIwBztsbXJTVLMbCgkhv6CCVBObupuiiVhIDTiSDCp1KkNjhj8XVFZWpqypDcJxiCCxXAZaD96fA7BsrEQN1bizONf2Qd5lApEUouFbFFM7M3bA2DszWYGms1xnisCeI63hoCajbYes58ekLe+uyt43udHlUxQxlD6QylMeRZxs6gw4VBh0MleDArKIa7DC48RprocE6ShLzbo9/pknjB5PiA2fABk2EJxQidCIRuQ0tgyoJ5WWDHOT4fIoSD2qNcja+OmA1L6nnJeH6De4cTytGUw+FN9kYTTp25zGMXnuSrn/tpnr98iY01CcpTeHAanG5StSGIti5UuKwVTEcFd+/e5urVtxlNTxhOE6xvY01FO4XnLp7hxSee49x2l2J4n3p6yORwl5O9B4yPD3GzGdaUSBKkapG2clyiSNs5Wa+N7nY4mIzZm1YcPjjGjmrmtUFpgUoEDkddG7R35FmGF5LSmEeiqioXINGhlUTKqAEiQttQXdvgf+yDH2HIoXryRNNOBZ1WRr+7jS0qjvcOcVVJFuNupSQkirzfIxtskvTXEXkGUVQwYAa3osbe6C24mKgGcEgPp7fW8RsvUNcliW7hhcS4MFeKmDxyQpDkORtJSm99h97WWaxKufbB+5iiQMioJaIFkjj2nMOrQKXVvTXy/hbFyQQfhayEh0QIitIyPjgh3eyy1h9wMh4iJaGnmmX7l496CAs8BCGZFP/4ccSqnww4EpdeQVQqDYu3tQ4vPFmS0lcK4z2Fm1MbIjiMb4iRaBPk4lerEIvvsPy8BpQtCx8Pmb2LZl+LXTeoOQaDNMGqWASIEZXR9EItqx5NDcM/VNpdLsh+NT4OeV3hVz5HrDwfVBLTRJFkKVpKyqKgrAqkKelqT56GiohbLBbhGEQ8vEe1udi0b+oq+G16AVLgjGVmDKauUalCZZ53r93iuV/OePX8GfaGhrODcNs44ZG7cPPdO9x/cERVQVGUVMKBThmcOsPgzFnWTp2jtdZ/CCU8TFUO/mI1jvFsxuF8Ti0UGZJMJcFUPJWkCjQC6T1KhAq3JYg5KBmMM5x3zOsa4z0m+mhlUpLEbFED0oXWJIM10sEm2foWva0dxsUUXU0x5ZzRwQH3du+yczxk+9S5H87ANAeuNXp7QG/QY20/JxEJUmVYW1JbTyvV5MIwvX+TjcuPADjGQHhxD0d8s8wGh9dF3ZmVhMnq11gCwObBBpwLF60zsDGgWWl0YYk9H6KOrkxGDaBcviM+vhjzK6mZhwb/p/exzBWE+yc8sqDZNmB5kXUPvcxeKJpeyEbgaik+4VmyE5okUnNvNpXWRnzih77aZ7o5B3mmKGbwV6+VHNSBMgQsky1Ckq3t0Dt1gdbGBpN7dxCzkkXvOLHnWApy75jfv8+d9z5m/YXPM9jcwkdVVJHA8PCQ29fe4mj/Y8bjfbApUqaYOqEcWqZ37/FHt/4V0/EJVV1QVyVVXVEbQ1nOMaJkVJUU9PkZOeDyy69gEci8zZnLj/P8F77EdHjEtJxglOP+7jDIfrsZs6MhENaNVEqMK9nZ6NAabLJz9mm++uqv8dxzL5Frh6wNYjLh6ve+y8d/9ieokwm9tL3wzGvWEe9Dgi51DnN8TMfDwXxG1vacPrUNKt4FixXx0VCOJ65EddvUxgSBMJeRtnKsqZnPTyjqOVneJks0VT1jdHwP0fJgUl558VnOTPc5fu8H9J0BYRaCDCCo3Bj53Jdpf/kf4KNgFCyXpIfYGQtaNos10mc5vcuP85//i/+Gqj4h0WsAJGmoOfpGKTlOIvn6Bvn6BqeApwHKA3Zv32RYlRSmopyccO/qe5TPPU0qNGqFkYFSiAtPIu+8RV1NqKVGi1BGT1DMh/sUe5/w4tMvc/f2dWS1Rmd6jT/4/rt899Ztfv6FJ7lpBZP2adJjx63b95jVU45v3OWlbzzLf/Vf/BKjD65y861vPZLrqLTCeU9dW2pjMCb0JRYqUMoS4cLUUM4pp4JCJFgbrSikxAmPFxonNV5qvFcQ+6SstUEDQIDQoGWYp7zQmAhAaZgzzoZ/O4vAIWUAjUImSJ+AEEgZAKPW0esx9lVK6dE6JU2DdYpxPvhNmgpBHYT6lCTI4oWDCQA0QcnGAsnhTKh4OmuCei+hH7JJTiol0UqFgF4QW5RCm5L3wZ7JGY+LfoJ1bbC2RnqDEAbrSopiDHz2wNEJxywWDMZ1iRSOnW6bnfVNZqMHXJWOqwcjnu5tcEbWJK5CpUmwbVISo2W49sJRmylVWaKNxk8kpZOgEqwo0Z01tM5ppynT+QjvDEU9xc4n1LMRRwdHHB/vMx2PKaYTisk+53Yu8tILL/MzP/01Xnn6KTY3FFYJ5h4qAaVvtHohIQBGHdeneQX3Dmuu3Dji4HjCwfiIqR3S2Vnj8cvP8svf/DovP/kkvW6Xk5N9jh7scXRnl9nuA8rhjFQmOKUp49qet1PSdhvd7aGyjKyVcVRP+fjeLT64s0u9N+J0nqBzSRqvszUeX1d0lUYBpTEU3tP9zK8imIVYi4/JYYUSoSpnvaesSxyKLMuR1oPz5FlKK9W0c81at4crHeP7x/h5EQom0U5IJZK020J2OrQ3tmivb2KVxjsfPFlxkaIa4gNjDNa5xXxrXfRjj72Q+IQkySI4i+rF3oe2gaic7L0LCRenSHXKk5cu4+qKjz6+gqlqHhwewMc1a3lG//RZiqi1IISgN1jHbZ/iZO8+pippRyzkPPjKkExn9Nc943YHlMLUZVPHWgn/xHKuD9WYcC4IrIMgnvXXbz8RcJSN6EQM7qKsDMZZhDNIr0mVJE81Wa0pnQ10DLHaH7VCORWrOeCleuUyOF0+14h8rFLiFq9fJlv/2nzyIngWK69rFtjVCiIrQaYQi8z+AmA2YJaGbrfS78UK79+FjKXwoCV4ayjrAm9KEhRGS6wKF8z7ZXUMWADzR7E5GRRmjTVYZxFK4qylNsFDTytJbSx1NWfqD/j1b/8+o41N/vGLnwN0CFBry8Hd16hH7+OLKSfjCcLV1Fh6/TX6O2fpbO3Q2VlHZCI2La+YNcWBV87nTMoH3Lh3h7947QN2p3MGp8/RkQnnd05z9vQ51tcGrHdzOjpcuKXnHo0OHMOTA4ajXW7cvcvhyNFa3yHt9LiwvcNa3qKTZ2Q6DJRaCmS7Tb6+znywTmdjGz8eMZ+OUcoynRc8uHuXyeEe2/YJSFIap2a/AFYeUk2206I/MORySiIdUgtcGRZipTQdYWFWP5oLuaAVLVUVw50YKmzBJH1FpW8BGpeJkCYTthgaQtD4fgXRvkhHcjYOlZUarngIMoZdLvfOsr9n9bmVpM1/aOXnU/t5+DkWE59QgbLlo1Kkj+cmqCl7RLQL8TGLuJg6RANvl+fJRzXWhW6tWPU5+2w3qYCpY3h1j2t33uVB7RAdHY9n5QyvteicO0NnfYNhnqCLmrqpTEZELfDkgCrGTHc/4vbHb2MHL7N1ZhMvBKaGej5juH/Ig3v7wRxahj6Ssjxh99q7HN6RVPUselaBswYZF5RECaZ2TtHpcPb5l9HdDOOD9HztLVna4pmXvxyEc4Sik/c53d9nb3cXbxxlZamtQyUS6y399dMMTp1m8+LTfOHVX+aVr30O1VKISqDqktsfvsnNN/6U/Ggv9M/4kDTycgmOpAJRFWTOspXM6Pg91ltzNs5ts765saCvN8DxUSUAct1ia/Mchyd3eTAaYb3BOYPG4qqSXt4FqRHzI+4Pd0mkQLQkn/vKL/DqE2eYfOfXaU0ciipkmCWAxCuJz87Qe/ZnQWaLbDfEadQ/NOqWmxDLBGsDRkRGlu7E87BCv5fyoT00LA7vHCSaV77xc3z88TsczsY4YDIZ88ff+XOefPElfuqJp2k3yq6x6igHO4jHv4iYHtGqTJiVfI11BmktsztXWN+8wHOf+wJH737CvBxxnBhe+vxPMUg3ee0vPuBuOeLcxnnufXAT377L819+gX62xnf+6Dco7h/zM6++CvzGZ34djfMYUzMvDXXtaHRHS2sRVpJqQWUts9kEzByftHGyT5LkWC0wwmAJiqxearzTRCJnmH/qCBylQmuJ0AleaIT1GBsoZEqEPkW8iLZFQexPqCjSIcM1UypBJylaJ8F7EoX0Aq3C3B+0J6HygLPUvkZggpaNDkBUqlix1Ala6RDj+cZ+w8Rqo0UstCvC7aekIE0S8jQlU4pEQAKkUqBkSOxWxuCqClPZlf5MUFqGag+OyjwK23iw3uCFx0nAWjq55LFul/sHx7xXTyBX9EWPVEu6tuR0niBkgkNgvWVWFWgXKj9aOFJhMHhOjo5ozUpkmmATS0sIks4AmaZkRqOURag2kgECgVaOvK1ZK3rUdUl3/UXOPfESL33+m1x8/DK+p9hXntrDTMSKowhMEglkQIon8QIjPPcnjiv3JhwOK4SVyFbOV7/+Kk9eeIbnnv86Z8+cJqsK5sf3mO3tUt27x+zOLsXBEdorbFlTTWYU8xlJu0d3fQ3dziHLmSGYzWdMJofMHtzFHA3p9Tfp9RTaFFhgWhl8WdFRCUmaMsFTekf7EbFynF8mKrx3sZ1EoZSHCMBqY/EU5FqR6OCH3G516bQGuEozOr6Pmw/JvSOytREC0jxFd3P6O9v0t89hVI6NVOzaORAe7y1SxB5jFxLUXnjqSOPSjX+pCNU85y1Nv7FwIQHtfLTAIox77yFJBR2Z4GzG+UuPUSC49fEVKmMYHh1z5f13yaSkt7VN7cAJidCh17F3epvD6xOENWEODx8AozFpecLGepe1zR7D+9NFPLdafGu2Jr4Qoulv5OE4/UdsP2HbVYRyfnWpClLO0hmcrRBeBfCoNdPKInygbS5h2UpA9NCvFRGP5so2QToN8BMr2VX/qYC42dOSkNaE0jLy8Vf9i8L7XQSxsQIiVr/VShWRJrsrFs80r2n6xJavDQGKdR5hBJKw2FhnqESJ9TXWi9B74JfNqk0/2go2fSSbi5xwnAo3IzbQYnzItgoRKnR1OSNPag4//gF/8q8lL+mEp77y+XAHlEcwfp+7V/6M/d0DvHMUpqTKNHW3S/vMBTYfewzdyX8oaGtO8eQAbt+8yu7hX/KtP/0T3r16TLZxBnXlKpcuPoavLaDJkpxM5egWtHSgsHofqE+JhPff/Zjf+d3/CZnsU3nH3lHKmcsv0t/axtWec6fPUJOx1oZMCYT0ZC2N6PbI1jfJjk8o+keo4SFVVaJxFMfH3Hrjr9jeWaPzzMsg9EKOGYi9Q4rkTJ+NHQ1iTq+bM7IlNm9jfQIUeOepyr85g/OTbs1d19C6QwUuCB7g7CLzEHp4g4JoA4ADaPQrd/LKXkUAXIFiEcaNj5ke3/gb8lCR41MHtcxmrVK8m+LmQrm4KR/+Nff5wykk/0OPLJJIUUUwCDTEY429iU3fTjjWkAV0jXdrYx7cnCXfAOcIHL2Mc0MzRzya6/jJ9evcevsdPnj7KibvIJzke39hOf0rkLUExbxApwl1pWhvnuXMM4+z/+FrmOEYh15UiJv5SnoP5YTR/Q9Jr16gc2qLbjYg2VBkGiajIYf7Jyja5KoOAhbUCHOCrU6YTSxSh6YaH3u7lBDITKIlJDJFJJ5qfEgvF2z0W2QapAGEIm1t8tznf4YkHXD1nTc42P2Ite2MB/ceUNWh5wpVs7WzydnzL7B99vNcfu4LPPn8s+T9hAIQpaQ6GHJy4z2Ob78FxRHaExd9QcPFFFJSVDPynuLZLz7Py19/lsHZOcW8ZHunS5iLG7+9T1ceP9ut382Z1A5qhRYaj8fOh3ghyDrr7Jw6R5JKToYHlFahsoStx5/hm7/0s3TuvcPs7lUyQoUDpXEigJbKFrjHfob8sZ8NVNAoiBPyt/5HDsSl5+4KeBSKZa9z0yryKWGx5v0NkFThPe1T2/yzf/5fgHH88etvsze23P7Bm/zL7L9j+k9+ja889RIbnS4+KryChNNP4W+/hxpP8PM5wlZU3iGUgtExJ3c+5NSFn+KLj23yvdfep93e4tJzn2fvgeanvniBKx98i++99/tkyQ7eWO7tfcJj21/h6PW77E72Wf/cLz2S6ziezTFGUdce6wU6SQmy+WCkwAnwzlLP5vi5J20JWp0eSkukCu04TgJag0vwtlH0DderAY9SKnRi0SKoNSIExikcGqcy8BpvNNYovDNoLUmSJFDxI2BM0iVobPoTpRRoJEJotBTxR6K1oK4Vwpco5dBRmVLJlEQlJCpFxaDRGYOtDbau8LYGb2i4I00YlmhNK8vp5Dm5Tkg8JHhyFVQqC2+x5YRiXlLNHdbOkdKTpRmp6pDIGmEtpn40YlXOBwVVYy15K6Pd6jEcTpnYGdOWZiAztjqKlAplDBQVppWCkMxri6UmRZAJRZL0aLdqZm7O9ARkYfB5jlYKhUJ3Dyinil6nT5pLRrMJebtPu7/B2sYm0/mcaVHS6q1x+uzjXHzqCTZ3dnAa5spT+XBfzbygAsoq2CkkKpJVo73VeAZHo3DtNgYdNuTj/NSXX+GJS9uc6naROmU6OWQyOmD24JDp7i6jmzeY7x7g51NKUzGdjHBFgROSfKNP0tqAJAttVO0OlbXY0kE5JfMFIJnWKWtpK6iQ2opet8O8ckzKilIKau9QyaNQclhG9IteIxFEoXxMYmSJpvYeWxcImZAlGa1Esdbqkoou1WSGnU/AzjGEmFsokK0U28roba/TP3sGq3OcFSglMNbEGDnQy1ES70N1PrCXQs9uw3TCi6j86hZJHWftIvHTeGpLIQIQ9RB8ty3tVgupEnrPtdnudLh65UNqW3Jjf5/pO2/whZc/z/b2GQoHUoNop3RP7TA5OIDxOAj4QWA21DWjg7uI/AyPnd/hdl2w9+AIJZpEtGfB8oyJ8cb324uVyujfsP3tr7KQsTIWFREf6jEKQZn0Bo0gFYJUht9GeMyi3hAW/UUIGINefEM7Wtb9VqtwC1NvIX6oWNFMZ+FpuQBfqwGpEI0nUrP31V0s39DQ9xZHK1aD87ivlWD4hztmmmAzBKxZktHJMlqqYuYKTD3BWvA6WexhJQ5++HQ/IkqVVQlCBIBoCFVRHORJGr5/FBKR3uHnRZhga8PtK7d4Y23Ay89dRt7e5db33uHk3iGUJXXtMAgKnXLq7AW65y+iNjZxacxeiJVr7Cz13pjxwZTdTz7mj//ij/n42jWKImE6qulubXMPhdQ5WdqilaQoBLgOdDSZFlSFpWUdV955gz/4oz/g42vvczTexSJIe+cYFYpzj5WoJMEozflTKVLlkIdsrhLQWsuZ9fska2ska2uodhc1m+ArQ1qUzO7e4d7bb/NU7xScPYuTYtFLFCSUobQZx7M2Z88/y5x7nMxLinmQWw6eBVCVj6bi2ICyReLDuwjsTPgdq2pCNFJMMWGzLEEugoDm7luMJRHEc5AhYGpAY+i9E59STG2SQXGCb4CMZ/EZD40RvxyDiw9d7Gv1D//Q6x+itItmThCxcTX8uEXiKRyAfAiXivjGhoK6TB41nxEEq2RYDOJYtm4JHh/F9q9+499w/95VDg8mnDt3EW06+EnO8CaculCSt7o44OhgyGFVond2WD97jpPDKdWkRMlmXgVEo3Rdc7h7A//+G5w7/wzV+iVM2ibXmkQnpDqhowRaEgSyvECLWIGQSbCUiP1LSmuU1qACZUe1UvqEefjxjTXO9DskDjIpSLWkrD2qt8bll79EtrHBg92zXBg/w7079xlNDFZI2t2EixfOMli7xNr682zuXCTJoaw91tckZYm5f4vx1Y8Y3ruFns9JZTvOicvZt7kr+punOPvyFxGXL3L15D6tdo/BxoAAGsXipxFlehSb91OGBwdgFXXTVyI1WbtHt5tBNULrNjrxyCTF5o6f+6X/iJdbhsmf/RlpYTGiRisZAnUhcM4gdy7R/tov4NVKEvU/5GZcoayKxUNi8S+/WHNX1z5oErKrwNI5S/+JF/jP/pv/M0/8we/x5muv8+H1G9z4y+/wL4/vMfyn/yU//+Uvsd1dRwoVLBzWBqgnXqE8uIcan+Dr0KtkvaP2BnfvJlunLrO2s8npS6eY7NXYxPMf/fOfZe2wzeuv/w4vvnAeI08xUae52JkzyDXbf/8fcf7eX5KMH3yGV2+5jScF3qd4FyjvSRponUK6ILSFo6w8RR1AeFt5dO5RDStBOoQWSDTO6fC9hYwJVBcpnBbqGqtLtEqD4bpWeBHWPUgDBc1abF3iXAASOok93FJF8BiA5BL8+6ikGOYxJ0NlMEkUaZbgXAtchcBGTkYEj0IF7QMX6Kk2mqg7W4GrgyCOD+TJ8B6BUpo0yciSnERIZNAtIY29X4UtKeYz5lNPXQK+Jk1AJC20lEhXYU2JdcUjuY5exIqrAK0kM1PzyXzKei64nLfwWoIxpL7k5GSIUy20gVa7haHCVHOsCKqbSb5GZkvK+ZRqNgpVJxlsRUohSLsdRLuHzXMsGZ1WF4mks7HDae8oTI3IMzr9Ab21HbI8DSw9oRY0wqoOrC+EJHMe7SFTKtAxraNGkCrBhfWEc9k2+lybjpZ0ux2kq5GmoirnMJ8yOz5gvHudvXff5vD2LeqTGX4+A1vjvQk2Dp0uqtOh1e+iOy3qRNNZ6zObWWYH81DNqi0Gz9iXnNQ1Awk9qahUwkk1Zeih62Any+go+Uj0qoUI91WzOe+RMb4WXoC1QeFUJSGOVZpenodkZj3CVxNEFOgKCTTIMgWthPbOKXpnLlMnbWpnQhInMo6C7ZSLtmdyAbacC4m4UGWMWMYtlQacC8kjCWEMR6qtFCyZHIASLgj7pII81XifsrH2PGtbG+zeuUtdzJjMhty5c5v1tXVk2qK2jqTTJukPyPob+Nkc60zs7fQkTmBmBdXJFNHbJM36SDXFGRsxWzyOeB6hiW2aGMvxoxKJq9vfGjg6RFAE44cFZxwESw7vkHhSKcm1ItcqqP801ZpP09ti/Las+sFKQnkB4R5aKf0P/1687lO7DpO1/1ShMlZcCDdA8/lNzp5FVXJxOMu/F4e4ROyLoqgIQNrGDG8rzeh12vRShbAzXDHDVCXOB7qn8KGiI1c+vTnoZUD/2W+pTJmjA9S3Fg9kOiPLUoypomJUeA4p8V6is3UuXHyCi2c2kQczPvne++xeP6YudPRtMsGodPsUaxcu0d85S38wQCdq8V2J9Asx1zjvuH77Df7wT3+fjz/ZZ2ZaVFWNredMj4YgNVmnRy9r0c1zUq1pp+epChl80UTCa2++y+vf+TbXbr3DyeiEkxNJbSErhxgS0naLrJWTtLpkqkUmt1BKk+sw6K1UZGs9dL+H7q+Rrw0w4yHCzBF1zfRwn/3bN9i+fo9+dh6xDl66xWILkOo1Lpz9JczRn6LvDUnTNlJMMbVDCU9lLbV9RFTVxeYjUAuBjFtUFWLFQXgWHobeB2U/VpMpK6DRf0q5OFbqvZJhMvWBXrQK7JZBKQ9ls1aR1gKb/k08wR95vz8MGlc+LQBi2QBBxaJ26n0UqnIhu9gopja9ABFghfEbHvDexYrjqveRwIVaA879zYf+d9k+uPYRUnkGpzY5Pr5P4lts9t/l5hsFdz6Gy1/5PNdv3mQ0OubByQnWwM6FZzB3j5kVu4uEjHPNvBbM291sxN6H73F7+3V6O0+Sdy9Sakkra7G902NzIMldm5ZuoUSo7EuVkOc5KslRWUKWJyRZikoydJqSt7qovA3tHmunLnL+yecZtDNyBcoFwCFTQekc5Clnn3qKrYtnKOcTXihrTkYFpXP0+m1aeYKWLQQdwAaanvf4YowdXef+h3/Mtde+T3FUsiEzpBBB6j/SIvE+0O6zNdLe80x4mv27ntHwmJ/58hNs7Zyn8eZtrt1Czv0RbPOij06DvYEqBXXlEUKjdI6QmmSwTbrWo3dieDCveemrf49/8NVXcd/7X/B790k1ICNBzYdgpRYV+TNfh84TYA1ChbWjqST+uEXibwoCVq2QPvUMjZexiAliKRXOOTrbp/n6P//f8NVf+Ud8+OYPeO/73+Xbb77NW9/7c5544jy97hotH30lUcizz6J23kNOhiEoKXxIbDkQszHF7odsPPELPPfCZdLsHhe++ArPbAz4v/+3/5IH6xX/4uv/gBu39ple+jV+diD4s2//9/zhn/w633h5jY/f/Yu/y+X6a7faiJC99xohLIqaRFmktEG5OPiLEbrPBM5pbO1BGYQAnYBABY9klyCqFIzBe0Nj1eVxOGuwVYmRCi09IkmD3UmSEhoyRBTUMWF+EgKpo3jNwm4oWA4tfGuXs2AkVQgaD9M0TfC+RSNMgwvqj6E5wUMU4rAmVBqdrYI1kKvxrib0NxIFcTRSaqRMECIhCLCF3jEfhX3KqmI+m1PMgxhQmKot3iu8yxdV3do8mvXRSYK1hNbBbxzDeq/Fdidnc62NtBVJBm2dMSmnTIo5vXYfmShqa1DOIBBYoRGyRZ73EOYu2heUtcVOYtWx2MBOS+xsxkwKDGvk3T5aJyTtHv3+GmiF06Gy44B5VSMTjXaAcSQSMiVp6WAxJKNadLPaehUKIh0dKY+Jppt00UDtDMp5lLPYyYjpjZvsvfs69975Kw4+vIaoPdR1tKEC6y1GCHq9Dr3tTVqDHul6F58mJN6iiiNu3b7K/aMRExOUujMtyLzCpJJh7Tg6OWHmJaQJ3UTRUpIjaxg8guvYJLGCENTyvvcQlHp9EFNMVUKeKLI0JU8TUuXx1ZhyfoSr6lB8wCESjUsk7UGbjTNnIVnDeI3UIUbyTkZ/yOgcIQQ22lAF8CfwQqFksAFx1uKJWhtSYV3oxfQu0ssjQAh7j3GL97FX0iOlxjkT9o/n4pmztNM+R8cHDA89xXzGyfCY7nYSktxS01nfoLd1lgcPHuCZo0X0ZPRgC4OflLTaCb3WBkIegSxjnBB6pb0LoLUpigWNhGhlIv/mNeUnAI5N11C8oIufKHXhHMJYhHAkWpMnCZ3MU5mgfESkvS0Uu2gCyibzv3hg8ZlNSLsIHL1nofqxQGwRvHr/sHH2ynMiVmTEw3WasMDFzOqqXUfz/hBjLuk8q1WU5VkgUu9CVcbhkUqT5ZpWplCupp7NsEWNtwJUnOib89l8PsTjC8uu+LQJ+Ge0KRzWA1LhyoIsz+m0MqypqOsaPFhnUUBtHUanrF24wBPPPcdaX3Dvuz/g5GifqqqZz0uMDb6UWbdHdu4C3TPnWD99hk4rb9ayICZgauzRlL3DCW9feZPf/b1/xwfvf4j1QZHLOEuepFSTSQDerQ4PkgxUGKR+Pmd7bY31tTVef/t9fu8PvsW9+1cppgdMTmZ42mghKcdTnFToVous1ULnHVKVkmiH9V36rYxKpmiv8O0Wut8l31inM9zGjYZMzRzmlno8Ye/2HdbP3KDKz7DRPo1uxR5PFSgHeUfx+VcvoscXeO+jN8LdEM3mrfXMa4vUj6jCsfqPRTAcwwC/BIHhGoT7vxGEkYg4CYuVKr1f2tewQmFr+guQISByLnp7xdf6leBzARibsdQAP7FMtjz0/F//7fzK3w+j0E8FTDJSVBf0vAiSm3f7SPWJwHGxm1iFWgDlBYOiAd1uMT81hIdHsZ1t6SC/X4xoiQrhp/z+b/0/STwIcmb/839PzZzBdo/Oznkubz/B2vnHuLt+BXt0gKpqsM1cFQCFsTV5IpjOTrj2+mukG4/z3KBN0j3LeFKwdmabr/3qzzEflySqw6C7jtYZSucM1tbJ8hZKa7KWIs0zkrxNK2vTbfdIsj5G5fg0I8k7pG0ZxDZE6N1wBrRqxM80rXyNPF1DONhYF8G2wFZAlAhHoDUIA3JmaRUjPn7rj3j9D/81x7du01J5tFSxC5ppcy2MFJQqo3P6eQZbLzGcX+GpJ1/iqWdfQsg2zs0ju4J4XR/NnArghSVtbVBP71AWhnZvg1TVzGbHTArLSGieWttAOU9nK+VXf/WfsTN5jeFHf0ZmPSr1eKVCP5mDwjv8Y19DPve1OK+sCCb9B4DGv8M3YVGdXSRGg2q1dw4H6MEGL33jl3jp1a9z6Td/g9/73ne4e+MBT597ipayUS7fQ5ajL79C/WAXXxUgK6jr0BMpBfP7d9g+ewebnqHaqVlv9/j13/wj0tNT/utf/b8w+nDIxL/HE2cuMbVDXvnqP4P33uSNd1/n3OPPwa3Dz/zbqyTHmhxrBLY2QIkxU7IsePwpkZBJjUpTPAqlUnAChCPRCtVKqJWlEgnGJKgkDeDP+IXPnAdwLqy5JTgM2rfQWYtEtxAyBd+In8UDi5UCYjJvNWMnEMskuFj2rC+lAeOcGSdgbx0Iu4iLvDWxummwdY0zgaLqI2jEGaTw4ftrHVSrlcaigv9hTN6XzjKvaygLZqWhKKcYa1FIpJd4C7YWwaKjlthaYcyjYVZVFkof6IaJN/RabXbyhLVckUrHIE3RzlOWNVVVMT4+Jmmv0XY5NZB6sCistaG9yDharT62VzItDFZocJ5yPEJlRwiZ0HIBVA9LB2mbjtcc1xavFK21Dq1WghQOoROE8GjvULFCK70nl2E9UipoDDRJTu+DWE4SuyySLDgYUFvK2mNmFXb/mDvvvsuH3/kt9j98m9HNu7Ssx7lG2i7cFUZI9GCDztZ5Ns6dZ217A9VfY1IVTPbvcPuj9zk+3udkWjIxNbl0pKmmnwarl1IJZghKb9AyQzhJZQxjJR8NcHQOJ0TsiSUyhATWWHA+Mhw93UTTauXkeYtOZ4AvC8bDMaIoyZr5iCDi1toa0NrcYi5TWnmOkTLQwX3QcvAx5mngXIOlmspeSJCEynvYr42+iUFh2K1WFrUO6qh43Aqrw1kTwhchQ1+ts8HeA8/ORh8rDVW5gR8NmU0ndDYGOAteaUg0g1NbTPbWObk/JnEeJaPIZu1gNKGzXiDWe9w+bDHcH5GoJLAQYvGviei8XJ3x4cdFOn97qupK0j9UBMUCGISY0AcRDWNCxUgldPOM2lqq6K8SpU0WsWWzMIUjX10M/Q999kPB7CpfdVX50PkFhbaBiCKi6kVFM+5wWQWJu1mWGsL3WQWxDbWvAbnxmBaHEz9NSh96ClJFngA+ePnMJ1OK0gEhQyeEjNWgJoANPWcP2XM8ou1UK6OuMqaFQcWsaW0MpjZY6yNIiGAvkZy5dJbnXnyKfi6ZPNhlfPA21fAG5egk9EAIi1MK3V9j/cwFNi5cpDtYX5znBjbUwxl3Pnyb73/yAb/3J3/Jtes3cV7EirRDqwTvQEmopmOO93axdcnJyQnT/SPa3tFtgfCW177/Nrd3H1C6GlcVZDJBCBOSG0own4wY7odGaecNmJLRyR47m+ucP3Wajf4GvXYPnSZk/S5q5xTVyRhzckxdjKGucMYx2j/i7pU3STa7bKgtQIcEiVJ4C1I70sGI2h3GfBFAsBGoncd5xaMK8JpKSlMdC7e3wvuQ2bQrKl5Lj1NLI6cu4+QoaPp2mvG8HAPN5wSOfmgKb0BKoF245WV+6GuKh8blqvhMM64WcHBZDlqeqzguHxqx8XuGnsYgSb/oeRAP9zWHtzfsiOVnr/Z3NWPNCxUKPAu1tGUvZwN74wT3SLbp7Y+iZ6SNfUeesamCD5sPohmJstwZedYsPHPpJbYe26B94Tz27nWoK5QI6sRSyAA+nEV6z5oSuPE9rv3g98nWoJ3/HP2NC7z8tX9I3jJUFpRMaCcZWmqESmIlQYfsuDM4ATrL6eYZudA4FyQ3ZJKQZwIdbxcElHUwSHamuYVCRVghcMaBFUhrwVhqb7FC0c4y6qpG1I51Z/nkzdf5q9/6HY6uXGdd5Rgf/V2bmdKH7K6UnmE1pXV2m7NPD1jbLBiMJc8//zyD9VOB5imXAvF+YeXif/SF+LturqI4GXFyPCTLuiANxgjSuP6sDTJme3cZjuAf/qf/lJ/eavHg3/8RHI7pr3VDRSpm1r0Q2F5K69WfR3Q28H+NuNint5Ul+oef+JFv+VHvWCzICGq8LxGiEx6JCtbeR4XQNOPVf/KfMJoPuf7hu5y89AKD9c3FcPEI5KWnEXcvI8YHSFXivA1BubPYsmR08016z51i2tth7+4fcKN/xH/2zf8d4vrHfO/qHzDKd/jcYMZjO+fQ1QVGzvHav3mN/YN3eRQKuSpJcU5hnaOqa5wtMHWg+eVZQqIhUxkybYPMcEIF2xSl0DqFHIoCrC+RVYLUGSQ2BMCRwtxQ6bEWSxnog96hhETKFNXEQrG6KGJyzHkfZXb8IibBN+07fjHzPXxl41zXPOAFwssV0OhwtcFUFaaqsHXwa3SuAY4mMM2UCNXGCByF0kHYRyU456gxFNYiywKfaGa1oagLyqpCOY/2EmEVziY4m2KMx9jQCvAotuPZnLICoRw73ZwLmSZNVTCClyBsTVE5RrVjUhmMmzCdj0iHnl67i8jazGoHdU2GxyBptft0toDRjMmsxlQVTEYUSuOso5hOSQ5SpMrIeuvQbeO0pre9TW16qH6ghiqRoqSglcilz7CAVIQqcwP8wzLqg0iT9XgbE8JKMDeOsjTI2Zzy/l0O3n2PN//9bzO68QNmJwe0RIaWQbjSuUC5tHhk3iHbPs3m488yOHuB3tYa49IwHw/5+Mp7HBzcp+UsbQ9TAVudFlu5IhOKiVFMTMXUg0g0qbPsVxWDPOX0I+odb5IlHiJzsYmTdRw3liRNyFNFJxNs9PtIIxnun1BNCtqo1T0F4bZaouuMjtckxRwtw9qQyJCfq50PTDsdXWo9kenikSq0ItTWgmj8HSNQjMOyGYlBndhExAkei5AerQTI4G/qbbQE8yEek96ihGGz30YU57h/POPg8D79zTZ5ex28RUhJ2tZsnTtNOT3Cjybh+8X4xhUFs6N7qLbnwvkNEiacHE/BJwv/xsU5aTRaZOh3NJ95j+MPLbhNX2E4UU1g5q3F1TVKKNpaUWcppTHYylC5ULd0LCc7VsFjrHosyrsxmFz0Nz3Uo8HisVC/i1CxuXgrwG9VE7GpHqwumTLudwkEYSnoshLFLmLTlVMfs3xSQqIkaaLIEklCTVWV1PMp86qksA4jBFrIqPwYIi6xqGzGrsYGBDyiQPWrWwO+7QzX5lOSOOHNTY1SkSeORapQ0RlsbnLhiWc4ffYs2xoY3mP3zlWOrl/BTQukJwyivEW6ucPmuUtsbJ9CZxoZ0zReeqhhPJxy5fY13n/vLabjIZ1Wm9FkgvcCJRu6i8AJj/JQHg8ZzuYUh8ccXrlOO02oymPu39/F+wSVhGBGyzzacjZqoh7lHMXREUemop7MOdl7wNrOaWaXL5EgyJMW3XYXlWp66+tU1jPb2KI8PmQ2PqScTcKArwyju7eY3L9OPfoceuvcInnhvEWikTLlxvABd44PqZwjSTVJrcAqqliFeTSbeCh+lHKpntXQU621i6DEe0dDN1pMviJUKMM4bsZYVCT1EWvSTNRqgSlDt4t5yBtySeNe/n5oyPDpfyy3HzpFMTOzbOJegkakxMdmcxoV1cWHNkFTkxhqBI1ikmZxjCLSdMN38nEia6aMwPuPVcqwZD0yiqNkhNahr1ICUgpa7RZ1PNJWmiKcpcBi5zOmJxPSi2c58/RTHN35mPHHE3ztglpbs0+pEM4irEHbI2YH73D1ewlSr/P4qz/LY6dfpqZAtfOgsIgN3nUQEg6IUBGxFidlqJoLifEeROydlW5xD0khQEKSCCAo3jkvqCILzTkX14hgBpQkCcprhE4RlScxoOqCG69/jz/5n/81ux9cY408AM7oO0Vs3BcqYe4d83JM/+IWL//CF3jylR5leoutBAa9HqAjcNThbogU1SV4/Oy3cnzAaOYwIiXxFUJmbG+ew58MeTA7YTqbkCSSzeef51f//t/HvvcHzK7cZJCl4OrQT+wtQjrmXpA98Q3UxmW8s4t78oe3ZaJluTLy0F8/6p8//om4QyRCJCzTmywCIqVDEg2d8LO/8M84/s3/D++88yZbX/0FMmVJZOh1RCmSyy9gD3bRpsDMg2CJFIJcKsb379Pavsb69nO8+dpNvnHuPJd7bf78YIOj63fwl1p8RI8vFnt8/53v8O1vvc/pbpf5/oTqERjHWW8wzoWA21d4H6pxprI4BBpHOxFkeYpMWxghMV4idUKS5iFekJZ5pRE6JFiETfHO4jDBy9C5ZhgBQSnS+BKFRqERmUAoDzRekPEnqgk7F1oS4gq7FKpemXEbCtoyGe1Dss8FhWlvLViDNSV1WWCqAldXC2qqswFAChxKiUWlUUYlVp2mpHlOluW4usbUjsIYfFVCmVB6T2kN0/kcX1ZkXpCrnER1gt9kcCkJYlmPYKtqh/eWVpLTyTtkiYq+dgrjQhtD6QXDylAjkYnl4PgeVoHI16AIitN1UdAWiq5QpK0eqcrIGVG7YwQGMR8zm40Y7t4mX9sk6/bwSqA7LfK1DZLBOtQz7GwDzClSmZCRkLR09NMOgbwlpkFEAC7SBlDirAPng2K7DGx2WXqUFfgp3PjBG9x6899y/fW3sPtz+rpFf/siGEc1nVCVJdYGtU+vJPnGGqefeoKdS4/TW99BZZpidJu9Ox/y4MFVqrpCVB4pHdoLUqHRMqGQCaN5zXBcIiK9tnaWTpKQJYpOoqkexYWMCRMiBXR5jzuk92RakSaKPGsx6K+hLIwe7OMnY/IYm7gFow+Yz5nef4AdzSnu3Q8FEq1QiSbRGisdqttF5i3qsgo2HypBSIHO8qAeoUQQAlMaqRVCEfugBU4qiKEKjqAh4mNBxofiloFgfSFk7ImMjMnaBBCLI/WSrcEAd+Ys9+6+x/WPrvD0ky+QD3KcdyR5CllK3h1Qzyp8HdSJhQfloJqMsMOcfG1Av7vOdFThLSghcZFaHqb55fhzDTPtb9h+IgmkpmHeNRW4GODJVdDnHd4FGWetoa0VZZ4G5aPKxsAkisgsEFzY3+LP5Scu+iibeVGIT1UCaPq0/GJfy1PRvFfE42cR+C8/YWX59M1R+GUhhGafDaJnUZlobshmR1JKUimDOl5dU5qK0pQU3lJE9SOBD3LyYknxaI5hURJfDJDPfntuW/GDYRGzmopaELIfUYZYipjl1B0Gm0/z2ONf4sK5c4hyxv4717n73i7MPdIYalvjhCRZW2f9wmMMTp2ns9YjyYnnMGTQK2OYzidUVQ1e0M5aHB0FqWAtAi9cNAF8zM5oKWFuKWYjhBDMrMF5Syvt47zEuPAa2SgAxv405yzUgZgxPhhTTA1Jd4xTGWdOX6IsBVUdJO29hFavjawqWoN1zNYO89Eh8+EJdVEibI2fzdm7cZ21dz/gwhcH6F7wnJJC4mvLbG7obm+i2wY5K0kTRakkjR2O+TG+OH+XbbXi2PQBNIkO5yPY+fTC3GR7VhCe8CvVbyEWNNdF/BheFCtLfqWYaBYN36uAsZEwefjjl6maRfJ7NQHTfCFYZBjD2PY0/YxipcroY6WxGcEhgeVYCt8sjnz5teO/FkYmgrAv/EoiKybAXGQueL8Yo49i29juIYVEimZKjoI9iAV4VSqh3+ohkxZ+csxkPqJ/9jSnn3gCjk4Y39uH2pJJtTinSgRxFVdV6MJi7l3n1vf/AiEVj3/pZZKNAYWpUFlIdnkJWnm0i6IoCFKdgZZIGZgAxPtNqnCGJeH6OrFsY9BSkiTLinK4/SPdVwq8iUboeExRklQ1eTnn1js/4M9+49fZe/dNetaj4ryhlQYffFqVVNR4TvDoU+e4+NNf49yXvsqeqygf3OexS8/TyVrh+0u1vAdEM1biav4ItlpIZEvRA8p5zanNMySZ4sFxiXOOyf4J5594iv/tP/1HXOIBd773XWQxQ7daiyQICryvsRtnyL/4K5D1fvx91yRKP8WoWaS//wZs+NA/Pv1vYGEIsCIqt/q8kIH61Dqzw8986ef4nT/9d/yPJyf8g1/8x5xpqVCdRCDOPY29/wnlyX2Uq5Fl8Cj01pHhmd79ENVa50z3Il/+pX9Ocf2I9777e/zeLcOXHzN88P1P+O/ULcToY9a2nuDw6BNMPodHMLeW9ZTaBEEgRIWQgRrnrMQZgUhAC0+mHCp1WKmwQiK1QicS4wTaSFSikDpBaRs4hniMt9HPzTWlCYQLiW3rDbWfB3EOZ5FpFhomnUK4BKGSWHVeJgtWw75m7m4oZ4vHRWR52dBXGQBjAI2uLjFVQV3NsXUVKKneLSqNeBv8GrVCJxqtk/CdkmDFkLZy0iynllCZIlzTqkJkNQaJsY6yqjGzOc4L0lyhE4fUHqE8TjoelQSA8NDSKVudDq1Oh6mvSWqDNwUil9TCczQrKSuLTjNs5TCmZngw5GbhWW93aSlFK0nIWil4z8wLRKtN4h15OaUYHjMfj5mOZ0iV4LsH2P4adLvk/R7ClMECxVbossA5E0RnJjNmnR7ZVpc80xjAJgLtQ+Wn8KCtp61AJhKDoBRQV44cSXnnHnJ6i+9+60+4+Z2/Iqv3WNMpnQsX8K7C1RXVZIKoK4QxaAGVA702YPDYk5x/+gW2zp0j67WYzo8Zj+4xO77H+GSEdRopNCoXZKXBW8VwVnKXmmJS01aaTWcQWOZ58G3vJWqFhvlZX8emPxDwYR2RBGZfnilaSpPqhFarjxY9Tg72qCZHtHDLdq8FIwtwLvh1z+ecHASV6sBiCu07lXfQyhFJRlVWpGmKVBoEqDTF+gA0RZIgtEZFATmZ6Dg2MlSiSbUmUcEfNU0SdCLQSoHQcRENok0hjBE4pRd9x1KG8ylSSfv8aXq5p5yMMIWlLmqs9yR4emsbFP05J0eTkPRpsv1eYCtDPS3Q7YREDUAMQdbxPITu2VVVbef9j1VUhZ8AODacXR//bhQUYxv3oqFURO8T7yq88WgpyVX4qaSj8rEX0cPy3c3C14BPArBaZM+WUHEZgDYVkhASPvRcXAcX+5VNpnn17m6QSnjfwwtvDEI/tep6v7rANp8f7wMhkdFHSTqL9yXOzDG2wroqTsJB0l4tdtOA0aaq4xdf4REl4lA7LTbvSbZbLWbSMirG1FJgEeAsaaIwCqok49TlL/D3vvAqF1s547c/4ujmbczMM54aCmcx3qO7PdpnztI6fYHBmfN0+52HQQCAd9QmSJC30xRfFGy2O5SVoTYBpFvvsD5MEVI0SQoQIiy0XggQCQIZegx1qDw11801oD9yzJsot55X9NdTMplgK4FOu7R7fRIdRAmcJ6iLbWxQj07obZ1ienhEMRmjpKGYTGD/AUc3b9Bbf4z1Jy8hO8FofjaacOPjayR4XnrmMu+XE/bKGWPngpqqCEHAo9iapMMy2xLHgG8myXhfwUqQsaSXN3Flk7UQiz+WEMuvilgSdyRDhk3GEeKx0edx5d6NiRYfL8xqcmWVtvrQl3noiGM2TIhQiYmWGwEshp6AxXHGARh+xQlxsdsmCeQ/9T0afLlyIlZe0qSxFh5qfqXP+jPeslQTMpLN8YMQ0U8NkFqTZhlZkrAx2KTXs1R2n2Sty9aFp/H7Y2xRMD54gPMJyBDkeBf6Z5yX1NMSKfco7n2XT147ADnmyS9/Az3ooRJJIjUyhUQR/dtsAILSg/AoJUg0WOuwMWupIkvYNz3gPlQrrfPYQqC0IFHE+zE8L+K6YS3Y0lDNTpDVmE/eeZ0/+83fYPfdN1mPC1pZ1qRJsrw/RFzYzQzR7XLhhW+Q7fwyVx90GBXvcXHQ4eKFZ8i6PawP/SLQgGBWKo2PZmLNNk8zEDPmxw+wdcbw4BClHaDZXttgZi2Xn3+Vzz3xInf/8P/N6OYuvVzhpMNJHXRxnMeg6TzxHN7ew+wfhYYmpUFLBOGkh561qPyb5iHFIFYpWU1qRD0EIv6Dt0/f6m6ZyAxTi1ucUOkFThjOv/hFvjnf5/3RhJ7xqDh+BB6vErKLz+Hu30BVc2xdoo0JfUESRnt30YNdnnv6Wd74N/8T/8N39nnmiYJ/8Y1XuWdKdsZ3EMe3+fb7V/B2H+vHjPevcPHy83/7C/VjttrMQi+0cEANPgZbQscfhfcOYwtc7SFJETqL/U4iJpNNGDNpirSExwHvbaj8eA/OhKphnMe9d9S+wnuHtSXKpIgkDVV5lSJVikpShEpIVFOJXKTEV36aed03E1nw0jYGZ6pQSTQGbypsVWDqABoDJdXG2MwihA3zvAqgWOoElaboNENnOSpLkYnCSYHxnso5rLO42iCrGiMk3tqYTFKoGNh5TATkHikNUj2aRI4XEqXTUM2ZTckTgcFiNBRGUTpLUTkMmtG8YlpZprMCLw4ZbAzYeuYpet1NBmlCN0/AOGxtmDuBSxN0r4OYn1CNKgJf32HdHFMpVAFGhZYlPU0w+NAvCEgvMQNPUUpaqWZ7vR2qXig0wdJkTQmsENTSUyICSKgdJIpPPhpz/P0PufPm/4trb33MZn+bta3zdPuDULkuJlTTEV54jHPoJKWoS1zWYu3C45x/8Ytceu4lsrU+s3rKcLjHlStv8caH7yCtJCHB6ZRZPaUQimnlaYmaU+02+9pipMQ5yXo7YaOVkUvIvCQTcPwIruOyPa1RWRGBVaYEWkmSRJHl4b6cjqdQVijvYs9gGCFuBWPIBrNogRA6MLGEDBRwa9FANZ5i3YREKdx0RhV7u0ObRGBLhhgkWuAIUEqGNg+tgwKyVGRJsM5JU43SIohK6SxU7XXIxoYqvkYmSdA/ic/J6O+KlOysb+LXN6lrQ1VbhJbU3pP2emyeOs383h0mMxZtZs5DXRn8eEp33ZJtbrF79IDRcJ9UJYF1EOd4Fy2eiCDyx0U5f3vgCCvVv7AciZVMZUMrbSJT4UOTP04irScVkEdJcec8diVQC3HtcpFnpccqGL6LT0easToXQeNq/OzjkYn4vuZ2EyvZuUVlppl2l3TYlbiVhz9x9R8B1DZmuFKGYEoLhRICgcX7GuwcW87xlSGXiizV5ErSBlLgR7HfHk14utzynad45pKmqva4cvsuU+soXPSeIhh3F87SP3+Ol37qCzx2eh2OHzDffZdy7xbl6ISyKoPymE5J1tbpnD7H4NJFettboQzfUDAiMgnCFpDonG4+4KWnn6UoDFVVUxlDVdcUZUllgjJdyH5YbGyAaAqwzoVAJdUStAq9s8bQEBGcb+iZIdA3eNJ2G6ESOlmbzfUtemsbtLodtAblBF56bJqQra3TWj9hPtygv7lFPR5SnlQooZjsHzK8dY3uxhb52S7dzimcNbQ6Oc+98hK9tM37b3xCL9mgzA3DkwlaaUpTxwX+s99cFLpZgsdlNcU1FhI+BP5hPCy6oGnUtJz3QW2tAWmr8GhB3RQRODWfESw6ZJMxIcSV3sa84OpYXLmbPcvs4Q+Fsqsc8ea18ZiFlEgll5XGZswv5p/mp9mtbJaJ2HcQz4NfAuJmDhIRGC4VnZf7XoTJC2z5aEZm3kqX/c3x86UQIQGlJFneptXpkGcZg7Uena5G9TxZu0/ryecoD444OLpDWo6xowq8i9TRsBh6IZF4ZqMHJPKEWk657lto1+PJL36e1tYAqSU6D9LvQgmUSkLvBaEfQ6moJunivCtApj6yWsTS4DhW/51zWOvRSpJKT12L4HvlPEk0AjDzCj2dcPudv+T7v/2/cP/9t2j7Eld7rHVIlcTAOlxpJ4Jaq0glW5sDetkpipMtJpXhzOVLvPyly7ROX4Am0yyjevVDgPHRza737+6ysdWmnSoyq6nqEYnM6fXO4qoZ3a0OX/3GF7E33mLv9XdRtqbSiiSRaOXxwgaVbamo3/sBxTvfBQReJJCmyDRYpQgr0aQID1a1KbIOqpWEhFy7g2u3kAmhX9WCUBle5MEqQCiUT1AiAa0WgQpah+qWCjVCdAwPmpMnNcReIREB6bJyG6wnyOHJv/crPLk4Iz6MWwivPfUk6syT+Ac3QSoq4fEi3Ku6hvrmB5idi4xvvccvf/VxfvGf/B/57X/z2+TXptjpA7792p8wLguU3WVnY51+b/BIrqNzVZydHMKHyluwnwjeiagABLwpAYN0NcoFQIipqJwPiRcBWmtEKsI4cRbpKpTLAmurBmdNBOGA91hn8M4E4GgVKsmQSYZUOV7VYAxJmpNkAqlEnJ6WybOGCrdIploXaIq1wZkyAMcIHm1dBqsPU4bqIoHa6SNTRcow/yilQoVFh+BWZRk6zxFJQuU9pq4pTB3EnPB4a5BlSY1AGEuuNLKlQjVNSCobkuhOCKx3CPloxqSSEqU8WkE30/RSwdQJrEiY1FBaQeEko9mcSVEwnk3I0hTwtHVCPS8RXctGbwMpPDNbhBgEC0lCb32TQZrRGmxSjEbUZYVUKoAQb6jnM9K8hVMzpnNL0fY4uQZZxcTPUek63TzFK0GShMSCjO1K2ntqGWi8tQi01cnuAUfDA67+4APctQ8pH1RcWjtNZ3OHpNPCSYkpS2Z1ja0txgFKUFqLzXM6Zy9z6fOv8sJXvsn61oBpvc/9vQ95690/4a9+8DYTkdEWgR47KQvmFgwKIwSt3jp1OSMVFbMK+llG1kpIBGgkUlryRxPmxASmXJKREHgnMMaH+ydxZAnkiWBSTfF1QaxLxbhAslB7J8hXuqbwFdlSSjZJlhAvZUKAErF1RGIjXXbZTrYoGy0TacaFuL8qYosLlDF2mBAT0Ah8jGnCzkUAh0ojlcRrBTpZgEgvNULrcCw6Ic0zRKLxQpFpRV2UbKUprZZkeiIX8XZzgGZaUA8f0Opqdnb6ODOknBsskqRhJ/lGuvCHqgQ/cvuJKo5NULga53nR1M4WJE88AdEGHUaP9p4UT0tFE0zrccZhrHt4fwvcGUPHJtMcnwvB4LJq0Uy4DxsbL3u9mpNCSHaFmzBmHJoMRBPDLpVTGzXFh4PZcHwhAPUNXVYIlIREQCIFiRIo4TGupi4LqmIWqGJe0lGKrla0ZQCOWQOuxBJANnUavwACn/3WvfQS6a0RXhyGgK8JtLwPBsbesLaxxjMvPsvlC2cZKPDlPocP3mdv7wZVOceaMgzBNKE12OLcY09y+vwFkkSHfPei+hx+6zRl/fRZnm+1ufzSKzjrGI1GjE+OmRdTZvOCogxg1FgTabyCqq5pt9vkWUaWZEiRUhcVdTXjZDqmrMsAjGTwTPOwMGZFhgqIlxqdt3jimZfYPvUMm5s9NlsynPM4C6QpyDwn72/QXt9gvr5JPjyiLmZ4WyOs4eD2NQZn15C8CJyKpvMCJQUXXnyCzVPPMTmomM5maJ2ilIlUqkczoy4N6ZeBQgNwmt+hEh6rdote3RhUxP5FpAvVrTh+mnHRVCAFzbhcDBSEl3ixlCwRVoYF1QWQKXygjIrVSp1fqjQ22w9ZAfjl443kfOi3XaqnNupwIU0bj1E2FccIfr1bLDTN+Vh8wCLD7yF6ky2Et4RafO/V7H04149mPGqp0UqitYq0VI0SAo1Aa03aatHq9Oj2unTaXfrrA1R/ADql2+1TnFzm3r2PqEcjcBNmswrnQ3XORdqCBpTQlCdTEn9IZT/k43mJmN3n0ue/hDh9Gm26dNZbVM4jlUelYZYUAoz14OyiflVbhzUSGXu7jbXUtSVJktAPaSLd2YIzYb6vK0uCYDYfURzvI473uf/uG3zv9/89hx+9z0AEgSBvg0+XFyFzKjxoKcI1SFp0187TW7tANyno9K9w+uwmz77yJE89+ySeUDGRCyGZkOh4mJ76aAJVryTjyrG5dRGpS6ZmysZOhjOCspXzzW/8DD91OmP42/9f1GxCkqR0M00ilolN70B6R1ZMyRxQe5BF5AArnLG40oIRuLJEehnGbiJxGkye4FqaNCF4rk4KnHGIJKdKEypn0Qg8ClRILAid4KSgcoDWJFkAkkKlyDxFKYXVKSppoZTGpV1yJ3DdDr7dRqHAqTDfagHtPqpy0OqACz2maFAuI+0kVFoG5c3IeXY+qCLWJ4cMP36LZ7/wIp8cPuCN3XeZ9C9yOPxdruz+gMHpAQN3lt39IeuDswyP3n401xEXewEtAovWkjRJybOcNE0RWmCa4NM7hDVYXyKsxQuNcVCZIFYVihICr0J1IfREJWjvMD60ujQ9UIsZxwbwZp0AY6G2oCxOVnhdIbMaZUwQpiEkwB8CjivxkLMOY4Ja6gIw2goXfRp949MYfX8bDokQSw/XUCUJAazQCTJJEUmCFZLCBMGf2vmgTCkIVNhiHih9DlIRaLvCg7OWuq7CpwgZ+sIWyYXPdsu0opflbKUpUgjmXjITkmktGE7nHE9nTOeh/6+H4GKnTWtzk5mRVLbm6GifQa/LvO5TC5hYyxqQJglWKiqt6axtktqafDKinE5wZYkp5rh5hUBiRmOqeUmpUmTfM1Ydct1jkG9ytrdBN89IUo/2ghywhP43KwQp0I/z7XhYUx9NmVy9Svdkl/vTY6alo6cyvJDhGvsaX1UQBY6cdzgpodWhf+Y8O194lc/9/C+zfvo8frrPR1e/zx+88Ze89Zd/STVL8FpglODElMycp5W3yaxn7uHjwyGFK9nuJGw7j5WCvWmwgeilmo52FOUj6XAEWLThBCYNi/5e5zzW1LQUtDC4VHNgQryWxcKBEA6xiMEcToqQNLPBOWBRZRNBPdURvGZDu0xgRzbsLu98XEmW1bkQQRAT7X4hPuO8J1FxTKzEDg2+8YFmB9QhsSPAOIv1i26tUAiJ+1aRVRUcdjRKSlKtsK0Uahss5oxZzAMCiXSe8uQY29H0O23s5gZ79w4grhsh2dvMG813+Ju3v4M4zhIoRty26PULJzq+ynuEdGghSSUYGSJ1qTRegxU1zhusc0EuXywDtYc/y3/qr5Wl33k8NgJYQjAZG8kbWQshWBiBi5UeAFaqEsTnGmC8xO0rpdsm7o7GcCFYDUpEiYJcg1Y13tfU9ZyyKDCVJVUpuc5oKU1HKLooWnFi+BTBdnnRxEPf8jPd/EaP1uY6XunQhK00wlYB4CtNlnfYPP045y6/yOmzG2Rmzv4HV7jx0VWm8zlVVSEdwfKi02fj/GU6a1vU3iJSogjA8np5IUAL8s0eer1PxwtKY1ira8rZlPl8Qmmq4JXjgwqYFQKZJGidoBNNliQkXmBKi6kMxlTMqxLrLYigdJWmGSqW3L0MgikCgVaaPG/R6/WDYbEO1d6aMOib061aEtXOkJ0O6doaab+PPGlhyxlYh5vM2Lt+nY2PrnCxfxaVtbE2iH1Irdg6s8HwToeD4yQAR1kihcQ+IqrqQ/fvSrCwAEpN9S1S/MRKMsVHKm8jtAB2hfLdVPCXFb2FKFWjnOaJRrnL3kohZDC29g4ROa7CL49xScP2i2uz+L1yHRplyYaeKuWy2thU5cIxNsfUHEMzXkIP9QIkL3bvF0OqUZn1MWASq5VK0Xy35rHmXD4aSlWSpGRpSpomIDxJkqIESOdIkpS83aHd7dDr9ei0e7Q7HdJuG5WnpMZw4ekzjA5f4uPJlOPiNsKDmZX///b+PEqWND3vw37fFhG51Hbr7rf79t49PejZAA4GAxAiQYgQCFEkBZuUZXMxz7FkOWlRlE3LsmyRoHx4DnnsY8sUXeZqkhKlY0k0QdGHCyiCWIUdA2CWnplepve+e+25RMS3+I/3i8ysOz09c2duTW/59KmuullRmZEZEV+8z/s+7/Oig/RGku23URYTSuK4oeUVJsfX+ezBm0yOxjzwie+hf/kyrnWUG1buDhqUUSidKIxCGZmEJV4NiuDlpmM0OCdKi/n8K7F5FZleVJhW0UuO9mjM/muvcPvVz3D7xV/nuV/6NW699DqDkFAago9dhjCfw9J/6lOESuGLHuXad7D20KfYfGLGlSde48Gn17lwdVuOb/T5POgC6nwKqPlqdCrHEGDv4Cbne09zONmkLK6xNqyItqDc2OSxy4/y/Z/6Tvyzv8bsK7foWUWvgKERf4AUdU62intq8h6izmsiGKtRUaFTInhJjrVRoSNUJlHEiNKWQkN3/scILRatIoZIr67px0A0Cq8Vqk0y8kUbfNMS2hZrDLrKhlHGoqwEPm2I8+x8VJrprKXplaTSUdYR2yYiLdOYmPUG9CYTJkWf1CZSDHhrqRI45SnaidSclUjFYgi0AXwL01dfhK3LWLfG7PothvZxPvvqhFlwDI9rrl2/zuaVc+zXt3jt1ss8ePnKfT+OCo0PLYSAUzKPuV/1KcoqV1ADUUm7hLEWkDlvwXuSSgRknnDrRbYtbRYyAxKj0dFCitgUCV0MFWXd1HnVJVcoYwB8IukAyhJ1TZzN8G4i9+3OHIyTZ3ZXeYwxEkPMCRmpZsbgidFDDHK+pZBjpa4P2WC6OY3GLUijceL0rDVRGTxaCgAxEcg9WshzyygPkEXEiZopIaZDwUtgrjVWlzhzCg5HgHVSRR9HaOuWMDHsTWsms5p6NqOJLdYqLmxsMCgGlE5iD0MgtVMKpWimB7x6M2J6fVy1Riwr6pjJvrNo12Nr8wz9rXPE2Zjp3h7jO3eYNfuk1pNoxW3VzwBLuXkWq0rsYBvKEl3I9V8oMnFEEm4qid99glLBOEBTK1JjCMctqfYYrUkx0XovkkytmNUTpvUMFHhrYH2DwfkrXP34d/Odv/v3sr19ken4mN/6/K/y7Bf/KS/+8hewdR9bOGYx0Qa5xje8x01ronG0oUG1ge1Bn4FRWBU4mI1R2lL1XL4eFMqWp3IcQc3ng0pUolEpEiMEIiEYUlA0k5qNjS2K0nG0exPTygiZGJJcR0mcnFutMIWjnUxwaUHpuugnppjn0ndFphwJdJmdXNQxShGTjOwg9yka4zBa55E2LSEJodPaEYPNlX2Ro+t5ijphkrjeujmJ7XbK0OnKLFnWnhLKy6z1VEfGx8c5SaXpPGhULhToEEl1S308g3KDFIdoPcEY6bVOmSiGbMCmurmTb4N7Jo566QnnIVoXELKU6cqHQansBqUTDqhy3Kly1SBIQw112xKSIiYjfRrzCuZbIC0I63yb/L/ODdJ7SdQZZ6WxnUg3701cvLoqiKILBiW+VHdVJ7qq2dKLqYRRCzmtUYZCO3oO+q5FMSW0U0gNSmuqsi/Bnysp0VRAzxgKZehqG8svMg9tUvq6B/CbxbkNuLRu2CoKekXF1E/o5jRFo2jdkMsPfJxPfMcnuLg+JD73CjeffYmj21NCLb0SSjuUdpy/8BBXH3yS7fMPsrG9hVIJsyzNTPkjVuI42yVRK5fL8WVBuz7Ax4BP4L1UDJIW4miMzaYYcsLGNkhFEUNShoDIfGzOjM4tObJEISHnrTNinGN1xCID3a3EtnR2AylCf61PPHOGdvc2w41NZrtrHO4f0tY1+Ja963d47dnn2LrwUTYf62eZjSzgj1wZMnmx4NrrjqqoOJ5NyczolBBzdS8Ts7QIjFW2cxe+lYmjEkcTGRQfsixJ/kZsrnPWGklrdaoBpXQef6DnPXgiBVG5d1jlRUtnCfiS+cMSeZxn2boTozvnF8xM9nlORCUpQ541iVJLbqp5ye0agbuPWUVSOllh6ghqlzWM3X6kRbAki6Y68Rl0LUKygT61w1iVPcrSUVUObQ3OFWiVhxq7gl6/T7/fp9frMRz06PcKSqcpLERjGG5v8fjHPk6sE785bZi8/jpFsNDIfDajdU7OJYpsnR/HBzhraMYtX5lZwsEhl59+hubBR1h78DxuY0BrNaqy9CtHsp0rKkiPqdx4lNbMgnzcOgLJk9CEhNjv+0CcNrhJwN+5zetf+hI3nvs8+6/9Bq+98OvsX7vFgApnNLOmkWOPKAW0kXUkAkVZMjUK+kPOPvEQ5z/5EezDh7hLb7B95QzOFoTQYoyZnxfL42qW7sSc1gXZHs640b7AQXENV1m2tjRlPeDqhcilC0MIR9x87jcposFoGb3ShARNoCrkejEJaEWySifVT2IoZKwMm45tovUwbT2g6AWZkWdNAAfEhGoTuomYxouEPBmRaIWW2IqxgyTYFAmPsuB0ydx5tjSowkBUtF562X3sglyJdF2osdMWpQzaJZrGUyWwB7t4NOV4Qu0jMSpcjOL9YzSmFOmV9B21xAg+RbTRhOkRx6+/wPnHPs6zn/0NfvNLP8HBnS9zFGtu3qzoDRz7bz7Hr795xAObG6dyHLW16DaQlMgdrXU457A2z2hLoLQkZayxhJQIracNiaQSUUugG2OUUVAEwIuyQyui0WisJCuVJigtpDNIj6FmkcAmSsuG5MgC0OJVTX3XLNvliGYesHSjybp7Q8oEMookdXGvzOs05Cqjw1iR5KpcbTROemyj0oSkMEoj/WEGnyI+BImrkkflCqZGQ7K5ehIW7tcaVHZ0NNZQFL1TOY4hwd7RmD1ktnjwntYHLIbtYQ9bDBlUjg1XMEuG/abBKVAxSHWZguODPXanE3rDM/R7gf7Z81gl44CmStRzJkRsSpRlSf/MBs30GD9z1G1NM6tpmpZZhGqwhrMlRVmAs+A0zsq8yAqRpzoUy+7PUYEl4ZSh8YE2JPqDHmtViTcWRaCpG2wIKCOzDXVRiNS/rDj34BNc+s5P8V2/63dQ9IdMd2/wuc/8E37ri7/GtRfv8OTwHAeFY7f2xHpCq1uKssfjzjFc6/PC4ZhJWzOoDJtGzpEZ0DcGYzUDp+n3Kqqyh7aWg1M4jjFG6R9UShIxueOmzWTnoGkxh8dsb/ZYB7YuXqR/aZu6aeS6ionQJuqmIbUtMQRmvkUN+mLUGYPcZHKCRYYl5tgngUoRk1LuGu8SO7IeQvZkyUFuCBKfapD1Aom/TFHQ1gUpNoDE2vNYCUApKX8piVViViOQ4pwH+DnJnbtACJtNWZGZt9dqHgyBUpIUHE8YnDGc3bjAwcGE2fG+/GlcGJ3Ku0ucGHb9Frhn4jjvZ5xn8VUOBufvV7bLK5gUzRIqegyKSmm6UEAyVApVaAyGmY9yIBLZFOerXQy72tzyAGT5ALsAF4hyA5vVQbgi4mTWqiQOXz7ShjRn8R3JlV2NS1lq9RavHFFKDCesUugkDoI96+g7KG2LDg2ohqFVKDuQm6oxGGWwaFzKyXy1REiXXqU7ZKcX3kDv8lWulF/gqtF8ZdBnf7YvC5NKHMfE2vZ5nvrEd/MdVx/C7k/YffFFmlvX0VNPUzciRUma9fV1+oMBs/Exk707xFdmTI72sK6HUhatHVo7Uori7qgRy3JjJPuptNxolUgfYmd6osFYl3u05IJoCVhjcK4Uuh/ARwVGYQsHAcmiki9urVBIosCnRLAGYw1eIYRdZclBJioJhbKaYBOUhnJtQDnsU60NmPYrmsN9Yl3DnufOq68wfvMNNi9fgX5OLmjFkx9+kuPPf44XtWJQOPbm18TpHMnUOWAtkUaVkxsg8s3OLWy50q5AbuKdBCMluvEdKt/cl2XpOqVFr7D88dKVI6RRZ5dguiHhMWQC2ZHFRKeHWGgTOHkRqJMVzrnpTb4aOmLaZdYW/Y75Y6Bzaw7QucTOn1/l6qqQ6i5UWn4fnUz+RFJq/shpWeNAUZRUVUl/4DBWY6zDGiPJplyJ7FU9yrKi16sYDPrYqgRToN2QIjRsXhzy5HcWTKdjvlAfEG/tgdL4unO9ze8rRpzWEDWpiQxsy+EbX+TLh9fZv/5Feg89jL34ABcefYq1S1coN7axa+sk7dE24MoKbRVFtuRXORHkW0g+YrUhRWjbQNN66oN9Dm6+Qrj5Gm9+7rO8/oUv0Ny8Tjy8xez4kA3Vk/YQH8SlON8otZYxHFEBVhPsgFSd54Gnn+KZ79vCPfkGh2sb6OFHiVwAJMkk9wKXg69uhMo8pOa0ZOMAzjhiPWHqG5qmxHvNlWqTO6+8yOShpwjTY+rmGJTDpCAV4Sj7pLyiykkNlEangA+JJih0kmHjOlj5nIKmbWdMW0WpDRNfo2xP1rKoUVETgya1idbLOW+swmlNTBq0w0UlxibRQxSzCdWl5VOAYCAUhDYxmzaMvSToipyALVTC54BJKcWsbTE5CReVogqRRmkhE1ERA3J8MvEiIr3lKUvmQ8oDvRXja69RbF/EBcWdl38dXSeGpeGgvs7ukcLpmqsPn2O7fzqEw1mHLgFtMFmR0bSNSMS0jMRQSROjknM/yOfcellDk47zdVMiUg8qkHQkGgXJ5LXS5EqezfJCMaiJyedgQEkCDrVYq7OKalmeilJLK6o6EYst6fVZTuKlZXMFhayzxmBdgXMl2pYk7aT3ylmUk9aEkKT3WpRjjpQsyTc0baRpamCGMUE8CJSeK4gk+Lc4J8YhSWnQBluWlNXpHMe2rvNwE0lalNaiehW9ckCvNCiTSDqxnwK+bVBRMevuLRGOm4jxU2a7dxi7A86eu8RRVVH2e/hkGCpF9C1KK3w9o18Ytoxh/dw2pYL9FGlCQzut5VzSQrj7PYcrFC4FqqSoksxv1EpMhhIKq1K+P4nKoNSajV6FGQ6ZjvuMexXWGuJsSlvXeC3O2BpPCJGxK7j60e/kw9/9O/iOT3+S/lrF8Ruv8tO/8BN85gs/yd5rL3Fwx5Cipp3so9DY1PDo+Q3O9DZxkxmH7ZhZrEFFCquxFuq2hQSVEdJrkHUjNQFfN7B+Cgey01FqMZKRe5lcCQ2JNiSuHY3ZnQWKO3uc2dpg6/xZWm1pQsIoMKVG90VqX2otRBtpFXM69zImIMjMb+07f5ZE8i3Ktygf8G1L9OJKrGMkhZTbWHLME3JlNC3oXUiJejaBVGOVqEoyOZJjvGQ0RloU4OZJ/C5mUZ0qRc1jKrld5B7qpBb0LBNeqxU+wmw8xR/usnHhEsONHofjfbkmtcpxZJbdxoj+OmPH7l2qOp+n1S1QeeHKrFl37DctaF8ioVMSho3MQIqATzKKXDuLNRZdt0yaQIiBeRNpJqaL4G8RvC0+5zRfJVWW4HkfmMxmMkNMtVmKAb4NzJqWkF/7REDfVSSW5sDN3zaLxVYjwahT0tdYWqgKKEwUaVlSlMpROYPVBfNSc5K+Sk33eXXLQpddnC/7c4J+WsQx6W3K8ixrzlJVDmcKZlokXmW/4kMf+ygffuIJtgoFd15heuM3OL7xBu14TGhaOdFUQqmW29df4ubBDXj+12g1tLMJRZa5GGPkQteakPX2tiwopJESZSzJaIJKRK1RtkBbkcYYa3N/m5g1xChE05gKa52QE8CUhdglayMDiueVMYUWSzi01ZhsASmLd+6zUwplLE6L3rtXOOrJmNneAfXNG9y69jKHu9eZjPcIzRgdW9qjKXuvvcytl36dsx+6SDm4SgoRtKI4O6RXVfRMkOx/9EKowulQjhQ7QgbdMjW3jMkyzy6KWNbqd9vPnwcWcu2l1MXciTRnYeeDY9Oil6aD9CTKGJcQQj5mXTbr5Lmclr/PSan8rPLCKL2/2XxK6/n1vqCBAlly5F9xvqDmNSp123Z/nIMqnVO55D68vDM6B7Mn0zdp/vmcFnW01tLr91hb76FUxFmHMYbCFRSuEClrVVH1evTWBth+SeoPoNyCRjPcGKJjg58GnvzYR2lnu3zpV3+Tya0jiqSIswayrEZ6xRUKg1YQYkthApPDN3nt87fQrz2LWrvA+PGP8fCTz7B+7kHSxhl6myXl0GDX1ymqDYyxWJOgaxOIMsk7tYnp4TEcHXNwc5c7b7zK/rUvcOOlz3L9hedpjo4oUkS1EZW64crioryUcBWnxphIVhMLR130eeAj38tjP/Bd9B7bw9kX2bJPc6n/ESq3DWSDrCQVGHKSb0Eau++ntaqCK3uYWDNtE6Gd4ZuSa29e58L5gmuvvcYrjzzC5pmHmN35Erqd0Q+KmYHCKXp1ok4Jow0xeEL0BF0QGk9PQ5MUJgVi1DRNwDeRGA0Yude2daCyFqciVim8SjQBrHJ4FXHR0ighLoUyRAttCCifZIaZUgQv5wmmQEdQAYwxlEVJUHJvdkgFbIZck1pDaBtistTBo1UkRFDiFYFFo6LM16UJhJTNrsj3a2XxiCGaSmDaBPUxt196js0rz/DAhatc373ONI5J5hhbVuhinU0zoLl9DQaX7/tx1MagXI4H2pa6bfDBY1uPtVa+jJH5erma3/qID9I2Q57jRgqoPKC8k8MrI/JcCQHk3MdYkskS0KYm5J5HyaFm2768SCWWg8YcOyxiznkcMz/Tu0hy/veL2CnmH2S/ZI6dLUqsqzCuIumCZMREKalIiI2oDHIVXCtDwkDSBB9pmxZUCySiVSI9TyJPTUFhVaTQmqoUh8lkLMpVaFsA/r4fx0G/pFBGEjIq0kYo+xVKRxoPKloSLa1vMYg3BdqIaQ+BcXMEsaZnNPhDmknF9RsK19+gWt8kKk0doG2mGDSqqCiVZX1gGWiNN5Aqje4VTL3C9AoUDT0zY73wbDnFGaVlVmMLh5MWXTlskaQenZetUidaoyicwvUsE2NE3j2bMdnfl/ujtRyphlQkhucf5V/6gd/DE5/4NFuPX6bXs1x7+Qv8+P97h//h1z/D7aMxumkoCk2bNINeyVplGA432S4tVWyIlcZ7qXJWrqCqLHXT4KO05RRKkodtCIQ6EozHFacjOVZ6yY9kqUqlO3VSviZmTU3TNkynY964eYtkLGCluq48zhrpA4xy/hqjsUZTFo5eWVC6gqpw9ArFWr+icn0UGqMTNkU5xgj5NwqcEsMcn8TUynupZnrf0raRpvE0vqX10lOMbzExEXzFuG1p83UuioAoZC/K+9NdEYDssp69GPQ8hmWemJ+3CeYLPqGyF4Tcl0Nu8zk+2sWtWzaHljvO0cwCSi+YR1ex/Hr4JiqOOaN2d3ZedXf9LkhbEL3lKodO0qhaIjfJkBTGlDjrUNqCmjFrWpqYSNlWpyOm3aJ4snVzEYJKhjkRg8e3DeOYdfy5LBxCzBI96NyDFouoPKvOAfYiQZ0/1FyRUUZhtKHUmlIlKhKVDRS6RqeIigEbLZXW9NEYZcgqrvm+qi7bl53UuuHry0VopVKeq3c6gaqqYZIKDgsnGVBraacK06u4cuEKT15+kIc21zijYO/aC1x/+YscHu3TtJ7G+yzVgcnBLul4n6BgFiNhXinKqvD5QPksG+vIAAmnrbjq5sqf7ypXXVUpJyYiKRujpBzku0xKxfFNG5M15Plzo8sKayGT2giB1VIdVqoLeDpSJUTTaEVhwClNbFoObt/m4PZN6qNDZsdiyxxSRGtLPNrnjS99hu2nHuLBCxdlECwKGgjWEAuLRgI4qzXmlFocY1wie6m7PuVtSYKDbDCSsgW8VNQl7ljkqIF5hb1LXnRImQCqJJmzlOeIpcUG8pq6qwLKi8aoRMIVuyQJ8/EX3TWVSHO35MXz5aZ3xHTIaFkDutH2iW5xy3upuuun++2yHHbxHjJ7np+fWsk+qigZvzgPrrpehjnrpJtb+43MOPpm4EpNUVkGgwFGp/nsJ2sdrpA+36Is6A37uLKEopIRDUmBK0ApqkGJ31qnmT3IIx/9PurQ4/lf/1XijZv0e5bxpJH33Q1gVMxnd7qUWNMQfUvc24PjGXt39gjPfZ5i/Qx2+zwXHn8IMyig6rG5fRlnHNPxAZsbQ6bTCb6usdZwcGefZv+A6a1b3Hr1dSa3b6LrXerJLoN6Rj9GseDoFvQY6czIZC2W67MOLa4aEIo1alvy0DMf4qM/+ATq8Q2uq8jjdoMPrT9MOdwkaitBepfYmMuU5yJ0ThLH01lXy40LML5BmbyYB80a6hSop2e4/vyLfHH7Ch//0DOsX7jMMDoG1kiwYhJryeO8F/LdeJrZlLY5hlnM1ZCWtvUch0CYTClMC0Yz8bU4Iw/WaAn0UwKfCLEmtIHUeDSaYCM+RSKBqiq5PeuzS8swHLNmFKVJ0CoO6pZDoyiLwPnNgkFVYGyiCDAJkZk2hBBpmkYUIDlYjN5L0I2iUsh5nJKMZDDiIJiSpkigU04GpkAbfJYUB5RKWCJlTOzdvk11pebK1av8+ss32GsCpVe45piD3X1u3t7n3Lpn+xSOYwhRTIiCJ3o/HymhW481Jn9ZjJX7jJCjPLonqnmCiizGlSBQzr8usdlJXmOKKNOpkuR+F2sh2jot7mmwkJ2Cms97PpHcW+Tf6Eyt5uqQ5cTbUqWyY//aGoxzOXErs+iUq0imIGhoQkPjxdxGaY/3Hhc8XcVEKZGdggHlCXnNDDHiY4SQxIEzRfrWUfQrknW0OJqvI437ZtEfVOikWANaIk2S+33rW/F2QGbTFsqSVKL2AR0TrnAkFB6JMVqtqKxjMhlTT2qKtSl2PKE/3GB7a4PkLIPKMYuBgzYxiy195YgbZ+j116m2WyZtxAzOsHZ2iwtrAzZVy+D4Dje+NKFpPW0s8MaxfmGTs+cqKbAlGf1A1AwsvB4Vr+yP2b29z/T2HY72b9Mc38bEROPOcObpR3nwmSd4+CPfxW/73h+EjZL4xjX++Rd+kf/2x/8u4y9/kcv9PhedZr8cEnSgnnnqGGkShKYhGE1UirH3jJs4T7DOPLRe4vkyKal2+lZIpHE4LQ6npwHVSbHjokCVkpKYBkm2dUqGGMXYJja+y4jkZ/G0Cjp/hPncaxLHSpysjZbryupEYTRGWVQmas4oBlVFr6rEmEZp1qqK/voaqten6G3hnKVtWkzwOGPoG8usFbms956+UUxv3iSExMbmBpNG1ntpg/LoEMVLo20JuTUqeZGWGxDlkPeoGLL0XOVupW7YCHM5eOdm7X3EWkudIqUrCY1iejxFxzAf3YHqzHQkYf/1cO8VR9Xdghe35xOdP0tViJRZmV6+TytE6gmUKgFG2LpSqNKhdUKriG48PpEdP7v5R2kpuJgzu3kA3CGEQF3XOeDLxhfdosvCJCTvDp3YdfEzuVohfxPyolgYTZmHehZKURAp8BQqYGKLVWASWAwmiaFASOSWc+b7LjGpkEf5jDojj0y5Vc4idhnF00ATaLQi9gqk7U0a98veGS5deJqza+c5s2nhtZd45Rc+w53rd1Ah0czq3K8gPRxCiuR99FTuDxWxeB4DoDDZnn9RsdFZW13PF4KkVCadBpuJBECMQQYmK5N7OTqCoxZJhCyTlDhfbvBKK5LSc+ObqA3OGNooozpkGpDsTfd+JJ0QUSEQg5hxeC8yvxJF8vlC8xE7axnfuslrn/08WxeeZvDII3QVrnLYx1Y9nC3plSWTts3z5E4B6a5rjwWJXDwuVUYhPplJLlFDCdo0iwpdtwQtrq+OOKWU0KL1ZGGWky1lEnTS0W6NWOj053mweVarW0NSrjgu1o242JUk16rWMSd7It1gSXm9nKmbzy5a6l/M5LT7bJajK5Xd05QyKBWlSbwz/ojCuhci1kUP92lh0HcMBwWDQS/PgxLiaKzJvVWWoihwpUWVjmRcNh9CHNGsQZWR4WYf0kO0dcWHgsOlGS995leY3DxC9Ryxlh4qo92JSrD0O0liJRFJYYYfT9ib3CBdt/iqx+tf2SRaizeOolqnsCUpNJSFpWlmBC8N/7PJGF3XMJkQJhOs9xRKEm82u+I2wc+NVhTdWgcgPdBtaAkO2sKg+pd44Mnv5OO/61G2HzzgZjOmXzzNmc2nKc9uCXHO8k5pJUiLkovYVrHke3eq6FU9Do4MxipQDRGpBE7GmvG44af/+3/MCy88xiOPPs7Dl8+z3i+IjWY46LO+sc6g18NaDRGsb2mmt3BuHWstoT0manGnLJWhUGKiow2SXHA9CqWgnaJo6YwkfNNAVOJg3nh0DLjxPq8/P+XL7YSn7S7nBuvSlz4eE3b3+cp4wtrGBls0oCtal/DNFK80TQCmMyaqovTSuiDOooGUZL1vnSb6hhQTPjpaH+ZqHm80pZEeMWlPSMRoiMnRxJrWGlTSFDHi/YzNK9sUg4peoaFVlL5Gx33Wi8DFtdORODatx9czQtPMnRcTKveIeoLWBGMwwaGMkGUfJTndLT2L5J30aXfVEZ0TnkqLjFdUYpIg013iVBtCa5HxHVES7lHWKUmhx0WauXN3z1hERsvpv06xsehHT0rlHkktihxnsNZibEceC7SrSLaQfubk8ch6HWKuqjQ1IIGtNQqlHSmF7EqZSEF6dH0IKJ/7yWMPpx2VGxBtQfBJKpUnCgL3B6VJlK4PWtNOx7Stl4pQzL3y0ZOSptGJUkXKoGm0YdoGlLGo2ZitXmK9KmmayAyPbRvGkyMOzJDtM5ewSeG2Noi6pibSaoMNinGKOBz9wTqD7Yq+NQy2L3H10kMYW3Cwe4PXnv8t6lnEVGucufwQlx97iP6wwCMjshqjGftIOgzsvjnh2leus//ydfa/8jr1G9fZO9ylLQPnHnqUJ5/4XfzQ7/u9XPiOK9BfowV+6Z9/med+YocvXPs51LHhYw89QGxnHBctt31kNp7BZML6xjZOi9nZ0SxyTOCw9dxpItOoCI2M9khJk5InaItBUzhLoTU+5hj7lBLkXSzRnSOiZAqkmDKJjPMkiEKBlnKMxAJZdaJytUNJTNHxiJQUPiRibDAaMXgjUSfpUzRG1GrWGtrQ0KSateE6/d4aaxtr9Ho9lBNDnNhGiFCaAmsV2mnWyiGNj8yahuhr+ptbjKdjrt++yaz1bG5sUA76hNbLqB1tcUrhtHAOadmATslJbMV8MkR862nqmqatiUnG7ngv4+naIAmbtm3l/l5WRNdnXBe0oc1JpVy8Up3yqhtx+Pb3ym9qjqMsW/JDV7dbTGrKi1jqDuISYUiSvBC6mOhpjUuKmkSTAlpZlDPoKLNhag9NgDYHASmlLM2DeXotLbJs3ZIZY8L73Oum5XHFEumMaVEtVXNhaA4yhcgopUhGz4MraxQ9Z+nnEr1L4kFgkQyaS54Shev08fkgdKGZoutpVPMFPyW5aXaYL/ZzGcrp9VTRM5iqYG1QsV5Y1p2m6VW4aovHn/oUH/+uTxOOWl78rc9x584ebatomhby4tLtl298lrvki1TJ7VWqi3KhtmFxk5PPVhzYuiRENz5HPuvQdXDMzcEDIjFRCZEaKMm6pChSG7nvdvKn3MSsFlJNpSAmT+PnWQ85T1OSTtvUeWZlE4KUFoPhMzGd14xztmo2mxH397j9xqvceOF1Hl5/BL0NONgaDtns9SnLEucc3ViJ00BnVEMmUSm7gZH3X8W4IFRRqjyKhYlNt2DIc6QFD+0C77T4d3ctRFj0THZOrd0Rjt1LL+Yidi5f8490mdiRr86u+thJUYQC0AkNu/cgMo7lrGa+YcR8PqVFUmv+Phbflj43yTDOs/4hzr+kVwEWvXECrbpz4f5jMOgx7JUMepVUJLJU21iRe1tjKIoCUxRgJVjtzL5EuaNJRYHGsh57qIuaXpxQxY+SUsuXf+uLtLf3qUj4WYsPCavd0ueVZcxZepeIuYKvIDUwqWkOd6UvXVnqZDHa4azisG0wCiDQ5n4fnRJWQakVxklSRzxgfJ7lKGvCsou2NrKG1CmhqwF6uEZa3+LC40/z1Hd/gupqySy9xFXruHj+CmsXLxNKWHS56jy66cRNoTvizF125/++/+gVgf75babjMeMJtF6cJfd3pTqgjeIrX/kKn/mVX2Rr8xJ1W5NUoC8lOqreOjoGUtuiGOKqwNbZ85S2oLWKC2c20WmCLtYoqzUqZxmsr7HR79ErHWVR5L5yTX9QUlQaV20wXNtiaEtpJTGaW7ev8YVbn8duPcL2Iw/SDjR17bEpEMfHbB7t8cQTz7BpNDCjD/R9zXo7IzSe1LRM20g9PsaqFh0SoWnl8w+eUM8Ikwm0njRtYNZAMyXWDXWM4CQ4a9tA0zTIn0XaBqZtYs9o0rDPdz30BL/8pTeJIVLvTTEzQ1lWbJzd5sL5h6iaW6dyHH3r8W1L8E1WSeXEbmJeqdBdUEfKlcOlO74C3a2zaOjuMapbewAd5+Ybopgx2KJC9YfE0ODrmraeEdsGctUhhkAMITuwBlh4sp6IF1Qn4esy1XmnlDJzQ52U1w9ldO6r1lmG67CuwtoSbEHUJudlZDRHUmKIFaKnbWeAy4m3FjojoBS7Ygg+SDxmopiWSdu9Q1GSksuyvsA3ZfL/daBioqmnHAXpUzNJ7ikhiVttaUWp4BOUaArtqWNLGxVGRzZc4kxVUijx49CqQo+PMUyIheZ4/ybHJRAbXG9If32d2LO41OKNzWNICopqQNEf0Dt3nlQobt16iTpEjo6PuXj+EmcfeJjtK49SrFmmAe5cA2YtE5N4+dYh0zfeZO/V5zl8/cuEW6+gDm9x6fIGj3z8X+fRT32UC888zZlLj9Mflhze9rz82Z/iJ7/8a3zmH/8iT6o9zhcVbsNhfc1+kzhMibH3KBRXL2zjqh7j2YwmRA78jGlbc2da0wSLNkZkmDG7mEaIWlyCKx3o4fEq0S8tpzSOU6TO88KR3IeV0vNiYmfuYnQ3YmJh5BZzO4/W5JngaR6Pd/F5dweJIWCMxhlDZQzOaKyzDAbrDDa3qIY9NjfW5B7tpbCwd+sOcVbj65a6bvNzZ9mzgbI/RJU9zKBPtT5Eb22Rhn3K6Llz7RYHB28Q2gbfijIkxUhlLUXhZOwLkXJ9g6o/JExnqNTSpkRZ9fAJTK+PWV8nIEUzo5DWv5iwMeYEXA0p0NQwnjZM2wa0mo//SrlVpPs89NeJV+/5So2JxUwS1fUgdr/tljA1t3id/+auE0qjKVC4riKJMGijEpXTWF1g24hqspZeK+mZaNrs/rioGnYmGPOXUIuaiRDLNH9c9mWZrHWN5EJaTgQWSUlfpoFBaVgvDWVKqKbBJNE3WyKORInM4HFSWsmEZnmn5DU6VyY6SVXqAt58yOYBuwTQp0Uco4XLj17h9ecHrL0cuby5wWR/zHBrgwevXuJgfMir125Sjg8Jsyn1bCofn1IURUnbBHRK8xlrKd8VjdEi1cmfZJrLAZZeOyWpJpNlGPmmmxOvEsyjaFMi5sHUPqUcXMgNwCYotJEh93k2lk/SX1WkRO7Lx+guWyXZKZsdF0OWxy1u9HmEBwqfFMSIUVpIbUqSaUOywylJQ3Uzrtl9/U3uvPIs564+wPrmI2ChcpqhLSiszVLZ7GZ6CtDd86qFPFPG2+aDEtM8WxezC2pH7FWXyMgyhYWMu7t28jk4/+eC1Kk8I1CTMtEQskE+J2LMbq8wP28WpDHvfDdTsrv+uoSJvKGT8mZk/5SOLFdZVX6KeY/k/MF8gyFfgh1xJdFJwyW5YDBoMWfK/8Vsi08mId223YiQ08DGxjr9fkVZOGxRoK3BOovSFqUN1hYiCXRO+jaMIWlhyiqvH0qXpAJoG9bODnH6Etq1eFdBsc7rv/UbcGeP+njCZDwT11Pt5skDpcziOk2QgtiFa6UplGbNZFdOyM6lkqVVluzMFklWaNzcGbVzmMsV4ZCvp7mkmRxwa0NQ0MQxodAMLzwFa8+w8dBFPvw9Q848tMvEbLLW+y4eP3+Z/oUL0JsvPPM1dD5OZs4dl1dQtXhzp7SylrZgPDlGBjIMMWmPFKSvXpuu4mQ5PpgyG7+C0gbnLJMUsTpSlhMqXTKtD3HFlOFGwZGGpk3s1g3u0ibX33iZ1lygV0Zm+7usX7pKkeDOzdeIykNxhvNnHqNM19k/fIONs1fpr5/nyQcu8eb1l3nt+h1ev73LBEtZVPyN2ZSZM7gQqAZ9inPn2BpWnOn/PINBj15VsdZbp9cvKCrpAyp7PQZOoYyiLNepSosbVLiioCgtzhpKV1KQKFNAqwoTZpSYeYU7RVA+oEOgnY6ZjA+Jsxmz6TE957h49hFu3675uV/4u+y++gqlWyM2x0yiSOa+/Pzn2FAzHrv85P0/kKFTruic5JAkotIaZx29qqQoHEppfEy0Kc3vFeREjjiLm2wMlM9VldOhKZAXZHTSoAzWiBN3WTq0At/WTMcT6ukYX4vcTZJbnpDnMZK8PGcSGf0iuNYLSSyLeE0ksTa3BWXiqBXaiHmStRbnSqytULogoAkhJ3iV9FxjFCZFiJHQtiglrx2aGa2fgGowTmXZahKzn9jKODQNyogbehs0tU80TcwB/f3HkU8k31CiKK0j+ECjhCAUxhKNxTqD856D6YzSanrOMdCGlHsw3wwRGyNRw8w3qAC9XkEfkVXv795md3cXN1inWt9ie2OTS+fXxXfBFMxIHE7G9JXm6NobvEnEGU81WOfsE09y8aGHqTbPcjhrCTemHB156llDmMy4Mx2Dq+jNppgKzj1yifKZy5y/dI4HHnmS8xeuYgYVAC88/wW+9JU3+K2f+Dw3n//7tNzhwcEW2hpUsDSxpVaW3WTYS4FL1lKUJWjNrJlhtSZ6zyx4pr5F+8BWuYZJgWkzoQmeWUzYZClCpB8jfaPpGy1KQU7WuO8nOs+PlMIiKa4WMUJc2hKYK6S6tqQuPl3Mac+kMkdEOru0Gq2xKuFMpN8rGfZLtrfPceH8wwRKSZZMZty6c536+JA4neJnNdp7kg8SoyBxfGejYF1BKgpUVTLY2kCVBYONPo9cusyl7SvcubPP/v4tXn71JQ4Ox9LDmMmxUZo2elR1B+dKSSimSFQKV1WEkESZ5MocTnmMUqJOspJATCmCjpRWcXw4ZjprmE6nzBP3Wa0j65IQvK/XkfPNEUdUdmTsRJ7d8Vq21eCr7s0JsqeOWjrBEgYpCacgWSpjJRtmlaYyCqukWbn1gUYpQtPOn1xMj1Imj3l577Jsi/RbfiV5oAtqSAg7zz1wVlsKncTNyntUXaOBol8xWHNUKhJnNW1qUVFTGINT4tBpJVeQ572kOaGWMHVh7EGup5EWWY75LmZ9+LKRx6khJXj4Mlc/9QyP3H6Nm4evUFV9trbWaSb77N9I9FVD3UyhcvTWB8zSjDrC3tGUQTWAVvTt43omBjZa49uG7d6QImS3PNXl+jMJiWlO5CNAzAO/kSoWIHbLqCzX0/iQUCFnWFNCR5uriCaTgERqfadrJqLxOTu83F9HSoTg81FaZKG00rlinw1f4snq2omkRwK0pmkjymoOdnd5/ku/zPDJc1x96AxDu8Gx9+i1AWXVkxlMRcnUt6dyGBeZoUXGeT5eI7/nEz1/c5K1CJ67xVQrNSecy5WoxXMvKoXyeeYMckx5rmnXwE62oV8E9sshu+xWl2KSCvv8Uk1LSoCuBxVypTRBkESQmiecFjeDE5/LPNsuVaygECkqy6sUc8WB6ENVx2Pzwhm7ZS3vvzq1a3Jjc4NeYUWW6rKzqjPZYdigi4LknAzKtlYCvhRzwqCr6oLSBWlgSCSqBy5zZtgTw4Zeyeaw4MXPfYH2xi2KvUOawxlGL63E8wr7oie9UyNLr5LP8nNNGxfJBw94I6sfIcyDbqsWvVXi+paVAMZIoDt/TXKPdIB+yfoDF+hdfgq79j2ce2Sb7Qfe4Pz5fVT5CJvDT1CeWSeVQYJmcj91Rxq7+8uJG1Ds3k3++bTScXDzzTfRRmzYyypCKilMg4xU0CLhix7vAyavPZOEDEHXYCdeAv6yYNMqjuop9XGk9IZxPeZLLx9g1Rbnz14gNLskV7F3vEe/MJTDIXUKXHrkMg9euMj0judoepOj/YpyeI7Z9DrHt29y87UbNJMpNJE70xlJKwqlOfQ1ddvgbAlEQgoUhSN5T117sN1IIzHiKAyS+FOSxFBFgS56uKLAOUPZH1D2xYG37A8YDPusDfsUZcnW5kOcHWzR+pu0PhCjp7CWtaqg0JreoOCFz/wU/+znfpMvvfQavXhMO6nFYISCjd4ZbM8wNKdDOHRM0l6Q72uyjmmsNpRlQa+qKIuSpBQ+RmxMr8g7UAAAQ5hJREFUc6MZ6AJdTUoa3wKtED6UzI0V7wUhjgaL1orCWsqypKwqjNH4UIJ2JG2JekLIlUeCl/MlaEgGpXOfZJbbQ8o+AVlN1SklTG730F3bR+dQjch+NNnlXM9d7UMI8ySvjNLNqW+fZBYjCaMziaynhHqMNhFjCqwuaJEgOoVA54sRFLRJqrp1UNRte2q940rr+aB0CaAlo+SKCqsdEUVoIrFpIQbaoDFJ4QA/bpl2QXpedlMqKYs1tG0wEVIbmcxmzGJLOLiBur7O7PwDhNk67fnzVGVf+kE12PoYd2i4cPYsG/1tppTEJjB59Q3ql98geCiJWBVzn7th80yP3oZl67GHWas+xOZmT4zRgAnw/MvHvPzzP81nfv4f8eXP/xqz6DlDy6W1Ch+3CTFyXEtlvw2JOtS01gFalA2qZtJaVHIMKihnM4JOjHVga/sMhCFFqompJqlIbBNWW9b6joFL9AxYlSB7efhTuj8as5jJLrcUJeaKZDXAUq57qQmGbu2X8DplY6qsIFMqK2WAGCmMpjCanrNsbW6ytr7G5lqPs1tnCE3k6MY1muNDDvf2mI4PITTS9zxXuzEfV9g9rUkQa1EPNPv7HN24gSssRz1HurzL2YtXuby+ztVLZzl36Ryf+cKXOToYiyQ1BbSCCkvjPXXTYlX28tAwO56QYiT4mOMVSUx1cY41Ij3vlHxFYQg+SPzbxbddPkuZRaKer3+H/KakqnMZ6lyC1n1MCxlmN6SSHADqtBS85pt4yp7c2hqsVtLgGzxt9Hhj8cqA1Xl+SkKbiHPiDJiyO0tIijbKkN3MH+dB/7x4xzKhzMGhEm20VSJvVDFRkNg2mouF48xgQOkcpdFUOgAzDtuG27QcqSBlXpWwWkn1JX84nQQyEPFJescMMTfddv1XXbFFZfIqDfApR6iLfVanFafmI+U4pMUMC85ub/Lq8S3qesLx7m0IE9q1HlVvSCjWqNyEGD3lZp8nv+NjvPbsl3no8iVeu3WTK089xf7RIdO64YnLV7jzm7/BBVfhkZtqUpC0zs7i0qcRuqpYVDL4OqVcuVJZoiozBnWeB9dlUCWQ17l3tDumCZU0RjM3Z4hzaSmLoeQ585Ty6pNilL5UpSGP7Qgx5EGqC8uibrGSrKsi5YHnjQ64ch1VR37zC8+x8akfogwwPHsZd+YSveFtNjePOWpvSvB4CpgPpJ2TNrW0MCzIohzxOQPKn0NH4rsKkDxL9/l0hLHrR1w8Lp+KqEaFNKolOiaFzkw2u+u+Ww/uwoIydg5iggXR7PqbYzZykTPXoJHB80sy4LTUS7xkQBFiFjynBbHoGK64A5OTYLImzMloVxFd2qPTohxFUVBWjqrsZYmqkcHrxoJ2YAowMswa7fK+SUVDzUkRgEhNGQzBt5Sb25wBqkrRXy9QwzVe+uyXad68RlPsMd4/yFLxLkPbmZGlpTe7VCUGOhmaVlaMI2Kk2NqmKCvawyNSU9NMJ/O+l25WlFTx1XwEz/watIYmtNi1dS4/9R1sPflh/OY5dJVYu9AwOHuJi1eepFdcRRc9lGPp/S5/V3Tz6E4eqbu2OaWsOMD6xjomW/DrwqCto7++jlGa6WTKZHpMjC2tDzIbEYMrLFZLVZckw9F9q9m9c4u2noDWVOUA7cCZAY898hH6Dt68+Qp74xrGx2w88BAmKiYHN3n9hTe49vINCC3NTHNmc8ZjH77C5M0pvf465y9Y2uu3oIxsrYuhgyIwm06YTqdSafINTWjxMdHESDSOLmFs5muIxrcyPN63npSOcMUa/X6F8zWHOSFoUJRWPG8DAV2WPPnRj/D0+jq/+LO/wGGYoE2PjcGQtWFFiDCLiRt3bnBnf0ybDNqCSorB1iXOXLzM2fU1rgw3CAd3OD6F46hjR/6EmElVQuGMVIjFkE2ULtnCb5H1QvocQ8zjObR8qSwJC9GjlUd0NeLQ6AyUTmMMUtWIMq8uKEuyJcF62hCIOvshKIeyCoXL+yfkTceQ18fcix5z6toWKGvFtRVkLaRLDuXe8yj33UCLjXItJSwBTVRRBpenhhQaYq5+KgzO5B5M7zFBYqJCWYy2ecZ3XjuT9Ea2wTNra1Q04kgaPD42yCTD+wyf1xkrzre2KFG+JaWWRGJgKgqjaVHUsWDceA7yLMeBNhRaY6wYjVhTiNWW8iI1TooQWprZjGPfUJiE45DJ7Td4c3yDG2+8jl3bQvcsa/0+w+FZzp7dYm/Wctwe4NyMwh1nWaXMsiwLR7+w9AcDTH9AOehTK8XNqLg9rTm68QZvvv4SX/7il3j51Td4/tkXYXKLtXDApfWKB7eG9F2f2kcmSaNUop55bk4aWuWwTrOePPp4wn4MDNYqvA+ossBUPTabhspoWtsy1gafoKc0ZSkjVHo2oZKRliBEBdSmRNQJrw1TNMP7fxSBrK6a37tBWynIhNj5VYjqRc/VVF0bR/dzIkTxUBE1msn9kQFrNT2rqFziwvY2Z7cfwbgSazx3buxx7ZU38OMx1oscf6C7TEJHTxfx0cl7pYwC0Uru8V0Q2h7VvP7lF7nz5i3s+hrr589w8dGH+OQnvpsvfvE19vavU9e3mdQNGidS9vzWQ1yoC0jknulOOWm7KhTd/EerNSEmZtNWDLhyzLScrIkxsBABqXkR52vh3oljgk6+peYPpHmvU4yRTkQ6z+3mwHVRqeieI5eStZHqXQyM64Zx29A6RywKfCsOQ1ZDr7AUhQYViR6SdtJv4z1NG6Q5PbvzdYExXdCiNNo6yZ57cXGryjLPEwwy8JXIWa35rv6A77z6CGc+9DTFsE/18vPsvvh5fuNwwhe1ZM/jkttZN6JDZ7IXydW2LjhHMg/de5+fZPOZK2neOA+LAHs5bL3fCE2DLS1l3zJrx+xODmjaCXduXWNv9yZl7zKTULH14OM8uvUg/TgD01KXFRuXLnL95de5tL3Brb09zly8xPFkQuM9F8+d4+azz7GlC2JQxBCgaQnjY1IzlXlPzZS6bWjbGu9rkY+0nrb1hCjGGUlNMKbl8KAlhiKPcslVrWQJKEIS19yUqx86pWxSEAmxlRlwOaUiZEYtzRdM0kCd7+tyo49SxcwZWlHjhEwu8pDlJEkKpRLeKNgPbAxKHtl8nKIYEHzizFMP8+h+Yn9X4WeRvcMp0+Z0iKOal+gy6YN5IzXz4CDLPVNX6RMsyGP3ZPJBqJwVF7lmt0Al5sNZ53+/sIVf2qM5gVuY1NCxdRZdQPLznI4trbbLMsY5oUHen0p55qbuJCbZ7ErLa8ylIlp6kFDZ/Xmp0t29Bzk3Aoogz54J1HyEw4kLUNxoT94V7h+qQY9er6SwfZLSkhm1eUi7EmmXsjLaR8yB8hy0lDXZeT0SdzUN2pKsQkWFO3OWtTVFb0PRW9vgzOZlrj37JXZf+wr62huM9/ZJTZ4/FXIipTMH647P0uchslhZ59oQKNfX2H7gAYztMy3u0IwPmNU10XucUTkDnEha41MCbahDoAk1ulRoV9DfOs+TH/skDz7529n1A/bV61y4+Dof/uiHeeDhj9ErN9GqyG2KuaWgK4fO5+7mcyennk6iu2Ppxfs5BTRAqRTTEHExyb2m6Ml9yRo2zm2x5hIHe0f4PMIAnSiqCjWrOa5rfFSEZooi5L65SOsnmELT6zleuf4lru8pZkfHWAK2dOw3LU5pxrOa1IJzjgpP4UqOJjd487XP0U4SNw8PODNcY09Fjq2hb2VsVN0Eun68ummwRkZNTZqWiEbpBCEQ20TK44WCVtK/HgNYxVpvDRsVx2NJGvQqS6GhSRoPFIVlc+McRSFOrUHVVP0BmG1cT2ETjBvPzTsH3No/pg0R4wYUFqKvqUPETGfs790hWcX12nNmts+ZrfP3/TiqebJNKnfGZAt8k3vmY8AHmZGmjcQVCQ/Ko7QkR9vgRaodc5VDR4JvSbHBqIDNFWerIs5EnI0oE2hipPYQgiJGjccRlCOoGqmxpzyiyC7W/xRBBZSRRLhOERVyVKskgaGy+3LIMxU7J0YRi+Q4LiC9WyZiTMpzDg0xRnyoiaEm+gZiyO0K2Y4uyvM4bbFGYZSFpCX26uZN5sC+aRtMPQETaaOiiYEQW05jAGDfikojGiVrKQrTzjjbd6yXA7Qu8LFmL2l8HUhWs6ESG/2KQX+Ncd1iK7mvTGrDLDRoFbGtRSfFQV0zaWp8PZN5nA4mswOaFryH5s2bRA1F0WPrzGV2z5+hLB1lNaTql7jSop30JZdFRa/XwzqL1gXGamazMcfHDUe7++zdvsmrrzzPnVtvMJ4cElNgUBg2+xXFsM/UGG5MAjo1FNbRKx3TtmW/CQQMGisGL3GMsgprKjSOTRdRG31mHrQbcDAZc3B0TBwoiqIvS22KOKNJVjN0JX0VxNxwfmwV3ueZu6cAlWOAlJVMnYt+t2/d6C4xiAnixA6LbYBOaRaJ6JSr9EESNj2n6Reai+e3uHj2CqG1TCc1h8d3OHjzNeJsKnPb5QUJUWIGNU9WCtmSFPZyjBQXYUTqXIYlQasV1ONjptNjju9coz3c47EPf4rBh57m+dcdu4eKm4eHjI9reohxnY9J+puXXrO7ljUmJ3+RGC5mY1At8ayxhbx+91l2u6qUxLpJrmlIqK9zIO99HIfuCGGYHxBQqBRyVUctKgxd2TMTOJVlYyIBy8YcCAnrgvoUPSHUTOMM71WesaLoFYZhPzFwmjQNNBOFVwU+N3prvSBrnQlIF/ZEElFplJUTySSRmFYusd6zbK2fZfPMNmcGQ64WJZ88s8l3Pf4Uvcc/CsMNeOPLHP6i4+Vf/VnK8TF97QjaELxHDG66gfLyWcyzefOIfGHMc9Jvo5MEdo6UCwltviLu9fB8w7BlAf6Q6fMv8sWvvMyzN/aYTgI2Kp59/llmpcaeO4O9dJmPPn6Fc3edR+eefhqAs/nfF5Z+t/Whp05unBCno7aBpgFfE9qG1rcE3xJD7D4hQkhMZhNSnKC1Z3d3QtsaQpALRs4ZcUpMiMRpubdjUe3qRk9kE5wo50U3EFw+80wc0fK77KBq6GzSAZMraa3M6NJKTJMCEVMamugZbGzyXd///Wxoyf5QOJ78nqdQB8dMrn2F6+ubHE1PIy+eF6aukDjvc8znTSdV0l3ALGSv62XMf7RUeQS19BwLF1w5FzuZuRxSNed73XbLNbnuuu8WTJa/Qx5Hw/y15tz1rhJ759Kr0pKJlZabRTezUak0ny/ZJco6mpCX12zYorPktSPRga7XYXkflsnS3B/2JLe97zDWoY1D9XpC+lJEpiubHHgJeSTXN5IS58mOPHWJgS4hSwoobUlOgXKYnsLYyKViTN9uMRyuce3cBoNr53nz5VeZ3N7FTGbEaUNoPQZRdcw/v7w2hSgZ1KQUrVIUgyHbVx8mrW1QDdbZ3Nrktee/TNBKzAVyj7FWijpGyqriqPU0haG//QhpuEZxdoOrj57nkSc+jLHn0LciD61v89SHL/HwU4+hzVkSldzouyAgn49C9Bfk8SQp7E7y5a9TPIjA/v4uKXqscXgtPdI6BtbW+wwVHI+PUBiqfp+EotcbSo/j5JBpCvTMAGYNdRuISUs1z7ciE0sGFfdpJnsoXVCVfXqVVHYO7lynV1iwCkUrjpj9IX56hEOx9+JLsDakrme8emOP5BRV0ihnMYWhpwpUDOI6mRrpCVegrcKgcfl+p40mhVbup8bQ02CNZjiwHB223BqP8cpQWEddNyRnSNGLpDNFqlmLawM3nn+NvUFBsbHO8Z7CpwO09+weTLi5t88sKgrrSL6lbiM+tSilaceHXJscoMuKenbI8XiPMw/e/x7H1CXe8qLQCfGD6sbGZDIdJXEVoifEBlSNNdLDGH1DaCMkhzUFUSV82+JDDTrhtDiIGx2xymOMR9mIwaKUISaN9zK8G+XQ2qFUk2fxSmKkS06LUkaMaayUS6XqqFWeb2xRxi1VFbrFW7Q5naO0SjJGRWuTVVmJqKSdxIcW39Sk0GK1kgqcsaikCSkgnhAiz21biESaXH3V2khfJBB8S11PSMrjkwy8j+p07DitivgQqdtIMgarYa0s2OhVrDnNrJ0xaQKND2gT2OyVDBVsDEqmTUtVFejCMqtnJFWTTIJkaH3B9OiAYz9h6qGXiwCBPm1j6A9nFCoSJkc0jcfrI2ZHB9x4U+bxGudAy7gPj8GZPGLMWdDQti31dMxsesRsPCXWM2LrKQtLr3QMXYUrLM4YVPIcjWdMlMZZTeU0672IInA4DexPa0oSlfP4SaKwUBUOZw0DC6ZuiMnTtpr9pqXVimQr6iZRlZIAaXxWjjiF1R4XWzByTc9ye1ATYNqGU6D/Akn6S9LSGnHYDyHk+7/06/qYR+nETqKa5u0Y3X2oixtSrrL1Cku/UFw5/wCXLzxMPZ6we/s1ptMpk4ND1GRML8+m7sbMxaWYR2lxAk8xsujFTPPYQpEl/UkKZXZu2CmEzQol4tZLrxOnLZc/9DhXL25SDTdQgztcv/4c/vAARQ+t5bwRBVaS0XYmG9zELpkqPcNKMZ9nTiaMnSdJdo3Iai29lFRfbqD72rh3Gyu9GHgZkey0VcxZbCdfFXQVtLzY0hFH5n1KMSEXdkzUMaCsxmFIs2O8bxlurXH+/BbDtT5b645NE5ndOOD2m1PGbcMUi9IR4xRWWbq6y8KpUfYpKGijp3CaM1XFhkr0VeLyxXN8+GOf4Inv+iSXLj/EmqnYXLf01odw4ECvwYcvMzvjOXzxt6hfeplifYtgDbO2nUtO4+IeM3/nkuxQ2ZVt8fjydvOTMC6FN1/ltHr/sX+wT/OT/4Sf/Hv/HZ+5cYMJfabTKUXwfOUrn+fG8S636glnJ2PenNzhka01hmrM2Z7CNOcxasDWWZczMIvxCFprYvBLWm8F2lGjCdahyx6gMBacFmdaBbQBptMpWinO9iqsFiHe/R/tfIpIgIHUBmzPcuXRPg+uD/nyYCjubaf2oixlJJYDZwn4dTe7MpPlqDsitBREn7guc4avI5FLszi7Cg9JzWXZc174FkF5zl8t7dPdgf3Sud6dM0vksQtm5oVS1SUHFjcNVMLMq/eLV5YZvzo/Jll/3W2Tch/m3CTp7gpVfs9f9Z5O54pUxkmPlNFgDRoHtlzsjdI5G6iFQCaDTCgXsyfJAXQmN+TPKaKMQ6rGFfQfQMV9el5xXl2hWKvYunyFc1cf5c6rrzG9do3pnTsc7u3j60Zm2IUIMVP27EZNXvuVc/TWNhlsXqCuKmzpCLOWw9mEoBTBKAk48+cbrGZja0hfGy5euMLFRz/NnXZAdbHlqaenDIoJt27eZuvCw3z0o09x4elNknaEkKT6oaCrJKosl+2SACdJY2JRcVxOB3SPnx55nE7GOZnTYFyFM4b1XgkRfJFQRUkKGms107Zh73DKcNCnkx17FKYsGTi5f0Xf4utGZK1eyGSwlrP9Hj2baELD5FDGfhzHIBWswmGaloNDJb1SGmbBsK4Ljg+mxFYLwQyRpMRPwDiHbhqM1hjraOvIbNbgU8RZTdO2zLzn7HCdvnYcTxpqa+kBpiyYTI6YjGtCFLKprcmGXFraOfIqMGvGDNf7OAOz6ZT14RmGw8Bk4jictEx8wpQlm8aRQmLa1iTyLNfc7qGBN958jbVI14x83zFP/mbFSghSYTQaorPSJ5iQhclHWu9p/RSlphQuoJG+OeUVzhoKK3KxVidRyeSEh9EGpxJGyzmuS4NyfaJ3+GMIbUuIEaXE7TQFS+trGXMBkEQp04Y2V+08QUdRGyiNUWKQoZWhc2Ag92TROdTnNTzFhRGY05rCarRRtEATIjHJtawRIuicOCvHkOWKgMqjDWhaQogELUaB0gMmxy9FT2wbcUtH9B7xlOw4a8AYR49AGyNWi1umUo7jtqWNkToGsIZtaxloy0RrJjGJgZAt2B0fM2tqUIrCaiosN3ePOPYN2mqGVUGlNMpGQtL0gqIfxKF9Fjx1Ejm+tVJ1ju2UyfGBzBv3kRhETixUTyr4MQZCkj5oow2Fs9heH+schTEYbXP8HMEa+sZg0USrqYgMcua7PZqwYRODykHylM5xdtBjrbDSJ5sCujTMVGTSNNS+5fb0mN1WURmL9y1N4zluPMaBUpbj2rNZiqHcOGpuhMRsPJHEbDid4yhVRj3nGDGb/kmyONPCFGVONyY7qaqsEItiEoOab69TotDQKy39quDSuS2uXLzC9DBy683b1JPbtNMxuo0YbaR9KY+DAzn/MdI/m6yVir41JC0KIHFAjkTvCW0rypMgLXHi9xHk2GUncHFh1dy+/hpRH3Dh4Y+zbS+ihony/Dlejw2HR55KKazO94Y8mkdpPSeqc/M+1RUMJGDSeZsYEzErJTpl1zyPtBR/vVVb0TLuOZpt2oBXQFGQjGHWtFgUVVHgQ6RpWzF4MIa2aUkxihw0l2i1kTHeIUYZyu4KmqCyRXeBtokU6jwAW97ULEY2N4Y8/NSDPLpWsvf5F/jC9Rdp2xmtrVA6SY9F9LkiMfcunUeJsXMQS5qiVTzRG/CJK5d49FPfyaXf8b08+MxHKHuLmVARmKQpfQ16AAePPcbt4ZBx8qwpsFpRq0UI0s2iy8dATvBcydBq8W+F9NzloixRCdGN3bZke3l1WiGq4Ff+f/+QX/2Zf8Eb40hBj8PDMRaop8eE5ojJ8RFHN28z+JVf4gvr66xXmgevaNa314nqY/zI9/9Rzp8DrMzXW95XbbLTZwCaxGsv7/PCi69wNN6bW4Eb68SBNYkEddpM2Ds4pA1QDtYpB5usrW2xtlawtlawsa5wFsJSKJ+6zA7MzThQksVLufSktaanNUVKtERaY6ispUoJglQwrTHiPAYs/U9eo7uAuiCVdDLuzAfbGDO/CJWRPpJhWXLx7AaDN/oUzt3X47e8A/K2l4LmlOg6S7qxG7LoynWhU1eVT/OqrLy/lInHonSlTnwembjkYL17+/MP5GS+aE4XVU6udJm45d8lTp7nc9Kouut3UV0iXzfzDDmIoRaRqDsnz5yiWiaa889iUXXq5LcpRTFn6vo48xtJXVauu3bzD+rE3t4/GFdKJkUlsbU3PaSyqBYynSxTVUmRlM3kcWl/OtI4r8SxuJmkUj7DoUPT0jcRs77BmcsPEGcz9h69xo2XX2bv+puUt2+xd3uX2fGYMJnipy2qDaRWqvsxBIy12LKkDonp4ZTN/oDJ7m1ef+UlpkcH4By1VgQNtdHE0jA8v40/f4EzZ7a4+sDj9HofpbnlWB++zoOXdhlsDijXL7K29gTbT54DLeNBjHH5qIR8jkqyQM6JjlDenTi5+8Z390V7OphNGlEO64D1AW8MIUSOJwVlWeBMotQi3URFLp2/xNH4gL2jGYRAHWq0ruiVfRS1VIs3Ngn1jFl9LLJ+regVSGvHrKUNkSbIlRS7Xn8kE2mtxhQVVas4HB/iJw226FHqHm2I9Ik4a9Eh5vm5EigNnMWlimnjadsWn6Qf3Deeo9DirMaVBlPX1BMZzYGxmOzMl4K0g8jYF0sbPX1XslYWOBRt45m0gdDuYrylaVpmLbRtQ6909IoBs2YKpkRbi7NA8DQhkYKirWsOU8tHrlw9leMY81whSXpnF1Q8rRb/gmQKuSajbNs0LY1v0TSk4HEqQBspkqMyhrIsaWNkWucxOkQ0GouYADptKVyJrnq0RR8ag582NFGkdzon3/V8nV70OKUo1YYYIjF5koroJOZyNqusRM2epXJBgmlZ3tTSmpZT7kl0DU5rlDVSpQiezmYudSqHnHwLUW71EUDlx5pINAEKOzckkdlzCXREa+kzi0rJsPZTGgBoiwqSI6UWYovSlmlQhEleS6ylMRFXGJQxjJtEVI6iLFEYjo+nBF9TakWjK0JhuP7G6zSNoez10UbumW09QaVI7T293pBZMrRtYNxGZiFQOpEPF6kjsIDS2Pz3Knae/mKmmJIm5vBcqmlWSGNRYvPoFhQoFVE6ERXUXobJ69IyayMH0wmDYZ+r2z1IM+qpByXJJK8ihYJpTByniD0+QM0S49rTtA3JW2qVOJxM0b5h6j06tQyc46i23MKDP+ZwpvFln6RKgo600Z/KcfwDP/qjp/K8y6gBvQ4XHtgEnjn113s7BKAELnGGS5zhmQ9/8h3dn7tx71JVU8i8qbUNlCs4PjpCkajW1mjqRuY6DQZUZUU4OiJ6T1lVYl6TSSUqMWtqlCso+wNSK8zclhbVTFB1w9qwhy0dx/WEG7cnuK2G3toWjzx0juHNA141X2EvtDRKKgZ1M6GZycBhLS4pQK6CpEjUBlOuYdIQp8/woXMP86999yc48y9/D/4jT5A6stO9T8AME7qfHziaMtGKpnI4peaLqzhRygXfzZKRv8/sPS0FxkoCO52j5ZjmBeN59ny50qHS6QSpAMe+wF1+nL0bu0ynhwxcxXg6JraeUAdcoTDjPVI7pj7c5bjQPPv6IbsEPva9Qz70YQ839mnXeri1gZTpO+tv0d2iYuTmi8/zj/7+j/OF557n8PiQ3YNDtBWJnTHbaL3N1tYaW2f7VGtb3NyteeX6HSZtZDprsU5TWi19E076unynvzbi9KY0WJ3o2QJtB6iqR9nv0R/02NoYcnF9QJ8ZU3+MOdPnqUcv80C/x/jOFILmkYcucubKWdRMpFXKiuvjiaD8HhEROfD22oBhv6QqTqHxHwkKZD4ocwId1VJA0J1XSwxNqYX7V0xqXlHsfteRPSFaXU9YToqkpdD8Lr44/9ecLX5tnKx2znnl8gZLP5/8ZffaIqWU6z9qlU2QZPSJymYUsEjoLF2Vc4rY6RNgTikXMhPI43HkN8sGQPcb0ybQs4ay6OVX6dajzvgnB1+yMuUkQPeVh9zPibGaJwm6/k01J/1gB2cYVOsMlJVK2N4tTL/CnV3n3PFDHB/scevaHneu7TLdPaA9PiKMx8RJzWw6k4yllcqocX1SqDl883WaZszAWspz52mSwhYlqTegHg6x22fonTtLWHOYYsLMgQ/HbFy4xNNXn+TBRxTl2S3OX72ANhtom83BdKdmWJy83TFcJHnmqSROno3d57Ocplje9v5Dd72wMc+EbYUkGVcwm2nKsiAN+ww2BmwWQ2YHB0yOpvSsIVUVRdtj0sxo4wyj5Nyc1lOK6CmrHlCw3S9xJjAbH8+r0DaJEU0Mgej94r7iNTFM8W2Dt4p+aWlbSxsDrurRqwpKZ0lJ0n9F4bBFRdNYkrMo19J4i1YJ37Q0TS39ex7KtDCDs9qQrPSYhyhzdlUl9vHT5BkMSy6sn2VgLPVsircKt7bBuTMX6MUpt2/CwfgWzhSUpsBHj3Ml2niMEwdzFSwugPWB4GsOQuLW7jUePIXj2PW2d60NUiHwkqjSMo7CJ1nHQkj4CDFpEgbvPTqBjZrSOvquoih6qBBxboYxNQqZW62VwaqCwvYoizVSMaTWBXUMzEJLHWtimGJTg0oNIfosU5XKH0qhjJJqiLb4ECF5MdYhy0xtQKkglYmQpELpZZ2WuXeLkUcqBlkPgxjraCUGZHrJkTUFkaw3jdQLu/aHlM1nfYr46CXmQqTUJKmsxeztYwqNKcT0I0VP25wO4Yg+EuIUn0eXhKBosh+Hs4a2BXRBGRST1JLQ9GLET2rq1hPbQN9paFsmzZjdCZTJsr5ecdzO8I3H+4TOFS5U5LiZMG3Ft2HcepF8RxnpkVI2ZTHQ+oDCSGVIi5PvPDGtMoXUGmss1oohk8lfgoQPLaEJtAQMifWyh0+Bo8MxYVDx4LkhPeUhGVyV8CExbVumCWim3GlqpsM+l73CJYOtSvpIH+xRjNQT6KdEGyGGhPYHGGN4IyY2rKbq91FK0zjNuPZEc3pJuRXePbhn4rh16QrBWGx/gLKWcrCOjol+r8K3nnJtRllVOGdx/SGEQFmUxCgXisz2iZRtIzORigIbImUUd1XXDsQ5z2iMs/TrGbvTI9pJYO/WLQ42NE2sUYXGVtIfQFujYo1KLTEEfJBKSzes2xqI0TObOtaLLZ587Af46O/8bZz5lx6ED1/CGkMNtPVMboKdU9oSl1SzMSEGktHMJyDk4Foj88kWwW4uAXcC54y4FFAnmAdBXak9FzgWwa06PfI4azRV/yzDjUvs7k0IfkIbIm0bJEOYEinO8MEzqwPBK2yhcIXj5S/+En/9r/0nfPrpT/Kh3/Y7ceQKR37ulLI7pdO8/sXfRI/fIDTX2Tvc5U4MTCZTPvzAFR668BiTcJVnPvkxjqbX+Plf+gw3dqFJA5oQKXs9TFESlTjs1lE09CGa/Ll5BoOCtV6J9xP2JoGk1wnTgmJWcNadhbZPs+fZrBzawOzmHoVqefp7P8bVi09w80bD9hULASbjBuN6FGuc5EvLHPLuEtldyJRZ/lFVMBhQOMNaeUrEUckMJZl3KEGcVkkMGQDJDOc6WgqZ/OicTT8ZaKslgtRZMy8TwK7PL6VFhfyt5Kmy7VKl9sQm8zrk/LXn/+pKjHeRzhP/umt/5uZHUZG0xqgsa0xd5XHZXnqxI3M32u49ziuTXSU2Sz/mL3l6SRxA+pdcBboAPGKAo/IeZev8bjlRioUpTrfOZYKUFOReyJQNM+QjtUDKfZF9lM0fgAF7JjK0LamKtBuGjXMVa9vneeBhR+ETYXrAeG+Pyf4xt2/t4WPEKBkLUBY92pDwKRJVpNUaVfYxrmQ43CQWa4y1o3EF1w6P+ewXfo0HLh3x2MefYc316LltHv7IJXpnS0CjSwvIjD/ySIFlAyfh8V1CYxlClU7WsRVvTRwVX22ec3/wo3/o3zyV5/12YOvKO/O6ETiz+SBnTmEc4zcL6Z33kAdjKyKkkBNVeY1SOpMmQFmMrUSPEYHUYrSmsH0K18foEkPE2h7WNajYkut6WNOjcGtYu06rBoSkCGlKJIBqgJn0RYaaFGqpvKsuAUi+82rQ4rAag4YY8rB2GaOjU4sYZAiZC8kQPISoRLKoIqhWrqKYeWPIhChL8qwtpH8sJHwQp2o97/4ElBiPRDwRUfMYo3DOidlGMBBkHIBSGmul97L1aZ7IvN/oty09E4kuESIYW6CVYnc6Zn+acAF6LpEKR6kShVHi4u89hTFUSuM0HDQ1tqlZo8fm5jaNPyK2LcPCYp1m1kqfZOU01srxCK3Gt5Gi51gf9AmtZxZyHxzSg2az5FQpSbgsmiZkVrQ24lzdjSbTWpLnSoGJXqSSCpzS9EpLUTqa8SHrvYrNjSF9GppEPvYOG1sSiqNZy63DY3RhGUaZNX0UG6ZFQfBj3GxCqNY4niUMLUWh6Nk+G6WlNB5jHSlpgnbUwTNpZ0za9i7t2beOnZ2d073xrvBN4Z6J4/DiFXHZyvN/qsG6zA9KUliv5sQoMewP5DTKWdGyy++nSJHlCTFGigQuB2y2qugN1/HBE1Nkbf0M6+2USX2bm6+8zhfbPcLtQybOoQdC8FyT6BlNURW0rceHkCWRFhSUhSWEyNE0UqmK7QuPMfjIJ2ifGeBsJMWEi6DvCu7jdJEFSwVAHtqrF/PqyHLaTqec37xIOrSaz6iLyyRyrsmWHxf5o6XAJhu4vNWMuvuBmA4YT29R9At6WxvcvjOliUluDingQ5CehphoW7BW4aK4NKbxLq+/8Rz7T39c5HXypubPvcyxLrgCd3xEoaANikl/m6e/+/v5/qtP8GBxmTtxm68cHfGZz7/Jy3c0Pg2IqsSUDldWeF3QNCnXThLeGoqiomctawPH1lqP2XTCpDmkVonGF/hgICqOboxZb2F76DiKiq3hkJ7VbK6fpd87x2ANrvaLXF6GXq/i4GBC8Jr+VkHSedSBisSo53258uYyI0rqREGs4xgKwChiGdBpjG6POQ3XuDmpyfLSrt40z0UkmX6kOh3RvK7WHaO09DyL9yC9kWZRYUzMg6a5W+qJZMmiVg4L4rhcDzp5hiwY+Mk63skdWUhPFymVefIlV7ZjvrRidk7NMd38b7tt5/uMOrHfi1Ek8jvVvUe6quXid6eFsiqxrrPrzlXEbDSh5gSxO6j557RM7NWJb/PPLJ+nsvzI9nOCnhMH2vYoN7bBWYLfQMWGtU2FqStKH2mOb3O4v0c9rTk7njFpa8pSESdjdq/dJBQ9Xt8/ZBzBbp1ht/bYpDDjCcc3DjiazDg8HvOVazd4de8FNn/nA1zYOMfViw9RbT3I4PwAgOA9SsuaOx9cntfIbk7jyfXwax0P9XV+v8IKb4+UhCgKWezWAOlxdEbjtMZqIY4KhdE2qz8UeBkdoJVBux5RFaSg8UmhTIm1PaLvEskOY/toMyDGEt86kk5YZaicAhdpjMe3M0IQYxqS9HYvLD+Q9UsrDE4eT11/F/gY0LHBYCjKAltWWKuop2KiIctCHpvV/ZdkHAg+EJWoOZx1hFaqW+KsrPP4IjHniUlGaHVO1QZNqaXqqnEkClTb5rGYuT+PKIT6lBQAm0PH1lBz1LT42CPiOLh5ncrWKDtgveqz1odJmBFahYotzhXYogBrWTOWUCsm+0c426dX9fBtKxV7W1DYEpMCM99SFpZBf40YoW7GzJqWXlFwZmOA0ZGkgtyXdOcKKv3pnTGkytXfLlGhSGircqU394kqTdIabQwumdyiYnEGCpMIszF951gfFAx1hNYy05II0VHRwzKpZ9w+nOBdiUYzO56hrKPWgdZPsjNnhDhFK4ctHb2yxEZDU4sUOoQa0NSp5ii2ROUXY89WeN/jnoljbcXOJMrQH3EL0koc0vPiJWMNFgOiu0CzW+YksBOrZt85X2biiJKgwSSNilJRGZgCbSrqgzEv1XukacOYilRVuGQYBIVOisbLYuW1NMNiZDmyRrJGTpXEZswXv/J5nnztAR5RH8ehswlEknEdyzihppOZjClf0lLRVPPIpitQkeU75OpiytucLFR1t6I4rynM/ziRpTHkKsjpILavMd59CX/QMIwtuuoxTjCZHDOeTml9i7GivfdK0baJEBUuKaKf0l/36PWKvXHJWSMGkPPKUQ7CVQsMN9HbG+y/9gqut86P/OAf4MOf/P2E24lXrr/JnfoOP/MbX+KVN8cYd46oDBGDUYZYa0iGGByJRFE6XL/ClgWDQY9hv2A8PeLgGKazNWZtpAlqLtEJkzEH4zHtuXX2XAOXz7Bx9QJq7RyHvscGQXrjurmkfUMZhty6/gbFtObiAxeZTgfsHxguXYSO4szfY1IsugkXp8ycLFWOzYsD3KDhcLLL1ilY/cQ8t6vjjwlEwpkgz4fvlC/dVku1YbUkSV0ioeTRMqq7wS+IV2eGs1zBu5sQpnTy57e6lyxkn8tXxtJ+LW/ZkUZ1FxFV86su75ciamT0hgyZnNeWYnZkAzlu8/7Gruq6tBuiIujkrIt65TIRvd8oSiuOvBhkWc7G3oslJr/2EoFUkUXlLd31nbxt9/cLGfny59ltoMwa1do688rduodpIE6O0VWkt1bQV4EzynPcjBmWcPzqy7z0wkvMmgG/+qUXeW23Ze2BR3lzf58wm9Eejzk+HNNOp7R1wzh4Yt8Qwscpqkc5d+kB7PqAblaosZ3sFsh9ZVJx1EuE0dz1Hu8+d+7+HMJd/7775/uDVWb8fYZOoip3/pxMk172wojhj8kJJoMCZTBGSe+eKtFBJPNRO5rs2N0mpNpmHIQ2V5gMSVsimuATbd0SDdiYqLQCq0laxozF0Erv6NKM4pQkvopLSTVxYM4GgbobARAwBJw1aFdhjIXkaWYepOMWGTaQRxYkJHncyrxDjBFiAPMklDYW58QBOrYNKcT5vUKTsEoKAiUKa0T6HJVBRS9mij47FgSPOaUex63v+BQAa0uPbT/11du9XUrXAk8+fj/36tuD8Vs8Nsnfz7zF7xycmMF4GhLwFd4fuHerxwHk5Q/5qbsJm/xv0Bg0C53nSVHR8v3VYCm+oZfdYvvEvze/pbP6NX7+l/46P/9L9/hng0us/8Dvnw8c/sb2/N2JTz/zDM9sX8K3fY4Oj/ns5z7Lrz73ZeqJEI9uWLqKIv0jSRbVao2OiVAMOajW+OLNAz6qhpzdKokhzq2PibB/e8LnJjNeaD3Vxcf40U//CJ/+1A/wymuKn3v5TZ6/dcBrb77Grb0WU50nRRmLoawjomixRFVgih6kSKMU2g5wawPKYZ9kFG2ToCgIbSt9PDHOHbA0njBpuXXziKKnqdMRk6LH4JzlgRlUreKM03MpZVKJ3ppmiy2a9ggoODic8S9++Q2++/u2WC8nbFXrFG5tURhGnSBHCrlxWqUY7x/wq8++wIs39phax9YpHMfWe7LvEyIVghhVnuUoX4txCkKuFjJSmYOoOg6RFlfzSQfVNCdm80LdiUqc/NSRnLzB/LFlqvrVuPs3J6uKC7nssonDYrtF1j33C3fRVBCbeRXzZ5KWiSNL7ykyl1lnEr1cIV2mjFLRPR1+4AoLRueV1eT3k1CYXHFUiCQ1k8dvCLLtgnSmE0R0ft4qcfRcbivFyZdeX6PiDBU1MIN0zKA+QqsZevomWo35/Je+xBdfvsFL1z3xhWs0sZUQtDP0CAGrFYPCsnt4xNGdFtW/gF3fXEiCtVkitV0VPAfuy9XmEwTx7lr28vdMgOff7/5cTislt8L7AWLMlxUned6hzPw2WKPRKYL3qDwfr0uFaSX9gN2A+DZFvG8kkYkYwciZa4hAG5FREG1D0jPa6PEKQmpJrReTG5X3I6bcbiAXbjfCaj72rFMyKZWNABXKJLm859XEOCfAziqiFcfY+dijvHeiOpJKpFKRlGx2ZPQyxgBQ2qJtQUqgo0ZFnddQ+b0BTAho32IwoAwxG+JIG3CeNRk8OpxOj+MKK6xw/3FaMwJWeJfjie/+IXFROZ7yws/8NM+GCZVuiG2N1Tr3bMZ5ZdQ5Jzp7DGWxhTUPcvHiE/jbJW3I2UIl82yIiugTUx25/OQTfNL8CGfPneWJJz7Fy1+u+a3P/hYvXh/z3O0j9vZLtB6SkiUhN92oRHPvk+j5EyLhmDWe5BPrxuKVQlOS9BqBY9rYkpQipISP4liXokZHQxsC01ZxXAd2pzcZ9te4cuYqzmrUeuJ8KbIcmS0aWVsfEKPI56ypMc0ddq/dZOuhfZx7jESfhM1E7K0ragDRFBzWazTtOTY3zVtv9C2iydIfpRf1u47gdcN5NRqlM+nJWfKukpfQ88z1iREGmWzMq4zzwZZ8dczdVQJzpfFklWwhqfpqLJOArky9REWXSaNSX+MpFu+r+3VMiPQnSVVOQqFcCe0aNWGpxzPNC/4n5JBqiSZ2r3NKdaWYlFRKVXZMRUlldGlf8g8syGP3/e6v7vN+6yrbMpGcH510chs5JF2F3SBpMg1KehEpN7APeori5/nyc7/CG4eeVA4J04Zi7ucjNYzCagwJHzxbvQE9N2DSioudTDFb7M/iPd5dSV2urnaP300c736vaunr7jEcqwLhCl8bIjvNJlSZOBoj6iWjgBAIqZZ1tVu3UpRttZhTpRBpYwN4kpL2HpmlJnOLIxofIrO2RmWyGaIYF828J/hA9EDMDsrC/nI1kbxedaOWurV/MdNYGYU2Gm3l5wR470mqJSUxZdEmO2xHnyv82WwsSsVRayNjW1Kg9UlIcPQin1QKcChl0NrKYwlQ0heqkyIFLzMIY5JKa1ahqWxQQ0qoEOE+E8eVAmCF08RoNPqLwG8DnkTGmU+BV4B/APzlnZ2dO0vbOmAEfBz4BPBhJDX7b+3s7PyNb+uO3yd8w8RxdSG+v9BW6zit+cIXXuAnf+rnuXbzJtYaCqWI1opb1zJPUHmYqHVECtKhp7ox4bGHL9JTYlA0M3B7rCimiQsbJRe2h1y68Dgf/ZDoPL74uSk/8Suv8tlrR7x+e8bevkfrTcR6WoPVEkArTVTd2IdICHKjscZgbCUVEiU9rE2TaJuAyo52SoNO4vSWsHgfMU4Ta4VVBWEc+dLn32BQWqYfPs9sbHBXYN3JwGTQ+NYTk6IoFOsbQ/7l7/4wavenuPncF5g9NmR762HmE/a6O7ZafFZKa0KKrJ2/wPd+/7+Cjxs896Vf4xq37vtx9CFkwrEIilNaDpgXJksdDcpO85k4dr20ZPK1TCpTNqBZBCRfNd9HnfxHJ/Bc8J38AaW7acwSUbiLeb8VEe+e80Ttct5PvVwpXPQyymfS5dC7kzkTjuX3MeesCzFq1/d4Ymfu+rP7iZiNYEweudHN9RMETpxkqLf4WnJXlRrrXdufJFPLVceufeAEOpINpNQ9byEtBt0xLWa46jxJbQBTiqKHUwFjJBHT5plv1jlKKy0BletTletMvGECFMpjugVGIe99vi+dFLl7/4llKat8LSdk7ibL3WeTOPmZrG5lK7w9fvcPfPodfPUuUdNhC3iHnItWWGGFt8K/D3wG+O+Bm4gW83uAHwP+7dFo9D07Ozuv5W0HwH+af74BXOc9rgReVRw/oDAxgYbJVLN9/mmGZY/DL3+OzaFndzqjIRFDm7OS0veAzq5pqaVnAsdv3KK98iDuuMa3r3JzreWVW5ucdxtcOlOSdKL1kagSrz3b8rM/e4NnXz/g5X3L7X1Iep02O+AqbQloITksQjupHgkxjFqGKIvrnCKkgCsi1khQ6PNgZXEXDRgt20WfMFZTT2dobdm9M+bnfvVFDqcN6enLOAoevQxrBoxKWGflwogB42rOXvWk/XV++XMVG1XF2rqh1EvVxmUuk793TeKDrSGPPvwghzef59r+/SeOca5J7YbCdHuxTByjuNp1xDEH6p2xQTc0XmzeWao2KZj3OOYKXbo7OF8mNGmJo7x1lfGra2B3bzenrnOyNye8b/H+u1dffO8knmlO8tLyX+f9n1Mqtagydj3ZnbnQCc6YCVa6W/V4n5BUR7KXvlT3i67StlxlXP5+4pmWvlj6/cnP+WQhs6tkMJ+NmObVVvVVpFKZCggQNNr1GQzPUB7fxgOqLElahoCUVclg2KeqKqrS0vMtsxt3aI73OWynHAJrBMoEZLlfV+n+agL49Rj716om3l19fKtt3r/4oGfGV1hhhRVOAes7Ozuzux8cjUZ/HviPgP8DspaCtJb+CPCbOzs710aj0Y8Bf/bbtaOngRVx/IDif/0n/92vfvDRRzkPnP8G/v6FV4954dW/A3//79zT624Cv60CHrinPzuJMSc7vwvuveF0Bvwa/PKvwS/fy9/90nPA37rHFzs9xBPyS2E1at7XBrA8xxBhB5AZgyQEVFKoufypqyouEc/uqyNPy2W3TDTyk86fej4CZP6/k3+yqDayYH1LG6S5a2AnqJSNhUud3DgtlziXPxe6qmO36XKVUZ5fejyVVNOXZKqKNCeSIAQ9hLtJ8/1DUsuVNZhLK9VyJfHuCmP3vSOWncHE3QTp7krlW6D7vOeGIHn7zg16fojzY2iULlC2AuWyXb8DJeoBrTTKWqpBhbGKmZ8xdIrZbJfDo1cZ1zeZpDF9FfOx+1pS1O6Fl9/n8vm2OO9PPvZW5NMs/fyBwQc6M36vWCmrVlhhha+HtyKNGf8NQhyfWNq2Af7Jt2O/vl1YEccVVngP44/8z/7QO70LK9wH/Kl/992TjLhXrD/zDN//DW770BUZ1veLf+8f8ot/7x+e3k6t0OEDnRlfYYV3O0aj0TbwrwP/KvARRJfcAJ9DstR/a2dn55S0LivcZ/xr+ftn39G9OGW854njPUpxHgZeepun+693dnb+J6e2s+8CrDKqK6ywwgofDHzQM+MrrPAewB8E/l/ANeCngFeBC8CPAn8D+D2j0egP7uzsnFKH/QrfLEaj0Z9GpphsIDzktyOk8S+8k/t12njPE0fuTYrT4bcQYnk3Pn96u7nCCvcPqwTA+wOr47jCO4QPRGZ8hRXeA3gO+H3AP1quLI5Go/8I+BXgf4SQyP/vO7N7K7wN/jRC8jv8U+B/vrOzc/8NLd5FeD8Qx3uR4nT4zZ2dnR/7NuzbCiussMIKK7yj+KBmxt/vGI1GLwMPfY1f39jZ2bn4bdydFb4J7Ozs/Iuv8fj10Wj0V4A/D/xOVsTxXYfu+hqNRheA70XW098YjUa/d2dn5zPv6M6dIt7zxPFepDgrrLDCCius8AHEBzIz/gHBAQtTo2Ucf5v3Y4X7jzZ/v7+DLle4r9jZ2bkB/PhoNPoMUkH+z4Fn3tm9Oj2854nj2+DtpDiXR6PR/xLYBu4Av7izs7OS7LxDuNfm8NFo9AQi3fhXkMTABWAP+CXgP93Z2fmpb+sbWGGFFVZ4F+ODmhn/gGB/paB6/2E0Glngj+Z//tN3cl9W+Maws7Pzymg0ehb4+Gg0Oruzs3P7nd6n08D7hjjeoxTnd+ev5b//aeCP7ezsvHq6e7rCW+Bem8P/z8C/ATwL/GNgF3gK6RP4faPR6N/b2dn5S9/et7ACwGg0+h8DvwOZBfcxYA34L3d2dv7wW2z7t4E/9nWe8l/s7Oz84H3ezRW+BYxGoweA/wT4YST5dg3pGf9zOzs7e+/grq3wdfBBy4yvsMJ7GH8BuTb/8c7Ozk+80zuzwjeMy/l7eNut3sN43xBHvjEpzgQhHf8A+Ep+7KOIkc4PAD85Go0+vrOzszwlcIXTx702h/9T4C/u7Oz8xvKTjEaj34GYJP1fRqPRf7uzs3Pt27HzK5zA/wkhjMfA68CH3mbbfwC8/DV+90eAR1m5PL6rMBqNHgN+ARn3+t8BXwK+G/j3gB8ejUbft+xkvcK7Ex+UzPgHBOVoNPrDwFVkwvFngZ/d2dl53wau73eMRqM/CfxvkfX1j7zDu7PCEkaj0YeQKv/1ux7XCL84D/zC+zmJ+r4hjt+IFGdnZ+cm8Gfu+tOfHY1GPwT8PPAp4H8B/D++bTu+wj03h+/s7Pztr7H9z+TK8e9GzoFVM/m3H/8+QhhfQCqPX1M2vLOz8w94C3fj0Wi0CfwHiFz5b9//XVzhW8AOcmP8kzs7O/9Z9+BoNPq/Icf+zwP/zju0byvcG973mfEPCC4C/8Vdj700Go3++M7Ozs+8Ezu0wjeP0Wj0J5AY9FngB3d2dnbf4V1a4SR+GClO/CzwItLudgGJdx4FrgP/1vIfjEaj/5BFEv3j+fsfH41Gvz3//PM7Ozt/45T3+75Bv9M7cL+xs7NzY2dn58eBH0JkVP/5N/A3HpFEAvxLp7h7K9w77rU5fNVM/g5iZ2fnp3Z2dp7/FmdO/RGgB/z9VSXk3YPRaPQosq6+DPw/7/r1n0WqHX9kNBoNvs27tsJbYDQafWg0Gn2Vq+ZoNNLZdfx9nxn/AOBvAT+IkMcB4hHwV4GHgX8yGo0+9s7t2gr3itFo9KeAv4yMhvuBu6taK7wr8M+Bv4bwix8F/neIKm4X+HPAd+zs7Dx719/8MNKW88cQRRZIcaN77LfzHsL7puJ4N74JKU4naV0FPe8S3Gtz+Gg0egi5iU6Anz3FXVvhdNFl6/7aO7oXK9yN35W//7NlSTnAzs7O0Wg0+h8QYvk9wE9+u3duha/CBz4z/n7Hzs7On7vroc8D/85oNDpGpI4/hhjPrfAux2g0+t8jSrnfBH73Kmn67sTOzs7ngT9xj3/zO09nb94ZvG+JY8a9SHG+J3//yttutcK3E99wc/hoNCqB/xIogf9glUV/b2I0Gn0ayZo/t3LHfdfhqfz9ua/x++cR4vgkK+L4bkCXGf8+JMu9iVSFn0OkjX/pLWRwP4wQy2V8b/7qsCKO7378FYQ4rhRU7wGMRqP/GDEc+3Xgh1by1BXezXhPE8d7bVIdjUafAn5jZ2enuWv734X05wD83VPf8RW+Lu6lOXw0GhkkEPo+4L8G/q+nvoMrnBb+7fz9r7+je7HCW2Ejfz/4Gr/vHt88/V1Z4ethlRn/QONm/r5SUL3LMRqN/hhCGgPwc8CfHI1Gd2/28tfydlhhhW833tPEkXuX4vxF4Duygcrr+bGPspBg/cc7Ozu/8G3Y7xXeBvfSHJ5J499FRnr8N8Af/hb761Z4hzAajTaAP8TKFOe9CpW/r66/FVZ4Z/Hp/H2loHr345H83QB/6mts8zOs7okrvEvwXieO9yrF+S8Qvf8ngd8DOOAGQjj+8s7Ozs992/Z8hbdEbg7/vyO9Gj+YnXC/1rYW+K8Q0vhfAX90ZUH+nsYfBvrA/2fV3/GuRFdR3Pgav1+/a7sVVljhlDAajb4DuHZ3YjX3+v/l/M+Vgupdjp2dnR9DelFXWOE9gfc0cbxXKc7Ozs7fBP7m6e3RCt8K7qU5fDQaFQjh//2Ic+4fv9uwY4X3HDp1wF99R/diha+FL+fvT36N3z+Rv3+tHsgVVljh/uEPAv/haDT6KeAl4Ah4DPhXgQr4x6zaNlZYYYX7jPfdOI4V3pvIzeF/AWkO/8GvQxpL4McR0vg3WZHG9zxy//HHEFOcn36Hd2eFt0ZnVvRDuY98jtFotIYoP6bAL327d2yFFT6A+CnkPvgI8D8F/jdIm87PIxb/v/duP4cVVlhhhW8V7+mK4wrvD3wTzeF/BfgR4DbwBvBn3mL7n14RkPcUOlOc1QiOdyl2dnZeHI1G/wxxTv0TwH+29Os/hxhx/NWdnZ3xO7F/K6zwQcLOzs7PIL1vK6ywwgrfNqyI4wrvBtxrc3i3/Vngz7zN8/70t7hfK9wjRqPRHwD+QP5nN3z806PR6G/nn2/v7Oz86bv+Zh34NxBTnL9z+nu5wreAEfALwF8ajUY/CHwR+BTwA4hE9f/4Du7bCiussMIKK6xwilAprQzwVlhhhfuD0Wj0Y8CffZtNXtnZ2Xn4rr/5XwE7iCnOv3l6e7fC/cBoNHoQUQj8MLANXAP+AfDnVvPHVlhhhRVWWOH9ixVxXGGFFVZYYYUVVlhhhRVWWOFtsTLHWWGFFVZYYYUVVlhhhRVWWOFtsSKOK6ywwgorrLDCCiussMIKK7wtVsRxhRVWWGGFFVZYYYUVVlhhhbfFijiusMIKK6ywwgorrLDCCius8LZYEccVVlhhhRVWWGGFFVZYYYUV3hYr4rjCCiussMIKK6ywwgorrLDC22JFHFdYYYUVVlhhhRVWWGGFFVZ4W6yI4worrLDCCiussMIKK6ywwgpvixVxXGGFFVZYYYUVVlhhhRVWWOFtsSKOK6ywwgorrLDCCiussMIKK7wt/v/yqeDEGOJOYQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 1152x676.8 with 32 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# ---- Get and show few images\n",
- "\n",
- "samples = [ random.randint(0,len(x_train)-1) for i in range(32)]\n",
- "pwk.plot_images(x_train,y_train, samples, columns=8, x_size=2, y_size=2, \n",
- " colorbar=False, y_pred=None, cm='binary', save_as='07-real-signs')\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7 - dataset cooking...\n",
- "\n",
- "Images **must** :\n",
- " - have the **same size** to match the size of the network,\n",
- " - be **normalized**. \n",
- " \n",
- "It is possible to work on **rgb** or **monochrome** images and to **equalize** the histograms. \n",
- "\n",
- "See : [Exposure with scikit-image](https://scikit-image.org/docs/dev/api/skimage.exposure.html) \n",
- "See : [Local histogram equalization](https://scikit-image.org/docs/dev/api/skimage.filters.rank.html#skimage.filters.rank.equalize) \n",
- "See : [Histogram equalization](https://scikit-image.org/docs/dev/api/skimage.exposure.html#skimage.exposure.equalize_hist) \n",
- "\n",
- "### 7.1 - Enhancement cooking"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:47:23.581991Z",
- "iopub.status.busy": "2021-03-01T17:47:23.580575Z",
- "iopub.status.idle": "2021-03-01T17:47:23.583588Z",
- "shell.execute_reply": "2021-03-01T17:47:23.584066Z"
- }
- },
- "outputs": [],
- "source": [
- "def images_enhancement(images, width=25, height=25, mode='RGB'):\n",
- " '''\n",
- " Resize and convert images - doesn't change originals.\n",
- " input images must be RGBA or RGB.\n",
- " Note : all outputs are fixed size numpy array of float64\n",
- " args:\n",
- " images : images list\n",
- " width,height : new images size (25,25)\n",
- " mode : RGB | RGB-HE | L | L-HE | L-LHE | L-CLAHE\n",
- " return:\n",
- " numpy array of enhanced images\n",
- " '''\n",
- " modes = { 'RGB':3, 'RGB-HE':3, 'L':1, 'L-HE':1, 'L-LHE':1, 'L-CLAHE':1}\n",
- " lz=modes[mode]\n",
- " \n",
- " out=[]\n",
- " for img in images:\n",
- " \n",
- " # ---- if RGBA, convert to RGB\n",
- " if img.shape[2]==4:\n",
- " img=color.rgba2rgb(img)\n",
- " \n",
- " # ---- Resize\n",
- " img = transform.resize(img, (width,height))\n",
- "\n",
- " # ---- RGB / Histogram Equalization\n",
- " if mode=='RGB-HE':\n",
- " hsv = color.rgb2hsv(img.reshape(width,height,3))\n",
- " hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])\n",
- " img = color.hsv2rgb(hsv)\n",
- " \n",
- " # ---- Grayscale\n",
- " if mode=='L':\n",
- " img=color.rgb2gray(img)\n",
- " \n",
- " # ---- Grayscale / Histogram Equalization\n",
- " if mode=='L-HE':\n",
- " img=color.rgb2gray(img)\n",
- " img=exposure.equalize_hist(img)\n",
- " \n",
- " # ---- Grayscale / Local Histogram Equalization\n",
- " if mode=='L-LHE': \n",
- " img=color.rgb2gray(img)\n",
- " img = img_as_ubyte(img)\n",
- " img=rank.equalize(img, disk(10))/255.\n",
- " \n",
- " # ---- Grayscale / Contrast Limited Adaptive Histogram Equalization (CLAHE)\n",
- " if mode=='L-CLAHE':\n",
- " img=color.rgb2gray(img)\n",
- " img=exposure.equalize_adapthist(img)\n",
- " \n",
- " # ---- Add image in list of list\n",
- " out.append(img)\n",
- " pwk.update_progress('Enhancement: ',len(out),len(images))\n",
- "\n",
- " # ---- Reshape images\n",
- " # (-1, width,height,1) for L\n",
- " # (-1, width,height,3) for RGB\n",
- " #\n",
- " out = np.array(out,dtype='float64')\n",
- " out = out.reshape(-1,width,height,lz)\n",
- " return out"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 7.2 - To get an idea of the different recipes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:47:23.607740Z",
- "iopub.status.busy": "2021-03-01T17:47:23.607256Z",
- "iopub.status.idle": "2021-03-01T17:47:35.167151Z",
- "shell.execute_reply": "2021-03-01T17:47:35.167645Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [#---------------] 6.2% of 16\r",
- "Enhancement: [##--------------] 12.5% of 16\r",
- "Enhancement: [###-------------] 18.8% of 16\r",
- "Enhancement: [####------------] 25.0% of 16\r",
- "Enhancement: [#####-----------] 31.2% of 16\r",
- "Enhancement: [######----------] 37.5% of 16\r",
- "Enhancement: [#######---------] 43.8% of 16\r",
- "Enhancement: [########--------] 50.0% of 16\r",
- "Enhancement: [#########-------] 56.2% of 16\r",
- "Enhancement: [##########------] 62.5% of 16\r",
- "Enhancement: [###########-----] 68.8% of 16\r",
- "Enhancement: [############----] 75.0% of 16\r",
- "Enhancement: [#############---] 81.2% of 16\r",
- "Enhancement: [##############--] 87.5% of 16\r",
- "Enhancement: [###############-] 93.8% of 16\r",
- "Enhancement: [################] 100.0% of 16\n",
- "Enhancement: [#---------------] 6.2% of 16\r",
- "Enhancement: [##--------------] 12.5% of 16\r",
- "Enhancement: [###-------------] 18.8% of 16\r",
- "Enhancement: [####------------] 25.0% of 16\r",
- "Enhancement: [#####-----------] 31.2% of 16\r",
- "Enhancement: [######----------] 37.5% of 16\r",
- "Enhancement: [#######---------] 43.8% of 16\r",
- "Enhancement: [########--------] 50.0% of 16\r",
- "Enhancement: [#########-------] 56.2% of 16\r",
- "Enhancement: [##########------] 62.5% of 16\r",
- "Enhancement: [###########-----] 68.8% of 16\r",
- "Enhancement: [############----] 75.0% of 16\r",
- "Enhancement: [#############---] 81.2% of 16\r",
- "Enhancement: [##############--] 87.5% of 16\r",
- "Enhancement: [###############-] 93.8% of 16\r",
- "Enhancement: [################] 100.0% of 16\n",
- "Enhancement: [#---------------] 6.2% of 16\r",
- "Enhancement: [##--------------] 12.5% of 16\r",
- "Enhancement: [###-------------] 18.8% of 16\r",
- "Enhancement: [####------------] 25.0% of 16\r",
- "Enhancement: [#####-----------] 31.2% of 16\r",
- "Enhancement: [######----------] 37.5% of 16\r",
- "Enhancement: [#######---------] 43.8% of 16\r",
- "Enhancement: [########--------] 50.0% of 16\r",
- "Enhancement: [#########-------] 56.2% of 16\r",
- "Enhancement: [##########------] 62.5% of 16\r",
- "Enhancement: [###########-----] 68.8% of 16\r",
- "Enhancement: [############----] 75.0% of 16\r",
- "Enhancement: [#############---] 81.2% of 16\r",
- "Enhancement: [##############--] 87.5% of 16\r",
- "Enhancement: [###############-] 93.8% of 16\r",
- "Enhancement: [################] 100.0% of 16\n",
- "Enhancement: [#---------------] 6.2% of 16\r",
- "Enhancement: [##--------------] 12.5% of 16\r",
- "Enhancement: [###-------------] 18.8% of 16\r",
- "Enhancement: [####------------] 25.0% of 16\r",
- "Enhancement: [#####-----------] 31.2% of 16\r",
- "Enhancement: [######----------] 37.5% of 16\r",
- "Enhancement: [#######---------] 43.8% of 16\r",
- "Enhancement: [########--------] 50.0% of 16\r",
- "Enhancement: [#########-------] 56.2% of 16\r",
- "Enhancement: [##########------] 62.5% of 16\r",
- "Enhancement: [###########-----] 68.8% of 16\r",
- "Enhancement: [############----] 75.0% of 16\r",
- "Enhancement: [#############---] 81.2% of 16\r",
- "Enhancement: [##############--] 87.5% of 16\r",
- "Enhancement: [###############-] 93.8% of 16\r",
- "Enhancement: [################] 100.0% of 16\n",
- "Enhancement: [#---------------] 6.2% of 16\r",
- "Enhancement: [##--------------] 12.5% of 16\r",
- "Enhancement: [###-------------] 18.8% of 16\r",
- "Enhancement: [####------------] 25.0% of 16\r",
- "Enhancement: [#####-----------] 31.2% of 16\r",
- "Enhancement: [######----------] 37.5% of 16\r",
- "Enhancement: [#######---------] 43.8% of 16\r",
- "Enhancement: [########--------] 50.0% of 16\r",
- "Enhancement: [#########-------] 56.2% of 16\r",
- "Enhancement: [##########------] 62.5% of 16\r",
- "Enhancement: [###########-----] 68.8% of 16\r",
- "Enhancement: [############----] 75.0% of 16\r",
- "Enhancement: [#############---] 81.2% of 16\r",
- "Enhancement: [##############--] 87.5% of 16\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [###############-] 93.8% of 16\r",
- "Enhancement: [################] 100.0% of 16\n",
- "Enhancement: [#---------------] 6.2% of 16\r",
- "Enhancement: [##--------------] 12.5% of 16\r",
- "Enhancement: [###-------------] 18.8% of 16\r",
- "Enhancement: [####------------] 25.0% of 16\r",
- "Enhancement: [#####-----------] 31.2% of 16\r",
- "Enhancement: [######----------] 37.5% of 16\r",
- "Enhancement: [#######---------] 43.8% of 16\r",
- "Enhancement: [########--------] 50.0% of 16\r",
- "Enhancement: [#########-------] 56.2% of 16\r",
- "Enhancement: [##########------] 62.5% of 16\r",
- "Enhancement: [###########-----] 68.8% of 16\r",
- "Enhancement: [############----] 75.0% of 16\r",
- "Enhancement: [#############---] 81.2% of 16\r",
- "Enhancement: [##############--] 87.5% of 16\r",
- "Enhancement: [###############-] 93.8% of 16\r",
- "Enhancement: [################] 100.0% of 16\n"
- ]
- },
- {
- "data": {
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\n",
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- "<br>**ORIGINAL**"
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\n",
- "text/plain": [
- "<Figure size 864x97.2 with 12 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**ENHANCED**"
- ],
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- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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- "output_type": "stream",
- "text": [
- "dataset : RGB min,max=[0.016,1.000] shape=(16, 25, 25, 3)\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GTSRB1-10-enhanced-RGB</div>"
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\n",
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\n",
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- "<Figure size 864x97.2 with 12 Axes>"
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- "dataset : L min,max=[0.019,1.000] shape=(16, 25, 25, 1)\n"
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\n",
- "text/plain": [
- "<Figure size 864x97.2 with 12 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
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- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dataset : L-HE min,max=[0.002,1.000] shape=(16, 25, 25, 1)\n"
- ]
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x97.2 with 12 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dataset : L-LHE min,max=[0.000,1.000] shape=(16, 25, 25, 1)\n"
- ]
- },
- {
- "data": {
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- "<div class=\"comment\">Saved: ./run/figs/GTSRB1-14-enhanced-L-LHE</div>"
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\n",
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- "text": [
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- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x97.2 with 12 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "i=random.randint(0,len(x_train)-16)\n",
- "x_samples = x_train[i:i+16]\n",
- "y_samples = y_train[i:i+16]\n",
- "\n",
- "datasets = {}\n",
- "\n",
- "datasets['RGB'] = images_enhancement( x_samples, width=25, height=25, mode='RGB' )\n",
- "datasets['RGB-HE'] = images_enhancement( x_samples, width=25, height=25, mode='RGB-HE' )\n",
- "datasets['L'] = images_enhancement( x_samples, width=25, height=25, mode='L' )\n",
- "datasets['L-HE'] = images_enhancement( x_samples, width=25, height=25, mode='L-HE' )\n",
- "datasets['L-LHE'] = images_enhancement( x_samples, width=25, height=25, mode='L-LHE' )\n",
- "datasets['L-CLAHE'] = images_enhancement( x_samples, width=25, height=25, mode='L-CLAHE' )\n",
- "\n",
- "pwk.subtitle('EXPECTED')\n",
- "x_expected=[ x_meta[i] for i in y_samples]\n",
- "pwk.plot_images(x_expected, y_samples, range(12), columns=12, x_size=1, y_size=1,\n",
- " colorbar=False, y_pred=None, cm='binary', save_as='08-expected')\n",
- "\n",
- "pwk.subtitle('ORIGINAL')\n",
- "pwk.plot_images(x_samples, y_samples, range(12), columns=12, x_size=1, y_size=1, \n",
- " colorbar=False, y_pred=None, cm='binary', save_as='09-original')\n",
- "\n",
- "pwk.subtitle('ENHANCED')\n",
- "n=10\n",
- "for k,d in datasets.items():\n",
- " print(\"dataset : {} min,max=[{:.3f},{:.3f}] shape={}\".format(k,d.min(),d.max(), d.shape))\n",
- " pwk.plot_images(d, y_samples, range(12), columns=12, x_size=1, y_size=1, \n",
- " colorbar=False, y_pred=None, cm='binary', save_as=f'{n}-enhanced-{k}')\n",
- " n+=1\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 7.3 - Cook and save\n",
- "A function to save a dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:47:35.173122Z",
- "iopub.status.busy": "2021-03-01T17:47:35.172640Z",
- "iopub.status.idle": "2021-03-01T17:47:35.174828Z",
- "shell.execute_reply": "2021-03-01T17:47:35.174333Z"
- }
- },
- "outputs": [],
- "source": [
- "def save_h5_dataset(x_train, y_train, x_test, y_test, x_meta,y_meta, filename):\n",
- " \n",
- " # ---- Create h5 file\n",
- " with h5py.File(filename, \"w\") as f:\n",
- " f.create_dataset(\"x_train\", data=x_train)\n",
- " f.create_dataset(\"y_train\", data=y_train)\n",
- " f.create_dataset(\"x_test\", data=x_test)\n",
- " f.create_dataset(\"y_test\", data=y_test)\n",
- " f.create_dataset(\"x_meta\", data=x_meta)\n",
- " f.create_dataset(\"y_meta\", data=y_meta)\n",
- " \n",
- " # ---- done\n",
- " size=os.path.getsize(filename)/(1024*1024)\n",
- " print('Dataset : {:24s} shape : {:22s} size : {:6.1f} Mo (saved)'.format(filename, str(x_train.shape),size))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate enhanced datasets :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:47:35.181842Z",
- "iopub.status.busy": "2021-03-01T17:47:35.181364Z",
- "iopub.status.idle": "2021-03-01T17:48:11.312247Z",
- "shell.execute_reply": "2021-03-01T17:48:11.312740Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**Parameters :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Scale is : 0.05\n",
- "x_train length is : 1960\n",
- "x_test length is : 631\n",
- "output dir is : ./data\n",
- "\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Running...**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Dataset : ./data/set-24x24-RGB.h5**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [#---------------------------------------] 2.5% of 1960\r",
- "Enhancement: [##--------------------------------------] 5.0% of 1960\r",
- "Enhancement: [###-------------------------------------] 7.5% of 1960\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [####------------------------------------] 10.0% of 1960\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [#####-----------------------------------] 12.5% of 1960\r",
- "Enhancement: [######----------------------------------] 15.0% of 1960\r",
- "Enhancement: [#######---------------------------------] 17.5% of 1960\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [########--------------------------------] 20.0% of 1960\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [#########-------------------------------] 22.5% of 1960\r",
- "Enhancement: [##########------------------------------] 25.0% of 1960\r",
- "Enhancement: [###########-----------------------------] 27.5% of 1960\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [############----------------------------] 30.0% of 1960\r"
- ]
- },
- {
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- "output_type": "stream",
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- "Enhancement: [#############---------------------------] 32.5% of 1960\r",
- "Enhancement: [##############--------------------------] 35.0% of 1960\r",
- "Enhancement: [###############-------------------------] 37.5% of 1960\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [################------------------------] 40.0% of 1960\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [#################-----------------------] 42.5% of 1960\r",
- "Enhancement: [##################----------------------] 45.0% of 1960\r",
- "Enhancement: [###################---------------------] 47.5% of 1960\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [####################--------------------] 50.0% of 1960\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [#####################-------------------] 52.5% of 1960\r",
- "Enhancement: [######################------------------] 55.0% of 1960\r",
- "Enhancement: [#######################-----------------] 57.5% of 1960\r"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Enhancement: [########################----------------] 60.0% of 1960\r"
- ]
- },
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- "name": "stdout",
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- "Enhancement: [#########################---------------] 62.5% of 1960\r",
- "Enhancement: [##########################--------------] 65.0% of 1960\r",
- "Enhancement: [###########################-------------] 67.5% of 1960\r"
- ]
- },
- {
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- "output_type": "stream",
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- "Enhancement: [############################------------] 70.0% of 1960\r"
- ]
- },
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- "Enhancement: [#############################-----------] 72.5% of 1960\r",
- "Enhancement: [##############################----------] 75.0% of 1960\r",
- "Enhancement: [###############################---------] 77.5% of 1960\r"
- ]
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- {
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- "Enhancement: [################################--------] 80.0% of 1960\r"
- ]
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- "Enhancement: [#################################-------] 82.5% of 1960\r",
- "Enhancement: [##################################------] 85.0% of 1960\r",
- "Enhancement: [###################################-----] 87.5% of 1960\r"
- ]
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- "Enhancement: [####################################----] 90.0% of 1960\r"
- ]
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- "Enhancement: [#####################################---] 92.5% of 1960\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Enhancement: [########################################] 100.0% of 1960\n",
- "Enhancement: [#---------------------------------------] 2.4% of 631\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "Enhancement: [##--------------------------------------] 4.8% of 631\r",
- "Enhancement: [###-------------------------------------] 7.1% of 631\r",
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- ]
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- "Enhancement: [###########-----------------------------] 28.5% of 631\r",
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- ]
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- "Enhancement: [##############--------------------------] 35.7% of 631\r",
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- "Enhancement: [################------------------------] 40.4% of 631\r",
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- ]
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- "Enhancement: [#######################-----------------] 57.1% of 631\r",
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [###################################-----] 88.0% of 631\r",
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- ]
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- "Enhancement: [######################################--] 95.1% of 631\r",
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- "Enhancement: [########################################] 100.0% of 631\n",
- "Enhancement: [#---------------------------------------] 2.3% of 43\r",
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- "Enhancement: [########################----------------] 60.5% of 43\r",
- "Enhancement: [#########################---------------] 62.8% of 43\r",
- "Enhancement: [##########################--------------] 65.1% of 43\r",
- "Enhancement: [###########################-------------] 67.4% of 43\r",
- "Enhancement: [############################------------] 69.8% of 43\r",
- "Enhancement: [#############################-----------] 72.1% of 43\r",
- "Enhancement: [##############################----------] 74.4% of 43\r",
- "Enhancement: [###############################---------] 76.7% of 43\r",
- "Enhancement: [################################--------] 79.1% of 43\r",
- "Enhancement: [#################################-------] 81.4% of 43\r",
- "Enhancement: [#################################-------] 83.7% of 43\r",
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- "Enhancement: [########################################] 100.0% of 43\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset : ./data/set-24x24-RGB.h5 shape : (1960, 24, 24, 3) size : 34.7 Mo (saved)\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Dataset : ./data/set-24x24-RGB-HE.h5**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [#---------------------------------------] 2.5% of 1960\r",
- "Enhancement: [##--------------------------------------] 5.0% of 1960\r"
- ]
- },
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- ]
- },
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- "Enhancement: [####------------------------------------] 10.0% of 1960\r",
- "Enhancement: [#####-----------------------------------] 12.5% of 1960\r"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Enhancement: [######----------------------------------] 15.0% of 1960\r"
- ]
- },
- {
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- "output_type": "stream",
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- "Enhancement: [#######---------------------------------] 17.5% of 1960\r",
- "Enhancement: [########--------------------------------] 20.0% of 1960\r"
- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
- },
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- ]
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- "Enhancement: [#######################################-] 97.7% of 43\r",
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- "Dataset : ./data/set-24x24-RGB-HE.h5 shape : (1960, 24, 24, 3) size : 34.7 Mo (saved)\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
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- "Dataset : ./data/set-24x24-L.h5 shape : (1960, 24, 24, 1) size : 12.0 Mo (saved)\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Dataset : ./data/set-24x24-L-LHE.h5**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
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- "Enhancement: [#---------------------------------------] 2.3% of 43\r",
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- "Enhancement: [#########################---------------] 62.8% of 43\r",
- "Enhancement: [##########################--------------] 65.1% of 43\r",
- "Enhancement: [###########################-------------] 67.4% of 43\r",
- "Enhancement: [############################------------] 69.8% of 43\r",
- "Enhancement: [#############################-----------] 72.1% of 43\r",
- "Enhancement: [##############################----------] 74.4% of 43\r",
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- "Enhancement: [################################--------] 79.1% of 43\r",
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- "Enhancement: [#######################################-] 97.7% of 43\r",
- "Enhancement: [########################################] 100.0% of 43\n",
- "Dataset : ./data/set-24x24-L-LHE.h5 shape : (1960, 24, 24, 1) size : 12.0 Mo (saved)\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Dataset : ./data/set-48x48-RGB.h5**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [#---------------------------------------] 2.5% of 1960\r"
- ]
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- "Enhancement: [#####-----------------------------------] 12.5% of 1960\r"
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- "Enhancement: [#########-------------------------------] 22.5% of 1960\r"
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- "Enhancement: [#####################-------------------] 52.5% of 1960\r"
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- ]
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- ]
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- "Enhancement: [##############################----------] 75.0% of 1960\r",
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- "Enhancement: [################################--------] 80.0% of 1960\r"
- ]
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- ]
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- ]
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- ]
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- "Enhancement: [######################################--] 95.0% of 1960\r",
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- "Enhancement: [########################################] 100.0% of 1960\n"
- ]
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- "Enhancement: [#---------------------------------------] 2.4% of 631\r",
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- ]
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- {
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- ]
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- ]
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- "Enhancement: [###################################-----] 88.0% of 631\r"
- ]
- },
- {
- "name": "stdout",
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- "Enhancement: [####################################----] 90.3% of 631\r",
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- "Enhancement: [######################################--] 95.1% of 631\r",
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- "Enhancement: [########################################] 100.0% of 631\n",
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- "Enhancement: [#########################---------------] 62.8% of 43\r",
- "Enhancement: [##########################--------------] 65.1% of 43\r",
- "Enhancement: [###########################-------------] 67.4% of 43\r",
- "Enhancement: [############################------------] 69.8% of 43\r",
- "Enhancement: [#############################-----------] 72.1% of 43\r",
- "Enhancement: [##############################----------] 74.4% of 43\r",
- "Enhancement: [###############################---------] 76.7% of 43\r",
- "Enhancement: [################################--------] 79.1% of 43\r",
- "Enhancement: [#################################-------] 81.4% of 43\r",
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- "Enhancement: [##################################------] 86.0% of 43\r",
- "Enhancement: [###################################-----] 88.4% of 43\r",
- "Enhancement: [####################################----] 90.7% of 43\r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [#####################################---] 93.0% of 43\r",
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- "Enhancement: [########################################] 100.0% of 43\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset : ./data/set-48x48-RGB.h5 shape : (1960, 48, 48, 3) size : 138.9 Mo (saved)\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Dataset : ./data/set-48x48-RGB-HE.h5**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enhancement: [#---------------------------------------] 2.5% of 1960\r"
- ]
- },
- {
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- "text": [
- "Enhancement: [##--------------------------------------] 5.0% of 1960\r"
- ]
- },
- {
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- "text": [
- "Enhancement: [###-------------------------------------] 7.5% of 1960\r"
- ]
- },
- {
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- "text": [
- "Enhancement: [####------------------------------------] 10.0% of 1960\r"
- ]
- },
- {
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- "text": [
- "Enhancement: [#####-----------------------------------] 12.5% of 1960\r"
- ]
- },
- {
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- "Enhancement: [######----------------------------------] 15.0% of 1960\r"
- ]
- },
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- ]
- },
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
- },
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- ]
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- ]
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- ]
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- ]
- },
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- "Enhancement: [##############################----------] 75.0% of 1960\r"
- ]
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- ]
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- ]
- },
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- "Enhancement: [#################################-------] 82.5% of 1960\r"
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- "Enhancement: [#########################---------------] 62.8% of 43\r",
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- "Enhancement: [########################################] 100.0% of 43\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset : ./data/set-48x48-RGB-HE.h5 shape : (1960, 48, 48, 3) size : 138.9 Mo (saved)\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Dataset : ./data/set-48x48-L.h5**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
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- ]
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- "text": [
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- "Enhancement: [########################################] 100.0% of 43\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset : ./data/set-48x48-L.h5 shape : (1960, 48, 48, 1) size : 47.8 Mo (saved)\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Dataset : ./data/set-48x48-L-LHE.h5**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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- "Enhancement: [#---------------------------------------] 2.5% of 1960\r"
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- "text": [
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset : ./data/set-48x48-L-LHE.h5 shape : (1960, 48, 48, 1) size : 47.8 Mo (saved)\n",
- "\n",
- "Duration : 00:00:36 129ms\n"
- ]
- }
- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "n_train = int( len(x_train)*scale )\n",
- "n_test = int( len(x_test)*scale )\n",
- "\n",
- "pwk.subtitle('Parameters :')\n",
- "print(f'Scale is : {scale}')\n",
- "print(f'x_train length is : {n_train}')\n",
- "print(f'x_test length is : {n_test}')\n",
- "print(f'output dir is : {output_dir}\\n')\n",
- "\n",
- "pwk.subtitle('Running...')\n",
- "\n",
- "pwk.mkdir(output_dir)\n",
- "\n",
- "for s in [24, 48]:\n",
- " for m in ['RGB', 'RGB-HE', 'L', 'L-LHE']:\n",
- " # ---- A nice dataset name\n",
- " filename = f'{output_dir}/set-{s}x{s}-{m}.h5'\n",
- " pwk.subtitle(f'Dataset : {filename}')\n",
- " \n",
- " # ---- Enhancement\n",
- " # Note : x_train is a numpy array of python objects (images with <> sizes)\n",
- " # but images_enhancement() return a real array of float64 numpy (images with same size)\n",
- " # so, we can save it in nice h5 files\n",
- " #\n",
- " x_train_new = images_enhancement( x_train[:n_train], width=s, height=s, mode=m )\n",
- " x_test_new = images_enhancement( x_test[:n_test], width=s, height=s, mode=m )\n",
- " x_meta_new = images_enhancement( x_meta, width=s, height=s, mode='RGB' )\n",
- " \n",
- " # ---- Save\n",
- " save_h5_dataset( x_train_new, y_train[:n_train], x_test_new, y_test[:n_test], x_meta_new,y_meta, filename)\n",
- "\n",
- "x_train_new,x_test_new=0,0\n",
- "\n",
- "pwk.chrono_show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class='todo'>\n",
- " Adapt the code below to read :\n",
- " <ul>\n",
- " <li>the different h5 datasets you saved in ./data,</li>\n",
- " <li>The h5 datasets available in the Fidle project datasets directory.</li>\n",
- " </ul>\n",
- " \n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 8 - Reload data to be sure ;-)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:11.344703Z",
- "iopub.status.busy": "2021-03-01T17:48:11.331603Z",
- "iopub.status.idle": "2021-03-01T17:48:17.607687Z",
- "shell.execute_reply": "2021-03-01T17:48:17.608195Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dataset loaded from h5 file.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GTSRB1-16-enhanced_images</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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EYqRtjY2BtskDCU9pAkn7yv+zCsXBC5fJZIKVCSUbQcTni4uLIZcGDx1jTE7U3d2dLS8vW6lUsn6/HxgqoRRqBWXcfOjENEHNdf53MEi1jpFnaGYB6AJ8y+Wy5fN5m0wm1uv1Qq4ecf7kMr5588ZqtZqZmZ2dndnZ2Znt7+/bx48frdPpBMvi8vJyQpBkMpnwTDxyZp+UA/I+X5IAwBrGqXl+rEEEgY6fWTLc1yyZPw64AjgCBmJGEAUHynsBafwubd1rOJG3jE4DDGmKh28Lgpp+0TeEP2td+6OkIcB/SkqTDdN4qgcOZg/D53SdqAzVPad91zAtVSpZgz7SgBArjVhJ4/1P6Zt/tq7pWHia0izBhj5D11FMTvi9E2ufjr/XlWJ7SXmizhefETGCh8vMQr7W+fl5uF+5XLa9vT3b2toKuXZqYPNyKbbepu1NbaeZJUAj/fKGHFVy9bmzoJjy7kErirafr6d4QXVdEAVTKBSC14iw0mazGfYjIa2ad21miTB7zc9WfgbgVO+UBwb6v665tHnVfqTp67pH/Tr/EgRog7xupvtIAWCMbzDOmnqhexDguLi4aGafwooXFxft+vo6/AaApu2J6T+sPU1/0TxCeKkaeDw/8LqANyxA+jzlN6zjWASJlxm8PxZZ9bPirtKU7xhjfCrgMrufhGkL3f8+TShNe960+yjgifUx9pzY38pcvbDR771w8pP50uTBlFKsH09l7LG14JWKaWBL/0dJIXm50+mEcExVahcXF0PiO5YitazHQIpXXPS53hMT23S+raqEwyi8EjQLGg6HIZRR+4uCQFvIn6CgDcU0sE73+32bTCZWKBRCWCD3vrm5CUUe8FbR7xhA9qGsfKfj7j26CDwYqAp0PH2rq6u2srIS2pHNZhPe55WVFavVavbmzRt7+/at7ezs2MrKivV6Pfv48aN9+PDBzs/PA9jG6ADIRjGeTD5ZGclBoWgCVrxZAMfY2OAd9BbNmJD3c0JfWK8ALE2Y1/XqowJ0P/J7jA36O1W4vPBW4a6CNk3w+bHQa1gPhPcgRBXY6Hr0SqkK1D81cIzJzWnKmJeFOjYKDHWuvVKl+3IaH57Gq1EoWJO6vmKy3N9L+5zWT68rxOYqTZ94adI15NdsDPzE5i+2R/X+/jc8h70WA5aPRXdwbT6ft1evXtlvfvMb29nZCXLS53D75/t1Q9tj+pjKR/1eDTSqA/o2p33+kqT8SscHI4ryDN8W3S8xpV73ncoxoqAo4Ebqhcou7xjxyr6OJ0aBq6sru7u7C4WVCI9lz3sQqe3z5HVZz/PTeFLMkBAbm5ekmPHBr1OVTWn8hzXpPeCep+J4yGQyoYigRnCpkV4NpdzLP4d2ARoHg4ENBgObTCYh8ktxD/fRPEpeOgcx/ctjjvF4/GgYswJMMwv5smn0s3Mcldl7ZVop5vVJW4AxwZW2gB8DNjGBFrvPY+SVo8cEoLYv1s+0Zzzn+p9DPiE4TZnRtvm26vX8H/MwqtIS81Z4oQNwuLm5CeCmXq/b5eWldTqd4OKfTCaBSd/e3oacNsL8PAON9SmmrHim4+/lwz084/IMLDZ+L0XeexBTsswsJE4vLCwEIaQhTQDLUqkUrJpUkCX/gtCamKUP0lCJNPJeNR96osyNkNlSqRSqhdFXlCC8jOvr6/bq1Svb2dmxUqlkmUzGzs7O7PDw0H788Uc7PDy0Xq8X1szy8nKw2I/H41C9zsyC4KcionpuZ0GquADOdXxifJG2qFKrXje+08qsCsz5jQddfs16RVXXd9o8e/Co/dNrzB4HUF6uMDY+rMePi46teq1i13wpegpPnwa00uQDfEkrS6sSj4FFlQ9VxrTAg75UKclkMgngCK9+TPGPyejH5Im2ne+94jMro2qsXV451DXE+vLRGDEDSQyYeXmBtZ/+YvijHYPBwJrNprVarcC72dMLCwu2tbVlS0tL9ubNG/sn/+Sf2Lt372x5eTkYXT2YSAN2XgGHVL57nkH/vcyPhcfp37PSdbSNOt5mFkLc1eMYAxqxyA5tt+oIEPmKpM5gdCRXEcXc898YmNHCOFTJzWazwRMGH9SQdK/3KMX4iwdk3vsVWyux+82KdC0pwSPU6GsWN64w17o/vfeNe6LvZDKZhHGatmAEZQ3xXAV2+jufNoRuxedq2PVAP032a19joeD02RuJNbqDuUYGKFidRs8GjjGAGEO4MdLF6X+nblK9r4Ye6SA+1sbYM/U53vqUBkx51zbHrp8mFD2jnQYiPHiclWVcFTmfaEybp/U5DRD5teEFY+yluTbk252fn9vp6akdHx/b8fGxHR0d2fn5ubXb7RDCODc3Z/l83iqVShCubHiscWwaDWvTjeP76vuvCooy5JiXLRbukqYMvxRxbwTSaDRKWJS4xuy+hPhgMAg5o1gvEXK1Wi1Y2hjv0Wj0IDk71g6vVKWNhV4fU3bNLIRjraysWLlctkKhYPPz84kQEzMLYat7e3v2+vVr29jYsEwmYxcXF3Z0dGQnJydB4A6HwwDMANEQ6wNhwfcqJBi/WZBXXDxY9IJbvboqJHQdari2H2MvaPh9zPrNMxgLfbYKY5ReFZ7+GTFDjt9/njdo6BFrM3bcTZpcUOCoQvRLUFqbPJ9J+zttjMweAns1wOjY6LrQsfdryPM7SJV/5aM+hDTW12nfPUUB1TWg60KB7ixIZZ7uS1W8/PqO6RQxHuif4/ml3+/MCcayRqNhp6en1mw2LZPJWKlUCl6LhYWFcDTHn/3Zn9nW1pbNzc1Zt9tNKLIAf7O4juF5kdfpvL6jxlp/XSz/btZyUdumugf8irlUgGtmD9Z/zFvneaQ+x+y+Yj2GbOaOexEx5Y9w8UZ1xod0kk6nYzc3N+FICI2+iRWLSdPRuLf/38tm/W0M0Cq/nrURJ8azmT8i0wCOHhByrcqvbDb7IJpH+wqvG4/HobCUHo1zc3MTdGdf7DOTySSK7xAxNZlMrN/vW6vVCgb7xcXFRO6/gkc/B3yvfAZeBD/3uYlpzhP/mZcbj+3Lz/I4eiVGO2dmCQXaL7KYEuQnOAbu9Pe+HV6gplHMOj0N+Gi7Y0LgKTRtEtLu8SWUmjSlPk24qeBI61MaMPQAEuWfzUVRF842uri4CMDx9PTUzs/P7fz8PByXgPJONdVutxvOqeP39Xrdtra2rFwuW7FYDB4m8tY4rkMtPH5t6f9pjDG2hnQjxsbzJWkymYQqquSAqlVNhSU5gBRRGA6Hlst9qva2srJixWLRVldXbW5uLiT5U3U1dlagNwpAMSYP00MRZNzUgwGz5tlLS0vhXEyYMeGzKysrocz81taWra+vW7FYtLu7Ozs7O7P379/bTz/9ZI1GI+QRwBjVUutBFu1C2CugA5zPgtIAgzJ4BQA6xqrMQoyrPwNPFfFYuI4acnwYKIJXha+uQYoGMZaAcF0Dnsd4ZVNBDUIaUIqwZy49//cUA0AxMDsLmrbnY2Dqc5Ro/Y1fM/5/FAr2mFmSX8cAp857TM7zXB+NkSZPnjImkNcNvGLH+yyBY+w5MYU0bd48b/Tzo5+rJ1iP1dCqjxj9MICtrq6GYxuIjOCIhl/84hf2+vVrm5+ft3a7najiyPPU2+xlPO2KjQt90z7pb+BVapiN3cOP9SzAR0wH0ed7fpAGlmP7M43/KK8CGCivxVjLOaU6TrQTIyUFAq+vr0MV1q2tLdvb27O1tbVwZIfqwH6N+n0Tm1cvf6btd//95/Cu51Lac/V7BVGQn3uz6aHesevZH1pQh+8orKe8DgOP9/CPx2NrtVqh2jEGOA19NbuPDjOzRD0PZLpGJ9AfdQSlyVz9W9dCjGfPBDhCKmCgNObjvTuxa5Q8w+Ueaco9zCqtnapoeFCjz4gxgdjzPMUGmmt9grteH9vgsXa8NMUWVAxYKzjQdqUJgzTAiOBDIHJQdavVCgeuXl5eBtDXbDZDYRwAEZuUdtzd3YUE436/b41Gwz5+/BjCFjc3N61arVqlUrFyuRzey+VyqMpJgrk/WJZn8HdM6VSlLC1fb9qaeQkCeGvittl98rgq/BrSAQPkPKlisRiAk54vpCXGGW+eiZfXC2K/dz3wAPhgtdNQyuXlZTOzRGis/laVo3fv3tn29raVSiW7vb21er1ux8fHdnBwYGdnZ9bpdBLhkirACcelr8ojYoBF1/AsKMa8VRlXhSIWssNYAwyy2WxQTHQtaJ4g86W/ZZx0LnXPeyDB2h6NRtbv9+3m5sby+byVSqUQUsV1nv/GLMP8jeJETg+KGB5H7qPrizZphIDyUe3Ll6Dn7PuYTOLzab957JnsL65HWWG9s7+4zstJ9rLm6rBnVIHxoJFnP1Xe+36pvFHQq0aDWfJVbYvvk3732Fry63JaGNh4fF8Zm1A29Ybh+SgUCiEkn/2ez+eDFwoZN5lMElW31Wig+ohXQH0/vQEfXmhmiXw9HzGikQLqDPBGTf+8l6YYj/C662PrSedZxynmYVU+ShVu5bvq7Y3pX/D5Xq9nFxcXdnl5abe3t1YqlWx9fd22t7dtbW0tgAuvN3pey7sHXX4te90uBqi9LKC9MSzwkhTTMTwY9G2GP6k+4nlUDGyNx+OEjOQzchPRQ2NrSR0i6GTsYQw/V1dXCUM6Rn2OWcEhks1mQzoOhiGNkooBQu2LN67q/HnwqWOr+zKNfpbH0T/QX5O2KT2Y9BQDn14B8dasmNDV6z2SjjGSxwRcbMM9RkyqMtJpY6XPmiUpyKEN2pa0l2ceaQwDZQPrKUdqNJtNazQadn5+bmdnZ3Z+fh6YIx5FzirCCkOIJNVAVZkm/IbzHieTiZ2cnNjBwYGVSiVbXV21SqVia2trVq1WrVarhdfa2loCRGrOG+1XBekpwFGFwKzDN8zuj+NAEVcPEwJO17wyU7N77xDrE6Y2Ho8DuGIc8GxiJbu5uQn5eMqIVdnJZD4ZdEajUSKchLYjSGGWhCBzP54zNzdnKysrViqVbGNjw3Z2dmx7e9sWFxdtMBjY0dGR/fTTT3ZwcGDtdtsmk0moAuu9V5rfq5Z3PqefmcynsyApjoPSPAuiLbGQcQWNPhxalVHGmrnz4akaasN68byRdRsLm9K1rl7LXq9nl5eX1u12w3P5jVe46YfuD8/D1eIOH2BNaJI/91Oe5YUp9/Ueklkrqp6ey8+9ojftfjFQAwG4mRMdFxQXVSB0zBQs6nyrMqTj/ZjH0bcLinl21LhAW3WtT/MavBR53UDb/BTQGPNwcR8KchHqhlcJRVK9DPR1aWnJCoWCmdkDAMqxVPD/TCYT8tPH40/525z1p0f+AAKZTx/qpjLD98PzIT9uOg4xQPNU0PZzSNuiIAMZ7wFJ2l5La19sfnVuWAMACY3a6Ha7DxR59t1gMAgG9X6/b7lcLlQSb7fbYX41kmp+ft7MLPUopzQgqG33/fZyIqY/e3A2C8Jgq/qL7i/fDk1/iumv3mAJIftU1lLYSGtC6BjwOz5jX6GP0R6emc/ng27F/kaHY51cX1/b/Py8jUYjm0wmoTbFNAeHN0wpzkI30LBYNUgrqWEojWZymrW3XPjv1GLzFMAUGyD931/3FIbOdWlCM61f0/739JT7zFLwTaPHwLduRL9BY0qBZ8CEFXa73XBGIGCR3MXT01O7uLgIxymw2fB0ZbNZW15etmKxaCsrK0GJZ30BSvXQe4DG7e2tNZvN4Ik8OTlJgMharWbr6+sBROKZ1IIoKEjT1tM0hUlpVkyVcYOhYJ3SRHnao6AO65V6E9UqrcADZgZIzefzD0I5YGy6Zuj39fW1dbtdGw6HZnYfAonXU63VCtpp7/z8vFUqFdvZ2bGtrS2r1WpWKBTs7u7ODg8P7eDgwA4PD+309NQ6nY6ZmRWLxVAdluegFPlcELNk9UYUHTUGqKfFK1cvRb54jQovNWJAjJceSZGmxHtQZmYPhI83BqklmefwvRqFCL+5vr621dVVy+fzwduoQorfodgocIF0/agSzb24xvNur8Sy3tVQofM+SyXH02PPmqaY+nvoPPl3vveKhJI3ZnmlISYDdP1pzQFvJPPGgDRA4L2aMRAZM1rE1vWXJPgj5Neg2b3iOQ1YKp8zu6/SaHbPSzWlIlb8JKb7xIwlauyhMrbmhCE3kZ0eaHE/zzNYdwqQplFaxFWaAv9SFOMPfozSdE4/12ntVH6i8wKPy2Q+pVngrTK7L5KCQRUPF/xZdZu7u7uQp0qRnEqlEorZVSoVq1artrKyEtYd4Y6abx7zMnke6scrBrRi63vWwHEwGCT64Q3gHjjGAC3/ZzKZRNVUfw3vOCQoznhxcRHqQih/y+VyIRpGj7xSwy06loaMq1EBryPHdAwGg3AciKadUOHeR4fodZoSZHbPb29vb204HNpkMgnV5FlrjAky+urqaup8fBZw9IJemVSaYEpjsGn3NUsHVT5EZhrjiS0IXSgxATRNsZgGiv1zmYQ0wPmPAUCa2YMNGNt8CgbMHsZGm1lQrofDoXU6neBZJFcRD+Pl5aW1Wq2QsD83N2eVSiV4CDkHiXw3DrnFM8a4sglhrhoKOxgMAuNVz4WeWwgDBjjyohhLNptNxLR7AK3j59fTNMXhpeeOsM5cLhcK2TA3tEGF0mQyCQrKtPwWvHGEXqDc8B2Wc0IiFRjwPM3LITQW8A9zQsHXcGas5JVKxTY2Nmx7e9u2t7fDWZ2Xl5cBNHLEBkIZYaleJl9am5cfH8aF347H44SAVwD6kqReaz9fMc8F/Yq9PFiDvND1+9crS5AHBQoCJ5OJraysBE8s3vtsNhsAooaz0T76pu3VPYPCu7CwEMY8BoSVFEiph9zMEiG7XDsLivF25aWetM2ejzzWzmmKLEYIzU3W/e/bYPbQOu9lQkzueuUo5hX0a8qvOT/vuh64H3350qDRP8eDR7PHw4m9p8vsvjIjhjtVDpF5y8vLCSUxloNKG73c1vYqQISPY2BVgIJHjLbxDA8OPfDVcYntY/0tf/t1HtMHX4Jia1vbwUtlWmx9Qv5/fz36D393u12r1+t2dHRkjUYjyB/1QqoBgrXOOtC5oM2Al263GzzN1Wo11CdYWFgIgLJQKCTSSbStqhPHDBH0T3lmDIDruMyKr/rQUX2Wf1cZofPl5w3eoh53NdReX19bu922y8tLazQawfvHHAES0YH0rGn2MvNBxAf6DXolIaq8VCdirWQyGRsMBiG6h/oP8Ab6iz6D3qa6HrosAJw51XN5+VzPgE6jZwNHb5XwSqdfmDAEncjnMIjYxOs7A/NYm/EUxGL7X1oQqfD1TGga8PxTgEcPDFUAeUXCbzxtM0p/t9u1y8tLOz09taOjoweVLTVhGA8fOYkbGxvhM4AQjFmVEbOkYq3eGK0aioWOv2HoMARyI5vNpp2dnVmlUgntIPGcvqkgjQkfH575pYgwXgV56hWgfVrURBUyHV+YoCr2zCteH7V06ZmZfOaZ783NjfV6Pbu7uwvKEAne5IAwJ1hkM5lMKATw9u1be/36tVUqFZufn7erqys7PT219+/f2x//+EdrNBo2mXw6C2l5eTlRWALFTAvhDIfDsI74TJkm46JWX0Cj5pDOYh4VNOp8xUB9DIjFPN9mDw196nVWi7Jeq0LY31cVv/n5+WBsMbtXnAaDgc3Pz9vKykoCFCtf1LWifeQ6iusAMLWqbxqY5l5qwcWw4o0psyC/9z3/9HMRe4/d47Fn+O9RWNTwoCGmnmLt9Iqavz+GI/7WcFKfX6OyMAZ+fPtZjxoZEcsPnPVcTgPnXg8yi3twWeesW52DXO7TofF6Ri3GNd7TIlpiYEDbM5nch83Bv/L5fPAu8r6wsJAIkdMXuhtzogqlPlPnHx6jbVNDk1IMiL80qRHQf54GOtLm3u8D5CY8p9VqhX5eX19bs9m0k5MTOzs7s6urK1tYWAjRMMViMVHAbHFxMVEwB88PQEP3MrKs2+0GIzjVVovFolWr1SC/CY1UXk5fPHjUvmmfY+ArZsSblXxMM2z7tqTtBX8f1XWy2WxCvrP2iVZrNBo2GAwsl/tU6Z18YiK7kEtaU0CPv1KDsHr71bgZq1MBX8XhQd2PlZWVkM9MjQ72XWw/edmqn9P/tLlPoxcLVU2zLvIdDMhTTAgy+eoNSRMQ/pkKUmPWEQ/cvOL0lIU/TaB4YJ32+1kKvKeSKgiqwHnQyLWQMh6ARa/Xs2azGUJSOUYDD6OCApL3q9Wqra2thVALQKN6pWB+aWBb24bAUnc/5Y8590rPgMQiSBhmv9+3fr8fmDEFY3TT81s/dgq+/djNEkgSzkAYwmAwSOThMX4+7FH3VDabDWM+Pz8ffq/XeosswMszRu9xVOUd4Mh1y8vLwUOFRY7Q5Gq1apubm7a3t2flctmur68Tx7McHx/bxcVFKGmtlUM15CqbzQZmrzk9KNFawYx2YQ0EBJGLOUui/7oXdV0zngreddy5TnmPhoT63EmepXyP32jYsir6gD1ylfv9vmUyn6o7lstlu7m5sfPzczs4OLDb21tbX1+33d3dRG6yrwDI396ggcKMcGcuGSfWKP3RPadrXcEi4zFLUrnCPKS9nnqvaZ/FFDkFjqoQ8bdX4B8DjWok5PnqJYvldnu56hU7r7z657E+NPRLw+54n+V8TpPzPN97Yj0xFsgYlEby0qhmTToGSqA3mMbaFRtTbbOCDDz3agzkRWisVjjnuVrVcTwehxxJXQf67te31xu8MULbOgtSfUafG+OVpH2YTTcA8b/KUIyMnU7HBoNBGGuM2ERPkS5TLBaDhwrvEV4rX3OAwoAKNkajUUgD6nQ6iaqct7e3Iee8VCrZ2tpaOLJFjTwxsOD3Vhpg9OQNQi9N8H3fBr/+vVeetntgyzXolt4rjz7Y7XZtNBpZNpsNTg6tiaEA0UdExMAsRkyVVbyrnqbROOh03W434STxsl0NVPTRzyeRVxrREcNufrwfzMdzJzC22HzOkBdKdOIx5uAZoAodv/lVSOkgmNmDza/30sF5jqXLKwSxTaKL0ysGXpHg71krMmmkAjsWxhGbSz5nLvHYce4ilrWLiwu7uLiwVqtlvV7PJpNJqLRYKBSsXC6H4jSUE9e8KLXIshl4Nm2fNm4onZyjxDETrVYrgEM8UvQb4GX2SQi02+1EuwAmqiTElD8Puv26eWlSpWQwGNhgMEgYaXx7dI2qZUvzR32ui99Xuu9iijLgS8NbYa4wOLNP4YOrq6tWrVat2WwGi/ju7q5tbm6GYgCDwcA+fvxo79+/t6OjI+v3+zaZTMLZjsobAIpYBznTkzAO+oKFD2FtZqHaL0d+KHDW8Z4FMS4e0DOmqhggILWIkLYL5QJPMPnCXMf+9YaNmOfcC77b21trNBr23Xff2enpqeXzefv2229DLuzR0ZF99913dnV1ZW/evAnGAQo30HbtI/OnRXBiCgDtQDj6SBPvodVQTcaQ+8wqTzU2H8pndd/5vsWUszTw4j9T2anhU4SZe4OJNxSqMcnLApXHqiRpuJZGhHj+EFPyYu2P9UWjALwhOY0HvwTFeDefYVSJyUz2maaojMfjRPE2oikwlGo4mx8znqv7149TTC7qHlK5xdxjRCB0jqOavKdEvZO0gYgRbYPOi/JLNUAp79Hfms0uVHWafqPvetQT4D32OyV4CnlwjUYj6Bhm916tcrlsKysrob4CFae9AVCBAPyJonEKGODTyP1erxdCEDEQkCaUy+WsWq0mKrHi5fRVr7VfMc8518X2MzQr7zFy3vOnWBt0/LyO6+UNFYwB53h22R+TyX0qEHuWdCr0Cg0j17FKI/+9N9jxfO07QJaUK/QXlXleR9C5UHn7GA95Cr1YVVX/uSrOvHuLYez3aQtBJ51rPcCEWfvrnip8vXUlTbilKc2x++iiVYGdBjq+FJD03jHIM4WYBQpLGPH7x8fHdnJyEiqkttvtB2GHKJBsPpgpIakohFrMgudq22LjE7smk7m3VqNEFQoFGwwG4SgQKtmxbugXnrJerxe8HnitCCtRIOnbwrzrGpmVggNghPHB7Pw4se7Yhwrg1HOgZ2uOx+NE+JHvh99jMNDx+FMeRqfTCUKK8tK69nO5XMjPGI1GIQRjZ2cnhKYOBgO7vLy0g4MD29/ft3a7bfPz8yF3gwq8ZhasgmpdQwnAMEDIuh7zwbrT8y01RMsrsrMgzxeUr00mkyA4tQou7VcFm7WL1RuDgL+v3tsrm54n8DfhURiKyBHWozhQVFutlh0dHYXoAg8wIB+Nwl7UdUrblPR7P1aTyX0einocY160l6aY8qAKAWstlrM5jR7jIwrs9KwvinJg6FPFSNujoXD+GawXgAWeMpRPeKGv4utlb+x/3z/tSxoA9YaPWdBjylQsdE73FWMwmUxsNBpZJpMJ4fR4nVZXV0PESEzOIpP8PXX80hRB/zf3QGYBMACMCwsLIT8eHsNntF8jbpAfMcAR05/8WH4pPYdnekOu/5v3NM+pkoK3q6urEI5ar9fDGKonmXxDDOUYNpUneeMSReMymUyYk7S+XV1dBV0mm80GXaDRaFin0wlRVjc3N1YqlUKoZTabTYQ5+vFS470n1b2VB89qXqnuHov6Ur3Cyxj1KPq0JnTYdrtt/X4/GBaRYURJUZF4fX3dKpVKwpngPbd+HNNwxzRdlv3JteRhTiYT63a7YW1AGqXEntRxiPGy2JzpvD/GW39WqKpXdKYN1FM+e+6zUc41b9HffxqjjU1eGlDxv/GTohOpEwFz1jZOE/4xEDcL8hbmmJDRPquVZjgcWrvdtouLCzs8PLSjoyM7Pz+3TqeTCNXAUpPP561cLoeNp7HZOnaqrD/HAqnjFFPumS/AI8+HYZDDRv+4Hx6z8XicAI8IA++BjCkaszYGEEpBuzUXKCYYYwo540X4C+dnorTouoitXbWSAtLa7XYobkOYjF9fZp+sacViMRS+4X9CorCoYsUlRITQKvWA8LmZJYSAmSWKKGn4sSalc40qENpHmHGM3/xc0twhzwN0bAFDAAHmSIv/0CdCy9I8XV74qmKqOb0QoVdEERSLRavVasGjmMvlbG9vzzKZjP3hD3+wVqtlp6entr6+bsvLyyHfEfIAkn4peIyNicocbbM3VirwMLtX+Dy/fknybaat9MuHy8Ys/nqvNAUjRgocIT1eR72PWk0Tr5NW9/TtUC/FZDIJB4+neRy9QuXHJ2YI0L/93Ol3MaX/pcnvkTSQ63UgP2codMVi0dbX121tbS3IPoxXXgFXPqljEVsLKjM9eblEG+EnChLVyOpfeO2Rh2rcVeCjz1Beo3vbk66Xxyo5fg4p39N26vyiRwIGzO7nLXY/fnN1dWWdTsdOT0/t4OAgFC6hmBvFafL5fKLIn0aKaN43Rk3vkVUe4XkH82lmIWKI3Obr62s7Pz+3q6sra7fb4d7qYDGzxNm4Xo9B1nkg7fnsrCI4IMaJZyqfQiYreIS8h10L8PV6PWu1WtZut4NxBCM3x78RrbOysmJra2shQiDGmzz498Z23Xv6Od/xrvtdnwOQRb/2R8dpbQtNPaK93njq+U2aMylGnx2qOs0iMw0YmT2sROV/5xVM3Uj6XN30/vcxsJj2igElfx+9FxtFFTltM9+Px+OERyg2Xl7p0bF9jtLwXPIM1bcnJgBvb28TxWTU09jtdkMsNgsXy1axWEyE5qjiTlt86J3ZPbNgTGhf7D1tzU0mk4RSowJxcXExnM3DURM6NggH9b4tLS2FMFfAqA8j8XtjFiE4EAofwInnDgaDBOD34EHHjOsbjUY4HgXvXEz59mvdAxoSuRcXFwNopNAJ86GUz+ctk/lkkWfdq2XR7NM8k49YKpWCwUgVIOZV+0w7yffSedVwHy0EoV4Txoc+Li0tWa/Xe/F5VCtibK2rgqChqFzLWGk+sAogva8Had4AxnNUSeLenMc6Ho9tZWXFqtVqCL0y+5SjmsvlgqWbMx4rlUqILqANrAfIt1Mt3mlCVj+LKTAxOTBLSgM2qhym8Vi9R+wzPwYxnufvyTMVKGo1PzWY6JrSMQeMkguXzX46Jol7aSEn3lmvMVmaBh7T1v1jYzFrei545DOz+6iKWq1mGxsbweDoZYRXQlnL6j3RcDRV6GNt8h5bfy/2iQeSGlGD8qm6jspDlXUxuUI7lLd4Y5UWvJoFcPT7z4+VD233IMQr1Yw/dR3q9bqdnp6GCqdbW1v27t07293dDcVvfI6uFpPTNjI33pukckzbrlFNGIsymUwiDDafz4c0EFKL6Pf19XUw6PEb7qG80nv4PI/7EgRv8R7HTCaTmEPlsaxL1c206A3V/ZG9AEUcCxgSAP7q7PD7V3kVshLDLm3GKK51KdgH0/gZHmwq6Pb7/YROambBK0m6Eg4b2u35SMwQkKYnRtv0OZPoO6kLOKboq+KjTGTawksDlvpsD66mgUSerd97r+FjIJNnqLKqTNU/D6AFOCGJWTeeV9J8P2cJHHmWjrl/52/60mg07OzsLFRLbTQaAUix4LBMr66uhlxGPEUADCU/5tMsHgpcYkpaGuhmrCeTSWCSapGh6qoe0quhDVhbPTimZLa31qh3I7aWX4q0HaVSKYwfFka/X7SdKJS9Xi8Un2k0Gokx1vBTM3sgXFj7MFkq6/b7/eCp9fmhOifMvSq3ujf8UR+a3M/3uVwuHLQLANS9lMlkwpmOzCEJ77FCKrTXhxNhhZwFcNT96IEfn+se0zbzHddh4DCzsNa9kqp7xhvfYsrseDwOuTzNZtNGo1HCc6JW1Lm5OVtdXbXV1VUbjUbWaDSs2WxaqVRKhOBAft8qP9D16vdRjEd68O2VWlUovgR5majz5w2O2pfHgCXkZYXKYazrevQC/1N5GIOZekQ1bJV9SNGsm5uboJCqwslh1YANyIcm+z6k8UW/DmL0pcCjKopp4JH2KJBiH1AtHG+ter1V74gR88i8APC1yJG2S4GiN6hpSLFZMheM79SwqnOnPFf7ahY38MTmzYMRvT/H5syCvIKsn/M+zYjO9zoH/X7f6vW6nZ2dWa/Xs6WlJdvZ2bFf/OIXoQo4ckv7mbZmVJnXZ8f64r/HMKpGM/YhtSXa7XaQjRRpGgwGoRJ5uVwORj/uGWun35M6RrMEk2r08kYK1Re0yJxvI8CRyv9nZ2d2fX0dQsgrlUoi7xhjPAYAJe23Es/gCC9et7e3IUSZcdawb/qi9+d+6Fjw4Xw+Hww48G7kKrmQc3NzQU9FPwIDaW0PxgnA/RTQaPZCVVU9CPQDoCWHVfFUhvkccKf3VquaXh+7h7e+ZTL3ZcX1+WltUGudTqZ/Jkow1ZmIgTezxNlifvOb3S+ULyEUPRDn2bqYYJi9Xs8ajYadnJzYycmJnZ6ehsqKWgQF0FgoFBLnIpLj5skzoli//XdpYxNjyrF7q8dYwU8ulwvHhWgOkCp7WLOw/pRKpdD/afmrs6Jc7lM5dyp93d3dJRR6DwiUMUwmkxDqCijQUE4f889vdE/yXaFQsEKhEEBoNpsNlQLVuq3zwmfsMQ3bAYAA7M0eesl1Htl37C1+yzpWpRlvo/IU+oFXEUCq1n0tpPPSlAYcIc/Y05Q27YsKDFWO1Iji50Xvz70mk0ngY2dnZ9ZoNCyXy4WcVFUmWTMI406nYxcXF1atVm1jYyNRYdXvE78mPMX2lSoIZvEoFf3ft/OlaRqPYnw1VEwVkBhoVEobE+aLecejrvnKChyRS71eLxyVQ3sUVCIPfK64mYW/vfFIj5VRg2qsH6p4xQCZl+NfkqY9LyZn/N/z8/NWKpVsfX3dqtVqMFwpqPTyUD8jTB+vsP6tCrTKtBhg1GMeMMJpaCo6j+ox/m/mMSa/Ce3XdRgz8HjvGOuUqqPtdttevXr1nCl6Nvl58kYyXp5XoAMxD1RdPzs7C+CrVqvZN998Y69fv7a1tbUQYcO9Y4YSNdboXsWopBVXtV2qM8eMgRDF4/A+dbvdRKg6UQccAM8z1RvKGvDGCZ4/bS+8JGE8USOitoF1H8MLyP/BYGCtVisUbpxMPoXcl8vlUHeDHFDug+cxl8tF60eoIQQvNIZzIg0ZYyrKoy8xXmn7BsKzqAYCDSHX8UDn4Xgz9i5efeZZDYMK/GNG4xh9dnEcL+j8dyoQOJCUMA0FajEGpYvAexhUmGissV7nv/Ov2BkrXolURqz3VYGvbdF3Cqvg2ajX65bL5UL55JigYbLSlKmXJl2wPAfmpMoX4Q2czXh6empnZ2fWbrcThwnD7KhkymbENT9tIaIUa3/TBJD+Pc06540X/nruDxDwRgKYjbcQ8/9oNApCj0JAPnwnbZ5fkgCweHNpsxf23kIMw8GDCnOiyq0aOCAdB90PKI3kHc7NzYX8t2KxGEBdbC0z74RJ6fEB/qVgRhUe1p5X2nkexWK0mq6SKlyE4zGeGnqHIjYLikUAeFI+o+tM+6NCEwUym80mhK72WX+jwMbsXpG9ubmxTqdjh4eHdnJyYqPRyDY3N219fd1WV1cDwNY9hMDFw1uv121raysUZfB9Vf6j36WNh34fs4L78VJ+N2tvYxoP98YbHybsAaP/+yk8hN8QJYFHkX2FsohlmoOl8TLB8zk7Dq8+vAWeoOej6gHYqrxxT+2/HxPP72N9ec5YvxTBV2Jryix+/ITuOzyN5XI58Ixpc637miiQdrttrVYrRMNokRM1jPBMHwnFHAE+9NgHDya1DSikns/HSPmFjgHXqyKvYzUej0Oo5/HxsdXr9ZkCR933MT0rxkO0rQCDbrdrFxcXIT1nYWHBqtWqvXr1ynZ3dxOeRm/4g5hn5pLaAkRmDIdDm5+fDxXHMbyzl9S5ENO9tU8AWPYjhn4MSBxqr0BEvVP8lqgVTzEMMAuCz9Nnr/t5LEF72E+ARoq63d7eWqlUCmCRiv8+igae6I3W2l+8uK1WK5z7CHhUr2A+nw/pAOvr61YoFBJ4KE2O0X8ljbLSeiXLy8vBcDiZTIL3kzn1RZZiOtNjoNHsM3Mc/SZIQ8l0MJ/PW6VSsVqtFmJ19dBTJp2XB48xgBmzjsUAnwd1KtT0t37C0kCn73NMCBDKwAH0nNuki0SVeFWIFdTMkmKhEWZJQTiZTEJVSzyNFMHRUsDqkSmVSuFcN84q0oXoF6UfC3/NUxSDWB/S+hz7HeCLcVdBgSClXZPJfY4eVblarVZIftcqlt67Pgti/TIXWLd0TdF39UyY3ecNmFnIR2WdwmwQJpo7B7gifINkcxK2KYRULpdtaWkprJEYU1cwSqU/hDUhc8rcvGCEdM9q3tVkMgnAkTbyW+UVmkTOuHoFSkMyX5o8oIntAa+QqRec77lexyptP/hn6P3UgNLr9ezg4MB++ukn63Q6trKyEsLvKHqkBYPI5UDh6Xa7dnZ2ZtVqNZx9hefKG6/Ue6k8SteNKnSeX+o46prgGZp/Nwt6DPjH5AXf+eti38Weh/KAEY/iZZzx5i3gvDSqQsOZvbGG8dNre71eAI4+H06VLt1Dn8MDvTyatVxUwpDhwR3tYp/ofGI41QqMZsm1yP/cE5BKWoufN63+rdUT9b7IqZgRTAEjBh0+p6BY7HgIH2Lp160WHtEIBj+GFPGAD1PRnCMjzs/PZxL+D/l2YSTV8cOj6IGjpquMRiNrtVpWr9fD2YwbGxv2+vVr29nZsdXV1QeFZtKer2PX7/ft9PTUPn78aMfHx9bv921paclqtZptbm7axsaGVavVUIVX8xk9uIiBR6qCsifhA5lMJqSG9Hq9wAM0BD0G/CGvA8+SPKCJrX9/HcZ/vMQU/bu6urKlpaVwfjg5jeg0GF80MiaN/2Cgo9BOvV63er0ewKMWLqQ+BgXm1tfXQw0I1k1sHNMMraqboevAbzD0IZuHw2EwJKjsixke/BqK0Ysex+GJRqBI1Gq1AB4pVby8vBwFaDEw6FF5zPOn35k9tPL538SYufbzMUHvGY3ZvTWA0Efis/3Ee8VOlahZkzIBP0a05fr6OlRPPT09DQxTE/QRnqosctQGIMRbZ3im7/u0tnqKGTC4T9ozFETpbzBuICx1fLLZbGCyKMcIZmVMhLhyja6rWRIhpVgOAfSqBKIAKQhj7pg3KplyD195jWctLi6GUvILCwuhHDm5HuVyOXijfHgWoDxNyQeUwuhYZzovmgjPGGtb2UM+F4hr6KNXuFBuaNfd3V2w1urampWAVHDDM72g5rsYb4v9Vr0B+gxdEx4ssOb5/urqyur1uh0cHNjFxYXNzc3Z9vZ28B6qsFGQncvlbHNzM4Szdzod29/fDxWJtZCTCmUda83j5L5+HUO+HzFertfNEjimKY5+fFTGeYrJHP8cHTOtQEx4sBaAAIRQ+Ao+4WWg2b333+xeJqlXCWUXoKj5cbyr4WUySXo00sZJ16Ou/2mGkC8BJFVn4PmsS9UlCN+u1WpWLpcTobxeL9G2M571et2Ojo5CCKRPldBne3kJMOM5yGRAPRXBh8NhUJKJLOGlAFKN3EoKGlFYNWonplNMJvfnJHPe8/n5uTWbzSA3Z0XT9ArGCblEBWKtVok87PV61mw2rdFoWK/Xs1wuZ7VazV6/fm27u7thvv2ahlTu8o63sdls2sHBgX3//fd2cHBgo9HIlpeXgwey0WjY3t6e7e3tWbVajYZ/+35pO5DbMUcLuct3d3fW6XQeGPFiPMgDxj8FcPRyJyYrMQYQbYSORN4iqTTsB18MKjaX+jycCziJ4L2EwyL74H/5fD5EqvHa2tqyarWa4JdmSZmhUYBm6QZz/V02m01EJsBjxuNxtPJ+bFyn0YvkOMYQqnacQSN8o1qthqIp5MjEgJi+++fp32kTHPuNX/Sxa72AigktDz5RaiaTScLyB9PRoipeaftTkFc0vWJ1fX1tnU7HLi8v7fz83C4vL4O1RosumFk4Vw/Xv55V5JlKzGr0XPJgPbb+vGdX+xrzerJOVWAwHh5oqmFDlXTGAu+jVuCaVXicZ5rKDDQ0T5Vq/W5hYSEo8+PxOIRXkE+Dkg7ArFariRDFTqcTigRMJhN79eqVvXr1yjY3N0OopyrqKBsIJs+g5+fngxWP9eUFvAIK9ZSwNgmzI3yYe6jHUD11KL8o4HpvPtOqkbMg5R9+L/o96vdT2vrXe+g4wn/MksYqVXLxgDQaDTs9PQ3FljY2NoICMz8/n7B2qjIyPz9va2tr9vbtW7u+vraPHz/axcWFnZychDAdvJVpgFn7772j+tm039EvD0ZmpehoX5RiwNqDCn+tJx0rz6Pu7u5CZeRms2kXFxcJ0IhioxWTPeiBdF+hfOgaIRKj3W6HPauFVTSKSMeCZ6WNke+XhuSlGVFmScp3vGLmI5fm5+dDioYWGokBc+57d3cXQhSZL85B5kzUmLIco1i0AikVAAdSGq6urkLYHHJcI4ioR0C//Jio0djPDXNGiB/G516vF0I8iVri/N1Zkl83+rd6x1nzXq4zR2dnZyFFJ5fLWbVatdevX9v29ratrq4G3YExipF6iwHSFxcX9vHjR3v//r29f//eTk9P7fr62gqFQihERgFC5XsKBGPGCN8GBcTKc/g98p65IxqQ53kDr/79pYCjf/l51f9Ze3qqgRb2Wl1dtZWVlWAsUU/jNJ7McwCNmqOrQB/gSK744uJiaAtRBBoBxbM0R1yfHfsbL7/X37kOr6M/QscseQTL59CLVFWloWrpVaUeBkQiKh4JGKxPvE5bEGkALu1a/5n+H7NiPvbSftE3wj9gQjAFEH6r1bJmsxmKhqQpLX6DfglAGVtwbAqO3cDt3uv1wgJEqQcQLC0tWbFYDJsRoalj58HaNCDlY/inCUxtOxSzpOi9vZDVZ7DhyNfTs82wzPmwOXIfWq1WwouOEUGvnwWhGMzPzwcLmPdex4Cj9h+wRqEcGNz19XUIc6pWq7a1tWVra2uWzWZDpWCUAKztWGDxBiKceEa/37ds9lPiOXtHlUssf1pwSOdIY/r5HeGyeianF2p4UgCDKGdY5CFVqBk7jCWzBI48y+/LNOGsPClmDPGfqcfRC38Fy2YWwmkwGo3HY6tUKra5uRlCVBHO5MyYWVBQKWO+s7MTckzq9XpQhiqVSsILpf1VpTQmsFWB8vxLlWb+hxepJ37W/DVNmCuQUCVOf+fvMU1R0rB6PLt4GjkAHMOl5t2b2QPPoCqHKP7sNYAm65/9p+fbYjAjzJ19fHNz82hucEzeqoFV9/OXoJhy7A0wKJpa1Xp1dTWRj+bXqdn9PhyNRnZycmIfPnyw8/PzAOiRG7pedA94Hh6j2FjqOZ6aY6fAUatWYvzVwmi6B/U+jI2eU3p1dRWileAj7XbbhsPhC89WOqXpqrr/WMMcXYAeR+7a+fm5HRwcWLPZDOGpr169SoSnehmvOmKM31xfX1uj0bAPHz7Y73//e/vuu+/s6OjI2u12eK56stSIamYhDcSvFb3GE7LO8xLmDH6O4VXnVQ0+Oo5fak/qc+AL03Qcf+QQ44d80vOgY172aX0aj+/zwPEyKmAENGolW1+UTB1JqtcQJuz3vteJdUwUdPrPGQv4eDb7KSzb56w+pmN7+mzgmIZ0PfG9hjNyKDhWGq2AqPdXEKULJbYR9Tf+3f+tDC92vWeMvGAyk8kkhIWQwIyQxMrXaDTs8PDQ9vf37eTkxC4vL20wGCQUGlXg1LLpwc0syAszHT9yZOr1ul1eXgbQqEoEisTi4mLwWOH2V4uJbgxVTh4jHSdPfvPoWol5Ac3uQ0NiFen0XYv9AIy90qPhf3d3d9Zut+34+Nj29/ftp59+so2NDVtdXQ3J1uSVzIIYC5Q3Kml5C5OubWVUAKLJZJIomIFnAm9yoVAIFjrCYynYgAUWbxSl59WahuJCvoHZpznWc7w0VHJxcTGED6nw4jpf8IO8Le23elNiOdOAbeZHi38ogFFBqnP/kqQeR8bMv+t+1ZDdmADXz1T4aNi8N5h467X2nWIN5NkgPDkEu9Vq2fX1dTAekLuRz+etVqvZ1tZWmBuMDXgcldS7ixHHywX/N//TV4Aie5f9EFM2Xpp0HlTRmQYanyJHlbiG+SRvjOIMgMZWq5XwMjKXyDL1snBf1gHjp1ZtrsFyryXy1XOF51+NEapAe5kQA426D7znkXZ8CQMAbeCdF2MDaCSvX9MzPJhgb93e3lqn07Hz83Pb39+3o6Mj6/V6ifFC19BDyJFhWinX8yP4lgJawJAWxmBva8ipB4FmFrzHFEDTMYcfKp/XcOnz8/OQ4gJgTDO8UWXypemx9aH6J+O1tLSUOLbh9PTULi4u7O7uziqViu3u7iYK4fiomTQDD3Rzc2PtdtsODg7su+++sz/84Q92eHhorVYryDstZMXeUnlN1AeVOmPRXTGeAnj0Og/GJDUmsJ81fNODDe7xJcCj6nY8z/NzlUuEg2IsYU+USiUrFothX2h9g5i+qcT9Fdi3Wq1wvMfZ2dmD9a5AFiOD8mKzpNzjOBAwk4JAHQc/LoqXWHNqyKHtWkArpktzj2n0bOAYY+yeFADRYDaB90AghHwImT+zTGP9FUDGBsqDPv2tJjvrK3at/5+JnEwmoeLc1tZWsFbx3XA4tLOzM/vpp5/sw4cP4egK8hZ0jFS582M4S1Jvio6lVqDynlIdD7P7c+2IFwc0AohVMXisT2nXoEDG2q8bwt/LM2q8v51OJ1FJVAWvAg5NQuddLU2aO8faNjNbWVkJjAnAhTd2FoSCFwNgCg7U8gTDwMhBzgagUftTKBSsXC6HsIdWqxUUk2w2G7zL+Xw+VJbDEooSn8ncH5UByEMZJLdA1yMeD5Qg+oAlGIXVHzmgIbA6n8wpggPFTqvB6nrVHEvGbG5uLnjSWq3Wi8+jV4zNHgLCNMNaWqiSV3TN7hVGMwuWzWkKPfsdpUrzeDCgEW7V6/VsY2MjeOyZV850xTNN4Y9yuZwQjGrMYP1AfmzU46z8QUPNFPj43NhZ8dfYnCmPnwYap1EMWLFO2+22XV5eBsDYbDZDARKs2+Ta393dBVCiXjPdZ4wZezCTSZ79ZXYfdufbqCCE52j4lYJA7ZfyLPgy869jFhuXWZBXpvR/VdIJ9SfaxucP+bUGaDw+PrYPHz7YxcWFjUajBI9TuVoul0M6j4bGMbcaEUP7MPaRElQqlRKGO7wwKLPK5/yemkwmoVou64E58mFw8OR6vW4fP360o6OjkOc1zWC8sbFh7969m8U0PiDtF23S+aKfWkG10WjYZDKxtbU129vbs+3t7WAcjUUNKOnaNrNQofr4+DiEpx4fHwfQ6PmU7nXmS48/83pkbM359rBuuYb76nFV/K3h5t7AFLvvLPmq8gANk+b5qvdTOZq1Dj9Tb6PPaXwMNDIPeGUBjo1Gw+r1etRI4sOT4X9qwMVQwBwDHonq0fFOa6OPvqGt+nnMGeJ15afo6mafCRwfu7EuXjxYl5eXdnx8bGtra6EQB4yW6wi3If5XLZiq6HvPoweKKnz0O/07BkZj15g9rP4IgymXywlrEEAYhnN6ehpyA4lnVo9Pmlv8Odbnn0u68VAqqQZLqKIfMxafehs1p0/7oQwlZq3i2ep99e1LY0p+0ZslFRqEWbfbDXl45+fngUn7Ywz43dLSklWrVVtbW0vkqqBAMb9a+AjLO5uSMud4XfL5vP31X//1505TKiEEUP40nFKZrK53XxCAcxfVuAMoxBLc6/Ws3+8HhrO1tWWrq6tWqVTs6urKSqWSvX792jY3NxMKp4I9jgagHTB3Dc1gngEUd3d3wTBDngD3gEew/6YJTfYb91HjB5Z45g3vJSG1hULBarWabWxsWKlUsh9//PHF55H1R8ESFdTMM9coDzS7L9qTBk7092mhkcrjMplMOBKAYliE4aEsEerS6/Xs/fv39vd///c2HA7tl7/8pdVqNatWq0FIAk7G408l+DOZTMiX5n66v7X/XlB6RcxbSbUPXKeeYsb0MSXhc8kDduX1fl58vx/j9142EfatZeDxOMK/tRAD+5r58Ie8x9aR5rgpeIztMZQjBTOqBxAazufeeKl9U8XHy/svIR9je0TXJPJPz9Al5C3mYUYWtdttOz09tYODA6vX68GIo4Y48sjX1tZCZBYRHp1OJzwnk8mE4hvIHjz+W1tbtr29HYq23N7eBu8zOa8YCbWgmu+7ziEGSuaZitlaIK5er9uHDx/s48ePdnl5GfJhvfxfWlqy9fV129vbsz//8z+3X/7yl3Z4eDiTefS6g/JRrRaOfCcfs16vW6fTMTMLoHFvb8/W1tYeHFnxmA7H+m+1WsHT+MMPPwRPow8N5TdEwKjhlWuVqAjqowjSiLazdjCIa7oKzhHl95Dfe7MCjEraDniqB1aMmQJHxgqDJuGpetb1NEBmlixmpEex4W3kqLpms5kwxigBENVwX6/XHxh0tL3kQqNzm6XjA/QtBYpaGVZDY/P5fAL3PNeY+rOqqqYxV94ZnG63GyZAS91irdNqjoRIdjqdoLxRsMN7Aj1wVOHDwKpSoZ+p8hGz5vK5AhPuzdk9tVotMBDPQDWfRM+l0/FKs9DE2v3SlGYtZkPgaYyBdBbo0tKSraysBOsIc6yC4imL0fd12m90g6dZMZm36+tra7VadnZ2ZkdHR3Z0dJQAjihKGoYxmUxC9bm7u7tweLOu56urq5CrxSZHEGUymVD62CuQswKOmUwmMPxms/mg6ABzqyG6jCOlpJXwIMJcCHm7vb0NhY/IdWO9UIK+WCwmQiBg7ljLGXddbyiwrCHWAYU2VlZWgkVf8xjVqBFjfAogUEJR9vg9+9bsPgSZNuNl3NzctHfv3oXczf/5P//nTObR7KGRyoMN2qseIn7nwZcCAi881fOjaxTPcqPRsP39fTs/P7elpaUQiseh0gsLCyFH6//+3/9rv/3tby2T+RQy/ebNG9vc3Azn9up5fhQpOD8/DwqYL9qg/yt5QafKD7yAcfAywBv+ZkV+z+tcKE9IMxo+dl/4jB5t0Gw2w0uL4SB/MFp6Y6W3UqtMREnzpMoQbQEoaug4L5RdnoOs1NBx7qUvjczxYdz++llQTMeBstlsyOGlMiOKu0ZP6RqF152fn9vR0ZFdXl4GIxF7cGlpyTY2Nuzbb7+1t2/fWq1WS+Std7vdB8UzVO4sLy/bzs6O/eIXv7Bvv/02GPEwAim44LeEPvq5V/6jyjX/U/1aozMolIax3J8DDBWLRXvz5o391V/9lf31X/+1/epXv7Lt7W377//9v7/oHEJpewwArBXvMZyfnJzYxcWFjcdjK5fLVqvVAmgkQiVt/ypIViWes3AJT93f30/oIrH1jOwEePLyBv9KpRK8aLH9raSyGe80hlh4Rq/XSxRJUrmk9/D9nCVvVSOM56EK0DFSc24zOdZ63iyg0ddXUZmp7wrc0e+pngpobDQaiXS0GLHnMOD7XMzRaGTj8Tjk3dJf2hnTdVTP1u9YM8hedJ7JZBJC2BU3qf7+GH3WOY5pN44pbmzObrcbkK4ex4HXkQFgAet5Rr1eL1gO0jyOKmyUdCN6pO43qgc6CpT0czxJhCPqeXa6YLW6o2722Fjp82YpEPVZ/n9ARLvdtn6/n/Dy4i01u1c+yeGbdjaMZyxpY+Atkv4a3Qx679h9J5NPhQewBB0cHNjh4aGdn5+HvvnNrZZwyoNzb3JYURgo50xugFot1ZL5JQiFhKIYhC+lGU9ub28DI2HOEEZ6DidHbeBpHI/Htrq6aru7u/b27Vvb2dmxQqEQwnjYy+RyesFGiKqCPZgVTNLni5hZqFZYrVZD2BOhrszRNIan60JBF55h+JOGABFNUC6XbW1tzTY3N21nZ8fW19dDTslLk+dpai1UD7EC5pgSqYBQP1MDhxZ/4DotgtHpdOzDhw/2/v17u729DZWw4XVzc3M2Go3s6OjI/uEf/sH+/u//3g4PD21pacn29/ftu+++C1Wz8SiWy2Xb2dmxfr8fok8QjLVaLawbLWCk8+d5s09vMIvzd33nmjRe8xLkZUWaAcArntPayj34Ts+PpRgDBRm0YBUREcw1HiOUKOWpvjCKGgh9/zC06O9QqlB+yHVbWFhI5PJo3h5Ft3QNe8Mw6z5NsZ41cIS0BgEh7ugx5MX5Uv4KGlj3HEPBGWvM6+LiotVqNXvz5o29e/fO3rx5Y6urq0EedTqdMGe6p9EtstmsVatVe/PmjX3zzTf25s0bq1arls1mEweA81vle5pH55VyX3lUj2BBzul9WTPa1slkEqosb29v27t37+xXv/qV/cVf/IX94he/sK2trZnxVch7q1g3yHANFcSLRDRMsVgMhlIfgTRNj4OItDg9PbX379/bd999FzyyHBHhDSNKgKG5ublQXZXP8YLt7e1ZrVazYrEYvp9WIVTHhT6iv5KixDPQe5Tnxu41S9CoxkHarfo+fWQ8Op1O0PUwRmI0Rmf1ETgeQOo+QUfC61+v1+3w8NAODg7CUVVglKfwJHUkse/UiEOfqUCOpx8eHiu4qLoy1y0uLtr19XXAWEQRErKrhZWeM3+fBRz1nQ5OIyadpOCTkxNbW1uzSqUS4vc1H4YkbJA9oarK3GLWx1hbPHDR9uhvH7OYwDTz+XzwNqoFnon3gBGBquFS08KknjOuP4cUENN2Qp80VFiBI2CIcaCkvlowY8A79uxYG7w1a1rbzR7mK6hidXl5aQcHB6H4AKBRN6aSfob1lI1HlTw8UFQVLRQKIfTHh0V4ilnvX4IArlTBJRQCLwAgSRWOyeS+CAPeIDMLlVM3Njbs5ubGTk5OQjghRQHevXtne3t7trKyEiyl5A6gBCpImUwmIXSEfaBeBFVeNGzR7J4BsufIjQMc6z7Staa/V0UI44gqNniQGcf5+flgXd7d3bVqtRpCsTW/76XJh1jTfrWyqnfHt0P5Gb9VxVWBtr6zFlD+Wq2Wffz40f74xz9ao9EIoJnjN7LZT7lSx8fH9tvf/tZ++9vf2v7+vpl9WovHx8dWKBRCAQksu9Vq1ZaWloKi1mw2bX9/3wqFQqKgiBoC0qzZ2veYhzbWx5ghZRYUa6u3jsfW0DQwq79DxpDXeH5+Hipf422kIjKeoEwmE/a55vVkMpmw7lhXfv2gdAF0AI1aSwCFR18Ys8wsgAwAEgbXlZWVVM8y16MgqsKvgGRW5HUCeJEqZOSw5/P5RG6jeuzG43FY7/V6PXiYlObn561Sqdje3p69efPGtre3Q3EpnXfloyj5+XzeJpNPUTLb29thr6pBG51Kz3REL8G7BG9RfqA5sNo3xoDfsLYoErS2thaMhBxm/+rVK/vNb35jf/mXf2m/+c1v7O3bt8Egy7FNs5pHs/sjl1ivyg8YW+SWevUIHV5bWwseODN7VOfhuXd3d4kQ3h9//NH29/etXq8HJf6pYENTqzTnUaMK/L3Uo+b5Dtdls59qDZTL5dD3RqORqElCzQfPx7wON6s9yb19ZAnf0Ud0oXa7be12266urkJBP1JvKIqjbU3TybXwF/rxxcVFAI37+/t2dnYWKgWTMvTcsUCXQs9mHkajkW1sbAT9SnUQDUv1a1DB4/LyctBpMTZR50MNAuhDT2n3ZwPHWMhgmnWYhYXljYNuq9VqyJspFothAeNy99ZXNoxXgvSZsc/0Hn7xqTCaBh4BtWtra7a1tRUO7lSPqR4JACNUD6mvLqduZ6/c6PssSPsLqCe3C2amll7eUUIIZdTCMTHlR61j+n0M0GvbpoVZeJDpLd8cqPvjjz/a8fFxYNIqsGNMFKFodh+awJzgCdFwJF8NWMNKtI9PyTn4XCLHBcaj7Vcvt+4f5halkPZzXA6AGM8buSi7u7u2vb1ty8vLNhgMwnljMGQtjATpusKLRrEFhB/KFcBTx9TMQnGVjY0Na7fbIe+OPtM/zy9UYKrSM5lMEsnrmUwmRBBUKhXb2toKHsaVlZWEwJwWhvJzKE1pp186Vp738r0KUPpp9hBMKq/hs/H40xmehFK1Wi1bW1uzN2/e2NbWVsh1vbm5sWazab///e/tt7/9rR0dHYV7ZrPZwN8PDw/t5OTElpaWgtI1NzdnOzs71mq1QsGqZrNp6+vrQSmjbTGA5a340/rmx1DXRxp4ewny8kafF/tf+ViMVFagvHAYOWf/cV4j0TnkAROWhFFWQYA+0+f7+31jliz17skbFwG3aiVnfefz+fC8TCaTsHirPNb59d5xfZ+lohoD/VpJlXAvPbdSr8NoBsjXc910/y0vL1ulUrH19XWrVqtWLBaDgqgVHAljg0dqUT6iMjgOhDHXXGXST5hPwADzhFFADYq6ZjS0D0O6huxyjjP5y+ST7e3t2S9/+Uv79a9/bd9++20I+ScsPs3Y+hKk8hv5hH6m/AJZoBEe6o1TORDz9ui60TXc6/VCocT379/b4eFhCGnUOgsaju2BnxaW0qgZBRjwfy0qCXl5GqNcLpcIWaVK83g8DkdYILfpp+937POXJOUL/O9fRGN0Op2g72HwV9AIv1HyOqW+rq+vg6eRtKfj4+MAsGP6ozdS6J7hOm90U31Zw2OpDbOyspJIrdJ2e+J5tAeDHbrPaDQKfOCx/ElPn+UG8Up9bBK8co9yMxwOrdVqhVLNFCBhcgkBwZvV7XbDhogpw9qGpyzapy54f2+YCIf8VioVW11dDW3H8kOVJYQ5yeFp7YuNW6wNL01eKSW+XY808BsIAkTpBvAgWPsSUwZ8X5UYK11nammm/TwXy8xgMLBms2nHx8f28eNH29/ft0ajYf1+/4FwSpsLFCwOieWF4KAPo9EojAMWXECjD1XVMN+XJtZXLpcLxhcsvX7elNRyDCNVwTA/Px/CJF69ehUKLRQKhWDV6/V64Ygdno3whSdgpSPRvlAoBGMRYbZEFAwGg4RyotZiPL2AR3IXdFx9qEkMIGDAgiHncjlbXV0NYHFzc9PW1taCh9ErCdOiBX4OqaVYga7ZvRBBadQ28DnKuH7vLcJqVVTPs9kni+fZ2VlQbpaWluz169f2zTff2OrqalBEu92uHR4e2g8//GAfPnywfr9vhUIh8BE8koBHlFk8WisrK7a7uxuUE9YHRUA0/M23V/ukQEL5joJrVeT5LdfPypDzGD2Fp3vepIY9Dpvm3LBmsxkUJcLK2W+Tyf2RGvBrf7wJyo2OKevMW+SV/8X4sP6evY2nEkWd/wGN8BttF8/T+6tyGJMxsyLPR8jFVsOpeuT0N+g7pN3o2W28SPnQAnOACCpwMsfqnWJOV1ZW7O7uLuhLmn4AaNVzmNVDrwWSaC8RJKPRKBhK1VtN+1hLgApAIzlyROq8evXK/vzP/9y+/fZbW19fTxgNFYjPKiKH9arKtnc8sG6R2Xrun4ZWe57hHRN6T+b+4uIihP1zXqdWqte1w9wQ6cX8UAxJvfvIMMAdz/ZzrMYfrT0QM7pggFhdXbVisRjaqQXpdN/H9uAsDTleh9TnMzZqZMEgrEfFqQdW5aifR416GI/HYS4PDw/t+Pg45DSi12BYYX8AzNSQqV75UqlkmUwm7G99Hsf50Ufutb29nTD4oKtOI9Yse3RtbS04BeARtH/aeHj67OI4/sbeYuhJreYUFzk5OQngi1AK9S5QIAdvhYbA6WaJAVd9rrcecr2CJ3+df3kvE25jcnNQpmEUv//97+3g4MDu7u6CR1K9HNo2314/1rMitabohtMqsTruLH6EiCr4MQHP/956E2sHpGEyPFMZvP4Giy7CmURlvB3+7MynEOfV1Wo129nZsd3dXdva2golzZlDLTOv1lj1ginNiqGqBUvXigJ6VbJRGAgBzWaziRAX1iuFS4rFYsidwHKJ8Lq9vQ2WvKWlpfA8ZXgYI8w+HVXCGa6Li4t2d3dn+Xw+cdZczALGultZWbGNjY1E4Q9dm7oOY4ABAcB4VatVK5fLtrW1ZZubm+H8zeXl5QdGEGiW+9Hf3+8dDybVS6n/++rG3FfzfMzucyuoBM0B3ZlMJhQEWl9fD2uEEPD9/X07PT21q6urxB5Q/sk5bo1GI4R2m30KW6QaL94pjubwylksZFXDrvU69bDc3d0FwargkvcvBRzTlLSnehq5FmWCPObLy0ur1+tWr9dDXqNWy0RealglOcSaj8Z+j/Fl5d8qA/0aVHCvRhzmgfvSHhRylcWTyeRBKBwKkVf4vWyZFV+N7R9ADqCIPE01UKg+ojUDSLnRccpkMg/OWiS0kXBj5hcDITwYIJ/P583MAiAbj8cht5VjtVqtVjDG+HBD5kD3mobze+CoHkraQJG8arVqZhaOniqVSra9vW17e3u2ublpy8vLgU/Rf73vLIg1o+tX96XKBnSJer1u3W43hGiyb+CdXsZ4Ax08tdFohMinDx8+2MnJSUiX4d7sS9YL1Y/VUwr4oRo4a4MoKor5KPhXeeH1tLQ9o+uRooeTySTMjaa+cD30JQw5qqMrL/AASwvMcBwaUYza9mnjwTrV8NTj4+MAGlutVgCG6nmnkCIFCpFx7DXqSGxubtrS0pK1Wq3AP3y4dLPZDH3TUFtCT9nvMUOxrkd4KU4vxkAjTzDk6BqeRs8GjtMs7h400Bm1gMA4B4NBKF6ChUM9jhsbG6FCKaXFeb63OsSALN+rQNQNz30U8OokKLNR6xxKtyapAohh1GdnZ3Z4eGiXl5fBWwWD58D0mPBLU/ZmQfSZ8CeqY2oIhRbFYfEiNLUkuG+zLl5CQ/TICrP06qiajzGt/xRc6Xa71mw27ezszE5OTsIBrFh4H9sAKlCI8yevCw8UlVXZXCTNY4FVr92XJizWMCsf8qjklQRyS9T6m8/nrVKp2Pb2dvCyss4VqI/H48QxGd4Yo4KQg28pvEOBAZhuJpMJDHowGAQA4RWcpaWlEEpK6BWWV0gVAYwe6tkws1AVmTmu1Woh5FxD53Q+Zy0YGX/9H4bO/7RLlWi+07n1bafd7EsNLye8HvCRzWZDQSDCyeDfKFWHh4fW6/VC7uvCwoKVy2VbWFhIKDQUbSG8mXWkxgGiNLrdrhUKhQRgYD3RR7OH51Xxmf/cW8E1ImLW4F9pGmh8yu9Yx8rr9LxGQlTZO3gHMplMwrgJv1YDrj7D8wzGywNHVdpQXrQ/+ns1QqLIqTfYh+Fh9EjLd42BxlkqqrRLdQCAI6DRFyDhbwzJzI2OBW2m/1rkCkPKcDgMcwyQw8MJGMAgbXYfjsjvCa3jb5RWBRG0VY0pGF/gC6wdCh8tLi6GvEbVg5CdGN0UFM3PzyfAKHURVJea1Z5UB4OCf9VPzCxhPDs+PrbhcBgibjR/1Rt0PCEjcYx8/PjRDg4O7OzszNrtdgAbRDSVSqVQxZ0aBcgz2obcW19fD97g09PTYJjjmA/P4wEZzIn34kN+//Lbcrls4/HYlpeXw9oiYsDLqtjfL0keJOpL51B1TDx86CixQl++zcoP2cOEpx4cHDwAjWYW5B8pGUQKEAnH/lPjJYYWdLdcLhciRpD5t7e31mw2bW5uzlZXV21zczNx/J/qW2lA2K97Uk4mk0nYjzG99bF5fDZw9PkRfvJ0Y8UmhIVHaCfnUDWbzVDWGnf59vZ2UCqw1uGC1g7qhokNgF9kj7moVQkxu7doY1VgQ5I7ouXI8dhQTILKa3d3d7a8vBwWnBeCOl4e4M6CFDhShEjLF2veiypoGu/uvUIx5XY0Glmr1Qq5aRq66xUW8gnK5XLCDe+VGhSpdrttx8fHwcPIMS5Yd6cpFTxPLcelUslqtVrwMnJ4Mgot4WIARTUkxKxvX4I0JM/MgiGAsEFfDMPMQjgLyoCZhb7v7OzY1taWVSoVW1paCvPIb6kqRtghIYa6hhEyFAxCaVSvB9cR6gtAwVgEuND1T6hrtVoNCrMWcPLrSr37Gsmwublp6+vrtrGxYWtrayFvwCyeO/clrKmqvKkA8OPqCYOORgDovtG2xwCC5rQQKqyVdRkTFaJYzjl64+3bt/bq1Subn5+34+Nj+/3vfx+MOMfHx/bq1asALOkjwLHT6djFxUXY9/pM2hlT0nRcVAFGEKbxTbVOz4LSlLLnrh0F9ZR+V8BI8QeMffBtxk29QxoOB1+PgUYlBUMKLjyI1GgLBVEKeJGPGFk1qkTXNZ97BVdlypeimHECnh8zGOoYIVcJ5ydENS3iRnUjACZ5rO12225vbx9UgmS8mT/GDJ5HVVvNLdc8Vd9mnQc1KAMeNXxV+68gVgsFqTER/nF3d/cAcOtYz4oYJ90H6nnPZDIhvePs7MwajUZC11NvqPIiz5O1cBVA4+jo6AHYUAM14bukZCH/zO7rQhAFtbOzY5VKxa6vr8NRR3d3d8GjTLQIkUKcBUqEVEyOeb0FeYLXEfmN4V/ljK6jWZPHGX4vkcaG7oquin4Xi8KJkY+Y6na7oTI/9TKIYsPAVSgUbGNjw7a3t21xcTEYtJGnCrK9MaBYLAajFIZAIrTwnqI/YxTUwofwm2n4x+yef6HTqYdRx/apkTjPBo4LCwsJayWEUFJm4AUNpFWrABdqdcZatba2Zjs7OwEImN1XwUpTDPznXhHUz/jcg8SYwCQshDArQvdgsggGcsNyuVxYAGafmArhWQocVTjqJPPZLImxJD+QRemt9cyTWgxRAn3bIQohXV5e2tnZWeJwVA03Yy5Q7HHhkzflQbUy5+PjY/vw4YN9+PDBLi8vQ24P4VBpBGhkLplPQlTxtngvlNn9sQUoDZo79KcgwkY1PAolEsMF7ZxMJsEryfUIyJ2dHXv9+rXVarVgEWatwtjYq51Ox3K5XDCK4B1CIKMEUWhJrduqnKiVDP5AeBdj6kMX8Vxubm4mCoEQGoLCCjDA0FGpVOzVq1f27bff2t7eXihAocDfC0Jv2ZwlqTIOY1elUBm8EsxelSK9h5J+poYjPCODwcCy2WwQtqocEHp1fHxsJycn1u127e3bt/Y3f/M39i//5b+0b775xsbjsX3//feWyWRCQZCPHz/aq1evQgEcQCMRJpz7RhgN65ExUSCiQh2lSueL9eHBUMw4GFPgZ00x8GsW917oXuPYDS2Ew9onbE0rIGoYHEo/99RXGnml0Hv/ze6VcT0fTZVKnSuMk2YWeI++uL+e6Qm4JY+OcYKmGVNegryBBR6ooMkbDtXbCB8mpB5eSt8AU7qeGafJZBKOVOn1emEcFMTqS3UcFEK/V3il5VLH9Cb1PDLHyFeOb9LcLvQJxkB1J8CkRhN9KYOcemq9B1v7igH97u4uRL7Bsx5TzrlHr9cLxjWM2YCByWQSQF21WrXt7W3b3t62fD4f9JdGo/HAsTE3N2eFQiEcZ5LJZBJeQDMLlcbRuTi6SvNifRSBJ/qIx5HxGA6HlslkQlVs5lhlJm2dpc6qxkQvl4k8os6ERrakRceljYMantEzj46O7OLiIuR9YrhZWloKzoZXr15ZNpsNxRg7nU7iuSrDzCysS8UW9Xo9VIM1sxD9p4Yc0m20WFOMvPELuaCFJNmfXD/N6Kr0bG2XxaOMSQebBvi4fxRrzcNByDDperg38bxra2u2vr5u/X4/oGyuj4FCP2gw1scWtG4A/YzB1gRwrVwGEwU45nK5UEqbAhwwjZiVN81qo//7A91fgpgXGJ6W4tbxos0sUA0jUqGqGxhX/fn5eUgmPjs7Cwn6ZvcHQZt9Cikgh3BtbS2qsHNfPQeOozawAqUdOOwJ8LG+vh5ezCtzjEdZxwBrnK82p1bbL00wAKymGuqCV1hDNYfDoWWz2dD/UqkUvI2cU6gWYiyeZvfeqevr61Dhi3LwmreruQZEGPg59Z8xdsyxV0R1P+fz+ZDkTVgXv4Up4p2s1Wrh6JytrS17/fq1ra+vB0VHlRdd1/47vWYWpHuMte6LxHhDHaSWafavB41qeFE+xJm5zWbTRqNRSBvgKADu3+12Q77O6elp4hDvv/qrv7JarRbufXZ2Zt9//70dHBzYH//4R1tfX7fNzU0rlUohWoGS/aQrnJ2dBTDv8510Xh6zqvJ/DGQrT5s1cIxZ8vn7Kb/Twip67AbAUY/dUMOqhg4qD2NMfNVFXe/aBvVsKX9jfcFXAKh6qLZ6zVUpR7Ezswf3pDgexmc96gfe5ed/1oqq8gR4qObkef6PLoPM5+gMDVFF9rNn2av8jpSXZrNpzWbTrq+vwxh7Iypt1L9Vr1DjrObNxfaF6iGsE0LJAX2q6/DSdeJzrrkna9KndDxnX3wuxdaP5w/stX6/H9Y0R62ogVzHK0aMG1E5eJDwOM/NzdnKykoo8ra9vW27u7vBMHJ+fv7gXFOz+8JU6KDk0nHd4uKinZ2dWavVCrJD5a8aIf0+j/Ep9iJna6IzaC6uetdVD5wVef3Br3uMNeACdHdeafqZl+mqm9/d3YWQY4oaqTEdz/H29ra9efPGXr16FUDe5eVlItKLNtNOnBvoX+VyOURdctYrv0EX0uI/6rzQvpjF5wH9guuJhMAoQZ9m5nGEgarCF2uoD0dBGcADoVXAWIA+p44FXKlUQriqhp/pIoHUqvIY+cWnYayK0inVz6HgenYjirqeg0XomC7W2MY1S9+8+t0sgKOGkTDeLCxVKmiTKhH0R8MfuQ+hVaenp/bx40f7+PGjnZ6ehjDV2JrBMm52n/ehz9Zwunq9bgcHByHZ3B94/xhlMplQjQ4GTkgqgN/HfXPvmBDkhXKhwvdLEIJiMpkEQcGapb+0j7W1tLRkGxsb9ubNG9vc3ExUB9T1oH3CE0i+ImHlAB1VePVQabwgXnnxCqDZ/VyjaAIiCHtiTOfm5kLRHjxlk8n9ERsosuvr6/b69Wvb2toKHq5CoZAQeGnW9jRj1FOscZ9DPgoCAYSnX/mSF9ReoKqHQZVCr6Td3t4GAw9hTqQJUC2ayIGTkxP7/vvv7YcffrDLy8tg/d7b27NKpRLaUywWQ5j3eDy2w8NDW11dtdevX9vm5mZQukulUgizIi/y5OQkhD9jvNAx4RnKP9SwlabE6P5FWZ/VPKata32Pkf+OfUTIYqPRCC9CVDW9gHA0721EUWA81eqdJrd1X+iaVw8k/6shjXc/N2otn0wmDwqL4WVmvWP8wWuVNm6zBo6QRploX2OeWA051bAy5au6N9Ef9CxF5rvf71sul7NyuRz2yzTl0OyhIgxP9YY6T54PwoMwTKqOw0v1BuaStaUGS+Vdqgd9CeCYli+v+wB9EoBMKlKapwqKGftVDmokF23heCvO3Nza2rJsNmuDwSDIYa//sFbwcOm5wprHd3h4aK1WK8x1LN1oGjEf6A8YnohkQqYDYuAFX8qwqmPh1zHGagxOqpvFgKPX92PPwqBAWgxGL60FsbW1FUDj5uZmcGyoHkl74XWajkGIN9hiZWUl1AOgfaTzmNmDvejn1c+B6qLqncQ4pbVENKrhsXn8LI8jN1bh4gW6In3dDChElLT2CdswT9yoVOGr1Woh8ZV3GLKGazAhMUrbPCwiiMFms+bzeSuXy1atVq1SqYQz62gnlg4t/MJCYQEo09fneHCpVUzNZndwvAJABQpeeOhCUo+jEuMHuDs/P7f9/X378OGDHR8fJ1zvntSoELu3hqaenp6G+56engYv9HOIOUFBZsPiZdECEMpYWGNqedbkflWEEKBfglAwUVjMLJEbiCKG0CE8d3t72zY3N0OoqQoZ+qtCttvtBosmINuHT5klz23U0vNECRDi5MkLcgwQ6nWEstn76mREI5A/AyPG0LO5uRkKuOAtUHARAyFpyhW/mRWxj1DKmAufy+D5iQJFNcCp5ZW2ayghB1PX63W7ubmxWq1mlUolkb/NNT/++KP98MMPdnJyEjy83tPO87TadLvdtoODA/v48aO9fv06zBu5lHt7ezYajWx/fz8Uf9nY2Egk8fsICJ2HxzxQPjxLweMsKQYgHwM6qoCNx+NQGbPX6yXOaKTqJqHg7HuEvx6boLxsmvKoY+OBYlo7/b7xz1C5ytozs8TxSUTm6JrX0MC0vcizZ2kA0P2iBkPG1oeomt2HzJE/rvUP/FhhFG61WiGFA2MBPI2ibB7wq97lxyltnU0zhnmlmr6od1HDVHW+1OOqa9230ffDr5VZkD9SSY3jzIk6Im5vb4MMiVX3VkU8BtL1O15qrK7VasHgRs7ieDx+oAsjk71OiD6azWYT8o7P0mSb2T0vfIwUpAKgVX6rvPHRC7MkHW8da+WV1EBBBqmRSuWUGkpi86jrA70U/W95edlqtZq9fv3a3rx5Y+/evbONjQ3L5/OhgJQWVILwOIMhkEsYxwGNpIloHiL6Jv3Sis60mTmIgUeze88160R5FUYTrnuMno1KqtVqFDRqmJAiWLUqKnAkXBCFXcunq0ULV+rq6moI/9AwEAaJgZhm9fDKhyfPEDxo5HBeLAYwU610xD2YUJQ+ZTh+nPhOPQQa6jEL8lYtbZe3EJpZYj6VQamQoWDN+fl5OA6DOPtp5JUW7g0zIFTr8PDQ9vf3g/Ka5sVNI5htqVSySqVia2trwcqjeRCeOel6Yhx0bSuo1n0xK2GopIw7m82GEBYED56IQqEQLJ0bGxtBeTezRHl0P68wZZQZckM5t1HzHbhWj9CBfDSBkipT/E+xApiw5zck8K+trSXyp6huRm4nvCUtXEr7mqasenA8C/LhW8of/NhogRMdE/YD/JP7quDge0qME+ZdKBSCpxDBNR6Prd1u28ePH+39+/d2cHAQqlsDPLW6HIYEFDCEL+XiDw8PrVwuh8PNc7lcsNoS1kXo5crKSuivhgPGlGPGTNeIenU8f5v1XPpn8byYXEqTVSjthCaRy6t5LhhnzO7P1tVjLbyyPk3BU0UzBibM7hUu5bvMMQe+6/7WtUwBLOaGfmgUUawYWxoI8kr8SxO8Udeeehp9ioLKcAVZmmPmATb91qqYOg66RtkvKJnksGvKjpeHsfGL8ZfHvC7obDHQqPtR955X7OkXESj6DK6ZBXnjCfNK35g3qhZfXV2Fg9LhU34epumP9NXsXldgT5LXqB6q5eXlxPnFmuJldm+I0Gg2BRCEpRMdAl/W+gx+fj3vTONB6A2Li4vhaDzWm+ctClhnQWlgHcLjSIg1UUeAqzT+m0asR7ALey6fz1u1Wg2A8e3bt7a1tRWOLjGzwCe88QSDt1ai172uIf+0lT1OmkehUAhHpRA27McojV+yDlROmFlCn4gZHGL0bOD47bffRhvpLV7KVFXJZnPpeTE6kHzvAQsFUziYFEBChSMVin5j+0HwqJz2MoC0FxdyrVYLFRjJNYiBRmWkah304Xz0ywNHBd8KImdB8/PzYZPRBrWQeGsxVjD1OnoLkCaYYx1/zCPolRUYJzH6FxcXdnR0FIpyXF5eWq/XC799jBTUkVu1t7dnr1+/tu3t7ZCvGmPaMcE2TeBq+78UsW7U04CBhmp8VFhbX1+3arUaqohq2Lcq5t5jxTqfTCah6vHS0lLYd2okQanFMqnjmeb18LyDe3GcB4LRF2Si0MDGxkYw5lQqFSuXy8HTj7DFU6BrN60N09597uBLka4l2psGbjx/ZN+itDD2XuExu88hJSqAgmScZUmV3Lu7O+v1enZ8fGw//vijHRwcWLvdDvdjvDS328wC4FdQMxgM7OzszI6OjmxnZydRYTWbvQ/FmZubC1ELxWIxobzzoi+PKQK65jwY8eM9S1IZE5NDnmKADICOrAFAaq6MRvfoeWWaexwDjr4NXi550OEje/hMI4A8kEH5wtPD9YAqD0S9AuPboG2fFa/1col95o2G3tAI74yNh46hvscqgKvRyOw+Lx/eBp8FWGh0k87dNEVw2vfei4qs8KGqd3f3Z6ameUHVmKV7UQH1rIAj+ocHOYwbfJN9hsfRG5J1jKaBR+YNgE8IdqFQsO3tbXv9+nVIn1hdXbVcLmfX19eJ1Bd9nvIC9ToqbwQAEFnU6XSsUqnY6upqAjhNIz//7Fu8oBT+8QZhbzyYFakjxXvfdZzM7EEF9xh4VvL80OMXHYdKpWK7u7v29u1be/PmjdVqtZC6Bq7xc6ltJv9VdWPtCxGWk8mnVL1yuRzyjImM0zBq1TeVD8X6BakRLFYL4Snz+Gzg+Pr164QFk82vAsBvMrXY8R2bUz0KeCwI/dT4dCoPKeKmkhyMl3ukDR4UY9K6AM0s5DWura1ZrVYL3kaSwDVsI2Z5gxDgPgyTv73gIaZ8VowUUuAYs54qeeAYG1OUAQC1z4NIIz9uMHFixQGNVGVV7+Vj1mZdZ4VCIXg3Xr16Zbu7u7a2tpbIVfFeqBigiXk89PsvTbo/EFQYKiaTiRWLxXC4PSHWzJXZw7MdVSHAyknxHcYRYaSAinnzYaqqwCupcsE9lFA2BoNBopIhvzWzEDYymUxCmAX5KSgHGK608q0+V/9PM4bNyquhpPwSQafhqnznlRg1VpB4rxZg+sH7aDSyRqMRzhUjZHRjYyPwN+a81WrZ0dGR7e/vW71eT+w95QeQrgHKuJt9qgx3fn5uBwcH9ubNG9vd3bVSqZQwmBEeS2gsBkXNj2X+Y4qx72sMjJglI0BmRTFFU797Cikg814ftRB7IKXAmP03TclN+/sp7fRGRr9e9V7e8q4gTL2ksb0Xa/+s+W3MsKxr3ufN6RhgvNHxSDNUxRQ3s3tjCaGImo8fMyrgRYjxi9jzvYKsMsCvX9X3vPcw5izQXE76qIZ5+qz3mJW+MxqNwl7QtjJHyDFkHQZmn984bb0pL+b3+XzelpeXA3ijqjcG63K5HJwQ7AXWlIZAM04KvP1+yuVyVq1WzeyT/Ot2u+FIOIzi+pvHiLFCFsGD9SxuDBRcOy064CXI8wLWooIy5H1aZJFZ0ujoeaf+rTnNHJ2xtLQUzvh+9eqV1Wq1xBmZ8DItEqZ76erqKpzLSFGdGG9bWFiwSqVi+XzeNjc3A87BY+nDr9PeH6OY/NTxnkafdRwHg87m01C32GT4SdGQBu7hBQsDT3U/XLzqpSSmmIX8lE7HBJEKCbNkQRzC8lCYs9ls1JKolnP65pWWGNiITZxv44cPH9In5DPJg8XHKE1RM0uGz6li8xQArAzh7u4uWP16vZ6dnZ3ZycmJXVxcWKvVevSYDaVs9lPYJrmMCF48KzBuFZbTGIm2VT9Xj48qEF+K1NoLA9f9wRrmoFn1HmKtVAXbW4YJKctkMqGSqk+iBmgAGPAksFa8wuFJgY23ImJE8DH9Zvd7jt9oMQ0fRszfaYDxKcBxliBSgbQqplrFTfkj7dDrtIqtL4ikQJxCHHieAdtqVOj1etZoNOz09DScQ+aNNmo45DOt6Mc+uL29tcvLS/vw4UM495ECPBSMICTz6OjIRqNRUJZRhr1Vdpqw0z57j6Pu2S9B0/imv8YbUdQyrIW3NHyK+dL9q0qVFjtDSX4qoI3JK/ilRhPExtnsvtBZLpdLeEZRxigUsra2FkKxpnnalb6EMQdSHqIRN/pCfsE/+V/fYzxGQYcS+VDVatV2d3dtd3fXNjY2bGVlxfr9fsiFbLfbdnNzYysrK1EdSHmZD0dT2a/AWKOjdG49aNQ597qE7j+VARirVWfgvrMgqpkqMPZ8SyNn1GOkczRtrzCm2Ww2ROSQY8+c1Go1e/PmTQI0wm9ZV/54F503NRrF5nBlZSUUHcN4qJEc04Cd5zv6N2CFisfIkbS1NivgqM9S2ROTNzEnyFONTypf9fzMlZWVRJX2Wq0WxtwblRS4+jYMh8NQEdsffafPB4OUSqVEyDs6j+dB0/oUo2lGuqfoOc8GjldXV4liNqpseiXLMyaYhoayYElW66oKJTMLnsnl5WUrFos2HA5DyCplvtNCImObIW2j0F42HCWZ/TlAHmzh8QGIqLJjFlceYv8/dXG/BHmBgNKnXsJpi8cLFhUEWMcf8zZCWFA5s4qcularZc1m07rd7rNBIx5GjnMh3Nifwcla84BQx90bFmJjoQrULAGGJ2VaeOUrlUqoFEueBhZD5pfPAFtm9/OAJRYwRiU1DDf8lucT9oP124OJTObewhtbE7Gx5r5aghrrKfkDACRdu4TrwLyh2LzE5su3zSt7s9qP2g7lMyhyOlbqTVJPMfPL+Gj72Z+EuJmZraysJCyYCjCpjHx8fGzn5+cP9qCGryk/x4Dgo0C63a4dHR3Zjz/+aG/evAlFeLSS4GAwsKOjo3D269bWVqIyqFm8gBbj4oFymgHnSyg4nuc/1Tin640ceyqQ63l4HJuDcYXfsg5UTjEHrCcPKnwbYsAtBjTMHoZeslYBWxrZcnd3l0g50ZxrjFJqCIJiysyX5K8KGn10irYzDVhpHyAPwpUymU+G8nK5bDs7O7a3txcqQ5tZOPdaK0rruE9bbwoIYmtUAQlt0/3k95fnjX7ekK8KquFfOlazAo7aH++lYR6Vf5nde3ufyyMIaeRM2q2trVCpvVqtBv3DnyaAgceHQfswc11Xsb7hnVZwx72f6iTw6xR9tlgsBqMkcjzGc2e1L/190V3QO/TMaLP09ajfx9qqRtm5uTmr1WpmZuHsRDCI5pBq5I1GUvBSp4LXuz1O0HXKbzQMHb6aFqbq/46R5+UeB8wEOMasHcpo/OR4sBWzTGnDCbcaDoehehAThMDEogNyxyPyGLN8Chhio/EcTUJVgarXam4B51ymWYZigDGtLbNUbjT0ib/13VsUYxZlSBVO5u8pYaoQnsaLiwu7uLgIc6oFFB4jHU/O+dve3ratra1g1dZ8RrN4Qr5673S9KBOPMQBvOPlSNB6PQwEpwqorlUoQWBhsvFEGwR3zHChprmQ+nw/eHtY7bSBsSs9pM3to8Y6tiZhCpW1iffR6vQfWYLP7BHa1zCrT9v3S5z0GHD3Nan6JujCzhBLl+Snvyou81Z/feH4zHt9XPh4MBgG44fnTMb+4uLAff/zRfvzxR7u8vHywB9U4pO3Q8DlfLrzb7drHjx/t+++/D0oU+bJ4Vg4PD0MJ9NPT06BAa7EB7/XSsdB1nTYOX4qeAx69IoMxkqgJDKWaG6N9ZR40B033iRobpikZj8klJc8HvbEok8kE4KiVnTkAnRQQzs3FgKFKadozla/MglRuK3iMzSXgw4MhrxT69iqv0z0OPyenESWV/dtut0PVVfVC+/Y/Fk0zDTRyjebKxhRL/T8N7GvkgQJHL5dmQRj8VdGmTcyrGs79GPlx8uTHlTVeLpfD58wlfFbnSvVIUjI0bNUsmQbkvX3avhgYUANjjBel6ca6HhW4cG8/TvDiWZGuWW+o0qMpvEFDf8vfj/FhXoSMLi8vB56Lvq85hrqudLz0OzVQeINJDC9pe3iO9iXNEPAYBtI+euPfU6MEzT4DOGrMNMJKB4WGeMbrF6/Gc6PkaWL/ZPLJ8zQajUKIKICuWCwmypRrHDbkO58mYLxigcAGpOJx1MIcuiFRWnEhe/TuxyBNkUj7O2b9fQliwzHGWjrcC767u7sEg/XKKOMI8+VeTxXqAAPc8YQ7ct+nEmsJazaWWs7e9EzbLL2am1/TZg+ty1ppDqXhS4NHcn5rtZptbW1ZpVIJ+8VbtFSYa2EcH36lXkSAqeYhebCFsQCDgQIQXefTFA/GN+bNn0wmITSLkFWK4dB+jgliHvCaepDpFdxYe6AvCTQUgJFTqsKd8YwBSb734VXeWHd7e2vdbtfq9br1+/1QKVqLk43HY+v3+3ZwcGC/+93v7KeffrJut/ugvWoBV+MKxyxo9T3o5ubGzs/P7Y9//KO9efPGtra2LJfLBf66vr5u33zzTYg+uLy8DCGtmt/ojZTKh7wHQ9eTrstZKaqetz9VsPvfaroEsk6t6mpx173sPY7qdWct+HBEpWleCb8m1eikckNlI7yDzwBDFLHCMKv8xew+rNYrN/rML7E305TuaXwtZmSdtg50f+PhwWCAB3Y4HNpkMrHBYGCdTicUceGQdgwrHih4o4o+3+toanjSefb9mjb2sTXCey6XC/IBXeFLAEfVN9PGSIHjY3305OUXxi7C/1nfPgJAQYBGyWi+L3w2VnBJDQNeT9S55Jo03dP3I0Yxb6dfW7PUfdLarN7ax3Tqp86n8s5cLhfqKNA37wzzMpe1prmlZklDnw89TmtHmhxJG6PY3zGDno5JTD96bB6fDRxLpVKCiWj1Qm30NNCoA+xzB9TigsI4Go3Cd/l83sbj+yMCGo3GA+AYUwxgGDHS9qlFAYs8Xk+UL91AeDU09DHNqjhN+MQmfZbCkfnT0uEKfHxCdsxb5P++u7tLVPx7qjAgHBEF5HOFCJu8UqnY5uamra+vhyRjXR/eqqPMVde2Ml4vTBV4x8buS9Hr16/D2YwamuqFmdm9l0itvZnMw3M08RyhrGBAieU2cmSAephi61rHxhuZFACowFSFyoNTjQBQiy3Fqli/yodiz0wDjUpfSkHVdisYVOuzXu8Flgoqz3vH43E42uby8jLkUBWLxaC0TyYTa7fb9tNPP9nvfvc7++GHH6zZbJqZhaJgELxD1zqgBcORzjN7aTgcWr1et4ODA9vb20ucnbqwsGDb29s2Go3s8PAwVICl+EeML7KmNJ9M1w7P1uv1fZb0OUKfvnGwN3lSPmKAdQJA1zFX7zCGHw2nmtb3tLZ6JVvBEWOtY89vUKSJFOBMZvU0+iqlk8kkEfoVA6vTAO5Lk/K0pz5T9RxPChzSDFUKSofDYdBz2F/Iurm5uZDu4xVN5QFPAY3qUaXful9iSmXaGvc6GMZIPTtR0wtmBRwVMGqbaY/KkJhR1PdJ+6v/az819F/5st7HP9fnV+q6iRmJvBFf7037YkBqGrhI05F5fky+6+9nNYe+vTxfw0D7/X6IOtI26fUe7MX6GpMZeq+09ug1zDnvMR6mqTuxcYuB0ueOU2x9xu7v+zkTj+Pq6uqDMETfUD9BfhAUles7Ll42MF6Pfr+fQPEI1MFgYK1WK1G4wSu2MfKKNe/cH2sfCbGaJ6ZucZ9vpQzHT3ja+z8G8oq7fqYu7DTQyP9abfY5eQtq7fscQuksFAq2sbERCgmUy+XgIUepVAaZpoj6MfGKqIaOeOHnx2nW9Pbt26D8a/t1D9JvvMHqidScJ+0jwJGDiymkooqb7k+S09MUdFXq/XimGVz8HsEwoYYk5SnsR+aCnAyAcQwwevCYti9nDThYZwoGUJ51T3KtVwBUUfF7+Pb207mNp6endnFxYePx2NbW1mx3d9eq1WoooDIcDu3w8ND+/u//3n73u9/Z2dlZeIZXgFWRoV1qOPIRB7Tl5ubGOp2OnZ6e2snJidVqtZA/Mx6Pw+HK3W435FVSZdsbLvTenk+lgcU0pfClKA1gPBV0sLcAjmb3Srda/ZX/6L3JMeZeShq6/dR+6H28nFOvpbaRtZvNZsMexfNCoTINp/dhXegB3Je94T1e0xSjn0sq/2JrRv+n7zGli/89iPKfeRmEQa7X61k2mw08j2t0fLQAjNJTwC7jrSF1PmQtBn79PWJ/e3kLz1Cvi4arzoJ0DFSm02/e1fCoxjsottZi/7O/1NPvZQfrWX9HG/xeYE1hpNZikCq/00BhTN/0f3udSMfLr4M0g81T+dvnknpXVVdl3auOHpPp/uXb7v9nXNXD+hTy+McDNOYVXjktxem5WCEG/p8j654jG58NHMvlcsK74ifDg0WzJLrVAfWWcbUOaenf0WgUOgYQKBaLtrW1ZcPh0AaDgd3d3UUB5DTSNrMJAaXkxGHVY/OSY4JQ9BXvHhMyuqi91dZ/P0slRxkVLxQR31a16MeUfO1LrE+zpGw2G6qHUip5e3vb1tfXQ4ibMurYetV3/dvPEfNLJS/WqIb7xizJsyTN/4p5VFWh9oq0GmwgFVTkNuJ55xmqYLAnvIc5ZlTya8kT16QJJ8ANuY6+CAzhfZPJJBwJonxFwWusbY8pyrRhFqSKkwrFmAXaA0k+0wqauna73a4dHx/b/v6+DYdD29jYCGdQUchrNBrZ+fm5fffdd/a73/3Ojo+PE7wgphD76AutcJfmRQDEXl5eWr1et06nY7VaLQhqLO9EeGDAKBQKYWx0nfCZjl1sjDxPnZWSE5OFn3MPxoF5LJfLAZSjcLBmFFCoMsL+hB/oGotFXOjzY38rQPcefb3WR+NQNKRQKNjq6mqidgDeSK8s63rwxpAvQbrO/Mt7VWOKpx83r9zruwdmhHy3Wi2bTD6Fp1KoT3NBddy9zqVt0Xb47zwYoG8xUBvzgkzj1f4+jAOh0t7gOgvy8t4DCvX4ISMAaL7tj4Ev/8xpPEeBmVky5NLze93LGuKohjTfJj8GSjGA4fuh64GoBfgS95g2zrMgz1d1XdJWNWjqPk0Du/p3DOBPI+8V9uvK50RnMpmEs8mH//q2xZ6f1jav3/rf+PlSPq6e9tjYxOizgKNvfIwhxlyhnvnEPJF0lM1DiXnAI1Zp4si3t7dD7oeGDqqSq+T/18mnnPHGxoatr6+HM3jMLFQj5LgIqqiq4FNmkCZweKkFmRAwD75m6b2CUVGljz7Qjthzfe6jMmNVZqbFbb8kkeNBBbO9vT3b3t4O5xfpvOjG0jl/TNB6RQGvinpCUJZR6r4kwYC8McYsGWIC819YWAiKpff4m1nC40VxDn9uo96bMdAwFsYuxsC8t5PP/XqJedkmk0mwvC8tLT3IWcVTMx7fV5BlTaIUeK8F9+Y99rm2f1aCUeeD5+gZt5AqJWoQYP4VnBMCzhmK9XrdSqWSvXv3LoSJYiTodDp2cHBg79+/t/39fet0Oon2+XWt57UqcIRHaoU7JSq2np+f28nJiV1eXtr29naYS7xNeKoGg4G12+1QidN7U1VIpyn6MSPXl6THZFBs7ePdJ0dwZWUlEe2iHhF/P2SmV56Ux09bx6ogahtVdjH3FBrzBig1AHCu3crKipVKpWCQpViIVn1UA40q9fryBudZEuOnci8mm70S60FZmmLtQQZ/c9D6cDgMx3JgGGN+FIRxL7PHFd/YvjC79xBqCGdMSff98vfVsdPPuT+Kqg+7nAWltZPv9MWeU3DG9WnAQ2WF5zP+Op3ftPaoE0LbhZxXHZf9nDZ20/bGU0Cj2UMDQMzIoH2c1X6cdl9koI6H8r/Y+kqbTw+qdXyfIzdiex7ZRk6/1y2n7YG0PT1t3aX1U/HH587hs4FjsViMKlbTFDF/TZpipsAr1kEApG6oUqlkm5ubwevhiwWopc8PMp8hkGDQGxsbtra2Zvl83rLZbFDA2u22tVotGw6HDyzjqqzTbp7vhY8HWbo51UM0SwCmAlnPhYnNlSqutNknm3uGO2vwSOWyWq1m29vboYLq2tpaOOjeM0Dtl1lS0ClT0LnU9QhDwsuoHhaUtS+tlJrdK3p+DapwxkOezWaDUh9TYDlOwcyCsu4LbKC4UgY7FpYYoxgQ1+tjcxOzlOGFInfVtw9lmzFQz2OaEh/jSbMEip40jJB9FctvVKHNu+dxGEqurq6s1WrZ5eVlOAtzc3PTtre3bXV1Nazn4XBoFxcXtr+/b0dHR9Zut4MxIk2ocX+de/aDHscB6d887+DgwI6Pj21vby+ciUWIZrFYtFarZe122+r1ejjTSuUP4MKvm1hEDN95o9esaNq6ipGuddYqxpX5+flwDivk598DYgxAeq3e2wNDDSv0eWFeJuGR4TMKfyioJ3yMuaSIl6aA6HEs/E7XHW3U8+10T8wSOKrs0zBUr4hOA1bqRX3MeOCvURljZonKjr7/MRCSpgB6vhrjs3pf/zmvtIIk00C28jddU1/K4whf9EYnNaBidEw74i2mQ9JvT2lKvI6X8naNAqO9vjCd6o4x+fgS5NcG6Shm9sDb7fvzJeSlrnH+XlpastXV1cCfdA41fP4xSgOUSp6f0iY1AMT2HwZvIuE+Z92rTuJxzbS14GWgejrVOJHWZ0/PBo5UGPIPmAYWoTRFLLbB6AwMCu/BaDQKzIn8iGq1mlBYAC4U90gTMMoAl5aWrFKp2MbGRsi7mZ+fD1aebrdrjUbDzs/PE4qVt5L6/AAFH5qfEkP8ChyVAc+CGN9Y2WB/phCU5loHSKg1bNagsVqtJs648jmN2ja1IvkNlslkHiiaKKQqZOg/oGUwGFi/37fhcBiKw6hH4EuRAkLmQdeVBxVm995CHR/6q7mNnFdEAQZVaKlySpi4Z15pipE3iMQYrn4eU4QI5RoMBgmFUsdEQzA1XE/3rAcWSp5Bz3pO1QLp5ys2LtpOxtMrqVQn7fV6trCwYKVSKZwHl8lkwn7FA3h0dGQXFxfRozQ8aX6vAkf2w7Tfj8dja7Vatr+/bx8+fLC9vb1wVA4RHtVq1brdrrXbbWu32+GInXK5HKp0YhDxhjsN1/RFYXS8ZkEvoUSp/NO8f83VxkgVU4RV+cXIB39OAxxm92veGwX1e5Vdd3d3ibb54z8oZsdxBMViMXGsj8oZBY38nrF4joL0UuT3oAc5MV1G58yf5eZljNm9N0MNk2bJ8wQ9IKdtMeAc473++zSDHPPOvT0gUmCs/VMAQdt1rNSIz31Vz1GDxCzIr59Yn3TNIzM03D4mQ5/yXH2W/5y1DL9XfUwjpQjzxjjD3vc8bJrsTdO7Y232hkIMrxQvBID5eddnzYL8HvDfUYFaQ/l91d7ntI2x8HsJXUlJx0znMhZxoFVzzexB256zvp6yHlXno42KT7wx7qnP/6xzHNMYz7TfpL2nWb9gKngREYLZbDbkl00mEysWi7a6umrX19fW7XZD2XKu8QzJA1025srKSjg4Ha9VJpOxq6sr6/f71m637eLiwk5OTuzi4sKGw2HUQwho0pe3vmmlUh0PBZuq3P/qV7967jQ9SjAlzaPyQo/qsihh2ledMz8OsxTs+XzeNjY2bG9vz169ehUOkCZnJhZumwZaIJ2rmGWSlxYswPvc6XSCojxLoJ9GylC9MUL7k2a8UMMKQmI8vj8bktAoFFD2jT/o3QtKz0yVgXlhrBRbO37PTiafQlYBRBzho2PC0TgaUqmg0SsSCl5j5Pv10oRyqc9QBZF2e7DAZ6r0Q+p1Wl5etrW1NSuVSiEXEv7abrft/PzcTk9PrdlsBuPcNGJslb9qru9jwPP29tZarZZ9/PjRdnd3rVar2erqaginpvpmq9Wyer1u7XbbLi8vQyQI94B03zPnzKX3ns2aRz0mfNMUOa+k6PzzOxQkPFF+reg+nkwmCa/j7e1twkgY84ip4qN8EIORNy6y99L2jVnSQKuhZPxGczJR+tRLBc9ShUzbPgvS8WGcY/w1dr3KT1XKYr+DFEDmcrkAuAnJBzh4vs76iMkdvcZ7TaG0cE1dD7G+xaIhPJBRvQCCD/u0l1kCR+2HlyX8Td+QLf1+P+Q5ej3C91PpMZDmQaiOO/tJ89p1rZslgXkaD+MZXi4/Za/oPeEZzJkasWa179IoZiDR7xhLMwsGKE1VScMtafQ58sEbVgCJalDTY1W0qupznvcc7KXXxwwNnt9741YaPRs4ttvtqPLilRj/v06et9xN+w25ZIDGxcXFYDHX8KZSqWRra2vW6/WC4kKIKYzSDwYMkHuWy+WQvL+0tBQW3WAwsGazaaenp/bx40c7OTkJVSR1cfp+mSU9jgpcprmpvUdmFsCRxeEXOMKAz3Uu1NqlwgVhP0tvYyaTseXlZVtfX7e3b9/a69evbWtry6rVajjvKqZcQ2rh1LCsGHjQDcR36o1rt9vWaDSs2WyG9eaV6C9FqtxNs15NJpME04Jxmd0X10CB0zh8zbkzu1fM9WgM3x6eFxOqClrT9r3eJ2ZZow1EGZAnhdfRW/fU6p0WZuOBbtoYzgpw6PM94/brNAa+Y1Z9DXXTc+H0OYPBIHgbT09Prd1uJ45zSCPPC5hXvy68oqU0HA7t+PjYPnz4YK9evbLNzc3Ad4gq6Ha7oZJuu922brdrpVLJstlPhXPUCqygCeVBBaOXVbOgNGu/f25sjmP3ov3wW8JWJ5NJQrmEZ3NvjD8+/98/y3vhFZzptdp+2qHRDn5fEzKmXmnmhzXCMSso7F4XUNCi3lKf7zgr0ueb3cs5lekY1BhL9Rr5aB7Pa3gGfeC3Kysr4YxLwha9dy/GF3WOYh7btGf73FEPNjwQ5l0V4pge6A21ahhXnXCaLvRzSSPCvIzXMYLn4JjAARGrWBvbq35vxz73v9X1RV4374wt46MRHs8BGjE5FqPY9xgWb29vQ/QAB9+nhTfO0pDj9TI+5zPdnz5t6inry89b7Bne6OLbp7qzVslVb7HWAfDz+TnzGpNnMQOH7kPVH6ZFeqXRs4Hjx48fEwq4es80/nraS6/RcrTKjM2SZ/1QSZWctmKxmDgjBfBYq9WC10SBn9l9HhHWSyaZqpwcFM/GZYAHg4E1Gg07OjqyH3/80Q4PDxPheWrVTVOWPdP0kx+zhM2SFDzpQo8JGK7PZrMBIFK8gvHUYjEUV3kpoggOnsY3b97Y5uamlcvlwOxZSzGKrS/PUFVZ8aCfv4n1x+PY7XYfhLR8afKC2luLuUb3aKzvWlSKPCSt2gaTZAzwsqZRTIFW5q5j5l+eGXpLGP9jGCJkXT1zrE2KAWnEgoJgVZx9u7+k9zhmjPMAVvmG9zJ65YsQ1MFgYNlsNlTH5bxGntlut21/f99++uknq9frNhgMnrSOdS7Viz0tP8gTRXlOTk7s+PjYXr16FaIG5uY+nTNZq9UCuCU8ut/vB4OAt6ybJfOwPSBRkDQr0jUaa5//PA1smllCXk0mkwAkVNHV/aHKkvaV9Q+oQPHjGRpSNS1UUb/XsKsYaGBNwJN03zJ3o9Eoau3W57DGFehohMyXIA+GvOGLtvooHg3JTTOqZrPZABg5roSQbDV++7C3p4ABlQVqZPDz6YGAepppH6BGozxifCnmGNDvvYGA8ZwFwRu9QuzHj+O8FhcXbTAYhBD/q6urED6fBhj1/Snk1wzjq4UK0VVJzxoMBjYYDB41zKcZoNL+jwEOPr++vg4ABy84Rcxia2iWwNHzuVwuWRRKX6w3dWaovhvr9zR67NrYHouBR42EwaCXlq+aNmcxfJF2rZ9neBdOg/F4/MBgxHWPzeOzgeMf/vCHhPLnz+LR//Vz/79eZ2YPGIpn0oRa7e3tWTabtbW1tUTnscSurq6GMFWQvXodzZLhGQjgYrFoxWIxHL9hdl86vtFo2MnJiR0eHtrR0ZHV63W7u7sLQEvL1scstWlg0ZMy7lluQvqmiqfmM2o4hAoA9QCr4MCKrLkBT1Ecn0IovRwhAGgslUqhsIL3NCqp0IgJtRhxD28tJUy12WwmQlSfYqGZFalQ1jXmAbEfB7MkM4ap+dxGmIgqoN1u1/r9ftTbqOPgQ1oUbNAG307+pm/+nno/PFyDwSAoaFyjwNF7mvVeMSVMr4kB8VnQY4qN/97PJYrG3NxcKIpzfn5uvV7PSqWSFQqFhBeZ/MfDw0P7/vvv7cOHD9ZsNp98nqoXeGpJ1RAvP39K4/HYer2e1et1Oz4+trOzM6tUKra8vBwUWYqqXF5e2nA4tE6nY/1+P+TaewCma92H8GlY36xD4/zfT1U+9G9dx4wzCjwVd+kv86EAUfc2fNvMEnuBPaKGF/XkAfT8vZG5rDuiFHzxGyqnAjZok4Zlx9a2D8v086iAbFakwMbLDpWRHnyhLNLnxcXFEJXCfZXUKL6zs2O1Ws0KhYLd3d2Fs0wxxnoe4ds67Xulx0Co3oP5JddOwym9nuOdCJ7X+3aqgWEWhAzz+YoqmzKZ+3Ogl5eXrdVqhegixn0aH9PPvexNG1czS8hhjAy8ABu0gzQsou/8XNOPafJS/5+2Ntjzg8EgGMcpZEV0j7/XU40Zn0sxwy58gegK+Ewmk3lQ7T6WOhEbu6e0AyOOJ36vuenqcYQXTyaTRLti+qjKNG2r739sftNApo/g1GfEeMk0+izgqIJKX/q5viuTjXkqPbii8fqZegwILS2VSuE4BmVsLHLeYQBKbFY27PLysq2srASrPNbwer1uh4eHdnh4GKzeMB4vzNIWn17rmZZSmtIxC1Lm45WFNKsqygNWOJ+wDWB/SSEAQ1hZWQnFFfAkYLXGMxYT7DFrTpoFEtL+ouCMx2Pr9/t2cXFhFxcX1u12Pzvx+iXJAwkvrBRcmCWNAvoZjJW94M9t5G+dZwWtntLWr+59r+g/RjEh7CscU/WN6wlFx3LL2kgrAKLPUkXnS4BHvb+Gj6WNEXOZydwf3TEej+3i4sJOT0/t/Pw8KKWlUimAxrm5Oev3+3ZycmLv378P3sZpIaoK0BSs0Karq6ug3LA2njKvV1dXdnl5afv7+/bmzRvb29uzWq32wINzd3dnvV7PGo2GlcvloJzTFuXJfl3Sbgxd3oDw0vTYGnnKGvLKmJc5yK/l5WVbXV0NhliMqTG+kM1mH1QdJ+KGI640Z1ENivBbfT5gk7W6uLiYkLscw4Ehinxp9VTyMrPE3HneoLqFeoxntR/h/4y913NUx1HPraZ+KHicn59/kDPHumQey+WyVSoVW11dDdfHwkd5lr6nAQnvZdR5NbNERduY4SrN26jhm/o7HRtdj96jEQPgsyB0A3229k+vw9BBig4h1TE55+cjTbGP8Rn/GbzJhzirkQugoTqW6jY/V3dUEDEefyoQBD8nVBUjkA+/nrVMNEvKY40qMvu05nAAEYUxmUwS4DFW+T1tzvQa3wae/1hbfT4w90VmYkjSeXzsnk/5zJPel9BjnDvcw3scvc4Yo2cDxw8fPoQbe8uHfu49VcpYlHmkoVu1DPG74XBozWbTFhcXg1DCSoTAU5f/0tKSFQqFcAYk7dI8PoDm8vJysMrjSr64uLDj42M7Ojqyo6Mjazabdnf3qZqr5rT5HAbtAxOQ5k30jEd/O0vlRsNr1RKvDF+9wswVeSmMr5kFQMlvXrLdLOarqytrNptmZtbpdEJIh+ZbxEIooNh4ekGin3nl+O7uzjqdjjWbzXAkS8yL9aUp5nGE/NrywhtCuOM1wKroFQI9iiRmwVNw4cdfxzcWjaCePb/3+X0MEGcymRDKA2j0hXIwDuna5jrPKLXtsf7NSkj6/qpCrfPgFTwv8Futlh0fH9vp6aldX1+HI2tQRjOZT7msFxcX9sMPP9gPP/xgp6enISojTdnRdjJeCKGrq6tg2ceiiXflsftNJhNrt9v2008/2dbWlr1588Z2dnYS/Jw12Wg07OzsLCi9a2trweuWZn3lnXaobJoFPXV9TFtLMauy9kWVxcXFRSuVSg+86n7vM5adTidYnAFxhULBKpVKMC4AClQ2EvIHKSCJedpUCQZQ4g3Qe6hxxBsrMUgiU4gw8MaVWZAHG6rX+KgqPQ4olq+muY4qH/GU6G806kdBOmOo+g6pBap30UZVrmmDeoQXFxcfnI+tOpmCRpX3mufmZaYH1siLyeS+4rPqg94TOAvSdAtv5NU9heEDAwqevtFo9MA7rnxZ39PI63r+bwWOGG/0/F7GDCBEFB3z6J8zjRRwpekLpHfhbcxms8Gxsri4+ABsPPXZP4dUNvp2s7a0oBTzRpVvZJKOVxo9Rf5N+16Bo+qnZsnCl7GqrzGdw7ftuWPN+lHj7u3tbSJqJIZJptGzgePl5WVCuY6heA8MtTFeAYoJcLWsqpWYjuOFKhQKIS+xXC7bZDJJeA/xIPb7/Qd5dzAKzqrTMNXb20+HVF9eXtrZ2Zmdn59bs9m0fr//AGjwd2yCYxszDTA+xTL1kjQajcImQKgpOPDx4Sxyb13OZDLBC6khAS/Vdo5Cub29tXa7HQSZKh0a0qYGAB9aoYLYKwUq/CA2HHkmnCunVVT/lKDR7GEeniqVqmSaWULJA2gw1wiHQqEQzW28ubkJeRZqrfKklmr/fNpLWCN5CrHjG7xA8sJeSfNAVFFTQQ9w1IgHxkP5UCwaIGbweWmindpfVQK9VwaQh+fm5ubGGo2GHRwc2IcPH2w4HFqlUrHd3V2rVqthr2IA2d/ftx9//NGOjo7CGV1ED6S1TwmrNNVYt7e3wz4DTCJAHwNp19fXdnJyYr///e/tm2++sdevXwcAk8/nrVarhdxGbe/8/HzwPnorcGwdAmoZ01nSU0Bh2nextR4zxGh+SlrqCOuE/aARIayfhYWFUJCF/c/Yc4wGRZW0XTq+HkTqC3DEPOn1Cih8FBMGLZVB5Ah97jloP5d4JjJPK8ua3Xti1dMI8CKf03tWYwYxxoV5WFhYsHK5bNVq1ZaXl+3u7s7a7baZ3YegxfQyeCGe6dXV1cALO51OAsD5NaPGdTzJeJ20eAvjEktDihky9DeznkMMWpPJJFFN2hubUKIBx1RWpTiX8sU0OZD2WZrs8kYgb3Rg3zA+yDlqDDDnT5VLagzxbVZAjKxH39UoAqI8dO/7Z8yCvJHCGzngLej+GPZJWSPqkAiBtPGapruqYXwagNO9A3hUvsdz1Gg+zYnmDeqPkccZ7M3BYGDtdtt6vZ6Nx+NgDPJHwPC7afRs4DgYDFJDYcwslVGkWTeUYtZ+LKNm996DwWBg9Xo9CLZ8Pm+TyaejOZaWlgLI5BgNPFQeOPL7SqUShGY2mw0DfHl5aY1GI8E8vEctbdPG+v0UcOmVg1ltxOFwGBbLwsJCKPkdyxlE4VLlHuvYZDIJ4QCaE+BDeT+X8DQPh0NrNBqp1y0tLVm5XLZyuRxyujAGACAReMrw1OoZ80bQv2azaZeXlyEU7x8DaKR92hcP+PR7FEU+175zJI0CR1X8yYnr9/uJPLgYU5vGlPFUdTqdYBWkqJI3QqUZWfTZXEOuo+Y5KN9gnetZo/Qvdm/dm1/CqqoKgFrvtf2qdMIXUeBGo5GdnZ3Z4eGhNZtNy+fztrOzY1tbW7a8vBzuOxgM7OzszD5+/Gj7+/vWbDZDyPdThZLZ/fEhZ2dn9oc//MFarZYdHBxYq9UKxcieQvCJq6sr29/ft++++87evHkTctoRbJPJxPr9vp2entrFxUXICSsUClOr++m+xkN6d3eX8BK9JHmeHRP2TxnnmLEi7TMMZygiKnv4HiMNYUooqeotJCUAfrmysmKlUilR3fOx9nlg+Njfuv8wXPj8OFX+8PApSJoFecMCwBbAiGK/uLiYCLUEOJLKwWtpaSnoECpf/DrV7wGkpORQNGdubs56vV4wqKrRljahsPJs9JyVlRUz+xS1w++VJ/q2zc/PB4Oi5kl7D5B6hzWMV72L2l+v+8xyHqetE12z9JU8R87uXltbS0S1aaj051KMJyCjfMi4GlamVXCfZlzVax7jP3d3dwGcmlnQneATMbA6zQHyEqQgm/Wmug5jhVcdr6yZJSLoVO5Pk3k6ljEw9ZgBUD3IauBTA3CaccU/y49BrJ36P9d5z79WG2eN8a41Tp4in54NHL1geszz+JQFpYBRlT1/DYJlNBqFanxYQwA+uKupKogg1OqXmUwmeCbL5bKtra3Z6uqqLS0t2WQyCUcuNJtNa7fbASz7AdXFpwxKN1ZsM6eNBX1OW6wvSYTrsbhXV1dtfX09xIP70FO1/JpZEFBmliiMw7l+Gv5KP2YJtEajkV1eXoYKuGwGLLVbW1u2ubkZCm9om1QZ1zXNHAyHQ7u8vLTz83Prdrv/aECj2cNDlWNWRSUFGyjTKjT13EZdh1RYI08wRsw7v1HLrl5D0Sn2lRcCnm88xqTNktZYFGINp9IcEviAD6f07dT+zHr9emOGVyJRBM3uPQxKWIhvbm7CURa1Ws1WVlYsk8mEvdxqtez09DSc2aiGAMYsFoYM8Tm8r9ls2j/8wz/Y7373O9vf37cffvgh5Eoi4KeRPmcwGNjHjx/tD3/4g1UqlRA1MD8/b9Vq1V69emUnJydB4FIyn3BV7qcAHGVdPWY+suAl6SlGjp9DsXuwp/P5vJXL5Qch3swVwAfCwOLBSqlUstXV1QBYUBqfopAqMFSl/LGXmT1QrvCkDgYDa7VaARj5ML5ZEB5a2ofegfKMR5F9pSBYI3JUjmpovlkybQfvCB5hfu9TacjXNkvyvNFoFMAsij7eF3JMyd/DSEfkByBB8/l0TWl9Ac0h17n36S0KInVtqFKrgG5W+5Gx9Sk39FFBBwVySqVSOKO50WhYvV635eVlq1QqCZ3mc/b0NL1OPY54q1jnjCkyyYPKmKFVn+cpTa5OJpPgdMEAqKHWaaHhswaOZg+NDjgo1HBF6D1H9tFP5L0aDrWgnn9OmryfplvpNchSf7yK6jc+NP8xR0sMWE7DF+ocQMbjGMDzqule3Ocp6/rZwNF30it5MdDI71SI+E6nLWK+V2aDK31ubs5OT08TXkOYW7FYDAm+VMCE8VP6em1tLbzwNhIOSywwlgs9o1AFiip72mb/tx8vzyjpo4JHfZ8FqbW5WCza5uZmIo7++vr6wYLEKolgUUssjHlWFdKgNOWJMLxOpxM+X1hYsPX19WB11SNZzJLz4kGM2SeAjfcZb8qs+/ccekrIne43D+zMLITopJ3bSL6HP5+Pe/IeA16x71BmNZ/HM1H9XWy/+WcAjgaDwYMCAxAKHV7Ou7u7hMI3bdxiDPolSUO/VOnWdalKrHrG8crf3NwE5UdBI78fjUbhWKHj42NrtVpBkOr45nK5J63xq6sru7i4CDkxR0dH4f/HLP2Q55f1et0+fPhgb968sVqtFsZ7fn7earWavXv3zswsofRSMIt7qNEK5VXD2r90eKPSz+HrMUMkazOXy4XCEF4ZYg+gQON99Lk3ZhYMSMhTcppie0HlvPbLA8gYeNRrY/8jY7ynWPNyFGS+NMUUclXC8Dr6CuO0iXFUr4LyOJUj3FeLeVB4jhBiPF4K0gaDgXU6nVAFFE8joFGBX6FQCJ5ajlRqt9uJ9AvkAQo4RnjClWOeZ99+zYMHoMb4thorZgk21LOjwFF5HSGsanwhD+z6+trq9XownpTL5Scr19A0sKgGSgwSelYi5GsB6PdeZvrnxtqiz6Yvfm2MRqNEJW5+7427er9ZkXdCeM84/dLQaowYZvcV2LVSvKbk6JzGjJDPAcasKw039gAXXRp9Og0/TBvjx4y7qjug18LvkdFeT3rqHD4bOHLjp4Qi6vea06Df+wmJIWkVJnQYRWs4HFq9XrezszPb3Ny01dXVkN+IQtXpdKzdbiesStVq1dbX1219fd1WV1dDsniv17NOp2ODwSAoVEyyho6lAV1V/vyYxcIhVdB6L8/nWLSeSpp7ks3eV6qtVCpBoJBLpP2AuTAW4/F9sSG8vlp976WIzThtPGLKLiFXKECavO8VW4QyjPz29jaEq/R6vcCI/pSKZxoxPjHPuF9Pk8kkHC1yfX0d8oU1fA/hoGfnxY5qmMZoYkYQ8m2KxWIIM8TCieWcPqQJ3NgeM7MAjlB6UL64VvOPUCQeU4T181kacRREexCkkQz+b5RIxs/MQoghQIJ7XV1dWb1et59++sn29/et1Wo9yOmNARMlBC6FPvr9fngO1T0RUj78+DEiROry8tIuLi6s3W5bPp8Pz1tcXLStrS0bDod2dnYWFGeMfrTNkyoas+SpMfpcgOh/m7bm+Y61jZxVL4sP/8xkMgFAargSuVXkeQHIfTRG7H9I5bzPb0wDicqXvPGEe8L7Nf9Lw6tmSexDnqXHmpjdRwOogsg4xgqBaXganxHR0ev1wlxQhM/LHPSUZrNpFxcXdnl5abe3t8GjqMdmEKqK8o+n/uLiIlG/gTBgdCuOKCP1o1gspnobNUwV4EhIpRoQlH95j+OsQAc8UsMBVR5o1WDW1+rqahiLZrNpvV7Pzs/PwziqweIx5X3a/vf8Vj1Aupe5lrWiayLt+TE+5/9XfoLu0+l07PLyMkRWAcJUN/C86EvyUy+vPV/XXGrWO3UU4Bv0RfGEl3fPWZcxGamRCcwl39EHNSbF+uXH+Tlt8foT0QnoV/7YlxhemUaf5XHUUBJFymkP95ZHhFGsk57UusLfMDUsYORMAfiwkhSLRVtfX7dut5sAQmtra7a+vm7VatUqlUpQYMnTI9xSK5ENBoPE5o4B52leRxilJsMylmqRReD6sJ1ZkC5WFA8snMVi0drt9oMFqHmeCE5yHzY2NmwymYTwXhRHfX1umCeL/zFiI8zNzQXPy+bmppXL5cBIvOCij/SNdXZ1dWXtdtsuLi6s3+/PLJzm55C3MMZCvPVv9h6WRfIC2AOaUzyZTILX3ntanyIw/drBuEB4eLFYDPuu1WpFDTQxq5vvlxLhKIQw+kJKMHM9tkCBqgeJac9/aWL96f5AATVLWpy1fXhPiZLQwmH0nb3TarVsf3/f3r9/b4eHh9bv9x/0VQVprN9aNGVra8vevn1re3t7ls/nrdPp2B//+Ef76aef7PT0NBEO/BQiLLHZbNr5+bldXl6G3PPJ5N6rls/nzcys3+9bp9OxlZWVACxVzjCGjC19VN71Jegx+eYB1TSAGFNItb/8XSgUouGAChw1rxGvFgYXPEvMoeauaQE1NWRAmgKhL+7pwaG+1PCFIs/8393dJc6ERPGZBbH3aAsebsaYcZtMJokiEwqcFxYWLJ/PJ7xdXlkEhJJ+42U/esb19bUtLi7a9fV1OL7m6OjIzs/PA+8cj8cJbyxzjoFnYWEh5ELv7+/b/v6+nZ+fJ87kVT1gbW3NqtVqiOTy3sY00IjnEeOEp5ihblbknwE/9MWa+B7DJnR7e2uXl5ch6ghwHivIZZbco7pXHwMiqlvGzijVaAkPHrWv08AcMjjWhtvbT8UHOcbp6uoqpHKpEdLLRy/jv9RcxtqhEXSMoeb2Me/wI43kmdbu5xo31Cih0U/sbZwWatSL8dFYPx8bH/1bIwFwoPV6Pctms4kjrVRvVDk5jX7W6bke/CmppcQLTq+0x1y6vBh0fdckfpSJTCYTPBfksc3Pz9vq6qptbm5ap9MJyiJhOHiifIUwBBbMczweW6FQCPlgepAx1z820Eyk93hB9FeB5GMetp9D19fXCcbJi1CNtbW1kCCuJbu1eh8V7sgX3d3dtVKpFEKDAWkot1pARy2u3JeN7a1sT2VIS0tLtr6+bmtra4GBlEolK5fL4egQDXf2HmRvwdKCMP4Mrn+MpEwmTXDoOiTWX3MbNaScedOiRzoXMaGh61iVRLN7oUzxIpgWChZnrvrwDe6f1ifaAjFvPM97HglnmUzuq0widGJAUvs6q/lHOdT8QniYhlcj6NgjgEaOWfC8EdDYbrft48eP9sMPP9jBwYF1u90wJ7ruvYfT09zcnK2urtq7d+/sr//6r+1v/uZv7N27d3Zzc2M//fRTMLiQGvAc4GhmoQgW4bTr6+uJ8DA1AlBxudvthmiHWPEKBYoKomZN09ZrbD1PA41p94wBzlwuF6ro6p7XXBdAGEVPqLipVajhEViq8SSpgdXPrwJ8In8oyKIGZ99233f+5rgRQszRCb4EcNSoJoqjUQ0avSKbzYYx9AVNNDRe5Zwq/JlMJoytjo1ZsgJ1s9m08fjTcTtnZ2ehSBTyWfcGc4cRrdvtWi6Xs+vr6+BRwqOv9RtQJKlSX6vVgmFddR5vaKW4mXoauU75KX2KGeZmxVdjIE494YwVegY8l3BfALNGo2EkUD7s92WsT2kARHmbgg1dCwqKzJI6dFq/vbzyXi+dx16vZ2dnZ3Z0dGTtdjuk+GxsbISqyr6gTAykfolorNia4X+i55aXlwNopHgUvIq5V77zHF2e50GxPjOXmquqhiB+pxGIvl+6b2JtSjMumt0fkcexKoBGIsw0YsNjtaf0/9nAcVqIasxqGJtcfz91h2MlZ/P4M6G0UhibCEUQizX5jxTOoZw7jLhYLNrq6moIg/Jtw1qolf02NjaCgqmKMf324xD7O6a4ew+DF6aZTMZardbzJukJxOHCvg+5XM5WVlasWq2GirSENdJeBAR9YjyowFetVhNFGG5vb8M9er1eQrgA4GJVNQEQWLpjDFfBb7FYtL29Pdva2rK5ubngPdTQBBRpXtwHbxvX4MWmAEfa2YV/anqqtQ+GwFgTrsC5R1q2nDVKzo16FyCNPOD5fKahlvo9lnrOStU1wh5GIHtA6PdRTFAr0ySfGV7i9zcWRyy3tC+NQU9j4C9BGrLGOLO2NbEeoIfS1mq1QtEmDGVqIcazfHh4aN9//719+PAhhKiiDKiX2Sx5LIdX5BcWFmxtbc3+/M//3P72b//W/tW/+le2s7NjrVbLcrlcCJ+r1+vWaDRC9MZTCeX44ODAtre3bXt722q1WugPii0l1/FWUwjEG01UYdWQx1krOGmy4Sm/SwOPaUI9pshh4Wat6PqCt1JJWc/mUyOagg9y/ckrJaxceSj8I5v9VBYfQIE+wPwpuFIe4vvHegMI0w90hlkCx/F4HAyG7LHT01Pr9Xo2NzdntVotFKIiPUbPwPQRUir3VWbCc1Buze55AV4CjHqDwcAuLi4CYFSerHt9PB4HIzoGuuvra+t2u9Zutx8cT8aeIUqnWq3axsaGVavVB94myMsIb0zwirBX0lVJVcPVLIm1qcYlb7hWYKlHqdAvjuhApiiggqbt+ZgOo4ZCDxy5txp/Ymk2sef6vRSj8fhTvi1Hz3FWdqVSsa2trVCkTEMaY0bU5/K5z6E0HUd5TyaTsaWlpYShDP6FZ133ngKnadFafu1PIzUCaGi98jxd99MMtTzvsfFVo8RkMkmAxkajERxnOMTAPrRLjatPmctnA8eYKzPNcjqtk3yvVi48H/pCmUSx1Dhws3u0T4VPtYCx6WGGLCS8HiSQm9mD3AP1ZC4vLz+YWN1A+tLx8J/TdwWOyvzTGMEsgKMHEWoNyeVyViwWbWNjI4QAq9ub6qnkJKkgQtAwdmzq6+trKxaLiepyCgo1FEoFkgI8s3vLG31QJl8qlaxWq4XKglic6BfjHIsrVw8oFXmbzWYoPPBcz8mXIl1r6lXzpAoKh70Toqqgkd+iuOA10lAm3r1H0OxeQZ9mvfLKQmwPpfXjKSAE4IiCqcAY4ceawcDgrY5fQhAqpVmPWZMKevmbY4nOz8/NzGxzc9M2NzdD6CZKwcnJib1//97ev39v5+fndnNzk/Aqad+ZlzQLLPtsc3PTtra2bHV11czMSqVSCP+Hr35umP3V1ZWdnZ3Zhw8fbHd3N1RDprLk+vp6KNR0cXFhnU4n5GfrmvRKF/xiloAjRl4eTLsm7TMPKKcZT/gcmalFWoj20FL17BVADrxY0yvgx/1+PxjT/Pl2rFXWnqZmsMZII/EelVif1Wpvdg+oAKBatOOlqdvtWr1eDwYQCuURqWN2H+HCeGghEVUS0U08j1HDrdl9dXKU3U6nEwzpzFOv10sUtEJum91HLcD/CF3PZDLh98wZAIn1gr5TrVZte3vbtra2QqSONxBC6FrD4dAGg0HQu7yBFcDm12vMy/LS5Mecz1Qf8G1g3SE7ACLj8ThRuXs8HtvKykpC13mO3NA9rR5FzeFFlqKToG+lHQvn+6rf+3HBwNBqtaxer4fUpNXVVavVasG5ouHT08ApYzcLihkWvOFJjZuMGfNlZkFmqPzPZDIBGKvMShu/p65TnU9eqg+rTvpYvmoaxeZC9VcMRfCnubm5UF8C4Kh6P+P5FCPOs4GjL8ChIWnKwFA0VSHxgpAJJI+QMuDFYtFKpVIIUVErkC+uw2LQimNY72C6mUwmhMtgaSc0ToumKOM2swBUfViT37B+Y8WsT97aph5Hz0CfY934XIIhAeL0M7xRhKyen58nQiTwOBKeQgWu29tb29raSswbhGAiZJS+eRCt86eHVXumqkwXLzTnj7FGmAtALeOs7z5kmGIwjUYjHO+BJfgfI6myGNv0fv2gQJhZCLFiT5vdr1Oq9hFybGYJJYVrdfxi3yufMLtXNmLWLq0a561zMUY+TYjRBzUiac6ICg81GsWEY2wcZ0Fp+UDKf1ACh8NhqIh4e3trlUrFdnZ2bH19PRGG22637fDw0D58+GCnp6dBgGhIKjxMn+m9BIwNew3AjUdkMpkEcKf7j7ziGPDxpO3o9/t2cnJi+/v7IQQeQ2ClUkmEyaG4qodFjUt+jerxHS9N04BhzEDy3HukKYb6DLN7ZR2jrHq3JpNJKPpBJAl5hNQG8IBQo0b0XGNVeOCpmUwmzA1H+Khnp1AohP03bQw05FPlJ5+lgc+XoP39fTs+PrazszNrt9sJvoohiogmQuFR2rQ4B31Q8KhAQUPYOAJBC17p/lOPAOtb+ZVe52WWrjs/7qyRzc1N29nZsZ2dHatUKg9AI/dRBZW9p7Ii5kFhPer/3kA1a+IZyH39LGZcyufzViwWQ2E8BY4aDaLr1D9vmoPFX+M9jug5tE+NiGlAI2as1fvzP8YFajhQib5cLod0Hw8aYwYELytmOY9pBgbPV+HxWuRraWkprFUi0TRCANK+an+meQW93kUbPGjUdaK6t86n13WmySlv6OaevV4vHCcDr87lciEEnWKgmncZwyzT6LNyHGMDrRuHxPtpFSi5HqsO+YS1Ws2q1aptbm5apVKxhYWFhNA3u4/9RzBxbAQKsCqneBlYPGaWsARgzVMmiEeGQfWLhommTb6SUxrx+/H4U+lnteoqM0sDkS9JPoFelQAUDJLkK5WKXV5eBs8jQkNLbhPiAFPC6+EZc9q4aBtQeHRdaZiyL3gS2wC+Lxqao8/UyppanOPi4iKU5P7HTH5dpn1mdr9vbm9vA1NFOVDAprmN5HbGLI5pQjGNuSN4SdCmCAEglRh8s4c8JnY//Vytx7oG6AdFNfRaBIxf/9z/S4BF7Q/rmH7ElCu8RQgGs0/FvgCNjCmhKo1Gw05PT61er4dQHQXlXuBBqgywNvjN1dWVNZtNOzk5CWebLS4uhnA1NTao4cw/M218FfQeHx/bwcGBVatVW1lZCft9dXXVNjY2rN/vW71eD2F6rB88ij6iA177JRRVpZjRNPbdtM/4PGaMTbseEI2XTw1/AEfC0QlVBOhBar3Xg+dVnnGdV2gpfmVmD/i5l3HeSMT1qnzFgNcs6Pvvvw+Wei+3yuWyvX371n71q1/Zt99+G/YdRh1yDvW8SXgNf3vgyN6fm5sLYFvTN3y/lfya4N0b9Lx8HI/HIYd0e3s7pHmgWPq1xTvyAWMN50D6PHjlGzyTtule9KDkpSkGpNRQqgZzCJ6GQwP+Qr+1Lgfjr+f1xeSH6kIxUKl7Q+/NutCQcrOkHJtmRPVtQIY0m01rNBrWarVCtFi5XA68Fu95Wq2NmL7xJSht3HwbtVAcBmTCvzOZTNAJlG/Bl8ySESuPGQEUm/CdOrs8GFXeGeObT5FPHjiOx+OgGxCeCl8vFotWrVYTclRlvO/LY/P5bODowwZ1EDTsyVsivQJhdj+whNOQkL2zsxNCJQB/GmZIKAeblMnnf3UBM3Gx0BaEaL/ft1arZRcXF+FQbLN7cOUZXaxP3ivgrW28e8YKEFNr0jSL0kuRz+3UEBPdMMvLy1atVq3RaNjFxUXwCOr1k8kkhDwgPGOV2NKUc/X8eS+IgnzNj/E5OTrWgEAdX9aNkn6PhZdDf7XS3P9fSNeUZ+bq1WXPMYYeJOjxDqr0qUWM9ap7QpkR13pwp7nGFJrAc0X+q18jXuCmKSLaPgxCPEMNDsoY5+fngyEkVrX0S5G3WDO+jKV6bMmzub6+DnnY6+vrwbOEIazT6dj5+bmdnZ1Zo9EISrCPoHgKr2FsybP66aefwllZl5eXtrS0ZPV63d6/f2+np6ehWE8stcGDx5jwz2QyAaBeXl4GowLKGSGzFONCJuiRArRZczhZe19yfmOyb9p1j82FKjNp94A0UiObzdpwOAyhxBjKUDAWFhaCkqxVQLUStYI89WB4GccaBMijwGGwmuYx0bFSy32awXoWdHh4mPifiJbt7W375S9/ab/+9a/tm2++sY2NjYTxbTKZhPQID/QA8L5fWs+B1JxcLpcwwCil6VOeZ8YAH78lbK1cLtvOzo7t7u7a1tZWKIQSmxs+g3fj0QDoElnAtWn3UB3HLKlsz4p0zGJA0uyhcQdPbKVSCeHb5Pgq8PegU/Un7pVm4NH/vaeM9UBYoeata4HBWF88KWAhDBrQeH19HbyrWjQydsafN1J448+XBJDKI1jT8Huz++Jyy8vLYX6ILhoOh3Z+fm6ZTObBWGLgUX2C5+mzY8R6QHfVYp5LS0vW7Xbt5uYm6CQAVcUBnrze4//nmVdXV9ZoNOzs7CzMK89HRyiXy8GAqPsOPqMOl2n02VVVvbLOewz4KCMDsChT1XjycrlslUolhDwhIFThUeWYxU0olQo0b5nURabMD+WEM40ApVTP0md6K15amKofE/29jo//TJ8xS1JBx7t6XuizhqxSqU/zFD0ABlj2+/1QPRPrpVcWFCx7sOOtHlxDgu/d3d2DMBEFPtxbi5/ofGioyng8tm63a81mM1Sa+8ea0+hJAV3MGMGaJPwJK3PsbCazey8dL/aVrnslnqVMjM/NkgJbwetoNApKFMBNcw1j/EXb6gVWjJhn9Tp6BRQFgVAy2h5j1LMkFey+X7p2sZqqUCCvWA0xgAI9gxNhFdsPyndi+5T/r6+vrdls2ocPH+zm5sbOzs6sWq3a/Px8OI7jw4cPD8L7GEP1MIzH4wRY9EBAPdHsSW/RxRhINV5vwFNvDu34EvyV56etG5WN09ZX7B5egdPPnkLwyMFgEM7sZT+Wy+UQLqk8OZPJBAWMnC8fUaIRLIS1DofDB3vcp2ek9Vnv7T3jj+39l6R8Pm/ffvut/cVf/IX9+te/tjdv3litVguhfF4vwSDN+tN1DThQQO8LAep5mqTiePCuvN0rtmm6GWM4Pz8fchk3NjZsY2MjHMeiMiHGh+GnVBjtdrs2HA4TSq/Ojepleg9tG9fNgqbpqR4IaPgv7xQ/8rKLqBkzS/BOs/sUJ//8WJuU4M8U/yqVSiFkOJPJBN5NSHNaMZO0+zN3gMZ2ux2KZJEetry8HIxzMQDsx1JplvsRBxKka98bHWkLPMkf05TL5QJoRs/RNBl1VqnH8Cl8R2Uc8okaKxzpcn19baVSydbW1qxUKgUe4uWu4hgvB3SvYwxoNpt2enpqZ2dnocI6xqFKpRL0cYzLysPV8Kp7IXU+njd9ZhsbG1MtKN4KoeCAiVQmSt4blhZF4QgdBkfPCaJgiQ64nzQmDlKUTXllQrmwzF9cXNjV1VU4w9ErZCxeDf/xm0y9XDAb9c6ooPHg0S/MWYVwaDt9+7QNc3NzoVLq1taWNZvNUCmRkCauHY/H4YBRzYPEeqYbgTYoUEXBoN9pSpNfW36hx0A4jEM/Y36ICacIwpdSSF6KPODQNcY4Auiz2WyiKI4H6lpAA0HG2PEsFZgaCuiNKF5pUoENCNLnkyvEc2Lzn+bp0HHwz2aP0mZVIBAUhLH7YlG+3bMi75n1vEANJ3gOCaXSvF4dj1wuF6pKj0Yjq9VqD/J92b96BArzmrbX8F71ej07Ojqyer1uZhZCY6+ursJceuOU8oqYoqnPo2LqycmJHR4e2vr6eshZYc/jOe90OkGh4sw75lDXOb+blaIao8cA5DTQmPZ5GnjUv/V/5fUoCf1+39rtdjgAfmlpyTqdTqIgFvwZObC0tBTWgQ8xZP/f3d0FJanZbIa1pNWsY0aFtL57z5Tyu1ntSeourK6u2tbWlv3lX/6l/Yt/8S/sN7/5jVUqFctkMolqs57vApRpL0YyXmo0V28jehA5WRQoU30nzQsZk1vMkeoqnMG6t7cXIhXgH/A+1WVo83g8DsZ2jurCYBPbyzpvUGzfz1Lesv+ZG9UVPalOy994ZpUvkh/I/5xhqfdWWRaTUbHnMk+kbW1tbZmZhWPPkFOcbauhsfoc7R/vmuJAZB1RBisrK1YsFh+c40rqVww4xto/7ZqfS9Qi8HtIX+QOIs9oD/JKq5yamZ2enlq73Q5jxtonLJj7xfr12JrV+SInX4/V2djYsFevXtn29nbYf17noT26R/xcY8hotVp2enoa+G0+n7dKpRJS/yqVSiiKA3A0u4+qpGAe3shp+edmnwEc/+k//acP0L6S96yhsGqREWVShLBgpQF0QKq0aLELNqtWvIQBk6yNx2QymSQmBwWMymNUlSIXaDAYBFCrsf46gSi6CBgtbc9k6rmFaYmwnpHqO+MLA3lJAhwgyL0XEZBv9mlxra2t2TfffGNmn4Tq4eGhnZ+fW7vdDvND25k/5r9QKDwAj348dS2hgOhhuP5YFp9PoPm1HPCuR4Yo8b9a31CY/v8GGhHssXarAGeuNWEcxd5b9yeTT/kaq6urCQVNGbY+Q7/3jDzN8OHn3b9iYWgKSGP3gjxw1JBnMwuKrwpsCsowTljjuN+sSZ+hHl6z+/5hMYavUdxIz1njXniP3r59aysrK/bLX/4y7HkttOGNW/Sd/1G29HeMDePL9XhuK5VKeBa8Fv7tjVUaEqm8R/fo2dmZ/fjjjwkhSB/JyyEH++LiwlZXV0NxCx9tonziS8xnbO0o6JumwKb973/3XJ4FX8Yr2G637erqykqlUghP1/QJVai0WJ3mP2GAIfQ8l8sFuY+c0yJUT/U4qoz0n6eN70vQn/3Zn9mvfvUr+8UvfmE7Ozu2t7cXgBbGZPQD8nu1+rTZ/X6FYmfzkS6ghnOK7gAaAY7oPX4fq6FUxxAZqud0FovFAIiJVPDHFaUZIzjSA2/Nc9I5vAFJ7z9LmZvL3VfH98fHeLmn0Qxm93wYY2u5XA7jjYEM3oU+Co8j3DMmx9LWL/xpeXnZNjY2bDQahWcitzkRoFar2draWqIAZFpf7u7uQorBycmJNZtNu7u7C3oAhmQ9msdHMqXpGNqnaQayn0taxI62xQwx8DavW6pRG6Mk6VWj0cjq9Xpi/+CBh9Sgncaz9Fr1NmKEh+eWy2Xb3Ny0b775JuRHs16811tBpPJ9NSJreKrZp+rqq6urtra2FjyNGAXAKej3WseF6tuq+6fRs4HjP//n//yBAqkLxisYV1dXwfrY6XRsMpkkjsDAAgawY5F7BQaGqZY3Dr8m5pyqfkyYlt8l9IOFhbUQbxOhqmdnZ9br9cJv1KLj0T5KEgmnFLzR+P9Op5MIs4pZ3x+jWQBHYq1VmVOF0uz+qBSSa1+9ehVCKfDkkp/mhYFa5fAS4+VS8kABRUQVFT2eBcusHtPCwkcRpZDD3NxcSA7WirkIYA5Dxos6Cy+EV3RmQWnAST3cGFcYP++FoY0YTHSeYuDegztvrY2Fuvh7TPvfh5PGXn4MfDtVoKryilDVkCQYveY7eFAdG+uXIgUSCnDUao/gQWHDGq7zBW9hz+oZtHov9Th6L6PygrSXv4da3lGgFRzy/bT/dZ1y38lkEvrord+AYwT/ycmJnZ2dBeWH12QySQAdM/sioeiPKVGfq2Q9RXnR/3U/aKEZgECr1bJisRh4oAJ6NXByL/aNj7rhvoS98pnZ/fp9SqiqthkFib3BHMYU/Zekv/mbv7F/9s/+mf3617+2zc3NkF+GR5VznvESEhKufWJNqyHVF8yhr+p9JEQVsIjOo8CRfeb3ohrNACEcVaNeB51HDXmMrS3mAD2OyBwNeY/Nm8qgaXrPLA1zWgXazEJYrbbV7KH8UCcD/INqwIwvoY7koupZ1eVyOXgG1fuoffZy0ew+SmRra8uWlpZCvQ2OIuJ+Gu4ck4c6D/1+387Ozuzg4MDOzs5sPB4HnRtDAvcnegW9IWZMSJOJswSOyrt8OoI+268tP+7ghPX1dTP7lHuN86PX69nFxUVi3WohRn+WZYx4PtEwFKKBf+RyOet2u1atVhPVwgHwWhFc26+6OfuRVIPz83O7uLiwyWRiGxsbtru7a7VaLcwxObI+OlKN/LTdG3bS6NnA8Te/+U1UaVTgCBghz+by8tLy+bw1Go3gEs1kMsEyqdUdKVbDBlcvIy+EGhaDXq8XDr/Gi2l2f44V1SGxNgEsFDhysO7l5aV1u93AhDUn0y9Okvy5ZmFhIcSgEwJE2XN9/mMCM/b3S9Pl5WUCOCrIMHt4fAYV92q1WgDtKtS63W7i/ppsqwIeJVcFl3osuU6VfpgGic6ASbyPMDoUEzaeekERcloZkDNudG5i5AVM2jV8r2tHN+osSC2D3uplZokYfrz7sdxG/laLFJ/FLNAemCkpU/K/iQFF/1s/3jEwGfuttjNNUKuXld8osyd/lr2gTHbW1nE/f7SX9sAbKQdPDgPrX+dKQ/W9F493VeT8i+9V8fPKn740FCz28qH6HrR6r6M+V4V9oVBIgBgS/3d3d0Oo7NHRUcgPWl5eThilvCV3lnMYI79PH1O20r7z94jd13s14El6NiCVMbXiJ+3UuVajgtn9ucl40fToKubORx2o0UHBaBr4MLMQsYQBGIPhrOfxb//2b+3t27e2sbFhpVLpQbVFDJasTTVya9/VIENffZE+eBL3RM6pp16jghSAq3KvMgd5qYZXNbbSFr9GIDWa3dzchKgsQKMPT9V54146zzEjBGM0a+CIDFbD4WNyPLYvc7lccBCQygR4BDhyvEWxWAxn3VYqlWDAot+xZ5pZCJOcn58P3kZvWEjLP/PAfDKZhCIwHz9+tOPjYxuNRqEITq1WCyGqgMbY2phGj+lFL0UU8wI46lgiK1VuM2ZmyfxZ+BFAEG9fv98P60OLPCroYg/p/p9mAMDTTQQcRUCvrq6sUCiEuiGEKGvBRyV1imhxH4x+OLoIQd/d3Q3nKaMnq/zTaKyYR3wmwPHdu3dTN7p6OPAIoojOz8/bYDAI16pAxwOCV2gyuS8EQbinjyUHsIxGo+DKZ9HALMzuDwTv9/shwZhQVQ56Pz8/DzHCvsqS7y9AZjKZBMGLhQFAq9Z3isXwf4w8YOKzWTFVZXg+XMzMEptK+00I4+7urpnd5wBQtRGQrtbQbDYb8uu4N4qtbj4sZGYPc3K0jT4sh7aRy4BypNZzjAQUg6CKJ/ebRk9hot4T4s8KfSxm/HOJsfMvBTswXCyLPndB3zXkw3+nIC6mrMbGJE258J/pdzFhFAOd+p2/xgMpL2j8/c3umT0Cl73wWNjGS5BX9Jk/eBiWbfKpCJ0qFotBuYsBclXeYopQmkVZ6blKgV8XqixO+40qlrHrkRPsWcapUCjY3t5eqIrcbrdDZWSiI5TXzFJRfSmKtXHaPOgeiF2noAeDm5kFmaR8S3OFoPF4HMImJ5NJAOQoPKxXjLvIaeUnMeNEzCil/cFIORqNAj+b1s+Xol//+tfByMZa9Oe+0Q+MOD68W4GxN9D6/DSzpD40mUyCl92HjKuSrOOBTFUPsIaiaVtUwfWkQF/z4jjXWIuYQX5fqTFJox2U/ypwnNVcaroBY8Jc0IYYr6E/yEr4DZ5H5nxxcdFarVaox9Htdq3b7drZ2VmiHsTq6moYF50jZJXKVsC+5+Fe/ioxfuiueKQuLi7s+PjYzs/P7fr62orFou3s7Nj29nYALjxPi9V5Q22aDuSND7M0yKknHVLDFG3RcM80IwHeVkI6u91uKOZ1d3cXnEfkgYIbbm9vgxEgBvJoq647jhQkrY10Kqpba99Un9O9rrowKQCk4+VyOVtbW7PNzU3b3d21jY2NcKSO95LqHMXW0lP34bO12Wq1mvifB+kEYR0nnAF0nc1mw2HxKGmaF4hwUlcsk8nnypARKioUKX1LqXbCCzhbCaapBQKwpl1cXAQGYGYPQjGYwFwuFxQ22oyA4TMshyw6LaF8d3eXWChKumkfU8x/DgHwFOQi+Gi7Ami19pDzyDmP6+vrdnFxEY7saDQa4eBRrDgc63B3d5ewAnpriDJJGCB5MySJmyVDphgz1qMqK71ez+r1evAk82LTKTN+CeHFmJRKJSsWi1YoFMI6nAV5r423arIGYVQIJPXaeKHgFUZV/FXQx5hymsKbZoRJ+yzWFs/09DP1bsSUIf87HSdv8V1cXAz7QkHoLCltXDT8j9yzxcVFW11dDWH57NmYEGBPeaVbr9F33x7dH2ltTlNi0owA/rtYW3xbFVTqdYxRuVy2ra0tOz8/D8d3tFqtkAKhIHmW9JR1Mk0B9Pd6Lm+KXafeMLP7FARCeTVXLwZouAeROuPxOPBkVWrMLEQE+TwjNfpN4wexedf7k+uUpvC/FLG/vPJFX3xb8UApeOQa73njGu895V66PpSf+f0R21Np3oS08dL50d+Nx58K4VxcXNj5+XnIBfOhsfpsJR/WrrqMej3SjHgvSRpG7/fetPXnoz4UmGiNAAzjGPVIfeJIhNvbW6vVasFrTRoBhgmcCT5n2AMI2hLzSKFjcRY5YZfkNM7NzVmtVrPd3V3b3t4O0RiEmisA82De692+ffrZrIyssf3gQ0h1DmNGKkhlpXqKW61WyN2F57B+kb+DwSBE+sT2r46d4hKcGUQppukp6iDRCEsInRidFy8mZzlvbW0FgwBtmxad4T97qqx5NnD0Z5xAXkhMJpNg2QTMadgGuY1Y1fSAThY+OYJ4Ib0XkPsxOMQU+7zDyWQSDtQdDodmZtZsNu3s7CzkxWjcPmBGn2eWnDT6Aci9uroKC1uLzHgGPs1Co3+zuGZFAEfGFmBGmxUIwzg1jCGbzYYzn1ZXV61arYYQjZWVlTCm5GMp2CfvUOOu1UKqXlmerQqKei+Zfxgfc4LQOzo6ssPDw+ARVZCcpjQzF+oV9YyJ+VFANT8/H6xYWohADxF+afKhN6w1rGGsN7UqMg/0U4V6TBmJGU+eo8jGFIy036eBWG2j3zMaxoMnxV+rSpPeUz1X9NWHTfvxmAWlgSxV5vQzeKYKOVVuHwP0j4H1p8xvDOg9tV/ahmn7UK/TcdD2028KR6DA4aH1RXLUEPLSFBO8n7NmvHL72L5JI1VCkF0ovpSEv729DYpkLHec52q+ntn9WcAYeb3sUyu5AqZpxggdLwVCZvfh2wrKZkWj0SiRwwjFwmv5G6OTel294u+9h0SjqOKu95xmYPD99/vR54Kl3UflG8b4fr9vjUbD6vV6iE6KAQnPO9VDqtFMtIHnPdcg8rmk+oMaT2iL99yaJSMk0oxt6EjksOkxSfl83szMOp2OHR4e2vX1dShkk8l8Cq9fX1+3jY2NYNjye07HzBstNJyfaDqADxF41Nlot9uWy+VsY2PD3r17Z7u7u1YulxPrTqOhFKCxjnSe/DrS98cMYT+XvKGGKDPkvo/W86R90zV/c3MT1nuz2QzjOB6Pw/6nVsvd3V1wBhDmS8SF3l/5nUZ5gFu0jbpf1UAHBsKrqMekgJ+Qe9Vq1TY3NxPH6ngjxFN4yFPp2cARBuKVQa94gZIZNF9umtC5YrEY4pcJixiPx+EAaRT8mOIDo9VQkkKhEM4tKRQKCQWCUvSj0cjOz8/tj3/8o71//96Oj4+t3++HTarWHQ/gsO7DYK+urkKI7M3NTWAibF68pXjveIZf5F6pUXAyCwKoa9y0WgZVCOF5SWMKVDfEAlIsFoN1DaDHhlDLJotbCyxgOQPomFkocqMbgXuSM6vx/5w1d3h4aB8+fLCzs7MQKvwY0QfyAQuFQoI5wBDUcg4Dw2pPQjK5JeQPzAI4qtAD6Konl2t8Lon+7b1uHqwpM9Rw67QwIw8EYkInZrXmWm8p9LxGv1PPfgyg+nv6z7T/2r/x+D7nSJXXWQIOVYz/P/b+PTbWNUvvw54q3i/FKt65ua9n9zmnp1vTmcZYtqxEiid2pMSRg7EHY8RJDChOHChiEjuTCJjYShQ5ghEPrAkkRyDsSBEGsmTHihLbSGQJtpSMZUUKYjj2WJqRuvuc0/vCvXlnkUUW76zKH9y/l08tvsXNvQ9rn+nT3wIIknX5vvd7L2s961nrXa+3GSIAvUllw1KplMqJ8xxRX/i6dafrJgAQv5+7ToxqOEhFcqDL2+ES+9XbGkmDOI/RUZ5yiR6HEENHOLDtlXxZ8OTP7PMC6bbecm1ot9vJHvm5rICNBw8eJJ1drVZT9NH38ES7zrXp48PDw6TXy+VyBxEJOSmpY39Sbg7G9vPssPU5gqtXQHVtbS3p9Jv0Iz/YBekqy8N1pZNtHnkkFQ5gGfdOdQN87yuu99B54JGLi4u0T9ijjJ6xECPGLv45d9TiuDmm67XDQfSctFtf9zHV1+d6jGrHjAX+xhkEu3Cu7vT0dNoL+vr1a21ubiYdNDY2lo4ymZqaSgQzbWKdsv0J4gsHx9OF2ZO3urqqjY2NlDHH3J2fn9fk5KQWFxe1uLioWq3WUejJ5SYylz5CYn/12mmkHdwrYi6PnObaGB07dyDRkaStkjnHFrj+/v6Ups2Zwk7EefAKB5aKp2TqsQXGbaXjfX48ExIn9vT0VMPDwylKjZ/D3lQqi/vpDj5ubyNm3/ZeTt7ZcTw4OOgw8j4ADqy9bLQrG48OePhWUkq74h5eAZHOQDkDAvjxvY1+iC6DxH5IDnpfXV3VF198oRcvXmhzc/NaFMsnqBtKInEMMM5tjIjhNBJBzVVr9BQ4Z7hyTM9dC2PmOdZunFFYsZiNt4lxJdpbLpdT6W+KJ7TbbW1vb3dUWEVw+tyQksbspbOlq2gvc8s3/cfKYgBGP6olsnb0tytPUkiq1apqtZomJiZS5JCCEs6eu6PtEVPmOH/3co8jgtJiXRF5c4ed/Uk+ht2Md1RortgYl0h0RPF5QhtdT8SoPJ91fRGBTkxtItXd7/U2Bz0Cn+g44jTG/Qd+37sWN8IORukfokSjo6PpzEIItpvEwX90vrq95m2K78XCG37MjhNQ0UGLaVg5R9B/u2709sStDbTFjylxIO9zKZJkvZAv49hEBzl+n+fy699E0jhx5+ftlUoljYyMpKJCgBGvuull/rkuOhjww7Wxg+gg6aooF+up29zLOUhxvbvddR3ey4yc7e3tjmrvMa3M9aM7ShGzuF6LDpM7xRDrsfLhbedPN3Dvv72/GMNWq5UcEaJUVE6lHgVzIWabRJvLus3pbggLxz5OyvVqLD2LyfvGI2ouru/dsYhOcyQuW61WGsOJiQm12+1UdAWHlfnB2tjb20vOANVS3S7iGLG9hCrIZHGxNjiSodFoqFwuJ0eCSrocyzA+Pp4I5Zy9idgTiRGyqMO7EQl3Lb7mGCeyDCliE8nRSHB2E+pRkB02Pj6uw8PDDhy1ubmZ9kJSv2V7ezvtYSyVSmkOSFd+BFmXkAyMsRe68rRY1iLbqZgDYFGeGUe3Wq0mcuE2fXib99/2uXdGs5SudSXu4AQF6Rs5yQ12ZwCjxiG5kjo2LUtXaXE5QOOv+UDgsMLkebvY77a6uqrV1VWtra0lBRlZfsSjNa70fRLHVAh3mG9iuH2QHJg6YOyVQgUsOIhyg+DOtxeXiGCC52PDME4bzhI52Nvb2wm8MAeckYTJpg2AQVLNWJB+DuH4+Hja5Dw6OtoxNpyFdHFxkSpZwRDBFkmd+y04NHV+fl7T09NpQXolOk9RBjDkxsr7qtsm6rsQJzUAIV4YiraT+kBKWbxGznFkDjsAd/KAfnQHP6e8ck6ag2Da7oA0gmQ36N5W1iaMb84pAMS5oXTjE+d0rISbM6a9EE9hc51Bu0kLPzs76yDXPP3NSZGbnPpu0u0Z6Suv1EiqYyzg404D7QLkxP6OANmNfJyXDgLJIIB9lZQIEuwKYBWwxOsfAuTk+u8m6Ta/IpHgjrHbqm62y7d/+DplX+ji4qKq1Wr6n0wddH7OcWy1Wtrf30+fwXEEOJVKpVS8AcLQyeKYcePPGp+FtcuWE9dd7zq330XQlScnJyla4O308YjEsRMbLq7beBb0FrjFK4XH/upmQ2K/SXniIa4nonC7u7uq1+spzZF1i83zeRfxCdeN/eP35HvdtgCAz3ohOKs5XIUNcCznUSNP33Wc4kQo3y2Xy0kn4kR6kShs2+HhYernra2tdDwG94vrg75kDMgSo/oqDufQ0JAePHiQDn2vVqsp04mInEdQc86923zHMzl88KHFx8l9CPqBI2Z8fN+1zVyLtYdOY00fHByo3W5rb29Ph4eHajQaqT2Qlh4N9EyCOKaeldgts5Lo9Pz8vB48eKC5ubn0nH19fSmwQbZfbn3lHMGbdGbOjmT76ta9+kbccYwXd8XiyhAgQXokziB7UTAK5+fnycC5YnaHIN4PIcTPwoQpIN3x/PxcjUZDq6urevXqldbW1rS1tZWcRjfI8frOluXaAEvAMzjLzuKDsXOHw8WZSe5xGwbhfcVzxKXrDI2zva7Q4n4MH28UYKvVSmdqVqtV3bt3T1tbWynnnrOOHGT6QcnIwcFBhzPk0TQWJ/tnp6enNTMzo/HxcfX1XVY+u3fvnkZHRzU/P5/Shf14CpxVnpFKlZOTk6pWq+lazqSSssLY5AyeG3pnvHohbrxz44Pz7pHHaEjjnHYlzfvOnpFyRho27CfRqJjWlBOUJN+RroyjG68IdNzZ9NcjmPPnYAziWOba43sDbtI3dy1OMjkQdWNSLl/uK+7r61OtVusg6LzysPeDH5nT7bn53U2f05/uODp4YU00m810nh1Mu8836bpjyHUdjEX9w9+k/GDEIQc5C4v9njC88YxL+pK50EvJzR9vg/dHt9f4fM5J8ciuj53bF7ZTkPnCmAGMOCaA1znzzxlsJ2c9WsLWklKplMCs9+vp6WlyvFjnRNZy6ZixD6IddMcRfRSJ5rsU1g7bJnCI3clwnBNJH1+PLm4reTYyb3hO7yu3eQ4+c8RKTlwPRwcXMtEdx1arlchiJ+MiYZFbrz6GEZvl2uX6pVf2MTqOHvCIUR+3AZ7uHok50kg9JbBUKqVoI5lL4ATPfqMYI1XdDw4OUuEV2su6i+m+vO9bYMBBExMTmpmZ0ezsrKampjqK4MXxiHrd15z3kdvOHAbP6bcPTa568Cna/m5t6mbnEPrM7cb4+LgWFhbU19ennZ2dtA8SH0bStfUe+zkXTPJ6GziZrD22E9y/f1/379/X5ORkWpOtVivZNu7BdWJ21E1jdhMeuEne2XHEy45KJC6uGP3zKByG5OTkJJ1B4tfCe3eGyH8iS41S5HqeJjk+Pp5Arh+9sbW1pf39/cRA55w578y3OcpRMUudEzYqzzhAvTKA3cQVUXQSo/LnGT1tg3FxUC5dOaBEBqenp/XRRx8lRUn++MbGRvrxfVouKHWUKkJaLe0cHh7Wo0eP9PHHH+vhw4eqVCopgsgh4US/PV3NFbJH7GB1XBH4/HRnjb+9iI7/9j7qpXCfyEzjEPf19V1LI4ygJ17PN+uTUuxrzj/b19d3LR0jp8gddMTDmQEQGOzYBneMuj1/TInkOh7V9HZ5f3kfOoj39dBLoOp61MfE2wKD7H0a5zOgg0wPnDgMi3+3m+MYHRYHirCqkHLoa/Q892XvmwN91+fxPq5bpM5ItKS0zwM233Wtp6NNTU1pdnZW8/PzGh0dTffxaEIv0xwR1xMu3ZzGbp+LjmOsgu39xrrzo6AApjgFpETWajVNT093OCxEL3KpoYA0AG+9XpfUmRmEOJnKEUAOimL/5+yNzzkvesU8pj29kHb7KipAKpoTYvSHrz1ft+7gu611hxngyOtxj7r/+FYI+uNtjmQEqJAI6ASPLlK9nPHkf7fzbgvd6YjOoxMabmNzffwh8I+TDRAZjC+kR3TOXEf4euVZcR75PGMxOjraERXyiJOkLC7mf+wb/c9rsY8HBwc1Pj7ekeUF6T0xMdERfXN7mdPvUXJYLhLfjv/idXslORt8enqaaqEwP28ixv13jnTJYTUP6FBss1arJdKccYKcc1wR1+Lx8XHyOYhO4/xBdBKpZO5ACpCi6iQamM6DX0i0pf78Pp7dfJu3yTs7jrDI7tjBPsIwxjRVV6BMzFiswzubzoggI4bOeXCUNwO4t7eXlMTIyIja7ct9doSY/axBV645ccMeJTJCvsgi80D7KRrD69FIIrdhE7+MRBY+Ojs+6XITzdsYUwidYSbfnznSaDRStbb19fUU+SWC4E58Li3L28SY9/f3J5Di7XJ2/fz8PIEhB2L+HS+G4kbBlWbOQOf2NSLvkzLxPuLj4MI6AwTw7PQdyo+ooT+zkwiukIku+GcccEaCIeeclEqlBGLZt+ZRRv6nP32de8QlEkrcOzqORKx4zT+XG6MciO9mbO9CnHS7ycDzHF5JTrpii3k+Ku05exojmH6PnESmHvAc03G4Ho4HxtSfB4DsusPnI2MeHXvaga7mLFJSwmgT5AYVjWdnZzUxMdGRkhajmR9Cvuy6j+x6twizj5M7Crloc7lc7tgjThob7/k6ka6TCF70hffjc/qaY1vB+Pj4tftxXSdrIuCLY3eTTb5LoY4BdiOXZcM4OCnnTr4TQrl17U6329FoT9y59/dy+svHzaMyFN0gK4BCh1zPnWHWk2ev5CJg9IXbikhmuHh7P6RtBLg3m83URvAqn/Gx4TWfx048eeZR/BxRRscTXI+z+2K6NnMEZ5LsOcaKrICxsTFNTk6qVquldErOOOWekeSOa8cxXG5scq95O7s5jb1ck75WSFVly0LEGL7eIvnqbe2WGea/wY+sQfRYXGNR3/oP7aby7dHRkYaHh1NaMdutJiYmOmyqO+jlcjldi7a1Wq10SoSPa44w8LHx58thDj5zk7yz4wiAc6DDRl+cM68MBNMJWHOQFwE44N3BQ2R9IiMQjSXRqePjY+3u7iYHA4fSq3B6KoIvhrgAcgo/B4yZXBgCD2MDYPr6+q5NZhS2D6wr7F4ITDwSx+ImJjOKK6Fu4JsUC0kaGhpK+2s8XYNUZpRhN0ZQ6gQc7Gecn59PFV2ZQz5X/XvSVSowhiCWO3am1xVMrm/i6x9SujmNrnwiscF79I+nj7lTyPqIa9b3S3VjIx2ARoAUz46S1JGW7BEqxgklTmTJ96m+zRlyQxrbyr1d10SHudt170pyxIiPH6+5xOdhDkC87ezsJJ0XowS557nt3I1zPkZR2NfshIA7Ob5uckaOtrrBPD091ejoaDqLjGOcnKnnLD2cS/rCQb/39YeUnC6Nts/F+wHg73uKIyj0ueBgA6DLb6qaO8nD+EhX0RlYbXfifMy9IJkTTfztxe/Gx8c1NTWV9o1T1dF1uK8xd8ByoIZ7EYXslQAGOY4jkqqMC85WHLNcimOOaI7PnSO0eGYn3d1xdOcuZpC4k8s84nl4Bq/+ylwCr7hw3ygRuLrOyq03b/OHIHHQFRQVI5UahyzaP28n85rPeFZRjjRmbkfnJTpurjt53x3Advsy4PHq1SvV63UNDw+njCraFjNQ4v0ce0cCHInY18X7I6cz4/dyDuldCM/FmvT1xJry9RnbETFbTmf53+6veFaLO9c8M/MH4tTbhH5gvhElJVqMjvTKsB7UYfwcP8UxiY5zN9LG/48kWM8dx2jwGTwYbg5fJlWJNKZuijAHwB3Exz14uYH3YiAOJIg6ohg5b8pBry9kruuDEDs9Rttim/g7RkN4v9vC4r3IePRKcs6gA8Fuld3orxgNdJbRr58D3ywSopE+VqTT5BxHbwPt5WB7D/EjLFyfVzG9jedlvsUFHA1FzkHMAcEIhHsJVHPzxNvh0ajcfPXv+PyTOsGMK8ycMck5p8xr2uEpa1JnwQiMpV/PgQyv5RRidK6iAY3PifAZn1/+/Hymm/N2V3LTdeM8c0fMdRnvU/GtXq8nx5GIQywOE+dm7Kc4njGthlQbwBiZJ378w9uMuJTfW+FG9eLiIrHtlLAnigA5SbU5qkOTzoo4QOoVwHHJ6dicrYjvIQ74IdN8vKUrYOsED8/p2QDodDID3BY5M07KuZQnM3AKmQue1eF7dDyiSaVCGPW47zQSIOCKaDe875gbOSfmLsSJaLKE3B4CYqN98qidZ8145DHOvajTnOT096IT41HI2H/urMaopx9fxNhHYsCvDSby53Ds59XO31UiCO+V8EySUntZI9EJ9D7Ivc7c93GI77sTw/Mxb5g7ObzkWJnxoXon6Y3NZlMjIyM6Pz9PWEW6Hinl7+jM+2dy33HJvR71Wvy7F+L2Ic45JwdzNiuOS/zpts3I349YNtor11Xut1CA0jMXvO6A63fPbIiEwrv00ds+E4nw3Nx4m7yz4xgNXtxfg3NGyVpPk4nOYS5dlcVMHjBpLVE50hY+A4tJipRXLcLgEnn0SGjswNhxUaF79AUnoxsDVypdpePRN24oc4bBJ3yuf+5KDg8PO8bCF4ofeOxj7U6AO4txf4ezNd4XznL4ax7hA3R4SmI0tj5OsXCAAynAZC7ltZsSieXQcyDb2xKVS+wz7tVLx9HJj3i/bvf1tRjH9SYwGx0y/66DSQCUp5lGYIEB9SNOYA59vXAvJxm8rXENce24pqNyjHPUCQW/lvdxr8Yx50Q5gMg5FW4kfbxj31DBj3LiLt2iOkg0Yr5G+YGQAWj7OVc+N3gmv3bOiWcejY+Pp0JVuSI3DmylzmJkHgWKAMnvddeSGyukGxDIfdZBiqeHklIeCb6op3wfjOtW7KWvSb+2s+dcy4mnGPGiiAuRD3cYOc+W9C4/mJp57dFgd5jivIx958VyeiGQjhwPhcPF/Xjf5zg6hH6NUdOb/ufZoh5wzBFtJn2dS4tk/jtx7vuvIo6LzqJvueA3zxcLHvp+5m77rKJdcZvVa2HsKpVKqnURCZioT7o5k91wQyRVo0McdfXbsB8p3rOzszo9PdXk5KT6+vo0OzursbGxaxjF8Vi8X1wj0Wb4a1FymDt+vtt371IiXpGuY/b4mZxzmBvHblHH+PmbnKuYPUVbyJ7yNFfIM0+BBUeh32OfdvNJ4nu+nnMOfdQ7OeL4NuP5XhHHCLicXYvKyo0fD4QiyoEl6QoUsS/G9wW6lMvlFKb38xVzx4B47jjHQsQoY4zM0K44aJ6e4JMYI0hbMawwuDxzDKt7X/iA99JxJDc6plnEQiVx8TiAd+XnbKr3hU/Ebs/iypNiKYxj3JdIX0WgzP1gvd3QxvsgcVHlwPnbgLUbclcCH1KpItGxiXMr97q3NbL7iM9p7wufw4BbUrGdhccJ9BQ71p9XFbvNPPE16kbEhfdyEUcHMd3u5fqKdcgz9gqoug6Kz+HP4I5YZENpO9crly9T0XZ3d/Xy5Uu9ePFCu7u76froa+8rqZPoiUaJ1z3VkXbFog++dlxnRF2S6wuOHpmfn9e9e/e0sLCg4eHhjr0k7jx4/3ikh+fxvpF0DTjelXQz9vG12xjoHLiJERKPCJXL5Wvp4K4TcfJ8O4WkjnkdAW2ObKG/OcdzbGwsnaWWOzaJ84A9NTbu6/Prxohb7FNfg72KODKfcRyZq5EI8xRs5ntMU40kQJybbu89BdjHwgkA718cx+jcXFxcdJyx6edIu/6OEcycTvX2EKmjjyIBETM3ckK/RFKqF0K/DwwMqFqtSlKK1sX55v3h+i/adH+2iBc8g4b3fc7nMlv47QQPa3ZhYUHVajXtxfTiVu6sdCM2u2FsJDpfOYlkWM4h6TXGKZWuqt1GXyIS2bf5iX3f7X3v47eRHN0wIteYmJjQ7Oyszs/PO87LdTsace679I//tFqtazrB24h9jkGZSIR0k/eKOMZF5MrDc3p9EdG4CCZuMgq5Dokg10GBK+d4H28/EREHweT9v82bzw0ECiE6DKTwEA2lSIuka06hP3ucwL2QyHpFdttBYo5x8+s4kJeuR+bcCLqSdFbThdC+G6qcE+Djl0s5oS/5bO75u/VJZGV4PyeMdS6V9bYL8X0lxxZ1ayfPFueZ92fOyOSch2gwLi4uKxf7uawOjnxO5FLF4xrlGjf1201j6G3LOc7RgeRzvI5jCwkR++GuxR3U6Bx7f9LG2BbXgx71aDQaWltb08uXL/X69Ws1m01JVxETdyCi4JDEuZCLyEfQ7OCJ+/Ea698dvTgXy+WyxsbGtLe3l863m5qayjqhEHPuRNFW7x/mXYyM3LW8DWDe9hr0E2DJyYsI9L1POH7H+zoCTQcpERxFx9H7E5tWKpU0MjKiiYmJdND42NhYh7Mat5sw/m4vuV4kYm/qM+agj/Vdi6+j09PTDifCUz9jvQf6i/ciceJ7lbqlnUbnxeeB/8TKqt52rk9aMsdB5MhQx2+OA6LNltTRJ/4d75tuayvqkkiU9UKcpGDf9djYWPZ4ohyZ42Pgbc4RYl6AiPoWrAf2uFGDw9O7GcPh4eF0HBifGR0d1djY2DVMEiU6iJH4iRgsPid9FfVVtDk5u5O7710Lcxhd7+uQ9uYwl2MxXyv+2dz/3DM6dHHd+G/vE4Trstd7fn4+6WO2VYBVfWy7zcVu4p+PEVTfv8v13S9zMvom/OjyXhHHKCieWMXNKwp5JM8BEgAkdpBHwnxAYW+kq30I0lWnOwvtLJ1f0xWBKwSu0w00S51FNGKVR+4djX6pVErG3N+LICeC2F45Gzy3O/P8jpMnF0nr5tR6e3MLzD/jBjM3WaNj3c0h4FooYlcYnmLji9+lmzHLEQ/dHJHYJ9G5jX3woST2n/8d34sRpzgv/Ro5JUt/sR5zc5kxdaciptv452K/+7r1sckZr24EVM6ocu1oIHgdfdXLMfRnj4rc250bF/7PARnOafPKfKxlUvfj3kfXPb4WADe851EGN0Q4aN5fMOVck3niLLKnJnP9/v5+7e3tqdlsXksLjEbSjWPUH7Qr7nu8a8nZx5sMvs/dnE7Bpjjoz5F5XCvn6Ln+ljojPjeRgpGJdj2HU0KF28nJyXTGqN/b7atvaQDMe4Gb2wKXbsD3LoU2kvWEuJPuzmO0d/6cCHjA+z3+zoFe/0ypdBVNJGrg6555Hqu8xvcjMepOrUdKI/aiDZGgz+kMJztyY+p6ulcOpDv9YK7BwcEU5PCsGF87OXvu9iFX/Ij9iPV6PVX7BJNwVByEuBMBOJBUHfatGNEe5rbuSNezbOJ78e+b1o9/JtpXpNsa7dU44jiOjIyk9M+cncxhVOZY9CUiRon6L2ImXxdOEPF+N4ede4yOjmp2dlajo6MqlUrXUlbjPfk/4q+cxHka9QY+kOsk5nIuS+BtuvWdHcd4UQeOKCx/PQe4XNG54XNHr1txEulyEqGw+aFjckqXiRbZQ8QnBIAmLpY4yeIzuwLxdrrx9OcDwOWMc+zjXogbdF8AOTBK37jRiAu123j7guJ7blhyc8mZTDfCkf3iOzc5bvH+vBbbF52GHGuUm1/0JYYpp2B7HeGIz+Rt7uYoIQ4AIqvqzlQOlMZ1lHNmpCulzxplzZRKpcS44YCQ6oaRjA4cxtH3VUeJYNqBDfOP9/i+z02fD7mUx14I/Z0jZSIwjGsOya1Bxpdz+2CxR0ZGJEn1el3r6+va29tL69H1W7lc7qhei5MgKR2AjE4la4O+dlsAk14ul7W/v6+dnR1J0tTUlEZHR7W/v5/a4M4nzoWn0Eed381gOxh3gMyek9/skltbOZDUbR64k+fgM+rOnK15G+HlbYjRMnfqc/rDnY1Izt3WaYz9c9fiUXsnrGJWgGMHBN3lqfkUcSOl17OeIvh0gQDyee/3dcc1On8eNYw6L+dw+nP4vOH5fY9st/53x9QxEff2uem4o1cOB0eyoY/QaURgGWO3026HnNz3IoySrvUzxcGIOGLr3JY58eDHj3kFcj/uxu1XHDved7mNYyhdJ/njNXI6pZsDk7P5dy2QaNiD2OZuDmPUkzkyIKcHc8SKRznjuPga7tY3pEuPjY2lNsfAWbT7XD8nEaM6CePz1lPgua9fwzGw6+Wb5J0dx7/wF/5C1/fGx8f1ySefvOslC/kK5O/+3b/7VTehkDuQX/7lX/6qm1DIHcgf+SN/pGfXnp2d1ezsbPa9jz76qGf3vWv5/PPP9fnnn3/VzbhRfvEXf/GrbsIHk62tLT179uyrbkZPZHV19atuwtdOcsC81/IH/+Af7Pk9Cum9/NIv/dJX3YRCTD7MKciFFFJIIYUUUkghhRRSSCGF/MhKqZfpV4UUUkghhRRSSCGFFFJIIYX86EsRcSykkEIKKaSQQgoppJBCCinkRikcx0IKKaSQQgoppJBCCimkkEJulMJxLKSQQgoppJBCCimkkEIKKeRGKRzHQgoppJBCCimkkEIKKaSQQm6U9zrH8TeTLC0t/bykf0DSdyX9lKSKpD+3vLz8T2Y++1DSPyfp75H0WNKkpG1Jn0v605L+7PLy8tmHaXkhLktLS78k6bdK+lTSjKQjSc8l/TuS/sTy8vJ2+Py4pF+U9POSPpJ0LOk/kfTLy8vL/96Ha3khLu+yHt98fkjSPy3p90p6KmlY0ktJ/4Eux/L5B2h2IUGKcfx6yDvax08k/Zyk/5qkTyTNS6pL+v9I+mPLy8v/rw/U7EJuKe+6Tgv5auVdcM7S0tITST+84XL/1vLy8j/Rs8YW0lV+3Mfx6xBx/F9J+p/oUnG+estnvyHpvytpT5cD/MuS/u+6dCL/tKR/f2lp6Ufemf4RlV+QNKZLoPnHJf05SeeS/rCk//yN0y9JWlpaqkn6m7oc+wtJ/5qkvyDpO5L+4tLS0j/zIRteSIfcej2+WWt/VdKf0CXg+Tcl/auSNiT9TyX92tLS0rd72dhCukoxjl8PeRf7+Eck/Uu6dBj/PV3ax/+3pN8j6f9Z6NXflPIu41vIVy+3xjkmvybpX8j8dD9UvZBey4/1OH4dnKRfkLQi6TNdMm83saJ/Q9Lk8vJyx8mzS0tLA5L+fUk/o0vG9c/3pKWF3CQTy8vLx/HFpaWlf1HSP6/LSPHSm5f/sKSflPR/k/TfWl5ePn/z2VlJ/19Jf3RpaekvLS8v/+BDNLyQDnmX9fiPSfov6dLp+N2+LpeWlv4FSX9I0h+Q9N/vWWsL6SbFOH495F3G8S9L+qXl5eX/1F9cWlr6B3QJkP7lpaWl/8vy8vJqrxpbyDvLu4xvIV+9vAvOQf6z5eXlP/wB2lbI7eXHehx/5B1HT59ZWorjdO2zp11eP1taWvp3dOk4fnKHzSvklpJbhG/kz+tyIfq4/Nyb338Ip/HNNTaXlpZ+WdL/QdL/SNL/ohdtLaS7vMt61GVKoyT9xUjmSPp3delwzN5d6wq5rRTj+PWQd7SPv9Ll9f9waWnpVyX9Lkn/RUn/17trYSFfRt5xnRbyFcs74pxCfpPKj/s4/sg7jnchS0tLfZL+G2/+/c+/yrYUck3+m29++7gsvPn9RebzvPYP9axFhdyV/Pqb3//w0tLSHw9Oxz/y5vdf+cBtKuTdpRjHr7+w9//8xk8VUkgh7yM5nIMsLi0t/T5J07qsyfE3l5eXC5z6m1N+LMbxx9JxXFpamtHlvoCSLpnw3yXpY0n/hqT/x1fYtB97WVpa+gOSxiVVdbn5+HfochH+S/axLUn3dFkU5zfCJYh+/ERvW1rIHchf1GW68c9J+ltLS0t/RdKpLotX/Q5dRo7/xFfXvEJuKcU4fo1laWnpsS6JuENJf+0rbk4hhfzIyy1xDvK73vz4939V0u9dXl5+0duWFnKT/LiO49ehOM77yIyk/40uU6h+vy6L5vxRSf+95eXl9lfZsEL0B3Q5Nv8zXS7Cv6zLfVOb9hmc+z/8JlosSVpaWpqW9D9/8+/Q0tLSSO+bW8j7ypu19vO63LP6TUn/jC7H/7+iS4D6bywvL198ZQ0s5FZSjOPXV95Uy/1zkoYk/eHl5eX6V9ykQgr5OshtcM6hLgtW/T26PAFgUlf7WH9G0l9dWloa+3BNLiQjP5bj+GMZcVxeXv67kkpvnI77uizu8L+V9DuWlpZ+z/Ly8s5X2sAfY1leXl6QpKWlpXld7qf5lyT9p0tLS//I8vLy/+/Nx/6QpN8t6R+X9K2lpaW/KmlU0s9K2tflQh3VZcXVQn6TytLS0rCkPyPpH5b0P9blfrhDXRZa+Vck/bWlpaV/fHl5+d/96lpZyNukGMevp7yxj/+6Lsfx39IluVpIIYV8SbkNzlleXt7QJdZx+WtLS0u/W9Jfl/TbdHkE0h//YA0vpEN+XMfxxzXiKElaXl6+WF5efrG8vPzHJf0+SX+/Lh3IQr5iWV5eXl9eXv63dekgTusSmPLemqS/V5egdEyX1at+VpeRyP+qpBFJe92KIRXym0b+l7p0/v/g8vLyv7a8vLy2vLzcWF5e/ku6jGAN6EdImf4YSzGOXzN54zT+WV2O65+X9E8W2TiFFHK3chPOueE755L+1Jt//8s9bF4ht5Qft3H8sXYcg/ylN79/5qtsRCGd8ubg8N+Q9Fve7E3l9c3l5eV/dnl5+eny8vLg8vLy/PLy8v9Al/seS5L+46+oyYXcXiiccq2E/PLy8q9J2pH0+E0KciG/eaUYx6+RvDmX89+U9E/oct//f8erVxdSSCF3K91wzg1CKuSPVIrj111+XMbxxzJVtYvcf/O7MJC/+WTxze/bpJ7+D9/8/nM9akshdydDb35fO6rhzd6qiTf/FpHj39xSjOPXRJaWlgZ1GWH8WV2y5v9U5oiVQgop5O7lXXDO3//md66yfCFfrXztx/HHKuK4tLT025aWlkYzr4/rKpXqL37YVhWytLT0E0tLSwuZ18tvDlSdk/Q3KMzw5vXxzOf/aUn/bUn/mQrH8UdB/qM3v//5Nw6Gyx/WJbH1Hy8vL+9/0FYV8q5SjOPXQN6M3b+tS6fx/6TCaSykkDuT98A5v+0NkRM//w9K+oU3//7ZXra5kOtSjOPXIOK4tLT0j0r6R9/8y2D+9qWlpV958/fW8vLyH3jz9z8n6WeWlpb+Q0kvdFnA4aEuizrUJP0NSf+7nje6kCj/dUn/8tLS0l+T9Lkuz7iZ12XlqaeS1nQVSZQuC9+sLy0t/QeSPnvz2u+U9Pe9+f4/try8fKZCPri843r8F3V57tE/JOnvLi0t/WVJR7osxvH3vfn7n+19qwuJUozj10PecRz/VV2eZ7wl6ZWkP5Q5VP5Xl5eXf7VHzS3kHeUdx7eQr1beFef8ki5THn9V0sqb1/4Lkv7BN3//r5eXl//GB2h3IZ3yYz+OP/KOo6TvSvq94bWnujrP77kuS+ZK0p+U1NRlYZWf0aUDUpf0n+gyPedPF3s5vhL5K5L+j7oEmj+lSye+Ken7uqzq96+ESrcnkv7Puix/zLk4n+uyLPL/fnl5+eDDNLuQjHxXt1yPy8vLr5aWln5a0i9K+j2S/ildZkGsSvoVSb/0pgJyIR9evqtiHL8O8l3d3j5+9Ob3jK5XAXT51TtqWyFfXr6r249vIV+tvCvO+dd1WfH/79VlcGNA0rouseqfWF5e/o9UyFchP/bjWGq3i0JphRRSSCGFFFJIIYUUUkghhXSXH6s9joUUUkghhRRSSCGFFFJIIYW8uxSOYyGFFFJIIYUUUkghhRRSSCE3SuE4FlJIIYUUUkghhRRSSCGFFHKjFI5jIYUUUkghhRRSSCGFFFJIITdK4TgWUkghhRRSSCGFFFJIIYUUcqMUjmMhhRRSSCGFFFJIIYUUUkghN0rhOBZSSCGFFFJIIYUUUkghhRRyoxSOYyGFFFJIIYUUUkghhRRSSCE3SuE4FlJIIYUUUkghhRRSSCGFFHKjFI5jIYUUUkghhRRSSCGFFFJIITdK4TgWUkghhRRSSCGFFFJIIYUUcqMUjmMhhRRSSCGFFFJIIYUUUkghN0rhOBZSSCGFFFJIIYUUUkghhRRyoxSOYyGFFFJIIYUUUkghhRRSSCE3SuE4FlJIIYUUUkghhRRSSCGFFHKjFI5jIYUUUkghhRRSSCGFFFJIITdK4TgWUkghhRRSSCGFFFJIIYUUcqMUjmMhhRRSSCGFFFJIIYUUUkghN0rhOBZSSCGFFFJIIYUUUkghhRRyo/Tf9oNLS0vtXjakkLfL8vJy6cteoxjHr16Kcfx6SDGOXw8pxvHrIcU4fj2kGMevhxTj+PWQ3DgWEcdCCimkkEIKKaSQQgoppJBCbpRbRxyRX/zFX9Tx8bFarZYGBgbU3395iXb7khhotVq6uLjQ+fm5Wq2WJKlcLquvr099fX0df5dKJV1cXKQfvntxcaF2u61S6dLRvbi4ULPZVLPZVKlUUqVS0eTkpCYmJjQyMpLa0Gq1dHZ2prOzM52fn+v8/Dz932631dfXp4GBAfX19eni4kKnp6c6PT2VJA0ODmpwcDC1qd1ua2BgQGNjYxoaGkrX9/bTtuPjY9Xrda2trWljY0P7+/vq6+tL7RwbG9Pg4GDqp1KppP7+fg0MDKhcLuv8/Dy1Y2BgQAMDAyqVSqlP/+gf/aPvMbQ3y2//7b9dIyMjGh8f1+joqE5OTrS+vq4XL17o+fPn+uKLL/Ty5Uttbm7q5OREp6enOj8/T226a6lUKpqentbMzIwWFxf19OlTzc3N6fj4WM+fP9dnn32mV69eaXd3V0dHR2q326nPHj16pN/5O3+nvvvd7+r4+Fh/+2//bf3ar/2aVldXdXx8LEkdn79rKZfLqlarmpub0+TkpMbHxzUwMKDh4WGNj49rbGxMAwMDOj8/v/N7//zP/7yky+fLraVWq5Xey61HSWktHB8f6/Dw8Npc5LP9/f0ql8vptVarpaOjIx0eHuri4iJdFymVSiqXyyqVSulv2tpqtdRqtVQqlTr0AWv4+Pg4rcGRkRGNjo5qYGAgtdefjXvxzKVSSaOjo6pUKhoZGdHg4GBqR39/v4aGhjQ8PJyux3W8z/jx+/T19WlwcFB/7I/9sTsfxz/zZ/5M6ieE+9Nn/sP7iPcDfenC99BhfJZ+8X7w9+K1eR0dyRjy4+17m+Ta6H/zPr993vhneb/bc8e/6bff9/t+363a+S7yC7/wC/rss890dHSkhYUF3b9/X5VKRe12Wzs7O/pbf+tv6a//9b+uv/k3/6ZevnypVqulubk5zc3NaXR0VK1WS8fHxzo7O1O5XNb4+Liq1aomJiY0ODiY1tze3p4ajUbSb+Pj4xofH1d/f79OT091dHSks7OzZNck6eDgQPV6XYeHhxocHNT4+LgGBwfV19en4eFhVSqVpKva7bZOTk50fHysoaEhTU9Pa2RkRLu7u1pbW1Oz2VS5XFZ/f3/q92gnWq2WTk5OdHR0lGztxcWFzs7O0jjcNKddd6DD+JyP68/93M/d+Tj+qT/1p9L94lyjv2q1mj766CP91E/9lH76p39aH3/8sSqVis7OznRwcKCDgwPt7e1pZ2cn9Xur1Uo6st1ud/RhuVxOOpTXWq2WTk9Ptbe3p83NTb169Uqrq6va3NxMujr2oa9rX9/+DC65Z/TxcP3MuNJ2ftw2DAwMaHBwUP39/R1r17+ba8vP/MzP3MHIdcrv//2/X+fn52o2mwk7oFMZy/7+/vQzMDCQ7MPQ0JCGhoau2Unsj6SkI8/OznRyciLpEkvyffDk2dlZ+i7PzzxhHYMZJyYmNDw8LElp3YAdGAOwrV+PdX98fKxSqaSxsTGNjY1pdHQ0revBwcE0Rj7veP6+vj612+1kg09OTtK6dlsYfxj7oaEh/cqv/Mqdj+PP/uzPamRkJPUrWL5cLifdAIY5OjrSyclJ0qHgaccNvsbiWolrEbwjSefn56nPWRe0w32cdrutw8ND7e7u6uDgQOVyWaOjoxoeHk7+ivsn6D3WDM9zfn7e0Sb0Ie33+YXNdP3Cs6LnpUs7OTQ01OGTuN/GM/7JP/knu47HOzuOx8fHHcD0JonOmoNbOpmG9vf3a3h4WOfn5zo8PLwGHEZGRiRJ+/v7Wl9fV7PZ1NzcnKanpxOoZMB4cEAi1/DJ0Wq1rjm3KAEWhzt3UXjt+PhYu7u72tjY0O7uri4uLjQ6OqqhoaHklI2MjCQjQLsY9NPT046Jw4RxRd0rccBH3/hi4adXDleuLVGR9/f3a3BwMCmNgYGBRFzE70u6ZsjccPVSHGi50YxG+q6lXC53ODnMfXd++JGu+ik6HThVX9y7cgABAABJREFUOFoYA1eGTuSwfjAqrBdfs8xfB37cG+XLOLGe3DF1gxTXp89b2uj3cAADUUSbS6VSh1PsjlAEYb72mY+9EBxevx9j1M1p9P7gc/zOzT36830krp+om7xt/B0dP/9u7lq5a/prOafBr+XAObYp1+ZeSLVa1ePHj9VsNjU8PJzm5vHxsba3t/X8+XO9fPky2Yrh4eFktxjH8/Nz7e7u6uTkRFNTU6pUKsnQHx8fpzXuQJbnGh4e1unpqer1uo6OjlSpVDQ/P59sEKTq8fFxAqvYKulyHgK0pEsQPDY2puHhYR0fH2ttbU3Pnj3T+fm5KpWKKpWKpCubiZ07PDxUo9FIQN1JIeyJOxOMT7d56zpNUoc+6oWwTrxvaYu36fT0NJHa0amAeGatjo+PJwcg5+R5H0hXRDgg2HVYN4Imt45i2296LRJX3fq5m21Dt5+ennboKAen0cnt5TjidO/u7mp/f1/n5+cJY2CncaawBZKSnZCUdL47f9hLPu+2D9zoGI7nhzThHtF54NrYM5y4k5OTjvsODw9rbGwsPePx8XGywzhwOI3gUXSIO4/YeMbZyWfGx0ljn/9O4nGdXo2l48EcpnNnmrFD/9KHEFjtdjs5n06KuyPpz+VkLD+ROGBe8z+EGYEu8IuTtx7w4ufs7KwDyzKPIL+deGN8uJYHJhwX+nNEe+lEjvtOb8MJ74yCvNE3iYNRj1DQMJw2jzowkS8uLhJ7w3VgQY+OjrS/v6/t7e10nenpaY2Pj6fOkpQUQmSM/J5RYbtEJt4dXZy9s7OzxATu7Ozo6OgosZGwPLAMztZxLSaKKwxfvL1Uqu4sxgVEtGxkZERDQ0OJweql+FxBseHAED2qVCqJyb3pOm4YUCS9FBacM/QQFrzfK8AKIHOn0ZnB6DS60o3OUrlc7mi3pI7IpJMtKGM3pNzD7xWBRwSLTlT4+vBrRSYvAkwUpwOm4eHhZCQjIeR95WwjesHbyvV7bRi519vIOJ4Doe0RkEbhcznnyz/TDVxGp6zbd9wYuSOc07Px726Oon/Os1FyRjCSRT7PELcTdy1jY2Pq7+9PWRGDg4O6uLhQvV7Xs2fP9IMf/EArKytJhwHgWFuuh/3/drudSMqdnR3t7e0lZ4Uo5Pn5uSYnJ9PzHRwc6Pz8XOPj4xoZGUmR+4ODg46+8b5g/hGR5MezC5xthzCDQMKmNZvN5DhKV8Das2u4t5MMPka5sXS80Gv7GB36KERUDw4OtLu7q93d3fQdjxbQlwMDAx1RIn9u14voc8AumSA4kN5nEURH/ZtbY75e/dlcx97k3MU1Ha/D/65PuZ5nKnCtXmML7DNjgl1wYjoSvfH5YzYKr6Fz3I54RA+7GglB1rt0hVXdeTs9Pe3oG5xDCIihoSFNTEwkPXF4eKixsbGU/QMJzA9OIxgrRrld7zr2ZI3zvIwZfeTOCZ/vhbhT7/PTdZjPS8ce9Cc6CqzpPoo705ISzsH3cDLNcYPbnNiPkHKO63FuHVd51Driokje+71dB/v7tMX7yx1HfrwffW16m7rJe9PnTEA3+HQOytG9+DjwdKJHt3hgQqhcxyfP6OhoR+oGTAwKgU7wSIZ730yIuJijgvU0ESKkpVKpg90hZWh/f1+np6fJ4cJp9CiZKySfdKQwMklJl+XZe7UQeU4HxTFVA6bqfaMUtxWULQrdxx+GyBeKA2DEJz2RZhj624DxLyMYpuPj4zTnh4eH01pgQaOU7vrePq9zqUCRIHH21yVGFlGqztrFe/l3PaVDyoM/b7crbe7h7Xfj5UxgXKv8YPzRIZ5mG51DjAltdxIlgkVXtL0iIW5i/nOSc3i5RjSi8bo33cv1eTdxIsKdvTh+fs2c4xgl5zT6XOg2r3Pg/iYH2pnhuxZA2dDQULJzjUZDKysr+sEPfpCijTgV/gysx6GhIc3MzEi6TOEfHBxMhClpio1GI9lPj5KMjIykdPm9vT0dHR2pXq93EJjcY2hoKOndWq2mSqWSyNuRkRHNzs6qVqup3W5rY2NDjUZDpVIpOafMPdIADw4OUsqUgzXIID7v0YwIAruNcZw/MYPiroXr+nPG++M47u/va3NzU9VqNeEAB2NONHtGiD87UUSIP6JMpN4x/qQg0zbIbLeJrjdv6h9fY9HhzBF0/vwu7kC5o8H89Pmd0/HdrnsXQtSXvvSgAZEcz1bz98BDTmq6E8U8drvjjpnrZuxTjOgNDAwkYkZSIgFxWDzNkD4bGBjQ6OioxsfHU8aCdLn+IRrAjmA5rhEdiIiBeQaPfDoepo2Ow4ic9lKv0o5Idvm8Qe+C22lTJLg9o4k2+7ghHp325+e+9BfX8PWG445/4uvaCSXHG6x5/AJ0s88Jd2adFHfy3vEymNPb6yQJ145+0dvW45fKu+IBnNHndwSs0SD4YqID3NvNhVaJiMCs7O/va39/P00WnC4Gbnh4OOUUu3KOCsH37nk6Bc7AyclJR6QUIwDT2mq1ErPDPTHMns6A0E9MOAaXiV8ulxM70kvxiQQ7PjQ0pJGRkRQtJXWRsHs3QPa+9ycNFWZ7bGxMpVJJ+/v7Ojw8VLPZ1Pb2tjY3N9M+EU8xlq4iYYxFqVRKCpP0hF7LxcVFijIwpuVyObGEkvTkyZOe3DsCrwggfA1Flimu1WhYoqPuaRWMgRtZ3/PczWFwveDRbjfKNzkfXD+Kt5freIQ/OlcoY3QC3/c+jIxxryJVNxlcB3ORPZSUnHWc2m7XyhkDB8jONPv1b2pbDrD6td8VSERnNL4XU4be5uhy/2626K6Fe2BbSBtdWVnRq1evVK/X1W63kxMHuCRaB+FVqVQS4CuVSjo8PNTOzo42Nze1urqqZrOpvr6+tH+euQybDbAkVZToJOmp1WpVlUol2d/JyUnVarW0hsfGxlKK1c7Ojra2trSzs6PDw8MO4Hx6eqr9/X3V63U1Go1ElEH+SeoAJU6++PO7898NK/h3vK97Ic7u9/f3d2QFIeABts/09fXp4OAgRRfRMYBFdxzdiaJ/Ir6IJPXh4WGyf94XpOK7PugG/nI6NJeREh3Jm3Sv68souahjJB17KcxFMBnz03+iI+VRHtaHPyPj6PY2fs/tDTiXMcZpBHO5bfF9wLSd9rfbl2morht8HybrH8zaarU6iNncGNI+J97jWObIG3eUnPzq1Zr0/mT+O0FK/0Pc4wzhQOKMeVpqzml2m+dZTFzHM178fV538pz1jG6PBEscd8bNfRLmEnrdgyyxForrWD5HG5mrPBdt9DVJP/D8N8k7O4400pn3GCF0oBUNQnwAdzydvYiFWCJ7A6PLYmEDqqd5kgfuxW0wqiw2v0+73U5pITwLqSiNRkMnJydpMdPxDCJpQDiLceMtBgK20aVUKqUiP84G9PX19dTpcQWAEiNFlY3VvYw4wohVKhVVq9XUf+12W9vb22o0GikFaGdnR/v7+8mJjwb89PRUh4eHaeHA1KEgep2u6kolGnNe74Xj6HM3sqI58iayvt0MvjPhrCvWRLd9KlHZelu6OV+eLuPXckXL/4yttz0aKmft+T7kUXSGHMRE4x/Ba6/Bas7JyukJF+9Pd767OdVRnKiLn0H/+Ge9PTlwn7ufP1OOEMhJN4IqzucIbuOz55yN+Jx3LX7/Vuuy0M3e3l5yrNBdpDB5wSfpihylzYCWw8PDVPTBiTOfu6VSqaNmQLVaTXaFyBjrmb1PZJiMj4+nYnO8dnFxodXVVa2srGh1dVXb29s6ODhI20i4P+0jIyFGNvic//bvI7exM8zDXtkkF+ZUvJfrU9JyNzc3E3lYrVYT0I/F+tBLrkf9PXcyAatEbomaEXHsRvK4QxTXg+vs6DjGZ/Qx9O0KPDu/c7bE9bOvP/9+LwkcxJ11niu2mXZ4llrOURoYGOjAbzEC5duy3KFxe+LXcaxAW/v6+tL6oi2Dg4OJ6CHaCPFEpAqs4djHX8tF6PwZsZvg1OgYI44zIpGSswN3JbnINbqU+Uk/R8fMHSkfH358Hvj6cJ3qhTS91gF+CdHssbGxhNvJwHCn2ov7SJ37U3Ew45iBq5xI8PUYCSq+48/C553UiESGy507jhHU+euxAlCOyfLPu1PFhHA2Jyd0LIvHlV/8Piki0TiPjIzo+PhYIyMjaRJirOv1uur1+mXn9PentNi9vb2UChTTfIgyshk3gnYH4aQRMJE8FcEH25nOXoiDRRYV0UYq+RHtcyDzLu1xpcj4ck8ca377HLm4uKxUe3BwoP39fR0cHHRUyooCuMKxJ13KmexeGynGmDb09/endtw0n+/ivlInmPE55OOWMwT+Gb9GXN8OeOI94nVI64j6IQcmo8PoP94Ob3+38WSO0d9eVIL16orTdVJ8Hv52x7ebQ3MX4iky3hY3HkjUpb7GbiIEuIavw9w143zgtWjQuknOacxJzsnr9n+OJHjbdSPgyY13L4RrO/HYbDavZbYwDwGc6AoqlnKtZrOpg4ODRIyNjY2lz56dnXUAVkAOVVJdlx4cHKhUKqVIo2cKcD3mBhHOly9famVlRbu7u6kdZFbQdp4Ve+Zr2CMtSJwfbici8cVr9F28x4dwIONciY7j0dFRSj+GKPCKmh5JdIDuv6Pj6D9O2nUD9De12+d+t988C6/5GMQftwERcHfTQblx8u/0aj16xMeduGhXPCXT5xXjBXBnPse+Za57wIDnxuYQhY5OgXSlK1x3MA5gM65FsIXoIpiY+3if+usxoiZdjyh7WmXE+W6P4hj3ej1Gm8NakK5spz+bpxcjrid9jLge65NrObns95NunrPu4JZKpTR2pVIpRYlLpVLHiQ8+P30uxj2pBENcZ9B2JxDQlx5pjXPAHU7HHNH25+SdHUcWnjfQJx8PGz1eJp2z4g5gXGlGw8FDwhwQpmUAvZMwnryG80j+N5tjR0dHUwrC0NCQpqam1N/fr/X1ddXrdQ0PD6taraYFBCs7Pj6eqtwxoLQn99zxGVAQnoPugNwnn9QJKO9a4mInclqpVJIR3NzcTOkU7+L8oERRcijwdrudUnKZyMfHx6kKoR9lwV4aZ4i8BDnii5voI8ysL4oPJcw1Z5t6ea/ooEVxUsYBXM4Z8bWZ24vDZ31uO5vsEULGxNm9yAa6ooqRL3fcchKVN/f0fQQx2wGD4iAijo+3LTqOvRLvf2+vP5uDmfg9/i6Xr1JO42fjPMk9d4wq5q7TTaLD9rbvOGnI/zf1cYxOeX/EcYwEQ44s6IV4W+Ka8/QowCj6lkjD4eFhIhexXewf5BoTExMql8va399P98SWoKvJ3Dg4ONDx8bH29/d1dnaW7B5r0PeUc2/Sa9fX19MWgWaz2VHR1dPFHOi4Y+zrmj6Ja5zvQbpyfcbKU7Z8Lfoa7bXcBBKx5x4d5jiuHLPvzmN0ItHlnrrq2MDnc+41b2+3KCK/o+PI54l6RR0f9X3UJfE6tAP9j12Ieue2ZNT7CvMFzEE/RxLTx9htlDuZOIfRgffvOP7N4T/u5UKQA9sFPjw9Pe2IbHEtT2UmdZk1T/oibWPLViwAFB0F/+E+XsvAbWEOI3CtXuFVD0oxJr7X1+cpY8Gze/aXO/g+x32dcR8Xns0dNvdJGHP6v7+/X6Ojo5LUEeXkvowz447+9gI9PqZuO7ALXsU5ptG22+10pJk/A2PnkWXmE/bgNjr1nUeZQfIB5PiIOBhR6aBAcPakznNLPETv4uAVr90jEN4hvm/Jc4txTmB+vb2cYzgwMKCtrS0dHBxocnIyGfyhoaFU8IbS5EwEj5rloja035+BSeoAO6Zf0g+9dBx9bGAziMJKlwpqfX29I42Wtt4k9AvXK5VKHce4+OsnJydqNpuSlMZhbGwspU5VKpW0CXxoaEg7OztqNBrJCEe2jHEntSc65B9CcLpjRLUXghONYfO14MACQOgMVM64RQeR16Tre4+j8cDYMa8xPJ5T73sAnGXLAQ9JHaCU77keccPu33dw6aQF7WRsuAfPy3PldNeHdBwdZHTrm0hSdbvW2+4Zr9kN4MTPREctB7BzINXFCcRorKP4Pd1hiffNgUHAVXRceiEAAxyxcrmsiYmJVIDm6OgoEWPVajWlknJERrPZTHv3SXmiWjMVUgEYFxcXiWirVCqamprSzMyM+vv70/eIeBKtnJmZSdVfR0ZGkiN6dHSU9jNCoJLp4cfuAIywvV7czgG1O0pOLnENfoioxFRA5kV0RhDXK72Qt60f1z08J+MVwaeDNXccHRu4Q+5z1x3MeL1uBFPs4xy24DqMZW5txb5wW4O43oztlvJHDX0IEkdShyPlqZgx0yv2VcRxTsb589LfHg2M6arxOjFi6WsF8oiCi+VyOW2DcgIf3A2Zg5MUI20QSLQ72jrazhqNdg6HJEbpHM/7c/QKazFmiDvzPg7+nO4kxueIexzjWMWsHCeZff6zjj1V1lP2PaonXc1HxtkrqaILR0dHOxxNL1AIlnE87To1F/X2PdFOCqDX2Q/r8/htRM57eSW+8TsuKO9gn7Ax7OsPDUvuoI8BRHjPPfZY8ZTFhwF25YyX7qF4BtOd2IODg47QrzM2RBqdBXJm0SVGbDyX2BkKfzb6yqVXTJwLz4BDx2sXFxeamprS5OSkdnZ2JOlGZ8ydUBi+kZGR9Jy+RxTw09fXlxxHN6j0O4seYMV5Yn5+2fn5uQ4ODrS9vS1JajQaqXy5R6M/lKA8vChErwgAHDXmja8zN0q+BzQqFtoc12xMOY9pnm7wUICsqajYncmj4IeDG9rghtsdRXeSIDD8GRFnHN1RYO3RZl/DOecrSi+NIvfutqZyDGCcz+5g+pjlAGO3KB9t8O/nwJ5/ppvD5p/P9Wt00P173QQ7kZOcc9qtT3spgNSjoyNtb29re3s7OX3T09PpyIbh4WHNzc2pr69P9Xo9HRbfaDTSXkQnYJh/6MRqtaqpqam0/vr6+jQxMZH0NVFDMi9KpVLaTz41NaXR0dFEmlYqlUSu7u/va2NjQ5ubmzo6OuogZXx+AVL8bDip88genHUHWr6Px9e5Xw8HinkG0x91FDail2MZ52hOsEE5sicHrl13+W9PZY4RcncYbiJ2chHdbuuPPuf/bnqB58sRUjn9wQ/zJJJ70VHzdt+1DA8Pd1QDR/eTKeZA23GcOwiMbdQtvg48w8qL0bjTH1MKHYceHR0lHbCzs6Pd3d1UiIpMLAIWHmygXU6+RGeHiJUXgHOn0YkGl0hm+Jj5d93W95LIcVvufocHM3hGz3bwdkJ08Tm/dlwrbms8SMVzxswpJ5A8zRzs1Wq10hY7/BTE1wFj6feAlOJ76FG+C2lBgMXJEtrgzwCmpnia9y1ExE3yXnscYaik66k5MRLgbKMrEHc+3CjwwDGqEDuYvQTsYcS4HB0dpaiiTyCckdHR0WvsmaR03hSKpd1udzg6DnaZpM5cxDAwAxAZWO+PqKSYOLznC6IX4gsLpYdDR5SRinnHx8caGBjQzs5OR3GEKPSJp70ylpwBNjY2psnJSY2OjialCZhqtVop158FwCJC+XmpewojvXz5MjmggC/ONosA90MJi5252WtxoOxG3JlOZ0YhWyIgcafLDUV0Gp2J9WvBwEnqmFt8xgs+uWJ3Isb1gRsIB5O87vtPnC30fpHy0VS/bjew47rqbSDyy0h0zLrdz9+Pr8frId2Y4hhB9utEJjdeN6Ym+ndv208ONrqNj/8dGfMIXL0vIojNgYNeSLt9ed7wxsaGnj9/nqpBt9ttLSwspIhgX1+fdnZ2tLOzo7W1tbSf2w/z9j4CNBKtxGkiUoF+dLLo/PxcAwMDmp+f18TEhCYmJtL6oDrj+fllZdCtrS1tbW2lYzwggTwFCnvIMQcTExOpWiV2wQve0Q6Y7dzc86NzsHuAJOlquwYAB/3CMSIfSrrNM48a+Gd9Peacx25OJH1Af/M9fjsJlgO/sY3xf9eHfo9oP6Kj2I3Yir99HfszfRU22M/lA5cwfzwjKDoCjmndyUb/YEMlJXwYUwrdMaT4CfOaCD7gHaeRYoB7e3tJZ7DWKGbFtZkjXmfDUxshdYh4oT9iNU6e13/76/SBEzyOeXlO7HcvJEbvI87p5is4xqW/PJU499y51xxDSVe4BSIPrI7O9THmdADw4Pj4eOo3x2MxhdZtlpMFjtn4HONN1FlShxPrWSPYbSKN77Mu3ytVNaYIxQiUD2A04PFzTMrY+G4TAcdid3dXW1tbOj09TYuWDgKYwv4AXDnzJjpsDDbhcD/nxQfM9yY6yMVQwjjEEHL830Ex7fDnc+XVK4fDFbpPXqKEktJxFlSUlZT+72ZEMOhcB2bbj6WA+Z6YmFCpVOqIHjrzCnPnm3txSDHYMDCvX7/W9vZ2B0PYLSr1oaWXzj+gKrfGcgwpa8HPGnPiQup05txh93WPInXiISohZ/0ciLgCjmkjuSioP0NkqLm3G/W44duBgZ8XGg2gP7unNfk+tV4x465Pb3IAc2C0m8RoYbe/uzmn/HadlPt8BIbdHLPYdh/LbsAlAuM4H3K//fPcy6PPH8JxPDk50ebmpr744gttbW2p1WppfHxcU1NTGh8f1/7+vl6+fKkXL15oZWVFOzs7ajabyQnxsfW+cWIOoHh6eqrd3V2dnJxob28vPSd2anh4WLOzs5qZmUm2k2OtBgYGtLa2pi+++CIdF4LT6OndtIdK5SMjI6pWq5qZmdHAwIDq9bparcv0rEqlopGRkbTmuOfu7m6y04yDr0tsAT/YWsTTy6XLIne9dBy72Qy3275n0d/PrRHHArwWSWWXmDXhOio6jzHrKaczcs5gnGu36Yt47aiT/fkioe594XKbNryPkDmFw+DRKtcbccycNGRe84yeRug2BdLGI+7MZY5a8HM5OV6F1PRGo5HSyqnH0WpdbvUgvR2s6RlspJxzHJwfqebEQKvVSicMgG993uVsbYzE0o9O3Hqf4rTctbjzl5PcnIxj7NHCGMzy3y7RpvE5/vejUhgzH2d+k3ZMlBF9x0+MMPr9YxAqPq+PEzrJ/RYvuAXBmOsLx3tvwznvVRyHQQR8SkoT3HPGIyNG4yLL3W2wotMlXXrRsKM7Ozs6OztLi9YnFZVPWUgx99cBsDMERCr5DiCAyBHX9UqjXJsBzkVauznSDDzvxQnRK4Xq94hMkU8awMHs7Gw6FmN3dzdFaL39OCRedTaCV2dA2AdIn/ueU37a7bYODw9ToSLGBobt8PDwmiFw1h0A9HWVqABz4vPU01Sc2ZI6j3XIrVt/3cG4ZyDENvg897RvvwZr14tjIe5guvH2zeAxiuo6xlOS+IynE/nzRWAV9YKv6V4LfdNNN+b6OQI4/5wTZlFyejpH5sW+9XnTjaTp5pQ6A5yTbs5gt+u5vfFr+1z3z/dKuCd73fb29tRsNpNerFarGhwc1MbGRjrqgordkX12cMh8xf6RAcPaOzo6Ur1e18XFhSYmJpLdGxwcTE4YGRiDg4OamprS/Py8Wq2W1tbWVK/Xtbq6mrI/WI8OwiD8OG93YmJC4+Pj6T10vu+JB4hwHZxSHx8+x2vMIS8Y0mq1OioSSlc2vpdjeRNxRTt9X6J/zudiJOlyf8e1E53C6Oi4I5lzfrqRLA7Ac85cNzLJr3NTn+Gs+TPQzpiZgPTScXQi1J0d2ovd837FaYxZYm5H47VdD+I0skcY55Gjecgs8H1mHpjACXR86XrdnQTs5snJSSLXIXg485yaEePj42l8PNU1EnRch/+lq6qksT0Rw/VCvH8ZjzhWHknlM9Fe+3ghMZL3NsLax8TxkAeYGo1GOjvXMyrBomyD4xq5szYhCHBGYwGcSGBIV/OaudvN4eT7Lox5TxxHX3zucLiByYkPBA8b0wgjGwb49/OM9vf3rzkvTBqPmDn74qkwnurkv1utVmIHSGsA0LLwGUg6HafErx+jLJH9iPftBpBin/VS3DjlmEhXRhws7XtEnYVCudDPvvcNI8sxH/v7+2n8R0ZGNDk5qenpac3MzGhmZkbVajUtOG8DZ4kxLyR1FFdgHA8ODrS7u5vu/3WWCOS7gRE3bjGa686gj5nPe1e8kXCQOhWW7+3gOqQpl0qljn0KzWYzpe3EQiaeUsSzesor89Yjqq7co6Hv5hzTP5I6DHJ8jl6Kt8n7x6VbGxykRVIOQBLBcGSbHYzGdjEm7ohzPT9/LpdZkWO14/VzQD333Llr+LN7NkW3a/RKfB2225dpZtPT05qYmFC1WlWpVNLOzo5WV1fTPsK+vr6UvuTFUzzSjZN0cXGR9nJDgBwfH6dzbycnJzU5OanBwUE1Gg1Jl3vL19bWtLu7q3v37umjjz7Sp59+qoWFBR0fH2t6ejoBylichX70s4rHx8eTA0c6HTbYSSBsJnqmVqtpdHQ0gWn6y6MfRFUGBwc7itn5XGZt54iVux7Hbgw/EtdSJIyjYxYdyNw1+T/3432Avo6OZe5Z/Hd03m7qQ8cw3a6XI926RYdyhNSHwDmxD+n/GGWK7fMgCfPbC+DgePoPtgkMQiX4w8NDbW1taWNjQ4eHh+nZwU+jo6MdfYnd417oBPRKqVRK+5JxOs/Pz1Mks9FopMKO1WpVk5OTmpubS8/kzpXb8vg3NU183cUAEGPfq1RV2uL9BS6J55q6c+lrkT51nE67sfdgi5iW6td3MoxoMfqu2Wx2kANS58kIENHoF4i2kZGR5Ez6XlXwLOPrfeEYx/0w5rbvsyyVroJhMapJWxw7vU3e2XFkDyB/S0qRx5h76387qOXBAfrxSAF/+OPj47QQDg4OktPoOev8Jv3RK89FZsgVoDOiklKFT4wazqGDSJwY2APf7E+n+6LjN/dxJisqVh+wqKx7Kc5eOAvq75HaVKlUVKlUrp0hxHg6qAQIRZYINt6BCsqgWq1qdnY2gS2UJApwfHxcjUYjMTmS0h4A0rYajUaKjkpXc86Zn6+LeBqhRxNzrDaKjzXHeDjgcxYvKiHPyfcfvsOc9jXjqdsAQ77nhYsODg7SXrBWq3UtzRxD4Q5cdEp8ncUN5rkUkG4Mv8/b2K+9TDvOydvmaiTbokHMATUXnz9+vQhYHTz5JnzfQ0Q1YQqyOEPqINXJoAiu30Xn5YjGm5435zD3QtwQV6vVFKmTpJ2dHb169UorKyup+ibEo4MW7Bv9RF9TNp3xgPCBRKnVaskhxHb64dXT09P6iZ/4CT19+lSDg4M6PDzU4uKiPvroI21vbydyNkYbHTABYjnX0XUGY04xD+mqlP7Y2Jiq1WpKXcVplpT2ddIX6ICYzXNycpIAUDfn5K7GUMoXk3IcER0m/43kXqc//TMxO8CBbyRf2u12x34oJ8L4HGs7gvvYpqjvHQ90c5i9f7x9nvbnr/n33Hlz/dILISPJST93mnIOtz+TR998LyPXiJEqAhyefsreZTIQyLTysxmJCI6OjmYjmqxznCQqrHrVZFJgIVwg13Fm9vb2UuSKo3lcp8c+4L7RifI+i/ufe+04uvNK28DvviY95dJttrczrmP/jBMLnhnjmFNSIgcIYlAVm2NS0OFOKrlzS3SZ0xpIe42Bl0jkRVJB6szQdNwlKflInmnFevAsF2zQ2wIt7+U48iCe1xw7GjbAnUhJ18CYdyLvYyQonLK/v5/2NNbrdZ2enqYwPGWKx8bGVKvVUhoNHcFEiD9S5/4omPSBgYEO7572+rmAXgqXxXtxcaFqtZqu6Urc8499gN2g+wTwPu6l5Pqlm4Pv6Q+VSiVV84zV5LzNHjaX1OFUojTp81qtpsnJSU1NTaWy9X4Oju+ZrFar1/awkR51fHyskZGRdH1SYnH0v4pzHT+EuHPD/HJFgmPOHHN21J1BSR3OvtS5DwDl52mfgCtSKvzsVAyrr+l6vX6NqQXo4tB6tUTG0Z0QjLkDaHcc3bhEB9cBrgPCCAKdTKHPerXn2CVnzN7Fqco9i78udRa2yX0v5zD6Bnw/JgeDThrl/v5+IvvQcXEORWPquiOSiN4+74e3Occ3rfNeknKRbMPR2d/f1/b2ttbX19ORQp5exfxyG8lPJFG47uDgoEZHRzU5Oanz83NNT09rdHQ0OXVHR0fJPs7MzOjx48daXFxUpVLRxcWFhoaGNDc3p48//jgVxSGd1MGjO0wAC69kCAmMXRgZGdHMzIxKpVI6A5LjRGDdj4+P0zicn5933Jf1iRPp6XiSOqqb92oMfb75Gsp99iZSIrfO4t+sA15zgO42Oa5lx1FxfeQco7ie4rPGKKY7pH6tnMNFGyN2iuL3ceexF0LBPG9XJPRjBMr7NJKR0d45gS1dBh/IdNrd3VWj0UhENmR5pVLR5ORk2oMIqcJrfuZizPyinbQRR9X3TjYaDe3s7KTzV3Fu2HN3eHio+fn5dD9wdHxu1rp0PdVZuqrA6dlLvRLu6/vzmLN+PJ/PXyeLPWshri/mAngCjOh6x4krHMJyuZwIAnAU2KdcLqex9Kgn4jr06Oiooy3cxyvk5ta+B8P8+Yl+xqyyHInk8xtywrO5usl7aV6/qEeXYAadGWBi+T60mCrGgPqeIs7A4jwswr/Hx8fJUM7MzKTKcO61e7g1ThJX8u12O3nmTAQcDm8vC4IJ4g4e7WRgSTfw58rd36MnpVKpo6S5V9/qlUQw4Mo0Li7G0p03KuQRRqfN9GFk+SQlhovrkH5Fmur8/LwWFhY0NTWVCjCwAFutliYnJzscjAhA2cfDPUqlUnJmGI+3LQgfox8lB9PHy8FONAROYqBYXCH5d/w6HunDoLgxjmdnRidBulSIsKNSJ9vtgJnr40SyTl1RMicdVPv7XD9Gs9AzGAPvu9xPdMB6FeGIEhl4jwxGh5D3EV/POaeqG/h1oOR97BFGIv8QPV4m/uLiQnt7e9rc3EyAqdFodOhH+j9GtGPEg+fLpZLFv2/rVOe+2ytxQgM7QeSBImzDw8MJiDgIjQCov7//2gHNR0dH2t3d1fDwsCYmJvTkyROdnZ1pZGREjUZDe3t7arVaKco3MzOj+/fv6/79+2lfoKfeEWnk/Ljd3d2ONTk6OqqJiQmNjIx0MNqQCFRvJCLoP5HIcbKKPoIkbjabiegC7GAXmZfsk3YMcdcSrxudsm5ERVyn3Zw2/z/ObwfpEcy7Po0AOrY3d39fT64ro77plqXlbXPxZwYUE4XJ2VIPPMR73KV4kCPnBEU951lovtfLHXu3WU6Msc6plAwRA14ZHh5WrVbT/Py8pqenNTw8nNoAuUNmgWNM3+oRtwjwjPQ562hmZkb1er2jUmuz2dTOzk7K3pudnU16fHJyMpFdEDIe3aI9ER85IeyRubsWdIbUeUYz/7vedNztuMAj8D4P+NujpR70IIKL8y1dFuYaHBxMvgNEKvfxbEj6EvG5QnYHuLrVaiXn30lbnpd56tfp5lB6YIZn5blcd0KCOMH/NoL8nR1HjEypdLUhns7KRfkkdThAPLg7UAymh3rZXIoxopxtrVbTvXv3NDs721EEwBc9nSHp2mTzSY/BltRh5LxtvphGRkbSxHF2gT0m7XY7tYnruLLPAaKouGh7r1Pickxyjv3jfZwxIrvtdrtjQgPGUSL+3Exg3yMzPj6eFlu1WlW1WlWtVlO1Wk1V+TxVibb6xHaGBQb78PAw3Qfm2iMeXjY+JyiQuCh/M4s7U25QosMYgYh0fS7mnMdc2hF9D/CF8XQw0E1J5/rU2UF38CKD7nsAPOoZQaSPn4Mod1Kd1PHoSkwT8uv22nGM6w/p1pc8q3+/2/VuciAdMDlg93UL2TM6Opr2Ivtavbi40O7ursbGxlSv19PfBwcHHdUn3UlyYtHXm+uOt2Vf5IBzrm+69VEvhLnGfr52u53WB04j7aPCIgRcTAtDIOVwDg8PDzU8PKyZmRlNT08nkPrFF1+o2WxqZGQk6dl79+5pcXExHYF0cXGR0uJgvh8/fqxGo6FWq6X19fWUglUulzU5OamJiQlJl1Gci4uLVFm1UqmkNFoHZ1Tw41m9CEhfX5/GxsY6snN8XhDZhuxjTsaUq17p55yjGPUj4gQZujISsnzOf8e/c86eXyNGLlwvul6KOp/vAAb9WWIEJ9oIB6PexthXUc8QTUaPe7udzPNr9ELIIItZDO48S1d1HMB6udQ98I0LpCZrmAAHleiJmrONanZ2VlNTUx1Vh+O+Qbc7MZ0/9ht9x/pAP3OmK+mye3t72tjY0OvXr1P1ZhwX6grQFo4scfvoYxSxkW+96lWqaq7uARjTbUckub2P32Yn3RkGJxKt3draSnsWR0ZGVCpdBpTAstTliJlS9Bfia4Czxw8PD1O/c/4vqa5gEp7dC4LFMfL1S/E0tuvgUzg5Xy6Xky0AJ3tx0JvkvRxHGhjTRRxceiqce/++qd3ZCgbq4OBAjUYjGcGDg4MU6ZuamtKjR4/08OFDVatVSVdnleSiAShUXxTuGLoi9whHjMLAuGP0MQ4MDmfu0P5qtZpSaCNrFZ1lv78XkHHQ1AuJCopn5TdGBUZ5cnIyjSWKdX9/P6WxlsvlDoeMRYuR96gwTmKtVksHVpOaSlEjdwz4TT/GueVRao4QgdCQLpUMVVpHRkY6Ss67MD8QyIwvA06i4e2VREMvqUNpRrbNhX7kM9GBi84i18CBJ52cQhlOwPg1ugGmbs/iziTrgaNiIDMiIRTBUS5KF0mb6DT6HpMIiHplGLv1Q+yT+CwOgNwo5CQHPHlOT0UFtLvj4esT8qhWqyWWHJKG1wEre3t7qXiDkzuw3l4oRbpilp3xjM4jY+e/c+L9kwP6vRQY5Y2NDe3t7aW0Ut6TlM5QPD8/T32UA4X+GsafbQPoYwribG5u6uXLlyqVSqnAmEfoEAfJ7MVcWFhIlQDL5bJ2d3dTOycmJjQwcHmUBgRiX19fKpQT01bd+QW0UYq+r69PExMTqlQqHfYZ8pnKrziWh4eHKQIK099rAic6eXEtuk6M5LTrk7dF03KOY+46b4uuxpRLrsc10I0+Ln7dmLHhf8d2RZzg7fKK6UTYcUzAOESduW83suwuJGKFiPscqzrmiamiHtFzDAexsbu7q+3t7ZQeyroEd0xNTXWsRyftb3IOvY89osZ7PKN/1o9DGx8f1+TkpA4PD1Wr1TQ8PKyXL19qb29P29vbHWmYzI/p6emk03Pz09sXbXqvtnLEMfPX4tYuxwM4s/G1aA/QQ9gnoow43pBglUpFU1NTqlarqajNxMSEarVaOtOWa3twg/5zmwXWZc9rqVRKGXOcGOBF5y4uLnRycpJ8EQhb1z9OWkGqg2Mdy7pO8C1G7Xa7I722m7yz48jEpHGuQH0SeX6wR9Qi+CdiRyiYs2yYzAzexMSE7t+/r08++UT37t1Tf39/2vQLgxlBr3vSKCrvTG+zl60l8ufAl053R9A/Szl09nNMTEyk/HEczDi56UMUUtyzAbDrheSMgDORgMmxsTFNT0939B2OHZEflBTOPuBvfHw8gUvGnjQ3HMaZmZmUa+8l3T3y4QbM+wZAicL3NnnUhHvWajWtr69rfX1dGxsb2t3dTZW0aB9zm7kJiH3fCHAswHTXkjMw0SnKOUDOfOF8M26xrZHV437SFVDY39/X3t6eSqVSSkl2Ysj3l0bnzY2o/zjxggGg/D/j7Swhvz1qmAM3fCfHrHf7/IeWHJnjv6Pcto2RWHBix8//qlQqac2QEUBaqu+LYY8jOrJWq6XoFzqdfTbock+fYh8QWSywoFzT9aWDEjfA3ZhkBxno0Q/hNLKe9vf3tb6+rp2dnaTzSNc+PT3VxMREah9GO3et3P+cYTg+Pt6hK8l+QecODQ2p1WolYrNcLqdtAIBjAO7MzEwigUgnR39KV9XLJycnO9pFxAX7iL1jXp2dnSUiAeIQ8AM5fH5+nkA2+nhsbCydednX16f9/X2Nj49rYGAgpT/3yj7mxsLnmxNt7gCgr3xMcvuL4v/+43Yvl4LvhBhtcLvsWIP7I9ExjBGN+BPbE8GpY66zs7NE9LPPj8wDtpign+N1erUmqdweAbyPM/0cC0KBHcFnHhFnfInc7ezspCi9pHRcDdWUcSxwGr3fok2Nzm3ut88FJxVyY0sU0k8beP78eRoXcBBbw3AeIWm7pTe7fvVsv14Izxn1OM/r65HP4Qz6euEZvP04ZOg+orB+BiMYkjRjSC0vwkf2FeMRs5ui7W6322lceA+dR7SYQjs4sycnJx2Fb5hPPj6+H9edZSfV+ZugC5kurg9ukndGsz4YADfYChQnneCKz9PJnLGRlKqskX+NkwYTPTExkVJtMLZUYfP9EDEvHINGZSvSTH2w6XBYVBYWoIjnI+3Aq74xISjIw+ZkNrzWarUEgJ1t5tkBu0w4+slTM3slOcMlqWMhwr450PfffH5g4LKkMKkPh4eHGhwc1MTEhObn5xNb3W63U54/RXD4DZPsjlZUUvz2he+K01OY6EsUOClVRIMdaLnB9jkKk+7pdd2Ybm8Hi5pFCIP5oSQ6j67QXYk5AMJIMp6sDX8fYsUVMd9rNBqp6A2pZZG98wI33kcOsoiA+Fmd9B1p8q4Y4zxhHDy7IQIvn0vdfuI45yIPdynR+ck5Q/He/v/bFD33oG/QgYB1IoqkObHvhUJV6Djf6+gZJ24kWTN+ELIfdI3juL29rc3NTe3s7Gh/f79jvzRgzfWsz2vvs25jEwGDS68iVj5/AJWNRkODg4Oq1WrJNqBfpKu9hl6Fz9Ospc6UOuY+acRcB0BBERqcMN93SGqVZ2S4ozY7O5sIXK85wLiQnjo0NJScY1JwvagHgLVcvtqP2Ww20/1HRkbS3i+eh3EcGBhIcxLHl+NCyOThfMpeFuTIAXp+ewQL55tIj+uuuMezm3MWHTGPIOYcTwfQjB/3A7S6fs05jPH+t2lfJHPd8aNa+tbWltbX1xNOY5z5vtsQx2y9kIODg2tEpXSFf5w88ew336dJCh/ve6SLSv8cLwYeZO8gxBvrlLFh7BzUe8qn61N3euP88KwsHwu3H07Cuu7mDFkwt2NTSZqamuqIoMXr+vrge70iAHw++n2xZ3EvLWs0zluehT4F21PxlmxH10Wcvzs/P6/Z2dlE1tEGMB/+ivehjx2fdyfXsyAlddQS4QxOsnOwm9RV4foQdfQNa8pxBG1y/N5qdRZ4cnt553scz87O1Gw2kxIvlUrJWfIQvHut7pjkJhapOuT2+gKYmJhIG/tnZ2clKUUk6WhnO3Be8NSbzabq9bq2tra0s7OTwsK+oV+6POh+amoqgSeYc/bKwW5iHGCVz87OEihotVra2dlJh7uSlsF3PHfcjY9Ht1zJ95IZzzmNvC5dVV309D0HnDjXOLwYc19wExMTaS8UjvPw8HAHA1etVlO0Mcd8IhEkxr8ldaQhl0qlxGqfnp4mQ+4b0EdGRhLb7VFFV0gAL5hUT+nw+9I3pOGQdgbA4/l7JblIiit0N0Sx73IK1xVs3ADvjij6gMrH7XY7RSvoM368cA7Xpc+8eAb9xVoEGA8NDaVqY/SrrxcHS5GBz80bN9o5B8VTWnrtOEaJrHjudf7n2R1cRnEmHQeCqGK1Wk3HGLEmJycnO6KM8UzcqHcldRilGG0mldzZ03q9njIAKORAtMIdSCdsHAR26y9/DWMane9eRqogt5h/rVYrMcXYzXK5nAAI0VvAjxcniG133YiD5UUZxsbGUtVU9h2WSqVE4pGGliMj0NkLCwsp82dtbS2BYkmJbe/r60u29uTkJO2DhehhnkCMsjUAYCQpAXLsB69JVzoVx5XoJSQk86hXNrLbdQGb6AvmEeOBY826gmyJEciYZcHajNEi/zs6cG7r3P7E6Ep0PKOzGF/z3/En115f80dHR2m7yPr6ehb7Rd0WwfVdSjwWiPt7FhAkPs4iGNTTU72gl6Rk47e3t7W1taX9/X2VSiVVKhVNT08nwg29Crjn+763G/0IcebFBmm779kDg7Em2FaAvvGtFo7By+VyRybCyMiInj17ps3NzXTO5O7ubkfgYnJyMmWV5GwS4+u4theSw6i+LtC5cW1GO85n6VOwHZFxKpxCyvE75w8gTkJEAtrb67a6W595Bh2ZWziRkGYE2PwcawImYCMvsASxVCqVOhxsT1+N86Unx3F4dE/Kp+O5kqFDnI32RYqTdXJykphONv0TacTTl65K65ZKpY5Fx56SV69epbQBwCvnP5KuwyD4gnLWfXx8XHNzc7p//77m5+cTS8uEIbXRU+gcpDcaDW1tbXWAJ5ha7zufcNFZor97KTnnEWPg7AMsnEf5UFx9fX1JcfH+yMiIpqenNTc3p4WFhVSIwUErQCM6ADlQkwOK8TXujRHHIeDMIvLTMerT09Pa2dnpIBlwHoeHhxNrf3x8rLW1NUlKn8GJAuDglMKENxqNBBQB3b0QFFFOUfE34+mpOLzmjLWDBgewzsT6PVqtViJ8tre3tbOzk5QZhoi+xQlw58IZ0zju5XI5KUwiF57bD1D1w3JZZ0RznJV1Z9HnjRc8KJfLaZxyKWJSbyNVt/1MzknKsb1xXeO0QOh4qrgXpgL4EvFx/R5TyH3NuiF0ptwj9oAiskxwWjc3N9NxS3t7eym9FaLPwRsML32S0xf+/LdxMu9KeP6RkZGka1ZWVlKaXqVS0YMHD1QulzUxMZHSN0n1z+n7CNLK5XIHA046aF9fnxYXFzU3N5cA0cXFRSJe7927p1qtltVFkJZUuF5cXEyZOmtra6mAg3RpDyBjOWIAwqy/vz/Zyr6+vsRmQ6IBxjggHRJ6ZGRElUolgRiKTKB3cLQpQAFRBfN+14J+8/8j2QtZ458ZHh7W5OSk7t27p7m5OdVqtYQ1XB+5Xsk5Y/zulibq349OnV/TX4v61X/znaiHc7YkOpXu2LC+X758Kakzqsb/3pZeOo3S1Xl7MVU1OsOSUuo0ZIs7xI6HyGTjiLj9/f20JqempjQ1NZXOZPR9aK4PSQ2FfGGtQZyBi+MRYkTyPVuEAoOQ8V7IzLeHSUrY14v/SNL29nbSGZCyntHj29P4Yc3HyGMvJGLmSEg4rvE57XPP5xlkHtvdqCaNDqRfvZgjVf5zRLP7OZ6NwGe8XXEN8H5ca+AyjxQzLpx5DU7z8+wjnnbbDY7Hj/PPg5NiYCQn75Wq6vsjJKWywrEz+Dyd5fm07jh6qJxoHQZwcXExberlvnQW+yMAG5ubm3r+/Lk+//xzra2tJQaBAgxU12KgPRLojCH/12o1ffzxx/r2t7+tR48eaXJyUlLnxnIUuw8WQGl3d1fr6+sd1eRIy+Q5mByecuMKrlcLkev7j0cl3KjkmBEmI8Cy3W537BMAgMzMzGhubk4TExMdm7ZRqDBGURnEedSt/Yg7tPQpi/bi4iI5jicnJwms1mq1lCoH48SBwWwqJwXZ99MCGJwdwiEmBevo6Eit1tVB9r1yHFFIzqzlFJCLz1tXLiiwdrud1qiznt7XGM+NjQ2trKxobW0tRfr9aA7f1+hj5nuBPGLoIATHE2WJE9loNFSr1VKKN2BTuirYgyPpUXA3ctL16n4xIhD7sFdsKm3IRUW5v//uJnHcEfrZ08RxOmZmZjr2MsKQQ+xEQiGuV+51E8kDYegb/c/OzpJhdkaV/znKA4cFHQ75kHtO/ve55o5l7O9eCIBieHhY09PTmpqaSo5Su91OTiL6Ev0xMDCQMnfe1rbBwUFNTk5qYWEhVTrl2rVaTcfHx1pZWVGj0dDY2Jjm5+c77KgXJIljiH2fmprSvXv30j41ooXsd2w0Gtre3k66kucGeJE+xvNgH6kmiZPBtoZKpaL5+flUkp73cBolJYIXAvnw8DBtP7lryZGSkRTxuYV+HBoaSmPD0VLsbYv2Drkpupdz1nKE1m0cPJfc/W6SeG93Rt0ZcrDrgYRukUUn9HohlUqlA3c60PcITX9/fyKZvZ6BR3QhsE5OThJxsre3J0kdmRroUMeSPD9BDvDE1taWNjc3tb29nYqJcaIA++tiJpRHDJ0Qn5ubSxWWcSLRq3HNDw0NpdoVjP3m5mZyHtFJ2F7PCpKuMtLcoe41XkV8/nl6r0fOPNrmJIBH29hGQNVvtqvFQnAQ1IyjX48zNH0fLA6/6z/HFfzt0WL3H1xHeH/7T39/f8Kr0dmLuoV7xjUGUe79BebrScTR8609gpALZ/PjKUseyfAcY98kPzs7q/n5eVWr1RSC5mFgq+v1eop2EGl89eqV1tbWtLW1lfKVc3vTMNykXeI4Hh8fp+eCUcURePz4cTpo+ezsLKUCMWH9AFEmkx/K6gfTO3Pp/eZsBg7pVyUoKt+74QVsPBWKcSYqhIEHhHp1P366MTCxDd3AX/wO14kOr+/rQhGSguBs0uDgYEexJRQDhSU86oqguDCYRF1Jg/aF3gtxJZMDO/RRZKDiWvXvOCOaO7oE47e5ualnz57p2bNnqtfrHXvaiDzitDjzGRnCbo5uu91OKW4wf9KVc+gRcNaKF8M6Pj7ucHz4Du2SrpxHT++gT6O8DVx9GYmRpshm5pzHm9aE62bpktzDYQTUOrAlrYrjNXAwIvjMzZsIPnOMKmsOJ5KsEoAPQIc2bG9va2RkRHt7ex1n9uUcq9iubiDU+6NXY8n1Sfskckv6tmegtNtt7e3tdZyh5QAoRm55TuYp5GmpVErjdXZ2ltjogYGBlDUzNzeXUkwd6MVxI6Lh2wzq9Xr6DpVgJaXiGb6HfGBgIGUOYQPZKgAR4eNB+8lQ8cir6w6IOHQtQPhtAOfLjKPvkeK1SILxHk5Hf39/2l7jIJ6tMdiVOA+7rSn/OxdljG3297o5hN3uGZ04f14n+WJkk74CdDJWTgjmyOccOXbXUqvVOrCnR8DBNpDYUme6vRPDtI8CQBy5cXFx0UHIVSqVRA55oT3StTlqbnNzU6urq3r9+rU2NjZUr9dTtJECi2Bl1lvcBkbGFxgLzLywsKDZ2dlEDE5NTaV0WXd+BgcHNT09ncaiVCppe3s7HbdTLpfTuvXoZS7i3Gu92i2YAlZhvnnbHHu53WGfOVHe09PTjiKK2ECP8rk+diL68PAwBR0oAMbfXq+FdseK5dQS8LR27hm3guBsOvHBlgHwGn3j2ZT8dvuSy0jw6q1vk/dyHOkQ9vqxn8ujd+4I+SZjBhgl4jnGzWZTo6Ojun//vp48eaKpqSn19/d37Bk7OzvT9vZ2chBhptfW1rS2tqZ6vZ42Y0tXew8i4I8TDyaUSQpIef36dUcJ+UePHqVcZ9/P4RvgJyYm0t4Nqnp5pNRZrRzIcgfoQ2z+7yYoTVKhYNuOj487lCmfRZmVSqXkkOEsxmIm8f43KZycc5h73fsufs5/BgYGUnVOFg7KuL+/X3t7ezo+Pk7pppSuJuUSACN1FgWROqNW3Qz0XUoOLKMY/L4ofQeO3mekAULkEOFhHvMdCmJsbm7q9evX+uEPf6i1tbW0odz3pAFwYyqE70Pw/ZTu6PM/BgylTqGB3DwiIgIzDJDxdc3a9mijR8/dSeE73s+9khj9RLqRcrwX55g7BTw3YHZ+fl6PHj3SvXv3kjNB1WNPzXGA4df2+0aJ5E4kMjwy7v3NvkpYXo9CQjAxjwBgHoXiXjnHNbYvOtO9EjfWbHnY39/X4OBg2ttJ9UUi876fyvdq+/OQ0jk0NJTO+wLYjYyM6PT0VNvb21pdXdXZ2ZlmZ2f1+PHjFJnMkXWxvzw6zTFMU1NTkpR0Avq/VqtJUse+LtJYOfLIiRrG3UEdNsarC3rWgK9DCq94oZKbzuS9i3G86T2eA33iUUfAPE41xClz0NPZpNtF3LrpoJscvrgmupGLfN71UMQivnc1OraS0hYN7Cffd5Lvts96V0LE0aNBEJvMvVi3wsfGSdnT09OOg+BbrZaGhoaS00iED0cUpwosQWbTxsaGXr58qRcvXmh1dTVFjjxLx/dA5siKqEeHhoa0tram169fa35+PmUZoOtnZ2ev7VfEeZyZmUnjWyqVEq7G7uJI+ZE+bl+Y070Wn3N+z25pyJ5VhZDViN9AACjaIcetrG9IafTP4eGh1tbW9PLlyxSs2tnZSUQAgTDvM/Q1RRvJ7GD/OW2gPWAdxJ1HrikpRajPz89TpDT6D6xF/BHe8322EJrusObkvcIg3Mj/j0o2gnqUKuDx9PQ0AdDNzU0dHR1pdHRUc3Nz+uijjzQ/P9+R3ge7Wq/X9erVK7148UJra2spP5kzdCjTzR4q0oL8J+6PwRtnQjhjj7Lw8vJPnjzR9PR0co48xAxYQCnBoDL52GMZUweicnAl/aGlm/Pl78eQNkw17A5G0/PpHegDDLpFU1zc+OWY2ds4wM7axgJEnuIJEOCoAJSMl0Ludj93QruxvXct3dITeI3n9bnuhsMBT64YAN9pty/PYeMg4ZWVlcSWorCkq0g+7XCWkvbm1qI/g7eVYkowqX6GkjuiPCfz0AFnjIr788MCsta8nT4vb3JK7kJiBNT7wSPENzmPbky9gFClUtHi4qKePHmihw8fan5+XnNzc5qenu44j8odZ64dx6ub4+W6KzLScZ07eUFbfby9GqXvDcNwY8S9LTmHKPaR69peAR23HaQ/eQomzheHcNN29KYzwrQTgMiZYUTeYcorlYrOz8/18uVLvXr1SicnJx2VyKempjqOAaBPPHrre1cBlJVKRXNzc0n3oRMB3Twv+2XK5XKyl+hM1riPIWC3Uqno9PQ0nRPKvkYIA+xvuVxO2TtEM8kO6tV6hKjIja8DwZjd4plRZKB4Khq60SMgUYfnHEG+k3MO4090/uLnHWjHNe4RN39W379aqVSSneOzpBYTxcERik5j7EtvQy+ErQpgKkgoUvx4Dgjvvr6+NK+c3IAw9fRAUhuJthMx8kIkh4eHKSWVCOOLFy/08uVLra6uJifbsw5ucrB9TD2CRnCD6pvgYRwYgj0UgHT9S9oqjvPZ2Zk2NjbUbDa1vb2dKmr7fk1f/zfp3rsS10/SlZ1Ed4I/HLPwf4yGg+OpyoyT5inlMVrOd9mOQ9989tln+sEPfpC26pBuzJYdqTMrDFIO525sbEwvXrzQ3NycZmdn0z5ZHP+ZmZlUUdrxAFkhjAMOMfaROc0YIWAxMHmsP+C458bxeNcBdE8UNt+jSZH99QY7M3VwcKD19XW9evVKu7u7Ka0GYBOrmbJncHV1NRW/2dra0vb29rW9jD5gkXUgEhIZV9goxNtPSWmKNZycnOijjz7SvXv3krHLgTxe55mZfLzfLX3Rox83sZ5fVt42QTCEtJvn92MWPA2JzcMwVTMzM4mt8ghlBHy59Kl3ae9tnDS/vt/bjaQ/M8rYnX5X2n5v1gDAIEYMbtPX7ytucFFQOeAe0+CYe953DihQLvQP58Otra3p1atXWllZSVWK+Y6njiKkkUAUdAM4/pvXYe5nZ2e1uLiYjnYhtRiwDUt2cXGRlL4XsvKoadzH6A4W7fMoiUePYn/fpXD9HAH3NoeRv71t5fLlfrDJyUnNzs7qwYMHevz4sRYXF1MxHMAAqUgemeW6HlGHXIupLLHPHHDFfo/9yVwYGBhQpVK55lS6LnXiJqadxT6Lc8qBTo6AuivxjBr0ChFCSEMyUqhC7NslvM3SlZNNRVH2o3qq0+joqJ4/f65f//Vf1+7ubiomR5TBz9GlbaRrwYjjrDMH+vr6NDY2prm5OR0eHmp1dTURRzgEktLWi0aj0cHK8wzUMTg5OVGpVNKjR48SOeApYqS9lkpXe/79bE8cucPDQ0lKYIo9nnctnnXAHEXX+zqL65MzbTk03Aky9BDXdvI1EkbdHL7c/91+5xzK+OOvx2uUy1cZRBQOIULj6Xs49V6MkLRLtw+xz3Lz/a7F+xK7x29ICdrkGMUjku12u+NsSlICncjxKFW5XE7ri/MdX79+rZWVFX3xxRd6/vx5Kt7oxUretx/QybGCNfsocRyZZzMzMwlfM9ZkEDx69CitbZxPbITbiai/6cNeSXRYpetFpXxvKutLuqqsyxwFt0KEeCaTBzmY25BV9Xo97Ufd3NzUq1ev9L3vfU9ffPGF9vb2OvAyuiKSTO12OxVK4+x3ttmRJjs1NaXFxUV98sknevr0aUdxSd/nCN5mDqBrmQMQWG77fE9jtJP0Z4zS5uS9Io7ObHsqmt8MRetKl8E8PDzU9va21tfXtbe3p76+Pk1NTWlhYSF52B61gLnhfCCq78GAwt76YHm6CxFFn+DOpHnanINndwC8whXXppgOLIUz576nDiXFpCI1whlwn1yxnG4vJAdEo7BwqPyF8nTj0G63O0AM6bsAnYmJiWugNAKjyO7l2neTOBDsFhnyhevKDsASlUVMy3VSxO8X901SCONDOY4xeubsFvfkM16KGWAgdVY89mIzMK3Hx8fa2trSq1evUloGWQJkG7gj6FGdONYoP3dkpU7WGZ3CnJqdndXc3Fzap+Ugl8iOr1/ADvrGQZ+n7MbUKwCegxr/rNS7KsfdFHVuXueMtT8joL9Wq2lhYUH379/Xw4cPU8oSKVW+F8cjIT4mzmrzE0EOn+M5nB2Ojrr/MG+kzmJJOXAgde5tdaDeLc039mOvWXFJqW3M4YuLC21vb+v169fJaeToJopk4CzhTPFs3neQc7VaLVUYr1arGhgYUKPR0MuXL/Xy5Uu1Wq2UETM/P58K4sT0axwcHAHa4Oln6PCpqalkl9fW1tIeKAjOVquV9vQAYD2LgXM8JycnE1hD73uhOA4iJ0Uee9vf358yfC4uLtK5amQTbW9v92Qso653ItvXXySZt7a29Pr16xSF8gqV9D/P6WR0XFuuE7s5g+/jOPpnfMtF/LzbE+nycPLcEREeycFpJluHfa7R8fbr8qy9EEB6jNz39/enPuf5S6VScvwcl/C8bFfiQHgiVVQR9kgjEanV1VWtrKzo+fPnqR7A+vp6WivdCOnbis8/+vT09DTtnYZg860nkjQ3N5eO02MODA4OampqqmO/Neftklrpabg5G9+rcXS87s8andhoC3gWdC0ONDrVo6mQIZGogjBfX19PUeOVlZWkc+v1ekdbIVy8n+IYe8ZmqVRKR6Ggc9lrin6Yn59PWRkxu8rTsSFVGXfmrj8XNtYzTnw99sxxZBG6g5MDxjlm6+joKIXu9/b2VC6X07ENtVotgT4Ew0P4HQNFBIy28KAcTBxTmVByMRQLE4ijF6tY+XdOT0+1ubkpSanKYyz4EkEnoN33aeJwIj54Ma/6Q0iO3XSn0SuIka6xs7OjnZ2dBDDa7XYKn8O+kQLnm5NzDjzzqN1ud6SR8pmb2ow4MHTSwq/hIEDq3EPqTDzRYQxh3Lcb78nrziDHaqK9jFQ5QPf2OGHD53zPhAMK33vL98rlsk5OTrSzs5OcxtevX3fs9/RxiECDNiCsM973NGf0BKQDFerY3E+hDlg2FCBgzsfdDT+Ghs8C4Pi+75/zNEG/zk2ponclb7vubYwzQMcBvxfDIc3X9x+TEsr3nUTz1GUnzHJgwUkD7y8vqOF97swun2X8SPWP1wdoEymjLczDuO7j+v8Q4sVbRkZGdH5+rpWVFX3++ecpUl6tVlMhJ4rY8HmcLvoRcgOgSv+x97DVaunFixd68eKFms1m2qMzNTWVUp+8wBo6jqwRMkawm84440xWKhXdu3dPjx49SmfZtlqtBLokpbM3mYORdMD++fnGVL6WroqOcB0noyqVihYWFiRd7rPs7+9PkVQyIe5asPnReYz/I9h82rOystJR6IIoK3YdME80yEGtg99uDmCU6ES67Ymv85q/xzO7zeD9/v7Ls0YhULEX6F7WpWMD0lVJneumH2L/3bUQoWZe+trEefR+cX3POgQjgjtZF1Tg9KKHEEL1ej05jT/84Q/1xRdfpDMT/divLyOe4eRp/dLVkXWcv4oOBy971MrnNFWBndQgcor9oI4A/RrJxl6I38fxjEeMpetzmPUWK+tSFImx8+AAuoc948+ePUv7UVdXV5PDyDYdFzAGmAIM4g6vY1zHjn4soWf3MQb379/X5OTktUjr4OBgymJptVra29tL85DARwxEOe5zH8pxz03yzo4jDfXG5ABVVFaAas5UZD/i+Ph4chxhIt3RPDk5SSWK8dJhCrxAj/+w0B28cy060/cXOPD2QXYQwntnZ2fa3d3Vy5cvOw7H9ihIZNmlzpSdcrl8LexfLpc7qpWyCHoV4fBx8t8OGonyxnz8vb09ra6uamtrKy1c0jVgWP35YEGokEnfooABUQAJLzR0U5vj30jOafTXnVnhGu12W6Ojo5qcnEzzhwVLKnWuDSilZrOpcrncQWoAAnPpfXclLHT/TftyYCcXwfI2ekopqRSwbOvr6yktnHGiHz26Tr+wvkgp9Pt6uxDKjFer1RRlpMw4ewDY++ROgusL5pe3Q1LH/T0bwCPhDm54Lp8jgJ5eyG3Z9+i0xfHu7+/vKM++sLCQUhdrtVrao+TnY3Jd1j66040tczneP7bZDXZ0QnAWiMQBqt1Y4/j61gfmKACbDBPak4uCvq1/ewVw6EcnpZrNpra2tjqKuZyenqZKiuxBZP04WIVIAbRdXFxod3c3ZeccHR2l/cblcllzc3MdZwgSbeS5WY/OSLNNw4tZeWRobGxMCwsLaZ/UxMREIgqJqELqDg4OJifD+73VaqnZbGptbS2db+l6C0KVa5JGSCpZrVbT6elpIishKD+0+HqLAtm6v7+vra2t5FhQ7II153jI0wrBODES1c1ZzN0/9yN1pp/nsJr/RFvC/7FWgdvJ/f39RDATbSTC1U1neJ/2Sq8yx5lf/hyeVhoJTyezPGp+cnKSUqs9RZU5DF7Y3NzU2tqanj17ps8++0wvXrzQxsZGWkNfRmg/Ke6+Rw/7SASYrKBGo9FB2qGL0S/S1dyAuJ2enk4RR69COj4+3lEPgO/2Uq/iAMb56/oFm+XBm4hzsDEcZ+L7+t0WHh8fa2dnR8+ePdP3v/99vXz5Uuvr61pbW9Pq6qq2t7fT1jb0fHRYpauIJ7VTYqaT39cDHxx15noCvcC93HYyZjwj5AS41CsHM0ZOvkqdZ1ffueNIGBbWngd2do7Oj4qLogCwUaR7ejU9X6w4moDVvr6+VPKbPXQxxcM9aZ/EdCKM2O7ubvouaaM+iC4+YTGIu7u7evHiRWLvvcqqRzfoK54fBg/2mXv6RPLJfhfMVE66GRnPlW80GtrY2NCzZ8/08uXLVCUKALCzs5OABulvtVpN7fZV1IaJieLB+Xcg6WWPLy4uEkNO5NXbnPvbpZvT2O2z/gO7DgBgs3m9Xu8ouOJtYJ7BGJHawp4egFmvGFXawe/IFkudTkHuM6wXT+/2c0hXV1e1ubmZ0l+kq+i4lwSHxXVA7ym/UfmRoiZdFZKoVCqamZlJUTL/e3p6Wn19fR0RXRQz18WhyO0zxXBC0GAwUOysgRyL6alOvZDc3IrSjSRBPEV8eno6EXIUwiE9NUYafewZs3gWp0f1Ypti2yPgZL752EVHzx0NxgZQB/jzwgYeCSWqEUmRXB9FO3XXEok4xoQiHURuOYP45OSko9S9g2h0vzuO+/v7Sc9Uq1Wdn5+nfTuVSiXtZSUVLQIiP2cMncAeNQCNRyhx5KvVqhYXFyVdprmR+rWxsZHI4OPjY1UqlfRdooySkm0jegG56NkcIyMj6eiSnZ2dFG2s1WoaGBhIxxSUy+XE9HPMyV3LTbYj5zw6AeeO1ObmZnKUORLBiWGfw56x4g5X1O/eBp/L3Wz6u2S7uO30+eCYD4zCfAZXbW1tJceR6Knre7++R3R7KdieVqvVUXDLsRa/iRjy/NhB9goSvRwdHU14x0kWopI7OztaW1vTyspKilbdldOIwwPunJ2d1b1793Tv3j1NTk6mthD19AJ24DZwF9usfL+qO1akw1MxnS1KEFK+JYQ+65X4mssRI45beRYwuGcb8nxeuIp5gXNKxeoXL17os88+0xdffKHXr1+n/Y2k1DsWiPbEbR6YiiinV28F+3ItAizo593d3XR9t6WOm9Dx2H7mMIEPJ0EgSQiEeSor/dItg9TlvVGQgypn433fShxwByQsoLgXEAWMUkUBU6FuZGSkI3rj3jj3iYqSex8cHGhzczOxmt4Ojzzwv0cZS6VSGmAM4ObmZnIePcLmAzA0NJQ2FntlTkBZTPfl+b1vey3RyPhmd5Tgq1ev0t4W0lJOT09T6mCMMLijFB0I+piFjaEkbcqfO5fW8q59Ep1JN8DedsAbTHGtVkuVf3kvB5wB3oAkB42AtV45jrdJ641Ah/FhvF3pomRZK6urqynSCMMGkCDCXK1WVavV0gZu+sPTLhz0Y4i8fSg9Pzx7cXExOT4zMzOpihht9fRa+j8e+wMAglWFqZXUoa/4rKes+t/838txlDqrufqY3eQ0OlHFvlD2pXFGlKeQ+2HG7uA7iHWnsVt6Kr9z887tA//Tn5HIcMAS0yTHxsY0PT2d9DX7rUm19D2z3drgfzsx0AuJwNOPZsAOoSdg7mMkxLdOEKmiLD5VqqemplQul1P2DFHBhw8fdhy/4boB8tIrXqK/WDfswQdIQRCNjIxocnIyOXhenI6jtKSrVD8c2kajob6+Pj18+FCPHz/WvXv3EiE0NjaW9lB7kR4IBs7TPT4+TmejHR8fa3BwsOOc5F7J24hKtyW8D6DE2djZ2dHm5mY6GB5yPBLVMTISyRXHNE7Yx/Y5/um2ZuN3u4kDbD+eAKyCgwJxsLm5qZ2dnVQYCL0RSSLHWB5l6YVAWNIXEJ0RX0idRZAgvbGDu7u7KT2bIiYezfe+IEvriy++0IsXL7Szs5PmBevKSUsn5QDzOSFgQuXNBw8e6OOPP9Ynn3yihYWFjr3DRMxwfjhrmWM2IHvJmqNSLmOD7p2YmFCj0Ugp1QR9/ExY8FM3W3AX4rbi4uLiGhYjSub6DjzumJvMNi/o6MdPQZizveCHP/yhVlZWtL6+njJEPPhBe3w+xXbzPgWI5ufnUwZQrVZLafeS0rEtu7u7qY7I6emp1tfXOyKjEW+jTzwbk+dBj2JjIcB97nmRwNvYx3d2HHEsaCiKgcV2U3qhM64AJB6C337cAQuXyNbs7GxSvIBXmGgHitFJAHQ0Gg0NDQ2laBr7NaIQAXTD7AAdkHJ8fKyNjY2UiuIpVoSGKWuOIm00Ggn8+n4GAFxceL0COZHBQQASAAyMP+knLMBWq6WxsbG0l8bBvU8+B91uFOl3GCFS1yJY93L8ub7wsY7Pl2Noc+LKH9BDuWTYIU/r6ybR+DlIJoXyrgX2utv6i9FFlIdHgz3KBONNta+NjY3EtsJMoZxQeoAiSASuiSPH/h3SZegb9AYs3OTkZEqv5MfPN8LhwwDjkKP02+2rVKFms5lY5rGxMY2PjycFybmkntvPj4MdZypbrVZHgYteiIPDCErjfEacqCJiU6vVND09rcnJyZSWCnnl6bkxLdWdB69a6vOo23rrBvz88+h89L+n3bDWicx5n1cqlUTAAF48Fcv3Xd1Geuk80l84gsPDw6n/6c+zszMNDw9rcnJSBwcHqS9x6llnXvEPwoO9v7VaLVVsbLfbmp+f10cffaSHDx+mCJ3bLCcE2XsmXRWAk5R0OzolOo+jo6OJOGStsYeGSo1U62V9kdr6kz/5k/qpn/qpVLCHVFUiVZxryRols6hUKiXdAQBCD0AY9ULcEXubU+Prk3lKXYZ6vZ7qAbAewTsjIyOJoPHDuyOx4u3x+3Vry23aHq8RP4c+JELnWUHMC9YjDnK9Xk9HEQDWb0q57aXDiEAwcl9PXUXceaXPy+VyKjKztbWlZrOpwcHBtPZyleLBlaurq3r+/LmeP3+uer2esBK4kPRlCunhlHkBF8hPhPU/Njam2dlZPXnyRN/61rf0W37Lb9FHH32UHD+3Y81mU7Ozs6la9fe+9z3t7u6mrUZkplSr1ZT2yvNAWlWr1RRBJmBwcHDQUfTQ59G7RLffRXzbmY8X44hTzXterBLCzLMfwARkCUpKvsTW1pZWVlb04sWLdIIDe1PRm+5kQwDgvMbaC9ix8fFxLS4u6unTp5qfn9fMzIwWFxe1uLiYqkPv7e0lwp6CPNvb2zo5OdHa2lrHntZ47jS6mmCVb5Xi2fkuc9335Ht9lbfJezmOztDTAAYoKi3/8c3yMAK5YyecvSNdjoOUKS3OxlZSBBlAB1h42aRTDAwM6OjoqONMGgdRdJgPPu+7Uucz7XZbh4eHqX04kJ6CCgDwSc/+N2d9HUg7g9Cr3H8p7zzSX37kBkULvBQ76RKzs7MJ4M/NzSUlFK/tTg2OPKnK/vwe3eA3kzlGzm4Sf57bfM9ZMxalV03zg3O7XScX2eP/XinUbo6MO5LuHLF2Wbf0u5MFpKD5nkZPt2Bf8uzsrGZmZtLh1m5IAcjsCWk2m4lYYY6dnp6mA5RnZmZSaiXzya/v6XO5Z0ZJcszPxsZGisQQgXPH0SNvrHcHovSL/5bU0Y67FI/Cx9ek7imhKH2MBQ44AKdarXYU8fKUHJ7Lf6JBzt3TU2ZcfE55e3mm6KRLSgVZuB7PgzhgmpqaSnOUNByPzHQDog7q/f9eiI8jkTocdk8PhehEvwN8YPTZl+LHbxA5HxoaSvsmV1ZW1G63U/n2Bw8epMiD6zScRtKg2u12yuAhwode9vQnruOEC8V3FhcXVSqVNDMzk9YxjhF7OHd3dzU2NqZPP/1UT58+1dTUVIp0QEL19/enrSOMCwVZWq1WOjcPZxg94iRzL8YxOhcuNzluEFqDg4OpjD+OI+mq1WpV1Wo1gU6cBl8fvJcDy25PI6DmNdqZs385QhFxp9EjFDiQEDXUO9jZ2UkpfESzIUl8C4BLzCLqpTgR5fYOXej6kLZD7pMhIyllInmKqjuOZKFRPIUTA1i/pHs/fvw4VRjG2dzc3ExELQ44fUl/ufPxne98Rz/1Uz+lxcVFDQ0NpQAH6wTds7i42GHvv//972tnZ0dbW1uamJhIDgzr0cki1jmp8FTMrdfriSDydNVekgDeD+4vQEZC6jI3se2kjnt9FIIB6FQ+R3BpY2MjVU4l1Zcgk+sf5o1nPvm6ZL6xNQ0d/emnn6ajkiheR7FMxz8rKyvpbMn19XU1m029evUq6U33YyLJNzw8nJxlz5p0wtbnlRcNzNn2KO9Fn3cDMC7u0fpixXiy14hIRUxbINWFTcaUCfZiNF7NMypS34+EIqf0eYwo0FYH2c66xlC2p4q0Wq2Ods7OzmpqaqrjsFTANg4ZE9k3j0vqAOco7F45jm9jLAljwzI1Go3E+kpXUToUFGCAsYz3whBRuVa6Yun4O45JNJTS2yOwzmxGg+XP7ArfQTNzgOciWsBcukk55t5jDGHJ71pwxOL9+dsj+s5gM++czIFxwzhgvJxlQ/Gi4KiEzFi6Y+Hpv4w/bCiGbnx8PEWtZ2ZmOg6n9zPomB+0g+vxrDDErvhxWkmPZhxYmxEgE/3OpYs6udEriXM7Gse4FviMp5RBdgBMvRAO67JbVMLJBt+H5SA6EhGu+5kDXm3Xx93JMNcR2AUHsnyHsQU0zc3NpX16MMCeouziqcU3RVfuUpxoZEx8DwvRM57LDb4XWPPnxv6QRnV4eKhWq5WiOyMjI7p//76ePHmihYWFa2lk7piw95q5QjQTYIje97Xh5MTZ2Zmq1WoifqkzgA0gY+Ps7Ez3799P645ULKLbRIr7+y8rpLKHEZvo5DQAqdlsqt2+ikx7xs5di6/znK3MzScn0qnnQNSRH/bGgXtwHog0kYLrdrBblK7bb2+f/86tdb7nhHsE4l6IBcAMGc/zkVrnFY89ShTJVJderkcIBr+37+2KRB0YzdO6cTLZmsF6Rj9hN7e3t1P18Xq9rna7rUqlkjJpHj58qG9/+9v6+OOPU+o66+3Vq1f67LPP9MMf/jAVI9zY2EhtGhgYSHb36dOn+smf/Ek9ffpUFxcXWl1dTQQv+JQssLGxMc3Pz0u61LOkFe/s7GhsbExra2u6d++epqamkrPFuiPDYWZmJukaxrzZbGpqairNJX73aiw9Qw0b5I4Z9pyMDSJ+REovLi5S9p/vN2bc2Z9KBoRH/HZ3d5NOHBkZSXuWqZpN1N1JOa5JJt3s7KyePn2qTz/9VJ988ommpqaSHgNbEwlFJ3tmGHNxd3dXpVKpI2Xaj5DB/hCQGxgY6HAe49rwmg85m95N3tlxBIh6VSUH9a6gonfL67AZpN2w18OdRhhVqjmSxgrriiH2YjRMAoAHSq5cLqcyyh5No0NhHDCUfj0voe17QpjIpdJl6hW5yRQQ4fPOHvo+TqrYAW7pK2eKu6Vn3pU4yx8ZTfoypocxnkxkNsHjIMdqWzwL1fb8fkQMMDAOUD2N0p07Z1eYUy5OWDhxIV3NzwiqPHUW4MaC8hz4dwUprnz9+JW7FB+7qLjpe3/uqNS8WAlGihRlFCHznqiWO3rDw8PpyJb9/f001s5EskY5dBhFSPrbzMyMZmZm0jWJPo6Pj6tcLicwBaPH2nYnBAa8Xq9re3tbe3t7aa3TN7SfMfXUcq7JnHHiSboidXrpODJmUWK2g4M/d1Aqlco1p5H91xhJX+M59tEdOp6329YCZyf5LNEtb1t0HOlv6apsPGyut8V/iEDhmKBvSb30CoLdhLnnfXjX4n2CDkFfnZ+fp/367OVlD3Cr1eqoRo3tYD4QTWePEnqW42o++ugjzc/PpzPlXD86YXl+fp6ITAAU+q5UKnUcju3pXpJSqh1gl2NTGHeqnu7v76vVaqX9tjicXuih0WhIUkclcZ6btZ7DGaSxQsQNDw/re9/7Xk/Gsptz2E3cgfOiMePj49re3k6Ak3WK/pmYmEjn7JHi6YXKcm3Czt3GqY2f8ch77vl87fpedhxdwPL+/r729vbS/lOKF3k6aCTbImbMRSPvUoiSS1dr0wlTsIBHuongeUE/zx6Ia5qiM8+ePdPz589TBWX2BT9+/Fjf+ta39JM/+ZP6xje+oenp6WSr0Y0PHjzoOMMcxxVsNDo6qoWFBX388cf65je/qfv372toaCgd94GDU6/XVSqV9PTpU33729/W/fv3ValUNDc3p48++kgvXrzQ8+fPtbu7mwIyi4uLaV80ziNzgMwrMLpXtga78vlekTiI2y7fQiGpo3gTNosxdLIMm8hzYu9PTk7StjgKAq6vr3fsaezv79fExETar12r1XRycpJSS7e3t9PZpZ5NODw8rOnpaT148EAPHz7UgwcP0prHvzk9PU1pyNPT0yloMTMzo4ODg1Tdlpovr1+/1szMTCpU5HiI5/LCR2xBos+Yv15f5rbRRuk9HEcPazKY7qXmgCvf83SqVquVwA0g09MFmNjssZqbm0uDF8ENneYRK0+zcSOI4wZDjSGN0Shng3FaPW+fVDv2WZLKyUTzSKsvKgwtjuzR0VFafBH891KpxvQwFzceHkWAzfKokR+x4WDSr+X9yLgAHp1l5vO5NMHoAHrU13+7Y+RG2J0o5lC5XE7jeHZ21kEc4Oj7vo53deIhSEgd7IUA6CPr5//z7LxHX+eY4Diu9DsRERg7QKev1b29vZR+TMUyKnkCED0iwb47L4BDQRcALYCXtkhKzF8kdahUyXEN/EidKSpExrkGRQ5wej2NkPUaDdVdS47pi2AVx9/T11hLFE9hrlEQxzMf3GEEOPmagtRjvsQUl1Kp1JGu5voD8s2vzVpyRxMnBT0M+PJ5GZ856g4/4xOjCnER09y7Sa/GUboC6/7s2MuDgwOdnp5qZmZGDx8+VKt1eQ4j+39x8mGqvYiDdLXeSX0jZfz+/fuamJi4VhUP/cYakpQyfdz2ekYNmTlsJfE+832oXK/dbqter+vVq1fp2KZy+fJoENLypEvbDYiDuCUNGb2A7nFbAzEFUAUM9ff3d1RxvUuJJLhLN2fO/3cwCplF6jjbWoj6UpUcwOm6KzeHI8mb0xsRR/C+k0Xxe76WWadERwHbktIcaTQaCfNANPIM3Jf7ddNtvRaAMbrSyXgPBtAngG0I1EajoaOjo7QW/aB46SqKh+P4+vXrdHYizt43v/lN/fRP/7S++c1vanR0NJFdODPo7o8++qijiCBOGlsu5ufn9fHHH+vx48dp+wfZNZwzuLGxoVKppOPj4/QsrGO2hFSrVb18+TKdz/z69Ws9fPhQs7OzHXrTcTA4yPdiHh0dpf2TH2JMmT/YC5+r0lVQC5uC3vMUU4+aM+6Mdb1e1/r6enLm0Dke+JmcnNSTJ0/07W9/W9PT02o2m3r+/Pm1wBJ1QFjbVDmnOvbQ0JC2trb0/e9/X7/+67+u09PTjhoP9+/f19TUVKoyv7GxkWwABPn6+rpmZ2dT0S2f5xAdIyMj14JkkjpssxPxjOPbSIB3dhxjbrcPKOKOm7M5lUolVTW6uLhI7A2fgSlgg+rr16+1tbWVwsye8sODORDxnHpna6UrthQGHhAGWIKR5xoYa9oGcGGSMTkBtBcXF9fSNXKpbYAwIiHNZrOjbDrP4Sm9vZC3haWdCAAM+mJlTw795GdS+j3ckfE0rnh/jCSOebcfJKZSRUMZ9yz4nPR28hnmJePsCtPT/zyfPbe4vB3sUSL1shfiezVi2o23yZleNwgQKvQnTDkggHEB1MI+Ushof38/7W/ByMK28sysc9KUSXdjz8j8/Hzaa1Gr1RIApi0U5mFcS6VSB5HhRwx4RVc/OgRDzA+p436dGEnzPvO52wuJazGCPp9XUfcC8Kik6oVxnEH2TALPmvDUU/SdZ0D4we6euo3+5HseZSD1lDXF/CL1n+9zfdaTF+SROlOTPIUKY0zqFQ6y69wIZBzA9grkUCzG9ZuPlXRltMmc4VBnHDcyUIhKLi4upsidExyQsFQ1Hhsb65hHjLOTL6x1J8P4PLadcSQrxo/M8LRoHD4cCM57PTo6SqBlb28vRTgHBgZStJjKuNzX15UTNWwF4dzn/f39ZDP56YX42s85YHFMEZ9X2LP9/f0E/Ggzzhjrgz6amJhIOhI7E9d9jBjm7PdNpHP8Xvw+Y0whF49i+154Io6kLsaiLt2itJHk7KWAVdzmRfvveoyI6unpaXKIcQDi0RXSVUEV9npy1rV0WTX50aNH+ta3vqWHDx+q3W7r+fPnWllZ0d7eXnIca7VacgDu37+fdDMVWs/OzlLGFoQghArFVDj24+DgoEO/cEQa6xHCaHh4OBXAe/HiRap4jO31MWM+gEV9vzSOleP8XoiTmr7lhjbRBsf1js/ZppLTNVSgXV1d1YsXL/T69et0vBDPD/6B8Hvy5Imq1WrCPqwTfANJ6TvUGwDbDA8Pq9ls6sWLF/qN3/gN/cZv/IZarZZqtZo2Nze1tbWlk5OTZBtwOolGU3eEowX39vaS85jb6zg0NNRxFIzjPj9qh0BRxNo5ea9U1bc5jbzuTh6LD4bx/Pw8gRre97Bxo9FIKWdEO2JKq6d/OthzsOWOJPd2Ft5TvTy6gtL0VDYmKO1EwUhKJcP39vbS+Vy+dxMQ5ef+OOD1VDK/fi9TqhzY3GSEeN2fg3z7e/fupUkNEMoxoUSEWejR0fF0RNJ0cpFXjI2XB4/A2J/D2R/u6eCccWi1WolVZCw8BXBqaiodC+BpnLl+cjacCpezs7OJieyVxHUZ5w5jECNFOEvtdjux5JS+5/MYIk979GI6gFMifkTxiIxI6qjaxTWYR+wNZk+j5/V7uqmnMFOJ8OLiIilGxtfL+QPgiGaw5phvRJudcHLWjX5ydrMXYxfXYHT+fVzRUx4ZYI6SDueH/nokziupOpHCj3S1NugP1i7gl7kU1xVj7EDMnUXfhO/PDcGDofc+Z035c/tWB4xxLtWGz9OeXgsZJG6L0GV9fX0dR2oADogCAMLa7cu08I8++kiffvqpFhcXU6VUB0xEEnxdOnCCgGQNQY4xh6J+cH3L2gOIeFYO+r+/vz+V6mdttdvtZDuxqb63BlIKMojXmEvMGQf2XkyOY6IAxb2KOL5Nl8bPSJ2pdP49zq5kzLxglzu+nJfpGT6+jSJ3f29DdFpj21zvd/suut6dRnCM7wHERkAC7O/vd6TnRVscicuoz3qJc8BununkUUdvF3Ofug5ExYlWeaYda4x5SWSHNTA1NaWHDx/q3r17kqRnz57pe9/7nj7//HPt7Ozo/Pyyervvf5ufn9fTp091eHiY9ktyFp/USbpj97a3t1MRF9b469evNTIyooWFBX3jG99IhJvXBymVSjo4OEiH2j9+/Fizs7OpECA6E9zujiN7yzlTFr3SDT9+WfH56bbD+8Oxnds59xU89Z3xY1vcixcv9PLlS62vr+vg4KADD4BlcPJnZmYSMYaeRYcxN9CTOI4ErSRpa2sr7Wfd3t6+lulVqVR07969ZBv4vmdM+hrke2QF0GbGHEKEsfJItGck9cxxzAH0uOhzSglASkSOh3IQj3ID6BEdAHSQJsCeOhiEHBvPgEdHwVMe3ZlDIdDRXjIZlh0Htr+/P6XjcF0qjHEUgEe5HCx5uNzbTBtcMeHU9EIcLN7EouYUAZGH6elpzc/PJ8Af9wI6eOJ/FFtMBeI+OMzS9XQhBzZIBLvRaLNXwMEon2ORA2BQxg5giDji/KEgSMeJgnMBmPOIQC8cR49IR0c79pMbcAAd+2pIkaIYElFzN/BuXGPaMACVCoitVivtEeH8QD+rkb73teaFQbyAgacx8x7RSIRItese7oEBcccR5xFwxrzNzUt3gHrpfLj+y81lF3caAXl+XmMk1Li+E148t6SODfbez7EfPJroex9LpauUYWf53VmMDoEzo4wT45YzYLTBtz342ZReCMPnqPdrrwUSqpsTzhogJZr0p4GBAR0fH6tWq+n+/fsaHx/X48eP9fDhwxQdJ8IjKelcyvx7VNZTt3HUfB+5Axwk6kO+n5tH6DeeF/sOaMaGAcBLpVIqLsF12UdJVg9rkjkT06gohOcp0JKuFXzoheRsT5xP/M2cdbtK9MrT/Ulbox9ZB2R10B+xgF7Uxy6+znN/++e6fd/XFmeIkunl6bcUxSFV1ffDxz2NUWfm7t2rtTk0NHQtCy1mptFOqXOrBoQzxJdn2zHOXlCOs44HBwdTtfnR0dHUX8+fP9f3vvc9ffbZZ1pdXdXx8XGKSrbb7URQV6vVhKvi/nRJHY4aawziFP1MRIpj1MheY3tGXKOcx0o2js9vsDr2GfuL/fRMiF7qWOyFp+N7hhB95FvJsDOxCAyfzdVTYctDxMFgQfAcupZigvV6PW1FwFmFKPJz3ilyxjFEkOCsm7GxsQ4SgmwE5gKOIxHnRqORPcIlZpVdXFx0ZFV5JpAH7m5D5NxZVVXEFSyN4Wy1ra0t1ev1lLPNPib/nqc9kWPuZ7DhOXPf6HjmmDXpKqXAU98wTAwq0S6vtsREhXnDkA4ODiZD584E1ekoGuPlcjGqDBj/M7lcyWJ8erUQo9MYHe/odHtKU39/f9q468cxdEvN8t/0Q7fPemQEUICS7gZ8HKz4tSAgHCR7//o13HC44SeaMzs7q8XFxcS0ce4Y9/NxQkFTQQ9l3QuhP7uJO8keZcah9kI4gBUvXx0dbY8IexSyXC4nkMN9iPYCgEqlUiqY4dFP3zPtKWr+XJ767SmptJHPOBjgOl4cxFNsiG7hIPu8dF3i2Qwx9fyuJBoqd5qinuM1dImfVYlx8fXtjqCn8XolU6mzwADjHckWT/2O+44BZdKVY+s/jAtOvpNqHkGN8zk+c4y2efaBZzzktglwvbcxqu8rkTRzQozq3sx5iibMz8+nghxzc3Nqt9tp39PAwIA2Nze1vr6ulZUVbW9va2hoSJ9++qkmJiZUq9WuZQEBLEhpAgDTPi+8krOftBdiyVPZHWg7AHH9Cfgm1Q+nie8wb9j/Mzw8nNY3eMDtM/ceGRlJJOv7Fix7F/E+ua2O9c9534Jntra2UiVpqkb7uvMiLOyH8wJmXDcSri65194mrC2vJh6P8QHgkpbJQeXMMeZVJPD8Ht3a1kvH0bdc+D5s5pa3z4nJmNoXU7vdSaM/ms1mcrgHBwe1t7enzz77TEdHR1pfX097Cl+9eqXDw8OUgj41NdXhuKHTYyYFeoV14vv4+/v7k16n9gZbNCDs/Rgq+uHk5KQj6OEZODhAXkzOMwQ9wtZLp9Ezfjx93u0cusDtm6epMpfdnkGW4/gTZY5BDwJV7E/mTFuivjiO+BaMFfaWNeGYFLyBvoZM8yrhXAsyh2Db8fFxajOp4l6NmfnsziPYzDG9YyffjvY2eWfH8W0XdgeAQUHR7O/vp8qoubAxDyMppXeQlhIjiIBBwIcDvpzjiGNHyk5/f38yWmxWxfEjv9z3SAGSJyYm0uSt1+sdEw0nktSN4+NjjY2NdYBuT8XiOlTvBGzj5PQywuELLi54Z69zY10ul5PjODc3p1qt1rFXVepOLkRnmDQY33vQ19eXxokUORSVO4JuqPxMHo80ME9gqj11D6XirAzfd0DM3gNYoUajoa2trY5nQ3EBCn3f0ducuy8jzI+c09FtrTIffU6yNnDyIsMtXR2b4CDGWfSxsbGkTHMMHY4069n72PeFRYeedeUl6z2FFiYYBe37IQHsOMk4jcydmIGAjuF/5mOMuty1RCAYoxkRkNIud6Di+bRxjbvjH1Pg0EekJvKa/8bxjKQL36XfnA3u1gbAiY971EcRkNAOj0LlHEfGPY6l92uv9GpMiyclqN1uJ3YZfVmtVjU3N6f79++rr69P+/v76diKi4sL7ezsaG1tLR1ETUXVe/fudRh5HDgkpnnnIvauH1wf+DzDAURvMq8APOxzJ32LegRU2CQVj/XOd/r6+lKbPGrsRzrRf3yeFCyIEohX9iH1SrxvpOvzxiONN+l4xoRCKhQqmZycTKnLnpU0NjZ2bZ8514nOflynb3uenI0Gi0AEepQEm+kgeXt7O20lYqw9YnGb9vha7xUBEB0L39dN9EbqDHj4ViHpKi3c8Spt9rRWtm1I0vb2tkZGRnRwcJCKylCMhjP5wMds78D2etaCB1Y8KiR17lXz9P9o+3BAyNDz4masIQIeZBrFeeJRV6/rQR/3yi4iOcfZ9/Mxvv7MXp8BO4m9cVvEljFPteaZmZde64IgD1FBxsyr4DIWzWZT29vbajQaCZ9wPaL5TgSRScXZ6ex5h9DxTA8ncCBvvP2xv7qNk8+X247nl0pVjSxnNPosDAAfYMHPBOKzfk0UFJ3twJKOYNHi1cfKqLkUifTQbyYSAwFbTwVBDDzt8onKJlfYUAyCO39MJi8WQXv9cFyUFM4GIWO+Uy6XexapipPDJ5sDuQiWfeLXarWOs/a4zm3EAY+zeQ6CYG9g1NwJAADTt6VSKQEOri+pYz+XpziwuJkL8bw17ycYQVI6cAb9M274HNTiNHbbp/JlhfkZo8MxYuWfj4yUdEX0oNyiE+MMoxduIHIwNTWV/m42m2l+UNyDcuOU83cD7pEL1pTv72UfwubmpnZ2dtIB6b52AaMeseQarFE/ENf7g7XG/WMajDtFvSIAIjC9yfGXOh09n7+xCrATeRh71z3cg/VBtkVsWy7S70aJtSV1OmfoFNeDGHUMsxu5CJCc0fZ7woB7kZTh4eHkKPmYeR/2GuC4HiiVSh0RJKoyTk1N6d69e5qfn9fk5KSGh4dTFJ21tbm5qe9///v6wQ9+oNevX+vg4EAjIyN68uSJHj58mNJZ45z1KII7HXEtR0fH+53+xnGEBEKfOZPN8TwPHjxI+/u3trbSnrejo6OOoiJO5rbb7XRcBWcfM2+c/Ijrz9d4ty0DdyU5Min2WdQJkbR2h53oxubmpl69etWxzYPxBEsQcXRAGNNAb9N+b29OGEvWlKd+YxNjEMCjjT7PIpnk983pM/534uMuBWwFdvCIO21yB9x1kBMkPn/d2cXpIlXQt300Go10FNfJyUmqhLm3t6d2u53STT3lX7qK/rgOpP1kAxGRJzIc9TY2z08PAPP43jYcJjIDIGWjQ+320R2vSBr2SnxeRZzvKfo4gtgYz2wBZ0YS0zG5k2zubPmeavZuszXHcYaTDe12W4eHh9rc3EzFvXBkuRYBF3APWxI8ko+9w18ZHBzUwcFBOteRopzYvmhTc/aVPvV559kkb5P3ijj6390UkxsnjyQQoh0dHb02OSPYR9yRoZNRaITjmdTOSsdqcLwGU8N7RBNZhCxOOpsJQapRtVpNm1yZnIBlzyH2SY1yok+YbJ7G5xFGV2S9EAeVUXG68+jA09OdvE9zkZhuYM0ncHyPdjnAgMHP3d/bzzVjxMT7k8XtKVCw5h61iAy+dDUn3VntJigjd1R6xahy7RjdiY5kHO8oOcciKti4twFjRcod6RSwrPPz88lpnJ+fV6VSSU6Kp13FNvIa9yOXf319Xdvb26naI6XAYULpC3c23NGN0VKe242Lr12PgtNvvUpVdSc5NzYRiLqj7ZvdPe3NI3jOpHs/MdaeQuhgyueFg0Pfg+aV2biPG6d4b2eDcyCFZ44RDD7jOtuPVOH5AV7o3Zyu6JUQXZOUDP7ExIQWFxdTdO6jjz7S06dPNT8/r1KppK2trRSlozDcD3/4Q/3Gb/yGVldXU7ro+Pi4Hjx4oE8++USPHj26dlA0/UkGQS51yXVpznGMY9ButxNgxc565KWv77Lgz8LCgra2tvTs2bMUKfPIPziA+c1YeeW/s7MzbWxsqL+/v+OcSUjh09PTtJfOM1B6JW4Lc863O0r+nW7Ai/nMEUavX79OWILK5H6EAvPbAaFHzSLxR9ve9kwIz+SZT4wF18ZGknpMpJH9XF5B3onKnJMV+9Hb3ivHEfuOXneiwW0Q+p41Qju7RRtpNxlNnsWCfoPsJOOAIIo7D9PT05qbm9PU1FTHofTuJII7/fxxSR3Bj5yT7jiE7RhODvg+YS8aF+dSxHo8e8SBb4u6fxnxKGucW/4ZJ988/dLtOUIf0a+e5eYOM3VOiDbiFMYCVvx4+7g+BAsZdE52OhknKe1fZH1ByHv6eLlcTvoQJ9L1RLf1FLERWXcR57xtHN9rj2M30O/vM1iEeEk1Y3KSkuMMAJ3GQc4AEyQCY+lq0pPi4QDZ9+pwL6JB7F2j5DlsH4rTvwNb1G6306ZlioCgYLa3t68BX/qInxgujqA27u+5zQB+GYmOY2T1Y3ujQxJBul/zpvvxt98f4+VGx9MBfJ8iAInNwwjpGK7kfc54O53B4bd/Lzow7GPY29tL1QNz0m63O1ijWq3WkWp010KUPCqAnPMY2+l/O1D3PVCSEgj3SmoQJpAlRH+q1Wqq7sXxEDMzM+lMQeZLTKmgHe5osHF9d3c3RRt3d3d1enqaFC5lxmP2QmTYPE085zjlwE6uXb0iAG6zfvx95jUsqle+8/RPn/cxkufPH1O/fY0Dhtwh9PWF8w5ryvdzxCLXA/xGxzB+rltEB33hlV6ZE74GuhFavVqPcQ/NyMiIFhcX9d3vfletVisVMRofH1er1dLa2pp2d3d1dHQk6XKt7ezspJLsVCUeHR3V7OysHjx4oMXFRU1PT6fKiN5vEcg6mPd5EPV2nOO+jog8eqE6z7IYHBxUtVrtSL2kwAQ2zglU9BRFsWDxj46OEjHE9oD79+8n3QyGIBOpl+uRPsutR18f9OtNUZe4ts/PL8993tzcTM8PKKV/IbI9guUANaffc86j68EItllHnrmALUSXgEmoU7Gzs5OK4hBlyxEU3m8+Rj6vcCZjxs5dSrlcTrUpqEsR92NGwszJWA82xLmAYwB+8ywZrk/qqgc+KGZGpP7Ro0d6/PixpqenU9EsnEyI51i5tVQqdUQOaZ/rN4IdzB93HPkec8K39OT0rTuIrt/A+djOXhGrOPFxnbm+ctsSHUDH3xEzuvMVx5E+Y8sN9Rxct2HLIC1dr+LAsQ0OEiDuzy+Xy+k6OKVEsnFGOU7Qj57CefVK8awr2h/7y+2/z3P64zYEwHs5jrcRBzVUu2Pvn3R1riKRR8Ao0QUcM5Qji4CFenh4KEmps30xsEClTuaGTsLQTU1N6ZNPPtGTJ0/UarXSxuTZ2dnEuuE4Up63VqtpcHBQ6+vrabL6fhI/ogAWhxRWFyYxE8DPqnOWrlfSbb9WdBo9FQ3mwwtwOPOUA2ndgFt8nfa4YvJrROc2pppIV444QKMbKOU+uUPgMWjeXmcWmUd+3fg/bCOpW81mM6Vh3bUAIlBQzpJ75DY6Qd4f0WBK1/fSeNTdC9M4uIC4AcyzJ4Cy7ihWV+4ufh/WOE4jLHej0UhtJO01p+ToE2fDXdF7Ck83YJOLhvXK4Yikit8vN45uCN1x9H0c0amIDqQ/u78f54cTCujknAMCWPHDhr2NtAcQE++bc/ZjtN77gXXs6aDsJ+oG+rnn2xz09xUyauiXSqWin/iJn9D8/HyHTXj9+nWqxgcRMj4+ntbF2NiYHj58mOzi6OioFhcXNTc3l6qy5thzQJBX6fP+jc6hv+9Ro6jfsGvoVoAGYzs8PKzZ2Vl94xvfUKPRULvd1traWkc7eEYH8ugPdwo54gN77cSepGvgqJcSMzVy/eNETXxP0jU9ihPQaDS0s7Oj9fX1jnNX2VsI4Q5ZB0GObfM+6OY05iTqGtchODUQfe12O2EyKjiyF86jv44BIiERhbnH1pxun7sLcTzgWWCAbHRRHOMYTYsknNtM3zYTdac/s6R0rt+9e/dSNs6DBw/08OFDzc3NpTP3OK8Ue1UqlVJaLCmP2FmqdeZIOrdzktJadZIN3eGkYhR3fCP55s5Jr8aRfcBxDJzIdLvBeDDe3j+u/z1Dw7eX8RzgGY5W40hArlMulxOpNzw8nDJH6Ev65/T0NB2bIV0FLtCF8blwNHE2STEmUgmRhtPvBbScLPQgQtT3cfy8/94md+Y4xkHxwcJr93RSOoGCGoA6Z4EYuFKplI4JoPIQaQfRE/fwO9FLmBo/e290dFTT09P69re/rU8++UQnJyfa2dnR6OioFhYWOqqcohzY00G1VJxSDmnlPv7DomWyOUPIZOjr60sMqg+wG/K7lqgE4ntedIT2DA0NqVKpaHJyMhUUuk2qavw/Oo/OiOYUkH/XFXRkC1EC5+fnKfXJi0hEFoloo7OrUmdlV+mqAIyzPQ5acKB94aHkqVbWq1QclGOr1epIefDfEURHsC7pGmlBX+cYTHcg/BgEIr4cbo2So2gLhturS+aMgJea3tra0sbGRkrnc0IpOn3uNPtvnysQAF54wqNvN5EfNzkjX1ZcL0jX9zjGe0eCgPXq+3j9uojP024kgov3nRvCaKR5D1BMxoiXaud77JmK2QyxnX7PXB/wmu/b8b1Ib+vrXohnorRarbQGqtWqXrx4oc3NzVTshrPcqtVqijgA9KTLsux7e3va3t5O+4inp6cTied6DecOsAG4RHwuRWef9+Prvj4hBJrNZsracXJqYGBAU1NT+uY3v5kcWA5DZ41SEIiCce32VXEb7D8pWRcXF9rd3dXq6mqHIzMxMdHhNJ2fn/fsfFz6xNek95FHzKLj2M15ZK2ASzivemNjQ7VaLYFC8BH9Dn5xfUWbvL1+vxwZFYkn1x2+Zxwn9vz8vOPoDa/A7YVXuCZ4CV3qdt3nqn9eyqfo34UcHh524FAnw3Pg2fGntz03tu48elr2TTI0NJQIlqdPn2pxcVELCwtaWFjQ7OysJGl3dzfVBJE6o1Z+BmokCx3jSFf7/D0SFffEe/TQnynnAOacR8cXvSRy2ObCc+Wcn2iT6LtY28Kf0zFkjkCWruNeggxDQ0Oanp7W06dPNT09nc5yX19fT/sOj46OkuMIGeDpqk6qk0GHw0+66uHhYUqR9aJV6M5I+rn4uLjOAEOxFqSrLVZkmN0kPYk4uqJAANPsk5A6z6PhAahcindPwZP9/X1tbm7q8ePHmp+fT1XHzs/POyJGpFV5BBAHEkYTB9T3SGCYRkZGUolkd+BgyIaGhnRxcaGZmRnt7u5qbW0t7UvZ399Ph53HsLiz71zTWXP6wQEBfdYLyaUz+th5yWOU4sjIiGZmZnT//n1NT0+nsH2MAkTpBg6RqKTo52iUy+VycsbdMKEsTk5OUqEJ2FLG19tWLnduNo572RgHFtrAwOVBrhyu7gRF7lkAB+wJGR4e1tnZWTIMdy04+nFvbRyXbg6Dtz3nZEqdUTDu6WdbYZy8CiLv8zfrgbZ6aoY7/0RNtre3tba2po2NDdXr9QSIJaV1GNsUjZs/CwSQ72nA4fUUsLgOI+DolfhavE36lq+r3HPnyAL+zr3eTXJzJt6DsfOU4DhnbgK28Zpva1ec2zkd5G27qS13KTE9yjNmdnd39erVK33++ed6/fq1ms1mR/bLw4cPU0o85Mb+/r7Oz881Ojqq+fl5zczMpIJpPld9H2IsABWfuZvT6I5R/B52legXzgZt6Ou7PNt3bm5OjUZDr1+/1uvXrxPI9aq/OHxO/HiWgt+LbSLNZjNV8qYCOs+wsrJy5+PInOmWtpWznVLn2cO+fj07p92+Ote6Xq+rUqloZ2dH29vbHUdbQVRXq9WOlFVPw8sRZ9wPiXrE1weOhJ/biH1rtVppDhJxBPzGaGO8v/ehO16+vj+EEKWF7HQnSrrqJ+ymj1G3sY/iDmQ3wsCJBjAu+xvn5+c1OzurarWaagd46jC2DkKG9U3WFOQc9+E3TqNXSofs9WrHvmdWuk5a5vR2tDXRAb1rAY/7D/0dsbY7/lK+xoPrvLhFw+coe1EpiklKealUSvvVZ2Zm0rpsNBpaXV3VysqKnj9/rrW1tRRlBHdAmkKE4XtA9tF+Is/UjCDbj2qsjvFuEsfI0hVJA2nkNsRx3E1y546jK1QaBKjHsBGh8AHDA6bq3NzcXHLOSqWS6vW6nj17lsL5VFJkgbDvzDvcnS5y9Ile4GSQJrK4uJgGkBSKOEGdSWNht1qt5JDCFMMYxo3ETAjAK5tsmRCSkscvXeU690K6gUzGzzfoM1knJyf18OFDPX78WPfu3dPo6OiNTuHbJnU3JVMqXZ3t56RCjEa6kqefYgqKb/b2Z/cITeyHnCML8BkdHc3ud4jP1G631Ww2Va/XNTAwoGaz2RPH0YuU5PL/pZtTlqIRcKczOmUQPbEQC+lXVDvtZkCdkCDq5IqyVLo6ZoeUNdLbPYKPscvdAxDrqbv+HhLXJBkKMS3JGbu3RbK+jOQc3fhcudfdeLzNcHdz/ngv9/lugg6MkfqbHA93Kn1u+rM58M05gf4dfz/em3EDDHwogQCkDa3W5TaEjY2NdMA0WzHIjBgeHtbk5KSmpqbS/rfnz5+r2WyqXL48DojqxBwK7mAJ+0okKPYDEoFXdKRjH0bwCKnjaziujZGREdVqtVQ11o/UIrXcKzdjw4lG8myk+1N9F6IHshLSpxuBdxfSTb9I6rBFsb8imEacND49vTz/j2O92DtYr9eT40aUoVKpdDjSnlIXndS4brqtYXS+O+xkiUSdfnBw0HHOHdUeowOGDvc5l8MYubnZqzVKIRnmSXR0I1kaHb2bnMecHooSv+uBDT97DyevVCqlSsWTk5MpNTJmyxAV8hMKYpSIeeZj5dgn2sa3RZmY692cx16KR/fRee7ogdmjTcjZRNd/OVIFncT6cMeRMzbpK7baOY7Y29vTs2fPNDk5qb/zd/6OXr9+nbAokXqCVBMTE6nyLvMBu8F2HTJ4wKG+LS8GDHz+Rt2NX0E/eVaoFxXi8zfJe2ndaIz89ZyS8AnHwzmrSKdLnTnF09PT2traUrPZ1M7Ojj7//HMNDAxod3dXk5OTHXsoHcwTATw5OUnvE8HAePf392tqaiopRzrVi7C4A4l3zgCgTGHhWq3WtUNSiXTCJLDoSfdwBzR6+ICrXhnG3Fj5IiyXy0mBPXz4UOVyOYXl/WiFu1D+fv9oVDi/Kzo3AAevnCWp4+gO5hlj68yn9PYICJ+JTobPV6n7XtTz88tDn7e3t3uWTuWRWW+/ryepexonf3tqsqfyMCbuPHNmI/OcqmGASsbEc++5tqcVkvbq++1IhwOg+LokzcPZx2gccgqUfoDQYT8cfYcuimmO0VD20kDmwMu7pP50cwhuut9tJYI/5kcsluB7ZGI7PPW0WwQn6qFu+ikK88fTy74q8XO5AACcSby5uamDg4OkWyl+Mz4+ruHh4ZQWuLGxoY2NDUnS4uKivvGNb+jTTz/V3NzctVR5dxy9kqkTCbE/nJGP7Hu0166P2+12AjMxLZo5QnbG/Py8FhYWtLKy0pGOShujjsHWVKvVpFfiXKrX6yl6SaSll+sx2of4d8Q5OZIjCmQGZCf7uHEeSVcFE/CclUqlI8Ue/U4/3Qb0x3Z4tJEf0h7duaUojqffgdsiAHfC5qasplx/90JwmKJd8HFjHrpT6c5AdB5dL0Gmxoqd3eTk5ER7e3t6+fJlas/Z2VkqpMexNKwf9jODUbtVSY0FfGh3TFVFb3tU0cniSD4jsS9yGS63sTvvK6enp6kYmqdWugMr5TP74hjmSBX/nttfzqgm2gj2cHzFGhgcHFSlUtHs7KympqZS+innMrKXkuKg6H4CEgSSsBuOocCztBPy3m1xjvBHfI1G0jba5pxDHeVLeyVxwUewxWdYYB5ixbGSlMA/jR8aGkobxsnz3d7e1suXLxPzyIL1nG0KcBwcHOji4iKVuiblgnNPvLAGg0QYGecDBYpTSHj/5OREa2trevbsWSoAABiGEfU9j9wLxoGJ4Cl99BV/e0peLyQH0nysJCUF9sknnyRG/NGjRyldKkYAc0zibdpxk1HxfaCuHPr7+1ME2yc6zodHbfkO7/uYe0TCwRY/niLih8/fRtrtdjKwvSIAiHrnnIYIanju2N8OIkgp9b2t3IcU8kqlov7+y6NwqPxF6hiKyBk4fjxK4grfHXzWKT9eUIS+xPBFI+/zzw0G92ANs48HZj8HfOJ68J9eSFyPtwWiOUa8GzDzPuenWxpbtzYyV2LUCQfG9yD7fMw5g7d1DLv1VSQOcvt0c3Jb5/p9JT63pATCj4+PE8Agk4P9O3t7e1pdXdXOzo76+vp0//59fec739Gnn36qx48fp/ODGQeeGafRI1F+b/87jo0D5gge/Lt83/fc4WTgyLbb7UT6zs3N6f79+1pZWUmM+cHBQQLYOJ70BboFQrjVaiW8cHp6ms5Bc1srXRGFvZA4L6MN8Yhijmzi9Uig+N+Q3Nvb25qcnExRDYAlGIfoBHsjfZ25bpfU0S6/n7eLMSCC4Xv90Qvsy+IIDj+30Yup5KRbNlO3ddcrxzFGF2M/odPBZ67DcMjd8XDdnHMcfQtGTnASyURoNptaX19PWXZPnz7Vw4cPNTExoQcPHujx48fa2NhI23MYf4Ibns4anT4cD9+LSjDCzwQkch8rtMa55UdZ5RytXgrPG8+DZQz8b36ifYh62bOoPGrnJBiO4+TkpCYmJlQul1Oatu9BbLfbGh8f1/T0tGZmZjQ/P6/j42Otr6/r1atXyXFn/CAEOGKD9FOP4jOG6HYEvQlWi8Ezd3xz4+Nj6njY1+bb7ON7RxxzXitGKz4An/MQOeyj733ic7CWHI58fn6eIh6VSiVVqCTv/vz8PFVwxKGgUt3MzEwCh7RBumR+AKak+bTb7VT+H8eTwWu326lE+MDAgFZWVvT5559rbW0tLbxY/c2VkbMJ7uzGFAPPVacveiE5JlDq3Gflm39nZmYSm1KtVhNjynPeNRCLbBBjSEovglL3vz3KyLPSrxhFXzA5Z8CVjKcy8nmICmd+c+IR7F4IKZ9+/6g4IuCJaxcQ4T+RlWTtVSqVRM5QPZH9FoCQUqnUkYZO/6E3WBOMK+L7D2G619fXtbGxkdYnYCt3xIk7kh7F5D5EyJy59MhZTPvgJ77XC4lOYo4VdYlGUboeUY/Xj8zk2xy4nMFxttPPbnQg6YSckwQO3GI1w9s4ybFf/B4+9vG9eN1eOo5eHZt7ub3D5rGXrVqtanp6Wn19fTo8PFSj0VC5XNaTJ0/0W3/rb9V3vvMdTU5OpqMaIsMPgIH0zOkhvhOBgfcP/7s+deDo3/PUWOyXRyxIvX306FHar7m6upr2Kfp+x/HxcU1OTqajPDibEj0LMUaWCVE4T9/rhfh8jHbS53Ju3t5mzrkjzpaG7e3trONItWCijmCWWCgOiXMgjp9nfvhxVF6p9+Li8ggWijNtbW0lXOTbjOL143PfZl3f5FB+WfHjiXgmyGaeXboqzuTkPhjCHSZ/jrh1o9sWFu+DVquVjvSiqu7Lly+1sLCgJ0+eJDtK1P5b3/pWyrzZ3t5OBDZbNuKeNzJrIDac3MdmUqDn3r17Wl9fT9WSIVNjxg5949ld0XHsNRkX+9NtmNs8f8+dI8d8/r1cRXLHRZ42TNGu/f39RPa8evUqnT177949PXr0SO12W9PT02kM5+fn0ykQZFPVarU0ftVqVbVaLe3lBhsxjuAh9L+fm1wqlRIJFzNA4vi7LkOPoz+jLbhzx/GmheEbTH2PUQSnKPyjo6PEevGA5BRTQY6UuMHBQc3MzOjhw4eanp7W+fm51tfXtbW1pb29vQ4nj5L9MzMzqtVq6boTExOamppSo9FIyoGIBqwoB42vr6+ndFIM5cTEhM7Pz1Wr1bS5uamNjQ3t7u525JsT4fSDuBkM2kG1NJ4t9qMroF45HN0UuSs5or5PnjxJ6Unk3BNyd8Pl0Z14j5sMh0/Y2JYciM0xwbE9DoCiY55Ly4rXzjHxgBlKlpN/nttv588WDftdijtI3RwBB4XeLzwvRsAdJCKKjClrMBIjABGM8NjY2DXnyp15/kYnQDiRUsNaBCCxzxGjFx3f2M8RJBHZh7DxKsAx7cOdyW6OZC/Z1TjH3chFQfG7w5aLPnItH+PbMMW+tvy3Z3f4xvpcWk2M4vo+lGjs/TljqltuPsc5Hfvhq5Ld3d2O40Y8XQz9NDg4mBhsokzosPHxcX3jG9/QwsKCvvvd7+rhw4cdNtSBgEcbvUIw70fH3B0d12/8H9eOf8cdJUAo9tuPowDYVCoVLS4upowEqo+Xy+WkJ3j+qakp1Wq1dL4l6e4e0YKVj8CwV+JzPjfvbsJBfPama/v8oFT/xsZGcqxJp3enjpTVWq3WkX7oY/c2O8Rzedp+rEgMObe/v5/OFPVoo9vZbvrG5xrCus7Z216Jn1dImjX7gMFsrCUvJOPVYr06e3we6lP4Qe5RfGy8FgOVw+v1ug4ODlQqldJ6wKGhGOHKyorq9XrH0W+tViuRTxMTE6kN9D1j6sdYEEWlGNLo6KharVZHkRa3365nnSxwW9LrMZTUcS+3TczBbsGL6DhK16ONrDcnTsAFo6OjaR8i2Q84jC9fvkyFzoaGhvTkyZO05QX7CCm2ubmZIsZE7HH6OCNyfHxcu7u7aYwg1dGDYBj3LfAn/AzY6DC6w+ypyl7dnjl+07ruGI+7HNwcyJc6FxiGjjQIT3Whs8bGxiRJs7Ozmp2d1eTkZFrkDvLo8FKplBbv0dFRUgoOkPr6Lqu+zc7OpopmdDZsMMry4OAg7dkaHBxMg8fG13a7rYODgw4wwOZ+qi658wjII9LCZMFI5thLd7x7Id0AKeNVKl1GO4k0+gHggIacYxVB/dvaH1kO/z6Tn2vk2JRohGlPLhoBmM2lL+SAE98FFDOvxsfH0/5Y7vdVCREOnFra7Smh7kh7/7oCdmcJ5UPKGJ+XrtYyxal4jzRQ1qZH0ukjZ7lJ3YNNw+HwkvPch0gMEQccFydlooPu4JLx8XQcdIMzjr7HM84xnqNXchMQjWvV1wxz09MVAZrxWrlo6m2dR/rTzwBzQIHz4hX6uu2Xka6KHXS7r0eJIwPOb9YykYKYqtnN+PUS6Ozt7aUMA3QH49Jut9MxUFNTUx2HQPM8RB5wpi4uLrJpwTwvINJBHRL1WXzPSZyoJ3OkjI8BNgCnA3DK2EE63r9/X7u7u9rb20vXc8exWq0mu+k2s1wup2eM0Z52u50+06sMgCiuX24iZ/ist7eb+Bo8OjrS+vp6eiY/T3F8fDw9L8WHPP3QHezoULvTj13AaUSP+h65crmcHKydnZ2Oo5DQzU5Q+PX9nvz2OcEz3xQRvWvxSAzZOehL1hJjig3ySCo61gkpJ3Io1BiLprxN0F1OkIyOjurhw4dJl5+fn6c9pV5Eh+0cR0dHqVYHZ7xSeGt0dFS1Wi0FX8CkRCwhA1qt1rWjJqID7LoCPeCF0XLk3l1LtFX87U5VxHdI3APP98EybF3xPdNgGt/7Wypdbq/b2dnRs2fP9L3vfU8//OEPtbGxocHBwZRGPz8/r7m5uZQ5gNPZaDQ6jhxkTnK2J+mt+/v7ad87mZT4Ch74gLCHAMBZddwVyVxP4+UcyFKpdA0fvm0e35njiIeOcoqgjQWGV0uRGI9i0WBA59TUlBYWFrS+vp4MCGdf9fX1pepEsCtUa6VcOcZnf38/TSoMt7MxhJ/57WfoYNhhinAoG41GR743iy9ubpeUDKCfwYPCjnvfIivSK4lOF+L3BWhXq9WO/Ppms9mhXFz55pjGt4kb5RzjkXNIc5Ezv1Zkh31zcXR0c6DKlSWfK5cvN0vXarVrqXmSsumTvRa/J88odaYtutJn3GNEDSXme484eJbrcR9A4fj4eOpb6Sp1hzUc2UjuUSqVUkEdDBhReww1aSLT09Mdih99QfoW6yfOHxwPB7K+EZ3v8qzuTHK9CBRvw8TdheQYex/L2D4itTjisMg5Isadx7etU3fgPHWQ/WWAV68W546766+oI7i+ixst5lAsBEMfeKTRIwUehen2fL10HL3QGmsDW8IRQaRzt9vtNM+dQaZAjEf1nIQkOkK2jD9zfEbvMxdn6XPOxk19xHdj1JHnQr9wtNXi4mJKwT0+Pu6oJgjR6sdA0F7Xa157YGhoKEUfPwRQjbqAPrsNKRoJ1Og4cZ3T01Pt7u526CpS2CqVSkc9h/Hx8ZTSz5zHTr2N3AI8etV0T1OFRN/d3dXW1pa2t7e1t7eX0mPjOZJcM+rFbv0TcQ32uVd6lX5D10OIQ+QwHv48bjt9j37sW/AA516SMtrtCLVuz4mjjnPuR4dQgJGKmzgWOI6VSkXValWPHj3Sxx9/nI6v4eg0zolk2xdnS6+urmpjY0OtVisV5alUKulYO8YvR5579qA7Kr1cjxEj8prjr3L5qtq62y1vPwQ27cVxrFar6exSHCuek3kDBm40Gtra2tLm5qbq9boajYaky7m2uLiYxos1BV6hqBRb5Ah2TU5O6vj4OBFDe3t7GhoaSpW079+/r2q1mr6Pjenv779WXCem6cazVt03Azu6L3DbgNU7O46e+oVi9L0/KFeMuk+2kZGRDnacBemlpWEB+vv7VavVNDc3p4WFBe3t7anVaml9fV2bm5vJ0BLFOzk5UV9fX2K+JaUjMra2tpJBApQyEcjjbzabCYA0Gg3V63WVSqU0mS4uLlLanKQ0+JLSESKzs7PXwv0AXqorHR4eJgc2gmv6DeXWS/G9Ttw7MkcONCWl6KzvFZCuF56ROpVIvEcUZ4q8yqbn1UM48HkAo5coh6VzhpDIhjuCHpJHcs6jM1SkBJD67EVV2u12ioj16tzNbuKMO2Pmfe/A0derG1P/m9QMiBScNa9mBtPFBm93xF0BRcdMUqoMuLOzo9evX2ttbS0RO17RDyDKIblemTXuAfJ9OZ4FAAhz407J//jccS24s+3yoYgBBxnRgWQd+JoBSLJxn/Fwo+6RRo88xnsgufnCj7fBCRq/jxsw/vcob3TU4/fdmff28cx+rqBHPOMYddNPvRB3nrlHf3+/qtWqZmZm9OrVqxTBmZ2d1cLCQto7jI3Y399PDiWp4XFuelq3A4OcRL0LOeOp6Og7xtGJom5zHjvgEVHa6pkJc3Nz6aieRqOhgYGBjkIwnqkgqaNCrHQ1fqzH/v7+ZCd7le2RI0Xje7Qt6tr4GX/PHafcPQ4PD1Wv19MxYTiORGPpV7bF+PjflKbtepk16PsbXT+z965erycHxdOhfa36c/EcUv7ootyai1t57lq8cA06hUCAO92uL52QBFv6czt5QMQI52t9fT31E8QX+9iGh4e1v7+fUk6lKx3Ofl+cn/7+fk1MTKhSqXTYN/bD4mRiA+/du6fvfOc7Gh8f18bGhs7PzzUxMaEnT57oyZMnqtVqarVaqUDk3t6eTk5OElFF1VDIG5+b9IFn1/ETncxejSOpnU7A0CeRAAM/g9v7+voSBo/FdXC+2BpXrVYTkc3zu9PqDiht8b2wXoSTCPfQ0JBqtVo6JvDZs2caHBzUgwcPNDo6mo62m56e1v3799MWuampqXRu/cXFhTY3N1ORKqrMEi2F/KFNjnWjj+XPhW/mOKpbVoXLl3IcETxzQI2H1Wk0g4mywlHzDbcOUiSlKkWzs7NqtS7z7jmeQ1IyIERG+J8Kjjs7Ox0TiUggKQB8lnxxFHCz2UzOKfsvObyZs3b4nleRg7VhAD21E0CAQiHKCmOD80Rf8Ty9WoiuTF1ywM+dNqLKKF7aymed0eF6ub9zwkSmb+MPCsIXcW5/Uwy5x7Q8V3Dxb1eA0RBSIAaF4wa/v78/lYvvZaW/KLmqu1HBYhAZRwcPgL7z8/PEZtdqtY6UO8/Z97RW9shI6hgPWCxPKycFCrZufX1dq6ur2t7eTgeKe6VTimAcHx+ntTI9Pa12+6pyox8LwpyMVeeIbkSgCjjw/QIRyPF3dFp6Id0IlZz43HQd1mw21Ww2r1VujiSQkwtO7uQIhwg4pas+AIj5azGFjTbG1FPmUkyLoq2Arty+IcaaNGev7BtTknL6zZ/vrsXT3CSleU3xl5cvX+r169cpDYp1xP4ZCr6RZjY5OdmRgo5+9C0frm9cz+X0HXYGJxVSx1NebyLUXFxXE3n0OQaoJq1fUoo8etYARCBRIGyLpNQ+JyyYR7cBOO8rPn9jlNHnFLbFP5tbR/Ha3of+nVarlZzHtbW1FJV13ei6d2RkJM1730Me7+Pr2FNVISfQzzgmOI6+RzWmqObSTl2Yt1En+O/bZD58GWF+uT10h8ftFnaS/hkYGEj2BAc9RlqJrOM4TkxMpHUF1p2ZmdE3vvGNtNfte9/7XqqUSj9wbjm2jow41khfX18iCNfX1/X69Wttbm6mzy0sLGh0dFQPHjxIDj8FWx48eJDwydraWrK5rEP24ZFSGSPCZMuBfZl7vicQ6aXj6PdAxzhWQ/Az8D/ccfSibvgF1FSZn59PxB5rKp6bisSoPYQbayhuHyAbY3t7O21zk6SnT59qenpa09PTHQUY+Q5ZXWtra2nc6/W6Tk9PEwaCWPLsK/wPP/LQcYDbXz8XkvVwU2Vg6T0cx25sk2+6dOPpoWXAJOkRNPD4+LijMhSL2pkcUiVQqhgZ96adSUBpoewA/ePj42q3r/YoEl10wE9EBGfz7OxMm5ubevnyZWItUEBMOPZistBZfF70A+cHUATT54vgQ0kOQLqDlTN+vOflgD3VJJeC8zZxI9ItqsDk90iKO4jxx9sfQbNHmFyiI8n1YwoQDiRziza5siWtRNK1zcp3Lcxz+tL7VLoasxiBdSNJSW7SNmq1mhqNRlKcXnUMZosfP7eO/sJh8TSP8/PzDkBEtHF7ezttKD85OekokFGpVNRut1N/S1fAnD1SpKoTcSN9DyNHZJTjD7wCmbPROaDneov3ehXh8PvE+3d7zfuc1F9/fphrdx5zazyuPfqA9eUREtYlaypeNxZoIcMjt3cWPY04KPdU6hihgeQ7ODhIe9Jz+69u6t9eEQDeTkmJJGSfGnbGCbmzszNtb29rbW0tZcdQHAWA45k8OGox0phzGnNC/7HdYmBgIJFzUafGZ8oRKTiynJtMO3BSAGZk2nBPB8OAbYCrXwOgynelS71HNfNeSeyHSJLymq8fj/K7k8T/b7sfYO/g4ED1el2bm5sd0aCJiYlEqhFx9IIubqsQd5qcvHes4mSCH4Xka8vnmtvWnO50nRr1jeuUXtlFbwdrjrGJEfZINrH+xsbGOoooHh4eJqLSsVBMvWbvINky8/Pz+vTTT/X06VPV63UNDQ3p+9//fnLu2OO2sLCg+fl5zczMaHBwUM1mM0WY/BzH7e1tffbZZ1pcXNTAwECqdD8+Pq6FhYX0ObKF+vv7tbu7q7W1Na2srGh9fT21cWJiQgsLC5qbm0vHbDl5zrVwhMDEPnfeRpTchXj6v9sJJzDcKaLttMmLt0Xik4gwUUeOADw/P0+pvWQKUssBXUX9lfPz8xSYYn/y2NhYwv5Eebe2tlKfMs8IkEFyOBHFcS3Pnj3T8+fPtbGxof39fbXb7XRE2tTUlMbGxtLYMX7sh3W/IxbCihVlPap6k7yz1vXGeQMBEy5RsTibQyexV4M8XU9pIv93ZmZG9Xpdu7u76b4AUQCSA3UHq3j2MLqU9UfZAlCkK1Dqezc2NjbSAt7e3tb5+Xka7JGRkZRuNDc3l5S7M1qIA3n2aPi+DiaL78vy7921eKpaN+cxMqvueAAKPD0mRvuiRAcnSg7cxt/uzOXATe7H9+8500+bcobd0wAhLDCgRL3GxsZSFCwaQhQNcz0WK7kriWlxud8+loBGhHHEgYSs4WyiZrOpcrmc1icst5MeTtgwPp7izBrf3d1N64g9Ajs7O8k59RRTUkpxauOc8fRXxgcnAkXJ85Da6vsyPE03l1YVAbIb015IjsjIgSqIAtcPXhzGo1AAorieWA+MUW7e8zfP7n3kOsB/uI8TPGRQYMz5nounGrnDEB1H1jzzif0/jHmuyiTtp6+QXhI5Pm/43/f+klqEsWcf/ubmplqtViqu4IQJ5I/rIl8vSIzo+PNHe4QTig11mx4dJhe/Ntdh7rEunQCgGijAyyNk2GR0ZalUSiSy4wbmQ3SKchkXdyE5ItTXkZOUnm75tghMjGA6UAdDMcYchcH+K/Qhenh0dDQdpRDnP3gots0LbgCApSvMc3BwkLbk+Dm6HoHIkb3uIEv5YibRJvl7vRKPYNMnTur7sSbeHkhUstkuLi7SsTcUhmP8wAELCwtaW1vT7u5uqgMxNjamubk5zc/P6+HDh7p//75GR0c1MzOTjnHAts7MzOjJkyeanZ3VxcWFVldX9ezZM62urqZtV61WS41GQ9///vdTts+3vvUtTU1NJScW8uXs7Ez7+/va2trS2tqaXr58qR/+8IdaXV3VwcFBOm7twYMHmp2d1ejoaEeUmHkEKSEpOU5+lFwO5961oP88W8WdRw8MIIwrY8/+ak+B933DBKnIuIKMrtfr2tra0sLCgoaHhzU9PZ3S78fHx9O1S6WS7t27p4cPH/7/2fvz4Fj3db8L+3ZrVkvq1ry0pj2cPZzjM9yBa7i+jvFNHEKAmCnG5SKpInESEisEbGJMjI1DIHbiJBBsiEyCi6IIJgRcsSEBjONLKqYgOI6dgM217/E+Zw9r0Dy11JrVnT+0Pj99+9Hvba21tnrttfd5n6qulrrfft/f+DzP95l+un//vsbHx1NxKXJKqbqNJxS5SDgxOlWncxW2vrq6qk8//VSfffaZvvjiC21vb6fq241GQ4uLi5qdnU2GSeYAnoycAHe4wQgdKKaERN0/R68MHF0ouAUaC7+kLqugJ7vzPcq0dOXdk5TCJiiPzLPq9boWFhaSwonn0Ct7stA9d61SqSRGSjs9Rh/Fin5EyyCDR4VVLBBMAJak+/fva2lpSY1Go0u5dqUZ5sMLb8nU1FQCyyxk/7ufiqoDtPhyBS4qXS4sAY6M+23AEcop5RG4xjw8L4bhYQO+wIvAbwwr8BCpHGiM3jOYJ0YH+s/RE9HSz3i4kSGeP3lX5PvKx8DH2MfTLUkIvsvLy67w0Kmpqa4qkChzWL7JEYyGBVeCaBshhbu7u9rY2NDGxoY2Nze1tbWlnZ2dFK8/ODiYlFcXTljDInhBkOBpw7DkxiR+i/WPYlQeouvJ/UVGCFcW+6XkeBt6PcdBOmPAWvV8YA9d873CvgWQs06ZL+4jXfNxH4doKOC+gHj2JryAcfOoCt8z0eIfjUc+9ni3yCNpNptqNpvJw++yIBqE+qmcFs0Pz2YcyB8+OTlRo9HQgwcPUijqwMCApqenNTw8nKz/Xqk0hoV6DmARj3blPoJZPCp4f1GwCUv0+0I5IAngoT7A2NhYF5iFx6CcVavVLq+4GyH8XEHCzz3yg/v5vftlkOM5bjyJ4xGBEAAjR94X/o/3ka73IMYRvI5eKAfvgHsdUY59L7HuuD981XPE0c8o0AJgjKCxyIuZGwf/LK6Zon3Yr/2J18WLSDGvKNi5dT0wcJVjDL/C4EElYTdw4ekjf3FnZyeFzvM965Tw83fffVebm5spZBSD7dzcXEqzWl1d1fr6unZ2dlKEXKfT0enpqdbW1pKOu7e3lwCnR+6R2rW6uqrnz5/ryZMnKRSzWq2mnDqcHp4jF/c1z2Yv51I8+jmPAGHf8/ABB/FuuMGhhA4uKXlifc69tsP09LSmp6e1t7eXcMLW1paePn2q2dlZPX78WAsLCzo/P1etVkuyEp758OFDffzxx5qdnU3H2RBmurOzk479k6SnT5+q0+no4OBAT5480ezsbCpORMrc6upqikTZ3d1NZ+c2Go0bhY88/BpZwbyx912vcsMse8HlfC96ZeDoFiwXam6xdFdwBI7V6vUBltVqNW1qhBmLng1eq9V07949NZtN7e3tpXAarAEoiS40sUxEq5iDD2ciXnyAz3DlAhouLy+TUJudnU2g8f79+1pcXExtZ3Kq1Wq2YAgClPA5rMoOft+EwuNhZlFBjlYkyJUQmCntZTO6N5Df+O+LyBcs3kHPl8uFFLqnw5VY7lMEHAmXdtAgXXtbvCiPK7Tu7WFdu7UGQEL7Tk5OUrVdjpi5a/J9FcF+DohHUO95Q4QxYUWTlIQflccajUYyjLhVPnpcUX4otLCxsZHORwU8IhDZczBUD2t0hugMjYgDwj+2t7e7QtiZJwSC5zayFjyUljlj3HiPe6Of+zFnxHBy4QgwgM/B07xYjFdL5DcAx5GRkRv71EO6vB1xj7kSnPNWxmJWDiAJn2V+PYJAurl/I2jEUIA88IqD7ilz4wVj5NSvUFXPyYwyhVwoSVpYWNA777yT8vcHBgZSjhHecZ9n9hOgsVdIrpQvdkab/DxA1gH7iXF2g1QvQ6DPDeGqGHpcuSY33n+DIbbT6STlDWDE2vCoDwxcbkTC63LX5OA09r/X/vTriwy/KGhRNsIDAHEeMsn5fhRZITeRCqteVTgWwmA/cS8/Lgy+i9GA1J29vT0dHBxk84aLQGCRvpIDk1E36xcRfYHXzI0qLifcaC9dnyfuehy6qheQqVQqKTLu0aNH2t/f18bGRgoxZU9g3JyYmEi6I+NNqsbo6KguLy+1tbWVQN7m5qb29/dveH052oGjNZ49e5aqbyLnz87OtLu7q+3tbW1ubqYqoJeXl6na8YMHDzQ7O3ujT/wepwnACB03Bzx8ju+aopwj0sDb4OvJc8c5yQCvMUdWuUykOA3RHswdHt4nT56kCtAPHz7URx99pEePHiUeTL2P2dlZzc3N6ezsTE+fPtXnn3+eQoMx8DBOOzs76XzbTz75RPV6PWEHl3WMf7vdTs60Bw8e6PHjx7p3714C/fAPvI3OM+EZ7PtYRd4NvR4tVkSvlSDgi8Q9Y4BHZ5Yo1GxYOg/qlZQqlXpIH4ruyMhI19mLCE42MJYkJsQtbd4Gr4LkBQUcrEbU7b/H+7KwsKClpaVU7XV+fj6dyUV7AYNYF2C+WEv8cF88rO4xdU9ovygqw1FB9udHBcJBkgM8+uh5blzvv/c2+N88H1CGNQiwzzk5rJt4EDkUPY7uafFCCx6ul7PUeO4sGzquLVfM8dZhuWu1WpqcnExnKfWDUDojRfCD8ub7dWBgIK05L5IDE8Hy5YoKFk3putKZezLZ5wjFra0tbWxsaHV1NXka19fXU5l3Qjba7auiENK1MGi3211HFbiVk2rIWOSxxrkxyC2Jfr5qtLrlwjKKwONtDPV1yfmAA3LIlXDALpZEBA3V3EiW91xk7wt8yp8DaPfE+JwHMAoVN4yhgCLcHbzx7tUGI19woeVCLVq/KYrj+Y2e7+f94u9oIOoXRUOEj9nQ0FAKEeTctXq9nkKJpOvIGzdAedSD57IxZkUAxfkU3zP3HBrNXvb8fviCU1QM/XkObAE81Wq1C0B6/gwy0Pmp507hbXSDqufYueEht3fvgly3cQ92LsIj/saLuOXGqkgORiMJpf9rtZp2dnZS2CrzRthqrVbr8joCkJhDDOF+6LvLTXQU8rkwyGCYf9n94rof8jQ3Tm5wgpf0S9fx3H5kWzSCuWEY8nQWCpogjzCOuEGTY8vu3bund955R5ubm6kI45MnTxK/ZYzQGev1egIogIjPP/88hSeurq4mh4nPp3umCXNfXFxMeYoAP/KHqfo+MDCQ8hofPnyYeBBAyiNBkOH7+/sJ3JKvnTu2o5/ksoRQTzeMco2vLRwQ8GSPHPMUMUK4OQ8TIzSg7ezsTJubm/rxj3+c9hugDT3FHWetVkufffaZ/vJf/sv60Y9+pN3d3a4Cj+hV7Ltms6n19fXUJvivR9dVKpVUaOzhw4d65513tLS0lIyNzB1r3b2sXgzLCwNFg6L/f9t8vjJwdMYZLUZFzDKCE48pJtRof38/CRAAFhM7OTmpe/fupYnEq0B7vBS751d5O3NMDGbhoTEeauJKx+Dg1UGr7733nh49epQsgLOzs2o0GmniYMgIOkAIyB/F1XOtYN4eltNvD0eck5wF1MfBBYIrsCh07mkipABwkrt/bIsrqRHsMA/ME1XgAOCuSOf6JnV7TWIYHH30BGo/o85j67nW2+55PFj8KpVKEvozMzOpQuhdUwT3OWJsPZTUw6Y8cdrngTBVT552zyXggPGhWEW73U7CjFALwlRRgLDo+RxhtNnb25OkFBYEyIgh7gClZrOZvCXM0+DgVTnzRqORQmwpjuWH+sZQ1ZwRpWiP3CUx9kXePLeOOhhBMBKyBH+Bx9BXB1OuINFn1jlzmuOXOa92VBTZP+6hoF/wQARi9CRzfwcG7Es8qZwj67lYfvZZfKavdwSrt7sf8+jRLm7EwDMxMDCgxcVFNRqNdOYw487+Yo5cwcBoGkMQc5RTBJxXsa8wPBBd06tYWwQ48fPLy8t0KLkbBzziAWWOsWI+Yrt8/miLR38UGXzuinytRJARZVUch6jA5sBlrg/+ew/JHh0d1dbWVgqjozq0y51Yyd73ghfTiUYzlEyiN3Z3d7uOe8D7nAOPcR28jGHY92S/eKmT83T+j4Yk2ufyHd5JqKIXR/FceQe+gA88j+12WxsbG3r27FnydjWbTa2traWQVfg+6RzPnz/X06dP9eTJk1TIBq8k5N4h9K9Wq6W1tbWUhsN1zB8yHq/agwcPtLS0lPLjAB/Sde4ngJNTDIgI4PoIQPopHxlj5IHLegdMUXYS5eeGUvQ6xxgU41tcXOzKnZeUzl6kGja/efjwYTK80Ybd3V199tln+it/5a/ohz/8oZ49e6b9/f0u+eQyiL0Of3fgzm8GBwdTlOKDBw/07rvv6sGDB0mvpD2MPwYFDy/2ooAuW2h71IV9veXotaqqMom5ieWaIisEoHBqaqorrInS/HRwamoqDQhMb3FxUa1WS51OJ4VvTE1NaXR0VM+ePUvFa3iOKyVMuPeBBZNj4i4kCCN6/Pix3n//fT148CDlCZB3QN9hxmw8gC6x7hT9wFLhHqOo0PdTMLoSEBXCXnPrY4o30KtVwawQ9FwXw91y93VypVLqLjyAQkJ1XgeOUSDFPuWYnDMcFDWEKS8sr3FNeYgATIn7HB4eppzc0dFRPXny5EvPW6QIAnp5BgAe0VuBUstngDQpDxZgTvQzju35+XkSgs+fP0/x/VtbW9ra2kp5je5V4lnE9mOt9VAU91J5eJYrlNLVHiQHYGFhQY1GI1W8Q/D52XHuhc3tB+lmLuldE1EKUfh5n7xNeM5dAXSPAXymXq/fsLR7/3x9xLL+zgOhXsoBez7ysGgU4+WGiAga2f/ubXNPI6DRjx/JeWtzCmo/reMYtSINDw93nQELaHQDAEaa+HvytDjCSuoObY6UAzP8Hfm4G5Pc2Oq/jcp3BJC+R4jYQFlzg4DLDebWZbGvcQeuDracd+XkyV1RNJxE8OTRNA6KfJ3zPffw37hn3fe670m8SdVqNek7CwsLyYCOQjgxMSHp2vjp5xPiTSFixEP0BwcHk0GGlAIiQbwys6fP5IwH8d37nJO78bt+6jluaPC8WffexRc8qVKppGqbhA2yTwDgHglClUv0VHjS2tqaPvvss3R2MSHHRMEMDg7q+Pg4FY3DyMqxGu4oiX1DyXdvv0c9SNf5mlTvXFpa0oMHD3Tv3r2UeoJuDBD1CuWdTieFReNtjIVx+g0cmUPkmPMJ51OeKuXRDOg3ntPrxby4jjBQdD5J2tzc1NnZmfb29vTpp5/q9PRUe3t7evDgQUrbIYJibW1NP/zhD7tCVA8ODlINGNfXnG9K10ZCiH4gLx49eqT33ntPDx8+TOHFMUzXjU5+pJEXLXN+xv1dH/AIziJ6LY+jAx2nTqe7ah6D4EoICyC6xUnk3dvbSxZyClpQ4ZTDMilBTCgcoYBffPGFdnZ2uty78Xm00a0lMBJ3XbMoh4eHNT09rYcPHyZvI0nIfv4JhMK7s7OTrEWVSiUl3lLut1qtJve2hyXl3vtBX9bixxwzdigBCC0PSQOc+AL1dhRRVGKI1Wbu3DORA0q3kXtCeMFw2Hycq9ZqtZLVjpBN/oYpeRVfwind+t4P4BiZdRTwUdnJMa+oyBBK4WMaPTk+dlJ3/jB5F+Q0koO4s7OTqpVJ10w8Wq1ReiicFQ07bo1jHWOcoES2h3NMT0+n/EyEH95r1pIrhnENFYVc3SV5rnMEsR66yXx76HWlUkmKEEyf8vFFR3MwpnHsUXpdCY6W0leh+Bs38PhapY9uxactrszs7e2l9YRQjvk//qwcRcB8l4RRMD4fOYLlG/7Bda7MuyLtYYSETfmej7LCwVwcY9YJIJxoDmQRayXmT94mj6LSgkLmhhnm2AtwSPkjb/x5sR0ejizdrEZ8V+SGorhHcspyXNfxOvpIP13PkNSlMPpnHAa+tbWVCgXiJcIYTl6s5zi7B5u8NEL1MTYhr72oS7PZ1OHhYdfZjTmDJH/7Wov7yv+Hl9H3OO+vw1tehhyUu4c1FluKfJU2jo2NaWZmJhXi2t/fT4Vs2OvwLLy/CwsLiedS0ZWjMLa3t1OFXA8dvLi4SAaB/f19HRwcdIGXlyGvVMxaQ0eZnJzU3Nxcqstx//59zc/Pp9Qa6bouAUWSMOB6ygdh0tGQ6WPZL/KxdoNv1FPcUAl/owgYvzk/P79h1MFwR9657+vNzU2dnp5qY2ND+/v7Wl1dTcY/IigrlYo2NzcTDiFMmBQa6drQFCMaHOjSF+labjx69Ejvv/9+Ks5DLrgb391hRZocIaoYKTEuRWM7ssgrtPei1/I4RibhHXfLYS7siXtUKtdJxSxYSUk5QJFAgUdRpHDA9PS0Dg8PNTs7m+L+6/W6njx5oq2tra58Jw+Fisoh7QPwYEXqdDqpwt23vvUtvffee7p//75mZ2dTsQ2vriVdbVyOHHj27JnW19d1cXGRKq4SnkAIIAwson4f535TFH6v8htJXXmOruTlPCdFoT25+xf9DZiXir1hUh6Eu3B3sADQxdNCHtXBwYH29/eTQKVKKsCfMwRdAWy328mKGK2a/aBe8+aeUP8MRsl3Dm5Zh5wb5QaYSNyXMXSPENZrBK6Hk3q+kwMiB6s5r4vPbWwTTB8mSzgHoNGBo1dXzXkWc2DntrH+shSPFXDvmx8b4m1yIx3rHN7i4S9Uu8xdj8IT++jl613oeRiQU1QY4zi5d9H75GvPraLwEl7n5+c6PDzU5uZmOvyaMGWvVJdrS/Rqe5/umqi0HNcoyhdj6N4P6VopcmUGIU/UQ666ZY7cW+ftQGFqtVppvwHkACl4SqLhqBe5/IAXkI/phj54ZeTDtNn3oPNnv86t8/3kq7lwRulm1Vyp2OMmFfMOt/AXGVcc7Hvo//T0dKoU7aFqAEhCV7kPaQgxr5X8qr29vWSEwYNPik2R7sZYMFYR8EcQGYFiBPz9mkeUZJ7pVYRdP4zPd88xhh2OjCKEmPOG3UvJmYzoFKRQdTodra6uJkN0LrICPbRo7F+GfI0NDAx0HW7/8OFDPX78WI8fP9bi4mJXbiP8FplB7rh0VQeEI2Fi4S4pH+nUD3I5GL2qUYZHwzJ4Ap4KMHLvMjK3VqtpaWnphmF7Y2MjnZUMRmFvYYQ7ODjQ9vZ2kkm84GmxIB/3d13LQezc3Jzee+89ffjhh3rnnXe6jv3DWA6v9P1MlCOeZi+6Ew23vk/9zM5e9FrnOHqIJY3GukV1KCgWWnBkDbObmZlJIGpraytVc2Ji2JAxTwIkDbPkzMenT5+mCosOYNwjJN20AMJcaX+j0dAHH3yg7373u7p//35iuv7yDXNycqKdnZ1UEavVaqUcIzYeE4gSEDeg55D1k2IIUVzIvQSjC3ssnhCb0jeu9wWmU+R5dKEJ3QY2c4qqtzmOJRsVywrV0ggJofAKR0YQusNvsWBhUUdIu9eSjR+ByV1TDkBFazbkn+cApQuydvsqOR7LXDQuRAOMH1qN8YSxI0+CYzfIjc1VF4Q3EGaVU15jKBRWVQ/noFqcVzD28xzdkBGNDq7kRI9NvxQciie5ddzzadw7WiSkWXP0z71Lo6OjqQ8+rlFpoo+sFfax81EHj241heLeZr+4ccKVAP+fMceYSPuJLiH8Gctvziqf61tUbvrlqWo0Gl1eNfqDAi9de5ecV+T4sHStwCIDo5Ie+xzXRo7HuocRBQolud2+yuPqpbTmnhEVVvrndQd8/7hSxW8cTLHmcspNbEc/KAJbqdtbEJVmv565LBrDaKQq6ofvS8LjVldXU5gjRrAYLon3ER6AruTFh0il2Nvb0+7ubvJykUfsxeE8Mszbz98AD+9Hbn1G8Ji77q7p5OTkhkLuhmMAhctEdFYv9kV9ClfQPTcQHo0Rjv2EjIOfP3nyRNvb28l4kwPYtyntvcifNTExobm5Od27d0/3799PoPH+/fspP87bCtDFgIDnktxGouwAPr5X+w0c6RfezlyOs/MM5zXeJg/jlq4dH76v0WsBmrF/pJ95pAsgMx5f4ryR/z0KI565zH041/ODDz7Qt7/9bX3rW99KhnD2Pd7qTqeTwto5QxRvOfPmxvoYSeS6IMXX3BCbo1cGjqOjo2nRMwAIbkIiaGQUZpGxsJmxaFDRrdVqJQsboTkoFtK10BkcHOwqT01Vq/fff187OzvJ3X98fJy8ISjDTChCmXxF7jc0NKSFhQV99NFHeu+99zQ+Pp4YKVZwBwsXFxfa2trSp59+qi+++EIHBweq1Wqan5/vOmuFSfYDVaN1yRdsvzai5xXlQOPLMHRX3PkbQMa4sEl9PQCoXKnz95zS04uih4H33PgxfzBIwA4eMYQFn5NjAOPC+3F0dKROp5MO+pW6Y9I5usXPPOoHoeRFoRyFks91BAL+PYIPBoqX0Nejf4/QYTxRRCgDvrOzk0JeXEh7riX38TBJ9paDpdx84snxamP3799PR6CgLLmFzr0GOeMG4+fj2E/wLynlfNMOhL8XMfE9G4n28TvP5fCwRICBGwmka2s8f/M/As2BRQSQubFzQYxi5QXB3OLuBo1o6POz7J49e6anT592VRrMgQs3Bkbg2G9FdWZmJoH0OCbu8Y2eEFeEvI1YjZ1/5LzikPfRAQwyyj3ZUalpt9vpSICXHaeoFLnC5nzR5QxrIvIAj4J42fnq13xGI1Hkq5DLNb+uyGPpoLGXEcoND/BC8uCePXuWztPFEItMpWIt0QIcXQAvxNNNSFs8D9WPOKNvUW/jf/dKF0VpROqXp7+IPF3E05WkayOw9yGuP8Alui1GZoAjeWa+pqnRIXUb15FB1WpVW1tbXWexftl1jHGOZzUajaR7Epr66NGjpIvGOhvtdjsdn0WkUKfTSWeO1+v1Lg+rrwuf037JSC/uElPPMDQ6T3VDZLvdTnpYq9VSq9VKTiXm1vUe+jE2NqaFhYV0z+HhYT19+jQZxHnF1Bv0KF9n8FuKkXnbpWsZMDw8rFqtpsXFRX3wwQf6+OOP9eGHH6Z8VD9KB37pFdXJp2S9UQQQ3dwjjmKUoKQb41hEr6zNwuBZqG5djHlIniAeAQkbjQEdHR3V1NSUBgYGkvCifD+bb25u7kZIF9dTcOfBgwc6PT1Nng6KKHi4HLlqMORqtaqpqSnNzc0l4Dg8PKyZmZlU8hZmSn9YZJeXl8ka/sUXX+izzz5L1TSXlpbSZq3X612VW50puzs7Wi37RTHkxq0Ouc0flTN/Zy5RcEdGRro8jwix2J9otY10GxO6zQqdaysWQw+nJGeq2Wx2tRlLEG3HukPf2u12su64Ajs6Oqp6vZ5+08uq/GVpZGTkRjujRVy6GcrhIADmiuBDGPLy6noR3DOeFGYhV4aXV0F2i160Bvq+cM8t+zPXN0DjwsJCOtcITyNgkVe0UDqfKlJUIwDKeYTuigCOMXojCkxXgJwcLMTcR3ItuC5aZF14cQ8HjggSXzMoljkB43PsHkUHrHzn4NWfg6K8v7+v9fV1ra6u6tmzZ3r+/Lm2trZ0eHiYwm7cm8HzbwP7/dqPyLCc8cuNLbShiNfTPqzhvn98ncY163zcFRSvDu339DbmQg5vo9ie6J1yRSUagLw/ubmKxqJotOqnIcf3fC7/0P/2NjpfcwAe21ok23v1iXDtjY2N5HUgQsqNMQBF2j48PJwKtMETCEd0Hcnz6nopjlH2xzWY4+1OOadCv3QdB0bwHXROKMrGKK/xNo6Pj6cxajabXQWgok4MeEM38mi2sbExffHFF9rc3Ewh6BjZX4cqlUpXOHKj0egqgLO0tKR79+4l0Eh+tfOAy8uronQbGxt6/vy5zs7OknGCVA8MFT5XbhTqp3EVo4ePtRvAPdXIx8PlPP978RhAXU6uVqtXRanu37+fnEtzc3PpnM7NzU2tra2lqrvsdzc+uF5GmDTzXKlU0pwh63nGe++9l5xWS0tLaf96OoeklHpAbjJFB8ltBDAjsx0o5s58pe13DhyxYLm7eHR0NDWKs2L84a60MbgoRnyOa50O4Kna29vT5uZm8gQtLCwky43nYpCYTP4b4MDP+iJ215VkR/peNcoZMO2RrpXV8fHx9Jy9vT198skn+uyzz9RqtdJie/jwYVcsOcADZiJdAzgsPqenp6pWq8mq0E+6TUHIWRvju//NXKAkRu8BYAvFMFeZi/do/XnZvkg3mb+DArwYFNnY3NzU9vZ2OrsHjzSW21qtpkrlOqQAxSsCMmcWklL4pHuK+kGeX+kMgH47OPSx8M/jeMPkRkZGugwasWgG80reBwwMDy5hIVF54vkOCCuV62ItbvWC8bKmuJZ9iJB89OiRHjx4oLm5uZQ4Ho++cYpGm7i22ZM5sNsPmpiY6PK6eqiqh3nG9RSNIm7EgQ8TguTh6TnwyRw4qPNcQ9/PRZZJX0MROMYCWQ5sfC2RoL+/v6/t7W1tbGx0FVqiPH3O4BR5SPy83+SevF7Pd/BTpGBL6pqnoutyPNqf0+l0ktEyRh4U3Sf3+csCSa4tMlBwnYNMD1t3rxC8x9vdbyXV2+d9in2E0Cdim/AIxGud3JAQyfvLHjk+Ptbe3p7W19c1OTmZqroTskq0CzySiCo/tsqP+gA4oiN5mkmv8S0yXOR+8zJrrV+GnMHBwa5COLl8cZ4fPY4uU9EP8TienJxoe3s73cvzPf2Im8nJyaTrUmBmfn5ei4uL+vzzz7WxsZHAKPopryJyuelpW9TRWFpa0sOHD3X//n3Nzc1pbm6u6xgX1oUbsDjO4/PPP9fa2lrymiFn+dtzBZ36vR/JD6e9tB3Z7ZXAaYu31f+mQi6RUM6z4nFKg4ODmpyc1NDQUAr9JZJqa2tLz5490+eff65nz551pcf5sVTcxw0mlUolgUQvttloNFIu6qNHj1IxTZf78EyiiYgW4EzsoaGhG7oP7aFwjofHSt35yi+j67xWVVVAEAPAQ6NXgga58IMp0UE2JxvLFzS/Izzj8PBQJycneuedd9JxHZ7kirfLn4sn0ysJ+aJAuacfhBe6JYwJwepEcRSKNXzxxRd68uRJCl+4f/9+UmKpfuZjUJRf4wodyla/GGoUVjklLEc58BhD12Bm/hkWNVcSXZEsUgxeFURy75hjGcPf9vb2tLOzk/I7CNvZ29vTxcWF6vV6ypmVrg4BPjg4SJsWIQRwAxhfXl6mKmZTU1NdFXf7QayZnBci9h9y40VOCeM+7CkvyuFz6GMdDQSEXwMGozB28OjAkTkcGBhIIeVY/rCis28bjUYSlEtLSyliAMuuVz2OIaquzDhY9DGL49GvvShdAUcH6Riu4E+e5xhBnwM5KIZmYXgDyAFwPMGeMfB96MY1V6zca50j39NupPPPGVPaDmCkMJXnNK6uriZPI8LZ13VOcen1f7+UnCJelQOSPh5F7YlgMafQF/UryrrcM3oZDeN9XpWKDI98x7riOzfoSt3eomjo4vt+7cnb5sMBnwPMot/1amc0rPEZ/WX8Sds5PDzU9vZ2OqKDCp8Y+1AY4dUYOzEkUXDJC+MgA/14o9w4xP9z/fI12msNvAkaGRlJIe2SutaXdL2GYnGtqOSjS9brdZ2fn2tzc1PNZjPNUeSh8Gk8kngtG42GFhcXkyfwyZMn6egNADyAxuUB8+j8H+8leWxeBOfevXuan5/vOvaDtnjkCGGOz549S86Pvb29pMOQr3dycpIcGr5n35RBzj2HbgiQ1GX8dYNk5MNgDj8P2j2F7XY7He/nMrZarSaHEtVp2Tv37t1LlY4do5ArTJuYTz9Cbnp6Wu+++64eP36cjD9EOc7NzWlqaqqrgq07mYg+IFqACsjgBrzhGDGIDPP1FY177E30q170ysCRxZdjKCiJrtS4BYBGxWtouMfq+6RVq9UUBsohme+++24CsGwCT+h2pcZz8FgkbELuz4BXq9V0DxYbzAElC+T+5MkTffLJJ/r88891eHioer2upaWlruRjH6s4Ua7AViqVVPUMRlMEMO+CvGgR726B8rmBipTpCBx9PhlfZ1SMA15HV+qj0loEIHNtkpQAajQUOMjxvFcKAbgLHysRTKLT6SSPmvctZxSRuitistH7aQBwBh4Bo3QzX6fXmPoaYE/7Qd30M4JBrNoTExOanJxMY0F+6G0VIXNGAxQejD9Y/ggbmZ6e1szMTGLcVFfG+AOD9uiICFx8jzFXRR7afhKFF5xnYTVECMTcNBec5E271zEWEWBvENUQx98trQ5s4IE5g0QOfPN+24s9iTUUBYX89ufPn+vZs2d69uyZNjY20vmfkO+rl1Vg+q3oFPGpHHCMBjOnnEXf33PXxmsiH39dUPM6vy1qi1+PTJWU+EPOsMCaJUfHiyj1c1/Gezto5N0NF0VWejeYxfvF8fDnxv7xvIuLiwQe19fXNTc3l/KfMDRxnyiz4RV4HKlMTDXVoiiCnE4Q+9VrDbueE2VTvPYuyY85iucudzqdG4ZP53Oun6FPEoJK5fXd3d0uee9eIfQXZCNey0ajoenpaS0uLurx48daXV1NkU+bm5va39/vOs7Dc5Phx8hEwkg5b3J+fl7z8/OpiIp7ni4uLrS/v59kcqfTUbPZ1BdffKFPPvlEX3zxhZrNZooq5CgSjsSbmprS7OxsV3581D36ReQR+ty4gcn1euRW5MUevYCTiWicnZ2dNMdECHpkDKCTgkjkfjLm9+/f1+bmZgJxAEjWGLmQjCFnut+/f19LS0tpfUxOTnad9R5lJvyQiv8OGqVrrzprHZ6BfsB68ggPeEqnc12c8zY5+VpVVX0y2FSdznXVODpIo31Bec4DTNEVUDwEvtkHBga0v7+fCiTgzVpYWOhyYWNRj2AHiwRhUJKSMoyyBQh1K49b7MkLIJRxfX1dP/rRj/TFF1/o6Ogond9DaCqT1kvZYixYnHjqALquSN41HR0dpfFx5Ti28TbK9St6I3KKrl/n37l1yBlAfM+1gft5vHtsk3sGK5XrEJRq9fqg8vPzc01PT6fkckKEYF4YGBwA81s2pYcduyHirilaJSOgdxAARYDO9T6W/I9y54LeBQaCsVarpXHgs9HR0eQh8kTseC8Xih7JwLMpXEXIBrkAWNspajUxMdFVORCjhIcTOX/w57pw8XFzxcHH6a6pVqt1PZtQZwRKPBPP9wC5a6zhaNxwHjYwMJA8F75OciGkPi7Ot3kvUhhuA0FuyEGJpXrq4eFh8jQ+ffpUa2tr2tzcvHEYdpFxK7YxrrV+k3ufIhUZafz6HBjP/RbKKeFFz8/9Nv5fBPSiTPXnvI6scGKdxfu6t5zvndfBq/tFuX75nkDHiXNzGzCKv3E9gO+jN9P3TLVa1cnJSUoL2NraSrlreBkAjz5mpBKRO0wFbAeOHjV2mwEkAoc4bkXf3zbGd0k+Dq5MS9c51a4b5OaNPHE8SAMDA5qYmEhVaff397ui5zxfPRrGPZQQEHH//v1UE4CzzJGZRO+4wQQgSmVdXl75lEJ+pGt1OlfhqF7R9fLyUtvb2/r000/1+eefq9lsSlKXE4U0tNnZWS0sLCTZRKXON8VXGeOoJ0g3PY7MHzKQeXSdiLMXXbdznbXT6STDsxuEfB7xQE5PT+vBgwddR9kQDiopnVF/dHSUPM7uwXSDuBs3INdDqN9CTQ6vH4KR3w3NrF8/nzdn5IoRHUVGMOiVgSPKRwRFdNDJGxCv8dA2YoGdEROnD0Ken59PFZ+ePXuWBnBxcVFjY2PpmYS8xrLIDC4b2UNU3UrBwsAKjouXsEbcvYRQnZ+fa2JiIp3R6CWa6XdOAcgJXBcMbMovU5q5F8EkvI0xLOhllK6cYPR7RSuQKyRu6XPFnI2Zq7qYMww4ePPcK1fMIrCsVquJ+bH2ACHn5+ean5/XO++8o0ePHqWcECyyzK8bTdyjhpGh2WwmZb5fAtIVqTgmkFuQInN14Z4zAkhKFroI3BGM/N4LA4yPj2tvby9Z3vAoUcKaOeVZ1Wr1RlipC1ivIugWPz9mA9DoDD96h+N69TUFvyiaq356jsfHx9MalbqLfnlRmRimSnsw2kXQ6/MqXXsd+S37xcc9etNzPCsH3KKBKAcS2KN+0LCHie/u7mptbU3Pnj3T6upqAoysG3hkbENshxuMctf0i4oUZTeSuLfM+bvvvTiORUYef+c5/jy/d9FvYvtf5fOi8fR+5O4RjTI5/uX7zWWiX/OmyHlfUZ9ehnIG0GgsgCf5s53nYpAk8onjj9A9PKzdDUnIKQAne4uCOK7z+HOL9Luiz3xsXL5EIHnbWrwL8nO5vQhKzhvra43PvLgYhfDQTaanp1WtVpPn0fWL09PTrrMz/ZlumAMEzszMpKgmDGl4iFw3Rp55+oLzakL9/agxwAyhjeTDEe6InEa3R6YfHBzo6dOnKZeO8aTGSDS6vooR6VWJczPds4t+jAxzj7xH3nQ6na7q/pKSsZtr8Nzt7u529cdDm30fYJjGMzs1NdUl23gejgPy8ik25HmwrqtEx4jv3dPTU+3u7mp9fV07Ozs6OztLeMcjk9CTOp1Ol6cxt68ZEzdMvIwx4JWBI7HO0fLkA+udzjXAGYp7a1whcq/O6OioFhcXVa1exVgTE873HJVxcXGRQByTU6lUuqwUHufM4osWMixzWBAODw9ThUgsN/v7+5LU5WVEeUVxdbdvTqGKAohzcy4uLlKMcTyj7K4I75m3KQcc3Qrq1+fmPyqpbkl2xdaf51UbXXGObeN/GAIUgaMzkuhxdPCINc2Tjgn9uLy81OzsrO7du6eZmRmdnp5qfn4+hafQJ+7lVkuYFGsljkk/KKeAREUnMvUipcXHFWLuPaLAya1dXgSAeHo8wAhYjoegejKKEOecMicARwChf06uMYJ5bGzsRk5jtJ75+vJ1KOkGSIr9LAIid0WTk5Nda8iVgxj+lIvaoA/RWBKJvcLf7D+v2ho9tDmBxu9zSoO/+wsPEYoMr4ODg3TkxtbWltbW1lJOI6HkGBCjYhvJeU4RwCz67V3Q5WX3+aM8i7FiLj2v06MGonKZ44FFfYr79zagGRX6+Hm8dxFAva0duWf6uOT65mkG7Idc6kY/0zloUy7sLY5RDtj72Ea51ksv4hr/zIEjCjMgAQ8E3ibkWjTaE+JGNXEAQ1HV7Mj/cms6jtXLjKfLxZf93esSqU/I/JgCxB70/rpROOoSHl1Xr9c1MjKS8kR3dnaSTkPeKYXOcIQwJ+i78PexsbHEiwGeFJdDfvI3v0d28j2G2ZOTk3SQO94vgGi8F/xqeno68UwviHN6eqr19fUUpkuIJecho2O/DNj4soT8AzBHPZo95o4i9GnGxysGDwwMpLMsAcrn5+fa29tL90MXd5Du/QT8uez2/c58z83NdQE06XpPOe7JyVqwAVGX29vbOj4+7tK1YrQBKXUxjYVxdIcGQBiskktFjPRaVVWPj4/VbrezzAm6zRIVwxld4CM4pe7qlAzOyMiIWq2WhoaG0qBwRuPo6KiazaZmZmZSomsstYsC4yGTMZzDD4A/PDxUu91OyioTf3l5qbm5OS0tLSXvFZPnClf0dMR8DtoDQyKczJW8fpBbOeI4QDAEB47OJIpCsxwI+gbxOXbg5eEGLiyLFNZIMHuYBBvD7+v/u2cHCxZGBemqrD5MBesUh3t7IQ/Pj4QIgfbx6RfRb99HOSXvNsXTlTSui2sPj73PDVZO5hKLF0UEEFSEzaytreng4CAl9bN3h4aGuioEsg8w8vBc+kwVOYw1fl5mDgRK3WGo9Jn/fQ3kFEWu79d+9BxHQkfdQh5fPmcIpBjG5+1GIERruhte8FDE8F7nYUUGowjOfT15iDghqYT9U6SDMC2qqMJ3KWzh4AriuVHh5bNeyky/DDkoqLln8Xn0fgMEct7ieK8c0LtN1rqyHikakHJyvAj05Z6dA5f+XW4OI1hxo47LgrgXc7LhLok5ijIujmlsW+7/yJf93cPE/DnxmX7vTuf6LMa9vb0b0RcUMaEPnNFHSOT+/n6q5BnL8hetuVzfIuXWZxwz78/L3PPLEPLcPXzOPy8uLlJYp7fHeT1GZry83Jc0FyKQOHx9Y2NDp6enyQvV6XSSLgpvcmOzG9ABOIA/PIcAQOYMwOjXxSMWnOD/fkQFfQNsAEQHBgZSAcjd3d3kldze3tbs7GxyqMQ11s+9iE7t4ZyMHfPnNSZoE2Mbx9T5LU6agYGBdMwT+fStVisZrnkRAuqyMvIGT7mJ/0vd6XmR50XZzd7d3NzU3t6eLi8vVavVUj6khyezzmNkjx/H5AbmCMaL5E6kVwaObDZ3bRZdlxMi7s73mNsipuxu3ImJidRJ9+gQe84GI7zg4OCg6/wXVwij0GEiUXDItdne3tb5+XlizPfu3dPg4GCqxkSCLCA6FqRwweJK1MXFRZo0gCMLDCtBP4kxYCOyeF3J8XGK45YTmHGePfGcBZsToDnrXs776SGt0rWXKHoZWJ8wjKgcxfbSLxjrwMD1ods8e2RkRPV6XYODg2md0cfckRMw75d1/b8uMZ4urHPCOfbZqQhEFj2PPcnaYR+7F8U9v7xTFbXZbCYmfHZ2lkqEk7PhAolneHQAfAGDUgzhjN7yHEXQJXWXz3dgzHP75W2UroBjNABEMOt9RAGhL3GvxDVQpHwzP86Xoscx53mMvIDn5Lz9GBvxQHPQOGFTfv6nV3j08vQxrJPx8LHw9nkYtLflZcDWlyUHPJDzf883iaDS2xyjK1x5j2Au9533N6eoF10b+5L7XRHlAIbzpiLQm5MH9D8HEIuA611R3G+RIp+I/c1d6/97P/keJTg3xjlwzZFjHBuFMkkkBlEd7fb1MR7sMQztKNLIVVeKoy52m7ElZ1goWlOxP/0iFHbnC76enF95uglyAWXaI2SiboqXd2xsTDs7OylUlJxtB/Yx78wBjXsJPXTYww1ddwY8slc8DBMnBsbHGNLqXlfXezqdTgLEnU5HExMTWl9fTzycKp5HR0fJ6xhBUj/II2LoF+Q8lFQWvnd9JOa0ul5KOHGn00m59PDqVquVjmfDOAMAZy/y7JwBLsrjKJ+YM/YYBgHaSz7zwcGBLi8vkwOLIjoenem6GGva9TCe7XvBI41edv5eGThK3WdL5QSklC+Ry0R6yFFOwYvCio5huWm325qcnEyTdnR0lGKNicUm2ZWSyW6xZoHncoa8nUzS0NBQCl2cn5/X0NBQyrnxkrd+j14hNM6kXFBwjxjT3g9COfRwAxayeyJ7AcYi4Mi8+DyzQHMeMQRVrK7oTBQrEfPmZZWxKnLPuDH8WAcEJOvKAZDHz0eGSF4d9wHsA0yjguOKTz8Fo9Tt1YwKWFRic3stKjExrzYqeewbxsk9ugg05swNCcPDw5qenk4VwxBuWGLxNPp4urBwr6OvT+ail+IVyQEF1uOo/Pl8ukLfD/IjgOhf9DA6r4rt8vmLwiunwLshht+yP8gBdiUj/u1Aks8RXH4WXKdzZdAjr+rg4CCBx2azqf39/a538hn9nMbILyI/yoUB9lrzb4riHHQ6naTAI6f8EHHmIYbvv4wBJPY1BwydevHv3LW530QeXnRd7vMcwHDjg5+B5gZHb1M/PY6+vr19cU5zhkE3Nkndhiz/zI08OVDj1+f62W63U3QUwGViYiKBFAoHXlxcdJXux+PvRUGQ+753iqIrvC25NR6NA0UU57NfFPmmjys6UKfTueGJgs95lJLPJXqGGzDb7XY6FH59fT0ZOJkfKu0DSPAgMh8O5D30EjnlhlWiRFzmoc/FI+AYb/rHHkOnmpiYSMXmCMG9vLxM7d7d3ZWkBIRnZ2fVaDS6nEhxTu+S6D/pKJVKpUvPkJQwAMdpOD9FB41nWEaHxtjYWJJ/jB1OKL6fnZ3V/Py8xsfHk94xOjqaiizmzrt2pwfrhjVHpNbg4GAKSW02m8khxlxJSvPP/mbc0bdd9kajt695905Gx9rLzOFrVVXFguFMNUc5q2kOxPk9opXLPV8sHpJBUSTPzs40PT2dKki1Wq1ksSEvDaDn9/VBA0hgsahWq+kcFdA9cesMMiGRUndcNYu0FzNkbC4uLrqS2aOy2i/yYz+8IpiUz+soAoxFigcLF6+dK/p8H5VxXxutVit5fLGSnp6epkPfOZ+IEBB+j/XMc0RPTk5SOwAwbG4Hz76ZaDt9It/OFW6Ao68DB83R0tQPAlREYZ2zckWB72vQcx48jMHJAZ0zGfeC0f+ckkUhAPeU5drnwNQFrxs4vD0wV79XbryjMhOZu1MMl3OQ1Q+i+h/kgCzuRcj3EnsshgLmrmO8HNQzbigScd2iKLFXPDTY+YcrRPDgVqul3d3ddBYq3x8eHiae7cpsDKHNAcci3poDF8igXkr4XVEEA/GZFJo4PDzs8hqztomWQQGIeyUH3uLajLKnaN1EORP5ehEQ9Pvk1gnXudLq+znyWb+H5+RgwELfgLdwP+/DXZOHE0f+ArkXS1IX3yyS4XHMHIR4aGzOYOwKL7/HKEMe3eTkZJKPGDrJufeD5nl5yk5OvjvPiHPrwDL2M/L+OE+5Pd0Pct0jx7ur1WoCIq70+xy5oT+2GaMlHkSi1dbW1rS/v6/Ly8suPoksu7y8TAYyD1GNESO+tgCMk5OTkpRqYkQ9BadA1FWcr7jHEi/o9PS06vV6co5g+AV84aVuNpuJvwMc+z2PlUol6XVe0JDxGhi4PmfdHTnOGz09gLF1h4Z0VaROUnI2YJxBNnEGonQF4uBV1Cgg5QS9iFcOBxExcHp6mnAIOcuARjfWAuJJ2XP85XocRlfWATyV0FocL250YIx7yVanVwaOOYDjk5trRO66qPDGMEQHj64U45lgw8PAsLS1Wi0dHh6mRX54eJieFz1AzqSZIKoTeQy7V3SkLQw6ExeZqC+UoslwZYwNIXV7q/pFxLC7cuoUPRu9FA8nFzgwpKg85UAj38FQt7a2UpnoL774Qs+fP9fx8bFmZ2f1+PFjPXjwQPfu3etaOyQyc4zB4OBgAo1eSdI9oR7f7d5Q1p4zAdYEIIv/o+IGY4mWyn54j93iFIVxjAqIygBKqls7XdD6vBTtGZ7tOQXSdfgLFr4iJSKC2Xidg8gIjp0c8Pp4+L19HmK/PMYfcutsbnzvkjz0Ruq2hPbag/63G+Jy18FTPA+PNZ/LBfYcVfYzgoujT+K5vhcXFynv6uDgoCs/ZmdnJ1UNZN3xv4ekMtbOG3yt0Vfe+dutyH4NfewnP4Uone9hzxB8BH4vXRc0ih5fP28rAgwX+MyXh8y7zIx71ccnN868+1jlvGpx3zpFXoNC6wVCojGEMUD5xujgRg7kSa4Nd00xbysCPj7PeUMhV2wjRUMVz4oG1tgml0/oHF5gql6va3d3V9PT0ykq6+zsTAcHB2o2myk83EHLy3gWvf9RNtBXN5bk1n6O+r0vkdM+T85jGGs/wsJBor+k7toNGLj39/e1vr6u1dVVra+vdxm8z87Ouo5aIMLA9yNr29Oy/DtARqVSuVHDgeOJfCyjngW4dW+m8/mRkZF0TwzzRNUdHx+nfqPXYHQAeLre1CvS7ssQ3lo3UoIBpOu6B170MvJO54vobYw9fJV31gD94zp4M/VPGINarabDw8N0tIbrLb7umBtqO+BZlK4jCM7Pz5MH03MXCZeNHtWcB9V1T4wSGDHgxwBsruX3L6OnvpbHMSqJPiFOUTg4gnbm6Mz1NiHmoNXDSUDRCOWJiYlUfIFwDCbflSVn4NzDc7h8wUpKG8yFd44RRQEXGRAbjPayQDqd65CJfimp0tU5jj5mbtnIgYUcvQzDdybGewz5YC6Ojo60t7entbU1ffrpp/qrf/Wv6pNPPtHnn3+u1dVVHR8fa2ZmJuU/SVcMpVqtptLTVOXEOgeTjpbaXO6ldBPsucLGXMO8OeMoGj0QFJ7HCxO6a4p9o39FSg9/X1xcJMOKV9ZzRtrrmXHP54xEcQ/7y5mT74sorN2C5kqutyPex0F83HduAPD1fnJykvYBYfHMq4OqfhH8JfYtKhFO8f+cYcx5bI7iPLtS4RbMdvv6rEfCmIqMAR6yfHR0lA3J8jD0nLJW5DmN6yb2LfazSAj2S1k9OztLygz7w5+FQSWuc/do+bpzcOcADA+1RzvEte9yNkZ7+DjFsXBlS7qOpuH/COJzv3ViHJA30UDkcx6NNKylojyqfu5Jb3/0Gty2flzOxXtxP/f6Q0WgkXvGvnMPPI/b29uam5tLhvNq9frMR4672dvb0/Hxcde8Or/kPc5lzqAQ7xHv47/1cfH57yeh+MO7mJPoFXKjTORHDujZl1Qw5ciKTz/9VKurqzo8PEzyCj0STxlyx1NkPPTf+ZuPE3oDhl6qfrqBJeos3COOu88Xug/3brfbqQgl+gvfsw87netaIg4ceXaUY3dF5BUCnpFHFIaBN0agFkGjk4OlOAd4ZtGHarVaCgVl78RKtRcXF9rf3086p68rngUfQzZirHeeTD0N0nqc79JHb787LtwDzVoFS3iKFwbfnO74MsacVwaOrjBigYjWHBrgv/HPfCNGZuMD4CAmCkPIlSWEMu/EIGPNBUgwYF7EwxG7CwcmywEiRBujII7kCo57ZGinx6SzWONv75p+9KMfpTGanJxMXtWcpTW36V6Gcr+h/9HocHZ2pt3dXT19+lSfffaZfuVXfkU//OEP9eTJE+3s7CSg2Gw29fTpU42MjGh6elpLS0vJ5b+3t9flASFcxzeydNPjmRPyEei4FwFAiBAgR8IVNDdMsKafPn36ymN4G7kFlHb3Uj7oPyFzVNdjL/M7/o/CyK/h+7hGnQcUgQv/ba8X10TgVrS2fBxcWLrSTYI7TJlrsE5jEIqehGhoukuKArfXnnsZg05OIY3XOO90SyVGMYSMCz28+Xgb3ZqP55l8YPqFoBodHe0KVaUyNkDSwxO5X06IxfWWA4e3gex+kYeX5kAOa9QBUjTERCOJC3OMmaOjo+p0OklxIXIChQclwb1Dlcp12JnztcgX49/879c7X/O94kq3P9f3su9LnhGt3iistA2lFT7seTz9IM+v9D3yMvvf5zkHkJxHx3xyfi/d9KBH46d07RnFW3F4eJhypFqtVgKOflbq/v5+Ku7mIDaCjpfpazSO5PSVCEJ7gdG7prOzs2SQLzKcRHkJH3P+D090vfHo6EgbGxv67LPP9OTJE7VaLQ0MDCT5MjMz02WoxGvkRmo3kHBv9mh8uRGw3W7fOKYohm86aMpFB8EfCGVutVppj3leHTqqpASevHiPR3T0az9yVrsXIoTn+Rqi/xg8uT56yX2soo5HKCzgFB2PvUSYLvzOj7tAljkPoD0Aa4+cIAUKhxWpeOQyxmPs6KMbdmNOvNRdXR/jBU4qnCruBImOk9sMAK9VVZWOuJB3oeGLNheS6ps4ClEaHnMMeI4PSNwgXR17MeEoh2xeAJq31185wRkXpQtQBjyG//jvfEJciUU5cyYViyL0i6l+8skn6YiJubm5xOByCuxtQORVKHo4XKE5PDzU9va21tbWtLa2pu3t7VREYnJyssvidXh4mJQkmBm5rV6emIRpNr4raow3DNAZjFuE3YrT6XSSp5EDWFkTXlzHBTrrph/AMa63ormKCikeISyHbv1ut9td50j18rblwF206uLZ9TbThqig+j39HkUANCo6EcDDpwhlIYeD+eJaNyAhPHmeK1hvCjjG8XCKhh0fmxzfcP6Zu84BC9fHdcV4OR+NXluuIU8EKy2RAjs7O6lAR3yhuBCq42s1GtO8z3FciqjfCioUjQs54Ohe9JzCHefF59qt4+wrV2oHBgZS7pOHdSFjPIweRTYaRovkqu8Zt2QjB9knOeMx5Pd3Lyv3Y/y4Jx5KHxe/d78UVd8PueiLXFu8j0X8kt+6ocWVV6eo7Bbxn7hX8IQdHBwk+YS8cgOhK8nR451rs4Oc2K6czhPbyLuv5whs7pqQ7zkjioMF10/dEOJ7wdc5IGFvb0/NZjOFF1KYCGMy8hMQhkfMC5l4eDpAwPMmJSVA4WcYujewSJ46H/EIhPhyQEhYI/Lf55CaEfSLV3Qc3TVVq9WUc+lhpTlHEvmcRFExb3FO3YjnkRluaACLoD/62mBc8T4zZ9wzGtH89xixAYluhM3lH/JM9yx7Skk01vHuXkpPiWNtEo0Z98Vt9MrA0S2FbDJnCp4/5tYWj8/3ifPr/fsiK3Jk1jmmzQB6GIAr9IAAmCYbFLDg4CI+K4JLZ/qxTVEp8wXkng1XumEedw3YIq2trWliYkIXFxfJFT8xMXFjnN+EsuXkCgGbi/mCyRLa64zArSoeDuLMxUGhb35nHvQ5x0yk6/ObOBx3aGgoWWzY/M4E6FMvReLLUJyrqGxArqxFBREhARM6OTlJBYmwnnk/ImhhDqLFM7bRldPo6Y9720P1PAw4p2TE5/l8e5vHxsZSURLOOnPDRdH4RuNQP6ioKp0DftoT96YD/iLGz9jHMfF9kbsn3zsvdcHtQlm6tqC6VfX8/Dx5eMmzIudqf39ftVot/U1VVeSCpK5Qn9v4UQ4wvynQKF0fw3MbcPD3+F1OgLssQcnzvcEceOEIN0B6xERu/UR5Fw0wMZTO28Y6iEqk38vJQ6xy/AP568ph5DUOovtBrhjGPvk1Tu4lj0Aqrss4tr32oM+LG8fiHHjFSfKHyVdDNtZqtbSvXPGXlIwZ9AWK3o7cGuLvXNviWORkQ7/mMVbIZC3FHDT4Y06vdTnkoZ3kiVar1ZSPNj09rdnZWY2Pj+vy8lK7u7va3NxM5wPS56j3ut5K++AlyC6K11Dp//z8POWP7+7u3ijsw9jGozjcKEO+HHoWR25UKpXkXeM8XQCly+YIVvpFgCP0LJeXPl+eInF0dJSMM1EXiwY+xxzwHXS72OcYDQGwdPmLTuHFE12fdO+fp8HRT18H3m53tPkc5/Yb38MnXX74swCPPranp6c95+O1QlXd0hjzKHwA+ByLpod1+mZ0pYBropIWFSZ/OSOLoMCVKiwyTL5vWP5mc3slN7c+uNcwMlEmhQUkdVd/84XmYbiMpyvUrqz1g9bX13V0dKRO5+qMwkaj0RUG0Eu5uc262OtaF6pR2aeC7czMjBYXF3V5eXUcCqGUeI5qtZrm5+c1MTGR5hjXPuWSp6enEzOPc+Ug3pUU6abHIAp5L+dMPhMCBUbgFSf7zVDdgsg45xQOB22uHLAOKWpCkYXV1VVtbW2lxG3uU6Q0ROVPUpew8urF7DP2WlRafR/kAGZ8diRXDvw6PMWTk5OanZ1N64c9FsP2olLUT0XV+Z3vD1+7TpH/5RRPv5c/I7cHuZfzclewXOFwIRhfhA4R2gTfZG9MTU2lvFrCVjmqA2PF7u6u9vf3b6wPBGoOlOWUUu9jP41wTsiHIgMAY+tVnr0P/nIgwtxRpdbn0hXjqOTwvcu6+L0bFNyo5PMfyfsTFX/nMW74yPGLqCzzt1dgdQOrn4H5Joh+xOc5T/V2Q1EviZ9F/cT5VY6X80w33uBFGxoaUq1W0/T0tObm5lSv15Pc89DxRqOhTufqbD5SOMiF9KJEMd+YdZgzrrHufB5z/Nwp7sMInO+SOp1OMrK02+2UyuLOBAeOtI/rMVpFgwCpHoATQOPCwoLm5uaSUb7T6Whvb0+VSiWNMe2hDYAIP1qMsGNqUUxPT2txcVFzc3NqNBrpfM6dnR198cUXarfbyfjLvDD3VBuN9SzcoEDqT7vd1uHhYZfHFA+jO3pcz42yqB/kRZyct7gu7joMuq3XDYlH/ES+xdxH/YF++1pmT/C3e/XQ/fy5UnfOOTwOeen5/+12O+mZXvHVvZi+F7mmKNzc+QjGIZcNHu3E3igypKdnvuoEViqVhEb9rBiEoVd59EmOzMSFV1x83NPRtr/zt79yVkgHjS4cnbxqoC+SaDX1e6HAxEqIrvTmFDD3XDiAcaYc+9ovhrqzs5O8jRMTE2q1Wl0C2QWc9y/2N1JU4IqAY/wtXs/p6ekUklCr1VJ1RhKSh4eHU8nxRqORrEPj4+OpKBJHdZDbyHrLgR634Oa8MVzr88imxrMIKEI5xiOaq5J210TbInBwRu7KYW6fMQ7Hx8fa2dnRs2fPtLq6qr29va4cHB9HH0P/3t8B2ggnjtHxqmXwjUql0hXWwysm/8fn+t9FoThcRzgRxYCoeAcTdy9a7FNunO+SbgM1cV/lDARxDeTApt/DlVc3VLlBMBoH47g6ca17G1l/VGPlYGyvrnpwcKCdnR1tbW1pY2MjWdNRytiXL+t5jILT+99v8OjAi2cWgX73+OfGLHrSJXVZpKVrQ6krvx6y5nLNq5TSvmhQjQA7Ft+J/XCZxv083DQSayQCKO+He7R9vbmxOY7zXVOOt+X4nn/mAD/K0aL78lncX/G5bozz6pzsq6mpKU1PT2tmZiYdx9FoNJIhc2JiQvPz8xobG9PJyYmmpqY0MTGRciHZkyiw7l3yKCvvg4MtH4vcZ7m+8n8/9yWAAsCDnuAFqmKboucYABUNQnhrjo+PU0G+RqOhmZkZTUxMdI1HrVbT/v5+Mo5hTJuYmNDExIQmJydTUZrT01Pt7e2lfTA0NKSZmZl0fuD09HSKluFojsPDQ+3u7urk5KQLNE5NTaU1gU7knvyYz865zK5P+xyh37rO7HKxX8ARfSUWt2TfXV5eFVfE6Nhut7sK6rCmyQeP50J7P1i/0QgYdSjWis8Te9N5HKCe1CaiQuC7g4ODXSHAkV87cPR8SvSrXkZRNyTwv/MS6hZAjk160ZcKVXVPjU/s0NBQV35YjhFGtM9i9nAXj7+PSmMEHzlFKoID9wS6Us0gVqvVrlhhb3MEGFEY+MsXV1TOvE3uvXTl7E2EOMI843mcPj/e/6J25ARsbhHfJkCHhq4Ot52dnU1AcGZmJuU/4XWkoA/gjJBVqXsjIDA9n8iV4ThnPi8+V6xzNwTElwtzrHtY+PqtqLrX0Mc29on32G73KFAVbGNjI5Walm6GSbzKmuSZAOx6vZ4sba1WKxX7kJSABL+D6XkoLf3L9dX3kF/n+5aQG8/3wRjgXlF+57+Pz75LckOV841ee7GoLTkeV8QrHRhG4JgDjL0oGrvic/HOc1A5wrTVaqXcIPKDUE6Ojo6SkMVKnwNURW0p+r9f5F79ome6wZD94TLKfxv/zvXdARzXs8f5jR+U7fzNc+pzvCQHYqOscwCXqwjuigj7NCcbc6AxjgH5Xc7z+0G99lavtRSBrn/uoNL/j7/zZ0TDlR/+PT4+nqovxrONKXg3MTGRdLRqtapGo6HT01PNzMxoZmamK+eYPGPeqRngxzjk+vOy4+m8JMrUfpGvXQ/h9jUbQSMUgYIbO8lnrNVqyYvnxcOIZkHvaDQa2t/fV6PR0ObmZgKbs7OzWlhY0NTUVKpk2mw2dXp6mkAcufmAP2o2ILOOj4/1/PnzrjoODhwXFha0tLSUQmjdixX12li1M9Yt8Wgg5/e9wMtdkNcjcJ7Di+MtDg4O0hFs1Wo1efQcqCHvc3Uv3JHDfLt3LicXHTgzVvAlryx+cXHR5VXm2ZyZ6e3ztevEOnPnlKSu6uc5Ph7XMvdxfsszpdv1nFcGju6yBR37g0DBLDQaxEJjMmgwmxhl0lE458t4vosrWL5wGajoGmbRePgn5ELWz/GjHy4g2FxYDLzP/hufhAgenWBgvfIZO51OEvZ3Te+//34KHSNsjwUdlZ6coMz1Pb4XKb9St6ufcaOoBkx5fn6+q3Q/IDvmg2Ft4hqEK4TA8xAE5jsyiZwBwpUwB1Bcj7HEj4MBOLqBoB/kjCB+5sDXlTQHjM5M2u121wG/zhBzxpSidZGbezyOk5OTCdhTxXdsbEydTicpKx4iUgRConLbq02+Tth3cf5isn0R4OoXAPF1V8QvYh/jNUWAORqEfM3kPIzRi+VtKBp3f/fvY1sRWHEf8jceaeaFgjpU/IN6eZy8//1USnNUBBz528O9PMokBxqdioAk8+vAjrkbGRlJctqjSTxEDw9mlF3cPxaNgnwdobhJ6gKNzk9z7Yu8qwhE+NrBuJOLPrhLivKtiPfEv/1/vA7+HWsy5wmPssf3q1eErtfrCSTiiWo0GslrVavVErAkh82rzOMpI2S82Wwmpfvg4ED7+/va39/X7u6ums1mV/9dl8q128ckp1znjFEeqXbX5DwLz0/k/77++I3LbE9l4BoM20tLS+lYhagvSFe6DHmmblTe3t5Wp9PR/Py8Hj16pPHxcR0fH2t3dzeFs2LYJBR5YmIi5dJR8Ie8VcKPPZeSaDLA6ezsrGq1WtLP3YNFug0eSep+eBEcB4w5R0o/5aPrbl7dlvbj+W23210Fvy4vL7tyOX1MvShYXN85bztrN4bPQw5OmX8vQNPpdLrwjT+f+WIucnwRIOzGj06n05XTyX38dAHnYXyWq68S+XIvem3gyI3dGwBD94bBPD1x14EairUPJJPENY78PXHUNyrP8YFwJbmXgs3z8BxFr6MPop/x5h5MKApe3ouU7MhEo3LUr4344YcfJpCDZRLrjLc9tj/3vbf7ZRTLnPKDtQ6Fx4WcH5vCyyuQAThgmPV6vSucKyqt7l2KoVq5MXcrl7efjey5BBw7QLI5be0n4Ij/x1cOOETvq3Tt5cNrQJ4aY+N9j8AqF8aRa5d0fc4l8804QZ6w7UI+Anva47+jb/GZOaEWFRn/TexfBGV3TQ6Qc6DR910vQ078zNvvgj56nIsUQZ9f/z/u4V7APs5ZzosJ72XfwP85VHtgYECHh4ddciDXTqde/LNf+9HXrAM7numhXr6ec6DMDZn83vubWyf8Hh7nYM2VPaJNAHzI5Fx7i2Qh13juaa9x9e/c2xT3M8+N+9fXSq95vwtiP+YiLGi3G7ZiG3NzGuVg5Jk5Xs4+IByVUEjeZ2dnNTMzk2Q4qRKEHbpB3NemFxHBy0jBqr29Pe3s7CRl29vihgbkts8lc+Me4chnuN6NVsiau6aogLP/Tk9Pb4Q+Og9mjDwiDKeBG4PRV3xMATTMJ+PoYIVxnJyc1PT0tIaHh5NOwz1cNyLNQ7rKf3R+iaEaXcQdJ6Ojo8mQACh0kO4gxOfSdRwvrlOkT/RLLkJeJ8WLWgJwAdou18AmnU4nhRJ7u31dOM9xcMq9JXXlXUvd3lrHQxFgS0reTfY8EWoeDeD71PeW66w8h757/q5jntw8uYcdPOYeSk+tunPgSEOwdgAKCSOMYA7l3FEtg++uWibS3ejSddGUdrvddfYQA3MbQnYPAu3yfjC5Hi8t3fQA8Jn/xielSJnLCXja4Yuw1z36QQ8ePEjMh9AxD1uFXPm6TUjH8SoCGU4udBgLZw7R+gOjYG2xsX1uuC/GBK+I6sefuIKWC4GLa8EVHaw6XIdFMeY28vx+zaMzJ2eEUYlxI0cOXDrFirAeNlpEOaujrweY+OHhoSQlQRb3H/dxAe/HpTCWURGLe1TqDm/x9kTrt9+DccoJR7ck3zUVrY8i/vG6z8iBRjesFSnxDiLiZ5E/xLWV82L6y5WquFeilff4+DgJchTYnNeR30bg62PRD3JFzNeOy4teIJu5Iecxtx6L+LDfx9e9n3WGwuBWaekaxESlKu7/ovUY5/5VAHtsd1yDt13fD+rVBwdFRfP4qm3LzR06AoVtAIr+AkRSCM4BowMBV449ysbBI2GufqQE7RoaGuoqnsO+dfDFu/MX37vxe+8jIZd3TdGQ7/sSIynXOGhinHy/0NZ2u61Wq6WdnR1tb2+nqqPVajUV/HLZ4roqIIBCbT5XMWIJuQc4dD14YGAgHTWHHuepFq7X0D9kKEUGz8/PdXx8rKOjo9QGnomDyD2lhC5LN89MflPA0Y34/lz6GfkvBMDnGm+/64xRF5XUZVzj2eiiPNt1DTCR89mYEua/iTgEXcjrL/gxLPSddrTb7S6d0yO23IBZqXQXmOP7oaGro15Y79LN+i2RXhk4siFAzN44qVtgOQh0Jd5DYqLHwzd2ZMpRoLginLuuiJH74uLdq0blLIUx/NVDbt0S0KvNsS2uhOZCeV4G+b8uzczMpMXplv6c4vgySgvX534XQ7BcgfeCJGwormETIOR8nXh1UwSP/+3hBCg9UZj6mMOAo3Um9peNDBHeyzhi9eO3OWPIXRPj4ELC2+vv/nkOLLjAg8l4jnEECW6x8j3g32NFPTg4SGXMSVxnb0VFxnMRPVrBIw6g3J5jDqPi4Iqxz0n0FEXgKHUfTH7XBJ+JfOTL7P/cfsuBxpzxjfF2PhlfRR56f5Yrjl69zeVCztpN+11Q8pnnGuWiPnJUtC/umuiXK1Vxz7ihKrcPvXgUv3NlJwdqHBy48stad4MZ3hBXkjwFxXmGv3rx/iJZ58/JfZf7/W3g0RWhrwI4uqcx8tecHpAzcN1GzBmhqYQbzs3Ndb3q9XoCjQ5CInCJ7UfXce8fBVLwUrFfK5VKOpoJXu0GDq/uymf+7p97/qoDz+HhYf3yL//yS43Nq9Do6Ggad/cCRQMlY160Jv06r/jt1TCr1Wo6vgLvJGk2Lot4ftRDCC3lWgxlnEs9NDTU1Qb2rANzNwRK3bouRnf3klIYiegfL0hH/rk7bFhTLgN4Tj/J+4SecHJycqNOgevyzCnr3XlnBL7On6KhxXlxznCKjhrzMKvVaipeBUagDzk9AzkL1vDK5OixHjGA04R0Nww9tNnTIRxM5vRx99K+DJ96LeAYPTe4gGmce9EcAPhn/C11x8/jckYRv7y8TEokHXRwWUQ+QWwwV+IdNKKMODr3+3Q6nRSOC8BiYnJW8pehXhPDxsa93Q+amprqWjBeITfOy6sAyAgWc54J1oCHqvB5fPc1FBWFqAQ7M3Zg4J7M6IGOBg9vSw5AxOs8R8utqtzb+9oPcjDFGs/tjV5KXU4Jc0EbAZQTY+peSgcV0nVuiaR0WG6z2ewSpK7MeE5rtAJL156m6L2K/XLrawSXDrIjyM0psP2eR55Be25T1ONvnHK/87UbPQJxDH083LrqClNR7ps/y0N+pJtg0fcq8oMQZulaHlARj7Ppjo6O0v1vG6uifdAvwOEA2CkKacYlAisHcxE4xv0Z+an/HYFjnGOUj+gJjffuRUXrzNvhz/fPnT/HvZu7f9y/b5piX339xr7Gecv1w+V6nF/4uB9PtbCwoPn5ec3Ozmpubk4LCwupeqcXSokRBL3640okwA8gMjY2lvYpBlnOV5XUBVLcUOsAJhpxI6B0PjQ4ONg34IiuV61WkzfH16Xrfa704yCJxrOLiwuNjo6m3NLNzc2kD+LBm5qakiRtb29rdXVVFxcXKTfV59q9v5JSyCm6397eXgKhrmt40SLnnT7/yDNP8/Hz+QCOBwcHXRF3MXyZe3i9DdocgWO/9qaDRvJzWY9uoPR2xZQ118t4OR9C56GvFxcXqTAjY8f4op/7uLgXkTkkpzXqDawXD4d2b6XUDVLdseVziXHddT8M9b5XY/46Y4L32/Ec9+pFrwwcf8/v+T2v+pOS3kL6g3/wD37VTSjpDuj3/b7fd6f3m5+f1/z8/J3e822kJ0+e6MmTJ191MxL97t/9u7/qJrz1NDAwoAcPHujBgwdfdVMK6Q//4T/8VTehpDug3/AbfsNX3YQuajabajab+tGPfvSVtSFXxAaj4G0Hhn9V9Af+wB94I8+p1WqSrs7HXl9fz17z/PnzG5999tln+nN/7s/dev/cbyONj4/r29/+dvYZn3322a2/v40ImZWkTz/9VJ9++umXvufL0j/4D/6Db+xZJd1Ob7bkXEkllVRSSSWVVFJJJZVUUklfO6r0Oza5pJJKKqmkkkoqqaSSSiqppK83lR7HkkoqqaSSSiqppJJKKqmkknpSCRxLKqmkkkoqqaSSSiqppJJK6kklcCyppJJKKqmkkkoqqaSSSiqpJ71yVdW3jZaXl3+TpF8v6acl/ZSkSUl/bGVl5b/Z4ze/IOn3Svp5SaOSPpH0L0n651ZWVnqfdF5SX2h5efkPSvo5SR9JmpN0LOlzSX9S0j+/srKyHa4fkfTflfT3SXpfV/P4RNL/XdI/vbKy8vkba3xJL03Ly8u/TtJvl/QLkmYk7Uj6S5L+2ZWVlX/3K2xaSS9Jy8vLf5ukf0jSr5I0K2lV0l+Q9M+srKz8v77KtpXUTa8qH0u++vbRq8zh8vLyI0m/W9JfJ+kdSdOStiX9SFc6zr+6srJy/mZaXtLL0uvosSW9ffSqeuzXlb4JHsffK+kf0NWGe3bbxcvLy3+HpD8r6W+U9Cck/e8kDUv630r61/vWypJuo98hqaYrBeUPSfpjki4k/ROS/rMXAlGStLy8PCjplyT987pisP8nSf+CpA1J/yNJ/+ny8vKvepONL+l2Wl5e/r263nt/StI/Len/qivl5he/upaV9LL0QjD+3yT9rK7m8A9J+ouS/g5J/9Hy8nKp6Lxd9NLyseSrby29io7zLUn/DUn7ulJW4bHv6Ao4/ukX81zS20WvpMeW9NbSS+uxX2f6JjCQ3yHpqa68hr9e0v+j6MLl5eUpSf+ipEtJv7iysvL/efH5Py7pP5D0m5aXl3/LyspKCSDfPE2trKycxA+Xl5d/v6R/TFdW1OUXH/9dkn6trpSc/8rKykrbrv+fSfp9kn6npN/a70aX9HK0vLz890j6pyT9GUl/98rKykH4fugraVhJL03Ly8v3dLWv1iX9YGVlZcO++y/qiof+k5L+1a+mhSVl6KXlo0q++rbSq8zhfyxp2udOSvz1T+vKQPd3S/o3+tLSkl6XXmWOS3p76VX02K8tfe2B48rKStpgy8u3zsdvkjQv6V8BNL64x8kLb8gvSfptKj2Pb5xym+0F/Ru62nAf2mfvv3j/d6KAlPRv6UrB+eafYv81oeXl5aqkPyjpSNLfG0GjJJXhU18LekdXUSp/zkGjdMWHl5eXD1Tuu7eKXlE+lnz1LaRXmcOVlZWzgs/Pl5eX/6SugOOHuWtK+uroFfdpSW8pvaIe+7Wlb0Ko6qvQf+nF+5/KfPdndaXY/sKLPI+S3g76jS/e/zP77D9/8f63vAAlTv+1F+9/pq+tKulV6BckvSfp35W0u7y8/LctLy//o8vLy//Q8vLyr/mK21bSy9Nfk3Qm6a9fXl6e8y+Wl5f/Rl2FN5b77utLJV/9htLy8vKApL/1xb//Wa9rSyqppDunnB77taWvvcfxFenjF+8/jF+srKxcLC8vfyrpu7qyvP6VN9mwkq5oeXn5d0qakFTXVZLxf0FXm+1/aZf9O5L+L7oKuflLy8vLf0ZXCu1f9+L6f05XeTolvR30q1+8r+sqH+77/uXy8vKflfSbVlZWNt90w0p6eVpZWdlZXl7+RyX9M5J++YUHY1tXeVV/u67yOv77X10LS/qSVPLVbwi9MOz8A5IquvIS/02SPpD0r+kqR7mkkkrqE72kHvu1pZ804Fh/8b5f8D2fN/rflJIK6HdKWrT//5Sk/5aDipWVlc6LKmS/T9I/rqvqjtAvSfrXyuq4bxUtvHj/H0j6VNJ/WdKf01Xo4z8t6W+W9G+qLJDz1tPKyso/u7y8/JmuCm389+yrTyT9yzGEtaSvD5V89RtFc5L+p/Z/R9L/RtI/trKy0vlqmlRSST8xdKse+3Wmn7RQ1duo8uK9ZKxfEa2srNxbWVmpSLqnK8v3+5L+v8vLyz/LNcvLy6OS/s+62pz/Q0lLujIK/K26AiN/9kX13JLeDhp48V7RlWfxl1ZWVg5XVlb+c10V5Hgq6deXYatvPy0vL/8uSX9c0r+sK09jTVceqR9L+mPLy8v/q6+udSV9GSr56jeHVlZW/uoLOTqoq7n7HZL+fl3N4cxX2riSSvqG08vosV9n+knzOOJRrBd8PxWuK+kropWVlXVJf2J5efkv6iq0+F+R9L0XX/9PJP09kv6hlZWV/7397N97YTH//+mqFPK/9eZaXFIP2n3x/uOVlZX/1L9YWVk5Xl5e/vcl/Xck/fWSynMA31JaXl7+RV0VOfoTKysr/7B99ReXl5f/Ll3t0//x8vLyv7CysvLjr6KNJX0pKvnqN4xeeIi/kPSHlpeX13V1xMo/qasw1pJKKqmPdIse+7WlnzSP46+8eP8ofvHibKP3dHXmSqn0vCX04sDpX5b0XSvIQaGGGyWrXwCTHUnvLC8vz76ZVpZ0C7Hv9gq+B1iO9b8pJX0J6rXvjiT9v3UlU37mTTaqpDujkq9+s+nfe/H+i19lI0oq6SeNCvTYry39pAHH/+DF+381893fKGlc0n+8srJy+uaaVNJL0P0X7+TXUPX2Rmn4FxVx8RxnS5OX9Mbpz+rKIPPh8vLycOZ7LHCfvbEWlfQ6VLjvwuflvvt6UslXv9n04MX7xVfaipJK+smkqMd+beknDTj+cUlbkn7L8is9HGIAAPMOSURBVPLyz/Hhi9yO//mLf//IV9Gwn2RaXl7+9ovDxePn1RcHpy7oCtDjmfoPX7z/Y5mjU/4JXYVg//nceYElvXlaWVnZ0lXuVF1XhTcSLS8v/026Ko6zr/wxOSW9PcS++/uXl5cf+BfLy8t/i64Ojz/R1SHkJX39qOSrX3NaXl7+G5aXl8czn0/oKsxYuqqeW1JJJd0hvYYe+7WlSqfz9a4Ds7y8/HdK+jtf/HtPV0roj3UtBLdWVlZ+Z7j+j+tKwfnXdRV+87fr6qiOPy7pN5dVx94sLS8v/3ZJ/2tdeaZ+pKsS/4uSfr2ukorXJP2GlZWVX35x/QNJ/4mkh7ryUv0pSce6Ulz/+hd//4aVlZUyX+4toeXl5QVJ/5GuSsL/h7oKa3xHV8VxOpL+3pWVlX/zq2thSbfRi7P9/n1dVcU9kPQndLU3v6OrMMeKpN++srLyhwpvUtIbpVeRjyVffTvpFefwT+oqFPX/qavcxiNJjyT9LbqqFv8fS/qbV1ZWDt9E20t6OXpVPbakt49eVY/9OtM3oTjOT0v6+8Jn7794SdLnuqoSJ0laWVn5k8vLy79e0u+R9F+XNKqrUvL/sKQ/XILGr4T+jKT/g64UlJ/SlYBr6SqZ+P+oq3nZ4eKVlZVnL6pT/aOS/jZJ/21dec9XdVXt8Q+urKz81TfY/pJuoZWVlY3l5eW/QdLv1RVY/HldgY9/R9L/YmVl5T/5KttX0u20srLSXl5e/lt1VXHzt+hqHsd1ZXz7d3W1T//0V9jEkm7ST+sl5WPJV99a+mm9vI7zL+pKdv5qXQHIcV3lkP8FSf+GpH9pZWWlDFV9++in9Qp6bElvJb2SHvt1pq+9x7GkkkoqqaSSSiqppJJKKqmk/tJPWo5jSSWVVFJJJZVUUkkllVRSSa9IJXAsqaSSSiqppJJKKqmkkkoqqSeVwLGkkkoqqaSSSiqppJJKKqmknlQCx5JKKqmkkkoqqaSSSiqppJJ6UgkcSyqppJJKKqmkkkoqqaSSSupJJXAsqaSSSiqppJJKKqmkkkoqqSeVwLGkkkoqqaSSSiqppJJKKqmknlQCx5JKKqmkkkoqqaSSSiqppJJ6UgkcSyqppJJKKqmkkkoqqaSSSupJJXAsqaSSSiqppJJKKqmkkkoqqSeVwLGkkkoqqaSSSiqppJJKKqmknlQCx5JKKqmkkkoqqaSSSiqppJJ6UgkcSyqppJJKKqmkkkoqqaSSSupJJXAsqaSSSiqppJJKKqmkkkoqqSeVwLGkkkoqqaSSSiqppJJKKqmknlQCx5JKKqmkkkoqqaSSSiqppJJ6UgkcSyqppJJKKqmkkkoqqaSSSupJJXAsqaSSSiqppJJKKqmkkkoqqSeVwLGkkkoqqaSSSiqppJJKKqmknjT4shcuLy93+tmQkm6nlZWVype9RzmPXz2V8/jNoHIevxlUzuM3g8p5/GZQOY/fDCrn8ZtBuXksPY4llVRSSSWVVFJJJZVUUkkl9aSX9jhCv/W3/lZVq1VVq1VVKhVVKhWdn59re3tbzWZT9Xpd7733nubm5tRut3V+fq5qtaqBgQFVq9c4tdPp9Hz36zqdjiqVK9B7cXGhy8vLdE+uabfb6b3dbktSemalUknvnU5Hl5eX6Tpe/tzLy8v04tlDQ0OqVqvpt5JUqVTS/1zrbalWqxoaGlKlUtHFxYVOTk50dHSkvb09HRwcaGBgQNPT05qamkp94x7+7H/73/63X3WabqXf/Jt/s6rVqtrttk5OTnRycqLz83NVKhUNDAyo3W7r7OxMnU5Ho6Ojmpyc1NjYmCTp9PRUZ2dnaYz57OTkRBcXFxoYGNDg4KAGBgY0NDSkkZERjY2NaXx8XMPDw2kuLi8v031qtZomJiY0NDSki4sLnZ2dpbnmnbE+OzvTwcGBjo6ONDg4qMnJSdXrddVqtXR/5pP+DA4Opvm/uLjQ+fm5zs/P1W630/oYHBzU4OBgWgPtdlsDAwOqVCo35vzs7EyXl5caGhrS4OBg2hOsOf72df+P/CP/yJ3P41/+y3859Y+20E76xf9xL7Cu+T0vn7/cO6/YX8bp4uJCFxcXXXvy/PxcZ2dn6d3n1/cg7+wBrjk/P0/zxthzj7OzM52enqbn8jv2JXsJcn7iY+X8gvVCH4eHh9Pa+jW/5tfc+Tz+0T/6R9M4joyM6N69e/rggw/04Ycf6vHjx3r33Xf18OFDzc7OanR0NLWPcTg7O9Px8bEODw91cHCg4+PjG/Pgfabf8W/vM/u2VqtpbGxMo6OjGhsb0+DgoM7Pz3V0dKSTk5O099n/kX/GZ+ZesT3Q0NCQhoeH0/rm/+Hh4cRT4LGsvVarpd3dXW1sbGh9fV2rq6t69uyZnj9/rs3NTe3v7+v09FS/+Iu/eMezKP3CL/xCWne+ln0/xj0YiT3r+8vlFrLF951fy171Z7hs5B7Mj68P51vxHpK6+GBsF/tkcHAw8Vnmn/vCO3zuGaPBwUGNjY1pZGSki4d7m7wvrLHf//t//53P42/7bb8t9bNofvi+1/qNlNtzkMssXy/MgV8X543fxWvj9bHN3o44J0XfF7W/Vxtyz4j74Hf9rt/Vc+xeh37jb/yNiWeMjIx08RBkmK+n8/PzLt3L173LIdbj6emp9vf3dXBwoJOTE52dnaVxHhwc1MjIiEZHR5P+KF3rl8izy8tLSUo8l/Xvcsl1V/TKTqejo6MjHRwc6PT0VJJS/9AF2Gvon81mU7u7u9rZ2VGr1Urt9OsvLy91fHyso6MjnZ6epr09NjamiYmJpAuyz09OTnRwcKBms6n9/X1NTk7e+Tz+kT/yR+78niVdEWvs4uJCkjQzM6Nf+2t/rR4+fFj4m1cGjrVaTQMDA10CQ1KXYKIxEWA6OIxgjc/9JV0LKr5no9FZPotCemhoKP3Gn31+fp6UHe6FwuF98o3dbrdvbHpXLp3x+BgAmgYHB3VxcaHj4+O0wWu1msbHxzU6Oto1ZnEMXkYYvQ6Njo4mpRIGJykxGJR8+jY8PKzR0dEu4Offn56eqtVq6fLyMjE+mCtgBmBarVZ1fn6u09NTVSoVjY+PJyUYBZA5OT097QICjOPx8XFi0s58mXPGM7cGLy8vdXp6mu4hKSmigF3WFgrQ2dmZKpWKhoeHU1vPz8+7lCufNwek/STWrW9+VzxRMiWl8eW7qCD0oqLrcgCAz2M7mFfGmO+iks1eis/nOwQW7yjLzC3Pdt7hfYjKvPObqNwjpFE0MJTcNfEshPjw8HCXAuDrLFIEBTljWKSX4Ss+F4BQ+KGD9KJnFSmP3g9fN27k4D2Cl6L15s+I4+hKI/worq+7otxcMDasHdamr7PYhyJgHT8rkhFRfjjQ4/kDAwNJEfX547vYHv6mD3HufP6K2pUDQkVAJ/6fM0L0i7/2aj/Pzv0mR72ApQOTXP96tc/5ma+HouvjPePfve4Rv+slO3LP6rVO+0mXl5dJlku6wdtZ+76vXIeL+ifkxu+oS+T4tz+Le/nc8/K9g+yJxiM3ymFEpU3IVp7vvIb7AWbRHQCMtNeNMs4zx8fHVavVkuGSfnDPkZERjY+P93U+S+o/gal60SsDx9PT0y5PnwvqSqWi4+NjbWxs6OTkJCkY3qCcEGAT+gZBSeH3voHZXNF7Ua1WNT4+rnq9rnq93sUwsNpgIdrf31er1dLR0ZHa7Xba4G6JPTo60ubmpra3t5M1nU04NTWl2dlZTU9Pa3R0tAvA8jxX6gFK7XZbtVpN09PTGhsbS8CHft1m0btLom3Dw8NpfE5OTpLyfXl5qZOTE7VaLZ2enqrT6SSPx8DAgI6PjxN4RDnjfwAkipuk5H2uVCpqNps6ODhIHg33zLJuolfKPSuAUO4Pg/O14syW+zLeR0dHOjw81PHxsTqdjkZGRtJaq9VqGhkZSfOGV8eNBHi4mCeEA+PqHrgiJfcuiH5Bbs1HWLiSSh8AbreRG02cHGDFF8+M4NF/6wq8zw1CC6U2KjRuOYciX4nAsMggExVrVy6iEGYt9xM48iwE9OTkpCYmJjQxMZG89dHD64Yv96S7h76XQtoLdPi6x4hzfHyc5td5tD8zGtCKnudrI7eect4v/79IER8aGkpy4OzsLFnlJyYmdHh4mBS8fpCPgY8FvMCNHTkg7WPjY+LjlVM+4+c+574HJN1QUIv2lfOQHHBw/gKxP5wPxN+5/uB7LvLvuDdpdxyjflAO/ECRn+fAT/x9NC76vfw3cW34d7lr3TBY1J5egLsXyPe+xvmPc9HrebGfRdf0k3wtOZCLMptoK4+AAZjRZ3iPAz90xqGhoRSZ4XpVBPfwA57P89CfeA6/dyeJOzeICnP5MTo62uW197UIyBsfH0/9Q39HzrkO615QACegkTbQF8bp+Pj4TUxpSXdI0UCCg6uIXhk4bm5udi1m3PAsrFarpSdPniShEcNCcwoci5ZwKEnJpX58fNwFTvHSnZ+f6+DgQLu7u2o2mzo5OdHY2JgePHigDz74IClakro2++XlpQ4ODlLoUrPZ1NDQkBYWFjQ9PZ0UVklqtVr68Y9/rL/0l/6Stra2UvjXxMSE3nvvPX33u9/V5OSkRkdHkxft8vKyK0xzcHCwi8lWq1XV63U1Gg0NDQ3p8PCwa6PSXreE9YPY3B52iPXZBcvx8XEKQeh0rsJWUWwHBgZSiBqeQe7h447HFZAgKSlzMGCAGmFveBOZN36HYgzYm5ycVKPR0MTERGKurqzgGXSwSb8Aju12O4XqAvYJu3IjxuXlpY6OjrrCJKvVqk5PT1OIlXulWG/Mez/IAa3UDfQiyHBheXFxkVVkoCjUcl6sCFBdMAJgaYN7AtljMQyZ/x00sq8i+VqN5GPB/1GBLVLEvF9uicVAgdC8a8L4MT4+rqmpKc3MzGh6elrT09NqNBrJmOHeR9rNGieE3MOw6U+OevEWN6AxJ75+Ihhn7nJKcc7AENelf+byxX8bw6YjkOKZDrCxymMAOzo60tnZWd/2Y857zl5jLCD3Mvt45owxPo4546uTPzuOPWPk+8CV5Ageud7bgKLqezwHTpgD96Ywf6wZv97bE/vrazmuozdBOa8wdBsPLQJfkXrNqf8uzn8EJUVUtK58Pfg9bgOG8TcOpNy75s+5rY13Tc4rfC4icPR2sR9Yn2608KgB7gNYxAExNjamsbGxZJx1uckYeAoMMjCOpz9zaGgo6ZYuM7lXtVpNqQU8G97tvFxSai9tcx2cyLPBwUHVajUNDg6mSDM8kug1Lq+5Nw6Fkr4+lNOF7tzj2Gw2E1MYGBhQrVbT1NSURkZGNDExocvLS7VarawXAUK40jgAANaMi4sL7e3t6ZNPPtH29nbKY5udnU0bBG/F1taWfvSjH2lnZ0f1el2VSkVLS0s6PT1VtVpNgJQQw8HBQR0fH+vzzz/XL//yL+vk5ET37t3T3Nxcej4bfXd3V1tbW/r000+1trbWNQ61Wk0ff/yxhoeHVavVknIM83AFCA8KAA2AXGR9lXqHt9wFHR8fJ2bAWBIDj3JF7hjzdHh4qGazmbwh5BQeHx/rxz/+sf7aX/trGh0d1QcffKCJiYkuxoVw4l6MA5YxGPHFxUXyAtO+Tucqjp7f47mEAbIe+L0L1ouLCx0cHGh/f1/Hx8eJ+dFfFGy34qEY1Wq1rrV6eHioo6OjBFyZn1arpVqtpnq9rrGxsS5AEwHLXZMrpexJ1pUrLlHxjxQVCYSaW7T9vlEYIqB5pq95D4lD4QSI45n3fvgcSuoKG+Z5CCy3zEblQOoOLfMw3thnnuNj5EAm3veuyUN9Go1GAox4HcfHxzUyMnIj38XzPmN+jvfd+xv/d3Ig5997PlwcH8YZQBANgzlPor/Tp6Ix9/mJ+TtxXlwxa7fbmpiY0PT0dFcOKMbIfpCvx+iZyCnu3te4p3xfx/8dTOXIvSVR4S261vmff+5zRdv43t/jODjwk5SUU3g6PCYa+uhrVGg8eiOuz7umeN8ir6rLtkhxXcfr49pwY/Ft/eu1ror6kPutU+Sx8Te5PuYAp9Q9V/H7fs1ZjoiOyhm+ioAj65LPHAjHPT08PKzJyUkNDw+niAIPT3XdIoJWgKWDQK9TAE/1+7nMZF/DC0dHRzUxMZF0S48+AWxKSg4fl9cYytHH8EiyZ91L62AXfZ17DA0NaX19/c1Mbkl9ISLqetErA0cWNaGEADks5cPDw6rX610u9VzDTk5OUphgrVZTo9HQzMyMJiYm1Gq1tLGxoVarpbW1tbR4a7WaZmZmNDMzo7GxMU1NTWlra0vNZlNPnz5NoVSuUPE7wiilK2/X8+fP9eTJE42Ojuq9995TvV7XxMRE18Zwq40ToaaNRiNZbiR1MSb66QosDAnhHPPjoDfBWAk5pr14YldXV7W7u5uANwUL8OAeHR1pZ2dHlUpF9XpdIyMjOj4+1l/4C39Bv/RLv6SlpSVNT0/rwYMHkpRi8GG8eP3Gx8cTk2PMYWRcAzAEWEjX6w5DgHTNeD18jfu0Wi3t7e2p2Wzq/Pw8eY9cqYbpSUq/oS14Hc/Pz9VsNrW3t5c2FUybNYBSC+NmXoss+XdBMbeTOWOdAdxYb64cuGfEKa5J1m0OgHItAo42MJ94f9zzipfUc2QjyKF9Pm4+T6zZavXKexqL+9BOhFrMdXEhDkXw4mPMffplACAMiEJPFHvCuOI5lijYeNIIm2asMKLkgEiR4u2AKwK7IrD3MmC66Jm5ZxR9DjH/vQwF0k2P28TEhNrtto6Pj7Wzs6P9/f2+7UdXsCR15YXSh6hA+lgyZxEk8lvu655CB+i+d/13RfMZ75ULNYbiGCPHvA1xbfg8ea4w/CRGHTAGca/lvGv9pF7rOierIyCPazqOd7xXDpjf1j4HsK8KqItAYBG/iJ/7nBeBx6Lnvmwb74JivQXfC5FiP3n3l0cydDpXoakY9uLa9KiZuN89EgsDtnStQ0jX6QtcL13rHB7ZVa1eO0gwMBKFgh7gKSGVSiXtQ/rlkTXxc/6Xrvc9bXCvI/K+pK8fRaPPbekcrwwcx8bGVKlUkjKPlVm6Vn58sfrGYdFRoIZ8OryWs7OzyT1Oku3h4aEuLi40MzMj6cqCNDU1pUajkbycVNIbGxtLeYQOPACOrVZLh4eHCZROTU3p8ePH+uijj7S4uJjy3NgotAMv0sDAgGZnZ/Xhhx/qZ37mZ/TOO+9ofHw89dVjv50BubD3qpCeYBzJAVA/6PT0NDGugYGBVKl0a2tL6+vrOjk50fDwsObm5lJo7fDwsM7Pz7W3t5fmcnR0VIeHh9rZ2dHa2lryQAJgYLIoIijg5DZ6KKNbrWCaLnw9j9AZcFSUJKU1QQXby8vLFP8fww1ZK35/vJN8RrgrlSTb7XbylNPe6GGB+gU2pGvjhIfYOoBkXDx/yK/JWYFd6Wb9MRc5wJ3zLPFMBJ8Dx6iwwiecXFH0NnqITy8gw/8UMIqhlDnFJYInf/5tHp4vS0QhTExMqF6va2pqKhlWHDSyVzCgkHvt+ddYv90zHJW1CCZylnjGscgTy1hFAFoETHspuEV/x7nJKbRuoPM15SHt8DDG9LZQnNcl9h3jguKVC+ON4w/FtZ+bP/6O8xuBpe8V6dpbFtvBWPK355Z73yL4ieHrzh/43L0i/n0OQEQvZi8vV3z2XVIO1PhzI5/PrddoFIkg7GWMxblr4pjFz3NGOJ+3orHNGRFjn/xvX3svAx4jsHoTRgAMzA7W3MBcZJzIGd1iZADvrgPze4+4i4DRjX9uOOA98luu9bBSiHah62IU9zBbbxf7eWBgIEWweAoAhl5vQwylj2kmyB+vE1HS15dw7PWiVwaOeIi6bmIWimjV8b9xnbO4RkZGupRaX4wAh/Pzc+3v72tjYyPlPHrYlHsXT09Pk/U9xmZDOzs72t7eVq1W08OHD/Xxxx/r8ePHCXTybO7tG3lyclLf+c539PM///P63ve+p7m5OUnqsuTkyIVILhzIFe9ejP0uya28UQA7o5KUQhhcAd/Z2dHBwYFGR0d1dHSkxcVFfe9739PMzEwqFuRlqIsYCmMGkB4aGlKtVktriXagEHt4rSeCu5eF/uHVvry81NjYmBqNhsbGxtTpXIW+OjOn6iK/lbqL7ESAgrVxampK09PTXWPEGOY8BXdNrsREAOZjwT5jTH2s2JturPB9QH8YL9/beGbZcwigCDpcuXCF3/ewC7xcPxz0QN5m3l0hoQ20L34fyflYVPRiu+6SWD+AR0LBo8cxKi7SNZ+IClFUgHKKbA5s517x+wh6copyBAm3/f1lKCp9sd/k4XuuTj/J++/yzb/3+fLP45qmXxD3dSNQXLNODkDdU+H38v/Z6z6G0YDj98wpp+7hj893T2k0XEWjW+7Z/vx+GnNiG5xfFoEsH/8io5y/F/0u6gFxn+TmGUImuCHA/871zf/u9Qy/X24ccsA/t+f9t/0Ej/CsGJHSa6zj5+wt/nd9xkGidL2fPPwU/oPeUqlUUgqQRyP485Gt0rWuxvdRxlYq3YZv5/uMtRsdpeujQlyf8rGJYDcajdDjvbp2zNMs6etDcQ/feagqyrErwzw0hq74d2wS8uZcEWVBn52dJcWWzSEphRhtb2+nqngx/47QRApE4NHEbV+tVlOey8DAgB49eqR3331X77zzjsbGxm6E1lJ9dWdnR+fn51pcXNR3vvMd/epf/av1gx/8QEtLSxoYGLiBzF0hzylGLkziOPrE3Wb9+7JEkSG3jE9MTGh+fl7S1ZgPDg6m6q+0lfxAPHnc591331WlclXkZnp6OlnA/FiUKMhgvh4nX6lUEvAEkHiYpAsAr6bqggjm6sehkCM7MjLSVVAGxuhew+gFqFavwrFrtVpXnhxFTBqNRhcjR3F1BtyvoioOFmMoqiuqUrfQ9vBQ2ktBIgeIkAshxsQNCRhvWEsucHg264e28bucshgFe6QcEK9UKilUhnGPHsdc+LyPkwvPqNjk2nlX5IYQqvIR7UD4kef0+V7wI2IYB2+/f1bkBYkAMAcGe31WdG+enXuPfxdRFGo55Y7/HTQCVLwirBsD+0VR6XRlDoqKWDSO8LfLk2jA8D74+HN/vx9jEtvic+b/FwE4/9v7E/viwNN5lOsMfr+i0GOuiYp5v2WkG81ie+ljbq5yey+2MQd2bwP8vsfiGPkz4jry38XnxWf1AqM5fhH7UWRYiL/3seXaflEsqBUNmb0MIFJxhIx0bWB1kOhedfoXPXjxeegfcZ9zH68LgS7B/SLQi/fm+hh6jqESueJyz/eqA0gH0P45YaslfTOoLx5Hzy2TbsbGusLM99H6i2vflR8WpHTtJfB8ypOTkxuFSaTrw0uxAp2enurg4EDb29spDJN8vtPTU42OjmppaUnDw8NaWFhIxXJ8c2JRIbSVQji/7tf9On300UeamJhQpVK5AWoi4ywiV869qAjjFS1G/aCZmZk0l1i9Jicnde/ePdVqtZS/ipePPo6OjiaQv7m5qcnJST18+FCLi4tp7jiixIFDBFZRISJ815PDPdyO/31MUEpcMeF7PJc8D8+NM0cHR1RJhSlSXazdbqcKY/V6PXlGh4eHuype+pzHYzpygvuuKCozPJc1RhI9Y+pCnzYzftHCGhV91qsbfAD9PjceohrnBeNSzvL7MqFnOWWNv1Gk4Qc+z6xntw47T/K2M2e5ogb9AhzxcGqEOuvWD5Bm3AHhCO5eIXS3AbtewKEIaMb7FwHCyMNyPK1IyY7X5F7ugfUXhsTDw8MUhs9Zs2+SfO1EEEC/pG5PIr8rul98j/Pi30eg7b/JGcr8e+g2UBCvcyXbgUL0SrjSWwSIciDM57tf5O3uZbDwvtwG/nLrPM6nf++/j4aC3LP8N0V6mt+j6HN/RfDqvCIaEOK9c2OUW4/9IgeMRSkIReAx8nxkpYN036vRiObyzY3g3Af9ij0Yw0uj95DPvH25Pe33cJmGTlCpVLpAY+Q70Wjk8++8M4LJfs9lSW+GOp2bdV0ivVZxHD+eQMon/Hs4KdfgcXBrhaSUjwKgdIX09PRUR0dHGh8fTx5GFqtbYEiCbrVa2tzcTIVupqenU3slaWJiQrOzs6k/R0dHXYABYVGpXBVwefz4se7du6cf/OAH+v73v696vZ68mQxyrw3M/1CMa88B7DexCRuNhi4vL9OxJ5eXV+cuNhoNjY+Pp4VDtUa8dM7M8PwODg6mHMhq9eq4Ec+vcyBCH52JuxeIeeBvVw6jQkW7vCAFYw/Y41wiNwgQZkFbONrl5OQkeV7xeuIRGxkZ0dTUVIrlJ/R1fHy8KwfA+9IvsOjkANANLT7mRQoCY+negajgOCF8GV+K4XAfBJQn2TM2eBcxELiA80IvrlQ4n/D1kWuf/++CEHA1PDzcpcT6euJ6N1jRZtaOh0T3gzjihheefPequ8LmXvIYghv3gr+iJyqOZS8QklNwnXKKsd87pzD2Ut6KrikCip5X1Ol0Emjc29vT7u6uNjY2tL+/n4776ddh1UXKdg6g+z7o5cGJno+czInyJMoRBxN+Lf9HkFQE8HsZDWJ/eKFcuncmKubwkNj/3JqI4dr9IDdYOvk6lG6er5kzlBSBw9w+ydFtRoJIuXvfBmx7tYH+xr7wcn4a+QnvRYarflI0dNK+23hO1Gedt8WIstgv3/eSboBNb4M7SNiDEXRG8vvyuzjmUcd0YzzA0Y2Rcb9HmZFri+uDJV2T617STYPD207oY73olbUgrA6ABuna1e7FXggRkq69TQ4eudbP7et0OqkQDffxoiQUvontQZFvt9tqNptaX1/XwsJCsqrA9IaHh1MFrMPDQ7Vara6zaaISjvdtcHBQCwsLGh0d7Sp77xvKBWTRuPm4OANBmHqoQS9l/y5oeHhYOzs7Wl1d1erqqiRpYWFB9Xq96/xJQoLpGwCAMWJep6amVK/Xu65zJcXHiM9cufBjOxhXr0jINQ50yGl1YwRjGi2Bnc61V5O1y72Pjo6SQokCT3u9wivFmFhXeGIBOVFhdGHVL+J50WLpDB2gx9+Mo695B9fcw/vDESi8qGzrwi/mSuCtHhgY0Pn5uY6OjtJ694PjMQ5hjHEjg68HBw28vAJkLlTe1xpr1teRC3i3TEtKewBPe66w0l3R3NycxsfHNTc3p9nZWU1PT6tWqyXe5OQKdxTeRcrMbUp+pNwznYqAewR2/n38Lgf+4hzHz5lvz8/2sGfPiSZqZGdnR7u7u9re3tbu7q4ODg50cXGhRqPxqtN0K/l+zwEfSTfmJKe0Rz7i1+fG1UEXeydajSOwifwS2V7UZn+P9ykCJW7QcN1Bug5PdY9K0X1yY/MyEQqvS1HBj2DA2+W8M3dd0d9xfeTkflTg/bc5Rb8IEEXQ5+TP8/b3MkIUAcA4L9GpUMSL+kW5Nnr7ikBz/DzOr1cV9dBV1/M8CiQ3NnxGOz2M1sfKo7S8bVCMGOIzbzvP4D4xbxFyh0zOUxv76c98E/P5tlO1Wk06JE6Lo6MjtVqtW/MG3xZCV+5Fr6wFxfPW2u12Orw9AilyFV3RcSKs9PDwsMsjyP2kqw2AYklIqSsSbIKhoSGdnZ2lSRoaGtLMzIympqaSsjwwMKB6vZ5Clw4PD9NhqdVqtavy3cDAQDpPDYXY8yCj4PdBl25a3yND8Wu8L0XC567p/PxcGxsb+uKLL7S2tpY8Kg6kIe8nyv7AwEDKwYKRslEclCFUATYeqsi4o+T4+ikCjgAV2kOFXl9rkm4oTa5sMo+ucFYqlZRjhnU83qNSqaS8TZQggIUfOh6FalQG7pKKhHdchzlB6euO76RuIemCzuceD54LSAdwKO4O8jEAcRB7q9VKL4AjIN0NCzG0MAc0vOiG/++AMgpi+uogzL2q3ItCW/0EjktLSxofH9fS0pIePXqkxcVFjY+Pp7aiHLgS4Lm9zF8OKPKeU/75/2XAI5RTTPnc10T8rgj85f7PvWIVP8+t9WrV0vU5jkdHR8njuLe3p8PDw1SJ9uHDh681V70oF+bl70WKc9yjUJGCznXOY6OyH++Vm4/cHDoocf7l7/H723id95O1Ac/2NI/o4SkCkb6e+kkvC2hz8jv+LvceAWruuf7Od7m54Lo4LjlglLuv63BRFvu9fL15H91jFsctZ1x4U0AjgsYceIvjzbW+nr0vDqxcN4FyDgLfr1J3hWM3+nBvj8CLcx33RpxzN5y7gcZ15jj+njvpfYzhrOg+/lza8yairN5mGhkZ0cLCgh4/fqzp6Wmdn5/r2bNn+vzzz7uAYz91wi9LOd030itrQXh4XGnx8C7fhLn8LhYwRzgcHBwk0Dg4OJiOicAzSagZnsfcmWV+Vlync1XNc2ZmRrOzs+kML0IaJycnNTc3p52dHe3s7CTgy9l7kYHwLHLbXFA7U5C6GUEUcH7PSH7tbdbXuyKOz9jY2OgqIuS5np6ADSOhbXhHyMeCXHlA2YUBwmB5B4Q4U4IBs3h9PGBarD/Whhd1YbycQUOunLoA8LAN+uNrmb4AdvGg4SWnf36fOOf9YhIe5lKk0MXk/LhO2Tc5pu/z4ULNlXi8htyXeaQYVadzVcXWQSIg8ujoKHn/OTsUhdJBBn10it5HB4oRNPq1Lkw9lIcxjGEmXoGuX4Jxfn5etVpNCwsLWlxc1MzMTNbTyjjklOoc5a7JARv/u9daLTJA+D6JoeBcH0GgpzxEr2EOHLKmHCzGomsYK+ANJycn2tvbS8W8kB/9ynMsMuTE7+K8+PrMXVsEGnMKZa+2+b197+cAbg4cFAGOou88EiT219uADI+g2fsb/+e3/aL4zNxeYh74Lhpy4n1y83SbbCh6brzGwaQb/3JjGucsgtHcvBUZGOL9I6jNreOX4TV3Qbk1E/dar9/4b33sXBf0qAcHpr5Goy5CZE4E6i6TfP9F0NlrjiNojLoM33k/PU3Dn+1pEq6H+bg4H/pJJ4Djd77zHb3//vs6OTnRr/zKrySnlqe4vc1058ARYIFC5dYMj7l2r5AvZO5xeHio7e1tHRwcSFLXYe9sTPfiAByxKvOssbGxdBD90dGRpqam9PDhQy0tLWliYiJ5CmHunL3n3/nRDGwQPJAeQuuWF9/QkXG+jPCOGzkqwLkwgruk9fV17e/vS5ImJyc1PT2dvL0eKhgZVaVy5ZkDmA8NDWl8fDzltHGth4+6AkxVVvcCOXOizx4KHH8vXTNexi5nqY7j5wVd+B3rwgGyM1eegxED0IQxwpVjX7OSuhSKflnGex24G4F/nBPIhRvGHtrsQMwFm5fjpvCMAwfGACWfo1F48T+AkmgCDEDOU+IcuRLibQewRi+o7zM8xG5NjSE5fm9JXXPfL4/jxMREOssxVrjrxTMi5b7LKXzxflHZySlWRcqv3yMCPgeFhIgTkQDgix7D28Cjg0jWG6ARgUdUAOfTHhwcJPnRS2n8suTyoUhZKwKB0TiQ8wr4b4qUQG+LGzeLnuveegdAUaHl3e9fJKNynivaGqNP2PO9jKYOVKDoObpLyoGhouuip8d5x8so1HF8cn3KAc7cfMbf+DqEnMfFEMTI5/0+kZcW8YgcOL2trf1WpCOfczCXa2fOCAIByLxQJPeGB/n+jbqOFz1DnkRDZpyfTqeTjGMYsUkVcZ3YdbacHuT9l7rPw0a+xbzQXBQD8t4jg/y+P6k0ODiosbExLS4u6sMPP1S1elUfAYPlF198oePj46+6mV+aXlkLarfbqeNsIEnpUPSBgYFUqAbwBTNy4IgCiZdBuvbuodw7OEBBiIKf8EJ+s7CwoG9961taXFzsqiTlCjGex6mpKe3t7eno6CiFXiJs2RRuBXUgEKlIuOSsbPydY8QRZPeL1tfXdXFxkQ4aJ6yXsSREOCoUjBMvFLTY/5xgkq6VGRQ9wgC9mI507ekCvDmD4j5ucXOGGz1VkaKgiNY9nxdnpu5tdK9HvLeHnbjVsR80Ojra9X9OoXQDj1ccjeRA2v/3vDH2kiv7PjfsU/Y+1ZApKEVOM+8o/YQPkjsSgWP0gEfgiHHIw8k9fMjHJBol3NKaWw9+bqiHud4l0RdyQQkdd+NZbh3lwGQRCIzKk49vDmj7tdESHgE2bXPgh8GAFx5mDAYoQvACzx/36ISikNX4GYYcxlO6rsjNMU3x3LS7ppx3rZdS7Lw18lrnqdw3zk8O1EUAGD1ice8wnn4/5jz2rQgIxc/iPorGG57t/fDqwN7OXtQvwFEExCO5ku57phfwiFRk2Ol1TVwjOe9wbKcDiZxR1tvrgIH58P4WtSXXpiIDQOTN/aC4/txI4+ApJxNzbed6yGWTdK2nwrv5zeXlVcX4sbGxdE4vzoioi+T+v7y8TEbWi4uLdGQTn7vhLO6rnDxnTFzXdvBYlNvo65v2ubGkn7z160DV6lXU2vj4eCqeeHl5mWq0HB8f64svvkjXFxmK3nZ6ZeBIqFClUuk6P8yPU8h5mwgBdMszirkr/CxCV9QrlUpSLFAm2KjHx8epsEmtVtPDhw/17rvvanp6OlljHHig1DYaDS0uLqbKe6enpxofH7+xAaKwLJrkuEEjo+ca/o/WUveE+P36BSBbrZYqlYomJyfVaDQSaPRCQZK6ADdGAc4vpKKo96lIYNJXrofZwUyxOvNbD5/MhVtEZcRzZXIWdG8DBMjz0ERvuzNeQmNhlh7y2ul0bjBbfhc9CXdNAEdXHCO48H7G8YyMPo6z5zIgFON4sP958fnp6amazaYODg7SUTo5rxP3ZRzhLy7o4SU5K6iHCfF3BEhQBD+AyJwSw/P88PhqtdqXJHfafXx8rGazmar7OlCP7SsCg64c+WcethtBYw445jxg0UProCeCvLOzs3SkEXnl5JYDHjEa8PLiW96u2O4IfP16ByjuhfZ10S/qBThyCrQrZs5n3fIPebujYpjjYeyhHKDw/ewV0qPRi70XjQ/x3hGsRnDKXPAsD2XOrTHf496eN0Uezp8jV7x9TKIMzK0HH8fcZ773igCWj78DfdcbouHB9ZCXuSYCj+h5yo1JDsh6H/1a73e/yNNk4hh7W6IxLAe4vD+sZedd6MAuM9CZKpWraLfx8fFUoBEHS2yLrx/XjR1EYlhst9upirmH7kfDufNyn2Pn6S5fIy/JyQSn3Lj+JBD6CjQ2NqalpSU9ePAgnRt+enqq9957T/v7+1pfX0/pYV9neq1QVRZHzGvChR4XZqVSSQv9+Pi461oWqisxAErAI4sYhdOB4/7+vjY3N3V6eqqFhQU9evRI8/PzKeyV57jFvtPpaGJiQnNzc8njyIZz0OJWKQRfjnk6o4kbp2hD+WcRlOaY110TB87GYiAOFKrVagKHKHQc2TE1NXUjfI8NlAttcIDHuiBUg/xStwC64PSwZ7+fX8Na8jnOCa4ckIiWtciwo4VteHi4KyzFhYUzXta1j+td09jYmKTrxHZJNxRoH0c3BPi8xPWZExDeB593PMcUqCKc+OjoKOUS+77Hy+jtpI0olc5XJGWFus9n3LOxbxEQxvXDdVHJZ16Z24GBgb4AR8Kb4HGEzzPurO2oXBaBwRw4dCXegfZtypRboiP5Xi9Sfr0trmwBHvFEHh0d3TAo+O/9nnF9577zV1wT/aLIc6JMiP1x/suad9knXctWv4fvmejZyyl9/O18nqgJPyInemxjPrTzT4/G6QWQ6GsMYYacx6IE49mP4bqxbzkv4F1QTub0MgpEPlIEznIgxj+P1+SMDVI3sHNw3ws8RsqtVX+Wv/O9z3mOX8RnOp+O7X8T4NGBfewb7Y0G30g+N4w3FZs5xsv1Ug9FJYUHIOnHOjlojEYhHzdoYGAgGYq9vQBU38/OP2m/6+45sOg8BDnhupHLEyeXNz9pHse4XhYXF/Xd735X3/72t5NjqlaraXp6Wvfv39f9+/f19OlTPX/+/I3Io37RKwNHB1JeiACGj7KMhRyGwuYh/LFaraaKnJ1O50bSqLvQIRQNR/iEHs3Pz+vDDz/UO++8o6mpqeTBylkGpSvgNDExoUajkTZ/q9VSu32d59VLqc6F7bgy55RTqlzwRwtdPwEjND09rVarpU7n6rwzL/xB3/EGDg0NJe8AnmO8zQApH6toKc/1FwEE4/KxdGFcZHkGdEjXilbOShwtbTmwlAOnPBeFxz2g7qmKANdBI/frV3ijdH3YvSv2zsRZk74PGeecRy6OEX3lO/YUAID5Zn+Tp9jpdHR0dJSOQCAknd8SosjYsWbcgOPjGZWUqHC55ymCFp9XSTcAkO83B98eRtRvarVaOjs708DAgI6OjjQ4OKizszMdHh4m0Br72yuf0K9xb5tf40XJHBxWq9V05EytVkt73tMHorLBGLkXy/c651GyTg4PD9Nn8Xeez54Dh1FBiUphBGdvUjhHYBPbRdtchkWF2+chtj23vlHwcqAxtoN7MN4YKtzoR8h4zJ+OoMH5ihvXXMn2NkZPowNZxgRjHOuR++RATj/3pcv3HGj09tOeHHkf+Z3PX7ynv+fIv3MvGHzZgYADopzXO7YzAhbvdw4A54BgrzGJOsCboNw4xz2S08G8zdwHuri40NHRUYqe8Og5eCYeRYAiOlPk5b4WfCz92d4m13HcgOMAGP7hey3KcHSBHNjP6ab+Od+5bvCTSt7/ubk5ffe739X3v/99PXr0SGNjY+p0OpqcnNTMzIwWFhb08OFDPXv2TEdHR9rd3f0KW/7l6LUrPcDkWYA5z5UzLN8cgA2u53B5yBmgh+CQH+O5LFQxGh4e1ve//309fvw4WWVyghcCIDYaDR0eHmptbU37+/u6vLxMB9jDjIt+H4WLb1CeX2RddKZQZOni2n7Q3NychoaG0tl6jAfeP0mpmqSkLmMAc+9Harj3iLmDXJmA/PuchyiCG2dsrhRyPX2IYVI8Oye0XKDTPgejzlCdWVYq3eHZ3CsnEFkjt4U+vS5xX1cM3JLq/fY+OaD19enGFlcOo3LuhaMAE6wBnuvHbVxeXqacWDxrKJKAB+aKMUV59f2RU7bcyxa9mDllzP9m7eLxi7/hvh4+2w/a2dnR0NBQGk/WI2vajVnRshy9dPQHQAB4dIDtlYg5aoQXIVX1ej0V7SHywAsnsNb8fw9bvri4UK1WS58dHh6qXq9rf3+/60ikWq2m0dHRrsqa3m4HJFGZ6aXoxXnMXd9PinwmeoNpX+Q1tL9I8fbf+56JsiQCHn7PWiHvk8INGBcGBgaS0kP7oyGQ50dPl/fXQaHvy9jXaLAEuBaBjAiy3wTl1k3kKbm5c7nk/+dAQe6ZUaGP3zl/gtiP8VzdIo9g7FMECEWg0Hllrn23UREov0vyGgRxzN3T7XLU5UA00LTb7XQ8HClOlUolFXes1Wpd/MxBoxtti6hIT4R6zYfzZBwnLheZfz+VID7bn9tr3/ne9QienzSiz41GQ9///vf10z/903r06FFKy6tUKpqYmNDl5aUODg50//59LS0taW1t7ScLOOIxlJQsKHicsBS6xwmF7Pz8PCmIeCmdmUYvFRvBN5sXOYBRjo2N6d69e5qdnU3uYZRYCEULAQegq1avch1PTk60tbWlo6MjVatVTUxMdBV9cfLN4eAxJxB6WcWdaeVAc78VnEajocHBQY2Pj6vdbmt8fLyrOI4zIgwEAwMDOjs7097eniRpamoqFRRyJuXKTAwpc4qgrcjyxXdOcaydgbqAjB6KCEijoJCUADR/e3tRyg8PDxPo9mM4otL2JpSbGK6VA7G0wz14zFFcu1HRZr/4OLgHC+XEj1hx4EjxqYGBq8JKFCxB4YlhQi7YXODm9pC3L3rZipQa/13MoeNz39Pwmn4qOdvb2xoZGUntBGy7V5B25XJFvfCLeyM9nNqtz/SVRP6pqSlJ16G5/M33AEc35jEWzvMxHKC4sDbPz88TAB0fH9fh4aEmJyd1cHCgWq2WKsqicA0ODurg4KDLsJEzCtymrPK7HK/oB0WwE/em77toHIgGG9ob17GPB8/o9Uy/L2PIesHz7+sJee38K3pJeA48NJLv5wgae/GrODa+v+Nc9zIMf1nKgbTcd3Et5gC285O4bnMAzj/rBRy5j+9z9jVGN16eRhGNLnGNSXlQVdTOKDuK9lfu837vR6I4coDL9Q+89m70jukPGOYocoIujKGNF4e/Y1h3+RVlbkwliIbcIuDoBrsY/YF+jRx2w1KlUklG2yJ9rOhzf77/7/r0Twp49PUzNDSkd999Vz/zMz+jjz/+WFNTU108fGRkRNPT0zo+Pta9e/eSY8rp6zZ2rwwcOZfNY7bxIOCFimFVMDevmupKhnStoLtC75tOUlc+hpc9bjQaevDggZaWljQ5OZnuw2+oaDQwMJDCCGCIVD+anJzU1tZWKtbAc1GAouUQyn3mzDNnuZN0Q5nhWmcA/VRUBwcHNTU1pVqtpna7nSxmVDjl+X5umnuJKpVKl2cXJuaKLeMeAbIrc65MuOcipyjy20jRMkib/F7+LAc+rDFnAhQlIQ/Uf0dOVrPZTNXTUHxj/iBz22/w6ILPBQvPj9ZmPodQ8KPwd2ETvQIOThCQKKTcA49Sq9VKFjjnB16VFQCJB4R59DxaH9MikM54xDXm60JSWsMufD2fg/s6+GF/9mMut7a2NDg4qFarpWazmQofRHAAkI2FEHi5B8JBZDSekC+DxxHwOD4+nir/1Wq1VMhhfHz8hoLic+HWblfquc5zfaiEzQvQ6M8FXAJq6AfzAJ9xr2Rsl88j1E8lNVJOMc8BiBgREdcsFL2VrE+e45Wpc/10BTgaHzhz9eTkJI0lnpLoAfT7xee4Ms6+iWkFOaDp4xPXauRb/srd6y7J+1zUV6iXsSDOP9cXgdK4v7wtcayILvAiRy67clS0X2I7fT3GfkIO7F9nf/VzT7I/PHItUuyb66+sXT/yBwdGp9NJRmZ0gImJia7oCfRhN4y02+2us2c9QsMN7j6mzlc9BxqjQPQuez98n9KOmAPZS6+KcjW3Rt6EnvO2UqPR0HvvvaePP/5Y8/PzCds4jY6OanZ2VnNzc5qenu46weHrSK8dqkpy8OXlZQqxYlPBxFA2csqmn7kldTMf6dry7Z5IvgcM4iGs1+uan59Xo9HQ0NBQV1jZ4eGhnj9/roODA42Ojur+/fvJGtTpXIXlTExMaHZ2Nh0S3Wq1EkPw/LQc43GQl6PcxnThWCT8/Pt+EJY4BDkgEWZYqVRSlUfCMjwP1c/jg0m657nVaml7e1tHR0fZ0IjcGBCSx/euCMffRjAI5ZSU6MF1C5xX7x0cHEw5nbTLFTtyrzgTzpVuQip9rb4J8lAcPMMRDLlyGQ0aRcqPlC/8gPBzYxACCDDO/j88PNT+/r5arVZSSCmS02w21Wq1uoAPBiiEMusRr6bnbzC3hHVSmMcNHrTJwZcLOgBrDLOFonLOGPRDOD59+jTtITzYcexpjx93EefR3+m3e2Phq5OTk6lyK1WSG41Gspa7QQ2lyJWXnNWZsWdPxbZgnHLPI+DU88339vbUaDTUbDaT4YGjW9yzyfmfGAVdOYxe0ag49YtyCnYEiNJNL2EEihFcxHn2z/3YGQftsV0xjDkeU+KKJAbUqAC7vPB7FwEO6Wa6Qex3rzGEn/nedb7UL4pz4fPlANHnAYr9jHslgsIcT+7Fl2mfg0ZATa6tLl9fdh9424quo9/RUHQbQPM29QIud0HRAOF/+/9x/0jX8gKDqB8phdzA4MbxGjGXMUb4MGfHx8c6PDzsqjoOj2M/osfG/Tc8PKyxsbHElz2XMnqYIWSl7yfAqvOeHE/yOXJjpn+fkwvfZGK9UEX13Xff1f379zU1NdUVieFrqVar6f79+/rggw/0+eefa3d3V6urq1kd922nVwaOhLGwyEdHR9PB8ZeXlwlk4EmYmJjoCnVBcBFWFb0k/ncMYWNwLy8vtb+/r/39fbXbbU1PT6fStyzwgYGr8/b29vb04x//WJubm2o0GhoeHtb09HSqjNnpdFKeZKvV0unpqVqtVvKo5hhtbqMUgZui/91y3Ev49IsowMEzAGnuOUQgUQHx7OwsWdUqlUpipmNjYwk4+W85KoUKmtHz6KEaPt+uhPhv/HcORmGKgBoP2Ymh09yXMea3XvSHe7qFkPtisJCUPCbRwBGfE/++SwJk8Qy3SPLu3oJoyIkKH+PCGHiola9TrKaMC99j1MGwwNEL0lWoOYDCj+Qh9EdS1zphfqnmi0CEIbM/aY/nTUtX6wQhjJEA0IFhxMFjDoTlcj77Qbu7u6mNbjGORhH3pkZvJHMX+YYDYAfd7OV6va7p6WnNzMykYjhjY2PpjFcKPUQQFv/mf/fK5kDT5eWlxsbGkvLDWiG0uV6vq9FoaH9/X81mU/v7+zo4OEjAd3BwMIW+UtisUql0pT/kcrp6tfuuyPdQVLhcOYsePDcS5kBWNAy459wNZbm2cH/WwMXFRQpR9fxGqfv4Jfi7ezScR/fibzlAUDQXuXvE3+WAYz8p8sMoh5xH5gBQ1A9y4DECz5zhIDeGDmZ8HjG+OY+Hx8WICu+H71UIOe7tjv3LzVHsa1y3cQycz/eDiiKZisjnBfnoehB6L3nAk5OTmpycTJ5Gr77tRmQvUnZ6eqr9/X1tb29re3tbe3t7ajabqaq0GwIqlUpXYTEiRWq1WnoueZXj4+NdERwuD5nnHLjjObftYV+jEVS+qX35tlGtVtPCwkJKl/Mj6iR1raVqtar5+Xl997vfTXN+enqqra2tdH1uHt5GemXgeHR0lDwFAwMDKaSKhY6ihdKGkkAVThYwoW1sBjap5xZyXQwvu7y81Pb2tra2tjQ8PKyFhQUtLCwkbyNC+ujoSBsbG/rxj3+s58+fa25uTjMzM3rw4EE6xgBlhCqjm5ubKUSK8LmiibzNExapyJrDpvPKdl7ltB/E8z2cIVZn9DBV2kalVcLcPA/Lw4xhWJeXl13lqiUVghcXYt5OxsdDn9zTNTAw0FXcxPNpYhin35dnuser07ku9kL/XPFmHMj9ohR2znLM/f39rsm9DYxNfC5j7cpXXKveXlfUYqg2ygiClP2D8OFcVDxBHmoIOPTxj8oH73h9HSxVq9UbZc1d6fKcDuaLaq5RiWe9eGgQ8xvD6VyZ6NeexBjjwCt6Cbz9cd8UgSQHJozR2NiY5ufntbi4qPn5ec3Ozmp2djZ5HAklpcgD4xdD36JyGwFPvM77xtxRgAsg6Z5IciDr9boODw+TzAH0c0Yk4dHOd6LinvMY9YPc0BLnzI03RVQka1jncW5z+yd3PzfYOtiIZzjSNvaSh7bGKoyxbfG5kafkxj7XZldOfV1FL3I/qVc7c/srtz+K+hs99w6ecoA8Ku8xzw7+ytrze7p8c29RL2+fywmn2A7vf9F4xTUQ5bnLpX5QDN/0MWKt+3y4niHphnce+YDRjeJhXu/D5ZB7hAGezWZTm5ub2tzc1N7eXoqqwDiP3oVO4n1AJx4aGtL4+LgmJydVr9eTkc9BJGkHsTAPfNfnCj0izq/zrzi/yE/Xkd/E3vwqiT5Do6Ojmpub08LCghqNRjopwp0a0rUeOzExocePH+vw8FC7u7sp8grd6OsAGqXXAI5UxBsaGtLMzIwmJydVrVYTOMB9j/ePjYYnx+OyYRooD3FB5ywkME1C4ZaWlnTv3j1NT08nbygK7O7urtbX17W6uqpnz57p9PRUDx480KNHj1JIFjQyMpIs3Xt7eyks0S3tRSGrccP5/9HC56Aphgc4w/Kw3n4QYbj0izZ77hlWTOm6YAx5kYQIci19l5SYWq1WU7PZTP1xJhMFbZFghiJDj+SbOSr9PMuFFYq0C1DWqod5uMDjGYwFIc+xre5d6HfMf2Q0OeDtFtbYnygYYsifMz4MCB62w9zzXBeyKJwR3PbqC0YTACIgEWDh+XDwFPd+4j2mWit5uDkLKYIdwwOhvm4I4TsXutzzLunhw4eJP8Rw3Ag26Iev+ZwC5zzFjSMOHGdmZlKo1eTkZLJa4+Gl/x7SFNeNfyZd79WoYDsY5joML3zH/gO4Tk1NpcICzHFUyJxHRoNJHBOXJ9vb23c0e9cU+VAEjbTnNsDH5z5eEYz5eBcZNVyBj0osY+fz4c9kzfE7v1f04jjdxtPjWOTaC8hh70WgFZ911+TKcATsvcC9/52b19jvCDxz3re434jq4Agfl2X+3Lhu2P+xL0VrMdfPormGorEr9j3OoyvZ/SDP/fN2OFiNYB5w4PuFvHvkEiH2eBkddCJDPJ2n1WqlCIrt7W1tbGxoe3u7K10DWRl1Pq/n4V7MarWaQvwBj3ghCaHlFXVYjLA+J54aFNdFbk5zxoWfNBobG9P09LTm5uZUr9eT7uFjE/dXrVbTw4cP9d3vflcbGxva2dnR06dPky71daBXBo6VSiXlwFCF0yv/efieCzIPv+p0OknB5HdecdUV9KiYkzdFeNXS0lIKU8X70elcnSEHaNzd3U15i0+fPtXCwoImJia0uLjYFZIxPj6uubm5FB51eHiodrudium4ch03TbTUxDFzpsS9UEwdXFarVZ2enmpvby+1ux9Ur9dTaDF5iB7S4p4YLGz1el0zMzMaGxvT2dlZcrV7niBjMjk5mQrkkB9J/9wb6ZusyFvA2Lii4u10wY5wjUqaew59/KXrUFeUVQ83cFCJZdetgJGRQ73CkO+SXEmIiibtl7o9kT5OjIELbwfNDqzxHhO6jLU7Z3jA8/+yoJHnxeIpHtKIBRVgw/yT2+OhjB6KLl0L35j35yAMLzvf+/mkjGW/BOXS0lJaVx5pUVTsIGcdZgxz78wNfG52dlbz8/Oanp7u8vaRMxMjPaLXwsfPv4sA0Q1mMVcnvtyISJumpqa6PNe9CgLRz9w8RfAo9Rc4ssf8leMFOUDiSnVuP3v/kJe5a3MgzEGjn5PpBgpXgNlXrtR0Op1ktMlFcuSAYQ7w5a6hPR4Bw7qIHmQfs7umGGrt5H2Me/B1+YPz49xY+d7jOAhAh89XEWDlty4nHIz6mvP+xDbm+hoNj70MIby/SY+jtyk+W+oGRYw16VgYR2knui/RGPDKGLlFLQRqZuzv72t3d1dbW1va3t7Wzs6ODg4Oku6cM2yip7gBKupNXtiKCAwMgEdHR0k2Is+iUajT6dyoHJ8zJuTWVQRHPwkg0sdlbGxMi4uLevjwoRYWFlSr1ZKhlWtze3pwcFDT09N699139Z3vfCcV5dzY2OiqWfE20ysDRzx7KHUsZKpK4oXBooHHrlKpdAFB3yBRSS/yfkhKLl4m7J133tH4+HgCBoODVwdn7+/v69mzZ1pbW1Or1VKlcuWFfPbsmSYmJrqqG6FMDg8Pa25uLoXaHRwcJGbhAjoCEyjH9KMS4B43mBGMq9O5yvfa2dnRZ599pmfPnunw8FDT09OvMbW9aXJyMh1iC3D0fD0YbqfTSco8SmWnc3XUwt7enlqtVvpuYmIizS8FRw4PD7W3t5eYmNTtPUSh92q8Rd66IuHi68QLhjhzjMDRGTFFQqanp1O4ga87chsR1G6pi8LTBbJbefslGGPuWfRwuzfhNoqKq3/u4VFYUT3UzUPEUXJelWgvwA8eQ/gN4JECAOzFgYGB7GHxUQiyzwmnJ1TelbJqtZoAFgKglzJ1V0Q1NsBzkdfReWevMY6/8f6NjY1pZmZGs7Ozmpyc7Dq/0fNI3WDiVbEjYItVWx3oujGKPkVDIXPOXiSndXR09MaRI7xiG1zx6kX9Vm56Pd+BY26v5ebTjWis2wie3ACWU+5yIXN4G1lvMQzV158bHZhP59e5/kdgWAQaocgjHew6f48Kdb8oeut6ASnp5dZVHI8c4C4CZR5qDP/lSCNCEWMOOBSNwBEAOlCJBomXoZwRq2hd+3p+E0CjyIDBGo9tRUa4cdSjlZBLzis9rQgjFw4Octl2d3e1s7Ojra0t7e3tdUVQ5MbPxzG3JqTrHH7C+D0cdnx8vMuoe3l5mYrpYPSJffd7xzHM6btF13yTifEZGhrS0tKSPvjgA73//vuam5vrqvwuqUuvcOMo+Gl+fl7vv/++NjY2Ui2QnZ2dvnrg74peGTjeu3ev65gMQCPCBKWa8FQ2GgM3Ojqqer0uSam4zsjISLLwsFEi0ISwAE1OTurx48daXFxM+ZIwpJOTE62uruqzzz7T+vp6Vzja1taWRkdH9fjxYz18+DBtIoDG1NSU5ufnEyg6OTlJoWluqXNypSXHgHPeM/+fsTk6OtLa2pqePXum9fV1HR8f9y3UkTHHy0ZoHwwwKteM0+npqSQlpndycpIOLx8YGNDk5GT6GzA5OTmZCl8UKVZFAtOFjx/nEJmUg04/d9Lny4G9t4Nzdubn5zU5OdklCPDK7u/v6/j4OAmKXOl7B6gIXvrRr3mMRgW3ckn58CC/1hUTQj1R5PE0ER2AVROB6uHUnv/5OuT7nRe8g9wNLKkOlt3LGEufx/4RXo0SxfrOKYfMZXxOv+jevXtpnnztuoeAccqBxuhpchAe+dHo6GjKjSFkH0XIAaOHJUcvXwRuDtp8vUWvos8tUSLwHfc6OmCgGI6Dztz5lEUKfo76peTEkDWe5bJBugk63KDlyqPzwPicaBzKkYN/jhIil0pSypcipxjjns9VlGtFIND5bByPyHcjsPR3vsfzQ1v8N6zTfiqrEQRFwxSUU6wjAIjX+is3vnENeEEjTxGg5gAFCgHVDhiQRxE8xrHzfevrMTcW3ldvs+/1XH963aMflAM09NNlpNQNsL1wlHS9H2LlUjeg4GnEw0jU2O7urvb29lIUGzqUAziXU1EmFfE05tSLD8K3+Yx55N6+1qKR2dsSdZuisY1//ySAR0mamZnRhx9+qO9+97v61re+pUajcUPnw7DuIc5uKJ2YmNCDBw/08ccfp1xXIvnednpl4Dg3N3fDM+RCv1K5tpjzcovmyMhIyquB0bTbbR0cHEjqDgPxPCeoUrmqMjU1NZW8Q7SlUrnyEjSbTa2ururp06fa3d1NoIgjRLa2tvT06VOtra2lJGJoeHhYjUZDs7OzaaMfHByo0+l0FYqIFqGoyEWF3S3v7gHxENFms6kf/vCHevr0qYaHh1PxiufPn7/qNN1KWM+mpqY0NjaW2gJQ8EJDKICdTicl8Q4OXp0DSa6pWzwRGq6kHhwcJM9FbEe0dkcFivl1r4evN2dwMTQyKi2RsQ0PD3d5G0dHR9P1gEashpKS58s9Xt6OGK7nXs1+0OzsbJfSzhjlSvRH77Yr/e5d86MgMOgcHh52VUv14kQIpdcl1kAMWRwcHEwhlJ67gcClDRiFPLTOi9xEMOYhzr53c0qGh/gWKfF3QQsLC2ksnG/w7EjOJ/179zAXhYISIeBhv0SKuNKUK6LiRYTcYJZTcNgHcW4d+HnFXA/9jnPUyyOTAy8vCyDvmuJ409bc2DjlPA7+dy8vTRwL/ywafjz0DhCOfPacUeZiYGCgaw/kDKG9FMyi0MvYTueVXJPzbvuz+xnJEUFFDvC/DPiN5Hs7Kt7u5aQNbsTBqwRoxPhCkRYMMXjJYhg3z3Cjpz+/CNT6tbm2u8Lshj++i0AoB+T6Rb5eIviNwFfq9hLxe+7BfvGzE9GLPIT44OBAzWZTzWYzgUeOpfIiU171lHkmGiCmVOQMSq5HxlB9jxCIUVD01wtWRj2JZ9y213MGy28iOQ8fHh7WvXv39PHHH+ujjz7S4uJiisRjLvwcc8Yk5pTisHj06JE2Nze1sbGRAOTb7nV8ZW2WRFusgZ1O50YxkWhtckbP/6BvSSlPDsuygyks46urq5KuvUPT09NJyXeBc3x8rO3tba2vr6dzBD0Utt1uq9Vq6enTp/rxj3+ser2eDr2n/YRy7ezspBzAgYGBLotejgndZqkhj44+kuMJID06OtLq6qrW1tb08OFDNRoNLS4u9gU4wjgoUuRWTULDXOBEgADzI77+9PQ0nZXp4MML5XCdjxfeBQ+7yilZvQSXAx/aJl0LCs+pdLA0NDTUlVzOWXXck7W0vb2tdrudks+ZL9oGI3AlyUME+b8fVKvVuhi9hxZ6e3IeEB8LN2j4mLrCGcNT3ar9uoRAJkKBEEU84bz8jCw8h1Ggeais5+WhuEjd+cnRm+fjGNe+X9cPmpiYSM+LSo33Mb7n+CxtjV4+BwqMsxv1mMtYeRMwEQFj3KsR9ETFjP993fk68rbH+0Xlifv43PLMIuXe79kvigYwPottj4bHIvJ+uxIbQzVzQNQNPwBG5o91wb3g40dHR7q8vEzGBZ9Ljy4o4tP0N4Ynvkxf/Xe3GSWiQnvX5PzltnbT3l7t6QXM4vc83xVR32/IV3hm9Dz5uZwOPt3j5PIg8v7Y7wj0c6AXigavonGJn/drLiMvic+KRieXGcgmKPLRuH+o58DZjFRLbbVaKR2I3yHr/ExxKq5Wq9Wu3ERC8739Lsf8xToYGhpKQDbm67ph1n/rvMv3b5EBJMffv6nA0alWq+nRo0f6+OOP9fDhw8QnocvLqwryzWZTZ2dnSc92nkn0JRGO77zzjtbW1rS2tqadnZ2+1Ta5K3pl4OjudVciHCy6hdIXqtSt4LFQ2aQDAwNdpb8Jd8QjODg4qFqtlvITCVGVlLwOe3t7evbsmTY2NnR8fNxlgaEt5+fnWl9f16effprOX5mcnEztGhwcVL1e1+zsrLa3t1N4gVvOXCmPm82FrTNoxkRSlxLmXpBoGfYjDO6SoveTNsFwHHg4mIKp+n2wXElK4bXj4+PJu+BFThg7xom+Q660R48iz4lKha8nBKlv0ghKWa9+HpJ7k5mbVquVCgBNTk5qeno65XFCEYC48hT71g/ytRWVdWdS0VNAWJoLDe87gMFBY/Q6FSn0L0sIZtaJh6b6i0IEnh/tnlLALIov4bRewMc9qgBPlGdXLljbGDN8D9+mHH4Z8pD8XgK4CBRFXpTztjLGnqfD+kTJjGX+XQH1fSsVFyhxJTOGEbvVnlfM3XTDi3/OvZ1Pw9ujcu3tuE0J6gf12hNFANLJlVkfF5/zyCfjvQERMewO/sX4EbrKGcbO96lj4IZTN0jEtjs/gXLe86L+Fo1V5LO9PLB3SUVrmzZFWc/nUt5r+SpGDNa6e5T8YHnkN3qCr/3cfsBI62Pq4elxrXnbbxuX2C/WUBHw93v0cw7jPir6Xro2lno1cN9jETS6t5E5ADhSQdUNMRjQqcbqhtDLy0sdHByk+fDzVd2IwNzQDs8nhzzaCUDr+5YCP14EzfXU3FrIeRb7bbx5m8hlXqPRSKczNBoNVSqVFKUhKRnrvGLuyMhIMt643kPq3tLSkh4/fqxPP/1UT58+/eYBR1cWcwLCmVDOuuYgy5Uzwpd4hm8GlLmxsTEtLCxoaWlJk5OTCXSyeU5PT/X8+XP96Ec/0traWvJuuXLLtYeHh3r69KmePn2q+/fvJyWKayYmJhKg3NvbSwwlhhy60u2bNyrtWCFOTk7U6XRSyB0b1zfiycmJtra29OzZs74pOhFw0H4UMhd6nvvoVrkY0sQ1eCywplHghI3k505FZdw/i4qwK5gOYnkuljzWBX1iXhlLwCygJHoosdodHx+rWq0m0Ej+IxQVgfi3A99+kQNVb5MDHRcMXI9FkxwiCEXcQWPuvDA3orwuxVB2vByA+Rieyj6iDbEgAKCRKr4YX9wrxTgRNuLeNvdcu2GLPRANLXdJrG3p9nyRojbkQKOvBwduWLsReuxLxu3k5KRLcYlKqO/NIqIN0VLvRSViiGo0Jjn/joDR2+IKsb/nPus35YyL/l1sR24MHXywFnn3ezA+bhyDPLcRw2fk54BFDC0YKrnPyclJmisP38NgmGuz9zn+HT97WWNMlAdRWe4n3bZ2fJ1F747rHC+rZPvziABAz/GQc3QFcupOTk66vJDRkI1x3Q06Lhsc7En5HEbWtVPkBfGaXnswZ/C4S4r7JbbN2+2GyFarpdPTU3U6naQveDoOYxfrABCqSnhqu91O6VCNRkMLCwuamZnpqvvhOh+6yMHBQZfMpe0U54sGb9fFkeHeZ36LXD86Okq1SdjP8AfGwdecz6U/y/WMN2HI+aqJ0xg4zQEHidS9XzG6ou8fHR11pQVI3eli4JqFhQXV6/U0/2/K0Pmq9NqJV1irolKSC1N1pcjB5dnZWfI2VCqVrrOiUP5x9VcqFS0tLemdd97RwsJC10GbML2DgwM9e/ZMn332mTY3NxOT9HA82nR+fq6dnR09efJE9+/fV61WSxVj6Rtex93d3aRAu1cr9hNyQYIX8eDgIFVOmpiYUL1eT1YfiDE8PT3V6upqYkyNRuN1p6mQImN3xRmLG2MWLb0wGRdOEfyh7OE1pkCOV/Rzhd7Xh98zgp6oMEJ++K7/3gEtSmk8hylazi8vL5PizLl2ExMTXR7PKHD8GS4M47V3TShv3N8tkJGhu3KJcSB6knKADI9eBBJfhpw3OGj0fMbJycnkbaQ/PJvqml60hzxMcgt4uZXWwUzkVYyBF8VxEP6ySu7rUM4r02vsnHw/RE8jn9NnL4ZCmLoXwvEDqONcu3KZM0Z4W/y50dPo7xE0+m99fbBm4Tvc1wE+98i9cu3rB+X2enxmBH69fst1RX2Bp+X4uVdSpcKidC2D4XPk8VPpkfXOfcnfcR5NDnwERc5rbjN8vM5eygHIftHLrpUITIoMAbcB5tzvXJb6ukcGc3TXxsaGWq1WAhUYbTESINsxHMD/4j7OjWmRbpPrH20uWse5v4v6fhfUay/6WmWc3Vt0dHSkavXqrETWvOc2ogu4x5EoJSrJc84f543fv39fMzMziZcxnsfHx12pWoODg0n3jakgo6Ojmp2d1b1791Sr1dL8MdcUWDk6OlKn00l6kYNHdDx3XHh+NmPFs3NgH906pxN9U8gNKdKV84qjrDiO0A3KnibD3FJlF6OOG6GJzpudndX9+/d1//59TU9Pa2NjI6WyvY30ysAxHkUQF5x7nxh0mJYzQFcKiP91yxeFbCia8/DhQ/3gBz/Qd77znVT6Vrqe2NPTU21vb2ttbU0bGxtpoqJS4uBgf39fn3/+uebm5jQzM5MsOEw4JXMPDg60urqqk5OTpORGD4+TM1CSo7e2tnR0dJTOSnOFyi2CnB23t7eX2twP4OhKoQs+FEcS7N0b53PL3/zG8zXJg2WOSeCv1+spD4ANJnUnoLvHMSpMDnSiouIMD6uPf+7rNXq3APDMBfeA6Y6OjmZDelyRioAxeuXdw3uX5Afc81z2UE7A+z5wBRIwhjXSj92IIYtfNqcxgjIv8FCv11NeM95G+uheUkJwjo6O1Gw2E6/AO+oCkr0l6YaV3Y1atMnn8k0oqDzH31+GcsLc/88Bt6j45NrhYYiA6LjePfSUtsTwZa7F2Ob5lV6NkN/zDAeHtMfD4bk+ekh8HNyzEOezn1QEanIGrwh2i9oXgXGkCELoOzwWZZTr4KEYNPGOYB0HWHiIcsyddq+XdL2vYr99fnL9fBm+6Lz/y4DOL0s5oC91R3tw3eu009dNBFlunKR65+Hhoba2trS2tqb19XUdHh5qaGhI9XpdjUZDjUYjgUjATaVS6aqvENvooD/OW8544f2HcpEJbwPljA4R8JLy4PoPhi4/ag4CvCN/CFEl2o2j3+bn55OHSpKOjo7SfsMTPDQ0pEajkUJbd3d3u/ghRvh6va6HDx/q0aNHmpqaSvsQEHN6eqqtrS3t7u6mEGbu46Hr8AWXE8haSWkMpO5QTddnnG/307D6VVHci8PDw5qZmdHCwkKqs+KpOz7GDrqPj49vYB70xoGBq9McHj58qHfffVeLi4v69NNPu9oA335b6LWAo28eV0BdWfGF5aGiLtBwhUM+QAi84eFhLS0taWJiQj/7sz+rDz74QFNTU+k3WN729vb0/PnzdIxFTkhHZnhycpJyHR8+fKjFxcXkwcHbNjMzk7wZ29vbyXIbhb8r64zB8fGx1tfX9fTpU+3v72t4eDiFO8LALy8vb7iuCdvd29vTwMCAfvCDH7zqNN1KzAHKgFfx4nMPk3IjgHtrJCWQf3p6qvHx8RSe6hZxDvOm4hjeWw/ncsOBkwsslJnI+H29ARqiZ4l+ABzHx8c1NTWVCuPAwF1R8Tm+zbLsAsU9M0VW2rug8fHxLuNMBNixOpsr9y4w8S5iaXUAFs/Ne92+wDgjAMGji7IDaATUsx+cNwBwDw8Pk8eENUh7MYKgDDM/8Bw3LNG+XJv7Bfpz1Avs30Y5EOWexljUwcEV13oIr+f6SNfjNTg4mPJj4F1ePMcLqHiOI6DRrdxFe833qxsl4QEOaJ3v5oDjmwKNPu6xLblroKhkR+CRA6K9+sQYeYVlLx5WrV7l+R8cHHRVfeSYAPLRIx/GCIMi63vK5yu2hXefk2iEy/FT5/m83NDaL576MhT1CidXsv2d73LrIjfP0QjD3+fn52o2m9rc3EwFNdxYPj09rfPzc42MjKQjptrtq2JvyAM/1J55c2NwBAy58XbA4N/ngKPzpl5GlX5T5OfeDoAjY0NkEsZ+PHfeZndwcAQH+WlTU1OamZlJ+kW73dbu7q7Ozs7S+dmSEk/EyD47O9vlIUT2kV6wsLCgBw8epJQt/y18m4idnZ2dVKPDi90BHonm8BoDblwFPOYMzj4O30TQGKlSqWh2dlaPHz/W/fv3NTU1pYGBga7zOL1YJKBcuhonChUR+uwpVMPDw5qfn9eHH36ojz76SJ999pm2t7ff2rF9rRxH/9s9QC4cei02Co8QCujVnVBuq9VqOqvx7OxM9XpdH330kebn57vCO6vVqk5OTrSxsaFPP/1U6+vryQXviiHt84qtTOba2pq++OIL3b9/PzEImCoWo+3tbR0cHCRvloeA5AhGvbW1pe3tbVWrV0m1MzMzyerkhX3cGg94Jam5H8Q8cYCsV/2i6BAVoQBfHsLn/QaIweRI8vawSQobefw2jDN6qd16FQWpKy4umPkbxg/gdaGEAguIbTQampqaSuBEUgrNRHgwN9wrjmFOcEo3hWO/rEW1Wq3LGINHAMUdj6IrAA4iAWLkPuBlZAwAYV6l9HXIAQDAZGhoSJOTkwm8Mx8urJ3BAmQRfngcvd0eUuvFDTxUywFFbv58X+cMRP2gIsXpVRUqN/SwL3JFHXKRGA6oPXecvvt9YnEdz5PEAOM5Ml4gx8O8IqBwBdMNP/TJ13luLnKKvH/+psHjbd8jZ3JyNV7by1DF/8hdL3BFtADX+BE7VH5k/0hXa4hCHvBxB+4YFgnlo42SuowCfO58MoLf28bJPZ5eE+FNGwOg2Jei58f+RqAUf+djAW9GnqKYoicRYgzw39nZ0fb2tvb39yUphSiSVzc2NpZ0C9YH756T5fMc+cPL8qF4fZGx403uRX9+nI84T24sHxkZSTIoVp/mOvc2EvmC3oSOUavVkiew1WqlMx1PTk7Sc0jRmJqa0sTERCqWQ/VrDDX1er3L2xX3HwCzXq/fMCBjUCcEdmJiIoX7VyqVLp0Zfg6YLjIYx3SjN2lkfRPkfPjhw4f6qZ/6Kf3Mz/yMHj16lPRkxp6QVNZANBJ1Op2uWgzIRfb86Oio3nvvPf38z/98Or7u008/7Trh4G2hVwaOHvISFS6ECoxIuvaisflYlD7AWFyk68qduO3feecdDQwMqFaraWFhISWj+nOPj4+1urqqTz/9VJubm6nITizg4d4qnnd2dqbd3V09ffpUT548Ub1e1/z8fJcSNTExoZmZmXSu48nJSQp1zAl9X0h41/Cm+AHzUamDSeCRxJrVD3IQRhno8/PzFL4JuMfFzsJlofNerVaTcj80NJRAR7PZTAq/J5ZTaIZKfuTJuJIavbexzXGsXSD4HLug5X9CPTi3EU8j1j029dnZWfKGwaC9qllOCLnC6wrOl/HS3Ubj4+NdXmKUfvZhVMyl7uq4KCYomYSlAhqjx7FXP1zpj5+78k/YD8KyXq+rXq8nocnRG+wHhBtzRLscJHo/HeQyX7GIkrfTPbGSutYiv+t0Ojd+d5f0ZYBjTkllHzlYK1IK2RvSdWl+N7L5d/CrmKvh9/PIgcjnImj0aBTawr3c8ER/ciA0eq9y+/NNKquxPy8DZH0M/e9ee0rKGzJiyJ17LTw3DoOLh6+xF7ziLUqrp6ng9Y/AIILGCJCLroXcaBP3NKGW/CZGDNwlRSNgzjB4GxWB2xyo8ud4f8k15/noLfyW6yj/7wZZgA9eSOpCSOoyAEbvtMuvor7GOaZfsW85MPGmASPPjPvptv55eL+HYktKRnePeiG3HpBPURz0OXIP8QIy1sjdwcFBTU5OJv4Gv0M/HhsbS2lVIyMjaW3QXk+rQceloj3pROg4h4eHKT8SXusOmWhIzRn5uA7Z2cuR8nWjyHenp6f17W9/Wz/3cz+n733ve+n8bC8oxLhyznWOt3te+eDgYALvrIXZ2Vn91E/9VNrHx8fHevLkSWrH2zLGr1UcxxdQbhF56KN0rZi5kAdcVCqVdLSGW9tQ2MbGxlL4mltZWKyEdK6vr2t9fT0d1C6pyzPlQCmCEYDnZ599psXFRTUaja6QGMIHDg8PuzYghTs8lAa3P89otVra2dlRp3OVtzg5OamRkZEEIN3Kk7PkA4LvmigaEkPKAMOAVgAI+Z+0zZVBV7Jd6JGf6gqfFz9BwPm6gHLCFsYfFRDfoDB8j+tH6R8eHk55dFNTU13hqZeX12fvYAkkvy4XTu1/u3IUr/Mx7gcRAgMAB1T5mEXQ6BZIB4jxCAYPO7xtHeaUYKn7WBKE2fDwsGq1Wpen0b2/flQD+x0vsh+/4SF4cV1Er6KkLo9jtAZ65EQkV5j7NY+3Acf4fYyk4D3m8zpozIEY9gv8x9cMAMLH2O8ZFWp4Au++X31+3OvhwNHb5gYgB/0RPFKkhTHyEPGice2n4upzEUFVrg2Mjbed3xYZnNwwkNt3KLUolu55d+WFYmUe2u0GKM/turi4SPwSwrPlSjbzkutrEaDPjY+Pp+esv0nwT7tcaYZyvM4/j/9HnaPoeu+vFyjjWpeTtCl6kA8PDyWpyxDtRzBI3YfG87wIWOI68zbHV65fuTX6VdBtoDF+Rps9HzyCaeSQA0eMMJLSmHuFWzxS6Bg8CwOvVz6GAHSVSiUZ3akhwTORzQMDV4VWqA0gKaUVYARCzzk8PNT4+HiX7umpKLlwY+nmOdsx/eWb5nGUrsbw4cOH+t73vqcPP/xQjUajy7nRbreTJ9HDwZ1i5NPx8XGK5nPPcbVa1fT0tD766COtr6/r+fPn2t3dTXv6bQCN0msAR8+PcYobMFZcdUUH5gdzjJ5BJoLNhtdvamoqVXhj4g4ODvT8+XOtrq4mIAKz5F5scr6DgXY618VgqLD6+PFj3bt3L/0eZaxer2tubi6FGCCYo8B3QTo4eHVu0ubmpra3t7vCtxqNRpermj7B7PvNaLEWo5T5WXnMCePmCh6gDGbD/1yPh1lSAtnj4+NJ4cQKilcpKjQ5Ic33rohG74Z/j9BzJodnFNA6MjLSFXp1fHzclXdAuWtXpmN7aIsrxN5W/mZ8+kFeJp+5dEXUDTGeh+bAy8NS/W+u6dV2hIqvY+cDruhT5MS9vtHbiGHCw4M94RwviedhemitR0Sg5Lpik1MAX0Z5LQJ2d0UvAxyjMhuV0ajcOWh3j298HtdALhCjp9nbEPMM+Zzn8X8c/xxw9Gv9O0CoA8IY/gpI9XDMqCj6uLxpJTbyB/888o9owIhj6+13pdzH1kPP/UxeCKDhR9YQDhUjhZxXE7ZXqVTSXqMaJEq2G0Nz4CI3/p73DDEGccyiIeJNzGUR6HuZ370Mf3GlPIapMjeuqDq4JGzfQaPn8FOpGiMdhlD2jEdruAyXumVrbt6KgHyRoeDLjufrUtwjEUi6/sJ6i/wl7jWML8ghrxYf+828IcOIanKDfaVS6SoWCI/DaAkoREfD8AMIZR6pqjs9PZ2i89Ar3fNJu9nbnpce566X8SPO39sCbO6SxsfH9fDhQ73//vvJ04iHuFqtpj2Ip1G6OVb+Qp85Pj5OOjR8lTVYr9f1/vvv61f9ql+lzc1NffLJJ29VldVXBo5xIXmyMMze8yJ6KWsMYgwrAzgyiJOTkymsEAaK9WR7e1uffvqpVldXU3GWxcVFPXjwQLVaTcfHx3r+/LmeP3+ui4sLzc7O6r333tO9e/d0eXmp58+f67PPPtPBwYE2Njb0/PlzPXz4MAlJ2usx6xThWF1dTUydMx+d0SJMz87OtLW1pbOzsxSCOz8/f8OLAxjzqq39IvIQUf5oq7ffzwiKioCHwUVw5IqcMyIPvSAE1MMgfdMVAUhXtPz/nCJKG1CCSBgfGxtLVnHCumCknc5VaK0fA5HzUBVZ2Nz44SF2/QKOUeg7+PPw0+g99OMXvEiSh5r3Ck1lTTg4ceHqLw/DxiKKl5HQVEJqPDf28vIyefmpVtdsNpOC5LmORAG44Pc152vRFXZf264gRQU9njfYj3ks+szfX0ZR9j0a5wbKefpywi7+H63RRcCsSKn330eZ4F5C38+ujHi/Yjgd37EvI+i+TYG/C4p7wNsY146vT7730GjaDkWw72PgoecolX4WHb91gyAKr1fYZh9QgAXPP6FyHEsEQIV/Oi8pMszwv4+Fz6fUbbTIrTlXquPauEvK8YGXeVaOh8QxyHnholIZz970dcQ1eLsoytJsNlPEE0b19fX1FOFEWByVVqvV6o184RzwuW1cisYg/i7H44o+v2sqArOAZ4+0kW7uL3/xm3jGMWNZqVS6PMaMtXSdJ05tCfL8fV0DJtlz8AQMaC73/PzVo6Oj5DmemZnR/Py86vV6Ao2ki9FWb7fr4S4Xow4HRUPmm5rHr4ImJyc1Pz+ve/fuaWpqKvFpT3FC13L+l5ONrJ0Y4eWFlyqVqzzVubk5ffjhh1pfX9fJyYmePHmSHGBfNb0ycCxyRztwYOGBzAFXlIX2RHwHngw0+XWEg87NzWl2dja503nG6elpqoq6vr6udrut6elpvfvuu/rZn/1Z3b9/X1tbW/rzf/7Pp6px9+/f18/93M/pO9/5jk5PT/UX/+Jf1M7OTkpYfv78uZ4+fZoO5QRgVCoV1Wo1zc7OqtVqJS9ns9lUrVZLBVfcSzIwMJDAx9HRkdbX1zU7O6vNzU0tLS2lilt4tdis/czdgAgzYuxRBJg7mB2CC8bj1mGYHZvFgYYrA85g3Bpaq9V0dnaWzirKeVEiuaLP/349TNDHj/b70RveNhSudrudrsOyd5vS4N6B6D3pdDqJufRLWXUPop+7yMs9Cg7wY5iqg3f3TKKsxTFwhc8LmPj69bAnQDsFAAg/p3CDW3hjRAIHXMMTPJTWC0h44SvahDHE++PnsHpYXVQQXIBy337NY1zrRcLnVSiCrLgnY4g9vJ0XayWGlLpRKCrKXpEv5+EsAhUROPKeuz4HTGM7isAX9+kXOSDKPTMqW96XyI8lJTnifMZ/h/xkHrFku7fRIzCI4nFegfIoXRv9MKbt7+93WcU9xJ82IzvYl0UKE33LKfE+90UeWkldymxRKO9dUAQMfBYNmj6fkeLvc/9zD9+PHhFSqVQSD4OPEfIIWKQWAfKLtJJ2u52qrxLiyP7EGBDXHrKzSN4V9eE2KpqnNw0a3TDO892YGg3dkY/xGy8+xT5z7y3FizgrGn1icnIygTwvokNROI/gktSVFgAfiJFB7n08Pj6WpK4z0jF6YgQims9lvUeXOG/xNR8j66KukzP0f12JfoyMjKjRaGh2djYd2efRalEPYQ0VGS2la8MPBgQ3OBCVdXl5mY5d+fDDD1O13q2trb6lr70KvRZwzAnBqJQCKFD4QO2Xl5cJqMHoXPHEooL1rF6va2ZmpgvpS1fgcnd3N5Wj3tvbS56NmZkZvffee3r//fdT0RzCIjkv5aOPPtLR0ZG++OKLFFp5cnKitbU1ff7551pcXEy5l/RzdHRUc3NzarVayQqAYPe8RghFamBgICnqm5ub2tzcTEDWLRds9Ny97ppgfAhpBwH87dVJc8Uw3NvHxvHvKpVKV9gNif1U/fLEfsbKlSYPn4sKVwSGkWHRbuaGYite+prrUHYA+n6mowsd75szSpSuovb0ss5+WYqhqBE0xhDOCBzjmU658EEf69sUCgdkCCss3g4Yp6amEhMGoDvopA3kNmF9ZZ9wX8LSyS+JHjaELqCy0+l05ccBMh1UOVj0v/tpzMmNaxHg6aW0RWU6pwBFy6dXz2Vs3ZoaPTv81sOC4XMeXhpzg2NfXCH3/3Mh37E/UemO941g5U1R7KePm6+fnGeKa1zZcA+q38fXJ0ogSqTv92gE8LwschxdYXbQ4IU0MPY1Go0ESry/bkx4GUAX+xTHwz/3+6FY91tJzc1NJNqcUw5zv+/VPweLvqc8qofwyP39/VStvdlsJgBCbhtzxPo5Pz/X4eGhdnd3E+jPGXXQ7dwo5O3Mgemisev1/5uk3Nywx+I6ivMXDaHOVzwPFTnqRxd5WsXl5WUyvHihm6OjIw0PD6fjp8bGxiQpHe0BuARMoMegm0lKnmfAIDoOQLRIvjr/d/DIWMU89hjK6+SRIV/3UNWou42Njen+/ft6/PhxKkoUoyw9pNwjy+Lacvnm+hcFQt3Qzn5eWFjQO++8o62tLe3s7Oj4+Fh7e3tvbkAK6LWO44ho2q1WbBYEGiBpYWFBjUZDlUolhZzBxPx1cXGRcs0IX6MsuDPqg4MDPXv2TM+fP1ez2UybE4bLGTx4NBwgYYW5uLjoimO/vLw6D3J1dVUbGxtaWlpKG5X7Tk1NaXp6OnlPxsfHNTc3l5KRc8RmPTo6SudmUXjGBS1t66dnw4lF7qGEgDuPu0eZJEQJoOWKizMMFzpYw5gD+jc5OalOp5MUE4SWh854qeKcouKM3NciG5qwHc/PcUMFY+DWdv8uKi2QP9fnz4FKVGz7peT4/R3IAgbcouVWbWd2HqLqCqQbCRxAuqIhqUtJ9VA7xgEAieXVwXsul4R7u/CmuANgGAXZj+WIYXc5BUjqPovWD6b3uXNFwfnTm1KCenk0nA/G/73dESxK3XPmwNGNWNK10cbDmHiWGyswuPA8t057u6NSFv/29eVtze2fqJRHxTx6rKKC3E+K8+SKqyuwvpd8rzhQdPDo988pgNEIJKlL7gFO8IQcHBykcG8fm8hXLy8v1Wq1UuG0RqPRFebo3jAPH81Z3eP4RCUz9tOv49r4mzdpFIAi//c2xP0WfwP5PnHjqqeOeKQDc9tsNlNKzcbGhg4ODjQwMJCqhMMniaogN+7y8up4h52dnTTOl5eXXcXunL9FIBCNQLF/EaAV8Z83bcxhjHO8wnUGd14Utd9/g9fIDW/8X6lUbswnhhfpKvRxcXFRkrqM2EQNNZvNdLZqp9PpOjMXfdYL6hBdUK1e1arg2DfyHNFt6ZcbmyJo9L5HoJPLl46G636l5LwpisBxbm5O3/rWt/Tee+8lR5J0vX4x1qOPwLv9PlE+sSYx9J2cnCTdm2upuDo7O6v79++nQjlbW1vJqfZV0msBR7cI+7t0FQK5tbWlSuUqR87jf1HkEXz8FuVDUko4lqSZmRnNzs52WTgHB68OOCW3cW1tLRuOI93Mn5C6lRHuRww47dzZ2dHq6qru3buXACiTPjQ0pKmpqa5COV4VKVrppG7PHGf+UA0L8Mp1KNn99G7wLAfZsZqrg5BKpZIsIm4R9Tai3PvagOG0Wq10LXlsg4ODqbJms9lMz3Lvnc9dNFhADmKioihdMWYqufq5ZIAcB0wuOKLSkwOP3k//LILGflriYjvd++D5o7SBMfYjLdzbGENVJKX95cDcnxu9UQhW6XpdOGj0Q+BZ824Z5Z54BdmD8RgOGC/FBvCg8HJw7EqRexzxXPK3KxceIoRi0S/gGBWrCDT8u6K/HZTEl9RdVdbXQE6x4r6+fqOhi3XC/HlEAtc574tGn9hP/zvu69hG97oV5eK4ccg/7yfRFvoZ95R7ACOviQp2HBPvN2sRBdYjDVBeMfTBh/EgYrhF4cyF/tMPUkukq0iRvb29VFAjKtkONjHkRsqtZ8bHx4B7YpxALkQ+/FUARynP673dRWstAuKYm+7GXPYWIP309FR7e3va2NjQ2tqaNjY2koGd8FQ3gPE3UVMHBwdda+7y8rKrWrqDCl+3UnfBqRyfiH3sZdx5k+Ax8kkHf97WoiiGIpAMv/HQRPYiui+6nBuv3cjmxnk/CsejhaTr8HHf+1I3aHPAEmWJ98l5EkblaKCLMsi9q0V5kegVMfrq60yjo6N69OiRPv74Yz169EgTExM3jAfoH8zfqxDzzh5mzyPXiMxbWFjQgwcPtLCwkMKZ8Th/VfSlgGNugbIgsX5MTk5KUqrwNDw8rEaj0aWsOzERtVpN9+7d0/z8fCq5zmY7Pz/X5uamPv/8c21tbSWrjCssDtpy3gIUQjb08PBwUqSazaaePXummZmZBDa4v3QFfhYWFnR4eKj19fXE9L30MtfGMDe8JC4kXBHD+9FvYi7w6qII0H739klKijPz5YoDIZ5YnN36xOaCWbLpAKm1Wk31el3n5+fa39/vWls5wBYVUN5zFm63klOIxefIlWdXhLm/5+sUMWTWEX9Hr4kz9n7QbcpwFBbucfQwFW+jg0afNwfAjBPrm3FlnWD5Zuw5bgMA755HLxdOm6Mwy+VfYm3l97FYE/wlgtvo6eQYHM/58b7FSAAU6bukInAVlSwHVDljVQRVDtyiASEWE3Ig5vdyyzNWVcJpvNQ/USFcE/lwTpHh2bH/fg3z521x5Yu29wLAPv/9NMqxbhw8er+dZ8Z+xv95ReU2Bz5igRpJqSaAe+fxNOKhh8d5Prevf9YNCk6z2UwFruDhcU1RBK7I2Ab/pa/ImV5Gkrie4jX9oLivit5zf0cqMspE3sw69/2ITuURS3t7ewn0T05Oam5uLim20RjUbncXG2TfeIRHp9NJXi3pWm7lDD9FsrBXv52XvWkqWjM5YOtzEvdh5M/RWOXfoXsQLhqjVjwlw3MsXeeIugTtcpAGoASAtlqtLt7oBQ+j8ZhXrq+us7quRAQZYCnm5X7dPY7M59DQkB4+fKiPP/5YH3zwQcIhLoepv0BIsVRsaIjYAzo/vzpvl6gOr+BLtMDc3Jzu37+ve/fupaiCryVwzAl4Fiul9gcHB1Wv11WpVDQ1NZUUu8HBQU1PTyf3OZYZFjaLb3R0NOU2srEqlatSwru7u1pfX9fW1lYqa+yKiW98JsAVKQcDbill0RweHurJkyeamJhILn/3pg0NDWlmZiadybO3t9flbvZnoHSyCb1wgSty9MHD5vpJPMvPGnKBHj21URnlNwgcvK4wmYGBgQTI3KLq1VsrlUrKfzs4OEjMOAK3aAnLgUnppjD3xGbWoHvRYuU6D1N1Ju5rKDKFaHX3drjltl9C05UMLwiDtyGOqe8NDzf0PkTvq/+OcEZyNngRCuwV3DAKUNGP8vAx19SZpY+bv2ISunR9xqm3zS2j7vWUug+9zs2pe+GdZ3gIc7+A48soVzlF1n+be0X+hqLgIUrxHrFd0UuBQulrjXn3XBiui/eNRqG4f709sd+xrc6X4uc+btHg0S+K3kT3uqLI5Z4fIzVyHtQIMAEFOeWN9Q2fw8tIQRUHjczr/7+9Nw+SbM3uwn6ZWVVZVZmVmZW1V1d3ve7Xb+l582akYROYAAGWbBgbkGxFEGFAksOAdeWQJUNgQViywEJIwWIJKzKEBEgg4QjEYoVthEa2JY0QwhAB2kaasd7MW7q79iWrcqvqWvL6j+zfl7976rtZS9et7p53T0RFVmXd5VvPOb+zfVbm6Fhms/1cub29PXdPNpt1hkGSrgs9AxWA2+u6t9l/O+c+sGgpKb4aZ7D0rVHL9+39PkUyDpgAUcMdC+Hs7Oxgd3fXRStlMhlXeGV+fh4LCwsoFovu/OR2ux3JoWK1TQAueoNglMYe9Y6pTqch/HZParvj/qd8zfb1psi2g+TjmRd5lu23GiKz2aw7AoVnJdL4wucrcGSIqq1kbvcf3235t8oOW+FTZbyVpXGFXOzYAIi0k9E9PH5CjbkvO3AkVatVvPrqq3jjjTfcCQ3q+PB5G338Qcn3P64LpnOpbkw9m4U6b9++jaWlJayurrqIqudFVyqOA0QPLlYGwYIX9EIxzCKTyTghQg9fGIZuw9Eienp6Gkn0pkWTG63VamFtbQ3r6+toNpuu3Li2T9tjrXdKbL/dRAcHB9je3sajR4+wsLCAhYWFiEU9m806UFkqldBqtdymAuCuA/qWfA0vsLlYfC/BnFp/kyL1RlABsdZqVZbJHPV7jjPnUEEjn8n507UD9MOTGMs9Njbm5oOfCtx8QmhQ34aHh1EulzEzM4Pp6WkUCoWIBV03PYBIyCLQzwFjv5WsgFTmrvOmazEpojKgnjOfcQfAGUXe9+PL87D9ZZgx83wJDLlnCST1MGLN07AMUhUpFW6+SrAqvFqtllOSaMRpNpuuYARw9oy4OMEIRI9QsbyDazzpfRm3vrWtPkXHgkSfcsNr9Pk69kD//DJd8z6F0So1qryoYqLjb6MvrKLM79XA59tXceOmSryOhfb1JuZPx0s9DL5UAP2dP+SjarTSvigf1RxlVd6Uj3Gf7O3tuf1i5aZtD9uvoBfoGT4bjUbEyGLBHvkvU0xU1lgQ4gMV7KOuS59ifxMARHmpb+345IPvfttH7Z+deyqhrPewvb2Nzc1NBxyPj4+dAa5SqWB+fh63bt3C+Pg42u02gH5xFtYQYHG6XC7nlE5GbRBwWEOizpnP2+vjLz4QHAdMeF+SNGjP23Wo69RGN8QZDfi76rtjY2OYmZlx3qFut+s8U9zX1tiueZJaaEV5pvIBH8DUvH0aaZl+odEAtvpnHGC0MoVHiDA3mvLYHvnzMpLyoZGREczNzeH111/H3bt3UalUImH3HAs9u9Gus8usc+qjPINV5zyT6R/NcffuXaysrGBtbc0dwfO86NqO4wDgOqmHzAJ9z5F6FvSz2+06SyYATE1NYXp62m06brQwDNFoNPD48WOsra05a5laZi3gsBZvKqG8HjjLBE9OTtDpdLC1tYXHjx9jcXHRFcXhs5jrODU1hVar5aw8QF+ZV7ClY2Y3rw0RvCngyL5TKSdDyGQy7rgMMjdep0qdWqxVEFqGq/NuGfDQ0FAEYFhLOJ8TJ6BUGVaPcLFYdMe4TExMRM4e0xADAJHiObSssw3qEdP367zZ/mpfVRFIgqi0aMEnH8i2xhUaaFTZ0z5q2KKuXwK/crnsforFIkqlUgQ4Wi+kr7KbtoUGHBVutjIsLegM7aAQo4LEolo8z4pClKHug4B8nHLHOSQluS/tXMUJ8zjgaBUL7a/m1vhyZLjmGWJllQcbxuoDjD7PsOayso2WvxGY0LCjnmCfILWKdhyYJV+wPMOObRJzyH7RGKEV85RXnaeMWyXW8lube0S5wvGjgkN+p1EWGqmTyWScosqx0jEj7+x0Oi5MmZEDOl+8j/xI152VxSqD9D2AvzqwrrOkjXI+wOGTR4Pu199VBtiQQQ0z5pxmMhmXvsHQVJ5hy1L+4+Pj7pgAPee63W473s5iSEylGR4ejoQq00umxDbwXGOS5Te+cbL708dXfZS0vqPt9gElNYRYg7n+X+/RUFDOB6uGz83NoVqtutoZ9O4yrJGReeoc8eURcm9pQTcNgycxgo8RXEwPoSFZo76Yh2l5CtA3uFljDwAHHDudjuuzehjZxhflrMHLkI7l+Pg4FhcX8eqrr2JhYcGNIUnTZDTNScmns/qI/Jf55zZ6jNeUSiW88sorqNfr2NzcxPb2NlqtlnP0XIQnXSddGjj6SAUGF52WZweiioaGZvIalpgOwxDz8/OYm5uLKMK0lO3s7ODx48fY3Nx0Hj4gqmyp0sT2AH1FyYI1nyWJYHZ1dRUPHz7EzMyMAyC8rlAoYHZ2Fp1Ox7XHWh993k4KDS2OE4b9YwZuyrNBJY8KBnNYgP75YWRWqthbIMz/2URx9gnoK1H6PRUQgvBKpeJCc2wxBN2c+nyfBZvu/UqlgkKh4DzdCpKpKGtFWYbaKfCy82fnkQqBzrtvrSdFfK8WwlCF2u5LJVXEOH/cu1pYQQtIEWTz7CmG4zCfg+PI632eaxsyaYvd6IHGmk9BIGkrqfqOIKHCrsoL+2cVGavk0RuuCm6Sc8i5sPOi5LOE83trrPAZc4A+LyIPpILK38lrqfCrt6zb7UaEmXpi2R7NoeU7bMilAkcdX+tlZASDBY1APOhTI5IFjpxDa9y6booLrbTKgwWI+nmet0NBuj23UT0VYRhG9pQtbMHx0OJs1jDMdh4dHbm9MTQ0hIODA0xMTEQMVYymCcPQFQZRg4MCJx0XfZe+0wc04kBJUuQb+0HXKvFaa2Dkd1RA7Xm7nBuGnapxjAXAmArAaA+C+JOTExfhkclkXKiyAkUejVQsFiM1HPRIAC1EZnUplSV2ndo1fdE5SmoeLegbBP7Vu66RAbxe7+Ga1hBVRuJMTk5icnISxWIRp6enkTM3GSau86BF+zKZjMsZz2azTt6q8ZXtsjxEQSaN8UNDQ25PAv0wcl+Kim9ufX2kjqoGRQAukuhFOC7iqpTNZlEul7GwsIBbt26hWq2eqTnC8FKGA/M+ks+hEPcu1cGogzPVR/WmkZERzMzM4I033sDOzo47gnBzczNW5iRJlwaO1lKh39vG+1C4/ZsWFJ5dQzf/1NSUs2jQ+ra3t4eNjQ1XhlrRdlzbfEqHxobTQpPP590humph2dzcxMOHD7G0tITZ2VkHHMMwdLmODAfpdDpnQj3IlBVYqKKuYEstXnFe3esivpOeGfu9raCo/To8PHSFMTh/NrlbGZWGYxwcHDhAwc9ut+tKD1PxV4VEFQqSZeiqoDBMkqWrqRxrKE422z+WRRmhb9P7vCtx1lQyASqtSW9oW+BAfyygjtuP3W7XlfMmk2LUAOeIwJDKBnMXtdCNhvtqOXmOv86dWkD1SAh6ERlep4oT1wYVZVr8qNxouFUmkzlTOts3T3b+NExMAYZVfq+bfOFu2m67Bi3PGwQ2rDKnlmRd09boYN+tXmlVsNTjr/vHKsqDwBLbaRVUXUMqYyx/V6WH+9vuV74vyX1pn229bL5327GIUzi4j8iP1fKtwBDo52gTlHDv+M7ltBE45F/K89XANDw87AWhajQg76CiqqkIg4y2dox8a9kaRJKgOJ1C1+ig+/R6Xx81H1vnTiMrCPioW3Q6HRcaRw/XxMSEk3MccxrvqDcdHByg1Wq5/xPAVCoVZwjQAmOUB5Tpyi9oCLS6nx0Dy1efF1kjvPJ4u7bVmaHX276Sz+h4aJ0Orm8a6Y6OjlxeoM739vY2CoUCpqamMDk5GYneqVQqkdQnTe9Qw5H2gfowjTwqe8MwdIUpM5mMi7YKw35UleW17C/rGZTLZYRh6PTcbreL0dHRCHhKWm9NmsbHx1GtVjE3N4epqSlXxVR5o0ZF2WhHS+fpC3qP6sjUk9WAkc/nsbCwgLfeesvVeHny5Anq9fp1df/CdCXgGMc0fIqLT+lQgBSGoWNsR0dHrvpioVCIALx2u+3KUO/t7bnE0PMEDzc4J19DqVix6NatW3j06JGzzPJZp6enaDQaWF9fx+rqKhYXF51lDugxhvHxcUxOTqJUKmF/f98JBG5yKtMaDmKFrIKZmwKOBIy0KAFw1iT19nFjaB6NMlitNqlKDcee406gwDAHCjH+f3R0FJVKxTFYBQS6riiINQRKPxnfr8eLKChgGwkcFeCQrFKt5FOsff8n8FDvSxJE4O1rqw034Txz/BiyQmWQ1ZB1zXKMuO71vE+tZKp73nppNZyFY0Lmqx7GdrvtQrOo3DQaDZeIr9Z55ljYkEwV2lwrYdivGqn5Orom1MvI9a1rz4blXDdpJIOSKp4+wOgzDHDfqUGBffIBTo38uIhHE4hWprXAkePM+7UNPqVf26Hv1fdb3q7KG+WIGrx8csGXq3Xd5AOFPhBv/6/t1Ht8oJfPseeYalVD9SrbyskX4UXWqKJGWso9gtGxsTG3h8i3c7lcpFDW8fFxxOhqx0A/7bhwDOxaSJLsvrJtjuuDj3TN8j7KQ84f50mPSCLg2N/fx/7+vgtV5TOtN0p/stmsywcn4BwaGkK73XZHqhB4sshOJpNxKUHq/dRKubq/LjIPzxs4qrHPZ1QlWUMF+b8vbUZ1H/4MDQ25sdFUF5V3GmKq80JdhHU0SqWSu0ZljvJSJb6DegeL7HCe2Jbh4WGnV2v9Bl9kkBLXxcTEBHK5Xi4no4Bs7t/LRnZv07M3Pz+ParXqQol1Lm0eKp/DZz1LhBKNNgcHB07PonzLZHpRjrdv38bbb7+Nra2tiFH9JunKwNFajvX//LQ/OqBULNvtNur1uhsonivEe2gBYzhovV53i973Xp/lW0OqWCyg3W5jYmICCwsLePPNN1Gv13F8fOyO12A/T05OUK/X8fDhQ8zNzaFQKGB6ejpSlKRYLKJSqWB3d9eF1ikYs1YvLR6hoEItWD4PxHUSFzwZHceOm4TX0HqsDJFnM3Y6HQwPD7u4fg1hVQWSgDSTybgKf9xgZGhkSKVSyeVkaIEaHwjyWcZGRkYwNjbmCu5wnuzGV0uqT1mm8NX/qeJrGYMKJRU4l1HWrkLMA1agpnvHF+qnCgYFi57/psJQwR+Zk3oLx8fH3fUjIyPOK0mPpd2PHMcwDJ1iQ89ivV7H9vY2dnd3XSEPDUdVhYp7yDeu3LtaYIfrDMCZOVVlm2tW1wb3BddhEqTVJ33g0fYv7lrdIz5PtO85FjjqPfY63Yu6htSgwrWnbdN9w3HW53OtqMeQ/RkEushfdB4tAFUgqfwtCdKx0P1o22/lpFVm2W81hihv5Z7lucCdTicy/uoFtM+z467PtqRrh/IwDEOMjY2h0+lgbGzM8VPOOS3nLDHPc1j1nE/tszX0krRtuk6t9z0JsnPnGxNS3DVKugbIlxT0qzFWDZ57e3totVqOJzYaDWQyGVSrVddOK78UwBOccM/RQ6FgnHyewIIAX/egjSqyssZnHFGj1yBK0hBg17XPEGfXlnp5rC7JT+oaBO2cLzWoKi/WvUF+RPBOz1IY9o94APpVw9Wo5tsragxnCLMaaXTvUo6qd1pTU+xaUn1GDVD6twXVLxNZ4FgsFjE3N4eFhQVUq1Xk8/lIHxn6Tc+/8vnrIPIHGsipN+uaLBQKuHv3Lj760Y+6olkbGxvOuHcTdOUcR2USvoWmsfG6sJVhhWHoPHqHh4fu6ATNNSPYYzwvS4EPDfkPwVSLtjIFKo3NZhPr6+tYW1tzMeavv/46Dg8P3Ybe2tpyZyQxRv3x48eYnp521bL0TJd8Po+pqSns7e250BIKzEGb0VdSWb14SRPngICQIM16kZSpcu5YeZYgjoVlVKCoZ5Hf8zwwAtJsNusqnjKkaWJiwuVyWIuO9cpyowH9Qjs8L5CghorUycmJE9ZhGMZ6eTg2ChwGeUv4f1UCkwSLSqxcSCBsLaIWcHP81APJUuwcD/UiU+B0u11nJWXoCgUfgSPDWU5OTlAsFs8ARwXWQG+vMv9jd3cX29vb2Nrawvb2tvM0Wst33DzofOg8aGSCza3TtUNlSIER96MKB1tI4rqIzx2kDFtjnRVYPiXOjpcqg1YJ9wlAu+dYdEOr5AJ90K1GCypKbLsFovzeB355nfJPXT9x86/GSe0rx0LHLwni+wcBMau42jYpSPcZqGj80jxfykK+1xpZ9H7yKhvZMgigA30FVT1l4+PjODg4iKQFqDGKbWTuDteF9TCTqGRrX3RsyLd8cvU6SdsWZ2jwrV0FTSS7dtWzaA12lDfUb1ggZ3d3F41GA51Oxymz9r1cG/zRcz1p/GKlVhbioZ4yNDTkctfp/SJgVH6qhlAFM88yD0kacuJIeagao9VzqLorf5S/MapJc0O1XgLXOY3ZjJTJZrMuPYpVyXncA71HnB/eq2k9bIfyb/LncrnsajtQjyYfYeVO1uIoFApnzpm0vFaNHDRSUS6rbOaYJCUfkyJde6Ojo1hcXMRrr72G5eVllMtlhwG4FsjTeDKC3s/ffXqflUEkn1GQ/OHw8DASTUC+OjQ05I4LWV1dxebmJp48eYKdnZ0b20tXOsfRCju1HKoHSxUJtfRycTJvcXt7G9lsFlNTU+6gTQ0XI9jjERy07FhLs22jbnK6e/f39/Hee+858Hfv3j3MzMzg7bffdhssDENsb2+7CqOMR3/48CHu3LnjKi2xz8PDw5icnMT09HTEfazWVWWsGkKkuVQqUJPegJp3qR4yjpUyQBv2dnx8jHK57AQemW2n04mAX+t10P4zTPHJkyfOO0VrK6ty0ptmQ+h0vDhfZORMTGeIAdt9cnLiGJ+WJldLjm2jBTlxiqe2S68HBluur4NY8YtWRCr3VEpUIOgn23NychIJVdG9TAXE9nVoaCgSWkXh2e12ncVV17B6mtRqyeIPOzs72N7exvb2NnZ2drC3txcxHFjvrbX4x5H2hW234F5zIi1wVOuzGiqSmEvrJWN7tZ8qdHyeOCvEdNwt/+VaYaEECkjf2lYPP+dUQ5aBfhQFx0+jKtSoYg0y2hfrRbIgWPulKQcaGmY92z5KUrhaz4DPI2QNACQ730o04Gj4oR7CrcSxYbEzep0IUvgu3Qt8hwWwalAgv81ms46f6rmsNBKq0Yn7/ODgIJJHR8OrKqp8hwWUKkdtHlZSfHXQc+Pmb9DcqSJu9wX3IlNgqJS3Wi0XhbG/v++qVdqIEB1nzTtlG/kOEvcPo392d3dRKpVclex8Pu8Ajxp+VLlV+aagKm5cBvGqpInry65tfnJ9KRDTOfNFy1BX0bQYpv9Qv2G0Sz6fR6lUAgDnFWQhHS0oxznSPEga5KlT6Z7kfiPvY5oOiyP55DmNr/RM0uNpcxyVx3JdMaVECznRMJXJZJzh+WUirsOhoSEsLS3hS7/0S/Fbf+tvxfLyMoaHhyP8UnNVaajzGfd85DMm+u4jcHzy5IlbU770MDqr7t+/j62tLQfokzhj2kfXik6owLKqIUEIrfZAP+ac3h+GJVYqFUxOTmJiYsIpHgBcaIWWoAXObnhf/pEqKlRw2u02Hj16FKmUdP/+fczPzyMMw8jmIKPudnvn8GxtbWF1dRW3b99GoVDA2NiY61OhUHBlsVm4h0nKakUF+iGgmsfn8/AlSXqOnoJ0MkRbyYtA4PT01AktJtdTCDHEV8OSrBKpCgD7rN4QAg96NDQB2SqNvDeT6edIVqtVlMtlx8R4LYsvMdeD4JTrgM/xebRUyPkUdiUVNjcxn8wv4lhp0SUNe9ZwGRUkzFMgQ1ILJZVVhkDoXtY5pQGHP1z7OqYa3sq91Wq1nDWdYeiaz2jPFrsoWFRhACDSd1XslXFb443uR1Uqut2uU/CSJttf9abp/wcJLp/hTv8maPR5cCzY0+gBGigUVOj1ukes90X3g95ngYAFkz7QqFZvzSGKU+ovuo6uSsrTbD987bDjpWOgbSUw4BmmVBSorGpIPr0LPLuxXq9jf38/co5cHA+z0S/qGdT9H4b9ox64FihT1BDItmukCSt++jyLvv5bg0OSgNGSNVDqdyTfGley3hvyNgUglEdUTvf29lxtBUZgUM4xHYAAj56QTCbjcr/5bD6XCjAQzeGn8Y7rZGJiwkX+KJgA+oVm1NCswCTOqGbHR0GZb7yuk3RfKY+3c2mjctQArDJd+0lvYKFQcIUVeawZQb3WWmA0Fb2OnHcL2oaGhlzhN6BXsIXvV0M3n6FpFNRntE/6N99lQavNb6Tst55FCyjVm21l6otM1khXrVbx4MEDfOITn8Cbb76JarUa0TOp47LAkRqbB/XZt87U6GLlhI47dWoagsiTM5l+Uc/FxUXcvXsXa2trTo/yRWJeN10JOFoGoRuScfW0WKkVggs3m83i8PDQ5TaOjY258sXWStLpdJwngoLHZzlSRUSZkr6TC6DRaOD99993QjKfz+Pu3buYmZnB/fv33YG7+/v7kRL++/v7ePToERYWFty5SZqHMTExgZmZGSfYGf5qj6/QzUdlWxVVn9U1CdKQT7WkWQ+Ugl8e9sqwxG6364qZNJvNSI6GjrnmSOjzVBBp2Cut0zRC6FzTQME5ZniqntloBSPzT7PZrAuxpILDOWFbdTNzjlQR4rVsi7aN/+c1alVPgvL5vFNKCOw51jZPU9cUmZOGIGrZbs4JhYZ62zhXNkeX1yhwVAssPSUs+LC3t4e9vT3s7Oxgd3cX+/v7ZwSU5hdcliiEVenUMVAvlc4jgTD/5ndJRgFcZL8P8qZZxVXXsxpELHjUfFdND+AzrSdIFRBd16rQWGOegiX15KtSx09Vyi2IUA+q9V5r/+x6uSnQyHGwyqnd/zZ6wfZZ20xibg1lC/MaNeqDe0+9SXt7e9jf33eHwIdheCZUnW3Sd2r0ggJ9HXcaf/U4B0biqGHw9PQ0EoHD9WNBoW++9Zqkw1N9xHdZz5qdJwuMrH7EdUtjsaaFZLNZ5yXi3DFvaWNjAzs7O3jy5IlT9HleLkGC8mECRxruCTSZnkFvL58FwBkAyJMJ7FlRkvOoawCAVx7qmLHvvv1o19tNkc9AxblV8BgXCqxGbgJHVnDX4m1AH2irk4GpHHyGAnOVAZlMxuk+lMEassi1w5BiGnTU4KM8XdvM9cZ225xMoJ8nq2c/qr5Kw53qOTfBX5OgoaEhLC4u4sGDB7h79y7Gx8cjvIeGmYODg4hBnOQDjz5jaRxZwyEQ5fcaqcE2Uf8ul8uYmprCwsIC1tbWXJHRpOnKmpC1NJFodaEbv1AouEVGUBCGoQtROz4+RrVaxezsLMbGxiKLnPH9q6ur2Nraiii2QH/CeD0QzTljGy0oOz3tHf/xwQcfYGRkBOVyGaVSCXNzc5ibm8P8/DyKxaJ7Fu8/ODjA6uoq3n//fZc8y0UG9MIG5+fnHfNuNpsOONqzYDgmqqypd5Qx8kltRLq7bclfjh0txLyWhwvTopbJZFAsFt13en4Yx5nKA4GeesQODg6wv7/vrmMYBoFnsVh0VjdbJEfXHNcUzxRUQ0W328XBwYEL1+p2u85COD4+7jaiKp2q1GkYo/YljklY5UEpKUMAQ76o6LHNNgRMPYgEhDQOaOVUXst9rHmFNAwpwFblWD3YVFLJCLWQByunsuADf9rtdqTw1bOsfVVE1aNmQ5EUdPA+3fNq+Egy+fxZ+uwT2BaYcQ+pEsh9R+80x8F6ZalEqYDj8+04Kw+xYIljyKNf2DYqPr6wau2jgkZV7HxAwu7FQXvzOsm2me2L4+U+RdZey77bqps0aiqPVd5ar9fRarXceFP5INDLZrORghkKimj0pcdKc2yoHHM/PHnyBPv7+y7P2eY7kh8wt4rGLh94VsXVKuq6LsnjfJFG10FWkePvum51bnz38v96nTUmWLBCb+PW1hbW19ddlBX3F89uZBipGlbDMHRKPvcxCxNxP6ms5BEpNIgdHBygXq87uWDPc7V7X+eXfJP9Pw9Y2z2ZlDHAPlfXuAU8lgcpSOJ64zM5JgrMCS5OTk7QaDTc3HIfUedgOxTUqTGG+ctaK4O6GvdmsVh0/FCruoZh6DybelSGrjsNadUwWd1fGtFB8Mgf9UCq7v2yAsdisYilpSXcu3cPU1NTbh/R4cMoAfI87lvlX6pDK5iOI+5/9fLyWRpF0G63I0WYuE6pqzFFbmZmBtVqFaurqwmOVJ+eyeNolQT9jtaQYrHoPG9qBel0OqjX6+h2u5iamsL09LRjhnzW4eEhNjc38cEHH2BjY8PlHPoWqM+iq4qNtpuT3G638fjxY7z77ru4c+cOZmdnvSGMtPSwSM/KygpWV1cxPz/vLDi8bnJyEp1OxynC9p18puYjANEkbT3+IKkyuxrbrgJAq1yenp5iZGTEha9kMj0PHg+x3d/fd8d35HI5xxjJSAhmVEknU2o0GqjX6847TWDKNcKcgDAM3Vgpc+dYUTDScmYt4xpCSyavFT/ZLp0HAI458j4+WxPnFciyrSSrVCdJFhxx7DlG6lWiBZmAkWuM4UwElb6KaewjhQUNHPQQ21wN3ktlUxPsCRYZPszxvw5jiVrJrZdM8259SqeuCfVw8Luk55J00ZCfOEsvlXxVGDQkSj3+CsQ04V+9JMqXFXirssX22HFVQwTfofNMnmfL22s4rfUyWkCh88Z2+D513JIgyxd860X/z7mwirbdb3qfehQAOB5M5abRaGBraws7OztubxMI0ihXKpUwNDSEZrOJ7e1tZ+RUYyIV1MnJSRQKBSeTNaKGILbVajm5T4VLZQEVYnq57DwoKAEQmXOg772h/ND1lwQp3+anBUfaft9c2z2s8t0qmqxUvrGxgdXVVaysrGB9fR37+/vodvs5aTyqDIDL1+f8AnBrgF4HzjfP361Wq5ifn8fs7Kw7UkUjuRgSaw2JCnh0fKg3cD50zFRP9I3VTfFSkr5T96euJcoI8jz1vHFNa1EwGtWLxWJk7Fm/g++irkKwxjm3Of+tVsvl++/u7kY8+dR1qL9wHWi14jAMnc6WzfaK7SgPBeDawnQkeySZzW1kvzSNyxaGseP7ohPbzlMBZmZmMDMz444c0b5xrjQ0N44U+A3i/b726N7h+mRBI/Lk8fFxd/3wcO8c+aWlJVfs075L+3qddCXgaC1KtmHcYBRWVMK4gFmliUKNHj+CSqAnaOhtXF1dRb1ej1jHqDzYxWoFrc+yBPQPZ+VxIK1WKxIySlJPBNAL69jZ2cHq6iqWlpZcrDgnmwVaqtUqms2mK4WtpAuR/1PllgKe4YNJkCqVFGJUPhqNhjuTkoyKij0ZHUMOtbIYGZqWqFaPmApNMsnT01PnmVbrpZaUpgWVOaeqQDFUhBZ0O8ZUVmkQIEC1QIXzrOE3BMQktRCrYYLj6btOQUcS5FPorQXLAijr2SZoVO8u77XHRHBeed/Y2FgkXEfz3qhQ0GLXarXQbDbdeWRUPgnsFfRfldlZsKzeLAXPwFmGahm9NTyp9y1JGhT2Ysl6MqywIGij0UP3Icecua28X8OP1cunIVX6XA2h8YWS6rhxf6tiyTArzhXboXsz7rlWwOteVn5/05ZwCzx8AEP3o7bRB0p84JHrm0ZNPfevXq+j2WxieHgYpVIJ1WrVhThWq1VMT09jeHgYOzs7ePjwIba3t3FycoKxsbEIH5+cnMTs7CwmJycBwHnE9vf33e+7u7sOxKgBifxF1496WRRw6LjYUHUb/murXiZBVrmzCp+uJ+uFtHNmDezWIMX0ns3NTTx+/NiBRhboo15BXssUjna77c7XYzE4KvgcbxqIx8bGXEjb8vIybt26hfHxcWc40OOPGJkzPj7u+IGeXW11Ps6rgrI4srrZTdJFeIDuLWtAYzqIevkZDszxol5LDzs9jxqmTMBHgMn5577d2NjA5uYmGo1GxOtLuUsAyogvG1FDGU2DLD2FjFDg/QSxauBhfzVMVSs08281nr/MxFodc3NzLtWJYcXcsxrpocCRctDuf+UVKkdVtln+YHU2EsP8G42GM1oQu2SzWZRKJdy6dQubm5tYXFzE1NSUi+RMcn6eGTja76zQ04XN0EEWwBgaGnLliDWUMwx7oaxra2vY2NhAq9VySo8qEfbdKoR84FHbxd81WV1BonoA1cIbhr28SyauV6tV56HkRI2NjWF6etopx+rSV2XaKunWojWIAT8rbW5uuhhpBeMEabQ4MmeF39PSxWsV6JLhUvlQyzPDk/idKqK0kNN6qucCkuHmcjlXWU7Hk2EXGk6l3geuEzJLhuTx/XZu1QAA9C3lOhfKKHyWVV1fth3XTfTeKOD1KVSWqdkQVRViavUH+nlb6qmk4NGqcmpJ1zAfeina7bY7Q5Wx+DSePOv4sH0EQspkNWdWS977FAnlFz7ekSQAiVs/ce2091hFVY0GnA/ln+qV1TMuNc9U15KCS/Iu5ZFUMrVojVaQ5DOUv+p6VIWa/bCgUYGjWusVbPiU9psCkNaY6ZOT55GdUzW4cjytceX09NRZp5nPmMlkMD4+7gADz9vlAddjY2PY3t5GoVBwJd25x+lt5LWTk5PIZDLY39/Hzs4O6vU69vb2MDQ05AArPY/NZjNyPp0an2yhDQv+9Xdr8ErSgu6bg7i5i1PI4q5X5VNBr3pjO52OO5KI+d4sZqT5Z6qzsIp5p9Nx9SFolD8+Pnb8jl6V2dlZLCws4NatW5ibm3PHbzDyiAZhglICUwARHU51MeoOOkc+Y07cvHI8kyKVg9oOa7TQsEPqK+ybyjQ1gijIVGM/xx3ogcO9vT0XBswzjvk/7mvuXeYl88iU09NT7O/vR9oM9HIlCeyVz3ONHBwcuKgehqsDiBwBYvMb1chNwKlg0ecAUVmapCHnusiuz3w+j5mZGQe66FhQeaVVbsl3KeuA6Dj4+IIaBq0+oYbUuP3A9INWq+X0bC2CVCwWcefOHXzsYx/Dzs4O2u02Hj58eIa3Xic903EccdYja02k54KFSra3t9HpdDAxMeEmi/fRG1Sv1/HBBx9gfX3dKe60NlrQaCfMBx71k+1T4aUeJzJb9VIoaOXxHI8fP8b8/DwqlYorqMG+MmR1e3vbtVEVOAIv9ZyqZSnpYhwMo6ASSFB/fHyMYrGIcrnsFjStawSUpVIpUqlNrV3MIWRVNv6fC/7k5MSFSZTLZadw1Ot1AL2Yba34ylLWtJhRcHHMNFRVi+3YMDsNnVIhoZtaAamemURGai17vG8QWaX7uknD/rhHrFDkjyrsWhSHSsbo6GgkNMxWUyUYU2+jhnar8KHVUnMb+cPjatSaqcr/ZcnnVaQA1xA9td5ahcbyMg3dUUrKgmeFroImq3TZNvjmWpWzOM8dFSDyrFwu5wohKUDRdujztSQ5f+K8gjq+2j6rdNmzIa33iX+rFdi+Jw443oRiY+dKKc6TyHnwAUY1wFDB1HA4XqtnJrLCIytNz87OYnFxEdPT05icnMTc3Bxu3bqFiYkJ7O3toVwuY2Njw4GVMAwdaFxYWMDc3BwqlQoAoF6vuxw5Vnvk/m40Gs46rtEH9hxflprnmaBx+1DljwINIGrcTYLi5nGQEYe/23mkHNIiI0D/iB/OXaPRcNEYnGNVNrkeCDLq9TpOT3vnTLOSOfcBQyhZOG5mZgZzc3NuDdgjG05OeserNBoNN0fMr6JXTfkAjb/W4Gq9jqpbKcC5KY+jD7hqu+y6oyxRPYLX69EM5JkEmhY4snAivYnNZhMAInJFeaWvEA3nhZFZeu3h4aE7C1IjDzgvBKo7OzsuXJXRfTy9QPeori8Ne9fKnupY0XFRXegmjDrXSdVqFcvLy7hz5447Q169ijTEUDem3glEzwm2ziugj2eAsxjEGk71XjWAch3SOKcGev4vl8thfn4eH/vYx1xu7MHBATY2Nlw/fY6NZ6ErAUcdPN9i0QFg52kNazab2NrawunpqXMP63lB3Gzb29t49OiRs4QqwrdkvSm6CYAoM4jzJChDtJ5GvZfgp16vY21tDVtbW1hYWHCVyPi+sbExVCoVVCoVV/lKQ6hsWW62n31QK34SxOMbaJWiR7FYLGJqasoV/eFm0fawCIIucj5TD4VnXwnEOLb0WBWLRRf2ZDeLgiBlUhq+pNYzehx1fAlOdJ2osqGbj4qufheG0bwH30b3hczZ9cXvkiB6FjhHtk3WmqqgkSBQK6RREFgPPK/V41LIxKyHXIEjLWX6wzMaNcfjvNyBOFKDCxmqLeyhnkYaF7i+1HPDOdO5tu9KinyAxq5/qyT72qM8l/fEgUbuW/Ib/q7rV/eeKlvaLp03ex3baZVv9fRzTdkiDdpeX9s13N8q6/zU9e8DltdNakG2Y6Nj4Wujz/hK3sTIBx5lo9X9MpmMC1GkxwiAk0EsPjc/P++8j0tLS6hUKmi3247nM7fqyZMnmJycxNLSEm7duoWZmRmUSiUHKDlX2WwWnU4HOzs7GBsbc1EEjUbDha7Tk6UgSZVjNUzo+ua+Bs5WpVU+nmRUju6ji5DqITq/ChzJ9wC49U6Qtr+/79JbFDRy7MIwdMYanr9Ib6WmaxBMjI6OurlfWFjA7OysM/pqKkkmk3HHntEgzEgRphOxgBbnTIEV50rrJVjDh9W77LwltR/J43yGgLj50r3LuQvD0DkH2FeuT3ooqftw7gjiCPbpsbK80xcxoe3iXGifCBz1KA3yThoB6LUkaJyYmHBpYQSNqqep95O6k0Yi+aIEktx/SZCug0qlgldffRUPHjzA7du3Xf4giePMIzisLFZ8oKBdQ3mtHOQn1wzTAqgvacVlHWstmkS9i8WSstleSPri4iLeeustFxFCI57t93XQlYCj3Vz8tBuSv3Nx2VAWWi4pXDKZXtXGRqOBzc1NrK+vo16vRypDkUnZgeB31hsUB8S0/SrAVGm01k69p91uY3t7GxsbG9jd3XXeFzLPoaEhFItFTE9PY2FhwVn5eA4lF4INAVChmKRVrlAoOO8pBRXbrPmVXMRc6ATwHFtVQFWhsMqZFRicD2VKqrgzjpvjrSA1m826RH/GpeuZVrTC8jgVHqJrLaNq+VMArBteC3X4lEGr+PkME+x/EmSZDBAFIRxPgmJV5NhnjhHv4zWqFHAcFCzacyJ1/G0xHHoarbeR6+eqoFEVIPU0UpBrzhbvAfpzqJEEwFkjkwUBSXmOLcUpOvbH8mJV2uy+UVDHcdc+cjzVy6EGNbbJPs8KyEFGFFUydb40HI/P0LaqB9SGqWq12ziQzWcmDRz1PXF73jcmVr7wU8PGCAz1jDfOtyp53W7XhZryfGQWoJuZmXE5i8Vi0YEBNbgcHh6iVCphenoalUrF5bmHYegKtDB/i6HqjDrgfPGMZspl8g32Q8/qVSVc50b3oBoTLMBMguLmT/eXbYNPPqgyTlCvcobAkcp6u912c6xGTxpDCTyVh+q7tHQ/q2uWSiUXIkneTp7LAim+IinWi6+GDLZJI1RonFP5cx4vSHIfAjhTVdSSzpmCJ/UAErQxn5Syj/yTRuzx8XF3RBmfd3R0FBlT3aMcK+Wf/N06DQgseGQVQ1GVdypw5DohHyiVSiiXy24/q6cSiBYT1DWoHkcLIF9mKhQKuHPnDt566y28+eabmJubc3uDa+Xo6CjibaQc5PyoHkQdg/docSWuEwCR9VIul12IOcNRqYtzrNXwqXJAK+ySRxYKBSwtLeGtt95yZ7++9957DnNcJz3TcRxKVslSKyLBGZVHMix6ihSMsLLY2toa6vW68zZaYWGBFZkQBZBeawWTPkPb6uuHEgUYmWer1cLm5ia2trZQKpXOHLsxOjqKubk5LC8v45VXXsF7773nQoE0v8syV12USVl01KLLhU0FAIhuGo35V6sjgYWGFAHR8Mm48WSfmWOn1jidGzLkg4MDJxTz+Tymp6ddmLAegksLLPMEVKmhYFAvDIlt5HyoR4TzroLSrh3tq4YnKGNOgtRyqV4LvpsWZSv82ebT01PndeT9tliKBWf6o0YZ9pOWMQsaO52Os7z68t8uQ752aagQQ2n1vDIlFda+PWg9bxzrm7KwquHKfmcNEgpUrNFAlVxV/GiY0eepBd33HqsYW3BJsuvf/qiFXivxavsV8HKd2Bw/fa8qXnyOeqxUFiUJHC9i7FO+ECdr2H9VFnQP6VrkdRw7Gu9YSZPHY5DXZrNZp9zQA0j+X6lUXPVTyuPT01MXaucrzQ/00xG4ZullabfbkZwcVg/ldxoBoXzZt9Y5XkpJKbD2vT5Q75Nvuq64filLee4m+0ElXD2wNiRZlVmGq3Hvag4jeTCrrxaLRQcK6ellQSO2nfNNmc7+qDdEjwVTHYlAlsBMiyCq8UYNtr4xtGN23WSNz769r4CR8on6iHr3mf+mR2Nw3RI8xBno+Hw18vqAOceNpL9zvClfOf/qFKGhhh7ikZERFAoFF/3GEHON7mA72X8Fjpqrrp5Rti1uTl9kymQyqFarePXVV/HGG29gaWnJFRriXuPcE4BbfYjzTaMLzzxlnjj5q2IGyiSGDFcqlUhuMo0A1Lv5PvVcK8/WSK8w7HnEy+UylpeX8eDBA2xubqLZbJ4xMF0HXVsSnU/JUiX78PAQ9Xodh4eHGBsbcx44MiEOzt7eHj744AOsrKyg3W67Z6sy4gOOlrgpbbipr83aXgotLg5udFWu+N2TJ0+wu7uL9fV15/mikkprQKVSwZ07d3D//n184QtfcMnKVAB8IXoaCpGUokoLM71qTMJXb5MNp1DBwj5ywfI6BVE+RVM3Eecnn887xsRFTqDK/FctUpPL5TA7O+sKE2kbWSW33W4jk8k48KDHq3DMue6U4RJYqhFAGbtl8iqIVDm2Y5CUZZxrU71H+mPBjlV8VMDpPtQ5syBaPYzaLyoTVJKY99RoNFxBLLVgXtVyybZwDau3UUuXaw6m9ll/t/Np36O85iZAowVgF1GcLUBUS75vnjnHagm1+4HgUd+rPNeG0ahH0jduuoZUMdUiFD6eQ2VaQ5q1wIgdC653G12iym9Se9ESeYAS/7bAke3j3uPvCjpsqCN5FpU7zp+1glNW0etVr9fRaDTOgHF9Bot6NJvNM/N5dHTkwqFYuI4KtRodqIyq0sXwL/VQUcHV3ONB+01l9k2SDxzye13/cUYPa5QE+p4efq+F4QhQtPgb0Jt3nkWs+4upGwoSCObJdxWcMzSvUCi4NaVpJHrMFUkNtNyTCoKtMcrqbiQfeEuC4tYI36detmaz6SoEUy8C4MamUChE1jmJeqJ6f/Q9dj10u91IHqOvjdYgof3h2HMf2VQq7inOI8PVNURV63ZQt1KPt/U4Knhk+3xjbR03LyKNj49jYWEBr732GpaXl1GpVCLtVoCmzh3KRYaBU7/gqQCMqFCsoONLY4+GDJNHqx6jsoH3k+/rESmcRzXAjY6OYnp6Gvfu3cPm5qbThTVy8zro0sDRZ2EYZDEFehPBcsOnp6eYmprC1NRUxLNBYbW7u+tyGxly47N+81NDIyhsrfCx+Y3WUmIZfiaTcYoNy1OzjdycvJ/n7uzs7GBycjJSpIVMZnp6Gnfv3sXdu3fx8OFD7O/vu5A9TqYqe9ls1nlOklJyyEDU68K5AuBCV/L5vBMGVBCUSfmUHgUwvrnTOVPgqEUChoaG3GY8PT11obXMFy2Xy679ZMDM0+BZVKwaaytpkvFSWHO+yHRVwQXgBTiquOra8lnKkwQdvnBDXd/8pEGA4WQaImOBo8+DpPPp2z98BoUPfyiAVAipdf0ypKBVQx0tcLShVzoOg4xN7A/Jgp8kAYfPYmtBhq/tCoh4rfJDfqqVm3vG16e4vuoaUAW/2+26PeozoCgv8IWoapVlnzdcgSMVF+2Pjp9P+VS++rxIjUg6p3FGJfZPvY0sDsZ71UClXg19Hnni3t6e2/fMh6ORR8czzpii80o+wuqqLG5G5Yf8SD0hamji8+jd0H2r/7f71ueNiVO6k6RB86htsqBZ6wJYIyYA54kolUoO4LHoHo/b0MgK5YVUaBmdw9BU3mePqmI/uHZoMGAhutPTUwc6rDGAIEnD6NgPrgOVtXGgUWVnkmSBrOqH3A+7u7vY2trCxsYGHj9+jK2tLYyMjLhaD5yfo6MjpxNppWDqDhqJ4zO68Zrh4WFX6ZR8GIjmh1tZ7pO3ACL6Iw169IBNT09jbm4OMzMzzlGj+5HXa2Ete16jhsBbg7JPh37RKZPJYGJiAouLi1heXsbs7GykSBfQP2edfec61nB+PYKMRhzOLZ/DImVMDTo5OXHhzATxqnvaOhHkgRx7rcJL4Kh8FujXV5mfn8e9e/ewurqK7e1tl3t+XfN0pRxHS7rY9TuNi2bIWi6Xw+TkpGOK3Nhh2M8b3NzcdCGtGvrJ51rgYhUIm38DxCfU241I4UhGr4V7gL7FTS1tu7u7WFtbc3kErEhKhaFUKmF5eRmvvvoqPvvZz2J9fd29W0Euv2OuIQsRJEH0wljXN9AvckNmyVANzqmCLJubwk1mgaMqH7pWCM4UOHI8NPyJ4ailUsm1UZVmCgECfVrabMlqKqNkCBqyYX+AaEgvSfsa970FV0kJSAsAtC9qCFHmZL1Quv7pxVBFkmSFBdDf+xqiqkfuMORKBZBaLi9KVI5oVLH5cbZoD7/3Afg4oO0DUVZRTGoeLQ/T91mvsbZZ+Z3+X5/FT/Is3Qv6HvWaANG1xDHWcCVrfPAZUizg1JxYFZQ6Dmqdp/zQwgEWZFpQ4TNq8dlJKzmcK59SFbfWbHusF4DeRioTCsS4p9SLT2Xy4ODAGWsPDg6wu7vrwtSA6HmpcaBR28j3EpDy3D8NV7W8W40DuofpeQT68kbrC/gMbWyXTTtJggbxbLsXfcBR5ySbzTrvuoJvzi/3Fo9O4fmZDClXzzx/VP4qINHzHvXcRzV2c9zIH8kH9HBxhvtr1U3OKd+nBkc18mi74sbRzl1SfJXKu+4n6jTMGdzc3MTa2hrW19exsrKCer3ulG86Azg2/J11E6w80Ug1jpkv7z6fz7vquWrI5tioXmxBqNVHFbQyD3Nubg6Li4uYm5tzZ7gSIKm3W3mMGnz5o6Ga6hhgG7VdLwPl83lXIIxHFJEfcvw5LhriaQ3TnEeCRuqpOj+M5GPYMNDzdrKireonXCusNUFDTiaTcToU0waos2t6Dp9Br2OlUsGtW7dw7949bG1tOUPFdZ0Lf2XgqJZAH0MPw341KIatHR0duSTdQqEQEVA8G3Ftbc2F0VjrthI3i7aJi5pMjDH32l57vVpsNcxJmR6ZpSoeCpparRYePnzomD+9lXzP2NgYFhcXcffuXSwtLWFlZcUxFI6htjGXy7lFmZTrnwvalm/3ucsJNnTMON/KiHR8fcqcVU74PUN0crmcU0T4XhZP0ipSOmYcHwqDTCYTCbHxCXZVrvQ5Pi+pVYbJGFRx1zFhyK99b1KWcStEdF1a4Mpx5n3adutxtJ5IO/9qwaVS/+TJk0jVVGu19FkuL0K0dGsYl1Z4VaGpeY4WmOgc23Bby8T1k/dyTq87XwA4uz7iwKMqJHG8Ua3PfJauT65j/d3uTQV9umYUJPqAhs/QYMddrarWSKH7U4sEqJdavQaqzMQZT7Rd1lN5E6TtU1Bk50gNYQTNmkvIfaBzYwso6J7X8xV5Jp8PrKvHw86fvY5rkG3k/qdBh33UKpNqnFPrOMFwPp/H4eFhxLvF/aZKrp1ntvkmSdcY/9Z5s/NHYw3z4yjnKHupo7CICdA7o4/zQDlojS1q0NEQV/JBrYJtwxm13QocVWby2bpW2X6CSfJ07TPln8+oamWq1SWToI2NDVeoiZVometJw8rOzo4LUWUe6ujoqLuPxaVGR0fdGcQ0Aug8aD8JLBXMFwoFtFot5xSw+f8cC/IoLYClpIYCTc9gbiuP3JmZmUG5XI6Epyroo6GXP1qTgOklNqWEYFH5uOpBLzoxlJPHEzE3UdcmZY+eekBvPnVkNdCoXsq5p05BuUVvL4Gnb8y4Hwnw6bTqdrsRI48CRx5tpMYkhrdXq1XcuXMH6+vr2N7edhjsOnjmM53j6LOqAtFQS1p1Op0OhoeHXXJuLpdzTDMMQzQaDTx69Airq6sut1Gfp9YNMjGr7FORUiGlTNeGrOqPfTb7w0VC64GGn5CB8swUHibKMFw+L5/Po1qt4pVXXsEbb7yBjY0N53213gJuaipMSQnGvb09t0nYTwo1DV/R9vkYv3q0lNTS7wO/ul4o+Gx58maz6cC4bgxuILY7DHsH3p6enkYss0DUA2A3qQ/A2jVtBW2csNNn+4RmkmTBhLVQAvDma2rbVGnUQhvW6mkBA5kYw1tYOVVzJOyxCRdd07qHqYiS8fqK83ANsRiIgha1yup3Vpn1KTHWg84zUK+T7Jj4DBf6P8un9FofWWAWxwdVMVY+a40v1gDk+5+201rmNbzShqQqWLSeRl8f2bY4ww/XnAWoSdAgvqDt0/9ruoa2V8NPVRlXXnV6euoKzVHWEGTasDnuVx948PFw2362RxUUAM4bowY57kPNM1bvo84BvY9UgLl3feNqldUkQUfcGvEBsDjjjeU/lLHqxWExIkb5qM5hIyPsPlJAaSNnLJ/zjZ/yeQX+2keVFVyXGillv9d9Fjc/NyUjP/e5z6HZbLozDTc2NlzuLlMpeARKGIZujoDoWddczyy4p8Zh5ZE0vtJ7pAbN8fFxjI+Po9PpuDOs9/f3Ua/XXRs4juR/wNlwaAIQ9Spr5dRqtYrJycnI0Su6dvh8ehoJGhuNBprNpjsWhmDWkvIou/5fNPBo9+bo6Cimpqbc0UQMAecaJ2CnwU6NYowo5PirRx6IhvTb6AL+ruuG46djx//xOjUeqHGCzpWxsTFXE4TXDQ8Pu1MHWERyYWEBu7u7zmDyrHTlHEeSWi+5abhRcrlcJDGTHWEuBK8/OjpCvV7Ho0ePsL6+7jYx0LdwWObFMA619mgoFhcAPSg6qVYJA3oeK4bHKjK3jFQZr4btHB4eYmdnx53rqMprGPZCbufn5/Hmm29ibW0N77zzTuQQZyV6X9fX16/NtWxpe3s7YonSMVChbJP3VXBRAVDFR8fVJ4Ss0svfNUeDRIumJqP7LDQa2gb0S4PHGQY0ZJPtsEBJBaP9ju3Q9ts14hPMSZC1QPv2p22PMib2hVY2oB8qZ3MaVNnjs5irRE+jTarXIgA6ZxcRMCpwaaUlePSNrVr0yLg535qbouOlilWc0q/KAYBEgKO+T9eVz0ijYeM+QOSbK34qiKLHUd9PYEADEvMzdN9re+wesG21IE5D22yVVF9oqhqx7BrinFjDhv1Ur3nSio31RJF0rVlwaA2HymcIIugh1vco7ywWi5FK3TpX+mz+WADik3G++3w8TyONbOSOekbiogAIpJguQeXMx785jhzjpMnXZ5JPrqniR49BHAgbGuoVfzs9PXUHd2ez2UiahfIAOzdx8se21f5fyT7f9kvfRS8pDfk+HcneE0c3MXcA8Eu/9EvOCL2/v++8jTYSRiMYqGuQj9IzR9CoxWXUGMk+25oJ9DBpSDLXO4+kazabLmyVMpXeXAUc1otJowxDIJnbymriqgcp39XjfTRMVcPPVb7avW51YdKLBhwtTUxMYHZ2FrOzs6hUKq4SLtvNlCmOv4I+a9yhgYF82dd367X3ReaoPNC/aVCjU8TyWYYZj4+PR/5PQMmjeCqVCubm5rC+vo6tra3nAxwt6QLSsIujoyOsrKy4DcsiMVoUh/d1Oh1sb29ja2vL5WQoGLTvUyWYQooUhiEODw+xsbGBd955B/l8HisrK2i1Wt4Qw263i1arhUePHrmwmpWVFWxubkaSY/X8GjJsKm70DOZyObz//vuO6U9NTTlFAegdOHr//n2sra25Q++13WxXvV7He++9h4cPHybmcWw0GpGQE4LlsbExTE1NudhvtXqrZVPd8/w/FRLOE0nBvA9QkiFxE/OZVKx0jCydnvbLJtN9z3ZYcMT7rXXQKr2DNrUlFRAqoHV9+pSx6yIbwqRttiDdZ63mvBwfH2N4eDiSz6rGHY6JMjf1EGmJflXUOS70DFOAanlvO/4AHKMmYGShI4aW8J06Vwzx9oXHMXyD33PurOLlA8ccK+t5S4J8gC9OiQPOnqmoz/ABJX6nIEQFlfXCqmKkY2DXh4bD6XNJGsbHeVfgaD2OanDwGR2Udyhw5rvs500YcQB/ro9PsbZhv9pO7Qv3AYGj9oGW8PHx8Ugf7brR+eXfuv/tPtD9oO20z7YgTufXAi5dP7xHIwbIg7iedC1ZhdUCtiSI/eF48VP1DftuBWo0YtmCTjTWcB7JmxhSCsB5k1jF02fEtHPN9qkewTZpLrOvn3qvlZF2LfBsSZVtPo/nRcZ30N/XRZ/5zGecrFC+kslkXO4oAYLm3lMfYcoRc8xYEIVGdxoigf6Y0Puj+g51LUau8X1U7AnWGCLJXEzeS7mluf2aQ8y+8FPD0rkeaBxmzpwNT+UPizay/Uo6v1yb+o4XjbRNlUoFy8vLeO2113D79m0XpUHi+qZXTh0iqqdqZJ7qxz4+zr/td75rLY+lbsuiRZYPHR8fu2NZqPdqhdVisYi5uTncvn0bOzs7eO+99yLRf1bnugxdKVRVmRiJzJEMZH19HR988AGazSZKpRLu37+PSqXiYv2B3oQ8efLEhRAQTHEgyPAs47OARTfu6Wmv4tvnPvc57O3tIZfLYW9vD1tbWy5WWJ8Thr0w2d/4jd/A6uoqcrmcO1xVN5B6Y6zniX/v7+/j8ePHDuFXq9WIlWB0dBTz8/O4f/++K2euBSfInBla0e12MT09fdkpuhAR6B4fH2NjYwMPHz5Eq9XC9PS0W4AEzWRYvE+VNUsquHSs1ENJspZQCltuGFVCVGCq8sqN3ul0nBfaKivKOLUtVqGxAlcVJ6usah8UNFrwljQzVQXLAgedExs+pEoB81IULKhnyAIRBfc2lFmfwVj/XC4XqQjGT95HgZrJZNz3FOyFQgETExPOikplSsdZhTOtrxr3T2+jForgePlAo41uYBt9IddJkV03dm0pKLL8iJ9x649jZsEjgaPOo3odKZwscLQKo0+Z5fNtWKqCRet15DqzRiY+l4qL/eG42PHwyZCbJN1PCootz2Sb1aCiQI9/n2f00vdafmzH1Pc8tsGn+FhDGdCXv7658q0XFo3wgVFfJICvzUkCR7bdKlj2U8fYKqK63n3GUABnvMTqTVAZZMfT8mXb9vP+1u/1mZlMNKecnjLKbwWMmUy/oiuBjC+CI2mgH0cbGxtnDBma2kA+SB2PwJKVSbVokxIVd4I8lQ2qpyhvVAWfP8fHx5iYmIgUj6OHU8Mk1cup53dqwSQNhba6uVZo1mI4ChgJmNT4q6TPVKMT//eigkegF6J6+/ZtvP322/jYxz6GW7duOQM020wgpuetasgoq0BbGWh1JOWXKpd8upT9sYZE8g7Lg7m/jo+P0Wq1InKb62poaAjz8/N48uQJtra2nNH9OujKwJGNtwuFgmBnZwfvvvsunjx5gjfffNOFmaliD/Q8X48fP8bKygoajUbE22jfB0RzG1Q5UMX98PAQjx8/xubmZsS7QSsNELXUE/TxGAhu9lKp5PUY8T61MLJtfB+Bj4KsoaEhd67j7u4uVlZWXB6o9mV8fBx3795FoVDAK6+8ctkpuhBxgTWbTbz77rv49V//dRwcHOD+/ftYXFyMKDk2VEKBuhUOPmVBlQn+j6TKBy0lBK1aIMMKJDL6VqvlDjnl+Nu8EgpjbS83mV5vhYu21SoqGhYwSIFJUrnh80lxAIL/U4VFx4bAkWOvCjvQz3lUy7llmrSE6vgxRI2hFFSWNaw4k+kX0shkMs4QQMHLvBBNTAf6RSMARMrUM5xErbEElDYngXtOycfw2c4k53KQkD6PbLvsmo27RnkT/6YHWD/t+lAF0irMCsCVdM3Y4zVUobZeLeW9Vhm182QFthXIN0Hadv0kWNTxsGNnlXeOpfJQCx55n0/x0P/Zde0Da/zdB36Ud1hjmQJHzXFUYxDnWiNBuN60CrLyeJ37uPYmzVt9oFF5qs6TjjvBn1bNtGHZJCqlnB+WzmeFcF2/amCIM95epE9W0dc1RN6tvJff+UAqjYS2SIcPsPp0gCTnUOWMeuDUiwf0j2Cg50+LztCgTaOkVtFkriCfoXvNnn+pnnSOPaOsdG0QkNIz6jvaxoJSn6GFPIdeTFsIh3mNjUbD1SVQ76vKctW77Zz6Pp83WRA7MzODBw8e4OMf/zju3buHUqkUMYJ0u13nibWpNWHYS0fb3993R7IAfb3I8lir+wL+KKDzZD7H2xqtrR4+NDQUkRuUGUNDvSPtlpaW8Prrr+MLX/gC3nvvPezu7jqn0FXn68rwM+6FCiqpNNL7xph/dvrk5ATNZhPb29toNBoAcKZ0N9AHOboprEdJQ2DYPg4wN39cOJUuIK1GpgxWN6UFPmwH0JvE6elpFIvFMwKbbZmZmcHt27cdcyBw5EQuLS3hK7/yK5HJZLCwsIBPf/rTV52mWKJVotFoYHNzE6urqzg5OcHS0pIL08hk+mc3+pQ37ZuGElkFSD3GPmGnmyMMw0jlOYaIMAxRGT2reDLvgmEkWlzBCklVyqwgs0JRwYkqnhZ88n5ep/+/SNjOs5CCoDhGYJVnC6qtZdl63NRSpgq+zWfShHAFBZaxqjWTeT7FYhGZTMZ5+/VsOLVms31kltls1gl1+6P5jlo0x46ZHSMFOVbJTpp0XVrlWH+34UJ231nFzLfmOQYEiBSgKpio7HOdcF6UOM/W6s538287vtpO3Su6r2ybffs17rk+QR0ntK+LztuDtk0+HgSczU2m/POFS+t+tSDZ7lsdK5KNltD94QOaDC21c0SlVxUark8qsTrHVq74Crz4FFWrgCdB5/FR5et2v/E7m7NLJV6PX+C8MKIJgMtr8nkqSBZQxskZ3zz75JLOseZeUmfh/lc+w+dpqKQNf477sQaSpOZxYmLCrSUfcFT5fnh46BwPWjWTx8UwH3FiYsLJOupunGsbmkxZZQuzKX9XQE75qJE8uk98soB/qx4LRI84s+crszosDe8EjTY9wFIceExyDp+VRkdHcevWLTx48ADLy8vI5/OOT1HnpIef8o3RNpwDjk2n0zmj8/l+jzM627HivFqDGe9TXmivVZxkc8OBPrDN5/N4/fXX0Wg0sLe3h3a7jffff9+Bx6vQM1VVVVLhx/ja+fl5h3h5bqMV3lT4FxYWMDExEbF0+EAaP32CxjfQKmj0k23Wa3SDa/t879AJtpOez+dRLBbdolTK5XpnEc7PzzvQxvh7MuPZ2VksLCw4RpUEcFTGzYpRtIABcBYLKyis546hDyMjI+7oDDJQjod1tesY2nmgAjIyMuIEbbPZdAoL26Q5UhRuBI6+kEKfAsQ5toq3GgrYLs0vsm3W5/B//L8qgkmQja/Xd+t65Vq2BhKOi4YGcg9wvtXjwWdZgEHLKT1/FnT5FH2+i3mJQ0NDODg4QD6fx5MnTyJ7XENpFZDSyMFCAfQ2Knjkpx6Ya9tkwaIFjkkBDZLdD5Y3+XiPrlmfkmmFuU8RV4pTTHXeNQ9SlUD1NlmyfFy/t8Y+C3gHkQ/0xwE0XncVL81l6TJ90GtUVgGIGDGBvsHHPtPXf15jLd+2UncYhhHZo2DV8jsNQ7dRKFwHVMi4F7XN5NWUr/xdz8ZTw7EqS+rds3shCVK+rmOsv8fpKapP6NzZfnHfKE+l95E5+8qPfOvbR742K8/wjZsquvl8HqVSyc0j+SzrGqhMoywhf7X72b6b97PPSQOOcrns1qyGdlI+UJ7Q25jNZl3emBoh6WnkfKq8ZCqGr9gX0yOsnNGQUjsOBJLKey355lj/R6+lehcVKPKH+XNsO3n8RXikztt5a/KmSY0gU1NTWF5exvLyMqrVquNFBP8aDcB71ICuqVE+HVZBoOUbqkv6ZDmfb2XqRXQA8lafh59GDQLhcrmMt99+262DdruNlZUVdy1wuaONLg0cVXEgKTMhIykWi6hWqyiVSqhWqygUCk7h181SqVTw+uuvY2FhwcUVq5DQCVHA51OifELFMiUfM+OkqFDm5rHWTRVmgD+UUSdV+0BmMDIygpmZGVf2mABJFXdfrPp1UqvVwuTkJCYmJnD79m10u113zmaz2cTa2lrkzJpsNuuKHh0fHztFvdPpYHNz050hROCoFh2CPI0bV+slx49jRAtfGIZOiNKjxfASClR6y9Q6yHmxirB6yOza8DE+e42GJqgQVIHA/2lIdpLCkZZqFeYEfQq8LDC2zFHXHqsbEjiyj/Q0sD8qONkGn1fR/o//Z3s1bIht1tA43Vf8m0YohsgqaGRxAPWkqiFA+RfbZcPxFBTdhFBkWJMaxwbxNFXwfcDXKnc6dvy08+Mba/td3Pf6DH0Pv/e9X3ki15Z9tu9ZqjTpd9Yo4VOobgI48l1W1th22P9xH5G/cY91u/2iJKTzeIoavLTaKteIGmG0ojJ5hRpmdA/ZwhxqZAKitQ74vUYUaJQQ26nh5OoVYj+1P0kZ4Czp/vDtfTWe+u5lqCd5LMddc3rJB3nOZhiGLsdMz9HjdeqJGgQafXvhPL2I7QPgUgvoZfMBR6s3UV/xzZ2Oix1b33XXSZVKJbJuGXnCg9szmYyrCk7AxvVJ2aKgkcfLcby4fil3aEhnwb6JiQk3njS0MHpK5Sj3JPUiNcIMOp7GR1xPzFtk1BbPjNzb28Pe3h5arZb3mCyrk/l4vBo/+N2gPfG8iBF+em6jhihbnYh6BdCPfuS88Xr2XX98eAQ465zST/7f98P/2U/7XE2zUl1F+0S+UywW8fGPfxxbW1t45513HHCkgytR4KihKBxI7Rw3wcjICCqVCkqlEorFYsQdr8yHRS+sdcw3qEo+ZUW/91GcJc5eoyDAAkW7ieIGXIEFSS0YpVIJU1NT2N7edgKCll8FWhrCcp307rvvusNiZ2ZmkMvlcHh4iG63i/X1dWxubiKfz2NiYsIdmErGube353JTKOy4gDUMgH3WyptqpeQY2jEjY+W1LI3c6XQcM9dcGnq5qJSo54VjboGj3fS8Jk5Z4CbV7613h6TKsPYtCbLMXgGjgifLHHSswjDE2NhY5H4KLxoK1OpNK516R3z7yP49CGSr0kGjgV5rPX8qhH19VQ+lDWvTdaYgVkkNZDcBOHj4NykOMPp4oo6VhsGxj6pwnwc49P36uyqKukbsdYOe53u3VXTj+LLvnrhnxF1zE2QBB78DogYWlYFAVLEn7+DaA+AUAAWcKpvi5oJeK75XPR4aAWQ9zDpeBHO6z1QRt558lYeq3Gi4nh0zVdx84Y4cK8tnkyK77+PWLduvn1T26dnS9aDeKP7N/HzmOR4dHaHdbmNvb89VjubetmvA1yarROrYWX5irwd6a2ZkZMSF7qkBQT2FPmOc5VVxAPGmgOP09LTTN6g78IgnyplutxsxthLM+fJyqYtoDiKjXtSAyUrgnU7HnV1O4E1jAvVLylfuR/WCKe+w/F73M/d4t9vFwcEB6vU66vV6BMTS08R6Hsyh9c0J38n5sXOppMbkm+KzceTjXdRx6ShR0M55YOQDAKcXqfHLGm3jwOB5ADBurfu+931n+aDqL3YOVIbQacPjSHgsIsNVLwMagSsARyaH2nwWCjBWbgJ6m7ZarTrLDuA/zFQRvaWLMBWrJAxSYuOutYLAMjnfdfps+0zfBuIEUbDwnBUuXpZfVtBlLc3XRZ/5zGcwMTEBoH+MQSaTQbvdxs7OjitUNDU1hZOTE0xNTTml5uDgwOWY8cgEPTeIP1wXCtpUqVfApuOrjJ7EcJJ2u+3GiRveZ6XmhiLT11CcQRtc2xS3mfhsfmp7raLBdXDZjXlRsgCcSp31uqmHTcmGgNkfzpd65XS/W0ZqxzKOWVrPra57tlPfSW+vHkPBKrqWf8RZA22/tK1ksBZ8qMXe7v3rJAsc+c440Of7pMGJR6pYj3OcQLO/+/4Xt2d89/po0P3AYKAXx799n3EGC187kqDzFCiVgzYUSpVE/o9HImWz2QhoiAPv7K8aHxn2aENS+R7yar4H6PMrX10AIHp2Gdeoggvtrw1xtV5DHRMLQOw12k/9vG5S0K7zGSfvfXvKtxbp5SVPpfFK00I4Z1otXMEmKS4iSeWTGgTYr0HAjroJK28SIPFegptMpn8WsCrOdjx8Y2THNEmam5s7Mw4nJydotVruvHDm/zGMk7oPHSDUkzKZTKQ6KceG8nBoaAjj4+MoFAoolUqRcxJ5eDwNLfTy63hyf/M76hY0LJC0H76q1J1OB3t7e2g0GhGgy/OV6SCw8naQ3hrHV9WYzDF6kSifz6NcLmNmZsZ5HMl7VS+gkYf7k3MaFzl2UYB4GTnp249Kyo/YB64/5as6l1xDo6OjrsAPT5solUrY2dmJTTEZRFfyOKpVUgUHLWgsWzs5OYlqtRqxsOgA6MK7KBPxhTP5lBySVQbj7rXPUMvaIGXJPj/umWy7CgmGgXJ8GI9MYWuF53XS5z//eSwuLjomp8yHC1BDauzm4RooFAqYmZlxCoNaKHXctM+svuYbG7VmAnBWEY4P0M+/VCWDc6HjT+DB9ur8+OZU59JW1tJ3sK0aYqKhltaKp0DruolWMrbVVrjTcVJSEKhePbsfqfBpbquuBZ0DC9bi/qdjw/HV/aQhdRSGGq/P9mtblOwetCDYjofPA6JrR8Prk1J2xsfHI3wxTiHTT9tX9VRdBjjG8St9h33XIIq7xiqtvmt9IDAOGA66ls/WPe1TAK6bFHBYUoOS8jhrILB7j7k4ylcIxng971GQweJhjUYjUmJfFX4antTo5QNFyu9onAjD/rFNDP+z4FH5gM/zr/3X6wYBHLtPkiDOozUYKf/Xa+26jtM7rIHK7i2OM8/Zo9eRsg84WxzDxytsrrb1VNpxo6xgxJAWVfGdr3kR3kTiGOj1SRlSLRUKBZyenrozhhlBo6HCrH67v7+Per2ORqPh/qehujS0qDGVxn3qJAoi1QjD9B7m8jKaR89n5PO0iJQF/pRNWnGe+5F9ZF4jC97wGvafskHP6Y4zjMStY+UHLxLp/gN6Oa63b9/G7du3MTk5GavL8F6tb8Lv4uSFT3YN+u483SEOnPNdyjO4JrSwpuquQD8ijuur0+lgbW0NGxsbODo6cjqi8paL0pVyHNkR3yd/Z2y4Vue0SjpwdmP4KE65iFOgfBNlJ0E/laGdBwTjGKMVtnymFToqeLTypHrkgP7ZO1apvU7Sw3Ap0IaHhzEzM4NKpYKjoyMXqkqv8fDwMAqFgitHncvlXP4KBRwXonoN+UkPmII53/jZsaTiZJVAm69mFURrlbECT9ehLURABs5+qgJz3nqy6ygpwMFwGxKFP5mC9bqxLQREcaBRBZUKTbXU6VirUsH3+kJGfSDWgkYqp2pVtUYLX3iGT/HV/g4SAqoo6n0UsuoVT4J8+/yiQE8/dT37AHscv/a9O+5vq8gPalNcf+LGUp896D1WSPNae4+vXUnOo89wZMknt3Sfat+63a5TLnVcuL94je5Nyg56rdrtduS4IjuGNt/HghqVW1RGmBulFnoarSw/8IENnQ/raYzzON4EP7XzosQxIA9SvuIDuhZg+n5XAxgQTe2gJ4yHkfM9ejzDoHXD8dLcSusdUnCsRg/1VPM+31zaMfOtGwuQ9dqkifm96nEjwLJeeZ6f3Wg0IgUD2W8t/sMigicnJ+68b86NHtmh6RCcVyrrHAsCTo2i4XtUN+Q+IVjluiCA4HpRT7HlGfzhsVucS+5h37xYWQucXcs3ZQg4j1RPzGQymJ+fx0c+8hHcu3cPY2NjEe85yeqElgbJNNKzrGWOn9UxLC+2+4nrRZ1O/J3YAujNH8OXV1dXnSdaz7C8LF0aONrFaD+5KDX3gf+zC88qajpYSnbx20G0C1eFi77Xvks3qwpGVZr12kGg0k6y3qseFIYiZDIZ7O7uotlsRtA/BbENAbhuevPNN7G0tORA4vHxsQN0LNRxeHjoksnJcFgtVj0yHB+GfhCA0vKsQlXjri0TUiWCGyObzTqLO9eLzYWx82k3H5+teXHW+mmrpiqA0oRpbSfXYRyo9HnZr5t4Lqn2UXOOfB6WOMuzMjBVEq3C7wMzvN+3N+1+ifP6WDCguVlqkY3zXqqQ1T5oX+IA/6B+ss1Jgkef1/Oin+yPWrr1O73e/k6yY2Lnwqf0+sCb79P33kH7we6j867X+RsEHi/y7mclXSv2O59yYvvnW3u6noGzBY/CsH/0A5VhBR30Muj1Gm2gYZBW/ur6J//N5XLO46F5UFqtUo1H53m8fZ5G682yvIX9uAnwoeRb43F8jXPnu5/jprKDc2fBDa+j3FMvFdcVDbNqVCNRcWTaC0EQ28exVWOrBZs++afGDB//0Ot9c58UL1XimXUcT4ZuaognvXQEju12GxMTE07uaPqHLbKTzWadV0/3h68oG9DXNeK+03HivuYa4tyHYYhOp4NWq+W+1/XDtmgBvNPT04hOoFXvlc/YtaMGDptexP9bOfA8SddqoVDA7OwslpaWMDU1FQHLPr36vD7E8Rq7B+KMRfqdfu/DHT790eovvNdiH3qg6Unf3d3F6uoq6vU6Dg8PsbOz40K1rxKmClwROFpmaJUxVaqp6NkFqkqqDoQOIICI0mdBhVUUVTmkoORmYxEV3quDqwVA1G2v/VNGqozWMn57Pzc/LQC5XM7ln7HSFYBIZUkNAUhqQ37Zl30ZlpeXMT8/j7GxMXeODceN4Q7NZhNDQ73jVWhJy2QyLlZeBZcF8FZYcDysEsPxVK8UST2WXAM+xYljTgbsE3bWqg1ELXq8zoYz+PqizMcKS8657omkLHIEjroe1RqtbdZ9ErfO1eKtxRh8irjOpTJk+6M5TtZSrs/jM60Rx8cnfG3m3mH7bTusB1nJB3Z0jpM04gBnj+MY1Daf0mX3kR2rQXzEgq7zrtd7LPmURB9ZcHdZigO/th8Xue86yQdadR+Q1NCogJD36H7Wa+2P7kUCRuZpNZtNBxZ0feke0bMFbcgaeSGNgIweUqWGCmir1UI226+QnMlk3PVxgFHbFfedjqdP2UoKOMbxG22Hby0N8lz4DFZcG6rwEeScnJy4iuFabId6gv5QPjIcMQz7xlvN6QPgQibJEzX/1faFa9LKdr1G+2YVWwuq9Xv2O0nwz7A8jivXPGUKgTr3TavVcrqcBen0INKAXiqVXL6iDRm0Mk31D5Urupfs+KoMDMPQeTy5Vrj3fJFF1ojI/nA+WHTRt1bVYHve3Ny04eY8Uj2OFW3Zf11zFqQBFwOAcYarOP6s5LuWf1uPo5UJFhMoD+FzeA/XTafTwfb2Nn7zN38Tn/vc57Czs+N49c7ODg4PD688zlfOcdQB0EZzIYdhiHa7Hckxs14AHTBVVHWTcdL5Dm5yKop6fpAFj9ls1iUrk+EODfUOJ280Gi4sgZtIAa/2yYaTAv2cAM0posVHGZPeH4b9c+f0rDu2jR4/ApckrXIPHjxwVaYY686z+A4PD1058Eaj4ZhpGIYoFAquXwTAPmXcghcdUzUgcB1oGKky1TAMneXk9PQ0cuafzhGvt0DDMlNlsHYDq3VcrXI+i7l9jioaKkCsZ+y6yY6bz+rNsaHCqGtZlVc7fnavWsZnv+f7bDu0bdYKG9dejqM1DllmbnmQ3qNh2PSSDAKt2Ww2su/If3S9JkXqubHtst/75lY/7e++vwfdexGe43ue78dHg8Cpgj9eEwcIfW2w91+0P9dFqiTr2tF17uNZvjbzO1Wweb16CLvdrqs6zQIfrVYLh4eHzqLMZ2muE2UpgYo16GUyGedZGR0ddbKNxiq2l/uMXk5GiPj2OJ8fZyg5b52zz8rLkiBVnm3bziO7fu3/+GnHB+jLDqZ78HxkHgehOa/kawT39CxSIaT39+TkBKOjo2i1Wi61hOcd02DM8EYa3HT87dqz82L76JMHceOQNPB49OjRGQOJ7gmgX3iP+hnHi0d8sQ5FPp935zLSkE7gqHonn606ho4FdT31SvqMJNbIq3yFz6G+CMBVdSVRxtl9orJbnRp2zSr/srLcJ/tfJBBJvhWGIXZ3d7G1teU8wRp6HWf48IE7q/Nc5P+WLLbQ71W+nZ72j3rJZDKRdavpBqpb6jxRZ9rf38fKygreffddrKysOHnRaDSulNtIuhJwtIsbiFZhowLWbDbd0Q10oetBo+z80dERdnd3sb+/j0wmg1Kp5Coc6cZoNBp4/PgxHj16hMPDQ1QqFSwtLWF+fh4TExNnlHiGt3Egj4+PMTIyguPjY2xtbeH999/H9vY2ut0uSqUSyuXymbBEDePhpOi5PbrwNI9Tqz+yD8ro+R3zBdlHKtdJ5zjygF89OkPP7iMo5OLLZrOuoi4AN/b0QGpRBAVMynCUFJyoAFLhxDW0tbWF3d1dZLNZzMzMOAYZpzhy/uOEMxAFmWwv54b/V2XeCgR9Dj+VWbC/alFOgrTAhk9Bs8qmz+LqC0dSOo9RKhOkt8t3HdeWeiF9ANPmXPm8jwp0VUBbAOwDw/xuUF6hzpmupaTm0ac86+9WobbXKD+1v9tnxr33In07D7hd9P7LgsZBz7fA8iL3JEVx7/UpysorfMq1VT58QIp7Wg/61vBUC7Qog3mdVoe0CjVDGnl8weHhIQqFAgqFwplS9UC/Cni73T6Tf6dn0VllJ24MfWT7c9VQq/PI8kOf4cLXVsuD7Fq2pLJTz6Dl2ObzeZRKJVd4yHquLS/VyAgNrQT6YbE0XKuSqjLBB2Tj5MOg8VDQ6JObNwE6VlZWHCiyeZ4EcBxnjR6rVCool8sOHBLosW7HyMgIut2ui9KiHOJc6DncPuClwNFWhY8Djyq/OLckddzQAWAdGTrPYdiPkCNoBvph42pAjQslVweSytcXgdjGg4MDbG5u4tGjRzg56Z1lqH3Q8Ns44OcDk75r7KcdC+WTceRzMuja8e0pu+fUOaNzRUeQjeq8Cl2pOI4dXG2susg1/NOXU0GBcnR0hEePHuHdd9/F+Pg4Xn31VUxOTjrlbnx8HCcnJ1hbW8Ov/uqv4ld+5Vewt7eHubk5fOITn3Ax6HqguYIPKs0KylqtFlZWVvDBBx8gl8theXkZExMTEW8fnwP0GTTDRQj4yJCA3mIhM2C+id5PJmEtBrQYcpNq+5OiTCbjLHBM2OYmopWTCgUXG3MuyPzGx8ddLgDQt4TomHNMrDAiWcDGsczlcjg4OMDGxgbeffddNBoNTExMuIR1a6WLU7CUWdv3K0MmKPBZgmyYg8+i5GuD9UomQZZ5WEZiLYv6owYdG66rgNuS9t+OV1y/rRLFsG2dRwpcC3ziPKD67jilJA7oxoFG21/elyRotO3Xv33zan+PA1z297h3WnB53vU+oBrXj/P6ZCkOANtrbDsGgcYk582StWD7SNuva9+3hnUt2/6Qx9LTxyMFbO4KvYz80cPAydOt8SiT6Z+rSENdoVBApVLB6elpRIFW3kjPp/U4qmHU9t0HKuLWEfmxGsGSIPXQ2P0Ut37VkGU9MnF7jXOfz+dRKBScN1DHncdd6fxoKgXfy7HmnOiaUpDB8GftJ+dbjRmWdw8yLupY+ebVt7YVeCRFDNXmugGiOpcCdRrM6Uigd1ENJJRrGvpqx5DPVkBodRHNWY2LgtG5tcDRyljq0lp/gilYqndS32a+J09AUC+cAkBtcxzpPN8krx1E3W7XFTza3NzEO++8g52dHUxNTaFarbr8VHXkDAK/Vp+4KvBSHZ9/+z45p8o/FTjqtfos1VFzuRzK5TIWFxddNdV6vX4tZ8NfG3C0HeBCZelhXquWkFwuh0KhgEajgffffx97e3sIw1445NTUFJ48eeI6T2F0cHCA7e1tbG1todPp4M6dOzg5OYkkAhNha5IymTBj++mNZFgHvXvKPHWz828guogowHQM1MtklWIyIj5PcxTUqmOVwesmVpiiUkFvLC1ZytDGxsaQz+fdOThDQ0OoVCouxl/HixuPi1Pj6q2l2ebjURHi3HU6HWxtbWF9fR0AMDs7i2KxGMnx4Ds11EKtYTqPJCsI+but4An0Sxpb5mKBkYJYpUGK9HWRBbBKCri0IIAPOGpfLNj2jZcN57LjoW2w/IJzZC3c1qJugaNVyM4bFwty43iXCmCbI3gTCo5vnQwCi3r9oDU2SAn33R/Hd3wCzjfXg/py3nP070HAwQpXH1j03XdeG6+DdB1bo4X9m9ercmmv12t9Rg0CNfU22rDTbrdfdZFFQDQH0hp3+S7KgaGhIWdk1DYz+kT3LN9DbyVlryqi/LR91rUQt7a4h8nLrkMJ8pFtryUfaLSfvj7b+3UsGR6pNRkYbkp5rV5WGnGtdwjoexe5DjU/zhrheL3yRrsW4/iub6/a9eyji/LwZ6VCoQDgrBGZHj+C7EwmE/G+FQoFN2bcdzSeU4bqXmOfgLMRBD65yv9bz7xtJ+/jp3o04yqWq67pk/1aOIXh7QSRBJvqvVbDhOpVBLA6vi8KcDw+Pna5fCsrK2i1WhgZGcGdO3fw0Y9+FHNzc+5a8jhr0BhEgzyD/B3wGwl98tZ3D4l7z/5f1x3nIpfLRY4um56extHREXZ2dvDo0SOX86ttvsqcXRo4+qxPPmZiXeN6P9ALiyyXy5idnUWj0UChUHAgjoCRJY3L5TJGRkZQLpddVU8AaLfb7jBTfTaBq26ysbExTExMOBA0OzuL27dvu43E59rcKCvYVQhTAedGU0+i3qfWJQJcBYk2vIHWgiQ34dTUlFM6ms2my4ehwsC4/mw2i4mJCVehqlKpuP5obqmdd+sut/OjDJzMSY0Seg+B6p07dzA3N+dKvnP8NCyY7SBZYW0FoDIAvV8tNzqXuvZ9lh9rQIhjBtdFNsfH513VfCDNcdTiN9bb4GOC7Ifd+9o3KjP2/2rJ0/bxGrZVyTJbzrl6vX3tUyOVVmO1oNGSVWK1bbat101x4CsOONp74sCWTxn30aD/+ebqou233/kUSgV/5wHdi+ynuPWgbUiKfGvbrjtrlKBSZkPWeL+2265vWtUbjQY6nU6kBoGOlXoh6O1nnn2r1XKhrZRB/GROHXn90dERWq2W+17lAGWBejjz+byLPLF9Y9uURyjgsXxV+61RE0mQb9/5rrHgVz/jnsdxsuteaylYGabgnPPAMdWwR5VZlI/aJtVZNFKK7+E7VJdRoOlbx7695Ruz8+5NgiqVCgBE9AWtPE79waYG0aPONU7dgQWGqDup0VXHR3VSGxpowaMFjtpWHU8tVEXDjJ5e4KvkquHmNBSr86bdbmNkZMRVaLXFH61ewPbzb59x+UWgMOzVWNna2oroqIeHh3j11VddXY+jo6PIXlEdcdB6HiTfbDvi9NE4AKhriGNtPaF2D5GfDA8Pu4JPuVzOHadXqVRcXvN10JU8joOENgUH42k7nU6EQdE7SG8V0B/Mw8ND1Ot1fOELX8D4+DgmJydd6NrIyIgLVaSlU4UUj44AcMZjls32YtiVEczPzyMMQ8zMzKDdbjuGwk0FIMJE1YsIIBLepxtGk9WVsesZQJxgGzOuHjQNPUmCisWiE9SHh4cubIkeRra7WCy6RPBqtepixDmfViHnOBAkU+ng3BMU+wAOFz8Vl9HRUReyPDMzg9u3b6NSqZwRVPTg6tzFWXd8oFFBuv1UYeMDnnGKsAqHJA0ATHD2GU34vYLEONBk+2Tnx3ed7/tsNpo3a5mdCh/dV+p55Pu5VqgQ8V4qvrpm4troW58XmQ+93wLfJGiQwuUzTAwSZKqM8n8XAY2DFF7f/wcB2UHt982XbWPcdYPIBzR89yYJHnX9AtEQbV2vupY1ysHye90vPuCo1SA1zJ/P5jPUE0Ejahj2Ckeo4jE2NuaiOsi/6QULw9CdH6fVPu17gX5ooA2BtyDLGhptX/m3Xm+NYEmQ9QCxzapU++SLfur9VmZaQMeQYwtGWPWUqSx8FmUeDQCst8DCR5lMJgJ69LxC6kuagsG28B0a0QOcrT6usl73v/JpJWtAIZ3Hm56VCoVCZNz4SX0QQMRDQ72lVCq53FLmP7L9JycnbhxtRWLLc9RIq//XMFWrDylwVEAKRFN7NEVLf9RhwX5St9VIMI10Y3tpVCBZgwHnTNurz0hKX70KHR8fY2dnB81mE51OB6Ojo7h3756LdKT3WHXObDZ7JvrqPLLrV/mAb91bR5oFi6prqFFJw/ItcOS+o6E8DEPneCNeOg+7XYYuDRwtM7eMlCVgt7e3I1VVqfwx5jqTyaDZbKLZbOLg4MBt5nq9jpWVFUxNTTmhdXh46OLRM5l+KWEOLq1nFHa6oTTmm6EtYRhibGwMi4uLKJVKaLVaLsyHXk8btsFNaAWf/t+GCyiwYVtYXMiXQM3nqRJNAXHddHBwELFY0ZNjvUlaip2Mh+EdNmyZC1OVFAo8VcDVmqWJ2zqWQE8oVioVjI6OYmpqyuWgailqG5Kj7wCilmq7fnXMdQ1boMB5tUJRGagKUusJ0/9dNylw5NjrGFiLp46VgttBP5chfe8gssaSOACu+a9qZFCl0e5J/q7zqEzTroc44WBB40VB51XovHFWBW8QmIsD9r4xUkUvrl86Pj4jjL1Gr/PNa1z77L3avkH9s0q6fZYKZa55Ow7XTdoWVf74P/1e+eZ5Hne2m+tZC86pnKVBld9bY2Amk3GVqUulkvNKAHBGQoJFpnfk83k8efIEe3t7LoeSxloCTLuXfQYDnVOrMGUymTOpAjZ6Qftulakk5tCO/XlGB5+Bwhrg1APCa8jTCDCo2J+e9srqkxeSZypPV0DDMwmB6NFm9JB1Oh0Xqkgjvi/MUQ3vFjhbWcp+2HGw99l74u69TmIFYLum9NgYtkG9NuVy2UW4sVAjALf2NPRTgb4FVUqWD/jCTa3Bmc/Vd3J9+KIDCOIYqcVTBVh1lwCZDhgNX2WUAFOvqOOpl1LbTgMGHRC2sOSLQPSkAsDk5CSq1aorNGX3J/dRnGcdiNflfDxNQaMv6kT3r95ro7PYJgv8fO3gHDBiU/fwdeqhl0YlylCUOVC5a7VaePjwIba2tpDP5zE1NYXJyUkUCgXnjSAwCsPQWUrL5TKWl5fdM/WIDKCvROpmnJycRLlcdpXe6MVkmKUqmgwx0Jw7AK7ATSaTcfdy8+gBvJpI7bMK68JjboJ6SvS9FI5k1MqU1Nup/7tuevjwoRuv4eFhl7tIBUCFuQowjmE2m42EIFFZAeCAKBmPteCoIq/WOu0vmRULMBC4KpMmsB3EBDj+ujl1DWlYAkmtybrR7Ds0FEi9CT6QlpRl3GfpVAZvQ018hgor2CkIfFb1OAX9omSBmOUjwNniSTb/IA5E+d416If9tPfYvg0CatdFce8YBMKUrKDS+33Ab9BzBrVReWpcSJ3++ECPb8/ZdvrAphWqFxkTK7gHgeSkSNcr363r/qKASxUNe9A3i3pQxqoRlNEuLDI3PDyMUqmEoaEhx/fL5bKLFGBeHQviUPE8OjpCLpdzxdL4Hg1XVcu9XQfsD0mBmMpUVXAs79bQepWX100+44Q1PpDsfGm/LsobyecUrLNip565p8CA8818tTAMXa5aGIauEBIABwr0SAqOp+bEcT0qEFFdxMdPztuDdqxucv8xskrfqZ44BUic25GRERQKhTPFcYA+cNQTAjT6zBr643iQb3wJJDlGthqqzh/Xi0bt8Fm8n+Dh4ODA6bmnp6cONNO7ZoEjU8UAOGeL7jXeR72QbQdwpv/Pi+w6K5fLePDgAV5//XVMTEy4Y2gIkrWQp8ooG2kUp8tZHsHfLWD0XWv3g5WpFvDFGWGILfSduVzOHWH3XENVJycnz1hqVEE+Pj5Go9FAo9HA7OwsqtUqXnnlFVSrVeRyORfq0mw23bW5XK8Azr17987kGGpHKdRmZmYwNDSE+/fv4/XXX8f8/DwmJyedFUUTwTmQzIXkxrQKv1ptKCT5SQWTG15zFEncgLyWQBOIhmJpGALfR6DG/3NMlWFdNz18+BCFQgHVahWVSgWTk5M4PDyMeJksEON4WaFqFz/bbxUA9YYRAPA+H1DOZDLOy0uFCIiWNaalXcGRBfK+zcl77I9VYPjJ9qqip0xaxyoOlCVBysS0Tzq+qqzqJxUPDdXlHqGnwgcSrwoedRx07qxirW1WMGKVa7Xc+97B9/jmwTLfQeAwDnxdJ+maiRtzey3JB4h998V951ufdlx0fes7lR9YMK9zp/terfO81nevtoP3qxclzrJvx0SFP79Lah71HUpq9WX/CPr0Gv7PylcdB83xA/rGT/aTiiArJo6NjTnDLXkqw1HpTSkUCu4YD6CX40XvI4GjHsXVbrcdqFHPI5Vx60WJW4uW5/oiJpRf2XWUJHC0c0XSNmlf9Pc4QwCfrZ8cL0ZYUcljtBVzWYH+OrIROjSuEViEYegiufi9L6+RQILX+Txf9jvto+37IPLxWB//vU4aGxtz65bjxQgq1krQcGvuDxr+rRGZPz6Po65X9V75Qk01Kk6/Y5sIEjWqS8NegX5BR50rzU1lO05OTlwoM9cLx0OBo4JHzrMCR/JQAkb1MPL91+XRelay8qNSqWB5eRm3bt3C2NiYM6pQZ9e837gII9VpVZ7o++z7+Wxdf6rncg58DgpLHF/dd3GyW3Vs8nueBWvbeBW6NHCcn593Z+iR0WgjuBGZT1gqlTA7O4vp6WkAPYGTyWRcJaejoyN3TtTi4iIajQa2trYi5YEBOHA3PT2Nj3zkI8jlcnjzzTfx4MEDzM3NOdBolWUSJ0gBTBzoUXDIA14pqChYtbKnhi+ooA/DMBKqqeGVNrTAp9RacHqdtLe3h52dHRwfH6NSqaBSqThrJdugyqLmLFoGxA1mFUs7pmop5fe2zDDvV2GobdExUbCjbeazldnbDW4VV+s9sZtcPV0+MK+gzMdEkppHq9xYQGzbof0nOOTc0eihgM7HXKzSYBVxO/f2O/7NufF5/XR+4gDCRRmfnVcLRnxhyC8ixc2Hj3QtDFLwBgHmuDbY91gl0Nde+x7fejhPEbXrWv/2CdM4Q8FF+vmsZNukMkfJZxQhDVLO1fBCTxUVS/I9WtMJ6GgcZY4j/6YHo9lsuvQSHqBNhYMpAsyT0+MIqHSyMrfu2UHgXvulxisF/D7lyGckuW7yrR3f/+18WblyHmi0sp6Ga0Y3cVxtFIY1GHL8NBdRdSf1UCtfBeDm3wJZXmMNAEqD+JFP6Y6TB0kRQ7BpdPalMrEeBdup6RFxuoMd67g1ovOknkXrAcpms04PVuCpeofW4FAwScpkMi4UlXNMxwcjDTgevFcr6xOsatVN6rxqrOO92meuk6QMOVch5R2ZTP/8Uu4VRkycnp464wH7pXvI6uGq8/LZ+qM6Iq/jHtb1l81mHTD3FTVTUh2Zz7V9VLKA1NZZeVa6NHD8wR/8wQtfu76+jl/7tV+77CuQy+Wwt7eHX/zFX/T+f2pqyj2fRzWkdDna2dkB0AOQn/3sZ59za1K6Kn3Xd33X825CStdA3/zN3/y8m5DSNdBl5GPSNDExAQCulsBFiMcXAIgYEpWGhoacDFa6zHtedPrO7/zO590EzMzMYGZm5rm9/+DgAJ///Ofx+c9//rm14VmpWCzG/i/OY03jyO7u7qXfd9HjYfb29i79bCUFAOyHFrW5KtHTfRWicSIJ+oZv+IZrec6nPvUpfOpTn7qWZ71sNDU1ha/+6q++lme9OCWQUkoppZRSSimllFJKKaWUUnohKXNTIQMppZRSSimllFJKKaWUUkopvZyUehxTSimllFJKKaWUUkoppZRSGkgpcEwppZRSSimllFJKKaWUUkppIKXAMaWUUkoppZRSSimllFJKKaWBlALHlFJKKaWUUkoppZRSSimllAbSpY/jeJEoCIIpAF8F4JMA3gZwC8ARgF8D8MMAfrhWqw086CkIgr8L4L98+udrtVrt5a09/RJTEAT/OYDfC+BLAHwcwASAf1ir1f6459rbAP4CgN8CYBnAJIAdAF8A8PcA/FitVkumLnRKl6IgCP4EgH/w9M8/VavV/o78bxhAgN6cfymAjwAYttel9GJREASfBPDfojdfUwDWAPw7AH+zVqv96+fZtpT6dFn5GATBawC+GsB/BOA1AHMA6gD+XwDfW6vVfvZGO5ASgCvN4ysA3hvwyH9Uq9X+WGINTunSFATB++jpMj7aqNVq8zfYnJQGUMpXX36P49cA+CEAvwPAvwHwvQD+KYCPAvg7AH48CILYE2qDIPhP0QONrcRbmtJ59D8A+G/QAxEr51z7KoD/AsA+gJ8A8DcA/B/oMd6/B+CngyB4qY0iXwz0FOD/L4jfXwX09uzXAZgHkB7K+oJTEATfA+D/BPAJAD8F4PsA/HsAfwTAvwqC4IyhJ6XnRpeVj/8TgO9GT7H5SfT46r9CT0H6mSAIvunGWp6S0lX1nF8B8Jc8P/8k+SandAXah3++/vrzbFRKZ+hDz1dfduX6NwH8YQD/3CD8vwjg3wL4z9BD+v/U3hgEwQx6k/+P0FNaf+9NNDilWPoWAI8BfB69uRhkhflFAJPWm/zUg/XTAL4cvXn/8URamtK59JRx/jB6nuB/BuDPeS7rAPhDAH65VqutBUHwHQD+xxtrZEqXoiAI5tGbxw0AH6vVapvyv98H4GcA/GUAP/Z8WpiSocvKx58C8D21Wu2X9CFBEPxeAP8XgL8WBME/rtVqazfR+JQcXVXP+eVarfYdN9XIlJ6Z9tL5einoQ89XX2rgWKvVfibm+/UgCH4AwF9BD0ScAY4AfvDp5zfG/D+lGyR11wdBcN61RzHfHwdB8BPozflr19i8lC5P3wTg96M3F7/fd8HTefwXN9imlJ6NltGLUvk3ChqB3v4NgqAJYOa5tCylM3RZ+Vir1X4k5vpPB0HwcwC+AsDvQiovb5SeUc9JKaWUrpFSvvqSA8dziDluJ/YfQRB8HYA/CuCrarXaznlAJaWXg4IgyKHnwQKAX32ebfkwUxAED9ALzfi+Wq3280EQeIFjSi8dvYNeLsdvD4JgularbfMfQRD8HvTykn/iObUtpctRrHy8putTuhkaNC+LQRD8GfTykHcA/OtarZbKxReX8k9D/e8AaKOnw/x8rVY7fb7NSukS9KHgq1+UwPFpftuffPrnT5n/LaOXl/NjtVrtJ264aSldIwVBMI1eXmQGPU/HVwC4D+B/RS8PK6Ubpqd770cBPATwF59zc1K6RqrVartBEPz3AP4mgN946t3fQS/n+A+jF3bzZ55fC1O6CA2SjzHXLwP4A+iFlv98gk1L6RJ0gXn8iqc/es/PAfjaWq32MNnWpXQFmkdPdiq9FwTB19dqtU8/jwaldHH6MPHVl704Thx9N3qJqj9Zq9U+xS+DIMgC+PvoFet46RJSUzpD0+jlxH07gG9AT4H96wC+rlarhc+zYR9i+nb0KqR+Xa1WO3jejUnpeqlWq30vevkbQwD+FIBvRa9YwCMAP2JDWFN6IckrH30UBEEewD8EkAfwHbVarX4D7UvpYhQ3jx30CnL8FvQqjk+iXzfgywH8P0EQFG62qSmdQz+MHoiYR69o3NsA/jaAVwD8iyAIPv78mpbSBelDw1e/6DyOTysU/VkAnwPwJ8y/vwU9BvrJl22iUjpLtVrtcwAyT0NUb6FXIvkvA/jdQRB8slar7T7XBn7IKAiC346el/FvpMcyfHFSEAR/HsB3AfhbAL4fvUq4bwL4qwD+YRAEX1Kr1f78c2xiSgPoHPlor82h5wH5D9ArIpdWd3xBaNA8PjXefLu55eeDIPhKAL+AXjXI/wq9yKuUXgCq1Wp/yXz1GQD/dRAELfTm+TvQ029SegHpw8ZXv6g8jkEQfCN6zPA3APw+BQ5Pz1L5K+idsfKTz6mJKSVAtVrttFarPazVat+HXqjcl6EHIFO6IZIQ1d8E8G3PuTkpJUBBEHw5gO8B8L/XarX/rlarvVur1Tq1Wu3fo6fUrAD4s0EQ3Hue7UzJT4Pko+faHHrVcb8GverUfzyN4ngx6DLzqFSr1U7QOy4AAH5PQs1L6XrpB55+pvP1gtKHka9+0XgcgyD4ZgD/M3qWmj/gCZl6Cz238NcHQfD1MY9552mhnK9K8x9fWmKVzi9/no34EFIRwOtPfz+MKTj1Q0EQ/BB6RXO++aYaltK10X/y9PPMUTm1Wq0TBMG/RQ9AfimAd2+yYSkNpgvIR712CL088a95+vkn0wIdLwZdZh5jaOvpZxqq+nIQ5zedrxeQPqx89YsCOD4t2PDdAH4ZwFdotT+h9wH83ZhHfBK92PJ/DKDx9NqUXk669fTzpapS9UVATxC/vz6BHpj4BQD/H4A0jPXlpPzTz7gjN/i997iclJ4PXVA+8toR9CzhfwTAPwDw9fa83JSeD11mHgfQlz39TA07Lwf9zqef6Xy9YPRh5qsvPXAMguDb0AtL/HcAvjLOTVyr1X4Zvbh+3zN+Dj3g+Bdrtdrnk2lpStdFQRD8DgC/VqvVOub7Ivp5G//8xhv2IaanhXDi9td3oAcc/36tVvs7vmtSeinoX6JXxfhPB0Hwt2u12gr/EQTBH0QvZ+MQwC8+p/alZOii8vHptXkA/wy9I43+LoA//TIrN19MdMl5/B0Afsmed/z0WKRvefrnjyXV1pQuR0EQvAVgzc7p06qb3//0z3S+XiD6sPPVlxo4BkHwtehN3il6Ss03eULk3o87gDOlF4eCIPij6J2tCfRAPAD8ziAIfuTp79u1Wu3PPf39LwD48iAIPo3esQ8dALcB/EEAFfQU17+aeKNTemYKguBb0SuuAgBf8vTz64Mg+N1Pf/+FFGy+MPRPAPzfAP5DAJ8NguB/Q684zgP0wlgzAL61VqvtPL8mpkS6gnz8AfSUm2308lW/3XP9z9VqtZ9LqMkpeegK8/g9AN56ahB//PS7jwHgebrfVqvVUuPOi0NfA+BbgyD4WQDvAWiiVyH+kwBGAfwkXsICKl+slPLVlxw4Arj79DMH4Jtjrvk0gB+5icak9Ez0JQC+1nx37+kPAHwAgMDxh9A7IPe3oZfLOA6gjp7158cB/L2nhQBSevHpP0av0rHS73r6Q0qB4wtAtVqtGwTBHwLwjQD+GHr5jOMAdtFTbv5WrVb76efYxJSidFn5yOuncbYqp9LPPWO7UrocXXYefxS9vfnb0DOmDgPYQE82fn+tVvuXSTU0pSvRzwJ4A72onN+JXj7jHnqpHT8K4EdfxgIqX8T0oeermTBM12NKKaWUUkoppZRSSimllFJK8fRFdRxHSimllFJKKaWUUkoppZRSStdPKXBMKaWUUkoppZRSSimllFJKaSClwDGllFJKKaWUUkoppZRSSimlgZQCx5RSSimllFJKKaWUUkoppZQGUgocU0oppZRSSimllFJKKaWUUhpIKXBMKaWUUkoppZRSSimllFJKaSClwDGllFJKKaWUUkoppZRSSimlgZQCx5RSSimllFJKKaWUUkoppZQGUgocU0oppZRSSimllFJKKaWUUhpI/z8rm4nLU24rAAAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 1152x507.6 with 24 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Duration : 00:00:06 286ms\n"
- ]
- }
- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "dataset='set-48x48-L'\n",
- "samples=range(24)\n",
- "\n",
- "with h5py.File(f'{output_dir}/{dataset}.h5','r') as f:\n",
- " x_tmp = f['x_train'][:]\n",
- " y_tmp = f['y_train'][:]\n",
- " print(\"dataset loaded from h5 file.\")\n",
- "\n",
- "pwk.plot_images(x_tmp,y_tmp, samples, columns=8, x_size=2, y_size=2, \n",
- " colorbar=False, y_pred=None, cm='binary', save_as='16-enhanced_images')\n",
- "x_tmp,y_tmp=0,0\n",
- "\n",
- "pwk.chrono_show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:17.611491Z",
- "iopub.status.busy": "2021-03-01T17:48:17.611022Z",
- "iopub.status.idle": "2021-03-01T17:48:17.613822Z",
- "shell.execute_reply": "2021-03-01T17:48:17.613334Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:48:17\n",
- "Duration is : 00:04:36 614ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/GTSRB/02-First-convolutions.ipynb b/GTSRB/02-First-convolutions.ipynb
index 4f88343..3b8cf7a 100644
--- a/GTSRB/02-First-convolutions.ipynb
+++ b/GTSRB/02-First-convolutions.ipynb
@@ -35,9 +35,99 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ "\n",
+ "div.warn { \n",
+ " background-color: #fcf2f2;\n",
+ " border-color: #dFb5b4;\n",
+ " border-left: 5px solid #dfb5b4;\n",
+ " padding: 0.5em;\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;;\n",
+ " }\n",
+ "\n",
+ "\n",
+ "\n",
+ "div.nota { \n",
+ " background-color: #DAFFDE;\n",
+ " border-left: 5px solid #92CC99;\n",
+ " padding: 0.5em;\n",
+ " }\n",
+ "\n",
+ "div.todo:before { content:url(data:image/svg+xml;base64,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);\n",
+ " float:left;\n",
+ " margin-right:20px;\n",
+ " margin-top:-20px;\n",
+ " margin-bottom:20px;\n",
+ "}\n",
+ "div.todo{\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;\n",
+ " margin-top:40px;\n",
+ "}\n",
+ "div.todo ul{\n",
+ " margin: 0.2em;\n",
+ "}\n",
+ "div.todo li{\n",
+ " margin-left:60px;\n",
+ " margin-top:0;\n",
+ " margin-bottom:0;\n",
+ "}\n",
+ "\n",
+ "div .comment{\n",
+ " font-size:0.8em;\n",
+ " color:#696969;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 2.0.25\n",
+ "Notebook id : GTSRB2\n",
+ "Run time : Saturday 30 October 2021, 17:05:46\n",
+ "Tensorflow log level : Info + Warning + Error (=0)\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run/GTSRB2.001\n",
+ "Update keras cache : False\n",
+ "tensorflow : 2.4.1\n",
+ "tensorflow.keras : 2.4.0\n",
+ "sklearn : 0.24.2\n",
+ "matplotlib : 3.4.3\n",
+ "pandas : 1.3.3\n"
+ ]
+ }
+ ],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@@ -63,23 +153,24 @@
"source": [
"### 1.2 - Parameters\n",
"`scale` is the proportion of the dataset that will be used during the training. (1 mean 100%) \n",
- "A 24x24 dataset, with 5 epochs and a scale of 1, need **3'30** on a CPU laptop."
+ "A 24x24 dataset, with 5 epochs and a scale of 1, need **3'30** on a CPU laptop.\\\n",
+ "`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"enhanced_dir = './data'\n",
"# enhanced_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
"\n",
- "dataset_name = 'set-24x24-L'\n",
- "batch_size = 64\n",
- "epochs = 5\n",
- "scale = 1\n",
- "\n"
+ "dataset_name = 'set-24x24-L'\n",
+ "batch_size = 64\n",
+ "epochs = 5\n",
+ "scale = 1\n",
+ "fit_verbosity = 1"
]
},
{
@@ -91,11 +182,11 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
- "pwk.override('enhanced_dir', 'dataset_name', 'batch_size', 'epochs', 'scale')"
+ "pwk.override('enhanced_dir', 'dataset_name', 'batch_size', 'epochs', 'scale', 'fit_verbosity')"
]
},
{
@@ -109,9 +200,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(7841, 24, 24, 1) (7841,)\n",
+ "Dataset \"set-24x24-L\" is loaded and shuffled. (46.2 Mo in 00:00:00 031ms)\n"
+ ]
+ }
+ ],
"source": [
"def read_dataset(enhanced_dir, dataset_name):\n",
" '''Reads h5 dataset\n",
@@ -158,9 +258,40 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_train : (7841, 24, 24, 1)\n",
+ "y_train : (7841,)\n",
+ "x_test : (2526, 24, 24, 1)\n",
+ "y_test : (2526,)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 864x338.4 with 12 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 864x291.6 with 36 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"print(\"x_train : \", x_train.shape)\n",
"print(\"y_train : \", y_train.shape)\n",
@@ -183,7 +314,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -273,9 +404,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Images of the dataset have this folowing shape : (24, 24, 1)\n"
+ ]
+ }
+ ],
"source": [
"(n,lx,ly,lz) = x_train.shape\n",
"print(\"Images of the dataset have this folowing shape : \",(lx,ly,lz))"
@@ -290,9 +429,54 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d (Conv2D) (None, 22, 22, 96) 960 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d (MaxPooling2D) (None, 11, 11, 96) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout (Dropout) (None, 11, 11, 96) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_1 (Conv2D) (None, 9, 9, 192) 166080 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 4, 4, 192) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_1 (Dropout) (None, 4, 4, 192) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten (Flatten) (None, 3072) 0 \n",
+ "_________________________________________________________________\n",
+ "dense (Dense) (None, 1500) 4609500 \n",
+ "_________________________________________________________________\n",
+ "dropout_2 (Dropout) (None, 1500) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 43) 64543 \n",
+ "=================================================================\n",
+ "Total params: 4,841,083\n",
+ "Trainable params: 4,841,083\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2021-10-30 17:05:48.977920: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
+ "2021-10-30 17:05:48.978105: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n",
+ "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+ "2021-10-30 17:05:48.978626: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
+ ]
+ }
+ ],
"source": [
"model = get_model_v1(lx,ly,lz)\n",
"\n",
@@ -312,9 +496,42 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/5\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2021-10-30 17:05:49.274102: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n",
+ "2021-10-30 17:05:49.274434: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2112000000 Hz\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "123/123 [==============================] - 21s 164ms/step - loss: 3.4347 - accuracy: 0.0945 - val_loss: 2.0063 - val_accuracy: 0.4794\n",
+ "Epoch 2/5\n",
+ "123/123 [==============================] - 21s 167ms/step - loss: 1.5982 - accuracy: 0.5571 - val_loss: 0.9999 - val_accuracy: 0.7478\n",
+ "Epoch 3/5\n",
+ "123/123 [==============================] - 21s 173ms/step - loss: 0.8116 - accuracy: 0.7569 - val_loss: 0.6751 - val_accuracy: 0.8337\n",
+ "Epoch 4/5\n",
+ "123/123 [==============================] - 23s 187ms/step - loss: 0.4775 - accuracy: 0.8590 - val_loss: 0.6036 - val_accuracy: 0.8496\n",
+ "Epoch 5/5\n",
+ "123/123 [==============================] - 21s 174ms/step - loss: 0.3680 - accuracy: 0.8924 - val_loss: 0.5160 - val_accuracy: 0.8678\n",
+ "\n",
+ "Duration : 00:01:47 405ms\n"
+ ]
+ }
+ ],
"source": [
"pwk.chrono_start()\n",
"\n",
@@ -325,7 +542,7 @@
"history = model.fit( x_train, y_train,\n",
" batch_size = batch_size,\n",
" epochs = epochs,\n",
- " verbose = 1,\n",
+ " verbose = fit_verbosity,\n",
" validation_data = (x_test, y_test))\n",
"\n",
"pwk.chrono_show()"
@@ -340,9 +557,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Max validation accuracy is : 0.8678\n"
+ ]
+ }
+ ],
"source": [
"max_val_accuracy = max(history.history[\"val_accuracy\"])\n",
"print(\"Max validation accuracy is : {:.4f}\".format(max_val_accuracy))"
@@ -350,9 +575,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test loss : 0.5160\n",
+ "Test accuracy : 0.8678\n"
+ ]
+ }
+ ],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"\n",
@@ -362,9 +596,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Saturday 30 October 2021, 17:07:38\n",
+ "Duration is : 00:01:52 100ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
"source": [
"pwk.end()"
]
@@ -394,9 +638,11 @@
}
],
"metadata": {
+ "interpreter": {
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
+ },
"kernelspec": {
- "display_name": "Python 3",
- "language": "python",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
@@ -409,7 +655,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.7"
}
},
"nbformat": 4,
diff --git a/GTSRB/02-First-convolutions==done==.ipynb b/GTSRB/02-First-convolutions==done==.ipynb
deleted file mode 100644
index a04e087..0000000
--- a/GTSRB/02-First-convolutions==done==.ipynb
+++ /dev/null
@@ -1,2104 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [GTSRB2] - First convolutions\n",
- "<!-- DESC --> Episode 2 : First convolutions and first classification of our traffic signs\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Recognizing traffic signs \n",
- " - Understand the **principles** and **architecture** of a **convolutional neural network** for image classification\n",
- " \n",
- "The German Traffic Sign Recognition Benchmark (GTSRB) is a dataset with more than 50,000 photos of road signs from about 40 classes. \n",
- "The final aim is to recognise them ! \n",
- "\n",
- "Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset\n",
- "\n",
- "\n",
- "**IMPORTANT :** To be able to use this notebook and the following, **you must have generated the enhanced datasets** in <dataset_dir>/enhanced via the notebook **[01-Preparation-of-data.ipynb](01-Preparation-of-data.ipynb)** \n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Read H5 dataset\n",
- " - Build a model\n",
- " - Train the model\n",
- " - Evaluate the model\n",
- "\n",
- "## Step 1 - Import and init\n",
- "### 1.1 - Python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:20.736698Z",
- "iopub.status.busy": "2021-03-01T17:48:20.736229Z",
- "iopub.status.idle": "2021-03-01T17:48:25.223259Z",
- "shell.execute_reply": "2021-03-01T17:48:25.223742Z"
- }
- },
- "outputs": [
- {
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- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
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- " margin-bottom:0;\n",
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- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
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- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "run_dir : ./run/GTSRB2_done\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : GTSRB2\n",
- "Run time : Monday 01 March 2021, 18:48:25\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/GTSRB2_done\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/GTSRB2_done/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "from tensorflow.keras.callbacks import TensorBoard\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import h5py\n",
- "import os,time,sys\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/GTSRB2.001'\n",
- "datasets_dir = pwk.init('GTSRB2', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "`scale` is the proportion of the dataset that will be used during the training. (1 mean 100%) \n",
- "A 24x24 dataset, with 5 epochs and a scale of 1, need **3'30** on a CPU laptop."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:25.227323Z",
- "iopub.status.busy": "2021-03-01T17:48:25.226846Z",
- "iopub.status.idle": "2021-03-01T17:48:25.228441Z",
- "shell.execute_reply": "2021-03-01T17:48:25.228919Z"
- }
- },
- "outputs": [],
- "source": [
- "enhanced_dir = './data'\n",
- "# enhanced_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
- "\n",
- "dataset_name = 'set-24x24-L'\n",
- "batch_size = 64\n",
- "epochs = 5\n",
- "scale = 1\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:25.232422Z",
- "iopub.status.busy": "2021-03-01T17:48:25.231959Z",
- "iopub.status.idle": "2021-03-01T17:48:25.235493Z",
- "shell.execute_reply": "2021-03-01T17:48:25.235001Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "enhanced_dir : /gpfswork/rech/mlh/uja62cb/datasets/GTSRB/enhanced\n",
- "dataset_name : set-24x24-L\n",
- "batch_size : 64\n",
- "epochs : 5\n",
- "scale : 1\n"
- ]
- }
- ],
- "source": [
- "pwk.override('enhanced_dir', 'dataset_name', 'batch_size', 'epochs', 'scale')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Load dataset\n",
- "We're going to retrieve a previously recorded dataset. \n",
- "For example: set-24x24-L"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:25.599848Z",
- "iopub.status.busy": "2021-03-01T17:48:25.598194Z",
- "iopub.status.idle": "2021-03-01T17:48:25.661850Z",
- "shell.execute_reply": "2021-03-01T17:48:25.661341Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(39209, 24, 24, 1) (39209,)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset \"set-24x24-L\" is loaded and shuffled. (228.8 Mo in 00:00:00 417ms)\n"
- ]
- }
- ],
- "source": [
- "def read_dataset(enhanced_dir, dataset_name):\n",
- " '''Reads h5 dataset\n",
- " Args:\n",
- " filename : datasets filename\n",
- " dataset_name : dataset name, without .h5\n",
- " Returns: x_train,y_train, x_test,y_test data, x_meta,y_meta'''\n",
- " # ---- Read dataset\n",
- " pwk.chrono_start()\n",
- " filename = f'{enhanced_dir}/{dataset_name}.h5'\n",
- " with h5py.File(filename,'r') as f:\n",
- " x_train = f['x_train'][:]\n",
- " y_train = f['y_train'][:]\n",
- " x_test = f['x_test'][:]\n",
- " y_test = f['y_test'][:]\n",
- " x_meta = f['x_meta'][:]\n",
- " y_meta = f['y_meta'][:]\n",
- " print(x_train.shape, y_train.shape)\n",
- " # ---- Shuffle\n",
- " x_train,y_train=pwk.shuffle_np_dataset(x_train,y_train)\n",
- "\n",
- " # ---- done\n",
- " duration = pwk.chrono_stop(hdelay=True)\n",
- " size = pwk.hsize(os.path.getsize(filename))\n",
- " print(f'Dataset \"{dataset_name}\" is loaded and shuffled. ({size} in {duration})')\n",
- " return x_train,y_train, x_test,y_test, x_meta,y_meta\n",
- "\n",
- "# ---- Read dataset\n",
- "#\n",
- "x_train,y_train,x_test,y_test, x_meta,y_meta = read_dataset(enhanced_dir, dataset_name)\n",
- "\n",
- "# ---- Rescale \n",
- "#\n",
- "x_train,y_train, x_test,y_test = pwk.rescale_dataset(x_train,y_train,x_test,y_test, scale=scale)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Have a look to the dataset\n",
- "We take a quick look as we go by..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:25.680242Z",
- "iopub.status.busy": "2021-03-01T17:48:25.679032Z",
- "iopub.status.idle": "2021-03-01T17:48:32.812121Z",
- "shell.execute_reply": "2021-03-01T17:48:32.812619Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_train : (39209, 24, 24, 1)\n",
- "y_train : (39209,)\n",
- "x_test : (12630, 24, 24, 1)\n",
- "y_test : (12630,)\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/GTSRB2_done/figs/GTSRB2-01-dataset-medium</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
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\n",
- "text/plain": [
- "<Figure size 864x291.6 with 36 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "print(\"x_train : \", x_train.shape)\n",
- "print(\"y_train : \", y_train.shape)\n",
- "print(\"x_test : \", x_test.shape)\n",
- "print(\"y_test : \", y_test.shape)\n",
- "\n",
- "pwk.plot_images(x_train, y_train, range(12), columns=6, x_size=2, y_size=2, save_as='01-dataset-medium')\n",
- "pwk.plot_images(x_train, y_train, range(36), columns=12, x_size=1, y_size=1, save_as='02-dataset-small')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Create model\n",
- "We will now build a model and train it...\n",
- "\n",
- "Some models :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:32.829882Z",
- "iopub.status.busy": "2021-03-01T17:48:32.827273Z",
- "iopub.status.idle": "2021-03-01T17:48:32.831970Z",
- "shell.execute_reply": "2021-03-01T17:48:32.831471Z"
- }
- },
- "outputs": [],
- "source": [
- "\n",
- "# A basic model\n",
- "#\n",
- "def get_model_v1(lx,ly,lz):\n",
- " \n",
- " model = keras.models.Sequential()\n",
- " \n",
- " model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Flatten()) \n",
- " model.add( keras.layers.Dense(1500, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model\n",
- " \n",
- "# A more sophisticated model\n",
- "#\n",
- "def get_model_v2(lx,ly,lz):\n",
- " model = keras.models.Sequential()\n",
- "\n",
- " model.add( keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=(lx,ly,lz), activation='relu'))\n",
- " model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
- " model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))\n",
- " model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Flatten())\n",
- " model.add( keras.layers.Dense(512, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model\n",
- "\n",
- "# My sphisticated model, but small and fast\n",
- "#\n",
- "def get_model_v3(lx,ly,lz):\n",
- " model = keras.models.Sequential()\n",
- " model.add( keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Flatten()) \n",
- " model.add( keras.layers.Dense(1152, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Train the model\n",
- "**Get the shape of my data :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:32.835259Z",
- "iopub.status.busy": "2021-03-01T17:48:32.834784Z",
- "iopub.status.idle": "2021-03-01T17:48:32.837421Z",
- "shell.execute_reply": "2021-03-01T17:48:32.836931Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Images of the dataset have this folowing shape : (24, 24, 1)\n"
- ]
- }
- ],
- "source": [
- "(n,lx,ly,lz) = x_train.shape\n",
- "print(\"Images of the dataset have this folowing shape : \",(lx,ly,lz))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Get and compile a model, with the data shape :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:32.840811Z",
- "iopub.status.busy": "2021-03-01T17:48:32.840350Z",
- "iopub.status.idle": "2021-03-01T17:48:34.042739Z",
- "shell.execute_reply": "2021-03-01T17:48:34.043240Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "conv2d (Conv2D) (None, 22, 22, 96) 960 \n",
- "_________________________________________________________________\n",
- "max_pooling2d (MaxPooling2D) (None, 11, 11, 96) 0 \n",
- "_________________________________________________________________\n",
- "dropout (Dropout) (None, 11, 11, 96) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_1 (Conv2D) (None, 9, 9, 192) 166080 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_1 (MaxPooling2 (None, 4, 4, 192) 0 \n",
- "_________________________________________________________________\n",
- "dropout_1 (Dropout) (None, 4, 4, 192) 0 \n",
- "_________________________________________________________________\n",
- "flatten (Flatten) (None, 3072) 0 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 1500) 4609500 \n",
- "_________________________________________________________________\n",
- "dropout_2 (Dropout) (None, 1500) 0 \n",
- "_________________________________________________________________\n",
- "dense_1 (Dense) (None, 43) 64543 \n",
- "=================================================================\n",
- "Total params: 4,841,083\n",
- "Trainable params: 4,841,083\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = get_model_v1(lx,ly,lz)\n",
- "\n",
- "model.summary()\n",
- "\n",
- "model.compile(optimizer = 'adam',\n",
- " loss = 'sparse_categorical_crossentropy',\n",
- " metrics = ['accuracy'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Train it :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:34.047487Z",
- "iopub.status.busy": "2021-03-01T17:48:34.047019Z",
- "iopub.status.idle": "2021-03-01T17:48:47.796939Z",
- "shell.execute_reply": "2021-03-01T17:48:47.797439Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/5\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/613 [..............................] - ETA: 29:47 - loss: 3.7620 - accuracy: 0.0000e+00"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 19/613 [..............................] - ETA: 1s - loss: 3.6511 - accuracy: 0.0539 "
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 38/613 [>.............................] - ETA: 1s - loss: 3.6190 - accuracy: 0.0573"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 57/613 [=>............................] - ETA: 1s - loss: 3.5909 - accuracy: 0.0622"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 76/613 [==>...........................] - ETA: 1s - loss: 3.5593 - accuracy: 0.0710"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 95/613 [===>..........................] - ETA: 1s - loss: 3.5176 - accuracy: 0.0823"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "114/613 [====>.........................] - ETA: 1s - loss: 3.4677 - accuracy: 0.0956"
- ]
- },
- {
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- "text": [
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- "133/613 [=====>........................] - ETA: 1s - loss: 3.4108 - accuracy: 0.1106"
- ]
- },
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- "text": [
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- "153/613 [======>.......................] - ETA: 1s - loss: 3.3455 - accuracy: 0.1272"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "173/613 [=======>......................] - ETA: 1s - loss: 3.2770 - accuracy: 0.1440"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "213/613 [=========>....................] - ETA: 1s - loss: 3.1378 - accuracy: 0.1778"
- ]
- },
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- ]
- },
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- ]
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- ]
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- "613/613 [==============================] - 6s 5ms/step - loss: 2.1913 - accuracy: 0.4140 - val_loss: 0.4812 - val_accuracy: 0.8812\n"
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- "Epoch 2/5\n",
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- "613/613 [==============================] - 2s 3ms/step - loss: 0.2814 - accuracy: 0.9191 - val_loss: 0.3312 - val_accuracy: 0.9226\n"
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- "Epoch 3/5\n",
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- "613/613 [==============================] - 2s 3ms/step - loss: 0.1367 - accuracy: 0.9618 - val_loss: 0.2893 - val_accuracy: 0.9329\n"
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- "Epoch 4/5\n",
- "\r",
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- "613/613 [==============================] - 2s 3ms/step - loss: 0.0949 - accuracy: 0.9714 - val_loss: 0.2745 - val_accuracy: 0.9371\n"
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- "Epoch 5/5\n",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "605/613 [============================>.] - ETA: 0s - loss: 0.0634 - accuracy: 0.9816"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "613/613 [==============================] - 2s 3ms/step - loss: 0.0634 - accuracy: 0.9816 - val_loss: 0.2507 - val_accuracy: 0.9433\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Duration : 00:00:14 747ms\n"
- ]
- }
- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "# ---- Shuffle train data\n",
- "x_train,y_train=pwk.shuffle_np_dataset(x_train,y_train)\n",
- "\n",
- "# ---- Train\n",
- "history = model.fit( x_train, y_train,\n",
- " batch_size = batch_size,\n",
- " epochs = epochs,\n",
- " verbose = 1,\n",
- " validation_data = (x_test, y_test))\n",
- "\n",
- "pwk.chrono_show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Evaluate"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:47.801137Z",
- "iopub.status.busy": "2021-03-01T17:48:47.800654Z",
- "iopub.status.idle": "2021-03-01T17:48:47.803230Z",
- "shell.execute_reply": "2021-03-01T17:48:47.802729Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Max validation accuracy is : 0.9433\n"
- ]
- }
- ],
- "source": [
- "max_val_accuracy = max(history.history[\"val_accuracy\"])\n",
- "print(\"Max validation accuracy is : {:.4f}\".format(max_val_accuracy))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:47.806663Z",
- "iopub.status.busy": "2021-03-01T17:48:47.806195Z",
- "iopub.status.idle": "2021-03-01T17:48:48.272279Z",
- "shell.execute_reply": "2021-03-01T17:48:48.272775Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Test loss : 0.2507\n",
- "Test accuracy : 0.9433\n"
- ]
- }
- ],
- "source": [
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('Test loss : {:5.4f}'.format(score[0]))\n",
- "print('Test accuracy : {:5.4f}'.format(score[1]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:48.276115Z",
- "iopub.status.busy": "2021-03-01T17:48:48.275641Z",
- "iopub.status.idle": "2021-03-01T17:48:48.277978Z",
- "shell.execute_reply": "2021-03-01T17:48:48.278460Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:48:48\n",
- "Duration is : 00:00:23 057ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"todo\">\n",
- " What you can do:\n",
- " <ul>\n",
- " <li>Try the different models</li>\n",
- " <li>Try with different datasets</li>\n",
- " <li>Test different hyperparameters (epochs, batch size, optimization, etc.)</li>\n",
- " <li>Create your own model</li>\n",
- " </ul>\n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/GTSRB/03-Tracking-and-visualizing.ipynb b/GTSRB/03-Tracking-and-visualizing.ipynb
index f24287e..e712a04 100644
--- a/GTSRB/03-Tracking-and-visualizing.ipynb
+++ b/GTSRB/03-Tracking-and-visualizing.ipynb
@@ -63,7 +63,8 @@
"source": [
"### 1.2 - Parameters\n",
"`scale` is the proportion of the dataset that will be used during the training. (1 mean 100%) \n",
- "A 24x24 dataset, with 5 epochs and a scale of 1, need 3'30 on a CPU laptop."
+ "A 24x24 dataset, with 5 epochs and a scale of 1, need 3'30 on a CPU laptop.\\\n",
+ "`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch"
]
},
{
@@ -79,7 +80,7 @@
"batch_size = 64\n",
"epochs = 10\n",
"scale = 1\n",
- "\n"
+ "fit_verbosity = 1"
]
},
{
@@ -95,7 +96,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.override('enhanced_dir', 'dataset_name', 'batch_size', 'epochs', 'scale')"
+ "pwk.override('enhanced_dir', 'dataset_name', 'batch_size', 'epochs', 'scale', 'fit_verbosity')"
]
},
{
@@ -316,7 +317,7 @@
"history = model.fit( x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
- " verbose=1,\n",
+ " verbose=fit_verbosity,\n",
" validation_data=(x_test, y_test),\n",
" callbacks=[tensorboard_callback, bestmodel_callback, savemodel_callback] )\n",
"\n",
@@ -512,17 +513,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<div class=\"todo\">\n",
- " What you can do:\n",
- " <ul>\n",
- " <li>Limit model saving: 1 save every 5 epochs </li>\n",
- " <li>Use a subset of the dataset</li>\n",
- " <li>Try different datasets</li>\n",
- " <li>Some exotic signs are waiting to be recognized in dataset_dir/extra !</li>\n",
- " <li>Test different hyperparameters (epochs, batch size, optimization, etc.)</li>\n",
- " </ul>\n",
- " \n",
- "</div>"
+ "## Step 10 - To go further ;-)\n",
+ "What you can do:\n",
+ "- Limit model saving: 1 save every 5 epochs\n",
+ "- Use a subset of the dataset\n",
+ "- Try different datasets\n",
+ "- Some exotic signs are waiting to be recognized in dataset_dir/extra !\n",
+ "- Test different hyperparameters (epochs, batch size, optimization, etc.\n",
+ " "
]
},
{
diff --git a/GTSRB/03-Tracking-and-visualizing==done==.ipynb b/GTSRB/03-Tracking-and-visualizing==done==.ipynb
deleted file mode 100644
index 06fdf4c..0000000
--- a/GTSRB/03-Tracking-and-visualizing==done==.ipynb
+++ /dev/null
@@ -1,2523 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [GTSRB3] - Training monitoring\n",
- "<!-- DESC --> Episode 3 : Monitoring, analysis and check points during a training session\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - **Understand** what happens during the **training** process\n",
- " - Implement **monitoring**, **backup** and **recovery** solutions\n",
- " \n",
- "The German Traffic Sign Recognition Benchmark (GTSRB) is a dataset with more than 50,000 photos of road signs from about 40 classes. \n",
- "The final aim is to recognise them ! \n",
- "Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Monitoring and understanding our model training \n",
- " - Add recovery points\n",
- " - Analyze the results \n",
- " - Restore and run recovery points\n",
- "\n",
- "## Step 1 - Import and init\n",
- "### 1.1 - Python stuffs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:50.410729Z",
- "iopub.status.busy": "2021-03-01T17:48:50.410258Z",
- "iopub.status.idle": "2021-03-01T17:48:54.312273Z",
- "shell.execute_reply": "2021-03-01T17:48:54.312755Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
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- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
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- "div.todo:before { 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- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
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- " margin-bottom:0;\n",
- "}\n",
- "\n",
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- " font-size:0.8em;\n",
- " color:#696969;\n",
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- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
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- "data": {
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- ],
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- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : GTSRB3\n",
- "Run time : Monday 01 March 2021, 18:48:54\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/GTSRB3_done\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/GTSRB3_done/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "from tensorflow.keras.callbacks import TensorBoard\n",
- "\n",
- "import numpy as np\n",
- "import h5py\n",
- "\n",
- "from sklearn.metrics import confusion_matrix\n",
- "from skimage import io, transform, color\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import os, sys, time, random\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/GTSRB3.001'\n",
- "datasets_dir = pwk.init('GTSRB3', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "`scale` is the proportion of the dataset that will be used during the training. (1 mean 100%) \n",
- "A 24x24 dataset, with 5 epochs and a scale of 1, need 3'30 on a CPU laptop."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:54.316311Z",
- "iopub.status.busy": "2021-03-01T17:48:54.315834Z",
- "iopub.status.idle": "2021-03-01T17:48:54.317483Z",
- "shell.execute_reply": "2021-03-01T17:48:54.317955Z"
- }
- },
- "outputs": [],
- "source": [
- "enhanced_dir = './data'\n",
- "# enhanced_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
- "\n",
- "dataset_name = 'set-24x24-L'\n",
- "batch_size = 64\n",
- "epochs = 10\n",
- "scale = 1\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:54.321421Z",
- "iopub.status.busy": "2021-03-01T17:48:54.320958Z",
- "iopub.status.idle": "2021-03-01T17:48:54.323965Z",
- "shell.execute_reply": "2021-03-01T17:48:54.324439Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "enhanced_dir : /gpfswork/rech/mlh/uja62cb/datasets/GTSRB/enhanced\n",
- "dataset_name : set-24x24-L\n",
- "batch_size : 64\n",
- "epochs : 5\n",
- "scale : 1\n"
- ]
- }
- ],
- "source": [
- "pwk.override('enhanced_dir', 'dataset_name', 'batch_size', 'epochs', 'scale')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Load dataset\n",
- "Dataset is one of the saved dataset: RGB25, RGB35, L25, L35, etc. \n",
- "First of all, we're going to use a smart dataset : **set-24x24-L** \n",
- "(with a GPU, it only takes 35'' compared to more than 5' with a CPU !)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:54.723486Z",
- "iopub.status.busy": "2021-03-01T17:48:54.722947Z",
- "iopub.status.idle": "2021-03-01T17:48:54.785527Z",
- "shell.execute_reply": "2021-03-01T17:48:54.786028Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(39209, 24, 24, 1) (39209,)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset \"set-24x24-L\" is loaded and shuffled. (228.8 Mo in 00:00:00 452ms)\n"
- ]
- }
- ],
- "source": [
- "def read_dataset(enhanced_dir, dataset_name):\n",
- " '''Reads h5 dataset\n",
- " Args:\n",
- " filename : datasets filename\n",
- " dataset_name : dataset name, without .h5\n",
- " Returns: x_train,y_train, x_test,y_test data, x_meta,y_meta'''\n",
- " # ---- Read dataset\n",
- " pwk.chrono_start()\n",
- " filename = f'{enhanced_dir}/{dataset_name}.h5'\n",
- " with h5py.File(filename,'r') as f:\n",
- " x_train = f['x_train'][:]\n",
- " y_train = f['y_train'][:]\n",
- " x_test = f['x_test'][:]\n",
- " y_test = f['y_test'][:]\n",
- " x_meta = f['x_meta'][:]\n",
- " y_meta = f['y_meta'][:]\n",
- " print(x_train.shape, y_train.shape)\n",
- " # ---- Shuffle\n",
- " x_train,y_train=pwk.shuffle_np_dataset(x_train,y_train)\n",
- "\n",
- " # ---- done\n",
- " duration = pwk.chrono_stop(hdelay=True)\n",
- " size = pwk.hsize(os.path.getsize(filename))\n",
- " print(f'Dataset \"{dataset_name}\" is loaded and shuffled. ({size} in {duration})')\n",
- " return x_train,y_train, x_test,y_test, x_meta,y_meta\n",
- "\n",
- "# ---- Read dataset\n",
- "#\n",
- "x_train,y_train,x_test,y_test, x_meta,y_meta = read_dataset(enhanced_dir, dataset_name)\n",
- "\n",
- "# ---- Rescale \n",
- "#\n",
- "x_train,y_train, x_test,y_test = pwk.rescale_dataset(x_train,y_train,x_test,y_test, scale=scale)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Have a look to the dataset\n",
- "Note: Data must be reshape for matplotlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:48:54.803654Z",
- "iopub.status.busy": "2021-03-01T17:48:54.791228Z",
- "iopub.status.idle": "2021-03-01T17:49:01.925903Z",
- "shell.execute_reply": "2021-03-01T17:49:01.926406Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_train : (39209, 24, 24, 1)\n",
- "y_train : (39209,)\n",
- "x_test : (12630, 24, 24, 1)\n",
- "y_test : (12630,)\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/GTSRB3_done/figs/GTSRB3-01-dataset-medium</div>"
- ],
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- "<IPython.core.display.HTML object>"
- ]
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pBtRDtVoNfr8fbre7a7rcSSCpF+nmSOrf2u7p9CutE8josT+lUgnNZhNutxuBQKCndmrHrRvJ0mvfepVuzgCJk8PDQ+zu7iKdTgOAEAK05+a+0PjgWY5/v/NoMt1aj5lAldE0E7ye1AZtOzWmod3lb5OsslqtT6Ta8HueJX2DVzbw6OgIyWQSGxsbyGQysjHNkL0GIZpdpUEg+NSnLbU0Gg1JjAcAt9sNr9cLj8cjwIihb3rBZE96kW7MpgZYetBNL6/bIjNZPO296sWvc2UJimkoWf1AA5x+Q80AZMwIDi0WyxMVBbq1kX3k4qpWqygUCtjf38fOzg4ODg5Qq9WkikEgEEAoFEIoFEIwGBRwoVMstCN0EhDodbPyECE3JwG6ni9TSeh5ooLVm0lXBQDaQLDRaCCbzUq0wel0IhAIwO/3SxoL+2kyrrqf/YA7HaLTQgdBGwGLxQKXy4VoNIqLFy/i6tWruHTpkjCuPp9PDlVqw2exWMSoRKNRTExMYH5+Hq+++io2NjZw//593L59G/fu3cPe3p4oPVOh9wMK+Dm2A2gzUDTgZDQYqqXOKJfLePDgAb797W/jwYMHqNfr8Pv9iMViOHfuHPx+P1qtllRFKRaLyGazODg4wPvvv487d+7IPL755pv47b/9t+Ott96Cy+V6Ikyp29kPONja2sLe3h5arZakQ1kslo7Qs/m8pzkDk5OTeO+993Dt2jX4/X4kk0ncvn0b9+/fx+7uLgqFQl95sy9Cuu21bkb6pLZpXar1qAk6uzmCJ4EE8+/dQO2zpFgsypgy35qRSOrJSqWCw8ND5HI5NBoNuFwuhMPhjjMhtH0nHZzlb4fDgWAwKAf6isUiCoVCR6qBx+N54hS36VD2Knr/mmNsEgLmXJgORrVaxcHBAZLJJNLpNJrNJoLBIMbGxjAyMgKfz9c1CkfR7SfjrNMmTgN8THnW/n2WjtYsOXXU4eEhKpUKLBYLisUicrlcx4FzEzuY5MqzHL9++khhX08iTWgHurVBYxadTqAxjh6XbntOP1s/h0D3NPai7wNb2qDk83lsbGzg0aNHyGQyHaeY2WAdIiZI4t802GHZJnZuaGhIlAGNTavVgtfrlVAnwQI3r8PhQL1e7yt5W1cLMNkjPdhkl/UzzE2jw+paiej0CA2IOK767+yPeRK+X9ADtOeMitIEOXymXmzMSQba6QTcnHt7e9je3sbGxga2t7eRyWTQbDbhdDqlTFo4HEY8Hsfk5CQmJiYQDAZFuRIA0viYG7hfI+twOEQBEszp+dDjSDGBq+mAaUVMY1Mul5HL5QTY8ju4/ln+hmvYLJlykhE/jZy0rvWapPPj9/sxPj6OV155BW+99RauXr2K2dlZBAIBcfC0x93NEJGFjcVimJ6exuzsLKanpzEyMoJgMIg7d+5gZ2cHpVKpo30EYv30UesBjm25XMbBwQFyuRwsFguCwaAYglQqheXlZdy5cwcrKysoFosIBAKYmprC5cuXMTc3h2AwiGq1isPDQ9En+/v74nzn83kkk0ncuXMH6+vrws6++uqriEajsNvtHQzC80ixWHzitdMAKDr70WgUw8PD8Pv9CAQCOH/+PL7yla/gypUr8Hg82N3dxfT0NM6fP4+dnR2k02mk02kcHBwgm80im80KaD5p7HV7/H7/c/XX/G5+/0ngxGyDGYXT7+d+IjDolsb1tHb0mxKSz+eFABgeHobD4RAwyzMFBwcHKBQKwjCGQiHEYjHEYjE4nU7Rqel0Wgga2hcNbJmjHo1G0Wq1UCwWkclkBCDncjkhPzweD4AnbUU/dqMbAWN+D/WOjlR1Sx2wWNqpLLlcTkD3wcEBNjY2cHh4CI/Hg2g0CgAC/LoRRLodOnJpkmanFdPmd/tbt9f1WqUTSna8Xq+LvqjVasjn8x2RYfPzpkPwvPqlm2j9bto2vdc1eWWG+AEIeUVbx8iXz+eDxXJ8wMtM3dS2XVeFMkm900hf4FWzcXxoqVTCxsYGlpeXO/K3tHKyWCxP1Au12+2IRCISnuPC5oEbAhyWymLtVLfbLSGYUCiEcDgspbWAJ08hnlY0GCXVrUt5EXBq6l3XNmM/+bvbRJjetK6OwPGkJ9nv6cmnSbFY7Khlq/tCUAkcs+X8TUO/ubmJtbU1PHz4ECsrK9je3kYulxOlTcDNPOSRkRHMz8/j4sWLmJ+fx9TUFCKRiCSJA08/nGOmiDxLdNiNwF87BBStkE1v0tzIOg2Am4z5ZqVSqaMqBsNiGvTr7+NzT2LyTiPdwmN03vidVqsVwWAQc3NzeOutt/DWW2/h0qVLiMViwr5rgwM82zHimEajUTidTgSDQYyMjCAUCuG73/0ulpeXZSy09NNH7ikdDWHfWYuQjGgmk8GtW7dw48YNVCoVXL58GbOzs4hEIsIc+/1+SUcKBAJyYGZmZgZXrlxBo9FAPp/H/fv38cEHH2BtbQ2/9Eu/hHw+j0KhgHfeeQcjIyMda8HUb5+FOJ1OjI2N4erVq3jttdcwPz8v+m9kZERClD6fD7FYDFevXhWgs7W1hbW1NTx69AgPHjwQkP8s8Xq9OHfuXM9t7UcHa/vCPan/zRq4ej/plA7qHvOwrf6tn/U0lus0/dM6Q5M6XE8HBwcAgFgshuHhYXg8HjkUyFzt7e1trK6uYmtrC+l0WtJw3G53BwEwNjaGUCgk6UmRSAROp1OinoeHh7I36NSZTGU/KUra+dasnIkDtO3QpIT+LtaFJ8FRr9dx8+ZN5PN5zMzMYH5+Xt7LOTqJKdf/18/oR3r9nH4/nRUyrJVKRdYgD/jS7jLSpcdRy/MQU09razfgqkU7HsDxYfVuh8VbrXYqTCaTkYiCz+fDyMgIYrGY5GHzuToiyFQBEkRcy5o8O428EPBKZimTyWBnZ0fYUXaUp9nNsC3QZnSY9zM1NQWbzYaVlRVkMhlUKpWOMAyNDQ1urVZDqVTC4eEhisWisF06KbofxanbqQEIRScg8/8ER0An66TBn1bIGsBqQESlw/Hid2mmQQOXfjZqsViUMSTzRqVEIK4XHL3GtbU1LC4u4v79+1hfX8fq6ip2dnZweHj41Of5/X6srKxgbW0NFy9exKVLl+SgUCgUkjIl3XJf+wHuGvQTvJqpHnoNc566eZ76xxTm9Pp8vg7GliVzyLzosmZ0FvT+6YdlNsMxmnHl2AUCAZw5cwbvvPMOvv71r+PSpUsYHh6W53XL7TXbpX90SonNZkMwGITX65U5BNrraGNjA6VSqUMR9RPKM4GHzsP1er1SEmtjYwPXr1/H7du3US6XMTc3h3fffReXL1+G3+8X48EDL0BnjjxDuB6PB7VaDefOnUM8Hsdv/MZvyAUBvMjh7bffxvj4uPRXz0WvBocVGk6bUsHUj7m5OWHRr127hrm5OYk06JCbz+dDMBiUtaedz9XVVczMzODmzZt4+PBhR8TMZFuj0SjOnTuHt956C9lstqc+At1BxtPWuwlcNXtjXhpjkgnaAOs62dp5NXUMf/cDGOjIMNWLbWFIv1QqiaM7MjKCeDwOh8OBcrmM7e1t7OzsYGNjAysrK3IQ8vDwUGpssnTd6OgoZmdnMTMzg4mJCYyPj0vKjz5oyxSFoaEhBAKBDjBp6tfTimm3TdCmwStwnNKm8x8JWMhEF4tFRKNROfC2vr6OXC6HtbU1ueyBz9Y2U8+fXif9OEmmnKSHn7a3tS7lQV7m8Xq9Xrjdbon0HB0dSZoHLxzqBlw/bTHZa/5bEyp63ky8QQeyWCxie3sbKysrKBQKCIVCmJmZQblcljk0bblOo9MXVOi/nZZ9fa46r3ow2HkaCSJri8UCr9crm4iASOcTOhwOxONxvPrqqzKp+Xwe6+vrKJfLwpYwnMJT/pVKRRK/i8Wi1IANBALStn5ZLdNgm0CK/dEDrgEsFS4PlHQrDaXzdZxOp/xfH85iH7p5I9pL6kXIbjcaDQEB2kO2Wq3iNdbrdRweHmJ5eRkfffQRPvzwQywvL4vDcBrWJp/Pdxw4yGazSKfTqFarOH/+PLxe7xMAQAOoXgEsQZM+iKa/y/QkNQNrhi90SgjBDw1nt1uJaDSptFgRw2S6+wE73frJ79MKxmJp1zudmJjAa6+9hq997Wu4cuUKwuFwR2hNHwZh3i7QmU+rvWGuU53aw8jJlStX5LO1Wg0rKytoNBriHPWbh6aFucyMxpRKJWxtbeHmzZu4desWLBYLvvrVr+LatWsYGRmB1douv6f3ie4/+8f1QId0eHgYb731FiKRCO7cuYPFxUV89NFHcjjmB37gBzA+Pv5ETn2v8+l0OlGpVE61h+12O8bGxvDWW2/h3XffxZUrVzAzMyOlvXQ5OxMkErjY7XY5mDc5OSkH9j788EPcvHkTa2trKBQKkurhdrtx9uxZvP3223jzzTfx6quv4h//43/cUx9NoPO09+i9T33Zrcwi1yIdfB7i0zqWB5qoB3R6TDd90i941RE0tlsf6rRY2ukEkUgEoVBIdOre3h5u376Njz76CIuLi9jc3EQ+n5czHdQRdMBXVlawuLiIsbExzM/P48qVK7h06RKmpqYkTSsajeLw8BClUgnZbFYu+9Hrvx97YTrwGjxqXaGZNv13jlG5XEY6nZaydU6nUxzfTCaDu3fvYnNzE7du3cL58+cxNjYmtl/PEzEExQSw/TLo/H5zvXX7Pm2fAHTUm9aOE3GC0+mUdZHL5eDxeMTOdnvOSSCabexVnuWAaGa9G4DUtoaXaezt7eHhw4e4ceMG9vf3EY1Gsbe3JykyuvymtiHaFvO5TJP51NMGTNGD2g3smd6tZnDC4TBmZmZw9uxZzM7OClOys7MjA0HRBZz1ZLNuXqvVEi9Vh/p7EX3Cjm00F5beyOam5kLQpSG096jHgL/1SUnNIFD0JjHHtB9QQBBmsRyH1XVISF8Xm8/n8ejRI3zwwQf4zne+g8XFRezv73fk/Jr5zZopA47Zkmw2i52dHQEfQ0NDApJ5wE6HvXX/exE9xnouTcOlN043JcW/sZA4mX+uLc3o6OdQadF5oaeqx0TL87IGJhvhcDgwPj6O1157De+++64wruwT+0qH0CxxZ7ZHO1oauHO92mztslpDQ0MolUpIpVJy5Sn71w+D3k1ozHnz1I0bN3D37l1YrVZcuXIFP/ADP4CJiQkUCgWkUikA6IhiaHZMs3k8AMa+RaNRRCIRjI6Owul04td//dfx/vvvw2q1wuVy4b333sPY2JiAp37Aj3kb30n6imkCb7/9Nn74h38YX/3qVzE9PQ232y17+ejoqGM9Asd5afrGMB74CYfDiMViiMfjkjfrcrmwtLSEfD4Pr9eLixcv4qtf/Sq+/vWv48qVKxgdHe0ZvGpnXa9RLea40anSp/Y1CDdLSjFNoNVqdexTOik6hE2jah5o6taO0wj3BNtDgMIDVB6PB5FIRNJscrkctra2cP36dXz729/G9evXsbGx8dT9b7FYpNYyK2Iw6vXaa69hdnYW0WhUSBtGKXO5nMw5x7VWqwmgOq3oueu2zrv93QwVA+2IXyqVQqVSkZs3SX5MT08jn89jZWUFq6urkgajdYyJI/Tzuz2vF3kW42r+XbPBzMXPZrPisFgsFiEFAIjNYCoJ8151GiX78rQ2Af05ICbuMIFst7QQ8zncX4eHh9je3sa9e/dw69Yt3Lp1C3t7e/D7/UilUjIGdrsdo6OjHc80z7ZwbFgxRpMwz5LnvmHL3PyazdCMHidPn3J3u92Ynp7G66+/jnPnzskVjsPDw5ibm8PGxoYku7OTpOfr9bqE/TgoZAJ1AfVeFzLLnTztx/xeKlszWZ1lokyga7KBFoulgz0gEOd46rC3/h4azl6F6Rha9MZn28rlMjY3N/Hxxx/jww8/xIMHD3BwcNCRq/k08Gx65QSq1WoV+/v7WF5eFsWq64tyDE3A3otoNvWkv5NNZVjfZFkJ6miMaEzN9c61zt+6yoDD4ZBnHB0dPQHQX0TIS4+TxXKcLvD222/j1VdflVxwDdbYL7OGcrd2ULHpkCjvYeezycCeO3cO165dQzqdxoMHDwTA9jOHmiHW41Qul7Gzs4Pr16/jo48+wtHREV5//XW8+eabGB4eRrlcFrZbh0vZdl0xwnRUdWjZ4/FgZmYGuVwOyWQSH3zwAd5//30EAgHEYjFEo1HJue3HUT7p/Tr8CrRL2127dg1f//rX8eabb2JiYgIOh6PD2JgGib81aNNisbQrUMTjcbz22msdN+xls1nMzc3ha1/7Gt59911cuHABsVis4xrd04oZ0TDbqAEJ20tjph1kOpE6TzuXy6FWq0mFGR394jO515rNppyX0E7t8+49zbxaLMd3xddqNdjtdgkdcy1ubW3hu9/9Lr75zW/i1q1b2N3dPdVzOI9HR0dIpVJYXFzsGCO32y3pNMyjzefzsFgsCIfDcvGIzos/rXSz7xw7M0JFMckrArxisQiXy4WJiQk4nU6sra2hWq3C4/EgHo/LnOqby0yAx+8029UL8Dmpj097RrcoAtcqc12p4xuNBtLpNAqFgpAzxDecH6Yq9aIb+7UXep7MOezWf/OgFQBJf9jd3cW9e/fw0Ucf4fbt29jY2JCqLXrPAu2r6EdGRuDxeGRv67HrZndOOx7PBV4106hBjAZ42lAzjENQxxyJ8+fPIxwOS2K70+nE9PQ0ZmZmsLe3J960vq++1WqJ8SH1ThB4EpN2GnG73RLm12DU7J/uvwZK9BJdLhf8fr8oLYaxCHS5iMwcVzIpHCttMPSkPs9hLuYbsTySBsb8f7Vaxe7uLu7evYsbN27g/v37SKVSwpiyH08Lb/A3ATvzaZvNJgqFAtbX10WZstYh14lm63rdqGSh+GwzVQA4VjqaGeLrPFBQLpcFuBL0dFNmdMrIAHE+uT45nnyeLpOi108/YubaMSR86dIlvPLKKxLepvNIA8gSP3y9G7jQyk4DezpX3HscW3raV69exd7eHtLpNA4PDyWM1qtwbDhuHPPd3V1cv34d169fRy6Xw9mzZ3HlyhVEIhFsb28jm81K3Uy2md/DfWzmQWqFTgBCYDg7O4t3330X+Xwei4uLuH37Nq5cuYLZ2VmMjo52rJ1ehGlQur/AMWinTExM4Ctf+Qq++tWvYmpqClarVdgNrrtuhpb7TrOW/BvH1OFwYGxsTNaIw+FAoVDA+fPn8dZbb+H8+fMIhUJPhGr7nUv9b72/dd91LhzBKtc356VUKmFvbw+VSkUOTnIczJQhvcfouPF9/RAcWnT6GJkpkifMOXa5XDg6OkI6nca9e/dw584dpFIphEIhuN1u0TNOp1OuZeY8pFIpHBwcoNVqIRgMYnJyEvF4HF6vF06nU3JnmZJAPcpIV7FYFJDENva6F08CS9rxoGj7TNF2z2ZrVz/xeDzY39/H9evXUalUcOnSJYl0HBwcCMAzbzDsxpR3Y0V7FfMzmiXUf6de1ABMpy5yD5VKJayvr2Nra0vwzOTkpFSjKBQKyOVycj6Hc3ISWNbt7Ae8mvrdZHiJRTTBoeet1WpXt9ja2sK9e/ck1Wh9fb2DlCkWi1hfX5e1RhJodHQUPp+vYy+b6Wma+DuNPHfagGl4CRS65TFoVo2e1szMDGKxGCqVClZXV1Gv1zEzM4NQKIQzZ84glUoJkOJhLSonfjcHzmQ0zUk6jeg8KvZBK0C9aDQVTmaKwJVFq+v1utyIwpwmGix65qyW4PP5RCFpBc5+atagl9wQUzKZTMetLPpgAzdRLpfD6uoq7t69i+XlZRwcHEi7ezHS+rtpfHjNKJPbeS0pa8SaHm6vAL1UKokh02w3v09vSL0+NbAjg39SzjK/S4fhefrd7Xaj2WxKHjM/p8GvDoOZztFphXtKf3cwGMTZs2dx4cIFDA8PdwBlOkbsl26HZi74Q+eJhyoByPyxvA9Doxxfsr7r6+tYXFzExsaG5PH1Kpx7gi3uzWQyiY8++ggbGxuYmprCpUuX4HA48ODBAywtLaFUKmF2dhZWqxVer7dj32pnRs+BaZzooFksFoRCIVy5cgX5fF6cmqWlJSwvL8Pn8wlI7jUKwnzck/oOACMjI7hw4QJeffVVzMzMwOl0ykUw+hyBjsqc9H2m4ed7bTYbQqEQXnnlFfj9ftTrdYyNjWFyclIOfPUrJhtGo2UaT8266qvBNfjmAUiWI+IhM+Zi87AuK0zoyJbe/9Tpmlyh9GMvgONcajqpVqtV2FC3241UKoWlpSU8fPgQ9Xpd8rJzuRwWFxeRTCYxOTmJN954AxcuXIDX6xWW9saNGygWizh79ix+8Ad/EG+88QbC4TAODw+xtbUlgMHlcsHj8cDtdkv9WLap1WqJPup3PvU8aYJK6zFtC02HodVqp/X5fD4Ui0U8ePAAH330EVqtdp55PB5HLBZDvV6X8DT3FtCZ8qTnSuvPbozh8/bVXKt677Cua6FQkMhAvV5HJpPB8vIylpeXJUJFBtxqtQo493q9HbroJCa0W7t6EZMsMcdRpzDp+dNpc6lUCvfv38dHH32EGzduYGNjA9VqVeq5u91ucd5WVlbkecQ8U1NT8Pl88v18LtekJmFOY/P7Bq/dFKFpkOlpkW3lpB0dHcHn8yEej2Nqagp2ux3b29u4deuW3NbF+8az2SxSqZTkr5n1+/Ri1Uqc7epVGem+UNgP9pvvYz/5mwdKyALk83lsb29jbW2to0g4WWQmNI+MjGB6evqJ0Dk3MT1OzTDrkEyv8ujRI7Ra7TJlHo9HQhdU5nQWHj58iKWlJSSTSTlYclr2RTMguvg9L7EgS55KpeD1ejE3N4eJiYmOpG6dntCL8PYanT/MH33jh+4L2fFSqSSlkfTVxjSqbI/5vQRaDMXr8mdm2J2inbleWS2uAa3s7Ha7nAyfm5uTgvzcFwQAZLq1s8f1VKvVZK55EETXJSQTwkN4ADrYV7fbLet5bGwMfr+/L2Bu9pOKvVqtYm9vDxsbG2g0Gpifn8fo6Ci2t7fxve99Dw8fPkSj0ZD9Njc3J+WEyIBzzPXJV841IyGsZMB9Go1GceHCBezs7GBpaUkuaZiamkI4HJY11IuQhec8EKgxDzkajeL111/He++9hzNnzsDj8XQ41XqM6CR2GzszCqH1JPdCq9VCOByWE+q6DjQ/24+u6QZuTEDAduj8c02CMNxMtoqXVNCJ4vfxsC73mr58wwwrmyzPSUzXs4SpcUBnNEfXZwWAvb09LC8vI5vNYnJyEm+99Ramp6exsrKCcrkMt9uNq1ev4t1338XExATcbjcmJiYkH7tYLOLatWt47bXXcPbsWYRCIble9t69e9jd3ZW62rxqVh8arVQq4gz0EwXRwIbrSh8SpI7TrCvHmwfImB7AA1pLS0u4d+8earUa4vE4JiYmEI1G4fP5sL29jWQyiWg0ing8/oRd18K1w3+/CDEjBXyuTke0WCxSp5bXhVutVhQKBezs7MhV2qyQMjY21lEiiweY9SFYE188rX29SDcWWdtovUepT7i3CFyXlpZw48YN3Lx5EysrK6jVavD5fBgbG8PY2Bi8Xi9yuRw2NjaQzWZx7949lEolOcBls9kkysPnd4v4nbZ/zwVeuWnNE/JaSWngx5Ac84FYt65er0udO5baCgaDuHTpEs6ePYuHDx/i0aNH0lkqbjKklUqlI2dLe2K9snZ6A2pm19yU7KfOmXS5XKI4CoWCnBBdW1uTzUtAyjFwu93IZDJykplGhBNMtoGT3+32sV5leXkZR0dHciKdhx04n6VSCTs7O3IFqC7dchqhYtPMHUMSNET1el1KirhcLkxNTWF8fBxutxuRSKTjWb3Ooa4CQOFccd5o4IDjdBMNXHXeNIErwSs/rwGEDoHwkEy9Xu9QVvzRYJX/71UZ6YNybKfb7UYsFsPk5CRGRkbEcGpGy2Jp58TqW9Y0wKFCpfFnsXNGUViTz2azSVSExcSpCN1uN6LRqNyYwzSMXkU7GHSqeFpZszVWqxVbW1tYXFzEw4cPUSqVsLy8jJ2dHbzzzju4fPky4vG4OFFaN1An8TYcAiC/398R8bBYLBK2TSaTciHCtWvXMDMzI05KL0J9wctZCJjT6TTK5TLm5+fxta99De+8844wUlyD2hHhHurmrOu9yP9zTXDt6LVN54Tv00avH71jPs/8Lu2EaVaW48474ln7myxXpVIRHa1TtnhdM4EAx0SfcjYBdS+gwRSOvwZvmrABICTG5uYmqtUqxsbGcO7cOcRiMaTTaYRCIfh8Prz66qsIhULY2NgA0D77MTMzg4sXL6JWq+HKlStwu91YXFyE1WpFJBIB0NZ3zEXkwTCCV0Za8vm89K9X5pWf0REkrTu6OfFMaWMka39/X8p3Wa1W7O3tib3P5XIIBoOYmZlBJBJBMBjE5uYm9vf3kUwmpW6o1qNmdLXb672Idqi6AVf9OvvYaDREX1YqFTkvk0qlsLGxIVdnM0d/a2tLak7z7Ec+n4ff7xd9043l1a/3u07pdOjv5vfwh4ccdcoNU/w2NjZw8+ZNqUpCRyQWi2F8fBzxeLzDYSqXy9jf35coMgmBVqsldca146MxoklCniTPfWCLioEMgl5QJr1OIOj3+zE3N4ezZ8/C7/eL5xIMBoWZ3d/fR61Wkw386NEjyT9stY5PxGllrnML+w0fmHmNutSPPmVOUMPwHfONHA4HisUiNjY2sLS0hM3NTVQqlQ5mS5/0J/Nps9lwdHSEfD4v11Nq5UPDpdvRL1tAYAUcAz29KZlHtbW1JfV29XybzzQ3kmYdNGvHfDX2icZ+Z2dH7pQfHR2VUKUZojmtmF4snSid90lFrBk9Mjtkh4FjB4mHt7Sh0spF52Ppm0cASNhI90Mz9t2YqNOIBr52u11uwIrFYlKCRjPOPMTk8/mkSLrO/2NbySIQyBSLRTF+wWAQfr9fvGxW+iBAouEKBAKYmJjA6Ogo9vb25OatXoTMGgEz91UqlUIgEJC1wnQP1h7kYY9yuSwO09zcHOLxOILBoIR6OW+lUknWe6VSQTQaxfz8POLxuKSAMJeNhnRrawuPHj3C5uYmLly4ICWJepF4PA6/3y9tslgs4sRGo1FcvnwZr7/+Os6ePQuXyyV6w8yjN42emcupT+Z3M5CmU05nhyCF0us+1J8hy2mCBL0vOM46jYclp7hOmU+uHUGOgWaw9KEv6mgdmmSfdZ3rfvYhD5NpZ5YOOw9rptNp7OzsIJlMolQqYXh4GDs7O1I+aWhoSOrpVioV3Lp1C9VqFW+88YbU9gWAaDSKQqGAGzduoFQqSWoQQeLe3h4ikYj8eDweSX/K5/NCJvQ6jxrgmNFWPXYcP52nTJtWKpXkkpBsNou7d+/i/v37YvvJ6k1NTWF0dBQjIyNYX1/H8vIyAOD8+fPiqOqbmbQOfp5UOvZD90e/bto+XdGBua4WiwWFQgFbW1vY3NyUA0yMBK2trUlKC207D3pRJ5tRRz0HzyO6DybTSUzCyggE4bVaDZlMBqurq7h58yZu3LiBR48eoVwud9Qt5i2HtBkjIyOCVQjc7927J7qk2WyKbjXXlRkZepo894EtM4dM52PqPEqGg/x+P+bn5/HOO+/g/PnzaDab2N7eRrPZxNWrV9FoNOSayb29PcTjcczPzyOZTEqZkWaz2XFIgSCBylyf/H8e0X3RoJyGns/Vp0orlQp2dnbkqlyfzyeHOg4ODsSQu1wuYY6CwSB8Pp8k6XNzjo+PC7hnniLZQ81Y9Cps08jIiJyA5ByS7UilUtjb2xPQcpKYnjxBOQ8OMCWBuWqa4aXUajXs7e1hc3MTBwcHwkprL74X4UbVede6fZp1pQdN1pWG0gwF6h8zVYXKRgNb9pVpE+aJ2eddn9rQ0nCz/FEwGOzIt9WOD9tMUJfL5dBqtRAIBDryQ/W8svA5nUa3290RNuS6ZP1lAJLTTo+8n6oYDDf6fD6Ew2Hk83msrq7KYZfp6Wn4fD4AbcM+MTGBTCYjzgNv3drf38f8/DwuXLiACxcuIB6Pd3j+VNKffPIJVlZWEAgE8Oabb+Ltt9+WQxZMo4jFYgiHw6hWq8KmZTKZvk7i87IDOhlkFe12O2ZmZnD58mVMTk52FDMngONrupIF547MeDcWSoMPDYBJLBDM0fli2kS/4WZKN7Bjtk2zd0DnhQR0YJgjSGeIYU1GHpjryTaTWSagJCDWudqauetnT5p2iJfu8HBOJpNBKpWSCyIymYw4htRx7EulUhHGijmfejwqlYqkEfB2SYLXcrmMvb09OUfCkmL8HMeS9uW0oqMP7K8mcMwoJ6vK5HI5qTvL2/gAYHV1Fd/73vdw9+5dqSTE/cdb8c6dO4d6vY4bN24gk8nI3uPBQj1PGlyzrb1KN0LGZGD1XOlrqkke8HzL7u6usP8ul0tSC7a2tjA6OorR0dGOuWHdV12P2GSRzf72KiapSKzGiCOdZo1tDg4O8OjRI1y/fh0ffvihAFePx4PR0VG57Y1OGMeHN8B5vV4pd5pMJmVdcK/E4/GOm7h0+z4z8Mo8RU0101gRvHKBh0IhXLhwAZcvX0YsFpO8EKfTifn5eQQCAXz88cdYX1+XfJFYLIZLly4hlUpJWNf0JLgpzXqV/eQSatGLV4N0TgDBC73NQqGAzc1N7O7uYmhoSAy4w+FANpsV4+t2uzE1NYV4PI5W6zj/JZfLAWgbDzJoOk9Wswj9Mq/8jAZb7DvLfvAedDPcaz6PrCYAqShAFoEginmUBBXM46U0m+2yN+l0Gvl8vuMigNN6YVry+bwYMG1U9PO0MmZpFqYLsD86PYDryzxIR+XNzcvx4HMJCHw+n2xc3bduyuk0osfPam3XvuTBPypCk9HiviyXyx1MM5WZLuullTeNn2Y49Wl9k82i0o5Gox1lenoVtlWHX8lozMzMiPfeaDQkj9Dn8yEajWJlZQV7e3vIZDI4ODiQn1Qqhfn5eczMzMhVrxynXC6H5eVl6UsoFEIwGEQ0GhUHIRKJyJ30zG3b399HOByWNI3TChnXZrOJVCqFra0t5PN5TE1NYXZ2VqqwaEaL883fjDYRiHL8TaeS60Az/QSHrVZLQs0MZ+p1yn/3CnqA48ofmtAAngzN6jXKfjLiwfQI2hg6xNT7JDKYT6n3FtcryxVRb+pIxPOwWjoFiPZAV48h68mLWba2trC7u4u5uTlcu3ZNwLN2jLWjrKuc6INsOpee7Tg8PEQmk0G5XBYnxzzFzvf2Iiex9eZ60nPMdUkng0RGJpPBvXv3cP/+fQGuBFGbm5v45JNPcP78eQF5tJu67JRek2bf+iUETHBoOjMab1gsFiF48vm8rLfDw0OpgR6JROB2u3FwcCCMeyqVwu7uLlKplFQiarWOc4JJgHUjpbRu7Ue0LtBpHToVhPud6Qxra2u4ffs2PvzwQ3zyySdS/SEWi2F0dBTDw8PSB129hGUT6UxubW1JKqLFYhF9Rbygybhe+tgzeNUIXg+EeZeyaVyBdjglFAphcnJScri2t7exsbEh4Q+/3w+HwyHXqXm9Xpw9e1aYiHQ6LR4AvWnNfHESTC/+tGIuWN1vzU7wu3WaAoEfCzGPj49jfHwcoVBIAIxma0OhEGw2m+TD8DACF8GFCxfEwALHqQt60/azWZmWkUwm5YYWGo1WqyXKNp/Pn5oxs1qtwrZGIhEMDw/D5/PBYrF0hJ3Ng1IEgGQOTOeD7+lFtra2pHqDCRS7OTT6Wj8zj5Vzq9eSdhy0kTFTCoA2INKpEzqnj0qqn3nkntIG3+v1CuOjQbHeE1RMFotFGCgaWr6XRphjb4YNzfCgOafAcTSCobB+hIaQuao0GAxbMb+vUqkgHA7j1VdfxejoKOLxOJxOJ6rVKrLZLFqtdh5asVjEysoKpqam8NZbb+Hq1auSCxsKhTA8PAyv14tsNovl5WWcOXMGFy5ckDq5AOD1ejE8PIxoNCo5bWR7/X5/T/07OjqCy+VCo9HA4eEhNjc30Wq1pBzSxMQEvF5vRz41fxP08IfRAkZmtEHge/h3rk+uaTI+1DNk0sm008j1c/Aul8t11D7We4frjCFmfr8Gr4zY6L6T/Sezqksm6txMni8g+KWDQYdapxz0Cwq4pwB09I/fxygMD5yxXzrNqJvoNATqZdPI08mg3SUryzQv7WBqR7nXvnZztLWDy+/UP3R0ucaj0SgajQZWVlZw7949IWkorVYLuVwOKysruH//PsbHx+HxeHD27FkBhJlMRtLszPVIvdtP7jmfb/67G3hl30ulEnK5HOr1Ovx+vxxqYhsvXrwIoG2LdAUbOqnhcFjAOSu4sKwZ9bce735JDgrXkO4jbRFrRnNdkoC7e/cuPv74Yzx8+BDZbFYOro6NjWF4eFicdW0z9Zjped/f30epVMLq6qrcmsr0M5IdOn3xNP3si3nVYJEDw7CODi3RwDFtgEWxp6am4PF4kEwmce/ePWxtbUnInIqAZUAcDgeGh4cxPj4uJXj29/flkAiVGQf+WUrhNGJ6kvw3+0iPnyErbiQqKpYQYsksMgBU1vxe5kNtb2+jXC5LWCWZTAoLaeZTPa8HBrRz7QjsWAgagCwgHqrShyIo+uADgI4UAebyBAIBRCIROe3Ow2rMQySLxzGgATDD8v0Cu6WlJczPz4sXyQ1KY8d1y3VMxXF0dCQOil5b2mkwFSND4mRlyf7T+DNXlqkQfK4G0/1ECEzvnMabc0jRDAkACRNRCeuT5WR3mGDPuSeANA2WZsXNeSKTq28z6lW0QqWTw3livrjdbhcDybqaut7nw4cPBfAeHR0hm81KPv3R0REuXrwo+4FsAssw7e7uys1bXO90Qv1+P7LZLGq12hMht9PK/v6+/Jv5uUB7T0UiEYTDYck/45rR/wY6QYO+5IRzoi/MAI6L9XMuyRJyzev9x/2h92Wvsr+/L+FCM3LVbDYlBYLPYBvNvcP2c33rvEq9Vrh++R6yfw6HA9VqtaPKht4D/eTzcvwZSdJhX/6NDq5O22E9aH3oSY+PPmBH3QUcHw4z7Szfz7MEBLV6bQC9neTWotvFudHRK9NRoh5hvq3H40EgEMDe3p7coNVsNqWmOoE508cePHiAmZkZXLhwAbOzs/B4PKjVajg4OJCccz5fp21pB7tX0Xtcj1G3FDey6foQM1MaU6kURkdHcebMGTmEubGxIaU+mT4wPDws6YIA5IYuj8cjEUuzL8+DafQa4Jrk69T1zL/d3t7G3bt3cevWLbmYiPn+Y2NjwqrqcdPONOe/2WzKoXCbzYbl5WWkUinUajU5uEaykvmvvdj7vphX7TmboRJuOp3rB0BY1ampKczPz8vtGrxudGJiQtgDhtI2NzdhsVgwOzsraH98fBzDw8PixXYzGJpV6MfL5GbSuYr8v2YF+HedV0ljpktL0fMmu0jFyk1QLpcxPDyMCxcuIJfL4fr16+LdkTngIjGZxH68zPfee68jxzGdTnf0VbONWnjYh8rS6XTKnEQiEdjtdgkp8yAKJZfLidJhn6hkdS6WBonaePYiyWRSmF+yDtpr1iESzRDQwTLzU/Xn9FxoI0qWh8Cf6RZmAXldnkpLr/Oow/UUGhlTCZvMsU5z0PuW6QE0/hwzrjc9N/r1bgZDg/9+mS2mczA1gM4inQzNMGsDxitdw+EwhoeHcfv2baytrcmcHB0dYXFxUVJ1Ll++DK/XKyCCUiqVJNfcBCU05hxfjkcvQkaJTCAlEAjIjTwcd1OXabCghetYO36aWQc6rwlljVSmdmmQxb2h2fZehak4/D6g80CZ2+2G3+8XvQOgA4Rzv3Ef09lin3hgUDOpBEZcH/rWKw2+TOawH6GDXKvVnkgbMdc914dujz48yN/6fcxH1lEjfXKbzDmBpRkF0Y5xv46yBth67Lq1m+PN66EZkbBYLFJG6eDgQAAtzxjo/OTt7W08evQIY2NjiMfjaDQa2NzclNSBaDTaYSO69bVX0YSGFu2oA3iiNJbD4UC9Xsf+/j62trZQKBQwMTEhdU95IQPzQovFIvb29rC9vS1pT3Q88vk8Dg8P5eZQ/fznAea6f7pfOoLGvu3s7ODBgwe4e/cuHj58iHQ6jUajIamAvEpa2wNTF5FoaLVaQiowlYX6O5/PI5lMYmJiAhMTEx0O26cGXjUI0CBHK3TN5NGj9/l8mJ6exoULFzA6OopKpYKNjQ2sra1JqINeIzdjqVTCxsYGHj58iKmpKYRCIQnFMxGcE2oCakqvSokneXUqhJnLow0VJ59gQl+7SQBKkEpDwcLLzWZTmEjmg9LYAJASMTabTdgJczP1wxi8/fbb8Hq9qNVqco0gNyIBOvuqx5Bjws0VjUYxOTmJ6elpBINBuT6Ol0kwVzeTyQgbzec8a4FqpqhX8EqFUi6XpT869YJGjOtU12WlcJ3rQ4h8nWvB6XR2lMKqVCpimMkKmSW7uqUvaIPVi/A5JnAyGRdTtHfLOdVAwAQ//E7NIhE8mM6baUyeBxyQ5dC1jrm/2We9Ttg+u90uNRWZWhQMBuXwwMHBAYrFIu7duyeh1lgshoODAwm5ApDSTJxXPsdMa+m3f9lsVljjarUqbfX7/R31STWr1O1Z2oEnwNdzSHZFv4efM51W7eToNBjzO04rWi8zTUXbCjPcr/MZCfJYzYJOFvcd30t2XNsmOmZss06X6KZ7+nWwAAgQ1ftQAwNGRKjXqSvoFOm1pUG1ZqP5fw3YuBfpgHJPdmNcTRvZj+jvpU7UlSzY92w2i7W1NaTTaak6k0qlcPfuXayurqLRaAjA4wFlt9uNVuv47Mft27cxPj6OqakpRCIRPHr0CBsbGwiHw3IgSI+TTsPqR05ipfXYMUqXyWRQLBbFTh4cHGBtbQ2bm5sA0BGF0Wck+P5yuYx0Oo29vT0JnRMLHBwcSO6rqdu1A9lvP09a5yQuyL6yNiv3qiY5NBmi1wPfq38Dx5UZOC7ED9wT3ANmasqzpG/wqo2JPuRhhnMYTonFYrh8+TLOnDkDq9WK3d1dJJNJAa46L0SXx8nn81haWsLU1BReffVVjIyM4Ny5c8jlctjc3BTDSSX/vJUG6D2b3gQ3iQawfBa9Us0e8jWWiqhUKpifn0csFpO+lkolYTw2NzeRy+XQbDaRz+cRDAYlV5RlTxie1AqsH9AzOjqKoaEhCS8yxEv2iTcqaWXLOaUhIQNCI6KvvKvVamL09/b2sL+/L//X4T1dsott0Hla/Srb8fHxJ/pHw0WD0I19AY7z7DQ7SwBqOkZkjxnO07enadBqpilQ2MduDNqzRDttmqEmMNfv0+NpAiENXs3PdwsNmsBXHx7U38u9oA/f9So6zGwy4d3YQG1MGbI6d+4cAoEAZmZmsLq6irW1NTx69AhbW1sAgPX1dVQqFQQCAQDoKOlFRc5DY3oMTRDdj/AwkWYmCXZMp6fb+jDBbKt1fJGJriltMiLA8eFKHS3ToMQEKv0yryzJRsOl80Op4zmmHEudn0f7wggNdYbpvOg20yZwbet1q0kIcwz7YbZ4UE7rCs2Ws+whQ8EUs/qHHgPuK4re4yZZwtdYM9zj8XQth6Xns1d2Utt8PU/cm5pRr9fbF888evQIVqsV58+fh9vtxq1bt/C9730POzs7iEajuHjxIkKhkOSLx2IxuN1u7Ozs4Pbt21hcXMTMzAzee+89RKNR2O12JJNJce5YqYM6Xa+Zfmy/JhRMQkzjHGIUMuCNRrsM1urqKpLJZMeNYBwru90uB1eDwaC0NZPJSISQJQoJ3jmX5jz32z/tGOrvoE7QlYz8fj/i8bikDvJaeB7YajQakhKh9b/WWdQpJAu2t7dRr9cRDAblEptz585hampKUivZntPuw77Aq17ENJqaVWMCMBcW8x4uXryI4eFh7O/vY3FxEdvb28J0EBiRqQQgOUssPRWPxyWfhEYlnU53hJi1UeFg9CK6zIwJ0jVroF+jIm61WnLCjoqrUqkgmUzKdzPlgadwfT4fIpGI9JkLiInpZA60YtOKt1/DyTHTJTro/TJvlX/T+cRsJ8uCZbNZmStuvGKxKAeDUqmU3G/P9cN1QdHsBIEzFYOey17mkMaOIRm9KXS+j/YYaYgajUZHceVuoX8KDRc/y3xCvtfMEdbzyDbo372KBpWsiUmmUqe7NJtNaatmSnQOogb2mhlmxIDOm+klm4CHY09lTN3Qq3Bf6VP2VJjaaOmxIAgnSOL1nMFgUO6N5xWarMGcTCaRTCbFEaMQWHI8df/0GGpd0auQKdV9MMH600Qb2pNSRvj9FotF0hO6nVEg+CLI4jqmI6ffe1rR4JGsi+6nCV51W1hyis+1WCxS+5e3Umk2lX3VwFADcv2jGUMt/YBXDZZ1+L7RaMgh5XA4LECen+OlGDqFie3W5IjOh+Wa1xFB5mGHQiG5JAc4Zv0INrmmeiUDtLPIuemmB4DjtLlyuQy/3w+Xy4VisYj79+/j4cOHsFqtmJ6exszMDJrNJvb29uRAHg9tb2xsIJPJYGVlBSsrK/B6vYjFYlhfX8fGxobo96mpqScOaPW7D/U+4v/522KxSHUW6jMCtcPDQ6yurmJ1dRUHBwcIBALSBputfe3y2bNn5aplv98vdpMHUMPhsIBaRi+pizweTwd47dfeZ7PZDgZXk40k2YjFAoEApqampP9kl3n4tdlsykVKZvRLO1fValVyfJnfT/x28eJFnD9/Xg7m6Xk4iR02pW/wqhe0ycYQtDGvyu/3Y3p6GlNTU2i1Wrh//z5u3LiBzc1NNJtNYfiY95HNZjtunCoWi9jc3MTa2ppcs3Z0dISDgwOpUcaO63Zy4HsV81CB3gzaSGuFSy+fp5YJfHK5HDKZjEwmQ/W7u7tiYEdHR2WjZzIZuVWElQoY2qRzoJOj+1nMvFaQnyd1z+/yer2IRqMIhUJIJpMd4JXzxNqfzONxOBwolUpyAxIBLHMonyZWq1Xyg5jrR0XdD2u3v78v9Q9prGk8qXDZFwIiDfBY87XZbMppcG2AubZ0np0u9MxNrAFCt7SDXnN8tJjKtdlsnxLlTVFsM5WJOY4a2OpIAvccmVN9ZzxwfNiEIFFHGbTSOTo6krVQrVb7Wqc6bE2mg/tEAxiTkdBsIQ233++XGpIjIyOIRCJwuVxYXl7uyIXV+oKhNH3AEDhe/2RVeLii1/SWbkKHzcw51zrW7KvJPupcYK4FHnClUdXlnChcDwRhBGY6P7pX0XPAHF79N5Nd1mlCbrdbdCmdQ+r7QqEgLCPXCSN47IvpfOm5NcfMdNh6Ec3yMu+fDDFTVrjmtAOtIzUE3larVQ7xsFKHzkfn33X6FudcV8ygM8tDRSZb24voz1B/akeJc0pbYLFYEI1G4fV6Ua1WsbGxga2tLTQa7dqe09PT8Hq92N7exs7OjlzMwwOKo6OjEvX48MMP5Sr58+fPy+l3v98vV+FqssncJ6cVE4RroE4Mcnh4iGKxKBinXq8jmUxiZWVFrlDX4Mvv9+PcuXMYHx+HzWaT8liZTAaLi4tSzo+RVaZgscYvibB+MIwpmUxG/s09RqLK4/F0pIyxJCDXjcPhkBsL0+l0x1kP4gcAAuoByOG0/f19Ab3BYBDT09O4dOmS1Numc2pG7z4V8NotJK8fqJkRnu6cmprC2bNnEQ6HkclkcPfuXdy9exf7+/tiVGmMWIuRJ9S5KNPpNJaXlxGPx6XywPb2NtLptJzkM9uicx176R/QedsM26gZJPaVh45ooL1eL8bGxlAqlWC1WiWPlwqpUChgbW0NS0tLaLXatSR56wa9Nl6PG41GEQgEZFEx8ZvscL9emM4R0ouG8+p2uxGPxzE2NtbBjrPffC5ZRvaVyey8Lu8kMcGG3W6XO6xZ9FiDvV6VEWvs6lCvBov6//w7FQeADtZGt1Uz/Fxv2sBrkE8AQANjhlRM8NprHzWIJnDKZrNymEBfZsH9qJ9HBcy1pHMCGUo6OjoSRt7n88l42u122a+aneZ+aTQayOfz2NnZwe7urtyr3qvQYNjtdpTLZan5zFzRTCaDaDTaAUA4NtyvBIGsPzwyMoLh4WF4PB7U63UpC1Ov15/IxW61WpITzvnmYZRMJiOnZsPhsESbehGuBz6X4CyTyUgEQ19QwDad9H9T+XNd6fJzWkebOkTvCw1edXi7V+GeopPEfaBTvDSrZOZQ8v361ipeLw0c26OhoXYdVxY9JxhoNBpwu91iJPWhSzNa0A+w43jqg2/aqQUg979PTU0hFothb28PAKTNHo9HLjEYHh7GuXPnhEBgLeGdnR243W4pBccam8ViEclkEuVyWcpQBgIBYUC5P7vlUPYqOn1E20WuP9rver2OaDQKl8uFfD4vB5l4K5PP5xMwbrMdlz1jZCcUCmFkZAT5fB4PHjzA2NgY3n33XczMzEiuaC6Xk1PwHo/niXXUq5j6Sa9BOhokBshsp9NprK6uYmNjQ86mcCzsdntHuUjtbHq9XrleOpVKYXt7W1JLOA5MK/T7/R0MZ7+ROh351Oc+OF7EBKw8Q5tl6gLqJo2NWN5LO1q5XA57e3sSmQ2FQlJBgoA+EAh06CFTjz1zznoaARwrC5Pl0A0gK1Wr1RAMBqXBDocDOzs7ePjwIZLJpBxSAI5P8RGo8so1GlzS8+Pj4zh37hzC4TCmpqZkYzDBWLepH/BKIQvBPtOT1yEBll9hDivDR6w/SSaP7z06OsLW1hbu37+PTz75BHa7HaOjoygUCigWixKKbzQaHSkD3OD0xGh8TjvJpnRjbmlQAIiSnJmZkY1JxULmTbNvzI3hjw6Dnkbcbrck54dCoY4SMqZHfBrJZDIIhUJShqPbd5lhRN6+Q7YRgBhFjhE/b6ZA8G98L71RllTi4Rv9bIJASr/sK9vEqEU6nZaQlFZ6OvzI9UxWWKcMUDRbx/FhexuNRkcpNc3GMmUgm81iZ2cHe3t7TzCap5XDw0Ok02kAwNTUFHw+H6ampkQpbm9vy8FBgrRuDIzWSZw7XaqFytkUjmk+nxcQzOem02mpNauvke1nDrVxo9PAusO8ehE4eY3o9WiCeH0qnWy8DvuetLcIZDmn/QrXnQY8+hlsG50sfXiL1/0y51iX4GNOLyN8DodDmDjONZlMj8cjlUcYJSGIM1m7XsWMsOgxo0NINvHs2bOYnp6WqKLWk5lMRv5/6dIlnDt3DlarFbdu3cLGxgZu3boFu92Oa9euYWxsDBMTE/B4PFhaWsL+/j6KxSJmZ2cxNTUlhAfXLcG7PlPQTz/1vtLRDUqhUJCIYiQSgc1mQzKZxN7entg0AFKFwGq1Su4qb1sqFouid1utdl7o8vIyxsbGMDk5ifPnz0sJRobpdd1Xs1LMaUWnt+g91Gq15OCxvpq+XC5jbW0NKysryGQyglU4r2QhGbnL5XIolUoS4SPuYVg9Go12sK+6QhHZV9PR7EV09QIdPdfRSF4koVNadP61zWbD0tISUqkUksmkjEu1WhWmvdVq4fDwEDs7O5Ir6/V6MTMzg1deeQXnz5/H2NiY2GZdzUnb59Psxb4PbGmjTSVp5mP6fD5ZcOPj42g0Gtjb25ObmzgwVqsVpVIJu7u7KJVKEo7WALJSqYin8+jRI7zyyitydez+/j52d3dlMjSC72chU3HrgzrsOxkM9lnfA0y2kR5UoVAQpZzP5/HJJ5+gXC5jcXFRcncI2ulZh8Nhud6PzK7FYpGT0ybF3o/CpaHVG1WDMuYonzlzBmtrazg8PJTbTcjWEeBxo+Xzedm0HMOT2qYXqMPhwOjoKGZnZzExMYFgMNjBlvbjfGxubmJ4eBixWExe6xbaZhvJQrJNdE7Mw03dIg78bg2UyAZ4vV54PB7xYvk+rTD6ZUFMD5z5yPTk9/f3O+rnWa1WCXXxEA+frXN19bjoepFUxgzPlstlCeVz7xME84a2ZDKJg4ODvtMG8vk8crmc5GTF43GcPXtWcuHo/I2Pj3eEVTUrZLPZUCwW5W75g4MDAUb6wCiAJ6IF+kpjq9WKYrGI7e1t7O7uwmKxSAiULFivThv1KJlpv9+P4eFhTE5OSh1mfUj0ad+j0yv4f/0a8KRT/6zvBPpjW7XwulsdedAMr16/OlrG9jEHkPNKcAoc1ywuFouSG6idMZY507qc60KHPvX+7hUUaDBgMrq0CXSwLl68iJWVFXFM7t69i1wuh2QyibW1NXEK9b68efMm7t69i6WlJSEP/H6/RAo2NzeRSqUQCAQQi8WkZGE2m0WhUJBDMgxZ92MTta3RbJ0GG4z8pNNpsWOVSkUOZlMvHh4eolwuIxKJSOnFQCCA4eFhSQcCjtNnqtUqdnd3sby8jEAgILdVrq2tIZvNIh6PdxBL/cwhgCccRH4HQaQ+pNVqteRQ2tramth5i8UipbBWV1dhs9ng9XpRLBYldE42lmH7QqEg4DUcDiMajYozXS6XUSqV4PV6JeVGR9t6kUKh0JHywT1h1oHWkTPOM8te+v1+2Gw2LC4uCjHBlCqmH1SrVYkilMtlhMNhzM7O4vLly1K3l9UinpYu8KmBVy1UBAzrABDPYnh4GHNzc5icnITdbsfe3h52dnbkPmAqMaAdgqahItNIz0Mr+Y2NDXz88cfweDwYHx/H9PQ01tbWsLGx0VFWhO3oJxxLg0wvSYNXoPO2LTO8YLPZ5PrCVqslbMDGxoZUFMhms/Jesgp2u1025/z8PCYnJ+WWIHp1pmfSD9PDPlKR6++gYiJTMD8/j7W1NblaEzgO/3E+WNNW50s+SzRgjMVimJ+fx9zcnDBYVK79suYbGxuYn5/vyPs0DSb7rnMAtYED0BF6p3Rj9vhvsplkenj4jIbSZF6fJ/VDt4ff2Wi076NeWVnB9PS0sBJ8Bh0t7gt62MBxbqTJkPF9TA3hfuA6MUs60cmk58189H7YHuYOEliHQiGp2HHz5k3U63Vcvny5I9Ss9wfnOp/PY3FxETdu3MDa2powwQT7JwlPB0ejUbnq8eHDh8hkMojH47hy5QpmZmbg8Xj6Mpha6vU6fD4fzp49i3Pnzsmd9rqcku6baaT1iWHz0F23Q3rdWI5uDme/YICyubnZ9VQy7YVOEdLpDUy38Xq9Elmj80FninWVedCX4UoyrEzz0uwqQZQ5Bux/r0ImlykoPp9PgB1Pp1ut7TzV2dlZvPHGGzg8PMTDhw/xwQcf4MaNG3J5Rq1Ww9raGu7duyc3E9FmEsDfvXsXe3t7+Na3viVs9OzsLKanp+W2OJ41oDPHtAF94UUvYjKsJivPw5m5XA6VSgWhUAg+n0/6+ejRI6lkc3BwIOXPeEmB1+tFvd6+UpVgiJGgarWK/f19PHr0CBMTE4jH4+Ko8Epvnq15VjThWX0EjjED/09yibmuTGHa3t7G2toadnd3pQKPxWKRK6ZbrRbW1tYEoDabTYyNjYkDwrVHcLy1tYV4PN4RLWTkgTXZtfTax42NDQGrJJ88Ho+U6iLWAY71i66nzVQrSrPZxPb2NjKZDIaG2rc7EnBT77tcLkxMTODy5cu4cuUKJicnpf6t6cyafftUwCu/3PSi9W06rVYLXq8X09PTOH/+PEZGRlAsFrG0tITl5WWpkcYNTiDDhF6Hw4Gtra2OwxJ8fz6fx/r6utz0EIvFcO7cOSSTSSwvL8utLGZ6w2mFBpm5Zsz10AWutSEwPTYdjiKgoMJYXV0Vip3GPxQKIR6PY2RkBCMjI5idncXs7Cz8fr8cAGq1WsJ4clP1G+bS86dBik75sNvtCAaDciUvD8yxMDMNB1k8OhgMmzyrXXxGNBrF7OwsLly4gLm5OfG+2Y5+UgYoNIScIw3y+KOZRs1EcUzYr27MjAkKLBaL1J+kQuB8sR/6N5/Xb/9M0MvvOjw8xPLyMiYmJuQaZu5JOoVMj2g2m2L0dW4j+wp0liric2gMCVz1Z7LZLJaWlnD//n2kUilhovoB6dx7DBHb7XaMj49jbGxM8v14iEKnQwBP5k1xzebzeanHCLTzTcn269PtDocDIyMjOHv2rIS5tre38eDBA6TTaZw9exZXrlzB6OioODy9ziVZmEKhgFQqJUxZpVKRC1po9PT+NAFXNyZG6we9n7rlmOnPdBPTMehF7ty5I7aBRpNhbK4hzb4wvYqn5pm/Wa/XcXh4KNdn61xzGnrmXBIUk9njs7Rjaqa5PWsMniZkXFkJgfuCEQr2ORqN4tKlS3Kb2q1bt544U5BKpbC2tib/17qpXq9jfX0d6+vrcLlcGBsbw1tvvYXJyUmcPXsWIyMjACB1Oi2W44id3he9SjeHRqfScf2Wy2UZ64ODAywuLuLWrVu4f/++nGcgwOFBYB5EXl1dxdjYmBzyoiPGw3m1Wg3j4+OYnZ0VsMVIrNPplM+yTf2KJifYPl2PGWinPTB1iHNHgqJWq0me/+rqqtjwcDgszCXXA21dqVSS6HEsFkM8HofNZpOKPcyL1rax13XK20qJ0VihYnR0VPJc9X7gHgIgFxORLWU7Wq2WXPvK1BU6km63G1NTU7h8+TJeffVVzM/PS7k4Ha07yQaeZp32BV71wOm0AV3SJBgMYm5uDnNzc/B4PFhfX8ft27exvLwsoRQdFgsEApicnMSFCxcEmW9tbSGdTsNqtXacKuZgZTIZzM3N4cKFC0ilUkin07Lo2c5eFzIVqsPhEANvnrplv01gwrEAjhlcj8eD4eFhTE1NSTiX+bF2ux2RSAQTExMYHR0Vat7lckk+CQAp7WPWZDTnotc5JKvG/mjDZrPZMDw8jFdeeQXVahVOpxPb29sCVvUtOAR5p20PwfHExATOnj2L8+fPSykRXVqKirtXmZubk1wjm832xJWpOoeHzCvbzU1tt9s7buUBngSs3RhXpgqYp5s5xtqx0YCu33nkd2imYHNzE48ePcL58+cxOjoqoVbNQtHYkzU3gbjZLu2k6vxrreyZ081Q58HBwXOxy2SQeOkEAAGwMzMzWF9fx9bWFh49eoRYLNbhPOv94fF45Lpglsp6+PAhhoaGMDc3J+VauFeZV0lHJBQKoV5v36JDozs5OYm5uTkJ4eq9f1p58803cfHiRezt7eHXfu3XkEwmsbS0hEajgfv37+Ps2bNotVqyHjl3JhDh3gWOa22aLArnUTtN3Vi4pwHVftboxx9/3FEGj1dJU89pY0pwyzKIzAOMRCLyfTw0o/tuMtLA8bXVoVAIoVCoo3wU96zWM8+z/4aGhsSZN9cCiQ+y/SMjI3j99ddlL12/fh0bGxsnHnDVLDvn3uFwYHp6Gm+88Qbee+89XL16VdZwPp9HJpNBpVKRsSPDaaZBnVb02GibwX/ncjlxVEdGRuB2u7G6uopbt25JKiBrl/PA1sHBgeSTM+Upn89jZGQEwWBQan8ODQ3JQdR0Oo1qtYrx8XGMjIxga2sL9+7dk4Ol1HO9pu9o4Rgz/Ym5qkB7bxUKBblc6fDwsGNu6ByVSiU0m00pEWmxWOQiAv3D72w0GpInOjo6ilAoJCF4Vh4ggGTqR69zyPNFFotFIgKFQgGtVqvj8g86Olpvt1otIZt0yqDNZsODBw+QyWQ6KjHY7XZMTEzg6tWreO211zA/Py+fJTvejSHvNe2jrwNberOzo0Tm3JTxeBwXLlzA+Pg4CoUCHjx4gEePHonnzBAG69ZxEoneWXKDBpI5HwSTDL83m03EYjHMzMzg/v37yGQyzxWK1Z6q+T1sLweYBsNMJ9BKlEWV4/E4zpw5IyEwKjMaR+bJ6utlaXj1gYBuirpX0WCMIWDguOg8NwcPyJDdcDgc2Nvbw97enmwsstynFd4wNj4+jldeeQWvv/663C/PTaHzqvrZqGfOnJEDGsxL5neZh/iogAlSaWh9Pp94zWQlzdPXbCOfQdZBp1bQUGrgqPv2vAy6+W8q3bW1Ndy+fRuBQEDC0HwPHQTmQ3LvmWkkFCobnqjWJ1HpIJbLZWxtbeHu3btYXFzE1taWhJ24b3oVRl5Yf5gyPj6O1157TaI59XodX/va1yRVJJ/PAzh2UrxeL2ZnZxGLxTA1NYXJyUk8evQIrVa7VuTY2JiAK9ZcJJi1Wtu5rg8ePMCDBw9gtVpx8eJFXL16FVNTUx2htF7B67vvvot33nlHbt/7D//hP+Dw8BAff/yx5C0yusR1xfXKeQM61yFDg2Zai44AddOP/a7BZwmZMR7O4LhSr9Fu0EFn5Gt4eBhHR0eYmJiQk/eMHPBKUV05gHaHe9Dr9cLv90vqDEENddnzMD5aCF519QNGCpkHzUNIXq9X1iKjbiMjI/jkk08kpYw1hfWZBO0cm7rz0qVLkm7FE/G5XA5Wa/sADnNgqePY5l5FgzNN4lDf5PN5IaFoxxwOB86ePSuHscbHxxEOh3F0dCQVhah3mTfJswrMz2ftcKvVijNnziAYDGJ4eBg2mw1bW1tYWVlBrVbD1NQUpqenJS//eYQA7/DwEPl8Hq1WO5eaNecfPnyI9fV1uVSI46CjL4z60W4ynL68vIxsNisHP8mS8xwC18jY2BharZakLWhmtB922eVyYXh4GIFAAPV6Xeqv7+7uyv6MRCICcDUhx3VIbGQeJH/w4IEc1PP7/ZiamsLVq1fxxhtv4Pz581J5QttAvf+edbbkJOmbedUbSycAOxwOBAIBzM3N4ZVXXkE0GsXa2hpSqRScTicmJiYkDE7ly1I8+Xxe8kXy+bycUmNIlpM2NDQkt0/wb5FIBNPT06K4OCi9ik4DsFgssvF1DhlD+dpL0BNDRaaBjtvtlmLV5lhSUZFtZSFkhgq00XoRwKfbuJBZ094Wc7UmJiZk3Ol9Uhn2Uh6I4GBqagrnz5/HtWvXcPXqVczNzYlnSuYCwIke2rNkdHRUQts06JwffcKe/dR5zcxZJivLfurcNv1eXSaHIRECQ84XWSWCPZMZfB7goBUAlazNZkM2m8WdO3ck/KPvywba65wHAXRqDNttGk6OYzfwX6lUsL29jTt37uDOnTvY3t6W0CjHsR+DqS8H0M+MRqO4cuUK1tfXcefOHRQKBczPz2NmZgYAOgAK552Kl+zqtWvXABxHWrhGeAoYOGYpt7a28O1vfxsrKyuYnZ3FD/3QD+Htt98Wtlfvy17k0qVLEiUgQPv2t7+NBw8eIJfLYWdnB/fu3euoh6ivRTVZn6cxF/ow12mcwhcFZungdotCkO1h/p2+NnJ8fFwc+JGREQFuBK9cq1y3VqtVgCtDm/pOec2KEuBrXdePkwwch/XJjFOHca21Wi2ped1qtQ+c2e12mdORkRGcP38eS0tLWF9fx/7+vhx+5bhRb/I8xNmzZzEzM4N4PC4Hn1hXmdHHYDAowF07L/04klwvHHN9oJU5m5VKRSrsOJ1OjI6O4vXXX8fc3JyQMCyp1O1wGwCZN84hAJk3jgNLKY6NjWF6ehoff/wxyuVyx+G8fkXvHZ7BKZfLQuiw/N/29jYODw8BHJ/ip33UY8Y+MT0ymUzK6XvevMnDrPV6XW6iYn13ltliWxjV0/brtOL1eqXMKA/fkvnOZDISbdKiMYHWpYySA5BIHBnYSCSCq1ev4u2338bZs2cFuJrnEjjWtL+fCXjlA7TS1AwAKwycOXNG7iVOp9OYmZmB3++XxU+g240RYzFjMo5A59WPFotFED437/j4OC5duoRkMtlRuLpXhaTZJAIPPl+zSAQxXNhaeTFpnrmFrIdpFq1vNpuSI0IDSPCgD/nQO9XMKNAfOKfocLeeQz3PBEPhcFgUND1bLjCGhTQI1MCfzgVzvljF4OLFi7hy5Qrm5uYkLGiejO4X2NEzZ44RAGm/Tm+haAeMQE0bN86jBgAEZno+tddN0SFR5tm+iD4SdPJ7uG4YKuWFF3fv3hUwPjk5KaWhyAjQ2dKsssks65PZut3NZlPqON65cwc3btzAgwcPOsJlZKD6Aa80bhxjfqfb7cbExAQuXbqEBw8eSCkhh8OBubk5yWFl7pzOgbda2zf5jI2NSd/5OvvG/h0eHmJjYwPf+c53cPfuXQQCAbz++ut49913MTs7K9dTasXci7DWIRkRMlbValVKAC4uLnbUgZ2enkYwGJS504fv2H4tnCeTyTuJdXzRDCzHiG1lGTBddojOH/cIK5AwHDk5OYmJiQkJOeubtbhurVarOI86lY1kgw7n6/Qrc//1w7xyLnRpRa59poAxf7Fer8vJ7Hg8jmg0iqmpKczOzmJ1dRW7u7tyIp+g3OfzIRwOY2xsDDMzM2L3NOjhBSXMzeQ4kRXW67pX0fZG63eGyHlwitUBbDabgBambmlgYp6R0elqfI5er9TNPCB8cHAgnwmHw6jVah31kJ8n8gpADqgyB50YhGlkXq9XCB2uIzMizXFjRIThdgJ+AHLrGvWu3W6XUpFDQ+0awCQHdJTUvAnwNEJSMRQKyQGzVuvJK5c1qQMc33LIg8fA8UFWphN4vV4MDw8jm80iGo3i1VdfxcWLF4XJ5fybjvXTbN9p1mnP4NXM0zMRs8/n66D2LRaL1HLTC1kzlcCTByw46RqwasZA52gQNE9MTACAHELpF7ya4KMbu0FPWvdDK1MubOaEam9X59ESzOj+UiFycREAcNFqj7UftoAKiApfM7k63MF5YQ1FzgEvMRgZGZGQAftCoKBZFRpoFupmdQHW7tRzroGdHq9ehAcXCJ65DnQ+L50RKgPdBr7O9xHEs116bHQJLM6pBrcaLJuep/5/rxIMBiV3Wn8Pw0N+v1/CiHfu3BEnaG5uTvKndL4sQSrBTre+6rQdpgpsbm7i1q1buHHjBlZWVqQkDPel1+uVG2R6FTq4+sAdfzudTrz66quo1Wr45je/ibt37yKTyeA3/abfhDfffLOjUgcPnLE/Jug3ASgNytraGv79v//3+OSTT+Dz+fDmm2/itddew8jIiIBjAB0GrBehk2G3t28Om5iYwIULF+TAB+9ML5fLEpotl8s4e/ashIOBkwEYX9c/Jxm9bqD3aX8/rfh8vg59arVapbIF50UbNe7Vg4MDYfV2d3dRKBQAABMTEx0glWuy21oF0DG/2ok8qW+9gjuy4QRY+nsYYQwEAlLLmxUJAEh7SMCMjo52VG+hHtVpSfrwDgAZH6YKMOxMZ8gc335sBnUb97120nmxRrFYBAA5ME29RAeCBA1JBJY5Y3u0XjZ1Kf9Oxw5oM7LpdBrhcBg+n09SDfp1JDk2Og2CIJOk1NDQEIaHh+UAEtcVx6Lb92nGUpMk5mfYTwJBk33mGPLiEh0hOo1wL7EOeDKZlIPzJ93kpQGsThMDjtP/WGlgampKSMXh4WFxsPVe0w6Jqa/MdfmpgFcTxPE1Cj0qXgt3dHTUUS5I55Kan9fhNxpH0yBoA6RPQbPcCI2nNuq9iGmI9ObRCdP0aGkQuOnItDK3NZvNIpPJSF/YNlNxEyxxLFjknsooGo1iZGSkI0zRL3jl9bD6wgOd86gVivZ+yWiFw2G57ndqagqbm5tSsqZWqwngpdJlRYXR0VE5TRmLxeD3+4U90KEJvSb6YZdZZojzZCoObka+plluzTxqIKcdFd1GPff6PUBnbdlugJDSD/tKtlpfjsFUCSoQllrJ5/NyEKhcLkulDqZVmOki3dqkDQrznVOpFO7fv4+7d+9ic3NTDmLabO1DQ1Rw3UJSpxEeVNIOCHC8JkZHR/Hee++h1WrfhLW6uorvfOc7yGQy0sdIJCJgQesWjpmun8i9u729ja2tLVy/fh0PHz6E1+vFu+++i69//euYm5uD3W7v0D383SsbQkaca44l6sbGxrC8vCwliHhXPHPUrl69inPnzmF0dBRer1f0K/tB0U61To35LIUXChBEksnSZxgY2WBNa/6Nt0c1Gg2pHMDrVnWfdcTOJFS0g6lZoBclDGdznHVVGgIeHj5idRZWTmDRehITLHWl9YpJLGhHptlsCii2WCyS48rT4zq8rO12r+uUa0ePr9avTL2hY8LbMcnCst0862Lm2Ov3kjHmeGkSinqKDL7NZpNby0ZGRjouGuhXmOvKcp4kYFqtltykNzs72xHR4PhyrrQuZR90SpYZ5eJ3cC03m03JSW00GuLs2e12rKysSDmtXoS5rbu7u8hms+Igx2IxhEIhSUtiu/nbBLBcD3p9cq3xkKvGDabO1va8Fya2m/SdNtDtgVxYvAI1m83KYtT5fnrhcgA4Scxb0eH7bs83mVUWhLbZbAKO2L5e+6c9I/3DttAb5gYDjkMd5XIZ+/v72Nvbk4LxvL5PU+/8HrK4PJxCr9XpdApgHR0dBQCh6p8GhE4jdCi0x8uxMsdcAy+G4j0eD8LhMIaHh+XKPipjDQrIjLOeHOvVMddSG1b9vG7MUS+iK1OQ0dY5myaY1Y6VBhN6PPQYaCVlpiBo9sDcnCexW93W+bMkHA53XC5ApltXAiBzCbT3x4MHD3BwcIAzZ85gdnZWgB0Pz5iKRHvKJnBNp9NYX1/H6uqq3JvNYtY0kDw0Q9DB27JOKwSdBD96XVD8fj/eeOMNWK1WfPOb38SNGzfw8ccfY2xsDG+++SZef/11jI6Oim7Sebvcx61WS0DB3t4e7ty5g48++ggHBweYnJzEO++8g7feeksOvpnsTr/pOzoFyGZrFzTXB8a4LyuVCpLJJA4PD7GysoK7d+/i9ddfx6uvvoqpqSkpQ6QjCEAnA6tzsJ83rNqLeL1eiUCRNaNTy/1ntVol39/tdmNsbAzxeLzjZiEeXNG39pCB0gBP90/vVR1tOUmf9KNLSQJQj2t2Uo8zQ8IEarw5jDqVDCKdUNMxJsgrl8tyAQEPYGknkVEVEzg8D2A3w/i678ylZMUF2n9+hg4T26SBjWZlmQqga2PrqhnUtZpRt1gsHeXQ9F7qV8gIs2KBTgmjruT/2UcdCdV/IytdKBREv3DemQtOZ4bgVJ/JoLPDNWPqnGg0eup+sRoEK0+wrnQkEkEwGOxIRdB2ycyVJ1bj39lWnRpBXavtdze7bkbT9LNPI32BV+3NaWROw59KpbC7u9vBdPF9OuzIzzFHlbmheqGaYIGf4fN1pYJWqyX5QZox60X0YSvmg+iDAcyjY8hAAw/ms2SzWayvr8tNQDs7O6jValJKSacBAJCk6YODAxkDgtepqSk0m+0SJNzMzzo5/CzRh4vMNA6dQ6fZYI4jwz7Mq4rH41I/kwAWQEctR10iQysDs/3dmPx+gB1Pp+twoc551X3m8/UzdJ/1JjM3o8mMmFEFM8xmvq6l13UaDAaFkaMwZYf1DgHIAZhyuSx3gpfLZeRyOUQiEcmDovetc6C1U8kT3gSwmUwG6XRa6kkSaGgAze+jYusVvDJ1xoyicJzJcsXjcXzta1+T2prvv/8+tra2YLPZkM/nJT8QgBwOIqg3Q5/pdBpbW1solUqYm5vDD/3QD+G9997D5ORkR3FtrgFt1HsFCDpCAUAY89HRUYyOjiKZTAKAOCi8LIT3i+/v7+PMmTNyEQxzQskU0fgxf5LpCX6/v2tbuzkHzyt0Urn3OWY0gJwPst6hUAjnz5/H9PR0x2FZHujkzT/UyVwjwLFu0kBVR1W6pRjovvfD2mk2l33Uh0R16FtXIdHAlKfqWZ1D60dt4AmImFZgsVjg8XgQjUYRjUYFuJoRAeD4PAHTwnoRk2HUxIDb7UYsFoPP5xNAZuIDk0XWc6+Z12azCY/Hg5GREVitVnF2zJxYba/0d+soYa+iASjnk/Zepzdovc02sE8sPcj6vjofmCW38vm86G06bPr8DH90iTXiDR2haDQa+H2/7/f11L9WqyV6mQcaeTBMvwc4trv6gLLuL98PHK8t2njaAg3uKeYeM9e5fv1Z0nfagBlW1yF1XvGqFUU3YGICCg1wNSurcy7M21r4m8qCyfD95r4wBEOjxsljCSvWM7NYLB1X7ul+VqtVHBwcYH19HY8ePUI6nUar1ZISMVTY9LRpOKnAGH5iuGxycrJjvE1GrFfRSeFaKXB+NLjSYSaTZddAloadIR0aykAgIEDBzGc1F60GAc9jSHkCnBtKGxQaEN0GAkp9St0EsJqx0UZRhz51H0xFwB8dCgM6c6p7Ea4fnuq3WCwCRMl2kZkZGhqStcccOeZOsRYki//zNDTz6xqNBtLpNPb391GtVgUIMMxHdpWKjjm3LP3G55us4GnkWUZW73+fz4dr167B6/Xi4sWLWFxcxNraGt5//3058ACgox5vq9USBkvrnMnJSfyW3/Jb5MRsLBbrODGrjaipiHsRfSUvAKn7PD09jfn5eaTTadhsNimhpPPmd3Z20Gw2sb+/j+3tbczOzuLcuXPCqFerVQkPplIpHB0dYXh4GGfPnhVj1U0n675oR81k3U4rdF7J/PL0Nvcb1yYAuUY1Ho9LGTICG9oC1njlPiVAbzabHYwuhXaJeaKaqdX9PMmIPku0DWOEkVEPnSZGvc1xoJ3SeY2sosC9pOdH62qrtX2Ii5EOXWXBJJQoBIf9gFc9Npo4Atp7lPuJ7dasm2btNPPK+eI1zWQ6Cah0XrquPdptnvRcagKhF6F+YPi7WCwK2KRd4PvYfh1ly+fz2N3dlYhrNptFPp+XtC7Oqz5EqlMHdGSJpIEGfzpXtp+9GAwGJc2PZxAIMOk8saoDMQAdR70GOAZcSyQzOHa6OoSOWnaz45yvbo7aaaRv8EqjzdeYo+nxeJ6oM8j36A2o8191+EiDUrKyZphPn+znxiC7w03N5O1eJ5nAlJOqDyIVi0VkMhlkMhk0m02EQiF5Fn84GQxz8RYOKhYqOa1kGTpi+Ixem2a8zLA1F3c/BZlpLLoxoNpo8f+anezGhOpNTK+TjAjnWgM6/VygM0zBZ5pGpRfRSlynBWjAyed122AapOi/aeCiDZF5uAA49kb1/7VC0oBYG9PTCmtasi4y88WYpqPHmHuGRbwZHaC3z6LmjUZD8glZnqjZbEqCPwC53cZisUhlA6a7VKtVYeQ1u8fQXq+iUz66sSk6vafZbNd7Hhsbw8WLF3Hr1i28//77uHfvntRTZGoRi3Tzs8yj5J3sb775Jn7kR34Ely9flnCoZtu7Gc5+JJvNwuFwCCDj+vN4PHLotV6vCzAAIKCI64+1rhmCZoFz5gA/evQI29vbqNVqcrXu+Pg4gO5GUO/JF9HPQCAg7aVR44n4er0ua4kgMxqNIhaLIRwOC5NOY0qAx3VL26BD8N1uteMtaiyUfhLw6Ud02zVo0rnzFstxvitTfJhOw8NLZO106oF2enWfCLB4MFNHf0y7azK9/YA7bae72XXuU9Ph1/aCupDfoQ8G8zNMwXC73cIwa/uh957peJmkQ6+iAanP55PrefXZFt3ner19E14ul0M6ncb29rbkyvMCpVwuJ2ue+EVHTk3RB7i7jb+2XWa62rMkFAphenoaMzMzHfXUi8UistmspOw0m03Zs3o96XnUuMg8I2L2q9trHMNu2EP/7VnyXKWyKFZrO2mXuTtM0id40B6F3oS6g0DnaTROerPZ7CgL1A0McQAICGl4GY7rRQgGtYEgfU/lohVNqVRCIBCQ26F0+2jQAYiBjMViHaVuWMyZfS0UCtja2pLC1gSoTKegkqDHc9LNLE8T7UmaLARwDEb5mjaspjLi3zguDHOYQE0v0m7g1VRGmuXsVajgtbI300+0d2kqDR2G0n8H8AQDwrZqsMr/sy1kXzQA4To5ySt9lpA15aE7i8XSUW6IzhQVZ71el5qPPFBJxoqO2eHhoQAKsmA0rMyTJpjU7BEvdKjVamKQCRrorJl3c59GmFtN9k6LNo7AcdqS0+lEPB7H22+/jenpabkbniXdarUaDg4OsLe3BwAYGxvD7Ows4vE4wuEwQqGQhO15OFLPJ/+vf/PfvRrNVColji33drPZlFukeNmLLt1GvddsHufvcj9rdo1RIoYrTWeLLHM3OQnA9iOsUsI2MSVKH+4g6PB6vRgfH5eDaDq8ChzX/AQg67darcqhJZ3TS0YUgIwrnTeOoan3+tmHek9zX9BB0rpH2y/WxmT6irZx5nrW0RraIZPZMpkrHQVi/wjsNYN3WuF4ch+YETjqSnM/8NknrTOtF03gaYJRs68mW67TCPqZR/0ZVtch868jEAAkfSWZTMq5lvX1dSGqLJbjclatVqvjvAX3nu43gI7X2R49htz/fE+vdpEEgs1mw+HhoVTvYApYqVQS/ERdS5yh0xWA432oQSz3uAl6u8lJILXXCEjP4FVPogY7PDVJ2h84zpHQAEwrUf2dmtHTIIcnJwEIKKKi4ODweyuVitT2NK8EPa3wu3Q7CVwZfqTiYL1C1uNj6Ebnk7L/VABcHLp+LY2T3sxcxGR9qcDJMOtc3H76SAWgx9EEjCa41AtLv9ZqHdeL06BVh5C6hQf43d0Wq+mc9CJmaoBWbCbzYKaHaFDE+WcdPH63Dl/p79LjoYGVTivQ809l1A9ADwaDaDabyOVyss7NCxgI+tgWskPsl15XJujWp2PJPNDoDg0NSfiL65rRBKbRsD36Dvt++jgzMyOOH8U00jTGbJPN1q4zSYaRp/SpbD/55BN861vfQrlcxqVLl/C1r30Ns7OzHc8gCKHh145GN1ZCt+u0ks1mpUYy58dms8nNQ7xxaGRkROrzanBDA0k9NDw8LCf1WYtxYmICwWAQVqsV4+PjUjvyWQb+eUErZXx8XNZ5sVgUANWthBZvIuShOH07lVkujSxsq9USMkFXcNHROpfLJWHbF9UvitZvmlmjndKOMudP1/vm3tWlv3TEEYDYoFKphFwuJ0Cd+/ekcnJaR1HP6/D0aUX3wdT9mvXVtugkMMt/a0ffHE+T8KDO6rZmNRn2PE6I2U5GkMy28cBcsVhEKpVCMpmUswQWi0UOMuu2aTKI88Y1SzbejMxp26ntJhnrXvtITMJKFzwgB6DjKnpd6lKvH2I4TV7R5vP9HD+NJ8x2mmuv298/NfBqislmacXJZH2GvhkG1ydPCfDYeU42cJx/wkNOvHlkaGhIcik1iNT5sN0Y3tMIjTWVECeWYfxmsyk1JJkXQxYqEonIARl9UImTzCv0rFarHGbR4Rje76zrUpJh1SEnfo5j2I9oh0ErJHOzaAWjPXodKuC46RwYXT1Be/wm+H3a4j7tIjZFM2Y0LN2UHPvEkJ52CPRvk5Xt5u3rPaDHSLPUmpXXzMrT2ImTJBqNolardThKzLkj66PLqnHN0cjoPG7mr9L5pDOqHQjgGHjTEHIeaRz5Hr6fp09PCvs/Sxg6Pgm8ms6rdpK0sHYiHdudnR0kk0ns7OxgZmZGojZaTEeEr+n1qtexbs9pheWDaAhKpZKkKTD/dWRkBHNzcx1pHGTZeTCPN+UQALJ2qNvtxszMjOgNMuA0Rs8KsZpOZj8yPj4ua/7o6AihUAiBQADZbPaJEnm8SYoMJg0j85R1/jrHT4fjTWea0QIynLrf2kHv1/kwP2PqTbZXjx11gJnCpB0jDRQJVMzvNuekm+7WrKjOre1nLs3n6jEzAa25bk76G1lotk3rGt1+HeHq1nezbf2cA+k2JmbaV6lUwvb2NpaWlnD//n2sra0hmUxKeoBZVYc5zWwfbQ2jXNlsFgcHB4KJ9KUamsnU/eRtib0K0724JhmlA44Bq77Gmc/j+61Wqzj/mujQURw915oANEXPVbcIyGmlZ/D6jW9841Tvo7IC2ouUJyL7EU4oQ6EApPg/hRO6s7ODnZ0d/Oqv/mpfz/qbf/Nv9vW55eXlJ177yle+gq985Ss9fc/8/DzefffdJ17f3t7GP/tn/6yvtpnykz/5ky/kez6v8jM/8zPf7yZ86nL9+nUAbY/6zJkz8jqvLQTaa+a0UigUenr/ZyE/+7M/+6l9N1MD9vb28Hf+zt/51J7zNPnggw+e+R5emf2yyr/+1/+6p/d/3tbgs+QXfuEXvt9N+NQlkUh8Zs/66KOPPrNnafmJn/iJvj4Xi8WkLKcWlsM6SZjeFI/H+3pur/Ld734X3/3udz+TZ31W8tkV/BvIQAYykIEMZCADGchAnlMsz5MfMpCBDGQgAxnIQAYykIF8ljJgXgcykIEMZCADGchABvLSyAC8DmQgAxnIQAYykIEM5KWRAXgdyEAGMpCBDGQgAxnISyMD8DqQgQxkIAMZyEAGMpCXRp67zuuzJJFI/OcAfgjAawCuAfAD+CcLCws/esL7nQD+EID/EsA8ABeADQD/FsDPLSwsrH3abX5RkkgkVgHMnPDn3YWFhdHPsDkvVBKJxI8B+J8e//cPLyws/H31NzuABNpz/jqASwDs5vteBkkkEhYAfwDAHwFwGYANwH0A/xDA/2NhYaH3ooKfc3na3H6eJZFI/AyAtwCcBzAMoAxgDcC/BPC3FxYW0sb7Xzpd82Xo40nyBdenvx9tnfI0aS4sLPReMPkzlEQiEQXwOwH8xwCuAJgAUAVwG+3+/cOFhYWm8RkfgD8D4D8HMAegAuAjtNfnL312rT+d9NrHl9Ee9oLbEonEOQC/C8D/HsA5AHEABwC+C+C/X1hY+LVPo42fBfP6fwPwx9AehK2nvTGRSAwB+PcA/jbag/XPAPxdAHsA/jiAW4lE4tKn2dhPQQ4B/KUuP3/9+9mo55FEIjEF4G8BKJzwFi+A/x7A7wcwCiD5mTTs05H/EcA/QFup/nMAfw+AA8DfAPDPH4PbL4ycYm4/z/ITaK+9f4v2/PwTAHUAPwXg48d9A/BS65ovQx+fJl84ffpYbqJ7v/4SABYt/+XvS8t6k9+Nto78CoD30bYD/x8ArwL4+wD+hdaZiUQiBOA7aOOEBoD/AcD/jDYo/DeJROJPfIZtP6301Ee8nPbw1LgNwH8H4K+iDVp/CcDPAfg22uD+Vz+tOfzUmVe0le0mgIdoI/mnofDfCeBraCvc32p4L38JwE8C+AaA/8un1toXL9mFhYWf+n434kXJ4035DwGkAfwvaM+HKSUA/wcANxcWFnYSicRPAfiLn1kjX5AkEonfAeDHAKwAeGdhYSH1+HU7gH8B4P+INqP1j75PTXyhcsq5/TxLYGFhoWK+mEgkfhrAnwfw59BmQICXV9d8Gfr4NPlC6VPKwsLCTbQB7BOSSCS+8/if/8/Pqj3PIQ8A/KcA/o2x3v48gO+hrTN/F9pgD2g7Xa+irW/+zwsLC/XH7489fv9fTyQSv7ywsLD0mfXg2dJrH19Ge9gLbvsVAD+zsLBwQ7+YSCR+CG0n+68lEon/98LCws6LbOCnDl41ZXyKmzrmH//+N2ZoAcD/irayffI6i4F8lvInAPwwgN/0+PcTsrCwUMXLwRI8S37X498/R+AKAAsLC7VEIvEXAPwOtBmsf/TZN+1TkWfO7edZuoG6x/Iv0AZ259RrL6Wu+TL0cSDHkkgkXgXwLtrs17/5PjfnmbKwsND1asuFhYVkIpH4uwB+Gm39QmBHHfuTBK6P37+fSCR+Du0o0H8F4L/51Brdo/Tax5fRHvaC2xYWFv7RCa9/M5FI/DqA3wLgqzie8xcin7cDW588/v3bEomE2bbf/vj3v/sM2/MixJlIJH40kUj8+UQi8eOJROI3JxKJz3Xe0kmSSCReQTs88DcWFhb+w/e7PZ+BMIfuUZe/8bU3Hoe+Xmr5gs/tf/L498fqtS+arvky9BH4AunTU8r/9fHvf/AFyK+vPf5dV6+dRsf+yKfWohcv3fr4ZZZPbTw+i7SBXuTfoB0++F0AbicSiX+HdiL0mwB+AG0v7G9//5rXl4wC+EXjtZVEIvEHFhYWvvn9aFA/8jh/7hcBrKPN8HwZhGzrXJe/zat/X0Q7Of2llC/a3CYSiW8A8AEIon246QfQBnV/Vb3tpdY1X4Y+niBfCH16GkkkEm4APwqgiXYu5Usrj3XMf/H4v7+i/pQCMIa2jr1rfIw69uKn27oXI0/p45dSEonEDNqORwnACydEPlfM68LCQgvtE4c/BeAC2mHMbwD4zWh3/p++ZN7nP0R78kbRTtq+gnZC+iyAX04kEte+f03rWX4S7ZOSv39hYaH8/W7MZyT/+vHvP5VIJCJ88bGS+kvqfeHPtFUvXr5oc/sNtHPK/iTaIO1X0M753OcbvgC65svQR1O+SPr0NPJ/AhAC8MsLCwsb3+e2PK/8VbRzW39pYWHh/6dep479Kc2gPz7R/6ce/9f5GMh/3uWkPn7p5HGVk38CwAngpxYWFg5e9DM+V8xrIpFwoV2i57cB+K/RzssqoX3o4G8C+A+JROJ3Lyws/K/fv1aeXhYWFv6S8dIdAP9VIpEooJ3D81NoH6r4XEsikXgHbUbu5xYWFr7zrPd/geT/hTbz8dsA3E0kEv8K7fX4vwNwBsAS2jmGLxMA6JAv4tyyZFIikYijnWv1VwHcSCQSv31hYeH647+91Lrmy9BHU74o+rQH+SOPf/8P39dWPKc8Pm3+3wC4h/YBWC0/CeC3on2C/5VEIvHvAXgA/GcA8mivVw8+5zr2GX38UsljJ+QX0dYz/xyfUiWQzxV4BfBn0V7EP76wsKA37C8/rjt2E+3yMC+Fsn2K/F20F/oPfr8b8ixRIeUHAP7C97k5n6ksLCw0E4nEfwrgx9FWSD+Gdg7Pb6BdZeBvow1e975vjXwO+aLP7cLCwi6A/28ikbiOdh//J7SZEeALomu+DH08hbw0+vS08riE2VfRPvH9uat1elpJJBL/Ndpr7C6AH1lYWMjovz8+5PQ22qWZ/hO0K2UcoM3I/ndo570ePj709LmUZ/XxyySPges/Rlvv/AsAP/o4AvTC5fMGXnmI4ImyDAsLC7cSiUQGwEwikYiaxbhfMiHY8X5fW3E68aFdEB0AKiecPPx7iUTi76F92OdPflYN+yzk8QnYn3v8I/I4jPUa2kXiP3nyky+FfCnmdmFhYS2RSNwF8FoikRh+XDniC6Vrvgx9fIq8TPr0tPLSH9RKJBJ/EsD/HW2G/EcWFha6OvmPU11+/PGP/vxvBmAB8MGn29L+5bR9/DLIYzLkn6INXP8pgP/i01y7nzfw6nz8+4nyLY9zKAKP//u59cJOKe89/t3thOXnTY7QLtLfTd5AO1fyf0P71qkvRNj5lPJjaN9W9D8uLCzUnvXmz6l8meZ2/PFvKtMvoq75MvSxm7xM+vSZ8jjd48fQPqh10v78XEsikfgzaKey3ATwW3SpwR7kDz/+/U9eVLtepLygPn4hJJFIONBmWv8ztKM/f6BLeb4XKp838PottENefz6RSHx7YWHhSP3tp9Bu7wcLCwv570fjepFEInEZwI4ZQnh8Ao8nfP/xZ96wHuXxAZ4/1O1vj4stv442gHupT8OeJIlEIrCwsJAzXnsbbaVVAPCXvy8NewHyRZrbRCJxEe0C9knjdSva4ccRAL+hDg68dLrmy9DHk+SLok9PKb8b7UOg//plPKj1uAb2X0b7itff+rQw+uO161lYWCgYr/8hAL8XbWD4uQOvvfTxiy6PHeH/Be2LGP4BgD/yaQNX4DMAr4n2LUW/4/F/WdPtvUQi8Y8e/zu1sLDAm3x+Gu28lx8BcC+RSPwK2mHZrwF45/G/O0ILn2P53QD+bCKR+DW0b2jKo33I5z9Gm7H7Jbz8VxqeKIlE4s/iuMTJa49//4FEIvEDj//9v70MoAjAv00kEmW0w0J5AJfR3qRHAH7XwsLCF4Lt+QLIf4T2TS7/AcAy2reExdG+HWYe7SsZ/7B6/8uoa74MfTxJvkz6lAe1XoYbtTokkUj8l2iDugbaztOf6JKOtKoK23sA7CYSiX+L9m1OAPB1tNfnMoDf+XmLbPXRx5fOHvaI2/4u2jYxhfZlGj/ZZTx+fWFh4ddfZBs/C+b1NbQPt2iZx3ENtzU8voZyYWFhK5FIvAHgz6CtlP4A2uW8dtC+xehnFhYW7n36TX4h8mtol6d5He2wlhdAFu0w7C8C+MVPK5H5cyL/EdpGVctXH/9QPjeb9SnyPwP4PWhXHXAD2Ea73X91YWFh9fvYroF0yr9D29h/DcA1tEsMFdE+xPSLAP6mZkdeUl3zZejjSfKl0KeJ9mUhP4CX96AWa2Lb0C7j1k2+ieNbCY/QruryA2jfxAS0QetfBPDzJiP7OZFe+wi8fPbwNZwSt+F4PIbRrh5xkvz6C2obAMDSar30+30gAxnIQAYykIEMZCBfEvlcXVIwkIEMZCADGchABjKQgTxNBuB1IAMZyEAGMpCBDGQgL40MwOtABjKQgQxkIAMZyEBeGhmA14EMZCADGchABjKQgbw0MgCvAxnIQAYykIEMZCADeWlkAF4HMpCBDGQgAxnIQAby0sgAvA5kIAMZyEAGMpCBDOSlkQF4HchABjKQgQxkIAMZyEsjA/A6kIEMZCADGchABjKQl0ZOfT1sIpF46a/iWlhYsJz0ty96/4Avfh+/CP0Dvvh9HKzTL3b/gC9+H78I/QO++H0crNMvbv8GzOtABjKQgQxkIAMZyEBeGjk180r5Pb/n98Dr9SIUCiESicDr9cJms6HVasFiOQbIzWYTtVoNpVIJ+XwehUIB5XIZjUYDlUoFW1tb2N7ehsViwdzcHM6cOYNwOAy73Q6r9RhT8zv1b/2cVqvTseDf+dNqtfAX/sJfOHX//spf+StoNptoNpvy3fp5jUYD1WoV1WoVtVoN9Xod9XodzWazow36szabTdrjcDjgcDgwNDSEer2OcrmMUqmEer0Oq9UKu90Oh8MBp9MJl8sFp9MJh8Mh32E+o9Vq4U//6T996v4BwM///M/DarV2jJP+Xkq3sdW/+XxKs9lEo9F45pxYrVZ5fqvVkrHjmPPz9XodR0dHqFar+Mt/+S+fun8/+7M/i3q93jEH+tm6XUNDQ7DZbLBarfJsPf+6PSeNh+6P+Vkt+rv1D9dIL+v0p3/6p5+YK3PdAUC1WkU2m8XW1hbS6TR8Ph/OnDmDsbEx2Gw2GadWq4VGoyGf5TiZY2CuAbN/3YTt+m//2//21P0DgD/4B/8gDg8Pkc1mkcvlUCqVAAB+vx8+nw/5fB6bm5solUrw+/3wer1oNBqo1WrSH5vNhuHhYUxOTiIYDKJQKGBtbQ0PHjzA8vIycrkcIpEIzp8/j/HxcdjtdgDA0NAQhoaGZI3Y7Xa4XC7Y7XZYLBbU63XZ/81mEzabDU6nE//yX/7LU/fvl3/5l+Hz+XDu3DmcP38e5XIZt2/fRiaTQTQaRSAQwNHREYaGhuDxeLC3t4eHDx9iaGgI165dw7lz5xAIBDA6OoqLFy9icnISdrsdTqcTlUoF77//Pn7lV34FuVwO165dw+XLl+FwOFCpVGC32xEKhRCNRuFyuUSfsd9czwBkTBuNBhYWFnqaw5//+Z+Xf+v1odcc9znnzWazodlsolwuo1AoYG9vD4uLi3jw4AEA4MKFC7h8+TLGxsbgdrthsVhQq9XkOxqNhqxh9sdms8k+Bzp1lbYVvfYxnU4DADweD8LhMLxer6wP3Q6bzYZarYZCoYB0Oo1isQiLxYJIJIK5uTm8/vrruHbtGuLxOCwWCxqNBo6OjrC9vY2PPvoI3/nOd7C8vAwAmJycxNzcnKyPfD6PSqUCq9XaYSs4huVyGRaLReyJxWLB/v7+qfv4p/7Un+qwNxStb9hHPrvRaIg+GRoagsPhkNe5p/S81et1AIDNZoPD4ejAAXyG1tP1eh0Wi0X2I20I1+43vvGNU/cPAL7xjW/A4/HAbrejVqshn88jl8uhXC7L9wJtfVoqlVCtVjswAvvYbDZRrVZhsVjgdrvhdDrRarVQLBZRLBbFznMsAIi95xjV63UUi0UUCgUcHR3B4XDA7XYjEAggHA6jVqvhww8/xMOHD0/dv1/91V/FwcEB8vk8arUaAMiYsT16z1O66X6N9WjTqGu1LeTf+VnOp7bF3ewI5ff+3t/71D71DF6dTif8fj+CwaBMNnAMPLigqZA4aUdHR2g2m7LIgfZCaDQayOfzyOfzcDqdcLvdsNvtsgj5Xm349UB2A1vdQFk/chJgZp/161RWADpAKCdTT2q9Xkcul5Nx4XfSQBK48jUNNE9qYy9iAmE9bnphauXE95lAV4NBm82GoaEhMSB6PfDf+v2cU/03vrder6NSqSCfz6NarfbUPw02tXGyWq0CFnX/tBLSn9OvnwRizY3aDbRq49itnea/TyPaKQLQ4QCwXXyfuW84vlrp0MA3Gg15XTsa5v7T/TLb1a0//azTqakphMNh5HI5HB4eiuKlfiAAMJ2FbgpYj1O9Xke1WpU9qxUwx81qtcrYdHNG+D00BHpd9yJHR0colUqoVCoCggGIUW+1WhgaGoLb7UYoFMLw8DCcTifC4TAcDgdqtZoY0lqthkqlgkKhIG2PRqOiiwgoaGhJOgAQh1rvQ6DTWad+60WGhoY69oWpa/Te53M1CLJarahWq/D7/bBarchms9jY2EAkEkEgEIDdbkez2USxWESlUpHvY19MmwCgAzRTH/Q7fy6XC0dHRzg6OkKhUJA5HBoagsvlgtfrhcvlAgABQxaLBU6nE16vF6OjoxgdHUUgEMDQ0JC0h+s6l8thbW0N9+7dw+rqKvx+PyKRiMwFgU6xWBRb4Xa74XK5OnSCzWYToqlQKPQ8h/wevf9PAh/mmHdzhLnXOFd6LfC9Wm+aNkKvD63P2dZepVQqid3l93K/mOSGBux6jEhIUZ/QGaMtI/7hd1er1Q6Hw9RDTqdT3k/SC4B8Vy9Ch4A6XpM77IvpLFBOmmMThOr55HtMgGxis26ky2mxW88z7fV6EQgE4PP5xFMwFx0nplQqoVgsolwu4+joSMAo33N0dIRKpYKDgwOkUilYLBZ4vV7ZfJwsc+GYQEgvcNMz7EeeBhQ1oCaAsNlsqFQqYnC04adoRq5arYqydblciMVi0m/thXHTvEjg2svnzEVGOQkk8DWr1drBPPB92jPj93QzaNz4xWIR+Xy+Z6NpeoXA8Rrq5hnqdpptM9dWt/aetk18No1mN2B8WunGJJmGxfx+cw6pnLmOW60WarWaREhMtlEDetNofBpCw+/3+zE8PIxSqSTsWqlUQi6Xk3Gg6DnUUQBzbimm48L/83s5PgT13eaM/e8H3HHc6/U6hoaG4Pf7BagPDQ1heHgYoVBIDPyZM2fgdrsRDocFJFFPFotF5HI5YVCtVismJiYQiUQQCoWknWRynE6n9Jl6jaCcfX1e8Kr3j3ZU+Vz9GsEbP0fn3+fzIRqNIh6Po1gsIpVKYX19HdFoFA6HA9VqFfl8Hs1mUxx+zhHbzOfruTQd7GcxQd3E6XR2MKo2m00YMq4hp9Mpz7JarQiFQsKYj4yMIBAIwO12o9VqCckDtMHuw4cP8a1vfQu/9mu/BgAYHR3F9PQ0CoUCLBaL6EjaHbfbjUajITa3VCqh2WwK0cRoXy/C/aUBnZ5fc6676ftuukfbcQ3cTT1D0oHrqJujYUbzehXab44920jgT2dCR6dM4kDjgWq1ikqlAgCCdVqtVocuJXts7gn20el0Cqng8Xjg8XhgsVhQrVZl755WCFw10CTbTeG46T3ZjQjQOtC0m3r8te3V0Wfzeebv00rP4DUYDMLn80n4wVyUBHDlcrkD0HEzMZSRy+VkcZRKJaTTadRqNVGqBHA6hE5DqsGsFlNR9qOMuFH0M0wApydKG08uUjIgZCAZStBhMX7e4XDA5/MhGAwKYNUKWI+t7ufziN5s5oIyF5f5Hm3EGU6kYTvJeyYI0iEl/QytrPR387PmWDxLNFjT32umDJjt7Qaqze/h37UX/bQxNse52wZ9XvBqeq38t24DFS+VMcOtxWIRpVJJ9imNYb1eh8vlgs/nE8NHsMR/67E4aU0+z1qlUdN7pFqtolwuI5vNIpvNwu12o9lswufzwe/3o9VqSaiUrAn7z/3ncrkQDoeRyWSErTtJWQOQUJ8Jvswx73UO7Xa7kADVahV2ux0jIyMAgEKhAL/fj/HxcUQiEdRqNfh8PmHW6Og6HA54vV4cHR3h4OAAxWJR+t1qtQQoeb1e2UeMDFGX6rXNdaIZaTqi/YDXcrn8RCqN6VxyXer3aTDdarXgcDgQCoUkfWJ1dRVOpxOpVEr6RB3K79Z90pExvtbNCe+VgXW73QCOGWruo0qlglKpBLvdLswsHYtgMIiZmRlMTEyg2Wwik8mgVCrBZrMhFAqJk5bP57G6uopkMinPc7lcqNVqyGazHekcZOddLhesVqvYIACyh2lrK5VKX3aRY6V1Szcbwfebr+vQPp0rDWK1XdRA1QR2fF3rZw1c+1mnlUpFUgAJLNkPnaqgHX7uHY6DCcK6AT/g2GElYGckiP/XOINpFG63G0NDQ2JrvV5vT/1zu91iL7jvzUik2Q8t3fSc7qeeO/4w+qH7YjoB5rNM/fo06Rm8BgIBYVz1A6mIyCpyA3ITM9+iXC5LuM3n8yEQCMDlcqHRaKBQKKBUKj0xAGQamAdG74WDpX8/r3BRmkbZBI98H//NsJwGpyazVavVYLPZxNC63W74/X6EQqEOL0uHg/UGfVH91aEps+9P+246HtVqFa1WS1jzbDYrjonNZoPL5YLb7RaFx3wwHR7S40inx8xB83g84iT1IlRCGmhr4Kifob17zQh026BaiXZrk/5OGmKtVE0DfZLTcBrRa0R7vmZahm6TVs78bKVSwf7+PrLZrORZMexM8Orz+eB2u+XfXq9XcrnM9vfLfHQT/Z10ZDhXtVoNHo9HHOloNIpQKIR6vY5MJiPjTZDG8CrDtj6fD+FwGKVSSRg+7lPTONLh0kxut3Xc6xw6HA74/X7Y7XaUy2U4HA4MDw8jFouhWCxiaGhIgCefSeeWTgT31MHBgfSV7y8UCk+0txvw1s4Z90C3fdDPvOZyOfm8Oa/6e/XeYJto6KhfqS+z2SwymQxu3ryJQCCAYDCI8fFxBIPBjpQz/ht4Mp/enLN+59Dj8cDr9YpzValUxMkjgaMdJ50WV6lUkEwmsby8DI/HA5vNhvHxcXHMmC51/vx5+Tv3XrFYFDBCcoepfAREJILI6h4eHqJUKvUEEPQ80ZnU6UUmEaL/rdcT95d2JLSu0HryJN3brc1at2sg2atwzROb0MbpKI4ZpdFt4Fjws4wYOBwOYbpp/zlvzWZTyC4ys9Q/fLbD4RDSjg4S11EvQuaW4JdYjc/V425G7YDjPaujXNTFOu/YtOFmSgHFTHswndrTSM/glQjefBgnjYwj2ZFMJiObhuEo5gORPTBzavRBqFbrONm5UCggFArJgQ1OoMly8Xe/AE97C3og9XO0J8ZFyL4TdHOiOV7MYSFgJTgjw0Dwx81gsgDdlE4/G7Xb95z0f81eZLNZ7O3tiVE8OjoSRas9VTLnXLAulwuHh4cYGRlBKBSC3W6XzaIZPNMLJ4vwPODVNNrdAB/7Z/Zfh1nYRs6Nbmu38TUVu36OyTL3s07N9d3t35oxACCe/cHBgRycPDw8FFBXqVQ6IiTsb7VahcPhQDabFWPI1CGd986+m+3pV3RKhFasBG5U7F6vF5OTkxgeHsbR0RHcbje8Xq+wkAztHh4eYm9vD/v7++IoE3gwd5LjpJ/N/nTTM/2COgACRFutFkqlkuS7R6NRyYuncWM76SSXy2Xk83lpCw9gxeNxBAIBMU6NRkPIBOpNAiF+1nTs9Nrka/p3L2ICim5gsZux44/T6UQkEhFnmQTI7u6uAPzR0VFEo1EEg0FhmHXI2gRaJ63RXllXAKLDScowFMzQMfeTdrAcDof0Y39/H+VyWXJnSfoQVMTjcXz1q19FsViUg0RME6hWqxKR9Pv9cDqdEj2p1+sd9qVQKMj5Ae6HfsQMO3NfdJtDresASE5oMpnE5uYmWq0WxsfHMTo62uEMm/rSTPU6iWR5WgToWaIBHdMda7VaB3ilHnU4HE/skWaziUqlIrn0dGbcbjeKxSKazXZeNr+rm02gaPBKx8RqtQqzT9vYi0SjUbRaLfh8PsEqzH3muRuKth9sH+eA8+B0OuHxeBAMBuH3+2GxWFAqlVAoFAQHEe/xczrHnrboaXb0WdIzeCXjYyo7AtZKpYJKpYJcLoe9vT0kk0nJ7QyHw8IyUnGbLCM9oFqt1gGIy+Uytre3kUqlEAqFRGExD0QzaJyAflggvQn1a+Z7GJYhSOLJWIZwGJolWCeQIItgsVhEgZhenU4675ZL0q1NvYg2DqZXaz6Dc1AqlbC7u4vt7W3kcjl5P/tA5pgslWZ9LBYLMpkM8vm8HMLRRophJHrlz8vcaWNl9kezzs/aMFoRd/NOtdI5CWSbz9SvPS94fdrrbLsJkqvVKra3t7GysiJORyAQQCAQgN/vlygI+6RzYfWBk3A4jPHxccTj8ScAw4sSveb12Jn73OVyIRKJYGRkRBjZsbExFAoFHBwcCJu8ubmJxcVFLC8vI5/PIxgMYmpqqoNJ5vef5HCY7dFh7n7ClTzYChyHLvl6LpfD/v4+Go0GQqGQsIuVSgU7OztIpVJotVrw+/0YHR2V3FgyyQTs6XQa+XwexWIRU1NTkoqldVK3/mow8LQUmaeJx+MB8GT+ebf9oveZjhAMDQ1JlI5MNMOosVhMHBegzTaTGNF2oVtI+iQWrRdh6kY2m8Xu7i7y+bzYOKaD8IDdxMSEpL7QwEciEUxMTAjjzpSQcDgsz9CHn0kIpVIpAR/ce3RGDw8PAaDjsBHJI4fDgWAw2FO1gW66kgDddOLMdWLqvGKxiMXFRXznO99Bq9XCu+++C5/PJzbP1CE6gtVtvZrpH/2SVsQePHhH+8XInw5101nXoE9XTSCwIytvsVgEF5E5JSnAXHfdP73uGW3SuedmdaPTCHVHLpdDPp9HKpVCo9FAqVTq0F20G+Y8kmElmPb7/YjH47L3rFYrMpkM9vf3kU6ncXh4KEBW7zWTqe8Ho1F6Bq8mqNN5j9VqFblcTjZXNptFs9mEy+VCKBTCxMSEHE7igOm8UO3VUYGRYchkMmKIOIHVahWRSERCh3rh6/zFXvv3tM9wEVMBMVGeJ35N5pHfqcGazvMy80VNpao3Y7e29TPxJrthMiAAhJnL5/PCrh4cHCCXy0l+DnMRdXUEAKIA9DyVSiVsbm6iUChgZGQEw8PDkqPGjar7pJVTr6IBmwkOTWXK1/RmJfvPXFDN5HL8dPiHnrjuQzfHoFsbnoe5M0WvD61sCbRZUodGNZfLSWja4/HICWX9A0Ccs3K53HG6nWCYoVuCJ65tjuvz9KcbM2gyoFyLDJNSfzCEenBwgNXVVdy5cweLi4tYXV1FsVhEOBxGo9EQBtfpdEp4TgMsc/10Y+P5mV6EjjpzE8mm8UxAKpVCOp0WXcP1pUEpcFwpwOVyyXusViu8Xi8ikYgAHrJJ2igC3UN4pk7udy59Pl/XaIWZr63TQQgKWq0WMpkMtre3sb+/j8PDQ2xvb2NnZwfJZFIM6qNHjyRVhPMZjUaF4GCo3gRGLwK86nQjsmKMrDWbTUmf4h4j2OW/o9EoxsbGJK0K6ARw/F6yWuFwGMPDw4hEItjd3ZVcWY6x3W5HMBgUkkSXYAyHw5J60Qt41aBKr3M9lubhXP1vrX82NzeF0LLZbMjn8zg4OBAbQjGJA+1IaZ3AtWuup15Fr08CTYvFIutGEyskaTRwrtfrkvKoHSy73S5AluTWSdENgtJmsynrhMBX6wUyuL3I7OysOPN7e3uoVCrIZrNPMNq0bRq7MDd/eHhYogfDw8MYHx/H5OQkYrEYLBaLOHDUW5lMBul0WkodHh0dCWmn932/NrC/uhLoZFwJ4iqVCjKZDNbX13F4eAiPx4PJycmO/E56MkT9pVJJQi7ccJrFY57P0NAQvF6veA7pdFqeOzY21gGeTgKCpxFutG4gUXtnfLYGrWSgdJ06k53TBoMGAjhWUt0ATze2+3k8FhOsmt9DpyGbzSKdTiOVSsmpf5fLhUAgIOCNeXbaeySQoEPDtIpMJoOVlRXs7+/j7NmzYnSZLgEcs879etAn9VUbYPa5Xq8/UXJFA9dcLifKQs8zPVAqXI/HIx63zgkyv1uvSb6u8yhflHDN6YMFlUoFqVQK1WoVsVhMlBAAySPn/mHUg+PPMJXFYpF5rdfrKBQKKBaLwnTqHEwz7eVF9xE4TiHQipZjzkOipVIJW1tb+Pjjj3Hr1i0kk0kBcXt7ewIMW61WB6DTpfq0I2z2g/v3pHy4Z7WfwrAknT1GNHQ96IODA8mntFrb6UcOh0Pmkwe6eGqaObQ8d0Ago9efGaHQKS0aGPQLXpkWwbXYjXnRYJZnJhjm/uSTT/Dd734Xm5ub4hjt7+/j4OAAALC5uYnbt2+L40GQw9q3Fy5cwNjYGILBoFSwMZnY5xHqPL/fj+npadhsNoyNjcHr9UrKAMFlPp+HxWLB2NgYYrGYgBqe+zgJnHFfOZ1OBAIB1Go1xGIxjIyMYH9/vwMYsG5vq9WS8wlWq1XSC2hbexW9trsBSa33KHwP5+Xg4AArKyuwWCz4+te/jmAwiFarnYurc4E1iNJkCseb64g6TjtYpnN7WiE4JVDV6107X0wPsFgsHVFCkhx0TIhZ6FwzP1+/V6fMEUsAx7VuAXRUJqINTaVSXcf7aTI6Oop0Oi11Y8293M3psFja6QGhUAjT09M4c+YM5ubmMDIygmg0iuHhYYTDYVm7JK1oNw8ODrC5uYnl5WVsbm4ik8kgl8t1ECt8jhnVOs2+7Au8cvFQ0ZbLZRSLRRwcHCCdTqNQKMBqtQrbSq+SpyTJBPCwD8GBxWKRHDbmZ4VCIcmr4IGR/f19pFIpUWAEENqQ9buItVI32R6Gb3QIwGRp+B6mEZCZ5YbQnhgVLoGgOXl6Qk0PidIPKNCskS4ODRzXDcxms0ilUshkMh0hFObdMQTEE9tmkWkqStYF9vv9aDabEtba2dnpqFdJ0MtxYfv6AbGcQ3NDmIyZmc9IkMD5JdtB54rzQPaRh2yYs0b2kqDcDKuZQEG36UWE203GjHuOe63ZbJfMYdoNc+R0LpUGn3r8yNb5fD5hGRiKJnNos9kk1840kM8LErqx5xpcci2TOclms9jZ2cH9+/dx8+ZN3LlzB2trazg6OhIHkxc43L9/X1JXpqamEAqFRCFrJ087m3ot9aNngHYpMK59KnPmuLpcLoyMjMDlcuHg4ECc/GKxKGssHA5LysDw8DDcbrfoZDI3wWBQHO5cLteRr8c1qh0drU90f/udv246C3jywBvBO0/ZP3jwAB9//DGuX7+OR48eIZfLCSggi8jIFdPKCBQYESDTdPHiRczPzyMSicja1Ay1GZ3pRRjy5oEph8MhNVsZfuac2u12+P1+jIyMYGpqSmrXst+0LQS9uuIKQ9EERbSLgUAAqVQKh4eHUq2Hujafzwszy/MlAHqum32S6PkzRZM0+rkHBwfwer147733MDo6iqWlJaytrSGVSsl61UCedtE8fKuZT/3TjxPJvpi6rpvt0YcZmZah1zgPuTqdTtmL1MU+n0/sJtML+WwzhYwkCoEt9TTXUq/rNRgMolQqdVzKw+dwnM2IrMfjQTwex/nz53Hp0iWcO3cO09PTiMViMk+mnicJxzSXra0txONxPHjwAOvr6xJFKRQKEnXvJp8aeNUMJAf18PBQcn54mn54eBh+v19KPKRSKTx69Aj37t3DysoKdnd3kc1mUalUOjYqb/CKx+OYmZnB/Pw8JiYmJPRCmnp/fx/FYhF7e3tCsTPH5Gkb62miF6wGG1yI7DP73Wq1RAHl83m5ESiZTGJ7e1vyj7hYPB4PRkZGxOBEIhE5JWqyjnox6wMIz8O66raQYQMgCqZcLiOTyWBvbw+ZTAa1Wk0O6eh+5vN5JJNJbGxsYH9/XwCMVuSxWEyKcFPRzszMyG1JKysryGQyEkLjaWGeju13Ds0wF1/r5uGRFSaTeHh4KGwJPU/zUgrthevSaEdHR/B6vR0glp8zIwEmcO3VkzaF38t28ftyuRw2NjaQzWYRiUQwPDwMr9fbAfZM9lAzKZqBo3Cfcm8znLW2toZqtSoOa7cctRctHD86nMz7293dxeLiIr773e/i+vXr2NraEoBOkEOHJZ/Py2019Xodc3NzCIfDHUwu22/mm+nx6bWP2nFjmwqFAjweD4aHh4URJ4NslrBi/cdIJCKHJng4likUPFQRj8c7QqCaGdcOlv4/38e8vBclplNOvcJ1tLe3hw8//BC//Mu/jK2tLYyOjuLMmTMShp+cnEQoFBLAy3nP5/PC3mWzWWxvb+PWrVuShlCv1wXAmgws29WrI0IASp3AaBT7xiiOx+PB1NQUpqamMDw8LIdwCGZSqZQcJtTpcbRpkUgEo6OjiMfjklPr8XgwOjoKv98vjBzHgXOoSxTqCGkvou2R/j/HjK9psof953vp/POyjWg0Knnqu7u7yOVy4lTqfGW9RnR0QNtHzcT264jQvpJ80RUR2AZWDNLhfX4WgJSt8/l8sNlschiWF1KwzJk+vK5JK90WphPxuUxncrvdiEajcDqdPaV+MBqsLyPRc2Wm1LhcLoyNjeHq1av4yle+gkuXLiEWi3WkmGmHATiOfOnyXsyPZVoZ9VIymZRyb2a6x2nnry+NxBxPslRk6niKmflG9AAzmQw2NjZw79493L59G/fv38f29racuOymMAhmJiYmcPHiRVy+fBnnzp3DxMQE3G43YrEYAODg4AAHBwcdpWPILjwPW2Cyk7p+H5kqKs9qtYpMJoOtrS2sra1hY2MD6+vr2NnZ6fB2mALBQtOTk5OYnp5GPB5HNBoVUGGGZZ42sf30kYuF3p3JWLCqAJUuTycfHh7KSVFe77u9vY1sNiteosViEQaV+Vy8/ndqagqBQABOp1NyYTKZDAKBAMbGxhCJREQRUfpN/TBZsW6pJOw/2RuyrEDndX/dbiPh2LPPBLKaJQCOD1NQugG5F8W8ct0whJPL5WR+7Ha7FLwvFovY3t5GtVqVwyE+n0/WszYW5jhSaZOlZf+Yg0lWjHl3rVar74NcZoRB70lzXXCf5vN5bGxsYHFxER988AE++ugjrK6uSp42D9IwBE12nQcYGLo/c+YMRkdHhV2gQibQIpDUbe2HDdGhUub3k1VlKhTXlNV6fOsO06y8Xq/kMrIfZLFarZaU4wqHw8LwEQBro8G50ka52WyKfu6n3jLnxYxi6X9zr9BY7+zs4ObNm/jggw+wsbEBv9+PixcvYnp6Wow5L15gP4aGhmS9M61nf38fzWYT29vbWFpakrztubk5OXdBYKdD0b0KgYa+rYu2gnV3nU4nRkdHMT8/L+k1JHMymQx2dnawvb2NZDIp4JUVBSwWi8xfLBZDPB7H2NiYkB+84ICO8t7eHorFYkeqgNVqFSfbrHfbr5j5qKbjQ2bRYrHImZVSqYR4PI5YLCZjzcsn1tbW5Kpmk0nV+sjUw3xNF9zvR9fodWBGzHSfiAU0+UAdp88PHB0dIZlMIpVKIRAIYHx8XA7EasdZ6w2TyGBqgY4oA+io2XxaIanEtBG2nfpFpzQ6nU6Mj4/j1VdfxVe/+lW89dZbmJ6ehtPplL2qz4OYjDiZZqfTKRUXmDbBtUqGlvqW/ef3nGYOewavJgtZKBSQzWblhhOyblTM6XQaDx8+xPe+9z18+OGHWFpakmsenwZMqtWqbGSeYCsWi2i1WpiYmIDX65VNwDqVPI2qPYt++mf+Xy9aDjjfV6lUkE6nsbW1hZWVFdy/fx9LS0sdhaUprO1HBUIATMXCE7PcDHoSzQX+PGwWlQINsM690XV6yXTYbDak02ksLy9jeXkZ9+/fx9ramjCzpmFi21ZXV+FyuTA7OyusAE/chsNhVKtV7O7uwmKxYGpqCsFgUG6jYV/7YUOo1DQjZzKMOuSoS5zovEfeb82cMvZNHxDyeDxy8hmAvI/t0MygnkMd9tLze1o5ycFhftn+/j4ePXqE7e1tAZQul6vjuslsNouJiQm88cYbEoYl68zvMdkMk/3nBRSMnpD5oVHVdQt7lWdFUDTbzBztnZ0d3LlzB9/+9rfx8ccfY2dnp+MaRqYfsTQRQ/bMKf3kk08E6DKCRKBDg0HnHUBHjl6vfbTZbB11OJkWQGeAucq6RA9ZnXK5DLvdLgyW1WqVetrUPUNDQ1I+yuv1dugeMy2E48A5p3Hl3mDoulcx17gJCvQ+TCaTuHnzJr71rW9hZ2cHc3NzuHDhAs6ePQu/3y/tY6knGk6CWI/HI8+KRCJ45ZVX4HK5sLa2hqWlpQ69G4/HxSEx85p7ERIadPp4wxUPxjidTkxMTODChQtyKrtWq8khwvv37+PRo0dy+IqEEMEB55HOAy+ueOWVV3D58mXMzs4KKGBEknqZ805nlnqrXq/j0aNHPc8l14sGeVqnapBH3TA0NISdnR3cvXsXR0dHmJubw9jYGCqVCnZ3d+Hz+TA+Pi4pFsQVBHHaplCXcy2ZYq7pXoTkkj78pllB4gCdAqJTGuhIBoNBDA0NIZPJYHNzE9vb24hEIlIBgyws+6rza/U5Et0fAJLGwNSsXgG6ebkMCQhGrIkFnE4nYrEYLl26hK985St47bXXMDExIXuLmI/51BoP0TbwjAQjykyB0hdT8LyTvuq213nrGbySrSJjlc/nsb+/j3w+L1UF2NFCoYCVlRW8//77+M53voPV1VVUKhUpeE5FQmVJ0ELDoW8v2djY6Lifd3Z2Vq6q5Y1ADGfToPYTXtcgR3t/WulzUWljzRCBPnGqjZpepPS6OZ40hBrUaA9Gv6bZtX7BK5+nT/8yrEQ2XB88KxQKWF9fx927d7G4uIiHDx8ik8nI95HB0HlzBHGVSgV37tzpOFl94cIFRCIRRKNRKdVB50CHM/tVRBQ9TpohIAPFDaQ9YF2iKJlMYm9vTxgUesLM/R0eHsbIyIiE8vTFDJw7XZVAe7da2b8oIbBuNNq1PRma4enQRqOBzc1N3LlzB9evX0cmk8GZM2ckp5whUH2ITM+FHks9N4woUAnu7+9L/c1AINBhdPrp00lrgPuyWCxid3cXrVYL9+/fxwcffICbN29ifX1dFDLzsJk3TzDANTs0NCRrf2lpSVgKMnn8HA9Q2e12AS0a8PUidBCp8KnUAYgzAEDyKHnal7qH99y7XC7JbyS4A46rEpAJ9/l84qRxv5mntLXh1gfI+i1hp4ErHXKtT2lMt7a28K1vfQu//uu/jrW1NYTDYVy5cgXz8/NSNYLtpIOtgSvniWMzNDQkALXRaGB7exurq6sCyDm2dNB1XepehPPO/nEOeTAlHA5jdnYW4+PjsFqtcrp+dXUVt27dwp07d7C+vo58Pi/rgPuIY8Q0NVaKWFlZkbMlpVIJMzMziEaj8Pv90q5yudyRE87x4eHSXoX9M6Md5t+0faQNKZVKWF9flyiPx+PB3bt3kc1m5QBQPB4XcocXbtD+Uz/zWRx3/l+TBP3qVfNwre6j7hf3g+kAEtN4vV7U63Vks1mxIZVKBaFQSH58Pl/HISxNcJhrQOthHXXpVcyIBzESDxYy4hgMBnH+/Hm89dZbeP311wVn8QpkXWfYZE41Y+1wOFCr1RAKhYQpHh4elv3PFCGmjZpyGl3aF3glEGHtR3p6DG1wQDY2NnDz5k3cvHkTmUxGTlnyhhCGTQgMgHYC/PT0NF555RVMTk7CarViZ2cHKysrwqrwdhyGy0KhkBweyWazwpQ8L3jt1me9AGi4WAkhFovBarViZGREDBsTl3Xx3mazfU3f3t6eGE990IfGVF/h2K0f/QI7npbU+aCtVvsyCFZxYO5mvV7H7u4uVldXsby8jPX1dbk1B0DHAR/t1fGwENBWPmtraxICoYPj8/mkHifTLmy29hWJOhTUqzIyw5/m6yzdRe+X/Tg6OsLe3h6WlpZw7949rK6uIpPJdHiZ3Pisczc1NYX5+XlMT09LqgwBDg8H6atVzfBsN4V5GtEMlvmaDskA6DigxBOrDGk1Gg0Bc6+88oqk+pBt43xqxkU/zwy1Md8bAEKhECKRCIDjesD99NFk6zRTUavVUCwWcXh4iEwmg48//hgff/wx9vb2hCFh+Iq5jsCxQWTfyWhQkT948EDYUBYrZ/4ox1IfrqEh6kV4FS/3G59NPUDQFovFYLPZJO2j1WoJURAMBjtq8DKNgCkOrDJBxqfZbKJQKMj6ZNRAM1xcQzr9xQzN9ioaYPD7OK6lUgm3b9/Gv/pX/wqLi4uYnZ3FlStXcObMGXGKzH2iQYwGj9pRarVaiMVikov46NEjrKysiB4/OjrCyMiIpM2Y1UJOI2Ruubd5yrpSqSAajcoBQOqEg4MDLC4u4uOPP8bS0hJ2d3cBtO2eBq0UDQTphNlsNmxvbws50Gg0JM2OTL4uUagP6XCue5VuVTf0IVbNUOr8UeoSOgk+nw+FQgEffvgh7ty5g4sXL+KHf/iHpVLD1tYWUqmU2HgAHakOpt7TjCIdl+dhXgnydeUKPleTUdz7HHt92Ukul0M6nUYul5PyWDs7O5IaSD2inVemd+hx1kQB+8qUg177SN2iD8bz8C3TIJjecunSJVy5cgXT09MIBAKiB1OplJTK1A6uZqj1vsxms9J+Hk5ldKBQKGB7extbW1sd+d1cT58KeNVMJPMEuUHoWRFw3r17F3fv3kWlUsHFixdx7do1zM/Pw2KxYHNzE/fv38eDBw+wtrYmSnt6ehpf//rX8eabb2J8fBw2mw3JZBLf+9738MEHH2BzcxP37t1DOBwWxof5TyzTwENQz5s6oEGG9rI0mGVozm63IxKJYH5+HjabTQ6x7e3tSe0zloogQ0vmgUCOIWitgLqF+Mx29ir6tCKpfNb9pGEji1gsFmWuVlZWcHh4KPksFAJXHaa32WxiEOhpbW9vd9wGMz09LVd68nvJArKdQO85TCYzqJUAQ748cMfxLZVKSKVSePjwIT755BPcv38fm5ubKBaLYjgYXgYgBcLJwuorf/kcHuQig63bp5mDftapdsw0qDs6OpIC0Qxj/v/Z+/PoSPPsOhC7EYEIxIrYA4F9BzITlZm1sbq6m93sbjVpaqThotHw6PioPaM5liwFbYqiSImWZYvUmBZ5xuJQpCYs2aIlUdIMxdGMSGtGa4vdZFV3sVhZlZU7MpHYdyD2DUts/iPyPrz4KjILX2RXZWU13jk4yAQCEd9ve++++5Yf2VgAUgwUiUSQz+dxcHCA27dvS3L95OSkVDTr56Vo5lgDB+YxEYywi8h3otiHY9XzxDPI1JNUKoXV1VUsLi5ic3NTWsJwv7EdESNHNLI6lGa1WuUM5HI53L17V5xihjJ11Tf3A1NPzIJXMv/lchk+n69tT1K3hEIhuZVJ97IOhUJS9UvgXqlUEA6HMTY2Bo/Hg1Qqha2tLezv77f14m02mxKW5v4wOorGaBEA08BOr58xCsJwZTqdxo0bN/Dtb38by8vLcDgcmJiYwNjYmNxcpZ+BOoaXSujokc4XpH5laz+u/crKCjY2NoRJYtEtn9NsLiGjjHSk2C2hp6cHAwMDGBkZkRzIQqGA9fV13Lx5Ew8ePMDJyQmmp6cxMDAAr9eLSqWCnZ0dbG5uimPpcrkQi8UwNDQkKVcMuWcyGayurordIEvGFAueDzLswKlTYnb9CO7InHF+tVPBvcPX0anM5/MIh8MYHR1FT08PVldXcfPmTbzxxhvY3NxEf3+/FGQzRz+Xy0lhEj9Tvzf/r8Pt2j6bFUacCIbZLUDraaZdMPJMMophcl5KxFRHVtQ3m02k02ns7u5KBIU2X5NXOsqqU2IIcHVrSbNrqFuSkmzzer0CwhldGh4exuzsLMbHx8V5oHNer9fb0of0ZRsAhM2l48y10Fcms4PB+Pg4JiYmpK0qc/HNEDldgVfgtCkvjTmVKxd9b28Pd+7cQTqdxsTEBD73uc/hypUriEQiqNVqcjOD3W6XcF04HMbLL7+ML33pSxgbGxNFPjw8LEVR9+/fx87ODtbW1sRrdjgcQmnr3qvdKFtjmFmzDxy/TtDXBpIelNvtRrVaFcaS4SzeQkaPVDOeuVxOmlv7/f4PANfvpPCA8DACEHaYjBTDX+zby/xJOil8jU5tIBDmfNHI22w2FAoF5PN5LC8vw+Vywe/3IxQKSc9YAgECBX1IuwF3RrBvdDq04uXe2tjYkEJCXufIcDoZRDKxLLzjzUZ2u13YLIJVGg9e46hB9dOmDGiQQeNJj5a54mQWCbzr9dZNTfPz8xIKXl1dRSqVwsLCghT5TE9PS5N1XYinAY0eCwGFzusiw1cqleRSkqcZoxG40gFh8dni4iJWV1el8wWZBObB6z2r8/QYimVoi3uY4PvBgwfSjYJ/AwD9/f1yNS6BrFmDosP3h4eHbc4I88r6+/slBYnhY7aYi0aj6OnpaesMQgPk8XgktEcmiFExPq/RMTfOvWbP6AiZFSNjr/Psq9UqHjx4gK9//eu4d+8ehoeHMTExgdnZWbmZi++hdbBRP+uQq3bKdUqE3++XjgUrKyvY2toCAAF7ZJjMCh1D2kM6bDo6yOtgt7a2pF6g0Wjg4sWLePXVVzE7OwuPxyNt2959913cu3cP6XQawWAQ8/PzeOWVVzA3N4dAIIBSqYQHDx7g/fffx+bmptQWuN1ujI+Pw+12tzHn7FZh7JRxVtHgVDsRnXIVdcE0a0Dy+TyGh4cxNTWFk5MTPHz4EBsbG9jc3ES1WsXdu3dx5coVqXsg6D04OEAoFGorlDSutTHE3036jh4P96YGkvw9ASRwqoMIXJlHf3R0JF0jmBJHh35vb090EUkP6mEyvgBE32icQYDL8ZqNgvA9eY7pLJVKJXEQYrEYhoeHEY/HEQwGYbfbBZscHh5KlJvsPQtM2QmF0SDWHTE1j+mCtAsk+li0vrOzg0Kh0OZAn8VedH3Dlr4elYUQrM4ulUpSed9sNnHp0iVcvXoVXq8XOzs70th8amoK5XJZbkjp7+/HhQsXMD4+jmq1ijt37qBcLmN8fBwDAwO4evWqvD9ZC24onXdJpa1zMMyMj5uWh0F/p8FhDpj2yLiJPR6PAGiCtmw2K5uGB0X3Q9VMnd68mnU1ArJuARBZMoYhuAm5gWjAqXB5JSzwwZYamrUAICCGHiyNV19fn7DjOzs72NjYwMTERFu4hGw+Wa5OoXEzotdNOx167Rg+pJfsdDrl8NIwsmin0WiIl8grSfk7m83W1qaGgI2hWxb86AIf4xyaFZ1OwvfjRSG8wpeMKl/n9XoxNjYmzhTQavS+srIiziIAKQShkA0ggNK3q/G9GUXQc5XJZMTj7laMwJWpAul0GhsbG1heXsbDhw+xv7/flpdMYEKQqdfeyCDrHDugtSbsjbuxsdG2VzhH7EbQbb4k34dAXKcKMLdscHBQGBpdeMbUKNYd8HzSyHG/UV/u7+9LCJ1FRZqN0YCeYJUMC8Oo+srSswrfS78/o1LLy8t44403cPv2bQDA5cuXMT8/L44T14XOGdk1XSSi9RHPOvU3O+LoLiCRSATValVu7Nra2pLWhd0AHzKQzMPldbB9fX0STbNYLDLetbU1VKtVTE9P4wtf+AIuXbokRp1pZ2S0WPX9mc98BvPz88IeezwezM7OyrhXV1exvLyMwcFBaUtIW8SIWrFYFJbZ7D7V6QJAu2Oj2U6eEe6bUqmExcVF9PT04OrVqwgEAnJZyPb2NgDg4OAAN27cwMzMDJxOJ8LhMAAglUphd3dXciX1M5Aw4bNoEN0pQnnWMdI+GLvD6H/TKaIupCPZ19fXljbFC0UikYiAPbZvY3cd4oRCodDWwUDvb+pdzaB3yy5TJxJrUFfzfcfHxzE+Pi7XFNNhLpfLsFgsknpFParTHE9OTiQlghEiPitTrxhdp+2Ix+MYGBhAX18fdnZ22ub7LHv0qS4p4GFwuVzSAok5VltbW8jlcohGo8Kwrqys4Nq1ayiXy7h06RIuXLggFLrT6ZRwZr1ex/LyMr71rW/JoXvppZcwNzcnaQS6FxzZQKfT2cZm6CtauxHjYdW5PjpsApzmzGhqnzQ6q9J9Pp+wAAzFEPgyX0YDZIbEOn09jdAzJrhn0QvnmuHvg4MDbGxsIJPJSMsLjpteos5HYqoA0zZYMEJ2KhQKCZipVCrI5/Po7+9vmzMeUt64ctb8l8etn2aVqAg4r41GQ7zDWq0Gp9MpVxizCpJ5klwXMrBcN64ZHTkqIZ1DpRlZrXiNwN+MaACsC+XYR/D4+LhNkdDrJuM+MDCAV155BUAL8LK1G8F4T08PJiYm2gorWRF+cHAAr9eL6elp+T0ZC0ZfWLTCgjGPx9PVGur5oWPKSy6WlpawsLCA5eVluUbVZrNJo3a2puG4dQjQ+N5cC15RSTCazWZRq9Wwvb0ta8n3qtfr6O/vh9/v/4BTdxbhXueZ4mfW63UBpATLOtLDYtZ6vS7t5iqVSpsTzPWw2Ww4Pj4WMNDX1ydXalerVYlWMZeS4IZnknufzppZ0Y4j/82c4t/7vd/D9evXYbFYMD09jbGxMQFePEvAKQAGIA4u9zNtEedJV9Rns1nJ02P+czgcxsTEBHw+nzTH1xetmE1xKZfLbeyr1WqVPuXs3c0C3bW1Nezv78PlcmFmZgZTU1M4Pj7Gu+++i3Q6jcHBQQwODmJ6elqiO0NDQ5icnESz2cRbb72FlZUVRKNRXLx4ESMjI21gaXd3F/l8Xogc2j+CeA0wzYjReTTqHqNdoj1gOg2jiUdHR7h58yauXbvWVrC8sLCAN954A9FoFF/84hfh9Xrlkp9isQi/3y9g2ZiaQIJHM8PdkjocC0kw/ZlGkkC33WPO9MnJiaQJVqtVhMNhDA4OSvcB1gft7+9/oAsIUwk5Jp0KRhvNiFI3Y2TnF74/r/Xme7rdbmFCybrqYk2OFYCwqQSrLA4kJsvlcgDQZu84RqvVKsXN3Be09TzvH1naAN+cDAgNPFmYSqUiVdocFABpar+9vS0hFg0CWaHvdDpRLpexsbGBxcVFAThzc3OIxWIAIGERMl7cSPRgOendpA5osKPDEvrAstMCvSPS3TQoOmRDb41J2jwY9CB1yyVdZGZkfPWXPqzdhtT14azXW9XprOAlI8xweqPREJaUt7mwlyuNEVmhUCiEkZERxONxNJutq/9YhNVoNETR2u12MTjsPWcM6evWGmZEKzIj2NfrSyb44OAABwcHsqfITDONgffKs1eeTqxvNptiHLg3dO4Pw6OdbhMx42WeZbwcE/OQCKKB9uuceQPTyMiIgF0qD80MayDAPbO/v4+7d+9Knp12uqhwGWpmkQAB89MKWwwtLy/jxo0buHHjBh4+fChthsg6Mv+Ya8V50SFAftdFhjyTZC0JVguFAo6OjrC1tdXG3hPk0TCYFbahajabUgnOLgYM8zMKw9fxOlF2AdnZ2RFdyzXkjW8cI53IdDqNbDYrt+SxcFYbSwJerhd1PMOcZoX7Rn+R8VxfX0elUkEkEpFUB14wwZ6uZI9ZncwcWDpZ3BPsfsIG+BZL6xIbAsaTkxPZqwQbjDqxUwxZUzNi/BvWYfCWNjq27J3N3P6TkxOsr69ja2sL3/72t7G/v48LFy7gi1/8IkZHRzE5OYl6vY5QKCQN6d966y288847GBsbg8vlwpUrV6QAem9vD3t7e0in08L6cg9roqQbfaqdhydFAWm72Dozn88jEAhIDvb29jbef/993Lt3D4VCQXKtWdMyOjoqkddAICBrXigUhBzjOaX9Mj6Dzu83O0aeaTKlGihyDrmHm82mFMnRrrPDAPujs9WbzWaT9A4WqvOiCVb8M/dUg2SjjdfXx5p1lPv6+nB8fCw5qOyKQKaZqXwE1VxrY7GckVzTxBX3dS6XQ61Wk7aE1CW0o9TN/DzqI3ZeOKt0ZVG4abiINNY9PT0oFArizTLRl7e+9PX14YUXXoDdbsf4+LgYQ95CRW9fV/3qXFaGsnQoiQeU/2bok0rXLDB4HMunNzJBgK6g1gdLgycqYObEMkmaXj5DSwQ6LKAwhs2NwPVpPEweVB5SHa7XoW2GLzwej7Q0oWfJO49pRNPpNJrNJvr7+zE9PS2XSXBdnU4nDg8Psbq6ipWVFXFe6LgwX4jPowG6WaaAHiYVDveqcS0bjYZcYcc2bszDrdfrElp0uVzSXJtroBlUhn0JkNxuN46OjiTEwnNi3E/dhPAofA7NKHLudN43HU1dAMDPdTqdGB4exmuvvYZQKCQs6czMjFwooTslMF2HOXuhUEhanhGwcm4I/Li+9NrNjpFzxXnOZDJYWlrCzZs3sbCwIDmu1WpVHCo2CteOoFa03BdG549zQ6OiC12AVsUuAazdbpdCmv7+/rbnPKswBYPnnrqMuWjMNdNgmaE+goTd3V3RtRwDdR/nkICbeYTM4WbEjEZTs+cEXgz5dboP/axryL3HCA+r8Vk8yILXw8ND7O7uYnV1VbrGxGIxOBwObG1tSY9wXlkZDAZRKBSwvLyMnZ0d2O12jI2NSUiUbakODg6EFeVFK9Q1NLh03LtxQtiDE2jZRpI5tEe0YXSEC4UCbt++LY7X5uYmAEixIKOZdEBoBxkdS6fTyOfzbSHuSqUi/dAZitV20e12y//Ndv4w2lGeJ20reW6azSbW19fx4MEDHBwcYGRkBPPz87DZbNLtgfaeRcmHh4fY3NzEtWvXcOHCBemXyiK3g4MDGQNTd3SbNyPQ6yZSp9NyjMXHXFfuFRJXBK8M/zPXFWjlxM/MzODChQvCStbrdayuriKdTovjz1tIqcN4jvU+1AQEU2DMnsVAICCX1RCvsWiReofzqKMcJF001iPLTOJGR/Wof/jFZyXjS2ecdQaM8pIcos4+iwPSdcEW/2306piDxVyjXC6Hra0tjI6OYnx8HBcvXpRwBq9P5f3LetOQTaXnzckoFArY2tqSPEJWc/LvgQ+2eel2fPr/GozygJAhNP6Of0OjTUMaCoXaksLJXgGnIUTm3nE++Qy6sEJ/dRty1qwgK6Z1Xho3D5+fzaR5ZS9zQR0OhxR18WrQkZERDAwMiEHm4eDBI2CkkdX5MVQODAF3k99D71sfBB0y1mwcr8Pd3NyUAqNIJIJmsyksMQs6yFrSA2baAPcnAYbOWeZn6wIZvW7dOFjA6b7Ue4IAQTO9msEH2m/8slgs6Ovrw9TUlFwx6XK5MDo6KqEjoL2BNwEkgRwvB9Hgle+t2ZlugY8WncdYLpdl32pFp3UHADF0nea5U+RC730NAjke5qiRqTNGaswIz5w2kGS6AQjwYWiaKUhWq1UuVchkMnJBjNaTxv1BR21/f1/SSch8MELCKIhun8Xv3TrLOs1Ct28qlUrCtDgcDrld6969e1haWpJzWK+3bmwk486zNzQ0BLvdjkwmI/00OUdk3tmKiDoIgABnzg0dAa6l2QgB9YvuUU3wz3aBdPqo45k3zxQYi8WCiYkJTE5OIhQKSRtF9izns/F9yQAyIsK9xNsmCYA0GKGNJkAwK1qHGHUKn4F2nYWOzWYTly9fRjQaxerqKt577z3JdaVQL5ycnODBgwf4/d//fUQiEbz66quIRqPI5XLY29sTZrBTGF+Du26FoXljVIa6gPtC31DHvcbLmHZ2doTsoB2Mx+MyZyyAXllZwdraGvr6+iQnn6kSOnRuZGFJmumLcM4qLpdLukLRHhNMshsA8YqxHkDbLRJxBK26c5HRVhsxitE+aLtC0qzT+zxOuo7lacCmJ5ceDJUWb305OjqSa+4ImAC0eVQ0gADa0LpurJ3NZiWdwG63S/6SBtB64rqRTmERPW5+Fo0EQQIZTIIW9i5jWNzlckmeC6ugAUhhS7FYBAAJW3ZiXjWz2y1rx+dnZeTu7m4bWGSPT3pozMcBIMwkjYLFYpFKRCpuq9UqOS5kOMi+Eqxyvvg8ZGh0GytuZrPgVYMzfWAoGugRjBJw6qIehji4vwhQmSdkZO2MX5r51PvECCyA7sAdxZhqwjESQHPP6nOqv1wul+Se81wzREvFwvFzrggKdc4bx6QZ8+8keCWrxas22eCarEWz2ZQIAiMdnZh7zg2NOZ+b7AHfkyCDBp/zGIvFMDg4KHfU8+/N6huCjEajIX2g2cSc4If55uw60NvbK1ePZjIZ6SMJQFh3fSsXcAqqWY9AxoihWILhSqUCj8cjpIH++251jTFHmJLNZrG0tAQAiEQiODo6wsLCAq5fvy5hV0ZkQqEQyuWygFg2uueeJoNMIZPMPOD+/n4cHx9LigAZbA3ItYE2K3SiuKfo1GqAwJCt/hlBqd/vx8zMDF5++WXE43GsrKzg3r17KJVKCAQCEunUzonWUYxEVioViVLSTmi9qvWQGdF6VJM1GthoppC63+/3IxwO4+TkBO+99x7eeust7O/vt6Vb6fne39/HO++8g4mJCczMzCAajQqDzvZZ1MX6xikCTzpZ3ayh1o8aWOl9z2Iti8UidRwsTs1kMtjb20O5XMbg4CBisRj8fr/8LaMddBaZoxyLxcQpcblcAo75uVp3a5LELHuuHWzislQq1eYkMeqinQE9fiPhwpQpYp5OeKXTM/Dfune6nnvu6w+TrroN6InVRkqzSvwZ6eHe3l4Ui0W5sYgefjwex+DgIHZ2dsSDBU4BCA0mAUGpVBJGbHx8vG3iyBJSeXQTQuhk3I1jJvgjE6UrBguFgnRdYOsUHlKGebiR2VKLlaU8gMYrC4HHF/d0Y1TIBjC8k81mhQXW60jjTg+UPfv0TWIErwwFZbNZWK2tPr/MAWKIkMqIoQp9rSqVqg5VPI2y1fNiXE+OUYfzaXR0jzqdO8kKZRpjfUc0P8NiOb1cQq+fzr3Vh5rP0U1qhN6PepwU7T1znYyOpo4esDCnWq0KKGQBDBPzc7kcSqWSPHOn3Cut9LUh6Jbt4RmmwWAvZeoEjoN5jcViUc6/z+cTw07wYDzPxvnkuS0UCsLc8fVsjzY/P4/5+XkMDg6K481oihnhXicT6Ha7EQgEpAUWAAFrvBu9p6dHrsE9ODiQugKOkdEDzRBRWKCVTqdxcHAgVdI0jExhIGDnM3STY6fHyPnRKSsnJyfIZrOSJsbcx0KhAJ/Ph/Hx8Tb2qlqtYmBgQK6vZDseVlB7vV5hTrXepzFmLji7iQQCASFVCCR1VOWsovO9NZOkbaORcaI4nU4pvvq+7/s+zM3NIZPJSPcMptro3FUt+kzz7BLcUDj3lUqlzbaYEe2AG0kr4PTMs0NGJpOB1+uV1lcHBwe4fv067t27JyQWAT3ZPJutVVS6tbWFGzdu4MqVK/B4PAJ+SbRYLBYB9HwWoy7rZq/qdeN+53djARUZSuZzMmWDV6CTqNKpUtSjTGniddbMUSYeovPJ/aJJOe7vblI/dBpmLpeTAs79/X1x9nkO9V7VzorFYhHswM48dB4ZdTSCVo2h9BzT1mtyRQPWs9iLrgu2NH1PFkuHTbjwgUBAwpDb29u4du0aNjc3MTs7i9dffx1jY2MYHx8XloPtr9hyirk/rH5jOgIAKdLiQAledfuabsb2OO+Nv9PhF7LI9CB4mxE9C3q8AKRvGtloKlWyWPx7fpbR69HeSafXnFX29vawtLSEW7duYX19HW63W3KNqPi4xgRpx8fH0uCbIX+yqZzv3t5eFAoFbG9vi3FiMYnD4ZDrVNksncUI3MRUGMbiAq2MzyLGw6MPodHJ4n5lg2n+jW6/BpyyCboyW4v2RNnGSIM2I3DUh7zbddRj4mfos2kEqhwbzwX/XjOLACQknU6npR1MX1+frGm9Xhegr+fBuEe1su9W9FzZbDb09fVhcHCwjX2gY8hcxnQ6LY4w+2ySUTWyRFpYSMrehZotc7vdGBoawqVLl6TzSTweb+t6YtZR1sDV6XRiYGBALu6goWHkguxMo9Fqgr69vY1UKiVFdtTB+lIFY8iPY2S3BgJAGlsCHEZe9Np1YlLMrB+fj+fc7XZLOzoySox0DA0NYXx8HP39/QiHw0JI8IvPTIaYOoL1BfwdQbJOyWK+rM/nkxxaMqd8nRlhcQsNMkXrGDrGei4bjQa8Xi/m5+fxla98BfPz8ygWi7h+/Tpu3LiB3d3djrnURtukbQGBjQY7/D0BYDdt6zo5fBqQMOq2vb2Ne/fuYWtrCwMDA7h48SIcDodcHkLwyWiF1n+0heVyGffu3cPv//7vIxAI4MKFC+jt7cX6+jqy2azoWAJeXdOgw/5mxWh/jHqMa6v3r849p14kg6ojpxS73Y5AIIDBwUH5Gf82EokIi0tdZbRdZJw15jmrELSm02mpW2Eva5KCmUwGuVwOx8fH0nGGe0o7yDwztHPERHxeXSfB52QUhXZRM/nETPo9zmIzugavOtdEJ+nabDZhangnd39/PzweDx4+fIi3334by8vLqNfreOmllxCNRoU6Z6N8q9UqTbiZR8lQNReVyk97PwzTU1l0k9+jPTntdejkd2N/M/ZxLZfLAkCpPGh09UE/ODhoazisARTHoxWfMZlaf+9GFhYW8I1vfAPLy8sIBAJSYMXLHqgIXC4XwuGw5IRms1lsbGzI1Zvam6T3SCVElieXy6HRaHUwoMdHA+31eoXpocFkfmk3B1SLVow8XEYGkDmGmrkhaKWHTODDZs28cSsYDCIcDgugoOLkbVuaVQZOPXs+25NCKmcRnXutgTjHqRUwcBoW5TMZFSIBEm8+KhaLqFar2NzclBQdm63Vpu74+FjyXI2iw8O6ytTs7VN8Lw3O6SCw0GF4eLgt79Vms8n1xuySYbFYJDebypcGSAuBJFNddHEXUxUuXLiAy5cvY3Z2Vm474nwZw+JnERpyn8+HgYEBjI2NtRXa8KpX3WqMeXMs2gFOo1QE9NyLNOzMx6ReKZfL2NnZkWbpwWBQrqdl71cNlnVYz6xo4Kpbgvl8PoTD4bYOJ/F4HFarVS4MIIus++/qdCMCVbK6Ouqh00Hq9Tp8Ph8ikQhmZ2cxPDwMAB/IHwXMp7ew840uWtFAnUCac8297PP5MDU1hUuXLqG/vx/7+/v4wz/8Q3zzm9/E4uIiACAcDotDxN7GrNLXbZM4bnat4Z7UrBdw2sLKLBnAeTE63nTsmOJSKpWwtLSEcrks3YF2d3dx69YtyXUl2NN7ych4Ly4uylWiQ0NDiMViUjzLfukEj1x/RmJ0Tr8Z0V1R+Gx67ugg62IjhsxJ0PDSpJGREYmS6Pkj689WkEwV3Nvbk6gSOw/oNA8d6WIqn9mzyO4PmUxGsArzVZvNpqRJkUQETq/epeNFx58OI3Da5xiAkF3EKzxfZGV5DvgzEpG6cwvPx0ea88pDY7VahX1knqTf75e8JCp2Imx6yzpR12q1Sk4S78ONRCLCbjDpe3d3V9igcDgsV4my7ZKxv1g34UojU0YDwMXj5tGKj8/NfovAaaiNAImhBs6R7oFGxoEsEeeNSlgDWB3O0IbdjCwtLeHGjRvSP/fzn/88+vv75Uo7eukM/WxubuLGjRvY3NwU1k0rajLPWmnoykKuA0EFr6jUPVJPTk4kbMp5YMixmzAXRRtdAjsKWYi+vj4Eg0FhXLlP6YSxBZHP55M9xmR7KhKtyDUzoNdNh/K18G/NjrETO09jSaDC9+4EPoyKggoGAKLRKPL5PFZXV7GxsSEAlmEtXgOtwTMAKSigIWg0GtKJwqzo9Ao+H3DqxbOn8MTEhDjKZH6YF1qr1RCLxSSnl+dVCxlXFkfp0Cs7TVy4cAGXLl3C+Pg4QqGQMA7dpLVQeFUkc+R6e3sFRPf09Ei6QDwel3vIWZSj89A1kaBBNIGrZneZHsDICVvDsWUPGWedy0yD3W3eMp1ZDTrdbrd05Wg2W3nXw8PDcLlcODw8xM7Ojlw/zs/m3tTG0kgUEMgYQWwgEMDAwAAmJyfhdDqRz+dFB2nGUuf6nkWM+aucL9ZpcF5ZVc21Gh8fx2uvvYbp6WnkcjncuXMHf/AHf4CHDx/KrXSazdcsGNdLAzXaXbbo4rMZbS9tjhkxRlc4TuMXdYPH48HAwACazSbee+89aQVmfE9jWgvfv1arYWFhAW+//Tbm5+clpc1ut0vOcjQabSMOui3QprDVHqN9wKnzzMgj9RrbxtlsrZsjeTnKyMgIJiYmEA6HEQgE2uaN4fVwOCx75PDwEOl0WjoXEBDzOmEdPeGcUaea1afGOgVGW4jd6vW6pH3ojgfEOpwXEky8TIV6s9FoyJmmzWR3BqaH8GptdigimCb72ylN8knSdbcBsptWq1VajTB8HAwGEYvFpCqNRmR0dFRCJFNTU9LHkMUHzHlJpVIYGBjA93zP98BisWBgYEDCgvv7+7DZWjdXBIPBtoKDdDoNi8UiTY0BdG1YNLtMAEV2h4qAQJzpA2TndBW9TuAnY0swQWXJ/olkV9iDUQMgDYT4724LDCwWi1RJDg8PY3BwUK4dZNiQ7CONpy6q09d9MmdNtxohUNKNjQEIqxKJRIRdBU7z4mjUmHtL5daNJ609ZgrXg+9ntbYaikciEWkIz7xJ5vbo/rvMG+KtI8ViUSpFCYL5bwJ2gknNumrpFJIzMz7uU82s+nw+FIvFtjw/Yz4RgReLfBhqBk5b/7DgwuVyIZVKyfXBei6NCodgicqq2Wx1bdjd3TU1PqBzix4aSp4ft9uN4eFh9Pf3y2Uo/Ex2kgDQBtR0mkWj0ZAKeAJC6gyfzydN4+fm5qR9WLPZlII2Ml7UfWZkdnYW0WhULjoAIADaYrFIIRJZHN6UlUqlJMrEc8J5YgU72SBtGGjomYPPtll+v1/aKxF06f3Vbc4ycMoGE7zqNAWPxyNFqoxm2O12yeldXV1FrVbD0NCQMIoAhGXTTjF7R/N8Uz9zPDabTVhsAG2AnoBVA4Wzyt7eXlveOOeJDjl1TCAQaLs6c2JiAi+88AJCoRDu37+PdDotoJZnrLe3VxjBSCSCy5cvo6enB5FIRG4iS6fTqFQqkpbFKKdmI6mLqZvNAnQCVuCU1NF6rFKpiP0jcA2FQsjn87h+/TrefvttybEeHh5Gb28vMpmM9Hynk9bf349Go3WjXTqdxvb2Nq5fvy43MVWrVayvryOVSkkhH8eo7WI3NpG54zpyC0BIKzopAIRFPzk5QTqdlmgPiQLN0uo5JE7grZuMTAKQyw54LjguAj+d2kUAa1Y4LuIM6nDaO+qNbDaL4eHhtqgBz7CO7vH5eGkB9wfHS/uju/Mwksn6J34eiyy1nvrQ8ZieAUAABRlFhvsZPqaXG41GpXdfOp3G6OgovvzlL6Narcpd4gsLC1hbW0M6nUa1WsXq6ioWFhbgdDoxODgIv9+Per2Oe/fuYWFhAZlMRoo2eG0cjQ+LpbjAQHchEopOGdAAgywAPWpWB3PiCYI00NfhXP2+Xq8XzWZTPDVuXt7O0ulZuAbd5NkBwNjYGL7whS/IOhQKBfFsdf4cALnG9zOf+QzsdjvW19elwEyHp4whDv3MDKuFw2FMT0/j8uXLGB8fl4sQeCh0lbNWQGYZH2NoS+dM6pQPAoRQKCTtbPb398UgaNaCKSJGoxQKhRCPxzE8PCzAlcwHgDaWRK+lfsZuwKvODdKpLg6HQ6qzGZbiuMlqaKnVasjlctjY2BDAx/1Hw8fwbiqVasuJNp4NDYYJ6tjOyMi8nHWM+rtxH5Dp5r3uvAGKYGVra0sanW9ubiIUCrU1j6fyZR9Ogm4AwgReuXIFMzMzknsJQAoUGYlhYZ/ZW8SuXr0qTD0jLmS8GYHi+WZR6MHBAXK5nHTjMIY2aVA3NjZEL9KwG4sgy+UyDg4OhAhg32bmvtIZehpdaqyOB07TP3TOHMdB4MWm5cw/Jni1Wq0CiEkk8Fyy5Q8dc15ikMlkBORSD+jUGjpddE7NyPb2tuQykjRh9EE7cYFAAP39/UilUrBYLJKqwfS3yclJxGIx9PS0uthsb29LXmIul8PAwABef/11XL58Wezr8fExtre3kc1mxV6SaGBXFIaGaauNhTFnEa2vdASHOqVQKGBnZ0ds8/j4OOx2O7a3t7GysiK9T1977TV89atfhdvtxrvvvotvf/vbODo6wgsvvCBX5R4eHuLGjRt47733kMlkcPv2bYyNjaG/vx/9/f0CFvVakSEkYOrGJnK/s588x8f1YOE4dY3VahW9xn7M6+vrYueGh4cFG/D9T05OsLe3hzt37mBpaQm5XE4itU6nE/39/bIvvF6v4AigHRR2w57rtAPWmejUSF4ssb29jf39fel0Qb3EiIzusMQ9ToefN3SSdNORMu51m82GSqWCdDqNvb096fPP27fMrGHXzCs3DAup9JV9Xq8XQ0NDmJ6exsrKCjY3N3Hr1i3YbDaMjo7C4/FIfsy1a9ewuroq1PXW1hauX78uyjUQCCCVSuG9997DnTt3UK/XJVcoEomI8eEmcLlcbTfemPXCjDl2OuSrN47T6UQgEBBFpYsk6KlyoXX7Je098TkJgqLRKMLhsOToaWPNz9ZAoZvxAa0eiZw7i8UiIQUW5jA0Wa1W4ff7MTo62pZvs7y8LEaFgB04Zcp03h0Np9/vx8jICC5evIgLFy4gFouJoS2Xy8JgksntFBY/qzxuTgjiNANut9vR19eHeDwuB5pFdXw23RKK0tvbi1gsJrmtZLD0uumCNmPFcLdMlnGMep549qLRqFwWwpuFjOdBj4UJ+xsbG23OCRlw5kHpYhZGF2jUgPY70JkXxh6IZBjMSKecV37XaUd2e+vCgGg0KpESOmLsvcjIDgAxKAQ8zJfn5zHi8OKLL+Lq1asYGBiQ8en8QSpvgqdSqWRqfENDQ8IGM72I66gLNmq1mjgj2uDpNdXGjWkQjP6QAdcAhMaaN8xR7/DqxkKh0NZBoZucXuC0z64uptE2hE4dX8NxMlqnGXaG3rknenp60NfXh3A4DIfDgZOTE2E5Gd2h06m7iHCemCtL55S62ozoOSVApLOuC8Wo/5jHW6+3rvYlOBgbG5N9SyZsd3cXm5ubuHfvHur11m1bvPylUCjg/v37uHv3Lo6PjzE4OIiRkRFhr0ko6XaNdEa7YV75XestjndnZwdra2soFosYHx/HwMAACoUCFhcXUSqVMDExgRdffBFf+9rX8P3f//2w2WySO14qlTA3N4fPf/7zmJiYQKVSwcDAADweD77xjW9gYWEBHo8H/f39mJ+fRygUkhx63t7Y19cnjF630Uh9uyX3Be2X7oJEfc7uM3t7eyiVSnLLZKFQQDweR29vL8bGxuT9+R65XA7r6+sSVaBOYT9bpiQxfUBHQjTZYZbQoX4iVmKfbJ2XzfPHgi4dwaC+Y+RRX9bA52KKgHaSiCnoqDabTbltlfUzzHs363R0BV55WGk4tDfMQp7h4WEJJ6ysrOCdd95BJpPB2NgYent7sb+/j6WlJSwvL7eFFNPpNG7fvo10Oo07d+7A4XDIlYEnJyeYm5vD/Pw8ZmZm0NfXJwnPpVIJPp8P0WgUHo+njSU1I5oF0GCTSpRKXytezonOHWMjd91+Qod2ybIMDg5idHQUY2NjGBoaEkVsVBj662mAKwDJvSGA0wVukUgEqVRKvH6r1YpwOIyBgQExAg6HA/fu3ROPWudy8pm4qXt7exEOh2XdJiYm4Pf7YbFYZO2azaa07aGhJUDpRozAjv/m2jLES+PCkBv3tM6ls1qtAmDpBfNmqomJCUxMTMi97MybJQNKR0r3fXzcM3bDFhgBMB1KRkDsdrs4lWQj+dlUzgSZvCnm+PgYm5ubAmDpnBpb8HQCw7pA0eVySbjbmD5yVjGy051YfY6BEQsaGypdFgew2KVer4vDwQJQOpk6DeHq1at46aWXRF8xrMWzws/nvmDhphlh9XsoFEKz2ZSKYBZ0kE2mceuUU6/Zk56e1o194XAYsVhMwnY0ODoCwb3ItC52HyBDwlQwRhkIgMyKNnBaT2g2iww62/Gtra2JTSAjzBSWaDQK4PSCkEgkgosXL8Jms6FUKqG/vx+Tk5MYHBwUB4w59MFgsG0/kHBggSXDwWakv79fALAR3OtQutvtxsjIiDB0hUIBDx8+RCaTQbPZlDQGq9Uq7cyy2aw4jrlcDsPDw1IvsLm5ifv372N/f1+q8pkzTAaR60f23NjR5qxCh5B7x1jEx+4kDodDbkS7desW3n33XVQqFbz++uv4sR/7MfzwD/+wnN/PfOYzGB0dRblchsvlknW1WFr5rIxkpVIpvPvuu5iYmJDibovFglQqJVcdczzG0LUZYV9opsDp/FJ9jTl7LfO6X9bgUJcAaOt9ykiUPgcEssYiyt3dXbkulUWa1Wq1LRLSLa5hX1m2o6tUKvK8jAozvYzgkumZTOWjDeC4tG7mMxlTJXV7MKYLMCVodXVVWnVpOStp1VXagN68+ipF5gFS0UxPT0vrmdXVVbzzzju4efMmALQl6+qwer1el415584d+ZxgMIi5uTlcvXpVbiKx2WxyL/3x8TEikYh4bbpCz4wYc2aYx6KrtMnC0fOn4WDeIwFgPp8Xr4zhGxraUCiEgYEBDA8PY2BgQPKEyHYZgbMGrtoL60aoqPUNX9yUDG9lMhlkMhkUCgUJAY+NjUmBXE9Pj1ypyrAbUyV0bqnH48HU1BRefPFFzM3NSdcIskiNRqtlDMGTBo086GbHqUEPRRt4FvvQuOs8HlZ4j46OYm9vD5lMRhQRGVifz4eRkRGMjY0hFotJlShZZ6vV2pYLq3NedUikE5jtRprN0ysTuVdYXc3CAObnEvAw7MoxMwzZaLSapjMXkcZKg3+bzYbBwUHptkAlz71O54gpPwQRZqUT09cJvHK/8bKFWq3WFrqyWCzY3NyUbhHlclm6WXDeCW4GBwdx6dIlXL16FdPT0/D5fLL2PPO6ItlqtUoI02ybpb6+Pvj9fkl3qNfryGQyYjTi8bjk9fHeehYBccw0alarVcY/NjaG0dFRMTZsg6OZHorNZsPh4aHkJPK6XxpOsiLddv8wFvdo1pxsjs5TPT4+FuBts9lw8eJFqZ+Ix+Oif9layG6348KFCxJJInj3er3Cohvzs5kmwpsdCRZ4x7oZicfj4tRyfvR54B5kRIR7ZX9/H4uLi216hXuVFeypVAqHh4fy97xiO51OY3l5GQcHB/D7/ZidnZWuBcBpRIH7kfu1W/ZcO6raHrH4kfn1bNqfSqXw9ttv480330SpVMLQ0BAqlQpu3bolAIb7gNGLO3fuyGccHx8jk8lIX+Xe3l5hkdlekQCMBBGfp9tonWbpT05OxHHXn0HnrtFoSOccfU0v9xfBHdv2MSWSTjPZW+aAkxzIZrPY29uT80c7pYsuuwHmAOS9t7e3Zb10PjQ7HfG5qIe4TrSLvb29MiecF03EaLKCtTssVCyVSlhbW8OdO3dw9+5dbGxsyLNoYZrLh0nXaQNkEZm7RKOQTqclFBsOhzE7OyuK6MGDB9jY2BCKXbNNOvTIIgpuilgshvn5eXz2s5/FlStX5NpAJgsz2Zp3rdML1jmiZ5UnUfLacBKEcKwE8j6fD/39/dJTLZ1OS5iIG5fgvr+/H9FoVLwsLhgBOw2zPhia4eq2upJFA9qL1qywTpzPZrNIpVIShiPrFAgEJN9K95bkYSB46+vrE5YyGo2i0WhID9FGo4GBgQGpBqcx5jj5XN20INJ/r39OI8nDyxwc9m91OBySKkHGShfE8GCxJRZbtdB71K23dJUscApcjQxiNwpXzw1zAwkEmJfV398vwISFS6xipmNAhRMMBtHT04N4PN7WwsT4mdppZVszKvlcLodarYZ4PI7+/n709fWhUCh0zaJz7bR3TxZId0jQz0aHcmhoqK1Kl3u9UCgAQBtoInCdnJzECy+8gEuXLmF4eFjSQJhLqZkTXeHcrSOpcxwJCJizzOIbq9WKra0tYeJqtVpb8Y0OldLI8NpXgjm+nvtM95Lme7D3dD6fl9QBXsVL4N6t4SQbrh0FXQhJkM1q+YmJCYng8Zxplpp7lsaVOaecA+0Aa33O63YzmYx0G2C0jsSB2X3KOdf7gCCIFewEdU6nE7FYTJi4hw8fSis6RoA4H9SnTE8bGhrC2NgYLBaLsLkMTc/Pz2NkZESa3BcKBWmlZWxzxHU3K3Q2OMdkXA8ODlAoFOD1eoV13dzcxPr6ujhbq6ur+J3f+R1885vflNQMFitr4KM7vQDAwMCAOCmTk5Po6+uT0DMvPWLxk9frbUuHMSu6z7eeN4LQRqMhjnq5XG4jNji3XH/+DW+wWl9fR6PRQDweFxYVaCcsGo1W1x7+TTAYRCQSgcvlkr2gHVWzYyQ+0y1FOf9kh3UrNYJyOlY6xYn7jOyxJmN4/nRhdL1el/7vd+/exfXr13H//n1JJaMOpNCGfZh03SpLgx32yGT7A3otHo8HIyMjgr49Hg8WFxext7eHYrHYNjm61xdD7wTAPKAvvPACxsbGhNpmwm9PTw/C4bCEh+hldmtUNKDQSh/44JVpwGnVOhk3hhB4uLjB6ZXwylGGq3RImcpB5/NxM3QCsN2IbqVivDaQzxmLxWSN6bUdHh6ir68P0WgUfr8fY2NjUjWpDwWNE8NlfX19YgxZgKAT23WqhGaHCM7Mglcje25MAyH4MhpkMo8MobAAg3/DddFMs76Kk9XhHK/T6WwzHrqFkQYcnfbUhwkNoO7N2Ww2xbtnugYrWhkVYdcLnSMGQFIn4vH4B1qhAaehXipPfca412u1GrxeL+LxOKLRKBwOBwqFQtd71aigjY5bp/NNoMxm9/w/len6+rrsPxpRv9+PyclJvPjii7hy5QpGRkakQMhYEKe/jOkUZoEPDSbZNbLUBDlerxf5fB7pdBqpVAqFQkH2KyNBND7NZlOKVpmKw7y8SqUir9FGRTvFzPXOZDIIBoNS+czbc3Ten1nRzhIdRr/fj8HBQbmZKJVKCQDy+/1tKR+suHe5XHK7ktZdOnLHsfBs6X3DOgoSCuy7OTw8LOFRs2uYTqfbzosGyzzbOvLBehA+P1lg5hNyP5C1Za7spUuXMDU1JWkvoVAIVqsVw8PDGB0dlShHqVRCNpttuyWN54h2xKyuIRGkHUSbzYZMJiMtzYaGhjA1NYVQKIR0Oo35+XnZc8xTJrGh59hisYiuJDgkXqBzTOwQCAQkd5MFduyZTrvKXGOzotNwjHsHOK1fqNfryGazAtp1iyfqcbvdjsPDQ6ytrSGXy2FtbQ1Wq1X62xYKBdEd1KMk7Fj8RkKLdpT5pJ2idmcR/i3nSve9ZuoO15psKKOv+Xweh4eHkrZCJ0PXJBgjxcApIOelKCsrK1hcXBTGlbiJThFtLAscP0y6uh4W+GDhFjcNe3ny4LjdbgwODsLhcCAcDmN0dFRuamJYWl/xStDAW1YmJycxNDQkDCVBRy6Xw/LyMg4PDyWEq+8S7jZfkmPTHqFmfYDTdiFU6mRVmd9I4x6Pxzt6SJwbHRonKNCei/6it2sMpXfjRWv2nOvGcfC9G40GfD4fhoeHpZAum81icHAQ0WhUQLff7/9Anz0dGuSYeBhSqRQASFP2SCQia0oDo5VPN9WjnFcjiOWY+RqmQ5DJKRaLcqsRCzDIBmungjlzBIwM0bO3McGh7sOoD7YxlNqNaNaBz0smmM9JYx8MBiWHkSwrE/Q513p/MeLBddSKiQqLRrFYLIqx9Pv9kgbDtAyt2MyK8e/0nPEckKHTe5prxVZXXEuuycrKCvL5vACBoaEhzM/P4/Lly20XBXCtdD6/TkfgudGg3uwaMgpARQ+cVjmTvSHYppNrDONqQJLP56XCmzmkR0dHolM4Dzyb3JNkwlgAS2aPoIiXQXSzhnoPEOjTYWDj+XQ6LUaVRYa6doHfdfQCaC8SNEY09DNwDPl8Xtj3SCSCiYkJDA0NCVg3K3ReOZ/aAHMddQcDkjLj4+Ny2cby8rJcp828Z4KiQCCA4eFhjIyMIBaLCRgcGhoCAMkn1MCVjBZTvDQ46OYccnxGkMKiqWaziVgshng8jp6eHoyNjeGP//E/ju/7vu+T+dAAWjOH/L0x+sR9zbNNgovV6foKZOaBaybXrPAZALTZQkaXGQI/OTlBKpUSp5DPyvll1KtarWJjY0OiXrSBNptN+jhrcoz7p1ariU6tVCpyDlikpvWAGaFjzK4pOlpG3UBdxPllZJkXRBQKBbktlV1CjOvGc0g2l+23NjY2sLm5KU4cc8W5nxgBI/EVCoXknD5OugKvxlAnF41tn46PjwWkhEIhuaEmHA4LKF1fX8fu7q7co85BMGw7PDyMqakpjI2NIRwOS34MWzrs7e3JnbzxeByRSEQ2iEb/3W5m45j5ftyoGsiQNTZ63xp8AqcVuzSw/FtdyUgDq0Frp5zXpxkbDa1mEzUQJxtut7du/eAlDMxpLBaLMiadz2hUbkagzUbyfX194pSQAeRnExBQugV4nf7OGIamAuGB4byzUtuYwwSgDXzS4LNdFtud6f3BeeDfPiktxYzwlqVCoSD5VJpN5v5jrpLRsBHoAqeKWwMxrVCB9m4Xugghn89LKyPmcOuWQU8TITACQiMwoZLUBUHaGPL/7JJB0Nbb24udnR1Uq1W5DIU3L5FZ0GBLs4Zk5jk3em+YdZiZV8wQMwsiGcYnG+NyuTAwMCBpBBqE6PFq4RliyJpzoAvZNENI48E0AzJIZLO6cSK5VkYdRkefl38cHh5icXFRcgMjkQjC4bCwbrpFFvPqtQ3S+0uninEM3IvpdBoHBweoVquS0jYzMyPh2W4Ij/n5+TZdTSeEXSiMwA+ApFUxdWx0dBS7u7vCcunrRkdGRjA1NSVtzBg1ikQiAuJZnJXL5YQMIjNG0WSH2XHy2Y1Ot/45gQpzOAlmtd7ge/G5jSBRR3loC4xFz2TimWNLhtaYCmNWeAYACAtOPUf7AEDa1ZE91X/HKDSjsEy50bctNhoNsTXa4QZOb3z0+/1yUZGRROuWtCK5QXvFPGK2wQLaex7rYjOmxGknkGk2uhaIr6HeIlZgvrLdbhcS0qi36VjzAohAIIA33njjiWPqOm3ASA/bbDZpZM9G2xxALBaTPCqGw4aGhoTlYk6VVmws4mF1H8Ng29vb2N7exvHxMfr7+zE8PIxYLPYBcKmf82nFCD44Zm0stWHn4dPtsTq9pz64BE5MEzDmuvJvqHyeNi1CM2gaFNDj18nvoVAIU1NTKBQKbZvTZrO1pX3QiHMjagaU6QM9Pa22UrrIifNFsGNMAjerjIxOAn+mxw+gbe0ACOuvc3logHRfPA36df6rsQjIyCTo8RiBmNl1pCesk++ZL8Z51eeJeelHR0cSymcenlZY/BsjS6PBgmaxjo6O4HK5pJG43++XPGANeLoxKNxr+u87efn6uwatWjHqzho+nw/r6+s4Pj5GMBjExMSEtKih80idpPfQk/aikYE4i7DNHhuEM++TLDnZY91UXKcxcF50dEPvff3MZFbICBqZQr2ndRcZnl2dE2dGNKjj/wlce3t7MTMzIw7wzZs3kclkBGg3m632O+xCwrFp8MoxUvSe0ZLL5bC9vY1cLge/34+pqSm55pdAj/vFjFy+fLnNUWU0Ym1tTVpAEoQzLYDpYkwLcDgc6O/vF3aNaUB8bSwWk3Q02h0ykiyIzmazEk7XIXCty6ifu+k2QL1G0gVokRE+nw/ZbFaqxvVa6+gIdSjJGp3y1Gw2BbjwnHIfkr1m6g//zfoLFjw6HI62CKZZ0YwwWWVdrOhwOKQFHZ1MHdpmCJyFqpwzAlmeOaOe0PmmzJ8OBoOIRqPwer3i2OpbrwDzly+xIJrdNxgtDwaD4vCQhOR50D3YGZVhVJ17XV95rJlrrjOZa0YlaUeNRBf1FHNqPR7Pdx68ag/aaIjtdrsAWHpjmUxGco10bgtbUxiNpAaBR0dHwsxS0WcyGblbfWxsDIODg215PTo3pxuD8mGi2U8qCR2q5UHV37ViNIYJ+H/muhkNi5FB1sZZe6tmRIfYtMEyMlrcvGyAHY1GRcHqsDwrNJmDRiWkDSGvjeP4jE2m9cbnz7rJz+LzP25ujEyoBgQ6N0srXgJYzhnHpC884Lzy7zXoMXrPjzO8ZoStnxhSZvELrxHlvuT+Yg66rlSn4qRoo2TM+TLmPDabTXlPtnzjlY18LxoS5ouZFf49z3MnAKvXi8+qAR5/x2temU81MjIiLLTf75f9z3XVDBrzxTQo5vj0XJo1KGQdacg1AGI6RDAYbGsHpPe20UnXjqB2HnjO6RxzTrjnjYZVO20Wi6UttcSscK2086BTq3iDG+d7Y2MDFotFQALngV98fu4FrSf12eb5PTk5QSaTwdraGlKpFHp7ezE+Po6ZmRn09/cLA6YBlxl59913pZ1cs9mUNB2r1So9M1ksA0BuoCQgpy5h1IZj0DZD60aSBVxnsls6pYWOlx4X11RHCs4qBMpaL3LvxGIxABBbTYac9k/vJ4a9q9Wq9BdmNxsWzVGncm5YvGN0vrifOOanITsAtO1/7huG9wmsSRCwzaQmRkje0VHRzKrev1p3NRoNyeFlgR1T0Nj9gOlh2lYC5i8MGR0dBQDR2WSSebsknQjd1pDrxXnm77jvqPc03gLac8w5LyzC64Sf+Df8vb6s6EnyVMwrv+tQKnPtLBaLVC0zb8vtdktFKQ2pBsDaUOp8CeZ/0DNh/k84HG4LF2jpNlRp/Dv9/07vaQTzmpnjxtehcx3S1YqXilkDjseBbz1nT8toGfMSjbQ/gSpBHjc+jTj70NGrpgHX6Rt8T7IJxkIX3cxfe15UTN2MUYMa/X89B50cAmNIll+64pprawyH6fcGTvMv9VkxPpfxb84qvFaZjAeZCCPQ0meT89rX14d6vdUkXYNXHR4yCouy6JyQIWDnDBpjrZwIBtl1ohsxhieNIWKdWtNJiRrTciKRiDjPGxsbcpEDiye0LiLrwYJLsrLc98x1JytjtqDJmKKjC2yq1aqE9/mdBpXn0hjGBdrZD44LOC3W0SCGhT/cO9pZJugFII6pWcaOz9FoNNr0GwEQxeFwYHBwEC+//DICgQB2d3elQwufv9lsyh7Te7sTaOU5rdVq0lZqe3sbPT09GB8fx8WLFzE0NCRONA1pN5GsX/mVX2lLNXrhhRfw5S9/Wbqn0I5xLrxer6SA+P1+Sfdh9E3nNRv3O/cFGUvmYRP0sLiuUCgIWNZCp6ebwjvOkRaCV4fDIdcQE6xqQKfXnOcqm81ibW0Ne3t78Hq9krbDPcpzpes9tE6xWq3COjMKqItjzQr1hrZPANqew2KxiKNLYE69RCedz8b9zvOk7Zgmh/iZFGOBL9eaua5GR/2sMjc31xZt5VmuVqvSjo9rxnXmM+j1p+1rNpsfaA9oJGv4Hpog0nrAiJvMOiBdt8rq5PXLmz4q1LLb7XKrA3NICEx1iMD48Jw03n7D3BL2fRsaGpJG9zq83UmJdyOdgIX+Mk46F5aTrot8jB6wESh1YrL1z/QcGxX004zRYrHIevB9WYjEG7Z4Z7H2lOj9coxk7zRDpRPZuUZkMHWjYx4MeoO8GScajYqnpg2qmbFp0NyJhe/E3gEfrHA3vh/XRudtaS9S70Mjm2NcM71fzY6RzMXW1pasgdfrlfxxI0vJzyLzw5/zGlLgtIJd70G+D5mder0uqRLBYFBugyOzQi+df8fQXjfOpJF5pSfPZ+d+ZKcKzjnnXUcPaPRYpEcQZGzRohW3dsb03ue8cqx8TrPMK4se9NqT7WAeOkkArgHTPpjrTCPEZ9UhbIIbjo0/o05lZ4j+/v42Z5HfuR94Vvl/M6L1o84T1s4zU1jYV5eOBW8ToxPB/rxcZ+1kaEaSzna9Xpd2hXa7XfpNT01NwefzSf4swWI3e3RhYQFWq1VuV/N6vSgWixgcHEQwGJT+umTXCEzIZPE5CTaN+oVgg04o9zTHSWc/EAjA7XZLalCpVGqzE9yrgHkGXb+HEcTwXNEh5hjImBIHGN8vm83K73w+n7T7epKQNdbrTkeS69+NvQAglzkYU8S4L8gwkoWlXtJ72cic68gHHUauGxlzTRjwdSR59Fnl+euU334WYeG1kWTR+cadfkcgb8Q9ANrmXNtPPW6jc6lZXCMBqj/7LGex6xu2OokeGBeexTx6o9ET1QttFA6SORq8HtDn80lLEyPL8p0Arh827scxeXoBjN7U4wBvJzaw08/1Zxifo1vRXiY/r1KpYGtrS/oPUiHrzceKQCou45zoTQ+chqs0W8Sx6tAYHZN6vS7hF10IZ3Zsel/wu3Zy9D7Vr9M/N8613qudDIBxLfXndponbaC6Aa+s0qaxprHXDAg/l8VVVMLMPwJO2XcaHZ13xnGyQTcAUT7NZlOYSZ27zR6ewGkRQzf7tZMDqEELdQSfV4NcXWxAkMc5puEoFArSkFyHr7jGDFUCp+CdxoMMApW1DiOeVcjiNptNMZwsRmk2m9I/WYdtAcjtPrVaTVhTNqYnaCBLow0Tw5Q0iNVqVfSqzvfVRaP6LHezhsY9xfXhGHW6F68xpt7f2NjAzs4OisWi5HUC7SFwFteQBeJYuT7MK52amsIrr7wiF6XwNdph60a++tWvAoDM/cWLFzE1NSU3RjUaDYTDYQnN+v1+jI+PY2hoSPIOdSW51oudnG6CRtpAgj9eysBe2/q8aEeEutuMaL3F96L9JTPJvabPhgbd/E6wQ9Cez+clx5tiBEScR86HDklzLQn8dMqTGTk6OoLT6fwAiCOJxr0FnDoYGozqKBDBqb5+VRe36hZpuoiJUVraSZ2Kp3UenV4zwo4URp1qJMT0uOk0ErxrMMp54N/RMdW5zXqu9N4A2oE+HY7HPcvjpGvm1QiyODDNSOkkZPZIM+byUJlxQNo48KpLNt2mITFW+HcarBGUnFU0wOn0fp2Ap54TPo8GLPx5pzDXWeaa/9ZK6WmAuq7cpHIDTq+K3NzcxMOHDyWkys/RwE23geKzsfCCXpXOnSOAJQMPnDYvB06v1CMbpJPYnwaod9qvxvk3GgqjQ6TzDPXvtbNiXAcdIuq0lzqBazNCtptGnDmwZOQo9N5zuRx2dnYk/UYn5hsZcBpDGiUCUlap6r3D7xynBs46x4tsvBkxOrWd9rpxfan8GWI35mdrnaPPkhEwWK3Wtub+nCfN/ACnedMMu5uR7e1tNBoN6Q/MFjvMg6UR47gJwNm7t9lsSh1BJ/CtIzjUvQStmlVh2gL1NA1MtVqVamEja3NWMYYV9dxpZkszidFotA30shdsNpuVtBWCFa6vTkkATgvD2Grq0qVLGB8fh8/nk3nQaTVmcwgpP/ZjPwYAst+j0aj0SiZ5Q8b85OREnHT27mTvXh1KNUbetA7hXuPcsHMBU4YYvqZzSptKoKkZWDPCtdI22mj3+F1HMLSjQrttt9uFHWaLtGw2K5cN8bsxR5RzQrsCQAqitG7uBryS3eT8MMqkL6khkCURZwTpdLqYVsB6nZ6e1sUGLOJLpVLSx5ct02KxWNs51hEk7mvaXdYtmBGtH570Xc8HATuZbY6RQFVHmzn3GucY7af+HA2CNW7q9CyPk65zXvkAWgEYPUW9uXWOJ3PmyM4wrMeiJWNo0Nhvk+9p/IxOPzcrnQCOkRnrxLDx33oBdBhFL+STAGwnz6jTnHb6/LMKQ8vaAdDAGji9ZSOVSolx06ylDp8QLLC5OvMKtaPicDhQqVSQyWTkekBdIcp8TLbV0H/bLSuiRb+PXl9t7PXvOLf8vW7rYSxmAj4ItLSyeNKe1QbK7Hi4djQOupCDn0GPn6kg29vbwmaRRdch/nA4jIGBAcTjcSm+tFgs6O/vl9vDqIw0m8Zx8fMJvJj3t7e3Z2p8ek6MCpJzRnaAoVgjy0/mAjhlOQkomHe/v78v1bzGFBnueV2wx31EtpmKvBvG58aNG7BarRgYGJDLPywWi3SRYEhR71uCcupM4LTXtga5RseSTJc2Jhq8auPCec3lctLPUrPTZoTGXleKa9HGmmKxtAoPBwcH4Xa7EY1Gsb6+js3NTTSbTbl1iXqClyno1B0COzpjLIhiBTnnjWDI7PmjTE5OyjPTtnGu6QgYwTl7LJdKJcmV1QVJuoiOc6OdK21bjGe4p6dHuo1ox1I7NGb3qbYRWl9pm6/1EYGeBrq6BsKY/6n3qPGcGwGO1jdGMkUDqm6E+4b7iSluzE2t1WoSBWg2m5LqpkF2T0+PXCnNnFVW6mezWTx8+BAPHjxAqVRCLBZDLBbrGK3SmEqTezxD3bLnncZ8lnkhcNV5vfr3mtzi8+uUhE7ny4iBzkrqUZ4KvOoHB9pRtd6cegOygCMUCsHn8wGAeM+6Z1uzeVrFrhOo9SbWn6mf5WmYOs3Q6Od+HCunn0ErF+OC8QBrQ6S/638b34/yNGyrFg1utDfNzUnGjWEDKmJdkFOr1UTR0rPnIWQeJL1Zi8UiFaZsN6KVEA0xjbIxSbybXDtKJ2fkcWDycfI4R0L/XgOtTuvTaY9oJqob8EpFT4+f97PTeAIQsGOxWCQ8nM/nsbS0JK1tCNLYxDqdTiOXy6Gvr0/+nhduMPeTit5YMEmFbzwf3TBbxnnq9HumB5Ct0W2GyC7qvWa1tqqk2Tqm2Wy2hZ07OcNkHzT7ToCnCw/MMlq8eaenpweTk5PivAOnBY90mPQ4dWP8w8PDNmCpmSnjntXzSONKVpApQtxTPK9sxdYteNW57fy/ZiIJvLUe4XMyRczv90uRXaPRkB6TdCaY0qJZcObla2ecXwQAOozarej0qEajIfOoc3z1GaejRGeJYIm529SfnfYS96FmxTin/JnFYpELJsiY0ZHTUVEzYtRpnaIU2u51Iq34d5rZ7ERSAZ1z7/X/dbSQn9/JMepGqN+cTmdbOhSf1el0Chbp6+sTJ5j7nCQMdRCZV7fbjUAgIIV11WpVIly6swKfgbaP/6ceAD5YO3FWedLfGAk5rjnnXOc56/QI/Td8H50nrHUwf6/fW+8hvs9Hyrx2AlXG8Ginh+z0t41Go+0edWPLJv1eetEeN8CnUUTGZ38S0NSfR4Ohje3jnudJjKIGM/pZ9PvoZ+l2rNq7JQCxWE7ZVACSQ8fXkBnQHqnL5ZLiqkqlAofDISwSPVZ6YPl8XpQXDSFBkPacO3nb3RQYaG9e76dOc2sEuNrhMv7N4w4X/67TXtfnQzMnnVIKzIxRszm8ACQWi4lh53v39PRIQ3SPxyPX/ZbLZQESRueq2Wy1/SkWi9jf30ckEhHnhDesaZZXrx3XiwaKV8+alU5sts61Bk4LD9njUvcmpOi1peHW66I7ZZAx6pQfRoa1p6dH0l+45gDk7JgZX7PZlDxUpnTwWTh+XWHOL50OwXZSPKuaBdHnh+vF56Vh4a2I2Wy2rbiNYIoMJnOezQh1Cw0e14o3StntdvT390svU66NNpxer1eukSaTzrViwRKdkE4pTdwDRgMKtOeudwMImMqgATnHS1DCs1WpVJDNZqWZfywWw/DwMPr6+mTP6dC6lkajIXtOnzHg1GHU0Q7mpesom35OM6J1XqdUHn4Z0wK5Rtp283fGIjQNqLl/jWlaWodqBlLrhG7tohF86ct3Go2G2Cc20LfZbNJFxW63i1NJxpxdHwqFApxOJ8LhMCYmJlCtVuH3+5HNZqUtFqOXGqzrudJgsJuWfMYxfpgYSTp+14WbnfBRJ9yjMVun89WJ7DnrOTQNXn/mZ37G7J88V/LjP/7jz/oRPnL5hV/4hQ99zdDQkFxBeBZhw2beZGOU3t5eTE5OSpjtcZJOp/Hv/t2/O/PndpKf/umffqq/fx7k/fffBwBZo3K5jGvXruHatWsf+rdDQ0P40R/90TN9TjQalTU7ODjAv/k3/6a7B+5CfvmXf/lj+6wHDx7gwYMHH9vnAcCrr74q/15dXcXq6uqZ/9br9cq/O7VFOovw/vW1tbWu/v4s8o//8T/+yN77kyC//uu//qwf4SOXv/JX/spH/hm/8Ru/gd/4jd/4yD/ncfKrv/qrH9l77+/vt/1/YGAAAwMDAFok0Y0bN3Djxo2P7PMB4Gd/9mc/0vd/FvL0yYTnci7nci7nci7nci7nci4fk1i+E2H2czmXczmXczmXczmXczmXj0POmddzOZdzOZdzOZdzOZdzeW7kHLyey7mcy7mcy7mcy7mcy3Mj5+D1XM7lXM7lXM7lXM7lXJ4bOQev53Iu53Iu53Iu53Iu5/LcyFNfUtCtJBKJVQBjj/n1XjKZjH+Mj9O1JBKJPwng+wC8COAqAB+Af5ZMJv/0E/7mcwD+OoDXATgBPATw/wXwa8lksrsmbs9AEomEBcCfAfDnAMwDsAG4D+AfAvjvnqexPEkSicQfA/AXAVwCEAawA+BdAL+cTCbfepbPdlZJJBK/BOBVALMAIgAOAawB+G0AfzeZTKYNr+8F8L8H8F8AmERrn24A+A8A/nYymfzo+it1KWbOYiKRmAHwJwD8bwDMAOgHkAXwBwB+JZlMfuNjeuwzi1ld82lfw8f8/a8D+K8e/XcmmUw+/Cie86OSRCIxDOBvAvhBnOqa3wbw88lkMvsMH+1MkkgkwgB+FMAfA3AZwBCAEwC30LIL/zCZTDbU65+7cwiY1jV2AIlHr30JLTtiB/Bnk8nkP/iYHvk7Jp8U7Pasmdc8gJ/v8PX/fJYPZVL+OoD/I1obc+vDXpxIJH4YwO8D+CKAfwngvwPgAPDfAvjNj+wpPxr5xwB+HcAEgH8O4P+D1lj+DoB//gjcPtfyCPT9LwBeBvBv0RrbewB+GMC3EonEmYzqJ0D+EgAPWsDl7wD4ZwBqAH4OwM1EIjHCFyYSiR4A/xHA30VLKf8PAP4egH0A/ycANxKJxKWP8+HPKGbO4n8N4BfRMpb/GsDfBvAttIzu7yYSiZ/46B6zaznz+L5L1rBNEonENz1sfgAAlExJREFUf4oWcC195x/ro5dEIjGFllP8ZwD8IVo2YRktx/mtR8Dwky7/OVp24DMA3gbwKwD+JwAvAPgHAH7LYBeex3MImNunHrTm4b8EEAew+xE+18clzxy7PTPm9ZHkksnkzz3jZ3ha+UsANtFiT78PwGM9xUQi0YfWwa4D+FIymbz26Of/VwC/C+BPJhKJP5VMJj/xIDaRSPwIgK8BWAHwWjKZTD36uR3AbwH4z9BifP7RM3rEp5ZEIhEH8NMA9gBcSSaT++p3X0Zrzf4mgH/6bJ7QlPQlk8kPXK2TSCR+AcBfA/B/RosdAFrMyefRAj8/YGBKfh7A/w2tefmv8MmSM59FtByRX0omk9f1DxOJxPehBfD/m0Qi8T8mk8mdj+phuxAz4/tuWEORRCIRRUu3/nO0AML3fVQP+BFKEkAMwE8kk8lf4w8TicQvozUvvwDgzz+jZzurPADwQwD+V8Oe+2toAfL/DC2m9X969Kvn8RwC5vZpBcB/AuD9ZDK5k0gkfg7A3/jIn/CjlWeO3Z41eH3uRYc1EonEk14KAH8SQBTAbxC4PnqPo0Qi8dfRMjR/Ac8HA/snHn3/2wSuAJBMJquPwPiPoMXw/KOP/9G+YzKGVnTibQ1cgda6JxKJIlrr+YmXTsD1kfwWWuB1Rv2M16C1GaBH8jtoAZ9P3LjNnMVkMvmPHvPz30skEt8E8P0APodTI/vMxaSu+dSvoUH+34++/zg+QWt2VkkkEpMAfgDAKlrROC1/A63UrK8lEom/nEwmu7tS7WOQZDL5u4/5+W4ikfh7aAHwL+HRGj2P5xAwrWtOAHx8VxN+l8izThvoTSQSfzqRSPy1RCLxFxOJxJcTiYS5i+yfL/nKo+//tsPvfh8tD+1zj3LVPunCvJblDr/jz15OJBKBj+dxPhJZRCtf67VEIhHRv0gkEl9EKxz79WfxYN9B+U8ffb+pfnbn0fc/mkgkjDrijz/6/ryP+0lSffS99kyf4unku2YNE4nEf4mWs/znjbnbz5HQNvx7o7ORTCaLaIXS3WjVSTyvYvZcfRrO4adVnjl2e9bMaxzAPzH8bCWRSPyZZDL5e8/igT5imXv0/QOXqCeTyVoikVhBq/BpEsC9j/PBuhCyrRMdfjep/n0BreT7506SyWQmkUj8VQC/DOBuIpH4bQBpAFNohcb+A4D/w7N7QvOSSCR+GoAXgB+tAq7vRQu4/qJ62f8K4H9Gi12/lUgkvo4WiH/l0et/Da1cyk+dJBKJMQB/BC1H8vef8eM8jXxXrOGj9fo7AP5pMpn87Wf8OE8jj7UNj2QRLWZ2Fq0I3XMlj3Kw/3eP/tuJvDG+/tNyDj+t8syx27NkXv8hWpszjlZC82UAfx/AOIB/k0gkrj67R/vIxP/oe/4xv+fPAx/9ozy1/C+Pvv9UIpEI8YePlNTPq9cFP9an+g5LMpn8FbQAQA+APwvgZ9EqStgA8I+M6QTPgfw0WmHIn0QLxPxbtHIiD/iCZDLZRCvF5efQMqo/8ejvvoyWIfnvPy2dJLQ8inj8MwC9AH7ueajufpx8N6zhI0b5H6NVoPVJLew5q3yabEMn+UW0irb+dTKZ/HdPeuGn6Rx+SuUTgd2eGfOaTCZ/3vCj2wD+fCKRKAH4y2gp3R/9uJ/rGQurMJvP9CnOJr8J4E8D+KNosZL/P7S85K+ixUwuopVH+bwbyL8C4P8B4FfRYqp20WKT/xaAf5ZIJF5MJpN/5Rk+oilhG5NEItGPVi7ZLwK4nkgk/ngymXzv0e+cAH4DrbX9cbRyJCtoFQD9KoDfTyQS/3kymfydZzCEj0Qehbz+CVpj/Od4vjqefEC+S9bwL6FVLPPHvgsAzvNkG9rkUceAvwxgAa0i3ye99lN1Dj+N8knBbs86baCT/D20JuCLz/pBPgKh9+x/zO/7DK/7xEoymWwkEokfQquNy9cefVUBfButLgN/Fy3w+rwxkyKJROJLAH4JwL9MJpM/pX71XiKR+FG0Qnx/OZFI/L1kMtkp9/cTK8lkcg/Av0wkEu+hNY7fQIsZAU7Z5b+YTCb/vvqzf/Oov+H7aIVqn2fgI/LIYP5TtMb8WwD+9CPm8nmWT/UaPuoP+gto9Q3918/6eb4D8qmxDVoSicSPo7XP7gL4I8lkMvOE134az+F3k3ys2O1ZF2x1EoIdzzN9io9G7j/6Pmv8xaNw+wRayenPBRBKJpO1ZDL5t5PJ5IvJZNKVTCb7ksnkD6KlqF5EqxH+nSe+ySdbWNjygTYoyWSyglbrFytajaefS3nUqP4ugHlVlPakcd8AkAEw9pz0nXyiPDp3/wOAPwXgvwfwv00mk5+GApFP+xrOoxVW/jOJRKKpv3DaJmvx0c9+5Jk95dnlsbbhkbAbyONyYj9xkkgkfhItEuM2gC8nk8nH9jf9FJ/D7yb5WLHbJxG8fvbR9+cCwJkUthH5wQ6/+yJa1aTfTiaTxx/fI30k8jW0bvP5rWQyWf2wF3+ChV0fHtdSiD8/+Rie5aOUwUffmeLx2HE/ykcjC/RcjzuRSDgA/Au0mJ7fAPC15z0PVMmnfQ1X0bogpdMXQdL/+Oj/qx//45kWOhk/YOwOkUgkfGiF0Q/xnBS/Pip0/W/RYvi//KTagE/5Ofxuko8Vuz2TtIFEIjEPYMcYQnhUYcgK2Oeh8btZ+RdohaH/VCKR+DV1SYETwP/90Wv+X8/q4cxKIpHoSyaTBcPPvgetPMoSWg38n2d5A61bVP5cIpH4+8lkUm5SSSQSfxQtg3KEVqrEJ1YSicQFtJpK7xp+bkXrhpsYWk4T8wbfQCuF4K8lEolvGZypn0NLb7zzqIXPcymPANz/jFbz8F8H8Oc69EN9nuVTvYbJZPJ9tK6+/YA86g8aB/DXnpfrYZPJ5FIikfj3aHUU+HG0ukFQfh4tNuvvf5J7vFIe9fn+m2jdFvYDH5Iq8Gk/h58q+SRhN0uz+fGnlDy6YeJn0fI2VwAU0Sry+WNoMXb/GsCPPmru+4mWRyGpH3n03zhadzQvo2U8ACCVTCZ/2vD6f4EW6PlNtMJ3P4RWRfC/APBjz0ueTyKReBstNuA2Wms4j5YSOgbwJz6sqvSTLo/A3b9DqwitiNZ1vrsALqIVlrUA+MlkMvl3ntlDnkEehe/+G7SqzJfQavfVj1Z4dRKtMf2RZDJ599Hrh9BieIbRYq3+LVrr/HkArz369x9JJpNvfZzj+DAxcxYTicQ/ROu6xhRaNxt1OnPfTCaT3/zIHtikmBzfp34Nn/Ae30Rrb888L+AVkOthv42WM/k7aLVL/AxaHSIeAPjcJ72PbSKR4K2KdbQAeKcc3VVeTvA8nkOgK7v/s2gV+gKtlLqraK314qOfvZlMJv/BR/rQ3wH5JGG3Z1Ww9Q20wNpLaFHNHgA5AG+iVWn4T54XAIfWRvwvDD+bxGmv0zW0WtQAAJLJ5G8nWlff/V/QuirPidYVcz8F4Fefo3EDLbD9p9DqOuACsI3W/dW/mEwmV5/hc31H5FFR2n+CFhPyp9CqoHSj5XD8a7TW698/w0c8q3wdrRuIPo+W0gwAKKNlEP8JWuMQTzqZTG4lEomXAfxVtJTSn0ErxWgHLcP0S8lkcuFjfP6zyos4+1lkf+IIWrdNPU6++R16tu+EvIgzju+7ZA0/VfKIfX0VLdbyB9EiAnbQ6g7x809iMD9BwnNlQ6sdXyf5PZzevPg8nkPA/D79QXzwyuLPPfqifOLBKz5B2O2ZMK/nci7nci7nci7nci7nci7dyCexYOtczuVczuVczuVczuVczqWjnIPXczmXczmXczmXczmXc3lu5By8nsu5nMu5nMu5nMu5nMtzI+fg9VzO5VzO5VzO5VzO5VyeGzkHr+dyLudyLudyLudyLufy3Mg5eD2XczmXczmXczmXczmX50bOweu5nMu5nMu5nMu5nMu5PDdyDl7P5VzO5VzO5VzO5VzO5bmRc/B6LudyLudyLudyLudyLs+NnPl62EQi8dxfxZVMJi2P+92nfXzAp3+Mn4bxAZ/+MZ7v00/3+IBP/xg/DeMDPv1jPN+nn97xnTOv53Iu53Iu53Iu53Iu5/LcyJmZV8rf+lt/Cz09PbBaW7i32WzCYrHAarXCarWi0WigVquh0WjAarXKa/k6i8UCm80Gi8WCw8ND3Lp1C9euXUOlUsHMzAwuXLiASCQCt9sNl8sln9tsNlGv19FoNNBoNGCxWNBsNlEul1EqlQAAPp9P/qbZbMrn/ORP/qSp8emx6c/k/4+Pj5FOp7G2toY7d+7g/v372Nvbw8nJiXxVq9XHvgfnodlsolqt4ujoCEdHR6jVanA6nQgGgxgeHsb09DReeuklXL16FWNjY3C5XDImfgeAn/7pnza1hhMTE/jKV76Cixcvore3F9VqVZ6rVquh2Wyi2Tx12LiONpvtA+/FZziLnOW1+nP5TADwEz/xE2f+nL/xN/4GrFYrbDabrCU/v6enBz09PfK+tVoNx8fHqFarqNVqcDgcCAQCsFqtuHv3Lv7Df/gPuHnzJlwuF15++WW89NJLCIVCKJVKKJVK6O3tRSQSgc/nQ09PD6rVKur1uswZ11p/NZtN1Go11Ot1AJBn/at/9a+eeYw/8RM/gWq1ipOTE9RqNVgsFjgcDvT29sJqtaJareL4+FiexWKxyM8qlQoKhQJKpRLq9Trsdjt6e3vR29sLl8uFvr4+OUsOh0PW/eTkBLlcDtlsFtVqFTabDbVaDYVCAYeHh3A6nYhGo+jv70coFJK/t9vtaDab+Kmf+qkzjw8AXn75ZfT19cFiseDo6AgWiwV+vx9+vx8ulwterxculwv1eh2lUgnHx8ew2Wyw2WwyVs4RAHg8Hvj9fjSbTZRKJdRqNXg8HvT29qJer8seqFarqFaraDQasNlsMjfUYwBgs9lgt9vlZ9yrv/Zrv3bm8fn9fplbi8WC3t5emXun0wmHwwGPx4NoNIpYLIbe3l5UKhWUSiWcnJygWCwilUohl8vh8PBQ1trhcMDpdMJqtaJer4s+pnAM+rm5vuVyGblcDoVCQebp3XffxcLCAgYGBvAjP/IjptZwcHBQdPHJyQkePHiAt99+G+vr62IznE4nZmZm8MILL6BcLuNb3/oWtra2ALR0eqPRQLlcht1ux/d+7/fia1/7Gr7whS+gUCjgX/2rf4Xf/M3fxMLCAgCgt7cXFotF1lyP+6zyF/7CXzjza7/61a8iEAjA7/fD5/PB4/HAbrfj6OgI+Xwe1WpVzgCfhTqop6cHdrsddrtdnrlQKGB/fx+7u7vY3d1FqVSCxWKB2+1GIBBAMBhEIBBAIBBAX1+ffB7tQbPZFPtYr9eRzWaxubmJjY0N5PN52O12BAIB/Mf/+B/PPMY/+2f/LOr1OlKpFO7cuYN33nkH169fx8OHD9FoNDA3N4cvfelLeOGFF+DxeLC/v4/79+/jxo0bePDggdhnoKXrBgYG8Nprr+ErX/kKLl++DKvVitu3b+PrX/86vvWtb2Fvb6/t9dyvDocD/f39ePXVV/GlL30Jc3NzcDqd8rrj42OsrKzg7bffht1uP/P4AOCXfumXZD9q+2/8Ak7tGNesVquhp6cHzWYTa2trePPNN/H1r38d9+/fRzAYxOTkJAqFAu7cuQOPx4Mf+qEfwg/+4A9ieHhY9gZ1jMPhEJxAzMD1pD1rNBqoVqv4mZ/5mTOP73Of+xzK5bLgk1Qqha2tLZRKJdl/h4eHqFar6Ovrw/z8PObn5+FyuZBKpdBsNhGNRuFwOJDJZFCtVuF2u1Gv11EoFGC32zE4OAiXy4VisSj6tVKpIJfL4eTkBF6vF7FYDJFIBF6vV+yjzWaDz+eDw+FAPp/H3t4ems0mfuu3fuuJYzINXrXQKBoXXP/e4XDIYjcaDTF+HNTCwgIePHiAk5MTWTC/3w+HwwGXywW32w2fz4dAIACPx4OentYj0zBZrVb09fXJM1BR04h1Ixpgcox8fn6v1Wool8vY2dnB0tISUqkUnE4nbDYbDg8PUS6X5bUarPD/xt8dHx8DgCgrv9+PUqkkoBbAB0AQf2ZWotEoQqEQ3G43gJbRstlscDgcT5wT/ez6c/WaU/h7Ixj9sNd0+r1Z0aDQuJb8oqLXv9NgVxu9o6MjHB4eIpPJiKIiQPB4PHC73aJU9HsaRc8hlVK349SfYVyTTopP/54g3mazoVKpIJvNol6vw+PxYGBgAKFQCF6vV4CAng8a3cPDQxSLRVQqFRweHsJut8PhcMh5r9VqMlf1ev2xa/wk4dmw2WxtBp8OYbVaFX2glbvFYhFgabVaBZTRoaSBsNvtcLlc6O3txeHhoRgi7ZBz7Pw/14+voZGkA2FGHA6HzAv3H8fGr1qtBqvVKrqQYzw+PhYnhaD16OgI9XpdnlU7T9r55pj0HtRrzPER6AQCATkvZsVms8kz8fO5N/izo6MjFAoF0dtayuVyG8AH0AbI7XY7fD4fent7xYZQl1I+TM90eu1ZhbZKOzFA65y4XC4BlnQk9HxrsMn157nkGeV7O51O9Pb2yn7X+4TrzP3Kv+e+8fv9ckYbjYa8x1mF+5xArFgsIp1OY2dnB8ViUZxXOnQEQw6HA+FwGOFwWIC92+1GLBbDhQsX0N/fL6+NRqO4ePEiDg8PsbGxgWKxiMPDQ+TzeVlPfQ75/oFAQOaFjnk4HEahUDA9Ruob/t+oT422jK+3Wq0CQKempnB4eIilpSW8//772NvbEz3VaDQQiUQwPT2NqakpeL1eIQE4D1q/dHpGoz4+q9DRodNL0Mr91Wg0UK/XUalUAAC5XA6VSkX0uZ4fOvbcqzyf3I8AZB9QL9GZczgc6Ovrk73ebDbbAPnx8TFOTk7OdA5Ng1fNPGhjQXE4HOjp6ZFNoD37YrGI9fV1LC4uYnNzE+l0Gul0GrlcDo1GA8fHx9jZ2ZHN2Nvbi3A4jOHhYUxMTGBkZESYE062NlqcPG0QzCojIyuhwYb+mVHpk4FzuVwol8vI5/PyPjSKmgHhRuA4fD6fPDtZMm2ANNDR4+oG+FCp8tmpPJ80J9y0NJRc/8d5pI8zFJ3A73da9BoCaHtezh2fkawQjSzQrhi4r0qlErLZLMrlMnp6ehAIBIQd4zxoA2I0mMa9o+etG1BAllCDRL6nNu4aaOl9wzFXKhWsrKwgnU7LHhwYGBAW4OTkRPZls9mEw+GA1+vF0dERstksisUigFPGyxh5oVGnPjAjNptNlCR1AteDjCrXFoAwWTw/XMdqtSpOodVqFa/f6XTC6XSip6dHFKZWqBQaHr2GHFsnHXhW0cDeOFc0isCps0HQrYGfz+cTwK3XiX+nhbrG+Hq+luBAs4JAa205r2ZFA87j42McHR0JGNH7XrPGRl3U29uLYDCI0dFRXLlyBaOjo+jr64PD4cDMzAxee+01uFwu7O7utkUGjM/QzTn7MCEzzP1IJrDRaMjZ4VwyImB8Fm3PNEjSeot7X+8T49nW0TiupcPhQCgUgsPhQKVSEdLHjPB9nU4nQqEQxsbGsLe3h62tLSwtLaHRaKBUKuHw8BAejwdWqxVutxvxeBzBYBDRaBSDg4Po7+8XhjoUCsHv96O3txcAMD4+DqfTiZGREezt7WF/fx/Ly8u4c+cONjY2ZL8eHx+jXC6jUqkIscPIDs/7k2zZ44Tzb5x7/TsAbbZDO/RcFxIA3KNra2t4+PAh+vv7EY/HMT8/j6GhIbjdbmEuOcdGcohf2nnTeMeMUO+RebXb7ejr60Nvby8ajYYQbtStOvLkcrlE51FXUSeTsT05OUGlUhGcAEDIDOq0QqGAQqGAfD6Pvr4+uFyuNgebn3vW/WkavOoDZfSS9cKTxaOROzg4kHBIKpVCNpvF8fGxhCm1wWGIkGFPspy7u7sIh8NtoTUaAA0M9QE3K0ZAYWQojOF/elzValWo/2q12vZ8VNo0xPRIqPQajYa8tlgsIpfLiVekQYFm9Z4G/K2srGBsbAxerxd2ux3lclmMyuHhoRhPht7cbjeCwaCEV202m4RbOScAzvRc3Rp6M6KVuNGYa8XDOdWKnwaEe9jhcEj4bXV1FUtLS21slGY/jNEHYxRCP4Oei27mRAM3nZbCf2tHiKLZZ6AF6orFohgij8eDYDCI2dlZ1Gq1DzCvGtw4nU643W6ZQ6YI6DnRn99N+NbtdothIqvW29sreocOHv/P6IEx0mG32+VZubZutxtOp1MUN/c6watm5skOarBKEElDAJgHR53Oiz7fZDbo1JNFtlgs4oC63W5JU9KAnc+ndaNmPY3g1eiA0Zgy6kAgZFb0+vMZabC1aGMYCoWQyWQQCAQwPj6OcDiMWCyGqakpvPDCC5iZmUEwGES1WsULL7yA3t5eTE9PY2NjAzs7O9jd3cXBwQGy2SxyuZxEwfTzdNIJj2O8niQnJyeyp7hXKUY7oYkLftcpPcViEdlsFvl8HoeHhzg+Pm5LhSHQYDqJx+ORaB+fXUeV+NnU04yYcK+fVbjudNrHxsYkIhKPx5HJZHBwcIAbN25gfHwcHo8H/f39kv43MDCAoaEh9Pf3i83R+4LPGY1GMTU1Jekwi4uLiMVikpZXLBZRr9eRyWSwubkJh8OB1dVVNJtNwQG7u7tYWVlBMBg0NUaOT4tRT/N5acc9Ho+kTuVyOXm+o6MjSW+gYxwIBDA4OIhwOIxSqYSlpSXRQYwuaxZUYxnjenZjP202G7xer5xpn88nKYuM4pBAZKSKaXQAhCCxWCwSaeTz+nw+eQ9GbLjHe3t7EYvFPhB50JiGzpmOEp1pvcxOgg5rUIwsEhVgtVrF7u4u7t69i8XFRRSLRbhcLkQiEYyPjwubokONVMD0AI6OjlAqlbC1tYWHDx/C7XZjcHAQU1NTGBkZgdfrFUVMj8DIrpkRrdQ6/a2ReW02W3mrlUoFBwcHsNvtqFQqsNlswuzozef1etHX1yceOr0MpkTwYPIZOn3+0zIIb775pnjI0WgUhUIB6+vrWFtbw+7uLiqVijAHDocD4+PjuHr1KgYHBwFA1qy3t7ftUOn56/Rdg7XHiRFwdSP6uYwsPZ9HsxxGJkN7wGShjo6OsLe3h8XFRVHO9J6ZH6o/F/hgWo3RYwe6Z6CZm0Swdnx8LIycMTIAnK4Dna1isYhMJoNMJoNGoyFpApVKBdvb24hGoxgZGYHL5ZJzqKMHTqcTsVhMWNienh54vV7Z70bvvZsUHubzlctllMtlCTmRsdDgUTPrWgfZ7Xb4/X709fUJ++t2u8VhLhaLKJfLAha1kiXI4poxvEcmloAMQFfjI0DUCp2K3G63w+l0wm63o1QqYWdnB+l0GoeHh4hGo5ienkY0GoXL5cLR0RHcbresjY70aJDaKfdVpxnosfJ98vk8isWiGCWzotMhnsRSk9kLBAK4dOkSBgcHMT09jcuXLwuDFw6HEQqFJE2gp6cHQ0ND8Pv9mJmZQaFQEKJkbW0NDx48wK1bt7C0tCQRAqNe55xzHc2ex0wmA4fDISlYWm/QYdIpLHTuaOu4Rw8PD7G/vy+heNZyHB8fy3kCAKfTKeece14DGq6lJl/IijHn0awQbBK4eL1eBINBjIyM4MKFC7h27Rpu3LiB999/H0dHR3jxxRcxPT2NcDgMr9cLn88Hv98Pr9fblptrJBaoh4LBoDCV4+PjWFpawuLiorCwuVwODx48wMHBgaTc8fwVCgUcHBzge7/3e02Nkftbp2Vo0ewgyQoCu1KphPX1dSwvLwtQDYVCUlfCNEm+79bWForFopAA0WgUQ0NDiEajcLvdH9ifnearkwN41vH5/X4Ui0U0Gg0Ui0VxcJxOJ6rVKvb398V5otOq97XP52sDr8FgUPQKdTGjz8fHx/D7/QiFQjg8PAQA9Pf3SwrD0dGRpJMQU9jt9jOxr10xr0A7za4PC5V/JpORBd3e3kaxWITNZkMwGMT4+DhGR0eFzTMyZTQK5XIZqVQKGxsbWF9fl/w8MpnFYhFDQ0MIBoMCgDWTxuc0K502rv6d0bvm89DDJSumw3gaXHs8HgHdVCY8CE6nE36/XxhOGlxtXIyH3qzcuXMHkUgETqcT4+PjKJVKuH37Nq5fv46VlRVhxP1+P2KxGGKxmGx+o9LUz9YJwHZ63sexx08LyilOp1Pm3JjeYUwD4c/5WjpRx8fHyOVyqNVqCIVCmJubkyJC5nvSaSkUCnA6nVJAxEOti3ooem+SoeTnmhEWWjFsQ6bNmHsHtK8L91mlUsHGxgZSqRQCgQAmJiYkP+nevXtoNptwu92YnJyUQjSdF63zTwlMmJfHuSd4pUIzKwShZOWoYI0Ml5G91pEfpnWQtazVam0hOxp3jo1MtjEtgb/XRVrGojuzuYT6mfWa6TAx9+rh4SHW19exsbGB/v5++Hw+RKNRmRPNslKv6FSHTs4k31uzzDwjmkjQ4WqzQr3MNdHFkkYg6fP5MDc3h+HhYTSbTUxOTmJubg7BYBAul+sD+oVsUl9fH+LxuDDkhUIBOzs7mJ2dxfDwMN5//30sLCxgf38flUqlzUHWzoPdbsfQ0JCp8dEg02nj89lsNgEiRkaJz3l0dCROSqlUQiaTQSqVEtZLr83x8TGKxSKsVquEYAOBACKRiBTAGPeSJoHI4lar1a6cEOoy2utQKIR4PI5oNAqrtZWDnUql0NfXh1gshunpacTjcWHTeYa0o2ZkFXUqDh3MgYEBTExMYHJyEpFIBNevX8f6+jo2NzeRzWbbamF4vo05z2eRYrEo46OO0eeTupqFm0dHR5JuVSqVsL+/j4ODAzQarRTCQCCAaDSKmZkZFItF7O7uIpPJoNlstqXtVSoVbG1tCdiLx+OSx6vXUtsqIwFyFtEEAMehc09ZWNjf3y+232KxoFKptOX161xuriFtAVMMrFYr9vf3sb+/j0wmg5OTE0kj8Xq9iEQiApxJPnDd+T5nwQJd5bzq3EBS9lqhFwoF3L17F9evX0c2m0U0GsXVq1cRDAbR19cneS9E2fq9gNMcL4arASAYDEqi+MHBAba3t4U5mp2dRTweF2OqmR6zoIDKxgi8jEqfi6ZZPSZFOxwOHB0diRHRSchHR0fC9AAQpaLDfPV6vY2u16zld0JKpRKWl5clkZ+A5d69e9je3obb7UYoFMLU1BQuXLiAS5cuibOhN6x2WjqBJSMDqFkgzUQY/+5pheE1PaedwKxWTNr5Ojk5QTabRSqVQr1ex/T0tFT5UsGRaSmVSsjlcmg2m/D7/YhEIvD7/W1shQ6RcO74MxbZmAU+Omke6FxQpw0F97VWmiyMGBkZwauvvgqr1YobN25gYWEBh4eHGBgYkPxX/XwscCqXy5JmokH6k5w9M8IqVhYO8XxpZ1AXHXDMnGOCXDK0nGcNspkzqp1EOp5kdMgM0HARWLBLB3BaoGBGdIRIz5dmPpvNJvr6+tDf34/NzU0cHBwgl8thfHwcExMT8Hq9Utxar9c/kEKgjZ4GT5xD6i4NKvkMzG+mru7mfLJDinYSjCDUarXC7/dLTiANIAt1jfOqwa+eS6C1N9npIhKJYGxsDBcvXsRbb72Ft956C4uLi6hUKh8Azk6nE6Ojo3jllVdMjc/tdrdF+wC07UGKZsxYvJLP54XNJMjUe9qYc8nK7lwuJzmMY2NjmJubEyZQA1cNgHVxolknRBNCxuhmIBDAhQsX4PV6UalU4Pf7MTIyglgsJgzd49LKHqcX+DqC2MHBQbjdbiF9vv3tb2NxcRG1Wg1DQ0OYnZ2F0+lENpvFxsaGaVYSQBuwpK4hgOQaaOC/v7+Pb33rW3j48CGcTqc4EYzEEQCzdsDpdCIejwOARGSZbpBKpYRxPzo6wsjICMLhsHxWJ6fErKTTacFF5XIZx8fHsNvtclZYIMf8ZEaGc7kcPB6PRAwIzpl/r513p9MpgJipL5ubm9jf38fIyAjGx8cxNDQEp9OJVColtk9jLEZgzrKGpsErjYY2yqymazabODg4wN27d3H79m2kUilhWufn5xGLxYQSLpVKSKfTYgAJ8AiGCRC8Xi/C4TCGhoaE0l5dXcXq6ipyuRzW19cBtIw56WgdCuvmoOpD9Tjjy/HrXEeGc2gIdKj1+PhYDMbx8bFsDuaZMuxLYxkIBNo+xxhy04DErNRqNWQyGezu7sLlcqFUKmFlZQU7OzsAgLGxMbz66qt4/fXXMTc3h3g83naYteLX/9Ysg352zQDpOe3khX8nhKBah+c6hWf5bLrCHECbg9Hf34++vj4MDQ0hEAi05QJzv56cnAiYI9CjAeZe1PmWHK9mRswCPDKDzIXTDK52vihG5oeVvNVqVXILbTYb9vb2sLKygkwmgwcPHiAej2N4eFjAqZ5TFk1ow6YrnQkcqfjMii4CGR4eht/vh9PpFGaFn0NHQVe7aiZVh9BpSDieQCCARqMh1e7aeWVRHAvzCBD1ZxgZTzOiwT7/zf3A92O6AvP8y+Uystks7t+/j8nJSYyNjbWN31gcqvcdcFrIQ+HvNJDkHiIrSMdNty48q/A88JwRCFO8Xi8mJibwPd/zPZidnZVCF80K61C33mP6jOscaxIfHo8HkUgEg4ODCAaDkiO6sLCAXC4nhANB7vz8PF566SVcu3btzOOLRCJoNptt7aq0zjY699QV+Xwe6XQajUZD6gd6e3sRjUbbnFx9jqiXSqWSpPJsbW3h6OgI+/v76O/vRyAQkPmm3mPol+9l1iYy3EwHTRdFOhwODAwMIBqNypmjc6j1LUVHYIw62Qhs9TkLhUJwOp2SxnR8fIx8Po+joyNJc2M+bze6hmefoWytIzhnTqcTh4eH2NnZwc2bN7G0tIR8Pi8OMeefTjP1us1mQyAQkCgEo3r7+/soFArChALAwcEBDg8PEYvFJCUGQJt+MZIwZ5FSqSTt8wqFAk5OTgRnsPNQoVCQVAGSkMQkzWYTxWJRdCFT6cjMer3etrmifWPxpN1ux8DAQFsXAo0hqAPJ+p6lwNc0eNVhJBosGtJCoYAbN27grbfeQrFYxNzcnORKejwemfjDw0Ps7u5icXER6+vrSKVS0haq2WzC6XTC5/MhHo9jcnISk5OTCIfD4ulNTU0hEAhga2tLWlUx746MGEG2WTGCMiPzCrQr+N7eXvh8PuRyOaTTafHqaeQ0gK7X61LxqZkibXCoZJjPqL1lY85ot2CPRRhkhZkPZbVaMTw8jO/5nu/BF77wBVy9ehWRSEQ+3whUO31p5a2ftRMzq1ls4+86rclZheBKh8q0ItRGRjNTOsRmsVjEC6XHr5k8FjT19vbC7/e35ZOVy2VRLsZKUX4WRRtjM6IVBfc8GXztDGimm0CAPV5pbJj/TLB+8eJFbG1tYXV1VVIwJiYmYLG0UhT49wwj6bCyZmXILBOMmZVmsyk9TAkyqXOMihI4rWhnyg0ZAl3JTvaW4Sky7Sxa5DpQ+dLx1A4xgZ3eQ9qpMbOGFL03jABZh/EPDw9xcHCAlZUVrK+vi/FhWNeYN2Z0cPW5NDqaOgSo15UGpa+vz/Qaaqem0WhIbhtleHgYX/nKV/DVr34VFy5cgNvt/gCQMc6T0cGgEwVASA8+P9s1vfzyy229wxcWFmC32zE1NYUXX3wR8/PzmJmZQTweNwVefT6fjJMgTxegGHUczwT1L8dAm8ciUWPkUANYFv+SzLl79y6WlpYwMzOD6elp6VNt1Mt8BrNRHhYm6fAwSSbN+BPE8UxRR+puAMa1pTyJheXf0dEhgH/77bdx/fp1XL9+XcAfW3C+9NJLpsYYDAbbCsl0friOhO7s7OD3fu/3sLq6Cr/fj9nZWfj9fklFMmIOTdRUKhVUKhWpA1pZWYHL5cL8/DxmZ2fh8XiEPNjb20O9XsfU1BQcDkdbZEgznmaEc1+r1YTltVgskkvu9XpxcHCAvb09iQowpYBF8wS97PnN6FgwGES9Xpf2kdwfZHpJlkUiESExOS46ena7XfbaWXRpV+AVON1selFv3ryJd955B3t7e9LWhNWgrGrO5XLY3t7GysoKHjx4gPX1dWQyGQGv3CjMY1pdXcXGxgZGRkak+XkwGJRWGwCwuLiInZ0dSeanlwOYT2ymkeIYOV7tIWoF5ff7MTQ0hMPDQzG0eiMT4PIQ6pYb2shT0dHDY2PyYDAo+VRcZP59t8IQE5WR1+vF+Pg4BgcHMTs7i+/93u/FlStXMDAw0NYmSFe1G3NHjeBUA+1OBlOHNI2KzeiBmx1roVD4QEqDZpz4XedhsV9puVxGs9mUNm1cR51LxTljtIHghxcXUEnRSdFth/T4+V7dOFk6NcEYGtYAjMwUDWK5XMbm5iby+bx0TXC73Uin0+JlT09Po9ls4vbt2xIKZB6WbkVERofArVqtSj6UBpTdADugZTR9Pp8oOg2GeQ65jwn42Ghbp/RwLtgrlQqZr+P7G0Gq3W5vy3/l/BAQE5iRFTEL0DVDYRSjDtKpADyHui8i97HxLPL12uHV55FAxvgz7kud66gZ07MK35/RAZ0CEI1G8dnPfhZf/epX8frrryMUCokzT13BOdai9Q+NKvckmWLdBshisSASieCll16S+e7v74fNZsPc3BxeeuklzM7OIhKJmD6LRsBPZonj1ulLLD6mHmGnADrHtBskCzSbrJ0b/oyAPZ1OI5/PY3V1FRaLBYODg9J/FjgN++vCMTNCwE9dw3Oic6IByBi5LgTwnXTck/S61l98LX/u9XoxMzMjPdZv3ryJTCYDAAKqzLYCAyD1N9x/PDe0EUdHRzg4OMCtW7ewuroKoBWhvHDhAlwul4BdspGM8jAXl2TA1tYWUqkU0um0kAKRSAT9/f0SVWL70Hv37uH4+FiiTjpn2Kwwmk2h7uD5GRgYQCwWg8ViEWdQdzthag/nIpfLCW5jkTrTshg9YN7y4eEhKpUKdnZ2EAwGZT8wXUbXqGgg+6FjMjsJmuqn8q/X61heXsY3v/lNbG9v4+LFi/jsZz+L8fHxtgKRhw8f4ubNm7h9+zZWVlZwcHAgt8WQNeKmdjgc2NrawuLiIt5//33MzMzg6tWreOGFFzAyMiIJ0ZOTkzg5OZGmyWyvQ9Bh1mhqIKmVhzYI9DA9Hg9isZgs9P7+vrTJYtgYaC8aAU6NlgZ0ulVET08PgsEghoaGEIvF5L204uEzdbOZh4aGpBm93++Hx+PB+Pg4vF4vRkdHMTU1Jd4Rn1cbUWNYspPXrMenlTvBtwbuxnF0UnRmJJfLSfU8mQBdFc55ZIiLaSzsH8i/Y/syrWg75Q5aLBbJ7aPXmMvlUK/XJQdKK2Qj29XJQH+YUEEwfEhQp+eQn6OrY9k/MZVKIRKJYGJiAh6PB1tbW8hms+I0MtKxubmJu3fvIhgMIh6Pi3Egq8Jw3eHhoRQjcs1pALoFr0zy9/l8sh/pFPCMG/vscl10OgjXmsCC7GWj0ZBiT5fLJUCQYI9OY09Pj4xBRyH0Oprdo/w7fbY4Br0f+HMCbIbogsGgtL4plUpiJHUvZu5dnRet87s1G2d8Jt29QTsi3Y5RF/vY7XbE43F89rOflVuWgsFgG5DXa/mk/wMtO8ROAwyLMpTNPFCrtZUjOjc3B4vFgkuXLqHRaEjhkd/vb3N4zyqM0hCwauCvbQcvOWFoVt+oROCqWU3tiBifie9ps9mk1mN7exu5XA4bGxvyt0wh4Hry/c2uI/cPIxsEN7qfJ4vTqAsIXrme1KV6DR8nncbLzwcgHSk2NjZw//593Lx5E16vF4FAQHSvWaGN5V5h1IJ7f319HW+//bbUioyMjGBwcLDNyahWq0ilUtjd3RUnIhKJ4Pj4GIuLi/jmN7+JhYUFWK1WTExM4OrVq7hy5YpEtY6OjqRzxtraGtbX12UsTLeh3TVbAKtvebNarQJkDw8PpQieN2Cxk4yu1/F6vYhGowAg7eh0X1g61DzDLGBma7BarSZngJEcpmaQvddR0rPYw65u2NKGolAoYG1tDdeuXcPdu3fh8/lw8eJFvPTSS3C5XNKY9sGDB3jzzTfxrW99C3fu3Om4wZg3xEOQSqXkd/fu3cPW1hZyuRxefvllTE5Owufzob+/H41GA0tLS9jd3cXa2hpCoRDC4bDp8AhwekCMrA2FypgVhfV6HS6XC6FQSG4qYv82vp9mNbRB0KFWHS4iS8TrAAmMtCHRYNKsfOYzn8HAwIA0jg4Gg8JYs3qTSk6DNoJWghdtCDuJBuO6mMDYrYAb35h2wHGaFbYq06FUAmWj08BUjkKhIH/H3DiyalQYVG5MsqfXyUgAc7T1IaxUKtKORIdLNUjRjPpZxefzoVAoSJ8+Y59jo1PD/VUsFrG6uop8Po+xsTGMjo7i+PgYDx8+xMbGBuLxuOy9kZERlEolPHjwAB6PBy+//DLC4bCMg0qefTQZVtaAjIx1NxXA7HDAXohkR3W6gAZkVITcuwSvBBbs67q5uYn3338fJycnuHDhAqampuByuQS4ct+TBaMuIKOie2sa82TNiDHCwLnTZ9yYy0kjxDHyWljtaLLjBcfCLxojGiRd0MMzwX1OsKOLTrphXnWUweVySVqK3W7Hl7/8Zbz++uuIx+OylkbdoKMnFO3As084HU+2SiJgDoVCbc8dDAZx5coVjI+PS7sgzhU7B5gVI9jXQlDDanKm63CNeCWz7hXMyAL/rx1mCv/v9XoF/C4vL0vHAu4Rv9/fFiHSf2tGdMSIebtMFXC5XPB4PAJQeI6M+azUc3pP8Ped1pm/45mgTnU4HIhGo5ifn8dnPvMZiXyRjNF9fc8qOgpi1MXHx8eCLY6OjjAzM4PZ2VlxhJlKRTCdTqeFdNJ1Pvl8HrlcToCyLvKkHSfZQSDM/FiG8bm/zNoL3kLaaDTkcgCSASxWY+4wUwjY3alcLov+ZYoh866ZEsKON0Brf0WjUfT09EiLrEqlApfLJcx9rVaTXsWMhOn0H+KnJ0lXzGutVhMjsbW1hTfeeANvv/02qtUqRkdHhRkFgO3tbdy9exdvvvkm3nzzTdy9e7ftruMnfYaW3d1dvP3228K8NJtNaZ8xNDSEYrGI/f195PN5ZDIZVCqVrkKymgk0HjD9b/aPtNtbN1VEo1Fp5s0iBRpQ4/vo96LCI9tHAEuj7XK5xEPhz2nU+P5m5ctf/nLbHcNMsNefT+FcaFZHVwg+SfgaerFUPLrNhhGoaoCp38eM9PT0CFjiWmjlqJWh7nfabDYFxOvDw2cnCNrY2MDbb7+N+/fvw+l04sKFC7h8+TKGh4elV6XT6ZQ8Sib16zw47Yx0kwJCJaBzPPnexrQM5p3V63VRoAAQDoeFdb116xZWVlYkRDU/P48rV67g+PgY9+/fx8LCQlujcRp6nkeyOrpns6587wa88kpXi8UivZH1rSwM0/FckPm12WxSBAlAnAquyb179/C7v/u7YmiZh8WwI4080304FipogiaGR9ltoRtmEmg37lxbnerCtdROIMN2bBVFJ02nbRDgss8nU2OYcwmgjTXRpISOmBBgncWgGMVut0sLN6fTienpabhcLni9Xrz00ksYGhqSlAIjE3yWc88olZ6/fD6P3d1dmcNIJCLzxpQfpgrp1J6zfqYW1iTodnDaiHPv08kkWOac6mgU0JkN1/K437E4jePKZrNtFxkQQFNXmBXuDe4znjvdPo7glPqWdkOPiY7f45w94891SpT+t9VqRTwexwsvvIDFxUW8++67sFqtuHjxIvr6+kxHeozpgtQxbGW1v78vl7iwq4DFYhHQevPmTbz33ntYXFyE3W7HK6+8gsPDQ2H8h4eHcenSJdhsNjmHCwsL0vJzbGxMQuos0JybmxMgfnBwAIvFIhECsw6ILqhinjadgHg8LsWAKysrbZFjzfju7+8jm82KreTa08azXRpZVPb5HRgYkOuDuf7cO4xkaTvBSNmHiWnwyupi9gHb2dnBm2++iQcPHuDy5ct45ZVXEIlExLPf3d3FjRs3cPfuXVQqFTHwxnAZFSzZJLfbLRPrcDhQKpWk+TRBBNlPj8cjbCtvqMpkMm2hirOKsXUDf8aNzYNPZcWCFxaUUAh4dE4Q8MHCHc3qcTGZK6OT5HUBhWZkuvGiddsyKnU9Rq1sdBGTvsFHj6eTGJkTY5qBMVVC74dOjLcZ8Xg8kqfIPnL6ZiY+F29/S6fTODk5gc/nkyb4BA38GwIHAgr23eSezGQyePnllzE6OirFLXa7Hfl8Xva3bqjP8XXLMGtwyOfTRsNYoMb+l1Q8bFdXrVaxtraGe/fuYXNzU1IuotEoRkdHMTMzI7fb7OzsYHR0FD6fT5ric62NuX50crhfujGYOmWD4yFY4BiNhZF6PxGUECxp1vXtt9/GyckJQqEQZmdnJbfc5XJJ/hZZWzLwXEOyA3qeu1lDfd46paXwPcmmEoATNDcaDenXyHQpprvoYlpNFrC47+joCOl0GhaLRVgUAG2GheeTc9lN6kc4HJaca44hGo1KipLOp3ySE6fPCaM/OoXAbrfD5/PB4/GgWCzKxQocD+sgtOGn3iPDz/GaETr7RsdDs910GPRZ0MQKW2RpPajfS+8H/ewApKNNvV6XC0NY2FcoFIR51ZGzbqJZfG5GkBjVIvFBZ4lXiVYqFckdZWoF10rXcDyOBOmUHkKbwT3AEPvw8DDeeust5PN5uN1uzM3N4datW6bGls/n2/pz85zs7e3h+vXr2N3dlRQTt9stIGt/fx9vv/02/uAP/gA7Ozuw2WzSYtDn88mcsJ86ADx8+FBs6vb2tnQPmZiYwNjYmNTuDA8Po1gsykUhrAGgHTIjh4eH4iwBEIeKrHm9XsfW1hYKhQK8Xi/6+/ul5sNut6NQKGB3d1dyy3VhKPUvIzsEsPV6XXAGP8N4VqijScrRwftI0gYYKuUDs/K1VCphfHwcV65cQTgcFu/v4cOHWFhYwPHxMV555RVMTk7C6/WKN86DdXBwgHv37mFhYQGFQgFDQ0N49dVXcfXqVfT19SGdTuPevXu4efMmNjY2cP36dWnjEw6HJQ+FFyLs7e1JONyMGAuqNBjQYXAd9ud8UKnqfBljvqtmWYzFIGSZdNiQi63Difr5ugF4ZNxo5HSerwYBTwKumqHhsxpDn/ydBhoEI9pT15+ngWu3SpbglWGIRqPR1uKFYyWgYw4a2T2taIFTpc18L6vVKgWE9+/fx+rqqjSrfvHFFzE2NibJ7TplgX1DdWsjo6N0ViFw1I6HdgYoVAy5XA6rq6s4ODiA1+uV3omZTAarq6vY3d2VFmr37t3DwMCAXGl46dIlrKysYHNzE319fbhw4QJisZgAA92vWSsovXe7SeGJRCJiSJhLy/cygnQypGS4yJ6witvtdiOVSuH27du4deuWMHPLy8t4+PAhRkZGMDAwIOCHoS3tpFJ0CyJ9XrrZr8YIQ6f0Geb3kmUNhUIYGBjAyMiItHAjgGB6CNeC4Ik3TAUCAQHyvF+cPbN50Qp1mn42hoTNysDAQFt6EQ2fvmWQYzfORycWjgwRK591pIx6imNnc3iCHV2AyfPOnG3mDZvVp9pxooPG88yUGV1kSLBnvLBBj7ETaO0kWt/yvT0ej0QdmGPYbDYlAtXNGHVER4f9eca4VwjUNCjlWaHDx2clUOQ4jPPQaV74MzrFbrcbQ0NDmJ6exsDAgIS9zdp8AEilUjI/7NXeaDSwtbWFe/fuoVwuy6UMfJ7j42Osr6/j3XffxdraGqampvDaa6/hwoULGBwclKiCzWaTmoHe3l65derFF1+ExWLBu+++i2vXrmFmZkaKpQOBgNhGrplR35kd38DAAJxOp9QIMTrd09ODcrmM3d1dcQCA04s3mFpQKpUkAsnOHXTsqQsZCcxms+KIs2MR7bsxEsG/5Vk8a5SuK+bVZrNJoQhD9C6XC4ODgxgeHobb7cbm5iaWlpZw584dbG5uIhKJ4HOf+xxeeeUVNJutfrBAy0C5XC6sr69Lb7HBwUHJZ5mcnITVasXQ0JD0PXvzzTexs7ODxcVFjI2NyYYbGhpCqVSSxrhDQ0PSosuM6LwrKiGg8+1WOoxPwKRz9VjsQcWqF8wIqMimkJIn8HiSEusm5NwpZ08DTg3aye4RSPMgaYBHr14XivALOGVvqcioyPnFQ6pZBZ2LZ1aMClb/TP+byf1UJuwgQDDIg8Y5oBfMdmgMx2UyGdy4cUPCSFNTU+K5Ejjp3Fuu6dOAVzo8VAhcT2MaAkM7uVwODx8+xMHBAUKhEEZGRtBsNuW2mp6eHsRiMTgcDhQKBdy+fRuxWAyXL1/GzMwMSqWSMAYslqTQMJLx0fuECqybCEE4HBbDS2cL+GBkgPuFOonFYyzIY4h/d3cXd+7cwerqqhSbplIprK6uYm9vT5Ssx+ORqmmy75qF5OdqUNttOzCK1ilcP11gw/zMgYEBeL1eXL16VVo7cf65t4zpAplMBvv7+xIyZy4vL1/gOS0UCnJugFOGkMUXvOLSjLA3MnPl2DdXs0BA5ygLAQuBArsKMHdUpzJpZ4bFQvV6Xfr3Mpfd6/UCgIAssoTsadnNGmo9oxl0XUzJZ2JxU6dIUKf3pRgdaq03dJTI4/GI80bwCkBqJ7oBr4/7XOA07Q1AR0fucQwsbVwnx8/o0Omf63no7e2VPvIzMzNYXV2Vq3XNSqFQEBvHdKV6vS5XMvPzGPkATtfH4XBgbGwMX/ziF/EDP/ADgkm0rnA4HNLWzu/3Y3BwEJcvX5YOLvx8MuP8t8ViEcedNkjbsrMKSbJ6vXX76cHBgYB97RRWq1X09vaK00tMxtsmdf479btOC2KUTxfONZtNhEIh0ZVMpWPKpe7GRPB6lvx60+CVTMvJyYm0fOCtJsxxsFqtyOfzWFxcxP3795HJZDA5OYnR0VH09/dLtRor80dGRqQZOe/5ffXVVxGPx7G3t4fd3V34fD6Mjo7i9ddfR6lUwq1bt/Dw4UNhiObm5iSEf3x8LEpOG9mzCA2AzoHiz4zFXJoxpHLlRicorVQqSKfTSKfTEs4ie8dcWS4swQeFjKEOt/OZqKy7AQX6St5OjAfHpwuz6C0TkGslQtDC/cHNzc/gIQRO0wcYKqOx1fOvx9iN0AARuAGnRUw6lNhoNJDP53FyciI3f9hsp1fyVioV7O3tYWdnR/YT2VxGFagoS6USFhYWkM1msby8LCGgeDyOWCwmbLcG+/pyBLPrSM+VTFMn9oJMHBPzeaPW1NQUIpGItNepVCoYHx/H7Owsenp68PDhQ6RSKdy/fx+Dg4MYGBhAf38/7t27h42NDWxsbGBoaEj2OEEljbVu8P1hRX1PEqvVKqCN1ancm9xber/oSyOazaYwtj09PQLeFxYWpIci05HW19exvr6O4eFhRCIR+Hw+AR16n2sHhM/AqIux24MZ0UwrdQrPvsPhkKKQSqUit2rNzs4Kk8Ln00CCrBdzug8ODqSPItuP8XYufube3p60emPKhNfrlffpJqeXjj3D2Cwi4j33dFI5Bi2aPMjlctjZ2ZEm7tRLOr/U+Hzcf2R6+Dc6/M1Wb6lUCplMRhjqswodDB1B0mSGvkGRBYU8H5qh7DRuHX3Sc2TUF9w3WvdaLKeX5Gh9R+bXjOjn1KlwxgI17uOentN+6xqIEcDqn2sQr+3bWZ/L4WhdaTo5OYlsNotsNovd3V1T4+Nn0o6Vy2XpFlOpVBCNRqUwUBMttVrrhq8vfOELsFgsePHFFzEyMgKPxyO2huRCT08P+vv7cfnyZdGZJAG++tWvYnp6Go1GQ67b1REKi8UiNzlSJ7Gm6KzicDjE1vFqc54Prev6+voQDAbhdrvbojZMGdIpeJwvOmY6TZPnkRfhcGwsXiuXy9IajH1hSXCdVc+YBq9srcTbPdLpNDweDwYGBiSZuF6vI5fLYWVlRbwhHrjd3V289957eOONN8RbZMIwG95OT09jdHQUe3t7+MY3voH79+9jdHQU3//934/h4WFpor67u4t79+5hZmYGly5dagtfMhRktlCEgJAT2Mkj1N41gRiLP7QxT6VSWFtbw9LSEjY2NpDL5eRqNm4UstUDAwPo6+uTqkAeaA0gjazo04BXI0g0hvl1lT3HzHAiUz34M834GdlZrdD43Lrdh1aGOnxgDMuakcPDw7b8YR4MnV/MQ0b2hs9IYKL38PXr1/Hw4UPk83nYbK2+d2y6rNmodDqNQqEg38mAMCebbIjOO+vkPJxFqBC0kuSccc04b7yYoFgsSoGLzWbDzs4ONjc34Xa7cfHiRbz66qtwOBzw+/24fv06VldXsbCwINeTjo+PY2NjA+vr6/D5fBgcHJTLG+jgMC9Uh6673adM0+D76HOngR7X0lgQR6bt8PAQDx48wB/+4R/KzX/cq6lUCktLS7h//z7Gx8el4TgVKj9Hh8a0w8U9rc/sWcUIXIxOKYEKmSuXy4VwOIxYLCaFc1x7zYgRLJCpzGazKJVKsFqt2N3dFcDKMHosFkOj0SokoqPN+WMvbTrlugPMWYQsDPM+GW4km6T1SyepVqvSG3xra0vGwQ4UOtLASBF1MQC5spK5gpxn/h3txcHBAW7fvo39/X0MDAyceXxMK9E6j/aD+dEEkRwvdZGOfBnXXkehtC3SekOff+57Vt5Tr2mHzufzwe/3y5qaEb4/9Y4uCtTCuWWuOcfBNdHMIp9b2yNtewhOOxEt1G02W+v2rfHxcayurgp4NatPiWnobJBoOj4+lpuzXC5XW777yckJgsEgXnvtNWkvZbfbxZHVEUQ6jXTeyuUyPB4P+vr68OKLL2J8fFxqC4w3V1ksFmQyGWxtbUlut1lSjqk2ZE99Ph/q9Xrb5Sxut1suLOBNZrqoUUegdBcZ6h+ND9h6kI4vnTyOh7Zf967m+xmL3B8npsFrKBSS8Cgn2+v1yhWiOlyxt7cnxTDFYhEbGxvY3NzEG2+8gW9961vweDy4cOEC8vk8stks9vf3JQUBaCU2v/nmm8IEDQ8Pi2cQCASwvLyMjY0NpFIpobtZsEDDZzbURSP5uJCNPrA6p4mAlKEZ5g5ev34d9+/fx+7uruSM8ODa7Xa5mpCAfXh4uC1dgDQ8GR8NAjuxbWcRGsXHebg6/KaVK3+uQwVGL14zA51yA4H2ClJ6bcbf678zK+wDqsM2WiHy88lMAqeXBWjGamdnBw8fPsT7778vDftZ+MGKUa6JBqRkmhgiZcUmDQoLHVipaWTczyIa3DBfWrOdZAUJtFmtGg6HEQgEpLI0lUohHA5jenoak5OTcgXi2toaFhcXcePGDcTjcVy4cAGvvvoqenp6sLW1JQ7VxYsXJYe92WxKuJbn4WkYdF0JztAx11KzXXyNTp1wu93Svmx1dRXXr1/Hu+++K1cg00k5OTnB/v4+lpaWsLq6iuHhYek8wHnVr2fesmbyAUiaz9MIQSd1BJ+vt7dXWiIRUFIfAO03/gEQo5ROp7G3tychcaB1hzvD10w3YORMA0kyIwTx7LRgFrwa89qMeZdG5834f95ItLOzg3K5LKFG1jNwzhkBYqcB7j/mbbOARjvUBK9k5hlZMANeuQ+NrLcGr4zWlMtlYZ+1TqLoGggdseBrdRGkZvV0upbuVsOx8gIgEjlnaQCvRe8rPhfX0QhetX41MrA6vYzrwznTEQ3q1p6enrY11nuDNobkAK8AZpQsFAqZGqPf75e8TDpcDKPri4/ohOket/F4HB6PR2w/HSj2N2VnJo5nfHxcbrRkzjC7chQKhbY0BeKNSqWC7e1tOJ1OzM3NmWbP6fg5nU6ZKxINPI88V8yJJ7ZpNBpte0vX9nANaetZnKlrBKiPdnZ2EAgExJnU+o5/y4uqzkI6mta2TGTm4tZqNXi9XjEUjUZD2j/oPpSbm5v49re/jcPDQ9y6dQsHBwfifbNp+M7OjhhfGpXt7W1JJt7Y2MDMzIyEr2mYs9msVNMxl4uHoFAomBqfBlI8JJ2+gFOFSe+Wm5ftNVZWVuTuad17kXOild3x8TFKpRIKhYLQ8loxUykwiVobTrOiGQIt2oBoRQW053Dx97pVhjG1QXvQDGcZE/SNYRUqQp3D1g1A59h0Q30dMtTMOp9LMzgnJyfY29vDwsKCFAgCLcctGo3KTTy7u7tYWloSg842KpFIBKOjo5idncX09DTi8ThsNpukVjDn7cOciCcJDzwNHgCZa702+XxeeiB7PB6MjIzA6XRiY2NDCrX01c2afcjlclhaWsKtW7cQj8clp3xvbw9bW1sIhUIYGxuTjh96P+mQrllGkkLGgrnIrPjnftGFaro7B3M0qQfW19dx8+ZN7O3tSU9RglDmAhYKBSwtLWFqakqYB55r7fToFkEcp7Gg66xidNqAdnbNCBRcLpcYQu3g6fNBnVQoFJBKpZDP58UQ0NFhCJIFW5oNLZVK8hn8PKYukXUyIwxP0lE0tuIzPr8WGvODgwNpNxcOhyWfnCkh/Jx8Pt+W48qIAY21DsNzrgneq9UqDg4OsL29bWp8nVIGgFPAyXSyTCYjLJdmrDTLpMGrJg40SNV62Qhe3W43/H5/2xzpVBs+j9muEdouGKNXxr1n/DvNduvIE3WNfq3NZpM87XK5LO3EjO9tjBqynyovVummK4bP55OwN59tdXVVQHQgEIDdbpf0MfYpZV9W2mzaPZIflUpFbjB0uVwChHmdKnWK3g9sPUh2lGMi+aX7AJ9V2K+VKVGNxmmnEp5BgkoAkqrEvcUIA3GALkoHTvEB7Spf73Q65ZmZAqZJQNYI6Yj3WesHTINXKnJuNib8s7pa5/nQW6/VatKGp1AoIJ/Pw+/3S5VgrVbDzs4O0um05FhRAXFyiMprtZoYfwBy5V6pVJLQEEOFBNFmhJ6iZgv1RuF49MLRgLGSb2dnB+vr6ygWiwiHw9Jeo9FoSFiZXtDAwACGh4eFFaDXyXZNNKCatqfx4nOaFQ2+OwHEx6UlUHFx7bXCNgJYvak1Q6Tn0wiSdSiYv+9GtAcNfBBIaY9PM+kcS7lcxsbGBm7fvo0HDx4AAF566SWMj48jEokgFArBbrfj7t270paNbVvGx8cxPDyMqakpzM3NYXR0VJQenRimt3A+unFEaOR40HXhoM6XJHg9ODjA0NAQBgcHUa/Xsb29jZ2dHezv78PpdGJlZUXaX928eRNra2sSfrx165YwV/39/RgeHsby8jIODg6wubmJQCAgoWedU0jh3jArdCzIjmmWmaL3JNeehUnNZhPpdBoPHjzA8vIy3G43Xn/9dWnobrVaceHCBVgsFqysrGB5eRlLS0vSrkZfN0ljrZlRvW/097OKsdhFs3g6jEqn8fj4WCp89WfqECydfx0OpIPJcDvPB0N0BJYaVDI/kwWHdMrNij5j1F1PijJwPI1GQ/JkmYPr9XrlKk3m7QIQQJ5KpXBwcIDj42NJaxkcHITP5/vAZ/K8sYZDd14xI48bD+0EwdjBwQE2Njaws7ODfD4vtlGzqdq2aL1o/DKSCmTGWDPC2xIJ6FhYylZ93ZxFfhafVeecf5juYq41haBIO0Tcp/q2QKanGRlXbR+o65gO0U0vYj4jw/QApPsKmUnaY80qk5ACIFcaNxoNcQ5TqZSQZ9QXAwMDuHTpklzFyvPHq1R5yUSj0br9j7qdZ5Qsp9l0SEb76vW61N0wwsQ2nxwL8951+zxiBZ1OqPcg14cpKs1mU5xVAlqudzqdljUOBAIChDsRhE8S0+C1Xq+LYeQ1hScnJ9Ic3Ah8uOHZf5Xs0+zsLF544QUMDw9Lh4BsNitXhTEETzCpc1zIRJBlYIUaD7EuvDHLFrCHJNC5XYfReHK83FhkcFZWVlCr1YSpY77M9va2VNOxUjIWi6Fer0u/RTIC+tYVeiT6s7sJqetxPU7paEXJ1+t11YZI/85oRI3sNd/L6Dl3UlBPIw6HQxwgne5gHIc2Avxs5rqurq5ibW0N1Wrr4o3Pf/7z0rat0Wggm81iY2MDgUAAY2NjGB8fx+XLlzExMYHh4WGMjo5iaGgIXq9XmoNT0ZLN0uM8a5K6fj0Bia7s57xybzBf8PDwUEJCOzs7WFpakkpk3hHOPN3FxUVsb29LWGl1dRU3b95EPB7H5OSkdB/Y2dnBgwcPpK8hjQ+fT8+52fEBrf3Nq5eZC66ZZjI5NHQA2tq4FItFLCws4L333sP29jaGh4fxyiuvyFWWFosFL7/8Mur1OnZ2drCxsYHFxUVcvHhRwogOh0MME/czAZk+fzon18wakjmmQ0jdSh2nwQwNmLHIRZ8x7m+r1Sohf5vNhkwmI6HBaDQqjcg1WKaxYbhSt8Ph7WbdiAavxmp3oz7g/3WHAuZQ+/1+uf2HuvH4+Fh6ULJNotPpRDwex+DgoDBmnBN+pk4d0v1KzaZ+GNMfKFrvsMCTHXhSqZQAuKdJqzEKQ9S6UMvtdsslDjoX04zw9Z3sQicx2hfuH/33uosLX8+OJXxG7numlj3OSSRxxNA9MYMZ4fmjsLCIJBgr86vVqjhzjUarbzRrddi/mOwyATHBItC6LIDMMM8ddQkLa7e3t+FyuRCPx+XZ6Jxo3GNGCBIByA2gun6JGIqplqzXIHFAUGqMyOp/a13P+eR5pwPNc91otIoHNfmgGdmzYJuuug0w15HKpFKptCVWd2LPuAB6MkdGRuDz+XD//n0sLy8jl8tJvpEx5EhvQ9PYdrtdihn4eq0gu1UMGthoAKSZLX0IaZhrtZooqd3dXfEEe3p6JHR0cHAgvXItFouEZwmKrVZrW/6gDofy8HBjPC3QMyoj/oxi9IQ0S6u9MSMI7KTIOz3nk5SfEeCaEX1YNCvH//MQd6psrNVavU43NzdRKpUwMDCAK1eu4NKlS5idnZUbqdhFIxqNYnJyEpcuXcLc3Bzi8bhcs8vkdN1KhMBVs+fdCIEOnTSeAZ6zZrOV68ZiHafTKXlg6+vrWFpaElagWq1idXVV9iG/ExDn83msra1Jd4/BwUFhkjY2NrC9vS1V8MCpQtOArhvwykhFrVaTZuv6ver1uoBXVsGSdbXb7djZ2cH777+PW7duIZVKyWUE+nYj7fnv7+9jcXERq6urEmpmNAVA23xrNp9jNZsewfNOIMZzzob+ZDoYAg+FQnKxCD+XQsNA/cvb/9i6rFwuo9ls3a4Tj8clXaK3t7eNlWW+W7PZFBBrtVrltWaFuoLpZsZQM3WDdny5tsy5q9frkjuuUwWof3O5HLLZLGq1moBzdqF5XDoHP7OT02BWjDqT342gnGvLUD5/pyNWnd5L/1/rXjomuiVcJBJBIBCA2+1u0zE8G8y9fZrxfZjN6aT/gVMGVq9xs3laBKbZPc6JjixpO2TcQwRaOvRtRkjK6Pdk+iLzNalrWNuzvb2NO3fuYHt7G5FIBK+99homJibQbDaldWgwGJQC3p6eHmE7jViJn8NraEkKagJI32Zmlj3n+XG5XPD7/YhGo9Kmimyu7oZEooXglTqPa9VJOhFQ+rX8OYk5kgOMeGgH4ix52aZPKpUPqXSv1yvGRIdiONF8GF1Y4XQ6EQ6H5Tq7zc1NrK6uSqNo4LTdkc775EFnqEcje10B3MlbO6sQRHKxeJCMoJWbmx40cEqL5/N5lMtlaRvWaDQkjYL5iPTeMpmM5PoyHM3G2qOjowI4dD4U55jz0q10Cyg5353A61kZ1KdlV58kNMbcM9x3OqkcOC24I5tGRcq+dlZrq7/wzMyM9Nek8rBYLIhGoxJGHx8fR39/vzS4prOhc9IISnRvUM0+mxENpJjDyQIx5msyNcButyMej8Pv96NUKmF1dRXr6+tyW57N1uqgwG4KDJ+xcAEA9vf3sbCwIP0Jh4eHMTw8jIODA6yvr8s8MEdNe9TG9JOzijFVgAZKXwrCNeNVjB6PB729vTg8PMTKyoq01KtUKqIjdHSA+4WM+8rKChYXF+UyFafTKfOg0zEYymeIjM9lRhji1Mae+pF5mMzN5QUFnF/qIoY2dcGo1WoVoMdWN2SSdMGT3ss0jqwy5tzSgHYqzjmLGFNiOjnLGsjpn2v9y/Ojo1HAKTgm6+bxeOTSGg22H6frjKHQpxEjC66fbXBwEL29vZI3bmSxOwHXxz2zBq+ayKFTFgwGxUlrNFq5/9QVZM+6HVenVLoniZHlZq4/f0ZAqJlErgMdGOPNhHwmPgv3q91ul7Gzx6jZcep/61qNQqGAzc1NlMtlBAIB1Ot1KcguFot44YUXpGjcYrFIahgLLtl+KhqNSi6+XmNGuXjuOs2vTkUxy7xy7r1er0QjmFqkGVFGOrjGRiLycSlueo+Q4NQRt0652ayVIDi2WCyyzh8JeAVO7xamIeYBIStIJcjcVNLQPGADAwOSV1atVqV/H40DB04jqg0+0H6FYbPZFGXPYhVueA1ozyrGRHn+38i2cDE46fwZ20MY8yzpbTHszMTw/f197O3twefzIRKJSOFAoVCQ1ib8LAASxtTKpBsxHo5OHv/jjI6m9jWoN76uE3PwpP93ep5umXOtGIxMDRWHXj8NBvT+6evrQyQSkZzuer11DeP8/DwmJydhsZy2QdH3yuv31AwPDbDeI/qQn1VYsGgs3mCv493dXTx48ADb29sIBAIYHx+H0+nE1taW5N1xbgjImDNeq9Xa2g0BQLFYxIMHD6QgbWhoCK+88gru3LmDnZ0dXLt2DScnJ5ienpZzyTmnoTErLCjQxppMIRk5Pi8VI5mdvb093L9/H0tLS8jlchISJytNw0dQxHywvb09LC4u4sKFC+jv75fKfs5Hb2+vtGjiXqGuMlu0FYlEPnB+LBaLhOspumBCv77RaEg7LIYuw+GwpFhwbXUbJ4ouCNVrxdcY89t12N2M0IEjENeRJSM7z7GeBRhpRpFj5v91+P9xukg/n86zNCudgIYGeWxO7/P5MDs7K2QGz7sGchr0Gt+v07+1E6bJHa1PuGbMvWUOpxnRbLIRzJxFPxsZaDKw2sbq13GOaHeNbJ8GSsCp3bXZbNJKrhvwqoWpSjabTXqqVioVrK+vS5RqcXERu7u7ApiDwaDYCdoVOt31el2cKx2N08692+3G4OBgW10PSTg6ko3G6dW7ZkSfwXw+j1wuJ84s85GNwFTvU73XjPYeaI8K6D2iCRyOMRAISEsuXuBTr9fbUiU/MvCqgaXb7W4rEOAHs4cZb2YhM+TxeCQfyW634+DgAHt7e7LZ9AYmGKjVanIBAAGuzlEiy8BqRTKcfJ0Z4aYiQCOg0cpQh3kIkKvVKtLpNHZ2dqQ/HNvVcON6vV6Ew2EArUKzVColm5BFA8biFOYvAZDbPXRObjcMplFJdvL4+dVJOWnvvxPzqhWNEcAZn7nTGM6qFJ80vsflLesxsaKahp6K1FhRqxt71+t1aVfEtig0esYcXw0qtaKmh63ZHrPjJatCJUnlxD2Zz+exsrKCXC6HS5cuIR6PSwus/f39DwAWpgH19va2XbHK+atWq9jb28Pdu3cRj8cRiUQwPj6OYrGI9fV1LCwsIBgMYmBgQJwaKrtugB2fjWecAIrAUxdzMeeV1fG5XA7379/HzZs3pXrc6HAxzG+xWOD3+yXdg+O5f/++3BbIz2KVLkGwZlu7ifKwIMO4B5xOpxQ6GJ0hHRViIRBbFjKXt9FoCPPDfUyjQ5aWTNXjGB5jYU43Dhbfi0aTqQvc/8BpQVcnXaT/Xt/0p4Vr8Dhn/MN+zobpbFF0lirnTvI43cZ9SUeM0ZtOeqkTOOwkj2Ov2ZqLtpKADjhNtWF3hW7GR33I/UfAoZ/jw86ATj3T7J5+D+D0enANfPgc3I96DY+OjiTCFI/HsbCwYHqM+jncbjfi8TjK5bL0xY3FYpIWyOiV1WqVTiw6x1wTcDyHtJv6THMcdHKmpqYEI/h8Pkkt0p1+uK/MiMVyWgyeSqXEKSArrG+e4+c9rqevJvX0mnSyf1qXMKWLDoZmXomzdA76h8lTgVeCRnoD+iYRMlbhcLjtyra+vj6Mj48jHo+jWq1id3cX2WxW3psH0Gq1IhgMSmUpgYZuaMzCLOYXavDabDbbrgM8q1Bh0xhpYKONiH6tzWaTfoTb29vI5/NSVddoNKQtDVMlyFazeC0YDErKAFm8vr4+uN1uYbN5CHSBBX9uVp4EejVLSPCuFYVmFR4HXp/EvBoLHHQKQif2txuArr0+AjsqQj47gREVBfcOWSHtnPCZjWFFPVedHAAWa/BcNJtN2Zs8rEaW+KxCJahD6mRX7HY70um0FOnEYjG4XC4sLi5icXFRCrWA9h6SzC3TtwJpQ1qr1STPa3R0FJcvX5Zb9fb397G7u4u9vT0AEAPNc6uZxLOKsU+wBj48C8wLZzN6q9WK/f193Lp1C3fu3BHdwuch4GcRRj6fRzAYxMTEBObm5nD//n0cHBxgYWEBExMT6O/vRzAYbLt2kw4nU6f4jGaBD/NaCTC5v/S10voaRq4zbwFKpVLY3NwUB5m5fmzPox167mndpskoRtZTOx7d5NkBp6FO9r0E0Bb96ATE9L916yDd6YFOzIedm06AysgwM02oG/Cq96fxHFPX8HN05woNlPTfaEBgBK96rrSO5TzRMeHcch9pJ7xbwgM4ZeutVqucAc6l8Vkf9xkaZFcqlTbWtdN4jeBVv792yHhrZSwWw+DgYFfj0+JyuTAwMIBMJoNms1U5PzExAbfbDbfbjUwmg42NDSkWazQa2NvbQ09PD6LRqERnqKe5RjyHRieHYf1oNCpttOho8+/4Xj6fzzSuASDFWDqSy7x49l8lWcDexLqAXNt9zSob9z//zc9hhMzn8yEUCiEWi0nhJYtGjQ7zR8q8ctEIIOkdHx8fS2XoxMQE1tbWkEqlpBfm0NAQLl++jKGhIRSLRaysrCCdTst75/N5bG1tyfWaX/7yl8XzefnllzE6Oio9Ud1uNyKRCKLRqBxM/o4UtdlFNoIzHhCtaHUun2YamVPCvm5U1sZ0AjIuDHMykZ+5PX6/X3rHaW+tUxiwmzw0rqEes/EwkcrX+Yraq9KAzhhG0OkSegyd5lqD8Q97rrOKNnJGA6kr88lUsTCkUCigWq22AVZtaLS3zNQNfXj166l4dP4g9wzDP5qdNBuSNRpg5lSnUinpMNBoNKR47Pj4GKurq1hZWWnrfUxAo/cZryLtFEYtlUpYXl7GnTt3JOl/bm5OlPe9e/fQaDQkBQaA5N+ZFafTKUpdg7hO7Btzjfl8t2/fxsrKipxBvf+oJ3Z2dnDnzh14vV4MDg7iypUrUpy2tLSEhw8fYmpqShgCzjFBPoGdTjkxI/oca+NNpoPRGIbaeU0j2+ns7e1J5brWwZlMRgo0HA6H3Nqj9ZUR1HXSATqfsls9Q2eDfSPpHHEOjWCM36lXdDTEqI90FMw4Du38ajHuHZ4Z3QHAjOi0B/3s1KFklciaNRoNMdgUIwjvpPOow4zgXrOPuusOU/S03aCNNLtPtY7TBUONRkPes9MzGfcYHTDmVnbquKAjdMa9YPwdv9gu02KxIBKJSJW+WdHP2tvbi0AggFKphHw+D4fDgXA4LPnUjHqkUimk02m8//772NrawszMDF5++WWMjIygXq9Lb1ZW+B8fHyOVSuH4+FgcTJ1Ko8GwLiju6+vD8PCwEC5mI8osxGJKHOeWOo1pUYFAQHKnbTabAFhGn406SxfX6ZQBrWfo5IdCIfT39yMUCkmUj6SCJrE0uH6SmAav9KS5cZjfyk2Uz+elEe/s7Cy2t7flmli73Y5YLCaVyWSCstmssEi5XA4PHjyQ9kRf+cpXMD8/D5vNJmzttWvXsLe3h76+PkxNTSEWi7VR62xLwfCiGdEhHSMFbmQldf6Jw+HA+Pg4Go2GXLFoNJz8PwApwGA4konSLAJxOBxS7ayVswZH3Yo2Yp0UD40K10QrTA3s9QbV763DBGTceEB1rhcZAc286nnudoz60BuZan3w+Gy8AQ447YdH0EAAavT+dShWi54LDV65F5kPSsaUwMAsSKfCYJUqAQEbtbPdCkPfbO21t7cnCfl8HzIcVFA0fATZHC/nlhc4jIyM4OWXX5ZihWvXruHOnTuSx6QZvm7WMhgMSkoA298QCOlG1nQYa7UaNjY2cPfuXele0mltCPTYzi4UCmF+fh4zMzNYXFzE0tISdnZ2sLKygs3NTQwNDUlbGWMIk9JNFERHUvRZ4lzRkNH5pTHkjVM0gjyr3Fe8QlXfQV6pVNoKxDRg4OfpegGCMs0MdyOMopHxpXPEnsd6XfS/O0VwjPrB6Oh2AnadmFctxWIRu7u72N/flwsozIg+S1ov0nF3uVxtjfkJ1ow6XAP5x4HXTrpGn12dGsDrc0ku6RZl3a6lJkw0kdNJjGCU/6aDxT3WiVE1vo9m8PQc8/1oM9lvPhgMdjU+oL3LEc8e63F6enowNTUFt9uN/f193Lt3D9vb23jw4AEePnwIr9eLVCqFWCyGvr4+lEoliXSxzSD7Zu/v7yMcDgvLSjKFfcS1/WEXjenpaUk/MRsF4ZXYnGNjrQRTCg4PD6WrCS+HYQtFPif1FvULI8jaJvK1jACwSJR6VF9aBECYWd5qdhYHyzR4Jbuq2atYLCZgbXd3FzZb6/7eyclJ6QW5trYm3ra+knFlZQXFYlHo6lKphIcPHyIQCMDv9+PixYsSaq/Varh37x7eeecdpNNp6RUbjUY/4EEAEI/djFB56cOpPX8AbTl4ZKjYQaFWq2F4eBjr6+sAWh4T70Wu11utN1jJzsbENEqk4WlkdK9HAiECik75QmeVxxkG/b5MldCNojVw139jfC+GsIBThcexazDD96LHplMQumUk+d5GMGCs8OfhAyAeKUEtwRvQuhO6WCwKAOXcEXhrBc6fU7kTWB4dHcmBpUPA+eJcmzWamu0jC9zT04NisYiHDx8in89jaGgIw8PDsFqtbSktANoAEwApFmDO6t7eHtbX1yViosHu8fExtra2cPv2bQwPD2Nubg4zMzNYX1/H/v4+dnZ2JHLALg9mLwsBWjlf1BkE/WSYCGDZXaG3txf7+/u4efOm9HU1ijZ6tVoN+/v7KJfLuHjxIi5cuAC/3y/V9uVyGevr63j48CFGRkYECNBQcq9ro2p2DZk7q5ks7VDqiAzPPsfNs8rikFqtJj1aWdTGtSL7SZa4EwDna8vlMkqlkhAT/F03DhYA0QNk4QmuS6VS27rq6BbQrlv4XQNXYwTIzLPxM05OTnBwcIDV1VVsb29LT2EzovNKdaoZnQm32y2MKPcKdQz/7sPGYQT0GhDS4dC62W63txVMG21aN+3AKNQz1GM6gvS459dCXczn0Ay7ZlgJrDTgpS7QzpZupeZ0OqVNWLfCz2c0x2q1Crbx+/2YmpqSW8zYs5VOci6Xw+joKPb29hCJRJDP53Hv3j0sLy8jHo9Lag+dt3Q6LTnrh4eHiMViuHjxotwSyt7KLDjlZ9IBNCP6lj0dGQROI3fUM7SRjNzwkg8CXOI+7nM6uTolUEejSWby2bneuk7A4XAgFAoJCXqWNewKvDIP02KxyE0mzB3a3t6W3IZIJILJyUlcuXIF+/v72NjYQCqVwrVr12C32+XKRuY0MVS9t7eHP/zDP0Q2m8XMzAxisRicTify+bxUUNNoXrp0CX6/H8ViEalUCsViUUAfD3a3YgxbaQBHA6GrvvlZ7DVIJeXxeOS2nlzu/9/et0bHlVVnfqWSLEulR0mlKj0s+SE/2+5Hug2dSYdFk7CYvBNgmIGZkBCyAitzmCEhw0omBBgCYQ2sGdbAkDkJIUCTQAIkgc5kgB4CTXcPTU8Tt7px2x673XpaD6vKUpWq9C65an7c+o52HV/Zda8k69H3W8urZKmq7rn37LPP3t9+nAxSqRTm5ubQ1tZWlhsTCoXQ0NCAzs5OdHR0GNaVgkAGRbYH86OI3Lxbm/GkkuKGR4NLGjGyM4P8LioVKhu+nwwmnyGNNvmdVFg2Y+oFbH4s58w2ECgjoVDI9MRkLhfzqLPZrFlM/IwMc0kngM+GBi6P8+N3MG9bOgkMO9tnQVc6h/K50BBaWVkxJ7TE43HE43FzCEgqlbrhO4hoNIp77rkH9957L2pqanD+/Hlz/LIdmi0Wi0ilUjh//rzJCY3H47jrrrtMnil7v9LxlM/JC2TKDOWC8sX1x2c7OjqKM2fO4OzZs8ZIl/cqN3E6UwBM7p10WOlgX7p0Cfv370cikUBLS8sNB2AQfoqZaPTK69qMlnT+isWiyYurq6tDU1MT0um0cQxYeNbc3IxQKGROGMxkMiZ8aecz02CUToE89UjmbHple/g5PnOuH46JhWn8u4R0MsjE8PpujJ2MCLn9X76P4BGePCZ5eXnZc5oZx0XZkkY+I0/19fWYn5/H7Owscrmc0W1kn+SY7Wcgn4WbzrGL2aizZQ9N6RT4cUDsNAAyu9z3mO4mjRe3+6iqqjJ5uHV1dUZH02DiP+kQAqt7h4xUVVVVGXllukBTU5PpEOD3HrnO2POYqWV0WkkksfioqakJuVzO9H9dWVnB8PCwIemuXr2KwcFBTE5Oora2FsePHzenwy0sLJji0HQ6jc7OTrS0tODYsWNmDfMAjrq6OrNeisWiZ+M1kUhgdnbWpAzYc0qdl8vlyvbwWCxmiAjAiR5LckoWV8liZKaUNDY2oqWlxeTTXr9+3bQ1lG1QWXjPPWxTcl5l0Us4HDbMYSaTKQuXU6C7urpw3333YWFhAT/4wQ8wPT2N733ve1hZWTHnPdOQIJtZKBSQTCaRTqdx+fJlI5A87nDfvn04ceIEjhw5gkQigerqaqTTacMU8VQRP82K7TCT3Ej4kGloM/TMzYAGWyQSQSKRMEYMGcd8Po/p6WlMT08jFAphamoK0WjUfGdLSws6OjrQ09NjzmmmQUxPnovbr+HK++ArFYssuiJksjnzW7ihUMDdwj5ynJLZ4YbJildb4dnPWoaIvILfbY/VnksZrmMuVk1NDY4ePWpOTGlpaXFlSWzWmb/nqT9UBNxIqCQkQyvZMi/gWnFjoegZMxeclbEybYWbGgtIqDQfeOABE2rk4SE831tGMRYXFzE8PIxnn33WNOg+duwYFhYW0NfXh8HBQSQSCTQ0NKC2ttaXE0mHQbLyMj+KCm/v3r3I5XIYGhrCpUuXMD4+jnw+b9KZeKKdzNej8uT3U0bkPWYyGfT392P//v3o7e01TARTNGi0kP30asCSkeO9SjmXDCzXXlVVlSlyIFnAnLt8Po/m5mbs27cPbW1thvHm5scm67IAhHJDA4h6hRskIxB0HPwY6OyGIQ1RMmUMIXJObKPDNtZoxNMQJOtGo00ahSQMqCPt58mTjPr7+3HlyhXX3OhK4OZgS2OUYV7m1TO3ng4y9YLbffNn+b18ljQaaVRx7qRDTN0mo02UdT/guBhS59qiw085Wev75f4JlLeblMYrjVpZ0CrJEmlsUTfF4/Gyg2H83Jv9M/dkFoAVi0Vks1ljhHd3d+Po0aOorq7GzMwMisUirl69ir6+PqRSKdTX15sahHQ6jYsXL6JYLJqjq3lwEdcEHUfaFYyGTE1NIRKJoLu72zCXXtu63XPPPchkMiatjIfXMFWGxmKhUDDGZSgUMnn3kmnN5XKYnZ01/erphLEQlhGxxsZGxGIxRKNRkxstnR2ZEkl9zFZilaxDXydsSaXPjZJ9LlkwcPXqVZM7cccddxjD5MyZM6aFj1Q8NttA4UylUkin0wiHw2hsbMSJEyfwile8Avfff79JzF5eXsbMzAxGR0cxMzODjo4OHDx4ELFYzDOjJZU6IQ0DGeoOhULG+JBdAZqbm5FIJJDP55HJZDA3N2cqm6enpw1Tks1m0dDQgMbGRnR3d5uE5sbGxjIBJfNkG6t+FinvUTKS/J2dC8v7kdeh8Ekl6jYOmWrA/0vWmpsi59oOCUpGwquyXVxcLDsdiIaFHaaXDHOhUDAh07q6Opw6dcqMs6GhwXTVAFAmr5JV4AadzWYxPT1twh/sHMH3cBxu7HOlkGkjkhlYXl5GY2OjOdechVo8BGQtZ4DyRsWyFuQGurS0ZNpkHTp0CIcPH0ZXVxfOnTtnDt/g8cd+ug0Aq6FYFjwWCgWjNOvq6tDY2IilpSUMDg7i4sWLGB0dNcyhNExtBkreD1+lDAKOcTkxMYGBgQEMDw+btUn2dWVlxShvbqReQINJGoZyHPIkMDLNrDZmalYsFsPMzIyJyMhiiKqqKkSjUZOXJtcAQeOGRjQNPua2sV2hX+OVBZDyerLAkI7FWkwpnwllnWkHExMTmJqaMmFMEgosFu7o6LjhxCw6jQCQTqdx6dIlXLp0qaxg2KtOZUGq3PylbuCGTFaKERnuHWQSbTZsLfZSkgMyjMv9gYar1EfS0LmZcXkryDGwRR2JG6kLbQJoLVDv0XiSKQQy4inTrPiM+SwBGEeIzJ2f+5P7VVVVlTH4mbpYV1eH+fl5c+hLa2srTp48aXK32fM1lUqZ41Z5WAKjHiMjI2X1ONQdNLwjkYgJl7OjCI1idmwhe+l1LR49etSkb01MTODFF19ELpcrI8IoW1yv+XweMzMzKBQKJvJEozqdTpt8b2kP0FllS9TOzk6zRrjnyffyPrhmWHdRifz4Ml5ls296zNFo1BwbmUwmzUbf1dWFjo4OszmSLRkZGcHU1JSh3G0B5aKmBZ9IJNDb24vTp0/jZS97GY4cOWLy09gyJpvNmkMQeKKJV0G2GRCZwygXL4Xc7klWU1ODaDSKzs7OMkaAm28oFDICyr81Nzejq6sLPT09aGtrQ1VVlWFxyRbJZGg+87WU/a1gPxO30KVt3PEzMrRjP6u1riVZLm4wknWVOU983muFoCoB2USGmMk0UbnTIOd306BmZTXnsK2tzeT7yO+RsiDZDLtnJICyI2HtueLzkyHsSiGLJ8LhsEmbyWaziMViOHjwIBobG01YVBp1AMp+LhQKGBsbw9NPP42FhQXU19fjxRdfxNDQkLkPmVdL1pf3PT09jWQyiX379qG1tRUHDhwwIbOBgQHs3bsXiUTC8zwC5Zs1x0zFTyNtfHwcfX196OvrQzKZNO+RTphbPpa8hixqkWt+dnYW4+PjGBkZueHULVmY6Cc3mxu2NKqA1Qp2Gq8yHEeFz0JZFkLwSEfKN//xWcm2SQRlTx7DKqMAjJDcilG7Gdh9htXbXC/sDMHC1Lq6OsOoAeVGIH/H8bJ/9tjYGHK5XJk+ZA0Go18EN1Xm7g0ODqKvrw/nzp0ra9XoNTWChiI3ZdsRl+yr7AZBw4Fdc5izKY1Z3jOfAeeexj/3CM4THToa0mRkKU8yb98LbMOaa4QyxvuifpD7yc1YZWCVGLIdHJmyR0OSz4YpLuxTyvZVlHc/cspnSz3NPa6+vh4HDx5EsVjE5cuXkclkTJ/So0ePAljV8bFYDHNzc4b5Z5oMxzs9PW3IgdbWVrS0tCCRSBjipK6uDh0dHYhEIpienkZ/fz8mJyfR0tJiTtv0y56TKGMbL85dMpksK54LhUJGDq9fv15GtvFZt7W1AYApFLbJroaGBtPLv7293UTtZL2OmyzItMFK9KmvbgNcSMVi0eRQ8LhBAGazZE4I2cWTJ0+itbXV9FO8cOEC+vv7kUqljPIFVilkPoTe3l7ceeedOHnyJA4dOmSOWGPo/fLlyxgZGUFNTQ16enrQ2dlpvF2v4UoZnpQbHw0Ye+HRIOICLhad/rLMeeWZ8uyjxkIFLurGxkbDWnV0dJgqb3rL0tuUlf/0Tvyyr0C5YSg9dQqPZAllo2IqEJnzaj8XvvIZyQ3VLv6wjWY/TKSENPJlqEmOnyEM3ltLS4sZJ5ksHpcqz+OWLAN/x4Upj9cjWy5TYezP28/bK4rF1W4fi4uLuHLlCubn53HgwAH09PQgHA6b/qu3Os98amoKfX19GBoaMoWTLNYiA0djimkQVFTxeNwwsbFYDKdPn0YoFDLtptjv2c/9cc2RbeGcMSxMw1seA8u0B2ngUyboLLPynQak7NhAOeBnMpkMhoeHMTo6ivb2djQ1NWHv3r1mrvP5vJkHL2hrazNOOzdvaWwwx40bKTubUIdQxjgnlEnJpFD2aUDKkC0NA4Z+OQ7JCEpGxg9kRwieWMY0MxpwHBt1rNQfdnRCRmX4N35GridbN3J9LS4uYnx8HBcuXMCzzz6L/v7+srXh1YmU7KHUEbwex0FGmEYSa0Cqq6sRjUZNf08eGw6UdxMgsy9PAguHw2XrkkweADOvMmWA4/Kra+yfmftKO6BQKJQ56msZsLeSJznHNJLl59g7m84KU5PkM/MKScbwOoVCwRQctba2orGxETMzM5idnTURmM7OToTDYXR2dhoWnPmi165dQzgcRjabxczMDOrr69He3o79+/fj5MmTOHbsGMLhMMbGxpBMJlFXV4d4PI7q6mpMTEzg4sWLKBQKOHLkCA4ePIiGhgaznrymDJ45cwbt7e3mMBYehjA5OWl67TNdgGlD7AtbXV2NyclJhEIhU3/EY+vZlhRw5LGhoQH79u0zRAaLtFZWVsrOArjZnAOVOZGejVdS4DKHr6qqypwtTfZpamoK4+PjCIVC5oHREt+/f7/5ubu7G8lk0nwvb4Thse7ubhw7dgwnTpxAb2+vOaqNHmx/fz8GBgawtLSErq4uHDhwAC0tLWVevBeQISZTJ9kYyQBwgdDQlp4jx06WdWlpCfF4HJ2dnWZzpBETjUYN48qFKVkCKhqytkC54erHeHULT0lDXRqTchySlZJ5fvK5yO+SBiMNBVsxS8NOGq5UIH7uT15DKkH+zP/z2jQUqACXl5eRTqcxPz+PbDZrcqnk91JJsUKbRT9VVVVmQ5EbP50oyUTLYjavssr7YFpDoVAwxVVsFzM/P4/JycmyvPKmpiZjuHMMNNb47JkDS0VKZoHHG5KdY/i+ubkZ8XjcpMxEo1HMzc1hdHTUKEc/jcMlM09HlBskW8nMzMwY5RsOh5FIJMwGLvMto9EolpeXceXKlbL0itraWtMblj2iaQhK9nV4eBhDQ0OG0WbBCp0WGiheEI1Gy9JpgNU2bzLExnEwIkNDhhs7HWjOJ2WDbBUjV1yzjATNzc0ZI1Y6qYxCyBDfeo3XUChk5IcdOGZmZrCwsGCMdJmjCsCcOsQ6AhpnJDVqa2uNwcB/ZLWYSwusMof5fB7j4+N45pln8NRTT5lCGcqa1PGVws1Advsbo4js8T07O2t0DNlZwGGz7Dxayj+jXsBqT3HbmZTRJuZRsiMC9+j1EAMSjFjJUD8ZWKlrgRsjaJUwsiQ++Hca5DRc+QyoC9YyliuBvA7HL39Ho4zFwEwfCIfD2LdvHw4ePGiMbNY8MHWSnQeYt3rs2DHcdddd6OnpwczMjDEY6+rqsGfPHkxMTJg+3Z2dneju7jZ2D1vPeTVemSaQzWYRj8eRSCQQj8fNoQHXrl0zzvjS0hLm5+eNM8iOBzJSIGt+aCMx6kF7LxQKGcfQjqpQz3HOeE9yLd9yzjw9ATi5QqTTOVlcbNXV1ejo6EA4HMbg4CDGxsYwOzuL+fl59Pb2Ih6Pm9zVgwcPIhqN4s477zTKTFaac2NkWEwercdUgYsXL2JwcNAYh8wbZY4WPVOvcDOq+Hu+yodO40zm5zDXqbraOUKUBTTccKiIyNIy6ZvCKfNEOA7JNkhjYyMgDT07jC+VEIWMC85mEjlO23t28/ilt2szrn43SwA3bOByrjg+O1WC98Y0laWlJaTTaUxOTiKdTpsj9Ni8uVh0Kj5ZpDg/P288Z24SVDA0NpgjxBChzE3zer/SAJZMB8OzdXV1Jl8qFovhxIkTpviMLBeT8eVRy3QyuCHaR6FKg1emgQCrucA1NTXG0GDHBRmarRS8niz0YURGFj0Ui0Xs27cPwGpbJ7LhMvWFzAkrg0+dOmWM84mJCcOY9/T0GGdR5mCxWrxQcCppyboytcLrpimNXbnObX0TDodRX19v5ppRHn4H1yEZZ+lI02Ci3JG1ZaEG1woNVlnsIxkyv0YB1xdzNNnFgf2GKYsA0NnZaZyrqiqnOK2npwetra1l6QV0nhKJRFkhpnREeeIi9cvy8jKuXbuGs2fP4vHHH8eZM2dM70tbX/iB1JE2iwSsFh8yP7Otrc0UcDHtjO0SZUSIssc1x7XKyAP1kbweiZdwOFzWyolr08+BIXbqgDQu5QFBdOipL9xkxiZP3KJ29vu4zljQxFQBWfDmRz4JWxfLvZDR1P379yMSiWB8fNzIL/dvGp4AjP4jYXf33Xeb42ubmprQ2tqKjo4ONDQ0IJ/PIxaLmfXHzjD5fB7Hjx/HqVOn0N3dXUYS+b3P5eVlTE5OIplMIpVK4dixYybFsq2tDcvLy6YINJvNGn3CY+xzuRxSqRRyuZx5HnQuAZgIJZ1iAMbBpxMdiUSMnpFFeXINVupA+mJeqWzkCR58oNzgq6qcxtSsdL1+/TqmpqbMDZANkH2/pCEjQ3dkrngkYiaTwejoKEZGRrCysmIYXFaF06C0G+FWAukBULnLfCY3llEmnMt8FMmKUHlxY6CHw/fI9iBUUDL/g+Ngjq3MO/YLN2UkWU87/8xmU4m1lJD97KVTIL9fhgbls/ULO6XCnitpkNO7ZJ4s8105dibhX7t2zRQIkX3M5/OmYpPyQoVtG+XcwMnQ8Vp+jXU6EDKExtNf2NKJFbEMx1KhUn5oBEijlJ9by7OXa1X+o5KamZnBysqK6VLA5+2n2IfjkmwTK1i5PiKRCHp7e9HU1GRyYrnZcU7luJk7KmWB419ZWcHhw4fLIg/8XH19PXp6etDc3GyeEatrWTjjVdfQiCCzBqBsTFz/NGQoS2RfaejwvrlpsrcnxyMdatm1hMajbNe2lkz63TBlekwul8PCwgKGh4cxNTVlHBF5qAjnVG52NEolsyxJCal/JIvNCMPi4iImJiZw9uxZPPbYY3jqqacwNDRk+rrKKI/fSJYdCbPXM+eU+p2su9wDKJuMMJBR5Fpl9IMpAsxNlHIn8w9pUDHlhPrIj/FqG4jyVdZlkISy2xS66WI7tcImh3g/knWm0x2JRMoO3eD3rCdCQDKOsiifa3W1cwAC7RpGl9ntgxGoaDSKxsZGc6JUPB43n5d2BABD2nFOisWiiWq3t7fjnnvuweHDh02kRBp5XkGdUCg4XZGmp6eRSqVMNIQ6jfKVyWRMJwTq9JUV5xCpkZERhEJOx454PG72gObmZkxOTpYZ/izMDIVCiEaj6O3tRW9vLzo7O81zlwSBm+O3Fnz1eaWipnByI+XFWTQFwHgqAwMDGB0dRSKRQHd3twn70Euk4LgJNzeddDptTuvKZrOIRCI4ePAgurq60NTUZDZcKgg/yojKgj9Lw9xm62zmFVhln/geGgTSG5b3Jw0dyYJxw5ffIfNGqTD8GAU27OfkRuHLjUEqHan05dzJ75bPQzopfHbyGa/XcAVgnAE719XOqZHeLMOpZLkoS4uLi0ilUqbgqampyXiqVKCcL/4sexXKuaeMyOdnhwgrBY3kubk5U/mZSCRMe6pw2CmivOOOO3D48OEy9kamwvBZMJTOvG0qOlkkIptY872yuTV/np2dNZXg7e3t5shBr5DOI5012XMwHA4jHo+bXELCjpxI2ZM5xjL3WVa+St1G+aVeYJgSQFnxn2RgK4XMT5SpAsBq6zrKMO9XRmT4OXnko+zxSYPJLvCh7pAMu0xz4TNbT7qADcrY3NycaatIx0Tm9+fzeTOnXE/22pCbHGEzw9w45+bmkEwm0dfXh+9+97t4+umnceXKlTJHxzY8vcDN4XeLQtkONPuHrqyslB1eUl9fj0wmg+Xl5bKuGnwW9nzZey8JEMowjzKlfGazWTQ1NXm+R7dX+XfKDo07GuKUM0lMcD25sfly7bLYieuSjJ1sPWgzwn4xNzdnUv3kvisJDv6ORdW1tbW4fPmyOamvq6vLFHdJeZLEAEkM5iNzbljzw6Oq2eudz01GMf3IKZ85jWwempRKpZDJZFBdXW26RrEwLJvNGnKErzxtiyz4wMAAAPfWk9RddD727t2Le++9F3V1dejq6jJ7jE1cVroWPRuv8/PzZkO2jQJOPABzNCW9ytHRUcOcMoQjF6VkSaXRxBtnwm8qlcLs7CxqamrKWmLR2KJC9rNZAii7F9uTpmck751/k0Y8JwJYrSi2N1JpJAOrPVXdQucybCv/zon3A3uBSSOTClbmT7mxsG7Gqn0N+9V2TqTAb4QHzevIMKKbgrTHLhUWAJMf2traiqqqKoyMjGBsbAyxWAxVVVWmKTY3ETLqNOwou7w/siTAKqPIZ+CWUnEr8HtZEZrNZs13r6ysYG5uDlVVVaZHMpUI71Mm0HNTm52dLcvNpcFhn75CZ5JhT7IC/E72WO3u7japPJKprxS248HrssUU1xzTH9YyROSGuZGQUSLZMaVSMLeez1Uaq1KHAOU5gFw3ZC1qamrKiq7YUgxYNYKl/iJDy2bplD+pA3h/GwneH41pHk7D9UFHjB0FmpqaDCtkO1wAynQq5VrK59zcHMbGxnD+/Hk8+eSTePrppzE8PGwiFfwOypibMXUrSEOR88jvlIVmQPkcMq2IETky4twHecoZv1+mVdl7lNyDbX3CPuzV1dVlKQpeYa8n/s42ykkacU5kGot0GN1yGyVJJOWfz9IuOtyo/QIoP42TP9sOLq+xd+9etLe3o6amxhT2ZrNZFApOC6mlpSWzd9TW1ho9KZloOiwAjOFYVVVl0h+j0SiKxaJrD10/xqss7mxoaDC2B3uRAzDRDBaSTk5OYnh4GMlkEjMzM8hkMpifnzcOs30QDEH5o2NGRp7P7u677y4rpJd7tZdcXs/GKwchK495QckSAM4DSyQSqK+vRywWw8jICK5cuYKLFy+aPFB2IiCLxQ2Z3glDcqFQyDTLPXTokMnfq6+vNzdNz4wTvR4vxfbouPHLamAuSnujoUEpF65UsFyM8nqAe96UrbSkYcux+Lk/vt7Ky3FTNrym27jl87PZG3k/NmPhNj5+t1dIYxtYDT3ZnivnUDIm9hxTGU9PT2N4eBj5fB4nT55ENBpFe3u7kVeGXWm88npkR+T9SEbRr/EqmSeGgpiaEwqttmOTBrkMvzHcncvlzBG4VLKyvyJ/R4NcMpjyXrlGqLCOHz9uQkQMDfuZR6lbeKIO79tmdWROmDQqpENGI0j+fi2DV86XHAdlhXImHVkvoMNhRzjcrm+vG8pbsegUk3BOqKfk+KgTWSQk04+koWyzhhsNzhc3UDJBDF3yUJnJyUmkUinTx5a5nbKVlM2yyh6qLFLKZDJ4/vnn8fjjj6Ovrw+Tk5OGmZT3KI1gP8YrHUPKJ/WBZI6lDpSGnr1vyAgNv48sHdlaaWhL/eHmpNfW1pp90m9nk5uRE3KP5BqSqVO8L8qmGznjJnMcJ8Pdcu3aY9oIeWUUWK4hub/ahnIoFEJTUxOOHTuG1tZWkxLDQnXmGdfX1yOZTGJkZATZbBZ79uwx/dzZB591CMzVjkQiZi+y4TdC0NTUZAxW2leSQOK981S+ZDKJgYEBXLx40XSDonNsM/4kH0kk8Do0UPP5PKampkze7NTUFNLptOnLS90nI9OVwLPxKkPbssIVKF+YcoKbm5sBAFeuXEE6nUYymUSxWN5YfXl5GS0tLYhEIsYgkJQyv5c9VJkLxYXNyeG1JevlBVTi/Bko31yA8sXI99kLkUrGXqxui1RuXHaY3mYR+ZylYewVt2JM+R55Pfu9tqPidv9uRrKdiuE2HvmM/GwostWZG6MkQxP2HNNjlPJTKBSQzWZNzjYAk3cmwx2yAl8az7YxbrMnfkC5YHV1PB7H1NSUyWWShqqULekYLiwsGOeQzKqMdPC9snWb/bwkI0HQqW1tbTUtVby2IALKz43nvCwtLd1gCNjPlOO0nSvpQHKTZV6hbLhufzeNPbl5SvidR3vNc7OUDDkNPvva0uDmWGVeHZ8BsHra3PXr15HNZo2RZ+dMrmW8rseglZ9lRKxYLJqIBvcT6n4yPLOzs6ZDBBu4M4dT3id1vSyeY0gznU5jaGgI58+fx9DQEABn/6Jcu82F3/sj6ysLj8mm2vpNvsp1aTtGdCQlk05ZpFFsO7/2XFJ+QqHVNBOpmyqBm152g9SF1dXVxoHgfVCHFgoFtLW1mZ6jdMIWFhaQy+WwsrJi8tuljNpyWOm4KgHlUjq68rvlGuf79uzZg66uLnR3d5uTsAYHB42Mkk1lIVQ6nTZ2Co+ybWxsRDQaNWkyAExUSxIf9n7rFSQGi8WiyR0mGcjr0llgTuzVq1cxMjJiDhI5evQoYrGYcSDZAYRGKx1MXosdRUZGRnDu3DmMjY0ZAz+ZTJqooEzTJOteCTwbrzwyk5v6rZiHUChkKkyHhoaQSqWwd+9ekzPIz5KFbWtrQ21trQl92hsKc96YW8PqQ36PZDb9hgltFkJeX/5dtpiRcPMO5Vhs9lQaqjYLaN8DFxYNhPUItA05BpsRtQ0GyV5IY3Wte3YzMCqFV+Ukx+jGZtuMr62guMhlSgM3JoZnGfaUHrJ9Tcl+rQXKq9cNhXIji7LGx8dx+fJl9Pf3Y2xszGwa3Kw5VttI4j9pmEpZ9Do2MsGsPpbsqBewwb00NDkuKUcyDMdnKXPlqAsYSmZ7IlZKV1dXY25uzhRQ8DNy07TlRTqngL8IAY9HlAy2lC06YW5Gqb0meD+yaTuNAjr5ZN4WFxdNc3J20bCdTvtevcoAIZmq2tpaw6YuLS2VOQY0KnO5nGlZxmdEw5tpBgwhc25ouPJa1IvMfZchypvpEj9GEHv82mz/Wmy+7fDZ+knKmf1Z29GxnQ3781zvbM3FVARGZSqFvCcb8tq8LxpcuVwOL7zwguk0cvXqVVy4cAFLS0s4deoUTp8+bVrPAcD09LQ5hYpGErtsuDkBxEawsPIa9pp2c+q43mT3FbLp+/fvN+slm80aPdPQ0IDOzk4cP34chw4dMjLMIkvqLq4LabBT59lRAz/3SYKFLK/sWEFdSDnj2jly5Aje+MY34q677jKkB4lJjo2pMHSuCgWnB/eTTz6JyclJjI2NlZEmc3NzZhyUXcp2JfrUs/EqW+1ID5YPnULOzZ5VpgMDA0ilUqiurkZ3dzf2799v2s3QSKMw0CKPRCI35CfxGszXy+fzJteQhiyZPa85aIS9aVOY+J2SxZHhPKCcpZQTYCt/e8HZBsTNFg6fl997tJWl3BRtlpK/txeMzR7c7H329eSzsNkyt+fjFdKAtNkptw3FHqt0hih7MkxOJkQyZPweGWriZwCURQWkM+Q3147XrK6uRiwWM150MplEJpMxDfu5qfhdC/K5cMy2jIdCqz1OFxYWjIFEY3A9TiQVI5ltyTBxDFTIdp9L+eyrqqpMg3f2oqShR/0hDVZZFClzF2VU5GbGRiWQ7A4NazrsUi+6OX2240UjkC0MuZECME4+mRVZ5CedAjcW3c2g9XOPfKZko2hIEwwfAuVHFUu2kI5gsVg0bDT3BKbH8N6ZnsBuGoTfVKu1IHPlZc9TuS9IXS4ZfhkKd9NN0ohxM2TXmhN5PRrvPPqaMuAVt5Jtt79PT0/j/PnzuHz5MorFojnsg3soT6riaZQLCwsYHx837eja2tpuSKVxI4s2ArLrBGWKhqLcm+X+wL9zP6itrUVHR4eR76mpKcM8ZzIZc3R9W1sbenp6ADj6kjIgZYlzLp2d9e6P0rBkqg67C8jUjHw+X1ZfUygUcOjQITz44IN45StfaQpymerDtSzTNSljiUQCU1NTeO655zAwMFB2XdnmDCivs9gU41Umz0tvy2bsstms2UjZViISiaCtrQ0HDhwwgikbfSeTSeORRyIRswFyo+EGZYfxpZciDVzm6XmFNF45oZwYWQUsNy3pEdkP3s6RA1YLbng9si58xpJFkgLMjZnhXj+K2GZE13oG8pU/y3u3DUS377INU5mS4ebRy/9Lw8AL3MZi34dtpMv/S2UlUzSkV8jwyNzcXFnDZXkSDo8wZG/HSCQCYNWolYyhV8j5CYfDRomwpydTcjYCchOVhYM0WmkAsTcuvXVpRPpZhzJsxjmThZ1ynmWYHcANm559H/y9zAEma8cwK+deGrVSHjjfknn3ArKhCwsLZkOYmZkxytsu9pGGrMx/lTmx/Gf386TzQtnIZrOGAJA5y3wPcbO1XQnkhgbApKQwaiErkbkOJDlA+WaxDtcei7kAlPXflc+J8uPG1K2HvZKQjqis2QDgarzSkCwUCuZIXKnzbsawyrQC+d61HA4AZfsK61P8OpNuuJlc5HI5DA0N4amnnsLs7Kzpz1xbW4tsNovx8fGy080WFxdNX+jGxkbTgtBtvvw6jGuBzjefuf3d9jO3CSrpgPD5ssfu8PAwnnnmGUxMTGBpaQn33XffDak+dtqHvK5tb/iZP1tPcG3IHuFyX+f7+VxYOAnAFHxRlphyAeAGkqSxsRE9PT04cuQIxsbGUFtbi66uLnR0dCAWixm2ltdnukwl8xqqdBErpTYni/82Qmu95hPZ7fcH7P573A33B+z+ewzkdHffH7D773E33B+w++8xkNPde38b2zsmQIAAAQIECBAgQIBNRMXMa4AAAQIECBAgQIAAW42AeQ0QIECAAAECBAiwYxAYrwECBAgQIECAAAF2DALjNUCAAAECBAgQIMCOQWC8BggQIECAAAECBNgx8NzndTOhlPoVAH9R+u/btNZ/vpXjWQ+UUjEArwPwcwDuArAPwDKA5wF8DsDntNb+jq3Zxtipc6iU+iiAlwE4BqANwAKAYQAPA/hjrfWUeG8PgN8HcBrAAQAtAKYA9AP4LIAvaK3Lz5/cQviVRaVUCMCvAngrgLsB1AG4CuCfALxXa/3CbbmBCrAR600p9RkAv17671Gt9YubN2J/8CinDwF4yy2+8lGt9as3ZbA+sJvXIQAopd4A4EEAPwLgHgCNAL6otX6zy3sfwg6bP8DbHJbeXwvgN+Dcay+AvQCuAPhHAB/TWg/ftsH7RCX7nlLqAQDvBfDP4Nzji3Dk9JNa6407OWMD4FFOjwJ4PYCfAnAUQDuANID/C+DjWuvvbsYYtw3zWlJEnwQwu9Vj2SD8SwCfBvCjAJ4G8HEAfwfgTgB/DuArJeNg12CHz+G7AETgKMxPAPgigBUAHwBwtnRvxGEAvwxgBo5C/hiAf4CzgX4WwLeUUtvJMfQsi0qpvQD+J4CHAHQA+KvS557A6sa0nbCu9aaU+gU4hut2l10vcvowgD9c499A6T3fvB2D9oDdvA4Bx3j5d3CMgrFbvPdh7Lz5AzzMYWl+vgPgj+EYSH8N4E8BJAH8ewA/VEqdvJ2D94pK9j2l1C/B0Z2vBPA1AP8DwB4A/w3Al27DML3Ci5x+CMBH4Bit34CzDp+EQyQ8qpR652YMcFss7NKm8jk4XvNXAbx7a0e0IXgBwC8C+LpkfJRS7wHwAwD/Ao638ndbM7yNxS6Ywyat9aL9S6XUhwG8Bw7Do0q//j6AFpvJU0rVAPgWgFfBmduvbOaAPcCPLH4MwM8D+M9wWFa3e91O8L3elFJxOIbvl+EY6g/ejgH7RMVyqrV+GI4BZL83CuB34TDTD23aSP1hN69DwDHsRuGwbg8CWJOV2qHzB3ibw9cB+HE4Buw/t9buHwJ4P5y95NexDVHJvqeUaoKjX64DeJXW+kzp9+8D8CiANyil3qS13k5GbMVyCuARAB/VWj8rf6mUehCOA/NflFJ/o7We2MgBbhfm9Z0AfhJOeHLuFu/dEdBaP6q1/gdbsWqtr8LxLAFHue4W7Og5dFO2JXDjOyreu+wWgi6FKB+237/V8CqLSqnDAH4TTnrAH9zkXrcN1rne/qz0+o5NGt6GwYuc3gS/AicF5Kta62sbMrANwm5ehwCgtf6u1vqy1no9Dda37fwBnmW0t/T6dZe5/PvSa3yjxrYJqGTfewOce/gSDVfAPKf3lv77bzdzkF7hRU611g/Zhmvp948DeAwOw/zARo9xy41XpdQdcCjnT2itn9jq8dwmcONfuem7dgh2+Rz+Qun17K3eqJQKA/jZSt+/TeAmi/8ajm74PIAmpdSblVK/r5R6u1LqyG0f4fqx5npTSv0agNcC+E07F2+HoWI5BfC20uuf3fRd2wu7fR16wU6cP8B9Ds+XXn9GKWXbIz9fev32po7KJzzsez9Zen3E5W9PAJgH8EAp93e3YdNsnS1NGyjlu/wlgBE44YRdj9I9/2rpv27CvKOw2+ZQKfVuAA0AmuHkdr4CjrL9iMt72+DkBYXgeNavAXAETn7o/7pNQ/aNm8jiy0uvzXCKX2Lib0Wl1J8AeOd2KzJww83Wm1LqAJycvC+UQrQ7Bl7k1Prcj8EpaHthswopNgIvpXXoBTtl/oCK5/DrcMLtrwfwvFLq23DSIU6X3v9JOPmw2woe973jpdcbCly11itKqUEAp+Cw0P9vI8e5lSjp11fDMc43nNTa6pzX9wO4F8ArtNYLWzyW24WPwCki+YbW+n9v9WA2ALttDt8NJ/GceATAr2mtUy7vbQPwn8T/iwD+K4D3rDMseLuwliwmSq8fhMN6vBvAEID7AXwKTr5aCk4BxnaH6z2WWJ7Pwymy2JSCgk2GFzmVeHvp9dObMqqNw0tpHXrBTpk/oII51FoXS5Xt7wfwPgCyOOs7AP5qmzrJXva95tLrzBp/5++jGzCubYESi/xFALUAfldrnd7oa2xZ2oBS6n44HsvHtNZPbdU4bidKVXf/AcBFOHlLOxq7cQ611h1a6xCcwp3Xw/GGn1VK3efy3oul91bDqXB+F5zN5QmlVOttHLZn3EIWw6XXCQCv01qf01rPaq0fhZO/VQDwO0qpPbdtwD5wi3t8F5xChLdthmLdbHiRU0Ip1QzgX2H7FvoYvFTWoRfspPkDKpvDUleTL8MxdN8BoBOOsfezcObyiVKl/rbBJux77IKyKxytUtrOX8IpxPsyHEdyw7ElzKug3F+A423teiil3gEnRHkBwKu11tNbPKR1YbfPodZ6EsDXlFJ9cO7xL+AweG7vvQ4nfPQJpdQknHYvH4QTytx2qEAWacw9YrMKWusflsJchwHcAeCHmz1eP7jZPZb6En4YTu/Xb2zREDcEXuQUwJsB1MMpHNl2hT5u2M3r0Ad23PwBt5zD/winzd1vaa0/JT72zRIj+xycdfz32Abwue+RWW1e4+9N1vt2LEqG6xfgzOlXALx5s6IfW8W8NsDpE3kHgEWlVJH/sBr++XTpdx/fojFuGJRSvw0nb+ccgJ8oVUDvdLwk5rDUIPsCgFOl3LpbgX0XX7Vpg1oHKpTFS6XXzBpfQ+O2bkMHt0Go4B5PwQlnvVXKbUl22Sbrcul3r71d414PKpRTFvp8ao2/b1vstnXoEzt2/oA155BFWTfk72qtfwhgGsAB5RxCsh3gZ9+jPr2hN3bJGD4Ep6BpwP77TkLpXv4awJvg5Jv/G631phWlb1XO6xKAz6zxt/vg5JJ8D86k7+hwtFLq9+Dk3T0H4DU7yWO+BV4ycwigq/RaSe7VvtLrtusk4UEWvwOnQfgNDFcpl4mtboY2fpTrQ4X3OIS1Zffn4IQ5/wZAFtvwHm+CNeVUKfWjcE7KeUFr/djtHNQGYlesQz/YJfMH3DiHrLC/oR1WSdeQlVze5HFVCj/73qNwDtP4aTjGncQr4bDpT2itlzZ8tLcJpRSyrwD4JTjM+lvd2thtJLbEeC2FIn/D7W9KqQ/AEYDP6x1ytOhaKDUh/iCAZ+A0YN7RqQISu2kOlVInAGRshq5U1PMhOAVM32duZGkjeV5rPW+9vwFOiAtwqmi3DTzK4jfhsAA/pZR6jdb6H8Xf3gcn/PX4dosgVHqPWuvnsLbsPgbHeH2P3mbHw3qVUwss9Nm27ZVeCutwHdj28wf4ktH/A8dJfo9S6knLgPsAHBvln7TWuU0ffAXwue/9LYCPAniTUuqTevWQgr0A/qj0nj/ZtEFvMkpOxlfh5Cl/BsDbN9twBba+28CuhVLqLXA20utwFug7lVL224a01g/d5qEFuBE/DecUkCfgtIaaglMl+yCcIoOrWA3ZAc4JMa9SSj0OJ8duHkAPgJ+BUzH6fTgnU20LeJVFrfVy6TPfgpN79jU4Z5O/HA5TkMLqZrot8BJZb17lFIA54eeNcNirz9+20XrHrl6HAFBKQ3lt6b8dpdcfU0o9VPr5mtb63dZndsr8Ad7n8MNw+r++GsBFpdQjABbgFPvcX/r5t27b6DcBWuusUuptcIzYx5RSX4KTDvGLcNpo/S2cwqZtA49y+qdwDNdrcI6Sfb+L7n1soyMGgfG6eThUeg0D+O013vM4dkDV6EsA34bDaPw4nNBcFM5pKS/ASc7/7xaL9+nS318OJ6euHk4e6DNwQief3cxcHx/wLIta6+8ppV4GJ4/rJ+A8k0k4z+lDWuvRTRqrX7wU1ptXOSV+Gc5Z89u90Ge3r0PAOSv+LdbverF60tQwbjxidKfMH+BxDrXWY6XuA78HJ2XnrXBqcSbgrNWPaq0v3sbxbwq01g8r57jUP4BzVPVeOEev/g6cZ7LdOg38CCqXU+reNjgtxNbCYxs0NgBAqFjcbs8sQIAAAQIECBAgQAB3bPnxsAECBAgQIECAAAECVIrAeA0QIECAAAECBAiwYxAYrwECBAgQIECAAAF2DALjNUCAAAECBAgQIMCOQWC8BggQIECAAAECBNgxCIzXAAECBAgQIECAADsGgfEaIECAAAECBAgQYMcgMF4DBAgQIECAAAEC7BgExmuAAAECBAgQIECAHYP/D12vLMpeHoPWAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 864x291.6 with 36 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "print(\"x_train : \", x_train.shape)\n",
- "print(\"y_train : \", y_train.shape)\n",
- "print(\"x_test : \", x_test.shape)\n",
- "print(\"y_test : \", y_test.shape)\n",
- "\n",
- "pwk.plot_images(x_train, y_train, range(12), columns=6, x_size=2, y_size=2, save_as='01-dataset-medium')\n",
- "pwk.plot_images(x_train, y_train, range(36), columns=12, x_size=1, y_size=1, save_as='02-dataset-small')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Create model\n",
- "We will now build a model and train it...\n",
- "\n",
- "Some models... "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:01.933060Z",
- "iopub.status.busy": "2021-03-01T17:49:01.932581Z",
- "iopub.status.idle": "2021-03-01T17:49:01.934235Z",
- "shell.execute_reply": "2021-03-01T17:49:01.934702Z"
- }
- },
- "outputs": [],
- "source": [
- "# A basic model\n",
- "#\n",
- "def get_model_v1(lx,ly,lz):\n",
- " \n",
- " model = keras.models.Sequential()\n",
- " \n",
- " model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Flatten()) \n",
- " model.add( keras.layers.Dense(1500, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Prepare callbacks \n",
- "We will add 2 callbacks : \n",
- "\n",
- "**TensorBoard** \n",
- "Training logs, which can be visualised using [Tensorboard tool](https://www.tensorflow.org/tensorboard). \n",
- "\n",
- "**Model backup** \n",
- " It is possible to save the model each xx epoch or at each improvement. \n",
- " The model can be saved completely or partially (weight). \n",
- " For full format, we can use HDF5 format."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:01.939567Z",
- "iopub.status.busy": "2021-03-01T17:49:01.939096Z",
- "iopub.status.idle": "2021-03-01T17:49:02.255158Z",
- "shell.execute_reply": "2021-03-01T17:49:02.255647Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "To run tensorboard :\n",
- "tensorboard --logdir /gpfsdswork/projects/rech/mlh/uja62cb/fidle/GTSRB/run/GTSRB3_done/logs\n"
- ]
- }
- ],
- "source": [
- "pwk.mkdir(run_dir + '/models')\n",
- "pwk.mkdir(run_dir + '/logs')\n",
- "\n",
- "# ---- Callback tensorboard\n",
- "log_dir = run_dir + \"/logs/tb_\" + pwk.tag_now()\n",
- "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
- "\n",
- "# ---- Callback ModelCheckpoint - Save best model\n",
- "save_dir = run_dir + \"/models/best-model.h5\"\n",
- "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)\n",
- "\n",
- "# ---- Callback ModelCheckpoint - Save model each epochs\n",
- "save_dir = run_dir + \"/models/model-{epoch:04d}.h5\"\n",
- "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0)\n",
- "\n",
- "path=os.path.abspath(f'{run_dir}/logs')\n",
- "print(f'To run tensorboard :\\ntensorboard --logdir {path}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Train the model\n",
- "**Get the shape of my data :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:02.259218Z",
- "iopub.status.busy": "2021-03-01T17:49:02.258741Z",
- "iopub.status.idle": "2021-03-01T17:49:02.260815Z",
- "shell.execute_reply": "2021-03-01T17:49:02.261299Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Images of the dataset have this folowing shape : (24, 24, 1)\n"
- ]
- }
- ],
- "source": [
- "(n,lx,ly,lz) = x_train.shape\n",
- "print(\"Images of the dataset have this folowing shape : \",(lx,ly,lz))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Get and compile a model, with the data shape :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:02.264586Z",
- "iopub.status.busy": "2021-03-01T17:49:02.264126Z",
- "iopub.status.idle": "2021-03-01T17:49:03.523200Z",
- "shell.execute_reply": "2021-03-01T17:49:03.523723Z"
- }
- },
- "outputs": [],
- "source": [
- "model = get_model_v1(lx,ly,lz)\n",
- "\n",
- "# model.summary()\n",
- "\n",
- "model.compile(optimizer='adam',\n",
- " loss='sparse_categorical_crossentropy',\n",
- " metrics=['accuracy'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Train it :** \n",
- "Note: The training curve is visible in real time with Tensorboard (see step 5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:03.529118Z",
- "iopub.status.busy": "2021-03-01T17:49:03.528620Z",
- "iopub.status.idle": "2021-03-01T17:49:21.885442Z",
- "shell.execute_reply": "2021-03-01T17:49:21.885977Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/5\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/613 [..............................] - ETA: 31:38 - loss: 3.7629 - accuracy: 0.0156"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/613 [..............................] - ETA: 53s - loss: 3.7349 - accuracy: 0.0321 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/613 [>.............................] - ETA: 6s - loss: 3.6315 - accuracy: 0.0393 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 39/613 [>.............................] - ETA: 4s - loss: 3.6103 - accuracy: 0.0443"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 57/613 [=>............................] - ETA: 3s - loss: 3.5882 - accuracy: 0.0496"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 75/613 [==>...........................] - ETA: 2s - loss: 3.5650 - accuracy: 0.0559"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 93/613 [===>..........................] - ETA: 2s - loss: 3.5334 - accuracy: 0.0652"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "112/613 [====>.........................] - ETA: 2s - loss: 3.4872 - accuracy: 0.0782"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "131/613 [=====>........................] - ETA: 1s - loss: 3.4313 - accuracy: 0.0933"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "150/613 [======>.......................] - ETA: 1s - loss: 3.3684 - accuracy: 0.1099"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "169/613 [=======>......................] - ETA: 1s - loss: 3.3013 - accuracy: 0.1269"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "188/613 [========>.....................] - ETA: 1s - loss: 3.2329 - accuracy: 0.1441"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "207/613 [=========>....................] - ETA: 1s - loss: 3.1644 - accuracy: 0.1611"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "226/613 [==========>...................] - ETA: 1s - loss: 3.0971 - accuracy: 0.1778"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "245/613 [==========>...................] - ETA: 1s - loss: 3.0318 - accuracy: 0.1940"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "264/613 [===========>..................] - ETA: 1s - loss: 2.9688 - accuracy: 0.2096"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "283/613 [============>.................] - ETA: 1s - loss: 2.9082 - accuracy: 0.2247"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "302/613 [=============>................] - ETA: 1s - loss: 2.8500 - accuracy: 0.2392"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "613/613 [==============================] - 6s 5ms/step - loss: 2.1656 - accuracy: 0.4151 - val_loss: 0.4663 - val_accuracy: 0.8804\n"
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- "Epoch 2/5\n",
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- "613/613 [==============================] - 2s 3ms/step - loss: 0.2527 - accuracy: 0.9257 - val_loss: 0.3143 - val_accuracy: 0.9187\n"
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- "Epoch 3/5\n",
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- "613/613 [==============================] - 2s 3ms/step - loss: 0.1282 - accuracy: 0.9638 - val_loss: 0.3121 - val_accuracy: 0.9260\n"
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- "Epoch 4/5\n",
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- "613/613 [==============================] - 2s 3ms/step - loss: 0.0864 - accuracy: 0.9741 - val_loss: 0.2334 - val_accuracy: 0.9437\n"
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- "Epoch 5/5\n",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "462/613 [=====================>........] - ETA: 0s - loss: 0.0636 - accuracy: 0.9812"
- ]
- },
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- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "480/613 [======================>.......] - ETA: 0s - loss: 0.0634 - accuracy: 0.9813"
- ]
- },
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- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "498/613 [=======================>......] - ETA: 0s - loss: 0.0634 - accuracy: 0.9813"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "516/613 [========================>.....] - ETA: 0s - loss: 0.0633 - accuracy: 0.9813"
- ]
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- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "534/613 [=========================>....] - ETA: 0s - loss: 0.0633 - accuracy: 0.9813"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "552/613 [==========================>...] - ETA: 0s - loss: 0.0632 - accuracy: 0.9813"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "570/613 [==========================>...] - ETA: 0s - loss: 0.0631 - accuracy: 0.9813"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "588/613 [===========================>..] - ETA: 0s - loss: 0.0631 - accuracy: 0.9814"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "606/613 [============================>.] - ETA: 0s - loss: 0.0630 - accuracy: 0.9814"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "613/613 [==============================] - 2s 3ms/step - loss: 0.0630 - accuracy: 0.9814 - val_loss: 0.2669 - val_accuracy: 0.9424\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Duration : 00:00:18 355ms\n"
- ]
- }
- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "# ---- Shuffle train data\n",
- "x_train,y_train=pwk.shuffle_np_dataset(x_train,y_train)\n",
- "\n",
- "# ---- Train\n",
- "# Note: To be faster in our example, we can take only 2000 values\n",
- "#\n",
- "history = model.fit( x_train, y_train,\n",
- " batch_size=batch_size,\n",
- " epochs=epochs,\n",
- " verbose=1,\n",
- " validation_data=(x_test, y_test),\n",
- " callbacks=[tensorboard_callback, bestmodel_callback, savemodel_callback] )\n",
- "\n",
- "model.save(f'{run_dir}/models/last-model.h5')\n",
- "\n",
- "pwk.chrono_show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Evaluate it :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:21.889952Z",
- "iopub.status.busy": "2021-03-01T17:49:21.889429Z",
- "iopub.status.idle": "2021-03-01T17:49:21.892339Z",
- "shell.execute_reply": "2021-03-01T17:49:21.891812Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Max validation accuracy is : 0.9437\n"
- ]
- }
- ],
- "source": [
- "max_val_accuracy = max(history.history[\"val_accuracy\"])\n",
- "print(\"Max validation accuracy is : {:.4f}\".format(max_val_accuracy))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:21.896661Z",
- "iopub.status.busy": "2021-03-01T17:49:21.895757Z",
- "iopub.status.idle": "2021-03-01T17:49:22.413060Z",
- "shell.execute_reply": "2021-03-01T17:49:22.412597Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Test loss : 0.2669\n",
- "Test accuracy : 0.9424\n"
- ]
- }
- ],
- "source": [
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('Test loss : {:5.4f}'.format(score[0]))\n",
- "print('Test accuracy : {:5.4f}'.format(score[1]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7 - History\n",
- "The return of model.fit() returns us the learning history"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:22.429378Z",
- "iopub.status.busy": "2021-03-01T17:49:22.428553Z",
- "iopub.status.idle": "2021-03-01T17:49:23.724291Z",
- "shell.execute_reply": "2021-03-01T17:49:23.724802Z"
- }
- },
- "outputs": [
- {
- "data": {
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- ],
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- "<IPython.core.display.HTML object>"
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J2w9UsfOQGhNFRMQfCgdxIj8rjfPmTWx//OQbJT5WIyIio5nCQRyJnFr4y+ZSGtSYKCIiPlA4iCOnTh9D8RivMbGusYXn3zroc0UiIjIaKRzEETOLut7Ck2pMFBERHygcxJlLT51CSpIBsLW0kt2Hj/tckYiIjDYKB3GmIDudc9WYKCIiPlI4iEPRjYn7aWhu9bEaEREZbRQO4tDiGUVMKswCoKahhTVqTBQRkWGkcBCHksxYubRj9ODJN9SYKCIiw8f3cGBmXzWz35jZLjNzZrZngK9zhZm9aGa1ZlYRfs2ZMS532KxYPIXkcGPimyXH2HOk2ueKRERktPA9HADfAS4GdgLHBvICZvYPwP8BmcCXgbuB5cALZjY5RnUOq8KcdM6ZM6H98VMb1JgoIiLDIx7CwcnOuSLn3KVAvy9HaGapwH1ACfAu51zQOXcXcBkwAbgjlsUOp8jGxGc37qepRY2JIiIy9HwPB865XYN8iQuAycDPnHM1Ea+7AVgFfDAcIEacpSeNZUJBJgA1Dc2s3XrI54pERGQ08D0cxMCy8P1LXTz3MpAHzBm+cmKnc2OiLuUsIiLDIRHCQVtPQWkXz7VtKx6mWmJuxeIpJJnXmLh5XwX7ymp6OUJERGRwEiEcZIXvG7t4rqHTPlHM7GYze21IqoqRotwMzp4zvv3xUzqtUUREhlgihIO68H16F89ldNoninPuAefcGUNSVQypMVFERIZTIoSDtjMcupo6aNvW1ZTDiHHaSeMYn+81Jh6vb+bFbYd9rkhERBJZIoSDdeH7c7p47mzgOLB9+MqJveQk4/IlHZdyfkJTCyIiMoRGVDgws0lmNs/MInsIngcOAjeZWU7EvouBC4HfOOeah7fS2FuxZArhBRPZuKec0vJafwsSEZGE5Xs4MLPrzezrZvZ1YByQ3/bYzK7vtPtdwFbgzLYN4S/+LwBTgTVmFjCzfwaeAY4C3xyWDzLExuVlcuasjsZEXW9BRESGiu/hAPg48O3wbTxQEPH44315Aefcb4Cr8M5Y+DfgVmANcJ5zbkT3G0RaGdGY+MzG/TS3hnysRkREElWK3wU45y7sx743Ajd289z/4V1fIWEtmzWOsXkZlB1voKquiZfePszyBZP8LktERBJMPIwcSB8lJyVFNyZqxUQRERkCCgcjzGVLphLuS+SN3WUcqFBjooiIxJbCwQgzPj+TZbPGtT/WpZxFRCTWFA5GoKjGxA37aVFjooiIxJDCwQh01uzxjMnxVos+VtvIy9u1YqKIiMSOwsEIdEJj4huaWhARkdhROBihLlva0Zi4fudRDlV2eW0pERGRflM4GKEmFmRx2sleY6IDntbogYiIxIjCwQh2xdKOqYWnNpTQGlJjooiIDJ7CwQh29pwJFGZ7jYkVNY28suOIzxWJiEgiUDgYwVKSk1ixZEr74ye1YqKIiMSAwsEIF3nWwrp3jnKkqt7HakREJBEoHIxwk8dks3TmWCDcmKgVE0VEZJAUDhLAFRErJqoxUUREBkvhIAGcM3cC+VlpAJQdb2DdO0d9rkhEREYyhYMEkJqcxIrFakwUEZHYUDhIECuXdkwtvPrOEY4eV2OiiIgMjMJBgiguymbxjCIAQs67WqOIiMhAKBwkkCuWdm5MdD5WIyIiI5XCQQI5d94E8jJTAThSVc/6XWpMFBGR/lM4SCBpKclcGtGY+IQaE0VEZAAUDhJMZGPiy9uPUF7d4GM1IiIyEikcJJipY3NYNG0MACHneGajGhNFRKR/FA4SUOSKiU++sY+QU2OiiIj0ncJBAjp//kRyMrzGxMOV9byxq8znikREZCRROEhAakwUEZHBUDhIUCuXdlzK+aXthzlW0+hjNSIiMpIoHCSo6eNyOWVqIQCtITUmiohI3ykcJLDI0xrVmCgiIn2lcJDAli+YRE5GCgAHj9WxcU+5zxWJiMhIoHCQwNJTk3n3IjUmiohI/ygcJLjIxsQXtx2islaNiSIi0jOFgwQ3c0Ie84sLAGgJOZ7dpMZEERHpmcLBKLAycsXE9SU4NSaKiEgPFA5GgQsWTCIr3WtMLK2oZdPeCp8rEhGReKZwMApkpKXw7kXF7Y/VmCgiIj1ROBglItc8eGHbIarqmnysRkRE4pnCwShx8sQ85kzOB6C5NcRf1JgoIiLdUDgYRSIv5fzE+n1qTBQRkS75Hg7MLMnMvmhm28yswcxKzOweM8vu4/FmZh82sxfNrMzMqs3sTTO73czyhrr+keTCUyaTmZYMQEl5LVtKjvlckYiIxCPfwwFwL/AD4C3gc8BvgM8D/2tmfanvTuARoB74F+DLwObwn58xMxuKokeizLQULlrY0Zj4pBoTRUSkC76GAzM7BS8Q/N459w/OuZ865/4R+EfgIuCaXo5PAW4B1gOXOufuc879xDl3DV5gOAtYPJSfYaSJnFpY/dZBjterMVFERKL5PXLwIcCAH3ba/lOgDriul+NTgUzgkHMu1Om5A+H72kHWmFBmT8pn1kRvtqW5NcRfN5f6XJGIiMQbv8PBMiAEvBq50TnXAGwIP98t51w9sBq43MxuNbNZZjbDzG4EAsDDzrkdQ1H4SKbGRBER6Ynf4WAyUOac6+pqQKXAWDNL6+U1rgX+BnwX2AHsBv4br5fhIz0daGY3m9lr/a56hLtw4WQyUr3GxL1Ha3hrvxoTRUSkg9/hIAvo7jKBDRH79KQR2AU8BHwYb6rid8DXgdt6OtA594Bz7ow+V5sgstNTuXDh5PbHT64v8bEaERGJN36HgzogvZvnMiL26ZKZZQEvAnnOuRucc79yzv3aOfd+4DHgW2Y2N6YVJ4joxsQD1DQ0+1iNiIjEE7/DwQG8qYOuAkIx3pRDT+30VwOz8U5/7Ow3eJ/v/EFXmYDmTMrnpAleY2JjixoTRUSkg9/hYF24hjMjN5pZBrAE6K0foO2k/eQunkvpdC8RzIwrTpva/liNiSIi0sbvcPAY4PDWKoj0Cbxeg0faNpjZJDObF55KaPNW+P6GLl67bdu62JSaeC5eWEx6ivdXYPeRat4+UOlvQSIiEhd8DQfOuc3A/cA/mNnvzewmM7sHb8XE54FHI3a/C9hK9CjD/+GdBnmFma02sy+Y2S1mthpYCfzGObd+WD7MCJSdkcoFp3Q0JupSziIiAv6PHIA3avAl4BS8oHANcB/wd10sbBTFOdcKXIIXHMYD38c7pbEQuBXv7AXpQWRj4qo3D1KrxkQRkVHP93DgnGt1zt3jnJvrnEt3zhU75/7ROVfTab8bnXPmnFvVaXu1c+4259y88PEZzrlFzrnvO+dahvXDjEDziguYMS4XgMbmVv665UAvR4iISKLzPRyIvzo3Jj6pxkQRkVFP4UC4eNEU0sKNiTsPH2fHwSqfKxIRET8pHAi5maksXzCp/bEaE0VERjeFAwGiGxP/tuUAdY1q1xARGa0UDgSABVMKmTY2B4CG5lZWvanGRBGR0UrhQACvMXFlxOjBk5paEBEZtRQOpN0li4pJTfb+Smw/WKXGRBGRUUrhQNrlZaXxrvkT2x8/+YZGD0RERiOFA4kS1Zi4+QD1TWpMFBEZbRQOJMrCaWOYUpQNQF1TC8+rMVFEZNRROJAoZsbKpRGNiW+U+FiNiIj4QeFATnDp4intjYnbSivZeei4zxWJiMhwUjiQE+RnpXHePDUmioiMVgoH0qWVERdj+svmUhqaW32sRkREhpPCgXRp8fQiJo/JAqCusYXVb6kxUURktFA4kC6d0Ji4Xo2JIiKjhcKBdGvF4imkJBkAb+0/xp4j1T5XJCIiw0HhQLpVkJ3OOXPVmCgiMtqk9PeAQCBQCEwCdgaDwcaI7R8F/h9QC/wwGAy+GqsixT9XnDaNNVsPAvDcpv187OJ5pKcm+1yViIgMpYGMHHwHeCXy2EAg8DngZ8CVwDXAqkAgsCAmFYqvlswsYmJBJgA1DS3tQUFERBLXQMLBecBfgsFgfcS2LwGlwHLgA+Ft/zjI2iQOJGnFRBGRUWcg4aAY2N32IDxCMBW4LxgMrg0Gg78F/hcvKEgCWLFkCsnhxsQt+yrYd1SNiSIiiWwg4SATaIh4fB7ggOcitu3ECxGSAMbkZHD2nAntjzV6ICKS2AYSDkqBeRGPLwOOAxsjthUCkdMOMsJFXsr52U37aWrRiokiIomq32crAH8DbggEAp/FG0G4CvhdMBgMRewzC9CvlwnktJPGMiE/k8NV9VTXN7N26yEuXqTBIRGRRDSQkYO7gBrg34EH8ALCHW1PBgKB8cAFwIsxqE/iRJIZly/tuN6C1jwQEUlc/Q4HwWBwN3AK8AXg88DCYDD4dsQu04H7gQdjUaDEjxWLp5JkXmPipr0V7C+v8bkiEREZCuac87sG3wUCAQcQDAb9LiXu3fHYa7y0/TAAV59zEp+4ZL7PFYmIyABZd08MpOegS4FAYCzwLqAOeC4YDKpjLQFdcdq09nDw7Mb93HDhHNJStGKiiEgi6fe0QiAQ+HQgEHglEAiMidh2OrAV+C3wBPBiIBDIjl2ZEi9OP3kc4/IyAKiqa+LFtw/7XJGIiMTaQBoSPwi4YDBYEbHtbrzTF3+OFw6WAZ8afHkSb5KTjMuXqDFRRCSRDSQczAY2tT0ITydcAPxXMBi8KRgMXgmsAz4cmxIl3qxYMpXwgols2F1OaUWtvwWJiEhMDSQcFAFHIh6fF77/Q8S2NXhnLUgCGp+fybJZ49sfP6UVE0VEEspAwkEFMDbi8QVAiOh1DRyQMYi6JM5Frpj4zMYSmltDPewtIiIjyUDOVtgKXBkIBL4GtOL1IKwLBoPHI/aZARwafHkSr5bNGkdRbjrl1Y1U1jbx8tuHedeCSX6XJSIiMTCQkYN/ByYB+/GWSJ4ItC8QEAgEkoHzib7WgiSY5KQkLlNjoohIQhrICol/wjsT4U3gbeBLwWDw4YhdLsGbUng6JhVK3Lp8ydT2FTRe31XGoWN1vtYjIiKxoRUS0QqJg/H1X73KuneOAnDNeSfz0Yvn9XKEiIjEiW5XSBzItEJMmVmSmX3RzLaZWYOZlZjZPWbW50WUzCzFzD5vZuvNrNbMqsJ//uRQ1i5wxdLIxsT9tKgxUURkxBvw8smBQOBs4CZgKVAAVAGvAz8PBoP9uSLjvXgXcPoDcA8wP/x4qZld4pzr8dvGzNKAPwEXAY8AP8H7XLPR6ZRD7szZ4xmTk05FTSMVNY28suMI582b6HdZIiIyCAMKB4FA4E7gq5w4JLEE+FggEPheMBi8rbfXMbNTgM8Bv3fOvS9i+27gR8A1wKO9vMw38PocLnXO/a3PH0JiIiU5iRWLp/DrF3YCXmOiwoGIyMg2kGsrvB+4DdiHN3JwEpAZvr8pvP3WQCDwgT683IfwAsYPO23/Kd4FnK7r6eDw1MMXgP9xzv3NPLl9/zQSCysjphZee+cohyvVmCgiMpINZOTgc8BhYFkwGCyL2L4H+O9AIPAnYAvwGeDxXl5rGd4CSq9GbnTONZjZhvDzPXkXkAu8bmb/DnwMyDGzMryAcbtzrqUvH0oGbmJhFqefNJbXd5XhgKc2lHDDhXP9LktERAZoIA2Ji4HfdgoG7cLbf4M3xdCbyUCZc66xi+dKgbHhnoLutH0D3QK8D/gK3qJML+JNe/xXT29uZjeb2Wt9qFN6sTJixcSnN5TQGlJjoojISDWQcJCCN+Tfkzr6NiqRBXQVDAAaIvbpTtsUwhjgEufcj51zjzvn3gusAj5iZgu6O9g594Bz7ow+1Cm9OGfOBAqz0wEor27k1R1Hfa5IREQGaiDh4B3g7wKBQJfHhrdfAezsw2vVAendPJcRsU936sP3LzvntnV67qHw/QV9qEMGKSU5iUsXT2l/rBUTRURGroGEg1/hnW74P4FAYHbkE4FA4GTgt8ACej/LAOAA3tRBVwGhGG/KoamH4/eH77u6jsPB8H1hH+qQGLh8acdyyuveOcKRqvoe9hYRkXg1kIbEHwCXA+8BVgYCgQN4X8QT8b7Qk4C14f16sw5YAZyJd5lnAMwsA69nYXUvx7c1Mk7p4rm2bUe6eE6GQPGYbJbMLGLD7nJCDp7ZUMJ1F8zxuywREemngVxboQm4FPgasBvvS3gZMDX8+GvAu8P79eYxvMs739Jp+yfweg0eadtgZpPMbJ6ZtfcgOOd2Ay8AZ5rZaRH7JodfowV4pp8fUQYhcsXEJzeU0BrS8twiIiPNoK+tEAgEcoB8oCoYDNaEt2UAaZ0u49x1AWb3AZ/FWyHxCTpWSHwBuLhthUQzexC4AbjIObcq4vileKMOTXgLJ5XjnbFwHvAt59w3+/AZdG2FGGluDXHtD/9CVZ2XDb91zRmcNXuCz1WJiEgXur22woCXT24TDgQ1nTb/GLi+j69/C94aCTfjTVWUAffhrVHQ6/lwzrk3zOxc4M7wa2UAW4GPOuce7MtnkNhJDTcm/valXQA8ub5E4UBEZIQZdDjoQbeJJJJzrhXvmgr39LLfjcCN3Ty3Cbiqf+XJUFm5dGp7OHhlxxHKjjcwNi+jl6NERCRe+H5VRkk8U4pyOHX6GABCzvHMxhKfKxIRkf5QOJAhcUXEiolPvaHGRBGRkUThQIbEefMmkpeZCsDhqnrW79KKiSIiI4XCgQyJtJRkLjk1csVETS2IiIwUCgcyZFZGrJj48vbDlFc39LC3iIjEiz6drRAIBFqHuhBJPNPG5bJw2hi27KugNeR4duN+rjl/lt9liYhIL/o6cmADuIlwRcTowZNv7CM0yEW3RERk6PVp5CAYDGr6QQbk/PmTCD79FjUNzRyqrOeN3WWcftI4v8sSEZEe6EtfhlR6ajKXnFrc/vjJ9WpMFBGJdwoHMuRWRlyM6cW3D3GsptHHakREpDcKBzLkZozPZcGUQgCvMXHTfp8rEhGRnigcyLCIXDFRjYkiIvFN4UCGxbsWTCI73et/PVBRx6Y95T5XJCIi3VE4kGGRkZrMxYsiGhO1YqKISNxSOJBhE9mY+MK2Q1TWqjFRRCQeKRzIsDl5Yh7zigsAaG4N8dymUn8LEhGRLikcyLCKakxcvw+nxkQRkbijcCDD6oIFk8hK8xoT91fUsnlfhc8ViYhIZwoHMqwy0lK4aNHk9sdPrN/nYzUiItIVhQMZdldENCau3XqI43VNPlYjIiKdKRzIsJs1KZ85k/KBcGPiZjUmiojEE4UD8cVKNSaKiMQthQPxxYWnTCYzLRmAfWU1vFlyzOeKRESkjcKB+CIrPYULT1FjoohIPFI4GAr/80t4fS20NPtdSVyLXPNgzdaDVNfrv5eISDxI8buAhFN+BP7vUXAOcvPh3Evg/Mth0lS/K4s7syflM2tiHu8cOk5TS4i/bt7Pe8+c6XdZIiKjnkYOYu2FZ7xgAFBdBU//Dr7xCfjeP8ELz0Jjg7/1xREzi2pMfGJ9iRoTRUTigMJBrJ13KVx5LRSOjd6+4034+T3wpQ/DL++DPTs6QsQodtHCyaSneo2Je45Ws7W00t+CRERE4SDmiibAe6+H7/0CvvBtOO08SE7ueL6+Dp7/M9z5OfjWZ+Gvf4K6Gv/q9Vl2eioXnjKp/bEaE0VE/KdwMFSSkmHRMgh8A77/S3jfx2FCcfQ+JTvh0SD804fhv+6G7ZtH5WhCZGPi6jcPUNugxkQRET+pIXE45I+Ble+Hy6+GHVtgzVPw2hpoDi8b3NwEL/3Fu00o9hoYz70E8gv9rXuYzJ1cwMzxuew+Uk1jS4i/binlyjNm+F2WiMiopZGD4WQGcxbBx78M9zwK134Gpp4cvc/hUvjdf8FXroP7vwWbXoVQqz/1DhMzixo9+PPrWjFRRMRPGjnwS1YOXHSld9u7A9Y8Da/81etJAGhthTde9G6FY+G8FXD+Chg70d+6h8jFi4r52XNbaWwJsftINW8fqGJecYHfZYmIjEoaOYgH02fDdZ+Ff3sUPvYlmL0w+vljZd7aCV/9KPzgNli3umNKIkHkZKSyfEHHiolPqjFRRMQ3GjmIJ+kZXq/BuZfAwRJY+zS8+Ky3XgJ4zYpvrfduOXlwzrvhXZfD5On+1h0jK0+byrOb9gOw6s0D3LxiPtnpqT5XJSIy+mjkIF5Nmgrvvwnufhg+/XVYuMzrWWhTcxye/QPc/km464uw9hloqPev3hhYMKWQ6eNyAGhobmXVlgM+VyQiMjopHMS7lFQ4/Xy45dvw3V/AVdfBmPHR++zcCg/+AL50LTz077D77RF5SmTnxkSteSAi4g+Fg5GkaLwXDr77c7jlTi80JEfMDDXUweon4V+/AP8SgL/8D9RU+1fvAFy8qJi0FO+v5TuHjrPjYJXPFYmIjD6+hwMzSzKzL5rZNjNrMLMSM7vHzLIH+HqPm5kzsy2xrjVuJCXDwjO86Ya7H4b3fwImdrqw0/7d8Ksfe8s1//R7sG0jhEL+1NsPeZlpvGu+VkwUEfGT7+EAuBf4AfAW8DngN8Dngf81s37VZ2Z/B7wPGNmT7/2RVwCXvQ++/QDceg+ceymkpXc839IMr/wN/u1W+NrH4YlfQ2W5b+X2ReTFmP62pZT6phYfqxERGX18PVvBzE7BCwS/d869L2L7buBHwDXAo318rRwgCNwPXBX7auOcGcw+xbtd8ylYtwpWP+WtodDm6EH4/YPwx4fg1LO8Mx0WnhF97Yc4sHBqIVOLsikpr6W+qZVVbx5g5dJpvR8oIiIx4ffIwYcAA37YaftPgTrgun681r/ihZ2vx6SykSwrGy54D3zjPrj9P7yFljIjZmlCIdjwEtz3Tbj1I/CHB73gECfUmCgi4i+/w8EyIAS8GrnROdcAbAg/3yszOxP4LHCLc+54jGsc2abN8pZpvudRb9nmOYuin68shz//2ltg6Z5/hldXxcUCS5ecOoXUZO+v5/YDVbyjxkQRkWHjdziYDJQ55xq7eK4UGGtmaT29gJml4I00POOce7w/b25mN5vZa/05ZsRKS/cWTfrK3XDnz+DyD0Bepws7bd0AD3zXOyXy1z+B0j1+VApAXlYa583rWCr6yTc0eiAiMlz8DgdZQFfBAKAhYp+efBmYDXymv2/unHvAOXdGf48b8SZOgas/5l1K+jO3w6lnQmTvZ201PPdH+Oan4Du3eKdHNtQNe5mRUwt/3XKABjUmiogMC7+XT64DxnfzXEbEPl0ys1nA7cCdzrldMa4t8aWkwNJzvVvFUW+p5rVPQ9nhjn12bfNuj/0nLLvAa2I8aV70ao1D5NTpYygek01pRS11jS08/9ZBLlsytfcDRURkUPweOTiAN3WQ3sVzxXhTDj1NgN8DVAB/MLNZbTe80JMWfjyph+OlzZhx8Hcfhu/8HP7xO14QSIm4rkFjgxcc7vqiN6Lw7B+8JZyHkJmx8rSOMKCLMYmIDA+/w8G6cA1nRm40swxgCdBbP8B0vL6FN4EdEbdivKmGHXj9CNJXSUmw4DT45Fe9BZY++EmY3Ok0wgN7vZGEL10LD9wFW98YsgWWLj11CilJ3ijF1tJKdh1Wv6mIyFDze1rhMeA24BZgTcT2T+D1GjzStiE8ApAP7HPOtU01fAko6OJ1g3g9C/8IxM85eiNNbj5c+vdwyf+DXVthzVPw6vPQFG4TaWn2Hr/6PIydCOdfBuddCoVjY1ZCQXY6586byOq3vB/jk2/s4zOXL+zlKBERGQxzPl+gx8zuwzsN8Q/AE8B8vBUSXwAuds6Fwvs9CNwAXOScW9XLa+4BapxzffoWCQQCDiAYDA7oM4wq9bWwbrUXFHa/feLzlgSLlnm9CYuWeX0Ng/TG7jL++eFXAMjJSOGRWy4hIzW+Fm4SERmBum0e83vkALxRgz3AzcB7gDLgPuD2tmAgcSQzG5av9G4lu7w+hJf+AnU13vMuBJte8W75hd5yzudfDhMmD/gtF88oYlJhFgeP1VHT0MKatw5y6eIpMfpAIiLSme8jB/FAIweD1NwE61+ANU/Dtg1d7zP3VG804fTzIbXHpSu69NgLO/nvv24D4JSphfzgxnMHUbCIiBDnIwcy0qWmwVkXebcjB2DtM/DCM1BV0bHP25u826NBOPtiLyhMPanPb7Fi8RR+septWkOON0uOsedINTPG5w7BhxEREb/PVpBEM34y/MON3gJLn/0mLD4reoGluhr465/gXwJw5+fh+Se8PoZeFOakc86cCe2PtWKiiMjQ0ciBDI3kZFhyjnc7VgYvPuf1J0Re4GnPdu8WucDSyfO7XWDpitOmsXbbIQCe21TKx989j7QUNSaKiMSawoEMvcKx8J5rYOUHvKmFNU95PQotzd7zTY3eNMQLz8CkafCuy7zrQOQWRL3M0pPGMqEgk8OV9dQ0NLN26yEuXlQ8/J9HRCTBaVpBhk9SEsxfAjf/M/zbI3DNp6B4RvQ+B/fB4z+FL10HP/lXePP19gWWksxYuVSXchYRGWoaORB/5OR5iyu9+72wezusfQpeWQWN9d7zrS3w2hrvVjQ+vMDSClYsnsJDq7YTco7N+yrYV1bDtLE5fn4SEZGEo1MZ0amMcaOhHl4LL7C0c+uJz1sSLDydx5Jm84tjBbRaMv9w9kw+eemC4a9VRGTk06mMMgJkZHojBOdfBqV7vHUTXnrOu4Q0eAssbV7HB1nHiqRMns2Zz9p1NTRdNFeNiSIiMaSRAzRyENeam+CNl7zRhK1vdLlLZfEcCi6/Ck47H9IzutxHRERO0O3IgcIBCgcjxtFD8MLT3iJLleUnPp+ZDWdf5J0SOW3W8NcnIjKyKBz0ROFghGltpWrdS7z56KOcVbebZLr4OzxtlhcSzroIsrKHv0YRkfinngNJIMnJ5J99Pk/vzeBHW3dxac02rm7ZTn5NWcc++96BR/4DfvNTOONdXlCYdUq3CyyJiEgHhQMZsVYuncrL2w/zeP7pPJ15No++ZwIpLzwNr6/1ehXAW2Dpxee828QpMPVkLyu3LemclASYFxq6vCX1c/+I49r3p4vXMkiyGL93p/07b2u/D+/fZQ1d7B+5X7c1h/enl/fs6r0zMgd0MS4RGToKBzJiLZs1jrF5GZQdb6CqvpkX3XiW3/QV+NCn4ZW/eU2MJbs6Dji037tJfLEkmDQVZsyGGXNg+iyYcpKaS0V8pHAgI1ZyUhKXL5nKw6t3AN6KicsXTILsXLj4KrjoSti7wwsJr6yChjp/C5auuRAc2OvdXnzO25aUBJOne4Fh+hyYPhumztQIg8gwUTiQEe2yJVN5dM0OQg7e2F3GgYpaJo8JNyCaeb+JzpgDH7gZtm30VmB0rusbrn2pZlwIXNt9T/uH73vaP3K/LvfvdFzUfm23Tvv3tdaheO9e9+/iOAjX22n/UAjq6zr2aRMKwf7d3m3tM9625GRvue220YXpc2DKDEhJHfxfJBGJonAgI9r4/EzOmDWeV3ccAeCpN0r42LvnnbhjeoZ3+WiJPw31ULIT9r4TvlLnDji8Pxw+IrS2wr6d3q1NSqoXEKaHA8OMOd6IQ4r+aRMZDP0fJCPeyqVT28PBMxv385EL55CSrGuKjRgZmTB7oXdrU1/rhYC9O7ywsHcHHC498diWZu/5PTs6tqWkwrSTvamI6bO9qYlJ07yRBxHpE4UDGfHOmj2eMTnpVNQ0cqy2kZe3H+b8+ZP8LksGIzMb5p7q3drU1XijC5GB4ejBE49taYZd27xbm7R070yVGeGwMH22d/ZKkgKDSFcUDmTEa2tMfHTtOwA88UaJwkEiysrxLvk9f0nHtppq2BcRFvZsh/IjJx7b1Ag73/JubdIzvMWyZkSMMIwv7jj9VGQUUziQhHDZ0qn8au07OGD9zqMcOlbHxMIsv8uSoZaTCwtO825tqquiRxf2bIdjZSce29gAO7Z4tzYZWeHehXBgmD4Hxk/S4lky6igcSEKYWJDF6SeP47WdR3HAUxtKuPGiuX6XJX7IzYeFZ3i3NlXHvKDQFhb27ICqihOPbaiDtzd5tzZZOR1nR8wI34+doMAgCU3hQBLGyqVTeW3nUQCe3lDC9RfMJllDxAKQXwinnund2lSWR48u7NkB1ZUnHltXA1s3eLc22bkRazCEz5IYM06BQRKGwoEkjLPnTKAwO51jtY1U1DTyyo4jnDt3ot9lSbwqKIIlRbDkbO+xc970Q2RY2LsDao6feGxtNby53ru1yc0Pjy5E9DAUFCkwyIikcCAJIyU5iRVLpvDYC9558E+u36dwIH1n5v32P2YcLD3X2+YcVBwJny4ZERjqak48vroKtqzzbm3yC08MDPljhufzyMjnnPd3rboSjld59xOneIuBDTGFA0koK5dOaw8H6945ypGqesbnZ/pclYxYZlA0wbudfr63zTkoO9QRGNp6Geq7WJ676hhsesW7tSkc2xEU2u5zC4bl40gcaGr0guTxY9Ff+scrvfvIbdVV0NoSffyV1yociPTXpMIsTjtpLOt3lXmNiW+U8JEL5/hdliQSMxg3ybstW+5tC4W8NRciRxf2vuMt193ZsTLvtuGljm1jxkeHhemzISdveD6PDE6o1TulNvILvv2LvqrTtqrBX+PleOXgju8jhQNJOCuXTmP9Lu/Utac3lnDt8llqTJShlZQEE4q921kXedtCIW9Vx7bRhT3bvVUfmxpPPL7iiHdb/0LHtrETTwwMWTnD83lGM+e8UBf5pd75Cz4yANQcP3Gp71jKyPL6WfIKvBGmqScN3XtFUDiQhHPO3AnkZ6VRVddE2fEG1r1zlLPnTPC7LBltksKXop40Fc55t7ct1AoHS6LPkijZBc1NJx5fdsi7vbamY9v4yRFrMMz2zpTIzB6ezzOStTSf+KXe03B+Vz+PWElOCX/R53tf9m1f+rkFkNd5W763uqcPFA4k4aQmJ7Fi8RR+89IuwGtMVDiQuJAUvrJk8Qw471JvW2urd7nqtoWb9mz3rkbZ0nzi8UcOeLdXn+/YNnFKxHUk5njXlchI8D6bUAjqaiO+1CtP/C0/8jf9rhpIYyk7N+JLPb/TF35Bx+O8Ai/MjYAzWBQOJCGtXDqtPRy8+s4Rjh6vZ1xegv+DKSNTcrI3VDz1JDj/Mm9bS7MXGNqbHt/xAkPn5jSAQ/u92yt/8x6beaMVkWswTD3JWy46njU2dGrGq+x+OL+mygtVQyUtvevf6CN/q28LAjn5CXkV0MT7RCJAcVE2S2YUsWFPOSEHT2/Yz3XLZ/tdlkjfpKR6132YNguWr/S2NTdB6Z7owFC62/stOpJzcGCfd3vpOW+bJcHkaV5QiAwMqWlD9xlaW735+M6/xbd/4R+LftzYMHS1WFJ4GD+/05d7wYnb8grjP0gNA4UDSVgrl05jw55ywFsx8UPnzyI5Kf6H80S6lJrmfanPmAO8x9vW1OiNKESeJXFgH7jOgSHkBYvSPfDCM9625GSYPCO66bF4RveBwTnvdM0T5ukro7/k2377rx3iRr3MrO6H7jt/6Wfn6oJa/aRwIAnr3HkTyMtM5Xh9M0eq6lm/6yjLZo33uyyR2ElLh5Pmebc2jQ1ek2PkGgwHS078om5thZKd3m3NU9625BSYMtMbVQi1RjTthX/L76oPIlZSUk9s0utpOH8oRz1E4UASV1pKMpcunsLvXt4NwBPr9ykcSOJLz4BZC7xbm4Z67zTKyMBwaP+Jx7a2dDwfCzl53QzdF5z4pZ+ZNSIa9UYLhQNJaCuXTmsPBy9vP0J5dQNFuZpPlFEmIxPmLPRubepqYd87EVer3OGdCdGT9Izo3967+o2+7c85+d7UhYxICgeS0KaOzeHU6WPYtLeCkHM8vaGED79LjYkiZGXDvMXerU1ttRcYSvdGB4G2AKBGvVFD4UAS3sql09i0twKAP766h9aQY8mMIuYWF5CWot9sRNpl58L8pd5NRjXfw4GZJQFfAD4JzACOAo8Dtzvnans5thD4CF7r7nxgLLAPeB74tnOuZOgql5Hi/PkTuf+pVGoamqmqa+Lh1Tt4ePUO0lOSOGXaGJbMKGLxjLHMnpSnZZZFRIiDcADcC3we+ANwD96X/OeBpWZ2iXOdz8mJclb4mL8A/wGUAQvxgsYHzOxc59xbQ1m8xL+0lGRuvGgOwafeJBTRsN3YEmL9rrLwdRjeJis9hVOnjWFxOCzMnJBLkhqkRGQU8jUcmNkpwOeA3zvn3hexfTfwI+Aa4NEeXmIbMNc5t7PT6/4ZeBb4FnB1rOuWkefKM2Zw/rxJbNxbzsY95WzYU8aBiuiro9U1tvDyjiO8vOMIAHmZqe1BYcmMIqYUZWMKCyIyCpgbykUqentzszuBrwHLnXNrIrZnAOXA8865Kwb42uXAUefcvN72DQQCDiAYDA7krWSEOlJV3x4UNuwup6y65xXainLTWTJjLItnFLFkRhETCrKGqVIRkSHR7W87fk8rLANCwKuRG51zDWa2Ifx8v5lZPpALbBlsgZK4xudncuniKVy6eArOOQ5U1HlBYY83ulBVF31ltvLqRv6yuZS/bC4FYFJhVntQWDyjiDE56uQWkcTgdziYDJQ557q4wDmlwLlmluac6+/1M78OpAK/GGyBMjqYGcVF2RQXZfOe06cTco69R6rbg8KmveXUNkZf9ObgsToOHqvjqTe8vtdpY3Paw8KpM4rIy9QKbiIyMvk9rbATSHXOTeviuYeA64FC51xlP17zaryzHZ4BVroePqCZ3Qzc/OlPf/p00LSCdK815Nh5qIoNe8rZsKecLfsqaGzu/qpwBpw8MS8cFsaycNoYstL9zuIiIlG6nVbwOxxsBsY75yZ08dzjwPuB9L6OHJjZFXhnPWwC3u2cO96X49RzIP3V3Bri7dLK8MhCGVv3V9Lc2v2JNUlmzC3OZ0m4uXH+lELSU7XGgoj4Km57Dg4AC8wsvYuphWK8KYe+BoPLgd8DbwIr+hoMRAYiNTmJhdPGsHDaGK5bPpuG5lbeKjnGxj1lbNxTztsHqghFBO+Qc2zdX8nW/ZX8au07pCYnMX9KgRcWZhYxd3IBKclaY0FE4oPf4WAdsAI4E+h8tsISYHVfXsTMLsMbMdgGXOKcOxbzSkV6kJGazGknjeW0k8YCUNvYzJZ9Fd7Iwu5ydh6OzqrNrSE27a1g094KHnreO35heEGmJTPHctKEPF1eWkR843c4eAy4DbiFiHAAfALIAh5p22Bmk4B8YJ9zri5i+wrgj8B2vKmEiiGvWqQX2empnDV7AmfN9mbMquqa2NS2xsLuMkrKoxf/bGhu5bWdR3lt51EAcjJSOHV6UfvqjdPH5WiNBREZNr72HACY2X3AZ/F+83+CjhUSXwAublsh0cweBG4ALnLOrQpvOwMvVBjwz3grJEZxzj3cWw3qOZDhVl7d0LHGwp5yDlfW97h/QXYai6d7owqLZxQxuTBLYUFEBituew7AGzXYA9yMd42EMuA+vGsr9LR0MnhLJbedXH5vN/v0Gg5EhltRbgYXLyrm4kXFABw6VsfGvd6owoY95VTURLfgVNY28fxbB3n+rYMAjMvL6FiQaWYR4/Iyh/0ziEji8n3kIB5o5EDiiXOOkvJaNoZXbty4t5zq+uYejykekx21IFNBdvowVSsiI1hcjxyISAQzY9rYHKaNzeHKM2YQco7dh493rLGwt4K6pugFmUoraimtqOWJ9fsAmDk+N3xdiCJOnV5ETkaqHx9FREYohQOROJdkxskT8zl5Yj7vO/skWkMhdhysYsNuLyy8WVJBU0v0DNzuI9XsPlLNH1/dQ5LBrIn54SmIsSycWkhGmv7XF5HuaVoBTSvIyNbU0sq20spwWChjW2klraHu/79OSTLmFnessTCvuIC0FC3IJDIKxecKifFC4UASSX1TC2+WHGPDbm9BpncOVdFDViAtJYkFUwvbV2+cMzmf5CQtyCQyCqjnQGS0yExL4YyTx3HGyeMAqGloZvPeCjaEV2/cfaQ6av+mlpA36rC7HICstBQWTg8vyDSjiJkT8kjSaZMio4rCgUiCy8lI5Zy5EzhnrrcgU2VtY3iNBW9RptKK6AWZ6ppaeHXHEV7dcQSA3MzU8BoL3oJMU4uytcaCSIJTOBAZZQqy07nglMlccMpkAI5U1bMxHBQ27Cnj6PGGqP2r65tZu+0Qa7cdAmBMTnr7Ms+LZxQxsSBr2D+DiAwt9RygngORNs45DhyriwoLlbU9X/tsQkFmeArCCwtFuRk97i8icUM9ByLSOzOjeEw2xWOyueK0aTjn2Hu0xluQaU85m/aWU9MQvcbC4cp6nt6wn6c37AdgalF2+6jC4ulF5GWl+fFRRGQQFA5EpFtmxozxucwYn8t7z5xJa8ix6/Dx9mWet+yroKG5NeqYkvJaSspr+d/X9gJw8oQ8Fs/0mhsXThtDdroWZBKJd5pWQNMKIgPV3Bpi+4HK9gbHt0qO0dza/SVRksyYMzm8INOMsSyYWkhGqtZYEPGJ1jnoicKBSGw0Nreydf+x8FLPZbxdWkWoh39jUpOTOHliHmNy0inITqcgK42C7DTy2/+cTkF2GrmZaSQn6QwJkRhTz4GIDL301GSWzBzLkpljgbnUNbawZV/HGgs7Dx0nMio0t4bYVlrZ6+sakJeVRn44PLSFhoKsdPKz06KCREF2OtnpKTrdUmQQFA5EZMhkpadw5uzxnDl7PADH65rYtLdjjYV9ZTV9eh0HVNU1UVXXxL6y3vdPSbJwaOgIDJHBIr8tTIS36VoTItH0f4SIDJu8rDTOnz+J8+dPAqCipoGSslqq6pqorG1sv6+sbaIyYltvl6zurCXkKK9upLy6sU/7p6cmdz8SkRU9zZGfnUZqspaXlsSmcCAivhmTk8GYnN7XRWhpDYWDQxOVdY1URYaH2ohgEd5W39Ta62tGamxu5XBlPYcr6/u0f05GCvlZncLDCX9Wv4SMXAoHIhL3UpKTKMrN6PMCSw3NrVS1j0R4gaIyMkSE/1xZ10RVbVOPZ1h0paahhZqGlhOWnu5KkkFuZkSvRNaJ0xyRoxbql5B4oHAgIgknIzWZjIIsJvRhaWfnHHVNLR3hodOURnuQqG0K9z009niVy85CrqNfYu/R3nssUpKsPTC0TWdE90+kdYxaZKfrVFAZEgoHIjKqmRnZ6alkp6dSPCa71/1DzlFd3xwVGDr6JE7cVtPQ/36JsuoGyqobet8ZLwhFB4aOIJHf6SyOvCz1S0jfKByIiPRDkhn54dMqp4/rff/m1hDHO/dLdA4WEWGi84qTvWlobuVQZT2H+tEv0d54md0pRERMeeRmppKbmUpaikYmRiOFAxGRIZQ6wH6JyMDQ+SyOqhj0S+zvQ78EQFpKEjkZqR23zFRy0lO8+4jtuRmpZLf9OfxcZlqy+idGKIUDEZE40u9+icaWLs/iOKEBs66R43VN/eqXAGhqCVFR00hFTd9OC42UZEZORjhIpKeeECjagkR2OGzkRmzPzkjVWR4+UjgQERmhzIzs8BdpcVHv/RKtIUd1fdMJZ2x0br6sqm2iuqGZmoZmWvubJiKEnON4fTPH+7lORZus9JSIIJHSMTqR2TFSkdseJrzn2wKIpkMGR+FARGSUSG4/EyK9T/0Szjkam1u9oFDfTE1ji3ffEH2rrm+mtqG5PVDUNrRQ3dBMYz/7Jzqra2yhrrGFI1V966eI1NV0SJdBoosRDU2HKByIiEg3zIyMtBQy0lIYl5fZ7+ObWlqpbWg5IUy0BYrIIOH9ubk9iNQ2tgyq9sFMhyQnWXuIyIkanUiNGp2IHNVoCyDZ6YkxHaJwICIiQyItJZm0nGQKc9L7fWxryOuniAoV9R2jE5HbuhrRGMx0SGvIta9NMRBZ6SndTneccMuMbOhMiZvpEIUDERGJO8lJ1n46ZX8552hobm2f7qhpiAwVPUyNNHpho7Glf2eAdNY2HXJ4ANMh6SlJ7SMQuV0EivlTClg2a/yg6usLhQMREUkoZkZmWgqZaSmQP/DpkOrI0YkupkbaRjLapk6qG5qpG+R0SGNLiMYeLhr2ntOnKRyIiIgMt8FOh7SNQLSPVHQzNXJCE2d9MyHX83RITnr/R1IGQuFAREQkRpKTjLzMNPIy0/p9rHOO+qbWLnoqOkLFomlFQ1D1iRQORERE4oCZkZWeQlZ6CuMHMB0SS7oCh4iIiERROBAREZEoCgciIiISReFAREREoigciIiISBSFAxEREYmicCAiIiJRFA5EREQkisKBiIiIRFE4EBERkSgKByIiIhJF11aIEAgE/C5BRERkuLhgMGhdPaGRAxEREYlirpdrR8vAmNlrzrkz/K5DounnEn/0M4lP+rnEn+H8mWjkQERERKIoHIiIiEgUhYOh84DfBUiX9HOJP/qZxCf9XOLPsP1M1HMgIiIiUTRyICIiIlEUDkRERCSKwkEMmVmSmX3RzLaZWYOZlZjZPWaW7Xdto5WZfdXMfmNmu8zMmdkev2sa7cxsjpl9y8xeNrOjZlZtZhvM7Gv6f8UfZjbXzB4xs61mVmVmdeF/x35gZpP8rk88ZpZlZrvD/5b9x1C+l1ZIjK17gc8DfwDuAeaHHy81s0uccyE/ixulvgNUAOuBAn9LkbCPAZ8B/gQ8AjQDFwF3Ah8ws7Odc/U+1jcaTQEm4f3btR9oARYBNwPXmNkS59wRH+sTz7eAscPxRgoHMWJmpwCfA37vnHtfxPbdwI+Aa4BHfSpvNDvZObcLwMy2ADk+1yPwW+Au51xVxLafmNkO4GvAx4Eh/a1Iojnn/gL8pfN2M1sNPA7cCHx/mMuSCGZ2GnAL8BW8Xz6HlKYVYudDgAE/7LT9p0AdcN1wFyTQFgwkfjjnXusUDNo8Fr5fOJz1SI/2hu8Lfa1ilDOzZLzvkqeA3w/He2rkIHaWASHg1ciNzrkGM9sQfl5EujclfH/Y1ypGMTPLwBtdywAWAN8LP/WEb0UJwBeBecD7etsxVjRyEDuTgTLnXGMXz5UCY80sbZhrEhkRwr8Z3Y43163pN//cBBwFSoCn8fp0rnPOrfGzqNHMzGYC/wJ8yzm3Z7jeVyMHsZMFdBUMABoi9mkannJERpQfAmcDtznn3va5ltHsj8A2vNGDpcBVwDg/CxJ+DOwGfjCcb6pwEDt1wPhunsuI2EdEIpjZt4HPAg845+7yu57RzDm3H+9sBYA/mtnvgHVmlqmfzfAzs+uAFcBy51zzcL63phVi5wDe1EF6F88V4005aNRAJIKZ3QF8Hfg58Cl/q5HOnHObgDeAgN+1jDbh75If4PV7HDKzWWY2C5ge3iU/vK1gKN5f4SB21uH99zwzcmO4wWcJ8JoPNYnELTP7JvBN4CHgJqcLvcSrTGCM30WMQpl4UzrvAXZE3FaFn78u/PimoXhzTSvEzmPAbXjnoUY273wCr9fgER9qEolLZnY7cAfwS+CjWiDMX2Y20Tl3qIvtF+GdWrpq2IuSWuD9XWwfBwTxTmv8L2DTULy5rsoYQ2Z2H97c6R/whoLaVkh8AbhY/wAOPzO7no5huM8BaXQsILLXOfdLXwobxczsM3iLHO0DvoF3CnCkw865Z4e9sFHMzP6At0LiX/HWNsgATsdbvK0OuNA5t8G3AqWdmc3Aa1C83zn32SF7H4WD2AmfjnUL3pKjM4AyvBGF251zNf5VNnqZ2Srggm6eft45d+HwVSMAZvYgcEMPu+jnMszM7AN4P5NT8X4zdXgh4VngbufcPh/LkwgKByIiIuILNSSKiIhIFIUDERERiaJwICIiIlEUDkRERCSKwoGIiIhEUTgQERGRKAoHIiIiEkXLJ4tIwggEAnfgXa/homAwuMrfakRGLoUDEWkXCAT6siqavnhFEpzCgYh05V96eG7PcBUhIv5QOBCREwSDwTv8rkFE/KNwICIDFjnHj3f1y1uAeUA18H/AbcFg8IRLAQcCgdl4V2R8N96FfsqA54BvB4PBHV3sn4x3+fPr8S4hnAaU4l1K+HvdHHM18JXw/g3AM8A/BYPB0kF8ZJFRQWcriEgsfBH4CbAR+CHwNvBR4MVAIDAucsdAILAMeA24DlgH/BvwMnAt8FogEDij0/5peNeu/zEwFXgU+BHwOvD3wHld1BMAHsabArkf2AJ8EHguEAikD/bDiiQ6jRyIyAnCIwJdaQgGg9/tYvtK4KxgMPhGxGvcizeS8F3g4+FtBjwE5AHXBYPBRyL2/yDwa+DhQCCwIBgMhsJP3QFcAvwv8P5gMNgYcUx6+LU6uxxYFgwGN0fs+yjwIeC9wOPdfXYR0ciBiHTtm93c/rmb/X8ZGQzC7gCqgA9H/LZ+Lt60w0uRwQAgGAw+BqwF5gLnQ/t0QgCoBz4VGQzCxzQGg8GjXdTzo8hgEPbT8P2Z3XwGEQnTyIGInCAYDFo/D3m+i9eoCgQCG4ALgPnABuC08NN/7eZ1/ooXDJYCq/GCRD7wSjAYPNCPel7rYltJ+L6wH68jMipp5EBEYuFwN9vbmhHzO90f7Gb/tu0Fne7720RY2cW2lvB9cj9fS2TUUTgQkViY0M32ieH7qk73E7vYF2BSp/0qw/fFA65MRPpN4UBEYuGCzhsCgUA+sATvNMKt4c1tfQkXdvM6bdvXh++34QWEUwOBwOTBlykifaFwICKxcH0gEFjaadsdeNMIv4poJHwB7zTH88PrELQLP14ObMdrTCQYDLYCQSAT+Enn0xADgUBa51MlRWTw1JAoIifo4VRGgD8Gg8ENnbY9CbwQCAQex+sbOD9820PEGQ7BYNAFAoEbgGeBxwKBwP/gjQ7MBf4f3uJJH4k4jRG8pZzPAq4EtgcCgf8L7zcVWAF8GXhwAB9TRLqhcCAiXflmD8/twTvzINK9wB/w1jX4IFCD94V9WzAYPBK5YzAYfCW8ENLX8dYvuBJvhcRf4a2Q+Han/ZsCgcDlwKeAjwA3AAYcCL/n2v5+OBHpmTnXl4uwiYicSJdIFklM6jkQERGRKAoHIiIiEkXhQERERKKo50BERESiaORAREREoigciIiISBSFAxEREYmicCAiIiJRFA5EREQkisKBiIiIRPn/rImRb/+eVXQAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, save_as='03-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 8 - Evaluation and confusion"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:23.729688Z",
- "iopub.status.busy": "2021-03-01T17:49:23.728778Z",
- "iopub.status.idle": "2021-03-01T17:49:50.874512Z",
- "shell.execute_reply": "2021-03-01T17:49:50.875040Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/GTSRB3_done/figs/GTSRB3-04-confusion-matrix</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x1152 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "y_sigmoid = model.predict(x_test)\n",
- "y_pred = np.argmax(y_sigmoid, axis=-1)\n",
- "\n",
- "pwk.plot_confusion_matrix(y_test,y_pred,range(43), figsize=(16, 16),normalize=False, save_as='04-confusion-matrix')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 9 - Restore and evaluate\n",
- "### 9.1 - List saved models :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:50.879141Z",
- "iopub.status.busy": "2021-03-01T17:49:50.878452Z",
- "iopub.status.idle": "2021-03-01T17:49:51.136832Z",
- "shell.execute_reply": "2021-03-01T17:49:51.137374Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "./run/GTSRB3_done/models/\r\n",
- "./run/GTSRB3_done/models/last-model.h5\r\n",
- "./run/GTSRB3_done/models/model-0002.h5\r\n",
- "./run/GTSRB3_done/models/model-0004.h5\r\n",
- "./run/GTSRB3_done/models/best-model.h5\r\n",
- "./run/GTSRB3_done/models/model-0005.h5\r\n",
- "./run/GTSRB3_done/models/model-0001.h5\r\n",
- "./run/GTSRB3_done/models/model-0003.h5\r\n"
- ]
- }
- ],
- "source": [
- "!find \"$run_dir\"/models/"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 9.2 - Restore a model :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:51.142044Z",
- "iopub.status.busy": "2021-03-01T17:49:51.141228Z",
- "iopub.status.idle": "2021-03-01T17:49:51.299315Z",
- "shell.execute_reply": "2021-03-01T17:49:51.299834Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loaded.\n"
- ]
- }
- ],
- "source": [
- "loaded_model = tf.keras.models.load_model(f'{run_dir}/models/best-model.h5')\n",
- "# loaded_model.summary()\n",
- "print(\"Loaded.\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 9.3 - Evaluate it :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:51.304655Z",
- "iopub.status.busy": "2021-03-01T17:49:51.303737Z",
- "iopub.status.idle": "2021-03-01T17:49:51.986530Z",
- "shell.execute_reply": "2021-03-01T17:49:51.987064Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Test loss : 0.2669\n",
- "Test accuracy : 0.9424\n"
- ]
- }
- ],
- "source": [
- "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('Test loss : {:5.4f}'.format(score[0]))\n",
- "print('Test accuracy : {:5.4f}'.format(score[1]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 9.4 - Make a prediction :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:51.995641Z",
- "iopub.status.busy": "2021-03-01T17:49:51.994849Z",
- "iopub.status.idle": "2021-03-01T17:49:54.028418Z",
- "shell.execute_reply": "2021-03-01T17:49:54.028940Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Output layer from model is (x100) :\n",
- "\n",
- "[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
- " 0. 0. 0. 0. 0. 0. 0. 0. 0.23 0. 0. 0. 0. 0. 0.\n",
- " 0. 99.77 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]]\n",
- "\n",
- "Graphically :\n",
- "\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/GTSRB3_done/figs/GTSRB3-05-prediction-proba</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
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- "The image : Prediction : Real stuff:\n"
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DoW3JZSkHoDWuUCNsD1DrIwnDKCLULcZOAB2G/9lLNDH3/5tn6Aal4uk7kOfhn56H3zUp7jUW+y3HuzUpbGp/1/Kdss6NUreVu5RyPA7nfPzNxNTNfraCn/IqTCVIzLm43eueupZrbpu52M3cgsPe3x2zt86JLs293uAk0O478mn/04FPAtHm17aDqmqKMR4OhKZlADhyDZEpB4K8TBKw1Qr86RPEH/4I8cc/gqcZAJArMghOMceqgtTwmuh7qLYjgXs8Qu734PtnqNUKarmEjGMwEUByAXA+JoOOgtCeA/NuWWo3kTnGhcWsX2zeT56HPwYPv7uFfm37e6xy+/M9yuWSq/2aYrex3O3jSCnHOLq9rXHHmwWAm/E+RXOx7LlruXSdl6597pzu/3Pbf1Sh6c7H1D2avG/38ChA2bWO0DC/KcYgQdaNHHrItoGqyE1p0LSkDcDRtbRjEIAtluCrFcTTFuLzZwS//AF8tQLAIIuUhG3bUpeqPAcrC+ozPfQvkbeenwkfe5mCJQlkGIIHAWQgACHAzbW4ysHYcErd3HfanUelj+UmGZ028wvOOfI8/DF5+Lu0TzX03grhVsV+STnaytx+n7Ou7aQ496aY/adi6VPjuOR6n5uruWt5yzxcUu72eD6CwHTn/WK+geVGG+fmPVxrrnVjvjY81feUCFQUGA57EoLP38jCMT2iBwkEAiyOwZZL8GwFvt5CbLcQT5/A12tyLSYJuT+rCvx4BD8ewI5HakPZtkA/AA1ZUfywg3xOIbMVWJqBLRdAnABxTL2oQ3VquAHbPamMJHvT3LCpubW+u+atsr9/ZPI87HnY0HdV6PfSrQuAS1btnHK6ZKnb5zXudONaN8qaa/eMm/RmLxCmrNpLSvWa6/21dI81/pYFw6PS1D0cv3Pv2a3zdKPQVO67lJAdZe/KghpYyN0z5PMz5GEPVRZA2wEMYGFElk2WkXWzWlGWsH6H0PCZfQ9ZFhgOB/D9Dmy/B/IjWF2R4O06qLKAPBDilswysCwD0hRYpsByCSQLMBeow4zcCMK53OFLz7k9T3OKacJ9/FEWoLeS5+GPycO/uUJ3Y7r3KPGpJLNrrmP3GHOK3FbgtjVuv1yF7h7zHre7TdcWAtfc8nPHvPadF4A/js6dkzhz642CUJ0QtWRVQR5JGA7PzxpR60g1u3IAEwIsocxgnq3olabgy+X4QhAAQkB0HWSRQzztMeyeIGy3Z9MCPWUiy/wIliwwpBmwWgGrNdhqTaAdaUeLWOXAadrSnJ2u9a74o6efgjwPz03Mj+PhH2ahvyZG6y4CLiVvTa1+LlnnRmHbn12Fbn6z36eO7aLHXXSBTYzbvtap3+6hS/u8ZqHgiWg2rIPZNf00nVkC6uxrNQyjq1IZvOv9DnJ/smxU25EbMooh0hR8taJXmoEtlmBxAkTR2HaSJwuoLINYb8iVud1CbjYWfrZOLuq68Zxsn4Lt1mCbLdhmC6wrsLYF63sqRxLi/PrVhES8cSo8J/525Hn48Xj4zc1ZgMvx3ynFdG37uW3nlPmc9W7e55Lh3GO77vU5F7s7PjsjHsALZLnXkHut9+57D10a66Mr+1uSh+a2naQ73ZKnfxSUBBQkxfOkJPzphkA4ZHGEPB6oI9V+D6mzgpmSQBCSBbNaQ2y2EOsNeJaBLRZAFALi/DHnC4pPBpst5PYJ6ukT9aI+HoGqpOSltqVXWUAej2CHA73yA1i5JWCQxZIELedU26uUdkUaOE0GsDssmwtzd81jZZ7NR+fXKfI87HnY0N0K/RZr+J7tb0kMc3+fO9+lBYStyO1EOPuYdgKcq9Cvxd5vtdLvoe8tnG65Z29dmPwM5PKA/Z2he+7F7Cr9wsOu9DmV4c2hp17QFaFeqeORXIqHAwnCqqKMXs7BkwRitdLWyhP4ZkPxwiSBCsIXCT8sDCGWS6hshWC7hXp6IuvmSDCZaBrIYaCxNI0jEDWc5moNLJdU18v5iI89Xszs5Ez/eGmXKU/Ve4eofnbyPOx5GPjBSXHX3O62lW22d/+fc8Pbx7hFmZt3o8zNu31OY4lPJTJMKfq5RcePJnf8Nl0KYzwizd1Lm8a5uNFyAeYFIrN+1wc/r9dVEmqQZFlUNSUMHQkeU2m8a0oiagClwLWbUmy2CD59QvDpEwnEbEU9oUUwkcELsDiCSFOo9QZq+wT5pK2bskDf1IS61TRQ/XDWEYsfD4SRvc6h0hQqjkkYml7VZn6cXJO7YpAvJvP6vE/du4/Cx56HPQ8berNC/y0tuDnFbr6bGstcrbntcjfHs13t5mXT3GS77n1jnU9Z6VNj/BFzOMc0bvjiIwjEm8g8kO77pV2AE5qWexzgJAihF4wA1CAhu466R5XFaNVIg3ilLRvV97TgjGNC1No+QXz6TK/tFny1AksWYGFwdp7RwglCnYCUga83EE9PhI9d5OBVBdU2GIYBkCSYZVkSNraB6lyvT8JQCBK4pmc1Y/rCHYH4CsvmbN70nGFGaXm6Qp6HH56HvzuwzC3HuNfF665aLlnnl5S5va3tYp9qtHLLtdjKfMq7cGm1dcmqv3bue8c6p9TvOdaHJbPwwYxL0qFxO/e+MnZ2LBNzlKa1ZFWR4DscoPY7qN2OBGKRj3jXCEOwxYLKe3StrjDWzWpNtbxhBMb5y7FyBoQRsKT9+WYL/nSEKHLIsoSsK7CWhLLqO6DWyFuHPfh+B7UywjCBDHRrS7MIZgyMnSyds+u3lMFdkoSNfzy9lTwPPywPv4vL/T1XGbck0E0pndda5+Z4bjb7pfj4VPzcnM9Y5vY47Rarcy6xuWuYo+/tWvwoin0qn2Mu+dLd726yLSPgDNBCQneN0njX0soIVrtnDZF50O0iW0ApqCDUvZ8zYL0B22xJqK03EJnJEI7BnAxeMxQpOFQUQ6UpsF6DbbeEupUfwYocTHe+wjAAdUOCeL+HWu0IeStNyYKKQqgggBQCEBwcHAqnuTwThPY83EruthfuxVuSSX9W8jzsedjQD7HQ7z3WW6xlty2qa51fUuZuPHxOkdtkAGaA8y5tRtlPueh/LzT1wH8Eoeh6e77rdTuCQNkvKSH7jgA48px6RD8/Qz5/OyX8aFclhI75LRZQaQaV6TrbbEWgHGkKniwARxAqpSCHgV4KkEJAxglkmkGtN8AmB9PIWygKsKampKK2oQ5Whz3UcwaZZmBpSlnIcQxEMXgY0ZgEoJgWTLCsG9vl+OZpnE5+de/dR4mjex72PAz8Rgr9mtX91uSxKVfyLe52ezVrK/M51/SUMrff3YWBTVMxdftY987re1giU/tduoZHITdnYPb3e1fhl8967rK0j2vig5Jij7KqIPOjRtT6Bvn8TFZFkVPfZyUBEULFMWSyxLBYQixTyMUSarkAkgV4kpwJQgDA0GPoB/TDgGHoMfQ9xTyFgEoSEqrrNbDeAKs92OEAFAXQtkDfA1VFLsvFMwnCNAVbpsBiqYUiQWoy4CyJid6/08JV36O5kNWl+/wzk+dhz8NT9GqX+6WD3+I2d39zXeC3rkouJaa53dPMyz7P1GrI3n/KNX/tWgC8qE0HzmFl34PuPc7UvLr/29f4yIp9jr+MEBzv5ZxgvFdQXngWbatDtS1kVWphuCPrZkTUqsh1yDkQxVDJgjpILZcYkgRDHGMII8gwhBLBebROSgxth65t0fYdumHAMEioYaB4YRgRHGa2Itfleg22z8COBxKCfU2xyDyHShJyVWYZWEYWFctWwLIDG4bpS1V4vXUzMfcjL+P03P0ekk5/S/I87HnYpe9iobtK8Np+cxb6PUrdvLvK3P1strVR4QxNLQJsa/4S4Iw7Htvd7rrdfyRdi6N9BGX+KroSAzujGwWlcVNSAFICVuxxOBww7HYkCA3edddRZ6gwAhYLIE2h0hRysUAfxWBBQCWXAATOH+6hbdHWFeq6Rt126OSAQdGpuVIQQkDECXiWEjzmeg2+WkHtU8hClxj1PdUUHw5k0WQrsoQ2G7ByA7ZqCBlsEh/72mRc2OPC3Ju9PKfeQJ6HH56Hv3sd+i3u9bnvrykU1wU+Z5m7ynyKzH5935+97AUAYwxhGL4AnnHHZBYjrpXuKsvf2h14bT7t8d2y/c9MFz0tsN1s85bQGFu7h5QarRnzUCup4THbloRNfsRw2I+IWirPgaYBV4qskDQF1huozZYydRdL9FFELsJhIJdn2xKvKgXZtujKAlVRoCpLVG2LTilIzsFEgFAIhJy6XPFlSoJwvaFzZDuC06wroK7H8h8cD8B+B+w3lIhU5uD1mq4hiaFkQLW9+pqhFFwH5k3TNbPlmdFw5YhzVs/PTp6HPQ+79JsBy1xLJpuy6l9zjqkkuClXu6t4jTLvug5d16FtW/R9P+4nhEAQBBiGAWEYIgiCF+Az9rEN2ee9FEf/3nSLkv4oCt1eSM0tyADr4XqjMnD3VspC0wIoC7dtoOoKsiioZne/127KA1RVgg89hAgg0iXYdgt8/gz5+TP6zRZ9mmIIAvRSoW0atGWBRkqEQQAuJWTboC0KVHmOqqpQty06MCAMESYxZByDBwFUFIEtU+pqtdmCbfeATmRCVUJ2lC2smpqSi3R9MTscwI855KYET2sgiQEhoDQgiBGE6n5ZeJHOnjtcvp+Pxsuehz0PT9GbFfotMeVLyWWX9rv1IXTd2lOx86nx2vsYZd40zfjquu5MoUdRhCiKxmNGUQQAL1zw7nW4Sv09Fi/2vtfm81Y3+kdR6PYD8lbLbfa5th5MBZAlYywbWDwrKeZISFbFWCc77HcYDgeooqAmEmAQSYxwvYH49Bn4wx8w/PIL8LRFv1yiF4L4tSjB+wEiKiG4AJMDhqZBW5SoywJ1VaEdekguIJIECRS4CBAHIRCG4IsFgtUK2G4hj0fgcNCZySVY20LWNTAM9F7kYMcj1PFACFx5DpVlZN0IARWCkLeAc7fiC76a8dq95n449/PRk+I8D3setundsNwvDcj+7taM7mtZ3FOu/Dll7h7HfhCUUuj7Hk3ToG1blGWJpmlQVRX6vscwDGCMIQgCdF13ZrW7QDSXxjnVwOVSPPu9BZCb7HZpO3f7R6SLCUU3fHd2LJwLxLmtTdYs8Z0ca3ZVTZ2olEaxIstmT0lEVQXeD+BBALFYItxsEfzyC9gf/oju8x8wbDbAYomecVRti7brIYsc0EAcahgwNC26ukRbVWjbFoN2e8ZSggchkoSSinhA4aRADkC9hSwKqFzDZJYlgYEMA2ULd50GDtGQnkf9yjKoZAEZBPSMCULekoafGDshfVkT+MICPN2oyVju3H3ySXGehz8yD7+LQr9nULdY6a4yv2WBYLvW7di5ndTmKimlFIZhGF3sZVmiqiqUZYm6rolxtEIPwxBxHKPvewAY8d6FEGdjtcfrvtuAM/Zc3HNjp+bgEk0tGm6JpT+6lX6V7BCR9QC/2MayYJh+nbk3tQAYrRqYxacijOmGYo4GUUvud1DaTSkLijtCSSAMwJdLiM0G4ukz2KfPkE9PYGkGJQJ0UqJuW5Rdh0YvRKWUkH2PoW3Rtw36toUcJJjgCJMFeBhC9j2gJDhjEGGAgHEEAFjXYdDjknlObtSKWk4qVVFmscbHHpHA1hsC61gswMIQknMgYuBGCOr38WXNlT2/jn1y0z2aEpgfnjwPfzgefneFPmU52y97mzmFdk2Z29u4teVTNedujNtWqLZCb5oGZVmiKApUZiU4DACAKIrGJDmjyO1GLsKql5xS6pfm43srzanFzKXzfgRlfvHajJDT77fOwgvXpbn31sOvQIhaShEAxwnv+gSPqfY74HgEDMoV40AYAoulrrNdQa3WkNkKMknQK6BrGjRti6IoUFYVmpZCRn3XY+g6yL4DdP5GGMdgQQglJRgDBOMIhEAYUG5IIDjYMICZHtaFFohlDllXBKPZD0DbQhUljX1nhGFGQCFRTPFHISAN8pYlCM+UhjVPE0t3d1btm/jiXn0k8jzsedilV8XQb3GBz9GtLvdrx3QV4zW89imFJqUck+CMm91Y6FVVoWma0UK34+mccwRBgDAMEYYhhmGAEOKq2918NrXodmLe91Cel1xvtyrzR1Tqcwsb25ujf3jlGSb8b0xDZOoYpBwkZEsuP3nMKRvYwGPuDxTbaw3edQAZRujjGDxJIOMFVJygiyI0QYhuGNAphabvUVcV8uMRZVloL1MHOfSAUgi4QBzH4GEIpnk4CgJEYYAwCBCEIYIwhAgDakKhAUJEkYMfj+DHI5hB3hpIUKuqHAW5fF5DZhnYMqUuWWFIVpkICHlLz8MLQfgWQWbdK3eB/F65Kr9H8jzseXiK3sVCv2aFuhbptePNHdf93lbk7mf3fK6Csre3LfS6rlHX9QuFbrvawzAcE+RMGZux1m8Z86U5mZuPe635exWy68W4Z9+fjWyBx2wrZsYjdPeCS5k/E/sYgTgMkK2Fd70jAA612wH5AayuwXqKCyIgYdiFEYYgAOMcEkCnFGopUUuJ1vI01VWJ4nikfJC2hZQSgnMkSYIwjiACCh8tkgSLJEESRYhM5UYYguuRq76HqEkYisMBw2EPfqQkJ2WQtzRQh9zvwbPnkzC0ULcQRoBS0127bKF41xxfiUs69/jR4uiehz0PT9G7lq3do8jNIO3P1yzyS+e5pMhdl7PZfhiGs+x2W6mbTHej0E25WRAEiKIIcRyjbdtRqfe6FaB9XZfyCK653W9V9Nfm1P1uyiswtd2jKvNJunbdr5kLIxAZALjxR4oLyqbBUBSEd717xvD8DHnYA0UB3rZUJqNLcYYoQh8GkIxKH4euQ9e0aBVQDwOarkPXE4pW1zRoqgpVWaJuGgBAFMWIEyAIIyyWC6RZhizLkC1TLJMEcRgiEAJMe5rAMJb/iO0Tgqc9gYSYBhtVRSAdXUfJUMcD5O55RN5iWUa1xsslWKJzWax5YPZEqfvneErVXFsMPzR5HvY8jHfqh37J+pyKJc8pjilFeM36dy30S8rcjp27ded2yVrbtuOr7/sxPm4y3c1vTdOcWelGoc+1X7WV6KX5eS29RiF/BBf7FNkLG5smr/8t7jT3qVWK+jObBhZFjmFPwlDuSdCwqgKGHoxzqCiCTBbokwRdEKIF0PY9uqZBX5XEw1Kh0nzbdS0JyaZBoxM7GecIwghCBIiTBGmaYb1eY71aI8tSLJIEURhCCM23nAOMek2zLANfr6m15XaLYbMB3+2giiNk05DLsq6h8iPknpC3mEbpwmoNljWQfQ8mJdTLyXjp1r2VbhF891qlPxl5HvY87NKbsdyvKfQXA9Q0p0Dm9pk6xy0tUYETzKv53bbMjWKeUuYmbm4eHBt0xvzeGjQjB2TG/uwmybnXYLdXfQ1dWyC5NHeuRxZ+P4psqwYAlMkMbqlf9KARteSOELVQ5GAGUSsIR6xrtViii2PUXKAaJJqmRScqDF2PXko0TY2qqtHUDZqmJkGosRREGAIMCMIQycJYNiusVhnS5RJJnCAMA6r5NchYjAFRRL2qsxW1s9xuITZbyNU3AuOoKLlIdSfkLbbfEYb2ZkNwmtUaLGs1nKaC4qd5GY0/T79r8jz88/Dwq7Hcp96vudiByy7hS0rc/X5KqV9yYZv9jGU+FTM3n42ytiFjTRzd1KLbVnqg8YenElWmEuWmlPm1sV+ax0tze8vx5pL5PgLZi62L+RyYeXAnLB/Lv6RDbNYz0ncjCIcscsgjdaQaDnuo4xGsLMH6Howx8DiCSlOw1QpYraCWKfo4Rss5ajmgaVr0XY9uGNDWNcqioAzhokBZUWJnLyUiPT4RCERxjGSxwHK5xGKxRJIsEEUReaFcBEPONZTmEmK1glhrYbjZEhZ2VUC2LbksmxaqKCCPB7A9dblixyPUuoSqayBZAGEIxdkJeUspkBNzZt5n5IiamXfg9vv5SOR52POwoTc1Z5mzzt3t5jIybxns3MJhLqvdHNeNEZv9XGVuZ7W7ytwkwpnzmri7bdnbELCuUjeZ71MhhLmFkDt2l659f4si/yiCbo7m+BHA21yTOPe+KQUoqBNEppRUJlNXlJRj6nYNklVJDSSUkkAYgS2WYCb+t32C2GzA0wyIE0gh0CuFpm3RtC2qokBxPOJwPOBwPCI/EkSmZKAEJF1ZIYR4kdQZBIF2UbIzSwwAEIRgcQKeZhBrGot8eoI8HoCyICEoJSAJTlPmuhf1YQ92PIAXR6hyRWVAYUhZ0o7H7N1MHH3vpnJGHo08D3senqI3K3TzfksM+LVJV5fc1fa7PQ4XL10pNSrptm1fKHOT1W7i5qb+3N7/kkK3Xe6uZT7XA33qmqaU+q3u8Wvz6/7+iILuFjp7QMYHkp2/T+2H6eeWWQLUuPqUUidXpVlw6pjjiKh1PEAd9vSe59Q8ou9JWEQx9WrebBB8+gT5+RfEmw26xRKdCNBBoe16qL5H2zSoigLH4wH7/R77/R7HwwFN04AJARFQ0qZUL7sGCs5JEM70F2CcAxHBafLVCmK7hXz6RAhgGnlL9T29dx1QVZDHI2FjHzRC1yonl2sYQXEGGYTgjBHyljmPmf7TTZoMTV6UMq5Sw7z372cnz8Oeh6foXRT69LjeZh3OWfy3WuhuEpxR6F3XjcrcfhmL3Xa1u2M2Fr6dGW+Dy9hQsLbF7q6wprwc7nXeM3+XFPlrFP9HoLN7YgvGK/NhC0Rmf+MIxBEicxSEPaSNqHU8QO33lG2bE860altAKiAUQJIA2Qp8+wT++RewP/4BbL0FkgUdu+swVBWaroMaBjR1jSLPcTiQMMyPR7RdhyAKEcYR+q7D0Bs4ZIVR1DB2ds0MJ4EzXqcIThbOZgv56TNBapYlQWe2DYahJyvHNL3QzTn4fk/dtJZLqCiCEpwyhYOATsD0MzLhtHT/v0klm/ugXaL01WMpc0Oehz0Pu/SqpLi5WDfwegVxydq/ZM261vlU3Nq1zm0L3ZSotbrW0bXwjYK2lfowDKMyn3rNueHn4vr29d2qyF+T1OYt9At0h9A/s3LGzJiX1tGZddMRopbSiFpyvydUrcMeyHONqNWTXA1CqMWSMmyfniB++QXBH/6IYLOlfs9KgdU1Bs5RNw0EY5CDtnLKEnmRIy9y9H2PcIgRJwvyPHUd+r7DMFiwmkpBYJrG6+QcLIrA0xR8s4EotSAsC3pVJQn6uoJqu/Ea1X4Htd9ArdeQyyVYnIAFARQXkJyDMwHoeCSzhbJSL6zOu1TyG13OPy15Hp6/zg/Cw2+y0A3d4hKYU2bXjm3/dot1PkV2idpUVrudzQ7grATNXSAopdB1HTjnaNv25PaZUOy2Qrfd7nNJclOLmUuK/F4Ffut+H4FeWDbWavgWUtBuSvPfaBKwUxxPKUilIIcBsjN9onPIw16jae0AXbOLtgEGSav+ONb9otfg2ycEnz5DfP4FarNFGMcQUgJliU4plFWFOIogdHJlr71QjeFrBWpH2bZouxZd22lL5yQQzywzCpqeldXaLkuxWlOJT1VBlgXFG/OcEqHaFmroKVkqP5Jls9tBrlbgy5QaXoQhEATgQkDpclBlPV9nglDPrbonQDlhqbp5LI9Cnoc9D7v0rsAyb7HObxnslFK/tq8NIGMrczeT3SRbXMowtC3pXmdxGmU9Z527FvqcZe0q9EsW+C3zfM+9+GgKfvYev2YebIE45a5UaozPyaLEcNCIWrtnyN1u7NOMrqPdwxBYLIAs06UzW/DtFsFmC7bdIogioB8wcIGmbVEul0gXCyySBeIoQhCEYFxAwSxkO7SdLrM0Nb66UmMYBgzawuF6rFJKSoDCyTsFxsCCECxZgGcDeNtC1BVlOB9OXbVkVQEN4WOjKKjr1u4ZfLWCTDOw5YL6TEcxXacKwCHGuTqzC8dHmk0Hfa+RJdwf0Rvledjz8BR9l25rZhC3/D4VP3bPc0mRX/IYuGhwdv24jc0eBAGUUrPJa1MeAZMBb8fM3YYtppzNkFkwTJ3j1lj6a1zt5nheyZ/oXa/PrKSVonIW8zV086ARgKPQEJlaEO53FItsamCQgKASG5amBG6x3oBvNuCrNdXRLlPwMEDUD0iGAcvFElmaYrXKsF6vsFqvka1WOB4PKEsC7FAA5DCg77tTowvLVXn2bBlhaBJCzQJSCEAI8DgGlIToO6CmbGC522HYPIPvd+DFkay4MwtnB6lRt1iaAosllf8kMZicSBrS1tVFl+MdVugj87HnYc/DLr3JQp+zYO3frrnR5/adcsfPYbbb+9nntWPnNiKcbZkbOFe3WxqAM+VtEurMeU1rVrO963Y3yty11OdWWlMK/dKNfM3DbI459/7a4/5sdC3xcGanK9bPya02xh2VgpIDVHdC1JL7/djEQh4PusyHIDIRhmcgGHy9AV+twNMMbLEAiyJACAhQO98kiZGlKTbrDconqt81r6qqxzij4VfbRTk+N4571SRAMShyU0KcrlsI8GRB1tp6BZFvMGyftMBegR32QFVTh6tW1/Qe9iQIVyuwbAVkK2qXuVxSAhJOhsy5hfN+9Ijuds/Dnoen6F0t9NeUiFyzsqeS4C4pczd5zShiY6EbV42xyI0CNvvZmemmcYuJl5tSNsNg9sLDtsrNay6ePpW0Z7vd3et+TyU7pcTdOf/IdOsD+WIbZ0cFaHjMAbJtIasKMj9q996O6nbznAArhgEQgQbA0NjTa3rxbAW2XILFJAgZTt3+kjhGukyx3WwomahuUFYVAXOUJdqWKjcABjmQt0oOg64ppvEyxsAMX9rXdpLo59dpBOIyBc/0ODdbDOs1+C6DKgtqczn0ULUu/9nvwbIdsF4D6w25YduOrCApASHG6TuP657Gc4tE+V7C9Gcjz8Mfl4ffVaHfqshvjZm729+aCGdb51NwrbZCDsPwzGVuXOS2W92OmZv/jbveHGdOoZvPdnKcrajdhYGLHjdFb1W8rjL/iIr8va9ZAeSuVNrKkQO5/TpC1FJloYXhAXJ/ILdlSXFHAGBhCL5YUp3seg2x2UCs1hBpCp4kYGE0Puicc4LATBKoQULKAV3fo2ob5EUx1vEWOWUJgwFSknXTDz0GOUApOc6DEYSccygN4AFjq01MEwt1LDJNwddrsm7WW/D1DrLIwZqWXLBtS72oDwcShvsN2OYAVmzAVzVUt4SKBighoAz8sVJeMd9Inoc9D7v0ZoV+q0t96rtbFLurzMfYiVUn7paVua52kwxnJ1LYFrqtgI0yNcrcHMdNXLOPYcbQtu2ZQjc9021r3Sjsqbmwv3dfUxb9a+kjKnCbLl6/jiFObmG5LJ2A0Fj5Y5JxVN+P8JiqLKjxw+FA1o1p39g0gJJUI7tIwLMVxHoDsdnSe5aRgIwT7Tok4gBBXQJgIL7s+h5FVWG92SHLVlgsNRwmF4BSGAZaoPZODBI4CZ4xgYhzQOLsWu35ULoEyLhWxWYD8bSF3G+h8pxik30PNfT0uSDkLYytK3PIsgRPU6gopkxhLQRHgTie0LkTc4t45/649Gg873nY8/AUvVqh3xuXmouXX9r+kmU+F0Oylbldd24s9b7vT0zA2KjQjeJljI2/m9apl8YFnJfF2Q1b7JdR8KbNqlvKZs/LlEKfyk+4hea2dd3+jybwvie57jMFg6qloJTOsG07qmktCYRDHo8EVKGFAbkpezDGwZMYPM2oPvbpiRpIrNb0XbIAj6LJcYgowoIxSDlgUS+RJAmSOEEURwjDCCIQIz8PQ4+269A0p0qP0VuFk8jhQlDLy4Hp6yEBz1xeFWIE6uDrLcTTJxL2BaGFqa4FKgIjsZG35OEAfjhCrXOoNCWgDs4hQ0LeGudVvR9Pet5+SZ6HH5OH361szVZG9rv7+ZZjmM9z9eaXlJvZz1WwUwp9qobcPa5rkdvK3LzbCwg3+c68TM90Y6VPJaHNKXTznbuYek3SnHteL+ymyRYQzP7fXkEzS2DglJCjdJ9oVVWQeU4Y14c91e4ej5ClRtRSoCSiZUrWzPYJ/OkT+GZLiUQLLQjFHGwGuQ5FEIILJ0djHLfCICX6jkA76pp6TFdlibqi3gXJMCCweV8EYNCJR/q6pL5uc9wRqGO5hNhsIPNPlDFcEEiHqmsMXQ90HUFqlpQdzfc0D/y4gUozqDiGEgEUA5QIIBkD19egYPGpmXvn3pj/L9/Ma8lgj0mehz8eD79Joc8p8Vvc8LZScre/ZJ27EK/2u53IZlvnxuVug8gYZW5o6liuJe5mV5r3YRgghDhT6sb97lroxkqfspDnFLoZr1kImLCAO/Zb/p/6/CGV+g0PyLlA1BKPMUcgcoBpoSHlCYBDl/gQ1vVOI2rpJKKqghp6MM4oiSjLwLdbiE+fyLrZbMGzDCxJgOD6I+pWcpyV9egxdX2HpqlRliXyPMfxeESeH7EqV4iTBMFyeX5QIcABDEqRK3EYYLKJAYAzBoSh7mK1JmzsoiDLrSggy5IyhQfC6pZVBaYTqvh+B7XZQGUZVJIQqhgn6EzoWCSYWw2ixvt2tgi+Ojtmw/s8ij8FeR72POzQm6FfXSvyrXRNkbsxZXtijLU8DMOZIjfwrk3TjExiMtvN9nYymn3OOSVuj5VzPrrSDXqcice7yvwSgpx73DmXu2vhz82H/f/U7x9SkQN3CfeTVWPta1s3CgQqoU6IWqptISvdvEL3iVa7HTWxKHKgaQApSZgsFpQR/PQJ4tNniKdP4JsN1btGMQmGC4K70Q2GctN2sizRNLVe1A4jD1MPgwZFUWis7B12ux2WaYowjMAZQ5Ik5zzBObi+LqaFIl02AzgDCwOwJIHKMohmS1ZNURBAR36ELAqwpqaGF00z4mOr/Q5qt4FMM/Ak0chbAuACXAhIaGFrWWpn2cp6Pu6SNo/G6p6HPQ9P0Jv6od/qVp+ywu3v3N9tN/fZKs1KhDNKzB6Pmwhn+pubV9M0Y7Kbca8b5WsDxdjnvKTI5xLXjKK2a9zdUjY7kc/1FEx5B8xvU4uAe93rH1aRG3rt9bueJMYApuOP0HxhXJVlqftE76A0CIc8HCge2bU0jDACS1NKyNk+kUDcPoGt1sBiCakBj4auBeNifJ6VUhj6Hk3boMwLEmzPz9TQIqeWk23bjuU9UkravqHmF/v9Ht9+/YY0zRDH8ej9SdMUSRyfAJEMvykFpuR4+QwKjHEwwaB0Ry3edeBVBZHnGI7UpYofKAua9T310C5LsvZ2O8jVGjzLIJdLIE6AKAICC1LTdlkaQTwO4M7794j87nnY8/AEvYuFfg+5itw+hmudu/Fqe1s39mzXnM/1OzcWOkAK3ShhF57VVqRTytD1CiilJuPvdtLdVF26fSy7/t1ck7uAccc5pdjthYWn70GW2xI4K3U1rkpZWZCSux3k8zMJxfxI7SWHQfdo1q7KtU4kenoaLRsVxZCcQXYdhrbB0A8jjxs+b5oGZVnguN/j27dveP72DfnxgLqq0PcdpBZgShE+dtOQdbPf7fBrmo6CcBgGtE2L9WaNLE2RJAvEcYxAl3QSn+osXvP8AZRJHIZgKgHXQl0VBYSGyxx2z+D5EUPTgPW9tnBoXvjuGTLLwJYpwYQmCVgcQ8lIz+m5S9Kt6/X0FvI8DDwmD7+LhX6LUr9kkbvHd93e15Lg3EQ4o8ztjmrm3birAx3XccFljGI2CtNWyGEYjszoKnazULCtaOMlmCtjMwo5DMMzK9+Nm9tzZpfJ2SVwc/Pj6b3IXl27v+jEomGANIhaeU6tJQ1E5mFP7rq2pRhaEIAvl7pkZjuW+bBsBbVYQoYBeqnQdi2aukJdVWjGzoAdmk4vWjUIx/FwwPPzN+R5jqapMfQayZCRMCGB16Aocux2O4RROHqjSKiWeCqesN1usV6vkWYrLJdLRHF8ulzjOrSJcyCMgMUCLFuBr9cQ2ycMmy2hhB325LodhpOFczhAps+EvpVlQJYCyxRssQSSQffmPo//0kS/0rrxpMnz8KPz8Lt1W5tyqbv72Ba5a52bz3OWuXtMN3nNLVOzlbr9MscLw/CFBW0r+ankODee3bbkdrLDAWYsQogxMa5pmlGR2zF1o9TtmLg9N24ynDvPdnLcLYrcDVNc28cvDq6TXtJSZvAwUDZsVVqIWnvIw25s/KD6Hiw4IWqJlQa10NCTWC6h4giScbRdS7HFwwH5gfpDF0WBsq7RtA0aw+t1g7qiRKGiKAjVUOoFJgAo0/K3RVEU46J1GORoIeV5jiLPUZWl3l8CjIELgdBOamJsFErGsoMQYFEMvlhAZasReUtsNpC7FXheQLYd0HdAQ/jYbL/UMJprarG5WkO2GVi/pGfs7htxYQ91PXnsI5PnYTwMD786y/2SAnfJVe6u8prK6p4qUTPHcpXtXCKc+zI1i8aqFUIgiqKzhYNtrU/FvW2r3cTe7cQ5Owvd9hq0bYsois5q1O1zmX1cpW0re9dNb8jed+5euQuoOcXulThmHh4G2I+oUmMTC6XIslG6vEWapJrDQQtDu8xHUYwtWWhXpYae1HjXKllABQH6QaLpOpRlSfHFX/+E3bdnHI4H5GWJumnQdj2GoR/BNoy107Ud5GCen1OyqFINgNMz03XtaB2RIM3pOel7XeJD/J4tlxDXMpWDgGp6tcXGNwZ9awOeH6mP9jBAdT2gkbeQ7kgQbjdgxRZsXUP1HSVb6fk9uSrf4K78iDztefhD8vC7tk89P/9L17j9veuuv6TQ5yzKKaU5lQhnAAhMyZqNCGeObSexRVFkrQAHxHGMpmkQRRHiOB6PaxYQYxs/0+EH5zFuu1bdrk23FwZ2lr07X/Z12xb71NxdUur22KYWVh+NXrjDbtxnfFDNvPe9FoQ1JRIVOdTRRtQiEA41nCwblmq8681GI2qtwJdLDFEEyQX6rkfTtSirEsfjEbvnHX799iv2ux3yskTVNOithaSSA+3TNiPWAlkJp2cEwMinfdehbVpUFZUBVWVJgrA9QSOP1zoMWCYJQg3yAaaRuOz54xwYkbcy8NUGYvOEYbsDPx6pnrlrCXmsaSCLHDjswQ57sMMBLM/Bywoqa6CSDioITr2mrT8z+Gfveo9/JvI87HnYplcr9HsVwJQL3v5/KnY+hdduW/Sm5pzKGeqzuLmJmbuQr+Z8RlEat7ux1uM4RpIkY5akWQlOlcDZte3G+rcBaIw1b8bsZuHPIcfNeTTcOZj63bXAp+6T7QmYWiR9JOU++7BYFo5yvlfACR5TSv2A24JQWzbHIyUW1TWhTTEGRBG5KddriO0WweYJgYbIhK7ZlUqh0+5Fk9Q5dqEqSyrraVsShnrlr6Tmr46wrqXUDSxGAKRhhM4cDF5C3+s+0w3a9oS6NZZsSqmFZo1VmmGRJIiiiBKNwlAjchExACoIwKIYbLkkfOztFuLwiaA0qxJoGsh+oLreqiL0scMebH+C01TVarTwFEAgHYpivwyw4DRvu7cfgTwPex429F0s9HsUwjXL/JJ17rra67oeM9ptxevCvdpu8yiKEIYhoig6U+ixLn0AzqFdjRK3LXQXic7GizcLBntxYAPQ2C59O7HOnsu50MRUSGJqfqfuy63W/EemscTGvFsejZFP+16jSJX0MB8PlDx0PFBG8Fjio3RrySXYagW+3SJ4ekKw3SJYr8GXKZW9cI6h69A5oaO2azH0A6AUOCeeUoyBK734VdSsYpDUjWoYJKS0+ETqqgnznJn3Qb/kaXspJfphQKvdmWVR4GmzwXq1RpqmWCyXiACEQsDG/2Kcn5peZCuI7RNkfqR5KQvIuqJmF3UFdC1kWYBpBDJ12EMej+CrlQbqIGGodBcr2+XInHfPr/Pkefhj8fC7dlu7Zdsp5WS+v6SsXGXkWry29Wy7xG0Af6PIjeKO43hU5LYyNy8T57a9AS6krK3QXeXedd3ZgsRu/GLgYI3r3W2teskqn5pT1+V+q6V9yVr/KGRbOC8yUhmjb5n5SkGa976H1A+3Moha+z29Dnq13ujWkpyPJT5iQ7jRVLO7hVitqOwlCKEAbVE0aHRWMIWKFEQgECcJeBAg1tbH2FGwbSCHU2Km0laP4TvOObiUkFb8kX5rCe2LQbsCFQZJi+SqqlDkBSU0ff6Mz58+Yfv0hLUckDEGJgR12LKmjAVCX2cKvtlClIVWFIVG36rA2pZikXVNqGNHitPywx5qtYJaLCB10qoKQ0ghzkA6YF76Hk1x7fikWFbqI5PnYc/DwBst9FsVwFw83f7/miJ3j2Gs+am6c9t6ti1lg9zmKnFbsbu/m4YtRqmP8Rsrq95W8K4FbyfN2crWMLEdR7dd77ZSv+Qen5rLqcXAtXsyt+9Dk+OSZPb3wCkbljEoxQir2eIFNfRQrbZs8qMWhDvqFa27UaExSUTGslmDawCO4OkTgu0TkK2AZAEEAVTXkTCsaxKG2sPEOEMcJwijiMYDYJASbdeNFogcJJq2AdOJQMIqtQQw1utK6z6PgB1tC7BifKbquqZEo+MBx8MeeX5Epd2kg5QANH8uFuS+ZIwSf7jOFl5mEJsWqqmA6tTcg+dHyLoE6oEwsquScML3e0IiW60gFwuwMIIUAuD0PIzIW3bc09wn+37h47jaAXgehudhm76rhX5p+7mY+VztuZvZPld3blvodkzbjpWb8jGjwO2e5bYyHVeFE2Vsc4luRplPxdkNU9pjt+Ps5hwjytHMHM55M6as7VuVuX3sa9s8Kr2IRdorY0bpLCZrVSkJ2fVUr1sUFG/c7aCen0+Y11VFGa9g1KoxTSlj9ulphMgUmw1gYo+MQTYN+rZFU1WoqxJNXWPoe3AukCwXVCERReAiwCAl6rpGfjyAM4a+76lEMgoR9xGUknoRG2kebCc7DkodZ1SaL6kMqEKe51RmpEuBmqZBN1D28DgHw0A5J2EIwTkY59SMI10Co7KowA9H8P0z2H4HludQbQeMFs5RQ2muIVcr8MUSMk6AMAREAIhz5C2498i5hx+ZPA9/bB7+blnuhqYsSKOwbbx0G+Z1zvIEXmaM2wp0zkIHTouCqf7ndh9y2wI3LnIXf92Owwshxk5qZkwmK96Aypj97RCA3aBlauFglPpURrqZO9tzYIcxbrHQ3bm95kX5UDR17Wb+GdN41/KEqJUTPKbcPUPutDDMTXtJSTWuSaJLYTbgT7qBxfYJWG+ANBuPT4KpRWsBcfRDD8Y5kiRGmmZI0xRhFGGQEkVRIAjECK7R1DXxLuOI40TzZYe2pcSguq5RNw06JxxlPEld16HhNcqqRFkWqIoSdV2h1VaWiWuOz23XY1hlWCwIwCMIQ7AoIsGoFGVOlyXkYYv+eQu2+kZZwU0N1bRA10IVGqhj9wyerQhKc7EA4phismEIFoanEiBzj8zLuUee4Hn4g/Lwuyv0W5K0XGU+ZaEbcpPCbKD+qZpzkxzXjDePFLpBY5sai1HgprGKiXO3bXvmErcVu4vQZteTu8rZnMu+Phs73l0suO72qfwBczx7nqaU+ke0tN+FlNJP3ckFpmCeP6XxrqmBBQnD/SgMCe+6AHQiEQsDclVqYSg2W8qe3azJsrF4SUlJ1s3oriThFgYhojhGtlphs9kgThJIKQlHYRhQlxXKRYGmaQAAi8VCh03JehnjiUWOPM9RFsWIzWCsHdszxjlHq4Vm3/ck/JU6vaTE0A9k4Q0DoKgPdWD6XgcBZUK3HeR6A7begq8JeYvtd2AVwYeyQQLNycKR2QosS4E0BRZLsGQBFSfkYoVjvdjC0PP5S/I8/OF4+N1i6LdYdK57eEqpX0r8cmvOpyBejVI32xnlZgBl7Pi3rbRHt03fn1nwLgb7XNc0W/GaenYT+7Hd6/bCxHZvTy0G3DaptpfCnk/7GG6GvHufbr1XnoCxeNTmc42oJbUrTloNLORuB7nfnyybfgALBBDFYOmS+kOvCVFLrNbgqY47WiQl1dd22lJpmwZdP2i+C5EkCbIsw2K5xKCBN8qyRJwkiJMEi8UCgQjABUcURgijEABD17UjBvbz8zP2QQjOjyjL4vwZHAYobUOYcBWgeYgzHYvFiCoGJcEZEIYB4iQ5n78gAF8uwNMUbLUCW6/B1mvw1RooCrJ82oayhssCcr8HSzNSENkKyFYErTn0gC6xMrfFq+8byfPwh+Lhd4uhu5Y0MK84XGvVVnBTsXPbkjaxaleZuwrdKHUDJGOOZxLRmqY5U7B935+ht9nK1VXmUy1RzT7mXMYNbu9v16S7Ct1uuWqfd0qx28e358kGy3G3v5Ql7+ky2Q8hlIbI1HjX1GpRA3Ac9pQcY1k2CAICqkgz8NWaaluzFT30y8XLc+kEn65t0bXaupC6/aNJ6oxjxEmCYRgQ1TEijTwYhREWiwWVXSYJsjTDUiNkdV2H4/GAb1mGKIqIVwBIdVpUj8bcmKdzwmAoywJMcACm5aMCZwxhILCIY6Rpimy9fhG+YXFM178kgcjXhLyFooBsW8qelgOBduRHsP0OWK3A1huwzQaqXkN1GZQuV/KW+OvI8/DH4OFXY7nfEpe13w1N1Zm7ynyqbMuOnU91UpsCkzGrsylr2D6uKSFzY+pT8XK7dt1+2Ql2tlI3zDHlTjeWtllkuAuHYRjOuri5c2uOYY45hTbnWuzuosu9l3as/kPQlaXyC0E46MxWg3dd5CeIzMOBQDlqgohkGqSCL1Nt2azBV7Ri56blons+qWObw6DBM4axTIcxwqUWIkAgdN8BoZN4tFcojmOIIMB6tcb26QmbzQZRHGPoexwOByyXyxFfQRnBLq1nr+9fPCvG3cmKcsw+DgUJwVWa4mmzPetkeEbCAHXoxhebLfh2DxQFWF1Dti1kXRGkqE7Mwm4HttmCHZ/ANhV401KttJRQWoib+8Iu3UDPw+PP47vn4Yfm4TdjubsKwFXW7vZuEtwld/tZWYIV5zYAMiPykH6VGvrPZJW753cXE3Y83pSn2XCthmzr21XgU+VvoW7ZB+AsG9N1qdueCjsMYF5BEMyGIdxrs481Z9VPze2t/z8unR6mF/EtWAKDsTOsa1VV2rI5CUJCidLNKwCqbzXdqFYbiPUaPCMADqYRtaZGo52i48gYGBizQjucnQZrPWemLDNJFliv1/j8yy/4/PkzlsslpJQ4Ho7UbpK9hD0WgUCoe1DbiUZmm0FKws2uK5RlZD1vFZrORvyaqKqIDFBHRq7a/BNYWZIQbGowDacpqwo4HglOc78HOx7Ai5xqpNsllEkqMgtSpbz/HYDnYc/Dht6l25r9vavQXeU+pdDd5ibAuTI3Cs9Y5kaZ53mO4/E4dugxCt0uEQNwlsVuj9Gc21jnrot/yiVuXO5T9ewGkMbUr9vocGeMp1Hh7PG49e0ujOyUtW3P/ZTXw7baL9GHttA1TT1PSpnSFj2vfU/wmJWDqHXYQ+W6eUV3XrPLVyuIzQZ8q5s8ZBnF5KJ4ehyMgXEOwQUCQZ4ayajqQQQCnAsw6MWgkjpjVy/6GCAEeZAWyyVWqxWenp6QrdZgDEjTbGxQobTgEtrjlCwSFHmBqq7QNi26vsNgIR5CnRQDoXSZ8+rZY4xAPSYmkolgzJAW2y1QlWBVBVZVNJdVBbQdlEbewvEIdtAgHUeN0rW0hKFB3pq4d+bzx+JeIs/DnoffpNCn4uVTlvCUVTxlndsK1F4AmCS4yuqqczwecTgcxvc8z1Hqtnm95XKxrWH3ZZeQ3aLQGWNncXRbscdxPMbu4zge3e+ue91uAmN/7yp1e47s+Ptc4puryO3XXJhkboEw9f8j0clqMF84cS3GrIxgAy85QLbGqjkXhPJ4gCyKk5uSC7BEI2qttxBPTwTEsV5DZBl4PG3Z0KnZ6AkycUbFoHkqggisPIxBJwIZa0RfGNf7x3GMxWKJ1SpDEARI4kTvS88bFwJxHGG5XGL3nOnniNpbVmWFuiFvl7RAPYIgRBTRseMkQZTEJ2xsCxf7jExryjSF2myBpgGrazCtVGRRAHUNtA1ZjkVOJUB7E9PdQC1TLQw5IW9xPiJvmXk7m0dnQfpoCt7zsOfhKXpTlvuUMp9S4O77nHVuyLYujWVulPnxeMR+v8dut8Nut8N+v8dR99itqmpUhrZCC4LgbAx2bN0kzdl13/Y1AaeJNl3R7HpHA99qA8sYhW4jzZnrAs5d7/a8TeG8u7X5r1Hq5pz29lMZ8I+sxOdJSxBLIJ6ANyiRxyQQqZoygg0qlNztCB1KJxGRZQOqOV1SJyr+9KThMZ/IulmmlGQzkRsBAIwzEjhJgsVygUFKKAYsl0uCIw5CMK6b/cgBw9CfCURlRecM0lYUxeCcI1uFdH3asgnDEIvFAlm2wnq9xvO3b3h+fsZu90zIVkqOzwpnDFFIVlCWZViv1lhvNlit1lhmGeI4gQimrwmMgUXkukXfgXcdZNNg0NnV/HAAKwqgbTXyVkUlQAeNWrbegC+XUJFG3mIADyw4Tc61dWVuqR01/gg87XnY8zDRmyz0qTiu+eyCx9ifb4mfAxhj5mVJje8PhwN2ux2+ffs2vox1bgBlxptnZYobt3MYhpBSjgrenN+4wKe6ntkv+9hGuRrFbsZqyulsKFkb+c3Mgztvdp6AnSQ3BYBjxjb12Rx7Sjm7tfFzZW0fipT+wxyBCOqSpEBlOLLrKF5W5BRr3O2gdgSRqY6EqIW+BzgDiyPwNKU63acniE8EwsHXa4jlklyVM2EQzjjCKESyWCBNMzDOocCwSFMsFgtq7csFoDA2pRiGAYM84TcY0AwpSTDa9zdbrUbYzCgKR7fmar3CMk0RxTEY5+j7AW3ToG1aSDZACEpWytIMm80Wnz5/xufPn/H06RNW6zUWywWCIJwUPYwz8DCCWkgwqSD7AUxbMXy/A9s9U/vJqoLsOsDgY49QmmvINAUzVqEQABcj8hbsBa6RI+adPZ51/oI8D3se1vQqhX7JMrf/dxW5mwBmx4mldTPN/13XoaqqUZk/Pz/j27dv+NOf/oTn52c8Pz/jcDicJcOZMdjK0ChVo7yNgjdKdKpczbbKXfe1Gas5Bud8TGIzVrubOOciv7nzApwn4pnzuaVs7oLDJruUzT2OIa/UiRQsl9b44JwEoUlcIUStgTJZq8qq132Gev42ImrBIGqFASXPrNbUwGKEx9yStbNcUoLNnDAUAmEUY7FcImtbBGEIMKaFY4o4iSECoZ8t3ZlKnlpMAqAYobIF4jmtViswZqybJbKMkLviOBl5uSpLFEUOUVUYBnqW4iRGlmV4enrC58+/4PMvf9DCcINkcco8fkGMgwUBBFtAgoFJcv2KPIfcPkOsv2FInyHLnOp1+17jYx+gds8EpZmmkEkCFkeQYQQEIZVTmecSjvtZx2P1AO7kjp+DPA97HnbpboV+KT7uKvYphT6V/OUqfGP1GnD94/E4Wua//vorfv311xfWuYmdAyeXtlHkRmmbd6XUWdmDGatRzrYStUvHbJe3bWkbBjL17ea8duKcbanbSt3OxjRhBttj4QLXuGA29jXY98gcz3Wv2657Q1NKfcpj8pA0IxDNVyaRSDYNZKnrdXc7yOdv5K7UiFpqRNSKKCtYN7DgW4LHFJsteJaRoAzC83inRVxwRDomKKUkoAtGTS0WyyWSOIEIAsKtVsalarspT6t8ijPKyfudZZkWcATkEccJuBAYhh5lUWK/22G3S2ghPAwIwwBJskCWZdhst/j06RM+6c5V2WpFWNi2MFTWeBh1tYIQEIxBKknWzWZDc7TZgK/XYPkRrO2orKppqBnGfgeWZRjSFNBwmiJOoOIYKorcqz4JQmV9+5j6/ESehz0Pa3qVQp9S1LaCc13vrrJ2LXRXyRtlXpblGDN/fn4eFfm3b9+w2+1wPB7PsNvNeU3imgGLcS3hKWXlfmcrVBOTN/8DL13e9svuomYrdbermm2pm+PYXgvznW2hux4EO+nNjHsuwc1N+rslKe5DKPZxJWwlEgFUdjMQNKRqrAYWI6LWDvJ41HjXA6224xgszahed7uF2G7pYV+tyLKJY7AgmBeG/GTdgDH0HXWpCqNYV1AQapaUAx1CqZM/jjFdHnRKtDGZvS4xxrBYLM48SEpJ1FWF3W6P1a/kgoyPMZSUCKMIcRJjmaZYZSusNxus1xtk2QqL5UI32xDnwkips9ggA6DCEDxJIFNd17zZgG8JTpMfDlBNA2jXr6oIH1ulO8g0IyCTNANbkmBkUp7V847i7yPwrEuehz0P4xUK3bao7Ti4W2JFY5q2zl34VbvG3NSZG1f7TsP8GXc7JTzszlzt1Gv3pBRNs5TIYPLilHlpLO4pT4OdTGe7r+3Yte0Wd8koWhOPN4sKc2028MyUhW32ta/lEuzsnDK3j2cnydnJcp4cUoByp0UpKDlQKYqJPR61MDRxx7IkQAmlwIIA3NSqrtcQa1PmQ4KQJ9R9iXFOD6yUJLTskIjQ8Ue5AOc6t0NQt6kois4SM5kRNO5LZ8++iMlNUBAESNN0XIgej0dst1usVmukaYZ8cYRSClF8wlpIFlSemSwSK3NZJ38aIagFlXtuBgBhBB4nUFogig0pDXk8QNUVZST3HdA0kPkR2O9ICK7WwHoNtlqTFWS7aPWxlZruLf0hyPPwh+fhuxW6a1m7/7vxYKPI59zttlVu15m7SXBGqZvM9tK0wptR5jYMqvkNOLnjbUVpf2fHqadc1q6FPmXZm+9M+ZztWjcLCheRzowNwFlynHmZ/0183ijnKbx3V5G7OQDub54s0k+VUjozuO+hmlbDOubautmfELUabdmMaFIEwsFXhKjFs4ySi5IF/W7HOJUCU0Yg0r3njMpqVEz5G0qqsYQnDEMwRi0mu7aF4OKMTzk78e4ZgNGVSzaWTpZlWK/X9NqssVqtkB+PAKAREuMToqIQYIzPWmkXz8c5MAJ1aOStpyfw4wG8LIG2hSwIAAVlSWVVaQas18B2C74tqaVlr/GxZ7KtPyx5Hv6wPHy3QjcoPHZ2t3l3O94Yq33OtW5/di3zw+GAw+FwZpUbN3ue56jr+uUka0awG6uYWkYD/GI+29aym0luM5KbFHePAnTd464itd3e7gLC3seeS9cLYq77kmvc3sc9v3ucj0LM/LG8fUqd0K1OiFotoTwVBXWkOhxoFZ7nkFVFDyygu1Fpy8aCyORpCpYsyE3phDgUFJRUJE+4IkuFn/oBCMHBcKrptXmlCUMEgbAEIgfjJs+CjkNuy9vmg3M+ZiWv12tsNltst1sURTHyS6QREJVUhNVtLTblMJx6PTNGmdImtDt1wiAATxLwNIPYbCCePpHrtyig6gqsbQkApa4h85wsHO3SlHkOXlVU8xvHgBAEpznyMmk0usdsEj3tEcjz8Dl5Hn6FQjelYXbdtWtJTlnkZp+pOnSj0A02uwGOMUrcWOW2ZT51M6MoOlPaBrnNfdlY7EZhu3XodmOUKSt9Tvm5CtLe3oaQdZPvbM+AvRixm8C4CXHuuGxyFx/XEhjdBYZ7LY9G+qrHTwpKe9qGMYkIGutaaaxrpQWhzHPI6uSmpF7RCw3CsSZUrdUaPM0IacvEHF8QnZ+sHDW6Ho2nRmi3o3D2NQmf4mwhavGEEQQXMKemci3Gmt7VCtvtFk9Pn1CVJTijUJAJYZnqk7IsUJYF4iQm/mSUOEQC8dzisEfAADBOHbx4moKvtTDMcxKGJSFvqbaF6qkXtYHTlIc9uKmZrlZQcUKoWwZ5SxE4CWMKj+589zzsediluxW6AW8xSrhpmjEpze5yZrvSDRSr6563rXc7Ec5GgjOK3K41d8ko8kRnO5puPeZ/+3uz7aVStTMX0Izr3Wx3C9n7u+79KcVuvjPeBbNIMYkfdoc315p3z2mTq8jtPAHbVf/oljpT6vRwjp4KQIFKZdQgoZqGrJeiINCNI7kolVmBNw3FDzmnVXqWUbxxs6V42moNlmq86zC8MBj9x5pyxjkMZ83xWBAEusWkZd0wSts5S4hS6kXZj7n37sJU6Izh1GQBf/6MpqnBOEdd1+Q+VQpN04xetCRZjG5MGpOYdWGeCUTOR5xwsd5QfLcstRWZE/51VZEwbCk7mx0PlGB01PchW1GmMLeQt8Y55WdZw4/G0Z6HPQ9PzukN25xRWZYYhmFU4nYbU9PpzFburpJ3Y+3mxhjFb7qnGaV+PB7HGnM3Ic1WeMvlcnxlGbXcWywW4zuVNcRninGup7mtuO3/pwBnzP/XyLXwXeVtW+h2jN+Mdy5cYD8sdjKiPc6p322lbsfhr7nvH43sRgkkCAmAQzU1raDzI0E37vf0EOZHcl/2HVkjJiNYu9zEp09UxrKmBhY8mrNsoIWGfmjZKZNW6d9MVu0UcSHAA0HY2CYeyPlZ/JS8YRKD89wYYSmVgrD4gzPKQVkuU6w3GxKGbQsFIM9z9F0PqRSqusbxeBxbXI6vOIGKonlMcXuxyBhYqGOQqxVEq2O8xi2soUjRNlQbXTdnbT7VYQ+ZrcDjmJC3APAggNSKwUBqPpoinyLPw56HDb1Kodsxb7eNqXGJG+Xu9ie3E8VoTtiZUndbo7r15YluQH/C+SWlnaYpVqsVsiwbFbqt2G0r160Jn1Pa5t1V3HMK/ZJid2NPrgKfcrsbt9RUi9Y5qFo3Lm7ItsAvud6vXcdDkHYNgrERgEMypts+ngA4VJ6foBv3OxKIGu8ag9RuyoQe5q1B0/pMZT6rFfhiARaGs4haZw/rxJwboTV1N4hfdGKnbnohRKDvMx+F3WCSUoHRYjLXPkUiDAgac7XCZrtFXdf0zAIoiwLDMKCuaxzzI8I4QrxYYLFMscxSpF0KqRSmrtYkaAE6oYjzMQYJJSH6nmKNRQF52GPY7Qh5qyyhhhqqa8n6OR70/diCZysC6ggCKM4hGSMhqO8pOD+VAD3aItXzsOfhCbpboRdFMcbOjYvcbWXq9ii3rXa7HtyNTxulbvDbTQKeiT8nSTIqOVuZGyW+2WywXq9H5Z6m6ajQXWVuJ7wB0y7qW+LTt8Szp45pX//Uu2up2252291uH9fNdnc/T7nbpxT6tWv46UkLwzH9hBl4TAXVD9RcoSxHAA71/Eyvw56ygtsGUJJW5wsC4CBh+JkE4mZDsUfdXnJuLqe+H12N5p6BTcstxiA4hxAa6yCMEEYhlFS6o5WBGpYjCpcRygwYhYZLgguEYTRmDJerFaqqGhfotOiuwThDFEVIlynKdY6m3pwqTqYydqUcy4AUcEIZCwKaw34Ar2uI/Aly+wSx2WB4zqjkp2spC7uuyE2520GtvhGUZpKARRpxSwN/cMagGB8tRW7u+SOR52HPwxP0KoXe9/1oRRslbrcyNZ3PjGK3J9JtJfqiPEGdZ8ebZDdTv2jc0MYqz7IMqxUB869WK2w2mzMrfblcnrmq7bj5nAJ3aer7OYX+FsU+FWufUvJTiw9DcwlvwDmC3JSF7rrnH1ap24sdwGpkISF7arogNd613D0TotbzM1k4eQ6YPI4gAF+Qu41vt4SodQaPGYEJPj+X9gNqLC6AhIaUgFJQXE0KF6aop0AYBIh1SGbQVSZJFCMKQohxdf9SGM5ZNwyUyBSKAFEYIY4ixFGIMNBCRtcPt4yhLivUVYm6qtDWDfquxdD3L+OtStE1DcPptNb1ssDOrl5r1C0CMmH7PbWo1P2mVZET/rgG5zDZ14hi8Cgi5C3GoNjJMlR6vh6KPA97Hp6gV7nc3QQ2A8FqYt5GqZsOaEahmyQ5Q7ZytWPCdoa5DahCje8pwc0oc1N3aCt129VuEuHm4s5vpSll7Cr2W+jSMaaO5ya0mXdjfbsK3cznlHVu72uP52GVunFfmfmDjtcNErLtCO86zyEPOxKCz9+gds8aIrOE6jqwIKSX7hfN1xudGazLfOJExx0ZCQKmH8vR/6gFgZlz87MioQxJfZqN++9FZyspIRgQBQEWcYJsuQQH8UUcRVgkMaIgoPiijquCn2puJ92kRghLSdabkhQDVeZ1Emqy7zH0Hfq2w9C26NoGfdOib1uKQdrjNccc46A0J7aQZ2EIHifgqa6BXq91lnUKVRaQQ09AHUUJediBLRdgyyXYcgEsF8BiCSQLIB5eCj6lADUPCPVTkudhz8MT9KosdxPnNpb5VH9y44a30dxMLPzFILTCNpa4UuqstMwuQzNu9tVqNfkybna3RM1tkWorQzeZzP08R/coYJfmXNxzVv5cbNw9nqvM7WPa/7vXfcnifzgaXYE4r98d+hFRSxY55N6CyDSuyqam/aMYLI4JPStbQZia3WwFvliCRxHA9cNuLBVyPp7F/0bZqOXk+ODKk8BW0HxgL0SlhGAccRQiXS4ghx5REEBKhTAKaUEbxwhFoN111A/b7r/8Ig6pSNANfYehI0HXty36riNhJOVo/QScQzDKyFVSQvY9uo6EYtdGiGLdjcvEHZXUQkoB8qUwBOeEQJYsqAxoRShlw3oNWRRU09u2UG0NdTxCxguwxRJIUyBbgWVrsCyDkkuaQuveaq3yHpzz+yHPw3QMz8Nn9GqFbpLWbJe7Uez298Y6v6QYjKI3kIBCCERRNFrY9st2s5t38znV7fnsePmICuS4r12yk8ZuoWuWq+vudr+f+uxaxZf2nTvfXJKbHdK45XoeUpHbZM+9XrGrrj/FHnMDkakRtfIcqqqpvaQQtBpfLMCMi221JkFoynzsFb7tfmQAGB8hL1+MRykw2wgy99KG2NTbmvBTmqbgjGORJJBKIRDUVcrgXHNGmcNKSkjGAEZoXi9clgqQw4Ch69E2LZqmRlPX6NqG2kUCCPXim1ykFMbinEEOA/q2o3aVYQTGCNsBjIFpJUAGHjtdr+2iZQwsEKRg0hRivcbw9ARxoCQutI2G0xwok/i4B/ZLYLUG22zBtgVY04D3/ZklM87jI5LnYc/DDr1KoRsQGPOyY+V2qZopT7tFORgL2ihyY2kbRW3+dxW6+d9OfpvLArddzeNkzbid5zDbp7Lgzfucde26xu1z2zQX05/abzIZZcLTcMnCnxrvQ7rYHRqRtPQLUlIjha6FrGvIsjxD1JL5UfeKNohaIdhiAZZmECuDd70+Na9IFhR71PG6k5tOkTAwU2zN9dmdYQS6pdTpvpzH0EiIiCBAHFMnqzCKxgoSSgrSVRERYVTDeGUAMFB87sW91ufp5YCu79C0LdquRdf3UFAQQYiEc3CdoJpmGZJFAiECSKXQdpQoKwLKilaATkDlZOiZZ08LdXW6XCIRUAlPmlEt9NOR5r4sqWa66yAN7rhuMsIOe7DjgWp+awPk0UOFp25g6tzWeQjyPOx5eIperdDtunMbKc5tLmLi45fc7SY27ipsuwzNKPQpJW/XmduIasDJBe3eeM75mdK+VvZlrseOXbuJbea7OYVu09Q5XuOiv0bXkvdeE+//2UmBjRaDUorgMdsWqE51u+p4HBG1lEloUWpMfhG6IQO1l9ySQMxWVLerSysBgHEOJQSFFxUAMLJsRjQqslYYCBiEQYcilbJkpskSNtsD4BwiDBFxBhGGiBO9eAZl//KxGoJqWqGPrw8IcMdVCZD7kHMoMAxKlwtJBTA6V8IYGOOIohBJQqGvZJmChyEGpVC3LURVg4mAro9zMCEQ6uOS5aafM8u6G4cVBEAcQ2QZ1HZL815XUBXBZ6qmBus6Akypa3CtsNjhAHk8ghclbbdooYIAKgy1C1jP3QOR52HPw1P0KuhX87KhXpVSZy5tamVH39kd2gCc1VlHUTTGx+1EN9ulbitzo7xNnNx2sdtW+aWuaG4296V3e/tLFu2Ui9215i+Nwz3nrXTp2HYynAtiY2f6zyn2R3W701qXkmzUMNCKuK6gypyEoG5eoY4HykjVrSWZhscUuk+0+PQJwafPENsnymhNM7DF4sX5mBCEEW1cjm6FBSMRePbVpQuw72sQIIjUKXZp/W4fQxp3qVIkmGYSQ3kQaKGoy2fCEEEcI9EHCwKd05IkiLX1xIRANwyommbcj4UhgigiATNxLjbeB+t6OQeLE/BU1/R2hLCl6hqqLgnCtK4Je7zvIasKyI/ghz0le+UHqGIFlSRQAcFoKi2EH408D3senqJXKXRjidslaMZlHscxlFIIgmCs6bOVq5u5bie7GYVug8TYSW4mOc4ocFeRG7c6gBefz27EBUU6ZznPKb4p5W4+233UzbXbx5yLs0+df2o8U7+7Y3Dr2V2420ulcPfkFPxMZAA4lJQkCDU8JmFdaxCOw57+L8sTolYUUVnK9gnB588IfvkDxGcLhGO5fJnJq4ldEECvJdv60R/mt9XuQ2a9JrcLAvAwhIgihEmCaLnEUilE/QJc57bEcUxNLvS1KinRDgOGusEAAEIgiGPEyyWSOzw/DKBa3OWSBLc0zUUayhLOc3ANiqKaBmjb8b6NSGiZwcYOIAEgCE9NPh6IPA97Hp6iV3dbs9HbTDtPmgeKfRhlbistY7kbRW4gTW2re8qdbie5mYQ5G2gFOM/udsu3gGkL+p6HfArf3RzjklvbPo+BWbVpyi0/p6Dd92vbmbHaVvmUQp9zuz9q2ZqCDq302vVVEhyj0tnAareD2u8IMrO2WksuFgSP+ekTxOc/QHz+BeLpE9WcLlOqJf2dki0AL91RJgREGCJMEiTLFGnfQ4QhlJQQphIljMAFH4GgWr3IBzoMSoEHIeKW4paXEnPdqOA4riAgXHGlKMmrriCPB4j9nhK8ihzoOqi+B6srci+bTO4sA08ScldyBpkAPAgfrsWq5+EL231gHr5boduJboyxMUvRRnCz3fAAxu3cdqYGktXGYTeWuik7M4kRbjMS29VtewtsRX4pIe+aIjX/m5fbP/xa/bmr/O3j2PvPjemW2PfUddjvc5jxrjKfuva57x6BJKDdlB01VChycnftdlDP3wiI43DQmaktlAJ4FFJscX3CuxZPT2eCcM6y+T3QzcKQsXHBvUyXABSSZAFAjc+vaXDRti3VPStFgm8YIBWVHHUaslleqZ19kVRkSAgSiG0DVTxh2H6C2PyKYbUG2+80UIe2fAy86e4ZKkshEw1XKgJwLiC5oJjoA5Hn4QvbfWAevpvLbRc7cJo8EzM3Lna7E46rzI2ythuoTDVRMfvYJVf2OexGL3ac3u0dbtOcAgTmcdzvebnlcVNK3VzLpcWBOf+UJT2liOfG7Cpwt3TvktJ+aIUuB8oIrnR5z35PQtBYNsdTAwumXZVseQLg4AaAY7EEi2JyB/6OPRq3Bk6YlAgYQxKGkMkCARj6pAcD05jbAgyMPHTDgAYA0323h34AVwpDHGNoW2oQ0nWAbln5YkxKt/hUirpNBcGZUGRBQNnWyxQ8y8BMSdViCRYewfoe6DqgKqEOR7JuUht5KwKiECwKHywlzvPwJfrIPHy3QjeKwE58mwMysePlxtK+ZJm7bUJtvHJjidttWZVS4+epbm52HN0euz3GKSV6y/fmeK7ivvb5khVvL4DM3NnnnusOZ7Zx3+cWGO6+92bc/+wk1QnvWpbVGL+Sex17NL2Km5bKdcKQanKTBVk4yyXV70YxWCAAEKAHmgYIBMA0n5l5VeqUzAM9t+Y1knpTZdWk28/+3YSexo30/bcydSEl0HUQQ48YChACYRRBqgAMDFxnGysl0QwDBgWEcoAYBoihh+p7cKbA2hZoG+r2VddQE720Vd9TA5GhB1OKLJAwAAujU5xWKxcmBMV+k4RKrXSNNHT7T9U02kLdkzBcksBEsgCShRaOj5UL4nkYnocn6G6FHlkrlSklYisdA7dqlLmpMTdY7G7v8qlsdQBnPdPN/0bBmw5tdkc31wXvxs+nlPItbu257ecSzC7Fql2Leyp5LdAM5FrUl1zmlyz1uWu6RI+o2AncQeNdawAOdTjQw2SyghsNwAFQxmsUkUsyikg4ci0ENZCHZIyEAOMvpZGpOxnrT5j2HxonorK2myF2flhl/VHmXX9nbtncvdNPg8Ufel4UZU3zrkPQdcDQI1AmvAZwqQBGzxb6Dn3XIu47xH2PoSeBGDAg7FqIugYvS6j4CMkYRJKQopCKsnu7FrJtyW2sFCVcBSFUFIGHISA4VD9ANvVoAYFrJK4oAgspM1l1HaC7WMnjESzdg2UZWJYB+sX6nrKzH4g8D9MBPQ+f090K3bQvtZWDbVEa97uryE2tue1yN5/dPt9GWRkL2yhuk8AAUIKZsc5NXbwppzMK3VbqhqZc2peUuWvV28e4pNDn4tVTrnNbkbvz5yYV2p/nlPQlr4KhOZCbR1TgLqnBZJ7WUFVBEJk51YGqgmp20bZQciArQFAZC3TpDnRmsawqsPAIJQewkppYnIksBrwQYXYd7QvpdoUmtiegDaUPrazN9D1nIKjOmeMxXRfM9LGkhs5kwwAxSHATPzQ8pMg9ObQNoqpEXNdYti1Y32Hoe3ClsGgaRGUBrmto+7aFiGOaQ6UwDD2Groc0cJxKt8sMA6gwggwpq1dJSQpLJ3apQSsnLsha0s8DJR3VUEVOAvF4ADuugfUGaGrwrqOksAciz8On43kePtHdCj1NUz03J4XnKiOjxM3LLTMz7/bL7GsnvhkrHMDYec0oNVvZG+vcro+3QW4uIaRdUuJTitA9hutWn3Kbz8XV7Ze5drv/uQ3QYyt+ewyXxjo1djtGNuVqN7//nmNpbyWTiCKrGrIoKRP4qF9FQSVAbXfCajZWiHbnybqCzI/kquw6sPxCi8lR6GkrRjm/nY9sZsRzVooWguN9PHdanhZ288ewhaWC9mhJBaWxuJnB5TLXoQAmB4i2Q9DUSKoKqqogmhZSDmCMI1YSkVLgkqyT/pgQzCinsiNabPeQfQ81yNEtKYMAPAhI0GnBKdsWssgx7Hd0b9rWQi3D2IRD1RVkEYHpe8nyI1AUlHikEboeiTwPnx/a8zDRqxS66642Csm2yG3lbStwo/Btd7zdOMV2Ixvrek6xulCtJsZurHY7297OLgem3e+XFLhLUy70S9b5JWVvFkRm/nrdQtCMyVb6lxYo5vOcop6iqWM9sjIHcAJ6qEqybI4niEyV5wT+0DaApMWkGiQw9FAtlQex/R4DGFTTgC0Oo+vM9jye3JK3CsNr5s3U/bCQs17sf4MgtDYdh65OQtFO32XWRkpJoB8QdB2itgXaFmHfQUrim6AOEFUVeJFDxjG6MIQMBBg7oTdKOUANkixIpXmY81EQmgQt9D1BmRY5hp0WiE1DddXDQO89HYsLAZnEQJqBrY9gRQ5ZlmB1BTQvwVJ+ZvI8/HJTz8OvUOir1UpP8rnVyDk/U+Z2nbntdrct8alSKts6H+/VBYt6Shnbteeu291OkrtVaV1LGrPbvZoFw9R1zdWDm3lxY/62Ig+C4CyT/9KY3Plz5/IesJhHBJYhQUhAHGTZHE6v/EhYy02tNwZY10I1NXVMEgG5K+sa/EB41wb4wexgpozk0AW35KvWTG+ziG49pBqPzSyZTdJQKUBJCSYHBNq1GQxWnNJ4kkSAISDErsFKXCIel1BSnSwVBhKWWhDStooEZtdC1TT/g4VLTuhbNVkuQkBCAWEEpAdyWeZHsKKAKstJ9LOfmTwPXz/kR+Thd1Polyx040K2FflUTHm8F44Css9nK07bsu26bhyD20L01iYxLjCNC0pjA8PY4zKIcHNx9Smr3I6V28cxHefsxYhdCjgFlnPpeuyxzn13af+HtNTriiyYsoDKbevmSMlFZUHuSs6oa1QQQJYVxbu0IJSHAyW2BKF+gLVLE8q5L7YweT29tF2Y+eB8PyEeL7HJ1bGZGKZzOOM9UwpCAdy+ZqYXjoxhYAxyPIBrRp2SoUaXqBGEZjupLaCOEsBUpduC5jlkVY4Y5WCckobCCOyYElBHnlM8uSygHkyhex52drhIH4eH71boWZaNn6eS4ey4uB0PtpX5JTf3lBJxFeXUQiIMwxeNYYQQZ1nv9jlcC9a47O3v7N8MGaVux5ptsr8327rbX3Od23Njj3dusXFN6X6khLebqKqBqiJIzFILxZJWweOque9PD2ZjGj4oyLaDCouTW00Iq2xmyiX5vS7ipZA6P6HF3+f/Xh0Xs45/yzhOBtzp3Ao4ZS7PzYc7MEbuXWb9Tm5TsoKo13dHsUZz72oC5xgVURzT90VJoCplSa9FccvF/Dzkedjz8AS9OoZuW562QjfQrK573bbM6brv55I5r8AwDCOGvBmTwZI38WijrOeUow1UYyxu872rYF3Fe+l6bkmqc+fJRcUzZCtxd1Fyy9xd+3xtzI9Cqm1Or6YBmobwsLuWHrZ+OGWUDgMJxraFBMDajhowmE5PjJGbzbJu6CS4WZicdrD/v4PmdnlNtITN/gN3jKfngL5j7GUM08QxlTWY2Yxl+5Rj/PM0kUopwGBj17W+h9RqEowBnRi7jqmW7ivqGqgren8g8jx867E+Fg+/SaHPuZDnGoFcSjqbs9inXNlTSt2Go7WT9IxCv4T17ipzF4HOpin42Tmyt7k0X+7r0uLnVnf71Ny683xpu4cm3ekI/UAPl6QVNAPAOIMSHIAYOzaN8zIM1Bsazvyb+/xI8+dei+E7GM/jy+d1Kodlil/H5KQ50hbNKHrN3Gppq4aeyrbk+T0Yx6ytIciB7nPXAW177wz8vsnz8HX6gDx8t0JfLBZnCnXuNRUjv6Qwrin0OfAVo9T7vkcYhuN+9m+24rY7sNnfGSVuu+jNMW239i2LE3N8e/z2NUwpcfu6pmLw1yz1qTDF1BxfUugfhvTKmeJdGt0pioEkISQtgIQepxaKCMNTzagCoORYamIbM7i4yLIsn5+cxqtgzDHizq9x/OSGpW46iXZTMopjgusMbMbITWxcmADdG0ZtKw1wCgsCnZikjyUfqw7d8/Db6FF5+FXAMq5idT+7VjUwrUDc2O6cK3uqRMxVekZxSynPkuQ455PW+ZxC55yPn80x7bFdUrT2Nc3Fx6e8GWb+7EXDtePe43KfCxl8WOKcEJ+iECxJCGZxtQJvaigwqGahhaEuQQkCXdLDaNU96JWzXUv6e59PW2q9m0zWccapY7Kzt7NPNw3A5m3OSRjawm0YwHqC6oS5V0kCvlqDZSuwNAWShDKGhQVl+ijkefjdDvpIPHy3Qjed1aaU+q1u9XsU0T2x37n9bUXmKkSj8O1rcvHgzb5zdeVT1zalcO3x2Ja5va+9yLDDAnOun2tzeUmZ33s/HobCkFbCixRstQZ/KoG+A+MccrmkeKR5wAzCFtcIWkoCpvZUynMhM8eTV6f3dzb/k9JtZtMLC1j7/U3n51zHfPlJIUlJMeJhIOHItXWTZdQOdPsEtlpTX+pYC8VHIs/Dl+mD8vCrFPol5XbJEjdkK1bzu/ud+X9KebmlXK6r3MDEurFzl2yL2P3elI/Z39nXaStjO9Y9l7jmeiPsGLkpUXO9ELaXYC7L/bXkLo4+lFKPE2DZg61X4F1LlooQYIslYTe31DABjI0ra8bJNUdxLalLUSz+uBpTu2VgP/geaCGkXnyHl9dmb6tO77BLnBiz9MSdi8ez+K4RhlaM0cy/8aBxDkQxdbJar8GfPoE/PYGt10CaQi2S2877s5Dn4Qun/7g8fLdCD8Pwhdt7ysV+6aLtmO/cdq4id61XN+7tIsS5yXDX3M3me7fDm/kNOIENuO/2PJj93Fj9lHI339m9412PgB1KmFvgTF3L1P/Xchg+jFJfLMCgNCwkCKpxsYTabAkDu9OQmSbexaxuTsoIQkWfR2K4Jg/fJuve4d7cKJBPccPbdryVD8/HMXdMK4aphSspJSupyPwm5Sg0GedUv7tYUGOL9QZsvQE2G2pysVhevIafjjwPX9zoo/LwqxQ6gBdK/JLScBO6blUeUwrMjXu7yrxt2xHL3VasUzH4ubG6eOnAOcztpSRAe6y2Er4GFmPH7c15jZXe9z2iKLrJQr+mzOcY9MMocwBYLPRqWYAHAipOgCwjQdg0lFVqoT/RA2gF75TS2anKcldeKmT5rQTZG86n3H+vHOPFz7fEFF+zLSylZN0HpZWR2Y5zsDAAophclMuUhGCa0f2O4+vn/JnI8/DVXT4iD9+t0O0e3VPv4/gvxNHvTeYCXlq1RhFOKXRjpdtZ7W5owFbAbix8KknPdrm7GfbmszkWgBfK3LyMkjbX43oRzPfGOncXAPfQm2JAj0zJAhA6USikpCKkGdA2QNdSKZD9oDE2Cjwi5TzYk768++kdrJ+b7vStHsP7N375hTVP98jCF/Ri/s1Lf8sJchNRRIIvWZAQTBJyT0fh7KF/SvI8fMcRPw4P363Q3XgzcN2VO/XdNeVklJur1G0l6brcjWI3gDI2cpzrKjcJcWYsroU9F1KYK5+zY+qXQgTmOlz3vMmwN9duY7fPJcTdS/cq9oddCESR1U4yICzrpD3V9g499Ty2g2+WlXM+LfY/d9yb72mt3OiWvESvuvOj/LMFoYlN3jywG85hC0N9TJNFrNtXIo6BKIaKYyAKoYIHU+ieh69u9BF5+G6F/md/9mf37uLJ0++K/oO///d/9BA8eXoTeR72NEUPVpzpyZMnT548fUxiHyoZypMnT548eXpQ8ha6J0+ePHny9ADkFbonT548efL0AOQVuidPnjx58vQAdHeW+6PRly9f/lMA/wqAfw7ALwAqAP8YwP8E4L/6+vXrr9a2IYAvAP4mgH8JwD8PIATwd79+/frf/KYD9+TJIs/Hnn528jz8dvIWOvDvAkgB/K8A/gsA/z2AHsA/BPCPvnz58k9b26YA/nMAfwfAXwfwF7/hOD15ukSejz397OR5+I304S10AOuvX7/W7pdfvnz5jwH8+wD+PdBKEABKAP8mgP/769ev/+TLly//EMB/+FsN1JOnC+T52NPPTp6H30gfXqFPMZCm/wHERP+stW0L4H/+LcblydM95PnY089OnoffTt7lPk//ln7/Rz90FJ48vY08H3v62cnz8I304S10Q1++fPl7ADIAG1Bixt8CMdB/8iPH5cnTPeT52NPPTp6HX09eoZ/o7wH4a9b//wuAv/P169e/+kHj8eTpNeT52NPPTp6HX0ne5a7p69evf/3r168MlDH5bwP4ZwD8X1++fPmXf+zIPHm6nTwfe/rZyfPw68krdIe+fv36l1+/fv0fAfwbAD4D+G9/8JA8ebqbPB97+tnJ8/D95BX6DH39+vUfA/h/APwLX758+eVHj8eTp9eQ52NPPzt5Hr6dvEK/TP+Ufh9+6Cg8eXobeT729LOT5+Eb6EMnxX358uVvANh9/fr1L5zvOYD/CMAfAfyfX79+ff4R4/Pk6RbyfOzpZyfPw+9DH1qhA/jbAP6zL1++/O8A/j8Av4KyK/91UCLGXwD4u/YOX758+fsA/ob+92/q93/ny5cvf0t//j8+Mpawpx9Cno89/ezkefgd6KMr9P8NwH8N4F8D8C8C2AIoAPy/AP47AP/l169fvzn7/G0Qk9n0r+qXoQ/FRJ5+OHk+9vSzk+fhdyCmlPrRY/DkyZMnT548vZF8UpwnT548efL0AOQVuidPnjx58vQA5BW6J0+ePHny9ADkFbonT548efL0AOQVuidPnjx58vQA5BW6J0+ePHny9ADkFbonT548efL0AOQVuidPnjx58vQA5BW6J0+ePHny9ADkFbonT548efL0APT/AxrWqqkcnL7jAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 648x169.2 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "YEEES ! that's right!\n"
- ]
- }
- ],
- "source": [
- "# ---- Get a random image\n",
- "#\n",
- "i = random.randint(1,len(x_test))\n",
- "x,y = x_test[i], y_test[i]\n",
- "\n",
- "# ---- Do prediction\n",
- "#\n",
- "predictions = loaded_model.predict( np.array([x]) )\n",
- "\n",
- "# ---- A prediction is just the output layer\n",
- "#\n",
- "print(\"\\nOutput layer from model is (x100) :\\n\")\n",
- "with np.printoptions(precision=2, suppress=True, linewidth=95):\n",
- " print(predictions*100)\n",
- "\n",
- "# ---- Graphic visualisation\n",
- "#\n",
- "print(\"\\nGraphically :\\n\")\n",
- "plt.figure(figsize=(12,2))\n",
- "plt.bar(range(43), predictions[0], align='center', alpha=0.5)\n",
- "plt.ylabel('Probability')\n",
- "plt.ylim((0,1))\n",
- "plt.xlabel('Class')\n",
- "plt.title('Trafic Sign prediction')\n",
- "pwk.save_fig('05-prediction-proba')\n",
- "plt.show()\n",
- "\n",
- "# ---- Predict class\n",
- "#\n",
- "p = np.argmax(predictions)\n",
- "\n",
- "# ---- Show result\n",
- "#\n",
- "print(\"\\nThe image : Prediction : Real stuff:\")\n",
- "pwk.plot_images([x,x_meta[p], x_meta[y]], [p,p,y], range(3), columns=3, x_size=3, y_size=2, save_as='06-prediction-images')\n",
- "\n",
- "if p==y:\n",
- " print(\"YEEES ! that's right!\")\n",
- "else:\n",
- " print(\"oups, that's wrong ;-(\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:54.032580Z",
- "iopub.status.busy": "2021-03-01T17:49:54.032018Z",
- "iopub.status.idle": "2021-03-01T17:49:54.034566Z",
- "shell.execute_reply": "2021-03-01T17:49:54.035080Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:49:54\n",
- "Duration is : 00:00:60 724ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"todo\">\n",
- " What you can do:\n",
- " <ul>\n",
- " <li>Limit model saving: 1 save every 5 epochs </li>\n",
- " <li>Use a subset of the dataset</li>\n",
- " <li>Try different datasets</li>\n",
- " <li>Some exotic signs are waiting to be recognized in dataset_dir/extra !</li>\n",
- " <li>Test different hyperparameters (epochs, batch size, optimization, etc.)</li>\n",
- " </ul>\n",
- " \n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/GTSRB/04-Data-augmentation.ipynb b/GTSRB/04-Data-augmentation.ipynb
index 289f1cb..2a5fa3f 100644
--- a/GTSRB/04-Data-augmentation.ipynb
+++ b/GTSRB/04-Data-augmentation.ipynb
@@ -60,7 +60,8 @@
"metadata": {},
"source": [
"### 1.2 - Parameters\n",
- "`scale` is the proportion of the dataset that will be used during the training. (1 mean 100%) "
+ "`scale` is the proportion of the dataset that will be used during the training. (1 mean 100%)\\\n",
+ "`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch"
]
},
{
@@ -72,11 +73,11 @@
"enhanced_dir = './data'\n",
"# enhanced_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
"\n",
- "dataset_name = 'set-24x24-L'\n",
- "batch_size = 64\n",
- "epochs = 20\n",
- "scale = 1\n",
- "\n"
+ "dataset_name = 'set-24x24-L'\n",
+ "batch_size = 64\n",
+ "epochs = 20\n",
+ "scale = 1\n",
+ "fit_verbosity = 1\n"
]
},
{
@@ -92,7 +93,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.override('enhanced_dir', 'dataset_name', 'batch_size', 'epochs', 'scale')"
+ "pwk.override('enhanced_dir', 'dataset_name', 'batch_size', 'epochs', 'scale', 'fit_verbosity')"
]
},
{
@@ -298,7 +299,7 @@
"history = model.fit( datagen.flow(x_train, y_train, batch_size=batch_size),\n",
" steps_per_epoch = int(x_train.shape[0]/batch_size),\n",
" epochs=epochs,\n",
- " verbose=1,\n",
+ " verbose=fit_verbosity,\n",
" validation_data=(x_test, y_test),\n",
" callbacks=[tensorboard_callback, bestmodel_callback, savemodel_callback] )\n",
"\n",
diff --git a/GTSRB/04-Data-augmentation==done==.ipynb b/GTSRB/04-Data-augmentation==done==.ipynb
deleted file mode 100644
index 523924d..0000000
--- a/GTSRB/04-Data-augmentation==done==.ipynb
+++ /dev/null
@@ -1,7062 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [GTSRB4] - Data augmentation \n",
- "<!-- DESC --> Episode 4 : Adding data by data augmentation when we lack it, to improve our results\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Trying to improve training by **enhancing the data**\n",
- " - Using Keras' **data augmentation utilities**, finding their limits...\n",
- " \n",
- "The German Traffic Sign Recognition Benchmark (GTSRB) is a dataset with more than 50,000 photos of road signs from about 40 classes. \n",
- "The final aim is to recognise them ! \n",
- "\n",
- "Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset\n",
- "\n",
- "\n",
- "## What we're going to do :\n",
- " - Increase and improve the training dataset\n",
- " - Identify the limits of these tools\n",
- "\n",
- "## Step 1 - Import and init\n",
- "### 1.1 - Python stuffs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:49:56.650823Z",
- "iopub.status.busy": "2021-03-01T17:49:56.650356Z",
- "iopub.status.idle": "2021-03-01T17:50:00.522797Z",
- "shell.execute_reply": "2021-03-01T17:50:00.523287Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { content:url(data:image/svg+xml;base64,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- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "run_dir : ./run/GTSRB4_done\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : GTSRB4\n",
- "Run time : Monday 01 March 2021, 18:50:00\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/GTSRB4_done\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/GTSRB4_done/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "from tensorflow.keras.callbacks import TensorBoard\n",
- "\n",
- "import numpy as np\n",
- "import h5py\n",
- "\n",
- "from sklearn.metrics import confusion_matrix\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import os, sys, time, random\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/GTSRB4.001'\n",
- "datasets_dir = pwk.init('GTSRB4', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "`scale` is the proportion of the dataset that will be used during the training. (1 mean 100%) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:00.526890Z",
- "iopub.status.busy": "2021-03-01T17:50:00.526417Z",
- "iopub.status.idle": "2021-03-01T17:50:00.528067Z",
- "shell.execute_reply": "2021-03-01T17:50:00.528537Z"
- }
- },
- "outputs": [],
- "source": [
- "enhanced_dir = './data'\n",
- "# enhanced_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
- "\n",
- "dataset_name = 'set-24x24-L'\n",
- "batch_size = 64\n",
- "epochs = 20\n",
- "scale = 1\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:00.532061Z",
- "iopub.status.busy": "2021-03-01T17:50:00.531584Z",
- "iopub.status.idle": "2021-03-01T17:50:00.534611Z",
- "shell.execute_reply": "2021-03-01T17:50:00.535098Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "enhanced_dir : /gpfswork/rech/mlh/uja62cb/datasets/GTSRB/enhanced\n",
- "dataset_name : set-24x24-L\n",
- "batch_size : 64\n",
- "epochs : 5\n",
- "scale : 1\n"
- ]
- }
- ],
- "source": [
- "pwk.override('enhanced_dir', 'dataset_name', 'batch_size', 'epochs', 'scale')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Load dataset\n",
- "Dataset is one of the saved dataset: RGB25, RGB35, L25, L35, etc. \n",
- "First of all, we're going to use a smart dataset : **set-24x24-L** \n",
- "(with a GPU, it only takes 35'' compared to more than 5' with a CPU !)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:00.968527Z",
- "iopub.status.busy": "2021-03-01T17:50:00.966390Z",
- "iopub.status.idle": "2021-03-01T17:50:01.031342Z",
- "shell.execute_reply": "2021-03-01T17:50:01.031834Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(39209, 24, 24, 1) (39209,)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset \"set-24x24-L\" is loaded and shuffled. (228.8 Mo in 00:00:00 487ms)\n"
- ]
- }
- ],
- "source": [
- "def read_dataset(enhanced_dir, dataset_name):\n",
- " '''Reads h5 dataset\n",
- " Args:\n",
- " filename : datasets filename\n",
- " dataset_name : dataset name, without .h5\n",
- " Returns: x_train,y_train, x_test,y_test data, x_meta,y_meta'''\n",
- " # ---- Read dataset\n",
- " pwk.chrono_start()\n",
- " filename = f'{enhanced_dir}/{dataset_name}.h5'\n",
- " with h5py.File(filename,'r') as f:\n",
- " x_train = f['x_train'][:]\n",
- " y_train = f['y_train'][:]\n",
- " x_test = f['x_test'][:]\n",
- " y_test = f['y_test'][:]\n",
- " x_meta = f['x_meta'][:]\n",
- " y_meta = f['y_meta'][:]\n",
- " print(x_train.shape, y_train.shape)\n",
- " # ---- Shuffle\n",
- " x_train,y_train=pwk.shuffle_np_dataset(x_train,y_train)\n",
- "\n",
- " # ---- done\n",
- " duration = pwk.chrono_stop(hdelay=True)\n",
- " size = pwk.hsize(os.path.getsize(filename))\n",
- " print(f'Dataset \"{dataset_name}\" is loaded and shuffled. ({size} in {duration})')\n",
- " return x_train,y_train, x_test,y_test, x_meta,y_meta\n",
- "\n",
- "# ---- Read dataset\n",
- "#\n",
- "x_train,y_train,x_test,y_test, x_meta,y_meta = read_dataset(enhanced_dir, dataset_name)\n",
- "\n",
- "# ---- Rescale \n",
- "#\n",
- "x_train,y_train, x_test,y_test = pwk.rescale_dataset(x_train,y_train,x_test,y_test, scale=scale)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Models\n",
- "We will now build a model and train it...\n",
- "\n",
- "This is my model ;-) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:01.038631Z",
- "iopub.status.busy": "2021-03-01T17:50:01.038156Z",
- "iopub.status.idle": "2021-03-01T17:50:01.039799Z",
- "shell.execute_reply": "2021-03-01T17:50:01.040282Z"
- }
- },
- "outputs": [],
- "source": [
- "# A basic model\n",
- "#\n",
- "def get_model_v1(lx,ly,lz):\n",
- " \n",
- " model = keras.models.Sequential()\n",
- " \n",
- " model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Flatten()) \n",
- " model.add( keras.layers.Dense(1500, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Callbacks \n",
- "We prepare 2 kind callbacks : TensorBoard and Model backup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:01.045223Z",
- "iopub.status.busy": "2021-03-01T17:50:01.044742Z",
- "iopub.status.idle": "2021-03-01T17:50:01.128903Z",
- "shell.execute_reply": "2021-03-01T17:50:01.129392Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "To run tensorboard :\n",
- "tensorboard --logdir /gpfsdswork/projects/rech/mlh/uja62cb/fidle/GTSRB/run/GTSRB4_done/logs\n"
- ]
- }
- ],
- "source": [
- "pwk.mkdir(run_dir + '/models')\n",
- "pwk.mkdir(run_dir + '/logs')\n",
- "\n",
- "# ---- Callback tensorboard\n",
- "log_dir = run_dir + \"/logs/tb_\" + pwk.tag_now()\n",
- "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
- "\n",
- "# ---- Callback ModelCheckpoint - Save best model\n",
- "save_dir = run_dir + \"/models/best-model.h5\"\n",
- "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)\n",
- "\n",
- "# ---- Callback ModelCheckpoint - Save model each epochs\n",
- "save_dir = run_dir + \"/models/model-{epoch:04d}.h5\"\n",
- "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0)\n",
- "\n",
- "path=os.path.abspath(f'{run_dir}/logs')\n",
- "print(f'To run tensorboard :\\ntensorboard --logdir {path}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Data generator"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:01.133334Z",
- "iopub.status.busy": "2021-03-01T17:50:01.132856Z",
- "iopub.status.idle": "2021-03-01T17:50:01.205911Z",
- "shell.execute_reply": "2021-03-01T17:50:01.205399Z"
- }
- },
- "outputs": [],
- "source": [
- "datagen = keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,\n",
- " featurewise_std_normalization=False,\n",
- " width_shift_range=0.1,\n",
- " height_shift_range=0.1,\n",
- " zoom_range=0.2,\n",
- " shear_range=0.1,\n",
- " rotation_range=10.)\n",
- "datagen.fit(x_train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Train the model\n",
- "**Get my data shape :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:01.209502Z",
- "iopub.status.busy": "2021-03-01T17:50:01.209027Z",
- "iopub.status.idle": "2021-03-01T17:50:01.211126Z",
- "shell.execute_reply": "2021-03-01T17:50:01.211600Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Images of the dataset have this folowing shape : (24, 24, 1)\n"
- ]
- }
- ],
- "source": [
- "(n,lx,ly,lz) = x_train.shape\n",
- "print(\"Images of the dataset have this folowing shape : \",(lx,ly,lz))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Get and compile a model, with the data shape :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:01.215044Z",
- "iopub.status.busy": "2021-03-01T17:50:01.214569Z",
- "iopub.status.idle": "2021-03-01T17:50:02.458056Z",
- "shell.execute_reply": "2021-03-01T17:50:02.458566Z"
- }
- },
- "outputs": [],
- "source": [
- "model = get_model_v1(lx,ly,lz)\n",
- "\n",
- "# model.summary()\n",
- "\n",
- "model.compile(optimizer='adam',\n",
- " loss='sparse_categorical_crossentropy',\n",
- " metrics=['accuracy'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Train it :** "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:02.463205Z",
- "iopub.status.busy": "2021-03-01T17:50:02.462719Z",
- "iopub.status.idle": "2021-03-01T17:50:59.164426Z",
- "shell.execute_reply": "2021-03-01T17:50:59.164928Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/5\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/612 [..............................] - ETA: 31:43 - loss: 3.7523 - accuracy: 0.0156"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/612 [..............................] - ETA: 34s - loss: 3.7342 - accuracy: 0.0243 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/612 [..............................] - ETA: 17s - loss: 3.7090 - accuracy: 0.0304"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 11/612 [..............................] - ETA: 13s - loss: 3.6837 - accuracy: 0.0354"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 15/612 [..............................] - ETA: 12s - loss: 3.6656 - accuracy: 0.0395"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 19/612 [..............................] - ETA: 11s - loss: 3.6512 - accuracy: 0.0428"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 23/612 [>.............................] - ETA: 10s - loss: 3.6404 - accuracy: 0.0454"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 27/612 [>.............................] - ETA: 10s - loss: 3.6305 - accuracy: 0.0475"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 31/612 [>.............................] - ETA: 10s - loss: 3.6231 - accuracy: 0.0491"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 35/612 [>.............................] - ETA: 9s - loss: 3.6158 - accuracy: 0.0501 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 39/612 [>.............................] - ETA: 9s - loss: 3.6096 - accuracy: 0.0509"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 43/612 [=>............................] - ETA: 9s - loss: 3.6042 - accuracy: 0.0518"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 47/612 [=>............................] - ETA: 9s - loss: 3.5989 - accuracy: 0.0527"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 51/612 [=>............................] - ETA: 9s - loss: 3.5940 - accuracy: 0.0536"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 55/612 [=>............................] - ETA: 9s - loss: 3.5889 - accuracy: 0.0546"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 59/612 [=>............................] - ETA: 8s - loss: 3.5839 - accuracy: 0.0556"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 63/612 [==>...........................] - ETA: 8s - loss: 3.5791 - accuracy: 0.0566"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 67/612 [==>...........................] - ETA: 8s - loss: 3.5744 - accuracy: 0.0576"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "375/612 [=================>............] - ETA: 3s - loss: 3.1715 - accuracy: 0.1523"
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- "379/612 [=================>............] - ETA: 3s - loss: 3.1654 - accuracy: 0.1538"
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- "383/612 [=================>............] - ETA: 3s - loss: 3.1593 - accuracy: 0.1552"
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- "387/612 [=================>............] - ETA: 3s - loss: 3.1532 - accuracy: 0.1567"
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- "391/612 [==================>...........] - ETA: 3s - loss: 3.1471 - accuracy: 0.1581"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "395/612 [==================>...........] - ETA: 3s - loss: 3.1411 - accuracy: 0.1595"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "399/612 [==================>...........] - ETA: 3s - loss: 3.1350 - accuracy: 0.1610"
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- "403/612 [==================>...........] - ETA: 3s - loss: 3.1290 - accuracy: 0.1624"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "407/612 [==================>...........] - ETA: 3s - loss: 3.1229 - accuracy: 0.1638"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "411/612 [===================>..........] - ETA: 3s - loss: 3.1169 - accuracy: 0.1653"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "415/612 [===================>..........] - ETA: 2s - loss: 3.1109 - accuracy: 0.1667"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "426/612 [===================>..........] - ETA: 2s - loss: 3.0946 - accuracy: 0.1706"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "434/612 [====================>.........] - ETA: 2s - loss: 3.0828 - accuracy: 0.1734"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "438/612 [====================>.........] - ETA: 2s - loss: 3.0769 - accuracy: 0.1748"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "442/612 [====================>.........] - ETA: 2s - loss: 3.0711 - accuracy: 0.1762"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "598/612 [============================>.] - ETA: 0s - loss: 2.8623 - accuracy: 0.2259"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "612/612 [==============================] - 14s 17ms/step - loss: 2.8441 - accuracy: 0.2302 - val_loss: 0.7962 - val_accuracy: 0.7733\n"
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- "Epoch 2/5\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "313/612 [==============>...............] - ETA: 4s - loss: 1.0485 - accuracy: 0.6818"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "509/612 [=======================>......] - ETA: 1s - loss: 1.0050 - accuracy: 0.6938"
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- "513/612 [========================>.....] - ETA: 1s - loss: 1.0042 - accuracy: 0.6940"
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- "517/612 [========================>.....] - ETA: 1s - loss: 1.0034 - accuracy: 0.6942"
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- "521/612 [========================>.....] - ETA: 1s - loss: 1.0025 - accuracy: 0.6944"
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- "525/612 [========================>.....] - ETA: 1s - loss: 1.0017 - accuracy: 0.6946"
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- "529/612 [========================>.....] - ETA: 1s - loss: 1.0009 - accuracy: 0.6949"
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- "533/612 [=========================>....] - ETA: 1s - loss: 1.0001 - accuracy: 0.6951"
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- "537/612 [=========================>....] - ETA: 1s - loss: 0.9993 - accuracy: 0.6953"
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- "541/612 [=========================>....] - ETA: 1s - loss: 0.9985 - accuracy: 0.6955"
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- "545/612 [=========================>....] - ETA: 1s - loss: 0.9977 - accuracy: 0.6958"
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- "549/612 [=========================>....] - ETA: 0s - loss: 0.9969 - accuracy: 0.6960"
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- "553/612 [==========================>...] - ETA: 0s - loss: 0.9961 - accuracy: 0.6962"
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- "557/612 [==========================>...] - ETA: 0s - loss: 0.9953 - accuracy: 0.6964"
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- "561/612 [==========================>...] - ETA: 0s - loss: 0.9945 - accuracy: 0.6966"
- ]
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- "565/612 [==========================>...] - ETA: 0s - loss: 0.9938 - accuracy: 0.6969"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "569/612 [==========================>...] - ETA: 0s - loss: 0.9930 - accuracy: 0.6971"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "573/612 [===========================>..] - ETA: 0s - loss: 0.9922 - accuracy: 0.6973"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "577/612 [===========================>..] - ETA: 0s - loss: 0.9915 - accuracy: 0.6975"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "581/612 [===========================>..] - ETA: 0s - loss: 0.9907 - accuracy: 0.6977"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "585/612 [===========================>..] - ETA: 0s - loss: 0.9899 - accuracy: 0.6979"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "589/612 [===========================>..] - ETA: 0s - loss: 0.9892 - accuracy: 0.6982"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "593/612 [============================>.] - ETA: 0s - loss: 0.9884 - accuracy: 0.6984"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "597/612 [============================>.] - ETA: 0s - loss: 0.9877 - accuracy: 0.6986"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "601/612 [============================>.] - ETA: 0s - loss: 0.9869 - accuracy: 0.6988"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "605/612 [============================>.] - ETA: 0s - loss: 0.9861 - accuracy: 0.6990"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "609/612 [============================>.] - ETA: 0s - loss: 0.9854 - accuracy: 0.6992"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "612/612 [==============================] - 10s 16ms/step - loss: 0.9846 - accuracy: 0.6994 - val_loss: 0.4180 - val_accuracy: 0.8839\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 3/5\n",
- "\r",
- " 1/612 [..............................] - ETA: 25s - loss: 0.9777 - accuracy: 0.6562"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/612 [..............................] - ETA: 9s - loss: 0.8317 - accuracy: 0.7175 "
- ]
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- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "553/612 [==========================>...] - ETA: 0s - loss: 0.6439 - accuracy: 0.8022"
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- "557/612 [==========================>...] - ETA: 0s - loss: 0.6435 - accuracy: 0.8023"
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- "565/612 [==========================>...] - ETA: 0s - loss: 0.6427 - accuracy: 0.8025"
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- "569/612 [==========================>...] - ETA: 0s - loss: 0.6423 - accuracy: 0.8026"
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- "577/612 [===========================>..] - ETA: 0s - loss: 0.6415 - accuracy: 0.8029"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "581/612 [===========================>..] - ETA: 0s - loss: 0.6412 - accuracy: 0.8030"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "585/612 [===========================>..] - ETA: 0s - loss: 0.6408 - accuracy: 0.8031"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "589/612 [===========================>..] - ETA: 0s - loss: 0.6404 - accuracy: 0.8032"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "593/612 [============================>.] - ETA: 0s - loss: 0.6400 - accuracy: 0.8033"
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "597/612 [============================>.] - ETA: 0s - loss: 0.6396 - accuracy: 0.8034"
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "612/612 [==============================] - 10s 16ms/step - loss: 0.6381 - accuracy: 0.8039 - val_loss: 0.3332 - val_accuracy: 0.9055\n"
- ]
- },
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- "output_type": "stream",
- "text": [
- "Epoch 4/5\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "597/612 [============================>.] - ETA: 0s - loss: 0.4634 - accuracy: 0.8534"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "609/612 [============================>.] - ETA: 0s - loss: 0.4630 - accuracy: 0.8536"
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "612/612 [==============================] - 10s 16ms/step - loss: 0.4628 - accuracy: 0.8536 - val_loss: 0.2505 - val_accuracy: 0.9306\n"
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- },
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- "Epoch 5/5\n",
- "\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 49/612 [=>............................] - ETA: 8s - loss: 0.3968 - accuracy: 0.8718"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 65/612 [==>...........................] - ETA: 8s - loss: 0.3964 - accuracy: 0.8722"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 69/612 [==>...........................] - ETA: 8s - loss: 0.3962 - accuracy: 0.8723"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "313/612 [==============>...............] - ETA: 4s - loss: 0.3822 - accuracy: 0.8791"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "545/612 [=========================>....] - ETA: 1s - loss: 0.3744 - accuracy: 0.8815"
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- "549/612 [=========================>....] - ETA: 0s - loss: 0.3742 - accuracy: 0.8816"
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- "553/612 [==========================>...] - ETA: 0s - loss: 0.3741 - accuracy: 0.8816"
- ]
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- "557/612 [==========================>...] - ETA: 0s - loss: 0.3740 - accuracy: 0.8817"
- ]
- },
- {
- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "561/612 [==========================>...] - ETA: 0s - loss: 0.3739 - accuracy: 0.8817"
- ]
- },
- {
- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "565/612 [==========================>...] - ETA: 0s - loss: 0.3737 - accuracy: 0.8817"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "569/612 [==========================>...] - ETA: 0s - loss: 0.3736 - accuracy: 0.8818"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "573/612 [===========================>..] - ETA: 0s - loss: 0.3735 - accuracy: 0.8818"
- ]
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "577/612 [===========================>..] - ETA: 0s - loss: 0.3733 - accuracy: 0.8819"
- ]
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "581/612 [===========================>..] - ETA: 0s - loss: 0.3732 - accuracy: 0.8819"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "585/612 [===========================>..] - ETA: 0s - loss: 0.3731 - accuracy: 0.8820"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "589/612 [===========================>..] - ETA: 0s - loss: 0.3729 - accuracy: 0.8820"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "593/612 [============================>.] - ETA: 0s - loss: 0.3728 - accuracy: 0.8820"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "597/612 [============================>.] - ETA: 0s - loss: 0.3727 - accuracy: 0.8821"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "601/612 [============================>.] - ETA: 0s - loss: 0.3725 - accuracy: 0.8821"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "605/612 [============================>.] - ETA: 0s - loss: 0.3724 - accuracy: 0.8822"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "609/612 [============================>.] - ETA: 0s - loss: 0.3723 - accuracy: 0.8822"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "612/612 [==============================] - 10s 16ms/step - loss: 0.3721 - accuracy: 0.8823 - val_loss: 0.2028 - val_accuracy: 0.9413\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Duration : 00:00:57 699ms\n"
- ]
- }
- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "history = model.fit( datagen.flow(x_train, y_train, batch_size=batch_size),\n",
- " steps_per_epoch = int(x_train.shape[0]/batch_size),\n",
- " epochs=epochs,\n",
- " verbose=1,\n",
- " validation_data=(x_test, y_test),\n",
- " callbacks=[tensorboard_callback, bestmodel_callback, savemodel_callback] )\n",
- "\n",
- "model.save(f'{run_dir}/models/last-model.h5')\n",
- "\n",
- "pwk.chrono_show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Evaluate it :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:59.168562Z",
- "iopub.status.busy": "2021-03-01T17:50:59.168095Z",
- "iopub.status.idle": "2021-03-01T17:50:59.170085Z",
- "shell.execute_reply": "2021-03-01T17:50:59.170560Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Max validation accuracy is : 0.9413\n"
- ]
- }
- ],
- "source": [
- "max_val_accuracy = max(history.history[\"val_accuracy\"])\n",
- "print(\"Max validation accuracy is : {:.4f}\".format(max_val_accuracy))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:59.173975Z",
- "iopub.status.busy": "2021-03-01T17:50:59.173501Z",
- "iopub.status.idle": "2021-03-01T17:50:59.655536Z",
- "shell.execute_reply": "2021-03-01T17:50:59.656042Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Test loss : 0.2028\n",
- "Test accuracy : 0.9413\n"
- ]
- }
- ],
- "source": [
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('Test loss : {:5.4f}'.format(score[0]))\n",
- "print('Test accuracy : {:5.4f}'.format(score[1]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7 - History\n",
- "The return of model.fit() returns us the learning history"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:50:59.673025Z",
- "iopub.status.busy": "2021-03-01T17:50:59.672542Z",
- "iopub.status.idle": "2021-03-01T17:51:00.809265Z",
- "shell.execute_reply": "2021-03-01T17:51:00.809756Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
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- ],
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, save_as='01-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 8 - Evaluate best model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 8.1 - Restore best model :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:00.813102Z",
- "iopub.status.busy": "2021-03-01T17:51:00.812631Z",
- "iopub.status.idle": "2021-03-01T17:51:00.961300Z",
- "shell.execute_reply": "2021-03-01T17:51:00.961789Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loaded.\n"
- ]
- }
- ],
- "source": [
- "loaded_model = tf.keras.models.load_model(f'{run_dir}/models/best-model.h5')\n",
- "# best_model.summary()\n",
- "print(\"Loaded.\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 8.2 - Evaluate it :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:00.965597Z",
- "iopub.status.busy": "2021-03-01T17:51:00.965132Z",
- "iopub.status.idle": "2021-03-01T17:51:01.648261Z",
- "shell.execute_reply": "2021-03-01T17:51:01.648759Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Test loss : 0.2028\n",
- "Test accuracy : 0.9413\n"
- ]
- }
- ],
- "source": [
- "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('Test loss : {:5.4f}'.format(score[0]))\n",
- "print('Test accuracy : {:5.4f}'.format(score[1]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Plot confusion matrix**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:01.653108Z",
- "iopub.status.busy": "2021-03-01T17:51:01.652630Z",
- "iopub.status.idle": "2021-03-01T17:51:29.633303Z",
- "shell.execute_reply": "2021-03-01T17:51:29.633808Z"
- }
- },
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1152x1152 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "y_sigmoid = model.predict(x_test)\n",
- "y_pred = np.argmax(y_sigmoid, axis=-1)\n",
- "\n",
- "cmap = plt.get_cmap('Oranges')\n",
- "pwk.plot_confusion_matrix(y_test,y_pred,range(43), figsize=(16, 16),normalize=False, cmap=cmap, save_as='02-confusion-matrix')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:29.637142Z",
- "iopub.status.busy": "2021-03-01T17:51:29.636670Z",
- "iopub.status.idle": "2021-03-01T17:51:29.639053Z",
- "shell.execute_reply": "2021-03-01T17:51:29.639528Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:51:29\n",
- "Duration is : 00:01:29 118ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"todo\">\n",
- " What you can do:\n",
- " <ul>\n",
- " <li>Try different datasets / models</li>\n",
- " <li>Test different hyperparameters (epochs, batch size, optimization, etc.)</li>\n",
- " <li>What's the best strategy? How to compare?</li>\n",
- " </ul>\n",
- " \n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/GTSRB/05-Full-convolutions.ipynb b/GTSRB/05-Full-convolutions.ipynb
index f684d94..a9e727e 100644
--- a/GTSRB/05-Full-convolutions.ipynb
+++ b/GTSRB/05-Full-convolutions.ipynb
@@ -62,7 +62,9 @@
"### 1.2 - Parameters\n",
"**Note:** \n",
"With a dataset of 20% and a scale of 0.1, it takes only 2-3' of a laptop... \n",
- "With a dataset of 100% and a scale of 1, it takes 30' on a V100 GPU ! "
+ "With a dataset of 100% and a scale of 1, it takes 30' on a V100 GPU !\n",
+ "\n",
+ "`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch"
]
},
{
@@ -75,31 +77,31 @@
"# enhanced_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
"\n",
"# ---- For tests\n",
- "datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-RGB']\n",
- "models = {'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}\n",
- "batch_size = 64\n",
- "epochs = 5\n",
- "scale = 0.1\n",
- "with_datagen = False\n",
- "verbose = 0\n",
+ "datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-RGB']\n",
+ "models = {'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}\n",
+ "batch_size = 64\n",
+ "epochs = 5\n",
+ "scale = 0.1\n",
+ "with_datagen = False\n",
+ "fit_verbosity = 0\n",
"\n",
"# ---- All possibilities\n",
- "# datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
- "# models = {'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}\n",
- "# batch_size = 64\n",
- "# epochs = 16\n",
- "# scale = 1\n",
- "# with_datagen = False\n",
- "# verbose = 0\n",
+ "# datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
+ "# models = {'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}\n",
+ "# batch_size = 64\n",
+ "# epochs = 16\n",
+ "# scale = 1\n",
+ "# with_datagen = False\n",
+ "# fit_verbosity = 0\n",
"\n",
"# ---- Data augmentation\n",
- "# datasets = ['set-48x48-RGB']\n",
- "# models = {'v2':'get_model_v2'}\n",
- "# batch_size = 64\n",
- "# epochs = 20\n",
- "# scale = 1\n",
- "# with_datagen = True\n",
- "# verbose = 0"
+ "# datasets = ['set-48x48-RGB']\n",
+ "# models = {'v2':'get_model_v2'}\n",
+ "# batch_size = 64\n",
+ "# epochs = 20\n",
+ "# scale = 1\n",
+ "# with_datagen = True\n",
+ "# fit_verbosity = 0"
]
},
{
@@ -115,7 +117,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.override('enhanced_dir', 'datasets', 'models', 'batch_size', 'epochs', 'scale', 'with_datagen', 'verbose')"
+ "pwk.override('enhanced_dir', 'datasets', 'models', 'batch_size', 'epochs', 'scale', 'with_datagen', 'fit_verbosity')"
]
},
{
@@ -300,7 +302,7 @@
"source": [
"def multi_run(enhanced_dir, datasets, models, datagen=None,\n",
" scale=1, batch_size=64, epochs=16, \n",
- " verbose=0, tag_id='last'):\n",
+ " fit_verbosity=0, tag_id='last'):\n",
" \"\"\"\n",
" Launches a dataset-model combination\n",
" args:\n",
@@ -311,7 +313,7 @@
" scale : % of dataset to use. 1 mean all. (1)\n",
" batch_size : Batch size (64)\n",
" epochs : Number of epochs (16)\n",
- " verbose : Verbose level (0)\n",
+ " fit_verbosity : Verbose level (0)\n",
" tag_id : postfix for report, logs and models dir (_last)\n",
" return:\n",
" report : Report as a dict for Pandas.\n",
@@ -369,7 +371,7 @@
" history = model.fit(x_train, y_train,\n",
" batch_size = batch_size,\n",
" epochs = epochs,\n",
- " verbose = verbose,\n",
+ " verbose = fit_verbosity,\n",
" validation_data = (x_test, y_test),\n",
" callbacks = [tensorboard_callback, bestmodel_callback])\n",
" else:\n",
@@ -378,7 +380,7 @@
" history = model.fit(datagen.flow(x_train, y_train, batch_size=batch_size),\n",
" steps_per_epoch = int(len(x_train)/batch_size),\n",
" epochs = epochs,\n",
- " verbose = verbose,\n",
+ " verbose = fit_verbosity,\n",
" validation_data = (x_test, y_test),\n",
" callbacks = [tensorboard_callback, bestmodel_callback])\n",
" \n",
@@ -438,7 +440,7 @@
" scale = scale,\n",
" batch_size = batch_size,\n",
" epochs = epochs,\n",
- " verbose = verbose,\n",
+ " fit_verbosity = fit_verbosity,\n",
" tag_id = tag_id)\n",
"\n",
"# ---- Save report\n",
diff --git a/GTSRB/05-Full-convolutions=1==done==.ipynb b/GTSRB/05-Full-convolutions=1==done==.ipynb
deleted file mode 100644
index ddef555..0000000
--- a/GTSRB/05-Full-convolutions=1==done==.ipynb
+++ /dev/null
@@ -1,1007 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [GTSRB5] - Full convolutions\n",
- "<!-- DESC --> Episode 5 : A lot of models, a lot of datasets and a lot of results.\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Try multiple solutions\n",
- " - Design a generic and batch-usable code\n",
- " \n",
- "The German Traffic Sign Recognition Benchmark (GTSRB) is a dataset with more than 50,000 photos of road signs from about 40 classes. \n",
- "The final aim is to recognise them ! \n",
- "Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset\n",
- "\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- "Our main steps:\n",
- " - Try n models with n datasets\n",
- " - Save a Pandas/h5 report\n",
- " - Write to be run in batch mode\n",
- "\n",
- "## Step 1 - Import and init\n",
- "### 1.1 - Python stuffs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:32.978130Z",
- "iopub.status.busy": "2021-03-01T17:51:32.977653Z",
- "iopub.status.idle": "2021-03-01T17:51:35.939404Z",
- "shell.execute_reply": "2021-03-01T17:51:35.939896Z"
- }
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- "outputs": [
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- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "run_dir : ./run/GTSRB5_done\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : GTSRB5\n",
- "Run time : Monday 01 March 2021, 18:51:35\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/GTSRB5_done\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/GTSRB5_done/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "import numpy as np\n",
- "import h5py\n",
- "import sys,os,time,json\n",
- "import random\n",
- "from IPython.display import display\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "VERSION='1.6'\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as ooo\n",
- "\n",
- "run_dir = './run/GTSRB5'\n",
- "datasets_dir = pwk.init('GTSRB5', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "**Note:** \n",
- "With a dataset of 20% and a scale of 0.1, it takes only 2-3' of a laptop... \n",
- "With a dataset of 100% and a scale of 1, it takes 30' on a V100 GPU ! "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:35.944300Z",
- "iopub.status.busy": "2021-03-01T17:51:35.943811Z",
- "iopub.status.idle": "2021-03-01T17:51:35.945447Z",
- "shell.execute_reply": "2021-03-01T17:51:35.945929Z"
- }
- },
- "outputs": [],
- "source": [
- "enhanced_dir = f'./data'\n",
- "# enhanced_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
- "\n",
- "# ---- For tests\n",
- "datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-RGB']\n",
- "models = {'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}\n",
- "batch_size = 64\n",
- "epochs = 5\n",
- "scale = 0.1\n",
- "with_datagen = False\n",
- "verbose = 0\n",
- "\n",
- "# ---- All possibilities\n",
- "# datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
- "# models = {'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}\n",
- "# batch_size = 64\n",
- "# epochs = 16\n",
- "# scale = 1\n",
- "# with_datagen = False\n",
- "# verbose = 0\n",
- "\n",
- "# ---- Data augmentation\n",
- "# datasets = ['set-48x48-RGB']\n",
- "# models = {'v2':'get_model_v2'}\n",
- "# batch_size = 64\n",
- "# epochs = 20\n",
- "# scale = 1\n",
- "# with_datagen = True\n",
- "# verbose = 0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:35.949612Z",
- "iopub.status.busy": "2021-03-01T17:51:35.949147Z",
- "iopub.status.idle": "2021-03-01T17:51:35.952385Z",
- "shell.execute_reply": "2021-03-01T17:51:35.952857Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "enhanced_dir : /gpfswork/rech/mlh/uja62cb/datasets/GTSRB/enhanced\n",
- "datasets : ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
- "models : {'v1': 'get_model_v1', 'v2': 'get_model_v2', 'v3': 'get_model_v3'}\n",
- "batch_size : 64\n",
- "epochs : 16\n",
- "scale : 1\n",
- "with_datagen : False\n",
- "verbose : 0\n"
- ]
- }
- ],
- "source": [
- "pwk.override('enhanced_dir', 'datasets', 'models', 'batch_size', 'epochs', 'scale', 'with_datagen', 'verbose')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Start"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:35.958073Z",
- "iopub.status.busy": "2021-03-01T17:51:35.957439Z",
- "iopub.status.idle": "2021-03-01T17:51:35.961045Z",
- "shell.execute_reply": "2021-03-01T17:51:35.961522Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Full Convolutions Notebook :\n",
- " Version : 1.6\n",
- " Now is : Monday 01 March 2021 - 18h51m35s\n",
- " OAR id : ??\n",
- " SLURM id : 2925\n",
- " Tag id : 036674\n",
- " Working directory : /gpfsdswork/projects/rech/mlh/uja62cb/fidle/GTSRB\n",
- " Output directory : ./run/GTSRB5_done\n",
- " for tensorboard : --logdir ./run/GTSRB5_done\n"
- ]
- }
- ],
- "source": [
- "random.seed(time.time())\n",
- "\n",
- "# ---- Where I am ?\n",
- "now = time.strftime(\"%A %d %B %Y - %Hh%Mm%Ss\")\n",
- "here = os.getcwd()\n",
- "tag_id = '{:06}'.format(random.randint(0,99999))\n",
- "\n",
- "# ---- Who I am ?\n",
- "oar_id = os.getenv(\"OAR_JOB_ID\", \"??\")\n",
- "slurm_id = os.getenv(\"SLURM_JOBID\", \"??\")\n",
- "\n",
- "print('Full Convolutions Notebook :')\n",
- "print(' Version : ', VERSION )\n",
- "print(' Now is : ', now )\n",
- "print(' OAR id : ', oar_id )\n",
- "print(' SLURM id : ', slurm_id )\n",
- "print(' Tag id : ', tag_id )\n",
- "print(' Working directory : ', here )\n",
- "print(' Output directory : ', run_dir )\n",
- "print(' for tensorboard : ', f'--logdir {run_dir}')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:35.964132Z",
- "iopub.status.busy": "2021-03-01T17:51:35.963659Z",
- "iopub.status.idle": "2021-03-01T17:51:35.965280Z",
- "shell.execute_reply": "2021-03-01T17:51:35.965752Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- Uncomment for batch tests\n",
- "#\n",
- "# print(\"\\n\\n*** Test mode - Exit before making big treatments... ***\\n\\n\")\n",
- "# sys.exit()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Dataset loading"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:35.970949Z",
- "iopub.status.busy": "2021-03-01T17:51:35.970464Z",
- "iopub.status.idle": "2021-03-01T17:51:35.972575Z",
- "shell.execute_reply": "2021-03-01T17:51:35.972094Z"
- }
- },
- "outputs": [],
- "source": [
- "def read_dataset(enhanced_dir, dataset_name):\n",
- " '''Reads h5 dataset from dataset_dir\n",
- " Args:\n",
- " dataset_dir : datasets dir\n",
- " name : dataset name, without .h5\n",
- " Returns: x_train,y_train,x_test,y_test data'''\n",
- " # ---- Read dataset\n",
- " filename = f'{enhanced_dir}/{dataset_name}.h5'\n",
- " size = os.path.getsize(filename)/(1024*1024)\n",
- "\n",
- " with h5py.File(filename,'r') as f:\n",
- " x_train = f['x_train'][:]\n",
- " y_train = f['y_train'][:]\n",
- " x_test = f['x_test'][:]\n",
- " y_test = f['y_test'][:]\n",
- "\n",
- " # ---- Shuffle\n",
- " x_train,y_train=pwk.shuffle_np_dataset(x_train,y_train)\n",
- "\n",
- " # ---- done\n",
- " return x_train,y_train,x_test,y_test,size"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Models collection"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:35.987163Z",
- "iopub.status.busy": "2021-03-01T17:51:35.979589Z",
- "iopub.status.idle": "2021-03-01T17:51:35.991659Z",
- "shell.execute_reply": "2021-03-01T17:51:35.991177Z"
- }
- },
- "outputs": [],
- "source": [
- "\n",
- "# A basic model\n",
- "#\n",
- "def get_model_v1(lx,ly,lz):\n",
- " \n",
- " model = keras.models.Sequential()\n",
- " \n",
- " model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Flatten()) \n",
- " model.add( keras.layers.Dense(1500, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model\n",
- " \n",
- "# A more sophisticated model\n",
- "#\n",
- "def get_model_v2(lx,ly,lz):\n",
- " model = keras.models.Sequential()\n",
- "\n",
- " model.add( keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=(lx,ly,lz), activation='relu'))\n",
- " model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
- " model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))\n",
- " model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Flatten())\n",
- " model.add( keras.layers.Dense(512, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model\n",
- "\n",
- "def get_model_v3(lx,ly,lz):\n",
- " model = keras.models.Sequential()\n",
- " model.add(tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation='relu', input_shape=(lx,ly,lz)))\n",
- " model.add(tf.keras.layers.BatchNormalization(axis=-1)) \n",
- " model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add(tf.keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add(tf.keras.layers.Conv2D(64, (5, 5), padding='same', activation='relu'))\n",
- " model.add(tf.keras.layers.BatchNormalization(axis=-1))\n",
- " model.add(tf.keras.layers.Conv2D(128, (5, 5), padding='same', activation='relu'))\n",
- " model.add(tf.keras.layers.BatchNormalization(axis=-1))\n",
- " model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add(tf.keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add(tf.keras.layers.Flatten())\n",
- " model.add(tf.keras.layers.Dense(512, activation='relu'))\n",
- " model.add(tf.keras.layers.BatchNormalization())\n",
- " model.add(tf.keras.layers.Dropout(0.4))\n",
- "\n",
- " model.add(tf.keras.layers.Dense(43, activation='softmax'))\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Multiple datasets, multiple models ;-)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:36.004683Z",
- "iopub.status.busy": "2021-03-01T17:51:36.002387Z",
- "iopub.status.idle": "2021-03-01T17:51:36.006733Z",
- "shell.execute_reply": "2021-03-01T17:51:36.006255Z"
- }
- },
- "outputs": [],
- "source": [
- "def multi_run(enhanced_dir, datasets, models, datagen=None,\n",
- " scale=1, batch_size=64, epochs=16, \n",
- " verbose=0, tag_id='last'):\n",
- " \"\"\"\n",
- " Launches a dataset-model combination\n",
- " args:\n",
- " enhanced_dir : Directory of the enhanced datasets\n",
- " datasets : List of dataset (whitout .h5)\n",
- " models : List of model like { \"model name\":get_model(), ...}\n",
- " datagen : Data generator or None (None)\n",
- " scale : % of dataset to use. 1 mean all. (1)\n",
- " batch_size : Batch size (64)\n",
- " epochs : Number of epochs (16)\n",
- " verbose : Verbose level (0)\n",
- " tag_id : postfix for report, logs and models dir (_last)\n",
- " return:\n",
- " report : Report as a dict for Pandas.\n",
- " \"\"\" \n",
- " # ---- Logs and models dir\n",
- " #\n",
- " os.makedirs(f'{run_dir}/logs_{tag_id}', mode=0o750, exist_ok=True)\n",
- " os.makedirs(f'{run_dir}/models_{tag_id}', mode=0o750, exist_ok=True)\n",
- " \n",
- " # ---- Columns of output\n",
- " #\n",
- " output={}\n",
- " output['Dataset'] = []\n",
- " output['Size'] = []\n",
- " for m in models:\n",
- " output[m+'_Accuracy'] = []\n",
- " output[m+'_Duration'] = []\n",
- "\n",
- " # ---- Let's go\n",
- " #\n",
- " for d_name in datasets:\n",
- " print(\"\\nDataset : \",d_name)\n",
- "\n",
- " # ---- Read dataset\n",
- " x_train,y_train,x_test,y_test, d_size = read_dataset(enhanced_dir, d_name)\n",
- " output['Dataset'].append(d_name)\n",
- " output['Size'].append(d_size)\n",
- " \n",
- " # ---- Rescale\n",
- " x_train,y_train,x_test,y_test = pwk.rescale_dataset(x_train,y_train,x_test,y_test, scale=scale)\n",
- " \n",
- " # ---- Get the shape\n",
- " (n,lx,ly,lz) = x_train.shape\n",
- "\n",
- " # ---- For each model\n",
- " for m_name,m_function in models.items():\n",
- " print(\" Run model {} : \".format(m_name), end='')\n",
- " # ---- get model\n",
- " try:\n",
- " # ---- get function by name\n",
- " m_function=globals()[m_function]\n",
- " model=m_function(lx,ly,lz)\n",
- " # ---- Compile it\n",
- " model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
- " # ---- Callbacks tensorboard\n",
- " log_dir = f'{run_dir}/logs_{tag_id}/tb_{d_name}_{m_name}'\n",
- " tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
- " # ---- Callbacks bestmodel\n",
- " save_dir = f'{run_dir}/models_{tag_id}/model_{d_name}_{m_name}.h5'\n",
- " bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)\n",
- " # ---- Train\n",
- " start_time = time.time()\n",
- " if datagen==None:\n",
- " # ---- No data augmentation (datagen=None) --------------------------------------\n",
- " history = model.fit(x_train, y_train,\n",
- " batch_size = batch_size,\n",
- " epochs = epochs,\n",
- " verbose = verbose,\n",
- " validation_data = (x_test, y_test),\n",
- " callbacks = [tensorboard_callback, bestmodel_callback])\n",
- " else:\n",
- " # ---- Data augmentation (datagen given) ----------------------------------------\n",
- " datagen.fit(x_train)\n",
- " history = model.fit(datagen.flow(x_train, y_train, batch_size=batch_size),\n",
- " steps_per_epoch = int(len(x_train)/batch_size),\n",
- " epochs = epochs,\n",
- " verbose = verbose,\n",
- " validation_data = (x_test, y_test),\n",
- " callbacks = [tensorboard_callback, bestmodel_callback])\n",
- " \n",
- " # ---- Result\n",
- " end_time = time.time()\n",
- " duration = end_time-start_time\n",
- " accuracy = max(history.history[\"val_accuracy\"])*100\n",
- " #\n",
- " output[m_name+'_Accuracy'].append(accuracy)\n",
- " output[m_name+'_Duration'].append(duration)\n",
- " print(f\"Accuracy={accuracy: 7.2f} Duration={duration: 7.2f}\")\n",
- " except:\n",
- " raise\n",
- " output[m_name+'_Accuracy'].append('0')\n",
- " output[m_name+'_Duration'].append('999')\n",
- " print('-')\n",
- " return output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Run !"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:51:36.012741Z",
- "iopub.status.busy": "2021-03-01T17:51:36.012261Z",
- "iopub.status.idle": "2021-03-01T18:23:11.857490Z",
- "shell.execute_reply": "2021-03-01T18:23:11.858007Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "---- Run --------------------------------------------------\n",
- "\n",
- "Dataset : set-24x24-L\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.65 Duration= 44.83\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.48 Duration= 48.64\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.39 Duration= 46.81\n",
- "\n",
- "Dataset : set-24x24-RGB\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.30 Duration= 41.46\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.22 Duration= 50.56\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.21 Duration= 48.00\n",
- "\n",
- "Dataset : set-48x48-L\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.90 Duration= 126.03\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.92 Duration= 110.96\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.48 Duration= 87.86\n",
- "\n",
- "Dataset : set-48x48-RGB\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.35 Duration= 130.73\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 98.79 Duration= 115.73\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.93 Duration= 89.29\n",
- "\n",
- "Dataset : set-24x24-L-LHE\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.77 Duration= 40.96\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.69 Duration= 49.11\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 94.99 Duration= 46.99\n",
- "\n",
- "Dataset : set-24x24-RGB-HE\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.04 Duration= 42.81\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.63 Duration= 50.85\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 94.46 Duration= 48.10\n",
- "\n",
- "Dataset : set-48x48-L-LHE\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.57 Duration= 125.16\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.71 Duration= 111.26\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.66 Duration= 86.69\n",
- "\n",
- "Dataset : set-48x48-RGB-HE\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.23 Duration= 131.88\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.65 Duration= 116.19\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.82 Duration= 90.30\n",
- "\n",
- "Report saved as ./run/GTSRB5_done/report_036674.json\n",
- "\n",
- "Duration : 00:31:36 842ms\n",
- "-----------------------------------------------------------\n"
- ]
- }
- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "print('\\n---- Run','-'*50)\n",
- "\n",
- "\n",
- "# ---- Data augmentation or not\n",
- "#\n",
- "if with_datagen :\n",
- " datagen = keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,\n",
- " featurewise_std_normalization=False,\n",
- " width_shift_range=0.1,\n",
- " height_shift_range=0.1,\n",
- " zoom_range=0.2,\n",
- " shear_range=0.1,\n",
- " rotation_range=10.)\n",
- "else:\n",
- " datagen=None\n",
- " \n",
- "# ---- Run\n",
- "#\n",
- "output = multi_run(enhanced_dir,\n",
- " datasets, \n",
- " models,\n",
- " datagen = datagen,\n",
- " scale = scale,\n",
- " batch_size = batch_size,\n",
- " epochs = epochs,\n",
- " verbose = verbose,\n",
- " tag_id = tag_id)\n",
- "\n",
- "# ---- Save report\n",
- "#\n",
- "report={}\n",
- "report['output']=output\n",
- "report['description'] = f'scale={scale} batch_size={batch_size} epochs={epochs} data_aug={with_datagen}'\n",
- "\n",
- "report_name=f'{run_dir}/report_{tag_id}.json'\n",
- "\n",
- "with open(report_name, 'w') as file:\n",
- " json.dump(report, file, indent=4)\n",
- "\n",
- "print('\\nReport saved as ',report_name)\n",
- "\n",
- "pwk.chrono_show()\n",
- "print('-'*59)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7 - That's all folks.."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:23:11.861464Z",
- "iopub.status.busy": "2021-03-01T18:23:11.860992Z",
- "iopub.status.idle": "2021-03-01T18:23:11.863813Z",
- "shell.execute_reply": "2021-03-01T18:23:11.863318Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 19:23:11\n",
- "Duration is : 00:31:36 926ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/GTSRB/05-Full-convolutions=2==done==.ipynb b/GTSRB/05-Full-convolutions=2==done==.ipynb
deleted file mode 100644
index 2f29f84..0000000
--- a/GTSRB/05-Full-convolutions=2==done==.ipynb
+++ /dev/null
@@ -1,1007 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [GTSRB5] - Full convolutions\n",
- "<!-- DESC --> Episode 5 : A lot of models, a lot of datasets and a lot of results.\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Try multiple solutions\n",
- " - Design a generic and batch-usable code\n",
- " \n",
- "The German Traffic Sign Recognition Benchmark (GTSRB) is a dataset with more than 50,000 photos of road signs from about 40 classes. \n",
- "The final aim is to recognise them ! \n",
- "Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset\n",
- "\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- "Our main steps:\n",
- " - Try n models with n datasets\n",
- " - Save a Pandas/h5 report\n",
- " - Write to be run in batch mode\n",
- "\n",
- "## Step 1 - Import and init\n",
- "### 1.1 - Python stuffs"
- ]
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- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
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- "metadata": {},
- "output_type": "display_data"
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "run_dir : ./run/GTSRB5_done\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : GTSRB5\n",
- "Run time : Monday 01 March 2021, 19:23:22\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/GTSRB5_done\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/GTSRB5_done/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "import numpy as np\n",
- "import h5py\n",
- "import sys,os,time,json\n",
- "import random\n",
- "from IPython.display import display\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "VERSION='1.6'\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as ooo\n",
- "\n",
- "run_dir = './run/GTSRB5'\n",
- "datasets_dir = pwk.init('GTSRB5', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "**Note:** \n",
- "With a dataset of 20% and a scale of 0.1, it takes only 2-3' of a laptop... \n",
- "With a dataset of 100% and a scale of 1, it takes 30' on a V100 GPU ! "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:23:22.354329Z",
- "iopub.status.busy": "2021-03-01T18:23:22.353844Z",
- "iopub.status.idle": "2021-03-01T18:23:22.355541Z",
- "shell.execute_reply": "2021-03-01T18:23:22.356018Z"
- }
- },
- "outputs": [],
- "source": [
- "enhanced_dir = f'./data'\n",
- "# enhanced_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
- "\n",
- "# ---- For tests\n",
- "datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-RGB']\n",
- "models = {'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}\n",
- "batch_size = 64\n",
- "epochs = 5\n",
- "scale = 0.1\n",
- "with_datagen = False\n",
- "verbose = 0\n",
- "\n",
- "# ---- All possibilities\n",
- "# datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
- "# models = {'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}\n",
- "# batch_size = 64\n",
- "# epochs = 16\n",
- "# scale = 1\n",
- "# with_datagen = False\n",
- "# verbose = 0\n",
- "\n",
- "# ---- Data augmentation\n",
- "# datasets = ['set-48x48-RGB']\n",
- "# models = {'v2':'get_model_v2'}\n",
- "# batch_size = 64\n",
- "# epochs = 20\n",
- "# scale = 1\n",
- "# with_datagen = True\n",
- "# verbose = 0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:23:22.359649Z",
- "iopub.status.busy": "2021-03-01T18:23:22.359184Z",
- "iopub.status.idle": "2021-03-01T18:23:22.362483Z",
- "shell.execute_reply": "2021-03-01T18:23:22.362972Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "enhanced_dir : /gpfswork/rech/mlh/uja62cb/datasets/GTSRB/enhanced\n",
- "datasets : ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
- "models : {'v1': 'get_model_v1', 'v2': 'get_model_v2', 'v3': 'get_model_v3'}\n",
- "batch_size : 64\n",
- "epochs : 16\n",
- "scale : 1\n",
- "with_datagen : False\n",
- "verbose : 0\n"
- ]
- }
- ],
- "source": [
- "pwk.override('enhanced_dir', 'datasets', 'models', 'batch_size', 'epochs', 'scale', 'with_datagen', 'verbose')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Start"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:23:22.368205Z",
- "iopub.status.busy": "2021-03-01T18:23:22.367565Z",
- "iopub.status.idle": "2021-03-01T18:23:22.371710Z",
- "shell.execute_reply": "2021-03-01T18:23:22.371220Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Full Convolutions Notebook :\n",
- " Version : 1.6\n",
- " Now is : Monday 01 March 2021 - 19h23m22s\n",
- " OAR id : ??\n",
- " SLURM id : 2925\n",
- " Tag id : 030435\n",
- " Working directory : /gpfsdswork/projects/rech/mlh/uja62cb/fidle/GTSRB\n",
- " Output directory : ./run/GTSRB5_done\n",
- " for tensorboard : --logdir ./run/GTSRB5_done\n"
- ]
- }
- ],
- "source": [
- "random.seed(time.time())\n",
- "\n",
- "# ---- Where I am ?\n",
- "now = time.strftime(\"%A %d %B %Y - %Hh%Mm%Ss\")\n",
- "here = os.getcwd()\n",
- "tag_id = '{:06}'.format(random.randint(0,99999))\n",
- "\n",
- "# ---- Who I am ?\n",
- "oar_id = os.getenv(\"OAR_JOB_ID\", \"??\")\n",
- "slurm_id = os.getenv(\"SLURM_JOBID\", \"??\")\n",
- "\n",
- "print('Full Convolutions Notebook :')\n",
- "print(' Version : ', VERSION )\n",
- "print(' Now is : ', now )\n",
- "print(' OAR id : ', oar_id )\n",
- "print(' SLURM id : ', slurm_id )\n",
- "print(' Tag id : ', tag_id )\n",
- "print(' Working directory : ', here )\n",
- "print(' Output directory : ', run_dir )\n",
- "print(' for tensorboard : ', f'--logdir {run_dir}')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:23:22.374310Z",
- "iopub.status.busy": "2021-03-01T18:23:22.373829Z",
- "iopub.status.idle": "2021-03-01T18:23:22.375465Z",
- "shell.execute_reply": "2021-03-01T18:23:22.375945Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- Uncomment for batch tests\n",
- "#\n",
- "# print(\"\\n\\n*** Test mode - Exit before making big treatments... ***\\n\\n\")\n",
- "# sys.exit()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Dataset loading"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:23:22.381106Z",
- "iopub.status.busy": "2021-03-01T18:23:22.380627Z",
- "iopub.status.idle": "2021-03-01T18:23:22.382269Z",
- "shell.execute_reply": "2021-03-01T18:23:22.382734Z"
- }
- },
- "outputs": [],
- "source": [
- "def read_dataset(enhanced_dir, dataset_name):\n",
- " '''Reads h5 dataset from dataset_dir\n",
- " Args:\n",
- " dataset_dir : datasets dir\n",
- " name : dataset name, without .h5\n",
- " Returns: x_train,y_train,x_test,y_test data'''\n",
- " # ---- Read dataset\n",
- " filename = f'{enhanced_dir}/{dataset_name}.h5'\n",
- " size = os.path.getsize(filename)/(1024*1024)\n",
- "\n",
- " with h5py.File(filename,'r') as f:\n",
- " x_train = f['x_train'][:]\n",
- " y_train = f['y_train'][:]\n",
- " x_test = f['x_test'][:]\n",
- " y_test = f['y_test'][:]\n",
- "\n",
- " # ---- Shuffle\n",
- " x_train,y_train=pwk.shuffle_np_dataset(x_train,y_train)\n",
- "\n",
- " # ---- done\n",
- " return x_train,y_train,x_test,y_test,size"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Models collection"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:23:22.399744Z",
- "iopub.status.busy": "2021-03-01T18:23:22.394832Z",
- "iopub.status.idle": "2021-03-01T18:23:22.401867Z",
- "shell.execute_reply": "2021-03-01T18:23:22.401382Z"
- }
- },
- "outputs": [],
- "source": [
- "\n",
- "# A basic model\n",
- "#\n",
- "def get_model_v1(lx,ly,lz):\n",
- " \n",
- " model = keras.models.Sequential()\n",
- " \n",
- " model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Flatten()) \n",
- " model.add( keras.layers.Dense(1500, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model\n",
- " \n",
- "# A more sophisticated model\n",
- "#\n",
- "def get_model_v2(lx,ly,lz):\n",
- " model = keras.models.Sequential()\n",
- "\n",
- " model.add( keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=(lx,ly,lz), activation='relu'))\n",
- " model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
- " model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))\n",
- " model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Flatten())\n",
- " model.add( keras.layers.Dense(512, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model\n",
- "\n",
- "def get_model_v3(lx,ly,lz):\n",
- " model = keras.models.Sequential()\n",
- " model.add(tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation='relu', input_shape=(lx,ly,lz)))\n",
- " model.add(tf.keras.layers.BatchNormalization(axis=-1)) \n",
- " model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add(tf.keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add(tf.keras.layers.Conv2D(64, (5, 5), padding='same', activation='relu'))\n",
- " model.add(tf.keras.layers.BatchNormalization(axis=-1))\n",
- " model.add(tf.keras.layers.Conv2D(128, (5, 5), padding='same', activation='relu'))\n",
- " model.add(tf.keras.layers.BatchNormalization(axis=-1))\n",
- " model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add(tf.keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add(tf.keras.layers.Flatten())\n",
- " model.add(tf.keras.layers.Dense(512, activation='relu'))\n",
- " model.add(tf.keras.layers.BatchNormalization())\n",
- " model.add(tf.keras.layers.Dropout(0.4))\n",
- "\n",
- " model.add(tf.keras.layers.Dense(43, activation='softmax'))\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Multiple datasets, multiple models ;-)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:23:22.412708Z",
- "iopub.status.busy": "2021-03-01T18:23:22.408971Z",
- "iopub.status.idle": "2021-03-01T18:23:22.416521Z",
- "shell.execute_reply": "2021-03-01T18:23:22.416994Z"
- }
- },
- "outputs": [],
- "source": [
- "def multi_run(enhanced_dir, datasets, models, datagen=None,\n",
- " scale=1, batch_size=64, epochs=16, \n",
- " verbose=0, tag_id='last'):\n",
- " \"\"\"\n",
- " Launches a dataset-model combination\n",
- " args:\n",
- " enhanced_dir : Directory of the enhanced datasets\n",
- " datasets : List of dataset (whitout .h5)\n",
- " models : List of model like { \"model name\":get_model(), ...}\n",
- " datagen : Data generator or None (None)\n",
- " scale : % of dataset to use. 1 mean all. (1)\n",
- " batch_size : Batch size (64)\n",
- " epochs : Number of epochs (16)\n",
- " verbose : Verbose level (0)\n",
- " tag_id : postfix for report, logs and models dir (_last)\n",
- " return:\n",
- " report : Report as a dict for Pandas.\n",
- " \"\"\" \n",
- " # ---- Logs and models dir\n",
- " #\n",
- " os.makedirs(f'{run_dir}/logs_{tag_id}', mode=0o750, exist_ok=True)\n",
- " os.makedirs(f'{run_dir}/models_{tag_id}', mode=0o750, exist_ok=True)\n",
- " \n",
- " # ---- Columns of output\n",
- " #\n",
- " output={}\n",
- " output['Dataset'] = []\n",
- " output['Size'] = []\n",
- " for m in models:\n",
- " output[m+'_Accuracy'] = []\n",
- " output[m+'_Duration'] = []\n",
- "\n",
- " # ---- Let's go\n",
- " #\n",
- " for d_name in datasets:\n",
- " print(\"\\nDataset : \",d_name)\n",
- "\n",
- " # ---- Read dataset\n",
- " x_train,y_train,x_test,y_test, d_size = read_dataset(enhanced_dir, d_name)\n",
- " output['Dataset'].append(d_name)\n",
- " output['Size'].append(d_size)\n",
- " \n",
- " # ---- Rescale\n",
- " x_train,y_train,x_test,y_test = pwk.rescale_dataset(x_train,y_train,x_test,y_test, scale=scale)\n",
- " \n",
- " # ---- Get the shape\n",
- " (n,lx,ly,lz) = x_train.shape\n",
- "\n",
- " # ---- For each model\n",
- " for m_name,m_function in models.items():\n",
- " print(\" Run model {} : \".format(m_name), end='')\n",
- " # ---- get model\n",
- " try:\n",
- " # ---- get function by name\n",
- " m_function=globals()[m_function]\n",
- " model=m_function(lx,ly,lz)\n",
- " # ---- Compile it\n",
- " model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
- " # ---- Callbacks tensorboard\n",
- " log_dir = f'{run_dir}/logs_{tag_id}/tb_{d_name}_{m_name}'\n",
- " tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
- " # ---- Callbacks bestmodel\n",
- " save_dir = f'{run_dir}/models_{tag_id}/model_{d_name}_{m_name}.h5'\n",
- " bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)\n",
- " # ---- Train\n",
- " start_time = time.time()\n",
- " if datagen==None:\n",
- " # ---- No data augmentation (datagen=None) --------------------------------------\n",
- " history = model.fit(x_train, y_train,\n",
- " batch_size = batch_size,\n",
- " epochs = epochs,\n",
- " verbose = verbose,\n",
- " validation_data = (x_test, y_test),\n",
- " callbacks = [tensorboard_callback, bestmodel_callback])\n",
- " else:\n",
- " # ---- Data augmentation (datagen given) ----------------------------------------\n",
- " datagen.fit(x_train)\n",
- " history = model.fit(datagen.flow(x_train, y_train, batch_size=batch_size),\n",
- " steps_per_epoch = int(len(x_train)/batch_size),\n",
- " epochs = epochs,\n",
- " verbose = verbose,\n",
- " validation_data = (x_test, y_test),\n",
- " callbacks = [tensorboard_callback, bestmodel_callback])\n",
- " \n",
- " # ---- Result\n",
- " end_time = time.time()\n",
- " duration = end_time-start_time\n",
- " accuracy = max(history.history[\"val_accuracy\"])*100\n",
- " #\n",
- " output[m_name+'_Accuracy'].append(accuracy)\n",
- " output[m_name+'_Duration'].append(duration)\n",
- " print(f\"Accuracy={accuracy: 7.2f} Duration={duration: 7.2f}\")\n",
- " except:\n",
- " raise\n",
- " output[m_name+'_Accuracy'].append('0')\n",
- " output[m_name+'_Duration'].append('999')\n",
- " print('-')\n",
- " return output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Run !"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:23:22.423037Z",
- "iopub.status.busy": "2021-03-01T18:23:22.422551Z",
- "iopub.status.idle": "2021-03-01T18:54:53.863523Z",
- "shell.execute_reply": "2021-03-01T18:54:53.864028Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "---- Run --------------------------------------------------\n",
- "\n",
- "Dataset : set-24x24-L\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.16 Duration= 42.29\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.94 Duration= 49.96\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.45 Duration= 45.31\n",
- "\n",
- "Dataset : set-24x24-RGB\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.44 Duration= 42.62\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.90 Duration= 50.83\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.33 Duration= 47.31\n",
- "\n",
- "Dataset : set-48x48-L\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.29 Duration= 127.53\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 98.15 Duration= 112.37\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.61 Duration= 86.86\n",
- "\n",
- "Dataset : set-48x48-RGB\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.43 Duration= 131.29\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 98.29 Duration= 116.55\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.80 Duration= 88.39\n",
- "\n",
- "Dataset : set-24x24-L-LHE\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.68 Duration= 39.48\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.88 Duration= 48.77\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.46 Duration= 46.59\n",
- "\n",
- "Dataset : set-24x24-RGB-HE\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.47 Duration= 41.96\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.70 Duration= 51.60\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 94.75 Duration= 47.60\n",
- "\n",
- "Dataset : set-48x48-L-LHE\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.83 Duration= 126.21\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 98.20 Duration= 110.61\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.89 Duration= 86.22\n",
- "\n",
- "Dataset : set-48x48-RGB-HE\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v1 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 95.28 Duration= 130.89\n",
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.94 Duration= 116.05\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 96.46 Duration= 89.50\n",
- "\n",
- "Report saved as ./run/GTSRB5_done/report_030435.json\n",
- "\n",
- "Duration : 00:31:31 438ms\n",
- "-----------------------------------------------------------\n"
- ]
- }
- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "print('\\n---- Run','-'*50)\n",
- "\n",
- "\n",
- "# ---- Data augmentation or not\n",
- "#\n",
- "if with_datagen :\n",
- " datagen = keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,\n",
- " featurewise_std_normalization=False,\n",
- " width_shift_range=0.1,\n",
- " height_shift_range=0.1,\n",
- " zoom_range=0.2,\n",
- " shear_range=0.1,\n",
- " rotation_range=10.)\n",
- "else:\n",
- " datagen=None\n",
- " \n",
- "# ---- Run\n",
- "#\n",
- "output = multi_run(enhanced_dir,\n",
- " datasets, \n",
- " models,\n",
- " datagen = datagen,\n",
- " scale = scale,\n",
- " batch_size = batch_size,\n",
- " epochs = epochs,\n",
- " verbose = verbose,\n",
- " tag_id = tag_id)\n",
- "\n",
- "# ---- Save report\n",
- "#\n",
- "report={}\n",
- "report['output']=output\n",
- "report['description'] = f'scale={scale} batch_size={batch_size} epochs={epochs} data_aug={with_datagen}'\n",
- "\n",
- "report_name=f'{run_dir}/report_{tag_id}.json'\n",
- "\n",
- "with open(report_name, 'w') as file:\n",
- " json.dump(report, file, indent=4)\n",
- "\n",
- "print('\\nReport saved as ',report_name)\n",
- "\n",
- "pwk.chrono_show()\n",
- "print('-'*59)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7 - That's all folks.."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:54:53.867403Z",
- "iopub.status.busy": "2021-03-01T18:54:53.866938Z",
- "iopub.status.idle": "2021-03-01T18:54:53.869791Z",
- "shell.execute_reply": "2021-03-01T18:54:53.869310Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 19:54:53\n",
- "Duration is : 00:31:32 522ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/GTSRB/05-Full-convolutions=3==done==.ipynb b/GTSRB/05-Full-convolutions=3==done==.ipynb
deleted file mode 100644
index 6b5e89c..0000000
--- a/GTSRB/05-Full-convolutions=3==done==.ipynb
+++ /dev/null
@@ -1,799 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [GTSRB5] - Full convolutions\n",
- "<!-- DESC --> Episode 5 : A lot of models, a lot of datasets and a lot of results.\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Try multiple solutions\n",
- " - Design a generic and batch-usable code\n",
- " \n",
- "The German Traffic Sign Recognition Benchmark (GTSRB) is a dataset with more than 50,000 photos of road signs from about 40 classes. \n",
- "The final aim is to recognise them ! \n",
- "Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset\n",
- "\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- "Our main steps:\n",
- " - Try n models with n datasets\n",
- " - Save a Pandas/h5 report\n",
- " - Write to be run in batch mode\n",
- "\n",
- "## Step 1 - Import and init\n",
- "### 1.1 - Python stuffs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:54:56.730624Z",
- "iopub.status.busy": "2021-03-01T18:54:56.730114Z",
- "iopub.status.idle": "2021-03-01T18:55:00.547340Z",
- "shell.execute_reply": "2021-03-01T18:55:00.546760Z"
- }
- },
- "outputs": [
- {
- "data": {
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- "**\\*\\* Overrided parameters : \\*\\***"
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- "text": [
- "run_dir : ./run/GTSRB5_done\n"
- ]
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- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
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- "text": [
- "Version : 2.0.17\n",
- "Notebook id : GTSRB5\n",
- "Run time : Monday 01 March 2021, 19:55:00\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/GTSRB5_done\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/GTSRB5_done/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "import numpy as np\n",
- "import h5py\n",
- "import sys,os,time,json\n",
- "import random\n",
- "from IPython.display import display\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "VERSION='1.6'\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as ooo\n",
- "\n",
- "run_dir = './run/GTSRB5'\n",
- "datasets_dir = pwk.init('GTSRB5', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "**Note:** \n",
- "With a dataset of 20% and a scale of 0.1, it takes only 2-3' of a laptop... \n",
- "With a dataset of 100% and a scale of 1, it takes 30' on a V100 GPU ! "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:55:00.551643Z",
- "iopub.status.busy": "2021-03-01T18:55:00.551168Z",
- "iopub.status.idle": "2021-03-01T18:55:00.552840Z",
- "shell.execute_reply": "2021-03-01T18:55:00.553324Z"
- }
- },
- "outputs": [],
- "source": [
- "enhanced_dir = f'./data'\n",
- "# enhanced_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
- "\n",
- "# ---- For tests\n",
- "datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-RGB']\n",
- "models = {'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}\n",
- "batch_size = 64\n",
- "epochs = 5\n",
- "scale = 0.1\n",
- "with_datagen = False\n",
- "verbose = 0\n",
- "\n",
- "# ---- All possibilities\n",
- "# datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
- "# models = {'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}\n",
- "# batch_size = 64\n",
- "# epochs = 16\n",
- "# scale = 1\n",
- "# with_datagen = False\n",
- "# verbose = 0\n",
- "\n",
- "# ---- Data augmentation\n",
- "# datasets = ['set-48x48-RGB']\n",
- "# models = {'v2':'get_model_v2'}\n",
- "# batch_size = 64\n",
- "# epochs = 20\n",
- "# scale = 1\n",
- "# with_datagen = True\n",
- "# verbose = 0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:55:00.556908Z",
- "iopub.status.busy": "2021-03-01T18:55:00.556437Z",
- "iopub.status.idle": "2021-03-01T18:55:00.559700Z",
- "shell.execute_reply": "2021-03-01T18:55:00.560179Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "enhanced_dir : /gpfswork/rech/mlh/uja62cb/datasets/GTSRB/enhanced\n",
- "datasets : ['set-48x48-L', 'set-48x48-RGB']\n",
- "models : {'v2': 'get_model_v2', 'v3': 'get_model_v3'}\n",
- "batch_size : 64\n",
- "epochs : 16\n",
- "scale : 1\n",
- "with_datagen : True\n",
- "verbose : 0\n"
- ]
- }
- ],
- "source": [
- "pwk.override('enhanced_dir', 'datasets', 'models', 'batch_size', 'epochs', 'scale', 'with_datagen', 'verbose')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Start"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:55:00.565807Z",
- "iopub.status.busy": "2021-03-01T18:55:00.565340Z",
- "iopub.status.idle": "2021-03-01T18:55:00.568836Z",
- "shell.execute_reply": "2021-03-01T18:55:00.568355Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Full Convolutions Notebook :\n",
- " Version : 1.6\n",
- " Now is : Monday 01 March 2021 - 19h55m00s\n",
- " OAR id : ??\n",
- " SLURM id : 2925\n",
- " Tag id : 012352\n",
- " Working directory : /gpfsdswork/projects/rech/mlh/uja62cb/fidle/GTSRB\n",
- " Output directory : ./run/GTSRB5_done\n",
- " for tensorboard : --logdir ./run/GTSRB5_done\n"
- ]
- }
- ],
- "source": [
- "random.seed(time.time())\n",
- "\n",
- "# ---- Where I am ?\n",
- "now = time.strftime(\"%A %d %B %Y - %Hh%Mm%Ss\")\n",
- "here = os.getcwd()\n",
- "tag_id = '{:06}'.format(random.randint(0,99999))\n",
- "\n",
- "# ---- Who I am ?\n",
- "oar_id = os.getenv(\"OAR_JOB_ID\", \"??\")\n",
- "slurm_id = os.getenv(\"SLURM_JOBID\", \"??\")\n",
- "\n",
- "print('Full Convolutions Notebook :')\n",
- "print(' Version : ', VERSION )\n",
- "print(' Now is : ', now )\n",
- "print(' OAR id : ', oar_id )\n",
- "print(' SLURM id : ', slurm_id )\n",
- "print(' Tag id : ', tag_id )\n",
- "print(' Working directory : ', here )\n",
- "print(' Output directory : ', run_dir )\n",
- "print(' for tensorboard : ', f'--logdir {run_dir}')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:55:00.571409Z",
- "iopub.status.busy": "2021-03-01T18:55:00.570947Z",
- "iopub.status.idle": "2021-03-01T18:55:00.572540Z",
- "shell.execute_reply": "2021-03-01T18:55:00.573010Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- Uncomment for batch tests\n",
- "#\n",
- "# print(\"\\n\\n*** Test mode - Exit before making big treatments... ***\\n\\n\")\n",
- "# sys.exit()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Dataset loading"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:55:00.578164Z",
- "iopub.status.busy": "2021-03-01T18:55:00.577684Z",
- "iopub.status.idle": "2021-03-01T18:55:00.579332Z",
- "shell.execute_reply": "2021-03-01T18:55:00.579794Z"
- }
- },
- "outputs": [],
- "source": [
- "def read_dataset(enhanced_dir, dataset_name):\n",
- " '''Reads h5 dataset from dataset_dir\n",
- " Args:\n",
- " dataset_dir : datasets dir\n",
- " name : dataset name, without .h5\n",
- " Returns: x_train,y_train,x_test,y_test data'''\n",
- " # ---- Read dataset\n",
- " filename = f'{enhanced_dir}/{dataset_name}.h5'\n",
- " size = os.path.getsize(filename)/(1024*1024)\n",
- "\n",
- " with h5py.File(filename,'r') as f:\n",
- " x_train = f['x_train'][:]\n",
- " y_train = f['y_train'][:]\n",
- " x_test = f['x_test'][:]\n",
- " y_test = f['y_test'][:]\n",
- "\n",
- " # ---- Shuffle\n",
- " x_train,y_train=pwk.shuffle_np_dataset(x_train,y_train)\n",
- "\n",
- " # ---- done\n",
- " return x_train,y_train,x_test,y_test,size"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Models collection"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:55:00.596775Z",
- "iopub.status.busy": "2021-03-01T18:55:00.591899Z",
- "iopub.status.idle": "2021-03-01T18:55:00.598353Z",
- "shell.execute_reply": "2021-03-01T18:55:00.598816Z"
- }
- },
- "outputs": [],
- "source": [
- "\n",
- "# A basic model\n",
- "#\n",
- "def get_model_v1(lx,ly,lz):\n",
- " \n",
- " model = keras.models.Sequential()\n",
- " \n",
- " model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D((2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Flatten()) \n",
- " model.add( keras.layers.Dense(1500, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model\n",
- " \n",
- "# A more sophisticated model\n",
- "#\n",
- "def get_model_v2(lx,ly,lz):\n",
- " model = keras.models.Sequential()\n",
- "\n",
- " model.add( keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=(lx,ly,lz), activation='relu'))\n",
- " model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
- " model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))\n",
- " model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
- " model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add( keras.layers.Flatten())\n",
- " model.add( keras.layers.Dense(512, activation='relu'))\n",
- " model.add( keras.layers.Dropout(0.5))\n",
- " model.add( keras.layers.Dense(43, activation='softmax'))\n",
- " return model\n",
- "\n",
- "def get_model_v3(lx,ly,lz):\n",
- " model = keras.models.Sequential()\n",
- " model.add(tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation='relu', input_shape=(lx,ly,lz)))\n",
- " model.add(tf.keras.layers.BatchNormalization(axis=-1)) \n",
- " model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add(tf.keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add(tf.keras.layers.Conv2D(64, (5, 5), padding='same', activation='relu'))\n",
- " model.add(tf.keras.layers.BatchNormalization(axis=-1))\n",
- " model.add(tf.keras.layers.Conv2D(128, (5, 5), padding='same', activation='relu'))\n",
- " model.add(tf.keras.layers.BatchNormalization(axis=-1))\n",
- " model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add(tf.keras.layers.Dropout(0.2))\n",
- "\n",
- " model.add(tf.keras.layers.Flatten())\n",
- " model.add(tf.keras.layers.Dense(512, activation='relu'))\n",
- " model.add(tf.keras.layers.BatchNormalization())\n",
- " model.add(tf.keras.layers.Dropout(0.4))\n",
- "\n",
- " model.add(tf.keras.layers.Dense(43, activation='softmax'))\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Multiple datasets, multiple models ;-)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:55:00.609613Z",
- "iopub.status.busy": "2021-03-01T18:55:00.605923Z",
- "iopub.status.idle": "2021-03-01T18:55:00.613370Z",
- "shell.execute_reply": "2021-03-01T18:55:00.613832Z"
- }
- },
- "outputs": [],
- "source": [
- "def multi_run(enhanced_dir, datasets, models, datagen=None,\n",
- " scale=1, batch_size=64, epochs=16, \n",
- " verbose=0, tag_id='last'):\n",
- " \"\"\"\n",
- " Launches a dataset-model combination\n",
- " args:\n",
- " enhanced_dir : Directory of the enhanced datasets\n",
- " datasets : List of dataset (whitout .h5)\n",
- " models : List of model like { \"model name\":get_model(), ...}\n",
- " datagen : Data generator or None (None)\n",
- " scale : % of dataset to use. 1 mean all. (1)\n",
- " batch_size : Batch size (64)\n",
- " epochs : Number of epochs (16)\n",
- " verbose : Verbose level (0)\n",
- " tag_id : postfix for report, logs and models dir (_last)\n",
- " return:\n",
- " report : Report as a dict for Pandas.\n",
- " \"\"\" \n",
- " # ---- Logs and models dir\n",
- " #\n",
- " os.makedirs(f'{run_dir}/logs_{tag_id}', mode=0o750, exist_ok=True)\n",
- " os.makedirs(f'{run_dir}/models_{tag_id}', mode=0o750, exist_ok=True)\n",
- " \n",
- " # ---- Columns of output\n",
- " #\n",
- " output={}\n",
- " output['Dataset'] = []\n",
- " output['Size'] = []\n",
- " for m in models:\n",
- " output[m+'_Accuracy'] = []\n",
- " output[m+'_Duration'] = []\n",
- "\n",
- " # ---- Let's go\n",
- " #\n",
- " for d_name in datasets:\n",
- " print(\"\\nDataset : \",d_name)\n",
- "\n",
- " # ---- Read dataset\n",
- " x_train,y_train,x_test,y_test, d_size = read_dataset(enhanced_dir, d_name)\n",
- " output['Dataset'].append(d_name)\n",
- " output['Size'].append(d_size)\n",
- " \n",
- " # ---- Rescale\n",
- " x_train,y_train,x_test,y_test = pwk.rescale_dataset(x_train,y_train,x_test,y_test, scale=scale)\n",
- " \n",
- " # ---- Get the shape\n",
- " (n,lx,ly,lz) = x_train.shape\n",
- "\n",
- " # ---- For each model\n",
- " for m_name,m_function in models.items():\n",
- " print(\" Run model {} : \".format(m_name), end='')\n",
- " # ---- get model\n",
- " try:\n",
- " # ---- get function by name\n",
- " m_function=globals()[m_function]\n",
- " model=m_function(lx,ly,lz)\n",
- " # ---- Compile it\n",
- " model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
- " # ---- Callbacks tensorboard\n",
- " log_dir = f'{run_dir}/logs_{tag_id}/tb_{d_name}_{m_name}'\n",
- " tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
- " # ---- Callbacks bestmodel\n",
- " save_dir = f'{run_dir}/models_{tag_id}/model_{d_name}_{m_name}.h5'\n",
- " bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)\n",
- " # ---- Train\n",
- " start_time = time.time()\n",
- " if datagen==None:\n",
- " # ---- No data augmentation (datagen=None) --------------------------------------\n",
- " history = model.fit(x_train, y_train,\n",
- " batch_size = batch_size,\n",
- " epochs = epochs,\n",
- " verbose = verbose,\n",
- " validation_data = (x_test, y_test),\n",
- " callbacks = [tensorboard_callback, bestmodel_callback])\n",
- " else:\n",
- " # ---- Data augmentation (datagen given) ----------------------------------------\n",
- " datagen.fit(x_train)\n",
- " history = model.fit(datagen.flow(x_train, y_train, batch_size=batch_size),\n",
- " steps_per_epoch = int(len(x_train)/batch_size),\n",
- " epochs = epochs,\n",
- " verbose = verbose,\n",
- " validation_data = (x_test, y_test),\n",
- " callbacks = [tensorboard_callback, bestmodel_callback])\n",
- " \n",
- " # ---- Result\n",
- " end_time = time.time()\n",
- " duration = end_time-start_time\n",
- " accuracy = max(history.history[\"val_accuracy\"])*100\n",
- " #\n",
- " output[m_name+'_Accuracy'].append(accuracy)\n",
- " output[m_name+'_Duration'].append(duration)\n",
- " print(f\"Accuracy={accuracy: 7.2f} Duration={duration: 7.2f}\")\n",
- " except:\n",
- " raise\n",
- " output[m_name+'_Accuracy'].append('0')\n",
- " output[m_name+'_Duration'].append('999')\n",
- " print('-')\n",
- " return output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Run !"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T18:55:00.620017Z",
- "iopub.status.busy": "2021-03-01T18:55:00.619537Z",
- "iopub.status.idle": "2021-03-01T19:19:32.853947Z",
- "shell.execute_reply": "2021-03-01T19:19:32.854446Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "---- Run --------------------------------------------------\n",
- "\n",
- "Dataset : set-48x48-L\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 98.95 Duration= 244.58\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 98.20 Duration= 248.09\n",
- "\n",
- "Dataset : set-48x48-RGB\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Run model v2 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 99.10 Duration= 487.52\n",
- " Run model v3 : "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy= 97.92 Duration= 486.87\n",
- "\n",
- "Report saved as ./run/GTSRB5_done/report_012352.json\n",
- "\n",
- "Duration : 00:24:32 232ms\n",
- "-----------------------------------------------------------\n"
- ]
- }
- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "print('\\n---- Run','-'*50)\n",
- "\n",
- "\n",
- "# ---- Data augmentation or not\n",
- "#\n",
- "if with_datagen :\n",
- " datagen = keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,\n",
- " featurewise_std_normalization=False,\n",
- " width_shift_range=0.1,\n",
- " height_shift_range=0.1,\n",
- " zoom_range=0.2,\n",
- " shear_range=0.1,\n",
- " rotation_range=10.)\n",
- "else:\n",
- " datagen=None\n",
- " \n",
- "# ---- Run\n",
- "#\n",
- "output = multi_run(enhanced_dir,\n",
- " datasets, \n",
- " models,\n",
- " datagen = datagen,\n",
- " scale = scale,\n",
- " batch_size = batch_size,\n",
- " epochs = epochs,\n",
- " verbose = verbose,\n",
- " tag_id = tag_id)\n",
- "\n",
- "# ---- Save report\n",
- "#\n",
- "report={}\n",
- "report['output']=output\n",
- "report['description'] = f'scale={scale} batch_size={batch_size} epochs={epochs} data_aug={with_datagen}'\n",
- "\n",
- "report_name=f'{run_dir}/report_{tag_id}.json'\n",
- "\n",
- "with open(report_name, 'w') as file:\n",
- " json.dump(report, file, indent=4)\n",
- "\n",
- "print('\\nReport saved as ',report_name)\n",
- "\n",
- "pwk.chrono_show()\n",
- "print('-'*59)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7 - That's all folks.."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:32.857821Z",
- "iopub.status.busy": "2021-03-01T19:19:32.857361Z",
- "iopub.status.idle": "2021-03-01T19:19:32.860186Z",
- "shell.execute_reply": "2021-03-01T19:19:32.859689Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 20:19:32\n",
- "Duration is : 00:24:32 315ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/GTSRB/06-Notebook-as-a-batch.ipynb b/GTSRB/06-Notebook-as-a-batch.ipynb
index 9955854..08825ec 100644
--- a/GTSRB/06-Notebook-as-a-batch.ipynb
+++ b/GTSRB/06-Notebook-as-a-batch.ipynb
@@ -106,7 +106,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Example at IDRIS\n",
+ "#### Example at IDRIS, using **slurm**\n",
"\n",
"On the frontal :\n",
"```bash\n",
@@ -131,7 +131,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Example at GRICAD\n",
+ "#### Example at GRICAD, using **oar**\n",
"\n",
"Have to be done on the frontal :\n",
"```bash\n",
diff --git a/GTSRB/06-Notebook-as-a-batch==done==.ipynb b/GTSRB/06-Notebook-as-a-batch==done==.ipynb
deleted file mode 100644
index ca02e67..0000000
--- a/GTSRB/06-Notebook-as-a-batch==done==.ipynb
+++ /dev/null
@@ -1,232 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [GTSRB6] - Full convolutions as a batch\n",
- "<!-- DESC --> Episode 6 : To compute bigger, use your notebook in batch mode\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Run a notebook code as a **job**\n",
- " - Follow up with Tensorboard\n",
- " \n",
- "The German Traffic Sign Recognition Benchmark (GTSRB) is a dataset with more than 50,000 photos of road signs from about 40 classes. \n",
- "The final aim is to recognise them ! \n",
- "Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset\n",
- "\n",
- "\n",
- "## What we're going to do :\n",
- "Our main steps:\n",
- " - Run Full-convolution.ipynb as a batch :\n",
- " - Notebook mode\n",
- " - Script mode \n",
- " - Tensorboard follow up"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - How to run a notebook in a command line ?\n",
- "\n",
- "Two simple solutions are possible :-)\n",
- "\n",
- " - **Option 1 - As a notebook ! (a good choice)**\n",
- "\n",
- " Very simple. \n",
- " The result is the executed notebook, so we can retrieve all the cell'soutputs of the notebook : \n",
- " ```jupyter nbconvert (...) --to notebook --execute <notebook>``` \n",
- "\n",
- " Example : \n",
- " ```jupyter nbconvert --ExecutePreprocessor.timeout=-1 --to notebook --execute my_notebook.ipynb'``` \n",
- " The result will be a notebook: 'my_notebook.nbconvert.ipynb'.\n",
- " \n",
- " See: [nbconvert documentation](https://nbconvert.readthedocs.io/en/latest/usage.html#convert-notebook)\n",
- "\n",
- " **Note :** Do not forget the option: --ExecutePreprocessor.timeout=-1\n",
- "\n",
- " - **Option 2 - As a script**\n",
- "\n",
- " Very simple too, but with some constraints on the notebook. \n",
- " We will convert the notebook to a Python script (IPython, to be precise) : \n",
- " ```jupyter nbconvert --to script <notebook>``` \n",
- " \n",
- " Then we can execute this script : \n",
- " ```ipython <script>```\n",
- " \n",
- " See: [nbconvert documentation](https://nbconvert.readthedocs.io/en/latest/usage.html#executable-script)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - How to run a notebook in a batch ?\n",
- "\n",
- "Maybe not always the best solution, but this solution is very rustic ! \n",
- "\n",
- "### 2.1 - As a notebook ! (better choice)\n",
- "\n",
- "A direct execution with nbconvert (see option 1) is probably the best solution. \n",
- "This allows to recover a complete notebook (graphics, traces, ...).\n",
- "\n",
- "### 2.2 - As a IPython script :\n",
- "**Important :** The generated python script must be executed with the iPython interpreter.\n",
- "\n",
- "Example :\n",
- "`$ ipython my_converted_notebook.py`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:36.018777Z",
- "iopub.status.busy": "2021-03-01T19:19:36.018318Z",
- "iopub.status.idle": "2021-03-01T19:19:39.522049Z",
- "shell.execute_reply": "2021-03-01T19:19:39.521449Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[NbConvertApp] Converting notebook 05-Full-convolutions.ipynb to script\r\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[NbConvertApp] Writing 13151 bytes to 05-full_convolutions.py\r\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-rw-r--r-- 1 uja62cb mlh 13151 Mar 1 20:19 05-full_convolutions.py\r\n"
- ]
- }
- ],
- "source": [
- "! jupyter nbconvert --to script --output='05-full_convolutions' '05-Full-convolutions.ipynb'\n",
- "! ls -l *.py"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 - Batch submission\n",
- "\n",
- " See the two examples of bash launch script :\n",
- " - [batch_slurm.sh](batch_slurm.sh) using Slurm (like at IDRIS)\n",
- " - [batch_oar.sh](batch_oar.sh) using OAR (like at GRICAD)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Example at IDRIS\n",
- "\n",
- "On the frontal :\n",
- "```bash\n",
- "# hostname\n",
- "jean-zay2\n",
- "\n",
- "# sbatch $WORK/fidle/GTSRB/batch_slurm.sh \n",
- "Submitted batch job 249794\n",
- "\n",
- "#squeue -u $USER\n",
- " JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)\n",
- " 249794 gpu_p1 GTSRB Fu jde45kb PD 0:00 1 (Resources)\n",
- "\n",
- "# ls -l _batch/\n",
- "total 32769\n",
- "-rw-r--r-- 1 jde45kb gensim07 13349 Sep 10 11:32 GTSRB_249794.err\n",
- "-rw-r--r-- 1 jde45kb gensim07 489 Sep 10 11:31 GTSRB_249794.out\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Example at GRICAD\n",
- "\n",
- "Have to be done on the frontal :\n",
- "```bash\n",
- "# hostname\n",
- "f-dahu\n",
- "\n",
- "# oarsub -S ~/fidle/GTSRB/batch_oar.sh\n",
- "[GPUNODE] Adding gpu node restriction\n",
- "[ADMISSION RULE] Modify resource description with type constraints\n",
- "\n",
- "#oarstat -u\n",
- "Job id S User Duration System message\n",
- "--------- - -------- ---------- ------------------------------------------------\n",
- "5878410 R watsonb 0:19:56 R=8,W=1:0:0,J=I,P=fidle,T=gpu (Karma=0.005,quota_ok)\n",
- "5896266 W watsonb 0:00:00 R=8,W=1:0:0,J=B,N=Full convolutions,P=fidle,T=gpu\n",
- "\n",
- "# ls -l\n",
- "total 8\n",
- "-rw-r--r-- 1 watsonb l-simap 0 Feb 28 15:58 batch_oar_5896266.err\n",
- "-rw-r--r-- 1 watsonb l-simap 5703 Feb 28 15:58 batch_oar_5896266.out\n",
- "```\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "----\n",
- "**What you can do :**\n",
- "\n",
- "(If you have a calculation environment with a scheduler...)\n",
- "Your mission if you accept it: Run our full_convolution code in batch mode.<br>\n",
- " For that :\n",
- " - Validate the full_convolution notebook on short tests</li>\n",
- " - Submit it in batch mode for validation</li>\n",
- " - Modify the notebook for a full run and submit it :-)</li>\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/GTSRB/07-Show-report==done==.ipynb b/GTSRB/07-Show-report==done==.ipynb
deleted file mode 100644
index 44ffcc9..0000000
--- a/GTSRB/07-Show-report==done==.ipynb
+++ /dev/null
@@ -1,640 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [GTSRB7] - Batch reports\n",
- "<!-- DESC --> Episode 7 : Displaying our jobs report, and the winner is...\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Compare the results of different dataset-model combinations\n",
- "\n",
- "Les rapports (format json) sont générés par les jobs \"Full convolution\" [GTS5][GTS6]\n",
- "\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Read json files and display results\n",
- "\n",
- "## Step 1 - Import and init\n",
- "### 1.1 - Python stuffs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:40.570271Z",
- "iopub.status.busy": "2021-03-01T19:19:40.569790Z",
- "iopub.status.idle": "2021-03-01T19:19:44.419683Z",
- "shell.execute_reply": "2021-03-01T19:19:44.420174Z"
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content:url(data:image/svg+xml;base64,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- " font-size:0.8em;\n",
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- ],
- "text/plain": [
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- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "run_dir : ./run/GTSRB7_done\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : GTSRB7\n",
- "Run time : Monday 01 March 2021, 20:19:44\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/GTSRB7_done\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/GTSRB7_done/figs\n"
- ]
- }
- ],
- "source": [
- "import pandas as pd\n",
- "import sys,os,glob,json\n",
- "from pathlib import Path\n",
- "from IPython.display import display, Markdown\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/GTSRB7'\n",
- "datasets_dir = pwk.init('GTSRB7', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "Where to find the report"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:44.423470Z",
- "iopub.status.busy": "2021-03-01T19:19:44.423007Z",
- "iopub.status.idle": "2021-03-01T19:19:44.424648Z",
- "shell.execute_reply": "2021-03-01T19:19:44.425133Z"
- }
- },
- "outputs": [],
- "source": [
- "report_dir = './run/GTSRB5'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:44.428055Z",
- "iopub.status.busy": "2021-03-01T19:19:44.427587Z",
- "iopub.status.idle": "2021-03-01T19:19:44.430599Z",
- "shell.execute_reply": "2021-03-01T19:19:44.431080Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**\\*\\* Overrided parameters : \\*\\***"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "report_dir : ./run/GTSRB5_done\n"
- ]
- }
- ],
- "source": [
- "pwk.override('report_dir')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Few nice functions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:44.438655Z",
- "iopub.status.busy": "2021-03-01T19:19:44.438173Z",
- "iopub.status.idle": "2021-03-01T19:19:44.439818Z",
- "shell.execute_reply": "2021-03-01T19:19:44.440288Z"
- }
- },
- "outputs": [],
- "source": [
- "def highlight_max(s):\n",
- " is_max = (s == s.max())\n",
- " return ['background-color: yellow' if v else '' for v in is_max]\n",
- "\n",
- "def show_report(file):\n",
- " # ---- Read json file\n",
- " with open(file) as infile:\n",
- " dict_report = json.load( infile )\n",
- " output = dict_report['output']\n",
- " description = dict_report['description']\n",
- " # ---- about\n",
- " pwk.subtitle(f'Report : {Path(file).stem}')\n",
- " print( \"Desc. : \",description,'\\n')\n",
- " # ---- Create a pandas\n",
- " report = pd.DataFrame (output)\n",
- " col_accuracy = [ c for c in output.keys() if c.endswith('Accuracy')]\n",
- " col_duration = [ c for c in output.keys() if c.endswith('Duration')]\n",
- " # ---- Build formats\n",
- " lambda_acc = lambda x : '{:.2f} %'.format(x) if (isinstance(x, float)) else '{:}'.format(x)\n",
- " lambda_dur = lambda x : '{:.1f} s'.format(x) if (isinstance(x, float)) else '{:}'.format(x)\n",
- " formats = {'Size':'{:.2f} Mo'}\n",
- " for c in col_accuracy: \n",
- " formats[c]=lambda_acc\n",
- " for c in col_duration:\n",
- " formats[c]=lambda_dur\n",
- " t=report.style.highlight_max(subset=col_accuracy).format(formats).hide_index()\n",
- " display(t)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Reports display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:44.443196Z",
- "iopub.status.busy": "2021-03-01T19:19:44.442722Z",
- "iopub.status.idle": "2021-03-01T19:19:44.645952Z",
- "shell.execute_reply": "2021-03-01T19:19:44.646439Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**Report : report_012352**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Desc. : scale=1 batch_size=64 epochs=16 data_aug=True \n",
- "\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "#T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow0_col4,#T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow1_col2{\n",
- " background-color: yellow;\n",
- " }</style><table id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44f\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v2_Accuracy</th> <th class=\"col_heading level0 col3\" >v2_Duration</th> <th class=\"col_heading level0 col4\" >v3_Accuracy</th> <th class=\"col_heading level0 col5\" >v3_Duration</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow0_col0\" class=\"data row0 col0\" >set-48x48-L</td>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow0_col1\" class=\"data row0 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow0_col2\" class=\"data row0 col2\" >98.95 %</td>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow0_col3\" class=\"data row0 col3\" >244.6 s</td>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow0_col4\" class=\"data row0 col4\" >98.20 %</td>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow0_col5\" class=\"data row0 col5\" >248.1 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow1_col0\" class=\"data row1 col0\" >set-48x48-RGB</td>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow1_col1\" class=\"data row1 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow1_col2\" class=\"data row1 col2\" >99.10 %</td>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow1_col3\" class=\"data row1 col3\" >487.5 s</td>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow1_col4\" class=\"data row1 col4\" >97.92 %</td>\n",
- " <td id=\"T_147cc0f4_7ac3_11eb_89be_0cc47af5a44frow1_col5\" class=\"data row1 col5\" >486.9 s</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x146aaac15a90>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Report : report_030435**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Desc. : scale=1 batch_size=64 epochs=16 data_aug=False \n",
- "\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "#T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow3_col4,#T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow6_col2,#T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow6_col6{\n",
- " background-color: yellow;\n",
- " }</style><table id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44f\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v1_Accuracy</th> <th class=\"col_heading level0 col3\" >v1_Duration</th> <th class=\"col_heading level0 col4\" >v2_Accuracy</th> <th class=\"col_heading level0 col5\" >v2_Duration</th> <th class=\"col_heading level0 col6\" >v3_Accuracy</th> <th class=\"col_heading level0 col7\" >v3_Duration</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow0_col2\" class=\"data row0 col2\" >96.16 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow0_col3\" class=\"data row0 col3\" >42.3 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow0_col4\" class=\"data row0 col4\" >96.94 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow0_col5\" class=\"data row0 col5\" >50.0 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow0_col6\" class=\"data row0 col6\" >95.45 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow0_col7\" class=\"data row0 col7\" >45.3 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow1_col2\" class=\"data row1 col2\" >96.44 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow1_col3\" class=\"data row1 col3\" >42.6 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow1_col4\" class=\"data row1 col4\" >96.90 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow1_col5\" class=\"data row1 col5\" >50.8 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow1_col6\" class=\"data row1 col6\" >96.33 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow1_col7\" class=\"data row1 col7\" >47.3 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow2_col0\" class=\"data row2 col0\" >set-48x48-L</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow2_col1\" class=\"data row2 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow2_col2\" class=\"data row2 col2\" >96.29 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow2_col3\" class=\"data row2 col3\" >127.5 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow2_col4\" class=\"data row2 col4\" >98.15 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow2_col5\" class=\"data row2 col5\" >112.4 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow2_col6\" class=\"data row2 col6\" >97.61 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow2_col7\" class=\"data row2 col7\" >86.9 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow3_col0\" class=\"data row3 col0\" >set-48x48-RGB</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow3_col1\" class=\"data row3 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow3_col2\" class=\"data row3 col2\" >96.43 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow3_col3\" class=\"data row3 col3\" >131.3 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow3_col4\" class=\"data row3 col4\" >98.29 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow3_col5\" class=\"data row3 col5\" >116.6 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow3_col6\" class=\"data row3 col6\" >97.80 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow3_col7\" class=\"data row3 col7\" >88.4 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow4_col0\" class=\"data row4 col0\" >set-24x24-L-LHE</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow4_col1\" class=\"data row4 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow4_col2\" class=\"data row4 col2\" >95.68 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow4_col3\" class=\"data row4 col3\" >39.5 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow4_col4\" class=\"data row4 col4\" >96.88 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow4_col5\" class=\"data row4 col5\" >48.8 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow4_col6\" class=\"data row4 col6\" >95.46 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow4_col7\" class=\"data row4 col7\" >46.6 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow5_col0\" class=\"data row5 col0\" >set-24x24-RGB-HE</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow5_col1\" class=\"data row5 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow5_col2\" class=\"data row5 col2\" >95.47 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow5_col3\" class=\"data row5 col3\" >42.0 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow5_col4\" class=\"data row5 col4\" >96.70 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow5_col5\" class=\"data row5 col5\" >51.6 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow5_col6\" class=\"data row5 col6\" >94.75 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow5_col7\" class=\"data row5 col7\" >47.6 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow6_col0\" class=\"data row6 col0\" >set-48x48-L-LHE</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow6_col1\" class=\"data row6 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow6_col2\" class=\"data row6 col2\" >96.83 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow6_col3\" class=\"data row6 col3\" >126.2 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow6_col4\" class=\"data row6 col4\" >98.20 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow6_col5\" class=\"data row6 col5\" >110.6 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow6_col6\" class=\"data row6 col6\" >97.89 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow6_col7\" class=\"data row6 col7\" >86.2 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow7_col0\" class=\"data row7 col0\" >set-48x48-RGB-HE</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow7_col1\" class=\"data row7 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow7_col2\" class=\"data row7 col2\" >95.28 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow7_col3\" class=\"data row7 col3\" >130.9 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow7_col4\" class=\"data row7 col4\" >97.94 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow7_col5\" class=\"data row7 col5\" >116.0 s</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow7_col6\" class=\"data row7 col6\" >96.46 %</td>\n",
- " <td id=\"T_1482a9f4_7ac3_11eb_85fe_0cc47af5a44frow7_col7\" class=\"data row7 col7\" >89.5 s</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x146aaa644bd0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Report : report_036674**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Desc. : scale=1 batch_size=64 epochs=16 data_aug=False \n",
- "\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "#T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow3_col4,#T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow3_col6,#T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow6_col2{\n",
- " background-color: yellow;\n",
- " }</style><table id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44f\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v1_Accuracy</th> <th class=\"col_heading level0 col3\" >v1_Duration</th> <th class=\"col_heading level0 col4\" >v2_Accuracy</th> <th class=\"col_heading level0 col5\" >v2_Duration</th> <th class=\"col_heading level0 col6\" >v3_Accuracy</th> <th class=\"col_heading level0 col7\" >v3_Duration</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow0_col2\" class=\"data row0 col2\" >95.65 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow0_col3\" class=\"data row0 col3\" >44.8 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow0_col4\" class=\"data row0 col4\" >96.48 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow0_col5\" class=\"data row0 col5\" >48.6 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow0_col6\" class=\"data row0 col6\" >95.39 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow0_col7\" class=\"data row0 col7\" >46.8 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow1_col2\" class=\"data row1 col2\" >96.30 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow1_col3\" class=\"data row1 col3\" >41.5 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow1_col4\" class=\"data row1 col4\" >97.22 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow1_col5\" class=\"data row1 col5\" >50.6 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow1_col6\" class=\"data row1 col6\" >96.21 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow1_col7\" class=\"data row1 col7\" >48.0 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow2_col0\" class=\"data row2 col0\" >set-48x48-L</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow2_col1\" class=\"data row2 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow2_col2\" class=\"data row2 col2\" >95.90 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow2_col3\" class=\"data row2 col3\" >126.0 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow2_col4\" class=\"data row2 col4\" >97.92 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow2_col5\" class=\"data row2 col5\" >111.0 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow2_col6\" class=\"data row2 col6\" >97.48 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow2_col7\" class=\"data row2 col7\" >87.9 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow3_col0\" class=\"data row3 col0\" >set-48x48-RGB</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow3_col1\" class=\"data row3 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow3_col2\" class=\"data row3 col2\" >96.35 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow3_col3\" class=\"data row3 col3\" >130.7 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow3_col4\" class=\"data row3 col4\" >98.79 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow3_col5\" class=\"data row3 col5\" >115.7 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow3_col6\" class=\"data row3 col6\" >97.93 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow3_col7\" class=\"data row3 col7\" >89.3 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow4_col0\" class=\"data row4 col0\" >set-24x24-L-LHE</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow4_col1\" class=\"data row4 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow4_col2\" class=\"data row4 col2\" >95.77 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow4_col3\" class=\"data row4 col3\" >41.0 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow4_col4\" class=\"data row4 col4\" >96.69 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow4_col5\" class=\"data row4 col5\" >49.1 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow4_col6\" class=\"data row4 col6\" >94.99 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow4_col7\" class=\"data row4 col7\" >47.0 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow5_col0\" class=\"data row5 col0\" >set-24x24-RGB-HE</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow5_col1\" class=\"data row5 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow5_col2\" class=\"data row5 col2\" >95.04 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow5_col3\" class=\"data row5 col3\" >42.8 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow5_col4\" class=\"data row5 col4\" >96.63 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow5_col5\" class=\"data row5 col5\" >50.8 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow5_col6\" class=\"data row5 col6\" >94.46 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow5_col7\" class=\"data row5 col7\" >48.1 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow6_col0\" class=\"data row6 col0\" >set-48x48-L-LHE</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow6_col1\" class=\"data row6 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow6_col2\" class=\"data row6 col2\" >96.57 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow6_col3\" class=\"data row6 col3\" >125.2 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow6_col4\" class=\"data row6 col4\" >97.71 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow6_col5\" class=\"data row6 col5\" >111.3 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow6_col6\" class=\"data row6 col6\" >97.66 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow6_col7\" class=\"data row6 col7\" >86.7 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow7_col0\" class=\"data row7 col0\" >set-48x48-RGB-HE</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow7_col1\" class=\"data row7 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow7_col2\" class=\"data row7 col2\" >95.23 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow7_col3\" class=\"data row7 col3\" >131.9 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow7_col4\" class=\"data row7 col4\" >97.65 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow7_col5\" class=\"data row7 col5\" >116.2 s</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow7_col6\" class=\"data row7 col6\" >96.82 %</td>\n",
- " <td id=\"T_1484a94c_7ac3_11eb_8acd_0cc47af5a44frow7_col7\" class=\"data row7 col7\" >90.3 s</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x146aaa48a810>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "for file in glob.glob(f'{report_dir}/*.json'):\n",
- " show_report(file)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:44.649950Z",
- "iopub.status.busy": "2021-03-01T19:19:44.649483Z",
- "iopub.status.idle": "2021-03-01T19:19:44.652296Z",
- "shell.execute_reply": "2021-03-01T19:19:44.651797Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 20:19:44\n",
- "Duration is : 00:00:00 234ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/IMDB/01-One-hot-encoding.ipynb b/IMDB/01-One-hot-encoding.ipynb
index 4df53aa..c8d8a3b 100644
--- a/IMDB/01-One-hot-encoding.ipynb
+++ b/IMDB/01-One-hot-encoding.ipynb
@@ -67,9 +67,10 @@
"metadata": {},
"source": [
"### 1.2 - Parameters\n",
- "The words in the vocabulary are classified from the most frequent to the rarest. \n",
- "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown). \n",
- "`hide_most_frequently` is the number of ignored words, among the most common ones "
+ "The words in the vocabulary are classified from the most frequent to the rarest.\\\n",
+ "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown).\\\n",
+ "`hide_most_frequently` is the number of ignored words, among the most common ones\\\n",
+ "`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch"
]
},
{
@@ -81,8 +82,9 @@
"vocab_size = 10000\n",
"hide_most_frequently = 0\n",
"\n",
- "epochs = 10\n",
- "batch_size = 512"
+ "epochs = 10\n",
+ "batch_size = 512\n",
+ "fit_verbosity = 1"
]
},
{
@@ -98,7 +100,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.override('vocab_size', 'hide_most_frequently', 'batch_size', 'epochs')"
+ "pwk.override('vocab_size', 'hide_most_frequently', 'batch_size', 'epochs', 'fit_verbosity')"
]
},
{
@@ -508,7 +510,7 @@
" epochs = epochs,\n",
" batch_size = batch_size,\n",
" validation_data = (x_test, y_test),\n",
- " verbose = 1,\n",
+ " verbose = fit_verbosity,\n",
" callbacks = [savemodel_callback])\n"
]
},
diff --git a/IMDB/01-One-hot-encoding==done==.ipynb b/IMDB/01-One-hot-encoding==done==.ipynb
deleted file mode 100644
index f75ab67..0000000
--- a/IMDB/01-One-hot-encoding==done==.ipynb
+++ /dev/null
@@ -1,2041 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [IMDB1] - Sentiment analysis with hot-one encoding\n",
- "<!-- DESC --> A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB)\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
- " - Understand the management of **textual data** and **sentiment analysis**\n",
- "\n",
- "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
- "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Retrieve data\n",
- " - Preparing the data\n",
- " - Build a model\n",
- " - Train the model\n",
- " - Evaluate the result\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Import and init\n",
- "### 1.1 - Python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:46.157492Z",
- "iopub.status.busy": "2021-03-01T19:19:46.157022Z",
- "iopub.status.idle": "2021-03-01T19:19:48.842852Z",
- "shell.execute_reply": "2021-03-01T19:19:48.842272Z"
- }
- },
- "outputs": [
- {
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- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : IMDB1\n",
- "Run time : Monday 01 March 2021, 20:19:48\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/IMDB1\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/IMDB1/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "import tensorflow as tf\n",
- "import tensorflow.keras as keras\n",
- "import tensorflow.keras.datasets.imdb as imdb\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "\n",
- "import pandas as pd\n",
- "\n",
- "import os,sys,h5py,json\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/IMDB1'\n",
- "datasets_dir = pwk.init('IMDB1', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "The words in the vocabulary are classified from the most frequent to the rarest. \n",
- "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown). \n",
- "`hide_most_frequently` is the number of ignored words, among the most common ones "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:48.846180Z",
- "iopub.status.busy": "2021-03-01T19:19:48.845709Z",
- "iopub.status.idle": "2021-03-01T19:19:48.847380Z",
- "shell.execute_reply": "2021-03-01T19:19:48.847849Z"
- }
- },
- "outputs": [],
- "source": [
- "vocab_size = 10000\n",
- "hide_most_frequently = 0\n",
- "\n",
- "epochs = 10\n",
- "batch_size = 512"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:48.851076Z",
- "iopub.status.busy": "2021-03-01T19:19:48.850602Z",
- "iopub.status.idle": "2021-03-01T19:19:48.852234Z",
- "shell.execute_reply": "2021-03-01T19:19:48.852707Z"
- }
- },
- "outputs": [],
- "source": [
- "pwk.override('vocab_size', 'hide_most_frequently', 'batch_size', 'epochs')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Understanding hot-one encoding\n",
- "#### We have a **sentence** and a **dictionary** :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:48.856175Z",
- "iopub.status.busy": "2021-03-01T19:19:48.855695Z",
- "iopub.status.idle": "2021-03-01T19:19:48.857366Z",
- "shell.execute_reply": "2021-03-01T19:19:48.857832Z"
- }
- },
- "outputs": [],
- "source": [
- "sentence = \"I've never seen a movie like this before\"\n",
- "\n",
- "dictionary = {\"a\":0, \"before\":1, \"fantastic\":2, \"i've\":3, \"is\":4, \"like\":5, \"movie\":6, \"never\":7, \"seen\":8, \"this\":9}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### We encode our sentence as a **numerical vector** :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:48.861190Z",
- "iopub.status.busy": "2021-03-01T19:19:48.860722Z",
- "iopub.status.idle": "2021-03-01T19:19:48.863258Z",
- "shell.execute_reply": "2021-03-01T19:19:48.863724Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Words sentence are : [\"i've\", 'never', 'seen', 'a', 'movie', 'like', 'this', 'before']\n",
- "Our vectorized sentence is : [3, 7, 8, 0, 6, 5, 9, 1]\n"
- ]
- }
- ],
- "source": [
- "sentence_words = sentence.lower().split()\n",
- "\n",
- "sentence_vect = [ dictionary[w] for w in sentence_words ]\n",
- "\n",
- "print('Words sentence are : ', sentence_words)\n",
- "print('Our vectorized sentence is : ', sentence_vect)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Next, we **one-hot** encode our vectorized sentence as a tensor :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:48.870450Z",
- "iopub.status.busy": "2021-03-01T19:19:48.869982Z",
- "iopub.status.idle": "2021-03-01T19:19:48.994103Z",
- "shell.execute_reply": "2021-03-01T19:19:48.994590Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "In a basic way :\n",
- "\n",
- " [[0. 0. 0. 1. 0. 0. 0. 0.]\n",
- " [0. 0. 0. 0. 0. 0. 0. 1.]\n",
- " [0. 0. 0. 0. 0. 0. 0. 0.]\n",
- " [1. 0. 0. 0. 0. 0. 0. 0.]\n",
- " [0. 0. 0. 0. 0. 0. 0. 0.]\n",
- " [0. 0. 0. 0. 0. 1. 0. 0.]\n",
- " [0. 0. 0. 0. 1. 0. 0. 0.]\n",
- " [0. 1. 0. 0. 0. 0. 0. 0.]\n",
- " [0. 0. 1. 0. 0. 0. 0. 0.]\n",
- " [0. 0. 0. 0. 0. 0. 1. 0.]] \n",
- "\n",
- "With a pandas wiew :\n",
- "\n"
- ]
- },
- {
- "data": {
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- " }</style><table id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44f\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >...i've...</th> <th class=\"col_heading level0 col1\" >..never...</th> <th class=\"col_heading level0 col2\" >...seen...</th> <th class=\"col_heading level0 col3\" >....a.....</th> <th class=\"col_heading level0 col4\" >..movie...</th> <th class=\"col_heading level0 col5\" >...like...</th> <th class=\"col_heading level0 col6\" >...this...</th> <th class=\"col_heading level0 col7\" >..before..</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44flevel0_row0\" class=\"row_heading level0 row0\" >a</th>\n",
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- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow0_col1\" class=\"data row0 col1\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow0_col2\" class=\"data row0 col2\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow0_col3\" class=\"data row0 col3\" >1</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow0_col4\" class=\"data row0 col4\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow0_col5\" class=\"data row0 col5\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow0_col6\" class=\"data row0 col6\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow0_col7\" class=\"data row0 col7\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44flevel0_row1\" class=\"row_heading level0 row1\" >before</th>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow1_col0\" class=\"data row1 col0\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow1_col1\" class=\"data row1 col1\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow1_col2\" class=\"data row1 col2\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow1_col3\" class=\"data row1 col3\" >0</td>\n",
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- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow1_col5\" class=\"data row1 col5\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow1_col6\" class=\"data row1 col6\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow1_col7\" class=\"data row1 col7\" >1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44flevel0_row2\" class=\"row_heading level0 row2\" >fantastic</th>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow2_col0\" class=\"data row2 col0\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow2_col1\" class=\"data row2 col1\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow2_col2\" class=\"data row2 col2\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow2_col3\" class=\"data row2 col3\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow2_col4\" class=\"data row2 col4\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow2_col5\" class=\"data row2 col5\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow2_col6\" class=\"data row2 col6\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow2_col7\" class=\"data row2 col7\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44flevel0_row3\" class=\"row_heading level0 row3\" >i've</th>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow3_col0\" class=\"data row3 col0\" >1</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow3_col1\" class=\"data row3 col1\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow3_col2\" class=\"data row3 col2\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow3_col3\" class=\"data row3 col3\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow3_col4\" class=\"data row3 col4\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow3_col5\" class=\"data row3 col5\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow3_col6\" class=\"data row3 col6\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow3_col7\" class=\"data row3 col7\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44flevel0_row4\" class=\"row_heading level0 row4\" >is</th>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow4_col0\" class=\"data row4 col0\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow4_col1\" class=\"data row4 col1\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow4_col2\" class=\"data row4 col2\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow4_col3\" class=\"data row4 col3\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow4_col4\" class=\"data row4 col4\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow4_col5\" class=\"data row4 col5\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow4_col6\" class=\"data row4 col6\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow4_col7\" class=\"data row4 col7\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44flevel0_row5\" class=\"row_heading level0 row5\" >like</th>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow5_col0\" class=\"data row5 col0\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow5_col1\" class=\"data row5 col1\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow5_col2\" class=\"data row5 col2\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow5_col3\" class=\"data row5 col3\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow5_col4\" class=\"data row5 col4\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow5_col5\" class=\"data row5 col5\" >1</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow5_col6\" class=\"data row5 col6\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow5_col7\" class=\"data row5 col7\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44flevel0_row6\" class=\"row_heading level0 row6\" >movie</th>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow6_col0\" class=\"data row6 col0\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow6_col1\" class=\"data row6 col1\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow6_col2\" class=\"data row6 col2\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow6_col3\" class=\"data row6 col3\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow6_col4\" class=\"data row6 col4\" >1</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow6_col5\" class=\"data row6 col5\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow6_col6\" class=\"data row6 col6\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow6_col7\" class=\"data row6 col7\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44flevel0_row7\" class=\"row_heading level0 row7\" >never</th>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow7_col0\" class=\"data row7 col0\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow7_col1\" class=\"data row7 col1\" >1</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow7_col2\" class=\"data row7 col2\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow7_col3\" class=\"data row7 col3\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow7_col4\" class=\"data row7 col4\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow7_col5\" class=\"data row7 col5\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow7_col6\" class=\"data row7 col6\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow7_col7\" class=\"data row7 col7\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44flevel0_row8\" class=\"row_heading level0 row8\" >seen</th>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow8_col0\" class=\"data row8 col0\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow8_col1\" class=\"data row8 col1\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow8_col2\" class=\"data row8 col2\" >1</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow8_col3\" class=\"data row8 col3\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow8_col4\" class=\"data row8 col4\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow8_col5\" class=\"data row8 col5\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow8_col6\" class=\"data row8 col6\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow8_col7\" class=\"data row8 col7\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44flevel0_row9\" class=\"row_heading level0 row9\" >this</th>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow9_col0\" class=\"data row9 col0\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow9_col1\" class=\"data row9 col1\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow9_col2\" class=\"data row9 col2\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow9_col3\" class=\"data row9 col3\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow9_col4\" class=\"data row9 col4\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow9_col5\" class=\"data row9 col5\" >0</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow9_col6\" class=\"data row9 col6\" >1</td>\n",
- " <td id=\"T_17192336_7ac3_11eb_bc6b_0cc47af5a44frow9_col7\" class=\"data row9 col7\" >0</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x148ffd836fd0>"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# ---- We get a (sentence length x vector size) matrix of zeros\n",
- "#\n",
- "onehot = np.zeros( (10,8) )\n",
- "\n",
- "# ---- We set some 1 for each word\n",
- "#\n",
- "for i,w in enumerate(sentence_vect):\n",
- " onehot[w,i]=1\n",
- "\n",
- "# --- Show it\n",
- "#\n",
- "print('In a basic way :\\n\\n', onehot, '\\n\\nWith a pandas wiew :\\n')\n",
- "data={ f'{sentence_words[i]:.^10}':onehot[:,i] for i,w in enumerate(sentence_vect) }\n",
- "df=pd.DataFrame(data)\n",
- "df.index=dictionary.keys()\n",
- "df.style.set_precision(0).highlight_max(axis=0).set_properties(**{'text-align': 'center'})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Retrieve data\n",
- "\n",
- "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
- "Note : Due to their nature, textual data can be somewhat complex.\n",
- "\n",
- "### 3.1 - Data structure : \n",
- "The dataset is composed of 2 parts: \n",
- "\n",
- " - **reviews**, this will be our **x**\n",
- " - **opinions** (positive/negative), this will be our **y**\n",
- "\n",
- "There are also a **dictionary**, because words are indexed in reviews\n",
- "\n",
- "```\n",
- "<dataset> = (<reviews>, <opinions>)\n",
- "\n",
- "with : <reviews> = [ <review1>, <review2>, ... ]\n",
- " <opinions> = [ <rate1>, <rate2>, ... ] where <ratei> = integer\n",
- "\n",
- "where : <reviewi> = [ <w1>, <w2>, ...] <wi> are the index (int) of the word in the dictionary\n",
- " <ratei> = int 0 for negative opinion, 1 for positive\n",
- "\n",
- "\n",
- "<dictionary> = [ <word1>:<w1>, <word2>:<w2>, ... ]\n",
- "\n",
- "with : <wordi> = word\n",
- " <wi> = int\n",
- "\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.2 - Load dataset\n",
- "For simplicity, we will use a pre-formatted dataset - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data) \n",
- "However, Keras offers some usefull tools for formatting textual data - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text) \n",
- "\n",
- "By default : \n",
- " - Start of a sequence will be marked with : 1\n",
- " - Out of vocabulary word will be : 2\n",
- " - First index will be : 3"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:19:48.999811Z",
- "iopub.status.busy": "2021-03-01T19:19:48.999340Z",
- "iopub.status.idle": "2021-03-01T19:20:06.206295Z",
- "shell.execute_reply": "2021-03-01T19:20:06.206788Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<string>:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/datasets/imdb.py:159: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/datasets/imdb.py:160: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Max(x_train,x_test) : 9999\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Min(x_train,x_test) : 1\n",
- "x_train : (25000,) y_train : (25000,)\n",
- "x_test : (25000,) y_test : (25000,)\n"
- ]
- }
- ],
- "source": [
- "# ----- Retrieve x,y\n",
- "#\n",
- "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words=vocab_size, skip_top=hide_most_frequently)\n",
- "\n",
- "y_train = np.asarray(y_train).astype('float32')\n",
- "y_test = np.asarray(y_test ).astype('float32')\n",
- "\n",
- "# ---- About\n",
- "#\n",
- "print(\"Max(x_train,x_test) : \", pwk.rmax([x_train,x_test]) )\n",
- "print(\"Min(x_train,x_test) : \", pwk.rmin([x_train,x_test]) )\n",
- "print(\"x_train : {} y_train : {}\".format(x_train.shape, y_train.shape))\n",
- "print(\"x_test : {} y_test : {}\".format(x_test.shape, y_test.shape))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - About our dataset\n",
- "When we loaded the dataset, we asked for using \\<start\\> as 1, \\<unknown word\\> as 2 \n",
- "So, we shifted the dataset by 3 with the parameter index_from=3\n",
- "\n",
- "### 4.1 - Sentences encoding"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:06.210615Z",
- "iopub.status.busy": "2021-03-01T19:20:06.210147Z",
- "iopub.status.idle": "2021-03-01T19:20:06.212460Z",
- "shell.execute_reply": "2021-03-01T19:20:06.212942Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Review example (x_train[12]) :\n",
- "\n",
- " [1, 13, 119, 954, 189, 1554, 13, 92, 459, 48, 4, 116, 9, 1492, 2291, 42, 726, 4, 1939, 168, 2031, 13, 423, 14, 20, 549, 18, 4, 2, 547, 32, 4, 96, 39, 4, 454, 7, 4, 22, 8, 4, 55, 130, 168, 13, 92, 359, 6, 158, 1511, 2, 42, 6, 1913, 19, 194, 4455, 4121, 6, 114, 8, 72, 21, 465, 9667, 304, 4, 51, 9, 14, 20, 44, 155, 8, 6, 226, 162, 616, 651, 51, 9, 14, 20, 44, 10, 10, 14, 218, 4843, 629, 42, 3017, 21, 48, 25, 28, 35, 534, 5, 6, 320, 8, 516, 5, 42, 25, 181, 8, 130, 56, 547, 3571, 5, 1471, 851, 14, 2286]\n",
- "\n",
- "Opinions (y_train) :\n",
- "\n",
- " [1. 0. 0. ... 0. 1. 0.]\n"
- ]
- }
- ],
- "source": [
- "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])\n",
- "print('\\nOpinions (y_train) :\\n\\n',y_train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.2 - Load dictionary"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:06.218170Z",
- "iopub.status.busy": "2021-03-01T19:20:06.217687Z",
- "iopub.status.idle": "2021-03-01T19:20:06.320328Z",
- "shell.execute_reply": "2021-03-01T19:20:06.320817Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Dictionary size : 88588\n",
- "\n",
- "Small extract :\n",
- "\n",
- " 440 : hope\n",
- " 441 : entertaining\n",
- " 442 : she's\n",
- " 443 : mr\n",
- " 444 : overall\n",
- " 445 : evil\n",
- " 446 : called\n",
- " 447 : loved\n",
- " 448 : based\n",
- " 449 : oh\n",
- " 450 : several\n",
- " 451 : fans\n",
- " 452 : mother\n",
- " 453 : drama\n",
- " 454 : beginning\n"
- ]
- }
- ],
- "source": [
- "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
- "#\n",
- "word_index = imdb.get_word_index()\n",
- "\n",
- "# ---- Shift the dictionary from +3\n",
- "#\n",
- "word_index = {w:(i+3) for w,i in word_index.items()}\n",
- "\n",
- "# ---- Add <pad>, <start> and <unknown> tags\n",
- "#\n",
- "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2, '<undef>':3,} )\n",
- "\n",
- "# ---- Create a reverse dictionary : {index:word}\n",
- "#\n",
- "index_word = {index:word for word,index in word_index.items()} \n",
- "\n",
- "# ---- About dictionary\n",
- "#\n",
- "print('\\nDictionary size : ', len(word_index))\n",
- "print('\\nSmall extract :\\n')\n",
- "for k in range(440,455):print(f' {k:2d} : {index_word[k]}' )\n",
- "\n",
- "# ---- Add a nice function to transpose :\n",
- "#\n",
- "def dataset2text(review):\n",
- " return ' '.join([index_word.get(i, '?') for i in review])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.3 - Have a look, for human"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:06.324607Z",
- "iopub.status.busy": "2021-03-01T19:20:06.324140Z",
- "iopub.status.idle": "2021-03-01T19:20:06.328447Z",
- "shell.execute_reply": "2021-03-01T19:20:06.328926Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**Review example :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1, 13, 119, 954, 189, 1554, 13, 92, 459, 48, 4, 116, 9, 1492, 2291, 42, 726, 4, 1939, 168, 2031, 13, 423, 14, 20, 549, 18, 4, 2, 547, 32, 4, 96, 39, 4, 454, 7, 4, 22, 8, 4, 55, 130, 168, 13, 92, 359, 6, 158, 1511, 2, 42, 6, 1913, 19, 194, 4455, 4121, 6, 114, 8, 72, 21, 465, 9667, 304, 4, 51, 9, 14, 20, 44, 155, 8, 6, 226, 162, 616, 651, 51, 9, 14, 20, 44, 10, 10, 14, 218, 4843, 629, 42, 3017, 21, 48, 25, 28, 35, 534, 5, 6, 320, 8, 516, 5, 42, 25, 181, 8, 130, 56, 547, 3571, 5, 1471, 851, 14, 2286]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**After translation :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<start> i love cheesy horror flicks i don't care if the acting is sub par or whether the monsters look corny i liked this movie except for the <unknown> feeling all the way from the beginning of the film to the very end look i don't need a 10 page <unknown> or a sign with big letters explaining a plot to me but dark floors takes the what is this movie about thing to a whole new annoying level what is this movie about br br this isn't exceptionally scary or thrilling but if you have an hour and a half to kill and or you want to end up feeling frustrated and confused rent this winner\n"
- ]
- }
- ],
- "source": [
- "pwk.subtitle('Review example :')\n",
- "print(x_train[12])\n",
- "pwk.subtitle('After translation :')\n",
- "print(dataset2text(x_train[12]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.4 - Few statistics"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:06.348997Z",
- "iopub.status.busy": "2021-03-01T19:20:06.348526Z",
- "iopub.status.idle": "2021-03-01T19:20:08.209541Z",
- "shell.execute_reply": "2021-03-01T19:20:08.210039Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/IMDB1/figs/IMDB1-01-stats-sizes</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "sizes=[len(i) for i in x_train]\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist(sizes, bins=400)\n",
- "plt.gca().set(title='Distribution of reviews by size - [{:5.2f}, {:5.2f}]'.format(min(sizes),max(sizes)), \n",
- " xlabel='Size', ylabel='Density', xlim=[0,1500])\n",
- "pwk.save_fig('01-stats-sizes')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:08.232295Z",
- "iopub.status.busy": "2021-03-01T19:20:08.222136Z",
- "iopub.status.idle": "2021-03-01T19:20:09.357264Z",
- "shell.execute_reply": "2021-03-01T19:20:09.357751Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/IMDB1/figs/IMDB1-02-stats-unknown</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "unk=[ 100*(s.count(2)/len(s)) for s in x_train]\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist(unk, bins=100)\n",
- "plt.gca().set(title='Percent of unknown words - [{:5.2f}, {:5.2f}]'.format(min(unk),max(unk)), \n",
- " xlabel='# unknown', ylabel='Density', xlim=[0,30])\n",
- "pwk.save_fig('02-stats-unknown')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Basic approach with \"one-hot\" vector encoding\n",
- "Basic approach. \n",
- "\n",
- "Each sentence is encoded with a **vector** of length equal to the **size of the dictionary**. \n",
- "\n",
- "Each sentence will therefore be encoded with a simple vector. \n",
- "The value of each component is 0 if the word is not present in the sentence or 1 if the word is present.\n",
- "\n",
- "For a sentence s=[3,4,7] and a dictionary of 10 words... \n",
- "We wil have a vector v=[0,0,0,1,1,0,0,1,0,0,0]\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.1 - Our one-hot encoder"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:09.362011Z",
- "iopub.status.busy": "2021-03-01T19:20:09.361533Z",
- "iopub.status.idle": "2021-03-01T19:20:09.363146Z",
- "shell.execute_reply": "2021-03-01T19:20:09.363612Z"
- }
- },
- "outputs": [],
- "source": [
- "def one_hot_encoder(x, vector_size=10000):\n",
- " \n",
- " # ---- Set all to 0\n",
- " #\n",
- " x_encoded = np.zeros((len(x), vector_size))\n",
- " \n",
- " # ---- For each sentence\n",
- " #\n",
- " for i,sentence in enumerate(x):\n",
- " for word in sentence:\n",
- " x_encoded[i, word] = 1.\n",
- "\n",
- " return x_encoded"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Encoding.."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:09.381505Z",
- "iopub.status.busy": "2021-03-01T19:20:09.371301Z",
- "iopub.status.idle": "2021-03-01T19:20:11.685826Z",
- "shell.execute_reply": "2021-03-01T19:20:11.686324Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "To have a look, x_train[12] became : [0. 1. 1. ... 0. 0. 0.]\n"
- ]
- }
- ],
- "source": [
- "x_train = one_hot_encoder(x_train)\n",
- "x_test = one_hot_encoder(x_test)\n",
- "\n",
- "print(\"To have a look, x_train[12] became :\", x_train[12] )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Build the model\n",
- "Few remarks :\n",
- " - We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
- " - **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly) \n",
- " In other words: we average the set of vectors/words of a sentence\n",
- " - L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling) \n",
- "Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer) \n",
- "ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks) \n",
- "\n",
- "More documentation about this model functions :\n",
- " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
- " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:11.691352Z",
- "iopub.status.busy": "2021-03-01T19:20:11.690883Z",
- "iopub.status.idle": "2021-03-01T19:20:11.692555Z",
- "shell.execute_reply": "2021-03-01T19:20:11.693036Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_model(vector_size=10000):\n",
- " \n",
- " model = keras.Sequential()\n",
- " model.add(keras.layers.Input( shape=(vector_size,) ))\n",
- " model.add(keras.layers.Dense( 32, activation='relu'))\n",
- " model.add(keras.layers.Dense( 32, activation='relu'))\n",
- " model.add(keras.layers.Dense( 1, activation='sigmoid'))\n",
- " \n",
- " model.compile(optimizer = 'rmsprop',\n",
- " loss = 'binary_crossentropy',\n",
- " metrics = ['accuracy'])\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7 - Train the model\n",
- "### 7.1 - Get it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:11.696102Z",
- "iopub.status.busy": "2021-03-01T19:20:11.695631Z",
- "iopub.status.idle": "2021-03-01T19:20:12.842068Z",
- "shell.execute_reply": "2021-03-01T19:20:12.842568Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "dense (Dense) (None, 32) 320032 \n",
- "_________________________________________________________________\n",
- "dense_1 (Dense) (None, 32) 1056 \n",
- "_________________________________________________________________\n",
- "dense_2 (Dense) (None, 1) 33 \n",
- "=================================================================\n",
- "Total params: 321,121\n",
- "Trainable params: 321,121\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = get_model(vector_size=vocab_size)\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 7.2 - Add callback"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:12.846257Z",
- "iopub.status.busy": "2021-03-01T19:20:12.845777Z",
- "iopub.status.idle": "2021-03-01T19:20:12.848165Z",
- "shell.execute_reply": "2021-03-01T19:20:12.848634Z"
- }
- },
- "outputs": [],
- "source": [
- "os.makedirs(f'{run_dir}/models', mode=0o750, exist_ok=True)\n",
- "save_dir = f'{run_dir}/models/best_model.h5'\n",
- "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 7.3 - Train it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:12.852468Z",
- "iopub.status.busy": "2021-03-01T19:20:12.852002Z",
- "iopub.status.idle": "2021-03-01T19:20:23.785622Z",
- "shell.execute_reply": "2021-03-01T19:20:23.786127Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 59s - loss: 0.6934 - accuracy: 0.4922"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.6659 - accuracy: 0.5854 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.6317 - accuracy: 0.6423"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.6021 - accuracy: 0.6791"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.5771 - accuracy: 0.7048"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.5562 - accuracy: 0.7239"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.5377 - accuracy: 0.7391"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 4s 50ms/step - loss: 0.5357 - accuracy: 0.7407 - val_loss: 0.3431 - val_accuracy: 0.8612\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 2/10\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.3098 - accuracy: 0.8945"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.2747 - accuracy: 0.9082"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.2679 - accuracy: 0.9094"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.2639 - accuracy: 0.9098"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.2616 - accuracy: 0.9096"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.2590 - accuracy: 0.9098"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.2569 - accuracy: 0.9100"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 15ms/step - loss: 0.2567 - accuracy: 0.9100 - val_loss: 0.2928 - val_accuracy: 0.8822\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 3/10\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.2240 - accuracy: 0.9062"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1944 - accuracy: 0.9269"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 15ms/step - loss: 0.1943 - accuracy: 0.9269 - val_loss: 0.3029 - val_accuracy: 0.8792\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 4/10\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1716 - accuracy: 0.9297"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "41/49 [========================>.....] - ETA: 0s - loss: 0.1556 - accuracy: 0.9424"
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- "49/49 [==============================] - ETA: 0s - loss: 0.1561 - accuracy: 0.9422"
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- "49/49 [==============================] - 1s 15ms/step - loss: 0.1562 - accuracy: 0.9421 - val_loss: 0.3046 - val_accuracy: 0.8800\n"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1303 - accuracy: 0.9557"
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- "33/49 [===================>..........] - ETA: 0s - loss: 0.1309 - accuracy: 0.9553"
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- "49/49 [==============================] - ETA: 0s - loss: 0.1322 - accuracy: 0.9544"
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- "49/49 [==============================] - 1s 15ms/step - loss: 0.1323 - accuracy: 0.9543 - val_loss: 0.3529 - val_accuracy: 0.8702\n"
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- },
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- "Epoch 6/10\n",
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- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1145 - accuracy: 0.9599"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.1154 - accuracy: 0.9592"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.1159 - accuracy: 0.9588"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1161 - accuracy: 0.9585"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.1162 - accuracy: 0.9585 - val_loss: 0.3627 - val_accuracy: 0.8725\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 7/10\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.0747 - accuracy: 0.9805"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.0877 - accuracy: 0.9722"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.0922 - accuracy: 0.9693"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 15ms/step - loss: 0.0923 - accuracy: 0.9693 - val_loss: 0.3997 - val_accuracy: 0.8669\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 8/10\n",
- "\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.0772 - accuracy: 0.9744"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.0773 - accuracy: 0.9743 - val_loss: 0.4484 - val_accuracy: 0.8576\n"
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- },
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- "output_type": "stream",
- "text": [
- "Epoch 9/10\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.0614 - accuracy: 0.9830"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.0635 - accuracy: 0.9819"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.0637 - accuracy: 0.9817"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.0645 - accuracy: 0.9812"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.0656 - accuracy: 0.9805"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.0661 - accuracy: 0.9800"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.0661 - accuracy: 0.9800 - val_loss: 0.5089 - val_accuracy: 0.8507\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 10/10\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.0721 - accuracy: 0.9844"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.0538 - accuracy: 0.9891"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.0514 - accuracy: 0.9885"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.0537 - accuracy: 0.9864"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.0542 - accuracy: 0.9856"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 0s - loss: 0.0550 - accuracy: 0.9848"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.0561 - accuracy: 0.9840 - val_loss: 0.4907 - val_accuracy: 0.8617\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 19.1 s, sys: 2.13 s, total: 21.3 s\n",
- "Wall time: 10.9 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "\n",
- "history = model.fit(x_train,\n",
- " y_train,\n",
- " epochs = epochs,\n",
- " batch_size = batch_size,\n",
- " validation_data = (x_test, y_test),\n",
- " verbose = 1,\n",
- " callbacks = [savemodel_callback])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 8 - Evaluate\n",
- "### 8.1 - Training history"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:23.801492Z",
- "iopub.status.busy": "2021-03-01T19:20:23.800291Z",
- "iopub.status.idle": "2021-03-01T19:20:24.941731Z",
- "shell.execute_reply": "2021-03-01T19:20:24.942231Z"
- }
- },
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, save_as='02-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 8.2 - Reload and evaluate best model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:24.948063Z",
- "iopub.status.busy": "2021-03-01T19:20:24.947575Z",
- "iopub.status.idle": "2021-03-01T19:20:30.169537Z",
- "shell.execute_reply": "2021-03-01T19:20:30.170042Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 0.2928\n",
- "x_test / accuracy : 0.8822\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "#### Accuracy donut is :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/IMDB1/figs/IMDB1-03-donut</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
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\n",
- "text/plain": [
- "<Figure size 576x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')\n",
- "\n",
- "# ---- Evaluate\n",
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / accuracy : {:5.4f}'.format(score[1]))\n",
- "\n",
- "values=[score[1], 1-score[1]]\n",
- "pwk.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\", save_as='03-donut')\n",
- "\n",
- "# ---- Confusion matrix\n",
- "\n",
- "y_sigmoid = model.predict(x_test)\n",
- "\n",
- "y_pred = y_sigmoid.copy()\n",
- "y_pred[ y_sigmoid< 0.5 ] = 0\n",
- "y_pred[ y_sigmoid>=0.5 ] = 1 \n",
- "\n",
- "pwk.display_confusion_matrix(y_test,y_pred,labels=range(2))\n",
- "pwk.plot_confusion_matrix(y_test,y_pred,range(2), figsize=(8, 8),normalize=False, save_as='04-confusion-matrix')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:30.173342Z",
- "iopub.status.busy": "2021-03-01T19:20:30.172869Z",
- "iopub.status.idle": "2021-03-01T19:20:30.175182Z",
- "shell.execute_reply": "2021-03-01T19:20:30.175656Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 20:20:30\n",
- "Duration is : 00:00:41 335ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/IMDB/02-Keras-embedding.ipynb b/IMDB/02-Keras-embedding.ipynb
index 6f29e85..2097ced 100644
--- a/IMDB/02-Keras-embedding.ipynb
+++ b/IMDB/02-Keras-embedding.ipynb
@@ -70,7 +70,8 @@
"`hide_most_frequently` is the number of ignored words, among the most common ones \n",
"`review_len` is the review length \n",
"`dense_vector_size` is the size of the generated dense vectors \n",
- "`output_dir` is where we will go to save our dictionaries. (./data is a good choice)"
+ "`output_dir` is where we will go to save our dictionaries. (./data is a good choice)\\\n",
+ "`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch"
]
},
{
@@ -88,7 +89,8 @@
"epochs = 30\n",
"batch_size = 512\n",
"\n",
- "output_dir = './data'"
+ "output_dir = './data'\n",
+ "fit_verbosity = 1"
]
},
{
@@ -105,7 +107,7 @@
"outputs": [],
"source": [
"pwk.override('vocab_size', 'hide_most_frequently', 'review_len', 'dense_vector_size')\n",
- "pwk.override('batch_size', 'epochs', 'output_dir')"
+ "pwk.override('batch_size', 'epochs', 'output_dir', 'fit_verbosity')"
]
},
{
@@ -329,7 +331,7 @@
" epochs = epochs,\n",
" batch_size = batch_size,\n",
" validation_data = (x_test, y_test),\n",
- " verbose = 1,\n",
+ " verbose = fit_verbosity,\n",
" callbacks = [savemodel_callback])\n"
]
},
diff --git a/IMDB/02-Keras-embedding==done==.ipynb b/IMDB/02-Keras-embedding==done==.ipynb
deleted file mode 100644
index 8ceff17..0000000
--- a/IMDB/02-Keras-embedding==done==.ipynb
+++ /dev/null
@@ -1,2883 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [IMDB2] - Sentiment analysis with text embedding\n",
- "<!-- DESC --> A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
- " - Understand the management of **textual data** and **sentiment analysis**\n",
- "\n",
- "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
- "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Retrieve data\n",
- " - Preparing the data\n",
- " - Build a model\n",
- " - Train the model\n",
- " - Evaluate the result\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Import and init\n",
- "### 1.1 - Python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:32.245181Z",
- "iopub.status.busy": "2021-03-01T19:20:32.244708Z",
- "iopub.status.idle": "2021-03-01T19:20:34.933746Z",
- "shell.execute_reply": "2021-03-01T19:20:34.934239Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
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- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { content:url(data:image/svg+xml;base64,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- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : IMDB2\n",
- "Run time : Monday 01 March 2021, 20:20:34\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/IMDB2\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/IMDB2/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "import tensorflow as tf\n",
- "import tensorflow.keras as keras\n",
- "import tensorflow.keras.datasets.imdb as imdb\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "\n",
- "import os,sys,h5py,json\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/IMDB2'\n",
- "datasets_dir = pwk.init('IMDB2', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "The words in the vocabulary are classified from the most frequent to the rarest. \n",
- "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown). \n",
- "`hide_most_frequently` is the number of ignored words, among the most common ones \n",
- "`review_len` is the review length \n",
- "`dense_vector_size` is the size of the generated dense vectors \n",
- "`output_dir` is where we will go to save our dictionaries. (./data is a good choice)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:34.937954Z",
- "iopub.status.busy": "2021-03-01T19:20:34.937480Z",
- "iopub.status.idle": "2021-03-01T19:20:34.939677Z",
- "shell.execute_reply": "2021-03-01T19:20:34.939198Z"
- }
- },
- "outputs": [],
- "source": [
- "vocab_size = 10000\n",
- "hide_most_frequently = 0\n",
- "\n",
- "review_len = 256\n",
- "dense_vector_size = 16\n",
- "\n",
- "epochs = 30\n",
- "batch_size = 512\n",
- "\n",
- "output_dir = './data'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:34.942848Z",
- "iopub.status.busy": "2021-03-01T19:20:34.942382Z",
- "iopub.status.idle": "2021-03-01T19:20:34.944000Z",
- "shell.execute_reply": "2021-03-01T19:20:34.944469Z"
- }
- },
- "outputs": [],
- "source": [
- "pwk.override('vocab_size', 'hide_most_frequently', 'review_len', 'dense_vector_size')\n",
- "pwk.override('batch_size', 'epochs', 'output_dir')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Retrieve data\n",
- "\n",
- "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
- "Note : Due to their nature, textual data can be somewhat complex.\n",
- "\n",
- "For more details about the management of this dataset, see notebook [IMDB1](01-One-hot-encoding.ipynb)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Get dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:34.949236Z",
- "iopub.status.busy": "2021-03-01T19:20:34.948760Z",
- "iopub.status.idle": "2021-03-01T19:20:51.233491Z",
- "shell.execute_reply": "2021-03-01T19:20:51.232989Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<string>:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/datasets/imdb.py:159: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/datasets/imdb.py:160: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Max(x_train,x_test) : 9999\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Min(x_train,x_test) : 1\n",
- "x_train : (25000,) y_train : (25000,)\n",
- "x_test : (25000,) y_test : (25000,)\n"
- ]
- }
- ],
- "source": [
- "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words=vocab_size, skip_top=hide_most_frequently, seed= 42,)\n",
- "\n",
- "y_train = np.asarray(y_train).astype('float32')\n",
- "y_test = np.asarray(y_test ).astype('float32')\n",
- "\n",
- "# ---- About\n",
- "#\n",
- "print(\"Max(x_train,x_test) : \", pwk.rmax([x_train,x_test]) )\n",
- "print(\"Min(x_train,x_test) : \", pwk.rmin([x_train,x_test]) )\n",
- "print(\"x_train : {} y_train : {}\".format(x_train.shape, y_train.shape))\n",
- "print(\"x_test : {} y_test : {}\".format(x_test.shape, y_test.shape))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Load dictionary\n",
- "Not essential, but nice if you want to take a closer look at our reviews ;-)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:51.238256Z",
- "iopub.status.busy": "2021-03-01T19:20:51.237776Z",
- "iopub.status.idle": "2021-03-01T19:20:51.319735Z",
- "shell.execute_reply": "2021-03-01T19:20:51.319214Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
- "# Shift the dictionary from +3\n",
- "# Add <pad>, <start> and <unknown> tags\n",
- "# Create a reverse dictionary : {index:word}\n",
- "#\n",
- "word_index = imdb.get_word_index()\n",
- "word_index = {w:(i+3) for w,i in word_index.items()}\n",
- "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2, '<undef>':3,} )\n",
- "index_word = {index:word for word,index in word_index.items()} \n",
- "\n",
- "# ---- A nice function to transpose :\n",
- "#\n",
- "def dataset2text(review):\n",
- " return ' '.join([index_word.get(i, '?') for i in review])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Preprocess the data (padding)\n",
- "In order to be processed by an NN, all entries must have the **same length.** \n",
- "We chose a review length of **review_len** \n",
- "We will therefore complete them with a padding (of \\<pad\\>\\) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:51.327330Z",
- "iopub.status.busy": "2021-03-01T19:20:51.326856Z",
- "iopub.status.idle": "2021-03-01T19:20:52.415575Z",
- "shell.execute_reply": "2021-03-01T19:20:52.415071Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**After padding :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ 1 14 22 1367 53 206 159 4 636 898 74 26 11 436\n",
- " 363 108 7 14 432 14 22 9 1055 34 8599 2 5 381\n",
- " 3705 4509 14 768 47 839 25 111 1517 2579 1991 438 2663 587\n",
- " 4 280 725 6 58 11 2714 201 4 206 16 702 5 5176\n",
- " 19 480 5920 157 13 64 219 4 2 11 107 665 1212 39\n",
- " 4 206 4 65 410 16 565 5 24 43 343 17 5602 8\n",
- " 169 101 85 206 108 8 3008 14 25 215 168 18 6 2579\n",
- " 1991 438 2 11 129 1609 36 26 66 290 3303 46 5 633\n",
- " 115 4363 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0]\n"
- ]
- }
- ],
- "source": [
- "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
- " value = 0,\n",
- " padding = 'post',\n",
- " maxlen = review_len)\n",
- "\n",
- "x_test = keras.preprocessing.sequence.pad_sequences(x_test,\n",
- " value = 0 ,\n",
- " padding = 'post',\n",
- " maxlen = review_len)\n",
- "\n",
- "pwk.subtitle('After padding :')\n",
- "print(x_train[12])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Save dataset and dictionary (For future use but not mandatory)**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:52.420397Z",
- "iopub.status.busy": "2021-03-01T19:20:52.419926Z",
- "iopub.status.idle": "2021-03-01T19:20:52.816317Z",
- "shell.execute_reply": "2021-03-01T19:20:52.815804Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Saved.\n"
- ]
- }
- ],
- "source": [
- "# ---- Write dataset in a h5 file, could be usefull\n",
- "#\n",
- "pwk.mkdir(output_dir)\n",
- "\n",
- "with h5py.File(f'{output_dir}/dataset_imdb.h5', 'w') as f:\n",
- " f.create_dataset(\"x_train\", data=x_train)\n",
- " f.create_dataset(\"y_train\", data=y_train)\n",
- " f.create_dataset(\"x_test\", data=x_test)\n",
- " f.create_dataset(\"y_test\", data=y_test)\n",
- "\n",
- "with open(f'{output_dir}/word_index.json', 'w') as fp:\n",
- " json.dump(word_index, fp)\n",
- "\n",
- "with open(f'{output_dir}/index_word.json', 'w') as fp:\n",
- " json.dump(index_word, fp)\n",
- "\n",
- "print('Saved.')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Build the model\n",
- "Few remarks :\n",
- " - We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
- " - **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly) \n",
- " In other words: we average the set of vectors/words of a sentence\n",
- " - L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling) \n",
- "Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer) \n",
- "ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks) \n",
- "\n",
- "More documentation about this model functions :\n",
- " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
- " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:52.821713Z",
- "iopub.status.busy": "2021-03-01T19:20:52.821240Z",
- "iopub.status.idle": "2021-03-01T19:20:52.823426Z",
- "shell.execute_reply": "2021-03-01T19:20:52.822943Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_model(vocab_size=10000, dense_vector_size=32, review_len=256):\n",
- " \n",
- " model = keras.Sequential()\n",
- " model.add(keras.layers.Input( shape=(review_len,) ))\n",
- " model.add(keras.layers.Embedding(input_dim = vocab_size, \n",
- " output_dim = dense_vector_size, \n",
- " input_length = review_len))\n",
- " model.add(keras.layers.GlobalAveragePooling1D())\n",
- " model.add(keras.layers.Dense(dense_vector_size, activation='relu'))\n",
- " model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
- "\n",
- " model.compile(optimizer = 'adam',\n",
- " loss = 'binary_crossentropy',\n",
- " metrics = ['accuracy'])\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Train the model\n",
- "### 5.1 - Get it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:52.826440Z",
- "iopub.status.busy": "2021-03-01T19:20:52.825972Z",
- "iopub.status.idle": "2021-03-01T19:20:54.067538Z",
- "shell.execute_reply": "2021-03-01T19:20:54.068038Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "embedding (Embedding) (None, 256, 16) 160000 \n",
- "_________________________________________________________________\n",
- "global_average_pooling1d (Gl (None, 16) 0 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 16) 272 \n",
- "_________________________________________________________________\n",
- "dense_1 (Dense) (None, 1) 17 \n",
- "=================================================================\n",
- "Total params: 160,289\n",
- "Trainable params: 160,289\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = get_model(vocab_size, dense_vector_size, review_len)\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Add callback"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:54.071702Z",
- "iopub.status.busy": "2021-03-01T19:20:54.071238Z",
- "iopub.status.idle": "2021-03-01T19:20:54.073975Z",
- "shell.execute_reply": "2021-03-01T19:20:54.074449Z"
- }
- },
- "outputs": [],
- "source": [
- "os.makedirs(f'{run_dir}/models', mode=0o750, exist_ok=True)\n",
- "save_dir = f'{run_dir}/models/best_model.h5'\n",
- "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.1 - Train it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:20:54.078524Z",
- "iopub.status.busy": "2021-03-01T19:20:54.078057Z",
- "iopub.status.idle": "2021-03-01T19:21:13.254697Z",
- "shell.execute_reply": "2021-03-01T19:21:13.255210Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/30\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 1:04 - loss: 0.6932 - accuracy: 0.4727"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 0s - loss: 0.6931 - accuracy: 0.4978 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 0s - loss: 0.6930 - accuracy: 0.5180"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 0s - loss: 0.6929 - accuracy: 0.5276"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "32/49 [==================>...........] - ETA: 0s - loss: 0.6927 - accuracy: 0.5389"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 0s - loss: 0.6926 - accuracy: 0.5516"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.6924 - accuracy: 0.5639"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 3s 27ms/step - loss: 0.6923 - accuracy: 0.5667 - val_loss: 0.6879 - val_accuracy: 0.7070\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 2/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.6872 - accuracy: 0.7324"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.6868 - accuracy: 0.7287"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.6863 - accuracy: 0.7254"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.6858 - accuracy: 0.7214"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.6852 - accuracy: 0.7168"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.6844 - accuracy: 0.7123"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.6836 - accuracy: 0.7101"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.6835 - accuracy: 0.7100 - val_loss: 0.6664 - val_accuracy: 0.7461\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 3/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.6633 - accuracy: 0.7734"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "17/49 [=========>....................] - ETA: 0s - loss: 0.6620 - accuracy: 0.7561"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.6603 - accuracy: 0.7562"
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- "49/49 [==============================] - ETA: 0s - loss: 0.6547 - accuracy: 0.7618"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.6545 - accuracy: 0.7620 - val_loss: 0.6210 - val_accuracy: 0.7759\n"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.6081 - accuracy: 0.7949"
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- "33/49 [===================>..........] - ETA: 0s - loss: 0.6054 - accuracy: 0.7962"
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- "41/49 [========================>.....] - ETA: 0s - loss: 0.6026 - accuracy: 0.7978"
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- "49/49 [==============================] - ETA: 0s - loss: 0.5997 - accuracy: 0.7994"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.5994 - accuracy: 0.7996 - val_loss: 0.5568 - val_accuracy: 0.8036\n"
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- "name": "stdout",
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- "text": [
- "Epoch 5/30\n",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.5369 - accuracy: 0.8309"
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.5341 - accuracy: 0.8301"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.5312 - accuracy: 0.8301"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.5282 - accuracy: 0.8306"
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.5278 - accuracy: 0.8307 - val_loss: 0.4907 - val_accuracy: 0.8282\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 6/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.4703 - accuracy: 0.8633"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.4642 - accuracy: 0.8633"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.4615 - accuracy: 0.8573"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.4551 - accuracy: 0.8561"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.4548 - accuracy: 0.8561 - val_loss: 0.4349 - val_accuracy: 0.8453\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 7/30\n",
- "\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "49/49 [==============================] - ETA: 0s - loss: 0.3984 - accuracy: 0.8715"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.3982 - accuracy: 0.8715 - val_loss: 0.3943 - val_accuracy: 0.8547\n"
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- "Epoch 8/30\n",
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.3589 - accuracy: 0.8849"
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- "33/49 [===================>..........] - ETA: 0s - loss: 0.3572 - accuracy: 0.8847"
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- "49/49 [==============================] - ETA: 0s - loss: 0.3542 - accuracy: 0.8844"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.3540 - accuracy: 0.8844 - val_loss: 0.3646 - val_accuracy: 0.8607\n"
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- "Epoch 9/30\n",
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- "17/49 [=========>....................] - ETA: 0s - loss: 0.3227 - accuracy: 0.8889"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.3210 - accuracy: 0.8903"
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.3200 - accuracy: 0.8907"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.3193 - accuracy: 0.8908"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.3186 - accuracy: 0.8909"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.3185 - accuracy: 0.8909 - val_loss: 0.3443 - val_accuracy: 0.8652\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 10/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.2851 - accuracy: 0.9062"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.2991 - accuracy: 0.8940"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.2985 - accuracy: 0.8943"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.2954 - accuracy: 0.8952"
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.2953 - accuracy: 0.8952 - val_loss: 0.3282 - val_accuracy: 0.8693\n"
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- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 11/30\n",
- "\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "output_type": "stream",
- "text": [
- "Epoch 12/30\n",
- "\r",
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- "17/49 [=========>....................] - ETA: 0s - loss: 0.2592 - accuracy: 0.9075"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.2568 - accuracy: 0.9082"
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- "33/49 [===================>..........] - ETA: 0s - loss: 0.2555 - accuracy: 0.9088"
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- "49/49 [==============================] - ETA: 0s - loss: 0.2547 - accuracy: 0.9090"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.2547 - accuracy: 0.9090 - val_loss: 0.3086 - val_accuracy: 0.8742\n"
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- "text": [
- "Epoch 13/30\n",
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- "17/49 [=========>....................] - ETA: 0s - loss: 0.2414 - accuracy: 0.9174"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.2418 - accuracy: 0.9168"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.2422 - accuracy: 0.9163"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.2423 - accuracy: 0.9160"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.2420 - accuracy: 0.9159"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.2419 - accuracy: 0.9159 - val_loss: 0.3013 - val_accuracy: 0.8768\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 14/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.2512 - accuracy: 0.9258"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.2327 - accuracy: 0.9218"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.2303 - accuracy: 0.9212"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.2293 - accuracy: 0.9212"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.2287 - accuracy: 0.9212"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.2285 - accuracy: 0.9208"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.2283 - accuracy: 0.9205"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.2283 - accuracy: 0.9205 - val_loss: 0.2961 - val_accuracy: 0.8786\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 15/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.2329 - accuracy: 0.9160"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.2140 - accuracy: 0.9251"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.2137 - accuracy: 0.9253"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.2148 - accuracy: 0.9242"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.2148 - accuracy: 0.9242 - val_loss: 0.2943 - val_accuracy: 0.8782\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 16/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.2576 - accuracy: 0.9023"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "49/49 [==============================] - ETA: 0s - loss: 0.2075 - accuracy: 0.9268"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.2075 - accuracy: 0.9268 - val_loss: 0.2900 - val_accuracy: 0.8816\n"
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- "Epoch 17/30\n",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.2017 - accuracy: 0.9291"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.2011 - accuracy: 0.9290"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.2009 - accuracy: 0.9291"
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- "49/49 [==============================] - ETA: 0s - loss: 0.2004 - accuracy: 0.9292"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.2003 - accuracy: 0.9292 - val_loss: 0.2884 - val_accuracy: 0.8826\n"
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- "name": "stdout",
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- "text": [
- "Epoch 18/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1817 - accuracy: 0.9375"
- ]
- },
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- " 9/49 [====>.........................] - ETA: 0s - loss: 0.1808 - accuracy: 0.9353"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.1817 - accuracy: 0.9357"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1821 - accuracy: 0.9360"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.1826 - accuracy: 0.9360"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.1832 - accuracy: 0.9358"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1840 - accuracy: 0.9356"
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.1841 - accuracy: 0.9355 - val_loss: 0.2871 - val_accuracy: 0.8828\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 19/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1765 - accuracy: 0.9473"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.1896 - accuracy: 0.9349"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1882 - accuracy: 0.9350"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1857 - accuracy: 0.9356"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.1856 - accuracy: 0.9356 - val_loss: 0.2869 - val_accuracy: 0.8835\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 20/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1732 - accuracy: 0.9453"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.1763 - accuracy: 0.9394 - val_loss: 0.2878 - val_accuracy: 0.8824\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 21/30\n",
- "\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "16/49 [========>.....................] - ETA: 0s - loss: 0.1711 - accuracy: 0.9391"
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- "40/49 [=======================>......] - ETA: 0s - loss: 0.1692 - accuracy: 0.9407"
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- "48/49 [============================>.] - ETA: 0s - loss: 0.1691 - accuracy: 0.9409"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.1691 - accuracy: 0.9410 - val_loss: 0.2882 - val_accuracy: 0.8834\n"
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- "Epoch 22/30\n",
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- " 9/49 [====>.........................] - ETA: 0s - loss: 0.1614 - accuracy: 0.9461"
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- "17/49 [=========>....................] - ETA: 0s - loss: 0.1635 - accuracy: 0.9448"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1635 - accuracy: 0.9446"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.1634 - accuracy: 0.9446"
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- "41/49 [========================>.....] - ETA: 0s - loss: 0.1631 - accuracy: 0.9447"
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- "49/49 [==============================] - ETA: 0s - loss: 0.1628 - accuracy: 0.9448"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.1628 - accuracy: 0.9448 - val_loss: 0.2901 - val_accuracy: 0.8828\n"
- ]
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- "text": [
- "Epoch 23/30\n"
- ]
- },
- {
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- "text": [
- "\r",
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- " 9/49 [====>.........................] - ETA: 0s - loss: 0.1560 - accuracy: 0.9483"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.1573 - accuracy: 0.9482"
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1579 - accuracy: 0.9477"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.1579 - accuracy: 0.9476"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.1578 - accuracy: 0.9475"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1576 - accuracy: 0.9475"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.1576 - accuracy: 0.9475 - val_loss: 0.2911 - val_accuracy: 0.8823\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 24/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1543 - accuracy: 0.9434"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.1553 - accuracy: 0.9478"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.1540 - accuracy: 0.9485"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1528 - accuracy: 0.9490"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.1521 - accuracy: 0.9493"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.1516 - accuracy: 0.9494"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1513 - accuracy: 0.9494"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.1513 - accuracy: 0.9494 - val_loss: 0.2944 - val_accuracy: 0.8818\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 25/30\n"
- ]
- },
- {
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- "text": [
- "\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1503 - accuracy: 0.9505"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "41/49 [========================>.....] - ETA: 0s - loss: 0.1478 - accuracy: 0.9514"
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- "49/49 [==============================] - ETA: 0s - loss: 0.1474 - accuracy: 0.9515"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.1474 - accuracy: 0.9515 - val_loss: 0.2958 - val_accuracy: 0.8824\n"
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- "Epoch 26/30\n",
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- "17/49 [=========>....................] - ETA: 0s - loss: 0.1454 - accuracy: 0.9522"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1440 - accuracy: 0.9520"
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- "33/49 [===================>..........] - ETA: 0s - loss: 0.1435 - accuracy: 0.9520"
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- "41/49 [========================>.....] - ETA: 0s - loss: 0.1431 - accuracy: 0.9520"
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- "49/49 [==============================] - ETA: 0s - loss: 0.1428 - accuracy: 0.9520"
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.1428 - accuracy: 0.9521 - val_loss: 0.3007 - val_accuracy: 0.8799\n"
- ]
- },
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- "name": "stdout",
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- "text": [
- "Epoch 27/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1331 - accuracy: 0.9570"
- ]
- },
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- " 9/49 [====>.........................] - ETA: 0s - loss: 0.1381 - accuracy: 0.9567"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.1375 - accuracy: 0.9570"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1371 - accuracy: 0.9568"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.1368 - accuracy: 0.9565"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.1368 - accuracy: 0.9562"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1369 - accuracy: 0.9559"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.1369 - accuracy: 0.9559 - val_loss: 0.3032 - val_accuracy: 0.8800\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 28/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1147 - accuracy: 0.9609"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.1233 - accuracy: 0.9589"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.1253 - accuracy: 0.9585"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1265 - accuracy: 0.9582"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.1275 - accuracy: 0.9578"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.1284 - accuracy: 0.9576"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1290 - accuracy: 0.9575"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.1291 - accuracy: 0.9574 - val_loss: 0.3085 - val_accuracy: 0.8780\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 29/30\n"
- ]
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- {
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- "text": [
- "\r",
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- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1275 - accuracy: 0.9590"
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "49/49 [==============================] - 1s 11ms/step - loss: 0.1271 - accuracy: 0.9593 - val_loss: 0.3094 - val_accuracy: 0.8804\n"
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- },
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- "output_type": "stream",
- "text": [
- "Epoch 30/30\n",
- "\r",
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 0s - loss: 0.1156 - accuracy: 0.9629"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 0s - loss: 0.1175 - accuracy: 0.9627"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1187 - accuracy: 0.9626"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 0s - loss: 0.1196 - accuracy: 0.9623"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 0s - loss: 0.1205 - accuracy: 0.9619"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1210 - accuracy: 0.9616"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 11ms/step - loss: 0.1211 - accuracy: 0.9616 - val_loss: 0.3134 - val_accuracy: 0.8781\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 31.9 s, sys: 2.62 s, total: 34.5 s\n",
- "Wall time: 19.2 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "\n",
- "history = model.fit(x_train,\n",
- " y_train,\n",
- " epochs = epochs,\n",
- " batch_size = batch_size,\n",
- " validation_data = (x_test, y_test),\n",
- " verbose = 1,\n",
- " callbacks = [savemodel_callback])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Evaluate\n",
- "### 6.1 - Training history"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:13.270116Z",
- "iopub.status.busy": "2021-03-01T19:21:13.269642Z",
- "iopub.status.idle": "2021-03-01T19:21:14.456570Z",
- "shell.execute_reply": "2021-03-01T19:21:14.457068Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/IMDB2/figs/IMDB2-02-history_0</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, save_as='02-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.2 - Reload and evaluate best model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:14.462825Z",
- "iopub.status.busy": "2021-03-01T19:21:14.462355Z",
- "iopub.status.idle": "2021-03-01T19:21:18.029786Z",
- "shell.execute_reply": "2021-03-01T19:21:18.030286Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 0.2869\n",
- "x_test / accuracy : 0.8835\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "#### Accuracy donut is :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
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- " <td id=\"T_4b70dba4_7ac3_11eb_a005_0cc47af5a44frow0_col0\" class=\"data row0 col0\" >0.89</td>\n",
- " <td id=\"T_4b70dba4_7ac3_11eb_a005_0cc47af5a44frow0_col1\" class=\"data row0 col1\" >0.11</td>\n",
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- " <th id=\"T_4b70dba4_7ac3_11eb_a005_0cc47af5a44flevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_4b70dba4_7ac3_11eb_a005_0cc47af5a44frow1_col0\" class=\"data row1 col0\" >0.12</td>\n",
- " <td id=\"T_4b70dba4_7ac3_11eb_a005_0cc47af5a44frow1_col1\" class=\"data row1 col1\" >0.88</td>\n",
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\n",
- "text/plain": [
- "<Figure size 576x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')\n",
- "\n",
- "# ---- Evaluate\n",
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / accuracy : {:5.4f}'.format(score[1]))\n",
- "\n",
- "values=[score[1], 1-score[1]]\n",
- "pwk.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\", save_as='03-donut')\n",
- "\n",
- "# ---- Confusion matrix\n",
- "\n",
- "y_sigmoid = model.predict(x_test)\n",
- "\n",
- "y_pred = y_sigmoid.copy()\n",
- "y_pred[ y_sigmoid< 0.5 ] = 0\n",
- "y_pred[ y_sigmoid>=0.5 ] = 1 \n",
- "\n",
- "pwk.display_confusion_matrix(y_test,y_pred,labels=range(2))\n",
- "pwk.plot_confusion_matrix(y_test,y_pred,range(2), figsize=(8, 8),normalize=False, save_as='04-confusion-matrix')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:18.033913Z",
- "iopub.status.busy": "2021-03-01T19:21:18.033442Z",
- "iopub.status.idle": "2021-03-01T19:21:18.035840Z",
- "shell.execute_reply": "2021-03-01T19:21:18.036321Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 20:21:18\n",
- "Duration is : 00:00:43 104ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/IMDB/03-Prediction==done==.ipynb b/IMDB/03-Prediction==done==.ipynb
deleted file mode 100644
index b6ca270..0000000
--- a/IMDB/03-Prediction==done==.ipynb
+++ /dev/null
@@ -1,525 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [IMDB3] - Reload and reuse a saved model\n",
- "<!-- DESC --> Retrieving a saved model to perform a sentiment analysis (movie review)\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - The objective is to guess whether our personal film reviews are **positive or negative** based on the analysis of the text. \n",
- " - For this, we will use our **previously saved model**.\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Preparing our data\n",
- " - Retrieve our saved model\n",
- " - Evaluate the result\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Init python stuff"
- ]
- },
- {
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- "metadata": {},
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- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
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- "metadata": {},
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- "text": [
- "Version : 2.0.17\n",
- "Notebook id : IMDB3\n",
- "Run time : Monday 01 March 2021, 20:21:23\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/IMDB2\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/IMDB2/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "import tensorflow as tf\n",
- "import tensorflow.keras as keras\n",
- "import tensorflow.keras.datasets.imdb as imdb\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "import pandas as pd\n",
- "\n",
- "import os,sys,h5py,json,re\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/IMDB2'\n",
- "datasets_dir = pwk.init('IMDB3', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "The words in the vocabulary are classified from the most frequent to the rarest. \n",
- "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown). \n",
- "`review_len` is the review length \n",
- "`dictionaries_dir` is where we will go to save our dictionaries. (./data is a good choice)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:23.040335Z",
- "iopub.status.busy": "2021-03-01T19:21:23.039861Z",
- "iopub.status.idle": "2021-03-01T19:21:23.041537Z",
- "shell.execute_reply": "2021-03-01T19:21:23.042015Z"
- }
- },
- "outputs": [],
- "source": [
- "vocab_size = 10000\n",
- "review_len = 256\n",
- "\n",
- "dictionaries_dir = './data'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:23.045235Z",
- "iopub.status.busy": "2021-03-01T19:21:23.044758Z",
- "iopub.status.idle": "2021-03-01T19:21:23.046424Z",
- "shell.execute_reply": "2021-03-01T19:21:23.046903Z"
- }
- },
- "outputs": [],
- "source": [
- "pwk.override('vocab_size', 'review_len', 'dictionaries_dir')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 : Preparing the data\n",
- "### 2.1 - Our reviews :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:23.049860Z",
- "iopub.status.busy": "2021-03-01T19:21:23.049398Z",
- "iopub.status.idle": "2021-03-01T19:21:23.051028Z",
- "shell.execute_reply": "2021-03-01T19:21:23.051495Z"
- }
- },
- "outputs": [],
- "source": [
- "reviews = [ \"This film is particularly nice, a must see.\",\n",
- " \"This film is a great classic that cannot be ignored.\",\n",
- " \"I don't remember ever having seen such a movie...\",\n",
- " \"This movie is just abominable and doesn't deserve to be seen!\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Retrieve dictionaries\n",
- "Note : This dictionary is generated by [01-Embedding-Keras](01-Embedding-Keras.ipynb) notebook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:23.055752Z",
- "iopub.status.busy": "2021-03-01T19:21:23.055290Z",
- "iopub.status.idle": "2021-03-01T19:21:23.130074Z",
- "shell.execute_reply": "2021-03-01T19:21:23.130564Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loaded. 88588 entries in word_index\n",
- "Loaded. 88588 entries in index_word\n"
- ]
- }
- ],
- "source": [
- "with open(f'{dictionaries_dir}/word_index.json', 'r') as fp:\n",
- " word_index = json.load(fp)\n",
- " word_index = { w:int(i) for w,i in word_index.items() }\n",
- " print('Loaded. ', len(word_index), 'entries in word_index' )\n",
- " index_word = { i:w for w,i in word_index.items() }\n",
- " print('Loaded. ', len(index_word), 'entries in index_word' )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 - Clean, index and padd\n",
- "Phases are split into words, punctuation is removed, sentence length is limited and padding is added... \n",
- "**Note** : 1 is \"Start\" and 2 is \"unknown\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:23.137719Z",
- "iopub.status.busy": "2021-03-01T19:21:23.137247Z",
- "iopub.status.idle": "2021-03-01T19:21:23.142213Z",
- "shell.execute_reply": "2021-03-01T19:21:23.142683Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Words are : 1 2 22 9 572 2 6 215 2 \n",
- "Words are : 1 2 22 9 6 87 356 15 566 30 2 \n",
- "Words are : 1 2 92 377 126 260 110 141 6 2 \n",
- "Words are : 1 2 20 9 43 2 5 152 1833 8 30 2 \n"
- ]
- }
- ],
- "source": [
- "nb_reviews = len(reviews)\n",
- "x_data = []\n",
- "\n",
- "# ---- For all reviews\n",
- "for review in reviews:\n",
- " print('Words are : ', end='')\n",
- " # ---- First index must be <start>\n",
- " index_review=[1]\n",
- " print('1 ', end='')\n",
- " # ---- For all words\n",
- " for w in review.split(' '):\n",
- " # ---- Clean it\n",
- " w_clean = re.sub(r\"[^a-zA-Z0-9]\", \"\", w)\n",
- " # ---- Not empty ?\n",
- " if len(w_clean)>0:\n",
- " # ---- Get the index\n",
- " w_index = word_index.get(w,2)\n",
- " if w_index>vocab_size : w_index=2\n",
- " # ---- Add the index if < vocab_size\n",
- " index_review.append(w_index)\n",
- " print(f'{w_index} ', end='')\n",
- " # ---- Add the indexed review\n",
- " x_data.append(index_review)\n",
- " print()\n",
- "\n",
- "# ---- Padding\n",
- "x_data = keras.preprocessing.sequence.pad_sequences(x_data, value = 0, padding = 'post', maxlen = review_len)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.4 - Have a look"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:23.148287Z",
- "iopub.status.busy": "2021-03-01T19:21:23.147811Z",
- "iopub.status.idle": "2021-03-01T19:21:23.152323Z",
- "shell.execute_reply": "2021-03-01T19:21:23.151828Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Text review : This film is particularly nice, a must see.\n",
- "x_train[0] : [1, 2, 22, 9, 572, 2, 6, 215, 2, 0, 0, 0, 0, 0] (...)\n",
- "Translation : <start> <unknown> film is particularly <unknown> a must <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n",
- "\n",
- "Text review : This film is a great classic that cannot be ignored.\n",
- "x_train[1] : [1, 2, 22, 9, 6, 87, 356, 15, 566, 30, 2, 0, 0, 0, 0, 0] (...)\n",
- "Translation : <start> <unknown> film is a great classic that cannot be <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n",
- "\n",
- "Text review : I don't remember ever having seen such a movie...\n",
- "x_train[2] : [1, 2, 92, 377, 126, 260, 110, 141, 6, 2, 0, 0, 0, 0, 0] (...)\n",
- "Translation : <start> <unknown> don't remember ever having seen such a <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n",
- "\n",
- "Text review : This movie is just abominable and doesn't deserve to be seen!\n",
- "x_train[3] : [1, 2, 20, 9, 43, 2, 5, 152, 1833, 8, 30, 2, 0, 0, 0, 0, 0] (...)\n",
- "Translation : <start> <unknown> movie is just <unknown> and doesn't deserve to be <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n"
- ]
- }
- ],
- "source": [
- "def translate(x):\n",
- " return ' '.join( [index_word.get(i,'?') for i in x] )\n",
- "\n",
- "for i in range(nb_reviews):\n",
- " imax=np.where(x_data[i]==0)[0][0]+5\n",
- " print(f'\\nText review :', reviews[i])\n",
- " print( f'x_train[{i:}] :', list(x_data[i][:imax]), '(...)')\n",
- " print( 'Translation :', translate(x_data[i][:imax]), '(...)')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Bring back the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:23.155235Z",
- "iopub.status.busy": "2021-03-01T19:21:23.154767Z",
- "iopub.status.idle": "2021-03-01T19:21:24.352952Z",
- "shell.execute_reply": "2021-03-01T19:21:24.352404Z"
- }
- },
- "outputs": [],
- "source": [
- "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Predict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:24.356283Z",
- "iopub.status.busy": "2021-03-01T19:21:24.355810Z",
- "iopub.status.idle": "2021-03-01T19:21:24.818928Z",
- "shell.execute_reply": "2021-03-01T19:21:24.819459Z"
- }
- },
- "outputs": [],
- "source": [
- "y_pred = model.predict(x_data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### And the winner is :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:24.823671Z",
- "iopub.status.busy": "2021-03-01T19:21:24.823205Z",
- "iopub.status.idle": "2021-03-01T19:21:24.825548Z",
- "shell.execute_reply": "2021-03-01T19:21:24.826031Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "This film is particularly nice, a must see. => 0.53 - POSITIVE :-)\n",
- "This film is a great classic that cannot be ignored. => 0.67 - POSITIVE :-)\n",
- "I don't remember ever having seen such a movie... => 0.50 - NEGATIVE :-(\n",
- "This movie is just abominable and doesn't deserve to be seen! => 0.33 - NEGATIVE :-(\n"
- ]
- }
- ],
- "source": [
- "for i,review in enumerate(reviews):\n",
- " rate = y_pred[i][0]\n",
- " opinion = 'NEGATIVE :-(' if rate<0.5 else 'POSITIVE :-)' \n",
- " print(f'{review:<70} => {rate:.2f} - {opinion}')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:24.829062Z",
- "iopub.status.busy": "2021-03-01T19:21:24.828597Z",
- "iopub.status.idle": "2021-03-01T19:21:24.830893Z",
- "shell.execute_reply": "2021-03-01T19:21:24.831366Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 20:21:24\n",
- "Duration is : 00:00:02 797ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/IMDB/04-Show-vectors==done==.ipynb b/IMDB/04-Show-vectors==done==.ipynb
deleted file mode 100644
index db9f475..0000000
--- a/IMDB/04-Show-vectors==done==.ipynb
+++ /dev/null
@@ -1,487 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [IMDB4] - Reload embedded vectors\n",
- "<!-- DESC --> Retrieving embedded vectors from our trained model\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - The objective is to retrieve and visualize our embedded vectors\n",
- " - For this, we will use our **previously saved model**.\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Retrieve our saved model\n",
- " - Extract vectors and play with\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Init python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:26.404210Z",
- "iopub.status.busy": "2021-03-01T19:21:26.403735Z",
- "iopub.status.idle": "2021-03-01T19:21:29.036859Z",
- "shell.execute_reply": "2021-03-01T19:21:29.037350Z"
- }
- },
- "outputs": [
- {
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- "metadata": {},
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- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : IMDB4\n",
- "Run time : Monday 01 March 2021, 20:21:29\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "import tensorflow as tf\n",
- "import tensorflow.keras as keras\n",
- "import tensorflow.keras.datasets.imdb as imdb\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "import pandas as pd\n",
- "\n",
- "import os,sys,h5py,json,re\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/IMDB2'\n",
- "datasets_dir = pwk.init('IMDB4')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters\n",
- "The words in the vocabulary are classified from the most frequent to the rarest. \n",
- "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown). \n",
- "`review_len` is the review length \n",
- "`dictionaries_dir` is where we will go to save our dictionaries. (./data is a good choice)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:29.040664Z",
- "iopub.status.busy": "2021-03-01T19:21:29.040200Z",
- "iopub.status.idle": "2021-03-01T19:21:29.041843Z",
- "shell.execute_reply": "2021-03-01T19:21:29.042319Z"
- }
- },
- "outputs": [],
- "source": [
- "vocab_size = 10000\n",
- "review_len = 256\n",
- "\n",
- "dictionaries_dir = './data'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:29.045503Z",
- "iopub.status.busy": "2021-03-01T19:21:29.045039Z",
- "iopub.status.idle": "2021-03-01T19:21:29.046672Z",
- "shell.execute_reply": "2021-03-01T19:21:29.047151Z"
- }
- },
- "outputs": [],
- "source": [
- "pwk.override('vocab_size', 'review_len', 'dictionaries_dir')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Get the embedding vectors !"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.1 - Load model and dictionaries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:29.051318Z",
- "iopub.status.busy": "2021-03-01T19:21:29.050837Z",
- "iopub.status.idle": "2021-03-01T19:21:30.307335Z",
- "shell.execute_reply": "2021-03-01T19:21:30.307829Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model loaded.\n",
- "Dictionary loaded.\n"
- ]
- }
- ],
- "source": [
- "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')\n",
- "print('Model loaded.')\n",
- "\n",
- "with open(f'{dictionaries_dir}/index_word.json', 'r') as fp:\n",
- " index_word = json.load(fp)\n",
- " index_word = { int(i):w for i,w in index_word.items() }\n",
- " word_index = { w:int(i) for i,w in index_word.items() }\n",
- " print('Dictionary loaded.')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Retrieve embeddings"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:30.311383Z",
- "iopub.status.busy": "2021-03-01T19:21:30.310914Z",
- "iopub.status.idle": "2021-03-01T19:21:30.314175Z",
- "shell.execute_reply": "2021-03-01T19:21:30.314650Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Shape of embeddings : (10000, 16)\n"
- ]
- }
- ],
- "source": [
- "embeddings = model.layers[0].get_weights()[0]\n",
- "print('Shape of embeddings : ',embeddings.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 - Build a nice dictionary"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:30.323658Z",
- "iopub.status.busy": "2021-03-01T19:21:30.321600Z",
- "iopub.status.idle": "2021-03-01T19:21:30.325823Z",
- "shell.execute_reply": "2021-03-01T19:21:30.325340Z"
- }
- },
- "outputs": [],
- "source": [
- "word_embedding = { index_word[i]:embeddings[i] for i in range(vocab_size) }"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Have a look !\n",
- "#### Show embedding of a word :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:30.329570Z",
- "iopub.status.busy": "2021-03-01T19:21:30.329111Z",
- "iopub.status.idle": "2021-03-01T19:21:30.331330Z",
- "shell.execute_reply": "2021-03-01T19:21:30.331801Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([-0.27725485, 0.21140432, -0.3213481 , -0.36671668, -0.3042682 ,\n",
- " 0.2842951 , 0.306909 , 0.33452982, 0.34216103, 0.33668107,\n",
- " -0.2911879 , -0.34277105, 0.2792769 , 0.3087132 , -0.3242576 ,\n",
- " 0.36817914], dtype=float32)"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "word_embedding['nice']"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Few usefull functions to play with"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:30.337791Z",
- "iopub.status.busy": "2021-03-01T19:21:30.337319Z",
- "iopub.status.idle": "2021-03-01T19:21:30.339003Z",
- "shell.execute_reply": "2021-03-01T19:21:30.339469Z"
- }
- },
- "outputs": [],
- "source": [
- "# Return a l2 distance between 2 words\n",
- "#\n",
- "def l2w(w1,w2):\n",
- " v1=word_embedding[w1]\n",
- " v2=word_embedding[w2]\n",
- " return np.linalg.norm(v2-v1)\n",
- "\n",
- "# Show distance between 2 words \n",
- "#\n",
- "def show_l2(w1,w2):\n",
- " print(f'\\nL2 between [{w1}] and [{w2}] : ',l2w(w1,w2))\n",
- "\n",
- "# Displays the 15 closest words to a given word\n",
- "#\n",
- "def neighbors(w1):\n",
- " v1=word_embedding[w1]\n",
- " dd={}\n",
- " for i in range(4, 1000):\n",
- " w2=index_word[i]\n",
- " dd[w2]=l2w(w1,w2)\n",
- " dd= {k: v for k, v in sorted(dd.items(), key=lambda item: item[1])}\n",
- " print(f'\\nNeighbors of [{w1}] : ', list(dd.keys())[1:15])\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Examples"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:30.356373Z",
- "iopub.status.busy": "2021-03-01T19:21:30.342050Z",
- "iopub.status.idle": "2021-03-01T19:21:30.364149Z",
- "shell.execute_reply": "2021-03-01T19:21:30.363659Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "L2 between [nice] and [pleasant] : 0.77101326\n",
- "\n",
- "L2 between [nice] and [horrible] : 4.4631104\n",
- "\n",
- "Neighbors of [horrible] : ['avoid', 'mess', 'annoying', 'terrible', 'save', 'worse', 'poor', 'ridiculous', 'badly', 'dull', 'predictable', 'fails', 'lame', 'poorly']\n",
- "\n",
- "Neighbors of [great] : ['amazing', '9', 'wonderful', 'loved', 'fantastic', 'today', 'perfectly', 'definitely', 'highly', 'enjoyable', 'superb', 'enjoyed', 'fun', 'brilliant']\n"
- ]
- }
- ],
- "source": [
- "show_l2('nice', 'pleasant')\n",
- "show_l2('nice', 'horrible')\n",
- "\n",
- "neighbors('horrible')\n",
- "neighbors('great')\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:30.367121Z",
- "iopub.status.busy": "2021-03-01T19:21:30.366654Z",
- "iopub.status.idle": "2021-03-01T19:21:30.368907Z",
- "shell.execute_reply": "2021-03-01T19:21:30.369383Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 20:21:30\n",
- "Duration is : 00:00:01 334ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/IMDB/05-LSTM-Keras.ipynb b/IMDB/05-LSTM-Keras.ipynb
index ad26669..70245f7 100644
--- a/IMDB/05-LSTM-Keras.ipynb
+++ b/IMDB/05-LSTM-Keras.ipynb
@@ -55,7 +55,8 @@
"sys.path.append('..')\n",
"import fidle.pwk as pwk\n",
"\n",
- "datasets_dir = pwk.init('IMDB3')"
+ "run_dir = './run/IMDB5'\n",
+ "datasets_dir = pwk.init('IMDB5', run_dir)"
]
},
{
@@ -68,7 +69,8 @@
"`hide_most_frequently` is the number of ignored words, among the most common ones \n",
"`review_len` is the review length \n",
"`dense_vector_size` is the size of the generated dense vectors \n",
- "`output_dir` is where we will go to save our dictionaries. (./data is a good choice)"
+ "`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch\\\n",
+ "`scale` is a dataset scale factor - note a scale=1 need a training time > 10'"
]
},
{
@@ -86,7 +88,8 @@
"epochs = 10\n",
"batch_size = 128\n",
"\n",
- "output_dir = './data'"
+ "fit_verbosity = 1\n",
+ "scale = 1"
]
},
{
@@ -103,7 +106,7 @@
"outputs": [],
"source": [
"pwk.override('vocab_size', 'hide_most_frequently', 'review_len', 'dense_vector_size')\n",
- "pwk.override('batch_size', 'epochs', 'output_dir')"
+ "pwk.override('batch_size', 'epochs', 'fit_verbosity', 'scale')"
]
},
{
@@ -138,6 +141,13 @@
"y_train = np.asarray(y_train).astype('float32')\n",
"y_test = np.asarray(y_test ).astype('float32')\n",
"\n",
+ "# ---- Rescale\n",
+ "#\n",
+ "n1 = int(scale * len(x_train))\n",
+ "n2 = int(scale * len(x_test))\n",
+ "x_train, y_train = x_train[:n1], y_train[:n1]\n",
+ "x_test, y_test = x_test[:n2], y_test[:n2]\n",
+ "\n",
"# ---- About\n",
"#\n",
"print(\"Max(x_train,x_test) : \", pwk.rmax([x_train,x_test]) )\n",
@@ -320,8 +330,8 @@
"metadata": {},
"outputs": [],
"source": [
- "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
- "save_dir = \"./run/models/best_model.h5\"\n",
+ "os.makedirs(f'{run_dir}/models', mode=0o750, exist_ok=True)\n",
+ "save_dir = f'{run_dir}/models/best_model.h5'\n",
"savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
]
},
@@ -346,7 +356,7 @@
" epochs = epochs,\n",
" batch_size = batch_size,\n",
" validation_data = (x_test, y_test),\n",
- " verbose = 1,\n",
+ " verbose = fit_verbosity,\n",
" callbacks = [savemodel_callback])\n",
"\n",
"pwk.chrono_show()"
@@ -382,7 +392,7 @@
"metadata": {},
"outputs": [],
"source": [
- "model = keras.models.load_model('./run/models/best_model.h5')\n",
+ "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')\n",
"\n",
"# ---- Evaluate\n",
"score = model.evaluate(x_test, y_test, verbose=0)\n",
@@ -424,9 +434,11 @@
}
],
"metadata": {
+ "interpreter": {
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
+ },
"kernelspec": {
- "display_name": "Python 3",
- "language": "python",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
@@ -439,7 +451,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.7"
}
},
"nbformat": 4,
diff --git a/IMDB/05-LSTM-Keras==done==.ipynb b/IMDB/05-LSTM-Keras==done==.ipynb
deleted file mode 100644
index 8a91a85..0000000
--- a/IMDB/05-LSTM-Keras==done==.ipynb
+++ /dev/null
@@ -1,5169 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [IMDB5] - Sentiment analysis with a LSTM network\n",
- "<!-- DESC --> Still the same problem, but with a network combining embedding and LSTM\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
- " - Use of a model combining embedding and LSTM\n",
- "\n",
- "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
- "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Retrieve data\n",
- " - Preparing the data\n",
- " - Build a Embedding/LSTM model\n",
- " - Train the model\n",
- " - Evaluate the result\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Init python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:32.923729Z",
- "iopub.status.busy": "2021-03-01T19:21:32.923218Z",
- "iopub.status.idle": "2021-03-01T19:21:35.565620Z",
- "shell.execute_reply": "2021-03-01T19:21:35.565041Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { 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- "\n",
- "</style>\n",
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- "metadata": {},
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- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : IMDB3\n",
- "Run time : Monday 01 March 2021, 20:21:35\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "import tensorflow as tf\n",
- "import tensorflow.keras as keras\n",
- "import tensorflow.keras.datasets.imdb as imdb\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "\n",
- "import os,sys,h5py,json\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('IMDB3')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Retrieve data\n",
- "\n",
- "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
- "Note : Due to their nature, textual data can be somewhat complex.\n",
- "\n",
- "### 2.1 - Data structure : \n",
- "The dataset is composed of 2 parts: \n",
- "\n",
- " - **reviews**, this will be our **x**\n",
- " - **opinions** (positive/negative), this will be our **y**\n",
- "\n",
- "There are also a **dictionary**, because words are indexed in reviews\n",
- "\n",
- "```\n",
- "<dataset> = (<reviews>, <opinions>)\n",
- "\n",
- "with : <reviews> = [ <review1>, <review2>, ... ]\n",
- " <opinions> = [ <rate1>, <rate2>, ... ] where <ratei> = integer\n",
- "\n",
- "where : <reviewi> = [ <w1>, <w2>, ...] <wi> are the index (int) of the word in the dictionary\n",
- " <ratei> = int 0 for negative opinion, 1 for positive\n",
- "\n",
- "\n",
- "<dictionary> = [ <word1>:<w1>, <word2>:<w2>, ... ]\n",
- "\n",
- "with : <wordi> = word\n",
- " <wi> = int\n",
- "\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Get dataset\n",
- "For simplicity, we will use a pre-formatted dataset - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data) \n",
- "However, Keras offers some usefull tools for formatting textual data - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text) \n",
- "\n",
- "**Load dataset :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:35.569733Z",
- "iopub.status.busy": "2021-03-01T19:21:35.569259Z",
- "iopub.status.idle": "2021-03-01T19:21:40.793993Z",
- "shell.execute_reply": "2021-03-01T19:21:40.794483Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<string>:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/datasets/imdb.py:159: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/datasets/imdb.py:160: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n"
- ]
- }
- ],
- "source": [
- "vocab_size = 10000\n",
- "\n",
- "# ----- Retrieve x,y\n",
- "\n",
- "# Uncomment this if you want to load dataset directly from keras (small size <20M)\n",
- "#\n",
- "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words = vocab_size,\n",
- " skip_top = 0,\n",
- " maxlen = None,\n",
- " seed = 42,\n",
- " start_char = 1,\n",
- " oov_char = 2,\n",
- " index_from = 3, )\n",
- "\n",
- "# To load a h5 version of the dataset :\n",
- "#\n",
- "# with h5py.File(f'{datasets_dir}/IMDB/origine/dataset_imdb.h5','r') as f:\n",
- "# x_train = f['x_train'][:]\n",
- "# y_train = f['y_train'][:]\n",
- "# x_test = f['x_test'][:]\n",
- "# y_test = f['y_test'][:]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**About this dataset :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:40.816852Z",
- "iopub.status.busy": "2021-03-01T19:21:40.806709Z",
- "iopub.status.idle": "2021-03-01T19:21:46.314688Z",
- "shell.execute_reply": "2021-03-01T19:21:46.315187Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Max(x_train,x_test) : 9999\n",
- " x_train : (25000,) y_train : (25000,)\n",
- " x_test : (25000,) y_test : (25000,)\n",
- "\n",
- "Review example (x_train[12]) :\n",
- "\n",
- " [1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]\n"
- ]
- }
- ],
- "source": [
- "print(\" Max(x_train,x_test) : \", pwk.rmax([x_train,x_test]) )\n",
- "print(\" x_train : {} y_train : {}\".format(x_train.shape, y_train.shape))\n",
- "print(\" x_test : {} y_test : {}\".format(x_test.shape, y_test.shape))\n",
- "\n",
- "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 - Have a look for humans (optional)\n",
- "When we loaded the dataset, we asked for using \\<start\\> as 1, \\<unknown word\\> as 2 \n",
- "So, we shifted the dataset by 3 with the parameter index_from=3\n",
- "\n",
- "**Load dictionary :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:46.319937Z",
- "iopub.status.busy": "2021-03-01T19:21:46.319457Z",
- "iopub.status.idle": "2021-03-01T19:21:46.401662Z",
- "shell.execute_reply": "2021-03-01T19:21:46.402170Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
- "#\n",
- "word_index = imdb.get_word_index()\n",
- "\n",
- "# ---- Shift the dictionary from +3\n",
- "#\n",
- "word_index = {w:(i+3) for w,i in word_index.items()}\n",
- "\n",
- "# ---- Add <pad>, <start> and unknown tags\n",
- "#\n",
- "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2} )\n",
- "\n",
- "# ---- Create a reverse dictionary : {index:word}\n",
- "#\n",
- "index_word = {index:word for word,index in word_index.items()} \n",
- "\n",
- "# ---- Add a nice function to transpose :\n",
- "#\n",
- "def dataset2text(review):\n",
- " return ' '.join([index_word.get(i, '?') for i in review])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Have a look :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:46.406442Z",
- "iopub.status.busy": "2021-03-01T19:21:46.405654Z",
- "iopub.status.idle": "2021-03-01T19:21:46.412683Z",
- "shell.execute_reply": "2021-03-01T19:21:46.412193Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Dictionary size : 88587\n",
- "440 : hope\n",
- "441 : entertaining\n",
- "442 : she's\n",
- "443 : mr\n",
- "444 : overall\n",
- "445 : evil\n",
- "446 : called\n",
- "447 : loved\n",
- "448 : based\n",
- "449 : oh\n",
- "450 : several\n",
- "451 : fans\n",
- "452 : mother\n",
- "453 : drama\n",
- "454 : beginning\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Review example :**"
- ],
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- "<IPython.core.display.Markdown object>"
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- "output_type": "stream",
- "text": [
- "[1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**After translation :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint\n"
- ]
- }
- ],
- "source": [
- "print('\\nDictionary size : ', len(word_index))\n",
- "for k in range(440,455):print(f'{k:2d} : {index_word[k]}' )\n",
- "pwk.subtitle('Review example :')\n",
- "print(x_train[12])\n",
- "pwk.subtitle('After translation :')\n",
- "print(dataset2text(x_train[12]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.4 - Have a look for NN"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:46.432761Z",
- "iopub.status.busy": "2021-03-01T19:21:46.428037Z",
- "iopub.status.idle": "2021-03-01T19:21:48.529955Z",
- "shell.execute_reply": "2021-03-01T19:21:48.529444Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/IMDB3-01-stats-sizes</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "sizes=[len(i) for i in x_train]\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist(sizes, bins=400)\n",
- "plt.gca().set(title='Distribution of reviews by size - [{:5.2f}, {:5.2f}]'.format(min(sizes),max(sizes)), \n",
- " xlabel='Size', ylabel='Density', xlim=[0,1500])\n",
- "pwk.save_fig('01-stats-sizes')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Preprocess the data (padding)\n",
- "In order to be processed by an NN, all entries must have the **same length.** \n",
- "We chose a review length of **review_len** \n",
- "We will therefore complete them with a padding (of \\<pad\\>\\) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:48.539000Z",
- "iopub.status.busy": "2021-03-01T19:21:48.538513Z",
- "iopub.status.idle": "2021-03-01T19:21:49.605945Z",
- "shell.execute_reply": "2021-03-01T19:21:49.606430Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**After padding :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ 1 14 22 1367 53 206 159 4 636 898 74 26 11 436\n",
- " 363 108 7 14 432 14 22 9 1055 34 8599 2 5 381\n",
- " 3705 4509 14 768 47 839 25 111 1517 2579 1991 438 2663 587\n",
- " 4 280 725 6 58 11 2714 201 4 206 16 702 5 5176\n",
- " 19 480 5920 157 13 64 219 4 2 11 107 665 1212 39\n",
- " 4 206 4 65 410 16 565 5 24 43 343 17 5602 8\n",
- " 169 101 85 206 108 8 3008 14 25 215 168 18 6 2579\n",
- " 1991 438 2 11 129 1609 36 26 66 290 3303 46 5 633\n",
- " 115 4363 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**In real words :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n"
- ]
- }
- ],
- "source": [
- "review_len = 256\n",
- "\n",
- "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
- " value = 0,\n",
- " padding = 'post',\n",
- " maxlen = review_len)\n",
- "\n",
- "x_test = keras.preprocessing.sequence.pad_sequences(x_test,\n",
- " value = 0 ,\n",
- " padding = 'post',\n",
- " maxlen = review_len)\n",
- "\n",
- "pwk.subtitle('After padding :')\n",
- "print(x_train[12])\n",
- "pwk.subtitle('In real words :')\n",
- "print(dataset2text(x_train[12]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Save dataset and dictionary (For future use but not mandatory)**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:49.611384Z",
- "iopub.status.busy": "2021-03-01T19:21:49.610910Z",
- "iopub.status.idle": "2021-03-01T19:21:50.858455Z",
- "shell.execute_reply": "2021-03-01T19:21:50.857953Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Saved.\n"
- ]
- }
- ],
- "source": [
- "# ---- Write dataset in a h5 file, could be usefull\n",
- "#\n",
- "output_dir = './data'\n",
- "pwk.mkdir(output_dir)\n",
- "\n",
- "with h5py.File(f'{output_dir}/dataset_imdb.h5', 'w') as f:\n",
- " f.create_dataset(\"x_train\", data=x_train)\n",
- " f.create_dataset(\"y_train\", data=y_train)\n",
- " f.create_dataset(\"x_test\", data=x_test)\n",
- " f.create_dataset(\"y_test\", data=y_test)\n",
- "\n",
- "with open(f'{output_dir}/word_index.json', 'w') as fp:\n",
- " json.dump(word_index, fp)\n",
- "\n",
- "with open(f'{output_dir}/index_word.json', 'w') as fp:\n",
- " json.dump(index_word, fp)\n",
- "\n",
- "print('Saved.')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Build the model\n",
- "Few remarks :\n",
- " - We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
- " - **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly) \n",
- " In other words: we average the set of vectors/words of a sentence\n",
- " - L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling) \n",
- "Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer) \n",
- "ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks) \n",
- "\n",
- "More documentation about this model functions :\n",
- " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
- " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:50.863334Z",
- "iopub.status.busy": "2021-03-01T19:21:50.862854Z",
- "iopub.status.idle": "2021-03-01T19:21:50.864515Z",
- "shell.execute_reply": "2021-03-01T19:21:50.864994Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_model(dense_vector_size=128):\n",
- " \n",
- " model = keras.Sequential()\n",
- " model.add(keras.layers.Embedding(input_dim = vocab_size, \n",
- " output_dim = dense_vector_size, \n",
- " input_length = review_len))\n",
- " model.add(keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2))\n",
- " model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
- "\n",
- " model.compile(optimizer = 'adam',\n",
- " loss = 'binary_crossentropy',\n",
- " metrics = ['accuracy'])\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Train the model\n",
- "### 5.1 - Get it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:50.868005Z",
- "iopub.status.busy": "2021-03-01T19:21:50.867527Z",
- "iopub.status.idle": "2021-03-01T19:21:52.052178Z",
- "shell.execute_reply": "2021-03-01T19:21:52.052669Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "embedding (Embedding) (None, 256, 32) 320000 \n",
- "_________________________________________________________________\n",
- "lstm (LSTM) (None, 128) 82432 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 1) 129 \n",
- "=================================================================\n",
- "Total params: 402,561\n",
- "Trainable params: 402,561\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = get_model(32)\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Add callback"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:52.056286Z",
- "iopub.status.busy": "2021-03-01T19:21:52.055812Z",
- "iopub.status.idle": "2021-03-01T19:21:52.061394Z",
- "shell.execute_reply": "2021-03-01T19:21:52.061863Z"
- }
- },
- "outputs": [],
- "source": [
- "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
- "save_dir = \"./run/models/best_model.h5\"\n",
- "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.1 - Train it\n",
- "GPU : batch_size=512 : 6' 30s \n",
- "CPU : batch_size=512 : 12' 57s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:21:52.065946Z",
- "iopub.status.busy": "2021-03-01T19:21:52.065472Z",
- "iopub.status.idle": "2021-03-01T19:28:00.577718Z",
- "shell.execute_reply": "2021-03-01T19:28:00.578236Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 4:25 - loss: 0.6931 - accuracy: 0.5039"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 29s - loss: 0.6931 - accuracy: 0.4971 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.6932 - accuracy: 0.4924"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.6932 - accuracy: 0.4925"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 27s - loss: 0.6932 - accuracy: 0.4906"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.6932 - accuracy: 0.4903"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.6933 - accuracy: 0.4899"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.6932 - accuracy: 0.4904"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.6932 - accuracy: 0.4911"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 24s - loss: 0.6932 - accuracy: 0.4921"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.6932 - accuracy: 0.4927"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 22s - loss: 0.6932 - accuracy: 0.4936"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 22s - loss: 0.6932 - accuracy: 0.4945"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "14/49 [=======>......................] - ETA: 21s - loss: 0.6932 - accuracy: 0.4953"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "15/49 [========>.....................] - ETA: 21s - loss: 0.6932 - accuracy: 0.4962"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 20s - loss: 0.6932 - accuracy: 0.4971"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 19s - loss: 0.6931 - accuracy: 0.4980"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 19s - loss: 0.6931 - accuracy: 0.4989"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 19s - loss: 0.6931 - accuracy: 0.4997"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "20/49 [===========>..................] - ETA: 18s - loss: 0.6931 - accuracy: 0.5006"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "21/49 [===========>..................] - ETA: 18s - loss: 0.6931 - accuracy: 0.5014"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "23/49 [=============>................] - ETA: 16s - loss: 0.6931 - accuracy: 0.5028"
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- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 16s - loss: 0.6931 - accuracy: 0.5034"
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "28/49 [================>.............] - ETA: 13s - loss: 0.6930 - accuracy: 0.5053"
- ]
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- "29/49 [================>.............] - ETA: 13s - loss: 0.6930 - accuracy: 0.5056"
- ]
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- "30/49 [=================>............] - ETA: 12s - loss: 0.6930 - accuracy: 0.5060"
- ]
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- "31/49 [=================>............] - ETA: 11s - loss: 0.6930 - accuracy: 0.5063"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "32/49 [==================>...........] - ETA: 11s - loss: 0.6930 - accuracy: 0.5066"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 10s - loss: 0.6930 - accuracy: 0.5069"
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.6930 - accuracy: 0.5071 "
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 9s - loss: 0.6929 - accuracy: 0.5074"
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 8s - loss: 0.6929 - accuracy: 0.5077"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.6929 - accuracy: 0.5079"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 7s - loss: 0.6929 - accuracy: 0.5081"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.6929 - accuracy: 0.5083"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 5s - loss: 0.6929 - accuracy: 0.5085"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 5s - loss: 0.6928 - accuracy: 0.5087"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.6928 - accuracy: 0.5088"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 3s - loss: 0.6928 - accuracy: 0.5091"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.6928 - accuracy: 0.5093"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.6927 - accuracy: 0.5095"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 2s - loss: 0.6927 - accuracy: 0.5097"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.6927 - accuracy: 0.5100"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.6927 - accuracy: 0.5102"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.6926 - accuracy: 0.5104"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 43s 777ms/step - loss: 0.6926 - accuracy: 0.5107 - val_loss: 0.6881 - val_accuracy: 0.5604\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 2/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.6874 - accuracy: 0.5508"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 29s - loss: 0.6871 - accuracy: 0.5527"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.6871 - accuracy: 0.5534"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.6872 - accuracy: 0.5529"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 27s - loss: 0.6872 - accuracy: 0.5513"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.6872 - accuracy: 0.5503"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.6872 - accuracy: 0.5502"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.6871 - accuracy: 0.5506"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.6870 - accuracy: 0.5514"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 24s - loss: 0.6869 - accuracy: 0.5521"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 24s - loss: 0.6868 - accuracy: 0.5529"
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- },
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- "12/49 [======>.......................] - ETA: 24s - loss: 0.6867 - accuracy: 0.5535"
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- "13/49 [======>.......................] - ETA: 24s - loss: 0.6865 - accuracy: 0.5540"
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- "14/49 [=======>......................] - ETA: 23s - loss: 0.6864 - accuracy: 0.5546"
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- "15/49 [========>.....................] - ETA: 23s - loss: 0.6864 - accuracy: 0.5548"
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- "16/49 [========>.....................] - ETA: 22s - loss: 0.6863 - accuracy: 0.5551"
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- "17/49 [=========>....................] - ETA: 21s - loss: 0.6862 - accuracy: 0.5553"
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- "18/49 [==========>...................] - ETA: 20s - loss: 0.6861 - accuracy: 0.5555"
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- "19/49 [==========>...................] - ETA: 20s - loss: 0.6860 - accuracy: 0.5558"
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- "20/49 [===========>..................] - ETA: 19s - loss: 0.6859 - accuracy: 0.5560"
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- "21/49 [===========>..................] - ETA: 18s - loss: 0.6858 - accuracy: 0.5561"
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- "22/49 [============>.................] - ETA: 17s - loss: 0.6857 - accuracy: 0.5564"
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- "23/49 [=============>................] - ETA: 17s - loss: 0.6854 - accuracy: 0.5569"
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- "24/49 [=============>................] - ETA: 16s - loss: 0.6853 - accuracy: 0.5575"
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- "25/49 [==============>...............] - ETA: 15s - loss: 0.6851 - accuracy: 0.5583"
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- "26/49 [==============>...............] - ETA: 15s - loss: 0.6848 - accuracy: 0.5592"
- ]
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- "27/49 [===============>..............] - ETA: 14s - loss: 0.6844 - accuracy: 0.5600"
- ]
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- "28/49 [================>.............] - ETA: 13s - loss: 0.6840 - accuracy: 0.5610"
- ]
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- "29/49 [================>.............] - ETA: 13s - loss: 0.6834 - accuracy: 0.5621"
- ]
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "30/49 [=================>............] - ETA: 12s - loss: 0.6828 - accuracy: 0.5634"
- ]
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 11s - loss: 0.6820 - accuracy: 0.5646"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "32/49 [==================>...........] - ETA: 11s - loss: 0.6813 - accuracy: 0.5660"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 10s - loss: 0.6805 - accuracy: 0.5674"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.6796 - accuracy: 0.5688 "
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 9s - loss: 0.6787 - accuracy: 0.5703"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 8s - loss: 0.6777 - accuracy: 0.5718"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.6767 - accuracy: 0.5733"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 7s - loss: 0.6756 - accuracy: 0.5749"
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.6746 - accuracy: 0.5764"
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- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "45/49 [==========================>...] - ETA: 2s - loss: 0.6680 - accuracy: 0.5859"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.6668 - accuracy: 0.5875"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.6656 - accuracy: 0.5891"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.6645 - accuracy: 0.5906"
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.6634 - accuracy: 0.5922"
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 35s 724ms/step - loss: 0.6623 - accuracy: 0.5936 - val_loss: 0.5006 - val_accuracy: 0.7664\n"
- ]
- },
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- "output_type": "stream",
- "text": [
- "Epoch 3/10\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.4821 - accuracy: 0.7852"
- ]
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 28s - loss: 0.4791 - accuracy: 0.7856"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.4701 - accuracy: 0.7939"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.4630 - accuracy: 0.8005"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 29s - loss: 0.4595 - accuracy: 0.8052"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 28s - loss: 0.4572 - accuracy: 0.8083"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 27s - loss: 0.4546 - accuracy: 0.8110"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 27s - loss: 0.4515 - accuracy: 0.8139"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 27s - loss: 0.4487 - accuracy: 0.8166"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 26s - loss: 0.4456 - accuracy: 0.8194"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 25s - loss: 0.4430 - accuracy: 0.8218"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 24s - loss: 0.4406 - accuracy: 0.8238"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 23s - loss: 0.4383 - accuracy: 0.8257"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "14/49 [=======>......................] - ETA: 23s - loss: 0.4363 - accuracy: 0.8272"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "15/49 [========>.....................] - ETA: 22s - loss: 0.4346 - accuracy: 0.8285"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 21s - loss: 0.4329 - accuracy: 0.8296"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 20s - loss: 0.4313 - accuracy: 0.8308"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 20s - loss: 0.4298 - accuracy: 0.8318"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 19s - loss: 0.4284 - accuracy: 0.8327"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "20/49 [===========>..................] - ETA: 19s - loss: 0.4271 - accuracy: 0.8336"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "21/49 [===========>..................] - ETA: 18s - loss: 0.4258 - accuracy: 0.8345"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "22/49 [============>.................] - ETA: 17s - loss: 0.4246 - accuracy: 0.8352"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "23/49 [=============>................] - ETA: 17s - loss: 0.4235 - accuracy: 0.8360"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 16s - loss: 0.4222 - accuracy: 0.8367"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 16s - loss: 0.4210 - accuracy: 0.8375"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "26/49 [==============>...............] - ETA: 15s - loss: 0.4198 - accuracy: 0.8382"
- ]
- },
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "27/49 [===============>..............] - ETA: 14s - loss: 0.4187 - accuracy: 0.8388"
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- },
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- "text": [
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- "28/49 [================>.............] - ETA: 14s - loss: 0.4177 - accuracy: 0.8394"
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- "29/49 [================>.............] - ETA: 13s - loss: 0.4166 - accuracy: 0.8401"
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- "30/49 [=================>............] - ETA: 12s - loss: 0.4155 - accuracy: 0.8407"
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- "31/49 [=================>............] - ETA: 12s - loss: 0.4145 - accuracy: 0.8412"
- ]
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- "32/49 [==================>...........] - ETA: 11s - loss: 0.4135 - accuracy: 0.8418"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 10s - loss: 0.4125 - accuracy: 0.8424"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.4116 - accuracy: 0.8428 "
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 9s - loss: 0.4106 - accuracy: 0.8433"
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- "36/49 [=====================>........] - ETA: 8s - loss: 0.4097 - accuracy: 0.8438"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.4089 - accuracy: 0.8442"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 7s - loss: 0.4081 - accuracy: 0.8446"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.4075 - accuracy: 0.8450"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 5s - loss: 0.4069 - accuracy: 0.8453"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 5s - loss: 0.4063 - accuracy: 0.8457"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.4057 - accuracy: 0.8460"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 3s - loss: 0.4051 - accuracy: 0.8464"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.4046 - accuracy: 0.8467"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.4040 - accuracy: 0.8470"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.4035 - accuracy: 0.8473"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.4029 - accuracy: 0.8476"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.4024 - accuracy: 0.8479"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.4019 - accuracy: 0.8482"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 36s 733ms/step - loss: 0.4014 - accuracy: 0.8484 - val_loss: 0.3748 - val_accuracy: 0.8512\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 4/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.3360 - accuracy: 0.8848"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 32s - loss: 0.3357 - accuracy: 0.8882"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 28s - loss: 0.3239 - accuracy: 0.8942"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 28s - loss: 0.3217 - accuracy: 0.8951"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 27s - loss: 0.3217 - accuracy: 0.8952"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 26s - loss: 0.3225 - accuracy: 0.8947"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.3241 - accuracy: 0.8940"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 25s - loss: 0.3251 - accuracy: 0.8935"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 24s - loss: 0.3261 - accuracy: 0.8929"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.3269 - accuracy: 0.8925"
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- "12/49 [======>.......................] - ETA: 23s - loss: 0.3279 - accuracy: 0.8923"
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- "13/49 [======>.......................] - ETA: 22s - loss: 0.3287 - accuracy: 0.8921"
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- "14/49 [=======>......................] - ETA: 21s - loss: 0.3294 - accuracy: 0.8919"
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- "15/49 [========>.....................] - ETA: 21s - loss: 0.3298 - accuracy: 0.8918"
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- "16/49 [========>.....................] - ETA: 20s - loss: 0.3301 - accuracy: 0.8918"
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- "17/49 [=========>....................] - ETA: 20s - loss: 0.3304 - accuracy: 0.8918"
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- "18/49 [==========>...................] - ETA: 19s - loss: 0.3311 - accuracy: 0.8916"
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- "19/49 [==========>...................] - ETA: 18s - loss: 0.3318 - accuracy: 0.8915"
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- "20/49 [===========>..................] - ETA: 18s - loss: 0.3323 - accuracy: 0.8913"
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- "21/49 [===========>..................] - ETA: 17s - loss: 0.3328 - accuracy: 0.8911"
- ]
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- "22/49 [============>.................] - ETA: 16s - loss: 0.3333 - accuracy: 0.8909"
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- "23/49 [=============>................] - ETA: 16s - loss: 0.3338 - accuracy: 0.8907"
- ]
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- "24/49 [=============>................] - ETA: 15s - loss: 0.3343 - accuracy: 0.8906"
- ]
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- "25/49 [==============>...............] - ETA: 14s - loss: 0.3347 - accuracy: 0.8904"
- ]
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- "26/49 [==============>...............] - ETA: 14s - loss: 0.3350 - accuracy: 0.8903"
- ]
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- {
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- "27/49 [===============>..............] - ETA: 13s - loss: 0.3352 - accuracy: 0.8902"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "28/49 [================>.............] - ETA: 13s - loss: 0.3353 - accuracy: 0.8901"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "29/49 [================>.............] - ETA: 12s - loss: 0.3354 - accuracy: 0.8900"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "30/49 [=================>............] - ETA: 11s - loss: 0.3355 - accuracy: 0.8899"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 11s - loss: 0.3355 - accuracy: 0.8899"
- ]
- },
- {
- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "32/49 [==================>...........] - ETA: 10s - loss: 0.3355 - accuracy: 0.8899"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.3354 - accuracy: 0.8899 "
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.3354 - accuracy: 0.8899"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 8s - loss: 0.3352 - accuracy: 0.8899"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 8s - loss: 0.3351 - accuracy: 0.8899"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.3349 - accuracy: 0.8900"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 6s - loss: 0.3348 - accuracy: 0.8900"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.3346 - accuracy: 0.8900"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 5s - loss: 0.3344 - accuracy: 0.8901"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 3s - loss: 0.3338 - accuracy: 0.8902"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "45/49 [==========================>...] - ETA: 2s - loss: 0.3333 - accuracy: 0.8903"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.3331 - accuracy: 0.8904"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.3328 - accuracy: 0.8904"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.3326 - accuracy: 0.8905"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.3324 - accuracy: 0.8905"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 34s 701ms/step - loss: 0.3321 - accuracy: 0.8905 - val_loss: 0.3588 - val_accuracy: 0.8655\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 5/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.2722 - accuracy: 0.9062"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 29s - loss: 0.2820 - accuracy: 0.9043"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.2861 - accuracy: 0.9041"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.2858 - accuracy: 0.9045"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 27s - loss: 0.2831 - accuracy: 0.9060"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.2809 - accuracy: 0.9071"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 26s - loss: 0.2798 - accuracy: 0.9078"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.2784 - accuracy: 0.9084"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.2772 - accuracy: 0.9089"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 24s - loss: 0.2762 - accuracy: 0.9092"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.2756 - accuracy: 0.9095"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 22s - loss: 0.2750 - accuracy: 0.9098"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 22s - loss: 0.2748 - accuracy: 0.9099"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "14/49 [=======>......................] - ETA: 21s - loss: 0.2746 - accuracy: 0.9100"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "15/49 [========>.....................] - ETA: 21s - loss: 0.2748 - accuracy: 0.9100"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 20s - loss: 0.2755 - accuracy: 0.9098"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 19s - loss: 0.2765 - accuracy: 0.9095"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 19s - loss: 0.2775 - accuracy: 0.9093"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 18s - loss: 0.2783 - accuracy: 0.9090"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "20/49 [===========>..................] - ETA: 17s - loss: 0.2790 - accuracy: 0.9089"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "21/49 [===========>..................] - ETA: 17s - loss: 0.2797 - accuracy: 0.9087"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "22/49 [============>.................] - ETA: 16s - loss: 0.2804 - accuracy: 0.9084"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "23/49 [=============>................] - ETA: 16s - loss: 0.2811 - accuracy: 0.9082"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 15s - loss: 0.2819 - accuracy: 0.9079"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 14s - loss: 0.2827 - accuracy: 0.9075"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "26/49 [==============>...............] - ETA: 14s - loss: 0.2834 - accuracy: 0.9072"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "27/49 [===============>..............] - ETA: 13s - loss: 0.2841 - accuracy: 0.9069"
- ]
- },
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- "text": [
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- "28/49 [================>.............] - ETA: 13s - loss: 0.2847 - accuracy: 0.9066"
- ]
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- "29/49 [================>.............] - ETA: 12s - loss: 0.2853 - accuracy: 0.9063"
- ]
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- "30/49 [=================>............] - ETA: 11s - loss: 0.2859 - accuracy: 0.9061"
- ]
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- "31/49 [=================>............] - ETA: 11s - loss: 0.2864 - accuracy: 0.9058"
- ]
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- "32/49 [==================>...........] - ETA: 10s - loss: 0.2869 - accuracy: 0.9056"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.2875 - accuracy: 0.9053 "
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.2880 - accuracy: 0.9051"
- ]
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- "35/49 [====================>.........] - ETA: 8s - loss: 0.2884 - accuracy: 0.9049"
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- "36/49 [=====================>........] - ETA: 8s - loss: 0.2888 - accuracy: 0.9047"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.2891 - accuracy: 0.9045"
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 7s - loss: 0.2894 - accuracy: 0.9044"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.2897 - accuracy: 0.9042"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 5s - loss: 0.2900 - accuracy: 0.9041"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 5s - loss: 0.2902 - accuracy: 0.9040"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.2904 - accuracy: 0.9039"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 3s - loss: 0.2906 - accuracy: 0.9038"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.2908 - accuracy: 0.9037"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.2910 - accuracy: 0.9036"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.2912 - accuracy: 0.9035"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.2913 - accuracy: 0.9034"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.2915 - accuracy: 0.9034"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.2916 - accuracy: 0.9033"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 36s 732ms/step - loss: 0.2917 - accuracy: 0.9033 - val_loss: 0.3586 - val_accuracy: 0.8687\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 6/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.2213 - accuracy: 0.9355"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 28s - loss: 0.2309 - accuracy: 0.9307"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 30s - loss: 0.2374 - accuracy: 0.9275"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 32s - loss: 0.2454 - accuracy: 0.9240"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 32s - loss: 0.2513 - accuracy: 0.9215"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 31s - loss: 0.2550 - accuracy: 0.9199"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 29s - loss: 0.2569 - accuracy: 0.9192"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 28s - loss: 0.2589 - accuracy: 0.9183"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 27s - loss: 0.2602 - accuracy: 0.9178"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 27s - loss: 0.2614 - accuracy: 0.9175"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 27s - loss: 0.2623 - accuracy: 0.9171"
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- },
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- "12/49 [======>.......................] - ETA: 27s - loss: 0.2630 - accuracy: 0.9168"
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- "13/49 [======>.......................] - ETA: 26s - loss: 0.2635 - accuracy: 0.9167"
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- "14/49 [=======>......................] - ETA: 25s - loss: 0.2637 - accuracy: 0.9166"
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- "15/49 [========>.....................] - ETA: 25s - loss: 0.2638 - accuracy: 0.9166"
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- "16/49 [========>.....................] - ETA: 24s - loss: 0.2638 - accuracy: 0.9166"
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- "17/49 [=========>....................] - ETA: 23s - loss: 0.2637 - accuracy: 0.9167"
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- "18/49 [==========>...................] - ETA: 23s - loss: 0.2636 - accuracy: 0.9167"
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- "19/49 [==========>...................] - ETA: 22s - loss: 0.2634 - accuracy: 0.9168"
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- "20/49 [===========>..................] - ETA: 22s - loss: 0.2634 - accuracy: 0.9168"
- ]
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- "21/49 [===========>..................] - ETA: 21s - loss: 0.2634 - accuracy: 0.9168"
- ]
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- "22/49 [============>.................] - ETA: 20s - loss: 0.2633 - accuracy: 0.9169"
- ]
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- "23/49 [=============>................] - ETA: 19s - loss: 0.2633 - accuracy: 0.9168"
- ]
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- "24/49 [=============>................] - ETA: 18s - loss: 0.2633 - accuracy: 0.9168"
- ]
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- "25/49 [==============>...............] - ETA: 17s - loss: 0.2633 - accuracy: 0.9168"
- ]
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- "26/49 [==============>...............] - ETA: 16s - loss: 0.2633 - accuracy: 0.9168"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "27/49 [===============>..............] - ETA: 16s - loss: 0.2633 - accuracy: 0.9168"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "28/49 [================>.............] - ETA: 15s - loss: 0.2633 - accuracy: 0.9168"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "29/49 [================>.............] - ETA: 14s - loss: 0.2633 - accuracy: 0.9168"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "30/49 [=================>............] - ETA: 13s - loss: 0.2632 - accuracy: 0.9168"
- ]
- },
- {
- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 12s - loss: 0.2630 - accuracy: 0.9169"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "32/49 [==================>...........] - ETA: 12s - loss: 0.2629 - accuracy: 0.9169"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 11s - loss: 0.2627 - accuracy: 0.9170"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 10s - loss: 0.2625 - accuracy: 0.9170"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 10s - loss: 0.2623 - accuracy: 0.9171"
- ]
- },
- {
- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 9s - loss: 0.2621 - accuracy: 0.9172 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 8s - loss: 0.2619 - accuracy: 0.9172"
- ]
- },
- {
- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 7s - loss: 0.2617 - accuracy: 0.9173"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 7s - loss: 0.2615 - accuracy: 0.9173"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 6s - loss: 0.2614 - accuracy: 0.9174"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 5s - loss: 0.2612 - accuracy: 0.9174"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.2610 - accuracy: 0.9175"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 4s - loss: 0.2609 - accuracy: 0.9175"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "45/49 [==========================>...] - ETA: 2s - loss: 0.2605 - accuracy: 0.9177"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 2s - loss: 0.2603 - accuracy: 0.9177"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.2601 - accuracy: 0.9178"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.2599 - accuracy: 0.9179"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.2597 - accuracy: 0.9179"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 37s 761ms/step - loss: 0.2595 - accuracy: 0.9180 - val_loss: 0.3350 - val_accuracy: 0.8731\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 7/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.2400 - accuracy: 0.9297"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 29s - loss: 0.2386 - accuracy: 0.9297"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.2357 - accuracy: 0.9310"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.2342 - accuracy: 0.9314"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 27s - loss: 0.2316 - accuracy: 0.9323"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.2298 - accuracy: 0.9328"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.2286 - accuracy: 0.9331"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.2288 - accuracy: 0.9331"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.2291 - accuracy: 0.9330"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 24s - loss: 0.2295 - accuracy: 0.9329"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.2297 - accuracy: 0.9328"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 22s - loss: 0.2297 - accuracy: 0.9329"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 22s - loss: 0.2294 - accuracy: 0.9330"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "14/49 [=======>......................] - ETA: 21s - loss: 0.2292 - accuracy: 0.9330"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "15/49 [========>.....................] - ETA: 20s - loss: 0.2289 - accuracy: 0.9331"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 20s - loss: 0.2284 - accuracy: 0.9332"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 19s - loss: 0.2278 - accuracy: 0.9333"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 19s - loss: 0.2273 - accuracy: 0.9334"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 18s - loss: 0.2269 - accuracy: 0.9335"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "20/49 [===========>..................] - ETA: 17s - loss: 0.2267 - accuracy: 0.9335"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "21/49 [===========>..................] - ETA: 17s - loss: 0.2265 - accuracy: 0.9335"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "22/49 [============>.................] - ETA: 16s - loss: 0.2263 - accuracy: 0.9335"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "23/49 [=============>................] - ETA: 16s - loss: 0.2261 - accuracy: 0.9335"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 15s - loss: 0.2260 - accuracy: 0.9334"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 14s - loss: 0.2258 - accuracy: 0.9335"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "26/49 [==============>...............] - ETA: 14s - loss: 0.2257 - accuracy: 0.9335"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "27/49 [===============>..............] - ETA: 13s - loss: 0.2255 - accuracy: 0.9335"
- ]
- },
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- "text": [
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- "28/49 [================>.............] - ETA: 12s - loss: 0.2254 - accuracy: 0.9335"
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- "29/49 [================>.............] - ETA: 12s - loss: 0.2253 - accuracy: 0.9335"
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- "30/49 [=================>............] - ETA: 11s - loss: 0.2252 - accuracy: 0.9335"
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- "31/49 [=================>............] - ETA: 11s - loss: 0.2250 - accuracy: 0.9335"
- ]
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- "32/49 [==================>...........] - ETA: 10s - loss: 0.2249 - accuracy: 0.9335"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 10s - loss: 0.2247 - accuracy: 0.9335"
- ]
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- "34/49 [===================>..........] - ETA: 9s - loss: 0.2246 - accuracy: 0.9335 "
- ]
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- "35/49 [====================>.........] - ETA: 8s - loss: 0.2245 - accuracy: 0.9335"
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- "36/49 [=====================>........] - ETA: 8s - loss: 0.2244 - accuracy: 0.9335"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.2243 - accuracy: 0.9335"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 6s - loss: 0.2242 - accuracy: 0.9335"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.2241 - accuracy: 0.9335"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 5s - loss: 0.2240 - accuracy: 0.9335"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 5s - loss: 0.2240 - accuracy: 0.9334"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.2239 - accuracy: 0.9334"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 3s - loss: 0.2238 - accuracy: 0.9334"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.2238 - accuracy: 0.9334"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.2237 - accuracy: 0.9333"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.2236 - accuracy: 0.9333"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.2235 - accuracy: 0.9333"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.2235 - accuracy: 0.9333"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.2234 - accuracy: 0.9333"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 36s 736ms/step - loss: 0.2233 - accuracy: 0.9333 - val_loss: 0.3772 - val_accuracy: 0.8661\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 8/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.2074 - accuracy: 0.9375"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 30s - loss: 0.2011 - accuracy: 0.9395"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 29s - loss: 0.1989 - accuracy: 0.9408"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 29s - loss: 0.1970 - accuracy: 0.9417"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 28s - loss: 0.1973 - accuracy: 0.9416"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 27s - loss: 0.1971 - accuracy: 0.9417"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 27s - loss: 0.1972 - accuracy: 0.9417"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 26s - loss: 0.1973 - accuracy: 0.9415"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 25s - loss: 0.1972 - accuracy: 0.9414"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 24s - loss: 0.1970 - accuracy: 0.9413"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 24s - loss: 0.1969 - accuracy: 0.9412"
- ]
- },
- {
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- "12/49 [======>.......................] - ETA: 24s - loss: 0.1967 - accuracy: 0.9412"
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- "13/49 [======>.......................] - ETA: 24s - loss: 0.1964 - accuracy: 0.9413"
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- "14/49 [=======>......................] - ETA: 23s - loss: 0.1964 - accuracy: 0.9412"
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- "15/49 [========>.....................] - ETA: 23s - loss: 0.1964 - accuracy: 0.9412"
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- "16/49 [========>.....................] - ETA: 22s - loss: 0.1962 - accuracy: 0.9412"
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- "17/49 [=========>....................] - ETA: 21s - loss: 0.1960 - accuracy: 0.9412"
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- "18/49 [==========>...................] - ETA: 21s - loss: 0.1959 - accuracy: 0.9413"
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- "19/49 [==========>...................] - ETA: 20s - loss: 0.1958 - accuracy: 0.9413"
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- "20/49 [===========>..................] - ETA: 19s - loss: 0.1957 - accuracy: 0.9413"
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- "21/49 [===========>..................] - ETA: 19s - loss: 0.1955 - accuracy: 0.9413"
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- "22/49 [============>.................] - ETA: 18s - loss: 0.1953 - accuracy: 0.9414"
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- "23/49 [=============>................] - ETA: 17s - loss: 0.1950 - accuracy: 0.9415"
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- "24/49 [=============>................] - ETA: 17s - loss: 0.1948 - accuracy: 0.9415"
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- "25/49 [==============>...............] - ETA: 16s - loss: 0.1946 - accuracy: 0.9416"
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- "26/49 [==============>...............] - ETA: 16s - loss: 0.1944 - accuracy: 0.9416"
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- "27/49 [===============>..............] - ETA: 15s - loss: 0.1942 - accuracy: 0.9416"
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- "28/49 [================>.............] - ETA: 14s - loss: 0.1940 - accuracy: 0.9417"
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- "29/49 [================>.............] - ETA: 13s - loss: 0.1938 - accuracy: 0.9417"
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- "30/49 [=================>............] - ETA: 13s - loss: 0.1936 - accuracy: 0.9418"
- ]
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- "31/49 [=================>............] - ETA: 12s - loss: 0.1935 - accuracy: 0.9418"
- ]
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- "32/49 [==================>...........] - ETA: 11s - loss: 0.1934 - accuracy: 0.9418"
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- "33/49 [===================>..........] - ETA: 11s - loss: 0.1932 - accuracy: 0.9418"
- ]
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- "34/49 [===================>..........] - ETA: 10s - loss: 0.1932 - accuracy: 0.9418"
- ]
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- "35/49 [====================>.........] - ETA: 9s - loss: 0.1931 - accuracy: 0.9418 "
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- "36/49 [=====================>........] - ETA: 9s - loss: 0.1931 - accuracy: 0.9417"
- ]
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- "37/49 [=====================>........] - ETA: 8s - loss: 0.1930 - accuracy: 0.9417"
- ]
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- "38/49 [======================>.......] - ETA: 7s - loss: 0.1930 - accuracy: 0.9417"
- ]
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- "39/49 [======================>.......] - ETA: 6s - loss: 0.1930 - accuracy: 0.9416"
- ]
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- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 6s - loss: 0.1930 - accuracy: 0.9416"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 5s - loss: 0.1930 - accuracy: 0.9416"
- ]
- },
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.1930 - accuracy: 0.9415"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 4s - loss: 0.1930 - accuracy: 0.9415"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.1931 - accuracy: 0.9414"
- ]
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- "45/49 [==========================>...] - ETA: 2s - loss: 0.1931 - accuracy: 0.9414"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 2s - loss: 0.1931 - accuracy: 0.9413"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.1931 - accuracy: 0.9413"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.1931 - accuracy: 0.9413"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1932 - accuracy: 0.9412"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 37s 764ms/step - loss: 0.1932 - accuracy: 0.9412 - val_loss: 0.3347 - val_accuracy: 0.8728\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 9/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.1136 - accuracy: 0.9785"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 28s - loss: 0.1255 - accuracy: 0.9697"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 33s - loss: 0.1333 - accuracy: 0.9661"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 33s - loss: 0.1384 - accuracy: 0.9639"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 34s - loss: 0.1405 - accuracy: 0.9628"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 32s - loss: 0.1416 - accuracy: 0.9622"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 32s - loss: 0.1424 - accuracy: 0.9616"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 32s - loss: 0.1441 - accuracy: 0.9608"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 31s - loss: 0.1454 - accuracy: 0.9602"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 30s - loss: 0.1466 - accuracy: 0.9597"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 30s - loss: 0.1476 - accuracy: 0.9592"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 29s - loss: 0.1486 - accuracy: 0.9589"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 28s - loss: 0.1496 - accuracy: 0.9585"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "14/49 [=======>......................] - ETA: 27s - loss: 0.1504 - accuracy: 0.9581"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "15/49 [========>.....................] - ETA: 25s - loss: 0.1511 - accuracy: 0.9578"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 24s - loss: 0.1518 - accuracy: 0.9576"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 24s - loss: 0.1523 - accuracy: 0.9573"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 23s - loss: 0.1528 - accuracy: 0.9572"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 23s - loss: 0.1532 - accuracy: 0.9570"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "20/49 [===========>..................] - ETA: 22s - loss: 0.1536 - accuracy: 0.9568"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "21/49 [===========>..................] - ETA: 21s - loss: 0.1540 - accuracy: 0.9566"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "22/49 [============>.................] - ETA: 20s - loss: 0.1543 - accuracy: 0.9565"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "23/49 [=============>................] - ETA: 19s - loss: 0.1546 - accuracy: 0.9563"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 18s - loss: 0.1549 - accuracy: 0.9562"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 17s - loss: 0.1551 - accuracy: 0.9561"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "26/49 [==============>...............] - ETA: 16s - loss: 0.1555 - accuracy: 0.9559"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "27/49 [===============>..............] - ETA: 16s - loss: 0.1558 - accuracy: 0.9558"
- ]
- },
- {
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- "text": [
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- "28/49 [================>.............] - ETA: 15s - loss: 0.1561 - accuracy: 0.9557"
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- "29/49 [================>.............] - ETA: 14s - loss: 0.1563 - accuracy: 0.9556"
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- "30/49 [=================>............] - ETA: 13s - loss: 0.1565 - accuracy: 0.9555"
- ]
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- "31/49 [=================>............] - ETA: 12s - loss: 0.1567 - accuracy: 0.9554"
- ]
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- "32/49 [==================>...........] - ETA: 12s - loss: 0.1570 - accuracy: 0.9553"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 11s - loss: 0.1573 - accuracy: 0.9552"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 10s - loss: 0.1576 - accuracy: 0.9551"
- ]
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- "35/49 [====================>.........] - ETA: 9s - loss: 0.1578 - accuracy: 0.9549 "
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- "36/49 [=====================>........] - ETA: 9s - loss: 0.1581 - accuracy: 0.9548"
- ]
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- "37/49 [=====================>........] - ETA: 8s - loss: 0.1584 - accuracy: 0.9547"
- ]
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- "38/49 [======================>.......] - ETA: 7s - loss: 0.1587 - accuracy: 0.9546"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 7s - loss: 0.1590 - accuracy: 0.9545"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 6s - loss: 0.1593 - accuracy: 0.9544"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 5s - loss: 0.1596 - accuracy: 0.9543"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.1599 - accuracy: 0.9541"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 4s - loss: 0.1602 - accuracy: 0.9540"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.1605 - accuracy: 0.9539"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.1608 - accuracy: 0.9538"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 2s - loss: 0.1611 - accuracy: 0.9537"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.1614 - accuracy: 0.9535"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.1617 - accuracy: 0.9534"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1620 - accuracy: 0.9533"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 38s 786ms/step - loss: 0.1623 - accuracy: 0.9532 - val_loss: 0.3484 - val_accuracy: 0.8672\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 10/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.1917 - accuracy: 0.9434"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 28s - loss: 0.1942 - accuracy: 0.9429"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.1950 - accuracy: 0.9424"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.1944 - accuracy: 0.9426"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 27s - loss: 0.1928 - accuracy: 0.9435"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.1896 - accuracy: 0.9447"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.1861 - accuracy: 0.9459"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.1829 - accuracy: 0.9472"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.1800 - accuracy: 0.9483"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 24s - loss: 0.1776 - accuracy: 0.9491"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.1762 - accuracy: 0.9497"
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- },
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- "12/49 [======>.......................] - ETA: 22s - loss: 0.1749 - accuracy: 0.9501"
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- "13/49 [======>.......................] - ETA: 22s - loss: 0.1742 - accuracy: 0.9504"
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- "14/49 [=======>......................] - ETA: 21s - loss: 0.1732 - accuracy: 0.9507"
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- "15/49 [========>.....................] - ETA: 21s - loss: 0.1722 - accuracy: 0.9511"
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- "16/49 [========>.....................] - ETA: 20s - loss: 0.1712 - accuracy: 0.9514"
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- "17/49 [=========>....................] - ETA: 19s - loss: 0.1705 - accuracy: 0.9516"
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- "18/49 [==========>...................] - ETA: 19s - loss: 0.1699 - accuracy: 0.9518"
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- "19/49 [==========>...................] - ETA: 18s - loss: 0.1693 - accuracy: 0.9520"
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- "20/49 [===========>..................] - ETA: 17s - loss: 0.1688 - accuracy: 0.9521"
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- "21/49 [===========>..................] - ETA: 17s - loss: 0.1682 - accuracy: 0.9523"
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- "22/49 [============>.................] - ETA: 17s - loss: 0.1677 - accuracy: 0.9524"
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- "23/49 [=============>................] - ETA: 16s - loss: 0.1673 - accuracy: 0.9526"
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- "24/49 [=============>................] - ETA: 15s - loss: 0.1669 - accuracy: 0.9527"
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- "25/49 [==============>...............] - ETA: 15s - loss: 0.1665 - accuracy: 0.9527"
- ]
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- "26/49 [==============>...............] - ETA: 14s - loss: 0.1662 - accuracy: 0.9528"
- ]
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- "27/49 [===============>..............] - ETA: 13s - loss: 0.1658 - accuracy: 0.9529"
- ]
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- "28/49 [================>.............] - ETA: 13s - loss: 0.1655 - accuracy: 0.9530"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "29/49 [================>.............] - ETA: 12s - loss: 0.1652 - accuracy: 0.9531"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "30/49 [=================>............] - ETA: 12s - loss: 0.1649 - accuracy: 0.9531"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 11s - loss: 0.1647 - accuracy: 0.9532"
- ]
- },
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- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "32/49 [==================>...........] - ETA: 10s - loss: 0.1645 - accuracy: 0.9532"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 10s - loss: 0.1643 - accuracy: 0.9533"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.1641 - accuracy: 0.9533 "
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 8s - loss: 0.1640 - accuracy: 0.9533"
- ]
- },
- {
- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 8s - loss: 0.1638 - accuracy: 0.9533"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.1637 - accuracy: 0.9534"
- ]
- },
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- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 6s - loss: 0.1635 - accuracy: 0.9534"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.1634 - accuracy: 0.9534"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.1625 - accuracy: 0.9535"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.1624 - accuracy: 0.9535"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.1623 - accuracy: 0.9536"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.1622 - accuracy: 0.9536"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1621 - accuracy: 0.9536"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 35s 720ms/step - loss: 0.1620 - accuracy: 0.9536 - val_loss: 0.3539 - val_accuracy: 0.8731\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 11min 34s, sys: 45.4 s, total: 12min 19s\n",
- "Wall time: 6min 8s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "\n",
- "n_epochs = 10\n",
- "batch_size = 512\n",
- "\n",
- "history = model.fit(x_train,\n",
- " y_train,\n",
- " epochs = n_epochs,\n",
- " batch_size = batch_size,\n",
- " validation_data = (x_test, y_test),\n",
- " verbose = 1,\n",
- " callbacks = [savemodel_callback])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Evaluate\n",
- "### 6.1 - Training history"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:28:00.600829Z",
- "iopub.status.busy": "2021-03-01T19:28:00.595900Z",
- "iopub.status.idle": "2021-03-01T19:28:01.701854Z",
- "shell.execute_reply": "2021-03-01T19:28:01.701336Z"
- }
- },
- "outputs": [
- {
- "data": {
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- "<div class=\"comment\">Saved: ./run/figs/IMDB3-02-history_0</div>"
- ],
- "text/plain": [
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, save_as='02-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.2 - Reload and evaluate best model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:28:01.707673Z",
- "iopub.status.busy": "2021-03-01T19:28:01.707192Z",
- "iopub.status.idle": "2021-03-01T19:29:53.208674Z",
- "shell.execute_reply": "2021-03-01T19:29:53.209195Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 0.3347\n",
- "x_test / accuracy : 0.8728\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "#### Accuracy donut is :"
- ],
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\n",
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- "#### Confusion matrix is :"
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- " <th id=\"T_7e88a0a2_7ac4_11eb_8167_0cc47af5a44flevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
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\n",
- "text/plain": [
- "<Figure size 576x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = keras.models.load_model('./run/models/best_model.h5')\n",
- "\n",
- "# ---- Evaluate\n",
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / accuracy : {:5.4f}'.format(score[1]))\n",
- "\n",
- "values=[score[1], 1-score[1]]\n",
- "pwk.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\", save_as='03-donut')\n",
- "\n",
- "# ---- Confusion matrix\n",
- "\n",
- "y_sigmoid = model.predict(x_test)\n",
- "\n",
- "y_pred = y_sigmoid.copy()\n",
- "y_pred[ y_sigmoid< 0.5 ] = 0\n",
- "y_pred[ y_sigmoid>=0.5 ] = 1 \n",
- "\n",
- "pwk.display_confusion_matrix(y_test,y_pred,labels=range(2))\n",
- "pwk.plot_confusion_matrix(y_test,y_pred,range(2), figsize=(8, 8),normalize=False, save_as='04-confusion-matrix')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T19:29:53.213428Z",
- "iopub.status.busy": "2021-03-01T19:29:53.212629Z",
- "iopub.status.idle": "2021-03-01T19:29:53.215924Z",
- "shell.execute_reply": "2021-03-01T19:29:53.215407Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 20:29:53\n",
- "Duration is : 00:08:18 651ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/MNIST/01-DNN-MNIST.ipynb b/MNIST/01-DNN-MNIST.ipynb
index fccd0cb..d6125a5 100644
--- a/MNIST/01-DNN-MNIST.ipynb
+++ b/MNIST/01-DNN-MNIST.ipynb
@@ -42,6 +42,9 @@
"metadata": {},
"outputs": [],
"source": [
+ "# import os\n",
+ "# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
+ "\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"\n",
@@ -56,6 +59,38 @@
"datasets_dir = pwk.init('MNIST1')"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fit_verbosity = 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Override parameters (batch mode) - Just forget this cell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.override('fit_verbosity')"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -171,7 +206,7 @@
"history = model.fit( x_train, y_train,\n",
" batch_size = batch_size,\n",
" epochs = epochs,\n",
- " verbose = 1,\n",
+ " verbose = fit_verbosity,\n",
" validation_data = (x_test, y_test))"
]
},
diff --git a/MNIST/01-DNN-MNIST==done==.ipynb b/MNIST/01-DNN-MNIST==done==.ipynb
deleted file mode 100644
index 1086192..0000000
--- a/MNIST/01-DNN-MNIST==done==.ipynb
+++ /dev/null
@@ -1,1500 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [MNIST1] - Simple classification with DNN\n",
- "<!-- DESC --> An example of classification using a dense neural network for the famous MNIST dataset\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Recognizing handwritten numbers\n",
- " - Understanding the principle of a classifier DNN network \n",
- " - Implementation with Keras \n",
- "\n",
- "\n",
- "The [MNIST dataset](http://yann.lecun.com/exdb/mnist/) (Modified National Institute of Standards and Technology) is a must for Deep Learning. \n",
- "It consists of 60,000 small images of handwritten numbers for learning and 10,000 for testing.\n",
- "\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Retrieve data\n",
- " - Preparing the data\n",
- " - Create a model\n",
- " - Train the model\n",
- " - Evaluate the result\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Init python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:55.132787Z",
- "iopub.status.busy": "2021-03-01T17:41:55.132315Z",
- "iopub.status.idle": "2021-03-01T17:41:57.766473Z",
- "shell.execute_reply": "2021-03-01T17:41:57.765887Z"
- }
- },
- "outputs": [
- {
- "data": {
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- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
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- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
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- "\n",
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- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : MNIST1\n",
- "Run time : Monday 01 March 2021, 18:41:57\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import sys,os\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('MNIST1')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Retrieve data\n",
- "MNIST is one of the most famous historic dataset. \n",
- "Include in [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:57.770301Z",
- "iopub.status.busy": "2021-03-01T17:41:57.769826Z",
- "iopub.status.idle": "2021-03-01T17:41:58.134815Z",
- "shell.execute_reply": "2021-03-01T17:41:58.135311Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_train : (60000, 28, 28)\n",
- "y_train : (60000,)\n",
- "x_test : (10000, 28, 28)\n",
- "y_test : (10000,)\n"
- ]
- }
- ],
- "source": [
- "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
- "\n",
- "print(\"x_train : \",x_train.shape)\n",
- "print(\"y_train : \",y_train.shape)\n",
- "print(\"x_test : \",x_test.shape)\n",
- "print(\"y_test : \",y_test.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Preparing the data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:58.139118Z",
- "iopub.status.busy": "2021-03-01T17:41:58.138646Z",
- "iopub.status.idle": "2021-03-01T17:41:58.503914Z",
- "shell.execute_reply": "2021-03-01T17:41:58.504408Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Before normalization : Min=0, max=255\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "After normalization : Min=0.0, max=1.0\n"
- ]
- }
- ],
- "source": [
- "print('Before normalization : Min={}, max={}'.format(x_train.min(),x_train.max()))\n",
- "\n",
- "xmax=x_train.max()\n",
- "x_train = x_train / xmax\n",
- "x_test = x_test / xmax\n",
- "\n",
- "print('After normalization : Min={}, max={}'.format(x_train.min(),x_train.max()))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Have a look"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:58.520818Z",
- "iopub.status.busy": "2021-03-01T17:41:58.508798Z",
- "iopub.status.idle": "2021-03-01T17:42:03.197110Z",
- "shell.execute_reply": "2021-03-01T17:42:03.197623Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-01-one-digit</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
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\n",
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\n",
- "text/plain": [
- "<Figure size 864x291.6 with 36 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_images(x_train, y_train, [27], x_size=5,y_size=5, colorbar=True, save_as='01-one-digit')\n",
- "pwk.plot_images(x_train, y_train, range(5,41), columns=12, save_as='02-many-digits')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Create model\n",
- "About informations about : \n",
- " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
- " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:03.202399Z",
- "iopub.status.busy": "2021-03-01T17:42:03.201924Z",
- "iopub.status.idle": "2021-03-01T17:42:04.346545Z",
- "shell.execute_reply": "2021-03-01T17:42:04.347094Z"
- }
- },
- "outputs": [],
- "source": [
- "hidden1 = 100\n",
- "hidden2 = 100\n",
- "\n",
- "model = keras.Sequential([\n",
- " keras.layers.Input((28,28)),\n",
- " keras.layers.Flatten(),\n",
- " keras.layers.Dense( hidden1, activation='relu'),\n",
- " keras.layers.Dense( hidden2, activation='relu'),\n",
- " keras.layers.Dense( 10, activation='softmax')\n",
- "])\n",
- "\n",
- "model.compile(optimizer='adam',\n",
- " loss='sparse_categorical_crossentropy',\n",
- " metrics=['accuracy'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Train the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:04.351326Z",
- "iopub.status.busy": "2021-03-01T17:42:04.350846Z",
- "iopub.status.idle": "2021-03-01T17:42:11.037410Z",
- "shell.execute_reply": "2021-03-01T17:42:11.036887Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/16\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/118 [..............................] - ETA: 1:34 - loss: 2.3281 - accuracy: 0.0879"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "118/118 [==============================] - 2s 9ms/step - loss: 1.0230 - accuracy: 0.7234 - val_loss: 0.2392 - val_accuracy: 0.9312\n"
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- "Epoch 2/16\n",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.2229 - accuracy: 0.9349 - val_loss: 0.1755 - val_accuracy: 0.9481\n"
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- "Epoch 3/16\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - ETA: 0s - loss: 0.1612 - accuracy: 0.9536"
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1611 - accuracy: 0.9536 - val_loss: 0.1513 - val_accuracy: 0.9558\n"
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- "output_type": "stream",
- "text": [
- "Epoch 4/16\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - ETA: 0s - loss: 0.1290 - accuracy: 0.9615"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1289 - accuracy: 0.9615 - val_loss: 0.1250 - val_accuracy: 0.9635\n"
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- "output_type": "stream",
- "text": [
- "Epoch 5/16\n"
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- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1039 - accuracy: 0.9691 - val_loss: 0.1108 - val_accuracy: 0.9659\n"
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- "Epoch 6/16\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "Epoch 7/16\n",
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- "117/118 [============================>.] - ETA: 0s - loss: 0.0738 - accuracy: 0.9787"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.0738 - accuracy: 0.9787 - val_loss: 0.0958 - val_accuracy: 0.9707\n"
- ]
- },
- {
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- "text": [
- "Epoch 8/16\n"
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- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "117/118 [============================>.] - ETA: 0s - loss: 0.0643 - accuracy: 0.9811"
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.0643 - accuracy: 0.9811 - val_loss: 0.0922 - val_accuracy: 0.9724\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 9/16\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "116/118 [============================>.] - ETA: 0s - loss: 0.0576 - accuracy: 0.9831"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.0576 - accuracy: 0.9831 - val_loss: 0.0939 - val_accuracy: 0.9719\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 10/16\n"
- ]
- },
- {
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- "\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "116/118 [============================>.] - ETA: 0s - loss: 0.0518 - accuracy: 0.9849"
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- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.0518 - accuracy: 0.9849 - val_loss: 0.0900 - val_accuracy: 0.9734\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 11/16\n",
- "\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.0462 - accuracy: 0.9862 - val_loss: 0.0854 - val_accuracy: 0.9744\n"
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- {
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- "output_type": "stream",
- "text": [
- "Epoch 12/16\n",
- "\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - ETA: 0s - loss: 0.0420 - accuracy: 0.9874"
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.0420 - accuracy: 0.9874 - val_loss: 0.0829 - val_accuracy: 0.9757\n"
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- "output_type": "stream",
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- "Epoch 13/16\n",
- "\r",
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 30/118 [======>.......................] - ETA: 0s - loss: 0.0332 - accuracy: 0.9901"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 59/118 [==============>...............] - ETA: 0s - loss: 0.0344 - accuracy: 0.9900"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 89/118 [=====================>........] - ETA: 0s - loss: 0.0350 - accuracy: 0.9900"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.0352 - accuracy: 0.9900 - val_loss: 0.0801 - val_accuracy: 0.9757\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 14/16\n",
- "\r",
- " 1/118 [..............................] - ETA: 0s - loss: 0.0283 - accuracy: 0.9863"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 30/118 [======>.......................] - ETA: 0s - loss: 0.0234 - accuracy: 0.9937"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 60/118 [==============>...............] - ETA: 0s - loss: 0.0256 - accuracy: 0.9930"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 89/118 [=====================>........] - ETA: 0s - loss: 0.0269 - accuracy: 0.9926"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - ETA: 0s - loss: 0.0278 - accuracy: 0.9924"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.0278 - accuracy: 0.9923 - val_loss: 0.0841 - val_accuracy: 0.9759\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 15/16\n",
- "\r",
- " 1/118 [..............................] - ETA: 0s - loss: 0.0285 - accuracy: 0.9961"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 30/118 [======>.......................] - ETA: 0s - loss: 0.0298 - accuracy: 0.9919"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 59/118 [==============>...............] - ETA: 0s - loss: 0.0293 - accuracy: 0.9918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 88/118 [=====================>........] - ETA: 0s - loss: 0.0291 - accuracy: 0.9918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - ETA: 0s - loss: 0.0290 - accuracy: 0.9919"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.0290 - accuracy: 0.9919 - val_loss: 0.0786 - val_accuracy: 0.9770\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 16/16\n",
- "\r",
- " 1/118 [..............................] - ETA: 0s - loss: 0.0161 - accuracy: 1.0000"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 30/118 [======>.......................] - ETA: 0s - loss: 0.0238 - accuracy: 0.9948"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 60/118 [==============>...............] - ETA: 0s - loss: 0.0234 - accuracy: 0.9946"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 89/118 [=====================>........] - ETA: 0s - loss: 0.0233 - accuracy: 0.9944"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "115/118 [============================>.] - ETA: 0s - loss: 0.0233 - accuracy: 0.9943"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.0233 - accuracy: 0.9943 - val_loss: 0.0793 - val_accuracy: 0.9772\n"
- ]
- }
- ],
- "source": [
- "batch_size = 512\n",
- "epochs = 16\n",
- "\n",
- "history = model.fit( x_train, y_train,\n",
- " batch_size = batch_size,\n",
- " epochs = epochs,\n",
- " verbose = 1,\n",
- " validation_data = (x_test, y_test))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Evaluate\n",
- "### 6.1 - Final loss and accuracy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:11.041202Z",
- "iopub.status.busy": "2021-03-01T17:42:11.040712Z",
- "iopub.status.idle": "2021-03-01T17:42:11.369019Z",
- "shell.execute_reply": "2021-03-01T17:42:11.369514Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Test loss : 0.07934584468603134\n",
- "Test accuracy : 0.9771999716758728\n"
- ]
- }
- ],
- "source": [
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('Test loss :', score[0])\n",
- "print('Test accuracy :', score[1])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.2 - Plot history"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:11.387241Z",
- "iopub.status.busy": "2021-03-01T17:42:11.385474Z",
- "iopub.status.idle": "2021-03-01T17:42:12.198760Z",
- "shell.execute_reply": "2021-03-01T17:42:12.199260Z"
- }
- },
- "outputs": [
- {
- "data": {
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\n",
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, figsize=(6,4), save_as='03-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.3 - Plot results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:12.203417Z",
- "iopub.status.busy": "2021-03-01T17:42:12.202944Z",
- "iopub.status.idle": "2021-03-01T17:42:34.030379Z",
- "shell.execute_reply": "2021-03-01T17:42:34.030889Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-04-predictions</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x1652.4 with 200 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#y_pred = model.predict_classes(x_test) Deprecated after 01/01/2021 !!\n",
- "\n",
- "y_sigmoid = model.predict(x_test)\n",
- "y_pred = np.argmax(y_sigmoid, axis=-1)\n",
- "\n",
- "pwk.plot_images(x_test, y_test, range(0,200), columns=12, x_size=1, y_size=1, y_pred=y_pred, save_as='04-predictions')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.4 - Plot some errors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:34.052023Z",
- "iopub.status.busy": "2021-03-01T17:42:34.050271Z",
- "iopub.status.idle": "2021-03-01T17:42:37.739845Z",
- "shell.execute_reply": "2021-03-01T17:42:37.740356Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-05-some-errors</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x507.6 with 15 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "errors=[ i for i in range(len(x_test)) if y_pred[i]!=y_test[i] ]\n",
- "errors=errors[:min(24,len(errors))]\n",
- "pwk.plot_images(x_test, y_test, errors[:15], columns=6, x_size=2, y_size=2, y_pred=y_pred, save_as='05-some-errors')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:37.743763Z",
- "iopub.status.busy": "2021-03-01T17:42:37.743290Z",
- "iopub.status.idle": "2021-03-01T17:42:40.661186Z",
- "shell.execute_reply": "2021-03-01T17:42:40.661683Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-06-confusion-matrix</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_confusion_matrix(y_test,y_pred,range(10),normalize=True, save_as='06-confusion-matrix')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:40.664961Z",
- "iopub.status.busy": "2021-03-01T17:42:40.664491Z",
- "iopub.status.idle": "2021-03-01T17:42:40.666815Z",
- "shell.execute_reply": "2021-03-01T17:42:40.667302Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:42:40\n",
- "Duration is : 00:00:43 903ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"todo\">\n",
- " A few things you can do for fun:\n",
- " <ul>\n",
- " <li>Changing the network architecture (layers, number of neurons, etc.)</li>\n",
- " <li>Display a summary of the network</li>\n",
- " <li>Retrieve and display the softmax output of the network, to evaluate its \"doubts\".</li>\n",
- " </ul>\n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/MNIST/02-CNN-MNIST.ipynb b/MNIST/02-CNN-MNIST.ipynb
index abb81c0..380970a 100644
--- a/MNIST/02-CNN-MNIST.ipynb
+++ b/MNIST/02-CNN-MNIST.ipynb
@@ -42,6 +42,9 @@
"metadata": {},
"outputs": [],
"source": [
+ "# import os\n",
+ "# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
+ "\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"\n",
@@ -56,6 +59,38 @@
"datasets_dir = pwk.init('MNIST1')"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fit_verbosity = 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Override parameters (batch mode) - Just forget this cell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.override('fit_verbosity')"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -190,7 +225,7 @@
"history = model.fit( x_train, y_train,\n",
" batch_size = batch_size,\n",
" epochs = epochs,\n",
- " verbose = 1,\n",
+ " verbose = fit_verbosity,\n",
" validation_data = (x_test, y_test))"
]
},
diff --git a/MNIST/02-CNN-MNIST==done==.ipynb b/MNIST/02-CNN-MNIST==done==.ipynb
deleted file mode 100644
index dae848d..0000000
--- a/MNIST/02-CNN-MNIST==done==.ipynb
+++ /dev/null
@@ -1,1697 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [MNIST2] - Simple classification with CNN\n",
- "<!-- DESC --> An example of classification using a convolutional neural network for the famous MNIST dataset\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Recognizing handwritten numbers\n",
- " - Understanding the principle of a classifier DNN network \n",
- " - Implementation with Keras \n",
- "\n",
- "\n",
- "The [MNIST dataset](http://yann.lecun.com/exdb/mnist/) (Modified National Institute of Standards and Technology) is a must for Deep Learning. \n",
- "It consists of 60,000 small images of handwritten numbers for learning and 10,000 for testing.\n",
- "\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Retrieve data\n",
- " - Preparing the data\n",
- " - Create a model\n",
- " - Train the model\n",
- " - Evaluate the result\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Init python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:43.238332Z",
- "iopub.status.busy": "2021-03-01T17:42:43.237851Z",
- "iopub.status.idle": "2021-03-01T17:42:45.880757Z",
- "shell.execute_reply": "2021-03-01T17:42:45.881253Z"
- }
- },
- "outputs": [
- {
- "data": {
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- "div.todo:before { content:url(data:image/svg+xml;base64,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- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
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- "div.todo ul{\n",
- " margin: 0.2em;\n",
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- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : MNIST1\n",
- "Run time : Monday 01 March 2021, 18:42:45\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import sys,os\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('MNIST1')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Retrieve data\n",
- "MNIST is one of the most famous historic dataset. \n",
- "Include in [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:45.885687Z",
- "iopub.status.busy": "2021-03-01T17:42:45.885212Z",
- "iopub.status.idle": "2021-03-01T17:42:46.221773Z",
- "shell.execute_reply": "2021-03-01T17:42:46.222265Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_train : (60000, 28, 28, 1)\n",
- "y_train : (60000,)\n",
- "x_test : (10000, 28, 28, 1)\n",
- "y_test : (10000,)\n"
- ]
- }
- ],
- "source": [
- "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
- "\n",
- "x_train = x_train.reshape(-1,28,28,1)\n",
- "x_test = x_test.reshape(-1,28,28,1)\n",
- "\n",
- "print(\"x_train : \",x_train.shape)\n",
- "print(\"y_train : \",y_train.shape)\n",
- "print(\"x_test : \",x_test.shape)\n",
- "print(\"y_test : \",y_test.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Preparing the data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:46.226056Z",
- "iopub.status.busy": "2021-03-01T17:42:46.225587Z",
- "iopub.status.idle": "2021-03-01T17:42:46.590656Z",
- "shell.execute_reply": "2021-03-01T17:42:46.591149Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Before normalization : Min=0, max=255\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "After normalization : Min=0.0, max=1.0\n"
- ]
- }
- ],
- "source": [
- "print('Before normalization : Min={}, max={}'.format(x_train.min(),x_train.max()))\n",
- "\n",
- "xmax=x_train.max()\n",
- "x_train = x_train / xmax\n",
- "x_test = x_test / xmax\n",
- "\n",
- "print('After normalization : Min={}, max={}'.format(x_train.min(),x_train.max()))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Have a look"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:46.607324Z",
- "iopub.status.busy": "2021-03-01T17:42:46.595281Z",
- "iopub.status.idle": "2021-03-01T17:42:51.355135Z",
- "shell.execute_reply": "2021-03-01T17:42:51.355640Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-01-one-digit</div>"
- ],
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- ]
- },
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\n",
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\n",
- "text/plain": [
- "<Figure size 864x291.6 with 36 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_images(x_train, y_train, [27], x_size=5,y_size=5, colorbar=True, save_as='01-one-digit')\n",
- "pwk.plot_images(x_train, y_train, range(5,41), columns=12, save_as='02-many-digits')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Create model\n",
- "About informations about : \n",
- " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
- " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:51.362126Z",
- "iopub.status.busy": "2021-03-01T17:42:51.361648Z",
- "iopub.status.idle": "2021-03-01T17:42:52.531716Z",
- "shell.execute_reply": "2021-03-01T17:42:52.532237Z"
- }
- },
- "outputs": [],
- "source": [
- "hidden1 = 100\n",
- "hidden2 = 100\n",
- "\n",
- "model = keras.models.Sequential()\n",
- "\n",
- "model.add( keras.layers.Input((28,28,1)) )\n",
- "\n",
- "model.add( keras.layers.Conv2D(8, (3,3), activation='relu') )\n",
- "model.add( keras.layers.MaxPooling2D((2,2)))\n",
- "model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- "model.add( keras.layers.Conv2D(16, (3,3), activation='relu') )\n",
- "model.add( keras.layers.MaxPooling2D((2,2)))\n",
- "model.add( keras.layers.Dropout(0.2))\n",
- "\n",
- "model.add( keras.layers.Flatten()) \n",
- "model.add( keras.layers.Dense(100, activation='relu'))\n",
- "model.add( keras.layers.Dropout(0.5))\n",
- "\n",
- "model.add( keras.layers.Dense(10, activation='softmax'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:52.537352Z",
- "iopub.status.busy": "2021-03-01T17:42:52.535266Z",
- "iopub.status.idle": "2021-03-01T17:42:52.548301Z",
- "shell.execute_reply": "2021-03-01T17:42:52.548770Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "conv2d (Conv2D) (None, 26, 26, 8) 80 \n",
- "_________________________________________________________________\n",
- "max_pooling2d (MaxPooling2D) (None, 13, 13, 8) 0 \n",
- "_________________________________________________________________\n",
- "dropout (Dropout) (None, 13, 13, 8) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_1 (Conv2D) (None, 11, 11, 16) 1168 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 16) 0 \n",
- "_________________________________________________________________\n",
- "dropout_1 (Dropout) (None, 5, 5, 16) 0 \n",
- "_________________________________________________________________\n",
- "flatten (Flatten) (None, 400) 0 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 100) 40100 \n",
- "_________________________________________________________________\n",
- "dropout_2 (Dropout) (None, 100) 0 \n",
- "_________________________________________________________________\n",
- "dense_1 (Dense) (None, 10) 1010 \n",
- "=================================================================\n",
- "Total params: 42,358\n",
- "Trainable params: 42,358\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model.summary()\n",
- "\n",
- "model.compile(optimizer='adam',\n",
- " loss='sparse_categorical_crossentropy',\n",
- " metrics=['accuracy'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Train the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:42:52.552385Z",
- "iopub.status.busy": "2021-03-01T17:42:52.551918Z",
- "iopub.status.idle": "2021-03-01T17:43:02.935246Z",
- "shell.execute_reply": "2021-03-01T17:43:02.935790Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/16\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/118 [..............................] - ETA: 5:49 - loss: 2.4933 - accuracy: 0.0723"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 22/118 [====>.........................] - ETA: 0s - loss: 2.2701 - accuracy: 0.1780 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 43/118 [=========>....................] - ETA: 0s - loss: 2.1020 - accuracy: 0.2597"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 64/118 [===============>..............] - ETA: 0s - loss: 1.9429 - accuracy: 0.3244"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 85/118 [====================>.........] - ETA: 0s - loss: 1.8063 - accuracy: 0.3766"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "106/118 [=========================>....] - ETA: 0s - loss: 1.6912 - accuracy: 0.4195"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - ETA: 0s - loss: 1.6336 - accuracy: 0.4407"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 4s 10ms/step - loss: 1.6292 - accuracy: 0.4424 - val_loss: 0.2711 - val_accuracy: 0.9273\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 2/16\n",
- "\r",
- " 1/118 [..............................] - ETA: 0s - loss: 0.4906 - accuracy: 0.8574"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 22/118 [====>.........................] - ETA: 0s - loss: 0.5000 - accuracy: 0.8472"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 43/118 [=========>....................] - ETA: 0s - loss: 0.4865 - accuracy: 0.8514"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 64/118 [===============>..............] - ETA: 0s - loss: 0.4740 - accuracy: 0.8550"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 84/118 [====================>.........] - ETA: 0s - loss: 0.4639 - accuracy: 0.8578"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "105/118 [=========================>....] - ETA: 0s - loss: 0.4543 - accuracy: 0.8606"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.4483 - accuracy: 0.8623 - val_loss: 0.1540 - val_accuracy: 0.9548\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 3/16\n",
- "\r",
- " 1/118 [..............................] - ETA: 0s - loss: 0.3151 - accuracy: 0.9043"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 22/118 [====>.........................] - ETA: 0s - loss: 0.3156 - accuracy: 0.9038"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 85/118 [====================>.........] - ETA: 0s - loss: 0.3046 - accuracy: 0.9062"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "105/118 [=========================>....] - ETA: 0s - loss: 0.3012 - accuracy: 0.9074"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.2990 - accuracy: 0.9082 - val_loss: 0.1112 - val_accuracy: 0.9661\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 4/16\n",
- "\r",
- " 1/118 [..............................] - ETA: 0s - loss: 0.2561 - accuracy: 0.9316"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "105/118 [=========================>....] - ETA: 0s - loss: 0.2391 - accuracy: 0.9270"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.2381 - accuracy: 0.9273 - val_loss: 0.0908 - val_accuracy: 0.9706\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 5/16\n",
- "\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "Epoch 6/16\n",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "Epoch 7/16\n",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1757 - accuracy: 0.9486 - val_loss: 0.0640 - val_accuracy: 0.9785\n"
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- "Epoch 8/16\n",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1716 - accuracy: 0.9499 - val_loss: 0.0575 - val_accuracy: 0.9803\n"
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- "Epoch 9/16\n",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "text": [
- "Epoch 10/16\n"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "118/118 [==============================] - 0s 3ms/step - loss: 0.1406 - accuracy: 0.9576 - val_loss: 0.0514 - val_accuracy: 0.9832\n"
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- "Epoch 11/16\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1336 - accuracy: 0.9592 - val_loss: 0.0494 - val_accuracy: 0.9842\n"
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- },
- {
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- "Epoch 12/16\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1305 - accuracy: 0.9619 - val_loss: 0.0481 - val_accuracy: 0.9843\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 13/16\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1204 - accuracy: 0.9633 - val_loss: 0.0469 - val_accuracy: 0.9848\n"
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- "output_type": "stream",
- "text": [
- "Epoch 14/16\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1159 - accuracy: 0.9642 - val_loss: 0.0430 - val_accuracy: 0.9862\n"
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- "Epoch 15/16\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "104/118 [=========================>....] - ETA: 0s - loss: 0.1174 - accuracy: 0.9665"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1172 - accuracy: 0.9665 - val_loss: 0.0421 - val_accuracy: 0.9864\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 16/16\n",
- "\r",
- " 1/118 [..............................] - ETA: 0s - loss: 0.1580 - accuracy: 0.9512"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/118 [====>.........................] - ETA: 0s - loss: 0.1144 - accuracy: 0.9648"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 41/118 [=========>....................] - ETA: 0s - loss: 0.1127 - accuracy: 0.9655"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 61/118 [==============>...............] - ETA: 0s - loss: 0.1123 - accuracy: 0.9659"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 81/118 [===================>..........] - ETA: 0s - loss: 0.1119 - accuracy: 0.9661"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "101/118 [========================>.....] - ETA: 0s - loss: 0.1117 - accuracy: 0.9663"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/118 [==============================] - 0s 3ms/step - loss: 0.1115 - accuracy: 0.9664 - val_loss: 0.0395 - val_accuracy: 0.9868\n"
- ]
- }
- ],
- "source": [
- "batch_size = 512\n",
- "epochs = 16\n",
- "\n",
- "history = model.fit( x_train, y_train,\n",
- " batch_size = batch_size,\n",
- " epochs = epochs,\n",
- " verbose = 1,\n",
- " validation_data = (x_test, y_test))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Evaluate\n",
- "### 6.1 - Final loss and accuracy\n",
- "Note : With a DNN, we had a precision of the order of : 97.7%"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:02.939782Z",
- "iopub.status.busy": "2021-03-01T17:43:02.939296Z",
- "iopub.status.idle": "2021-03-01T17:43:03.329445Z",
- "shell.execute_reply": "2021-03-01T17:43:03.329955Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Test loss : 0.0395\n",
- "Test accuracy : 0.9868\n"
- ]
- }
- ],
- "source": [
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print(f'Test loss : {score[0]:4.4f}')\n",
- "print(f'Test accuracy : {score[1]:4.4f}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.2 - Plot history"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:03.345332Z",
- "iopub.status.busy": "2021-03-01T17:43:03.340614Z",
- "iopub.status.idle": "2021-03-01T17:43:04.164767Z",
- "shell.execute_reply": "2021-03-01T17:43:04.165287Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-03-history_0</div>"
- ],
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RfuaZZ/L666+z1157UVlZyVlnnsmXzv4CJBKc/OlPsebdVew+dZK/6SezmroOtto6X0xUN9qvO5boCSA7YGQ+UdTW+ycNFReJFJy54VasUATpKY7DMOyS/sorr7DbbrsVJU9FlUpF/RISGf0WotfJdF8F70sX/g+z9tiNM044fmDv2VFHkFEhW5HdsifGK//4J7tNnhAFlFFRBbGIFFHOb2d6cilHeQSPnnzkqGOpr63l8m9+vZsjzN/8062TYnGIZ7zuSI91NsvsTWUlTJ3Zt2NFpFvJVIr2RIq2ZIpEMsWEUTW9n5QHBZdSlu530bzV95JOZvaSHgAziMV59o9/2DZ4dHkdU5GTSD+0J1NsbmpjS3M7m5ra2NzUxubmNjY1tdHUmqA9maItkaI9Cgzt0evs7S7HZKQnkik/XFmGP1zwSSoK+P+q4FJqUinf1LapEZob+xdIouDh+yjEu/ZdiMc7O4WJSK/aEsnOINHcxuYm/3pLFCy67vP7m9ryaORSIO2JFNWVhStmVnApBamk77fR1OifUnob6kPBQ0Yo5xytiRRNre00tSZoak3Q2p6kpT1JWyLV8bo1Y2lJdN3uPMYf35pIdjmvr9XQff0XSWY/IgwTFWZUxiuojBmVsRiJVIpqFFwkkfCBpKnRdwzs7j8iFvN9KGpqOwOJgocUQVsiydYWHxC2ZgSHra0Jmlrbo3WCpraEP64t2t/S3vG6qTUxfG7Wg5iNCjNG11Yytq6KMXVVjKmtjNZVjKqJUxmPURmroCpeQWWsgnjMryvjWeus9HjG61jF4N4DFFxGkva2zrGnWpu7Py5e2dlru7pGgUQGzDlHc1uSxhYfFBpb2nt47YPH1hYfGBqj1+3J8hwvrcKMsXVVncEiHSiiYNFlX5RWXxMvaP1HMSi4DGfO+eFJ0gGlvYehSqqqOwNKZZUCivRJS1uCNZtbWLelpWO9NuP1luY2Glv8k8VweWCojFVQXxOnrjpOXVWc6soYNZUxqjOWmsoY1fEcaZUxqisrch5TUxmjMt63b/T59OCIVVhZTnSn4DLcOBeNfRUFlER3k2ia7xFeFw0dUlnZZfRjKW/OOba0tLN2c4tftqTXzazd0srazc2s29JCY8vQVRzHK4y66jj1NZXUV3cGh/qayozXudPT59VWxaiKq2/TSKDgMpy0t8PaVb75cC5mneNR1dX7upNhSHPDDIxzjvZkipa2JM1tCZrbkrS0+3VzW6IjvaU96fe1JWhu83UX6SePtZtbaEsUthiqpjJGfU2c+upK6mvijKqp7HxdXemDRsdrHwxGpYNJTSXV8Yqy/AZfrnQH6KszDxu8a99wny/6Wru6y5ApX7v0f9l+5kyCL8yHulEs+MFlWEUFixcvZsOGDbS3t3PJJZcwb968Xt+isbGRefPm5Tzv5ptv5rLLLsPM2Guvvfj5z3/O6tWrOfvss3nzzTcBuOaaa5g+fTpHHnkkL730EgCXXXYZjY2NLFiwgLlz53LAAQfw2GOPcfTRR7PzzjtzySWX0NbWxsSJE7n11luZMmVKzjlnNm7cyEsvvcSVV14JwPXXX88rr7zCFVdcUeif9JBqSyTZuLWNjVtb/bqptcv2lpb2KDB0DRrNbUlSQzRyRmWsgomjq2kYU0vD6BoaxtT49egaJo6pYVxdVceTRjxWuClwpfQpuAwHG9b60X7TzGDUGE489XTO/fo3CC64CIBf3XEH9913H+eddx5jxoxh7dq1zJkzh6OPPrrXb4Q1NTUsWrRom/NefvllvvOd7/DYY4/R0NDQMTfMl7/8ZQ488EAWLVpEMpmksbGx1/lhNm7cyKOPPuo/0oYNPPHEE5gZN9xwAz/4wQ+4/PLLc845U1VVxV577cUPfvADKisrufHGG7nuuuv6+9McNCnn2NLczobG1o5AsakjcGwbRJpah76vQqbaqhiTxtQyMTNoZASPhjE1jK2r0tOEDAoFl+EgM7DE49AwDWpqmbX/FN577z1WrlzJmjVrGD9+PNOmTeO8885j8eLFVFRUsGLFClavXs3UqVN7fAvnHOeff/425z300EMcd9xxNDQ0AJ1ztTz00EMd87PEYjHGjh3ba3A54YQTOl4vX76cE044gVWrVtHW1tYx90x3c84cfPDB3HPPPey22260t7fzwQ9+sI8/vP5rSyTZ1NTGpq1tbGr2683Nndubm3wnt/SyualtyCq1K2MV1FTFqK2KU1MZ63hdWxmjpipObXpfVYyays7tCaOrmRQ9ddRXa9h+KR4Fl7664b7CXq+lORpFOOPbbW0dNEztUpdy3HHHceedd/Luu+9y4okncuutt7JmzRqeffZZKisr2WGHHbaZoyWX7s7rbq6WXOLxOKmMDpo9zQ1zzjnn8NWvfpWjjz6aRx55hAULFgDdzw1z5pln8t3vfpddd911QDNaJlMpVq5vYvm6rWxsau02cGza2kZL++CO2hyrMMbVVzGurtqv66sZW1/F+Hq/Paa2ygeMqlgUQOIdAaNSRVAywim4DDXnYPMG2LCOLr2wxk2EsRO2aUJ84oknctZZZ7F27VoeffRRfvWrXzF58mQqKyt5+OGHefvtt/v0tps2bcp53iGHHMKnPvUpzjvvPCZOnNgxV8shhxzCNddcw7nnnksymWTr1q1MmeKfpNatW8eoUaO45557OOyw3HVRmXPD/OxnP+tI727Omf32249ly5bx3HPP8cILL/T6edJB5J21jby9Zgtvr/Hr5eu2Dmp/ilE1lR2BYlxdVefrjnU6vZpRNXEVOUnZUnAZSskkrFvtK+/TKmIwaaqfWySHPfbYgy1btjBjxgymTZvGySefzFFHHcU+++zD3nvvza677tqnt+7uvD322INvfvObHHjggcRiMWbNmsVNN93E//3f/zF//nx+8pOfEIvFuOaaa9h///258MIL2W+//dhxxx17fO8FCxZw/PHHM2PGDObMmcNbb70F0O2cMwD//u//zpIlS7pMz+yc8yO3JlK0RsVYZ1+3uCBBJFbhO7ellzHdvM5M0xOFSN9oPheGaD6X1hZfDJbZb6W6BiZN05S2+CByxJFHEnzxy+z/bwfSlkjRlvDjRWX+ja58+02+92DPM1FOHF3N+xpG0xC1dsoOFGPqqhhXV0VdtZ4sRApA87kUzZZNsP69rt16x4yD8Q19n8ekxDjnaEuk2NrazsrVaznq0Lnsstue7Lz3fqzb0nsdEvggsv2k0Ww/aTTvaxjF9pNGsf2k0YyqUbAWKTYFl8GUSvmg0ri5M80qoGGKn0lxELz44ouccsopXdKqq6t58sknB+X98pFKOT8oYTTWVCIq1qqqHcX9f36m2/Pi0QB9VfEYjXWVXHHa/goiIsOcgksv8mlN1UV7my8Ga8sYD6yyGiZP82N/DZIPfvCDLFmyZNCun6+2RDIa0ND3Iu+pGDYziFTHK6iqjFEVryBW4Z/unHOsq4qz23YThir7ItJPCi49qKmpYd26dUycODG/ALN1i6+4z5xXpX4MTJzs54MvYSnnaG5L0NTihyNpS3Tf3Leiwqir8sOI1PXSA9w5x7p166ipKexUrCIyOBRcejBz5kyWL1/OmjVr+naCc53zq3QwXwTWnIS1PXdCHKmSKUdbNOGSr4Dv/th4zKiq9E8mlbEK2s3Y1Mf3qampYebMmQXJs4gMLgWXHlRWVnb0LO/VhrVw3XfhHy93pjVMhf/4Jmy/0+BksEiSKcerKzbw1Bvv8fQ/1vDP1Zu7PbY6XsHeOzaw706Tmf3+SUwZVzeEORWRYlFwKYRXnoeFl/pWYWl7z4HT/3PQKu6LYd2WFn7/7Dvc+9w7bNja/dwy08bXse8HJjP7A5P40A4TNUS6SBlScBmIVAru/SX85uedzYwrKuBTp8Nhx5XMhF2vrtjA3U8tZfHLq3JOMRuvMPbcfgL7fmAy+35gMjMn1qv/iEiZU3AZiBsvh8f/1Lk9djzM/wbsslfx8lQgbYkkf355FXc/vZTXV25bKzK+vpr9dprMvjtNZu8dJ2qQRBHpQsFlIOYcAk885J9adtkL5n/djw82gq3b0sK9z73D75/NXfS1x3bjOWbfHTlglyma30NEuqXgMhB7fBiO/qzvy3LMqRAbuXUL6aKvP7+8ikRW0VdlrIK5e05n3uwd2Gna2CLlUERGEgWXgTrq5GLnoN/SRV+/efptXlu5cZv9DaNrOHKf7Tl81naMq68e+gyKyIil4FKG+lL0NW/2DvzrrlNV9CUi/aLgUkZeXbGB30StvlT0JSKDScGlxKWc45GXVnL3U0u7Lfo64iPv45Mffp+KvkSkYBRcSlgyleJ7dz3Pn195d5t9KvoSkcGk4FKiUs5xxe9e6BJYKmMVzN1jOvP2VdGXiAwuBZcS5JwjvO/vPPjCio60w2dtx2kH7aKiLxEZEgouJejGh17jd8+83bF92Kzt+MoRH9SQLCIyZIpe2G5mFWZ2npm9amYtZrbMzC43s/o+nl9pZueb2Stm1mpm68zs12a262DnfTj6xV/+we1//WfH9tw9pvPlTyqwiMjQKnpwAa4ErgBeBs4B7gC+DPzOrOcJ5s3fMX8DfAd4DTgPuBr4KPCEme0+iPkedu5+6i1uevi1ju05O03mv+Z9iFiFAouIDK2iFouZ2R74gHKXc+7TGelv4YPEicBtPVxiHnA4sNA594WM838OvBRd4+ODkPVh5/4ly7jm/s65ZPbecSLfPO7DagkmIkVR7DvPSYABV2WlXw80AZ/t5fyDovWNmYnOuTeBPwOHmNn7Bp7N4e3Rv6/kqnte6NjefeZ4Fvz7PppHRUSKptjBZTaQAp7KTHTOtQBLov09STd9asqxL5223wDyN+w9+cZqvn/3EtId7j8wdQwXnzSb2iq11RCR4il2cJkOrHXO5ZrWcAXQYGZVPZz/92h9cGaimdXRGVS26+5kM5tvZs/kkd9hZcnStVx8x3MdE3i9r2EU3/nMvoyq0dwqIlJcxQ4udUB38+W2ZBzTnVuA94D/MbOzzGxHM5sN3Ak09Ha+c26hc26fPPM8LLyyfAMX/fIZ2pMpAKaOq+V7J++nfiwiMiwUO7g00Vm0la0m45icnHMb8BX2/wQWAm/ii9jqge9Hh20uSE6HkX++u4kLfvEULe1JACaOrub7n51Dw5iaXs4UERkaxQ4uK/FFX7kCzAx8kVlbTxdwzr3onJsF7AQcCOzknDuQzqD1aiEzXGzL1jbyjVuforElAcDYuiouPXk/po7v6QFPRGRoFTu4PB3lYd/MRDOrAfYG+lwf4pz7h3NusXPuH1HS4finlscKk9Xie3djE1+/9Uk2Nfl4W18d57uf2Zf3TRpd5JyJiHRV7OByO+CAc7PSz8LXldyaTjCzaWa2a1RZ3yMzOwfYE7jSObe1cNktnnVbWvj6LU+ydrOviqqpjHHJZ/blAxqAUkSGoaK2V3XOvWhmPwa+ZGZ3AfcCu+F76D9K1w6U3wNOxfdteSSdaGb34utaXsYHqkOBY4Df43vuj3ibmtr4+i1PsmqDr36qjFWw4IR92H3m+CLnTEQkt+HQGeJcYCkwHzgCWAv8ELjQOZfqw/mPAycAp0XbrwBfBK5zziULnNcht7WlnfNvfZJ31jYCEKswLjjuw8zasaGXM0VEisecc70fBQRB8DfgWuCWMAy3DGquhlgQBA4gDMNiZ6WLlrYE59/2FH9ftgHwQxl8/VOzmLvn9OJmTESkU87BC/Opc9kd+BGwMgiC64MgGJH9Q0aKtkSSb9/xbEdgATj3yA8qsIjIiJBPsdhM4AzgzGj9+SAIngeuA24Lw7AkKs6Hg0QyxXd//TzPvbm2I+3sQ3fnsFklP0yaiJSIPheLZQqC4BP4OpKjgBjQiG/ZtTAMwyWFzOBQGE7FYsmU439/s4SHX1rZkXbq3J35zL/tVMRciYh0K2exWL+CS1oQBFPwTzFnADtEyU/j62Z+GYZhSzenDivDKbgsfOBlfv3EWx3bx+//L5xxyK6a7EtEhqsB17lsIwzD1fgmwl/F97Y3fIfInwDLgiA4dyDXLzdNrQnufmppx/aRH3mfAouIjEj9Di5BEMwIguAi4G3gLmAq8Ft8H5OLgSRweRAEFxcgn2VhxfqtHSMcT59QxxcP31OBRURGpLz6uQRBYPhhVb4QrePAauC7+PqWZdGhvw2C4HLgT/gis28VLMclbPm6xo7XO0waTYUCi4iMUH0OLkEQXIBvKbYdvvhrMRACd4VhmMg+PgzDLUEQ/A5YUJislr7l6zob3M2YUF/EnIiIDEw+Ty7/gx8IMgSuCcPw5V6OB3gWuLk/GStHmcFlu4ZRRcyJiMjA5BNc/gPfO7/P/VnCMLwXP16Y9MGK9XpyEZHS0OfgEobhdYOZkXLnnOtS5zJzooKLiIxc+dS5fBg4ErguaoKcvX8qvmPlb0diR8piW9/YSnObH2dzVE2csXVVRc6RiEj/5dMU+f/hK/Tf62b/anzLsK8ONFPlqGtl/ig1QRaRES2f4LI/8HAYhjm79EfpDwH/WoiMlZvM+hYViYnISJdPcJkKLO/lmJXAtP5np3ypvkVESkk+waUJmNTLMZOA1v5np3ypj4uIlJJ8gssSYF4QBDk7YARBMAaYFx0neVqxLrNYTH1cRGRkyye4LMQ/mTwQBMFemTuCIPgQ8EegITpO8pBIpli1salje8aEuiLmRkRk4PLp53J7EASHA58Dng+CYDWwApgBTMEPCfOzMAx/MSg5LWHvbmzqGLCyYUwNNVV5DfkmIjLs5DUqchiGpwFnAy/jK/g/Eq3/DswPw/D0QmewHCxfp5ZiIlJa8v6KHIbhQmBhEAR1wDhgYxiGTT2fJT3pElxUmS8iJaDf5S9RQFFQKYCufVxUmS8iI9+AZqKUwlAfFxEpNflOFlYPBMAn8BX51TkOc2EYvr8AeSsb6uMiIqUmn4ErxwF/AXbHz+syBtgEVAG10WErgfbCZrG0NbUmWN/o+53GK4wp49QMWURGvnyKxS7AB5YzgPFR2pXAKOAA4Dngn8Buhcxgqcusb5k+oZ5YhQasFJGRL5/gcjSwOAzDGzMHrwzD0IVh+ATwSWBX4JsFzmNJy6xvUZGYiJSKfILLdvink7QUGXUuYRi+B/wBOLEwWSsPK9THRURKUL4DVyYztjfhO1BmWo2v6Jc+WqbgIiIlKJ/gsgz/9JL2MvCxIAhiGWkfBd4tRMbKhfq4iEgpyie4PAocGARBusb5duD9wO+DIPhiEAR3AHOAewucx5LlnFMfFxEpSfn0c/kZvtnxTPxTzLXAwcAxwKHRMY/hW5VJH6xvbKW5zZc01lfHGVtXVeQciYgURj6jIj8H/EfGdgI4NgiCjwAfAJYCT4dhmCp0JktVdpGYmZohi0hpyKcT5ceAzWEYLslMD8PwWeDZAuerLGg0ZBEpVfnUuTwMzB+sjJQj9XERkVKVT3BZCzQPVkbKkfq4iEipyie4PIIf5kUKRMViIlKq8h1bbJcgCC4OgqBysDJULhLJFKs2dk6Ho2IxESkl+TRF/gbwEnA+cEYQBH/Dd5h0Wce5MAzPKFD+Sta7G5tIpvyPrmFMDTVV/Z63TURk2MnnjnZaxuupbDv0S5rDj5wsPdDUxiJSyvIJLjsOWi7KUNc+LgouIlJa8ulE+fZgZqTcdJl9UmOKiUiJyadCXwqoy5hiKhYTkRKTTw/99/X12DAM3+nrsWZWAXwF+AKwA7AG+BVwoXNuaw+nps834CTgS8DO+Dlm3sEPrHmVc25zX/MylNQMWURKWT51LkvZtmVYLi7P614JfBlYBFyOnyb5y8AsM/u4c663scouwbdgewj4NtAOzI1ef9LM9nfO9SXfQ6apNcH6xlYA4hXGlHG1Rc6RiEhh5RMEbiZ3cBkH7A1sj+9o2ee6GTPbAzgHuMs59+mM9LeAq/GzWt7Ww/lx4Fz8DJn/X0YgutbMEsDJwIeAJX3N01DIrMyfPqGeWIVKJ0WktORToX9ad/uCIKgAvgWcDZyax/ufBBhwVVb69cClwGfpIbgAlUAt8G6OJ5yV0brXorWhpjHFRKTUFeQrcxiGqTAMv40vOrs0j1NnAyngqcxE51wL/mljdk8nO+eagcXAYWb2NTP7gJntYGanAQFwi3PujTzyMyQ0ppiIlLpCl8f8lc6Jw/piOrDWOdeaY98KoMHMeptB62T8iM2XAm8AbwE/xdflfK6nE81svpk9k0d+C2K5+riISIkrdHCZAORzt6wDcgUWgJaMY3rSCryJrxP6DL6o7df4sdDO7+lE59xC59w+fc5tgaiPi4iUuoINaBUEwceBE/Djj/VVEzC5m301GcfkZGZ1+Kel55xzJ2bs+qWZ/RL4HzO70zn3Wh55GlTOuS7FYtvpyUVESlA+/Vwe6uEa2wHpfjD/k8f7rwR2N7PqHEVjM/BFZm09nH8csBN+UM1sd+CD3UeBYRNc1je20tSWAKC+Os7Yut5K/URERp58nlzmdpPugA3A/cBlYRh2F4RyeRpfR7Mv8Od0opnV4Js3L+7l/BnROpZjXzxrPSxkNkOeMbEe3wdURKS05NMUeTA6Y9yOrxc5l4zgApyFr2u5NZ1gZtOAscA7zrl0UdnL0fpUfK/+TOkm0U8XNssDs7xLkZjqW0SkNBX1W71z7kUz+zHwJTO7C7iXzh76j9K1j8v38AHjIHxnTYB78M2YP2lmi/EV+QYcC/wbcIdz7rkh+Ch9pj4uIlIOhkOR0bn4/jHzgSOAtcAP8WOL9Tj0i3MuaWYfx9e5HAv8AF9M9wbwNeCKQct1P61Y17VYTESkFOVToX8BcBGwQxiGK3Lsn44PEheGYdjnjpTOuSR+TLHLeznuNLpOWJZO34IvWuux2fFwsVwtxUSkDORTj3IU8EiuwAIQhuFKfGfGeYXIWClKJFOs2tjZslrFYiJSqvIJLh+gswK9Oy9Hx0kOqzc2k0z5sT8bxtRQUzUcSiVFRAovn+BSRw8dGiMtwOj+Z6e0LdMEYSJSJvIJLsuAOb0cMwc/JpjkkN3HRUSkVOUTXO4DPhYEwQm5dgZBcCJwIPCHQmSsFHWdfVJ9XESkdOVT6P99/AjEt0UB5j78U8oM4HDgaGA9+Q25X1aWq1hMRMpEn59colZin8DPT38McA3w22g9Dz8D5SfCMFxe+GyWBhWLiUi5yGtIlzAMnwF2xg8YeTnwk2h9HLBLGIbPFjyHJaKpNcG6LX5szniFMXVcbZFzJCIyePJuCxuGYTtwV7RIH2U+tUwbX0esYjCGahMRGR50hxsiK1SZLyJlpOjDv5SLLpX5qm8RkRKn4V+GyHJV5otIGdHwL0NExWIiUk40/MsQcM517UCpPi4iUuI0/MsQ2LC1laa2BAB11XHG1VcVOUciIoNLw78Mga7DvtRjZkXMjYjI4NPwL0NARWIiUm40/MsQyOxAqcp8ESkHBR3+BXg+CAI1Rc6yfG1nHxc1QxaRclCQ4V+CINgeuBA4HZgGxAqVwVKQ2cdFxWIiUg76Pc9uEAQxfHHYfODj+KcgBzxYmKyVhkQyxaoNnS249eQiIuUg7+ASBMG/AGcCpwFTouS1wHXAT8IwfLtguSsBqzc2k0w5ABpG11Bb1e94LiIyYvTpThcEQRz4FP4p5SD8U0obvmjs08BvwjC8cLAyOZItX6/6FhEpPz0GlyAIdgLOAk4FGgADngNuAm4Lw3B9EASpwc7kSJbdx0VEpBz09uTyGr4e5T3gSuDGMAz/Pui5KiHq4yIi5agvTZEdcC9wpwJL/tTHRUTKUW9PLt8CPo9vYnxaEASv4YvEfh6G4apBzltJyJzHRXUuIlIuegwuYRh+B/hOEASfwNe9HIUf3uU7QRD8EfjZ4Gdx5GpuS7BuSysA8Qpj6rjaIudIRGRo9KmHfhiG94dheBywHXA+fqiXw4Ff4IvN9g6C4CODlssRKrO+Zdr4OmIVmlVaRMpDXp0uwjB8D//kcmkQBIfgmybPA/YBngqC4AXghjAMf1zwnI5AmROEzVB9i4iUkX5/lQ7D8E9hGJ4AzAT+G3gd+BBwdYHyNuJ1GfZF9S0iUkYG3F08DMO1wGXAZUEQzMX33he6VuYruIhIOSnoWCRhGD4CPFLIa45kK9THRUTKlGqYB4lzLqt3vupcRKR8KLgMkg1bW2lqSwBQVx1nXH1VkXMkIjJ0FFwGSXaRmJkVMTciIkNLwWWQLNOAlSJSxhRcBknmmGLq4yIi5UbBZZBoNGQRKWcKLoNEfVxEpJwpuAyCZCrFqg1NHdsaDVlEyo2CyyB4d0MzyZQDoGF0DbVVBe2rKiIy7BU9uJhZhZmdZ2avmlmLmS0zs8vNrNev+2Y218xcL8u/DsXnyLR8veZwEZHyNhy+Ul8JfBlYBFwO7BZtzzKzjzvnUj2c+wpwSo70amAhsBZ4qrDZ7V2X0ZBVmS8iZaiowcXM9gDOAe5yzn06I/0t/OjKJwK3dXe+c241cEuO656Efyq72TnXXuh89yazj8t2enIRkTJU7GKxkwADrspKvx5oAj7bz+umR2a+oZ/nD0jXPi4KLiJSfoodXGYDKbKKrpxzLcCSaH9ezGxH4CDgL8651wqQx7x1aYY8QR0oRaT8FDu4TAfWOudac+xbATSYWb4jPn4e/zTU61OLmc03s2fyvH6PmtsSrNviP06swpg6vraQlxcRGRGKHVzqgFyBBaAl45g+MbMYcBqwGbijt+Odcwudc/v09fp9kVmZP218HbGKYv+IRUSGXrHvfE34ll251GQc01efwE+7/AvnXD7nFYzmcBERKX5wWYkv+soVYGbgi8za8rjeGdG6KBX5AMvXazRkEZFiB5enozzsm5loZjXA3kCf60PMbDJwFPCCc66g9Sj5WJFRma8+LiJSroodXG4HHHBuVvpZ+LqWW9MJZjbNzHY1s+7qYD4HVFLEpxboWiymPi4iUq6KGlyccy8CPwaONbO7zOxMM7scuAJ4lK4dKL+H75G/77ZXAnwrsRZydKocKs65LsVi6uMiIuVqOAz/ci6wFJgPHIEfsuWHwIW9DP3SwcwOwA8bc5tzbsPgZLN3G7a20tSaAKCuKs74+u7aKoiIlLaiBxfnXBI/ptjlvRx3Gr6Zca59f8X3bSmqFVlTG5sVPUsiIkVR7DqXkqIiMRERT8GlgNTHRUTEU3ApoC7BRc2QRaSMKbgUUOaAlSoWE5FypuBSIMlUilUbOkecUe98ESlnCi4F8u7GZpIpB8DE0dXUVhW9IZ6ISNEouBTIcg37IiLSQcGlQFaopZiISAcFlwLRaMgiIp0UXApk+ToFFxGRNAWXAulSLDZBxWIiUt4UXAqguS3B2i1+VuZYhTFlXG2RcyQiUlwKLgWQ+dQybXwd8Zh+rCJS3nQXLIAulflqhiwiouBSCJmV+Rr2RUREwaUgVmR0oFQfFxERBZeCUDNkEZGuFFwGyDmnDpQiIlkUXAZo49Y2mloTANRVxRlfX13kHImIFJ+CywBlz+FiZkXMjYjI8KDgMkAqEhMR2ZaCywCt0NTGIiLbUHAZoGXq4yIisg0FlwFSHxcRkW0puAxAMpVi1Yamjm3NQCki4im4DMC7G5tJpBwAE0dXU1cdL3KORESGBwWXAciszNdTi4hIJwWXAViu+hYRkZxUjjMAH91tGg1jalm+rpGdpo0tdnZERIYNBZcBmDy2lsljNeukiEg2FYuJiEjBKbiIiEjBKbiIiEjBKbiIiEjBKbiIiEjBKbiIiEjBqSlyhiAIip0FEZGRxoVhuM0siXpyERGRgjPnXLHzMOKZ2TPOuX2KnQ/pSr+X4Ue/k+FpMH4venIREZGCU3AREZGCU3ApjIXFzoDkpN/L8KPfyfBU8N+L6lxERKTg9OQiIiIFp+AiIiIFp+AiIiIFp+DST2ZWYWbnmdmrZtZiZsvM7HIzqy923sqVmblulsbez5aBMrNvmNkdZvZm9HNf2svxu5jZ3Wa2wcy2mtmfzezgIcpuWcjnd2JmC3r4H/p/+b63hn/pvyuBLwOLgMuB3aLtWWb2cedcqpiZK2N/ZtuWL+3FyEgZ+i6wHngOGNfTgWb2fuCvQAL4AbAJOAu438wOd849OLhZLRt9/p1kOA9Ym5X2bL5vrODSD2a2B3AOcJdz7tMZ6W8BVwMnArcVKXvl7k3n3C3FzkSZer9z7k0AM3sJGNXDsd/D3+w+4pxbEp1zM/B34MdmtqtTU9ZCyOd3kna3c27pQN9YxWL9cxJgwFVZ6dcDTcBnhzpD0snMqsysL/9EUkDpm1hvoqLjo4FH0oElOr8RuAHYGZg9GHksN339nWQzszFmNqCHDwWX/pkNpICnMhOdcy3AEvSPUUzH4QP8FjN7z8x+aGZji50p6WIvoBp4PMe+J6K1/oeK5wV8MWWLmf3VzA7vz0VULNY/04G1zrnWHPtWAAeYWZVzrm2I81XungLuAP4BjAE+CXwJONDMDoi+GUvxTY/WK3LsS6fNGKK8SKeN+PrKvwIbgF2Ac4Hfm9nnnXM35XMxBZf+qQNyBRaAloxjFFyGkHNuv6ykm83sBeA7wFeitRRfXbTO9T/UknWMDBHn3FXZaWb2U+Al4EozuzOfL2gqFuufJvxjfS41GcdI8f0vPsgfUeyMSIf0/0au/yH9/wwjzrl1wLX4xhcH5HOugkv/rAQazCzXP8cMfJGZnlqGAedcO9Hvq9h5kQ4ro3Wuoq90Wq4iMymOpdE6r/8hBZf+eRr/s9s3M9HMaoC9gWeKkCfJIfqdzARWFzsv0uFFfJHY/jn2zYnW+h8aPnaK1nn9Dym49M/tgMNXdmU6C19WfOtQZ6jcmdnEbnZdjK9b/N0QZkd6EJXb/w6Ya2YfSqdHzcfPBN4gqyWmDC4zi+dqVWlm2wH/AazDV/T3mSr0+8E596KZ/Rj4kpndBdxLZw/9R1EHymK4wMzmAA8D7+A7i30SOAh4EvhhEfNWFszsFGD7aHMSUGVmF0Tbbzvnfp5x+DeAQ4A/mtmVwGb8l7MZwBHqQFkYefxORgFvmdndwCt0thY7M9p3knOuOa/31u+wf8wshn9ymQ/sgB8u4XbgQjV5HXpmNg8IgD2BiUAS/w34V8AVUR8kGURm9ghwYDe7H3XOzc06fjfg0uicKvwQJQs09Evh9PV3EtUf/xjYD1+MPAp/T3sM+IFzLu8nSQUXEREpONW5iIhIwSm4iIhIwSm4iIhIwSm4iIhIwSm4iIhIwSm4iIhIwSm4iIhIwamHvkiZCYJgAXARcFAYho8UNzdSqhRcRPIUBEFfeh7rxi1lTcFFpP++3cO+pUOVCZHhSMFFpJ/CMFxQ7DyIDFcKLiKDLLOOAz9C7bnArsAW4B7g/DAM381x3k7At/CjB0/CDyT4IHBxGIZv5Dg+hh9Z+BT8AJ5V+Em3HgG+3805xwH/HR3fAvwR+M8wDDVZlwyIWouJDJ3z8FPG/g24CngNOB34axAEkzIPDIJgNn7CrM/iJ6e7DHgCOBl4JgiCfbKOrwLuA64BtsNP+3A18CzwKeBfc+QnAG7BF+H9GD9X+gnAg0EQdDeNt0if6MlFpJ+iJ5JcWsIwvDRH+uHAfmEYPp9xjSvxTzKXAmdEaQbcDIwBPhuG4a0Zx58A/BK4JQiC3cMwTEW7FgAfx0/CdXwYhq0Z51RH18p2GDA7DMMXM469DTgJmIefrkCkX/TkItJ/F3WzfL2b43+eGVgiC4BNwGcynhYOwBebPZ4ZWADCMLwd+At+IqePQkdxWAA0A2dnBpbonNYwDNfkyM/VmYElcn203jf7YJF86MlFpJ/CMLQ8T3k0xzU2BUGwBD+h027AEuDD0e6HurnOQ/jAMgtYjA9EY4EnwzBcmUd+cs1Tvyxaj8/jOiLb0JOLyNBZ3U16ujJ/bNZ6VTfHp9PHZa3zrYTfmCMtEa1jeV5LpAsFF5GhM6Wb9KnRelPWemqOYwGmZR23MVrP6HfORApMwUVk6Gwzl3kQBGOBvfHNgF+JktP1MnO7uU46/blo/So+wOwVBMH0gWdTZOAUXESGzilBEMzKSluALwb7RUZF/GP4ZsofjfqhdIi2Pwa8jq/YJwzDJBACtcC12c2IgyCoym7qLDLYVKEv0k89NEUGuDsMwyVZaX8AHguC4Ff4epOPRstSMlqYhWHogiA4FXgAuD0Igt/gn052AY7Bd778XEYzZPBD0ewHHAW8HgTBPdFx2wGHAv8F3NSPjynSLwouIv13UQ/7luJbfmW6EliE79dyAtCIv+GfH4bhe5kHhmH4ZNSR8gJ8/5Wj8D30f4Hvof9a1vFtQRAcBpwNfA44FTBgZfSef8n3w4kMhDnXlwFeRaS/NMS9lCPVuYiISMEpuIiISMEpuIiISMGpzkVERApOTy4iIlJwCi4iIlJwCi4iIlJwCi4iIlJwCi4iIlJw/z85yhZNLZ8FEgAAAABJRU5ErkJggg==\n",
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, figsize=(6,4), save_as='03-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.3 - Plot results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:04.169481Z",
- "iopub.status.busy": "2021-03-01T17:43:04.168991Z",
- "iopub.status.idle": "2021-03-01T17:43:26.253447Z",
- "shell.execute_reply": "2021-03-01T17:43:26.253976Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-04-predictions</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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OAZCdnc3+/fs5cOAAOTk5gDMf/2Gfp1Kp0Ol0zM7OUl1dzccff0xNTQ16vZ7vf//7PPfcc4SGhgKLvxcp15jdbpfG3OHDh6mvr6elpQWLxUJvb6/0Wik1UJcCHtZWlUrF8PAwV69elYbrfIbqfMZAdHS0dILY7Xb8/f0ZHx9nbGyMlJQUtztHXD2Is7OzTE1NYTab6erqoqKigvr6erq7uzEajWRmZrJp06YHKHUC7rIblN/b29tLRUUFtbW1mEwmqee6ceNGXn/9dVJSUtDpdPj7+xMcHIyvr6+UM9PpdNTW1rJs2TKOHj0qFYlGRkY4efIk0dHRPP/885Lq4U6HiMViwWQyERISQnZ2NnFxcVK67XHOjoVq91OxIARHR4Q+Tpw4wa9+9Svu3r0LgJ+fHz4+PoSHh3Pt2jWpbSrIyyqVCqvVKheWq9yC+J0ItajVaiIiIoiPjyc0NHTRBtWVtrBs2TI2btzI1NQUk5OTbN26lT179hAdHQ1AX18fYWFh6HQ6RkZGOHz4MEePHmVwcBBwLOiAgACCgoKckkOWkkHvCrGxjo2NUVlZydjYGBs3bqSkpMQpg9aTjFeTySQ9F4GBgZw9e5Z33nmHkZERpwQzJR9UeGYvXLhAfX09fn5+JCUl8dJLL7Fx40b5fk/U7hPyZYODg0RHRztdtB6mFarsQ1BQEOHh4dhsNgYGBpyMV0+HMou2ra2N06dP09HRIV8PCgpi27ZtrF69Wv7OnRQeQa+ZnJykoqKCzz77jL6+PoKCgti1axf79++XhqtQQHmU59uVknTmzBk+//xzdDod+/fv5wc/+AFFRUXy9cWeu0o6w/T0NKdOneJXv/rVA17IsbExrly5Qnp6OoGBgdLz76kQXFOtVotGo2F6epq+vj40Gg2hoaEEBwfj4+PD9PS05ESCg5JkNptRq9VERkai1+vx9/cnJCSE4OBgAgMDCQsLIyEhgZiYGPR6vYz0mc1mjEYj2dnZTl75xZjPgpus3C/BEYYWlKXe3l6MRiNdXV1UVVVRX18vz8bU1FQaGxtJT0+XXsyoqCiCgoKk3SCeKyyeMStslNnZWfr6+ujr63PKdUlPT2fHjh288sorBAcHP9LoLC0txdfXV+a/jIyMYLVauXjxInFxcRQWFjpdShcDwigVc6SxsVHacYWFheTn58u5NF/fxDwXNuF8FA+lDNw3kbt7asYrIAf19u3bXLx4UW5Eglze1dVFdXW1TGaKjY2VIQ1hvM73MJS/s9vt+Pr6kp+fz/PPP09JSQlhYWFPoxuPDdGmxMRE9u3bx6ZNmwCHaK/wWIDjIBTeRqPRSHNzMz09PdJQFWLGrqoMnhh2/ioIPpJarZbhr9HRUbRaLQUFBWRlZUlvjzs9WfNBuTEMDg5y5swZamtrAQgNDXV6Xcz12dlZ+vv7GRkZkX2OjIxkdnaW6OhosrKyPCp8Lkr7Cdmva9euoVar2bZtGytXrpTve1gSjHJOGo1G+vr66OjoICIiAqPRuGj9+KZQhssbGxs5evQojY2N8vWgoCAKCwtJTk6Wv3NnYqFYM8osc4CAgACpigAOJYLHKa6gvCTX1NRw8uRJurq6yMnJYcOGDaSlpQGewb1vbW3l888/l4arXq+X3tbBwUE+/PBD/Pz82Lt3rzRePVmyTjzL6elpTp48yd27d0lKSmL9+vVSJcLf359ly5Zx//59QkJC0Ol0TE9Pk5KSwvLly4mJiSE2NpbU1FQyMjKIjo5Gq9Xi6+srKze6eqKVxuNiOQ5EO8SeMTc3R3d3N9evX+f06dNcv36dtrY2jEajVCZStrm9vV1yePV6PTExMURGRpKSksKePXvYu3evDGcrk6cWA0onk7LNycnJ7Nmzh82bNztRWOY7z8U8Xblypcw5OHnyJA0NDYyOjlJbW0tDQwMJCQkEBga6ZR2OjY1x9epV6uvr0Wg05OXlSeqfcCS6tksoMIizb76z5Gkl8z+VWay0nn18fFi+fDm7du1ifHyc8PBwAgICmJmZoaenR0p+iNuGMBKeBCqVivb2djQaDeHh4axevdqJb7iYm1dISMgDt36LxYJWqyUgIABw3EJv3rxJU1MT09PT8jCMiooiISFBer6UXoelBmWiTmdnJ+fOnaOjo4Pk5GQKCwtlBQ5PysYXHn4hxNzW1sa//Mu/UFlZKd9jMplkKFZECASExqJAX18f7777LgMDA/zbf/tvJel+ZmbGbSVyld64mZkZTpw4wT/90z9RV1dHYWEhBw4ckMbro8ZGaRT09PRQXV3NxMQEy5Yt81hjYT4o11ZTUxNVVVVOWswhISEUFRURGRnp5C1b7DWp9BDPzc3R1tbG/fv3MZlM6PV69u3bJ8dNVNr6quIf4pI8OTnJ3bt3effddzlz5gzg8Kqkp6fLPWsxxcbhywNe7CH9/f1cvHhR0nJEWW2R5GS327l7967sy4YNGygoKCA2NpbAwECP2kfFmPn4+DA0NMS7777L73//e6anp9mzZw/FxcXSCMrOzubtt99m27ZtREZGSvWAuLg4cnNzCQkJISAggIiIiCf2Ni+0U0QYNErDZXp6mvr6eioqKqiqquLevXvS66pEeHg4GRkZWCwW6uvrMRqNcg5OTU3R3NxMf38/bW1tBAcHk5OTQ3Fx8aJfLJWc16CgILlfqtVqduzYwd69e6WOq4gSi34I20RZEESj0VBaWopKpaK/v5+GhgbAMf8rKirIzs4mOzsb+DLCvVBjqKQcTU5OcvbsWbn+hPylkn6ihNVqxWq14ufn5/Sa2WxmcnJSvq7VaomMjHwqJYyfuvGq0Wh48cUXSU1NpaOjg+DgYOLj45mcnKS5uZne3l66urqoq6ujoaEBs9lMfHw8vr6+9Pf3Mz4+Li13kRgTGhpKaGgoU1NTNDU1Ybfb6e3tpaGhgZ6eHukhWCzuk9JDqvxOEVIQlU5EGOjkyZP86U9/klxXu91OdHQ069evZ8WKFZLvqvQKLSW4blpNTU0cP36ckZER8vPzSUlJke/1hEQQAUFFEcbXsWPHeOedd+jv75eEfJG8JQ5+X19fAgICCAsLIywsTHrFbDYbZrOZgYEB3n33XTZu3CiNV3CfN13wO+12OzU1NXzwwQecP38evV7P7t272bhxo/SSPMx4FeMr5mZPT48TJUg5Zz3FYHgYhNpCW1sbzc3NMuQnEB4eLuer6Lc71CTEM52YmOD+/ftcuHCBu3fvolKpeOWVV3jrrbeIiYmR81cYna5Q7lFinx4fH+fYsWO8++67zM3NkZ2dzfr160lNTZV9FXNmsSDWouj3tWvXOHLkCMPDw8CXiZTCcLVarZhMJm7dusWtW7e4dOkS3/3ud3n99ddlJM4TLsrKPlmtVk6cOMEvf/lL6urqJD9eWUAgISGBffv2yUQZcHhqhXdVCRHihUevO3EuLfTaVF4+BC2iurqaS5cucerUKe7duyfbq9Vq8ff3Jykpifj4eLKysigoKKC7u5t3332X0dFRSkpKGB0d5ebNm9JgN5vN3Lp1i0OHDqHRaCgsLESlUi24YScgvNhms5m2tjZ6enoAWLFiBVu3bqWoqMjJoJ3vPBfPSMxprVZLSUmJ1Hg1Go2MjIxQUVHBxo0bnYzXhaQVCuMSHBJZn3zyCY2NjahUKiIiIuR+CM7RShEN9/X1xWazYTQasdvtDA8P09jYSHd3N1NTU8zNzREYGEhCQgJpaWnExcV9o7ynp2IpuX55VFQUISEhFBYWSoqAzWajsLAQs9mMyWSSmqfj4+NkZmYSFBREfX09fX19+Pv7o9PpmJmZkZtrTEwMx48f57//9//O9PQ0Op2O0NBQp87PR25fSCgPBmU2JTgmmtFo5O7du/zxj3/kxIkTzMzMoFKp0Ov1lJaW8sorrzgZdkvRcIUHq910d3fT0tICOIwbd3kdvwrKcNrAwADt7e0MDQ3JA0ccfkojNi8vj+XLl5Oenk5ERARzc3M0NTVx//597t69K0PoNTU1dHV1kZCQIPu/2CFNZZ3p/v5+jh07xrlz59Bqtbz11lscOHBAesQfFU4U3gaB3t5emWCwVEp0irFUqVRMTExw7949OUcF9Ho9ycnJTgbPYnuVxQElPDSXL1/mww8/5NKlS7IgQWlpKfn5+XKP9Pf3f+gYiLkraCPgiBC0trbKMd2wYQPbtm2Te5GYN+7I5AZHpOLu3btcuXKF8fFxqeQRGhqKr6+vkySUwJ07d4iOjmbLli0yG3q+sOZiQjxHjUaD0Wjko48+kuU1w8LC2LVrFxs2bCAuLk56EFUq1QMe1a+6mIj/V1JCxO9gceawMMLAsZceOXKECxcu0NLSQk9PD21tbXK+paSkEBoaSnFxMa+99hoZGRno9XpSUlKkd3Z2dpa33nqLhoYGmpqaGBkZITIykpCQEJqbm3n33XeJjo6Wkk3CebKQc1bZx+bmZg4dOsStW7fQ6/VkZ2cTHR3tpHf9OFC+LyEhgZycHClhKPotsNDREKVjaXBwkIqKCvr6+ggODqakpISCggInr7pQZxEFRMBB9zh06BBNTU2Mj48zPDzM0NAQMzMz+Pj4oNPpCAwMJC8vj+3bt7Np0yZZjUypiPM4WBBrSchjKT0WouECaWlp5OfnMz09TWxsLACrV6+WskNarRaj0YjFYiEjIwNwePRE2DYyMpLVq1eTlZXlxOdZzMNmvu8SBopWq5VZ3VVVVdLDo1ariY2NZdOmTWzZsoXg4GC5ybrbS/BNIAyf3t5eOjs7ZTjdVRDbU7KClYZkY2Mjx44d4+7du9LAEdmjIuwOjnJ4+/btY+PGjcTExEiOYVdXFx9++CG3b98GHJGCW7du8Zvf/IbXX39d8hLdZQyAQ9T+5MmT9PX1kZOTw969e1mxYgXg4LD6+fk9dO2I+QwOSogwXMFxsLpmAnsilF44q9VKXV0d7e3t8hkFBASwceNGNmzYIDdod2i7Kuel3W6ns7OT27dvS15uamoqeXl50sAJCAiY14CBLys1iQQLo9FIXV0dJ0+e5N69e/j6+vL888/zne98xylK4K4IgfjelpYWJ694UlISZWVlZGZmMjk5ya1bt7h37x4jIyP4+/tjsViko+Czzz4jJCRERvPcCeU43rx5k/fff59Tp04BsGfPHn74wx9SUlLiVM1MjJ2QKxNjKKCUm/KkM0PpXT5//jzvvPMOV65ccdr/Q0NDWbduHStWrCA0NJS8vDyef/55pzUWHR2N3W5Hp9Oxdu1aNmzYQEtLC++99x79/f0sW7aM8fFxxsfHaWpqkut6Mc4VpfHY2dnJtWvXmJyclOVgAwICnGhGXzU2ymRei8VCamoqK1asoL29HZPJxPj4+GNpcD8NuDoo1Gq1TMT19/dnx44dbN++XZZCV85DX19fpqenuX//Pp999hmfffaZE/VuPty6dYvBwUEiIiKcjNcnmc8LsjM/rgEZHBzsRGwWoVhX2O127t+/z507dySVIDMzk5KSEkkgdpe3wLWdygk2OjpKT0+Pk6h0ZGQkJSUlrFq1SvbVUwy6rwNx2Go0GsxmM62trU5GgZIXBJ4TVhbttlgsXLp0iV/+8pfcv39fZgNbrVbsdjtarZagoCDS09N58cUX2bdvH7m5uczNzWEymfD39ycmJob6+nrCw8MZHx+X4UyROSt4UErd0IW8ZAkjRqvVYrPZqKur4+LFi9LojImJkeLu4v0Pg6ALaDQaBgYGOHz4MNevXwcc6zwzM9NpzXrK+LpCuTGbTCZaW1vp7++XvwsMDGT79u3s2LEDf39/+QwX23h19TyIyIU46AIDAx9a1cb1p3KeCaPi2LFjnDlzhvr6evR6Pa+++io7duwAvrzELHaflQkwMzMzdHd3S7qAWq1m165d/PznPyc1NZWenh5u3rzJuXPnOH36NG1tbfKi3NfXx29+8xssFgt//ud/Ls8GJeVlMSCeu4+PDyaTiRs3bvDRRx9RUVEBOMZn5cqVbNiwAY1G84AqiYj8uPILPTGRV/RVrVYzPj7OxYsX+fDDD7l+/bqT4RUTE8PWrVvZt2+fTFAT54ay/0KWaWhoiIGBAVavXs0rr7zC3bt3qaiocPK6u/IrFxriHLNYLAwPD8v+BQUFER8f7yRV9jgeROXrGo2GpKQk0tLS8Pf3l4nAi0nJUn6+soRwbGwsxcXFMjdHnHsCk5OT/PGPf+QXv/gF9+/ff4AamJKSQkxMDL29vZI6aTKZuHnzJtXV1axdu/ah0YVHYcFWtLDklYMoqmQIfpOQUbBarTKpREwQsZGCo27wL37xCz766CPUajXLly9n27ZtpKenL1TzvxbEbWt0dJSqqirOnTvHuXPnpAchODiYDRs2cODAASc5Gk9KLnhSKA9IlcpRrae7uxur1UpSUpKs/CPgKdQIYbDNzc1JbpaYm3q9XkoUlZaWsmPHDnJzc8nKyiIrKwv4kksKDg5TQUEBGzduZHx8XIZ66urq+MMf/oDdbicoKIjk5ORFuagoudMzMzPcuXOH69evMzk5SVRUFPn5+Y9dj1r5Wb29vZw+fZqrV6/i6+tLWVkZmzZtchpfT/EEuUJ5WWhtbaWmpoa+vj6n13NycqQXQLn/LDaUc0SIgAcGBmKz2aQ8m6CnBAUFkZubS0REBBaLxUkXdnJykuHhYQYGBqiurub48ePcunVLSoPFxsZKdQFwHMrKssiLBeX3Ce6cUBXYvHkzr732GqmpqQDExcWxfft2AgIC6O/vp7u7W54dVqtVju3MzAzwoEdpMftjsViorq7m0KFD/OlPf6K7u5ukpCReeOEFtm3bJteVq3D/wwwfTz4jLBYLFRUVfPDBB5w+fVqOX0xMDCUlJZSVlbFu3TonT7OA8t8hISFs2rSJ69ev895773Ht2jVaW1vlfBdjmZSURHJysnxui+EMEDqtDQ0N1NTUSBWl5ORkcnNzSUxMBL7eZUmtVuPn54der3fq02JFklUqlYyMC/3aqakpAHJycpycHYLiMjg4SE1NDVeuXOHTTz91krNLTEwkMzOT5cuXk5OTQ2xsLNPT09TW1nL+/HkuXLjAwMAAPT09TExMfC3u64JZEvO5zR82oPMlRCj/tqGhgVOnTtHX14evry+bNm1i8+bNTrJU7jaKREYpODifv/rVrzh+/LischIQEMD69evZv38/O3fulOE+d8vRfFMoD9rp6Wkp6QOwfv16du3aJUNBSkK/p0AkvOj1eoxGo7z9+/r6UlRUxPe+9z3eeustJ1kWAWVfMjIy2LNnD319fZw6dUomedXX13Pt2jX2798PPJgkthBQHtZGo5HGxkaqqqowGo1s2bKFnTt3SqoOPHrtiLlpNpupq6ujtrZW6hNv2rSJrVu3Sjkxd3gqHxeiXePj49y/f99JSxMciVrK/WSxDR4llJu4iAQIzc7KykomJyc5fvw4Pj4+5Obmsn37dqKjoxkbG5MZvz4+PvT29nL//n0aGhpobW2lra1NKiskJCSwfft2p8jXo7izCwmlR9FqtUrNS51OR1FRkTSwp6enCQgIIDAwkPz8fKKiotBqtXKtqVQqkpOTyc7OdurXYvXJNWt6aGhIykZ2d3ejVqs5cOAAP//5z4mNjX0sXV5PhWuOx+DgIHfv3uXy5cvy8q5Wq4mKiuLll19m//79hISEyKxzsQcKmovNZmNiYoK+vj7S09NpaWnho48+oqur64G9MjIyUhYwEFjIZC3lBX5qaopbt25x/vx5uru7JX0xLy9v3vc/CaxWq5Nk2GJdvAT1QiSY37hxg4sXLzI0NCQLtiipYVarlb6+Pm7evMmhQ4f4/PPPmZqaQq1WExoaSnR0NGvWrOH555+nrKyMqKgo+fejo6PY7Xbu3LnD1NQU4+Pj0kiGJ1urbj9pXEMhImtQGLR1dXXcunVLekmsVithYWHExsbKOt6e4LlUutmnpqaoqqqSmowAy5Yt4/nnn2fr1q1OLnJ3t/ubQhnSEreq5uZmYmJiWLduHfn5+U4Goaf01zUaoJQlslgsFBQU8P3vf5+XX35ZLjwhF6Lk/Ij5GxoaysqVK1m1ahV1dXVO1de0Wq30TgrjdSGh/PypqSm6u7slhWHFihXs2rULnU6H2WyWh//DINbYzZs3uXjxogy1i2QJkeRjNps9kvuqPGRtNhudnZ20t7c7SZwJ75CyMpq7jHClt8XHx4cVK1awdu1aKisrpfeppaWFlpYWgoKCGBwclIb42NiYLK0ppHd6enowm83SMwmOCNDGjRvZt2+fjAa482LpKrN34cIFGV7s6+ujsbHxAb1lnU5HX18fw8PDaLVaub/k5uZSXFwsOcGLnQehdEaMjY05VXDLyMiQpT/B4fAQxuvDEn+V8ETaADjaNTMzw+TkpDwHBS3A398fHx8fBgYGuH//Ps3NzYyNjWGz2fDz85Mlt3t6emhqamJqagqtVktnZydtbW1yzJVzMyEhgZdffvmB6OVCQelJnZ6e5vbt29y8eRNwFBrYvHmzU4ntr9sWV+fIYtEJlXPWbDbT0dEhE+xiYmIeKC/c29vLmTNnOHbsGJcvX5bGZ3R0NDt37mTr1q3k5eWRlpbmFJUDBzU0KSmJ8PBwpqammJ6efkDn/nHhduP1UeGR4eFhTpw4wfHjx2UYqKSkRPJCwHNKqQYGBgJflsYVIQURAsrMzGTdunVS3sZiscgs2qUIV09bb28vN2/e5Nq1a9hsNimHIUIRyo3ZEyButL6+vvLwA8ehWFJSwmuvvca+ffuIioqSN2Lh0VJCyLT4+PgQGRlJeno6cXFxjI6OyvdMT09TUVHBpk2bpAd3sTAyMkJrayvgSO7Jy8uTYyJC0oKnpeyTyWSSvDuj0UhTUxO1tbVyo4qNjXXasD2Vty1oLWKujo6OMjw87DSOa9as4YUXXnhsb/RCQqlW4uPjQ35+Prt376avr48rV65IQffh4WEmJyepr6+nvr7e6e/F3A4MDCQyMpLp6WkpQWiz2fD39ycvL4+NGzcSEBCwKNGAR0HpXZqcnGRgYED24+7du1RVVbFlyxYnZ8X9+/dpbGyUnjxwPLPJyUkmJyedtDUXa266RtIGBgZobGxkampK0iG6u7vp6+uTCT4C81EFxLgopa6EpB14jtKHSqWSVb4iIiLo7OyU+0R/fz+nT5/m3r179PT00NHRwdTUlCyTKvSnRVEbAb1ej06nk+MoaFpxcXG88sorlJWVERwcLJ/RQkYvlfNnZmZGJiSHh4dTVlZGaWmpVFR6mkl0ix0xAMcaGh8fZ3p6GpVKhb+//wMOjqamJj7++GNOnDiB3W5Hr9eTkZHB2rVr2bt3L9u2bZOODJEbIiJIAwMDMnKn1+sJCwtzKjDidrWBbwJlskF1dTUnTpyQXIodO3bwwx/+kC1btsiF727jVSmfMTMzw+9//3t++9vfSk+x3W4nNTWV0tJSyYl5FqAMkdntdrq7u6mvr5cbUGRkpDToxfs8CUoZGSXxPyQkhH/zb/4NBw4cAJBeK6WX1RVK77M4wJTJCtXV1fzhD38gMDCQ9evXo1I5ih0sVMRAuRlNTk5KLcL169c7SbMpS/e5tkN56M/NzTE1NcXExAR2u53AwECnUBC4fx0+DK4GxeDgID09PXJ8dDodBQUFbNiwgbCwMI9R/lCOS3FxMX/9139Nc3OzrGx248YNeXiAIymiqKgIf39/+vr6sNlsbNu2jby8PA4dOsQHH3yA1WolNDSUlJQUkpKS5B4qol3ugvKiEBcXR1lZGW1tbVy9epWOjg66uroYHBwkNDSU27dvU19fz/Xr1+nu7pZ/JzzLN27cIDU1VUr7LPa+Mzs7K72E7e3t3Llzh7GxMdRqNW1tbRw7dkxm3SclJTnRG+bDfIa3J1wUlTQPX19fIiMjKSoqIj8/n9raWhnZaG9vZ2xsDJVKhcViYWZm5gHHh2t1LfiyZLfQ9I2MjGTjxo0cOHCAzZs3yxLs7qh+J/oeGxtLRkaG5MmLogTfZM65w/Oq/B5B35iampIXWuVZYDabqa2t5c6dO/J3BQUFvP7667z22mukpaU9kKCtjCQIBR8/Pz/JFRZ82id9fh5jvCpvmEIi5t69ezQ3N8v3rF69mt27d8sF7wmlAMVN2MfHh/b2dk6dOiWzSnU6HZGRkbz00ku8+OKLMiwpMsGXMpTGqyiV2tnZyczMDJGRkWRkZDiFYRdDQPpJIG71omSvgFardZINmpmZeWT5YSFnIwjsLS0tdHd3y4QFcHgRIiMj3UIX0Wq1hIaGolKpCAoKcjI4H9UG11roQiLLbDZLmTulHqW7jb2HQblxdnV1cePGDaeqWjqdjri4OLmBLpbY+VdBeSEKCAggNzeX3NxcRkdH6e3tZfny5eTl5dHV1UV0dDQrVqwgIyODgIAAent7MZlMbN26lcjISO7fvy8/Nzk5mRdffJHi4mL5O3fzlIWWst1uJy4ujq1bt3L69GnAwdmur6/n17/+NT4+PjICIKg5ot56b28v58+fx2KxcOfOHS5fvkxkZCQxMTGyfwsd+VF6RcFBeRD0B39/f6amprhx4wZzc3PcuHGDjIwMVq1aJcXpzWazjMiFhIQ4Je+YTCasVitBQUFOIXTB9XXX+lM+z/T0dPbu3UtiYiI9PT20tLRw//596dAQ9CphlJrNZqcqVIK6IlQnhAGckJDAtm3beOONN9i5c6eT0bsYlEFXuooow+vn5/e1suQf9h0ajcbJLtBqtYsyrq6e16mpKUlREvNZzLl79+5x+/ZthoaGUKvVbNy4kRdffJFXXnmFzMxMAKdIpUqlcjpLhIKCUE2Jioqatx2PA48yXpUkbpFsICZ+bm4uOTk50nC1Wq1uNVzFRihCsIIu0NXVJd/j5+dHQUEBzz//PGvXrgU8Q9LracB1oo2NjdHR0YHFYiExMfEBT7OnhLiU/KWxsTHOnTsn+UvgWKyDg4Pk5OQAPJIH6Mpvu3v3Lrdv36anp0catAD5+fl8//vfZ/ny5Q9UEloIKJ9zREQE6enpVFZWUltbS21tLatWrXpAVskVygIHnZ2dkmAPDo5oYmKik2fd3ZfIh0HsJ1arlYaGBq5fvy4NCnAYFcrN1Z2JWvPBdXzCwsIIDQ0lOzubV155RR76Yn0JfrlQzejv73cqxVlWVsb+/fvJycmRxpa7jVfAKfybkJDgtHecP3+e06dPy7ERpTdTUlL4/ve/zwsvvCC1JcfGxhgeHubcuXOkp6dLr9hCVyeaD65rwtfXl6mpKS5dusS5c+cIDQ1l06ZNbN++nbi4OCYmJhgbGyM4OJi0tDRiYmIIDQ2VEoQzMzOkpKSQlZUljRxXys9iQzl3goKC2LNnD3v37mV0dJRz585x+PBh6urqsNlshISEEBUVRXBwMHa7nYmJCXp7e2lpaaGrq0smbonLDHzJb/3Od77D2rVrnaSoFmveKsdxfHycmZkZbDab/O9pQORTKC8/NpvNLWWahfEJX9KX/P39GRwc5MyZM9y+fRu73U5KSgrbtm3ju9/9rqRciXNDeW4qzxJlFE9wvb8u3L9rfQGRCCMWZX9/P9XV1fT395OcnMyePXtYsWLFAxwgd7YXvvQ43bp1i6NHjzrJ72g0GpYtW+YkM+Fph+PXhdLrPTs7S1dXl6xYVFhYyPr162X51IU21p4ESuN1YmKC69evU1lZKcdldnZW8qvh4V4ps9nM7Oys5F43Njby2WefcePGDckDEt5XrVbrJMS8mAoT/v7+hISEoFaraW1t5dChQ2i1WtatWyc5Y+LA0Gg0mEwmxsbG6O/vl0VC7t696xQBEZu2O0JcTwqlbJEIhwn4+fmRlJTk5Fn3RCNcjBEgL76PojaIPdRisXD8+HGuXbuGSqXi+eef55VXXpFyb55wmRRQPveAgAB27dpFZ2cnR44ccRoz8XpZWRkvv/wy3/3ud4mKiqKmpoaMjAzu3LlDX18ft2/fZvPmzWzfvt3pIrmQcDWoUlNTyc7Opr6+XiYiBQcHS/H3sbExrl+/Tn9/P/7+/hiNRoxGI3q9nvDwcBkpsVqtjIyMMDc3R0JCAsnJyaSmprJx40ZSU1Nl9Guxk9OUUOq9guPS/Nxzz5GUlCQ1e3U6nUziCg4Opr+/n5MnTzIyMiL1W8X+6OvrS0lJCS+++CI7d+6kqKhISlXNzs4uauRSOKhGRka4efOmzCH4pnxqZeIvOCJDra2tMv9ienpaekDF9y00hAGt5IxrNBp0Oh2jo6NUVlbS09MjOas6nU7mPkxOTmK1Wp0irqIfgjtrNBq5desWNTU1LF++/LEjgfPBMywKnMN7k5OTNDU1UVNTAziqce3atUtmF3pCzWrlhtjf38+VK1e4dOmSk8JAfHw8+fn5Trymr2PEuSY7KUP27jqAlBtVc3Mz9+7dY2pqisDAQAoKCqS8zWJk1z8JlG2xWCwyY9k1NKR8j1KOB5CLeWxsjJ6eHlkO8cyZM4yNjcnqcmazmdDQUOLj45mYmGDZsmWL4oFWfr4w0DIzM6mrq+PTTz+ltbWV559/nvT0dEwmEzMzM+j1eimV0t3dTVdXF2NjY4yPjzM5OSkTEMEx9iEhIU7PyZMMIXhQtshoNDI5Oem0b8TGxjoVOgHPiRAooVRfERCHjNgXlNXDxLjcuXOHw4cPU1VVRVhYGFu3bqW4uFjyzsSFxROgFOhXq9Vs374dHx8fjEYj586dk3ugVqulrKyMt956i1deeUXqFSckJLBjxw4mJiZoaGigp6dHRhpycnIW5bxwNV7z8/N5++23+fTTT6mqqsJkMjkVrAHH2aEslvFVEBz9zMxMTCYTP/zhD7+xB+tpQFmARRjSwcHBrFmz5qF/ExgYyLFjx6QxL85UUT79+9//Prt375ahZXHpX6yzX5nsabfb6ejooLq6WkYyvullQXmmm81mqqurnZK94+PjFz265ePjI0saWywWeTGyWq0EBgaSkpJCZGQkbW1tdHZ20tDQQEtLC5mZmQQFBTnNQ/H8goODZdsvX77M2bNnsVgspKeny1LO4ruXlFSWgJiQU1NTUsRWUAby8vJkiU2lp8hdUHodR0ZGOH/+PFevXpUJBDqdjtjYWF544QX27dsnBbbh63EDlYcUfHkwu+Pgcf3u/v5+Lly4IHl1GRkZxMfHy/crs589Ea6bvuttenZ2FovFglqtxmg0YrfbpR7o4OAgH3zwARcuXKCurk563UVpx8LCQnbu3Mnu3bvl7XQxvCPKeRESEsKOHTswGo2oVCoqKyuprKyktbVVliZWeqPn5uYwm83yP/EslJtocnIyGzZsIDIy0u0en0dBGRLv6emhqqrKKaM5OTmZsrIyp/XpKcbcoyC8U8psdBFy12g0zM7O0trayqlTpzh//jzg8MCLaneLmYH/pBAGilarZe3atfz4xz8mJSWFuro6RkZGSEpK4sCBA+zevVsarrOzsxQXF+Pj48PIyAgDAwNMTExw9epVEhIS8Pf3l4mKC+n4cOWeJicn8/bbb7Nu3To+++wz3nvvPenc+Lre4Lm5Oale8tlnnxEdHe2kee5uRReRIPlVbRgZGeHIkSOcPn1aUu2E4bp161a+853v8NxzzzkZrs8alMZad3c3VVVVVFRUYLfbyc3NZe/evU5atovB7xUFTpTfabVamZycJC4ujh/84Aeykt3U1BQ1NTWcO3eOkJAQli1b5pRwL6QYxZo4c+YM//iP/0hfXx/Z2dns3LnTKYH4Sfdej9iplQL/VVVV/Mu//AunT59GrVZTVlbG2rVrZWhPWdnIXVB6N+rr6/nggw9kXXtw8NJeeOEFXnnlFTIyMgBkCFmj0TxUEkLpYRU/xS3oYXAtxbYYcJWDaWhoYGBgQBLytVqtE7fX0zxZAqI4gdA8hQe92UFBQfL5K71f9fX1HDt2jOPHj3Pp0iX5+5CQEKanp5meniY2NlbKuoDzTX6h+wWO+aTT6VixYgU6nY6oqCiuX79OY2OjpAWIZ2Cz2bBYLAQHB1NQUEBcXJzkifb09DgloInqNuDwGLgmGngCXPmctbW1nDp1yonvmpyczOrVq4mIiFgUyZ2nBRH1eNi+MD09TV1dHXfv3nXS5Q0PD5cVtDzxsgHOl/vg4GAZeq6oqGBoaIjk5GQnY21iYoLg4GB8fHwoKipi3bp13L9/nytXrnDz5k0iIyMpKyuTh6SQh1sMCKM5JSWF6Oho/P39aW5uxs/PTyZz+fr6Mjc3x+DgIOPj406OEYvF8oCBq9frJaf57t27nD17lvT0dPk8lJq57oCrZ1QUJBCXBq1WS29vLwcPHuS3v/0tt27dApByhf7+/uzatYt9+/bh7+/vRLNy9zmi1Wrx9/d/6PN9nL1d8HqV4zo8PMyNGzdobm52yuDfs2cPmZmZC743Kds8OTnJzMyMk3SkSKALDw8nKyuLN998k56eHs6cOcOdO3dkJbl169axcuVK0tPT8fHxkUmIc3NzVFZW8otf/IILFy6g0+l46aWX2Lp1K2FhYV+7fx5hvAouS0dHB6dOneLYsWNMT0+TlJTEiy++yIYNG5wOR3ceMGLyCeO1o6PDyUsMjtJob7zxBmvXrpUud61W68RxeVzj9avgjgXtGnqfmpqSRozQ7vPUw1EJ4SV41AVgvudbU1PD3/7t33Lr1i0mJyed+K3CCA4JCSExMdEp8UR56VkMiLb7+vpSWFhIeno6O3bsoLGxkd7eXumN1el0sqZ4VFQUpaWl5OXlMTo6yocffsif/vQnqqqqZJljT/XaPQyi0pnwaoiQZlRUlIwSLDYX+WlDmRQhqqoJDnpISAgrV66UWe3geCaeduGYD0FBQaxYsYLc3FwpQ6XkybmqYuTk5LB8+XIqKyuZmpqitbVVFugA99FbcnJy+Hf/7t9Jrdrq6mo6OjrQ6XS0tbXx+eefc+fOHaanp/Hx8ZHRNlcoaQfT09MMDAw48SI9zUMpPP1irg0NDXH27Fk++eQT7ty5AzgMcrEHp6enU1hYKHMJRMliT4CSqvMwPM78Eue/oK3dunWLEydO0NDQADjWqxD5F9+7kMar8qweHh6WHGUBlUrlNAbbt28nIiKCv//7v+fDDz/kzp071NbWcvPmTQwGg5PDrq6ujqtXr/LZZ59x8eJFdDodO3fuZMuWLd9YOtStxqvgEwqu3aeffsqhQ4ekIZiSksKaNWtkWM9sNnuEsL9y8prNZnmoCwQGBspyccqb6Ncx6IaHh2loaMBsNksv4MzMDD4+PqSmpjpJTSwGXD1aY2NjNDQ0MDo6SnR0NPn5+aSkpMg+e5qX52GyIErdQuV4Xrlyhfv37zMyMkJwcDBarZYrV65w+PBhhoeHCQkJwdfXVxql4m9ffPFFvv/978tELXclrSlVPITkUnp6OjMzM/LAEKFmIWAvKAJhYWGsXLmSW7duyY0VHHNyYGCAZcuWeexFRckTbW5udqrWo9PpSExMJD4+3qN5u08Cpcdtbm6OhoYG6urq0Ol0rF+/nh07dsjDEDwzMc0VSoqS69oRnjzRZ4vFgkajkcUXrl+/LjVWa2pqKC4uJiEhYdG9ksLzqNVqnRJZysrKmJ6eRqfTUV1dTWdnpxTwt9ls6PV6UlNTycnJYdmyZQwMDFBbW0tLS4vkIi7mRfjrQjnPzGYzFy5c4F/+5V+4evWqVMQQF/+MjAz27dtHdna2/BtPukxqNBonpSBXaTSj0SgvWK4OEWWSpdIeuHXrFkeOHOHixYu0tbUREBDAiy++yMaNG6UBr1RfWAgoP9vX11dekFQqFaOjowwODsq5JtbdqlWrMBgMxMbGUlNTg8ViYeXKlaxYsQK1Wk1PTw8XL17k4sWLnD59mrq6OvR6PS+++CJvv/0269evl2v662r1eoTnFeDu3bt8+umnUiM1Ozub0tJSJ06EpxwuynYIvTer1So9puPj49y+fZt169bJDDy9Xi+9O4A0EpSadTMzM7JcmsgyvXLlCqdPn2Z0dJSwsDA0Gg1DQ0NoNBpeeOEFXn/99UXtu/BWCnR1dXH37l3AEebLy8uTJGx386/mg7I9Pj4+BAUFodVqZSjHbDZz8+ZNEhMTGRsb4/e//z0HDx6UpV5dIcj1Su9BUFAQ+/btk3QBkcjmjo1Y6eEXG4RIKJsPglMu5qWg9CgPIZPJxPj4uExA8zSIeadSqZienpZGgUBQUBBr1qyhoKDAKeHJ07xWTwLl5j86OkpTU5OskrZq1SrWr1/vVAVoKXB7lWvVNQFvvnknkkPWrVvHjh076Onpob+/n8OHDxMTE8PevXulNrFyji8klMayazKL4OwmJSWxcuVKqquraWtrAxznQ3p6Ort376a4uJjKykpMJhM9PT2STmC1Wp3OFE+FiDi2tbVx5swZTpw4Ic9EgeDgYF599VX2799PfHy8E23OXVDumyqViqioKJKTk4mKiqK3t/cBjrOfn5+ThNl8UFIkBZXp5MmTkvdbUlLCgQMHKCkpcSqMs1j7bHx8PIWFhTIxrampiYqKCmpraykpKcHHx4eZmRn8/f1Zs2YNhYWFUkYxPT2diIgIKisrOXLkCB9//DE3b97EbrcTGRnJli1bOHDgADt37pQOkm+i1euWHUwIgQvphFOnTvHBBx9IdQG9Xs/69evZs2ePk8yUOyfyw6DMuhcGTFtbG3/zN3/DsmXLJM1AZPgajUY0Gg1ZWVkUFBQQHh6Oj48PU1NT3L9/n7q6OmZnZ6WMUVdXF52dnYyPj0stuOHhYTQaDWNjY/J2tlhQZtSOjo7S1tYmDTuRqOTJm6krp27NmjW0tbXR0NCAyWTCbDZz8OBBrly5gt1up76+/qGGq/JZKDmhq1atckoC8gQDXkmPcCXlu75P3LyVY6l8bkFBQVLXT8xtT/LkKS9N4lKo9ITExMTw3HPPUVJSIrNpPaGq1jeBOBAnJye5ffu2TB6dm5sjNjZWhujGx8elzM1SgTJc+zCur2uVrp07d9LY2MhHH33EpUuXiIqKoqSkxKkU5WJCGJsiwdF1H8rLy5NRGnB48e7du0dQUBBjY2NUV1dTXV0tzw+hAeppa+9hGBkZkVQWZTlfq9VKUlISr7/+Om+++abcNz2BwqOMxmm1WiIiIigsLCQpKYl79+49oMP6VTQcq9VKZWUl9fX1DA0N0dDQwJkzZ6ThKiqIbtiwQWbuL8ZzUNpV0dHRvPXWWxiNRn73u98xNDTE9evX0ev1dHR0sHr1anQ6HUajkYiICHQ6HRqNhr6+PpkfUVdXR01NjSxbnZKSwv79+3nxxRcpKiqShqtYz18XbjFexYEnQo4nT57kD3/4A+DYhJcvX87WrVspLS2VZf8WI9Pu60DZJsH7HBkZ4ezZs4/8u5iYGAoKCvDz88NmszE5OenEK5wPWq0WPz8//P39mZ2dZWhoyEl/c6EhJptarcZkMnH//n2nBJiAgACp5ybe72kJW8pDLjQ0lO3bt9PY2CgvTlarlbq6Ourq6uT79Hq9nKvC2BHVYYRR5OfnJysdPffcc07Vp5S8PHfhcQ85ocMIjmclJMKUm3RYWJjcgNyRMPhVUHq4rFYro6OjTheQhIQEioqKpOdrMZN4njaEI0Bcji9dusTnn38ulS9iY2NlMg849DeXGh5n7govleDyrly5ki1btnDp0iUGBwe5ffs2V69eJS4uTiZ4LSZUqgerKoqws1arJS0tjcTERGmYmkwmOjo66Ovr4/r164yMjDjJ1YFDR3b16tVSyQQ8Sy1DGbUxmUxcunSJ2tpaeekXRuyGDRv48Y9/7KQo5EnrUckPj4mJkcaeoKRkZ2cTGhoq6XzKKI6gzU1OTlJfX8/58+e5desWnZ2dTE9PSz52WFiYrFQVEBCwqBdqZYKgVqtl1apVGI1GxsbGOHLkCPX19VIOc+vWrWRkZMik7MHBQerr62lsbKShocFJ9i0mJoaioiJ27NjBzp07KSgocNKG/6ZJeG6Z6crNaHx8nKGhIfladnY2b775JmVlZXKSeNJEdr01R0ZGkpubKzk8rjp+D8PY2Bi3bt2ShtBX/V1wcDDFxcWkpqbi6+uLzWYjISGBvLw8GWpaaCi5rhMTE9y7d4+mpib5uiDbK0NznpYUIuadSLrLy8sjJycHvV7P1NSU5CyJjVW8F77kLwsj3t/fX3JZy8rKePPNN1m9ejWBgYFO2r6e6BmZj2MkaC/zlV1WGq+uf+tJlxNwlkOamJjg/v37MnkJeECjdilDWQREZPWKBNKQkBCWL19OZGSkfL/Yo+bjkT5LCAsLY/369bz66qt8+OGH9PX18fHHHxMVFcVzzz2Hn5/fonm2HgZlyD88PJzCwkJWrFhBZWWl3H8sFgttbW0PeIpTUlLYu3cvBw4cICEhQa5JTzorldWnent7OXbsGG1tbU7nf35+PuvXryc9PR34MtrjiXsmOC664qzu7+/n448/prq6mrCwMLkPin1UrEuVSsXQ0BCNjY20tbUxNTXlxFvW6/Vs2LCBgoICWW7WHfNSq9VKh9OGDRvw9/dHrVbzhz/8AZvNxvXr12loaJD758zMDBMTE1JSURl9jImJ4c033+S1116joKCAoKAgJxWcp3FmLOruJTYLIQ/S3NzMJ598Ir1eISEhbN++nZdeeonU1FS5YD1tIivd7Hl5efzkJz9h2bJlVFZWYrfbnbyP4PDI9vf3OykSmEwmOdgxMTGyXnJgYCDR0dHExcUREBAgKzklJiaSl5dHXFycVC4Q7z1y5Mii9Fu5gY6MjMgEAnCEG7Kzs50yCD2ZPiAQEhLCqlWreOmll2R5SaHFqNfr0Wq1ku7h2p/Y2FhWrFhBTk4O27ZtY9euXR5JbZkPD9s8XMnzWq2WoKAgJ51XZXazJ0I5TuPj41IaTKVSERgY+ICou6cZ318X4gAR0ZuIiAhyc3OJi4vDZDJRXV3NzZs30Wg0lJaWsnz5cuDL0qmets8+KVw5kvn5+ezZs4fGxkbOnTvH8ePHKSoqYv369fj5+UnD310Gn2vyzrp16xgYGMBms1FRUSGNG3FOqFQqwsLCKCws5Pnnn+e1116T+QUPk190B4RxEhgYiMVioa6ujnfeeYeqqir5HrPZTHx8PPv373faNz1BEssVykuer68v+fn5VFRU0N3dzc2bN6VBp9TKFj9FgRvX818gPz9flggWnmfX71xMiJwHnU7HypUr+Vf/6l+h0+moqalhfHycwcFBWWEMvozC2e12YmJiiI2NJSsri8LCQjZv3szq1avley0Wy1OVz1z0JyQOjampKY4cOcLf/u3f0tnZSVRUFHv37uWVV16RWbFfV8h5oSHCAna7ndjYWL7//e8TGxvL+fPnmZ2dJSAgwKmy1OjoKPfu3ePatWtSSgmQOrYlJSWyPGBgYCCFhYWsWrVKJsQos25dB30xF7qr8Xr//n26u7vRaDQUFxdTWFjotOg82bOjPLCKi4v56U9/yv3797l27RonT55kYGAAk8mESqWSYXNh9AiPQk5ODm+99RY7d+6UoTtPTFJ7ErgK2JtMJiYmJpxK5ippEJ7eV9H+6elpAJms9LBEmqUOJZ95dnZW8iUrKio4cuQIt2/fJjMzk9jYWGm8erKn6+tA0NL8/PwoKiqiuLiYc+fOYbVaZWZ/ZGTk185yftoQhmd6ejqvvfaa1HsVWswi/J6QkMCKFSvYu3cvu3fvltEdT6g4qYQIP6tUKnp6evibv/kbSQsMCAhgenoatVpNSUkJr732GpmZmcCXakKeNheV51hERAS7d+/Gbrdz8uRJ6uvrGR0dlRdGQd9x3RddHR8qlYrMzEx27NjBG2+8wbp165ze565noIwuqlQqtm3bRlpaGleuXKGrq4uRkRFOnDhBTU0NJSUlbNq0CavVSn9/P4GBgZSUlFBWVkZ8fPwDUdenHYVdNOtCbJBClufevXtcv35d1jQOCQlhzZo15Ofny8ni6QejgEqloqysjMTERCfPsuADiizR9vZ2ZmZmZIgvICCAtLQ0kpKS8PX1ldnBrjw1T4GrRJjg64gEic2bNxMYGOhxpSfng3JuRUREsHbtWvLz8yksLCQlJYUrV67Q0tLixOnNy8sjLy8PjUaD3W5nxYoVlJSUSMN1bm4Ok8mETqfzqMPkSeCqKdjW1iafBcDq1audPASPSv5yF5RjK+rJCy5hQEAAkZGRz4xElhJ2u11Kv4EjeevMmTNcuXJFcrRTU1NZv369k4rLUp2rD4NSLD46OprvfOc7Ujnk1q1bnD59mtjYWJkg5Uley5SUFP7sz/6MzMxMbt68KcuFrl69mq1bt5KVlUVmZqY0XAW9wJPGULkftLe3OxXwEZfgNWvW8Nprr5GVlTXv33kSlO3S6/UUFRWxbNky8vLyePfddzl79qyTtxXmvxAL2bOIiAhWrFhBYWEhycnJTtJgnjAH4Us6kq+vLykpKQQHBzMxMYHVamXz5s309fWRnJxMamoqNpuN0dFRNBoNiYmJTvKdQipOKTH2tLBo1oVILABoaWnh2LFjsqQoIMMMgvPxNAi9CwllLWe73U5AQIDUdn0aENwfsQgET8idnlfld4WHh7NmzRqpDLF9+3ZJGbBYLB61mX4VVCoVfn5++Pn5sWzZMtLT0ykpKeHy5ctcuHCB7u5uUlNT2bVrFxs3biQ0NBSLxYKfnx8RERGyDJ4y2WmpQiSRCM9kU1MT9+7dA2D37t28+OKLTqEgT7ygKOfesmXLKC0tpampifr6eoKCgqQ+rYCn7jFPChFWjo6Opquri+npacbGxrDb7SxbtowNGzawf/9+tm3b5nTh8lSj4etC9MdoNOLn58eaNWuwWq1cu3aN2tpajh8/TklJiTRev4lcz9Nsr2hHVlYWWVlZ5Ofnk5iYyOTkJFu3bmXLli3Al4VylDq3ngBxVok2tbW1cePGDadoo91uJyQkhJdeeomdO3ei0+lkvz2pL49CUFAQwcHBpKSkYDQamZycpKury8lpJTyYJpMJi8VCYWEhGzZsID4+noyMDLZs2SIVP5Q8X0/Zi5QUCLvdTnh4uNQpFp7yh0E47URS/oK1ccE+2QXKDeX69escPnyYmpoaJ3KzeFCeEMp5EiwEudq1drkye/FJKnA9TSjd/unp6fz85z9nenqa8PBwJ66rJxo0T4KEhARCQkIoKChg7969GI1GAgICiImJISoqCp1OJ2+mYiyUyWxLHcr1J7hMZWVl/Pmf/zlFRUWyVLOnUiRcZZNefvllJicnOXbsGMuWLSMyMtLpPZ7Yh8eF0ujSaDRs376dwcFBDh8+zPj4OAUFBaxbt46CggLS09NJSUlxUhxYyn3/KihDtQkJCWRlZVFbW0t1dTX379+npKREGhCe8Bxc9/X8/HyioqKw2WxOigLKxEpPchKIM0sYLNeuXeOzzz6jt7dXvic0NJRt27axefNm2afF0tx9GlB66HU6HXv37qW4uNip0I1SZUecE8HBwURGRuLn50dgYKCTVJ043z3F+6/E17HHlJVCF7I/Cz5jlCK7s7OztLe3U11dTVdX1wOl7sRt0hMH8WFQZk4+TDZIGDeupeXEc3FduJ52oxZQepsDAwMpKChwel0p27OUIOaa2HzVajVBQUEEBQU5VSVSYqn18XHhqqNZWlqKv78/paWlbN26dcmsS+XhXlBQwL59+0hISJBVmJS0gaU8lso1qVarWbVqFRqNhpiYGMbHx1mzZg3bt293SrhTavMulfH8OnAVwd+3bx8NDQ3U1tZy4cIFtm7dSk5OjsdE+IQhKjj2Wq1WJmSBg6olJLc88XxwLZhQW1srPa+CtpOfn8+BAwck3xpYUsofSv1ocCRbK/V5HxcWi0VWxfPUKoXgHMUSzjQhEam0XYTxvZh0wQX/FuXt0GazyTJ4yhJkIvEpKCjogWpASwmPmoAPE9deSoa6wMP6udQPw8WsZOKpcNUW3L59O5s2bUKv17uVsvKkcB3LoqIiyS1zPfyfhTEXa1KtVlNYWEh6ejpzc3P4+fk9YBwsFS/XN4UY15mZGUJDQ/nud79LV1cXf/M3fyN1U5Xnk6dEEh7GI/dEg9UVrlEbYcyKJKaCggJJvVrKKhffdM/QaDQemS/wKAhbxdfXV0Z8xHoR/VjM9bMonleBubk5RkdH6evrk6LhGo1GivOuWLHigYexlPCogfOETfFpQuhFis1fZJcuZSjbLyRDlJVslIv2WYfos+ACg7Pe7VJ5BsJbIGTonnXMzc2h0WicdIZtNhtms1mu06W4t34TKA2N1157Da1WS3h4+NfymC0GlNQwseZEwRBPhuuFMT8/n7y8PCkhuWLFCjZv3iyr8y1lvrXSAyvOwUdBOK9ExGMp9lupY+sJWHDjVdlRUXs6Li6O0NBQxsbG2LBhAwaDgZ07dwI4eWS98Fyo1eolVWLySSHKF3takYXFglJQGpxL4S4lKA8JZV+eVTxs3Jbi2D0tCM+z3W4nOzvbKbtbCU+bF666tZ4O18jbqlWr2L17N319fajVal577TXWrVvnxNFe6vi2ry13YsGfuvLw8PX1paCggB/96Eds374do9FIamqq1DiDZ2NCe+HFswKRCbtYpQoXCkrpuqUWrntSiKiBkDzz7qlfPpNv62V0MeC6ppKSknjzzTfJzs5GpVKxevVq4uPj5euedlnwYmnhiXc1g8HwVBtQXV3NZ5999lQ/85vgaffPE/Gs9/FZ7x94+/gs4FnvHzz7fXzW+wdPr49Xrlx5Kp+zEHjWx/FZ7N+z637wwgsvvPDCCy+88OKZg2opZvV74YUXXnjhhRdeePHthNfz6oUXXnjhhRdeeOHFkoHXePXCCy+88MILL7zwYsnAa7x64YUXXnjhhRdeeLFk4DYNFYPB8APgN1/xtrny8vIlqc9jMBgigL3AS0AhEA9YgCoc/f5NeXn5o5WNPRwGg+F1YDOwAigCgoA/lJeXv+3Odj1tGAyGBOD/AJ4HIoBe4GPgP5WXl4+6sWkLAoPB8F3g3S/++efl5eW/cmd7vim+LfNUiWdwDFXAD4GfAPmAD1CPYy/9h/Ly8vlrcy8hGAyGNiD5IS/3l5eXe2ZVhcfEt2EM4dtzXhgMho3AXwHrgXBgBId987fl5eVHFvr73el5rQD+00P+O/PFe466pWVPB/uBXwKlwHXgb4GDQAHwK+CPXyzmpYx/D/wFDqOg271NWRgYDIZ04DaOTfcG8P8DWoD/Gbj6xSXlmYHBYEgE/jsw5e62PEU88/NUiWd0DH8L/BpIBT7Asbdqgf8b+OAZ2EsFxpn/TPz/uLNRTwnP/Bh+W84Lg8Hw74ELwCbgGPDfgM+AMGDLYrTBbZ7X8vLyChwG7AMwGAxXv/jfXyxWexYADcAe4LDSw2owGP5XHJN6H/AaDoN2qeL/AXQBTTg8W2fd25wFQTmwDPjL8vLy/y5+aTAY/r84+v9fgJ+5qW1PFV8cHr8BhoFDwL9zb4ueGr4N8xR4NsfQYDC8CnwXaAVKysvLh774vS/wRxx76feBd9zUxKeJsfLy8v/o7kY8bXyLxvCZPy8MBsN+4D8Dp4DXysvLJ11eX5SycB7HeTUYDAXAWhweksNubs7XRnl5+Zny8vLPXKkB5eXlfcA/ffHPLYvesKeI8vLys+Xl5Y3l5eXPpN6awWBIA3YCbcA/uLz8H4Bp4LsGgyFgkZu2UPhLYBsOr8G0m9vy1PCsz1MXPItj+NoXP/+bMHoAysvLrcD//sU//82it8qLJ8EzP4bfhvPCYDCogf83MAO85Wq4ghzTBYfHGa/AT7/4+etnhQMzD8Tg2tzaCi++Ctu++HlinkvIJHAZ8Mdx2VrSMBgMucD/Bfzf5eXlF9zdHi+eHM/wGAquZ8s8r4nfrTQYDKGL05wFhc5gMLxtMBj+V4PB8D8bDIatBoNhSeZ9uODbMIbfhvNiPQ7axxFg1GAwvGQwGP76i7m6bjEb4lHGq8Fg8APeBuZw8EKfORgMBg3wvS/+ecydbfHiK5H9xc+Gh7ze+MXPrEVoy4Lhizn5O6AD+F/d3Bwvvgae8TEUnrrUeV5LU/x/ziK0ZaERg2Mc/wuOPIkzQKPBYNjszkY9BXwbxvDbcF6s+eJnP3AH+BzHhflvgSsGg+G8wWCIWoyGeJTxChwAQoGj5eXlnW5uy0Lh/8KRtHWkvLz8uLsb48UjEfLFz/GHvC5+H7rwTVlQ/L+AYuAH5eXlRnc3xouvhWd5DD//4ufPDQZDuPjlFwb7f1K8L2xRW/X08RtgOw4DNgCHSs3/AFKAowaDoch9TfvG+DaM4bfhvFj2xc+fAX7ADhzqLQXAcRwJXB8uRkPclrD1EPzki5//w62tWCAYDIa/BP4tUIeDvO7F0obIjl2yXEqDwVCCw1P338rLy69+1fu98Dx8C8bwfRwRuReAGoPB8CkOzt0OIB2HRysTWNI0s/Ly8v/k8qtq4GcGg2EKx7nxH3HILy5FfCvG8Cuw5M8LHPJm4OjL6+Xl5ZVf/Pu+wWDYi8PrvNlgMKxb6L3IYzyvBoMhDwefogsHn+KZgsFg+J9wSILUAFvLy8tH3NwkL74a4qYc8pDXg13et6SgCDU38GXShBdLCN+GMfyCP7gHh3JCH46L/7/CcVaU4VBWABhwSwMXHiLBd5NbW/EN8C0Zw2f6vPgCQqe2RWG4AvBFxEdEk0sWuiGe5Hl9ZhO1DAbDX+HQe6sGtpeXly/lBfptQv0XPx/GUcr84ufDOE6ejkC+7JvJYDDM955fGgyGX+JIAvqrxWqYF4+Nb8UYlpeX23BoSf435e+/yJNYARiB+4vfskWBOC+WbJY6fCvG8Fk/L+DLPo495HVh3PotdEM8wng1GAx6HDexORwixs8MDAbDX+PguVYAzyllQrzweAg90J0Gg0HtotcbBGzAseFec0fjngLMPHy9rcTBobyEY8N6FsPRzwK+7WP4XUAP/HaxJHrcAJHFPV+m/rOAZ2UMn/XzAhyFCWxApsFg0JaXl1tcXi/44mfbQjfEI4xXHNWowoDPn6VELYPB8L/jKBN3G9jppQosLZSXlzcbDIYTOLT7/iccVYsE/hMOT8j/KC8vX5J6ml+EeX4832sGg+E/4jB8frvUS4s+y/i2jKHBYAguLy+fcPndGhyOgSkc++yShcFgyAd6Xc8Ig8GQDPz9F//8/aI37CniWR/DZ/28ACgvLx8yGAwfAH+GI0n034vXDAbDc8AuHLSIBVdS8hTjVSRqLeWKWk4wGAzfx7EYZ4GLwF/OE9JrKy8vf2eRm/bU8EXVlFe/+KfQ8VtnMBje+eL/h8rLy5d6hR8DcAX4O4PBsB2oxVHydyuO8M//5sa2efEY+JbM02cdJw0GgxEH9WoSyAdexOF5fq28vHypeyX3A/9Pg8FwFkcVqkkciUwv4fBKHmHpl4h91scQvh3nxc9x9Ol/MxgMm3BUDE3GkUw4C/x5eXn52EI3wu3G6xfC2mU8e4laQs/OB/irh7znPEu7HN4KHCX9lEjjS92+dpZ4ecovbtOrcVxEnsex2fYCfwf8J683fUlgBc/4PP0W4E/Amzgy1v2AHhxa4P9XeXl5mxvb9bRwFodOaDEOmkAADl7hJRwJeb97BirEPetj+K04L8rLywcMBkMpDq/rXhxFFyZxVET9r+Xl5YtCi1DZ7Ut9PXjhhRdeeOGFF1548W2Bx0hleeGFF1544YUXXnjhxVfBa7x64YUXXnjhhRdeeLFk4DVevfDCCy+88MILL7xYMvAar1544YUXXnjhhRdeLBl4jVcvvPDCCy+88MILL5YMvMarF1544YUXXnjhhRdLBo+t82owGJa8plZ5ebnqYa896/2DZ7+Pz0L/4Nnvo3eePtv9g2e/j89C/+DZ76N3nj67/fN6Xr3wwgsvvPDCCy+8WDJ44gpb5eXlC9GOBcU8ZVkfime9f/Ds93Ep9g+e/T5656kznvX+wbPfx6XYP/CMPooCSeKnWv30fGmLPU9FH1SqRzp6nyo8YQwXEl/VP7eXh/Xi2cDc3BxWqxWbzYbdbkej0aDVap/qhuROzM3NYbPZmJubY3Z2FrvdLv8TUKvVqFQq1Go1Go0GjUazqJuZF8827Ha7XF9qtRofHx/v/PJiyULMXeUcnpubA56uIbsY8K7DxYfXeF1EzGfwuC7gpboI1Go1Op0OnU7n9Hu73b4k+qQcE9eSycIg1Wq1i90sL7yQe4ZarcbX19fpNXHYq1SqJbHOFgve5+K5sNvtGI1GTCYTVquV2dlZfHx8CA4Oxs/Pz93NeyLMzc1hNBqZnp5GrVYTFBT0wBn4bcR8ts6jIBw/TwKv8bqIEN47V++JGDgfHx93N/Gpwx3hlK8D5WJTelbFGGk03qXihXswNzfH3NzcvN4om80G8IBR+22Gcg37+Pg8k/vqUoNwYtjtdvr6+rh48SJXr16lra2Nvr4+oqKieOutt9i7dy86nQ6bzeaRkQXX82xkZISjR49y6NAhwsLC+PGPf8z69esBHrpmvw0QEUoRrXSliCgvlT4+Pmi12idep94TeYEgDhyxgQojyMfH55E3s7m5uSXpLRgfH6enp4f+/n5sNhuRkZGkpqYSEhICeP5CVrZtPkN1ZmaGkZERRkdHMRqNWCwWrFYrJpMJi8UiPbO+vr4EBgYSGxvLsmXLlsQtXMxVMV/FQSM8zuI/LxYPdrtd7gVi37BYLPT19WE0GgkICCAqKsppflmtVlQq1bf6oiXm7rNmzAuDXOyjS2WMlU4AgO7ubs6cOcOhQ4c4f/48Y2Nj8r3T09OEh4dTVlaGv7+//HtPOguFR18YWnV1dbz77rucOnUKjUbDpk2bKCkpQaPRPLbXcSnD9ewQjh6xZz1JtPJJx3pprIAlCHH4P6khulSMhNnZWekxnpub4/79+3zwwQccO3aMkZERysrK+Ku/+is2b94MODxEGo1myfRPYG5ujra2Nu7fv8/t27e5d+8era2tjI2NYbVaMRqNmM1mgoODSUhIIDQ0lJSUFHbu3MnmzZuJjIz0qM13PijHZD7j1dPb/yxCPH+r1YpGo8FsNlNRUcG5c+fo7e0lOTmZ9evXU1xcLEOtS21tLQSe1bkq1iIsrXEWPG1hxNTV1XHw4EEuXrzoZLgCtLa2curUKdLT00lPTwccFzJPomuJS4QwXqurq7l69SoAqampBAQEyLPuWZ2LSrieHYvZZ48wXkUofXZ29oHXhCXvetP0tBuZgNJzqnSDj4+P09fXR2NjI42NjUxMTBAWFkZKSgphYWHo9Xri4uKIj48HPD/crjRy5ubmaG5u5tq1azQ0NADw+eefs2XLFmm8evIt1GQyMTIywuTkJMPDwwwMDGCz2fD19aWjo4M7d+5QX19PV1cXnZ2dTn+r1WqJiYkhKyuLwsJCwsPDCQ8PJyYmBp1O57HjZ7FYmJubw9fXV0YGHnUoCk+gJ4RgXZMD53vGYv0pX3P1WHliNEC5f6jVaiYmJrh9+zY3btygoqKClpYWZmZmCAsLo6qqiry8PPLz8yktLSUqKgpAhuo86dBfaCgNisHBQe7fv4/NZiM1NZWEhATpofbUc2M+iLCrSqWSya9ivgojSsznpdKvlpYWbt++zejoKHq9nqSkJFauXElKSor0vHryWeHK46yvr2d6epro6Gh+8IMfsHz58mfO66+EzWbDarVit9vR6XTznh0TExMMDAzQ1dVFe3s7w8PDzMzMMDs7i9lsxmw24+fnJ+khcXFxbN++nYyMDMB5LT8KHmG8zmecfhU8daEqNxeTycTo6CidnZ10dHRQV1fH6dOnOX/+POAwfEpLS4mNjSUkJIQNGzbwwgsvsGzZMnd24bEgjBm1Ws3c3BxdXV0MDQ2h1+sxmUzYbDbu3LnD3bt3KSws9LjwubgdA3R0dHDixAm6uroYGBigrq6O0dFRfH19GRwcpK+vDwC9Xo+fnx8mkwl/f3/WrFnDli1bWL58OYWFhXLxLQUoDRsxlsrwj9LrKoxATzBc4eHJgV/nczwNykjN0NAQly5d4r333uPIkSNMT08DjrFrbGzkxo0bBAQEUFpayne/+11eeukloqKivpU8T4vFIj3QXV1d/PM//zNGo5HXX3+dsLAwOVc85QL2OHAdR3EpUaqZCHjieSjoAlqtFpPJxN27d6moqGBqagqAkJAQ1q9fz2uvvUZhYSFTU1PMzs4SGRkpP8PT6BFqtVoar319fXR0dABQWlrKc889R1ZWlhwLTzbCvy5c5504MzQaDVarlebmZioqKqioqOD69etUVFQ84GEHB0ffarUCEBwczL//9/+eH/3oR4SHhz/2XHbrzFCGnh8HFosF+PLgVfIs3A2lF8dsNlNTU8Ply5e5c+cOnZ2dTE5OMjU1RXt7u/wbi8XCzZs38fX1xc/Pj56eHlQqFS+88IL0ojzuLWSxoeyv3W6ntbWVlpYW1Gq1NGBra2u5fv06cXFxREdHA85Go7tgt9uxWCyyHQ0NDfzqV7+iubmZ0NBQxsbGmJqaQq1WExISQk5ODgkJCYSHhxMQEEBaWhr5+fmkpKQQFRVFZGQker3erX16XFgsFnx9feWam5qaoq2tjba2Nurq6mhpaWFqaorAwEASEhJISkoiJyeH/Px8aRw86br1JAhvltITZ7FYnPha7uiXKzewtbWVY8eOcfjwYS5fviwNV/hyHwQHT/DOnTuMjY1x48YNXn31VbZu3Sq9Pzabbclylu12uzzglHN2PojENXA8k6qqKiYmJigrK5OfIT7T0zE7O+tEEwBob2/n8uXLNDU1kZmZyfPPP09YWJhMTHP3njoflCH/2tpa/umf/okzZ85gsVgIDAykuLiYAwcOsGPHDnx9feUYKvvtSfNWmQBotVqprKykq6sLtVrNsmXLCAkJcZqjS3F/fBLYbDbu379PZ2cnra2tNDc3097eLs8TYbSqVCoyMjKIiYkhPj6eqKgopqamuHbtGrW1tUxMTHDmzBny8/PZsWPHY0eM3DLjxQYijLKxsTFGR0cxmUxO71Gr1fj7+xMaGkpwcLBHGq0CSmOupqaG999/n08//ZS6uroH3hsYGAh8eZOenp5mcnKSU6dOAUijCDzXeFWGT+x2O+Pj44DjOYhx9PPzIzAw0CmM4ikLWhDvAUZHR6mqqpJtDwwMJCsri+LiYnJzc0lOTpY0AI1GQ3Z2tpN3XKhIiHCK2OA8KVtWjJVYQ6Ojo7S0tFBbW8vdu3e5desW9+7dc7olBwQEyIMyICCA3Nxc+Vkic9gdniyLxcLIyAgjIyPzrg8RMvf390er1cp2+vv7ExISgo+Pj6QeaLXaeT24SvUJV0NioaDcQyYmJqirq+PmzZtyTETyo0qlIiAgAB8fH0wmEwMDA9y5c4eamhrGxsYwmUysWrWKmJgYadQsxURQES4XeFRoXDk+IyMj8jmYzWante6pcE3QA8ccGBwcpLW1lZs3b3Ly5Elqa2vJzMxkZmaG3bt3S6eA1WqVScGeAuVFoba2lmPHjjEwMIBarWbNmjU899xzrF+/Hl9fX8mN9WQngJKm0dvby/Xr12ltbUWr1RIaGopGo3FaZw+bq64XqPky8D0RVquVmZkZZmZmGBwcpKKigps3b1JdXU11dTVDQ0PyvaGhoeTm5pKenk5RURF5eXnEx8eTkZFBfHw83d3d/O53v+M3v/kN/f39Tvbd48ItxqtygLq6ujh16hQnTpygublZehUEpyI3N5cdO3awfft2YmNjAYdXwmw2k5KSIrMS3cVfU4Zx5ubmaGho4JNPPuGPf/wjbW1tAOh0OsxmM+AwCBISEhgbG5Ph6NDQUKanpzGbzdy4cYPu7m6nz/dUKMdxvk1z/fr17Nu3Dz8/P+np9ITN1TUEHhMTQ0lJCdeuXZNexc2bN/OXf/mX5ObmMj09jUqlws/PD7vd/gCnScw7sQkLr6QnbURKI29kZITf/OY3fPbZZ/T39zMwMMDIyMgDfzM9PU1FRQUzMzPExsYSFhZGdHS0k0GkNPAWqx+tra289957fPLJJ0xPTxMUFOR0mbJarURFRZGfn098fDwWiwWz2UxWVhY7d+4kLi6O5uZm+vv7iY2NlckhSggu92JCuX8FBweTmJhIREQEAwMDAISFhVFYWEhsbCxRUVHo9XoqKyv5/PPPMZlMmEwmjh49SnNzMy+//DKvvvoqBQUFwJfeck9YfwsBsSZnZ2fp6OhgbGwMX19fJ5keT4e4CNrtdnp6erh06RInTpygoqKCnp4exsfHsdls9PX1MTg4iNls5jvf+Q5ms5muri6ZNCrORHdDGCM9PT10d3dLD7iPjw+lpaVs27aN4OBggHn3VU+D0ngdGhqioqKCwcFBwsPDycjIICEhAbVajcViQavVPnRPdP39Yu+hTwLluTE+Ps7Zs2c5cuQIFRUVDA0NySilQHh4OPn5+Wzbto0NGzbIPczf3x8fHx95OQkNDSUmJoawsDBCQkJ4/fXX2bFjB4B8fl+FRTVehfaXOOQrKys5cuQIp0+f5urVqxiNxgf+5saNG7S1tTE8PMyqVasYGxvjypUrzM3NsXPnTtavX49er3fbwCs3xrGxMU6cOMGf/vQnabgKjiRAVFQUzz//PHl5edy7d4/Dhw8zMTHBxMQE/v7+WK1WxsbGpGcWHGFdT038UXpe5+O1ZGVlyVDzzMwMgYGBHhMGUnrbli9fzk9+8hP8/Pw4e/Yso6Ojki4gxLOVi1gQz10TKTylb0qIw1tsuq2trRw9epSDBw/KLFmNRsO6devIz88nKioKPz8/fH19aWlp4ejRozQ0NPCHP/yB6OhoXn31VXQ6HePj4wQGBjoZsgvVf+Vnz8zMUFtby+HDh6moqHjk3927d4+EhAQsFgsmk4nU1FTa29uJjY2lpaWFgYEBYmJiyM3NJSMjg2XLlhEUFERAQIDcbN0FwSVTztOioiL+7M/+jLS0NHx9ffH19aWwsJCAgADOnz8vQ3U3btxgcnKS0dFRXnnlFUpLS+XneKp+poDyEB8dHaW5uRkfHx8yMzPlvjjfQS/mx/T0tIzihYSEEBkZ6SR874n9FnuLMN7Onz/P8ePHqaiooKqqiq6uLvlevV6P1Wqlrq6ODz74gMnJSWZmZujr6yMtLY0dO3ZQXFwsuZlKKspiQaxXlUqFxWKhpaWFrq4u2b/Y2FhWr17N8uXLUalUGI1Gjy9OIBLkBIaHh7l//z4AQUFBpKWlSYNLqa7g+hnw4BxUjs98lBF3wmq1yn2wp6eH9957j48++ki+HhUVRU5ODpmZmWRmZhIZGUlSUhLFxcUkJSU5fZbov81mY3x8nPDwcHbu3MmyZcuk4QrMm7g/HxbVeBULymg00tjYyB/+8Afee+89uru7H3nrqq+v5/Lly7S0tFBdXc3ly5dlhn5KSgppaWlyQrgzg7impoZjx445TerJyUn5/y+++CI/+9nPKCoq4ubNmwB8+umnzMzMyFupRqNxMuI9LdFpPqhUqnnDPUqD1tOkQ9RqteSLRUdHs2fPHtrb27l69Somk0mG1AsLCwHnS4qPj4/HeDe+CkqP5NTUFB999BG/+MUvaG1tBRzzcs+ePXz3u9+loKAAvV6PXq8nICCAjo4OoqOjeeeddxgYGKC/v1+KjIs1uGrVKoKDgxd0zSmf/ezsLBaLhaCgoK/8O6HLKz6jo6ODq1evymQBwf+NiYmhsLCQlStXkpOTQ1RUFKGhoSxbtozo6OhFyVR3lRRqaWnhzJkz1NfXo9VqWb58Obt37+aVV15Bq9VKpYWsrCySkpLQ6/X8/ve/l5V+6urqGBoakmHasrIy+T2eDCUNpb6+nl//+tf4+vry05/+lKKioodSVUS/RAY0OLw78fHxTs4ATzEKXCHmVnV1Nb/5zW94//33MZvND+yrIoIHyJCt1Wplenqa5ORkNBoNhYWFaLXaRxa3WEgov3N2dpaenh7q6+uZmJhAr9ezdu1acnNzl5T0l6tB2dzcLJO1YmNjiYiIeKzP+Cp4WmREaUhOTEzQ1NQEOPqyfPly1q9fz86dOykqKmLZsmWy2MCjxlSj0UgaWnFxsYwiCTzuRWbRjFdxu/Tx8aGmpoby8nKOHDkiQ+cBAQHs2LGDxMREeajMzMzQ2dlJVFQUmZmZdHV1cevWLcxmM8PDw5hMpse20hcSIyMjNDQ0cOTIEaqrqwFHyMRqtRIUFMTq1avZtWsXGzdupLi4GJ1Ox+rVq/nZz35GYWEhly9f5vLly3Jjev/99wkMDOTFF1+Uh/TjutIXC8oJqtFoeP755+nt7aWqqkoml9y+fZurV6+ycuXKR3pN3Alx6EVERFBaWsqqVau4fPmyTEBLS0sDkF4wwWldChqoykN+dnaW69evc+nSJerr6wGHZ/xf/+t/zUsvvURycvID8yspKYlXX32V4OBgrFYrJSUlGI1GLly4wN/93d8xODjIvn37+NnPfiZv2QsxT5UbekBAAMXFxbz22muEhobS19eHXq93SpbQaDQMDw9TW1tLb2+vVCaYnJyU4XUBs9lMe3s7Y2NjdHZ2cuPGDZkcFB0dzdq1a2X4ayEvLK7Ga2trK2fOnMFqtZKcnMwrr7zCtm3b5Os+Pj6YzWZ8fX0pKirijTfeAODKlSt0dXUxNjbG4OAgx48fl8buhg0bpJPA02TCXLnFRqORhoYGLl68iFqtZuPGjeTm5j4yHAtfRkVEUl5oaKikD3gSlUfw48U5UV9fz5UrVzh79izXr1+XZ4FyrgIyiqJWqzEajU6OjtbWViorK+U8EsbrYkOpWGKxWGhtbeXevXuYTCZKSkrYtWsXKSkp8v2eThcQa0Wv12OxWLh16xaXL1+WY1NQUCDpD/BoA3Rubo7JyUkmJiZk9GRwcJDx8XHi4+NJSEgAPOecVPYlJCSEjIwM7t+/T0BAACkpKWzatMmJ/qGE4JsLupKSrufn50d8fLzTnmoymZ4oyrzgxqty0xB8pA8//JB3331XErSDg4NZtWoV+/fvp7CwEKPRKN30fX19qFQqzGYzPT090thYtWoVRUVFMrFpMRMrlJidnaW7u5ujR49y+PBh6ekQm1Nubi4//vGPeeuttwCknq1Wq2XTpk2UlZXxxz/+ka6uLsbHxzEajXz00UeMjIwQHR3NqlWrZP88CcowuUqlIikpidTUVKcEtYaGBq5cuUJSUpLUr1XyhtwNZTWiubk50tPTKSkp4fbt2xiNRnQ6HQEBAV/5OeIC5Wm3ZqWBcufOHf70pz9RWVkJODaP3bt389Of/hQ/Pz+ZOOjv7y8VIex2OxkZGSQmJuLj44PRaOTcuXN88MEHXL58GXDo+R44cEAarw8LmX1TCI+br68vmZmZ6PV6YmJi6OjowMfHh6ioKHlg+/j4MDw8zL179+js7ESr1RIQEIDJZKK/v5+Ojg5GRkbk3mSxWBgfH6eyslI+H4GbN28yNTXF9773PbnRLtRF0tXDLKIxYWFhlJSUkJaWJhPNdDqd9Fao1Wo2bNhAVFQUCQkJfPLJJ1RXV2MymRgcHOTjjz/GYrEQEBDAmjVrgIUbp28C5YE9ODhIc3MzPT09+Pv7Mzg4yOTkJBEREfMKowsDtaGhgfr6emw2G8uWLSM8PBxwDmW7E2KMhcE2OTkp6XMHDx6UOtngSOwVNBZBA7FYLE7KCr6+vuj1eiYnJ9Hr9URHR8s+ustYVz7npqYmqqqq6O3tBRxC/vn5+YSGhjq935Mh+OLgGK/r169z69YtAFasWEFRUdFDjVcldcNut9PQ0MC9e/cYGBiQl8qGhgb6+vrIzc1l165dLF++3O25PALK+ePn5ydzHoxGIxEREWRkZMhcEFEBTlz+HxU11mq1Tsm0Sj7s42LBrQix0LRaLc3NzfzqV7/it7/9rfx9RkYG27dvZ+fOnZSWlhIeHu5k4E1PT1NZWcmhQ4c4e/YsVquVnJwcdu3aRWlpqVwEi2kUKTdZq9VKb2+v5CcpJ2psbCxlZWWsWLHC6W+Vh5RarWb58uWsWrWKkZERKYJfWVnJ73//e1QqlfTWuovDNB+UEi2zs7NcuHCBI0eOSNUBwEmiRsCTjHCxuQtJHnG7FgbN1NSUFPMXho7ZbJZZ7PCluoJarfY43pZIIhSVa44ePUp7ezupqan863/9r/mzP/sz2WZhAAhDXDyPgIAAQkJCsNlsXL9+nV/84hecO3cOcMzdgoICpxD+Qs1N14tpYmIiQUFBzMzMAF+Gi5Uen23btjE9PS35hFarlaGhIe7du0dNTY3kLYsCG0oJKgFR1Wrfvn1yr1kIw2++JMKUlBTa2towmUxOtCil1JeAVquloKBAevI6OzulZ2hkZIRz585RWFhIXFwcsbGxHmm4KhVk+vv7aWhoYHJyktDQUIKCguThptx/ldWMTCYT165d4/r16wBkZmbKS5WY1+7eOwWHUPTz7t27/Pa3v+Xs2bP09PQ4vXfHjh08//zz+Pr6cvXqVc6dO0dbW5vTmSr0xLOzs3nppZd4/fXXnbzzi9lfsbeLc7ilpYVLly5JGh04aEp+fn4ed9F/FFwvlePj4zLBNSEhgfT0dCcnx8P61tPTw6effspHH33E2NgYGo2G8fFxRkdHmZqawtfXl8rKSv7dv/t3rF+//oHvdje6u7uprq7GYrHIJOf8/HwZJRfr8Ekuiq6qLk+CBbP2xGEvFtLk5CRnz57l4MGD9PX14efnx6pVq9iwYQOvvPIKa9askZNe3KLVajWBgYEMDAxw7Ngx6urqJKdn9erV0sW+2FCGY8fGxmhoaKCjo0NOtLm5OfR6PWVlZbz66qskJibKUIq4cYubio+PD4mJibzxxhtMTk7S29uLzWbDbDZz7NgxEhMTKSwslM/E3TcxASVdY3Z2ljt37kjDVei8arVagoODPVIqC5yTmXQ6Hb29vdTW1srQ48mTJ2lvb5dedBH+ENqn8fHxREREOG1cJpPJrXqh4JwhOjMzQ3V1Nbdu3ZIaw8uWLWPPnj3ExcUBjuSDiIiIeQ0aYdDZbDZJ2zGZTAQEBJCfn09eXt6iGK+AvGiI/oWGhjp5cFzxsGIfQr7FYrGgUqlobGwkIyODnp4e+vr66OvrY25ujrGxMSwWC3fv3uXEiRPs37+fwMDABZPzUT67mJgYli9fLhM/RfhXXBZHRkbo6+tDo9EQGxsrLyoJCQls2rSJqqoqWYLTZrMxPDxMS0sLPT09xMTEAEh9W0/g1btyWVtaWmQUp6CggNzcXLnOXDmvYp1ZLBa6urokFzE8PFxezjxl31Qm9dy6dYv333/fiT6n1+tJTU1l1apVbN68mczMTFkwZT5PqnAQREdH8/rrr1NaWgrwlRnvCwHXhKSenh5qa2sZHByU7xHRLHHZUCosiPUtLqqLbXw/DsQYKC8QD5ODFH1RqVSMjo5y/fp1Lly4wLVr1+R7hfSi1WqloaGBjz/+mNTUVCIjI0lPT5fzfLFpBOL7xN7Q3t7O6dOnuXv3LgB5eXkUFhbK9aXRaL7WhfibJDovmPEqDhkxqLdv3+bIkSPydpmdnc2uXbvYs2cPWVlZD3hNhbE2MDAgw3zgeEgiSUs5YRbzJqc0DoaGhrh//77TAtVoNKSmprJ161a2bNki5TOU3hWxUE0mE0FBQezcuZPW1lauXbtGZ2cnVquV0dFRJiYm5Oe6em3dCVcxZqUBJwxbUfbXU9rsCuXztNlsXL16latXr0od0Q8++MDJ6yoM2LS0NEpLS1m9ejUlJSUUFRU5fa67w5PK5y0Ml9HRUVkNLSYmxokL9yjjRYylKF8cHBzM0NCQTHgSHvjFglhD3+T5JiQkEBMT40RB2rNnD1NTU1RVVXHkyBGuXLkiJarGxsY4c+YMiYmJbNu2TUZWnqbGratnOSAgQIa8hR60QGdnJxcuXODMmTOEhoby3HPPERMTw8TEhMzeXrFiBaOjo1y5cgVw7EnT09OMj4/Lfov54AkQzxMcc66xsZH29naSkpLYunUr2dnZTu8VUM4DIVno6+vrcfqursUETp8+za9+9SsuXLjgpI+ZkpKCwWBg48aNNDY2cvDgQaqrq2lqaqK/v98pmiX+PyAggPT0dEnNAvdILLruA1NTU4yMjMgLcHh4OKmpqU55BIK65eoZd/ce+jAo147498P2QFFiHBxr9tSpU9TU1MjXAwIC2L9/P6+++ipms5l33nmHo0eP8umnn5KdnU1qaio+Pj5O4fjFgjKSbTKZuHXrFteuXcNoNBIWFkZeXp7cn8A9TqkFM15FaGRmZoY7d+7wwQcfcPHiRaanp8nJyWH37t28/PLLFBYWOnVcGHlCTuvmzZtcuXJFEtPDwsLIzMwkPT1dhnEXW0pKOVEHBgZoaGhgfHxcHgaBgYGsXLmSwsJCOcnnu0WKiSlQXFzM2rVrMRqNDA0NybJrnriIXb0A8xmvvr6+HivLozxIBgcHZVi9v78fcPTBZDLh5+dHbGws/v7+0itXWVlJW1sbd+/e5fDhw6SlpbFt2zZ27drl5JVzV3Uj143UbDYzNjaGj48PhYWFbNy40Yni8Cher/JSKbiW4juio6NJSkpyMn4Xo6/KELo4pJUUEBG6mu9QEeUqlQeBVqslLCwMcCSphYSEMDk5KRPboqOjKSwsJCkpyYlP+LT7qlwngYGBsk3d3d00NzcDDoPg/PnzvPvuu9y5c4eoqCg6OzsJCgpiYmICm80mdZX7+/ul4WCxWKipqeHatWukpaWRlpbmNLZms1lGDNwB5X4i+K5jY2NkZ2eTkZEh15XJZJLzUCS/6HQ6goOD5TwXc1NZZtTdxpByvrS2tvLZZ5/x6aefyvGJjIxkxYoVrF+/HrVazdGjR7l//75MHlVC0Hvm5uYIDg5mx44d7NmzxykK4Y4kKFfqR19fHzU1NVILdfv27ZSUlMhxUCbrPOwS6IkFiZR7irh0fdUFvqGhgQsXLsixzM3N5fnnn+fNN9+kpKQEs9kstWObmpqorq52qpK32Dq4yv7Mzc0xNTXF5OSkzC9QqrCAe+g4C7JTKRMa6urq+O1vf8vJkyflDTMhIYEXXnhBSp9YrVY5MMJwBcfkP3v2LOfPn2diYoLg4GBKSkooLCx0a6hLuQn29vbS1dXlJNS7bNkySktLnTIqRWjEFcoJGR4eTnFxMbW1tQwNDWGxWB76d+6GqxdYyRdUVjASnFFPg6iuZLfbOXr0KP/n//l/0tjYSEBAABkZGcTGxhIbG0tycjKZmZkEBwfT3NzM2bNnaWpqwmq10tfXJyXPTp8+jcVikZ52ITnlDriOS29vL62trVitVpYvX862bducNPjmO9RFmFWUbayoqODKlSsMDw8DSEN47dq1TrSBxTxkHnUxEF64x4UoGyvoTDk5OfK18PBwVq1aRVZWFoDcr562MaTsS0REBHl5eWRmZjI4OMjY2BhjY2MMDw9z9uxZLly4INvtatwoITxbwngNDg4mNDSUjRs3Sk+6UGNwJ5R9F6L2drudmJgYJ46uXq93clYoa60PDQ1RV1eHxWIhPz+fjIwM+ZnuvEQrI3UdHR188sknXL582WnP1Gg0rFq1iszMTN59911OnjwJOPorjFW1Wi3zQcQaT0tLY+/evbz88suybKlwGiw2lN5zcJzfgu8aGxvLypUriYqKknuLOBuE08fVi+6JtAFXzEflUBYustvttLW1cefOHXkZjomJ4a233uLVV1+V+4yPj48U7O/t7WVgYMCj9G9FlNhqteLv7090dPRjJTMvJBbEeFVO4OHhYaqrq2W2IThI24J3NT09LR+IkiM7ODjIsWPHuHr1qgzfCa1DZUUcd3gKlJSBjo4O2T6x4LKysli1ahXx8fGSG/MwD6rr75Qbj8VikeXYhBSQJ3oxXaEUlJ+cnPTI2uLiOQ4MDHDjxg0aGxsBh/f7Rz/6EcuXL5cG6LJly9DpdIyMjFBWVsbw8DBDQ0NUVlZy6tQp7ty5Q3V1Nf/xP/5H0tLSKCoqYt++fSxfvhxAShot1kasXH8jIyPcvXtXau5GRUURGRn5lW0RB6SPjw/j4+McPXqUY8eOOdFYIiIiiI+Px8fHxylxxtMhDlnhqVV6dwS3V6xpcOxht27dIjExkczMTCfe+tPqr5InBw5ZmpKSEl5//XUGBwdZuXIloaGh0pBV9sX1c5TcQ1FsYXZ2Vobjf/vb33Lw4EESExN54YUXeOmll5yypRe7JLV4jkL+q6urS6rMJCUlSbULZR8FlPt/TU0Nra2t6HQ6srKy5BkD7k3UEoUhABobG2XZcGGYms1mzGYzV69e5f79+9y4cUP+rV6vZ82aNajVatra2mhvb5eJeLGxsaxZs4bCwkL5+e52FCjno3LfNxqNdHR0cO3aNZqamjAajXR2dtLU1MTExIS8KAv5sIyMDMrKyigqKpIGnKcU2FB+v/CAu/5OePrHx8e5du0aFy9exGazERMTww9/+ENef/11MjIy5PwVziCxt/T393P79m3Kysrkc1lMKOXOzGYzTU1N1NbWApCcnEx2djaBgYFS0k1cQEQ/FqOa5lO3/Fw9cmIxiWxfu90uLXfASUBaYHx8nCNHjvD+++9LUVxwKBNs2rSJqKgoWYnCXZusqBzS0dEhva46nY6CggI2bdpERkbGA3ye+aDkzc3NzTEzMyMNXqPRSHd3Nz09PdJ49aSb6MM2ETGh4+LiyMrKWrSEnieB2DSsVispKSmsWbMGHx8f3njjDd544415b7zR0dFy3gKsWbOGtWvX0tDQwPXr1zl79ixVVVVcvnyZqakp3n77bacowWJpTSrX3/T0tFSwAJxUK0TJXsHhFH8rPDziGUxNTUlBdIHY2Fiio6Ol0ePuQ/NJMN++If7d3NzMwYMHnZIqhoaGaG9vZ3h4mMzMTGBxSjpmZ2fz5ptvSqoVIAtD3Lt3j97eXqkVOT09/UD40m63S81lge7ubqfy04ODg0xPT0taRHR09KJ77ZT8uv7+fu7cuUN7ezuRkZEUFRURFxfHzMwMQ0ND8nA3Go1SaQKgra2Nzz77jI6ODsLCwkhOTnYSjneXwSM4kQIDAwNUVlZKA1T8tFgs0puu1WpJSkoiLCyMsrIyVq9eTVVVFR0dHU79yM3N5bnnnnNKXHa3ioSyfb6+vrI0+sjICFeuXKGtrQ0/Pz+Gh4epqqqatyw1OAykpqYmXnvtNdatW0dQUJDHSCwqPcyCgqTc/5Rc15mZGS5evMilS5dQq9W88cYb/PjHPyYtLQ2LxeIUxVEa+0IVJT09ndjYWPm9sDhzWWm8zs3N0dLSgtlsRq1WU1paytq1a79yronntFCUnac+G1wbqfy38uE/rHxaW1sbly9f5oMPPuDKlSvSEPL19SUyMlIO5GJ7B8DZ0BTi5kNDQ9Ig8/PzY+vWrezcudOJzPy4BpswbpTvF7SElJQUqafmKXA9KF2xfv169u7di1qtlt5HT+EuiXkZFRXF22+/ze7du1GpVA+Uk3wUUlNTSUxMZNeuXezYsYP8/HxOnz5NY2Mj77zzDj09PfzVX/2VzAB+mgk+j4uHXXhcL5lKpQxXObbm5mYnT2RKSooUjVd+z1KFclzu3bvHJ598wr179+TrkZGR7Nixg7Vr18pM44XyMis/U6fTkZeX5ySNFRERwSuvvIJOp+PatWuMjIxgs9lobm52Svx5XFy5coWamhpZCOGNN95YdKF08TzBMddOnz7NxMQE2dnZBAQEUFdXx9WrV6mqqpJGQVdXl+TFimfT0dEhhc4FbUmJxc7YFlB+p7+/P5GRkQ812sAh8bVnzx42bdpEcXEx4+PjXL9+nebmZkk1CAsLY82aNWzevJmIiAgpweUpzgFwft5ms5nm5mZJcVE6aeDB5MH29nbef/99BgcHMZvNbNu2TYapF/vsV46fj48PQUFBBAcHMzIywvT0NNPT004XFOW+Kjyo4ODTr1+/XiasKemVDzNM3c3TBsfYiZyjuLg4IiIipFzgyMiIPGPm5uYICQlxSmZbSHtlUa4ydrtdGqEqlYrBwUEOHz5MamoqQ0ND2Gw2/P39ZXbs4cOHpZdHeC+Fp0eQvN2xSF313oxGIzabTQ6yqJwlss8fJ5lsvmxZZd8EbUAsdE81EuabpHFxcbIvIiHEkzZXcIxZTEyMU4jRZrNJvrEwUsQNVGR+C2624LWuWrWKqKgotm3bxvHjx/n1r3/NH//4R/z8/LBarRQUFDxS1ulpwnUslIeE2IgEL1dAHAbip0ajoa+vj/Pnz/P55587eeuSk5N57rnnJAdU+XdLEWJNjY+P09LSQktLC+C4MGu1WlasWEFpaamTJNNCe4GUmpmCOyeMgezsbLRaLTk5OYyOjmI2m2ltbaW5uVka4sKrI7LvdTodGo2GkZERWltbJU9/YmKCiYkJOjo6sFgshIaGsmvXLmJjY524iAu57yjna3d3txTqt9vtHD9+nI8++ojr16/T3d1NWFgYAQEBTlqbrpicnOTGjRtkZmYSEREhIz/uMlyVeQ25ubn88Ic/5Pz587LoTlhYGOHh4YSFhREREUFaWhrr1q2T+uCCLykM16SkJKkBK+TgPGX9uUZ9hGfZbrfLCKVQ44mIiMDPz4+MjAxSU1Pp7+/n3r17dHV1yaI9R44cARzPcdu2bZJeuJhQPtvAwECKi4tZs2YNbW1tXLhwgZycHIqKiggMDHQy2Do6Ovjoo48k13XNmjVOlEdXL7WgzYCDF7tixQqpDLPYSWtKTn9LS4ukKlksFs6dO0d3d7d0Sok9SqPRyGhrWloaMTEx8rxfiOTlBdmBlQ9ZqAO0trYyMDCA3W7n7t27/Jf/8l/Q6XROB+vExAR9fX2yHjkgaQa5ublkZmZ61CK1Wq1OUlAajcZJW1KZnf24EBwaAaHa4GlG3+NAGdbzBK7S40KZBKKEUlzcFbOzs8THx5OUlERaWhozMzP88pe/5L333mN8fJy3336bLVu2yAzyhfQCKT9Xr9cTHx9PVVUVALW1tdy8eZPo6GhpTLtqYE5PT9PR0cH58+f57W9/y7Vr1x7QIF21ahXh4eHzRguWCpSJFRaLhcrKSmpqapwoFCtWrKCsrMzJG78YWb+P8sAEBASQl5cnPd+CbiSKZVitVsbHxzGZTGg0Gvz8/AgLC8PPz4/a2loOHjzI559/7mQQgSO59o9//CNzc3Ps3LmThISEBd9vXS9aMzMzklfd0dFBT0+PNHoCAwMJDw8nOjpaVifSarXMzs7S1NQktbZHRkb47LPPZJnxLVu2yLnuDu+rci/JysriJz/5CaWlpdTX18uktMTEREkVELxPgEuXLj1An0tPT+d73/semzdvBtwThXwYXA0yjUYjuaoBAQEEBQWxfPlyNm7cSHp6OjqdjpSUFLKyshgZGeHy5ctcvHiR27dvy5Kyx48flwm0y5cvXxDO+aMg9jaLxYJOp2P9+vXU1NTw6aefMj09zccff8ybb75JXFyclI2cm5vj0qVLfPbZZ4yPj5OWlsby5csfiMiK9ttsNoaGhmRCbGFhoayuKWyCxRhj8UzFnB0fH6e1tVVGdQYGBvj0009lFVTl3FapVISHh7NixQp27tzJ9u3bycrKQq/Xywv408SCGK/KzT0nJ4fvfe97zM7OcujQIcxmMwMDA05hSCX0ej3f+973CAkJ4cKFC1RWVhISEsKmTZucKlW5Y7EqjUqz2UxHRwd9fX1Og/Kk7XL15rpqOmq1Wo/ZmFzhGk5xhbLutrulah4GcQkRIUZXGaWvgri8iFLH4PBMHjhwgKGhIT7++GOOHDmC2WxmYmKCjRs3kpiYuKAGkJKSk5iYyE9/+lPm5uY4ceIEp06dAhyb0Jo1a0hKSmJ6elrqMU5NTVFXV8fNmzeprKyUiWzCwAMH1UJc0kRygqfO0UdBqS0pDJ4zZ87IC7XNZqOwsJBNmzYREBAgN/bFlqxxpXGINgv4+PgQEhJCSEiI/J0oQOGKVatWodPpyM7O5vLly5w+fZq2tja0Wi0Wi4UbN27ItfDqq6/K8tvK0P5CQiTNgYMPGhsby9q1a1mxYgXJyclERkYSHBwsE0Kio6OZmprinXfe4f3335eFUubm5jh69CgBAQHEx8fLsrjCAFlsKMcxPDycDRs2SB5zQEDAvLXhOzs7OXv2LCdOnJAFKfz8/MjKyqKgoAD4soywJ6w/1zmalZVFcXExN2/eRKVSSaNGGK6iWEFoaCgqlUqWw83NzeXYsWOMjIzQ1NTE9PQ0XV1ddHd3k5OTI9ffYtKwxB6v1WrR6/WkpaURHR1NR0cHw8PD3Llzh4KCAhkZttlsThFTcfESlBZx7ov2NzQ0cOvWLaamplixYgUlJSVyni6mc0CZOCpyA/R6vcxNCg4OJjw8XOYcCbk6se76+vo4duwY1dXVXL9+XUpIJicny/fD04mcL8hupFKpZLZyUFAQzz33nAxX3rp1i8nJSeDLB+Tv78/k5CSzs7McOHCAt99+m8HBQSlDFBcXx+rVq0lOTgYWbyN1hasE0cDAgFPoSiTBCDyOseaaOSsyUJXf86iEL3dC+TyUHnQBV+1XT+LrCgj+9ddNdFBWhYMvOaM5OTn84Ac/ICQkhGPHjnHmzBkGBwcZHR3ljTfekNzthZjLSl52UFAQr776KhaLhaamJlpaWjh69Ci3b9+muLiY4uJimQks1C1aW1tlVZ+YmBj5e0DyzsUm7YkXkseFCGPNzs5y69YtTp06JStaAcTHx1NYWEheXp6UDFvsfUcZshcHqBijpqYmyX0MDg6Wl18l1UWsOaVIfkFBAQUFBaxatYrIyEg+/vhj6uvrmZubw2g0cvXqVUJDQ1m9evWCG6+uB3NycjKbNm3izp07ZGRk8OKLL/Liiy9SVFT0SOm58+fPyxB1dHQ0QUFBWCwWjEajU9Kau/Yg5XjMzc2h1WrlHiAgqvOJcpsVFRXcvXtXOnp0Oh1bt25l27ZtMhLgCUargGsiZFZWFiUlJbS3tzM+Pk5ERASbN29m06ZNANIAEvaCyWQiLCyMsLAwWXK8qakJvV4vIwruHD9l38LDwykqKpL75tGjR/H392fnzp0yXK5MwtLpdJLCosyRsdvt9PX1ceLECU6fPs3c3Bxbt25l5cqVTpGhxU6IFbZZYGAgOTk57Ny5U1I3U1JSCA4OxsfHB6PRyNjYGL29vbS3t9PW1kZPTw9dXV0cPHiQ5uZm7HY7b775ptPl+mlgwXZiV8t627ZtxMbGUlFRQX19PQMDA5I7FhoaSlpaGnl5eQQGBlJfX8/x48elNENiYqLkJC40CfhxISaz8oAQh4vyPU8CrVYruWkCvr6++Pn5OUlqeKLBMJ/xqvRweMq4LSSUWahBQUFs3ryZ1NRU4uPj+fu//3tu3LiBWq2mrKxMHlwLyZ9UrsHS0lJ++MMfcujQIXkgHj9+nAsXLkgNPz8/P/R6PSEhIezZs4ecnBxu3LjBkSNHsFqtBAYGkpGRQWJiosfoD34diHESElKXL1/mk08+keVzwZGUtmvXLtasWeMUpnQnjEYjDQ0NXLp0iSNHjlBXV0dJSQlarZaNGzc6aUKLg0+su/nWX0FBAf/L//K/kJGRwX/9r/9VhqZNJhO1tbXcv3+fwsLCh9JonhaUl/WioiL+w3/4DwwMDBASEkJmZqaTXvbDMDMzI/mCJSUlvPDCCwQFBaHT6Zw0jd2dsa4cC9d9XHCUwRGuPX36NHfu3JF7SmRkJPv27ePVV1+VnnJPi8y5JhyGhobi7+/PwMAALS0tsgzufFDuV6IwyJ07d2SZZndH75RzJy4uji1bttDY2EhdXR0nTpxgcHCQxsZGvvOd7xAaGkpdXZ0scRwfH09mZqaT8g44EtPOnj3LJ598Ql9fHykpKZSWlsoiTGKMF5uWpXzOGRkZ/OAHP2BychK9Xi/Xlbh0zM7OMj09zejoKJ2dndTW1nL9+nWuXLnCvXv3OHjwIGFhYezYsUPSJp5GueYFW8mi8+Lm4e/vz6pVqygsLKSpqYne3l6sVqtMHFmzZg1+fn7U19fz+9//njNnzgAOvcOYmBhptbtz8rpKgBmNRnkIisQtZfhnPoPuUZ/Z399Pc3OzLDUbGhpKXl4eCQkJThJHnmC8unqEBJdTidbWVsbHxwkJCUGv13vUJusKpTTI111USq6Q8K6IzOHbt29LD5dSp3MhvepCH3Bubo6oqCi+853vkJ6ezsWLF6mpqXEqhBEYGEhiYiLR0dEkJyeza9cuIiIi5DoFB12guLiY1NRUpz57wnx8Uih1Nz/66CNOnjzpNBa5ubm8/fbbFBcXy9+5o2qRcpOfmZmhpqaGgwcPcu7cOcAhYxYWFobFYmHNmjWPnZw0NTVFQEAAYWFhvPzyyzQ2NvLBBx/Q2dmJzWZjYGCAEydOEBUVxZYtWxY01K5cb1FRUdLbq4TZbJaeOmVmuq+vL1VVVTIpJjw8nDVr1vDqq68SGxuL1Wp18lq523hVJq2I/Ub8FGM3ODjI0aNHuXDhgrxQBQYGSmk+ESXyxIic8nwyGo1MTU3Jc7C7u9uJLmg0GmUyocjrEH8/OTmJ0WjEYrF84335aUFcDlUqFfHx8ezYsYOmpiba29tlxKKpqYnx8XHS09Od5PYCAwNlxTdRprmvr4/jx4/z3nvvUVVVRVhYGK+99hrFxcUPJNAuNpT7h5+fn9Oe/yisWrWK9evXo9fraWhoYHBwkIaGBqqqqmSeBHi48Sq/wGWzEFmyGRkZMmQnvD7gEJq+desW4OC/pqamEhcXJ41Xd3s/BIQhOZ+24pN8hpJs39nZycWLF2lvbyc0NJTdu3fzwgsvOE0cT+m/0mjx8fEhLS2NxMREurq6ZBtv3rzJxx9/zEsvvSQXriclFyjxtI0w5cIMDg6WlJfFFpwWm75YS0lJSWzatInGxkYp8yaoO2lpaURFRaFWqwkODqa7u9vpAhYSEkJRUdFjVY7zVLiut8OHD3P69Gna29ulUeDr6+tUqtPdpVMFxPwU4XFwGDq//vWvmZmZISIigoKCAjnmj5rPgsMLDvmtH/zgB5jNZv7xH/8Rm83G9PQ0ly5dIjk5mbVr18rLs7sglB/EfqvValGpVIyNjdHQ0CDpHtnZ2eTk5MhDUlSn8kQoHTwiGdRisfD+++/zz//8z9JrBw6jYPfu3U6GvTsuU08CnU6HXq93OieUa8g1EVm8b2ZmRuplj46OotPp5Di6e68R576Pjw/5+fns378fq9UqKUeDg4P85je/ISAgQEYCwGG4379/H6PRSEtLCxUVFVRWVnLp0iWp5LJ//35+9KMfkZaW5sQ5XQpQ7qtms5nOzk6pLx4REUF0dLRTtO5pnLULvhuLRgo+lpjA4qBwzUIbHByUnNjQ0FBKS0tZvXq1JAy78wamXHihoaFs2rTJSfTbZrNx5coVUlNTiYmJmZeE7wpXrbeOjg7AEbYUWn/idU+4fQoo26LRaIiIiPj/s/ff4W1e+YEv/gEIorD33jtFUiRVKFK9y1Xu62TGjj0zSWYWm3o3d39z9+7uTXKf3Cd3b7JJNlnM7Mxmmj0uY3ts2ZZl9V6oSlGUSIqkRIm99waA4O8P6By9gChZskjiBY3P8+ihSADkOe9p3/OtREZGutRT7+3tpaWlxSVwyxO5Tu/HXCVRVmptRQCY1WqVc7azs1NWmAsICHA5dBZCa6kUzLVaLYmJiSQmJspo7Pu1o6+vzyVjRHh4OAUFBSQlJcmLl7dU1RKI+edwODh58iQffPCBDEqzWq0YjUZefvllnnnmGTm/PZkpQ7neQ0NDKSkpYevWrdjtdtra2qRmvKenR0b1wt1AHqGtFIigIaW5WaNxVrKKioqSWkoRNf1Vqf7mGpGfVaxL9/SB7nvH9PS0LJur1+spKCggJydHame9oXiGuyKgpqaGqqoq+XpISAirV69mx44dhIWFMTU1hV6v9/hlyh13jXJGRgZr166lurqalpYWpqenqa6uprKykrKyMinMiET94pw7deoUX3zxhcwJOzU1JTWwnsZ9rJYvX47JZKK0tJRLly7JQFdlWjC73c61a9f4yU9+gslk4vbt29TX18sI/piYGF599VV+7/d+jyVLlgB3c76rhampKWldFmtLCKyiit/g4CC1tbUcPHiQ48ePMzo6ikajobi4mDVr1sgCP3OV9mvBZv/90gwpI2lF1RSDwcDk5CRhYWGsWbOGVatWyUpVD0pXNN8oD4awsDB27tzJ8PCwvPkPDg7yzjvvoNVq+d73vkdISIj0g53tluw+iCMjI3KBxsXFyRKlgKxzrKYJLQ4Ym80ma68rNXXBwcFERka6BEOpqf3Km61SAH0YLawyqbT7hcLPz08e+n19fZw8eZKjR48CTj9DZeWfhZ7LynYrx0XpE6nRaJicnJS+ZgK9Xu+SCk5ZGUntiLnq7+/P5OQklZWVfPrpp5w9e9blUFy9ejXf//73Wb16tYuA7kmEAKbT6ViyZAmvvvoqGo2G9957T74nLCyMwcFBJicnZSniBwU4KV2QBgcHOXHiBNXV1S4Hc3x8PCkpKbL/C5WW6KtcFJRaHiHsiiIoCQkJJCQkyKpHnh67h0G5f1y5ckW6jYHTjaK8vJyNGzfKuA+leV2NiPMuKCiIbdu20dPTQ39/P2fPnuXDDz+UF6qysjIAF+vluXPn+PWvf83evXulAGgymYiJiSE2NnbBL/7uuP/NkJAQmQ2jvr6e/fv3ExwczLlz51xcXbq6uvjkk0/k53Q6HYGBgeTm5vKd73yH1157jbCwsDmNxp8LxDwzGAwPXJdDQ0NUVVXx7rvv8tlnn8nqfwUFBVRUVFBYWOjiXulVwqsSsXEqb1yTk5McPXrUxU8kLy+PJUuWSA2mska0JxELLSIigk2bNtHS0sKuXbtoaGjg4sWLpKWlsXHjRkpLS12KGCgRKYZMJhOjo6McO3aMvXv3utStVpbOVcOt0x3RppaWFq5cueISqa3X6zGZTJhMpllNQ55GGT09OjpKTU0Ng4ODREVFkZycTHh4uIs/mjA9inQfIkWWSJ3ijkbjrMR0+vRpPvroI7q6uli1ahV/8Ad/4JKoeiHT9oiLlLgwKg9Bh8MhNbH+/v709vZSU1PjEmAxOTmpynn4MCh9rOrq6vj5z3/O3r17XQT2devW8frrr0s/V/ecy57C/TJVWFjIwMAAZ8+epaGhAZ1OR3NzM7/61a84efIkBQUFlJeXP7AohsPh4NSpUzK37bVr11zWryjCoozwVuPYuwejKYPL1NheJUoT9MTEBIcPH+a9997j0qVL8j2FhYW8/vrrskofqN9dQHlGGwwGnn/+eTo7O7l69So9PT0cOHCAiIgI+vv7ycnJITw8nJ6eHo4cOcKnn37KiRMn6OrqYmZmhtDQUCoqKnjyyScpKSmRqabmOuH9o+Kews5oNJKXl0dISAg7duzg6tWr/OQnP+Hw4cMun4uKimLFihWkpaWRnZ3NihUrWLZsmTzr1XQpmZ6eloFyDzqnLly4wLFjxzhx4gSnTp2SZ0Z+fj6vvPIK69atk3NW9UUKvgqxGSt9Xrq7u/nyyy/Zv38/k5OTFBUVsWrVKpd8hWq5jShv85mZmTz11FO0tbVJ82N3dze3b98mPz8fo9EoXSZEn4W2QAg9zc3N/OY3v+Hzzz/HarVSXl4uA9gEnq5ZLRAHgpiMfX19HD161KWcJjhNsB0dHQwMDLhoY9VyoCjbcenSJT7++GPGx8fZvHkzhYWFLq4cGs2Dc3sKYVar1TI+Ps7IyAj9/f1UVVXx8ccfc/XqVdLS0njzzTd59tln0Wg0UlO0kHP6Qf0QrhPi4GltbeXEiRM0NTXJ93iius3jIsxvIt1MTU0Nb731Frt27WJwcFCawBITE3n55Zd57rnnZJJxtWjtlL6Roi+FhYW8/PLLDA0NcebMGU6fPs2lS5dISEhg5cqVdHR0sHTpUln1R6zBgIAAHA4HV69eZe/evZw4cYLGxkYX1x5wCiBRUVFER0e7uBeoDZFjWLRNTa5VD4NQYoiL7ttvvy33nOjoaMrKyti6dSuhoaHy4ql24VUZYBgQEEB4eDhPPfUUFy9e5KOPPuLGjRv85je/4datWyxbtoyEhAQ6Ojr48MMPpfIqOjqa5ORkiouLZcJ7IeDNRbDP46K8UCoze4j4lKKiIurr6zl9+rRUSOn1elatWsXGjRuJjIwkOztb+tVbrdavPGcWCnFh9/PzkzLI9PQ0IyMjMg+9n58fNpuN+vp6Pv30U37729/Ky29iYiK5ubls27aNnTt3kpWVdY+wPxd4dHdWChA3btzg8uXLMm9qVlYW+fn5LrlC1aB1FYi26/V6kpKSWL58OVeuXKGmpoaenh6ampoYGBggPj5e3qxnS648Pj5OdXU1VVVVjI2NERERwfbt2+WGJTYytRykQmMubmI1NTXs2rWLpqYm6ScnzD3CT+brBrTNB8rnOTMzQ1NTE1988QUffPABoaGhFBYWPpJpamZmhurqavr7+9Hr9TQ3N3P27FnpStLf309ubi7PP/88W7Zskb9Pbb6i7sGDDQ0NnDt3jpaWFrRaLaGhoaSlpbmkelFT+++HsojCyMgIb731Fj/96U+lX73D4SAmJoZt27axZs0amTVDJCRXEyLYz2q1Eh4ezhtvvIFGo6G5uZnOzk4mJydpbW2lt7eXCxcuEBISIitQiecg3K9GRkYYGBhgeHjYJbBEkJ2dzYsvvsiWLVswGo2qEBhmQym8Kv11wfN7zVehFFa6urro7e2VbQ4JCaGsrIzly5e75Mf0hjUnEEF+Go2GvLw8/uiP/gitVstvfvMbbty4wcjICOfPn8fPz08WIgBnZP7q1avZuXMn5eXlJCUlucSPqOUsdMd9fWRkZFBUVMT58+ddrM23bt2ioaGB1tZW9Ho9RUVFMgDR0yiD0JX9aWhooLa2lrGxMex2O/39/Vy/fp26ujqamprk2KWkpPDSSy/x1FNPUVRURGRkpDxT5nr/8JjbgHLhdnZ2UldX51I/XeQ8VcOAzoZygxTuA4ODg3R3d1NfX8++ffuIiYlh8+bNJCQkuGhRxeLr7u7m6NGjfPbZZzIyr6CggFWrVpGdnQ3cdWZXE2IS2mw2qqurZRk/QAquFRUVbNmyhdLSUlVdQJR+rVarlba2NqqqqmQ9bVGBKi8vj8TERAICArDZbPT398sqWgMDA7S3tzM5OUlPTw9VVVV0dXWh1+sZHByUVWHi4+PZuHEjTz/9NGvXrpWBTuD55+COUuva29vL7du35YZkMBjIysoiOzvbxZVFrWvTHXFZbG5u5syZM1JwFYfrxo0befPNN2XFI1CvaVa4soCzjc888ww9PT2cPHmS3t5eOY9FidWHxd/fn+DgYEJDQ2VxgG3btsm1K4RfNaAUSkdHR+nq6mJiYgI/Pz9ZXlYkURfvV9tcFTldNRoNDQ0NfPjhhy7uAuHh4VRUVFBUVOTiEqF2gVyJeP6iqtm6detkAOjp06dpaWlx8e8NCwsjLy+PsrIytmzZwurVq2WWGrjrNqi2sRTrQhQyEeb25cuX89prr6HVaqmsrMRms1FbW8v4+Dg6nQ6bzUZbWxsZGRku8oGn5quyKAI45bIrV67Q2NhIX18fvb29dHZ2Mjw8TFdXF1euXJFpFGNiYigvL2f9+vVs2LCB5cuX36OZnms8KryK//f399PV1eWSAkYEVygPebVNWrhbzzcyMhKr1cqVK1fYtWsXR48eZXx8nLa2Nl599VWZXmhwcJChoSEGBwc5f/4877zzjsxpm5eXx9q1a118ItWG8DVyOBw0NDRw6dIll+TuACtWrOCHP/yhrASjnLiePgCVc0ikiBKBEABffvklR48eJTU1lVWrVhEfH09fX580n0dGRtLa2kplZSUTExMYDAbpFgLOILX4+HjS0tJYt24dO3fupKCgQLqLqHEOC0Taq4GBAUZHR+VBaTAYWLJkCUuWLFFl5ovZcHdvaW5u5tChQzLCF5ybalZWFk8//bSs+uMJd45HRZm2KjY2lh/+8IcyyreyspL6+nra29vp7e2dVavqjl6vJyMjg5ycHJYsWcKGDRsoKytzyd2sJmFe6YcsNEB9fX2YTCb6+/vp7OyUFi9Qp/AqhLDBwUEOHjzIz372M1kkApxjkpKSQnJystw7PJ2k/+si9gxRoCU0NJTMzEw++eQTamtrCQwMpKCggLy8PFasWMETTzwhlTdKwUetGleB0j0pODiYJUuWEBkZKVN8NTc3ExkZSVJSEikpKeTn55OXl+eiEBC/xxMoL0adnZ188skn/PSnP+XatWskJycTGBgoA9OVuZbj4+N5/vnn+da3vkVpaek97mXztZeqYjYIs5bosHB+LioqIiAgQN761bhwlTfBoqIifvCDHxAZGcm+ffs4ffo0t27doqamhldeeYXy8nKuXLnCBx98wI0bN+jt7ZW3baPRSHl5OZs2bXIpG6jGBSsm+fDwsExnAs5a6EuXLpV+PaKEqPiMGsZP2QZ/f3+ysrJ46aWXMBgMVFZW0tPTg91up729nffff5/x8XF5uzSZTDIIRtR8Tk5OlhkErFYrcXFxFBYWkpycTEZGhkt1H7WiHJvp6WlGR0eZnJyUm5PRaGTt2rWsXbsWg8Egg93Upj1W4u7ecuTIEX75y19y69Yt/P39sdlsREZGsnnzZpYtWyY/9zCZJtSCMj92fn4+QUFBrFixgs7OTlnBcHBwkIsXL9LT00Nqaiomk4nW1lbsdjvR0dGEhYURExNDamoqSUlJxMfHk5mZ6ZLGSG0uLkrhdWJigr6+PvkzEU+gVg2lWDsix/mpU6f4/PPP5eVYzM3x8XEXn0O1um08LMJsHhwczMqVK4mIiGD58uV0d3cTEBBAbGws4eHhxMXFyZzYcHf+qXmvcUd50YuNjeX5558nOztbprOLjo6W/uRRUVELGrQ7G2JuCffGqqoqvvjiC3bv3i3lExHPI9Bqtaxbt47y8nKWLFlCcXGxzCgASHfB+dw7PJptQJjARD5QYVIoLCykrKxMpuWx2Wyq8QlxRxlMERgYyBNPPEFSUhLR0dEcOXKEW7du8fHHH1NXV8emTZvo7u7m008/lbXjw8LCZOWibdu2UVxcLP171CLwuSP8YSIjI8nPz6euro7ly5fzp3/6p2zcuFG+b3JyUrp+qK0fYsGGh4ezfft2kpKSKCkpobOzE5PJRFdXF6dPn6a+vl4KqRERERiNRmJjY0lKSpIbUWlpKQkJCUxNTckABYHIUuCekFtNKOeZ3W5ndHSUqakpadqKjo6muLhYmpAnJyfv0RaoCTE/DQYDVquVs2fPsmvXLi5fvuzyvszMTJ544gnS09PlM1Cbn+v90Gg0UrARWv/k5GSSk5MBp1Cn1+sZGRlh//79NDc3U1RURFBQEHV1ddjtdnJzc0lPTycmJsblogmuqcXUhvsFVAS+6vV6EhISXLSu7u9XE01NTezdu5cLFy7I+SfOuuLiYqKjo12yYXgzyjPA39+fvLw8FzcdJcLvUqQc9DaU5nKNRkN6evpDV6jyBEqXoPHxcS5cuMAXX3zholgTVvCgoCDp3rF9+3aeeOIJuefAXTfHhVC6eUR4VUazTU9PU1NTw9GjR+nt7SU1NZV169a5aKzU6OfijrJ9ubm5vPbaa6xdu5ba2loOHz7MiRMnOH/+PMnJyRQVFQHOzba0tJTy8nLS09NJTEx0yQGq1j6LdiUlJfHHf/zHvPLKK4SHh5Obm3vP+9QqgCsFSb1eT2FhIYmJiVJom5iY4JVXXmF4eBidTkdQUJC8QAUEBBASEoJOp8Pf398loGI2lJc1NaLU6kxOTtLU1ERLSwsGg4GJiQkSExNdfLLUKoQLpqampEBz7do1/uEf/oFPP/0UuJs0XKPRUFhYyIoVK2Q1HG8SXgXKQhtKxHiFhYWxdetWxsbGCAsLQ6fTkZOTI7Vg7kKr8veqdc66V6/LysoiJSWFuLg4lwpwogCMGvrgHnjb2trKoUOHOHPmjCyZKgTVF198ke9+97supYm9SfP4IIRA96AxEQFDap1/D4syFZqaUT5jnU5HTEwMhYWFjI+P09XVRVhYGCtWrGD16tXk5uYSHh5OUFAQMTExLnm/ldaPhTgjPKp5Ff8fGBiQjtuRkZEkJia63LjUPvjgWsAAnNrjwsJC1qxZQ2lpKWlpaZw/f56CggKefvppgoODmZycJDs7W1bVEKh90Yq2mUwm8vPzyc/Pl6+Nj4/LBOnecGsWVXh0Ot09NdUf9rYsUmUJYV2Znskb5q4wzYEz9dlHH33E7t27AeetW5k8G9S/HpUb5/nz5zl8+DAOhwOj0cjMzAwGg4HNmzfz1FNPSX9n4bvsbSgFbjEPxc+V1gWlNUCZl1hUyhF7jjAfqnmMlW2LioqSmVlCQ0NdXEDUhHuqoLa2Ns6ePcvFixdxOByEhYURHx/P0qVL+c53vsO2bds83OL5QfTfvQKcsEypfe49Cu6FNJSCu4gdUYM7pPJ5BwQEsHLlSqKioigvL6exsRGDwUBFRQUbNmy4p2qoyA/u5+e34LECHs3zCnfLVaalpVFTU8P4+Pg9OQe9CffkyeHh4WzYsIFly5bJAJ+goCDphD+bWc7Tk/lxUHOGiNmYi83D3bzqbaY+pdA2Pj7uUlNd+EEqNXRq7ZfS9O9wOGhqauLGjRtS8Lbb7QQFBVFRUcEPfvADNm/e7PI5bz80lZq9h7V6KNPGie/Vzmz7a0VFhbSKCNSsRTeZTAQFBREQEIDVauXJJ5/kueeeo6ioyMXnc7EiShQr5503zL2vw2z5W5V5iT2NuxtOamoqiYmJrFq1yiXeYzZllNhvPdEPjwivSgFPq9WSnp5OTk6OjLYMDQ11Sa/kTSh9B4Xfjr+/PxEREff9jKjb7C2aOoG4dYkUJkaj0avaL3AfM6G1EmlPxE1ZbLDu37s7pathQ3oUlId8VFQU3/ve96ipqSEmJoaMjAyWLFniopX2hjEWwqhOp5Mlbo1GIzt27ODf/Jt/w8aNG6Um9n7lm70NdwFA6XsncviKuS3MmWo6RL8OGo0Go9Hook1WVs9TC+5jk5SUxHPPPSfT561cuZJVq1bJc09ZkGIxspiFVXe8ra/iYuF++ZuenmZqakruHcIP1lN985jmVfn/xMREli9fTn9/P2lpaRQWFrqYudTuYzcb7puOODBmSzGkZg3Bg/iq+unehvuYeYPbw1ygnH/x8fH88Ic/fOD71Sq8KoMn/f39SU5OZsmSJeTm5lJfX8+mTZt488032bx5M3q9Xgo5i0FwfRDu1iC1jt/joNTgqVHgcz/gIyIi2LJlC1u2bHH5+cNU9PPhYyFQZuwQyoD7+ch7Ao9pXgVCeH322WcpLS0lODiYvLy8RSU4KP17hDZI4OkazT58CJQVtmZD7b7YAmXS8NWrV/Of//N/ZmBggPT0dJYvX+7iI+rD+1GmWlRbWq9HRVixfGeCD08h4jbEPyG4qu3S+8jCq9lsno92qIbF3j9Y/H1c7P0DXx+/DhcuXODDDz+c09/5OPjG0PtZ7P0DXx8XA4uxf77rnQ8fPnz48OHDhw+vQaPWSiQ+fPjw4cOHDx8+fLjj07z68OHDhw8fPnz48Bp8wqsPHz58+PDhw4cPr8EnvPrw4cOHDx8+fPjwGjyWEM9sNmuA7wB/CBQAfkA98HPgf1gsFu+r1TgLZrM5Cfhr4AkgEugAPgH+ymKxDHiwaY+N2Wx+GdgAlADFQDDwa4vF8pon2zWXmM3mZuB+JW+6LBZL3AI2Z85Z7OvQbDa/ibMvD8JhsVjUlQfmETGbzf8vsALIAaKACeAWzr3mXywWS5/nWjc/mM3m14Ff3fn2DywWy//yZHsel8W+FpWYzeZ1wJ8Bq4EIoB+4AvyjxWL5woNNmxMWc//UciZ6MpvzL4HXgW7gfWAM2Ar8E7DebDa/YrFYvDqazGw2ZwKngBhgF1AHlAF/CjxhNpvXePmh8p9wCq2jQCuQ59nmzBtDwD/O8vPRBW7HfLDY12EV8Ff3eW0dsBnYs2CtmT/+HLgI7Mc5loFAOfCXwB+azeZyi8XS4rnmzS1mszkZ+GecazDIw82ZKxb7WgTAbDb/J+D/BnqBz3EqdKKAUmAj4O3C3aLu3x08fiZ6RHg1m83P41ykN4Eyi8XSe+fn/sBvgJeAN4BfeKJ9c4gFp+D6JxaL5Z/FD81m83/Dedj8DfADD7VtLvhznEJrI04N7GHPNmfeGLRYLH/p6UbMNd+EdWixWKpwCrD3YDabT9/5708Wqj3zSIjFYpl0/6HZbP4b4D8C/wewKJI93tFQ/hzoA34L/IVnW/T4fBPWIoDZbH4Fp2B3AHjRYrGMuL3u1aXFFnv/FHj8TPSUz+uLd77+vVikABaLxQb85zvf/vGCt2oOMZvNGcB2oBn4H24v/184b9Wvm83mwAVu2pxhsVgOWyyWhsWgDfiGsujX4f0wm82FODWTbcBuDzfnsZlNcL3Db+58zV6otiwAf4JTY/4dnPvoYmDRr0Wz2awF/l9gHPiWu2AHsr9eyWLvn9rwlNuA8Im4Mctr4mfLzGZzmMViGVyYJs05m+983WexWFzqUFoslhGz2XwSp3BbDhxc6Mb5eCQMZrP5NSAF52FZDRxbBD5o34R1eD++f+frvy6CcXwQz975Wu3RVswRZrM5H/hb4J8sFssxs9m8+as+4yV8E9biaiAd+BAYMJvNTwOFwCRw1mKxnH7Qh72Axd4/JR4/Ez0lvIqbZfosr2Uo/p8HnJn/5swLuXe+Xr/P6w04hdccfMKr2okD3nL72U2z2fwdi8Vy1BMNmiO+CevwHsxmswl4DXAAXh3k447ZbP4LnD6goTgDuNbiPFj+1pPtmgvMZrMO5zq8jdMVYjHxTViLK+987cLpn12kfNFsNh8DXrZYLD0L3bA5YrH3T4nHz0RPuQ18fufr/2Y2myPED+9sTsrgivAFbdXcEnrn69B9Xhc/D5v/pvh4DH4ObMG5WANxbkj/E0gD9pjN5mLPNe2x+Sasw9n4NzjX3Z7FFMR0h7/A6Zb0ZzgF1y+B7YvkwPwvOINe3rRYLBOebswc801YizF3vv4AMOEMRgvGqZ3cC6wHPvBM0+aExd4/gSrORE9pXt/Dqfl4ErhmNps/xeknshXIxKmVzAYWszlPc+erz19UxVgsFvdI9RrgB2azeRT49zijuV9Y6HbNEd/UdfiHd77+T4+2Yh4QaWrMZnMsTjPm3wKXzGbzMxaL5aJHG/cYmM3mMpza1r9fZOZXwTdhLYp0dBqcGsjLd76/ajabX8BppdxgNpsrvHSMF3v/APWciR7RvN7xAd2JU0vQiTPK8rs4I9fX4owiBWfKEG9FaFZD7/N6iNv7fHgXP77zdb1HW/EYfEPWoQtms3kJTqGulcWRsmZWLBZLl8Vi+Rina1Ikd/Oheh0Kd4Hr3A1eWlR8Q9aiyGt+QyHYAXBHk773zrdlC9qquWOx9++rWNAz0WN5Xi0Wix34+zv/JHf80UpwJtm+uvAtmzPq73zNuc/rIvr3fj6xPtSNOES8NlsEfCPWoTvflEAtACwWyy2z2XwNKDGbzVHKSHYvIoi7++ik2Txrxq+fms3mn+IM5PqzhWrYXPINWIviTBy8z+tC+DPNf1PmhcXev69iQc9ENZaHfR0wAr/x8rQSIufp9jspNCRmszkYWINzM/JW5/tvOhV3vs4WHbwYWCzrUGI2m404++UA/tXDzVlIEu589VZhfQrneM3279Kd95y4873XmmMfwGJZi8cAO5BtNpv1s7xeeOdr84K1aG5Z7P37Khb0TPRkedgQi8Uy7PazlTh9tEZxllT1WiwWS5PZbN6H02z373BWgxH8Fc7byf+0WCyLJU/hosNsNhcAHRaLpd/t56nAv9z59u0Fb9gcstjXoRuv4Ax4+XwxBWqZzeY8nEnDO91+rsWZMD0GOOWt5ajvmFx/f7bXzGbzX+IM4vrlIigPu6jXosVi6TWbze8D38YZfPefxGtms3kbsAOnG92Xnmnh47HY+wfqOhM9WR52v9lsnsDp7DuCs5bzUzhv2S9aLJbFoNEy4ywP+9/NZvMWoBZYBWzC6S7wf3qwbY/Nnaowz9/5VuQprDCbzb+48/9ei8XizdVvXgF+aDabD+OsfDOCM3jiaZyakC+Av/Nc8+aEb8I6FIhArcVQUUvJE8D/dycVTxNO/8hYnFXvMnD6UP6B55rn4yH5JqzF/w3nGfh/ms3m9cBZIBVngM808AdenMcWFn//VHMmetJt4EOcaSRewzngRThzLhZYLJa9D/qgt2CxWJpw5lr8Bc4J/e9xDvR/ByosFkvf/T/tFZTgLFn4Bs5bJTgPS/Gzlz3TrDnjMPAxztyL38I5TzfgNFG+ATxjsVisnmvenLDo1yHI5PZrWZyBWgdwCuSROCs1/e84y4n247TyFFgslmuea56Ph2TRr0WLxdKN8yz8ByCZu9XSdgPrLBaLV6eSWuz9Q0VnomZmxpepyYcPHz58+PDhw4d3oMaALR8+fPjw4cOHDx8+ZsUnvPrw4cOHDx8+fPjwGnzCqw8fPnz48OHDhw+vwSe8+vDhw4cPHz58+PAafMKrDx8+fPjw4cOHD6/BJ7z68OHDhw8fPnz48BoeukiB2Wz2+pxaFotFc7/XFnv/YPH3cTH0DxZ/H33zdHH3DxZ/HxdD/2Dx99E3Txdv/3yaVx8+fPjw4cOHDx9ewyOXh7VYLPPRjnnFbDY/9HsXe/9g8ffRG/sHi7+PvnnqymLvHyz+Pnpj/2Dx99E3T11ZjP17ZOHVx+Nht9ux2+34+fmh0+nQaDS4VznTaB5o6fDhw8dD4nA4mJqawmaz4efnh9FoxM/Pz9PN8uHDhw+vxuFwMD09jcPhwOFwMDMzI/8BUraZmZlBq9ViMBjQ6eZO5PSY8OpwOADQar9Zngs6ne6eAXQXVpWD78OzzMzM4HA40Gg08t9iQGw2X8VsffamZ6DVajGZTJhMJvmzmZkZr+qDDyfKw1Gj0Xjt2eF+yM+Gct15+1x1F2i8vT/fZMS5odVq5b+v+/nHnQceEV5nZmaw2WwA+Pv7e+0mNF/4hFf14HA4sFqtaLVa/P39F8WYiD7Z7XZ5mCgPUuX3SgsBeNdl0+FwzNpe3/ryTsS8BacSwJvmojs2mw273Q4w6/rz8/OT/7yd6elppqen5YXj6wg9PtSBzWbD4XDg7+//tbSoYt7r9Xr8/f0fqy0LLryKA8VgMCz0n14w3DU74nuHw0FHRwe9vb2EhYURHx+PVqvFZrPJDUz5bJQTxbfYFxahLfDz83PR2n0V09PTUihU6wGr1WoxGo1f+/NK7ZcaBUCxx2i1WkZHR6mvr6e9vZ2goCDy8/OJi4tzeZ83IUx0SjPd/XAXFtQ4Vg+DmGuzrUVv0qILYVWn02EwGBb1GQh3x2Y2a+Nc/g1hGVuItSwUb9PT0/Jy8ahry13zPjMzw/T0tMtadlcaeJLp6WlsNht6vd5lzo6OjjIwMMDo6Kh0hxQuWjMzM+h0OnnhNJlMZGRkEBsbK3+H6PPXnRsLLryqYTDmm9nMrHa7nebmZj766CMqKytZtmwZ27ZtQ6fTMTIygk6nw2azYTAYyMjIIC4uDj8/P6/anBcbX+e5i01UrYLdXKB2c+bU1JQUcpqbm/l//p//h9/+9rekpaXx93//97z44ov3vM9bUB7Ss/mXKbV4SuFVrWP1MHhz25Us5j1hNkRf5/OS6An3Efd19ahj6r5/uguyyjNEDczWnq6uLi5dusT58+dpbGxkYGCAkZERenp6GBgYYHp6mqCgIKanpxkYGCAlJQWz2cx3v/tdqXH1GrcBu90ubxJtbW1UVlZiNBopLS0lLi7uno4IU4NWq8XPz081A3k/ZrsBTk5OYjAY0Gg0dHZ28vbbb/PLX/6Szs5Obt68ybVr19BqtdIsLQ7T5cuX88wzz5Cbm4ter5e/X+3PYDEgbsHiNlhdXc358+eJiIhg2bJlJCYmSlOeckyUm44atXnKA6Szs5OjR4/S0NCAv78/er1e3oLFhclqtaLT6UhNTSU1NRWTyYS/vz9hYWFERUXJDUiN81L40wNMTExw/fp1wCnINjY2qlprPBtiTioPTDXOsblG7KlivTU0NFBZWcnExAQ5OTmUlpYSEhICIDVhasRms+Hv7y/b19HRwcmTJ2loaECj0WAymaRZ3d/fH5PJRFRUFKmpqcTFxREUFCTNtGrtoxJx1gOMj49z6NAhbt++TUJCAmlpaURFRREVFfVY1h9hfXCXDeZzPxL9ms3cPTk5KcdwtrYozwchEwgetJ49ub86HA5p4hdyyJdffklNTQ39/f00Nzdz7do1mpubGRoaeuDvGhwc5Ne//jWtra2EhIRQVlbGhg0bgK+vgV0w4XVycpKgoCDAuQn94z/+I9HR0fzwhz8kPj7+nkHyNn8fYdZSouxTW1sb7777Ls3NzYDzGVRVVc36u6qqqpiamuL1118nOTlZmirEBFIjDxv8o3ZEdLpYSIcPH+Zv/uZvyM3N5S/+4i+Ijo6W2jqlkCv6Zrfb5SamJrcBu90ufXYbGxv527/9Wzn/dDqdNGm6U1hYSHl5OWFhYeh0OhITE1mzZg2lpaWAOv1Hlc/cz8+P6OhoAEwmE9euXePgwYOsWbNGjqPa3QfEXILZ3QZmy1giPicu/2ru3/0QlygxTufPn+ev//qvaW9v5/nnn+f73/8+FRUV82aSniuU7Wtra2PPnj386Ec/4uLFi7O+39/fn/z8fNatW0d5eTlFRUWkp6d7jZuBuEhoNBomJyc5evQou3btIiYmhnXr1rFu3TpWrVolhddHWX9KIXBkZITx8XECAwMJCgqad22lTqeTc1IIzmKPf1RBXHnBFlH7anMb0Gq1TE9Py+9PnDjBf/2v/5XDhw/f8z5xOZtN0SjOlosXL3L8+HEAfv/3f5+VK1cSEBAg9zPVCq/KgWltbaWxsRF/f3+5IJUTWE0H4VchJp64jU1MTDA+Po5er3fph9VqZWBgQH6vnBTu3Lhxg1/96ldcuHCBjRs3sn79enJycuavE18ToRESfp7ufVKaN4Wfl9oPUXez7K1bt+jp6UGj0dDR0YHNZrvH1Cw2a5vNxueff87+/fuJi4tj+/btlJaWzjrHPYnNZqOnp0d+fz/BFaCmpobe3l6CgoKYmJggODiYW7du4efnx5IlS1QvOIiLhMlkQqfT8emnn2K1WsnIyCAjIwNwrs3H0QLNJ8o5MzIyQm1tLfX19TQ1NXH79m0mJyfR6/XygqvVauWeFB0dTUFBAUVFRWRkZBAVFaXqC7A77j69Y2NjtLa2MjExwalTp8jJySE8PJyCggKpOFDLGoO7GleNRoPVamXPnj3s3buXCxcuUFNT88DPVVdX097ezu3btxkfHyc2NlZqmUWqRbWek8p2+fv7ExkZydTUFGfPnsXPz4/MzEyXPedhFB9wN1JdjHVnZyfnzp3D4XBQUFDA0qVLHzsIaDYmJyfl/tDS0sI///M/U1dXR0lJCatXr6a4uJiEhISH/n09PT00NDRI61ZTUxMXLlxgcHAQm82GTqdjzZo17Ny5k4SEBJd0UwuBcg1NT09z5swZjh07xp49ezh58uQ97xdWkCVLlpCUlITJZHKxmHR1dVFZWcnBgwcZGxsDnBbNAwcOsGXLFgIDA79WO+f95BEPIjg4GHCaTM6cOUNPTw+rVq0iKioKwCX3KTg36rGxMQwGgzSbqAnlhBJBVzdu3ODKlSu0trYSFBREdna2vGEGBgaSk5MjBSGDweBy+9JqtS6BCfX19dTX19Pd3c3SpUtd3r9Qjulw70VC3JJEO4UzvrdoBR6EGE+j0YjdbufixYt0dHTg7+9PbGysvHkLxJiJHKJ+fn5UVlbyox/9iJiYGMLDw8nLy3NxUPfUwao0k4eFhbFy5Ura2toACAoKknNPaABEkODExASdnZ0uv2tkZAQ/Pz9ee+01CgsLAVdToadRPmOj0UhsbCzR0dHcvn0bgHPnzjE+Pi7f8yDh3VMoNUx2u53W1lbOnTvHyZMnqa6upra29p5xmY2SkhIKCwtZt24dTz/9NImJiYC6xut+KDXOAGlpaaxbt44jR47Q09PDuXPnKC4uZsmSJTIgVk3Cq3KvqKur4+233+ajjz6Sl3mTyYTBYJABu0FBQURFRcmLcm9vL/v27UOr1ZKcnMzWrVu9IuOJUrA2GAysWrWKs2fP8sknn9DV1SX7+6goLzNTU1NcunSJd955h56eHl588UWys7Px9/d/aGH4YVGe01arlatXr7Jnzx727dvHli1beOKJJ1i9ejVRUVEuedzFZ8VZ6XA46O/v59q1a1y5coXx8XE0Gg2XL1/m9OnTLn+zra2N/Px8YmNjpSvXQqB0v5mamqKhoYGPPvqIn/70pwwNDWE0GomJiUGj0RAVFUVubi6FhYWsXLmSkpISYmNj7/mdY2NjxMXF0d7eLvesqakpzp8/T0ZGhjxDxPn4sHNj3ncv4TMBzsbt27ePzz//HLvdTnh4OAEBAcDdW5XYrC5cuMD58+dJSEigoqKC1NRUuSmpZYNSCiONjY28//777N27l/r6esLCwti+fTsGg4Hy8nIiIiJYuXIlzc3NdHd3Mzk5CdzV3Or1epeJLtDpdBiNxntU+PPN/UyRwKztXAwINwDho3zgwAHOnz+PVqslPj6esLAwlz67LzKtVivnb3d3t9yoBcpNcKFRtjsqKorNmzfT2dnJ2bNnGR0dlcn7RYYLuDcdkZgTbW1tvPPOO+Tl5blsPGoRhpTtiI2N5amnnsJgMLBnzx66urrQ6XRMTEzI96hhL3FHuHkA9PX18cEHH/Db3/6WmzdvMj4+7tL+B1FXV8fly5dpbGwkKSlJCq+enIsPi7vwmpmZyYYNG2hsbOTGjRsMDw/f42eoJsT4DQ8Pc+nSJWpqauSeGhAQQHh4ODExMUxMTKDRaCgvL2fbtm0A7N+/nw8++IDBwUEOHTpEQkICoaGhlJaWSsuPGv3NwXVf9PPzIywsTLodKXNmPypCsLHb7dTW1nLq1Cn27NkDQEpKClNTU8DDa3IfFqViJiEhgT/+4z9Gr9eza9cu9uzZQ0NDAx9++KGLa49oh1LBJVzSxsbGGB4eZmpqCofDwfDw8D1/c2BggFu3blFYWEhkZKSLcmu+UArd4+PjXLhwgS+++ILPP/+coaEhdDodS5cuJS0tjaSkJFavXi39zk0m0301qIGBgaxcuZJjx45x6tQppqam5Lp9HBli3k8b5UM/f/48n3zyCUNDQ+zYsYNnn31WTgzxPkF/fz8XL17k2rVrBAQEEBcXh8lkkk78njxwlNopgKamJj766CM+/PBDrl69Cjgn34kTJyguLiY3N5fY2FhWrlzJxYsXpcYLnJqh6elpufDA9TAdHBzk+PHjxMXFkZqaKv/+fPn3iIoZ4obvcDjkTVloF8VlZHJykv7+frq6urh16xZ9fX3A3c0rICCAmZkZJicniY2Nvce8oraNV5hsAHp7ezlz5gyNjY0A0q9KudjExiTmQVtbm/SX1Wg0jI2NqUarp3zW4eHh0udz6dKlHDt2jLq6OsB54AYHBzM8PCwvWIKQkBBmZmYYHh6mpaWF+vp6+vv7iYiIUJVlRDlGERERbNy4kdDQUOx2O7t27cJqtXLp0iVycnIIDQ2VJkE1CQNK4XVkZISTJ09y5swZl/cYjUaSk5OJjY2Vh93U1JTsj5+fH0NDQ8zMzHDp0iUOHDhARkYGOTk5XhMIqmxbYGAg4eHhco2GhIQQHBw8q++5mhDaLGF9DAwMZNWqVaxYsYKkpCTGx8fRarWUlJRQXl7OyMgI7e3tHDx4kMHBQUZGRmhubqalpYX8/HwXX201KhCUSp3p6Wnq6uq4du2ai8vVo16elPN0aGiIuro66uvr5esi/+98IJ6xzWYjICCAJ598kuDgYAICAjhw4ACNjY3ynPi6BAUFERAQwMjIiAz0Hh8fd9mD5zu+QKwhu93OhQsXeOedd/j444/p6uoCYM2aNezYsYOcnBySkpIoKiqSyke4m1JLnIuTk5PS6h4TE0N2djZxcXHcunWL6elpQkJCXD7/qP2aN+FVPGjxQLq6ujhz5gxNTU1UVFTwl3/5l5SVlUm/SRHxLIiJiSEsLExO/E2bNn3tiT8fiH7V1NTw4Ycf8tFHH7ksJnDmQWtubqavr4+srCyKiopYtWoVfX19NDY2MjU1dY9wI5zchQBUVVXF5OQkKSkpUngVwVvzMYlFcMfIyAgOh4PAwEAXs7egs7OTixcvcvnyZaqqqjh79izNzc1SmJuZmcFkMsnfU1ZWxg9/+EOeeOIJeQlRG+JAHxoaorq6mhs3bgBOgS4uLo7IyEgXTZAykf/4+DjXrl2jqakJu92OyWRSVUS7uxCwdOlSiouLWbt2LX5+flJ4jYuLY9myZVJbNDg4KD83MTEh++zv709zczNVVVWsXr36awVfLAQ6nY6kpCSCgoIYGhri6tWrXL58mY8//piUlBS2b98utSJKfzpP4669CggIkO0UazImJoa1a9dSXFzM9evXOXToEFNTUwQGBtLb2+sydjMzM5w/f56lS5eSkpIiDw01acxnQym02O12FyFlcnKSqakpl/mmpn1FPNvw8HCWLl3K2rVrmZqaIiIigt/5nd/hhRdeIDw8nImJCel+pdFoGB0dxd/f/x63MvegHrWitGCNjY1x5swZKisrgbsKjUe1Iirn6dDQEI2NjXR0dADONZ6ZmTlnKZjuh3KdFBUV8Qd/8AekpKSwf/9+rl696qKAelREtgJhURGKlAdZ+uYK5Z5ttVq5cOECH3zwgXTz0Gg0FBQUsG7dOp599llyc3Nn3TPcg+yVmXcMBgMpKSlkZGRIX/2JiYnHkuXmXXgVD7ynp4ehoSGys7NZt24dy5Ytk6+LG6Sy43FxccTExFBZWUlra6uc7ELz6gmEWl20s7q6mvfee4+PP/5YHv5KLc7ExAQtLS10dHSQmZlJSkoK3/72tykrK2Pfvn18/PHH9Pb2ApCfn49Wq+X27dvSp1A8w+vXr1NdXc2OHTsIDQ2dF62riKA0Go1oNBrOnj1LV1cXzzzzjAwUsNlsNDQ0cP78eSorK6mqqqKuro7e3l78/f0JCQnBYDBgMpmw2WxycwFnEFptbS2lpaWkpaW5uCV4WsAT/n9+fn6Mjo7ywQcf8O6770p/V6ERyc/Pl4eKu4A2OTlJW1sb7e3tAPKyoQyQUYNQJ/oqNp/GxkbZZoCMjAy+/e1vExcXR2VlJSdPnqS+vp6bN29KLYDQwldVVXHgwAHS0tJk8JMn/XofhMlkkuZLh8PBoUOHWLNmDVu3bpVCoZo0Wcp2hISEsHr1ajo7O+nu7iY7O5tly5aRnp5OZmYmcXFxFBQUsGzZMkwmE319fXzxxRcyrZQQyoUVRZlTUs3CkPvlLyAggISEBOLj47l+/TqnT59m69atPPPMMwCqE+6U6yA7O5vXX3+dtWvXotfrKS0tJTw8HMAlAHR0dJSpqal75qHdbleF0uZhUPq8trW1cfPmTfna1w3aVQqvvb29UrkQEhIig5rF2Ttf54k4sxwOB8HBwZSXlxMfH8/y5ctpampieHhYniNDQ0MyKFa4fExNTTE5OUlgYCCBgYE0NTVx6NAhGhoapMVTEBQURGBg4D0WrfnomxiPvr4+aR3//PPP6e/vp6Kigq1bt5Kfn09OTg5ZWVmyTcK/V7jLuY+re7YEZWzMxMQEIyMjLgL/o1qB5lV4VR7yzc3NdHZ2kpiYSGZmpjSv3k/bERQUREhICBMTE/T397vcuD25QYmHe+PGDT744APef/99qaHT6XRMTU3J9g0MDHDjxg0aGxvJy8sjKiqK4uJiiouLSUtLY3x8nD179pCSksKzzz5LYGAgx48f5+TJkwwODsq/pdfruX79OpWVlaxZs0b6lsyllsvPz09Oyt7eXs6fP8/Q0BArV64kJCSE69evc/bsWS5evMjhw4epr6/HZrMRFBRESUkJFRUVZGVlodfrCQsLY3Jykt27d7N79255izx79iyFhYWkpaUBzInfy1ygbEdLSwu7d+/m0KFDAGRlZbFhwwYqKipkqrfZBLSxsTFu3bpFe3s7Go2GxMREEhISXIRXTwvp4Gq1OHHiBD/96U/Zt28fOp2O7Oxstm7dyo4dOwgJCWHVqlWUl5ezb98+PvjgA3lBCwgIYGxsjLq6Os6fP89LL7006+/3JO45Quvr6zl16pS8LFqtVlpbW+/xT1MLyjURHBzMxo0b0el0tLe3U1hYyObNm2WwK0B6ejrgvGDu2bMHg8Eg/ZgdDgd6vZ5ly5axbNkyeYCoYe09DKJiXVhYGMXFxTIoRMzB8fFxqdFT0xgqs5aYTCZKS0tlijm4a8kSAoC/vz9BQUGMjY25aKWMRiOJiYnEx8e7+F+q8ZIId10ArVYr7e3tjI6OytdEBaZH2SfcFVZTU1O0trYyNjZGWFgYFRUVVFRUYDQa590NRrlmTCYTeXl5ZGdnS3O/8NPu6+uTaTFzcnKIjo6WAltYWBhBQUHs2bOHCxcuyHPcz8+PwMBACgoK2LhxI8nJyS7C61z3S+l/PDMzQ3NzM5999hm/+MUvmJycJC8vj+9+97u88sorhIaGAq775FdVTlPmphbzQTyjsLAwYmNjXfxkVeM2oGRkZITKykoOHz5MSUmJ1O65I34mfC79/PyYmJiQVRtEWceFFgLE5BIDJRy0d+3a5SK4KoPOhJajqamJGzduMDQ0RGRkpGx7UVER3//+9ykvL5c+UDExMRQUFEjNkDCJTU9P09DQwOXLlyksLJQDPhdarpmZGRf/urq6Oj799FNu3rxJfHw8lZWV7N27l1OnTlFVVUV/fz8Oh4PU1FTy8/NZv3495eXlJCcnS5eDiIgIJiYmMBgMjI6OUlVVRWdnJ3v37iU3N5eKigqZe1MNAp1er8dms9HZ2UllZaXcdAB54RDthbvmI2Vk5tDQELW1tbS0tMj0MMHBwQti9nkYxKau1+uxWq1cu3aNzz//nDNnzkif5FdeeYVXX31Vatr1ej1r164lNDSU5uZmmpubpUuLoLu72yUoTS3Cg5jX4vnX1tayd+9ebt26Jd+jXD9qabdAua71ej3Z2dnyAAwNDZVaO3e++OIL/uVf/oXq6mrZf5vNRmZmJtu3b2f58uWAU3jX6/WqFYCUKC1eoqS2YGhoiGvXrrFixYp7TO1qR2j8rVari/KgpaWFy5cvMzg4iMFgYMuWLezcuZNVq1YRGBj4WEFPC4G4MPX19dHa2urivvJ1tOPufVVaP3U6HWlpacTHxwO4+HzPJ0ohWQSlKYmMjJTBkeK8DgwMlBfOoaEh2tvb6e/vB+7KPMXFxXz/+99n06ZNBAcHz2tqO3dB32q10tXVJS1sa9eupby8XAqu4jOCr5p/ynGzWq0MDw9L+SEnJ4f169eTnJz8tV225k14VTa8paWFS5cucePGDSoqKkhOTpY+V0rJX3yv1WoZGxujvb2d9vZ2DAYDt2/fJj8/3yPaAqVQ3dDQwG9+8xvef/99rl27BtwVZpRR2mJT6uvrY3R0VPZTRBiaTCbWrVvHypUrsVqtUmDYtm0bAwMD2O12Tp8+zejoqEyxcfv2bQYHB2XQ01wcuO7aiiNHjvDee++h0WiIj4/n888/p7OzE61WS2BgICUlJSxZsoS8vDzy8vJYtWrVrGmygoODWb9+PYODgwwODtLb28vk5CSXL1/m8uXLLkniPYXNZpNO+Hq9ntraWj755BMZUBcbG8vq1aspKSkhMDDwnkA5pd+Ww+GQeXz9/PxITk4mJSVFNZoSZYW7/v5+PvvsM3bt2kV/fz8ajYYlS5awceNGsrOzAaebj3ADKSwsZNOmTdTX13Px4kWXIIKRkRFaW1vlRqhW/8nZtAQixZ14LmoTYJUYDAaXNDTiQj0xMSEjly9dusQ//MM/cPToUZfPhoWFUVZWRkFBgfzZQmYueVyUh6TJZCI5OVl+f+PGDT799FOMRiOFhYX4+fnNa0Dr10HMLZETW/xMKESUaSTPnTvHoUOHOH36NMPDwyQlJbF06VKKioqk5UetQXbKYF+tVsutW7c4ffq0i/tYZmYmBQUFLpevhznPlZfMlpYWKRDn5+eTlJQk37dQa1h5BohzX/xMCGJCaJ2ZmZGWZp1Oh9VqpampidraWulaYDAYCAwMJDs7m82bN8szXqkYmI8+KOdRVFQUK1asoLe3l9jYWJlaT1w4lEHqD4NSmTc5OUlnZ6dU9kVFRVFUVAQ4zybVCK9CI6XVahkaGuL8+fO0traSkpLCxo0bycvLA5y3JGUCd2WqrMbGRqqqqqSGQPgCGQwGjwmvw8PDMrmuyCog8vQpb/zKhP2ZmZmkpqYSEhIiBW/lIWo0Gl1uigaDgZdeeomZmRkaGhqkycVms0lhPi8vT5oZHhetVis1j1euXOHChQvy74pNNyoqimeeeYZt27ZRVFQkSxber1SeGP/U1FSeeOIJWQMZnJq6qqoqcnJySElJcXm/pxFjOzY2Rk5ODs899xwvv/wy+fn5UptwP5T+PBqNhuzsbJYsWQLcTXLtac2roLOzk+PHj0s3gOLiYtatW+dyCChzK2s0GlavXk1dXR0NDQ0uxTbGxsaoqqqioKCAvLw8l4ucJ4V190tuWloaFRUVDA0NydQ03lp5SrleRkdHOXjwIMeOHePy5ctyXxJaPYCysjKeeuopF+uBN+VlVo6TRqOhqKiI9evXc+HCBS5fvozBYGDlypUuOYfVlg9Vma1FmMGVa7K7u5v/9b/+F//6r/9KV1eXdJMLDQ0lOTnZ5aKvhr1yNtwVIRcuXODAgQN0dnZiMBjIz89ny5YtrF+/Xlb1+ypllNJiNDMzQ11dHSdOnKCxsZGwsDBWr17tcplZ6Gdzv3LgyrknLvXKjBgDAwP09fXJgG33iluz/X+ucU+DGB8fz86dO1m5ciWhoaFkZ2cTEhLytTX9wvfZbrdTV1fHhQsXpDwzF3LcvAivSnNdVVUVu3btYmhoiI0bN1JWVibf535LEt8PDQ1x4sQJLl++TExMDJs3byYzM9Pj1WH0ej06nc4lwbm/v790OhY3aavVKlOfvPjii2zcuFFqVpWCq/viHRkZkSk4hLZPMDg4yPXr16mrq6OoqEgmL34clInKBwYGOHv2LFeuXGFycpLo6GiysrJITU2luLiYjRs3ulSLEthsNhlgIMyQ4+PjGAwG9Ho9sbGxhIWFYTQamZycZHx8nI6ODhc/KE8EyojqN/7+/gwODvLuu+/yySefyAogQUFBrF+/nhUrVshF7r5JKQX33t5eRkZG5PeRkZHy/+Jm6cnDVBzm4+PjXL9+3UUbsmzZMjZt2kRcXJxspzgsRdoW4e7xxRdfMDAwIC82ExMTNDQ0cOvWLXJzc2UAphq0mMrxCgkJIT4+3sUEptfr5XxWm7/kbAgtj3K9XL58mY8++ohDhw7JpOfKgibFxcX87u/+Ljt27JACgND6eXpOPizKNs7MzBAUFERkZCR6vZ6xsTG6u7vluhXvUSOiH+L5g/Pyd/78efbu3cu7777r4tYCTp/75cuXEx8fL7MtCKWBmsZOqXiyWq2cPn2ao0ePShestLQ0nn32WSm4wr2av9lQurQNDw9z4sQJTpw4ITPwxMbGuih/FvqZPOjvKYU+YeXx8/OjqamJw4cPc+HCBXlJsdlsMj2hyDiw0MHpQUFB5Ofnk5eX59Kvr3PBV8oWly5d4uc//zkHDhwAIDk5mby8PNm3r1IM3Y95tfFNT09z6tQp9u/fT1JSEps3b5a+KcB9I+n6+/upqamhq6tLRrsVFBS4RPt5osrUyMgI/f39LiY3kQNV5DYT7/X392fdunW8+eabJCUlzVr9xd2MKQQGq9VKdXW1y4Y8MTHB7du3aWtrY2RkhOjo6Dl9BkJtHxcXR0VFBStWrOCpp56ipKSEiIgIF7ONOPQ0Go0UAJUIwRXuVi9SVu3wtF/azMyMFF4B9u3bx1//9V9LDUFcXBzr16+nsLBQHvLKTVQg+iF8hUTUvnvmDE/iXpXt2rVrnDp1SmpP8/PzWbZsGcXFxQQHB0vBU6npEnM6KyuLZcuW0dXVJc1dDoeDsbExxsbGXIQGTwsQwkwr+nHr1i0qKytdcixPTEwwMTGhurRm90PMK7H/jIyMcOXKFQ4dOsTg4CBarZaAgAB5MUxMTKS0tJTY2FjGxsZc9htv6K9AKWRrNBpsNhvj4+PyOYSEhHhcsfGwCIEGnNHdZ86c4e233+bzzz9nbGxMlkwfHR1Fr9dLv3mxPyvzYqoJkb4RnBYsUd5cEBYWxubNm8nJyZFr82FcjJRnxcjICNXV1S4xCX19fbPmQlUD4uwQ575Wq+X69esy0FvkhhWBZgkJCaSlpUn3EE8FVD7uvqBUXgwNDbF3717efvttJicnycnJ4Xd/93fZuHGjPE+/bqnjORVelSp+cKZ4OnfuHCMjIzJPWGhoqDwwlAOj3JRDQkKk1lGv12M0Guc9h9v9+mOz2aR25tSpU/zkJz/h2rVr+Pv7uxQXEO0SPipxcXHk5uZKU6xy03JH5HsNDAxkaGiIjz/+mJ///Od0dHTIBaAUGOfqGSiff2hoKBs3bpSbS3x8PBkZGS4Cm3geQri5n+uCe/tEiqKxsTGPC65iIxGH/MmTJ/noo49k2bqQkBC+973v8Xu/93sueXWFtktcQgC5adrtdlk5Be4VDDwtKIiNZHBwkHPnznHw4EHa2tpIT0/n2WefZc2aNTLgwL2dygMmLi6ODRs2cPv2bY4cOQLc3XjUdGjAvYGIjY2NHD582CV4pLGxkVOnTrFu3TqplfQGlEnvIyIiZLvdA0u1Wi03b97kF7/4BYcPH2bFihXk5OQQHBxMdHS0tAapHeX60WicZSljYmLuec9s/1cLYoy0Wi2Dg4N8+eWX7N69m4aGBpqamuTeERgYiEajwWg0otPpqKqq4sc//jFbt25lw4YNMmgZcLmAexpx5o+OjnLmzBmOHz/uYt0RGRPgboqlR/WPn5ycZGRkRJ6xk5OTDA8Pz2uBgsfF4XAwPj4u/ZorKyv51a9+JQVXnU5HfHy8TMe4bt06l+CvhY4hEIKnkDceVXgW56Pdbqe3t5cTJ05w6NAhJicnCQ0N5emnn+bFF1+kqKgIjUbj4mL6qMzpk1H6GnV0dPDZZ59x6dIlkpKS2LFjh0yRBEjzh2j85OQkDoeDoKAggoKCiI+PJy4uDj8/P0ZGRqRJfSFz+bkLrzU1NZw9exZwqtjtdrtL7kuhZUxOTmbHjh0sXbpU/i6lqUhpohQCoPgboaGh1NbWcuzYMcAp+A0PD7scUHMVbKF0MDcajWRnZ8uAHYHVapUmIWV1rYfFZDKRlpZGcnIyHR0dUlPtKV9DZbni5uZmfvnLX3L06FHp1mCz2TAajTgcDrq6ujAajS6mZvcocHCas0SQHeCSSxNctdULjWjv5OQkNTU1VFZWUlNTAzhzTwrtunIjme3zgAwoUFpP7ufz5WncLwxBQUEkJiZis9mYnp5mcnKSGzducOzYMZKSkqTLg9qCfb6KsrIyXn/9dfbu3UtPT49LVTeRY9putxMWFkZtbS2ZmZmEhISwdu1aNm/ejE6nk+ZMNY4j4DIeWq2W/Px8KioqOHfuHMPDw3R3d9Pb26tqNwhlTMfo6Cj79u3j7bffBpz7iMlkYmJiwuVyBc4zp66uTpYVX7VqFZmZmbKqnadzZSvXy+DgICdOnODkyZN0dHTItEjJyclUVFRIoexRBG7lnOzv72dgYEAK+vHx8aSnp0tNpfv7PY2QVYTg2tDQwKeffioFV39/f2w2G1FRUTz//PNs375dXk6U7j0Lydf9eyLdpFLDfPLkSQ4ePMjVq1cJDAxk+/btPPnkk9KK/rjMi/AKzo3z448/prm5ma1bt8rIMnCtFiL8uJSRmFqtloSEBLKysmRScbEhL3QiauVDdtdCKtshtHHR0dHs2LGDN998kxUrVsj3znaDUQqwSgICAqQwpVTBW63WR86R9zA8aCIJk9WjTDZ314jU1FTS0tK4dOkSU1NTHjOHgKtZqa+vj9raWlnvXq/XMzw8zM9+9jOOHz9OVlYW27Zto7y8XObwDQoKuicVS11dHVevXpUaAOV89SRK7UZnZ6f0IwenJqSgoEBm8BBtftC4iPRvyvmnVm2l0gIEzrQvf/EXf8HBgwc5efIkN2/eZHBwkIaGBnp6esjNzQU8H2j2MCj7lZ+fz3/5L/+FDRs2sGvXLk6cOOFiVhXzcGhoiLNnz3L06FE0Gg1dXV1SaFc7Yu8Rwbvx8fFSi1xXV0dXVxe3b9+mt7dXulOpVYiFu0oRgXt1SXfsdjtnzpzh+vXrJCQk8NRTT/E7v/M7UtHgySppSv/r9vZ2vvzyS86ePSstkiEhIbz++uu88cYbLnEADyvAirVot9vp7Ozk9u3b8jVRIVCZ71hN2U5EQQJw5pn+b//tv0m/z+DgYBkjERUVRX5+votWXc3zdzaU++bY2BgHDhzgvffeo7GxkaGhITIzM9m4cSMrVqxwkSkeRw6Yc7cBwdTUlKyJ29HRwZUrV4iIiCA0NJSAgACX2xK4Rr/6+/uzdu1arl27xs2bN+no6JCpNRbavOeepkWgNB8Ls2lSUhLPPvssr732GqtWrZKDKbSNQrh3rzwhSq3a7XZGR0epr6+XwqvYsKempsjKyiI3N5eYmJg5D7gQZlZx2ImggK8zuZRt8vPzIzw8nPDwcLRaLVarVVbzUv5tTzAxMUFfXx+A1E6Jutl1dXWkpaXR19fH5cuX0Wq1TE1NER4eTkpKCklJSfj7+3Pz5k0OHz7skkIqLi7OZa54ym1Aeai1tLRw8uRJrl69itFopKKigmXLlkltyP00qMo5JlK+uJvp1CjAimcuNENpaWm8/PLL6PV6enp66OnpYXBwkMbGRpfsCeKyqPbDQ1w29Ho90dHRPP/884SFhZGbm0tTUxO9vb309vbS19cnqxt2d3fLzx85coSCggJiY2OlUKEmM/RsKPeM+Ph4IiIiAOf8u337Nrdu3SI8PFzVfQBkrIBAJO4PDw8nNzdXBtz5+fkxPDxMS0sLPT09jI6Ocvv2bfr6+hgbG+OVV15h+fLlHhXYlGt/cnKSqqoqmQ4JIDw8nFWrVklBe2hoyKUKpUCsN2WQk/szGh4eZmhoSP4sMjKS9PR0qcF0P1c9hVACKAXXH//4x3z44Ycyd68QXNPS0li/fr3MvCNy/qolZuKrED694tlPTExw+PBhvvzyS06fPg1AREQE69ato6ysTMpxSh/pr8ucznrlA4+Ojmb9+vV0dnbS1tbG7t276ezsJDIykuTkZMrKyu7xW1ISGBhISEgITU1NHD9+nM2bN7NkyRLa2toYHx8nPDyc0NDQed+olAtMqU0Tt3vlYsnJyeG1115j9erVwN1UYELgVi4ucUDabDaOHDnC3/3d31FTUyPziYpUVUIgSktLY+vWraxfv15u2rMFEX1d7hd89bg4HA5Zg1w8A/dNxlMbjnAJaGlpAe4199++fVvmvBUXlMDAQHJyckhLS2NsbEzmHBwaGpKa2eXLl89a1MCTdHV1UVdXJ6uiFRcXy5Rrgq/aMMVN2Vs2VncCAgKIj48nJSWFqqoqRkdHaW5udjHVeton+2ERKe4EOp2OTZs2sWLFCoaGhhgcHKSpqYnz589z7tw56uvr6ezslBq/27dvs2/fPlasWCH3q+npadULfgL3jBZdXV00NDSQmZkpD0i1pOCDey14sbGxpKWlyXGcnp5mzZo1PPXUU1Kxo9fruXr1Krt27ZKxIxqNhoaGBn76058yOTlJUFCQ1J574tKl/HvKQE5wus9t3LjRxV3QYDC4ZPj4qt8tNNLCtU6p5JqtJKkaUCoNbt68yT/+4z/y/vvvMzAwIJVS4Cwh/vrrr7Nz506Zw1kNZ8WjoHRbsdlsHDhwgLfffptLly7J91RUVPDcc8+5WHnmYp+Z0yelbFBycjI/+MEPyMzMpKqqCq1WS11dHf39/dhsNuLi4oiIiJC+lGIRi5Kxg4ODVFdX09XVxd69e2lvbyctLQ2j0UhcXBxlZWVUVFRIk4EazH0mk0kKluBUn+v1+nuE3Bs3btDW1iZv0l988YUcbOVBajAYpPklNTWVdevWuWwEarhlzoZyLKanp2lvb+f27dtYrVbS0tJITU31mJ+S8m+JsrwBAQF0d3dLLSwgTenuWsapqSnOnDlDZWWly8IV/8/JyWHTpk0uOVM9tSEpD4fR0VF5sOh0Okwm0z0339nmk/slwz0yVK1zUKBsnzhUlPXVrVari8nWG1JmKZmenpbWGVHpJywsjNTUVDIyMliyZAlr167l6tWrnD17lpqaGpmxpKamhkOHDpGQkEBqauqCVCZ6HNznWlBQEKGhoQwNDdHV1UVLS4tUbIBnUvDdD+UZEBISwnPPPUdBQYFUgszMzJCZmUleXp5MOajT6UhKSpLJ4a9evSp9Pvv7+9m1axcajYYXX3yRZcuWERAQsODn4NTUFDdv3qSmpoYvv/xSWjFCQkLYuXMnv/d7vyfz7wKPnNZqYmJCygXKlEqiSI67BdeTCK2xwWBgZmaGCxcu8Otf/5pPPvlEPhchI4WEhLBjxw5eeuklmQ9cmeVFrYi0j8LyLPK1jo+Pc/ToUT788EOOHz/O0NAQaWlplJeX88ILL7Bq1SqCg4NdAs8flzk9VcWDdzgcBAYGUl5eTlZWFleuXKG1tZWOjg5qa2tpaGigqqrK2YA7B/vU1JT0Z9VoNNJMMDExgc1m49SpU9y8eZOMjAz8/PzuiVxfiANnNq2M8rZrtVpdcn2KwgRjY2PSofnatWscO3aMhoYGurq6aG5u5vbt21I7K/yB4W4VnLi4OFavXk1hYaEsgaiMKlYbStOrzWbj1q1bXL9+HYfDQXx8PFlZWfcNgppvlIdZYmIir776Krm5udTV1XHz5k26urqYmpqSVWK6urpkoJly8xTCLSADgACZVUNZBU0NB6jIiAB3zf/KFDPi5w/aVISPunswmpp9DJV98vPzk0GWYuzEOAs8nRniUfHz85MuKkLwVqZVEod8WVkZ/v7+tLa2SrPurVu3uHTpEsuXLycmJkamYVKTxlKJe/Bgeno6SUlJDA0NMTQ0pIpsJvfD3f2svLyc8vLyr/xccnIy27ZtIyIigr1797Jv3z7GxsbQarU0NzfzzjvvkJqayooVK1zcZOYT5ZoaHx+nqqqKd955h2PHjkmzfmRkJHl5ebKq2/DwsKwIJ94jhHa9Xi+1z+Pj4zJwOyYmRgqn4mwVe1ZOTo5L7ndPBsUKhPDq5+dHe3s7b731Fj/72c8YHR3FZDLJbAkAJSUlrFmzRhZsul86Rk+hfJ7K2JzZZI6pqSmOHTvGz372M/bs2cPY2BhpaWns3LmTF198keXLl7uk/xI8bpnjeZF+lI2JioqivLxc5oIcHh6mr6+PkZERlwS9Su2cn58ft2/f5uzZswwMDFBcXMzLL79MSUkJBoOB0NBQEhMTXQSghdhslRqa2R64zWaTk1PkWrRarRw9epTjx4/T399Pd3c3LS0t9PX1MT4+zvj4uPT/EJuOKKcGzkX66quv8sILL5CSkiIr53h6oT4IpXAzPT1NT0+PTJsSFhZGTEwMJpPJI5Gyyo1dVH1JSkpi3bp1cjzsdjsBAQFoNBrq6uo4ffo0t2/fZnR0lOnpaaKjo4mJiZEBP42NjUxOTuLv709GRobUqIhiFWq9ZDxMNKtybGZzldFqtYSGhsqLmviMGuem0q/b01Ha84H7vqDUPOp0OgYGBmRtcYGot648NNWq/VG2y2QykZWVRXJyMteuXZPBiWpt+6OgHEOdTkdmZiZZWVmkp6czMTHBwYMH5Tkjym4r8zLPd9uU86q/v5+rV69SXV0ttYvCelpfX8+nn37K9PS0LMhTU1PD+fPnmZmZkdUpo6KiiI+PZ2RkhOvXrzM2NkZZWRlvvPGGdGnp6Ojgxo0bMoG/CPpREyJzR19fH8ePH+fs2bNSDggMDGRmZkae65GRkSQmJt6Te1ktKGN6hCA7W1U+m83G3r17ee+992R1Sp1Ox8aNG3nxxRdZtWrVrBYdEfyr0Wi+9rqdN+FVRFQKzYDJZHKJCnRHJOEH58M6e/YsHR0ddHZ2Eh8fzw9+8IMHfn6+UE6o0NBQAgMDZToa98XT2dnJ3r17aWpqYnx8XKYAO378OEePHnVxNlciBB3xf2WwT2ZmJs888wylpaXyZ2rUijwIkSUBICEhgcTERLRa7UOVB5xv9Ho9kZGRLpGwSvLz80lLS6OtrY3+/n7A6cIRExNDdXU1o6Oj3Lx5E0D6xApB4EG5fdWAMDkbjUZpGRAotXji56LAhHK8YmJiKCkpISsry+UAVYsQ4S5oBwUFERwc7BLFvFhQjovSfGy326msrOTs2bPcvn1b7jG5ubls2LCB/Px8mbf66yYMXwiU7dLpdISFhclLkwgkVEMg6MMgUrYp8fPzu6dylvLyu3btWkZGRjAYDBw8eJDu7m70ej3Xrl3j6NGjlJWVyQDM+VJuiAugmGsNDQ0cOXKEnp4emWxfo3FW8jt27BjV1dU4HA5pWbx06dI96cDA1UUOnJU5u7q6eO6550hJSaGxsZHTp0/T29tLTEwMaWlpLnmKPTlvhUAPTre/gwcP8t5773H9+nXAOYa9vb2AU3lTUVHBjh07iIuLc1lznjwHhVUN7hZDma09XV1d9Pf3S4vV1atXeffdd/nyyy8ZHR0lLy+PHTt28Mwzz1BQUICfnx9Wq9XF5UycIY9bonreVELu0YJfhclkckmKf+vWLRmtp9FoGB0dlcLrQmkdxa1AkJ6ezpo1azh58qTMNSei/sHpy/qTn/wEnU4nDw+HwyEr+dzvbyhN0UpBNjo6mtzcXBc/WjX49n4V7oeGyCMbGhpKTk6OTFatVvOkEr1ez/LlyyktLZW3ZrFptre3u2gAAgMDVVvFSHmo2+12Ojo66Orqcpl7SkTbxXxTugcoxzc9PZ2KigrS09OBuxHraum3EmVqPoGaxmiuEKkHxV567do1Dh48SF1dnZzD69ev59vf/jY7duyY1aSndoQ/ujgUBwYGaGtrc9ln1Sy8Ch9OJbM9f7GfiHX45JNPEhYWxvT0NPv372dwcJBPPvmEiYkJAgICWLNmjYuAMNcoBTWAtrY2Gfwocp9PTU0xMDDgUkZanFlKAVVJQEAAer0em81GQEAAU1NT7Nq1iwsXLrBkyRJmZmY4d+4cMzMzZGRksG3bNrKysmSbPHmOiDk4Pj5OfX09X375JXv37mVqakoWnRAa2Jdffpl//+//PSkpKfeMkSfXn9gHlXv8+Pi4LChlMpmor6/n4MGDNDY2Sjnlxo0b8kISFRXF7/zO7/Dd736XpKQkqZxyL68+V/2cV3um0nwl/s12eAhhTzmQQ0NDTE9Pk5WVRUFBgdSQCNNtUFDQvPvauQuvmZmZrFu3jtu3b1NXVwc4bylCqyjSQM2GSJGl9BtU+g+KCHzxu5KSknj55Zd5/vnnCQ8Pl7ddbzhglG0Ukc8hISEUFxe7RByqQXAQm7GYl2I8xFcRLCD+r2R6epqJiQnGx8fx9/enqKiI5ORk+braxkusr8nJSS5dukR+fj7bt28HnBuLMPko/UCVrgAhISFYrVaGh4flz1JSUlz6rLaIdXHoiwO9qamJa9euuQg5ygNVTeP1qIiMHkajUY7f5cuXeeedd9i/f79LuenQ0FA2bNggAwu9IVhEiThPxFyz2WzU1tbKuQnco9lUE4+6942MjGA0GjEYDJSWlrJ9+3a6u7s5cuQIIyMjnD9/nsbGRpYuXeriwjMf7VbOk/j4eHJycjh//rwU0ACXPLYiRzY44zeys7OJjY2V1rfY2FhZQl2n0xEdHU1HRwcnTpzg1q1btLW1YbVa5e8XvrR6vV7OA0+5ZolgSXDKJqdOneLMmTOyv6IIkb+/PwUFBTzxxBPSz1VtKOUvURmrp6eHkJAQDAYDzc3N1NTU0NPTIy01IyMjUqvs5+dHT08PN27ckMHryrmiLLzQ3t5OTU0Ner1ezgcxhg+rnFyQEX+YKjzKIgUCkR+0qKhIauvE7W4hblruG0xMTAw5OTku5dtEEMiDouiEC4VyQYvfL7Szdrud0NBQgoKCCAgIYNOmTbz55psUFxcDyFuMN+AuvLa1teHn58eSJUtcBB01+IKKW/PDzCe73S5NYODcoIeHhxkbGyMqKspFqyx+t1rGzGq1ygug3W6nvr6eEydOsGnTJtauXcv09DRGo9HlObgHEdy+fZv6+npaW1vRaDQUFhZSUFDg4uaiNk26uCiLdXb16lWXg1ZooYeHhwkJCVHdheNhEHuP0gw3PDzMxYsX2bVrF5988gnNzc2yX2FhYeTn58uofLvdruqgu/shFB6i3SKNovL1xYJSIB0bGyMgIEBaJgVDQ0NyHiurOc7luLpbVJcvX84bb7yBw+Hg4sWLgPOcDA0NJTg4mNDQUOlOMDMzQ0JCAkuXLiU3NxeTyYTNZiM+Pp60tDTpExoaGorD4eCZZ57h6NGj7N27l3PnzmG324mKiiIlJUX23dPadeWzvXz5MocPH6alpUVWD52amiIlJYXNmzezadMml2JNjxuwNJfMzMwwMTHB8PAw169f56233uLtt9++J6h3NsTe2tXVxW9+8xtu377N9u3bWblyJTExMdK9B5BuBGfOnOHzzz8nMjKSZ599lvDwcHUKrw+LssGZmZkUFBRISV8p/C70piQeZkBAAFFRUS6+Nv7+/i5R3CLwSrmoRIlVd/R6PQaDQWoL0tPTef755yksLCQrK0um0AD1CQUPQrkg+/r6ZKlAUaRiLtNlLARCeyeqSwnhdWBggJaWFhezlZq0Pe45isU8Fevn3Llz/N3f/R0nT55kw4YNMvp5bGyMGzduSC3e8PAw1dXVnD9/nurqam7evElUVBRbt25l48aNsvwhqONCokSZ+kqZZUH8bHx8nIaGBq5du0ZpaalLDkq1zk8RAKOckyaTSc7B1tZW3n33Xfbv309TU5P01Z6ZmaGkpIQ/+qM/Yvv27QQFBam+MIES5XwWAR9K69ViEVbdq02658VubGxkz549UlgU71uING9i3xbrIykpiZ07dxIcHMzly5fR6XTExsYSEREhhRaRTmlmZkZq5EQxCYfDIYNjAZe9pKSkhJGREY4ePeriMxofHy8zY7i7MS0UQhHg7+/P8PAwZ86c4Z133pHFhoRWWavVUlpayquvvkp5eTlRUVFSiaWGogpiT2xtbeXs2bNUVlZSW1tLdXX1rIKr2N+VsQIZGRlMTEzQ1tZGT08PlZWVdHR08PnnnxMWFkZaWhoVFRWsWLECvV7PxYsXqaqqwmAwEBUVJYXfR0U1J41QrwsSExOJjY3lwIEDXL58ma6uLukn4qnbllarJTExkU2bNtHT08OVK1cYGRmROWptNtusPj1arVYeEGKzFZNGvD8nJ4fnn3+eN954wyWXq4ii9fQkfxjEuIi+3rhxg0OHDslAPNFf98AgtfdNXJxE5RtwtrutrY3W1lYAadJTk+nVPdgwNjaWrq4uufEPDAxIv7KWlhampqaIjIzk0qVLXLlyRQag9ff3c/bsWaqqquR8Xb58OZs3b5aaBLX6uiqtPlqtloyMDPLz87l+/TqTk5PY7XZ6e3vp7OxkcnJS1cKrMg2W2CuV+0p7ezu9vb0cOXKEt99+m+rqauBuXt+UlBTeeOMNvve978nfeT8fRDXiHrAVHBxMYGCgdN1yn39qG7/ZEEKqUiCczRIk0kT19vby5Zdfsn//fjo7O+XBPzo66uJGMd8on21KSgovvvgi27ZtQ6PRYDKZXAJXvwphmVRaIYU1JywsjLy8PJYtW4bVamXNmjUsWbLkngqGC41yX7l58ybvv/8+u3btkhW0xLrKz8/nySefZMuWLS4WW0+izBih0Wi4efMmH3/8MQcOHKCqqkpmjTCZTBgMBnlZnpiYwG63YzQaiY+PJyQkhJycHAoLC+no6ODAgQM0NzfT3d3tUs0vIiKCa9eucfnyZaanp2loaCAmJoYnn3ySjIwMgoKCvpbvr2qEV3A9MESgU09PD0FBQXR0dJCSkuKRSGbRppmZGdLS0njjjTcICgri7//+72lubpabhrtbgEAUYYC7wqvy/SkpKXznO9/h5ZdfJjU1VX7O2/zQhN+o0Ezu37+fn/3sZ/T395OWlobD4XAx63na5POoKOfe9evXuXHjhryBBgcHk52d7VKf2tNjp/z7WVlZrFmzhmPHjjE6Oupyc25tbeWjjz7iwoULGAwGurq66Ovrc9H+KC9aqampbNy4kfz8fPk71CoouAuvubm5LF26lPb2dqlZED6+au2D4EGatZs3b/Kv//qvnD59mvb2dtrb2+VrWq2WjRs38uabb/LUU0+5fO5xSzQuJMo84kajkaysLJdiIKDeeXg/3HMmu7ffarXS1tbG9evXqaqq4vz585w/f57u7m4MBoOMsTAajQQEBHjs8hUcHOyiNX1UxPoTmmbR/pSUFF599VXWr1+PRqMhMTFRllIVn1tIlH6b4Iy+P3v2LOfOnZNZFMS5nZmZyVNPPcXGjRul4KqGIGUR7CguAJWVlVgsFtra2lzSWomLhLslOTU1lZdffpnNmzeTl5dHQEAA9fX1REVF8d5779Hc3Ozy9/r7+zl8+DDHjx9Hq9WSkpJCWVkZGzZsIDQ01CXVG3ip8Kq8fUZGRhIVFcXU1BT9/f33VMHxBGJhxcfH89xzz9Hf388HH3xAXV2d9KEzGo3yxmm1WmXdaiVGo5GYmBgiIiJISUlhy5YtPPPMM2RkZADIwRTVubwFEfgkuHnzJm1tbYCzvO3y5ctdfEI9Ldw9CkpXh+HhYRoaGqQpC5CaLVEWVg2aO+XzTU5Opri4mNOnT0uhLSIiAofDwfDwMJ2dnXR2dn7l70xJSeGFF17g6aefdhlLtbkLzIZGo5GpskR7xaHv6bF6EEJoFePZ1tYm07Pp9Xra29s5cuQIX375JfX19fJz4eHh5ObmUlRUxI4dO3juuefQ6XRYrVYmJyfnNbBnPpmamsJkMpGcnExiYqLcc2ZmZlzmoZovx0KImW3dtLa2ygqM7e3t1NbWcuPGDZqammhsbJRCkrhMRkdHs2XLFgoKClx8QRfCNWu2lHpCs+fusgN3LazKy6LQNguU/w8NDaW4uFjGfrj/7YWcv8q/193dTXV1NWfOnJGBZeC8CIv0gxUVFTz99NPSkupJq7ESpfA9NjbGxYsX5TkNzrii8fFxF4VFYGCgdGdcvXo1TzzxhIv/bllZGUajkaioKE6ePEl7eztDQ0P09/czMDDA5OQkiYmJFBYWUl5ezvr162WefqPR6N1uA2JSTE1NSV/Q4uJigoKCaG9vl+ZZ8JzQo3SCT05O5t/+239LREQEP/nJT7hy5QrgNPOL5PSz5ZAUB0pOTg7l5eU88cQTMs2Q2NDUXqbxYVFuzGlpaaxfv57w8HB58/PWftpsNsbGxlwuJf7+/jIPMKijNKVynQQHB5ORkUFqaqosGDExMXFP9g8lIghICLtxcXE8//zz/O7v/i7Lly938VXylouIKFIg2j05OXnfDCFqQRx6ovrenj172L17N3q9npGREc6cOcPAwIB0T7LZbBiNRlatWsW3vvUtNm/e7HLR8Pf39yqNqxJlPkpwCjfCbcBdQ6RmlELX+Pg4nZ2d+Pv7MzY2xuHDhzl58iT9/f20trbS0NDA5OSkzMqj/B3p6ek89dRTvPTSSyxbtmzBx/V+AvJCBVQvJKKYEDiDV99++2327t1Ld3e3tAaI8QkPD2fFihWUlJSg0WhUVURDKMbAeRFWug45HA6XmABwKmbWrVvHSy+9xMaNG0lOTp61tPjSpUvJy8tj586dnD59mnPnztHY2EhnZyfR0dHs3LmTp556iqSkpHsubaopUvA4KB9aQkICWVlZ7Nmzh3fffZeEhATWrl2LTqfzWIJ7peAVHR3Njh07sNlsXLlyRVZjEu8D54FfUlJCVFQUVquV2NhYmdQ9KytLlhEFdZgUHgfl4SEqqgkyMjLuWwjAG1BulAaDgbCwMOk7Cs6KKSL9i9KnyJMoNwSj0UhZWRkBAQF8/vnnvPvuuzJdlAhGdDgcshABIKN/wTmPX3nlFb71rW9JwdX9b6gR98M1ODiY2NhYwsLCpF/WzMwM0dHRLn50aumXMhhwbGyMU6dOsXv3bvbv3y994YUmTrgvlZWVyejm0tJSaQ0QQU6Pmxzc0yjHJiQkhPT0dGprazEYDPT09MjX1BiIJoI/RbBdbW0tJ06coLa2VvoWNjY2cuPGDcbHx136I5Qher2esrIyli9fztKlSyktLSU3N9dlP/JGjfr9UKYxFGe+J9wF7Ha7FNrGxsa4evWqtFZpNBp5qTKZTJSUlLhkYhHCqxpQnk8xMTEUFxezceNGxsfH0Wq1tLW1MTY2RnJyMqWlpZSWlrJkyRIKCwtd3OKmpqZcLAii8EB2drbMaNLX18fY2BihoaFSDhKIcf26MT3qeJoKlOYEoX09ePAgu3fvZtWqVaxduxZApstaaAFBq9W6aAwTEhJ49dVX2bJlC6dPn8bf359z584RFBREeno6y5YtY8uWLaSlpckEzKmpqS4+MGIAvVUbIlCO3a1bt2QN9TVr1lBSUiLNfWoumfogxAYaFBREZmami49XXl4e8fHx8nu1aYAMBgM5OTmyJnhnZyd79uyRgRVCABJaOZFuZ2JigsjISJ555hm+/e1vs2LFClUJ6F+FclMUAZerV69mdHRUJtouLS0lLS1N5i5Uk/+re6qyAwcOUF1dLS+G/v7+cj8KCAggJyeHl19+mW9961tyPlqtVunX6+2Cq3uqpuTkZDZv3kx9fT03b97kzJkzlJaWumiH1ODCo8RutzM2Nsa1a9f49NNP+fDDD2lsbJz1vTqdjoCAAKl1DwwMpKysjOeee45t27a5pB5UFrBRU38fF0+c8+6453xvaWmRa9C9EEF+fj6bNm0iMzNTvl9tFymxd4eFhbFu3TqXbBXCNUUEmxUWFsrPCR/Y2fYSZQGL6OhoeWlWItKKCmH3ccZVdRKEMm+fyWRi6dKllJWV0dLS8lAVSRYKMdAisjI2NpbY2FhKS0sZGBjAz8+PoKAgwsPDiY2NlbWN3TdfkWRZbRvso6LcOMfHx6mpqeH69ev4+/uzZs0aVqxYgcFgkDdQbxVexU0zPT2dxMREGQ27fPlyWakI1KO5Eyifd2JiIm+++Sbr169neHiYkydPcvz4ccLDw2Uu3tLSUoqKiuTnkpKSSE9Pn7f8kfOFexujoqIoLy8nKyuLl156ienpaZKTk6VGQE2CK7hegqamphgcHHQpsGCz2QgMDKSoqIhNmzaxZs0aiouLiY2Nle8RWiFvXHOzoSy/nJGRwVNPPcXevXtpbGzkypUrdHd3yxLUoI65qgz0GR8f58qVKxw5coRLly65+Bu6I9IMCRee9evXU1FRQXh4uGqi1xc7Yuz0ej1Wq5UrV65w7tw5mYJOWdQFnMLrunXrXKyqnha+lbgL4jk5OcTExADOvo6OjmKz2QgJCXFRyIjXlYG87q99VUn02Xykvy6q283cTZ0lJSV873vfo6+vj7KyMvmap/1HlOZIYdoTAuxXIQRWZQotb0dorMCpdT127Bg3b94kMjKSnJwcuQjUlAf1UVE6uvv5+bFu3TrA6RKxfPlyqenxREaMh0Fke9BoNGzbtg29Xk93dzfp6enk5uYSHh5OXl4eKSkpFBUVyQ3N/XeotX8Pg06nk8VPsrOz73nd00KOO8r2mEwmIiMjMZlMaDQawsPDycnJIT8/n9WrV7Nx40aXkplTU1MYDIZFs8coERd+4Sbx+uuvc/z4ccrKymQgiNpQCrDCZLt27Vqys7Npa2tjcnISf39/mTDeZDJJ9wBwurwIyyMg0w4KDZa3rklvQpjGrVYrXV1dANJqBbBs2TKZiUXksQX1KTNEcLzD4cBgMDxQbpmYmJB+sg/aS5SZXYSQq5R1RPXGuULVwqufnx/Z2dmkpqa6pGACdUU3P+qB5wlf3flGqSGqr6/n5MmTMm+osjCB2szpj4K7mWPTpk1UVFRI061y7qptswJnm0SdatGPtLQ0vvWtb/Hiiy/KMoYPCujxpuISiwF3/87s7GwKCwsxmUxkZGTwwgsvsH37dhISElzWltAULVaUzyUyMpI//dM/5Q//8A8xGo2ydPhs7/UU7peQ0tJSCgoKpADhHtwrLokifzTc2w+9Xq8KrfJiRzxfcaEoLCxkzZo17N69m9bWVpkVKS0tjd/5nd9hy5Yt8gKl5rF5WDlEzL9H6ctsvslz/SzUIwHeh9lMzGpdsCLTwPT0tMsNW/h3eFtlqUdB9Gl0dJRbt27JVD7BwcFERka6aKm9HTG+RqPR6zImiM1K3Ix1Oh2BgYH3lJoU2h/hJuHv7+81xTK+ChEYo6ySo1ZBT7n5h4WFSZNxX18fMTExrFy5UgZRaDQaJiYm5FipQWibb4RWJzQ0VLUaV3ceZ++wWq0y6G6xrEdvQbmeNmzYwH/4D/+BwcFBAgMDsVqtxMXFsXr1apnyEtQtvArcM3gos5u4u0Q8CvMt66heeIW7Ao8yL5wa+SpfzHgiiwABAABJREFUTrW2ey4QfRsfH6e3t1f65YlgmMWE8rbqPje9BaXfk/uFQmw6yujlxYTQoHtD8JK7xi4vL4+8vDyX9yjnoDJjwjcBpZnS25QD7jlQZ8O9P3q9XrUXrcWOsgBEcnIyf/RHf+ThFs0N7j6w3oLqWyxSVLiXRfShLsQG7Ofnx/DwMHa7na1bt/LGG2/IPLagvqjLx0FZY95btSBifSmtBe6lmn2oG5FvWI3leRcKm83mEgyqdq2z0h/wfon+lf6soF5f+m8SwsXDd4HwPI8svJrN5vloh2pY7P2Dhenj97//fQB++ctf8stf/nLe/54S3xguDhZ7Hxd7/2Dx93Gx9w98fVwMLMb++a5xPnz48OHDhw8fPrwGzWIIoPHhw4cPHz58+PDxzcCnefXhw4cPHz58+PDhNfiEVx8+fPjw4cOHDx9eg0949eHDhw8fPnz48OE1+IRXHz58+PDhw4cPH16DqvK8ms3m14Ff3fn2DywWy//yZHvmArPZnAT8NfAEEAl0AJ8Af2WxWAY82LTHxmw2vwn8/Cve5rBYLIsqaahvnnoXZrP5ZWADUAIUA8HAry0Wy2uebNdcYjab/19gBZADRAETwC2cY/gvFoulz3Ote3x8Y+j9Yzgbi2kv/Sach2azORJ4AXgaKAISAStwBWfff26xWBwL0RbVaF7NZnMy8M/AqKfbMleYzeZM4ALwHeAs8A/ADeBPgdN3JoI3UwX81X3+Hbrznj0eadk84ZunXsl/Av4Ip+DT5tmmzBt/DgQC+4F/An4N2IG/BKrvzFtvxjeG3j+GLizCvbSKxX8evgL8FFgFVAL/CHwEFAL/C/iN2WxekEopqtC83unsz4E+4LfAX3i2RXOGBYgB/sRisfyz+KHZbP5vODeqvwF+4KG2PTYWi6UK54K9B7PZfPrOf3+yUO2Zb3zz1Gv5c6AVaMSpvTvs2ebMCyEWi2XS/Ydms/lvgP8I/B+AN2cq942h94+hZDHupd+Q8/A6sBPYrdSwms3m/4hT8fES8CJOgXZeUYvm9U+AzTg1P2MebsucYDabM4DtQDPwP9xe/r9w9vN1s9kcuMBNm3fMZnMhUI5TQ7Lbw82ZS3zz1AuxWCyHLRZLg8ViWbRJrWcTeu7wmztfsxeqLfOBbwwBLx9DNxbdXno/FtN5aLFYDlksls/cXQMsFksn8OM7325ciLZ4XHg1m835wN8C/2SxWI55uj1zyOY7X/fNMtAjwEkgAOekXmx8/87Xf7VYLNMebckc4Zuni3KefhN49s7Xao+2wsfjsKjGcBHvpfdj0Z2H98F256t9If6YR90GzGazDngLuI3TLLKYyL3z9fp9Xm/AqfHKAQ4uSIsWALPZbAJeAxw4fWC8Ht88XXzzdLFiNpv/AggCQnEG/6zFKfT8rSfb5ePhWcxjuMj30ntYjOfhbNwZ19+78+2XC/E3Pe3z+l+AUmCtxWKZ8HBb5prQO1+H7vO6+HnY/DdlQfk3OPu022KxtHi4LXOFb54uvnm6WPkLIFbx/ZfAmxaLpcdD7fHx6CzmMVzMe+lsLMbzcDb+FmfQ1hcWi2XvQvxBj7kNmM3mMpw3r7+3WCynv+r9ixARkbfYfLj+8M7X/+nRVswRvnm6aOfposRiscRZLBYNEIczcCIDuGQ2m5d5tmU+HpbFOobf0L10UZ2Hs2E2m/8E+PdAHfD6Qv1djwivCtPBdeA/e6INC4DQWIXe5/UQt/d5PWazeQmwGmdU8Bcebs5j45unwCKcp98ELBZLl8Vi+Riny0ckd3Np+vASFtMYfkP2UhcW23k4G2az+d/hTOt2DdhksVj6F+pve8ptIAinDx3ApNk8a/aPn5rN5p/idOr+s4Vq2BxSf+drzn1eF5Gj9/M19EYWm2O6b54uznn6jcFisdwym83XgBKz2RxlsVh6Pd0mH4/GIhnDb8Je6s5iOw9dMJvNf4YzJ3gNsMVisXQv5N/3lPA6BfzrfV5bhtMn5gTOg9VbzQsiD+F2s9msdcuJFgyswVlB5YwnGjfXmM1mI06TgYP7j6234Zuni2yefkNJuPN10R2g3yC8fQy/CXupZJGehxKz2fz/w+nnWgVs88SFyiPC6x1H7d+f7TWz2fyXOCfyL725VJzFYmkym837cJp8/h3OSiKCv8JZSeV/WiyWxZLj7hUgHPh8sTim++bpopyniw6z2ZwHDN7Jtaj8uRb4v3EWoDjl7WV+FzOLfQy/CXupG4vuPBSYzeb/jLOU+AVg+0K6CijxdLaBxY4ZOAX8d7PZvAWoxVlWbRNOM+z/6cG2zTXCMd3bK4h8E1nU89RsNj8PPH/n27g7XyvMZvMv7vy/12KxeHOFnyeA/89sNh8DmnBWLYrFWYkqA+gE/sBzzXt8fGPo/WP4DWNRnodms/kNnILrNHAc+JNZXECaLRbLL+a7LT7hdR65o9VagXOwnwCeAjqA/w78laduLHPNnaTTa1nEjumLmW/APC0B3nD7WcadfwC38O7ylAdwHpJrgGKcqXnGcF483gL+u28MVc83YQy/ESzy8zD9zlc/4M/u856jwC/muyGamRlfBhwfPnz48OHDhw8f3oHHy8P68OHDhw8fPnz48PGw+IRXHz58+PDhw4cPH16DT3j14cOHDx8+fPjw4TX4hFcfPnz48OHDhw8fXoNPePXhw4cPHz58+PDhNTx0qiyz2ez1aQksFovmfq8t9v7B4u/jYugfLP4++ubp4u4fLP4+Lob+weLvo2+eLt7++TSvPnz48OHDhw8fPryGRy5SYLFY5qMd88osFSDuy2LvHyz+Pnpj/2Dx99E3T11Z7P2Dxd9Hb+wfLP4++uapK/Pdv5mZGTSaByq5H5mv6p+vwpaPOcVms2Gz2XA4HOj1evR6vaebNOeIPvr7++Pv7+/p5swZNpuNmZkZ/P3953wj8uFjrpmZmZH/ADQaDVqtdxkTp6ensdlsTE9P43A40Gg0+Pn5odPp0Ol0vnXoJYhxtNvtaDQaeTZ46/gJYXRmZoa2tjbOnTtHXV0d/f39DA0NYbVaSU1NZfXq1VRUVBASEiI/Jz473333Ca/zyMzMDA6HA0AOprdO5vuhPDy0Wu2iE+jcEcLdYuyjsk/zcZP24WMumW0/VQqyaka008/PDz8/Pw+3ZmERZ6JAjJXax+xBLJZxFGMjLoHV1dUcOnSIS5cucfPmTVpbW+ns7GRycpLw8HAuXLjAzZs32bBhA+np6RiNxgUbR5/wOo9MT09jtVoB0Ol0i1LgmZmZwWazodFo7qtlXSyCkPIisphwOBz3aKwWy5j5+GYxPT0tL5hqxuFwMDMzg073zTqCHQ4HdrvdReEh/nkji22fFONjNBoB2LNnD++99x7FxcW8+OKLDA4OMjIywsTEBLW1tVy4cIErV65w6tQpXnnlFTZs2OCihZ3PZ/PNWjkLjDD9LEbEIaHT6TAYDIDT7NzV1UVzczMTExOkp6eTmZmJRqOZVUDyNkT7b926RXt7O/Hx8aSlpQFgtVq9zkykPECEechqtRIfH4/JZJLv8aY+LXamp6ddTMw6ne6RND52u12uXWGe9tbxnZ6exm63Y7PZsFqt6PV6goKC5Otq3HPEBViM2eDgIK2trfT39zM+Po6/vz/h4eFER0cTFhaGyWRaNGfIzMwMWq32vkoOIdA/CLVYMN1N4729vbS0tNDf349eryc+Pp6EhAQCAgJc3q92lJZUgKGhIWZmZigvL+e1117Dbrdjt9txOBxUVlby29/+lsOHD/PZZ5/R29uLw+HgySefRK/XS9e6+er34lgVPhYc9w1E3MQOHDjAu+++S3t7O2+88QZ/9md/RkJCgosvjDci2t7X18cvf/lLvvjiC1566SX+/M//HJ1Oh91u9zpBQPhnifZ/9tln3L59m5dffpnly5cDTgFhsRyeiwGlX+fXOcTFRUX835vmq5Lp6Wl6e3tpa2uTQkNCQgJr164lMDBQvkdtwqtyD+zr62P//v188sknVFZW0tvbS3BwMMXFxWzdupWtW7eSm5u7aNbfV8018fpsrh9KYVENc1a5L05NTXHkyBHefvttLly4QGhoKE899RSvv/46RUVF97xfzWi1Wpd2rl69mqioKFatWkVwcDCAPOueeOIJlixZwqpVq/jFL37B8ePHCQ4OJjIykpUrV0ql1nyd+/P2NB0Oh7zhiw1XbCQzMzPy1jw9PS0/495BrVYrb6jCkV1tm5ES4eMq2lxVVcWZM2eYmpoiJyeH4uJi4uLi5AGihkX4KDgcDqxWKxqNRk7M9vZ2Lly4QH19PTdu3OD8+fNUVVUBTpNDYWEhzz77LOHh4YBzEXuTb5DQ3gjN5O7du3n77bdpaGggOTmZGzdukJOTI2/YatT23A+hvQPnuFy+fJkjR44QHBxMYmIicXFxciPzxvnqjtiTxD9ln8SmrfYLyP1MrA87PrN9Xgh5au43uK4tu91OS0sLBw8e5OjRo3R1dZGamsrNmzfZvHkzWVlZ0nVADXPXXeNaU1PD7t272bt3LydOnMBmswEwOjpKR0cHU1NTFBYWUlhYCHjfvilwPxNbWlq4du0a4+PjBAcHExYWRmRkJJGRkQQHBz/03in2Lk+Nq1I72dnZybFjx9i3bx8TExO0trZitVpZunQpS5Yswc/P7x4/X7XiLmOtXr2akpISwsLC5M/EfqHX68nKyiI6OhqAX/3qV1RXV/PZZ5+RnJxMamoq4LRKCnlhLpk34fVBfixCmFW7X9KjMjMzg91ulwv19OnT/PVf/zWdnZ1s27aNb3/722zZsoWkpCSXW6anN9aHRavVSl+YmZkZWltb+eCDD3jrrbeora2VWQbEe7u7uzl+/DgrV66Uwqvy+XgDYqFqtVquX7/Oe++9R0NDA+DctD777DOeeeYZsrOzvUZoVaIMlggODqavr48vv/yS3Nxcnn/+efz8/FxcRLwNdwHVm/cdcelXCt7CbeBh9xBh9oO7bk3etB4FYq8Vwt7Vq1e5ePEi169fZ2Jigt///d8nODhYvk8NYy4Enu7ubt577z1+9KMfSTOzXq/HYDAwOjrKzMwMjY2N1NXVUVJSQmxsrBwjbzov4O6cFe2/evUqP/7xj7lx4wahoaHExMSQmppKdnY2qamphIWFubgPiL6aTCZiYmKIjY1Fr9eraq8dGhqitbWVmZkZ9Ho9VquVW7duce7cOVasWEFOTo50lfCG8VO2T1wslAhBVIxrSEgIL774Inq9nn/5l3/h008/paSkRAqv84XXnUbC1OkNG67ysK+srCQgIIDg4GCp0RKLVO19EUKp8vZ0/vx5fv3rX7Nr1y6am5td3q/X6/Hz88NmszE2NiYPS3C9sXob/f39sq86nY6mpiZ+/vOfc/XqVV588UW2bt0qhXtv0JQoL5h+fn4UFRWRlJTE1atXuXDhAjt37pRaA4fD4TXCqxBYlL6hYk4+DNPT06pJu6TUNvb393Pq1CmOHTtGa2srycnJbN++nXXr1t1zoMzG8PAwhw4d4rPPPmNiYoJNmzbx3HPPERMTAyB91NSKcjz8/PxIS0vjpZdeIjk5mXfeeYejR49y9epVmpqaGB0dlWZOT+85yjHs6enhs88+49NPP6W/v5+wsDCWLVuGyWSitbVVCt9DQ0Ps37+fpKQknnvuOa86Lx5ER0cHly9fvufMiIiIID4+nqCgIKampgBn9hNh9RKm6CeeeIJly5a5nEWeEAiVbnChoaGEhISg1+ulJdlms1FZWUleXh4xMTFEREQA3uM+MBvKC4Xo/+TkJH5+fsTGxrJx40a++OILPvvsM95//32Sk5MpKyt7qL3p6zDnT1F00Gq1Mjo6itVqRafTYTQaMRgM6HQ6pqenGR8fZ2hoiNHRUXlYKDvm5+eHyWTCZDLJgJ/AwEDVO0ArN9ikpCRKS0uprKxkYGCA8+fPU1FRId8jhAK1b0biQJucnJRajrfeeovf/OY3ACQmJpKcnIzRaGRiYoJbt27R2dmJv78/QUFBLg76ahyz2RDzy9/fX2pCrl69yuTkJOC8RHV0dNDR0UFvby8pKSmsW7cOo9F4j7ZBrbgLr8XFxRQXF1NfX09bW5vUoj9MIIWnEWtJ7COzpTObmJhgbGyMsbExJiYmXNwm9Ho9YWFhhIWFqWrclM99bGyMy5cv89Zbb9HV1SUDe8rLyzEYDPfMO2WuRofDQUdHB4cPH+ZnP/sZADdu3CAzM5PNmzcD96YwUjN+fn7ExcURFxdHSUkJo6OjXLx4kZGREXp6eujp6SE+Ph7w/J5js9nkAX7z5k0OHjzI0NAQpaWlrF+/nvLycux2O59++inXrl0DYHx8XAriov3ecl4oUZr2hZZ8dHQUo9FIamoqBoOBqakpeW5MTU2h0+nQ6/VMTk4yNTWFXq8nIyMDo9GIzWZjZGSE1atXExQU5LFYCuW6jIiIICUlheDgYNra2mSQUnV1Nfv27aOoqIi1a9cuaPvmCqXAqnzGSquPUE7FxsaydetWmpqaqKqqYv/+/RQXF6PX6++xSs8F83IFGB4epq6ujgsXLtDZ2UlISAhpaWkkJycTGBjI4OAg165d4/jx41y5coWpqSmMRqMMHBEmzPT0dHJycjAYDDgcDpYuXcq6desICAjwinx+er0eo9EoB290dJSJiQnZdm8QCJQ3/RMnTvDOO+9w+vRp6urqAEhLS+ONN96goqICk8lEbW0tH3/8MV1dXTJNmBo0WI+K0l1genqaM2fOcODAAQYHB+95r8lkwmAwqHouzobSz1Gj0RAfH09qaiqBgYGyYIE34J6sfjY6OjqoqanhypUrVFdXU19fz+joqLxYJyUlsX79erZv305ubq78nJp8mMWBIbT7Ihrd/VARKINcpqen6enpYWJiAq1Wi8PhoL6+nitXrrBs2TLVCe1fhXs+ypiYGEJDQ6XwKtapGgJ8lPNS+P+tWbOGjRs3snbtWpYsWSKtW7du3eLy5ctYrVbph60UWD3dl8dheHiYW7duMTQ0xIoVK/jBD35AUVERBoOBrq4uTp48SXd3N/Hx8QQGBnLr1i06OjqIiYmhpKSE/v5+jh07xtjYGAkJCdIf2BMo904/Pz9SUlJITk6mu7tbjvfExASXL1/m/PnzZGRkkJCQsKhiCARibgYEBPDss88yODjIL37xCxoaGqQMoMx5P1fMifAqTHTidllVVcVHH33EmTNnGBgYIDw8nPT0dKKjo5menqarq4va2lopAN2PY8eOERERQUREBEajkbS0NOrr69m5c6dMUSQi39SCcqNSBqaBU3PS0tLCjRs3yM7Oxs/PT7UCgkhrAk4frQMHDvDjH/+Y48ePYzQaKS4uJi8vjzVr1txz4B89elSam9Xav69CKQw5HA7a2tpobm5mcnJSHv7CaT04OBi9Xu/1m5EyaEmY7LwBoW0Vm+jIyAhtbW0MDAwwNDREb28vTU1N1NfX09DQQE1NjdSgK39HQ0MDPT09vPDCCxQUFKhiTJWa1MHBQW7fvs3U1BTh4eGUlJSQkpIi9z93QU259nQ6nexzbGwsHR0d2O12BgcHGRsbIywszGvGW2C1WjEajfT29nL79m3GxsYAiI6Olj72X3WpWQiU55NwAwgICKCwsJCEhATAad3asWMH09PT/Pa3v+X48eOMjo7S1dVFV1cXSUlJct/xNsSctFqtDA8PY7PZiI2Npby8nJycHACWLFlCYWEhQ0NDxMTEYDAY6OzspLu7m8jISDIyMqipqaGtrY2AgACX2AtPoFwrGo2G5ORk0tLSuHLlipyH4FyzN27coK2tjZiYGK8TXh8mQ4S4HPv5+ZGUlERBQQF2u53a2lqam5ulz+xc7y9zIvUJjQBAW1sbX375JR988AGdnZ3odDqam5uprq6W0rcy+0BQUBCJiYkYDAba2toYGhpCr9dL94KRkRHGx8dxOBzU1NRw6dIlgoOD+c53vgOo24dEGVQBTkG7ubmZ5uZmUlJSpKlPjSiDHCorK/nRj37EiRMnCAsL43d/93d57rnnyMrKIjQ0lKioKMDpVjA6OipdQWJjY0lKSpI5Q8E7tbAajYbAwECCgoJcFrOIXvf393+gBkztCEHcaDTKsRofH2diYkIVAtyj0Nvby/nz5zly5AgXL17k5s2b9Pf3MzExIctwijUnzF5arVZqSaampoiIiCAyMpKUlBRpdoeFH1Nx+RV0dXVx+vRpOjs7CQsLk0Kack0p/++u8evu7qa9vZ2RkRHAaTEIDw+XScXVpGX+KpQpfYaHh2lsbGRwcJCMjAw2btxIenq6h1t4F+X5lJqaSnJysotPtXCbi42N5aWXXmJgYIBz587R1dUl/UMTExNV44f9qChTswkXsomJCbq7u8nKypJ9ioyMlJHr4HxWiYmJ8vklJyfz/PPPExAQ4PHxVY6DwWAgJyeH7OxsDAYDY2Nj8qJht9tpbW2lq6vLxf1Dref+10XsHcLVTqfT0dbWxunTp8nJySE4OHjOU2c9ltTncDiYmpqSB97Q0BCffPIJe/fupbOz0/kH7tz4hfYxMDCQsrIycnJySE9PJzU1lYiICGZmZhgaGmJqakpqGiYmJmhvb+fKlSscPXqU7u5u2trauHDhAmvWrCE7O/u+CY/VgN1ux2q1utyWZ2ZmMBgMqkrjMhtKbU9fXx/h4eG88MILbNu2jU2bNpGTk3PPRjo5OcnExAT9/f3MzMywdOlSNm3aRGxsrHyPWi8aD0Kj0UjBThwgytQ3K1asYOXKlVIb4C0BhUqNiNFoJCgoiIyMDMCZ0qalpYXQ0FBVmF7vh8PhYGBggJs3b1JTU0NdXZ3Url6/fn3WzyQlJbFmzRrpf3748GE+/vhjwLmHiYTxSjy1Tt0F0OHhYflzEQNwP4FGq9XK7AKiCs7Vq1elZigyMpLExEQZ2KRmRYA77hrm3t5e/Pz82LBhA9u2bZPFCtS0FsUcUrZHnKF+fn4YDAaGh4fp6Oigq6sLcFrrxsfHVZMx4XFQZrawWq1S2SGEGuVZLoQh5XwMCgpi+fLlLsGXyrzFC4ly79Tr9aSnp1NSUkJiYiL9/f2A83I4NjbGlStXqK+vZ8eOHdLa6m2+y49CcnIyy5cv59SpU5w7d45ly5axevVqwCkTzVVavsfaqcShDs7Jtm/fPn7729/KVELg3BCDgoKw2+2kpaXx5JNPsn37drKzs0lJSXmoBfnpp5/S0tJCd3e31H4NDQ25LGi1CIHKNhgMBgIDA10OF7vdzsDAAGNjY4+U126hUOblFRtramoqb7zxBkuWLCE/Px9wrfQjxiAoKIjIyEjZp4KCAtavXw84LyIGg0F1/X0YHA6H1CiL/goNXlpaGlu3bmXlypXA3c3Mm/ophFdw+jBrNBppriwsLFRlX8Q8tdvtNDQ08N577/HZZ59x48YN+Z7g4GCioqKIjY0lMjJSHhyJiYls27aNJ598UgoTFy5c4Pbt24SGhhIaGiqfh8ATe4vSagNIwRWcl6bAwEACAwNd2iYOc3E4arVaGSR06NAhlyhvcWERKLW8akYZmDc5OSmFA5PJREVFBVlZWYDTevAg4X6hUQbPiQNcq9XKIOSpqSmuXbvGhQsX5Geio6PlOeGthV6Ugp7wgfT390ev12MymeT5YbPZZNo3ZU54pW/pbAHbnnweSsVUQUEBK1eupKOjg8HBQTnWjY2NHD16VApx3noOPgjlc0hLSyMvL4/q6mqXixjMrRvP1xZexSENTm3FBx98wPvvv8+VK1dcUiOZTCY2b97M+vXrKSoqIiMjg7i4ODkJH4aMjAyZJFen05GZmUleXh7+/v7zXoLsUXA360RFRZGens7Fixflz0Ri7aSkJFnFSEThqaUPyv+HhoZSWlqKVquV5kXxmjB/KAVeg8GA0WjEaDTK9CCAi7nW2xgaGqKlpeWeRWgymSgqKiI7O1v+3Bt90mZDDb6CD0IIZyLf8MGDB10EV39/f773ve/x5JNPyqBJIajpdDpiY2OlxWjt2rVcvnyZd955R2q51KIVEcJre3s7t27dklodnU5HRESEdBtQmvxnZmakIADQ1NTEiRMnuH37NnBXiBL/9zbsdjt6vR673c7x48f50Y9+xPXr18nIyJCCK6h3LSoDDN0VG5OTk1LAA6cgEBcX55JBwltwd5vr6elheHhYWnKE9lX4TIrMRMLsLH6H0u1AbSiVbwkJCTz11FO0trayb98+qVybmZnh2LFjcgw3bdok+6/T6VTZr0dFOS9tNht9fX309/cTHx8/b9acr/1bxUBMTU1x4MABfvnLX3LixAn5ekhICCkpKZSVlfH000+zefNmlyoNIshLWd1F3KhnZmYYHx8nNDQUnU7H2NgYo6OjgFP7EBERIU1doqa8WlBOxLi4OJYsWcKRI0fkwTo4OEhdXR3d3d3yfcqbuJoQmnVl7lLlBiMOTdFukUWit7eXuLg46QsL3leKUlRampiY4NKlS1y9etXFZAt3NXvKi5g39VGgbLPIsWg0Gl20HGpDCK8Gg4GgoCACAwMJCwsjLS0No9FIXl4er7/+OsuWLbvv77Db7VITu2zZMr788ktaWlqoq6ujp6eH5ORk+bc87ToxPT3NxMSEnIP+/v4kJiZKlxyxj4qxEmu2ubmZI0eOUFVVxdjYGP7+/tjtdoxGI/Hx8S4aZrUI7PdDCENCaXL9+nU+/vhjjh07RnR0NN/61rdcLpJqdClz16YrLx0iIE+4rMTFxRETEyODz4RQq8Z+zYZSOzowMEBDQwMdHR1oNBoCAgIICQmR57hOp5u1CtODCh2pAXGBttlsBAcHs3btWk6cOMG+ffsAp5ukw+FgaGiII0eO8MQTT7Bp0ybAu9x0vgqlEstqtdLa2kpHRwe5ubnzNl8f+cmJqDJhsjly5AgfffQRV65cke+Ji4tj3bp17Ny5k9WrVxMfH+8StAPIG8f9yjGK10S1CiHsKfOKifepBdEPMYjBwcHExsbKWttw15XAWzYgJUJYUI6Xsn54fX09u3fvpru7m1WrVpGXlyff5y2L1N0f6ebNmxw7dszFFUaUcjQajQQGBroc+mrZVL8u7jmIQZ19UgrU+fn5PPPMM1RUVMjKLhERES5auNkQFyohEIkUfJcvX+bWrVtS8FVDIJPYD/39/ZmamkKr1UqhBpCBg8oDsbW1lffff5/du3fT1tYG3J27KSkplJeXy2h3UL/wquxbZ2cnu3fvZv/+/QQHB/ODH/yAP/mTPyEqKgqbzSZzhaoZEdAj2tnQ0MCBAwe4efMmGo2GkJAQAgIC5PmhxnX4IJRnw+DgIJcvX5ZKHFGJajGgDKyMjY0lPj5e9k8ZnCXWp8DbxvNhEXl7YX6tH48sUSgPdrvdTmNjI7W1tYyPjxMTEyP9OtasWcPKlStdggFE7joRwfygA0EkLz579iyHDx+WwmthYaFLRKKnD5Wvwj1i2OFwyMAmb0CZn8297jEgs0L09PRw5swZamtrSUlJ4ZVXXqG4uFi+Ty2uHV+Fu/Da1dXFtWvX6OvrA3AJ1srKymL58uUuQoTaBYCvQqvV4ufnx+joqFxzalxjyuecmJjIyy+/zNTUFAkJCXJ/EGsNcLHsgHM+it8hAkcEwpypZKG1z0JgFpkQamtruXXrlkt7lOMi5qTSRCsq3dTU1AB391SA3NxcNm/e7FLC0RsumCJJ/fHjx9m9ezeNjY3k5+dTXl4uLT1K30lvQRRCuXLlCoODg9KypSy5LbTm3sLU1JS0io6MjHDmzBn6+/sxmUz09/dz4MABWlpaCAwMlC4TYWFhslSssqQqqFvYU+5HmZmZFBcXc+7cOYaHh6VGWZjTh4aGCA0N9ZozUeCen165/yhTKzY1NdHX14dOpyMxMdHlfJxLC+wj71bKP+zn50dycjLFxcWEh4eTm5vL008/zYYNG6TQKvzHRMWsr0JU1NBoNExNTXH48GE++eQTBgcHSUtLY8uWLTI3HKjzYBXodDqZwF4wMDBAY2OjzMagdu4XrSuiBsVkvHjxIsePH0ej0bB582Z27NhBSEiINM2qybXjUejs7KSlpYXh4WEZiCUEm9zcXMrLy4mIiHAJIvFmlMJrT08Po6Oj0ldUTYeI8jnrdDoXLb9ApP/6KkTFP1G0oLCw0EWo84TLi1JrNTIywvnz56mpqZEX4enpaalFBVz+39fXx65du3j33XelRUz4EU5NTaHRaMjLy6OkpETGDYhxVyNirxEJ+ysrK/n1r3/N2bNnCQ0NZfny5S7+9d6y1yj3i97eXlpbW+UlWRTsaWho4Pjx4yxZsoTg4OCHOkPVgnLNdHZ2yuwfIvvFtWvXCAoKwmQyYbfbGR0dJTo6mu3bt/PCCy+wdOlSKeApL55qw90VpLi4mOeff57h4WHq6+vlmh0cHKS6uprq6mpWrFghx9JbgvBmC5BTxrsALi4DQUFBLF26VObkh7mV1x5ZeFVucAaDgbKyMhITExkfH5c+Z0JwhbsRrA97qxdFB4TAW1dXJwW9jIwMiouLXXwp1bjhirx9/v7+ZGdnk56ejslkYmpqCrvdTktLC729vS7lG70N5WYyPT3N+fPnuXTpEvHx8RQUFMjblrf1T+nbODg4SENDg9x03Q9Ff3//Oc9d52mUG9Rsmna1MZtJX2inHkb7JvzVBgcHZUL0zZs3s3TpUvm6J4qJKP+ew+FgYmLCRRvsHlAnhFmHw8HZs2f58MMPOX78OICsEiesPenp6WRkZLhcStS8RoULi7g8VlZWsnv3bux2O6+++irf//73KSkpkRpX5ToV5497jk0xrp4+P0S7JiYmGBwclDl4DQYDo6Oj7N27l9raWkpKStiwYQOrVq0iLi5OBsDO5ieqFkwmkyxLXFlZKVO02Ww2eaaLIF+R2UbkQR8eHuYP//APPVpF62ERZ4Y4E7Oysti5cyfNzc10d3e7VGWsqqriiy++IDw8XPZNzb6vIi5JnAVKi4/dbndRNiqt6mNjYxgMBjIzM4mJiZEW6Lns52NpXrVaLfHx8bKGtEAcHvdzwn4QyhxuNTU1tLS0yNdyc3MpLCwkNDRU1RGIyqo4SUlJxMXFERISwsDAAFarVfqEeHOuN2E+9/Pzo7q6mpMnT9LW1kZpaSlJSUkuE/5hUPpYCo2upxBzfGxsjM7OThkk4y6IW61WRkZGiI+P97qAtPshBCCDwUBAQICMlvV0wNL9UProiu+/aoMUxQqMRiMajYaBgQFu3bqFw+EgLy+PsrIyWTfdZrN5pFCDu4UrNDTUJa2VqIQmEJrHo0eP8tZbb1FZWenyXuFfaDKZSE5OdlEAqHVsBcq1dezYMb744gvsdjv5+fk8//zzlJeXA85zx2azSWFoNm3ybPuKGi6e4nAX83hoaEgKPc3NzVy5coWWlhZ6enrYsmXLPVkV1HQOigxAWq2WsbExPv/8c7744gs5ByMiIsjMzCQ+Pl4GcQtlz9WrVzl9+jQ//elPCQ0NxWQykZ6ePmvqLLUhrCV+fn4yrWRkZCSDg4MYjUasViuNjY0cPnyYNWvWSOFVjVkxlK6C7jEd4uKk3IPEhVBYsPR6vSw3Ll73uPD6MLgHLj0M4kYsbs1nz57lZz/7mdR6FRYWsnLlSgoKCqRJRa1mWveAJqVgpnyPWhfhgxCTVEzCxsZGPv74Yy5evEhQUBCFhYUkJSW5aPAeRYBV06XEfX4p/ZdFNL5S8yo+420o+zgwMCADXpTBlGo+NJQ8TDsdDodLbtva2lpqa2uJj49n9erVcsP15BpV/l29Xk9MTAzR0dHS79Fut7vkfQWn9u6TTz7h/fffx+FwyANT6VKg1+sJCQlxcadQ87iKA8/hcHDo0CH+6Z/+iXPnzhEdHc3rr7/OihUrANf1J1yVvGGPFe3T6XTEx8eTlpb2/2fvv6Pjvu4DbfyZAQaDjkHvjeiFIAgCJNhFUmySLJGSZSmW5BbbsSdK2V3n/N7dd/fd7Pue3WQ3uznJJp7EGyeWIsuSLFuyRImU2CvYQRAkUUgCRCV6LzOY+vtjeC+/MwQpUgQwg9E85/CAmIZ757bP/VTa2tru0YSPjY1x+PBhurq6sNvtZGdny3WrdDHxBpSR552dnRw+fJgzZ84AkJeXx9q1a9mxYwdLly4lMjKS6elptFotOp2OEydO8Gd/9mc0NTXx85//nPj4eF566SUSExO9OoAUXC0Yov/KM0387OjokO4h4J39cXfT6OvrY3R0FJVKxfDwMFarldLSUpfLB8ClS5c4d+4cOp2O6upqMjIy5q2Njy28ipRXQp2sDIR4FJSBMlNTUxw9epQPPviAwcFBsrOz+drXvkZ1dbUUbj1hyvsyiMTMSsd78A7h7MvgcDhcqqp1dXVx4cIF7HY7a9asYd26daSnp7vknrzf4hQmCXHr9pboYKUA7X5TFHNblA8VVgdvNv18EcpCH+3t7UxPTxMbG3vfTCDeyKOsJ61WKy8dt2/f5uTJk9y6dYu8vDwKCgpcArnAMz6Uyj00LCyMyspKent7ZfUwk8lEfX09paWlJCQk0N3dzcGDBzl48KDcZ0S0s3sKrYyMDJKSkuTne+NeZLVaXTSK7e3tfPjhh3z22WfEx8fzh3/4h/z+7/8+CQkJwL1+h+CczyMjI9hsNrm3iOA8s9lMWFgYcXFxHvM9VP6tiIgINmzYQHBwMNevX6e/v5+hoSF6e3ul9WdsbIzz588THh5OaGiorF7obfumaE9bWxuffvqpS57zlStX8vu///usWbNm1nn35JNPsnPnTukuePToUSoqKoiOjvaaft4P5WVJXBiVBWvE92M0Guno6KCzs5Pk5GSvUsCJNoq1ZDKZOHfuHHv37pUZd6anp5mZmaG0tJSVK1dSXl4utcjHjx9n//79pKWlsW7dOhf3wbneZx77tHU3X31ZlJ0bGhqSfqHgjCYWJUnBe/OizoYyqEnJYhC874dyEt68eZOBgQFKSkrYtWsXVVVVcj58kUbAW30qldpG5YVDaNHVajVZWVksWbJEbqjihr0Y5qTAPYn48PAwfX19mM1moqKiiImJISAgQPbNmzbZL4tSIJqcnOTo0aOcPXuW8fFx4uPjSU5OloKtVqv1WNUpMY9EMZjCwkKMRiMNDQ10dXUxPj7O3r17CQ4OJicnh4MHD/LOO+8wOTkptbNi7gYFBUlBPD4+nsrKSlkGGDxXYvNBOBwOubaGhobYv38/R44cwWKxUF1dzcaNG2VWib6+PoaHh6USJTg4GIfDQVtbGzdv3sRisUg3kJGREXp6ejAajRQWFrJz5055rnjqAupwOGSO0JUrV9LT00Nvb6/0FT106BANDQ1MTU2hUqk4ceIEt2/fZmBggN///d+X83Q+BIRHQWlqBmce3pqaGnmOl5WVsX37dtatWwe4utcJVCoV69at48KFC5w4cYL29nba2tooKSlx2Wu99dxQatJjY2OJj4+nra1NBko6HA6sVisNDQ00NjaSkJDgcl56eo9VWjAmJyfZv38/77zzDqdOnZLjKLKWHD16lMzMTJ555hm+973vSX/07u5u1q9fLwPuxOfNdd+8RlWk3EBNJpNLbfH4+HhZZWRiYgKr1UpERMSi0HQJ4XWxpcWYDbFpaLVaLBYLx44dY8+ePQwPD7Nx40Y2bdrkkhbDXWNls9lcNCrKnLHNzc2cPHmSqakpKisrWb58OSEhIR4VCGfzcbXb7cTExLBixQqys7Plc4stNc9siM03ODiYhIQEafIRApynN9bHQTmPmpubOXHiBJ2dnbS3t3P79m0AEhISiI2NlcLrbNq8hUY5p9LS0li1ahWdnZ2cPXuWCxcuMDg4SGpqKq2trTLdl8ViISsri5mZGXp6elwCvXJzc6msrJQ+st5QoVAc6OKyqNFoXPaOmpoa3nvvPa5du0ZERAQWi4UTJ07Q0tLCwMAAra2tDAwMMD09zcTEhFyzIghPuFCIoKCxsTEAVq9eTVpaGtnZ2R79DtxdRDIzM8nIyGBycpKioiIKCwt57733OH36NCaTCZPJRHNzMw0NDVLD5w0Cnfv3FxcXx5IlS2htbSUyMpIXX3yRbdu2yedF2jal37pGo2HlypWy6t3U1BRms9llH/a2i5ZAKbwGBASQnZ1NQUGBtJSA80w0m800NzfT2trK5s2bgbsuaZ7cY5V/v7e3l48//pi3336bjo4OqqurKSkpkVYrUbzn7NmzvPnmm9TV1REfH099fT1ZWVmsX7+e/Px8GSw6H4oqr5D+lFW2RkdHqa2tlbkJ165dy8svv0xOTg6ASyaDxYCYlEq/s8WK0k+wubmZt99+m8OHD5Oenk5FRYUcI2UEotDaCUd25eIUlUeuXLnChx9+yCeffEJERARhYWEy2tuTQW3uvsri/6WlpTz55JMuKUA8LeQ8DkpTl0iaHhsbK03L3h6N/jAIrZpKpaKuro6/+Iu/oLOzkyVLlmC1WtFqtUxNTdHQ0IDRaCQxMZGEhAQZtOYplH9bo9FQUVGB3W4nLi6OM2fOyOhsjUYjq/kUFBTIGuufffaZzOCSmJhIUVGRSxCFJ4NFlCVS3b9ji8XC+Pg4165dY8+ePZw+fRpwpg07duwYp06dIjIyUqZ1E0qN4OBgtFqti1VBBKzZ7XaZ+L+np4dLly7x29/+ltjYWNasWeNxs7TSwiECzwoKCigoKGBoaIgrV664KHWUe443XJzdg6oqKipQqVSyIuY3vvENEhISpIbcvUS8EHJiY2MJDw/HYrFITbpyfnhDX2dDaWFVqVQUFRVRUVHBwYMHGRkZAe5mkbh16xYNDQ10d3e7+Nh7ErPZLF1oGhsbeeONNzh9+jSbNm3iT//0T9mwYYPL65ubm/nggw/49a9/TU1NjdxLnnvuOTZs2ODiqqMs1DRXeMWJq/zS6urq+D//5/9w7tw5kpKS+MlPfsKuXbvu+15vN9WKiGWhWVjMiM1pamqKkydPcuTIEWw2G+vWraO8vFy+TmghHQ6HPDTctSnT09NcvnyZAwcO8Nlnn1FfX49araa8vJzs7GyvKE3qLrwGBQURHR3NihUrZES6MrPGYkPpfuNwOGROW3FJma2++GJFaQ6zWCwMDw9jsVhoa2uTc+3cuXO0t7cTGhpKTEwMpaWlrF+/noqKCmJiYjy+zwQHB1NUVMSSJUtYtWoVhw8fZv/+/dy4cUPmbq2urqasrAyr1conn3wi36vVasnOziYzM9NlrnpSWyfccNyFxsnJSU6fPs2RI0e4ePEiDQ0NLtpjkfJrYmKCpUuXsn79emw2G1qtlurqavLy8jAajVKYDQwMxGQySaF/ZmaGvXv38t5773HgwAEyMjIoLy8nKCjIo/NcaX10/250Ot09Ar43unuAqxC2dOlS4uLi0Gg00j/Z/cwWvwslRUdHBx0dHR7L9PFlEe2cmZlBq9USGxvLsmXLSE1Npa2tDbi73kZHRzlz5gwHDhzgmWeeISkpySX2wBN9Vs6lGzdu0NzcTGpqKs8++ywrVqy45/UFBQV897vfpbCwkLfeeosPP/wQwCWnv7CqzAcePXHtdruLM73D4aC+vp4jR44ATmHBbrczPj5OT08Pw8PDhIWFoVI5S6+mpKS4vNcbJ7m4ecbGxtLb2ysH0hvbej+E5kZ81xcvXuTjjz+mvb2dsrIytm7dKm+PYkzFZqSMah4dHaW9vZ3Ozk5u3brFkSNH2LdvHyaTiSVLlvD000+zc+dOli9fLhe5Jw5XZXCLTqeTpf6ElkhENAPSJ3QxoowKdjgcDAwMSDNzYGDgos6g4I67+X39+vV89tlnzMzMSPOlODTF68+fP09jYyMvvPACO3fuJCoqCri7HhYasZbCw8OlZrywsJBbt24xPDxMTk4OTz31FFqtlrfffpumpiaX4C2xhpW5Tz15SIrvUCQ27+vrk/EO58+flxWYBAkJCYSEhBAZGYlOpyMpKYknnniClStXolKpCA0Npaio6KHakJ+fT3h4OKdOnXLZozwlDAotuMjkoixGcPv2ba5fvy5dPETKqZmZGSnwecv5Jy7EwooxMzNDVVWVdMURgp37e0Q/JicnuXz5Mo2NjYBTBjAajczMzCw6qys4cyqvXLmSlpYW+vv75V4DTs3loUOHWLJkibRyibPTE2MpxsBoNNLW1obZbGbNmjWsX78ecMZEiEBskW0nMjKSiIgIIiMjCQkJwWg0Ul9fz8cff8zv/d7vkZSURFRUlMzwIlKozUX/PC68KnPxXbt2jaamJvkFDQ8P81d/9Vf8/Oc/d5HeHQ4H69ev5wc/+IGM9ha3NG9AOfnUajXx8fGkpqYyMjIiNQjeemueDWUgw9jYGIcOHZIJ0EU9+YiICJdqWu6T8/bt2+zfv59PP/2Uy5cvMzIyIh3Aq6ur0ev1bN68WaYDEnhSMxQWFkZCQgLJycm0t7fL5Nq1tbUcO3aMbdu2ERcXJzNfeMPh8ago07iMj4/LNCiLKdPAo1JQUMCmTZvo7u52iYQWpKamYrPZ6Orq4oMPPiA0NJTVq1d7XHh1R5TCtVqtTExMyBRYHR0dHD9+nKtXr0rhVQhFwcHBHveNFGl4xPyqq6vjk08+4cKFC1IQN5vN0jcVnAVqKioqKC4uJjc3l6ysLJKTk0lISJAmyUfpV2pqKq+//jrbt2+XOY0f9TPmEvcgUVEatrW1lUOHDnHq1CkGBgbketVqtcTFxXnNmSf2P1GBsLa2lnfeeQer1UpUVBRLly4FmDUAUim8WiwWmpubaW5uxmq1EhISIt3PFgtKN7fw8HCqq6tpaGjg0KFDLiVzJycnaW5ulj734FnfZdHusbExent7mZycJCEhgZycHMLCwqSFXJwNIyMjfPrpp/zjP/4j165dQ6fTERcXx8DAAP/4j//I1NQUr7/+OjqdzmVez9W54pEdWGxeyjrcx48f5+jRo9TU1BAUFMTMzAyTk5MyP5w7FouFl19+WQqvwlfP2xCppaanp10W4GISdoQWzmKxcOTIEfbu3cv09DQ7duxgx44dMurX/UBvamqisbGRjo4OGhoaOHPmDFeuXJGVYSoqKti4cSM7d+5k3bp1LtoGT34/So2j+y1YWAcOHjzIkiVLZLL3xSjwKTdKu91Oe3s7ra2tWK1WaeGAxVclbTaUF7D4+HjCw8PRaDRER0eTlpZGbm4u4eHhREdHo9VquXLlCocOHZJ+d96CMMOJQhLCHUe5dqxWK3FxccTHxzM9PY3D4SA9PZ0NGzZQUVHhomn0hD+5+Jujo6OcP3+ejz76iGPHjtHR0eGSu1atVrNt2zZWrlxJSkqKHKfk5GR5kXBHVCITgbLu1gNRrjwyMpLY2FhiY2PnubcPRrgmKatKgjM12IULFzh16hS1tbXcuHFDau0SEhJ45pln2LlzpxxLTwdTKpVHt2/floUJMjMzXS4hswlmQpMs0iV2dnbS19cHON0lUlJSXAp0ePry9UW4l65OSUkhISHBxVoXFBTE+Pg4XV1dLqXiPemi5W4RAacCR+RyVQZjT0xM8Omnn/Lf/tt/o7GxkejoaF577TVycnI4cuQIH330ET/96U+x2Wy88sor5Obmuvg4z4WQ7jH1gdhM2tvbef/993n77bepr6+XAl5gYKBLDlCVSiVTNIBT6+AtGjp33JMVd3d3c+vWLRe/rcVwk3RPkSRu0xcuXKCwsJDvf//7fO1rXyMgIEAG3dntdqanp7l27RoffvghH3/8Mc3NzfL7yMjIoKioiIKCAp544gm2b98uJ7Uwi4FnTdXib9tsNsxms0ukqMPhYGJigps3b0rNMSzeVFliXITwOjg4SHBwMLGxsS7C62Lq1xdx69Ytzp8/T1tbG2lpabz88sts3bpVWg9GR0eJjIzEZrMRERHBk08+6SIseVJQEPuhO8q1Exsby/Lly2lsbKSrqwur1Up6ejpPPvkkBQUFgDOIQlQYWyjsdruLkqGmpob/+T//JzU1NXJfFz6OFouFVatW8YMf/IDnn39+1nbO5tYSEhLiIsg/CG9wi1H6nINzXG7dusWhQ4fYs2cP58+fZ3R0VF6Mw8PD2b59O6+//jrLly8HPJ9iSVyoxLjevHmTY8eOMT4+TkFBAYmJiQ98vzIe4vr169y8eVM+l52dTWFhIaGhoV6RDuxR0Wq1xMfHExMT4yK/iDknsmV0d3eTmprq0aw1VqsVjUZDXFwciYmJqFQqOjo6aG9vJzMzk/b2dtrb25mamqKlpYX33nuPxsZG4uLi+MEPfsCPf/xj0tPTWbp0KSaTic8//5w333yT8fFxnnvuOcrKyoiMjJyzCpoLKryKyHsh2Z8/f55//ud/5qOPPnK5fQCEhoYSGxtLeno6RUVF5ObmEhsbK4UEpdYLPJNI/H64C6/T09NMTU25ZBzwJm3O/XAXXi9fvszx48cBZ7LpDRs2SJ/Pjo4O+vv7aWlpobGxkYsXL3Lx4kX6+/sBSExMZPny5Wzbto3Vq1eTkJAgo7nh7tyYraTjQiPGJiAgAK1WO2s1IlFidLGjnKciECYtLY3k5GSfEF7FhULkwmxvb+e9997j888/Z2xsjCeeeIL169dTVVUFODdwkW3giSeeICQkhNTUVBd/O29wGQBXP8mZmRm5diwWCwMDA4yMjLi4DSgjfj0xd92FnMbGRhnfoHzNhg0bWLVqFdXV1axdu3bWuSeK47jn0X6Uan6iSqMnLCZKzZNKpaKtrY3jx49z+vRp2tvb6e3tpb29XZaIBSgvL2fTpk08++yz0gwv3u9J3PcHoTlNT0+nqqrqnvSJYk4KQU5cNmpqavjFL34hMw2Vl5dTVVVFamoqsDjOTHA9IzQaDXl5eSxbtoyMjAxu3LghlSKCpqYmTp48ydatW2UKO0+nPsvPzycpKYljx47xZ3/2Z6SlpTEwMMDNmzfp7e1ldHQUi8XC8uXLefnll3n22WdJT08HYMWKFfzoRz8iLy+Py5cvc/jwYc6dO8fSpUvZtm0ba9asISoqCpPJhEajuSfrxMOy4MKrKD0Jzs3r8OHDTE9Pk5+fj8lkoqurC4fDQW5uLhs3bqSwsJDi4mIptd8PTws8SpSLWaVSkZiYSGpqKj09PffktvNmlBqBtrY2zpw5w+DgIOXl5WzduhWNRsPAwACXLl3iypUrXL9+nXPnznHt2jWZGHz16tUsW7aMgoICiouLWb58uXQzAKemISAgAI1G4yIkegOBgYGEhobKQ1/49Gq1WkJCQrzqwjQX2O12WeJXWU98sWk7lCiDB9Vqtcxw0dnZSXl5Odu2baO4uFi+XgSDRkREkJeXN+tnelpYECgjtIODg+X/zWYzt2/fpre3VwZXxMfHuxz+3rBfarVaoqKiMBqNpKamSqXE17/+dZ5++mnS0tKAu0JOYGCgFFQftziOWq32GjezGzduyJyaly5duud5nU7H8uXLefbZZ3n66afJy8uTFwHxnXga5ZoQcy45OZmMjAx58RMZFMS+qdSQ37hxg/fff59//dd/xWw2U1hYyMsvv8zatWvlOHvDnH1YxPiIAKXi4mIKCgq4ffu2vHgJGhoaOHjwIAUFBVJ49YQbpHI9LV++nF27drF3714+/PBDl74EBgYSHx9PRUUFzz77LNu3bycpKUleCAMDA9m4cSOlpaWcP3+et99+m08//ZQLFy5w6dIl1q1bR1VVFcXFxY9VPnbBVQjKhRYVFcX69etJSUkhNTWVmpoaPvzwQ2w2G6tXr+Z73/semZmZBAQEfGnp3BMo1eIBAQHk5+dTVlbG+Pi4i/DqzTdJ4UQfEBDA0NAQhw8f5uTJk6jVatauXUtmZiYnTpzg8OHDnD9/nq6uLsbGxqSmIDU1laeeeoqnn36asrIyaTZxF1CV6Zi8RSgQ2O12LBaLvCUrTezePHZfBovFwuTkpKzmlJ2d7aLNWgxuLrOhHCdhku3t7SUrK4tdu3bx9NNPExsbe8/ButhQ9lOj0bgEPubm5lJdXe1iqfKE9ti93PKyZcv47ne/y9TUFBkZGWRlZZGXl0deXp48xOGu0KK8THvbXvGwKAOb7HY7ra2t/Pa3v+XNN9+kqakJcI6fsNLpdDp27tzJSy+9xIYNG6QWU2kR8waU+0NsbCwRERGYzWbGx8eloCasa8o15nA4qKur43e/+x2HDx+We21xcTGbN2+WucM97RrxqLgXONHpdBQXF9PU1ERLS4sMoLRardJiqdSye2K/VY5LUVERP/rRj8jIyODzzz/n9u3bMvVXVVUVhYWFJCQkEBcXJ31iAZfLdGpqKnFxcbIM85kzZ6itreXMmTM8++yz/Nt/+29ldbsvc/4v6A7mnox66dKlJCQkEB0dTXd3N4cOHWJycpKYmBiWLl0q6+VarVYp9ClL0Hm6Msz9cE9WnJGRQU5ODpcuXWJ4eBjwHtPj/RBJsgH6+/s5e/YsAwMDhIeH09fXxwcffMDVq1c5ceKEHBuNRkNZWRnLly9n5cqVrFq1itLSUpfUKFarFavVKueCN38PQqATlYtE1KTZbJb9WMwoLQSTk5MYjUbCwsJITU2VORnFoeGN6+xhECZhu93O+fPnOXXqFFarle3bt/Pss8+6+OMpL9bCLUQcQt5+cCqFV6VwBE5hQqkBE69ZaNz9dUtKSoiMjGR6epqYmBgyMjJc8kOK1DrevEc8Kkq3uZmZGS5dusThw4dpbW1FrVYTGRkp95vw8HC2bdvGN7/5TbZt2yY1cUr/Zm/A/SIfFRVFaGgovb29XLx4kZycHCoqKqS2fHh4mPHxcYaGhqivr+fAgQMcOXKEvr4+kpKSWL16NS+++KL0z16seaaV8zYxMZHq6mpOnDghfXojIyPlOZuYmOiioPNksLIIBhVKp7KyMgYGBoiMjKSwsPCedHTC/UaZtk3MUa1Wy+bNm0lOTmbt2rXU1NTQ0dFBeXk5iYmJj5Vb2SOaVyFlL1myhKysLLq6uqivr+fChQuAc1CVN2+hsvb2A+R+CEFbiTcvRvfKOy0tLZw+fZqxsTGCgoL45JNP7hHeEhISWLNmDTt37uSJJ56QJRfdeVxz30IiNEWivUajUfoizZXTuSdRrqebN2/S2tqKyWRyKZEqorMXm/Dq7l/f1NTEBx98wNmzZ0lJSWHLli3ycjybVse9qIa3o9xPzGazzJkKzoNQXLg8jXIexcTEuPhDKp8Tfsq+hjK3ssVioauri56eHqlJHR8fx263ExISwo4dO/j2t7/N5s2bpeDqrrn0BpSCCzij0rOzs2lra2Pv3r3SnFxUVER7ezvnzp3jxo0btLS0yMBJgOTkZHbs2MF3v/td1qxZIwOBAwICFs2ZoUSlUslqYjqdjhUrVrBkyRJZLW56elrmOF+1apXMnASeU265j2VaWhopKSkuFgN33NvqfkkNCAigpKSEoqIivvGNb0irrnjflz1bPJYqS2gn1Wo1Q0NDXL9+XabHiI+PdwkuEKm1FgvuKvDp6Wmmp6ddBl6YKb1RIFdOYKPRyIULF7hy5QpwN61JWFgYlZWVJCcnEx8fT2FhIStWrKCoqMjFp1WUxhU+h97YX3fE2IWEhFBQUEBpaSmdnZ0uCaZjYmJcfLAXkyCrDEgD5/wUfsuAS/ogpQZ+MaG8gFksFk6dOsXRo0cJCQnhySefpLKyUmY0EQL6YkYpvNpsNqampmSWDKEJ8UbXD+U+abPZZBDWYhRWHgaltSMgIACdTkdoaKhLMO+mTZt46qmnWLlyJSUlJdLVymw2e+08VfpnZmdns23bNq5evcqFCxfo7Ozk+vXrpKSk0NfXR2Njo8y+o1arycrKory8nLVr17Jx40YqKipctHiLHTHeiYmJrF+/noaGBurq6uT6FHlwhbULPO/fq0zh5n62mc1mLBbLF1pPhQJByBPu5eEfF4/sEMoNy2q1MjU1JUv5qdVqmVlAsJhKxMG9AVvR0dHodLp7So16eoI+CHFzGh4eRqvVkpGRQVdXF1lZWRQUFFBeXk5ZWRlxcXHypi205eK2OR8TdiFQLtaysjJ27dqFxWLh9OnTqFQqKisr2bBhAykpKfJ1i1E7KTCbzWi1WvLy8khISJBRo4sZpQB048YNjh8/Tnt7Ozt27OD555+XfRSXqsWOu/A6Pj4uBSKtVktQUJBX9lPpw7oY94pHRTkGWq2WpUuXsnnzZkZHR2lra2PVqlX8yZ/8Cc899xxwV8nhTQFms6EsJBAdHc3mzZtpbGyku7ub3t5ePvroI/laIRBlZGSwevVqGdRbUVEhFQIi8GexX2KULoQajYYnnngCh8PB559/zqlTpwgICKCyspLMzEy0Wu09BTw82W5lDnCxTsU8fJi5qPT7FcoEpdLucbN8eHxmCCFHaOgiIyNZsmSJi9uAN266D0K0VyzA4uJiVqxYwZ49e+js7CQpKYnk5GT5Om+8XYpJFR0dzYsvvkh1dTUmk4nQ0FB0Oh0xMTFERUVJ86pykxGmMW/VLD8I940jKSlJ1nYWeV1jYmJISkpy0TAvpjnqvmGEhYWxY8cOCgsLCQ4OlqV+gYfOmelNKKOwh4aGOH78OOfPnycwMJCqqiqWL19OQECALFXp6YNirhH+ogIhFHpbP91TCi6mNfRlUfYxMDCQoqIivv/977Nz506mpqaIjY118SkUvsveGNDqjrJ9CQkJfPOb3yQ7O5uTJ09y6tQpbt26RVpaGqtWrZJV0kpKSsjMzJTFQZQshj5/Ee6uMNnZ2URHR7N69WoGBgZQq9WkpqbKdGDetgaU6fhEwOSXGRPl54jfH3d8PSK8KhstfFmFilnk1nSPSFyMk1hZcGHlypW89NJL5OfnU1hYSFlZmXydNx4sgtDQUHJzc8nNzX3g60QaKV+4LQtEf5KSkmTtaV9Eo9GQmZnpIrQKh/vFdvkA1/KtPT09HD9+nNu3b1NSUkJxcbFPmSQFyjUXFRXFhg0baGxspKmpiaGhISYnJ13S83hD371Bw+RpwsLCyMnJkVH1ApG3dzGtQTGWQlNcWFhIbm4uy5cvp7q6mr6+PhITEykrK5NCnPKcV5YX95UzRIlKpSIoKEjmOF8MuGdN+LLMR4yIx2eIe3CQ8HvyRv+sR0W5MJOSkvjDP/xDZmZmCAoKcoks9DYH/C/DYtlgH4Wv8sG6mA8PZWDS8PAwjY2NhIWF8eSTT1JYWCif86U5K/YQm81GTEwMr732Gna7nTfffNMntcu+zmI+E9y1y0uXLiU/P99FOTXb2hNKLD9+HgaP+7yCMyVIeHg4QUFBxMfHk5ub65KTUOk3spgQbRaaIPciC4shZ6EwQYoa7+IGpTRFLtbxeVisVisWi0UKRUK77InKPPOBCOgxm82o1WpCQkIWdb/cc7tarVaZdDsrK0seootZQLgfom8xMTHs3r1b5rAtKytzuTB7m3nyq4qIxBcBdQEBATL2Y7Gj1KS652kX/o9w19/5q6KJF5l6xHkSFBTktWk/vRmPCa/KgKaEhARKSkqorq6msLCQ6upqIiMj7ylPulhROi0LF4jFslBFyprFGHE+V/iSK8RsCHOWNweEPArK/SIqKoqqqirKy8tZsWIFcFczu9j3ldkQY+hwOMjKyiIrK2vW1/mCcOQLPEoAzGLDPQ5CIBQeX9U56OvnyULhFZpXnU7Hli1bWLJkiUyoPdvrFjNCu6XMW/dVXbx+/MwnyotWTk4OP/jBD1x8zHx97SlLU/rx42mUUebKHNm+dL77WXgeWXjV6/Xz0Q6vwdf7B77fR1/vH/j76Av4ev/A9/vo6/0Dfx99AV/sn++qH/z48ePHjx8/fvz4HCp/dJ8fP378+PHjx4+fxYJf8+rHjx8/fvz48eNn0eAXXv348ePHjx8/fvwsGvzCqx8/fvz48ePHj59Fg1949ePHjx8/fvz48bNo8FimXL1e/x3gF1/wMrvBYFi0mcT1ev3XgY1AObAMiADeNhgMr3qyXXOFXq+PBXYDTwNLgVTADFzBOba/MBgMi7rO71dgDH29f9/Bx/eZ2dDr9a8B/3rn1x8YDIafe7I9j8tXZK/570AlkA/EAUagHfgd8PcGg2HIc62bG/R6fRuQeZ+n+wwGQ9ICNmfO+Qrsp16zDj1Z5qEO+C/3eW49sBnYt2CtmR/+I84JPAl0AYUPfvmi40XgH4Ae4AjQASQCzwM/B3bq9foXDQbDYk5p4etj6Ov9q8P39xkX9Hp9OvB3OMc03MPNmSu+CnvNvwFqgQNAPxAGVAN/DvxQr9dXGwyGTs81b84YA/5mlscnF7gd84Gv76desw49JrwaDIY6nAfLPej1+tN3/vt/Fqo988S/wTmBb+K8jR3xbHPmnOvAs8CnytuWXq//D8A54AWck/q3nmnenODrY+jT/fuK7DMSvV6vwqkBGQI+AH7i2RbNGV+FvSbSYDCY3B/U6/X/FfgPwL8HfCHb/KjBYPhzTzdinvDp/RQvWodeV2BXr9eX4rxtdgOferg5j4XBYJAT1xcrXBgMhsP3ebxXr9f/I/BfgSdYxAfKV2AMfbp/98OX9hk3/hinNvmJOz99gq/IXnOP4HqHX+MUXvMWsDl+vgS+vp960zr0OuEV+IM7P//ZYDDYPNoSP4+D5c5Pq0db4cfP7PjcPqPX64uAvwT+1mAwHNfr9T4jvH4Bvr7XfO3Oz3qPtmLu0Or1+leBDGAKZ7+O+8o6/AqzoOvQq4RXvV4fArwK2HH6T/hZhOj1+kDgW3d+/cyTbfHjxx1f3GfurLm3cPqg/QcPN2fB8MW9Rq/X/wSnr3IUzgCudTgFvL/0ZLvmkCScc1XJLb1e/12DwXDMEw3y83h4Yh16W6qsbwA6YJ+POKZ/VflLoBTYazAYPvd0Y/z4ccMX95n/B1gOfMdgMBg93ZgFxBf3mp8A/xn4U5yC62fANoPBMODJRs0RvwC24BRgw3BGrP8MyAL26fX6ZZ5rmp/HYMHXoVdpXoEf3vn5M4+2ws+XRq/X/zHw74Am4DUPN8ePn9nwqX1Gr9evxKlt/V8Gg+H0F73eV/DVvUaki9Lr9YnAGpyCwSW9Xv+MwWCo9WjjHhODweCe+eMq8CO9Xj+Jcyz/HGcqJj+LBE+tQ6/RvOr1+mKcC7UL2Ovh5vj5Euj1+j8E/hZoADYZDIZhDzfJjx8XfG2fUbgLXAf+k4ebs2B8FfYag8HQZzAYPgS2AbHczdvri/zjnZ8bPNoKP4+EJ9eh1wiv+GAAxVcJvV7/p8Df47xJbzIYDL2ebZEfP7Pia/tMOM6k9kWASa/XO8Q/nKZngH+689jfeKqRc8lXba8xGAztOIWDEr1eH+fp9swT/Xd+hnm0FX4eGk+vQ69wG9Dr9cE41c124J893Bw/j4her///4TRt1QFbDQbDoGdb5MfPvfjoPjPD/ftSgdMP9iTQDCx6l4Kv8F6TcuenL1y4ZmP1nZ+tHm2Fn4fCG9ahVwivOKs2RAOf+FAAxVcCvV7/n4D/F7iIM6jA58x3fnwGn9tn7gRnfX+25/R6/Z/jFF7fXOzlYcG39xq9Xl+IM3l/r9vjauD/AxKAGoPBMOKJ9s0Fer2+BOhxHze9Xp+JU4MH8MsFb5ifR8Jb1qG3CK8igMJnKt0A6PX6XcCuO7+Kms2r9Xr9G3f+P2gwGBZtBRy9Xv9tnJPYBpwA/niWxMxtBoPhjQVu2pzxFRjDXfhw/9zwyX3mq8BXYK/ZAfyVXq8/DrTgrJCWiLNK0xKgF/iB55o3J7wI/F96vf4IcAuYAHKAp4FgnD7o/9NzzXt8fH0/9aZ16HHh9U5i7XX4SACFG+XAt90eW3LnH0A7i7t8Y/adnwE407rMxjHgjYVozDxRjm+PYTm+3T/A5/eZrwK+vtccxHmpWgssw5nKbQpnIN5bwP/2AU3zEaAApzVgNU7/1lGcbi1vAW8ZDAaHx1o3N5Tj2/up16xDlcOx2OeKHz9+/Pjx48ePn68K3pRtwI8fP378+PHjx4+fB+IXXv348ePHjx8/fvwsGvzCqx8/fvz48ePHj59Fg1949ePHjx8/fvz48bNo8Auvfvz48ePHjx8/fhYND50q6065wUWNwWBQ3e85X+8f+H4ffaF/4Pt99M9T3+4f+H4ffaF/4Pt99M9T3+2fX/Pqx48fP378+PHjZ9HwyEUKDAbDfLRjXpmlAsR98fX+gWf66HA4cDgcqFQqVKoHXoZnxdfHEHy/j4thnj4uCz2Gdrv9S6+pL4N/DF1ZjP0D3++jf5664ov983iFLT++i81mAyAgIGBBD1g/fh4Gh8OBxWLBarVis9mw2+0EBAQQFBSERqPx2vlqsVhQqVQEBgaiVrsaz+x2+z2P+fHjx4+v4RdeHxO73Q7gF85mISAgAACz2czk5CRms5ng4GDCw8MJDPRPPV9HaNq9DWEFUKvVBAUFERQU5OkmPRRir9FoNABMTU0xMTGBSqUiIiKC0NDQr5zgarfbUVaJFPuwN847gZh/SkuUN7d3oRBjqVKpvnLz2M+j45cgHgOHw4HVasVutxMYGOgXyO6gFFomJyc5f/48H374IdevX2fNmjW88sor5OTkyEPHv3H7Jt46vna7HZvNtmiEVoHZbJYCt81m47333uP9998nJCSEb33rW+zatQtwambVarW8PPoyNptN7sFCGy2Ee2/F4XBgNpux2Wyyvd62RhYapRUkKCjIL7z6+UIWXNpyOBz3aCsfZuGKW5ndbpemscDAQI8uepVK5XIAftnDWtzCBYt14So1Wna7naamJs6cOcOJEyd49913MZlMjI+Ps3nzZpYsWeLp5sr22mw2l+//YVCpVAQEBCzasXpclNoj5bp0OBwEBASg0Wg89t0o2yLWonJuBgYGEhAQgNVqpauri4GBAUZGRpiYmCAyMpLc3FxSUlLQarUeab87Yr8MDg4GnBfCffv28ctf/pIjR44AUF5eLoVXpbuOr6CcY4AUzjUazazCqrdenMDZdjGWc4G4jIlzcbHtS2KdarVar1lzfhYOcfkUe/PDsuDCqzAJPGrwjniP+Axv3JS+LL7SF6UQPj4+zp49e3jjjTfo6enBZDIBEBcXN6cb9+Mgvnfl3HqU9/rKuH0ZRP/dL17e4CqgvBSLtigvzODUYp47d479+/dz9epVmpqa6OzspLS0lB/96Ec89dRTxMfHy8/0ZL+E4CYEkvfffx+DwUBTUxMA4eHhLmvKF+emuyn5i/rna/1/EIv9bFxs7fUzt4h1/ajzYMGEV/fF9bANtdlscuMS2i73z12oye+uyenv7+fSpUsMDAxQXl7O0qVLAafZ7lG0wt6sJXgUxCS02+00NjZSU1MjD9jAwEBSUlJYsWIFWVlZqFQqObYL0W+hYVVenu43px4FZaDPYtJ2PCrCRcZms8kbsvj+ZvNRczgcjIyMEBAQQEhIyIKYcpXryH1Mle1raWnh448/5vDhw9y+fZuoqCgyMjJIS0ujsLCQ9PT0ey5YnhRcAwICCAgIYGhoiL179/LGG29w4cIFwKmNzc/PJzExUb7HV+eh+7gODw/T0NDAzZs3GRkZISwsjNzcXIqKikhOTgacWp3HXeNzicPhoKuri5qaGjo7O1myZAkrV64kLS3N5TWPOt/EHFlMKK0hQ0NDHDp0iM7OTpYvX86qVasICwvzdBPnDHFOiD1K6fMsLEFfBYQSQZwlQUFB95ydNpvtob6PBRNe3c13D+uYLTqhNPF6ym1A+CgBTE9Pc+rUKf72b/+WtrY2/v2///dSeDWbzTLC/n6I/jscDjo6Oujs7CQhIYH8/Hxg4VPgPC5Wq1V+Nzdv3mT//v20trbK52NiYli7di3r1q0jLi4O4JG1nY+D2CSUv8NdE7NYVI/yeeLA8NWNx/3COZuJVnxvZrMZh8NBYGAgg4ODXLlyhaGhIVJTUyksLCQhIWHe26vcY8T6UV5YLBYLra2tvP/++7z11lsMDw+zfv16nnrqKSoqKsjIyECn03mVL6zFYpHt2b9/P3/9139NfX293Duio6OprKwkJydHvkdcKnwJk8lEb28vVquVxMRELBYLR48e5Te/+Q0HDx5kYGAAgJ07d/Lqq6+ydetW4uPjCQwMfOS1PdcoteZjY2OcOHGCv/qrv6Kuro6Kigp+8pOfsGvXLkJCQgDXc+ZhUMZeLBa3AaXldWZmhtOnT/MXf/EX1NXVsXv3bsLCwqisrJR768MKNN6KL58TX4TyHBHzUrnH2mw2F6XIw35P8y68KoUwm83GrVu3aG9vx2w2Ex0dTUpKComJiff4uignq81mo6Wlhc7OTlpaWrhx4waJiYns2rWL3NxcwFV4mi+Uf2NsbIyDBw9y/PhxAIxGo3yduyl1NsQGZbVaOXHiBL/61a/Iz89Hr9dLAXYxIfpjMpmoqanhww8/5ObNm7KPJSUlvPrqq2zYsEG+JzAwcEEFWHAegiaTiYCAAEwmE62trVy5coWOjg7MZrNcVFar9b6fERAQQHJyMitXrmTp0qVotVrpN/tFlxZvxGazYbFY5KEnfOjE2Ij0UUpGRkbo7e1laGiIgYEBrl27RlNTE0ajEZvNxtjYGFFRUWzYsIH4+PgFEV7BGYHf1NREa2urDP5QqVRMT0/T0dHBhQsXaGpqIjw8nGeffZbnnnuOpUuXEh0dvSDte1jE4a7RaJicnKSuro4DBw5w7do17HY7ISEhJCYm8uSTT/Laa69RVVUl3+cLh6RS+2ixWLh58ybvvvsuV65cYenSpcTFxXH06FGOHDnC+Pi4fN++ffvQarVoNBq2bNlCTEyMPDA95fqhFF5v3rzJ0aNHuXXrFgC1tbUcO3aMNWvWkJmZ6RIT8qDPU7rtNDY2cuzYMZqbm0lLS2PTpk2UlJR4jXvWbFitVnkRvnHjBnv27JFWumPHjlFZWUlxcTERERFSabVY5rUIIgQe+jLhK9ZXdywWC2azGYCQkBAXl1FwnscHDx6koaGBnJwcdu7cSWho6EPJBfMuvAqzAEB/fz979+7l1KlT2O12dDodhYWFPP300xQWFgJ3BUSlxrWuro5PP/2UpqYmmpqauHTpEsuWLWPlypULKryKQAhwBk1cvXpV/h4ZGSn//7ABaOIzb9y4wb59+7h06RKVlZWkpqZKk4m3521UOtsDdHZ2Ul9fT0NDAxaLhZCQECorK3n++efZvHkzGo1G3rSEYDHfbQOnpry1tZXm5mZpzjYajTQ1NXHu3Dlu3LghhdeAgACXzVLZRrEppaWl0dzcTFVVFTqdjoyMDEpKSlw0ut48bnBXQ+muFRCBMErMZjMmk4nJyUlaWlqoq6vjxo0bdHV1cfv2bfnT4XAQERFBYWEhhYWFxMTEzOu6VH7PAwMDnDx5kgMHDtDa2irztc7MzADOOdDf309SUhK7d+/mxRdfJCkpCXCOq9lslu4Qnr6EmM1mtFotKpUKs9nM8ePHqa2tlc+r1WoqKyt58cUXWbduHXBXA+ft0faPis1mo6enh8OHD3P69GlOnDhBamqq3H91Oh1LlixhYmKCGzdusHfvXsB5cFZXV5OSkkJwcLDHBQOLxcKVK1c4e/asDPY1m81cvXqVwcFBMjMzH+pzhJYqICAAi8XCsWPH+Nu//Vuam5vJzs5maGiIkJAQiouLXV7v6f4LRGYBMU9bWlo4evQoVquV4OBghoeHuXLlCiaTSQqvntaePwjRPiHrzKZlnZqaYnp6WgplIrOCRqMhMjKS8PBw+VneMk6PgnsAL9w9R5T7kcVikdk2WltbOX78OIcOHWJwcJCnnnqKrVu3PvTfnHfhVQyi0Wjk7Nmz/PrXv+b8+fOEhIQwNTVFenq69DUDmJmZkQeHxWKhrq6Od955hw8//JDh4WGp4RwfH3fRdi70gNvt9sf6++L1NpuN0NBQgoKCGBwcpL6+nnXr1knhVWw83orSxGWxWGhoaKC1tVUK+kVFRfz4xz/mueeek5oAtVq9IH0SAgA4heqf//znHDp0iMnJSWkJMBqNjI+PSwFHcL/IR6vVisPhYHJykr6+Pj7++GOioqJ47rnnSEhIICkpSWoKvHnc4F4LwczMDOPj4+h0OpcNx2g0cvnyZWpra7l69SpXr17lxo0bjI6OykC8/Px8nnvuOakR3Lx5MytWrECr1c6rBkjkDga4evUq//AP/8ClS5dISkoiLS2NyclJRkdHSUpKorq6moyMDHJycigrK3PxExXaZW/Ju6kcl/Hxca5fv053d7d8PDIykrVr11JZWSlf5+4as5gRlypwzsuRkRG5nkRmCEF1dTUvv/wyJpOJv/mbv6GpqYl9+/ZhNBrp6elh165d0q3CExo8Zbtv3brFrVu3mJiYcHFBe1ThTMyD4eFhampqaG5uBqC3t5djx45RXV0thVchJHl6TitR9ndiYoLOzk6pgBIBiN6+fwoepLAwGo0MDAxw+fJlrl27htlsRqVSMTw8zPDwMImJiWzZsoUNGzYQEhIiteqLpe8CZfCu6IO7IC6UHDabjaamJj766CPOnTuHVqtl06ZN8rwQn/dFzNtOp0ybMzMzI9MlnTt3zkWVfOvWLemvBHcd7AF6enr48MMPeeedd+jt7XVteGAgPT09TExMEBERseB+au4TbHp6Wv7/YW65ygkfERFBQkICXV1dNDU10dvbS1ZWFrCwfqFfBmEGMJvN1NXV8fnnn1NTU4PdbqesrIzXXnuNr33ta0RFRQFOM8FCmbMsFotcDN3d3fz617+mp6dn1teK3ILChG632+UcnQ2z2UxfX5/8XaVSkZiYyLPPPktaWpoU/rzxJi00H6LPExMTnD9/ntraWsbGxkhOTiY1NZXw8HDMZjNtbW3U1NRw6tQpae4ESE9PJyUlhYyMDNasWcPKlSsJCwsjIiJiQVKhiX6I+dTe3s7JkycJDw/nmWeeoaioiPHxcUwmE/Hx8SxfvpycnByXQBDhQqLRaLxC8BMHYHBwMA6Hg+bmZj7++GMuX77MxMSE1Pzn5eWxbNkyYmJicDgcGI1GQkNDvW6ufVmUwmt7eztHjx6lv7+f8PBwJicnUavVLF26lLKyMnbs2MHXvvY1xsbGOHXqFE1NTczMzHD58mWys7NdLqaeEF7FmExNTTE5OcnU1BRw14qTlZUlNW8Pg9hbpqenOXv2rBRcwZnNJTY21sUNz9vnhEhbJ6ioqGDlypVyXXtTwJ0SEX+j0WhctK1dXV20tbXR3d3NwMAA/f39NDU1cf36dcbHx7Hb7XIuxMfHMz4+TkhICFVVVdLv2dvODREXIvosLspCBhIKKaVMNDMzQ2trK0NDQ/T29nLx4kWuXbvG1NQUYWFhWK1WcnJySE9PZ/fu3axbtw6tVvvQVst5262FcGe322lubuaDDz5g7969WCwW2fCZmRni4+PR6XQu7xMD19HRwdGjR6XgqtPpGB0dBZw+p5cvX6asrIzly5e7SP4LcWtxN2UI37/4+PiHuuW6p/CxWCwAtLa23lfA8iaUAXcOh4Pr16+zd+9e9u7dy8DAABqNhhdeeIFvfetbREdHY7Va5zy/4aOg9ENSqVQEBwe7aM7FjfhRLgvK1zc1NfHuu+8SHx/Piy++CHhvsnjlYTAxMcEnn3zCv/zLv3Du3DlMJhPR0dFER0cTHByMyWRidHSUkZERZmZmUKvV5OXlUVVVRWVlJUuXLiU7O5uEhATp0+QpRkdHcTgcVFZW8u1vf5vCwkIpnIo+u69Lb/MLFOskMDAQi8XCe++9xz/+4z+6XJTy8vJYv369vOD6ksZVIObR1NQUp06dYs+ePXR1dcl+RkZGsn37dp577jmWL19OWFgYAQEBVFdXc+3aNerq6piZmWFmZuaB/usLgTKqPDg4WLqyBAcHk5WVRWlpqYvP9f3ODmHREd9BU1MTR44cYXBwUL4mNjaWLVu2SEsm4PF86F+EEPwsFguhoaFUVFRQXl4uFVLeKry67yc2m43r16+zf/9+Pv/8cxobG+W+KQLqlCn7HA4HnZ2dfP755wQHB6PT6SgrKwPwOvcfIZgqx2G2OSViHqxWK7W1tXzwwQecO3dOWul6enqwWq08/fTTfO973yMhIQGVSkVhYSGhoaGP1KZ52fGU/qddXV3s3buXY8eOSVOP6HR4eDirV6+WmzDcDeJRqVRMTEy4mIeEUOhwOKQPY1dXF8uWLSMgIOChAqXmCuFfJujq6qKxsZHw8PBHuj2J9opJffv2bbq7u6VGWWgAZ6tj7klEhRih7Wlra+P06dO0t7cD8MQTT7Bx40ZiYmIA5y3sUSfn46Jc/KmpqezevZvDhw9LIUz43opDQfjoZmZmUlhYSGpqKkFBQVgsFvlveHiYq1ev0tjYyMzMjAzWstls97iyLOR8fFiMRiPBwcEEBAQwPDzMv/7rv/LOO+9w+fJlqaHq6+tzEZYAsrOzqaysJDc3l+LiYkpKSkhNTb1vIJbZbJb7wHyZLEVAEzj3HNHm2NhYmaN1NuFUCIjetJ4EQqsh/i8sMYLAwEBWrFjB9u3bZTooYFbLk0hHcz8zpHJuin1V/H1PRUcLzaiw2B0/fpzPP/+crq4u4K62MiwsjOTkZHJzc6UmXavVsm3bNikoXL58mUuXLlFbW0tmZiZRUVEe6ZP4njUajfRjFr8nJyeTlpYm+/AglxX3/aSjo4OGhgYmJyflY9HR0RQXF0tf7oVS5swVISEhREVFER4e/kh5fRcSZcCcWq1mbGyM2tpaamtraW5ulvNOKKQEarVaXvQnJycZHBykv7+ftrY29u7dS2lpKfn5+V7tMuE+DsJtzGg00tbWxoULF6RbTENDAzU1NZhMJtLT01m2bBlPPfUUOp2OkpISli1bRlpaGlqt1qW/D9v3eRFelbfDGzduyAAKIYCJxZadnU1VVRUpKSnyvUqBLzo6moiICPnczMyMXLxBQUHExsai0+nk6x9Vc/Y4uG8kw8PD9Pb2YjKZpPCqNH096HOUN0vhgzk1NUVERITLZudNiI0YYHBwkIaGBhobGwFYu3Yt3/rWtygpKZGv90TOPqXwmpGRwbe//W1ycnJobGzEZrO5+Nc4HA5mZmYIDw+nuLiY6upqcnNzCQoKkmYMlUrF9evXefPNN2ltbWVqakoKB4GBgdJ3WeANvpPuCL9Om83G3r17+dnPfkZTU5Psg7jxh4aGotVqCQ8Pp6CggCeffJJ169aRnZ1NbGysS05f5WYu/gUFBS2IK49YN2NjY4yNjUm/3b6+PmJjYzGZTPeYtsR73AsXwL3pwbwNjUZDUVERVVVVBAcHPzCn9FyUrF7olH3K4CKj0cjFixe5cuWKvASJy+H09DRTU1MYjUbsdrt0EcrNzWXDhg0cOHCAuro66urq+Pjjj4mLi2Pz5s3yAr2Q7gNi/hmNRkwmk8scCw8PJzw8/JHHyWKx0NPTQ3t7u0u2BZVKJdeuMiWltyO+E7VaTVRUFJGRkV4pwCkvA2azmZ6eHs6ePcunn37K/v375UUzKCiI8PBwYmJiyMjIIDQ0lLCwMAoLC8nOzmZwcJDLly9z9uxZ2trauHnzJleuXKGtrY28vDyv67vdbmd6epqZmRk0Gg1ms5mOjg66u7uZmZlhcHCQ8+fPc+DAATo7O+X7QkJCqK6uln69VVVVREdHMz09TW9vL5OTky4p4h5lTc6L8Kp0RL9+/TptbW0ysCIwMFD6EmZmZlJZWekS8avUPISHh7v47igXeFRUFOvWrWP58uXSPWGhTbTKTWE2Tc6X2TTcS2u6f4anN6PZXDPq6uqoqamhq6uL4OBg1q1bx7Zt24iLi5PmeE+YQJRtDA8PlwUSRkdHsdvtcj4J1wchCOh0OqKjo2f1v4mJiUGr1bpkIhCHrfv4e1OEr4juFRqtd999l5/97Gdcv34dcPZDaLXS09NZuXIlZWVllJSUsGTJEhITE4mOjp71kFVe4h42f/Nco3ThUQpboh3KPJhivJVtFXuI0p/LG03xAQEB0qUDHt41xWg0YrFYXC76ot/CciBM7wJhkbify8V8454TXDke4pKl0WhkOwWhoaHodDoiIiKYmJjg5MmTpKenU1FRIYVXq9U672eF8tI7MzNDR0cHPT09cq4qS90+Klarlf7+ftrb2118esPDw4mIiHBxo/M23MdSaa0KDg6msLBQZl4QMRLesI8qMyQIf+NPPvmE06dP09HR4WIhKS8vp6SkhOXLl7N8+XLi4uJk6j4RKHrmzBkZU+BwOBgYGOD27dukpaV5PPuA8u/abDYZT3Dx4kVMJhMajYaBgQE6OjqYmppiYmKCnp4el7lYWlrKjh07qK6uJj8/n4yMDBn7EhoaSmJi4he6ITyIOd2dlQLk2NgYp0+f5vTp04yMjADOL0EpxCQmJpKfny83lMDAQCIiIjAajQwNDXHmzBmXCaFc5EFBQWRkZEjNrDI4xxPMluj+YTYOd6HXbDYzPT0tBYmhoSGmp6eJiIiQWmZPT2iVSsXo6CidnZ1YLBY6OjpkIM/GjRvZvn27NCcHBQW5HCyeRKvVkpKS4qLp/yLE2ExOTtLc3Mzhw4epqanBaDS6CDxxcXGsWLHCJUevN6XLEutufHycAwcO8MYbb3D69Gngbh8zMzNZtmwZFRUVrFixgrKyMjIyMuRnCAFfIPIXeqKP4rsVqdfa2tro6urC4XC4mKHE719GC6xMf7OQ6025b7jvKRqN5oHafSHI2Ww2pqampIBz+/ZtGSgk3APEuhR7TUZGhkxvFhoaSkxMzIJfOpV9Eda16OhoOjs7mZiYQK1Wo9VqWblyJaWlpSQkJNyT2k2j0Uj3lnPnzjEwMEB9fb3sP7Age5Iy48jo6CgXLlzg2rVrcnytVisTExMYjcaHOiuUlkWr1YrRaJTCQkxMDMuWLWPr1q0uWTS8QeibDY1Gg9VqpaWlhfr6ejke5eXlFBQUyNd5U4osMcfMZjOnTp3iV7/6FZ999pmUUYTgXVlZKc8CpQuHO2VlZaSkpBAREcHMzAyjo6OMjY25uDp5yvdVaEFVKmeqvtraWn79619z/PhxF1dOQXBwsAz0TUxMJDU1lcrKStasWUN6erpLBimRBcjdGvuo58icCq/KxdrX18dnn33G0aNHXTqrXKRig1RisVi4cOECBw4c4ODBg3R3d8vnlFK93W6X/hbg+UU6lzdcsUkNDg5y5MgR2tvbWbt2LatXrwbu3gAXus8iyADg+vXr/PSnPyUsLIyUlBRCQ0MpLCzk1Vdf5YknngDudYlYrExOTnL+/Hk++OAD9uzZI/16o6KiGBsbA5wRwy+88AIVFRWA6y3dkwifXqHpOHDgAD/96U85f/68fI1araa0tJTvfve77Nq1i9jYWAICAmYtkeot2kjlXjM1NUV9fb3UIgs/ZCF8Ki8RwrdZ9G1mZobh4WEZSBobGyv/hlJL6Slms7w8aK8RLiGdnZ0yJ+qVK1fo6+vDaDRiNptRq9Uu/voi/VtWVpYMwCssLGTVqlUugsRC4N43oVkWLi2BgYFs2bKFV155hdWrV896KYmNjaWqqoobN25w9uxZwDlHlILQQmgklX9P5JRua2u7x0rgrtV+0HxTPifMreBUBH3ve9/j6aefJioqykX48CaUFqDBwUH279/PgQMHsFqtcs4pMy94yx4qzjCr1UpNTQ1vv/02H330kQwg12g0VFZW8p3vfIfNmzeTmJg4a0VCZTzQ1NSUrMZptVrp7e2lra2NkZER6bPvKZQuj1arle7ubnp6emS5b6vVisViISIiguLiYsrKyigrK5PudkKx4R7EO5c5tOdceBWDNTExQW1trfR/UCanF0xPTzMyMoJOp6OlpYVr167JSNG6ujpZJUcIc2Ijs9vtJCQkuEjuC71I3f9eXFycFOIEDxLaxHOTk5MyfQY4A4uKiooICQnh1KlT/PrXv5YLWyAO5YXus3LsxsbGqKmpobu7m5UrV5KcnMyOHTvYtGkTKpUKk8kk0/coC054ygQp/r4ySEutVrscfgMDA5hMJkJDQ+np6aG2tlYe+o2NjZw4ccLlMqX8PkSeUFFdSjk3hcDgCcFPGaFst9upr6/n9OnT0vQTHh7OypUr+cY3vsHTTz8ttTbicijWn0j4722HoRKlsCmCHoKDg2W/z5w5Q0NDA1arVZrxurq6GBkZQaVSERERQWZmJmVlZaxYsYIlS5Z4XHPuLmSNjo66mFnB2V+lgG61Wjl58iS//vWvuXjxIv39/bN+dlhYmBQURkdHuXbtGjdu3CApKYmcnBwuXbpERUUFxcXFLFmyZEGqkCnn18DAAA0NDXR2drqY2NevX8/u3btdLG1KISE8PJyMjIxZLyILiVJIDQwMZGZmxsVyITR1SmuNSNMn3i8sG+IMFJo4dytmQEAAubm5coxEvnRvQ3mpt9lsnD59mitXrgCQkpJCVFSUy/fm6fWnbMP09DRnzpzhjTfeYM+ePVJwzczMZMeOHTz99NNUV1e7CJ4Wi0Vq/IWLTlRUFGq1mt7eXrq6upicnMRut9Pd3c2BAwcIDw9ny5YtZGdneyzlotKP3mw2k5yczIoVK0hJSSEhIYHk5GTsdjuhoaEUFxeTl5dHcnKyLP2uRJyTYh6Lz33cPs3paapcLCLPmfI5i8XicuDfunWLAwcOkJmZSX19PXv27OH8+fNSo+p+GzWZTKhUKtLS0li1apVLLklP+PYoF1ZYWBg6nc4lW8JsKAMSrFYrPT09dHZ2Sj9goYm+cOECb7/9NnV1dWzevNklsthTwp+46Q8NDdHS0oLFYsFoNHL69Glef/11vv71r0vhJzg42OvSEN3Pl9FsNtPb28ulS5cYHh4mPDycS5cu8fbbb9PR0SFzbs7MzMjF555tYnBwkL1799LU1ERYWBgVFRUyMbonM0WI/s7MzHDx4kVu3Lgh15dOp2PdunX84Ac/YOfOnYBrJL63jZ8S5fcZHBzMkiVLSEtLo6mpSVYDm56epquri4aGBvbu3cvnn39OR0cHGo2GuLg4mT1CCAmi7OrSpUvZtGkTW7ZsoaKiQgpBnrgwuv+96OjoL8za0dzczGeffcbJkyel1SskJIT8/HxiY2MJCgoiOjqajIwMoqOjpaa2rq6Oa9eu0dHRQUdHB7W1tRw8eJB169bx8ssvs3Hjxnnrp0C5jw8NDdHY2EhfX58c74CAADIyMqTgOjk56aKpU/rHelp4U46d2N+1Wq3UAgcHB7Nx40aqq6vla5SXaff2i8/r6+ujrq5OWhrEa90vNd7OwMCAS97oiIgIGaTsDSh9lh0OBy0tLezfv59PP/3URXDdtm0bf/zHfyyLQphMJhlordFoXFKBis80Go3cvHmTtrY2eY709/dz4sQJYmNjycnJISsryyULyELOZ7HehoaG6OnpkW5xQ0NDlJWVsWbNGpnyLTo6+p4gWLjr0nS/dj/uXjonwqsIctBoNNjtdi5dusTx48ddoiCF0Kq8eV67do1/+Id/ICIigsHBQVpbW11cA8TAic8V5q0VK1awdetWlzQ9nkyBAs5B7uzsJC0tzWWyur9eCOChoaEYjUZu3brl0u+uri5+8YtfyPKzy5cvZ/fu3ZSWlsrPWWhTirgti4T277zzDu+99x7Dw8OA88a8fPlySkpKZMJ0pUkLFqZ876NisVhoaWmhtraW8+fPc/nyZYaHh2W1s46ODgA5ZmFhYQQFBbmkFBKC4dDQEO+//z7gNOE999xzsmCBcjMWgSILIQSJVF7grD71s5/9jCNHjkiN67p16/jOd77Dk08+Kd9zvzESGmtPpVByRym8ajQa4uPjiY2NJTAwUKauERHAN2/elELOE088QWJiImFhYYSFhUn/zunpaVpaWujo6KC/v58333yTY8eO8corr/DCCy+QmJjokQujcjzS09OpqqoiLy9PPib2SBGEdu3aNX73u99x9uxZKbjqdDqWL1/OSy+9REVFhTxQRCW1wMBAjEYj58+f5+OPP+b48eP09PTIDA5dXV3k5OQsiPCqxGq1ysw07oei4H6uAN4QqKQcu5aWFnp6elxc3YKCglxKwj7IL1ukSwQ4f/48P/vZzzh58iRw9/KmzMzjrRYSpQLk3LlzMh4GnPmL16xZIy9Uns6RrQwWdDgcDA8Pu/iOZ2Vl8eqrr/L1r39dCq7Cp1OUunXfT9VqNUNDQ5w+fZr9+/e7KPgAmbg/JyfHJWjbE8qP0dFRGhsbaWlpkdl5CgoKKCkpkUo25ZwDpIJSWBrn86ybM+FVeUNpa2tz0fAALhor0RlRIk2JWMAiOlj5XnBqJtesWcOqVasICQnx2IHq7ntmMpmYmZlxmawiel0gBCBBQEAAfX19dHd3u9y+9u3bBzgrjXzrW99i06ZNMvJdGSW/UAjBU6SvOXz4sNw4CwsLefLJJykuLpbjpFarGRwcxGw2MzY2Rnd3N6Ojo+h0OnJzc0lLSyMwMNAjNbeV/kszMzPcvHmTzz77jAMHDtxTxU34ZpnNZlkVRRn0oWRwcFAmC29qasJmszE0NMSSJUvIzMyUmsGFMgO5R2DfunWLzz77TFYpeuKJJ/j+97/PU089BSBdV4TwJ3LehoaGStcP5RrzdDCa+O5EO3Q6nXQFuHTpktyHmpqa0Ol0rFy5ks2bN7N69WoSExNRq9VER0e7mLna29tpbm7m7Nmz7Nmzh0uXLkmh4aWXXpIm2fn2Z3bPYiIQ/uXKA0Pp02uz2bh48aJLYRdwaoc2b97M008/TVpa2n3/blJSEjqdjtjYWM6dO0dnZ6dcu7dv357jXs6O2GeENcQ9qbvwWVa+XqAMaJrNV3ihcM+O0N/fz82bN+no6GB8fFy2LTExUfrMj4+PS3cj4QYiLhni0gzO8q8nT57k008/xeFwEBQURGFhIaWlpS7WOW8TXpUp1+x2O42NjVy6dEn2Pzo6mmXLlpGeng7cvXh7w0VZINy/oqOj6e3tlbLIsmXLAKTSRrnHT05OYjKZ6O3tpb+/n+npabq7uzl27BgnT55kfHycoKAgqZlfvnw5Tz75pLzUCG38Qoyn8kyanp6mubmZkydP0tvbS2FhIYWFhURGRhIQECDdzsT6FD6uC6k1nxMpSClcqlQqYmNjSUhIuK+Q9aCNxGKxyIIDyg1L+PiUlpa6VCRZiJQnD0NQUBChoaGPZGrt7+/n9u3bjI+Py5uN0MAWFRXxk5/8hOeff15OCE9FdrvnC1SWwi0sLGTjxo2kpqbKx27fvs1nn33GhQsX6OnpkbXIY2Nj2b59O7t372bJkiUe18QKv87R0VFpBhKHp7IwwZfh8uXLNDU1odVqycnJYefOnbz88suyIIfyQjAfKM01DoeDkZERuUGGhYVRWVkpq7m0trbS1NQkc+9duHCB8fFx8vPzKS0tpbi4mNzcXCIjI13+hqfTtsFdN5yEhARZWKK7u5u6ujoSEhJ45ZVX2L59O8uXLyc5OfmBpaQzMzNJTU2loqKCNWvW8NZbb/Hb3/6Wf/qnfyIvL48tW7YA8x8BrdwflfOvv7+furo6F0258H8X73Mv7ALOlDVbtmxxWaOzERISwoYNGwgKCiIgIECuCXiwVnAucE8b1dDQwMmTJ6W/rqg45o43aFi/iKmpKcbGxuS+KYLjEhISOHPmDB0dHXKvCQgIcOlrdHQ0hYWFLFmyhKmpKY4dO8alS5dcUmGFhYURHx/vNeb22VDuFUKwF3ERwcHBbN++3SU40FurginXm91up7Ozk76+PiIjI5mcnMRqtcrLZUtLC+fOnaOhoYEbN27I4kMTExOMjo4yPT0tL2rgtKysW7fOJcPLQqIcI7PZTHd3NxcvXmRqaorq6mpWrFghBVcx95TFoRbcL3cuPkSpPVOr1RQVFVFRUcGxY8cYHR2VAp0ySEYcrmKSCp9CEcUGd9XlQisZERFBenq6i0P0Qn5Z7j64ytrLN2/e5PPPPycwMJDly5dL/1fxHpGXr6enh+HhYYaGhmQFivHxcdRqtfSprKqq4nvf+x7PPfec1LiKPLmeQDm+IkWUIDg4mKSkJOLj4zEajVy9epXjx49z6tQpGhoa6OjocPHFEj6G3/3ud+Vh6u7rNZ+4mxZNJhPDw8PSSqD0Y9XpdDKlR15eHikpKXIsRHoe8f7+/n5GR0cZHh5mfHzcRYgQ2qvw8HBeeOEFkpOTF2RzFhqA27dvy5K94vH29nY+/PBDzGYz165do62tDZPJxODgIDdu3ACcQm5paSlFRUUUFBRQWlpKSUkJmZmZXhFIAXfXZFBQEOvWreP111+nra2NsLAwcnNzWb16NeXl5S7vEXPAZrPJwicqlQqtVktgYCBxcXFs2bIFrVbL5cuXqa2t5Z/+6Z+Ijo6moqJCJoCfLz805bxQfv7w8DDXrl1z0YIqtcCiD5GRkYSGhso5nZCQQEZGBhaLhcnJSZk6TOzBymp5wcHBpKam3pMFZr4FI2VaMnC6uRw+fJi+vj5CQkJkzIROp/tCn18xNsq17qmcy0L5MpuSpaenh0OHDt2j/BGXZ5vNRkREBNnZ2WRnZ2M0Gjl37hzNzc2EhoZKl4qAgABCQ0Pv0UJ7E+6BOrGxsYSGhjI1NUVRURGvvPIK5eXl0ud+oc6DR0FEzgtXh5GRET7//HNaWlqkNScwMFBmGrh69SonTpzg2rVr97gHCIS7j0ajISsri9zcXBeXu4XcZ5XpBaOiokhPT0er1XLhwgX2799PXl4eK1asWPBKmfdjzjSvcHcjTUxMJCMjQ26qyhRL4vAXKAdHGUWv1CYoNbAzMzMLnvJktraGhIRQUFBAU1MTY2NjnD17lsbGRnp6evja175GUVGRDN7SaDTSD+/MmTNcvXqVzs5ORkdHZdoe8dkajYatW7eya9cuQkNDpUnQk8Ez4qAzmUzU19e7uHqI3LxqtVpmILh8+TI6nY4nnniCtrY2zp49K7U4V69eBaCyslIKryJ580IjNACz/e2EhAQ2btxIXFwciYmJPPPMM6xYsQJwmvi0Wi1arZbJyUkaGhq4dOkS165do76+nubmZvr7+13mcH19PXv37iU1NZXNmzfLZM3zYX4XrhFqtbOa3cWLF13GYHR0lA8++IDf/OY3snKKyL0XERFBUlISZrMZo9HIpUuXOHfuHFqtlvLycr7+9a/z3HPPkZubK/smvktPoDywly1bJv2u4W5aFndEW2cL3hPaL5E+7Omnn+batWu89957MpWUyFG5EEEU7vlIRaYM977A3ZLV7lHtRqORyclJkpOTZaSz8n3uKX1MJpPMNAHOlHCzRRHPJULgFIjSpyKwKSwsjLi4OFatWuViHp/NeiMEJffvxlNaWnF+Kc+93t5emXLvfkEtSpcQZc7NgIAAOS/E5yrd7MTj3oYyT7iIE7BarWRnZ7N582YZACT8RT2N+6UiMTGRrKwsYmNjZaD14OAge/bskWsqKCiImJgYgoODGRoaYmBg4L6fLwpsiHFLTEwkMzPTo1mUxDwKCgqSBWrefvttfvGLXzA6Osp//s//WbpJLGSVutmYU7ut+6YqNE/iphUWFnaPJu5BJjj3g9FkMjE+Pu4S1LWQi1S5wcfGxvLcc88xMjLCnj17AKdQc+bMGQYHB0lKSnLJPGA0Gmlvb6elpeWe1DWiig04g59KSkpk1P709LRLNO1CIi4TYoLW1NTw/vvv09bWBjg1k9nZ2dLMERwczPLly0lPTyc6OpqUlBQaGhpwOBycOHFCajqbm5upqalhxYoVC27uci90UVBQwEsvvSRv/SIlW2ZmJitXriQmJoawsDCXdDZK87lINSWSMnd1dVFTU8NvfvMbWltbXVxBhoeHpT+pYD7mr1LLMzw8zN69ezl27Jj8u5OTk1Jrk5CQQFFREbm5uSQmJpKYmEhCQgIqlYr29nYuXLjAiRMnGB4e5syZMzK5/datW8nPz/dI2d/7IYJG3RGaRaWlR7zeHeFTGRoaSkREBFu3buXs2bMcPHiQ+vp6hoaGSEpKmtd9R3npUdatFyjb7Z7JYnJykr6+PhfXgaamJs6fPy/zL4JrMJ/y/WK/Evlvg4ODee6551i5cuXcdnIWlN/pxMSE1BwHBQWRnp7OE088wVNPPeVSdlq5dwgBVWjulHPBU8nuVSoVISEhMmUb3NUCK8f5Qe0TwcrK35WfbzKZmJiY8JpiMLOhDMByOBzU1tZy/PhxAKmFFXiL4K3URKpUKtLT09m2bRujo6P87ne/o729/Z4YCCGjKMnOzqakpISkpCRsNhsDAwPcvHlTuquJ/VpU5AoNDfVYLI8yOMxsNhMSEiJ9cj/88EO0Wi0lJSUkJCRQXV0tXc88EQMxp8KrclMNDQ0lNzeX3t5esrKy2LlzJwkJCTQ0NHD16lU5YO7morGxMYaGhlxKGYrJrNPpyMjIcBEeFvILExNJmHOefPJJ+vr6uHHjBo2NjahUKm7duuWS/mM2xGamUqlk2hSxIcXFxbkIBJ40n4h2iX5fv36d48eP09fXR0REBFVVVVRWVkr/48jISDZs2ODyGWFhYbz77rsuFxZRga2vr4+YmJh78sDOJ8r5otVqKSoqIjMzU7psCO2ViEQXKHMUuweGaDQaUlNTSU1NZcWKFRQWFjI9Pc1vfvMbGTijUqkYGxujtbWVyclJqcmab+2daF9ISIj0VYqKiqKwsJDc3FyysrIoLy+nrKyMhIQEl0NkenqaixcvUlxczN69e7l8+TKXLl1Cq9USGhrqMlc9fQuHuyZaZXCIyL/7sCg1PiJ3ZlVVFadPn2Z0dJT29naSkpLmra/u2h73ZOXCzaO5uVlqv5VBTYmJiWRnZzM2NiYFmubmZo4cOSKr/4i+CUQuX6HdCwoKkgJTYmIi1dXVLhkO5gul0KIU1sC5t1RXV7Nu3Tpg9tzJ4ruz2WxMT0+7BAwvpA+l8txSqVRERkayZMkSiouLGRkZwWKxyHytYWFhUgAX/Q8ICCAyMpLo6GjCw8Ox2WzSJUloK8U/cPrFJiQk3FN5zVvp7+/n1KlTMge88KMUZ6K3uCQJxDgGBwdTXV1NSEgIOp2Ozz//nPb2dhdriBA4AwMDiYyMJDMzU+6vmZmZ2O126urq+PDDDxkcHJRjKNamyKAkLEALjVLGGR0dJT4+nu3bt3PhwgU0Gg2NjY2cO3cOnU7H1NSUVHh4Yr7NqfCqnHT5+fn8/u//Pi+99BIlJSVkZ2fjcDikP6Ayt6LZbCY8PJyAgACOHDnCW2+9xdWrV+UkEJtQUVER27dvd0kv4smgn4iICDZv3szg4CDvvvsu165d+8L3pKenU1BQQGFhISaTicOHD9Pa2go4hSn3/KjecguFu0mWwXlbrqyslAeoeN4di8XiEvgBTvNIWVkZJSUl0hS2UBGVSsSG9DAmKmXf3T/DndzcXL7//e8TERHBG2+8ISuTNDU1sWfPHnbs2CEDt+Yjcl15iCUnJ/PDH/6QuLg4Dh06RGhoKGvXrqWyspL09HRiYmKIioqaVYMaGhrK+vXrpQ+kSqWS+SVv3LjhohVc6DyEs6EMHBB82XYJYTwyMpK4uDg0Gg2jo6P09fW5VPeaa4TLAjj3tq1bt9LV1cWZM2ekNnLPnj0MDw/zwgsvUF1dLS9CgYGBPPHEE4SEhHDkyBFOnz5Na2sr4+Pj1NTUoFaraWlpYdWqVTKq2263y+Iv4nvq7++nv79fXgKEIOzuB7tQiDY+SLOovDwZjUZaW1ulWR6c60C5LhZCQBJCT3h4OKtWrWJiYoLo6Ghqamro7e0lPz+fZ599lqKiIqanp5mZmZHBcqGhoTJuQpyTt27d4pNPPnGJ0o+IiGDNmjWsW7fORanj6WBYd8QeNzk5yYULF+SZB/cqA7zpzAPXuaJWqykuLiYhIYGnn36aW7du0dbWxtDQEEFBQaSkpMh0mcK1LDo6moiICMLCwrDZbIyMjDAzMyOz0yxdupTnnnuOoqIi+Xc8vZeq1Wri4uJYv3498fHxbNmyRV4Mz58/z9WrVzl9+jTZ2dls3bpVnh8LmRJz3oTXzMxMdu3add+cp/cjOjqaxsZGurq6XNKKAKSlpVFRUSHzwHnqlqacWLm5uXzjG99ArVbz+eefMzExcY8gEBgYKB2gY2JiyM7OpqKiQvqDCvLy8qiqqiIlJUU+5ulbqPL7Dw8Pl5GUKpXqnoTSwjxuNBoJDAwkPDycoaEhl1x+Op1ORrErg9k8pWFWmjtmZmZk4Aog/f4CAwPvqSkvUAZkCP+zyMhIli1bxvj4OJ9++ik9PT3yhi2qeCn//lwjvldhGl66dClRUVHSn2rVqlX3RLRarVYXzfLMzAwajUbmkFy1ahXXrl2jpaVFJknv6+uTG657xR9PMJfZOMR3KFLfCOFIXLKV2s65RGz8Im3ZM888w9TUFE1NTUxMTBAYGEhHRwednZ1yr9mxYweA1JTu3LmTjIwM4uPj2bt3L1euXKGpqYn29nZqa2vZsmULzzzzDKtXr5ZaeeU8HBgYoLm5mcnJSYKDgxkYGLjHFLqQaLVadDqdTLP0RSUmh4eHaWpq4ubNm1LY2LBhw4ILd+5nl6hgV1BQwO3bt8nNzZXxEUajEZPJJC/xgYGBhISESA2cCACqra2lpqYGs9mMTqdj9erVbNq0SRbsWcjg14fBPbCxra2Nmpoaurq6AKc/dUZGhmyzu6+yNyH2eq1WKy1tVVVVdHR0MDg4KIXXB13ybt26RVNTEz09PfKxyspKdu3aJTWz93N/WkjEpaugoICUlBTGxsYIDw9HrVZTWFjIr371K9ra2rh48SJVVVUuFrhFKby6T7pHFVzFZyQmJhIVFSX9QQTj4+P09fWRnp7u8cGFu9kTcnNz+fa3v83mzZulVlksQvGamJgYMjIyZERweHg40dHRLiUM09PTKS8vdzEVevoG5p62Rwg4RqORzs5OhoeHZSUppU+TaHdQUJCLZjMjI4OKigqXueGpsXTX0nV0dDA6OkphYaEUzJV5dZXjKt7v7scmhCeLxUJvb69LkIbIN6oc8/lc6MpDLD09nW984xsuWTKUuCeUVmrihAuPENwA6urqXDZgT/kUzhfKINTp6Wnsdjvx8fFyrgtT4XwJCiK7iHDPKSwslGVSQ0JCMBqNnDx5ktLSUjZu3CgjlGdmZoiMjGTVqlWEhoaiVquxWCw0NjZiNBqpra3lxo0b9Pb2EhgYyJo1a4C7l+T+/n5qa2u5ePEi4Jyz6enpMsBwoVCui+joaJYvX05ubq5sp/slxX2f6uvrk9XU1q9fz5NPPumy5yy08AoQHx8vcw2LC3tYWJh0bZntOxbrbXp6mo6ODvr6+qTFIy8vj82bN7u4dHj6vHBHCHyiXYODg1y5coXW1lZ0Oh3PPPMMa9euld+Vp6pHPgz3C6xLT08nJSXlgYF3ok+XLl3is88+k+5kSUlJlJSUUFxcTGBgoAzI86ZxDA8PJzg4WJ7TSUlJhIeHMzg4yK1bt1z86xdSaz4vK9h9IxG3RxElqdTwiAAQoe1qa2ujo6NDfiFKU9H169c5cuQIiYmJ8qbpycpNyoNe3MTgbm5GYe4RN2l3oqOjWblyJbW1tYyPj3Pz5k2uXLlCVVWVDNiaTxPlo6KMYrZarXR3d9Pc3ExOTg4xMTFyciuFI3ezbVJSEuXl5Qt+kNwPMYb9/f3Sr3PdunXs2rVLVl+aDbEhib4p+zgwMMDHH3/Mb3/7W/r6+uTfWbJkCdXV1S7C63z7uyrTrCn7IjTFyrkpLlvC71c8bjKZuHLlClevXpXr0T01jzchtMhfpkSo2J+E0Hft2jUuX77M9PQ0CQkJ0ifNPRXTXKP8bvPy8vjBD37AzMwMx48fl7kxJycnqamp4eDBg2zdupXg4GAX7aLIjJCamkpNTQ0NDQ3cunWLkZERDhw4wNjYGJs2bWLt2rWkpaUxMjLCJ598wi9+8QvsdjtRUVHs3r2bDRs2uFQznC/cA9EEIkWZe2UxJVNTU0RGRqJSqWSasImJCbRaLVlZWbJikSdclJSBZMKUrES4bYi0SaKdDocDrVaLxWLh888/56233qKurk6+LywsjISEBJfUSt4o+LkH4rW0tGA2m0lISGD79u2sXbtWpsT0hrK+D0J8vyLDQ0BAgEyxB879QwSIiny94uIPTvnm/PnzjI+Pk5SUxBNPPMHSpUvlJdibhHdxPgj/anG+9/T0cOHCBZqamkhPT3exJC4k83L6KL/8oKAgF+3EbFo2Ua0BnAFbosa8RqORPpHgzJd54cIFFzPJQqqpvwgx2Mo+ui9Es9ks+wVOt4Nly5Zx/vx5bty4wenTp3n66aelOVY5aTxNYGCg3HjNZjO3b9+WvnRr166VvshCQ9vX18e5c+dcfF7T0tIoLS0lMTHR42mWhBbVYrHQ1NTEwYMH+eyzz7h8+TJqtZrnnnuO6OhoGWDhvsGIzARwV/M6MjLCkSNHeOONN2QVMnDOjYSEBAoKClwEjPneqJVp1pTf9/3WjPhOxDjbbDauXr3KqVOnOHv2LEajkYSEBLZt2ybXIHiXj91sKbAeFrGfCGGirq6Oo0ePymTwYrzm2zdPeXEIDQ3lxRdfZGhoiNraWiYnJ2Vg2pUrV/jFL37BwMCAFEKVpufCwkJSUlIoLCzkxIkTtLW1MT4+TlNTE/v37+fMmTNs2bKFFStW0N7ezvvvv09vby/Jycl8+9vf5pvf/CYlJSULfoF2z8cs8n8La4gQ9MTeERoaKv/f0dEhNVuBgYGEhoZKAc8TFgJ3U7gyobv7eaFUiIiLonAZ2LdvHyaTSSpFRPzIfGcveRzcMymYTCbpRpaQkEBpaancV5UlcL0dd7lGEBAQ4HKZEOXEwZlvu7u7W/Y/KSmJJ598Ukbsi/d7yxgq56JyDK9fv86FCxcAp1bWU/mFvWKmKLWrk5OT0h/mfge7t9xMBMI0Aq5lcN1fA7ikf5mZmaGlpYWmpiZMJhNJSUnk5+e7mI88eQt19ymOjo4mLS2Nrq4uZmZmaG9vZ2xsjIaGBi5evMimTZsoLi5mZmaG+vp6Tp48yYULF+jo6CAwMJDc3FyWL18uTa/K0nILjfLgM5vNjI+PSx/X2tpafv3rX9Pf3098fDxTU1NYLBbCwsJISkqitLSUyMhI6uvruXjxIna7Xfph3759mwsXLkgNiUajkaVW8/LyKC4uJiIiQv79hRQKlAeJmKcio4QwgSsj3UdHR/nkk0/4+OOPuXjxoswYUVJSwvPPP++SPsxbLljuuFduckd8J0IAsNls8lBSq9X09vZK9wiltWChfPOU+8jatWt59dVXOXLkCD09PYyPjzMyMsKJEycYHBzk+PHjREdHk56ezrJly6T1Rgh7+fn5pKSkMDMzI4Xcuro6fvvb33L8+HGGh4cxGo1s3ryZH/7wh6xcuZLk5OQFm6PuFjvBwMAAJ0+eJD4+nqqqKhmdLuav0qR59OhR3n77bS5fvgw4TbrKvLCeRukfL7IluKdkgruZXgQqleoeDZfZbHbJx7sYEOMGzrzMSv9Qb7EwPirulxElok8DAwMcPnyYxsZG+VxcXBxlZWUkJSVJC7RSseVNKNemuBjqdDo2b97sYpVZyLPco8Kr2FSFs6+IYBOBP8oUWoDMvSk2ZfAOH58HabLcEdVtwNn/hoYGmci4tLSUtWvXkpSUJF/vaZO6ciHl5OSwfft2IiIiuHr1Kjdv3mRgYIAbN25QX1/PrVu3qKqqwmw2c+bMGY4dOyYFnoyMDHbt2uXin+fJRepuooyMjHTJp3vkyBFu3LhBeHi41CSHh4ezZMkSqqqqiIqK4vTp09TU1GC324mNjZXaaBGpLTIZiJx4O3bsIC0tTR667u2Ya5QHoBhL9/nknuNzcnISo9HI8PAwZ8+e5V/+5V84duyYfE1WVhabN29mzZo1REREeDRwUtlu4Z7icDi4ePEig4ODlJWVyeBHYZoVbjxCUy5cP5T7yODgoNRqnj9/nvDwcCorK1m3bp38/haqYpNYKw6Hs6To97//fZKTk9m3bx+3bt1ibGyMwcFBTpw4wYkTJwDnGK1du5aEhAQmJydlIRStVis1llNTU8THx5OWlkZPTw+jo6PExcWxevVqfvSjH7Fp0yYAaTJcaB9R5XwaGRmhvr6e1atXy3a4uyYB1NfX8/bbb/PLX/4Sk8lEdnY2u3btYunSpfK1nj4vHuTPqOy/MuuECOaKjIxkfHxc9jcqKoro6GiX78LbBR8RCJqbm0txcfE9qekWI/e7yCrXzcjICDdv3nSpjhcXFyfPeuGm5K0oL1KRkZGEhYWxe/duvv71r5OQkCDdcRZSXvGo8DozMyM3597eXv7u7/6Od999Vw6ixWJxUc1nZmayevVql2j8xWJmECiriNlstntyEYpUKd6Au0Ai6tuXlpayf/9+9uzZw82bNwGkufzixYsyR5wyt6vI9SsOEm/JCwrOC4VIzi8wmUwyWlkQFBREa2sr586dQ61WS7OdSqWit7cXi8Uiy42Cc6yTkpJ46aWXeO2118jJyZH+mAvRd6V/OTg32Qf5+7W2tlJXVydzMV+5ckUWpADnGL7yyis8++yzMqjQG7Qlop+ihOjbb7/NoUOH+OEPf8iPf/xj6WsvtBsiGG22ClwDAwNSE9nY2Ehvby/r16/nT//0T9myZYs05y7k3BWXkIiICJYtWyazSLS0tHDr1i2uXr0qtSHg9KsbGBiQfoQOh0O6Uoix12g0xMTEkJuby7p164iNjaW0tJQ1a9a4aNQDAwMXxNTufgFKTU2VVp7p6Wmmp6dn1TDabDba2to4ceIE+/bt48yZM9INbdu2bbzyyivk5+fLNemtFgIlyoDXiYkJTpw4QV1dHdPT0/I1YvwSExO92ufVfVxHR0cxm81ERUURERGxKMZjLhDnvXCBVKlULsUZvMVV4GEIDg4mPj6e8vJyKXx7IsuFRyU/5aS+dOkSBw4ckClZREStu8QfGxsra4uD9y3WL8LdxKA8BIUPpbdFbov2hoaGkpWVRVJSkoxCbmpqoq2tjfr6enp7e138WwsLC2V6pk2bNknB1b34gTeQlJTEjh07UKlUXL9+nZ6eHoaGhqQAbrPZpAZEWR5XIC4hkZGRBAcHS4f8p556im9+85vSVUJZ2nG+EBYN97KfguHhYSnMTU9PMzw8LKtp1dbWcu3aNem6A85xzM7OZt26dTK1DyxMUYlHRa1WExISQkdHBz//+c/RaDTs3r37vgFHNpuNrq4uuru76enp4ezZs+zdu5fr168TExPD+vXreeWVV9i2bZvLexZy7ipTgAUGBlJUVIROp2N4eJiuri5aW1vp6emhv7+fjo4Oenp6MBqN0nIl9kpROhaclfzWrFnD6tWrycvLIz4+nqysLOnrbDQaZYDRQlxQ3NOcbdiwgY6ODt555x26uroYGxvj3LlzqFTOSkdms5m+vj4GBwfp6OjgwoUL1NTUMDU1RVxcHM888wzf/OY3KS0tBe7NFOLNKPfG4eFhrl27Rmtrq4vwLiwJ4eHhXl2cQFQ7A2hsbKS5uZnR0VEiIyNlH3wV5Tne19dHc3OzzPKRnp5OUlKSV4+dQBkDcevWLa5cueIS7wGeUSJ6RHgVh6sQQpuamjhx4oRLqTVR9UU5oDMzM5hMJheHfV9CCLPeNond2xMcHExVVRXl5eVMTk5SW1vLr371K44fPy4P9tjYWDZv3sz27dvJzs4mJiZG5oidy3ycj4My5VVUVBS7du1izZo11NXVcfr0aW7evMnw8LAMFunu7qalpeW+l4uoqCiWLVtGUlIS4+Pj5OXlSc0P3BV65nuhi+92amqKyclJVCoVYWFhsjpTQ0ODzGnb19dHY2MjjY2NdHd3Mz09LddVUFAQpaWlPPXUU2zfvp2lS5fKiG7l9+dp3IW7b37zm4yPj/PP//zP/Pf//t9xOBxs27aNsLAwdDoddrudzs5OmYP46tWrnDlzRn4HDoeD0tJSnnnmGZ5//nnKy8uBu/uWp/NoRkdHS/9bZaWlrq4ujh49Kgu8hIaGumReUKlUUntXWVnJpk2bSE5OlkKucjyV2ryFQJliLjAwkLKyMr75zW9y9epVurq6qK+vp7+/n4MHD8qUWdevX6e9vV1eME0mEyEhIWzdupXXX3+dFStWAN5h5fmyiACg2UpoT09PMzIygslkcqnS5S3rUunXPzMzQ29vL729vTLobLFZTR8V5QXk3LlzHD9+nJGREcLCwsjJySEtLc0lANQbzkR3lGVqL126xJtvvslvf/tbzGYzvb29dHR0uOTpXUg8MnuEj5qycsjNmzelVgCQkaUREREuPrAajWZRC67KQyIgIOAeU7W3aV2VKHP2iX/BwcGsX7+eiIgIdu7cKQXwoKAgMjMzKSwsdJnY3nyQqFTOHMMbNmwgKyuLoaEhJicnMZlMzMzM0NfXx61btxgeHmZ6eprx8XGZRSA6OprIyEgKCgrIyMjAYrGg0+mktlm4E8xn30VaFpEv0GAw8MEHHxAaGkpKSgqTk5PSJ1fcmoXrgyAmJobi4mLy8/PJzc0lNzeX0tJS8vPzF7SM76MgNn0xt0pLS/nOd77D7du3OXbsGD/96U95//33CQoKIjIyEqvVSl9fH8PDwy5FKoKCgqiqqmLp0qWsXr2aVatWyUpoItBmNiFiIRHtVZr/hXY9Ly+PsLAwqqqqCAoKQqvVSg27WK/CbJmSkuKS9QKcfRQV3zy1RpX7X15eHqtWraKmpoaxsTF6e3sZGxtjdHRUXiaVREVF8dJLL/Gd73zHRXBdbGeF8ruPjY2luLiYjIwMGQSanp5ObGwsOp1O+iuLIF9vsmiJwGWR2F+Uo46MjCQqKkpWEFO+3hdQnpFjY2McOHCAw4cPy7SJNpuN5ORk0tLSvLb/7qkge3t7OXXqlCzEBLjsE55YYx4RXpUaVZXKWb87MTFRCjnCP0sIDeBMq7F06VIZrLVY3QaUmtXQ0FA2b94sK6cIXy1v9QNSBvyIC4jD4SAkJITVq1ezevXqWd+nDOrxlo1ViTLCV/SnoKDA5TVms1kGMg0ODjIwMMDQ0BDBwcFkZWURHx+PRqNBp9PNWmp1IYQepSDmcDi4desWZ86cAZzaOuGGI6qFabVaoqKiiI2NJSwsjPT0dIqKili2bBlLly4lJyfHJfOF8BddDGuutLSU119/nczMTE6dOkVDQ4PMJCB8zU0mExERERQVFbF8+XKKioooKCggNzeX5ORklzrf3pI43D3ATvwTY5+SkuISE/AghPuKGFNv6KPSL1etVrNmzRo6Ojo4dOgQHR0dGI1G6YetVquJjIyU+8rGjRv57ne/S3V1NXC3wtxiRLkXFRcXU1JSwmeffYbZbEalUlFVVUVBQQHR0dGebuoDEXmmham8vLxclje+ffs2JpNJ7pferLh5FERee3DGguzbt4+amhoCAwOxWq2kpKRQUlJCbm6uy1nvTZpXZQrSsbEx9u/fz29/+1tZ1nfdunVUVlZK5dtXRngVgySi8aqqqqTJa2BgAK1WS1hYmPQjVKvV7N69m6efflomeF8MB+hsiMlqtVpl/sapqSkZsR4fH++STN5b+6lMu/QgTZzyYPV2xIExm4Am8vpFRUWRlpaGyWTCbDZLv7P7XTiUCcrnG2WaFY1Gw49//GNWr15Nb2+vDNyZmZmRFd+io6OldUMI3eHh4URFRREVFXWPP7bSX9sbUbY3ODiYNWvWkJ2dzfPPP8/Y2Jj0e4S7/dFoNERHRxMfHy+158qxnO9iBF8W0S6l8Pqo46IMLPQWs6VyDAMDAykvLycuLo6VK1fyzjvvuGS+SE5OpqCggPz8fEpKSqiurpaWjoVac/OFsvpfYmKiTDHY2NjI0NAQqampbNmyhaSkJK9JrTgb7q5/KSkpJCYmUl9fz4ULF3j22Wflme4rwquS27dvc/nyZUZGRmRp1Y0bN7Jp0yYXq463uQsqA8t7enrYs2cPR48eRa1W8/LLL/Pyyy+zdu1auVd6Yt551OnEaDRKP8jc3FzGxsYAp6+eWq0mKiqKxMREtmzZwu/93u+xbNky+WV500B/GYTgHhoayrPPPovdbker1ZKXl+eiLfDWfrr7rYp0UspgNFEtxVs1ye4oTTjC3Aq4WAlEUnH3PikFefE9CP/WhRpDMR4i8nPp0qXyMLdarajVaqanp5mZmUGn033hhmOxWGQ/FsJXd64QB39wcDDZ2dlkZ2c/0vutVquLqd0bhaAH5a1V5pye7XlgUYynRqMhPj6e+Ph4cnNzZZW44eFh0tPTycvLIysri4KCApYvXy7dIMT8Xyz7zmy4WyfLy8v53ve+x+HDh4mLi2PFihWUlJTIPnqrJVJkOBFkZmbywgsvkJOTQ1FRkctz3rjOHgVx9gUHB+NwOGhpaZEKOXCeEenp6axbt04GvYr3eRvuOZdHR0cJDAzk61//On/0R3/EqlWrZGpCT10UPbp7KRfabAnxN27cyKuvvkp1dTVardbrFubjoBRQU1NT+c53vgM4AyUWYz9nK+u3GPshUJZrfJh+uAu4yscWmtkObSGohIeHu+SzfRCBgYFerWm9H4/bXm8wnz8OD9P2xTamERERvPjii+zcuVOaNEVeSXdB1duF8ofBXRjIzs7mD/7gD3jttddmtfZ463i6Z9TJy8vjRz/6kcz3qtyLPB0I+bgItyoRy3Px4kVOnDghMyjpdDqysrLIyMhwmaPeKLQr51NcXBzr16+nqqqK7373u1Lwdve/X2g8usqVAlx0dDR/9Ed/xLVr14iOjiYjI4Ply5ezYcMG+RplQNdiRzizm81mtFrtQwsU3spiSUPzsHyRAKo0J7svYE9/D+Lvi9yYGo1GCqKirUKzrDQ9C99B8W8xj6koBGGxWO6bfk4ZlCAuX96SCePLsljH636I0rBqtZrQ0FAXlyolIqAuKChoUY/f/QgMDCQiIkJaKsE1gHaxjPv9rFa+MGazxfJER0cTGBgoS2o/9dRT5OTkEBAQIPvtjX1XWgxjY2P5+te/LqtEAnJP9aRPuUeFV+UkTk1N5d/9u3836+uUqbV8CWX+NG81+/iZHW/ccNy530EP3ucbNx+IPcPX9o2vEsJNR+BeMlX8XOza8odB6QcrLqGLUcvsnut8MeylD4MYE6EIWLlyJf39/UxOThIZGclrr73Gxo0bF8V+5F6cp6ysDLgrp3iDS45XzPwHpU8SeQx99UYNd7ULwIL6SPrx48fPYkHkW1bmD1VWS/NlRFJ4kbnFk+nMHgdlIR5RSMVXzjtlMKhKpSI8PJxNmzaRkJAgBcDFILgqcdeKCzllUQqver1+PtrhNfh6/8D3++jr/QN/H30BX+8f+H4ffb1/4O/jXPGrX/1q3v/G/fDFMfTt66ofP378+PHjx48fn0LljWka/Pjx48ePHz9+/PiZDb/m1Y8fP378+PHjx8+iwS+8+vHjx48fP378+Fk0+IVXP378+PHjx48fP4sGv/Dqx48fP378+PHjZ9HgFXleBXq9/jXgX+/8+gODwfBzT7ZnrvHl/un1+vXAnwJrgBhgGLgC/I3BYNjrwaY9Fnq9/r8DlUA+EAcYgXbgd8DfGwyGIc+1bm7Q6/VtQOZ9nu4zGAxJC9icOcfX+6fEV9chgF6vTwP+X2AHEAv04FyH/8VgMIx4sGnzhi+dGXq9/uvARqAcWAZEAG8bDIZXPdmuuUSv16uA7wI/BEqAAKAZ+AXwU4PBYPNg8+YEb9lPvUZ41ev16cDfAZPA4q6VOgu+3D+9Xv8fgf8PGAQ+wXmoxAHLgSeAxXxo/hugFjgA9ANhQDXw58AP9Xp9tcFg6PRc8+aMMeBvZnl8coHbMV/4ev98eh3q9focoAZIAD4CmoCVwJ8AO/R6/VpfuEgq8cEz4z/iFFongS6g0LPNmRfeBF7DeVa8B0wBTwJ/C2zQ6/UvGgwGX0jx5PH91CuE1zu3lV8AQ8AHwE8826K5xZf7p9frX8R5YB4EnjcYDBNuz3u+FMfjEWkwGEzuD+r1+v8K/Afg3wO+kAF61GAw/LmnGzGP+HT/vgLr0IBTcP1jg8Hwd+JBvV7/1zgvmP8V+JGH2jbn+OiZ8W9wCq03cWpgj3i2OXOLXq/fhVNwvQWsNBgMg3ce1wC/Bl4Avg284aEmziUe30+9xef1j4HNONXtUx5uy3zgk/3T6/Vq4L8D08A33Q9MAIPBYFnwhs0hswmud/j1nZ95C9UWP35mw9fXoV6vXwJsA9qAn7o9/Z9x7qmv6fX6sAVu2nzic2eGwWA4YjAYbviI5nE2nr/z838JwRXk2vtPd379owVvlY/icc2rXq8vAv4S+FuDwXBcr9dv9nSb5hIf798aIBv4DTCi1+ufBkoBE3DOYDCc9mTj5pmv3flZ79FWzB1avV7/KpCB87CsB477go/WHXy5f76+DsWeud9gMNiVTxgMhgm9Xn8Kp3BbDRxa6MbNNT5+ZvgywtezdZbnxGMVer1eZzAYRhemSfOGx/dTjwqver0+EHgL6MBpgvUpfL1/QNWdn304/UKXKp/U6/XHga8bDIaBhW7YXKPX63+C0+8sCmcA1zqcC/YvPdmuOSQJ51xVckuv13/XYDAc80SD5hhf7p+vr8OCOz+v3+f5GziF13wWufD6FTgzfBmhbc2e5bkliv8XAmfmvznzisf3U0+7Dfw/OIMJvmMwGIwebst84Ov9S7jz80dACE7H9AicWp/PgQ3A+55p2pzzE5wmyj/FKbh+BmxbxAKBkl8AW3BuSGE4hZ+fAVnAPr1ev8xzTZsTfL1/vr4Oo+78HLvP8+Jx3fw3Zd7x9TPDl/nkzs9/q9frY8SDdy4k/0XxuugFbdXc4xX7qcc0r3q9fiXOm+X/8gGz1j34ev/uEHDnpwqnZufynd+v6fX63Tg1JRv1ev3qxf4diPQfer0+EaeZ9i+BS3q9/hmDwVDr0cY9JgaD4b+4PXQV+JFer58E/h3OzAq7F7pdc4Wv94+v0Dq8D6o7Pxe1L+VX5MzwZd4FXgV2Ag16vf5jnH7oTwI5OC0EecCidlXylv3UI5pXhWnkOncdmX0GX++fApFbsVVxYAJwR2vw+Z1fVy5oq+YRg8HQZzAYPsRppozlbg5GX+Qf7/zc4NFWzB++0j9fX4dCsxp1n+cj3V636PgKnRk+yx1/7GdxWul6cWYe+B7ODAvrcGaOAGcaLV9kQfdTT2lew3H6JwGY9PpZMw39k16v/yecTut/ulANmyN8vX+C5js/R+/zvDhUQ+a/KQuLwWBo1+v1DUC5Xq+PU0aX+hBik/WlKG4lvtI/X1+Hon/593leZPy4n0/sYuCrcmb4NAaDwQr8rzv/JHq9PgRncQYjcG3hW7YgLOh+6inhdQb45/s8V4HT5+ckzk1rMZpPfL1/guOAFcjT6/VBBoPB7PZ86Z2fbQvaqoUj5c7PRW0GegCr7/ycLXrWF/CV/vn6OhT5QLfp9Xq1MuOAXq+PANbiFAoWcxDMV+XM+KryGhAMvLmY09Z9AQu6n3pEeL1jyvr+bM/p9fo/x7lQ31yspfB8vX8Cg8EwqNfr3wNewRlo8B/Fc3q9fiuwHacp7zPPtPDx0Ov1hTiTMfe6Pa7GmRA+AahZzKUp9Xp9CdBjMBiG3R7PBP7+zq+/XPCGzRG+3j/w/XVoMBha9Hr9fpyuOn+Is+qU4L/g1PT8zGAwLNp8qF+VM8PX0ev1kQaDYdztsSqcMRKTOMsbL1q8aT/1eJ5XP4uefwusAv5vvV6/ATiHs+7xbpwayR8s4px2O4C/upNqqAWnz1IizuowS3D6Nf3Ac82bE14E/i+9Xn8EZ2WYCZzBBU/j1BTsBf6n55r32Ph6/wS+vA7BWcWuBvjfer1+C9CIs7+bcLoL/N8ebJufh+BOBapdd34VOVFX6/X6N+78f9BgMCz2SmIH9Hq9EWcQ0wRQAjyFU7P+vMFgWOxWHq/ZT/3Cq5/HwmAw9Ov1+lU4tT27cSYKnwA+Bf7CYDAsZlPeQeD/4DRLLsOZimcK52H5FvC/3W+gi5AjOPNoLsdp9gnD6Tt5Emcf31rkFXF8vX+Az69DoX2txKm52oFTIOgB/jfwX3xgHX4VKMdZHlXJEu7mQG1n8ZfB/Q3wMs6sAyHAbeDnwF8aDIY2D7ZrrvCa/VTlcCz6fduPHz9+/Pjx48fPVwRPFynw48ePHz9+/Pjx4+eh8Quvfvz48ePHjx8/fhYNfuHVjx8/fvz48ePHz6LBL7z68ePHjx8/fvz4WTT4hVc/fvz48ePHjx8/i4aHTpWl1+sXfVoCg8Ggut9zvt4/8P0++kL/wPf76J+nvt0/8P0++kL/wPf76J+nvts/v+bVjx8/fvz48ePHz6LhkYsUGAyG+WjHvKLX6x/6tb7eP/D9Ps5l/6xWKzabjYCAAAID57emh3+euuLrfZzv/tntdtTqudVP+MfQlcXYP/D9PvrnqSu+2D9/hS0/fh5AYGAgAQEBqFQPtD758eN1zLXg6sePHz/egl949eNnFkTlOZVKJQVXh8OB1WpFrVYTEBDgyeb58SOx2+1MT0/T0dFBS0sLYWFhVFZWEhkZCSAtB378+PHzuMzMzGCxWHA4HKjVapRVWgMCAggKClqQ/cYvvPrxMwtCYJ2enmZmZoagoCDCwsLQaDSAq3DrZ3HjcDikiX2xjKfD4cBisRAUFIRaraatrY3333+f2tpaysvLKSwslMKr1Wr1C6/cHWdwrltPaqZnK8vucDhwOBzywqxsq5iXi2V++vE9xNzUarVotdoHvtZut7vMZZj7uesXXv34cUNoqsbHx9m7dy+1tbWUlpayY8cOEhISAOdCttlsUpj1s3ixWCzYbDYCAwMXzXgqhVeAU6dO8cYbb2A0GiktLfW7DMyCzWbDbDbjcDjQaDRoNBqPCoM2m01qr1QqFWazGZvNRlBQECqVCovFgt1ud3Fd8l9C/HgKIYw+DBaLBavVikajkXN2rueuX3idZ8QNRCAG3263y5u1O0Ir4K2aILvdjtVqlX0TbQVcblui/d7Yh9kQ2reAgAAmJyc5efIk77//PqdPn6agoID+/n6qqqrIy8sjJSVF9tlms3lck+Pn0RHjLQTAxYK4XIWFhQFw4sQJfve739HR0UFkZCRhYWEuwYWLZf3NB2KPEgGX8x10+SDcNavubQkODnb5/X5tXaxWH3HmuZ+H4vwQ34943P11i62/voZarcZisXDr1i3a2tqwWq0EBwfLi7RarSYmJobs7GxiY2Nn1c7abDb5WY87nn7hdZ6536J7kLCjFPy8EdE+sREr2yp+LjbBFZzmVSHI9Pb2cvDgQerq6ujr66Ovr4/m5mZWrlzJM888w7Zt28jIyAD8gTGLlcU0N5XMzMwQGhoKwI0bN/jpT3/KZ599BkBhYSE6nc5Fy7FY+zkXuPvkeZK5Mp8u1vFUKjgEynPQ/bxYrP30Vex2O1evXuWf/umfeO+995ieniYhIQG73c7Y2BharZaKigpeffVVvvGNb8wqvCrH+nF5LOHVbrdjs9mk86643SpvTV9FU4e4icxmlhIC38N+J+6mI0+iHNNH0WJYLBYsFguANM16+8Yk5rbZbJYa8p6eHmpqahgdHaW1tZWNGzeyYsUK4uLi5PsexbQyH4igMpvNJjUdYr4FBATIfsHdC8ZsFyX3QDV3/6X59meaa5QaOOHyIebv8ePHaW5upqioiMrKSqlN8NY+hYSEADA+Ps7vfvc7PvroIwDKy8t58cUX2bBhg9TKwv01eL6McrxVKhUmk4lLly5x8eJFrFYr5eXlVFZWEh4evqBjrVKpmJmZYXBwkL6+Pqanp9HpdAQFBXHx4kW6urooLy8nNjaW8+fPMzIyQl5eHqmpqURHR5OYmEhkZKTLGevtl2eLxSLPjPu19YvSuol9DZzmZ2/vs69gtVqlPFNbW8vbb7/NO++8w9jYGAAdHR3ytRMTExw/fhyz2cyVK1coLS0lNTWVyMhIOXcjIiLmrG2PtasJdf/D+ond7wbsrYfE4yA0eDabjfb2dgYGBgCkMJGenk5ycjJwd3ErBYFH/W4XAqXgIvwExQHh7jYgDg2VSiX9y7wd5eUgPj6edevWcePGDfr6+rBYLGi1WkZHRzl27BgXL17k6tWr/N7v/R5PPPGEHEtPCz2e/L493fcHoTzsrFYrFotFCnWffPIJb731Frt37yY5OZklS5a4uPd4y0EpAq9UKhXDw8O8//77/OY3vwGcGtddu3bx/PPPs2TJEvl6oUz4qiFMnGJNt7W18atf/Yq///u/B+DHP/4x+fn5hIeHz/v3pJxDg4OD1NXVUV9fT3d3N3a7nZSUFKxWK//6r/9KU1MTu3fvJisrizfffJPh4WEqKipYsWIFSUlJ5OTkUF5eTnFxsVzj3jRHZ0MZ5GoymZiYmJBBsDqdDq1We4+GXHnWiN8Xak/7spr6+82fxejmIeZUYGAgNpuNhoYGfvnLX/LLX/6SqakpNBoNgYGBGI1Gl/dZrVaOHj3K0aNHiY+Pp7q6muzsbOLj4yksLKS8vJz09HSplX2cM2PBruQmk0nenNwPBuHU+6haSW9DaOqUvkunT5/mN7/5DfX19TIyGCArK4vs7GwcDgdms1k+rlariY2NJTs7m/z8fGmaFp/vCd9K91Q7V69e5ZNPPuH06dPY7Xby8/NlX8bHx9HpdOTn55OSkkJcXBxJSUkL2t4vi5iDANHR0WzZsoWZmRkALl26xPDwsNQgT05OcuDAASYnJ7l9+zZbt24lNzcXrVaL1Wpd1PP4y2Cz2ZiZmSEwMNDrfUiF5ln5+9DQEA0NDdy8eZOMjAyvFQxExPyFCxd47733uH79Otu2beOFF15g9erVUnCF+wdI2Gw2efEUnwl43CfUndk0/g/zHvHaqakpenp6GB8f5/Lly5w7d06+TljGxHvmE/H5U1NTHDp0iH/5l3+hvr6e2NhYcnJy6Orq4ubNmzQ1NQFw4cIFGhoaGB4eBqCuro7h4WHsdjsBAQE888wz/Mmf/Ak5OTnz2u7HxWQyERAQIL/nxsZGTp06xeXLlxkaGiI+Pp6VK1eycuVKkpKSmJycZGxsDJVKRUhIiNxTAgICiI2NJTY2Vn72fJ2FZrMZs9kMuArQSsWScr64x3go3enEeaK0TIvXePvZYDab5X6gVqs5duwYe/fuZWpqCnD6ZwvXJaUAK2Q8gIGBAY4fPy5ln9jYWFauXMmOHTtYsWIFOp3usS4kX3qncjgczMzMMD4+zvDwMDMzM0RGRhITE0NAQICcAEFBQYSGht7jjP5FKCMxvenweBABAQHSpNfV1UVtbS379u3jgw8+oL+//57Xh4SEuExioQ1KT09n+fLllJWVkZ+fT1ZWFkuWLEGn0wELr+ESmyZAf38/+/bt480336S5uRmdTkdfXx9XrlxheHiYgYEBgoODyczMJDU1lfT0dAoKCsjMzCQ6Opq4uDji4uK86pB0R3y/MTEx7Ny5k8DAQCIjIzl37hwTExMEBwdjMpkYHBzkyJEjDAwMMD4+zvbt21m2bJmLyXahx8pqtTI1NcX4+DhGo1GuQ51OR1hYGNPT00xMTMi5KrS0gYGBUjAS0dhCGyUiRwMDA9FqtdhsNqxWK1qtVt7MNRqN3MyEdcGbNA1GoxGLxUJERARarVZeQgCSkpJISUnBbDYzMDAgo2SVASSeRHyfYs309PRw5swZLl68SGRkJLt37+bVV1+V+6TIRazcN4WrhNKFZDY8rSVSpth5VL9/pTuIw+Ggvr6eI0eO0NPTQ2trK/X19QCsXr2aJ598Upow50uQUAoxVquVpqYmPv30U/bv3w84/erVajUTExO0tbXJ93V2dsr/BwUFYTabXZ5/++23iY2N5Rvf+AY5OTnysugtlg/hZiXOfJvNxsWLF9mzZw8HDx7kypUrTE1NERYWxtWrV2lpaSE7O5uxsTF6enpkxgUhA2i1WpKSkigoKCA/P5+kpCQ5ZnO114jvLigoaM4v37OddcJ9y93i6i37ptC6Aly7do1jx47JeRkWFsbExAQTExOAc/+MjIzEbDYzNjYm00sCjI2NSReDGzdu0NjYyO3btwkICGDbtm3AXVnvUWWCLy1BjI2N0dTUxPHjx9m7dy+dnZ2sXr2aHTt2EBYWJs3kYtIVFBQ80ucrc9wtBpQ3+Z6eHv75n/+Zjz76iLa2NkZHR2d9j7vKHZzBGE1NTfT09HDy5EkCAwMpLCzkD/7gD3juueeAhTcHio3C4XBw6dIlDh48CMDrr79OeXk5w8PDNDY2SnPswMAAR48eRaVSER4eTnh4OMHBwSxZsoQXXniB3bt3ExcX5/GDcjbcN4/Y2Fi2b98u2z8xMYFKpaK7u5uzZ8/S3d3N1atXcTgcTE9PExYWxrJlywAWXANrNpvp6OigpqaGo0eP0tLSQl9fHxqNhrVr11JWVkZzczPnz58nPDxcRoUmJycTHR0tzXoOhwOdTkdcXBwBAQEMDQ0xNDSETqcjLS1NCsfCD296epqkpCSpYRebsqcvKMJiYLVaqauro7OzkxUrVpCTk+PSNnGQiA3bm+Yj3NW2ija3t7dz+fJlZmZmKC4uprKyUgqqZrP5voev0s/3QSgvq57gy2q7xZwzGo1cu3aNDz/8kE8++YTx8XEGBwex2+2Ul5fzk5/8hCeffJKgoCCpEZsPlN/39PQ0N27coL293eU1TU1NLhcpd2Z7bnh4mL/+679meHiYH/3oRxQWFs5twx8TsZYE+/bt46233qKmpka6YIFTE3369GkaGxuJjIzEYrFIwUecDcKlIDAwkMzMTLZs2cIzzzzDsmXL5PjNxXqdD8FfaJFFvmV33Oe5Nwiu4vIo9pCmpibeffddrly5Muvrs7Ky+NGPfsSaNWvo6emRLgONjY2zvn5sbIxPPvmEkpISF+FVub89LF/6dLl9+zYnT55k3759HDt2DHAuqtHRUYKCgqTJIzY2liVLlpCbm0t0dLQMgBGO68HBweTl5ZGcnCw1OAkJCS4bsJjs3u43OT4+TldXFx999BHvvPMOzc3NgPMWExcXR2xsrNwohSbMaDQyNTVFUFAQ4eHh2Gw2Ojs76ezslDeWW7dukZiYSEpKily0MP83bXGBEAuspaWFY8eO0dLSQllZGa+//joFBQXcvn1bmoKMRiO3b9+Wm/To6CiXL1+ms7OTqKgoGYC2EO1/HMSC0mg0REdHs2HDBrKysjCZTAB0d3ej0+nYt28fPT09XLt2DbPZTEREBBEREWRnZ0sN0Hy3U8ypiYkJzp07x9tvvy21O4KmpiYKCgpoa2uTph+NRoNOpyM2Npa4uDipMbbb7YSHh6PT6QgMDGRkZITR0VEiIyNJTExkYmKC0dFR4uLi5JoOCgqipKSETZs2SdO1OMQ8McZC4AsICMBoNHL8+HGOHDnC6Ogo8fHxLtpxs9nMzMzMrEKbN8xPpalyeHiYo0ePcunSJRITE1mzZo28NIgcpu6IQ1L4mfX19dHY2EhnZycmk4mQkBCSk5MpKCggLS1NmjuF5WshUGpNxd80Go1SC/dF2rCZmRmX/v3qV7/i3Xffpbe3F3Bqv7KysnjhhRfYvHkzkZGRLnNkvhHfZWpqKiUlJRiNRgICAmS1ovDwcAIDA4mNjSU9PZ2ZmRmuXr1KT08ParVaKjpCQ0MZHR1lfHycY8eOsX79ehkUI/6OJ7XmytzXImPLBx98wNGjRxkZGZGvFW00mUz09vbKcYK7Jmm73c7o6Ki0HnV1ddHd3c3Q0BDbtm1j3bp1xMTEAHetll+272LONTQ00NTUhM1mIzg4WKaFDAgIQKvVSquyMnjMbDYzOTmJSqVCp9MRHh7OxMQEnZ2dWK1WkpKSpEwTHBxMUlISaWlpX5jo3xMo1/zw8DAfffQR7733Hu3t7dLKZjabiY6Opri4mF27dvF7v/d7pKamYrfbSU9PZ8mSJVy4cIHm5mZ6e3sZHR1FpVIRHBzMyMgIJpOJs2fPcvHiRVasWEFQUNB904Y+iC8tvE5MTNDa2ir9dMB5k9q3b989jtfKgB4xuYWWJyQkhF27drFq1SpMJhPBwcFUVFSwbNkyuSAXQ215k8nEuXPn+NWvfsXBgwfp7u6WzyUkJLB06VKWLl0qJ2xhYSEZGRkMDg7S29tLREQECQkJ2Gw26uvrOXfuHE1NTXR0dGA0Gjl27BjJycnEx8eTlZUF4JKkfD5QVh4CpwBUW1uLzWYjOztbJuxPSkpCp9MRGhqKw+GQG7NWq2VoaIhPP/2UpqYmcnJyWLlyJeHh4YB33DTvh7tpNSYmhqioKPmc0WiU/Th06BDt7e3cuHGDjz76iMjISJ5//nkyMjJQqVTzWp5TudYsFgvDw8OMj4/f8zqr1cq1a9dcHhNa8oGBAZcgO3hwtgExL2w2G2FhYajVasbHx1GpVPyP//E/+MlPfgI4hSmtVutR4RWch+r169c5cOAAycnJbNq0iczMTDkmyj3KXfjzBrcB4QJgtVqlKby3t5cdO3awadMmOS81Gs0XHgJdXV18+umn/O53v6Ourg6r1UpoaCjZ2dm8+uqrfO9730OtVstMFQvpsiX+lt1u58qVK7S1tRETE0N+fj7x8fH3BIXej9u3b3P16lWpQFGr1RQVFfHaa6+xa9cu6X413/EDys/WarXk5eWxefNmsrKypKVOrVbLmIfY2FgqKyuprq5mbGyM3/zmN5w4cYLe3l6uX79Ob2+vFOTAub5aW1sZHh52KQXsSYuHWFO3b9/mzTff5M0336S1tdXFFxJc15VGoyE4OPj/z95/h0d95Xm++EtSBZVyzjkjgkCAwCKbDDbJsbvtbrt7Okz13r2zM7O7c+/d/e3uvc8+O7P7zOz07Extu4PbdrvbbmMbjAGDwWQRRBKSQBGUcyrlClLp90dxjr9VCIyNpCrJ9X4eP8IVz6mTPufzeX/eHwICAkhKSiI3N5f4+HipKVpVVUVDQwMDAwPU19fz7rvv0tDQgJ+fH5s2bZKfNxWG+9GjR/nv//2/09PTQ1hYGFarFbPZjFqtJjg4GI1Gw8jICCMjI/LiIyKOwcHBJCQkEBwczNDQEJ2dnQwMDKBWq4mPjychIYHY2FgWLlzIpk2bmDdv3hO1daqhjEIYjUZOnz7N8ePHqa2tBezzeXx8HK1WS2FhIT/5yU/Yvn07arVannEFBQUsXLiQzs5Orl69ytGjRzlz5oy0YwTKysr47W9/C8DSpUvl/vZ1bL1vPMsjIyMJCwujp6dHPjZZiEN5iAgoJ/Lo6Cjnz5+nrq6OkZERJiYmOHbsGAsWLKCgoIBly5aRmpoqP2t4eBiVSvW1ObRTDWeuTXNzM+fOnePkyZMOnKWQkBB27NjBCy+8QEJCgsyAjY2NJSwsjKGhIYaHh/H19cXX1xcvLy/y8/NZtmwZJ06c4NSpU5SXl1NXV8ft27elNxYeTDyZajiPXX19PXfv3kWj0RAXFycnure3t+Q7enl5yX+D3fP+/PPP093dTUBAAEFBQQ5Gg7tD6Z1RGqA6nY7Nmzej0Wjo6uqSnub6+nqKi4spLCyUyXbTabwqPzc4OJjVq1ejVqtZvHgxHR0dcvxGRkYwmUxotVp0Oh02mw2z2Sz5qx0dHVRXV3/t7x8aGpL/npiY4Pbt27S3txMTE+PyELzou1arleM4Ojr6UAPPWUDdHaCcOyKprLOzk6ysLHbv3k1hYSEqlUpSiZTzQUQPfHx8aGxs5OTJk5w9e5by8nJu3Ljh8D2NjY2kpKSwYcMG0tPTZ+RiKeSPRLh/YmJCtvPDDz+kr6+PHTt2EBUVRWRkpMP7nJNpRKJkTU0Nzc3NFBQU4OPjI3nq6enpbNq0iczMTAfHyXT2UWm8ajQaMjIyCA8PZ2BgQGpKq1Qq6ckTlwi1Wo1Op+O1114jPT2dkydPPuCdFIbv8PCwg0HrKijpKtXV1bz55pscOHDAYU/RarXExsYSHx+PWq2mvr6exsZGFi5cyAsvvEBWVha+vr5ER0cTHh7O2NgYXV1d9PT00NXVRUlJCUePHqW2tpaioiKZFJybmztl0cjBwUFJeezu7paPC5WER0E4Ah72XGNjIxEREZSVlXHx4kVycnJITEwkICAAPz8/GV0NCgpCp9PN+CVE/G5C8uqDDz6Q+4SXl5ecZ4mJiezcuZP169dLL7s4W1QqlaQKRkdHExkZyeDgoJTU8vX1xcfHh5aWFo4cOcKiRYtYunQp8GWEaNqMVzE54uPjWbVqFevXr+fkyZP4+voSHByM2WyWNxEl6V64nIeGhh44IJqbm2lubpb/X1ZWxunTp8nPz2f79u1s3ryZ3NxcgoKCpLfL1RA3PXFYtLW1UVdXJ4nKABEREWzYsIHXXnuNNWvWTPo5wcHB0nMyOjqKTqdDp9Oh1Wq5cuWK/D3F89/Evf5N4ZywMjIywujoqNxcxOISyXsimQccCek6nc5BNWE2YTLOqriJe3t7U1hYSE1Njdxce3t7aWpqcgiRTadBpFTu0Ol0Ukanurqau3fvypCN2NydK7uJEHFzczNFRUVUVlbK6k1jY2NSJUSogoyPj9Pb20tfXx9Wq1UmKI6Ojkrqy8DAANHR0S41XpXjNjg4KC+bQsPV3YzUh0F4I8C+x1y7do2+vj6WLl3K8uXL5RoU2cHw5XwTF67e3l4+//xz/uEf/kFy0UJCQqQDQBhFDQ0NVFRUEBsbi5+f37R7XZXyR6Ojo5SXl3Pq1CkOHz5McXExsbGxMoKjnEfKfyvD/mazmaamJrRaLatXr2ZoaIjLly8TFhbGwoULSUhIkO+faU6vt7e3pBQ9CiJULtZSdnY2169ff2AdCUdRcHCwgyPHVetNzL3W1lY+/PBDfve739HZ2YlWqyUjI4PAwECCg4PJysoiLy8PPz8/bty4QWlpKStXruSHP/yhjOQpkZGRIf9dUVFBZGQkf/zjH6moqODAgQMEBwfzyiuvOFCVnsToW7ZsGfv27aO8vByTySQvccqLrXN0SjgCBCVS6GyLbH2RD9Hb2yujAZcvX0aj0ZCTk0NUVBRarZa4uDjpec/MzJwx41V5QR4YGODUqVO8//77nDlzhv7+ftRqNRqNRtLNCgoKWL9+PYGBgZKuoaRhCWqgv78/GzZsoLGxkdraWsrKyhz0ehsbG7l9+zZ1dXWSZvd15u/X/nXEh2s0GlatWkViYiJlZWU0NDTI5+rr62lubpaNEVyQzs5OPv/888e6KZrNZi5fvsy9e/coLi5mz549vPjiiw6L351kbMQmqvQ6bty4kb/5m79h8eLFj/V+m80mw0llZWUUFRVx9+5d4EuX/Uwar84QB2R0dDTR0dEPhPGUC1zpVVHWN54LELXHRQjl1VdfxcfHh//4H/8jbW1t3L17d8aMVwHlotdoNOTm5pKUlITZbJaXR2XI3znLdXR0lB07dsjkQiFVI3jsarVaeo0+/vhjPv74Y1kSUKzD7Oxs8vLySE5OxsvL62uHgaYKyszh8fFxKisraW9vx8fHB5PJRG9vr8Nlys/PDz8/P8xmszR03QFiDQmqUXNzMxcuXKClpYWNGzfKiwM4jr/YJ8SF5dq1a+zfv18arjExMezatYuMjAxKS0s5ePAgQ0NDMmHmUQlEU9Uv53bX1dXxy1/+kqKiIkJCQvjud7/L0qVLWX9fQ1nMXWePsHL/9/HxIS4ujq6uLu7evSsPy/Xr17NlyxZJFwD3LdzQ0tLCrVu3GB4eJigoSPZFUIGU/OeIiAiZSyIw0+ehkv5QV1eHwWDg008/lR5IrVbLc889J8PLWq1WXmwLCgro6ekhPDx8UsPVGfPmzeNf/at/hc1m4z//5/9MXV0dn332GZGRkWzZskUaQE+CNWvWkJ6eTnd3N6Ojo9IBp4xCirPO29sbnU5Hf38/d+7cob29nbGxMbq7u+np6cHf35/w8HCGh4e5efOmA8US7OdpVVUVzc3NspCPSP4NDw8nPj5evnY6uczKC2BVVRVvv/02R44ckbREERnQ6XTk5eWxbt06ucc7J+eKuamspLVt2zbGx8f53e9+R3FxsUPkXazT8PBwSX15XLvuG4+0zWYjKCiIBQsWsGDBAnp6emSWYF1dHa2trQ4TKTAwkPb2dkJDQ7l9+zb+/v74+vrKQevp6aG1tVUuUl9fX8xmMx0dHRw+fJj+/n66urpYuXIlWVlZREREuDSBS7mJgN1gUHoewe5eX7BgAWC/0QjCtjIbUxitarVa3l5OnTrFm2++yaVLl2SCkCuE55UHxfDwMK2trfT39xMcHExkZKRsj6gP7+zRUBLSnZO/3BHCWzyZXI9zCVybzYbJZMLf3x+dTkdaWpqc7729vfKWOlMQ81FURFGr1Q/Ncp0MwcHBD9XjVYYE+/v7uXTpkowweHl5yXrWW7duZe3atS5PRFBuxgMDAzQ0NNDR0cHY2BgWi4WRkRGHDTQmJob4+Hiam5u5d+8eFovFQfbLlVDuMZ2dnXR0dJCSksKaNWscjDHlby4qHU5MTFBeXs7Bgwe5dOkSKpWKhQsX8uyzz/LCCy9IL7n4LaKiokhOTiY4OHjaEraUh3Bvb69cQ1evXuXIkSOMjo7yve99j5dffpnk5GQHTv9kh7dyrDUaDUajkTNnznDx4kXu3LlDVlYWzzzzDCtWrECtVjsYWzMN8ZsKQ0h5iezv76ekpISioiJqamoYGhrCZrMxODhIW1ubvAxrNBq59tLT01m0aBGBgYEu0wBXzs/y8nI++eQTampqALt3f926dTzzzDMsX778gfeGhoZKrVoRrRUaqMqxtlqtmEwmQkJCCAkJYfv27RQVFcny3SLp7Yc//KGsmPZN+iGcbMp19bhYsGABnZ2deHl5SclIPz8/QkJCGBkZITc3l+vXr0u1he7ubtra2jCbzQ7R2kuXLuHv709UVBS7du2SVRG/STb+V0H0WXxuX18fly9fpqioSF5g/fz8ZCJodHQ0e/fuZd26dfLi7EzfVEZTxGfExMTw3HPP0dDQQENDg0M+UF9fH83NzYyMjMyc8ersSVMKCEdFRUmvm4Ag5O7YsQOTySTlL8bGxmhtbaWoqIgjR45QVFTE8PAwJpNJPg9w9epVzp8/T1JSEj/72c947bXXiI2NdWl2pfJ7lRq3ApWVlXz++eesWrVKUgPAUXrKOUmkvb2dAwcO8MknnzA0NCR/g7CwMNLS0hxoEzMR1hPfYTQaZda50Wh0IKzDV8vruFON8YdB2d+HaU0Kj6KXl5e8bNTU1HDu3DlJSHe+2Mxk+8VGNJVrQhgQZrOZK1euUF1d7aDjvGjRIl588UV2794tvUDTsdk+LpyT2Pr6+hgYGMDb25uQkBBCQ0MdjL2MjAxSU1O5desWlZWVsm+T8fVnEsrxBLvn1d/fn127dvG9730PtVrN6OiolBMCx4vG7du3+d//+39z6NAhhoaGyMjIYNu2bbz++uukpKRgsVjw9/dHq9ViMpmIjo5m/vz5gN2YsNlsU3oRUSoKgN3LU1RUhEajobq6Gp1OR2FhIc899xyZmZmP9ZmC0uLl5cXw8DCHDx/mV7/6FUajkczMTFatWsXixYtnrCDBo6BMeBQqCl5eXnR3d/P555/z7rvvcv78eZkDMTExIeeimIfCOZKSksKSJUtk8q5wiMyU8SoMDI1GIz2GFy9edOCFbtu2jT/7sz+TDpxHQVmRcTKKiPJsSUtL49VXX2ViYoITJ05w8+ZNUlNT+e53v/uNjdcn3S+Tk5MlLUVJmRPjUVhYSH9/P0ajkerqaikpVV9fLx1UQj/85MmTZGZmsnPnTnx8fL6xlNRXQUmDsNlsUodVGXlR7n+ZmZmsWbOGtLQ06Sj5qjaJ3zUwMJD09HTS0tLo6+uTzh2hJPJN5u0T/Rrj4+OSF+Lr6ysNlId5CUXBAmfExsYSERFBQkICq1evllqiYiFERkbKMERjYyNnz55l+fLlhIWFyc3VVZmxwsjJyMhg7dq1MnxgMpm4evWqVBFYtmwZYE9wEaRlMbAWi4XS0lKqq6u5ceMGX3zxhUyEGRsbIzAwkLVr17Jjxw5ZhhRmVjrM39+fnJwcIiMjKSkp4fz58+Tn50vlCMGbEb/HxMQENTU1dHZ2EhMT41ASzl0hPMdfRXEQC3ZsbIzLly9LKRhxg1bKTs00nPmAyo1cSRkQnjmldJngaon3gr2vgqpz8eJF3nrrLa5cueLwHQsXLmTt2rXScBWbn6u87MpN1WQyUVFRQX19vZQiCgoKcpiLIvl0aGiInp4eeWF2JTdW7Ge+vr5ynhUXFzMxMUFmZqZc+8pKWaLNAmVlZRw5coSWlhaSkpLYt2+fLD0K9rkSGxtLdHQ0/f39mM1meTl25kZPFUT7RkZGuH79Om+//TaDg4MkJiayYcMGdu/eTW5uLoDkED4qOVc53+/du0dJSYn0UgYEBBAbG+tw4XcH+pKSgw72dv/pT3/is88+k485a4ArIyuRkZFs2rTJwZs50/1SRtJ6e3v59NNP+eijjzAajQQGBrJgwQJ27drFunXrUKlUDA8PS6+cUnlIOD0eRS0Te7Lgk+p0OrKysqSxCHYP3lQkMI+NjcmL26OgzDXw8fHB19f3kYZcWFiYlPVKS0sjPj6eRYsWcefOHc6dO0dVVZU0XsFeqEKslelKJHVOfBwYGGBwcNBh3xaX/hUrVvDCCy9ITeHHMfaVlylBf/L393f4nfz9/QkJCfnKCMtkeCLjVSR2KPFVX6ycFEoeU3JyMsnJyezbt4+bN2+iUqnYv38/YE+4EB5IEfoRBqKrjFcB0aaAgACefvppqXV6/vx5Ojo6uHDhAgUFBSQnJ0t9SeXE7+rq4saNG3z++eccOnSIe/fuyd8G7OHcNWvW8PLLL0uemzBAZqK/oh0hISEUFhZy+vRpzp49y+eff84zzzwj+bzi4iJ4Ml5eXpSWlnLq1Cmys7PZtm0bGRkZDp5ad6MQCCNcuQmKBSgiB8LwGxoa4vz583zwwQccP36ctrY2wG4IrVq1ShoI4jNcga/6XpGl/VUYHx+nqqqKEydOSJkmLy8vgoODKSgoID8/n6CgIDkv3UmPua6ujuvXrz9AzVEWFenr66O/v1+G+ZRqGK6K6litVjk2vb29nDp1itLSUsnPFXAOsQrh9pqaGoqLi2UirDh8RGaveG9kZCRRUVEyK1wkXk7X/iI+s6uri7q6OsrLy2W7X3/9dVatWiUvDY+SAVTymsE+hvX19dKIEEbIo4x7V0LsNc3NzXz66aecOnUK+JKCJYwYcUZOTNgrEGVmZrJlyxa2bdvmUB52po1XYYAMDw9TXFzMqVOnJF1gyZIl/PjHP2bDhg3ydZNd5r9um8VvI1RDRkZG5HMBAQFTMl+ns0SymHtCFWb16tU0NTWh0Wior693UBIKDQ11oKlNN4QDUtAalQgJCeGll17i5ZdflhQVQU16FJTyZWJNCoUbgeHhYQctX/G+x8GUjpIyWcf55qK8OSqJz5N5ahcvXsyf//mfo9Vq+eMf/ygXsihPKaQlnD/fFVAOoFar5dlnn8VoNFJVVUVjYyNNTU388Y9/RKVS8fOf/1y202q10tDQwP79+6W8ljBcwb6Zp6SksH79evbu3cvKlSslz2QmD1Xl94SFhREXFwd8KQmVmppKcHAwKpUKq9XqMDGHhoa4ffs2PT09pKWlkZKSIr2z7mC8OvP6qqurOX/+PGVlZRiNRoKDg0lPT5eC0oLX3NfXJ8NkJSUl0nAFe+nJ119/nYULF8rH3MmYA2TFIZF8p+RqT3ag3Llzh9/97nccOXJEljlWq9Xs27ePV155hezsbLy8vGRUYTq1hx8FsVEKcfHa2lpOnToldQrhSw7s8PAwgYGBmM1mqbIQFRXF/PnzpdHoCg6hgHL/HBoakpWYVqxYQXJysnxOiPqLA0Vw7j7//HOOHz8uq0pt27ZNVjkUhrugbI2MjJCdne1QqWm6aT7OZ4SXl70an0ajkaoqX0dZZnh4mPHxcWJjY4mNjaWpqQlvb29CQ0MdPLeuGk9neltHRwcXL17k008/5cSJEzLSJjyMAiL3w2azERISwl/91V/JkPl0J9ZNBudCBDdu3OBPf/qTLMgDkJCQwMaNG4mJiXHg4E8VJnMyuDoBTxmlUf5b6agSXlqlRzIxMZH09HT8/PykJqxIxBPvFUmxM9GHyb4nMzOThQsXygickPv8up8NSO+5QGVlJRcvXmTVqlUyae9xFSOmdMSVN4WHbRIPa5SYkFarFV9fXzZs2ICfnx89PT0ynDI2NsbQ0BANDQ00NTU5TN6ZqpTiDNHn0dFRKWS8bt067t27xxdffEFlZSVXr16VItXp6elSwqiqqorf/va3DhpooaGhxMTEkJKSIg+dgoICgEcaGNPZNwFvb2+Cg4MJCAjAarXS0dFBb2+v5PMqx91sNtPd3c3AwAD+/v6y7Kg7QYRjRJsrKip45513OH/+vHxNVlYWOTk5khuo0Whoa2vj5s2b0qslZGDS09PZu3cvGzdulDdN5yS2mYZyE52YmKC6upqrV6/S3NyMr68vsbGxstKb1WrF39+fhIQEqSbR2NjIkSNH+OCDD2hpaZG/VWZmJs8//zwbNmxwWd+coSybLAqmHD16VHpoxsfHuXz5Mp2dnVJL02q1UlZWRl1dHWlpaaSlpTkctK40dgQEPWtiYgKdTjepUaeUuxkZGeHGjRtUVFSgUqlYv349a9asISAggJGREYf+1dbW0traSk5ODhkZGfK56b4cO3vobTabrCqYmJgoHxeSi0ovjtLbLDivkZGRJCcno9VqJd0sLS2NBQsWOGjEuvIyIrynYFeA+OUvf8mJEyeAL6MgwlEjoDQoVCqVQ/VJV+i7CgeUGLuKigo+++wzent7UalU6HQ6UlNTpZNjOqg3Yu4o54+rz5evcigpzwAhLenl5UVzc7NMJhWvy8zMJCUlxSFjfzrmrZhXgldbUlLC1atXHSgrCxcuZOfOnQ7qLN/kIqJct8pcn87OTq5fv+5QL+Bx6R9upRciqlUI5Obm8txzz2E0Grly5Yr8sW/dusUnn3zCzp07HcrDudLDJYjrZrOZnJwc/s2/+TdER0fzL//yLzQ1NVFeXs7f//3fo9VqaW1tlcUJlJl3sbGxbN26lU2bNjF//nxCQ0MdEr1mOozp/F0RERHExcWhVquxWCwPHEAi5GKz2bh69SrXrl1jYGCAZcuWkZSU5JBM5CpP+aMgMl6VuHfvHp2dnQ5lKy0WiwzxBAYGsmzZMp5++mkKCgrIy8uT/XQHfp3y0BweHubYsWO89dZbdHR0yOpYghJhsVgICgoiJydHCtVXVlZSUlJCa2ur/LyIiAgpDC7CskpvgrPXYaagvLGPjo7KOSi8ekL38/Lly/I3EfuNKN4gCoWI9rtDMqjgBAqvsXKjV3p7BMbHx+X8DA0NJSsryyGxR/xGQpu6t7cXX19fBxnC6Ug6FAfY+Pg4g4ODDgUuBgYGKC8vZ9myZQ7Gq0hEUiqAKBPZjEYjJpNJcls7OjowGo2EhIRQUFDAggUL3OIiKTyWot3FxcXScFWr1Xh5eTlknYvfX2mgGo1GDAYDTU1NrFixgrS0NJdEsJTeOaFfCvbzYfXq1axYsUK2abo8+Ep+PsyOKpzgWKq5ubmZzz//nPPnz8uCCGq1mvT0dClFBdPneXU2Xq9evUpZWZnDa/Ly8njmmWdISkpyuDx+XSj30omJCQdbT6l4Ip5/HLiN8So6phSk1mg0pKamkpKSwo0bNxwWd0VFBe3t7Q4ixjMFcfsUGYUajeaBRJ/ExETCw8MJCAiQPJKzZ88+cFvOysqShmpqaqpc/EoNR+Gmn+lNSoyD8OokJiZSUFBATk4ON2/epKysjObmZkmcHxkZwcfHh76+PkpKSigpKWFwcJD4+HhpvCqz+F0N5zbk5OTw0ksvkZKSImXbmpubHSqtCKhUKikqvWPHDjZu3OhgIIB7GK9KjIyMcPv2bUpKSh75utraWqKjo5mYmKCjo0MWZRBGgK+vLwMDAxw8eJBr164REREhLzZpaWlSeWSmlUCcuY3COBL/9vb2Ji4ujtDQUHx8fAgICGBsbEzyzZxv/K705Ci/W4TafH195Z7iDOckC7FXTkxM4O/vLy+ZInrQ2dnJ/v37KSoqwtfXl7y8PNLS0hyMjqnuj3I/GRgYoKOjQz5vsVi4ceMG/v7+DA8PSznEh8kWic/r6uri8uXLaLVauru7Zfg6KSmJtLQ0+X5XR32cPZBCTxnse4lzgpYyzCqysU0mE8eOHaO2tpYXXniBH/zgBzJJ0lVSWco9LjQ0lKeffpply5Y5qARM1W8vLjCjo6MMDw87cF4jIyPdbr+dDCaTSVIeh4aGKCoqctiP58+fz/r168nNzZ12z6vSoPTy8pKJxkqZR+GQetKCM1ar9bES4b4O3MZ4FVDeTm02G0NDQ5NW5VKpVFRVVZGXl0dgYOCMlosVHkflgCo35+HhYU6fPs2ZM2fo6+uTIr9KLwLYJXq+853vsGvXLhITE6VmqPMkcTVnUilHs3TpUtauXcvNmzf57LPPyM/PZ/ny5bISjo+PD8PDw9y9e5eGhgZ8fX3x8/OTxrigd7jDLdm5DQsWLJAG6d27d2lqauLGjRvcuHGD3t5eJiYmZMWpsbExgoODWbJkCZs2bZI8xMeRD5lJKPvo7e1NfHw8MTExDqUmnWEymSSVRaw7ZfJLR0cHnZ2dnDhxgsDAQJmImJKSwvPPP8+zzz4rtUKd2zBTcN7ww8LCyMrKYuvWrSxcuBCdTkdsbCxDQ0P8+te/5tChQ7IIhzvMTSUEnSoqKoqFCxc66PE6G5si/C68s4GBgQ7Grkh4uXTpEm+//TYtLS3k5OSwceNGmQAkkram8sB0noe+vr4OF/TR0VHu3LlDY2MjpaWlrFixgtWrV7Ny5Uqio6MdPquqqkom2F27do2PP/5YCr23tLRIjWPncXTluDr/lvPnzycjI4Pa2toHDFdnODs87t27x2effUZubq7kMc+0VJaA8jcNDAwkPj6eqKgovLy8HHjYTwolLaa/v5+KigpZhj0oKIiYmJhZUXZcUD5MJhNlZWVUVVXR19eHt7c38+bNY9u2bWzevFlSXUREeTr6pJwvPj4+5Ofn89RTT3H58mV58W9oaOD69etSEQq+WblzJdfXy8sLq9Uqz4eoqKhvVCXOfU7Z+1A2XIRNlC5lAYvFwsWLF8nIyGDNmjUOVTCm+gYmKl+JzxYTULR1cHCQiooKWZyhpqaGO3fuUFVVJY0EYbBOTEyg1WpZuHAhu3fvZvfu3Q7JPcrvE0ayqxej8vtDQ0PJzc0lJSVFkq1LS0tZsmSJ/N3HxsaoqqrCZDLh6+sr5bTcEeK3FtmTYWFhrFixgoyMDAYGBigsLJTex46ODq5cucK1a9dobm6mqanJofqNSKJwN+NVbFABAQHs2rWL+Ph4ampqaGpqoru7m8HBQYaHh+nv75cafMrwluBoiTFUUiv6+/tliPrWrVsOHnphnMykB1Z8l1hvAQEBrFmzhqefflqKusfGxqJSqWTJw4iICAf6g6vXmzMEbWB0dJTW1la6u7tlOUzla8A+HvX19bS3t6PRaCgsLHTgq42NjVFdXU1RUZFMZJs3b56DOsZ0rVVl1nxqaipr166loqLCIapmNBopLS2lo6OD0tJSPvvsM8LCwqQepMVikVERb29vOjo6qKmpoaOjQxp5IoFtsnPDVXDOzt69ezf+/v589NFHVFZWEhAQgE6nw2QySe65cNCIM0RZpvPOnTscPXqUlJQUli9f7hYyhKOjo/T19TE4OCgdGU8CkeQk6FyC1tLa2srhw4e5ffs2AQEBpKWlERYW5hbRvIfBYrFIbXur1cr777/PBx984EDHysrKYtWqVQ5ymNO5FzknmQ0ODtLT0+NwWRLUIrFPirY+ztgq266k3SmpJGIfUPLSH/f8dJ9T9j6UG6e3t7f0cDrDZDLJTX26IQ5xAavVisViwWq10tzcTGlpKcXFxZw5c4Zbt245vE9kX6tUKsxmM8PDw5jNZnx8fIiIiJBeBWEgCI+HO2xGAs4e5oULF7J+/Xqampq4cOECb731FmazmaVLl2Iymbh+/brkQTnzIN0FSvqC+K3F5UetVhMTE0NMTAxZWVnyPX19fcTExGCxWOjs7MRoNHLhwgW2bdvGggUL3E4mCnAYN19fX5YsWcKSJUtobm6WiUqjo6O0t7dz7tw5Wltb5W8gKtsAsrSjMuIg5IxEPXatVotKpXqggtVMGa9Kw1OtVjN//nyCg4NleUrl5Uq0T5S/FQa5uxiuzpxXlUpFY2MjxcXF7Nq1Sz5nMpnQ6XSyb2azmZ6eHoxGI1FRUSxfvtzhMGxpaeHw4cMcPnwYk8lETk4OhYWFDlJGM3H5Cg0NdShYc+3aNcA+buJCJOS7HgeCB2uz2YiKiiIlJcUtErUElCozKpWKmJgYXnnlFcLCwqQEmjJxMigoCI1Gw8WLF/noo4/o6upifHxcGhYWi4VLly6RkpJCVFSU3KdmOvdDua8LB48IRysdNkoNcOf3TfZZSolCpXxVf38/RUVFXLlyRUaDnBMt3WUNKyFohRMTE5w7d4533nmH06dPy+czMzN56qmnyM7OdqCATOdadDZem5qaqKurk/kswnmo1AIXr30cKPnYXV1d1NfX09XV5fD+jIwM8vPzHUocP+6lx+2MVyWUoTDnHyw4OJidO3dSWFgIfFlZZqq9rpMdvMLjeOPGDWpqaujp6aGvr0+GMcBeGz4zM5OcnBwWLlyI1WrlxIkTlJWVMTQ0RHFxMUFBQSxatEhKRCg1Ut0JYhyEJ3XJkiXs3LmTK1eucPPmTd599136+vpITk6mo6ODoaEhQkJCpMC62Wx2+eHhjMnG1Tmr2RmhoaHk5+dTWVnJ+fPnMZvNGI1GBgcHpRHhrnDuT0JCAgEBASxcuBCtVktVVRV3796luLhY8rjFQRgeHk5MTAzz5s0jNzeXwMBAaexptVpZxlKlUrFo0SKWLl06o5XgBJRRiqCgIH7wgx9gtVpJTEx8YF9wPkTFuLvbJUtAHB6ivKSA1Wp1kA1UGvBKr7sYo8uXL3PgwAEqKioAu7Tb+vXrCQ8Pl4fNdHEHnRPgEhISeO655xgfHycoKIj6+nqp4PE4EBcssI+fn5+fpP1s2bLFoUqXu+w/zmH0devWkZeXJx0xwtMoLixLliwhJyeHt956i5KSEoeLZVdXF7W1tbIoA8x8OWPn9SISH0VblMUMnI1XpWQmOBYtcJ4rNpuN8+fPc/DgQYeCMN7e3ixdupSnnnpKViVzp/NTRPaEg+STTz7ht7/9LTdv3pSvyc7O5vXXX+eZZ54hNjZ2WpIlJ4Mz5zU6OprY2FiGh4cd9hjntjzO7yvGcWJigpaWFr744guOHz9OTU2NQ+4S8I2jXW5nvCo7IW4Ak/HQUlJSWLFihfx/s9k8pfqSysxWoQrQ09NDa2urFGW+fv26w8CKEq5CMzE5OZnc3FxSU1MpKSnh8uXL8kARi/H27dsUFhbi5eWFn5+f22yyk0HZthUrVvDSSy8Bds3Xc+fOyWo9CQkJqFQqmagmbt7uAOeEMVFUws/Pj6CgIAe+teAaikPdz8+PjIwMB2NIJOS5S/++CqJPPj4+so73xMQEN27ckAkQExMTDA8PExAQwMqVK1m+fDkJCQlkZGQwf/58AgMDGR0ddZCmEv8WFzHxXTP5uygr96jVaodkTlGfWxi4YpyFN0vQdJTGhSsPQeV3a7VaqfIxMDDA7du32b59OzqdjuDgYBkiF1wyEbHq7++no6NDJvxcuXKFt99+m8uXLwOwcuVKtm7dSk5OjqSHOH/3dEDsCSqVivj4eHbv3k1sbCxVVVXU1dVJ+pXQvYyMjCQmJgatVivDr1FRUURHRzM4OEhxcTFXr15lYGCA7OxsycMXHmd3MmjEHBWcP39//0dW4wsLCyM8PJwbN27IxB5xzg0PD9PU1ORQknUmjFfnuanVajGbzYyOjnL58mXCw8NlBczJ3uesTiKg3CvGxsYYHBxkYGBAllT97LPP+PjjjyVNKSUlhc2bN7N582Y51o+rEToTUFKvxsbGKCoq4p133uHo0aOy9HJYWBi7du3i5ZdfdtBvngkojVdvb2+SkpLIzMyU/HGwO+HEpUpAyJqKf4vPUiYZKmkCV69e5a233uLMmTMPvFaUzH2UsfwwuMcoPwRKjoRyogcGBpKWljatIsXCCwp23sc777zDhQsX6O7uprOzk56eHocfOSEhgfXr17Njxw6WLFkiM/d0Oh1tbW2Ul5dTV1fnsNFotVrJM1RW1HBXKC8HkZGR/OAHP2D+/PmcOHGCqqoqcnNz2bdvH/7+/vz+97+XFdBmSmT5caDUKOzv7+fEiRN0d3ezePFimfwHPGDIKA8dYQiJz1NGBtzVcyegrHAmcPbsWd59911KS0vlYzqdjtWrV/Paa6+xZcsW/Pz8GB8fl2Wgg4KCHvu7ZhqTfa/gXStll8DOVx8cHHTwBLkDlPtZUFAQq1evlmWZDx8+THp6Ovv27ZPjYTKZ5LwW3tOBgQHq6+vp6OhgeHiYo0ePSs3sPXv28KMf/YjCwkJpuM7U/uOcgZ6amkpUVBQbN24EoL29nRMnTnD+/HnUajXr169n7dq1REREyORdcaj6+Phw/fp1/u7v/o7Tp0/LvlosFgcxfXeDmI/Ov7vy/8WFMDY21kF1QUjUWa1WKisrJUULpt94VV78AOLj41m1ahXXr1+noaGBf/mXf6Gqqoof//jHbNu2bVJljK+aZyaTidraWioqKigvL6e8vJw7d+7Q1NQkM+FjY2P58Y9/zA9/+ENiYmJkwpq7GK7gWCXv5s2bvPHGGxQVFckxio2N5Tvf+Q4vvfSSNFxnMsnV2fMaEhJCWFiYw/4pDE1le5TOH2fjVeyx4vHGxkbOnDkjDVdnjWI/Pz8CAwMdxm3WJmyJSejlZRdt7u3tlXwfsG/MaWlpZGRkOCzUqd6klJurxWKhubmZS5cuyR9dhEcmJiaIiYlh7dq1MmPXYrHIKj6tra3cuHGDc+fO0dTUJHUkR0dH8fb2JjAwUBoCou/uDJGUpNFoiImJYfv27SQlJVFXV0dycjKLFy9mbGyM2NhYeSg6Sxi5C4qLi3nvvffQaDRkZmZKIwCQB6MYj8bGRurr66mpqaG0tFQ+LvR6XZlZ/3Uh+Fcmk4mbN2/ywQcfcPToUXp7e9FoNOTk5LBixQo2b97M008/7cBHEkaec9hdbF5KXVlX/hZingpvqzI0pbxMKSv1CA+sM5XAFVB6hkXp6ebmZlpbW6mrq+PQoUP4+PhQWFhIYmKizNb19fWV3jCAkpISfvWrX2G1Wjlz5gxeXl6sWLGCF198ka1bt8oQ9EwXeRGcSOGlCQgIkIZOZGQkgYGBZGVl4e3tzYIFCxySzpyxdetWaegIhZClS5eSmpo6oyo0XwfO8odDQ0MEBATIOdrR0YGXl5eMZAj5OUAm74E9wU0pGaXUw50OOPP68/Pzeemll+jr6+PmzZvYbDYuXrxIQEAA7e3t5OTkEBoaik6nw9/fX0padXR00NXVJT2T4+PjGI1GWltbaW5uprGxkYaGBqqqqqisrJTfFxMTw9KlS1mzZg0vvviiVN5wh4qNzhBrUPDMjx8/7nDRSE1NZe/eveTl5QEzz1d2phjFx8eTnp5OSEiIlHLr7u6mpaXFwVGovBQ6G7UCLS0tFBcXc/bsWT799FO5jwpqh8ViISEhgVWrVrFixYq5x3nt7e2lrKyM2tpaB25TVFQUaWlpDgL+Uw3lDxgREcGqVau4d++evDkpPXAxMTEEBgZisVg4c+YMd+/elR7V+vp6Ghoa5Gbj6+sr/52dnU1mZqb8Lnf2FAgIPqSAj48PixYtYtGiRfKxkZEREhISiImJobGxUXK4XAmxuYnft6WlhZMnT/LFF1+wZs0a5s2bh7+/v4NGq9AUFGVGz507R01NDUajUS5unU7nwLV2J+N8MghuOEBzczOffPIJp0+flptqdHQ0L7zwAvv27SMtLc3hAiI2u4dtWM7yVK6E8zxVQmmUKvuj0WgceLPOfXUFxNoJDw9nzZo1XL16lXPnznHmzBmam5tpaGjg5ZdfJikpCYvFQl1dHe3t7fISdvv2be7evSv/PzExkdWrVzNv3jy5Fly1Np0jakpPZEpKygMKCEoql0i4U6lUDA4OEh0dTVpaGl1dXVRUVNDZ2SnXsvKi5erxFFAari0tLdy+fZuIiAgWL16Mt7c3YWFhsv02m81Ba1oZYvX39/9GMkPfFEp+qpeXF3FxcWzZsoXS0lLq6uro7+9nZGSEjz/+mKtXr0plmrCwMCIiIkhPT8fX11eq8ahUKoKCgjCbzdy7d4/bt2/T2tr6gNZoQEAA8fHxLF++nOeee44tW7ZIDzS4Xk5SCaUR2tXVxf79+/nss88kN1mobTz99NOyJLMrLsrOHv7MzExyc3Md5lNfXx9lZWVcvXpVXpIFjUCZnCd0XM1mM62trVy6dIn9+/dz+vRpKbWp0WikMk9CQgJ79uzh+eeflxJ9Yq+bdZ5XsbGIA6e/v5+TJ09SVFTkoEkpjEXnW/VUH5rC8yGM5d27d8vvULr+wc75/Pzzz6UeaGdnJxaLRaoLKCEOkaeffpqf//znLF26VD7nqprw3xSTTTaxqYlyqvBlGBNcZ9wpEwfALsR/5coVye0Unh1lO69fv861a9e4efMmN2/epLq6WvKtwH6zXr16NdnZ2W61eT4Kop0TExOUlpZy+vRp6dkICAggPz+f9fdFsgFZm1x4U52NDXc0DB4Hoq1ms1l6rhISEoiLi/tGIazpgvL3jomJIS8vj4qKCkpKSujo6GB0dBSz2UxGRgb9/f3cvn2bK1euOHh4lCVHbTYboaGhD1TTciUEh078FQedEsqMZzEXVSoVTU1NXLx4kfPnz9PV1QXY9yCz2exWUlkCwrAR43rv3j3++Mc/cvbsWVQqFTt37uTVV18lODgYtVrNsWPHeO+99/j888/lZ/T19aFSqfD392fDhg2ySAwwYxEP5cUuMTGRH/zgB8THx3PmzBnOnTuHyWSiqamJ3t5ebt++LUvHhoeHo1ar6e7upqurS+qDj4+P09HR8UB5XF9fXzZu3EhhYSHz5s0jKSmJrKwsOT/cwTHijImJCWkQlpeXc+TIEUpKSuS+kpaWxmuvvcbLL7/ssA5dRXkQChjC+6qEl5cX5eXl/K//9b+wWCx897vfZXR0lKKiInp6emT0eWxsDKPRSEVFBZWVlXR0dNDQ0OBQwVC5ly1YsIAf/vCHLF68WD72dfvvNsarslQZwKlTp3jrrbe4ceMG8GVYMiAggKysLLKzs+XNa7qqT4nNVCSibN26ldHRUcLDw7lz5w59fX1STqi8vHzSzxDt9vX1xdfXF5VKxbx58/jZz37Gvn37AKQ4urt4rR4XytCm4PeIsm8tLS1S2F8cJjqdzmV9VN5sh4eHuXbtGnfu3AHsG+DZs2eJiYmhra1NSmEVFRVx5swZadyJ5BGtVktoaCjLli1j165dzJ8/X36Pu46hko4zMDDAhQsX+PTTT6mrqwPsh8T69et5/vnnmTdvHoC8WT9sU3mYB9adITzwXl5eDA0NUV9fT1tbG1qtlvT0dJKSkhxkfVw9nsrf3t/fn4KCAjo7OwGoq6ujqqqKf/qnf0Kn02E2m+nu7sZms6HT6WQFsbGxMUZHR/Hy8iIzM5OkpCQHzvJMZTc/DMo5prxcCa+a0OQVUCYCdnd3c/LkSU6ePEl9fT1gv4SFhYVJ54bSY+tqiP6ZTCYaGho4ceIEJ06c4Ny5c4DdERIREcGWLVu4desW/+N//A9OnToFfElVMxqNjI2NsXLlSr7//e+zYMEC+fkzJbEo1oUw0pYvX05ycjIxMTEEBQVRXl5Oe3s7AwMDDzhwHgaNRkN0dDQ6nY6xsTE0Gg0rVqxg3759FBYWEhcXJ18raBfuJCkpLmBirl67do1Dhw5x+/Zt6S3PyMhgz549fPe735UOEyGR6ar5qVz74eHhpKamUllZiVqtRqfT0dfXx+XLl9HpdFKn9sSJE3R0dKDT6aQXtqenh/LycocLiDKZT6vVEhERQUpKCi+88AJLliwB7GtB0ES/DtzGeBXGhdiUiouL5W3T19dXhugzMzPJy8uTmYxiEk8HnA/uuLg4vvvd77Jp0ybpSm9vb2dwcJDKykru3bsns9LhS0NcVGNasGAB69atY82aNQ4bjjsUInhSKD1vnZ2dlJWV0draip+fnwwrgHsYOX19fVy/fl16asrLy/mLv/gLWf50ZGQEs9ksMyHBPkYLFy4kLy9P0j2ysrJITU11yBZ2Ny+AgHKDqqur4/e//z2HDx+WsiWJiYns2rWLF154QWZ1q1Qqt+3PN4WSG9fb20tFRQVtbW1oNBoiIyMd+Oeu5u2Co2EZFBREQUEBkZGRLFmyRHLKlIl2YJd0W7x4MQkJCXR0dFBSUoJarWbVqlW88sorbNiwwUFw3B0LM8DDw/3KPb+/v5979+45ROdCQ0NJSkqSHFp3yEAXXFTn8622ttYhiffu3bu89957Us1GOG/A3m+lHN+8efPYtWuXNBCcCyHMBJQXvaioKPbt28fKlSspLS3l+PHjFBcXMzw8LL2r3d3djI6OEhwcTFhYmFQ2UavVpKenk5OTQ1hYGH5+fkRHRzN//nxyc3MfUGRw9aVyMigTQUdHRzl06BDvv/++jIJ4eXnx9NNP8/3vf18aroKT70oovz80NJRnnnmGkZERbt265TA3L126xN27d/Hy8mJwcFAqtIC97xaL5YFqcMoIdWRkJHv27OGll15yUIr6pvKgbmO8jo2NYTKZZFal8kfw8fHBZrORmJjI1q1bZUgTZmYSC9kkUYEpLCxMarh2d3djsVhoa2ujra1NegWE12BsbAyLxUJgYCDZ2dmsWrVKFiZQVt2YzXAuFmG1WhkdHWViYkIeJEIwXNyoXQlRgUdAVBpSQoS50tLSmDdvHvPnzycnJ4eUlBRSU1Mf0A51x4QBJZQGQE9PDxUVFbIEYEZGBtu2bWPt2rUOt1937s83hdKIFxcUoV8skojAPS5ZAqItPj4++Pv7ywSm3NxcFixYQFlZmQwlBwYGEhcXR1paGuHh4fT09FBWVobVamXlypVs2LDBIXPdnfqphFD7+CqIw1/sqd7e3ixcuFCGP91FRcLZ8xsWFkZoaCgDAwP09vYSEBDA0NAQVquVs2fPYjKZ5MVSSPgJ2Siwe+EXLVrkUGDFFd5z0SdxoQgODiY4OJiUlBRiYmJYtmwZFosFf39/JiYmMBqNDtKEExP2kttCVSEuLo7AwED8/PwICwtzmKtKD507XapF2FycgT09PRw6dIjjx4/LS1VoaKiMbAn7ZXR0VKqFuBKi3WNjY4SHh/PCCy/Q2trK2bNnAaQTQ1k2/GEQ4xMYGIiXlxdGoxF/f3+SkpJYsWIFzzzzDKtXr5bf53wh+zpwG6vJmeekXIgi7LBgwQJ27txJYmKifG4mDKGHadItWLDgKzOTlY85C4C72oibSij7GRgYKA3WgIAAh1rsSgkyVyEkJITly5dz7do17t275/CcSqVi/fr1pKamMjY2RlpaGuvXr2f+/PmEhoY6JC8p4eoN6GEQRqvYoNra2qiurnbgA65Zs4aXX37ZIUHG1WM0ExClnpXeTXetCOeMoKAglixZQl5engxXKtUUhLE0MTEh6UmTFSBwV+P1UVCOjclkYmhoCF9fXzIyMoiKiiIvL8/B6HGHtSl+Z2GwLFiwgKCgII4dO0ZDQwNgN0hFmWYlBgYG0Gg0cs2Ghoayd+9eh3wJV0fvnL2HOp2OtWvXsmrVKgePsyh/q9FoZDKooCc5Z787w12VI5SwWCx8/PHH/PM//zN3796Vjy9cuBC9Xs/atWsBpNybu0AoAKhUKiIjI1m8eDGxsbG0tbUBXy3B5u/vL6PhISEhZGZmotFo6OjoICQkhIKCAhYtWkRmZqakmjxpZM8tjFcxmTUaDSaTiQ8//JBLly4BjrI2KSkpLF26VL5OWTZuJtsqvLDfNKzoqhDPdMJZhzcuLg4/Pz+MRqPk6Dm/biahTCzz8/Pj2WefJSkpiba2NmnECA9BRkYGoaGhWCwW6UUQi0yZ7SwMBnceR6VR093dzaFDh3j33XdpamoiOjqap556imeffZYlS5Y40AXc4cCfbghjTnAJo6KiZAKFO4SaJ4MwVMWFWsiePQrOzyv5z7MNSi4s2GkDtbW1mEwmcnNziYmJITw8/IH16i4QZ51QkFBWFFPOPZEfYbFYGBgYkJHIFStW8MILL7Bu3TrS09PlWLqLJ1KUbBWGifMaUlaDE3hY25UVutxxLcKXnF+1Wo3RaOTgwYMPaGbHx8ezZs0aFi1a5HAJcac+OXNON2zYwH/7b/+Nd999l+rqasLCwujp6aGpqYmkpCTWr1/PyMgIDQ0NeHt7ExoaSlRUFJmZmcybN4+oqCjGx8cZGRkhICCAhIQEwsLCUKvVDxSM+aZwi19PadUfPXqUf/iHf+DmzZt4e3vj7+8veRcBAQEOtxVXeEfUarXDLVN44h4nbCMOHHcimU8VlJNQo9EQEhKCTqejt7eXxsZGqWPoKn6P0qPm5eVFdnY22dnZj/Ve5YEpDn132ngeBqWHuLW1VWYuFxUVAZCUlMQzzzzDqlWr5Lr6NnhcBaxWK1arlYCAABITEx08z0oDyZ2gPOiF12oyz6uYp8qwudLgnc1wpn6YTCaCgoLkmg4LC3PbZEJR7QzshndeXp5MCO3s7JRjJZJevLy88PX1xWQykZaWxp/92Z/x/e9/323XqdKhpNR9ftQYKBPqlJeNySKe7gZhvAKUlpbyzjvvSMcb2KUH9+7dy6ZNm+SYuevZIZKtBX3gBz/4AWFhYZw6dYq4uDiampo4deoUK1as4PXXX2d4eJiKigpJsQsLC6OwsFBSeKYbLv0VleXTTCYTly9f5sMPP5R1fwUJGOxGo7txtZQEbeW/J4Pg07mbJ2CqoOyToIDodDqpCzeb++zMm5stXislx/XgwYO88cYbDoLfPj4+REdHy3CPu/N2pwLKNTo+Po5Op5NJIaLEJLhHqPmroDQOlDqoSt7uZHN3NsOZCxseHs7atWtZsGABr732GuHh4Q94zd2pzyL0bbPZiIiI4G//9m9ZuXIlv/rVr6ioqHjg9Wq1mo0bN5KSkkJOTg4bN250W8PVGUrP6Vc5dpTqHu6kDvEwiDYK0f3m5maKioqorKyUJZrDw8NZtWoVu3fvZsWKFdKT7s7j53xh2LBhAzk5Oeh0OkZHR3nxxRelIsHY2BgLFy6U71Gr1Q7FBqYbLjVelRWlhBXf2NiITqeTeqFWq5WEhAR27tzJ+vXr5XvdIRNYOciz3ZvxpFBW6BkdHaWlpYX6+nqsViuRkZEyW1RoyrkKSpFtoR2p3FiFV1VsosJT5U4H4FdBKSXU09PDZ599xkcffSRDWUFBQcTFxbF+/XoSEhJmVd+eFMp1GhQUxPLly0lLS3NIKgT3Ej1/GGaDZ2o6oByb7OxsfvSjH5GTk0NWVpYLW/X1IKQDU1JS0Ov1AHzxxReYzWZUKhXDw8OMj48zf/58tm7dyvz58wkPD5eFecT56M7j/ziUltkKZYKWKL184MAByRHVarUUFhby/PPPs2zZMhnZcmcuvTjvNBqNQ1QqMzNTvkb5b+CB8r82m03SIpW5BNNBRXOp8ao0Pn18fPDz85MkbiHmHBYWxs6dO/n3//7fk5qa+kBmnwfuAeckCqFFGBwc7ODRcpdwrFikk20m7n7r/yoohbvv3r3Lr3/9a86dOyelwEJDQ/ne977Hd77zHYfkx9nc58eF8jCNjo5m06ZNskSpck+Zq4fuXIBynITyhzt7syaDMvlIo9Hwox/9iO985zvSoyc86RqNRp6LSurTbIn+zFUo6RBWq5W7d+9SXV0NIPVp9+7dy9NPP+12EeOHQdm2b2psKrmzSuN1OvrtVuSLsbExhoeHJd/HYrGwZMkSdu3aRWpqKuB6QW0PJodyogcEBPDUU0/xb//tvyUgIECK3oP7eLRmu4H6KCgvCN3d3Vy4cAFAlulLTk5m/fr1DmX5vm2HociAdqeMXw++HpSVtsDuzRTKGu4+lwW9Y2RkREoLKastTYbpNAQ8+HpQ7pcajYZFixaxY8cOBgYGyMjIYM2aNQ78T2U0bDZA0HNsNhtjY2PS7hKRU+H4UVKSxHtmim/uVsarWq0mKipK8itiY2PZuXMnS5cudZAomm237G8DlMZrcHAwGzduZOPGjfIxcUudi8lq7gblWISEhJCXl8fNmzdRq9UsWbLkgZKS38bLoHJTnW2lbT2ww3nMZtve4u3tLcOuD0v4VV6yPXPUfaCkvul0OrZu3Up2djbDw8OkpqaSnJwM4HZqEF8XzvaW8t+upq241HhVDqifnx8rVqwgMjKSXbt2YTQaiYmJYeXKlURGRjI2NobNZpt1G5QHdoibp2cDnn4ovdsZGRn8p//0n6ivr8fX15fY2FhSU1Md+J2zdWN9UggVCSW32YPZBTGGs31vEVneStoAIIvYzPb+zTU4JyhHRkYSHh6OxWKZFXq0cwFf23gV5PKZwL1797h48eKMfR/MbP9chbnex7neP/jmfbx169YUt2T6MNfHca73D+Z+H+d6/8DTx7mAudg/901V9MADDzzwwAMPPPDAAyd4fRv5bh544IEHHnjggQcezE54PK8eeOCBBx544IEHHswaeIxXDzzwwAMPPPDAAw9mDTzGqwceeOCBBx544IEHswYuk8rS6/WvAb/7ipfZDAbDrNav0ev1O4H/E8gFwoE24DrwDwaD4ZIr2/ak0Ov1XsDrwE+A+YAPUIV9XP/FYDC4RzmtJ4Ber/87YBmQBUQAo0ADcBD4Z4PB0OO61j05vi3r0Bl6vf5V4J37//tjg8HwG1e2Z6qg1+vXAH8BFAJhQC9QBvyjwWA46sKmTTnm2hjO9b1GQK/XJwD/L7CNL8/Eg8B/MRgMfS5s2hPh27SXusMYutLzWgL8l4f8d+r+az5zScumCPc3o8NAPnAM+AVwA9gNFOn1+ldc2LypwNvAb4FU4E/ArwEN9n7+6b5xO9vxbwB/4AT2fv0BGAP+M1Cq1+sTH/7WWYES5vg6dMb9MftfwJCr2zKV0Ov1/wE4B6zFvt/8PfApEAqsd13Lph5zdAzn+l6DXq9Px+68eR0oBv4ncA+7g+eSXq8Pd2HznhQlfAv2UncZQ5d5Xg0GQwn2wX4Aer1eeCR/NVPtmWro9foY4K+BDmCRwWDoVDy3Aftk/n+Bd13TwieDXq/fA7wK1AEFBoOh+/7jauAD4DngB8BbLmriVCHIYDCYnB/U6/X/Ffi/gf8LmLUienN9HTrj/oXqd0AP8DH2NTrrodfrXwD+P+AksM9gMAw6Pe8edZmnAHN1DJnje819GIAo4F8bDIb/JR7U6/X/gN14/6/Az1zUtifCt2gvdYsxdDvOq16vXwCsBFqAIy5uzpMgGfvve0VpuAIYDIbTwCAQOdkbZwn23f/798JwBTAYDFbgP97/3/9jxls1xZjsMLmPD+7/zZyptswk5tA6dMa/Bp7G7jUYdnFbpgR6vVZM9MkAAQAASURBVN4b+DtgBPius+EKcl3OFcy5MYS5v9fo9fo0YAtQD/yL09P/CftYvqrX6/1nuGnTirm0l7rTGLqd8Qr89P7f385yzmQNYAEK9Hp9hPIJvV6/FgjE7iWZrYi5//feJM+Jx/L1en3IzDRnxvHs/b+lLm3F9GGurEMJvV4/D/hb4BcGg+Gcq9szhSjETt05CvTp9fqder3+3+v1+v9Tr9c/5eK2TSnm8Bg+CnNlr3n6/t/PDQaDTfnE/QtXEeCH3dCbS5hLe6nbjKHLaAOTQa/X64BXABswq8n3BoOhV6/X/3vgH4A7er3+IPYwVzqwCzuv6acP/wS3h/C2pk7yXJri3znA5elvzvRCr9f/NRAABGNPqliN/TD5W1e2azowl9ahgF6vVwG/Bxqxh2DnEpbf/9uBnVO/UPmkXq8/BzxvMBi6ZrphU4k5PoYSc3ivyb7/t/ohz9dg9+plAV/MSIumGXNwL3WbMXQ3z+uLQAjwmcFgaHJxW54YBoPhH7GH11XAj4G/AV4AmoC3nOkEswyH7//9S71eHyYevH/A/BfF60JntFXTh7/GHhb5C+yHyTFgy2w3CB6CObUO7+P/BywBXjMYDKOubswUI+r+358BOmAT9sjOAuA49gSu/a5p2pRiLo+hEnN1rwm+/7f/Ic+Lx0Omvykzhrm2l7rNGLqb8fqT+3/fcGkrpgh6vf7fAR9iT1pKx55JuhR7WP0Per3+v7uudU+M97FnTqZj9yz/Sq/X/yN2wvoO7DcwgNkeJgHAYDDEGAwGL+x0iX3Yvcs39Xp9vmtbNi2Ya+uwALun7u9nuzzdQyCkd7ywe1i/MBgMQwaD4TawF2gG1s1mCsG3YAwlvmV7jRJCnWYu1ayfU3vpY2DGxtBtjFe9Xp+LnbvVjJ27Nauh1+vXY0+iOGQwGP7SYDDcMxgMIwaD4Qb2A6UF+Kv7BOhZh/t8l13YvQTt2JUHfoh9/FZjp0gAzGbv8gMwGAwdBoPhAPbQSDhf6kzOCczBdShCzdV8mUg41yB0Fe8ZDIZbyifueyiP3//fghlt1RThWzKGD2AO7jXCKxf8kOeDnF43qzHX9tL7cJsxdCfO61wiNQM8c//vaecnDAbDiF6vL8ZuxC5h8qQnt4fBYBjDriX598rH7/N8FmMX2b498y2bfhgMhga9Xn8HWKzX6yOUiguzHHNtHQZg518BmPT6SZWGfq3X63+NPQnoL2aqYVOIqvt/jQ95Xhi3uulvyrTg2zCGD8Uc2mvEPM16yPNCTeFhfMrZhrm2l4IbjaFbGK96vd4Xu+fOhl30fi5Ae//vw+SwxOOWGWjLTONVwBd4e45J9Dgj7v7fObExzdF1aObhfcnHfnm8gH1Tnq3h6HPYxewz9Xq9xmAwOO8pC+7/rZ/RVk0dvg1j+FWYC3uNcORs0ev13spsdb1eHwiswu7wmAsJvnNxLwU3GkO3MF6xJzGFAofnCKkZ4Dzwr4Cf6PX6NwwGQ4t4Qq/Xb8c+yCbgoova98TQ6/VBBoNhwOmx5dizYoewF2GYtdDr9TmA0WAwtDs97o1dED4KuDibSxo6Yc6tw/th8z+b7Dm9Xv+fsRs+b8/m0qIGg6Fbr9f/Cfge9qSm/yCe0+v1m4Gt2MN4x1zTwifDt2EMvw17jcFguKvX6z/HToP4OfYKaQL/BXtOyBsGg2EuaPfOub0U3GsM3cV4FaTmuVB9QuBD7Dqum4AKvV5/ADs3dB52SoEX8DezvF71Cb1ePwqUYy+6MB97spYZe5WfWUmHUGAb8D/uSw3dxc7jjQbWYU+iaMeuIjFXMBfX4bcFfwmsAP6f+zrSxdgLpezF7q37scFgMLqueR58Bb4te40eu8Pmn/R6/UagAvu83YA91Pz/uLBtU4m5vJe6xRi6PGHrvuj0auYWqVkkNO3AXi7tDvZD5K+wi/ceBbYaDIZfuK6FU4IPsUvyvIL98FyIXctuvsFgOP6oN84SnMS++YRjz/r9t9jL3vZiv2XONxgMd1zXvKnDXF2H3xbcl91bgb3OeCJfVqE6AqwxGAxzQSprLuNbsdcYDIa72LVr38I+X/8Ku2LNPwFPzXJnDjD391J3GUOviYm5pErhgQceeOCBBx544MFchss9rx544IEHHnjggQceePC48BivHnjggQceeOCBBx7MGniMVw888MADDzzwwAMPZg08xqsHHnjggQceeOCBB7MGHuPVAw888MADDzzwwINZA4/x6oEHHnjggQceeODBrMFjFynQ6/WzXlPLYDB4Pey5ud4/mPt9nAv9g7nfR888ndv9g7nfx7nQP5j7ffTM07nbP4/n1QMPPPDAAw888MCDWYOvXR7WYDBMRzumFXq9/rFfO9f7B3O/j7OxfzD3++iZp46Y6/2Dud/H2dg/cN8+TkxM4OX1SGfpY8Hd56koDvUkfXXXMZwqfFX/vrbx+nUxMTHB+Pg4VqsVm83GZBW9nB/z8vLCy8sLHx8fvL29UalUeHt7nMQeeDCdsFqtjI2NMTExIf/z8fFBo9Hg4+Pj6uZ54IEHcwjCLvDy8kKr1QIPGnNTZczOFIS9Y7PZHOwdYceIfdS5TxaLBR8fH88++zUwrcarmHgqlQqV6sm+ymazAXiMWDfFxMSEHCNx+ZhNm863GWKdqtVq1Gq1q5vjgQcezGEIg05prNlsNkwmE1arFW9vbzQaDVqt1u3PEHHJhy/PvUfZOlarFYvFgtVqZWJiAo1Gg5+fHxqNZqaaPGcwbcarGNSpmnzj4+OAx3h1V4yNjckF6ePjMyUXFg9mBjabDW9v769cq7PNC+KBBx64J2w2m4OXsa6ujmvXrlFfX49WqyU/P5+CggJ8fX0B+/nvjl5J4WkV596j2tjX10d1dTVlZWU0NDQwMTFBZmYmGzZsICkpCbD308vLy2PnPAamzboQtxCz2UxPTw/t7e0YjUY5OIIWAHaXuc1mk251Ly8vdDod/v7+BAYGEhERgb+/v/zs2XaI2mw2OcEfRZvw9vaetaEDj9du9kHpAZmYmKCtrY2enh7MZjOjo6NYrVZCQkJISEggPDxcbqizbf19myEiIiKUKfZdsc964MFMQ5z/AwMDdHR0UFVVxdWrV7lx4wZVVVV4e3vz/PPPM2/ePHx9faWB6E7nojjLvb29HQzN4eFhhoaG6Onpobm5GaPRiEqlYnx8nI6ODmpqaigvL6eurg6r1UpaWhptbW1s376drKwsdDqdC3s1uzBtxqvNZqOnp4fa2lqKi4s5efIkt27dwmQyOfA+vL29sVgsTExMoFKp5MGYkJBAWloaaWlprFu3jo0bN0rjaLYdnqKfj2O8zqZ+eTC7Ieaa1WqlpqaG48ePc/nyZdra2mhpaWFkZISlS5fy8ssvs2PHDsLCwgC7d8DjVZ8dEIaCOGDFvz37jAczCeczu62tjQsXLnDy5EmuXbtGe3s74+PjdHV1AXDt2jX6+/uJjIyU73c3OK+hrq4uSktLpXf10qVL3Lt3T1IgrFYrZrOZsbExLBYLY2NjdHR00NDQQH19PT/96U9ZsmQJYHfoeagEj8aUnkDK21FTUxPHjh3jwoULVFVVUVpaitlsfuh7fX19MZlM8v/b29u5du0aMTExVFZW0tLSwqpVq4iPj0er1c6KJC7h6VB6mR8X7m6gK/s2PDzMzZs3uX79OoODg6SkpLB48WLS0tLw8/NzdVNnBDabDYvFwvj4uORsuZOnQImJiQnGxsZQq9WMj49z6dIlPv74Yy5evEhVVRUDAwPytUeOHEGr1ZKens5TTz0l3z+bILyOYD9wnCMdcxWCDuLsHZorEB5lMb4Pm5fCQyaoTO7wWwhvotgvHhW1mpiYwGw2Y7PZ0Gg0s/LiqOSFtra28tFHH/Hee+9x+/ZtBgcHAQgKCpKv7+zslPbAw5w+roCzSkBzczM1NTV0dnZSU1NDWVkZLS0tdHR0UFtb+5WfZ7VaaWho4NKlS7zwwgvy8bGxMZcbrzabTSbwKpPN3AVTugqUE+zOnTv87ne/48qVK4DdOBXeRxGympiYIC0tjY0bNxIXF0dlZSU1NTVYLBZMJhNGo5G+vj4OHTrEnTt32LNnD88//zwLFy6cFSFM0UaReSg2WGWblf8vDtPZ4BlR8iS7u7t5//33+Zd/+RcAcnJy+NGPfsTzzz9PSkqKfP1cTuLy9vaW/Cx3hjI5a2xsjJs3b/LLX/6SgwcPEh4ezrx582hpaaGvr08mF1y5coXz58+TkpJCbGzsrIuAzFYqzpNC7D9KA08klMyG3+NhBouYc8Ion410pa+TyOzl5TUr9pbJoLwoArS0tPDpp5/ym9/8hrKyMgA0Gg0WiwWLxSLfFxoa6qBA4C77jGiHyWSitraWU6dOUVRURENDA83NzbS0tDzwWnGmK20Wi8WCVquVigsmk4nh4eGZ79AjIJww7oppucKNj4/T09NDXV2dfExwV4TbPDQ0lC1btrB3716WLVuGVqulr6+P/v5+bDYbRqOR6upqLl68yJkzZ6iuruaTTz4hLCyMuLg44uPjAeQkcCco+TCDg4OUl5dz+/Zt2tvb6ejowGg0olar8fPzY2RkBKPRiI+PD+np6axatYqVK1fKcIkwEt0Z/f393Lt3T/5/e3s7FRUVdHZ2Ohivs+HA/CaYTSEeq9Uq21pdXc0vf/lL3n//fQDWrFnDjh075HNXr17l/PnzdHR0cOHCBXJzc9m8ebNcb+42poLfqeSYf5UHfHx8nLGxMQcu6FzBxMQEnZ2dchzr6+uJi4tj/fr1PPXUU0RFRcnXwZNpTk4VxBiOjY0xNjYG4HDRF5d8tVr9RGPljhcvs9ksnTrK9jmfb7PhTBAQhpqvry+jo6McP36cf/qnf6KiogKw2wUiKmIymfD39yciIoLly5cTGBgoP8cd+qvc5ysqKvjtb3/L+fPn6enpkVxXJSYmJoiOjiYyMpLMzExycnLw8/Ojt7eX+vp66urqqK2txWq1MjAwQF1dHQMDAwQFBbnleSIuF97e3nKfhQf3DeUcBqYteXtKP1F0wsvLi5iYGJYvX86pU6cYHx9nZGREGq3R0dHk5eXx0ksv8cwzz8ibc0JCgsPn9fT0kJeXR0xMDIcOHaK6upqDBw8SGxvLrl27CAkJcSvDVWm0enl50dnZSVFREcePH+f69eu0t7fT2toqB90ZOp2O3t5ekpKSpPEqwkruBuVteGxszKGNERERjI6OMjIyIh9zh7CP0vOthNLT/bCxedjnqdVqNBoNZrOZlpYWhoaGCAwMJCoqSiYZusNBKdogNsX6+nreffddPvzwQwICAnj55Zd5+eWXWbZsGYGBgdy9e5fg4GCam5u5ffs2bW1tdHd3S9UPYWS42thTHvRKA1TpjRPJZ+I1Yh5otVp8fX0d+iD6NRszfgUdRKVS4eXlRWNjIydOnODw4cN8/vnnjI6OAvaD18fHhx07dsiDyJXeLTGnREROjOFX7e2jo6MMDg4yPDzM6OiovIQoIeaHWq0mJCSE0NBQdDqdy9ej1WplcHCQkZERfH19CQ8Pf2h/x8fHJQc9JiaGkJAQwP2NWHEWCq/xnTt3+PTTT6XhGhwcTH9/PwAhISHk5OSQlZVFRkYGhYWFkkbgLmtReW4MDw/T0NBAeXm5w5mh0+kICAggNDSU2NhY4uLiSE5OJi8vjyVLlhASEkJPTw9Xr17ls88+o6amBgCj0SgN2vnz50tjz5Vnh9lspre3l/HxccLCwp6YAjjVfZkW49Xb25tFixbxyiuvEBUVxY0bN+ju7iY2Npa1a9eyfv165s2bR3x8/CNDPuHh4WzatImQkBAmJib46KOPuH79Om+99RZqtZq9e/fKhWG1Wt0ifCQmuMVi4dy5c7z55ptcvXqVgYEBKSX1MIyOjlJRUUFDQwN5eXkz1eRvBHHYjY+PMzw8jNVqlc8JY9bVB4QzhGEiFpHS4yQoLeJ1yvcIKN8DjnPuxo0b/OIXv+D69essX76cn/70p6xbtw5A8ktdCdFWLy8vhoaGOHz4MO+//z5ms5kf/vCH/M3f/A1JSUnSyMvIyGDr1q1cvHiR6upqQkNDiYmJkZuqu4TylBfmydDY2MjNmzfp7OxErVajUqkYHR1lfHychIQEFi9eTHx8vMPnuMNl45tAaZx3d3fz6aef8tvf/pbS0lKHA/b48eMsW7aMbdu24e3tLS/IrjIQlAUxHhc9PT3cunWLa9euUVpayt27d+nv73fYd0TfzGYzERERrF+/nj179lBQUODgHZqJfjvrlNfV1XH69GkqKytJTU1l+/btZGZmTvre6upq/uf//J/cvXuXn/zkJ7z00kuAe/AiHwXlvtfa2sqRI0coKSkB7FQBwWmNiYnh+eefZ/v27WRnZxMUFIROp3OgSrj6kgw47OFZWVns27ePrq4uSY0EyM7O5umnn2bVqlXExMQQGBhIaGgoQUFBBAQE4O3tTXBwMG1tbfj7+8t+TUxMMDIywvDwsENC7EzuRcrvGh0dpaSkhKNHjzI4OMi2bdtYvXq1VJAKDAx0+Zk2LcYrQFRUFBs3biQmJob8/HyMRiOxsbEsW7bMwTATMlkieUQYFzabDZ1Oh7e3N8uWLcNsNmM2m/n4448pKioiJiaG7Oxs8vPzp7IL3xjiFuzj40Nvby+nT5/mD3/4A2fOnJEej4dNQl9fXzQaDSMjIzQ0NFBcXExOTg7p6eluxy903vCbm5u5ceMG7e3t8jUmk+kbJalNNx4VFha/79dps+CNXrhwgT/84Q8cPHgQs9lMY2MjhYWFrF69Gh8fn6/lzZ0JiGSR8PBw4uPj2bhxo9QZFF6s2NhYMjIyCA8PR6PRkJKSQkZGhsNh6UpviHI9CENkZGREytS0t7dL7cjr16/T1dUlw81ms5mJiQmSkpJYvHix9Pbk5OSQkpIiD47ZVBjFbDbLRFaTycSRI0f4/e9/L42FiIgIkpKSKCsrw2q10t3dLfsp5AtnGmLPFO2wWq1yDOvq6mhtbZX8bGXVt5GREZqamigvL6esrIyamppHJgMD1NbW0tnZSXx8PIsWLUKn08mzZqaMV+V3Xbp0iTfeeIO6ujrmzZtHW1sb8+bNIzg4GD8/P7RaLSMjIwwMDHDhwgXeffddRkdHSU9PJz8/n8zMTLkW3dUD6+XlhdVqpa+vj8OHD3Pw4EG6u7vRarVyvNasWcPu3btZt24dixcvdutkNKViUFRUFLt378bLy4vExESMRqO8DK9du5ZFixY9cJYonSP9/f309fXJqMOjEg5nCsrLRn19PR988AEffvghY2NjtLa2UlpaikajwWq1EhoaSlJSEjqdjv7+fnx9fQkNDWVwcJDBwUECAwOlRFhCQgLp6ekPOIyeFNNmvAJERkaydu1aVq9e/VDjQHkYOj9ns9mkx2j16tX09fVRW1vL+fPnuXnzJqdPnyY8PJzk5GSXG3nKW3BpaSm//vWvOXXqlKwY4jw5hcE6ODiIyWSSB6pQWVi4cCExMTEydOIu8kTC8BGbZUNDA1euXHEwXt0pO1TAZrNhNpulRAkg56MIHztDHKZjY2My4dBisTAyMoJGo0Gn03Hjxg3+6Z/+iRMnTkhOkFarZWxsTIbV3eHSIULJYJ97a9euRavVMjg4SHR0tHydmJcAAwMDjIyMoFarCQwMlGEjZZjeHSA21+rqaiorK7l9+za3bt2iqqqK3t7eh75PZPkGBASwcOFCdu/ezbPPPktWVhbwJbdrNkC53srLy3nzzTcpLi4G7HQsYaBbrVbKysokJ9iV46hss8lk4tatW7S1tdHa2srnn3/O+fPn8fb2JiAgQHKTfXx8GB8fx2QyMTIy8pVGK9jn/tjYmLxob9q0iZycnBkdX7FvinOqvLycmzdvOvw7ODiYxMREyXk0m820tbVRX1+PyWRCo9Fw69YtvvjiC+Li4vD3939gP3YHCOqKSqVieHiYkydP8v7771NeXu6g/JGamspf//Vfs2vXLgCH6J27QrlewsLC2LdvH+vWrZPeyPDw8EnPEuUFw9vbW+rYKyXsXA3l/Gxvb+fWrVuS5njgwAE++eQTGc0IDw8nOzsbPz8/2tvb0Wq1REVF0dfX53CmWK1WduzYwauvvkpUVNSU2mfTYg05C/g6Lyyr1Sp/qK/ydClvA6mpqeTm5srQ+rlz55g3bx6JiYkOBqIrPH7iOwcHB7l16xZXr151MFwBkpKSyM7OJiMjg+joaMxmM1VVVZSXl0vui9VqpbOzk9bWVofsS3cyBpVtsVgsDA8PP9BWd7hJKqXbjEYjly9f5urVq7S3t+Pj4yMvRk8//TTbt29neHiYO3fuyHBWe3s7lZWVDA4OEhcXh06no62tja6uLilZI+bh0NAQXl5e+Pv7s2DBAlJTU10eVlFCuQbVajXz588nLi5OcnRHR0ex2WySA2qz2Th79iz37t2Th2R/fz8xMTFuofQhbvFCS/rmzZsUFRVx9epV6uvr5XycP38+q1evJiYmxqEYysjICO3t7ZSUlFBRUcH58+fp7e2loaGBgoICVq1aRWpqqltwQh8FMQZarZaBgQFu377N73//ey5evAjYQ7LPPfccmzZtQqVSMTg4SFVVFXfv3uXChQusWrVqxstwKh0ZIyMj3LhxgzNnzlBeXk5vby89PT1UVVXJ7Ovu7u5Hfp5KpUKn08l5K8ZLXB61Wi1GoxGLxUJFRQUlJSVER0cTGhoqC3RMd//Fniig3C8FX9dkMtHR0YFOp5MXyOHhYcbGxqR4fWlpKefOnWP9+vXk5ORMa5u/KcQlQ4yxSFgWxqlGoyE/P59XX32VjRs3yvcpL1QiQ9/dERQU5CDxJSCcF8rokLe3N2NjY2i1WtauXUtzczMnT55kcHBQ9tmVl2Wl3RQaGkpOTg41NTU0NzdLh49Ae3u75JgLbV5nCC+tSEbfsGEDwcHBwNScHdNivCo3emeeoUimeJyDXdxQBPz9/UlPTyciIoLu7m7q6uro7Ox0cMfPdBKJMJB8fHwwm80UFxdz5coV6REQkzEsLEyGSNavX09kZCR9fX18+OGHMvvQYrHg5eVFX18f7e3tbmu8KiedRqPB39/fwYMuFqqrD3vlXOjv7+fChQu89dZbtLW1ObyuurqaoaEhent7KSoqkkkETU1NVFZWAnaPZEBAwKQHqdKYS0xMZPv27SxatOiB590JGo2G6OhoB6+rQF9fH6dOneK9996jvr6emJgYYmJiHlizrhhf4WUSfOsbN25w8OBBrl69yr179xgaGiI8PJyUlBRycnLYtGkTmzdvfkDsfHBwkLa2NoqLi/nggw84ceIEt2/fprq6muLiYiYmJkhNTQW+lHlzNxoMfDkGRqORoqIi3n77bU6ePImvry8REREUFhaye/duNmzYANjF3w8ePMidO3e4dOkSGRkZxMbGAl96zKYbytDhvXv3+NOf/sQHH3xAZ2enw+uEB2uyg05JIxgfH5daoZNBeF7B3kfny/bDvmMq4Zx0pJxLQUFBqFQqSY0TFe6cIR6rq6ujvb2dnJwct7xUCW49wM2bN6mqqpJ7qpeXF7m5ufz85z/nxRdflB5mjUYzK+XAJnPSKKkwzrBYLKhUKvz9/UlOTn7g3HQllPMzKiqK7OxsvvjiC8Auf5mUlERXVxf9/f3ExcWRlpaG1Wrl9u3btLa2otPp0Ol0WCwWuru7pfpCTU0N58+fJysrSxqvU5EHMq07lVK6Rnhivwk/R3xOTEwM8+fPJzIyksrKSunZdCWUeqdms5mysjLu3LnjsDkGBQWRn5/Ppk2beOqpp+RBGhoaSldXlzRcxSYrVAmUnzFbwpfuCm9vb7RarUOZYYGSkhI6OzsZGRmhra3tAckTsIc1lUU0lBAh+YiICLZv386LL74oDR/xvDtjdHRUXhJNJhNvvPEGf/jDH2hqaiIgIID58+dTWFhIRkYGgENGuyvR2NjI8ePHee+99ySnfsuWLWzevJkFCxYQHh5ORESE3DDhywNCeEzi4uIIDAxEp9Nx7NgxBgcHuXr1Kjdv3mT9+vUkJia6pYEgMDExQUdHBxcvXuS9997jyJEjTExMsGHDBjZu3Eh+fj6LFy+Wrw8LC0OtVjMwMEBfX59D2H2mLshiz+7r65MUD2fDVamMMVmBCWV7BY3gYVB6v9LT01m4cKGUCZspvqiSb3/nzh36+vrkc3FxcWzYsEHqLt+9e5fu7u4HkkMFlDJF7gThORX73Weffcbbb79NcXGxnGcJCQls3bqVFStWyPEVVAJ3vBw+CuLypByLr0r49PPzw2azUV9fT3FxsVTkUUaNXbXXKMdgaGiIu3fv0tjYiK+vL1u2bOG5557Dz88Pk8mEn5+fvGwMDQ3R3d2Nr68vycnJ1NbW8l//63/l/Pnz8rONRqPDGp2KvWZaT1Wl7MmTQGwwOp2OxMREedBqtdoHZE9meuCVB5uPjw+hoaGEhobKDVGj0eDt7U1QUBAZGRn4+/vT0NDAxMQEd+7c4ezZs9TX18v3ixBRYGCgw+/mzoenM8dVeWlxJZS/X3BwMGvWrCEgIID6+noGBgbw8/MjODiY3t5empqaMJvNJCQk0Nvby/DwMFqtVvI8hVKE8LAbjUbMZjP9/f1YLBYCAwN5+umn2bt3L9nZ2QAO+qHuBmXbRPJKd3c3hw4d4s0335Q0lsLCQnbs2MHSpUvle10RBXDOSK+trWX//v2cPHkSo9FIVFQUu3bt4pVXXmH16tUO7xXViQDpsVWpVGg0GgIDA9m4cSPj4+P09fVx8uRJbDYb7e3ttLW1ERsbK8tWuwuUdJjm5mY++eQTPvroI86dO4fNZmPVqlW8/vrrPPfcc3h7e2O1Wrl79y5VVVXcvHlTGgsdHR0OHsvp7qNzsufAwADNzc10dHTI1wQGBmKz2RgeHnao9PZVEBx0kT8ADypRBAQEkJqayvz58/Hy8pLVg6bbeFV+R19fHxUVFQ59DgkJYcuWLURFRZGbm0tNTQ1tbW0O4dr+/n6qq6sBu2fT1U6bySD2fGG8Xr16lQMHDsiIYnh4ONu2bWPv3r3Ex8dLI89Zgmkyp5e75A4o8XXsG6Uyjclk4syZM5w6dUo6SsTZLy4AroDSG1pXV8f58+cxmUykpKSwfPly1q5d+1ifExERQWhoqPz/yMhI0tPTHZwIUzF/3dsldB9KLUeLxfIATcCVB4ty4vr5+bFx40YaGhooKSmRSVhGo5G6ujoaGxux2Wxcv36dxsZGmpubuXXrlny/2WzG29ub+fPnk5+fT0BAwKTf404QxvtkG4urNxvlAgkODmbVqlUsXbpUZkRqNBrCwsIkz3h0dJTh4WE6OzsZHBxEpVKhVqulaLrguVZXV3P27FkqKysljSA7O1sW3ACkB8KdjB4B50SdiYkJbt26xR//+Ec++ugj6urqUKvV5Ofns2fPHjZv3uxA33EFl1eU3xXtKCoq4he/+AUdHR1ER0ezadMmdu7cSUFBwQPv1Wg0k4b2hOh4YGAgTz31FEVFRZw6dQqbzcbo6Cgmk+mhQtyuhGhLX18fJ06c4I033qC8vByAJUuW8Oyzz7J+/Xo5/2/fvs1nn33G559/TmlpKcPDwwQGBtLf3z/jWszK39HX11fKIgl8lZyggFqtJiAggNHRUTQaDUuXLiU0NJSrV6/S1NQkX6P8PKFcoNRTnYk+Kz1xonKk0uOt0WhITk5m4cKFzJs3D6PRiNFopKuri6CgIEZHR3nvvfek8eqOhtxkEGsW7AbMypUr2b59O8uXL5f8z8ku90qKhauTCqcKShtmbGyMpqYmmpqapNoAIAsXKc+tmey32NdFYRNReGjJkiWkp6c/1meYzWbOnDnjEEmJi4sjPz/fgaI2FfbMrDBexU2uv7+fmpoaeSMXmd9KzLSxoNRF0+l0JCQksGzZMkJCQjAajTLcU1dXx3vvvYdOp6Ouro6Ojg6Gh4fp6+tzMCLAnpgmqnEIuONNG5Cl7ZSEbhGid5dwuTKpRavVOtwABUQYEexjqaRsjI2NYbVaCQkJwdfXl4sXL3Lnzh2uXbsGQGZmJnv37qWwsFB6MYUHwt02XSVHG6CyslJWsTt37hwNDQ0ArFq1ih//+Mds2LCBoKAgTCaTy8sFKjf6pqYm6b2yWCyEhIQQEREh2yc8Gv7+/g89/MSlRKVSERwcTFhYGEFBQdK4MJlMDt/park60VZvb2+Gh4c5dOgQv/rVr6Thun79er7zne+wbNkyWltbuXLlCrW1tdy6dYuSkhIqKytl6C47O5uCggJJYYLpvyAr9zlBsykoKKC2thadTsfo6CiNjY309fURGRnJ/PnzAft6VCa0aLVakpOTmTdvHhMT9mITTz31FAMDA7S1tT1gvIrkzImJCendctXeJDi6zlEp0Z7AwEACAwNJTEx04CAL5QhwH9H+yaA8f/39/SUVTq1WExYWRmRkpEOGvXJOWK1WOb6TGbXunDj5VVC2u62tjaqqKtra2uT+olarSUtLIzs7W85bJXd4OuGsDnH69GmOHj3KwMAA+fn5PPPMM6SkpMhz0GKxSM1sMWYqlQqbzSajO0qVl+DgYJKTkwkICJhS+UH3sC4eAucErNbWVm7duiVlmQICAtwiiQQcByMpKYn169dz5MgRh3K3hw8fnvS9Wq1WyoJpNBri4+NJTk5Gq9XKjd5dNyu1Wo2vr6/DYSBkqdxF+kQ5Jya73DhrzwniuRLK0oAWi0V6RwIDA9m0aRNbtmwhPDwc+FIP0N1RXl7On/70J/bv309VVZXDGAYEBJCZmSmTecQ8dKUB5+xJFwgNDUWj0UijQMgrfVVbRTgSkJuy8IiJBBJ3ou4oE1+bmpo4cOCAFEjPy8tj+fLlpKWl0dfXx/Hjxzlx4gT19fUYjcYHwrOZmZns2bOHtLQ0eaDMlEEn1oaPjw95eXmMj4+Tn59PT08P586do7S0lKeeeopt27bJBFZxuAr97/T0dJYsWeIwJ+7cueNwHoj5Ki7WIpHG1XupswGm3C+UaglK41XpHDCbzTI65EwvczWU/RIqCmA/J5z3VGe6n/JirNR8F+vU1eP2TSDWq5iXPT09XL58mbKyMqmmARAfH09ubq7MyZjJokvOMnvvv/8+ly5dAmDjxo1s3ryZ2NhYufaco3DKcRSSiso5qax46PZSWVMFZ+O1v7+fhoYGWltb0Wq15OTkkJyc7KCf5iqepXKiJSUl8ed//uds3LiR8vJyzpw541CFYzII70BMTIyU/wK7oeROJXCdDWnR3uvXr8tQwfDwsMyIFVCGTVyF8fFxedMV7XBWwhAHi/IwER7H4eFhSktLOXr0KKWlpYB9zkVGRsoqcGL+CakUpYHkSijbYrFYKC4u5sCBA3z++efcvXsXwMELUFNTw/79+wkKCiI7O1v+JiKxcKb75OyJSU9P58UXX8Rms8nCJwkJCQ4b8ePMNbG/dHZ20tjYKDO6U1JSSE9PR6PRyIpxrj48xfwcHx+nvr5eep69vLzQ6XS0t7fzwQcf0NzczJ07d6QXXbxGzGGwJ26JBDzhnZyp/jkbb/PnzyclJQWTycTSpUvp6uoiPj6elJQU4Es6lTDyhO6waG9XVxdlZWUcOXJE5g8ADgoDANHR0URFRTlwYV2ZIyGg9MQKT5YwXgWUjoCenh6Ki4tJTU0lPz8fLy8vtyiooZxD9+7do7W1VT4XHx/PsmXLSElJkXvRw9oq+nf79m0GBgZITU3l6aefJjk5GZg5VYypgLPj5OrVqxw7dszht0lMTGTjxo0OSb4zsb86G9YtLS2cPXuWs2fPApCcnEx+fr60RSar6DaZCshMUTlnxwy4D1EPemxsjKysLFavXk1ubq6DR8hVi1fJ0QkKCqKgoICCggLq6urw9fVlZGSExsZGKZSv5NIJb4+vry/5+fnk5ubKz3U3D56zESEq1oSFhcnHhoeHqa6uprm52eUGqxLfJHlQOaesVislJSVcunSJlpYWwD5mIvNSeYFxpwsHOGZVi+SsO3fuyJAzfFlcwWq1UlVVxaFDh8jOziYxMVFWblJqGs7k2Hp5eTkcWNnZ2bz++uuEhISQlpZGSEjI1w6zKedDQ0MDVVVVgN2rm5WVRXx8POCelbZEtTCwj0N1dTW1tbUOUm6+vr7S2LVYLPT19eHr68vixYtZsWKF7M9McyiVoWIR9hfrJS4u7ivfr5zLd+/e5eDBgxw6dIjS0lL6+/vleaAU78/IyJCcaDHmrpDzU3qdBZR658pom7Ocl3DODA4OUlNTQ2trq6wwqdQUdQWUSWnK/V8gNzeXVatWkZCQAHxJXxJ9tVqtstBIeXk5V69e5fr167S2tpKXlydDz+K73AVKD7Hz7+/j44PJZGJiYkLSl+7du8fly5cdkhGXLVvGc889R1JSkgPFbaYhElujo6PRarVs3LhRFmyByZUUnLX1xRgajUbAPp+jo6Md9F2nCm5tvDrfUpWlx3Jzc8nPz5ehWhFyd7Wh5Pz9SUlJbNq0icHBQU6cOEFNTY3DAKrVarlJ5eTksH79eocN3J2E7pUQB4O/vz+xsbEPZIz29vZKyoTYpNwdImSnPMydDRaLxeLgBQkKCpKeV3eG8rDUarUsWbKEwsJCiouLJT9p4cKFUrdPlIS9ffs2/+2//TfCwsJYuXIlTz31lPycmZS3cb40JSUlERsb+9ia0c5Qtr2xsZFLly7JBIWlS5c6JCi4g2YxID3APj4+ZGZmOlwYH1ZJTKkE4uvry65du/jZz34mx1GZHe4KfJPf1Wq1ysO9sbGRL774guLiYsnnFR5aEXrNzc1l7969PPfcc1JpQClx6Goox0h4rpTeVGcIb5m7eh+FkSP2SVFKNCYmBviS9yv2WcGVfPPNNzlw4ABGo1EWljCbzdy6dYtz586RlZVFTk6Oy6tpOkNZXMEZyjG02Wx0dXXR19fnQGXJzs5mzZo10kEgKDLTDWX00dvbW9oq4uJUUFAgox8w+SVXeWaC/TJ5+fJlOjo68PLyIi0tjdTUVAed7VlHGxAhya972AmZjZ6eHqqrq2lqapK1dZVJNq6WZVJCeDp8fHzQaDRkZ2eTlZXFqVOn5AYrZF1EBvWyZcvYs2cPGzZscDiU3CHk/DCIiajRaCZtpytlP74OlF57kQXb39+P1WqVAtr+/v40NzfLaigajUaWmh0cHKSjo4ORkRFp7BqNRsbGxggJCSEsLMyliU7w5UYlQm7Jycns3bsXHx8fbt26RVhYGPn5+YyPj1NdXU1kZCTx8fEcO3aMjz/+mJCQEF588UVMJhMLFy4kICDApYen0mhVRjG+amMUHEgxX9va2jh69CjHjh3DZrOxYcMGXnnlFRYuXCjf446qEfHx8WzYsIGBgQEGBwcZGhqSXtXx8XFOnDghaTu+vr6kpqaybt06vve977Fu3Tr5OcIgdgWcQ6oigU4kVIn5JcZVWX5ZKGR8+umn3L59W3LSBa3F29sbk8mE1Wpl3rx5bN26lQULFsjvmyl9V2eIPUZ5ARYUI2dMFpIF+3x0jvS4OpFJGKETExP4+fmRlZUlnTBCX1tpdCpD0PX19Xz88cf88Y9/lFQYJa1gaGiIo0ePEhERwU9+8hN57guHlSsgjM+volCJPg8MDPDZZ59x7tw5LBYLvr6+Us3EbDbLue6sGzsTUM6r/Px8wsLCGBsbIzo62iHS9rD1otwb+/r6aGtrkxGVydQ9pmovnbHT5+saYUoNuM7OTr744guOHz/OvXv38PX1dVgM4PpkCiW8vLxkyA6go6OD+vp6enp65GvUarXk12VkZPDnf/7n7Ny5k8DAQJe0+UnwMI6LO43Jo6C8OVqtVi5dukRFRYWDUaTVaunu7qa0tJTOzk65efX19XHx4kVMJhOBgYEEBAQwPDxMWVkZYM8AX7dunYNMiCs9BkqDc/78+eTk5Dgk7IjSzX5+frS1tVFSUsL4+DgdHR3s37+fhoYGduzY4cBBczW+jhFis9kc6ncfOHCAX/3qV9y+fZtly5bx+uuvs2vXLodKMO6iGqGMLKlUKvbu3cuiRYtoaWmhtbWV6OhoCgoKqK6ulmWQwe5Rf+aZZ3jllVdIS0sDvgzbuktkR3jWH3VOKPl5JSUlGAwGPv30U7q6uiQfW7xOzGmNRkNqaqpDyNmZgjLdUM4djUZDREQEycnJMqv868Jms02qWOBqiJwCUQ5UFPhQhtaVrxW4evUq+/fvdzBchUdPq9ViNpupqanh9OnTvPjii9J4nYyDOVP4uvPn2LFj/OM//iOXL1+WSc7CkaU07lxxCVGuOW9vb9LS0r7WGaV8nbioiCRRoXOvfO2s8LyKg1B5ixY1nH18fBwmgHNiy/DwsNQ57e/v5/333+fYsWMALF68mAULFjhkvbmLh9JZF7KyspI//elPHDhwQPIkAQfOy6JFi1i9erW85cwWQrqSuzYZ3CUs91UQc0fIhLz55puUlZURERFBQEAAFotFenLa2tro7OyU42w2m2X2qPCIiKppOp2O4ODgBzTu3CHcJTxPzvNMuZHGxsby3e9+l4iICE6cOMH169c5ceIEFosFf39/QkJCZDhopj1ZygTAx03Mcl5XV69e5Z133uHWrVvodDq2bt3K5s2bp1xMe6og+ikoO0lJSURFRTE0NCQzgbu6uqioqJCcs8WLF/PSSy+xe/duabiKfdld9syvghhrYbheuXKFt99+myNHjjgkrQEOiiBg16gUnPyJiQnMZvOMlyFVzk9/f38WL16Mn58fQUFBHD9+nKioqAeMsIc5A5SJpM6vcXV0wNlr6FxBSxkqF2NQV1fHlStXpIZtSEiIPBujoqIICwujvb0do9HI0NCQQxKbK/or+iLm4qlTpzh58iQjIyP4+fk5XCrGx8dlNODatWtcvnwZsK8/q9VKREQEO3fuZOPGjZjNZumpdNW6VHL7v+n5JGTOAMLDw1mxYoUDb3Yq+zYtFpIyi00MstDZ02g0DuL7zhB8H6VH4OrVq7LUWFhYGNu3b2fdunUOh4w7bMTOOnQ1NTUcOnRIShGBnS4wNjbG2NgYoaGhLFu2jK1bt8qKFK4uuvB18FXGg7v3xdmIHBwcpLW1ldraWvnfV0FoUwoI40+lUpGdnU10dLTbyLkpIXiBYrN1TnYUoa1ly5axbNky8vPz+ed//meOHz/OrVu3WLp0KYWFhdJ4VSbHzAS+zg1eJJOIYgWDg4PcunWLDz/8UKqA7Nmzh+3bt0tenghDu5PxKuAsQxMeHo6XlxcjIyN8+umn/PrXv6a9vV0mKe3atUsqC4h+uYvH9XGg5M3funWLt956iw8//JDu7m7ppVOqYfj6+pKTk0Nubi5r165lyZIlcl26mr6j1WpJSkoiKSmJ+Ph4UlNT0el0DuFZUW3JGc57qfP8d/W+4ryHiCIEggKgNGbVajVDQ0NcvXqVyspKaTgZjUY0Gg0LFiygoKAAm83G6dOnMRqN+Pv7u1y6TvRvcHCQ69ev88///M8cOHDgsd/r7+8vq9olJCSwZ88e1q9fLy/VQsvYVZSWqYBIQA8PD6egoIDMzEyH73Bb2oA4EJUejoGBAa5du8bY2BiFhYUPNV4nJiYYHh5Go9HIzOdPPvmE3/3ud/T19eHt7c3atWtZs2aNLME5k2K+j4Kz4VpRUcHBgwf5+OOPpRQR2I3XuLg4QkNDWbBgAbt376awsBB/f3/Gx8dnPKQ1nVCGhtwRSs+xl5cXkZGRrF27lr6+Pv74xz9KOSxnOEuyBQYGEhkZSVBQELGxscyfP59FixaRnZ1NbGysA4dZ+b2uhvCUKDOVhRHgvJFt27aN9vZ27ty5Q3t7O52dnTNenembQIRYhbHW2trK559/zrFjxygqKiI2NpY9e/bw+uuvs3jxYvk+Vxs5jwMxTiI0e/36dc6ePSsTz5KSkigoKJBSN2J/cQfP/+PA2VN68+ZNfv3rX3P48GEHVQWw07DEoZmQkMCPfvQjNm3aRFRUlEMypTtdRrKzs6XEntgjviqaNZsgfuvR0VFJkYMvLyMjIyPcuXNHFpUQ79m6dSs//OEPyc3N5eLFi1Jz1NUavcKoHB0d5eDBgxgMBulNfRw42ylGo5E7d+6Ql5cnZbIEH9iVNo2z7vnXhTOXfbow5VaSML6sVivNzc00NzdTXl7O6dOnGRoaory8nJUrV0qx2+DgYMkRDQkJceB8lpaW8sc//pGTJ08CsG7dOnbu3Ckrr4D7bEZKw7WkpIQPPviAAwcOUFtbi0qlkslLiYmJPP/882RlZZGUlMTy5cvlbVIpOTHb4DzRJyt1564Qm6mPjw85OTmEhYURHx/P+fPnaW1tlckRzc3NUo4H7FGA3Nxcli5dSlpaGkFBQURHRzN//nySkpJc3KuvxuNUzOrt7SUoKAiVSsWKFSskx7Kmpoa+vj75One7qIhLtIjiTExMUFlZyaeffsqf/vQnysvLSUhI4IUXXuC73/2uTOYZGRmRc8E5i1gYimJDFh4lVxwyzpJL9fX1FBcXO+icBgcHk5KSgq+vr9RynU0eV5H0IXD58mUOHjxIe3s7arWasbExSScQiVopKSl85zvf4cUXX3TbNai8UIkiIPDkdCJ3NHhtNhvDw8PU1tZy584dFi1aJOeg0WikrKyM6upqxsbG8Pf3Z9myZbz66qvs2bMHsI+5oMC4qlCBUpe2v79fUsuE4SoKRQg6pHgP2M9Bb29vhoaGMJvNDmWB+/r6+PTTT+nu7mbVqlXk5+eTkpLi4KF29mbPBJ50PxPvt1qtDA8PS27vVGNKjVelu7umpoaPPvqI48ePU1tbi9FoxGKxcOHCBeLj48nIyCArK4vw8HDUajXp6els3LhRUgHKy8s5dOiQgw5lTk4Oq1evJioqalK9PFfAOXReU1PDxx9/zDvvvCM5rkJZAOyVcL7zne+Qnp7+wKDOVo+r8wLTaDSSj6csBeiucJ5DUVFRPP/88zzzzDMMDAzg7+9PcHAwJ0+e5C//8i+l8bp582ZefvllVqxYIS9h4iI2V6Csnubr60tUVBQWi4WqqioH75e7Ga8CIgRbXFzMe++9x7Fjx6itrSU2Npbvfe97/OxnPyMuLg6r1So9HpPxacUcVxqvrs7wFu0Fez3yO3fuOIxJTEyMlBIU3tnZcJkEHvD+l5SUcP36dYcLE9j3GkFPCgkJ4fXXX+e11157LL1YV+FhFx533iO/CZTn2ZUrV0hISMBisUiZtuHhYe7duyf5yUuXLmXv3r2sXr0asHvaP//8c5qamtBqtWRnZztIMs7U2a/kGV+/fp0333yToqIi+fzw8LC87IqonHiPUDaZjAZiMpkoKSnh2rVrfPLJJ7z66qt8//vfl9x0VxZdmgpMd2W0KbGWlFJDw8PD3LlzhyNHjvDhhx9y+/ZtwC7tEhkZKWtsV1ZWEh0dzfj4OP7+/uTl5dHR0cHSpUsZGRnh2LFjHD16VPIJU1JSyMrKkgMrpKjcweATZPS7d+9y+PBhPvnkE2m4KjNKk5KSWLRokdSQFMULtFqty43wJ4FzxmxISAjZ2dlSkBpmx8YsPCIi8UpEAwQWL14sy/f5+/uzevVqnnnmmUnn4MjICOPj45KE7y79/zqC+84JTvfu3ZM84LCwMIeDxJ2MIkElUqlUmM1mTpw4wSeffMKZM2fo6OhgwYIF7N27l5/+9KfSyPmmerGuSnxylpxpbGzkxo0btLS0EBYWxtKlS1m5cqWcvyL6MxsgaCxWq5X29nYuX77MZ599xoULF2Sfxe8uKlKB/YxYu3atHNOBgQH8/Pzc4oxwhlK6bmJi4rEULZSUD3dabwLObUpNTWX58uVcvXqV6upqDh06JMvabtmyBavV6uDACQ0NJSYmhrGxMS5dusRvfvMbPvjgAwDJ3VZSsKY7iqCsSjgxMUF5eTmHDx+mqKgIq9Uqy0cPDw/LOahUbvH29pa64IGBgSxatIjh4WHu3r3L4OCggxf23r17HDlyBICCggIWL15MXFyc5MCCe+2xSij1YsV8hi/LMU/X/jglq1op+NzT08Nnn33Ge++9R319PWq1mpycHF544QXi4+M5e/YspaWlqFQqWSmku7ubjo4OGhoaSEtLw8vLi5qaGik3lJqaynPPPce6deukS93V4S/nzLy7d+9y4MABDhw4QGVlJYCU+bBarYSEhLB+/XqppSkmpa+vr9tOyseFCI0ICKPPmdvsLgbcwzBZycLh4WFpsNbW1srNVlALxMHofAlxLtrgLnjcuSYOVOFFaG1t5cyZM1y+fJmAgAB27tzpQMR39XpUQslxLS8v54033uDYsWOMjY0RERFBbGwskZGRdHV14eXlxfDwMBMTE0RERBASEiK1RpXJBUqKgMViobOzk46ODgICAsjMzJzx/gtpKYDm5mZu3bpFaWkpNpuNqKgo9u3bx+bNm+XcFX1wdygvTEajkaNHj/L2229z7do1rFarpKUJbVQR6UhJSWHTpk0OHledTueWhqsSX6d9zoUM3A3O+2dBQQHPP/88RqNRnufNzc10dXXR09PD0NCQw4Wqvb2d69evYzQaqays5NChQ1itVvz9/aU8H9g9ljNxbirP+PHxcW7fvk1JSYnk75pMpkmT5pS8VfH+TZs2sW/fPnp6ejh79iwNDQ309vbS3t4uz5Tr169TU1NDfn4+L7/8Mnv27JHljN2REgKOChijo6MMDg7KXIiAgAAiIiLkHvR11GEeB1PmeRUNE57X6upqfH19efnll3nxxRfJy8uTgtEDAwOMj4/T2dnJpUuXOHHiBHV1ddy6dYvGxsYHSqNlZGTw6quvsmjRIsA9pKQEp06j0TA+Ps7FixfZv38/5eXl0nunHKTExES2bt3K8uXLARzEtGcrRNsnk/cQcjxKzJZEEcChhO/o6ChdXV3cuHGDoaEhgAeqhgk5LWXJQ3eC1WqVmdiPugkLGRehU2y1WqmoqOD8+fMcPHiQ0dFRNm3axEsvvURaWtqk6iCugvjdBW2jtraWjz76iHPnzsmwXXd3N8XFxTQ3N3PgwAH8/PzkWoyJiSEwMNBBL1QcVLGxseTm5hISEkJzczMXLlygtbWVzZs3o9frCQ4OnrEDRlx+fXx86Ovr48yZM5SUlMj5GhwczPLlyyWfUsjwuNucVGKy3662tpajR4/KhB0BZRg2OjqarVu3smXLFvLz86VaBLjXhWoqIS6U7mbAKr3JKpWK9PR0tmzZwpUrV6ipqQHsPE9RXluUhBXvrampob+/n4CAAAYGBiQFJjEx0eFS4qp+Ky+yzo/BlxQrcUZYrVby8vJ4+umneeaZZ1i0aBGjo6MUFBTQ2dlJfX09165d4+bNm1JXXHBqx8bGGB0d5ZlnniE9PV1qF7uaqjQZRHuMRiNtbW2y/4mJicybNw8/Pz9ZcW0qE2GnzAJUhnOETptOpyMvL4/169dLTValsLkI67S0tNDd3c3o6OgDnCawe7FE+GtsbMyhIoUrIPhYPj4+WCwWiouLOXbsGCUlJYB9EpvNZsbGxtBqtaSmprJ3715WrVrl4I2crYarMrGsqamJoqIiuQmJ54ODgx28j+646B4F50XW19dHS0uLvCWPjY051B738/OT73GXfipD2n19fdTV1aHRaEhISCAkJER6FpWhSGX4vLOzk2vXrnH+/HkuXrwo+edLly6VNdUFfccdDAVlsYmysjIpYq9SqRxqqo+MjHDv3j2ampoICAiQ1B6z2SyF1oXMT0hICCaTCbPZTF5eHosWLaK6upozZ84A9gPq9ddfd4nxCtDS0sK5c+fk2KjVajIyMqSEmfhdZgOEV9VisVBaWsr+/fullJmXl71QiKgVL7BkyRJ+8IMfsGHDBlc1e8Yh6HLOl1B32XeU45Odnc3u3bsZGRmhpKSEjo4O+R/g4Env7e11KHPs5eVFVlYW27Ztc0hsc7USiLIAhvDKOlMgEhMTyc7OZtu2bezdu1fSHcVzYN+TFyxYQEJCAtHR0XR1dTEwMEBnZyfnz5+nq6tLqhPpdLoZLcf9OFDaAYODg9y8eVPq9YKdPiguHcLZN5WYMgtQuXCUZd1KS0s5c+YMq1atctCys1qtlJaWUlJSQldXl1yQkxGb6+rq2L9/P5s3byYuLk6KAbsq01cp31JRUcGbb77J2bNn5fNKSZq0tDR+/OMf89JLLzncHt1pEn4dOEsPXbhwgd///vcOmqghISEsW7bM4aIy2/qrTD6cmJigr6+P3t5ehwXoXLfaVfp8D4Nys6uvr+fw4cOMjIxQWFjI6tWriY6OlrQWZVIWQFdXlyyuoRS9F7WqBdypEIVoh9ls5ubNm3zyySe0tbXx7LPPUlhYKD3JgpslKvSEhoYyOjpKeXk5HR0dqNVq+vr68PX1Zfny5TIruL29nfT0dAdD3dmYmgkov89oNFJbW0tTUxMajYYNGzawefNmudcK+oc7QxmpGB8f59atW/zmN79h//79UiJRJMQI2oZKpSInJ4dNmzaxZMkS+VnutganA4Iy4q79VK4PnU7Hiy++SFpaGvv37+eDDz6Qld9E8aKHZaMnJCTw7LPP8sorr8hKgDMtJalcayJ6JegRYl4qs+vBnlj+wx/+kKeeeoqUlBQHw1uJ0NBQVq9eTWpqKrt27aKnp4d79+5x8uRJDh8+TGVlJUVFRaxYsYKFCxe63TpWUiMaGho4deqUgyyocOBNF6b811CpVNJLarVaOXDgAL29vVRXV5OdnY1KpcJoNNLS0kJpaSk3btygvr5eHo4REREsWrQIf39/ampqqKyslJJZY2Nj7Ny506FGtSsSJpSL89atW5w+fZquri55ExOLMTIyklWrVkmjG77k68xmKBe0UlII7BvrggULWLlyJTExMdKz564b7cMgQnOif3V1dVRUVEg+j0ajcZh37shJUrbp7t27nDx5EqPRSHBwsOSPabVaB5pOf38/tbW1nD17lkOHDjlcyrKysnj55ZdZuXKlfMwdPK4CSo3MqKgonn76aUZHR9mzZw8rVqyQiXMajUbyeQFZRa2mpoaenh7UajWDg4NoNBoWLlyI0WgkNTWV0dFRcnNzGR0dJTY2loaGBjZu3CijSjNlxIuIDtjnYVdXF2C/SBQUFLBmzRq5B7sLpeNREMar2Wzm6tWrvP/++xw4cEBG4TQazQMGTkpKCi+99JIs5SvCkrPtkvy4EL/RxMQEJpOJjo4OqXoinnc3iIIYvr6+rF69Wor0nz59mlu3bjE6OupA/1CpVAQFBZGTk0NSUhLR0dGsWbOGvLw8YOb7qLyYe3t7k5OTw6JFiygrK8NmsznMSV9fX5KTk0lPT2fHjh3s3btXeljFZVlEukSf1Wo1Wq2WtLQ06ZltbW2Vako3b95kYGCAxsZG0tLSpIyou1zQlM6bgYEBBzsO7LaZMjo51fvjlBivykEOCAiQA1xeXo7RaOTIkSMUFxcTEhIiN6KBgQEGBwcdSM9qtZqNGzfy05/+lMjISI4dO8Ybb7xBbW0tN27ckDePwMBAh1rVMwVl9qHVauXmzZsUFRXR09PzQFu8vLxYvHjxA0kErg55TAWUkzA2NpaUlBRqa2sZHByUigopKSnAl0agOyy2rwPlpchms1FWVsalS5ew2WwEBQWRmJjo9skwSipPX18flZWVGI1GybNas2YNKpWK4eFhxsbGaG1t5YsvvmD//v0yMUEcmMHBwWzcuJHvfe97ZGVlTVqMxF3g6+vLmjVryMzMZHR0lPDwcMLCwh7g0jujoKDAISlGUCni4uJISEhApVIREBCA2Wxmw4YNDA4OEhwcLKlAM2W8Kr9ncHBQ0rR8fHxITU11UGRx5/1GqZ9pNpu5fv067777Lvv376e3t1d6GIWRIKSDgoKCWLVqFfv27XugcthcgvI8Eb+V4EVWVlbS3Nz8wPPuBOfxWLFiBZmZmSxevJhf/vKXnD59Wj6em5uLVqslMjKSdevWsXz5cvz8/Bzm+kxHeMTvKTi8K1asYGhoiMrKStl2gSVLlvDiiy+yfft20tLSpOSessKYwKMuWHFxcWzcuBG1Wk1WVhb+/v5otVqHueAuFxXleKjV6kkroE3nnJySk0fJZwwLC2Pr1q2kpaVRWVnJkSNHuHz5Mm1tbbS1tU36/pSUFJYvX86iRYtYtWoVq1evliL34+PjvPXWW1RWVlJcXExdXR3d3d08//zzLF68WB5IM5Eko9wgbt26xRtvvMHhw4dluTeR7OLl5UV2djabN29m3bp1hIWFzYkELQHl4hGJdt3d3Wi1WqKioggPD3fbbPvHhTIhwmaz0d3dLf8/KSmJFStWyJs18FhSNzMNpRZyUFCQ9MB9+umnNDU1kZycjFarlXIuPT090sAVmD9/PmvXrmXevHksX75cKgy4I4dZ2R5/f38pSecsK+UMpVrKZH1Sq9VSLxXs/GYldWKmIPY4Pz8/bDYb1dXVFBUVSeNufHxcRnXa2tpoaGggJiaG2NjYGd0nHxeCtgF2/t/x48c5duyY5D2KZFgBm81GRESElKgT4wvueXmcSijDrxaLhfr6eskbhS/lxdwRFotFRgAiIyPZsmULAQEB7NixA5VKRWpqqvS8+vn5kZKS4nB+iDngDl71goIC/vqv/5o9e/bI0tpqtZqUlBTy8/Md5CFFJOBR7RY0PLEuxW+0YcMGsrOzGR8fJzw83CFa645zXaVSodFoHC4swhYSmGqje8qMVwE/Pz/y8vLIy8vDbDbLwbx7966DZALYJ6UI7+3du5eCggLpybHZbKSlpfHKK6/g6+vLb37zG27fvo1Wq6W5uZn6+nrmzZsnN+WZ8AIpf/z29nauXLlCZ2cnYOd5ikM/Pj6erVu3smnTJpn9Km5icwlWq5WWlhbq6uoAewh6fHzcQb8O3Oem+HWhFKhXzq3w8HDmzZtHdHS0fMwdNxTlupw3bx47d+7k3LlzDA0N0djYSGNjIyqVShp3Ihs0MTERq9VKeHg4u3btcqhAJTyuX7UpuxrC468sLvAwKLnNzp5XpYbhZIbfTGpuKjnMFouF2tpaqqur5XpTq9XcvXuXTz75hGvXrjE+Ps62bduIjIz8Sq+zK+Cs1tHZ2cnw8LDcK5X9Avt+4+fnR0FBAU899RRqtVpePNwxAvCkUM41kRQpuPWAQ8lVd95jhUEj1ldISAg7duxgx44dD32PMNbdYZ9R2iSBgYGPbLfS0H6cSMBk8oxgdwI6lxUXcMdLisViYXR01OHsF3QqAbekDTwMWq2WXbt2MX/+fIaHh+UkEIeAzWaToQIRlhPPi8UYHx/Pyy+/TEFBAX19fajVallDXvnjzMQEVyYqxcXFER0dze3bt9HpdGi1Wsl5zc/P55lnnmHhwoXyve7oqfomUPZDrVazYMECduzYwb179zCZTA4enrkEJW9QbFDuPp7KNufk5PDv/t2/40c/+pHcZERGvRJarRadTie9ADExMQ6Jd7NpXJVKCo8DZWUc8f7JDFtXQXnIabVacnNzJT0H7HvgkSNHeOedd2htbZUSPUKqxt3oO8o9OzAwkBUrVnD37l2uXLnC4OCgVIFQemi9vb0JDw8nIiJC0g3c0TCfaqhUKtRqtQPP0p3G8nEgolmP027hSXanSMHjrH/lOvsm7XbHdfowOCeOtrS0yGhAcnIyycnJ8gyajkv+tBivSqmZR90gnGE2m6VcjxDx9/LyIjo62sHLpcRUC98+CsrBioyMJCIiArDfgMUteMuWLXzve99j+fLlMinEZrPNGc+AUhwdYOXKlQQGBnLv3j3a29vx8fFhwYIF8jXuWg3mcaD0wikl3NRqNUNDQw94PtxlkxVQSmH5+/szb968b/xZZrPZbSraPQ4e5tF4FB51AXaHOSzml0j6TEtLY/ny5YSGhtLf34/RaJT11sPCwpg/fz6JiYnSUeBq49sZSo9WaGgo27dvp7u7m6qqKgYGBrBarZJjbDab8ff3f6D+u7snoz0JlOPV398vuc0Cs6VimoByTQqqEnxZjUqc44/rtZxpiP1BSOopk5SFZ/xJ90elLaOkE7ja+/xV6Ozs5N69e0xMTBAQEEBycjLR0dEPcGCnEtNyEgkD9OvCuYzm4xilM2kwOPM3RM17kfUZEBDACy+8wPPPPy9Lwwlv7FyCsj8RERGsWrWKgoICWRpOq9XK18yVvjv3QymR4s6YqoudO5W4/bZDOQ6pqamsWLGC5uZmGWpdsWIFe/fuZfv27VKmR1ktzN0gPKvR0dEUFhZKOpaQIRNGzsKFC3nppZdYunSpA6Xn24C51k+lTu1si0pOZqRORx/EmeOuv41yPxkaGpJFJUTBl8jIyGkd42kxXp1vD+KmMlnjBV/JucSo+Bz4sjCB+Axxy5npW7fy+wICAnj22WfJzMykr6+P/v5+0tLS2LJli0OocS5DSTKfbCzc0Rv5uFDqKKpUKjZu3MiFCxdoaWmhra2NsLAwhyQedzfSbTYbFotFJhk8am4qN021Wj0nL2CzGcqDMyEhgddff53FixfT29tLaGgoeXl5D1SbAvc9BJXeGSHHFhoaysmTJyWfPiUlhd27d1NYWCjX3WzeX74KzpGDDRs2cPfuXc6cOUNvb6809pWvn02Ybe1VYqbaPpvmtlBdEbSWBQsWOCTUw9RTO6c9Bujj4/PEmedCzNjVUP74YWFh7N27d9LXiU11rnOxHpZJOBe8IkI9QIi879u3j97eXn7xi18QGxtLcnIyAQEBDnqw7gxvb+9Zry/sgR3O+9DWrVvZunWrC1v0ZFCWWo6IiGD79u2kpqYSGBjIH/7wB7y9vfnOd77Dzp07HS6McxlijEVy5Pbt2wkMDEStVnPx4kWWLFnC4sWLHV4/m/dbD2YflPMtIyODTZs28cUXX7B48WIKCwulGsh0lad2vUU4C/FVN36hC/dt2EwECV/pzXOHDNEnhTLL3MfHh/DwcF588UWSkpLw9/cnKytLvu7bMM4euB++ah9y11roD4NYa35+fixatAgfHx/y8vJkEmxaWhoajWZG8xzcCQUFBfz85z9n165dxMXFOXDYZ/t+68HsgzLampmZyc9//nOeffZZwsLCHrhYTQe+tvGq1+unox1ug7neP5j7fZyJ/h07dmzav+NRmOtjCHO/j3O9fzB1fbx48eKUfM5UwzOGcwNzvY8z3b9Dhw5N+3fMXuKJBx544IEHHnjggQffOnjN9aQiDzzwwAMPPPDAAw/mDjyeVw888MADDzzwwAMPZg08xqsHHnjggQceeOCBB7MGHuPVAw888MADDzzwwINZA5dIZen1+nBgL7ATWAjEAxagDPgd8DuDwWBzRdumEnq93gt4HfgJMB/wAaqw9/FfDAbD7Krv5wS9Xv88sA5YDOQBgcAfDAbDK65s11RBr9e/hn2sHgWbwWCY1To1er3+74BlQBYQAYwCDcBB4J8NBkOP61r3ZPDsNXNmr3mNObwW53r/nKHX69cAfwEUAmFAL/Y1+Y8Gg+GoC5v2jfFt2GvcaZ66Suf1BeB/A23AaaARiAb2Ab8Btuv1+hcMBsNszyZ7G3gV6AT+BAwDm4BfAGvnQB//A3ajdQhoBnJc25wpRwnwXx7y3BrgaeCzGWvN9OHfADeAE9jnqj+wEvjPwE/0ev1Kg8HQ5LrmPRE8e83c2GtKmNtrsYS53T8JvV7/H4D/D+gGDmNfmxHAEmA9MCuNV74de00JbjJPXWW8VgO7gCPKm4her/+/gWLgOewD/pFrmvfk0Ov1e7AfJnVAgcFg6L7/uBr4AHsffwC85aImTgX+DXajtRa7B/a0a5sztTAYDCXYF+sD0Ov1l+7/81cz1Z5pRJDBYDA5P6jX6/8r8H8D/xcwW4UQPXvNHNhr5vpanOv9E9Dr9S9gN1xPAvsMBsOg0/MzW/N9ajHn9xp3mqcu4bwaDIZTBoPhU2cXusFgaAd+ef9/1894w6YW++7//XtxmAAYDAYr8B/v/+//MeOtmkIYDIbTBoOhZpbfJL829Hr9AuyeyRbgiIub88SYzHC9jw/u/82cqbZMNTx7zdzYax6GubYWnTGX+qfX672BvwNGgO86G64g5+ysxLdkr5kUrpin7lgeVkzeMZe24skRc//vvUmeE4/l6/X6EIPBYJyZJnkwRfjp/b+/ne1cwq/As/f/lrq0FdMHz14z+zHX1+Jc6l8hkAp8CPTp9fqdwALABBQbDIZLj3rzLMdc2Wsehhmfp25lvOr1ehXw/fv/69r6m08O4QFJneS5NMW/c4DL098cD6YCer1eB7wC2LDzmOYM9Hr9XwMBQDD2BK7V2A3Xv3Vlu6YDnr1m9mMur0WYk/1bfv9vB3aO/ULlk3q9/hzwvMFg6Jrphk0n5the8wBcNU/dTSrrb7HfxI4aDIbjrm7ME+Lw/b9/qdfrw8SD9yeykvAcOqOt8uBJ8SIQAnw2i5OYHoa/Bv4T9izg1dg32i1z7TC5D89eM/sxl9cizL3+Rd3/+zNAhz2hMBD7OjwOrAX2u6Zp04q5tNdMBpfMU7fxvOr1+n8N/BVQiT35YLbjfey3ke3AHb1efwg712cTkA7UYOcSzvZQ0LcNP7n/9w2XtmIaYDAYYgD+/+z9d3jUV5rvi36qpKpSKecAEhJCQhICIUSwRDI5GBuwjXPsdru7p2bPzJ6+s+fM3Wf2ufvs++x95tzz7JmePTPVM52m2wHbYMBkE0zOCKGAUEIB5YiyVFUqVd0/irX4VSFsbCNVSa7v8/iRUQWt9Vvvete73vB9DQZDDI4Q398BNw0Gw7NGo7HArYN7gvDqmimDKbsX72OqzU/QJ6lweFiL7v+71GAwPI+j4Olpg8GQN1VSCKagrhkLbpFTj/C8GgyGP8VB6XIbWG00Gu+5eUjfG/eTtrfi8Ga14hDcH+Oozl8OCO7MdrcM0ItvDYPBMAeHUdfI5KVz+UYYjcY2o9G4D9gARAAfuHlITwxeXTM1MNX34hSdX/f9nzUKwxUAo9E4jMP7CrBkQkc1TpiKusYV7pRTt3teDQbDfwT+AbgFrDUajVNGwRqNRivwP+//J3E/RyQbBxl86cSPzIvviKlUPPGNMBqNdw0Gw20g22AwRCor2ScjvLpmSumaqb4Xp+L8Ku7/7HnE68K41Y//UMYXU1nXuMBtcupWz6vBYPjfcCxwIY6byVRdYFe8BfgBuyYzNcgPCQaDwQ/HutmA37l5OBOJafd/TuoD1Ktrpo6umep7cQrP7xyOavtUg8GgHeP1ufd/1k3YiMYBPxRd4245dZvxajAY/guOnLobOG4mk9qrMxYMBkPwGL9bjGPeA8B/m/BBefFd8RKOgpcjU6R4AgCDwZBuMBhix/i9+n6TgmjgktFo7H7405MDXl0z5XTNlNyLCkzJ+d3fd5/hYDP5P5SvGQyG9cBGoJdJXJH/Q9A1CrhVTt2SNmAwGN7BoUxHgfPAnxsMDzXwqTMajX+Y4KE9aZwwGAzDOEIH/Th6jj8DmHF0FxmLl3HS4H5nn+33/ykMoDyDwfCH+//faTQa/2qChzVeEEnpk77LjQs2Af/PfZqaahz5kTE4OqYl48ihfN99w/t+8OqaqaFrXDBV96LAVJ7fL4CngP/dYDCsxNF5KhF4HscefX+ychH/gHSNgFvl1F05r4KP0AcHLc9YOMskbmd4H58Dr+KoBNYDzTh40P7OaDTWuXFcTwrZONpOKpHMA27JuziKSCY1DAZDBo7Cl6lUPCFwEofyWQbMx0F5Moij8vdD4H9N8kIDr66ZGroGmPJ7ccrPz2g0thsMhqeAv8VhsObiuGwdBv4vo9E4mXmIfyi6xiPkVGW3/6A6e3rhhRdeeOGFF154MYnhEVRZXnjhhRdeeOGFF1548TjwGq9eeOGFF1544YUXXkwaeI1XL7zwwgsvvPDCCy8mDbzGqxdeeOGFF1544YUXkwZe49ULL7zwwgsvvPDCi0kDr/HqhRdeeOGFF1544cWkwWPzvBoMhknPqWU0GlWPem2qzw+m/hynwvxg6s/RK6dTe34w9ec4FeYHU3+OXjmduvPzel698MILL7zwwgsvvJg0+NYdtoxG43iMY1wxRou2R2Kqzw+m/hwn4/xg6s/RK6fOmOrzg6k/x8k4P5j6c/TKqTOm4vzc1R7WCy+8cCPsdjsqlQqr1Up9fT1XrlyhuLiYkJAQ1q5dS3Z2NlqtVr5vKsNms2G1WrFarYyOjqJWq9FqtWg0GncPzYtHwGazMTo6Ktfo3r17XLp0iYqKCkJCQliyZAmzZ8/Gz8/PzSP14ocEm83GyMgIIyMj8nd2ux2NRuOVxSeMSWe82u12lC1txcE6FQ5Ym80m56ZSqVCrvVkd4w0hT4/bJlmlUsn/JiOEMapSqRgaGqKsrIzTp0/zySefUFBQAMBPfvITfvazn7FgwQJ8fHwAh2xOVXkUxqpWq3X3UJ4IvkmmJ7sMg2MOGo0Gu91Oc3Mzp0+f5qOPPuLMmTMkJSXx85//nPDwcOLj4wGc9KoXE4ev06uuazIV1katVqPT6dDpdO4eisfAbrdjs9mcfvckdJDHG69CCYuD0263Mzo6Kh+Gj4+PPGAnO0ZHR7FYLABoNJopc5h6KoQsif/GUrQqlcpJyfr4+KDRaCatoh0ZGZFy1dTUxK5duzhw4ADl5eXyPR988AFRUVGkpaURFBQk99tUNV6/DpNx3mK9lHpSQKVS4evrO6m9yiMjI3L8VVVVfPjhh5w4cYKCggJGRkZobGykrq6Orq4ur/HqZrjKn4Dr5UqtVk+Zc/zrMDo6+oOYpxKjo6OYzWan34lz9Ps8C48yXoXnURgTPj4+aLVaJ4WjVqvHPEzcpZyUY/2+QqnRaB46VH6Iwj6eELdA4dn29fXF1/fbb4PJehgqD4w7d+7w5ZdfUl5eTlhYGFlZWZSVldHe3s6VK1cwmUzSeH1cz/RkRH9/P62trbS1tdHf34+/vz/JyckkJCSgVqsnxVqPjo4CjkPhceVZ7ANPnpcrxPkg9GRBQQF79uyhrKyMgIAAwsPDSUtLIz4+3unyP5nm6ApxxrgagmKtlXMT+k2tVrttzsqUjm97dlmtVukkmIwYGBigrq6OlpYW4IG8JiQkkJmZiY+Pz0MOucmMr9ONQl6/6xn7TfAo41WtVsuN920PS6WHbCIhxjpegjgVBNzT8CQObHfJ23eFSBcQ4az29nZu3rxJfX09AGlpaTz//PPk5OSwa9cuIiMj5QEymQ/+sSCUqthbdXV17Nu3j0OHDlFSUkJSUhJ//dd/zY9+9CPA4ekTFx1PxXfVmZNpbYUM+/n5YbVauXXrFidOnKC2tpaoqCjy8vKYMWMGycnJ5ObmEhcXJz87mebpCnEuus5hrDl5QrrZ9xmDO43u7wJlTYDJZKKwsJDf//73HDlyBJVKhclkYmhoiPfee49f/vKXaLVarFYrNpttSqQWfN1ajfc6ul0bi1ulyGESnlVxUPT29jIwMEBcXBxqtZqmpiZqamro6OjAx8eH6OhoEhISiImJmdBQmPL2JMYq5vFtNq7wrA4ODnL9+nWuXbvG6OgoCxcuZOnSpQQGBgKOG6m7Dk/XVA2VSiWLXES4RxycSk+A8jkIr7q7btTKXE9heLa3t9PU1MSdO3doamrCbrej1+sB5Nx8fX2xWq0MDw/j6+vLjBkzmD9/PsnJyQ+lFHgybDabfPatra3s3buXQ4cOMTw8jFarJSoqirlz57JhwwYWLFhAYGAg/v7+AE7rOxUxNDTE3bt3uXnzJlarlfLyctra2uTrYxkOngCxB319feWhf+/ePe7evUtbWxsdHR309vbK94+OjqLX65k5cybz5s0jNjZWfs9kMGRHR0elDmxpaWHXrl3s3LkTk8nEypUrefPNN0lOTiYgIIDp06cTFBTk5hF/P1itVoDHTvMQ6RRiHSdaNwmvr4hCDg0NUV9fj9VqlTpEGOIDAwOYTCbAMb/g4GASExPlmgnZ9vS0QKvVKtemo6OD06dPc/ToUSf9AVBTUyPXQ1nbMhkxOjoq10bsR9fCXmXE+OrVq5w9exaLxYKfnx+jo6MkJiby1FNPkZCQ8J3tGrcbr67COTIygt1uR6vV0tTUxM2bNwkNDSUmJoaWlhY+++wzzp8/T2trKz4+PqSmprJkyRIWL15MSkoKwcHBE3LzFAs1ODgoF0UYPt+mQtvHxwer1crt27f57W9/y8cffwzAs88+i7+/P8uXLx+fCXwLiDw5V3ybm6O7vQFKCAOlsLCQgoICTp8+TWFh4Td+TqVSsWjRIt566y127NghPTuToSJfqSzr6uo4ePAgly9fBiAhIYGIiAjCwsLIyMggIyPDKdfTk9ZuPKDVaomMjGTGjBnU1NQQExNDSEiIfN1TDTtRaAYOmW5ububatWtcvHiRoqIibt26RUdHh9NntFotq1ev5u2332bz5s2EhYV55NzGghhnT08Pp06dYs+ePZhMJvR6PatWrWLNmjVERER47GXj20LoXGHIifQdMTdfX198fHykfAojyl2XEaWOsdvtXL16lXPnzjE0NIRWq5UGzejoKJ2dnfJi5e/vT0xMDAsWLGDhwoUkJCSg0+kmRc2HK+NFSUkJvb296HQ6goKC6OzsBCA2NnbKFKaNdaFQzslms8lLTFtbGx9++CH/8i//4vT+nJwc/vZv/5aoqCjpoPu2mFDjVVlEIB6ActLV1dVUVlZy69YtSkpKaG9vJzU1lXfeeQdfX1+uXr3K73//e0pLSwkMDMRut3P79m1u3bpFVVUVW7ZsYfHixQQHB8u/Nx4Hr/B2WCwWDhw4wPnz51m7di0vvvgiABaL5aFcXVcoiw7a29s5ceIEV69ela9fvXqVffv24e/vT05ODr6+vpMuV8Zut2OxWKSSFRBezYmah1Lh37t3j4KCAk6cOMH169dpbm6WofPH+Z7r168TFhZGXFwca9asITw8fFIoI+ElNplMtLa20traKl+bNWsWCxcuJCoqSv5ussjYd4XSOyUOe3FYigiQ63s9BUqPHDjy7C5dusTJkye5efMmNTU1NDY2yuJPJSwWC2fOnJERhY0bNxITEwN4bn69Mp+3q6uLI0eO8Ktf/YrKykri4uL4kz/5E1588UUiIiKAyS+7JpPJKV/0+vXr7N+/n9LSUnx8fOS5tmLFCp577jni4uLw9/dHpVLR2tpKbW0t06dPZ8aMGcDErKvyrG1vb+f06dPs37+fgoIChoaGHvr7w8PDsognICAArVbLoUOHyM7O5tVXX2Xz5s1On5kMhZPKaKRWq5XOLHBcGpVpWJPR8yo8rsJpZbPZaGtrw8fHh9DQUEmtODIygsVioaamhlOnTnH+/PmHvquvr4/BwUG5t78LJsR4FQvlarCI6vqBgQHKysqkAVdVVUV3d7e8jQnl6u/vT0REBH5+fqhUKkZGRhgcHCQ/Px+TyUR6ejpZWVny+5+0wIvvE4ZkeXk5u3fvZt++fXR1dZGVlUVqaupDRvmjvkugvLyckydP0tDQQGBgIGq1mq6uLvbu3UtERASzZ88mMDBQ3mgmchML3jqTycTw8LD0ig8NDdHT04NWqyUoKEgm2oeGhqLRaLDZbPj7+0tBN5lMMs/nUaGG8YAylAXQ0NDA4cOH+fTTT6UBp1KpCA8PJyIigsDAQHlACFitVnp6eqSRW19fT3V1NTk5OYSHh8vn5IkeOrH3lCkDd+7ckSG7oKAgFixYwKpVq5g+ffqYIaGpCGUaia+vLzabjcHBQcAhq0ql6mkHjVgXUVmfn5/P/v37OXLkCN3d3YDjsIyLiyMkJETqy+HhYTo7O+ns7OT48eOoVCpmzpwp9atYd0+DMhWrvr6e48ePy4v+5s2bef/994mNjZXeycmaS6jM6QXHAX/jxg0OHjzI3r176erqIjk5ma6uLpqamujt7WXFihWkpKQADuaFzz77jIqKCrZv3y6NV2FQjaduGhkZkc+9urqanTt3cuDAgcf6bE9Pj/z/0tJSzGYzdrudnJwcIiMj0el0Hm+4gmNf+vn5YbPZ6O3tdTpDRJpEQECAx+mTb4KwO4QTymaz0dDQwO3bt6mqqsJutzN37lxWrFghjfSioiIOHz7MmTNnaG1tJSQkBK1WS3d3N1arlbS0NKZNmyZl/bvYAhNyOrkOSixuS0sLdXV1FBUVcenSJfLz8+ns7GTmzJn8yZ/8CRs3biQpKUneqJcsWcJbb73F8PAwN27ccBKOgIAAaf0/6u9+XwgCc4DOzk6OHTtGSUkJ4Kh6/fjjj3n55ZdJT0+X83zUphOHhNls5s6dO5SXl2M2m1Gr1QQEBNDX10ddXR3FxcX09PR8Z9f694HNZpPjuHXrFpWVlahUKqKioqioqODs2bPExcWRk5NDb28vWq2WFStWEBERgcViYe7cuUybNo3e3l6KiooYHBwkJSWF1NRU+f0TkaCvVBbDw8P09vY6eaWCg4PJyspi8+bNLFmyhICAAIaHh1Gr1QQFBVFTU8NvfvMbabzq9fpv9Kx7CkR+lTB4amtruXr1Ki0tLeh0OhYtWsTSpUvJyMhArVbL5+KJRsyTguslw2KxYLFYnAx9T1xbpQdNhGX37t3L6dOnqa2tdcpvzcnJIS8vj4yMDBmyrKqqkh7anp4erl69Sl1dHStWrJDf6UlQOj3AkVN448YNbt++DUBUVBTz5s0jOjpavt/T5vBtYLFYpAE4OjrK4cOHOXjwIA0NDcyaNYuf/OQnbN68mZKSEnbu3ElSUhLTpk0DHDnA//qv/8q//du/odFoWLBgwUPyMp4yrXzuokZACZ1Ox8jIyCOps5Q4duwYLS0trFu3jm3btrFgwQLg4UJLT4W4+LvqUGXNyGSSU5vNhtlslmdIQUEBv/vd7ygpKaG5uRmVSsVLL71EXl4eWq2Wnp4e9uzZw6effsq9e/ekTTY6OkpAQABhYWHMnTuXpKQkdDrdQw6mx8W4Gq+uRTptbW0UFBRQWVlJX18fXV1dlJaWUlRURHd3N9OnT+eZZ55h1apVvPjii/LmCNDc3Ex1dTWtra0MDAxIQUhKSiIjI4NnnnmG7OxsWWQCT/4AVgpcTU0NJ0+epK6uDj8/P1paWti3bx+hoaEEBgZKmh3xOVfFIcZmMpkYGBiQm314eNjJczDRG1U51tHRUdrb27l69SrHjh2jqKgIX19fIiIiqK2tpbW1lZKSEsrLyxkYGECn03Hnzh38/f2x2+0sXryYOXPmUF9fz9WrV+nv72fevHmsX7+eBQsWEBAQ8NDfHA8on2FsbCwbNmwgMTGR7u5u7HY7AQEBpKSksGzZMtLS0h76/KxZs9i9e7f8t7+/v0yBmUwYGhri9u3bXLt2jd7eXpKSkli1ahXZ2dlSHidDntmTgOiCU19fz+nTp8nPz5eGu9lsll5YT4JYo97eXq5fv84nn3zC7t276e/vByAuLo7MzEwyMjKYP38+qampJCYmEhkZCTiMva6uLq5evUpPT89DBV2eeKAKPS+KYXbv3k1hYSHR0dG88cYbPP3009KpMBbV4GSC0Putra2cO3eOY8eO0draSnp6OitXruSFF14gICCAjIwMrFYrYWFhRERE0NHRgdFo5Pe//z2Dg4Ns3ryZzMxMaby60mmNB5TPPSEhgRdffJHIyEhu3rwpHTOuEHpZePUEi0R/fz8XL16kp6eH5ORkabx+l4LoiYSS5tN1nIGBgbIYbSzSfk+GkuqqubmZQ4cOsWfPHplPP336dGJiYqTttWfPHg4fPiwjm0KvarVacnJyWLt2LZs2bZI1I991TcfVeFWr1TK039nZyblz5/jggw+4fPmyzAs1mUz4+voyd+5cXnzxRbZt20Z6errTZrh37x5Hjx7l448/5sKFCzJEkZGRwfr169myZQtZWVmEhYWN53ScFIDILRJtJVUqFZWVlZw7d46UlBSio6OlS3ysW4X4rr6+Pnn4gOOZiZBuUlISWVlZDxWPjCeUhqTNZpOcn0ePHpW5WPX19XKM4AjFi1zcEydOYLFYUKvV3Lhxg5iYGLq7u+Vnrl+/TmNjI4AsRvsut67HhauXLTExkYSEBKk8xHxdSbKVz6GqqoqBgQH5mqBO8lQl+ij09fVRX18vPcjR0dHMnTtXVp1PhsKz7wtxoAs53rdvHzt37uTWrVtyPT3Nq+6aktLQ0MC5c+c4deqU1B2pqaksX76c559/nqVLlxIWFiYroQVlT19fH+3t7dIwVzJKgGfl9oqojI+PD729vVy5coXdu3dz/PhxAJYtW8aPf/xj5s6dCzguHJM1XUBZA9Dd3c2nn37Kl19+SVBQEM888wyLFi0iPT1dXvaFUScifLt37+Y3v/kNPT09vPrqq/zFX/wFixcvlpX9ExFFEWwCKpWKpKQk3nrrLZYsWcKePXv4wx/+QFNTE4DMtQYeSs0aGBiQ+aIWi4XGxsaHWD88PSKkNMSUl0GLxeIUifWkvfZ1EGtqs9moqKjgwIEDHDx4kI6ODgIDA5k7dy7r1q1j69atAHz22Wf867/+K1VVVeh0OpmGBjBnzhxefvllXnvtNXnmuDJkfBuMi/GqDFdYLBby8/M5duwYp0+fpqioSFriFouFJUuWsHr1apYvX052drbsiAKOEOe1a9coKCjgypUrnDt3Tr72zDPPsGXLFvLy8khNTZ3wG7cwWgV8fX0xm83U1dXR2NjotDGVQix6Hms0Gvr7+7lx4waFhYXyZmqz2TCZTERGRrJp0yZWrlwpldZ34XL8PrDb7QwODlJXVyeNVbVaLcfq5+eHRqOR4SCbzSbX1mazUVtbS11dndOYGxsbOXnyJLNmzWLGjBkkJCRMqEISBNiP+psmk0myR1itVi5cuMCnn34q00MAQkNDmT9/vlNkwJMNWfH8xaEgIPptizymx6Eys9vtThe28SKgHi8MDw/LFJy2tjZ27drFrVu3gAeH6ZIlS8jMzJSfcXcagesFLC4ujpkzZxIXF0dQUBCbNm0iLy+P+Ph40tLSZMGq0ove3d3N0aNH2b9/P319fURFRfH0008ze/Zs+R5PkmGr1SrH39/fz9WrV7l48SIA69ev5+2332bOnDny/Z5u1Hwd1Go1VqsVu91OUVER586d4969e2zatIkXXniB6OhopyIZnU5Heno606ZNo6WlhfLycrRaLc8//zzvvfceubm58rsn2sMnjB1/f3+ysrKw2+3ExMRw8+ZNWltb6e/vp7Ozk6amJienjXK8Yi0nI+eriMiNjo46OXiKioowGo1s3ryZ+Pj4SXHRGh0dpb+/n/r6em7cuMHVq1e5du0aRUVF6PV6duzYwRtvvEFGRgY6nY4zZ87w2WefSeYelUolIwQrVqzg2WefZfPmzdJwhe+nc8b91KmsrOSLL75g9+7d0o0cFhbGrFmzyMrKYv369WzatInQ0FDgQSVtY2MjX331Fbt37+bcuXOYTCZ0Oh2pqank5uayZcsW1q9fLw074cmdKLge2oICxGKxMDQ0JI1UIcjKIiXlOMvKyrh165b0QAujICMjg3Xr1pGdne3UvGEiN7MowJo/fz7V1dU0NDRIr7eYq3KDKsemPHDtdjt+fn7Y7XbMZjMNDQ0UFxdTXV39kId6vA9QoWDGapupVqvx8/OT42lra+PUqVPs3r2bgYEB9Ho9ycnJrFmzRjJAiEPHU8OVwlgHR7TAlffTbDbLsOs3XYzEweQanp0sXLeuTB0FBQVS0Qr+wXnz5vHiiy+ycOFC+T5RoOkuiOcqCqoiIiLYtGkTZrMZi8XCM888I4t2RLWv0uNhMpk4e/Ysx48fp729HXAY6G+88Qbz5s2Tf8eTZFisk9lslpR2nZ2dZGZm8tZbb7FlyxZ5kXYtAgXPl0VwLibVarXcu3ePmpoaenp6SEhIYOXKlfKCLOapUqlkDYgovCsrKyM5OZlnn32W7Oxsp9zQibyQuPJ8ms1msrOzycnJoaamhv3790vDp6enRxqv4nwT/y/SsSwWy6RKzRKRAo1Gg9lsxmQyyd8VFhby93//94yOjvLee++h0+nkfvYkWXUtzqqpqWHv3r0cP36c27dvMzg4SGBgIKtXr+b1119n3bp1gIMd6YMPPuDSpUuAQ5cI22DBggX86Ec/YsOGDTLlTrQp95j2sOJwE56cO3fucPDgQY4ePepEzZOens4777zDypUriY+PdyKT9vHxobm5mY8++oiPP/6YxsZG+RBWr17Nn/3ZnzFnzhyCgoKccibF4TsRuZMiBOe6sYRxqcxpcW3rp1wstVrN0NCQnJ/4vtjYWLKzs5k/f74M6yk9EeMJpbLz8fFh1qxZ/Pmf/zkrV67kt7/9LadPnyYqKopZs2bJQiybzUZAQABBQUFynUWRkCiiEMa9eM1isUjDb6KhpEdS5veOjIzIyuy2tjZZ1SxSBhISEvjZz37Gtm3bJMMC4LGeR2UEpL6+npMnT1JRUSFfF6Gs7wvlGnsqBL2dv78/fX19fPnllxw+fFi+rtVqWbp0Kdu3b2fdunWSNszdrTaVUB50cXFxvPjii9hsNpnTKvRff38/FRUVNDU1Se7JK1euUF1dLb8rJSWF1atXExIS4lEtOZV5miMjI5w7d47PP/+c4uJitFotycnJTpG2yZQ76AqRI6mMUg4MDBAUFMSsWbMkCwQ8MAxFUdfAwABHjhzhl7/8JYWFheTm5hIaGirPC3EhdafcKp0RAQEBtLe3U1FRQW9vr5PTw3UNxWeGh4elEwg8vyBPRKLEPrXZbGi1WinLtbW18nISGBjoETrFFeLyK860S5cusXfvXhoaGmS60YoVK/ibv/kbcnJy5Ofq6uq4dOkSbW1t8jI5MjJCcHAwixYtYvny5fj7+0uPtLANvs8zeKKnrrLbRHNzM59//jmff/45d+7cARzWeFpaGhs3bpRMAoCsVNfr9bS3t7Nv3z727NlDeXk5ACEhISxdupT333+fZ555Rv49i8UiQ8DjaUAo86/sdjtVVVWUlJQ4hT0ETYuYpzCsBfeZwODgIP39/fj6+lJeXk5tba1TPqVGoyE0NJTo6GjpjXYX1Go1kZGRREZGkpycTH9/P4GBgcTHx5OTk0NfXx8nT56ktraWtLQ0oqKiuHXrFhUVFYyMjDh1URE0W+AwzlNTU4mLi3Py9kzEZn6cUHdNTQ3Hjx/nwIED1NfX4+fnh8lkIjw8nAULFjilCwi4swPao6A8GDs6OigsLKS5uVm+LgzOr6tIVnqyVCoVZrOZ1tZW2eEuLi5OhoEEibqnPQdwPiA7Ozs5cuQI+fn50nvu4+PD4sWLWb9+vVPqkicdMMqLllqtduLlVb7e3NzMvn37KCoqkkV69+7dAx4UdeXl5clcek/KdVaGjQcHBykuLubixYu0trYSHh5OfHy81K3wII3JlTdcGIYiouIp81NCFJmBIx/9+PHjXLt2jeTkZNavX++k/4XhJsLNdXV1HDlyREYOUlJSiIqKkt5Wd8/Xx8dHRrAA2TyjoKDga6M8orIdYPbs2U4tfj1hXq5wTQns7e2VTDXCCFc6udrb26mrqyMyMvJ70USNB4TRqtfrMZlMnDp1ikOHDlFWVibfExAQwMqVK1m6dCngSOlpbm7m1q1bTmeLmLuPjw9BQUFS1wg2pSeBJ3rKKBegpKSEffv2yVxBjUZDSkoK77zzDi+++CKJiYkPBnH/gQEcP36cv//7v6e2tla+npOTw3/6T/+J1atXO/29iQpzuYY0KioqKC4udqpIFoaC8Lwqhbqrq0t6YCsrKykoKKC7u5vGxkZZVQkPwpMip1CZU+sOuG6o1atXM2fOHCIjI4mPj8dkMpGdnU1LSwtxcXFERkbS2NhIYWEh+fn5XL58mbq6OsA5UT8xMZF58+aRmpoqfz8RFbFjwTXMWF1dza5du9i7dy/l5eVOnvOBgQGuX78uO76Fh4c75Wd5MoTRPpZx+k2hVmXYury8nIMHD3LhwgX0er0smExMTHQK+XkalAZ1Q0MDXV1dDA8PSwMnISGBuLi4hxSrJxwqrvg6D2l3dzenT5/m888/p7a29qHWopmZmRgMBtasWSN/50npAkIehR4VOZLwQG8ITzPgtP9c05Y8uThGqfN6enrYv38/v/vd7zCZTDIlDpwb3iiLfM+cOcO1a9cAeO6553jnnXdYsGCBRxhESj50i8VCQUEBR48epaysTObcjyVzon7Cbrczffp0Nm3aJJkGwDMjXK7PWFnsrEz3E6itraWwsJCUlBSnS78nzE3IpN1u58SJE/yv//W/nBonhYeHy1RGgd7eXk6fPs3Fixelo0qkBYDD8RgcHCzX2263Mzw8jJ+f3/c+M5/IExMbRSxAfX09JSUl3L17V77n6aef5tVXX2Xbtm1ERkZis9no7u52Cv+fOnWKL774QhquAQEBrF27lrfffpuVK1cCjtwfq9VKQEDAhG5OpTE6MDDgREKs5G2z2Ww0Nzdz5coVpk+fTn19PTdv3pS0Es3NzZSWltLT08Pw8DAtLS3ye4Vx5ynCLEIfwoBTdm0Bh7JZunQpQ0NDUhhTU1OJj49Hq9VSVVUljVdlBebo6CgRERHSi+AOr6UyHGwymbhz5w6VlZXk5+fz1VdfUVJS8hC9S3NzM3v27KG4uJiEhARmzpwp0ztE0YXVanXyOAgouwRNFJT7Q0Qnvu1zVirj27dvc/DgQfbs2UNpaSkBAQG0trZSXV3N9u3bWbFiBb6+vh7pgbVarXR0dFBSUsKJEye4e/eujAYkJCSwZMkSMjIyCA4O/s68g+5AX1+fZD5pamqioaGBGzduyGYFVquV0NBQ1Gq1vGzHxsZKT4jFYvnO1b7jAaH7xHnS1NRET08PoaGhbNy4kVWrVhEbG+vURlxgZGSEvr4+VCoVwcHBTvLnaU1ElDrv3Llz/Pu//zsDAwO8+eab0nCFB+sj5tnR0cEXX3zBhx9+SGNjI4sWLeK1115j1apVTvN152VayXIxNDTE0aNHOXz4MH19fdJZoUwHEFCOWa/XExcX58Qg5Clrp4TymcfGxrJ+/Xo6OjrIz8+XeZ1C9ux2OxUVFVy7dk3KMbg/ajcyMoKPjw96vV5Sfn7yySdcu3ZNpqlER0ezcuVKXn31VRYsWCDPutu3b3PkyBGuXbvG6OiojFKOjIwQFBTE8uXLpe1msVg4fvw4dXV1zJo1i9zcXMLCwr5znvoTNV7F//f19TE0NOTUHm3OnDmsWLFCUrj4+voSEhIiD4irV6/yL//yL5IKRa1Ws3jxYv70T/+UDRs2AA/yfdxRqad8sCLFQTlngcHBQQoLC7l3754UhsLCQkkPpVKpHiJw1mg00kDU6XTMmTOHjIwMJy+Quw7SsZL+lfMVlaXKLi6CSUHpmRbGok6nk1XSyu+YaIg59ff3U1RUxIkTJzh16pS8WAjjS8xpdHSUvr4+Ll68yMWLF/H19SUlJYXNmzdjMplYtGiRU0tHAWUe+ERDuU7iEqL05n9d/phyT5tMJoqKijh06BBffvmlTAvp6+vjypUr3Lhxg66uLkk+PdGsGF8H5fNvb2/nwIEDHDhwgJaWFnn5jIqKYu7cuaSnp0ud5SnjHwvK4tSOjg5+97vfsXfvXurq6saM1vT29kpjobq6mmPHjhESEsKcOXNkWpOnhC6VHtSysjIZFs/IyGD16tVkZGQ4vRcch29bWxuVlZXU1NQwOjrK9OnTSU5Oloa6p1xEhB4UnZhKS0vZt28fZWVlvPvuu/ziF78AkA4BwYxhs9moq6vjwIEDfPDBBxQVFZGSksK2bdtYunSpNH7cbQiJsQpYrVZaWlpobm7+xroNZdSmr69P5m3PnDlTfq8nXUDgQbTUZrMRFxfH9u3bqays5PLly8ADb7Iw2nt6erhz585DZ6M7oYxSnD9/nr/7u7+joaEBeLAfFy5cyMsvv8zGjRvRaDTY7XbpiCsoKJDzEcYrOKg+N27cyNy5cxkeHubAgQP89re/pby8nA0bNhAbGysvJ27rsOVKxh8fH8+sWbOIiIigvr4eX19fiouL+c1vfkNGRgbp6enk5OTg5+eHxWLhyy+/5NNPP+XUqVMy//OVV17h/fff5+mnn/7OkxsPCM+h6yEhQqYdHR0MDg5SUlIivR3K9neuUBrAvr6+zJs3j61bt7J69WqCgoLcavy4QmxSZahZbF6LxSKVZkVFBQcPHqSsrEx6USwWC3q9njVr1vDmm29KjkKY2LClMg+0t7eXkydPcuzYMa5fv86dO3ek/On1etkAIyIigsbGRm7fvi2jCVarlTt37nD06FEaGxt58cUXeeWVV5xC7MKrKxLYBfWYMOLHe01dw6g+Pj5OFxHx+lj7Sul1Lygo4De/+Q1ffvklg4ODTlEGcBgPhw8fRq1W85Of/ISlS5dKmhRPqab18fFhZGSEuro6yTnp+rq7D/3HhfDogIOV5dNPP5Xcva6YPn2603ybmpr44IMPsFgsvPfee8yaNctJB7kz1AwPiPpv3rzJH/7wByoqKtBoNCQlJTnxXYsL/7Vr1zhy5AiVlZUyFWtgYICwsDBmzpzJihUr2LZtmzSA3G3cCQeMSqViYGCATz75hAsXLrBgwQLWrl0r36dkxuju7ub69et8+eWXnDx5ktbWVpYuXcqOHTt47rnnZDTMU6IFyj2v1+tZunQpt27dkvzugmLRbDZjs9nkWSp+qlQq2tvbuXjxInl5eZIPXOR6e1qKlkj1E0wgY7EQKQ1zcYa6G8LhJMZ74cIF9uzZIw1XYXBrtVpSU1PJyMhwKpQUa6e0hcT/a7VaZs2aRVBQEJcuXeLChQucPHmSs2fPYrPZqKmpoa2tDZPJNGak8nHwRHexUKqhoaFkZWWRmprK7du3MZvNnDlzhjNnzpCZmcnq1au5e/cuycnJ1NfX88EHH3Ds2DFGRkaIi4tjxYoV/OIXv2DRokWA4xYWEBDgEblZPj4++Pv74+/v76QoxOE4ODj4UHceEa5VMhEoiZqFV0Sv1zNjxgxycnIIDw8HHGkS33VxnzQeZUQLbys4PM/Xr1+XN0+RczcyMsKMGTPYuHEj27dvR6vVuqXKWWm8ms1mysrKOH78uDRKo6OjSU9PJyEhgdTUVHJycoiLi6Ouro7CwkLq6upoaGigqqqK1tZWysvLZYexgIAAVqxYIVNafHx8pPd8ZGSEoqIienp6SElJkcWK40kP5up5tVqtD93yXXNexfu0Wi1qtVpeRA4dOiRzD3U6HcHBwTIKMjQ0RGdnJzt37mTatGmkp6dL+XUXxLzEs62srOTkyZNOFfcCgp94rC5AngilvPj7+5OZmYnJZCImJobg4GC6urqwWq2kpqYye/ZsamtruX79Ot3d3ZhMJmpra9m7dy96vZ7t27eTkZHhdsNdHIbCKC8oKODYsWP09/fLSI0y1Nzd3c2tW7f45JNP+Oijj2TxrFarRaPRUFdXx82bN6mpqWH27NkeY7wqvdwtLS1cuHABHx8f3nvvPRlhBEfKXG9vL3fv3uXSpUscOXKE69evo9VqWb16Ndu3b2f9+vUy/9cT2AUElOe0v78/GzdulKHk/Px8TCaTE9sAPEi9E0VsgjNdyVLkCQbfWFCeYSMjI4SHhxMSEiLbkLvWAQjd6m4odX93dzdHjhzhzJkz+Pr6EhgYKGkVfXx8CAwMlMWRglFBtElXRsLFHg0ICGBwcJDTp0/T0tLClStXaG5udkq1NJvNTgw/3xbjVrAVHx/PihUrqKqq4ubNm/L3paWlNDU1ceLECZlCcPfuXdlpYdOmTfzlX/6l5B9UJqx7AkQHkG9jSCsNJtfvEsLjqRvzmyCML5EqcPr0aackb0GJFRoaypw5c0hLS3NrC1LlOvj7+5OamkpsbKw0Xp9++mneeOMNsrKyCAgIwM/PD71eT1paGsuXL6e3t5eqqiqOHTvGrl27JG/mpUuX6Ojo4M033+SNN95wKioBx632xIkT6PV6J5JmceiMB1xzXgVtixKuB57SeLXZbFy8eJEjR47IanVwVKyL5iADAwNcunSJK1euYDabKSkp4dq1a9KIF9/pjjbHYh52u50jR47wD//wD3R2djpxDqvVakJCQggJCZlw5ovvCuVldt68efzyl7+ktrYWk8lEbGyspHHTaDQEBQXR1dXFxYsXOXToEPn5+XR0dFBRUcFvfvMbbDYbr7/+OsnJyV/LOjHRUHIxC90oxtff38+XX37JJ598wpUrV6ThGhYWxurVq0lMTKSsrIwvv/ySqqoqCgoKWL58OcHBwW73TCqLJuvr6+nr6yMuLk7yClutVnp7e+np6eHSpUucOnWKU6dOUV9fT3BwMFu3buX1118nOzvbyRPt7nmNBbHv4+LiePbZZwGHYSscG+AoAtJoNAwODjqx7gAycjU8PIxer/cY4/zr4O/vT0ZGBosXL+bKlStyTjqdzuPOehGJE+krpaWl8jwTOecit7yrq4uqqir6+/sJCwsjODiYtrY2WYzuCuHEKi4uZmho6KG1DQ4OJigo6Hs5JJ+o8ao8GIODg1m3bh2jo6MkJSXJAiVxm1KG0oUxY7VaGRwcpLu7m4aGBnx8fJg2bZp8n2jLOdEb1fWGcu3aNZmQrdfr5Q0CkG54MU7hWR3Lq6NM5AbHoeTv7++UE+uJSkkJ0bQAkIbMrVu3ZKh8cHDQKZd32rRpbk2FcPVa5ebm8tOf/pTMzEwCAwNZu3Yt69atc2qbCUhDIDY2loSEBEJDQ4mMjOTSpUvyQpafn09CQgLLly9Hp9PR19dHbW0t586d48qVKwQEBLBu3TpJMg7jayQpn29sbCyLFy+mrKxMUtf5+PhIijcREVAWdRUVFXHx4kXKysqw2WwkJCSQm5vLggULWLx4McnJydy5c4fa2lrZuKKtrY3S0lJp/MP4GuhfB6WXub29XXqOlcwXERERzJkzh3nz5jkZA57gGXkUhM4QxYGzZ89m9uzZ0gHgivj4eJKSkkhISGDfvn0cPHiQzs5OGhoaqKiooLW1lRkzZrjdeFX+Tb1eLzuFmUwmzGYzdrud7u5uTp48ya9+9SvOnz8POGR7/vz5rFy5UnYkPHbsGDU1NVRVVXH27Fnmzp3r1NTmUQ6F8YbSAOvt7WVoaAiz2cy5c+eoq6vjypUrtLa2YjabqampobS0VBb1zpw5ky1btshUupGREalHPdGoU+6h+Ph4tm3bho+PD6GhoTKXdc6cOdy7d49Lly5RXl7uFB0Sxc9VVVXMmzdP5lp6MkRNy8DAABaLxel8FxD61t0Q6zMwMEBHR4cT9adgYgHH/rt27RpdXV0EBwcTFxdHaGgobW1tXLp0SXpolXO1WCxO3Rz9/PzQarUMDAwQHBxMQkICSUlJMvf7u/CEj4vn1W53dBxKT08nNjaWtWvX0trayt27d7l58yaXL1+msrJSWuPC8LPb7Rw7doza2lrmz5/P008/zerVq5k+fTqAWwxXV/T29lJaWirDGYGBgU6HpAhBKpWJa4GTCM+K/xevT5s2jQULFpCQkCDf78mHKDy4sNhsNm7dukVxcTHNzc0PHaRxcXGkp6cTHx/vlGs50ZtYuS5qtZrExETeeecd3nzzTQB58fg66HQ6li5dSm5uLpcvX+Yf/uEf2LdvH+DgEL1w4QKNjY2Ul5dz6NAhLl68SExMDH/2Z3/Gs88+S0xMjDToxjOEKW7VdrujReOKFSu4cOECp06dknN1LUoT+6u6upo9e/Zw5coVeUguX76cv/zLvyQzM1MWLPr6+so8L7PZTHd3N11dXU6Kyx2KWsl+olKpWLJkCevXr+fSpUsMDg5KOdDpdAQGBj6UBuSJxoASylQdgUd5MSwWCyEhITz33HNoNBrZrcrX15ehoSFaWlpkLia4zzOk/LvK9pri4Ovo6KChoYHf/e530nCdMWMGy5YtY9u2bWzYsIGwsDCamprQaDT4+/tjt9spKyujoKCAvLw8txuvyjmGhISg1+spLi7mH//xH7Hb7ZSUlKDT6UhKSkKn06HX62URTEJCghPbi9VqdSqK9lSIy1BcXBzbtm0jLS2N9vZ2pk+fTmpqKtXV1ahUKjo6OmhtbZUXS4vFwo0bNzh58iShoaHMmDHjIT3lCVA6twSv8pUrVwBH+Fy0T1emRniSfhFjUeps5fgE3dm1a9dQq9VEREQQEhLC0NAQXV1dmM1macuMxSIh6h/AURybmZnJkiVLnGT5u+iccU/+CQ0NJTQ0lKSkJLKzs9mwYQMHDx7kn/7pnygvL0en00nFGR4ezr1797h+/br8r6CggFWrVpGTk+NEHj6RUApbcHAwq1atory8nLKyMmmAixuEcLO7LkZWVhZLlixBo9FQWlpKUVERvb29+Pr6SuN32rRpLFmyxIkD11ONV1eqmiNHjvD73//eKUVkYGAAtVrN3LlzefHFF1myZIkk3va0zkVKZSguIMK4VBYviVQPjUaDWq2WMt3Y2EhJSQkVFRV88skn6PV6amtruXv3LjNmzOC1117j+eefd+qaMxFQ0j6FhIQ8xGOqTF3RaDT09fVRWFjIiRMn+OKLL+Qe3bFjBz/72c9YvHix0+cDAwOl1xUeKEB3r6vSeAVYvnw5AwMDdHd3k5+fLwsku7q6uHnzJnPnziU2NtYph9CTDkgRRheHhJIAXaQ/CJoycdkQsmsymeQ+FQ0KRPSrqKiI9PR0Vq5cKRlARPGJO2GxWKQnSIQYi4uLuXTpEmfPngUcc3nhhRfYsGGDk+dc6FRxkIaGhsrwtIC75RMcJPybN2+mr69PNuSZPXs2q1evJi0tjdbWVq5evUp/fz9BQUFER0dPCmPVFcqc+ujoaCIiIjCZTDIVQLQbLSkpobW1FbvdLg324uJi9u7dS3Z2tjR2RPGvJ0FZxKqseREFZoJPWhjyniB/AsoiYwGRdwwPOmKCY35tbW20tbU5fYfSjhFz9vf3R61WyxznwMBA8vLyeOWVV1izZo3Uz9/VDhgX43Usr6Ovr688GGbMmCEflNlsJjQ0lNmzZ5Obm8vAwAAFBQV0dHTQ3NzMv/7rv3Lq1ClZWZmWliYNxYkSAuXDDQ8P59VXXyUsLIwPP/xQtg8VXgKVSoVWq0Wr1RIeHk54eDixsbGsWrWKLVu2ALB3715aWlro7e19KF9EtHMU3Gru7rL1KCh5PPv7+zl8+LD0Popb2cjICKGhoWzZsoVXX31V9l/3JAjjTigXkQfkWiSnNHCFIaFWqwkKCmLdunXU19dTWVlJe3u7zBvy8/Nj5cqVvPXWW+zYsYPQ0NDvHCL5PlAaOkFBQej1eoaHh+np6aG4uFh2TFOpVPT19fHpp59y6NAhWXWamJjI66+/zooVKwBH6oygOLl37x5DQ0NSHmbPnk16erqk+BHPbqLhyh1otVoxmUwPVcZarVb6+voYGBh4bBoxd+BRVdaigFTA19f3ISpBpdGm0WhYtGgRd+7c4eTJk9TV1Y3ZcMUdUOrygIAAQkNDpVe4paWFkpISzpw5w8jICDk5OWzZsoUdO3aQlZUFOBP/azQahoaG0Gg0LFmyhNWrVzvpUncVbSnXMCEhgZdffpnQ0FCKi4vR6XSsXbuWZ599Fl9fXz799FO+/PJLent7yc7OZsmSJU6FkO6sHfg2cJVbUciqbMk9f/58ZsyYwcWLF4EHnSnNZjOdnZ0MDQ3Jz3va3oQHsuvaQWqs3G3BWOT6WXfC9cwTzhslh7/Q44I9QayrOD8FxJz7+/vlnAMCAsjLy+PVV1/l+eefl7L7fWqaxnUHi7ws8WDsdjs1NTUUFBTIPAmA+fPns23bNtavX49Op6Orq4uBgQFu3LjBr371K4qKirh79y4NDQ0899xzLFy4kOjo6AnzSro+2KioKLZv386cOXPYs2cPu3fv5s6dO2i1WiIiIkhISGDOnDmS+DwkJITIyEimT5/O8PAwmZmZxMXFUVVV5ZRyUF1dzW9+8xuGhoZISUnh3XffJS8vD/A8T5AQPrvdzrVr16ioqHB6XaxNUlISWVlZTlx9nkZ3oqT/epxxKdtOguNCExYW5rQ+8fHxLF26lNdff501a9ZIr5Y7FVVoaCgZGRnk5uZy48YNqqur2blzJ2azmZiYGKZPn87Q0BDl5eVOhuvatWvlxcNuf9CiUhQCiU5kKSkpbNmyhQ0bNhARESEvmO6SW2WzjzNnzvCb3/yGW7duOXFiJiQksHHjRjZv3kxMTMxDDVfcjSedfyqiBq4E/u6GKzdwcnIyubm5NDY2UldXx9DQEBaLxYmRZtu2bdJwFS23lWeOaB8+Z84c5s+fDyCLf9y1D5X6RUSlYmJi6Ovrw8fHh8jISKxWK+fOnePgwYMUFhYSFhbGc889x/PPP09UVJTkDfWk8+C7QLnmrnRSImcyPDycvLw8p9oXT9mbj4Ox9tZYVJvuhEqlIjw8nHnz5lFdXU1jYyMmk0nKmDLFzfViK7zJQk8p97D4f51Ox4oVK/jRj37Es88+62Q7eEzBlitc87IsFguNjY00NjbKm1RISAirVq3ihRdekOHy1NRUAPLy8ggKCuJ3v/sdBQUFfPDBB9y7d4/w8HDZ81hJ2D0REOGooKAgFi5cSHh4OEFBQVRWVuLn5ydbp86ZM4fs7GwnMn6bzYa/v/9DPZsF321tbS2VlZWAQ8kqc1A8yXgVgtrb28vZs2edjHdRLQsOY2n+/PlOlczfRFQ90Rgrd/BREPMW7+/o6KC6upri4mKuXLki1ysgIIDU1FTWrVvH2rVrCQwMZGRkROaoTeTBqYxOaLVacnNzqauro66ujtraWsrKyjh27BhBQUEsWLCAiooK2Q0O4Pnnn8dgMMg9KcLQt27dIj8/n2PHjlFaWgo4CkoWLFgg2RRE3rM7DAW73S4P+d7eXi5fvizbaYo8NF9fX9LS0lizZo28XIkQu7svV8rwot3uaPwyPDyMr68vQUFB36pRi5IXdnR0lNLSUioqKlCpVISFhZGWluYUhnWHnlFeBsHRlGDDhg2UlJSQn58voxkajYbU1FQWLVokK/TBkb6iTO0StIWuVEWe4OWCBwe7Vqtl+vTpsq4D4LPPPuN//I//QXFxMeDYgzt27CA6Otrp854yF1co+cDFv5Uc00qPq6+vL7W1tRw5coTbt2/L7xCRzISEBBYuXOiUbuUp5+BYEHMXEEXbSuNOp9M5FYe6+/Lo4+NDUlIS27ZtIzAwkC+//JLbt2872VZms/mhonPluJWyqFKp8PPzIyYmRhblrVy5ko0bN0o9Iy6R3wfjfoVRbjKNRkNcXBxxcXHSABBdeYThqnx/QEAABoOB7Oxs/vmf/5lPPvmE48ePk5eXJ5scTDQEVZZAUlISf/qnfyqLtATZuU6nczKKRKhAo9Ewffp0WUkLDgUmNjQ4vA4bNmxwCrN7gqJS5qn29/dz6dIlPvzwQw4dOuSUwyQUz+zZs8nLy5sUObyPA2VBYm1tLYcPH+bgwYPcunVLHpTgUM4hISHExsZKRevuMKW4/CQnJ5OXl8fx48dlG+aqqiqMRqNUqEpS+1mzZknD1WQy0dHRweXLlzl06BDXr1+nsrISm81GbGysJKUWcFcVtPCg6/V6hoaGKCoqkp5keNBievr06cybN8+J1kxJZeROKL3GAwMDXL58mdraWqKjo1m8eLHM/3scfk+l8dbV1cW1a9eora2VKQQLFixwezc/MX5x4QkJCWH+/PmkpaVRWloqGVjsdjv+/v4EBAQ4UbD19/dLnSpaFg8MDKDVahkcHKS9vZ3o6GiP8dop10vpmOjs7OTMmTPScN22bRt/9md/Jpu62Gw2j+A7/zqIS5dSXz5KPk0mE6dPn+aPf/yjjIr4+vrKM0Sv1xMSEuJ0WfOE/fl1eJzxeVLqg6+vL9OnTyc+Pp6ZM2cSFxfHF198QXV1NXq9Hp1OR3t7uxPnLuB0uRbQ6/VERkaSmprKqlWrWL16NampqQQHBzt1L3wSttuE7mTBB6oMW4lKNQFRxGS1WgkJCUGlUrF06VJaW1tlu8BPPvmEuLg4nnvuOZlbN5Hk06J6TlBlKQ1RJWw2m1Mlnhhfa2srfX19Tu8TSElJ4bXXXuO1115zuo17wm1T6TW9d+8eFy9e5MqVK1LRiJuZVquVxRSrVq16yDiYbBDhb3AYErdv3+bQoUN8/PHH0gCEB7mFVquVmTNnkp6ejlqtlrLpzkNHGdrJzMzk5Zdfxmq1ysYJgrpOpVI5HRTnz58nNTWV8PBwSd1z8+ZNbty4ISl8AgMDWbduHRs3biQ+Pl4aFe5aa2VjD8EJWlJSIsmwhSGUmZnJs88+y6xZsyQNn6fIp9J4bW5u5sCBAxQWFvLUU08xa9Ysabx+nWEgQujC8yPa/NbU1MjXp0+fLivbBdx5wVTKaUxMjGSsEftMo9FInnCz2UxgYCANDQ00NTVJp0FDQwMXL15Er9czd+5cUlNT5cHpaYaPSHEQ7V8//vhj9uzZAzgM17/6q78iOzsbQBbmeaoDQFkcOtYYxdoq6wdu377N+fPnuXXrFvBw8U5cXBzTpk1zyuv2xPkruYiVOkQwDYj27+DYz2fOnCE6OpqoqCi3NyFSOhlmzZrFCy+8QHR0NJWVlQQEBBAcHExzczM3btygvLyclpYWhoaGnDq6iYZD0dHRrFixgmeeeYZly5aRkJDgtJ6PovP7LphQz+vIyAitra10dnbKnI979+45cb4KS1+JoaEhIiIiSE9Pp7CwkPz8fE6fPs3KlSvdYrzCA5ohcFa4AiIMptfrnTwEDQ0NHD9+XFaXArKQRCz8pk2bSEtLAx4stidsWKWR3dfXR01NDX19fU5dUQDmzp3Lj370I7Zu3ep0yILnHR7fBOXaDQ8Pk5+fzx/+8Af2799Pb2+vDEEr+6zrdDoyMzOdPJbuNoqUlGbR0dG88cYbaDQa/vEf/1GG/cWlUdn95uDBg5SWlpKamkpvb+9D+ephYWGsX7+eV155hQ0bNshCC3emuSg9AVVVVZw5c4Y7d+6gVqsJDQ2VxmtMTAwrV67E19dX5lN6wiURnPdJdXU1ly5doqioiMjISCfCb2XxoCuEl06QwB89epRDhw7Ji7Ovry9RUVFMmzbNKZrkTl2jPEj9/f3JycmhuLiYzs5O+vv7GR4epqSkhLa2NioqKrDb7VRWVo7Z8jc7O5tXX33VKefckzxeItomDOtTp07xz//8z3R2dvKjH/2IX/ziF8ydOxfAicrMU6HMbx8dHaWvrw+LxUJgYKDsOijOfbVazZ07dzh8+DD5+fnSgBfGPCApw+bMmYO/v/9DtQaeAuWYNBoNycnJZGdnU1paKll5RPoAOGyAr776isTERDZu3CiNV3d1fxP7TegRwcnb29uLXq/H39+frq4uSkpKOH/+PIcOHZKsQj4+Pk7pIWq1mtjYWHJycpzYIYR+eZIOnAl/UoKgV8ktpkxedhVMq9VKd3c3zc3NkmRcENx6grdA5NYpaSKEMCs3shhfVVUVhw8f5vbt29IbJw7TBQsWsH37dlmEIL7PU+B6gxLrptFo0Gq1Tsarsue2J+Xrflu4GkJ79+7lww8/xG638/zzz5Odnc3hw4e5du2aVDzTp09/KD/NUyBkMSQkhBUrVlBaWkpnZydtbW0PFREEBgZiMpmorKyks7MTi8XiZLimpaWxYcMGtm7dylNPPSUVlLsOGKF89Xo9o6OjlJWVceDAAdkS1pXCzmKxOB0WnrROYr8MDAzQ1NQkcz4F+bmA0ngV81M2TBHf09HRwf79+zl48KBTv/I5c+Ywc+ZM1Gr1E/WKfFco9YRWq2XJkiXYbDYCAwM5evSoDF22t7dTWlqKzWZzcn4osWTJEp5//nkZwVIypLgbShYegLt373LkyBHa29t5++23+R//43/I3PHJoD+VYxRNB4qLi+nt7SU6Opply5aRnZ1NZ2cnt27doqOjg7KyMo4ePUplZaU0XpUOEl9fX8LCwmR9iNBdnnQmgjMtn6+vL2vXrmV0dJQPPvhA8hErG6PAA4+s2G9j0WtOJJQMCEKHKnNSo6OjWb9+PZmZmfT09EjjVZk2oFaraWpq4sKFC6SmphIVFUV4ePi4tYIf950sBE1UlolOL2KxXYsPhPfDYrHIhG4/Pz/q6+spLy9HpVKxePFili5d6pRf5y6lJKixvg4i1xUcSqqwsFAmrIvnExgYSE5ODsuWLSMwMFBWynqS0lJuroCAAFkk4Zr6ERERIfNcRWccT5rHt4FKpaKnp4fy8nJ27tzJzp07UalUbN++nbfeeguz2czx48eBB9XrS5cudaqO9aS5K8cybdo0tm7dik6nkx1+TCYTKpWKgYEBJw+f4PULDQ0lJCSEadOmsWrVKrZu3crixYulZ0F06XKHh115SRweHubatWtcv35dXqpEsZlarSY6OprMzExMJpPch54QFXDlgbRarU4MD4KGT0BpoIrPifdarVY6Ozvp6enh1KlTXL16VXrVFy1axAsvvMCKFSukbvKE+QsIgzwyMpINGzZIuTt58iTNzc0MDg4+xDUJDyjFUlNTycvLkw1fTCYTPj4+bjfOBURaiI+PD42NjXz44YdcuXKF1NRU3n33XWm4igJlT4fSeL116xYfffQRly5dYnh4mIiICCoqKli+fDldXV1cunSJ+vp62tvbqaurk98hWlILGZ01axZJSUlumM13g7BdZsyYwYsvvkhRUZFs8uLn5yc97RqNRnJj9/b2Eh4e7lZWFnAuXFYa0qKGxWKxEBwczLRp08jMzJSf02g0klYLHA6BK1euYLVasVgsbN++nfj4eCce2CeFCbP4RB5afHw8WVlZxMbG0tHRgc1mo7Gxkfr6eqdCLrHg4EgbaGhooLOzE39/f2bMmEFsbKzHkU4/CkIoBwcHaWpqkmE7QYYPjvy7efPmObUO9WQIxTvWoS9yfJSH6mSCMjLQ19fHuXPn+PTTT/n000+x2+288cYb/If/8B9ob29nz5490rMHjmK7LVu2kJycLH/nKQcmOHvy9Xo9S5YsITs7m5deeokTJ05QVVUFIKu8lRC8tbm5ucyZM4fMzEySkpKclK6nGOqCWNtkMjE6OopOp8NsNjM8PExSUhI//elPeemll+TaCJold0PIndJDruR7dj3kvs7orq2t5cCBA1y4cIGqqioaGxsBxzquWbOG9957j+joaHmoeIpXEpzlVKvVsnjxYuLi4li+fDmnT5/m+PHjUlaVsNlszJo1i82bNztFsB6nc95EQsmReerUKT7++GPMZjMvv/yyk+5wdz7k40LITmdnJ1euXOHs2bPSS97X10dbWxsnTpyQvMomk+mhbkyiXTU4Wv4+88wzTowSniSfY0Hk/Ar+byWzjJJ9wWw209jYyNWrV2XR5PTp050cfZ50Zrp2Ng0JCSElJYU7d+7IdCtXwzQ/Px+LxUJfXx8vvPCCrP94kumDEyYNysFmZmayefNm7t69S19fH4cPHyY1NZWXX35Zvk/pVRWdi0wmE/Hx8cyaNYtp06Y5FcN40mILKKmVrFYrd+7cobm52SllwtfXl6ysLF599VVyc3PlZ0XuoCdBmTdZVlZGTU0NAwMDTmET5Q1Mr9ePyQ3nyVAWHVitVgoLC/noo4/YvXs3AMuWLWPt2rUMDAxw9OhRjh07JjsBhYWFkZuby9KlSwkLC3NKJfFEKIvIwsPDiYqKkp6QO3fucOnSJfr6+tDpdPj4+DBt2jQWLVpEVlYWCQkJ8mB1R+OFb4LIr+vp6XHyIIMj9J6amirZPNxJ6fUoiGiNSqUiPj5eMkHcu3ePyspKEhMTCQoKkkT8Op1Ozre/v5+Ojg7Onz/Prl27ZE4zwLx581i+fDnbt2+XqS2e0FHrURC6Q6fTMWvWLBITE0lISGDmzJlUVFTItRV8lH5+fmRkZJCXl8fMmTPdXjzoCnEmjIyM0NbWxtWrV9m5cyd3795l/fr17Nixg8jISEklpCxU8kQoebvNZjPl5eWUl5fLdQkICMBisTgVhSrh5+cn6yVE+hzAihUr2Lp1KxkZGU462dMhzmzXdEhR16JSqfD396enp4ezZ8/S1tYm6dKAcQmvf1soIz/gsEVcOcyXL1/OvXv3uHfvnuTxV+a2mkwmScOYlpYm2TJETvOTmN+E7WhlakBsbCwvv/wy7e3t/OEPf+DKlSt89tlnpKenSzJpcEz0q6++4oMPPuDy5cuoVCoWLVrE4sWLZYW/sirX06C8QY2OjjI4OMjw8LA0alQqFXq9Xua6JiUlyZy179p1YjygJG632WzcvXuXmzdvUllZKYuVxEaNj49n2rRpTsVsnjKPb4v29nYuXLgg0wKSkpJYt24dXV1d7N69mxs3bkjDNSYmhk2bNrFhwwYnDl9Phuu6CMMAHEb6W2+95bR+rrncAp5qnPv4+EgDW5lz5ufnR39/P0NDQ/j7+3vkoai8uIaEhMjLfFVVFfv376e5uZmkpCR5cffx8WFkZISGhgZKS0spKyvj7t27khFCrVYzb948duzYwSuvvCKLCW02m0fxLrvCdW18fX1ZvHgxOTk5Tt4skXsnZNTTciNdPU6tra0cOHCAnTt3Ul5ezlNPPcVbb73F0qVLZf7xZMh1Vcqp1Wqlv7+fnp4emarjyg3qChEZEXtTdGLaunUrWVlZcl09zZEzFpRGn6+vLzExMWRkZDA8PMzw8DANDQ3Y7XaGh4cl3/3IyAhr166V3/Go4kt3w5WDef369dy5c4cLFy44eV2VKYTiQjleDRkm1OoT1YRarZasrCxee+012traOHr0KPv27cPHx4cdO3aQlpaGxWKhsLCQPXv2SONh+fLlvPnmm7ICEzwrJOsKJcemTqdj9uzZJCYmyoNG8NeJvtXwMMmxJ8JmszE8PCyLdwQNT0BAAEuXLmXhwoVOntjJoHgEhCFgtVppa2uT3UbAkZdcVlYmuU7F76Ojo3nmmWd49913Wbx4sdy8nqiEXKFMXVHmYH/T2JWFQp4yT+U4RAOCN998k6eeeorLly9TU1PDnDlzZJtpk8kk+297GpQXC51OJ/Piuru7OXHiBGVlZcyYMUN6rcxmMyMjI/T09NDQ0CDbvc6cOZNVq1Yxf/584uPjJXUUILmpPXH+rhCpSOLy5KkOi0dBGNpi3KWlpezbt48rV66QlZXFz372M5599lmnqvXJAKXs6HQ6YmNjZZMIcBi0AQEBBAQEyOiA0B1DQ0OSchIcF+Zt27aRk5PD7NmzJfewuz2RjwslU4avr6+MwFmtVqqqqjh27BhlZWVOBv3MmTOlw8D1OzwJysthREQEK1asoKWlhYGBAQoLCyWjjpISLCYmhqVLl0rmJHiyaWUTzvOqpEzIzc3lz/7sz4iMjOTgwYMcPHiQ1tZWFi5cSF9fH/n5+dTV1aHRaCT/6TPPPINer5ceP08XatGf2d/fn8jISGbOnCmNuZ6eHsLDw6Ux6O/v/5DL3hPg6kHu6Oigs7PTiSIDHAVAGzZskAbc47Zb9RQo5yl6ajc3N0tlc+vWrYf4CKOjo9m0aRNvvfUWK1eulN/lLtqTbwvXntZiLV37VcODQ8QTPVvgrBg1Gg3z5s1j3rx5dHV1kZiYSHFxMRs3buSFF16QFGeeCuVc9Ho9OTk5VFRUUFpayuDgoAzPjgW9Xk9iYiKpqaksW7aMLVu2sHjxYvm64C32ZI+rK5TyJiqjx/LICf3paUaA2E9CJ1RWVsqc8tWrV0vKJGX3qckAZU6nr68vM2fOJC8vj6KiIioqKlCr1QwNDY3pwPD19ZWFaTNmzODHP/4x77777pi5n560lo+Ca+tf0QXObrdTVlZGeHg4p0+f5tatWwwNDZGXl8eOHTuYN2+e/JySesrToIweJCQksGXLFrRaLSdOnCA/P5+2tjZ0Oh1arZawsDDWrFnDli1bZMrAk7YH3Hq6BgQEsHLlSlJTU1m6dCl79uyhoaGB06dP09/fj9VqJTc3l0WLFpGTk8PixYslfYMnHp6Pg+DgYEJDQ2WlbFBQEDExMXIunugJUVZxm0wm8vPzuXHjhhMfqFarZf78+U4pHZOBm1AJpbIUlw4lNZQSUVFRJCcns3jxYrZv387y5cudvsfTL1VjQen1F54ipSIVdDaeeMFyhY+PDxEREbJi/c0336Snp4dp06ZJ+fTkOSgvPiEhIWzcuBEfHx/Onz9Pa2sr9fX1TpXaqamp+Pv7o9VqmTVrFitXriQnJ4dp06ZJA0FASRY/GSHG/3VpA56YrqTcSwMDA9jtdtLT01mwYIGUSU8c9+NAnA9BQUG88MILzJ8/n/z8fE6cOMGJEyec8lkFpk2bxp/8yZ/w1FNPodfrSUpKcjr7Jis3uICSamrevHlER0ezefNmenp6GB0dJSoqiqSkJMLCwpw+56nzdbVLEhISeOONN1i9ejUfffQRO3fuJCAggE2bNrFkyRLmzJlDSkrKuF3E3Gq82u12AgICSElJISkpicTERK5du0ZnZycjIyNERUWxaNEiWY0HD7wGk0Xxuno4EhMTeeedd7h586ZMn1i5cqVTgZqnGa/grFQFP52fnx8DAwMEBgayePFiNm3aNKlbwbomqc+YMYN169bJNILAwEB8fHwICgoiKSmJ5ORksrKyyM7ORqPRyI5GkyXk5wrlvpos++vroDS2ExISZHhOFA14snwqL1FarZb09HT8/f2ZNWsW3d3dNDU1cfPmTfr7+0lNTWX27NmyuGfatGksXLjQ6VAUF03X4ovJCE9fu7Hgyvvt7+9Pbm4uubm5LFy4UBbNCRaXyQiRRhQVFUVUVBQLFiwgPj6ehIQEqqursVqtkvUDHGkCb7/9thOtoMVikdRwk22NXaHkTtXpdMyYMUNyn092iFz5gIAAwsPDefPNN9HpdAQEBLB582Ynb7LI3X7SRrlbjVflJhXkvkuXLpUJvoLj1bWvsafeTB4FpRdl7ty5zJw5U+YZ6nQ6WZUv4GnKSyl4er2ep59+mq6uLtra2hgYGECtVrNixQrWrl1LUFCQE8vCZIKr8ZqRkUFiYiLvv/++DHkIz45ocazVap3mORlSBX5IGEtfeFpI+eugHGd8fDwxMTGydkBwKIsuWuKw9/HxeSglQNlAwouJh7Jlak9PD4mJibz00kssX76cOXPmAJ7JevFt4Gps6nQ6Nm3axMqVK2WnKSWpvV6vlywaAlNJf04mJ9u3hStFX0ZGBr/4xS9QqVQyV1lgvJ6BWyVFpVLJghGRDztWHpbwaD2qZ/JkwqPm6MkQz9xsNqPT6Zg/fz6dnZ0cOXKEuro6+vr6CA4OloTSIn93skOj0RASEvKQgnWFJ1JFeeGAULCieluj0UzKdRLeC+VFXhmtGev9gjTdU9pL/9AhDDeNRsOCBQtQq9VOnrjJKJdjQXB/6nQ6/Pz8vjFsLBwggpJvKkEZQbFarVitVpmvLZwfk3XOKpVKUoL5+/s7nZPCOTee/Mpuv+aMVTCizHUR/002L97XYaw5TjYkJSWxcOFC8vPzCQ0N9fi0h++KR9G0uNJHeeHZEN7JyQrXA85VhyghPD6T9VCcylCpVAQHB8scVyWmih5xddB8nQ5VqVQEBgZO5PDcAmHDTGYdNBaUa63URxPhoHO78aqE8lYCzkp4Mhp4Y8Fms0kvkDB8JkuekzKkExcXxzvvvENOTg6+vr4sWbJEvjZVNqgIz4o0lrG4JKdCNMCLyQFlkZKSDcI1B3QysLB44cBkIuD/LhBnupBbZdqAaJIyVc72HzLEWQkT183uWxuvBoNhPMbhMZjq84PxmaNrK1F3wruGUwNTfY5TfX4w9ec41ecH3jlOBUzF+XldRl544YUXXnjhhRdeTBqoPJUQ1wsvvPDCCy+88MILL1zh9bx64YUXXnjhhRdeeDFp4DVevfDCCy+88MILL7yYNPAar1544YUXXnjhhRdeTBq4lSrLYDBsAf4CmANEAC3ADeDvjUbjZXeObTxgMBjeAj64/8/3jUbjb905nu8Lg8HwLvDv3/A2m9FonLQ8MAaD4f8GFgGzgUhgGLgLfAH8s9Fo7HLf6J4sDAbDCuA/AkuBcOAeUAL80mg0HnHj0L4XDAbDDuBpIBuYDwQBHxuNxjfdOa4niR+KnE5VGQUwGAwq4EfAT4FMwAeowKFj/8VoNI66cXhPBFNdTn8IusYV7rJr3OZ5vS/Eh4Ac4EvgH4ECYBtw0WAwTKnFNhgMCcA/AQPuHssTRCHwfz7iv1P333PULSN7cvhLIAA4gUNGPwaswH8Fiu+v66SHwWD4W+AcsBLHfvyfwEEgDFjlvpE9Efwt8B9wHChN7h3KuGHKy+kUl1GAPwK/A2YCnwG/AbQ41vOz+8btZMdUl9Mfgq6RcKdd4xbPq8FgiAX+CmgDsoxGY7vitdU4DJ//BnzkjvE9adxXOv8OdAF7ccx90sNoNBbiMGAfgsFgEJ7zX0/UeMYJwUaj0eT6S4PB8N+B/wz8v4FJTaJnMBheAv6/wEngBaPR2O/y+mTvOvGXQCNwB4dX5LR7hzMumNJyOtVl1GAwbAfeAmqBJUajsfP+7zXALuBF4B3gD24a4pPClJZTfhi6BnC/XeMuz2vi/b99VWm4AhiNxtNAPxDljoGNE/4cWIMjJDTo5rGMOwwGw1wgF8fN87Cbh/O9MJaivY9d93+mTtRYxgMGg0EN/N/AEPC6q1EAYDQaRyZ8YE8QRqPxtNForDIajVOWF3Aqy+kPQUaBF+7//J/CcAU5r/9y/59/NuGjesKYynIKPwxdo4Bb7Rp35bxWARZgicFgiFRuVoPBsBJHnsgXbhrbE4XBYMgA/g74R6PReM5gMKxx95gmAD+7//N3UyFP6xF47v7PYreO4vtjKY4w5edA9/089LmACbg2FXPPf2CYCnL6Q5DR2Ps/a8Z4Tfwux2AwhBqNxp6JGdKEYirI6Q8GnmDXuMV4NRqN9wwGw/8G/D1w22AwfIHD9TwL2IojH+Znj/6GyQGDweALfAjU4wiJTHkYDAY98CZgAyZ1QZoSBoPhr4BAIARHwcFyHIr279w5rieAxfd/tuHIOZ+nfNFgMJwDdhiNxo6JHpgX3x5TVE5/CDIqHDgzx3gtWfH/6cCV8R/O+GKKyukPAp5i17itYMtoNP4SR6jEF3gf+BvgJaAB+INrOsEkxf8BLADeNRqNw+4ezAThZSAUOGo0GhvcPJYnib8C/j84Kp2X4ygY2TDJD0yA6Ps/fw7ogXU4Ih9zgWM4imN2u2doXnwHTEU5/SHI6KH7P39hMBjCxS/vGwr/p+J9YRM6qvHDVJTTHwo8wq5xJ9vAX+MIA/0Bh8c1AFiII0TyscFg+P+5a2xPAgaDYQmOW8n/nCJhrcfFT+///De3juIJw2g0xhqNRhWO8N4LOLwhNw0GQ457R/a9IWjMVDi8V18ZjcYBo9FYCjyPo/jgaYPBkOe2EXrx2JiicvpDkNFPcTCzzMIRjfy1wWD4JY6C2GdwpNoBTIk0rCkqp1MenmTXuMV4NRgMq3Ak4B8wGo2/MBqNNUajcchoNBbgUEZNwP/LYDAkf933eCoUbvVKHiTbT3kYDIY5OPLTGoFJzbn4KBiNxjaj0bgP2ICDm/iDb/iIp6P7/s8ao9FYpHzh/q362P1/LpnQUXnxvTDF5HTKy6jRaLThSJn7K6AVB/PAj3Ho0uU40uoApkJEUmKKyemUhqfZNe4q2Hr2/s+HaCSMRuOQwWC4hsOIXcDYCeyejkAcJMwAJoNhTOaP3xgMht/gSHj+jxM1sHHGD6FQCwCj0XjXYDDcBrJdiw4nGSru/+x5xOvCcNCP/1C8eNKYInL6g5BRo9FoxcFd+z+Vv79fR5CNg9C/dOJHNv6YInI61eFRdo27jFfd/Z+PosMSv7dMwFjGA2YcZNNjIQeHUX4Bh1KeEikFBoPBD4e3wMaj5z7VMO3+z8lsqJ/DQRKeajAYtEaj0XXPzb3/s25CR+XFk8Rkl9Mfuoy+BfgBf5wClGBfh8kup1MdHmXXuMt4PY+jC8VPDQbDvxmNRtmJwmAwbAaW4aBBueSm8X0v3A9l/WSs1wwGw3/Fsch/nOztYV3wEo5igkNTpVDLYDCkAz1Go7HV5fdqHITp0cAlo9HYPdbnJwOMRmOnwWD4DHgDRyL+34rXDAbDemAj0IujoMILD8RUl9MfiowaDIZgo9HY5/K7xTgq8AdwNO6ZtJjqcjrV4Wl2jbuM189xdEpZB5QZDIZ9OPJ8MnCkFKiAv5nsfY5/YBCFWpO9o5YSm4D/5z4VTzWOvLMYHJ1TknHI7PvuG94Twy+Ap4D//T7P8jUcjUSex+EFeX8yc0ve7160/f4/BZ9mnsFg+MP9/+80Go2TuevdD0FOp7SM3scJg8EwDNzC0agnE0exlhlHV7HJmEKnxJSX0x+ArvEYuIvn1WYwGJ4B/hR4FYcC8gfu4Sj0+V9Go/G4O8bmxbfHfcLi5Uy9Qq2TOIzxZcB8HBRggzgS1j/EIaf33Da6JwSj0dhuMBiewuHReh5Hd7R+HN3R/i+j0TjZeSWzcbTWVCKZB/yZd5ncLZunvJz+AGQUHE6dV3HwZOuBZhxc2X9nNBrr3DiuJ4UpL6dMfV3jMVDZ7T+ELmZeeOGFF1544YUXXkwFuI3n1QsvvPDCCy+88MILL74tvMarF1544YUXXnjhhReTBl7j1QsvvPDCCy+88MKLSQOv8eqFF1544YUXXnjhxaSB13j1wgsvvPDCCy+88GLSwGu8euGFF1544YUXXngxafDYPK8Gg2HSc2oZjUbVo16b6vODqT/HqTA/mPpz9Mrp1J4fTP05ToX5wdSfo1dOp+78vJ5XL7zwwgsvvPDCCy8mDb51hy2j0Tge4xhXGAyGx37vVJ8fTOwcRRMM8VOlUqFSfe1leExM9TWEqT9HT5bTJwXvGjpjqs9xMs4Ppv4cvXLqjKk4P7e0h/ViasNisWCz2fDz85OGqqvBOjo6is1mQ6PRuGOIXijQ19dHcXExN2/eRK/X8/TTT5OamgqAzWZDrfYGaLyYXLDb7VgsFkZHR9Fqtfj6eo86LyYONpsNm83m9O/R0VHUarXUp2q1Gh8fH3cNccIg9qFGo3mi+3DCd/To6CjgWLjv4oHzwjNht9ux2+2o1Wq0Wi3g2LBDQ0OYTCasVisqlQqtVou/vz86nU5uXLvd7pWFCYRQpOLiUF1dze9//3t27dpFTEwMo6OjREVFERoaKtdlqq2RzWaT0QClLhJy/F0jBF54BlQqFTqdTv57qsmvF54FZWQRcDJSv+lz4rPi81NFTsWeE/bAk8aEGq82mw2TyQSAn5/fD+LW8UOB3W5nZGQEjUYjN+3t27c5efIkhYWFtLa2olarSUtLY/369axZswY/Pz/AcTNTHjRejC+UyrG3t5eLFy9y8uRJBgcHuXfvHhUVFdTW1pKdnY1KpXIy6KYKRkdHGRkZQaVSOXkEhMfEx8dnSs33hw4RQfCuqRfjAXEZ/raeRavVitVqlbLp4+MzZeyisc4MVyP/+2DCjFehPAICAuTvrFYr8GCSkzG0Y7fbsdlsqFSqbxVeFcI+2RWqcu46nY7R0VFaWlooLi5m7969HD16lIaGBvn+L7/8krt372KxWFi9ejUhISEea7gKD6WIFrhCKJrJFFa3Wq34+vqi0Wjo7Ozk0KFDHD58GI1Gw/r161m0aBGzZ8+e1DL5dRgdHcXHxweNRuOUsiLkWHl4iENlMq3v10FcQlwvI3a7Xcq4uKyIZzFZ9dPg4CC1tbX09/eTmJjItGnTAM9Lg/kmHSN062TTM1Md4twfHR3F19fXKYposVgYGRlheHhY6pW+vj4GBgYICgoiODgYm82GXq8nODj4odQ58d2T0YhV6ha1Wo3VaqW+vp6enh6mTZtGbGws8GT24YRZi2MpQOEin8xenW9rtCo/N1VCBMo5VFRU8Pnnn7Nr1y5u376NTqdj+vTp+Pr60t3dTV9fH0ePHqWnp4fu7m5efPFFgoODAaTn1lOgXFvXw168NtkOFIvFIi+JJSUl/OpXv+Lu3bu88MILvPPOO2RmZqJSqaTShakVynrUPMb6/WQ13B4F5VyUa6sMXbq+Z7LMX7k/R0dHqays5N/+7d8oKirilVde4cc//rE0Gjxpz6rV6oeKWpWYrHpmqkPsDVcDU6VSSedNXV0ddrsdHx8fampquHv3LomJiSQlJWGxWEhOTmbDhg0EBgaO+d2TEcIhJ/6/vLycnTt3Ultby2uvvcbWrVsBx1n/fZ1W4268Wq1WGYJrampi7969WK1Wtm3bRnJy8kPvnyyGrM1mw2KxOBUliRDA10EIszIPRBgUk0lBiWR0Meby8nKKioq4evUqO3fupK2tjaioKN566y0yMzPx9/enu7ubEydOcPz4cS5evEhISAixsbGsWLGCwMBAt3verVYro6OjMgogPB7fdAMWXpPJcFMWz9hqtXL58mUKCgpIT09nx44dPPXUU24e3fhApAjodDrUajVDQ0Ncv36dkpIStFotixcvJjs7G4CysjI6OjqYOXMmCQkJAAwPD6PVaj1ifYW3ZyxDZywoPSFarXbMg/G7XsCfNOx2O1arVXqdvo0+UHqq+vr6uHnzJpcuXaKkpASr1Up0dDRbt26VhoKIQLgLIyMj2O12KVePI1uT8ZxwhVhfJYSnEZyNQnefB2NBjFV5oRgdHeXatWuEhoYye/ZsqqurMRqNlJeXo9fr8fX1xWw2YzabuXnzptyT8+bN486dOwQFBTE0NER8fDxLlixh5syZ8rs97bL1TVCOtbi4mI8++ohDhw4RHR392DrrcTFhxitAW1sb//7v/05NTQ0mk4l3332XkJAQLBYLKpWKgIAAjxTYsaBWq2XOptLgedzxi7QBV0N2MkEcgk1NTRw+fJhdu3ZRXFyMyWQiJCSE//gf/yM///nPCQ8PBxzPKSUlBYvFwuHDhzl16hSxsbFERkayaNEiJy+QOzas6/opQ3pjXaqEgvUEo+abIA5rrVaL2Wzm9OnTXLhwgcjISDZs2MCiRYsAMJlMMqw+VaA0Dvr7+7l48SJ/+MMf+OKLL9DpdLz77ru88sorABw7doyKigry8vJ4/fXXiYqKQqfTecyF+vt64cYyFMYyiN2Rfyfyj78LRDoIQEdHB0VFRQwPDwOQn5/P7t27yc3NlcbryMiIW88aMU+lwa6E0hOuVqvl3p2MUM5lspzvj4LYFyaTiaGhIXx9fSksLOTzzz8nPDyc1atXc+zYMY4dOwaAVqvFYrEQExNDSkoK9+7do6WlheDgYAYHBzlx4gR37tyhq6uLRYsWMTw8jE6nk2kukw2CUaG9vZ1jx47xySefMDAwwOrVq0lKSpLvexLn+7hLkrLwYdq0aezYsYPdu3fz8ccf09zcTFpaGu3t7YSFhfHaa6/JnAiLxeKxm1UZ3m5paaGwsJCIiAhycnIee3MWFhbS3t7OvHnzmD59OjC5bllinE1NTXz55ZccP36ca9euAZCRkcFPfvITXnnlFWm4gkMWli9fTm1tLTdu3KC1tZWamhr6+/ulcehafekuDA0NUVZWxtmzZykoKGBoaEheVkwmE4GBgWRnZ7N27VqysrLk2K1Wq0cafsoISH9/PwcOHKCwsJAFCxawbt06p9QNwCPn8G3h6hHv6+tj9+7d7N69m5s3b0pvyGeffUZ1dTX+/v7U1tZy9+5dioqKaGtr4/3332fmzJmA56W1fFt0dnZSU1NDV1cXZrOZgIAAAgMDaWpq4tq1a/T19REQEMDo6CjBwcEsX76cxYsXExYW5u6hfyOUxl9fXx9VVVV0dXVJ46Gzs1PKNowdop8ICKNZnIklJSWcOHGC8vJyzGazPD9MJhO+vr4EBgaSkZHBxo0bmT17NuBsqHs6rFYrFosFAL1e/60ugSMjI1itVo+JeggHgM1mo6qqiq+++ori4mKpMwIDAzl37hzV1dWo1WqysrKIj4+nubmZ4OBg5s+fT0hICHq9nvj4eCIiIigrK+Ojjz6irq6O5uZmqZcWLFgg7Ql3OnS+DUZHR+nu7qalpYWioiIuXLhAW1sbkZGRREZGEhQUJN87KQq2hLIfHR0lNjaWv/mbvyE8PJz/9t/+Gzt37iQ+Pp66ujoyMzNZv369NF6F0HoShIGl0WiwWq1UVlZy+PBh8vPzmT59Onfv3iUuLg61Wu1ExQMPPK06nY7Ozk7OnTtHXV0d8+fPZ+PGjaSlpcnF9eTUCZvN5pSvUlpayp49ezh58iQAeXl5/OQnP+H5558nLCxMcr4K406v17NixQpefvllCgsLyc3NJTY29pF8sBMxH3A888HBQTo6OmhoaKCmpobi4mKOHTtGeXn5mJ8Vm7O/v5+cnBz8/f091rgR8qdSqTCZTNy+fZu2tjaSkpKkcQY4KZjJCmXxlc1mo6enh6GhIfLz89m5cyenTp0CIDQ0FHBEhA4fPkxSUhLBwcH09/fT0dHBxx9/THh4OG+88QYxMTFuz88XbC0DAwPYbDaZR97W1oZOpyM2NhY/Pz9ZgNXW1kZ7e7tkAqmpqaG8vJy2tjb6+vrQaDQEBgZSXV1NQUEBAIGBgVitVoKCgmhrayMoKIhFixZNiFxbrVb6+/vlRXGsYpZHQWm82u12BgYG6O7uBiAqKoo5c+bg7+8v3+MuY0jMp6enh5KSEo4ePcrnn39OVVXVIz+TlpZGW1sbW7duJTMzk4CAgCdatT0eUBZhKx06AwMDmM1mpzQPi8WC2WwGkJGswMBAAgMD5fNyTVNzB0ZHR2lubub27ducO3eOU6dOcfXq1YfeFxsby3vvvceqVasIDg6mra0Ni8XCtGnTSE1NZebMmej1egCWLVuGxWKR63njxg1u3LhBR0cHKSkphIeHOxV3exqU6+Lj40NHRwdnzpzh3LlzXL9+HavVir+//7h43CfMhy88P2q1msWLF5OTk8O1a9e4ffs2VquVgYGBiRrKd4bJZJJCd/fuXf7Lf/kvHDx4ED8/P0JDQ9m/fz8BAQGoVKqvrSAdHR3FbDbT3t7OgQMHKCoq4j/9p//EkiVLAOf8LU+DEFJRRXjt2jUuX76MzWYjOTmZv/iLv+CFF15Ao9HISkzXkFhKSgp//dd/zeDgIEFBQYSFhbmtOEilUmE2m2lpaeHatWucOnWK/Px8WlpaMJvNXyuXtbW1fPzxxzQ1NfHGG2+wceNGqWDcnVP3dRAeR71e/9CN2NMPxceB8uIrCgQLCgqorKyktLRUvi88PBx/f396enrw9fVl8eLFxMXFYbFYKC8vp7W1ld/97nf09PTw/vvvk5iYCLjPA9vd3U1BQQE3b96kp6cHPz8/mpqauH37NoGBgcydO5egoCCZj19RUcHt27cZHh5Go9FIw9dqtUruZVcIebfb7VRVVVFbW8v8+fPHZb6uRVa1tbWcPn2a6upq5s+fz7p162Su3LeRR6XBpNFomDt3LgsXLnQqjHGnfhW1H3v37qWqqoqOjg6n1zUaDf7+/vT29gJQWVnJL3/5S4qKivjP//k/k5eXJy+hIhrkaRhrvWpqarh+/Tq1tbVS/4gQc2trK76+vpIDfOHChWzevNnJ6+8uyj4REbXb7dy4cYN/+qd/4vLly/j4+ODn5yfpPwHi4uJ4++23eeONN5g1a5a8OAoarcDAQKfxBwcHk5OTQ1FREaWlpRQUFDA8PEx8fDzd3d1OkUtPhGsE4+7du1y4cIHLly/T2toKQGRkJAkJCU77b1KkDYwFvV7P9OnTCQkJkRtXp9M5bURXg8cToFT2nZ2dHDx4kJGREUmL8U3FWo9CUVERPT098t+eEDYfC2azGZ1Oh6+vL319fRw/fpyDBw/S09NDaGgoL7zwAuvWrZOGq9lsxt/f30lQ7XY7Go1Gpkq4C8K4VKlU9Pb2cvLkSQ4cOMCFCxec1kLknLnKo1BaTU1NfPHFF2i1WqKjo8nJyfFIz7kyVCk8AVFRUcyaNYuoqCjgmz3+IjdSCU+shhaG6/DwMKdPn2bnzp3k5+djMpmw2+2kpaWRmZlJeHg4FRUVACQlJbF+/XpmzZrF0NAQ/f39NDU1UV5ezpEjR9i2bZs0XpUNHsYTdrud4eFhGW6trKzk888/5+LFi5IbubW1lc7OTsBRNGmz2eju7v5afaTRaIiJiZH5vP7+/mi1WlklDQ55SUhIICYmZtwuYcoUm/7+fs6cOcNvf/tbWlpasNvtLF26VD6Hb9pPSvlTzl2lUhEREUFMTIxTJG8i96fysnPr1i327dvH3r17KSwsBBzn4bJlywgKCsLf35/MzExiY2O5c+cOJSUl3Lhxg+7ubq5cuUJDQwN5eXmAe72Qj4PBwUGam5upr6+nrq6OiooKrl+/TmlpKUNDQ/KZiIuUoFo0m83MmTOHsrIynn76aVJSUpgxY4bbmtqI89hqtdLY2MjZs2edzgM/Pz/S09NZsGABS5YsYdWqVaSnpz/y+0wmE8PDw9Iw9/f3l1EGkafd3NzMvXv3mDVrlsedJUqI6JbJZKKiooITJ05w+fJlmpqa8PX1ZebMmaxdu5ZFixY5XUQmlfGqNMhGR0cZHByUoQJ4uFLfExdM+cD9/PyYOXMmlZWVgEMB9ff3P/Z36fV6Kajh4eEelyIxFpRrUlxczIEDB7h27RohISH86Ec/4s0335ShWHEr/brvcCeUntGqqioOHDjA6dOnGRwclO/5uhxck8kk8+kGBwc5f/48KSkphISEkJKSAniGF9O1y0lfXx83btygqamJGTNmkJWVhVarfSgdZCwII9W1sMdToFxTk8nE0aNH+eijj+SFRK1WM2vWLLZu3coLL7yAyWRi165ddHR0kJWVRUZGBnPnzqWjo4OmpiaampoARyOH27dvk56eTlBQ0ITtVZVKhb+/P6Ojo9TV1XHs2DGOHDlCY2OjLOIR+YQA9fX1TvIaGhpKeno6kZGRMuolvETz5s1j0aJFhISEEBgYiNlsZv/+/TISlpKSwrp161i4cKGUjydtLCkvAYODg9y8eZP8/HzsdjttbW20tbUxffp0p+KmR8mbcmw9PT1Ounh0dBSr1eo2p4DIW2xsbGTPnj386le/oq2tDXCs8fz583n11VdJT08nICCAGTNmEB0dLb1Yfn5+HDlyBIvFwo0bN1iwYAGpqalSDj3lsizSdcRYqqur2bVrFwcOHKCiokI2AHF1BAQEBJCUlIRGo6G7u5vW1lZKS0upqKjg5MmTvPTSS+zYsYMZM2YAyIjeREEZFQwMDCQuLk7qBp1OR3p6Oj/96U/Zvn07UVFR37hPXHN4/fz8iIiIICoqiqioKDo6OjCbzTQ2NjJnzhwnbnxPgsi9VqvV3Lp1i507d3LkyBHJ6x4XF8czzzzDtm3bmDdvHvBk0z8mTAKUg/Xx8SE4OFjSTNntdoaGhuQBAcjwvCdB6W2Ji4vjxz/+Mfv27aOurg6bzUZGRgbZ2dmMjIxw69Yt6urqGBkZkYaBj4+PrPbVarXSeFVWYXpKwZISQkjFmE+cOMEnn3zC9evXAZg/fz4bNmxg/vz5gKPYydXjqoSy77iobJ9oL4IwrJuamjh16hTXrl1jcHAQPz8/dDod/f392Gw2pk+fTlpaGjNmzGDmzJlERkZy+/ZtvvrqK1pbW/Hx8WF4eJiWlhby8/NZtGiRNF7FRnVniNJ1DMXFxZw4cYK2tjbmz59PTEzMQ+9VQoS94IFHc6yD0vXgcgdErqePjw/FxcV8+umnHD9+nKGhIcBxSC5fvpwXX3yRJUuW0N/fj6+vLytXriQ6Opq0tDRCQ0NZtmwZw8PDhISEUFJSgtls5pNPPsFiscgDaryLKJQhYYvFwr59+/j4449pbGyU77FYLGg0GgICAhgeHsbX15esrCzmzp1LWFgY8fHxpKamyja/Ip3AbrcTGxtLfHw8fn5+0rgaHBykq6sLgA0bNrBu3Tp5GR0PnaSUFbVajV6vJywsjHv37lFcXMzFixeZMWMGcXFxwOMbLcPDw06OEXBuBzxREB5XQVK/f/9+vvjiC2m4pqSksGrVKtavX09ubi5RUVFOhk1KSgqhoaEEBwfT29vL+fPn+fWvf42Pjw/vvvuuLODylDQzoSt0Oh3t7e2cOHFCcnwKpKenk5eXR2xsrKQmjI2NJSEhQUYaent7uX79OkePHuXKlSuMjo6i1WrZsmULiYmJE56OpWywlJqayurVq2lqaiI9PZ24uDgSEhJYs2aNlFNAXpaEZ1LoCmHsKfVGQkICTz31FJ2dnTQ2NsrUicLCQlJSUqThpyy69QSI8Yh6gv3791NTUyNft9vtxMXFjVuUdcKkQLm5oqKiWLRoEeXl5TJRvbu7m0OHDhEYGMiqVavQarUeV2WnNF4jIiLYsWMH0dHRnD9/XhbtrF69mpGREU6dOiXbot65c4e+vj6n77JarahUjv7bqamphISEAJ5JUKzMNSovL+fDDz/k888/B2DBggU888wzTmGSbyIfFvN2B5QhvLa2Nnbt2sUXX3wh01fMZjMqlYqoqCiSk5PJzc0lNzdXeuVUKhXt7e1ERUXxhz/8gbq6OsBxsJaXl1NSUiIrtD1Bdl27tdTV1XHr1i3AIcNf50V09drabDZZbCFkVHRWc+c8hdEt1rWgoIBPPvmEc+fOSU96WFgYubm5bNu2jQULFqBSqQgKCmL58uUPfV98fDxbt24lKSmJs2fP8vnnn3Ps2DF6enoktZjIbR8viGc+ODjIoUOH+Oyzz7hz5w4ajYa8vDySk5Pp7e3lxo0b1NfXExgYyJo1a9i6dStLly5l+vTpBAQEfKNRIy4mKpWKBQsWyBC74OAWB/d4GEdKmdFoNMTFxclcv9raWsrKyujv738s41V81/DwMENDQ07zio2NZcaMGU46ZyLkVWks37p1i/3791NYWIharSY6OpqFCxfy9ttvs2LFCsCZRUB41IW81dXVUV5eTkdHB6dPn2bJkiXEx8d/rZNgIiEMNR8fHwYGBrh8+TIHDx6UhmtiYiJpaWls2rSJLVu2kJCQIB1XSq50gZqaGrKysvj444+5ceMGw8PDjI6O8tZbb8k8UHfknqemprJlyxbsdjtLliwhKSnJiZNV6HylnD5qfcTeiomJITc3l66uLvr7+2lsbMRisVBbWysvk8r3u9s+EOPQ6XSYzWauX7/O6dOnnQxXcESUxYVM2djnSV0i3WK8RkZGsmzZMvLz87lw4QIjIyN0dXVx8OBBdDqdFHRPg7LDko+PDzNnziQ0NJTc3FxGRkYICwsjNjYWm81GUlISbW1t5Ofn88c//lHmpYlcyZGREWbMmMHWrVt56aWXnCq+PeEWDQ+Ur9iId+7c4auvvnIqelmyZAnbt28nKSnJqcrbU6HMs2tra+PAgQPcvHkTQOa2xsfH8+yzz7J161bS0tLw8/NzSrSPjo7mjTfekF1UwPGsamtrOXfuHJmZmSxdulTST7k7rKdUFoODg9hsNsLDw0lKSpJr63ppcqX9stls3L59m1u3btHf349Wq0WtVhMSEkJqaiozZsxwW3hLOT+z2cyBAwf44x//KKvNAbKzs/nzP/9zVqxYIXOyx5JT8fvw8HAWL16M2WymqKiIW7ducfXqVb744gsSExPJyckBnrw3RNmStr29nX/+53/m97//vQxTpqSk8Oqrr/Lqq6/S3t7Of/2v/5X6+nrmzp3LX/7lX7JkyRInDupvgjhMVCoV0dHRREZGuuViqUzREbL4bZ6pMBD6+vro6emRlxa1Wk1aWhrz5s1Dq9U6eUPHC+JwF4d2aWkpx48fl/nVNpuN+fPn88wzzzB37lz5OSUHr3JOgYGBvPXWW0RHR7N//37AUVg3MDCAv7+/fHbgvjQekYMNjnzNw4cPS+rErKwstm3bxurVq5kzZ45TtOdRSE5OxmAwMDg4yK1bt7h16xZFRUW8+OKLTrzhE2G8utou69evR61WP0Qhp8yzfpx1UHaiioyMZPPmzdjtdv74xz/S0tLC7Nmznfaxpzi2RHMmcKTofPbZZ5w4ccLpPSkpKWzZsoW8vDwZqRKXzyc1hwn1v4uCD41GQ1paGgsXLuTChQvU1dXR399PZ2cnRUVF1NTUkJiY6FHk4Eooc2AiIiKIiIhwet1qtZKQkEBCQgL+/v4cOHBAvubn5yfpo/z9/Vm0aBG5ublOtxNPmbPITxIK4saNG3zxxRfyNj137lxyc3PlRUMc5J5svCoPB6vVyt27d+W/g4KCCA8P57nnnuO9995jzpw58jUR0rJYLDKv9eWXX6arq4v8/Hx5WBYUFHD06FHi4+PlweQp3K+jo6M0NTVhNptlBbZIz1ESxAuiafHv6upqzp07x+nTp6moqGBoaIiAgADCwsLQ6XRERESwfv16nnvuOUnjM1FeZ3GRtNvtdHR0cPLkSfbv3+9kuPr5+bF06VI2bNiAWq3GYrFIGR0ZGXkofUXsw4CAAEnh19zczNWrV2W1tNJ4fZLeEHGZ6O/v5/Tp0+zatUsarvHx8cTHx0uGjrCwMF588UV8fX15+umnWbVqlRzT8PCwTBUQxrDy8i28Q8q9qlx/cdBMlFdPVGSL8ZhMJvr6+mTKx+NCXCSU6TLR0dGSJmsivHXKBjSjo6NcvXqVU6dOSfYAjUbDggULWLt2raQTdKWUEp53kW4WFRXFm2++SUZGBnfv3iU+Pt5tBWhjQYy9p6eHU6dOcebMGYaHh0lJSeH555/n5ZdflvpURHCUew2Qe9NsNhMeHk5wcDChoaHExsYSFRVFXl6eEzPKRJ0zYnwigiXOe5EOKPbSd5WrkZERtFotcXFxJCcn09PTQ0NDg6TW9DQoL5lVVVVcunSJ7u5uGaWLjo7m+eef57XXXiMjIwMYH/mcUOPV1TDLzMxk2bJlDAwMyAT7oaEhqqurmTt3rmzP6An5dGPhUeMRKQFC0JUbThwkNpuNwcFBBgcHZUMGT5uf0qNlMpkoLy+XtFgpKSm89NJL5Obmyvd4KjXUo+Dr6+vkLZw5cyZr1qzh3XffdTJc4UGqg06nkxv36aefpqenh7a2NskF29HRwaVLl1i5cqU0Xj0lh7m/v587d+7Q3t7O+vXrmTNnjtMBKIxA5YFSVFTERx99xOeff05dXR0hISGyQl2v19Pa2srx48dpbm4mPj6e5cuXT3glt9g7Z8+e5Ze//KWsmAcICQlhw4YNrFy5UhpiSoPM1YATYxdGTnx8PBs3bpR0PuKSLfCk1lbpmQBHlOPixYtOBYRWq5WOjg727t3LnTt3yMzMJDIykp/+9KdERETQ399PUFCQNISEJ0gYrkoP3ddB+f7xhPL7xd/T6XSyg1Ftba3TJeTrxu7akUppqCubE0yEbCqL22w2G3fv3pUpEGq1mpSUFLKyspya0zxqbq4GUU5ODvPmzXsoNO0uiCJJHx8fent7OXDgALt27aK2tpagoCBWrFjBCy+8IPWpeL/gI4YHazI6OkpAQAABAQEMDQ3JC3NkZCR/+qd/yvvvvw88YL2ZaIeA60VOXPC/615R8saDozC0tLSUzs5OqYc9iXVJjFev1zMyMkJFRQVfffWVTG2w2+3ExMSwdu1aXnjhBZmrqyzsepKYcOlXHhTx8fFkZmZy4cIF+buuri7Onz/PnDlzpPE60dWF3waiCELcJIOCgpxc/ZGRkcyePZvg4GD6+vpkARcgbyoqlUp+x1g5iGMpNvG78fSMiLUaHR2lpKREUvGAIwy7adMm0tLSZGtJT10jJZQKr7293UkeFyxYwI4dO+Sms1gskmQZnD3u4vNlZWWSWkv0sL5z5w4XLlwgLS2NjIwMt1cFC1mx2WyS4N7f318W8giIg0Ws45UrV/jVr37F/v37sdvtrF+/nkWLFhEUFIROpyMyMpJ79+5x5swZWlpa+PjjjwGkATsRBQbKudXW1pKfn4/NZiMgIACbzcaCBQt4/fXXWbJkifSSKuX0UQeP8nczZ85k+fLlXLhwgfz8fGpraxkcHCQgIOCJel2VezkwMJCoqCinsba2ttLa2kphYSH79u2ThR7p6enyYpWRkcG8efMIDQ2VefQCj9OZaSKjP8PDw/JiL6jAysvLpbHp7+//UNj0URBezqqqKiorK2XOqKBeepzveFJQFt3a7Xb6+vrkhSc2NtbJcIWHL1DCmBVOG8HLC46iQ6FPlLR17sqFVDJ89Pf3c+nSJUkllZuby5YtW6Th6ho6dh2vsmXv7du3+fWvf82xY8dYtGgRCxculO9zt7NH2bnv+5y/YvyDg4OUl5fz1Vdf8fnnn9PS0kJ8fDwzZsx44tyo3wfiMiyaNB06dIiPP/7Y6TI/a9Ys1qxZ4+T8Ga+OcBPueVUiKCiI1NRUFi5cSF9fH21tbTQ2NnL+/HmWLVvG8uXLPZ5CSuSXKZWsKPoROVYi9DEwMMDg4CB2u53Q0FBWrFhBbm6uk/fPU+arFLi2tjYZLgUHE0RycrLkoFOr1U7eDU+Ea/FRfX09t27dkqE8vV4vefrA4WnW6XRO6yGMdI1GQ39/P59++im//vWvJReneAZ9fX2cPHmS2NhYoqOjiY6OBtyXPuBa1S2KlUTKgPAUKS+J+fn5GI1Gdu3ahZ+fH2+//TY//vGPycrKekgRPffcc/z3//7f2b9/P3FxcbIISuRGjechoyzUAcf+MZlMqFQq8vLyePnll3n66adl1fyjGoe4QjwHsQ9mz55NQkIC58+fp62tjYGBgSdatCUOc2GwpKamsm7dOi5fvizzqkNDQzGZTHJMbW1t7NmzRxpKUVFRPPXUUyxevJjU1FTmzZtHenq6nMvjHCATaRAoxyPqA8R+zM7OJi8vzyk/cqyxCcYFlUrF0NAQN2/epLi4WBqvnhCxU6Y+xMTEkJGRIbmVAaf0Dngg0+L5CE+lKzwhPUvpWLFardy7d0/K8NKlS1m1apWkdNNoNGOGwZUePXDo5iNHjnDq1CmsVutDjQDGO2f5m/B9n7trEW1NTQ27d++WERVwGIGLFy+WHUefxN/9vhBnPTyof1F2htPr9cyZM4dFixYRGBj40Jn7pOFWV5nI/YmJiSEtLY1f/epXNDQ00NLSwu3btykqKmL+/Plu91x9HVz5D1tbWykqKmJ4eJjw8HA6Ozu5du0a7e3t8v3gKHTasWOH0w3FUwxXcFZKQ0NDtLa2yoNFkGl70ni/CcoQlc1mo6Wlhbt370qlqNVqCQwMdMqFFIpW9NgWhlh1dTV79+5l37590mhS5nMLqrRLly7x/PPPu9V4VR7ePT099PX1odPpmDFjhjTohoeHCQgIkNWjN27c4Le//S2HDx9m5syZvP766zz//PNOxSVKzJo1i5SUFPr6+pyqYyciXUKsV09Pj5PnJiAggFdeeYU333wTf39/qTu+7QEgvMchISGEhYWh1+vR6/XSIzKeYb3MzEx+/vOfs3z5culZtVqt8uDr7++nqKiIEydO0NvbS0dHB4cOHaK4uJhp06bx1FNPsXbtWknpI8YsaHzccZFSplTpdDqGhobo6uri9OnTsiNPaGgoS5YsYcWKFYSEhEiPnc1mk/sNkLy1IjoimksMDg7KS8qTLBB5XLjKvfLCJFryKqkglbRK35fZwd1npJhrQEAACQkJMj+0r6+PwMBA/Pz8pAdP1FRYrVYpm/39/Xz22Wf88Y9/pLe3Fz8/P6Kjo52eibs9kF8HpdddmaajHLPr+hQXF3P8+HFpuIIj3WnmzJlS/t1JhybmpExp2rNnj2St0Wg0+Pn5MX36dLKyskhLS0OlUkkH0HjJo1uNV5FMHx0djVarZdeuXZLgtqKiggsXLjBt2jQZYvHE9AG1Ws3w8DANDQ0UFhZSWFjInTt3MJvNBAQE0N/fz/Xr1526NoFj7l1dXXz11VfSwFAWVigVoDInSlSBC0MpMjJyQuYoCsrAEc5ThjPA/QUD3wUq1QN+XYvFQn19PR0dHURFRTndHDUajTzo6+vr2bVrF//+7/9OdXU1Op1OFiCAY12FB1aZIuIuKJVmR0cH3d3daDQaaVADsgALHJR1X3zxBSdOnCAmJoaf/vSnvPXWW0RERMhGIsJLOzg4KOUvKiqKmJgYbDYbfX19BAcHj6vhoCxEGhoaoqKigrq6Oulx8/f3Z/HixTLlo6+v76Ew+tdBrL24wDQ2NtLe3o5WqyUiIkJ+r2uO8PeFsgI5MDCQ5557jq1bt4753tHRUcrKysjLy+PUqVNUVVXR29tLfX29jCzU1tbKA2Xx4sXMnj3brTpUyWddVlbG9evXKSkpobCwkKGhIdRqtWTrWLZsmWTscC1oElB68qKiooiIiHCiCDObzd+58+F3hassCFkBhxzW1dXR3NxMYmKik4wJjIyMYDab5U9BTyeMW2Eg6vV6OVdRcOhOPayci6uBGRERIcc21jqOjIxw7949vvrqK3bv3i1pl/Ly8lizZg1JSUnyve72QAqISxUgL2Sunn7XtCxAXsT0ej0DAwNUVlZSUVHh1MlRGH7gHo5iJZT8veAoSj548CDd3d0EBgYSFBREcnIya9asYenSpXLO4305dqslqLyRugp0Y2OjTHJXvt9TjFdlRWlfXx+///3v+fWvf01PT49UuPCgm5grLl++THl5uRPLgKjuVQq88OgJ4RWVwODwgD711FPjspmVY/Dz8yMoKEgqJFH1/CiaJU+EcoxqtZpp06YxY8YMucGGh4e5ceMGZ8+eZdOmTZIaS2mY1NXVcfDgQQ4cOMCdO3eclJeACFlPnz6dzMxMt/dTV857YGCAkZGRh9ZKqRibmpq4evUqgYGB/PznP+eVV16R3hPhFRJGqXIvRkVFERcXR09PD+Xl5eTk5EhlNx7eIMGIIOZVWFgoqYjE3/w+uY5KT8fQ0BB37tzh7t27mM1mRkdHMZlMsjf7eMj+4+wpHx8fWdj69NNPU1FRQWVlJWfPnuXMmTMMDAxw4sQJLly4QHR0NJs3b+bVV1+VqTEwfvlornCVgdLSUv74xz/y+eef09HRIZ+paK4QHh7uJD8DAwNOha9jYSwGBdcL5EQYAUoSerVaTWpqKunp6VRWVnL37l3OnTtHeHg4ZrOZuLg4YmNjZRRE8IK3tbXR1dVFXV0ddXV1mEwm/P39pcddpVIxb948cnJyCA0NJTw8nNmzZztxMrvTQykK7trb24mOjv5GWS4oKGDfvn0cO3ZM7mNBQ7lt2zZiY2MZHR39TtGT8YI4Hx7HK6p0Oil1Z1tbGy0tLU4RBXjAgqL8O+6CsqisoaGBkpISGhoaZI1OeHg469ev57333iM+Ph6YmMYZbrUElUplYGDAaQEHBwfp7e11an/oKVXbrhAh58jISHnLFvQ28ODQF4ILjjCnqzf2cSE6fSQnJ5OUlCS91U8SymctqtBFS9TExEQSExOd0gaU1bXiZqn8Hlfl5XrIjDfE3xfJ/gkJCaSkpDjNs6GhgfPnz8tCGHAUZdXX11NRUcGNGze4fPkyNTU1BAQESM+ImI8Ig2m1WjIzM8nJyXG6yEz0YaIsvhH0SUIpKveVCGGaTCbOnj3L7du3eeqpp3jllVeIiYnBbDZLMnExB41G4+RREpW3AwMDtLe3YzKZnELrT3qtXT3abW1ttLe3S+/G0NCQTHOBb8+EoVwrf39/kpOTiYuL4/r163R1dTEwMIBerx/3S9ujvPfid3q9npCQEBYuXEhGRgYtLS3MmzeP2bNnc+XKFSorK7l37x737t2ThndnZyc5OTnExsZOKN2QzWajra2NW7ducerUKQ4dOiTzecUlwNfXV0akmpqaiI+Px2w209bWRlBQEAkJCfj6+mIymVCr1TKtQ6/X097eztWrV2lqapLRj8TERMkLKsYx3lAW8vj6+rJmzRra29v54x//SGNjI5WVlRw7doyioiLCw8PJycnhqaeeoqenh/Pnz1NeXs7AwADd3d1UV1fLBiquOHXqFFlZWcTGxrJ48WICAwOlh9IdxuvIyIg80ywWCxcvXiQwMJDc3FwZ+he6QLBhdHZ2UlpaytmzZ2XXQnA4TJKSkpgzZ47M+xQXVnc3RBGy5cp4YLFYMJlMTo09xD51LeQGh46qqqp6aH21Wi0pKSkPRTfdASUvcl1dHR9++CHHjx+XTsX+/n6io6PJycmRhqugvRvvtEK3Gq9KReLaIlTwpn1drog7ofSMhIWF8dZbb5GXl0dDQwM3btzg3LlzVFZWYjKZnDx0yu5EjwOdTkdMTIzk1AwNDWXhwoWsXbuWrKwsQkND+Yu/+IsnPj+lUdfb28vdu3fp6upCq9USFhb2ULGK0qv5OF003LWWVqtVbqq4uDgnhdLR0cG1a9cIDw/HYrEQGhpKWVkZR48e5dSpU9TX1wPIXu9Kr6tI+xCV7hkZGWRmZjoZeBPtMVA+Yx8fH0lvNTo66kRBJMZYVVVFQUEBnZ2dsu2o+J6x1lP5LMXcx+pdPt6wWCySdk6MMy4u7hs9dV8H8exENfX06dOJiYmRB5Iy/D2e+DahN39/f2bNmiVbGs+fP58TJ06Qn59PY2MjtbW1fPDBB1RWVvLuu++yfft2dDqdvFSPh1GgrEY3mUx89dVXfPDBB1y5ckV6EH19faVB0NXVxblz57h48SIhISGEhobKVBSdTkdYWJgTDZiASqVidHSUe/fu0d3djcViQa/Xk5mZ6dS2cyL0jniO4qI8d+5ctm7dSkFBAY2NjZhMJkpLS7l69SrgYPUoLi6mvb2ds2fP0tPTI3XM16U8DAwMcO7cOcDhDFm2bJmT8ToRUD5PX19foqKiCAgIYHBwkDNnzlBUVER2djapqamSAUTkO3d3d1NYWEh5ebnT3gUH28b8+fNJTEyUv/OE1qiis9RYl+eqqiqam5sxm80y1Ux0Bps1axY5OTlO54HgCBcXOHAYuatXr2b9+vUytcudkU1RaAdw9+5ddu7cKWkhweEdnz9/vmSGAmS76fGGW41XV+9GSkoKZWVlwAOL31NCBK5wFSbhjRwaGiIzM5Pp06fzxRdfcO3aNSfCdteE7tDQUCnos2fPlgbP8PAwNpuNqKgoEhISiI6OlqwG8fHxTjlA4wGl8FVXV5Ofny9zGc1mMwMDA07ezJGRERlGHev5eAqUMhceHs6SJUuorKzEZrNJAv/Dhw9TVFREYGCgpMMShivwkNdS6WkGR0VxdnY2mZmZMgfWXfloykKymTNnkpaWRm1trcwzzMjIkOHZwMBAoqOj0el0tLS0UFhYyPLly2WRjSgW0mg0D6VMKHt2TwRtj3Ida2trqaqqkhGInJwcXnrpJac98m3zr5Seq9raWg4fPszZs2cJCAhg9uzZsrvOk855/bYQBo4oqPDx8cHPz4/MzExJy3Ts2DEOHjxIUVERPT09HD9+XHKNzp49e1zHp6RLGx4e5uLFi07deFx5WAX1IDhSeYQnTkDZVOSboNfrCQ0NdTIYJhJKmUhPT2fbtm309PRQUlKCr68vcXFxjIyMMDw8LL2tfn5+xMTEyM+mpKSwYMECAgMDpYEn9p/JZCI/P18ywSjTZCbKeFV616Kionj77bfx9/dn//79tLS00N3dzenTp7l06ZKMQgl5VUYeExMTCQoKoq6ujoGBAUJDQ8nJySEpKUmmtogIoLvg2lChsbGRK1euUFVVxb1792hqaqKmpobBwUF5+RD0mDNmzGDGjBmYTCaSkpLYtm0bfX19nDp1Snas1Gq15OTk8NZbb7FhwwYnikZ32UFKGVYW5Op0OmbOnEleXh7PPfccycnJWCwWmbozEfrQrcarMpQXFhbGM888Q29vL1evXqWtrY2Ojg6n6mVPNYjgQacdf39/srOzZQ6luF2LsLKoXA4ICCA8PJxp06ah1WoJCAjgqaeeYsWKFQQFBck82ZCQEKKiopyqU4GHPH/jhZGREaqrq2VlIThumdXV1cybN4/AwEC0Wq1TrlV/fz99fX0MDg4+5DkQz8i1ecNEQWnEhIeH8/zzzzM0NMTJkyfp6emhq6uLzs5Orl+/PubnhSIJDw+Xeb9VVVXSkxkeHk5WVhZz586VCsedRVtKT2hSUhKzZ8/m6NGjXLhwgdzcXNkBxW63ExcXR0ZGBn5+fnR1dcnDZazD31WhChL/yMhIedESGA+Pnvjb/f393L1718l7sWTJEjZs2OBER/RdjVer1cq1a9f4l3/5F8rLy5k5cyapqanyfe5OZRLFlEqIvObIyEiWL19OQECArMovLS3FZrNx/fp1zp8/T3h4uKSjGo/8V1edLTrw2O12yRAwMjJCSEgIMTExDA0NMTAwQFJSEmFhYdTW1sq1VdYHCLkWHn+r1crIyIhkBoEHBTXu2n9KmfP392fDhg2MjIxw7tw56awROazKlrK+vr4MDAxgsVjIyspi2bJlhIWF0dPTg81mIzAwkNHRUe7cuYPJZOLmzZtYLBYnQ3+ijB2xt0dGRggICGDt2rWEh4fj6+vLiRMnaGtrk+vQ09MjozV+fn6EhIQQHBzM8uXLycnJob6+ns8++4yBgQHCw8OZN2+e9OgJD747bQBR52IymaipqeH06dPs37+fS5cuOV0cBJTON/GZvr4+pk+fLtf35s2b0jkQGhpKRkYGeXl50tA3mUyP3ep5PCAcG/fu3aO5uZmAgAA6OjrQ6/Vs3LiR119/nYULF8p5TqQ+dDtVFiB7rW/fvp3BwUEqKytl4npdXR2LFi1yyrebDBhrkwnFk5GRwdq1a1m4cCERERHSoIuOjiYiIgJfX18nIuRHYaI8PhaLReYjWywWGv//7P13dJtXeqiLPyAJEuy9iWKnRFISRVKNqlaX1UbuGtkeZzwzyWSCJHMmmZN7z++0m5yz7rqZk5PJ5GSCmXiKW8ZFtixLliVZkkVVqzdShWKvYqfYiY7fH9De+gBRzSKJjzCetbRAAR+AvbH39+53v/stzc3U19czNDTk4pczODjIjRs3OHPmjMsRGdzdqFgsFtLT03nmmWdYs2YN0dHRE2q5UiqUERERrF69muDgYKZMmcKVK1eor69/oHUnKCiItWvX8vLLL5OcnMyJEyd4++23pfI6ffp0li5d6nJU6ckgQ6Uw0Wg0xMfHo9FoqKio4OrVqy6BgEpXAX9/fxd/3dEQSm1ra6ss8yzql/v7+0uH/vEcW2UAo0D0SXnvPOocUwZPOhwOmpubuX79ulQMxLGoQG0yabQiA/n5+bzyyiukpaXx2WefcezYMS5dusShQ4eYM2eOVF6FlXQsUR71BgcHs3btWlJTU9HpdERFRUmLvUg/JhRQ4TIwMDBAeXk5lZWVBAUFkZSUhJ+fH729vbLMr6hideXKFWpqaqQ/nlqMHWJTkJqaygsvvMCyZcvQarVERUXh5+cnMyIIv3GRN1vIKBEwGRsbKyvKgTPISSg/JpOJM2fOkJOTw9y5c6V1VnnqN564F3v5i7/4C4qLi6mvr0ej0dDf3091dTVDQ0MkJSUxY8YM4uPjiYyMZNasWSQkJHDgwAH2799PR0cHISEh0odSBDt5So4qfVjLysrYvn07R48e5datW9KXXLBy5UqZjzkpKYmcnBwiIiIICQmRpbNv3rzJ/v37uXTpknyvso9qkCnKPg8NDXHu3DmXMugRERGUlJTI4E9PbC48HrovooKDg4NJSkpi7ty5JCUl0dbWRmdnJyaTSRUWrIehFNIi36AysbI4cg0KCiIvL4+NGzcyf/78UT9LGeDiHgAlLF7+/v4TkmdVq9WSmprKzJkzKSsrw2azSQdzsehVVlZSVlZGc3MzV65c4ciRIy6WMHdOnDghF6ilS5dKJWiigww0Gmey/qeeeoqEhAQqKyu5ceOG7IsIyNJqtaSkpMia8mvXruWFF16Qn3Hw4EGZoy83N5d58+YRExMzqhI10bj/nmlpaeTm5nLu3DkuX75MeXk58+fPlwtDcHAwCQkJ9PX1cfDgQYKCgmSaG3GcK5SIkJAQqqqq+NWvfsWhQ4eYPXs2K1eulBua8TxSF7+tTqe7xwdNKJ5i7B5HqIrPEe4eFy5c4Pjx4xiNRlJTU1m4cKGLRVcNfnhKlJHMIrrZ399fnpJ0dXVx/vx5bt++zZUrVygvLyc3N5fw8PDxqYKjUDiCg4N56qmnKCkpQavVEhER8Uj3+5w5c7hx44b0PQ4ICJBKg1arlRUAAW7duiWVV09b6gTKYgPJyckuG9tHQcRJCHkk5H5ra6s8cjaZTFRVVVFTU0NxcbE8Yp8omSpSeFksFnQ6Hbm5uSQnJ9Pa2irz+dbW1jI8PCxPeGJiYlzaFhUV5bKmibHz9OmGKLYCzvm1fft2mZxfp9PJTA8rV65k8eLFhIeHY7FYSEpKYurUqTKThODDDz/kD3/4g1RcIyMj6evrkxbqiS5nPBoiJ7lG4ywyUFtby+XLl+VJeHJystxcgNNCPJZFWx4Fjyuv7oECsbGx5Ofnc/nyZRn4o5b0WA9CuUgL5VLZL7Eoiv4pK2e44x4I5e4APRETxN1fa9myZXL3LGovgzPI6de//jWfffYZ3d3dGI1GaaUNCwuTPnUtLS20t7fLzzx69CgJCQnk5uZKp3xlLrnxRjk2Op2OgoICCgoK6Ovro6qqioaGBvr6+hgcHCQyMpJ58+aRmZkpfT8FCQkJMg+xRqMhISFBWpaUCdk9hXt07uzZs9m4cSONjY1cuHCBt99+G39/f+bMmQPArFmz2Lx5Mzt37uTtt9+murqaBQsWEBQURHd3N/7+/qSkpFBQUEBMTAyffvopb731FlFRUfz5n/85GzZskN81ngmqxQZPWPCUVmKRIxMef+OgtFo2Nzdz4sQJSktLAefG5Omnn5ZzWu2ySemDLBZFZdq9W7ducf36derq6pgxY4ZLENp4pf96mDV/NHQ6HbNnzwbujqfIw6zc5F+/fv2eDBjKGIPJijLtlrvcCg0Npbe3VyoZ7icdE6lMuG/kIiIiXFIOZmdnyzETp67KuWY2m10MQC0tLeTk5HhchirLpLtnQNLpdMyfP5+//uu/Zt26ddjtdpmjdTQf3X379vH2229z69Yt+ZyowCZ0ITVYXpXj2NvbK0tT2+12EhMTmTlzpkvebE/EdKhC8ip9gxITE1m1ahWXLl2iqamJixcvsnjxYvLy8lSTw2403CO772f+z8vLY+XKlaSmproknxYRzO7WAvH3RE8MZdvj4uLIysqSFrWRkRFOnTrFz3/+c1pbW/nkk0/kUXtcXBwLFy5k2rRpZGZmkpOTQ0hICOfOnWPHjh20tLQwPDxMe3s7J06c4LXXXpPKqycs68pKPODcXMTGxpKXl8fQ0BAmk0kGMikRJWH9/f2l765QZsS1apin7lHzWVlZbNiwgQsXLrBnzx62b99OeHg4UVFRZGVlMW3aNH70ox8RExPD8ePHpa9aZGQkQUFB0gJRVlZGXV0d586do6CggM2bN7N582bCwsKkcj+eip3yd50+fTovvvgiVquVsrIy9uzZQ2xsLNOmTZO+4mK87ofwmxSf29DQwN69e118n1NTUykoKCAkJEQqgWpWXoUrCDgVg+PHj/PVV1/JOT84OIjVaiU8PFxukMc7slkok8IqeL8E7MocmsoiIcp0g0qFRvhRqnk8AFnoQ2m0cQ/iVWb3EIYQ8bfY3N++fZvW1laXlGmpqakuwV6esNqJDYOodqcM4HU3TAwNDWG32wkPD78nL/PAwAC1tbVykzyasjveCPktqiZeuHCBs2fPuqxTWVlZbNq0iRUrVgD35ho2m80MDAzQ2dnJ+fPneeeddzh06BDgnLMi2Flc29fXR39/v/z8iR5DMe/E711fX8/Bgwc5deqUTP+ZmZnJggULpDsLeKY6qCrudOUOOSEhgfnz55OXl0dFRQXHjx9n0aJFZGZmEhwcLE3rnlYK3FHeVPcTyhqNhqlTp8ogGbFTVWOZVfdMEGlpaS7HpaWlpRw/flz6qEVERDB79myefvpp1q9fLy05yrRUZ86ccSnJarFYXHaxnhC27n6Roh1RUVH3HPcIYSbS8IBzMRLHlKGhoSQmJrr4cqvh2BJcj94KCgrYsGEDjY2N3Lhxg08++QSz2czmzZspKioiJyeH7373uyxYsICOjg5psRPj1draSm1tLdevXyclJYW//Mu/ZNWqVfL4fiIUCGWAQFZWFq+++io2m036y//7v/87Tz/9NCtXrgR4YHCj8AsUEcKNjY0cOHCAjz/+mLKyMgICApg9ezYLFiyQ1ga1jOtoKJVQ8fe1a9f44osvOH36tFwsw8PDyc7OJjMzE3AunuN98qFUjr+ONU057kqZKwK2lKciarS63q9S2MNQ+mKbTCauXbtGZWWlVHSEe1dSUpJHlVfxvY+ypul0OheZq7Q6Go1G2tvb6e3tlXl6J3osRVuGhoa4cuUKH3/8MQcOHJBuc1FRUSxfvpyVK1fKIEKh7AocDgc1NTXs27ePzz//nLKyMgIDA8nMzCQpKQmr1Up/fz9NTU309vbKUz9RNt4TucGVJxrNzc2cP3/eJT5itPnlifK1qlBewVXQhIaGSguPKF6gNiH0dRDpQVpbW8nIyJiQlEJfF3EzAvLYfOXKlTQ1NXHz5k0Xf14/Pz+Kiop45ZVXeO655+6xUorPE5VyxGfOnTvXRSH2dPJpYU1TWjsEVqtVBogouXXrlixluHr1aoqLi+VravG5A1craEhICM899xyhoaF88MEHfPXVV/zqV7/ixIkTrFixgoULF5KcnCwDalpbW6mqqqK8vJyamhosFgtxcXFSCV6xYoVUeiZ6Yymq7oky0tHR0bS2ttLR0cEHH3wgy8SGhYXJnIs6nU7Ob2EJE9bzkZERDh8+zN69e7ly5QojIyPk5OTwwx/+kOeee05aXSdaUD+qxUkocMIK2d7ezvHjx9m9ezdnz56VLj1JSUls3LjRpdqW2gwCSoQsUhZDUSrC7qnbxHNqVGC/DsrxN5lM9PT0SFkqmDJlivSnFXPU0/JH/PbKsbifG4D7eIqUWhONmEtCZp48eZI333yTU6dO0djYKDOzPPPMM2zZsoW0tDQsFosss62UtRUVFXz44Yfs2LFDpltcsGCBzAs/PDxMeXm5LMnd0tJCd3e3xwLU3N04c3JymDNnDseOHaO1tRWAmzdvsm/fPvLz85kyZQowPsGeD0M1yqsSs9ksLXIidYi30NzczNWrV0lISPBY/sGvw9SpU3n22Wfp6+vjnXfeobW1VaapEUq4RqNhcHCQgIAA+vr6GBoaws/Pj/b2dr744guuXr0qk/ivW7eOZ599Vk5+8Hxg02iLt9Li4T4PW1pauHDhAg0NDfLYTlnNR03KgFgURM7Z5ORkXnrpJcLDw4mIiOD48eOUlZXR1tbG2bNniYqKIiwsjOjoaPz9/amurubq1at0dXWRlJREcXEx27ZtY/78+QQFBcnPHe961vfDbrcTGRlJfn4+g4ODDA0NsXPnThlclp6efo/bgNL3Dpxjfe7cOT7//HNOnDghK3TNnj2bDRs2yE3Zw1wQxgPlqY77Ii9OeTQajcsYNDU1UVpaKjcoyjLVy5Yt43vf+x4zZ86UC4+nxu5RcL+X3GVFQEAAQUFBLhY/EdSqvNbTytzXRemCNDw8THNzs0sayfj4eKZMmSLnpSc2WKPxMCuwUjlVBiaLkxBP9ME9u8FXX33FBx984HJNXl4er776KkuXLr3nvcPDw9y+fZubN2+yfft23nrrLUwmE0lJScyaNYuVK1eydetWKU9mzZpFUFCQrBoninZ4IvOA0v9Yq9XKIHplWsuenh4uXbrkUsFQpED7RgVsPQwRpe8Nu2dwVqvq6upiaGhIKq+eTHL+IJRWDj8/P/Lz89m8eTNXrlyR/lYirVdZWRkmk4nTp08THh7O8PAwJpOJkZER6uvrqa6ulnlD586dy5YtW1i5cqX0d/K0U/5oKK0GwlKnPFK5evUqp06dYnBwEIvFwtWrV6murmbWrFnAxFshHwWlb2NISAgbNmwgKyuLNWvWcO7cOaqrq2ltbeXChQsMDg6Snp4uI4PXr19PSkoKhYWFzJ49W0Z+i9/EE/NYfF9gYCD5+fksX76czs5OysvL6ezs5MyZMxw/fpyoqCiXAAN3qqurOXfuHAcPHuTMmTP09vbi5+dHSUkJa9eulUUJwLOpz9xx35QIOjs7+eyzz/jggw8oLy93ySU6Z84c1q1bJyv+CBmrtvvvQbjPtaCgIGJiYlz88NwV/cmM0hLY398vZQ04j9/z8vJcDAGTYb0cza1OKK+i5Loyx6nSF3giEQFVgPR/DQwMdHErM5lM9Pb2ykpbFy5c4MKFCxw+fBir1UpycjI/+tGPXE61BNHR0Tz77LPExsbS3t5OSkqKdE1T+kVP5DxW3jdGo9HFvQ+4Z6PoiXFRjxTG1VlY/DDh4eGEhYWp2iowGsIi4s7IyIhMUKy8FtRrFVAepxcWFvLqq68SGBjIjRs3ZA3u27dvc/r0aU6fPn3fz8nLy2Pp0qUsX76c5cuXyxtYrQun+3i4K6+dnZ10dHTIZPZXrlzh5s2bUtCo8chSqeyImvBFRUXk5uZSUlJCVVUVtbW11NbW0tPTQ2xsLDk5OaSnp5Oamkp2draLgiCsdp5S0pXfm5mZyfLly2loaKChoYH+/n4qKyvZsWMHXV1dTJ8+nYiICGlVHh4e5tatW7S1tVFbWyvn7+3bt0lKSmLt2rWsW7eO+fPnu3zPRMsim83G8PAwWq1WLuZiYyQ2fcLiJkr83rhxg5MnT3L+/HnpKuDn58eiRYt47bXXePrpp+XmeaLKOY4lSsuzyPIxc+ZMCgoKaGpqoqurC6PRKPPBCtScbvFBKMfHZDLR3d0tc0sL//zJdJIH9yqjOp1O3luigpPIyjPReUSV97sIPq6rq5P+4u3t7Vy+fJmAgABpZa2traWzs5OhoSF6enro7e0lJyeHuLg4Nm7cyHPPPUdWVhbgnIfiJCQ0NJTY2FiefvppmdHFXcZ4MmBbq9WSlZXF9evXZf8tFgt1dXXyFEq4Yk0kqlJeReeVicdNJpOMyp9M3G/HHxgYKBNRK69VM8pIz/DwcF588UVycnI4cuQIVVVVNDc3c/HiRbq6ukZ9f1RUFCUlJWzatIm1a9cyffp0GaQHnnUXeBzcxykoKMhld9zf309bWxvt7e0ymbraLK8Cd+EYHBxMQUEBM2fOlEq6MvDnfmVfPW2FVAYlBQUFMWPGDL71rW9hMpnYt28fPT097Nq1i9OnTxMdHU1cXBx5eXlkZWXR3NzMyZMnuXXrFn5+fgwPD9Pf309QUBAbN27kL//yL5k1a5ZHxlB57Ds4OMjly5cJCQmhuLhYLuLKvoPT//rNN9/kD3/4g3TrERHc4eHhzJw5k1deeYVt27ZJK7QnSxePBSIfpVarZebMmRQWFnLx4kU6Ojqw2+0EBwePmjt0suGezUap6ClzgE8m3DdMIksBONeM3NxcwNk/cYw+EbifApaUlLB161a2b99OXV0dAG1tbbz33ntcvHiRwcFBTp06RXNzM2lpaRQWFlJcXExsbCxJSUlkZWWRnp4uS8Erx0n5GwQHB7vkTlUL06ZN4/nnn8disVBaWsrQ0BB9fX3U1NRQVVU1IWn2RkOVyqsySW9FRQUXL15k9erVLhU31IZ7eivhbyX8QsXgZmRkMHPmTBmopFaXgdEQR1c6nY6SkhISEhLo6Oigr69PRoYCss8Oh0MmI09LSyMnJ8flaEstfllfF+Fnp/RHvHXrFg0NDcTGxk6K0wLlkbHy34MwmUxybNUwfsr7JyYmhsWLF6PRaLDZbOzZs4fBwUGZpxDg3LlzZGRkyPyFSlJSUigpKeGZZ56hqKhoIrvhgjIAoq+vjy+//BKj0YjFYqGoqAiNRoPRaJT11BsbG7l+/TrHjx+XgSHg/G0WLlzIihUrmDdvHvPnz5eKqxpSuT0pyrEX2RPCw8Mxm81kZWWxceNGsrOz5TWT4Z58GCJlllCE3JXZyYTSNauxsVGmXJwyZcrXygk8Frgrr/n5+Wzbto3g4GCuXLmC3W7n2rVr7Nu3j5aWFmJjY6mpqSEmJoZvf/vbrFixQuafnjJlijwtEYFgykA6sQEVJylquR+VWT1iYmLYsmULfn5+9PX1UVZWJkugJycnExAQ4BL7MlGoSnkVE1mn07kcTXZ0dMijL7XinirLbDZjNBrlrlikcBFFCgICAqTioMZUWaMhFHDhS5aVlSWPQR4V4U4hcuFOZkTFGyX9/f10dnYyMjLikdyEj4tyDNxzb4p2C6EkHieqkMTjIlw6EhISWLRoESaTifDwcCoqKmhtbWVwcBC73c7w8DAVFRX4+fkRERFBYGAgoaGhZGVlUVJSwuLFi5k7d678XE/4Tioj541GI1evXuXKlSt0dXWxbNkyYmNjMRqNVFZWcu7cOc6ePeuiiAcHBxMaGsrMmTN58cUXeeGFF2Qkunvk/mRG2QebzUZaWhoLFixgaGiI1atXs3nzZqKjo6WbzGTts9JgY7FYGBkZkWvKyMgIXV1dDAwMjJrpZTIwMDBATU2NzKCQkpLiouB5YtyUboxFRUWEhYWxfv16hoaG2LNnD19++SUzZ86URpm8vDx+8pOfEBISIq3IgYGBUpaKYCx3RECwGhHp8yIiIli5ciXd3d0UFRVRUFDAunXrpBFOBGxNJKr5xZT+gcnJycyePZuFCxdis9mYO3eui4OzWhUBgcPhwGw2uzh6K1EmtVejFflhPMkuy10xmmyMFh0rBI9QcCZrgKEynY1ybopHT+yuHwdlu+Li4li5ciVz585lcHCQyspKmpqaAGfi7ePHjwMwb9480tPTSUhIIC8vj+zsbCIiImQOSk/5oysX66CgIKKjo+np6WHHjh2cPHmSyMhI/P396evro7Oz8x6XnenTp7NhwwbWrl1LQUGBS0q6yTg374e7+1V6ejrf//73eeaZZ0hKSpJGEDWkjXoS3Nvu5+dHcHCw9K28cuUKdXV10so8GZR0pSxpbW2Vm6/w8HDi4uJcFDpPjJ37d4rjf6vVSlpaGs8//zwRERGEh4djNBqJjIyUfsfKXLdis/goa57a4l/cC0i98MILDA8PExER4bJR8oTyrRrlVTlYYWFhLFmyhLCwMAIDA2WVDXGdGo4q3XGPfBXFCK5fv47RaJTHCNOmTfP4jvJJUVpgRTCXUOSUwUpwV6ETqabUOHaPg/sxZWRkpMx5K1KshIeHu1gn1SKIHoaajq2+Dsrf2c/Pj/j4eKm0zZ49m7a2NgICAmhra2P27NlYLBZmzJjB1KlTiYuLc8lGoPT39QTKRSM6OprNmzczODjIoUOHqKysvOf6sLAwYmNjiY6OlhVwNm/eLDNfiHy2omylN+Ln50dISIgs3wuuJz2TGaVykJCQwLJly2hvb5dVjybjhlnIGpPJRHNzsyz2kpKSQlxcnGp8lZWWU1GEICcnh5ycnHuuNZvN8j4T/Xucuae2tUL0QQTMKd3+xPOeOtFQjfLq3vkZM2aQk5MjJ4tyAqhxgRUKm3ADKCgo4Fvf+hYajYauri4SEhLIzc1l0aJFLuX+JpvAUSKUUofD8cCI5Yfl+pssuG82UlNTmT17Njdv3qSiogJwuoXEx8ej0+m86nh2sqPRaOSxeWxsrKyzLvJIu89NT89VISPsdjsRERFs3LiRyMhIEhMTOXv2LFVVVfT09AB09lhKAAEAAElEQVTOGvI5OTnMmzePtWvXMm/ePGJiYlyizyfzkfmToFZjx+Mi1gqbzUZCQgJbt25leHiYhoYGhoeHWbt27WO7cHmS0fLWtra2otVqyc/PJycnR27gPG3keRxZoNVq7wmo9Abudw958kRDNcqrQAz2aEnhJ9NRs1arZeHChcTExDAwMEBkZCRTpkwhNjbWpd7zZOnPaHiLUvqouAvQ9PR0Nm/ezNSpU6moqMDf35/FixeTkpICfHN+F7UifM9tNpuMSBcWFPe0QjabTfoQumcD8SRCHgYFBbFkyRJiY2NZs2aNzGphsViIjIwkLi6OrKwsiouLCQsLk+83mUzSAPBNmI/C11BYiiZLPMGjIuZDREQEGzZsIDQ0FI1GQ1FREampqfI6tczf+6FU6sxmM11dXTQ0NODn58fixYtZvHgxAQEBmM1mjxUrUKJMzQbIsujCGivkinuifm+550Q/RJyO8N/1ZP9Up7w+6MdQ+0Rw3+VHRkYyf/58D7bIx3gSFRXFggULXEpsKlH7fPV2/Pz8XJKcw72LkPj/o2RZ8ATKqN+goCAKCwspLCx84HuUfVNrcN14IXwNvU1pBWfflEGg06ZNY9q0aaNeq8a5fD+0Wi0xMTHk5OSg0+lYsmSJ7KdQXtWCkBfeOscehpoCy9TTEjdE1DNMPl88pY+MDx8+1IXw1Ya7skXNG43RUuw8CLPZLBUdNffLx9dDrI2jKRKT5XRSuTaGhoayZMkSmQd1xowZ8rVvooLo49F4bOVVr9ePRztUg7f3D7y/j97eP/D10Rvw9v6B9/fR2/sHE9/HvXv3Tuj3gfePozf2z2ca9OHDhw8fPnz48DFp0HhLNJwPHz58+PDhw4cP78dnefXhw4cPHz58+PAxafAprz58+PDhw4cPHz4mDT7l1YcPHz58+PDhw8ekwWOpsvR6/YvAcqAIKATCgT8YDIbveKpNY4ler38dePMhl9kNBsPkScj3APR6/TLgJ8BiIAboAcqBXxgMhokPHx0j9Hq9Bvge8ENgJuAP3MQ5tv9qMBhsHmzemOKtYwig1+unAv8DWA/EAq3Ap8DfGQyG2x5s2pig1+vrgfT7vNxuMBiSJrA5E4Jer38NeOfOf//EYDD81pPteVK8XdZ8A/oXCzwHbAIKgBTAjFOGvgm8aTAY7J5r4digFt3Nk5bX/wr8Bc4foMWD7RgvLgN/d59/h+9cs88jLRtj9Hr9fwWOAU8B+4F/BD4DooEVnmvZmPA28DsgE/gQ+A0QCPwz8OEdgTzp8eYx1Ov12cAFnAvnWeCfgFrgPwCn7iw63kAfo8ub/+3JRo0Her0+FfgXYNDTbRlDvF3WeHv/XsLZpxLgDPALYAcwC/gtsN0L+ggq0d08WaTgr4BmoBqnFl/qwbaMOQaD4TJOBfYe9Hr9qTt/vjFR7Rkv9Hr9S8D/BA4BzxsMhgG317UeadgYoNfrnwVeA+qABQaDoevO81pgO/AC8F3gLQ81cUzw5jG8gwFIAH5sMBj+RTyp1+t/jlMO/b/AjzzUtrGk12Aw/K2nGzHe3FEA3gS6gU+A/+jZFj053i5rvL1/d6gEtgCfKy2ser3+P+PcNL8API9ToZ3MqEJ385jl1WAwlBoMhiqDwfCNytWl1+tnAQtx7lg+93Bzngi9Xu8H/AwYBl5xV3oADAaDZcIbNnY8f+fxH4WwBdmn/3bnv3854a0aQ7x9DPV6fRawDqgH/tXt5f8HGAJe0+v1oRPcNB9fnx8Dq3Ba0oc83Jaxwttljbf3D4PBcNhgMHzm7hpgMBjagF/f+e+KCW/YGKMW3U215WG9mD+98/i7ye7jg9M3MhP4GLit1+s34TwiMQJnDQbDqQe9eRIg/ARrR3lNPDdHr9dHGQyG3olp0pjj7WO46s7jgVEWlQG9Xn8Sp3K7EPhyohs3xgTp9frvAGk4lboy4JgXyBmJXq/PB/4e+GeDwXBMr9eveth7JgneLmu8vX8PQxgArB5thRfhU14nEL1eHwx8B7Dj9IGZ7My/89gOXMTppC7R6/XHgBcNBkPnRDdsjBAWgsxRXstS/J0HnB7/5owL3j6GuXceK+/zehVO5XU6k195TQLedXuuTq/Xf89gMBz1RIPGEr1eH4Czf43Af/Zwc8Yab5c13t6/+3Jn3v7Rnf/u92RbvAlfqqyJZSsQBewzGAxNHm7LWJBw5/FHQDCwBmfk4SzgC5zBPx95pmljwp47j3+t1+tjxJN3hNHfKa6LntBWjS3ePoaRdx777vO6eD5q/JsyrrwJrMapwIbi3IT8G5AB7NPr9YWea9qY8d+BYuB1g8Ew4unGjDHeLmu8vX8P4u9xytO9BoPhC083xlvwWV4nlh/eefw3j7Zi7BBpvjQ4rXNX7vz/ml6vfw6ntWu5Xq9fNEmPnz/AaSnfAFzX6/W7cfqGrgGycVrtpgGT+VjW28fwYYjo30nte28wGP7O7amrwI/0ev0g8FPgb3Gm8ZmU6PX6BTitrf/opfPQ22WNt/dvVPR6/Y9x3n8VOAPWfIwRPsvrBKHX62fg9C9sBiZ1zkwFIj9mrULpAeCOZUTsMhdMaKvGiDs+kltwRjO34RQ+38c5hktxRjsDdHikgWODV48hdy2rkfd5PcLtOm9DBIo85dFWPAEKd4FK7gb3eBXeLmu8vX+jodfr/xxnGrDrwEqDwdDj4SZ5FT7L68ThTYFagpt3Hnvv87pQjILHvynjg8FgsOLMefqPyufv+C8XASPAtYlv2Zjh7WMo+jf9Pq9Pu/N4P5/YyY5QBiZzNoUw7o6fUa/Xj3bNb/R6/W9wBnL9ZKIaNpZ4u6zx9v4p0ev1P8GZT/oqsNpgMHiNUq4WfJbXCUCv1+tw7jTtOJM0ewvHcEZPTtPr9YGjvD7rzmP9hLVo4ngN0AHbJ3MqKbx/DEUOwnV30oJJ9Hp9OLAE56LpVUEiChbdeRwtynuyYMIpN0f7d+nONSfu/N8bXQq8RdbcD6/qn16v/79xKq6XcVpcfYrrOOBTXieGl3A6ou/1kkAtAO7k6/sQ55Hsf1e+ptfr1wJP4zyOnbQRlnq9PmKU5+bjdMIfxFlydNLi7WNoMBhqgAM4A5f+3O3lv8NpkXzHYDBM2nyher1+pjIIRvF8OvDLO//994lt1dhhMBhGDAbDH4/2D9h957K37zz3oSfb+iR4u6zx9v4B6PX6/4azPxdwWly7HvIWH18Tj7kN3Km48eyd/4occIv0ev1bd/7uMhgMk75yyh1EoNakr6g1Cn+Nsxzef9Hr9U/hrCSSjjM4xIaz5niv55r3xBzU6/UjOI9/BnDW5N6I0xr0vMFgmMwWLYG3j6Ee+Ar4P3q9fjVwA2d/V+J0F/gvHmzbWPAS8J/0en0pzgpGAziDYDbhtGjtxQtLxHoh3i5rvLp/er3+uzgVcBtwHPjxKC4u9QaD4a0JbtqYohbdzZM+r0U4y8EpyeJuzrcGvKPsXz5Oh3RvCtSSGAyGDr1eX4Kz3vFzOJO9D+CsHvb/GQyGyX4c+zGwDWekbDBwC2eO3r83GAz1HmzXmOHtY2gwGGr0ev08nAvLepwLZivwf4C/84JAilKc+WyLcboJhOL0YT6BM9DpXU9Xw/HxSHi7rPH2/okctv7AT+5zzVEmdwlcUInupnE4fDLNhw8fPnz48OHDx+TA5/Pqw4cPHz58+PDhY9LgU159+PDhw4cPHz58TBp8yqsPHz58+PDhw4ePSYNPefXhw4cPHz58+PAxafAprz58+PDhw4cPHz4mDT7l1YcPHz58+PDhw8ek4ZHzvOr1+kmfU8tgMGju95q39w+8v4/e0D/w/j765ql39w+8v4/e0D/w/j765qn39s9nefXhw4cPHz58+PAxaXjsClsGg2E82jGujFKi7b54e//A+/s4GfsH3t9H3zx1xdv7B97fx8nYP/D+PvrmqSve2D9Plof9xmA2mzGbzQAEBgYSGBjo4RZNDA6HA6vVis1mw+Fw4Ofnh0ajwc/Pj4AA39Tz4cPHk2O32zGZTNhsNrRaLYGBgWg0Dzwt9uHDxyTHp0FMAN8khdVutwOg0WjQaDRotVq0Wu2o1zocDt8i4wHsdjvuZaFHGwfxnG+MfKgRMYf9/PwIDg72cGueDIfD4dIfH6Pjvr74UA/u64owVo0XPuV1HHmQcma3271SSAkrq7+/P/7+/g+91meBnVhsNhtWqxWr1SoFjbCGK4WNWBzGWwD58PEkeMsG2G63S9mp1Wq9cm0YCx5nffExsSjXFY1GQ2Bg4Liu7x7XHOx2u8s/gZ+f36SdoEqlwGazUV9fT2NjIxqNhoyMDNLT0/Hz85O7bSGo7HY7VqsVYFL13eFwYLfb8ff3d7GyGo1Gent76erqwmazodPp0Gq1BAcHk5CQICe2zWZTbV+Vc1ModMpx8/f3n1SLp5hXQUFBj/we5Xz24XnEBkQsEmLj8U3ZaIh+i399fX3U1tYyMDBAcnIyqamp6HQ6TzfzsXCX996ilI8Vwthzv1M8teNwOLDZbC56jr+/PwEBAZNmnB0OBxaLBUDOVbGZCAoKGvWE2Wq1YrfbCQgIGPMNmceVV2WHhGIgnp+su0+LxSL9Om02G9u3b+cXv/gFGo2Gv/qrv+KnP/0pAQEBcgESAy4WIvH3ZMH9CMfhcNDU1ERVVRWXLl3i7NmzDA4OkpSURFRUFJmZmWzYsIGcnBwAqfiqEXcLpLvyOpnG6evyTejjZEKj0eDv7y83VN8kxRVcFTuj0cjRo0f5xS9+QV1dHa+99hp//Md/TFpa2qjX+5icTPbxE/ep0lg12e5Zd/1EuRbej/HUZzymvFosFjQajdTIH6SoTjbho7QgA5SXl9PR0QFARUWFfN194MXvoUTtfVcuoP39/VRWVlJeXk5FRQUVFRWUlZVRX18POH1/IyMjyczMpKenh+eee45Zs2bJ3bQaXClEkJk4vhOKwoMQ4+npto+Gu9W0qamJa9euUVdXx+DgIP7+/oSEhBAZGUlsbCxhYWGAc0cdFBREcnIySUlJk9biAa7Wc5vNhs1mc7mnlEGEanZjcbe0joYIkBQLi5i/at0cfh00Gg12u52enh4uXLjAW2+9RWlpKQC1tbUMDQ25yE01ylBlm8xmM42NjVRUVOBwOCgqKiI1NRVwjqea56RAWNiEzBwrxKmcsLBfvHgRo9FIdnY2aWlpqraw2+12LBaLtEze77612WyqV2SHh4cJCQm5Zy6K/w8NDXH06FFu3rzJyMgIUVFRzJ8/n/nz58s+m81mqeOMRV89dleIxVBpTnd3WhdCV82D+jDsdrvLDZadnX3f3chobgNq7btyIXU4HNy6dYuTJ0+yZ88ejh49SmNjIw6Hg4CAAAIDA/Hz88Nms9HZ2UlnZyft7e0MDw/zox/9iMzMTODuTexJRJCZQCg9ynkp3CTEHPV0mx+Eu/La0NDARx99xCeffEJvby8AOp2O5ORksrOziYuLk0dBMTEx5Ofns2DBAgoKCggPDwfUscl4HCbzKY4SMYZCTig3jkJWqF0BfxKUiszg4CDHjh3j3//939m5cycAKSkpFBUVyQ2YQI0y1Gq1Sjlz+/ZtDh06xM6dOwkJCeEnP/mJVF4tFsukGM/xaqPZbJbBeF1dXbz11ltUVVXxzDPPsHXr1nvWDjWNtZ+fn4t7ltg4i/tWuA2oeWMp1vmQkBDgrgIqXCK1Wi1Wq5WzZ8/ywQcf8MUXX9DR0UFoaCh//Md/zJQpU0hJScFmswGMqRFkQu8K4TMhdmcOh4OKigrOnDlDRUUFvb29DA0NAU4lb9myZSxYsGDSLZqBgYFysKqrqwHIy8tj5cqVrFu3Tk5WPz8/6UMCcPbsWQ4cOICfnx9LliyhpKREThq1+YUqF87u7m527NjB+++/T3l5OYODgwBkZGRQUlJCYmIidrudtrY2ysvLuXnzJg0NDZSWljJv3jwSEhIIDQ1VVf8Aent7qaio4Nq1a3R0dBAeHk58fDwDAwN0d3eTkpLCwoULyc7OdlEs1DZHlQI9KiqK+Ph4lw2V0WikpaWF4eFhtFqtHL/g4GBKS0u5ePEiP/jBD1i+fDmgzj6Ohtls/lpp2ZT+zWpAbJ7E/fHVV1/x2WefUVtbS3x8PLNmzWL69Omkp6eTkZExqa3kD0JYlP38/KipqeHjjz+WimteXh6vvvoq69evJzEx0cXyrJZxVCIWfoDu7m7Onj1LWVkZhYWFj3xvCWOPJ/uoHBNlu8aqPcqTyZ6eHsrKyrh8+TIOh4N58+ZJ5VVsBjz1Oyj7LAKXlDK2q6uLU6dOceHCBRobG9FqtcyfP5+1a9eSnp7ukTY/Cn19fURFRQHOPvzmN7/BZDIxb948rFYrV69e5datW4yMjDA0NERSUhIdHR0MDQ1x5MgRoqOjmTFjBikpKcyZM0d+rtADn4QJU16F8hUYGIjJZKKqqorKykrKysooLS3l1KlTLopcVFQUfX19pKenS+XVarWqOuWUw+HAbDbLI4LKyko+/PBDrly5Qn5+Pi+99BILFiyQ14rdi+D06dMYDAaKi4tZsmSJKnfcQpiIhbS1tZXdu3fzxhtvcPXqVcC5kOTn5/PUU08xe/ZsQkNDCQoKorOzk+3bt3Pz5k0A2tvbqaqq4tatW0ybNk0KwIk+5lN+3+DgINevX6exsZGenh4qKio4e/YstbW16HQ6EhISMBqN9PT0MHXqVOrr69m0aRMzZ85UZX5J94UwLS2NzZs3ExUVRV1dnbQcm81mHA4H/f391NXV0dTURGtrKwCdnZ1MmzaNzMxMUlNT5bxU43Es3LXCCFkxMjKCyWTCarXS39/P8PCwtMYKRTwkJISoqCjCw8NVp5gLa424544cOcL/+l//C3BuTAoLC5k1axa5ublkZ2cTHx8v3xMZGUlWVhZJSUmAesfsQYjNhBjP6upqPvnkEz7//HMAkpOTeeWVV3jppZfIzc31ZFMfGaVr2dDQEE1NTdhsNqZMmUJkZKR87UFz0ZNKqzLoCODChQsMDg5SWFgolZ0nMbiIeSqsrp2dnVy8eJG+vj4AampqpCuesj2eQmyWRHBSQEAAdrud5uZmKioqOHXqFMeOHaOsrIyuri4Ajh49SktLC8899xwzZswgICBAdcGxERER8u9PP/2Uf/mXf8FisbBy5UoASktL6erqIiAggHnz5hEeHk5iYiLt7e1cuXKFuro6MjIyWLx4McPDwyxZsoTg4OAx0W0mTDuyWq1yItfW1vLLX/6S0tJSBgYGGBwcdFFcwWn1unLlCm1tbTKw50GOwWpA+EuKo4LDhw/zy1/+kt7eXgoLC8nOzpbXWiyWe5SdyspK2tvbmTdvnpwcQ0NDqrNKioV0eHiYzz//nH/6p3+SCulTTz3Fiy++yJo1a0hNTUWj0WC1WomIiKCtrU36psFd1xBPo0zZ1dTUxO9//3v27dsn51tXVxcjIyMA1NXVSdcBoeD19PTwyiuvUFxcLPujVutkeHg48+bNIz8/Xx4BKV1V+vv7uXDhAh9//DG7du3CbrczMjLCpUuXmD17NgkJCdKioNZAO4vFIttoNpv56quvaGxspKOjg9OnT1NRUUFgYCBBQUHy2vz8fFatWsWyZctIS0tTzeIBd7N5CJSLdmBgIBUVFdTW1hIRESHddMDZ97S0NL773e+ydetWwsLCVLc4PipCmWlvb2f79u385je/ob+/n7i4OF544QU2bNhwj+I6Wfo4NDREf38/IyMjLsYMNSNkR1BQEHa7nV//+tdUVVXxD//wD8yfPx8Ak8kkTw4fF7EBFbL28OHD7Nixg87OTsBp3FLKHjWMtfsm89KlS7z11lscOnSI5uZmAgMDiYqKIiYmhv7+fm7evMk///M/YzQa+cu//EumTJmimlMf4Wvt5+fH4OAg77zzDm+88YY0aIgT4tu3b8vrL1++TGBgIEajUX5Of38/ZWVltLe3c+vWLW7dusWWLVvkBudJfLrHXXkVQjcgIACj0UhtbS2ffPIJX375pTxSB6dFqKCggIiICGpqajh//jytra3cunVrvJs4JohdZmhoKODckWzfvp3e3l7Cw8PJysoa9UYWpvNLly5x6dIlAgICmDFjhnxdCABPT2a4u4AIJefYsWPs2LFDKq4bNmxg27ZtrFu3Tlp6BEajUe48BaGhoaSmpsprhcI30X1VbopsNhu9vb00Nja6XBMWFkZCQgKBgYEMDQ3R1taGxWKhvr6ezz77jMTERDIyMoiLi5N+3GpUXjUaDTqd7r6BDsnJycTGxlJRUcG+ffswGo0EBwfT1tZGXV2dVPaU6dHUgpg/om/Nzc0cPHiQI0eOUFtbS3NzswwedEcEsbW2trJu3TpycnLQ6XT3HIl6ArGAC9LS0oiJiaGnp4egoCBMJhP9/f309/ff8976+npiY2NJTU1l4cKF8hRrMlhgxX0kFrf6+no++ugj3n77bdra2khISODb3/42W7duZdasWYBTqRJH8mo8uRIIA0dfXx/Xr1+ns7MTk8kk238/hKuA1WpleHgYm81GWFiY/LyJGlelglVXV8fRo0epqqqiqalJKq9PYmxSxr/09vZy6tQpSktLsVqt8gg6MTFRXu9pOSSMcyKL0LFjx/jNb37DZ599htVqZfbs2SxatEiejBiNRj766CP27dvH+fPnaW9vZ8qUKYBnFXFlJh2TyURdXR2ffvopb731Fjdv3pSyX1jAxcY/JSXFJcBZo9EQGhqK2Wzm2rVrXL58md27dxMVFcXy5csnh/IqBP/t27c5c+YMO3bs4Msvv6S5uVlek5iYyJo1a3j55ZeJjIxk586dtLe3ExwcrGo3ASUWi0XeQDdv3uSNN96gtLQUjUbD888/z6pVq1xM8EKp12q1NDU18eGHH3L69GkXf1kYP0f4r4OwFoNzIdm5cyenTp1Co9Ewf/58Nm/ezKZNm4iNjQWcCqtQJA4fPsyvfvUrvvrqK8BpAZw5cyZ5eXlyQfWUwqf8zqSkJDZt2kR/fz+lpaUYjUaCgoKYM2cOTz31FAkJCRw/fpxPP/1Uvkdstp555hni4uImvP1jic1m49atW/T29so5arPZiIqKIi4uTs5xNVgH3FFau1taWvj973/P+++/T319PSaT6b6LqUajYWBggCNHjtDQ0EB/fz/f/va3pULkno95onHf0C1cuJBnn32WPXv2SCtsYGCgLEEtjizF+J0+fZqkpCTS0tKkdVK4N00W+vv72bVrF//0T/9Ea2srCQkJvPDCC2zdupVFixbh7+8v/YLVvGYII4e/vz8Wi4WysjJOnz5NW1sbOp2OyMhIF19A93tM/L+7u5sLFy7Q29tLSUkJ06ZNA1wDwcYT8RvbbDauXLmCzWYjIiKClpYWTCYTQUFBT5QJQDnnm5qaaGxslCdE8fHxFBUVSWUP8Ghws9L32G63s2fPHv7xH/+REydOEBUVxWuvvcarr75KUVGRVOoAYmJiuHXrFiaTiaamJnJzc6WBy1Mnd0Kum81mysrK+O1vfyuNcAEBAZjNZilXwsPDmTt3Lhs3bmTJkiUkJyej1Woxm82YzWaioqLo7e3lnXfeoaKiAqPRiMlkcvm+J9ngjJtmJFK2KI/Q33jjDc6ePSujnAEWLFjAunXr2Lx5M0VFRVgsFmkhiYqKcrFCqlngBgUF4XA4aGlpYffu3Rw/fhyAgoICNm7cyIIFC2TeV39/f3kjglOxP3DgAADp6elyVwJ41AndHeXR5eDgIHV1dfT19REWFsacOXMoKipyafvIyAhXrlzh4MGD7N+/n5MnT8rXNmzYwOuvv8706dPlc57aPSsTLsfFxbF582ZMJhPd3d309PSwbNkyVq1axdKlSwkJCeH27dvs3r0buKs09PT0MDw87JH2Py7KdFFCcdNqtYSFhXHr1i0++OADjhw5IoWnVqtl6dKlrFixwuX0wNPWDoHydAecpxg7duxg165dVFRUABAdHU1hYSFTp04lNjaWmJgYLBYLTU1NXL9+natXrzIyMkJdXR0ff/wx+fn5UnkVeZs9qbzCXavavHnzZEaIXbt20dvbS0pKCmFhYVRWVsoFQlhsW1tbOXXqFK+//rr8TKV7kxoRclKc2B06dIhPP/1UHluuXr2abdu2ScVVmf1DzShPK2w2Gw0NDVRWVjIyMkJeXh65ublER0fL60frj81mo6qqinfeeYfm5mYiIiKk8mo2m8dVeXXfyIkTGXF/XL16latXrzJnzhy5ofg6Gz/R766uLq5du0Z3d7d8LTAwUJ5yCTx1b4rAI61Wi81mk250J06cIDk5me9///u89NJLFBYW3vPeadOmkZ6eLmVQUVGRzE/sCUOO2WyWm5IbN27w5ptv8uGHH9LX1yddHIWldOnSpaxcuZIFCxaQm5tLWlraqHNVWYxJq9WSlJTkIneeRLcZN+VVCB5w5jn94IMPpIIWFRWFVqslIyODTZs2sXXrVvLy8gCkcDYajdy+fZu+vj6am5vp7u4mIiLC5dhLXO9JRCoToQR8+umnvPvuu7S0tJCUlMTKlSspKipyCXIBV6HU2NhIU1MTALm5uaSkpMjXPN0/Jcq2BAcHy8ACs9lMcnIyWVlZUmBZrVZqamp4//33+f3vf8/AwAABAQFER0dTUFDAd77zHTZs2CDfL9JpeRKxYEZHRzNv3jwaGxvx8/NjzZo1FBYWyrkXHh4ux1FYu9QYrHU/3PN+isVyZGSEEydO8Pnnn1NeXg44x7ygoICnnnqKqVOnAnct6mrpr7AyCsF75MgRfve739HW1gY4x2vdunU8//zzTJkyhbCwMJKSkjCbzTJo9PLly5w7d07mJz5x4gQrVqxgypQpqkm1JRQfnU7H4sWLsdvthIWFUV1dTUZGBjExMXz11VecPXsWf39/YmJi6OzslG4uSqOB2lEG+5SVlfHBBx9w+vRpwsLCWLVqFS+88AILFiy4Z8EcGBiQkd5qmqOjYbPZMJvN8gg2JyeHRYsWkZycfM86oVQs2tvbOXLkCLt378Zut7uMq9jUjGe/lVbB27dv09DQQFdXF0ajkeHhYRkfIHgc65q7q0hdXR3Hjx+X6yM4U6JlZGTI9ceT8QVKg8758+f51a9+xbFjx4iJieHP/uzPeP3110lNTZX+sMPDw7LdAwMDOBwOeX+KTEvgmfgeMb9u3brFrl27+Pjjj+nr60On00mLa1hYGIsWLWLr1q1s2bKFhIQE4K5BRImoLtrQ0IDJZJIBacr58STjNi7KqzINQnl5Ob/61a+kJVKn07Fu3TpeeuklsrOzCQkJkcqaWIBSU1OZMWMG+/fv54033mBoaIjk5GS+9a1vqS6vm3KSNTQ0cOjQIbnwr127lm3btpGVlSWvEYMloijr6+s5duwYg4ODREREMHPmTBfrpVotCWFhYcTHxwN3k7yLPjU3N3Pt2jU+//xz9uzZw8DAAABTpkxh/fr1PPvssyxZskR+llr6qHTRmD59Ot/73vfw9/cnPj5e9s1qtUqFVYkafCMfF6PRKIMuAN59911+/etfU1lZKa+ZPXs2zzzzjCos5I/CyMgIra2tUnGNj49n4cKFPPfcc2zYsIHQ0FCZtcThcBAXF8fSpUuprq7mzTffpLu7m87OTk6ePMn777/P1q1bZc5NT6erU84vkWonNTWVgYEBQkJCCAwM5JlnnqG7u5vh4WEaGhrYu3cvhw4dkv5rog9qTaelrItut9upqalhz549HDt2DKPRyIoVK3j99dfZsGEDOp1OLorCD3/37t00NTVRWFjIggULpHVOLcYOJTabDYvFIhfzzMxMiouL8fPzw2g0otVqXeabzWbDaDRy6tQp9uzZg8lkIi8vz8X3c7zLjY4WPHjjxg16e3uJj48nNTWVuLg42YZHKfLyoM+vqanh+PHj1NXVodPpyMvL46mnniI3N1dmC/GEoifmqZCdNTU1fPjhhxw5cgSdTscrr7zCs88+K2XHaJWoTCYTJpMJPz8/wsLCHuguMp4oN0a3b9/mww8/ZPv27XR2dhIQECBdrnQ6Hdu2bePFF19kzpw5cv0HZB535X0mLO7CfcRqtVJVVUVra6vU41RneRVKQEtLC7t27eKTTz6hvb0dnU4nAwdefPFFl/coq1G0tbVx7tw5vvzyS06dOsXg4KCs1iB4WFmy8cY9fcvNmzfZvn0758+fB5zpotasWcP8+fPx9/fHbDbfI1gGBwc5deoUX375JUajkUWLFrF27VoXy6taFxmRikin02Gz2bh+/TpffPEFISEhVFZWcurUKQ4fPkxPTw9arZa4uDgSExMJDQ1Fq9XKo2qdTqcaZUhZgCAkJETeYODckNlsNmpra2lpabln7hmNRrkIqdEfdDSUPmmnT5/mgw8+4NKlS4DTGpuXl8cLL7zApk2biIyMlAJbbXNSyJuRkRHOnTvn4k+fkZHBc889x+rVq6XlPCAgQPqoBwcHc/v2bRobG2XKF41GQ11dHSdPnmTJkiUu1Y48OVeVuYTBuYEUmVgEyjnb0tKC0Wjkxo0b9PX1cfPmTerq6sjOzpZyS22BW+JYUqPRyKo9n3/+OZ2dneTm5rJhwwaWLVsm5+7IyIi0ZA0PD3PgwAEOHjzIypUriYiIYOnSpYB6cvcKq6IoKFFXV0d7ezuJiYnMmjVLKkPKnKHKU4ULFy7w0UcfcfnyZWmBV64XExEjoVQue3t7aWlpAZxzqauri7a2NjIzMwkKCpIp+L6Oi8rg4CDV1dXS9ScqKopFixYxb948F9cKTyBkgZ+fH7du3WLHjh3s2bOHwMBAnn32WV599VVyc3OlNVKMi3IDOjg4SFtbGxqNhpSUlEdOkTbWKNey2tpa9u/fz7Vr12TQo9VqJTg4mC1btvDd735X3lM2m80lUM197plMJm7fvu1i7Onu7nax0D5JirMxnenKAgQ9PT189tln7N+/X6a3ENq7OC4RN2RfXx/9/f20t7dTU1PD2bNnKS0t5caNGzLtQmdnJz09PfK71KC8KoVKaWkp//Zv/0ZHRwc5OTm8+uqrLFy4UC52Iu2E0s3AZrNRV1cn86POnj2bVatWodFoMBqNqq6Wo0zE3NfXx4kTJ6itrZXpbNra2qQyJzYl/v7+1NbWcuDAARwOB4sWLXJJJq4G7mdB1Wq1jIyMUF1dTUNDwz1zz2q13pPubTJgsViklbGmpkY+X1RUxJ/92Z+xYsUKYmJiVGtVVkartrS0UFpaKu8nnU7HvHnzWLNmjbTAKU9s/Pz8GB4e5ne/+x1vvPGGVHqVR7ZqmZdKHrVNMTEx5OXlMWXKFDo6OmhpaaGzs5PMzEyXlG5q2Ty609zczIkTJygvLyc2NpaNGzeyfv16GRAKrhuwrq4umpqaZEq+1atX3+MT62mUx/rDw8NcunQJo9HIU089RX5+vrxOWJOFvzU4c2rv2bOHffv2YbFYmDlzJrNmzXJReiZ6LIeHh6WPdU9PD/v37yclJYXi4uKvpbAKo4jJZKKsrIyamhoZH6LT6cjOzpbuS+L6idQD3Dfwvb29HD58mI8//pjW1lZWrVrFyy+/zNy5c6UfrPJ+FX8bjUauXLlCbW0tU6dOJSYmxiWeYCLljtIHu6amhrq6OuDumhYREcG2bdt47bXXXAoNKI13AuWpuyjMcO3aNfl6YmKiSyYi1Siv7gE9ZWVlVFdXSz9Ik8lES0sL+/btY2hoiLS0NKxWK7dv3+b27dv09PTQ3t5OdXU1N27cwG63ExoaytDQEPX19S5ps8RO2lMolWez2UxVVZWM/M3MzGTDhg1Mnz5d+rqIAVUutmLnLXYmyvySohqZmpRX5e8t3ASioqIYHBykqanJxS8JnD5cQUFBXLt2jba2NtLT08nMzCQvL4/k5GQ58dWmICitHiaTSUaHBgQE0NPTw+3bt++56WJiYuSi6umN1aNgNBqpr6/n5MmTfP7555w+fZrh4WGSkpKYPn06L730Ehs2bJClNtW0wVCiHIeRkREaGhqor6+XQQXKkwyr1Sr7IO6tgYEBLly4INP2BQQEoNVqiYmJISUlRY49eC4oxB2lBVYExgo5YbVa5UZRyB1haR4ZGbnH5UUtfXJXCrq7uzl27BgnT57EYrEwY8YMVqxYQW5uLhqNhpGREZeI9lu3bnH8+HHpLiJOQsTCrJZ7UhmAW15eLlPyCdcksfhbLBaXoLqrV6/y7rvv8tlnn0k3rLy8PEpKSqTfIUy88iqsaCJwtampSaZOFM8rr1UGiiqj9AFp4QPnxuXAgQMuqRV1Oh05OTmkpaXJ9XGiYw2Em4K43y5evMgf/vAHzp07x+zZs9m2bRvLly+X89h9PPz9/enq6mL//v3s2rWLwcFBYmNjCQ0NHVXJnQiEDBgZGaG7u1veQ+J+ycrKYvPmzdLiOjIyQmBg4KhzTXmPDQwMcP36daqqqgBnGsbc3FwXt0jVuA0oG261Wunt7eX27dtYLBaZj7Cnp4fjx49z/fp1IiMjZb174bSuxP14Ui2CFu76FlmtVi5cuOCiuOXk5MiCBMLaKvqivGFv374trV1ZWVkuO0r3oBo1oPz9Q0JCmDp1KlFRUTJ3pjI5uk6nIyIigqioKLlzLi4u5oUXXnA5ylOLRWQ0lBVPwHlC0NTURFdXl4tvT2JiInl5eS7Hd56u+OKOcpEQ7g8ff/wx7733Hjdv3kSj0RAdHc3ChQv53ve+x/LlywkNDfV4+cmH4S5zhoeHGRgYIDIykpKSEgoLC2XbRQopUblHWL6UJzpiroaHh7sEhYC65A/ctVIpUbomiftL+J4JH0phbXUPjvEkoykFu3btoqamhqlTp7JixQry8/OlMidqqgvf2BMnTvDRRx/R0NCARqMhKyuLmJgY+fmensNK5VxYXZuamrBarbKEsRgjcLUodnZ28rvf/Y5f//rX8iQyOjqa3Nxc5syZI4tteCLbQlBQEGFhYS73RltbGxcuXGDx4sVynom596D2KWVma2srR48elfEj4DQKKSsxekLG2u12l3vmq6++4vDhwwQHB7Nx40ZWrlxJcHCwDFoW42kymdBqtdKfubS0lDNnzgAwdepU4uPjpR8+TKzyKr5TbNqnTJkiNyAFBQVs2LBBZl6Bu6cCD/vM+vp6GhsbGRoaQqvVMnfuXObOnetikX8SF7RxU17F4ImdvjAxazQaTCYTbW1tUsN/EENDQwQHB7NkyRIXk/V4O6Y/CKUyKpSAo0ePotPp2Lx5My+++KLcXSidugEZAWs2mzl58iRNTU0EBASwZs0a8vPzpRBXuiSAOixfygkbHBxMRkaGi++ROCYRjvSVlZWyLG5JSQkpKSkuPnoTfeTzdRBCymazUV5ezpkzZ1wKGIgkzbNmzZJWA1CfoqMUuGazmfLyco4fPy6FlFarJT4+ntmzZ7N8+XIiIyNlWVXlTlnN9PX10d7eDiBLvmq1Wpd7Z2RkRFpTa2treeuttzh79iw6nU76YoNTOZg5c6ZLUILaNpOjoVRYxcZQlKyMioqSBgOhuIp0hp6Up+AaqGOxWDh9+jTHjh0jMDCQ9evXs2HDBhd/XpF2EJxrxKVLlzhy5AgOh4O0tDRmz55NcnKyvN7Twb1Ki79WqyUkJIT09HSCg4OlQqSU96IYysmTJ/nDH/7A3r17peIaFRXF/PnzmT17trQ8e+KYGZzWtLy8PJqammT7rl27xs9+9jMWLlxIZmYmoaGhZGdnU1RU9MglwJVFUQICApgzZw4rVqzwqJUZXOV6Y2MjFy9exGw28+yzz/LCCy+4HIkrN4UiWj84OJiwsDAXv8+UlBSys7PlCclEb0LEOAQGBjJ37lx++MMfsnPnTgYGBnjmmWd47bXXyMzMlIa30Ta7Yh0XFbbKyso4duyYXCsjIyNZvHgxy5YtIzw83CXf8ddlTJVXZUPCwsIoLi6moqKCyspKl5JhcDcK0d/fn/DwcEJCQggICJClUHt6emTH4+LiKCgokDnQwHPKgTiuEMprU1MTX3zxBR0dHSQkJLBlyxaWL18uU0IEBwdLC63YgYLT8X779u1UV1eTlZXFsmXLyMzMlP3y9/eXPqPiM9SAWBBjYmKYM2cOCxcu5MaNG7S1tcnqMHa7HbPZTGZmJuvWreO1115ziVa3WCwyYEEt/XJHaXG02+3U1dVx5swZLl68eE9Kk5ycHJlnER4/wnYicD+SCgkJIT4+noSEBDo6OjCbzdLBvr6+nsLCQoKDg6VCrobN08Ow2WzS/060VaS5EoEvyp3+l19+yd69e2XwoBDKSUlJLF26lLy8PHQ6nVSq1LYhgbsWSzFXlQuCn58fZrOZ/v5+goKCKCwspKCg4L6f5ckxFsEv4DxOv3TpEkNDQ0ydOpUFCxZQUlIC3B1jUZbU39+fW7du0dzcLBfQ8PBw5syZ45LlRVgAPY0yIE24T5WXl9Pc3Extba1U9tra2jh//jy///3v2blzJ4AMngkPD2fZsmXMnj1bfu5EyRshtwXTp09nzZo1VFdXy1KhHR0dHDhwgMuXL5OZmUlISAgLFy7EbrcTGxtLfX093d3d6HQ6wsLCsNvtMsYjLCyM7u5ujhw5Ij/Pz8+PwsJCFi9eLN2YJrLPAjHflBUmy8vLCQ8PZ8mSJcycOVO6R4q878KyLoJFwXmqIDbZM2fOpLi42CUrxkTPU2VmiJycHF555RVSU1Npbm5mxYoV0uDknmdfeYqsLGAEzg3MmTNnpCtldnY2xcXFLi5cTzp+Y6q8KhsfHx/Pq6++SlBQEO+//z61tbUMDw9LJTYuLo7g4GBCQkIoLCwkJydHprWJjY3lt7/9LT/72c/k5w0NDbkkgfekoFUeV/T391NbWws4j1CmTJnikptPOGG7pwCpqKjg8OHDgDOF1Jw5c+SOTBztHT9+nKCgIJYuXSqfE/5snsD9946NjaWoqIiSkhKOHj0qq3DY7XaZVuNv/uZvXPwGwfNWkEdBWarW4XDQ2NjIzZs3pWVOSUpKCqmpqS5WPrUpOkrfP51Ox1NPPSVT75SWltLb20t9fT379u3DarXy2muvsWjRIvl+NQf2CCIjI11KDSt9KIeHhwkKCiIoKIiRkRF27NjBJ598IsdT/C5z5sxh06ZNrF+/XhXlGh+G2FyJv5UMDg5SVVVFc3MzOp3OZfPf29t7j0XdUzJVea91dHRw4cIFGTznXtBFKATBwcH4+/vT0dHBpUuX5CIJTuW1uLiY9PR0l+NbNYyjUvYFBwdTWFjIV199xcWLF3nvvfcYGRkhJSWFy5cvs2vXLo4dOwY4T3iULgOFhYVkZGTIdWUilVdA+q4mJyezevVqrl69SmNjI729vXK8Ojo66OzsxOFw0NnZKe+1srIyWltbCQ4OJjQ0lMHBQaxWqzzpuHz5MhUVFS7rfWhoKPHx8S6bz4mWscL9EZBp6Kqrq4mLiyMhIeEedwFxwiqMBQDHjx/n5z//OYcOHWL69Ol8//vfZ8WKFfI7PJHJxf2+SEpKYv369ZhMJpeqoO5tE7JHrJHK57u7u7l16xYWi4X4+HhmzJjhYpUei3txXJydxO4yLS2N5557joiICJqbm+nt7eXWrVsEBASQkZFBWFgYOp2O6dOnk5KSQkxMDOnp6QAu/p86nY6oqCiPReO54x64JCLRdTqdi9+jcpeovNFMJhNVVVUyuXRxcbGsJObv7097ezulpaW8//77FBcXS98hcdznCad8YbUSKWwqKyu5cuUKZWVl9Pf335MKJCgoiI6ODqqqqmSQBaDqDApKlLtKjUZDW1sbzc3NLgF3VquV/Px8ZsyYQXJysrSwq7l/wn0gMjKStWvX4u/vT2xsLEeOHKG6uprq6mq6urrkyUdJSck9VVHUhPJeDAoKIioqSi4iTU1N1NXVERsbK4Xw0NAQu3fv5p133qG8vFwGmmg0GrKzs3nuued4/vnnpaKnVouzcm6K+WaxWKiqqmJoaIigoCBu3rzJyZMnGRwcRKPRUFpais1mo7e3l9bWVgIDA0lMTKSgoIAZM2bII2hP+qGLIgzi+7u6ujh79iwZGRnMnz+fmJgYlwW1sbGR3bt3u0Q0p6WlyXUE1OV/rlSi/f39Wbx4MRcuXODw4cPs3LmT7u5uMjIyqK6u5quvvsJsNhMeHk5YWJisLpaamkpGRoYcd09sLJX3RWZmJk8//TQ1NTXs27cPgIiICPz9/aX1tKKigr6+PoxGo8w+5E5kZCR9fX2cOnXqnteUgV6jtWG8EW42glu3bsl2hoeHS8VMo9G4rPvCcCPSYv7hD3/giy++wGq1kpeXx4YNG5g6dapMFeppOSuU0fDwcGktFlXbRC5XcXIaFBTkMu96e3ul3Ll+/bo8OU9ISCA9Pf2++tDXZVzzvIJzYn/ve9/DbDYzPDzM8PAw/v7+hIWFERQUJIWv0lze3Nwso3/BOakLCgpcBJIn/SWV3ysmHTgXj/7+fvmayO0Kd48vwXkjC2utOIIXRw2nT59m586d7Ny5k4aGBulrIr7XU31WLpCXLl3igw8+4PPPP6e+vl4ugqKSWE9PD01NTfzqV7+ivLyc//pf/ytPP/00gEwqPpno7++X2S5EbjuAWbNm8dxzzzFv3jyXLBFqRnmvhYaG8q1vfUsu9kePHuXmzZu0tbXx4YcfcvToUb797W/z2muvueSgVJNCp2yHTqcjKSmJxMRE6uvrOXHiBDExMfj7+zNv3jwAdu3axRtvvMGZM2dkDkpwKhIpKSlMnz7dJem7Wi3OyghtUTO8qqqKvXv3UlFRQXBwMN3d3Vy8eBFwLp6//vWv+f3vfy+PNgMCAsjPz+fVV18lLS1NKq8TXZpS+OWC8zRnwYIFfPHFF5w5c4ahoSG++OILOjs7WbduHatWrZJH62azmdOnT3Po0CE6OzvR6XQUFRWxdOlS1QbauQe9zp07l/Xr19PY2Eh1dTWHDx8mJCSEwcFBwHlCmZmZidVqZWRkRPr0imA0T60JyjRyGo2GkpISamtruXLlCs3NzfT397vEAFit1nuy0ShJSkoiNTWVsLAwMjIyZBCwQE1jCLic6pjNZhfLv7sBo7Ozk927d/P2229z8eJFrFYrKSkpFBYWupzuqEGmjvY7K0/UhduE3W5nZGREZsWor6+nvr6eoaEhqqqqOH36tIx3iouLIz093cV1QrXKK+BiPhfJwIODg11y9I2GSK+gnOj+/v6EhoZK4aqmCOihoSHpm9rZ2cm//uu/cv78eaZMmSIT8+t0Oqmot7e3c/ToUZlLLTAwkNLSUtrb2wkICODSpUucPHkScObadM8VO5FWBPE7KxdvZd14cbRnsVgoKSlhw4YNBAQEsH//fqmcnzx5ksOHD8sMDEIBUnOWAbg38f3p06dd/OoA5s2bx0svveQSSKK2BP73Q0Rqg1MJDw0NZenSpVy/fp29e/eyb98+2tra+P3vf4/VauUHP/gBxcXFAKqJUAfX33vKlCksW7aMM2fOUF9fT3l5OVqtlubmZrKzs7FYLJw4cYJTp07dkzLKbrfT3t5Oe3u7TFEkgp/UqLyCcwwvXbrEsWPHqK+vl9WOmpqa0Gq10mAQHh6O3W5naGjIpd9paWnMnTuX6dOnu1h8PHFfCiXMz8+PnJwcli9fztmzZ2VFnpMnT9LX10dtbS2FhYWEhIRQXl7O4cOHpSUvODiYDRs2sGnTJmnxcvfR9DTKTBB+fn6kpqayevVqbt++zZEjR6ioqJDWSoBly5axZs0aLl26RE1NDYGBgSQkJLicQnoC0Q9hlYuNjWX58uVcvHiR999/36Voi/u6LdZC8U+4BwQEBJCXl0dERARnzpzh8uXLGI1G/Pz8iIyMlFkNlG4mE9lfpawRpzQGg4GWlhYMBgO9vb0UFhYSFhYm03vW1dVx8+ZNrl27RllZmQzaWrZsGYsXL3YJuFOTnHGP0QHXNdvPz4/r169z9OhR2traaG1tpaWlhcHBQQYGBlz0t8zMTObNm0diYqJcP1WtvCoH273esvvfYiKKiR0SEuIykELLFzk3R/ucicJdGCYkJLBkyRKuXLnC8PAw77zzDu+88w5FRUUUFBSQmJhITEyM9C9raWnh4sWLcmdpNBo5duyY9G0S3zF//nx++MMfsmrVKpcAjInur5iww8PDXLx4kY8++ogdO3bQ0tJCaGgoqampzJ07l2eeeYZNmzYRGBiI1Wrl3LlzVFdXExYWxpEjR0hMTOT1118nJiZGHsGoUXl19yGrq6ujtLSUS5cuMTIy4pLKZsaMGeTl5QHIvJNq7JM7IvMH3A34ycnJIScnh5KSEpKTk4mOjubQoUPSCivSuBQWFrocV6qhv6IPoaGhLFq0iA0bNtDU1ERVVRUXL16U1kclIpVbYGAgAwMD9PX1UV1dTX19vbSsu/tyeRrl7z08PMyZM2fYsWMHe/fulZthdzQajfQ11Ol0REZGEh0dTXFxMWvXrmXx4sVkZmbeY12ZaJR+4v7+/ixbtozOzk4+/PBDysrK6O3t5cSJE5w4cYKMjAyCgoKoqqpy8bkLDQ2lqKhIBmopA2bVjKhkN23aNK5cuUJlZSU1NTVERkbyzDPPsGLFCpqbmxkZGWHKlClkZ2e7FGfwZP+UQbc5OTm8/PLL2Gw29u/fT1dXlyw4oLyP3IO3ARkvMXXqVIKDg2WuZnAey6ekpMhMA8qN90QiYhrAqZC9+uqr3L59m7fffptTp07R09PDnDlzmDp1KkajkZs3b1JRUUFbW5vLPRUaGsq8efOYP3++3CR7Is3Zgxhtw2ez2aT1v7a2lo8++oidO3fS2Nh4z5iKzbBI6SZSbYlsCmOxoRz3LakyUMk9lZbyb2Wgi0jdIhDPq0UIKQXH4sWL+ad/+icOHz7Me++9J5MqC6dzEUEZHh4uK1YMDg5Kf1cRRBIQEEBWVhYlJSXMnz9fOuQrj79gYgWV2FDU19dz5MgR9u7dy5dffklPTw/BwcGsWbOGTZs2sWDBAtLS0qQ1YM2aNfT19fHRRx9x6tQpzp49S2JiIgsXLmTx4sWqDGgSCMVaCJLm5mYuX74srSEiqnLOnDnk5ubK96llbj4OynRKguDgYNavX090dDRRUVHs2bOH+vp63njjDex2Oz/96U9JTU1V1QZEOZ+ioqJ45ZVXiI2N5fe//z0nTpy45/qZM2dSUFBAdHQ0t27d4tKlS/T19Xm88MnDUP7e9fX1vP3223z88ccu2S+UCH9ewfTp09myZQtz584lJyeH1NRUF/niSXcQ9xRK06ZN4+WXXyYpKYmPP/6YQ4cOSfcs9yNlsZYoFRzl82rE3X1g1qxZZGRksHz5cgYHB+nv78fPz4/8/Hx6enqorq7GZrNRXFxMUVGRSxlZTyo97rJj2bJlBAcHk5uby82bN6mvr+fGjRv39XMVzJgxg4ULF5KcnMz169eprKyUmUNSUlKYOnWqy6bZUygthzNmzOC1114jJiaGc+fO0dLSwunTp4mPjyc2NlZmSZg7d67MfWs2m0lJSSE/P1+eQou86GpEuRYODAxQWlrK0aNHZfWzW7dujToeDoeD4OBgUlNTXdywxlK+jLvy6m5ufxgicX93d7d8zmw2Y7FYVOV4L4KYwsLCmD9/PikpKYSHh3P+/Hlqa2tpa2vDZDJhNBplvWd3kpKSWLNmDbNnzyY0NJTk5GTmzp3rEhXsqQAgo9Eob6jq6mrefvttjhw5AjiF1IoVK9i2bRsbNmyQC6DFYsFms5GWlsbmzZspLy+XTu0dHR20t7czODh4T1JrNTGaY/7169cZGhqSFp7p06ezdetWl7RDnna0/zooTxGUJZtFXmWj0UhLS4v0Zfriiy+YN28emzdvJioqSr7X0z6wyhyaAQEBTJkyhW3btuHv7092djb9/f1SAEdFRUnldXh4mIMHD3LlyhXg7kZSzXNT/NbCh1Aorjk5Ofj5+dHf309vb69c+MPDw4mPj2fmzJk89dRTrFmzxqVwg5jrajEOKH2QMzMzZW7atLQ0qbSaTCY6OztpaWmRRoDZs2fzzDPPSB9CmDwuPHB37o3mVnfhwgWqqqrw9/cnLS2NpKQkjx2f3w/RFp1Ox9KlS8nMzKSuro6amhpu3rwps1709fURHBwsLawmk0kqvRs3bmR4eJj29naZ4zU6OlpmIhJ4sr9K2RAQECANN6tWreLKlSs0Njbi7+/P1KlTSUxMJDo6mt7eXvbu3cvp06cBp9VWOU/VMH73Q3kKWVNTwwcffMDHH38sX9doNOh0OhnfI4K6xKbZ3T1rLNcKVTgDKfOmtrS0cPLkSZdjMHe3AzWgzEsITveB733ve/zgBz+goqKCs2fPyoXlypUrspJPX18f/f39hIeHs3LlSv7iL/6CkpISTCaTjCxV4ml/LZvNRmVlJRcuXACcRx6rVq3ipZdeYv369ffU1RYbjLi4OJe+iKocnnT5eFSUm6Senh4aGhpcXp82bRpPP/00mZmZcm56epyeFHHiIcZEq9VKf21BX18fx44dIz09nWXLlqHRaEaNAvYUyiPM4OBgXnzxRTZt2iRdk0Qy+KioKIKCgrh06ZLMbysQvl5qZ+rUqaxevZrOzk6sVivz5s0jPT1d5iNuamrCZDKRkZGBXq9n27ZtxMTEuJTIBfUVXnDfOERHR7NhwwaWLVvG0NAQ/v7+mEwmDh06xM9//nN6e3vx8/PjW9/6Ft/5zndITU2VFurJpLzej/7+frlJ8ff3l2V+QV1Kj/u4paSkEBcXx9y5c7HZbHR1dVFbW0tHR4cMyoqOjpbBhlOnTiUiIoKuri7CwsKkPI2MjCQ5OVm1BWCCgoJIS0sjOTmZxYsXY7FYsFgsMg1ob28vBw8epLu7G41GQ0REBFOnTr1nnVcbyopw4DyBPHjwIMePH5fXCGOOcAsRWVuU87KlpYXKykra29tJTEx00QGeFFWsuEplYXh4mI6ODpd8mtHR0aSmpspqTsKU7embV1hARPJecYMVFhYydepUhoaGCAkJYdOmTRw6dIi3336bpqYm/Pz8WLVqFa+88opMvi123uD01bLZbOh0uglXioSlV6fTYbFY+OKLL/jyyy9dKg+tXr2ajRs3ytKvIoOEMg1WSEiIi1+SsPKpbbF0x/0YTlm2WBTRyM7OdkmAPhmUnYehFDpGo5GmpibOnj3rsokMDQ11qZqnNpRBJP7+/oSEhDwwsEVZshmc4ysCTNWIUjmPiYnhxRdfJCsri6GhIbKysoiOjubEiRM0NTXR2tqKyWSSxUTE0Z1Go8FoNEr5qdZNlwiWCwgIGHUcb926JccvICCAadOmkZqaCiBPENQ6jqMhrOoWiwWj0UhwcDBarZaWlhZ57K7T6VSTLvJ+CEu+v7+/y2mUUFiHhoYIDAy878ZCnPyIeSmqkalxniqDDAMDA0f1w42IiMBisTAwMIDdbiciIoKYmJh71ka1oSxt29PTw/vvv8+bb75Je3s7oaGhxMTEMDAwQG9vLw6HQ/ruWiwWwsLCmDVrFsPDw9y4cYPPPvuMlJQUXnzxRZlSTOmO8HVRxYxwz9UYGRnpYvGJiYkhJSVFavpj0fGxQqmwKSdzbGwsOp2O0NBQWT3jN7/5DeAUzIsWLWLNmjWAU1lVClvlLnOiUbopNDY28t5773HgwAHgbgWOoqIiebwlbkhw9W+uqqq6J3F4WFiYvGnVqPCJsRPHIBcvXpRZE8CpvJWUlFBcXCxvbLVYHccCcV85HA4uX77Mzp07XVLWgdNfNDs7W/5fTVYQgXJhUFbHEjkKlSmhlH6uIyMjDA4OysBQteH+W0+fPp1p06a5RLCLwEKz2UxUVBRTpkxxMQ7Y7XYXC7taUaYWFLJCHE23t7dTXl4uo9STk5Ndxmsy3pPKgGW4O4dv377N+fPn6e3tJT09nYyMDLmeKKt1qQX3ddk9eb0YJ/G82EwFBga63KPKUsEmk0lVLoOC0eaZsgKnRqNhZGTEpepdaGgosbGxHs/u8TBERhmNRiPdHsRaEBsbK/spEGkHdTodq1at4oUXXqC1tZWf//znXLt2jY8++oiioiKpvFosFu9TXsUuRrkzE7vR0a5XEyLnmVarvWdnqdPpXNqtTP0lhLBIru7p6FFwLhTXrl3j6tWrMufgypUreeGFF2RqKIfDcc8OsqWlhTNnzrB//35KS0sBSE9PZ+HChWRlZd2zIKkJZYm76upq3nvvPc6cOSNfDwsLY/Xq1SxZskSOkVp8zh4X93kmFHeA7u5uLl26xOnTp10sz0FBQRQUFJCRkQF4zh/7UbFardINYDTlVaTzEf1ub2/n+vXrtLW1yTyaahxb5dgp22ez2aioqODcuXOA874rLi4mNjZWHqVPRsVOHEsGBwdjNps5f/48Bw8epL29naCgIObMmePiE6mGU7knQamodXd3ywIMkZGRZGVlSeur8PFWI8KQIzaIgPSJFGk0RcEdEYEeGBgoXXvE+iDKASvjENS2dgijjWi3SOKv1WoZHh6mt7dXBvxOmTJF5ikWqFF5dc9lrwwKHR4elpt8rVbrMsZLly7lJz/5CStWrGD//v2yn83NzS4p4MZiDFUx85UdMZvNDAwMuCirQ0NDsuNqjlR3PzpQ9ksEFojrlFViRMEG8NxiKRZEpdW1rq7O5Yi4qKiILVu2uFQ/6+/vx2g0YrPZaG1t5dixY+zatUsGdyUlJfHSSy/xrW99y+V9ahxD5aJRW1vLwYMHqaqqks+FhISQn58vi2Uod9iTBeXpgPi/8oios7OTAwcOcOrUKReBFRgYSHFxsXSX8GTBjEdltGpuyjEWlXFiYmJoaWkBoLW1VfoUgvoWSnCVESIqPSwsDIfDIaswAWRlZbF06VKmT58urRyT0Q9U6cphtVq5fPkyp06dwmg0ysAYZfWeyXQ/uuMeMGo0GuXaodPp5KYK1FU5zB2xSRpNzitT9YGzX0oXEOVrISEhxMTEuJzEqm3tEK5mypSWoj86nY6Ojg6Z93T69Onk5eURGhoqTxLU1h9wvYf8/f1dSkkbjUapnwmDT0hICIWFhbz66qusuFPuVqfTyblstVrp6+uTa+ZYnJyrQnl193lV1kEG5w9kNptV5S7wKCgXPpEjDWDBggUu1cKEO4RahK5QaMxms8vYmEwm2Qej0Uh9fT0XL17k5s2bdHd309raytWrV6msrAScN3FJSQmbN292SXDvHuymFpS/f29vL11dXS6vi99lMjPagqeM1j9x4gS//e1vOX/+vHxep9OxefNmXn75ZVn+WM2byAehHOPk5GQWLVpERUUF5eXlAPek6VM7Skuq+301Y8YMZs2aRWBg4KQPLBTysaamhosXL8rUSxqNRga7Kq/1FpQ++Eq3LG9C5GlWKrziSDouLo5p06bJeBdQn/LqjtIYYDabaWxspL+/n7CwMHJycu4xfqi9P+6GCuFOoExR9+yzz/L8888zbdo0eZ047QKn32xtbS2NjY1kZWWNySZadZLMbDbT29vrYnkVKaesVuukOhJyT6shBjs1NZWQkBCX3LZqQbQpOjqasLAw6dKg1WqprKzkD3/4A9OnT8disVBRUcGZM2e4du2ai3+rVqslPz+fkpIS1q9fLxVXUG+5TXBV7EJCQu4Zl76+PpmGSM0plUbDPRH2yMgIRqOR6OhoWdL3yJEjvP/++1y6dEkuHoGBgZSUlLBt2zZWr16t2qTaj4pS6EZERJCenn5PLuWuri65yVJ7H5XztKWlRZ5QLVq0iMWLF0v/SLUVXHgchLuD2Wyms7PTZW2IiYm5J3p7Mt2XD8Nut8sNs7tVdrKsgw9D2Q/hyiPGWBRlUJ5Uqn18hWJmtVo5e/YsN27cACA+Pp709HSXAjFqxV1WKDOyDA4OytO7OXPmsGHDBrZu3SoLEQhCQ0PJysqiubkZi8XChQsXyM3NJSEhQd6vT2KQVJ3yKqIUlRYCo9EoXQmUSoOarJWj4V6IQQxSdXU1tbW15OTkEB0drYo+KPM+BgQEEBMTQ2pqKqmpqbS0tGC1Wjl+/DjHjx+XgqS/v9+leopWqyU7O5sZM2Ywf/581q5dS3FxsfRzEteoFeU4uFudwWmBVGaAmEzKgLIYgclk4syZM/T19ZGfn09ERAQnTpzgV7/6FaWlpS7R93PmzGHr1q0sXrx40pT2fRSUwZ9KhUBkO+nv7yc6OtrF6qWG+3Q0RNsaGxtpbGwkMDCQFStWuGwaJ6tvtjvuClxWVhbLly+XQYRKOesNKI0bk9Ff+XExmUwu7kqTUcYKQ9W1a9f48ssvuXnzJlqtlvnz58uMGOD5NJgPwr34RE5ODmfPnsVkMslYh4ULF/Ltb3+bLVu2yNz0w8PD0ic7PDycvLw8ysvL6e3tpba2loaGBkwmk1Ren8SYpYpfT/lDCWtIdXW1DBQKCAjAZrNNuiNbpaAJDAyUA3bu3DmqqqpktgE1KQNKQVlcXMyf/MmfEBQUxNGjR+VuWGn5iIuLY/78+cTFxTFlyhRZnjE2Npa0tDT5ecqjFLUhFn+dTofD4aC+vp6KigqXSPXIyEhKSkrIzs52UWgmC0JQ9vf3c+zYMXbv3k17ezvZ2dmEhIRw8eJFvvrqK9mnjIwMFixYwObNm1m5cqWMElV+ljfgrpT6+/sTHR0tAw3Ukpbvfog8u35+fiQkJBAaGoqfn59ML+Ve7ngyIjJCBAYGEhQURH9/v3wtPT2doqIigoODR63HPhlRjlV8fDxFRUWcOXNGZgIRTCb58yCU/RCpsgTt7e20tbXJjDziejXej8oiIuBcJ9va2rh9+zb5+fls3ryZpKQk2traiIqKUm1VLcAlliMmJobvfe97stS7KESxfPly5s6dK2NZ3AtJabVadDqdLCkbExNDRkbGmJU2VsUqpFTcwsLCmDJlCjExMTQ3N8vnlRH8Sv8YNaNc5CMiIlixYgVtbW1EREQQHx/v0ZRY90OMhc1mIyUlhVdffRV/f3+MRiO1tbWEhYXh7++P2WwmLi6O5cuXs3LlSlJSUoiPj3fZWYpcjWpPGK5c3C0WC7W1tdTW1kr/XofDQWRkJOnp6S6VUdR87CNwF/QDAwPcvHmTPXv2uAT3CMQYLl++nM2bN1NSUiIVOTVtssaC0YLOQkJCSE5OJjAwUHVp+UZDKK9arVZWpDIajQwODsqoZzW3/3GJi4tjxowZVFRUkJWVxdy5c10yQ0x2hU6ZGB7ulvStqamhq6vLRXGfDPLncRHpJcPDwxkYGJDBWpNF7gh563A4GBwclG53fn5+dHV1UV1dTUREhDzFA3XKVdEekcN+2bJlREVFkZycTFRUFJs3b5ZrvfJUVSlrQkJCyM7OZvr06bS3tzNjxgxycnLGLPhOFcqrcnHV6XT3VGcKDAx0sYZMFkuCUgjFxsbygx/8gA0bNqDRaEhPT5eDqLZcfXB3UgUFBbFp0yZyc3MZHh6W+fhEvsi4uDipiI82JpNhMXFP1RYbG0t8fDzh4eFy19jd3U1HR4eqU7aMhuibOOpJSUkhNzd31ITaU6dOlZXTZsyYQUxMzAOT/E9WlHlR3StsWSwWqSCozR/9YWg0Gpf5OVncqx6G0uUhLS2Nv/qrv+Lll1+Wie+Vaesm03jdD3HsbLfbmTJlCmvXrmXnzp3U19dLZQgmh/x5XEQVqhkzZhAXF8ezzz5LSUkJ4eHh0gKv1rmsVELtdjuVlZU0NTURHBzMjRs3+D//5//w4osv8u1vf3vMrI/jjXJNz8/PJyoqisDAQFn0BO5uokarOvrCCy8QFxdHeXk5iYmJ0iAwFqd3qlBe3Tucn58vF83IyEhWrlxJZmbmpFBYlSgXj8DAQHJycsjJyXG5Rq0Li0ajkZbTuLg4GfjxIJT+gyKn32TAPbAuIyOD9evXY7FYOHfuHM3NzdhsNoKCgibtgqGM6F20aBHf//73OXLkCEFBQdKyUVBQwKZNm5g/f758n8lkIjAwcNIpcg9CCNugoCASEhJccoT29/dz/vx5GSiiDLRU630qxsVoNBIWFkZkZKRM/i6uAfXKmoehbH9ISAizZs26JzhEjdarJ0Hp7pGfn8/WrVupra0lOTlZXqPm06zHQemWExAQQHFxMWFhYaSmprJo0SJ5f6r5PnTH4XDIFJ/K1HtxcXEkJydLFzU1K+OAdFURKbGEtVUEEo5WNVOpoKakpLBt2zaKioro6ekZ0+IvqlBehcXAbrcTEhLCvHnzyM7O5ubNm2zatInvfOc7LuU41TzYo/Gg9qq5L48b5KG8Xs39ehjh4eGsWLGCadOmcfjwYU6cOEFAQACrVq1yiUyfTL6fSktrbGwsP/nJT/jTP/1TKTyFT6G7pXU0C+1kR2zKAgMDmTZtGllZWURGRtLX10dfX5+sDieCgNRs8VFaG7VaLfPmzWNoaIjw8HAZFewNx+nwYJniTYoruI5rWFgYf/qnf4rFYnE5kVSzz+TjoJSjwcHBLF++nCVLlsgy5QK1G6+UMsLPz4/c3Fzy8/MZHBykoKCA73//+6xdu9YlA8hkQJn/3f250frgfr34LYSLk/trXxfVrL7KgY+Pj+eFF16gsLCQefPm3bPLnoyCSuxelCmIJsPOWWwsRivRJ/yzhJVVrQv84yJSJGVkZLBx40ays7MJDAwkPT3dZfFQuzAdDbFbjoiIcEk/o0RkkBAuIt6G0tdVBGgpj/vS09NdAtTU/BsoF5DAwEDWr1/PzJkziYmJISoqyis2k0ocDoeMeBZVmSbTJvJxET7Xynt1sqWMfFSUgbNKxFir+T4EVwuyv78/c+bM4Yc//CGrV68mNzeXp59++p64ncmC+O2FH/2juDoK66zIwKBcL8ei/6q5690n55o1a2Q0vjcgLFuT0ZLl7+/vlb6PD8PhcJCUlOSiyCiZTMJHoEzz5W6NE5sPZd1tb8R9EYyPj6egoIArV66wYMEC6aYE6i+Bq+yLRqOhsLCQwsLCUa+djPPVndGUG29GmdlE5NZU83x8EpTzU6ncTJb+ut9fKSkpsqiLYDK4CjyIxzG4ic3laIxF/1U3K5Q1uAVqPrbz4Z14S9qd0XA4HLJiHeBiLZhMvspfF/eNcnFxMX/zN39DZ2cnGRkZzJw5U742WWTOZLPk+Hg8RBoibz0NUSJcCEWmDG+Z16JCoxoDtMcbZRqxser7Yyuver1+TL5YrXh7/8D7++jt/QNfH8eLc+fO8dFHH03Id/nGcPLj7f0DXx+9AW/sn3dv4Xz48OHDhw8fPnx4FRpviEL14cOHDx8+fPjw8c3AZ3n14cOHDx8+fPjwMWnwKa8+fPjw4cOHDx8+Jg0+5dWHDx8+fPjw4cPHpMFjqbL0er0G+B7wQ2Am4A/cBN4E/tVgMNge8PZJiV6vfw14585//8RgMPzWk+0ZC/R6/SbgPwAzgFigFbgA/NxgMJzyZNueFL1e/zrO+fgg7AaDYfJVK1Cg1+t/BswDpgNxwAjQAHwK/NJgMHR7rnVPjrfLmm/KPHVBo/kfwP8FTMPhaHqCz/kp8PdAAQ5HxRi1bszwxjVDibf17xsga2KB54BNQAGQApiBcpx9fNNgMNjv/wljhyctr28DvwMygQ+B3wCBwD8DH96ZBF6DXq9PBf4FGPR0W8aKO0rPHmAOsB/n2F0EngFO6vX673iweWPBZeDv7vPv8J1r9nmkZWPLXwGhwEGcY/gHwAr8LVB2Z+5OZrxd1lxGzfPUmdzx+2g0p9FoBtBohtFoLqHR/BiN5vEVao0mFfiPwBsPVVw1mtfQaBx3/v3xKFcYgA7gfz92O8YZb1wzlHhp/7xd1ryEs08lwBngF8AOYBbwW2D7RPXRI5ZXvV7/LPAaUAcsMBgMXXee1wLbgReA7wJveaJ9Y82dwXwT6AY+wSl4JzV6vT4JZz/agdkGg6FD8dpKnIvm/wD+3TMtfHIMBsNlnIrBPej1emFVfmOi2jOORBgMBqP7k3q9/v8F/jPw/wMmZaLAb4KsmQTz9G2cY9CBc0EfAtbgXNCfQqN56Z5ybw/mvwFBwD888CqnkiuUo7BRr3E4RtBo/hn4GRrNYhyOrx6jHeOGN64ZSryxf98EWQNUAluAz5UWVr1e/5+Bszj7+DxOhXZc8ZTl9fk7j/8oBhjAYDBYcAomgL+c8FaNHz8GVuE8ThjycFvGinSc8+eMUnEFMBgMpcAAEO+Jho03er1+FrAQaAE+93BznpjRFNc7bL/zOG2i2jIOfNNkjUQV81SjeZa7C/pMHI4/xuH4D0ARTrcUsaA/6udFAq8CXz7Q6uos4yOUo18/5FP/HbChrg2aN64ZSryxf14vawwGw2GDwfCZu2uAwWBo4+59tmIi2uIp5VUUi68d5TXx3By9Xh81Mc0ZP/R6fT5On6p/NhgMxzzdnjGkCqevywK9Xh+nfEGv1z8FhAOHPNGwCeBP7zz+brL7MD2Eb915LPNoK56Mb4ysGQU1zFO5oONwyAUdh+PrLugvAyE4LbgP4tGVI4fjFnAceBGNJuIx2jIuePGaAXh1/77JsgbAcufROhFf5qmALSHEMkd5LUvxdx5wevybMz7o9foA4F2gEefxq9dgMBh69Hr9/w38HLiu1+s/xWnlyMZ5rHCQu4un16DX64OB7wB2nD4+XoNer/+POI9XI3EGcC3Fqbj+vSfb9YR8I2SNOyqap4+0oKPRROFw9D7C562583jivldoNFI5wuE4hkaz6hE+9yROi9FTOP34PYI3rxng9f37RsoakOP6R3f+u38ivtNTllchHP5ar9fHiCfv/AB/p7guekJbNfb8d6AYeN1gMIx4ujFjjcFg+AVOy0oA8CfAf8Lp0N0EvOXuTuAlbAWigH0Gg+HrRzmrk/8I/D/AT3AqrvuBdQaDodOTjXpCvimyxh21zNPHWdAfhaU4XZIqR31Vo/m6ytG5O49PPcZ7xgOvXjPw7v59U2UNODeLs4C9BoPhi4n4Qk8prx/gjH7Nxmm1e0Ov1/8CZ9DBRpxH0uD0Q5qU6PX6BTiF5z9O9pRR90Ov1/9fwMc4HdCzcUasz8VpUfmDXq//X55r3bjxwzuP/+bRVowDBoMhyWAwaHBay57HqVxc0uv1czzbsifC62XNfVDLPJULOhqNXNDvKJmPt6BrNIFAItD2gAAvqRzhcDyOctR25zHtMd4zpnj7muHt/eMbKmv0ev2PgZ8CFTj92ycEjyivd5x9t+C09LTh7PD3gWacO2uRV3JSWu4URyOV3PXr8ir0ev0K4GfAboPB8NcGg6HWYDAMGwyGizjzwLUAP9Xr9VkP+pzJhF6vnwEsxjlP93q4OeOGwWBoNxgMO4F1OHP3vvOQt6gWb5c1o6GyeeqyoKPRvIFG8wu+3oIee+fx9qivajRSOcLheFzlqOfOY9wDrxonvH3N8Pb+wTdW1vw5zqwh14GVBoOh5yFvGTM0j5ehZPy546vVAziAyDuRepOKOw7ZowvYe/lng8Hwk/Frzfig1+v/N87d1o8NBsO/jPL6JziV2BcNBsO4p82YCPR6/T/jDAT5O4PB8Lcebs6EoNfrL+GMDI9XRtB6A94ga0ZDdfPUaWX9DzgX81ycgR1fAf8F+CXOjAjFOByXH/I5UTjlahkOR+Eo33ENpxJcjMNhUrz2tzjdYf4Eh2N0/1+NZjZwBdiDw/GtUa8ZR7x9zfD2/j0Mb5Q1er3+J8A/AVeB1RPtJuixClsP4DVAB7w9iQfYhDNR8WjMwXmsdQJn5Y3JenwSdOfxfumwxPPmCWjLuKPX63U456ad+4+tNzLlzqNXHXXdwRtkjQuqnKcOhxX4xzv/7qLRBOPcGI3gVDwf9jm9aDRm7lpglYThrBAHYEQzap7036DR/AZnINdP3F4Tn+kpq5i3rxne3r+H4VWy5k6w9t/jPEFZ6wnDhifLw0YYDIZ+t+fm4/xBBnEmuJ+U3HFEH62aC3q9/m9x3qhvT/JSeMeBvwB+qNfr/81gMLSIF/R6/QZgCWDEaWHxBl7C6Ze3x5sCtfR6fR7QeydPn/J5P+B/AgnAVwaD4VGtJqrDm2XNKEymeSoX9Dupsx6FcqAYjSYCh0M5pk+qHImAscuP2I4xxdvXDG/vn+CbIGv0ev1/w9mPCzgDeifMVUCJJy2vB/V6/QhOk/MAzjrAG3EKoecNBsNoqVV8qIePceZxXQPc0Ov1O3H6+eQDmwEN8J8MBkP3/T9iUiECYLyhopaS9cA/6PX6Y0ANTr+sRGA5zoCtNpyZJCYz3yRZo755eq+iCRrN113Qj+AMCl2AMo+0MzhrVOXojttAMU4l+X7K0cI7j6WP0RYfPtzxalmj1+u/i/N+teE0YP1Yr7+ntke9wWB4a7zb4knl9WNgG85chMHALZz5CP/eYDDUe7BdPh4Bg8Fg1+v1G4E/xzmOz+FMHt6DM0jk/xgMhgMebOKYcSep9lLUEQAz1hzCqegsAQpxplcawhlY8S7OcfTIznoM+UbIGhXP04NoNPdd0HE4HmdB34HT1/5pxqoIikbjB6wGbuJwXB2Tz/TxTcXbZY1IeeePM6XiaBxlAkrgqi5gy4cPHz58eBEazd/gXNCzubugfwH8PQ5H/df4vIs407ml4nA83Bf7YQFbGs26O+35KxyOXzx2e3z48DHh+JRXHz58+PAxedBoXgbew2m13TkGn7cDp5tMNg5H3xN/ng8fPsYdn/Lqw4cPHz4mDxqNBmfQlTNbwZMsYhpNEXAR+DEOxy/HpH0+fPgYdzxVYcuHDx8+fPh4fJzK6g+BndxN5fZ1ScaZNP/XT9osHz58TBw+y6sPHz58+PDhw4ePSYPP8urDhw8fPnz48OFj0uBTXn348OHDhw8fPnxMGh45z6ter5/0/gUGg2HUmoHg/f0D7++jN/QPvL+Pvnnq3f0D7++jN/QPvL+Pvnnqvf3zWV59+PDhw4cPHz58TBoeu8KWwWAYj3aMK6OUL7sv3t4/8P4+Tsb+gff30TdPXfH2/oH393Ey9g+8v4+TYZ46HA4cDgd+fl/PhjgZxlAkBHA4HGg0mjtZ7h6Nh/XPk+Vhv3E4HA6sVisWiwW73Y6fnx9BQUH4+/t7umk+HgGbzYbJZCIkJASAsrIy3nvvPWpra1mwYAFbt24lLS1NXusbVx8TiW/O+fChXiwWCw6HA39/f/z9/V2UOavVCkBAwORXyex2u+yb6J9SabXZnEXxnlRWqeaXcjgc2O12wNlRobGPpq0/jvauFsTOQ6vVotVqPd0cH18Du91OUFAQACaTiT179vCzn/0MgLa2NtauXUtaWprcpPgUCXUj5A2MLmcmA0rrjZhvdrvdxdIxGfs1Fog1RDnOSjQazde2evkYO5Tr/qPMVXdrnnivGhH3or+/v8u6b7FYGBwcxGQyERYWRlhYGODap8mEUl8T95TVamVkZISRkRFsNhtBQUGEhYURGBg4Jt+pKuXVbDbLgbbb7djtdrRaLQEBAS5FVPz8/CblAPuYvFgsFil8uru72bVrF7t37wYgJSWFdevWkZycLK/3LYrqxuFwYLPZpJUgICBgUsoU0QflgmAymQCnFccbLDlPgs1mkydd4GoYEQqFb5PpOZT3YUBAwCOPhfLe9fPzU61OIOaeTqeT7RsaGqK8vJxPP/2Ua9eusXbtWv74j/+YkJAQafSYbOuH1WqVchSgr6+PyspKTp06xcmTJ+np6aGoqIjnnnuOxYsXu7zv68qocZVswppqs9lclE/xt1arxc/PD7vdjr+/P8HBwePZHI+i0WgwGo10d3fT3t7OwMAA4eHhZGRkEBMTA6hv1yUsiEJIiJtKTW0cb8RRrFarxWazcf78eY4dO8a+ffs4c+YMOp2OH/3oR/zZn/0ZsbGxAC43sQ/14HA4sFgsAAQGBk7KExD3xV7MM7PZzODgIBEREffIUaG4TbYF8XERv41QaMR9qOZ7UVjm4K5iLdZN8fej4OfnR0BAwKQZY2WflfehcpMhDFhwd+6KsVXzmIrx8/f3lyd1AE1NTVRVVVFVVcWFCxf47LPPaGtro76+nqioKDZv3ix1AeFWqGaEXhcQEOAyhm1tbZSWlnLs2DHOnj3LxYsXATh79ixWq5Xw8HAKCgqAJ7OYj+sMEMcAfn5+oyqvyte9EaUyarPZqKio4MCBA+zfv5+qqirmzJnDT37yE1auXAk4d2ljZVIfC5Tjo3z8JmGxWKQ1oKmpiX/913/l0KFD9Pb2As4N2LRp06TiajQaXXbZPtSDN8gasYkUiproT2VlJVevXiU/P5/CwsJ73vNNQDm+k6XP7muj0jIslFfRl9H+FtdONtl8v7aK38P9XlW7e4ASsW4qx6uxsZH33nuPAwcOUFdXR1dXl9xIX716lX/4h38gPj6eDRs2AM7TE7Ub89zn7vDwMB0dHRw9epR33nmHr776CqvVSlBQECaTif7+fvbu3UtiYiLBwcFkZ2fLtfXrGO7GRXlVauTCpH8/RkZGCA4OpqGhgb1792I2m8nMzKSrq4uenh7y8vKYNm0acPfoNiwsjKioKEJCQlQ9mYXwMZlMVFdXs2/fPj766CMuXboEOAe/r6/P5Xq1IHZ+7jtc4d4Bd29SsaCqeSy+LjqdDnAeg5SWlnLq1ClaW1sBSExM5JlnnmHGjBnyek8pR1ar9R7fRx93EbJDzOfOzk6uXr1KT08PUVFRZGVlMXXqVGlBUNspiBJhrert7WVgYIDbt2+zb98+Tpw4QUZGBrNnzyY4OJjExERmzZrl4s7iLbifCrlbHsUpV1NTE9evX6e9vZ3AwEBCQkKw2WzYbDZSUlJYsGCBDLIcb2uXzWbDarXKgJXAwMBR3VWeZJOl5nkLrroBQEdHB9euXeP27dvk5+eTn5/voqiOJsu6u7tpbm5maGgInU5HdHQ0MTExREREeLTvSiuxn58f1dXV3Lp1i5GREc6ePcuOHTsoKyuT63xUVBRRUVG0t7dz7do1Ojs75WeJ30mNY2k0GgGkkaa3t5fr169z/fp1rl27xqVLl7hy5Yq8TqvVEhERQX9/P21tbZSVlbFgwQKysrKkki8s1Y/DuCivykYIQaE8ChE7ivLyclpbW0lISODEiRP89//+3xkaGmLJkiXU1tbS2NjIypUrWbNmDXa7XS40c+bMYf78+QQHB6tycJUC1eFwUFVVxe7du9m1axc1NTVERkYSHx/PypUrSUlJke9Tk9Ih2u7uNuB+FOKtWK1WKYSsViulpaV89tlndHV1ARAdHc33v/99Xn/9dbKzs+XC5ynLuZqP0dSAUhno7Ozkiy++4LPPPqO2tpacnBy2bNnCunXrpAX96wjT8UZYWjUaDR0dHZw6dYqysjLq6+s5c+YM165dAyAsLIzw8HDmzJnDs88+y5o1a8jIyABcI4EnM+7HzcqNf0dHB+Xl5VRWVnLp0iX27dtHc3PzPZ+xYMEC/u7v/k4qr1ardVzvXyE/lYh1UTke4rj8cd0GtFrtpBhXIau6u7v58ssveffdd+ns7OR73/se06ZNIyAgAKPRKBX7vr4+2tvbGRoawmg0UllZyenTp2lvbycjI4OVK1dSUlJCRESER/ulVDY7OjrYv38/Bw4coLOzU7oHgKt1PT4+nujoaHJycuQ8BFTtgy8MOna7nYaGBk6fPs2RI0c4e/YsNTU1DAwMAHdlrt1up7+/H4CQkBASExOJjIx06d/X6euYrnjuO9fOzk5Onz7NuXPnaG1txWKxoNFoCAwMxN/fn7a2NgYHBwkLC+PWrVvyKPbKlSvyB7h8+TIDAwMYjUZaWlpISEggICCAWbNmufh+qQmlW0RfXx9fffUVO3bs4Pr16+Tm5rJp0yYWLVpEZmbmPRN2tM9S7ujGe0IrA5O6urr46KOPuHz5MomJiSxevJj58+cTFxc36nuVOy21LfyPi/C58vPzo6mpibKyMs6fP09vby85OTm88sorvPzyy0yfPh2467A+0QJHDb5Rou9qHHPl8fqtW7c4ePAgR48epaqqSh7fjYyMMGPGDBYtWqRK5VXIOZvNRnt7O1999RVHjhzhxo0btLe309HRIWUnwODgIIODg3z55Ze0trbS1tbGli1byM7OVv1p1WjY7XaZSkjcY+6y0mg00tHRQW1tLadOneLUqVM0NTVx+/btURVXgNu3b8vPhfE7+XpQaqCrV6/S3NyMVqslJCSEkZERmpubuXnzJq2trZhMJnmqJU5X4K7ybrFYsNlsFBQU8MILL5Cfny+/Uy3zF3AxXgH09/dz/Phx3nnnHfbv3w/A3LlzuXjxIikpKfT09NDZ2UlzczM1NTXU1dVx+/ZthoaG6O7upqamhqGhIUpKSliyZAnR0dFoNBqP9lt8b0NDA1988QWffPIJp06dkusiuCquOp2O5cuXs3TpUrKyssjJyZHXqdFApDToAJw8eZJ33nmHsrIyGhsbaW9vd7mHIiIimDVrFhaLhTNnzgCQlJTE2rVrmTt3LnBX3/C48qr076yvr6e0tJRPP/2UkydPugjX+zYmIACbzcbt27flc7dv3+b8+fPy/729vVy9epWmpiZSU1PlhFHDzSp2yqIdzc3N7N27l/fee4+ysjLi4+NZs2YNf/RHfySFzMOYaKVAqbz29PSwfft2jh49SmRkJHV1dTQ0NDBt2jQSExOJj4+Ximp4eLjckYFruh5PK1ePg/sRRkNDA5999hmHDx+mra2NyMhINm/ezOuvv05mZqbcWHhqAyUCHvv7+zGbzYSEhBASEjLuv7kyBYzou5oCg8SpgXAVaGlp4cMPP+SXv/wldXV1Ltf29PTQ09PjklLpfumVJhoxF/39/RkcHJQLxsGDB2Ubg4KCSEhIICIiQp6Y9Pb20tnZycWLF4mMjGTmzJnk5ORIGW02m1VpqVMqOHD3CN3dImqxWOjr66Orq4uuri5aW1upr6/nxo0bnDt3jqtXr7pcHxoaSlhYGKGhofL0aOnSpUyZMkVeM9bzVsg/IUuGh4fp6upicHAQq9VKQ0MDp06dor6+ntDQUEJDQxkeHqa+vp7Lly+7HCM/jK+++orExESmTJlCZGSkKu5BgYgo9/f3p729nRs3blBbW8uJEyc4e/Ys4Bzn69ev88EHH5CYmEhfXx91dXVUVFRQVVXF0NCQ/LyAgACioqJIS0tj3rx55OTkuFgDJ1oPUMoai8XC6dOneffddzlz5oyMYxG6QXh4OA6Hg6GhITIzM1m6dCnPP/+8nN9qkqECMY/Fvenn58fVq1d59913effdd2V2EyFrR0ZG8PPzY+XKlWzYsIGKigpu3rxJb28vixYtoqSkRH62Ut94XMZkxVUeRdntdi5evMiuXbvYu3cvV65cwW63Ex8fT3BwMJ2dnYyMjIz6Ocpd8P3QarWcOXOG6dOnM3v2bCIjIwHXwBpPISZxYGAgNpuNvXv38j//5/+kubmZqKgoNmzYwMaNG6UPrxpRLmb+/v7yKKavr49Dhw5x4sQJgoODmT59OvPmzSM6Olq6cih9P0H9/lejISx1Yi5duHCBN998k8uXLxMVFcW2bdvYunUr6enpLu+ZaCwWizxaGh4eZt++fdTU1FBQUMDChQtJTEwc9zaM1m+1+G0rFxSAffv28S//8i/y6E7J7du3aW9vd7GQqKUfyqCs+vp69u/fz7lz57Db7YSGhpKYmMj06dMpKChg1qxZhIaG0tLSwpEjRzh48CCDg4P09fVJ62xISIhq+nY/3JVXJRaLhaGhISorKzl27BgXL17k1q1bdHV10d/fj8lkclF0BHFxcZSUlJCfny8zMmRlZbmcfI21wmCz2eTGzmq1cvLkSXbt2sXly5cZHh7GbrfT29tLf38/QUFBBAQEYLVaMZvN8pj1URkYGKCuro7W1lYX3081nMwof4cTJ07wxhtvUFVVhcVicUnrdvXqVerr62VmnqGhoXvGMjU1ldmzZzN79mzmz59PYWGhywbEExlExEmAzWajpqaGEydOcO7cOam4igDL5ORkiouLmTp1KkajkZSUFNLT00cNTFMTSr2mr6+Pzz//nHfffZeLFy+6xL/4+fkxMjJCZGQka9eu5a/+6q8oLi7m448/pqioiJSUFF566SWioqLkZz+Jm86YKK9CwPr7+2Oz2Th37hyffPIJN27cAJwTasaMGYSFhXHu3DlGRkaIj48nMDAQq9VKQkICOp2O/v5+QkJCmDJlijwm0el0DA4OUllZSUdHB0ajkdraWnbt2sWiRYt4+umnAXX4iwoLgd1up6ysjCNHjsgjq1WrVvHSSy8xf/58AgICXG7a0fygRFqfwcFBhoeHCQwMJDw8fNz9fJU3f1xcHFu3bsVut3Ps2DHa29vlaxUVFdy4cYOkpCRCQkI4e/YsBQUFZGRkMH369HtuysmA2FUGBgZiNBo5c+YMu3btklacRYsWsXXrVhYsWCDnp6csrsoFwWg0cvz4cfbs2cOcOXPw9/dn9erVBAcHYzKZHit/4mgIHzxhFRCWQD8/P0wmE83NzQQHB8tFRHm06QnEuAQHB2O1Wvn888/57W9/S0NDA2vXruWpp57i1q1b3Lhxg+bmZpqbm7lx4wZnzpwhNTWV8PBwj2f9ELECdrudkZERqqur2bNnD6dPn5a+/9u2bWPJkiVERUWRnJzMtGnTCAoKoquri9TUVAIDAzl8+DDd3d188cUXdHR08NRTT7Fs2TLZP6UfrRoYLcC3s7OTpqYmmpqa6OjooLu7m9raWsrLy7l586bLSZ1Ap9MRGxtLREQEqampMk4iJyeH0NBQtFot4eHhLn6SYy2vxOeJNfH9999nx44dj6yYJiUlERUVhdVqZWhoiMHBQelOFxoaSnh4uFTycnNzyc7OJjY29ol9CccKs9lMQEAAQUFBDA0NcejQId59910OHDhwz7U2m00GHwqioqKYOXMmUVFRhIWFkZaWRlFREbm5uaSlpbmcJHhqHhuNRoKCgtBoNLS0tLB9+3b27t2LyWRCq9VK5S4zM5M/+ZM/YfHixQQHB2Oz2QgJCSEuLo6RkRG0Wi2BgYGqXDOV92RlZSW7du2Srh7ChbCrqwuTyUR4eDhPP/00r732msznWlxczA9+8APS09OZM2cOcG8A7ddhTC2v4u+qqiqqq6vl6xaLBT8/PyIiImR0YFFREWFhYdjtdgoKCpgyZQr9/f0kJydTWFgoFVudTkdNTQ2ffPIJpaWl8nN7e3u5ceMGM2bMIDU11eNRwkrzd1tbm7SQAJSUlLBx40YWL14sLcXCwX40xNGHVqvlxo0bnD9/nqSkJJYsWUJKSsqoqVPGCuURcFRUFM8++yzgPPKqqKjAZDLR29srx7m+vh6bzcaBAwcICwsjLy+P5557jueff57p06erZlF8FMxms/Q1amxs5N///d/ZuXMndrudhQsXsmXLFoqKilSxUVK6ZdhsNurq6mhqaqKlpYX8/Hxyc3PJyckZk6NhIbzcA0vMZrN01p86dSrPPfccMTEx97jPTDTKTcXx48f5h3/4B86ePcvTTz/Nf/kv/4WlS5fS3t7O8ePH+fLLL/nss8+oqKhg9+7dTJkyhZUrV7oodxPZD+VRs0ajoampiZMnT7J3714uXrwoLcdxcXF8+9vfZsWKFYAz8XloaCgOh4OUlBS2bNmCTqfDZDJx8uRJdu7cycGDB6mvrycxMZG8vDyX71MbVqtVRiefPXuWo0ePUlFRQXd3N52dnfT398s56U5oaCj5+flMmzaNGTNmsHjxYgoLC4mMjHxgVpSx+h2U7lIOh4PGxkaOHj1KaWmpVFwDAwOlYjMaERERFBcXk56eztDQED09PbS0tMgj9KGhIXnqYrfbSUpKYv78+cTHxwPOVEtCqfIkQuk5duwYP//5zzl27Jic3+KkNTIykszMTCIiIjCbzVitVmJiYsjMzGT69OkkJycTExNDSkoKmZmZhIaG3vM9npI1Qr4ODQ1x8OBBWS7cz8+P8PBwenp6AKcO8OMf/5jQ0FBMJhN2u131qbDAVZYKX14RGApO32Uxx+Lj49m4cSOvvvqqi2uAyCKh3FyMxXiNmdlImZuut7dX5jATRyEBAQHk5uaSnp5OeHg4s2fPJjw8HKvVSmJiIjExMZjNZiIiImSiXkFKSgrBwcHk5uZSVVWF0WgkNTWVnJwcVSgS4OojJ4SVULSnT58ud5CCB7VbKK9+fn7cvHmT3/3udyxcuJAFCxbg5+cnF9TxFExiPMPCwli7di2xsbE0NDQwNDREV1cXV65c4cKFC3R0dMj3DAwMcO7cOZqamqirq+Opp55i9uzZZGdnS4Gj1sUSXI8r6+rqOH/+vLR0lJSUsHTpUpcxFJkI3C2TIm3PRFll7XY7g4OD8u+KigquX7/O1KlTpYAUln73/KBCyVTOR2WUs/I1Zdm/GzducOLECfbt28fVq1eZPn06AQEBrF+/XrosTLQFVhyR6nQ6qVj/4he/4OTJkxQVFaHX61m6dCngTHM2f/58GhoaiIqKoqWlhcuXL1NRUcGSJUukn9pEK6/C/Unc/8ePH+fXv/415eXlDAwM4HA4iI+P5+mnn5YKKDgtjUp/bX9/f1asWCHduXbu3Mng4CAnTpwgIiKCJUuWMGPGDLKzs+WRp6hoONEoN2H19fVcu3aNuro62traaG5uprq6mubmZvr6+jCZTFLpE/dbdHQ0U6dOJTExkZSUFKZPn05WVhbJyckkJyeTnp4+oQEw7jLObDZjNBqlsiYsv1qtltjYWLnZjI2NZWRkhKGhIZKTkykqKiI6OpqBgQFGRkYYHBzk6tWrHDt2jDNnzsh7Xpx6qsVqJ9Z7sQE8fPgwb7/9NhcuXAAgPT2dlStXMmXKFIaHhwkPDyc3N5eYmBg5piEhIURFRRETEyPjKdyVPVGRUxQ7mkiMRqM0MDkcDt577z3+9V//lZs3bwK4+H7m5OSwcuVKuQaqMRjrfoh519XVxaFDh9i1axeVlZUEBQURGBgo18ecnBx++tOfsnr1auLj42W52yfxaX0YY7K6KqOs/fz8iI+Pl3m9xA0bFhbG7NmzmTlzJgkJCS5KgDsiOlNE5oWEhPDUU09RWFhIT08PVquV4OBgwsPD79mFeUoxEopKb28vZWVlchInJSUxc+ZMkpOT78kD545QGsSC2dDQwJEjR7h69SoFBQXSKX0ijkiEn47dbicxMZENGzZgt9vp7u6mp6eH8vJycnJyuHr1Kna7ncjISJnGpLW1le3bt3PhwgW2bNnCH/3RH5GZmQng4oeoFpRKj81m4/Lly5SWlsq0WFOnTpULvUajkcdhD8thPJ4ox1+r1ZKTk8OJEycAqK6u5siRI0RHRzN37lxCQ0OfSGDabDa5APf29lJVVcXhw4fZvXu3dA3q6upCo9GQlJQkXXmUfrkTgdlslkdvg4ODbN++nd27d6PT6di6dStr164FnAJZp9MRGBhIYGCg/G2Gh4cZGBjwaLCWcj41NDRw8eJFKisr6e/vJzg4mIyMDDZs2MAzzzwjF3NlzIFw3bLZbAQHB7N+/Xr6+/vp7u7m9OnT0v2qrKyMTZs2/f/Z++/wuK/szBP/IFUVcs45BwIESAIMIClGMSeRklpZcltt9Xzb7vW6x17/7FmvvfPMjGd+a3c7dPW6s7rVylmkmBOYE4hEIudM5FwooID9o3gvv1UEJUoiUAWo3ufhA6IS7q2bzj3nPe9h165dpKSkzCjjNNsQRp6TkxPj4+PcunWLI0eOcPLkSWpra+nt7ZWHoxqC2qPT6YiKiiI5OZnk5GRSU1NlWNn6XJjLRBi1RJCzszORkZEkJSXh7+8v9UnFhSEmJoZ169bx3HPPERkZSU9PDwaDAT8/PxmlE+jv7yc0NJTu7m5u3bpFf38/7u7upKenk5SUJKWkxNy2FdRRoebmZt555x0+/vhjxsfHiY2NZePGjbz00kssWbIEo9GIi4sL/v7+X/q5alkxW8oSgqVUW11dHW+++SbFxcWAOSoizo6goCA2btxIRkaGNOSEcoi67L29FmFwcnKSGvyffvqp7KM1FW3nzp18//vfB5C8bZ1OJ7+j2ZDoeyTGq/oQd3Z2ZsmSJeTn53P58mWpMuDj40NYWBjR0dEzustF58TAWnsWnZycpKivPUG01cXFhYmJCYqLizl58iQdHR3Ex8fz6quvsmfPHsLCwoAvTgQRhuvAwABXr17lrbfe4uOPP8bX15f09HQ8PDwA201wcTEJDAwkPDycpUuXShFmnU6Hq6srTU1NnDt3ToY5XVxcyM/Pl8arvWRxqyEMaiFt9vHHH/P666/T2dlJfHw8u3btIjc318JYEIvTVlDTAby8vDhw4ADDw8PSEyoym+vr69mwYQPR0dFfa9709/fT1NREVVUVFRUV1NXVyX/Nzc2AeV4MDQ1JMXgBNS93riDWV1tbm7xArlq1ipUrV1qM10zr0JbVmcTaF9+X0BXu7e0lPT2doaEhQkNDefbZZ3niiSdISEjAzc2N8fHx+4qJWHv+8vLyWL9+vSzKIETFBc9Oq9VaJCDOVZKP+iI7ODjI2bNn+eijjygpKfnCkPr09DT5+fns3buXrKwsvLy88PLykpqZX6Slqqa+zPY4iz1C0KnUxqgIm4uISHBwMK6uroSGhs7IpTcYDFy8eJETJ05QVFQkk4GysrIkfSQgIIDh4WGb0QXEvBN5H/X19Rw6dIirV68yPj6Ok5MTGzZs4Hvf+x55eXlf6bI0U8lcWzgOxPwRF96uri4OHjxIXV2dfI068dzLy0tyr9UOPlGwQn2BsyfuuYCrqyt37tyhoKBAGq7i7NHpdOTk5JCTk8Pu3bvle4TjS43ZGKtHZryqByY2NpbU1FTKysqk8erl5YW/v780AKxvHKJzag+AOrypvs0J2EJX0xpqL+jIyAjXrl3j+PHjGAwG8vPzeeGFFywyWmeCoFiI8EdlZaUk9xuNRgICAggMDJQb/Vz1WXhyRFUtscAEf9nHx4eEhASL96xatYqUlBQmJyf59a9/TVlZGefPnycjI4Pw8HC70+QF5EVpenqa4uJiCgoK6OjoAMzagwcOHJC1mCcmJqQRNDExQUtLC0NDQ4yOjjI8PMz09DR+fn5ERkYSFBQ0a2Mm1ohoz9atW+nq6uLChQuMjIxQV1dHf38/d+7cob29nZiYGFxcXPD09CQgIACtVis9dL6+vlInUWzOPT09VFdXU1VVZWGwtrW1Wej5enh4MDIywtTUFB4eHhYG4lzSBeCeeHZNTQ1/+MMfKCkpkZeP1NRUedFU70Fquof6e51rTE1NyeQ6MCdEfvDBB4SHh6PVajEYDHh7e7Ny5UoyMzO/8LPUvHXBh9ywYQOVlZUcPnyY0dFRRkZGKC4uZmxsjN7eXnbt2kVGRsachjTV80OE0AMCAtDpdBalskXyVVBQEJ6enoSFhbFp0yYOHDgwozNjYmLCgneqlnObS6j7FxMTw+rVq2lpaaGjo0Mmh/b29nLu3Dmio6PZsGHDfXvk2NgYtbW1nDp1Shqu4tKYnJzM9u3b2bdvHwkJCRiNRmkk2gLijBbRlnPnzvGrX/2K4uJidDoda9eulY4AFxcXDAaDzP0QYyagPmvE+WoPFEFhcwgu84cffsi7775LX1+fNNr7+/vx9PRk0aJFPPbYYyxdulQar0Kz2NYJoQ+C2i6bmpqirq6O69evS0eAk5OTtFdiY2N55plnWL9+PbGxsYyOjkqPuKurq0WxDfHZak7/Nx3PR7ai1bdZUeFFbW2PjIxIGQ8Bd3d36U203mDUHVZrhdo6k9kaasOnsrKS69evy4zJiIgImYFtNBpnLLcqIPozOjpKQUEB58+fl94HsShs2We1EfZl7QgPD5dh9oqKCk6dOkVGRga7d++W423LTH0BdfY8mAtifP755zIpxs/Pj8WLF5OZmYmbmxsGg8GCF3jz5k0+/fRTGhoa6OzspKamhvHxcRYvXsyTTz7Jli1biIyMnNVNV/TBzc2NRYsWkZuby6FDh5iamqKvr48LFy5QWlqKRqOR4+br60tgYCAmk4nx8XHS09NZsmSJVMEQnNaLFy/S2toqeb0TExP3GXojIyNMTk7i5+dHXl6exUVtrkoGCyNcGK+ff/45//Zv/8bIyAhPPPEE69atIyIigqmpKRlWBfuW+nJxcaGvr4+mpibZ3qioKLy9veVrHoY/LvbV5cuXy+zmP/zhD/T390v+uuCXPvfcc+Tn58tEI2FgzRbE+jeZTDI5dGRkRCYmCa9kREQEzz//PFu2bCE6OhqtVouHh8cDKyqJcKytzwi1gRIQEMCzzz6Lk5MT77//vtxjhHbv8PAwfX197Nu3j+joaPm+a9eu8fvf/56zZ8/S1dUlvxOArKwstm7dKtec4NHaCuqCOqOjo5SXl0tvXW5uLn/5l3/Jhg0bpNdZfdEVZ5wath4/a6j57+3t7Rw5coT33nuPK1euMD09jaenp/SoRkdH8/zzz/PMM8/g7+8vqZDiHLeH+TkT1BcQZ2dnCgsLOX78ON3d3XJuTUxM4OXlxfr169m3b5/Fnq/2JMO9MVR7y9X23DfBI+O8qv8fHBxMSEiIxaFdVFTE+Pg4oaGhcgDDwsIIDQ2VX1ZoaCghISEEBAQQFRVl8f7Jyck5OwwfBtaGz40bN3jnnXek6HJcXBxJSUlyg37QoWgymeSkaGlp4aOPPuLYsWO0trYCZl27zZs3SwMK5j5cMpPBKhapWqpESF8IQ114RYqKiigvL+fAgQPAvYo59mC8qg/osrIyjh49SlNTE9HR0ezdu5fHH39chvvUHtejR4/yzjvvyKpbwlML5o3N39+fuLg4wsPDZ7WQhvqwSkpK4k/+5E+IiYnhzJkzVFVVzaiVaA1RxcbZ2VnqaJaUlMg5HhYWxvj4OH19fbi6uuLp6YnRaJR6zeHh4ezcuZPdu3eTmJgoP3eu+K7q+dnf38/ly5cZGRkhJiaGnTt3yso1wgunhkg8ANsars7OzvJiV1dXR01NjUVEZsOGDezfv5/w8HALTesv+n7VCXYeHh4sX76c1tZWDh06JBVDpqamaG5u5uTJkyxevFgmtInn5mKvEf3x8fEhMDDQIikLzJSzlStXyrap3yfaqM5itoeIHNwbG8HFzs7OZmBggJaWFgYHB+nq6pIFRq5du8bQ0BANDQ3k5eURFhYmS4wePnyYjo4OGa728/MjKyuLnTt3kpWVJfdcWxedUNMHGxsbLZJ5s7KyJOcc7jkvbJl09VWhLoDQ3d1NUVER1dXVct8Q+6ygc6xdu5aQkBDg/r2npaWFsrIyDAaDpBaIsqk6nc5mFALrs7mvr4+amhqZZzQ5OSnzGnbu3CnLTs+UmPUw3vJvYsQ/UuNVHM7h4eFkZWWRnZ1NV1cXo6OjlJSUUFRUZPG+oKAg6Zl0cXEhIiKCmJgYEhMTWb58OVlZWdKDa2tDxxpicxcH361btzh06BD19fVSNigvL0++fqYbsclksjiITp48yU9+8hML/szixYt56aWXLDwi9rDIrd3+6oPO+mAdHh6mvr6erq4uwsPD7eZwUXsKAJmMBGaFi927d7Ny5UpZEUUkgZw+fZp//Md/5MKFC/K9IlwiDPqioiIuXbpEZmam5DvPhvEq5oTJZCIgIIBdu3aRkJCAv78/x44do6qqirGxMXnZEFB//0LrVECEfDQaDcuXLycyMpKKigqGh4eZmJhgbGxMGhexsbE89dRTHDhwgMzMTJllOpeeBZE8MDU1RVFREfX19Wg0Gg4cOMD27dvvS3xRQ20o2WpOqudFb28vV65cobS0VNIzcnNz+f73v8/mzZtlkYGv0lb1YZScnMzq1atlMpSgJLS3t9PY2CglltTcwtmG6PvY2BhVVVU0NjZaPO/u7i4VLEQEQKvV2l3i54OgPrvS0tLYuHEjNTU13Llzh+npadzd3eXcLSsrY/HixURGRtLS0kJdXR2Dg4NSWstgMJCdnc3LL7/Mzp075XoT6hi2hNj/R0dHaWlpkcar8OKVl5eTnp5uMd/tNXw+E9Tf7/j4OMPDw/LcEzkvYNY13bhxozRcAZmYptFo6O7u5vPPP+f48eMMDAzg5+dHWFgYMTExLFmyhBUrVshxnWu1E2uvaF9fn/T2C2fFsmXL+OM//mMLOSz1WlR7l0UOk5oyIOy5b2qgz0p5WICMjAyefvpppqenOXPmDAMDA/e9vru7m/7+fpmoUFtbi6enJ76+vpw5c4alS5eSm5vL8uXL5eZlr3XU1VVRYmNj2b59Ozk5OfJ59YQQFSuEMTExMUFdXR3Xrl2ThqtOp2PZsmXs2bOHJUuWWHB/7a3vYNk/b29v4uLiZJ14gOrqai5evMjjjz+Oj4+PXW5a1kRzYcQ5OTnh4eHB5OQkN27c4O2335YedoHo6Giee+45NBoNn376KUVFRZw6dYrVq1dL43U2jSPrtffCCy+QmppKSUkJFRUVFBcX09TUJF8jNBVnqnYXHBxMXl4eqampuLq6Ul9fLzOkRSYpwPr163n22WdZu3YtcXFxFomYc2UICsPMycmJzs5Orl27Rm1tLb6+vhbVd8Tr1B6CgYEBKisrZZKZyAC3tRGgvmg4OTmRmJhIdna29Mx+1UQ4Ydg4OTmRkJDAa6+9hoeHB5999pncl0WFpurqajIzM+c0yiX+jtgTRYhVGGzqvVK996sPRHu4DD8I4oIJ99bWxYsXuXr1qoz+CE3eyclJSkpKqK2tlcoXgn8IZsrP0qVL2bhxo8WZOJeqHl8GcZkU56HJZOLGjRv81//6X1myZAl5eXnExsYSFRVlYfQI+o+tPcgPAzGe6lC6m5sb6enpPP300+zatQs/Pz85P4eHh/nkk08oLi6mra2Nuro6WltbpbA/mG2ILVu2EBcXZyE1NVfnvYiAu7q6MjQ0xPnz57l8+TIGg0HyksHsdBRa/ILnKnjqra2t9PT04OzsLCvhiWIwRqMRX19fEhISWLduHStXrpRSY1/HKTdrxmtISAg5OTmcPXtWbpBeXl5oNBrGxsakt0h8YWDOOB0cHKS9vZ2KigquX79OcXExjY2NrF69mtTUVLmBz4b0wleB+obS19dHb2+vnNDJyclSVknIDOl0OguqgeAn9fb2UlRUxIkTJ7h06ZL8/IyMDF588UV27txpIftiD17XB0EcOkJgOyoqSj43PT3N6OgoBoPBIvPSltwf6/kjMrfhnm7m+Pi4NMp6eno4fvw4ly9flgeOSFTasWMHL774IiaTibq6Om7evEl5eTm1tbXk5+fLUNBs9UN4HkUygKhCk5OTQ2lpKVFRUdKb5+/vj06nk5WLROaoSA7csGEDGzduxM3NjWPHjnHz5k0aGxuldqOQvHn22Wf53ve+Z3HLnus1ae05r6mpoauri4CAAIsL0vj4OC4uLnL/GBkZobCwkKKiInp7e+V3aAvjVf33jEajFOIHM/1IrZMMX12xQxhPExMTeHh4sHbtWiYmJujr6+PMmTPyAlNZWcmJEyfw9PQkPj5ezqnZHlPRH3HpjY2Npb6+Xl6SxsbGOHfunNQEDQsLIywszMJgU2vc2iPUnL/4+Hg2btxIZ2cnJSUltLe3yzNSp9MxOTkpf3dzc5OqDElJSaxZs+Y+Xqy9GHqijxqNhujoaJYsWUJJSQkDAwNcvnyZy5cvc+rUKTZu3MjixYtJTU0lOTmZoKAgdDqdLCIB9wob2et4iu9cnfCp0WgICwsjJSXF4uwzGo0UFBTwi1/84j6nB5jntzAMjx49ypYtW4iNjZ1TrXCwvBQLR0BlZSUTExMWHNawsDB5Jopzraenh0uXLlFYWEhvby9ubm4MDw9z+/Ztrl69alF6W1SNy8nJkdJhX4eiNGvfjMlkkqXrBMTBqk7+EBvQTBtyR0cHJ0+epLS0lAsXLvDqq6+yadMm4N6NxFZ0AvWG0d7eTmlpKZ2dnbL2snWoUtywrcMAt2/f5r333uPQoUMyi3TRokXs3r2brVu3EhkZCdy72dnLRjUT1DJnkZGRFtp9ycnJ5OfnExISIg9SdRKRLaC+gAiDQSwyQV9RexMnJibo6Oigr69PGnwpKSns2LGDAwcOkJaWRlNTk5S9UWdazlV/1HPL3d2dtLQ0YmNjWb16NaOjozJZoqWlherqanp6evDy8pLh45CQEFavXo2npycnT56ksLCQ6upqObZgNqj27dtHfn6+BWXIFjwt9fdrMBikvqK1J0rNzx4ZGeHQoUN88MEHlJeXy0uLreaiOqzf399PYWEhJSUluLm5yeIkwhMDX18RQX04ZGRksHHjRqqqquR31tjYyJEjR0hNTbXQZZ5Lr156ejr5+fncuXNHnh1tbW389Kc/xdvbm/j4eFatWsWqVauIi4sjNDQUnU5nF5fhL4L6u/fw8GDz5s1ERERw7tw5Pv30U1mNURQNEfNVGHGhoaFs3ryZV155hWXLlgGWMo32AnGRjY6O5rvf/S7+/v788pe/lDkcnZ2dfPrpp5w7d46goCDCw8NJSEhg2bJlbN++XUapxLyzp75ZY6ZLndFotLB5uru7OXjwIO+88859tEkBsa+CmWJ35MgRIiMj2bBhg0w+nOuLmdFolOcFmOelr68vK1asYOXKlbi4uEjVEldXV06fPs2//du/yX1LOAIE1UUN4WQQl9OvS1GaNctvamoKLy8vVq9ezcjIiHQdi3JpQUFBuLq60t/fL+sAi1unyBx2c3NjcHCQ4eFhWltbCQ8PJzU1laioKJtXqbD22AkxbScnsx6tCJmrpXnAfKj29vbS29tLdXU1x48f5+zZs9JwDQ8PZ+vWrezZs0eSocXn2DusJ6E6JBQYGCj7I2gTtqYOqMewp6eHzs5O2X5BoFdjfHyctrY2mZyVlJTE/v37eeKJJ0hJSQHMXnixoAWnW11cYjbHUW28isPP1dUVb29viyx1gMTERFJSUjAajXh4eODs7IzBYJDVURobG7l27ZosQqHVaiX3KS0tjaeeeorU1FR5a7aHZErBIQfk5ULA3d2d6elpmpubOX36NB988AGnT5+2qFJkq0iO+m8K+TFhhAnhfbE3ft0DXT03RGg2KChIeqLBPHdv3bplkWgzF1q96s8Xa0qr1XLhwgUqKyvp6+uTCi7V1dX09fVRWVlJcHAwS5YsYcOGDYSFhcn+zVWi2deBGNfg4GDWrVtHZGSkNOAuXLggueeCd2w0GmVSV09PD6Ojo/Kz7M3TrJ7H7u7uZGdn4+3tjZ+fn6wI1t3dTUVFheTZFxUVERoaKmkSW7ZsITU11e5LpwqDUj3PvLy8SEhIICgoSFIgTp8+ze9+9zuuXr2Kk5OTVCQQBVJcXV0xGAyMjo7KxLXq6mpqa2tZt26dLIE8F2OtdiC2tbVRVlYmL7Zgjqjm5eWRnZ2NwWDgxIkT3Lx5E5PJJCUmHwRvb298fX3l/BYlcuHrJ8rOGm3A1dWVpKQkoqKi2LBhAwUFBXz44YdcvHhRenc0Go3cnEQVoPb2dqqrqxkbG7M4NMfHxzl16hRRUVE89dRTMmwy14TmmSA4H2C+WdbV1dHd3U1QUBBw7xY5PT1NfX09N27c4NKlS9y8eZOKigppDEVFRbF9+3b27NkjdUXFArHXzVgN64NfvRh6enpoaGiQdAp7SMAT36nRaKS9vZ2enh7APHdFtS01RkdHLZJJNm7cyP79+y0E3vv6+qirq2NycpKYmBiLC4j1580mvuz71Wg0xMbGysNUUCTEPG5sbOT27dsMDAzcN/+ysrJYsmQJbm5uUlnC1oYrmPusLiQhDFkwe1sbGhr49NNP+fjjjykvL2d6ehpvb28mJiZk+U5brDP1/hUUFMSKFSs4ceIEjY2NUoIHHp0hKbx1M43Z4OCgNBRhbkLS6u/cz8+PzZs3k5SUREpKCm+88QZlZWXy+fHxca5duyarya1duxaj0ciOHTsIDg6Wr7NXD6y1hzgpKYmQkBAiIyMxGAwWiZMiMjk1NUVvby+HDx/Gy8sLJycnVqxYYVG9yJ7OB3UfExIS+PM//3NptN6+fZsbN25w8eJFKYfW2dnJyZMnqayspKKighdeeIGVK1dKuos9jiPcH+ny9vaWiepOTk6cPXuWDz74gMLCQkZGRuQ+6efnR0ZGBqGhofIiJvTwBVXAOtox29+BiAoL1NXVcebMmftyHYaHh2lubqauro633nqLDz/8EKPReF++g5omMz09jcFgsOiDer6qX/9VMGsWhGiou7s7qampsv709u3b8fHxISkpCY1GQ2trK0NDQ/j4+KDVaikqKuL999+nsLBQenqE16GkpIS33noLb29v9u/fj5+fn01vn6KPIgMbzJvryZMnGR0dZd26dSQnJ9Pd3U1VVRXNzc00NjZSW1tLc3MzXV1dFiGGdevW8b3vfU9qbppMJrtduA/Cg9rb3NxMYWEh/v7+FnxEW21O1hnet2/fltqLImxqbShMTExIz0dCQgIrV64kKSlJ3sINBgO3bt2irKwMDw8PMjIyLA5UW/RTbErinyDGa7Xa+4xO9f9ra2u5desWExMTeHp6MjIygq+vL7t27WLHjh1ys7J1TXX13/bx8SE5OZng4GBMJhPl5eW0trbi5ubG+fPn+eyzzzh//jw1NTUAbN++nbVr13Lx4kUOHjxIb28vJpNpzqvAqfsgxPqDgoKoq6ujsbGRuro60tPTZbTpm6wZMe/9/PwkpeTy5cvy+bGxMSorKykvLyc1NXVOq8iJkLmPjw+LFy/G09OTwMBASktL6e/vp6qqihs3bliEIYuKinj99ddpbm5m8+bNZGdny7lpD46NB2F8fFyWMh4dHaW6ulpG3wApIQXm80+UZr58+TLLli1j6dKl0nhVF/OxF4jommhjUFAQ+fn5JCQkkJeXx4YNG7h69Srnz5/n2rVrTExMUF9fz2effUZ3dzcrVqxg8eLFpKenExkZaXfnoKCcWYf8m5qaGBwcRKfTSSeViMpOTU3h7+/Pyy+/zI4dOxgdHeWTTz6hoqLC4rO9vLweaAzOJqzVFIRtIvb4/v5+Tp06JVVJCgsLZejfYDBIR6PIY1q7di3Z2dnU1dVRUFAgo1ziM7/pmM6q+0udxBESEsL27dvZvn37F74nKytLimmXlpZKt7WzszOTk5MUFRVx9epVcnNz8ff3t4vbpyCbC1y6dIlLly5x7tw58vPz6ejo4MqVK7S2tlp4g8A8UV1dXUlNTWXnzp1SXktIa9jbpvRVoF4MfX19tLe3Mzg4SEBAgA1bZYb60jM8PExDQwOtra3odDry8vJYvHixTE5SGw3iPRERETLxzMnJCYPBQElJCadPn6a9vZ28vDxWrFhhkWhjqyo/D0P8V3vDOzo6qK6ulhGBkZERNBoNa9as4bXXXrPQArW1caCmpkRERLB27VrOnj3L+fPn+eCDDwDz+jx9+jRHjx5lenoaLy8vsrKyZF8GBwc5ePAgIyMj9PX1MTo6+lC11h8V1HvX6Ogo7e3tsiCGSFxVFyj4Jh5YddJoZmYma9asoaysjKqqKiYmJpiYmODy5cskJCTg4+NzH+d+NmHtlUxMTCQqKore3l5qa2s5c+YMHh4e1NfXMzw8zNDQEIODg5w+fZrq6mru3LkjS2mLvtp6fj4IQioI4PLly/z+97+nsLBQyggJ3VbhtQIzlUAk2gm6z1zz6h8WTk5Ocr6IMqiurq6ycE9OTg7p6ekEBQURFBREQ0MDHR0dtLS08N5771FQUMC6det49dVXZfKTvSkNqat+Tk9P09PTw8mTJwkKCiIpKYmbN2/S3t7O1NQUOp2OiIgIvvOd7/Daa68RGxvLlStX6OvrY3h4WHooRYUuUa0Rvj4n9KvAmjKl0+lwd3eXTjlBKystLaW0tFS+TshDigqBbm5u8mK8d+9eli1bxvHjx7l69aqFo866otrXwayfpl9VBiEqKopnnnmGjRs3cunSJd544w2uXbsmO2oymejp6ZFyDAJzbbyqK33FxcWxefNmamtrLRQDrl27xu3bt6UEykxYvnw5u3btYtmyZRZlH+ej4areWPr6+uSNE8yT3M/Pz4JjZy8Qi9ZoNOLq6kp4eDhhYWE4OTkxOTkpjVd1gmB9fb2F9FRnZydHjx7l0qVLeHp6smbNGnJzc/H09JRz0x6oEmqIjVcYJi4uLvT29nL8+HEKCwulV8HZ2Zm8vDz27NkjQ2Iigcse5qjYPLVaLWvWrCElJYXz589TUlIitTTHxsbQarWyxOpLL73E3r17GRsbs/AINDY20tnZKY22ufb4TE5OMjo6Ko3MhoYGamtrZXlY+GbFFKzDkevXr6e5uZk7d+5IhYOGhgZu3LjBtm3b5PegLqYy21DvrVqtlvDwcAIDAwkODmb9+vUYjUauX7/Ob37zG+m1amlp4cSJE2RnZ0tD3551YMVe0N7eTllZmdSXFrkfERERxMbG0tnZaeGZGx0dpauri6GhISnFZM8QBpla1gzMY5uWloafnx8HDhygpaWFP/zhD3zwwQcMDg7S2dnJqVOnyM/Pl8UNxH5jK+NV/V2rE1iFBN/ExARVVVX84he/wM/Pj/7+fumsysnJ4Qc/+AGbNm3CxcWFEydOSBplb2+v7JfJZGJ4eHhGCcO5xpft7WrFBTBro+fn55OXl0d4eDh+fn40NDRQXl4uJVEF3N3dv/HZMaunqTW3USgMCCNH7ZkVIU2j0UhoaCihoaEkJydTUlIiwwoi2766uppTp04RHBxMVlaWTTih6gPAy8tLqiDExcVRXl6OwWBgbGxMVhUDc+ahEDZ2dXVlyZIlPPnkkzz33HPScytkKezN0HkYiDYbjUbKysos+FsJCQksWrRICjfbWpvQmp/t6emJj4+PLPPa1NREbGwsWq3WIhtc0B1aW1upra2lr68PHx8fLly4wOHDh6XXdf369bLalNFotHmC4UxQKwWIg354eJgTJ05ITqEokfvMM8+wZcsWmfVurzxsT09Ptm/fTkNDAzU1NQwMDGAymaRetNAZ3Lp1K2A2BtSJg42NjVRWVpKYmIivr69FhbzZmqvW1IfMzEzS0tIs9hH15Vc9XmpD70FQt13QSMbGxtDpdGRkZLBq1So+/PBDabyGh4cTGRlpEbqcy7FWlwIXBouQf0tNTQXM1J7R0VE+/fRT6urqGBgYoL6+njfffBNXV1f27t0rK/zZE31A8KqdnZ3p6+vj6NGjnD9/Xh7s4+Pj5Ofns2PHDmJiYigtLZXFb4aHh+ns7KShoYG2tjaio6Pt3snh5ORksb7EmArPrMgXSE1NZWhoiI6ODm7evElHR4csXSzyR2ydFGrtmfTy8pJGK5gNMoPBIOlnaoicguLiYm7evMmZM2e4fv26TGAXMoeizLawa2Bu1p71JUgoBVhDo9FI281oNGI0GvHx8SE7O5sdO3awceNGPDw8KCws5PPPP6e2tpaamhoZPfDy8mLp0qVs2LBB7i9fN2diTi0kEQaZqaEioWliYkJuNup6wGCeHENDQ5SVlTE9PU14eDhBQUFERkZabHhzMcGt/0ZUVBQHDhxg1apVlJaWUlVVxejoKFqtlpGREW7fvs3FixdlX1avXs1zzz3Hzp07LSgH9iQ2/VWg9nz39fVRXl4uOVxhYWFkZ2dLY05cWmwJ9Yag0+mIjIwkKiqKlpYWjh8/TmZmJkuWLMHLy0vOQ19fX9LS0qSsTXV1NSdPnsRkMvHBBx9QXFyMn58fq1atkocs2I8O44OgHovu7m7Ky8tliCc7O5u9e/eyfft2mXxmT8YA3E/H2LVrFykpKVy6dInS0lI0Gg3r169nzZo1eHt7Mzo6Krmcfn5+pKenk5SURE1NDcPDw7S0tNDX14evr++c8M1ERrEIBW/evJmbN29y+PBh+V2r56t1ssOXwZrX7OzsLD3tItNZjZUrV7J7925Z4AFmrhA42/iiEHFkZCR//ud/TlBQED/96U8ZHx/HYDBw6tQptFoteXl50jNpb/NVjMetW7d46623OHHihBxTLy8v9u3bx5/92Z/h5OREdnY2CQkJvPfeexw+fJjJyUlqa2u5ceMG0dHRREREWMxRe99r4B7f0XpdpaSksGLFCurq6iRlqaenRxYdsbUnXT2HAgICSEtLIyQkRBqgX7RPFBcX8zd/8zcy/C4cW4BMiALIy8vju9/9LuvXrwdsJxn2IOUV4VwTjkhfX18ee+wxnnnmGbZu3YpWq+XgwYP87Gc/4/Llyzg5OeHj4yPfLzzQ27dvl7SXr2uczwltQBz+1m5mYXELLqF1R0XFqZn0wEwmE+7u7jaX1FDTIry8vEhJSSEyMpJFixYxOjpKd3c3p06dor6+Xpb5y8nJ4emnn2bPnj1S185gMMjiBfMR6oVrMpkYGRlhcHAQJycn0tPTWbRokQx7igPUllBXvQkNDWXVqlXcuHGDy5cv09/fz6VLl7hy5Qrr1q2TxlFcXBy7d++moaGBCxcuyAo5JpOJy5cvMz4+zsqVK1m5ciU6nY6RkRE8PDxsLgn2IIiLnlq4v7i4WHrgwOwJjIqKui+T2x4hNladTsfixYuJioqSYxEbGysNVl9fX0ZHR6UBFxgYaKGjaguvsri4izb6+PgwNjZmUUpRvE7IzFmvObEPCVoH3DMUROhW/C7+zsWLFzlx4oSkTohCARkZGXh4eMy5fq/6vJjpIi+qwvn6+sqoQHh4OHfu3JHendbWVoukLnuYr2I81LSj06dPU1JSIqWQxMV39erVst+pqakEBwdTUlLC4cOHAXN04P3330ej0bBv3z6pamMvUMtIWY+f+nchAyaqM3V1ddHT02MxXq6uroSEhODm5iY9trY6O9QGZHBwMBs3bmRgYIB33nmHyspKWYnK09OT8fFxxsbGZBTVYDDcp3eqVrURCW0vvfQSa9eula+Zq7lrPU5Css/a+yr2KTGfQ0NDyczMlDlKRUVFfPLJJzIJdHp6WhbcSE9P54knnmDTpk0W/H27NV5nCoHPdIsQ2dAajQaTyURRURGff/45lZWV8jVqcnRiYiJLliyRyT9Go9EmovfqG4rYhDw9PUlLS6O2tpYjR45YZBSuWbNGVs4ShqtYwAsFrq6uaLVaSY+IiIggNDTUYiHa2ngFS65kRkYGaWlp+Pr6MjAwwK1bt3j//feZmpqStaaNRiNpaWnk5uZy8+ZNGhoaaGhosCidNzY2JpUVwsLCSExMJDg42C49IoKy4+zszMTEBMeOHeOzzz6Tsi2ATIoZGRmRyWf22Bew9A5OT08TEBBgkRwo9o+ZNIbF+pucnLSoeDOXUO+Lzs7OeHp6MjAwQF1dHc3NzXIeibZbU1/UsF5f4ncx511dXamqquLgwYMcP35cysRNTExI+TOYe8PvyyhT1pd7T09PEhMTaWxslB6wwMBAi/G1h/lqnftx5coVTp8+bWHQiNwJQTMTVLmAgABCQkLk3tTf38/p06cJCQlh6dKlBAQEWFzGbY2HTaoSVCqj0Uh5eTlHjx7lzJkztLe3A+axTU9PlwUzRGEiW54dagWInJwcqQzw+uuvU1FRIZWHxsfHpW2gTjQTXtbJyUmCgoJISUnB3d2dmJgYXnjhBR577DHgnj0z1zxzAbWyjvX6EZEi0ae+vj6OHDlCUVERN2/elOeHoFWIKpXbt29n9+7dFsmw36R/dkOsbGlp4eTJk3R3d6PT6SgrK+P06dMWvEmRZSuqM6m/hLnU0HwQBP9FeHGuXbvGG2+8IQ1XNzc3yQ2ZyyxeW0G9Yc+V3MdXgXVoJCAggPDwcAYGBmhububo0aN0dnZy+vRpwsPD8fHxkQUp/Pz8pLdKbeiIDNLVq1fz2GOP4e7ujlarva/imr1A9L+yspJPPvmEY8eOWSQLiI3X3sbuiyAMNGuunTDKROazQGhoKOnp6VRWVtLV1cXNmzfJz8+XNJe5KJGqzs4Gs8dt06ZNnDhxguLiYi5cuEBQUBCBgYFSzkudNOLn54ebmxsjIyNMTEzICFZ/fz9jY2N4eXnh5eVFdXU1BQUFtLW10dfXx7lz52hra5OeFIPBYLEnzeW4z0T5EjQyQFLJRCjV2dkZHx8foqOjLdaXkIGzJ6j71dbWxo0bNygtLWV0dFSuL3d3d1kq1ZpWlZaWxqpVqygsLJRFJIaHhxkdHZWXMVsb6cJrPtPlQ00JdHV1tVAiOHbsGG+++SZXrlyhqalJcj+fffZZmawFtqGuWMP6O05MTOSZZ54hKiqK9957j2PHjmEwGHB2dpbZ+mINubm54e7uLvfUvLw8nn76aVnJMTk5WX6uLfZb9d8cGRmRXlfri4igOExPT9Pa2sqRI0eYnJyko6NDPqfT6eT7vL29WbNmDVu2bJF9VKv4fF3MivGq5jEMDg7KMI5Op8PDw0NWuxHC8BMTExQXF/PRRx/R1NSEl5cX/f39MnwpvHiTk5MytObj4/OVuV+zDa1WKw/MpqYmCgoKKC8vB8zlRpctW8b69evvk/5YqBB9E8aEerzswRiyDg8nJyezZ88eeXmqr6+nvr4eMC9Af39/yVkymUwyq9nT01NqEQ8PD+Pt7U1gYCCBgYEWfCZ7g3ru9ff309TUZCFnAuYQe0hIiIXkl73PWfXhqaYoWW+WwljKyspi48aNXL16lVu3blFaWio5d+IzZjtcaR1iXbx4Ma+88gqBgYFSQujTTz+V60ar1cqLPJg5oN7e3jQ3NzMxMSG9VYKu5O/vj1ar5fr163z88cfU1tbKIhMA/v7+TE5OEh4eTlxcnAWNYrYhxsHJyUnqSzo5masRWXufrA0YUXlKHd4cGRmxuzWn9nwLvW919SLxnLpYhrhoTU9Pk5aWxurVq2loaJDGq4+Pj91wecUYCn3ykZERaazB/Ylbo6Oj3Llzh5KSEt58803effdd+Rl+fn7s2rWL7373uyxZskRSYuwpiVlNYYyLiyMuLg4/Pz/GxsY4d+6cRZlYse+MjY1Jx4C7uzuxsbFs375dVuMU4fiZ9qrZhrW3PDIykrS0NCoqKuSlX3Dl1RgeHmZwcFB+hkajkSVhNRoNeXl5ssT14sWL5fseBZVuVmaDOizX2NjIu+++S21tLZ6ennh7e8sQXUdHh1zEgu9iNBrv8/QI752oQb5s2TLWrFlzX6KTLSGSApycnOjq6uLo0aPcvHlTPp+Tk8Orr74qwwJqzdCFBrEJCw4izI1W3VeFOFCEcbJs2TJCQkJISUnhzTff5NSpU/K1Q0NDFtJf0dHRbN++na1bt5KUlIS7uzsmk4nx8XEptC4Kb9iDx2AmqHlpkZGRxMTEyM1HQCREqmkt9ubVehC+bL6JC7W3tzcrVqyQkZzBwUGLSM5se13Vf0fA19eXDRs2kJKSQktLC3V1dZw+fZpz584xPDyMn58fOp1O8uqio6Px8vKiqamJiYkJkpOTmZqaora2ltHRUZkc0dXVJTloYpx9fHzYtGkTaWlphIaGytrlMDdjrRazb2lp4fz580xMTLB48WKys7MfeJA3Nzdz4sQJTp06ZRGhE5dle8ZM88k6J0R41gUv3c/Pz2758yIaCuYz/8aNGyQnJ5OTkwNYetUNBgPXrl3j8OHDnDp1ioqKCnnGJycns3XrVvbv3y+lI0WY2p5gPVYAK1as4KWXXsLPz4/z589L+oMw6tWFJ7RarbycCUxNTdn0rFC3ZfXq1fzt3/4t7777LufOnZP5Oi4uLhZ9FjRPEQ1SRyGXLFnCn/7pn7J27Vp0Op3FOn4U++msWHzqQ8NgMNDY2MiFCxcwGAzSS2Uymejt7b3v9ilq/wqNP1G5SAjgLlu2jO985zts2rTJIrnL1sar4OMAdHV1UVBQQEVFBe7u7sTFxZGbm8vKlSvx8vKSIUx7lE/6upicnJT97+zs5Pr167S2tqLRaGYstWpv3juxwcTHx7N79258fHzIzc2lq6tLXqYEP02n0xEXF8fatWtZs2aN3RqnXwa1VzIyMpK4uDhCQkLo6uqSmbCBgYHExMRIL998UsP4MqNTrT0p+Mnnz59nfHycgYEBadyPjIxYhMHmqu1eXl5SHio6Opr+/n7q6+steGUCDQ0NshIaQFVV1YyfK0pYiqQ1YSQeOHCApUuXSmNeYC76bF1G+vz58zQ0NFBcXExFRYWUhBIXQzArmhQXF3PixAlqamosPsPPz88ikdceLs0iYc7V1ZXo6GiioqLw9/env79ftk+UGRfnmjoy1NXVRU1NjfRygfnMEQUA7AkFBQUcOnSIXbt2SeO1qamJtrY2urq6aG1tpaioiOPHj8vIlpeXFzk5OWzYsEHqSavViezR0SPaJeyVgIAAtm7dSlBQEPHx8Vy7do2ioiJZbtnV1ZXg4GBCQ0NZvHgxeXl5cuzUc9tWUCd6RkVFsX//fkJCQggMDOTQoUMWibwzwdXVlbCwMAICAoiMjGTPnj08/vjjcj951BeQWbH41IZkeHg4y5cvp6qqiqtXr3Lnzh1pzFgnRTg5OckMW7UIuvAQLF26lOeff569e/cSHR0N2I9sj3pghoeHqa6upr+/H41GQ1paGhkZGRaJFrY2th8l1BnCYM72vXTpkiwtKjJFrb3p9gJr71JISAj79+9n3759D2yzEJWeL17IL4OoXW0dbg0ODpZhLXV0YSFAbYg7OTmxZMkSTp48SUtLCxcvXuSxxx6TZUptPc4pKSkyjHr8+HEKCgqor6+nt7cXFxcXYmJiCA8PZ2xsjI6ODgYGBqRxLqhWKSkpLF++nNTUVLy8vBgfH8fV1VV6yITRZMv8AXFZuH79OufPn+fYsWOEhIRI49XNzU1qpDY0NEhFE/EvNjaW5cuXyzkrPtPWUKs9REREkJGRQWxsLGNjY/JSPDY2xp07d+jr65O0jenpaVpaWigoKODSpUvSEAJz6NnT03NOtIgfBPE3xQW+srKSM2fOcOLECQICAli3bh1ubm4cO3aMY8eOUVpaSl9fn0VxEJ1Ox6JFi9i1axc7d+4kOTlZft582GusVQjWr19PWloay5cv57333uOTTz7BaDSydOlSlixZQmZmJitWrCAtLU2Os704ssRaMZlMeHh4sGXLFnx9fTEYDHz44Yczar+C2emYkZFBbm4uS5cuJTU1laSkpFmlm82KBaV2LUdFRbF7927q6uooKSmxWKyurq74+flJLVRxCxXvVX9RK1eu5Pnnn2ffvn2SMyr+lr1BiGqDeVKKm7Z6E7XHdn8TqA27iYkJGZoEJM/ZWm/S3iBCH0JC6WHGSBh9AuJ7EJmlIixvj/1Ve3ZaWlpoamqSXE8nJyeSk5Mt1tpCg9oQd3V1JSMjg5ycHFpaWvjwww+Ji4sjPj5eeg7mOrlSnZwkJL02b95MQkICmzdvpru7G4PBgJOTE97e3nh7ezM1NcXQ0JA0DMReLCSHoqKi8PPzk48LiouaFjLXc1W9zry9vWUte4PBQFVVlYUXWRSqsV5z09PTxMbG8uyzz3LgwAFZoMBevHYzJfqkp6fT1tYm+YF37tzh2LFjTE9Pk5OTg8lkoq6ujtLSUgoLC2lsbJQXi+DgYOLj44mPj8fFxcVmpVPVfGWj0UhnZyc9PT309/dz/Phx3NzccHNzo6ioiJKSEqkIAWa6yurVq1m6dClpaWnS6BEi+GCf58SDIL4LEZnz9/cnJCSEdevWMTk5SUxMDJGRkYSFhREREWEXl6oHQS13tmzZMhRFYe3atZK6KZ4TZ6anpydhYWFER0cTHR0ti0qIYgcPe55+Fcya+0/d+djYWDZu3Cir1wwNDdHf38/g4KAFbUCr1aLT6dDpdJJ/ZjAYSEpK4sCBA+zevduiXKE96IUKqD2pAQEBrF+/Hjc3N4KDg8nJySEsLOy+UNZ8WphfBnVfdDodgYGB9PX14e3tTWxsLFFRUbIQhT2NmxrqBSY8/zN5XkX7hYE6X73ownidnJyktbWVtrY2+Vx8fDxr1qwhISFBPraQvK5wf6GK5ORk1q1bR0VFBTU1Nbz11lv4+/vz1FNPSYNqLqGej+r9NCEhwWJcHgWs5/ZcQv33/P39WbFiBT09PVy9epW2tjYZoRMyXtPT0/dxycPCwti5cyfPPPMMWVlZ8vXCU2trWCfkxcfHk5uby7Vr17hz5w5Go5H29nb5T+RL3Lx506I8LJhpEWvXrmXt2rUWiju2gLVDwsfHh4SEBHx8fKivr+enP/2pxevd3d3R6XQEBQWRm5vLrl27WLt2rU0KDT1qqPM7wMxbF+M0E+y5n+JMExffL+rHgyAiDbN1oZq1U9c6kzs/P5/Y2Fh6e3upra3lzJkzHDt2jM7OTovXZGZmEhwcTEpKCjExMUxOTqLT6YiJiZH6dwL2NPDqjTQiIoI/+qM/4oknnsDV1ZXAwEACAgLs3vP4qCDqc4P5MFq0aJGs1jM+Pm73mrbCUPiiSmBqnbv5CnXSgbgMCsTExLBq1SpZvhFszyt/1FAfNiLpaceOHUxPT3PkyBGuXr3KX/3VX3HkyBH+7u/+jnXr1gG2kbeb7YRH9WfP9d6kPid8fX1Zv349S5Ysoby8nIKCAmpra5mcnOTOnTs0NDQwPj5OREQE0dHRuLu74+3tTV5eHps3b7aQG7Kn+apWXnF2dpbqMwkJCdTU1Fi8VlR6E4o81liyZAkvv/wyq1evlo8Jx8BcQ72G3NzcSEhI4LHHHqOqqoojR45YvFZUs1u8eDG5ubksWbKEuLg4C4qHmoc+HyHa/2UJ2fOln19XJnAu+jdrq1s0Wniv1ILhK1askKXtrl27Rn9/PxkZGWzdupVFixYREBBgcWiqIbwP9mY0qBexTqeTUjXfFqg3ztHRURobGwFzAoZaFUJdRchesdA4yQ+CWEsajWZGQf/o6Gh5sMyHjfabwsXFhYyMDLy8vFi0aBGHDx/m+PHjNjMM1FA7AwSdQFywxPPqy4h1e9XRAmsvoD1FQrRaLVqtlqCgIBITE4mOjqahocHCeJ2YmCAkJITIyEi8vLzw9/cnKytLzlWj0SjpBfYG9XedkZHBM888g4+PD3V1dYyPjzM4OEhzczMNDQ3yPXFxcbI8alxcHHv37mXTpk1S4URQnWzZX7E/+Pj4sHz5csbGxoiPj6ehoYGpqSmZxBMbG0tmZibZ2dnSayz6MNPcnG+wNvSE9jvci6SItTwf+inOQdGPB+2DwpEj/s0FVW7WT2g1f0VAp9Oxbt068vLyMBgMUlBclNL8IsPU3gfc3ts3F7D2SD6I5O2AbaG+HYeEhJCUlCR1aTUajVQGmS9egq8L635FR0cTFhbGqlWr+Ku/+itcXFweWVWYRwG13uKDuIEzGa8zvc7ekZmZSVpamkwKFQeo4AELQ0At82NrQ+6LoKaBBAcH89JLL8niA+3t7dTW1nL48GFpvMbFxbFz504SExMJDw8nJyeHlJQUaQDPZlj2q0B9+YmMjGT//v3s3LlTGm6izLGovqheQ7YoxzxXUJd2ttfch4fBwyQnz3X/5sS9JCQYRC1jjUYj/z0IoqwYmL84Nze3eSPTo64mIrx4C40vqIYQJxZISUnhL//yL6mqqmLRokWSpwz2k1XpgKW3LjAwkK1bt8rs5ry8PGJiYmzcwrmFuiKc2J/URqu9GPDz+RB8GAgjVcglPoxxJvZcezZc1RCVplxcXEhLSyM8PJyenh46OzvJysri9u3bgNk7m52dTVhYGIGBgRZRLFFC1J4gojkeHh54eHh84euEvKI9GN+zhflOLROwx8vFnMVGhYadGtY8LvUXJLIU5yOsjblvA6xLW/7N3/zNjK/7tn0v9gz1enN3d2fNmjVkZWUxNDSEp6cnwcHB8vn5YBB8U1gbhQ8Kyzswu3iQQfMgb7L4/3zaW9TyVmDm+vr6+pKQkMCqVase+D61p90e+2tdRXEmLrWgTthj+x2YP7AZsU/wtgQndj5ICznwxVB7rqxhXR7WAfuDSC5UJ1DAt8NwVUMk6gkReFGucSF4UOYbxAVC/BMJlGru6Hz1bglvsVon9csgihLYs+EnKB7qSmdivMS/+TheDtgXvrLxqijKbLTDbrDQ+wcLv48LvX/g6ONCwELvHyz8Pi70/oGjjwsBC7F/DleYAw444IADDjjggAPzBk62loBxwAEHHHDAAQcccMCBh4XD8+qAAw444IADDjjgwLyBw3h1wAEHHHDAAQcccGDewGG8OuCAAw444IADDjgwb2AzqSxFURqAmWvAQqderw+bw+bMCRRFeRH43d1fv6fX639py/Z8UyiK8grwmy952ZRer5/3uiiKoqwF/hzIBwKAXqAU+Iler//chk37Rvg2jKGiKE7AHwF/AiwCXIBKzP3+qV6vN9mweY8MiqJEAf83sA0IBNqBj4F/0Ov1fTZs2iPBt+HMUBRlJ/C/ARncG8MbwD/r9fpLtmzbo8C3YQxh4Z4Xati6j7Yu4D4A/GSGx4fnuB2zDkVRooF/w9w3Lxs351GhCPiHBzy3FtgIHJ6z1swSFEX5L8B/BbqBg5gPlCBgCbAemM+bURELfwxfB14E7gDvACPAZuBfgMcURXlKr9fP68xVRVESgYtACPAJUAEsx2wIbVMUZbVer++xYRMfFRbsmaEoyv8E/growXzp6AaSgL3AAUVRXtLr9W/YroWPDAt2DGHBnxeAffTR1sZrv16v/3sbt2HWcdfz8xvMm9KHwH+2bYseDfR6fRFm4+c+KIoivAQ/n6v2zAYURXkK8yI9AezX6/VDVs/PzzJwd7HQx1BRlH2YDdd6YLler++++7gb8C5wAHgZ+K2NmviooMdsuP5Qr9f/m3hQUZR/Bv534L8B37dR2x4lFuSZoShKGOZzoRNYrNfr76ie2wCcwuxVXwjG64IcQ1j45wXYTx8dnNe5wQ8xe7D+CLPXZ0FDUZRMYCXQChyycXO+NhRFcQb+JzAKPGe9SAH0ev3EnDdsDrBQxhDYf/fnPwnDFeS4/Z93f/2zOW/VI4SiKAnAFqAB+KnV0/8X5j3nRUVRPOe4aQ48PGIxn8dX1IYrgF6vPw0MAcEzvdEB+8C34bywpz7a2vOqVRTlBSAG8wZbAhQsFA4agKIo6cA/Av+i1+sLFEXZaOs2zQFeu/vzV/N8LPOBeOB9oO8uHy0TMABXFwIH7QuwUMZQcOjqZnhOPLZUURQ/vV7fPzdNeuQQe8oxvV4/pX5Cr9cPKYpyAbNxuxI4OdeNe8RYqGdGNWAEliuKEqS+aCmK8hjgjZlKsBCwUMfw23Be2E0fbW28hgG/t3qsXlGUP9Lr9Wdt0aBHCUVRXDH3rwn4Gxs3Z06gKIo78AIwBczrhDQg7+7PTqAQyFI/qShKAfCkXq/vmuuGzSYW2BgKIyB+hucSVP9PAy7PfnNmBal3f1Y94PlqzMZrCvPfeF2QZ4Zer+9VFOX/AP4ZuK0oyseYaWaJwB7gOPculPMdC3IM+XacF3bTR1vSBn4DbMI8kT0xfwn/AcQBhxVFybZd0x4Z/g4zgfkVvV4/ZuvGzBGeBvyAw3q9vtnGbfmmCLn78/uAO+YkH2/MN82jwGPAe7Zp2qxiIY3hwbs//0JRlADx4N2LpTpRzX9OW/Vo4Xv358ADnheP+81+U2YVC/rM0Ov1P8FMc3EFvgf8NfAU0Az81ppOME+xkMfw23Be2E0fbeZ51ev11hnOZcD3FUUZBn4E/D3wxFy361FBUZTlmL2t/7RAwgUPiz+5+/M/bNqKRwMhD+WE+TZZfPf3W4qiPIHZ07VOUZRVC2yMF9IYvo3Zi7wds0frU8x8rc2YvVrVQDIw30OWXwSnuz/ntaLCt+DM+CvgvwP/Cvw70IE5IvA/gD8oipKj1+v/yoZN/MZY4GP4bTgv7KaP9piw9f/e/fmYTVvxDaCiC1RxLylkwUNRlAzMnJgWFoAcCCC0MetUixSAu570o3d/XT6nrZpFLLQxvMsB3YM5k7sDs/LAdzH3bw3m0CyYZbTmK4Rn1fcBz/tYvW6hYSGcGesxJ8J8qtfr/0Kv19fp9fpRvV5fiNmYawV+dDc5byFi3o8h347zwm76aGvO60wQh8h8zoz1wswvAzAoijLTa36hKMovMCdy/flcNWyWsVCSfAQq7/7sf8DzYiG7z35T5gwLbQzR6/WTwD/d/Sdxl9ubA4wBt+a+ZY8MYp6mPOD55Ls/H8SJne9YCGfGrrs/T1s/odfrRxVFuYrZiF3CzMmH8x0LYQy/DeeF3fTRHj2vq+7+nM8LdBz41QP+3bz7mvN3f5+v4QMLKIqiw+zVmsLcr4WAAmASSFYURTPD85l3fzbMWYtmEQt0DL8ILwI64N15LmEjDJ4td6VsJBRF8QZWYzbQ52tC2pdhIZwZ2rs/HySHJR43zkFbbIGFMIbfhvPCbvpoE+NVUZRF6uQJ1eOxmLk+MI/FmPV6/Zher391pn/Ap3df9vrdx96xZVsfIZ7CnPTy+QJI8gHgrlzNO5jDsX+nfk5RlMeBrZhDsUfmvnWzggU3hgCKovjM8FgeZgm7Yczi7/MWer2+FjiGOenlB1ZP/wNmb9bv9Hr9vNWYXuhnBnDu7s8/URQlUv2EoijbMV9ADJirqM1LLPQx/DacF/bUR1vRBp4C/lpRlNOYK98MYU6e2InZE/I58P/YqG0OfD2IJJ95W43pAfgLYAXwt3f1Fq9iFhR/AnOSz/fmsT6oNRbqGB5XFGUMc3LIELAI2IE5QrJfr9fPZ2+PgILZsPlXRVE2AeWY5+0GzHSBv7Vh2x4FFvqZ8T7mikWbgXJFUT7CzNFOx0wpcAL+ep6X+F3oYwjfjvPCLvpoK9rAaeAjzNqLz2H+MtZhDqW/DOzS6/ULNTyy4HC3EMMaFkiSjxp35WlWAD8GorlXLe0QsFav18936RNgYY8hZsPAG7PqwF9gluf5JbBIr9cf/aI3zhfc9b7mYi5zuwJz5nYi5sz1VfPc6IEFfmbcTSzcgbmU723MhsCPMBeW+BzYqtfr/8V2LXwkWNBjCN+O88Je+ug0PT2v1VMccMABBxxwwAEHHPgWwR4TthxwwAEHHHDAAQcccGBGOIxXBxxwwAEHHHDAAQfmDRzGqwMOOOCAAw444IAD8wYO49UBBxxwwAEHHHDAgXkDh/HqgAMOOOCAAw444MC8gcN4dcABBxxwwAEHHHBg3uChixQoijLvNbX0er3Tg55b6P2Dhd/HhdA/WPh9dMzThd0/WPh9XAj9g4XfR8c8Xbj9c3heHXDAAQcccMABBxyYN/jK5WH1ev1stGNWoSjKQ792ofcPFn4f52P/YOH30TFPLfFN+zc9PY2T0xc6lh45HGNoibns3/T0tBzzbzru9trHRwXHPLXEQuzfVzZe5wqTk5NMTEwwNTWFqAImFq5Go0Gj0di4hQ5822AymTAajUxOTuLs7IxWq8XV1W6X0NfG1NSU7KeLiwtubm4Lsp9qmEwmxsfHmZ6etvtxHR8fR6vV4uTkxMjICCdOnGBoaIi0tDTi4uLw8fFx7I8LCGqDda4vKw448DCYnp6W9pqLiwuurq6zPlftZocWt0oAZ2dnXF1d7e4AUbfRsZF8OcT39aASxM7OzvPqO3RxccHd3d3iMfV8mG9Qe3LUcHZ2RqfT3fdagfnY1y+Di4sLHh4etm7GQ0E9FtevX+cXv/gFTU1NbNq0ib1795KTk4NGo7GJZ9aBRwe10Wo0GhkdHcVoNOLm5oanp6fjgmJjqNfhF51zC/3ioXYqziXsyjqcnJxkenoajUZjdwM9PT3N5OQkU1NT0rh24IshbmNiXOGe4SO+Qzc3N1s28RtjamoKMBs/8xHCi+zi4oLJZAKYcW5PTU0xNTXlmPc2hNh7dDodk5OTlJSUcPDgQa5fv05nZydDQ0PExsaSmpqKn5+fw3idx5iamsJkMuHm5obRaOTq1at8/vnn1NXVkZmZyb59+8jMzJSvdXZ2pK/YAiIyPDk5KfdPgenpaZydnRd05Gp6ehqTyTRj/2Z7/5nTb3R6eloe9uJ30UFXV1dpuU9PT9PW1kZ7ezsDAwPS8BHhsvj4eGJiYnB1dZ2zDdrJyek+Q0sMnJikD2rHt+HWJSCMHCcnJ1xcXNBqtWi12i/8DHvefNX96+vro6amho6ODjw9PUlNTSUyMvK+180HmEwmXFxcLG7LYgMaGhqioaGBO3fu4OXlRVxcHKGhodJAn299fRDEXHV2dqa7u5uqqiomJyeJi4sjMjLS7i5W6nViMBgoLCykqKiIwcFBABoaGuju7n6gB2ihwGQyye/iq14axZljr/sN3FtfYv41NDTwySef8POf/5zBwUFMJhM7d+6Ur7Xn/XOhYWpqSl74XV1d5fx7WONUGLjz1dmhhtqRNzY2RktLCwaDAX9/f0JCQizsudk4L+bUeHVycrpvkZlMpvs6VllZyeHDhzl27Bi3b99mYmJCHjDBwcG89tprfPe73yUiImIum38fRH/E4HwbN5CZQs5fdbLasyEkODwGg4FLly7xk5/8hOPHjxMbG8sPf/hDXnrpJYKCguYdfeBB7ezr6+PcuXO8/vrrXLx4kZSUFJ577jm2bNlCTEwMLi4u86aPXwaxZoeGhjh06BA//vGPcXZ25kc/+hH79++3MF7tzWAXl0PxD0Cn0y1YD48aYp/9OuMxHxwJak9Wb28vZ86c4cSJEwwODpKcnMyaNWuIiYkB5s9+s1Dg7Ows/33d9y8UTExMSMdUWVkZf/jDHxgaGmLDhg1s27aNoKAggAd6Zr8p5mynE1a69WITnerq6uL27dsUFxdTVFREYWEhxcXF931OS0sL165d47HHHiMwMFB+ebN5+5yenmZgYIC6ujo6Ozvx9vaW3qiH9c6IBDRhrFsvAnX42Z4NBJPJJPvg4uKCs7Oz7JuTkxM6nU4eph0dHZSWllJTU8PQ0JAkcgN4eXmRnp7OkiVLJI9UeAPtAWrPzvT0NPX19Zw7d46zZ88C0NjYyO9//3ump6d54oknSEhIsHifvcJoNKLRaHB2dqa9vZ3i4mK8vLxISEigtbWVgoICzp49y7FjxxgfH6e7uxuA4uJi8vPzWb9+PRERERbe9fkGQWNxc3PDycmJyspK3nzzTUpLS/H398fNze2+OWlv69HFxQUvLy88PDws5pt1dGuhYHJyUvI93dzcLObdl10s1N+JeJ/YszQajd3NYbFHtrW1cerUKT777DPa2trIycnh5ZdfZv/+/QQGBsrX21v7FypE5FeMT3t7O6Ojo4yOjlJbW0tLS4vcV8R89fPzk4mUvr6+eHp6AvdoiPYW3fkqEN9DZ2cnn3/+OR9++CFBQUGsWLFiTvbLOTNexQYrQu3CgDMajTQ3N1NQUMBnn33GhQsX6Orqku8TlAL1YIuwZnZ29pwZr+3t7bz99tscP36chIQEtm7dSmpqKsHBwQQFBeHt7S0PPOuwnejDQvCKqD09AtZ9GxwcpLKykhMnTvDZZ59x7do1JicnLd7j4+PDd77zHfz9/UlPTwfueTntAepw3NTUFI2NjdTV1aHT6TAajTg7O1NUVITBYCA+Pl4ar5OTk3adSCE2y+HhYT777DPeeecdYmJiWLp0KdeuXePjjz9maGjIImmyoKCAgoICrl+/ztTUFDt37rQ4PO3NK/kwUBt458+f59KlSwAkJSUxPj5OT08PwcHBdkuVGBsbo6Ojgzt37mA0GgHzmvLy8pJ74kKiD6j3GEEbEJfnLxsX9SVLfCf2uB9PTk7KNvX393Ps2DFef/11rl27RkhICLt27WLfvn1ER0cD9y6i9jQvZwv2ENlyc3Njenoag8FASUkJhYWFdHd3y0t/eXn5fe9xd3dnz549rFmzhsTERBYtWkRMTIykhdjbvvIwEPPUxcWFoaEhjh8/zsGDB2lubiY5OZmIiAiLhN/ZssvmZPUKQ6Crq4uGhgYGBgZwc3NjcHCQiooKiouLKS0tpa6ujrGxMfk+Ly8vdDodzs7OjI2NMTIygk6nY3p6mvHx8TnxMIjJZTAYKC8vp6ioiJKSErq7u/Hw8MDFxYXk5GRWrFhBRkYGGo1GcmLAvNi8vLyIiIj4SoaZkCmy54k9NDTE+Pg4AQEBsr91dXV8/PHHHDp0iKtXrzI8PDzjewcHB7l48SJr164lMjISHx8fuztM1JicnJTj6uXlxfT0NCMjI7S1tUnOIdivwSC8VmI+ffjhh/zyl7+ktLSU6upqLl26RGVlJWDepBMTE5menqaxsVF+RmlpKR999BFRUVFs3LhRfu5MFxp7hqurK87OzgwMDHD58mXOnj3LyMgITk5OtLS08MYbb6DT6fjOd74DIGlLtu6jOkfgzp07XLlyhStXrjA5OYmvry/Z2dmkp6cTHBwM2Ndl8FHixIkTVFVVkZGRQV5eHj4+PvK5LzIGjEYjTU1NGAwGUlJS5CVzfHzcLoxA9fjW1NRw7Ngxzpw5A0BKSgorVqyQHHuwTQhaXBweFQSN44suIUajUToMhD0wlxB7p7OzMxUVFfzmN7+hoKCAsbExRkdHaW5uxmAwzPjesbExPv/8c27dukV0dDQrV65k+/btLFu2TFLs5psHVk0DGBoa4urVq5SUlADg6+uLVqudkzGaNWtBnZ3s5OREcXExx48fp7KyUk7Erq4uysrKaGlpke/T6XRSb3F4ePg+42doaAidTkdAQICFsTNbG4+TkxPT09P4+fmxYsUKbt26RX19PQUFBRZtLi8vZ+nSpTI7VN0ef39/6Z3z8vLCZDJJI0ir1UpjHMDDwwNfX1/ZN3u4manbMDw8TF1dHbW1tfT09GAwGIiIiCAtLY3x8XGOHDnCe++9x61bt9BqteTl5ZGdnU1QUBBGo5GhoSFu377NlStXaG1tpbCwkJycHDIzM+V3bS8JFervXafTodVqGR8ft7hgWUvW2HqsHga3b9/m7bff5tq1a4A5/DU5OUloaChZWVmkpaUREhJCX18f169fl2t2dHSU8+fPk56eTnZ2NoGBgXZ/wbKGmgZw9epVfvOb33D58mV0Oh1OTk60t7fT3t5Ofn6+xXvAvsKzPT093L59W0Y0goODiY+PJzY2Vr5mPtMH1J620dFRhoeHmZ6e5tatW7zxxhtcv36dvLw8pqamWLNmDe7u7vdJv4kInxjXK1euUFpayuTkJAkJCSxatIioqCh8fX1t2dX7ErRaWlo4d+6cjAYEBQWRn59Peno6Tk5O0vM1V5d9cZZbc6xn4+9YX/5FUql6j52rM1EtATUxMUF1dTW//OUv+fGPfyxfIyKuERERhISEEBwcLOecRqOhr6+PGzduUFZWRllZGZWVlfT39zM6OsqyZcvw8vKyiErPh71Uva8YjUa6urpk9MfJyUnSI2cbszb71bpnwpvxq1/9irGxMfz9/TEajYyMjDAxMSHfI7giM3mvYmJimJqaQqfTkZ6eTnx8vEUW+2wO+tTUFOHh4Xz/+98nOTmZ//7f/7u8aYA587egoICSkhKmpqakp0a0ydXVFR8fH9zd3aUHJygoSHJhJicnJV8mIyODtWvXkpKS8rWSn2YD4u/39/dz7tw53nzzTS5fviwNAU9PT0JDQ5mcnKSxsRE3Nzf279/PunXryM7OJjIyUh4uw8PDHDt2jK6uLqqrq+no6LjvgmJv3kv192+9cdtbW62hPhidnJwYGhqiqKiIpqYm+RqTySQT0J588kkZ3ZiamuLq1aucPXtWRh06Ojo4efIkiYmJbN++XSZN2jvXV0C0sbOzk0uXLnHy5Em6u7vl2gRISEiwSAa1Jz1i0Q5r9RPB4bSm58xXqL07HR0dfP7551y9epXq6mrq6uro7u6WRmhiYqKk7VivVZPJRHt7O4cOHeI3v/kNdXV1MjqWnp7OD37wA3bv3o2Tk5PNOPfCmSHm382bN/n8889paGjA19eXPXv2sGXLFuLj43F2dmZiYmJOzwUh2zXbHHex53xZv+YqoqAel9bWVv71X/+VX//614D5TA8LCyMvL4+srCyCgoKIi4sjPT1drksfHx8qKyv52c9+xieffCLzZt577z2am5t5+umn2bNnD1qt1kIabT7Bek7M5V45K8brxMSEJNXX1tbyq1/9ijfeeEOGV+/cuSNfK7gRBoNBGrJRUVGEhYXh5eWFt7c38fHxREVFAWYDNzMzk7i4OIuBnu2DU6fTodPpOHDgAD09Pfz2t7+lpaUFV1dXRkZG6O3ttfDIWaO9vf2+xyoqKoiIiGB8fJzW1lbAfHCWlZXx+OOPs27dOry9vQHbJDOJm7D4u8XFxbz11lt88skn9/X11q1b8v+7d+/mz/7szyy8VwIhISFERERISoiXl9d9wv/2YigIiNCOtV7t9PQ0Y2NjFhcwezNmxXcpNnxPT0/CwsIICQmRYybk5x577DGZxSywdetWIiMjKSkpwcfHh6NHj3Lt2jU8PDzQaDRs27ZNehvsGWqPa19fH2fPnuXcuXMyIU3QkMLDw9mzZw+5ubnyvfZCZxHr0cnJCT8/P7y8vO57jdrjYe9j8rBoa2vjk08+4cSJE/IxPz8/YmJi8Pf3lwe/OhkLkLy8mpoazpw5w5UrVyzWZ1xcnIUH0x7WbltbG+fOnePKlSu4ubmxfft2XnjhBRlmBubcwFF/R3fu3GF0dPRrf1di3xRj5uzsjLu7O35+fnh6et53xk1MTFBeXk55eTk+Pj7k5+dLT7maIzwbEJ89MjLC+fPnOXToEBMTE6xfv56NGzfi7+9PamoqycnJaDQaAgIC7ivusnLlSsbHx0lLS6O4uJizZ8/S0tLChx9+iIuLC97e3qxZs2ZeeWDVbbM2XsX+M28TtkRnWltb+c1vfsOPf/xjDAaD7KjaOzA+Pm4xAZOSkti3bx+LFy8mJiaGtLQ0QkNDMZlMkgf6TaQqvg7Uf8vV1ZWnnnqK0NBQjh49Smdnp8xYrqiokK9/mJBdQ0OD5BROTU2h0Wjo6Ojg4sWLtLe3k5GRIY1Xo9F4n5E321CHiwDKy8u5cuXKFxrpYWFhrFy5Uh7+YtyEl7yxsZHCwkKamprQ6XTExMRYGD/2umgfNOcEF8reIaIBzs7OZGVlERYWJp9zd3fH3d3dwgMuLqAajYbc3FwZCbh58yYDAwNcuXKF7OxsFi1ahJ+fnzxQ7XXjnZiYkPO4v7+fI0eOcOnSJZycnPD29pYX66CgIHJycqQnD+7Jv9ka6u/VYDBY7DFhYWGkpaVZJNLNh3n5IIi+jo6OUlpaKpVnnJyciIuLIycnh507d7JixQoCAwMt9ik17WhiYoLS0lLKy8vRarWSm7h9+3ZeeeUVli9fLv/mXDsH1MljYJ6Xp06d4vTp0wwNDZGZmcm2bdtYs2YNbm5uUlPcFpepsbExbty4wfXr17lz546ce8IY/ToQmflBQUEkJiaSlJSEr6+vhde9sbGR3/72t/ziF78gMzOTN954g6ysLMC8Bma6wH1TWAvv19XVcf78ebq7uwkKCmLDhg28+OKLREVFyQvxF0We1q5dS15eHoWFhTg7O/PWW28xNTXF2bNnZWLs5s2bAfukKH0RrNVN5nLvf6SrwGAwSEJ1U1MT//AP/8Bvf/tb2TlhwKiN1+npaUJDQ0lOTpYyC1u2bCE6Ohpvb285iLZMChELVCSnBAcH8/jjjxMVFcXg4CAuLi7U19dz5MgRjh07ZpHAA2YvAZg3Jz8/P+Lj47lz5w6tra0WC1/wZQGqq6stjAlbHZ7qyejl5UVQUBBtbW3yIuHq6irDHWFhYaxatYrly5dLjtLIyAgGg4GQkBCmp6cpLCzknXfekRtBbGwsISEhwL2btL0ZP6I9agNWjEdcXBz+/v73vdbeoP5e6+vr6evrk88JL5ZaucNoNMrLi7u7Oz4+PvJwAfNhJl4zH2C955SXlzMyMiKTQMSa1Wq192Xs21ob1Lr8YmNjI+fPn6enp0e+JjExkXXr1slMdJjZYywOZjW/UKih2IOxK+hmo6OjNDY2cvHiRT7++GP6+/sBWLFiBXv27GHDhg0kJSXh7e1tIRYP9zyTk5OTXLhwgYKCAioqKjCZTMTHx7NixQqef/55Nm/ejE6ns/BozzXURlJhYSFvv/02169fx93dndWrV5OdnW3zUPKVK1f4/e9/z4ULF+jo6JBlal1cXL7S+hfzTZTZFrrEXl5eBAYGEhAQIHMIXFxcmJiYYHR0lDNnzjA1NUVVVZXMDYHZ43SrKY9gvgAJ55souiPmnfX7xOVIfIarqytarRYPDw+WL1/O8PAwWq2Wo0eP0t7ezunTp1m8eDG5ubnzsiqe2E9sgUdqvKo7MTg4yI0bN+QE8/LyksaYRqNBq9UyMjJCQEAAmzZtYseOHcTFxREdHU14eDiANBjF4SGMyLn2vAJy4gr4+PiwYsUK+bvBYCA0NJSpqSlKS0ulnpurq6vktTY0NBAdHU1OTg7t7e2cP3+ejo4ODAYDBoPB4vvTaDTSkAXbeFGsLwt5eXns3LmTrq4u6uvrJc9Oq9WSkZEhqQ5ZWVkyjOfj4yOzgW/dusXRo0elhzorK4uEhAS5OdurIaQ+8NXjEBUVRU5OjgU/0t42HnUFOzDTV27evGmRJBkfH8/KlStJSkoCzHNNzF/1HAwKCiI9PZ3r16/LQ9/Wht3DQj1u3d3dDAwMAPeiCwIjIyMMDAwwNjaGh4eH7JstDxVrjdLbt29z6dIladCBeT9KT0/H1dVVJsTOdLiq58JMsFU/1Ya0k5MTnZ2dnDhxgnfeeYfr16/j7OzM8uXL2b9/P88995zMuheXaPEZwhicmpri/Pnz/O53v+Py5cvSqEhOTuaVV17hsccekyFeWxnt1h6+27dvS3WBhIQEVq1aRXR0tDSEbOW8aWxs5NSpU5SXl+Pv7y+TGx92nqgTccV7BNXO2lD8IqxYsUJGImH26DzWofDIyEiys7M5efKkzNMxGAxSfUbwPJ2cnGaMjgramUajYdOmTbi7uzM4OMiHH35IR0cHDQ0NdHR04OfnZ1f8+oeBLbW+H+noq427yMhI/uzP/ozXX3+dS5cuWXgR/f392bBhA9HR0QQGBpKXl0dOTg6enp4Wt0wxIYTRICa5rUN4M4UIdDod69atIyYmhr6+PgsNQk9PT+lN0Gg0BAYGYjKZqKqq4vTp0xw/fpxbt25JY0C44tXheVtMaPE3hUc0NTWVvXv3Ul9fz8DAAP39/UxNTeHt7c22bdt49dVXJTd5amqKkZERaQSVlJTwk5/8hD/84Q8AbN68mVdffZWMjAz59+yFWyggNluj0UhfX5/sr0BwcDBJSUmykgjYn/GqxkwHhZOTE1lZWaxatUpKLKmhnuchISFs3LiRW7duUVZWRlNTE3fu3LHgntmbPJMYQw8PD8DM2bt8+TIjIyPyNeoxHR8ft0uPsnrvGxgYoLe31+Ji8TB7or2NjRrC+ynmUW1tLadPn+bWrVtMTk6SnJzM/v37efHFFy0oL9aKM8J4r6ys5KOPPuKTTz6xiDLExsayYsUK3N3dbZ4koz74e3p6aGpqkvNy8eLFZGVlSUqELfWjMzIyUBSFwcFBfHx8ZCLcw0IUshHrcGRkhOLiYiorKxkfH2dyclIqE/X19VFfX2/xfiH0v3fvXgICAuTjszVuwu4QSWqhoaGsX7+eixcv0tHRQXp6OuHh4TJC9WXlz+EebcnNzU16WgsKCuju7qavr0/yiO35/BBQ7zXWlxhxxsyFjfZIrQWxkQhFgT/+4z9mcnKSmzdvMjw8jJubm+TehYWFsXnzZrKzswkNDZWfMT09Las1OTs72zxkMhPEgS4WnXjM29ubxYsXP/TnxMTEcOvWLYaHh6UHUwy6MA6t/6atsWjRIp544gkGBgY4cuQIY2NjODs7Mzo6SmVlJSMjI/j6+hIWFoanpydTU1PcuHGDN954g7fffhuj0UhwcDBPPfUUTzzxhMz8tZewpYB6I+nv76esrIzy8nKLC4VWq8XX1/c+kr69QR36FnJlIoweEhJCUlKS9Lpa67aqjR13d3fCwsKk7FlzczOtra2Mj4/L78DeDCR1xrDJZOLGjRucO3dO0gREoRSBlJQUYmNjpYfHHmgDakxMTEi9bGGURUVFWSTaWdNb1DJH4+PjNDU10dTURHt7OyMjI0RGRrJ8+XJCQkKks8CWfR4cHKS4uJhr164xNDREcnIyBw4cYN++fdJwHR0dldW24F71IzCXVD158iRnzpyht7cXMDsXRDa4OuHHVhCRKVdXV3p6ejh8+DCXL1+WCcnr16+Xsme21hjOzMy0cDJ8VeNEbQi6ubkxPj5OSUmJlOATlxZvb296eno4ffo0Fy9epLu7Gzc3N9LT0zlw4ADbt2+3oGjNhbND9DM9PZ2nn36a/v5+VqxYYVEh88vWiYjaijUpHFg+Pj50d3dL/rp67dkz1O0TOS0CIrlvXiZsWU/qxMREMjMzpZg2mDenmzdvyk1XbbyK27OtvasPA+Fdha/ncaupqaGgoICamhrAbBwITo/Q8xOw5YRWe9VcXV3ZuHGjRenXvr4+3nnnHU6ePElubi67du1i586dODs7U1JSwo9//GM+/fRTxsbG0Gg0ki4gPAn25uWyhtCmFfJSIiFPKBDYc/tn8mhduXKFjo4OnJ2diY+PJywsTB6O1vNMbcRPTk4yNjYm+zs8PExfX999fFJ7g2jT8PAwFRUVVFRUMDIyIr+T4eFhXF1dycjIYP369WRmZtqVMa42JBsbGykqKpIFJcLDw1m7di1LliyR9BzrbGARtRoeHqa4uJijR49y4cIFSktL6e7uJjc3lx/+8Ifs3r1bhi6tw7yzCXVIfHh4mIsXL8qkVYA1a9bwyiuvkJqaCpgNeA8Pj/s8QAI1NTWcPXvWQg7O19eXxx9/3EIBxZbnjDqBsLW1lffff59z587h5eXFtm3b2Lx5MwEBAXIMbD0Hv6ljQd1+rVbL0qVLpba3+C48PDxkXsjw8DCnTp1ifHycmJgYVq9eTVpaGjB3lcXUbdbpdOzatUvmAAg87PdivY+oiz3Yu7FqDXV7h4aGLKLq3ySB76vikRuv6sQCMIdptm/fTmdnpwwHmEwmSkpK6O/v59atW+Tk5JCUlERwcDChoaGEhoZauOLVtcjtCdbeCRFGELdMAfWknZiYkElOZWVlkv8Jlgd/SkqKBZfSHowCo9Eo5a3y8/PZtGkTg4OD3LlzR3pzAFatWsXIyAj19fX87Gc/4+DBg4yMjBAcHMy2bdvYtWuXXdMFBMR3Pjk5ydDQkHxcJCoIw9UexuZhMDQ0xI0bNygoKKC/v5/o6Gjy8vIsvHbWh6Tag+ft7U1ycjJxcXEA9PX1MTAwYBFCtLfvQl1S9Pbt29y4cYPa2lomJyfR6XQynBkUFMSiRYvIysq6LxJkS6gpGfX19XzwwQdcvHhRPi8KtoikULhfa9HJyYn+/n4++ugjDh48SHl5OXV1dfKifP36dX76058yMDDAE088QWRk5JweqOoKQ83NzRw+fJizZ88CkJOTw8aNGy0MVzU/VlycxJlTVVXFkSNHKCwstIhcxcbGsm3bNvLy8uTftOW+o77wGQwGeRlxdnYmMzNTql2IJGgBdWn1uYKgV3yTtaCW7BMRVWEEqvvn5+eHyWSS1RuB+0Lzc+0sUEt6qR/7KhcKYdQ5OZmrdfb392MwGHB2dpZVqeYL1HOvo6ODjo4O+fvExISMJM82Zk0qSwxUYmIiO3bsoLi42ILLMjAwQHFxMcXFxURHR5OZmUliYiLx8fGkpKSQnZ0tM2fVVXzsmRciMpdnglj4YpJWVlZy69Yti5Dl6OgoYKYTZGVl2V21HPUCi4+P58knn6Svr493330XQPJb29raOH36NJ999hm//OUvAXNoc+3atbz44os8/vjjgG024q8DZ2dni76LsXzYsJEtoW7bnTt3qK6uljrLfn5+xMbGWsgrzdQXk8nE+Pg4Hh4eZGZmkpmZiaurKwMDA4yOjtrF3LSG8ByqyxafPHmSGzduSOqH4O5NT0+j0+lITU0lKSnJ4lCytcdLbWTV1tZy6NAh6urq5GE4MDDA0NCQRXlKtaSg0CI+ffo0P//5z7l8+TJgXstCCm1kZIQrV64wPDyMi4sLe/fulRdntaE4W1DzTnt6eqRjIzg4mH379rFq1SoLo0dtRKmdGn19fXz88cd88skntLW1AfdKHW/bto3c3Fw5H2w9Z4WxPT09TV1dnTwH0tLSZN4AWGr7gm3m42wnSBsMBjQaDc7OzvT29nLt2jWKioqYnJwkODhYXjIF5nq/VX/nX9cTrva8Dg0N0dHRQV9fHxqNhqCgIEmHmIv19k0h5sL4+Dj9/f0Wl0Th3JqLi+Gsl4cVHJ5NmzZRV1dHU1MTJpPJYrNtbm5meHiYyspKnJ2d8ff3Z/v27ezYsYOYmBgCAgKk8TAyMoKbm9u8uqnAPX6saPfNmzc5fPgwHR0dsrKY4Pr6+Pjg7+8/ZxXEHhaiDSaTCU9PT/Lz8+np6aG3t5erV68yNjZGc3Mzn376KUePHqW8vFy+d82aNbz22musW7dOPmbPRqvaKO3v77fYPIWn0cvLCx8fnzkpU/xNINo0MTFxn9TMw1ZlUh/2X8TxtbWnUg1h8ExMTFBSUkJBQYGFyoL1WC1atIiUlBTgnsfL1hJZM0n2qDExMYG3t7dFFvbExISM8LS1tXH48GHeffddioqK5Gs8PDyIiYlBp9PR3t5OU1MTt27d4te//jUajYZ9+/YRGBg4586C0dFRacysWLGCTZs2kZiYCFiOifDACiNwbGyMEydOcPDgQcrKygBz6c7U1FSeffZZ9u/fLyX5YO6F/gXEZUSj0TA+Ps7x48d544036OzsJDMzk2effVbyzwHJUxZ8UbXxLbx/9ryPPgjCPhDr09nZWX4n7e3t0iBKTExk6dKlFhdsW3nMRQT4q3A61cmfaqnCiooKxsfH8fLyIjw8XCoszYdKherzRCguCMTFxbF06VK8vLzkHjRb/Zm1WaCWZNHpdKxevRpXV1daWlooLCzk7NmzFlyJvr4+i6zQ0dFROjs7ZaGClStXyspMcH/1J3uAdaadmLRi01ELZ9+8eZOrV68CSDUCEcIMCwu7L3RnTxNatMXDw4Nt27ZhMpkYGBjg2rVrdHV10dXVJV8bEhLCkiVL2Lt3rzRcBUnfHi8g1h675uZmrly5QnNzs8VrdDodiYmJLFq0yKI2uj2Nk4BaX1G98bu5ueHr6ysz8eHBnCX1XBS8V7HGrUPU9obR0VGqqqooLi5meHhYenKGh4dxdnYmICCADRs2kJOTY+umWsCaluTu7o6/v7+U0XN2dmbp0qWsXr3aIkrj4eGByWSitraWgwcP8vrrr8ty1gEBAYSHh5OSksLixYvx9fWlrq6OgwcP0tDQwPXr17l58yaPPfaYNBjmckwHBwelSklsbKwFhUMNtfE5MjLC0aNHefvttyktLZWPu7u7k5GRwZYtWyTVZXR01EICba4hvOJOTk4MDw9z9OhRDh06BEB2djabN28mIiJCRqWs90i13qg97jUPC/V5qNVq5SVEo9FY7FFhYWEsXrzYQmHCVv1+WKNZfYaoKZQmk4nr16/z4YcfUlhYCJjlJ9PS0uzKjnkQxEVWRHR6e3vp7u6Wdo6rqyuJiYlStUZcSmarb7NmvFpPsLi4OBITE9FoNBw9epSenh4ZwpoJTU1NvPXWW0xPT5OWlsbTTz/Nyy+/LL8Y4S2yt0FXG67CwLYewMLCQslxgvs5ajExMaSkpFgYFfbUT5E9Kvg6mZmZFoeMUJXQaDQsW7aM73//+2zfvh24l5xhj0YOWGrygZnecfLkSZqbm6WxJpQhFi9ezLJlywBzCGU+VNtSz6PQ0FBycnIkPedhi0SI5+0pC38mqNdif3+/pEu4ublJvuTU1BR79+7lL/7iLyw8XvZ4sRJ0rNHRUXQ6HTk5Oezfv58NGzZYyLWB2cg5duwYP/vZz2RCqJeXFxkZGezevZvc3FxycnIICAjg8uXLXLx4kYaGBvl35jKsLuaQ0Wikrq6OxsZGtFotoaGhch2KqJQaExMTdHZ2cvnyZd59910KCgqkfi+YjdfQ0FCLSIGt91FrT7o6qczHx8dC5UK9l0xNTdHS0kJTUxNarZakpCSLzPv5DDU1wvriLLix6nGz5z0H7il8WJ8FZWVlvPHGG/zqV79ibGyMlJQU9uzZQ2Zmpuy/veX1qCH6JMaptraWmzdvymIp1nbAbFPq5sz/7uPjIzuyefNmuru7iY+Pp76+np6eHkwmE1qtVoYM1Aky165dw9nZmdbWVlauXMnKlSstPA22gjj81B6omSSGwMzxnZiYoKKigk8//dQipC5uKGD2wnp4eNitIaQWTJ+enqaxsfG+cKxWq5XZo+Hh4SxevFguypGRkVkp6fcood5ABwYGaGlpYWRkRPKyRHasOpRl6wSQh4HYVAV8fX1lSFZQCB6mD+pKXGr1AfE37AVqSTs1txws12dqaiqZmZmAeX66u7vb5doTMoLi+w4PDycpKUl6pUwmEy0tLfT29lJaWspHH30kDde0tDTWrFlDfn4++fn5hIeHS3UCEa6Fe974uZIhEl7E6elpKisrKSwsZHBwkA0bNrBlyxa5z6s5sQaDgba2NgoLCykrK5OHqJBWEglg6enp5OXl4e3tbfG3bAm1ioO4CAsEBgZaJAWJEHphYSHl5eX09fXJ8SkrKyMxMVFWprRno+fL8CDVCDDvUd7e3hZcZ3s1XoUtILzK/f39tLS00N/fT2trK5cuXeLEiROSc+/j44OHh4cFPclek9PB8uyfmpqirKyMgoICent7iYmJYd26dbJ4CNxzEswW5uy0tQ5/vfLKK2zZsoWbN29SVFQk+UxXr17ls88+k+8RHJObN29y5coVvL29+eEPf8h/+k//yeKLsgWEduKXDVBpaSlVVVX09/dTXFzM8ePHpZcDLPViDQYD9fX11NbWkpCQIG/i9iDZA/fqvE9PT9PU1MTvf/97fv3rX1NfX28hyQP3DkU1PcTeDTxraDQaPD09payO2pun5o/a64ZqDWsPxteZU+LAdXNzw8vLy269IurEOvW8E1JnAgMDA1KAfS4keL4u1KFWnU5HWFgY3t7eUjpoenqakpISDh06xJUrV6irqwPMyZKPP/44r776qtShFmoZZWVlfPjhh9JjKbKF5+ISoq4uNTIywq1bt2TeQ3JysoXaCpg9VyUlJQwPD3Pr1i0++ugjmpubLcKzExMTuLu7k52dLcvIBgUFWUTBbAl1tKK/v18aMnFxcaSkpEgPuslkoq2tjY8//pif//znVFZWkpyczNq1a6UT5OLFi6xevZrNmzfLJK+vmgVvD1CPycDAgCzUoNVqiYyMJCIiQhY6sOcEWWvj+vLlyxw5coSqqirq6upoaGiwODNqamo4dOgQWq2WDRs2EBYWZpfl0QWsjdempia5x0RERJCRkXGf53U2MaeWhCAvi5tGZGQkAQEBpKenMzo6iouLCxs3bmTTpk188sknXLx4UVbgEBgaGuLXv/41AwMDbN26laVLlxIaGjqnC1btPle3q7Ozkzt37sgkpq6uLlpbW2lsbKS3txeDwUBnZydtbW33eYLExJiYmKC2tpaWlhaLW7k9GK9qwfbq6mp+//vf84tf/EJKZQjxelHudnBwkJaWFqmiMF9gLXMmvODWoVTrDFh73XS+CA9jpKhpL8PDw9TV1eHs7MySJUtYvny5RbKQreeoGsI4Ev8eBIPBwOjoKD4+PjY3bh4WMTExbNmyheXLl8u+ubi40NraysWLF6mqqsLf359Vq1bx+OOPs3HjRosCKqKwyNtvv83rr79uccG8c+eO3Hvmak5PTk7K6IZGo+HatWv8j//xP/D09JR7ZVtbG+3t7RiNRu7cuSN56NbrMikpiZdeeol9+/ZJY1BdodGW0Gq1MuHs5s2bcu9cuXIl8fHxcv4ZjUauXbvGe++9R2VlJZ6enuzYsYMtW7ag0+m4ceMGn3/+Oa2trURHR0vj1Wg0zlii1J4hPHSjo6OUlpZKfV93d3cCAgIkVVBdEMgeIfa+/v5+SWU5d+4cXV1dFnQWgf7+fg4fPszt27cpKCjgxRdfZP369XPc6oeHWhlocHBQFgAB81j5+PjcF9mY955XYfSI27EQ6HVyMtcCFqFLgfz8fPlYUVGRDE1qNBoMBgPt7e38+7//O5WVlSiKwp49e4B7PNPZ9u6JjdBgMDAwMEBHRwc1NTVUVFTQ2Ngotc+qq6tnnLQzQYQJ/Pz8iIuLs6BZgG09Wmo+0uTkJM3Nzbzxxhv8+7//O319fURGRrJs2TICAwPp6+ujrq6O27dvMzk5SWdnp8Vt86uUFbQV1IecUIGwrnyiTkgU77GHw/FRwzrcWllZSXV1NU5OTqSnp5ORkYG7u7vdiKnDvculVqtlfHyc0tJS6SEA85iKeRgbG0tcXJxMurO3bN8HzSkPDw8iIiIsDguj0Uhvby+dnZ1MT0+TkZHBk08+yd69e6UBICriabVaysvLuXr1qoVOo7u7OyEhIZJvP5uhWvX3rNVqSUxMJDExUSaN3bx580vfL/j1QoUhNjaWffv28cQTTxAWFib3rNkOYX4Z1FEAMF8Qampq6OnpkRrDajWE9vZ2PvnkE27evEl0dDTf+973eOWVVyQ/PSIigtOnT3Pp0iUaGxvl+4TneT7A+jvp7Oykurpacig9PT3x9PS0e6eAuoodQEtLC6dOneLEiRPygiUkpPz9/fH29pZUJjEPampqMBqNuLq6smjRIjw9PW1WDvhBEGvIYDDIAicCQvJLyGXOhUrJrBuv1pIeRqNRZptbZxaqsWXLFgIDAykqKqKwsJDS0lIaGxstpH2OHz9ObGwsS5cutRBany2oQzKDg4OcOXOGgoICamtraW5upru7m+HhYdlHa++qGmpPnqurKzExMeTl5ZGTk0NMTAxJSUkWB5MtD1S18VpRUcEbb7zBL3/5S/r6+oiLi+OVV15h06ZNBAQEUFNTw4cffig5vWNjY1JzEWwnUfN18aAFaG2s2vsGK2CtkflliTliUxbZ0TU1NbS2tsoQrOBo2VP/x8fH5QHe3NzM7373O06dOiXpAyIsGRUVJYXrxevtyXAFy3mlnnPNzc2cOHECb29v4uPj0el0dHV10dbWxtDQEP7+/qxZs4Zt27ZJwxXMh0xNTQ2XLl3i5MmT1NbWyud8fX3ZvXs3O3fuvC9kPxtQc5Ld3d1ZunQpS5cu5ciRIxbKM1/0fq1WK50hAQEBPPfcc7z88ssW2en2cKECy7H09PTE29tbSmGpS92CmWp26tQpPDw8+OEPf8iLL75oEZIViWiDg4MWka25rIz2qCGcQuJ8Dw8Pt0hKs+folrpdGo0GnU5nETlNT09n06ZNrFy5ksjISAwGAy0tLRQXF3PmzBkKCwv56KOP6O7u5sknn2Tz5s0WeT22HFNr47yiooLDhw9bOASCg4NJTU1Fp9PJ9TjbZ/2sGq/igFNX6RFhIfVrxsbG5GtFnV8R8lq1ahVXrlzhZz/7GZWVlbKOurhpHzx4kIiICF544QWSk5NlDfPZ4MaoD36TyUR7ezunTp2y0E+cCWrOndAnVBuvERERHDhwgN27d5OcnCwTnmxtvKqlMeBeWd/PPvuM3t5evL29eeqpp3j22WelPmZQUBA3b97ExcVF6orW1tZSU1NjURJ2Pm2w6gIZD/O6+QI1V/BBbVdf2Jqamjh9+jTl5eW4uLig0+nuE1G3B6iNldu3b3P69Gm6u7vRaDR4eXnJcFdUVBRbtmwhOztbvt6eOXVwbx/o6uri/PnzpKSkyLVXXV1NY2OjrGr4/PPPW4STx8fHqaur47PPPuPIkSPcunVL0gW8vb3ZunUrr7zyCmvXrpUHz1wYDGJd+fj4sH79emprazl8+DDNzc0zXq7EXmpdrjgyMpItW7bIqJ2Q3LKH8VQrKri5ueHn58fixYsJDQ2luLiYiooKGaUT4+Tq6kp+fj5PPvkkoaGhGAwGhoeHJT1L6MXai3H+daAeGyGZJYpWpKWlWeS12OvatF4j0dHRrFu3jpqaGgoLC6Xhmp+fz9KlS+XrpqenWb58Oampqbz11lucOnWKI0eOYDQaCQwMJDw8HI1GY3NZUJEoqjZeDx48KBWTfH19iYuLk2M1V1UnZ814tbbWW1tbGRgYIC0tzcIQE/IQYgMSNavHxsakC1oQ7kWoz2AwyM21o6OD//W//hfh4eEkJycD5sUvDMBHCXW7PT09yczMJD09XRqvwkidic8qbmHWg6rT6VizZg3PPfeclMxQJzLYEtaap/X19Vy+fJmOjg5cXV3ZsGEDmzdvlocn3POGiH4aDAYqKiqoqqoiOjraIlN9Pm+6CwEPcxCIedjb20tBQQFHjhxhfHycgIAAmekuLoy2lkATIX9hkJeVlXHhwgVpoE1PT8u16eXlRVZWFunp6TJEbm+UAWtYe/v7+vosVFlaW1vp6+sjLS2NF198Ua7LoaEhenp6KC0t5eDBgxw5coTOzk5Jh/H29uaJJ57ghRdeYOXKlXN+wVRnkufm5kqPpDUXF+7J9KkLpoCZ7pCcnGyhAGKPY6mWo0tISCAgIACACxcucODAAXJycmhtbaWrq4tFixaxfv16aRS4uLhYXDZ1Oh1+fn73FbOxRwPvYSDk6wwGA56ensTHx89pAtA3hZiX7u7uskT6li1byM/PJyUl5T7KnJOTEykpKRiNRhobG2lubqa6uporV66watUqHnvsMQICAmxOSRM2icCdO3dkZNXf35/c3Nz7bIB5a7yq+W+ChH348GFqa2uJjIwkKSmJoKAgUlJSyMjIeKB7+c6dOzQ1NXHs2DEpMC7g5OSEp6cnfX19GAwGC/LwbGkUqj3IWq2W7OxsnnvuOQBOnDhBV1eXPBxFHfjg4GCampqora2Vt+XJyUkZKtu4cSMHDhyQUj0PE8qdK1hn6BYWFnL48GH6+vrIzs5m165d8iY5NjaGVqtFp9PJpAQwG68lJSVkZGSwZs0aadjOx6zY+XooCFhn3ff399PU1ISfn58UT4d7Sgpubm64urrKspvvvvuupICoZXrAPrKc1RtmeXk5r7/+Oh9//DHDw8P4+PgwODjIxMQEoaGhvPTSSzz11FNERkbOWqTmUUOUshUGTHt7O1euXCEhIYGoqChGR0eZmJigra2NGzdu4OPjQ19fHzU1NVRXV1NSUkJhYaHUFnV2dsbPz49169bx/PPPs3HjRguN17n8PoRzQqPRkJqaygsvvEBoaCj9/f14eHgwOTlJR0cH5eXllJWVyeQusd+mpqayfv16aQwCdscZtIanp6cMi1dXV9Pc3MzQ0JDkQLq4uBAcHCzXrNFolIbq4OAgnp6eJCcnW/TZ1mvwq0J9QTKZTBiNRiYmJnBzc8PT09Pm0mZfBWrj2sPDg9WrVwPIC5WgiIj9RkgvhoeHk5mZyZUrV6iurmZkZITW1lapRAG2lyBU901d5S8wMJANGzawbNkyi2I489Z4VXsO29raOHbsGO+//z63bt0CzPyIp59+2iIkMDQ0JG+TJpOJzs5OLl26xGeffcbZs2fvK+soOKU6ne4+d/xcyd14e3uzceNGyW/54IMPmJ6eJigoiGXLlrFv3z5SUlK4ePEi7733HmVlZfJmLF63d+9eHn/8ccCydKA9QH2Dqq+v5+rVq1LiKyIigry8PAuvuLOzM15eXoSGhhIdHU11dTUGg4Hbt29TVVV1n3rCfIOtN5BvCmt5r76+Pm7dukVUVJQ8AMVlRXBAjUYjN27c4P333+f06dPAPUNBLepv62QYdbJYV1cX586d47PPPpM6p2reZ3h4OK+++qr0Fojwsj1CfWj4+fmRkpJCQ0MDDQ0NNDY28sknnzAxMcGSJUvo7OzEaDRSUVHBv/zLv3D9+nU8PT0pLS3l2rVrsuSmk5MTfn5+xMfHs3TpUnbs2MGqVassLi9zHfkRsoMCQs9bjcLCQn72s59RX1/PyMiIxXrMzc1l06ZNFlxdezTk1NEJZ2dnMjMz+fTTT5mcnOTatWvk5eVJiaimpiYGBwdlwpmYo93d3RQUFNDX10dqaqosLSo+f77AOhmwp6eHtrY2WeBGlDmeL7DeA4XRqo5gzmSMC0UbtXdTGLn2ALXTo7u72yLao9PpyMjIICUlBRcXl4cudPMoMGcJW2rvqslkYvHixWRmZtLR0cHvf/97zp49i0ajwc/PT3J7WltbqampYXBwUL5XePYEP2jZsmX8/d//PWvXrpWTZLaNP2F8Ojk54eHhIUMEPj4+tLe3k5CQwKZNm1izZg2enp64ubnR1NREY2OjhfpAQEAAKSkp0lAYGxuzkB2yJdQh1O7ubs6fP09FRYV8Pioq6oEhnbi4OFavXs3k5CT19fVMTEzQ19cnDVZbh0G+CoQnfKaQsnXyk732Sc23U0cv+vv7qaqqIjc3Vxqvau8OwKVLl/j4448timokJSWxZ88ei1CRLRPxZkooOH/+vEWioPoQ1Gq1FoUy7Pkipd7LkpOTefbZZ5mcnOS9995jaGiIjo4Ozpw5I7OVRQSqtrYWg8GAu7s79fX1FuPu4eHBpk2bePLJJ1m0aJHUiwXbUyeEd3km43lkZITa2loGBwdlsRAwl1V97LHHSE9PlzJU9qqXqVY9cHd3Z8eOHdTV1fH2229z7NgxMjIyeOaZZ/jud7/LkSNHGB4eprm5mYSEBPkZZ86c4Y033qC/v99CSQLmn/Gqnm+1tbXcuHGDoaEhAgMDLcZ4PkMkoQkjUDgRhGRoc3Mz169flxUA4V5JeVtDXflrdHSUkpISC3ULQEboxOvnCrNuvAqNRbXHJyUlBWdnZ0pKSrhy5Qr/+q//auFZVUNwSIQ0lUjoEli2bBlr164F7unIzpVUiAiVenp6smHDBqKjo+ns7CQwMJCcnBwpHO7p6Ymvr+99G4uoriFgT5n46kloNBotxKMF9cPPzw+4J3guEB8fz7Jly6ipqaG+vh6YHxJZAupDT2w46kRA8bg1d9seD0uwTIgJDQ2VyYJ9fX1cu3aNxMREacC4ubnR39/PwMAAlZWVHDx4kFOnTkmvnVarZenSpeTl5eHi4oLRaLyPjjDXEJcLsb7Ky8u5du2aNMRFaFnIKo2MjNDR0SG9dPZ+4Av6jpeXF+vXr8dgMDA5OSkjIXV1dRaZvwLC6wzmC2V8fDxarRZ/f3/27dvHU089ZRGyFYowtoQYC5PJJJNbJycnqaqq4s033+T69esYDAYZds3IyOA73/kO+fn5Fh5Ne4Vax1Wj0bBixQra29spKyujqKiITz/9lEWLFpGbm8u2bdvo6+vD1dWVsbExuru7OXfuHG+88Qbl5eXk5eWxdOlSC+PVHnIlHhbWF/7Ozk55Xmi1Wrvui5reN9O+r35ezcMXv6ttFJFLIvoeEBBAZGSkRXl4W50twusP5ryH69evWzgyAgMDbVYxc9Znh0iSENptAI2NjfzTP/0TLi4uDA0NfaHhKiREhPEKZu+r4HktWbJEvn4uN1+RhS9uJj4+PixevFhWy1Jr2tbW1lJWVnafWL/14rUnz504RKanpwkJCSE7O1t6WjUaDQEBAXJzEd4SAeH9sBbxn88QBoS6T/MN4eHhrFu3jvr6ei5evEhnZyenTp2Sm+zmzZsBOHfuHKdOnaKsrIyWlhY6OjowGAxERUWxe/duduzYIb2ztk7Sgvt54k1NTTJKIA4J9cVDlF+eL7De01avXk1oaCiXL1/mnXfe4ezZs1/4/uTkZF588UXWrVtHWFgYGo0Gf3//+3SL7Uk1YmJiQnLny8rK+M1vfsPnn38uQ5YuLi5kZGTw9NNP89RTT0mFAVtmZX8VqL/n7Oxsdu7cSX9/P1evXuXv/u7v2L17t3SIDA4OUlJSQkFBAQcPHqSnp4f8/Hxeeukl1q9fj7e3t90L+D8MXFxc7jNY7WU+zgT1d652XphMJikXJc5FnU4349hUV1dz+fJlbt++zcjICDqdjrVr17JixQoZDbF1EqzA0NAQlZWVUrsWzIa2mnM9l1GsWTFe1YMk9Pt+8IMfcPnyZU6ePEl7e7usogFmj0hQUJA0dCcnJxkcHJS/C8+JRqNhzZo15ObmEh8fT2RkJImJiYyNjeHu7j7nNzVrCSXhZRZhAa1Wy9TUFDU1NZSVlckwniBiWw+0PRmvgIUhnpWVJTmOvb291NTUUFtbS1paGlqtVhozXV1dnD17lvPnz1tcWKwNXHuGup1C7kuj0eDu7o6Liwvd3d0y6U79Hnvsn3pTdXNzY926dfT19dHU1ERfXx9jY2MUFhai1Wplqc3CwkILfrPAkiVLZEa6reVb1LD+7tXzLigoiKGhIek5joyMZNu2bRY6oPbs4VFDzDdvb2+WLFkiveXp6ekMDAzg6ekpD04hO6jValm2bBmbN28mPT3d4vOEZ9NeQpTWEPNW7KHt7e1yrKKioti6dSvbt2+Xhqs9zckvg/ryFBkZyZNPPsnExAS//vWvuX79OiUlJVy8eJH4+Hj6+/tpaGigt7cXrVbL3r17ZelbwYO1ZyPvYSH2WLhnANrbnqpWA3rQBfhBFf0GBgYkn/zOnTsyAVqof4CZ671//35WrFghP9+W1eHUf3dyclIm8gq50v7+fptV0Jx149XX15edO3eyb98+6uvrefnllzl37hyArIAzMTEhB++LjICNGzfy13/91+Tl5cmkLHvYdK03DnXmNtzTWPyy99kzfHx8SEhIICgoiO7ubj755BM8PDzYvn078fHxjI+P09LSws2bNzl69CinT5+W/F5fX18SExMttCPtYdweBiKRxGQyMTo6+kDaANjneIo2CSJ9cHAwK1eu5MKFC9TV1TEyMkJvby+ff/45R44cAe5JvghvHJhFttevXy9VMYRkkT0YCtbfu5qKIy7DYDYY9uzZw1NPPWXhLZgvXljrAzE8PJxnn32Wp59+2uJxdVERsdZmGid77re1pjaYjW1nZ2diYmJYv349jz/+uEXS4HzZUwSEIaTRaMjJyWFsbIz+/n5OnDhBW1sbR48ela91d3dn5cqVvPDCC2zbtu2+kuj2uPd8VQiqCMy8v9oDhIzn6OiojLiq95sHoba2lqtXr8r+FRUVcenSJSorK2VBjpUrV/L000+zZcsWebm2tQSheo4JBaXKykoZLW9ra6O5uZmlS5fi6uo6p+fBrLscdDqd7FB8fDzPP/+8rMwTGBhIT08PFy5csPBCurq6smfPHjZt2oSrq6vUFV25ciVr1qyxiwNzJqgPDbWREx0dTWxsLF1dXUxMTODt7Y2zszMhISH3afTZE9RGpuhHUlIS3d3dVFRU8NZbb1FRUUFoaCgmk4m+vj6am5stEtOcnZ1l1Rt1GMReD07rEqeRkZFkZGRw9epViwtIcnKyXVbxeRCcnJxkckRCQgJ//Md/THBwMEeOHKGsrOyB70tPT2fFihWsXLmS1atXS36TvZSChftL9WZlZZGSkkJVVZW8FEdFRbFjxw727dtHTEyMlHOxZ67yg6BOgrXO0v8iCCqPWH/2Mn4zQd221tZWmczi6urKmjVreOqpp1i6dCnu7u7SuLXn/jwI6rmXlZXFa6+9xrZt2+jo6ODOnTsYDAZcXFzw8/MjLS2NNWvWyFwDteTgfJvDM0GU4gbLs8de6Cxi/5yYmODo0aNcuHCBjIwMNm3aJMtLOzs7U1tbS0lJCf39/VIemdT/MgABAABJREFUq7a2lsrKSoaGhjCZTBgMBgIDA9m6dSuhoaEEBgaSnp5Obm6uxbli636Lc3p6epqYmBh27txJYWGhfD4iIgJ3d3fGx8dlbshcYdaNV5HoAubNc+/evSQkJFBdXS0z2Ly8vLh48SJubm4EBASQlZXFj370I/Lz82f8TLEB25sHT0w0tVSXi4sLubm5bN26lc7OTqnll52dzerVq+2abG99sCcnJ7Np0yb6+/upqKiQ/2aCSFbbsGEDL7/8spQyMxgMD+T/2AOsDaH4+Hi2bt1Ke3s7J06cwM3NjSVLlrBt2zaLbHt7zW4WEAf7xMQEnp6erF27lrCwMNzd3dHpdHR0dEhvh4h+xMTEsGnTJrZv386iRYuk4Wpdec3WsNZoXblyJa+88gqffvop5eXlhIeH853vfIf9+/cTGRkpEwztbb09LNReKWtu3UwQBq51CVJ7hpiv/f39tLS0SJqZSHJav3497u7u83oc1RdAkZAnSuR+EUTmuq0TJR81rGkDgtZiL/uqWhlBOG+CgoIoLy+XhTWcnZ25cuUK586dk3utSGwVOTuiutrOnTvZunUriYmJMkHNuq/2sMdOTU1JWdLc3Fzy8/O5fPkyERER7N+/n+TkZDkP53KsZn3mi86IkE9YWJgcPDB/MWvXrqW7uxtnZ2dZOi8tLW3GzxOZxdYJU/YE61Dt4sWLMZlMNDY28sYbbwBmQ3D37t1ERUVJjpq9HSzWN9/U1FSeeuopPD09OX78OOXl5XR0dMz43oSEBPbt28fevXvv0+CdT/Dz82PDhg0kJCTwgx/8ACcnJwICAggLC5uXEjXqzTApKYnvfve77NixQ6oGwL0EKHd3d0JDQ2WZQvGcva056w0+MTGRF198kU2bNtHX14e7u7ssXyiq+NjDofBNIcbiQSV61Tqa9nTZ+CoQfRSHY0REBLGxsdLIEbJY8xnWUm8P83p744J+HVjPycDAQGJiYqitrWVgYICWlhYLmUxx7ts6hO7s7ExGRgarVq2ipKSEN998U+oOiwiXgJ+fH3FxcXL/jI6OZunSpSxevJjU1FQLXWIB68qWtoa1bfLMM8+Qm5uLp6cnsbGxBAQEfGmZ8dnAnKx6dWhrenoanU5nwRNRiyyrIQoXCCPVzc0NjUYzbwwg9QaTnZ3Nd77zHYxGI11dXezevZuMjAzAbOTac5/EohRVxYTaQ3V1NXV1dfT09MgFJyqHLV++nKefflrqE46Ojtq1x/VBcHZ2vi+jcj5D7YF1dXUlLi6OuLi4L32f4MHOh0pUWq2WqKgooqKiZnzentfaV4GIEsx3420miFrq/v7+eHt7y3Dy6tWriY+Pl6+bL5fGL4LaAysSQa096sLLqtVq7c7J8XVhbYiGhoaSkZFBZWUlra2tXLp0iWXLlpGSkmIzOSY1BCVQq9Xy2GOPERwczIULFygqKqK3t1dGmDUaDb6+voSHh5OWlkZCQoKkKcXExLBo0SILXdTx8XE5B4RH3d72WJH3ARAbG0tsbKyNWzRHxqsaX2VQ1DpnX/W99gD1JuPs7MzmzZtZsWIFJpPJohjBXFUE+7qwNjiTkpKIiYmRHCW1hqva0FX3cT4argsZX5Un5xg7B+YSaiWPkJAQgoODiY2NZffu3RbG60Iz3IUBM5Nn1Z7PiK8Da+M1PDycjIwMzp8/T2trK0VFRZw8eZKkpCSWL18+Z/rtXwRRBTQqKoqwsDBWrlyJwWCwOAPVl0pB1RGGr7Wyh5OTk028ll8Hc1X29WFhk5UviOYC6kQCcQPRarUL4lYN9zhK1h5ntTdrPkAtRfOwG4nBYJA1nMF+yPdfBaLmtjhQxcZk7zzXL4IYD1GCcSZ9PhEumm/9FBEAcbESIuELxWP1bYB6rNLS0vjTP/1TfHx8yM/Px9PT0+54148K8zGB8OtCTSl0c3OTuu0+Pj7yNUK9JiwsjNTUVACb85zFGIkosEjWelgYjUYptzXXSU7fFELDfXx8XO6rtkoYtMkMsE6KWWi3Z2uo+6euyjHfjPOZyqM+6CYmFri1jMh83Ji/irE+3zCfkngeFuriJg7MTwgVFhFqfe211yyen4/7iAMzQxijTk5OJCcnExkZKZ/r7++ntrZW6jQD0vCzB4gz0LpIj/r/1nN1vtOW7GVvtY8Z8C2B8OBNT0+j0WjsZgF+VQjPuUieUxvkwiMispsdh4wDDjjwdaAWhLd+3LGvLByonSKenp7k5uZy48YN6urq0Gg0hIaGymIM1q+3FcTcnKmYgpibgr+qbq+96tfOR3xl60lRlNloh91gofcPFn4fF3r/wNHHhYCF3j9Y+H1c6P0D2/Rx06ZNbNq0CTB7ZvV6/az+vYU+jguxf44rgAMOOOCAAw444IAD8wZO9pQ95oADDjjggAMOOOCAA18Eh+fVAQcccMABBxxwwIF5A4fx6oADDjjggAMOOODAvIHDeHXAAQcccMABBxxwYN7AYbw64IADDjjggAMOODBvYDOhUUVRGoAHFcjt1Ov1YXPYnEcORVFeAX7zJS+b0uv186tSgRUURdkJ/G9ABhAItAM3gH/W6/WXbNm2RwFFUZ4E1gE5QDbgDfxBr9e/YMt2PWooihIF/N/ANu6N48fAP+j1+j4bNu0b49syhgCKoqwF/hzIBwKAXqAU+Iler//chk175FAU5UXgd3d//Z5er/+lLdvzqLBQx/DbcCYqivI/gVwgBQgCxoBGzHvpv+v1+h7bte7RwF72U1ur5A8AP5nh8eE5bsdsoAj4hwc8txbYCByes9bMAu4u1L8CejAvzm4gCdgLHFAU5SW9Xv+G7Vr4SPBfMC/QYaAFSLNtcx49FEVJBC4CIcAnQAWwHPOlZJuiKKvn+aa74McQQFGU/wL8V8zr8CDmC0gQsARYD8xbw8caiqJEA/+GeUy9bNycR4YFPoZFLPAzEfjfgULgOHAH8ARWAn8P/ImiKCv1en2z7Zr3SGAX+6mtjdd+vV7/9zZuw6xAr9cXYV6s90FRFOGR/PlctedRQ1GUMOA/A53AYr1ef0f13AbgFGZP3nw3Xv93zAu0BvNt87RtmzMr0GM2XH+o1+v/TTyoKMo/Y+7/fwO+b6O2PQos+DFUFOUpzEbPCWC/Xq8fsnre9vUcHxEURXHC7MHrAT7EvA/Neyz0MVzoZ+Jd+Oj1eoP1g4qi/Dfgb4D/HzDfKwbYxX5qa+P1WwdFUTIx38RagUM2bs43QSxmzvQVteEKoNfrTyuKMgQE26RljxB6vV4uzIVYpURRlARgC9AA/NTq6f8L+BPgRUVRfqTX60fmuHmPBN+CMXQG/icwCjxnbfQA6PX6iTlv2Ozhh5i9dOvv/pz3+BaOocQCOhOZyXC9i3cxG6/Jc9icWYG97Ke2Nl61iqK8AMQAI0AJUKDX6022bdas4rW7P381z/tZDRiB5YqiBOn1+m7xhKIoj2HmwXxso7Y58PAQh/8xvV4/pX5Cr9cPKYpyAbNxuxI4OdeNc+ChkA/EA+8DfXd56JmAAbi6ELjnAoqipAP/CPyLXq8vUBRlQRivfIvGcAYslDPxi7D77s8Sm7ZiAcHWxmsY8Hurx+oVRfkjvV5/1hYNmk0oiuIOvABMAfM6uUCv1/cqivJ/AP8M3FYU5WPMYbxEYA9mzs9rD/4EB+wEqXd/Vj3g+WrMxmsKDuPVXpF392cnZr5dlvpJRVEKgCf1en3XXDfsUUJRFFfM50UTZi/WQsK3YgytsZDORDUURfnPmLnYvpgTuNZgNlz/0ZbtWkiwpVTWb4BNmA1YT8yL9T+AOOCwoijZtmvarOFpwA84vABI2+j1+p8A+zFfgr4H/DXwFNAM/NaaTuCAXcL37s+BBzwvHveb/aY48DURcvfn9wF3YDPmyEcmcBR4DHjPNk17pPg7zIlLr+j1+jFbN+YR49syhtZYUGeiCv8ZM+3qzzEbrkeALQvt8mFL2MzzqtfrrbMOy4DvK4oyDPwIc3beE3PdrlnGn9z9+R82bcUjgqIofwX8d+BfgX8HOjBnHv4P4A+KouTo9fq/smETHfjmcLr7c9qmrXDgiyCkhZwwe+eK7/5+S1GUJzB71dcpirJqvoafFUVZjtnb+k/ztQ9fggU/hg/AgjoTBYTUp6IooZgpIf8I3FQUZZdery+0aeMWCOyxSMH/e/fnYzZtxSOGoigZmCdxC/Nb7gQARVHWY04w+FSv1/+FXq+v0+v1o3cX5hOYyfc/upsQ5ID9QnhWfR/wvI/V6xywPwgd3jqV0QPAXQ/l0bu/Lp/TVj0iqOgCVcD/aePmzBYW9BjOhIV2Js4EvV7fqdfrP8JMvQrkni6xA98Q9mi8ilCzp01b8eix0Ejpu+7+vE8mQ6/XjwJXMc+vJXPZKAe+Mirv/kx5wPMiO/ZBnFgHbA8xhv0PeF4YRu6z35RZgRfm+ZkOGBRFmRb/MIdmAX5x97Gf2KqR3xALfQxnwkI7Ex8IvV7fCNwGFimKEmTr9iwE2KPxuuruzzqbtuIRQlEUHfAiZlL6r2zcnEcF7d2fD5LDEo8b56AtDnx9iMvHlrtyPRKKongDqzFXibk81w1z4KFRAEwCyYqiaGZ4PvPuz4Y5a9GjxTjmfXOmfzfvvub83d/na0h9oY+hBRbomfhliLj7c0Eb6nMFm3BeFUVZBLTr9fpeq8djMXMnYf6L26vxFOAPHFxApPRzwJ9irhryH3q9vlU8oSjKdsxGjwFz5SYH7BR6vb5WUZRjmMNaP8BctUjgHzBHQP5jvmq8fhug1+u7FUV5B3gec1LTfxHPKYryOLAVM+3jiG1a+M1wN2z+6kzPKYry95ijO6/P5/KwC30MZ8CCOxMVRUnDXHipw+pxZ8zFJ0KAi/O93La9wFYJW08Bf60oymmgHhjCLLG0E9Bh5r/8PzZq22xAkNLne/UQNd7HXAlmM1CuKMpHmBO20jFTCpyAv57nZUVRFGUfsO/ur2F3f65SFOW3d//frdfr53uFHwXzJeNfFUXZBJQDK4ANmOkCf2vDtn1jfEvG8C8wj9nf3tVZvoq5kMgTmD0939Pr9f22a54DD4Fv0xguxDNxG/D/vytrVotZOjIUcxWqBMzn4/ds17xHA3vZT21FGzgNfIRZlPk5zIt2HebQz8vALr1evyDCzXdFtdewwEjpdwXtd2AuFXcb8wb7I8xi9p8DW/V6/b/YroWPDDmY5+TLmL0fYN6IxGNP2qZZjw56vb4WsxbhbzEfnj/CfJn8V2DVfL+A8O0YwzuYx+7HQDT3qlAdAtbq9fqFKLO0oPBtGcOFeiZidub8HHNi1n7gL4EDQC/mKNYivV5/23bNe2TIwQ72U6fpaYcCjgMOOOCAAw444IAD/x97/x0d53Wm+aK/QlUh5wwi50SQAAnmLJJikkSRSpYoWbJs2e7qds/pc86a7pk7PXN61uqZ071uz7XbFjzOtmSLEpVIUcxiBEUCjCByzjnnyoX7R2lvfQWSEiURQAGqZy0tiKiAvb+997vf+LzzA85YsOWCCy644IILLrjgggv3hEt5dcEFF1xwwQUXXHBh3sClvLrgggsuuOCCCy64MG/gUl5dcMEFF1xwwQUXXJg3cCmvLrjgggsuuOCCCy7MGzwwz+tnrfjmNQoKClT3e22hzw8W/hwXwvxg4c/RtU8X9vxg4c9xIcwPFv4cXft04c7P5Xl1wQUXXHDBBRdccGHe4Ct32CooKJiJccwodDrdA793oc8PFv4cZ3p+U1NTTOdHVqlUDj+/DpxpjjMB1z51xEKfHyz8Oc7H+cHCn6NrnzpiIc5vrtrDfmtgtVoxGAyoVCo8PT1xc3M5u+cbrFYrarVa/lulUn2hkmoymVCpVGi12tkY3ozAbDZjsVjkPJTzd8EFF1z4urBYLA7yxcPDY17Lyi/C1NQUJpO9Wai7u/s3cm7MV0xNTWG1WpmampJ3p8ViwWKx4ObmhlarRaP56qqoS3mdYajVanx8fOS/xQIuVCi9kkrvpEqlmleKu5iHm5sbarWaqakpeeCMRiMGg8Fhnm5ubnh4eODn54e7u/scj/7rQ+xPrVbrcKEon4cLzo/7RQfmo+xRnrMv6gipnN98nOe3BRqN5mspK/MNQpZ6eHjM9VBmFdPvfzc3t7vWW61Wf+PnsvB30BziXoqq1Wpd0AdXKHk2mw2bzSY3sFqtxt3dfd4oPzabDbPZjEajwWaz0dnZSXt7O11dXdy6dYvbt2+j1+vRaDRYrVa8vLzIz89n//795OXlye+Zb8bK/cYrLOf5sn7fdghvh81mA+zKnEajmVd7UUAorcJ4hM+VU+UFKQxNV5RgfmK+ycovg8ViucujvNDmeC+I82q1WgFmzJmzcLUoJ4BKpWJgYICqqioAli5dip+fH3B3KHo+Y2pqCpvNJi+Q+e55FAaGWq1mYGCATz/9lPLyctrb22loaKCoqIjR0dG7Pnvnzh1MJhM2m434+Hg8PDzw9vaeV+vs5uaGzWajq6uLzs5O1Go1sbGxhIWFAZ8r9Vqtdt4osjabzUGRUyo6CwVKo1GtVqPRaO65PsKoFM/AGWG1WmVIWWnwfhVvjXKtXXAOCNkxNDRER0cH/f39uLu7k5CQQFxcHGq1Wio8ItqlDDXPF4i7XavVYjKZaGhoQKVSERcXh7e391wPb8ahPK8ABoOBgYEBLBYLnp6eqFQq+vv76e/vx9fXl5iYGIKDg7+yU8+lvD5kKC0rvV7PiRMn+Od//mf8/f357W9/S05OjnzN19d3Lof60CCEy3wSMF8E4XEFuHr1Kj/72c8oLy/HZDJhMBgwGAz3/FxXVxeHDh2is7OTDRs2sGbNGtLT0+Uhdlare/q4GhoaOHz4MKdPn8bLy4snn3ySp556ioCAANzc3OadB1a5P8V/82n8DwLlnL5ojz3Ie+YaQrH+JmN05vl9myA842J/9vX1cezYMQ4ePEhZWRlZWVn86Ec/Ii4uTn5GGGDz9U4xmUx4eXkB0NLSwk9/+lN8fHz4yU9+QmJiooOCvtBhMBi4du0a169fZ2JigsjISNRqNVeuXOHKlStERUXxwgsvsGfPHqKiooAHd+zNqfKq9IYIK2v6f2Df+MKb4MwLLjw8IlRQWlrKn//8Z6qrqwkICMBsNju8dz5DWNEAHh4euLm5YbVaaWxspKGhgd7eXkZHRxkbG8NqtZKSksLatWuJjY11EEhWqxWz2czU1NTXTtx+WLBYLHLflZeXc+HCBT766CMuXLgg1yswMJCUlBTUajVjY2MMDQ0xNDQE2C/d5uZmRkdHmZiYICgoiEWLFhESEgI4r/IqUgKmpqaorq7m0KFDHDlyhLq6Onx8fDAYDAwODrJjxw5ycnLw9PSc6yF/JYiQ+XSIMPR8LkgTaydko5hHW1sbLS0tDA8PYzQa8fX1JTU1lYSEBKnAKuWrM0B4W4XXCmBycpLu7m5GR0dpamqiqakJm80mDX+9Xo/VasXf35/IyEgiIiJISEggIiICmH+52sqcXmXqh1qtxs3NTXrXlYrdvfKAlcaMiIwBsx5xEAVLQma0trby/vvvc/HiRQCCg4OJjY2VY73fOokUtPlwTpVjvHr1Kh999BGJiYl897vfBe69XgsBZrOZyclJRkdH6e/vp6GhgdLSUkpLS2ltbcVisRAYGIhGo6GlpYXGxkaqq6tRq9VkZ2dL5dVisTin8qq8wBdifpJSee3q6qKmpgaAtLQ0h5DBfJ+3KFAC+5qOj49TUVHBuXPnuHz5MlVVVQwODsrw+sqVK/H392fRokUyT1Ss/1w/C7EnhYLT3d3N22+/zb/9279hMBikYhAZGcn27dtJTExkbGyMjo4OGhoaqK6uZnh4WF4QQ0ND3L59m/T0dFauXCmVV6vV6lSXqLgExbxramr48MMPefPNN2ltbQXsYz5z5gyVlZW0trby8ssvs3TpUumBneu1exAIQ0sYW6IYbSEUjojnLwwvq9VKXV0dp0+fpri4mK6uLgwGA2FhYaxfv55t27aRk5ODl5cXKpXKqS5SIReEbBgeHubq1atUV1fT1dXFhQsXuH79+j0/GxoaSnp6Ounp6WzYsIGNGzcSHx8/L/anEkpD4l4RgvmWkqWUL/39/Vy6dIkbN24AsHfvXv7Lf/kv5OfnA3aPpbu7+z3XzJnk5v0g7hGxRu3t7ZSXlzM4OMiiRYvkXShk50KA0ijSarW0tbVx5coVKisrpcdV5KlrNBr5/15eXmi1WsxmMxUVFQwMDMjvFJ7pL8OsSW6RxyTyrb4JbYSzerCmIzIyksTERJqbmxfUhhU5jwLNzc2cPn2as2fPUl5eTmdnJyMjIw6f6e3tpb+/H4PBgK+vr7xspysPJpNp1hVaZRGdxWKhsLCQ06dPy/SAlJQU1qxZw6pVq1ixYgWhoaFMTEzIvND6+nouXbrE1atXJQtBc3MzxcXFPP/88w5/x5koYaxWq8Pl8umnn3L48GHa29vle4Sw6ejo4MiRIzQ3N/PII4+wadMmUlNTnTL1RSkfzGYz9fX1fPLJJ1y9ehV3d3dyc3NZs2YNy5Ytk+thMpnuWRXrjBB5q2KswmNeVFREaWkptbW1NDY20t3dLYsKPTw8qKyspKSkhH379rFlyxYiIyMdFNi5kqnKMKHRaKSoqIhr165RU1NDV1cXIyMjjIyM0NDQcN/v6O/vZ2xsjLq6OmpqamhoaJDFkyqVSu51Z1WCnH18XwfintBoNAwODvLnP/+ZP/zhDwwNDbFp0yZeeuklFi9ezNTUFF1dXej1ery9vR285UJmBgUFSaXQWe//6ePS6/WYzWbUajUTExO0trYyNjYm616cdR5fBOHFF44Lg8HgEAmprKzk4MGD1NXVMTIyIu8PwOH/9Xq9lL3x8fHymcCDGyqzJqnvpZCMjo5KBUGEF4SCKzwlIpzs6+tLYGAgfn5+TrvgSkXAbDYzMTEhF2j6Qs5HRVYYHiIRvaOjg8bGRq5fv87x48cpKipySI3w8PDAaDQCdkurq6uLrq4uUlNTpdd2bGyMgYEBTCYTUVFRchOLvzUbUKlU2Gw2TCYTn376KYcOHaKsrAwfHx+io6NZvnw5+/fvZ+vWrTKXCSAzMxOTycTw8DCxsbGMjIxQVVWFXq8HoKqqik8//ZTY2FiCgoIcPNXOsIdF9bnZbOb69eucP3+eqqoqbDYbERERREVFoVKpGBsbo7e3l9bWVlpbW2lra8PLy4tFixZJoeWsBYhWq5WBgQGuXbvGwYMHATh27BiPPPIIu3fvZsOGDcTHx88rj5ay2Kqvr4/S0lJOnjzJxx9/THV1tXyfSOcRedojIyO0tLTQ3NyM1WrlxRdfBD5XnOZq/YQstFgsXLt2jTfeeIMjR44wPDzs8D6VSkVYWBje3t74+vri4eHB6Ogo3d3dGAwGzGYzvb299Pb2YjKZSE9Pl8wfzipvhSwQz154nYTCPTY2htlsxsfHBy8vLyYmJhgYGGBwcJCBgQFUKhWxsbGEh4djtVql8e/u7i7vHovFIgtJ3d3dZ83gVBrrZWVlvPPOO9TW1uLl5UVYWBjNzc28+eabDA0NMTo6Kp0I4pkIjnQPDw8yMzNZsWIFycnJTq/EKiHWYWBggJqaGvr6+u6645x9DkJhFcaVUk6IaPLU1BQ3b97k7NmzlJSUMDw8LOWU8M66u7vj6emJt7c34eHhREREEBkZycqVK0lISJDf+aAOhDlzMzQ3N3Pt2jWampoAu7LX3d3N4OCgrE7r7e3FarUSEhJCXl4eTzzxBBs3bpTWGThPvhZ8rryazWaqqqo4e/YsV69eBcDT09PBHT4fLWylQnn79m1+9atfcfXqVcbHx6WQVUIorgATExO0t7fT09NDamoqYLe+Lly4wMGDBxkYGECn07F3717Anug9000dxIFUq9WMjo5y6tQp/vCHP3D+/HksFgspKSnk5eWxZs0asrOzHRRXAXd3d8LDw9mwYQPNzc2YTCbKy8sBGB4e5t133yUgIIDnnnsOtVp9l9dsLmCz2bBYLPISuHz5Mr/85S+5du2aXLO8vDx0Oh3h4eEUFhZy6NAhGbLt7u6mrKyMjRs3ytxCZ1JelTJBo9EQFRXFmjVr6Ozs5MaNG/T393Po0CGKi4t55JFHOHDgAFu2bHGIjjiTXBGYfll3d3dz7Ngx3n77bYqKihgfHwcgPDychIQEfH19GR0dpaKiAr1eL72s169fp6ioiKeeegovL69ZNRSVEH9Xo9EwNjbGlStXePvttzl58uRdiitAREQEeXl5ZGRksGTJEkJDQzl16hS/+93vZCXz1NQURqNR5qNPTEzg4+PjtOupjPqMjY3R3t6O0WhEo9EwNDTEzZs36enpIScnh7i4OEpKSrh+/Tp9fX309fVhtVrJysqShUDj4+NotVqpGLi5uTE4OEhXVxcmk4m0tDQeeeSRWZmfMNZ7enq4evUq9fX1gN2Rde3aNYqKirBYLBgMBoxGo9zfIiInXtNqtaxatQqj0UhkZKSUW85IO6mcw9jYGBMTE6hUKvR6PR0dHQ77eq7O3deBzWaThsjU1BTDw8NYLBbJQlNSUsK//uu/cvbsWYxGo4PjCuy1IuHh4SQmJrJ06VKWL19OamoqISEh+Pr6OnhenUJ5FQtps9loa2ujp6cHi8VCT08Pt27d4ubNm/T19aFWqzGbzfT39zM4OMjk5KRDQVNdXR1VVVWy6CA5OXkmh/21oLxYbDYbo6OjtLW1MTY2Btit3/b2dpYuXTrvui8pcwWNRiOffvopf/7znzl8+LAsVhIIDQ3Fy8sLf39/oqKi0Ov1VFVVSe+Ot7c3RqORhoYGLl26xIULFzhy5AgGg4HExETy8/OJjo6etW4kohhiYGCA8+fPc+rUKWw2Gxs3bmTr1q3k5uaSmJhIcHAw4FhkaLVa8fDwQKVSER4eTmpqqnyfQGdnJ52dnVIpUnLfOgvKy8s5fvw4ExMTAKxatYqnnnqKxx57TM7Nw8OD6Ohobt26RW9vLzdv3qSyspLMzEzAOZU9sAvCuLg49uzZQ3p6OlVVVVRXV1NeXs6dO3c4ePAgbW1tdHR0sG3bNhYtWgQ478VitVrR6/W0tbVx8eJF3njjDWkg5+bmsm3bNlJTUwkICECj0TA8PMynn37KhQsXaGlpYWpqiujoaOLi4qQxPVfeH2Uhz8TEBMePH+fIkSMMDQ0RFBREfHw8MTExuLu74+HhQUxMDHl5eaSnpxMSEkJbWxsGg8Ehp07IKOGBNRqN+Pj4OGXR1vR8UFEvMDY2hkajYWBggFu3btHT00NycjIhISFUVVXR19cHfJ5DePv27bu+OzQ0lNjYWLRaLd3d3bS2tuLj48Pu3bvJzs6e0XlZLBYZ1dHr9Vy+fJlLly5JxW18fFwaWmAv2oqMjJTzmZqawtPTU0YO/P39ycnJISoqykGxcTaZo9QBpqamGB0dZXBwEIvFIr3I0x08zgplwbyItoJ97YqLiykuLsZoNLJu3TpycnK4evUqp06dknm9Go2G4OBg4uPjJRXaokWLiI+PJy0tjYyMjHs6g74KZlR5FYprS0sLJ0+e5NKlS/T399PT00NTU5O0EsX74PPqWfi8MtJsNjMyMsK1a9fIy8sjMDBQFsE4yyUjrK2pqSk8PDxYtGiRQ07Z2NgYt27dIikpiczMTLkZnGX8XwYhNOrq6vjVr37FoUOHALtHWaR+REZGkp+fT0pKCvHx8SxdupSJiQnOnDlDb28vmZmZuLu7c/PmTd5++20+/vhjWVACdkXvzp07BAQEyNDWTD8fIWwmJiYYGhrCZrPh6enJmjVrePHFF0lKSpIsEnB3+ouSFm1kZMTB2gR7uoSyOt8ZimREzrnFYqG8vJyamhqpuCYlJfGDH/yAffv2yZSCxMREfvCDHxAbG0tfXx+tra3cuXOHs2fPynVWWuXOdqlotVri4uKIi4tjy5YtGAwGKisrOXToEL/73e/45JNP6OjoICAgQHr+lZ7puYayulytVtPY2MiHH37I4cOHuXPnDl5eXqxbt46nnnqKPXv2EBsbKz9js9lYtWoVYWFhHDx4ELVazYsvvsjevXvlGZsrj7nyXIsCj6GhISIiIsjNzWX16tVs3ryZ6OhobDYbXl5ekq1kcHBQ5reK1LL4+HjCwsKoqKigr6+P6upqenp6CA4OdjoZq4xU9Pf38/HHH/PGG29QWFgoGTCUhnJlZaWUG56enqSkpJCamorVauXmzZt0dHQAn/M0Cx5NJfz8/AgMDJzxfS3mplKpJFXSzZs3HdLmIiMjJftFamoqcXFxuLu7YzKZsFqt+Pr6EhQUJP8LDQ3F39/fQbY4S6TnfhBhcrEm9+NedkZMpykzmUwMDAxw/fp1Dh06xEcffcT4+Dg7duxg3759GAwGsrOzZYFWaGgoW7ZsYffu3SxdulSun0gzeBh3xIwor8qD2dXVxdmzZzl27JgMdygv73tZIp6enpK+Rhxes9lMZWUlZ86cISMjQyqvznTJTE1NYTab8fDwICkpiYiICJkfOjIyQmFhoVRewfm7FolnLxSTvr4+Tp8+zbVr1+R7tFotUVFRpKWlsXLlSlavXk1cXBwBAQEEBgZitVpJSEhgYGAAs9nMjRs3uHLlChcvXpQpIwBBQUFkZ2ffxcow089G7EVvb2+5p0wmEyqVSlJ3CIqa6VAewN7eXurq6uju7pa/MxqNjI6OOii0c624KovtKioqeOONNzh27BgajYbExEQOHDjAo48+SkhIiMy5Cw4OxsvLi/T0dJkHq9frOXXqFGFhYcTExBAZGSm/35nOo/DkiPC08P7n5+fj7e3N+Pg4v/zlL6mqqqK+vl7KrrleJyWE8STCsI2NjRw5coSSkhKio6P57ne/y65du0hJSXHYs2C/4BcvXsyLL75IQkICRqOR5cuXO/BqChaM2TI6RBRG7JPTp0/zu9/9jqqqKjw9PcnIyOCJJ55g+/btpKSk3HNcfn5+cj9aLBY8PDxYu3Ytmzdv5tq1a/zlL3+hvLycy5cvExYWRmhoqPzbzqT02Gw2ioqKeOONNzh//jzweSpSZGQkUVFRREdHy4iWu7s7/v7+8nWbzUZzczMdHR3SS2k0GqmtraW6ulpGtLKzs4mLiyMqKsph7WcCyrMzPDxMZWUlvb29hIaGsmvXLrn/AgMDCQ4OJiwsDD8/PwfPq1arva9nzlkdPsoz5ObmRlJSkvQw9vb2yijffILNZqOxsZGrV69y/fp1qqqqqKurkxHl2tpajEYjW7ZsIS0tjWvXrtHT00N0dDTr1q0jLy/vrmgkfF7ALwq/vk73vxlRXpU8XR0dHZw5c4azZ89iMBhwc3OTYRyr1So7qIi+8KGhoQQFBUlrxWq10tnZSW1tLWNjY4yPjztt4ZOyJRrY8zx8fHwwmUxMTExQUVFBQ0ODvOCV/HvOCvF8JyYm+Pjjjzl8+LC06H18fEhNTWXdunVs2LCBlStXEh8f7/BZlUpFYGAgbW1tnDx5kqNHj3Ljxg16enrkd/j7+7Nu3Tp2795NSkoKYFf8ZrOLU2hoKCtWrODRRx9laGiIgIAAjEajzAn8IkxNTdHW1kZ5eTk9PT3Su+rh4UFERAQhISHyYM61V1J5XhobGzl+/DjNzc14eXnx9NNP8/zzzxMbGwvYFR9/f3/5fl9fX1atWkVHRwdXr16lsbGRsrIyWaAGD05zMlO4X7hLQITwVCoVGRkZvPjii9TV1XHt2jVZkJaYmOhUxXXKS7Grq4ubN2/Krn1r167l1VdflefGYDA4FLSAfR1zcnJYvHgxer1ePgNlNfhsQtwPopjs+PHjMpITFhaGv78/WVlZMjd+cnISd3d3SaVlMBjw8fFh7dq11NTUcPr0aYaHh/H09GT79u2kpqZy69Ytrl+/ztWrV1mzZo1UXh+UQ3KmIRQZo9FIa2srN2/eBOyOm507d7Jq1SoSExMJCwsjLi5Oru+9sHbt2rt+J1J7JicnycrKkg6T2YBSZre3t0v2kuXLl/OTn/yEFStWPND3iBSr6Ry1zqi4wueyXdRrREZGkpOTg4+Pj3zdWccuoGQSsFgsVFZWcuzYMT766CNu374tHTFubm54e3uzZMkScnNzycvLIy8vj+XLlzMyMkJwcDDh4eGAI8+wsujrm57DGZdawnsjQsOZmZnExMRgMpnQaDTExsaSmJhIZGQkPj4+hISEEBYWxtTUFO7u7pjNZg4dOsTrr7+Ov78/K1euJCYmRn6/MwgigekVu9HR0eTm5nLz5k1GR0flfxMTEzKnc64vxvtB6aHo7Ozk+PHjvPvuu7KoJzo6mq1bt7Jr1y7y8/MJDw93UHTg88Pc2trKu+++y/vvv09NTQ3j4+MyPL9x40Z27drF2rVrHXKxZiPnVVmg4+vry9atW0lKSmJ8fJxFixY5hPu/iHvQYDDQ3t5OXV2dQ/et0NBQnn/+eXbv3i0/P9dePaHM6fV6uru7Zf5cdHQ0ixcvJiEhQaYLCMVGGJHR0dHs37+f0dFRKisrGRkZuUv5mWtjUkQ/gHu2EtVoNExMTGCz2QgKCmLFihWsWbOG+vp62traaGpqIiYmxiGtZy5ljMgfBHu3nnfeeYdjx45hNBqJiopiyZIlREdHy/eLy+d+xPUiqiEU9ZSUFOk1V/6tmYTyXA8PD0tDFuxnymw23+WgEJffdEMyPz+fHTt28M4773Dp0iVqamrw8fHBw8MDg8HA0NCQg3E11/tTQMxDMAMIWRIUFMSWLVvYt28fISEhDjREXwXh4eFs3rwZi8XiUAwzkxCGnkgFnJycpKGhQRoWWVlZMqccHszjL9brfnvaGaE04ENCQqQcmu6Mcda7Xzjbamtr+fDDD/noo49obGx0OJPe3t6sXLmSPXv2ONzbUVFRMvojoJRJSs/0N8WMSCqlsI+MjGTXrl0y12bx4sVERkZKz1pCQgJJSUmEhYXd87IRdFNms5mgoCCSk5MJDAy859+aa0wvxIqPj2fDhg309vZSUVHBxMQEer2e8fFxgoKC5GecEYKfDqC+vp7333+fTz/9VFpeKSkpvPDCC+zYsUN+RlSxC4E0OjrKnTt3OHbs2F00PgkJCaxZs4bHH3+c7du3S8/IbPNtKg9TTEyMg2EkcL+DJgpOvLy88PDwcChCALtCvHbtWmmBTk5O4u3tPSdrrqRlGRgY4MKFC5w5c0bmJ23YsIGEhAS5f6cLWVFtmpSURG5uLlFRUYyMjMgUAoG59iwom2dYLBYGBgakseTt7U1ERAQBAQHy/VqtViq8np6e+Pj4zPkclFBWVJeVlXHw4EFKSkrw8fFh+/btrFq1yqEYUDQfUMJsNsucO8GGcfToUbq7u9m5cyd79uy5p+ydKYh8SIDBwUGH8Xp4eBAeHu6QeuLl5SXXRDSYALtCk5KSwiuvvEJHRwfV1dX85je/ISQkhNbWVgcDxNkgnvfo6Kg0IN3d3dmzZw/r16+X0Q9hOCqbbAjvo3gmgl5y+veLsLvNZkOv10uH0EwWCysVlP7+ftrb26V8EFRtoaGheHh4OLTNFnMTXjnhoXOms/h1oFKpMJlMgD3tTqkAzna6zpdBOCy8vLy4c+cOf/rTnzhy5Ig0Lj09PVGr1URERLBmzRoeeeQR1qxZI5kFhPHr4eHh4MGdKR1tRjQEpeIRFxfHK6+8wvPPPy/znIQ3Ryh798t3MBgMvPfeexw9ehSDwYC3tzcTExMYDAZ5MJ0hrCcw3ZO6aNEicnJyuHTpkvydyPUQ73dGa3J6OsP4+DjNzc3SkIiJiWHDhg0O4R8l7Y3oYf3RRx/xl7/8hdu3b8uCILDnqz399NO8+uqrJCQkOFycwmqfD1CO835edKWwcoZ5mc1mSktL+dOf/sTRo0cBeOyxx3jiiSfIzMy8p2U8/YyFh4eTnp5Oc3Oz5O5NS0u763NziZGREUpKSrhx4wZNTU0YjUYiIiJYtmwZ2dnZREdH4+vrS29vL83NzWg0GpYtW0ZeXp4MmTlDO2ohS3t6eigtLZUd+6Kjo9m+fTtr167F29v7rsYhSihpboqKivhf/+t/8fHHHxMYGCgZXLKyshyaHsz0XhXfL7i8BaKjo9mwYYNDmFzISeWYRL2Al5cX27ZtY3h4mD/96U+cPn1a0vZ5eXndlco112dQGdESxVYi7eGxxx7jJz/5CUuWLJHyVESHpiud0+ngpt8jynPo5uYmo0izqSwJ1gAfHx96eno4ffo08fHxREREODAGKcPpzqTMPQwIzzrYaww6OztZtmwZ4HxGlRjP1NQUt27d4r333mNwcBB3d3fc3d2l023Hjh386Ec/IiMjw+G+UCqqs3EPzDhVllqtxs/P70tDF8KtLB6Wv78/ZrOZc+fOUVJSAiA53pQH1Rk3uhA8gYGBREdHy5wXQFKAgPOEsB4EYmOLXLNVq1ZJgSjoaoTXp6+vj0uXLvHee+/JIgQvLy+CgoIIDg5m06ZNPPXUUzIPy2KxyMt3tpUf0d9eeDiUXaeE8jL9IhBE4GIdL168yNmzZ6WFLeDr6zvn3L7i0leS2ldXV0slCOx5hsuWLSMoKOiuvungSOkj3p+WlsbFixclzZTysp1tiLWzWCz09fVRXFzMxYsXKSsro7W1lYGBARl+jYiIIC0tTRYHiuYLcXFxZGdny4tmtkLo94NSeR4dHeX06dN88sknkq916dKlZGZmOuTTKWGz2ZicnESr1cp9euHCBX7zm99w5swZwB6yHx8fR6/Xz2ohkzJkHhIS4uBljYqKIi8vj0WLFt1zLwoox6rRaNi0aRNtbW3cuXNHGsqenp6y8Y2zQDAIqNVqKisrOXLkCBcuXAAgJyeHJUuWAPacf6H4fN30stnwfikhxij2UnR0NPn5+aSnp9PY2Eh7eztvvvkmvb29kvorJSWF7Oxsh2iIMGicpfDzq0L5rP38/MjMzKS6upr29nauXbtGfn4+kZGRTuOoEeslPKaXLl3i9OnTDA4OAkhjHpAUmMHBwfc17sV9OtPsCjNOlQWfK6b3suiVhSwqlQpfX1+pFClDKoBMG/D393dqMnFlgrm3t7fDJTgf28QqFThRhR8bGyu932K9pqamaG9v58KFCxw9epQ7d+7I75iammLRokXs27eP/fv3S08dMKc95r/O31YyRPT19fHOO+/w4YcfMjExIanDfH19yczMvGel5Wxi+jkRzT+UQicwMFCmsYi0jy96JkFBQURERODu7s7Y2BiDg4Po9XqHkPtsRkREnmRXVxcXLlzgjTfe4NKlS2g0GiIiIoiOjkatVjM5OUldXR2lpaV8/PHHxMTE4Ovri5ubG2vWrCExMXFWxvsgmE5ef+PGDVnUs3LlSjZu3ChDy3C3ISzSJ4S3rrGxkV//+td8+OGHMkTr7u5OcHAwUVFR0ikwG3n4SuU1IiLiriJPpYfxy4jozWazbESxceNGzp8/T1dXF2APnSvzScX3zwXEsxUK2cDAAMXFxdK4T0hIIDExEZPJJKOT33Qd5ioKIsLhGo2GFStWsGvXLrq7u6mqqqKxsZHXX39djm/Hjh08/fTTrF27ltjYWLy9vecVB/q9oHzuAQEBLFmyhMuXL9PX18eNGze4ffs2GzdulIbnXLMnKPP6BQNNYWGhVD5Fy3ORIldXV8e7774reVvT09MJDg6W53q27vIZ/yui7abIiRBeIKWgFB4bb29vqQjV1NTw4Ycf0tbWJr9LrVaTlJSERqORXgVn3OjThY5SURgcHGR0dJTo6GinVLwFlGNT5hHq9XoaGhro6upi6dKlDp+ZmpqisLCQv/zlL9y5c0fmNQuv6po1a9i1axfZ2dmyKAiY9TUU7Ye/blhYmeZQWlpKVVWV9PaYTCY8PDzIzc1l3bp1MpcX5i4/W6lITk5O0tfXJzu/5ObmsnTp0q8cVlSr1Wi1WsbHx+nv72d4eHhO80XHx8cpKyvj8uXLjIyMsHbtWlauXMnSpUslBdrk5CT19fVcvHiRM2fOUF9fj6enJ8nJyTJv2Rkh1kN49sPCwkhISJDeKiUPMXwewhPnSuSvCcYXYWCZTCaCg4OlEiz27kxDaQxrtVpWrFjBxo0buXTpEk1NTVRUVJCamoqXlxcWi0UWuAruU2E8urm5odfr8fDwwMPDg4yMDElkr9VqCQgIcKDdm0soFXaAoaEhysrK6OjowNfXl507d5KdnS0b9ExfT2dJx3kQKBVvHx8fnnzySRITEykrK6O4uJhPPvlEFk2eOHGChoYG0tLSWLZsGdu3b2f9+vXyu74oHWY+QK1W4+vri6+vr5S7ojuVwFwbVOL59vf3c/nyZa5evcrg4CABAQG4u7szOjoqu54NDQ1RWFjIrVu38PX1lUX2S5YsYe3atZK+TuxXk8kkmXceNmZceRX5NsrK7ftBbNSenh4++OADfv3rX9Pa2oqbmxuZmZls2bJFeojm04ZWbs6xsTEHweSsXljluPR6veycoVarKS8v58iRIwQFBZGTk4O3tzc2m43Lly/z/vvvc/ny5btyXLdv385jjz1Genq6FGxzsYbi4lO2Luzr68NoNMpQhygmEILH399ftv0V4xb8d6LdqJiLKKyIj48nOzvbgYFhrtIGlJ7inp4eysvLaW1tJTIykh07drB8+XI5/gdRsE0mk1T+jUYjjY2NtLW1sWjRIpmfONNV+sqw/uDgIGfOnOHTTz9lamqKvXv3smrVKlavXi3lhcDU1BSJiYk0NzdTW1uLwWCgo6ODoqIi2YkrISFBthp1Bh5mJTm9wWCgs7OTiooKMjIySElJcchpFO8XrVFramp44403OHz4MKOjo3h5ecl9LBo3TP87Mw1lwZbNZiM7O5vNmzdTVVVFS0sL165dIyMjg6VLl8p8O+VnlRCec7AXlnZ1dclUoPr6eoKDgx1k0VytpbK+YXh4mKKiIq5fv47RaGTr1q28/PLLrF69ek7G9rChLCRTq9XExsYSGxvLunXrWL58OUuXLpWNFQYGBqitraW2tpaLFy/S2dmJ1WplxYoVDoV68wn3ii6L331dTtPZgMFgYGJiQp6XkZERh9dNJtNdRckCFy9epKamhiVLlpCQkEBmZiapqakOSuvDjsY5VWPgpqYmOjo6aGlpoaioiObmZsCeC/Q3f/M3PP744/K9wgp3RigXSZkbCXardDoPo7NDeTnabDa6u7s5ePAgDQ0NfP/732fTpk2Ulpbyhz/8gfPnzzM5OSk/6+/vz/e//31eeukl6U2BuSu0UypVw8PDvPvuuxw/flz2FPfx8SE6OhovLy+8vb3Jzs4mNzcXLy8vOS/hEWpoaODOnTuyilQogKLhQWBg4D27cc0VhoeHKS8v58aNG4C9oDA3N1d63h50TYQXyd3dHYPBQF1dHW1tbaxYsUJSgc303lZ+/5UrV3j99dcxmUx873vfY9euXURGRt6XvUTZCc3T0xOr1crly5epr6+noaGBH/zgByxevNhp6HlsNhs2mw0fHx/Gx8e5ffs2er0em83GY489JhkyhOHV3d3NhQsXZL1AdXW1nK+o/E5ISOC5556TOZbi87MBpeLq5uZGSEgIcXFxBAcHU1tbS3FxMWFhYVgsli/lBJ2amqKlpYXi4mJOnDhxV6vUW7duUVZWxiOPPIJWq52TPEplWLivr49Tp07x9ttvy25EomXmQsP0/RQUFMT69evJy8vDZDLR29vL9evXOXnyJBcvXmRwcJB3332XmpoannrqKZ566im5t52tucSDQslrCs515yuLJkUhdn5+PqmpqQ4R7wdBb28vH330EWfOnCEwMJBly5bxwgsv8NhjjwGfN4x5mA6rWeF5HRwcpKWlhfb2dhkWcXNzkxvS3d2dwcFBmdTc39/v0H1J5LMVFhYSGBhIaGgoixYtcvBqORPrgBKhoaEkJydLr0l/fz8NDQ3ExcVJ746zQZmbBZCRkcFTTz3FH//4R0k4DUiqpTt37lBdXc358+cZHh7Gw8MDf39/IiMj2bJlC6+88oq8JOcqV1koIkajUYbORVHZxYsXHd4rLkCNRkN2djalpaVotVrGxsbks7HZbAwMDNDa2sr4+DhTU1OYTCbCwsLYsmUL69evJzw8/K7Cp9mesxCaY2NjXLlyhaKiImlZJycnk5GRIcPPX5ZfKCDOrPhuo9Ho0ClvJve0kqVkamqK0tJS3n77bS5fvszKlSsdGmWYTCap5Hl4eDAyMsKVK1f48MMPaWlpIT4+nl27dqFWqzl16hT19fX89re/xWQy8cMf/pDc3Fz5PbPd2lG5DgEBAWzYsIGmpiaOHTsGQFVVFceOHaOvr4+oqCiMRqM0MsfGxigqKuL06dPYbDbc3d1JTU1laGhINhhJSUlhxYoVkuMVZj+tRdwDAQEBJCUlsWjRImpqarh69ar0MFdWVspQpIeHh4xs+Pr64uHhIbkoT5w4QUVFBWazmSVLlpCTk4Ner6e9vZ1Tp05htVrZs2ePQ+RntpR1kQMKdq7ec+fOUVxcLKMH7e3tvPfeewQGBtLb2yuZEry8vIiLiyM1NVV2a5wPUBa+imZDQhZ5e3vLVI7o6GgSEhLIzc1l+/btHD58mNOnT1NYWIjZbCY2NlYyYswnxVUp/0wmE4ODg9KLqVKpHNr+Ohtyc3N54YUXiIuLY3R0VNKsCa+s2WzGaDQyNjYm96rIKxc89j09PXR0dDAxMUFfXx+PPPII8fHxUmaDE7eHVcJkMtHY2MjBgwf56KOPZDI9OCqcQrkQXgZxSU1NTVFdXc0///M/s2jRIrKysli6dClLly4lIyODhIQEAgMDnVJxBbvympeXx6pVq2RL1OvXr5OWlkZycrLczM5GESK62QguxR//+Me0tLTw5ptvAsjw4/nz57ly5YpU3gDZfvPZZ59l27ZtDsTUc6HAib+rUqno6emhsLCQwsJCrl27Rl1d3X0/a7FYKCsrk92Mpitl4vJRKjXr16/ntddeY/Xq1V+LXPxhQqmMdnR0SO8i2Nc3PDzcwev6oMqr+G6h1CUkJBAZGelQfDlTUBaUWSwW2b0vMDBQCkkxPpVKJRXzkZERbt68yaFDh7h06RKBgYE8++yz6HQ6aaQcOnSICxcu8Mtf/hK1Ws3f//3fExMTMyehS3FZm81m/P39eeqppxgbG6OyspKmpiZUKhU3btzg2rVrqNXqu5QF8ZxiY2PJysrC09OT27dvS+XVy8sLf39/B+/0bJ9N5QWelpbG6tWraWpqorm5mdu3b1NZWcnBgwfJzc1ly5YtREREMDIygl6vJzQ0FK1Wy6effsrbb7+NwWBAq9WSl5fHgQMHePnllwkNDeXQoUP8p//0n/iXf/kXbDYbkZGRhIaGOpDfz1aBGtg9r42NjbKS22q1cvLkST755JO7ukkJD9bu3bvZs2ePLCj8IhYGZ8B0hpb7KZ5TU1P4+flJozMkJISRkRGKi4upqanh2LFjREVFsWbNGgCHPe7MUK73xMQE9fX19Pb2AvZ6CWeJ6AgojSJ/f3+efvpptm/fzvj4OAaDgfHxcQYHBxkYGGBsbIzR0VFqa2u5fPkyLS0tsshQSWlnNBr5+OOPqaqqwmw288Mf/hCwy7OHxeM+48rr1NQU4+PjNDU1OXhTHwSCukHk2LW0tNDS0kJVVRVXrlwhJSWFvXv3smfPHtRqtYOFN5dQChaNRkNycjJZWVny8mhvb3fIHZnrasP7QXnIoqOj2bdvHx0dHZSWlsoKRIvFIkOSAhEREWzatInNmzfLsM9sNx9QzkE0EwB78crrr79OZWWlpPgSXJcBAQHo9XpZBGK1WqmqqnIg4Z8O4XH09fVl2bJlPPbYY6xatWrOFVdwFKLCAzB9LkpuzwcVqGNjY3R3dzM4OIifnx9LliyRuZcws4waynHabDY6OztlW9DHH39cRmPGxsYcipbq6up48803OXToEP7+/nzve9/je9/7HgkJCQA8++yzpKSkEBwczAcffMAbb7xBREQEP/jBD4iMjGRqakoWO80mlOlHO3fuRK/X89FHH1FcXCzz0JVebyU2bdrEM888g8lk4uLFiw7d3/z9/YmJicHHx8dBYZpNTKdfEywkt27d4siRI7S2tmI0Gjl//jwdHR3SYBYRu7GxMVpbWwGIjY1l27ZtbN682aFQctu2bRw/fpyzZ8/S2NhIV1cXoaGhs5abPR3d3d20tLRI7l3RTUxZwOPp6YnNZmN8fJzLly8zNDTEokWLpPIqwrzOprxOdxTc7z0mk0k6RpTn6fHHH0er1fLGG29w8uRJjh07RmpqKkuXLpV1Fc54T06HUu7q9XpaWlrkvwXRvzNCnKuAgAAH6jKr1Yper2dsbEyuXXt7O8nJyVy5coXu7m46OjoYGhoC7DUuVquViYkJ2tra5O/h4XZKmxFNQpmfMjg4SEVFhcMC+vr6YrFYJF+m8DoqSXJFD2uVSoWPjw/u7u4yAVxQTAwNDTE0NORgRTuDRaM8vIKvzt/fH3d3d6m8Dg8Py/ffj0ZsrqG8XGw2G+vWrUOr1XLs2DHOnj0rLw4PDw/ZdlN8brqSOpfCVgi8iYkJLl26xLVr1wBkakRMTAybNm0iJiZGdiMS1B+XL1/m5MmTDA0NSatZ6f0TCkFwcDDr1q1j9erVktPYmTwFk5OTDA0NSYUnODgYf39/h57TX3ThWCwW+byGhoaoqalheHiY+Ph4YmNjJSWYspPXTED53aKQs6WlhZCQEGnVg53+SygELS0tvPvuu3z00UcA0uOakpIijeuQkBC2b9+Ou7s7VquVI0eO8L//9//G09OTV155hdDQUJkuMpteLxEBsdlsREdH84Mf/EByLFZUVDAwMIBarSYnJ4fExERGRkaorq4mICCAl19+mb179/Lxxx9TX1/vIHNCQkJISkoCPvdmz/Y+Vf49rVZLfn4++fn5bN26lYCAAN566y3pKa6rq3NQCgQ7TVJSEllZWaxfv57NmzdL5gyj0YiHhwe+vr784Ac/IDY2lt7eXm7fvk18fLzD3p8piLPg4eHB5OQkhYWFHD16VNZyqFQqwsLCMJlMeHl5kZmZSV5eHt7e3litVnp6ejh+/Dg3btxwuD9FsaSzQcmKIbiDRacvJdXi9Fx0vV6PRqPBy8uLffv2MTExQVVVFTU1NZw+fZpVq1axfv36eZM2oYTFYnFwVImIlTOmDQidTTgBhZxTMiYIJCUlERkZSW5uLs3NzZLrfHx8nPHxcblWIpdd4GHKzhk5AULJBHu48sKFC5LzU6vV4uvri0qlkt47sD8wg8GAm5ubQ+VzUFCQDEuKhgdLliwhLS0NHx8fIiIinKZv/L0grHslWXhFRYVD+oQzbmQlxGYODg5m8+bNREZG4unpyVtvvUVvb+9dykpraytnzpwhLS1NeraEQTObCqwyp3pgYIDjx49z/fp1+bq7uzuZmZns3r2bJ598kri4OEnn5uPjg16vJzY2Fg8PD86cOUNHR4dDzqsSgsjfmYi1lWehp6eHmpoaBgcH8fX1lTRSYk8KIXUvTPek9vb2yiYHIodNye86k5hOs7R161a5thMTE4SEhLB48WLAfskXFRXxwQcf8OGHH+Ll5cV3v/tdfvSjH8kOTiInVGDdunWSaeKDDz7gpz/9KSqVih/96Ef4+vpKT5koPJwNKAW+p6cnjz76KJGRkVRXV9PW1oZKpZLeb5PJRFdXF/7+/mzevBmtVsvg4CBdXV3S0PL393doxz3XMnO64R4bG8trr73GkiVLuHLlCuXl5fT390sHh9FoJCEhge3bt7N48WL8/f2JiIggNjZWKnUqlYrx8XF8fX1Zv349Go1GevQAtm/fLnuwz5RHz2g0yn3S2dnJL37xCz7++GM5vqSkJB599FHy8vIIDQ0lNDSUqKgoWYxcVlZGfX09g4ODTkGt9EVQOqzu3LnDuXPnMBgMLFu2jHXr1kkF5l6FV9Ply+rVq9m2bRsNDQ1cu3aNjz/+mNjYWHlm5zN9ltlsxmAwOG2xuYDSIXg/Z0RsbCxRUVGYzWZiYmIwGAxcuXJFMioJKsqZuvNnRHlVDlYknefn5+Pm5kZ8fDyBgYFoNBr8/Pyw2WyUlJRw584dJicnJS/Y2rVrWb16NaGhofj7+xMaGiq7NGVkZDgUa8HMe3y+CpQXuZubG9HR0SQnJ0uPcU9Pz109jp1RIAmI+YguRcuXL6esrIx33nkHsFNseHh4yGc/OjpKcXExwcHBqFQq1q9fT2BgoPyO2fLwKI2ovr4+rl69SkdHhwzXxcfH8+Mf/5gDBw44dIDr6emhoaGB+vp6mpubMRqNDuF10c1JuWYGg4G2tjaqq6sJDg4mKCjIKTyuAuLSB7vSnp2dTWZmpsMlcC/lVcxX5FIKerDu7m7A7sENDQ2VRRgzHYZV8ge6u7uTlJREfn4+H330EcePH8fPz4/nnnuOgIAArl27xh/+8AcuXrxIeHg4r7zyCq+++qpUbsV3iJxRs9mMp6cn69evl/8+evQoP//5z1m0aBHPP/88Wq1WdpyZLW+l+DvCAxsaGsojjzxCXl6eNIIjIyPv8n4LjI6OynSRwMBA8vPzycjIkK/PtlE5HUr2AaPRiLu7O3FxccTGxpKQkEBlZaX0pAqvXlJSEjt27LhLiRGe8enNCeLj42Xe7+TkJHFxcVJ5na3ufoImKSwsjPz8fNavX8+jjz7KkiVL7vm3jUYj0dHRuLu709HRgV6vx8vLa046EX4ZxHg6Ozu5ePEihw8fJjQ0lNTUVIe9da97ThhRk5OTeHl5kZKSwsqVK3n33Xfp7e2lt7fXId3J2Z09Skx3CoSHh5OQkODQddNZ1lKszb3kmogOi/tAKKfCOBONCsRnRVRyaGiIsbGxu/7Gw8CMKK9KgZKamipprmw2G3Fxcfj5+cmCkbGxMX7+85/T0NAgK/KioqL4zne+w6uvvoparWZwcBAvLy/pIblXyMRZNoASer0eb29vgoKCyM3NlRf8F/WpdkYIz4h4xiJvS1BHLVmyhICAANra2mhra5PE4gcPHqStrQ0/Pz82bdoE4JB/OpsQVZMifxogMTGRnTt3SsW1p6eH2tpabt26xcWLF7l+/TpDQ0Oo1WrMZrP0gIl0ACWMRiNNTU1cvHgRk8nEmjVrZtyz81UQFhZGYmIi1dXVMh9pejvbe+1DJb1JW1sbH3zwAWfOnJHpB0IYC1it1lnxiijHunTpUp599ll+9atf8Zvf/IbKykqSk5O5fv26TBHZt28ff/3Xf01ycrKMhig9rqJSX3iGxH4dGhri8uXL/Pu//zt+fn489thjuLm5SeNzNtd1+qUSFBQkc9OUvxceSvE7kUMJduU1JyfHYc3mem8KiJCysvAvLy+P7Oxs+W9xgd6vQY1yLsoCRI1Gg6+vL0NDQ9y5c8eBNeWrFCp+FUxve/u3f/u35ObmMjExIdMclLmF0yEikwaDgZaWFmprayX3rbPcGcqIml6vp7S0lJs3b2I0GsnPz2fTpk0EBQXJO+SLnrNSrsbFxREWFsbQ0NBd83WWuT8IpofJExISWLJkCT4+PtK54izz+aJxTJ+HsuXw+Pg4xcXFVFdXMzIy4sBjO5OtiWfM8yqEjLCkUlJS7nmJC47I4eFhTCYTiYmJPPvss+zcuVNudNEhR8BqtUovkrN22QIcQgOBgYEOXqPW1lb6+/tlLp2zXCD3gtKbNjAwwKlTpzh69CgjIyNs2LCB1157jdjYWC5fvsx7771HTU2NDFHevn3bIV9rNueptPJEVy1lcVlnZydHjhwhOTmZrq4uamtraWlpoa6ujpqamnsWaonvFDm8wiOp1+upqamRHKKpqakOnp256N6kFDbe3t6Eh4cTGhoqqZRycnJYvHixbDZgsVhkAY/wLCu/Y3BwkJs3b1JZWcnU1BShoaGSzma2Ic781NQUYWFhPPPMM7S0tPDTn/6UkydPEhcXh7+/P+vWrWPt2rU888wzJCcnA/bzN11miHkKr7qHhwcbNmzgRz/6EePj4xQVFfGP//iP9Pb28txzz0nPicg/nI0LSOmhFEqDMsojLkO9Xo/FYpGK0fTqb2etVFeOSxhWolXqvWCxWOQZFVyaynz76YqsSqWSNHkNDQ3S8z5TETvlpe3j48PGjRtlXnJiYqJcH6GkCko3jUbD4OAgZWVl1NbWolLZW3KHh4c7zMUZ1lB5NxgMBlpbW+nq6iIwMJAVK1YQHR0N2Nk+fH19v1B5VTo1QkNDycnJobGxkYqKCnp6emTERJl/P9/grJyvSigji2J9lQaEGP/IyAi3bt3izJkznDlzhrq6Omko6/V6tFotS5Yskbn14rNOnfMKdxctif8X4R+AU6dO8Ytf/ILLly/LYoLNmzfzyiuvEBcXJ8Pp0ze84ItzdiiFp+C3BfszqKqqorKyUnYSAeflqlUKqK6uLs6fP8+tW7cAe0XzSy+9BNhDc2azmVOnTlFSUiJDgLW1tdTU1JCSkuLQoGGm56r8flE4ofxdZWUl/+N//A/Z110wDYhuWyLkoyTl9/f3Z9GiRYSGhjIxMUFXVxfd3d0YDAa6u7sZGBggKChIFpqIuc41RKhHq9ViNBq5ceMG6enpbN++ncTERDw8PBzYApTrZLVaJaF4Y2MjY2NjuLu7k5+fz4oVKxwuktkuJBE8g9HR0Tz22GM0NjZSWloqcyK3bdtGVlaWVDanpr64VeH0/fnoo49iMpn4+c9/TklJCf/0T/+EWq3m5ZdfBhyLG2YL9wrrqVSft3lUelr1ej1DQ0PSaLNarYyNjTk0EnFGuTNdObnXGRKpZ/fDdOMtOTmZzMxMSktLuXXrFpcuXWLdunVS/s50hESj0UjGAPj8uQulTVDPgX1fl5WVUVlZiUajYenSpdIYFswtzli1Lri0jUajpAMD+578smerjNoInnC9Xs/t27cdakSEXuBse/ZBIPhSQ0JCnNZh9WXe0snJSTo6Orh9+zYfffQRx44dk/qbj4+PdFwtW7aMffv2sWzZMvlZjUbj3GwDSgjOVuGhElp9f38/169f5+LFizInYvXq1WzevJnU1FTAruQJ6itlVbSzLvoXYToxsTNXHU6HUkgMDQ05dN9QXh7BwcHs3buXhIQETpw4IWleTp06RVhYGM8++6xDKH2mha/y+4ODg1m+fDnl5eX09fUBSKXsfhDeLBFqjY+PJywsjOzsbFJSUmhra+PkyZOcPXtW5oCKhhqNjY2sWbMGT09Pp6gMFtazEmVlZVy8eJGQkBD8/f3vGcEYHByksLCQ8+fPU1RURHV1NWCvNn3yySd57LHHCAgIkJy3sz1XpSxYvnw5f//3f093dzd+fn6kpqY6hMdFMd6D7Dvh3QoJCWHfvn0EBwfzb//2b1y+fJmf/vSneHt78/jjj0vFY66KSO5F+q1UbltbW2lvb5dpDiJ8rvRyObMSIO4P4QVSRrOmdy+cDrEXrVYrvr6+PProowwNDWEwGCgrK+P06dMkJyc7UFDNVoRE3Af3Sx8bHByksbERg8GAu7v7Xe1/nQXK8+fh4UFkZCS+vr5UV1dz7NgxsrKyWLZsmWzIMzk5eZf3TaS5TExMSAdDb28vJSUlgL0ToLJi3VlqWx4E0z3kQUFBREREAJ+nZM3VXJQGgNVqpbu7G5PJRGRk5F0FqZOTk4yNjWE2mykpKeHkyZPcunVLss6AfV0E49CaNWt4+eWX2bFjh+R5f9h1STN+0yiLDcTl1t/fz7Fjxxy4B3Nzc3n++edZv369nJzSu+qMVuZXwfTWhN7e3l8aRnE2CC+cEsrCs4CAAJYtW0Zubi4+Pj709vZy8eJFbty4QU5ODjt27CAqKmrW+BWVzzY8PJyNGzdy+/ZtmQcpcuem82QKb4ifnx+RkZEsWbKETZs2kZqaSmBgIKmpqXh7e9Pe3k5FRYV8JkLwjo2NUV5eTlVVFYsXL3YIcc+moFI+34iICFavXk1zczODg4MYjUbZ0k+tVrNx40aCgoJkcZbBYKCjo4M7d+5w4sQJDh8+LBW0/Px8du3axY4dOxwK8eYCykK6wMBA1q5d6/C6SIH4qoq1eK/RaCQoKIgnn3ySqakpBgYGKCkp4Wc/+xkRERFs3LgRmL1c3+mYvp+Ue2xoaIjy8nIHY9Pd3Z3g4GAH2htnVgS+rCjOaDQyMDDgkHvp6ekpZavw0mm1WiIiIggLC8PT01Omqc3kvp0ecezp6UGj0UiGHFEoON2oGhgY4ObNm5JSy8vL6y6l3VnWTEmv5O3tLVORzp07x3vvvYe3tzc//OEPyc/P/9KIqTAcJiYm+OSTT7h06RKAQ/oBzH5055tguqERGBgo5ynk0lxBRBeFvL98+TJ1dXVkZWWRlpaGt7c3Hh4eDA0NUVZWRn9/PwaDgVu3bnH27Fn6+vrkeVOr1TKas2LFCl5++WX2799PWFiY/HsPe8/O2pMzGo1y45aXl/OLX/yCkpISVCoV+fn57N27l8cff1xawXO9sDON+eBxnQ6VSoWvr68D08PIyAiTk5MOQsnNzQ1fX1+H3+n1egdlara8B8J7qtVqSUtLY/ny5Vy6dIna2loHxVuJoKAgli9fztq1a2Wv54iICFmZLoTP2NgYRqNR5ucJypeRkRHa29vp7u4mLS1NKjWzTYiupI6Ljo5m7969sj1uZWUl/f39nD9/nv7+fm7fvk1CQoK8WO/cucOlS5dkMwKh4G/atInvfOc7bN261cGr6aw5aN80UqNcr/Xr1/P973+ff/3Xf6W4uJjDhw8THx8vK9mdAcrio6GhIerq6hxSWET6yPTzN5/CsOPj47K46/bt2xw5coTBwUEiIiJYtGgRS5YsITc3V66dOH8XLlzg/fffp7a2loyMDPLy8ggKCpLf+7DPpkhpAejv7+e9995Do9Gwd+9e4uPj5VoYjUZUKpWMYt24cYOjR49KOrqsrCyZ7wrOU2CnhPBaC/qvoqIiioqKOHToEOPj4+zdu5fVq1c75D/eC+Pj47z11lv8+c9/xmazERUVxeLFix3y6ueTI0vZNQ24750zF1AqrxaLhdu3b/Puu+8SEhIimwuB/e7u7OxkeHgYs9mMXq+X3LXibFmtViIiIli+fDl79+5l586dUnFVcsY+TMyadijc0MPDwxQXF1NaWgrYCeL379/P008/7ZAPNJ826P2gvCBEBT44kuaL4hBnhnIe4eHhkvZrYmKCGzdu8Jvf/IaIiAiptPb09HDlyhWHPKXJyUl5wU8/0DMNsZfc3d3ZuHEjRqORmzdv0tbWxujoqExpMRqN+Pn5sXz5cjZt2kReXp7kFpwOk8mEyWQiJiaGlStXcv36dQcSeD8/P4KDgx28cXNx6YiQq2D3WLt2LRUVFdTX1zM6Osrw8DCFhYW0tLSwaNEiIiIiUKlUkiRcIC4ujq1bt7Jr1y42b94sBZNoNDLX51UUiQoeVpED+k1TGUQbWovFQmhoKPv27aO/v5+33nqLEydOEBUVxauvvkpISIhThXPBvjajo6MOnbUsFsu8NJzBLkMEG4hgAWlsbOT06dM0NDSQmZnJyy+/THR0tDSc3dzc0Ov1lJWVceLECQoLCxkeHsbLy4uQkBAH2fuwz6dSeR0dHeXkyZO0tbVhMBh45plnSExMvCudp7+/n5s3b3L16lWGh4fJzs5m37595OTkyPc4Y3ct5RlbvXo1P/nJT4iKiuLKlSucPHmS6upqVq1axYYNG8jMzMTHx0dGeUThnUhRevvtt2lqaiIwMJAnn3ySRx55xMGD5+xOLaUc8PLyklEOX19fh3C8M6Q/iL8vIhMWi4Xr169LPnRl3dK9IBw36enp7N+/n61bt5KZmUlkZCSAQ7rnw8aM7wLhQVWp7E0Jzpw5w7lz52RP+HXr1rF9+3aZ56rskzvfMb2DjGBNEKFM4clzdijXIjQ0lDVr1lBZWcn58+c5d+4cV69exWaz4e/vT2JiIpOTk9TV1TmEuoKCgmaV2F05dqHYgN2LkZKSQkVFBbW1tUxOTkpS8MnJSYKCgsjPz3cgsb/Xpebu7k5UVBSbNm0iMjKS8PBw3n//fQwGAxkZGaxdu5a0tDTZsWmuQn3Txx4eHs7SpUu5c+eO9Mi5ubnR3t4ujY3pRZJeXl7s2bOHV199lZycHHnhT6ebmmsIfs+HPSZRwCaI5V966SX0ej2///3v+c1vfkNoaChPP/30FxYPzQVEwxflWioZC5wdgrFGSdF35swZ/vKXv3Dnzh1UKhVBQUGMjY1hsVjo7e3Fz88PPz8/BgYGZL7s9evXOXv2LFeuXJGtKoVCMZPPQXn23N3d8fHxobq6mtdff52xsTH279/PkiVL5B3Q3d3NiRMnOH/+PG1tbbi7u7N+/Xoef/xx4uPj56Sd7YNC1KWINXn++edJSEjgww8/5OzZs5SVlVFaWsrVq1fJy8vD09OTyclJqbz29fVJXm2wp6Dt2LGD/fv3s379esC5OhY+KCYnJyWtYEZGhkP6w1zPQ8lC4uXlxfbt2+nq6uK3v/2tPCfu7u4Yjca7mtAIb6pge9m/fz9/93d/R1hYmMznnmke7BlXXpUae0tLC8ePH+f06dMAbNmyhT179pCWljbTw5gTKL1u4eHh7N+/n9LSUkZHR7HZbKSmpuLp6Smpe5xVYVcKS29vb1atWkVbWxvNzc1UV1dLupq+vj7Gx8fx8PBwoKTatWsXTz/9tEw3UHZRmy0oC1vc3d3Jy8sjPj5e0gsJKi3RCENAyecpLGWxTsHBwfj5+REXF0dCQgJr165ldHSUxMREVq1aJb/HWQjgRdvbzZs34+Pjw9atW+nt7aWsrIySkhIHajBfX1/y8/OJjY0lKyuLRx55hNzcXIdqaGe8RGcCQgALQzwrK4unn36aoqIiiouL+e///b/j4eHBCy+8MMcjdYTIMfTx8ZG53YKJQGlYOpvH+H4QVFe1tbWAfdzKinaVSsWFCxdobW2V7C4qlYqWlhbZrQpg8eLFbNmyhZycHIfc34d90SqNqEWLFvHjH/8Yq9XK0aNHef311ykpKSExMRFfX1+mpqZoaWnh5s2bcn4rVqxg48aNkuLNWdqffxFE2opKpWLVqlUEBwezcuVKWeMyNjbG7du3aWpqkpFIJXx9fVm3bh1btmxhzZo15ObmytfmWtl7UCjlYk1NDWVlZYDdOymKl8T75vJumE6fl56ezne/+10SExO5fv06DQ0NNDQ00NHR4WDkxcXFsWjRIoKCgoiMjGTp0qXs2LFDescFL/pMY8aVV3HZjY6OUlpaSnl5uXwtLS2N9evX4+/vLzvWOJMn55tCzN1msxEUFMTTTz9Na2srp06dIjs7W7rWlQfeGaGsSFSr1cTGxrJlyxb6+vo4duwYDQ0NkidU0E0JZGRk8PLLL/PYY48Bn1dlz7bio7QchVWorGBVYjq/3f0gQtJeXl6EhYXdVSw0/W/PJZSdTzIyMkhNTWV8fJyWlhbOnz9PdHQ0AwMDgF34REZGsmzZMlavXk1GRgYBAQEO5PfOyq08k1DSYuXl5fH8888zODiIXq//QtaKuYLFYmFsbIyhoSF5mUxMTKDVaucF28C9KrWXL1/O/v37uXjxosx9FS03u7u7OXjw4H2/Lzg4mMzMTLZv387u3buJj48HPu/E97Cfg5Bxok3sjh07UKvVjI2NcfnyZdkqVgl3d3cCAwNZunQpTz75JPn5+Q68tc66VuBYXCe8pBkZGWRkZLB48WJWrVpFR0cH/f39eHp6Ul9fL7uFCeXpkUceYdOmTeTm5koeXKE4OYMcfRAo732bzcbixYux2WysW7dOMg2A881Ho9GwePFisrOz2bRpExcuXODq1avU1NTQ09MD2I2wvLw8srKyiImJISUlhezsbHk3iMLV2bgfZkx5nb7hGhsbuXnzpgxVarVaoqOjHWgjRCrBQsF0QRMTE8Pf/M3f8NRTTzkkRDt7kwIBUQCkUqlYvHgxAQEBLFq0iA8++IDbt2875HyCvbjnlVdeYfPmzfJ3cz1PoZh+UQK5kppnrsc7U1Cr1QQEBJCdnU1YWBhbt251KDwTl6hoogGfC2NnIUifbQiOQpVKhY+PD88++yx5eXmMj49L79hcQ+mZM5vNjI+PO5xLkact5K4y/Ofs8Pb2Zv369SxevJjr169z/vx5ObeioiKampru+9nw8HB0Oh07d+4kMjLSQYmYaQ+YUoYsXbqUV199lbS0NKqqqmhsbKSzsxOtVsvixYtZt26dNC7T09MltaDAfDh3wkOsHGtmZiYRERGMj48zOTnJ4OAgExMTUpa4ubkRGBhIZGQkISEhDk6D+VRMCI45uRs2bCAmJgaVSkViYqJDVM/Z7hbxjFUqFVlZWYSEhLBx40bGx8dlIw0RmQwICMDLyws/P785S0WaUc+rUiAODw/T1dUliwdSUlJYtGiR9AAsZE+OqEJXqVQkJyfLi06E7uZb+FV4TxMTE9m7dy/u7u6kpaXR19cnKxHDwsJ47rnnePzxx/Hy8pINC+Z6rkrvwL0u7AflAVVCMBqYzWZsNhsajWZOvMsPCiV3plarJSoq6q5LUgkRFZnpHCZnx/R982XPbS6gXJ/AwECWLVuG0WikoqKCsbExNm7cyLJly2T19kxVAs8ENBoNgYGBBAYGSmaM0dFRySRSWFiIRqMhICAAs9ksKRpNJhMrVqzglVdekd5W4SWajRxKrVYrZYS3tzePPvooS5culRy8fX19aDQa0tLSyM3NJTY2dl5HIFWqz9vAWiwW6Y0LDg6+b7RrOkSHJ41G47Ry9Mvg5uZGTEyMg6PK2SGo20QBl9LIux9E04zZLqSbsb82PT9nfHycrq4u2YFo5cqVpKen30VWvFBxr7nNx0OpFExg9yYfOHCAp59+WlLwiJC7srrSGYvwhMfpyyoqH+R7lFEDZ1cGvqqCPh/36UzDWddXuVZRUVHs37+fPXv2YDKZmJqakvzFAvPJGJk+1jVr1khvz+rVq/nxj398194W99C95j2bHj0hIzQaDT4+PoSFhZGRkSGVO7DLSGdnnvmqEEVBX3WfiTV01nO2UCFSwr7Kes1VyuOMKq9KzsGmpiZu3LiB2WxmxYoVPP300+Tl5cn3flsuSJPJJD2X89W6FvktgjHBx8dHtt+cDmevEH1YxtPX8djOFabPU1BBKcN94rKd66ICZ4R4HhaLRXYBnAmWg28CIVN9fX0dipLmOywWi2yjqnzeHh4eMkfyi6DX62VkZLb3tfLvidSce+0Zs9ksWRbm+/lTGvKCpk14vaeHmdVq9ZytzUzBbDZjMplkobCz03xNXy+xZsqCZ0GNONfRuFl7khaLBX9/f6Kjo9m3bx/btm2TLeOUfHgLHc52yX1dKIvrlPQZ098zXxS6bzPmoq3rQoAzPzdlNEF5Lp09KvBluN8z/7JKfDHvuaDr+yJMXxtYuCl0znpWZhKzVbw0E3Bm+QYzqLxOr4zcvHkzISEh+Pr6yl7HML88Vi7cDZHLJSxq8TslSbwLLrgw+xBnU3i5RA7hVw0LOiuUHiGRqwf3bpnrbB49IS+VcxAeLWcZowsuODO+smah0+m+8R/95JNPvvF3zBQexvycHQt9jgt9fuCa40LAQp8fLPw5LvT5gWuOCwELcX7z3/x2wQUXXHDBBRdccOFbA9V84PdzwQUXXHDBBRdccMEFcHleXXDBBRdccMEFF1yYR3Apry644IILLrjgggsuzBu4lFcXXHDBBRdccMEFF+YNXMqrCy644IILLrjgggvzBnNGwqnT6V4B/vAlb7MVFBTMWxJYnU73NLAJyAWWAn7AXwoKCl6cy3E9TOh0uj3AfwCygBCgC7gJ/K+CgoKrczm2bwqdThcC7AP2ADlANGACyrDv3T8UFBTY7v8N8wc6nS4G+O/ATj5fx8PAPxUUFAzN4dC+Eb4ta6jT6VTA94AfAtmAGqjBPsfXCwoKrHM4vG+MhT4/AJ1O1wzE3+flnoKCgshZHM6MYKHP8Vty5zfjBGs4lwzyJcA/3ee1DcAjwIlZG83M4L9g38DjQDuQMbfDebjQ6XT/AvxHYAC7otMPpAB7gad0Ot13CwoK/jx3I/zGeAb4JXZF7jzQCkQA+4HfArt0Ot0zBQUF85qyQ6fTJQNXgHDgCFANrMRulOzU6XTrCgoKBuZwiN8E34o1BP4EvAT0Au8AE8A24GfAxgUwx4U+P4ER4Kf3+P34LI9jJrGQ57ig73wF5nwN50x5LSgoKMGuwN4FnU4nPHa/nq3xzBD+DvsGrsdujZ2f2+E8POh0ukjg/wZ6gCUFBQW9ite2AOewe/Lms/JaCzwBHFN653Q63X8GrgFPYVeC3p+b4T00FGBXXP+2oKDg5+KXOp3uf2Hfw/8M/HiOxvZNseDXUKfTPYldsWsCVhYUFPR/9nstcAj7HF8G/jhHQ/xGWOjzm4bhgoKC/2euBzHDWMhzXLB3/jTM+Ro6Xc6rTqdbDKwGOoBjczycb4SCgoLzBQUFdQvEIzAd8dj3T7FScQX7vIExIGwuBvawUFBQcK6goODo9LByQUFBN/C/P/vn5lkf2EOETqdLAh4FmoHXp73837B7uF7S6XQ+szy0h4JvwxpiV74B/k0odgAFBQVm4B8/++dPZn1UDw8LfX4uLBAs8DvfqeCMjed/9NnP3y2EPKYFjDrsuYMrdTpdqPJS0el0G7Hn+hyeo7HNBsyf/bTM6Si+OR757Ofpeyh4Yzqd7lPsyu1q4OxsD26GsVDWUOSYNd7jNfG7ZTqdLrCgoGB4dob0ULHQ56eEh06nexGIw244lgKXFthd+G2Y40LHnK+hU3ledTqdF/AiYMOej+aCk6KgoGAQ+Hvs+YOVOp3u1zqd7n/qdLpDwGngDJ8bIgsKOp1OA3z3s3+enMuxPASkf/az9j6v1332M20WxjJrWGBrKAzHxHu8lqT4//maf7fQ56dEJPAm9lSdn2JPv6rT6XSb5nJQDxnfhjkudMz5GjqV8go8CwQCJwoKCtrmeCwufAkKCgp+ij2kpwFeA/4Be4FMG/DH6ekECwj/L7AYOF5QUHBqrgfzDRHw2c+R+7wufh8480OZVSykNfz4s5//p06nCxa//ExBVxbFBs3qqB4eFvr8BP4AbMWuGPhgZ8f4FZAAnNDpdEvnbmgPDd+GOS50OMUaOlvawA8/+/mrOR2FCw8EnU73H4H/Afw78AugG7v3438Cf9HpdLkFBQX/cQ6H+NCh0+n+Fvi/sFfkvzTHw5kNqD77uWByuBbgGr6NPWK1C3sU5CNgEns1fjJ273kqMF/Dsgt9fgAUFBRMZ98pB36s0+nGse/X/wc77du8xbdhjgsdzrKGTuN51el0WcBa7JV6x+d4OC58CXQ63WbgX4CPCgoK/s+CgoLGgoKCyYKCglvYN24H8H99VhC0IKDT6f4aOzVPJbDls9SJ+Q7hWQ24z+v+0943r7EQ1/CzXOUnsLN/dGNXyF/FLkvXY6eyAzvN1LzDQp/fA0AUFm6c01HMLL4Nc1zomNU1dCbPq6tQa37hsc9+3kUFUlBQMKnT6a5hV2LzuHehxbyCTqf7P4D/H3Yrc+sCSomo+ezn/XJaUz/7eb+c2HmDBbyGFBQUWIB/++w/ic/qCHIBPVAx+yN7OFjo8/sSiH06Lxk/HhDfhjkudMzqGjqF51Wn03lit6ZtwO/meDguPBg8Pvt5Pzos8XvTLIxlRqHT6f4eu9JTgt1bt2CUHj43Ph7V6XQO8kCn0/kB67ArBkWzPbCHiQW+hl+ElwBP4NBn1FILDQt9fgBrPvs5750AX4BvwxwXOmZ1DZ1CecVe5BOEvXjCVag1P1D42c8f6nS6aOULOp1uF3alx4C9c9O8hU6n+0fsxT03sXvr+r/kI/MKBQUFDdjZIRKAv5728j9ht6LfKCgomJjloT00LPQ1BNDpdP73+N0K7PMex94wZN7iWzC/bGUxmuL38djrCWB+N3z5VsxxocOZ1tBZ0gZEodZ876jlgM86wzz52T8FV+EanU73x8/+v7+goOD/nuVhPSy8B3yCvWiiSqfTfYg9Hy0Te0qBCviHedxWFJ1O9zL2S9GKXVn/W51ON/1tzQUFBX+c5aE9bOiwGxn/rtPptgJVwCpgC/Z0gf/PHI7tG+FbtIZndDqdHntKxBiQDewGjMD+goKC+e7RWujzewb4B51Odx57J7Ex7MVoe7B7lo8D/9+5G95DwYKf4wK/88GJ1nDOlVedTpeJPel+IRZq5WJvW6hEEp9zE7ZgL0KYdygoKLDpdLrd2L1138Ge3+oNDGJfx38vKCg4PYdDfBgQvJJq4P+4z3suMs/bUhYUFDTodLp87EreTuxKQRd2Fol/mudFTd+KNcRuTH4He1W+F9CJnSv7/y0oKGiew3E9LCz0+Z3Hzrmchz386gMMA5ex82m+uQC6Nn0b5pjLAr3zP4PTrKFqamq+7xUXXHDBBRdccMEFF74tcJacVxdccMEFF1xwwQUXXPhSuJRXF1xwwQUXXHDBBRfmDVzKqwsuuOCCCy644IIL8wYu5dUFF1xwwQUXXHDBhXkDl/LqggsuuOCCCy644MK8wQNTZel0unlPS1BQUKC632sLfX6w8Oe4EOYHC3+Orn26sOcHC3+OC2F+sPDn6NqnC3d+Ls+rCy644IILLrjgggvzBl+5SUFBQcFXer/RaMTd3R2VSkVzczP/+I//yHvvvUdWVhb/8A//wDPPPAOAyWRCrVajVqu/6pC+FPfoqHNffNX5OQO+yvxg4c9xPs4PFv4cXfvUEQt9frDw5/hN5zc1NYXgWlepVKhUX+gofGhw7VNHLPQ5LsT5zWqHrdHRUerr6zEYDJSVldHX1ydfUx7ihQir1YrNZkOtVuPmNj8c3mI9pqamsFqtDuujXC+1Wo1Go5k1wevCN4fRaMRsNqNSqfDw8ECjmfNme3MKm82G0WjEy8sLgL6+PkpLS5mcnMRkMmE0GklJSSE/Px83NzdsNhvAvDnLLjgn7qWw2my2b82+EvciON4pbm5u8+qudGH2Mas3lkajwc/PT/5bbNpvA5ReZZvNNqtW9leBECBubm5yfCqV6oGEiFhPZ53bw4TNZmNqauqBn42zQIzZw8MDDw+PuR6O08DNzU0qrgMDA5w6dYqPP/6Y9vZ2TCYTQUFBPPPMMyxbtgw3NzeHczJXEGO411m73/mb7iBQev2mKw8L/QzPJYSxNDk5idFoRKVS4efnh6+v77ySJ1+EL3NIqVSqGYu2ziXEvMXZ/Dbch3OBGVdelZvX3d1denjMZjNWq9XhfQvZ86qExWJx2kNrtVqxWq0y1eOrwGKxADjt3B4mrFYrFosFlUqFu7v7vLlwhPf/fq/Nl3k8TJjNZrRaLQDt7e38+c9/5sSJE5SWljI8PAzAtm3bCAoKcjDo5lpeCa+VUtGcbnhOh1LOTk1NSUPazc0Nq9UqvfFKWe3CN8d0I6Onp4fi4mIuXLjA7du38ff35+WXX+bpp58G7HtSeB/nK2w2m7wTlOdGvKbVahekUjc1NYXJZMJqtaJWq7/WXerCl2PGpZPy8HV0dDAwMABAeHg4/v7+8rWFaOmLw2symRgbG8NsNhMaGoq3tzdgD91OTU05hfIjLjSNRoNGo2Fqagq9Xo+bmxtGo5GRkREmJyelFWk2mzGZTLi5uREYGEhERMS3wpMnlAWtVisVHuXvnRVifGq1GpPJRG1tLR0dHXh7e5OcnMyiRYukRxHu77lbSFB6RkwmE319fRw+fJg33niDqqoqAIKCgsjLy2Pfvn0sXbpUrvFcyiuxll+kXArFVqTzPEiUQFy0AkK5nQ0FSsjKqakpmYb0TaBMdXKGlKbpnvq2tjZOnz7NwYMHGR4extfXl0cffVS+XxgV8xEiAvcgToz+/n4mJiYAu/PDaDQC4OvrS2Bg4LzyRCujFp6eng6vfdn9oIxaKn+6cH/MmPIqFlJc8K2trVy9epXOzk4A1q1bR1JSknz/fLYw7wc3Nzfc3NzQ6/WUlJRQWVlJTk6OFFLOEHq8F6xWK7W1tfT19eHu7k5fXx/l5eV0dXUB9rUaHx+nv78frVZLTk4OW7ZsYcWKFQ4K7P1CmvMZ95uPs8/TbDZLD0Bvby8///nPOXjwIElJSfz93/89zz//vHzflylGCwVizfR6PWVlZRw/fpxjx45RU1ODRqNh8eLF7Ny5k23btrF48WJCQkIc1nmu1vxB/q5QVL/JZTib4U4x3oclD5Xf5wxnc7qs7+3tpa6ujtHRUXx8fNi1axeLFy+W75/P5++LnrfRaESv1+Pl5UVnZyeFhYW0tLSgUqkYHx+nr68PNzc3YmNjyc3NZfny5URHRwM4vWH9TaIxzqYDzAfM2AkROYHiEDY1NXH16lXa29uJi4tjzZo1JCcny/cvpMUT6RDCgxAWFobVauXkyZOcOnWKoaEhHn/8cemBBeasAGS6d6WmpoYzZ85QWlqKXq9Ho9EwMjJCZ2cng4OD8lKYnJxkeHgYtVpNTU0NFRUVLF68mMWLF7NkyRISExMdQpnOKnAeFMrw7NjYGHfu3KG+vp7AwEDWrl1LeHg4YPceOOPFo1REmpubKS4ulvP4NhVOgqMXZGxsjJs3b/Lhhx9y5MgRWlpa8PT0ZNeuXTzzzDOsWbOGmJgYp1hT5R40m82Ul5fT29tLYGAggYGBmM1mjEYjoaGhxMfHA2AwGGRahNFoZHR0lPHxccxmM3q9nuHhYTw9PQkMDKSrq4vy8nI0Gg35+fmsXLlSemJncl/fz5P8TeWGUpaazeY598Aqz5Ver2dkZASbzUZycjJr166d9/fhdAW9pqaGmpoabDYbgYGBWK1WqqqqaGlpQavVYjAYqKiooLu7Gzc3NwwGA5OTk3h4eODp6UlJSQk2m00qr1arVebJOhOUd6hKpaKtrY3CwkKam5tJSEhg69atREREAI7naGpqSqYQKtdb6E6wMCPSDwsz6nlVHta2tjYqKysBCA0NJTMzk8jISACZG7JQIOZiMplQqVTo9Xr6+vqoq6ujpaWF8fFxVCoVjz76KP7+/g5esdmGzWbDZDLJYpXCwkL+63/9rwwNDT3wdwwNDVFaWsrHH3/M1q1b+f73v098fDxqtVoexPm+vlarVXpybt68ya9+9SsuXLhAfn4+8fHxUnk1mUxOoegICAVAKCHt7e3cvn0bk8kEQGxsLL6+vvL9C724QKm4DgwMcOXKFT788EMuXLjA4OAgkZGR5Ofn88orr/DEE08AdsXHGYwSi8Ui8wTHxsY4cuQIV65cISYmhoSEBKkQpaSksG3bNnx8fKivr2dychJPT0/Gxsbo6uqiv78fg8HAyMgI7e3tBAQEsGjRIiorKzl58iQAr732GmFhYaSnpwP2/T9T8xfsDdPTBr7JPpyeNqBM8ZkrKOcTEBBAeHg4Go2G3t5eWltbGR4eJi4uDnB+L+O9oEwpq6ys5L333uPixYvYbDbCwsIwGo1UVVXR3Nz8hd+h1WoxmUyUlZWRm5vLk08+CThvrcjU1BRms1mOq6ioiP/23/4b9fX1pKamMjY2xv79+wkLC5PvEXL5XvtyPhouc4EZk8ZKF/rY2Bjt7e20trYC9nyWiIgImVu5kJRXMRer1cqNGzc4d+4cTU1N1NbW0tPTA9hTKN566y2uXLlCaGgo+fn5PPLII7i7u2Oz2WSu2mxBaWSMjo5+JcVVSRs0MTHB4OAger1eCl1RCDLfodVqpefg6NGjnDx5kuHhYby8vBxSJZxN8CjzzwDKy8spLCyUa+zt7f3AnlahYIh8PK1W63Tz/SIoDerx8XEuXLjA7373O65evYqnpyfr169n8+bNrFmzhry8PPk5ZyjOmo7Ozk7u3LnD2bNnCQ4OJiwsjMnJSQwGA4GBgZw+fRpPT0+Gh4cxGo2o1WqMRiPj4+MYDAbp9ZmYmECr1eLj4yOL0wCuXLnCsmXLZjSfXWkQVFZW8vbbbzMyMsLmzZvZsmULwcHBwIOxsyi9tFarVaapjY+Ps3nzZtLS0gC7canM650tTD8nqamprF+/nsrKSpqamjh//jxr1qxhyZIlcg7z6Wwpn2tpaSmvv/46Z86cYWBgAI1GI+/EsbGxL/weUewkoHwGzqzIi7FZLBaqq6upr68HoK6ujjfffBMvLy8OHDggn4PZbJZ5sePj43R3d2Oz2fD29sbb25vAwEA59/loyAg9RkCkUD5MzKjyKi7Mrq4uSTkD9nye6QnN8x3KgpjR0VGuXr3KBx98wLFjx+jo6JDvCwgIYGRkhCNHjgDg7+/PSy+9RExMDIsXL3ZQBmcDKpXKQVFevHgxjz/+OLdv35aXi0ajkYJJbEp3d3dCQkJISkrCzc2NsrIy6uvrGRsbY2RkBIvFgru7u/Q+z1fjRGlYmc1murq6qKqqYnR0lHXr1vGd73xHeksAp/DwKCEUNjGHvr4+GhoaGBwcBOwGh16vf6DvcnNzu+viny8pIWIdxfm8ePEif/zjHzlx4gR+fn5s2rSJvXv3smXLFhkRMpvNwNyv6XTv+eDgILdv36a1tRWbzUZ/fz/9/f3y/T09PdTU1Hylv9HX1ycp1IxGIxUVFfzpT3/C19eX/fv3O6Q4Paz5iMtseHiYs2fP8vrrrzM8PMzQ0BDx8fEOyuuXyQ+lZ3hwcJDTp0/zl7/8hfHxcfR6PaGhoQQHB8+ZHFIq1mq1Wsr78PBwmpqaaG5ulmcS5lfBlvA8iv1ZX1/Phx9+yPDwMFqtVkYuRAhcq9Xi6+uLl5cXFotFPhNxF3l6euLr68vixYvJz8+Xf0e8xxkh9vLk5KSDPI2MjMTLy+suB46npycmk4mqqiquX79ObW2t9Cx7e3uTm5vLxo0bCQkJmdV5fBMo00bupaxOV2jhc2/911FsZ1R5FVW8AwMDDlY93M03ON8hlDWA69ev89Of/pTCwkJZSSkwMTEh6UMAeZFGR0fj7u5OWloaGo1m1nJgpyska9euxd/fnwsXLtDY2EhwcDDJycmEh4ejVqsdqE+Cg4PJyspibGyMX/7yl5SXl3Pnzh1KS0vp7+9n0aJFTlmQ9lUwXVharVaMRiP+/v5s3LiRnTt34u3tLTvJOaOSPv2sKS/GbxqGmy9eATE+g8HApUuXKCgo4PTp0wQGBvL444/z9NNPs2bNGsLCwuRnnCXfbHpeekVFBefOnaO9vf2u9wpDUSljlK8pqYru9XdEtbf4O9XV1VKJf1gyWxj6Qi6UlJRQXFwsL/2qqiqqqqpIT0/Hz8/vgdZA6cXt7u7m1KlTXL58Ga1Wy/vvv4+HhwfPPfccQUFBgCM92lxAePOFQycuLo7Q0FD5urPsva8Dm83moKxZLBZ8fHyIjY1lbGwMk8nEsmXLyMrKwmAwYLVa8fHxkbnXiYmJ8nkon8lcp+18EZRsPcr7NCMjg5/85Cds3boVtVoti9XAHm34+c9/zunTpxkdHcXNzU3qSTt27MDPz48NGzbg4eGxIPQl5b5QyqKvu89nVHmFz3MqlaGAhQQhiN3d3WWqwFtvvcXZs2cxm814eXkRFxeHwWCQNGEpKSlkZGQwMjLClStXKC8v57333iMmJoaYmJiH6uX4KvMAe0rH6tWrCQ0Npa2tjYCAAOLi4mSl9b08baKCVKzxzZs3uXbtGo8//jgeHh6SkHs+8t2ZzWYZMh0dHaWyspKGhgZsNhv+/v5SEH0RYfxcQmnV6vV6ent7GRkZkQoJfLFSYjabpfI0OjpKZ2cnAwMD+Pr6kpaWJpUBZ0z9EaFxkd4gztuvfvUrTp48iYeHBwcOHOC5554jOztbevqMRqMMdToDpueNl5WVcfHiRXp7e6USKHINxZwFvLy8pFKoTJvw9PQkNDRUpm1pNBoZJUlKSiIhIYGQkBCys7NnRGkQZ2ViYoK2tjaGh4fx9/enr6+P9vZ2mpqaGB0dfWDlVamMDw0N0dbWJpXDoqIiQkNDefTRR+V+FftitiDmK9ZQsA0MDAzg4eFBbm4usbGx8v0Porw6K1uNu7s7Pj4+jI2N4ebmRlhYGPn5+Wzfvh2r1crg4CAZGRlkZGRIz6uXlxfu7u4EBAQQHR390Iv3ZhJKRWxoaIjR0VH52uLFi9m+fbu8J8Q+HRwc5MSJE3zwwQcMDw8THR3NokWL6O/vl5ECT09Pp5FBX4TpzRgsFgvj4+NMTk5K2eLv74+Pj899ZYlISRPR6wdZ6xk3ZcSBdbYD9rCgLLYaHR3lnXfe4ejRo/ICsVqtZGVlERkZSVNTE56enjz55JNs2bKFxsZG/vVf/5UTJ05w69YtPvroI6Kioli7dq1UYGfr0E5fn5SUFJKSku7KNVOpVA5FLw0NDbz33nucP39evqejo4OTJ08SGRnJ6tWr5zXZtjhsk5OTMhWkpaUFsAug9vZ2YmNjnZZwWzmmoaEhmpubaWtrc+AAvVcoR0Cr1TI+Pk5tbS03b97kxo0b1NbWEhkZyYEDB9i5c6fMXXc2KAsiRkdHKSws5Ne//jUff/wxALt27eKll15i5cqVgKMh6kxrKYwHgd7eXpqamgBkas69PKmhoaHExMTQ3d1Nd3e3/L2npydLlixh6dKlaDQaDAaDzIn19fVl586dbNy4EXd3dywWi7x4H+YzUdKUjY2NMTY2xuTkpHxdKTMeRAaKdZ6amqK3t1demhaLBbPZTF9f3wMbbDOB6Ypmf38/9fX1DAwMEBYWRk5ODjExMfL9D3JfOmtxpdKjrNFoSEhI4JFHHuF73/segYGBcr96enretQ7zjYpQWXhlNBqpqamhuroagNzcXFatWuXwfh8fH0wmE5988gkfffQRw8PD5Obm8sILL5CZmSk90YmJiSxZssRpZasSwqllNBplWprY225ubgQFBRETE0NiYiKhoaHSmeDu7i691II7XXzfg2DW/PDOuvkeJnp7eykvL6evr096PLRaLatWrWLfvn10dXWh1+tZvnw5YWFhREZG8sILL9DV1UVJSQnvvfceKpUKb29v1q5dC8xsle+9ICp+lcU4wpsjiq+EpQyfV6/X1tYC9svUbDZz+fJlDAYD/f397N69Wx5Cg8EwL/KdlZ6S0dFRzp49y1tvvcWNGzfkeyYnJ5mcnPzSrkZzCaW3zWazMTY2Jg0rkc88HW5ubjL/Snj03n33XT744ANGRkYYGRnB29sbf39/goODWb58ufROO2OzhvHxcS5dusRvf/tbjh49CsDq1at5/vnnyc3NlcJXFMk42zpOV16Va6YsJktISGDz5s0kJSWh1+sJCAggKiqK4eFhydGs0Wjw8PAgOTmZ5ORk2bRCeEy8vb1JS0tzYKAQY5gJ5XV8fJyamhpKSkqYmJggMDCQFStWsHjxYukJ/yIom4YA1NbWUlJSQn9/v8Mzm+s1na68GgwGenp6mJiYIDMzk7Vr18o0K2X+6HSIYh9AylGRyjS9ccpsQvl8RYMMQNZH+Pn5ERgYCOAwt3uti/DCzQfHl9KDPzo6Kvnc/fz82LVrF4888ggeHh6MjIzg7++PSqWipaWFU6dOUVRUREBAAE888QTf/e53iYiIYHJy8q4mB3O9d+8HEenRaDQMDg5SWlrK5cuXuXjxIlVVVdhsNmJjY4mJiXHIb/by8iIiIoKYmBji4uJIS0sjIyPjK+/dWdGKFjJ3pPC4mc1mmpqapPdAr9fj4+PD2rVrWbduHWlpabLiFZCLvn37doxGI//+7/9OaWkpR48eJScnh6SkJFk4MptQXozKfEaxsQT9lUBUVBSLFy+moqKC8vJyTCaT9PRUVVXR29uLWq1mx44dDpeMs2J6+H9wcJCzZ8/yi1/8gqKiItzd3WVeoPAeKL2XziZohOC32Wx0d3c7hLTERahcT2WBl5jL9evXOXz4sDRQVCoVBoOBqqoq6urqyMrKcirlVcxnamqKoaEhrly5wu9//3tZJLl69Wpee+011q9fL8d9P9oaZ4DyeVqtVsbHxx3+PTU1RXh4ONu3b+eHP/yhLHJRGorTK7i/zCAWCpJQHh7mvhYe1cHBQYqKiiQrAEB0dDTLli0jPT39gVgBlPtteHiYy5cvc+7cOdra2hzeJ4zyuYTy73d2dsqK9JCQEDIyMuR77qW8is8qc9RFDqFWq52TVDMllHObzkJiMpkYHR1lcHCQ4OBgjEYjNpvtrpQr+NybPBeMEF8HStnZ0dHBtWvXaGlpIS0tjdWrV7No0SLA8V5Q0oZGRUWRk5MjeWCVyroz1hMIQ1p5l3d3d1NUVMTRo0c5fvy4jPKkpqYSERFBcHAwExMTlJWVSa802LsXJiQksHLlSh555BFWrFhBREQEnp6eD3SHzIry6kwP/2FBSSIM9g1ZU1PjUKC1Zs0a/vqv/9qBdkdAbPrg4GCefvppIiIi+Jd/+RcuX77MhQsXWLVqFf7+/lIozbZSIDbpvZKqleOIi4vjsccek/8uLy+X/2+z2bhw4QLj4+O0trby7LPPEhQU5JBn54wQSeX9/f2cOnWKX/3qVxQVFaHValm7di1tbW1UVVU5KBHOmJOlzENtbW3l4sWL8sIEJKm9UgDbbDaZ52uz2Th9+jS//vWvZZga7HP18vIiMjKS8PBwB6XPGZ6B6GpXW1vLpUuXOHz4MOfOnQPsiuuPfvQjdu7cSWRkpNPvRfjcoNTr9RQVFcmLDz5XSjMzM9m9e7dDlyal9+arKgNCvj1MhU+pbIGdjuuPf/yjw3zEWjzoeij3W11dHZ988gm3bt0C7PM3GAyAPfd3Lo0q5XOcmJigsrKS0tJSgLsu63udoemsIQCNjY309vaSkZHxQF7qmYRyfsoxCm5sZbHO9GJCZb62UnFzRk7X6RBrNTU1RVVVFTdv3gTsSqmy+FO5piJiBxARESE90uCYLuOMENFTDw8PmZZTXFzMoUOHOH/+PN3d3aSmpnLgwAHWr1+Pv7+/TG0qKSmRNT719fUMDQ0xNDRERUUF58+fZ926dezZs4e1a9cSFRX1pWOZE4n9dYWICFkrqVZmgj/sQTBdmHR2dspuIWCnyHjsscfYvXs3Wq1WKrVeXl4yHCJygwICAnjssccoKSnh8uXLNDQ00N7e7jAvJZvBbODL6K3EGnh6epKbmyvpTS5cuCALSfr7+6mpqaGwsJDBwUEmJibYt28fiYmJDrk8zqDwwOcVyCLP89SpU/zsZz+jpKQErVbL97//fTZs2MCHH34owyLKRPy59jhOh1J57enp4ebNmw60bRqN5i7hqVyLlpYWjhw5wscff4zZbCYwMFAqexkZGaxevZqsrCz5DGBu2zyLBhHCs1hUVMRvfvMbysrKAFi1ahWvvfYau3fvlk0lplPFORuUazg4OEhFRYUkeVemDCQlJbF69Wo8PT0ZGRmRBobwmoqiL6UxOv3cidenV0w/LAh5J5TXjo4OLl68KC9Do9FIf38/XV1djIyMOHz2fjJCnDlBY1ddXe0QGQF7/v6mTZsICAiQn5vLNRfPWNmcYXJyUp7D6SF4+PxctbS00NHRQU9PD1VVVXR2dhIbG8vKlStJT08nKChIej5nSx4pPaVWq5W2tjaHmg+1Wo2Xlxf+/v6APe/TbDZjMBhk9f0XpZIpm1c4i4xV0tdNTU3R3NxMWVmZZAAR6yCgHLdybfR6vYPDS+wHEdGbayjprYRO4OPjA9i7pl66dIkTJ05w5swZBgcHyc3N5fvf/z4HDhyQxZECK1asYMuWLVRXV0svbGtrKw0NDdTW1tLR0cHw8DB+fn7Oq7x+XR5TobAqq9vmajNPT4UQycrDw8MEBQWxe/duVq9eLTewMjFZQCjjAkIJEBaJksh4rkNe0zH9YKWlpREVFcWzzz6LxWKhq6uLY8eOcfjwYaqrq6moqOBf/uVfGBoa4sc//jHR0dFOp7wq9+WNGzf4wx/+QElJCYGBgbz66qv87d/+LcHBwRQXFwOftysE51uf6dBoNPj4+DiQzQv6GiUdjVarRa1WyzBPfX29Q45dQkKCTBXJz88nPDzcaXrIK8fQ39/P1atXpeK6efNmXnrpJXbu3CkVV2cY85dBSZE1OjoqK/PBvqbKIiQhW8QaKwvPpssrZ5i38mJUNtPQarV35fXeD+I1MXelPBXGzI4dO3jmmWfu2uezCaWh4enpSWxsLMnJydTV1WE2mx3meL91ampq4ujRoxw9epTa2lpMJhMGgwGDwUBaWhovvPACu3btIj4+Hh8fn1m5G5VFjkajkZs3b3Lr1i0HhgvBtNPU1ERsbCyDg4MMDAzQ29tLb28vUVFRpKWlScae0NBQB+PJWeSLEspzaTabqauro7q6GqvVKhkThJIHdzdbEP+emJhwaiYmMU6VSuVwtgYGBvj973/Pn//8Z3p6etDr9YSFhfHcc8/x8ssv4+fnd0+HTnJyMnFxcWzcuJGxsTHa2tq4ffs2V69e5c6dO3JfPAjmRHn9Khe9IDgWAu1eB9JoNM56nozyIImNrNVqmZqawt/fn+TkZMLCwhyqEe/1HdO/B+yX1M2bNykvL2fFihVfKYw221DS+AQEBEjvhqDXioyM5N1335Ue2ffeew9/f3+ee+45Se4/1zRLwkvg4eHB5OQkhYWFvP766xQWFpKQkMCPf/xj9u3bJ/vFC6Ek0geCg4Mdmjg4i3dAuWdiYmJYt24d5eXlsnhnampKVnpPp/IZHh6mubnZITXC39+f7du3s3z5ciIjI4mLi5uRSvSvCrEHhRektbWV999/n08//RSwh9SfeuopHnvsMam4wucKxXQjEu6WUdMjPbMxXyXVF9hTPy5cuEBbWxt+fn7A57mpokvTpk2bHHLllQWYc335u7m5yf3S3NxMbW2tHJPZbEaj0ZCRkcGKFSsIDw93oGkTnxf7VJwzUVgo7onpUKnsfNSCp1p8brafxXRHR3d3t8zL9fT0vKeio+SuvXz5siwYFQ1klCgtLWVychKbzcaLL75IQEDArFBpKeWd0Wikra2N7u5u+TuNRsPw8LDs6ufv78/k5CQTExOSViooKIioqCh5T8bFxREXF0dERARJSUl3EfU7gyKrVF5NJhPNzc3U1dVhsVjIy8tj8+bNDuwRytoYQSMF9vQC0SJeq9XexXQyF3ejKLgTbDRKw7C6upqGhgZu3rzJkSNHZBTI19eX/Px8Nm7cKGWTmK/FYsFoNOLp6Ymnp6dkGfD39yc6Opr09HRWrlxJTU0Ner2ezMzMBxrnrFBlgaNFHBYW5hBq/CIoKyiVl4xQ/NRq9Yy0LvwyKD0Zogpfr9fLHMLpBTz38zIq/618Jrdu3aKmpoa0tDRCQkKcRiGajvsZE1qtlpSUFNm2cmhoiKqqKqqrq3nrrbfIzc2VyqvJZHrg/TATEMVJer2eM2fO8G//9m8UFhaSlJTE3/3d3/Hiiy/KkN74+LjM9+no6KCqqsqhMtqZlFdlt7bw8HDy8/M5ceIERUVFgD1k1dLSwsDAgMM+ValUjIyM0NjY6NAqOCAggNWrV7Nlyxan7HajUqkYGxvj6NGj/PKXv6S+vp74+HiefPJJNm/eLBVXZShWhHCd2TgUGB0dlbnHgt5KoLq6mnfeeYfh4WHWrVtHUlIS3t7eTjMvoYipVCo6Ojr44IMPuHjxokNTBC8vLxYvXszmzZtl5f2XKV9CKfiiNdTr9YyPj895XqiA0Wikvb1d5uMK2SPC6uLcKnOxjx8/zi9/+UsAByaI2NhYMjIy6OzslLmHOTk5REZGzop3Wbk27u7uMg1JnDG9Xk9HRwctLS0OdIpfhPj4eJKTk0lPT2fjxo2sXbuWiIgIudbOIHeUaThWq5X29nbq6+uZmpoiLy+PrVu34ufnJ1szi3Xs7++XclUwa6SkpMi1slgsjI2NySK8uXDqTC8KFCw15eXlnDhxgmPHjlFWVibnr0yfbG5ulvzfSqfe9PtdacwFBQWxfPlyli9f/pXGOeOSTWmVgd1rlZmZ6RDCuRdMJpPDQzQajZw5c4YrV65gsVgICwsjKiqKFStWkJ6eLj83W8qDWFSxKXt6eigpKQHsQjgwMNBhwZRho/tBuVFVKhVBQUFSaXL2ohLhJVKSDbu5uREQEMDevXsZHx+nv79f5mtNz2mbCwgLUwiOwsJCfvazn1FYWEhKSgr/4T/8B6m4isvXw8MDPz8/fH19pdGiFKbOmGwv9p2fn5+Dh0fknSkh9lljYyPFxcUOObJubm74+PjIfT3dWzlXUBZ51NTUcOXKFVmYtn79ep566ikWL16M2WxGr9dLD567u/ucGL4PiukRm8TERLZu3cp7772H2WzGbDbLuQwMDFBUVERXVxeXL18mOzubbdu2ObTXFN7Nubj8lfJrdHSU2tpauru7ZcTCZDJJL7KSAeLLxipeV6vV+Pn53fP8eXh4SAo/Jc3fbEIp/318fMjIyCAmJob29nb6+/upq6sjIiLCwbsvxlxcXCyLu8Cu3O3evZukpCTS0tLIzs7mwoUL/OQnP6GkpITjx4+TmppKenq6pECbKWNTmXPs6elJfn4+hYWF0jN8r5agX4aWlhZGRkZkHunp06dZtmyZzLGfbQ70e0FEBASVYlNTEwaDgcDAQFJTU+/K9xRoaGjg1q1b9PX1ERcXR3x8vGQaAHvR4W9/+1uio6N57bXX8Pf3d6A2nOk5KZ/n0NAQLS0tVFdXU15eLjvuKRkDxJ4eGRmhqKiIiYkJLl++zKJFi4iJiSEjI4OlS5fKMyeiKcroydc1smbsaSg9jUpl0svL6ws9AspEaLBr8u3t7TQ0NHDkyBEOHz4sixEyMzPZunUrO3fuJCcnZ1Z7VysF68TEBHfu3JHJ2klJSeTk5Dh4eu4nPO6X3yTa5CnzapxZeVVetOLyEcn4qampbNu2TSpDU1NTdHV1MTo6ir+/v1NQFLW3t3Pw4EHOnz9PdHQ0f/M3f8Pzzz9PYGAger2eyclJQkJC0Gq1hIeHExYWhtlsZnBwkPHxccmiMDk56VSdUZQRAg8PD7y9vaVnR6vVYrVaaWhooKmpSRbStbe3c+3aNcrLyx3SBoKDgx3YLx6k5/xsQKQ/VFRU8OGHH3LlyhXAzvaxc+dOSUN0Lx5Mm80mO46p1WomJyfp7++XbZwtFgseHh6EhYXJ+Xt6es7aWVSONyMjg9deew1vb29OnDhBf3+/VE5EwVJXVxfFxcUkJyczODiIzWYjPT0dDw8PmT8/vWPXbEB5B/j5+ZGUlERAQIBMYRGyr6qqiuPHj7Nq1Sr8/PzuolFSenuE10ukDzQ2Njr0lQe77O3t7WVgYMChCGQuwrFijlqtloSEBFJTU2lvb2dgYIDGxkaWLl2Kr6+vVPRVKhVVVVW89dZblJSUoFKpiI6OZtu2bRw4cIDMzEx5T4r7pq+vj87OTgfnwEynSgh6L61Wi6+vL9nZ2aSkpFBfX09UVBQajUZG45QRHrEP4XNvn9lsZmhoiOHhYYaHh2lsbOTTTz/lypUrdHR04O3tTVZWlpzXXMkfURsAdoadmpoaAPLz80lOTpbvE/msIpTe1tZGaWkpNpsNX19fvLy8GBwcpK+vj7a2No4ePcrZs2fZunWrA+XfbECZwlNTU8PNmzepqKjg5s2bXLlyRTo6PD098ff3x83NDYPBIPlb9Xo9V65cobCwEG9vb2JjY1m1ahV79+5l06ZNBAcH3/euVxbjP2h3wxmVwEK7Bhzc5k1NTYyMjMgFVUJpoXd1dfHLX/6Sw4cPMzk5KXPzwK4Y3bp1i4qKCo4fP85rr73GSy+9RHBw8IyTjYsHDDA2NsbFixdlYQjYPSRpaWmSUeBBBeX08So9BF+3yG0uIeavUqnIzMzkySefpLm5maamJm7fvs2mTZtYvHixfJazaUmLNVGr1XR3d/POO+9w4sQJPD09+d73vsfzzz9PSEgIJpPpLuERGhpKYGAgd+7c4fbt2+zZs4fY2FjZCtnd3d0plDpwVF7Dw8Olpd/V1SV5UI8cOYKXlxd/9Vd/RUxMDDdu3ODTTz91qIINCgoiKSlJ5nEpcxHnAtNz7W7fvs3hw4d555136OrqIjw8nBdffJE9e/bIQpB7rUlJSQnXrl2jr68PjUZDX18flZWVdHd3YzQaMRqNBAQEkJOTQ05ODpmZmeTl5REdHX3XOGYCSkXN19eXdevWERkZydKlSzl+/LhMI+jr65MpHlNTU7S0tHDo0CFqamrYsGEDGzZsICsr6y6FcLagvLSioqLYsWMHpaWl0ovj7u6OzWbj1q1bTExMcPr0adm+VniephdnwefP32az0dzcfFexh81m4+zZsyQkJJCYmCgjD8JYmU0on/nk5KRUtC0WC/39/fJOFPLQZDJRWlrKyZMn6ejoIDAwkGeffZYDBw6Qk5Pj8Bxu3LjBwMAAgYGB5Ofny/0JzLi3fTozTVJSEvv376enp4egoCBpYN2v5atYC29vb4aHh7lx4wY3btxwoGurqanhyJEjxMbGEhISQkRExFfqwPawoCwsBHu6zqlTp6itrSU2NpZHH32U7OxsB0YkZX7y8PCwZCQS+fnHjh3j0qVLXL58GU9PT/bs2cN3vvMdmR4y0122lDJscnKS4uJi3n33Xc6fP09/fz/j4+NScfXw8GDJkiVkZ2cTERHByMgIXV1dMopVW1tLZWUlJpOJpqYm2traqK2tpaWlhb1795KYmHjX31Q+K3CCDlvKXNfpgqK5uZmBgQF5ESoPoUajYXJykpqaGg4fPsyhQ4dkUrCnpyfp6enExcXh4eFBfX29rGS/ceMGu3btIjg4eMY3stLiGxwcpKamhsHBQfm61WqVirlQXu+HexVsCTh7BfuXQVnEFBwczKpVq0hPT6e6upqmpiZ6enqkV0i8b7YuFGWebX19PSdPnqSnp4ctW7bw7LPPSq+5SC5XkoB7e3tjtVoxmUz09vbKC1GtVjuEi5yhsEAZIXBzc2PNmjVUVFRw5MgRhoeHUalUNDU18cEHH5CRkcGSJUsoKipy4INVqVT4+fkRFhYmC/LUavWcpg0IAxWQBSHnzp2TnjytVusQvhsfH5dRHKPRiMVioaamhqtXr9LQ0CCt/paWlntW/zY0NHD9+nXy8/Px9fWVyoFIG5lp5UDAw8ODrKwswsLCiI2NpaWlBZVKRXd3N3fu3KG+vp7Ozk4mJiZoaWmhtbWV1tZW6uvryc/PZ+3atdJzBbObo60MtWZnZ/Pcc8/J7mdC8TaZTFRUVNDY2ChlgTJ9zGq1yvSk6bLTZDI5RAo0Gg02m42Ojg5OnDhBeHg4u3fvZtGiRQ4sLrNxRqdHIENCQkhLS6Oqqoq+vj7u3LnD8uXLiYmJQa1WMzIywqVLlzh9+rTc0/Hx8WzZsoUlS5bIQjyr1UphYSEHDx6ku7ubRx99lJ07dxIdHT2rHnalcRITE8Nzzz2H0WiURZTifE1/JkKxFdHWyclJli9fzsqVK6msrKSxsVG2466urubtt9+WBb9arfau9L2ZhnIOo6OjfPrppxw7doyRkRESExPJzc11MByUaVpgl1VCERwcHOTDDz9kcnKS9vZ2PD092bVrF9/73vdYunQp8MVR24cFwesN9lSBw4cP8/vf/16merq7u7N69WrS09NlV6ykpCTCw8MxGo309PTg5eUlldfOzk68vb2pra3lnXfe4erVq0xNTZGeni6VV+Xf/LrMUTOaNiAsMlFlJiDYAZTvVX6mo6OD3/3ud/zmN7/BZDIRGhpKcnIy4eHh5ObmsnXrVrRaLW+++Sa1tbXYbDa8vb1njQpG+d3u7u4EBgY6eJFFG9jpLRa/yvfe69/zEeLC0mg0+Pv7S2Wiq6uLtrY2jEbjrCuv072GfX19VFRUAPaKekF5JqpCRe6Zr68vNpuNiooKaawYjUYGBgbkdyk9DM6wfsrEezc3N1auXMno6CgtLS1cuHBBnpnGxkY+/vhjqquruX37NqOjow4h2snJSUZGRpiYmLjrrM0FlGT3er2e6upqh65KZrPZYV2uXbvGmTNnKC4uZmRkBI1GQ09PDz09PQQGBhIZGSk7Uk1XXlUqe0ex6upqxsfH2b59u3xtJvMJvwhhYWE88cQTcnyiPeMnn3zCuXPnqK6ulmtYUlJCVVUVly5doru7W1L5wOwqr+I+EJGr3bt3ywKf9957T3r6x8fHHZRQ8Vn4aga9UJiEZ160KX366acl88BsKT7TL+fMzEw2b97M7du3KSsro7CwkHXr1rFp0yYA6Tk/evQoRqORtLQ0duzYQWZmplTK3dzcmJyc5ObNmxQXF+Pp6cny5ctltfZspvYo5xYaGnoXQ8CDQMxpw4YNDA4OUlxczJtvvsno6CiTk5MYjUYKCwvJzMzk2WefBRwr42cD4lkaDAZu377N2bNnpaGfm5sr62+UhpoovisvL5f6Ctjpprq6uvD09GTlypV897vfvYsRZTbWTnkXGo1Gurq6pOIaGBjIqlWrOHDgAJs3byY4ONiBB1qZuzo1NUVubq7Ue86fP09JSQnFxcUMDAzcdaa/KWZsxYXVp9VqSU5OJiEhweF1ZdtKkS8jqpwrKyul6xnsXSgOHDhAVlYWERERpKamSte7aNGZmJgoqYzE982UUFZStoSGhpKdne1AT3Ovlpv3g/K1uVYIZgJKoS3WCqCmpoZbt26xc+dOWWWrVEhmGspnPTExQV9fH2API//P//k/pVUvPGtWq1XSmPT19dHT0wPY6Yt+/etfU1xcLHuMJyQksH79esmmcK+Q2WxDeEnd3d1ZuXIlTzzxBAMDAzLdxWKxcP36dcrKyhgeHpZULoJLdGRkhMHBQUZHRx06x8wVlOtns9kYHh5mYmJCkt2bzWY6Ojo4evQo586do7m5Wa5BVFQUubm5krYnJCSEsLAwjEYjVqsVg8HA9evXuXjxIl1dXWi1WlkgZTKZ5tTjLLxY0/PCQkNDZUrBsmXLqKiooKSkhJKSEpqbmzEajVRVVfHee+8xOTnJo48+Sk5OjkODitmCOFNqtZoVK1bw9NNPExAQQHNz812NNATu5bX7MnmpfM/ExASLFi0iOjpaKsKzWbk+nc3D3d2dgIAAedE3NzfT3d0tFYGamhru3Lkjc1c3b97MM888c1e0UuQkCoqhrKws6e2by8r8r/N3lfs5ODiYLVu2yKYW165dkykmgr8XZrf1vFKOd3Z2cu7cOS5cuADAE088wcsvvyxlfmdnJw0NDfT09NDZ2UlLSwt1dXUOeo1Wq2XVqlWsWbOGLVu2sHHjRhnhm02jWHk3CY56wcyRlpbGihUryM3NvWeaJzhywYr93NbWxqVLl2Qqy7Jlyxw80g9jXjOqvAqLKDQ0lKioKPz8/BgbG2NoaIgbN24QFRVFSEiIAz9rf38/t2/fdvCarFixgieffJLY2FjAvjFOnz4tN05CQgJZWVkOBQkzDWFZabVaIiIiHBb2Qdr9id8LZW1kZIT+/n6H73c2ZXZ68cSDbMDpyrmyuruhocGh2n02cyiVglLQSBUXF9PV1UVHR4cDv6aAMv9OYHBwkCNHjnDhwgXZ2i8/P5+kpCQHHtu5Vl6VeW/BwcE8/vjjDA0N0dfXJw1BkZ4Djsn7Wq2WjIwMMjIyHCzuuczrVZ6vgYEBxsbGHDymwjvV2NjI5cuX0Wq10tMXGxvLli1bvpBp4OzZs3R2dtLZ2Smpa4A58bKCYwGs2EvKghdxtrKyssjKymLTpk18+umnxMXFce3aNZqbmxkcHOTOnTt0dHTQ2dnJgQMHZBtq8R2zfVlOTU2xevVqUlJSJAn/Rx99REdHBx4eHrIaWaT4GAwGJicnHZoz3A/KtKVNmzbx4osvsnbtWskSMpftR6c3VZiamqK3t5ebN29iNBr59NNPpUGdnp7OU089JZkjRkZGMJlMtLa2cvDgQc6ePYtarWbbtm2sXLnSIeo5l9X4XzUSKpwEokjS09OTzMxMkpOTJZMP2GtBjEaj7FY5W3NUKq89PT1cuXKFrq4uYmJi2L59O8nJyTKVsbKykuLiYpk/LxqLKOVWamoqf/VXf8WTTz4p97dg6pnN+0J5zwUEBPDkk0+yfPly9Ho9KSkpUpHV6/UOtF/KuQijenx8nFu3bvHmm2/y9ttvYzAYWLFiBdu2bSM1NVW+/2GcuxnNeVVuKi8vL2JiYqirq6Ompoaf/exnGI1GDhw44BBeHx0dpbS0lLKyMgIDA3n++ed57bXXpOLa29vLuXPnePvtt6muriYiIoIXXniBvLw8+R3TiX5nGtPzeZSXyv0Kx4TwdHd3Z2xsjPPnz8ue3MBdHVLmOgQtjBFlHtWDHDDluJUFfGAP0U+nB5sNqFQqB8Vl+fLl/Of//J85ceIEdXV1+Pn5ER8fL4sNlJXNgk+xtLQUrVZLYmIi4eHh0nDSaDTEx8c77Om5XjvlGIQATklJYffu3bITVU9Pj8ytA6QHc2pqivDwcL773e9y4MABQkND5XfMpfIqBO7Y2BiVlZV0dnY6vC4Mho0bN5KamoparSYlJYWAgAD8/Py+lCIrJiaGtLQ0SkpK6O3tdfBczzaPrzJnUNkic3r6i1L5DAsLY9u2beTk5FBaWsqpU6c4duyYpGa6evUqERERxMTESOX1XgbbTELIktDQUAICAoiIiCA7O5t9+/Zx/Phxrly5gq+vLxs2bCA/P5+pqSmuX7/OJ598QmNjI25ubvj6+soUkbGxMYfv9/f3Z+3atWzdupUNGzaQnp4un91ccTGLs+Pj40N8fLxDy9qqqioOHjzI2NgYly9fpru7m9jYWJ5//nnWr18vP3/+/HmuXr1KU1MTV65cYWRkhPDwcLZv305aWhpgL7wRzCKzDbEvlZzsX1RALfatuB+E8gr2+762ttahpmR4eJj29nZ5rmdLvirv+NbWVukJ9vLyorOzk4MHD1JSUkJFRQV9fX0O8jQqKoqIiAhJPwX2NIO1a9c60EiJqO5cYGrKzqkcGBiIj48PFotFjs1oNGIymfDw8HBoGiIg0ulOnTrFyZMnuXXrFgaDAa1WS1ZW1l1RHqdWXqf3Vk5NTWXTpk0MDQ3R3d1NSUkJn3zyCenp6WRmZkqus/b2diorK6UnYf369SQlJcl+uGVlZZw5c4aSkhKio6M5cOCADKdM7wM9k1Ball5eXg6KnPJiv1+YUVn9XFdXx6FDh2S1YWRkJMuXL3dI9p7r6vX79coWvG33ep94RiJ9xGKxyLw2Ly8vkpOT///s/Xd41FeeoIu/kqqkKpVyzjkhiSSLIHIGA24HsN3ttrvt7TDz1Pb0nZ3Z3+ydG3fufea5M3Pv3d0701M7PZ5u2912OxvcxoBtjMhIIEAEISGEJCSUcyhVlSr9/ijO8bcKgbENqipR7/PwCFXSOXXS53yiW0aF2dxoxSbhcDiIj4/niSeeICsri/b2dumaEhUVJYVXIQy1t7fzj//4jzQ2NqLValm5cqX0BRLfRXp6+h3Rvr6C8jsuKyvjxRdfpLKykqamJs6cOcPly5cZHh52G9PMzEw2btwoUw1NTU15rU/CrCrGr7e3l9bWVjdLDbjWXW9vL9nZ2W4XW8Hk5CTT09MEBQWh0Wiklg9cGob29naMRqOb9i4jI4MtW7a45ZV+2JWrxME+U6T2TJpDp9Mpc2RHREQQERFBcnIyg4OD0lIFLp/zs2fPsm3bNukjOZtuO3BnvfeYmBhiY2PJysoiMzOTwsJCIiIiWLt2rXQJKy0tJSsri+bmZoKDg0lOTpbZCU6cOOE2b9PT09m2bRs/+tGP3A7hsLAwr2okBYWFhWzbto0bN27IvModHR1YrVYp4IgYgSNHjhAaGkpbWxuHDh3i4MGDjI+PA8gSuAsWLJCf7c0Ls9D6fpszyzOd3Uwa8tHRUfr6+sjPz/9WgT7fFqGg6Ozs5PLly3LPuXHjBq+99hpGo1FeoESKyKSkJHJzc1myZAmhoaF8/PHH3Lx5E7VaTXp6ury8iMwT3izWo5wznuMQFhbmduF3Op0YjUYmJycZGxujpaWFL774QmZ7Adf5snLlSjZv3kxmZuYDn5MPVXiFrw6byspKxsfHaWxslGbKxsZG3nnnHSorKykrKyM4OJjTp0/T0dEBuL6glpYWPvroI44fP05NTQ0dHR0YjUYSExN5+eWX+eEPf0hhYaGcxLN1a1FWQElPT3dzGxBVMkRlrJkWsVK4vXTpEidOnGBwcBCdTsfatWvZsGGD263cW8KCUpsjhDOlm4enhl0pvCr9doxGIy0tLXJsc3JyyM7OdlsQs60l8DR9l5eXU1xcPGO1HqHxSk9Pl2aU8PBwysrK2LhxI+np6VitVlkGT/l+X6m4Be5tCQ8Pl5G9Q0NDHD58mHfffZfDhw/LTTguLo7CwkK3bAvevEh5boAieMCzNLTZbKampoZDhw7J4A74aj/SaDRurgDKC5fQIBw5ckS68mg0GjZv3swvf/lLysvL5aXMW+vyXq5InpYntVpNbGys23dkMpkYGRlxc9vxppuS55wqKioiIyPDraQsuNIwpaenMzExgcPhIDk5WboS1NbWSteRsLAwCgsLKSsrc+v3bJYQnwnx951OVxnxH/7whxiNRv7+7/8eo9FIR0eHnJdBQUG0trbyL//yL7z66quAa0+dnJyUSoCSkhK+973v8fTTT7tlOvGF4hvT09PST16r1ZKcnPyNv/+cnByWLFlCU1OTFOgBWc1yNoQ9ZXW40dFRqqurOXXqlAxqcjgcUqaJiYmhuLiYiooKli5dSklJCSkpKSQnJ9PS0sKxY8eAr/y+xb4jlDu+zrVr12TgVU9PDw0NDVy9elVWzxTnRm5uLs888wzf//73KSoqui8f9W/KQ995RYM1Gg3Lly/nJz/5CWq1muPHj8uo7YsXL5KWloZOp5P1gcG1eL/88kvsdjuXL1+WN83CwkJ2797NCy+8ILUGStODN1BuHK2trRw/fpykpCRZkMFsNt/RxtHRUS5evEhdXZ1MFWM0GomNjaW4uJjw8HB52HpDYFBOtv7+fo4cOUJTUxMZGRlUVlZSUFDgJtR4ojzYOzs7+fTTT6mtrSU4OJicnBySkpJ8QshTVtpSpuRRJom2WCxERka6PSbMf8IcMlMSfF9HHHLJycmsXr2atrY26urq5CZUXFzM/Pnz3cbJm330dH/Izc2lvLycxMRE+vr6UKlUhIeHMz4+ztmzZ3nttdcYHh6moKAAnU5Hbm4uKSkpbhcUk8kkfdM6Ojqora3liy++kIdlQkICy5cv54knnqC8vBz4yo3mYc1ZZYoco9FIU1MTRqNRpqoRPr2i/KR4rTgkHA4HJpNJmuY9c32K+e1LfvVindntdnQ6nZvrzdTUFEFBQWi12juy14ik6Z4pxXJzcykpKZFVkERddV/AbDZLgW7Tpk1UV1dTXV0thW8xjjNlXgDXRbuiooIVK1awdu1a6S7wsIOV74XSL3tsbIxTp05x8OBBuru7qaqq4vnnnyc9PR2z2YzD4ZC5fZWaVVFpTRl0ZjKZ3PzZRdEYz/y/DwtlzILRaOTy5cs0NjYSGhpKQUEB+fn5REdHy8JC+fn5ZGdnk5OTI11yRDtFm7VaLWq1Wn5nDzuX6/1gtVrlfJuenpbKwPHxcTo6Orhy5Qrt7e2YTCbpoy1yuIKrEMOGDRuorKykuLiYJUuW3JGW70Hy0IVXMSltNhtxcXG89NJLREREMDk5KcsZ9vX1zdgxkcpGkJmZycqVK3n88cdZvXq19IP1Bp4+PCUlJWzevJkTJ07Q3d3N/v37SU1NZdOmTcCdvnJTU1M0Njayb98+Tp48KV0LoqOjSUpKcssT620cDgdtbW28/fbb7N27l6ysLHbt2sXatWtlsmIh+Ck1r2IjFWP92WefMTExQWhoKDk5OaSlpbkJQt4SXmcycd3N7KVso8j1OjU15Zb83F8EWGX1NrVaTUJCAnl5eW5am7CwMLRarZsgocyx6i2UwRMLFy5k/vz5NDc3Mz09LbWJJpOJmpoaJiYmSEpKIisrizVr1lBaWirN7na7nXPnznH48GEuXLjAzZs35UVSp9ORlpbG8uXL2blz5x3BMA/zO1Dmxh4aGuKtt96iqamJ1atX89RTTzFv3jypQb5bij0xZv39/fT19d1RCvhhFnL5NggXDoHS6uN5SRbuWELz5llMRKVSER8fL8uQewbRehvlHpGdnS1LaJ89e1ZG2IvxERcPp9OJTqejtLSUDRs2sGnTJsrKytysft7soxgri8VCU1MTe/bskRrjgYEBtmzZQnp6OhqN5q4VIz0LvNTV1XHo0CFZvRJc3114eLj8Dh92oKHys0UA+rx589BoNKxfv166teh0Ojc/Y6VMMzQ0xPnz52VRkbKyMgoLC9322tleiyJXsvA1Pnv2LDU1NQwODmKxWKQioK+vj9OnT7tlShCo1Wri4uLIy8ujqqqKnTt3smLFCrn3KMfmQc/NWbN5KTeW9evXYzabycjI4MqVK4AratuzOoqS4uJivv/977Nr1y456J7pR2YT5QJzOp2sWbNG+nEeP36cxsZGPvroI6anp1m9evUdaSaGh4c5d+4cJ06ckJGUy5cv59lnn2XLli1uie+9iZjYIl0SuJzVP/roI06dOkVmZiYpKSmEhoaSkJBAUVERWVlZaDQajEYjw8PDtLS0cPz4cWlaCQsLIzs7m/z8fLfqR94+SIU2zTPAQOnf7BnhLcraieeEq4QvHZT3wul0YjabpeAQHx/vtlZv3LjBxYsX2bFjh3xMBIMoTeezPU+Vfy8xMZHNmzfT0dHByZMn5QYr0nw1NzfT0tIifebj4uIwGo0YjUbMZjMDAwN0dXW5bczLli1j7dq1LF68WBZGiYqKkoL7wxb8lH7yU1NTnD17lhMnTtDY2Ai41mVJScl9taGhoYFDhw7J9QeucZ/NMrffFJHZwzNqXuz1QkMrhFdPVwlvZhK4H5QZIxITE1m/fj2NjY2cPXsWcO2RZWVlVFVVsWjRIjQaDSaTCZVKRVpaGjk5OWRmZkoBbjbz9c6E8u8PDg5SX19Pc3OzfH5wcFBmT4A7rTdKM7oYtzNnzrBv3z7OnDnD5OQkoaGh6HQ6SkpKpPA4G7l6lZ8dFRXF9u3bqaioQKvVkpmZKQtLeKJMxN/a2sqBAweoq6sjJiaGrVu3UlVV5XZZm+35KgLERCrAd999l3/6p3+Se4PdbiczM5OEhARGR0fRaDRul8ikpCQWL17MypUrKSsrIz09naysLDdXDhFo+jAUOrO2c4nG22w2YmJi2LVrF8nJydTV1cnKGufOnWN8fJzIyEhGRkYYHBwkMjKSzMxM5s+fz1NPPSXNdiIowdtCj9BaRUdHs2nTJoKCgujr62P//v2yUszQ0BCVlZVotVrp6FxbW8vRo0fdEquvWLGCX/7yl9Ik6I3620qEcBISEkJqaqpbrt6Ojg5u3rxJbW2t1ConJiayYMECGYg1Pj5Ob2+vrKYlDuSMjAzy8/OlVkQIfN7mbkFpSlcPpWCqUqlQq9XSFBkcHOyVaPTvgsi8IDboyMhIedEKDg6ms7OT2tparl27JqujREREeH28lJthaGgoa9askXler169KivZKIME+/r67qgcJgS42NhYEhMTycjIkAEWq1evpqCgQL5+Ni/Jnn9HmBvb29v5+OOPMRqNMpg1KipKBo0qTe/CZ/LDDz/k2LFj0nQuqlGNjo5Kvz3RP19BmUbQ83FwzUHR3omJCYaGhtw0XSInr8CX+iZQplsUqSQFKSkprFixgqeffpqqqqq7CmfKy5Q3UVpjJiYmZN5aQXBwMNXV1TidTnJzc+Xl1+FwEB0djU6nk2Zrq9VKQ0MDH330ESdOnHBLM5WTk8OCBQtkvtvZcH1RfrdarVaWihaIIEmBWIeeF1Bx8dRoNJSWlsrz1NtBhODqw8TEBE6nk8TERBISEjCZTGRnZzN//nyio6PdNMohISHEx8ezcOFCli5dKoV0p9MpY2PUavXDvVQ8tE+eAeXmHxoayooVK1iwYIGsTT05OSlv2zabTQqGYWFh6HQ6N79SX9EYeB7ihYWFFBQUyEPh8OHDjI6Okpqa6parsLW1la6uLmnKi4iIkL548FW+N28iNpfg4GA5iXNycmhvb3dzwBYLt7OzU1ZGEdoRq9UqzevgigBet26dm1Dg7X5+E5RaWM+fnv/3F5RtDg8PJysri2vXrkm/yK6uLg4fPkxqaioFBQVSozlTYNtsIzbJnJwcnnnmGQoLCzl58iRHjx7lzJkzbqby0NBQOQ+Li4vZunUr+fn5mEwm7HY7+fn5lJaWEhcX5ybEewPPA7O0tJSrV68yPj5OfX09bW1tHD58mKKiIje/OxEBLMrD1tXVceXKFRl4piyrOpuR2g8SsScFBQXR39/PgQMHqKmpccv9OlM5Ul9GFMEQaLVaQkJC6O/vp6WlhZycHDctnUCZys+bePobC2FUcOvWLX7729/yxRdfsGDBArKzs5menmZ6epolS5awatUqBgcHOXXqlEzmX1dX55b7PCIigpKSEvLy8uRj3kwtpURUjoOvxuRu6TOFttjbKP3g1Wo1u3fvpqKiguzsbOLj42Wch0gF6Wn9EDEfngHcYkwe9ryc1ZNHmIDErcTT8V5o4u6FCFDw9qEpEJu/OBRFZZDBwUEaGxsZGRmhtrYWcPU/KioKo9HoFlmoUqkoLCy8o3KRL2xIQjhXqVRs2LCBzs5OWUd7JoQ5VvkZoh86nY7169fz/PPPu6Ub8pWxvBeeG6RwEZienr7vamq+inKeRUVFkZubi06nk7kVQ0NDpemuqKhICklGo5GsrCxWrVol/c9n23yp1IinpaWRkpJCbm4uRUVFLF26lFu3bhEUFERycjIRERFSO1tWVsaqVavIyMhgYmICo9FIQkLCHQKCcKmY7SAYpdYxNjaWrVu30tHRwaFDh7BYLPT39zMwMMCVK1fIysoiLS2N8PBwpqamMJvNmM1mOjo6ZMyA0DCL4J9FixbxwgsvuAVU+Eow09eh1PL19fVx/vx5twIb4ApAFFlBwPt76d0Q7dJoNCQlJREdHc3Y2BiXL1/GYrHQ2dlJd3c369evl8HJosKUtwKzZkKpxImPj2flypV0dXXx6aef0tnZycjICCMjI3R1ddHW1kZmZqbcQ9vb22lpaaG/v5+TJ09y48YNBgYG5F4aExNDRkYGS5YsYdu2bRQWFkqfd28Ir0IhA8gL/NfFTCizZoggbm+jbF9ISAgbN25k27Zt33hOCf9zkbd+tqxyXvkGZ8pbeL/4QgqQmRAbf2hoKBs2bKCiokJqrH7zm9/Q1NQk/QuVgqtOpyM/P5/Vq1fLiFHwXiUfJcqUVyqVioULF/LKK68wMDDABx98IA/2u20eyhuoqN/81FNPyfrdSp82f8TTH89fUW5WUVFRlJeXU1RURE1NDeAKNqitreXkyZMEBwcTHh4uU9WsWrVKVq0C7wdzBQcHU1hYSH5+Ps8///yMCbXF72LexcbGull1lMyk7ZoNRNscDgdRUVGsWrWKhoYGzpw5w/j4+B1FGoRJ8m5WAGW+6czMTF555RV++ctfyudmu6rPg0bZ9oSEBJYtW3ZHRR9fXKdijOLi4liyZAlbtmzhiy++YHR0lM7OTtLT02VZbTEnfNE1SakBFsUhRAT+73//e1nyNzQ0VF68RH9EcJcQZsWaDQoKIi0tjYqKCtatWyeDLYXfpbcyDN1PVhlhtRQIRQe4LlZJSUlur/UFvq1s5a1Lr1d3KzFZlYngxYRUqp2FkPCwE4I/KMLCwkhOTiY5OZmEhAR0Oh2ffPIJFy5cYGRkhKioKNLT0ykuLqasrIySkhKKiorIz8+Xn+FrAp3QqJWVlaHX66moqKC7u5vR0VF6enpob29naGhIbqwiv59g6dKlPPvss1RVVcnHfG0DvhfKeScKaKSmppKZmem1QgsPCuVci4qKYt26ddhsNiIiIjh06NAdJk2lf5dWq3UrpuGtjVh5WAitx/2sIWUuYrvdLvvpCy4RSpKTk3nqqaeYnp7mjTfekInARbCFMgsCuPtfm0wmrFYrDoeDlJQUXnzxRZ5++mn52b6WdeCbEBIScocFLysri02bNsn4CPE6XxESBJ5uGxUVFfziF79g27ZtDA4OEhISQnp6OiUlJbKID/je2TATYWFhLFq0SKbEOnv2rCxQ1N7efodwJxDnpsPhQKvVsmrVKp544gnpzqMUGv1pzirdBmJjY92yt/gKIkhSuIspLwbKn55ymbfOPK/uzmKDnWsIIVylUpGVlcXzzz9PRkYGBw4coKWlhejoaEpKSliyZAnLly93u4X5IuIQF6aBqqoqqqqqGBoakoLr5cuX6ezslK+9desWjY2NjI6OUlJSwq5du9i2bRupqaluJWb9BbHRqlQqpqamMJlMMruCTqfzeq3074LYmETUblFREXFxccTGxhIWFsbp06cBlznQZrNhMpnQarUsXLiQp59+2icuXZ4afM88pp4HndC8KgVUXxw/EQ8QHBzM4sWLSUpKIigoiNdee43e3l6pMbXZbPLwEYhLhogSjoyM5Omnn+bll18mMzNzxqIj/obVasVoNLoJpiEhIeTn55OUlCS/E19VfCgP/vj4eNasWcOaNWu82KJvj2d2luDgYObPn09CQgJVVVUykf3169fp6urCaDQSHByM1WqVOVMXLlwoC09oNBo2bNjAkiVLANdYK1Pk+Tqe2S/E78PDwzPm7vU2wj3KX1I9+scs8EOEJjkkJET6wRYXF8s8p5GRkcTFxbklMfZ1PJ2w4+PjiY2NJTs7m4ULF2IymeRzU1NTjI+PY7VaiYyMJCMjQx68yqARf0EIr8pDMDo6WpZMFQF2/tYvJcq2JyQksHXrVkpKShgaGpI5ReErN5LY2FiSk5MfeM3q74oQWpXaHWUCf4EyuED87osoBZz09HT+6q/+iqVLl/L73/+e+vp6WeHHM4+rYP78+WzatIlly5axcOFCWWpVCBj+hnIMbTYbFovF7bGJiQk3S0EA7yAsVDk5OTITzfj4OEajEZPJJK2ugCxnHB0dLdMnBgUFucWBzFYg0IPA051Bp9NJ7fnY2JibVTLAtyMgvD4ElBoccZBGRES4+bQKRJ5QoQny5UUpbrwiSEmYVr9pZLYvCDjfFOVtf+nSpbz00kusXLnSLa2UvyPmnjg44uLi3IJe7oUvHSjfNHWQr7T76zCZTISGhhITE8NTTz1FeHg4p0+fxul00tPTI6sTajQaKZimpqaydOlSmVlBILRd/ointjI/P18GxarVapYuXeqX1hChJVb6fQrNumeFNF9GKbiJS4VOp5NFP74p/uqTrWxvWloaTz75JElJSSQlJZGSkiKf85f56Wv412zwQ77OoVxo67zheP5tEYvSX9r7IFAe9Nu3b2fdunWEhYXJx/3F1HI/fBsN8qM0F7yFZy7ItWvXsmzZMgCZWlAIrUofWM/k4uAfGT7uhmi70+kkOzubjRs3cunSJYxGI1u2bOGHP/wheXl5flcwRIyXZ9olfzobPHkQ7fYnwV2Jp/D64osvsnv3boKDg93iBObS2TGb+O8O5ico/QlF0IRyk/JHU7PnYSAKKijdAcRjwrf1YScsng3EgRIeHu4mDPiS1vFBoNTAKqPUPc3uc2Vc/QWx7kwmE+AKlvsmEcLCrWemkrL+hnAJCQkJoby8nJdeeon169fz2GOPsWjRIoKDg938f/0FfxG0vykit6k4J2YqLjBT0SGRDsufEWdgRESETwZq+SuBU2eWCA4O9tk0X98VX6jwMht4BiT4u1bk6wgJCXHLpBDAN1COyf0UyBBzdC6NpTJAT6vV8vjjj9/xmsClyncQZ8SjOCZ3OzcCfDcevZkUIMB3xG63S5NkwOQTwFsIf3lwr9qjPBiFkOevptevY6aiGHPNEhLA/1GmofJG0ZO5yDcWXvV6/cNoh88w1/sHc7+Pc71/EOjjXGCu9w/mfh/nev8g0Me5wFzsX0D0DxAgQIAAAQIECOA3BPla1ZEAAQIECBAgQIAAAe5GQPMaIECAAAECBAgQwG8ICK8BAgQIECBAgAAB/IaA8BogQIAAAQIECBDAbwgIrwECBAgQIECAAAH8Bq/nedXr9auBPwdWAHHAMHAZ+G8Gg2G/F5v2QNDr9RnA/wFsA+KBHmAv8DcGg2HEi017IOj1+r8HKoEiIAEwATdx9fFXBoNhyHutezjo9fqXgN/d/vVnBoPh37zZngfNXO3fXN5r9Hp9O5B9l6f7DAZDyl2e81sC89T/CMxT/5+nvjKGXhVe9Xr9/wL8n8AgsA+XYJcALAbWAf6+UPOBU0AS8DHQBCwF/gdgm16vXzkHhLv/AJwHvgD6AR2wHPjPwM/1ev1yg8HQ6b3mPVj0en0m8E/AJDDnav3N1f7N9b3mNmPAf5vh8clZbsdDJzBP/ZrAPPV/vD6GXhNe9Xr9s7gW6SHgGYPBMOHx/FwoXWTAJbj+0mAw/JN4UK/X/xdcQt/fAn/qpbY9KKIMBoPZ80G9Xv+3wP8E/DUwJzIk6/X6IOA1YAj4CPiP3m3Rg2Wu9u8R2WsARg0Gw3/2diMeNoF56vcE5qn/4/Ux9IrPq16vDwb+HpgCXvBcpAAGg8E66w17gOj1+jxgC9AO/LPH0/87YARe0uv1ullu2gNlJsH1Nu/d/lk4W22ZBX4JbABewTV+c405179HYa95BAnM0wD+wJybp76EtzSvK4Bc4ANgRK/X7wDKATNwxmAwnPZSux4kG27//NxgMDiUTxgMhgm9Xn8Sl3C7HPhyths3Czxx++clr7biAaHX6+cBfwf8fwaD4Zher9/wde/xJ+Zw/x6FvUYQptfrXwSycB2Wl4BjBoPB7t1mPTgC83ROEJin/o/Xx9BbwuuS2z/7cPlLzlc+qdfrjwG7DQbDwGw37AFSfPtn812ev45LeC1iDgiver3+P+Ly6YnGFcC1CteE/jtvtutBoNfrVcDvgQ5crhBzijnev0dhrxGk4BpHJW16vf4Vg8Fw1BsNepAE5mlgnvoDc3yeCrw+ht5KlZV0++efAlpgExCJ66b5GbAGeN87TXtgRN/+OXaX58XjMQ+/KbPCf8TlDvHnuATXg8CWObLZ/m+4AiZeNhgMJm835iEwl/v3KOw14PKt24jrUNHhEn5+DeQAB/R6/ULvNe2BEZin/k9gnvo/PjGG3tK8htz+GYTrNnnx9u8Ner3+aVzayrV6vb5qjplLlATd/un0aiseECI9hl6vT8ZlAvs74IJer99pMBjOe7Vx3wG9Xr8U1+35/52Lc3Gu949HZK8xGAx/4/HQFeBP9Xr9JPCXuLJ/PD3b7XpQBOZpYJ76A4/APPWZMfSW5lXkN21VLFIAbt9UPrv969JZbdWDRWhWo+/yfJTH6+YEBoOhz2Aw7MHlEhHPV/nt/A6F+acZ+F+93JwHzlzv320ehb3mXvzL7Z9rvNqK70BgngbmqT/wiMzTezGrY+gtzeu12z9H7/K8WMjah9+Uh4boY9FdnhdR+HfzifVrDAbDTb1efxVYpNfrEwwGw6C32/QtiOCr8TPr9TNm/HpVr9e/issx/89nq2EPiLneP3g09pp70X/7pz9nNQnM08A89QcehXl6L2Z1DL0lvB4DbEChXq8PNRgM0x7Pl9/+2T6rrXqwVN/+uUWv1wcrMw7o9fpIYCWualQ13mjcLJF2+6e/RpFagN/c5bkKXH5NJ3AdPP5oIprr/YNHY6+5F1W3f7Z6tRXfjcA8DcxTf+BRmKf3YlbH0CvCq8FgGNTr9e8CP8Tl3Py/iOf0ev1mYCsuc/pBb7TvQWAwGG7o9frPcZnP/z2uKhuCv8F1O/m1wWDw2/xver2+BFey4l6Px4NxJdtOAk75axnc2+a6n870nF6v/8+4NqM3/LXc31zvHzwae41ery8DegwGw7DH49nAr27/+uasN+wBEZingXnqDzwK89SXxtCb5WH/AlgG/M96vX4NcAZXvdyncWnqfmYwGEa917wHgh5Xedh/1Ov1G4FGXH1ej8td4H/2YtseBNuA//t2GpcbuCqJJANrgTygF/iZ95oXIAAw9/eaZ4H/Ua/XVwNtwASQD+wANLhKiv4/3mtegPskME8D89TX8Zkx9FbAFgaDoR/XQv2vQCZfVaP4FFhtMBj8Pi2IwWC4gSvn6eu4+vqXuAb6H4Eqg8Ew5L3WPRAOAf+KKzDrGeD/B+wChnFpl8sMBsNV7zUvQIBHYq+pBvbgSnL/Ai4haC0uE+WPgZ0zmKED+BiBeRqYp36Az4xhkNM5JzI1BQgQIECAAAECBHgE8JrmNUCAAAECBAgQIECAb0pAeA0QIECAAAECBAjgNwSE1wABAgQIECBAgAB+Q0B4DRAgQIAAAQIECOA3BITXAAECBAgQIECAAH7Dfed51ev1fp+WwGAwBN3tubneP5j7fZwL/YO538fAPJ3b/YO538e50D+Y+30MzNO527+A5jVAgAABAgQIECCA3/CNK2wZDIaH0Y6Hil6vv+/XzvX+wdzvoz/2D+Z+HwPz1J253j+Y+330x/7B3O9jYJ66Mxf7F9C8BggQIECAAAECBPAbvrHm9WFgt9ux2WwAhISEEBISQlDQPV1VAgQI8IBxOBxMT09js9kIDg5GpVLhdDqx2WzMVIkvKCgIlUqFSqUiJCTECy0OAMgxstlsOBwOOVZBQUFyHFUqVWBP9SOcTicWiwWn0xkYvzmC3W7HbrcTHBw8J2Qc5T4jfleeH6GhoQ/1XPAJ4VUIrI8iDocDgODgua8Ef5T66st4CqLi9+DgYDQazR2vDwsLm5V2PSicTqfbxir+fzcBXPzzV4KCglCr1ajV6nu+zvOwCeC7BAUFzbgW5wpOp1OeB0rE3PT3NanE6XQSFBQ05+Qc5fiIPoaFhbmdF+Lxh4FXhdeH2TF/QWi11Gr1nBbqxK1MHLRzua/+gDg4nE4ndrudoKAgQkNDv/Xn+dJadjgc2O124CvhVWglRRvF/9Vq9Xfqtz8h+uwr4xRgZhwOx5zfH+12O9PT024XSmEpCAkJQaXyCb3ad0bsrzP1x5f2zO/K3foID6+fXpkhojOiQ0NDQwwMDGCz2YiNjSUhIcHvtD3w1UQFvnbxiQ3qUTg4bTYbKpXKrzQJNpsNu92O0+kkODiY4ODgO7QC/rTxOBwOOefEASEQc3ViYoLW1lb6+/vR6XQkJCQwPT3N8PAwVquVkJAQqdV0OByo1Wri4+NJT08nNjbWW12TwqrT6ZTaDU8Nx9etMyHA+6PQYDQaGRgYYHBwELPZLMcaQKfTkZycTHJy8qxpRB4WYn8VY61EuLD4s2ZLuT4nJia4fv06FouFtLQ0UlJS/PJM9ESYzYUrxNe9FvDbMRXjqVKpcDgcDA0NYTQa0Wq1xMXFSUuJv6xF4d4pXB6mp6ex2+1oNBpUKhUWi4UbN27Q29tLdHQ0OTk5xMfHAw/H6uoV4VVoAIKDgzEajRw5coT9+/djMplYs2YN27dvJysry+/MXN/k8POXPj0I/FEgCA4OloKaUnj1vHj5C2Ju3m0sxsfHOXbsGG+88QbHjh0jKyuLFStWMDo6ypkzZxgZGZGbsFiX0dHRVFZWsnPnTlavXk16err8vNnckEXflJrVb/MZ/jKmSs3c1NQUFy5c4IsvvuDEiRN0dnZiMpmw2WxoNBry8vJYt24dW7duZfHixX53YCq516VRebn0V6anp+UFv7+/n3/7t3+jt7eXp556im3btpGUlAT4twb9m7TbH88NJUJQB+jt7eXgwYM0NzeTm5vL9u3byczMBFyKkq9z+fEVxCXRbrdz8+ZNKaimpqbS3t7Oq6++yhdffEFhYSE///nPeeKJJwgPDycoKMjt+3gQeEV4FRPY4XBw7do1Pv/8c/7whz9gNpsJCQnhscceIyMjw01Y8BeUgzOT+cdut8uby/j4ODU1NUxPT/PYY4+RmpoK+P7BIoLrlALeTA7oVqtVugjcunWLEydOEBERQVVVFfHx8T7dz7sJesr2WiwWtxulOECFf5MvoLwAijXX09PD0NAQVqsVq9VKR0cHly5d4vLly5w+fZqBgQH6+/sZGxvDZDJx69atGT+7t7eXmzdvcuPGDUZHR/nBD35AdHS0FHBn6zvw9Cfr7Ozk5s2b2O12tFot09PT3Lhxg56eHiIiIlCpVAwMDKDRaFi8eDEVFRXExcXNSlsfNE1NTXz66afs37+fS5cu3fH8rVu36O7uprOzkyeeeIKqqiri4uL8SjAQVpCwsLCv9RsUWmd/NDsrtclarZbQ0NA5ExCp1CoDnDp1ivr6eiwWixTcwsPDiY6OJikpiYKCAnkeKt/vT4g52N/fz6FDh/jd737HmTNnWL58OSUlJVJ4tdvtPie8Kt3JgDssWk6nk8uXL3P48GEsFgvx8fEMDw9TXV1NZ2cnnZ2dJCYmkpaWRlVV1UOZw15Z4WISDg0N0dTURGNjI2azGYDBwUEmJiaw2Wx+Z1JXDnhISMiMi03cPoKCgmhtbeVXv/oVZrOZv/7rvyY1NVWaZH15s7rfg0EIrwA1NTX8zd/8DQUFBWRkZBAfHy83JF8UYO12O1arFeAOM7vQ9PmLGU9cEhwOB5cvX+bkyZPcuHEDh8OB0WiktrZWCj5KLeb169flZ6hUKjmfhQuBVqtlYmKCmpoaEhMT2bZt26wLr8q+jY6OcuXKFY4cOcLFixdxOp0kJyfjcDg4efIkDQ0NxMTEEBUVRWdnJyqVitWrV7N79242bNhATk6OT4+pUgCw2Ww0NDTwySefcPDgQa5du4ZKpXLTrIJLm9fU1ERXVxf9/f3Y7Xa2bdsmNXzCpceXUZqYlVkVlPtGcHCwvCj7m5AjUK6X6elp0tLSyMjIoKKiQl72wT81kso9ob29nd///ve8/vrr8twH0Gg05OTkMG/ePLZv386OHTvcBFh/QaypoKAgxsbG+Pzzz/nwww85fvw4DoeD5uZmRkZGvN3MeyI0rAKHw4HJZGJ6eprp6Wmam5s5ceIEBw4c4ObNm/J1yrl55MgRkpKS0Gq1VFZWyn3a83XfllndtTzdAEwmE6Ojo5hMJvkaMclnigz2RZTaw9bWVs6cOUN4eDjr1q0jOjrazfQMSA2lw+HgypUr1NXVoVarGRoa8mY37gvP26/RaCQsLExOcs/n1Wo1drud3t5ejh07RlNTEzExMXLD8jWhVYzl2NgYhw8fZt++fZhMJhYsWMC8efPQarXY7Xaio6MpKioiISHhrp/lcDikkOct4UDp4jAwMEBtbS379++npqaGsbExQkNDMZvNtLe3u71nprVXXFxMQUEBdrudpqYmbt26dcdcED5qs8n09DSdnZ2cPXuWo0ePcvbsWbq6upieniYyMpK0tDTi4+OJjIwkMzOToKAgUlNTycvLo6enh5MnT3L16lXq6up4/vnnWb58OVFRUT5pFRDmxaCgICYnJ9m7dy9vvvkmXV1dOJ1OsrKy2Lx5M/n5+bS1tXH8+HEaGhoAlz/z0aNHyc3NZcWKFWg0mrtGfPsKnufFyMgIBw8e5PDhw/T39xMWFib98AoKCti2bRurV6/2K8FciRBebTYbp0+fZv/+/RQVFREREQFw3/EUvohKpWJqaoqOjg4+//xzTp065Sa4ApjNZpqamqQ5ure3lyeffJL58+dLKx/43rnhiTJ4aXx8nJMnT1JTUyPXWkJCAuHh4fL1vtYf5d7ndDqZmprCaDRy6tQpPvnkE7q6uggKCqK7u5u+vj6394pYCIC+vj727NlDfHw8aWlppKWlPdB2zvoqUH4x09PTdxx699Ja+iKiP06nk+vXr/PWW2+h1WpJSUlh2bJld7xWbFA9PT3cuHEDo9FIQkKCDDrx5fxvynG7fPkyV69eJS0tjeXLl6PT6eTzVqtVaoGsViufffYZn3/+OQCFhYXSidtXNiPl926xWDh9+jSvvvoqBw4cAODEiRMsXryY8PBwbDYb8fHxlJeXU1RURHR0NMHBwURERBAXFyed13U6nde1eFarVVovLl68yK9+9Ss+++yzO14XGhpKamoqMTExhIWFERwcjFarBVwCQ1xcHOvWrSM3N5dr167R0dGB2Wx2O3ySkpJkf5XpqR40nkLl4OAgNTU17Nmzh7Nnz2K1WsnKyiIjI4PY2FhiYmJISUmRmv7JyUkSEhLIzs6mra2N3/72txw9epTf/va3OBwOYmJiqKiouOuFzJsozYsWi4WLFy/S0tJCbGwsixYtYsmSJezcuZOioiKpNR8ZGWFoaAiz2czo6Cj19fVcvXqVpKQkn3JvmYmgoCBsNhtWq5Vbt27x5Zdf8umnn3Lt2jVCQkKIjIzEZrPR29vLqVOnaGlpwW63s2XLFp8Zs/tBzGnR5tHRUS5fvszRo0fp6Ojg2WefJSMjw02A8xdEpg+1Wo3D4aCmpoa3336b5uZmNBoN0dHRcq+02WxMTEwwMjLC6dOn6enpISkpifnz5wOu/UwEQPkyyguhxWKhu7ubgYEBVCoVWq2WtLQ0IiMjvdjCeyP218nJSc6dO0dbWxsWi4WjR4/y9ttv3/F6rVZLTEwMMTExREdHExERweTkJI2Njdy6dYt9+/aRm5vLjh073AK4vusa9YrwKpiYmGBgYMBN8wr+ETzhGbhjNBrp7OzkypUrAFy6dImysjIiIiKkplXpizUwMMDNmzeZmpoiNDTUpzcmpXBns9k4ceIEr7/+OrW1tSxatAibzcaqVavQ6XSAa8EqIxJPnDjBtWvXiIyMpLy8XN7AfEUwEEmVg4KCmJqaorq6mlOnTsnnOzs7GR0dlWOkUqk4ePAgWq2WkJAQ1Go1JSUlVFRUkJSURHx8PJWVlaSkpHitT8rMFwBtbW1UV1fL34VrQGhoKBUVFWzatEmOTWxsLDqdDofDwcTEBDqdjvT0dLq6umhra5PuFILExERycnJmRZvgmb91ZGSEnp4egoOD2bRpEytXrmTRokXExsZKV4fQ0FCpjRNzTqvVsmTJEtLS0tBqtRw8eJADBw5QWlpKaWkpERER8uD1Jfcl5b4jLguJiYns3r2b3bt3y6CehIQEKZh++eWXNDc3A65L85kzZ8jIyKCgoEBmkADvXyI9cTqdDA0NcfbsWT777DPq6uoIDw/npz/9KStWrJAX5itXrvBf/+t/5ZNPPiE8PJz4+HifvYDcD06nk/DwcDQaDaOjo1y8eJEFCxaQnJwM+GfAltPpZHBwkIaGBs6cOYPD4SAhIYFly5ZRWVlJVlYW4+PjtLa2Stef9vZ2mpqa3FyD/Gkc4StlnCj4YjabMRqNd+yhvoDy+x0eHubIkSO8+eabXLhwAa1We4emHFz9KysrY8OGDaxdu5acnByGhoY4cOAAo6OjjI2N0dzcTHV1NQsXLpTC64PYV2fdbUDpzzk2NsatW7cYGxuTrxGDOz097dNVRTzdAbq7u2lsbKSvr4/IyEgmJiaYmpqSJh9l+hrx+paWFjdT4L1ypXkTpZ+uMKl/8MEHmEwmgoODaWxsZP78+VJ4VWqPL1y4QH19PQAVFRUsWrRIvs4Xx1ZslDExMYSGhuJ0OhkfH2diYsLtdcPDw26/C9/tuLg4YmJiOH/+PMuXLyc/P5/k5GQ5D2YLz+9Wq9VSWlpKTEwM69evl4dJZGQkxcXFLFy4kPz8/LtqBKxWK3V1dTKgS5CRkcGTTz7J448/Lsf1YV7EPPsVFRVFeXk5ycnJ5OXlsXLlyvuaV1arFY1Gw6ZNmxgZGeHmzZs0NjZSV1eH2Wx2W7e+hOibUjs+Pj5OQkKCFFynpqYIDw+nqqpKCuB9fX2MjY1hNpvp7e1123N9SXgVF2VwXYKF4NrW1kZhYSFr165l165dxMTEyPcsXLiQy5cvc+PGDVpaWrh27Rrl5eVotVp5ifNloUdpog0KCpKplOLj47HZbNLXUOCLLi0zMT09TWhoqHSL27t3L8eOHZNrKjExkc2bN7Nlyxbi4+OZmJhgcHCQy5cvY7FYOH78OM3NzTQ0NFBeXi7PSV9H2UZx0VCpVFJJ5ynz+IrSSrlOmpqaeO+999i3b58UtENDQ9FqtYSFhZGenk5xcTHFxcXk5eVRUlLCsmXLUKvVDA4O8u6778pA37i4uIdihZxVScnztqjRaIiKinKTwMfHx+no6GBkZERqcnxpcxV4Cq83btzg3LlzmM1mcnNzSU1NlYe58j2Cvr4+2traAGSgga8dlAJluxwOB+3t7XIhikNE+ZqwsDCCgoLo7e3lxIkTdHV1kZSUxNatWykvL5ev8xWTpVLY1mq1PPbYY1y6dInjx48zOTkpg0HudVu2WCw0NDTIOX706FE+//xz1q9fz44dO3jsscdQq9V3zJuHifIiVFZWxi9+8QtKS0upqqoCXNYCtVotL4l3W1+dnZ188sknvPfee9TW1sobeEpKCtu3b+dP//RP5biKIL2HtVaVnxsUFERWVhbJycmYzeb7/rue6ywyMpLExEQaGxvvKIXrKweLJ0IoBdc+arFY3J4TAuyKFStobGzkyy+/ZGxsTFZR87ZLy91QBnFarVZ6e3ux2+1s3LiRTZs2UVZWRnBwsFsOUIvFQklJCUuXLpWmZ2/4X39bPIVRu92OyWTCaDSi0+mIjY31aR/Ju6F0W7p+/Tq/+93vuHDhAmq1mpycHLZs2cLWrVspLCwEICIigpycHFJSUrh69Spnz56ltbWV+vp6srKyiIqKAnxfk+5ZeEGZbtHhcDA1NeV2GfEVlG5Jra2t1NTUYLVapfAt2hwdHc2KFSv42c9+xpIlSwCXIlKc50NDQ1y9epXJyUkAFi1axObNm2V2BeCBZFeYdeFVeaCKm/TFixdlxNrw8DDnz59n0aJFMm+k8jbuK3hWzrh58yZXr14FICcnh4KCAim8isnreSiKDVYE9yirAvkSynaHhYW5Hf7JycmkpaW5ba7KHJSjo6PSD7GsrMwnMyp4Bpnl5eWRnJwsF19ISAg6nQ6LxSJN0cq0IVarFZPJ5CZADA8Py6j+oKAgCgoKSEhIkH2fbeG1sLCQjIwMEhMT5WOelytAmrTEpeTEiRP84Q9/kGY8Ibimp6eze/duXnrpJbcLyWwdKsoDX1mSULmO4Ku15HA4pGAqxhNcWpDPP/+cS5cukZKSwqJFi+R7LRaLT61FT8E9Li6OqqoqNm3axMKFC+XFISQk5I6L1tTUFOCay8JNSflZviKkKy+SoaGhPPbYYxQUFJCdnU1ubq583fj4OHa7nYSEBOk7KbJfCKFe4Ct9uxtKn1en00l3dzc3b97EbDYTFRUlg9L8DeGqMzIyQm1tLa2trfK5Xbt28corr0jBFb5SZiQlJZGQkIBarcZsNjM9Pe2zip2vQwQ02+12t4CtmfZeX0Osm8jISJxOp9xTwsLCSEpKcgvAioyMlHuK8IEVCLeC2NhY6bf8IM5+r9iohY9hXFwcixcvdovanpiYoK2tjcHBQfmYr6ZUUrZHGVUuNlP4Skj1HKz4+HhycnLo6elhfHyc7u5uxsbGpI+seK8v9Fl5S2poaHAzoUdERFBQUEBsbOwdVYrCwsLQ6XQyub2v+ml5CgU6nY7MzExycnJob2/H6XTel5+S0o80PDyc0dFRent7uXjxoltg3mwdpsp+RUREEBER4VY6VbxGmV5IeQkZGhriiy++4KOPPqKvr09ePtPS0tixYwcvv/yyFPYsFovMwzmbfVNmRxAa8vu51dtsNiYnJ6mtraW6upqpqSl+8IMf8MILL0gBX6PR+NShqfxuw8LCWL58OYsWLWLTpk1kZGTI50R+UOVrExIS6O3txWq13iEM+JJwJ+ah0+kkLCyMxYsXy+eUcQMxMTGyD9PT07S3t9PX10dWVha5ublyDij3I19FzGFxiejr66O3txen01VSu6+vj/Hxcenz6ivnwt0Q553Qip89e5YrV67I+avT6VizZg1FRUWA6yISFRXlFvArxlpctnxF0XE/KMdGuECKS390dDSFhYVuMo+vjKWyHUlJSeTm5tLZ2cn4+Ljb68bGxmhtbaWrq0sqGCcmJqR1sqmpyU0Wio2NlRUYH2TVNK/leRUO2AMDA1IrAF/5xfo6noJYamoqOTk59Pb2MjU15aZFFf+UA5aenk55eTkNDQ0MDg7S2NhIT0+PnAziO/DmxBZtEKXfvvzyS9577z2ZfgdcmtecnBzps+uZZ1G0f2JiQm7CUVFRbsmOvb14PYXXmJgYNmzYQHR0NMePH+fChQsyqf+9EH3yPCx9SWhXri+lSUsg2nnx4kU+/PBD9uzZQ39/P/BVVoKnnnqKl19+mYULFwJ4XYsuhC/lXBLrb6ZL79TUFOfPn+fcuXMcO3aMgYEBli1bxu7du8nPz3d7bXBwMNPT0z7hb6eMAQgPD2fDhg0EBwffkbJNaeFyOBwsWbKEJ554gt7eXsbHx+UFyx9RKgNCQkJkQOjx48ex2+1s3ryZTZs2yej1+ylD6mt4pioSRRr8BeVlyG63Mzo6Sl9fn9QeL1q06A7hTbzHarUyOjoqNevCRcSf+i/mm9PplJcqQVpaGiUlJW5FUXzlcqVsh7BCiUI0ZrOZsLAwnE4no6OjHDt2jB/84Afy9T09PTQ1NXHs2DFOnjxJS0uLdOtSamEfpBJy1gO2lIdlS0sLx48fp7u72+114eHhPuuTdTdMJpO8XSn9I4WWQxkt6XA4CAsLIyYmBq1Wy9DQEH19fW4aze9S6vJBIjaVoaEhDh06xHvvvYfJZEKj0TBv3jxKS0vvWtdemLuElqu/v5+JiQnpu+RLGh9BUFAQ0dHRLFmyhHnz5lFWVkZNTQ2dnZ1ynITvoFqtxmazMTQ0xMjIiBx/4X8p8AUTrfibd6tQJA6HqakpGhsb+fDDD/nggw+kqS8mJobi4mI2bNjAc889JwVXi8Vyh6ZvNvG8FIo1pnzMYrHQ09NDd3c3w8PDdHZ2cubMGU6ePClTSsXGxnL8+HEGBgawWCxERERQXl5OQUGBFPS87b6kdIEICQlxKxc6U1Up0d7i4mKWLVvGRx99REdHh9f7cT94auFEhR6lvyu4qhfV1dXR0tJCUlISixcvluZq4ebjbwgLhsPhIDw8nPT0dDcBwBfOhXvhub+Nj49LDV5WVpZMrSgQMRJib2poaOD8+fNYLBbi4uKIiopyG0df77/Y741GI8PDw4yOjsrnioqKZDYUz9d7G+WekJqaypYtW4iOjub06dOcOXOGsbEx+d13dXVx+vRptmzZIufrmTNneP/992Wg1rx589i9ezcVFRXyc0Xcx4Ng1le20t9PCDSemle73e6Tgs3dGBwc5MyZM9TX1xMcHExhYaFMkyT8mJTaN/GYqBQjTCO+tkBFm51OpywZKgK1MjIy2LRpE4sWLZJt9cxRK0x2ynQhvrJQ74aI9hXpajZu3Mjy5csZGRmhr6+PwcFBOV+np6dpbW3l5MmTnD9/XgrqntpnpWnbW/P6foTmsbExzp07x549e2SZP3D5MBUXF/P888/z/PPPS18nUbLTV/C02Igcp729vZw5c4bDhw9z9epVedFQpn65cuUKLS0t8iBNTExkw4YNvPzyyyxfvhzwncuW5xq6mzZDHEYTExP09/djNpul9tYfrFswc0J+pabfarXK1Hw6nW5GK4I/IfZckd82ISGB0tJSkpOTZzXY87sgXKNE0N3NmzflJTEnJ4eysjJ58QL3+dzc3MyBAweorq7G6XSSn59PcXExERERd7il+Rqe57wIulNettLS0igqKnLL6uIrF0nl9xofH8+GDRsoLy8nNzeX6elpTp065WZ9fP/994mJieHll1/GZDLR1dUlFXAxMTFs3bqVn/zkJ2RnZ0t3tQdpwZp14VU5UJmZmVRWVnLq1Ck6Ojrk41ar1acd1EXQR0hICOPj4xw5coQTJ04ArhtLaWmpXJxCYPM0HStNgGJBKhe9eNzbiI3UYrG4LcK4uDjKy8vJyMiQ5h3R/tDQUDlZxXNxcXHk5+e7aYt8dRNSolarZQJmkYtQ3JovXbrEpUuXGBgYkBoiISQKATcqKoqCggLpS+rNMfX829PT09K3MCQkhKtXr/Lhhx9SXV0t84JmZ2ezatUq1q5dy8aNG6XgKjYxX9h4lT6v4NJ8X7hwgdOnT9Pc3Mz4+DhGo5Hr16/LDB8AS5cupbS0lKioKIaGhmhoaODGjRtMTEwwOjpKZ2cn/f39/OhHP2LDhg1ERUXJC6cvmN1tNpu8GCrH1jMgsq6ujoMHD9LT00NiYqJMsSXwpYAtT2ZaL577qE6nQ6vVSmHpXu/1B8SeCy7/UFFQQih1/GHfFIyPj9Pe3o7RaAQgKytLBq+CS8DTarVYrVaOHj3Ku+++y7Fjx+Q4JicnS1eemdzSfAllbI7D4aCxsZELFy7IwF/4qvqWUqnli4isJFlZWWzcuJGhoSGcTifXrl3DaDQyOTlJS0sLf/jDH6SF9eLFi4yNjaHRaCgpKWHRokVkZ2fLz3vQMt2sZxsA16EpfLXWrVsnq+OAS2JPT0+XpmXxPl8aZJGbFaC3t5e6ujpZMi0zMxO1Ws34+DharZb29nYp3IBLixUcHCzTgYkBFZuS5yHkbUS7PANhYmNjKS4udkucrTQJiEoook/R0dGkpqbKpOi+lvzdE89xEL+LeWk0Gjly5AiffPIJzc3NUnANCwvDYrFIrcnOnTt5/PHHZQCfLxWjUJrX+/v7+eKLL/jss8/kRTI6OpqNGzfyk5/8hCVLlsjxV5YA9AWUqfRsNhvV1dW88cYbfPHFFzIfb3JyMpGRkTJ/6/r169m9ezfr1q0jKyuLiYkJzp8/z5kzZ7h48SLt7e3cuHGD/fv3Mzk5SXR0NOvXr0elUvnMxfpuJnFlsAu4ClScP39e5t20Wq1uWmdfmY/3i7K9NpsNo9GI2WwmKCjIbV76W79mQukW4y/9UWr3W1pa6OrqAlx9ycnJkcKoxWJxS830+uuv89ZbbwEupUFaWhrZ2dk+Zd25F8KaERISgt1u5+rVq5w/f95NeFVqjn1JpvFEqVxKTU1l69atxMTEUFNTw8mTJ2lsbARcmvLf/va32Gw2WWY8NDSUBQsWUFhYKPccePBljb3uEKTRaNw0AZmZmaxdu9YtcMKX/ZYcDgfT09PSvyooKEgefsItoq+vj6mpKVQqFRqNRqYAaW9vZ2JiQvZfOZl9STMpNlDlJiI0isrXKH8CMm0PuLRhyqhFX+nb/SDGVwiuLS0t/Ou//it//OMfaWtrk5uWUuPlcDhYsGABzz33HGvXrpUbmi+U/xVpW8TBUV9fzzvvvCMFV4fDQXJyMt/73vf4/ve/7ya4irx/vjR+ytRsZ8+e5Y033uDdd991e01fXx99fX0UFRWxefNmnnnmGZYuXSqF2cjISNauXUtRURFbt25lcnKSuro63n77ba5fv84nn3xCeno6RUVFMnvGTH6m3kKZzcPTD9hzX5mcnLyjqqE/ocxH2dfXx6VLlxgfHycxMXHGlH3+iLf3iG+D0q8eoL29nQsXLsjMQcK1Q+yjIrDQaDSyb98+qcACVzqpjRs3umWb8AUrzzdBuLSI8yE2Npbs7GzS0tKkJtIXzoOZEJp+YWEW6SMXLlxISkoKH3zwAc3NzZhMJlpaWtzeW1ZWxrZt26isrJQZJ0TZ8QeJV3ZepSZV5AEVxMXFMW/ePJ/28VHegEWOT3Goj4+Pc+LECW7cuHFHFaa7YbVa6erqoqWlhYqKCsLDw30qNZjD4cBisbjdIME1dqIyis1mkzlgxeE+OjrK1NQUNpuN8fFxenp6mJycJCIiwqeyDXwdIlcfuMxce/fu5de//jXj4+Oy9Ki4bJjNZoKDg1mwYAFPPvkkK1askH31dqCM+K6VkdrNzc288847/O53v6OnpwdwrcHHH3+cH/3oR7JqivA38yWNK9zpQ9/e3k5vb69MxJ+YmCgFzZSUFKqqqnj66adZvXo1gHSJUavVMptCamoq4IqKdjqdvP7663z55ZckJCTw8ssvS9cJb81bsS8qrTXKPVK46whzrPIA1Wq15OXluZUu9vX1p0SZHxtcAlJNTQ1BQUEUFxe7BcL4m7CjRJwxysqMvuzeAXcWExofH5dBuuCal21tbTQ3NzN//nxCQ0MxmUzs2bOHTz75hNHRUdlHYZUtLS2VFzNfH0/PeA+RnlA8LoRXEXynLEvuayi/76CgIMLDwwkPDycxMZHU1FQyMjIwGAxcunQJQAYvZ2VlsWrVKioqKqTG9WH513tdbeA5eCJhvxCCfHGxKjfP3t5e6uvrGRkZISwsjK6urjuCQbKzs4mNjWVwcJCBgQHpawhIgefcuXPy4FyxYoV06PYV4c7hcLj1qb29nbfeeot58+ZJjazy5u1wOBgaGqKtrQ2TyUR0dDSTk5OyZK4v9Ol+UKZwuXbtGl9++SV79+6VWmRhQlYG3Anh73vf+550F4AHU1Xku6D8zk0mEydPnuS9997j+PHjMp1LbGwszz33HD/4wQ+orKyUbfbFS6QnarWa7OxsduzYwfz58wkJCSEqKoq4uDhyc3PJz88nISHBbUyES8xMB2N4eDiLFy+mpqaGgwcP8t577xEVFcUzzzwjBVhv5C/+OiFGjJPJZOLy5cucO3dOuoKUlJTw+OOPk5WVJROP+7L7jieegnprayv9/f2UlZVRUlLiNra+LuzcDc/z0F+C6zzXQXx8PGlpaXJ+OZ1Ojh49SmJiIikpKcTExPCb3/yG3/zmNzK1kvIMUQYMi8/3F5SpzpR98hVLzbdB+Ninp6fz4osvcvToUSm8gquvMTExpKamuu0pD2vcvP5NitKoAmW1KWVQk6/gmYd2YGCAy5cvAy4fHlFlKScnh+LiYkpKSigrKyMyMpLh4WGsVivDw8N89tln1NbWys8ym83U19dz8eJFSkpKpPDqzRyaykWXmprKvHnzOHLkCKOjo5w9e5bBwUFycnKIjo6WZgZwX7jXr1+XQq9IkO75+b6IMhjGZrNx9OhRPvzwQ2pra+nu7pZaLXGYij5qtVp27tzJk08+SU5ODvBVAn9vauqU1eB6e3s5evQoH3/8MdXV1fT29gKuxNQ7duzgBz/4AVVVVdKHWZj3fBHl3hEREcH8+fNJT09ncnJS5vmMi4uTgQMCZaUX5UZrtVqlUKdWq1m8eDHr16/n6NGjXL58mQsXLrB27Vo34dUb61PZb2H5EIK4MJ339fWxb98+Dh48CLhqya9cuZLi4mLAf3JqC4SWXavVYrPZqKuro7GxkeTkZCorK8nLy0Oj0bhVrPI3xPkn3LSUly2lIOSLiP1NrLvMzEwqKirIzs6WAaBtbW189NFHgCu+5Y9//CP19fWAKzgtIiKCyclJrly5wvvvv09GRgbr1q0D3NesL6JUNAlro7JUsafVwJ8QrmYqlQqr1crJkyfdguzDwsKkBbm5uZmJiQlpwRLz4UHj9RNppopDvqpKnwm1Wo1Wq8VoNBIcHExoaCiZmZk8/vjjPP7447KCmNKUd/PmTdra2qitrQVcWjtRhaO3t1dGZoL3E8ALsrKy2L59OwMDA3z88ccMDAzQ1tZGd3e324IUEbHCv1e4GkRERBAZGXnHjcxXN2PlHDx//jz/+q//KjddjUYj02J5Xr7WrVvHK6+8In21RDS/NxHCp81mo7u7m88//5w33niDuro6edmKjIxk586dvPLKK3doXP1pLcbHxxMfH+82r2Zq/9204OJxu91OaGgooaGhbNy4kePHj1NdXc3o6Khb3kZvVv8zGo10dHTQ1taG0WgkLCyM2NhYFi9eTEREBNevX+eLL77g1q1bREdH88wzz7B8+XJ5mIj9yl+wWq1yLQ0ODvLFF19w48YNFi5cyMqVK2UmE3+as+BeYQuQAWixsbFkZmbKyHxfF14Fyqw5paWlLFu2jKtXr9LV1SUtWP/wD/9wx7lWWFhIVlYWV65cobW1lT/+8Y8sWrRICq9i3vrCefh1TE9PMz4+LouCgH9pjpWIAGux9v7whz/wD//wD1JpB19ZIAcHB/nyyy/ZsWOHrKD2sPC68OpZPWRqakom7I+Pj/e5AffUQmVlZbFt2zbq6upISEigoqKCyspK5s+fT1FRkdSCKBfcyMiIW0ECnU5HcnIy0dHRmM1mKVCAd7WTnmagJUuWoFKpyMjIoKamhu7ubvr6+hgaGnI7xIXJR+kjm5mZycKFC+UBIzYiXxtf+EogcTqdHDp0iF//+tfs379fPm+xWNzyGFqtVrKzs9myZQvPPfccS5YskenCvKn98TzEu7q6eOedd3j77be5dOmSnFuLFy/mySef5PHHH2f+/Pkyyfu3ab/D4ZDppGw2m0ydNhsCvNK86DmvxDiFhITcceGYCfEdAOTn5/OLX/wCu93OpUuXuHLlipvP7GxppcUhMjQ0REtLCw0NDTQ0NNDR0YHJZCIsLEzGDKSkpHDhwgUZTJGfn8/atWtZuHAhQUFBMgrYn7STSi3x5OQkjY2NTExMUFVVxfz586W/q6cw6OsIDbhIA9XZ2SlTusXHx9+R2szXUV7mYmJiWLdunczcMTY2JvurjHVZtGgRL730EiUlJXz55Zf86le/Ynp6mrNnz3Lu3Dkee+wxQkND/WZM7XY7U1NTmEwmvxZexR4u9u/GxkY+/fRTKbiK7DqiMI/VauX69evs2bOHtLQ0Fi5c+J3Ok3vhNeFVHDSeN/+RkRGamppYsGAB8fHxwFf53Xxl8JWHVX5+Pj/84Q9ZtGgRkZGRrF69mnnz5snnhXAuBr+9vZ2jR4+6qdyzs7NZunQpMTExpKWluW1WvpAXVJkGavny5WRkZLBs2TKamppob29nZGREalGF1tXpdNLd3c2ZM2dkYJcwV4qNy9dM0cpNxmQyce7cOf71X/+VDz74AEC6CohbZlhYmAzQWr16Nf/pP/0nmSVDmJ19AavVSnt7OwcOHODNN9/kypUrgEtAS0pK4qmnnuLP/uzPiI2NveMg/TqU2iBhqvWWNk85X8WcFfuGZ6q3+/ksp9OJ0WgkIiKCZcuWkZSURENDg5vGYTYLqogMJXV1dVRXV1NTU0NLS8sdZYtTUlKIiIjAarUyOTmJSqUiOTmZ9PR0N3ckf0f0ISwsjMjISL9LKSVQttdsNtPc3MzNmzdJTU31y8A6ZXlUgIULF/LEE08wPj7OyZMnZaCrw+FAo9FQXFzMc889x7PPPktmZiaRkZF0dHTw6aefcvXqVT7++GPptw6+tbfeDavVysTEBOPj49KlB/xvbiqzIVy8eJF3332XhoYGmdfdYrGQnJxMXl4eUVFRtLa2cv36dQ4cOEBOTg55eXnExMQ8lPSYXpEelLdiT1/AkZERrl+/LtNrADLS2VcWr1KLGhcXx8qVK1mwYAEqleqOOuOefmUWi4Xx8XHp+6nRaFi6dCk///nPycrKYmpqyi3owBcEPM/vPSMjg9jYWBYtWsTU1JT0/RQHuTCrNzc38/vf/579+/fL3L4CX1zEQqB2OBycOnWKf/7nf+bAgQPAzC4OFosFjUZDUVERS5YsITMzE/C+L6FSC261Wrly5QoffvghH374IU1NTYBLI5Kbm8vatWvZvHmzm9bqm9yQhX+o8Ie626HyMMfbMyl/T08PAwMDJCcny8P/22jixIVNJOe+evUqMLuBQMoMFUNDQxw7doy9e/dSW1vLyMjIjO8RPsxiLgvNt7Ikp78Lr2q1Go1GI33SlQK8L+4t90J5tvX393Pjxg2mpqbIy8sjLy9PrkVfyhF9P4i5m5CQwMqVKxkbG2Nqaorq6mrMZjPZ2dm88MILbN68maKiIhITEwFXUOGSJUs4fvw4N2/e5MKFCzz55JNSeFWmSvNVpqenGRoakinpxDnuL+vOM43XlStX+MMf/sB7773H0NAQoaGhmM1moqKi+NnPfsauXbsYGxvjgw8+YHBwkJ6eHq5du0ZPT4/MrvCg5TevSEaiRjUgNXeCoaEhmpub5Qbs6wQHBxMZGelW7k2UKxSHuXKhxcTEEBsbKydzWloaixcv5rHHHpOv+Tp/vdlGGYAlBBudTud2GM6EVqvl8OHDqFQqxsbG6Ovrk9+TL5krxUIV2vHW1lb279/PgQMHMJvNsrCE8EUWqbGmp6eJjo5mx44dbNq0yc3Xy5t+rsJSAS5B5pNPPuHdd9+VJmSNRkNZWRlbtmxhy5YtzJs3z01TKd6r3GjFxUQ5N4X/mbKvk5OTDA8PMz4+zsDAAEajkYKCAkpKSh5qn0WbBwcHOXDgAD09PezYscMtaOB+Dzyn0ynHXa1W093dzZtvvsm1a9eYN28ey5Ytk699kOUOZ0IplPf391NbW8uJEyeYnJwkMzOTsrIyUlJS0Gq1TE5O0tDQQHt7O1NTU/JCqVarCQsLc7Po+MshejdGRkbo7OzEYrGQmJh4RwYJf0IpvIqD3+FwkJiYSEJCgltSe38SXuGrs0OlUklFhyA2Npbvfe97svzy1NSUTD0YFRUl3Qm7u7vvelHzVUTxDIG/zUkhn0xMTFBfX8/evXv59NNPZSECcOV6f+qpp3jppZekf2t3dzd1dXUymPLIkSNER0eTlpb2wOMoZlV49Uxi3N/fz5UrVxgYGJCvmZycpLe3l76+PjcNki8IcfeL54Gm1J6I6jbiRpaamkpsbKybKcRXgrQ8+aYRvDdv3pSlAY1GI11dXRQUFPhc9LpyfvX19XHo0CFqa2vl5ikyDwiUQYalpaVs376d0tJS4Ktyh95EjJHdbqe+vp4DBw64JZLWaDSsW7eOH/zgB+Tk5NxVqFOauoTWR/R7pkN0eHiYEydOcPr0aVpaWrh48SJWq5U/+7M/e2jCq3JtDQ4OcvDgQfbt24dWq2XTpk1ur7tf4dVznp8+fZq9e/ei0WjQ6/Vs2bJFPvew/fCU3/Pg4KAsfhIREcH27dt55plnmD9/PgkJCXR2dvL666/z3nvvuR0yQujxd4FVOSa3bt1ieHiYxMREFi9eTHp6ut/kA70Xyrk3PT0tfQ796fwTTE9Py32+s7OTPXv2cOLECWmhrKyslK6BcGd6KTGOU1NTjI2Nydf5gyDoz9kuxHfvdDo5fvw4r7/+OtXV1W5jALBr1y7+9E//lLy8PPlYaWkpVVVVskDFnj17yMnJkdlZlFl8vitelSCUJUQFIphCmE8yMzN9fhIoc/F59gfuPGCvXLlCT0+P1MpOT09LdwHhTuCrG7CnX6HQyIlKGk6nk8jISGw2G21tbTQ1NclE6X19fXcUKfAmQjgVbTlx4gR79uzh1KlTtLe3Exoa6maSFEKpuHhUVVXx0ksvuVWB8aY5S3l4OxwOzp8/z+eff861a9cAl4tOSkoKK1asYOPGjRQWFgLIvMPKQBelO8+9NpuOjg7po3fjxg3OnTsn/ZxDQ0NZt26d/DsPA6E1B1dWiD/+8Y+EhISwbdu2b1SlT7g/ANLMPjk5yaVLl3j//fdpaGhg69at7Ny5k6SkJClUzNZ4OxwOhoeHuXXrFuAq3bt06VI2bdok95uYmBhprlQGfU5PT9PR0UF7ezvl5eWA93MOfxM8rRk9PT2MjIxQVVVFSUkJCxculK/11Yv/vVBeUOLj40lNTZV7jT+7QygvS1NTU9JlSa1W88ILL7B7926Sk5OZnp6WJmq73S4rGor9OTIy0q1ymr98D77uJvd19PT08OWXX/LJJ5/IVJAiyLOyspLt27ezaNEiHA4HfX19aLVasrOzWbNmDadPn6a3t5czZ85w/fp1tm7dKv1kH5Q8N6vCq/JmJSpQJCQkuE1McWh2dXVx7do1IiMjSUxM9Omb572CWzxzuw0ODtLQ0MD4+DharZawsDA306sQmHwd0WelUBAcHCz7Ksr+Cd/lqakpent7GR4elkUKvJ18WulD1tjYyG9/+1tee+01wDUPlWm/7Ha7W5aB0tJSfvSjH/Hss8+i0+mkEOVNjbJwFxClaI8fP86XX34phW273c6GDRvQ6/UsWrRIvu9uTvRCGBb/F77NarUai8VCY2Mj+/btY9++fVy/fl3mApyamiIpKYlnn32Wf//v/71bAOPD7PvJkyc5e/Ysf/Inf8KPf/xjwGXJCQ8Pv69xUX4PPT09HD9+nD/+8Y8cOXKElJQUVq1aRUZGBjB7aYvE37Db7YyMjMhAT7vdTlRUlNx3mpub+fzzz/n000/lmouJicFkMmGxWGhoaODQoUNkZWVRWlrq5oPnD8oBocmy2+00NTUxOjrKmjVrqKyslL6SvnxG3AvlPAoPDyciIgKtVut3Qvi9UKvVREREMDg4iEqlYv369VRVVQEu07Qy4E5UNBTjqdVq3dxC/MWC4NlOXxdglYVoJicnqa+v58KFC27FiZxOJ2vXruXnP/8569evl3tmbGys/P/69eu5fPkyV69eZWxsjLa2NkZGRmT2qAe1Tr1WHhZcvqFGo9Htdmm1WhkcHKS5uZm2tjYWLVokJ/VspqV5mCirMmVnZ5OZmXmHEOivKCemzWaTmmSR9065ELxNUFAQZrOZy5cvs3fvXqqrq+VzQhupUqlkOhAxbpWVlfzsZz/jySefJCIiAvCNjUlslkLz2tLSQmNjo3w+JCSEBQsWUFlZKV9/t7k2NDREa2urtAxcvnyZ5uZm2c+xsTFu3rxJbW2tzHuamZlJQUEBOTk5VFRUsHnzZplh4kEjLhFib+jt7aWtrY3h4WG3g04I3PfCM/PF1atX+eMf/8hHH33E5cuXyc7O5gc/+AHPPPOMfJ03qv8p0+g5HA56e3u5ceOGzHn60UcfyUwSK1eupLKykuHhYaqrq7l16xYff/wxKSkpREdH31G0wZdRfs9Op5Nbt27R3Nw8J84CcE8t1dfXR3d3NxMTE3cU0PA3n1fl+IiKhAKln7ywbil9Ii0Wi3y/RqPxy7H23Hd8PU+v0moxMjJCc3Mz/f39bq/R6XRS66pMJ6icp9HR0ezevVsWmmhsbKS9vZ34+Hi3ILDvildnhNBUefoTisCDy5cv3+G35u+TWCTvB1f/RdlKcRj6m3+vEs/FGR4eTnR0NGNjY8TExJCTkyO1JOCdzVgpsI2OjlJTU8OePXs4fPgwfX19MrpcjIXwO9NqtdKk98ILL/DCCy8QEREhfXh8wQyr/P4dDoc0MQOyMpFybg0NDcnMEGNjY1gsFoKCgjAajfLmbLVaMRqN1NbWyko44NKkhIaGEhISQnZ2NosXL6aqqkpGSJeWlsrPVqaKe1B4rpGQkBBiY2PR6XR0dna6mZqnp6dltTDxPSnXmdiDrFYrFy5c4J133uHDDz+ko6OD4uJiXnzxRX74wx/KaOfZzFEs/kZISAgxMTGkp6fT1dWFzWajtraWqakphoaGOHz4MBcuXABg2bJl/PjHP2b16tXcunULq9XKO++8Q3t7O8eOHaOiooKEhAQ5Jr5+WVauWbPZTENDAzU1NWg0GplH09f7cC/EmSbK+ba2tqJSqcjNzSUtLc3Nfcef+ikEmsHBQVpbW6UFCHALwAoNDZU5Qru7uzl//jzHjx+np6dHVtgSgZfgXy4vAhHn4ctnu7JtImd0VFTUHa+5desWx48fJz8//44UpsICOTU1RWJiImq1mtbWVurr68nNzSUuLg54MBYfr0iCIu9ZREQEZWVl5OTkUFNT42rQ7fJjIyMj1NfX09XVJc2OvnxruRuek3VyclIm7w8JCSE8PNwtie+DvJnMNp6Cd1JSEllZWXR0dJCSksLSpUtlLtHp6Wmvlkw1m83U1NTw61//msOHD2MymaRZciZhq6CggJ07d7Js2TIWLVokNa6+fJgo3U+EG0RDQwMHDx7E4XBgNBpRq9V0dHRQV1cnhSK73c74+DhjY2OYTCasVqub5kStVlNZWUlBQQFFRUVUVFRQVFREdHQ0Go2G8PBweYN/2PmZxYVWmPWvXr1KU1MTH330EZs2bZJ+5GazWV5MhHuFUtPQ3d1NdXU1e/fu5fTp00xOTrJmzRpefvllduzY4Va9aTbNueK7Cw4OJicnh2XLlnH48GEmJyfZv38/J0+exGw209PTA8D8+fPZvXs3W7ZsITs7m9jYWCoqKjhz5gytra3cuHGDixcvkpOTQ3Z2NqGhoV533/k6lMFmRqOR+vp6rl27xjPPPEN2dvYD96WbLTxTvA0MDHD9+nUmJyfJz8+nqqpKCm3eLjH9TVDmiRaxD21tbW5WA2U/xPl38uRJ3nvvPerq6jh79iwA27Zt40/+5E+Ii4uTvrH+osBSyiszVRL1NcQ6cjqdJCUlsXbtWhmACy7ZbGJignfffZeTJ08yb948GSgpCsCItKbT09NcvXoVp9PJwMAAdXV1LFu2TAqvDyLnq9fyvIqBzM7OprKykvr6em7evClr5NpsNiYnJ91ua/6wcGdCGT3a09NDX18fgHRQF4va3zZfTzw1A+np6RQWFlJfX8/4+Li8bXtuXrOB54Z68eJF/vjHP1JdXc34+LjU0onFJ9wbYmNjWbZsGVu3bmXTpk2UlpbKz/C1yGZl3W+VSsWaNWu4cOECvb29ch2dO3eOqakp4Kv8yT09PVy8ePGu6Wi0Wi3JycnExcWRlZXFkiVLZIR3ZmamWyJ1gXAXedgFC5TzTWga3333XX79618TERHB1q1bZZlXgefhd/XqVV577TXeeecdurq6KC0tZffu3WzevJmNGzfKi4qoSuUt4uPjSU9PR6vVMjo6yvDwMMPDw4Ar33RVVRU7d+5k/fr1ZGVlAa75W1VVxc2bN/nwww/p7e3l8uXLbN26VV7QfHEuK1FeiicmJujv7yckJISysjKpGbJYLHdYFvwNUQUuPDyc7Oxst4BDZdyEr6MU0ux2u0ydJ9adpwV1YmJCVmV68803ZfXJrVu38uKLL0qBx/OzfZmgoCC5V4j9x2q1uo2jL/ZFxC0Ii1phYSGxsbGMjIwQGRkpMyW1tLRw69YtUlJSZNpIpeLNbDYzOTkpC6XcvHnzjrLa3xWvCK/KACe1Ws3SpUtpbm7mo48+ksEGmZmZrFy50u1g9NXN9esQ1URE9QkxiDabjYGBAQYHB0lKSnKrEOOPm7Cn5jUuLo7s7Gy0Wi1Xr17l4MGDJCcnk5OTI1NxiPc9bMRiCQ4OZmpqihMnTlBdXS1z8YnboliADoeDmJgYnn76aV555RUWLlxIeHi4nLe+ePtXpmhTqVQ899xzWK1WfvWrX0nfpatXr3Lp0iW3sRLVT2aivLychQsXotPpyM/PZ/369ZSUlMjct3e7cM2WkKdWq2Xmi5SUFLZu3crBgwf5/PPPycjIoLCw0C2VC7jWXU9PDzqdDqPRyLvvvss//dM/YbFYKCsr40//9E/53ve+R0ZGhlu6MG8Irsq1ERERQVJSEtHR0VLTGhsbK/dKYRlQViYMDQ3lscceo6+vj1OnTnHhwgWOHz9OVVUV2dnZREdH+/x+ozQTd3R0EBMTw6JFi0hPT/diqx4OQgHgy+PxdSjbHhQUJPMlKwW3mzdvMjU1hc1m49ChQ3zyySccO3aMiYkJVCoVu3bt4i/+4i9YunQpgCy24S+IADX4Kn4iJCTETYbxxTH23M8zMzMpKSnhwoULdyg3zGYzN2/edHtsprSK4eHhREZG3uG//V3xWpEC+CqFVHl5OSUlJVKLoNPpZCoNkUDdM7LdH1CmyJqcnOTixYtcv35dulllf1IAAKRlSURBVEY4HA66urro6emhqKhIvtYf070IlJMyNDSUmJgYIiMjaWtr49y5c2zcuJGcnBzgK/eR2eircjGJRdfe3i79WYVmUgi5hYWFPPHEEzz33HMsXbpU9stqtc5am78poo3CJzMvL4+nn36ayclJ6urqGB4edvNb9aSoqIicnBxCQ0NJTEykvLycefPmERsbS3BwMImJidLvU4nFYpFzVhQgmc2NWaRsU6lUZGdns3r1ai5dusTHH39MT08Py5Yto6ysjISEBEwmEw0NDVy4cEGm5Tt9+jRBQUE88cQTvPLKK6xcudLNTUAEdHnjsFHueRkZGWzfvp2JiQkSEhKIj4+nsrKS0tJSioqKyM7OdiuWIuaqVqslIyNDHqbt7e381V/9FZ988gm/+MUv2LZtm7Qm+NIeKy5VQni9cOECX3zxBVFRURQXF8tLS3BwsFeLgnwXlNo3EdQ6OTnJ9PS030TVe6JcJ2q1mpycHJKSkmRgdnh4OF9++SUdHR3Y7XbOnz9PbW0tVquVoqIi1q9fL/ddQFom/WmMIyIiZGJ+oVHPzc118931RUurpwUmJyeHkpIS6TrgyUzaY+W8zcjIoLy8nGXLlrlVH/X7PK/iwIuNjaWkpISlS5dy5coVKTRUVFQAXyU79sXBvl+EpstqtboJSuHh4W4aPfFaf8QzFVpUVBSZmZkkJibS2trKyZMnqa+vZ+XKlTJYyxumk+DgYDQaDRqNBrPZjMPhkOUmrVYrGo2G1atX89Of/pR58+ZJH10RWODrKDeGgoICXn75ZTZs2EBjYyOpqamcO3cOp9NJRESErE+dmprKunXrZKBVWVmZW/5agVJgEHPa24eK0g8uNDSUrVu3Mjk5yd69ezl48CBnzpyhvLyctLQ0pqamaG5u5tatW8TGxpKVlUV6ejrbtm3jJz/5CfPnzwdcB6YQbr2t8RHfeWhoKBUVFahUKubNm0d0dDRLliyR6bvA/cKsLKAQExPDkiVLMBqNDA8P097eTk1NDTt37nQrauHLwuu1a9c4dOgQ09PT5OXl0dPTQ3d39x0acn/aPz0FvfT0dJYuXcq8efPkZQN8U9C5G56X6LS0NMrLyykrK6O2tpbh4WH27dvn9h6NRsPChQvZunUrTzzxBAsWLJBCkLf3l/tFac0KDw/nscceY9OmTdTX15OXl8f27dspKCiQr/fFAC5P62lOTg6rVq2iubmZ+vp6dDodGo1GXihman9ISAgRERFkZ2dTVlbGwoULZSEV5Wu+K17dqZQb5ZIlS/gv/+W/MDk5SWpqqltic38VXJUa5sjISFauXMm5c+f49NNPAVd1rRUrVrBgwQJpVlGWzvVXRNoTnU7HwoULKSwslJvWmTNn2LFjB7m5ufc0Oz9olItMp9ORlZVFTk4O169fx2KxoNFoyMrKIjk5meTkZNasWSM1xMoiFP6AZzCE0MotWLCAdevWSbcVkeFCBKglJSXJfIuiHrUn4sKhzL3p7Q1YmYs2ODiYhQsXEh8fT1lZGdXV1TQ2NtLX1yfLE6empsqSzIsXLyY2Npbo6Gg3rbJwHfGFtejpSz5v3jyp1VGmBQP3Es6e/uc///nPee655zCbzQwPDxMcHExZWZl8ja9dzDwvthaLhdbWVoaHhwkPD2fRokVev1h8FzzHNS0tjV27dkk/a+Vh74/9VLpYrVixgp/97GdYLJY7rD+ZmZns2LGDDRs2sHjxYjIzM2X8gT+h9PkMDg5m9erVZGdnMzo6ik6nIyMjwy3bji/sLZ547uXx8fE8/vjjlJWVMTo6KmUxsf/fbe9Xq9WEh4cTFRVFdHQ0ERERbpcQv3UbECg1dQkJCW6LFZA+iL5oov0miPQR6enpPP7447S3t9Pb28vmzZtZu3atDDpQak3mCrm5uezcuZOJiQlGR0cpKSlxy0c6WygXmkajIScnh8LCQlpbW2VwoN1up6KigpUrV1JeXi7b5+9zUGS1CA8PdzNbfR1TU1NuacCEu4SvfhfiMAgJCSEvL4+cnByWL1/OhQsXuHr1KuPj4yQkJJCbm0txcTHz5893E9hsNpuMgvUlYUHMXRFMoVar5V4pIn2Dg4NRq9V31ZxGRETcM+euMkG5L+GZ41UEG05OThIXFyeFAXFh9vZF6puibG94ePgd/tkzXUT8BU/B/KmnnsJkMrFnzx5sNpssjV5SUsKaNWsoLy/3+RLp98LT8jiTTONvqNVqUlNTv9G5MVv4hI3obhuOL6rVvw3Kg3DFihWUlZVht9tlNRWBr2k+vi3K/gYHB/PEE0+wfv16HA4HGo0GnU4HzK57hKeWIysri0WLFtHS0iLzY2o0GnJzc1myZIkMBvE1P8DZRKSw8dc1GBwcTGFhIZmZmWzdulWaxZU5apV4w1/3mzCTVUZcLr5rm32xz56aHa1WS35+PoODg2zYsIHi4mI3gWEu4o9C692Ij4/n5ZdfZvfu3QBubjkajcbng5nuF39uuz/hM6eySBllt9tRq9WEhYXNmYUrTJDCb01pOoDZTXo+m4h8mkLr500851JGRgZbtmwhLS2NlpYWLBYLeXl5LF++nOTk5Lu+z18R80+ZrkVZJEIERCq1zf7adxHABUhfZmU1GIHVapWRwELb6utrUIzZN3XbEH7bQnsr0qRpNBqf0jIrUWr9AcrKyviLv/gLABYsWEBmZqZ8zt+0dDMhggPFvukP8/F+EfM1IiLCTWGjxGazyaqGc6HfdrtdrjmlZcTf+iYqZYo9Q3lu3A2xNz3MIF6fEV6FaXOuojT9CLO5mAhzVbOnPFA8++xNgoKCiIuLIy4uTpZKvRv+KsB5IubfXJ1rSjzNrJ6VteArwcgfrR3fJierLwTWfVM898by8nLKy8tnfO1cEF6VuUHnGsqAOk/BZ66egyEhIWi1Wm834zujUql8cmx8r0VzHHEbEzdMf/TT+qaI2tX+1GdfCdYJ8N0QWkZROUapEQiMr39wr3zQ/pZd4FFHnAXwldAqLtWB9Rjgm/CNhVe9Xv8w2uEzzPX+wdzv41zvHwT6OBeY6/2Dud/Hud4/CPRxLjAX+xe46gQIECBAgAABAgTwG4LmapRmgAABAgQIECBAgLlHQPMaIECAAAECBAgQwG8ICK8BAgQIECBAgAAB/IaA8BogQIAAAQIECBDAb/Baqiy9Xr8bWAssAhYCkcBbBoPhRW+16WGj1+tfAn53+9efGQyGf/Nmex4Gc6mPer3+ZeC1r3mZw2Aw+G2SSb1eHw88DewA5gPpwDRwGVffXzMYDA7vtfC7o9fr/x6oBIqABMAE3AT2Ar8yGAxD3mvdd+cRGcOXmftr8ZE4E/V6/Q7gfwBKgXigBzgH/BeDwXDam237rgTGcPbG0Jua1/8F+AWuQe7yYjtmBb1enwn8EzDp7bY8LOZgH+uBv7nLv8O3X3PAKy17cDwLvAosA2qB/wZ8CJQD/wa8p9fr/T2R5n8AdMAXwP8HvAXYgP8MXLo9b/2ZR2EM65n7a3HOn4m3L5L7gArgIK71eB54Ejip1+v9XcgLjOEsjaE3ixT8B+AW0ILrplLtxbY8VG4fHK8BQ8BHwH/0bosePHOxjwaDoR7XoXkHer1e3C7/dbba85BoBr4HfKrUzun1+v8JOAPsAp7BJQz5K1EGg8Hs+aBer/9b4H8C/hrw50SIc34MH5G1OKfPRL1en4LrXOgDFhgMhn7Fc+txXUL+D+BN77TwgRAYw1kaQ68JrwaDQQ7qXEyg68EvgQ3Auts/5yKPQh8B0Ov15cByXDfrT73cnO+EwWA4fJfHe/V6/b8Af4trTP1Z8LlDcL3Ne7iE18JZbM4D51EYw7sxx9biXD8Ts3FZe2uVQg+4+q7X6yeARK+07AERGMPZG8NAwNZDRq/XzwP+Dvj/DAbDMW+352HwKPTRgz+5/fM3BoPB7tWWPFyst3/avNqKh8cTt39e8morHi5zfQwflbU4F7iOyxd7qV6vT1A+odfr1+DyDz3kjYYFuG98Zgy96TYw59Hr9Srg90AHLg3PnONR6KMSvV6vBV4EHLj8Ceckt8f1R7d/PejNtjwo9Hr9fwQigGhcAVyrcAmuf+fNdj0s5uIYKnlU1uJcwWAwDOv1+v8E/Bfgql6v34vLzSwfl9vLF3x1GQngg/jSGAY0rw+X/w1YDLxsMBhM3m7MQ+JR6KOS54AY4IDBYOj0clseJn+HK+Bnv8Fg+MzbjXlA/Efgfwf+HJfgehDYYjAYBrzZqIfIXBxDJY/KWpwzGAyG/4bL/1oF/Az4H3EFHHYCr3uaogP4Hr4yhgHh9SGh1+uX4tJE/r/+nv7jbjwKfZyBn9/++WuvtuIhotfrfwn8JdAEvOTl5jwwDAZDisFgCAJScG2+ecAFvV5f4d2WPXjm6hh6MOfX4lxDr9f/FfAB8DoubZ0OeAxoBd7S6/X/4L3WBbgffGUMA24DDwGFKb0Z+F+93JyHwqPQR0/0en0psAJXNOl+LzfnoaDX6/89rtQnV4GNBoNh2MtNeuAYDIY+YI9erz+Pa/7+DpeGck7wKIzho7AW5xp6vX4d8PfAHoPB8BeKp87r9fqnca3Fv9Tr9f9iMBhavdHGAPfGl8YwoHl9OETgSog+DzDr9Xqn+IfLbAnw6u3H/pu3GvkdeRT66MmcDg7R6/V/DvwKuAKsNxgMvd5t0cPFYDDcxCXglXkGH/grj9AYzum1OEfZefvnHemjDAbDFK60bsG43NAC+CY+M4YBzevDwQL85i7PVeAa2BPANcBfze2PQh8ler1eg8v86uDu/fZbbjvh/x2uXJqbDQbDoHdbNGuk3f7p9wLQozKGc30tzmHCbv+8Wyol8fj0LLQlwLfDZ8YwILw+BG4HLv10puf0ev1/xiXYveHPpVMfhT568CwQC+yba8Eher3+f8WVWPocrgCmOWNm1uv1JcCopwZSr9cHA/8nkAScMhgMI95o34NiLo/hDMzZtTjHOY6r+tTP9Xr9rw0Gg6xApdfrHwdWAmbglJfaF+Dr8Zkx9JrwqtfrnwKeuv1ryu2fVXq9/vXb/x80GAx+X6UpwJxBBIf4exUfN/R6/Y9xCT12XBvTL2dIrt1uMBhen+WmPSi2Af+3Xq8/BtzAldYlGVf1mzygF1fErN/yCIyhJ3N1LT7F3D4TP8CVA3QT0KjX6/fgWn/zcJmjg4D/0WAwDHmvid+NwBjO3hh6U/O6CPixx2N5t/8B3GQOlBgN4P/cLsKwirkZHJJ7+2cIrhRSM3EUV2SpP3IIl5CzEliIK7WSEVdgwe+Bf5wDWsq5PoaSOb4WFzGHz0SDweDQ6/XbgX8PfB94GggHhnGN5T8aDIbPvdjEB8EiAmM4K2MY5HQ6Z+PvBAgQIECAAAECBAjwnQlkGwgQIECAAAECBAjgNwSE1wABAgQIECBAgAB+Q0B4DRAgQIAAAQIECOA3BITXAAECBAgQIECAAH5DQHgNECBAgAABAgQI4DcEhNcAAQIECBAgQIAAfsN953m9XbPerzEYDEF3e26u9w/mfh/nQv9g7vcxME/ndv9g7vdxLvQP5n4fA/N07vYvoHkNECBAgAABAgQI4Dd84wpbBoPhYbTjoTJDqcS7Mtf7B3O/j/7YP5j7fQzMU3fmev9g7vfRH/sHc7+PgXnqzlzsnzfLwz4yOJ1O7HY7TqdT/t/hcCCqmwUFBREcHExoaCghISFebm0AJTabDZvNJsdP4HA4AORjKpWK0NBQVCoVQUH3tFT5JHa73e13MU9tNhsOhwOVSiX/+WP/5hIOhwOHw0FQUBAOh0POT0+U4+V0OgkODiY4OGBsC+A9nE6n3FOCgoJQq9WB/cQPmJ6exul0olarfWYP8Snh1el0SqEgKChI/vMnlAKO0+mUfVCp7u+rVgq0cw0hvIv/++oYi3aKcbvfsfP8DPD9cRT9nOnSpFKpCAsLu+f7/B3lngO+v+94CqEhISGo1epv/Bm+2j9PPMdH4Nl+8bu/9OteeJ6DviIsPAiEwPoo40/jK/aK0NDQOx7zNj4lvNpsNqxWK06nU27K/qaJFBpVpQD0TRarzWYjODjY7/p9PwiNkdA8i+8mJCTEJxaDQGgcQ0NDv3W7RP98qV+eCC1ISEiIT2+gDxOHw8H09LQcL/Fd+Op38l0PDuXl2h8Qlg/PC4bnHjmXtMp2ux2LxQJAaGjonOnX3XA4HHO+j0qcTqfUZAqLna9it9vvUN44HA6fkE98QnhVCnn+eiu7lwZramqKsbExbDYbZrOZ8fFxpqamsFqtgEt7Eh0dTXZ2NrGxsYB/CD/3QgipQlOkUqkICQn5Vpqi2cJqtd6hbe3r66O7u5vR0VF5sXA6nVitVnnZCgoKIjExkby8POLi4tzcP2w2mzS3+BLiwBDtMplMsi+if2NjY/T29mIymYiJiSE1NZXY2Fg0Go3fzkv4SmgXApBWq/V2k+6J0NQIgXpiYoKpqSlCQkIwmUz09fUxPj4ux9RmswGQmJhIWloaKpUKu91OeHg4ERERXu7N/aGcn9907ShdsoKDg/1mrtrt9hmtPcKFR+wpnhcYf+kfgNlsZmhoiJGREcLDw0lPT5fWnUdBiBXnoeee4yvaTIEYC5VKhcPhoK2tDavVSkZGhs/sIT4hvPrSoD0oJicnUalUTE1NUV9fz9WrVzEajfT19dHY2Eh7ezujo6MAaDQaysrKeOWVV9i1axeAmybIHxFCnlis/oCnCae9vZ3PP/+cAwcOcOnSJcbHx+V4TE9PAy6hz2KxsHjxYp599lkqKytJS0sjNzcXrVZLSEjIjP6I3kTpvgHQ1tZGQ0MDFosFrVaLw+FgYmKChoYGvvjiCzo6Opg/fz6bN29myZIllJWVkZiY6PZ5/rSGxTj727y02Wz09/dz/vx5uru7CQsLo7u7myNHjtDY2IjVaiU0NJTJyUmcTierV69m+/bt6HQ6TCYT6enpLFiwgPT0dDleviowfJf5FBwc7JeX/7uZkMXj/ugaobx4Ady6dYv9+/dz/vx5iouLeeWVV0hJSQFcyoO7uSnNFe42dr42pjabTfoj9/f389prr9HX18euXbvYsGGDT2iLvSq8KjdOo9HIhQsXuHTpEkajkezsbBYuXEhOTo6c0L54SCp9Nx0OBz09PZw9e1b2w2az0dnZSXd3N3a7ncnJSW7evMnExITb57S3txMZGUlCQgJLliwhPDzcSz36dojAEaWPjFLwHhsbo6+vj/7+fnp7exkZGSEyMpLS0lLy8vK8epuzWq2EhIRIbcelS5c4duwYTU1NXLx4kfr6eiYnJ93eo9FoyM3NRaVScevWLW7cuMHrr7/OwYMHycvLY9OmTWzZsoWEhAT5uUrtibcQay4kJASj0ci5c+f4+OOPOX/+PGFhYaSmphIaGsrU1BSdnZ1cunQJs9nM8PAwvb29XLhwgZ07d7J161bi4+OlkOAPCE25uFQAcs8ZHR0lKiqK1NRUUlNTycjIID4+3sstdl2ShPtKV1cXb731FseOHWNychK1Ws3AwAANDQ0zvnf//v309/cTGRnJ+Pg4kZGRbNmyhd27d5ObmzvLPbk/xBoJCgrCarVSW1vLlStXpEDudDoJDQ0lISFBzlWHw0FcXBwpKSmEh4cTFhb2rfzUvYVYQ+LCX1tby8mTJ7FarSxbtozVq1ffsz9WqxW73S41Zb50GVG6JoHrnPvkk0+oq6vj8ccfx2w2y9fO5Ns8V1D6uN64cYNjx44xOjrKggULqKioIC4uDvCdWAmlK9+VK1c4ffo058+fZ3BwkOnpaVavXk1sbKy0YHljznl1hU9PT6PRaAAYHR3lD3/4A7/+9a9xOBysXbuWn//85yQnJ7uZFbx9+Hui9AkxmUwcOnSI119/nZqaGreF6YnILiA+w263c+jQIVQqFTqdjsrKSgAsFotf3EaV/YGvhPqpqSlu3bolBcELFy5w4cIFBgYGWLx4MT//+c9JS0tzE15n+5Ki/Ht9fX28/vrrvPbaa1IzDhAWFib90MrLy3n++edZsWIFERERtLe3c+DAAT744AOuXbvG0aNHaWtrY3JykqeeeoqkpCT5d7yN8sJ4/fp1Pv74Y6qrqzGZTCQnJ2MymaQLT1FREXl5eQwPD9Pc3ExjYyNNTU0MDg7icDjYunWr1MD6qgZPiTDHOp1OxsbGuHjxIh9//DEffvghHR0daDQaFi1axOrVq3n66aepqqoCvjLnznb/hLlYcP78eX7zm9/Q2trqdmEWh4fnmrFardTU1KBSqaQrQWdnJ6GhoTzzzDNkZmbKPvmKYkAIYULj89Zbb/H666+77aVqtZq8vDxKS0sJDw/H4XCQlZVFWVkZsbGxhIeHExMTQ0ZGhtva84X+zYRom8Ph4MKFC/z617/m9ddfB+B73/sewcHBVFVVoVarpSVL2R9fd7dT7ntjY2N0dXUxNjYGeF9Ie9go3VcAWltbeeuttzAYDPT39/P444/z53/+52zatMmnLAaivaOjowwPDzM1NcXo6Ch79+4lOTmZwsJCYmNjvSqPeV3zKpiamqKxsVE+ZrFYsFgsPmdy9UT4VwUFBdHb20t1dTVHjhz52velpqZSUFAgg5iMRiM3btzg3Llz9Pb2ytfZbDafFV6F0C1u/EquXbtGTU0Nly5dor+/n+HhYTo7O7l+/boUAiMjI0lJSSE6Olp+3mwGb3lGUtbX1/P222+zd+9eN8EVvnITiIqKYsOGDbz88stkZGQAUFlZSWJiIiMjI3z22WeYzWZqamoYGRmhsbGRp556ilWrVknfQ8Bri16M09jYGK2trVgsFrZu3cqSJUuIi4vDZrPJC6VGoyEmJoaBgQH27t3L3r17aW5u5osvvmBqaors7GwpvAqXA19EBAiKA76trY1/+qd/4tixY0xMTNDR0QEgx02j0bBixQr5fm9dOoKCguTav3XrFrW1tbS3t8s2iXap1Wq0Wq3sn9B2CZcWIbiCa12++uqrBAcH89JLLxETEyOFc28fmODuo3rt2jUuXLhwhxLAarVKtyshyEVERHD06FEiIiKYnp5Gp9OxefNmnnzySbKysgB8LjBUXE5UKhVms5ljx47x5ptvcuDAAfmazz//HLvdzksvvcSqVasA11kphAutVktaWprb53rrsnU/hIWFERkZSVhYmBy7uYyI+xAXj08//ZTf/OY39Pf3A1BdXU1RURG5ubkUFhb6zJhZrVbUajUxMTEUFBS47e29vb309PRQUFDg1UuTV4VX5QHe19eH0WgEXBGWmZmZRERE+MxgeuIp+IyMjHDixAnOnTsHuA6UsLAw6fuakJBAbGwswcHB6HQ6Fi5cSElJiRT+bDYbDQ0NBAUFERkZKf+Or2malYgALPgqKG18fJyOjg5qamrYv38/NTU1bu+JiYlhwYIFzJ8/n02bNrFo0SKvLQBhRg4PD8fpdHLgwAH++3//70xMTKDRaGRgknCJUKvVFBcXU1FRIQVXYdZds2aNzLe5b98+zGYzFy9epKmpiaCgIJYvXy4PT6UZbbZQampEtGtMTAxr167lscceIy8v767vLSgokPlC3377bTo6Ojh58iTt7e2sXr0a+GqT9iXhQOkiYbfbZaDTp59+yquvvir3m8TERHQ6HVqtlqSkJCoqKkhOTpaf4w3BTlzkRIBWXV0d165dQ6vVYjQa5WFiNpvlRX8mhDYnLCwMrVbL6OgoDQ0NnDp1ih07dhATEzOLvbo7ytR04BLWL126xPDwMODaT0NDQ+WYWSwW+vr63D6jpaXF7ffu7m5CQkJ49tlnSU1NBVxr3ldcCjz7u2/fPt577z0sFgsxMTEEBQUxMjLCp59+yvT0NLdu3QJgYmKCkJAQHA4HMTExPPbYYxQVFcnIdWHF8hUTtBKHw4HVamV6eloK2XMZpYDe2trKl19+SWdnJwARERFMTk5y5MgRysvLiYqKkvuOty1ZSsXivHnzWLx4MTU1NZhMJm7evMnhw4dJSEhgwYIFgHfWlU8Ir1arlYGBAandEulRfPlWJgZX6Tv32WefyQ0mKChI+klmZmayfft2Nm/eLE11ERERMrOAEJBGRkawWCxuB6eval2VTExMcPr0aY4fP861a9fo6Oigu7tb3i4FWq2WJ598ku9///uUlpYSExPj5i4w2wKd0lfnxo0bNDU1SV9krVZLdnY22dnZREVFYbPZ0Gq1zJ8/n4qKCrfPmZqaIjw8nM2bN3P48GH27dsnn7NYLDQ2NtLa2sq8efNm1FLPBp7RyXFxcSxbtkxqrb6OJUuWMDw8THV1NR0dHYSGhjI2NuYWFe5rB5G4GILrcnn06FFOnDhBTU2NFIIA8vPz2bp1K6WlpWRkZJCZmem2Br19gRTZH6xWq9uh4hl4NxPKYhpKK9bExATd3d1kZmb6hMnZM7hzcHCQjo4OpqamAGT/vwkXL17krbfeoqSkRAqvVqvVJ4RXodkClxXk5MmTnD9/Xl5CLBaL29geP36cy5cvA18JpU6nE51OR1FREUVFRWRlZVFRUUFFRYUUfr2N5xwdHx+np6fHLWXiXEUpgPb19XH48GFu3rzp9hrhA/vll1+ycOFCue94O3hNuZ9rtVqeffZZTCYT7777LvX19YyNjaHT6dxiP2Ybr65iYTJRq9UkJCRILabD4cBsNvuFACsOtv7+frq6uuQBMT09TXFxMcuWLaO0tJSlS5eyZs2aex6ECQkJdzzmCxvQTNhsNgYHB2lsbKS2tpba2loaGxvp7u52C0YrLCykoKCApKQksrKyWL9+PevXr3f7LG9o7MRNMSQkhPHxcQ4dOsSVK1fk86GhocTGxrJixQqWLl0qb9ApKSmkpaW5vV9cugBWrlxJRUUF58+fl49dvXoVg8HA7t27ZTCet7SU4sAQFgBwae9EAFBYWJhbVLPZbJbBgxaLRQoQWq2WkZERbt26RVZWlk8IQAJlDmGAnp4evvjiCz766COOHTvGyMgIUVFR5ObmMn/+fFatWkVVVRUFBQVugZImk4ng4GCvHCJKrYtOp6OkpISMjAxMJpNsW1hYGGq1Gp1OR0pKCllZWYSFhTE6Okpvby+jo6OYzWaMRiPT09Nu83RgYID6+npycnKkFcEXAgqVKFNGhYaGUl5eTlFREVqtluHhYfr6+qSPtkhdJy6k3d3djIyMcObMGQ4ePEhhYSG5ubnSJcbbKL/nM2fOsGfPHhoaGmQAmhjfmJgYgoODmZycdHMnU3Lr1i0uXLhAWloadXV1LF++nK1btzJv3jzgK4HdG/uNp9VCp9MRExMjTc/t7e0yeNAXLhUPEqUAeuPGDY4ePUpvby+hoaFMT09jNpsJCwvDaDS6ZR8C7weviflpMpnQarUsX76c1tZWPvjgA8DlevXpp58SExPDSy+9JPda5aXsYeN14VWQlJTkdkj4oibHE2X7VCoVWq2WsLAwKbytW7eOv/7rvyY7O5vp6emvNQMIc64Qin3tO1AebsPDwxw4cIB3332X06dPMz4+fkf6jKKiInbs2MGOHTsoLy8nIiJixontjT4qzRxXrlxh3759XL9+3a1NRUVFrFixQvqaWa3WGZOjKzWXGzZswG6387d/+7ecPXsWgI6ODn71q19hsVhISkqipKTEa+M6U/L9sLAwGdGudC0QbjF2u53m5mYuXbrEyMgI4LqciYIi4FuXLFHOFlyXyr179/LOO+9QW1uLxWJBo9GQn5/PM888w3PPPUd+fv6MrgHeFHREW4Rbyvz581m8eDHJycn09fVJDXFYWBixsbEsW7aMtWvXEhUVRWtrK/X19bS2tnLz5k2uXr16h+AzMjJCf3+/FIbBNwIKlQgLHEBZWRl/9md/xgsvvEBoaCjt7e2cOnWKoaEhgoKCGB0dxWg0otFoGBkZob6+nra2NjQaDT09PZw5c4b4+HiioqLkZ3tDWFIG2JnNZi5cuMCHH37IkSNHGBsbkxdi4YIkLATKrDZi/QoBx2q1MjIyIvtdU1PD+Pg4P/7xj8nOzvZ6SWflfpmZmcljjz3GjRs36OzspKGhgQULFhAfHy/Hw9fcj74tymBI5ToUAXbKYjjCD1jgK/1X7glKN87g4GAuXrxIamoqjz/+uDwDhXvdbOBV4VW5eXR2drqZ8vxtAgtNj/LAM5lMcoMJDQ2Vmivhg+dwOIiMjJSagyNHjnDy5ElUKhXLly+nqqpKasd8QSsyPT0tfe06Ozt55513+Pzzz92eB5dWbunSpXzve99jy5YtlJSUuI21SO3ybUuvfhfEvNJoNAwPD3Pu3DneeustTp8+7aaZCg0NZfv27VJwBe4peAuTbnh4OBs3buTy5ctMTEzQ1dUlLzNNTU00NzeTlZUlx3W2fJuU68lzXSkFN5vN5hbFLg7S3t5eOjo6pHYgOTmZ/Px8qbUTOUa9idhoxZzq6Ojgs88+4/333+fYsWOAawxXr17N7t272bJlCzk5OfL9ouqNOER8bf+prKzkRz/6EQMDAxQVFVFYWEhkZCQajYbMzEzpt5yfn8+iRYtobW3l2LFjDA4O3iG85uTkUFlZ6TPuEZ5uAz09PVy+fJmuri7i4uLYsmWLW37JnJwcIiMjmZqaIigoCJPJhNlsRq1WYzQa2bBhA+Pj47JaY3p6ulv/vDW2yvUuIs/ff/99GX2v3OeVgXaCmcrlajQa6c8McPPmTf7whz9gs9n48Y9/TH5+PjC7WjGBZ67ypKQkiouLiY2NZXh4mO7ubsbGxoiPj7/j4uzvKC0/zc3N0tcVXHuUODOSk5MpLy93s7x6+6wXKM+miIgIMjMzGR4eRqVSMTk5SUNDg7TgwMxn5MPCJ4TXyclJN59X8D0twNchzMdCMwWu1Davvvoqzz33HIsWLSIsLOwOE6TVasVoNHLlyhXef/999u3bR0ZGBgkJCTz22GNuQo63J7RSUz44OMiZM2cA1+YZFhbG2NgYCQkJVFZW8uSTT/Lkk09KXzOLxeJWMcdbZmaTySRNw5OTk7z66qu8//77gCuTgPA5W7ZsmXRvEJXR7uUbqqwcJgTY7u5u3n77bfkam81GS0sLFRUVsy68KoVss9ksLQHiIhUUFIROp5N9UGp4nE4n/f39dHR0SFNXZWUlCxYskHPS22YucE9bJ1xB3nzzTY4fPw4g/XyfffZZnnvuOXQ6HU6nE7PZjFarlYLR9PQ0k5OT2O12NBoN4eHhXs8OAa7AuX/37/4dNpuN5ORkt0IR8JVvbFhYGBkZGUxOTmK1WqXfqDLdW1paGsuWLZP+3N4uiOIplHV2dlJfXw+4UtNt2rSJtLQ0qfHXaDTEx8ffNRfvY489Jv8vXF2U36W3+qrMTmM0GqmtrZVBaTExMYyNjUnrQExMDGFhYVJDJ9awWJd2u53ExERKSkqwWCwcP36ckZERVCoV165d4/XXX0er1fLiiy+SkZHhlo1itvC0aMTGxpKbm0t8fDxNTU20t7czMDBAbm4uQUFB9+XH7eso012JlHzK4ENP6928efOoqqqSZyX4pvCamZnJunXrmJiY4Nq1a4ArldaZM2fIy8ujvLxcrrHZONd8Is9rREQEubm58kAHZuVA/y543gwTEhJISkqSh0NQUBAtLS28/fbb0ueovLwccL9Z1tXV8fnnn1NbW8uJEyeYnJxk9erVzJs3z83/zhcms7LPyoACp9PJ1NQU6enpvPDCCzzxxBMUFRXdEbEtDihvjq0y6EOlUrm5CphMJiIjI9m6dSs/+clP3LI+fJ1JyzMgKjMzk6ysLHkgBwcHMzw8zI0bNxgbGyMzMxN4+EKfp0arqamJkydP0tzcjEqlIjw8nPHxcSIiIti2bZtMEaXsT19fH3V1dZw7dw6LxUJlZSWrVq2SlXHAN/zVxBqx2+1cvXqVI0eOcPz4cZxOJ5mZmTz99NM88cQTVFZWul0elH01Go0cO3aMgwcPMjo6ypo1a9i6datbVarZ1Awp10pkZCQRERF3TYMkaqWLVFIfffQR77//Pi0tLdKfWZmVwJdyvHoKLcJPNzg4mEWLFlFWVoZKpfpWUeq+5I+t/K51Op3buhHmfafTSWVlJX/yJ3/CggULGB4exmQyuVn1hLZZlBW/dOkSY2NjHD16VF7iOjs7+Zd/+RdsNhu//OUviY6Olhec2UaMWWRkJMXFxaSkpNDY2EhjYyOdnZ0sXboUcC/t66+I7EGDg4NcunSJ6upqWlpaZL9E9hpwXVgWL17M8uXLiY2NdTsvfAHl2klPT2f79u20trZK4dVms9HU1ERLSwtlZWXA7I2hz/i8Jicn3+Fj5suT2PNGmZWVxeOPP05ISAi1tbW0tbVhNptlAvuwsDCef/555s+fT1CQq1rOuXPn+OSTTzhy5IhM85KSksLGjRtZsWIFarVamnp8YTKL8TEajTLiHFyCrE6nkyVSlyxZArgERZEOxtsmZYEQSJ1OJ0eOHGFoaAhAftchISE8+eSTbN26FXD1VXnI3O3QFI8L4TwhIYF58+axYMECTp48icPh4Nq1a3fkZHzY5nbPSN+LFy9SXV1Ne3s7U1NTDA0NMTExQUJCArdu3aK1tVXm9bPb7fT29lJXV8fRo0cZHBwEkGauqKioO1IceQOl363VauXy5ct8+umnnDlzBqfTSUxMDNu2beP73/++LDwgop09/cqbm5t55513+N3vfge49qji4mKSkpLkOHlL2FPuASLHsnjcbrdLq87Q0BAHDhzgzTffpLGxUb5eaGBLSkooLy+/I2OKt1HOVafTKYsNpKWlyb6FhISg1WoJDg6W7keevtoibZvI7CGEfV/YQ4Wvo7C2KXPYjo6O4nA4KCkpYffu3bz44ov3/bnJycncvHmTqakpLl68KMsEd3R0UFtbi9lslsKrN3Kni8wfwcHBpKWlyUw7Q0NDjI2NuflK+vK5fy+UAYMqlYqrV6+yZ88eamtr6ezsnNFtKywsjOTkZKnM8GbFqpkQlym73U54eDhLly5l2bJlHDlyhOHhYSYmJrhx4wZdXV3yPZ4lgR8WXhVelYMoApWU+PIk9jy8UlJSePrpp1m7di379u3j3/7t37h8+TIWi4WGhgaMRqMsSKDT6Xj77bd566233FJnBAUFsWDBAoqLi+VCFgesN/H0f2xqauL69evSzSM0NJTFixezadOmO8pO+oJGDu4MtnrjjTf453/+Zzo7O9HpdLIvarXazRz7TTd68V1pNBqWLl3Krl27UKlUHDt2DIfDQVtbmxQixOsfJspNcHx8nOnpaVJTU0lPT6e3t5dTp05hsViki0N1dTVLly4lJyeHsbExzp07R0tLizxkNRoNKSkpJCcnS/9nb2vuxN82m800NTWxb98+9u7dy7Vr10hISGD79u3s2rWLhQsXur1HzE0h/N24cYMDBw5QXV0tXzc5OUl/f78MnALf2JeUB1xQUJDbPO3p6eHkyZPyQqzRaDCbzTgcDvLy8vjpT3/K008/LX3sfDFZvBBAPd02xB4KuPVfKbyKQge+JAQo22w0Gjlw4ABvvPEGbW1tsp3CHeSpp55i+/bt3+jzVSoVP/3pT3E6nfxf/9f/JYUlp9NJZGSkT/j7evqkw1fuVt7W/D8IlGPsdDo5d+4cBw4coKenR44FuLvIzFSZ0tdQFtPQ6XQUFBRQVFTE5cuXMRqNtLW1ueVcni3XD69KFsoOioobgpCQEEJDQ90e8+UJLiZgeno6zz//PDqdTgY0CTPe3r17uXXrFhqNRuZEBZfAJAIoduzYQVFRkfxcbwuunoiMCOJwAZdguGTJErZs2UJsbKx8zlcEV3CfOzdu3ODTTz+lrq4OcOVpFZv8D3/4Q7cgnm9aNUo5X1NTU1m6dKk01TscDoaHhzl16hQFBQXExcW5uYY8TIQma/HixRQUFBAREcHExATl5eV8/PHH0n+5vb2d8fFxoqKisNvt9PT0SBNXdnY227ZtY8eOHcTHx7u5I3gLZdR4Y2Mjb7zxBocOHaKhoQGA0tJSdu3axZo1a9BqtW6XmOnpaRloaTQa2bNnD7///e9lYIXQ0nterH3hgPHcC4Xvss1m4/r16zQ3N0utfmRkpLx8zJs3jw0bNsjgrtmuane/CNNjSEgIOp1OVuFTq9VuGuOv0+5YLBYZwe9txHfc19fH0aNHqa6uluPicDhIT0/nueee49lnnyUvLw+LxYLJZEKlUsliE0pE2V+j0UhycjIqlYr58+dLlxjhxx4VFeUm6HsbpXBjMpncfNX92edVeVlqbGykpqZGKqfCwsLczsugoCASExOpqKiQFeDA96rAzYSwpIqiIRMTE/T09DA0NERSUpKbf/ZDbcdD/wv3QNnBqakpN+2BzWbzyUPjXojDIyYmhhdffJGYmBhMJhNnz57FaDRKHx/4aqI7HA5iY2Opqqri+9//PmvXrpXmHfAdk54nSr+79PR0KioqZF5Bi8Xic8UVxOZoMplobGyUxSTgq3n1wgsv8Jd/+ZcUFBRIbeI3vTx4prFJSUlxq2LkdDo5duwY6enp7Ny5UwrHM0UWP0hEYYK4uDi3x0Xkb3x8POPj40xNTdHa2irLkEZFRTE+Pi77smvXLjZv3uzWR1/AaDRy4sQJ3n33XXp7ewkLCyM3N5cNGzZQVVWFVquVYyrWlFL4qamp4b333qOpqcntM8PCwkhKSnKbB94W2JUoyzNbrVbOnj3LZ599JsdvenpaRqFnZmayePFi6as8W+a9b4PQnlosFnp7e7lx4wZZWVn09/fLy0VsbCwxMTGYzWYGBgaw2+2kpaWRnJwsU8L5wj6kvOQNDw9z9uxZLl++fEfZ21WrVvHv/t2/o7y8XGa9uJ/2K5UEwkog/u7k5CRTU1NuxSq8hTjvRV53cAnynZ2dPnlm3C+eGuWBgQHOnj3rZkqHr8o9m81mnE4n0dHRrFmzRlapAt8972dCuf+LinirV6+etRSDXhFexSEiDu6hoSFOnz4tfeoAuege9qH+IFH6oQHs3LmTiIgIDAYDn3zyidtm5XS66h2HhISwePFidu3axfr16918Mn3lBqbc8Ox2O4ODg9y6dUvWs1+1ahUFBQXyNb60AD3zkI6MjNDV1eU2FtHR0Sxfvpzvfe97sh8iyO7bCiri/aLsqPIyYjKZmJyc9InLWHJyMj/72c94/PHHOXToEHv37nXbdIVWxGazYbFY6OnpkQeNUhs02wKdcM8QdeHPnj1LbW2tTAlVWlrKiy++yM6dO6UbiHAV8BRiP//8c/7lX/5FFqkQScSdTid5eXmUlpai1WrdcnT6Cna7XUbfm0wm9u/fz3vvvcfY2BharRaz2SwDdJYsWcKqVaukFhN8RxD3DELT6XSEh4fT39/Phx9+yOXLl4mMjGRiYkJGbet0OiIjI7FarUxMTBAUFERaWhrZ2dkUFhaydu1aOfbeTNSv9GtvaWlh//79blY3p9NJTk4Oy5Yto7CwEPhmGnEhLHR0dHDp0iVZ2VGp6fP2XqN07QgPD2fBggVkZGRw69Yt6uvruXbtmsxe4ivn3jdBeV6Pj48zNjZGTEwMqampjI+PYzQaUalUZGRkYDabuXXrFg6Hg6ysLBkH4e1g5rvhmYkkPT2dBQsW0NraKlO8iZKxOTk5bmfoTHnFHxReE17hq7Ko169f5/Lly/KLAGRFJqVZ1dcntVCl2+12TCYTarXaLd0JIE14IrhEq9VSXFzM6tWriYyMlMmpfekW6rnxORwOWU9dq9USERHhdiAKs4gvCLHCX0dozlpbWzl58qRb3svc3Fy3ijTw7d01RACJMBN1dHTQ29srF7DZbCYxMZGMjAw3jclszG1hkhObpDKoxel0yopZoqyvVqtFrVbLRPYtLS384Q9/QKVSsX37dq+WoFSWfp2cnOT8+fNcvXoVtVpNREQES5YsYefOndIFZ2pqCq1WKw9Rm83G+Pg4dXV1vPrqq+zfv5/p6Wm3dGnZ2dmUlZVJTaW3SzYqEeMo/AXNZjOHDh3iwIEDch/VaDSy8lRubi7r169n2bJlbhXefGGNwp3Ca1JSEhkZGdy8eZPz58+7Vaz7us/JzMykrKyMoaEhdu7cSXp6OiqVSs772ZyzngFSIsBFpLQSF4/MzExSU1PdAkO/ToGhfH56epqBgQH6+/ulT71arSY0NNQn3AaUlz6tVsuCBQuorKzk1q1btLS00NjYSF5eHjqdzmfm5DdB+b2q1WoiIyOJioq6o/BAcHAw8fHxqNVqli1bRk5Ojs/sKXfDMyB33rx57Nixg+vXr0sLZkdHB4f+/+39eXhTV57gjX8s27LkVd5tjI1tbGzABmxWsxO2AAECSapCNlJVSXXNnerqmq5+3/lVdfdvut555p3umemlllZ3dZJKqkgqCVkgIQtr2MMaVgPGxgvYeMe7LVmSpfcPcQ5XwiQksS3J3M/z8AhL1/I595z7Pd/zPd9l714mTpxIVlaWHMPhnG9+4ZToHfUaEhJCcXExixcvllaQb2MFGwnUuyaXy0V1dTWHDx9m7969nDx5UvrmWSwWGVkpLAHeQQi+iAb9MtTCRK/Xk5eXx9SpUzl06JA8rt2wYYNU/vwp3YlIDSOU0QsXLrB9+3aPErYRERFkZmZ65Iz8pg+dWsHp7Ozk448/5vDhw9L65XK5SEtLIysra8SPotXWD7VC//LLL/Pb3/6WmzdvykIhBoOBmTNnEhUVRV1dHVevXqWnp4fdu3cTHh5OdHQ0CxYskJuWkT4pUB9Z2Ww26urqqKmpweFwkJeXR3FxMUlJSfIa9RG5y+WipqaGbdu2sXXrVi5cuCDniHBfio6OZsaMGTLBu78h5rWwuu3du5ff/va3HiWORc7p9PR01q5dy+LFi6ULy0hWwrkfvLO3ZGZmMmXKFMrKymhpaQHu+Hh+GS6Xi/r6ejo6Orh69SpHjx5l06ZNLF68GKPROOKVtbz7JRRKkfYL3P2Kjo4mMjJSytqvKz+dTiddXV10d3fLzVdYWBj5+fnk5+d7BAX5Qjn0vg8pKSlkZmZiMBjo6uqipaWFvr4+mUvb+8TMn/FuY3x8PGlpabK4i8gjbbfbqaioYMKECTz55JMea+Zg3+NviEwI4eHhzJkzhyVLlnD9+nUqKyuxWCxcuHCBPXv2kJGRwaxZs4Y9oNcvlFfxQIuHSqfTkZKS4pE0PFB2Yw6HgwsXLrBt2zbee+896eMqPjMYDCQkJNDd3U1vby8dHR1UVlby+eefs3jxYuLi4vxuJyYmnljg8/PzmTt3Lrt27eLs2bNcu3aNt99+W1YGEyUYxQIrrHu+Qq3otLS0eCiu4vPx48fLIKVvGmzmnXrpxIkT7N69WwYPARQWFlJcXMy4ceOkBXCkjzMHBgawWq10dnZy7tw5Xn/9dc6fPw+4fVyF8rdo0SJMJhOVlZXs3r2bnTt34nK5aGxspKen5y5/9JG2aAnsdjstLS1SWcvKyiInJwen0ymzBKhzSF+7do333nuPV199lfLycgBZHlcs/KISkHpD4y+bZ2ExF1kEDh48yO9//3sOHjyI0+kkMjISu90u+7J48WKeeeYZmYcRPE8WvP0hR9o6Kf6maENQUJAMLPviiy+k8ioUV51OR0ZGBrm5ucTGxsrCG+3t7VRVVdHW1kZXVxddXV1UVVXR2tqKy+Vi2bJlck3xVXnY+vp62tra5NiEhoYyadIkli1bRmFhobxOnIZ8GWp3hKamJvbt28eFCxfkvXQ4HEyZMoVZs2bJTY6vrO3eOZJNJhMpKSmEh4fT2dlJU1OTRw5ifz1C/zLUxV6EfBfuaWpfV6fTyfz58z1SSgZCxgX1BsRkMrFixQqampp46623pDvZxYsXOX78uEf1xeHaKPuF8ioCndRCdLBk2v6MGNRbt26xf/9+/vCHP1BbW4tOpyMyMlIGvQgXgerqapmJ4OzZs2zbtg2TycRDDz0E3HkQ/KnvamE6ceJElixZQkdHB9XV1bz++us0NDTwox/9iOXLl8uyt/5g3VELhcGcydPS0jx8zZxO59de2LzH6+DBg7z77rvStw1g1qxZfPe735U5fEcS9WLgdDq5cuUKBw4c4NChQzJIKS0tjYULF/LII48wY8YMaR1uamoiNDSUuro6SktLZVlfX6bf8c4JqvZhTk5OJjs7m7i4OOm7KtpXU1PDO++8w7//+7/LwB+9Xn+XMpOQkEBubq5HsJ2/bKDV0dkHDx7kn//5nzlw4IBUant7e2V/p0yZwooVK+RCabPZ7loo/UnGiIU8IiKCuXPnsmDBAurq6mhtbSUsLExGac+bN48NGzaQnZ0tfcgrKirYs2cP165dY2BggO7ubplnOyoqivz8fJllYSSUVzHvDAYDNpuNixcvcvDgQcrKymTMQ3JyMosWLeLJJ58kISFBbvjFZurLUFeo/Oyzz3jjjTdkoB4gLWCFhYUyS4OvfLZFX4QiI6yTkZGR3Lhxg8uXL9PU1ORRvMXbWuvviE2f0+mkpaXlLmVcyKyIiAgPueKLsr3fBCH/RHuLioqora3l448/pqGhAXDP+c7OTo9CGMN1CusXyqt351wul8fxkD9PYiGgxKTt7u6mvr5eDmZYWJgc9MjISEpKSti8eTN2u50JEyawfft2qqur2bt3L3PnzpXKq8PhGFZn52+CWtinpaWxYsUKqqqqqK6uxul08sUXX/Dyyy9TWlpKSUkJc+fO9ahkJFLfjDTeSaEFERER5ObmsmTJEnnE8U0CBvr7+2Xkusvl4vDhw/z+979n586dMrgkNjaWlStXsmnTJo8ygCNl/RElJ4OCgtDr9ezYsYNXXnlFWqFzcnJ47LHH2LhxI5MmTfIohZucnMzYsWNJS0ujurpalu6cNm2atLKP9DG0+rkICwsjPT2dkJAQaf0W7QJ33tO2tjZu3rzJsWPHePPNN6XiKqre1dbWSgU4MjKS2bNns2DBApKSkvym6o1wJwoKCpI5et944w2OHDlCf38/er0eg8Eg/Xvnzp3L008/7ZEzVOSEFfLqXlZ/X7lqqZ+/5ORknnzySQoLC2VAVnBwMOHh4dKtQIyzzWYjNzeX3Nxc2tvb0el0tLe3s2vXLrZv386JEyc4ePAgY8aMwWAwjMhctdlsUt5YrVZ27drF/v37PaowZmZmUlhY6FHX/qsQ8yA4OJj+/n62bt3K7373Ow/Fdc6cOTz++OMsWbLEIx7B16hdBHNychg3bhw3btzg5MmTHD16lHHjxpGYmOjXa743TqdTuotZrVYOHDjAH//4R86cOSNdXaxWqzyBHDdunEcKxkDpp6C/v18+P2PHjvXQ36KjowkPD/fo03D1zy+UV9E58epdkcmfc7+prTpOp5POzk5sNhuRkZF0dHRgtVqxWq1ER0ezdOlSHn74YWbMmAG4/X5sNhsvv/wyjY2NnDx5kvnz58udsr9NahHk43Q6MRqNFBUVMW/ePMrKyuRx3aeffsqRI0d45JFH0Ol0LFy4UFaV8dXu8l5zJzo6mtmzZ5OXlyctMV/nyFT4KqsV4kOHDvGHP/yBTz75RGbPMJlMzJkzh0WLFknF1Wq13pXHeDhRB4CUl5ezZ88eucHKycmhpKSE9evXyzKNIk1USEgIfX196HQ6j6pbVVVVHu4XI502S70JioqKkhams2fPcuXKFb744gvS09OprKzk6tWr1NTUUF5ezunTp2lra5MZJjIyMqipqZHZMyIjI5kzZw7Lli3zyIXqa8VVIPp948YNXn/9dbZt2wbcKUQg/CjHjRvH+vXr+e53v4ter6e/v19uXLy/S5w2CKXIYDD4rJqY2uczKCiI4uJiiouL73m9sNDp9XrGjh0rjyrBbSFKT0/HaDTKaoZxcXGsXbuW4OBg+fwO1wZSLXdqamo4dOiQLMvscDgIDw+nsLCQ3NxcrFar3Fzeqz3inqjTm129epV3331X5mk2mUykp6fzne98h+eee07mY/a3UzxwW4anT5/O0aNHqaur48SJE8yePZvY2NivLMftL4jTjrCwMOx2O6dPn+a1117j7bffBtzPksiIAlBcXMyKFSs8Ki0GgtVVjXoeGY1GWS0NoLW1lba2No+4nVFtefWenP48Wb1RH8fa7XZqa2upq6vzKFvodDqlk7awrIJ7gVm6dCmVlZXs37+fnTt3YjKZ+OEPfygduf3xSEEosXFxcTzxxBNER0fzyiuvSAHa3d3NZ599BrgLAuTn5zN+/HjS0tLkd/jKp0n9UPX393P9+nXq6uo8fE/vVxHr7e2VwRcAR44c4ZVXXmHHjh0ytybAhAkTeOSRR5g0aZJ8b6T8XIXgEO4SV69e5Z133pGKK7h9In/0ox8xffp0+Z73nBMKutPpJCkpiQkTJngIrZG2qKv/XkREBMXFxcyaNYtz587JADnh6tDa2kpPT48ck7S0NJ577jlKSkqora3lxo0b8rMxY8awdu1aj3vh62Ib3pkBRFBSdXW1vCY8PFwukHq9npKSEmbNmuXh4zmYHGlsbKS5uZmuri7a2toICgqisLBQFurwVRGD+/17wrAx2KYzNDSUefPmYTAYcDqd7N69mzFjxrBq1SrpKjIcxVTU7gLgzu0qSoarF/KEhASKi4uZPHkyoaGh0j/7y+SiaLPIx/zxxx97zIOioiIef/xxVq1a5eGv7S9KoPq5FRvIY8eOceLECWpqarh58yaFhYVyTPw1D7FAnGiBO9fpn/70J3bv3i0/9y7ksnTpUp555hmio6OlC4+v5cvXRd1esdlvamrixo0bXLlyBZPJxKZNm2Swq8PhGJYS6H5x19RH7+BOJK/2mfBnhPAUwTfXr1+npqYGi8UiBVVaWhrz589n3rx5REVFyeTRERERzJ49m5qaGs6fP09DQwPbt29n/vz5Unn1h/KwatRWZnEE8sQTT9Dc3ExQUBAWi4WWlhYaGhp48803OX78ONOmTWP16tWsWbPG48h8pNvsTVtbGwcPHqSwsFAKIIvF4lFGFu6MsfgnxkTk5O3o6GDv3r188MEH7N69m46ODpmTEtwCe/HixaSmptLf349OpxuxMRXuGkLgVFRUcOrUKanoTJgwgQULFkhlra+vj7CwMA9hExQURFtbG42NjTidTtLT05kwYYJP84Wq56FOpyM9PZ3Vq1fT1NTE/v372bVrl7w2LCyM2NhYsrKyyMzM5NFHH2XdunVYrVa++OILj4IVEyZMYOHChaSmpko//OEQvF8HdYaIzs5OSktLOXr0qAxQA6R1PDo6mlWrVvHcc895KODCfae/v5++vj4aGhq4evUqV69elemVOjo65PH7kiVLmDNnjvTNE0rscCLcxYQ/qHBx8M5TKhR5kSdbzcDAgMczZjQaSU9PJyIigsbGRmpraz1iK4bDKuSdCrKqqoorV67IuAexgY6OjiY7O1vKkXtlmXE6nVIBCAkJobe3l0OHDrF7925OnDghN15xcXHMnDmTjRs3kpKS4nEv/QUhhwYGBoiIiKCkpISTJ09y5swZzp07x+eff87UqVNlrlB/V17Fve3p6WHXrl18+umntLe3y9zeav/0xMREJk+eLF1dRB5Uf+7fYHjHBghZumXLFiwWC2VlZVy9elX62Q9X//xCefXHI437RW2lczgc3Lx5U0bHwp20X4sWLZLVjYSirtPpGDNmDDNnzmTcuHE0NDTQ0tJCZWUlt27dIj4+3m93ZepjaJPJxGOPPcaMGTNoampiz549fPjhh3R3d3P9+nWqq6tpaGjAYDBIi4B3dPFwol6gvK2qFouFxsbGuzZQ3n0VryIiXa3Q/PGPf+Tf/u3fqKmp8fBtEiQkJEhLlnfe2ZEmNDQUu91OV1cXiYmJLFq0yCNdi/DRVvt5trS0yON4h8OB0Wj0m3kpTgEMBgPLli0jLi6OmJgY/vSnP0m/8nnz5pGWlkZCQgJFRUUsXLgQgI8++ojdu3fLjAMmk4ns7Gx5QuAPaevUJxQtLS0cPHiQXbt2cezYMZqamjzyB0dHR7Ny5Uo2b97MnDlz7vqumzdvUl1dTV1dHSdPnuTw4cPU1NRIVwOdTkd/fz/79u2jpqaGMWPGSOVVLLTDjRhPgfD791Yy7yUz1GXFRRGNxsZG6uvrAWTQ10gisiGI+yxkZ2hoqEce83vNNe95WFlZyZYtW/jss8/o6uqSY79gwQLmzp3rUZTDX7HZbBiNRuk6EBsbS3NzM7t372bRokVSefVXd0FxUhccHExfXx8fffQR27Ztk/nDRVopkWdZpN5Tp+/zVdGMb4v6pNloNDJz5kzOnz8v3w8ODqa6upqKigrGjx8vT+2Geq33ixXI6XRitVqlI/vkyZNJTk6Wn/sidcv94l19qqOjg87OTrn4h4WFkZOTQ3Z2tkzeL1JoiKCY3NxcJk6cyPHjxxkYGKCsrIwrV654HPv5W+oQteXL5XIxYcIE8vLy6OrqIjY2lvT0dJxOJ7W1tWzfvp3jx48zZswYMjIypPKgdq0YKbxLMgIyEfr06dOl76CwmgqfJXUbIyIipH/T+++/zwcffEBFRYX83Ol00t7eTkJCAosXL2bz5s1ykTIYDCOqEKnbbbVa6enpobu7G6vVSmpqKgUFBSQlJcmiGuoiBsHBwVitVi5fvuxRRCQ0NNQjENH77/iK8PBw5s+fD7izYfT19ZGbm0t+fj6xsbEYjUZZcADcfoilpaVSCI8dO5bExEQpbP2hmpY6b3B1dTXvvvsu+/bto6enRwZ1qoNgwsLCaGlpYe/evdhsNplSq729nTNnzlBeXk5XV5fMzziYi0xKSgoZGRke6cVGKg+xek4JZS80NPS+/r76BCwoKEhWgevs7JRW6pEqXanmy6xr6vuv/r8660lQkLsaZW9vL9XV1bz33nscPXpUFhMBd3q4xx9/nEWLFkml3Z8teurxzM/PZ968eWzbto1r165x4cIFZs+eTXJyst9skr1Ru9P09/dz4cIFLl26JGW7w+EgIiKC/v5+mdt77dq15OXlSfnqbykxvy5ivkZERBAUFCRzhIeEhNDR0UFraytZWVnDZqjy+cxQ+waJgIlZs2Yxbtw4eY0/l4zzTjkTFRVFVFQUNptNBm5lZGSQlZUlj4fA0/Te1tYm8792dHRw6dIlzp07x+TJk6Xy6k9BI2q822QymXj44YdZtGgRbW1t7N27l8uXL3P+/Hk+//xzli5dyrx58+SiO1KLokBt6RBWyIqKCl5//XUZIGIwGO5a5ERwh8vlwmazcfLkSf7n//yf7NmzB7gTMCO+Nz4+nk2bNvGLX/xCRhKLoIyRXlRE/3t6emhqaqKpqQlwH1umpaWRkpIiK4KJa3t7e6mtreXatWscOnRIpvyKiYkhLy9PBsJ4/w1f4P23hYuOCOZRzzHhV9jR0UFPT490TwoNDSUnJ4ecnBwPq7gv++VdnampqYlTp07R3t4uq/mJQCxwL5hnz56VR+NWq5WQkBDCwsLo7u6moqJCHjGrT07EzzExMUyZMoUNGzawevVqMjIy5N8eCdcJ0V+htH3Z3/R2I1C/qpVdh8Phobz6wpInguEG+9vquamea2qF1+VyUVtby/Hjx9m7dy+ff/65fIb1ej3Z2dls2LCBlStXeljKfe3u8mWo17/k5GRKSko4ffo0LS0t1NfX09LSQmJiokd6P39c/wTC5UVsnLzlRn5+PsuWLWPs2LHSDcRfFfP7RfRR5HKPjIykp6cHq9VKfX09zc3NHv0c6mfPZ3fPW6EQyoKwjvhj/e3BUCvWBoOBoqIirl69yuHDh7HZbFIpVSuu4vcASktLefvttzl69Cg9PT0EBQXR3NzskboHRj6a+5tgs9lwOp0yWvnixYvs2bNHHqWIogyCkcgi4R29O3nyZJYvX87hw4flEX9DQwNvvfWWtGRNnTrV4ztaW1tlIJ6wWJ0/f55jx44BeESTpqam8vzzzzNt2rS7UuD4eh4Ly5Z6JyysdeJzwbVr13j55Ze5fPkyzc3N1NfXk5SUxGOPPcaGDRsYP368VPL8JRhEnULI24onEHlQq6qqPCxXJpOJxYsXs3DhQgwGg+yTrxcY75Md9RG/+hpxmlNRUUFFRQUDAwMefqreMQTie10uF7GxsUyZMoXZs2cze/ZsZs2aJaP2h3Ns1d8tFLS9e/dy7do1lixZwvLlywf9PXUBBmEdV2821eNeWlrKoUOHuHjxIoBHFhehwA834jRDfc/F61fJBKfTyZkzZ9i6dSt79uzh5s2b9PX1yXlQUFDAc889x2OPPSbdBcD3QYZfhXpOhYSEEBUVRWRkpCwiI4qgqGWVP6NWWIVhRmwsMzIymD17tsxhC/7fn/tBrfeMGzeOgoICTp8+TVdXF8eOHSMnJ4cFCxZIeTrUfvM+meHqICeXy0VzczM3b96UR+n3k6DZX1APhiipWVZWxueffw64B7ihoYEzZ84QGhpKW1ubDPZpbW1l586dfPLJJ7Kso16vJzMzk9zc3HsKZH9AnYtXKApBQXeKEnR0dLB161beeecd4I7FLjk5WQqkkXAH8VZA8vPzWblyJbW1tZSVlclNQWNjIx999BFBQUGsWbOGCRMmYLVaqampobKykoqKCm7cuMH169epqKigp6dHfqf4jrFjx7J582YURZGpUMSxkTr90EijTo4dExNDQkICV69epauri7Nnz5Kfn09qaio3btxgYGCAyMhIDh06xLvvvis3Hnq9nsWLF/Pcc8/d5U/pL8+qOvWTmJtCWRd+xiJNUVNTEw0NDfK61NRUZs2aJcfNYrF4WJZ9hfre6vV6oqKiZLvVn4nk9kJJFUqdGPuQkBCpQIWGhhIXF0dCQgLx8fFkZ2cze/Zs5s6dK+WOOuhouFArr3a7nevXr/PRRx9x8OBBGhsbSUxMJD09HYfDQWRkpOxTWFjYXT7jLpeL3t5e7HY7FouFtrY2ampq+Pzzzzl06JC8Ti1TR8plSfjAeyuUwjqu7oMYV5vNxq1bt7h06RK7du1i69at3Lx5U36fyFH9ne98h02bNklfSjFvfb1R/joYjUZMJhMRERFYrVYuXbpEVVUV06ZNGzar3VCiDjQUbishISHk5OSQnp5OamrqXeXA/UVmfhvUBrWUlBTGjh1LTU0NjY2N0sij3nwMtbucT5RXtY/WwMAAZ86c4ejRo1itVuk/8VU1rP0F9ZFdSEgIGRkZpKenS2tWe3s727ZtY+fOnVgsFtrb2+WOW/hGikhhp9NJfHw8a9as4dFHHyU2NlbuPv1tJ+1dSELUWQ8KCqK9vZ0PP/yQU6dOyc9TU1NZunQpxcXF8p6NlMuAWlBERUWRnZ3NxIkTaWhooLOzU35eV1fHH/7wB3bs2EFERISMXLZYLFgsFmlZ9rZiOZ1OvvOd7/DjH/+YWbNmefgyCaXBV6it2waDgfDwcEwmE7GxsVy/fp0tW7ZgtVopKCjg3LlzVFRUEBERQVtbmyywADBt2jSWL1/uke5rJCLQvwlqq6vYIKkXP7vdzq1bt2htbZUnBZMmTfKwkvvj4iIseHDHX/yrrhXHz+KoMijInRh/3rx5LFq0iIKCApKTk4mIiCAyMlIusCPdf/FcBQUF0dXVxY4dO2hra2P8+PFkZWWRnZ1NUFAQkZGRzJgx466NRW1tLWfOnJHBWeXl5VRWVnLz5k152iMWWO8AzOFA/TdMJhNJSUlERETIUrXgduO5cuUKeXl5hIaG0tzcLC2qLS0tfP755xw4cEBWnwK3PElISGDq1Kls2LCBtWvXSsXVO+e0PyPWO3C7LxUUFJCbm8vp06c5e/Ysx48fZ/Xq1XIj5e/H7OJ5VLvDzZs3j6lTp97lsuIvJ1VDiZAv3mvdcK7xPlNe1cdF9fX13LhxA0A6QIuHOFAQg2Y0GsnJyWHixIk0NjbS0dEhj5e/jKCgIGJjY1mwYAGLFi2SC6k6YMNfEErLYJaplpYWPvzwQ7Zu3UpjY6NUHFwuFzNnzpQVjXzlwxQSEkJ+fj7f//73ycjI4N1335UWDYfDIWuifxUGg4Hp06eTlZVFQkICq1atYsGCBQCyNGVUVNRdvmu+Flp6vV76+rpcLqqrq3nzzTfJzc2lvr6eyspKD+U8LS2NWbNmydRJ6opp/spggVZqJVsoSm1tbTidTmbMmMHy5cs9lFd/9BcUaaPAfZIhUtOFhIR4JKIfGBjAYrFIP23xO9HR0cyaNYsFCxYwf/58iouLPcpUwp0o6ZH2RQ8NDSU1NZWxY8ficDhobW3lgw8+wGQykZeXR1ZWlnRxOX78uExlJqplXb58mTNnztDQ0EBNTY1U9gSpqals3ryZRx55RCpBw7WxVPvbCgtcQUGBdIUTxT+am5t57733qKmpITY2lo6ODjo6OtDpdHR3d3PhwgXKyso8Kr/NnDmTWbNmMX36dObNmydPCqxWq08yKXwbxPzU6/VMmDCBadOm8fbbb+N0Oqmrq5Pz1h+LFHnHusTExGAymeju7pb+xhaLhY6ODpmpY6TdVYYbtYyIjIxk3LhxlJWVSXes/v5+6urqmDRp0rC4YPlsK6PehahrAHd2dnLt2jVZnQgCo86xum3Z2dnMmzeP69evU1VVBdxJi6FWDMQkDgkJITk5mfnz5/Poo496BKv5U44+wWDWNqfTSWVlJXv27GHr1q18/vnnHkInLS2N7OxsGdE+XImLv4qwsDAmTpzIxIkTyc/Pp7Ozk7fffhuLxfK1vmfSpEn89Kc/5fHHHwfw+P3g4GCpXKjxxfz1fm4SExPJyMiQzvUAzc3NNDY2yk2JsIqYTCbWrVvHU089RW5urjwVAd/7735d1IuFyKQgjqMXLVrE+vXrCQ8Pl2nQ/LF/6rEUCo0IylJvBtWVo8TpSGxsLKtXr+Y73/kOc+bMkSU4vRlJ65b3qdW4cePIzc0lPT1dlu/t6urixIkTnDlzxqNYg8PhYGBggKSkJKn4CReXwZgxYwbPP/88eXl5wJ3gyeEcZ7vdLtMpTZw4UWbQEaV329ra2LNnD8ePH8dkMmG326XyI9YK9XiMHTuWjRs38sQTT5CQkOAxfr7IojCUhIaGYjAYiI6OpqOjA5PJ5JfPoEDt8hYWFkZhYSGXL1+mu7sbi8WC1Wpl3759XLlyheLi4kFT1wU66rk5btw4VqxYQUNDA2VlZYC7LPeVK1dIT08nKipq9CivakSKBYPBQFdXF8ePH+fhhx9m8eLFADKQxp/xDkCbNGkSzz33HE1NTVy6dIkzZ854+EmCe/BTUlKYOnUqCxYsoKSkhNzcXKKjo+Vi5E8PsIjUBigvL+ett96ivr6ejIwM9Hq9LLZQUVHhoaSvXLmS559/XkYv3yuYZiRQK8w5OTn88Ic/ZPr06Vy4cIEPPvjAI4hnMFatWsXcuXPJz89n6dKl8n2j0SjdP4T7hD/gPX8mTpzIunXrsNlsVFVVER0dza1btzh9+jTd3d2YTCaZj1BYlOfNm0dQUJDPc54OFcHBwZhMJuLj47HZbEyfPl1moejv75cKhz+gHr+oqCiSkpJkPmGBUFDVVsTIyEhSU1PlEfOcOXOYNGkSubm50tqqNhwEBwf7LO+k6GN4eDiLFy9Gp9Nx7do1mpubuXbtGpcuXfKQnWrZ0tTURFtbm4ev74oVKygqKsJisXD9+nXi4+NZv369VFzVf3M4Ud/LhIQECgsLOX36NO3t7fT29sqy2SJ1nUhNpyY0NJSJEyeSnZ1NSUkJK1as8EgjKdZGf5E3Xxexqezr6+PKlSt0dHSg1+vJycmRstqX68W9UPviGo1GFi9ejNVqpbm5mVu3bmG1WmlsbMRisZCTk+P3xrdvglgTXC4XMTExlJSUcOHCBfbu3UtbW5uUL+LZVJ+2D8W98InyOpgfojrVEODhNuCPxwbeqIVhSEgIubm5FBUVodfrOXDgABEREVy5csWjEk5SUhKTJk1i5cqVLFu2TFbe8Ifj5cFQW0tv3brFp59+yvHjxwc9BtHpdOj1eubOncuPf/xjHnnkEeCOy4GvhJEIYhGpx0pKSigpKZF5Wt98801cLnfpW1G+z2az0drayowZM3jhhRfYuHGj/D6x+IeFhXmk4fI3xH0PDw9n6dKlREVFUV9fj16vp7W1ldzcXC5fvsyYMWNYvXo1BQUFGI1GkpKSPKyt/jgv7wf18xkWFkZqaiqxsbGUlpZy/vx5NmzYAHhGo/sa70U7LS2NlStXysh8QJ5kBAW5qzn19fWRnJxMUVERmZmZZGZm8vDDD8uNozoLQVBQkF9Y7NRjU1BQQEFBAY2NjVy5coXS0lLOnTtHTU0NPT09dHV1SXcscMtasTgmJiYyb948nn/+eRYvXkx9fT0VFRVkZ2dLdyVRNngkTn3Uf0MU0bh16xa7d++WQXdCbqqD7YRrzsDAAHl5eTzyyCM89NBDTJw4UZZ89ddYiK+DWg8QgaJjx46luLiYGTNmeGRB8ScjjhrhIhAbG8vSpUtpbGzEbrfzxRdfYLFY0Ov1hIeHe7j+jRa3AW9iY2OZNWsWjz76KLW1tRQXF5OdnS3n85cVAvom+GTmqy2KISEhzJ07l2PHjnH8+HHAnafRF3Xgvw3eFpK8vDxZiSg6OpqioiI6Oztl0IRI9B4TE0NKSopUXME/g0XAUxhnZmby3HPPYTAYOHDgwF3XJiQksHHjRp5++mlmzJgh3/cHISSCWdT3OTc3l7/8y7/kscceA9x9FQuESFEUFxdHfn6+x3eJ7/H3PITebZs6dSr5+fnodDpsNhslJSV0dHRgMBhITU3FZDJ5CJpAF7befohpaWnExcXR0tLCK6+8wrhx49i4caP0S/SXABHvo7nvfe97rF69Wp5WeafMGhgYwGAwEBsbS3h4OJGRkR5VfcR89vf5KtIlTp48mWXLlsmyzRcvXuS3v/0tJ0+eJCwsTG4eTSYT3/3ud9m4cSNz5szBaDQSERFBWlqaTFM40kYQtRtHREQECxYsICoqijFjxrBnzx7Onz8/aBn09PR0Jk2aRFRUFNOnT2fNmjVkZ2fLPgi3kEC35qn1AKPRyObNm3n44YeJj48nMzPTw2XOH57FwVC3MSkpie985zukpKTw/vvvc/z4cWJjY8nOziY1NXVEAgVHGm8ZUlxcTGpqKhaLhejoaJKTkwdNxTgU+GRGqI85goODKSkp4fvf/z5JSUlERUWxceNGCgsL5fX+ZA25H0QCcXALroSEBI9gkMEQu3B/VtRFYIjL5SI1NZUXXniBiRMnMmvWLCoqKnC5XISFheF0OklPT2fTpk1ScbVarX7jSyiswoAs32cwGMjPz79LOR0Mq9VKUJC7go8/pFO6H9QWDnBbH9XWAFG6+Kt+f7QQHx/PokWLuHHjhsx64o9BourNg9FoJCsri6ysrK/1HQMDA1itVmlx9FdFAO5YhnU6HUajUVr/BTk5OTQ1NTFmzBiio6Pp7++npaWFKVOm8MQTTzBnzhxZljMoKMijjvxIWVy9EcpKZGQk8+fPJy4ujszMTE6dOkVdXZ2MVO/t7SUyMpKSkhKmTp2KyWRi/PjxMj+oMHyoczUHMuq5HRoaKq3ugYSwotpsNsLCwkhLS2PdunWYTCYKCwsJDQ2lpKSEtLS0u4K8RgOiT8JlyWQy3RUEOlz4zG1ATWpqKk899RTr16+XlRq8zeyByv1O0uDgYL91F1Cj3jWGhoYyf/58ioqK7qrbLRJPC/xFcfVG+Dd+nfvuj5Ho94s/jsFIIhS3sLAwNmzYwLJly3C5XNJK6X2dv/Bt5IK/+WF/GcI15V5tjYyM5M/+7M947rnnpAuQiFgXuWDh7vHz5Xh69yUvL49x48bx2GOPeaQbFNZwg8EgS9uqLXv+5vc5VATCvPwygoKCPNaEiIgIHnroIebNmwe4ZU0grxn3g08yB434XxwEkXRZXUs70FEfywmrjrrKihDQwcHBspxhID3E6ohmdTU0b/wtAMYb9UKpTtGmTvAOd6pRDXeE8nCjtnaoM36I6G2xYPrreA0lg8kcfz5OF4UIBsunqEZtnRN+kYEynt6uKsISK+SNwWCQJbgHQ22dhDvj6Q+yVV0B7uusd6L8ZqCM4TdFBPeI0sD++hwOhphfIi2W98nWg4JIzSdyTIeEhAzbOPqF8gqei2kgTdp7oe7DvXKiBjJqf6XBfMnEIhRID/CDJHC8rVujfWEcDPW8FffDn2WPsPCMdiuOQOSG9Laafpm88b7Wn8ZT/Yx5l4pV4/1s+mO6xOFgNMhf9ViNRJU6f0NUwBsJ/EJ5FZYfsWseTGBp+CeiFOdglTWERVlDwx8RSf+Fz50/+5tr3EG9VgDSyhMo8kZ90uF9uiOsxt4V4jQCC3FiAPg0u85o5mtriIqiDEc7/IbR3j8Y/X0c7f0DrY+jgdHePxj9fRzt/QOtj6OB0dg//9+mamhoaGhoaGhoaNwmKNDzN2poaGhoaGhoaDw4aJZXDQ0NDQ0NDQ2NgEFTXjU0NDQ0NDQ0NAIGTXnV0NDQ0NDQ0NAIGHyWj0pRlMeBRcA0YCoQBbxhNpuf8VWbhgNFUdYAfwFMAuKBBuAL4J/MZvMxX7ZtKFAUpQYYd4+Pm8xmc8oINmdYUBRlLPD/AA9zZwy3A780m83tPmzakDGa56miKEHA94AfApOBYOAq8Crwr2azecCHzfvWKIoSD2wA1gCFQBpgAy7i7uOrZrP53lUNAgRN1gS2rHkQ1nytjyOHLy2vfwP8GPcNuOnDdgwbiqL8A/ARUAzsBH4FnAHWA0cVRRktE7oT+OUg//6PLxs1FCiKMh63Evc94CTwz0AVbkXv2G3FIaB5AObpH4BXgCzgbeAlQI+7n2/fVm4DmSdw92k2cAL4F+A9oAB4Gdg6Cvoo0GRN4DLq13y0Po4YvqwE8F+AOuAabi1+vw/bMuQoipIC/BXQBEwxm83Nqs+WAJ/h3mG/7psWDikdZrP573zdiGHCDCQBPzGbzb8RbyqK8k+45/D/AH7ko7Z9a0b7PFUU5VHgWaAamGU2m1tvvx8KbAUeAzYDr/moiUNBObAO+FhtYVUU5Re4laDHgI24FdpAR5M1gcuoXvNvo/VxhPCZ5dVsNu83m80VZrN5tObqGof7/p5QKwTg7jvQDST6omEa94eiKNnACqAG+Fevj/8b0As8qyjK/RUp909G+zzdePv1H4XiCmA2m+3A397+8c9HvFVDiNls/sxsNu/wdg0wm82NwL/f/nHxiDdM4755EGTNA7Dma30cQbQarMNHBW6/s1mKoiSoF05FURbi9hPZ7qO2DTVht4+WM3AL2QvAoUD3JQQeuv26exDFoFtRlKO4F5w5wL6RbtwQMdrnqfCDrBrkM/FesaIoJrPZ3DEyTRpR7LdfHT5txdChyZrAlTUaGkOGprwOE2azuU1RlP8K/BNwWVGU7cAtYDzuI749wJ/5roVDSgqwxeu9akVRvmc2mw/6okFDRN7t1/J7fF6Be0GZQIAuKA/APBXKeNYgn2Wr/p8PHB/+5owciqKEAM/d/nGnL9syhGiyJkBljYbGUKKlyhpGzGbzv+A+tgwBXgT+f7iDK2qB17yPaQOUV4GluBeVCNzRzr8DMoFPFUWZ6rumfWtibr923uNz8b5p+JsyfIzyefrR7de/VBQlTrx5W7H7peq62BFt1cjw97iDtj4xm827fN2YIUCTNQEuazQ0hgrN8jqMKIryfwP/L/Br4LdAI24Lz/8E3lAUZZrZbP6/fdjEb43ZbP6l11ulwI8URekBfgb8He40PqMREcEd0P5No3yevgU8A6zCbVn+EOgDluG2LlcAuUCgHzt7oCjKT3A/f2W4A9YCHk3WAAEuazQ0hgrN8jpMKIqyGPgH4EOz2fyXZrO5ymw295nN5jO4BexN4Ge3HfVHIyJQZKFPW/HtENaOmHt8Hu11XcAx2ufpbf/BdbgzKjTiVuS+jztadj5uFwmAQLYue6Aoyn/GnQbsMrDEbDa3+bhJw40mazQ0HjA0y+vw8cjt17vSSJjN5j5FUU7iVg6KGDyYJNARykDARsfiTmQPbj+zwci9/XovP7VAYNTPU7PZ7AD+8fY/iaIoRty5Ci3ApZFv2dCjKMpPcecHLQWWBrjLx/2iyRoNjQcMzfI6fITdfr1XmiHxvm0E2uILSm6/BqTCcxuh0K1QFMXjWVEUJQqYh1vxCeRAnwd5nj4LGICtt1NnBTS3A+/+GTiH2+L6ICiuoMkaDY0HDk15HT4O3379oaIoaeoPFEVZhVsYWYHPR7phQ4WiKJPVQTCq98fh9p2EAE1uD2A2myuB3bgDQv6z18e/xG3p+aPZbO4d4aYNJQ/CPI0e5L2ZuAOaenAXYQhoFEX5W9z9+QK3xbX1K34loNBkzaiQNRoaQ0aQy+Ub/+/blW8evf1jCrAS985ZLKatZrP5r0a+ZUPD7d3zLtyBId3ANtw+dxNxH9UGAT81m82/8lkjvyWKovwd7sj0/bgrGHXjDoJZg9ui9QmwwWw2B6zV7nbJxs9xV775ALiCuwznEtxHeHPNZvOte3+Df/OAzNMTuK1Wpbj7OBlYDfQDGwM9El9RFFEhbAD4DYP7RdaYzebXRrBZQ4oma0aFrHmUUbzmg9bH2++NSB996fM6DXdZRjXZ3Mm9eB13kEVAYjabnYqirMa9i34St99gONCGW9D+2mw27/ZhE4eC/bjzExbhPrqLADqAI7hzMW7xdRWOb4vZbK5UFGUGbuvcw7iVngbckfm/DPRgmAdknr6Lu2/PAEagHngZ+Huz2Vzjw3YNFSKHbTDw03tcc5DALoGryZoAlzWM8jX/NtPQ+jgiffSZ5VVDQ0NDQ0NDQ0Pj66L5vGpoaGhoaGhoaAQMmvKqoaGhoaGhoaERMGjKq4aGhoaGhoaGRsCgKa8aGhoaGhoaGhoBg6a8amhoaGhoaGhoBAya8qqhoaGhoaGhoREw3HeeV0VRAj6nltlsDrrXZ6O9fzD6+zga+gejv4/aPB3d/YMHo48aGhq+Q7O8amhoaGhoaGhoBAxfu8KW2WwejnYMK4qi3Pe1o71/MPr7GIj9g9Hfx0CZp6Jwi8vlIijIbXwTr1+FNoaePAh91NDQGHl8WR72a+F0OnE4HNjtdrmoBAcHExoaSnBwsK+bp6ER8LhcLux2Ow6Hg5CQEPR6va+bNGI4nU76+/sJCwtDp3MfSHkrrGplVsM3uFwuXC6XXAucTqccE51OR0hIiFwPtHVBQ2P04tfKqxBUQUFB6HQ69Hr9oAuqsJRoC8vI43Q6uVeJYZ1Op41JABEUFOTxjD0Iz5XL5cLpdBIcHIzRaASgq6uLvr4+nE4nISEhGI1GIiIipFKr4TuCgoLumqdfhrbh0NAYnfi98jowMEBQUBAhIfduqrhG22mPPAMDAzgcDg8LiFgw9Hr9l46bhv8jFLvRisvlwmKxEBkZCUB3dzcvvfQS+/fvp6enh7i4OB566CE2bdpEfHw8AHa7ndDQUF82W+M+cTqd2iZaQ2MU4peahcPhQKfTyX8APT091NfX09jYiNVqRa/Xk5CQQHp6OjExMYDvLEVOp5OBgYG7rJDi/zqdjuDgYI/+BCqin2JDERoa+qULubg3ov/+inoMnU6nfF9Y/YODg0e1EgdgsVioq6ujtbWVxMREcnJyCA4O9ngeRwt2u52QkBB0Oh2RkZE4nU66urrYtm0bL7/8MmVlZfJah8PB6tWrpfLqcDj8SnkV1uOBgQHgjrwJdIVN/SyGhITgdDqx2+1YrVZaW1u5efMmfX19hISEEBQUhNFoJDk5mejoaIKDg4mMjPQ4RQj0+6GhoXEHv1Re1Qul3W6nqamJCxcusGvXLvbt20dDQwMxMTFMnz6dp556inXr1hEcHCyF+Ehb+3Q6nUeQh3hV+2IJJSjQuZc/4L0Q/fb3vnuPjxi/QGn/N2FgYEDOTafTyaVLl/jjH//I0aNHWbNmDb/4xS8wGAxSqR9NPrBqa5zFYuHs2bPs3LmTHTt2eCiuAHV1dVitVvmzenPjD4g5KtysRgtifIQ87+zs5MaNG9y4cYPDhw+zY8cO6uvrCQ8PJzg4mDFjxvDQQw+Rk5ODwWBg6tSpFBYWAndc0Ebjc6yh8SDiN8qry+W6K2CitLSUAwcOcOnSJRobGzl37hw1NTUAtLW1UV1djcPhICoqirlz5xIeHj6iwsnhcEh3hfu1zAW6H6HaPaOtrY3S0lLKy8vp7OyU1tWuri7Cw8OZM2cOc+fOldfbbDaCgoL8xmolAj/UltXRbl1Vo/Ypb29vZ/fu3bz22mt0d3cTExPD2bNnKSkpISwszNdNHTKsVqtHkOfVq1f57LPP2L17N8eOHaO5uRmDwUB0dDTNzc2AW3nt7++X3+EvCqLaX/erXKu8UQe/BgcHo9fr/U4mqfvkdDq5cuUKn332GWfPnuWLL77g+vXrgNtHGeDmzZt0dHQQHx+PXq9n0aJFBAUFkZeX5zcyR0NDY2jwK+XVYDAAbkF1+fJl3nnnHV577TVu3LgBIH0odTqdtLBWVlayd+9ekpKSmDJlCuAWzCNxbCYEq/DNHRgYuKfbgGi3vy0QX4e+vj7sdjs6nY7W1laOHz/O9u3b2b9/Py0tLXdd//3vf5/g4GAyMjIICwsjNjb2Lr9YX6JWpNVjKMZRbXUdjYqtsCDqdDrsdjtnz56lu7sbgObmZt555x30ej3Tp08HPC21gYjL5UKv18sN7pUrV3jrrbd4/fXXqaqqAtxzQlhZg4KCcLlcjBs3zkOB95f+qzeS6nkr5M2XtTMkJMTv/dEdDgednZ20tbVx/fp1Dh48yK5du7h8+TK9vb0ys4BQ4nU6HTU1NVRUVADQ1NREcHAwTz/9NDk5OfI7/b3fGhoaX41fPMV9fX2Eh4cDbuHy8ccf8/HHH3P48GGpuIJ78YmMjMRqtZKamsq0adNISkpCp9PR29vrcd1wohaADoeD0tJSjhw5wuXLl+no6ECn02Gz2eju7iY0NJT8/HxmzZrFpEmTSE9PJyoqSrbTXxbCwVBbiZ1OJ1VVVZw7d466ujqqq6u5ePEiV65coaOjY9Df379/Pw0NDaSkpLBq1SqeeOIJwH3PbDabHHNfIPxwwd3PCxcu8MUXX3DhwgXKy8vp7u7GYDCQmJhIXl4e8+bNo6ioiLi4OL8es6+D+pg5PDwch8MhP6urq2Pbtm1ERkaSkJDAuHHjAlpx7e3tJTQ0VLo+vP/++2zdupWTJ09SXV0NgMlkknPZZDLx/PPPk5WVRXp6OmlpafK7xCbbX2htbeXkyZOcPHmSvr4+CgsLWbJkCWPHjgU85/qXIRRAX6JuQ3l5Oe+++y7Hjh2jt7eX1tZWqqur5eZC+LQK39iBgQFphRW/v3//fqZPny6VV39z+dDQ0Phm+FR5VS+c4D7+2bdvH6+//jr79u2js7OT0NBQYmJicDqd9Pb2ysWlt7eX8PBwJk+ezPjx46WgBobN6iqUzZCQEBwOB3V1dVy8eJEjR47w8ccfc+nSpUF/7/Tp05w+fZpp06axatUqFi1aJI/p/GHBuBdiUQgNDUWn01FeXs6WLVs4d+4cLS0tcvwMBoPsj7Bg9vb2Ul1dLRWD3t5e0tLSmDNnjl9YfcT8sFqtHD58mHfffZczZ85w69Yturu76e3txWq14nK5SElJobW1lcjISKZNmyaVF3/ffHwVautcTU2Nh19nZ2cnnZ2dnD9/nurqapKSkmQqqUDqt2hrRESEfG/fvn3827/9G3v37gXcJzoOh0PKlrS0NH7wgx/wZ3/2Z4wZM0b+nlACfW2BFzLD6XRy48YN9uzZw/vvv8/58+dJTU0lNjYWu91+1++px62trY2mpib6+/uJjY0lNTXVr4KbbDYb165dY/v27Zw9e1a+r9PpCAsLw26333PTHBERwcDAAFarlatXr3Lo0CEyMzPJz8/3qz5qaGh8c3yqQdhsNnkc19XVxa9//Wvef/99amtr6ezsBNwLyeTJkwkPD6e0tJQrV64AbmvD/v37SUxMZMWKFaSnpw+7U77dbpfCr7m5mddff52PP/6Y69ev09DQAIDRaCQlJYWUlBTGjRtHZGQkN2/e5MKFC5w5c4aWlhaio6OZPXs2gN8qruBpmRsYGODSpUscPnwYi8Vy17VRUVEkJiai1+u5ceOGhyUc4MCBA1itVp544gnWrFlDbGzsiPTBG3Xmg7a2Nj755BPeeOMNTp8+TVxcHCtWrGDOnDnExMRw9epVPv74Yy5fvszp06cpLCxkwoQJUnkdGBjwuRL+TRDjKpSwmpoa9uzZI/3JvXE4HH7j5/l1EW4uYpw+/PBDfvWrX3Hs2DF5jdFolPImMzOT//iP/2D58uV39dnXz6ralcXlcnHz5k127NjB7373Oy5dukRCQgLLly9nzZo1jBkzxqOYC9yZr11dXXz00Uds2bKFtrY21q1bx49+9COSk5MBT7nsi76BO4ius7MTm80mr9Hr9fK6L5uPavct4csdHx9PQkICKSkpgP9ljNDQ0Ph6+GzlVeePtNls7N27lw8++MBjl52Tk8PatWuZO3cuer2euro6SktLOXfuHGfOnKGxsZHDhw9TXFxMYmIiCQkJUrANx65aCFan08m5c+f46KOPOHHihPx88uTJrFu3jqKiIkwmE3FxccTExFBXV8d7773HW2+9xY4dO9DpdFgsFmbNmoXBYJCC1J8sAS6XSy74fX19HDlyhGPHjmG324mNjSU5OZnGxkY6OjqwWq1MmDCB5cuXExsbS3l5OZcuXaKjo4Pe3l5u3LhBc3Mzu3fvxmKx0NLSwsyZM8nOziYxMXFEFxG73S7nXX19Pe+++y4XL16kpKSEpUuXMnfuXGbOnCmvTUtL4/e//z0VFRWcOXOGtWvXenxXICqv4Hk8K4IfB7NkhYWFfaX/pD8jNptWq5VDhw7xH//xH3z22WcAMpOC1WolIiKC7OxsfvKTn7B8+XLArQR1dHQQFRXlFwFN6s2SzWbjiy++4JNPPpEnPhkZGSxbtkz6/nvnow0JCcFms3Hy5Em2bt3K3r17CQ0NZfHixX63ObHZbNLHHtzjKDYZdrudmJgYxo8fT0xMjNxc9fT0UFVVJV0HDAYDVquV8+fPk5SUxJIlS6Ty6m/91dDQ+Hr4ZOVV5wlta2tj165dvP3229LRHtyCeOXKlbzwwgtMmjRJLrYWi4W33nqLl156ibNnz1JaWsorr7xCbGws69atAzxzOA51e10uF+fOnWP37t2Ul5cDkJKSQkZGBiUlJWzatEmmZ+nu7iYqKoqcnBzGjx9PVFQUL730Em+++aYMUCsuLvbLoyy19aW0tJR///d/59NPPyUmJob58+djNBplNLbBYOChhx7ixRdfJCEhgaamJurq6qisrOTUqVPs3buXsrIybDYbBw4coKKiglWrVvHd736X2NhYucCOhAuF2q+ztbWVEydOEB8fz9/8zd8wa9Ys+vv7ZdaL0NBQ5s+fz5UrVygtLaW0tJSOjg5SU1NlewMV9TzT6/VERUUNam0TBSgCbbEX5aTFs7V3717+z//5Px4W1/7+fvkcrlixgp///OceG5fQ0FASEhJ80v7BEOMQFBREb28vJ06c4Ny5c4B7k5GUlOThjyvGTO2jf+PGDT788EMOHjwIwNq1a1m1ahUmk0n+nj9YJEXpbyEPbDab/H9cXByLFi1i3bp1pKWlYbFYcLlclJWV8dprr0nlNTw8XLrC1NbWSnkF/hN0p6Gh8c0YceVVWL6CgoLo6Ojg0KFDbNu2jV27dsniA3PnzmXp0qUsW7aMSZMmAXesnkajkVWrVtHQ0MCNGze4efMmR44cYe3atdhstmHJRSmirIODgxkYGJA5Idvb25k2bRrPPPMMkyZNksE9AlG1ByA9PZ0f/vCHOBwOtmzZwmeffUZGRgaZmZnSGuCL47rBECmkRFuam5vlou9wOGTyekFmZiZTpkyRC31ycjLJycmMHz+eCRMmMHHiRPbs2cMnn3xCV1cXNTU1tLW1YbfbPRaRkVCQ1H/DZrNJZVTMM71eT3d3t8xmkZWVxeLFi9m2bRuVlZWUlZUxceJEYPTUTg8ODr6ndTWQc4cK5dVms3HmzBmpsBmNRkJDQ2VQXlFREevWrfNQXHt6enzm2nIv1OPQ1dXFlStXaGxsBNzuVampqV8aTGa32zl37hwHDhygp6eHlJQUVqxYwbx58zAYDFJh94V7hPfcMxgM5Ofn8/DDDxMSEkJ9fT16vZ7x48ezfPlyli5dyuTJk6VFXFT0U8tc9fMpMhJoaGiMDkZUeXW5XHJX39bWxqFDh9i6dSuHDx+WO+S5c+fy1FNPsWrVKhmEJVJfWSwW9Ho9KSkpzJ8/n23btnHz5k1cLhd9fX309vZ6CLOhQgi94OBgnE4n165d4+rVqwBMmDCBRx99lPHjxwPuBUJUuhHXW61WwsPDyczM5Pvf/z4Wi4U//elPvPfee+Tm5rJp0yaPFD6+xrvUbkJCAjNnzmTHjh3ExsbS1tZGf3+/vC+FhYXSGgnu4Cyj0YjJZGLatGlMmTKF2NhYampqOH78OAaDga6uLurr67FYLB4BUMONul9xcXHMmjWLlJQU2tvbiYyMxGazyQVQjGN2djaxsbFUVVVRVlZGT0/PqKreI0r8Dnb/A7G4hrDgh4eHY7fbOXnyJBcvXpSfW61WLBYL0dHRLFu2jLVr11JSUkJ3dzcRERGEhob6neIKngpee3s7t27dkj8nJydTWFhIYmKifE+Mm7C6dnR0cOPGDWprawG3n/qYMWNkIN7AwIBfWF3BrbxOmjSJp59+mvHjx1NaWorT6WTWrFmsW7eOpKQkwC1rDAYDOp2OqKgoDzce72cykJ9RDQ0NT0ZMeVVbRS0Wi4yQ3bt3L21tbURHR/P444/z6KOPUlRUJKN8hYIkotjFApuSkkJWVhanT58G7hQMENcOF0JRFvT399PQ0CCVV5FBQK0kiRKbISEh0o+3rKyMvXv38qtf/QqdTsdjjz3mkS7M176UagtOYWEhv/zlL1m+fDnHjh3j7NmzVFdX09vbS15eHhMmTCA6Olper644Jn6ePXs2Tz75JAAnTpxgz549REZGyjRiI4XaMj9u3Dj+4i/+Ar1eL++9yOsqErjDHf9fu91OW1sbnZ2dREZGSuUg0JVXYWkXz9pgATGB1D+73S5PDVpbW9m5cycnT54EIDo6Wh4r5+fn8/Of/5wZM2bQ09Pj9+4R6lRlbW1tHi4w+fn5LFiwwCM7gvfJgAiCEpsykZnAH9HpdJhMJgoLC0lPT5cBdHFxcVJxBfe8FCdjer3eo8/qOetd9llDQyOwGTENSezo+/v72b9/P2+//TaHDx+mra0NcAvfp556iqVLlwJIFwJ1aU6j0ehRcUW90IgI8uFA3Ybg4GDy8vLIzs6mqqqKkydP8s4772A0Gpk+fbr0i1UngBcLqTiWW7x4MfX19dTV1XH27Fn27dvH6tWrCQ8Pl/W7fa28wp2o3YiICIqKisjKyqKvr48DBw7IZPYpKSnk5OR4LILCkgN3ItXHjBnDhg0baGpq4vjx4zgcDhlsFx8fT1ZWlkfRh+FSltT3NSEhgdWrV8vk9eDp7ycWQrX10W63eygNgYjYCIr+NTU1ceXKFVpbW6VvtwiU8d40ivf8WZlVt+306dPs2bNH5osWG8+YmBgef/xxZsyYAbjTK/lr+VB1ij5wK64NDQ0e83Ds2LFMmTKF0NBQbDbbXT7/ra2tHDp0iGPHjmGz2YiJiSEnJ+euTae/ERYWJt2QBKIaY2hoqMeGv66uziPLiTpTQWRkpIdLgT9vUjQ0NL6aYZdWQsAGBQVht9vZtWsXr7zyCvv376e1tRWDwUBmZiZz58718Bcd7LhSvXNuaWmRVXFCQ0OJiIiQi9ZQL6zq3I7BwcEsXryYZ555hrS0NBoaGvjwww85cuQIra2tsp2DCUex+Oj1etasWcOmTZtITk6mqalJLqr+JFS93S9aW1uxWCweymlycjITJkzwCPjwRihCwsc3JiYGcFuCDh06xKlTp+TYimTjI0FQUBAGgwGj0Tio/6o6TZhQIPxZabtfhEIquH79OidPnsRisRAWFnZP65W/I/ok/FwPHTrEe++9x+XLl+U1DoeDhIQE/uqv/opNmzbJ9/3ZPcLb77ixsdEjqj4sLAyTySQ3YIPJn2vXrrF161b27NlDf38/BQUFzJgxg/j4eHlNoPhwq4NnBRcuXGD//v3SBxjubFSio6MpKioiPT19xNuqoaExPIyIeU/4oF2+fJlt27bx8ccf09/fT2RkJFOnTuXhhx9mxYoVMuBHJMb3Jjg4GJvNRnNzM2fOnKG+vh6A+Ph4EhMTPSpXDaV1SCxq4jh/0qRJrF27lpqaGrZs2UJNTQ1vvfUWer2eFStWSBcCcO/+RX5Yp9PJxYsXaWxspKioiCVLlvDRRx9RW1tLeXk56enpMsrWHxD97u/vp7q6mgMHDvDFF1/Q1dWFXq9n8uTJLFiwgAkTJsj+qX8P7i4YodfrMRgM8viyqqqK+vp6D4v6SB3vCQu5WnER80Z9UnD+/HkaGhruGZUfSAqeQL3wd3d3y41XSEiIh2IbFRWFyWTymJP+2l+1NfnWrVu89957bNu2jZ6eHpm43mazMWPGDJYtW8bYsWNl4GBUVJSUO/4QNKnGOy/vzZs3OXfunJR/RUVFHhXABvOf7+np8ahWmJWVJf29xd/wR+VVXbZZ+OKLAENwj3l5eTkffPAB+/bt49atW/Kz/v5+mSJr/fr1HvfIH/uqoaFx/wyb8ioUECFISktLef/99zl69Cj9/f2AW4AuXbqUTZs2eQQ8eStvasGl0+morKzkzJkz0rVA5AwdbtSLdn5+Phs2bMBut7Nz506OHz9OQ0MDN2/e5PHHH2fSpEno9Xr0er08wuvo6GDnzp189tlnPP300+Tl5ZGcnCwrdI0dO5a8vDxCQkIGVQRHErUi0NzczOHDh9m3bx+nT5+mtbWVyZMns3nzZtavXy83HfeKVlf3ITo6mvT0dDo6Oujp6eHWrVt0dXXJ9FQjiXdgmkDd95s3b3L27FkaGxsxmUyMGTNGHrWK6/xVmbtfjEYjUVFRdHV1eSi1er2eMWPGkJGRQWhoqPQt9Lf+ig2HsDy2t7ezc+dOPv30U2mdFMfJK1eu5MUXX5S5UOPi4nzT6K+Bt/JaX18vAwfFnFT3QwS4ius7Ozvp6Ojw8GOPj49n/PjxBAcHy0IO/qjQCSur2t1HvUZUVFTwxhtv8P7771NRUYHD4cBgMEj5GRcXx7Jly1i+fDlGo1EaIPyxrxoaGvfPsGpGwoLT39/Pzp07+eMf/0h1dTVGo5GMjAxmzpzJkiVLyMzMvNOgQZQ1h8Mh/ZeCgoLo7u6mtraWgYEBiouLeeSRRzxcDoYrqbpa4EVERLBw4UJ+/OMf88Mf/pDExESuX7/Ov/7rv/JP//RPvPHGG5SVlXn0yW63U1dXx7Fjx3jvvfcoLy9n0qRJmEwm3n//fbZv3y4Ve19XNVL7012/fp2dO3dy5MgReSyXl5cnK5uJ6wc7Vlf76oE7ZdicOXNkYElrayvXr1+nsrIS8HTR8BXq+97f34/VaiUsLIz09HRyc3MJDw/HZrN5lFMNZLxdBcR8DQsLIyUlRQbIqIPY/ImBgQGPcqjvv/8+//AP/0BFRYW09AseeughNm7cKH0lB8PhcPjd2Krvu9Vqlf3t6Ojg8uXLHsfl3d3d0ie9v7+fa9euceHCBVlFDNw5UMVmMZDToZ0+fZrt27dz5coVecInjB1wZ7MsXJ0GK5uroaEReAyL5VUIj9DQUHp6ejhw4AC7d++W5ScjIyOZOHEiCxcupLCwUEZ332tHrM49WFdXR1lZGbW1tfT09JCTk8OSJUtITk6WCtdwBzsJS09cXBxz584lNjYWnU7Hm2++KV0Jjh49yrp161izZg1jx44lPj6erq4uwsPDGRgY4NSpU8yePZtly5bR0NDAli1bOHHihIefpa+srt45Ebu7u6mqqqK9vd3jOpEeSxzH3s99N5lMpKamShcPcPsvNzY2Mn78eMLCwnzqe+jd95aWFpqamkhPT2fZsmUyF6g6TZb6ZCAQ8PbvbGtrG1RZCw4O9ghy8ddofFGQQFjjTp48KVPZRUZGSkVuxowZ5ObmYrVaaWxspKGhgfDwcAwGA/39/dhsNpKSksjIyJCnH/5wnO69KYyIiCA5OZmWlhY6OzspKyvj2LFjLF++nJSUFA8/1rCwMCwWC9evX5fBsdHR0URHR3sEJPqbNX0wxElHaGgonZ2dHDp0iHfffVf6NIs0iaJ89bhx45g7d66Hu4A/BMJqaGh8e4blSVYnuD969Chms5lTp07Jz8VR/8yZM4mLi5PCczA/VfUR7uXLl3nzzTf55JNPqKysxGAwkJ6eLvPBjpQFwbutEyZM4IUXXiA/P5+PPvqIPXv2UFVVxUsvvcShQ4dIS0tj2rRpTJgwgb6+PnQ6Ha2trXR2dpKbm8v06dN5/fXXsVqtHhH3/qIoiNrwISEh0jLc399/X1H3QhlU+yJWVlZ6lCKNiIiQJTiH2l/5myDuu91u59ixYxw7doysrCxKSko8orP1er3MPhBIR5HqDBgVFRVcu3ZNKq9qxd2f5uDXQe12JBS2DRs28MILL9DY2MhTTz3FtWvXsFgsBAcHy9zFzc3NbNq0id/85jcyR3N/fz8RERG+6gpwt3tLZmYmhYWF1NTUSGvq4cOHefXVV3nhhRc8cr0KBgYG5NiaTCaPAK9AG+eBgQEOHTrE//pf/4sjR44A7jFXpxg0GAwsXbqU559/nokTJ8r++Us8gYaGxrdjSJVXcWwjFsaysjJ27tzJ4cOH6enpQafTkZyczPLly1m2bBnZ2dlSSfHeEastWv39/VRVVfHRRx/x7rvvyuP4VatW8dBDD8kjIbWv6HAj8rmKSkzZ2dkkJSWRmZnJ9OnTOXHiBEePHuXMmTOcOXOGEydOkJGRQWdnp1QAL1++zMmTJ2lqaiI8PByXy0V9fb30MfSVBdLbMmez2XA6ndJvcPLkySxcuFAuBF8VZKYek5qaGk6fPi2DTaKiokhNTSU9PV3mbPRVyiLxd8PCwrDb7XIMb968SVpaGvX19Rw4cICgoCCMRiPjxo0jOTlZ9l0UqPiyKke+Rp01wWKxUFdXR1NT0z2VF/XY+auFTmyuwB1hPlgbHQ4HVVVV7Nmzhw8//PCe37Vt2zYWLlzIY489RmhoqF9sSLyDCadMmcKNGzc4fPgwN2/eBNxlX7dv305kZCQlJSVYrVZsNhv9/f2cOHGCq1ev0tPTQ0hICGlpaR5lb/11XAXCgBEcHExXVxf79+/npZdekoqreGZ7enpwOBwYjUZKSkpYtWoVU6ZMkXLF3/upoaFx/wyp8qquJd7S0sL777/PgQMHpFUnMjKSJ554gmeffZYpU6Z4pHbxVlYGBgbkglRZWcmf/vQnPvnkE6m4FhQU8MILL7Bs2TLgjhP/SC426vyv4O7f3LlzKSkpoba2li1btvDmm2/KSjjnzp3zsFaeOHGC8PBw2tra6O3tpbOzk/LyclJSUnxevUncx+7ubhoaGmQ0uihz+93vfpeYmBg5dvdSXr2P4bu6urhx44b0kTUajcTExMijTl+WcVSX562srOTVV1/l4MGDOBwObt68ydtvv014eDgWi4X4+HgWL17MqlWryMrKAggo6yvc2SB+2f0OhMVeRJ8PDAxQWlpKXV2d/EykzdqxYwe7d+/2yP05GM3Nzfzv//2/cTgcPPPMMzLFnz8ENAkZFx0dTUlJCdOnT+fq1as4HA66u7u5cOECv/3tb/nTn/6Ey+XCbrdjt9ulWw648zIXFhZ6HKX7YxCeGnHfe3t7OXToEL/97W/Zu3cv4B5fh8PhIVfz8vJ4/vnnWbp0qexXcHBwQFmXNTQ0vpwhVV6FwtXW1sauXbvYsWMH58+fx+VyYTQamT17NqtXr5aJwYViplYAB1tMa2pq2LdvH2fOnAHcAlj9PeL3fHUkpLYSC4U7Ozub733ve0ydOpXm5ma6urqora2lqqqKy5cvU1VVRV9fH319fdIS29HRQXNzsyyDK77bFwuLGJNbt25RVVXFrVu3CAoKYtmyZaxYsUImDVdvWO6FejzV1bdEoJA/KAbgWZmptraWDz/8UEarWywWLl26JK04wcHBXLx4kbfeeovMzEwee+wx1q9fT1BQkFxM/dkCK/g61ih/PF5Wb3xbWlo4d+6ch/IqrLIOh0M+Z3Fxcej1eqnQhYWFMWnSJHp6eqioqJAnJevXrycqKspv+q3ObDF27Fg2b95MVFQUn332GVevXsVqtVJeXv6l32EymZg5cya5ubke3+sP/VMjNrFCse7p6eHTTz/l97//PQcOHADcLgChoaHY7XYPGTNlyhSWLVsmC6cIpd+fFXQNDY2vx5Aorw6HQ1ohHQ4H+/fvZ+vWrVy6dEkqXwsWLODpp5+muLhY/p7aF1Jcp4427+vro7y8nF27dnH27FnAXRXpO9/5DmvXrvUIJvFlbka10ipKbQYFBZGRkUFGRgbgtupZLBbOnDnDq6++SlVVFZGRkRQXF2O32ykrK6OlpYUbN27Q09MjE/kPZpUeTryT8dvtdjo6OrDZbOTm5jJ79uxB66d/Gepr1GV8g4ODGTt2LMnJyR4Lla8WGfXfNRgMjB07lp6eHoqKijwyYoBbma2pqeHw4cMcPnyYlpYWUlNTmTVrVkAFhfiLYvZNUT8ffX19NDc309PTIz/v7+/3yN1rs9mkHyzcqRAXGhoqXVnAnWHj888/Z968eVLO+NoXW8hFEZz20EMPkZKSQkpKinRvaW9vl371whAQFBQkfcxjY2OZNGmSdBvwJ8XOex6K/ra3t3Pw4EF+//vfs3PnTsD9fNpsNnp7ez2K00RFRWGz2SgrKyMyMpLQ0NCAeh41NDTujyF5qtULYFtbG0ePHuXw4cMyyjc9PZ2VK1eyYcMGoqKipLKrtrYJpUYImubmZo4cOcL27dvZsWMHVquVuLg4nnzySTZt2kRRUZE8Mhqu1FjfhHtFZIucr4WFhRQUFMhStxMnTiQxMZEjR45w+vRpLl++TGtrqzzWU7tPjCRqq7hQKMPDw4mKivJoz1fd9+DgYIxGIy6Xi7KyMsrLy2Xlm/DwcBYvXsysWbM8yu/6aizVG6Dc3Fz+7u/+ju7ubsaMGUN8fLy0EjscDux2O7du3eLcuXMcOHCAmpoa/vt//+889dRTcmMlFAd/mZv3wt/b92Wo2z5YfXvva3U6nbTSPf/88/z5n/85ly9f5je/+Q3Nzc3yutraWk6dOsX48ePJyckBfPcseuOdb3rz5s2sXLmS1tZWmpqa6OrqkhlcampqOHbsmAyYTU5O9nDR8aeNi6iupy5t29vby969e3n55Zc9LK7e7Rbrh81m4+jRozQ3N1NcXMy8efMoKSmRJ0UjbQzQ0NAYHoZEEovjeovFwtmzZ7l48eJdO/2ioiKZHklUnBJ+WXq9Xn5HW1sb5eXlnDt3jl27dvHhhx/idDrJysriySefZN26dRQVFUlFwx8WEzVqy7GIVnY6nTIFlE6nIzY2lrCwMJxOJyaTiezsbNLT07l06RI2m83Df8tXi4tYIG02G93d3dhsNmJjYxk7dqxH9PWXLQQ2m026FFy7do0//elP7N+/X353UVERK1eupKioyOM43leLi3oupaSksH79+q/8nRUrVrBq1SpeffVVPv30U373u9/R3d3NmjVrPDYg/uAWcS/8SYH5uqjbbrfb6e3t9fBrFZlBXC4X/f39GI1GsrOzeeihh/jBD37A+PHj2bdvH7W1tQQHB8v8ykFBQWRnZ3sk//cXpUfMJWE1TU9Pl/mW7Xa7tDzrdDrOnj3LjRs3pPLqnabOn/DO8dzc3MyBAwd47bXX2LNnDy6XS7riiDgKIUvEPLBardTW1lJbW8vp06eprKwkODiYpUuXfmluXw0NjcDiW2l+3mmQKisrOXjwoEcZwrS0NEpKSu4qXygWFfWCcOvWLfbs2cO2bdu4cOECNTU1OJ1O8vPz+U//6T+xbt06xo4d63FE789WI2EJEhkJBA6Hw8Mqp9PpiIiIICMjg+zsbI/FZaQXTDGeIlClqamJ2tpawJ1lYObMmYSEhMjE/V92/9WBFp9//rnMFGEwGCgpKWHFihUUFBTI71DPpUCisLCQH//4x8TFxfHqq6/y93//9+h0Ol544QXArVAEYr8CFbVCGxIS4lFcIS8vjx//+Mds3ryZ69ev87Of/YytW7fS19eH0WiUp0XTp0/niSeeIDQ0FKvV6ldlmwWDbdxDQ0MxmUxSto4ZM8Zjs2kymTyUOH/YuKjluMPhoLm5mc7OTk6fPs2WLVvYv38/LpdLpsP6su8BpHW9q6uLzz77jKysLHJzc8nNzb3LTU1DQyMwGVLl9caNG5w7d05GpoP7WGvJkiWkpqbS19cny6UKdDodPT09nDp1isOHD3PkyBGOHTsmrQfp6ek8+eSTrF+/nnHjxgH4bZnKwRjMkqi27giBazAYSEpKIjU11WNx8ZXyCnf8Ojs7O0lKSmLChAkyLdlgi57ok8Vi8Ug5tXfvXj744ANZpMJqtZKZmcnMmTM9Eqr7k4LndDql0qO23sGdoDPhsmIwGMjOzubFF1/k8uXLvPXWWxw+fJhly5aRmZnp8+C7e/FlYxgoeFdvW7JkCRcuXODatWsA0j1JjNWUKVMYM2YMe/fu5fXXX+eNN96Q3yMU1zlz5rB+/XqprPrr/VD7pXsXVBApBhsbG6UsVfua+xuiauKZM2coKyujubmZa9eucerUKenjGxwcfFcxDTGHhevYwMAA0dHRBAUF0d7eTm9vr3wN1M2xhobG3XzrM3f1Atjc3Ex1dTVtbW2EhoaSnZ3N/PnzWbBgAXDHH1St9FqtVo4cOcLvf/97PvjgA2w2m1TYYmNjWbNmDQ8//LCH5TYQ/AjVqIWmCEpTBxk4HA6ZecDXZWG972tISAgxMTH09/ffJfi9FR3xf7Xf4ZUrV3jvvfc4cOCA9DWMiooiOzubzMxMQkJCBs064WvU+Yq/DKfTSV9fH+Hh4SQnJ/PII49w+fJlrly5ws6dO/nud78ro57VVaD8AfUcDGRsNhsDAwMYjUYWL17skcfVYDBgtVqlT6ROp2Pnzp1s2bJFVowzmUzSzWnGjBn8/Oc/Z/ny5VJO+TIY9H5Qzyn1M9nV1UVdXZ3sp+iHr1LReaOOV2hubmbr1q188MEHVFdX09XVJa3Hws9cxEp4IyytYm3p7e2VpcnHjh1LdnY28fHxX8tXX0NDw7/51sqrEAK9vb3U19dTXV0NQExMDMXFxUyZMkVeq9Pp6O3tpaWlhbq6OmpraykvL+f8+fMcOHBA+qrl5uaSlZVFQUGB9HEV6W68c6sGMi6XC5vNRn19PVeuXKGurs5DefcF3nlrCwoKSEtLo7S0lNLSUpmwX/grC9QLvFBcy8rKeOuttzh+/LhcQCdPnszq1atZs2aNRxBFIFtE1JuNwsJCioqK+PDDD/nwww9ZtGiR3yqvAu/NklBu/C2g51545wZWW+cMBoOcp3a7XeadFvMxOjpapkMzmUw88sgjzJ8/n7CwMFmcI5Dmprq9/f39tLW1yf4lJyeTkJDg0R9fKnHqALhbt27x2Wefce7cOY9rXC6XdP0QCN9YkVdZFFBRV8YDMBqNFBUVMXv2bJktRF2FS0NDI3D5Vk+xWvD19/fT3d0tcymGhoYSGRlJe3s7dXV1xMXFyTyM5eXl1NbWUlpaypkzZ2SJw5iYGKZMmcLMmTOZNm0ahYWFTJw4UR67+jISfagQAlZYSAYGBqTFemBggMTERI8UYCPdX3UAhE6nw2AwoNfraWtr48iRIxw5coR169YNGvxgsVikxaOmpoZ33nmHjz76iIaGBnlNcXExL7744l15JgMV4a8M7nsmksC/8847lJaWSiUJ/Mfi5Y16jgnFQFjavS3r/vj8ebdTBBX29vbKXK4ik4lwXYmIiMBut0vFrqCggLVr17Jq1Srpcy6CSgMJdXtFQZDGxkZ0Oh1ZWVkeMQO+Pu0QG7menh4uXrzI9evX5fsRERHS+jowMOBR0GZgYMAjby+4+xIdHY1Op6Ovr4/Q0FBmzpzJqlWrmDp16sh3TkNDY1gZsi2o98JmsVgoLS2lv7+f0tJSYmNjaWpq4siRI1y7dk0KH6G4mkwm1q9fz7Jly8jLyyM9PZ3ExMRR52DvHVTQ29tLT08Pvb29jB8/noKCAmJiYqRf70grduqArd7eXq5evSrzYl68eJEdO3ZQWFjIpEmTPH6vvr6eqqoqLBYLV69e5eDBg5w+fZqmpiZpCUlMTGTSpEnSCiIU3UBWXgUiCC8xMZEZM2aQlJQky1UK/FURUrfL6XT6pXX4yxBWOHD35YknnsBut/PrX/9azl1xMiAKUeh0OnnSM2/ePH72s58xZ84cTCaT31gmvwlqa2ZtbS3Hjx+nsbGRyMhIsrKySElJ8Xn/hGwTMvD06dMeqRUdDgcWi0UGtQp55B0noCYzM5P8/HxZQjY+Pp7Vq1ezdu1akpKS5HWa1VVDY3TwrQO2hCARUbkCnU4ny4qWl5djNBppb2/nwoULHhaorKwsMjMzmTFjBitWrGDKlCnExMTIxUb8jdHiKiAShxuNRnp7ezl79qwU2rm5uWRnZ8vrwLeK3cDAAFarlZCQEKKiouju7ubYsWO8/fbbTJ48GZfLRXx8PAaDgVOnTnHx4kUsFgu1tbV88cUXcj5ERESQl5fHvHnzZLYCYFSNqzpYZty4cSQlJWGz2fxa+fH2vwZ3P4T/p8Vi8bjeX90I1JHqISEh5Obm8vTTT9PS0sLevXux2+00NDTI/vT399Pf309iYiKLFy/m2Wef5eGHHw44pf2rqKur48qVK4DbfSIvL4/MzEyPfvri+RPzSCiibW1tNDc3y82EOO0RQWdqlwFwuzONHTuWnJwc9Ho9YWFhFBQUMHHiRIKCgujr68NkMjF16lTpmiQyfowWeaOh8aAzZNvQkJAQEhISiIyMpKenx6P0qTiqA0/L46RJk1i+fDkPPfQQ06ZNIzk5+a4FxF+PKr8pQimNioqiubmZgwcP0tHRgcViITo6WuYx9JWioBbuERERTJ48mfT0dLmANDQ0sGXLFvR6PUajkcTERAwGA1VVVVRWVuJwOAgODpYLkclkoqioiGXLlrFw4UImT54sx3M0WUHU0d6iupHwmfQn1M+SXq8nPj4ek8kk31MrFZmZmbLSG+D3GT7Uczc9PZ3vf//7LFiwgIqKCnbu3MnRo0fl5xkZGbz44ov89Kc/JTIyUh5FB/qcFPegs7OTtrY2uTEOCQkhIyODsWPHAndyMPuq9LQ6WDA9PZ3x48dz6tQpent7pawRmz8hB00mExkZGaSlpTF37lweeeQR0tPTsVqtGI1G4uLiPEp1qzf+o21joqHxoPOtfV6FUAgJCWHdunU4nU5ef/11rl69Kq9T75zj4uJYsGAB06ZNY+LEieTn5zN+/HgPP0/1ztyfF8tvglBmRJGCa9eu0dvbC+ATNwFv1MUJ9Ho9EydOJD09XSrdLpeLlpYWbDYbYWFhXLt2jYGBARl85nQ6cTgcGAwGCgoKmD9/PvPnz6eoqIjU1FSZamu0oS64ce3aNRoaGmRpUn9DjJFeryc3N5f8/HyMRiMWi4WEhARWrVpFVlYWCQkJTJw4Uf6ev5QRvRdq5VWv1zNlyhQmTJhAU1MTU6ZMoaKiQvY7Pj6eBQsWSLkzMDDgNxW0vg0ie0dTUxPNzc0ewUtGo1HeI1/OS/U46XQ6JkyYwOLFiykrK2Pv3r0yswC414vs7GxSU1MpKChgypQpJCYmkp6ezoQJE+76bnWVRgistIoaGhr3z5AEbIksAAUFBdKfccuWLXR2dsrACKvVSlRUFLNnz2bjxo0sWbLEI8enEKZCYR2twkZY6ITSL2pzjxs3joKCAil4/UXgBgcHU1hYyKJFizh69KgcS8DjmC8vL4/w8HDq6uqw2+0UFhaycuVKli9fTmFhoUchgtF0dCc2WsJifuvWLU6cOEFvby/p6ekegW3+MJ4C8bwZjUbmzJnDmjVruHTpEosWLUJRFAoLCxkYGLgryjtQEEpMZGQkkZGRjB8/ftDr7HY7QUFBMiAoUBFV3ISvenl5OXV1dYSGhtLf309YWJjfKeZCFsTExDBz5kxaW1sJDw/n3LlztLe3k5qayrx58ygqKiInJ4eCggJZSQyQm2YY3P3BH4wBGhoaw8OQSDN1+qrIyEg2b97MwoULZWogp9Mpq0yJEqPeR5XCIukvSttwIaJlhbIDMHXqVH7wgx/wyCOPSGE7WP3ukUS9mM+dO5e//uu/5s0332T79u2yBrygoKCA5cuXExoaysWLFxkzZgxPPPEEU6ZMITw8fNBcsKMFUaxA3K/y8nIOHTpESkoKa9asITU1VV7rL8qDTqfzOEadNm0af/u3f0tXVxeJiYnk5OQAd5frDCTUm+EvQwQFBTrexUUqKys9lNeYmBgPmesP7izq+z5mzBjWr1/PnDlzaG9vl6V8ExISiIuLk5sQb8T4efdHHeCloaEx+hgy5RXu+Cipa23fC4fDIa0FIl/faEUtQMPCwggPD/cQtkVFRTzxxBMyuEBYSnwteIVlIyoqisWLF2MwGIiJiaGsrIyuri50Oh3p6enMnDmTGTNm4HA4yM3NJTMzk/nz5wPuxaW/v5/Q0NBRuzER876yspLf/OY37Nq1i5UrV/LII4/IMQX/8rtTJ3aPjo72yMcMbgUoJCREJpEPNNRtFtXS1JkfRBEKfy9A8E0QypyormUwGJgxY4b0dwXfBGp5o07LFxISQkpKCikpKfe8XowjICtujeZ1Q0ND494MqSno6yzOIrAlEBfGr4u6j1FRUaSnp5OUlITBYCAjI4Pi4mKZW9KfrAXBwcEe1t/p06eTl5cny1GCe8yFEuByuSguLr4rmtnffSW/KSJbgsvl4sKFC7z33nt88sknOJ1O6c+t1+tlIJA/KAxqRNsHQ2yeRsO4Cd987wpLo6FvAu9Ay7y8PMaMGcPVq1eZOXMmDz/8sN9upO53HNQxFqNp7DQ0NL4+Q6q8qmtt22y2u/LyiQhQvV7/wCiu4ClojUYjBQUFPPvss8ybN4+kpCSmT58uFx/vgANfI8rXOp1O9Hq9rBZ1L8TxuRh/kVNzNKPX64mMjGTixIn84Ac/IDw8nOXLl8tjWn+e58JKJ0qshoSEjLoxG22K6mCoxyssLIxp06bx4osvsmDBAiZPnsycOXMwGo3Sz9Qfx1cEaokS2TqdTp7KiTaP9nHU0NC4P4ZFSwoNDfWrnb0/IY7av8ytwt8WFrUyPVgKr8GUg0APgLkfRJ/DwsLIy8sjLy+PZ5999q7r/P1oU1RS0whcvP3Kx4wZw6ZNm+66zt9kixrhpqKhoaHxVWiSQuO+ET5nwlcS7kT0CgXNny07GhoaGhoaGoHP11ZeFUUZjnb4DaO9fzD6+zja+wdaH0cDo71/8GD0UUNDY+TRzGMaGhoaGhoaGhoBQ5A/VgDS0NDQ0NDQ0NDQGAzN8qqhoaGhoaGhoREwaMqrhoaGhoaGhoZGwKAprxoaGhoaGhoaGgGDT1NlKYoyFvh/gIeBeKAB2A780mw2t/uwaUOCoihBwPeAHwKTgWDgKvAq8K9ms3nAh80bEh6AMfwHYAYwAUgALMB13H38rdlsvuW71n17FEV5HFgETAOmAlHAG2az+Rlftms4UBRlAfBTYC4QB7QBF4F/MZvNn/iwaUOOoijPAn+8/eOLZrP5ZV+259uiKEo8sAFYAxQCaYAN9/i9CrxqNpud9/4GDQ2N0YTPLK+KoowHvsCt3J0E/hmoAv4COHZbWAU6fwBeAbKAt4GXAD3wK+Dt28ptwPKAjOF/ASKAPbjH7Q3AAfwdcEFRlHtXmwgM/gb4MW7l9aZvmzJ8KIryN8AhYCGwE/hHYAcQCyz2XcuGnttz8jdAj6/bMoQ8gVt+zgZOAP8CvAcUAC8DWwNdnmpoaNw/vrS8moEk4Cdms/k34k1FUf4Jt8LwP4Af+aht3xpFUR4FngWqgVlms7n19vuhwFbgMWAz8JqPmjgUjOoxvE202Wy2er+pKMr/AH4B/BwI5GSW/wWoA67htsDu921zhh5FUZ4A/juwF9hoNpu7vT4fNeUAbytwrwK3gPeBv/Jti4aMcmAd8LHawqooyi9wb5wfAzbiVmg1NDRGOT6xvCqKkg2sAGqAf/X6+L8BvcCziqJEjHDThpKNt1//USiuAGaz2Q787e0f/3zEWzVEPCBjyGCK62223n7NHam2DAdms3m/2WyuMJvNozJnnqIoOuAfgD7gKW/FFeQzOVr4CfAQ7tOQXh+3Zcgwm82fmc3mHd6uAWazuRH499s/Lh7xhmloaPgEX7kNPHT7dfcgwqgbOAqEA3NGumFDSMrt16pBPhPvFSuKYhqZ5gw5D8IYfhlrb79e8GkrNL6Kubjddj4B2hVFWaMoyn9VFOUvFEUp8XHbhhRFUSYCfw/8ymw2H/J1e0YQsflw+LQVGhoaI4av3Abybr+W3+PzCtxWvQnAvhFp0dAjrK1Zg3yWrfp/PnB8+Jsz5DwIYyhRFOWvgEggBncA13zciuvf+7JdGl/JzNuvTcAZ3ME+EkVRDgGPm83mlpFu2FCiKEoIsAW4gdud5YHgdr+fu/3jTl+2RUNDY+TwleU15vZr5z0+F++bhr8pw8ZHt1//UlGUOPHmbWH7S9V1sSPaqqHjQRhDNX+F2x3ip7gV153AikBXeh4Akm6//ggwAstwZ1QoAHbhDuB6xzdNG1L+/0AR8LzZbLb4ujEjyN/jHstPzGbzLl83RkNDY2Tw1zyvImo0kP3w3gI+BcYDlxVF+Q9FUf4FOAesxm2ZBAj4dFn3YDSMocRsNqeYzeYg3O4gG3Fbz88qilLs25ZpfAXBt1+DcFtY95nN5h6z2XwJd+qlOmBRILsQKIoyC7e19R/NZvMxX7dnpFAU5SfAz4Ay3MGxGhoaDwi+Ul6FVS7mHp9He10XcNz2A12H22LXiFu4fh/3YjkfdzQwQLNPGvjtGfVjOBhms7nJbDZvw+0SEc+dXJoa/onINVxlNpvPqz+4baEU1rpZI9qqIULlLlDOnUDQUY+iKP8Zd+q6y8ASs9nc5uMmaWhojCC+8nm9evt1wj0+FxHc9/KnDAjMZrMDdz7Jf1S/ryiKEXdeTQtwaeRbNiQ8EGN4L8xm83VFUS4D0xRFSVBnlNDwK8Q87bjH50K5NQ5/U4aFSO48g1ZFGTRr20uKoryEO5DrpyPVsOFCUZSf4s4pXQosNZvNgWoA0NDQ+Ib4yvIqckmuuJ3KRqIoShQwD7diF4iBTPfDs4AB2BrAaXoe9DEEGHP7dbS6fowGDuGOQs9VFEU/yOcFt19rRqxFQ0s/7kIog/07e/uaI7d/DniXAkVR/ituxfUcbourprhqaDyA+MTyajabKxVF2Y376PU/464GI/gl7opGvzObzQGdp1BRlGiz2dzl9d5M3EEGPbjLqgYkD8IYKoqSD3TcziWpfl+HO+l9EvD5aCiDO1oxm82tiqK8DTyNO6jpb8RniqIsB1bidm0JyEj1264PLwz2maIof4c7iOsPgV4eFkBRlL/FLTO/wB0sqbkKaGg8oPiywpYCfA78WlGUpcAV3KX/luA+av5rH7ZtqNijKIoF9/FWNzAZd7BWP+5KP4PlgA0kRvsYPgz879vplCpx+ykn465ElY3bl/lF3zXv23O7Etyjt38UuYlLFEV57fb/W81mc6BXafpL3PPyrxVFWYi7ItM43AFbA8CLZrO5w3fN0/gqFEXZjFtxHQAOAz8ZxEWixmw2vzbCTdPQ0PABPss2YDabK3Hny3wN98LyM9yR+b8GSsxm8617/3bA8C7utDzP4F5AC3HX4Z48GtK6PABjuBf4D9yBWRuB/wt3Gco23NblyWaz+bLvmjckTMNdpngzbiskuBVz8d7jvmnW0HH7aHk27uPmdO5UofoYWGA2m0dDqqzRjsiXHYw7Xd1/G+Tf875omIaGxsgT5HKNikxGGhoaGhoaGhoaDwD+mudVQ0NDQ0NDQ0ND4y405VVDQ0NDQ0NDQyNg0JRXDQ0NDQ0NDQ2NgEFTXjU0NDQ0NDQ0NAIGTXnV0NDQ0NDQ0NAIGDTlVUNDQ0NDQ0NDI2DQlFcNDQ0NDQ0NDY2AQVNeNTQ0NDQ0NDQ0AgZNedXQ0NDQ0NDQ0AgYNOVVQ0NDQ0NDQ0MjYPj/AIv935tgHy1hAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 864x1652.4 with 200 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#y_pred = model.predict_classes(x_test) Deprecated after 01/01/2021 !!\n",
- "\n",
- "y_sigmoid = model.predict(x_test)\n",
- "y_pred = np.argmax(y_sigmoid, axis=-1)\n",
- "\n",
- "pwk.plot_images(x_test, y_test, range(0,200), columns=12, x_size=1, y_size=1, y_pred=y_pred, save_as='04-predictions')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.4 - Plot some errors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:26.274844Z",
- "iopub.status.busy": "2021-03-01T17:43:26.273344Z",
- "iopub.status.idle": "2021-03-01T17:43:30.002019Z",
- "shell.execute_reply": "2021-03-01T17:43:30.002541Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-05-some-errors</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x507.6 with 15 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "errors=[ i for i in range(len(x_test)) if y_pred[i]!=y_test[i] ]\n",
- "errors=errors[:min(24,len(errors))]\n",
- "pwk.plot_images(x_test, y_test, errors[:15], columns=6, x_size=2, y_size=2, y_pred=y_pred, save_as='05-some-errors')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:30.005960Z",
- "iopub.status.busy": "2021-03-01T17:43:30.005484Z",
- "iopub.status.idle": "2021-03-01T17:43:32.917974Z",
- "shell.execute_reply": "2021-03-01T17:43:32.918476Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-06-confusion-matrix</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_confusion_matrix(y_test,y_pred,range(10),normalize=True, save_as='06-confusion-matrix')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:43:32.921787Z",
- "iopub.status.busy": "2021-03-01T17:43:32.921323Z",
- "iopub.status.idle": "2021-03-01T17:43:32.923588Z",
- "shell.execute_reply": "2021-03-01T17:43:32.924076Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:43:32\n",
- "Duration is : 00:00:47 045ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"todo\">\n",
- " A few things you can do for fun:\n",
- " <ul>\n",
- " <li>Changing the network architecture (layers, number of neurons, etc.)</li>\n",
- " <li>Display a summary of the network</li>\n",
- " <li>Retrieve and display the softmax output of the network, to evaluate its \"doubts\".</li>\n",
- " </ul>\n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/README.ipynb b/README.ipynb
index fa4ce4e..d54ae3c 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "18d935c7",
+ "id": "56d23ddb",
"metadata": {
"execution": {
- "iopub.execute_input": "2021-10-22T13:43:17.380919Z",
- "iopub.status.busy": "2021-10-22T13:43:17.377572Z",
- "iopub.status.idle": "2021-10-22T13:43:17.397089Z",
- "shell.execute_reply": "2021-10-22T13:43:17.396194Z"
+ "iopub.execute_input": "2021-10-31T16:15:55.515130Z",
+ "iopub.status.busy": "2021-10-31T16:15:55.511675Z",
+ "iopub.status.idle": "2021-10-31T16:15:55.528086Z",
+ "shell.execute_reply": "2021-10-31T16:15:55.527647Z"
},
"jupyter": {
"source_hidden": true
@@ -40,16 +40,17 @@
" - Understanding **Tensorflow/Keras** and **Jupyter lab** technologies\n",
" - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU\n",
"\n",
- "For more information, you can contact [Fidle team](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20team) at : \n",
- "[<img width=\"200px\" style=\"vertical-align:middle\" src=\"fidle/img/00-Mail_contact.svg\"></img>](#top)\\\n",
- "Don't forget to look at the [Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)\n",
+ "For more information, see **https://fidle.cnrs.fr** :\n",
+ "- **[Presentation of the training](https://fidle.cnrs.fr/presentation)**\n",
+ "- **[Program 2021/2022](https://fidle.cnrs.fr/programme)**\n",
+ "- [Subscribe to the list](https://fidle.cnrs.fr/listeinfo), to stay informed !\n",
+ "- [Find us on youtube](https://fidle.cnrs.fr/youtube)\n",
"\n",
- "A propos de [Fidle à distance (fevrier-avril 2021)](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref)\\\n",
- "Voir le [programme](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/Pr%C3%A9sentation#programme-)\\\n",
- "Voir ou revoir les [vidéos](https://www.youtube.com/channel/UC4Sukzudhbwr6fs10cXrJsQ)\n",
+ "For more information, you can contact us at : \n",
+ "[<img width=\"200px\" style=\"vertical-align:middle\" src=\"fidle/img/00-Mail_contact.svg\"></img>](#top)\n",
"\n",
"Current Version : <!-- VERSION_BEGIN -->\n",
- "**2.0.24**\n",
+ "**2.0.26**\n",
"<!-- VERSION_END -->\n",
"\n",
"\n",
@@ -57,9 +58,9 @@
"\n",
"| | | |\n",
"|:--:|:--:|:--:|\n",
- "| **[<img width=\"50px\" src=\"fidle/img/00-Fidle-pdf.svg\"></img><br>Course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**<br>The course in pdf format<br>(12 Mo)| **[<img width=\"50px\" src=\"fidle/img/00-Notebooks.svg\"></img><br>Notebooks](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/archive/master/fidle-master.zip)**<br> Get a Zip or clone this repository <br>(40 Mo)| **[<img width=\"50px\" src=\"fidle/img/00-Datasets-tar.svg\"></img><br>Datasets](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**<br>All the needed datasets<br>(1.2 Go)|\n",
+ "| **[<img width=\"50px\" src=\"fidle/img/00-Fidle-pdf.svg\"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width=\"50px\" src=\"fidle/img/00-Notebooks.svg\"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> Get a Zip or clone this repository <br>(40 Mo)| **[<img width=\"50px\" src=\"fidle/img/00-Datasets-tar.svg\"></img><br>Datasets](https://fidle.cnrs.fr/datasets)**<br>All the needed datasets<br>(1.2 Go)|\n",
"\n",
- "Have a look about **[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/install%20fidle)** these notebooks and datasets.\n",
+ "Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.\n",
"\n",
"\n",
"## Jupyter notebooks\n",
@@ -184,7 +185,7 @@
"\n",
"## Installation\n",
"\n",
- "Have a look about **[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/install%20fidle)** these notebooks and datasets.\n",
+ "Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.\n",
"\n",
"## Licence\n",
"\n",
diff --git a/README.md b/README.md
index 9f58b2c..bb04bde 100644
--- a/README.md
+++ b/README.md
@@ -19,16 +19,17 @@ The objectives of this training are :
- Understanding **Tensorflow/Keras** and **Jupyter lab** technologies
- Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU
-For more information, you can contact [Fidle team](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20team) at :
-[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)\
-Don't forget to look at the [Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)
+For more information, see **https://fidle.cnrs.fr** :
+- **[Presentation of the training](https://fidle.cnrs.fr/presentation)**
+- **[Program 2021/2022](https://fidle.cnrs.fr/programme)**
+- [Subscribe to the list](https://fidle.cnrs.fr/listeinfo), to stay informed !
+- [Find us on youtube](https://fidle.cnrs.fr/youtube)
-A propos de [Fidle à distance (fevrier-avril 2021)](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref)\
-Voir le [programme](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/Pr%C3%A9sentation#programme-)\
-Voir ou revoir les [vidéos](https://www.youtube.com/channel/UC4Sukzudhbwr6fs10cXrJsQ)
+For more information, you can contact us at :
+[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
Current Version : <!-- VERSION_BEGIN -->
-**2.0.24**
+**2.0.26**
<!-- VERSION_END -->
@@ -36,9 +37,9 @@ Current Version : <!-- VERSION_BEGIN -->
| | | |
|:--:|:--:|:--:|
-| **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/archive/master/fidle-master.zip)**<br> Get a Zip or clone this repository <br>(40 Mo)| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**<br>All the needed datasets<br>(1.2 Go)|
+| **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> Get a Zip or clone this repository <br>(40 Mo)| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://fidle.cnrs.fr/datasets)**<br>All the needed datasets<br>(1.2 Go)|
-Have a look about **[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/install%20fidle)** these notebooks and datasets.
+Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
## Jupyter notebooks
@@ -163,7 +164,7 @@ A scratchbook for small examples
## Installation
-Have a look about **[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/install%20fidle)** these notebooks and datasets.
+Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
## Licence
diff --git a/SYNOP/LADYB1-Ladybug.ipynb b/SYNOP/LADYB1-Ladybug.ipynb
index a2eabb2..8c6598f 100644
--- a/SYNOP/LADYB1-Ladybug.ipynb
+++ b/SYNOP/LADYB1-Ladybug.ipynb
@@ -49,8 +49,8 @@
"sys.path.append('..')\n",
"import fidle.pwk as pwk\n",
"\n",
- "run_dir = './run/LADYBUG1'\n",
- "datasets_dir = pwk.init('LADYBUG1', run_dir)"
+ "run_dir = './run/LADYB1'\n",
+ "datasets_dir = pwk.init('LADYB1', run_dir)"
]
},
{
@@ -78,10 +78,11 @@
"\n",
"# ---- About training\n",
"#\n",
- "scale = 1 # Percentage of dataset to be used (1=all)\n",
- "train_prop = .8 # Percentage for train (the rest being for the test)\n",
- "batch_size = 32\n",
- "epochs = 5"
+ "scale = 1 # Percentage of dataset to be used (1=all)\n",
+ "train_prop = .8 # Percentage for train (the rest being for the test)\n",
+ "batch_size = 32\n",
+ "epochs = 5\n",
+ "fit_verbosity = 1 # 0 = silent, 1 = progress bar, 2 = one line per epoch"
]
},
{
@@ -323,8 +324,8 @@
"pwk.chrono_start()\n",
"\n",
"history=model.fit(train_generator, \n",
- " epochs=epochs, \n",
- " verbose=1,\n",
+ " epochs = epochs, \n",
+ " verbose = fit_verbosity,\n",
" validation_data = test_generator,\n",
" callbacks = [bestmodel_callback])\n",
"\n",
@@ -466,9 +467,11 @@
}
],
"metadata": {
+ "interpreter": {
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
+ },
"kernelspec": {
- "display_name": "Python 3",
- "language": "python",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
@@ -481,7 +484,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.7"
}
},
"nbformat": 4,
diff --git a/SYNOP/LADYB1-Ladybug==done==.ipynb b/SYNOP/LADYB1-Ladybug==done==.ipynb
deleted file mode 100644
index d601f41..0000000
--- a/SYNOP/LADYB1-Ladybug==done==.ipynb
+++ /dev/null
@@ -1,21176 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [LADYB1] - Prediction of a 2D trajectory via RNN\n",
- "<!-- DESC --> Artificial dataset generation and prediction attempt via a recurrent network\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Understanding the use of a recurrent neural network\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Generate an artificial dataset\n",
- " - dataset preparation\n",
- " - Doing our training\n",
- " - Making predictions\n",
- "\n",
- "## Step 1 - Import and init\n",
- "### 1.1 - Python"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:44.800326Z",
- "iopub.status.busy": "2021-03-09T21:32:44.799922Z",
- "iopub.status.idle": "2021-03-09T21:32:46.950980Z",
- "shell.execute_reply": "2021-03-09T21:32:46.951434Z"
- }
- },
- "outputs": [
- {
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- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.19\n",
- "Notebook id : LADYBUG1\n",
- "Run time : Tuesday 09 March 2021, 22:32:46\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Run dir : ./run/LADYBUG1\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/LADYBUG1/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "from tensorflow.keras.callbacks import TensorBoard\n",
- "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
- "\n",
- "import numpy as np\n",
- "import math, random\n",
- "from math import sin,cos,pi\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import pandas as pd\n",
- "import h5py, json\n",
- "import os,time,sys\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/LADYBUG1'\n",
- "datasets_dir = pwk.init('LADYBUG1', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:46.956147Z",
- "iopub.status.busy": "2021-03-09T21:32:46.955762Z",
- "iopub.status.idle": "2021-03-09T21:32:46.957784Z",
- "shell.execute_reply": "2021-03-09T21:32:46.958157Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- About dataset\n",
- "#\n",
- "max_t = 1000\n",
- "delta_t = 0.02\n",
- "features_len = 2\n",
- "\n",
- "\n",
- "sequence_len = 20\n",
- "predict_len = 5\n",
- "\n",
- "# ---- About training\n",
- "#\n",
- "scale = 1 # Percentage of dataset to be used (1=all)\n",
- "train_prop = .8 # Percentage for train (the rest being for the test)\n",
- "batch_size = 32\n",
- "epochs = 5"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:46.961654Z",
- "iopub.status.busy": "2021-03-09T21:32:46.961270Z",
- "iopub.status.idle": "2021-03-09T21:32:46.964356Z",
- "shell.execute_reply": "2021-03-09T21:32:46.963946Z"
- }
- },
- "outputs": [],
- "source": [
- "pwk.override('scale', 'train_prop', 'sequence_len', 'predict_len', 'batch_size', 'epochs')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Generation of a fun dataset\n",
- "### 2.1 - Virtual trajectory of our ladybug"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:46.972623Z",
- "iopub.status.busy": "2021-03-09T21:32:46.972202Z",
- "iopub.status.idle": "2021-03-09T21:32:46.974644Z",
- "shell.execute_reply": "2021-03-09T21:32:46.974928Z"
- }
- },
- "outputs": [],
- "source": [
- "def ladybug_init(s=122):\n",
- " \n",
- " if s>0 : random.seed(s)\n",
- " ladybug_init.params_x = [ random.gauss(0.,1.) for u in range(8)]\n",
- " ladybug_init.params_y = [ random.gauss(0.,1.) for u in range(8)]\n",
- " \n",
- "def ladybug_move(t):\n",
- " k=0.5\n",
- " [ax1, ax2, ax3, ax4, kx1, kx2, kx3, kx4] = ladybug_init.params_x\n",
- " [ay1, ay2, ay3, ay4, ky1, ky2, ky3, ky4] = ladybug_init.params_y\n",
- " \n",
- " x = ax1*sin(t*(kx1+20)) + ax2*cos(t*(kx2+10)) + ax3*sin(t*(kx3+5)) + ax4*cos(t*(kx4+5))\n",
- " y = ay1*cos(t*(ky1+20)) + ay2*sin(t*(ky2+10)) + ay3*cos(t*(ky3+5)) + ay4*sin(t*(ky4+5)) \n",
- "\n",
- "\n",
- " return x,y"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Get some positions, and build a rescaled and normalized dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:46.979469Z",
- "iopub.status.busy": "2021-03-09T21:32:46.979086Z",
- "iopub.status.idle": "2021-03-09T21:32:47.254151Z",
- "shell.execute_reply": "2021-03-09T21:32:47.253870Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset generated.\n",
- "Train shape is : (40000, 2)\n",
- "Test shape is : (10000, 2)\n"
- ]
- }
- ],
- "source": [
- "# ---- Get positions\n",
- "#\n",
- "ladybug_init(s=16)\n",
- "x,y = 0,0\n",
- "positions=[]\n",
- "for t in np.arange(0., max_t, delta_t):\n",
- " positions.append([x,y])\n",
- " x,y = ladybug_move(t)\n",
- "# (x,y) = (x+dx, y+dy)\n",
- "\n",
- "# ---- Build rescaled dataset\n",
- "#\n",
- "n = int( len(positions)*scale )\n",
- "dataset = np.array(positions[:n])\n",
- "\n",
- "k = int(len(dataset)*train_prop)\n",
- "x_train = dataset[:k]\n",
- "x_test = dataset[k:]\n",
- "\n",
- "# ---- Normalize\n",
- "#\n",
- "mean = x_train.mean()\n",
- "std = x_train.std()\n",
- "x_train = (x_train - mean) / std\n",
- "x_test = (x_test - mean) / std\n",
- "\n",
- "print(\"Dataset generated.\")\n",
- "print(\"Train shape is : \", x_train.shape)\n",
- "print(\"Test shape is : \", x_test.shape)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 - Have a look\n",
- "An extract from the data we have: the virtual trajectory of our ladybug \n",
- "And what we want to predict (in red), from a segment (in blue)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:47.268886Z",
- "iopub.status.busy": "2021-03-09T21:32:47.268563Z",
- "iopub.status.idle": "2021-03-09T21:32:48.209238Z",
- "shell.execute_reply": "2021-03-09T21:32:48.209567Z"
- }
- },
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x864 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_2d_serie(x_train[:1000], figsize=(12,12), lw=1,ms=4,save_as='01-dataset')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:48.213054Z",
- "iopub.status.busy": "2021-03-09T21:32:48.212642Z",
- "iopub.status.idle": "2021-03-09T21:32:48.430602Z",
- "shell.execute_reply": "2021-03-09T21:32:48.430229Z"
- }
- },
- "outputs": [
- {
- "data": {
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- ],
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- ]
- },
- "metadata": {},
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- {
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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "k1,k2 = sequence_len, predict_len\n",
- "i = random.randint(0,len(x_test)-k1-k2)\n",
- "j = i+k1\n",
- "\n",
- "pwk.plot_2d_segment( x_test[i:j+k2], x_test[j:j+k2],ms=6, save_as='02-objectives')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.4 - Prepare some nice data generator"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:48.434877Z",
- "iopub.status.busy": "2021-03-09T21:32:48.434440Z",
- "iopub.status.idle": "2021-03-09T21:32:48.443691Z",
- "shell.execute_reply": "2021-03-09T21:32:48.443368Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**About the splitting of our dataset :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of batch trains available : 1250\n",
- "batch x shape : (32, 20, 2)\n",
- "batch y shape : (32, 2)\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**What a batch looks like (x) :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[ 1.973e-04 1.973e-04]\n",
- " [ 1.511e+00 6.019e-01]\n",
- " [ 1.097e+00 5.694e-01]\n",
- " [ 6.732e-01 4.095e-01]\n",
- " [ 2.772e-01 1.534e-01]\n",
- " [-6.037e-02 -1.521e-01]\n",
- " [-3.214e-01 -4.520e-01]\n",
- " [-5.014e-01 -6.919e-01]\n",
- " [-6.096e-01 -8.267e-01]\n",
- " [-6.663e-01 -8.277e-01]\n",
- " [-6.991e-01 -6.870e-01]\n",
- " [-7.382e-01 -4.191e-01]\n",
- " [-8.108e-01 -5.863e-02]\n",
- " [-9.365e-01 3.454e-01]\n",
- " [-1.124e+00 7.367e-01]\n",
- " [-1.368e+00 1.061e+00]\n",
- " [-1.652e+00 1.274e+00]\n",
- " [-1.946e+00 1.349e+00]\n",
- " [-2.214e+00 1.281e+00]\n",
- " [-2.420e+00 1.086e+00]]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**What a batch looks like (y) :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[-2.528 0.801]\n"
- ]
- }
- ],
- "source": [
- "# ---- Train generator\n",
- "#\n",
- "train_generator = TimeseriesGenerator(x_train, x_train, length=sequence_len, batch_size=batch_size)\n",
- "test_generator = TimeseriesGenerator(x_test, x_test, length=sequence_len, batch_size=batch_size)\n",
- "\n",
- "# ---- About\n",
- "#\n",
- "pwk.subtitle('About the splitting of our dataset :')\n",
- "\n",
- "x,y=train_generator[0]\n",
- "print(f'Number of batch trains available : ', len(train_generator))\n",
- "print('batch x shape : ',x.shape)\n",
- "print('batch y shape : ',y.shape)\n",
- "\n",
- "x,y=train_generator[0]\n",
- "pwk.subtitle('What a batch looks like (x) :')\n",
- "pwk.np_print(x[0] )\n",
- "pwk.subtitle('What a batch looks like (y) :')\n",
- "pwk.np_print(y[0])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Create a model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:48.446767Z",
- "iopub.status.busy": "2021-03-09T21:32:48.446457Z",
- "iopub.status.idle": "2021-03-09T21:32:48.536831Z",
- "shell.execute_reply": "2021-03-09T21:32:48.537089Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "gru (GRU) (None, 200) 122400 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 2) 402 \n",
- "=================================================================\n",
- "Total params: 122,802\n",
- "Trainable params: 122,802\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = keras.models.Sequential()\n",
- "model.add( keras.layers.InputLayer(input_shape=(sequence_len, features_len)) )\n",
- "# model.add( keras.layers.GRU(200, dropout=.1, recurrent_dropout=0.5, return_sequences=False, activation='relu') )\n",
- "model.add( keras.layers.GRU(200, return_sequences=False, activation='relu') )\n",
- "model.add( keras.layers.Dense(features_len) )\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Compile and run"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.1 - Add callback"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:48.540172Z",
- "iopub.status.busy": "2021-03-09T21:32:48.539738Z",
- "iopub.status.idle": "2021-03-09T21:32:48.543251Z",
- "shell.execute_reply": "2021-03-09T21:32:48.542911Z"
- }
- },
- "outputs": [],
- "source": [
- "pwk.mkdir('./run/models')\n",
- "save_dir = './run/models/best_model.h5'\n",
- "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.2 - Compile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:48.549875Z",
- "iopub.status.busy": "2021-03-09T21:32:48.549536Z",
- "iopub.status.idle": "2021-03-09T21:32:48.555024Z",
- "shell.execute_reply": "2021-03-09T21:32:48.554662Z"
- }
- },
- "outputs": [],
- "source": [
- "model.compile(optimizer='rmsprop', \n",
- " loss='mse', \n",
- " metrics = ['mae'] )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.3 - Fit\n",
- "3' with a CPU (laptop) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:32:48.559653Z",
- "iopub.status.busy": "2021-03-09T21:32:48.559008Z",
- "iopub.status.idle": "2021-03-09T21:35:39.476040Z",
- "shell.execute_reply": "2021-03-09T21:35:39.476476Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/5\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/1250 [..............................] - ETA: 0s - loss: 0.4516 - mae: 0.5741"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/1250 [..............................] - ETA: 22s - loss: 0.7420 - mae: 0.7200"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- ]
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 20/1250 [..............................] - ETA: 27s - loss: 0.4573 - mae: 0.5478"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 23/1250 [..............................] - ETA: 27s - loss: 0.4339 - mae: 0.5311"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- ]
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- ]
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- ]
- },
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- ]
- },
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- ]
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- " 386/1250 [========>.....................] - ETA: 18s - loss: 0.0854 - mae: 0.1962"
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- },
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- " 623/1250 [=============>................] - ETA: 13s - loss: 0.0560 - mae: 0.1480"
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- " 626/1250 [==============>...............] - ETA: 12s - loss: 0.0557 - mae: 0.1476"
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- " 629/1250 [==============>...............] - ETA: 12s - loss: 0.0555 - mae: 0.1471"
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- " 632/1250 [==============>...............] - ETA: 12s - loss: 0.0552 - mae: 0.1466"
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- " 635/1250 [==============>...............] - ETA: 12s - loss: 0.0550 - mae: 0.1462"
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- " 644/1250 [==============>...............] - ETA: 12s - loss: 0.0543 - mae: 0.1451"
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- " 653/1250 [==============>...............] - ETA: 12s - loss: 0.0537 - mae: 0.1440"
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- " 656/1250 [==============>...............] - ETA: 12s - loss: 0.0535 - mae: 0.1436"
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- " 659/1250 [==============>...............] - ETA: 12s - loss: 0.0532 - mae: 0.1431"
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- " 662/1250 [==============>...............] - ETA: 12s - loss: 0.0530 - mae: 0.1428"
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- " 665/1250 [==============>...............] - ETA: 12s - loss: 0.0528 - mae: 0.1425"
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- " 674/1250 [===============>..............] - ETA: 11s - loss: 0.0522 - mae: 0.1413"
- ]
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- " 677/1250 [===============>..............] - ETA: 11s - loss: 0.0520 - mae: 0.1409"
- ]
- },
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- ]
- },
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- " 683/1250 [===============>..............] - ETA: 11s - loss: 0.0516 - mae: 0.1401"
- ]
- },
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- ]
- },
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- ]
- },
- {
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 695/1250 [===============>..............] - ETA: 11s - loss: 0.0508 - mae: 0.1387"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 698/1250 [===============>..............] - ETA: 11s - loss: 0.0506 - mae: 0.1385"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 716/1250 [================>.............] - ETA: 10s - loss: 0.0495 - mae: 0.1364"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 719/1250 [================>.............] - ETA: 10s - loss: 0.0493 - mae: 0.1360"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 722/1250 [================>.............] - ETA: 10s - loss: 0.0491 - mae: 0.1357"
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- },
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- },
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- " 728/1250 [================>.............] - ETA: 10s - loss: 0.0487 - mae: 0.1351"
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- },
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- " 730/1250 [================>.............] - ETA: 10s - loss: 0.0486 - mae: 0.1349"
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- " 740/1250 [================>.............] - ETA: 10s - loss: 0.0480 - mae: 0.1339"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 743/1250 [================>.............] - ETA: 10s - loss: 0.0479 - mae: 0.1335"
- ]
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- " 746/1250 [================>.............] - ETA: 10s - loss: 0.0477 - mae: 0.1332"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 749/1250 [================>.............] - ETA: 10s - loss: 0.0475 - mae: 0.1328"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 752/1250 [=================>............] - ETA: 10s - loss: 0.0473 - mae: 0.1324"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 755/1250 [=================>............] - ETA: 10s - loss: 0.0472 - mae: 0.1322"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 758/1250 [=================>............] - ETA: 10s - loss: 0.0470 - mae: 0.1318"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 761/1250 [=================>............] - ETA: 10s - loss: 0.0468 - mae: 0.1315"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 764/1250 [=================>............] - ETA: 10s - loss: 0.0467 - mae: 0.1313"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 767/1250 [=================>............] - ETA: 10s - loss: 0.0465 - mae: 0.1310"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 770/1250 [=================>............] - ETA: 9s - loss: 0.0463 - mae: 0.1307 "
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 773/1250 [=================>............] - ETA: 9s - loss: 0.0462 - mae: 0.1304"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 776/1250 [=================>............] - ETA: 9s - loss: 0.0460 - mae: 0.1300"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 779/1250 [=================>............] - ETA: 9s - loss: 0.0458 - mae: 0.1297"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 782/1250 [=================>............] - ETA: 9s - loss: 0.0457 - mae: 0.1294"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 785/1250 [=================>............] - ETA: 9s - loss: 0.0455 - mae: 0.1291"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 788/1250 [=================>............] - ETA: 9s - loss: 0.0454 - mae: 0.1288"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 791/1250 [=================>............] - ETA: 9s - loss: 0.0452 - mae: 0.1286"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 794/1250 [==================>...........] - ETA: 9s - loss: 0.0451 - mae: 0.1283"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 797/1250 [==================>...........] - ETA: 9s - loss: 0.0449 - mae: 0.1280"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 800/1250 [==================>...........] - ETA: 9s - loss: 0.0448 - mae: 0.1277"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 803/1250 [==================>...........] - ETA: 9s - loss: 0.0446 - mae: 0.1274"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 806/1250 [==================>...........] - ETA: 9s - loss: 0.0445 - mae: 0.1272"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 808/1250 [==================>...........] - ETA: 9s - loss: 0.0444 - mae: 0.1270"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 811/1250 [==================>...........] - ETA: 9s - loss: 0.0442 - mae: 0.1267"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 814/1250 [==================>...........] - ETA: 9s - loss: 0.0441 - mae: 0.1264"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 817/1250 [==================>...........] - ETA: 8s - loss: 0.0439 - mae: 0.1260"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 820/1250 [==================>...........] - ETA: 8s - loss: 0.0438 - mae: 0.1258"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 823/1250 [==================>...........] - ETA: 8s - loss: 0.0436 - mae: 0.1256"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 826/1250 [==================>...........] - ETA: 8s - loss: 0.0435 - mae: 0.1253"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 829/1250 [==================>...........] - ETA: 8s - loss: 0.0434 - mae: 0.1250"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 832/1250 [==================>...........] - ETA: 8s - loss: 0.0432 - mae: 0.1247"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 838/1250 [===================>..........] - ETA: 8s - loss: 0.0429 - mae: 0.1242"
- ]
- },
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- },
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- ]
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- ]
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- ]
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- " 862/1250 [===================>..........] - ETA: 8s - loss: 0.0418 - mae: 0.1221"
- ]
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- " 865/1250 [===================>..........] - ETA: 7s - loss: 0.0417 - mae: 0.1218"
- ]
- },
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- " 868/1250 [===================>..........] - ETA: 7s - loss: 0.0416 - mae: 0.1216"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 871/1250 [===================>..........] - ETA: 7s - loss: 0.0414 - mae: 0.1213"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 874/1250 [===================>..........] - ETA: 7s - loss: 0.0413 - mae: 0.1211"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 877/1250 [====================>.........] - ETA: 7s - loss: 0.0412 - mae: 0.1209"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 880/1250 [====================>.........] - ETA: 7s - loss: 0.0411 - mae: 0.1207"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 883/1250 [====================>.........] - ETA: 7s - loss: 0.0410 - mae: 0.1206"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 885/1250 [====================>.........] - ETA: 7s - loss: 0.0409 - mae: 0.1204"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 888/1250 [====================>.........] - ETA: 7s - loss: 0.0407 - mae: 0.1201"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 891/1250 [====================>.........] - ETA: 7s - loss: 0.0406 - mae: 0.1198"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 894/1250 [====================>.........] - ETA: 7s - loss: 0.0405 - mae: 0.1196"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 897/1250 [====================>.........] - ETA: 7s - loss: 0.0404 - mae: 0.1193"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 900/1250 [====================>.........] - ETA: 7s - loss: 0.0402 - mae: 0.1191"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 903/1250 [====================>.........] - ETA: 7s - loss: 0.0401 - mae: 0.1188"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 906/1250 [====================>.........] - ETA: 7s - loss: 0.0400 - mae: 0.1186"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 909/1250 [====================>.........] - ETA: 7s - loss: 0.0399 - mae: 0.1184"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 912/1250 [====================>.........] - ETA: 6s - loss: 0.0398 - mae: 0.1181"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 915/1250 [====================>.........] - ETA: 6s - loss: 0.0396 - mae: 0.1178"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 918/1250 [=====================>........] - ETA: 6s - loss: 0.0395 - mae: 0.1176"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 921/1250 [=====================>........] - ETA: 6s - loss: 0.0394 - mae: 0.1174"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 924/1250 [=====================>........] - ETA: 6s - loss: 0.0393 - mae: 0.1172"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 927/1250 [=====================>........] - ETA: 6s - loss: 0.0392 - mae: 0.1169"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 930/1250 [=====================>........] - ETA: 6s - loss: 0.0391 - mae: 0.1167"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- " 969/1250 [======================>.......] - ETA: 5s - loss: 0.0376 - mae: 0.1139"
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- " 975/1250 [======================>.......] - ETA: 5s - loss: 0.0374 - mae: 0.1134"
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- " 978/1250 [======================>.......] - ETA: 5s - loss: 0.0373 - mae: 0.1132"
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- " 981/1250 [======================>.......] - ETA: 5s - loss: 0.0372 - mae: 0.1130"
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- " 984/1250 [======================>.......] - ETA: 5s - loss: 0.0371 - mae: 0.1127"
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- " 987/1250 [======================>.......] - ETA: 5s - loss: 0.0370 - mae: 0.1125"
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- " 990/1250 [======================>.......] - ETA: 5s - loss: 0.0369 - mae: 0.1124"
- ]
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- " 993/1250 [======================>.......] - ETA: 5s - loss: 0.0368 - mae: 0.1121"
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- },
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1002/1250 [=======================>......] - ETA: 5s - loss: 0.0365 - mae: 0.1116"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1005/1250 [=======================>......] - ETA: 5s - loss: 0.0364 - mae: 0.1113"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1008/1250 [=======================>......] - ETA: 4s - loss: 0.0363 - mae: 0.1111"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1011/1250 [=======================>......] - ETA: 4s - loss: 0.0362 - mae: 0.1109"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1020/1250 [=======================>......] - ETA: 4s - loss: 0.0359 - mae: 0.1102"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1023/1250 [=======================>......] - ETA: 4s - loss: 0.0358 - mae: 0.1101"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1026/1250 [=======================>......] - ETA: 4s - loss: 0.0357 - mae: 0.1099"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1029/1250 [=======================>......] - ETA: 4s - loss: 0.0356 - mae: 0.1097"
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- "1179/1250 [===========================>..] - ETA: 1s - loss: 0.0315 - mae: 0.1010"
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- "1182/1250 [===========================>..] - ETA: 1s - loss: 0.0314 - mae: 0.1008"
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- "1185/1250 [===========================>..] - ETA: 1s - loss: 0.0313 - mae: 0.1007"
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- "1191/1250 [===========================>..] - ETA: 1s - loss: 0.0312 - mae: 0.1004"
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- "1194/1250 [===========================>..] - ETA: 1s - loss: 0.0311 - mae: 0.1002"
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- "1197/1250 [===========================>..] - ETA: 1s - loss: 0.0310 - mae: 0.1000"
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- "1200/1250 [===========================>..] - ETA: 1s - loss: 0.0310 - mae: 0.0999"
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- "1203/1250 [===========================>..] - ETA: 0s - loss: 0.0309 - mae: 0.0998"
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- "1209/1250 [============================>.] - ETA: 0s - loss: 0.0308 - mae: 0.0994"
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- "1212/1250 [============================>.] - ETA: 0s - loss: 0.0307 - mae: 0.0993"
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- "1215/1250 [============================>.] - ETA: 0s - loss: 0.0306 - mae: 0.0992"
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- "1218/1250 [============================>.] - ETA: 0s - loss: 0.0306 - mae: 0.0990"
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- "1221/1250 [============================>.] - ETA: 0s - loss: 0.0305 - mae: 0.0989"
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- "1224/1250 [============================>.] - ETA: 0s - loss: 0.0304 - mae: 0.0988"
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- "1227/1250 [============================>.] - ETA: 0s - loss: 0.0303 - mae: 0.0986"
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- "1230/1250 [============================>.] - ETA: 0s - loss: 0.0303 - mae: 0.0984"
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- "1233/1250 [============================>.] - ETA: 0s - loss: 0.0302 - mae: 0.0983"
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- "1236/1250 [============================>.] - ETA: 0s - loss: 0.0301 - mae: 0.0981"
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- "1239/1250 [============================>.] - ETA: 0s - loss: 0.0301 - mae: 0.0980"
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- "1242/1250 [============================>.] - ETA: 0s - loss: 0.0300 - mae: 0.0979"
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- "1245/1250 [============================>.] - ETA: 0s - loss: 0.0299 - mae: 0.0977"
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1248/1250 [============================>.] - ETA: 0s - loss: 0.0299 - mae: 0.0976"
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- "text": [
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- "1250/1250 [==============================] - 28s 22ms/step - loss: 0.0298 - mae: 0.0975 - val_loss: 7.1813e-04 - val_mae: 0.0207\n"
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- "output_type": "stream",
- "text": [
- "Epoch 2/5\n",
- "\r",
- " 1/1250 [..............................] - ETA: 0s - loss: 5.6774e-04 - mae: 0.0206"
- ]
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- " 4/1250 [..............................] - ETA: 21s - loss: 0.0011 - mae: 0.0266 "
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- " 7/1250 [..............................] - ETA: 22s - loss: 0.0017 - mae: 0.0327"
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- " 10/1250 [..............................] - ETA: 23s - loss: 0.0021 - mae: 0.0377"
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- },
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- ]
- },
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 31/1250 [..............................] - ETA: 23s - loss: 0.0023 - mae: 0.0386"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 179/1250 [===>..........................] - ETA: 22s - loss: 0.0023 - mae: 0.0377"
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- " 192/1250 [===>..........................] - ETA: 22s - loss: 0.0024 - mae: 0.0377"
- ]
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- " 199/1250 [===>..........................] - ETA: 22s - loss: 0.0023 - mae: 0.0374"
- ]
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- " 201/1250 [===>..........................] - ETA: 22s - loss: 0.0023 - mae: 0.0375"
- ]
- },
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- ]
- },
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- " 205/1250 [===>..........................] - ETA: 22s - loss: 0.0023 - mae: 0.0375"
- ]
- },
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- " 207/1250 [===>..........................] - ETA: 22s - loss: 0.0024 - mae: 0.0376"
- ]
- },
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- " 209/1250 [====>.........................] - ETA: 22s - loss: 0.0023 - mae: 0.0374"
- ]
- },
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- ]
- },
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- " 213/1250 [====>.........................] - ETA: 22s - loss: 0.0023 - mae: 0.0374"
- ]
- },
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 571/1250 [============>.................] - ETA: 16s - loss: 0.0021 - mae: 0.0348"
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 623/1250 [=============>................] - ETA: 14s - loss: 0.0020 - mae: 0.0345"
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- " 626/1250 [==============>...............] - ETA: 14s - loss: 0.0021 - mae: 0.0346"
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- " 629/1250 [==============>...............] - ETA: 14s - loss: 0.0021 - mae: 0.0346"
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- " 632/1250 [==============>...............] - ETA: 14s - loss: 0.0021 - mae: 0.0346"
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 635/1250 [==============>...............] - ETA: 14s - loss: 0.0021 - mae: 0.0345"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 638/1250 [==============>...............] - ETA: 14s - loss: 0.0020 - mae: 0.0344"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 641/1250 [==============>...............] - ETA: 14s - loss: 0.0020 - mae: 0.0343"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 644/1250 [==============>...............] - ETA: 14s - loss: 0.0020 - mae: 0.0343"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 647/1250 [==============>...............] - ETA: 14s - loss: 0.0020 - mae: 0.0343"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 650/1250 [==============>...............] - ETA: 14s - loss: 0.0020 - mae: 0.0343"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 653/1250 [==============>...............] - ETA: 14s - loss: 0.0020 - mae: 0.0343"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 655/1250 [==============>...............] - ETA: 14s - loss: 0.0020 - mae: 0.0343"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 658/1250 [==============>...............] - ETA: 14s - loss: 0.0020 - mae: 0.0343"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 661/1250 [==============>...............] - ETA: 14s - loss: 0.0020 - mae: 0.0342"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 664/1250 [==============>...............] - ETA: 14s - loss: 0.0020 - mae: 0.0342"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 694/1250 [===============>..............] - ETA: 13s - loss: 0.0020 - mae: 0.0342"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 697/1250 [===============>..............] - ETA: 13s - loss: 0.0020 - mae: 0.0342"
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- " 700/1250 [===============>..............] - ETA: 13s - loss: 0.0020 - mae: 0.0342"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 703/1250 [===============>..............] - ETA: 13s - loss: 0.0020 - mae: 0.0342"
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 706/1250 [===============>..............] - ETA: 13s - loss: 0.0020 - mae: 0.0341"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 709/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 712/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 715/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 718/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 721/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 724/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 727/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 730/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 732/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0339"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 735/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0339"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 738/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0339"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 741/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0339"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 744/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0339"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 747/1250 [================>.............] - ETA: 12s - loss: 0.0020 - mae: 0.0339"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 750/1250 [=================>............] - ETA: 11s - loss: 0.0020 - mae: 0.0338"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 752/1250 [=================>............] - ETA: 11s - loss: 0.0020 - mae: 0.0338"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 754/1250 [=================>............] - ETA: 11s - loss: 0.0020 - mae: 0.0338"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 756/1250 [=================>............] - ETA: 11s - loss: 0.0020 - mae: 0.0338"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 759/1250 [=================>............] - ETA: 11s - loss: 0.0020 - mae: 0.0338"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 762/1250 [=================>............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 765/1250 [=================>............] - ETA: 11s - loss: 0.0020 - mae: 0.0338"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 768/1250 [=================>............] - ETA: 11s - loss: 0.0020 - mae: 0.0338"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 805/1250 [==================>...........] - ETA: 10s - loss: 0.0020 - mae: 0.0336"
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- " 811/1250 [==================>...........] - ETA: 10s - loss: 0.0020 - mae: 0.0337"
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- " 817/1250 [==================>...........] - ETA: 10s - loss: 0.0020 - mae: 0.0337"
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- " 819/1250 [==================>...........] - ETA: 10s - loss: 0.0019 - mae: 0.0336"
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- " 822/1250 [==================>...........] - ETA: 10s - loss: 0.0019 - mae: 0.0336"
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- " 828/1250 [==================>...........] - ETA: 10s - loss: 0.0019 - mae: 0.0335"
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- " 831/1250 [==================>...........] - ETA: 10s - loss: 0.0019 - mae: 0.0335"
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- },
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- },
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- " 990/1250 [======================>.......] - ETA: 6s - loss: 0.0019 - mae: 0.0327"
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- " 993/1250 [======================>.......] - ETA: 6s - loss: 0.0019 - mae: 0.0326"
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- " 995/1250 [======================>.......] - ETA: 6s - loss: 0.0019 - mae: 0.0326"
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- " 997/1250 [======================>.......] - ETA: 6s - loss: 0.0019 - mae: 0.0327"
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- "1002/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0327"
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- "1004/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0327"
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- "1006/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0327"
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- "1008/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0327"
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- "1011/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0327"
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- "1013/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0326"
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- "1016/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0326"
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- "1019/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0326"
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- "1022/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0326"
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- "1024/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0326"
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- "1027/1250 [=======================>......] - ETA: 5s - loss: 0.0019 - mae: 0.0326"
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- "1030/1250 [=======================>......] - ETA: 5s - loss: 0.0018 - mae: 0.0326"
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- "1032/1250 [=======================>......] - ETA: 5s - loss: 0.0018 - mae: 0.0326"
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- "1238/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0318"
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- "1241/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0318"
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- "1244/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0317"
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- "1247/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0317"
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- "1250/1250 [==============================] - ETA: 0s - loss: 0.0018 - mae: 0.0317"
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- "1250/1250 [==============================] - 33s 26ms/step - loss: 0.0018 - mae: 0.0317 - val_loss: 0.0012 - val_mae: 0.0284\n"
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- "output_type": "stream",
- "text": [
- "Epoch 3/5\n",
- "\r",
- " 1/1250 [..............................] - ETA: 0s - loss: 0.0013 - mae: 0.0310"
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- },
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- " 4/1250 [..............................] - ETA: 21s - loss: 0.0022 - mae: 0.0390"
- ]
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- " 7/1250 [..............................] - ETA: 24s - loss: 0.0016 - mae: 0.0318"
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- " 10/1250 [..............................] - ETA: 26s - loss: 0.0018 - mae: 0.0343"
- ]
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 27/1250 [..............................] - ETA: 28s - loss: 0.0017 - mae: 0.0311"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 29/1250 [..............................] - ETA: 28s - loss: 0.0016 - mae: 0.0301"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 31/1250 [..............................] - ETA: 28s - loss: 0.0015 - mae: 0.0295"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 171/1250 [===>..........................] - ETA: 27s - loss: 0.0013 - mae: 0.0269"
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- " 182/1250 [===>..........................] - ETA: 26s - loss: 0.0013 - mae: 0.0268"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 188/1250 [===>..........................] - ETA: 26s - loss: 0.0013 - mae: 0.0270"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 460/1250 [==========>...................] - ETA: 20s - loss: 0.0012 - mae: 0.0258"
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- " 463/1250 [==========>...................] - ETA: 20s - loss: 0.0012 - mae: 0.0258"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 538/1250 [===========>..................] - ETA: 18s - loss: 0.0012 - mae: 0.0256"
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- " 540/1250 [===========>..................] - ETA: 18s - loss: 0.0012 - mae: 0.0255"
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- " 548/1250 [============>.................] - ETA: 18s - loss: 0.0012 - mae: 0.0256"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 551/1250 [============>.................] - ETA: 18s - loss: 0.0012 - mae: 0.0256"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 554/1250 [============>.................] - ETA: 18s - loss: 0.0012 - mae: 0.0256"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 557/1250 [============>.................] - ETA: 18s - loss: 0.0012 - mae: 0.0256"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 559/1250 [============>.................] - ETA: 18s - loss: 0.0012 - mae: 0.0256"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 561/1250 [============>.................] - ETA: 18s - loss: 0.0012 - mae: 0.0256"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 563/1250 [============>.................] - ETA: 18s - loss: 0.0012 - mae: 0.0256"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 565/1250 [============>.................] - ETA: 17s - loss: 0.0012 - mae: 0.0256"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 567/1250 [============>.................] - ETA: 17s - loss: 0.0012 - mae: 0.0255"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 570/1250 [============>.................] - ETA: 17s - loss: 0.0012 - mae: 0.0255"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 573/1250 [============>.................] - ETA: 17s - loss: 0.0012 - mae: 0.0256"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 576/1250 [============>.................] - ETA: 17s - loss: 0.0012 - mae: 0.0255"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 579/1250 [============>.................] - ETA: 17s - loss: 0.0012 - mae: 0.0255"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 582/1250 [============>.................] - ETA: 17s - loss: 0.0012 - mae: 0.0255"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 603/1250 [=============>................] - ETA: 16s - loss: 0.0012 - mae: 0.0255"
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- " 612/1250 [=============>................] - ETA: 16s - loss: 0.0012 - mae: 0.0255"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 614/1250 [=============>................] - ETA: 16s - loss: 0.0012 - mae: 0.0255"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 617/1250 [=============>................] - ETA: 16s - loss: 0.0012 - mae: 0.0254"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 620/1250 [=============>................] - ETA: 16s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 623/1250 [=============>................] - ETA: 16s - loss: 0.0012 - mae: 0.0254"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 626/1250 [==============>...............] - ETA: 16s - loss: 0.0012 - mae: 0.0254"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 629/1250 [==============>...............] - ETA: 16s - loss: 0.0012 - mae: 0.0254"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 632/1250 [==============>...............] - ETA: 16s - loss: 0.0012 - mae: 0.0255"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 635/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0255"
- ]
- },
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 638/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0255"
- ]
- },
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 641/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 644/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 646/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 649/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 652/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 655/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 658/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 660/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 662/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0253"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 664/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0253"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 666/1250 [==============>...............] - ETA: 15s - loss: 0.0012 - mae: 0.0253"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 668/1250 [===============>..............] - ETA: 15s - loss: 0.0012 - mae: 0.0253"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 670/1250 [===============>..............] - ETA: 15s - loss: 0.0012 - mae: 0.0253"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 672/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 674/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0253"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 676/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 678/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 680/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 682/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0254"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 684/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0253"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 686/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0253"
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- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 693/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0253"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 695/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0253"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 697/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0253"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 699/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0253"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 702/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0253"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 705/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0253"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 707/1250 [===============>..............] - ETA: 14s - loss: 0.0012 - mae: 0.0252"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 709/1250 [================>.............] - ETA: 14s - loss: 0.0012 - mae: 0.0252"
- ]
- },
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 711/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 713/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 715/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0251"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 717/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 720/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 723/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\r",
- " 725/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 727/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 729/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 731/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0253"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 733/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 736/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 739/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 741/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 743/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 745/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 746/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 748/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 749/1250 [================>.............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 751/1250 [=================>............] - ETA: 13s - loss: 0.0012 - mae: 0.0252"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 753/1250 [=================>............] - ETA: 13s - loss: 0.0012 - mae: 0.0251"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 755/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0251"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 757/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0251"
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- },
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 759/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0251"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 763/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0252"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 765/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0252"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 767/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0252"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 769/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0252"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 771/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0252"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 773/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0251"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 775/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0251"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 777/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0251"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 779/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0250"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 781/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0251"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 783/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0251"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 785/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0251"
- ]
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 787/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0251"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 789/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 791/1250 [=================>............] - ETA: 12s - loss: 0.0012 - mae: 0.0250"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 793/1250 [==================>...........] - ETA: 12s - loss: 0.0012 - mae: 0.0250"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 795/1250 [==================>...........] - ETA: 12s - loss: 0.0012 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 797/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0251"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 799/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0251"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 801/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0250"
- ]
- },
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 803/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 805/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0251"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 807/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0251"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 809/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0251"
- ]
- },
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- " 811/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0250"
- ]
- },
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- " 813/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0250"
- ]
- },
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- " 815/1250 [==================>...........] - ETA: 11s - loss: 0.0011 - mae: 0.0250"
- ]
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- " 817/1250 [==================>...........] - ETA: 11s - loss: 0.0011 - mae: 0.0250"
- ]
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- " 819/1250 [==================>...........] - ETA: 11s - loss: 0.0011 - mae: 0.0250"
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- " 821/1250 [==================>...........] - ETA: 11s - loss: 0.0011 - mae: 0.0250"
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- " 823/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0250"
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- " 825/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0250"
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- " 827/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0250"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 829/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 831/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 833/1250 [==================>...........] - ETA: 11s - loss: 0.0012 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 835/1250 [===================>..........] - ETA: 11s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 837/1250 [===================>..........] - ETA: 11s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 839/1250 [===================>..........] - ETA: 11s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 840/1250 [===================>..........] - ETA: 11s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 842/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 844/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 846/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 848/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 850/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 852/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 854/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 856/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 858/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 860/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 862/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 864/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 866/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 868/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0250"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 870/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 872/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 874/1250 [===================>..........] - ETA: 10s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 876/1250 [====================>.........] - ETA: 10s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 878/1250 [====================>.........] - ETA: 10s - loss: 0.0011 - mae: 0.0249"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 880/1250 [====================>.........] - ETA: 10s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 882/1250 [====================>.........] - ETA: 10s - loss: 0.0011 - mae: 0.0249"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 894/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- " 896/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
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- " 898/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- " 900/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 902/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- " 904/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- " 906/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- " 908/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- " 911/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 913/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 915/1250 [====================>.........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 917/1250 [=====================>........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 919/1250 [=====================>........] - ETA: 9s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 922/1250 [=====================>........] - ETA: 8s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 924/1250 [=====================>........] - ETA: 8s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 926/1250 [=====================>........] - ETA: 8s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 929/1250 [=====================>........] - ETA: 8s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 932/1250 [=====================>........] - ETA: 8s - loss: 0.0011 - mae: 0.0249"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 935/1250 [=====================>........] - ETA: 8s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 937/1250 [=====================>........] - ETA: 8s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 939/1250 [=====================>........] - ETA: 8s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 942/1250 [=====================>........] - ETA: 8s - loss: 0.0011 - mae: 0.0249"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 944/1250 [=====================>........] - ETA: 8s - loss: 0.0011 - mae: 0.0249"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- " 968/1250 [======================>.......] - ETA: 7s - loss: 0.0011 - mae: 0.0248"
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- " 971/1250 [======================>.......] - ETA: 7s - loss: 0.0011 - mae: 0.0248"
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- " 974/1250 [======================>.......] - ETA: 7s - loss: 0.0011 - mae: 0.0248"
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- " 976/1250 [======================>.......] - ETA: 7s - loss: 0.0011 - mae: 0.0248"
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- " 982/1250 [======================>.......] - ETA: 7s - loss: 0.0011 - mae: 0.0248"
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- " 988/1250 [======================>.......] - ETA: 7s - loss: 0.0011 - mae: 0.0248"
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- " 990/1250 [======================>.......] - ETA: 7s - loss: 0.0011 - mae: 0.0248"
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- " 992/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0248"
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- " 995/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0248"
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- " 997/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0247"
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- " 999/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0247"
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- "1002/1250 [=======================>......] - ETA: 6s - loss: 0.0011 - mae: 0.0247"
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- "1005/1250 [=======================>......] - ETA: 6s - loss: 0.0011 - mae: 0.0248"
- ]
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- "1008/1250 [=======================>......] - ETA: 6s - loss: 0.0011 - mae: 0.0248"
- ]
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- "1011/1250 [=======================>......] - ETA: 6s - loss: 0.0011 - mae: 0.0248"
- ]
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- "1014/1250 [=======================>......] - ETA: 6s - loss: 0.0011 - mae: 0.0248"
- ]
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- "1017/1250 [=======================>......] - ETA: 6s - loss: 0.0011 - mae: 0.0247"
- ]
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- "1019/1250 [=======================>......] - ETA: 6s - loss: 0.0011 - mae: 0.0247"
- ]
- },
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- "1021/1250 [=======================>......] - ETA: 6s - loss: 0.0011 - mae: 0.0247"
- ]
- },
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- "1024/1250 [=======================>......] - ETA: 6s - loss: 0.0011 - mae: 0.0247"
- ]
- },
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- "1027/1250 [=======================>......] - ETA: 6s - loss: 0.0011 - mae: 0.0247"
- ]
- },
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- "1030/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0247"
- ]
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- "1033/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0247"
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- "1036/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0247"
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- "1038/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0247"
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- "1200/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0246"
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- "1223/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0245"
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- "1226/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0245"
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- "1229/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0245"
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- "1232/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0245"
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- "1234/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0246"
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- "1237/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0245"
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- "1239/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0245"
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- "1241/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0245"
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- "1243/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0245"
- ]
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- "1245/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0245"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1247/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0245"
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1249/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0244"
- ]
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- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1250/1250 [==============================] - 37s 30ms/step - loss: 0.0011 - mae: 0.0244 - val_loss: 5.8311e-04 - val_mae: 0.0196\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 4/5\n",
- "\r",
- " 1/1250 [..............................] - ETA: 0s - loss: 7.2268e-04 - mae: 0.0226"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/1250 [..............................] - ETA: 33s - loss: 0.0018 - mae: 0.0332 "
- ]
- },
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- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/1250 [..............................] - ETA: 41s - loss: 0.0016 - mae: 0.0331"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/1250 [..............................] - ETA: 41s - loss: 0.0017 - mae: 0.0337"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/1250 [..............................] - ETA: 41s - loss: 0.0015 - mae: 0.0312"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 11/1250 [..............................] - ETA: 41s - loss: 0.0013 - mae: 0.0286"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/1250 [..............................] - ETA: 41s - loss: 0.0012 - mae: 0.0269"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\r",
- " 15/1250 [..............................] - ETA: 41s - loss: 0.0012 - mae: 0.0267"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 17/1250 [..............................] - ETA: 41s - loss: 0.0011 - mae: 0.0248"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 19/1250 [..............................] - ETA: 40s - loss: 0.0010 - mae: 0.0242"
- ]
- },
- {
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- ]
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- {
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- ]
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- {
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- {
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- " 563/1250 [============>.................] - ETA: 18s - loss: 8.4583e-04 - mae: 0.0218"
- ]
- },
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- ]
- },
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- ]
- },
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- ]
- },
- {
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- " 571/1250 [============>.................] - ETA: 18s - loss: 8.4935e-04 - mae: 0.0219"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 579/1250 [============>.................] - ETA: 18s - loss: 8.4712e-04 - mae: 0.0219"
- ]
- },
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- " 581/1250 [============>.................] - ETA: 18s - loss: 8.4672e-04 - mae: 0.0219"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 619/1250 [=============>................] - ETA: 17s - loss: 8.5243e-04 - mae: 0.0219"
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- " 621/1250 [=============>................] - ETA: 17s - loss: 8.5094e-04 - mae: 0.0219"
- ]
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- " 623/1250 [=============>................] - ETA: 17s - loss: 8.4900e-04 - mae: 0.0218"
- ]
- },
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- " 625/1250 [==============>...............] - ETA: 17s - loss: 8.4775e-04 - mae: 0.0218"
- ]
- },
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- " 627/1250 [==============>...............] - ETA: 17s - loss: 8.4800e-04 - mae: 0.0218"
- ]
- },
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- " 630/1250 [==============>...............] - ETA: 17s - loss: 8.5180e-04 - mae: 0.0219"
- ]
- },
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- " 633/1250 [==============>...............] - ETA: 17s - loss: 8.5514e-04 - mae: 0.0219"
- ]
- },
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- " 635/1250 [==============>...............] - ETA: 17s - loss: 8.5414e-04 - mae: 0.0219"
- ]
- },
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- " 637/1250 [==============>...............] - ETA: 16s - loss: 8.5558e-04 - mae: 0.0219"
- ]
- },
- {
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- " 639/1250 [==============>...............] - ETA: 16s - loss: 8.5420e-04 - mae: 0.0219"
- ]
- },
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- " 641/1250 [==============>...............] - ETA: 16s - loss: 8.5279e-04 - mae: 0.0219"
- ]
- },
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- " 643/1250 [==============>...............] - ETA: 16s - loss: 8.5193e-04 - mae: 0.0219"
- ]
- },
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- " 645/1250 [==============>...............] - ETA: 16s - loss: 8.5004e-04 - mae: 0.0218"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 647/1250 [==============>...............] - ETA: 16s - loss: 8.4949e-04 - mae: 0.0218"
- ]
- },
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- " 650/1250 [==============>...............] - ETA: 16s - loss: 8.4677e-04 - mae: 0.0218"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 653/1250 [==============>...............] - ETA: 16s - loss: 8.4743e-04 - mae: 0.0218"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 656/1250 [==============>...............] - ETA: 16s - loss: 8.4923e-04 - mae: 0.0218"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 659/1250 [==============>...............] - ETA: 16s - loss: 8.4973e-04 - mae: 0.0218"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 662/1250 [==============>...............] - ETA: 16s - loss: 8.5180e-04 - mae: 0.0218"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 665/1250 [==============>...............] - ETA: 16s - loss: 8.4959e-04 - mae: 0.0218"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 677/1250 [===============>..............] - ETA: 15s - loss: 8.4598e-04 - mae: 0.0218"
- ]
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- ]
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- ]
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- " 686/1250 [===============>..............] - ETA: 15s - loss: 8.5017e-04 - mae: 0.0218"
- ]
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- " 689/1250 [===============>..............] - ETA: 15s - loss: 8.4973e-04 - mae: 0.0218"
- ]
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- " 692/1250 [===============>..............] - ETA: 15s - loss: 8.4979e-04 - mae: 0.0218"
- ]
- },
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- " 695/1250 [===============>..............] - ETA: 15s - loss: 8.4883e-04 - mae: 0.0218"
- ]
- },
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- " 697/1250 [===============>..............] - ETA: 15s - loss: 8.4781e-04 - mae: 0.0218"
- ]
- },
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- " 700/1250 [===============>..............] - ETA: 15s - loss: 8.4881e-04 - mae: 0.0218"
- ]
- },
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- " 703/1250 [===============>..............] - ETA: 14s - loss: 8.4797e-04 - mae: 0.0218"
- ]
- },
- {
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- " 706/1250 [===============>..............] - ETA: 14s - loss: 8.4605e-04 - mae: 0.0218"
- ]
- },
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- " 708/1250 [===============>..............] - ETA: 14s - loss: 8.4884e-04 - mae: 0.0218"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 710/1250 [================>.............] - ETA: 14s - loss: 8.5201e-04 - mae: 0.0218"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 713/1250 [================>.............] - ETA: 14s - loss: 8.4994e-04 - mae: 0.0218"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 716/1250 [================>.............] - ETA: 14s - loss: 8.5061e-04 - mae: 0.0218"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 719/1250 [================>.............] - ETA: 14s - loss: 8.4858e-04 - mae: 0.0218"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 722/1250 [================>.............] - ETA: 14s - loss: 8.4673e-04 - mae: 0.0217"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 725/1250 [================>.............] - ETA: 14s - loss: 8.4817e-04 - mae: 0.0218"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 728/1250 [================>.............] - ETA: 14s - loss: 8.4720e-04 - mae: 0.0218"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 731/1250 [================>.............] - ETA: 14s - loss: 8.4510e-04 - mae: 0.0217"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 734/1250 [================>.............] - ETA: 14s - loss: 8.4541e-04 - mae: 0.0217"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 737/1250 [================>.............] - ETA: 13s - loss: 8.4671e-04 - mae: 0.0218"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 740/1250 [================>.............] - ETA: 13s - loss: 8.4661e-04 - mae: 0.0218"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 743/1250 [================>.............] - ETA: 13s - loss: 8.4433e-04 - mae: 0.0217"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 746/1250 [================>.............] - ETA: 13s - loss: 8.4293e-04 - mae: 0.0217"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 749/1250 [================>.............] - ETA: 13s - loss: 8.4525e-04 - mae: 0.0217"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 752/1250 [=================>............] - ETA: 13s - loss: 8.4584e-04 - mae: 0.0217"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 755/1250 [=================>............] - ETA: 13s - loss: 8.4381e-04 - mae: 0.0217"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 758/1250 [=================>............] - ETA: 13s - loss: 8.4190e-04 - mae: 0.0217"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 761/1250 [=================>............] - ETA: 13s - loss: 8.3927e-04 - mae: 0.0216"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 764/1250 [=================>............] - ETA: 13s - loss: 8.3726e-04 - mae: 0.0216"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 767/1250 [=================>............] - ETA: 13s - loss: 8.3793e-04 - mae: 0.0216"
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- " 783/1250 [=================>............] - ETA: 12s - loss: 8.3859e-04 - mae: 0.0216"
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- " 786/1250 [=================>............] - ETA: 12s - loss: 8.3931e-04 - mae: 0.0216"
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- " 789/1250 [=================>............] - ETA: 12s - loss: 8.3741e-04 - mae: 0.0216"
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- " 791/1250 [=================>............] - ETA: 12s - loss: 8.3588e-04 - mae: 0.0216"
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- " 793/1250 [==================>...........] - ETA: 12s - loss: 8.3593e-04 - mae: 0.0216"
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- " 795/1250 [==================>...........] - ETA: 12s - loss: 8.3442e-04 - mae: 0.0215"
- ]
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- " 797/1250 [==================>...........] - ETA: 12s - loss: 8.3357e-04 - mae: 0.0215"
- ]
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- " 799/1250 [==================>...........] - ETA: 12s - loss: 8.3323e-04 - mae: 0.0215"
- ]
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- " 801/1250 [==================>...........] - ETA: 12s - loss: 8.3339e-04 - mae: 0.0215"
- ]
- },
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- " 803/1250 [==================>...........] - ETA: 12s - loss: 8.3379e-04 - mae: 0.0215"
- ]
- },
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- " 805/1250 [==================>...........] - ETA: 12s - loss: 8.3332e-04 - mae: 0.0215"
- ]
- },
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- " 807/1250 [==================>...........] - ETA: 11s - loss: 8.3445e-04 - mae: 0.0216"
- ]
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- " 809/1250 [==================>...........] - ETA: 11s - loss: 8.3508e-04 - mae: 0.0216"
- ]
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- " 811/1250 [==================>...........] - ETA: 11s - loss: 8.3627e-04 - mae: 0.0216"
- ]
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- " 813/1250 [==================>...........] - ETA: 11s - loss: 8.3543e-04 - mae: 0.0216"
- ]
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- " 815/1250 [==================>...........] - ETA: 11s - loss: 8.3555e-04 - mae: 0.0216"
- ]
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- " 817/1250 [==================>...........] - ETA: 11s - loss: 8.3557e-04 - mae: 0.0216"
- ]
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- " 819/1250 [==================>...........] - ETA: 11s - loss: 8.3437e-04 - mae: 0.0216"
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- " 821/1250 [==================>...........] - ETA: 11s - loss: 8.3302e-04 - mae: 0.0215"
- ]
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- " 823/1250 [==================>...........] - ETA: 11s - loss: 8.3391e-04 - mae: 0.0215"
- ]
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- " 825/1250 [==================>...........] - ETA: 11s - loss: 8.3443e-04 - mae: 0.0216"
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- " 827/1250 [==================>...........] - ETA: 11s - loss: 8.3484e-04 - mae: 0.0216"
- ]
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- " 829/1250 [==================>...........] - ETA: 11s - loss: 8.3492e-04 - mae: 0.0216"
- ]
- },
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- " 831/1250 [==================>...........] - ETA: 11s - loss: 8.3527e-04 - mae: 0.0216"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- ]
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- ]
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- " 859/1250 [===================>..........] - ETA: 10s - loss: 8.3296e-04 - mae: 0.0216"
- ]
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- " 861/1250 [===================>..........] - ETA: 10s - loss: 8.3272e-04 - mae: 0.0216"
- ]
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- " 863/1250 [===================>..........] - ETA: 10s - loss: 8.3184e-04 - mae: 0.0215"
- ]
- },
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- " 865/1250 [===================>..........] - ETA: 10s - loss: 8.3053e-04 - mae: 0.0215"
- ]
- },
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- " 867/1250 [===================>..........] - ETA: 10s - loss: 8.2900e-04 - mae: 0.0215"
- ]
- },
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- " 869/1250 [===================>..........] - ETA: 10s - loss: 8.2869e-04 - mae: 0.0215"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 871/1250 [===================>..........] - ETA: 10s - loss: 8.2920e-04 - mae: 0.0215"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 873/1250 [===================>..........] - ETA: 10s - loss: 8.2819e-04 - mae: 0.0215"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 879/1250 [====================>.........] - ETA: 10s - loss: 8.2812e-04 - mae: 0.0215"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 881/1250 [====================>.........] - ETA: 9s - loss: 8.3221e-04 - mae: 0.0215 "
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 883/1250 [====================>.........] - ETA: 9s - loss: 8.3300e-04 - mae: 0.0215"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 895/1250 [====================>.........] - ETA: 9s - loss: 8.3647e-04 - mae: 0.0216"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 897/1250 [====================>.........] - ETA: 9s - loss: 8.3669e-04 - mae: 0.0216"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 901/1250 [====================>.........] - ETA: 9s - loss: 8.3399e-04 - mae: 0.0215"
- ]
- },
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- " 903/1250 [====================>.........] - ETA: 9s - loss: 8.3339e-04 - mae: 0.0215"
- ]
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- " 907/1250 [====================>.........] - ETA: 9s - loss: 8.3224e-04 - mae: 0.0215"
- ]
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- " 909/1250 [====================>.........] - ETA: 9s - loss: 8.3198e-04 - mae: 0.0215"
- ]
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- " 913/1250 [====================>.........] - ETA: 9s - loss: 8.3066e-04 - mae: 0.0215"
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- " 915/1250 [====================>.........] - ETA: 9s - loss: 8.3013e-04 - mae: 0.0215"
- ]
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- " 917/1250 [=====================>........] - ETA: 8s - loss: 8.3036e-04 - mae: 0.0215"
- ]
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- " 919/1250 [=====================>........] - ETA: 8s - loss: 8.3051e-04 - mae: 0.0215"
- ]
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- " 921/1250 [=====================>........] - ETA: 8s - loss: 8.2986e-04 - mae: 0.0215"
- ]
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- " 923/1250 [=====================>........] - ETA: 8s - loss: 8.2947e-04 - mae: 0.0215"
- ]
- },
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- " 925/1250 [=====================>........] - ETA: 8s - loss: 8.3228e-04 - mae: 0.0215"
- ]
- },
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- " 927/1250 [=====================>........] - ETA: 8s - loss: 8.3158e-04 - mae: 0.0215"
- ]
- },
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- " 929/1250 [=====================>........] - ETA: 8s - loss: 8.3254e-04 - mae: 0.0215"
- ]
- },
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- " 931/1250 [=====================>........] - ETA: 8s - loss: 8.3273e-04 - mae: 0.0215"
- ]
- },
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- " 933/1250 [=====================>........] - ETA: 8s - loss: 8.3137e-04 - mae: 0.0215"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 935/1250 [=====================>........] - ETA: 8s - loss: 8.2996e-04 - mae: 0.0215"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 937/1250 [=====================>........] - ETA: 8s - loss: 8.3155e-04 - mae: 0.0215"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 939/1250 [=====================>........] - ETA: 8s - loss: 8.3328e-04 - mae: 0.0215"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 941/1250 [=====================>........] - ETA: 8s - loss: 8.3603e-04 - mae: 0.0216"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 943/1250 [=====================>........] - ETA: 8s - loss: 8.3600e-04 - mae: 0.0216"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 945/1250 [=====================>........] - ETA: 8s - loss: 8.3651e-04 - mae: 0.0216"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 947/1250 [=====================>........] - ETA: 8s - loss: 8.3558e-04 - mae: 0.0216"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 951/1250 [=====================>........] - ETA: 8s - loss: 8.3412e-04 - mae: 0.0215"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 995/1250 [======================>.......] - ETA: 6s - loss: 8.3025e-04 - mae: 0.0215"
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- " 997/1250 [======================>.......] - ETA: 6s - loss: 8.2930e-04 - mae: 0.0214"
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- "1001/1250 [=======================>......] - ETA: 6s - loss: 8.2848e-04 - mae: 0.0214"
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- "1003/1250 [=======================>......] - ETA: 6s - loss: 8.2889e-04 - mae: 0.0214"
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- "1005/1250 [=======================>......] - ETA: 6s - loss: 8.2826e-04 - mae: 0.0214"
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- "1007/1250 [=======================>......] - ETA: 6s - loss: 8.2791e-04 - mae: 0.0214"
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- "1009/1250 [=======================>......] - ETA: 6s - loss: 8.2719e-04 - mae: 0.0214"
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- "1011/1250 [=======================>......] - ETA: 6s - loss: 8.2730e-04 - mae: 0.0214"
- ]
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- "1013/1250 [=======================>......] - ETA: 6s - loss: 8.2754e-04 - mae: 0.0214"
- ]
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- "1015/1250 [=======================>......] - ETA: 6s - loss: 8.2674e-04 - mae: 0.0214"
- ]
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- "1017/1250 [=======================>......] - ETA: 6s - loss: 8.2582e-04 - mae: 0.0214"
- ]
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- "1019/1250 [=======================>......] - ETA: 6s - loss: 8.2502e-04 - mae: 0.0214"
- ]
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- "1021/1250 [=======================>......] - ETA: 6s - loss: 8.2547e-04 - mae: 0.0214"
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- "1023/1250 [=======================>......] - ETA: 6s - loss: 8.2493e-04 - mae: 0.0214"
- ]
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- "1025/1250 [=======================>......] - ETA: 6s - loss: 8.2439e-04 - mae: 0.0214"
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- "1027/1250 [=======================>......] - ETA: 6s - loss: 8.2530e-04 - mae: 0.0214"
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- "1029/1250 [=======================>......] - ETA: 5s - loss: 8.2846e-04 - mae: 0.0215"
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- "1031/1250 [=======================>......] - ETA: 5s - loss: 8.2731e-04 - mae: 0.0214"
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- "1205/1250 [===========================>..] - ETA: 1s - loss: 8.1689e-04 - mae: 0.0212"
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- "1209/1250 [============================>.] - ETA: 1s - loss: 8.1576e-04 - mae: 0.0212"
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- "1211/1250 [============================>.] - ETA: 1s - loss: 8.1553e-04 - mae: 0.0212"
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- "1213/1250 [============================>.] - ETA: 1s - loss: 8.1533e-04 - mae: 0.0212"
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- "1215/1250 [============================>.] - ETA: 0s - loss: 8.1458e-04 - mae: 0.0212"
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- "1217/1250 [============================>.] - ETA: 0s - loss: 8.1383e-04 - mae: 0.0212"
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- "1219/1250 [============================>.] - ETA: 0s - loss: 8.1438e-04 - mae: 0.0212"
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- "1221/1250 [============================>.] - ETA: 0s - loss: 8.1522e-04 - mae: 0.0212"
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- "1223/1250 [============================>.] - ETA: 0s - loss: 8.1502e-04 - mae: 0.0212"
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- "1225/1250 [============================>.] - ETA: 0s - loss: 8.1530e-04 - mae: 0.0212"
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- "1229/1250 [============================>.] - ETA: 0s - loss: 8.1501e-04 - mae: 0.0212"
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- "1231/1250 [============================>.] - ETA: 0s - loss: 8.1653e-04 - mae: 0.0212"
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- "1235/1250 [============================>.] - ETA: 0s - loss: 8.1688e-04 - mae: 0.0213"
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- "1237/1250 [============================>.] - ETA: 0s - loss: 8.1581e-04 - mae: 0.0212"
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- "1239/1250 [============================>.] - ETA: 0s - loss: 8.1475e-04 - mae: 0.0212"
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- "1241/1250 [============================>.] - ETA: 0s - loss: 8.1412e-04 - mae: 0.0212"
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- "1243/1250 [============================>.] - ETA: 0s - loss: 8.1494e-04 - mae: 0.0212"
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- "1245/1250 [============================>.] - ETA: 0s - loss: 8.1464e-04 - mae: 0.0212"
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- "1247/1250 [============================>.] - ETA: 0s - loss: 8.1404e-04 - mae: 0.0212"
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- "1249/1250 [============================>.] - ETA: 0s - loss: 8.1399e-04 - mae: 0.0212"
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- "1250/1250 [==============================] - 37s 30ms/step - loss: 8.1399e-04 - mae: 0.0212 - val_loss: 0.0011 - val_mae: 0.0242\n"
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- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 5/5\n",
- "\r",
- " 1/1250 [..............................] - ETA: 0s - loss: 0.0013 - mae: 0.0277"
- ]
- },
- {
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- " 3/1250 [..............................] - ETA: 26s - loss: 8.7683e-04 - mae: 0.0220"
- ]
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- "output_type": "stream",
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- " 5/1250 [..............................] - ETA: 30s - loss: 6.0737e-04 - mae: 0.0178"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/1250 [..............................] - ETA: 31s - loss: 5.4412e-04 - mae: 0.0173"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/1250 [..............................] - ETA: 32s - loss: 5.1764e-04 - mae: 0.0172"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 11/1250 [..............................] - ETA: 32s - loss: 6.7131e-04 - mae: 0.0193"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/1250 [..............................] - ETA: 32s - loss: 6.3273e-04 - mae: 0.0186"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 15/1250 [..............................] - ETA: 33s - loss: 5.9780e-04 - mae: 0.0181"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 17/1250 [..............................] - ETA: 34s - loss: 5.6348e-04 - mae: 0.0176"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 19/1250 [..............................] - ETA: 34s - loss: 5.2155e-04 - mae: 0.0168"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/1250 [..............................] - ETA: 35s - loss: 5.6816e-04 - mae: 0.0175"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 23/1250 [..............................] - ETA: 35s - loss: 6.6325e-04 - mae: 0.0189"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 25/1250 [..............................] - ETA: 35s - loss: 6.4043e-04 - mae: 0.0185"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 27/1250 [..............................] - ETA: 35s - loss: 6.5483e-04 - mae: 0.0188"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 29/1250 [..............................] - ETA: 35s - loss: 6.6359e-04 - mae: 0.0190"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 31/1250 [..............................] - ETA: 35s - loss: 6.9729e-04 - mae: 0.0194"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 33/1250 [..............................] - ETA: 35s - loss: 6.9424e-04 - mae: 0.0196"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 35/1250 [..............................] - ETA: 35s - loss: 6.9363e-04 - mae: 0.0196"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 37/1250 [..............................] - ETA: 35s - loss: 6.8378e-04 - mae: 0.0196"
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- " 39/1250 [..............................] - ETA: 35s - loss: 6.6980e-04 - mae: 0.0193"
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- ]
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- ]
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- ]
- },
- {
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- " 201/1250 [===>..........................] - ETA: 30s - loss: 7.1129e-04 - mae: 0.0197"
- ]
- },
- {
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- ]
- },
- {
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- ]
- },
- {
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- ]
- },
- {
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- ]
- },
- {
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- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- " 558/1250 [============>.................] - ETA: 19s - loss: 6.9438e-04 - mae: 0.0194"
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- " 563/1250 [============>.................] - ETA: 19s - loss: 6.9373e-04 - mae: 0.0194"
- ]
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- " 566/1250 [============>.................] - ETA: 18s - loss: 6.9949e-04 - mae: 0.0195"
- ]
- },
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- " 569/1250 [============>.................] - ETA: 18s - loss: 6.9721e-04 - mae: 0.0195"
- ]
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- " 571/1250 [============>.................] - ETA: 18s - loss: 6.9551e-04 - mae: 0.0195"
- ]
- },
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- " 573/1250 [============>.................] - ETA: 18s - loss: 6.9498e-04 - mae: 0.0194"
- ]
- },
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- " 575/1250 [============>.................] - ETA: 18s - loss: 6.9525e-04 - mae: 0.0194"
- ]
- },
- {
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- " 577/1250 [============>.................] - ETA: 18s - loss: 6.9568e-04 - mae: 0.0195"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 579/1250 [============>.................] - ETA: 18s - loss: 6.9534e-04 - mae: 0.0195"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 581/1250 [============>.................] - ETA: 18s - loss: 6.9567e-04 - mae: 0.0195"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 583/1250 [============>.................] - ETA: 18s - loss: 6.9470e-04 - mae: 0.0195"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 589/1250 [=============>................] - ETA: 18s - loss: 6.9488e-04 - mae: 0.0195"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 591/1250 [=============>................] - ETA: 18s - loss: 6.9614e-04 - mae: 0.0195"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 593/1250 [=============>................] - ETA: 18s - loss: 6.9630e-04 - mae: 0.0195"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 603/1250 [=============>................] - ETA: 17s - loss: 6.9208e-04 - mae: 0.0194"
- ]
- },
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- " 621/1250 [=============>................] - ETA: 17s - loss: 6.9096e-04 - mae: 0.0194"
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- " 627/1250 [==============>...............] - ETA: 17s - loss: 6.8850e-04 - mae: 0.0194"
- ]
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- " 630/1250 [==============>...............] - ETA: 17s - loss: 6.8897e-04 - mae: 0.0194"
- ]
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- " 633/1250 [==============>...............] - ETA: 17s - loss: 6.8753e-04 - mae: 0.0194"
- ]
- },
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- " 636/1250 [==============>...............] - ETA: 16s - loss: 6.8566e-04 - mae: 0.0194"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 639/1250 [==============>...............] - ETA: 16s - loss: 6.8486e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 641/1250 [==============>...............] - ETA: 16s - loss: 6.8467e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 643/1250 [==============>...............] - ETA: 16s - loss: 6.8404e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 645/1250 [==============>...............] - ETA: 16s - loss: 6.8266e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 647/1250 [==============>...............] - ETA: 16s - loss: 6.8145e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 650/1250 [==============>...............] - ETA: 16s - loss: 6.8225e-04 - mae: 0.0193"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 653/1250 [==============>...............] - ETA: 16s - loss: 6.8772e-04 - mae: 0.0194"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 656/1250 [==============>...............] - ETA: 16s - loss: 6.8857e-04 - mae: 0.0194"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 659/1250 [==============>...............] - ETA: 16s - loss: 6.8747e-04 - mae: 0.0194"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 661/1250 [==============>...............] - ETA: 16s - loss: 6.9114e-04 - mae: 0.0195"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 664/1250 [==============>...............] - ETA: 16s - loss: 6.9038e-04 - mae: 0.0195"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 667/1250 [===============>..............] - ETA: 15s - loss: 6.8921e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 670/1250 [===============>..............] - ETA: 15s - loss: 6.8686e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 673/1250 [===============>..............] - ETA: 15s - loss: 6.8518e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 676/1250 [===============>..............] - ETA: 15s - loss: 6.8446e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 679/1250 [===============>..............] - ETA: 15s - loss: 6.8225e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 682/1250 [===============>..............] - ETA: 15s - loss: 6.8255e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 685/1250 [===============>..............] - ETA: 15s - loss: 6.8311e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 687/1250 [===============>..............] - ETA: 15s - loss: 6.8416e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 690/1250 [===============>..............] - ETA: 15s - loss: 6.8621e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- " 696/1250 [===============>..............] - ETA: 15s - loss: 6.8816e-04 - mae: 0.0194"
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- " 699/1250 [===============>..............] - ETA: 15s - loss: 6.8643e-04 - mae: 0.0194"
- ]
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- " 705/1250 [===============>..............] - ETA: 14s - loss: 6.8546e-04 - mae: 0.0194"
- ]
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- " 708/1250 [===============>..............] - ETA: 14s - loss: 6.8419e-04 - mae: 0.0194"
- ]
- },
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- " 711/1250 [================>.............] - ETA: 14s - loss: 6.8383e-04 - mae: 0.0194"
- ]
- },
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- " 714/1250 [================>.............] - ETA: 14s - loss: 6.8557e-04 - mae: 0.0194"
- ]
- },
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- " 717/1250 [================>.............] - ETA: 14s - loss: 6.8594e-04 - mae: 0.0194"
- ]
- },
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- " 720/1250 [================>.............] - ETA: 14s - loss: 6.8405e-04 - mae: 0.0194"
- ]
- },
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- " 723/1250 [================>.............] - ETA: 14s - loss: 6.8223e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 726/1250 [================>.............] - ETA: 14s - loss: 6.8510e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 729/1250 [================>.............] - ETA: 14s - loss: 6.8425e-04 - mae: 0.0194"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 732/1250 [================>.............] - ETA: 14s - loss: 6.8523e-04 - mae: 0.0194"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 734/1250 [================>.............] - ETA: 13s - loss: 6.8791e-04 - mae: 0.0194"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 736/1250 [================>.............] - ETA: 13s - loss: 6.8692e-04 - mae: 0.0194"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 739/1250 [================>.............] - ETA: 13s - loss: 6.8627e-04 - mae: 0.0194"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 741/1250 [================>.............] - ETA: 13s - loss: 6.8516e-04 - mae: 0.0194"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 743/1250 [================>.............] - ETA: 13s - loss: 6.8514e-04 - mae: 0.0194"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 746/1250 [================>.............] - ETA: 13s - loss: 6.8370e-04 - mae: 0.0194"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 748/1250 [================>.............] - ETA: 13s - loss: 6.8318e-04 - mae: 0.0194"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 751/1250 [=================>............] - ETA: 13s - loss: 6.8201e-04 - mae: 0.0194"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 753/1250 [=================>............] - ETA: 13s - loss: 6.8088e-04 - mae: 0.0193"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 756/1250 [=================>............] - ETA: 13s - loss: 6.7903e-04 - mae: 0.0193"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 759/1250 [=================>............] - ETA: 13s - loss: 6.8062e-04 - mae: 0.0193"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 762/1250 [=================>............] - ETA: 13s - loss: 6.8201e-04 - mae: 0.0194"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 765/1250 [=================>............] - ETA: 13s - loss: 6.8070e-04 - mae: 0.0193"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 767/1250 [=================>............] - ETA: 13s - loss: 6.7921e-04 - mae: 0.0193"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 769/1250 [=================>............] - ETA: 12s - loss: 6.7926e-04 - mae: 0.0193"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 771/1250 [=================>............] - ETA: 12s - loss: 6.7896e-04 - mae: 0.0193"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 774/1250 [=================>............] - ETA: 12s - loss: 6.8375e-04 - mae: 0.0194"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 776/1250 [=================>............] - ETA: 12s - loss: 6.8402e-04 - mae: 0.0194"
- ]
- },
- {
- "name": "stdout",
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- " 804/1250 [==================>...........] - ETA: 11s - loss: 6.7870e-04 - mae: 0.0193"
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- " 807/1250 [==================>...........] - ETA: 11s - loss: 6.8014e-04 - mae: 0.0193"
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- " 810/1250 [==================>...........] - ETA: 11s - loss: 6.8174e-04 - mae: 0.0193"
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- " 813/1250 [==================>...........] - ETA: 11s - loss: 6.8076e-04 - mae: 0.0193"
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- " 816/1250 [==================>...........] - ETA: 11s - loss: 6.7990e-04 - mae: 0.0193"
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- " 819/1250 [==================>...........] - ETA: 11s - loss: 6.7945e-04 - mae: 0.0193"
- ]
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- " 822/1250 [==================>...........] - ETA: 11s - loss: 6.7847e-04 - mae: 0.0193"
- ]
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- " 824/1250 [==================>...........] - ETA: 11s - loss: 6.7822e-04 - mae: 0.0193"
- ]
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- " 826/1250 [==================>...........] - ETA: 11s - loss: 6.7839e-04 - mae: 0.0193"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 828/1250 [==================>...........] - ETA: 11s - loss: 6.7759e-04 - mae: 0.0193"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 830/1250 [==================>...........] - ETA: 11s - loss: 6.7747e-04 - mae: 0.0193"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 832/1250 [==================>...........] - ETA: 11s - loss: 6.7640e-04 - mae: 0.0193"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 834/1250 [===================>..........] - ETA: 11s - loss: 6.7648e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 836/1250 [===================>..........] - ETA: 11s - loss: 6.7914e-04 - mae: 0.0193"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 838/1250 [===================>..........] - ETA: 11s - loss: 6.7846e-04 - mae: 0.0193"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 840/1250 [===================>..........] - ETA: 11s - loss: 6.7914e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 842/1250 [===================>..........] - ETA: 10s - loss: 6.7821e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 844/1250 [===================>..........] - ETA: 10s - loss: 6.7802e-04 - mae: 0.0193"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 846/1250 [===================>..........] - ETA: 10s - loss: 6.7929e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 848/1250 [===================>..........] - ETA: 10s - loss: 6.7917e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 853/1250 [===================>..........] - ETA: 10s - loss: 6.7856e-04 - mae: 0.0193"
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- },
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- ]
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- ]
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- " 884/1250 [====================>.........] - ETA: 9s - loss: 6.7784e-04 - mae: 0.0193"
- ]
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- " 887/1250 [====================>.........] - ETA: 9s - loss: 6.7702e-04 - mae: 0.0193"
- ]
- },
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- " 890/1250 [====================>.........] - ETA: 9s - loss: 6.7604e-04 - mae: 0.0193"
- ]
- },
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- " 893/1250 [====================>.........] - ETA: 9s - loss: 6.7576e-04 - mae: 0.0193"
- ]
- },
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- " 896/1250 [====================>.........] - ETA: 9s - loss: 6.7629e-04 - mae: 0.0193"
- ]
- },
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- " 899/1250 [====================>.........] - ETA: 9s - loss: 6.7457e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 901/1250 [====================>.........] - ETA: 9s - loss: 6.7382e-04 - mae: 0.0192"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 903/1250 [====================>.........] - ETA: 9s - loss: 6.7262e-04 - mae: 0.0192"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 905/1250 [====================>.........] - ETA: 9s - loss: 6.7246e-04 - mae: 0.0192"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 907/1250 [====================>.........] - ETA: 9s - loss: 6.7667e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 909/1250 [====================>.........] - ETA: 9s - loss: 6.7742e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 914/1250 [====================>.........] - ETA: 8s - loss: 6.7659e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 917/1250 [=====================>........] - ETA: 8s - loss: 6.7530e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 920/1250 [=====================>........] - ETA: 8s - loss: 6.7804e-04 - mae: 0.0193"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 926/1250 [=====================>........] - ETA: 8s - loss: 6.7816e-04 - mae: 0.0193"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 929/1250 [=====================>........] - ETA: 8s - loss: 6.7990e-04 - mae: 0.0193"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 931/1250 [=====================>........] - ETA: 8s - loss: 6.7970e-04 - mae: 0.0193"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 967/1250 [======================>.......] - ETA: 7s - loss: 6.7552e-04 - mae: 0.0192"
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- " 974/1250 [======================>.......] - ETA: 7s - loss: 6.7469e-04 - mae: 0.0192"
- ]
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- " 977/1250 [======================>.......] - ETA: 7s - loss: 6.7449e-04 - mae: 0.0192"
- ]
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- " 979/1250 [======================>.......] - ETA: 7s - loss: 6.7479e-04 - mae: 0.0192"
- ]
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- ]
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- " 983/1250 [======================>.......] - ETA: 7s - loss: 6.7261e-04 - mae: 0.0192"
- ]
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- " 985/1250 [======================>.......] - ETA: 7s - loss: 6.7169e-04 - mae: 0.0192"
- ]
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- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "1188/1250 [===========================>..] - ETA: 1s - loss: 6.6364e-04 - mae: 0.0190"
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- "1191/1250 [===========================>..] - ETA: 1s - loss: 6.6500e-04 - mae: 0.0190"
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- "1203/1250 [===========================>..] - ETA: 1s - loss: 6.6519e-04 - mae: 0.0190"
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- "1209/1250 [============================>.] - ETA: 1s - loss: 6.6299e-04 - mae: 0.0190"
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- "1212/1250 [============================>.] - ETA: 0s - loss: 6.6224e-04 - mae: 0.0190"
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- "1215/1250 [============================>.] - ETA: 0s - loss: 6.6188e-04 - mae: 0.0190"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1218/1250 [============================>.] - ETA: 0s - loss: 6.6130e-04 - mae: 0.0190"
- ]
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1221/1250 [============================>.] - ETA: 0s - loss: 6.6013e-04 - mae: 0.0189"
- ]
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- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1224/1250 [============================>.] - ETA: 0s - loss: 6.6169e-04 - mae: 0.0190"
- ]
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- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1227/1250 [============================>.] - ETA: 0s - loss: 6.6515e-04 - mae: 0.0190"
- ]
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- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1230/1250 [============================>.] - ETA: 0s - loss: 6.6444e-04 - mae: 0.0190"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1233/1250 [============================>.] - ETA: 0s - loss: 6.6362e-04 - mae: 0.0190"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1236/1250 [============================>.] - ETA: 0s - loss: 6.6308e-04 - mae: 0.0190"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1239/1250 [============================>.] - ETA: 0s - loss: 6.6400e-04 - mae: 0.0190"
- ]
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- "name": "stdout",
- "output_type": "stream",
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- "1242/1250 [============================>.] - ETA: 0s - loss: 6.6567e-04 - mae: 0.0190"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1245/1250 [============================>.] - ETA: 0s - loss: 6.6465e-04 - mae: 0.0190"
- ]
- },
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- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1248/1250 [============================>.] - ETA: 0s - loss: 6.6347e-04 - mae: 0.0190"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "1250/1250 [==============================] - 35s 28ms/step - loss: 6.6253e-04 - mae: 0.0189 - val_loss: 1.4133e-04 - val_mae: 0.0094\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Duration : 00:02:51 915ms\n"
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- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "history=model.fit(train_generator, \n",
- " epochs=epochs, \n",
- " verbose=1,\n",
- " validation_data = test_generator,\n",
- " callbacks = [bestmodel_callback])\n",
- "\n",
- "pwk.chrono_show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:39.503438Z",
- "iopub.status.busy": "2021-03-09T21:35:39.502653Z",
- "iopub.status.idle": "2021-03-09T21:35:40.380264Z",
- "shell.execute_reply": "2021-03-09T21:35:40.379912Z"
- }
- },
- "outputs": [
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history,plot={'loss':['loss','val_loss'], 'mae':['mae','val_mae']}, save_as='03-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Predict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.1 - Load model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:40.389951Z",
- "iopub.status.busy": "2021-03-09T21:35:40.389624Z",
- "iopub.status.idle": "2021-03-09T21:35:40.472565Z",
- "shell.execute_reply": "2021-03-09T21:35:40.472167Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loaded.\n"
- ]
- }
- ],
- "source": [
- "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')\n",
- "print('Loaded.')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Make a 1-step prediction\n",
- "A simple prediction on a single iteration"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:40.476974Z",
- "iopub.status.busy": "2021-03-09T21:35:40.476612Z",
- "iopub.status.idle": "2021-03-09T21:35:41.279415Z",
- "shell.execute_reply": "2021-03-09T21:35:41.279037Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/LADYBUG1/figs/LADYBUG1-fig_00</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
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\n",
- "text/plain": [
- "<Figure size 1080x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s=random.randint(0,len(x_test)-sequence_len)\n",
- "\n",
- "sequence = x_test[s:s+sequence_len]\n",
- "sequence_true = x_test[s:s+sequence_len+1]\n",
- "\n",
- "sequence_pred = loaded_model.predict( np.array([sequence]) )\n",
- "\n",
- "pwk.plot_2d_segment(sequence_true, sequence_pred)\n",
- "pwk.plot_multivariate_serie(sequence_true, predictions=sequence_pred, labels=['Axis=0', 'Axis=1'],save_as='04-one-step-prediction')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.3 - Make n-steps prediction\n",
- "A longer term prediction, via a nice iteration function :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:41.283428Z",
- "iopub.status.busy": "2021-03-09T21:35:41.283085Z",
- "iopub.status.idle": "2021-03-09T21:35:41.285221Z",
- "shell.execute_reply": "2021-03-09T21:35:41.284904Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_prediction(dataset, model, iterations=4):\n",
- "\n",
- " # ---- Initial sequence\n",
- " #\n",
- " s=random.randint(0,len(dataset)-sequence_len-iterations)\n",
- "\n",
- " sequence_pred = dataset[s:s+sequence_len].copy()\n",
- " sequence_true = dataset[s:s+sequence_len+iterations].copy()\n",
- "\n",
- " # ---- Iterate \n",
- " #\n",
- " sequence_pred = list(sequence_pred)\n",
- "\n",
- " for i in range(iterations):\n",
- " sequence = sequence_pred[-sequence_len:]\n",
- " prediction = model.predict( np.array([sequence]) )\n",
- " sequence_pred.append(prediction[0])\n",
- "\n",
- " # ---- Extract the predictions \n",
- " #\n",
- " prediction = np.array(sequence_pred[-iterations:])\n",
- "\n",
- " return sequence_true,prediction"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "An n-steps prediction :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:41.288627Z",
- "iopub.status.busy": "2021-03-09T21:35:41.288272Z",
- "iopub.status.idle": "2021-03-09T21:35:41.878196Z",
- "shell.execute_reply": "2021-03-09T21:35:41.877843Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/LADYBUG1/figs/LADYBUG1-02-prediction-norm</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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Bmyoi7DYmj+1OpbuGNg57qxqNOtLxFjmOG5DMgUo3Nz4/m2FdOzBxWBpdEmNNqFREpHVZnr+XX53WzewypImCNkzVKSpz0TnAdu41t+NNe153Zo/a8NmN/y7ZzAPv/cgpbR1MHJbOsG4JOuZGRKQFVFRVk7ujmF6d2phdijRRUE7zHaq4zNXqDxU+dNoz1mGvd5FjTISdy0Z14bXfnM55Azvz9vfruc75HZ8szjvuuisREWmc1VsLyegYo6PDgkDQ/wSLy13EnMCaoWBXN+15Ii0QbCFWzuibxOl9TmHllkI+XLiRN2evY9yAZC4YkqpjbUREmsHyvL30S2lndhnSDII/TJVV1dsVXBpmsVjo07ktfTq3paCwjI9/2MTUF75ncEY8Fw9Lo0dSG7NLFBEJWD9pvVTQCPowVaRpvmaRGOdg6rjeTB7Tjf8u3cJj05cQHxPOxcPSGNm9IyFWrasSETlRFS631ksFkaAOU9U1NZRVuokM1zRfc4kMD+WSEelcPCyVeWt2Mn3BRqZ9tZqLhqYxbkAnIsP0/1pE5HhWbd2v9VJBJKh/iiXlVURHhGrUpAWEWK2c1iuR03olsnprIR8u2sTb36/n7H6duHBoKh3bOMwuUUTEb/2Ur/VSwSSow1RxmYvoCI2UtLSeneK4r1McO/eX8cniPG55cS79U9oxcXgavTrFYVFrBRGRw2i9VHAJ6jBVVK7F577UoY2DG8/uxa9O68YXy7fwt0+WExNhZ+KwNEb37IgtJOg7cYiIHJfWSwWfoA5T6jFlDkeYjYuGpjFhcCqL1u1k+qJNvPj1ai4cksp5gzoTpTVsItKKab1U8Anqn2RxuUsjUyYKsVoY2aMjI3t0ZH1BER8u3MjV//yGM/omcdHQNJLaRppdooiIz2m9VPAJ6nkXrZnyH10TY7nr4oH8+6YxRITa+N3L83jwvR/5KX8vHo/H7PJERHxGYSr4BPXIVFGZi7jIMLPLkEO0jwlnypk9uOLULnz50zaembmCcHsIE4elcVrvUwjVuioRCWIH10slx5ldijSjoA5TxeVVpMRHm12GHEO43caEwSmcn9mZH9bv4qNFm3jpmzVMGJzK+YM6E6PpWREJQgfXS4WGmF2KNKOgDFPlLjfZ83P5dsU2vly+lRe/Xs2EzBSyRmYcPNRX/IPVYmF4tw4M79aB3B3FfLRoE9c++y1jep/CxUPTSG4fZXaJIiLN5qf8vfTXFF/QCbo5lXKXm1tfnkf2go24a7xrcYrLqshesJFbX55HucttcoVSn4yOMfzhwv5MmzqGWIedP7y+gPvf+YElG/doXZWIBIWf8vfSL1VhKtgEXZjKnp9LQWEZLnfNYfe73DUUFJaRPT/XpMrkRLWNCufqsd15/TdnMLJHR56btZKpL3zPrGVbcLmrzS5PROSk1K2X6tlJ66WCTdCFqRk5+UcFqToudw0zczb7uCI5WWGhIZw3sDMv3Hwa15/Vk9mrCpj8j295c/Y69h+oNLs8EZFGWbm1UOulglSzLyAyDOMuYJzT6Tyjub/2iSgpq2rwenGZy0eVSHOxWCwMzohncEY8ebtK+OiHTVzn/I7RPRK5eFgaqQnaZCAi/u+nPK2XClYtsRq7BzCmBb7uCYl2hFLcQKDSLrHAlpoQzW3j+3Ht6d35NGcz97y1iNSEaCYOSyMzIx5r7TmAdZsQZuTkU1JWRbQjVJsQRMRUP+XvY/JYnccXjIJumm9CZgp227G/LbvNyvjMzj6uSFpCm8gwrjytK6/95nTO6JPES1+v4abn5/DZks3sP1B5cBNCcVkVHrQJQUTMVeFys3Gn1ksFq+O+RTcM4+FGfs2BJ1lLs8gamcHcNTuOWoQeYrWQGOcga2SGidVJc7PbQji7fyfO6pfE8ry9fLhoE8/PWom7xkN1zeE7AA/dhDB5bHeTKhaR1kjrpYLbicx3/BHwAJZGfF3T9rFH2G08M2UU2fNzmZmzmeIyF44wGzUeD0/8apimeIKUxWJhQFp7BqS1Z9LfvqCy4thTvXWbEBSmRMSXtF4quJ1IsigHtgGPneDXvB4YedIVNYMIu43JY7sf9oL5lw+XMjNnM1eN0Xx1sDtQT5Cqo00IIuJrWi8V3E4kTK0AujidztdO5AsahjEWk8PUsUw5ozu/fnEu4wYkkxAbYXY50oKOtwlBh1+LiC9pvVTwO5EF6MuAOMMwklu4lhbVoY2DCZkpvPLNGrNLkRbW0CYEq8U71fffpZuPWlMlItISVm4tpEtirNZLBbETCVOLgWKg5wl+zbnA6yddUQu6dFQGy/P3snprodmlSAvKGplBYpzjqEBlt1lJbh/Fw5cP4cvlW/n1tO/Jyd1tUpUi0lr8lLeXfiltzS5DWpDFjDPPDMPwADidTp8/95fLt/JpTj5PXTsSi6Uxa+olkNT1marbhBDjsDM+s/PBPlMej4f5a3fy4terOSUukhvO6qnmnyLSIm57ZT5Xj+3GgLT2ZpciTVNvaGh1W9vO7JfEJ4vz+G7ldk7vk2R2OdJCjrUJ4VAWi4VRPToytGsCn+bkc+cbCxnZvQOTx3ajbVS4j6sVkWBVt16qh9ZLBbWga9p5PFaLhZvP6cVLX6+hokqH5rZ2oSFWLhqaxkvGWBxhNm58fg5vzVmvvxsi0iy0Xqp1aHVhCqBP57b0SIpj+oKNZpcifiI6IpQbz+7FP68bTd7uEq5zfseXy7dSY8I0uIgEj+VaL9UqnFSYMgzjasMwvqnvdiC4/swefPTDJvYUV5hdiviRxDgH900axH2TBvFpTj63TJvLsk17zC5LRALUT/lq1tkanOzIVCqHH2Z85G2/1zHOwS8GdeaVb9UqQY7Wq1McT107kstHd+GpmT/xp3cXs3l3idlliUgAKXe52bSzROulWoFWOc1X5/JRXViycQ9rt+83uxTxQxaLhdN6JTJt6hj6pbTjD68v5J+frWD/gUqzSxORALBqi9ZLtRatOkw5wmxcc3p3np+1CjNaREhgsNtCuGREOi9OHYMtxMoNz83mvXkbqNQidRFpwPJ8rZdqLVp1mAI4q18nXO5qZq8qMLsU8XMxDjtTx/Xm6WtHsXbbfq5/bjbfrNimReoickxaL9V6tPowFWK1cFNtqwSNNMiJSGoXyZ8uHcydF/bno0WbuPXleazYvM/sskTEj9Stl9J5fK1Dqw9TAP1S2tEtMZbpC9UqQU5c35R2PHPdKC4emsZfP17Gw+//yNa9pWaXJSJ+YNWWQromxhKm9VKtgsJUrevP6smHizaxt0StEuTEWS0WzuibxEvGGLonxXHbK/N5btZKistcZpcmIibyrpfSFF9roTBVKzHOwXkDO/PKt2vNLkUCkN0WwmWjMpg2dQzVNR6uf2422Qtycbk1dSzSGv2Uv5d+qVp83looTB3i8tEZ5OTuZn1BkdmlSIBqExnGLef14e+Th/Nz/j5ueG42s1du125RkVbk4HqpJK2Xai0Upg4RGRbKVWO68dyslXrxkybpHB/NQ5cP4bbx/Xh/fi63vTKfVVsLzS5LRHxA66VaH4WpI4wbkExZpZu5q3eYXYoEgQFp7fnn9aM5PzOFx6Yv4dEPllBQWGZ2WSLSgrReqvU52TCVB8xp4HbACrFauHlcL6Z9vVrrXaRZWC0Wzu7fiZeMsaR3iOa3L83lhS9XUVJeZXZpItICtF6q9bGYMZ1lGIYHwOl0+vy5T9RD7/9Ij6Q2XDaqi9mlSJDZV1rBG7PXM2/NDn45ugvjB6cQGqJBYpFAVu5ykz0/lxk/5lNcXkVMRCgTBqeQNTKDCLvN7PKkeVjqu6Df4PW4/qyefLBgI/tK1SpBmlfbqHBuPb8vf71qOD/m7ubG52czd3WB1umJBKhyl5tbX55H9oKNFNeOOBeXV5G9YCO3vjyPcpfb5AqlpTVbmDIMI84wjMjm+npmS2obyTkDknnt23VmlyJBKjUhmseuGMot5/bhzTnr+cPrC3XotkgAyp6fS0FhGS53zWH3u9w1FBSWkT0/16TKxFcaFaYMwzjTMIy/GoYRd8h9CYZhzAb2APsMw/i/5i7SLFeM7sKi9bvYoFYJ0oIyM+J59oZTObtfEg+9/yOPf7SUnfu1SF0kUMzIyT8qSNVxuWuYmbPZxxWJrzV2Ivc3QB+n03nnIff9HTgVWA9EA7cahrHQ6XS+30w1miYyPJTLRqXzUPaPVLiqKSmvItoRyoRMzYNL8wqxWjh3YGfG9D6FDxZs5NcvzuW8gZ25fFQGkeGhZpcnIg0oKWt4M4lORAh+jZ3m6w/MrbthGEYEcAnwpdPp7A50B7YANzdbhSYqd7n575It7C6qoLi8Cg9QXKZ5cGk5EXYbV43pxvM3nsb+A5Vc55zNfxbn4a4+9rteETFftKPhNzwxDruPKhGzNDZMJQDbD7k9DAgHXgVwOp0lwEy8oSrgZc/PpWB/GUcuC9Y8uLS09jHh/P6C/jx2xRDmrd3BTf+ew8J1O7VIXcQPTchMwRZy7I1edpuV8ZmdfVyR+Fpjw1QlEHHI7VMBD4f3mCoGgqLBhubBxWwZHWN5/Mph3HR2L176eg13vblIxx2J+JlJI9KxWCzYrIcHKrvNSmKcg6yRGSZVJr7S2EU/m4AzDrk9CVjvdDq3HXJfMt7F6AFP8+DiDywWC0O7JpCZ0Z7Pl27hT+8uZlB6e645vTvxMRHH/wIi0qKW5O6hc7sohnVL4NOczRSXuYhx2Bmf2Vnra1uJxv6EXwOeNgxjEeAC+gIPHfGYQcDaZqjNdNGOUIobCFSaBxdfCrFaOT8zhbF9TuH9eblMfeF7xmemcOnIDBxh+mUtYobqGg+vfbeWm87pxZAuCVw9NihWuUgjNXaa7zngXWAwMArv+qgn6i4ahjEU6Al810z1mWpCZgp227H/F9msFs2Diykiw0K59oweOG84lV1F5Vzn/I7PlmymukaL1EV87ZsV24iNDGNwRrzZpYiJGvV21ul0VgFXGIZxM+CpXXB+qI3AQLxn9QW8rJEZzF2z46hmbLYQKzU1NaTER5tYnbR2CbER3HnRANYXFPHCl6v4+IdN3HBWTwZnxGOx1HvqgYg0k6rqGt6Ys447Lhygf3OtnM7mO46685ZmHjEP3j+1HY9NX8qdFw3QOxIxncfjYeG6Xbz41WoS2kRww1k9Se8QY3ZZIkFtxo95LFi3iz9fMdTsUsQ36k3MClNNsHLLPh56P4d7Jg5kYFp7s8sRwV1dw6dLNvP29+sZ3rUDk8d2o110uNlliQSdiqpqpjz7LQ9dNoSuibFmlyO+UW+YanCazzCMjXhbH5zldDo31d4+ER6n0xn0e0F7J7fl/ksG8cgHS7hv0iD6p7YzuyRp5WwhVi4ckspZfZN4Z+4Gbvr3HC4aksolI9IJ144ikWYzY3EePZLiFKQEOP4CdOsRj7HiTWbH+9NsByj7u74p7bh34kAem76EnzfvM7scEcB7FNL1Z/XkX9ePZsveA0xxfsesZVuorvFQ7nLz+ndryXryC8595FOynvyC179bq47+IifoQKX3JIyrx3YzuxTxE5rmayY5G3fzxEfLeOiywfTsFHf8TxDxoTXbCnnhy9WUlldRUVVN4YHKwzZV1DUXfGbKKPXEETmON2evY3thGXdeNMDsUsS36p3mazUjSC0tMz2eOy7szwPv/ci67fvNLkfkMD2S4njy6hF0jo9iZ1H5UZ39dUSSyIkpLnPx8eI8rhqjUSn5n0aFKcMwTmiVtWEYg0+unMA2pEsCt0/ox/3vLmaDjvwQP2OxWFiev7fe6zoiSeT43p+fy2m9EkmMc5hdiviRxo5MLTMMY0xDDzAM43Zg7smXFNiGd+vAb87rwx/fWczGncVmlyNyGB2RJHLy9pZU8PmyLVwxuqvZpYifaeziiLbAV4ZhPAI84nQ6Dy64MgyjLfAqMB7vGX6t1uieiVTXeLjv7R/4y5XDSE1Qc0/xD8c7IikqPNSH1YgElnfmbuDs/p1oH6N2I3K4xo5MDQXWAQ8AXxuG0RHAMIzRwDK8QeoDvF3QW7UxvU/hhrN6cu/bi9i8p9TsckSAho9ICrFaCKksY/kzT+MqK/NxZSL+bUdhGd+t3M5lI4O+64+chEaFKafT+TPec/leB8YCyw3D+BfwDdAemOp0Oi91Op2a3wLO6JvEtaf34J43F7Ft7wGzyxEha2QGiXGOowKV3WalU7tInrwyk9BdW9n/hyms/+prk6oU8T9vzlnPhMEptIkMM7sU8UON3s3ndDrLnU7ntcAdQDwwFSgEhjidzn83c30B7+z+nfjVmK7c/dYiCgr1bl/MFWG38cyUUWSNSCfWYccCxDrsZI1I55kpo+iUlkyvx/7OvgumYP/4NZ56bz77SivMLlvEVJt3l/DDhl1cMjzd7FLET51UnynDMM7BOzqVABQD0cCbgOF0Oo87BBOMfaaOZ8aP+WTPz+Vvk4fToY12gYj/q6is4q25uaR/No32ffrQe/JVWEPUg0pan0c/WELXxFguG6UpvlauefpMGYYRYhjG48BngAO4AugCfAFcBeQYhjHg5OsMXhMGpzBxeBp3vbmI3cXlZpcjclzhYaFcd2YPul1zHY4VC9h6+w1szVlidlkiPrWhoIiVW/Zx4ZAUs0sRP9bYab7vgTuB5cAgp9P5rtPp3ON0Os8D7gbSgAWGYfy2mesMChcNTWN8Zgp3vrGQvSWaOpHAkNS3F2l/f579w8cx45PZ/PuLlZSXaFOFtA6vfbeWy0d30dmW0qDGhqnhwL+AEU6nc8OhF5xO51+B04AdwFPNU17wuWREOucOSObONxZSWFppdjkiJ8RqtdLvl5fzy7sMInbkU37HNaz9+D94amqO/8kiAWrlln3k7y7lvIHJZpcifq6xYWqS0+n8rdPpPGZnP6fTuQhvW4SPmlxZELtsVBfO6JPEnW8sZP8BBSoJHG0iw5g8+RfsvfJ2HF9lk3v3b9m1fafZZYk0O4/Hw6vfruXK07pit4WYXY74OR10bKLXvl3LgnU7+etVw4lx2M0uR6RRXJWVLHnrfZ7aG8+1vaI468wh2MK1bVyCQ87G3Tg/X8kLN59GiFXH2ArQwAL0k5oENgwjETgTSAKO9dvT43Q6HzmZr92aTB7bDXeNh3veWsTjvxpOdIS6T0vgsIeFMXzKVTy17wBbnv4Le/7rpPzSqaSddqrZpYk0icfj4dVv1nLVmG4KUnJCGh2mDMN4CO9i80M/1wJ4jvhvhanjsFgsTDmjO+6aGu59exGPXzmMSB3nIQHmlLaRJD70CD/P+IzEd/7B/B9z6DPVICZCo60SmBas3Ym7xsNpvRLNLkUCRGNbI1wJ3I93V98leIPTa3hbJEwDaoB3gTOat8zgZbFYuPGsnvRMiuO+t3/gQGXDB9GK+COLxULfC84n4vGXWN95EL979ktWvfMunupqs0sTaZTqGg+vfreWq8d2w2qpd1ZH5DCNHb+cCmwFznU6nXWLzPNqWyTcjPdsvkuBmGasMehZLBamjutFescY7n9nMeUut9kliZyUyNgYrr7kNO77RTfCF31J/h03seOnn80uS+SEzV65HUeYjWFdE8wuRQJIY8NUX+Azp9N56Kv9wW0OTqdzFjAL71Ez0ggWi4VbzutDcrso/vTuYiqq9I5eAldGr66k/P3fFPYfg935AO//90dcbv2dFv/mrq7h9dnruOb07lg0KiWN0NgwFQrsPeR2ORB7xGN+Bvo3pajWymqxcOv4viTERvDgez9SqUAlASzEZmPg1VdR/ehLrC21MOOhx9nw3/+aXZZIvb5YvpUObSIYkNre7FIkwDQ2TBUAh67I2wz0O+IxSYDmqU6S1WLh9gn9aRNp5+HsHL2bl4AX374N92dl0nPsKBwz32D9vbexf/Nms8sSOYzLXc1b36/n2tO7m12KBKDGhqmleKf66nwDnGoYxlWGYUQahnE+MKn2cXKSQqwW7riwPxF2G498sISqanWZlsDX68yxtP3byxR1TONL58vM+DGfGnVQFz8xM2czXTrG0iMpzuxSJAA1NkzNBHobhpFWe/txoAh4FSgG/oN3h98fm6vA1irEauXuiwcQarXw5+lLcCtQSRAId4Qz+Le/Zcjtv2flD8vZ/rtr2bZokdllSStX7nLz/rxcrh7bzexSJEA1uQN6bbD6PZAB5AFOp9O54jifow7oJ6iquoZHs3MItYVwz8QBaiAnQaOmpobl0z+m89dvsyu5Nym/vh1HmyOXYIq0vLe/X0/+7lLumTjQ7FLEv9W7K0HHyQQAl7uah97PISo8lDsvGkCIVbtMJHjs31vI6hen8WJoX24cm8HQAV2w6E2D+EhJeRVTnv2Wp68dRVK7SLPLEf9W74uvfmMFALsthD9lZVJU5uL/Ziynusb3AVikpbRpF8eIu+7ktxOHUvXeNPLuNNizZq3ZZUkrkb0gl5E9OipISZMoTAWIsNAQHrxsMLuKynnm05+oMWFEUaQl9U9tx9BHHmNP9yHYn7qbZdOmaa2gtKjC0ko+W7KZK0/tanYpEuA0zRdgyl1u7nv7B1Lio7nhrB58sGAjM3LyKSmrItoRyoTMFLJGZhBhP6kzrEX8wo68rXz430X8bGnHHb1CdHiytIjnZq0EYOq43iZXIgFCa6aCSVmlm7vfXMj2fWVUuqtxuf/37t1us5IY5+CZKaMUqCSgeTweFi34idQ3/0Jx+2QSp95OdKIOnpXmsauoHGPa90y7eQxxUWFmlyOBQWumgokjzEb/lHaUVlQdFqQAXO4aCgrLyJ6fa1J1Is3DYrEwfGR/oh9/kaLYDvCgwffzlmPGG0AJPm/NWc8vBnVWkJJmoTAVoD5fvoX6XlJc7hpm5qjDtASHyJgohvz+D+z63d95d1Ux7/zfi+xcqr7AcvK27T3AgnU7yRqRYXYpEiQUpgJUSVlVg9eLy1w+qkTENzJ6ZvCP60bRJTEO+/OPsuavj+IqLjK7LAlAr89ex0VDU4mOCDW7FAkSClMBKtrR8C+BGIfdR5WI+E6I1crQKy6h5qF/U1ruYv5jfyZn426zy5IAsnFnMcvz9nLxsLTjP1jkBClMBagJmSnYbcf+8VktFs4dkOzjikR8p13H9gx+4GEipvyO1z+az7p7b2P/po1mlyUB4LXv1nHpKO14lualMBWgskZmkBjnOCpQhYZYibCHMHv1dtZsKzSpOhHfGNY9kSd+fR6FSd2wPn47a5/7J9WVlWaXJX5q9dZCNuwoYnxmZ7NLkSCjaB6gIuw2npkyiuz5uczM2UxxmYsYh53xmZ3JGpnBjxt288B7PzI+M4Vfju6CLUS5WYJTeLidYb+eypZ151L6yrM8+NI3XD1hKF2S25ldmpis3OUme37uwV58VquFzPT2OkVCmp36TAWxvSUVPDnjJ0rLq7jrogE6LkGCXo3HwxfLtmB761+kxNhIuvk2HAnxZpclJih3ubn15XkUFJapF580F/WZao3aRYfz2C+HcGa/JG57dT6f5uSrR48ENavFwrkDOzP43vvYZ4/Bff+NrM/O1t/7Vih7fu5RQQrUi09ahsJUkLNYLFw4JJW/Tx7OZ0s286f3fqSwVGtKJLi1aRvLsLvvpuC6B/hi/T4eeHcxezboxbM1mZGTf1SQqqNefNLcFKZaic7x0Tw9ZRQZHWIwpn3P/LU7zC5JpMV1HzqAG++6gYFxYPvbHax76gncB0rNLkt8QL34xJcUplqR0BAr15zenT9eMogXvlzNUzN+oqzSbXZZIi0qNMTKxecOpeKPz1Kydz/Ff7iW9ctXmV2WtDD14hNfUphqhXont8V5w6l48GBM+56VW/aZXZJIi+uYnMigR/5M3qTf8uA3W3nvtU8o2aKpnmA1OD2h3mt2m1XtEaRZKUy1Uo4wG7dP6M+NZ/XkkewlvPrtWtzVx15fIBIsLBYLg846lRemnk6HA7vg0VtZ//ILeKo05RNMlm7aw4+5u+jQJuKoXnx1u/myRupcPmk+ao0g7Cut4KkZP1F4wMWdFw2gc/sos0sS8YmNK9dR9sozVNojSbj9TyTr737AW7ppD3/5cCn3XzKILomx9fbiU1sEOQn1tkZQmBIAPB4Pny7ZzGvfruVXY7pxweAULJZ6/96IBI3q6mo+m7+W9+dv5J6QFWRcdxNhEREwKxu+nQmlJRAVDaePh3FZEB5hdslSj2Wb9vDn2iDVN0VNW6XZKUzJidm6t5QnPl5GdISd30/oR7vocLNLEvGJPbsL2fD8P+m5dSkREWHYK8vg0Om/UDvEJ8K9TytQ+aG6IPXHSwbRT0FKWoaadsqJ6dQuiqeuGUnPpDb8etpcvl9dYHZJIj7RPj6O4ff/iYo+w7CV7j88SAFUuags2MpHD/+Nt+asp9ylnbD+YlmegpSYS2FKjmILsXLVmG48eFkmL3+zhr9/spwDlQ33bBEJFh1yl9T7izHMU80FB35m064S7npjkQKVH1iet5c/T1/KfZMUpMQ8ClNSrx5JcThvOJVQm5WpL3zPis1qoSCtQGlJg5dDykq4b9JAEmIj+HDhJh8VJceyPG8vj01fwn2TBtE/VUFKzKMwJQ2KsNu49fy+GON68+fpS3jp6zW43NVmlyXScqKij3M9BovFwuWjMpi1fItvapKj/JTvDVL3ThqoICWmU5iSEzK8Wweeu/FUNu8p5daX55O3q+F37yIB6/Tx3sXmxxJqh7HnA5CaEM3uogofFiZ1fsrfy6MfeIPUgNT2ZpcjojAlJ65NZBgPXprJBUNSuPONhXy4aBM1JuwGFWlR47K8u/aODFR1u/nGZQGQt6uE9lE6ksTXDgapiQpS4j8UpqRRLBYL5w3szFPXjmTOyu3c89YidheXm12WSPMJj/C2Pzj3EirCoqjBgicqFs695GBbBI/Hwzuz1zJqx2K+WpKHGS1mWqMVtUHqnokDGZCmICX+Q32m5KRV19Tw3rxcPlmcx9RxvRnb+xSzSxJpVuUuN3e9sYiE2AguH5VBakI0ebtKeHdeLruKyrlxdGec323ioj0LGXD5pSRkpJpdctBasXkfj2TncM/EgQxUkBJzqGmntJx12/fzxMfL6JoYyy3n9SEqvOHT2kUCSbnLzYcLNzFr+RZ2F1UQHxvOuP7JTByeRoTdhrvKzaqXppG6dBa5g8fT79qrCbHp30BzUpASP6EwJS2roqqaF79azaL1u/j9Bf20lkFanZ1r13Pghb8zp/0ARl1zBV0TY80uKSjUBam7Lx7IoHT9XhFT1RumdNKjNIvw0BBuOa8Pw7ru4q8fL2Ns71O45vTu2G0hZpcm4hMdunfF81cnG1Zs5eMX3uaMmAp63zyV8AgdyXSyfq4NUnddPEBBSvyaFqBLsxrSJYHnbjyNHfvL+e1L89i4s9jskkR8xhISwjkDUrjx+guI2b2ZPXdcz5rvF5hdVkBauWUfD9cGqcz0eLPLEWmQwpQ0u1iHnfsvGcTE4Wnc/eYishfkqoWCtCqxSUl0fez/qBx3KWUfvcVfP1pKUZnr+J8ogDdIPfR+DnddpCAlgUFhSlqExWLhnP7J/GPKKBas3cldbyxkV5FaKEgrYrGQMeECej7+NHHhVrbfbbBs5iy1UTiOw4JUhoKUBAaFKWlRHeMc/G3yCAZnxHPLi3P5ZsU2vZhIqxJht3HDef2JmvQrkj97mSX338PO7TvNLssv1QWpOxWkJMAoTEmLC7FauGxUF/58xVDembuBP3+4lOJyTXlI65I8Ziyxf3uJ6Lg2PPnyF3w4fz3V1Trnss6qrYUHg9RgBSkJMApT4jNdEmP51/WjaRsVxtQXvmfJxj1mlyTiU7bIKLr9/m5unToRxzcf8vPdt5G3bpPZZZlu1dZCHnzvR+64sL+ClAQkhSnxqbDQEKaO683tE/rx5IzlPDdrJZVVencurUtSu0jG3fk7orp0o+2TtzPn369Q0Ur/HRwapIZ0STC7HJGToqadYprichf//Oxn8naVcNdFA0hqF0n2/Fxm5ORTUlZFtCOUCZkpZI3MIMKulmgSnIrWrWXBzK95196T289Ip1+vFLNL8pnVWwt5QEFKAoc6oIt/8ng8fLNiG89/sQqr1UJZpRuXu+bgdbvNSmKcg2emjFKgkqCW81MuXZx3sKzrGAbeeCMx0RFml9SiFKQkANUbpjTNJ6ayWCyc2a8TZ/RNoqjMdViQAnC5aygoLCN7fq5JFYr4Rma/DOz3/4OMvRvYc8/NLJi3LGh3vq7Z5g1Sf7hAQUqCg8KU+IVvft5Gfa8bLncNM3M2+7YgERNEJHWi02P/IPz8S3hn2R6efOVLdu7eb3ZZzWrNtkL+9O6P/P6CfgztqiAlwUFhSvxCSVlVg9eL1T1aWguLhVPOm8CTN5/BuSU/U/3AVGZ//AXVNYE/SrVm2/6DQWpY1w5mlyPSbBSmxC9EO0IbvB5qs7Jzf5mPqhExX2iIlT633o79susZ8MU0Zj38F3J3BO5Zl2u27eeB9xZz+wQFKQk+ClPiFyZkpmC3HfuvY2iIlYyOMdzy4lwem76ENdv2+7Y4ERO1H3Mm0X97mZiRY7jvzQX89+3/BFw7kbXb9/Ondxdz2/h+DO+mICXBR9ujxC9kjcxg7podFBSWHXM331+uHIbHA58v28Kfpy+hfUw4lwxPZ1i3DoRY691gIRIUrJHRjD5nFH2651Pz5D0sXfItUdfeQp/eGWaXdlx1Qer2CQpSErzUGkH8RrnLTfb8XGbmbKa4zEWMw874zM5H9Zmqrqlh7uodTF+4iZIKFxcPTeOc/p0IV+sEaQ0qK9j26gtELf2Od8+8jV+OH0ZMhN3sqo5p3fb93K8RKQke6jMlwcfj8bBqayHTF2zk5y2FnDswmQuHpNIuOtzs0kRaXNnWzby8opiKH+Yy6pyRDB/eF4vFf0Zp64LU787vx4juClISFOr9B6a38hKwLBYLvZPb0ju5Ldv2HeDjHzZx4/NzGNGtAxOHp5HeIcbsEkVajKNTZ27pBDtKlxP12gN8OvcMhl4/hYS4SLNLY31BEfe/u5hbz++rICWtgkamJKgUl7v4LGcznyzOIzUhmonD0hicEe9X79hFmlvVtnwK//UEc2riCZ10DeMHp/pkLWHd1PyhR0CN6t6R+Wt38Lvx/RjZvWOL1yDiQ5rmk9bF5a5m9soCpi/cSI3Hw6Th6Zze5xTsthCzSxNpGTU1bCnYy6szcxi4czm9r7uBtKS2LfZ05S43t74876hNIwDxMeFMmzpGR0BJsNFxMtK62G0hnN2/E8/deCo3n9ObOasKuPqf3/LWnPUUqQGoBCOrleSkeO7LymQou7H++bfM+OALXO6WaaOQPT/3mEEKoKjMpSOgpFVRmJKgZrFYGJTenseuGMpfrhzGzqIypjz7Lf/4bAVb95aaXZ5Is7O2jSfh/r/RNusqRsx5nd85v+an/L3N/jwzcvKPGaRAR0BJ66MwJa1GakI0t0/oz7SpY4h12Ln91QU88O5ifsrfG7QHykorZbEQPXYc7Z9+jV+d04+CZ//O9FemU1rR8LFNjaEjoET+RxPa0uq0jQrn6rHduWxUF776aSvPzFxBRJiNScPTOLVnIrYQvceQIGENYWSPjlRcdgFVrz7D4pULCL/iZoYPzDjpTRkl5VXM+DHvuI+Lcfhn7yuRlqAF6NLq1Xg8/LB+F9MXbqSgsIwLh6byi4GdiQxv+LxAkYBSUcbe157j0502cruP4pbzehMfE3HCn76rqJyPftjEl8u3MrxbB8JsVr5YvvWYU312m5WsEelMHtu9Ob8DEbNpN5/IiVi3fT/TF27ix9zdnNO/ExcNTaVDG4fZZYk0G5e7mnnvfULE4m8ouugGzh7TD2sDo1R5u0r4YMFGFq7fyTn9O3HxsDTiYyLq3c1XdwTUM1NGaTefBBuFKZHG2FVUzieL85i1bAsD09ozaXg6PZLamF2WSPOoclH0/qtY537OzJQzGHHtZFIPaXLr8Xj4eUsh2fNzWV9QxAVDUhmfmUJ0xOGjtSd6BJRIkFCYEjkZByqrmLV0Cx//kKfDlSXo1GzOZUv2O9zpGcL5g5K57NTu5OTu5v0FuRSVubhkeDpn9++k/mwiXgpTIk2hw5UlmBXsLaHsj1P5Oqo7H0UP4N5LBjOyR0e9aRA5nM7mE2mKEKuVMb1P4bReiazcUsj0hRt5c856Ha4sAe1ARRWfLtnMxz9sIjPzSi7LncmZuz9g/vIIBqW31yYMkROkMCXSCBaLhT6d29Knc1u27T3ARz9s4sbnZzOiW0cdriwBY29JBR8t2sTny7YwJCOeRy4fSkbHGPCcS8W3n2HdUc1vnF9x3dm9GdW3s9nlivg9TfOJNFFxuYtPczbzHx2uLH5uy55SPliwkblrdnBWvyQuHpZGx3p2q279KBvrVx/zeb+JXDDxDNrPnwHfzoTSEoiKhtPHw7gsCD/x9goiAU5rpkRamg5XFn+1emsh78/PZdXWQiYMTuWCwSkn1FSz6oc5VL3+T9yuKiIt1YRUH9L1PNQO8Ylw79MKVNJaaM2USEurO1z5rH5JLNm0h+kLN/Hqt2sZn5nC+MEpxKojtPhQjcfD4g27eH/+RnYXl3PJ8HTuungg4aEnHu5Dh55G6OZ11Hz5EdbqIw5MrnLB7gKYlQ0XTm7m6kUCi8KUSDOzWCxkpseTmR5P3q4SPly0kSnPfsuY3qcwcVgandpFmV2iBLGq6hq++3k72QtysVmtXDoyg1N7dSTEepLHJM394uggdfDJXPDdpwpT0uopTIm0oLrDla85vTszFudz+6sL6JnUhkkj0unbuS0Wi+Vg48MZOfmUlFUR7QhlQmaKGh9Ko5RVuvl86WY+XLSJpHaR3HROLwaltW/62r3SkuNcL27a1xcJAvpNLeIDbaPCufr07lw22nu48tMzV+AIszFhcGc+WLCJHfv/dyRHcVkV2bWLhHUkhxxPYWklnyzO47Mlm+mf2o4HLh1M18TY5nuCqOgGA1N5aAQ1FVVYrRY+XLiJWcu2sLu4nPiYCMYNSGbi8DT9HZagpwXoIiao8XhYtG4Xz37+M7uLK475GB0WKw3Ztu8A0xduZPbKAsb2TmTi8HSS2kY2/xN98jp8/oF3Su8IHmsIVViZlngWSzv0J61DDJePyiA1IZq8XSW8MzeXXUXlPHHVMAUqCQb1DvOe5CS6iDSF1WJhRPcOVLrrWYsCuNw1zMzZ7MOqJBCs276fRz9Ywm2vzCc2ws5Lxhh+84u+LROkwNv+ID7Ru3vvUKF2LB07Yb/zcSItNaS0jeDeiQPJ6BhLiNVKRsdY7ps0kITYCD5cuKllahPxE3qrIGKikrKqBq8Xlbm4682FdO0YS9dE75/EOId6WLUyHo+HnI17yJ6fy7Z9B5g4PJ3fX9DPN6M94RHe9gezsr2LzUuLISoGxp5/sM/UN2138MDYHkf9vbRYLFw+KoOHP8jhytO6tnytIiZRmBIxUbQjlOIGAlVMRCiThqWzYUcR363czrSvVlPucpNRF65qPya2dWBVwAo61TU1zF5ZQPaCjdTUeMgamc7Y3qdgC/HxpEJ4hHfHXj279nYXV5CaEH3Ma6kJ0ewuOvZUtkiwUJgSMdGEzBSyF2w8uPj8UHablQmDUxjaNYGhXRMO3r//QCXrC4pYX1DEnNUFvPzNGkoqqujSMYYuhwSspHaRClgBqsLlZtayLUxftImEmAiuPb07Q7r4b1f9+JgI8naVkNHx6IXvebtKaB+jsysluClMiZgoa2QGc9fsoKCw7LBAZbdZSYxzkDUy46jPaRMZxpAuCQzp8r+AVVTmYkNtwJq3ZgevfbeW4rIq0jvG1I5gxdQGrChCrP75gtwaHK8NRlGZi/8szmPGj/n0SY7jnosH0rNTnNllH9e4Acm8MzeX+yYNPCzweTwe3p6zlpDSQj5fuplzBiQr4EtQ0m4+EZPVvcDOzNlMcZmLGIed8Zmdm9xnqrjcxYaC4oOjWBt2FLH/QCXpHbzBqkvtCFZyewUsXyh3ubn15XnHDM7xMRH0T23LnFU7GN2zI5cMTye5feA0dy13ubnrjUUkxEYctpvv3Xm57CrYzWMbXmN+XC++SD4V4/wBxxzBEgkAOptPRKCkvIrcHUUHA9b6HUXsK6kkrUP0wQXuXTvG0jk+6uQ7Zssxvf7d2nqndAF6dYrjj5cMol10YE6Jlbvc3j5Ty7ewu6iC+NhwxvWv7TNVVoTnzX+R54nk7qr+jOl1CleP7UZkeKjZZYs0hsKUiBzbgYoqNuw4ZASroIjdJRWkJfwvYHXpGEtKfJTvFz4HuIqqavaXVrLvQCX3vfUDZS53vY+Nddh5//dn+7A6H/N4wF1F8Y6drH37Lf5l68/ks/tyRt8kv10LJnIEHXQsIscWGR5K/9R29E9td/C+A5VVbKwNWMs2ebfk7yquIDU+mi6JMQdHsFISogltZQGr3OWmsLSSwgOV7D/gYl9pJfsP1N4uraTwgKv2WiXuag9xUWHERYY1GKQAisuObooZVCwWCLUTExfLkLYWpq17B+cXe/h8WRd+fW6fencDigQChSkROUpkWCh9U9rRN+V/Aaus0s3Gnd6AtSJ/Hx8u3MTO/WWkxEd7dxHW/kmJj8JuCzmh5/GHcwk9Hg9lLjf7S70hqC4IeUOSi8JDwlLhARd4PLSpDUhtIsNoGxVGm0g7qfHRxKW2r71mJy4yDEeY7eCoS9aTXzTcBsNhr/daUImKgRvuwr58EbdOf4XPRgzmzjcWck7/Tlx5Wld1SpeApGk+ETlpFS43uTuLa3cSeoNWQeEBkttHHRaw0hKijwpYDS3IToxzNOlcQo/Hw4FK9yFByHXIyFFl7ciS62BIslosxNWGorqQFBcZVjuqZK+95r0vwh5yUtNSDa2ZarVHB9XUgNVK5UtPMcOdyEcV8dx8di9G9+yoqT/xR5rmE5HmF2630Tu5Lb2T2x68r6Kq+uAI1tpt+5n5Yz7b9x2gU7so7/qr2mnCBWt3HhWkwHuMTkFhGdnzcw8LFx6Ph5KKqqOm0grrRpAOhiTv7dAQK22iDg1H3v/ukhh7SFDy3h/ug9GQk2mDEfRqNzmEjTqDS157mjM7pPPI1y7+u7Qtvz63D0ntWuiIHJFmppEpEWlxlVXVbNp16CL3YnJ3Fjf4OaEhVgamtfMGp9oRJntoCG0jww5OpR06glQ3qhRXez089MSmGn2ppdpgBIXKCvj4Nao7JvNhaDfen5fL+MGpXD66C2F++LOUVkm7+UTEv5z7yKcc77fPQ5cN/t+oUlTYCa/FkgC3bCGV3/2X59qfztLCGqaO683wbh3Mrkqk3jDVurbhiIjfiHY03GMo1mFneLcO9EhqQ4c2DgWp1qT3IMJS0/ldznM8lLSPF75YxQPv/ciO/WVmVyZyTApTImKKCZkp2G3H/hVkt1kZn9nZxxWJ3wi1w0VXw21/JrV0G8/fOIoeHaO45cW5vP39elzuarMrFDmMwpSImCJrZAaJcY6jAlWrXpAth+ucAZNvxR4Swi+/fYaXuxexbus+pv77e3I27ja7OpGDFKZExBQRdhvPTBlF1oh0Yh12LHin9rJGpDepLYIEoZAQuOleYlYu4MH89/hNZhzPfLqCx6YvYU9xhdnViWgBuoiIBIiaGvh2BnTrS0XbDry/MJ8ZS7dy2aguXDQ0VccdSUvTAnQREQlwViuceSEkpxO+4Esmz3Pyr3GnsGTjboxp37Mif6/ZFUorpTAlIiKB58wL4YwJdHj5ER6LWs+vTuvG4x8v468fL6OwtNLs6qSVUZgSEZHAY7HA6HHwgBNLxyRO65XIi5f2JC4qjJv+PYdPFudRXeP7ZSzSOilMiYhI4GrTDoadDmUHiHjyDm7YO4e/XTaQ71cV8NuX5rJ6a6HZFUoroDAlIiKBzxEJDziheD8pz97B385OYuKwNB7OzuHpmT9RXOYyu0IJYgpTIiISHKJj4Ya74MpfY2mXwJkJMO2awdhtIdzw/Gz+u3QzNSbsYJfgpzAlIiLBpc9gCAuHJfOI+vMtGImlPPbLoXy+dAu3vzKfDQVFZlcoQUZd8UREJDj94jJI7wGvPU2XQaN46trrmLVsC/e98wOn9Urk6rHdiQpv+IxIkROhkSkREQlePfrDg8/BqLOxVldzXkgB0246jSp3DTc8N5uvf9qKGc2rJbhoZEpERIJbWDickgJ7d8EHLxHToRO/u/LXrBmYzD8/+5nPl23h1+f2ITUh2uxKJUBpZEpERFqHdglw/78gKRUeMugRWcM/rhvNqT0TufONhUz7ajXlLrfZVUoA0siUiIi0HqF2uGgynHoutGlHSM5cLkjtwqnpQ1j14jSqsucTXl0OUTFYTh8P47IgPMLsqsXPKUyJiEjr0y7B+3Hfbnj9GeJsNkaVHYDq2n5UpcXU/Dcba848uPdpBSppkKb5RESk9Tr7Yhg6Bor3Q9XhjT2t7ircO7dR9dn75tQmAUNhSkREWrfFc6CeHX22ajflsz5m4bqdPi5KAonClIiItG6lJQ1ejq6u4IUvV/PAu4vZUVjmo6IkkChMiYhI6xbVcEsES3QMz990Kt2T2nDLS3N5+/v1uNzVPipOAoHClIiItG6nj/fu8juWUDvEtsU+93OuGJXBv64fzdrtRdz87+/Jyd3t2zrFbylMiYhI6zYuC+ITjw5UoXbv/Vf9FhZ8DY/fTsfiAh66bDA3nt2Tf3y2gkc/yGF3cbk5dYvfsJjRRt8wDA+A0+n0+XOLiIgcpaIcZmXDd59CaTFExcDY8//XZ6qmBuZ9ASE2GHEmuCqptIby7rwNzPwxn0tHZnDuwGT+szifWcu2sLu4nPiYCMYNSGbi8DQi7OpEFAQs9V5QmBIREWmEjWvh+Ufh8pth4Ei27SvjH5+tIHdHMQPS2vHL0V1ITYgmb1cJ78zNZVdROU9cNUyBKvDVG6Y0zSciItIY6d3hujvgo9fgnw+SFAF9U9rSP7Ud900aREbHWEKsVjI6xnLfpIEkxEbw4cJNZlctLUhhSkREpLG694MHnoWBIyA8gi+W5HPFqV2wWA4fvLBYLFw+KoNZy7eYVKj4gsYcRUREToYt1HvGH7C7xEVqwrFbLKQmRLO7qMKXlYmPaWRKRESkieJjI8jbdezmn3m7SggLtfLat2spLncd8zES2BSmREREmmjcgGTemZvLkZu6PB4P787L5dyBndlXWsmUZ79TqApCClMiIiJNNHF4GruKynls+lI2FBThrq5hQ0ERj733A7v2FnH12G7cNqEf/7xu9MFQ9apCVdBQawQREZFmUO5y8+HCTcxavoXdRRXEx4Qzrk05E5e/R8RD//L2rqq1o7CMd+ZtYN6aHYzPTGHisDRiHPV0YRd/oT5TIiIipnBVgj0MZrwFKV2g37CDlw4NVecP6syk4ekKVf5LfaZERERMYQ/zfkzvCe/+G5yPwD7vuX4d4xzcNr4f/7p+NEVlLqY4v+OVb9ZQXKbpv0CiMCUiIuILvQfBQ89DUqr3aBqA2tmhjm0c/O6IUPXyN2soUqgKCApTIiIivhJqhwuvgglXwtZN8MgtsHHNwcuHhqqS8iquU6gKCApTIiIiZkhKhXMmwbMPwZv/goqyg5c6tnFw6/l9FaoChMKUiIiIGSwWGH4GPPwCOCLBYoWS/Qen/uB/oerZQ0PV1wpV/kZhSkRExEyR0TDxWggL9y5Q/797YMfWwx7S4dBQVaFQ5W8UpkRERPzFlD9A36Hw+O3w7QzvfRXl8Mnr8LtL6fCHSdz65SO8mryVygOlTHn2O176eg37D1SaW3crpz5TIiIi/mbfbijaB/GnwKO3QFEhVB0yChVqh/hEdt/yF975cRuzVxbwi0GdmTQ8jTaRYebVHdzUZ0pERCRgtI2HtO7wyRuwZ+fhQQq8t3cXEL9gJr/9RV+eu/FUyiqruM45mxe/Wq2RKh9TmBIREfFXi7+r/1qVC777FICE2Ah+Uxuqyl1uhSofU5gSERHxV6Ulx7lefNjNQ0NVRVW1QpWPKEyJiIj4q6johq978C5QLyuF6uqDdyfERnDLeX0UqnxEYUpERMRfnT7eu9j8WELtcO4lEB4Bsz+DO34Fbzu9HdVrN5fVharnb1KoakkKUyIiIv5qXBbEJx4dqGp38zH+Cu/t8y6Fu56E6FhvN3VXJWzPhx1bAIiPOTpUTVOoajZqjSAiIuLPKsphVrZ3sXlpMUTFwNjzvUErPKL+z5v/FUx/CeLaw9DTDxvl2l1czvvzc5m/PI/b7esYmLcQ64ES77Ti6eOP/7Vbp3pbIyhMiYiIBKvqalizHJYtgF/eDCuXQPF+GDQSLFbcj/4WdhVgq3H/73PqRr3ufVqB6nD1himbL6sQERERHwoJgd6DvH/AG5SWzoN3n4c27bDt3QGHBimAKheeXduxzMqGCydT7nLz4cJNzFq2hd3F5cTHRDBuQDITh6cRYVeMAIUpERGR1qNHf++f0mK4+2qoqjrmwyzuKsq/+ITCUyfy+EfL6dAmggcuzSQ1IZq8XSW8MzeXu95YxBNXDVOgQgvQRUREWp+oGKioaPAh4ZVlTH1hLu2iw7h34kAyOsYSYrWS0TGW+yYNJCE2gg8XbvJRwf5NYUpERKQ1Ok4PK0t0DNERofzqtK5YLIcvF7JYLFw+KoNZy7e0ZIUBQ2FKRESkNTpeD6ux57O3pILUhGOHrtSEaHYXVeByVx/zemuiMCUiItIaHa+H1bgs4mMiyNt17CNt8naVEGEPIevvX3LH6wt47du15GzcTbnLfczHBzOtGhMREWmNwiO87Q8a6GE1bkAy78zN5b5JAw+b6vN4PLw7L5dJw9O5aFgqq7fuZ0X+Xt7+fgMbCoroHB9Fn85t6du5LX2S2xLjqGcELEioz5SIiIgcU7nLzV1vLCIhNoLLR2Uc3M337rxcdhWVH3M3n8tdzdrtRazI38vPWwpZvbWQ+Jhwb7Cq/RMfE5D9q9S0U0RERBrvYJ+p5VvYXVRBfGw44/qfeJ+p6poaNu4s8Yarzfv4eUshEfYQ+nZuR5/OcfTt3I5T2jqOWuTuhxSmRERExHwej4fNe0r5efM+VtT+qanx0Du5LX1TvNOCaR2isR4nXJnQTFRhSkRERPyPx+NhZ1H5wXD1c/4+9pdV0iu57cGpwa6JsYSG/G/PXN30Y4c2h08/vjO3/unHZqAwJSIiIoGhsLSydkpwHyvy97G98ADdTmlzMFz9lLeXrfsOcO/EoxfGPzZ9KWkJ0Vx5WtfmLktn84mIiEhgiIsK49ReiZzaKxGA0ooqVm0pZMXmfbz+3To27izm/64ZUW8z0Yc/yGmJMFUvhSkRERHxa1HhoQztmsDQrgkAnPfop8dtJupLatopIiIiAeV4zUTjY8N9Wo/ClIiIiASUumaiR677rmsmOq5/sk/rUZgSERGRgDJxeBq7isp5bPpSNhQU4a6uYUNBEY9NX8quonImDk/zaT1aMyUiIiIBJcJu44mrhvHhwk08/EHOYc1Ef39Bv5bqM1UvhSkREREJOBF2G1ee1tWnu/bqo2k+ERERkSZQmBIRERFpAoUpERERkSZQmBIRERFpAoUpERERkSZQmBIRERFpAoUpERERkSZQmBIRERFpAoUpERERkSZQmBIRERFpAoUpERERkSZQmBIRERFpAlMPOjYMw8ynFxERETlRHqfTaTnWBY1MiYiIiDSBxePxmF2DiIiISMDSyJSIiIhIEyhMiYiIiDSBwpSIBAzDMPIMw8gzuw4RkUOZuptPRCQQGYbRC3gQGAvEAPnAu8DjTqez3LzKRMQMGpkSkUByZu0f0xiGMQxYDFwEfAU8AxQDfwK+NAwjzLzqRMQM2s0nInKCDMMIAVYAPYELnU7nf2rvtwLvA5OAe5xO5+PmVSkivqYwJSLNyjCMa4AJwEAgEajCG0Ceczqdbx7yuInAdGARcKrT6aw65Fof4AdgPzDA6XTuqr0/D8DpdKYe8lg7cDNwDZAGhAG7gOXAP51O51fN+L2dAXwNzHE6nWOOuJYO5OKd8ktzOp365SrSSmiaT0Sa23NAKjAHeBrvWqIU4A3DMB6pe5DT6fwQeBYYBjxWd79hGA7gPbyh6Fd1QaoBr+KdagsFXgf+UfvcfYFzm+H7OdQZtR8/P/KC0+ncCKzD+72mN/Pziogf0wJ0EWlufZxOZ+6hd9SOHv0XuNswjOedTue22ku/B0YCfzAM4xun0/k53oDVC3jY6XR+09ATGYYRC1wO5ADDnE5n9RHX2x1x+xq8Qe9E5TmdzlcPud299uO6eh6/HuhW+ye3nseISJBRmBKRZnVkkKq9z2UYxrN4R3bOxDuChNPprDQM4zJgCfC6YRh/xTtdNwd4+ASezgNYgEqg5hjPu/eIu64Bxhz5uAbMxjvyVSe29mNRPY+vu79NI55DRAKcwpSINCvDMDoDd+ENTZ2BiCMeknToDafTud4wjJuAt4C/AXuAK44cZToWp9NZbBjGDLxrtJYZhjEd+B5Y5HQ6y47x+LGN/44ape4QVK2XEmlFFKZEpNnULsL+AYjDG2q+wDtaU413eu1qvGuhjvQl3vYCMUD2IdOAJ+IyvOHtCuCh2vsqDMP4APiD0+nc2fjvpF51I0+x9VyPOeJxItIKKEyJSHO6HWgHXHvEWiMMw/gl3jDFEfdb8E77xeAdlbrRMIx3nU7nnBN5wtommQ8CDxqGkQychnc671d4A9yphzzXNTRtzdTa2o/d6nl819qP9a2pEpEgpDAlIs2pS+3H6ce4Vt9apTvw7rp7C3gC78jW24ZhDHA6nXsa8+ROp3ML8JZhGO8Aa4DRhmG0O2Tt1DUN1HEsR66Z+ga4r7bevxz6wNpRuW54WyNsbEzdIhLYFKZEpDnl1X4cC8you9MwjHHA9Uc+uLab+KPABmCq0+ksMQzjNrztFV41DGNCQ/2aDMOIB9KdTueiIy5FAtGAG3DV3dkMa6ZmA6uB0wzDuOCIpp1P1D7mefWYEmldFKZEpDk5gWuB7NrF4NuAPnhHct7Hu74JAMMw2uDtQeUBLnc6nSUATqfzecMwzgQuwTtt+GQDz5cELDQMYzXeHYFb8E4Xjgc6Av+o+7rN8s05ndWGYVyLd4Tqg9p1WZvxLrYfDMwDnmqu5xORwKCmnSLSbJxO50/A6cB84BfAVLzhZiLw/BEPfwnv+qW7nU5nzhHXrgc2AX8xDGNoA0+ZBzwA7Kh93ttrn2sT3gXpvzvpb6YetaNgQ4BPgHOA2/AuSH8YONvpdFY293OKiH/TcTIiIiIiTaCRKREREZEmUJgSERERaQKFKREREZEmUJgSERERaQKFKREREZEmUJgSERERaQKFKREREZEmUJgSERERaQKFKREREZEmUJgSERERaYL/B7P7Kbb/Nm6EAAAAAElFTkSuQmCC\n",
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\n",
- "text/plain": [
- "<Figure size 1080x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sequence_true, sequence_pred = get_prediction(x_test, loaded_model, iterations=5)\n",
- "\n",
- "pwk.plot_2d_segment(sequence_true, sequence_pred, ms=8, save_as='02-prediction-norm')\n",
- "pwk.plot_multivariate_serie(sequence_true, predictions=sequence_pred, hide_ticks=True, labels=['Axis=0', 'Axis=1'],save_as='02-prediction-norm')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:41.881137Z",
- "iopub.status.busy": "2021-03-09T21:35:41.880638Z",
- "iopub.status.idle": "2021-03-09T21:35:41.884090Z",
- "shell.execute_reply": "2021-03-09T21:35:41.883701Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Tuesday 09 March 2021, 22:35:41\n",
- "Duration is : 00:02:55 936ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/SYNOP/SYNOP1-Preparation-of-data.ipynb b/SYNOP/SYNOP1-Preparation-of-data.ipynb
index 3f2bcb3..5f09b9f 100644
--- a/SYNOP/SYNOP1-Preparation-of-data.ipynb
+++ b/SYNOP/SYNOP1-Preparation-of-data.ipynb
@@ -341,9 +341,11 @@
}
],
"metadata": {
+ "interpreter": {
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
+ },
"kernelspec": {
- "display_name": "Python 3",
- "language": "python",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
@@ -356,7 +358,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.7"
}
},
"nbformat": 4,
diff --git a/SYNOP/SYNOP1-Preparation-of-data==done==.ipynb b/SYNOP/SYNOP1-Preparation-of-data==done==.ipynb
deleted file mode 100644
index 313b265..0000000
--- a/SYNOP/SYNOP1-Preparation-of-data==done==.ipynb
+++ /dev/null
@@ -1,3192 +0,0 @@
-{
- "cells": [
- {
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- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [SYNOP1] - Preparation of data\n",
- "<!-- DESC --> Episode 1 : Data analysis and preparation of a usuable meteorological dataset (SYNOP)\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Undestand the data\n",
- " - cleanup a usable dataset\n",
- "\n",
- "\n",
- "SYNOP meteorological data, can be found on : \n",
- "https://public.opendatasoft.com \n",
- "\n",
- "About SYNOP datasets : \n",
- "https://public.opendatasoft.com/explore/dataset/donnees-synop-essentielles-omm/information/?sort=date\n",
- "\n",
- "This dataset contains a set of measurements (temperature, pressure, ...) made every 3 hours at the LYS airport. \n",
- "The objective will be to predict the evolution of the weather !\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Read the data\n",
- " - Cleanup and build a usable dataset\n",
- "\n",
- "## Step 1 - Import and init"
- ]
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- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
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- ]
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- "text": [
- "Version : 2.0.19\n",
- "Notebook id : SYNOP1\n",
- "Run time : Tuesday 09 March 2021, 22:35:45\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Run dir : ./run/SYNOP\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/SYNOP/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "from tensorflow.keras.callbacks import TensorBoard\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import pandas as pd\n",
- "import h5py, json\n",
- "import os,time,sys\n",
- "import math, random\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/SYNOP'\n",
- "datasets_dir = pwk.init('SYNOP1', run_dir)\n",
- "\n",
- "pd.set_option('display.max_rows',200)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Parameters\n",
- "`output_dir` : where to save our enhanced dataset. \n",
- "./data is a good choice because our dataset will be very small."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:45.725337Z",
- "iopub.status.busy": "2021-03-09T21:35:45.724996Z",
- "iopub.status.idle": "2021-03-09T21:35:45.727272Z",
- "shell.execute_reply": "2021-03-09T21:35:45.726955Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- Our future enhanced dataset (no need to change)\n",
- "#\n",
- "dataset_filename = 'synop-LYS.csv'\n",
- "description_filename = 'synop.json'\n",
- "output_dir = './data'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:45.729865Z",
- "iopub.status.busy": "2021-03-09T21:35:45.729568Z",
- "iopub.status.idle": "2021-03-09T21:35:45.731949Z",
- "shell.execute_reply": "2021-03-09T21:35:45.731632Z"
- }
- },
- "outputs": [],
- "source": [
- "pwk.override('output_dir')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Retrieve the dataset\n",
- "There are two parts to recover:\n",
- " - The data itself (csv)\n",
- " - Description of the data (json)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:45.734423Z",
- "iopub.status.busy": "2021-03-09T21:35:45.734108Z",
- "iopub.status.idle": "2021-03-09T21:35:45.736873Z",
- "shell.execute_reply": "2021-03-09T21:35:45.736588Z"
- }
- },
- "outputs": [],
- "source": [
- "data_filename = 'origine/donnees-synop-essentielles-omm-LYS.csv'\n",
- "schema_filename = 'origine/schema.json'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.1 - Read dataset description\n",
- "We need the list and description of the columns."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:45.740771Z",
- "iopub.status.busy": "2021-03-09T21:35:45.740462Z",
- "iopub.status.idle": "2021-03-09T21:35:45.742676Z",
- "shell.execute_reply": "2021-03-09T21:35:45.742350Z"
- }
- },
- "outputs": [],
- "source": [
- "with open(f'{datasets_dir}/SYNOP/{schema_filename}','r') as json_file:\n",
- " schema = json.load(json_file)\n",
- "\n",
- "synop_codes=list( schema['definitions']['donnees-synop-essentielles-omm_records']['properties']['fields']['properties'].keys() )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.2 - Read data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
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- "shell.execute_reply": "2021-03-09T21:35:46.069123Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**Raw data :**"
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- " <td>45.7265</td>\n",
- " <td>Colombier-Saugnieu</td>\n",
- " <td>69299</td>\n",
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- " <td>246900575</td>\n",
- " <td>Rhône</td>\n",
- " <td>69</td>\n",
- " <td>Auvergne-Rhône-Alpes</td>\n",
- " <td>84</td>\n",
- " </tr>\n",
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- " <th>29157</th>\n",
- " <td>7481</td>\n",
- " <td>2020-02-12T16:00:00+01:00</td>\n",
- " <td>102460.0</td>\n",
- " <td>-180.0</td>\n",
- " <td>6.0</td>\n",
- " <td>360.0</td>\n",
- " <td>2.3</td>\n",
- " <td>283.45</td>\n",
- " <td>271.75</td>\n",
- " <td>44.0</td>\n",
- " <td>...</td>\n",
- " <td>5.077833</td>\n",
- " <td>45.7265</td>\n",
- " <td>Colombier-Saugnieu</td>\n",
- " <td>69299</td>\n",
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- " <td>246900575</td>\n",
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- " <td>69</td>\n",
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- " <td>84</td>\n",
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- " <th>29158</th>\n",
- " <td>7481</td>\n",
- " <td>2020-02-13T04:00:00+01:00</td>\n",
- " <td>102100.0</td>\n",
- " <td>-240.0</td>\n",
- " <td>8.0</td>\n",
- " <td>150.0</td>\n",
- " <td>4.9</td>\n",
- " <td>274.75</td>\n",
- " <td>271.15</td>\n",
- " <td>77.0</td>\n",
- " <td>...</td>\n",
- " <td>5.077833</td>\n",
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- " <td>246900575</td>\n",
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- " <td>69</td>\n",
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- " <td>84</td>\n",
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- " <th>29159</th>\n",
- " <td>7481</td>\n",
- " <td>2020-02-14T01:00:00+01:00</td>\n",
- " <td>102080.0</td>\n",
- " <td>230.0</td>\n",
- " <td>1.0</td>\n",
- " <td>280.0</td>\n",
- " <td>4.5</td>\n",
- " <td>283.15</td>\n",
- " <td>276.15</td>\n",
- " <td>62.0</td>\n",
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- " <th>29160</th>\n",
- " <td>7481</td>\n",
- " <td>2020-02-14T07:00:00+01:00</td>\n",
- " <td>102430.0</td>\n",
- " <td>210.0</td>\n",
- " <td>2.0</td>\n",
- " <td>140.0</td>\n",
- " <td>3.4</td>\n",
- " <td>280.15</td>\n",
- " <td>278.45</td>\n",
- " <td>89.0</td>\n",
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- " <td>69</td>\n",
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- " <td>84</td>\n",
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- " <th>29161</th>\n",
- " <td>7481</td>\n",
- " <td>2020-02-15T16:00:00+01:00</td>\n",
- " <td>102190.0</td>\n",
- " <td>-160.0</td>\n",
- " <td>6.0</td>\n",
- " <td>180.0</td>\n",
- " <td>6.9</td>\n",
- " <td>290.15</td>\n",
- " <td>273.75</td>\n",
- " <td>33.0</td>\n",
- " <td>...</td>\n",
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- " <td>246900575</td>\n",
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- " <td>69</td>\n",
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- " <td>84</td>\n",
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- " <th>29162</th>\n",
- " <td>7481</td>\n",
- " <td>2020-01-25T22:00:00+01:00</td>\n",
- " <td>102030.0</td>\n",
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- " <td>1.0</td>\n",
- " <td>140.0</td>\n",
- " <td>4.9</td>\n",
- " <td>281.45</td>\n",
- " <td>278.55</td>\n",
- " <td>82.0</td>\n",
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- " <td>7481</td>\n",
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- " <th>29164</th>\n",
- " <td>7481</td>\n",
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- " ID OMM station Date Pression au niveau mer \\\n",
- "29155 7481 2019-11-16T01:00:00+01:00 100640.0 \n",
- "29156 7481 2019-11-16T19:00:00+01:00 101090.0 \n",
- "29157 7481 2020-02-12T16:00:00+01:00 102460.0 \n",
- "29158 7481 2020-02-13T04:00:00+01:00 102100.0 \n",
- "29159 7481 2020-02-14T01:00:00+01:00 102080.0 \n",
- "29160 7481 2020-02-14T07:00:00+01:00 102430.0 \n",
- "29161 7481 2020-02-15T16:00:00+01:00 102190.0 \n",
- "29162 7481 2020-01-25T22:00:00+01:00 102030.0 \n",
- "29163 7481 2020-01-26T19:00:00+01:00 102010.0 \n",
- "29164 7481 2020-02-08T19:00:00+01:00 102540.0 \n",
- "\n",
- " Variation de pression en 3 heures Type de tendance barométrique \\\n",
- "29155 130.0 1.0 \n",
- "29156 90.0 3.0 \n",
- "29157 -180.0 6.0 \n",
- "29158 -240.0 8.0 \n",
- "29159 230.0 1.0 \n",
- "29160 210.0 2.0 \n",
- "29161 -160.0 6.0 \n",
- "29162 20.0 1.0 \n",
- "29163 80.0 3.0 \n",
- "29164 150.0 2.0 \n",
- "\n",
- " Direction du vent moyen 10 mn Vitesse du vent moyen 10 mn \\\n",
- "29155 190.0 1.0 \n",
- "29156 130.0 3.5 \n",
- "29157 360.0 2.3 \n",
- "29158 150.0 4.9 \n",
- "29159 280.0 4.5 \n",
- "29160 140.0 3.4 \n",
- "29161 180.0 6.9 \n",
- "29162 140.0 4.9 \n",
- "29163 170.0 3.7 \n",
- "29164 190.0 6.2 \n",
- "\n",
- " Température Point de rosée Humidité ... Longitude Latitude \\\n",
- "29155 272.75 272.75 100.0 ... 5.077833 45.7265 \n",
- "29156 276.95 274.65 85.0 ... 5.077833 45.7265 \n",
- "29157 283.45 271.75 44.0 ... 5.077833 45.7265 \n",
- "29158 274.75 271.15 77.0 ... 5.077833 45.7265 \n",
- "29159 283.15 276.15 62.0 ... 5.077833 45.7265 \n",
- "29160 280.15 278.45 89.0 ... 5.077833 45.7265 \n",
- "29161 290.15 273.75 33.0 ... 5.077833 45.7265 \n",
- "29162 281.45 278.55 82.0 ... 5.077833 45.7265 \n",
- "29163 282.85 279.15 78.0 ... 5.077833 45.7265 \n",
- "29164 283.75 277.65 66.0 ... 5.077833 45.7265 \n",
- "\n",
- " communes (name) communes (code) EPCI (name) \\\n",
- "29155 Colombier-Saugnieu 69299 CC de l'Est Lyonnais (CCEL) \n",
- "29156 Colombier-Saugnieu 69299 CC de l'Est Lyonnais (CCEL) \n",
- "29157 Colombier-Saugnieu 69299 CC de l'Est Lyonnais (CCEL) \n",
- "29158 Colombier-Saugnieu 69299 CC de l'Est Lyonnais (CCEL) \n",
- "29159 Colombier-Saugnieu 69299 CC de l'Est Lyonnais (CCEL) \n",
- "29160 Colombier-Saugnieu 69299 CC de l'Est Lyonnais (CCEL) \n",
- "29161 Colombier-Saugnieu 69299 CC de l'Est Lyonnais (CCEL) \n",
- "29162 Colombier-Saugnieu 69299 CC de l'Est Lyonnais (CCEL) \n",
- "29163 Colombier-Saugnieu 69299 CC de l'Est Lyonnais (CCEL) \n",
- "29164 Colombier-Saugnieu 69299 CC de l'Est Lyonnais (CCEL) \n",
- "\n",
- " EPCI (code) department (name) department (code) \\\n",
- "29155 246900575 Rhône 69 \n",
- "29156 246900575 Rhône 69 \n",
- "29157 246900575 Rhône 69 \n",
- "29158 246900575 Rhône 69 \n",
- "29159 246900575 Rhône 69 \n",
- "29160 246900575 Rhône 69 \n",
- "29161 246900575 Rhône 69 \n",
- "29162 246900575 Rhône 69 \n",
- "29163 246900575 Rhône 69 \n",
- "29164 246900575 Rhône 69 \n",
- "\n",
- " region (name) region (code) \n",
- "29155 Auvergne-Rhône-Alpes 84 \n",
- "29156 Auvergne-Rhône-Alpes 84 \n",
- "29157 Auvergne-Rhône-Alpes 84 \n",
- "29158 Auvergne-Rhône-Alpes 84 \n",
- "29159 Auvergne-Rhône-Alpes 84 \n",
- "29160 Auvergne-Rhône-Alpes 84 \n",
- "29161 Auvergne-Rhône-Alpes 84 \n",
- "29162 Auvergne-Rhône-Alpes 84 \n",
- "29163 Auvergne-Rhône-Alpes 84 \n",
- "29164 Auvergne-Rhône-Alpes 84 \n",
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- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_379cd_row1_col0\" class=\"data row1 col0\" >date</td>\n",
- " <td id=\"T_379cd_row1_col1\" class=\"data row1 col1\" >Date</td>\n",
- " <td id=\"T_379cd_row1_col2\" class=\"data row1 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_379cd_row2_col0\" class=\"data row2 col0\" >pmer</td>\n",
- " <td id=\"T_379cd_row2_col1\" class=\"data row2 col1\" >Pression au niveau mer</td>\n",
- " <td id=\"T_379cd_row2_col2\" class=\"data row2 col2\" >17</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_379cd_row3_col0\" class=\"data row3 col0\" >tend</td>\n",
- " <td id=\"T_379cd_row3_col1\" class=\"data row3 col1\" >Variation de pression en 3 heures</td>\n",
- " <td id=\"T_379cd_row3_col2\" class=\"data row3 col2\" >2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_379cd_row4_col0\" class=\"data row4 col0\" >cod_tend</td>\n",
- " <td id=\"T_379cd_row4_col1\" class=\"data row4 col1\" >Type de tendance barométrique</td>\n",
- " <td id=\"T_379cd_row4_col2\" class=\"data row4 col2\" >2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
- " <td id=\"T_379cd_row5_col0\" class=\"data row5 col0\" >dd</td>\n",
- " <td id=\"T_379cd_row5_col1\" class=\"data row5 col1\" >Direction du vent moyen 10 mn</td>\n",
- " <td id=\"T_379cd_row5_col2\" class=\"data row5 col2\" >3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
- " <td id=\"T_379cd_row6_col0\" class=\"data row6 col0\" >ff</td>\n",
- " <td id=\"T_379cd_row6_col1\" class=\"data row6 col1\" >Vitesse du vent moyen 10 mn</td>\n",
- " <td id=\"T_379cd_row6_col2\" class=\"data row6 col2\" >2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
- " <td id=\"T_379cd_row7_col0\" class=\"data row7 col0\" >t</td>\n",
- " <td id=\"T_379cd_row7_col1\" class=\"data row7 col1\" >Température</td>\n",
- " <td id=\"T_379cd_row7_col2\" class=\"data row7 col2\" >14</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
- " <td id=\"T_379cd_row8_col0\" class=\"data row8 col0\" >td</td>\n",
- " <td id=\"T_379cd_row8_col1\" class=\"data row8 col1\" >Point de rosée</td>\n",
- " <td id=\"T_379cd_row8_col2\" class=\"data row8 col2\" >17</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
- " <td id=\"T_379cd_row9_col0\" class=\"data row9 col0\" >u</td>\n",
- " <td id=\"T_379cd_row9_col1\" class=\"data row9 col1\" >Humidité</td>\n",
- " <td id=\"T_379cd_row9_col2\" class=\"data row9 col2\" >17</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
- " <td id=\"T_379cd_row10_col0\" class=\"data row10 col0\" >vv</td>\n",
- " <td id=\"T_379cd_row10_col1\" class=\"data row10 col1\" >Visibilité horizontale</td>\n",
- " <td id=\"T_379cd_row10_col2\" class=\"data row10 col2\" >31</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
- " <td id=\"T_379cd_row11_col0\" class=\"data row11 col0\" >ww</td>\n",
- " <td id=\"T_379cd_row11_col1\" class=\"data row11 col1\" >Temps présent</td>\n",
- " <td id=\"T_379cd_row11_col2\" class=\"data row11 col2\" >1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
- " <td id=\"T_379cd_row12_col0\" class=\"data row12 col0\" >w1</td>\n",
- " <td id=\"T_379cd_row12_col1\" class=\"data row12 col1\" >Temps passé 1</td>\n",
- " <td id=\"T_379cd_row12_col2\" class=\"data row12 col2\" >542</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
- " <td id=\"T_379cd_row13_col0\" class=\"data row13 col0\" >w2</td>\n",
- " <td id=\"T_379cd_row13_col1\" class=\"data row13 col1\" >Temps passé 2</td>\n",
- " <td id=\"T_379cd_row13_col2\" class=\"data row13 col2\" >552</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
- " <td id=\"T_379cd_row14_col0\" class=\"data row14 col0\" >n</td>\n",
- " <td id=\"T_379cd_row14_col1\" class=\"data row14 col1\" >Nebulosité totale</td>\n",
- " <td id=\"T_379cd_row14_col2\" class=\"data row14 col2\" >801</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
- " <td id=\"T_379cd_row15_col0\" class=\"data row15 col0\" >nbas</td>\n",
- " <td id=\"T_379cd_row15_col1\" class=\"data row15 col1\" >Nébulosité des nuages de l' étage inférieur</td>\n",
- " <td id=\"T_379cd_row15_col2\" class=\"data row15 col2\" >2381</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
- " <td id=\"T_379cd_row16_col0\" class=\"data row16 col0\" >hbas</td>\n",
- " <td id=\"T_379cd_row16_col1\" class=\"data row16 col1\" >Hauteur de la base des nuages de l'étage inférieur</td>\n",
- " <td id=\"T_379cd_row16_col2\" class=\"data row16 col2\" >8861</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
- " <td id=\"T_379cd_row17_col0\" class=\"data row17 col0\" >cl</td>\n",
- " <td id=\"T_379cd_row17_col1\" class=\"data row17 col1\" >Type des nuages de l'étage inférieur</td>\n",
- " <td id=\"T_379cd_row17_col2\" class=\"data row17 col2\" >3377</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
- " <td id=\"T_379cd_row18_col0\" class=\"data row18 col0\" >cm</td>\n",
- " <td id=\"T_379cd_row18_col1\" class=\"data row18 col1\" >Type des nuages de l'étage moyen</td>\n",
- " <td id=\"T_379cd_row18_col2\" class=\"data row18 col2\" >6912</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
- " <td id=\"T_379cd_row19_col0\" class=\"data row19 col0\" >ch</td>\n",
- " <td id=\"T_379cd_row19_col1\" class=\"data row19 col1\" >Type des nuages de l'étage supérieur</td>\n",
- " <td id=\"T_379cd_row19_col2\" class=\"data row19 col2\" >8494</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
- " <td id=\"T_379cd_row20_col0\" class=\"data row20 col0\" >pres</td>\n",
- " <td id=\"T_379cd_row20_col1\" class=\"data row20 col1\" >Pression station</td>\n",
- " <td id=\"T_379cd_row20_col2\" class=\"data row20 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
- " <td id=\"T_379cd_row21_col0\" class=\"data row21 col0\" >niv_bar</td>\n",
- " <td id=\"T_379cd_row21_col1\" class=\"data row21 col1\" >Niveau barométrique</td>\n",
- " <td id=\"T_379cd_row21_col2\" class=\"data row21 col2\" >29165</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
- " <td id=\"T_379cd_row22_col0\" class=\"data row22 col0\" >geop</td>\n",
- " <td id=\"T_379cd_row22_col1\" class=\"data row22 col1\" >Géopotentiel</td>\n",
- " <td id=\"T_379cd_row22_col2\" class=\"data row22 col2\" >29165</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
- " <td id=\"T_379cd_row23_col0\" class=\"data row23 col0\" >tend24</td>\n",
- " <td id=\"T_379cd_row23_col1\" class=\"data row23 col1\" >Variation de pression en 24 heures</td>\n",
- " <td id=\"T_379cd_row23_col2\" class=\"data row23 col2\" >14443</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
- " <td id=\"T_379cd_row24_col0\" class=\"data row24 col0\" >tn12</td>\n",
- " <td id=\"T_379cd_row24_col1\" class=\"data row24 col1\" >Température minimale sur 12 heures</td>\n",
- " <td id=\"T_379cd_row24_col2\" class=\"data row24 col2\" >21883</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
- " <td id=\"T_379cd_row25_col0\" class=\"data row25 col0\" >tn24</td>\n",
- " <td id=\"T_379cd_row25_col1\" class=\"data row25 col1\" >Température minimale sur 24 heures</td>\n",
- " <td id=\"T_379cd_row25_col2\" class=\"data row25 col2\" >29165</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
- " <td id=\"T_379cd_row26_col0\" class=\"data row26 col0\" >tx12</td>\n",
- " <td id=\"T_379cd_row26_col1\" class=\"data row26 col1\" >Température maximale sur 12 heures</td>\n",
- " <td id=\"T_379cd_row26_col2\" class=\"data row26 col2\" >21883</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
- " <td id=\"T_379cd_row27_col0\" class=\"data row27 col0\" >tx24</td>\n",
- " <td id=\"T_379cd_row27_col1\" class=\"data row27 col1\" >Température maximale sur 24 heures</td>\n",
- " <td id=\"T_379cd_row27_col2\" class=\"data row27 col2\" >29165</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
- " <td id=\"T_379cd_row28_col0\" class=\"data row28 col0\" >tminsol</td>\n",
- " <td id=\"T_379cd_row28_col1\" class=\"data row28 col1\" >Température minimale du sol sur 12 heures</td>\n",
- " <td id=\"T_379cd_row28_col2\" class=\"data row28 col2\" >27364</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
- " <td id=\"T_379cd_row29_col0\" class=\"data row29 col0\" >sw</td>\n",
- " <td id=\"T_379cd_row29_col1\" class=\"data row29 col1\" >Méthode de mesure Température du thermomètre mouillé</td>\n",
- " <td id=\"T_379cd_row29_col2\" class=\"data row29 col2\" >29165</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
- " <td id=\"T_379cd_row30_col0\" class=\"data row30 col0\" >tw</td>\n",
- " <td id=\"T_379cd_row30_col1\" class=\"data row30 col1\" >Température du thermomètre mouillé</td>\n",
- " <td id=\"T_379cd_row30_col2\" class=\"data row30 col2\" >29165</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
- " <td id=\"T_379cd_row31_col0\" class=\"data row31 col0\" >raf10</td>\n",
- " <td id=\"T_379cd_row31_col1\" class=\"data row31 col1\" >Rafale sur les 10 dernières minutes</td>\n",
- " <td id=\"T_379cd_row31_col2\" class=\"data row31 col2\" >14127</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
- " <td id=\"T_379cd_row32_col0\" class=\"data row32 col0\" >rafper</td>\n",
- " <td id=\"T_379cd_row32_col1\" class=\"data row32 col1\" >Rafales sur une période</td>\n",
- " <td id=\"T_379cd_row32_col2\" class=\"data row32 col2\" >9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
- " <td id=\"T_379cd_row33_col0\" class=\"data row33 col0\" >per</td>\n",
- " <td id=\"T_379cd_row33_col1\" class=\"data row33 col1\" >Periode de mesure de la rafale</td>\n",
- " <td id=\"T_379cd_row33_col2\" class=\"data row33 col2\" >8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
- " <td id=\"T_379cd_row34_col0\" class=\"data row34 col0\" >etat_sol</td>\n",
- " <td id=\"T_379cd_row34_col1\" class=\"data row34 col1\" >Etat du sol</td>\n",
- " <td id=\"T_379cd_row34_col2\" class=\"data row34 col2\" >12278</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
- " <td id=\"T_379cd_row35_col0\" class=\"data row35 col0\" >ht_neige</td>\n",
- " <td id=\"T_379cd_row35_col1\" class=\"data row35 col1\" >Hauteur totale de la couche de neige, glace, autre au sol</td>\n",
- " <td id=\"T_379cd_row35_col2\" class=\"data row35 col2\" >12083</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
- " <td id=\"T_379cd_row36_col0\" class=\"data row36 col0\" >ssfrai</td>\n",
- " <td id=\"T_379cd_row36_col1\" class=\"data row36 col1\" >Hauteur de la neige fraîche</td>\n",
- " <td id=\"T_379cd_row36_col2\" class=\"data row36 col2\" >2914</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
- " <td id=\"T_379cd_row37_col0\" class=\"data row37 col0\" >perssfrai</td>\n",
- " <td id=\"T_379cd_row37_col1\" class=\"data row37 col1\" >Periode de mesure de la neige fraiche</td>\n",
- " <td id=\"T_379cd_row37_col2\" class=\"data row37 col2\" >4489</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
- " <td id=\"T_379cd_row38_col0\" class=\"data row38 col0\" >rr1</td>\n",
- " <td id=\"T_379cd_row38_col1\" class=\"data row38 col1\" >Précipitations dans la dernière heure</td>\n",
- " <td id=\"T_379cd_row38_col2\" class=\"data row38 col2\" >95</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
- " <td id=\"T_379cd_row39_col0\" class=\"data row39 col0\" >rr3</td>\n",
- " <td id=\"T_379cd_row39_col1\" class=\"data row39 col1\" >Précipitations dans les 3 dernières heures</td>\n",
- " <td id=\"T_379cd_row39_col2\" class=\"data row39 col2\" >73</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
- " <td id=\"T_379cd_row40_col0\" class=\"data row40 col0\" >rr6</td>\n",
- " <td id=\"T_379cd_row40_col1\" class=\"data row40 col1\" >Précipitations dans les 6 dernières heures</td>\n",
- " <td id=\"T_379cd_row40_col2\" class=\"data row40 col2\" >10869</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
- " <td id=\"T_379cd_row41_col0\" class=\"data row41 col0\" >rr12</td>\n",
- " <td id=\"T_379cd_row41_col1\" class=\"data row41 col1\" >Précipitations dans les 12 dernières heures</td>\n",
- " <td id=\"T_379cd_row41_col2\" class=\"data row41 col2\" >10919</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
- " <td id=\"T_379cd_row42_col0\" class=\"data row42 col0\" >rr24</td>\n",
- " <td id=\"T_379cd_row42_col1\" class=\"data row42 col1\" >Précipitations dans les 24 dernières heures</td>\n",
- " <td id=\"T_379cd_row42_col2\" class=\"data row42 col2\" >12730</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
- " <td id=\"T_379cd_row43_col0\" class=\"data row43 col0\" >phenspe1</td>\n",
- " <td id=\"T_379cd_row43_col1\" class=\"data row43 col1\" >Phénomène spécial 1</td>\n",
- " <td id=\"T_379cd_row43_col2\" class=\"data row43 col2\" >14818</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
- " <td id=\"T_379cd_row44_col0\" class=\"data row44 col0\" >phenspe2</td>\n",
- " <td id=\"T_379cd_row44_col1\" class=\"data row44 col1\" >Phénomène spécial 2</td>\n",
- " <td id=\"T_379cd_row44_col2\" class=\"data row44 col2\" >14826</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
- " <td id=\"T_379cd_row45_col0\" class=\"data row45 col0\" >phenspe3</td>\n",
- " <td id=\"T_379cd_row45_col1\" class=\"data row45 col1\" >Phénomène spécial 3</td>\n",
- " <td id=\"T_379cd_row45_col2\" class=\"data row45 col2\" >15515</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
- " <td id=\"T_379cd_row46_col0\" class=\"data row46 col0\" >phenspe4</td>\n",
- " <td id=\"T_379cd_row46_col1\" class=\"data row46 col1\" >Phénomène spécial 4</td>\n",
- " <td id=\"T_379cd_row46_col2\" class=\"data row46 col2\" >28869</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
- " <td id=\"T_379cd_row47_col0\" class=\"data row47 col0\" >nnuage1</td>\n",
- " <td id=\"T_379cd_row47_col1\" class=\"data row47 col1\" >Nébulosité couche nuageuse 1</td>\n",
- " <td id=\"T_379cd_row47_col2\" class=\"data row47 col2\" >4753</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
- " <td id=\"T_379cd_row48_col0\" class=\"data row48 col0\" >ctype1</td>\n",
- " <td id=\"T_379cd_row48_col1\" class=\"data row48 col1\" >Type nuage 1</td>\n",
- " <td id=\"T_379cd_row48_col2\" class=\"data row48 col2\" >5699</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
- " <td id=\"T_379cd_row49_col0\" class=\"data row49 col0\" >hnuage1</td>\n",
- " <td id=\"T_379cd_row49_col1\" class=\"data row49 col1\" >Hauteur de base 1</td>\n",
- " <td id=\"T_379cd_row49_col2\" class=\"data row49 col2\" >5439</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
- " <td id=\"T_379cd_row50_col0\" class=\"data row50 col0\" >nnuage2</td>\n",
- " <td id=\"T_379cd_row50_col1\" class=\"data row50 col1\" >Nébulosité couche nuageuse 2</td>\n",
- " <td id=\"T_379cd_row50_col2\" class=\"data row50 col2\" >16112</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
- " <td id=\"T_379cd_row51_col0\" class=\"data row51 col0\" >ctype2</td>\n",
- " <td id=\"T_379cd_row51_col1\" class=\"data row51 col1\" >Type nuage 2</td>\n",
- " <td id=\"T_379cd_row51_col2\" class=\"data row51 col2\" >16643</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
- " <td id=\"T_379cd_row52_col0\" class=\"data row52 col0\" >hnuage2</td>\n",
- " <td id=\"T_379cd_row52_col1\" class=\"data row52 col1\" >Hauteur de base 2</td>\n",
- " <td id=\"T_379cd_row52_col2\" class=\"data row52 col2\" >16317</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
- " <td id=\"T_379cd_row53_col0\" class=\"data row53 col0\" >nnuage3</td>\n",
- " <td id=\"T_379cd_row53_col1\" class=\"data row53 col1\" >Nébulosité couche nuageuse 3</td>\n",
- " <td id=\"T_379cd_row53_col2\" class=\"data row53 col2\" >25387</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
- " <td id=\"T_379cd_row54_col0\" class=\"data row54 col0\" >ctype3</td>\n",
- " <td id=\"T_379cd_row54_col1\" class=\"data row54 col1\" >Type nuage 3</td>\n",
- " <td id=\"T_379cd_row54_col2\" class=\"data row54 col2\" >25642</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
- " <td id=\"T_379cd_row55_col0\" class=\"data row55 col0\" >hnuage3</td>\n",
- " <td id=\"T_379cd_row55_col1\" class=\"data row55 col1\" >Hauteur de base 3</td>\n",
- " <td id=\"T_379cd_row55_col2\" class=\"data row55 col2\" >25431</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
- " <td id=\"T_379cd_row56_col0\" class=\"data row56 col0\" >nnuage4</td>\n",
- " <td id=\"T_379cd_row56_col1\" class=\"data row56 col1\" >Nébulosité couche nuageuse 4</td>\n",
- " <td id=\"T_379cd_row56_col2\" class=\"data row56 col2\" >28850</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
- " <td id=\"T_379cd_row57_col0\" class=\"data row57 col0\" >ctype4</td>\n",
- " <td id=\"T_379cd_row57_col1\" class=\"data row57 col1\" >Type nuage 4</td>\n",
- " <td id=\"T_379cd_row57_col2\" class=\"data row57 col2\" >28780</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
- " <td id=\"T_379cd_row58_col0\" class=\"data row58 col0\" >hnuage4</td>\n",
- " <td id=\"T_379cd_row58_col1\" class=\"data row58 col1\" >Hauteur de base 4</td>\n",
- " <td id=\"T_379cd_row58_col2\" class=\"data row58 col2\" >28850</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
- " <td id=\"T_379cd_row59_col0\" class=\"data row59 col0\" >coordonnees</td>\n",
- " <td id=\"T_379cd_row59_col1\" class=\"data row59 col1\" >Coordonnees</td>\n",
- " <td id=\"T_379cd_row59_col2\" class=\"data row59 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row60\" class=\"row_heading level0 row60\" >60</th>\n",
- " <td id=\"T_379cd_row60_col0\" class=\"data row60 col0\" >nom</td>\n",
- " <td id=\"T_379cd_row60_col1\" class=\"data row60 col1\" >Nom</td>\n",
- " <td id=\"T_379cd_row60_col2\" class=\"data row60 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row61\" class=\"row_heading level0 row61\" >61</th>\n",
- " <td id=\"T_379cd_row61_col0\" class=\"data row61 col0\" >type_de_tendance_barometrique</td>\n",
- " <td id=\"T_379cd_row61_col1\" class=\"data row61 col1\" >Type de tendance barométrique.1</td>\n",
- " <td id=\"T_379cd_row61_col2\" class=\"data row61 col2\" >2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row62\" class=\"row_heading level0 row62\" >62</th>\n",
- " <td id=\"T_379cd_row62_col0\" class=\"data row62 col0\" >temps_passe_1</td>\n",
- " <td id=\"T_379cd_row62_col1\" class=\"data row62 col1\" >Temps passé 1.1</td>\n",
- " <td id=\"T_379cd_row62_col2\" class=\"data row62 col2\" >542</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row63\" class=\"row_heading level0 row63\" >63</th>\n",
- " <td id=\"T_379cd_row63_col0\" class=\"data row63 col0\" >temps_present</td>\n",
- " <td id=\"T_379cd_row63_col1\" class=\"data row63 col1\" >Temps présent.1</td>\n",
- " <td id=\"T_379cd_row63_col2\" class=\"data row63 col2\" >1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row64\" class=\"row_heading level0 row64\" >64</th>\n",
- " <td id=\"T_379cd_row64_col0\" class=\"data row64 col0\" >tc</td>\n",
- " <td id=\"T_379cd_row64_col1\" class=\"data row64 col1\" >Température (°C)</td>\n",
- " <td id=\"T_379cd_row64_col2\" class=\"data row64 col2\" >14</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row65\" class=\"row_heading level0 row65\" >65</th>\n",
- " <td id=\"T_379cd_row65_col0\" class=\"data row65 col0\" >tn12c</td>\n",
- " <td id=\"T_379cd_row65_col1\" class=\"data row65 col1\" >Température minimale sur 12 heures (°C)</td>\n",
- " <td id=\"T_379cd_row65_col2\" class=\"data row65 col2\" >21883</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row66\" class=\"row_heading level0 row66\" >66</th>\n",
- " <td id=\"T_379cd_row66_col0\" class=\"data row66 col0\" >tn24c</td>\n",
- " <td id=\"T_379cd_row66_col1\" class=\"data row66 col1\" >Température minimale sur 24 heures (°C)</td>\n",
- " <td id=\"T_379cd_row66_col2\" class=\"data row66 col2\" >29165</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row67\" class=\"row_heading level0 row67\" >67</th>\n",
- " <td id=\"T_379cd_row67_col0\" class=\"data row67 col0\" >tx12c</td>\n",
- " <td id=\"T_379cd_row67_col1\" class=\"data row67 col1\" >Température maximale sur 12 heures (°C)</td>\n",
- " <td id=\"T_379cd_row67_col2\" class=\"data row67 col2\" >21883</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row68\" class=\"row_heading level0 row68\" >68</th>\n",
- " <td id=\"T_379cd_row68_col0\" class=\"data row68 col0\" >tx24c</td>\n",
- " <td id=\"T_379cd_row68_col1\" class=\"data row68 col1\" >Température maximale sur 24 heures (°C)</td>\n",
- " <td id=\"T_379cd_row68_col2\" class=\"data row68 col2\" >29165</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row69\" class=\"row_heading level0 row69\" >69</th>\n",
- " <td id=\"T_379cd_row69_col0\" class=\"data row69 col0\" >tminsolc</td>\n",
- " <td id=\"T_379cd_row69_col1\" class=\"data row69 col1\" >Température minimale du sol sur 12 heures (en °C)</td>\n",
- " <td id=\"T_379cd_row69_col2\" class=\"data row69 col2\" >27364</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row70\" class=\"row_heading level0 row70\" >70</th>\n",
- " <td id=\"T_379cd_row70_col0\" class=\"data row70 col0\" >altitude</td>\n",
- " <td id=\"T_379cd_row70_col1\" class=\"data row70 col1\" >Altitude</td>\n",
- " <td id=\"T_379cd_row70_col2\" class=\"data row70 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row71\" class=\"row_heading level0 row71\" >71</th>\n",
- " <td id=\"T_379cd_row71_col0\" class=\"data row71 col0\" >longitude</td>\n",
- " <td id=\"T_379cd_row71_col1\" class=\"data row71 col1\" >Longitude</td>\n",
- " <td id=\"T_379cd_row71_col2\" class=\"data row71 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row72\" class=\"row_heading level0 row72\" >72</th>\n",
- " <td id=\"T_379cd_row72_col0\" class=\"data row72 col0\" >latitude</td>\n",
- " <td id=\"T_379cd_row72_col1\" class=\"data row72 col1\" >Latitude</td>\n",
- " <td id=\"T_379cd_row72_col2\" class=\"data row72 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row73\" class=\"row_heading level0 row73\" >73</th>\n",
- " <td id=\"T_379cd_row73_col0\" class=\"data row73 col0\" >libgeo</td>\n",
- " <td id=\"T_379cd_row73_col1\" class=\"data row73 col1\" >communes (name)</td>\n",
- " <td id=\"T_379cd_row73_col2\" class=\"data row73 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row74\" class=\"row_heading level0 row74\" >74</th>\n",
- " <td id=\"T_379cd_row74_col0\" class=\"data row74 col0\" >codegeo</td>\n",
- " <td id=\"T_379cd_row74_col1\" class=\"data row74 col1\" >communes (code)</td>\n",
- " <td id=\"T_379cd_row74_col2\" class=\"data row74 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row75\" class=\"row_heading level0 row75\" >75</th>\n",
- " <td id=\"T_379cd_row75_col0\" class=\"data row75 col0\" >nom_epci</td>\n",
- " <td id=\"T_379cd_row75_col1\" class=\"data row75 col1\" >EPCI (name)</td>\n",
- " <td id=\"T_379cd_row75_col2\" class=\"data row75 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row76\" class=\"row_heading level0 row76\" >76</th>\n",
- " <td id=\"T_379cd_row76_col0\" class=\"data row76 col0\" >code_epci</td>\n",
- " <td id=\"T_379cd_row76_col1\" class=\"data row76 col1\" >EPCI (code)</td>\n",
- " <td id=\"T_379cd_row76_col2\" class=\"data row76 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row77\" class=\"row_heading level0 row77\" >77</th>\n",
- " <td id=\"T_379cd_row77_col0\" class=\"data row77 col0\" >nom_dept</td>\n",
- " <td id=\"T_379cd_row77_col1\" class=\"data row77 col1\" >department (name)</td>\n",
- " <td id=\"T_379cd_row77_col2\" class=\"data row77 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row78\" class=\"row_heading level0 row78\" >78</th>\n",
- " <td id=\"T_379cd_row78_col0\" class=\"data row78 col0\" >code_dep</td>\n",
- " <td id=\"T_379cd_row78_col1\" class=\"data row78 col1\" >department (code)</td>\n",
- " <td id=\"T_379cd_row78_col2\" class=\"data row78 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row79\" class=\"row_heading level0 row79\" >79</th>\n",
- " <td id=\"T_379cd_row79_col0\" class=\"data row79 col0\" >nom_reg</td>\n",
- " <td id=\"T_379cd_row79_col1\" class=\"data row79 col1\" >region (name)</td>\n",
- " <td id=\"T_379cd_row79_col2\" class=\"data row79 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_379cd_level0_row80\" class=\"row_heading level0 row80\" >80</th>\n",
- " <td id=\"T_379cd_row80_col0\" class=\"data row80 col0\" >code_reg</td>\n",
- " <td id=\"T_379cd_row80_col1\" class=\"data row80 col1\" >region (code)</td>\n",
- " <td id=\"T_379cd_row80_col2\" class=\"data row80 col2\" >0</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fdaf77d1fa0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Shape is : (29165, 81)\n"
- ]
- }
- ],
- "source": [
- "df = pd.read_csv(f'{datasets_dir}/SYNOP/{data_filename}', header=0, sep=';')\n",
- "pwk.subtitle('Raw data :')\n",
- "display(df.tail(10))\n",
- "\n",
- "# ---- Get the columns name as descriptions\n",
- "synop_desc = list(df.columns)\n",
- "\n",
- "# ---- Set Codes as columns name\n",
- "df.columns = synop_codes\n",
- "code2desc = dict(zip(synop_codes, synop_desc))\n",
- "\n",
- "# ---- Count the na values by columns\n",
- "columns_na = df.isna().sum().tolist()\n",
- "\n",
- "# ---- Show all of that\n",
- "df_desc=pd.DataFrame({'Code':synop_codes, 'Description':synop_desc, 'Na':columns_na})\n",
- "\n",
- "pwk.subtitle('List of columns :')\n",
- "display(df_desc.style.set_properties(**{'text-align': 'left'}))\n",
- "\n",
- "print('Shape is : ', df.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Prepare dataset\n",
- "### 4.1 - Keep only certain columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:46.075091Z",
- "iopub.status.busy": "2021-03-09T21:35:46.073325Z",
- "iopub.status.idle": "2021-03-09T21:35:46.142466Z",
- "shell.execute_reply": "2021-03-09T21:35:46.142113Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**Our selected columns :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>date</th>\n",
- " <th>pmer</th>\n",
- " <th>tend</th>\n",
- " <th>cod_tend</th>\n",
- " <th>dd</th>\n",
- " <th>ff</th>\n",
- " <th>td</th>\n",
- " <th>u</th>\n",
- " <th>ww</th>\n",
- " <th>pres</th>\n",
- " <th>rafper</th>\n",
- " <th>per</th>\n",
- " <th>rr1</th>\n",
- " <th>rr3</th>\n",
- " <th>tc</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>2015-06-12T17:00:00+02:00</td>\n",
- " <td>101050.0</td>\n",
- " <td>-230.0</td>\n",
- " <td>6.0</td>\n",
- " <td>140.0</td>\n",
- " <td>3.6</td>\n",
- " <td>286.25</td>\n",
- " <td>50.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98330.0</td>\n",
- " <td>5.1</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>-0.1</td>\n",
- " <td>24.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2015-06-05T17:00:00+02:00</td>\n",
- " <td>101590.0</td>\n",
- " <td>-220.0</td>\n",
- " <td>8.0</td>\n",
- " <td>190.0</td>\n",
- " <td>3.9</td>\n",
- " <td>286.95</td>\n",
- " <td>32.0</td>\n",
- " <td>3.0</td>\n",
- " <td>98930.0</td>\n",
- " <td>9.9</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>32.6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2015-06-15T11:00:00+02:00</td>\n",
- " <td>101420.0</td>\n",
- " <td>90.0</td>\n",
- " <td>1.0</td>\n",
- " <td>270.0</td>\n",
- " <td>1.5</td>\n",
- " <td>286.85</td>\n",
- " <td>64.0</td>\n",
- " <td>3.0</td>\n",
- " <td>98660.0</td>\n",
- " <td>4.5</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>20.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2015-06-15T14:00:00+02:00</td>\n",
- " <td>101430.0</td>\n",
- " <td>20.0</td>\n",
- " <td>1.0</td>\n",
- " <td>10.0</td>\n",
- " <td>2.5</td>\n",
- " <td>286.45</td>\n",
- " <td>55.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98680.0</td>\n",
- " <td>5.1</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>22.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2015-06-20T05:00:00+02:00</td>\n",
- " <td>102030.0</td>\n",
- " <td>0.0</td>\n",
- " <td>4.0</td>\n",
- " <td>50.0</td>\n",
- " <td>0.7</td>\n",
- " <td>282.95</td>\n",
- " <td>82.0</td>\n",
- " <td>2.0</td>\n",
- " <td>99170.0</td>\n",
- " <td>2.4</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>12.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>2015-06-22T05:00:00+02:00</td>\n",
- " <td>101680.0</td>\n",
- " <td>-120.0</td>\n",
- " <td>6.0</td>\n",
- " <td>180.0</td>\n",
- " <td>0.7</td>\n",
- " <td>286.15</td>\n",
- " <td>80.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98870.0</td>\n",
- " <td>4.7</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>-0.1</td>\n",
- " <td>16.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>2015-06-23T02:00:00+02:00</td>\n",
- " <td>101270.0</td>\n",
- " <td>150.0</td>\n",
- " <td>2.0</td>\n",
- " <td>20.0</td>\n",
- " <td>4.5</td>\n",
- " <td>282.95</td>\n",
- " <td>54.0</td>\n",
- " <td>0.0</td>\n",
- " <td>98490.0</td>\n",
- " <td>10.2</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>19.3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>2015-06-25T14:00:00+02:00</td>\n",
- " <td>102180.0</td>\n",
- " <td>-40.0</td>\n",
- " <td>8.0</td>\n",
- " <td>10.0</td>\n",
- " <td>2.3</td>\n",
- " <td>283.25</td>\n",
- " <td>38.0</td>\n",
- " <td>1.0</td>\n",
- " <td>99430.0</td>\n",
- " <td>7.5</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>25.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>2015-07-05T20:00:00+02:00</td>\n",
- " <td>101410.0</td>\n",
- " <td>50.0</td>\n",
- " <td>3.0</td>\n",
- " <td>190.0</td>\n",
- " <td>8.3</td>\n",
- " <td>288.05</td>\n",
- " <td>33.0</td>\n",
- " <td>3.0</td>\n",
- " <td>98760.0</td>\n",
- " <td>13.4</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>33.4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>2015-05-14T17:00:00+02:00</td>\n",
- " <td>101070.0</td>\n",
- " <td>-150.0</td>\n",
- " <td>6.0</td>\n",
- " <td>20.0</td>\n",
- " <td>6.2</td>\n",
- " <td>284.95</td>\n",
- " <td>60.0</td>\n",
- " <td>3.0</td>\n",
- " <td>98300.0</td>\n",
- " <td>11.1</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>19.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>2015-03-16T22:00:00+01:00</td>\n",
- " <td>102150.0</td>\n",
- " <td>40.0</td>\n",
- " <td>1.0</td>\n",
- " <td>50.0</td>\n",
- " <td>1.7</td>\n",
- " <td>275.05</td>\n",
- " <td>62.0</td>\n",
- " <td>1.0</td>\n",
- " <td>99240.0</td>\n",
- " <td>4.6</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>8.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>2015-03-26T01:00:00+01:00</td>\n",
- " <td>101140.0</td>\n",
- " <td>100.0</td>\n",
- " <td>1.0</td>\n",
- " <td>330.0</td>\n",
- " <td>5.9</td>\n",
- " <td>275.45</td>\n",
- " <td>82.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98220.0</td>\n",
- " <td>8.1</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>5.1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>2015-04-03T17:00:00+02:00</td>\n",
- " <td>101690.0</td>\n",
- " <td>-250.0</td>\n",
- " <td>7.0</td>\n",
- " <td>340.0</td>\n",
- " <td>3.5</td>\n",
- " <td>278.15</td>\n",
- " <td>55.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98850.0</td>\n",
- " <td>6.4</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>13.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>2015-04-05T20:00:00+02:00</td>\n",
- " <td>101850.0</td>\n",
- " <td>140.0</td>\n",
- " <td>3.0</td>\n",
- " <td>10.0</td>\n",
- " <td>7.8</td>\n",
- " <td>268.45</td>\n",
- " <td>38.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98950.0</td>\n",
- " <td>13.5</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>8.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>2014-10-22T17:00:00+02:00</td>\n",
- " <td>102670.0</td>\n",
- " <td>-70.0</td>\n",
- " <td>8.0</td>\n",
- " <td>20.0</td>\n",
- " <td>4.6</td>\n",
- " <td>275.35</td>\n",
- " <td>55.0</td>\n",
- " <td>1.0</td>\n",
- " <td>99770.0</td>\n",
- " <td>7.2</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>10.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>2015-02-08T16:00:00+01:00</td>\n",
- " <td>102570.0</td>\n",
- " <td>20.0</td>\n",
- " <td>3.0</td>\n",
- " <td>350.0</td>\n",
- " <td>12.3</td>\n",
- " <td>271.55</td>\n",
- " <td>68.0</td>\n",
- " <td>0.0</td>\n",
- " <td>99590.0</td>\n",
- " <td>19.9</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>3.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>2015-02-11T07:00:00+01:00</td>\n",
- " <td>102670.0</td>\n",
- " <td>-10.0</td>\n",
- " <td>7.0</td>\n",
- " <td>290.0</td>\n",
- " <td>2.0</td>\n",
- " <td>267.55</td>\n",
- " <td>88.0</td>\n",
- " <td>10.0</td>\n",
- " <td>99610.0</td>\n",
- " <td>3.3</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>-3.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>2015-02-07T04:00:00+01:00</td>\n",
- " <td>101900.0</td>\n",
- " <td>160.0</td>\n",
- " <td>1.0</td>\n",
- " <td>310.0</td>\n",
- " <td>2.0</td>\n",
- " <td>268.65</td>\n",
- " <td>74.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98900.0</td>\n",
- " <td>3.2</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>-0.4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>2015-02-13T04:00:00+01:00</td>\n",
- " <td>102140.0</td>\n",
- " <td>-50.0</td>\n",
- " <td>8.0</td>\n",
- " <td>140.0</td>\n",
- " <td>2.1</td>\n",
- " <td>273.55</td>\n",
- " <td>74.0</td>\n",
- " <td>0.0</td>\n",
- " <td>99190.0</td>\n",
- " <td>4.9</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>4.6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>2015-02-16T19:00:00+01:00</td>\n",
- " <td>102060.0</td>\n",
- " <td>100.0</td>\n",
- " <td>3.0</td>\n",
- " <td>20.0</td>\n",
- " <td>3.2</td>\n",
- " <td>275.65</td>\n",
- " <td>88.0</td>\n",
- " <td>1.0</td>\n",
- " <td>99110.0</td>\n",
- " <td>5.1</td>\n",
- " <td>-10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>4.3</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " date pmer tend cod_tend dd ff td \\\n",
- "0 2015-06-12T17:00:00+02:00 101050.0 -230.0 6.0 140.0 3.6 286.25 \n",
- "1 2015-06-05T17:00:00+02:00 101590.0 -220.0 8.0 190.0 3.9 286.95 \n",
- "2 2015-06-15T11:00:00+02:00 101420.0 90.0 1.0 270.0 1.5 286.85 \n",
- "3 2015-06-15T14:00:00+02:00 101430.0 20.0 1.0 10.0 2.5 286.45 \n",
- "4 2015-06-20T05:00:00+02:00 102030.0 0.0 4.0 50.0 0.7 282.95 \n",
- "5 2015-06-22T05:00:00+02:00 101680.0 -120.0 6.0 180.0 0.7 286.15 \n",
- "6 2015-06-23T02:00:00+02:00 101270.0 150.0 2.0 20.0 4.5 282.95 \n",
- "7 2015-06-25T14:00:00+02:00 102180.0 -40.0 8.0 10.0 2.3 283.25 \n",
- "8 2015-07-05T20:00:00+02:00 101410.0 50.0 3.0 190.0 8.3 288.05 \n",
- "9 2015-05-14T17:00:00+02:00 101070.0 -150.0 6.0 20.0 6.2 284.95 \n",
- "10 2015-03-16T22:00:00+01:00 102150.0 40.0 1.0 50.0 1.7 275.05 \n",
- "11 2015-03-26T01:00:00+01:00 101140.0 100.0 1.0 330.0 5.9 275.45 \n",
- "12 2015-04-03T17:00:00+02:00 101690.0 -250.0 7.0 340.0 3.5 278.15 \n",
- "13 2015-04-05T20:00:00+02:00 101850.0 140.0 3.0 10.0 7.8 268.45 \n",
- "14 2014-10-22T17:00:00+02:00 102670.0 -70.0 8.0 20.0 4.6 275.35 \n",
- "15 2015-02-08T16:00:00+01:00 102570.0 20.0 3.0 350.0 12.3 271.55 \n",
- "16 2015-02-11T07:00:00+01:00 102670.0 -10.0 7.0 290.0 2.0 267.55 \n",
- "17 2015-02-07T04:00:00+01:00 101900.0 160.0 1.0 310.0 2.0 268.65 \n",
- "18 2015-02-13T04:00:00+01:00 102140.0 -50.0 8.0 140.0 2.1 273.55 \n",
- "19 2015-02-16T19:00:00+01:00 102060.0 100.0 3.0 20.0 3.2 275.65 \n",
- "\n",
- " u ww pres rafper per rr1 rr3 tc \n",
- "0 50.0 2.0 98330.0 5.1 -10.0 0.0 -0.1 24.2 \n",
- "1 32.0 3.0 98930.0 9.9 -10.0 0.0 0.0 32.6 \n",
- "2 64.0 3.0 98660.0 4.5 -10.0 0.0 0.0 20.8 \n",
- "3 55.0 1.0 98680.0 5.1 -10.0 0.0 0.0 22.8 \n",
- "4 82.0 2.0 99170.0 2.4 -10.0 0.0 0.0 12.8 \n",
- "5 80.0 1.0 98870.0 4.7 -10.0 0.0 -0.1 16.5 \n",
- "6 54.0 0.0 98490.0 10.2 -10.0 0.0 0.0 19.3 \n",
- "7 38.0 1.0 99430.0 7.5 -10.0 0.0 0.0 25.5 \n",
- "8 33.0 3.0 98760.0 13.4 -10.0 0.0 0.0 33.4 \n",
- "9 60.0 3.0 98300.0 11.1 -10.0 0.0 0.0 19.8 \n",
- "10 62.0 1.0 99240.0 4.6 -10.0 0.0 0.0 8.8 \n",
- "11 82.0 1.0 98220.0 8.1 -10.0 0.0 0.0 5.1 \n",
- "12 55.0 1.0 98850.0 6.4 -10.0 0.0 0.0 13.9 \n",
- "13 38.0 1.0 98950.0 13.5 -10.0 0.0 0.0 8.9 \n",
- "14 55.0 1.0 99770.0 7.2 -10.0 0.0 0.0 10.9 \n",
- "15 68.0 0.0 99590.0 19.9 -10.0 0.0 0.0 3.8 \n",
- "16 88.0 10.0 99610.0 3.3 -10.0 0.0 0.0 -3.9 \n",
- "17 74.0 2.0 98900.0 3.2 -10.0 0.0 0.0 -0.4 \n",
- "18 74.0 0.0 99190.0 4.9 -10.0 0.0 0.0 4.6 \n",
- "19 88.0 1.0 99110.0 5.1 -10.0 0.0 0.0 4.3 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Few statistics :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_cf3c5_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >pmer</th> <th class=\"col_heading level0 col1\" >tend</th> <th class=\"col_heading level0 col2\" >cod_tend</th> <th class=\"col_heading level0 col3\" >dd</th> <th class=\"col_heading level0 col4\" >ff</th> <th class=\"col_heading level0 col5\" >td</th> <th class=\"col_heading level0 col6\" >u</th> <th class=\"col_heading level0 col7\" >ww</th> <th class=\"col_heading level0 col8\" >pres</th> <th class=\"col_heading level0 col9\" >rafper</th> <th class=\"col_heading level0 col10\" >per</th> <th class=\"col_heading level0 col11\" >rr1</th> <th class=\"col_heading level0 col12\" >rr3</th> <th class=\"col_heading level0 col13\" >tc</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_cf3c5_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_cf3c5_row0_col0\" class=\"data row0 col0\" >29148.00</td>\n",
- " <td id=\"T_cf3c5_row0_col1\" class=\"data row0 col1\" >29163.00</td>\n",
- " <td id=\"T_cf3c5_row0_col2\" class=\"data row0 col2\" >29163.00</td>\n",
- " <td id=\"T_cf3c5_row0_col3\" class=\"data row0 col3\" >29162.00</td>\n",
- " <td id=\"T_cf3c5_row0_col4\" class=\"data row0 col4\" >29163.00</td>\n",
- " <td id=\"T_cf3c5_row0_col5\" class=\"data row0 col5\" >29148.00</td>\n",
- " <td id=\"T_cf3c5_row0_col6\" class=\"data row0 col6\" >29148.00</td>\n",
- " <td id=\"T_cf3c5_row0_col7\" class=\"data row0 col7\" >29164.00</td>\n",
- " <td id=\"T_cf3c5_row0_col8\" class=\"data row0 col8\" >29165.00</td>\n",
- " <td id=\"T_cf3c5_row0_col9\" class=\"data row0 col9\" >29156.00</td>\n",
- " <td id=\"T_cf3c5_row0_col10\" class=\"data row0 col10\" >29157.00</td>\n",
- " <td id=\"T_cf3c5_row0_col11\" class=\"data row0 col11\" >29070.00</td>\n",
- " <td id=\"T_cf3c5_row0_col12\" class=\"data row0 col12\" >29092.00</td>\n",
- " <td id=\"T_cf3c5_row0_col13\" class=\"data row0 col13\" >29151.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cf3c5_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_cf3c5_row1_col0\" class=\"data row1 col0\" >101753.55</td>\n",
- " <td id=\"T_cf3c5_row1_col1\" class=\"data row1 col1\" >0.26</td>\n",
- " <td id=\"T_cf3c5_row1_col2\" class=\"data row1 col2\" >4.31</td>\n",
- " <td id=\"T_cf3c5_row1_col3\" class=\"data row1 col3\" >204.09</td>\n",
- " <td id=\"T_cf3c5_row1_col4\" class=\"data row1 col4\" >3.40</td>\n",
- " <td id=\"T_cf3c5_row1_col5\" class=\"data row1 col5\" >280.03</td>\n",
- " <td id=\"T_cf3c5_row1_col6\" class=\"data row1 col6\" >71.02</td>\n",
- " <td id=\"T_cf3c5_row1_col7\" class=\"data row1 col7\" >10.11</td>\n",
- " <td id=\"T_cf3c5_row1_col8\" class=\"data row1 col8\" >98894.60</td>\n",
- " <td id=\"T_cf3c5_row1_col9\" class=\"data row1 col9\" >6.30</td>\n",
- " <td id=\"T_cf3c5_row1_col10\" class=\"data row1 col10\" >-10.00</td>\n",
- " <td id=\"T_cf3c5_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
- " <td id=\"T_cf3c5_row1_col12\" class=\"data row1 col12\" >0.28</td>\n",
- " <td id=\"T_cf3c5_row1_col13\" class=\"data row1 col13\" >12.69</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cf3c5_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_cf3c5_row2_col0\" class=\"data row2 col0\" >798.09</td>\n",
- " <td id=\"T_cf3c5_row2_col1\" class=\"data row2 col1\" >111.44</td>\n",
- " <td id=\"T_cf3c5_row2_col2\" class=\"data row2 col2\" >2.72</td>\n",
- " <td id=\"T_cf3c5_row2_col3\" class=\"data row2 col3\" >115.42</td>\n",
- " <td id=\"T_cf3c5_row2_col4\" class=\"data row2 col4\" >2.47</td>\n",
- " <td id=\"T_cf3c5_row2_col5\" class=\"data row2 col5\" >5.86</td>\n",
- " <td id=\"T_cf3c5_row2_col6\" class=\"data row2 col6\" >18.28</td>\n",
- " <td id=\"T_cf3c5_row2_col7\" class=\"data row2 col7\" >19.40</td>\n",
- " <td id=\"T_cf3c5_row2_col8\" class=\"data row2 col8\" >761.59</td>\n",
- " <td id=\"T_cf3c5_row2_col9\" class=\"data row2 col9\" >3.85</td>\n",
- " <td id=\"T_cf3c5_row2_col10\" class=\"data row2 col10\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row2_col11\" class=\"data row2 col11\" >0.61</td>\n",
- " <td id=\"T_cf3c5_row2_col12\" class=\"data row2 col12\" >1.41</td>\n",
- " <td id=\"T_cf3c5_row2_col13\" class=\"data row2 col13\" >8.15</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cf3c5_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_cf3c5_row3_col0\" class=\"data row3 col0\" >97960.00</td>\n",
- " <td id=\"T_cf3c5_row3_col1\" class=\"data row3 col1\" >-750.00</td>\n",
- " <td id=\"T_cf3c5_row3_col2\" class=\"data row3 col2\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row3_col4\" class=\"data row3 col4\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row3_col5\" class=\"data row3 col5\" >249.25</td>\n",
- " <td id=\"T_cf3c5_row3_col6\" class=\"data row3 col6\" >2.00</td>\n",
- " <td id=\"T_cf3c5_row3_col7\" class=\"data row3 col7\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row3_col8\" class=\"data row3 col8\" >95170.00</td>\n",
- " <td id=\"T_cf3c5_row3_col9\" class=\"data row3 col9\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row3_col10\" class=\"data row3 col10\" >-10.00</td>\n",
- " <td id=\"T_cf3c5_row3_col11\" class=\"data row3 col11\" >-0.10</td>\n",
- " <td id=\"T_cf3c5_row3_col12\" class=\"data row3 col12\" >-0.10</td>\n",
- " <td id=\"T_cf3c5_row3_col13\" class=\"data row3 col13\" >-12.10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cf3c5_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_cf3c5_row4_col0\" class=\"data row4 col0\" >101300.00</td>\n",
- " <td id=\"T_cf3c5_row4_col1\" class=\"data row4 col1\" >-70.00</td>\n",
- " <td id=\"T_cf3c5_row4_col2\" class=\"data row4 col2\" >2.00</td>\n",
- " <td id=\"T_cf3c5_row4_col3\" class=\"data row4 col3\" >130.00</td>\n",
- " <td id=\"T_cf3c5_row4_col4\" class=\"data row4 col4\" >1.50</td>\n",
- " <td id=\"T_cf3c5_row4_col5\" class=\"data row4 col5\" >275.83</td>\n",
- " <td id=\"T_cf3c5_row4_col6\" class=\"data row4 col6\" >58.00</td>\n",
- " <td id=\"T_cf3c5_row4_col7\" class=\"data row4 col7\" >2.00</td>\n",
- " <td id=\"T_cf3c5_row4_col8\" class=\"data row4 col8\" >98480.00</td>\n",
- " <td id=\"T_cf3c5_row4_col9\" class=\"data row4 col9\" >3.60</td>\n",
- " <td id=\"T_cf3c5_row4_col10\" class=\"data row4 col10\" >-10.00</td>\n",
- " <td id=\"T_cf3c5_row4_col11\" class=\"data row4 col11\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row4_col13\" class=\"data row4 col13\" >6.60</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cf3c5_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_cf3c5_row5_col0\" class=\"data row5 col0\" >101740.00</td>\n",
- " <td id=\"T_cf3c5_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row5_col2\" class=\"data row5 col2\" >3.00</td>\n",
- " <td id=\"T_cf3c5_row5_col3\" class=\"data row5 col3\" >190.00</td>\n",
- " <td id=\"T_cf3c5_row5_col4\" class=\"data row5 col4\" >2.90</td>\n",
- " <td id=\"T_cf3c5_row5_col5\" class=\"data row5 col5\" >280.25</td>\n",
- " <td id=\"T_cf3c5_row5_col6\" class=\"data row5 col6\" >74.00</td>\n",
- " <td id=\"T_cf3c5_row5_col7\" class=\"data row5 col7\" >2.00</td>\n",
- " <td id=\"T_cf3c5_row5_col8\" class=\"data row5 col8\" >98920.00</td>\n",
- " <td id=\"T_cf3c5_row5_col9\" class=\"data row5 col9\" >5.30</td>\n",
- " <td id=\"T_cf3c5_row5_col10\" class=\"data row5 col10\" >-10.00</td>\n",
- " <td id=\"T_cf3c5_row5_col11\" class=\"data row5 col11\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row5_col12\" class=\"data row5 col12\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row5_col13\" class=\"data row5 col13\" >12.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cf3c5_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_cf3c5_row6_col0\" class=\"data row6 col0\" >102240.00</td>\n",
- " <td id=\"T_cf3c5_row6_col1\" class=\"data row6 col1\" >70.00</td>\n",
- " <td id=\"T_cf3c5_row6_col2\" class=\"data row6 col2\" >7.00</td>\n",
- " <td id=\"T_cf3c5_row6_col3\" class=\"data row6 col3\" >330.00</td>\n",
- " <td id=\"T_cf3c5_row6_col4\" class=\"data row6 col4\" >4.60</td>\n",
- " <td id=\"T_cf3c5_row6_col5\" class=\"data row6 col5\" >284.55</td>\n",
- " <td id=\"T_cf3c5_row6_col6\" class=\"data row6 col6\" >86.00</td>\n",
- " <td id=\"T_cf3c5_row6_col7\" class=\"data row6 col7\" >3.00</td>\n",
- " <td id=\"T_cf3c5_row6_col8\" class=\"data row6 col8\" >99360.00</td>\n",
- " <td id=\"T_cf3c5_row6_col9\" class=\"data row6 col9\" >8.20</td>\n",
- " <td id=\"T_cf3c5_row6_col10\" class=\"data row6 col10\" >-10.00</td>\n",
- " <td id=\"T_cf3c5_row6_col11\" class=\"data row6 col11\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row6_col12\" class=\"data row6 col12\" >0.00</td>\n",
- " <td id=\"T_cf3c5_row6_col13\" class=\"data row6 col13\" >18.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cf3c5_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_cf3c5_row7_col0\" class=\"data row7 col0\" >104280.00</td>\n",
- " <td id=\"T_cf3c5_row7_col1\" class=\"data row7 col1\" >810.00</td>\n",
- " <td id=\"T_cf3c5_row7_col2\" class=\"data row7 col2\" >8.00</td>\n",
- " <td id=\"T_cf3c5_row7_col3\" class=\"data row7 col3\" >360.00</td>\n",
- " <td id=\"T_cf3c5_row7_col4\" class=\"data row7 col4\" >18.80</td>\n",
- " <td id=\"T_cf3c5_row7_col5\" class=\"data row7 col5\" >295.95</td>\n",
- " <td id=\"T_cf3c5_row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_cf3c5_row7_col7\" class=\"data row7 col7\" >97.00</td>\n",
- " <td id=\"T_cf3c5_row7_col8\" class=\"data row7 col8\" >101210.00</td>\n",
- " <td id=\"T_cf3c5_row7_col9\" class=\"data row7 col9\" >30.20</td>\n",
- " <td id=\"T_cf3c5_row7_col10\" class=\"data row7 col10\" >-10.00</td>\n",
- " <td id=\"T_cf3c5_row7_col11\" class=\"data row7 col11\" >19.00</td>\n",
- " <td id=\"T_cf3c5_row7_col12\" class=\"data row7 col12\" >45.00</td>\n",
- " <td id=\"T_cf3c5_row7_col13\" class=\"data row7 col13\" >38.90</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fdb741b9bb0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "columns_used=['date','pmer','tend','cod_tend','dd','ff','td','u','ww','pres','rafper','per','rr1','rr3','tc']\n",
- "\n",
- "# ---- Drop unused columns\n",
- "\n",
- "to_drop = df.columns.difference(columns_used)\n",
- "df.drop( to_drop, axis=1, inplace=True)\n",
- "\n",
- "# ---- Show all of that\n",
- "\n",
- "pwk.subtitle('Our selected columns :')\n",
- "display(df.head(20))\n",
- "\n",
- "pwk.subtitle('Few statistics :')\n",
- "display(df.describe().style.format('{:.2f}'))\n",
- "\n",
- "# ---- 'per' column is constant, we can drop it\n",
- "\n",
- "df.drop(['per'],axis=1,inplace=True)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.2 - Cleanup dataset\n",
- "Let's sort it and cook up some NaN values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:46.146981Z",
- "iopub.status.busy": "2021-03-09T21:35:46.146660Z",
- "iopub.status.idle": "2021-03-09T21:35:46.204073Z",
- "shell.execute_reply": "2021-03-09T21:35:46.203780Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**Before :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
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- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>date</th>\n",
- " <th>pmer</th>\n",
- " <th>tend</th>\n",
- " <th>cod_tend</th>\n",
- " <th>dd</th>\n",
- " <th>ff</th>\n",
- " <th>td</th>\n",
- " <th>u</th>\n",
- " <th>ww</th>\n",
- " <th>pres</th>\n",
- " <th>rafper</th>\n",
- " <th>rr1</th>\n",
- " <th>rr3</th>\n",
- " <th>tc</th>\n",
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- " <tbody>\n",
- " <tr>\n",
- " <th>396</th>\n",
- " <td>2010-02-19T16:00:00+01:00</td>\n",
- " <td>99760.0</td>\n",
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- " <td>3.0</td>\n",
- " <td>330.0</td>\n",
- " <td>4.6</td>\n",
- " <td>275.85</td>\n",
- " <td>79.0</td>\n",
- " <td>21.0</td>\n",
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- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>279.25</td>\n",
- " <td>77.0</td>\n",
- " <td>2.0</td>\n",
- " <td>97470.0</td>\n",
- " <td>NaN</td>\n",
- " <td>0.2</td>\n",
- " <td>0.2</td>\n",
- " <td>9.9</td>\n",
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- " <tr>\n",
- " <th>477</th>\n",
- " <td>2010-03-01T19:00:00+01:00</td>\n",
- " <td>101400.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>340.0</td>\n",
- " <td>2.6</td>\n",
- " <td>275.45</td>\n",
- " <td>61.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98520.0</td>\n",
- " <td>5.7</td>\n",
- " <td>0.0</td>\n",
- " <td>NaN</td>\n",
- " <td>9.4</td>\n",
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- " <th>734</th>\n",
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- " <td>2.0</td>\n",
- " <td>98680.0</td>\n",
- " <td>12.3</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>10.9</td>\n",
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- " <tr>\n",
- " <th>1061</th>\n",
- " <td>2010-05-13T23:00:00+02:00</td>\n",
- " <td>NaN</td>\n",
- " <td>60.0</td>\n",
- " <td>2.0</td>\n",
- " <td>330.0</td>\n",
- " <td>4.6</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>2.0</td>\n",
- " <td>98220.0</td>\n",
- " <td>7.7</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>9.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1063</th>\n",
- " <td>2010-05-14T05:00:00+02:00</td>\n",
- " <td>NaN</td>\n",
- " <td>-50.0</td>\n",
- " <td>5.0</td>\n",
- " <td>350.0</td>\n",
- " <td>4.1</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>2.0</td>\n",
- " <td>98110.0</td>\n",
- " <td>7.2</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>8.1</td>\n",
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- " <tr>\n",
- " <th>1064</th>\n",
- " <td>2010-05-14T08:00:00+02:00</td>\n",
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- " <td>5.0</td>\n",
- " <td>350.0</td>\n",
- " <td>4.6</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>2.0</td>\n",
- " <td>98110.0</td>\n",
- " <td>6.7</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>8.1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2268</th>\n",
- " <td>2010-10-11T20:00:00+02:00</td>\n",
- " <td>NaN</td>\n",
- " <td>150.0</td>\n",
- " <td>2.0</td>\n",
- " <td>10.0</td>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>2.0</td>\n",
- " <td>98060.0</td>\n",
- " <td>3.1</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
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- " <tr>\n",
- " <th>2269</th>\n",
- " <td>2010-10-11T23:00:00+02:00</td>\n",
- " <td>NaN</td>\n",
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- " <td>3.0</td>\n",
- " <td>80.0</td>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>2.0</td>\n",
- " <td>98190.0</td>\n",
- " <td>2.6</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
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- " <tr>\n",
- " <th>2270</th>\n",
- " <td>2010-10-12T02:00:00+02:00</td>\n",
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- " <td>70.0</td>\n",
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- " <td>NaN</td>\n",
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- " <td>98260.0</td>\n",
- " <td>1.5</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
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- "text/plain": [
- " date pmer tend cod_tend dd ff \\\n",
- "396 2010-02-19T16:00:00+01:00 99760.0 180.0 3.0 330.0 4.6 \n",
- "434 2010-02-24T10:00:00+01:00 100310.0 60.0 1.0 NaN NaN \n",
- "477 2010-03-01T19:00:00+01:00 101400.0 NaN NaN 340.0 2.6 \n",
- "734 2010-04-03T02:00:00+02:00 101550.0 50.0 0.0 190.0 7.7 \n",
- "1061 2010-05-13T23:00:00+02:00 NaN 60.0 2.0 330.0 4.6 \n",
- "1063 2010-05-14T05:00:00+02:00 NaN -50.0 5.0 350.0 4.1 \n",
- "1064 2010-05-14T08:00:00+02:00 NaN 0.0 5.0 350.0 4.6 \n",
- "2268 2010-10-11T20:00:00+02:00 NaN 150.0 2.0 10.0 1.0 \n",
- "2269 2010-10-11T23:00:00+02:00 NaN 130.0 3.0 80.0 1.0 \n",
- "2270 2010-10-12T02:00:00+02:00 NaN 70.0 1.0 0.0 0.0 \n",
- "\n",
- " td u ww pres rafper rr1 rr3 tc \n",
- "396 275.85 79.0 21.0 96890.0 NaN 0.0 1.0 6.1 \n",
- "434 279.25 77.0 2.0 97470.0 NaN 0.2 0.2 9.9 \n",
- "477 275.45 61.0 2.0 98520.0 5.7 0.0 NaN 9.4 \n",
- "734 277.55 64.0 2.0 98680.0 12.3 NaN NaN 10.9 \n",
- "1061 NaN NaN 2.0 98220.0 7.7 0.0 0.0 9.9 \n",
- "1063 NaN NaN 2.0 98110.0 7.2 0.0 0.0 8.1 \n",
- "1064 NaN NaN 2.0 98110.0 6.7 0.0 0.0 8.1 \n",
- "2268 NaN NaN 2.0 98060.0 3.1 NaN NaN NaN \n",
- "2269 NaN NaN 2.0 98190.0 2.6 NaN NaN NaN \n",
- "2270 NaN NaN 2.0 98260.0 1.5 NaN NaN NaN "
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- "<br>**After :**"
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- " <th>477</th>\n",
- " <td>2010-03-01T19:00:00+01:00</td>\n",
- " <td>101400.000000</td>\n",
- " <td>195.0</td>\n",
- " <td>4.0</td>\n",
- " <td>340.0</td>\n",
- " <td>2.60</td>\n",
- " <td>275.45</td>\n",
- " <td>61.000000</td>\n",
- " <td>2.0</td>\n",
- " <td>98520.0</td>\n",
- " <td>5.70</td>\n",
- " <td>0.0</td>\n",
- " <td>0.5</td>\n",
- " <td>9.40</td>\n",
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- " <th>734</th>\n",
- " <td>2010-04-03T02:00:00+02:00</td>\n",
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- " <td>50.0</td>\n",
- " <td>0.0</td>\n",
- " <td>190.0</td>\n",
- " <td>7.70</td>\n",
- " <td>277.55</td>\n",
- " <td>64.000000</td>\n",
- " <td>2.0</td>\n",
- " <td>98680.0</td>\n",
- " <td>12.30</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>10.90</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1061</th>\n",
- " <td>2010-05-13T23:00:00+02:00</td>\n",
- " <td>101020.000000</td>\n",
- " <td>60.0</td>\n",
- " <td>2.0</td>\n",
- " <td>330.0</td>\n",
- " <td>4.60</td>\n",
- " <td>281.25</td>\n",
- " <td>86.500000</td>\n",
- " <td>2.0</td>\n",
- " <td>98220.0</td>\n",
- " <td>7.70</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>9.90</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1063</th>\n",
- " <td>2010-05-14T05:00:00+02:00</td>\n",
- " <td>101040.000000</td>\n",
- " <td>-50.0</td>\n",
- " <td>5.0</td>\n",
- " <td>350.0</td>\n",
- " <td>4.10</td>\n",
- " <td>279.15</td>\n",
- " <td>80.666667</td>\n",
- " <td>2.0</td>\n",
- " <td>98110.0</td>\n",
- " <td>7.20</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>8.10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1064</th>\n",
- " <td>2010-05-14T08:00:00+02:00</td>\n",
- " <td>101040.000000</td>\n",
- " <td>0.0</td>\n",
- " <td>5.0</td>\n",
- " <td>350.0</td>\n",
- " <td>4.60</td>\n",
- " <td>279.35</td>\n",
- " <td>79.333333</td>\n",
- " <td>2.0</td>\n",
- " <td>98110.0</td>\n",
- " <td>6.70</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>8.10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2268</th>\n",
- " <td>2010-10-11T20:00:00+02:00</td>\n",
- " <td>100786.666667</td>\n",
- " <td>150.0</td>\n",
- " <td>2.0</td>\n",
- " <td>10.0</td>\n",
- " <td>1.00</td>\n",
- " <td>284.75</td>\n",
- " <td>83.333333</td>\n",
- " <td>2.0</td>\n",
- " <td>98060.0</td>\n",
- " <td>3.10</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>14.45</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2269</th>\n",
- " <td>2010-10-11T23:00:00+02:00</td>\n",
- " <td>100863.333333</td>\n",
- " <td>130.0</td>\n",
- " <td>3.0</td>\n",
- " <td>80.0</td>\n",
- " <td>1.00</td>\n",
- " <td>284.45</td>\n",
- " <td>84.666667</td>\n",
- " <td>2.0</td>\n",
- " <td>98190.0</td>\n",
- " <td>2.60</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>13.90</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2270</th>\n",
- " <td>2010-10-12T02:00:00+02:00</td>\n",
- " <td>100940.000000</td>\n",
- " <td>70.0</td>\n",
- " <td>1.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.00</td>\n",
- " <td>284.15</td>\n",
- " <td>86.000000</td>\n",
- " <td>2.0</td>\n",
- " <td>98260.0</td>\n",
- " <td>1.50</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>13.35</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " date pmer tend cod_tend dd ff \\\n",
- "396 2010-02-19T16:00:00+01:00 99760.000000 180.0 3.0 330.0 4.60 \n",
- "434 2010-02-24T10:00:00+01:00 100310.000000 60.0 1.0 170.0 4.15 \n",
- "477 2010-03-01T19:00:00+01:00 101400.000000 195.0 4.0 340.0 2.60 \n",
- "734 2010-04-03T02:00:00+02:00 101550.000000 50.0 0.0 190.0 7.70 \n",
- "1061 2010-05-13T23:00:00+02:00 101020.000000 60.0 2.0 330.0 4.60 \n",
- "1063 2010-05-14T05:00:00+02:00 101040.000000 -50.0 5.0 350.0 4.10 \n",
- "1064 2010-05-14T08:00:00+02:00 101040.000000 0.0 5.0 350.0 4.60 \n",
- "2268 2010-10-11T20:00:00+02:00 100786.666667 150.0 2.0 10.0 1.00 \n",
- "2269 2010-10-11T23:00:00+02:00 100863.333333 130.0 3.0 80.0 1.00 \n",
- "2270 2010-10-12T02:00:00+02:00 100940.000000 70.0 1.0 0.0 0.00 \n",
- "\n",
- " td u ww pres rafper rr1 rr3 tc \n",
- "396 275.85 79.000000 21.0 96890.0 8.25 0.0 1.0 6.10 \n",
- "434 279.25 77.000000 2.0 97470.0 6.65 0.2 0.2 9.90 \n",
- "477 275.45 61.000000 2.0 98520.0 5.70 0.0 0.5 9.40 \n",
- "734 277.55 64.000000 2.0 98680.0 12.30 0.0 0.0 10.90 \n",
- "1061 281.25 86.500000 2.0 98220.0 7.70 0.0 0.0 9.90 \n",
- "1063 279.15 80.666667 2.0 98110.0 7.20 0.0 0.0 8.10 \n",
- "1064 279.35 79.333333 2.0 98110.0 6.70 0.0 0.0 8.10 \n",
- "2268 284.75 83.333333 2.0 98060.0 3.10 0.0 0.0 14.45 \n",
- "2269 284.45 84.666667 2.0 98190.0 2.60 0.0 0.0 13.90 \n",
- "2270 284.15 86.000000 2.0 98260.0 1.50 0.0 0.0 13.35 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# ---- First of all, we have to sort on the date\n",
- "\n",
- "df.sort_values(['date'], inplace=True)\n",
- "df.reset_index(drop=True, inplace=True)\n",
- "\n",
- "# ---- Before : Lines with NaN\n",
- "\n",
- "na_rows=df.isna().any(axis=1)\n",
- "pwk.subtitle('Before :')\n",
- "display( df[na_rows].head(10) )\n",
- "\n",
- "# ---- Nice interpolation for plugging holes\n",
- "\n",
- "df.interpolate(inplace=True)\n",
- "\n",
- "# ---- After\n",
- "\n",
- "pwk.subtitle('After :')\n",
- "display(df[na_rows].head(10))\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - About our enhanced dataset\n",
- "### 5.1 - Summarize it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:46.209161Z",
- "iopub.status.busy": "2021-03-09T21:35:46.208784Z",
- "iopub.status.idle": "2021-03-09T21:35:46.244009Z",
- "shell.execute_reply": "2021-03-09T21:35:46.243678Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**Dataset columns :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
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- "data": {
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- "#T_c7dca_row0_col0,#T_c7dca_row0_col1,#T_c7dca_row0_col2,#T_c7dca_row1_col0,#T_c7dca_row1_col1,#T_c7dca_row1_col2,#T_c7dca_row2_col0,#T_c7dca_row2_col1,#T_c7dca_row2_col2,#T_c7dca_row3_col0,#T_c7dca_row3_col1,#T_c7dca_row3_col2,#T_c7dca_row4_col0,#T_c7dca_row4_col1,#T_c7dca_row4_col2,#T_c7dca_row5_col0,#T_c7dca_row5_col1,#T_c7dca_row5_col2,#T_c7dca_row6_col0,#T_c7dca_row6_col1,#T_c7dca_row6_col2,#T_c7dca_row7_col0,#T_c7dca_row7_col1,#T_c7dca_row7_col2,#T_c7dca_row8_col0,#T_c7dca_row8_col1,#T_c7dca_row8_col2,#T_c7dca_row9_col0,#T_c7dca_row9_col1,#T_c7dca_row9_col2,#T_c7dca_row10_col0,#T_c7dca_row10_col1,#T_c7dca_row10_col2,#T_c7dca_row11_col0,#T_c7dca_row11_col1,#T_c7dca_row11_col2,#T_c7dca_row12_col0,#T_c7dca_row12_col1,#T_c7dca_row12_col2,#T_c7dca_row13_col0,#T_c7dca_row13_col1,#T_c7dca_row13_col2{\n",
- " text-align: left;\n",
- " }</style><table id=\"T_c7dca_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Columns</th> <th class=\"col_heading level0 col1\" >Description</th> <th class=\"col_heading level0 col2\" >Na</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_c7dca_row0_col0\" class=\"data row0 col0\" >date</td>\n",
- " <td id=\"T_c7dca_row0_col1\" class=\"data row0 col1\" >Date</td>\n",
- " <td id=\"T_c7dca_row0_col2\" class=\"data row0 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_c7dca_row1_col0\" class=\"data row1 col0\" >pmer</td>\n",
- " <td id=\"T_c7dca_row1_col1\" class=\"data row1 col1\" >Pression au niveau mer</td>\n",
- " <td id=\"T_c7dca_row1_col2\" class=\"data row1 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_c7dca_row2_col0\" class=\"data row2 col0\" >tend</td>\n",
- " <td id=\"T_c7dca_row2_col1\" class=\"data row2 col1\" >Variation de pression en 3 heures</td>\n",
- " <td id=\"T_c7dca_row2_col2\" class=\"data row2 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_c7dca_row3_col0\" class=\"data row3 col0\" >cod_tend</td>\n",
- " <td id=\"T_c7dca_row3_col1\" class=\"data row3 col1\" >Type de tendance barométrique</td>\n",
- " <td id=\"T_c7dca_row3_col2\" class=\"data row3 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_c7dca_row4_col0\" class=\"data row4 col0\" >dd</td>\n",
- " <td id=\"T_c7dca_row4_col1\" class=\"data row4 col1\" >Direction du vent moyen 10 mn</td>\n",
- " <td id=\"T_c7dca_row4_col2\" class=\"data row4 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
- " <td id=\"T_c7dca_row5_col0\" class=\"data row5 col0\" >ff</td>\n",
- " <td id=\"T_c7dca_row5_col1\" class=\"data row5 col1\" >Vitesse du vent moyen 10 mn</td>\n",
- " <td id=\"T_c7dca_row5_col2\" class=\"data row5 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
- " <td id=\"T_c7dca_row6_col0\" class=\"data row6 col0\" >td</td>\n",
- " <td id=\"T_c7dca_row6_col1\" class=\"data row6 col1\" >Point de rosée</td>\n",
- " <td id=\"T_c7dca_row6_col2\" class=\"data row6 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
- " <td id=\"T_c7dca_row7_col0\" class=\"data row7 col0\" >u</td>\n",
- " <td id=\"T_c7dca_row7_col1\" class=\"data row7 col1\" >Humidité</td>\n",
- " <td id=\"T_c7dca_row7_col2\" class=\"data row7 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
- " <td id=\"T_c7dca_row8_col0\" class=\"data row8 col0\" >ww</td>\n",
- " <td id=\"T_c7dca_row8_col1\" class=\"data row8 col1\" >Temps présent</td>\n",
- " <td id=\"T_c7dca_row8_col2\" class=\"data row8 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
- " <td id=\"T_c7dca_row9_col0\" class=\"data row9 col0\" >pres</td>\n",
- " <td id=\"T_c7dca_row9_col1\" class=\"data row9 col1\" >Pression station</td>\n",
- " <td id=\"T_c7dca_row9_col2\" class=\"data row9 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
- " <td id=\"T_c7dca_row10_col0\" class=\"data row10 col0\" >rafper</td>\n",
- " <td id=\"T_c7dca_row10_col1\" class=\"data row10 col1\" >Rafales sur une période</td>\n",
- " <td id=\"T_c7dca_row10_col2\" class=\"data row10 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
- " <td id=\"T_c7dca_row11_col0\" class=\"data row11 col0\" >rr1</td>\n",
- " <td id=\"T_c7dca_row11_col1\" class=\"data row11 col1\" >Précipitations dans la dernière heure</td>\n",
- " <td id=\"T_c7dca_row11_col2\" class=\"data row11 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
- " <td id=\"T_c7dca_row12_col0\" class=\"data row12 col0\" >rr3</td>\n",
- " <td id=\"T_c7dca_row12_col1\" class=\"data row12 col1\" >Précipitations dans les 3 dernières heures</td>\n",
- " <td id=\"T_c7dca_row12_col2\" class=\"data row12 col2\" >0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_c7dca_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
- " <td id=\"T_c7dca_row13_col0\" class=\"data row13 col0\" >tc</td>\n",
- " <td id=\"T_c7dca_row13_col1\" class=\"data row13 col1\" >Température (°C)</td>\n",
- " <td id=\"T_c7dca_row13_col2\" class=\"data row13 col2\" >0</td>\n",
- " </tr>\n",
- " </tbody></table>"
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- "<pandas.io.formats.style.Styler at 0x7fdb741cc190>"
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- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Have a look :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
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- "data": {
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- "<div>\n",
- "<style scoped>\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>date</th>\n",
- " <th>pmer</th>\n",
- " <th>tend</th>\n",
- " <th>cod_tend</th>\n",
- " <th>dd</th>\n",
- " <th>ff</th>\n",
- " <th>td</th>\n",
- " <th>u</th>\n",
- " <th>ww</th>\n",
- " <th>pres</th>\n",
- " <th>rafper</th>\n",
- " <th>rr1</th>\n",
- " <th>rr3</th>\n",
- " <th>tc</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>29145</th>\n",
- " <td>2020-02-24T13:00:00+01:00</td>\n",
- " <td>102380.0</td>\n",
- " <td>-220.0</td>\n",
- " <td>8.0</td>\n",
- " <td>120.0</td>\n",
- " <td>1.6</td>\n",
- " <td>281.15</td>\n",
- " <td>59.0</td>\n",
- " <td>0.0</td>\n",
- " <td>99540.0</td>\n",
- " <td>3.7</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>16.0</td>\n",
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- " <tr>\n",
- " <th>29146</th>\n",
- " <td>2020-02-24T16:00:00+01:00</td>\n",
- " <td>101990.0</td>\n",
- " <td>-350.0</td>\n",
- " <td>6.0</td>\n",
- " <td>110.0</td>\n",
- " <td>1.6</td>\n",
- " <td>281.55</td>\n",
- " <td>50.0</td>\n",
- " <td>3.0</td>\n",
- " <td>99190.0</td>\n",
- " <td>3.3</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>19.1</td>\n",
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- " <tr>\n",
- " <th>29147</th>\n",
- " <td>2020-02-24T19:00:00+01:00</td>\n",
- " <td>101800.0</td>\n",
- " <td>-220.0</td>\n",
- " <td>6.0</td>\n",
- " <td>150.0</td>\n",
- " <td>2.9</td>\n",
- " <td>280.05</td>\n",
- " <td>55.0</td>\n",
- " <td>3.0</td>\n",
- " <td>98970.0</td>\n",
- " <td>4.1</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>15.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29148</th>\n",
- " <td>2020-02-24T22:00:00+01:00</td>\n",
- " <td>101740.0</td>\n",
- " <td>-80.0</td>\n",
- " <td>6.0</td>\n",
- " <td>170.0</td>\n",
- " <td>1.8</td>\n",
- " <td>280.35</td>\n",
- " <td>67.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98890.0</td>\n",
- " <td>4.3</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>13.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29149</th>\n",
- " <td>2020-02-25T01:00:00+01:00</td>\n",
- " <td>101640.0</td>\n",
- " <td>-150.0</td>\n",
- " <td>8.0</td>\n",
- " <td>170.0</td>\n",
- " <td>2.5</td>\n",
- " <td>278.85</td>\n",
- " <td>83.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98740.0</td>\n",
- " <td>4.7</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>8.4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29150</th>\n",
- " <td>2020-02-25T04:00:00+01:00</td>\n",
- " <td>101450.0</td>\n",
- " <td>-200.0</td>\n",
- " <td>6.0</td>\n",
- " <td>150.0</td>\n",
- " <td>3.7</td>\n",
- " <td>277.75</td>\n",
- " <td>87.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98540.0</td>\n",
- " <td>4.8</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>6.6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29151</th>\n",
- " <td>2020-02-25T07:00:00+01:00</td>\n",
- " <td>101530.0</td>\n",
- " <td>60.0</td>\n",
- " <td>3.0</td>\n",
- " <td>30.0</td>\n",
- " <td>4.0</td>\n",
- " <td>276.95</td>\n",
- " <td>92.0</td>\n",
- " <td>3.0</td>\n",
- " <td>98600.0</td>\n",
- " <td>6.1</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>5.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29152</th>\n",
- " <td>2020-02-25T10:00:00+01:00</td>\n",
- " <td>101490.0</td>\n",
- " <td>-20.0</td>\n",
- " <td>8.0</td>\n",
- " <td>200.0</td>\n",
- " <td>1.8</td>\n",
- " <td>277.55</td>\n",
- " <td>87.0</td>\n",
- " <td>3.0</td>\n",
- " <td>98580.0</td>\n",
- " <td>5.5</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>6.4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29153</th>\n",
- " <td>2020-02-25T13:00:00+01:00</td>\n",
- " <td>101330.0</td>\n",
- " <td>-140.0</td>\n",
- " <td>8.0</td>\n",
- " <td>150.0</td>\n",
- " <td>3.8</td>\n",
- " <td>278.95</td>\n",
- " <td>85.0</td>\n",
- " <td>21.0</td>\n",
- " <td>98440.0</td>\n",
- " <td>7.1</td>\n",
- " <td>0.6</td>\n",
- " <td>2.0</td>\n",
- " <td>8.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29154</th>\n",
- " <td>2020-02-25T16:00:00+01:00</td>\n",
- " <td>100990.0</td>\n",
- " <td>-290.0</td>\n",
- " <td>6.0</td>\n",
- " <td>140.0</td>\n",
- " <td>4.4</td>\n",
- " <td>279.55</td>\n",
- " <td>69.0</td>\n",
- " <td>3.0</td>\n",
- " <td>98150.0</td>\n",
- " <td>7.2</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>11.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29155</th>\n",
- " <td>2020-02-25T19:00:00+01:00</td>\n",
- " <td>100910.0</td>\n",
- " <td>-90.0</td>\n",
- " <td>5.0</td>\n",
- " <td>260.0</td>\n",
- " <td>4.3</td>\n",
- " <td>278.95</td>\n",
- " <td>69.0</td>\n",
- " <td>25.0</td>\n",
- " <td>98060.0</td>\n",
- " <td>8.4</td>\n",
- " <td>-0.1</td>\n",
- " <td>-0.1</td>\n",
- " <td>11.3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29156</th>\n",
- " <td>2020-02-25T22:00:00+01:00</td>\n",
- " <td>100980.0</td>\n",
- " <td>60.0</td>\n",
- " <td>3.0</td>\n",
- " <td>280.0</td>\n",
- " <td>8.0</td>\n",
- " <td>273.65</td>\n",
- " <td>51.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98120.0</td>\n",
- " <td>11.3</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>10.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29157</th>\n",
- " <td>2020-02-26T01:00:00+01:00</td>\n",
- " <td>101040.0</td>\n",
- " <td>30.0</td>\n",
- " <td>2.0</td>\n",
- " <td>230.0</td>\n",
- " <td>2.8</td>\n",
- " <td>275.65</td>\n",
- " <td>69.0</td>\n",
- " <td>25.0</td>\n",
- " <td>98150.0</td>\n",
- " <td>10.7</td>\n",
- " <td>-0.1</td>\n",
- " <td>-0.1</td>\n",
- " <td>7.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29158</th>\n",
- " <td>2020-02-26T04:00:00+01:00</td>\n",
- " <td>101060.0</td>\n",
- " <td>-10.0</td>\n",
- " <td>8.0</td>\n",
- " <td>230.0</td>\n",
- " <td>3.0</td>\n",
- " <td>275.85</td>\n",
- " <td>86.0</td>\n",
- " <td>25.0</td>\n",
- " <td>98140.0</td>\n",
- " <td>13.6</td>\n",
- " <td>0.4</td>\n",
- " <td>1.8</td>\n",
- " <td>4.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29159</th>\n",
- " <td>2020-02-26T07:00:00+01:00</td>\n",
- " <td>100940.0</td>\n",
- " <td>-110.0</td>\n",
- " <td>6.0</td>\n",
- " <td>210.0</td>\n",
- " <td>3.3</td>\n",
- " <td>274.85</td>\n",
- " <td>78.0</td>\n",
- " <td>21.0</td>\n",
- " <td>98030.0</td>\n",
- " <td>7.4</td>\n",
- " <td>-0.1</td>\n",
- " <td>-0.1</td>\n",
- " <td>5.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29160</th>\n",
- " <td>2020-02-26T10:00:00+01:00</td>\n",
- " <td>101100.0</td>\n",
- " <td>160.0</td>\n",
- " <td>3.0</td>\n",
- " <td>230.0</td>\n",
- " <td>6.8</td>\n",
- " <td>274.45</td>\n",
- " <td>74.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98190.0</td>\n",
- " <td>10.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>5.6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29161</th>\n",
- " <td>2020-02-26T13:00:00+01:00</td>\n",
- " <td>101200.0</td>\n",
- " <td>100.0</td>\n",
- " <td>3.0</td>\n",
- " <td>310.0</td>\n",
- " <td>10.3</td>\n",
- " <td>270.55</td>\n",
- " <td>52.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98290.0</td>\n",
- " <td>19.5</td>\n",
- " <td>0.0</td>\n",
- " <td>-0.1</td>\n",
- " <td>6.6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29162</th>\n",
- " <td>2020-02-26T16:00:00+01:00</td>\n",
- " <td>101290.0</td>\n",
- " <td>100.0</td>\n",
- " <td>3.0</td>\n",
- " <td>310.0</td>\n",
- " <td>8.9</td>\n",
- " <td>270.55</td>\n",
- " <td>47.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98390.0</td>\n",
- " <td>14.3</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>8.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29163</th>\n",
- " <td>2020-02-26T19:00:00+01:00</td>\n",
- " <td>101550.0</td>\n",
- " <td>230.0</td>\n",
- " <td>2.0</td>\n",
- " <td>300.0</td>\n",
- " <td>2.8</td>\n",
- " <td>272.05</td>\n",
- " <td>64.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98620.0</td>\n",
- " <td>7.4</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>5.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29164</th>\n",
- " <td>2020-02-26T22:00:00+01:00</td>\n",
- " <td>101780.0</td>\n",
- " <td>200.0</td>\n",
- " <td>2.0</td>\n",
- " <td>50.0</td>\n",
- " <td>3.2</td>\n",
- " <td>274.05</td>\n",
- " <td>84.0</td>\n",
- " <td>1.0</td>\n",
- " <td>98820.0</td>\n",
- " <td>8.2</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>3.3</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " date pmer tend cod_tend dd ff \\\n",
- "29145 2020-02-24T13:00:00+01:00 102380.0 -220.0 8.0 120.0 1.6 \n",
- "29146 2020-02-24T16:00:00+01:00 101990.0 -350.0 6.0 110.0 1.6 \n",
- "29147 2020-02-24T19:00:00+01:00 101800.0 -220.0 6.0 150.0 2.9 \n",
- "29148 2020-02-24T22:00:00+01:00 101740.0 -80.0 6.0 170.0 1.8 \n",
- "29149 2020-02-25T01:00:00+01:00 101640.0 -150.0 8.0 170.0 2.5 \n",
- "29150 2020-02-25T04:00:00+01:00 101450.0 -200.0 6.0 150.0 3.7 \n",
- "29151 2020-02-25T07:00:00+01:00 101530.0 60.0 3.0 30.0 4.0 \n",
- "29152 2020-02-25T10:00:00+01:00 101490.0 -20.0 8.0 200.0 1.8 \n",
- "29153 2020-02-25T13:00:00+01:00 101330.0 -140.0 8.0 150.0 3.8 \n",
- "29154 2020-02-25T16:00:00+01:00 100990.0 -290.0 6.0 140.0 4.4 \n",
- "29155 2020-02-25T19:00:00+01:00 100910.0 -90.0 5.0 260.0 4.3 \n",
- "29156 2020-02-25T22:00:00+01:00 100980.0 60.0 3.0 280.0 8.0 \n",
- "29157 2020-02-26T01:00:00+01:00 101040.0 30.0 2.0 230.0 2.8 \n",
- "29158 2020-02-26T04:00:00+01:00 101060.0 -10.0 8.0 230.0 3.0 \n",
- "29159 2020-02-26T07:00:00+01:00 100940.0 -110.0 6.0 210.0 3.3 \n",
- "29160 2020-02-26T10:00:00+01:00 101100.0 160.0 3.0 230.0 6.8 \n",
- "29161 2020-02-26T13:00:00+01:00 101200.0 100.0 3.0 310.0 10.3 \n",
- "29162 2020-02-26T16:00:00+01:00 101290.0 100.0 3.0 310.0 8.9 \n",
- "29163 2020-02-26T19:00:00+01:00 101550.0 230.0 2.0 300.0 2.8 \n",
- "29164 2020-02-26T22:00:00+01:00 101780.0 200.0 2.0 50.0 3.2 \n",
- "\n",
- " td u ww pres rafper rr1 rr3 tc \n",
- "29145 281.15 59.0 0.0 99540.0 3.7 0.0 0.0 16.0 \n",
- "29146 281.55 50.0 3.0 99190.0 3.3 0.0 0.0 19.1 \n",
- "29147 280.05 55.0 3.0 98970.0 4.1 0.0 0.0 15.9 \n",
- "29148 280.35 67.0 2.0 98890.0 4.3 0.0 0.0 13.2 \n",
- "29149 278.85 83.0 2.0 98740.0 4.7 0.0 0.0 8.4 \n",
- "29150 277.75 87.0 2.0 98540.0 4.8 0.0 0.0 6.6 \n",
- "29151 276.95 92.0 3.0 98600.0 6.1 0.0 0.0 5.0 \n",
- "29152 277.55 87.0 3.0 98580.0 5.5 0.0 0.0 6.4 \n",
- "29153 278.95 85.0 21.0 98440.0 7.1 0.6 2.0 8.2 \n",
- "29154 279.55 69.0 3.0 98150.0 7.2 0.0 0.0 11.9 \n",
- "29155 278.95 69.0 25.0 98060.0 8.4 -0.1 -0.1 11.3 \n",
- "29156 273.65 51.0 1.0 98120.0 11.3 0.0 0.0 10.2 \n",
- "29157 275.65 69.0 25.0 98150.0 10.7 -0.1 -0.1 7.8 \n",
- "29158 275.85 86.0 25.0 98140.0 13.6 0.4 1.8 4.8 \n",
- "29159 274.85 78.0 21.0 98030.0 7.4 -0.1 -0.1 5.2 \n",
- "29160 274.45 74.0 1.0 98190.0 10.0 0.0 0.0 5.6 \n",
- "29161 270.55 52.0 1.0 98290.0 19.5 0.0 -0.1 6.6 \n",
- "29162 270.55 47.0 1.0 98390.0 14.3 0.0 0.0 8.0 \n",
- "29163 272.05 64.0 1.0 98620.0 7.4 0.0 0.0 5.2 \n",
- "29164 274.05 84.0 1.0 98820.0 8.2 0.0 0.0 3.3 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Shape is : (29165, 14)\n"
- ]
- }
- ],
- "source": [
- "# ---- Count the na values by columns\n",
- "dataset_na = df.isna().sum().tolist()\n",
- "dataset_cols = df.columns.tolist()\n",
- "dataset_desc = [ code2desc[c] for c in dataset_cols ]\n",
- "\n",
- "# ---- Show all of that\n",
- "df_desc=pd.DataFrame({'Columns':dataset_cols, 'Description':dataset_desc, 'Na':dataset_na})\n",
- "pwk.subtitle('Dataset columns :')\n",
- "display(df_desc.style.set_properties(**{'text-align': 'left'}))\n",
- "\n",
- "pwk.subtitle('Have a look :')\n",
- "display(df.tail(20))\n",
- "print('Shape is : ', df.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Have a look (1 month)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:46.247361Z",
- "iopub.status.busy": "2021-03-09T21:35:46.246955Z",
- "iopub.status.idle": "2021-03-09T21:35:49.233422Z",
- "shell.execute_reply": "2021-03-09T21:35:49.233752Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/SYNOP/figs/SYNOP1-01-one-month</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1152x1440 with 13 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "i=random.randint(0,len(df)-240)\n",
- "df.iloc[i:i+240].plot(subplots=True, fontsize=12, figsize=(16,20))\n",
- "pwk.save_fig('01-one-month')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Save it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:49.238196Z",
- "iopub.status.busy": "2021-03-09T21:35:49.237883Z",
- "iopub.status.idle": "2021-03-09T21:35:49.430709Z",
- "shell.execute_reply": "2021-03-09T21:35:49.430377Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset saved. (3.0 Mo)\n",
- "Synop description saved.\n"
- ]
- }
- ],
- "source": [
- "# ---- Save it\n",
- "#\n",
- "pwk.mkdir(output_dir)\n",
- "\n",
- "filedata = f'{output_dir}/{dataset_filename}'\n",
- "filedesc = f'{output_dir}/{description_filename}'\n",
- "\n",
- "df.to_csv(filedata, sep=';', index=False)\n",
- "size=os.path.getsize(filedata)/(1024*1024)\n",
- "print(f'Dataset saved. ({size:0.1f} Mo)')\n",
- "\n",
- "with open(filedesc, 'w', encoding='utf-8') as f:\n",
- " json.dump(code2desc, f, indent=4)\n",
- "print('Synop description saved.')\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:49.433825Z",
- "iopub.status.busy": "2021-03-09T21:35:49.433450Z",
- "iopub.status.idle": "2021-03-09T21:35:49.437267Z",
- "shell.execute_reply": "2021-03-09T21:35:49.436923Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Tuesday 09 March 2021, 22:35:49\n",
- "Duration is : 00:00:04 719ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
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- "nbformat": 4,
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-}
diff --git a/SYNOP/SYNOP2-First-predictions.ipynb b/SYNOP/SYNOP2-First-predictions.ipynb
index 86c0c0e..1d26012 100644
--- a/SYNOP/SYNOP2-First-predictions.ipynb
+++ b/SYNOP/SYNOP2-First-predictions.ipynb
@@ -83,7 +83,8 @@
"train_prop = .8 # Percentage for train (the rest being for the test)\n",
"sequence_len = 16\n",
"batch_size = 32\n",
- "epochs = 10"
+ "epochs = 10\n",
+ "fit_verbosity = 1 # 0 = silent, 1 = progress bar, 2 = one line per epoch"
]
},
{
@@ -99,7 +100,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.override('scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs')"
+ "pwk.override('scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs', 'fit_verbosity')"
]
},
{
@@ -266,8 +267,8 @@
"pwk.chrono_start()\n",
"\n",
"history=model.fit(train_generator, \n",
- " epochs=epochs, \n",
- " verbose=1,\n",
+ " epochs = epochs, \n",
+ " verbose = fit_verbosity,\n",
" validation_data = test_generator,\n",
" callbacks = [bestmodel_callback])\n",
"\n",
@@ -396,9 +397,11 @@
}
],
"metadata": {
+ "interpreter": {
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
+ },
"kernelspec": {
- "display_name": "Python 3",
- "language": "python",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
@@ -411,7 +414,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.7"
}
},
"nbformat": 4,
diff --git a/SYNOP/SYNOP2-First-predictions==done==.ipynb b/SYNOP/SYNOP2-First-predictions==done==.ipynb
deleted file mode 100644
index c64b600..0000000
--- a/SYNOP/SYNOP2-First-predictions==done==.ipynb
+++ /dev/null
@@ -1,16422 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [SYNOP2] - First predictions at 3h\n",
- "<!-- DESC --> Episode 2 : RNN training session for weather prediction attempt at 3h\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Make a simple prediction (3h)\n",
- " - Understanding the use of a recurrent neural network\n",
- "\n",
- "\n",
- "SYNOP meteorological data, available at: https://public.opendatasoft.com\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Read our dataset\n",
- " - Select our data and normalize it\n",
- " - Doing our training\n",
- " - Making simple predictions\n",
- "\n",
- "## Step 1 - Import and init\n",
- "### 1.1 - Python"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:51.332259Z",
- "iopub.status.busy": "2021-03-09T21:35:51.331936Z",
- "iopub.status.idle": "2021-03-09T21:35:52.686328Z",
- "shell.execute_reply": "2021-03-09T21:35:52.686643Z"
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- "}\n",
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- " margin: 0.2em;\n",
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- " font-size:0.8em;\n",
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- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.19\n",
- "Notebook id : SYNOP2\n",
- "Run time : Tuesday 09 March 2021, 22:35:52\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Run dir : ./run/SYNOP\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/SYNOP/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "from tensorflow.keras.callbacks import TensorBoard\n",
- "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
- "\n",
- "import numpy as np\n",
- "import math, random\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import pandas as pd\n",
- "import h5py, json\n",
- "import os,time,sys\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/SYNOP'\n",
- "datasets_dir = pwk.init('SYNOP2', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:52.690120Z",
- "iopub.status.busy": "2021-03-09T21:35:52.689812Z",
- "iopub.status.idle": "2021-03-09T21:35:52.691978Z",
- "shell.execute_reply": "2021-03-09T21:35:52.691640Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- About dataset (no need to change)\n",
- "#\n",
- "dataset_dir = './data' # Enhanced dataset is very small, so ./data in a good choice :-)\n",
- "dataset_filename = 'synop-LYS.csv'\n",
- "schema_filename = 'synop.json'\n",
- "features = ['tend', 'cod_tend', 'dd', 'ff', 'td', 'u', 'ww', 'pres', 'rafper', 'rr1', 'rr3', 'tc']\n",
- "features_len = len(features)\n",
- "\n",
- "# ---- About training (Can be changed !)\n",
- "#\n",
- "scale = 1 # Percentage of dataset to be used (1=all)\n",
- "train_prop = .8 # Percentage for train (the rest being for the test)\n",
- "sequence_len = 16\n",
- "batch_size = 32\n",
- "epochs = 10"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:52.694714Z",
- "iopub.status.busy": "2021-03-09T21:35:52.694407Z",
- "iopub.status.idle": "2021-03-09T21:35:52.696773Z",
- "shell.execute_reply": "2021-03-09T21:35:52.696455Z"
- }
- },
- "outputs": [],
- "source": [
- "pwk.override('scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Read and prepare dataset\n",
- "### 2.1 - Read it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:52.702215Z",
- "iopub.status.busy": "2021-03-09T21:35:52.701833Z",
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- "shell.execute_reply": "2021-03-09T21:35:52.867424Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**Train dataset example :**"
- ],
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- " <th>tend</th>\n",
- " <th>cod_tend</th>\n",
- " <th>dd</th>\n",
- " <th>ff</th>\n",
- " <th>td</th>\n",
- " <th>u</th>\n",
- " <th>ww</th>\n",
- " <th>pres</th>\n",
- " <th>rafper</th>\n",
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- " <td>88.0</td>\n",
- " <td>60.0</td>\n",
- " <td>96250.0</td>\n",
- " <td>4.1</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>7.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>-150.0</td>\n",
- " <td>6.0</td>\n",
- " <td>60.0</td>\n",
- " <td>1.0</td>\n",
- " <td>278.65</td>\n",
- " <td>93.0</td>\n",
- " <td>61.0</td>\n",
- " <td>96100.0</td>\n",
- " <td>2.6</td>\n",
- " <td>0.2</td>\n",
- " <td>0.6</td>\n",
- " <td>6.6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>10.0</td>\n",
- " <td>3.0</td>\n",
- " <td>280.0</td>\n",
- " <td>2.1</td>\n",
- " <td>278.85</td>\n",
- " <td>95.0</td>\n",
- " <td>58.0</td>\n",
- " <td>96110.0</td>\n",
- " <td>2.6</td>\n",
- " <td>0.0</td>\n",
- " <td>0.4</td>\n",
- " <td>6.4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>230.0</td>\n",
- " <td>3.0</td>\n",
- " <td>310.0</td>\n",
- " <td>2.6</td>\n",
- " <td>279.15</td>\n",
- " <td>96.0</td>\n",
- " <td>50.0</td>\n",
- " <td>96340.0</td>\n",
- " <td>5.7</td>\n",
- " <td>0.0</td>\n",
- " <td>3.0</td>\n",
- " <td>6.6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>280.0</td>\n",
- " <td>1.0</td>\n",
- " <td>330.0</td>\n",
- " <td>4.6</td>\n",
- " <td>278.15</td>\n",
- " <td>94.0</td>\n",
- " <td>21.0</td>\n",
- " <td>96620.0</td>\n",
- " <td>8.7</td>\n",
- " <td>0.4</td>\n",
- " <td>0.8</td>\n",
- " <td>5.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>480.0</td>\n",
- " <td>3.0</td>\n",
- " <td>350.0</td>\n",
- " <td>5.1</td>\n",
- " <td>276.95</td>\n",
- " <td>91.0</td>\n",
- " <td>60.0</td>\n",
- " <td>97100.0</td>\n",
- " <td>8.2</td>\n",
- " <td>0.2</td>\n",
- " <td>0.4</td>\n",
- " <td>5.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>530.0</td>\n",
- " <td>2.0</td>\n",
- " <td>350.0</td>\n",
- " <td>3.1</td>\n",
- " <td>274.05</td>\n",
- " <td>83.0</td>\n",
- " <td>21.0</td>\n",
- " <td>97630.0</td>\n",
- " <td>7.2</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>3.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>450.0</td>\n",
- " <td>2.0</td>\n",
- " <td>340.0</td>\n",
- " <td>6.2</td>\n",
- " <td>272.15</td>\n",
- " <td>81.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98080.0</td>\n",
- " <td>9.3</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>280.0</td>\n",
- " <td>1.0</td>\n",
- " <td>320.0</td>\n",
- " <td>6.2</td>\n",
- " <td>270.15</td>\n",
- " <td>74.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98360.0</td>\n",
- " <td>10.3</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1.1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>220.0</td>\n",
- " <td>1.0</td>\n",
- " <td>290.0</td>\n",
- " <td>2.6</td>\n",
- " <td>269.65</td>\n",
- " <td>72.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98580.0</td>\n",
- " <td>5.1</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>100.0</td>\n",
- " <td>1.0</td>\n",
- " <td>350.0</td>\n",
- " <td>3.1</td>\n",
- " <td>270.45</td>\n",
- " <td>79.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98680.0</td>\n",
- " <td>4.1</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>300.0</td>\n",
- " <td>3.0</td>\n",
- " <td>350.0</td>\n",
- " <td>5.1</td>\n",
- " <td>268.55</td>\n",
- " <td>70.0</td>\n",
- " <td>2.0</td>\n",
- " <td>98980.0</td>\n",
- " <td>6.7</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>-0.3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>130.0</td>\n",
- " <td>1.0</td>\n",
- " <td>10.0</td>\n",
- " <td>4.6</td>\n",
- " <td>267.45</td>\n",
- " <td>60.0</td>\n",
- " <td>2.0</td>\n",
- " <td>99110.0</td>\n",
- " <td>7.7</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>150.0</td>\n",
- " <td>3.0</td>\n",
- " <td>10.0</td>\n",
- " <td>5.7</td>\n",
- " <td>267.45</td>\n",
- " <td>59.0</td>\n",
- " <td>2.0</td>\n",
- " <td>99260.0</td>\n",
- " <td>8.7</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>140.0</td>\n",
- " <td>1.0</td>\n",
- " <td>50.0</td>\n",
- " <td>2.6</td>\n",
- " <td>268.15</td>\n",
- " <td>70.0</td>\n",
- " <td>2.0</td>\n",
- " <td>99400.0</td>\n",
- " <td>5.7</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>-0.8</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " tend cod_tend dd ff td u ww pres rafper rr1 \\\n",
- "0 -120.0 6.0 0.0 0.0 278.75 88.0 60.0 96250.0 4.1 0.0 \n",
- "1 -150.0 6.0 60.0 1.0 278.65 93.0 61.0 96100.0 2.6 0.2 \n",
- "2 10.0 3.0 280.0 2.1 278.85 95.0 58.0 96110.0 2.6 0.0 \n",
- "3 230.0 3.0 310.0 2.6 279.15 96.0 50.0 96340.0 5.7 0.0 \n",
- "4 280.0 1.0 330.0 4.6 278.15 94.0 21.0 96620.0 8.7 0.4 \n",
- "5 480.0 3.0 350.0 5.1 276.95 91.0 60.0 97100.0 8.2 0.2 \n",
- "6 530.0 2.0 350.0 3.1 274.05 83.0 21.0 97630.0 7.2 0.0 \n",
- "7 450.0 2.0 340.0 6.2 272.15 81.0 2.0 98080.0 9.3 0.0 \n",
- "8 280.0 1.0 320.0 6.2 270.15 74.0 2.0 98360.0 10.3 0.0 \n",
- "9 220.0 1.0 290.0 2.6 269.65 72.0 2.0 98580.0 5.1 0.0 \n",
- "10 100.0 1.0 350.0 3.1 270.45 79.0 2.0 98680.0 4.1 0.0 \n",
- "11 300.0 3.0 350.0 5.1 268.55 70.0 2.0 98980.0 6.7 0.0 \n",
- "12 130.0 1.0 10.0 4.6 267.45 60.0 2.0 99110.0 7.7 0.0 \n",
- "13 150.0 3.0 10.0 5.7 267.45 59.0 2.0 99260.0 8.7 0.0 \n",
- "14 140.0 1.0 50.0 2.6 268.15 70.0 2.0 99400.0 5.7 0.0 \n",
- "\n",
- " rr3 tc \n",
- "0 0.0 7.5 \n",
- "1 0.6 6.6 \n",
- "2 0.4 6.4 \n",
- "3 3.0 6.6 \n",
- "4 0.8 5.9 \n",
- "5 0.4 5.2 \n",
- "6 0.0 3.5 \n",
- "7 0.0 1.9 \n",
- "8 0.0 1.1 \n",
- "9 0.0 1.0 \n",
- "10 0.0 0.5 \n",
- "11 0.0 -0.3 \n",
- "12 0.0 1.2 \n",
- "13 0.0 1.5 \n",
- "14 0.0 -0.8 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**After normalization :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_5113e_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >tend</th> <th class=\"col_heading level0 col1\" >cod_tend</th> <th class=\"col_heading level0 col2\" >dd</th> <th class=\"col_heading level0 col3\" >ff</th> <th class=\"col_heading level0 col4\" >td</th> <th class=\"col_heading level0 col5\" >u</th> <th class=\"col_heading level0 col6\" >ww</th> <th class=\"col_heading level0 col7\" >pres</th> <th class=\"col_heading level0 col8\" >rafper</th> <th class=\"col_heading level0 col9\" >rr1</th> <th class=\"col_heading level0 col10\" >rr3</th> <th class=\"col_heading level0 col11\" >tc</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_5113e_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_5113e_row0_col0\" class=\"data row0 col0\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col1\" class=\"data row0 col1\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col2\" class=\"data row0 col2\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col3\" class=\"data row0 col3\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col4\" class=\"data row0 col4\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col5\" class=\"data row0 col5\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col6\" class=\"data row0 col6\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col7\" class=\"data row0 col7\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col8\" class=\"data row0 col8\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col9\" class=\"data row0 col9\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col10\" class=\"data row0 col10\" >23332.00</td>\n",
- " <td id=\"T_5113e_row0_col11\" class=\"data row0 col11\" >23332.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5113e_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_5113e_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
- " <td id=\"T_5113e_row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
- " <td id=\"T_5113e_row1_col2\" class=\"data row1 col2\" >-0.00</td>\n",
- " <td id=\"T_5113e_row1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
- " <td id=\"T_5113e_row1_col4\" class=\"data row1 col4\" >0.00</td>\n",
- " <td id=\"T_5113e_row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
- " <td id=\"T_5113e_row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
- " <td id=\"T_5113e_row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
- " <td id=\"T_5113e_row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
- " <td id=\"T_5113e_row1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
- " <td id=\"T_5113e_row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_5113e_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5113e_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_5113e_row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_5113e_row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5113e_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_5113e_row3_col0\" class=\"data row3 col0\" >-6.79</td>\n",
- " <td id=\"T_5113e_row3_col1\" class=\"data row3 col1\" >-1.59</td>\n",
- " <td id=\"T_5113e_row3_col2\" class=\"data row3 col2\" >-1.74</td>\n",
- " <td id=\"T_5113e_row3_col3\" class=\"data row3 col3\" >-1.36</td>\n",
- " <td id=\"T_5113e_row3_col4\" class=\"data row3 col4\" >-5.22</td>\n",
- " <td id=\"T_5113e_row3_col5\" class=\"data row3 col5\" >-3.85</td>\n",
- " <td id=\"T_5113e_row3_col6\" class=\"data row3 col6\" >-0.53</td>\n",
- " <td id=\"T_5113e_row3_col7\" class=\"data row3 col7\" >-4.97</td>\n",
- " <td id=\"T_5113e_row3_col8\" class=\"data row3 col8\" >-1.62</td>\n",
- " <td id=\"T_5113e_row3_col9\" class=\"data row3 col9\" >-0.32</td>\n",
- " <td id=\"T_5113e_row3_col10\" class=\"data row3 col10\" >-0.27</td>\n",
- " <td id=\"T_5113e_row3_col11\" class=\"data row3 col11\" >-3.04</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5113e_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_5113e_row4_col0\" class=\"data row4 col0\" >-0.63</td>\n",
- " <td id=\"T_5113e_row4_col1\" class=\"data row4 col1\" >-0.85</td>\n",
- " <td id=\"T_5113e_row4_col2\" class=\"data row4 col2\" >-0.62</td>\n",
- " <td id=\"T_5113e_row4_col3\" class=\"data row4 col3\" >-0.75</td>\n",
- " <td id=\"T_5113e_row4_col4\" class=\"data row4 col4\" >-0.72</td>\n",
- " <td id=\"T_5113e_row4_col5\" class=\"data row4 col5\" >-0.68</td>\n",
- " <td id=\"T_5113e_row4_col6\" class=\"data row4 col6\" >-0.42</td>\n",
- " <td id=\"T_5113e_row4_col7\" class=\"data row4 col7\" >-0.55</td>\n",
- " <td id=\"T_5113e_row4_col8\" class=\"data row4 col8\" >-0.69</td>\n",
- " <td id=\"T_5113e_row4_col9\" class=\"data row4 col9\" >-0.16</td>\n",
- " <td id=\"T_5113e_row4_col10\" class=\"data row4 col10\" >-0.20</td>\n",
- " <td id=\"T_5113e_row4_col11\" class=\"data row4 col11\" >-0.75</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5113e_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_5113e_row5_col0\" class=\"data row5 col0\" >-0.00</td>\n",
- " <td id=\"T_5113e_row5_col1\" class=\"data row5 col1\" >-0.48</td>\n",
- " <td id=\"T_5113e_row5_col2\" class=\"data row5 col2\" >-0.11</td>\n",
- " <td id=\"T_5113e_row5_col3\" class=\"data row5 col3\" >-0.19</td>\n",
- " <td id=\"T_5113e_row5_col4\" class=\"data row5 col4\" >0.04</td>\n",
- " <td id=\"T_5113e_row5_col5\" class=\"data row5 col5\" >0.21</td>\n",
- " <td id=\"T_5113e_row5_col6\" class=\"data row5 col6\" >-0.42</td>\n",
- " <td id=\"T_5113e_row5_col7\" class=\"data row5 col7\" >0.04</td>\n",
- " <td id=\"T_5113e_row5_col8\" class=\"data row5 col8\" >-0.29</td>\n",
- " <td id=\"T_5113e_row5_col9\" class=\"data row5 col9\" >-0.16</td>\n",
- " <td id=\"T_5113e_row5_col10\" class=\"data row5 col10\" >-0.20</td>\n",
- " <td id=\"T_5113e_row5_col11\" class=\"data row5 col11\" >-0.01</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5113e_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_5113e_row6_col0\" class=\"data row6 col0\" >0.63</td>\n",
- " <td id=\"T_5113e_row6_col1\" class=\"data row6 col1\" >0.99</td>\n",
- " <td id=\"T_5113e_row6_col2\" class=\"data row6 col2\" >1.10</td>\n",
- " <td id=\"T_5113e_row6_col3\" class=\"data row6 col3\" >0.50</td>\n",
- " <td id=\"T_5113e_row6_col4\" class=\"data row6 col4\" >0.77</td>\n",
- " <td id=\"T_5113e_row6_col5\" class=\"data row6 col5\" >0.82</td>\n",
- " <td id=\"T_5113e_row6_col6\" class=\"data row6 col6\" >-0.37</td>\n",
- " <td id=\"T_5113e_row6_col7\" class=\"data row6 col7\" >0.62</td>\n",
- " <td id=\"T_5113e_row6_col8\" class=\"data row6 col8\" >0.51</td>\n",
- " <td id=\"T_5113e_row6_col9\" class=\"data row6 col9\" >-0.16</td>\n",
- " <td id=\"T_5113e_row6_col10\" class=\"data row6 col10\" >-0.20</td>\n",
- " <td id=\"T_5113e_row6_col11\" class=\"data row6 col11\" >0.71</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5113e_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_5113e_row7_col0\" class=\"data row7 col0\" >7.14</td>\n",
- " <td id=\"T_5113e_row7_col1\" class=\"data row7 col1\" >1.36</td>\n",
- " <td id=\"T_5113e_row7_col2\" class=\"data row7 col2\" >1.35</td>\n",
- " <td id=\"T_5113e_row7_col3\" class=\"data row7 col3\" >6.24</td>\n",
- " <td id=\"T_5113e_row7_col4\" class=\"data row7 col4\" >2.44</td>\n",
- " <td id=\"T_5113e_row7_col5\" class=\"data row7 col5\" >1.59</td>\n",
- " <td id=\"T_5113e_row7_col6\" class=\"data row7 col6\" >4.45</td>\n",
- " <td id=\"T_5113e_row7_col7\" class=\"data row7 col7\" >3.08</td>\n",
- " <td id=\"T_5113e_row7_col8\" class=\"data row7 col8\" >6.25</td>\n",
- " <td id=\"T_5113e_row7_col9\" class=\"data row7 col9\" >29.82</td>\n",
- " <td id=\"T_5113e_row7_col10\" class=\"data row7 col10\" >31.17</td>\n",
- " <td id=\"T_5113e_row7_col11\" class=\"data row7 col11\" >3.07</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f6a827dd250>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Shapes :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset : (29165, 14)\n",
- "Train dataset : (23332, 12)\n",
- "Test dataset : (5833, 12)\n"
- ]
- }
- ],
- "source": [
- "# ---- Read dataset from ./data\n",
- "\n",
- "df = pd.read_csv(f'{dataset_dir}/{dataset_filename}', header=0, sep=';')\n",
- "\n",
- "# ---- Scaling\n",
- "\n",
- "df = df[:int(scale*len(df))]\n",
- "train_len=int(train_prop*len(df))\n",
- "\n",
- "# ---- Train / Test\n",
- "dataset_train = df.loc[ :train_len-1, features ]\n",
- "dataset_test = df.loc[train_len:, features ]\n",
- "pwk.subtitle('Train dataset example :')\n",
- "display(dataset_train.head(15))\n",
- "\n",
- "# ---- Normalize, and convert to numpy array\n",
- "\n",
- "mean = dataset_train.mean()\n",
- "std = dataset_train.std()\n",
- "dataset_train = (dataset_train - mean) / std\n",
- "dataset_test = (dataset_test - mean) / std\n",
- "\n",
- "pwk.subtitle('After normalization :')\n",
- "display(dataset_train.describe().style.format(\"{0:.2f}\"))\n",
- "\n",
- "dataset_train = dataset_train.to_numpy()\n",
- "dataset_test = dataset_test.to_numpy()\n",
- "\n",
- "pwk.subtitle('Shapes :')\n",
- "print('Dataset : ',df.shape)\n",
- "print('Train dataset : ',dataset_train.shape)\n",
- "print('Test dataset : ',dataset_test.shape)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Prepare data generator"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:52.872217Z",
- "iopub.status.busy": "2021-03-09T21:35:52.871802Z",
- "iopub.status.idle": "2021-03-09T21:35:52.881309Z",
- "shell.execute_reply": "2021-03-09T21:35:52.881555Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**About the splitting of our dataset :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nombre de train batchs disponibles : 729\n",
- "batch x shape : (32, 16, 12)\n",
- "batch y shape : (32, 12)\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**What a batch looks like (x) :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[-1.087 0.623 -1.743 -1.361 -0.212 0.928 2.554 -3.533 -0.555 -0.156 -0.199 -0.624]\n",
- " [-1.358 0.623 -1.227 -0.957 -0.229 1.206 2.605 -3.733 -0.946 0.17 0.219 -0.735]\n",
- " [ 0.089 -0.482 0.666 -0.512 -0.195 1.317 2.451 -3.72 -0.946 -0.156 0.08 -0.76 ]\n",
- " [ 2.079 -0.482 0.924 -0.31 -0.144 1.372 2.04 -3.413 -0.137 -0.156 1.892 -0.735]\n",
- " [ 2.531 -1.219 1.096 0.499 -0.313 1.261 0.552 -3.04 0.645 0.495 0.358 -0.821]\n",
- " [ 4.34 -0.482 1.268 0.701 -0.517 1.095 2.554 -2.401 0.515 0.17 0.08 -0.907]\n",
- " [ 4.792 -0.85 1.268 -0.107 -1.01 0.65 0.552 -1.694 0.254 -0.156 -0.199 -1.117]\n",
- " [ 4.069 -0.85 1.182 1.146 -1.333 0.539 -0.424 -1.094 0.802 -0.156 -0.199 -1.314]\n",
- " [ 2.531 -1.219 1.01 1.146 -1.673 0.15 -0.424 -0.721 1.063 -0.156 -0.199 -1.412]\n",
- " [ 1.988 -1.219 0.752 -0.31 -1.758 0.039 -0.424 -0.428 -0.294 -0.156 -0.199 -1.425]\n",
- " [ 0.903 -1.219 1.268 -0.107 -1.622 0.428 -0.424 -0.295 -0.555 -0.156 -0.199 -1.486]\n",
- " [ 2.712 -0.482 1.268 0.701 -1.944 -0.072 -0.424 0.105 0.123 -0.156 -0.199 -1.585]\n",
- " [ 1.174 -1.219 -1.657 0.499 -2.131 -0.628 -0.424 0.278 0.384 -0.156 -0.199 -1.4 ]\n",
- " [ 1.355 -0.482 -1.657 0.944 -2.131 -0.683 -0.424 0.478 0.645 -0.156 -0.199 -1.363]\n",
- " [ 1.265 -1.219 -1.313 -0.31 -2.012 -0.072 -0.424 0.665 -0.137 -0.156 -0.199 -1.646]\n",
- " [-0.182 0.255 0.666 -0.957 -2.063 0.428 -0.424 0.638 -1.233 -0.156 -0.199 -1.856]]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**What a batch looks like (y) :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ 0.179 -1.219 0.58 -0.755 -2.063 0.65 -0.424 0.665 -1.233 -0.156 -0.199 -1.93 ]\n"
- ]
- }
- ],
- "source": [
- "# ---- Train generator\n",
- "train_generator = TimeseriesGenerator(dataset_train, dataset_train, length=sequence_len, batch_size=batch_size)\n",
- "test_generator = TimeseriesGenerator(dataset_test, dataset_test, length=sequence_len, batch_size=batch_size)\n",
- "\n",
- "# ---- About\n",
- "\n",
- "pwk.subtitle('About the splitting of our dataset :')\n",
- "\n",
- "x,y=train_generator[0]\n",
- "print(f'Nombre de train batchs disponibles : ', len(train_generator))\n",
- "print('batch x shape : ',x.shape)\n",
- "print('batch y shape : ',y.shape)\n",
- "\n",
- "x,y=train_generator[0]\n",
- "pwk.subtitle('What a batch looks like (x) :')\n",
- "pwk.np_print(x[0] )\n",
- "pwk.subtitle('What a batch looks like (y) :')\n",
- "pwk.np_print(y[0])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Create a model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:52.885097Z",
- "iopub.status.busy": "2021-03-09T21:35:52.884713Z",
- "iopub.status.idle": "2021-03-09T21:35:52.979074Z",
- "shell.execute_reply": "2021-03-09T21:35:52.979326Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "lstm (LSTM) (None, 100) 45200 \n",
- "_________________________________________________________________\n",
- "dropout (Dropout) (None, 100) 0 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 12) 1212 \n",
- "=================================================================\n",
- "Total params: 46,412\n",
- "Trainable params: 46,412\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = keras.models.Sequential()\n",
- "model.add( keras.layers.InputLayer(input_shape=(sequence_len, features_len)) )\n",
- "model.add( keras.layers.LSTM(100, activation='relu') )\n",
- "model.add( keras.layers.Dropout(0.2) )\n",
- "model.add( keras.layers.Dense(features_len) )\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Compile and train"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.1 - Callback"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:52.982534Z",
- "iopub.status.busy": "2021-03-09T21:35:52.982232Z",
- "iopub.status.idle": "2021-03-09T21:35:52.985097Z",
- "shell.execute_reply": "2021-03-09T21:35:52.984792Z"
- }
- },
- "outputs": [],
- "source": [
- "pwk.mkdir(run_dir)\n",
- "save_dir = f'{run_dir}/best_model.h5'\n",
- "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.2 - Compile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:52.991776Z",
- "iopub.status.busy": "2021-03-09T21:35:52.991077Z",
- "iopub.status.idle": "2021-03-09T21:35:52.996508Z",
- "shell.execute_reply": "2021-03-09T21:35:52.996221Z"
- }
- },
- "outputs": [],
- "source": [
- "model.compile(optimizer='adam', \n",
- " loss='mse', \n",
- " metrics = ['mae'] )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.3 - Fit\n",
- "6' with a CPU (laptop) \n",
- "2' with a GPU"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:35:52.999970Z",
- "iopub.status.busy": "2021-03-09T21:35:52.999667Z",
- "iopub.status.idle": "2021-03-09T21:37:55.885925Z",
- "shell.execute_reply": "2021-03-09T21:37:55.885444Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/729 [..............................] - ETA: 0s - loss: 0.7364 - mae: 0.6778"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/729 [..............................] - ETA: 9s - loss: 0.8963 - mae: 0.7027"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/729 [..............................] - ETA: 10s - loss: 1.1341 - mae: 0.7488"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 11/729 [..............................] - ETA: 10s - loss: 1.0436 - mae: 0.7371"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 15/729 [..............................] - ETA: 9s - loss: 0.9259 - mae: 0.7013 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 19/729 [..............................] - ETA: 9s - loss: 0.8821 - mae: 0.6889"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 23/729 [..............................] - ETA: 9s - loss: 0.9174 - mae: 0.6970"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 27/729 [>.............................] - ETA: 9s - loss: 0.9914 - mae: 0.6934"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 31/729 [>.............................] - ETA: 9s - loss: 0.9765 - mae: 0.6928"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 35/729 [>.............................] - ETA: 9s - loss: 0.9913 - mae: 0.6988"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 39/729 [>.............................] - ETA: 9s - loss: 0.9518 - mae: 0.6900"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 43/729 [>.............................] - ETA: 9s - loss: 0.9111 - mae: 0.6777"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 47/729 [>.............................] - ETA: 9s - loss: 0.8887 - mae: 0.6724"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 52/729 [=>............................] - ETA: 8s - loss: 0.8632 - mae: 0.6651"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 57/729 [=>............................] - ETA: 8s - loss: 0.8352 - mae: 0.6555"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 62/729 [=>............................] - ETA: 8s - loss: 0.8383 - mae: 0.6508"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 67/729 [=>............................] - ETA: 8s - loss: 0.8221 - mae: 0.6443"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 72/729 [=>............................] - ETA: 8s - loss: 0.8062 - mae: 0.6375"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 77/729 [==>...........................] - ETA: 8s - loss: 0.7857 - mae: 0.6295"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 82/729 [==>...........................] - ETA: 8s - loss: 0.8029 - mae: 0.6286"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 87/729 [==>...........................] - ETA: 8s - loss: 0.7928 - mae: 0.6239"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 92/729 [==>...........................] - ETA: 8s - loss: 0.7942 - mae: 0.6217"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 97/729 [==>...........................] - ETA: 7s - loss: 0.7815 - mae: 0.6182"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "102/729 [===>..........................] - ETA: 7s - loss: 0.7745 - mae: 0.6157"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "107/729 [===>..........................] - ETA: 7s - loss: 0.8002 - mae: 0.6152"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "112/729 [===>..........................] - ETA: 7s - loss: 0.7972 - mae: 0.6129"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "117/729 [===>..........................] - ETA: 7s - loss: 0.7828 - mae: 0.6082"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "122/729 [====>.........................] - ETA: 7s - loss: 0.8097 - mae: 0.6080"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "127/729 [====>.........................] - ETA: 7s - loss: 0.7992 - mae: 0.6049"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "132/729 [====>.........................] - ETA: 7s - loss: 0.7877 - mae: 0.6009"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "137/729 [====>.........................] - ETA: 7s - loss: 0.7761 - mae: 0.5974"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "142/729 [====>.........................] - ETA: 7s - loss: 0.7647 - mae: 0.5936"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "147/729 [=====>........................] - ETA: 7s - loss: 0.7607 - mae: 0.5921"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "152/729 [=====>........................] - ETA: 7s - loss: 0.7548 - mae: 0.5900"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "157/729 [=====>........................] - ETA: 7s - loss: 0.7486 - mae: 0.5875"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "162/729 [=====>........................] - ETA: 6s - loss: 0.7424 - mae: 0.5856"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "167/729 [=====>........................] - ETA: 6s - loss: 0.7409 - mae: 0.5843"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "172/729 [======>.......................] - ETA: 6s - loss: 0.7349 - mae: 0.5827"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "177/729 [======>.......................] - ETA: 6s - loss: 0.7289 - mae: 0.5805"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "182/729 [======>.......................] - ETA: 6s - loss: 0.7253 - mae: 0.5787"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "187/729 [======>.......................] - ETA: 6s - loss: 0.7208 - mae: 0.5773"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "192/729 [======>.......................] - ETA: 6s - loss: 0.7133 - mae: 0.5751"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "197/729 [=======>......................] - ETA: 6s - loss: 0.7075 - mae: 0.5732"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "202/729 [=======>......................] - ETA: 6s - loss: 0.7087 - mae: 0.5738"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "207/729 [=======>......................] - ETA: 6s - loss: 0.7028 - mae: 0.5717"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "212/729 [=======>......................] - ETA: 6s - loss: 0.6972 - mae: 0.5696"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "217/729 [=======>......................] - ETA: 6s - loss: 0.6949 - mae: 0.5693"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "222/729 [========>.....................] - ETA: 6s - loss: 0.7006 - mae: 0.5682"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "227/729 [========>.....................] - ETA: 6s - loss: 0.6969 - mae: 0.5669"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "232/729 [========>.....................] - ETA: 6s - loss: 0.6930 - mae: 0.5654"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "236/729 [========>.....................] - ETA: 6s - loss: 0.6889 - mae: 0.5640"
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- "240/729 [========>.....................] - ETA: 5s - loss: 0.6846 - mae: 0.5625"
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- "244/729 [=========>....................] - ETA: 5s - loss: 0.6819 - mae: 0.5612"
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- "248/729 [=========>....................] - ETA: 5s - loss: 0.6841 - mae: 0.5613"
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- "252/729 [=========>....................] - ETA: 5s - loss: 0.6816 - mae: 0.5603"
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- "256/729 [=========>....................] - ETA: 5s - loss: 0.6777 - mae: 0.5586"
- ]
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- "260/729 [=========>....................] - ETA: 5s - loss: 0.6763 - mae: 0.5578"
- ]
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- "264/729 [=========>....................] - ETA: 5s - loss: 0.6731 - mae: 0.5567"
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- "268/729 [==========>...................] - ETA: 5s - loss: 0.6723 - mae: 0.5567"
- ]
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- "272/729 [==========>...................] - ETA: 5s - loss: 0.6701 - mae: 0.5556"
- ]
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- "276/729 [==========>...................] - ETA: 5s - loss: 0.6688 - mae: 0.5547"
- ]
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- "280/729 [==========>...................] - ETA: 5s - loss: 0.6736 - mae: 0.5551"
- ]
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- "284/729 [==========>...................] - ETA: 5s - loss: 0.6704 - mae: 0.5539"
- ]
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- "288/729 [==========>...................] - ETA: 5s - loss: 0.6676 - mae: 0.5525"
- ]
- },
- {
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- "292/729 [===========>..................] - ETA: 5s - loss: 0.6644 - mae: 0.5516"
- ]
- },
- {
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- "296/729 [===========>..................] - ETA: 5s - loss: 0.6615 - mae: 0.5505"
- ]
- },
- {
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- "300/729 [===========>..................] - ETA: 5s - loss: 0.6587 - mae: 0.5494"
- ]
- },
- {
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- "output_type": "stream",
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- "304/729 [===========>..................] - ETA: 5s - loss: 0.6574 - mae: 0.5490"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "308/729 [===========>..................] - ETA: 5s - loss: 0.6557 - mae: 0.5482"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "312/729 [===========>..................] - ETA: 5s - loss: 0.6539 - mae: 0.5476"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "316/729 [============>.................] - ETA: 5s - loss: 0.6508 - mae: 0.5463"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "320/729 [============>.................] - ETA: 5s - loss: 0.6490 - mae: 0.5456"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "324/729 [============>.................] - ETA: 5s - loss: 0.6558 - mae: 0.5460"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "328/729 [============>.................] - ETA: 5s - loss: 0.6607 - mae: 0.5464"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "332/729 [============>.................] - ETA: 5s - loss: 0.6657 - mae: 0.5468"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "336/729 [============>.................] - ETA: 5s - loss: 0.6750 - mae: 0.5477"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "340/729 [============>.................] - ETA: 5s - loss: 0.6785 - mae: 0.5485"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "344/729 [=============>................] - ETA: 4s - loss: 0.6764 - mae: 0.5482"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "348/729 [=============>................] - ETA: 4s - loss: 0.6762 - mae: 0.5481"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "352/729 [=============>................] - ETA: 4s - loss: 0.6751 - mae: 0.5478"
- ]
- },
- {
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- "output_type": "stream",
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- "356/729 [=============>................] - ETA: 4s - loss: 0.6724 - mae: 0.5468"
- ]
- },
- {
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- "output_type": "stream",
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- "360/729 [=============>................] - ETA: 4s - loss: 0.6686 - mae: 0.5453"
- ]
- },
- {
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- "output_type": "stream",
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- "364/729 [=============>................] - ETA: 4s - loss: 0.6673 - mae: 0.5449"
- ]
- },
- {
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- "output_type": "stream",
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- "368/729 [==============>...............] - ETA: 4s - loss: 0.6652 - mae: 0.5441"
- ]
- },
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- "372/729 [==============>...............] - ETA: 4s - loss: 0.6631 - mae: 0.5434"
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- "376/729 [==============>...............] - ETA: 4s - loss: 0.6604 - mae: 0.5423"
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- "380/729 [==============>...............] - ETA: 4s - loss: 0.6581 - mae: 0.5417"
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- "384/729 [==============>...............] - ETA: 4s - loss: 0.6585 - mae: 0.5417"
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- "388/729 [==============>...............] - ETA: 4s - loss: 0.6565 - mae: 0.5411"
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- "392/729 [===============>..............] - ETA: 4s - loss: 0.6577 - mae: 0.5408"
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- "396/729 [===============>..............] - ETA: 4s - loss: 0.6552 - mae: 0.5398"
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- "400/729 [===============>..............] - ETA: 4s - loss: 0.6531 - mae: 0.5390"
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- "404/729 [===============>..............] - ETA: 4s - loss: 0.6501 - mae: 0.5378"
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- "408/729 [===============>..............] - ETA: 4s - loss: 0.6476 - mae: 0.5369"
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- "412/729 [===============>..............] - ETA: 4s - loss: 0.6469 - mae: 0.5364"
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- "416/729 [================>.............] - ETA: 4s - loss: 0.6516 - mae: 0.5359"
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- "420/729 [================>.............] - ETA: 4s - loss: 0.6567 - mae: 0.5359"
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- "424/729 [================>.............] - ETA: 4s - loss: 0.6553 - mae: 0.5357"
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- {
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- "text": [
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- "428/729 [================>.............] - ETA: 3s - loss: 0.6590 - mae: 0.5358"
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- "432/729 [================>.............] - ETA: 3s - loss: 0.6582 - mae: 0.5353"
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- {
- "name": "stdout",
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- "436/729 [================>.............] - ETA: 3s - loss: 0.6595 - mae: 0.5356"
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- {
- "name": "stdout",
- "output_type": "stream",
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- "440/729 [=================>............] - ETA: 3s - loss: 0.6591 - mae: 0.5353"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "444/729 [=================>............] - ETA: 3s - loss: 0.6565 - mae: 0.5344"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "448/729 [=================>............] - ETA: 3s - loss: 0.6545 - mae: 0.5339"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "452/729 [=================>............] - ETA: 3s - loss: 0.6533 - mae: 0.5336"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "456/729 [=================>............] - ETA: 3s - loss: 0.6518 - mae: 0.5331"
- ]
- },
- {
- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "460/729 [=================>............] - ETA: 3s - loss: 0.6496 - mae: 0.5323"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "464/729 [==================>...........] - ETA: 3s - loss: 0.6492 - mae: 0.5322"
- ]
- },
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- "name": "stdout",
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- "467/729 [==================>...........] - ETA: 3s - loss: 0.6481 - mae: 0.5318"
- ]
- },
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- "name": "stdout",
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- "471/729 [==================>...........] - ETA: 3s - loss: 0.6460 - mae: 0.5312"
- ]
- },
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- "name": "stdout",
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- "475/729 [==================>...........] - ETA: 3s - loss: 0.6442 - mae: 0.5306"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "479/729 [==================>...........] - ETA: 3s - loss: 0.6429 - mae: 0.5303"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "483/729 [==================>...........] - ETA: 3s - loss: 0.6414 - mae: 0.5298"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "487/729 [===================>..........] - ETA: 3s - loss: 0.6391 - mae: 0.5288"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "491/729 [===================>..........] - ETA: 3s - loss: 0.6392 - mae: 0.5284"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "495/729 [===================>..........] - ETA: 3s - loss: 0.6379 - mae: 0.5280"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "499/729 [===================>..........] - ETA: 3s - loss: 0.6368 - mae: 0.5276"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
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- "507/729 [===================>..........] - ETA: 2s - loss: 0.6345 - mae: 0.5267"
- ]
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- "511/729 [====================>.........] - ETA: 2s - loss: 0.6345 - mae: 0.5269"
- ]
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- {
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- "515/729 [====================>.........] - ETA: 2s - loss: 0.6372 - mae: 0.5266"
- ]
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- {
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- "519/729 [====================>.........] - ETA: 2s - loss: 0.6349 - mae: 0.5257"
- ]
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- "523/729 [====================>.........] - ETA: 2s - loss: 0.6366 - mae: 0.5257"
- ]
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- "527/729 [====================>.........] - ETA: 2s - loss: 0.6365 - mae: 0.5255"
- ]
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- "531/729 [====================>.........] - ETA: 2s - loss: 0.6363 - mae: 0.5255"
- ]
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- "535/729 [=====================>........] - ETA: 2s - loss: 0.6353 - mae: 0.5252"
- ]
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- "539/729 [=====================>........] - ETA: 2s - loss: 0.6343 - mae: 0.5250"
- ]
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- "543/729 [=====================>........] - ETA: 2s - loss: 0.6334 - mae: 0.5247"
- ]
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- "547/729 [=====================>........] - ETA: 2s - loss: 0.6403 - mae: 0.5257"
- ]
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- "551/729 [=====================>........] - ETA: 2s - loss: 0.6425 - mae: 0.5254"
- ]
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- "555/729 [=====================>........] - ETA: 2s - loss: 0.6408 - mae: 0.5251"
- ]
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- "559/729 [======================>.......] - ETA: 2s - loss: 0.6400 - mae: 0.5249"
- ]
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- "563/729 [======================>.......] - ETA: 2s - loss: 0.6387 - mae: 0.5246"
- ]
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- "567/729 [======================>.......] - ETA: 2s - loss: 0.6375 - mae: 0.5243"
- ]
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- "571/729 [======================>.......] - ETA: 2s - loss: 0.6374 - mae: 0.5239"
- ]
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- "name": "stdout",
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- "575/729 [======================>.......] - ETA: 2s - loss: 0.6371 - mae: 0.5235"
- ]
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- "name": "stdout",
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- "579/729 [======================>.......] - ETA: 2s - loss: 0.6352 - mae: 0.5227"
- ]
- },
- {
- "name": "stdout",
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- "583/729 [======================>.......] - ETA: 1s - loss: 0.6348 - mae: 0.5224"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "587/729 [=======================>......] - ETA: 1s - loss: 0.6338 - mae: 0.5220"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "591/729 [=======================>......] - ETA: 1s - loss: 0.6320 - mae: 0.5214"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "595/729 [=======================>......] - ETA: 1s - loss: 0.6311 - mae: 0.5210"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "599/729 [=======================>......] - ETA: 1s - loss: 0.6305 - mae: 0.5208"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "603/729 [=======================>......] - ETA: 1s - loss: 0.6296 - mae: 0.5205"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "607/729 [=======================>......] - ETA: 1s - loss: 0.6282 - mae: 0.5201"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "611/729 [========================>.....] - ETA: 1s - loss: 0.6266 - mae: 0.5195"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "615/729 [========================>.....] - ETA: 1s - loss: 0.6324 - mae: 0.5198"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "619/729 [========================>.....] - ETA: 1s - loss: 0.6313 - mae: 0.5195"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "623/729 [========================>.....] - ETA: 1s - loss: 0.6297 - mae: 0.5191"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "627/729 [========================>.....] - ETA: 1s - loss: 0.6290 - mae: 0.5188"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "631/729 [========================>.....] - ETA: 1s - loss: 0.6271 - mae: 0.5181"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "635/729 [=========================>....] - ETA: 1s - loss: 0.6251 - mae: 0.5173"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "639/729 [=========================>....] - ETA: 1s - loss: 0.6238 - mae: 0.5167"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "643/729 [=========================>....] - ETA: 1s - loss: 0.6219 - mae: 0.5159"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "647/729 [=========================>....] - ETA: 1s - loss: 0.6207 - mae: 0.5155"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "651/729 [=========================>....] - ETA: 1s - loss: 0.6197 - mae: 0.5150"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "655/729 [=========================>....] - ETA: 1s - loss: 0.6186 - mae: 0.5147"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "659/729 [==========================>...] - ETA: 0s - loss: 0.6172 - mae: 0.5142"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "663/729 [==========================>...] - ETA: 0s - loss: 0.6177 - mae: 0.5143"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "667/729 [==========================>...] - ETA: 0s - loss: 0.6169 - mae: 0.5139"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "671/729 [==========================>...] - ETA: 0s - loss: 0.6165 - mae: 0.5138"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "675/729 [==========================>...] - ETA: 0s - loss: 0.6166 - mae: 0.5137"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "679/729 [==========================>...] - ETA: 0s - loss: 0.6156 - mae: 0.5134"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "683/729 [===========================>..] - ETA: 0s - loss: 0.6146 - mae: 0.5130"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "687/729 [===========================>..] - ETA: 0s - loss: 0.6139 - mae: 0.5126"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "691/729 [===========================>..] - ETA: 0s - loss: 0.6128 - mae: 0.5122"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "695/729 [===========================>..] - ETA: 0s - loss: 0.6114 - mae: 0.5117"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "699/729 [===========================>..] - ETA: 0s - loss: 0.6104 - mae: 0.5113"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "703/729 [===========================>..] - ETA: 0s - loss: 0.6092 - mae: 0.5108"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "707/729 [============================>.] - ETA: 0s - loss: 0.6085 - mae: 0.5104"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "711/729 [============================>.] - ETA: 0s - loss: 0.6071 - mae: 0.5097"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "715/729 [============================>.] - ETA: 0s - loss: 0.6062 - mae: 0.5094"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "719/729 [============================>.] - ETA: 0s - loss: 0.6059 - mae: 0.5090"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "723/729 [============================>.] - ETA: 0s - loss: 0.6046 - mae: 0.5085"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "727/729 [============================>.] - ETA: 0s - loss: 0.6033 - mae: 0.5080"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - 11s 16ms/step - loss: 0.6027 - mae: 0.5078 - val_loss: 0.4866 - val_mae: 0.4223\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 2/10\n",
- "\r",
- " 1/729 [..............................] - ETA: 0s - loss: 0.4111 - mae: 0.4386"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/729 [..............................] - ETA: 8s - loss: 0.4623 - mae: 0.4563"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/729 [..............................] - ETA: 9s - loss: 0.5709 - mae: 0.4552"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/729 [..............................] - ETA: 9s - loss: 0.5252 - mae: 0.4509"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 17/729 [..............................] - ETA: 10s - loss: 0.5143 - mae: 0.4549"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/729 [..............................] - ETA: 10s - loss: 0.4855 - mae: 0.4474"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 25/729 [>.............................] - ETA: 10s - loss: 0.4601 - mae: 0.4388"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 29/729 [>.............................] - ETA: 10s - loss: 0.4983 - mae: 0.4446"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 33/729 [>.............................] - ETA: 10s - loss: 0.4883 - mae: 0.4440"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 37/729 [>.............................] - ETA: 10s - loss: 0.4909 - mae: 0.4471"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 41/729 [>.............................] - ETA: 10s - loss: 0.4811 - mae: 0.4457"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 44/729 [>.............................] - ETA: 10s - loss: 0.4765 - mae: 0.4450"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 47/729 [>.............................] - ETA: 10s - loss: 0.4840 - mae: 0.4477"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 51/729 [=>............................] - ETA: 10s - loss: 0.4836 - mae: 0.4459"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 55/729 [=>............................] - ETA: 10s - loss: 0.4837 - mae: 0.4457"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 59/729 [=>............................] - ETA: 10s - loss: 0.4791 - mae: 0.4459"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 63/729 [=>............................] - ETA: 10s - loss: 0.4752 - mae: 0.4448"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 67/729 [=>............................] - ETA: 10s - loss: 0.4937 - mae: 0.4481"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 71/729 [=>............................] - ETA: 9s - loss: 0.4913 - mae: 0.4476 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 75/729 [==>...........................] - ETA: 9s - loss: 0.4849 - mae: 0.4466"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 79/729 [==>...........................] - ETA: 9s - loss: 0.4810 - mae: 0.4452"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 83/729 [==>...........................] - ETA: 9s - loss: 0.4764 - mae: 0.4434"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 87/729 [==>...........................] - ETA: 9s - loss: 0.4731 - mae: 0.4422"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 91/729 [==>...........................] - ETA: 9s - loss: 0.5167 - mae: 0.4450"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 95/729 [==>...........................] - ETA: 9s - loss: 0.5126 - mae: 0.4447"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 99/729 [===>..........................] - ETA: 9s - loss: 0.5131 - mae: 0.4465"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "103/729 [===>..........................] - ETA: 9s - loss: 0.5068 - mae: 0.4450"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "107/729 [===>..........................] - ETA: 9s - loss: 0.5049 - mae: 0.4450"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "111/729 [===>..........................] - ETA: 9s - loss: 0.4965 - mae: 0.4421"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "115/729 [===>..........................] - ETA: 9s - loss: 0.4938 - mae: 0.4420"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "119/729 [===>..........................] - ETA: 9s - loss: 0.4904 - mae: 0.4412"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "123/729 [====>.........................] - ETA: 8s - loss: 0.4892 - mae: 0.4415"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "127/729 [====>.........................] - ETA: 8s - loss: 0.4908 - mae: 0.4435"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "131/729 [====>.........................] - ETA: 8s - loss: 0.4921 - mae: 0.4449"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "135/729 [====>.........................] - ETA: 8s - loss: 0.4923 - mae: 0.4457"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "139/729 [====>.........................] - ETA: 8s - loss: 0.4970 - mae: 0.4462"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "143/729 [====>.........................] - ETA: 8s - loss: 0.4964 - mae: 0.4461"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "147/729 [=====>........................] - ETA: 8s - loss: 0.4922 - mae: 0.4452"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "151/729 [=====>........................] - ETA: 8s - loss: 0.4949 - mae: 0.4467"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "155/729 [=====>........................] - ETA: 8s - loss: 0.4945 - mae: 0.4467"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "159/729 [=====>........................] - ETA: 8s - loss: 0.4983 - mae: 0.4465"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "163/729 [=====>........................] - ETA: 8s - loss: 0.4938 - mae: 0.4451"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "167/729 [=====>........................] - ETA: 8s - loss: 0.4952 - mae: 0.4463"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "171/729 [======>.......................] - ETA: 8s - loss: 0.4994 - mae: 0.4470"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "175/729 [======>.......................] - ETA: 8s - loss: 0.4985 - mae: 0.4472"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "179/729 [======>.......................] - ETA: 8s - loss: 0.4970 - mae: 0.4469"
- ]
- },
- {
- "name": "stdout",
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- "183/729 [======>.......................] - ETA: 8s - loss: 0.4977 - mae: 0.4470"
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- "187/729 [======>.......................] - ETA: 8s - loss: 0.4976 - mae: 0.4473"
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- "190/729 [======>.......................] - ETA: 8s - loss: 0.4974 - mae: 0.4479"
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- "194/729 [======>.......................] - ETA: 8s - loss: 0.4965 - mae: 0.4474"
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- "198/729 [=======>......................] - ETA: 7s - loss: 0.4923 - mae: 0.4457"
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- "202/729 [=======>......................] - ETA: 7s - loss: 0.4938 - mae: 0.4460"
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- "206/729 [=======>......................] - ETA: 7s - loss: 0.5005 - mae: 0.4465"
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- "210/729 [=======>......................] - ETA: 7s - loss: 0.4987 - mae: 0.4458"
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- "214/729 [=======>......................] - ETA: 7s - loss: 0.4975 - mae: 0.4460"
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- "218/729 [=======>......................] - ETA: 7s - loss: 0.4986 - mae: 0.4469"
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- "222/729 [========>.....................] - ETA: 7s - loss: 0.4972 - mae: 0.4470"
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- "226/729 [========>.....................] - ETA: 7s - loss: 0.4950 - mae: 0.4466"
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- "230/729 [========>.....................] - ETA: 7s - loss: 0.4955 - mae: 0.4469"
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- "234/729 [========>.....................] - ETA: 7s - loss: 0.4949 - mae: 0.4467"
- ]
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- "238/729 [========>.....................] - ETA: 7s - loss: 0.4932 - mae: 0.4465"
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- "242/729 [========>.....................] - ETA: 7s - loss: 0.4999 - mae: 0.4468"
- ]
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- "246/729 [=========>....................] - ETA: 7s - loss: 0.5006 - mae: 0.4475"
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- "250/729 [=========>....................] - ETA: 7s - loss: 0.4996 - mae: 0.4471"
- ]
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- "254/729 [=========>....................] - ETA: 7s - loss: 0.4975 - mae: 0.4467"
- ]
- },
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- "258/729 [=========>....................] - ETA: 7s - loss: 0.4961 - mae: 0.4462"
- ]
- },
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- "262/729 [=========>....................] - ETA: 7s - loss: 0.4955 - mae: 0.4463"
- ]
- },
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- "266/729 [=========>....................] - ETA: 6s - loss: 0.4947 - mae: 0.4462"
- ]
- },
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- "270/729 [==========>...................] - ETA: 6s - loss: 0.4955 - mae: 0.4464"
- ]
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- "274/729 [==========>...................] - ETA: 6s - loss: 0.4939 - mae: 0.4460"
- ]
- },
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- "278/729 [==========>...................] - ETA: 6s - loss: 0.4963 - mae: 0.4463"
- ]
- },
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- "282/729 [==========>...................] - ETA: 6s - loss: 0.4957 - mae: 0.4462"
- ]
- },
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- "286/729 [==========>...................] - ETA: 6s - loss: 0.4945 - mae: 0.4458"
- ]
- },
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- "290/729 [==========>...................] - ETA: 6s - loss: 0.4934 - mae: 0.4458"
- ]
- },
- {
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- "294/729 [===========>..................] - ETA: 6s - loss: 0.4921 - mae: 0.4454"
- ]
- },
- {
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- "298/729 [===========>..................] - ETA: 6s - loss: 0.4912 - mae: 0.4451"
- ]
- },
- {
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- "302/729 [===========>..................] - ETA: 6s - loss: 0.4915 - mae: 0.4452"
- ]
- },
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- "306/729 [===========>..................] - ETA: 6s - loss: 0.4909 - mae: 0.4452"
- ]
- },
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- ]
- },
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- "318/729 [============>.................] - ETA: 6s - loss: 0.4907 - mae: 0.4442"
- ]
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- "output_type": "stream",
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- "322/729 [============>.................] - ETA: 6s - loss: 0.4899 - mae: 0.4437"
- ]
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- "output_type": "stream",
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- "326/729 [============>.................] - ETA: 6s - loss: 0.4889 - mae: 0.4434"
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- "330/729 [============>.................] - ETA: 6s - loss: 0.4923 - mae: 0.4440"
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- "334/729 [============>.................] - ETA: 5s - loss: 0.4919 - mae: 0.4439"
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- "338/729 [============>.................] - ETA: 5s - loss: 0.4902 - mae: 0.4435"
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- "342/729 [=============>................] - ETA: 5s - loss: 0.4893 - mae: 0.4432"
- ]
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- "346/729 [=============>................] - ETA: 5s - loss: 0.4873 - mae: 0.4425"
- ]
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- "350/729 [=============>................] - ETA: 5s - loss: 0.4863 - mae: 0.4423"
- ]
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- "354/729 [=============>................] - ETA: 5s - loss: 0.4857 - mae: 0.4422"
- ]
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- "358/729 [=============>................] - ETA: 5s - loss: 0.4850 - mae: 0.4420"
- ]
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- "362/729 [=============>................] - ETA: 5s - loss: 0.4839 - mae: 0.4418"
- ]
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- "366/729 [==============>...............] - ETA: 5s - loss: 0.4846 - mae: 0.4419"
- ]
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- "370/729 [==============>...............] - ETA: 5s - loss: 0.4839 - mae: 0.4418"
- ]
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- "374/729 [==============>...............] - ETA: 5s - loss: 0.4842 - mae: 0.4419"
- ]
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- "378/729 [==============>...............] - ETA: 5s - loss: 0.4833 - mae: 0.4416"
- ]
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- "name": "stdout",
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- "382/729 [==============>...............] - ETA: 5s - loss: 0.4819 - mae: 0.4412"
- ]
- },
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- "name": "stdout",
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- "386/729 [==============>...............] - ETA: 5s - loss: 0.4812 - mae: 0.4409"
- ]
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- "name": "stdout",
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- "text": [
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- "390/729 [===============>..............] - ETA: 5s - loss: 0.4807 - mae: 0.4407"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "394/729 [===============>..............] - ETA: 5s - loss: 0.4815 - mae: 0.4410"
- ]
- },
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- "name": "stdout",
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- "398/729 [===============>..............] - ETA: 5s - loss: 0.4801 - mae: 0.4405"
- ]
- },
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- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "402/729 [===============>..............] - ETA: 4s - loss: 0.4799 - mae: 0.4405"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "406/729 [===============>..............] - ETA: 4s - loss: 0.4808 - mae: 0.4411"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "410/729 [===============>..............] - ETA: 4s - loss: 0.4891 - mae: 0.4412"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "414/729 [================>.............] - ETA: 4s - loss: 0.4890 - mae: 0.4410"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "418/729 [================>.............] - ETA: 4s - loss: 0.4892 - mae: 0.4415"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "422/729 [================>.............] - ETA: 4s - loss: 0.4894 - mae: 0.4417"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "426/729 [================>.............] - ETA: 4s - loss: 0.4898 - mae: 0.4421"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "430/729 [================>.............] - ETA: 4s - loss: 0.4888 - mae: 0.4418"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "434/729 [================>.............] - ETA: 4s - loss: 0.4944 - mae: 0.4422"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "438/729 [=================>............] - ETA: 4s - loss: 0.4952 - mae: 0.4427"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "442/729 [=================>............] - ETA: 4s - loss: 0.4951 - mae: 0.4429"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "446/729 [=================>............] - ETA: 4s - loss: 0.4980 - mae: 0.4431"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "450/729 [=================>............] - ETA: 4s - loss: 0.4982 - mae: 0.4431"
- ]
- },
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- "454/729 [=================>............] - ETA: 4s - loss: 0.4980 - mae: 0.4431"
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- "458/729 [=================>............] - ETA: 4s - loss: 0.4976 - mae: 0.4430"
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- "462/729 [==================>...........] - ETA: 4s - loss: 0.4978 - mae: 0.4433"
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- "466/729 [==================>...........] - ETA: 3s - loss: 0.4973 - mae: 0.4432"
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- "470/729 [==================>...........] - ETA: 3s - loss: 0.4971 - mae: 0.4433"
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- "474/729 [==================>...........] - ETA: 3s - loss: 0.4988 - mae: 0.4439"
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- "478/729 [==================>...........] - ETA: 3s - loss: 0.4974 - mae: 0.4435"
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- "482/729 [==================>...........] - ETA: 3s - loss: 0.4963 - mae: 0.4433"
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- "485/729 [==================>...........] - ETA: 3s - loss: 0.5014 - mae: 0.4437"
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- "488/729 [===================>..........] - ETA: 3s - loss: 0.5011 - mae: 0.4436"
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- "491/729 [===================>..........] - ETA: 3s - loss: 0.5045 - mae: 0.4436"
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- "494/729 [===================>..........] - ETA: 3s - loss: 0.5051 - mae: 0.4440"
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- "497/729 [===================>..........] - ETA: 3s - loss: 0.5046 - mae: 0.4439"
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- "500/729 [===================>..........] - ETA: 3s - loss: 0.5067 - mae: 0.4441"
- ]
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- "503/729 [===================>..........] - ETA: 3s - loss: 0.5074 - mae: 0.4443"
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- "506/729 [===================>..........] - ETA: 3s - loss: 0.5069 - mae: 0.4444"
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- "510/729 [===================>..........] - ETA: 3s - loss: 0.5093 - mae: 0.4451"
- ]
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- "514/729 [====================>.........] - ETA: 3s - loss: 0.5075 - mae: 0.4445"
- ]
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- "517/729 [====================>.........] - ETA: 3s - loss: 0.5068 - mae: 0.4444"
- ]
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- "521/729 [====================>.........] - ETA: 3s - loss: 0.5081 - mae: 0.4445"
- ]
- },
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- "name": "stdout",
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- "525/729 [====================>.........] - ETA: 3s - loss: 0.5102 - mae: 0.4446"
- ]
- },
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- "name": "stdout",
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- "529/729 [====================>.........] - ETA: 3s - loss: 0.5100 - mae: 0.4446"
- ]
- },
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- "name": "stdout",
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- "533/729 [====================>.........] - ETA: 2s - loss: 0.5085 - mae: 0.4442"
- ]
- },
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- "name": "stdout",
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- "537/729 [=====================>........] - ETA: 2s - loss: 0.5076 - mae: 0.4440"
- ]
- },
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- "name": "stdout",
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- "541/729 [=====================>........] - ETA: 2s - loss: 0.5063 - mae: 0.4435"
- ]
- },
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- "545/729 [=====================>........] - ETA: 2s - loss: 0.5054 - mae: 0.4432"
- ]
- },
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- "549/729 [=====================>........] - ETA: 2s - loss: 0.5051 - mae: 0.4432"
- ]
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- "553/729 [=====================>........] - ETA: 2s - loss: 0.5041 - mae: 0.4431"
- ]
- },
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- "557/729 [=====================>........] - ETA: 2s - loss: 0.5031 - mae: 0.4429"
- ]
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- "561/729 [======================>.......] - ETA: 2s - loss: 0.5026 - mae: 0.4429"
- ]
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- "565/729 [======================>.......] - ETA: 2s - loss: 0.5015 - mae: 0.4425"
- ]
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- "569/729 [======================>.......] - ETA: 2s - loss: 0.5003 - mae: 0.4421"
- ]
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- "573/729 [======================>.......] - ETA: 2s - loss: 0.5001 - mae: 0.4419"
- ]
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- "577/729 [======================>.......] - ETA: 2s - loss: 0.5005 - mae: 0.4419"
- ]
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- "617/729 [========================>.....] - ETA: 1s - loss: 0.5076 - mae: 0.4427"
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- "621/729 [========================>.....] - ETA: 1s - loss: 0.5069 - mae: 0.4426"
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- "625/729 [========================>.....] - ETA: 1s - loss: 0.5061 - mae: 0.4424"
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- "629/729 [========================>.....] - ETA: 1s - loss: 0.5064 - mae: 0.4427"
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- "633/729 [=========================>....] - ETA: 1s - loss: 0.5050 - mae: 0.4423"
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- "637/729 [=========================>....] - ETA: 1s - loss: 0.5045 - mae: 0.4422"
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- "641/729 [=========================>....] - ETA: 1s - loss: 0.5035 - mae: 0.4421"
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- "645/729 [=========================>....] - ETA: 1s - loss: 0.5035 - mae: 0.4422"
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- "649/729 [=========================>....] - ETA: 1s - loss: 0.5063 - mae: 0.4422"
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- "653/729 [=========================>....] - ETA: 1s - loss: 0.5052 - mae: 0.4419"
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- "657/729 [==========================>...] - ETA: 1s - loss: 0.5055 - mae: 0.4418"
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- "661/729 [==========================>...] - ETA: 1s - loss: 0.5058 - mae: 0.4420"
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- "665/729 [==========================>...] - ETA: 0s - loss: 0.5062 - mae: 0.4422"
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- "669/729 [==========================>...] - ETA: 0s - loss: 0.5057 - mae: 0.4421"
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- "673/729 [==========================>...] - ETA: 0s - loss: 0.5048 - mae: 0.4419"
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- "677/729 [==========================>...] - ETA: 0s - loss: 0.5051 - mae: 0.4418"
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- "681/729 [===========================>..] - ETA: 0s - loss: 0.5044 - mae: 0.4416"
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- "685/729 [===========================>..] - ETA: 0s - loss: 0.5065 - mae: 0.4423"
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- "689/729 [===========================>..] - ETA: 0s - loss: 0.5060 - mae: 0.4423"
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "717/729 [============================>.] - ETA: 0s - loss: 0.5057 - mae: 0.4415"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "721/729 [============================>.] - ETA: 0s - loss: 0.5051 - mae: 0.4414"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "725/729 [============================>.] - ETA: 0s - loss: 0.5082 - mae: 0.4413"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - ETA: 0s - loss: 0.5075 - mae: 0.4412"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - 12s 17ms/step - loss: 0.5075 - mae: 0.4412 - val_loss: 0.4676 - val_mae: 0.4079\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 3/10\n",
- "\r",
- " 1/729 [..............................] - ETA: 0s - loss: 0.3293 - mae: 0.3903"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/729 [..............................] - ETA: 9s - loss: 0.3410 - mae: 0.3884"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/729 [..............................] - ETA: 9s - loss: 0.3518 - mae: 0.3944"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/729 [..............................] - ETA: 10s - loss: 0.4960 - mae: 0.4291"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 17/729 [..............................] - ETA: 10s - loss: 0.5091 - mae: 0.4396"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/729 [..............................] - ETA: 10s - loss: 0.4844 - mae: 0.4323"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 25/729 [>.............................] - ETA: 10s - loss: 0.5344 - mae: 0.4445"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 29/729 [>.............................] - ETA: 10s - loss: 0.5815 - mae: 0.4553"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 32/729 [>.............................] - ETA: 10s - loss: 0.5554 - mae: 0.4486"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 35/729 [>.............................] - ETA: 10s - loss: 0.5466 - mae: 0.4496"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 38/729 [>.............................] - ETA: 10s - loss: 0.5443 - mae: 0.4503"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 42/729 [>.............................] - ETA: 10s - loss: 0.5317 - mae: 0.4469"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 46/729 [>.............................] - ETA: 10s - loss: 0.5110 - mae: 0.4395"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 49/729 [=>............................] - ETA: 10s - loss: 0.4986 - mae: 0.4358"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 52/729 [=>............................] - ETA: 10s - loss: 0.4970 - mae: 0.4375"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 55/729 [=>............................] - ETA: 10s - loss: 0.4843 - mae: 0.4328"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 58/729 [=>............................] - ETA: 10s - loss: 0.4793 - mae: 0.4323"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 62/729 [=>............................] - ETA: 10s - loss: 0.4753 - mae: 0.4311"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 66/729 [=>............................] - ETA: 10s - loss: 0.4684 - mae: 0.4291"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 70/729 [=>............................] - ETA: 10s - loss: 0.4675 - mae: 0.4309"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 74/729 [==>...........................] - ETA: 10s - loss: 0.4679 - mae: 0.4311"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 78/729 [==>...........................] - ETA: 10s - loss: 0.4634 - mae: 0.4296"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 82/729 [==>...........................] - ETA: 10s - loss: 0.4575 - mae: 0.4274"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 86/729 [==>...........................] - ETA: 10s - loss: 0.4534 - mae: 0.4257"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 90/729 [==>...........................] - ETA: 10s - loss: 0.4554 - mae: 0.4256"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 94/729 [==>...........................] - ETA: 9s - loss: 0.4547 - mae: 0.4255 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 98/729 [===>..........................] - ETA: 9s - loss: 0.4618 - mae: 0.4267"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "102/729 [===>..........................] - ETA: 9s - loss: 0.4578 - mae: 0.4256"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "105/729 [===>..........................] - ETA: 9s - loss: 0.4565 - mae: 0.4258"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "109/729 [===>..........................] - ETA: 9s - loss: 0.4719 - mae: 0.4256"
- ]
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- "113/729 [===>..........................] - ETA: 9s - loss: 0.4679 - mae: 0.4246"
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- "117/729 [===>..........................] - ETA: 9s - loss: 0.4681 - mae: 0.4253"
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- "121/729 [===>..........................] - ETA: 9s - loss: 0.4639 - mae: 0.4245"
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- "125/729 [====>.........................] - ETA: 9s - loss: 0.4593 - mae: 0.4230"
- ]
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- "129/729 [====>.........................] - ETA: 9s - loss: 0.4583 - mae: 0.4232"
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- "133/729 [====>.........................] - ETA: 9s - loss: 0.4610 - mae: 0.4253"
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- "137/729 [====>.........................] - ETA: 9s - loss: 0.4696 - mae: 0.4276"
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- "141/729 [====>.........................] - ETA: 9s - loss: 0.4668 - mae: 0.4273"
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- "145/729 [====>.........................] - ETA: 9s - loss: 0.4639 - mae: 0.4269"
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- "149/729 [=====>........................] - ETA: 9s - loss: 0.4641 - mae: 0.4278"
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- "153/729 [=====>........................] - ETA: 8s - loss: 0.4598 - mae: 0.4264"
- ]
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- "157/729 [=====>........................] - ETA: 8s - loss: 0.4552 - mae: 0.4247"
- ]
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- "161/729 [=====>........................] - ETA: 8s - loss: 0.4532 - mae: 0.4239"
- ]
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- "165/729 [=====>........................] - ETA: 8s - loss: 0.4538 - mae: 0.4242"
- ]
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- "169/729 [=====>........................] - ETA: 8s - loss: 0.4511 - mae: 0.4232"
- ]
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- "173/729 [======>.......................] - ETA: 8s - loss: 0.4533 - mae: 0.4240"
- ]
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- "177/729 [======>.......................] - ETA: 8s - loss: 0.4540 - mae: 0.4241"
- ]
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- "181/729 [======>.......................] - ETA: 8s - loss: 0.4539 - mae: 0.4242"
- ]
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- "185/729 [======>.......................] - ETA: 8s - loss: 0.4508 - mae: 0.4230"
- ]
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- "188/729 [======>.......................] - ETA: 8s - loss: 0.4573 - mae: 0.4246"
- ]
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- "191/729 [======>.......................] - ETA: 8s - loss: 0.4582 - mae: 0.4252"
- ]
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- "194/729 [======>.......................] - ETA: 8s - loss: 0.4557 - mae: 0.4244"
- ]
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- "197/729 [=======>......................] - ETA: 8s - loss: 0.4545 - mae: 0.4243"
- ]
- },
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- "200/729 [=======>......................] - ETA: 8s - loss: 0.4624 - mae: 0.4247"
- ]
- },
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- "203/729 [=======>......................] - ETA: 8s - loss: 0.4602 - mae: 0.4240"
- ]
- },
- {
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- "206/729 [=======>......................] - ETA: 8s - loss: 0.4797 - mae: 0.4276"
- ]
- },
- {
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- "209/729 [=======>......................] - ETA: 8s - loss: 0.4784 - mae: 0.4276"
- ]
- },
- {
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- "213/729 [=======>......................] - ETA: 8s - loss: 0.4765 - mae: 0.4275"
- ]
- },
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- "217/729 [=======>......................] - ETA: 8s - loss: 0.4741 - mae: 0.4264"
- ]
- },
- {
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- "221/729 [========>.....................] - ETA: 7s - loss: 0.4730 - mae: 0.4266"
- ]
- },
- {
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- "225/729 [========>.....................] - ETA: 7s - loss: 0.4714 - mae: 0.4262"
- ]
- },
- {
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- "229/729 [========>.....................] - ETA: 7s - loss: 0.4703 - mae: 0.4259"
- ]
- },
- {
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- "233/729 [========>.....................] - ETA: 7s - loss: 0.4691 - mae: 0.4255"
- ]
- },
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- "237/729 [========>.....................] - ETA: 7s - loss: 0.4676 - mae: 0.4254"
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- "241/729 [========>.....................] - ETA: 7s - loss: 0.4681 - mae: 0.4263"
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- "244/729 [=========>....................] - ETA: 7s - loss: 0.4674 - mae: 0.4264"
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- "247/729 [=========>....................] - ETA: 7s - loss: 0.4682 - mae: 0.4264"
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- "250/729 [=========>....................] - ETA: 7s - loss: 0.4671 - mae: 0.4262"
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- "254/729 [=========>....................] - ETA: 7s - loss: 0.4686 - mae: 0.4271"
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- "258/729 [=========>....................] - ETA: 7s - loss: 0.4702 - mae: 0.4273"
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- "262/729 [=========>....................] - ETA: 7s - loss: 0.4745 - mae: 0.4276"
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- "266/729 [=========>....................] - ETA: 7s - loss: 0.4717 - mae: 0.4266"
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- "270/729 [==========>...................] - ETA: 7s - loss: 0.4696 - mae: 0.4260"
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- "274/729 [==========>...................] - ETA: 7s - loss: 0.4724 - mae: 0.4273"
- ]
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- "277/729 [==========>...................] - ETA: 7s - loss: 0.4703 - mae: 0.4265"
- ]
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- "280/729 [==========>...................] - ETA: 7s - loss: 0.4728 - mae: 0.4268"
- ]
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- "283/729 [==========>...................] - ETA: 7s - loss: 0.4753 - mae: 0.4278"
- ]
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- "286/729 [==========>...................] - ETA: 7s - loss: 0.4740 - mae: 0.4276"
- ]
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- "289/729 [==========>...................] - ETA: 7s - loss: 0.4734 - mae: 0.4275"
- ]
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- "292/729 [===========>..................] - ETA: 6s - loss: 0.4726 - mae: 0.4274"
- ]
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- "296/729 [===========>..................] - ETA: 6s - loss: 0.4752 - mae: 0.4276"
- ]
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- "300/729 [===========>..................] - ETA: 6s - loss: 0.4748 - mae: 0.4280"
- ]
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- "304/729 [===========>..................] - ETA: 6s - loss: 0.4733 - mae: 0.4279"
- ]
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- "308/729 [===========>..................] - ETA: 6s - loss: 0.4841 - mae: 0.4291"
- ]
- },
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- "312/729 [===========>..................] - ETA: 6s - loss: 0.4840 - mae: 0.4293"
- ]
- },
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- "316/729 [============>.................] - ETA: 6s - loss: 0.4883 - mae: 0.4308"
- ]
- },
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- "name": "stdout",
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- "319/729 [============>.................] - ETA: 6s - loss: 0.4865 - mae: 0.4303"
- ]
- },
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- "name": "stdout",
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- "323/729 [============>.................] - ETA: 6s - loss: 0.4859 - mae: 0.4302"
- ]
- },
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- "327/729 [============>.................] - ETA: 6s - loss: 0.4829 - mae: 0.4291"
- ]
- },
- {
- "name": "stdout",
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- "331/729 [============>.................] - ETA: 6s - loss: 0.4818 - mae: 0.4289"
- ]
- },
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- "335/729 [============>.................] - ETA: 6s - loss: 0.4808 - mae: 0.4288"
- ]
- },
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- "338/729 [============>.................] - ETA: 6s - loss: 0.4796 - mae: 0.4286"
- ]
- },
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- "342/729 [=============>................] - ETA: 6s - loss: 0.4777 - mae: 0.4281"
- ]
- },
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- "346/729 [=============>................] - ETA: 6s - loss: 0.4762 - mae: 0.4277"
- ]
- },
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- "350/729 [=============>................] - ETA: 6s - loss: 0.4742 - mae: 0.4269"
- ]
- },
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- "354/729 [=============>................] - ETA: 5s - loss: 0.4732 - mae: 0.4268"
- ]
- },
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- },
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- "362/729 [=============>................] - ETA: 5s - loss: 0.4718 - mae: 0.4267"
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- "366/729 [==============>...............] - ETA: 5s - loss: 0.4718 - mae: 0.4270"
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- "370/729 [==============>...............] - ETA: 5s - loss: 0.4722 - mae: 0.4274"
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- "374/729 [==============>...............] - ETA: 5s - loss: 0.4753 - mae: 0.4275"
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- "378/729 [==============>...............] - ETA: 5s - loss: 0.4748 - mae: 0.4273"
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- "382/729 [==============>...............] - ETA: 5s - loss: 0.4760 - mae: 0.4273"
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- "386/729 [==============>...............] - ETA: 5s - loss: 0.4743 - mae: 0.4269"
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- "390/729 [===============>..............] - ETA: 5s - loss: 0.4749 - mae: 0.4269"
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- "394/729 [===============>..............] - ETA: 5s - loss: 0.4739 - mae: 0.4264"
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- "398/729 [===============>..............] - ETA: 5s - loss: 0.4748 - mae: 0.4267"
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- "402/729 [===============>..............] - ETA: 5s - loss: 0.4739 - mae: 0.4266"
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- "406/729 [===============>..............] - ETA: 5s - loss: 0.4722 - mae: 0.4259"
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- "410/729 [===============>..............] - ETA: 5s - loss: 0.4716 - mae: 0.4255"
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- "414/729 [================>.............] - ETA: 4s - loss: 0.4741 - mae: 0.4257"
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- "418/729 [================>.............] - ETA: 4s - loss: 0.4737 - mae: 0.4258"
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- "422/729 [================>.............] - ETA: 4s - loss: 0.4748 - mae: 0.4260"
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- "426/729 [================>.............] - ETA: 4s - loss: 0.4734 - mae: 0.4256"
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- "430/729 [================>.............] - ETA: 4s - loss: 0.4728 - mae: 0.4253"
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- "434/729 [================>.............] - ETA: 4s - loss: 0.4725 - mae: 0.4251"
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- "438/729 [=================>............] - ETA: 4s - loss: 0.4715 - mae: 0.4248"
- ]
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- "442/729 [=================>............] - ETA: 4s - loss: 0.4700 - mae: 0.4243"
- ]
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- "446/729 [=================>............] - ETA: 4s - loss: 0.4695 - mae: 0.4239"
- ]
- },
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- "450/729 [=================>............] - ETA: 4s - loss: 0.4694 - mae: 0.4239"
- ]
- },
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- "454/729 [=================>............] - ETA: 4s - loss: 0.4681 - mae: 0.4235"
- ]
- },
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- "458/729 [=================>............] - ETA: 4s - loss: 0.4686 - mae: 0.4240"
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- "462/729 [==================>...........] - ETA: 4s - loss: 0.4678 - mae: 0.4238"
- ]
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- "466/729 [==================>...........] - ETA: 4s - loss: 0.4671 - mae: 0.4237"
- ]
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- "470/729 [==================>...........] - ETA: 4s - loss: 0.4677 - mae: 0.4240"
- ]
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- "474/729 [==================>...........] - ETA: 3s - loss: 0.4666 - mae: 0.4237"
- ]
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- "478/729 [==================>...........] - ETA: 3s - loss: 0.4666 - mae: 0.4239"
- ]
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- "482/729 [==================>...........] - ETA: 3s - loss: 0.4675 - mae: 0.4239"
- ]
- },
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- "486/729 [===================>..........] - ETA: 3s - loss: 0.4669 - mae: 0.4238"
- ]
- },
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- "output_type": "stream",
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- "490/729 [===================>..........] - ETA: 3s - loss: 0.4675 - mae: 0.4241"
- ]
- },
- {
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- "494/729 [===================>..........] - ETA: 3s - loss: 0.4674 - mae: 0.4240"
- ]
- },
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- "498/729 [===================>..........] - ETA: 3s - loss: 0.4704 - mae: 0.4241"
- ]
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- "502/729 [===================>..........] - ETA: 3s - loss: 0.4708 - mae: 0.4240"
- ]
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- "506/729 [===================>..........] - ETA: 3s - loss: 0.4698 - mae: 0.4237"
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- "510/729 [===================>..........] - ETA: 3s - loss: 0.4684 - mae: 0.4234"
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- "514/729 [====================>.........] - ETA: 3s - loss: 0.4682 - mae: 0.4234"
- ]
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- "518/729 [====================>.........] - ETA: 3s - loss: 0.4687 - mae: 0.4235"
- ]
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- "521/729 [====================>.........] - ETA: 3s - loss: 0.4700 - mae: 0.4233"
- ]
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- "525/729 [====================>.........] - ETA: 3s - loss: 0.4754 - mae: 0.4234"
- ]
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- "529/729 [====================>.........] - ETA: 3s - loss: 0.4748 - mae: 0.4232"
- ]
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- "533/729 [====================>.........] - ETA: 3s - loss: 0.4740 - mae: 0.4231"
- ]
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- "537/729 [=====================>........] - ETA: 2s - loss: 0.4768 - mae: 0.4233"
- ]
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- "name": "stdout",
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- "541/729 [=====================>........] - ETA: 2s - loss: 0.4764 - mae: 0.4236"
- ]
- },
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- "name": "stdout",
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- "545/729 [=====================>........] - ETA: 2s - loss: 0.4845 - mae: 0.4241"
- ]
- },
- {
- "name": "stdout",
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- "549/729 [=====================>........] - ETA: 2s - loss: 0.4838 - mae: 0.4239"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "553/729 [=====================>........] - ETA: 2s - loss: 0.4923 - mae: 0.4245"
- ]
- },
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- "name": "stdout",
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- "557/729 [=====================>........] - ETA: 2s - loss: 0.4916 - mae: 0.4246"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "561/729 [======================>.......] - ETA: 2s - loss: 0.4907 - mae: 0.4245"
- ]
- },
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- "name": "stdout",
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- "565/729 [======================>.......] - ETA: 2s - loss: 0.4909 - mae: 0.4246"
- ]
- },
- {
- "name": "stdout",
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- "569/729 [======================>.......] - ETA: 2s - loss: 0.4912 - mae: 0.4248"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "573/729 [======================>.......] - ETA: 2s - loss: 0.4924 - mae: 0.4252"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "577/729 [======================>.......] - ETA: 2s - loss: 0.4911 - mae: 0.4248"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "581/729 [======================>.......] - ETA: 2s - loss: 0.4928 - mae: 0.4251"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "585/729 [=======================>......] - ETA: 2s - loss: 0.4924 - mae: 0.4251"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "589/729 [=======================>......] - ETA: 2s - loss: 0.4908 - mae: 0.4245"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "593/729 [=======================>......] - ETA: 2s - loss: 0.4893 - mae: 0.4240"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "597/729 [=======================>......] - ETA: 2s - loss: 0.4902 - mae: 0.4247"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "601/729 [=======================>......] - ETA: 1s - loss: 0.4912 - mae: 0.4251"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "605/729 [=======================>......] - ETA: 1s - loss: 0.4908 - mae: 0.4249"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "609/729 [========================>.....] - ETA: 1s - loss: 0.4901 - mae: 0.4247"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "613/729 [========================>.....] - ETA: 1s - loss: 0.4910 - mae: 0.4251"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "617/729 [========================>.....] - ETA: 1s - loss: 0.4947 - mae: 0.4251"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "621/729 [========================>.....] - ETA: 1s - loss: 0.4941 - mae: 0.4252"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "625/729 [========================>.....] - ETA: 1s - loss: 0.4946 - mae: 0.4255"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "629/729 [========================>.....] - ETA: 1s - loss: 0.4942 - mae: 0.4255"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "633/729 [=========================>....] - ETA: 1s - loss: 0.4936 - mae: 0.4254"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "636/729 [=========================>....] - ETA: 1s - loss: 0.4940 - mae: 0.4254"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "639/729 [=========================>....] - ETA: 1s - loss: 0.4935 - mae: 0.4253"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "642/729 [=========================>....] - ETA: 1s - loss: 0.4932 - mae: 0.4253"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "645/729 [=========================>....] - ETA: 1s - loss: 0.4931 - mae: 0.4254"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "648/729 [=========================>....] - ETA: 1s - loss: 0.4928 - mae: 0.4253"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "651/729 [=========================>....] - ETA: 1s - loss: 0.4916 - mae: 0.4249"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "655/729 [=========================>....] - ETA: 1s - loss: 0.4902 - mae: 0.4244"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "659/729 [==========================>...] - ETA: 1s - loss: 0.4907 - mae: 0.4246"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "663/729 [==========================>...] - ETA: 1s - loss: 0.4897 - mae: 0.4242"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "667/729 [==========================>...] - ETA: 0s - loss: 0.4901 - mae: 0.4247"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "671/729 [==========================>...] - ETA: 0s - loss: 0.4891 - mae: 0.4243"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "675/729 [==========================>...] - ETA: 0s - loss: 0.4881 - mae: 0.4241"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "679/729 [==========================>...] - ETA: 0s - loss: 0.4875 - mae: 0.4241"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "683/729 [===========================>..] - ETA: 0s - loss: 0.4866 - mae: 0.4238"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "687/729 [===========================>..] - ETA: 0s - loss: 0.4859 - mae: 0.4236"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "691/729 [===========================>..] - ETA: 0s - loss: 0.4852 - mae: 0.4235"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "694/729 [===========================>..] - ETA: 0s - loss: 0.4851 - mae: 0.4236"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "697/729 [===========================>..] - ETA: 0s - loss: 0.4853 - mae: 0.4236"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "700/729 [===========================>..] - ETA: 0s - loss: 0.4866 - mae: 0.4239"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "703/729 [===========================>..] - ETA: 0s - loss: 0.4861 - mae: 0.4239"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "707/729 [============================>.] - ETA: 0s - loss: 0.4862 - mae: 0.4241"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "711/729 [============================>.] - ETA: 0s - loss: 0.4866 - mae: 0.4242"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "715/729 [============================>.] - ETA: 0s - loss: 0.4861 - mae: 0.4242"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "719/729 [============================>.] - ETA: 0s - loss: 0.4856 - mae: 0.4240"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "723/729 [============================>.] - ETA: 0s - loss: 0.4845 - mae: 0.4237"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "727/729 [============================>.] - ETA: 0s - loss: 0.4835 - mae: 0.4234"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - 12s 17ms/step - loss: 0.4833 - mae: 0.4233 - val_loss: 0.4483 - val_mae: 0.3840\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 4/10\n",
- "\r",
- " 1/729 [..............................] - ETA: 0s - loss: 0.2831 - mae: 0.3501"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/729 [..............................] - ETA: 9s - loss: 0.4026 - mae: 0.4002"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/729 [..............................] - ETA: 10s - loss: 0.4545 - mae: 0.4052"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/729 [..............................] - ETA: 10s - loss: 0.4941 - mae: 0.4093"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 17/729 [..............................] - ETA: 10s - loss: 0.5986 - mae: 0.4301"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/729 [..............................] - ETA: 10s - loss: 0.5746 - mae: 0.4319"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 25/729 [>.............................] - ETA: 10s - loss: 0.5359 - mae: 0.4268"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 29/729 [>.............................] - ETA: 10s - loss: 0.5279 - mae: 0.4273"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 33/729 [>.............................] - ETA: 10s - loss: 0.6441 - mae: 0.4365"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 37/729 [>.............................] - ETA: 10s - loss: 0.6088 - mae: 0.4299"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 41/729 [>.............................] - ETA: 10s - loss: 0.6556 - mae: 0.4310"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 45/729 [>.............................] - ETA: 10s - loss: 0.6234 - mae: 0.4245"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 49/729 [=>............................] - ETA: 10s - loss: 0.6038 - mae: 0.4240"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 53/729 [=>............................] - ETA: 10s - loss: 0.5839 - mae: 0.4204"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 57/729 [=>............................] - ETA: 10s - loss: 0.5749 - mae: 0.4202"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 61/729 [=>............................] - ETA: 10s - loss: 0.5538 - mae: 0.4148"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 65/729 [=>............................] - ETA: 10s - loss: 0.5401 - mae: 0.4130"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 69/729 [=>............................] - ETA: 10s - loss: 0.5615 - mae: 0.4134"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 73/729 [==>...........................] - ETA: 10s - loss: 0.5529 - mae: 0.4123"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 77/729 [==>...........................] - ETA: 10s - loss: 0.5442 - mae: 0.4114"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 81/729 [==>...........................] - ETA: 10s - loss: 0.5343 - mae: 0.4103"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 85/729 [==>...........................] - ETA: 9s - loss: 0.5320 - mae: 0.4123 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 89/729 [==>...........................] - ETA: 9s - loss: 0.5252 - mae: 0.4122"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 93/729 [==>...........................] - ETA: 9s - loss: 0.5194 - mae: 0.4119"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 97/729 [==>...........................] - ETA: 9s - loss: 0.5092 - mae: 0.4101"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "101/729 [===>..........................] - ETA: 9s - loss: 0.4987 - mae: 0.4072"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "105/729 [===>..........................] - ETA: 9s - loss: 0.4931 - mae: 0.4064"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "109/729 [===>..........................] - ETA: 9s - loss: 0.4881 - mae: 0.4061"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "113/729 [===>..........................] - ETA: 9s - loss: 0.4918 - mae: 0.4064"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "117/729 [===>..........................] - ETA: 9s - loss: 0.4897 - mae: 0.4067"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "121/729 [===>..........................] - ETA: 9s - loss: 0.4855 - mae: 0.4057"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "125/729 [====>.........................] - ETA: 9s - loss: 0.4812 - mae: 0.4055"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "129/729 [====>.........................] - ETA: 9s - loss: 0.4777 - mae: 0.4057"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "133/729 [====>.........................] - ETA: 9s - loss: 0.4746 - mae: 0.4059"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "137/729 [====>.........................] - ETA: 9s - loss: 0.4770 - mae: 0.4076"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "141/729 [====>.........................] - ETA: 9s - loss: 0.4765 - mae: 0.4079"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "145/729 [====>.........................] - ETA: 9s - loss: 0.4818 - mae: 0.4066"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "149/729 [=====>........................] - ETA: 8s - loss: 0.4789 - mae: 0.4064"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "152/729 [=====>........................] - ETA: 8s - loss: 0.4759 - mae: 0.4057"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "156/729 [=====>........................] - ETA: 8s - loss: 0.4772 - mae: 0.4074"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "160/729 [=====>........................] - ETA: 8s - loss: 0.4871 - mae: 0.4092"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "164/729 [=====>........................] - ETA: 8s - loss: 0.4930 - mae: 0.4109"
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- "168/729 [=====>........................] - ETA: 8s - loss: 0.4898 - mae: 0.4107"
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- "172/729 [======>.......................] - ETA: 8s - loss: 0.4888 - mae: 0.4114"
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- "176/729 [======>.......................] - ETA: 8s - loss: 0.4925 - mae: 0.4126"
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- "180/729 [======>.......................] - ETA: 8s - loss: 0.4880 - mae: 0.4114"
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- "184/729 [======>.......................] - ETA: 8s - loss: 0.4843 - mae: 0.4106"
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- "188/729 [======>.......................] - ETA: 8s - loss: 0.4822 - mae: 0.4104"
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- "192/729 [======>.......................] - ETA: 8s - loss: 0.4804 - mae: 0.4106"
- ]
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- "195/729 [=======>......................] - ETA: 8s - loss: 0.4770 - mae: 0.4095"
- ]
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- "198/729 [=======>......................] - ETA: 8s - loss: 0.4764 - mae: 0.4098"
- ]
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- "201/729 [=======>......................] - ETA: 8s - loss: 0.4759 - mae: 0.4100"
- ]
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- "205/729 [=======>......................] - ETA: 8s - loss: 0.4723 - mae: 0.4091"
- ]
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- "208/729 [=======>......................] - ETA: 8s - loss: 0.4721 - mae: 0.4091"
- ]
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- "211/729 [=======>......................] - ETA: 8s - loss: 0.4715 - mae: 0.4093"
- ]
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- "214/729 [=======>......................] - ETA: 8s - loss: 0.4702 - mae: 0.4092"
- ]
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- "217/729 [=======>......................] - ETA: 7s - loss: 0.4707 - mae: 0.4095"
- ]
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- "221/729 [========>.....................] - ETA: 7s - loss: 0.4690 - mae: 0.4096"
- ]
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- "225/729 [========>.....................] - ETA: 7s - loss: 0.4701 - mae: 0.4101"
- ]
- },
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- "229/729 [========>.....................] - ETA: 7s - loss: 0.4705 - mae: 0.4098"
- ]
- },
- {
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- "233/729 [========>.....................] - ETA: 7s - loss: 0.4710 - mae: 0.4105"
- ]
- },
- {
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- "237/729 [========>.....................] - ETA: 7s - loss: 0.4703 - mae: 0.4106"
- ]
- },
- {
- "name": "stdout",
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- "241/729 [========>.....................] - ETA: 7s - loss: 0.4700 - mae: 0.4112"
- ]
- },
- {
- "name": "stdout",
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- "245/729 [=========>....................] - ETA: 7s - loss: 0.4721 - mae: 0.4123"
- ]
- },
- {
- "name": "stdout",
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- "249/729 [=========>....................] - ETA: 7s - loss: 0.4701 - mae: 0.4120"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "253/729 [=========>....................] - ETA: 7s - loss: 0.4695 - mae: 0.4122"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "257/729 [=========>....................] - ETA: 7s - loss: 0.4676 - mae: 0.4120"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "261/729 [=========>....................] - ETA: 7s - loss: 0.4664 - mae: 0.4117"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "265/729 [=========>....................] - ETA: 7s - loss: 0.4645 - mae: 0.4114"
- ]
- },
- {
- "name": "stdout",
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- "269/729 [==========>...................] - ETA: 7s - loss: 0.4634 - mae: 0.4114"
- ]
- },
- {
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- "273/729 [==========>...................] - ETA: 7s - loss: 0.4646 - mae: 0.4118"
- ]
- },
- {
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- "output_type": "stream",
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- "277/729 [==========>...................] - ETA: 7s - loss: 0.4646 - mae: 0.4114"
- ]
- },
- {
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- "281/729 [==========>...................] - ETA: 6s - loss: 0.4626 - mae: 0.4109"
- ]
- },
- {
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- "output_type": "stream",
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- "285/729 [==========>...................] - ETA: 6s - loss: 0.4607 - mae: 0.4105"
- ]
- },
- {
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- "289/729 [==========>...................] - ETA: 6s - loss: 0.4609 - mae: 0.4107"
- ]
- },
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- "293/729 [===========>..................] - ETA: 6s - loss: 0.4616 - mae: 0.4114"
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- "297/729 [===========>..................] - ETA: 6s - loss: 0.4603 - mae: 0.4111"
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- "301/729 [===========>..................] - ETA: 6s - loss: 0.4604 - mae: 0.4115"
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- "305/729 [===========>..................] - ETA: 6s - loss: 0.4711 - mae: 0.4130"
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- "309/729 [===========>..................] - ETA: 6s - loss: 0.4696 - mae: 0.4129"
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- "313/729 [===========>..................] - ETA: 6s - loss: 0.4687 - mae: 0.4129"
- ]
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- "317/729 [============>.................] - ETA: 6s - loss: 0.4679 - mae: 0.4130"
- ]
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- "321/729 [============>.................] - ETA: 6s - loss: 0.4666 - mae: 0.4128"
- ]
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- "325/729 [============>.................] - ETA: 6s - loss: 0.4676 - mae: 0.4135"
- ]
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- "329/729 [============>.................] - ETA: 6s - loss: 0.4698 - mae: 0.4146"
- ]
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- "333/729 [============>.................] - ETA: 6s - loss: 0.4762 - mae: 0.4151"
- ]
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- "337/729 [============>.................] - ETA: 6s - loss: 0.4769 - mae: 0.4155"
- ]
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- "340/729 [============>.................] - ETA: 6s - loss: 0.4755 - mae: 0.4151"
- ]
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- "344/729 [=============>................] - ETA: 5s - loss: 0.4756 - mae: 0.4156"
- ]
- },
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- "text": [
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- "348/729 [=============>................] - ETA: 5s - loss: 0.4738 - mae: 0.4153"
- ]
- },
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- "text": [
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- "352/729 [=============>................] - ETA: 5s - loss: 0.4743 - mae: 0.4156"
- ]
- },
- {
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- "356/729 [=============>................] - ETA: 5s - loss: 0.4754 - mae: 0.4156"
- ]
- },
- {
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- "output_type": "stream",
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- "360/729 [=============>................] - ETA: 5s - loss: 0.4742 - mae: 0.4152"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "364/729 [=============>................] - ETA: 5s - loss: 0.4764 - mae: 0.4156"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "368/729 [==============>...............] - ETA: 5s - loss: 0.4767 - mae: 0.4159"
- ]
- },
- {
- "name": "stdout",
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- "372/729 [==============>...............] - ETA: 5s - loss: 0.4770 - mae: 0.4161"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "376/729 [==============>...............] - ETA: 5s - loss: 0.4757 - mae: 0.4159"
- ]
- },
- {
- "name": "stdout",
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- "380/729 [==============>...............] - ETA: 5s - loss: 0.4760 - mae: 0.4161"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "384/729 [==============>...............] - ETA: 5s - loss: 0.4748 - mae: 0.4159"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "388/729 [==============>...............] - ETA: 5s - loss: 0.4732 - mae: 0.4155"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "392/729 [===============>..............] - ETA: 5s - loss: 0.4726 - mae: 0.4155"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "396/729 [===============>..............] - ETA: 5s - loss: 0.4721 - mae: 0.4152"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "400/729 [===============>..............] - ETA: 5s - loss: 0.4712 - mae: 0.4150"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "404/729 [===============>..............] - ETA: 5s - loss: 0.4697 - mae: 0.4146"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "408/729 [===============>..............] - ETA: 4s - loss: 0.4707 - mae: 0.4146"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "412/729 [===============>..............] - ETA: 4s - loss: 0.4694 - mae: 0.4142"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "416/729 [================>.............] - ETA: 4s - loss: 0.4682 - mae: 0.4140"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "420/729 [================>.............] - ETA: 4s - loss: 0.4666 - mae: 0.4136"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "424/729 [================>.............] - ETA: 4s - loss: 0.4654 - mae: 0.4132"
- ]
- },
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- "428/729 [================>.............] - ETA: 4s - loss: 0.4658 - mae: 0.4136"
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- "432/729 [================>.............] - ETA: 4s - loss: 0.4658 - mae: 0.4138"
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- "name": "stdout",
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- "436/729 [================>.............] - ETA: 4s - loss: 0.4665 - mae: 0.4141"
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- "440/729 [=================>............] - ETA: 4s - loss: 0.4667 - mae: 0.4139"
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- "444/729 [=================>............] - ETA: 4s - loss: 0.4658 - mae: 0.4139"
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- "448/729 [=================>............] - ETA: 4s - loss: 0.4678 - mae: 0.4146"
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- "452/729 [=================>............] - ETA: 4s - loss: 0.4662 - mae: 0.4140"
- ]
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- "name": "stdout",
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- "456/729 [=================>............] - ETA: 4s - loss: 0.4656 - mae: 0.4139"
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- "460/729 [=================>............] - ETA: 4s - loss: 0.4661 - mae: 0.4144"
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- "464/729 [==================>...........] - ETA: 4s - loss: 0.4650 - mae: 0.4141"
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- "467/729 [==================>...........] - ETA: 4s - loss: 0.4643 - mae: 0.4140"
- ]
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- "471/729 [==================>...........] - ETA: 3s - loss: 0.4630 - mae: 0.4135"
- ]
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- "475/729 [==================>...........] - ETA: 3s - loss: 0.4617 - mae: 0.4132"
- ]
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- "name": "stdout",
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- "479/729 [==================>...........] - ETA: 3s - loss: 0.4608 - mae: 0.4129"
- ]
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- {
- "name": "stdout",
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- "483/729 [==================>...........] - ETA: 3s - loss: 0.4597 - mae: 0.4125"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "487/729 [===================>..........] - ETA: 3s - loss: 0.4586 - mae: 0.4121"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "491/729 [===================>..........] - ETA: 3s - loss: 0.4587 - mae: 0.4121"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "495/729 [===================>..........] - ETA: 3s - loss: 0.4577 - mae: 0.4119"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "499/729 [===================>..........] - ETA: 3s - loss: 0.4600 - mae: 0.4124"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "503/729 [===================>..........] - ETA: 3s - loss: 0.4595 - mae: 0.4124"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "507/729 [===================>..........] - ETA: 3s - loss: 0.4594 - mae: 0.4124"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "510/729 [===================>..........] - ETA: 3s - loss: 0.4588 - mae: 0.4123"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "514/729 [====================>.........] - ETA: 3s - loss: 0.4605 - mae: 0.4128"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "517/729 [====================>.........] - ETA: 3s - loss: 0.4595 - mae: 0.4125"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "521/729 [====================>.........] - ETA: 3s - loss: 0.4588 - mae: 0.4123"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "525/729 [====================>.........] - ETA: 3s - loss: 0.4580 - mae: 0.4121"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "529/729 [====================>.........] - ETA: 3s - loss: 0.4580 - mae: 0.4123"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "533/729 [====================>.........] - ETA: 3s - loss: 0.4577 - mae: 0.4122"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "537/729 [=====================>........] - ETA: 2s - loss: 0.4580 - mae: 0.4124"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "541/729 [=====================>........] - ETA: 2s - loss: 0.4582 - mae: 0.4125"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "545/729 [=====================>........] - ETA: 2s - loss: 0.4591 - mae: 0.4132"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "549/729 [=====================>........] - ETA: 2s - loss: 0.4586 - mae: 0.4128"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "553/729 [=====================>........] - ETA: 2s - loss: 0.4575 - mae: 0.4125"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "557/729 [=====================>........] - ETA: 2s - loss: 0.4569 - mae: 0.4124"
- ]
- },
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- "609/729 [========================>.....] - ETA: 1s - loss: 0.4563 - mae: 0.4113"
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- "613/729 [========================>.....] - ETA: 1s - loss: 0.4557 - mae: 0.4111"
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- "617/729 [========================>.....] - ETA: 1s - loss: 0.4563 - mae: 0.4112"
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- "621/729 [========================>.....] - ETA: 1s - loss: 0.4551 - mae: 0.4107"
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- "625/729 [========================>.....] - ETA: 1s - loss: 0.4547 - mae: 0.4107"
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- "629/729 [========================>.....] - ETA: 1s - loss: 0.4577 - mae: 0.4112"
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- "633/729 [=========================>....] - ETA: 1s - loss: 0.4566 - mae: 0.4108"
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- "637/729 [=========================>....] - ETA: 1s - loss: 0.4565 - mae: 0.4109"
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- "641/729 [=========================>....] - ETA: 1s - loss: 0.4565 - mae: 0.4110"
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- "645/729 [=========================>....] - ETA: 1s - loss: 0.4560 - mae: 0.4109"
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- "649/729 [=========================>....] - ETA: 1s - loss: 0.4561 - mae: 0.4110"
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- "653/729 [=========================>....] - ETA: 1s - loss: 0.4553 - mae: 0.4107"
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- "657/729 [==========================>...] - ETA: 1s - loss: 0.4546 - mae: 0.4105"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "697/729 [===========================>..] - ETA: 0s - loss: 0.4666 - mae: 0.4112"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "701/729 [===========================>..] - ETA: 0s - loss: 0.4727 - mae: 0.4119"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "705/729 [============================>.] - ETA: 0s - loss: 0.4716 - mae: 0.4116"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "709/729 [============================>.] - ETA: 0s - loss: 0.4716 - mae: 0.4117"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "713/729 [============================>.] - ETA: 0s - loss: 0.4707 - mae: 0.4116"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "717/729 [============================>.] - ETA: 0s - loss: 0.4701 - mae: 0.4116"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "721/729 [============================>.] - ETA: 0s - loss: 0.4696 - mae: 0.4115"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "725/729 [============================>.] - ETA: 0s - loss: 0.4709 - mae: 0.4117"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - ETA: 0s - loss: 0.4704 - mae: 0.4115"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - 12s 17ms/step - loss: 0.4704 - mae: 0.4115 - val_loss: 0.4431 - val_mae: 0.3898\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 5/10\n",
- "\r",
- " 1/729 [..............................] - ETA: 0s - loss: 0.1900 - mae: 0.2947"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/729 [..............................] - ETA: 8s - loss: 0.7350 - mae: 0.3977"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/729 [..............................] - ETA: 9s - loss: 0.9163 - mae: 0.4542"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/729 [..............................] - ETA: 9s - loss: 0.7437 - mae: 0.4348"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 17/729 [..............................] - ETA: 9s - loss: 0.6408 - mae: 0.4198"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/729 [..............................] - ETA: 10s - loss: 0.6551 - mae: 0.4318"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 25/729 [>.............................] - ETA: 10s - loss: 0.6087 - mae: 0.4312"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 29/729 [>.............................] - ETA: 9s - loss: 0.6156 - mae: 0.4323 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 33/729 [>.............................] - ETA: 9s - loss: 0.5872 - mae: 0.4267"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 37/729 [>.............................] - ETA: 9s - loss: 0.5535 - mae: 0.4179"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 41/729 [>.............................] - ETA: 10s - loss: 0.5408 - mae: 0.4175"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 45/729 [>.............................] - ETA: 10s - loss: 0.5173 - mae: 0.4115"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 49/729 [=>............................] - ETA: 9s - loss: 0.5130 - mae: 0.4102 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 53/729 [=>............................] - ETA: 9s - loss: 0.4956 - mae: 0.4057"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 57/729 [=>............................] - ETA: 9s - loss: 0.4859 - mae: 0.4060"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 61/729 [=>............................] - ETA: 9s - loss: 0.4797 - mae: 0.4067"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 65/729 [=>............................] - ETA: 9s - loss: 0.4766 - mae: 0.4071"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 69/729 [=>............................] - ETA: 9s - loss: 0.5165 - mae: 0.4102"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 73/729 [==>...........................] - ETA: 9s - loss: 0.5107 - mae: 0.4106"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 77/729 [==>...........................] - ETA: 9s - loss: 0.5056 - mae: 0.4112"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 81/729 [==>...........................] - ETA: 9s - loss: 0.5145 - mae: 0.4135"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 85/729 [==>...........................] - ETA: 9s - loss: 0.5188 - mae: 0.4169"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 89/729 [==>...........................] - ETA: 9s - loss: 0.5091 - mae: 0.4154"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 93/729 [==>...........................] - ETA: 9s - loss: 0.5097 - mae: 0.4156"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 97/729 [==>...........................] - ETA: 9s - loss: 0.5014 - mae: 0.4135"
- ]
- },
- {
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- "101/729 [===>..........................] - ETA: 9s - loss: 0.4949 - mae: 0.4128"
- ]
- },
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- "105/729 [===>..........................] - ETA: 9s - loss: 0.4973 - mae: 0.4148"
- ]
- },
- {
- "name": "stdout",
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- "109/729 [===>..........................] - ETA: 9s - loss: 0.4999 - mae: 0.4157"
- ]
- },
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- "name": "stdout",
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- "113/729 [===>..........................] - ETA: 9s - loss: 0.4923 - mae: 0.4135"
- ]
- },
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- "117/729 [===>..........................] - ETA: 9s - loss: 0.4924 - mae: 0.4142"
- ]
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- "121/729 [===>..........................] - ETA: 8s - loss: 0.4975 - mae: 0.4148"
- ]
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- "125/729 [====>.........................] - ETA: 8s - loss: 0.4924 - mae: 0.4139"
- ]
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- {
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- "129/729 [====>.........................] - ETA: 8s - loss: 0.4868 - mae: 0.4118"
- ]
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- {
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- "133/729 [====>.........................] - ETA: 8s - loss: 0.4949 - mae: 0.4122"
- ]
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- {
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- "137/729 [====>.........................] - ETA: 8s - loss: 0.4887 - mae: 0.4104"
- ]
- },
- {
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- "141/729 [====>.........................] - ETA: 8s - loss: 0.4857 - mae: 0.4105"
- ]
- },
- {
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- "145/729 [====>.........................] - ETA: 8s - loss: 0.5144 - mae: 0.4120"
- ]
- },
- {
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- "149/729 [=====>........................] - ETA: 8s - loss: 0.5111 - mae: 0.4116"
- ]
- },
- {
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- "153/729 [=====>........................] - ETA: 8s - loss: 0.5132 - mae: 0.4132"
- ]
- },
- {
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- "157/729 [=====>........................] - ETA: 8s - loss: 0.5107 - mae: 0.4138"
- ]
- },
- {
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- "161/729 [=====>........................] - ETA: 8s - loss: 0.5069 - mae: 0.4138"
- ]
- },
- {
- "name": "stdout",
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- "165/729 [=====>........................] - ETA: 8s - loss: 0.5069 - mae: 0.4142"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "169/729 [=====>........................] - ETA: 8s - loss: 0.5023 - mae: 0.4135"
- ]
- },
- {
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- "173/729 [======>.......................] - ETA: 8s - loss: 0.5021 - mae: 0.4135"
- ]
- },
- {
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- "177/729 [======>.......................] - ETA: 8s - loss: 0.4975 - mae: 0.4125"
- ]
- },
- {
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- "181/729 [======>.......................] - ETA: 8s - loss: 0.4971 - mae: 0.4132"
- ]
- },
- {
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- "185/729 [======>.......................] - ETA: 8s - loss: 0.4951 - mae: 0.4127"
- ]
- },
- {
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- "189/729 [======>.......................] - ETA: 8s - loss: 0.4939 - mae: 0.4124"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "193/729 [======>.......................] - ETA: 7s - loss: 0.4932 - mae: 0.4128"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "197/729 [=======>......................] - ETA: 7s - loss: 0.4910 - mae: 0.4119"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "201/729 [=======>......................] - ETA: 7s - loss: 0.4921 - mae: 0.4128"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "205/729 [=======>......................] - ETA: 7s - loss: 0.4952 - mae: 0.4139"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "209/729 [=======>......................] - ETA: 7s - loss: 0.4930 - mae: 0.4138"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "213/729 [=======>......................] - ETA: 7s - loss: 0.5020 - mae: 0.4154"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "217/729 [=======>......................] - ETA: 7s - loss: 0.5031 - mae: 0.4158"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "221/729 [========>.....................] - ETA: 7s - loss: 0.5006 - mae: 0.4154"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "225/729 [========>.....................] - ETA: 7s - loss: 0.5159 - mae: 0.4164"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "229/729 [========>.....................] - ETA: 7s - loss: 0.5113 - mae: 0.4150"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "233/729 [========>.....................] - ETA: 7s - loss: 0.5089 - mae: 0.4146"
- ]
- },
- {
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- "237/729 [========>.....................] - ETA: 7s - loss: 0.5062 - mae: 0.4139"
- ]
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- "241/729 [========>.....................] - ETA: 7s - loss: 0.5025 - mae: 0.4129"
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- "245/729 [=========>....................] - ETA: 7s - loss: 0.5002 - mae: 0.4124"
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- "249/729 [=========>....................] - ETA: 7s - loss: 0.5007 - mae: 0.4127"
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- "253/729 [=========>....................] - ETA: 7s - loss: 0.5000 - mae: 0.4130"
- ]
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- "257/729 [=========>....................] - ETA: 7s - loss: 0.4976 - mae: 0.4124"
- ]
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- "261/729 [=========>....................] - ETA: 7s - loss: 0.4987 - mae: 0.4128"
- ]
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- "265/729 [=========>....................] - ETA: 6s - loss: 0.4965 - mae: 0.4125"
- ]
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- "269/729 [==========>...................] - ETA: 6s - loss: 0.4930 - mae: 0.4113"
- ]
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- "273/729 [==========>...................] - ETA: 6s - loss: 0.4912 - mae: 0.4107"
- ]
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- "277/729 [==========>...................] - ETA: 6s - loss: 0.4919 - mae: 0.4109"
- ]
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- "281/729 [==========>...................] - ETA: 6s - loss: 0.4887 - mae: 0.4098"
- ]
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- "285/729 [==========>...................] - ETA: 6s - loss: 0.4878 - mae: 0.4099"
- ]
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- "289/729 [==========>...................] - ETA: 6s - loss: 0.4849 - mae: 0.4091"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "293/729 [===========>..................] - ETA: 6s - loss: 0.4828 - mae: 0.4087"
- ]
- },
- {
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- "text": [
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- "297/729 [===========>..................] - ETA: 6s - loss: 0.4857 - mae: 0.4082"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "301/729 [===========>..................] - ETA: 6s - loss: 0.4882 - mae: 0.4082"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "305/729 [===========>..................] - ETA: 6s - loss: 0.4868 - mae: 0.4083"
- ]
- },
- {
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- "output_type": "stream",
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- "309/729 [===========>..................] - ETA: 6s - loss: 0.4843 - mae: 0.4076"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "313/729 [===========>..................] - ETA: 6s - loss: 0.4855 - mae: 0.4077"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "316/729 [============>.................] - ETA: 6s - loss: 0.4849 - mae: 0.4078"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "319/729 [============>.................] - ETA: 6s - loss: 0.4862 - mae: 0.4080"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "322/729 [============>.................] - ETA: 6s - loss: 0.4846 - mae: 0.4076"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "325/729 [============>.................] - ETA: 6s - loss: 0.4849 - mae: 0.4079"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "328/729 [============>.................] - ETA: 6s - loss: 0.4828 - mae: 0.4072"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "332/729 [============>.................] - ETA: 6s - loss: 0.4811 - mae: 0.4070"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "336/729 [============>.................] - ETA: 5s - loss: 0.4884 - mae: 0.4073"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "340/729 [============>.................] - ETA: 5s - loss: 0.4864 - mae: 0.4069"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "344/729 [=============>................] - ETA: 5s - loss: 0.4871 - mae: 0.4075"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "348/729 [=============>................] - ETA: 5s - loss: 0.4851 - mae: 0.4071"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "352/729 [=============>................] - ETA: 5s - loss: 0.4833 - mae: 0.4068"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "356/729 [=============>................] - ETA: 5s - loss: 0.4856 - mae: 0.4073"
- ]
- },
- {
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- "output_type": "stream",
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- "360/729 [=============>................] - ETA: 5s - loss: 0.4856 - mae: 0.4069"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "364/729 [=============>................] - ETA: 5s - loss: 0.4847 - mae: 0.4071"
- ]
- },
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- "368/729 [==============>...............] - ETA: 5s - loss: 0.4839 - mae: 0.4068"
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- "372/729 [==============>...............] - ETA: 5s - loss: 0.4832 - mae: 0.4069"
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- "376/729 [==============>...............] - ETA: 5s - loss: 0.4842 - mae: 0.4070"
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- "380/729 [==============>...............] - ETA: 5s - loss: 0.4858 - mae: 0.4074"
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- "384/729 [==============>...............] - ETA: 5s - loss: 0.4840 - mae: 0.4071"
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- "388/729 [==============>...............] - ETA: 5s - loss: 0.4836 - mae: 0.4070"
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- "392/729 [===============>..............] - ETA: 5s - loss: 0.4829 - mae: 0.4071"
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- "396/729 [===============>..............] - ETA: 5s - loss: 0.4816 - mae: 0.4068"
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- "400/729 [===============>..............] - ETA: 4s - loss: 0.4842 - mae: 0.4070"
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- "404/729 [===============>..............] - ETA: 4s - loss: 0.4822 - mae: 0.4063"
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- "408/729 [===============>..............] - ETA: 4s - loss: 0.4799 - mae: 0.4055"
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- "412/729 [===============>..............] - ETA: 4s - loss: 0.4801 - mae: 0.4062"
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- "416/729 [================>.............] - ETA: 4s - loss: 0.4820 - mae: 0.4070"
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- {
- "name": "stdout",
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- "420/729 [================>.............] - ETA: 4s - loss: 0.4805 - mae: 0.4068"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "424/729 [================>.............] - ETA: 4s - loss: 0.4812 - mae: 0.4068"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "428/729 [================>.............] - ETA: 4s - loss: 0.4798 - mae: 0.4064"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "432/729 [================>.............] - ETA: 4s - loss: 0.4789 - mae: 0.4064"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "436/729 [================>.............] - ETA: 4s - loss: 0.4768 - mae: 0.4058"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "440/729 [=================>............] - ETA: 4s - loss: 0.4757 - mae: 0.4056"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "444/729 [=================>............] - ETA: 4s - loss: 0.4750 - mae: 0.4052"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "448/729 [=================>............] - ETA: 4s - loss: 0.4741 - mae: 0.4050"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "452/729 [=================>............] - ETA: 4s - loss: 0.4731 - mae: 0.4049"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "456/729 [=================>............] - ETA: 4s - loss: 0.4750 - mae: 0.4057"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "460/729 [=================>............] - ETA: 4s - loss: 0.4749 - mae: 0.4057"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "464/729 [==================>...........] - ETA: 4s - loss: 0.4742 - mae: 0.4054"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "468/729 [==================>...........] - ETA: 3s - loss: 0.4724 - mae: 0.4050"
- ]
- },
- {
- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "472/729 [==================>...........] - ETA: 3s - loss: 0.4707 - mae: 0.4044"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "476/729 [==================>...........] - ETA: 3s - loss: 0.4704 - mae: 0.4045"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "480/729 [==================>...........] - ETA: 3s - loss: 0.4707 - mae: 0.4048"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "484/729 [==================>...........] - ETA: 3s - loss: 0.4696 - mae: 0.4047"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "488/729 [===================>..........] - ETA: 3s - loss: 0.4692 - mae: 0.4047"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "492/729 [===================>..........] - ETA: 3s - loss: 0.4680 - mae: 0.4043"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "496/729 [===================>..........] - ETA: 3s - loss: 0.4660 - mae: 0.4035"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "500/729 [===================>..........] - ETA: 3s - loss: 0.4660 - mae: 0.4035"
- ]
- },
- {
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- "504/729 [===================>..........] - ETA: 3s - loss: 0.4650 - mae: 0.4034"
- ]
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- "508/729 [===================>..........] - ETA: 3s - loss: 0.4652 - mae: 0.4036"
- ]
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- "512/729 [====================>.........] - ETA: 3s - loss: 0.4635 - mae: 0.4030"
- ]
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- {
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- "516/729 [====================>.........] - ETA: 3s - loss: 0.4630 - mae: 0.4030"
- ]
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- {
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- "520/729 [====================>.........] - ETA: 3s - loss: 0.4626 - mae: 0.4028"
- ]
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- {
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- "524/729 [====================>.........] - ETA: 3s - loss: 0.4626 - mae: 0.4031"
- ]
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- {
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- "528/729 [====================>.........] - ETA: 3s - loss: 0.4624 - mae: 0.4034"
- ]
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- {
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- "532/729 [====================>.........] - ETA: 2s - loss: 0.4611 - mae: 0.4030"
- ]
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- {
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- "535/729 [=====================>........] - ETA: 2s - loss: 0.4597 - mae: 0.4026"
- ]
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- "539/729 [=====================>........] - ETA: 2s - loss: 0.4595 - mae: 0.4025"
- ]
- },
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- "543/729 [=====================>........] - ETA: 2s - loss: 0.4581 - mae: 0.4021"
- ]
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- "547/729 [=====================>........] - ETA: 2s - loss: 0.4571 - mae: 0.4019"
- ]
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- "551/729 [=====================>........] - ETA: 2s - loss: 0.4571 - mae: 0.4022"
- ]
- },
- {
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- "555/729 [=====================>........] - ETA: 2s - loss: 0.4568 - mae: 0.4023"
- ]
- },
- {
- "name": "stdout",
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- "559/729 [======================>.......] - ETA: 2s - loss: 0.4559 - mae: 0.4021"
- ]
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- "name": "stdout",
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- "563/729 [======================>.......] - ETA: 2s - loss: 0.4554 - mae: 0.4020"
- ]
- },
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- "name": "stdout",
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- "567/729 [======================>.......] - ETA: 2s - loss: 0.4543 - mae: 0.4017"
- ]
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- "name": "stdout",
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- "571/729 [======================>.......] - ETA: 2s - loss: 0.4535 - mae: 0.4015"
- ]
- },
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- "name": "stdout",
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- "575/729 [======================>.......] - ETA: 2s - loss: 0.4544 - mae: 0.4021"
- ]
- },
- {
- "name": "stdout",
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- "579/729 [======================>.......] - ETA: 2s - loss: 0.4536 - mae: 0.4020"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "582/729 [======================>.......] - ETA: 2s - loss: 0.4533 - mae: 0.4019"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "585/729 [=======================>......] - ETA: 2s - loss: 0.4530 - mae: 0.4019"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "589/729 [=======================>......] - ETA: 2s - loss: 0.4523 - mae: 0.4018"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "593/729 [=======================>......] - ETA: 2s - loss: 0.4522 - mae: 0.4017"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "597/729 [=======================>......] - ETA: 2s - loss: 0.4516 - mae: 0.4015"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "601/729 [=======================>......] - ETA: 1s - loss: 0.4526 - mae: 0.4020"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "604/729 [=======================>......] - ETA: 1s - loss: 0.4524 - mae: 0.4019"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "607/729 [=======================>......] - ETA: 1s - loss: 0.4519 - mae: 0.4018"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "611/729 [========================>.....] - ETA: 1s - loss: 0.4520 - mae: 0.4019"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "614/729 [========================>.....] - ETA: 1s - loss: 0.4514 - mae: 0.4018"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "618/729 [========================>.....] - ETA: 1s - loss: 0.4516 - mae: 0.4022"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "622/729 [========================>.....] - ETA: 1s - loss: 0.4530 - mae: 0.4023"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "626/729 [========================>.....] - ETA: 1s - loss: 0.4563 - mae: 0.4025"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "630/729 [========================>.....] - ETA: 1s - loss: 0.4566 - mae: 0.4028"
- ]
- },
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- "output_type": "stream",
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- "634/729 [=========================>....] - ETA: 1s - loss: 0.4556 - mae: 0.4025"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "637/729 [=========================>....] - ETA: 1s - loss: 0.4557 - mae: 0.4026"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "640/729 [=========================>....] - ETA: 1s - loss: 0.4550 - mae: 0.4025"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "643/729 [=========================>....] - ETA: 1s - loss: 0.4540 - mae: 0.4022"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "646/729 [=========================>....] - ETA: 1s - loss: 0.4534 - mae: 0.4021"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "650/729 [=========================>....] - ETA: 1s - loss: 0.4528 - mae: 0.4020"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "654/729 [=========================>....] - ETA: 1s - loss: 0.4534 - mae: 0.4023"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "658/729 [==========================>...] - ETA: 1s - loss: 0.4548 - mae: 0.4029"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "662/729 [==========================>...] - ETA: 1s - loss: 0.4554 - mae: 0.4029"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "666/729 [==========================>...] - ETA: 0s - loss: 0.4552 - mae: 0.4029"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "670/729 [==========================>...] - ETA: 0s - loss: 0.4549 - mae: 0.4029"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "674/729 [==========================>...] - ETA: 0s - loss: 0.4543 - mae: 0.4028"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "678/729 [==========================>...] - ETA: 0s - loss: 0.4555 - mae: 0.4032"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "682/729 [===========================>..] - ETA: 0s - loss: 0.4546 - mae: 0.4029"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "686/729 [===========================>..] - ETA: 0s - loss: 0.4542 - mae: 0.4029"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "690/729 [===========================>..] - ETA: 0s - loss: 0.4536 - mae: 0.4028"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "694/729 [===========================>..] - ETA: 0s - loss: 0.4547 - mae: 0.4031"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "698/729 [===========================>..] - ETA: 0s - loss: 0.4563 - mae: 0.4032"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "702/729 [===========================>..] - ETA: 0s - loss: 0.4588 - mae: 0.4038"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "706/729 [============================>.] - ETA: 0s - loss: 0.4605 - mae: 0.4044"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "710/729 [============================>.] - ETA: 0s - loss: 0.4600 - mae: 0.4043"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "714/729 [============================>.] - ETA: 0s - loss: 0.4598 - mae: 0.4044"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "718/729 [============================>.] - ETA: 0s - loss: 0.4610 - mae: 0.4048"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "722/729 [============================>.] - ETA: 0s - loss: 0.4604 - mae: 0.4046"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "726/729 [============================>.] - ETA: 0s - loss: 0.4603 - mae: 0.4047"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - 12s 17ms/step - loss: 0.4597 - mae: 0.4047 - val_loss: 0.4369 - val_mae: 0.3822\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 6/10\n",
- "\r",
- " 1/729 [..............................] - ETA: 0s - loss: 1.6886 - mae: 0.6142"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/729 [..............................] - ETA: 8s - loss: 0.5879 - mae: 0.4195"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/729 [..............................] - ETA: 9s - loss: 0.4441 - mae: 0.3862"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/729 [..............................] - ETA: 9s - loss: 0.4433 - mae: 0.3921"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 17/729 [..............................] - ETA: 9s - loss: 0.4483 - mae: 0.4035"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/729 [..............................] - ETA: 9s - loss: 0.4191 - mae: 0.3960"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 25/729 [>.............................] - ETA: 9s - loss: 0.4120 - mae: 0.3915"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 29/729 [>.............................] - ETA: 9s - loss: 0.3890 - mae: 0.3825"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 33/729 [>.............................] - ETA: 9s - loss: 0.3800 - mae: 0.3791"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 37/729 [>.............................] - ETA: 9s - loss: 0.3862 - mae: 0.3855"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 41/729 [>.............................] - ETA: 9s - loss: 0.3779 - mae: 0.3830"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 45/729 [>.............................] - ETA: 9s - loss: 0.3836 - mae: 0.3847"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 49/729 [=>............................] - ETA: 9s - loss: 0.3906 - mae: 0.3878"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 53/729 [=>............................] - ETA: 9s - loss: 0.3880 - mae: 0.3886"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 57/729 [=>............................] - ETA: 9s - loss: 0.3860 - mae: 0.3887"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 61/729 [=>............................] - ETA: 9s - loss: 0.3954 - mae: 0.3910"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 65/729 [=>............................] - ETA: 9s - loss: 0.3889 - mae: 0.3887"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 69/729 [=>............................] - ETA: 9s - loss: 0.3918 - mae: 0.3909"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 73/729 [==>...........................] - ETA: 9s - loss: 0.3976 - mae: 0.3924"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 77/729 [==>...........................] - ETA: 9s - loss: 0.3958 - mae: 0.3923"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 81/729 [==>...........................] - ETA: 9s - loss: 0.3939 - mae: 0.3917"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 85/729 [==>...........................] - ETA: 9s - loss: 0.4231 - mae: 0.3947"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 89/729 [==>...........................] - ETA: 9s - loss: 0.4196 - mae: 0.3938"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 93/729 [==>...........................] - ETA: 9s - loss: 0.4188 - mae: 0.3927"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 97/729 [==>...........................] - ETA: 9s - loss: 0.4175 - mae: 0.3935"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "101/729 [===>..........................] - ETA: 9s - loss: 0.4413 - mae: 0.3938"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "105/729 [===>..........................] - ETA: 9s - loss: 0.4385 - mae: 0.3941"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "109/729 [===>..........................] - ETA: 9s - loss: 0.4405 - mae: 0.3967"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "113/729 [===>..........................] - ETA: 9s - loss: 0.4617 - mae: 0.3973"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "117/729 [===>..........................] - ETA: 8s - loss: 0.4546 - mae: 0.3956"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "121/729 [===>..........................] - ETA: 8s - loss: 0.4707 - mae: 0.3994"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "125/729 [====>.........................] - ETA: 8s - loss: 0.4710 - mae: 0.4009"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "129/729 [====>.........................] - ETA: 8s - loss: 0.4704 - mae: 0.4016"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "133/729 [====>.........................] - ETA: 8s - loss: 0.4697 - mae: 0.4021"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "137/729 [====>.........................] - ETA: 8s - loss: 0.4673 - mae: 0.4016"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "141/729 [====>.........................] - ETA: 8s - loss: 0.4672 - mae: 0.4027"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "145/729 [====>.........................] - ETA: 8s - loss: 0.4629 - mae: 0.4015"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "149/729 [=====>........................] - ETA: 8s - loss: 0.4599 - mae: 0.4003"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "153/729 [=====>........................] - ETA: 8s - loss: 0.4704 - mae: 0.4016"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "157/729 [=====>........................] - ETA: 8s - loss: 0.4675 - mae: 0.4012"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "161/729 [=====>........................] - ETA: 8s - loss: 0.4657 - mae: 0.4011"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "165/729 [=====>........................] - ETA: 8s - loss: 0.4682 - mae: 0.4025"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "169/729 [=====>........................] - ETA: 8s - loss: 0.4681 - mae: 0.4027"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "173/729 [======>.......................] - ETA: 8s - loss: 0.4744 - mae: 0.4043"
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- "177/729 [======>.......................] - ETA: 8s - loss: 0.4731 - mae: 0.4039"
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- "181/729 [======>.......................] - ETA: 8s - loss: 0.4699 - mae: 0.4032"
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- "185/729 [======>.......................] - ETA: 7s - loss: 0.4652 - mae: 0.4018"
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- "189/729 [======>.......................] - ETA: 7s - loss: 0.4617 - mae: 0.4006"
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- "193/729 [======>.......................] - ETA: 7s - loss: 0.4594 - mae: 0.4005"
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- "196/729 [=======>......................] - ETA: 7s - loss: 0.4581 - mae: 0.4003"
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- "200/729 [=======>......................] - ETA: 7s - loss: 0.4606 - mae: 0.4005"
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- "204/729 [=======>......................] - ETA: 7s - loss: 0.4588 - mae: 0.4003"
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- "208/729 [=======>......................] - ETA: 7s - loss: 0.4561 - mae: 0.3996"
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- "212/729 [=======>......................] - ETA: 7s - loss: 0.4553 - mae: 0.4002"
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- "216/729 [=======>......................] - ETA: 7s - loss: 0.4570 - mae: 0.4010"
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- "220/729 [========>.....................] - ETA: 7s - loss: 0.4677 - mae: 0.4019"
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- "224/729 [========>.....................] - ETA: 7s - loss: 0.4644 - mae: 0.4009"
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- "228/729 [========>.....................] - ETA: 7s - loss: 0.4742 - mae: 0.4010"
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- "232/729 [========>.....................] - ETA: 7s - loss: 0.4752 - mae: 0.4023"
- ]
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- "236/729 [========>.....................] - ETA: 7s - loss: 0.4748 - mae: 0.4031"
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- "240/729 [========>.....................] - ETA: 7s - loss: 0.4723 - mae: 0.4026"
- ]
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- "243/729 [=========>....................] - ETA: 7s - loss: 0.4713 - mae: 0.4028"
- ]
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- "246/729 [=========>....................] - ETA: 7s - loss: 0.4731 - mae: 0.4036"
- ]
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- "249/729 [=========>....................] - ETA: 7s - loss: 0.4715 - mae: 0.4029"
- ]
- },
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- "253/729 [=========>....................] - ETA: 7s - loss: 0.4722 - mae: 0.4029"
- ]
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- "257/729 [=========>....................] - ETA: 7s - loss: 0.4707 - mae: 0.4028"
- ]
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- "260/729 [=========>....................] - ETA: 7s - loss: 0.4692 - mae: 0.4026"
- ]
- },
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- "263/729 [=========>....................] - ETA: 7s - loss: 0.4700 - mae: 0.4032"
- ]
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- "266/729 [=========>....................] - ETA: 6s - loss: 0.4738 - mae: 0.4038"
- ]
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- "269/729 [==========>...................] - ETA: 6s - loss: 0.4736 - mae: 0.4040"
- ]
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- "273/729 [==========>...................] - ETA: 6s - loss: 0.4736 - mae: 0.4041"
- ]
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- "276/729 [==========>...................] - ETA: 6s - loss: 0.4766 - mae: 0.4050"
- ]
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- "280/729 [==========>...................] - ETA: 6s - loss: 0.4739 - mae: 0.4043"
- ]
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- "283/729 [==========>...................] - ETA: 6s - loss: 0.4753 - mae: 0.4051"
- ]
- },
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- "287/729 [==========>...................] - ETA: 6s - loss: 0.4725 - mae: 0.4043"
- ]
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- "291/729 [==========>...................] - ETA: 6s - loss: 0.4708 - mae: 0.4041"
- ]
- },
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- "295/729 [===========>..................] - ETA: 6s - loss: 0.4696 - mae: 0.4041"
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- "299/729 [===========>..................] - ETA: 6s - loss: 0.4681 - mae: 0.4036"
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- "303/729 [===========>..................] - ETA: 6s - loss: 0.4682 - mae: 0.4041"
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- "307/729 [===========>..................] - ETA: 6s - loss: 0.4667 - mae: 0.4042"
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- "311/729 [===========>..................] - ETA: 6s - loss: 0.4646 - mae: 0.4036"
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- "315/729 [===========>..................] - ETA: 6s - loss: 0.4633 - mae: 0.4034"
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- "319/729 [============>.................] - ETA: 6s - loss: 0.4609 - mae: 0.4026"
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- "323/729 [============>.................] - ETA: 6s - loss: 0.4589 - mae: 0.4019"
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- "327/729 [============>.................] - ETA: 6s - loss: 0.4564 - mae: 0.4011"
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- "331/729 [============>.................] - ETA: 6s - loss: 0.4588 - mae: 0.4020"
- ]
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- "335/729 [============>.................] - ETA: 6s - loss: 0.4594 - mae: 0.4021"
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- "339/729 [============>.................] - ETA: 5s - loss: 0.4624 - mae: 0.4023"
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- "343/729 [=============>................] - ETA: 5s - loss: 0.4613 - mae: 0.4021"
- ]
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- "347/729 [=============>................] - ETA: 5s - loss: 0.4625 - mae: 0.4026"
- ]
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- "351/729 [=============>................] - ETA: 5s - loss: 0.4613 - mae: 0.4023"
- ]
- },
- {
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- "355/729 [=============>................] - ETA: 5s - loss: 0.4605 - mae: 0.4022"
- ]
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- "359/729 [=============>................] - ETA: 5s - loss: 0.4604 - mae: 0.4022"
- ]
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- "text": [
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- "363/729 [=============>................] - ETA: 5s - loss: 0.4589 - mae: 0.4019"
- ]
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- "367/729 [==============>...............] - ETA: 5s - loss: 0.4578 - mae: 0.4016"
- ]
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- "name": "stdout",
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- "371/729 [==============>...............] - ETA: 5s - loss: 0.4560 - mae: 0.4011"
- ]
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- "375/729 [==============>...............] - ETA: 5s - loss: 0.4573 - mae: 0.4012"
- ]
- },
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- "name": "stdout",
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- "379/729 [==============>...............] - ETA: 5s - loss: 0.4577 - mae: 0.4015"
- ]
- },
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- "name": "stdout",
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- "383/729 [==============>...............] - ETA: 5s - loss: 0.4581 - mae: 0.4019"
- ]
- },
- {
- "name": "stdout",
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- "387/729 [==============>...............] - ETA: 5s - loss: 0.4575 - mae: 0.4023"
- ]
- },
- {
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- "text": [
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- "390/729 [===============>..............] - ETA: 5s - loss: 0.4556 - mae: 0.4015"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "393/729 [===============>..............] - ETA: 5s - loss: 0.4543 - mae: 0.4013"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "397/729 [===============>..............] - ETA: 5s - loss: 0.4539 - mae: 0.4014"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "401/729 [===============>..............] - ETA: 5s - loss: 0.4539 - mae: 0.4015"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "405/729 [===============>..............] - ETA: 4s - loss: 0.4528 - mae: 0.4013"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "409/729 [===============>..............] - ETA: 4s - loss: 0.4512 - mae: 0.4009"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "413/729 [===============>..............] - ETA: 4s - loss: 0.4506 - mae: 0.4010"
- ]
- },
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- "output_type": "stream",
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- "417/729 [================>.............] - ETA: 4s - loss: 0.4488 - mae: 0.4004"
- ]
- },
- {
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- "output_type": "stream",
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- "421/729 [================>.............] - ETA: 4s - loss: 0.4487 - mae: 0.4006"
- ]
- },
- {
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- "425/729 [================>.............] - ETA: 4s - loss: 0.4505 - mae: 0.4007"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "433/729 [================>.............] - ETA: 4s - loss: 0.4512 - mae: 0.4005"
- ]
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- "437/729 [================>.............] - ETA: 4s - loss: 0.4521 - mae: 0.4006"
- ]
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- "441/729 [=================>............] - ETA: 4s - loss: 0.4509 - mae: 0.4004"
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- "445/729 [=================>............] - ETA: 4s - loss: 0.4549 - mae: 0.4009"
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- "449/729 [=================>............] - ETA: 4s - loss: 0.4547 - mae: 0.4012"
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- "453/729 [=================>............] - ETA: 4s - loss: 0.4528 - mae: 0.4005"
- ]
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- "457/729 [=================>............] - ETA: 4s - loss: 0.4525 - mae: 0.4005"
- ]
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- "name": "stdout",
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- "461/729 [=================>............] - ETA: 4s - loss: 0.4518 - mae: 0.4004"
- ]
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- "465/729 [==================>...........] - ETA: 4s - loss: 0.4597 - mae: 0.4011"
- ]
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- "469/729 [==================>...........] - ETA: 3s - loss: 0.4598 - mae: 0.4012"
- ]
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- "473/729 [==================>...........] - ETA: 3s - loss: 0.4599 - mae: 0.4015"
- ]
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- "name": "stdout",
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- "477/729 [==================>...........] - ETA: 3s - loss: 0.4586 - mae: 0.4012"
- ]
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- "name": "stdout",
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- "481/729 [==================>...........] - ETA: 3s - loss: 0.4606 - mae: 0.4018"
- ]
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- {
- "name": "stdout",
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- "text": [
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- "485/729 [==================>...........] - ETA: 3s - loss: 0.4594 - mae: 0.4015"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "489/729 [===================>..........] - ETA: 3s - loss: 0.4594 - mae: 0.4015"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "493/729 [===================>..........] - ETA: 3s - loss: 0.4589 - mae: 0.4013"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "497/729 [===================>..........] - ETA: 3s - loss: 0.4622 - mae: 0.4017"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "501/729 [===================>..........] - ETA: 3s - loss: 0.4608 - mae: 0.4013"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "505/729 [===================>..........] - ETA: 3s - loss: 0.4591 - mae: 0.4007"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "509/729 [===================>..........] - ETA: 3s - loss: 0.4572 - mae: 0.4000"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "513/729 [====================>.........] - ETA: 3s - loss: 0.4558 - mae: 0.3995"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "517/729 [====================>.........] - ETA: 3s - loss: 0.4562 - mae: 0.3999"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "521/729 [====================>.........] - ETA: 3s - loss: 0.4549 - mae: 0.3995"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "524/729 [====================>.........] - ETA: 3s - loss: 0.4543 - mae: 0.3993"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "528/729 [====================>.........] - ETA: 3s - loss: 0.4560 - mae: 0.3998"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "532/729 [====================>.........] - ETA: 3s - loss: 0.4566 - mae: 0.4002"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "536/729 [=====================>........] - ETA: 2s - loss: 0.4606 - mae: 0.4008"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "540/729 [=====================>........] - ETA: 2s - loss: 0.4603 - mae: 0.4007"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "544/729 [=====================>........] - ETA: 2s - loss: 0.4595 - mae: 0.4007"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "548/729 [=====================>........] - ETA: 2s - loss: 0.4584 - mae: 0.4005"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "551/729 [=====================>........] - ETA: 2s - loss: 0.4574 - mae: 0.4002"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "555/729 [=====================>........] - ETA: 2s - loss: 0.4581 - mae: 0.4004"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "559/729 [======================>.......] - ETA: 2s - loss: 0.4567 - mae: 0.4000"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "563/729 [======================>.......] - ETA: 2s - loss: 0.4567 - mae: 0.3999"
- ]
- },
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- "622/729 [========================>.....] - ETA: 1s - loss: 0.4482 - mae: 0.3986"
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "700/729 [===========================>..] - ETA: 0s - loss: 0.4537 - mae: 0.3997"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "703/729 [===========================>..] - ETA: 0s - loss: 0.4530 - mae: 0.3996"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "706/729 [============================>.] - ETA: 0s - loss: 0.4551 - mae: 0.4001"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "709/729 [============================>.] - ETA: 0s - loss: 0.4548 - mae: 0.4000"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "713/729 [============================>.] - ETA: 0s - loss: 0.4541 - mae: 0.3998"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "717/729 [============================>.] - ETA: 0s - loss: 0.4536 - mae: 0.3997"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "721/729 [============================>.] - ETA: 0s - loss: 0.4534 - mae: 0.3997"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "725/729 [============================>.] - ETA: 0s - loss: 0.4528 - mae: 0.3996"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - ETA: 0s - loss: 0.4516 - mae: 0.3991"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - 12s 17ms/step - loss: 0.4516 - mae: 0.3991 - val_loss: 0.4304 - val_mae: 0.3697\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 7/10\n",
- "\r",
- " 1/729 [..............................] - ETA: 0s - loss: 0.3274 - mae: 0.3362"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/729 [..............................] - ETA: 9s - loss: 0.6662 - mae: 0.3771"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/729 [..............................] - ETA: 10s - loss: 0.5619 - mae: 0.3925"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/729 [..............................] - ETA: 10s - loss: 0.4916 - mae: 0.3917"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 17/729 [..............................] - ETA: 10s - loss: 0.4529 - mae: 0.3907"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/729 [..............................] - ETA: 10s - loss: 0.4324 - mae: 0.3898"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 25/729 [>.............................] - ETA: 10s - loss: 0.4192 - mae: 0.3871"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 29/729 [>.............................] - ETA: 10s - loss: 0.4132 - mae: 0.3900"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 33/729 [>.............................] - ETA: 10s - loss: 0.4235 - mae: 0.3927"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 37/729 [>.............................] - ETA: 10s - loss: 0.4107 - mae: 0.3880"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 41/729 [>.............................] - ETA: 10s - loss: 0.4070 - mae: 0.3873"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 45/729 [>.............................] - ETA: 10s - loss: 0.4049 - mae: 0.3887"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 49/729 [=>............................] - ETA: 10s - loss: 0.3940 - mae: 0.3848"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 53/729 [=>............................] - ETA: 10s - loss: 0.3830 - mae: 0.3804"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 57/729 [=>............................] - ETA: 10s - loss: 0.3770 - mae: 0.3789"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 61/729 [=>............................] - ETA: 10s - loss: 0.3895 - mae: 0.3835"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 65/729 [=>............................] - ETA: 10s - loss: 0.3858 - mae: 0.3819"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 69/729 [=>............................] - ETA: 10s - loss: 0.3853 - mae: 0.3833"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 73/729 [==>...........................] - ETA: 9s - loss: 0.3932 - mae: 0.3839 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 77/729 [==>...........................] - ETA: 9s - loss: 0.3967 - mae: 0.3864"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 81/729 [==>...........................] - ETA: 9s - loss: 0.3925 - mae: 0.3858"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 84/729 [==>...........................] - ETA: 9s - loss: 0.3903 - mae: 0.3851"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 88/729 [==>...........................] - ETA: 9s - loss: 0.3867 - mae: 0.3839"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 92/729 [==>...........................] - ETA: 9s - loss: 0.3853 - mae: 0.3839"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 96/729 [==>...........................] - ETA: 9s - loss: 0.3997 - mae: 0.3871"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 99/729 [===>..........................] - ETA: 9s - loss: 0.3967 - mae: 0.3863"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "103/729 [===>..........................] - ETA: 9s - loss: 0.4257 - mae: 0.3895"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "106/729 [===>..........................] - ETA: 9s - loss: 0.4242 - mae: 0.3895"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "109/729 [===>..........................] - ETA: 9s - loss: 0.4210 - mae: 0.3887"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "113/729 [===>..........................] - ETA: 9s - loss: 0.4238 - mae: 0.3907"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "117/729 [===>..........................] - ETA: 9s - loss: 0.4192 - mae: 0.3895"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "120/729 [===>..........................] - ETA: 9s - loss: 0.4160 - mae: 0.3884"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "123/729 [====>.........................] - ETA: 9s - loss: 0.4120 - mae: 0.3873"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "126/729 [====>.........................] - ETA: 9s - loss: 0.4099 - mae: 0.3870"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "129/729 [====>.........................] - ETA: 9s - loss: 0.4077 - mae: 0.3865"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "132/729 [====>.........................] - ETA: 9s - loss: 0.4090 - mae: 0.3867"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "136/729 [====>.........................] - ETA: 9s - loss: 0.4093 - mae: 0.3866"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "139/729 [====>.........................] - ETA: 9s - loss: 0.4092 - mae: 0.3873"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "142/729 [====>.........................] - ETA: 9s - loss: 0.4120 - mae: 0.3864"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "146/729 [=====>........................] - ETA: 9s - loss: 0.4120 - mae: 0.3866"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "150/729 [=====>........................] - ETA: 9s - loss: 0.4160 - mae: 0.3871"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "154/729 [=====>........................] - ETA: 9s - loss: 0.4164 - mae: 0.3872"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "158/729 [=====>........................] - ETA: 9s - loss: 0.4185 - mae: 0.3889"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "162/729 [=====>........................] - ETA: 8s - loss: 0.4238 - mae: 0.3907"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "166/729 [=====>........................] - ETA: 8s - loss: 0.4219 - mae: 0.3905"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "170/729 [=====>........................] - ETA: 8s - loss: 0.4218 - mae: 0.3906"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "174/729 [======>.......................] - ETA: 8s - loss: 0.4519 - mae: 0.3947"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "178/729 [======>.......................] - ETA: 8s - loss: 0.4525 - mae: 0.3961"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "182/729 [======>.......................] - ETA: 8s - loss: 0.4571 - mae: 0.3978"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "186/729 [======>.......................] - ETA: 8s - loss: 0.4601 - mae: 0.3988"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "190/729 [======>.......................] - ETA: 8s - loss: 0.4619 - mae: 0.4003"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "194/729 [======>.......................] - ETA: 8s - loss: 0.4569 - mae: 0.3983"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "198/729 [=======>......................] - ETA: 8s - loss: 0.4630 - mae: 0.4000"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "202/729 [=======>......................] - ETA: 8s - loss: 0.4604 - mae: 0.3997"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "206/729 [=======>......................] - ETA: 8s - loss: 0.4556 - mae: 0.3979"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "210/729 [=======>......................] - ETA: 8s - loss: 0.4540 - mae: 0.3976"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "214/729 [=======>......................] - ETA: 8s - loss: 0.4629 - mae: 0.3989"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "218/729 [=======>......................] - ETA: 8s - loss: 0.4617 - mae: 0.3989"
- ]
- },
- {
- "name": "stdout",
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- "222/729 [========>.....................] - ETA: 7s - loss: 0.4619 - mae: 0.3986"
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- "226/729 [========>.....................] - ETA: 7s - loss: 0.4589 - mae: 0.3977"
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- "230/729 [========>.....................] - ETA: 7s - loss: 0.4583 - mae: 0.3975"
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- "234/729 [========>.....................] - ETA: 7s - loss: 0.4574 - mae: 0.3971"
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- "238/729 [========>.....................] - ETA: 7s - loss: 0.4565 - mae: 0.3973"
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- "242/729 [========>.....................] - ETA: 7s - loss: 0.4568 - mae: 0.3979"
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- "246/729 [=========>....................] - ETA: 7s - loss: 0.4745 - mae: 0.3992"
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- "249/729 [=========>....................] - ETA: 7s - loss: 0.4739 - mae: 0.3988"
- ]
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- "253/729 [=========>....................] - ETA: 7s - loss: 0.4742 - mae: 0.3993"
- ]
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- "257/729 [=========>....................] - ETA: 7s - loss: 0.4713 - mae: 0.3987"
- ]
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- "261/729 [=========>....................] - ETA: 7s - loss: 0.4693 - mae: 0.3986"
- ]
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- "265/729 [=========>....................] - ETA: 7s - loss: 0.4698 - mae: 0.3993"
- ]
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- "269/729 [==========>...................] - ETA: 7s - loss: 0.4693 - mae: 0.3992"
- ]
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- "273/729 [==========>...................] - ETA: 7s - loss: 0.4678 - mae: 0.3986"
- ]
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- "277/729 [==========>...................] - ETA: 7s - loss: 0.4672 - mae: 0.3986"
- ]
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- "281/729 [==========>...................] - ETA: 6s - loss: 0.4656 - mae: 0.3986"
- ]
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- "285/729 [==========>...................] - ETA: 6s - loss: 0.4634 - mae: 0.3980"
- ]
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- "289/729 [==========>...................] - ETA: 6s - loss: 0.4621 - mae: 0.3978"
- ]
- },
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- "293/729 [===========>..................] - ETA: 6s - loss: 0.4591 - mae: 0.3966"
- ]
- },
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- "297/729 [===========>..................] - ETA: 6s - loss: 0.4621 - mae: 0.3968"
- ]
- },
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- "301/729 [===========>..................] - ETA: 6s - loss: 0.4623 - mae: 0.3972"
- ]
- },
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- "305/729 [===========>..................] - ETA: 6s - loss: 0.4603 - mae: 0.3967"
- ]
- },
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- "309/729 [===========>..................] - ETA: 6s - loss: 0.4583 - mae: 0.3965"
- ]
- },
- {
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- "313/729 [===========>..................] - ETA: 6s - loss: 0.4565 - mae: 0.3960"
- ]
- },
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- "317/729 [============>.................] - ETA: 6s - loss: 0.4578 - mae: 0.3968"
- ]
- },
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- "321/729 [============>.................] - ETA: 6s - loss: 0.4648 - mae: 0.3985"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "325/729 [============>.................] - ETA: 6s - loss: 0.4629 - mae: 0.3978"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "329/729 [============>.................] - ETA: 6s - loss: 0.4638 - mae: 0.3980"
- ]
- },
- {
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- "output_type": "stream",
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- "333/729 [============>.................] - ETA: 6s - loss: 0.4618 - mae: 0.3975"
- ]
- },
- {
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- "337/729 [============>.................] - ETA: 6s - loss: 0.4606 - mae: 0.3972"
- ]
- },
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- "341/729 [=============>................] - ETA: 5s - loss: 0.4589 - mae: 0.3968"
- ]
- },
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- "345/729 [=============>................] - ETA: 5s - loss: 0.4611 - mae: 0.3971"
- ]
- },
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- ]
- },
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- "357/729 [=============>................] - ETA: 5s - loss: 0.4587 - mae: 0.3965"
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- "361/729 [=============>................] - ETA: 5s - loss: 0.4572 - mae: 0.3962"
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- "365/729 [==============>...............] - ETA: 5s - loss: 0.4561 - mae: 0.3961"
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- "369/729 [==============>...............] - ETA: 5s - loss: 0.4544 - mae: 0.3956"
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- "373/729 [==============>...............] - ETA: 5s - loss: 0.4523 - mae: 0.3950"
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- "377/729 [==============>...............] - ETA: 5s - loss: 0.4508 - mae: 0.3947"
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- "381/729 [==============>...............] - ETA: 5s - loss: 0.4491 - mae: 0.3942"
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- "385/729 [==============>...............] - ETA: 5s - loss: 0.4481 - mae: 0.3941"
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- "389/729 [===============>..............] - ETA: 5s - loss: 0.4513 - mae: 0.3950"
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- "393/729 [===============>..............] - ETA: 5s - loss: 0.4515 - mae: 0.3950"
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- "397/729 [===============>..............] - ETA: 5s - loss: 0.4511 - mae: 0.3952"
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- "401/729 [===============>..............] - ETA: 5s - loss: 0.4498 - mae: 0.3950"
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- "405/729 [===============>..............] - ETA: 4s - loss: 0.4488 - mae: 0.3949"
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- "409/729 [===============>..............] - ETA: 4s - loss: 0.4476 - mae: 0.3946"
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- "413/729 [===============>..............] - ETA: 4s - loss: 0.4473 - mae: 0.3947"
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- "417/729 [================>.............] - ETA: 4s - loss: 0.4466 - mae: 0.3946"
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- "421/729 [================>.............] - ETA: 4s - loss: 0.4462 - mae: 0.3945"
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- "425/729 [================>.............] - ETA: 4s - loss: 0.4466 - mae: 0.3948"
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- "429/729 [================>.............] - ETA: 4s - loss: 0.4479 - mae: 0.3957"
- ]
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- "433/729 [================>.............] - ETA: 4s - loss: 0.4469 - mae: 0.3955"
- ]
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- "436/729 [================>.............] - ETA: 4s - loss: 0.4462 - mae: 0.3955"
- ]
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- "440/729 [=================>............] - ETA: 4s - loss: 0.4457 - mae: 0.3957"
- ]
- },
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- "444/729 [=================>............] - ETA: 4s - loss: 0.4440 - mae: 0.3952"
- ]
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- "448/729 [=================>............] - ETA: 4s - loss: 0.4491 - mae: 0.3952"
- ]
- },
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- "451/729 [=================>............] - ETA: 4s - loss: 0.4485 - mae: 0.3951"
- ]
- },
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- "455/729 [=================>............] - ETA: 4s - loss: 0.4474 - mae: 0.3947"
- ]
- },
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- "458/729 [=================>............] - ETA: 4s - loss: 0.4464 - mae: 0.3945"
- ]
- },
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- "462/729 [==================>...........] - ETA: 4s - loss: 0.4455 - mae: 0.3944"
- ]
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- "466/729 [==================>...........] - ETA: 4s - loss: 0.4483 - mae: 0.3944"
- ]
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- "470/729 [==================>...........] - ETA: 3s - loss: 0.4496 - mae: 0.3945"
- ]
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- "474/729 [==================>...........] - ETA: 3s - loss: 0.4479 - mae: 0.3941"
- ]
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- "478/729 [==================>...........] - ETA: 3s - loss: 0.4470 - mae: 0.3939"
- ]
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- ]
- },
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- "486/729 [===================>..........] - ETA: 3s - loss: 0.4463 - mae: 0.3937"
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- "490/729 [===================>..........] - ETA: 3s - loss: 0.4476 - mae: 0.3945"
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- "494/729 [===================>..........] - ETA: 3s - loss: 0.4474 - mae: 0.3943"
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- "498/729 [===================>..........] - ETA: 3s - loss: 0.4466 - mae: 0.3941"
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- "502/729 [===================>..........] - ETA: 3s - loss: 0.4468 - mae: 0.3943"
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- "506/729 [===================>..........] - ETA: 3s - loss: 0.4466 - mae: 0.3942"
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- "510/729 [===================>..........] - ETA: 3s - loss: 0.4465 - mae: 0.3944"
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- "514/729 [====================>.........] - ETA: 3s - loss: 0.4474 - mae: 0.3948"
- ]
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- "518/729 [====================>.........] - ETA: 3s - loss: 0.4468 - mae: 0.3946"
- ]
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- "522/729 [====================>.........] - ETA: 3s - loss: 0.4456 - mae: 0.3943"
- ]
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- "526/729 [====================>.........] - ETA: 3s - loss: 0.4452 - mae: 0.3941"
- ]
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- "530/729 [====================>.........] - ETA: 3s - loss: 0.4448 - mae: 0.3940"
- ]
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- "534/729 [====================>.........] - ETA: 3s - loss: 0.4435 - mae: 0.3936"
- ]
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- "538/729 [=====================>........] - ETA: 2s - loss: 0.4429 - mae: 0.3933"
- ]
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- "542/729 [=====================>........] - ETA: 2s - loss: 0.4427 - mae: 0.3933"
- ]
- },
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- "546/729 [=====================>........] - ETA: 2s - loss: 0.4432 - mae: 0.3937"
- ]
- },
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- "550/729 [=====================>........] - ETA: 2s - loss: 0.4421 - mae: 0.3934"
- ]
- },
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- "554/729 [=====================>........] - ETA: 2s - loss: 0.4466 - mae: 0.3935"
- ]
- },
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- "name": "stdout",
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- "558/729 [=====================>........] - ETA: 2s - loss: 0.4457 - mae: 0.3933"
- ]
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- "name": "stdout",
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- "562/729 [======================>.......] - ETA: 2s - loss: 0.4456 - mae: 0.3935"
- ]
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- "name": "stdout",
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- "566/729 [======================>.......] - ETA: 2s - loss: 0.4480 - mae: 0.3939"
- ]
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- "570/729 [======================>.......] - ETA: 2s - loss: 0.4473 - mae: 0.3938"
- ]
- },
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- "574/729 [======================>.......] - ETA: 2s - loss: 0.4477 - mae: 0.3939"
- ]
- },
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- "578/729 [======================>.......] - ETA: 2s - loss: 0.4470 - mae: 0.3938"
- ]
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- "582/729 [======================>.......] - ETA: 2s - loss: 0.4473 - mae: 0.3941"
- ]
- },
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- "586/729 [=======================>......] - ETA: 2s - loss: 0.4480 - mae: 0.3941"
- ]
- },
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- "590/729 [=======================>......] - ETA: 2s - loss: 0.4478 - mae: 0.3941"
- ]
- },
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- "594/729 [=======================>......] - ETA: 2s - loss: 0.4468 - mae: 0.3939"
- ]
- },
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- "598/729 [=======================>......] - ETA: 2s - loss: 0.4457 - mae: 0.3935"
- ]
- },
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- "text": [
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- "602/729 [=======================>......] - ETA: 1s - loss: 0.4449 - mae: 0.3933"
- ]
- },
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- "output_type": "stream",
- "text": [
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- ]
- },
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- "output_type": "stream",
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- "610/729 [========================>.....] - ETA: 1s - loss: 0.4433 - mae: 0.3930"
- ]
- },
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- "output_type": "stream",
- "text": [
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- "614/729 [========================>.....] - ETA: 1s - loss: 0.4455 - mae: 0.3932"
- ]
- },
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- "text": [
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- "618/729 [========================>.....] - ETA: 1s - loss: 0.4465 - mae: 0.3933"
- ]
- },
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- "626/729 [========================>.....] - ETA: 1s - loss: 0.4449 - mae: 0.3929"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "630/729 [========================>.....] - ETA: 1s - loss: 0.4445 - mae: 0.3930"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "634/729 [=========================>....] - ETA: 1s - loss: 0.4431 - mae: 0.3924"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "638/729 [=========================>....] - ETA: 1s - loss: 0.4421 - mae: 0.3920"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "642/729 [=========================>....] - ETA: 1s - loss: 0.4418 - mae: 0.3920"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "646/729 [=========================>....] - ETA: 1s - loss: 0.4412 - mae: 0.3919"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "650/729 [=========================>....] - ETA: 1s - loss: 0.4406 - mae: 0.3919"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "654/729 [=========================>....] - ETA: 1s - loss: 0.4414 - mae: 0.3920"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "658/729 [==========================>...] - ETA: 1s - loss: 0.4407 - mae: 0.3918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "662/729 [==========================>...] - ETA: 1s - loss: 0.4415 - mae: 0.3921"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "666/729 [==========================>...] - ETA: 0s - loss: 0.4419 - mae: 0.3922"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "670/729 [==========================>...] - ETA: 0s - loss: 0.4415 - mae: 0.3923"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "674/729 [==========================>...] - ETA: 0s - loss: 0.4424 - mae: 0.3929"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "678/729 [==========================>...] - ETA: 0s - loss: 0.4421 - mae: 0.3929"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "682/729 [===========================>..] - ETA: 0s - loss: 0.4422 - mae: 0.3927"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "686/729 [===========================>..] - ETA: 0s - loss: 0.4424 - mae: 0.3929"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "690/729 [===========================>..] - ETA: 0s - loss: 0.4442 - mae: 0.3930"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "694/729 [===========================>..] - ETA: 0s - loss: 0.4445 - mae: 0.3932"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "698/729 [===========================>..] - ETA: 0s - loss: 0.4446 - mae: 0.3935"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "702/729 [===========================>..] - ETA: 0s - loss: 0.4467 - mae: 0.3939"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "706/729 [============================>.] - ETA: 0s - loss: 0.4468 - mae: 0.3938"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "710/729 [============================>.] - ETA: 0s - loss: 0.4459 - mae: 0.3936"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "714/729 [============================>.] - ETA: 0s - loss: 0.4457 - mae: 0.3935"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "718/729 [============================>.] - ETA: 0s - loss: 0.4448 - mae: 0.3933"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "722/729 [============================>.] - ETA: 0s - loss: 0.4452 - mae: 0.3936"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "726/729 [============================>.] - ETA: 0s - loss: 0.4443 - mae: 0.3932"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - 12s 17ms/step - loss: 0.4448 - mae: 0.3933 - val_loss: 0.4258 - val_mae: 0.3677\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 8/10\n",
- "\r",
- " 1/729 [..............................] - ETA: 0s - loss: 0.2510 - mae: 0.3279"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/729 [..............................] - ETA: 9s - loss: 0.2328 - mae: 0.3146"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/729 [..............................] - ETA: 10s - loss: 0.2634 - mae: 0.3362"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/729 [..............................] - ETA: 10s - loss: 0.2827 - mae: 0.3470"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 17/729 [..............................] - ETA: 10s - loss: 0.3482 - mae: 0.3650"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/729 [..............................] - ETA: 10s - loss: 0.3530 - mae: 0.3685"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 25/729 [>.............................] - ETA: 10s - loss: 0.3626 - mae: 0.3730"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 29/729 [>.............................] - ETA: 10s - loss: 0.3463 - mae: 0.3664"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 33/729 [>.............................] - ETA: 10s - loss: 0.3454 - mae: 0.3651"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 37/729 [>.............................] - ETA: 10s - loss: 0.3410 - mae: 0.3640"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 41/729 [>.............................] - ETA: 10s - loss: 0.3485 - mae: 0.3660"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 45/729 [>.............................] - ETA: 10s - loss: 0.3431 - mae: 0.3634"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 49/729 [=>............................] - ETA: 10s - loss: 0.3411 - mae: 0.3622"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 53/729 [=>............................] - ETA: 10s - loss: 0.3394 - mae: 0.3613"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 57/729 [=>............................] - ETA: 10s - loss: 0.3464 - mae: 0.3636"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 61/729 [=>............................] - ETA: 9s - loss: 0.3514 - mae: 0.3670 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 65/729 [=>............................] - ETA: 9s - loss: 0.3632 - mae: 0.3679"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 69/729 [=>............................] - ETA: 9s - loss: 0.3697 - mae: 0.3700"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 73/729 [==>...........................] - ETA: 9s - loss: 0.3689 - mae: 0.3712"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 76/729 [==>...........................] - ETA: 9s - loss: 0.3671 - mae: 0.3712"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 80/729 [==>...........................] - ETA: 9s - loss: 0.3702 - mae: 0.3731"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 84/729 [==>...........................] - ETA: 9s - loss: 0.3705 - mae: 0.3743"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 88/729 [==>...........................] - ETA: 9s - loss: 0.3687 - mae: 0.3738"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 92/729 [==>...........................] - ETA: 9s - loss: 0.3696 - mae: 0.3754"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 96/729 [==>...........................] - ETA: 9s - loss: 0.3778 - mae: 0.3758"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "100/729 [===>..........................] - ETA: 9s - loss: 0.3736 - mae: 0.3746"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "104/729 [===>..........................] - ETA: 9s - loss: 0.3735 - mae: 0.3755"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "108/729 [===>..........................] - ETA: 9s - loss: 0.3847 - mae: 0.3786"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "112/729 [===>..........................] - ETA: 9s - loss: 0.3856 - mae: 0.3793"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "116/729 [===>..........................] - ETA: 9s - loss: 0.3854 - mae: 0.3795"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "120/729 [===>..........................] - ETA: 9s - loss: 0.3854 - mae: 0.3789"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "124/729 [====>.........................] - ETA: 9s - loss: 0.3851 - mae: 0.3792"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "128/729 [====>.........................] - ETA: 9s - loss: 0.3847 - mae: 0.3798"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "132/729 [====>.........................] - ETA: 8s - loss: 0.3919 - mae: 0.3825"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "136/729 [====>.........................] - ETA: 8s - loss: 0.4053 - mae: 0.3848"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "140/729 [====>.........................] - ETA: 8s - loss: 0.4072 - mae: 0.3863"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "144/729 [====>.........................] - ETA: 8s - loss: 0.4063 - mae: 0.3866"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "148/729 [=====>........................] - ETA: 8s - loss: 0.4037 - mae: 0.3859"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "152/729 [=====>........................] - ETA: 8s - loss: 0.4094 - mae: 0.3869"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "156/729 [=====>........................] - ETA: 8s - loss: 0.4087 - mae: 0.3865"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "160/729 [=====>........................] - ETA: 8s - loss: 0.4079 - mae: 0.3866"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "164/729 [=====>........................] - ETA: 8s - loss: 0.4059 - mae: 0.3860"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "168/729 [=====>........................] - ETA: 8s - loss: 0.4148 - mae: 0.3864"
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- "172/729 [======>.......................] - ETA: 8s - loss: 0.4175 - mae: 0.3884"
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- "176/729 [======>.......................] - ETA: 8s - loss: 0.4253 - mae: 0.3894"
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- "180/729 [======>.......................] - ETA: 8s - loss: 0.4267 - mae: 0.3907"
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- "184/729 [======>.......................] - ETA: 8s - loss: 0.4222 - mae: 0.3888"
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- "188/729 [======>.......................] - ETA: 8s - loss: 0.4225 - mae: 0.3893"
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- "192/729 [======>.......................] - ETA: 8s - loss: 0.4186 - mae: 0.3879"
- ]
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- "196/729 [=======>......................] - ETA: 7s - loss: 0.4154 - mae: 0.3868"
- ]
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- "200/729 [=======>......................] - ETA: 7s - loss: 0.4167 - mae: 0.3870"
- ]
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- "204/729 [=======>......................] - ETA: 7s - loss: 0.4170 - mae: 0.3879"
- ]
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- "208/729 [=======>......................] - ETA: 7s - loss: 0.4170 - mae: 0.3879"
- ]
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- "212/729 [=======>......................] - ETA: 7s - loss: 0.4164 - mae: 0.3881"
- ]
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- "216/729 [=======>......................] - ETA: 7s - loss: 0.4143 - mae: 0.3875"
- ]
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- "220/729 [========>.....................] - ETA: 7s - loss: 0.4167 - mae: 0.3881"
- ]
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- "224/729 [========>.....................] - ETA: 7s - loss: 0.4172 - mae: 0.3885"
- ]
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- "228/729 [========>.....................] - ETA: 7s - loss: 0.4229 - mae: 0.3888"
- ]
- },
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- "232/729 [========>.....................] - ETA: 7s - loss: 0.4212 - mae: 0.3881"
- ]
- },
- {
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- "output_type": "stream",
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- "236/729 [========>.....................] - ETA: 7s - loss: 0.4198 - mae: 0.3880"
- ]
- },
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- "240/729 [========>.....................] - ETA: 7s - loss: 0.4179 - mae: 0.3875"
- ]
- },
- {
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- "244/729 [=========>....................] - ETA: 7s - loss: 0.4177 - mae: 0.3875"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "248/729 [=========>....................] - ETA: 7s - loss: 0.4164 - mae: 0.3872"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "252/729 [=========>....................] - ETA: 7s - loss: 0.4163 - mae: 0.3874"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "256/729 [=========>....................] - ETA: 7s - loss: 0.4150 - mae: 0.3870"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "260/729 [=========>....................] - ETA: 6s - loss: 0.4125 - mae: 0.3863"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "264/729 [=========>....................] - ETA: 6s - loss: 0.4115 - mae: 0.3862"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "268/729 [==========>...................] - ETA: 6s - loss: 0.4158 - mae: 0.3869"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "272/729 [==========>...................] - ETA: 6s - loss: 0.4187 - mae: 0.3865"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "276/729 [==========>...................] - ETA: 6s - loss: 0.4205 - mae: 0.3865"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "280/729 [==========>...................] - ETA: 6s - loss: 0.4199 - mae: 0.3865"
- ]
- },
- {
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- "output_type": "stream",
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- "284/729 [==========>...................] - ETA: 6s - loss: 0.4179 - mae: 0.3859"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "287/729 [==========>...................] - ETA: 6s - loss: 0.4196 - mae: 0.3862"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "291/729 [==========>...................] - ETA: 6s - loss: 0.4265 - mae: 0.3874"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "295/729 [===========>..................] - ETA: 6s - loss: 0.4261 - mae: 0.3871"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "299/729 [===========>..................] - ETA: 6s - loss: 0.4260 - mae: 0.3875"
- ]
- },
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- "303/729 [===========>..................] - ETA: 6s - loss: 0.4265 - mae: 0.3875"
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- "307/729 [===========>..................] - ETA: 6s - loss: 0.4269 - mae: 0.3878"
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- "311/729 [===========>..................] - ETA: 6s - loss: 0.4259 - mae: 0.3877"
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- "315/729 [===========>..................] - ETA: 6s - loss: 0.4269 - mae: 0.3881"
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- "319/729 [============>.................] - ETA: 6s - loss: 0.4268 - mae: 0.3884"
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- "323/729 [============>.................] - ETA: 6s - loss: 0.4261 - mae: 0.3884"
- ]
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- "326/729 [============>.................] - ETA: 6s - loss: 0.4253 - mae: 0.3882"
- ]
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- "330/729 [============>.................] - ETA: 5s - loss: 0.4260 - mae: 0.3884"
- ]
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- "334/729 [============>.................] - ETA: 5s - loss: 0.4252 - mae: 0.3884"
- ]
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- "338/729 [============>.................] - ETA: 5s - loss: 0.4240 - mae: 0.3879"
- ]
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- "342/729 [=============>................] - ETA: 5s - loss: 0.4302 - mae: 0.3887"
- ]
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- "346/729 [=============>................] - ETA: 5s - loss: 0.4299 - mae: 0.3887"
- ]
- },
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- "350/729 [=============>................] - ETA: 5s - loss: 0.4281 - mae: 0.3882"
- ]
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- "354/729 [=============>................] - ETA: 5s - loss: 0.4271 - mae: 0.3880"
- ]
- },
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- "358/729 [=============>................] - ETA: 5s - loss: 0.4249 - mae: 0.3872"
- ]
- },
- {
- "name": "stdout",
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- "361/729 [=============>................] - ETA: 5s - loss: 0.4261 - mae: 0.3872"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "365/729 [==============>...............] - ETA: 5s - loss: 0.4250 - mae: 0.3871"
- ]
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- {
- "name": "stdout",
- "output_type": "stream",
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- "369/729 [==============>...............] - ETA: 5s - loss: 0.4242 - mae: 0.3872"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "373/729 [==============>...............] - ETA: 5s - loss: 0.4243 - mae: 0.3873"
- ]
- },
- {
- "name": "stdout",
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- "376/729 [==============>...............] - ETA: 5s - loss: 0.4243 - mae: 0.3877"
- ]
- },
- {
- "name": "stdout",
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- "380/729 [==============>...............] - ETA: 5s - loss: 0.4250 - mae: 0.3882"
- ]
- },
- {
- "name": "stdout",
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- "383/729 [==============>...............] - ETA: 5s - loss: 0.4253 - mae: 0.3883"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "387/729 [==============>...............] - ETA: 5s - loss: 0.4263 - mae: 0.3884"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "391/729 [===============>..............] - ETA: 5s - loss: 0.4274 - mae: 0.3888"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "395/729 [===============>..............] - ETA: 5s - loss: 0.4276 - mae: 0.3890"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "398/729 [===============>..............] - ETA: 5s - loss: 0.4273 - mae: 0.3888"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "401/729 [===============>..............] - ETA: 4s - loss: 0.4340 - mae: 0.3893"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "404/729 [===============>..............] - ETA: 4s - loss: 0.4335 - mae: 0.3892"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "407/729 [===============>..............] - ETA: 4s - loss: 0.4333 - mae: 0.3894"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "410/729 [===============>..............] - ETA: 4s - loss: 0.4326 - mae: 0.3894"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "412/729 [===============>..............] - ETA: 4s - loss: 0.4335 - mae: 0.3896"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "414/729 [================>.............] - ETA: 4s - loss: 0.4327 - mae: 0.3894"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "417/729 [================>.............] - ETA: 4s - loss: 0.4326 - mae: 0.3895"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "420/729 [================>.............] - ETA: 4s - loss: 0.4320 - mae: 0.3893"
- ]
- },
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- "423/729 [================>.............] - ETA: 4s - loss: 0.4318 - mae: 0.3894"
- ]
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- "426/729 [================>.............] - ETA: 4s - loss: 0.4312 - mae: 0.3895"
- ]
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- {
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- "429/729 [================>.............] - ETA: 4s - loss: 0.4313 - mae: 0.3899"
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- "432/729 [================>.............] - ETA: 4s - loss: 0.4312 - mae: 0.3901"
- ]
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- "435/729 [================>.............] - ETA: 4s - loss: 0.4313 - mae: 0.3905"
- ]
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- {
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- "438/729 [=================>............] - ETA: 4s - loss: 0.4301 - mae: 0.3901"
- ]
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- "441/729 [=================>............] - ETA: 4s - loss: 0.4293 - mae: 0.3898"
- ]
- },
- {
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- "text": [
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- "444/729 [=================>............] - ETA: 4s - loss: 0.4293 - mae: 0.3900"
- ]
- },
- {
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- "text": [
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- "447/729 [=================>............] - ETA: 4s - loss: 0.4314 - mae: 0.3905"
- ]
- },
- {
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- "text": [
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- "450/729 [=================>............] - ETA: 4s - loss: 0.4300 - mae: 0.3900"
- ]
- },
- {
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- "text": [
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- "453/729 [=================>............] - ETA: 4s - loss: 0.4295 - mae: 0.3899"
- ]
- },
- {
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- "456/729 [=================>............] - ETA: 4s - loss: 0.4410 - mae: 0.3908"
- ]
- },
- {
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- "459/729 [=================>............] - ETA: 4s - loss: 0.4404 - mae: 0.3908"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "462/729 [==================>...........] - ETA: 4s - loss: 0.4392 - mae: 0.3904"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "465/729 [==================>...........] - ETA: 4s - loss: 0.4388 - mae: 0.3905"
- ]
- },
- {
- "name": "stdout",
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- "468/729 [==================>...........] - ETA: 4s - loss: 0.4389 - mae: 0.3908"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "471/729 [==================>...........] - ETA: 4s - loss: 0.4387 - mae: 0.3911"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "474/729 [==================>...........] - ETA: 4s - loss: 0.4380 - mae: 0.3910"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "477/729 [==================>...........] - ETA: 4s - loss: 0.4369 - mae: 0.3907"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "480/729 [==================>...........] - ETA: 3s - loss: 0.4370 - mae: 0.3908"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "483/729 [==================>...........] - ETA: 3s - loss: 0.4369 - mae: 0.3909"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "486/729 [===================>..........] - ETA: 3s - loss: 0.4363 - mae: 0.3909"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "489/729 [===================>..........] - ETA: 3s - loss: 0.4369 - mae: 0.3912"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "492/729 [===================>..........] - ETA: 3s - loss: 0.4366 - mae: 0.3912"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "495/729 [===================>..........] - ETA: 3s - loss: 0.4356 - mae: 0.3909"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "498/729 [===================>..........] - ETA: 3s - loss: 0.4353 - mae: 0.3909"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "501/729 [===================>..........] - ETA: 3s - loss: 0.4347 - mae: 0.3908"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "504/729 [===================>..........] - ETA: 3s - loss: 0.4351 - mae: 0.3911"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "507/729 [===================>..........] - ETA: 3s - loss: 0.4349 - mae: 0.3910"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "510/729 [===================>..........] - ETA: 3s - loss: 0.4346 - mae: 0.3908"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "513/729 [====================>.........] - ETA: 3s - loss: 0.4343 - mae: 0.3909"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "516/729 [====================>.........] - ETA: 3s - loss: 0.4337 - mae: 0.3909"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "519/729 [====================>.........] - ETA: 3s - loss: 0.4335 - mae: 0.3910"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "522/729 [====================>.........] - ETA: 3s - loss: 0.4327 - mae: 0.3908"
- ]
- },
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- "525/729 [====================>.........] - ETA: 3s - loss: 0.4324 - mae: 0.3908"
- ]
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- "528/729 [====================>.........] - ETA: 3s - loss: 0.4334 - mae: 0.3912"
- ]
- },
- {
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- "531/729 [====================>.........] - ETA: 3s - loss: 0.4328 - mae: 0.3910"
- ]
- },
- {
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- "534/729 [====================>.........] - ETA: 3s - loss: 0.4324 - mae: 0.3910"
- ]
- },
- {
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- "537/729 [=====================>........] - ETA: 3s - loss: 0.4341 - mae: 0.3913"
- ]
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- "540/729 [=====================>........] - ETA: 3s - loss: 0.4336 - mae: 0.3913"
- ]
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- "543/729 [=====================>........] - ETA: 3s - loss: 0.4335 - mae: 0.3914"
- ]
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- "546/729 [=====================>........] - ETA: 2s - loss: 0.4348 - mae: 0.3918"
- ]
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- "549/729 [=====================>........] - ETA: 2s - loss: 0.4414 - mae: 0.3923"
- ]
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- "552/729 [=====================>........] - ETA: 2s - loss: 0.4427 - mae: 0.3931"
- ]
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- "555/729 [=====================>........] - ETA: 2s - loss: 0.4444 - mae: 0.3934"
- ]
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- "558/729 [=====================>........] - ETA: 2s - loss: 0.4441 - mae: 0.3934"
- ]
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- "561/729 [======================>.......] - ETA: 2s - loss: 0.4433 - mae: 0.3932"
- ]
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- "564/729 [======================>.......] - ETA: 2s - loss: 0.4427 - mae: 0.3931"
- ]
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- "567/729 [======================>.......] - ETA: 2s - loss: 0.4427 - mae: 0.3933"
- ]
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- "570/729 [======================>.......] - ETA: 2s - loss: 0.4423 - mae: 0.3933"
- ]
- },
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- "574/729 [======================>.......] - ETA: 2s - loss: 0.4428 - mae: 0.3934"
- ]
- },
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- "578/729 [======================>.......] - ETA: 2s - loss: 0.4423 - mae: 0.3932"
- ]
- },
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- "name": "stdout",
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- "582/729 [======================>.......] - ETA: 2s - loss: 0.4413 - mae: 0.3930"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "586/729 [=======================>......] - ETA: 2s - loss: 0.4403 - mae: 0.3927"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "590/729 [=======================>......] - ETA: 2s - loss: 0.4398 - mae: 0.3923"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "593/729 [=======================>......] - ETA: 2s - loss: 0.4397 - mae: 0.3924"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "596/729 [=======================>......] - ETA: 2s - loss: 0.4388 - mae: 0.3922"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "600/729 [=======================>......] - ETA: 2s - loss: 0.4395 - mae: 0.3925"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "604/729 [=======================>......] - ETA: 2s - loss: 0.4411 - mae: 0.3929"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "608/729 [========================>.....] - ETA: 1s - loss: 0.4400 - mae: 0.3925"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "612/729 [========================>.....] - ETA: 1s - loss: 0.4389 - mae: 0.3921"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "615/729 [========================>.....] - ETA: 1s - loss: 0.4399 - mae: 0.3922"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "618/729 [========================>.....] - ETA: 1s - loss: 0.4390 - mae: 0.3920"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "621/729 [========================>.....] - ETA: 1s - loss: 0.4381 - mae: 0.3916"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "624/729 [========================>.....] - ETA: 1s - loss: 0.4445 - mae: 0.3920"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "628/729 [========================>.....] - ETA: 1s - loss: 0.4435 - mae: 0.3917"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "632/729 [=========================>....] - ETA: 1s - loss: 0.4438 - mae: 0.3916"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "636/729 [=========================>....] - ETA: 1s - loss: 0.4431 - mae: 0.3914"
- ]
- },
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- "output_type": "stream",
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- "640/729 [=========================>....] - ETA: 1s - loss: 0.4429 - mae: 0.3917"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "644/729 [=========================>....] - ETA: 1s - loss: 0.4423 - mae: 0.3916"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "648/729 [=========================>....] - ETA: 1s - loss: 0.4417 - mae: 0.3915"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "652/729 [=========================>....] - ETA: 1s - loss: 0.4418 - mae: 0.3916"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "656/729 [=========================>....] - ETA: 1s - loss: 0.4429 - mae: 0.3921"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "660/729 [==========================>...] - ETA: 1s - loss: 0.4434 - mae: 0.3920"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "664/729 [==========================>...] - ETA: 1s - loss: 0.4427 - mae: 0.3919"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "668/729 [==========================>...] - ETA: 0s - loss: 0.4420 - mae: 0.3918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "672/729 [==========================>...] - ETA: 0s - loss: 0.4409 - mae: 0.3914"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "676/729 [==========================>...] - ETA: 0s - loss: 0.4433 - mae: 0.3918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "680/729 [==========================>...] - ETA: 0s - loss: 0.4426 - mae: 0.3917"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "684/729 [===========================>..] - ETA: 0s - loss: 0.4433 - mae: 0.3917"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "688/729 [===========================>..] - ETA: 0s - loss: 0.4423 - mae: 0.3915"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "692/729 [===========================>..] - ETA: 0s - loss: 0.4424 - mae: 0.3919"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "696/729 [===========================>..] - ETA: 0s - loss: 0.4417 - mae: 0.3917"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "700/729 [===========================>..] - ETA: 0s - loss: 0.4415 - mae: 0.3918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "704/729 [===========================>..] - ETA: 0s - loss: 0.4417 - mae: 0.3918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "708/729 [============================>.] - ETA: 0s - loss: 0.4415 - mae: 0.3918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "712/729 [============================>.] - ETA: 0s - loss: 0.4411 - mae: 0.3918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "716/729 [============================>.] - ETA: 0s - loss: 0.4416 - mae: 0.3919"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "720/729 [============================>.] - ETA: 0s - loss: 0.4408 - mae: 0.3916"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "724/729 [============================>.] - ETA: 0s - loss: 0.4404 - mae: 0.3915"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "728/729 [============================>.] - ETA: 0s - loss: 0.4396 - mae: 0.3912"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - 13s 17ms/step - loss: 0.4394 - mae: 0.3912 - val_loss: 0.4245 - val_mae: 0.3662\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 9/10\n",
- "\r",
- " 1/729 [..............................] - ETA: 0s - loss: 0.2047 - mae: 0.3104"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/729 [..............................] - ETA: 9s - loss: 0.4371 - mae: 0.3966"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/729 [..............................] - ETA: 10s - loss: 0.3931 - mae: 0.3820"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/729 [..............................] - ETA: 10s - loss: 0.3823 - mae: 0.3792"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 16/729 [..............................] - ETA: 10s - loss: 0.3897 - mae: 0.3854"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 20/729 [..............................] - ETA: 10s - loss: 0.4037 - mae: 0.3975"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 24/729 [..............................] - ETA: 11s - loss: 0.4332 - mae: 0.3977"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 27/729 [>.............................] - ETA: 11s - loss: 0.4846 - mae: 0.4063"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 30/729 [>.............................] - ETA: 11s - loss: 0.4726 - mae: 0.4022"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 33/729 [>.............................] - ETA: 11s - loss: 0.4657 - mae: 0.4010"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 37/729 [>.............................] - ETA: 11s - loss: 0.4750 - mae: 0.4027"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 41/729 [>.............................] - ETA: 10s - loss: 0.4532 - mae: 0.3951"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 45/729 [>.............................] - ETA: 10s - loss: 0.4530 - mae: 0.3941"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 49/729 [=>............................] - ETA: 10s - loss: 0.4372 - mae: 0.3884"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 53/729 [=>............................] - ETA: 10s - loss: 0.4431 - mae: 0.3927"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 57/729 [=>............................] - ETA: 10s - loss: 0.4606 - mae: 0.3954"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 61/729 [=>............................] - ETA: 10s - loss: 0.4668 - mae: 0.3996"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 65/729 [=>............................] - ETA: 10s - loss: 0.5017 - mae: 0.4013"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 69/729 [=>............................] - ETA: 10s - loss: 0.5102 - mae: 0.4014"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 73/729 [==>...........................] - ETA: 10s - loss: 0.4991 - mae: 0.3996"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 77/729 [==>...........................] - ETA: 10s - loss: 0.4954 - mae: 0.4001"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 81/729 [==>...........................] - ETA: 10s - loss: 0.4949 - mae: 0.3993"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 84/729 [==>...........................] - ETA: 10s - loss: 0.4925 - mae: 0.3997"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 88/729 [==>...........................] - ETA: 9s - loss: 0.4965 - mae: 0.4004 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 91/729 [==>...........................] - ETA: 9s - loss: 0.4896 - mae: 0.3990"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 95/729 [==>...........................] - ETA: 9s - loss: 0.4842 - mae: 0.3983"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 99/729 [===>..........................] - ETA: 9s - loss: 0.4904 - mae: 0.4003"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "103/729 [===>..........................] - ETA: 9s - loss: 0.4906 - mae: 0.4011"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "107/729 [===>..........................] - ETA: 9s - loss: 0.4838 - mae: 0.3995"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "111/729 [===>..........................] - ETA: 9s - loss: 0.4796 - mae: 0.3986"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "115/729 [===>..........................] - ETA: 9s - loss: 0.4722 - mae: 0.3966"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "119/729 [===>..........................] - ETA: 9s - loss: 0.4702 - mae: 0.3971"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "123/729 [====>.........................] - ETA: 9s - loss: 0.4710 - mae: 0.3981"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "126/729 [====>.........................] - ETA: 9s - loss: 0.4686 - mae: 0.3982"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "129/729 [====>.........................] - ETA: 9s - loss: 0.4644 - mae: 0.3965"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "132/729 [====>.........................] - ETA: 9s - loss: 0.4639 - mae: 0.3974"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "135/729 [====>.........................] - ETA: 9s - loss: 0.4674 - mae: 0.3982"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "138/729 [====>.........................] - ETA: 9s - loss: 0.4627 - mae: 0.3963"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "141/729 [====>.........................] - ETA: 9s - loss: 0.4582 - mae: 0.3949"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "144/729 [====>.........................] - ETA: 9s - loss: 0.4534 - mae: 0.3930"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "147/729 [=====>........................] - ETA: 9s - loss: 0.4524 - mae: 0.3933"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "151/729 [=====>........................] - ETA: 9s - loss: 0.4547 - mae: 0.3932"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "155/729 [=====>........................] - ETA: 9s - loss: 0.4525 - mae: 0.3936"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "159/729 [=====>........................] - ETA: 9s - loss: 0.4517 - mae: 0.3937"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "163/729 [=====>........................] - ETA: 9s - loss: 0.4503 - mae: 0.3939"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "167/729 [=====>........................] - ETA: 8s - loss: 0.4676 - mae: 0.3943"
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- "171/729 [======>.......................] - ETA: 8s - loss: 0.4678 - mae: 0.3960"
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- "175/729 [======>.......................] - ETA: 8s - loss: 0.4687 - mae: 0.3968"
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- "179/729 [======>.......................] - ETA: 8s - loss: 0.4686 - mae: 0.3979"
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- "183/729 [======>.......................] - ETA: 8s - loss: 0.4685 - mae: 0.3984"
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- "187/729 [======>.......................] - ETA: 8s - loss: 0.4715 - mae: 0.3985"
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- "191/729 [======>.......................] - ETA: 8s - loss: 0.4689 - mae: 0.3984"
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- "195/729 [=======>......................] - ETA: 8s - loss: 0.4706 - mae: 0.3992"
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- "199/729 [=======>......................] - ETA: 8s - loss: 0.4663 - mae: 0.3978"
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- "203/729 [=======>......................] - ETA: 8s - loss: 0.4645 - mae: 0.3981"
- ]
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- "207/729 [=======>......................] - ETA: 8s - loss: 0.4652 - mae: 0.3981"
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- "211/729 [=======>......................] - ETA: 8s - loss: 0.4776 - mae: 0.3988"
- ]
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- "215/729 [=======>......................] - ETA: 8s - loss: 0.4759 - mae: 0.3983"
- ]
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- "219/729 [========>.....................] - ETA: 8s - loss: 0.4758 - mae: 0.3994"
- ]
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- "223/729 [========>.....................] - ETA: 8s - loss: 0.4722 - mae: 0.3982"
- ]
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- "227/729 [========>.....................] - ETA: 7s - loss: 0.4698 - mae: 0.3980"
- ]
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- "231/729 [========>.....................] - ETA: 7s - loss: 0.4686 - mae: 0.3980"
- ]
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- "235/729 [========>.....................] - ETA: 7s - loss: 0.4654 - mae: 0.3971"
- ]
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- "239/729 [========>.....................] - ETA: 7s - loss: 0.4631 - mae: 0.3967"
- ]
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- "243/729 [=========>....................] - ETA: 7s - loss: 0.4599 - mae: 0.3959"
- ]
- },
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- "247/729 [=========>....................] - ETA: 7s - loss: 0.4630 - mae: 0.3965"
- ]
- },
- {
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- "251/729 [=========>....................] - ETA: 7s - loss: 0.4602 - mae: 0.3955"
- ]
- },
- {
- "name": "stdout",
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- "255/729 [=========>....................] - ETA: 7s - loss: 0.4595 - mae: 0.3951"
- ]
- },
- {
- "name": "stdout",
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- "259/729 [=========>....................] - ETA: 7s - loss: 0.4584 - mae: 0.3949"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "263/729 [=========>....................] - ETA: 7s - loss: 0.4556 - mae: 0.3942"
- ]
- },
- {
- "name": "stdout",
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- "267/729 [=========>....................] - ETA: 7s - loss: 0.4542 - mae: 0.3937"
- ]
- },
- {
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- "271/729 [==========>...................] - ETA: 7s - loss: 0.4600 - mae: 0.3947"
- ]
- },
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- "275/729 [==========>...................] - ETA: 7s - loss: 0.4590 - mae: 0.3949"
- ]
- },
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- "279/729 [==========>...................] - ETA: 7s - loss: 0.4585 - mae: 0.3953"
- ]
- },
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- "283/729 [==========>...................] - ETA: 7s - loss: 0.4582 - mae: 0.3957"
- ]
- },
- {
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- "output_type": "stream",
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- "287/729 [==========>...................] - ETA: 6s - loss: 0.4565 - mae: 0.3955"
- ]
- },
- {
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- "291/729 [==========>...................] - ETA: 6s - loss: 0.4535 - mae: 0.3945"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "295/729 [===========>..................] - ETA: 6s - loss: 0.4513 - mae: 0.3940"
- ]
- },
- {
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- "299/729 [===========>..................] - ETA: 6s - loss: 0.4518 - mae: 0.3945"
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- "303/729 [===========>..................] - ETA: 6s - loss: 0.4505 - mae: 0.3945"
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- "307/729 [===========>..................] - ETA: 6s - loss: 0.4487 - mae: 0.3940"
- ]
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- "311/729 [===========>..................] - ETA: 6s - loss: 0.4463 - mae: 0.3932"
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- "315/729 [===========>..................] - ETA: 6s - loss: 0.4467 - mae: 0.3936"
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- "319/729 [============>.................] - ETA: 6s - loss: 0.4470 - mae: 0.3940"
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- "323/729 [============>.................] - ETA: 6s - loss: 0.4462 - mae: 0.3938"
- ]
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- "output_type": "stream",
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- "327/729 [============>.................] - ETA: 6s - loss: 0.4444 - mae: 0.3934"
- ]
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- "331/729 [============>.................] - ETA: 6s - loss: 0.4442 - mae: 0.3934"
- ]
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- "335/729 [============>.................] - ETA: 6s - loss: 0.4426 - mae: 0.3929"
- ]
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- "339/729 [============>.................] - ETA: 6s - loss: 0.4419 - mae: 0.3926"
- ]
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- "343/729 [=============>................] - ETA: 6s - loss: 0.4397 - mae: 0.3917"
- ]
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- "346/729 [=============>................] - ETA: 6s - loss: 0.4387 - mae: 0.3913"
- ]
- },
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- "text": [
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- "350/729 [=============>................] - ETA: 5s - loss: 0.4381 - mae: 0.3913"
- ]
- },
- {
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- "text": [
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- "354/729 [=============>................] - ETA: 5s - loss: 0.4371 - mae: 0.3911"
- ]
- },
- {
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- "358/729 [=============>................] - ETA: 5s - loss: 0.4362 - mae: 0.3912"
- ]
- },
- {
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- "text": [
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- "362/729 [=============>................] - ETA: 5s - loss: 0.4367 - mae: 0.3913"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "366/729 [==============>...............] - ETA: 5s - loss: 0.4362 - mae: 0.3914"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "370/729 [==============>...............] - ETA: 5s - loss: 0.4356 - mae: 0.3913"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "374/729 [==============>...............] - ETA: 5s - loss: 0.4349 - mae: 0.3911"
- ]
- },
- {
- "name": "stdout",
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- "378/729 [==============>...............] - ETA: 5s - loss: 0.4331 - mae: 0.3903"
- ]
- },
- {
- "name": "stdout",
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- "381/729 [==============>...............] - ETA: 5s - loss: 0.4323 - mae: 0.3900"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "384/729 [==============>...............] - ETA: 5s - loss: 0.4307 - mae: 0.3895"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "387/729 [==============>...............] - ETA: 5s - loss: 0.4311 - mae: 0.3899"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "391/729 [===============>..............] - ETA: 5s - loss: 0.4311 - mae: 0.3896"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "395/729 [===============>..............] - ETA: 5s - loss: 0.4301 - mae: 0.3895"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "399/729 [===============>..............] - ETA: 5s - loss: 0.4296 - mae: 0.3896"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "403/729 [===============>..............] - ETA: 5s - loss: 0.4283 - mae: 0.3891"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "407/729 [===============>..............] - ETA: 5s - loss: 0.4288 - mae: 0.3892"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "411/729 [===============>..............] - ETA: 4s - loss: 0.4295 - mae: 0.3897"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "415/729 [================>.............] - ETA: 4s - loss: 0.4307 - mae: 0.3904"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "419/729 [================>.............] - ETA: 4s - loss: 0.4297 - mae: 0.3902"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "423/729 [================>.............] - ETA: 4s - loss: 0.4295 - mae: 0.3904"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "427/729 [================>.............] - ETA: 4s - loss: 0.4286 - mae: 0.3902"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "431/729 [================>.............] - ETA: 4s - loss: 0.4296 - mae: 0.3908"
- ]
- },
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- "435/729 [================>.............] - ETA: 4s - loss: 0.4317 - mae: 0.3907"
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- "439/729 [=================>............] - ETA: 4s - loss: 0.4330 - mae: 0.3913"
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- "443/729 [=================>............] - ETA: 4s - loss: 0.4329 - mae: 0.3913"
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- "446/729 [=================>............] - ETA: 4s - loss: 0.4322 - mae: 0.3911"
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- "449/729 [=================>............] - ETA: 4s - loss: 0.4322 - mae: 0.3913"
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- "452/729 [=================>............] - ETA: 4s - loss: 0.4315 - mae: 0.3912"
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- "456/729 [=================>............] - ETA: 4s - loss: 0.4305 - mae: 0.3909"
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- "460/729 [=================>............] - ETA: 4s - loss: 0.4312 - mae: 0.3914"
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- "464/729 [==================>...........] - ETA: 4s - loss: 0.4297 - mae: 0.3907"
- ]
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- "468/729 [==================>...........] - ETA: 4s - loss: 0.4303 - mae: 0.3909"
- ]
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- "472/729 [==================>...........] - ETA: 4s - loss: 0.4299 - mae: 0.3908"
- ]
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- "476/729 [==================>...........] - ETA: 3s - loss: 0.4324 - mae: 0.3916"
- ]
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- "480/729 [==================>...........] - ETA: 3s - loss: 0.4329 - mae: 0.3919"
- ]
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- "484/729 [==================>...........] - ETA: 3s - loss: 0.4315 - mae: 0.3914"
- ]
- },
- {
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- "text": [
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- "488/729 [===================>..........] - ETA: 3s - loss: 0.4322 - mae: 0.3916"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "492/729 [===================>..........] - ETA: 3s - loss: 0.4312 - mae: 0.3915"
- ]
- },
- {
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- "496/729 [===================>..........] - ETA: 3s - loss: 0.4317 - mae: 0.3918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "500/729 [===================>..........] - ETA: 3s - loss: 0.4303 - mae: 0.3913"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "504/729 [===================>..........] - ETA: 3s - loss: 0.4295 - mae: 0.3911"
- ]
- },
- {
- "name": "stdout",
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- "text": [
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- "508/729 [===================>..........] - ETA: 3s - loss: 0.4326 - mae: 0.3912"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "512/729 [====================>.........] - ETA: 3s - loss: 0.4382 - mae: 0.3921"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "516/729 [====================>.........] - ETA: 3s - loss: 0.4374 - mae: 0.3919"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "520/729 [====================>.........] - ETA: 3s - loss: 0.4369 - mae: 0.3919"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "524/729 [====================>.........] - ETA: 3s - loss: 0.4362 - mae: 0.3920"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "528/729 [====================>.........] - ETA: 3s - loss: 0.4358 - mae: 0.3918"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "532/729 [====================>.........] - ETA: 3s - loss: 0.4362 - mae: 0.3921"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "536/729 [=====================>........] - ETA: 3s - loss: 0.4372 - mae: 0.3923"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "540/729 [=====================>........] - ETA: 2s - loss: 0.4377 - mae: 0.3924"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "544/729 [=====================>........] - ETA: 2s - loss: 0.4397 - mae: 0.3922"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "548/729 [=====================>........] - ETA: 2s - loss: 0.4398 - mae: 0.3925"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "552/729 [=====================>........] - ETA: 2s - loss: 0.4391 - mae: 0.3924"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "556/729 [=====================>........] - ETA: 2s - loss: 0.4388 - mae: 0.3924"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "560/729 [======================>.......] - ETA: 2s - loss: 0.4373 - mae: 0.3919"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "564/729 [======================>.......] - ETA: 2s - loss: 0.4404 - mae: 0.3926"
- ]
- },
- {
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- "568/729 [======================>.......] - ETA: 2s - loss: 0.4400 - mae: 0.3926"
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- "616/729 [========================>.....] - ETA: 1s - loss: 0.4340 - mae: 0.3905"
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- "620/729 [========================>.....] - ETA: 1s - loss: 0.4330 - mae: 0.3901"
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- "624/729 [========================>.....] - ETA: 1s - loss: 0.4365 - mae: 0.3901"
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- "628/729 [========================>.....] - ETA: 1s - loss: 0.4365 - mae: 0.3902"
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- "632/729 [=========================>....] - ETA: 1s - loss: 0.4384 - mae: 0.3906"
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- "636/729 [=========================>....] - ETA: 1s - loss: 0.4380 - mae: 0.3905"
- ]
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- "640/729 [=========================>....] - ETA: 1s - loss: 0.4380 - mae: 0.3906"
- ]
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- "643/729 [=========================>....] - ETA: 1s - loss: 0.4373 - mae: 0.3903"
- ]
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- "647/729 [=========================>....] - ETA: 1s - loss: 0.4377 - mae: 0.3908"
- ]
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- "651/729 [=========================>....] - ETA: 1s - loss: 0.4372 - mae: 0.3907"
- ]
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- "655/729 [=========================>....] - ETA: 1s - loss: 0.4361 - mae: 0.3903"
- ]
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- "659/729 [==========================>...] - ETA: 1s - loss: 0.4351 - mae: 0.3899"
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- "663/729 [==========================>...] - ETA: 1s - loss: 0.4352 - mae: 0.3899"
- ]
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- "667/729 [==========================>...] - ETA: 0s - loss: 0.4344 - mae: 0.3897"
- ]
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- "671/729 [==========================>...] - ETA: 0s - loss: 0.4338 - mae: 0.3896"
- ]
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- "675/729 [==========================>...] - ETA: 0s - loss: 0.4342 - mae: 0.3897"
- ]
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- "679/729 [==========================>...] - ETA: 0s - loss: 0.4333 - mae: 0.3894"
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- },
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "702/729 [===========================>..] - ETA: 0s - loss: 0.4312 - mae: 0.3890"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "706/729 [============================>.] - ETA: 0s - loss: 0.4304 - mae: 0.3887"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "710/729 [============================>.] - ETA: 0s - loss: 0.4304 - mae: 0.3889"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "713/729 [============================>.] - ETA: 0s - loss: 0.4310 - mae: 0.3889"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "716/729 [============================>.] - ETA: 0s - loss: 0.4306 - mae: 0.3888"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "720/729 [============================>.] - ETA: 0s - loss: 0.4333 - mae: 0.3895"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "724/729 [============================>.] - ETA: 0s - loss: 0.4327 - mae: 0.3892"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "728/729 [============================>.] - ETA: 0s - loss: 0.4353 - mae: 0.3893"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - 12s 17ms/step - loss: 0.4362 - mae: 0.3894 - val_loss: 0.4255 - val_mae: 0.3693\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 10/10\n",
- "\r",
- " 1/729 [..............................] - ETA: 0s - loss: 0.2179 - mae: 0.3053"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/729 [..............................] - ETA: 8s - loss: 0.3299 - mae: 0.3581"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/729 [..............................] - ETA: 9s - loss: 0.4788 - mae: 0.3573"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 13/729 [..............................] - ETA: 9s - loss: 0.4855 - mae: 0.3603"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 17/729 [..............................] - ETA: 9s - loss: 0.4376 - mae: 0.3541"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 21/729 [..............................] - ETA: 10s - loss: 0.4027 - mae: 0.3491"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 25/729 [>.............................] - ETA: 10s - loss: 0.3935 - mae: 0.3566"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 29/729 [>.............................] - ETA: 10s - loss: 0.3852 - mae: 0.3556"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 33/729 [>.............................] - ETA: 10s - loss: 0.3679 - mae: 0.3500"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 37/729 [>.............................] - ETA: 10s - loss: 0.3794 - mae: 0.3578"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 41/729 [>.............................] - ETA: 10s - loss: 0.3799 - mae: 0.3583"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 44/729 [>.............................] - ETA: 10s - loss: 0.3742 - mae: 0.3583"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 47/729 [>.............................] - ETA: 10s - loss: 0.3812 - mae: 0.3606"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 50/729 [=>............................] - ETA: 10s - loss: 0.3929 - mae: 0.3676"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 53/729 [=>............................] - ETA: 10s - loss: 0.3840 - mae: 0.3650"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 56/729 [=>............................] - ETA: 10s - loss: 0.3761 - mae: 0.3624"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 59/729 [=>............................] - ETA: 10s - loss: 0.3720 - mae: 0.3615"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 62/729 [=>............................] - ETA: 10s - loss: 0.3665 - mae: 0.3594"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 65/729 [=>............................] - ETA: 10s - loss: 0.3663 - mae: 0.3616"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 69/729 [=>............................] - ETA: 10s - loss: 0.3682 - mae: 0.3637"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 73/729 [==>...........................] - ETA: 10s - loss: 0.3672 - mae: 0.3646"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 76/729 [==>...........................] - ETA: 10s - loss: 0.3636 - mae: 0.3636"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 79/729 [==>...........................] - ETA: 10s - loss: 0.3616 - mae: 0.3631"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 82/729 [==>...........................] - ETA: 10s - loss: 0.3683 - mae: 0.3642"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 86/729 [==>...........................] - ETA: 10s - loss: 0.3752 - mae: 0.3677"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 90/729 [==>...........................] - ETA: 10s - loss: 0.3835 - mae: 0.3707"
- ]
- },
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- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 94/729 [==>...........................] - ETA: 10s - loss: 0.3831 - mae: 0.3698"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 98/729 [===>..........................] - ETA: 10s - loss: 0.3810 - mae: 0.3700"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "102/729 [===>..........................] - ETA: 9s - loss: 0.3781 - mae: 0.3695 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "106/729 [===>..........................] - ETA: 9s - loss: 0.3751 - mae: 0.3693"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "110/729 [===>..........................] - ETA: 9s - loss: 0.3727 - mae: 0.3694"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "114/729 [===>..........................] - ETA: 9s - loss: 0.3802 - mae: 0.3716"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "118/729 [===>..........................] - ETA: 9s - loss: 0.3797 - mae: 0.3724"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "122/729 [====>.........................] - ETA: 9s - loss: 0.3810 - mae: 0.3738"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "126/729 [====>.........................] - ETA: 9s - loss: 0.3813 - mae: 0.3736"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "130/729 [====>.........................] - ETA: 9s - loss: 0.3772 - mae: 0.3722"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "134/729 [====>.........................] - ETA: 9s - loss: 0.3724 - mae: 0.3700"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "138/729 [====>.........................] - ETA: 9s - loss: 0.3705 - mae: 0.3693"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "142/729 [====>.........................] - ETA: 9s - loss: 0.3720 - mae: 0.3704"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "146/729 [=====>........................] - ETA: 9s - loss: 0.3689 - mae: 0.3693"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "150/729 [=====>........................] - ETA: 9s - loss: 0.3694 - mae: 0.3698"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "154/729 [=====>........................] - ETA: 9s - loss: 0.3734 - mae: 0.3721"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "158/729 [=====>........................] - ETA: 9s - loss: 0.3813 - mae: 0.3737"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "162/729 [=====>........................] - ETA: 8s - loss: 0.3793 - mae: 0.3731"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "166/729 [=====>........................] - ETA: 8s - loss: 0.3763 - mae: 0.3723"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "170/729 [=====>........................] - ETA: 8s - loss: 0.3820 - mae: 0.3731"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "174/729 [======>.......................] - ETA: 8s - loss: 0.3807 - mae: 0.3727"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "178/729 [======>.......................] - ETA: 8s - loss: 0.3780 - mae: 0.3720"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "182/729 [======>.......................] - ETA: 8s - loss: 0.3747 - mae: 0.3710"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "186/729 [======>.......................] - ETA: 8s - loss: 0.3828 - mae: 0.3729"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "190/729 [======>.......................] - ETA: 8s - loss: 0.3824 - mae: 0.3730"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "194/729 [======>.......................] - ETA: 8s - loss: 0.3803 - mae: 0.3726"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "198/729 [=======>......................] - ETA: 8s - loss: 0.3789 - mae: 0.3722"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "202/729 [=======>......................] - ETA: 8s - loss: 0.3778 - mae: 0.3720"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "206/729 [=======>......................] - ETA: 8s - loss: 0.3789 - mae: 0.3727"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "209/729 [=======>......................] - ETA: 8s - loss: 0.3808 - mae: 0.3730"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "213/729 [=======>......................] - ETA: 8s - loss: 0.3794 - mae: 0.3728"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "217/729 [=======>......................] - ETA: 8s - loss: 0.3812 - mae: 0.3730"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "221/729 [========>.....................] - ETA: 7s - loss: 0.3802 - mae: 0.3727"
- ]
- },
- {
- "name": "stdout",
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- "225/729 [========>.....................] - ETA: 7s - loss: 0.3905 - mae: 0.3741"
- ]
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- {
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- "output_type": "stream",
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- "228/729 [========>.....................] - ETA: 7s - loss: 0.3901 - mae: 0.3744"
- ]
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- {
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- "232/729 [========>.....................] - ETA: 7s - loss: 0.3911 - mae: 0.3755"
- ]
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- {
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- "236/729 [========>.....................] - ETA: 7s - loss: 0.3932 - mae: 0.3767"
- ]
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- "240/729 [========>.....................] - ETA: 7s - loss: 0.3926 - mae: 0.3766"
- ]
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- "244/729 [=========>....................] - ETA: 7s - loss: 0.3922 - mae: 0.3768"
- ]
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- {
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- "248/729 [=========>....................] - ETA: 7s - loss: 0.3918 - mae: 0.3771"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "252/729 [=========>....................] - ETA: 7s - loss: 0.3954 - mae: 0.3786"
- ]
- },
- {
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- "output_type": "stream",
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- "256/729 [=========>....................] - ETA: 7s - loss: 0.3947 - mae: 0.3788"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "259/729 [=========>....................] - ETA: 7s - loss: 0.3939 - mae: 0.3789"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "263/729 [=========>....................] - ETA: 7s - loss: 0.3933 - mae: 0.3786"
- ]
- },
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- "output_type": "stream",
- "text": [
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- "267/729 [=========>....................] - ETA: 7s - loss: 0.4049 - mae: 0.3790"
- ]
- },
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- "output_type": "stream",
- "text": [
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- "271/729 [==========>...................] - ETA: 7s - loss: 0.4030 - mae: 0.3784"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "275/729 [==========>...................] - ETA: 7s - loss: 0.4077 - mae: 0.3793"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "279/729 [==========>...................] - ETA: 7s - loss: 0.4063 - mae: 0.3790"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "282/729 [==========>...................] - ETA: 7s - loss: 0.4062 - mae: 0.3790"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "286/729 [==========>...................] - ETA: 6s - loss: 0.4094 - mae: 0.3799"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "290/729 [==========>...................] - ETA: 6s - loss: 0.4103 - mae: 0.3807"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "294/729 [===========>..................] - ETA: 6s - loss: 0.4095 - mae: 0.3804"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "298/729 [===========>..................] - ETA: 6s - loss: 0.4085 - mae: 0.3801"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "302/729 [===========>..................] - ETA: 6s - loss: 0.4086 - mae: 0.3805"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "305/729 [===========>..................] - ETA: 6s - loss: 0.4091 - mae: 0.3808"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "309/729 [===========>..................] - ETA: 6s - loss: 0.4089 - mae: 0.3809"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "313/729 [===========>..................] - ETA: 6s - loss: 0.4146 - mae: 0.3817"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "317/729 [============>.................] - ETA: 6s - loss: 0.4128 - mae: 0.3812"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "320/729 [============>.................] - ETA: 6s - loss: 0.4125 - mae: 0.3811"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "324/729 [============>.................] - ETA: 6s - loss: 0.4115 - mae: 0.3810"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "328/729 [============>.................] - ETA: 6s - loss: 0.4114 - mae: 0.3814"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "332/729 [============>.................] - ETA: 6s - loss: 0.4110 - mae: 0.3815"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "336/729 [============>.................] - ETA: 6s - loss: 0.4108 - mae: 0.3814"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "340/729 [============>.................] - ETA: 6s - loss: 0.4116 - mae: 0.3812"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "343/729 [=============>................] - ETA: 6s - loss: 0.4118 - mae: 0.3817"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "346/729 [=============>................] - ETA: 6s - loss: 0.4128 - mae: 0.3820"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "349/729 [=============>................] - ETA: 5s - loss: 0.4139 - mae: 0.3824"
- ]
- },
- {
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- "353/729 [=============>................] - ETA: 5s - loss: 0.4125 - mae: 0.3821"
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- "357/729 [=============>................] - ETA: 5s - loss: 0.4137 - mae: 0.3824"
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- "361/729 [=============>................] - ETA: 5s - loss: 0.4129 - mae: 0.3825"
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- "365/729 [==============>...............] - ETA: 5s - loss: 0.4120 - mae: 0.3825"
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- "369/729 [==============>...............] - ETA: 5s - loss: 0.4108 - mae: 0.3822"
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- "373/729 [==============>...............] - ETA: 5s - loss: 0.4113 - mae: 0.3829"
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- "376/729 [==============>...............] - ETA: 5s - loss: 0.4112 - mae: 0.3829"
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- "379/729 [==============>...............] - ETA: 5s - loss: 0.4107 - mae: 0.3827"
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- "383/729 [==============>...............] - ETA: 5s - loss: 0.4103 - mae: 0.3826"
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- "387/729 [==============>...............] - ETA: 5s - loss: 0.4085 - mae: 0.3819"
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- "391/729 [===============>..............] - ETA: 5s - loss: 0.4077 - mae: 0.3817"
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- "395/729 [===============>..............] - ETA: 5s - loss: 0.4069 - mae: 0.3815"
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- "399/729 [===============>..............] - ETA: 5s - loss: 0.4054 - mae: 0.3809"
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- "403/729 [===============>..............] - ETA: 5s - loss: 0.4047 - mae: 0.3807"
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- "407/729 [===============>..............] - ETA: 5s - loss: 0.4036 - mae: 0.3804"
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- "411/729 [===============>..............] - ETA: 5s - loss: 0.4038 - mae: 0.3806"
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- "415/729 [================>.............] - ETA: 4s - loss: 0.4037 - mae: 0.3807"
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- "419/729 [================>.............] - ETA: 4s - loss: 0.4026 - mae: 0.3804"
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- "423/729 [================>.............] - ETA: 4s - loss: 0.4031 - mae: 0.3807"
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- "427/729 [================>.............] - ETA: 4s - loss: 0.4017 - mae: 0.3803"
- ]
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- "name": "stdout",
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- "431/729 [================>.............] - ETA: 4s - loss: 0.4016 - mae: 0.3802"
- ]
- },
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- "name": "stdout",
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- "435/729 [================>.............] - ETA: 4s - loss: 0.4009 - mae: 0.3803"
- ]
- },
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- "name": "stdout",
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- "439/729 [=================>............] - ETA: 4s - loss: 0.3995 - mae: 0.3797"
- ]
- },
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- "443/729 [=================>............] - ETA: 4s - loss: 0.4024 - mae: 0.3804"
- ]
- },
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- "name": "stdout",
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- "447/729 [=================>............] - ETA: 4s - loss: 0.4022 - mae: 0.3806"
- ]
- },
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- "name": "stdout",
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- "451/729 [=================>............] - ETA: 4s - loss: 0.4076 - mae: 0.3808"
- ]
- },
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- "name": "stdout",
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- "455/729 [=================>............] - ETA: 4s - loss: 0.4152 - mae: 0.3815"
- ]
- },
- {
- "name": "stdout",
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- "459/729 [=================>............] - ETA: 4s - loss: 0.4145 - mae: 0.3813"
- ]
- },
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- "name": "stdout",
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- "463/729 [==================>...........] - ETA: 4s - loss: 0.4139 - mae: 0.3813"
- ]
- },
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- "467/729 [==================>...........] - ETA: 4s - loss: 0.4133 - mae: 0.3813"
- ]
- },
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- "471/729 [==================>...........] - ETA: 4s - loss: 0.4133 - mae: 0.3815"
- ]
- },
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- "475/729 [==================>...........] - ETA: 3s - loss: 0.4179 - mae: 0.3819"
- ]
- },
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- "479/729 [==================>...........] - ETA: 3s - loss: 0.4232 - mae: 0.3823"
- ]
- },
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- "483/729 [==================>...........] - ETA: 3s - loss: 0.4293 - mae: 0.3829"
- ]
- },
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- "487/729 [===================>..........] - ETA: 3s - loss: 0.4279 - mae: 0.3824"
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- "491/729 [===================>..........] - ETA: 3s - loss: 0.4276 - mae: 0.3824"
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- "495/729 [===================>..........] - ETA: 3s - loss: 0.4285 - mae: 0.3830"
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- "499/729 [===================>..........] - ETA: 3s - loss: 0.4295 - mae: 0.3835"
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- "503/729 [===================>..........] - ETA: 3s - loss: 0.4316 - mae: 0.3837"
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- "507/729 [===================>..........] - ETA: 3s - loss: 0.4317 - mae: 0.3839"
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- "511/729 [====================>.........] - ETA: 3s - loss: 0.4312 - mae: 0.3839"
- ]
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- "515/729 [====================>.........] - ETA: 3s - loss: 0.4324 - mae: 0.3843"
- ]
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- "519/729 [====================>.........] - ETA: 3s - loss: 0.4312 - mae: 0.3840"
- ]
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- "523/729 [====================>.........] - ETA: 3s - loss: 0.4318 - mae: 0.3843"
- ]
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- "527/729 [====================>.........] - ETA: 3s - loss: 0.4311 - mae: 0.3842"
- ]
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- "531/729 [====================>.........] - ETA: 3s - loss: 0.4315 - mae: 0.3842"
- ]
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- "535/729 [=====================>........] - ETA: 3s - loss: 0.4306 - mae: 0.3839"
- ]
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- "539/729 [=====================>........] - ETA: 2s - loss: 0.4313 - mae: 0.3844"
- ]
- },
- {
- "name": "stdout",
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- "543/729 [=====================>........] - ETA: 2s - loss: 0.4314 - mae: 0.3847"
- ]
- },
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- "547/729 [=====================>........] - ETA: 2s - loss: 0.4307 - mae: 0.3846"
- ]
- },
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- "name": "stdout",
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- "551/729 [=====================>........] - ETA: 2s - loss: 0.4304 - mae: 0.3845"
- ]
- },
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- "name": "stdout",
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- "555/729 [=====================>........] - ETA: 2s - loss: 0.4311 - mae: 0.3846"
- ]
- },
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- "name": "stdout",
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- "559/729 [======================>.......] - ETA: 2s - loss: 0.4298 - mae: 0.3842"
- ]
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- "name": "stdout",
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- "563/729 [======================>.......] - ETA: 2s - loss: 0.4297 - mae: 0.3843"
- ]
- },
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- "name": "stdout",
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- "567/729 [======================>.......] - ETA: 2s - loss: 0.4297 - mae: 0.3846"
- ]
- },
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- "name": "stdout",
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- "571/729 [======================>.......] - ETA: 2s - loss: 0.4288 - mae: 0.3844"
- ]
- },
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- "name": "stdout",
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- "575/729 [======================>.......] - ETA: 2s - loss: 0.4281 - mae: 0.3843"
- ]
- },
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- "name": "stdout",
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- "579/729 [======================>.......] - ETA: 2s - loss: 0.4278 - mae: 0.3843"
- ]
- },
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- "583/729 [======================>.......] - ETA: 2s - loss: 0.4270 - mae: 0.3842"
- ]
- },
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- "587/729 [=======================>......] - ETA: 2s - loss: 0.4271 - mae: 0.3844"
- ]
- },
- {
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- "591/729 [=======================>......] - ETA: 2s - loss: 0.4258 - mae: 0.3840"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "595/729 [=======================>......] - ETA: 2s - loss: 0.4251 - mae: 0.3838"
- ]
- },
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- "output_type": "stream",
- "text": [
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- "599/729 [=======================>......] - ETA: 2s - loss: 0.4257 - mae: 0.3840"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "602/729 [=======================>......] - ETA: 1s - loss: 0.4279 - mae: 0.3844"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "606/729 [=======================>......] - ETA: 1s - loss: 0.4276 - mae: 0.3844"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "609/729 [========================>.....] - ETA: 1s - loss: 0.4276 - mae: 0.3846"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "613/729 [========================>.....] - ETA: 1s - loss: 0.4275 - mae: 0.3847"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "617/729 [========================>.....] - ETA: 1s - loss: 0.4276 - mae: 0.3850"
- ]
- },
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- "621/729 [========================>.....] - ETA: 1s - loss: 0.4282 - mae: 0.3855"
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- "625/729 [========================>.....] - ETA: 1s - loss: 0.4276 - mae: 0.3855"
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- "633/729 [=========================>....] - ETA: 1s - loss: 0.4267 - mae: 0.3855"
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- "653/729 [=========================>....] - ETA: 1s - loss: 0.4277 - mae: 0.3858"
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- "657/729 [==========================>...] - ETA: 1s - loss: 0.4275 - mae: 0.3858"
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- "661/729 [==========================>...] - ETA: 1s - loss: 0.4271 - mae: 0.3857"
- ]
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- "665/729 [==========================>...] - ETA: 0s - loss: 0.4299 - mae: 0.3861"
- ]
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- "669/729 [==========================>...] - ETA: 0s - loss: 0.4302 - mae: 0.3863"
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- "673/729 [==========================>...] - ETA: 0s - loss: 0.4301 - mae: 0.3865"
- ]
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- "676/729 [==========================>...] - ETA: 0s - loss: 0.4294 - mae: 0.3862"
- ]
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- "680/729 [==========================>...] - ETA: 0s - loss: 0.4287 - mae: 0.3861"
- ]
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- "684/729 [===========================>..] - ETA: 0s - loss: 0.4292 - mae: 0.3862"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "688/729 [===========================>..] - ETA: 0s - loss: 0.4289 - mae: 0.3861"
- ]
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "692/729 [===========================>..] - ETA: 0s - loss: 0.4304 - mae: 0.3864"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "696/729 [===========================>..] - ETA: 0s - loss: 0.4297 - mae: 0.3861"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "700/729 [===========================>..] - ETA: 0s - loss: 0.4296 - mae: 0.3863"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "704/729 [===========================>..] - ETA: 0s - loss: 0.4290 - mae: 0.3862"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "708/729 [============================>.] - ETA: 0s - loss: 0.4320 - mae: 0.3869"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "712/729 [============================>.] - ETA: 0s - loss: 0.4335 - mae: 0.3872"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "716/729 [============================>.] - ETA: 0s - loss: 0.4327 - mae: 0.3870"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "720/729 [============================>.] - ETA: 0s - loss: 0.4329 - mae: 0.3870"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "724/729 [============================>.] - ETA: 0s - loss: 0.4330 - mae: 0.3872"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "728/729 [============================>.] - ETA: 0s - loss: 0.4321 - mae: 0.3870"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "729/729 [==============================] - 12s 17ms/step - loss: 0.4321 - mae: 0.3870 - val_loss: 0.4327 - val_mae: 0.3805\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Duration : 00:02:03 883ms\n"
- ]
- }
- ],
- "source": [
- "pwk.chrono_start()\n",
- "\n",
- "history=model.fit(train_generator, \n",
- " epochs=epochs, \n",
- " verbose=1,\n",
- " validation_data = test_generator,\n",
- " callbacks = [bestmodel_callback])\n",
- "\n",
- "pwk.chrono_show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
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tjQcffJDMzExmz57NI488Qnp6Ov379+fmm2/mvffe44YbbiAlJYX09HR++9vfNvnzRESkfTEdq966IpGIA4hGo7XKP/74Y8aPH3/Q93fOsXrrnupt04f0zu1wy2pb6nslIiJtps7fgDXU0s6ZmQ6NExGRdqNj/WrcSXXJTGN3kT9Lr6ikHLq2zucsXry4esVKlczMTN55553W+UAREelwFDw6gNgej5KycsorKklLbfnOrMmTJ7Nw4cIWv6+IiHQeGmoJUUvNr0lNSak1r6MjnVarOUgiIh2LgkdIsrKyKCwsbLEfrB1xnodzjsLCQrKyssJuioiItBANtYRk8ODB5Ofns23btha5X1lFJdv3lAKQkgI7umXXPaW4HcnKymLw4MFhN0NERFqIgkdI0tPTOeSQQ1rsfpXOcf6dL7Fzn59k+uv/OZZxg7q32P1FRERagoZaOogUM6aN7FP9et6KrSG2RkREJDEFjw5k+si+1X+et7JlhnBERERakoJHB3L4yN6kBBM7lm7YWb23h4iISLJQ8OhAumVnMDaY1+GA+avU6yEiIslFwaODmRYz3PLeCgUPERFJLgoeHcz0UTETTFduo1IbcImISBJR8OhgRg/IIy8nA4BdRftZsWlXyC0SERGpoeDRwcQvq9Vwi4iIJBMFjw6o1n4eWlYrIiJJRMGjAzpiZJ/q7dKXbtjB7mItqxURkeSg4NEB5eXULKutdPD+qoJwGyQiIhJQ8OigptfaPl3DLSIikhwUPDqoaVpWKyIiSUjBo4MaPaA73bLTAdixr5SVm3eH3CIREZEkCB5mlmJm15vZUjMrMbP1Zna7mXVp5PtfNTNXx2NaE+o6M3uxufdONqkpxhG1ltXqtFoREQlfWtgNAO4ErgGeBm4Hxgevp5rZLOdcZSPuUQBcn6B8VdzrnwB/SFDvXOAM4F8Hce+kM31kH175cCPgh1u+cvzokFskIiKdXajBw8wmAlcDTznnzoopXw3cDZwHPNaIW+1zzj3SUCXn3IuJys3se0ApkOgejbp3MqpaVuuAj/N3srekjNys9LCbJSIinVjYQy3nAwbcFVd+P1AEXNjYGwVDNt3MzBquXet9xwNjgaedc9tb8t5h694lk9ED8wCodE7LakVEJHRhB4/pQCXwbmyhc64EWBhcb4xBwF5gF7DXzJ4ys3GNfO/XgudEQzAHe+/QTa91Wq3meYiISLjCnuMxEChwzpUmuLYBOMbMMpxz9W29uRqYAywCKoAjgW8Cnzaz45xzi+t6o5l1A84J7vFyS947WUwb1YdH31gO+HkezjnaWceNiIh0IGH3eOTg51YkUhJTp07OuUudc991zs12zj3pnLsBOBnIBe5o4PPPD+7/J+cO3OjiYO5tZleY2bwGPr/VjR3Yna7Bstrte0tZtUXLakVEJDxhB48iILOOa1kxdZrEOfcG8Dow08yy66n6NXxPxgMtfW/n3H3OudCX3KamGEeM0Gm1IiKSHMIOHhuB3maWKHwMwg/DNPeEszVAKtAj0UUzm4yfQ/K8c25DS9472ei0WhERSRZhB4/3gjbMiC00syxgCnAwQxWjgXIg4UoV4LLgua5JpQdz76QSGzyWrN/BvpKyEFsjIiKdWdjBYzZ+m4nr4sovx8+9eLSqwMwGmNk4M8uJKcszs9T4m5rZ6cCxwIvBCpn465nABcAW4JlEDWvuvZNRj9xMRg+IWVa7WstqRUQkHKGuanHOLTaze4FvmtlTwLPU7Fz6GrU3D7sNuBiYCbwalM0E7jCzf+F3Ei3H955ciN9x9Lo6PvoLQC/gF8658jrqNPfeSWn6yD4s37QL8KfVHj9+QMgtEhGRzijs5bTgf4CvAa4ATsf/UL8HuLkR26V/AszHb3feD0gH8oHfAT+tZ+5G1d4df2yFeyelaaP68NibKwAtqxURkfCEHjyccxX4M1pub6DeJcAlcWUf4/fhaOpnntyIOs26d7IaN6g7uVlp7C0pp2BPCau37mFEv25hN0tERDqZsOd4SBtJTUnhcC2rFRGRkCl4dCLTR8Uuq9X26SIi0vYUPDqRA5bVlmpZrYiItC0Fj06kZ24Wo/r7eR0VlY6FqwtDbpGIiHQ2Ch6dTGyvh06rFRGRtqbg0clMG9W3+s/vBctqRURE2oqCRyczYXB3umT6VdQFu0tYu21vyC0SEZHORMGjk/HLantXv9Zwi4iItCUFj05oesxwi06rFRGRtqTg0QnFTjD9cN12ikrrOq5GRESkZSl4dEK9umZVb5deXulYuEan1YqISNtQ8Oikpo/U9ukiItL2FDw6qWm1tk/XsloREWkbCh6d1ITBPcgJltVu3VXMugItqxURkdan4NFJpaWmMPWQ2GW1Gm4REZHWp+DRiU2PG24RERFpbQoenVj8stri/VpWKyIirUvBoxPr0y2bQ/p2BaCsopIP1ui0WhERaV0KHp2cTqsVEZG2pODRyU3XabUiItKGFDw6uQlDepCdkQrAlp3FrC/cF3KLRESkI1Pw6OTS45bVztNwi4iItCIFD9FptSIi0mYUPKTWBNNFa7dTUlYRYmtERKQjU/AQ+uZlM6xPLlC1rFan1YqISOtQ8BAgbnWLtk8XEZFWouAhAEwfqdNqRUSk9Sl4COCX1Wal+2W1m3YUsWG7ltWKiEjLCz14mFmKmV1vZkvNrMTM1pvZ7WbWpZHvf9XMXB2PaXF1P1VP3WfquP9pZvaWme0zs+1m9oSZHdISX3syyUhLZYpOqxURkVaWFnYDgDuBa4CngduB8cHrqWY2yzlX2Yh7FADXJyhfVUf9+4A34sry4yuZ2ZeAJ4EPgBuAPOA6YI6ZTXPObWxE29qN6aP6MHfZFsAPt3zxyA6Xr0REJGShBg8zmwhcDTzlnDsrpnw1cDdwHvBYI261zzn3SBM++u2G6ptZOnAPsB443jm3Nyh/DpgP3AJc0YTPTHrTay2rLaS0rILMYPhFRESkJYQ91HI+YMBdceX3A0XAhY29UTBk083MrJH1u5hZVj1VTgQGAn+oCh0AzrmFwKvAuUE46TD6dc9haG+/rHZ/eSWL1uq0WhERaVlhB4/pQCXwbmyhc64EWBhcb4xBwF5gF7DXzJ4ys3H11P91UL/YzJaZ2bUJAkvVZ7+d4P1zgW7AmEa2r92YNir2tFrN8xARkZYVdvAYCBQ450oTXNsA9DazjAbusRr4BXApcA4QBU4F3jGzyXF1y4B/At8GzgSuBHbie1z+lKBtVe1I1DbwgSchM7vCzOY10PakM31k7Gm1OrdFRERaVtjBIwdIFDoASmLq1Mk5d6lz7rvOudnOuSedczcAJwO5wB1xdec45z7vnPu9c+5fzrnfA0cB/wEuMbPj4tpGHe1rsG3Oufucc9Pqup6sJg3tUT2vY+N2LasVEZGWFXbwKAIy67iWFVOnSZxzbwCvAzPNLLuBupXAbcHL0+LaRh3ta3bbkl1GWipThveqfq3TakVEpCWFHTw24odTEv1wH4QfhtnfzHuvAVKBHo2sC9A7pqxqqWyi4ZSqskTDMO3e9FG1dzEVERFpKWEHj/eCNsyILQxWm0wBDmaOxGigHNjeyLoAW+LaBnB0gvpHAbuBZc1uXRKLnefxwZpC9pfrtFoREWkZYQeP2YDDb8oV63L8/IlHqwrMbICZjTOznJiyPDM7YKMJMzsdOBZ4MVghU1XeK0HdTPyeHAD/irn0GrAJuMzMcmPqHwZ8CnjCOVfWqK+ynenfI4fBvfzGsaXllSxa25jsJiIi0rBQNxBzzi02s3uBb5rZU8Cz1Oxc+hq1Nw+7DbgYmInfR4Pgz3eY2b/wu5SW43tPLsTvZnpd3Ec+b2Yb8RuAbcSvXLkQ3+Nxj3Ouelmvc67MzK7Fh6M3zOx+/BLa64FtwA9a4FuQtKaP6kt+4WoA3luxlWkxm4uJiIg0VzJsmX4dfo7FFcDp+MBwD3BzI7ZL/wQfIs4A+gHp+K3Pfwf81DkXPwfjSeAL+N1SuwP7gAXAD5xzf4m/uXPuCTMrBr4H/Aq/wuW/wI0J7t2hTB/Zh6ff8cFj3optcErIDRIRkQ4h9ODhnKvAn9FyewP1LgEuiSv7GL93R2M/6+fAz5vYvmeAhAfIdWSTh/UkMy2F0vJK8rfvY9OOIgb0qHdls4iISIPCnuMhSSojLZXDYpbVvqdltSIi0gIUPKRO00bVrG7RsloREWkJCh5Sp9jTahdqWa2IiLQABQ+p08CeXRjUM1hWW1bB4nVaVisiIgdHwUPqVWsXU51WKyIiB0nBQ+oVu3+HJpiKiMjBUvCQeh06rBcZaf6vyfrCfWze2eHOxRMRkTak4CH1ykxP5dBhsctqNdwiIiLNp+AhDdJptSIi0lIUPKRBsafVLlxdoGW1IiLSbAoe0qBBvbpUb5deUlbBkvU7Qm6RiIi0Vwoe0iixwy1a3SIiIs2l4CGNEjvcogmmIiLSXAoe0iiHDu9Feqr/67KuYC9bdxWH3CIREWmPFDykUbLSUzl0WM/q1xpuERGR5lDwkEbTabUiInKwFDyk0WJPq12wuoCyisoQWyMiIu2Rgoc02uBeXejfPRuA4v0VLFmv02pFRKRpFDyk0cyM6bHDLVrdIiIiTaTgIU1S+7RaBQ8REWkaBQ9pkikxy2rXbNujZbUiItIkCh7SJFkZaUwaWrOsVqtbRESkKRQ8pMl0Wq2IiDSXgoc0Wfyy2nItqxURkUZS8JAmG9I7l355flltUWk5H+XrtFoREWkcBQ9pMjNj2iitbhERkaZT8JBmqX1arc5tERGRxgk9eJhZipldb2ZLzazEzNab2e1m1qWR73/VzFwdj2lxdU80s3vNbLGZ7TGzbWY2x8zONzM7mHt3NlMO6UVaiv+Wrd66h4LdJSG3SERE2oO0sBsA3AlcAzwN3A6MD15PNbNZzrnGzFwsAK5PUL4q7vXPgcHBZy0GugDnAo8BJwGXH8S9O5XsYFntwjWFAMxbuZXPTh0acqtERCTZhRo8zGwicDXwlHPurJjy1cDdwHn4UNCQfc65RxpR70bgTedcRcxn/Rp4BbjMzH7tnPuwmffudKaN6hMTPLYpeIiISIPCHmo5HzDgrrjy+4Ei4MLG3igYsumWaMikinPutdjQEZRVAk8GLyc1996dUew8j/dXFVBRqWW1IiJSv7CDx3SgEng3ttA5VwIsDK43xiBgL7AL2GtmT5nZuCa0Y3DwvKUV7t1hDeuTS59uWQDsKy3no/yd4TZIRESSXtjBYyBQ4JwrTXBtA9DbzDIauMdq4BfApcA5QBQ4FXjHzCY31AAzGwh8HT9n482WvHdHF39arVa3iIhIQ8IOHjlAotABUBJTp07OuUudc991zs12zj3pnLsBOBnIBe6o771mloOfaNoFuMQ5V9aC977CzObVV6cjiN3FdJ728xARkQaEHTyKgMw6rmXF1GkS59wbwOvATDPLTlTHzLKAvwPTgEuD97TIvYN69znnOvyS2ymH9CY1WFa7cstuCvdoWa2IiNQt7OCxET+ckih8DMIPw+xv5r3XAKlAj/gLMaFjFnB5M1at1HnvziYnM42JQ2q+DTo0TkRE6hN28HgvaMOM2MIgGEwBDmaoYjRQDmyPu3cmfnjlZOAK59yfWurenVXteR4KHiIiUrcW28cjEomMw0+8LAL+Go1GdzXibbOBm4DrgNihjsvxczserSowswFAHrDOOVcUlOUBe+OXyJrZ6cCxwHPBCpmq8kx8T8cpwJXOuT/U1bCm3rszmz6yD3/871IAFqzeRkVlJakpYWdaERFJRk0OHpFI5GbgKmBiNBrdHpTNAv4FVK1A+XYkEpkRjUYL67uXc26xmd0LfNPMngKepWbn0teovXnYbcDFwEzg1aBsJnCHmf0LvyqlHN97ciF+x9Hr4j7yUeCzwEtAkZnF7xOyyDm3qJn37rSG9+1K765ZFOwpYW9JOUs37GTikJ5hN0tERJJQc3o8TgWWVoWOwG2AA34A9AciwLXAzY2433X4ORNXAKfjf6jfA9zciO3SPwHmA2cA/YB0IB/4HfBT59yGuPpVkz1nBY94PwSqgkdT791pVZ1W+/yC9YAfblHwEBGRRJoTPIbj50gAEIlEBgFHAHdEo9Fbg7JxwBdoRPAIhjJuDx711bsEuCSu7GP8/hqN4pwb3oS6Tbp3Zzd9ZGzw2MolM8eG3CIREUlGzRmI70HtSZXH4ns7nokpmw/o4I5OZOqImmW1KzbvZvteTX8REZEDNSd4bMMvda0yEygD3okpy2jmvaWd6pKZXmtZ7fyVBSG2RkREklVzhloWAmdGIpFJ+N1FzwXejEajxTF1hgObDrp10q5MG9mHRWt9Z9h7K7bymcMGN/AOERHpbJrTK/EL/LLWD/ATMPOImZ8RiUSygE9xcHtwSDs0Lea02vmrCqiodCG2RkREklGTg0c0Gn0Dv9Lj7/hJpmdHo9HnYqocg1+l8vQBb5YObUS/rvTq6jeh3VtSxicbd4bbIBERSTrN2kAsGo0+Dzxfx7WXgakH0yhpn8yMaSP78J+F+YAfbpkwuNPvKi8iIjFadAJoJBLpEYlEurTkPaV9mR4z3KLTakVEJF5zdi79NH7L8dui0eiOoKwv8ARwHFAeiUTujUaj/9uiLZV2YeqI3qSYUekcyzbtYue+Urp3qesAYhER6Wya0+NxNfClqtAR+BVwPLACKASujUQiX26B9kk7k5uVzgSdVisiInVoTvA4DHiz6kUkEskGzgZejEajY4GxwHrgyhZpobQ700b2qf6zTqsVEZFYzQkefYGNMa+PBLKABwGi0ege/C6m2jO7k5oeEzzeX7VNy2pFRKRac4JHKZAd8/p4/Jbpr8eU7QZ0SlgnNbJ/N3rm+nkdu4vLWL5pZ7gNEhGRpNGc4LEaOCnm9VnA8mg0Gnta6xD8KbPSCZkZR2i4RUREEmhO8HgImByJRN6JRCJvAJOBx+LqHI7f1VQ6qekKHiIikkBzgsdvgb8C0/An0z4D/LzqYiQSmQGMB15tgfZJO3X4iD4Eh9WybONOdu4rDbdBIiKSFJq8j0c0Gi0DvhKJRK4EXDCZNNYq/M6law6+edJedc1OZ/zgHixZvwMHvL+qgJMmD2rwfSIi0rE1a8t0gGg0uruO8gI0v0Pwy2qXrPfbvby3YquCh4iIND94RCKRHOBL+N6N7sAu4H3g6Wg0uq9FWift2vRRfXno1WWAP6220jlSzEJulYiIhKlZZ7VEIpHTgLX4iabXA5cC1wWv10QikTNaqoHSfo3s343uXTIA2FW0n+WbdoXcIhERCVuTg0ckEjkceArfy/Eo8D/AqcHzo0H5k5FI5IgWa6W0SynBabVVtLpFRESaM9TyXfyGYcdHo9G5cdcejEQi9+JXtNyE3+NDOrHpI/vy0iK/xcs/3l3NtJG9GTeoRwPvEhGRjqo5Qy3HA08kCB0ARKPRd4Ang3rSyU0f1Ydu2emA38X02w/PZe6yLSG3SkREwtKc4JGHPwSuPuuAbs24t3QwXbLS+fH506vDR2l5JT98fB7PLVgXcstERCQMzQkeG4EZDdSZBmxqxr2lAxo3qAd3XnoM/bv7I34qHdz1zGIeeW0ZzukAORGRzqQ5weNZ4KRIJPKdSCSSGnshEomkRCKRbwGzgnoiAAzulcudlx7DqP41HWF/fn05d/17MRWVlSG2TERE2lJzJpf+GPgC8BPg68F5LZuA/sBxwHBgM3BryzRROoqeuVn88qtHc+uT85m/yu8x9/yC9ezYW8pNX5pKVkazt5UREZF2osk9HtFodDP+jJaXgGHAhcANwEXAIUH5cdFoVEMtcoCczDR+eN50Ph2zi+k7y7dy4yPv6DwXEZFOwA5mjD0SiQzC71yah9+5dEE0Gt3QQm3rECKRiAOIRqNhNyWpOOd44JVPmD1nZXXZoJ5d+MlXZjCgR06ILRMRkRZQ5zbVBxU8WoKZpQDXAl/HD9NsAx4HbnbONbj1upm9CpxYx+Xpzrl5cfXz8MNAXwJ6ASuB3wC/cwm+GWZ2GvA94DCgFPgv8G3n3OpGfHkKHg34x3tr+O3zS6j6xvfoksmPz5/O6AF5obZLREQOSp3Bo8FB9Ugk8qdmfqiLRqNfa0S9O4FrgKeB24HxweupZjbLOdeYmYcF+K3b462KfWFmGcCL+F6ae4CP8buuRoF+wC1x9b+E35PkA/xwUh5+a/g5ZjbNObexEW2Tenx++nB65Wbys6cXUlZRyY59pdzw8Nt8/+wjOCJm11MREekYGjOb75Jm3tsB9QYPM5sIXA085Zw7K6Z8NXA3cB7wWCM+a59z7pFG1LsMmA5c45y7Jyi738z+BtxkZg8459YGbUjHh5P1wPHOub1B+XPAfHxIuaIRnykNOG78AH7WJZMfzH6PvSXlFO+v4Pt/fY/rzziUzxw2OOzmiYhIC2rM5NJDmvkY0Yh7n4/vjrkrrvx+oAg/cbVRzCzFzLqZ1Xv86VeC+94fV34XkA6cG1N2IjAQ+ENV6ABwzi3Ebwl/bhBOpAVMGtqTOy45hj7dsgCoqHT86p8fMHvOCu31ISLSgTTY4xGNRte24udPByqBd2MLnXMlZrYwuN4Yg4C9QDZQZGb/AW5yzi2tqhDMJTkceN85VxL3/neDdsR+XtWf307weXOBk4AxwJJGtlEaMKxPV+669Fi+95d3Wb11DwB/evkTtu0u4apTJpKaUl+mFBGR9qA5G4i1pIFAgXMu0TrKDUDvYF5GfVYDvwAuBc7Bz9c4FXjHzCbH1OuBDyYHrLoJPr8QH2Bi20ai+jFlgxJcA8DMrjCzeXVdl8R6d8viVxcfzaHDelaX/WveWn7yt/cpLasIsWUiItISwg4eOfiVIomUxNSpk3PuUufcd51zs51zTzrnbgBOBnKBO+I+iwY+L/az6qvfYNucc/c556bV13ZJLDcrnZ98ZQYnTBhQXTZn6Wb+79F32F28P8SWiYjIwQo7eBQBmXVcy4qp0yTOuTeA14GZZpYdd5/6Pi/2s+qr3+y2SeNkpKXyf1+ayhePPKS6bMn6HXzrwbfZuqs4xJaJiMjBCDt4bMQPpyT64T4IPwzT3F9x1wCp+CEWgB1AMQmGR4LP70XtYZWqpbKJhlOqyrRZWitKMePKkydw+azx1WXrCvZy3QNzWLVld4gtExGR5go7eLwXtKHWabdmlgVMAQ5mjsRooBzYDhDsB/I+fn+Q+KAzI2hH7Oe9FzwfneDeRwG7gWUH0T5ppLOPHsF3vjiFtGByaeGeUr710Nt8sKYw5JaJiEhThR08ZuP3+7gurvxy/PyJR6sKzGyAmY0zs5yYsjwzS417L2Z2Ov48mRfjVrD8Jbhv/P4b1+FDyuMxZa/hD7+7zMxyY+59GPAp4AnnXFmjvko5aDMnDeInX5lBTnCQXFFpOd997F1eXaI93ERE2pNQg4dzbjFwL/AlM3vKzC4zs9vxk0Jfo/bmYbfhdxqN7R2ZCSw3s1+b2bVm9g0zewj4J3430+viPvJ+/OZfd5jZ7cHnPYXfPv1nsdugB6HiWmAI8IaZRczsO8AL+G3df9BC3wZppCmH9OZXFx9Nz1zfYVVWUcltTy3gqXcatXu9iIgkgWQ4h/w6/HyMK4DT8YHhHvxZLQ1tl/4JPkicgd/yPB3IB34H/NQ5V2sOhnNuv5nNwp/Vcj41Z7VcjQ9AxNV/wsyK8We1/Iqas1pujL+3tI2R/btx16XH8N3H3mV9oT/K5/cvfETB7mIumzWelHr3jxMRkbCFfkhcR6dD4lrH7uL9/OCv8/gof0d12acmDuRbZx5KRtoBo28iItK26vwtMOw5HiLN0i07g59deCTHjO1XXfbqko18/y/vsa9EU29ERJKVgoe0W5npqXzv7CM444ih1WUL1xTyrYfepnBP/K74IiKSDBQ8pF1LTTG+eeokLpk5trps9dY9XPfAW6zbtifElomISCIKHtLumRnnHzeKb515aPXk0q27irn+wbdZsn57yK0TEZFYCh7SYZx82BB+dN40stL95NK9JWV855F3eGvp5pBbJiIiVRQ8pEOZPqovv/zqUXTv4g813l9eyY+fnM+/5q0NuWUiIgIKHtIBjRnYnTsvOYaBPf0mt5UOfvPchzzw8lK0fFxEJFwKHtIhDezZhTsvOYYxA/Oqy/46ZyW3/3MR5RUN7UsnIiKtRcFDOqzuXTL55UVHMWNUn+qyFxflc/PseRTvLw+xZSIinZeCh3RoWRlp3HLuNE6ZMri6bP7Kbdzw8Fx27C0NsWUiIp2Tgod0eKkpKVx/xqFccPzo6rLlm3Zx/YNvsSE470VERNqGgkd7szkfCrQ8tKnMjK9+agzXnj6ZlOAEgU07irj+wbdYumFnqG0TEelMFDzak/Iy+P1t8MMIvP1f0AqNJjvt8KHcfM40MtP8X/1dRfv59p/n8s7yLSG3TESkc1DwaE/+8WdYvxKKi+CPv4T7fgb7tC14Ux09th8/u+goumWnA1BaVsEts+fz/IJ1IbdMRKTjU/BoT6YcDX0G1Lx+7zW45Sr4eEF4bWqnJgzuwR2XHEO/7tkAVDrHnc8s5pHXl2uvDxGRVqTg0Z6MHA8/iMLxn60p21EAt/8fzL4PyvaH17Z2aEjvXO685BhG9utWXfbn15Zx97MfUlGpvT5ERFqDgkd7k5UNF18H37gZcms2x+LFp+DWayB/dWhNa496dc3ilxcfxdRDeleXPfv+On70+HxKyipCbJmISMek4NFeTT0GfvhbmDS9pmzDGh8+Xvgb6Df2RuuSmc6Pz5/OpycPqi6bu3wr3/nzXHbu014fIiItScGjPcvrCdf+CC74BqT7Q9EoL4PH74c7boLt28JtXzuSnprC//v8YXz5mJHVZR9v2MnF97zCfS9+ROGekhBbJyLScSh4tHdmMPNzcPO9MHRUTfnShX7i6buvhda09ibFjK99ehyRUyYQbPVBSVkFf5u7movveYVf/3sxG7drwzERkYNhmsHfuiKRiAOIRqOt/2HlZfDPR+G5x8HFDLUcdRJ85RuQ06X129BBvLdiK394aSlrttVerpxicMKEgZx77EhGxExKFRGRWqzOCwoeratNg0eV5R/6fT4KYjbF6tkXLrsBxkxuu3a0c5XO8e7yrfz1zRV8nGB30yNH9+XcY0cycUjPtm+ciEhyU/AISyjBA6B4H/zlt/DWSzVlZnDKOfCFiyAtvW3b044551i0djuz56xg/qqCA64fOqwn5x47iiNG9Maszv/XREQ6EwWPsIQWPKrMex0evhuK9taUDRkJl38bBg4Lp03t2LKNO5k9ZyVzlm4m/v+cUf27cd5xozh2XH9SFEBEpHNT8AhL6MED/CZjf7q99g6n6Rlw9tfgpDN9T4g0ybqCvTz+1kpeXryBisra/w8N7tWFLx8zkpMmDyI9VfO3RaRTUvAIS1IED/D7erz8T3jyj34SapWJR8Cl/wvde4XXtnZsy84i/jZ3Nc8tWMf+8tp7p/TplsU5R4/glKlDyUpPDamFIiKhUPAIS9IEjyob1sD9P6+9w2luN7joGjjiuNCa1d7t3FfK0++s5p/z1lJUWl7rWl5OBl+YMZwzpw8nN0tza0SkU6gzeITeD2xmKWZ2vZktNbMSM1tvZrebWbPWfprZ42bmzOzDBNdeDa7V9XixCfWnNfdrDtWg4fDdX8MpZ9UMsezdDb+9FR64A0qKQm1ee9W9SyaXnjSOR645if85aSx5ORnV13YV7eehV5dx0a9f5o//XcqOvdoNVUQ6r9B7PMzs18A1wNPAc8B44GrgDWCWc67Re3+b2RnAP4BSYJVzblLc9c8A/RK89VzgDOBa59zdMfVfBSYC1yd4z7POue0NtSnpejxiLf0A/vSr2juc9hkAX7sBRk0Ir10dQGlZBf9ZuJ4n3l7F1l3Fta5lpKVwypQhnH30CPp3zwmphSIirSo5h1rMbCKwGHjaOXdWTPnVwN3ABc65xxp5r1zgI+DvwJnA3vjgUc97lwLDgYGxYSIIHsOdc8Mbc59Ekjp4gF/t8shv4N1Xa8osBU4/F864ANLSQmtaR1BeUckrH25k9pwVrC+svetpihkzJ/nNyIb16RpSC0VEWkXSDrWcj2/cXXHl9wNFwIVNuNdPgDTge01pgJkdD4zFh5+EPRjBcFA364ibNOTkwhXfgctvhOxgdMtVwjN/gZ/9L2zOD7d97VxaagqfOWww9111IjefcwRjBtScKFzpHP9dvIErfvc6P3x8Hp9s3BleQ0VE2kjYwWM6UAm8G1vonCsBFgbXG2RmM4BvAtc553Y3sQ1fC57/UMf1QcBeYBew18yeMrNxTfyM5HfkTLjlt7V3Nl2zDH70DXjt36BJyAclxYxjx/Xn7q8dy20XHMlhw2uvInrrky1c88c53PjIXBauLiDsIVARkdYS9lDLYqCvc+6AeRdm9jhwDpDpnNtfzz3SgPnARufcqUHZGhox1GJm3YBNwBZgpIv7ZpjZA8BGYBFQARyJDzj7geOcc4sb+hqTfqglXmUFvPAUPP0QVMSszjj0SLj4OsjrEVrTOpqP83fw1zkrmbtsywHXxg7sznnHjeSoMf20GZmItEdJO9SSg58ImkhJTJ363ACMBr7RjM8/P7j/n+JDB4Bz7lLn3Hedc7Odc086524ATgZygTvqu7GZXWFm85rRpnClpMJnz/ErX2J3Nl30DtxyJSycG17bOpjxg3vww3On8fuvn8BJkwbWChifbNzJDx+fz5W/f53/LsqnorLRc6xFRJJau+7xMLNR+MmptzrnfhJTvobG9Xi8CxwODHPObWhCu18Bjge6OueK66vb7no8Yu0vhacegJf+Xrv8xNPgy1dAZlYozeqoNu0o4om3V/LCwnzKKmoHjf7dszn76JGcMmUwGWnajExEkl7S9nhsBHqbWWaCa4OAgvqGWYDbge3A02Y2quqBn2SaEbwekOiNZjYZP4fk+aaEjsAaIBXo2OMOGZlw3pVw/U9r72z62rN+7seqT8JrWwc0oEcO15w2mYeunsk5R48gO6MmYGzeWcxvnvuQr979Co+/tZJ9pWX13ElEJHmFHTzeC9owI7bQzLKAKUBDQxXDgIHAEmB5zGMQfvhlOX6FTCKXBc91TSqtz2igHB96Or6Jh/uJp7E7m27ZAD+7Hv71KFRUhNe2DqhX1ywumzWeP1/zab564hi6ZdfsdrpjXyl//O9Svnr3yzz0yifs3KfNyESkfQl7qGUy8AF17+NxkXPukaBsAJAHrHPOFQVls4DuCW4dxc8R+V9gk3NuTtznZgIb8OFhsHOuPP4GZpaHH66piCs/HXgGeM45d1pDX2O7HmqJ5xy8/V94LFp7h9MR4+Cyb0PfgeG1rQMr2V/OswvW87e3V1Gwp6TWtcy0FE49fChnHTWCvnnZIbVQROQAybmBGICZ3YNfKfI08Cx+59JrgDnASVU7l5rZg8DFwEzn3KsN3HMN9czxMLNzgb8Cv3DO3VhHnS/gJ5D+C1iFDykz8HuLbAeOdc4ta+jr61DBo8q2zfDHX8CKj2rKMrPh/Cvh2JN12m0r2V9ewcuLN/D4W6vYsL32ZmRpKcanDx3El48ZyeBeuSG1UESkWlIHj1TgOuAK/O6hBcBs4Gbn3N6Yeg/ScsHjBeAzwNi6woOZjQd+hJ982g9IB/KB54GfNnZeSIcMHuCX3T73BPzzz7WHWqYeA1+9Frrm1f1eOSgVlY43P97E7DkrWbml9rY1BswY3ZdTpw5lxug+pKaEPZoqIp1U8gaPjq7DBo8qa5bBH35Re4fTvB5w6bdgUvs8R6+9cM4xb+U2/jpnJR+uO3C6Ua+umZx82BA+O2UI/XvoTBgRaVMKHmHp8MEDoLQEnvwDvPJM7fKTzoSz/kfLbtvAh+u2M3vOCt5dse2AawZMHdGbU6cO5eix/UhPVS+IiLQ6BY+wdIrgUWXRu/DAHbBnZ03ZgKFw2Q0wbHRozepMNhTu4/mF63nxg3x2JFjxkpeTwWcOG8ypU4doLoiItCYFj7B0quABPnQ89GtY+HZNWWoafP4i+OzZfmdUaXXlFZXMXbaF5xasZ/7KbST6v3zy0J6cOnUIx40fQGa6/ruISItS8AhLpwse4JfdvvE8zP69H4apMmYyfO0G6NU3vLZ1Qlt2FvGfhfn854P1FOwuOeB6blY6n548iFOnDuGQft1CaKGIdEAKHmHplMGjypaNftntqqU1ZTm5cNE1MP2E8NrVSVVUOuat3Mpz76/nneVbqUzw//64Qd05deoQTpw4kOyMtBBaKSIdhIJHWDp18AC/1PaZx+CZv4CLOX/kmFnwlQhkabVFGAr3lPDCB/k8v2Adm3ceeNxQTkYan5o0kFOnDmH0gDxMe7OISNMoeISl0wePKis+gj/8HApijoDvM8DveDpyfHjt6uQqnWPh6kKeW7COt5ZuprzywH8PRvbrxqmHD+GkSYPokpWe4C4iIgdQ8AiLgkeMon3w2L0w9+WaspQU+NwFcPp5mngasp37Snlp0QaeW7CO/MJ9B1zPTEvhhAkDOfXwIUwY3EO9ICJSHwWPsCh4JPDOK/DIPVAcc97L6Il+4mnv/uG1SwC/MdmH63fw3PvreOPjTewvrzygztDeuZw6dQizDh1Mt5yMEFopIklOwSMsCh51KNjsdzyNPe8lOwcuvBqOnBleu6SWPcVlvPzhBp57fx2rt+454Hp6agrHjuvPqVOHcOjwXqSoF0REPAWPsCh41KOiAp79K/zrUaiM+a36qJPggm9Adpfw2ia1OOf4ZOMunluwjlc/3EhJWcUBdQb0yOHUqUP4zGGD6Zmr3WpFOjkFj7AoeDTCyo9978e2TTVlvfv7iaejJoTXLkmoqLScV5ds5LkF61i2cdcB11NTjKNG9+XUw4dy+Ig+pKaoF0SkE1LwCIuCRyMV74O//BbeeqmmLCUFzvgKnH4+pGriaTJauXk3zy1Yx8uLN7CvtPyA633zsjnlsMGcPGUIffOyQ2ihiIREwSMsCh5N9O5r8Oe7fRCpMnKC7/3oo4mnyaq0rII3Pt7EcwvWJzwpN8Vg2sg+nDp1KDNG9yVNB9WJdHQKHmFR8GiGwq1+6GX5hzVl2TlwwTf9/A9JausK9vL8gnW8tGgDu4r2H3C9Z25mcFDdUAb00AZyIh2UgkdYFDyaqbICnnsC/vFw7YmnR870ASRHE0+T3f7yCt7+xB9Ut2B1QcI6Uw7pxalTh3LM2H5kpGk4TaQDUfAIi4LHQVr1Cdz/s9oTT3v19UMvoyeF1y5pkk07inh+wTpe+CCf7XtLD7ielZ7KESN6c+SYfhw5ui/du2SG0EoRaUEKHmFR8GgBJUXwl9/BnBdqyizF73b6uQs08bQdqais5J3lW3luwXrmrdhKgh3aMWDc4O4cNbofR43px7A+udolVaT9UfAIi4JHC5r3Ojx8NxTtrSkbMc73fvQdGF67pFm27irmhQ/yeXnxBjZsP3CL9ir9u2dz1Jh+HDm6H5OH9SRdE1NF2gMFj7AoeLSw7dv8xNNli2vKMrP9hmNHfxr0m3G7tL5gL3OXb2Husq18tH57wp4QgJzMNKaN7MPRY/oxbVQfumVru3aRJKXgERYFj1ZQWQHPP+knnlbE7KA5/US46GrIyQ2vbXLQdhft590VW5m7bCvzV26jaP+B+4MApJgxcUgPjhrTj6PG9GVwL/13F0kiCh5hUfBoRWuWwf0/hy0basp69oXLboAxk8Nrl7SYsopKFq0t5J1lW5m7fAtbdhbXWXdwzy4cNbYfR43uy4QhPUhN0ZCMSIgUPMKi4NHKSoph9u/hjedryiwFTvsyfO5CSEsLr23SopxzrNm6h7nLt/LOsi0s3bCTuv716pqdzoxRfTlydF+mjexDl6z0Nm2riCh4hEbBo43MfxMeuqv2xNNDxsJlN0I/TTztiHbsLQ2GZLYwf1UBpQkOrgN/dsyhw3px1Ji+HDW6H/21aZlIW1DwCIuCRxvavg3+dDssXVhTlpkNX7kKjvmMJp52YKVlFXywppC3l23hneVbKNxz4F4hVYb36cqRY/py1Jh+jB3YXYfYibQOBY+wKHi0scpKeOFv8PRDUBEzKXHa8XDRNdCla3htkzbhnGPF5t3MXbaFucu2sGLz7jrrdu+SwYxRPoQcPqI32RkamhNpIQoeYVHwCMma5X7H09iJpz16w9dugHGHhdcuaXPbdhfzTjAvZMHqQsoqKhPWS09NYcohvThytF8l06ebTtMVOQgKHmFR8AhRaYmfePr6czVlZvDZc+DzF0GaJhx2NiX7y3l/VQFzl2/hneVb2bnvwEPsqozq340jR/fj6LH9GNW/m3ZPFWma5A0eZpYCXAt8HRgObAMeB252ztW9nWHd93scOAdY4pybFHftU8Ardbz13865MxLc7zTge8BhQCnwX+DbzrnVjWmPgkcSeH+On3i6b09N2fDRfuJp/8GhNUvCVekcn2zYydxlPoSs3rqnzrq9umZW94RMGd6bzHRt0y/SgKQOHr8GrgGeBp4DxgNXA28As5xziftFE9/rDOAf+ICwqp7gcV9w/1j5zrlX4+p/CXgS+AC4H8gDrgMqgGnOuY0NtUnBI0nsLIQ//hI+XlhTlpEJ518Fx52iiafC5p1FvLNsC28v28ritYWU17F9amZ6KlMP6c20kX04bFhPhvTWWTIiCSRn8DCzicBi4Gnn3Fkx5VcDdwMXOOcea+S9coGPgL8DZwJ76wkelzrnHmzgfunAGqAcmOic2xuUTwHmA390zl3RULsUPJJIZSW8+DQ89UDtiadHHAcXXQu5mngq3r7SMuavLGDusi28u2Ire4rL6qzbvUsGk4f25NBhvTh0WC+G9sklRUFEJGmDx63Ad4ETnHNvxJRnAYXAa8650xp5r1/jh1jGAYtoIHgATwAVzrmSOu43C3gRP+Tz47hr/wWmAb2dc3X/i4SCR1JatxLu+xlsXl9T1qM3/M//g/FTQmuWJKeKyko+yt9ZvUomv7D+EeC8nAwmDe3JocN8GBnet6uCiHRGSRs8/gPMAnKcc6Vx1+YAY5xzfRpxnxnA28D5zrnHzWwN9QeP3UC3oHg5cC9wt4v5ZpjZ/wE/BT7jnHsp7j4/AW4CJjnnltTXNgWPJFVaAk/8AV59pqbMDE45G77wVU08lTrlF+7l3RXbWLy2kMXrttfbGwJ+F9XJQ3syeVgvDh3ak0P6ddPeIdIZ1PmXPOxF6wOBgvjQEdgAHGNmGc65Oqeem1kafv7FC865xxv4vDLgn8CzwMbg878G3AVMwfeExLatqh2J2gYwCKg3eEiSysyCC78Jk46AB++CvbvAOXj+CfhoAVxxI/QfEnYrJQkN7pXL4F65fOnIQ6gMtnFfvLaQD9Zu58N129lVVPufqz3FZbz1yRbe+mQLALlZaUwa0pNDh/uhmREKItLJhB08cvATQRMpialT95o3uAEYDXyxoQ9zzs0BPh9bZmb344PIJWb2R+fcmzGfSx3tK4mrcwAzuwK44qqrrmqoWRKmKUfDLWPggdthyfu+bN0K+NE34dyvwwmnauKp1CnFjBH9ujGiXzc+P8MHkXXb9rJobSGL1m5n0drCA4LI3pJy5i7fytzlWwHIyUzzQzNDfRgZ1b+bDriTDi3soZbFQF/nXL8E16qWxWbW1eNhZqPwk1Nvdc79JKZ8DQmGWuppx4nAq8BtzrmbgrJ7gG8CE5xzH8fVj+CHZ05xzr1Q37011NJOVFbCf/8Bf/sTlMd0nefkwtCRMGQkDBvl/9x/MKRoOaU0zDnH+oK9fLB2e/XQzPa9dW/nDpCTkcbEoT2YPLQXhw7ryegBeaSlKohIu5O0Qy0bgQlmlplguGUQfhimvt6O24HtwNNBCKmSBmQEZfucc5saaMea4Ll3XNuq2lEreARlkHgYRtqjlBT4zBf9rqb3/ww2rvPlRXth6Qf+USUjEwYf4sPI0OAx+BBIzwin7ZK0zIyhfboytE9XPjdtGM458gv3sXjd9qBXpPCAc2WK9pfz3optvLdiGwBZ6alMHNKDQ4f1YvKwnowZ2J10BRFpx8IOHu8BJwMziNlXI1jVMgV4vYH3D8PPxahrnsVy4N/AARuDxRkdPG+JaxvA0cBLtatzFH6C6rIG7ivtzZAR8L174O8Pw5v/qX3abZX9pbBqqX9USUmBAUNrekeqAklObtu1XZKemTGkdy5Deudy2uFDcc6xcUcRi9YWsnjtdj5YW0jB7toL7UrKKpi/qoD5qwoAv4/IhME9qlfNjBmYR0aaeuCk/Qh7qGUyfnOuuvbxuMg590hQNgC/gdc651xRUDYL6J7g1lH8PIz/BTYFczsws17OucK4NmTidyM9FjjSOfduUJ4OrMVPSI3dx+Mw4H3gAefcZQ19jRpqacecg8Itfvlt1WP9SthR0Ph79O4fhJBRNWGke6/Wa7O0a845Nu8srg4ii9YWsmVXcb3vyUhLYcLgHkwe1ovDhvVk7KDuCiKSDJJzOS3UmkvxNH6S53j8TqZzgJOqdi41sweBi4GZ8TuMJrjnGhIvp30PP4Qyn5pVLRfiezzucc5dE1f/HGA2NTuXdgOuBxxwhHOuwaEWBY8OaPdOH0DWrYS1K/yftzRh1K1bj5oQMnQkDBkFffr7XhOROJt3FlWHkEVrC9m8s/4gkp6awvjB3auHZsYP6qEt3iUMSR08UvHbkF+BP6ulAP/D/uaqXoag3oMcfPC4EfgCMArfU7IPWADc55z7Sx33OgN/Vsuh1JzVcqNzbmVjvj4Fj06ipAjWr6rdO7Jxbe0dUuuTleOHeaoDySg/dJMW9mioJJutu4pZXLVqZl0hG7cX1Vs/PTWFsYO6c+jQnkwY0oPRA/Lo3iWzjVornVjyBo+OTsGjEyvbD5vW1QSRtSsgf5XfvKwx0tJh4DAfRKpW1Awe4fcgEQkU7C7xQzPrtrNoTSH52xs+W7NPtyxGD8ir9VAYkRam4BEWBQ+ppbICtm6s3TOybqXfwKwxzKDf4NpDNUNHQW63ht8rnULhnpLqVTOL125nXUGCCdIJ9O6Wxej+eYwZqDAiLULBIywKHtIg5/yE1arJq+tW+k3MCrc2/h49+9SsqBk5HkaMh5wurddmaTd27C1l8brtLF5XyPKNu1i5ZTf7yxt36HdVGIntGemRqzAijaLgERYFD2m2vXuCILKipmdkcz64RvzQMPPDNKMmBI+JfoWNdmHt9CoqK1m3bS/LN+9i+Sb/WLV5N6WNDSNdsxhVHUS6MXpAHj1zNfwnB1DwCIuCh7So0hLIXx3TO7IC8tfU3m21Lnk9YGRMEBk6UofhCXDwYaRX10zfMzKwu8KIVFHwCIuCh7S68nLYvN6HkTXLYMVHfoVNQz0j6RkwfIwPIlWBRHNFJFBRWcn6gn3VQWT5pl2s3Lyr6WFkQF51D0mvrgojnYiCR1gUPCQUJcWweqkPISs+glUfQ3H9yy4BfyJv9fDMBD+RVcMzEkgYRrbsprSsolHv75mbecBqGoWRDkvBIywKHpIUKitgw1pY+TGsWOLDSMHmht+Xmwejxtf0iAwfozNppJaKSn8Q3vJNu1gRDNWs2Ny0MDJqQB5jFEY6GgWPsCh4SNLaWVg7iKxbARUN/LBITfN7ioyaWDNEk9ejbdor7UZFpSO/cC/LNh5cGBndP49hfXLp1z2bPt2y6ZGbSYp64NoLBY+wKHhIu7G/NJgjsgRWfAwrP4J9exp+X58BNUFk1AS/46q2f5c4VWGk9pyR3ZQ0MoyA34W1T14Wfbtl0zcvm3552fTtnl39uk9etk7uTR4KHmFR8JB2q7LSL99dsaSmZ6QxZ9Lk5Pp9RKqCyCFjtduqJFRR6dhQFUY272bZxp1NDiOxDOiRm+kDSdyjqqxLllZytREFj7AoeEiHsmdnTW/IiiWwZnnDS3lTU/05NKMm1swV6dG7TZor7U9sGFmxeTebdhSxdVcxW3YVs7ekEcvGG9AlM+2AQNInJphoOKfFKHiERcFDOrSy/X5uSNXqmRVLYE8jtn/v1TcIIuP98+DhkKITVKV+RaXlbN1V7B+7i9m60weSbbv98/Y9JVQe5I+09NQUenfLOqDXpCqg9OmWRUaa/q42goJHWBQ8pFNxDrZuCoZngiCycV3D78vOgZETYcxEGD1Jq2ekWcorKinYXcLW3cVs2RkTUHb5kLJ1d3Gjt4uvS9VwTvwQTuxrDecACh7hUfCQTm/fnmCOyEc+jKz+xE9krU9aOowYB6ODIDJqAmTltE17pcNyzrGraD9bYoLI1l0+pFT1muwpPvjhnNysdIb06sLgXrkM7tWFQb26MKRXLgN75nSm3hIFj7AoeIjEKS/3271XTVhdvgR2ba//PZbit3gfPcmHkTGToGv3NmmudC7F+2uGc7ZUDevEPAoPYjgnxaBvXnZ1IBncK7c6oPTqmol1rLklCh5hUfAQaYBzsG0TLP/QP5Z9CFs3Nvy+/kN8ABk9EcZMhl79Wr+t0umVV1RSsKekVhjZsquYbTFBpTnDOVnpqdVhxD/X/Dk7I60VvpJWp+ARFgUPkWbYWeh7Q5YFYSR/tQ8o9enZx/eIjJnknwcM1Xbv0uaccxTsKWFD4T7WF+4jv3Av+cHzlp3FNOcnbu+uWdVDNrG9JH3zsklNSdq/4woeYVHwEGkBRXv9HJGqILJmGVSU1/+e3G41QzOjJ8HQUX5pr0hI9pdXsHF7EesL97KhcF91IFlfuK9ZS4XTU1MY2DOnumdkSK/cIJx0oVt26JOzFTzCouAh0gr2l/pJqssW+zkiKz+C0pL635OZ5fcRqQoiI8ZBRmbbtFekHlWTXvNjekjWF+5jQ+FeNu4ooqIZk0rycjIOGLIZ3CuXAT1yDtzdtaTY/780fAx0694yX5SCR3gUPETaQEWFn7BaFUSWL4G9Dewnkprm/6EdPckv4x010e+6KpJEKior2byjmPUxQzb5QW/Jjn0NrA5LIMWMQXkZHJW2g6kl6zmkcCU9tq7BKitwl1yPHXdKSzW9zuDRLmesiIjUkprqQ8TwMXDyWX4+yKb1sHxxMDyzBLZvrf2einLfU7LyI3gePx9k8CG154nk9QzlyxGpkpqSwqBgfke8fSVl5G/fR35BTS9JfuFeNmzfV2uCqznHiLICphavZ0rJeiav3UiWO3Co8sP/vMzklgsedVLwEJGOxwwGDvWPE0/3ZYVbauaILF8Cm+I2NnMO1q/yj5f/6cv6DqwdRPoM0IRVSRpdstIZO7A7Ywd2r1VeWVnJ9pWr2LdwHmnLF9EzfynZ+4vqvdfK9N4U9xrUiq2toeAhIp1Dr35wdD84+tP+9Z6dwbBMEEbWrfQH48XautE/5rzgX+f1rAkiw8f4YKONzSQZ7CyEpR/ARwtIWbqQ3tu3Ud+JSMV5fcjvO5qPug5nbko/lu6Gy44f1yZN1RyPVqY5HiLtREmR39Ssai+RVUsbPgAP/Lkzg4bDwGEwaJh/HjBUE1eldRXtg08WwccLYOnCho8m6Nodxk+pefTuX+uyc45K50hNSUnw5mbRHA8RkXpl5cDEI/wD/AF4a5bX9IisWALFCbqrC7f6x6J3a8rM/LDMoOG+V6QqmPQf7LeDF2mqsv3+7+DHC/1jzXJw9WxUlpXjN9abMNUHjYHD6h0mNDNS22gYUcFDRCSR9Ixg6e1E4FyorID8NT6ErPzI/3lLvl9RE8+5mmGaBW/VlKemQt9BNT0jVYGk70DtMSK1VVbAmhWwdAF8tNCHjvp64NLS/WnPVT0aw8cm7d8pBQ8RkcZISfXnxQwdCZ/+vC8rL4MtG3w394Y1sHGtf966KfFvoxUVflLrpnXAGzXlaem+NyQ2jAwa5rvDW67rW5KZc/7vRVWPxieLoHhf3fXNYNgoGBf0aIya4PeqaQcUPEREmist3QeFQcNh+gk15WX7/XLe6jCy1j8XbE58n/Iyvy18/ura5RmZfr5I7PyRQcP99vBaXdP+FW4NgsYCPzG0ocMS+w/2IWPcFBh7GOR2bYNGtrzQg4eZpQDXAl8HhgPbgMeBm51z9cS9Ou/3OHAOsMQ5Nynu2onAl4ETgs8qAZYBvwH+6uJm2prZq8CJdXzUdOfcvKa2T0Q6gfSMmt6RWCXF/rfaqiBSFUx2FCS+z/5SWLvcP2Jl5QTLheN6SPJ6KpAks727fcD4eKGfELplQ/31e/T2IWP8FBh3mA+cHUDowQO4E7gGeBq4HRgfvJ5qZrOcq2/2TG1mdgZwFlBcR5WfA4ODz1oMdAHOBR4DTgIuT/CeAuD6BOWrGtsuEREAsrLhkLH+Eatob9xwTRBMdu9IfJ+SIr/qZtXS2uU5uQeusBk0HLrmtcIXIw0qLfFzgj5e4MPG+lX1H3aYkwtjDw3maUz1PRwdMEiGGjzMbCJwNfCUc+6smPLVwN3AefhQ0Jh75QJR4F7gzDqq3Qi86ZyriHnfr4FXgMvM7NfOuQ/j3rPPOfdII78kEZGmy8n1Y/SjJtQu37MrpmckCCYb1vigkkjR3ppVOLG6dvcBpHtPyMyGrCzfa5KZFbzOrvlz1evYsrRk+B01iVRW+N6o0lL/vL8keC6F0mK/4uTjhT4Y1neYYXqG3xemakLo0JF+LlEHF/bfpvPxa33viiu/H/gZcCGNDB7AT/Bfz/eoI3g4515LUFZpZk/ih18mAfHBo2o4KBfYEz8cIyLSarrm+d+Axx5aU+acnwsQ2zNSFUxK6tidcs9O37XfXGnpPoRkZScIKlkxQSW7JtjEhpgDyrJaZ1mxc36+TFUI2B8XDEoTlZXUXT/+UVW3Mfu7JJKS4jeeq+rRGDneh49OJuzgMR2oBN6NLXTOlZjZwuB6g8xsBvBN4Hzn3G5retfU4OB5S4Jrg4C9QDZQZGb/AW5yzi1NUFdEpHWZQfde/jHh8Jpy52D7ttpzRzas9XNK9jf9MLFaysv8Y9+eg7tPrNS0ukNJbGBxlXE//OsIBlWhoPGj821j0PCYCaGTIfvAM1c6m7CDx0CgwDmX6P+KDcAxZpbhnNtf1w3MLA3fQ/KCc+7xpjbAzAbiJ7auAt6Mu7wamAMsAiqAI/EB59NmdpxzbnE9970CuOKqq65qapNERJrOzO+i2qsvTI75na2y0q+m2bTeB4fSYigpgdKi4Lk4pqzYT4AtLfY/yEuC163xw7yi3LenJcNMWzHzK46qH1m1X/fs4yeDjpsCeT3Cbm3SCTt45AB1RfGSmDp1Bg/gBmA08MWmfriZ5eAnmnYBPuecq9V/5py7NO4tT5rZP4FXgTuAz9R1b+fcfcB9VVumi4iEIiXFb1DWd2Dz3l81fFFSR1CpCiuxr2NDywH1S/y94s/FaSmpaXGhINMPB8WXZWRBZoLQEB8kMuPKMoNhog446bOthB08ioC+dVzLiqmTkJmNAm4GbnXONWmViZllAX8HpgEXO+feqP8dnnPuDTN7HZhpZtnOubpW0IiItH9mfh5Cega01LYR1WEmLqjE9riUBGUpKXWHgPhgkZ6pibDtQNj/hTYCE8wsM8FwyyD8MEx9vR23A9uBp4MQUiUNyAjK9jnnNsW+KSZ0zAIua8aqlTXAp4Ae1L10V0REEqkVZrTUt7MJey/e94I2zIgtDILBFKChDbqG4eeJLAGWxzwG4YdfluPnf8TeOxM/vHIycIVz7k/NaPdooBwfekRERKSRwu7xmA3cBFxHrYMLuBw/t+PRqgIzGwDkAeucc1XDL/8P6J7gvlH8HJH/Bap7O4LQ8XfgFOBK59wf6mqYmeUBe2P3/AjKTweOBZ5zzpUkfLOIiIgkFGrwcM4tNrN7gW+a2VPAs9TsXPoatffwuA24GJiJn9yJc+6lRPc1s1/hQ8OTcZceBT4LvIRfGnth3PVFzrlFwZ9nAneY2b/wK17K8T0zF+J3M72uqV+viIhIZxd2jwf4H+BrgCuA0/E/1O/Bn9XS0tOepwXPs4JHvB/il84CfALMB84A+gHpQD7wO+CnzrkGNtkXERGReKEHj2Ao4/bgUV+9S4BLGnnP4U0pr6Pux/jD5kRERKSFhD25VERERDoRBQ8RERFpMwoeIiIi0mYUPERERKTNKHiIiIhIm1HwEBERkTYT+nLaziISiYTdBBERkbbiotFowiN81eMhIiIibcacc2G3QZrIzOY556Y1XFMOhr7PbUff67ah73Pb0Pe5furxEBERkTaj4CEiIiJtRsGjfbov7AZ0Evo+tx19r9uGvs9tQ9/nemiOh4iIiLQZ9XiIiIhIm1HwEBERkTaj4NFOmFmKmV1vZkvNrMTM1pvZ7WbWJey2dRRmNsbMfmRmc81sm5ntMbOFZvZdfZ9bl5nlmNlqM3Nm9puw29ORmFlPM/uVma0I/u3YZmavmNnxYbetozCzXDO7ycwWB/9uFJjZW2Z2iZkl3ESrM9POpe3HncA1wNPA7cD44PVUM5vlnKsMs3EdxP8A3wD+CTwKlAEzgVuBL5vZUc654hDb15H9COgddiM6GjMbBrwK5AJ/BJYBecChwKDwWtZxmFkK8BxwDPAQcA+QA5wPPID/t/rG0BqYhDS5tB0ws4nAYuBp59xZMeVXA3cDFzjnHgurfR2FmU0DljvndsWV3wp8F7jaOaffxluYmR0OvAt8Gx+q73XOfTPcVnUMZvYGMByY4ZzbFHJzOiQzOxp4C7jLOXd9THkGsBTo6ZzrHlLzkpKGWtqH8wED7oorvx8oAi5s6wZ1RM65efGhIzA7eJ7Ulu3pDMwsFf/3+HngqZCb06GY2QnAccAvnHObzCzdzHLCblcH1C143hhb6JzbDxQA+9q8RUlOwaN9mA5U4n8rrOacKwEWBtel9QwOnreE2oqO6XpgHKAejpZ3WvC8zsz+BRQD+8xsmZnpl5WW8y6wE/i2mZ1jZkPNbKyZ3QYcAdwSZuOSkYJH+zAQKHDOlSa4tgHoHXTrSQsLfiO/GSgHNJzVgszsEOCHwI+cc2tCbk5HNDZ4vh/oCVwMfA3YD/zZzC4Nq2EdiXNuB3AmsB14HFiLH2L5BnCWc+7+EJuXlDS5tH3IARKFDoCSmDr726Y5ncpdwFHATc65T0JuS0fzW2A1cEfYDemgugbPe4CZQdc/ZvY0sAr4qZk9pInpLWIv8CF+Yvpb+KD3DeAxM/u8c+7FMBuXbNTj0T4UAZl1XMuKqSMtyMx+jB8CuM85d1vY7elIgq7+k4ErnXNlYbeng6pagfWXqtAB1b+h/xPoT02viDSTmU3Gh40XnXM3OOeeds79ET+/ZjNwf9BzKgEFj/ZhI344JVH4GIQfhlFvRwsys1uA7+GXw10Zbms6luDv8R3As8BmMxtlZqOAYUGVvKCse1ht7CDyg+fNCa5VrXDp0UZt6ciux/8C+ERsoXOuCPg3/u/18LZvVvJS8Ggf3sP/t5oRW2hmWcAUYF4IbeqwzOwHwA+Ah4HLnNact7RsoA9wOrA85vFqcP3C4PVlYTSuA6majD44wbWqsq1t1JaOrGo/lES9Gmlxz4KCR3sxG3DAdXHll+Pndjza1g3qqMzsZvws9D8Dl2r8u1XsA85J8IgE158PXv8zlNZ1HH/Hz++40MxyqwrNbADwBfyeNSvCaVqH8lHwfElsYdBj93lgB7CybZuU3LSBWDthZvfg5xs8je+irtq5dA5wkn5AHjwz+wbwG2Ad8H38EuZYWzRJrPWY2XD8ZFNtINZCzOwK4PfAEuBPQAZwFTAAOMM590KIzesQgt1h38cPWz2K/ze5J/4Xw+HAN5xz0dAamITU/dN+XAesAa7Ad1EX4LfmvVmho8VU7YcyFL/1cbzXAAUPaTecc/eZWQF+V9gf48P028BXnHNzQm1cB+GcW2tmM/DL7j8NnIef2LsQ+JZzThvjxVGPh4iIiLQZzfEQERGRNqPgISIiIm1GwUNERETajIKHiIiItBkFDxEREWkzCh4iIiLSZhQ8REREpM1oAzERkUaIRCK34M/wmRmNRl8NtzUi7ZeCh4i0iUgk0pjdCvVDXaSDU/AQkbb2w3qurWmrRohIOBQ8RKRNRaPRW8Jug4iER8FDRJJS7JwKYBj+oMRx+KPenwFuikajmxO8bzT+dOFPA33wByq+BPw4Go0uT1A/FX+S6EXAJPwJrhuAV4Gf1/Ges/EHr00CSoAXgG9Fo9ENB/Eli3QKWtUiIsnueuB3wAfAXcAnwKXAW5FIpE9sxUgkMh2YB1wIvAf8CpgLXADMi0Qi0+LqZwDPA78FhgCPAXcD84EvAscmaE8EeAQ/LHQv8CFwLvBSJBLJPNgvVqSjU4+HiLSpoCcjkZJoNPqzBOWnAkdGo9EFMfe4E98D8jPga0GZAQ8D3YALo9HoozH1zwX+CjwSiUQmRKPRyuDSLcAs4F/AOdFotDTmPZnBveJ9FpgejUYXx9R9DDgf+DzweF1fu4iox0NE2t4P6nh8p476f44NHYFbgF3AV2J6GY7BD8W8HRs6AKLR6GzgTWAscBxUD7FEgGLgytjQEbynNBqNbkvQnrtjQ0fg/uB5Rh1fg4gE1OMhIm0qGo1aE9/yWoJ77IpEIguBE4HxwELg8ODyy3Xc52V86JgKvI4PKXnAO9FodGMT2jMvQdn64LlHE+4j0impx0NEkt2WOsqrJpbmxT1vqqN+VXn3uOemTgjdmaCsPHhObeK9RDodBQ8RSXb96ijvHzzvinvun6AuwIC4ejuD50HNbpmINJmCh4gkuxPjCyKRSB4wBb+U9eOguGoeyKfquE9V+fvB81J8+Dg0EokMPPhmikhjKHiISLK7KBKJTI0ruwU/tPKXmEmhc/BLbY8L9tmoFrw+AViGn2RKNBqtAKJANvC7+KWwkUgkI365rogcPE0uFZE2Vc9yWoC/R6PRhXFlzwFzIpHI4/h5GscFjzXErISJRqMuEolcDLwIzI5EIv/A92qMBb6A33jsqzFLacFv334k8DlgWSQSeSaoNwQ4GbgBeLAZX6aI1EHBQ0Ta2g/qubYGv0Il1p3A0/h9O84F9uLDwE3RaHRrbMVoNPpOsInY9/D7c3wOv3PpX/A7l34SV39/JBL5LHAl8FXgYsCAjcFnvtnUL05E6mfONebASBGRtqVj6EU6Js3xEBERkTaj4CEiIiJtRsFDRERE2ozmeIiIiEibUY+HiIiItBkFDxEREWkzCh4iIiLSZhQ8REREpM0oeIiIiEibUfAQERGRNvP/Aac1pykFhjqqAAAAAElFTkSuQmCC\n",
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history,plot={'loss':['loss','val_loss'], 'mae':['mae','val_mae']}, save_as='01-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Predict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.1 - Load model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:37:56.672671Z",
- "iopub.status.busy": "2021-03-09T21:37:56.672279Z",
- "iopub.status.idle": "2021-03-09T21:37:56.755377Z",
- "shell.execute_reply": "2021-03-09T21:37:56.755629Z"
- }
- },
- "outputs": [],
- "source": [
- "loaded_model = tf.keras.models.load_model(f'{run_dir}/best_model.h5')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 Make a prediction\n",
- "A basic prediction, with normalized values (so humanly not very understandable)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:37:56.759933Z",
- "iopub.status.busy": "2021-03-09T21:37:56.759255Z",
- "iopub.status.idle": "2021-03-09T21:37:59.123110Z",
- "shell.execute_reply": "2021-03-09T21:37:59.123488Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/SYNOP/figs/SYNOP2-02-prediction-norm</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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3QV1qW6cV/MdtuClyPiuEv+N8Og0KDDcsruTk4Bj9Ix4DRiVE9vFM+hmZ8FFdZp/3cRazCX8gmPDrtLS0sG/fvhl/nnzySQB8/iAWc2iF0GwyEQgW9vwl4ueZ9OOZ9OOanQp9wRXwzMOgNRuXVXP20ip+9FD6ijrkkC8B24BXgEEjT5yr1yO5Ou5sM+bx8bun2/nwfz/E1+59kdWNLj72+g1xF2pJtLiLYUVhYuAPBJmY9M+o6p0wmx2ueSucOpH8uRZQ0LfJ7HY7/f39VFVV5e0+A601/f392O3zX+jls46+US5cVZP0eYrMJs5fUcPTR3q49twlBoxMiMSkoo9puOBMdbkd8wJLdEVTK4ap4IuwYmg0ucjLb91TvTfn/BtZtBw+8mrhkQ9dvY4PffshrmppYkVdeZpHmdVWaq2PAiil9gOJV26bJpeuuYbHJ+kb8eAPBDGbFNbAREFfR8VjT2sXu/a20Tv173Dn1mYWVZWw+9kTPPLyKc5bUcPN167nnKWv/h4UW4vmPGe+PX/hn8XznOnPu+MPrUxMBpIrCrOAkQkfZcUW4woWXjvVy1DrUAZEihR0YLho0SI6Ozvp7e3N9FBSym63n2kEXYg6+0d588XLDTnXxatreeTl0xIYioxJ1cWVe3wSpRTlxQvf3bSYTYx5/Ya99nThVhWQmsBQbpblvxk9DKdTCmoa4MgBWL0BV4mN925Zwzf+sJ/bbtwkFaenhINCo+XKNZdn0s/whI/w/SONpmfUT21dA8ZcSeSvSFU///O3+ygrtvCWS1bw3Zu2UFFqm/O8bS1NcQdniTwn/LyugTG0hvdsSd3WIPf4JOXFBqSRTvenX0HAD69/h7HnnaagA0OLxcLy5fLPPJ9prekaGGOxAamkABesquWb9x1g0h/AWmR84Q2RX1KxVycVF1fBoKZ/1ENFiY3R3oV3GEz6A4x6/AxF+IBP1uCYF4e1CJvFzMiED5MJSmwGpOJMU+g3y/Jd9+z9hdMN9cN3/hP+3/dBKV533hL+/EInf97XITf8UixXrrmi718bY+vZ8jsyn0gVRjVgt5h5x+ZVmRlUBE6HleO98/cKT5Z7fNKYwjPTnXUe3P7PcPX1YDH43FMKOjAU+a9vxEOxtYgSI3K8CU0my2vLePH4ABesTD49VeS9LwEDwHOAy4gTpuLi6t/vfp5ap4P3X7w2psf3uCf4xK7H+PGtVxk6DoD3f+sBPv+357O0poyfPXKEMa+f918V27iEAOgeGo8eGDYtA6sNjrXBirWYlOJjr9vAZ3/6FJua642/kCsgSqkPAR+66aabMj2UpESrUJmqypX5JOp7N5xdtRmcDivu8cmUvsZwKgLDxSugcSk8/RBc+hpjzz2l4IvPiPxmVOGZ6S5eXcuTh7sNPafIWyu11lVa66uBk5keTCTPHe3jYOcg77o89ru5VWU23OOThu8z1FrTM/zqak+5w8pwij+8Rf7pcU9E71sbrk769ENnDq1qcLJ1QxPf/evBNI0wP2mt79JaX5DpcSQrWoXKVFWuzBc97omobcGy7b1zlqQ+MHRPGFSRdLbrboAiY7NoppMVQ5HXOvpGWVRlyL75My5eXcfnf/40N19jfAEQkV9StVcnWdOLA5hMir+5cBn2OEr2m00mKsts9LonaKw07sbL4JiXYmvRmbGUF1tS/uEt8k+oh+E8F6JbtsPkzH5m77lyDe+5Yw87v7mX00PjLK4u5YbNq9i6IbWl4UX22bm1ma/e8+KMG1+pqlyZL1pPDPClXz/H5nX1PH6oG68vu987l8PG0FiKA8OxFKwYAjSfHSpAMz4GDmMXPkBWDEWe6+w3bn9h2NKaUtBwvHfU0PMKkQ7h4gA97gk0EAhqdj93gj2tXXGdp85ZHHEfTjJmr/Q4HVaGJyQwFPEJ9TCMUHwmzFUFwQD0nT5z6MnD3ditZj5+XQv3fOZ13Hzter63t429++P7dyFyX6ioSSPWotAlcq3Tzq3bW1JSuTIf/OG5E3zxl8/yyTeczWeuP49bt59NrbMYRaivYDa+d2lJJZ1IUWAIoYyH7/6/lJxaVgxFXuvsHzV8L6BSioum0kmX1ZYZem4hUi1ScQCvL8CuvW1xfXjXOovpNjgwPD00s2iIpJKKeE36A4xM+KgsW6Aw0pMPgGcC3v4hAH76yBE+9TfnsHFZNQAbl1XziTeczZ33HZBVwwJktxRx49ZmfvX4UW5776XRU5MLmC8Q5Nt/OsAL7f189cZNZ7KzEq0Wmk7lDgujHh9BrVNWjdg9Pklzoysl52bjJfDT/4KTJ6DR2IJIsmIo8lpH/xiLq41NJYXwPsMew88rhFLqQ0qpZ1J1fqMKK9Q5HXQPpWDF0DV7xdBn6GuI/Bbuxbngxd4Fl8OzD0MwlPLW0TfKhsWVMx6yYXElHX2SGVKIjvUMs6y2jGW1ZbT3pLZ6ZS4aGvPymR89Se+wh6+//zLDt+ykmtlkwmErYiSFny/u8RTtMYRQAa0t2+GvvzH81BIYirzl8QUYGvOm5E7fOcuqONY9IvufhOFSXcDBqMIKda7UpJLWTxtHWbGFkYnQXV0hYjFvq4rpmpaBvRiOvQzA4upS9ncMzHjI/o6BlNxYzHZKqR1KqX9WSv0zUAM4w98rpXZkenypprWmvWeE5VOB4TEJDGd45bSbW/7nUdYvruBf/vYCw9sJpYvTYcU95l34gQlKSVXS6ba+IdS+wmCSSiryVlf/GA0VjqhVspJhLTJzzrIqnjnSw1VnSz80kTveeflKbr93/4xjiRQHqHMW81f3uJFDo3tonPNXvJr6Hbqra2bU4zO+UbDIS6Hm9jHe5HjfP0BNPQA3bF7F1+55kU+84Ww2LK5kf8cAX7vnRW7MsqIZafJ+4MpZx7449fVB4IfpHU56DYx6UUpRUWJjeW0Zzx/ty/SQMmZ6obIaZzGXrK7lwZdOcfO169myvjHTw0tKqvcZpqSP4XTlLti4CbraQze6DCKBochbHf2jLE5hesPFa0LppBIYilzS3jNKy5IKut2eMx/2O7c2x70nJBV7DCNVkwzvM5TAUMQiVMBonsIz0y1dBYf3w6r1Z/YR3nnfATr6RllcXcqNW5sLcn+h1npLpseQSe09IyyrLUMpxfLacu5+4limh5QR4UJl4T3pPe4Jfv/McW7cuibng0IAl8PKUAoDw+FUppKG9XTBbZ+B//d9wxreS2Ao8lYqehhOd9GqWr7714P4A0GKzJKVLbLf4VNuHjhwkrs+cmXSdzJrnMUMjHgJBIOYTcn//mutQ6s9s9IAy4tln6GIXffQOOetiLHgmFLwo2/Ae26FVWexdUNTQQaCYqZjU2mkAEuqS+kaGCvIz/lIhcoA/vBcBzdsXp2BERnLWWJL2YqhZ9IPgN1iTsn5z2hcCktWwpN7YfM1hpxSAkORtzr7R2ekpRmtqsxOY0UJBzoGOWdZVcpeR+Suqf04S6e+rQGsU/t2AI5rrdOWkhUIau74Qyvv27bWkPQWi9mE02Glf8SYfbwjEz6KzIoS+8z9KlKZVMRjwR6Gs11wBTzzEKw6K3WDEjmlvWeEsxZXAKE0+5ryYjr7xwquCrlRhcqyVWiPYWo+W9zjkzhLFqiMbJTXXg//exv8/scw0AuVNXD9jXDJtoROV1i3P0RBCacDpdLFU20rhIji/YT25nwRqAVc075/fzoH8ofnTmAxm7j6HONSn+tcxXQPGbPPsDtKCqCzOPX9pkT+iLn4TNgFl8Ozj5ypTirEsZ7hMyuGQMFWJjWqUFm2SuUew+EJH+XFaSrK4x6E0WEY6AF06OsPvg5P7EnodBIYiryktaZrwPjm9rNdvKZO2laIqLTWW7TWKsqfLekax+Colx8+eIiPvW6DoT2bjNxn2D00HvGCvsxhkSb3Iia+QBD32CTV5fbYn9S4FN57KyCVb0Uos6Kjb5SlNa8GhstryzjWM5zBUWXGzq3NWMwzPy8SKVSWrVIZGA6NeVNbeGa633wf/LO2W0x64e7vJXQ6CQxFXuof8WK3FM1JSzPayvpyxr1+uvrHUvo6QiTjrr+8xGvPWcTyunJDz1vnLDasl2G0FEBnsZXhcdljKBbWN+yhstQW/57X1Rugsz0lYxK55eTAGBWlNhy2V3daFeqK4baWJpobXZTaLShCNwJv3d6S9c3rY+UssTI0npp2FSlvVTHdQG98xxcgewxFXuroH2VxdWpXCwFMSnHR6lqePNLD9VXLU/56QsRr37E+9ncMctdHrjD83HUuB21dQ4acqydKCmC5w8qpQWPbYoj81O0eT2y/a38P3PF5+M8fggGFlETuClUknXkDbXltGcd6Cy8wDASDdPSPcecHN1PnirHSbw5xpXKP4YQv9RVJwyprptJIIxxPgMyAIi919o+yKIWtKqaTfYYiW036A3zjj/u56ZqzKLYafx+wzlnMaYN6GZ4emoh4UV9eLKmkIjZx9TCcrnEJlJTBKy8ZPyiRU6ZXJA1rqChhaGySca8/Q6PKjJc6Bqkus+dlUAjgdKSuKqk7namk198I1lmFbqy20PEESGAo8lKqW1VMd+7yatq6hhjzSrqbyC6/evwoiypLuLS5PiXnr3UW02PQHsMe90TEC5BUNyEW+SPUwzDBwhgXXA5PP2TsgETOae8ZnlN91GxSLKkupb3AVg0fP9TNpua6TA8jZcodFobHJ9Ha+P3FwxO+9AWGl2yD93wcKmsBFfr6no8nXJVUUklFXuroS22riumKrUWsX1zJs6/0ccVZDWl5TSEWcmpwnN88eYxvfmBzyl6j1llMr9tDUOuki9pEKz4j7SpErLqHJmhZWpnYky97LQwNGDsgkXOO9Y7w3ghtKcL7DM9aVJGBUaWf1prHD3Xz2evPy/RQUsZaZMZqMTPm9VNqcD0Kdzqa2093ybaEA8HZZMVQ5KV0rhiCpJOK7KK15pt/3M9bN61MaRqQzWKm1G5hcDS5DfyjHh9BrSmLUN5bGtyLWCW8xxBC+3GqamGwz9hBiZzhmfTTP+yhqXLutUOhVSY90TeKzx9kZb2xBcuyTap6GbrTWXzGYLJiKPKOxxdgcMyb1rz4i1bX8qOHDhMIasym+FZO9rR2sWtvG73uCWqcxezc2pw3Vb9EZjxy8DQ97gmuvyT1BZHqXMWcHhqnqiyOFgGzdE/tL1QRVh3Lii2MTPgMWZUU+S3uHoazPfxHGB6Cd95s2JhE7jjeN0pTVSlF5rlrJstqy3jiUOHc/H3iUDeXrKmLOCfnE5cjVJm0yeCFhLRWJTWYrBiKvNPVP0ZDhSPuAC0Z9S4HFSU22k4OxfW8Pa1d3L67lR73BJrQHpnbd7eyp7UrJeMU+W/c6+fbf3mJW16/AUuECxyjGbHPMNr+QoAiswmHzcyoR1YNRXSBYJCBEW9yzbeLLLD3XvjA6+Af35Nwg2iRm9ojFJ4JC60YjqRkP1o2erwtv/cXhqVqD3surxhKYCjyTjorkk538epanozzjuKuvW14fYEZx7y+ALv2thk5NFFAfvDgIc5dXk3L0qq0vJ4RvQy73ZH3F4aVFVsZkV6GYh79I6EqgAnfDHliD/z+R6CDgA6Vf//B1yU4LCDHekbmFJ4JqyixYVKKgSTT5nPBwKiHjv5Rzk7TZ0gmOUuMDwyDWjPq8UXcGpELJDAUeacjzfsLwy5eU8uThyP0kplHtJWWXoMqPYrC8sppN3v3d/GBq9am7TXrXMV0J/n7ulAKoNNhxS0tK8Q8upOpSApw9/dgctZF/6Q3dFwUhGM9w1FXDJVSLJtaNcx3Tx7u4fwVNWnJOMk0p8Nm+B7D0QkfxdYizDnaEzU3Ry3EPDr7R1mcgRXDtU0VDIx6F0yrC2rNE4e6+YcfPE60bNek0qFEQQpqzR1/2M+NW5txldgWfoJB6pyO5APDKD0Mw6QyqVhI99B4Yj0MwwZ64zsu8k77PCuGUDgFaAoljRRSk0rqHp/ElaNppCDFZ0Qe6uwf400XLUv765pNigtW1vDk4R7ecMHSOT/3+gLc39rF3U8cxWYx85ZLVvDacxbxjT8emJFOai0ysXNrczqHLnJYuHhRj3uCIrMJS1F67/fVOovpGUquyf18ewwhdU3upfBT/kiqhyGEqpIORMj4qExP2yORWUNjXvyBINXzFNFaVlvG/hP53dLEM+mn9fgA//imjZkeSlo4HVaOdhsb7A9PpLlVhcFyIjBUSq0B3g28FlgJ2IFXgF8Ct2utxzI4PJFFtNYZ22MIUGIr4q6/vMS3/rj/zIXmeSuq+f3Tx9n93HGaG1187PUtnL208ky1L7PJdObi1G41s6bBKRenIibh4kXhGwv+QJA7du/HhErb71CdK1R8RmudcAW7aD0Mw8pTcFd39nsXLvwEyL+/HNQ9NEFzkyvxE1x/Y2hP4fR0UqstdFzkvdD+wvJ557DltWXc+8zxNI4q/Z492seaJqfhff2ylavEypDRK4ZjEhimw/uAjwK/B34M+ICtwP8F/lYpdYnWWjZlCfpHvNgtRRmZ1Pa0dvGnfR1M+oNA6ELzy797AYtZcdXZi/jyezaxpHpuwLqtpenMhejIhI/33/nAgiktQsD8xYvSFdwUW4uwWcyh9JkEUlgnJv14fQFcJdE/SJ3FVoYNLj6TDe+dME63e4LLz2pI/ATh5tB3fy+UPlpZEwoKDWoaLbLbsXkqkoYtrSmjo2+UQDCYs/vHFvL4oW4uXVMYaaQQ7mNobEEh94SkkqbDr4B/11q7px37tlLqMPBZ4P3ANzMyMpFVQquF6S88A1MXmlNBYVioabeNj29viekcZcUWbti8iu/ef5D/e8NFqRimyCPRihSlu3hRrbOY00MTCQWG3UOhNM757tSXO6ycSjJddbZsee+EMZJOJYVQECiBYEFq7xlmTaNr3scUW4uoLLPTNTAe8SZvrgsENU8d7mHHFaszPZS0ScUew+Hx3F4xzIlbHlrrZ2YFhWE/n/q6IZ3jEdmrI4OBYbQLyv4RT1znue6CpXQNjPHsUSl6IOYXrUhRuosX1bkcCfcy7ImhKXl5scXw4jPZ8t6J5AW1jun3SIhoYlkxBFhWU0Z7nlYmfalzkKoy+7z7vfONs8SGe3zS0P6UQ+OTlDtyNxU3JwLDeSya+hpf8ziRtzr7x1icoTt5Rl1oWswm3r9tLd/5y0ECwcJopisSs3NrMzaLecYxm8Wc9uJFoV6Gia3odbvHF7wQScVd3Wx570TyBke9lNiL5vz/FCIWQa050TvKspqFA8N8rkz6xKFuNhVQGimA3WJGKYVn1raCZAyPT+JypK8yuNFyNjBUSpmBzwN+4CcZHo7IEpnqYQjGXmhetraeEruFv77YadTwRB7a1tLErdtbqHUWowildN66vSXte+SS6WW4UKsKmGpwP2HsHsPwexfu1VVTbs/IeyeSF+qDWTirHMJYpwbHKXdYKYmhNsGy2vxcMdRaF1SbiulcDquhvQyHZcUwY24HLgE+r7Vui/QApdSHlFLPpHVUIqM6+zJXkdTIi3SlFB98zTq+/0AbE5N+4wcr8sa2liZ+eMs27vvcdn54y7aMBDa1ziQCwxhSAFOxYgih967WWUyRSfGld10sQWGO6onh5oIQ0cRT7G15nja57+gbxesPsKq+PNNDSbtyh7GVSYfGJ3Hm8B7DXCk+M4NS6ovA3wF3aa3/PdrjtNZ3AXfdfPPNko9XALy+AINjXuqTaXKcpOkVRpO1tslFy5Iqfv34Ud595RpDzilEKtQ5HfQMJbHHcIF/s2XFFkY9PoJaY0qwJUYkWmv6Rjwsryunf8STlwUlCkG3ezyj877Ibcd6RlgeQxopQFNVCQMjHjyTfuzWnLyEjujxQz1sWlOXcMuhXBa68WhcZdLh8UnKi3M3MMy5FUOl1BeAfwZ2AR/J7GhENukaGKPe5cirMtI7tzXz26fb4y5gI0Q6Te9lGK+eGNIAi8wm7BYzYx5jV8/HvX5MChZXlci/sRzWbURFUlGw2nuGY14xNJtMLKoqpb13NMWjSq/HD50uuP2FYUZnpAyP+3DO034p2+XUFbRS6l+AfwF+AHxAG1lGSOS8jr7RjBWeSZV6l4NrNy7m+w9EzJYWIiuU2i0oBSOe+PYBTvoDjEz4qCxbeKN+ucNqeGXS/hEPVaV2qsrs9I8Y28tKpE/30MKrzkJEE2tF0rDQPsP8KUAzOOrlRO8oLUsrMz2UjHCWGLfHcNIfYNIfwJHDq8k5ExgqpT4PfAH4IbBTax2c/xmi0HRmsPBMKt2weRVPHe7laHf+fBCJ/FPrLI47nbTHPUF1uT2m9FCnw4p7wuDAcNRLZZmNyjI7A6OyYpirYll1FiISry9Aj3uCRXHcVM63fYZPHO7m/JU1WIsKs6qvy8AVw+FxH+UOa06n5OZEYKiU+ijwr8AJ4K/AO5VS75725+rMjlBkg47+URZnqPBMKpXYLbzz8lV8568HDe21I4SR6lyOuAvQdA/F3nsuFb0M+0c8VJXZqSq10TcsgWEu0lpLKqlI2Im+URorSs5UJ45FvlUmfaKt8NpUTOc0sPiMO8cLz0COBIbAhVNflwDfJ7RqOP3PZzM0LpFFQj0M82/FEOD15y2hxz3BM69I03uRneoSqEzaHUPhmbByh5Vho1cMw4FhmZ1+WTHMSe7xSaxFJhy23E3dEpkTT0XSsOW15XmzYuiZ9PPi8QEuXFWb6aFkjNNhM2zFUALDNNFa36i1VvP82ZLpMYrM0lrT2Z+5VhWpVmQ28YGr1nHXXw4SCEoWtcg+da74m9x3D41TG2MKYHkKWlb0j3ipKrNTXWZnQPYY5qRY2p0IEc2xnuG49hcCVJXZCAQ1Q2O5P2c8d7SPNY1Oyopzt+9esozcYxjqYSiBoRAZ1z/ixWYxUxpDg9pcdcmaWlwlVv60T5rei+xT6wxVJo1HTxwX9eXFVkbGjW1yHyo+Y6OyzMbAqFdStXNQTxzpyELMlsiKoVIqb/YZPn6oMJvaT2dkuwr3hKwYCpEVOvN0f+F0Sik+dPVZ/PDBQ4x7pem9yC71LgfdcRafiSeVNDXFZ0KppNYiM3armeEJYwNPkXqn3ePUuaTwjEhMvBVJw5blQWAYCGqePNzDJQW8vxCMLj4jgaEQWaEjTyuSzra6wcm5y6v55WOvZHooQsxQm+gew4wWnwmlkgJUldqlAE0O6pHCMyJBw+OTeHyBhH5/ludBy4qDnYNUltqoL/AbKw5bEf6AZtIfSPpcbkklFSI75PP+wtlu3NrMPc8ep3c4votwIVKpvNiCPxBkzBvbqpsvEGRo1Et1uT228zushq7oBbVmYMRD1VQPxapyaVmRi6SHoUjUsZ4RltWUJdRaIB9WDJ+QNFIglI1V7rAwZMA+Qyk+I0SW6MjjiqSz1TqLWb+ogg/c+SDXfnE3O+7Yw57WrkwPSxQ4pVRcvQz7hkNpnGZTbB9D5cXGNrgfHp+k2FZ0pndXVamN/hEJDHNNPPtUhZiuvWc47v2FYctqyzjRO0owh/clP17gbSqmM6oyqaSSCpElCmnFcE9rF88f68PjC6AJXRjdvrtVgkORcfWu2NNJQxVJY7+gdxpclbR/xEtV6aurlVVldvqlMmlO0VpPrRgWdiqcSEyi+wsBSmwWnA4rpwbjq8ScLU70jeLxBVjd4Mz0ULKCUZ8v7vFJyotzOzCUxj8i53l9AQZGvNQXSDrRrr1teP0zW1Z4fQF27W1jW0tThkYlRHz7DOMpPANQVmxh1OMjqDWmBFK/ZuuflkYKoRL0R7tzOzWs0Ix6/KDI62rUInXae0a4KonPzHCj+6bKxLOV9rR2sWtvG73uCWqcxezc2pyWz/EnDnVzyZrahNJo85HTYcVtQPsRSSUVIgt0DYzRUOGIOSUt1/VGufCOdlyIdKlzOWLuZdg9FF/RkCKzCbvFzJjHmIq84YqkYVVldgYklTSndA+NSxqpSEhQa9p7R1ia4IohJL/PcE9rF7fvbqXHPZH27J/H27oLvhrpdK6S5FcMtdZTfQxz+0ZVYVxJi7zW2T/G4gKoSBpWE+VCKNpxIdIlnl6GPe6JuKvhlTuM22c4vSIpTKWSjkoqaS6R/YUiUT1DEzhsRUml/S2rSa4y6a69bXh9MythhrN/Umlw1Mvx3hHOWVaV0tfJJU6HlaEkP1vGvX6sReYz+9ZzlQSGIud19BXO/kKAnVubsVlmTjy2IhM7tzZnaERChNS7imPuZdjtjm+PIUwVoDGol+GcVNJSuxSfyTGn3bK/UCTmWM8Iy2rLkzpHsk3uM5X98+Thbs5bUZPzAYyRjNhj6M6D1UKQwFDkgc7+URZXF05guK2liVu3t1DrLEYB1iITWzc0yv5CkXFx7zGMMzB0OiyGFaAZGPHMKD5TURq6MAgEg/M8S2QT6WEoEnWsZzjhwjNhi6pL6XFPzFn1i1Wmsn8eP9TDpjW1KX2NXBPaY5jcZ8vwxCROh23hB2Y5KT4jcl5n/xhvvHBZpoeRVttams4Egk8d7uGHDx3K8IiEAFeJjYlJP55JP3Zr9I+XQDBI/7An7gugUC9DYwLDvhEPVdN6KJpNJpwOK4OjkzH3VhSZ1T00zvpFFZkehshBx3tHuWBlTVLnsJhNNFaUcKJvNKHqnju3NvOV379AIPhqywubxZyy7J89rV38756X6R32cOS0G6WU3FCe4ixJvl3F0NgkTlkxFCKztNZ09o8VVCrpbOevrME9NsnhU+5MD0UUOJNS1JYvvM+wf8SLq8SGxRzfR1Col6ExTe4HRme2q4DwPkNJJ80VPXFWthUizIgVQ3i1MmkizltRTZFJUV1mRwFKwXu3rElJsBYudNM7HJrf+oY90uZqGiNSSYcnJinP8YqkICuGOSNTJY2z3cCoF6vFRFlx7t+lSZTZpHjdeUvY/exxbr3u7EwPRxS42qlehktqol90xdvDMMyo4jOBYBD3+CQVpTM/xKXJfW7pllRSkQBfIMipwXGW1CR/Qzm0zzCxAjS/fuIYr924mL973QYAvvPXgwymqADWfIVu5FoSXA4r7vHk3vt8aFUBsmKYEzJZ0jjbFVrhmWiu2biIhw+eYsxrzGqKEImqi2GfYbw9DMOcDituA1JJB0dDH+CzW9xUlkkBmlwx7vUz6Q/mxYWYSK+OvlHqnMWGFF9JdMXQPT7JH58/wd9euvLMsdeft4Q/v9DJpD+xPYvzkTZX8ysttjAxGcAfSHyPuXtMAkORJpkqaZwLOvrHWFRArSqiqSy1c+7yava0nsz0UESBq3UuXJk03h6GYWXFFkYMWDHsG5nZwzCsqsxO/4i0rMgF4VYV0qBbxKvdgIqkYYlWJv3Nk8e4fF3DjHmwqbKEVfXlPPzSKUPGNp20uZqfSSnKipMrbpYvqaQSGOYAudMTXWf/KItlxRCA7ecvZfezx9FaL/xgIVKk3uVYcI9hIj0MIbximPyqeKgi6dzqcdVlkkqaK04Pjcv+QpGQYz0jhuwvhNCNMI8vEFeK+8iEj3ufPc7bL1s552fXnb+Ue589YcjYpnv3FavnHEtloZtclOw+Q/e4T1YMRXrInZ7oZMXwVecsq2LSH+Rg11CmhyIKWGjFcHzex5xOoIchhIvPJL9i2D/qoTLaiqE0uc8J0qpCJKrdoMIzAEopltXEt2r426eOcWlzXcSbYxevqaVneIKj3YntW4xmzOtndUP5mTZXtc5ibt3eIvsLp3EmuYfdPe6VwFCkR6ih+cz/VXKnJ0RWDF9lUorXnbeY3c8ez/RQxBSllEkp9Qml1MtKKY9SqkMpdZtSKm/vZtS5Ft5j2JNAD0OAcofFkHYV/SPeiKmklaV2BmTFMCeE+mBKc/tUyPd5K9Tc3pjAEML7DGML5MY8Pn7/dDvvuGxVxJ+bTSZed+4S7jXwc9zrC/DLx17hE9edzQ9v2cZ9n9vOD2/ZJkHhLE6HjaFkUknHfZQXS2Ao0mBbSxM7tzZjmtpK4XJY5U4PoclucNRLfYXcNQ577TmLeeJQt2G93kTSvgZ8FXgJ+BjwS+AW4B6lVF7Ov5WldkYmfFELKAS1ptftSXjFcGTCRzDJdOnQHsO5qaRVZTb6JDDMCd1Did1cEDHJ23lr1ONjZMJHfYVxNxXi2Wf4u6fbuWh1LY2V0WPsazcu5sEDJxn3+g0Z3337OljT6GJlffy9FguJqyTZVFIpPiPSyOmwcWlzPddsXMR7pVUFAF0DY9S7HHMqCxYyp8PKRatq+euLUrE205RS6wldVN2ttb5ea/0drfUngU8CW4F3ZHSAKWI2KarL7fS6IwdYg6NeSu0WbJb4KwIWmU3YLWbGPMldMA2MeKiOsGJY7rDimQykpCqgMFa3W/YYpkK+z1vtPSMsrSnDZGDRouUxViYd9/r57VPRVwvDqsvtnLOsmvsNqDw/6Q/wi8de4V2Xz/+aYmqP4VhigWEgGGRi0k9pHrROkyvqHLG/Y4D1Syqpdzk4NTj//p1C0Sn7CyOSIjRZ4wZAAbfPOv4dYBx4d7oHlC6187SsSLb3XLnDmvSKeP+Il8rSuYGhSSkqS20MSGXSrCd7DFMmr+ctIwvPhC2rLed47+iCmQz3PNPOucurWVy98PaX6wz6HP/zC52sqC1jTaMrqfMUAmcSvQyHx32U2i2G3nDIFAkMc8SBE4NsWFxBvcvB6QUKOxQK2V8Y2frFFZiU4sXjA5keSqG7EAgCT00/qLX2APumfp6XQr0MI89TiTa3DzOiAE3/aORUUoDKMhv9o5JOms08vgDjXj8VESrLiqTl9bzV3jNs6P5CCLXRcdiK6JmnTc/EpJ+7nzzGDZtjW7nbuDxUTO6lzsGEx+ULBPn5o6/wzsvnViQVcyVTlTRf0khBAsOcMDLho9s9zsr6choqHJyWFUNgqrl9tawYzqaU4rrzl/CH54wveS3i0gj0aa0j3YLsAqqVUvnxSTJLnbM46kVSqFVF4oGh05Fcr6lJfwDPZCBqv6mqUjt9wxIYZrMe9wQ15cV5cXc+C+X1vJWKFUMIFaCZb5/hvc8ep2VJVcxBqUkptp+/hHufSbwIzf0vdrK4qoR1iyoSPkchcSaxx3B4QgJDkUYvdQ7Q3OTCbDLJiuE0oVRSWTGM5KqzF/HMKz0MjUlKXAY5gGj/AzzTHjODUupDSqlnUjaqNKhzOaKmkp5OsLl9WNlUAZpEhdJIbVGDCmlZkf26pYdhKiU0b0H2z11a66nm9sYHhqECNJErk3p8Ae5+4hjvjHOf39XnLOLJw4l9jvsDQX76yBFZLYyDy2FjKME9hu6x/GhuDxIY5oRQGmklEKqa5PUHGfMm3+Q5l2mtZY/hPErtFi5trudP+zozPZRCNg5Ey3WzT3vMDFrru7TWF6RsVGkw3x7DHvdEUhf1yTYh7h/xUBkljRRCgaG0rMhusr8wpRKatyD7567eYQ/WIjOuEuNTkJfNU4Dmj8+dYF2TixV15XGds7zYyqXN9fzlhfg/x/fuP0mdy8GGJZVxP7dQJZVKKiuGIp32dwywfiowVEpR7yrm9OD8fcLy3cCoF0uRKS96xqTK9vOX8sfnTyRd2l8k7CShtKtIVyFNhNK18rKvSJ0repP77qHxpPrPlSfZhLh/xENVhMIzYVVlNvolMMxq0qoipfJ23krVaiFEb1kx6Q/wy8cT3+d33QVL2P1cfJ/jgWBotfBdsloYl3KHhVFPYu2QhmWPoUiXSX+AV04Ps7bJdeZYg6ST0tE/GlNlr0LW3OikxFbE80f7Mj2UQvU0oTn2oukHlVJ2YCOQtSlXyaouszM0Nok/EJxxXGud9GpPebEFdxJVSftHIze3D6sqs0svwywXam4vgWGK5O28lar9hQCLq0s5PTQ+p9XNfc93sLreyaqGxHoINje6KLEV8Vwcn+MPHjhFRamNs5fKamE8zCYTDltRQlsV3OOSSirS5PApN4urS3HYis4cq6+QlhWSRrowpdSZ1hUiI34OaODWWcc/SGiPzo/TPaB0KTKbqCi1zSni4h6fxGoxz5jP4lXusDKS7IrhfIGhtKvIeqEehsY1KBcz5O28lYqKpGHWIjP1LgcdfWNnjk36A/z8sVd45xWJr9yFP8djLUITCGp+8vBh3nX5apQUZ4pbqJdh/PO/e3wSZx70MAQJDLPe/hODrF88s6KUFKCZqkgqgeGCtqxv5IXj/VJlMQO01q3At4DrlVJ3K6U+oJS6Dfgq8CDwk4wOMMUi7TM0YqXH6bDiTqr4TPRWFQBV5XZpV5HlZI9h6uTzvJXKFUMI7zN8tQDNX17oZHltGc1J9hDctqGR/R0D9ETZtz3dwwdPUVps4dzlVUm9ZqFKdJ/hsKwYinQ50DFwpvBMWEOFBIad/WPSwzAGDlsRV65v5L59HZkeSqG6FfgUsJ7QxdY7gG8A12mtg/M8L+dF6mVoxN6wZPsYLrRi6LAWoTWMe/0Jv4ZInUl/gOFx37z/D0XSbiXP5i1/IEjXwBhLalIXGE7fZ2hkD0G7tYitGxr54/Pzt6AKalktTJbLYWUogc8X9/hkSooaZYIEhlksqDUHOgZZvyTCimGBppLuae1ixx17eOaVXu7Y3cqe1q5MDynrbT9vKfc9f4JAMCc/z3Oa1jqgtb5Na92stbZprZu01p/UWo9memypFqmXoREpgOUOC8NJ7DEcGPFSNU9jdKWU7DPMYr3DoRVfs0kufFMlH+etzv4xasqLsVvMKXuNZbVltPeGAsP7X+yksbKEswzqIRj6HO+Ys297ukdfPo3NYuaClTWGvGYhcpbYEloxdI9PUi6ppCLVTvSOUu6wUDmrgl69K5SiVWjVJve0dnH77tYz6RT9o15ul+BwQSvry6kus/P0kd5MD0UUkFpXMadnp5Im2cMQQiuGIxOJVY7TWtM34qGqfP7Vpqoym7SsyFJG/A6JwpPKiqRhy2vLOdYzcqaH4LuS2Fs427LaMhorS3i8rTviz4Na8+OHZLUwWaE9homlkkpVUpFy09tUTGe3FuGwFTFYYE2Yd+1tw+ubWfHL6wuwa29bhkaUO15//hIpQiPSqs7pmLMnJtkehhAqbGOzmBnzxJ/qOT4Zeo7DOn/xm8pSu7SsyFI9UnhGxGlPaxdf/0Mrj758mh137EnZzeQDnQP0D3vY/qU/0j/ipTeGPYHxuO78Jdwb5XP8iUPdmE2Ki1fXGvqahSaRPYaeqc8V+wKfK7lCAsMsduDEABsWR05DaHAVXmXSaJOs0ZNvPrrirEYOdg0V/N5UkT51zrm9DI3qP+d0WBNKJ+0fCbWqWOiOenW5nf4Cu/GWK6SHoYhHONMovGe4xz2RkkyjPa1d3LF7P+E8Bl8gaPjrXLa2nvbeETr6Zmb0alktNEwigWE+taoACQyz2oGOwYgrhgB1BViZtCbKxUC04+JVdouZq1qauO95KUIj0qPGaad/xEsgGLpUCvcwNGK1J9ECNAMLVCQNqyqVJvfZqtuAVWdRONKVaZSO17EWmbnmnMXsfm5mEZqnjvQQCGouaa4z7LUKlbPEytB4fDcFhyd8eZNGChIYZq0e9wQeXyBqS4aGisIrQLP9vCVzjtksZnZubc7AaHLP9vOW8Punj/Hur9/PtV/cndKUGiGsRWbKii1nAqwRjw8UlNqT36Bf7rAkVCCgf8QzZ892JJVlkkqaraRVhYjFpD/A3v1dUVs8GJ1plK6Mptefv4T7X+zEMxWEhlYLj/DOy1djktXCpLkS2GPozqP9hSCBYdZ6qSPUvzBaWkBDhYNTBbRiGAhqHj/UzTUbF1HrLEYR6pN26/YWtrU0ZXp4OeHI6WHGJwP0DnvQpC6lRoiwOlfxmQuzHgNTAMMFaOLVN+KleoHCMwBVZaHVTpF9ut0T1Dtlj6GI7OTAGN/960He/fU93LevI2rTcaMzjdKV0VTvcrC2ycWDB04C8NzRPiYm/WxeV2/o6xQqpyP+qqTuMW9epZLmx07JPBSt8ExYnauY0y8Uzt66e55pp8hs4tbrzpa7YgnatbeN2YUcw6kuElyLVKhzOugeGmfDkkpDmtuHJdqEeGDUE9MYqsukyX02CgSDDI7GFtyL7LantYtde9vodU9Q4yxm59bmBT+Hoj0nEAzyxKEedj97nCOnh3nN2U189cZNLKoqPbPHcHqaZyoyjXZubU7L6wAsqi7ljj+08rV7XsRsNvG6cxfJdZFByh0Whscn0VrHvF/TnWeppBIYZqkDHYO85uzok2RDAfUy7HFP8OOHDnPbjZfK5JcEKd4j0i3U5D70+9Vt0P5CgLJiS0J7DPtHPKyLoa9YZamNgRFvXBcHIvV6hz24SqwUmSXZKZfNDtbC2StA1OAw0nO+du+LPHzwFG0nh6hzOrju/CV84e0XYC16tVdh+HzxBqHxStfr7GntYvczx/EHQnd5/YEgf97XyVmLKuUGrwGsRWasFjNjXn/M2x7yqVUFSGCYlcY8Pk4OjLGy3hn1MdXlxbjHJ5n0B2ZMgvlGa803/rifN120nCXVpZkeTk6rcRZH3G8hxXtEqtS6ijl8yg1A99C4YXvDnA4rPW533M8LVyVdiM1ixmYx511RgVwn+wvzQ7RCLd/568GovQa/89eDc54z6Q/y/LE+vnrjpayoK4/6ettamtISNKXjdXbtbcPrn9nk3usPSuaPgcK9DGMNDN3jk6ysj/77l2skMMxCL3UOsqbRiWWeu6Jmk6K63E730ASL8zhgevClU3QPjfP5t52f6aHkvHSmuggBoRXDR18+DYQu6jcsiZ4eH49yR2JVSftHPFTHEBhCqMl9/4hHAsMs0j00Qb30MMx50bJUBka9/Odv90X9WSSeycC8QWG+kcyf1HM5QpVJm6IUf5zNPT6Jszh/PickMMxCBzoG2TDP/sKw+qmWFfkaGA5PTPLff36Jz73t/HmDZBGb8N3E//rTAYYnfNSmKNVFiLBQL8OpVFKDi8+44yw+o7VmYNRLZenC7Spgap/hiKegLjqzXbesGOaFaNkrtc5ivv3hKyI+Z8cdeyTjBcn8SYd497APSx9DkWoHOgZYH8Od9YaK/O5l+N2/HuSytfWcFcOeIBGbbS1N3L7zMuqcxfzwlm0SFIqUqnU56B2eIKi1oXsMnQmsGA5P+M6kiMaisswedZVCZEb30Lj0MMwDO7c2z/l3uFD2SiLPyUfyPqSesyS+wDDf2lXIimGW8QWCHDrpZt0i14KPrXc5OJWnBWj2tffx7NE+7vpI5LuHInENlQ5GJnx5d5dLZB+7xUyxtYiu/jF8gSDlUUrHx6vcYWF4Ir7AsD/G5vZhVaU2+oalMmk26XFPsGVDY6aHIZKUSKGWdBV3yXbyPqSe02GLq5ehBIYipY6cctNUWUKJbeELqHpXMW0nh1I/qDTz+gJ8fXcrf3fthpjeBxEfk1KsqC/nyOlhzltRnenhiDxX5yqm9cQAdc5iwyp8lk31MYynamg8+wsh1MuwvXck0SGKFJAehvkjkUIt6Soik+3kfUgtp8NK/0hsNwWDWjPq8VFm0E3PbCCppFlmf8cA65fEljrZUJGfLSt+/PBhVtaVs6m5LtNDyVur6st55XT8VR2FiFeds5jW4/2GpgBazCZsUyXFY9U/4qEyzsBQmtxnj0BQ0zfsocYpPQyFEKkTzx7D0QkfxdaivGqhkz9/kzxx4MTgvI3tp6t3OTg1NI6e3bU8hx3tHua+5zu46Zr1mR5KXltV7+TI6eFMD0MUgDqXgxenVgyNFG+BgP4RL1UxFp6BUGA4EONdY5F6A6Meyooted2eSQiRea4SK0MxfrbkWxopSGCYVbTWocIzi2NbMQwvXY944qvOl60CQc3X7n2RG7c2x9RrTCRuVX05R2TFUKRBrbOYvmEPtQanAJYXx1eApn/UE9e8UlVmo08Cw6zR4zauqq0QQkQT6mMYW7bI8MQk5Y78SSMF2WOYVTr6xyi2FVFTHtuHn1KKepeD7qEJyvOgh8rvn27HVmTm2nMXZ3ooeW9JTSm9wx7GvX4cNpkGROqEKyf/756XuffZ44YVSoi3AE3/iJcLVtTE/PiKEhvu8UkCwSBmU/L3UPe0dknBCBJ7H/a0dp1ps7Pjjj0F+94JIVIvnmwU99gkTkfsmSi5QFYMs8iBjoGY+hdO1+AqzovKpN1D4/zk4cN8fHsLJoMKVIjozCYTS2tKOdot6aQidfa0dnHvM8fPfN/jnuD23a3sae1K+tyhFcPYsyXi3WNYZDZRXmxlKI7qdNHsae3i9t2t9Lgn0Bj7PuSSRN6H8HOGp/pWFup7J4RID+fUTcFYtmm5JyZxyophZiilTMDHgQ8Dy4Be4BfA57XWYxkcmmH2n4g9jTSsviJ3W1ZMv3NsKTJx8epaFleXZnpYBWNVvZNXTrvZEEPPTCESsWtvG5P+4IxjXl+AXXvbkl7xiX+PYXztKiCUThp6XnKp7bv2tuH1BWYcM+p9yCXR3odv3bc/6v/LHz10SN47IUTa2C1mlFJ4fAGKrfOHScPjk3mRsTddLq0Yfg34KvAS8DHgl8AtwD1TQWPOO9AxGPdFer0rN5vcz75zPOkP8tThHrkLnEarG5wclgI0IoV63RNxHY9HWXHsqaSBYBD3+CQVJfEGhsZUJk3l+5BLov19Rz1+Tg+NR/wz6olcebbQ3jshRPq4HNaYehm6xydxluRXYJgTK4ZKqfWEgsG7tdZvmXb8GHAH8A7gJxkaniH6RzyMenxxr5g1VDh4/FB3ikaVOhHvHPuDchc4jVbWl3PPtDQ/IYxW4yymJ8IFfI0BRUScDis9p2IroDQ0FqocF29J8aoyuyEFaFL5PuSS6nI7vcNz389aZ3HUStSPtXXLeyeESCunI1SZtL5i/qJp7vFJltWWpWlU6ZErK203AAq4fdbx7wDjwLvTPSCjHegYZP2iirj319WlYMVwT2sXO+7Yw7Vf3M2OO/akZBVP7qBn3vLaMrr6R5n0BxZ+sBAJ2Lm1GZtlZnsBm8XMzq3NSZ+73BF7VdK+EQ+VcbSqCKsqtRnSsmLn1maKTDPndqPeh1xy1qIKZr0NC74PqfwdEkKISJwlVtzjC2eLSCpp5lwIBIGnph/UWnuAfVM/z2kHOgZYn8Ber3pXMb1uD4GgMb0M01UkIdrdXrkLnD7WIjONlSW094xkeigiT21raeLW7S3UOotRhFaGbt3eYkxV0mLrmYIkC0l0n2BlmZ3+0eQDw20tTaxpdJ5pMVRZajPsfcgV3UPjPH+sjw+8Zl1cvw+p/B0SQohIYt3D7h735V0fw5xIJQUagT6tdaTwvQu4VCll1VonXz4uQ/afGODma+Nv6m4tMlPusNA/4qHWgKAqXUUSdm5t5iu/f2FGQCt3gdMv3Oh+TaMr00MReWpbS1NKLuLjKT7TP+JNKDCsNmiPIcDwuI8v77iEe549Tr3LUXCBzZ1/eok3XbSct1yygrdcsiKu56bqd0gIISJxxrzH0Jt3gWGurBg6gGifzp5pj5lBKfUhpdQzKRuVQca9fjr7x1jd4Ezo+fUu4yqTpivF87wV1RSZFVVlNrkLnEGrGqTRvchN8fQxHBjxUJVIKulUVdJkjXl89I14WFJTyqY1dTzelnv7wpPxWNtpOvtGedul8QWEQgiRCU6HLaYbj8N5uGKYK4HhOBDtU90+7TEzaK3v0lpfkLJRGeRg1yCrGpxYi8wLPzgCIyuTpivF89dPHOPqsxfxk1tfw32f284Pb9kmQWEGrKx38opUJhU5qKzYysiEL6ZeU/2jHqrK418xDFUlTT4wPHzKzcr6cswmE+csq+J47wiDo8asRGY7z6Sf//rTS3zs9RsS/owTQoh0cjosDC0QGE76A0z6AzhsuZJ8GZtcCQxPAtVKqUjBYROhNNOcTSM9cGIw7v6F0zVUODht0Irhzq3NWMypLZLgHp/kj8+f4O2XrTLsnCIxK+vKOdYzQiAYXPjBQmQRi9mEzWJmzBu5ncF0fSNeqkrjDwzLHVbGvf6kCzQdOuU+kxFiLTJz3ooanjxcGKuGP3roMBsWV7BxeXWmhyKEEDGJZcVweNxHucOKirNoZLbLlcDwaUJjvWj6QaWUHdgIZH266HwOdAywYXHiTcbrXQ5OGbRiuK2liXOXV1MydQdEKdi5rdnQ1bxfP3GUy9c1GLInUiTHYSuiusxOR99YpociRNxi3Wc4kEBzewCTUlSW2RlIcnXv0Mkhmqft4920ppbHD/Ukdc5c0N4zwp9f6ORDV5+V6aEIIUTMnCUL7zF0j0/mXRop5E5g+HNAA7fOOv5BQnsLf5zuARnFHwjSdnKIdYsSXzGsrzC2ZcWY18/n3nY+f/rcdm64bBWHTxq3B214YpI/PHeCt1+20rBziuSsqpd9hiI3lRVbGIlhn2GiVUkh1LIi2XTS6SuGABeuruXF9n48vvxtFRPUmjv+0MqOK1dTkcD+TiGEyJTQTcf5bwgOT0xSLoFhZmitW4FvAdcrpe5WSn1AKXUb8FXgQXK4uf0r3cPUuxxnypgnosHl4PSgMcVhfIEgR04Pn7m7/bZLV/L8sT4Ox9hIeiG/eeIYl62tp941f9NQkT6rGkKVSYXINbGsGE76A0xMBhL+AK9MsjKpe3ySkQkfTVUlZ46VF1tZ3ejkuaO9CZ832/3lhU58gSCvP29ppocihBBxccXw2eIekxXDTLsV+BSwnlCQ+A7gG8B1Wuuc3SB14MRAUvsLASrLbIx6fIbcfX7ltJvGCseZzbQOWxE7rlzDXX95KaYiD/MZmfBx77PHuUH2FmaVlfXlvCIrhiIHlRdbGR6fv5fhwIiXilIbpgT3gSRbmfTQySFWNzjnvP4la+p44lB+7jMcHp/kf/e8zC2vb8E8u6O9EEJkOYetCH9Az7u/3D0hgWFGaa0DWuvbtNbNWmub1rpJa/1JrfVopseWjP0dg6xPYn8hhPbB1LmKDSlA81Ln3LTWazYuwj0+yRNJ7on5zZPH2NRcR32FrBZmk3Avw2CSgb8Q6VbusC7YsqJ/NLFWFWHVSVYmPXzKzZoIrYguXVPHk4d7ZvRyzRf/c//LXHlWY8ItmIQQIpOUUpQ7LAzNs89weHyS8mIJDIWBtNahwjNLkgsMwbiWFQc7BzlrVmBoNpn44GvW8d2/HsQfSGxxdtTj455n2nmHrBZmHafDSqndYlgvTCHSpbzYsmC6T/+Il8oE9xcCVJYmFxi2nXSzZlrhmbD6CgcVJTZe7hpM+NzZ6EDHAE+/0sN7t6zJ9FCEECJhC1UmDRWfSXwbWLaSwDCDTg6MYzGbDKnO2WBQAZqDnYOsW+Sac/yClTXUuorZ/dyJhM7726fauXh1HY2VJQs/WKTdyrpy6Wcoco7TYWV4wcDQQ3USgWFVmZ3+JKqSHj41xJrGyCtn+dbs3h8Icsfu/Xzo6rMoseffBZMQonAstIc9FBjmX2EtCQwzaH/HQNJppGH1LkfSKz59wx68vgBNEYI3pRQffM06fvLwYcY88+/pmW3M4+P3T7dzw2ZZLcxWqxqcHDGowJAQ6VJebGV4Yv75qD/BVhVhyewx7B/x4PMHqYty8++S5joez6N9hr956hhVZTauPKsh00MRQoikOB1W3GPRbwoOj0tVUmGwUBppcoVnwupdxZweSq4yaWi1sCJqs84VdeVcvLqWnz5yJK7z/u7pdi5YWTOjKp/ILqvqyznSLSuGIreUx7hiWJlAc/uwqjI7AwlWJT00lUYabU5d3eBkYtJPR19Ob5UHoMc9wS8efYWPvm5D3jV8FkIUHldJLCuG+ZcZIYFhBuxp7WLHHXv4075OfvLwEfa0diV9zoYKR9LFZ17qGlywn+J7tzRz376OmNNWx7w+fvuUrBZmu1X1oRXDZCvPCpFOsbSr6B/1JtzDEKDEVkRAa8a9/rife+jkUMTCM2EmpfKmOul//ekAf3PhsogZJ0IIkWucDitDkkoqUm1Paxe3726lxx1a3RsY9XL77takg8Nw8ZlkLuyj7S+crqrMzpsuXMauPW0xnfP3Tx/nvBXVLK4uTXhcIvWqymwoBX1JNvIWIp1CDe4XTiWtTiKVVCmVcDrpoVORC89Mt2lN7qeTPnGom+O9o/ztZSszPRQhhDDEfDcetdZTqaSyYiiStGtvG95Z/Qa9vgC79sYWaEVTYrdgKTItePc8mkl/gKPdI2ca28/nrZtW0Hqin5e7huZ93LjXz2+ePMY7ZbUw6ymlWFnvlAI0IqeE21XMd0NsYCS5FUOAqlI7/aPxBYZa69CKYZTCM2HnLKuivWeEoXn2smSjcObLtV/czb/+4lkuW1uPtcic6WEJIYQhQnsMI19Tj0/6sRSZ8nLOk8AwSdM/HHfcsWfOyp8/EOTwKTe/e7qdf7/7+TMrhbP1Rjkej2RaVrxyephFlSUUW4sWfKzdWsR7tzQv2PT+nmeOc+7yapbUlCU0JpFeq+rLpQCNyCkWswmbxcxYlDTPca+fgNY4bAvPa/NJZJ9ht3uCIrNpwaDUWmTmvBXVPHk4uT6x6TQ980UDQa353dPthmyLEEKIbOAsid6uwj2Wn83tAZL7tCxw4Q/H8Apgj3uCr937IodPu7GYTRzsHOTwKTe1zmLWLargvBXVvHi8n4EIpc9rDGhZEa5MurYp/oI2saSRTveasxfxmyeP8ejLp9m8bm4FuolJP3c/eZT/3HFJ3GMRmbGq3ikXdiLnlBdbGB6fpDRCe4RwRdJki6Ekkkp6KEr/wkguWVPHIwdPc83GxQmMLv3my3zZ1tKUoVEJIYRx5kslHZ7Iz4qkIIFhUiJ9OE76g9zzzHH+dtNK3n7ZKtY2uWZcsFjMphnBJIDNYmbn1uakx9NQkXjLipc6h7hkTW3MjzebFB+8eh3f/ON+Ll5Th8U8c/H53meOc/bSKpbKamHOWFVfzl1/eSnTwxAiLuF00kbmFj3pH/VQlURF0rCqMnvUbI9oFio8M91Fq2v51h8P4PEFsFuyPzUpWoaLEZkvQgiRDVwOK+7xyJkiocIz+RkYSippEqJ9CPr9Qd6zZQ0XrKyZcxd7W0sTt25vodZZjAJqncXcur3FkLus9a5iuhNsWXEwhoqks52/ooamyhLufeb4jOOeST+/fkL2FuaahgoH415/wvtUhciE+e7qGrG/EKb2GMaZSnr4lHvB/YVh5cVWVjWU8/zRvkSGl3bRMlyMyHwRQohsUFpsYWIygD8QnPMzCQxFRIl+OG5raeKHt2zjvs9t54e3bDMs9aa+wsGpBPYY9g5P4PMHaaxwxP3cD1y1jp8+cmRGZcDdz51g/eIKlteVx30+kTmhAjTlHDkt+wyNopT6sFLqx0qpl5VSAaWU9AMxWHmxleHxyJVJ+5Jsbh9WVWZjII7iM0GtpwJDV8zP2dRcnzNtK94RofqoUZkvQgiRDUxKUVZsiXjj0Z2nze1BAsOk7NzajNk0c+9KJj8cEy0+c7BziHVN0Zswz2dZbRmXra3np48cBsDjC/Crx4/yritWx30ukXmhfoZSmdRAnwHeCPQAJzM8lrwUTiWNJLTH0JhU0nj2GJ4cGKO02BLXHeVNa+p44nA3gWD23zs4fMrNecurUpL5IoQQ2SJaRsrwuA9ncX4GhrLHMAlbNzTyX386gNlsYmjUS42zmJ1bmzP24VjrLGZgxIs/EKTIHHvMHyo8E3/BmrAdV65m5zf38sCBk/SPeLEVmWjvGWGFrBjmnJX15TyVQ9URc8AW4ITWOqiUuhdYlOHx5J3yKHd0AfpHvKxLoBjXbJVloVRSrXVMN9AOnXTHvL8wrKHCgcth4+WuQdYvrkx0qCl3oGOAp4708J2PXElJhII/QgiRL6IFhu5xLw0JZNnlAgkMk9B20k15sZXv3nxl0lXvjGAxm6gotdE77InrF/Zg5yA7t61N+HX3HevHF9Bn9uB4/UFu390KIHeQc8yqeic/efhIpoeRN7TW7ZkeQ74rd1jpjdJ/c2DUQ1V58iuGdosZm8XEyIQvpvShWBrbR7KpuY4nDvVkbWDoDwT5xh/288HXrJOgUAiR95wOW8Rehu5xHy5JJRWzPXjgJFeub8yKoDCs3lUcV2XSSX+Aoz0jNMdYJCGSXXvb5qQ/hUuXi9yyuLqEvhEPY97Ie7aEyDbO4ujFZ/pGPFSVJr/HEKCyNPZ00ngqkk53yZo6Hm87Hffz0uW3T7XjKrGxZX1jpociUkj2RgsR4iqJXJl0WPYYitmCWvPQS6e4cv3cHn6ZFO8+w8On3CyuKsEeQ2P7aKR0ef4wm0wsry3jaPdIpociREzKHVaGIwSGWmsGRrxUGrDHEKb2GUboQTtbIBjkldPDrE4gMFzT6GTM66ezfzSRIaZUj3uCnz96hL973fqsuhkqUkL2RgtBKJV0qMCKz0gqaYIOdAxSVmzJuj59DRUOTsexYniwcyip/YUQqsIaqceXlC7PTavqyzlyyk3LkuxMZ0s3pZQLuDWOp9yhtR5I4vU+BHzopptuSvQUBaW82BKx+MzIhA+bxWRYX8DqGAvQnOgdpbrMnlCqpUmp0KrhoW7etqk0kWGmzLf//BJvvHAZi6qya1wiJbYge6OFwOmw0t4790a5e3wyb1NJJTBMUDiNNNvUuxw8HkfJ84Odg1zaXJfUa+7c2sztu1vx+gJnjknp8ty1st7JSx2DmR5GNnEB/xLH438EJBwYaq3vAu66+eabJX0rBqEVw7mpz0ZVJA2rLLPFFBgeiqN/YSSXNtfx00eO8LZNc1tCZMqTh7s51jPMp9+8MdNDEWkge6OFCHFGyEgJBIOMe/15u89aUkkTEAgGefjgKa48K7vSSCHUyzDWVFKtNQe7BjkryUIH21qauHV7i5QuzxOrG5zSy3AarXW71lrF8Ueq96RRuF2F1jPj6P5RY5rbh8XasuLQySFWJ1B4JuycZVUc6xlhaGzhtNV08PgCfOu+A/zdtRuwFhmz+iqEELnAWTJ3D/vIhI+yYsucdnX5QlYME7CvvZ9aZzGNlSWZHsocDa7YU0l7hz0Egpp6V/Ipn9tamiQQzBNLa0o5OTCG1xfAZlAanhCpYjGbsBWZ59zB7R/xUFVqYGBYauP5o30LPu7QKTdbNyQ+F1qLzJy3vJonD/dwzcbFCZ/HKD99+DBrG12cv7Im00MRQoi0cjlsDM2qSjo0NhlXj9pcI4FhAh46cIorz8q+NFIIVVDy+oOMeX2U2OZf5n6pc5B1TRVSSEDMYC0y01RVSnvvCM1JrHwIkS7ljlAvw9mBYWWZMRVJAarL7fSPzr9i6AsEOd4zwqr65Hq4bmqu49GXT2c8MDzRO8IfnjvBtz98RUbHIeKX7r3RU68p+6NFXonUx3B4In8Lz4AEhnHzBYI82naaO6+4PNNDiUgpRb2rmNODE6ysnz8wTLaxvchf4QI0EhgmRyn1BuCcqW9XTR3756nvh7TW38zIwPJMOJ20kVezOPpHPIYWB4ulXUV7zwgNFclVeQa4aFUt37rvQEZX7bXWfOOP+3nXFasNTckVaeMijXujQfZHi/xT7rAw6vER1BrT1CKKe3wSZ3F+7i8E2WMYt2df6WVJdSm1WVxxs97loDuGfYYHO4c4a5Er9QMSOWdVg5MjUZqGi7i8Bfji1J9wNabw95/K1KDyTXmEXob9I8buMawsDaUUze7ZOt2hk0NJFZ4JK3dYWVVfzvPHFk5dTZX7W7sY9/p5wwVLMzYGkTjZGy1E8swmEw5bESMTrxY4c49P4iwxLhsl20hgGKcHD5zM+ua+DRUOTi0QGE76A7T3jiRVJEHkr1X15VKAxgBa6xvnuRBblunx5QtnhMqkoaqkxn14F5lNlBVb5i0Kc+hkchVJp9s01bYiE4YnJvnuX1/mlu0tmE1ymSCEKFxOhxX3tHl/eHySclkxFABeX4AnD/dw+brsq0Y6Xb3LwakFCtAcPuVmSXWpYT2+RH5ZUVfO8d5R/IFgpocixILCqaTT9Y96qDSw+AxAVamdgXma3IdaVbgMea1L1tTx5KEegjr9WXm79rSxeV29pJILIQre7H2G7nEpPiOmPHWkh9WNTipKs3sJud7l4LmjvfM+5qXOQdZJGqmIothaRE25nY6+UZbXJVBI44k9cPf3YKAXKmvg+hvhkm1GD1MIINTkfvoHdyCocY9NUmnwXF1Vbqdv2MPqhrmrgh5fgK7+UZbXGrOvsbGyhHKHhZe7hjgrjXvBD3YO8sShbr5z05Vpe02RXWRvtBCvcjmsDM0KDCN9BuQLCQzjkAtppDCVSrrAiuHBziE2r61P04hELlpVH9pnGHdg+MQe+MHXYXJqZWWgJ/Q9SHAoUqLcYaV32p7YoTEvZcVWiszGJsVUldqiViY92j3MkpoyQ3v9NbgcfPYnTzHh9VPjLGbn1uYF2wLtae1i1942et0TCT3HbFJce+5iSvO0ebOIyVuA98469sWpr8cBCQxFwXCW2GbceBzO8xVDSSWN0bjXz7NH+7gsB4KpOlcx3e6JqClIWmsOdg6m9S60yD2rGhLcZ3j3914NCsMmvaHjQqSAc1bxGaP3F4bN1+T+0MkhQ+8i72nt4tmjfYx7/Wigxz3B7btb2dPaNe9zbt/dSo97IuHn+IOav7zYOe9zRH6TvdFCvCq0x1BSScUsTxzqZsOSSsqLs/+XodhahMNWxOBo5Kp8Pe4JtA4FkEJEs6reyROHeuJ/4kCUNOZox4VIUpnDwvCMwNBLZQpaLFSV2WnrGor4s0Mn3Zy9tNKw19q1tw3frD2+Xl+Ab963n74owenPHj2C1xcw4DlBdu1tW3ClUQgh8p3TYZ2Rhecelz6GglAa6ZVnZXfRmenqXQ5OD41HDAwPdg6xbpFLGtuLea2sL+fo6eEZ/Xti4qqEof65xytrjBucENM4i2cWn+kf9VCVgr3gVWW2qAHWoZNDvOWSFYa9Vq97IuLxMY9/RhA8+2dGPSfa6wshRCFxOqy8PO2G4PD4JC4JDAvbyISPF08M8I9v3pjpocQsXJl0/eK5d7APdklje7Gw8mIrZcUWTg2M01RVsvATwt7wLvjpneCfdsFptYUK0AiRAuWz2lX0j3ioTsWKYZQm92NeH73DHpbVlhr2WjXOYnoiBGe1zmI+8Jp1EZ/z4EunDHtOTRb36hVCiHRxllgZGg9tj/H4AmjAlscV/WWPYQweazvNucurKbHlzmb8hgoHp6MUoAlVJJXAUCxsVX05h2PdZ9jfA9/6P3Dpa+DGT0JlLaBCX9/zcSk8I1Im3K5CT+2rHkhhKmmkdhVHTg2zvK7M0J5/O7c2z7n4sFnM7NzanPHnCCFEoXBN22PoHvNS7rDmdcadrBjG4IEDJ3nduUsyPYy41LuK2X9icM5xry/A8d5R1uRxqV1hnFUNTl45PbxwNd7BPvjKP8HWN4DFGgoCJRAUaWIxm7AVmRn3+imxW+gb8XBpCorPOEusjHl8TPoDM6qPHjo1xJoGl6GvFd7fF0+F0XQ9RwghCoXT8WpV0uEJX16nkYIEhgsaGvPS1jXEv/ztBZkeSlzqKxz85cW5VeUOnXKztLo0r5fBhXFW1pfz26fa53+Q3wdf/f/gitfDa69Py7iEmK3cEeplWGK3hKqSGtzcHsCkFBWlNgZGvdS7HGeOHzrp5uLVtYa/3raWprgDtHQ9RwghCkH5VHEzrXXeF54BSSVd0MMHT3PhqlrsORZIhYvPzHawc5CzFksaqYjNqnonR065z6TozeH3QZEFPvL/wevelt7BCTFN+bQCNAOjXqrLjQ8MIXLLisOn3JKFIYQQechaZMZqMTPmDRXxyoXuBMmQwHABudLUfraacjvusUkm/TPLkB/sHGRdkwSGIjZVZXbMJhO9wxEqMY644f/eAsfaoGlZ2scmxHThAjST/gBjHl/K7upWldoYGHl1n+Hw+CTusUkWVRtXeEYIIUT2CPcyHMrzHoYggeG8+oY9HOsZ4fyV1ZkeStzMJhPV5fYZ1eZCje1DrSqEiFXERvejI6H00ZYLYdmazAxMiGmcjlCT+4FRLxWltvharMShssxO/+irN0oOn3KzqqE8Za8nhBAis1yOUGXSYQkMC9tDB09xaXPdjCIDuSTcsiKse2gCpUKly4WI1ar6UAGaGX79P7BuI1y/E+SCWGSBsmILwxOTof2FKahIGhZKJX11xfDQKTdrGl0pez0hhBCZFb7xKHsMC9yDB05yZQ6mkYbVu4pn7DMMt6nI5zK7wngr68s5cmpqxXBiLJRC+o6PwNs+IEGhyBpOh5Xh8UkGRrwpDQyrZ+0xPHRyiNWyv1AIIfKWs+TVwFBWDAvU6cFxTg2Os3FZVaaHkrCGCgenh15NJQ01tndlbkAiJ/W6J3jqSA/XfnE3O77yR/b89i9gs0tQKLJKqJehj/5RD1UpaFURVllmmxkYnnLTLCuGQgiRt5wOG+6xSUklLWQPvnSSzevqKTLn7ls0O5X0YOcQZ0ljexGHPa1dfO+BNoIaNNBDMbd3lrCndW4rFCEyqbw4dEe3bzg1rSrCqkpfXTEcGPXg9QWod0l6vhBC5KvpqaQSGBaoBw+cyslqpNPVVzjonkol9fgCnOgblZQnEZdde9vw+oIzjnn9QXbtbcvQiISIrNxhYWQiVHwm5XsMR0N7DA+dDLWpkPR8IYTIXzP3GFoyPZyUkgb3EZzoG2VwzMv6xZWZHkpSGqatGB4+OcSymrKcLaQjMqN3WlXbWI4LkSnOqRVDk1IpDQxL7UUEAkEmJv0cOumWm21CCJHnXCVWBse8jEz4pI9hIXrowEmuOKsBsym37wKXFVvQGkYmfLwkbSpEAmqiVLB12IoIBIMRfyZEJoT7GIaqkqZuj6FSKtSyYsTD4VNDUpFUCCHynNNhpWtgDIfNnNNbzGKR33+7BGiteSBHm9rPppSivsLB6aHxUGN72V8o4rRzazM2y8xVZmtRqEfmrbse43jvSIZGJsRMr7arSG0qKYTSSftGPLSddLOmUVYMhRAinzkdVrqHJvK+VQVIYDjDntYu3nn7/XT0j/Glu5/LiwIb9a5iTg2Oc7BrUArPiLhta2ni1u0t1DqLUYR6YH7iurP57w9fwbUbF/MPP3iCXz72CoGgzvRQRYGzFpmxFZkJBIOU2FK7S6Kq1EZb1xAmpahOcRAqhBAis5wloSyUfC88A7LH8Iw9rV3cvrsVry8AQI/bw+27W4HQxXGuqq9w8PyxPswmRU25XMCI+G1raYr4b2D7+Us5f0UNt93zAo+2nebv33AOi6tLMzBCIULKHBZMSqW8GExVuZ3H27pZ0yiFZ4QQIt/ZLWZsFjPOPN9fCLJieEao+mJgxjGvL5Dz1RcbXA4ePniKdU3S2F4Yr77Cwf/bcQlbNzTxye89xt1PHCWoZfVQZIaz2JqWFbyqUjsHu4ZYI4VnhBCiILgc1oJIJZUVwyn5WH1xT2sXP3roMCMTPva197GntSunVz9FdjIpxd9cuIwLVtZw2+9f4NG2bi5truNP+zro6BtlcXUpN2xexdYN8rsnUmdPaxftvSNM+oPsuGMPO7c2p2S+29Paxc8fPQLA7585TmNlicyrQgiRx/a0dtE/4uHPL3Syr70/ZZ8v2UACwyk1zmJ6IgSB0aoyZrvZqbGjHn9epMaK7NVUWcKX37OJ2363j18+dpRPX7+RDYsr2d8xwNfueRFAgkOREuH5btIfqpTb455IyXw3e151j0/KvCqEEHksPO/7p2oppOrzJVtIKumUSNUXbRYzO7c2Z2hEycnX1FiR3cwmxZHuYT59/UY2LqumyGxi47JqPvGGs/npI0cyPTyRp9I138m8KoQQhaXQ5v2sXzFUSjUB7wGuBdYA5UA78AfgP7TW/Ua8Tjjq37W3jV73BDXO4pxeKs7H1FiRGzr6RtmwuHLGsQ2LK+noG83QiES+S9d8J/OqEEIUlkKb97M+MATeAHwB2A18GRgBLgJuBd6ulLpIa33aiBeKVn0xF+VbaqzIHYurS9nfMcDGZdVnju3vGJCKpSJl0jXfybwqhBCFpdDm/VxIJX0YWKq1vl5r/VWt9Xe01h8EbgIWA5/K7PCyU76lxorcccPmVXztnhfZ196HPxBkX3sfX7vnRW7YvCrTQxN5Kl3zncyrQghRWApt3s/6FUOt9YEoP/o58B1gQxqHkzPyLTVW5I5wgZk77ztwpirpjVubpfCMSJl0zXcyrwohRGEptHk/6wPDeSya+tqd0VFksXxKjRW5ZeuGJgkERVqla76TeVUIIQpLIc37uZBKGs2/Tn39fkZHIYQQQgghhBA5Lm0rhkopF6GCMbG6Q2s9EOVcfw+8DbhLa71nntf8EPChm266KY6XFUIIIYQQQojCorTW6XkhpZYBx+J4ymqt9ZzGZ0qpDwB3EWpX8WattW+hE918883p+UsKIZJy5513qkyPIVvIvCVEbpB5ayaZu4TIftHmrbStGGqt24GkJk+l1PsIBYV/Bt4SS1AohBBCCCGEEGJ+aVsxTJZSaifwXeB+4I1aa08KX+sZrfUFqTp/rpD3IUTehxB5H7Kb/P8JkfchRN6HEHkfspv8/wmR9yFE3oeQTL4POVF8Ril1I6GgcC/wN6kMCoUQQgghhBCi0GR9uwql1BuB/wGGCfUufItSMzJSR7XWv83A0IQQQgghhBAiL2R9YAicR2hl00Vof+Fsx4HfGvyakV6nEMn7ECLvQ4i8D9lN/v+EyPsQIu9DiLwP2U3+/4TI+xAi70NIxt6HnNljKIQQQgghhBAiNXJij6EQQgghhBBCiNSRwFAIIYQQQgghCpwEhoBSyqSU+oRS6mWllEcp1aGUuk0pVZLpsaWTUkpH+TOa6bGlglLqM0qpXyqljk79PdsXeHyzUuq3SqlBpdSYUuphpdS2NA03ZeJ5H5RSX5jn9+RTaRx2wZN561WFNHfJvPUqmbtyk8xdIYU0b4HMXWHZPm/lQvGZdPgacAvwG+A2YN3U9+cqpV6jtQ5mcnBp9jBzN736MjGQNPgSMAA8R6i4UVRKqZXAY4Af+E/ADXwQ+JNS6nVa67+mdqgpFfP7MM0ngL5Zx541cExiYTJvzVQoc5fMW6+SuSs3ydz1qkKZt0DmrrCsnrcKPjBUSq0HPgbcrbV+y7Tjx4A7gHcAP8nQ8DLhqNb6R5keRJqs1FofBVBK7QdK53nsvxP6B3y+1nrf1HN+ABwAvqWUWqtzt5JTPO9D2G+11u0pHZWISuatiApl7pJ561Uyd+UYmbvmKJR5C2TuCsvqeUtSSeEGQAG3zzr+HWAceHe6B5RpSimrUiqWX9ScFv6HuZCp9JY3Ag+EJ6ip548C3wXWABemYozpEOv7MJtSqlwpVfA3lzJE5q0ICmHuknnrVTJ35SSZu2YphHkLZO4Ky/Z5SwLD0C9XEHhq+kGttQfYRw7/8iXorYQm5xGlVI9S6htKKWemB5VhZwM24PEIP3ti6muh/Z68SCi1w6OUekwp9bpMD6jAyLw1l8xdM8m8FZnMXZklc9dMMm/NJXPXXGmbt+SOGTQCfVprb4SfdQGXKqWsWuvJNI8rE54CfgkcAcqB1wN/B1yplLp06m5NIWqc+toV4WfhY01pGkumDRHaD/EYMAg0A7cCu5VS79Nafy9jIyssMm/NJHPXXDJvzTSEzF3ZQOauV8m8FZnMXa8aIs3zlgSG4AAiTVAAnmmPyftJSmt98axDP1BKvQj8G/Dxqa+FyDH1NdLviWfWY/Ka1vr22ceUUv8L7Ae+ppT6VQF/mKWTzFvTyNwVkcxb08jclTVk7poi81ZUMndNycS8JamkoSV8W5Sf2ac9plB9mdAEvT3TA8mg8P//SL8nBf87orXuB75NaKP4pZkdTcGQeWthhT53yby1AJm7MkLmrvkV+rwFMnfNK9XzlgSGcBKoVkpF+gVsIpTykPd3rqLRWvuYeo8yPZYMOjn1NVLqQvhYpJSHQtI+9bWQf0/SSeatBcjcJfNWjNqnvhbq70m6ydw1D5m3AJm7YtE+9dXw3xMJDOFpQu/DRdMPKqXswEbgmQyMKWtMvQ+LgO5MjyWDWgmlNGyK8LNLpr4W9O8JsHrqayH/nqSTzFsLkLlL5q0YydyVXjJ3zUPmLUDmrlikbN6SwBB+DmhCmzmn+yChHOYfp3tAmaCUqoryoy8S2ot6TxqHk1Wm8rfvAbYopc4JH58qL/0B4DCzKqzlI6VUUaRqaUqpxcBNQD+hDdIi9WTemiJzV2Qyb71K5q6sInMXMm/NR+aukEzNWwVffEZr3aqU+hbwd0qpu4E/AOuAW4AHKZxGq/+slLoE2AucINRw8/XAVuBJ4BsZHFtKKKV2AEunvq0BrEqpf576/rjW+ofTHv4Z4Crgz0qprwHDhD7ImoDtOdxoNZ73oRQ4ppT6LXCQVytkfWDqZzdorSfSNvACJvPWDAU1d8m89SqZu3KPzF1nFNS8BTJ3hWX9vKW1Lvg/gBn4e6CN0PJ1F/BVoDTTY0vje/A3wJ+m/u4eYIxQT6H/D7Bnenwp+js/QOjOZaQ/D0R4/Drgd4TKB48DjwCvyfTfI13vA6GN4N8llOYxCPiAU8CvgIsy/fcotD8yb515Hwpq7pJ5K/73Quau7Pojc1fhzVtTf2eZu+J4HzI1b6mpFxdCCCGEEEIIUaBkj6EQQgghhBBCFDgJDIUQQgghhBCiwElgKIQQQgghhBAFTgJDIYQQQgghhChwEhgKIYQQQgghRIGTwFAIIYQQQgghCpwEhkIIIYQQQghR4CQwFEIIIYQQQogCJ4GhEEIIIYQQQhQ4CQyFEEIIIYQQosBJYCiEEEIIIYQQBU4CQyGEEEKIPKCU+oxS6pdKqaNKKa2Uak/wPK9XSj2mlBpTSg1MnXO5wcMVQmQZpbXO9BiEEEIIIUSSlFIaGACeA84HhrXWy+I8x/XAr4AXgO8ATuBWIABcoLU+aeCQhRBZRAJDIYQQQog8oJRaobU+OvXf+4HSeAJDpZQFaAf8wHqt9ejU8Y3As8D/aK0/ZPCwhRBZQlJJhRBCCCHyQDgoTMKVQCPw3XBQOHXefcADwNungkchRB4qiMDw5ptv1jfffLMsjQohcobMW0KIDLhw6uvjEX72BFAOrJnvBDJ3CZG7ijI9gDSTiUqI7KUyPYAsJfOWENkr3+atxqmvXRF+Fj7WBByI4VwydwmRnaLOWwWxYiiEEEIIIRbkmPrqjfAzz6zHzKCU+pBS6pmUjEoIkRYSGAohhBBCCIDxqa+2CD+zz3rMDFrru7TWF6RkVEKItJDAUAghhBBCAIRbUTRF+Fn4WKQ0UyFEHpDAUAghhBBCADw99XVThJ9dAgwDh9I3HCFEOhVa8ZkZfD4fnZ2deDyehR+cQXa7nUWLFmGxSIVoIQpdrsxbs8k8JkR2UUo1EGpef0JrHU4PfRA4BXxAKfW1aX0MzwG2ALu01r5EXi8X5i6Zp0ShK+jAsLOzk7KyMpYtW4ZS2VlYTGtNf38/nZ2dLF++PNPDEQvY09rFrr1t9LonqHEWs3NrM9taImXkCJGYTM1bw+OT9I148AeCFJlNVJfZKXdYY3quzGNCpIdSagewdOrbGsCqlPrnqe+Pa61/OO3h/w68F9hKqEchWmufUurjwM+Bh5VS3yHUouITQC/wL4mOLduvuabPU8dGrfJZLgpSQaeSejweqqqqsnKCClNKUVVVldV32ETIntYubt/dSo97Ag30uCe4fXcre1plO0YuUkqZlFKfUEq9rJTyKKU6lFK3KaVK4jjH65VSjymlxpRSA0qpXyqlkoqMMjFvDY9P0u2ewB8IAuAPBOl2TzA8PhnT82UeEyJt3g98cepPLeCa9v37YzmB1vqXwBsJVSb9CvBPwMPAZVrrhD/Qsv2aKzxPDQ2PyWe5KFgFHRgCWTtBTZcLYxSwa28bXl9gxjGvL8CuvW0ZGpFI0teArwIvAR8DfgncAtyjlFpw7lRKXQ/cCxQD/wB8GbgCeFQp1Tjfc2M4dzJPj1vfiAetZ7Yk01rTOzyB1xeY87PphscnOdo9zOFTbvqGPXJxJUQKaa23aK1VlD9bZj32xqnjD0Q4z71a60u01g6tdYXW+q1a61eSHV+2X88opRj1+OSzXBSsgg8MM+2OO+5g3bp13HDDDbzmNa9h48aN/PznP8/0sEQCet0TcR0X2UsptZ5QMHi31vp6rfV3tNafBD5JKO3qHQs83wJ8A+gALtda36m1/nfgGqAO+EIqx2+08Eph2LDbzY+/910CQc2pwXGOnB7mRN8o3e5x3vnuHfzkZz8nqPWclcZAUMuddyFEVgsEI9/oks9yUQgkMMywO++8kz/84Q98/OMfx+fzsW/fPt7+9rdnelgiATXO4riOi6x2A6CA22cd/w6hHl7vXuD5VwKNwHfDxRsAtNb7CO3leftU8JgTiswzPyqGh9389Af/Q5HZxLLaMlbUlVFdZsdaZCYQ1LjHJ3nl9DDd7ok5q4ly510Ikc3MpsirmvJZLgqBBIYZ9JGPfISjR4/y+te/nssuu4x9+/axceNGXnkl6WwNkQE7tzZjLZr5T8piNrFza3OGRiSScCEQBJ6aflBr7QH2Tf18oecDPB7hZ08QKuawJrkhpo/dYp7x/W1f+gInjrfzpqsv5x/+4R/4+C23cMG5Z/Puv72ekaEBasqLWVlXHjXFVO68CyHSrb29nQ0bNpz5/itf+Qpf+MIX5jyu1F40Jzi0WczyWS4KggSGGfTtb3+bxsZGHn74Ye6//34uv/xy9u3bx8qVKzM9NJGAbS1NvGXTCorMCgW4HFYcNjOb19Vnemgifo1An9baG+FnXUC1Umq+kpyN0x4b6fkQuYF01hn3+vH4AtQ6i8+sHP7TP/8rK5av4MUXX2DTpk20tbXR2trKd77zHR577DEATCY1Z6UxTO68CyGyld1aRGWpDYc1VLi/1lnMrdtbpCqpKAgF3a5itmu+uNvwc/7pc9sNP6fIXlWlNq4+exG3Xnc2AP/nF8/wq8eP8s7LV2d4ZCJODkIV+SLxTHtMtLKcjqmvkc7hmfWYGZRSHwI+dNNNN8UwzNTOW/5AkNND49Q5iymxW3CV2ACwTg5hmrqj/tBDD3HDDTdgNptpbGxk27ZtZ85TXWafk04qd96FEJC911zBoGZi0s//ueFCvvvXg3z9fZcZMDIhcoMEhtNIECeS1d4zwrLasjPff+i1Z/F3332Eq1qaqHNFjANEdhonVOo9Evu0x8z3fABbvM/XWt8F3HXzzTdHL/U5TarmLa013e4JyootlNjn3w4ZrdJguM9hz/AEwaDGbFJy510IAaT/mquoqIhg8NVCWtHa50z6A5y9tIracjt9I9JiRxQWSSUVwkDtvaMsq3k1MKx3OXjTRcu56y8HMzgqkYCThNJFIwV2TYTSTOdr4ndy2mMjPR8ip5lmjaGxSQIBTVWZfc7PysrKGBkZAeCKK67gZz/7GYFAgFOnTrF3794Zjy13WKkpt1PusFJdbpegUAiREXV1dfT09NDf34/X6+Xee++N+LhJf5DzVlRTWWZnaNRLcJ52PELkGwkMhTCI1nrOiiHA2zat4MhpN88d7cvQyEQCniY0P140/aBSyg5sBJ6J4fkAmyL87BJgGDiU3BBTxzPpZ2DUS0NFMaYIq4FVVVVcdtllbNiwgccff5zVq1fT0tLCTTfdxJVXXjnn8RazGZ8/OOe4EEKki8Vi4fOf/zwXX3wx1113HWvXrp3zGK11KDBcXoPFbKLEbsE9Nt89QCHyi6SSZlh7ezsAW7ZsYcuWLRkdi0hO/4iXIrM6sw8rzGYx8+HXnsWd9+3nvz58BZYoBTlEVvk58P8BtwIPTzv+QUJ7A38cPqCUagCcwAmtdTg99EHgFPABpdTXwi0rlFLnAFuAXVprX4r/DgkJBIOcGhqn1mnHUmSO+rif/OQnMZ/TYjbhC0hgKITIrFtuuYVbbrkl6s8np25gNVaGtn5UldnpH/FQURopeUSI/CNXqEIYpL13ZEYa6XSb1tRR53Lwu6fa0zsokRCtdSvwLeB6pdTdSqkPKKVuA75KKOibHhX9O3CQaauLU0Hfx4HFwMNKqZuVUp8G/gz0Av+Snr9JfLTW9Lg9OKxFlBXPV3Q1PkVmRSCoo7avEEKIbDA+6cdqMZ3ZN11VZpN9hqKgSGAohEEipZGGKaW46Zqz+PmjR+iXD5lccSvwKWA9oSDxHcA3gOu01gsuf2mtfwm8kVBl0q8A/0Ro9fEyrXVW7i8cnvAx6QtQU25sOwmlFBaziUBQAkMhRPYa9/qxTsuUqCq1MzAarUC1EPlHAkMhDNLeM8LSKCuGAIuqSnnduUv4n/tfTuOoRKK01gGt9W1a62attU1r3aS1/mQ4LXTa427UWiut9QMRznGv1voSrbVDa12htX6r1vqVtP0l4uD1Begb9lBf4TjTisJIEhgKIbKZ1qE2FdZp2z0qy2xyM1cUlIIPDHMhtSkXxpjt9rR2seOOPVz7xd3suGMPe1qNX7Bp7x1heZQVw7AbLl/FC8f72X9iwPDXF4XD6DkhGNScHhqnutyGzRJ9X2EywumkQojClc3XM57JAEUmNePGWPXUHkMhCkVBB4Z2u53+/v6snqi01vT392O3zy0ZL2Kzp7WL23e30uOeQAM97glu391qaHAYCGpO9I2ypKZ03scVW4v44FXr+NZ9B+QiWSQkFfNW78gE1iIT5QbuK5xOa83EqJthr/zOC1Gosv2aa9TrI+gZnXG9VSWBoSgwBV2VdNGiRXR2dtLb25vpoczLbrezaNGiTA8jZ+3a24bXF5hxzOsLsGtvm2E91U4PjeNyWCmxzd8IHODK9Q3sfu44f3juOG+4YJkhry8KhxHzlmfSz6jHTyCoMU3dHqwqszN02vgU0rAJv+KR4x62XpyylxBCZLFsv+YaGPVQ4nCwfs2KM8dCgaHsMRSFo6ADQ4vFwvLlyzM9DJFive6JuI4nYr7CM7Mppbj5mvX804+e5IqzGnE6UrNKI/JTsvPWntYubv9j64ybJRaziU++4eyUNp9v7xmh/eHTKTu/ECK7ZfM115jHx2e+fj+/+PurZ7TpqSy10T+auhXDPa1d7NrbRq97ghpnMTu3Nqd0HhZiIQWdSioKQ40zcoXFaMcT0d4TvVVFJMvrytm6oZHv7W0zbAxCxCLSCrovEGRXin8XGyocdA9NSAq1ECLrvNDez7pFFTMqkgK4SmyMTvjwp6APazq2uQgRLwkMRd7bubWZollVFm0WMzu3Nhv2Gu29sa8Yhu24cg1PHOrm0Mkhw8YhxELSsYIeic1iptxhkf06Qois89yxPs5bXj3nuNmkcJXYUtKyYr5tLkJkigSGIu9ta2li3SIX1qLQr3uts5hbt7cYmq6xUKuKSErtFm7c2sy37jtAMEs344v8k44V9GgaKko4NTie8tcRQoh4PHe0j/NWzA0MIXUtKzJ1k06I+UhgKArCqMfPZ99yHmXFFn7wsa2GBoWT/gCnh8ZZXF0S93OvPmcR7jEvf3vbX1LaSkOIsJ1bm8/cJAkzegU9mgaXg1ODYyl/HSGEiFX30DijHh/L68oj/ryqNDWVSTN5k06IaFIeGCqlPqOU+qVS6qhSSiul2hM4xwNTz43054IUDFvkEc+kn5OD45y/sgaHtYiuAWMvTDv7x6h3OebsTYjFA/tP0jfiZWTCJ3sMRFpsa2li05o67BYzitSsoEfTUOHgpKwYCiGyyPPH+jh3eTUmFbkqc3W5nf4UpJLu3No8p29sum7SCRFNOqqSfgkYAJ4DXEmcpw/4RITjR5M4pygAR04Ps7SmFIvZxNomFwc7h1hUNX+/wXgkkkYatmtvG75Zm9qNbqUhxGxdA2P86zsuYOOyyKlTqdJQ4eCJQ91pfU0hhJjPc0f7OH9lTdSfV5amJpU0/Bn/1XtexBcIooCbrzlLPvtFRqUjlXSl1rpKa301cDKJ84xprX8U4c+AUQMV+enQySGaG10ArF1Uwctdg4aeP55WFbPJHgORbh19owyMemlZUpX2126ocMgeQyFE1ghqzb72fs6NUHgmLJVN7re1NFHjtPPdm67kwlU1CWUeCWGklAeGWmvDVvSUUialVLlSUdb7hYig7aSbNY1OANY1uXi5a8jQ87f3jrA8wcBQ9hiIdHvwpVNccVYDZlP6p1FJJRVCZJNXTg9TVmyhdp7P3FQ2uQ9qTa/bQ62zmG0tTdwv20hEhuVS8ZkmYBRwA6NKqbuVUmszPCaRAw6denXFcGV9OR39Y3gm/Yadv703vh6G08keA5FOWmse2N/FlesbM/L6ToeVYFAzMuHLyOsLIcR081UjDatO4Yrh0JiXEnsRNouZTc31vNw1yGAK9jMKEatcCQyPAf8J7ATeBtwJvA54UinVksmBiew2PDHJ0OjkmT2F1iIzy2vLOHzKbcj5x71+hsYmqa9wJPT8bS1N3Lq9hZpyOwC1TnvaCoGIwnOsZ4RJf5B1Ta6MvL5SivoKB6eHZNVQCJF5zx3r5bzl0fcXQqhdxcBoagLDHvcEteWh1Uq7xczFq+t44EAyu66ESE5OBIZa651a689qrX+utf6V1vofgNcCpcBXoz1PKfUhpdQzaRuoyDqHT7pZ1VA+I21ubZOLgwalkx7vHWFJdWlSaXnbWpr40cevorzYwjfev1mCQpEyDxw4yRVnNZDJbPyGCgcnDa4MLIQQ8fL6ArR1DXH2ssp5H1dmt+D1BfHMakZvhJ6pNNKwq1qapCq5yKicCAwj0Vo/DDwEbFVKRUwO11rfpbWWdhYFrO3kEGum0kjD1ja5eLnTmAI0yaSRzuYqsTE0NmnIuYSYTWvNgwdOsiVDaaRhjbJiKITIAvs7BlhRV06JzTLv45RSVKWoyX23e3xGYLhxeRV9Ix46+kYNf61stqe1ix137JF+zlkgZwPDKe2AGajI8DhEljp00s2aBueMY+uaKjjYNYTWOunzt/eMsLTWmNYXrhIrQ2Oyt0CkRttJN0UmEyvrIzdxTpd6KUAjhMgCzx/tm7ca6XRVZXYGUhAY9rgnZgSGZpOJLesb2bO/cAKjPa1d3L67lR73hPRzzgK5HhiuBvyE+iQKMcf0wjNhda7iUCWw4eQneaNXDAclMBQp8uBLJ7lifWbTSEFaVgghskMshWfCUlWZtMftoW5WRdRtU+mkRty8zgW79rbhnZWmG+7nLNIvqwJDpVSDUmqtUsox7ZhTKTWnsYtSajtwGfAXrXVqdgWLnNY/4sHnD1LnmjnpKqVY21RhSNuK9p4RltcaswJTIamkIkWCWvPQgVMZTyMFaKwo4bQEhkKIDBoa83J6aHzOjeNoqsrs9KVoxXB2e6pV9eVYzCZeMmjLS7aTfs7ZpSjVL6CU2gEsnfq2BrAqpf556vvjWusfTnv4vwPvBbYCD0wd2wp8VSl1D3CU0ArhRcC7gT7g1lSOX+Su8P7CSCsk65pcHOwa5IqzGhI+/9CYF38gSFWZLZlhnuEqscqKoUiJAx2DlBVbWGrQ6nYyasrtDIx68QWCWMxZdW9SCFEgnj/WR8vSKopinIOqSm30p6AyaY97Ys6KoVLqzKrh+sXzF8bJB9GCbunnnBkpDwyB9wNXzjr2xamvDwI/ZH5twLPAdUAdYAE6gW8DX9JaSxKyiOjQtMb2s61d5OL7ew8ldf723hGW1pQZlprnKrHRZlC1VCGme/DAyYz1LpytyGyiutxO99D4mTYyuWRPaxe79rbRO3Wnf+fWZqkkLESOiSeNFELBy5HTw4aOYdzrxxcIUlY8t/jNtg1N3PK/j/KRa9bn9Q203uEJglpjNikCwZmpsxsWS/mQTEj5b5vWeovWWkX5s2XWY2+cOv7AtGMHtdZv01qv1FqXaq1tU//9UQkKxXzaTs7dXxi2psHFK93D+ALBhM/f3jPCslrjVmAqZI+hSIFAMMjDB09xZRKr40bL1X2GUiRBiNyntea5Y32cF2PhGQjvMTR2xTC8Whjp5nJ9hYNFVSU8+0qvoa+ZTbr6x/j77z3O9Rcv51NvPIdaZzEKqHUW85FrzuLQSTfff6AtK/ZaFlLV1HSsGAqRdlprDp10Rw0MHbYiGiscHO0ejnmPwWztPSOGVniUVFKRCvva+6l1FtNYWZLpoZxR78rNwHC+IgmyaihEbujoD/VRXVQV+5xYVWYzfI/h7Iqks13V0sT9rV1csqbO0NfNBq+cdvPPP32a92xZw+vOXQIwZw7dur6Rz/7kKUYmfNx87XpMGSqcFr4hGJ77wzcEYe6Y80H+rk+LgnZycJxiq5mK0uj7/9Ytqkiqn6GRFUkhtGLoluIzwmDZ0LtwtsYcXTGUIglC5L7np1YL49kGUlkaaldh5OpV9wKB4eVnNfDMK72MeXyGvWY22H9igM/8+Cluvmb9maAwEleJjf/ccQnHekb4z9/uw59EhlcyCq1qqgSGIi8ditDYfra1TS4OJrinT2vN8Z5RlhqYShpeMcyGtAmRH3yBII+1dSdVZCkVGnK0l2G0YghSJEGI3BHv/kIIZRmZTSbGvH7DxtG7QGBYXmzlnKVVPPLyacNeM9OeOtzD//nls/zTmzZyeQyfSyV2C//2zosY8/r5118+OydAS4dCuyEogaHIS6E00siFZ8LWNbkSblnR456g2GamvNia0PMjsVuLMCnFxGT6Jz6Rn559pZcl1aXUlGdX4NKQoy0rdm5tnlMIwmxS7NzanKERCSHi4Q8EaT3eH3Nj++mqymyG7jPsdk9QW26f9zFXTVUnzQd793dx2z0v8IW3X8D5K2tifp7dYuZf3nY+JbYi/r+fPJX2FdRCuyEogaHIS20xrBguqi5leHySoQT29RmdRhpWUSoFaIRxsjGNFKaKzwyN59zq+NYNjdSU2ygvtqCA6jI7dkuoyqoQIvu1nRyi3uXAVRJ/mymjexn2uCeodTnmfczFa2o5cnqYvuHcbtd9zzPH+e5fX+Y/3nUxZy2Kv9pokdnEP75pI8try/jHHz6R0HVbonZubcY8K+1YAW/btDxtY0gnCQxF3gkEgxztHmZ1w/wrhialWJvgqmG7wWmkYS6HNa0TnshfHl+AJw/3cPm67EojhVBalt1iZmA0t37Xn3mllyKzmZ998mru+9x2fnzrVXzm+vP4f7/dh3tc9gcLke0SSSMNqyqzMzBi3JzVMzy3h+Fs1iIzm9fWs/dAbq4aaq35ycOH+fUTR/nKezexvC7xgn0mpfjoteu5aHUtf/+9x+lJUyrnhatqKTKHbgSGqqba2byunrufbM/JzJeFSFVSkXdO9I5SVWqn1D63N9BszU0uDnYOxl31q71nmI0JpKIsxFViY0gK0AgDPH24hzWNrnkLMGVSY4WD00PjVJXlxmqb1prv7W3jPVvWYDa9evf4wlW1bF3fyG2/f4F/ffsFhvU1FUIY77mjfbz7itUJPbey1LhUUn8giHtskqqyhefnbS1NfPvPL/G2TSsNee1Um97r1W41U2Ir4o73bzZkrldK8d4tzZTZLdz8nYexmE0MjnpT2lP2T/s62LyukX9808YZx3//dDt//4PH+bcbLjK0dVmmyYqhyDuhNNL5VwvD1jVV8PLJobhfo713lOUpmAgklVQY5YEDJ9myPvtWC8PqKxycHMidu62PtXUDcNna+jk/u3FrM+7xSX7zVHuaRyXETEopk1LqE0qpl5VSHqVUh1LqNqVUTL0ZlFIPKKV0lD8XpHr8qTTm8XG0e5gNSyoTen51uZ3+UWMCw74RDxWlNsymhS/DW5ZWMjwxybHuYUNeO5Vm93qdmAwwPOHjhfZ+Q1/HVWJjYtLPwKg3pT1lA0HN759p528uWjbnZ2+8cBnv37aWT//oSV7uSrzCfbaRwFDknbaTbpqbXDE9dm2Ti0Mn3QSCse91CgSDdPaPsqS6NMERRudyWBnKsfQ6kX3GvD6eO9bHpRGCmGyRS03uA0HN9x8IrRZG6qVVZDbxmTefy88eOcLhU+4MjFCIM74GfBV4CfgY8EvgFuAepVSs13x9wI4If44aPto0euF4P2sXubBZzAk9v6rUTr9BqaQ97glqYtybbFKKresb2bP/pCGvnUqRWjtM+oOGt3bYtbcNf2DmdVsqWkg8eaibyhJb1H7X21qa+MQbWvj8z57huaN9hr52pkhgKPJOLK0qwsodVipKbHT0jcZ8/q6BUPqb3Wp8Jrar1MaQ7FUSSXqirZuWJZWGVs01WmNFCaeHciMwfOilkzisRVy0qjbqY+orHPzd6zbwpbufY8ybX33HRG5QSq0nFAzerbW+Xmv9Ha31J4FPAluBd8R4qjGt9Y8i/BlI1djTIdS/MPZqmLNVGliVtHto4f2F013V0sTe/V0Es7xgV7paO6TrdX77dOTVwukuXl3H5952Pv/xm+d55OApQ18/EyQwFHll0h+go2+UlXFscA71M4w9DaC9JzUVSSG0YjgoK4YiSQ++dCorq5FOV1/h4OTgWKaHsaBAMMiPHjzMe7c2L7h/8IqzGti4rJpv/GF/zlVcFXnhBkIFE2+fdfw7wDjw7lhPNJWSWq7yaNNsMoVnIFR8xqjAsHd4/h6Gsy2vK6fUbmH/ieyOzdPV2iEdr3Ose5jO/lE2x1DArWVJJV9650V8674D/Glfh2FjyAQJDEVeOdo9zKKq0rhSRdYtcvFy51DMj2/vGUnJ/kII7TGUFUORjOGJSVpPDMRdUCndGnMklfT+1i4qy2xsXFYV0+M/8tqzONY9wp9f6EzxyISY40IgCDw1/aDW2gPsm/p5LJqAUcANjCql7lZKrTVwnGnX455gZMLHyvrEq2JWltoYHPUasmrXvUBz+0iuamni/izvaXjj1jXMvpNgs5gN7/W6c2vznOs8kzK2p+zvnm7nuvOXzuldG82qBidffs8l/PihUBXWXCWBocgrbSfdMReeCVvbVBHfimHvSEpaVcBUVVJZMRRJeOzl05y3vBqHLbuLTleW2pjw+pmY9Gd6KFH5AkF+9NBh3rNl4dXCMJvFzGeuP5f/uf9lTvSOpHiEQszQCPRprSN9iHQB1UqphfLLjwH/CewE3gbcCbwOeFIp1WLkYNNlT2sXN3/nYdzjk7z3G3sTLlBiLTJTYrcwbMDN254EAsMtGxp55OBpJv2BhR+cIXVOB84SK7XOcGuHYm7d3mJ4tdBtLU3cur2FWmcxCqgpD/WUXWpQNtfw+CQPHzzF689bEtfzFlWV8pX3buKPz53gCz9/mh133M+1X9zNjjv2GF4YJ1Wy+8pBiDgdOjnE+sXxVRxbXltG99AEYx4fJTG0uDjeM8KyK9ckOsR5uUqsUpVUJOWBA6fYHueHWSYopahzhVYNVyTR2yqV/rSvg0VVpbTEWcVwWW0ZO7c186W7n+fr77ss4WIXQsTJAUT7APFMe0zUyEZrvXPWoV8ppX4PPECoqM3V0Z6rlPoQ8KGbbrop1vGmXLhKZrggSrh6JZBQsFJZaqNv2IOrJLk2QIkEhjXlxaysL8/a/rQAv32qnXduXsXfXJT65u/bWppm/D/83VPH2LX3Zf7vDRclfe4/Pt/BpjX1Cf1/rnUW86aLl/PNP+wnvLac7O9dOsmKocgrh066aY5zxbDIbGJVg5O2kwtXE/T6AvQMT7CoKqbK33Ers1vw+gJZfUdQZK/BUS+HTg5x4eroRVKySTank076A/z04SO8d0tiN4Gu3biYpTVl3PWXlwwemRBRjQPRrmTt0x4TF631w8BDwFalVNRoRmt9l9Y6q1paRKqSmUz1yqqy5FtWaK3pdcdXfCbsqpamrF156nFP8PyxPl5zzqKMvP7rz19KR99o0q0xAsEg90RpURGrnz/6CrMTjlNRNTUVJDAUeWPM66PbPZFQKsG6JldMfWhO9I3SVFlCUYw55/FSSuEssUqTe5GQhw+e4uLVtdhzZIWqobIkawPD3c+eYHWDM2qZ8oUopbjl9Rt49mgfD+dBpTqRE04SSheNFBw2EUozTfTDpR0wAxUJPj8jjK5eWV2WfMsK9/gkNos5ocrmm9fWs6+9n+GJ7LtGuPfZ47zm7CZKbAtnXqWCxWzixq3N/M/9LydV/Ouxtm5qncWsbohvkWG6dFVNTQUJDEXeOHzKzYq6soSCtlBl0qEFH9feM2JYDns0FSU23FKARiTggQMnuTLLq5FO1+Aq5lQWVib1TPr5xWOv8J4EVwvDSuwWPnP9uXzzj/u5+8mj7LhjT87tNxE55WlC13UzcumUUnZgI/BMEudeDfiB7C6LOYvR1Ssry2wMJFmZNJE00rASu4XzV9TwyMHTSY3BaF5fgPue7+CNFyzL6DiuXN9IIBjkkZcTf39+91Q7b0oyFTZd1VlTQQJDkTdCaaSuhJ67blEFL3cOLniXqb13hGUpKjwT5iqxScsKEbfe4QlO9I1y/srE+3SlW0NFdq4Y/u7p47QsqTRk72Nzo4tzl1dz118O0uOeQPPqfhMJDoXBfg5o4NZZxz9IaG/hj8MHlFINSqm1SinHtGNOpdScdAOl1HbgMuAvUxVOc8a7Ll8151gyVTKryuz0JRkYJlKRdLpsrE66d38XzY1OmlK0zSZWJqV437a1fG9PG/5AMO7nv3LazamhcS5tTq6qd6SqqamozpoKEhiKvHHo5FDCgWFVmR2bxbzgRWoqW1WESQEakYiHXjrFZc31MZfWzgYNFQ5OZllgOObx8esnjvJuAwtMHegYYPY9p1zZbyJyh9a6FfgWcP1Ui4kPKKVuI1Q05kHgJ9Me/u/AQWauLm4FDiulvq6U+rhS6qNKqe8Dvwf6mBtwZj1rkZnltWVnqlcmWyWzqtROf5I3bnuTDAxHPJMc6BjgmizJPtBa89un2tNScCYW562optppT6if4O+ebucN5y9NervQ9KqpAE6HNSXVWVNBqpKKvHHopJsbk7gbs7bJxctdQzRWRr/j1d6buub2YRUlNtljKGK2p7WLXXvb6HFP4HJY2dPalRMfPgB1rmL6hj0EgkHMpuwIaH/z5DEuXFXDkupSw87Z6468wpAL+01EzrmV0H7ADwHbCQV03wA+r7VeaAmlDXgWuA6oAyxAJ/Bt4Eta6+xaporB/a1d/O2lKw2bE6vL7UmnkiazYrintYtv/vHAmRtN2VDtsvXEAP5AkPNXVGfk9WdTSvH+bWv5wi+e4aqWppj3cg6NeXn05dP870e3GjKOcNXU//7LS7gctpz5XM6OT+ICs6e1S/aaGGxozMuoxzdvULeQhfoZjnp8jE74qHWlNkfcVWJjSFYMRQzCpdh7pgKMofHJnEpRtBaZcZVYowZO6TY8Mcnvnm7nXZevNvS8ubzfROQWrXVAa32b1rpZa23TWjdprT+ptR6d9bgbtdZKa/3AtGMHtdZv01qv1FqXTj1/pdb6o7kYFA6OejnYOZh0WuB0laW2pIvPJLNiaHSVVSOEVguXxdzrNR3WNLpYv7iS3zzVHvNz/vh8B5etrcfpWKjVZ3xW1JZzrGfY0HOmkgSGaTb9Qk72mhjn0Ek3qxudmJKYmNYtcvFy51DUn4cLzyTzGrFwlVglMBQxycaLhHhlUzrprx4/yuZ1DUndYIokl/ebCJGrHnzpJJesqUuo+mc0rhIbIxOTCe1fC0tmxTBalkGPe4JAMPFKnInqHhrnxeP9vObszLSomM+NW5q5+4mjMRXz8weC3PvMcf7mQuPTYZfXlnGse8Tw86aKBIZplg8Xctmo7eQQzQ2upM6xqt7J8b7ROf9/wtp7U7+/EEKppIOSSipikMslscMaK0o4PZT5wHBozMsfnjvBDZvnFqtI1uz9JiYFH712fc6kFgmRi+5PQVq92RRqKTWQxD7DngR7GEL0LIMis4n33/kA9z57POo1TCrc88xxrj57EcUGBt9Gaaoq4cr1jfzskSMLPvbRl0/TWOlgZX3yBcdmW1JTyqnBMXxJ3ExIJwkM0ywfLuSy0aGTQ6yJs7H9bDaLmaXVpRw5HbnRfXvPCEvTEBjKiqGIVT6kKNZXODg5kPmWFT9/9BWuamlKqijEfLa1NPHDW7bxp89tZ/O6BnqHsyN9Voh81NE3Sq/bw7nLqww/d1WpnYEEm9x7Jv14fIGE0xWjZR/8/RvO5h/+5hyePtzDjd/cy08fOcLIhC+h14iVZ9LPn/Z1/P/s3Xd4nNWV+PHv1aiXGfVq2ZabbFxpxgYDtklCaAkhEEICBCeEXUihbJJNNqRsSNskBIckLD82uyaVUEIKOIWAbWKDwTbEIGNbtmzJSLJ6GbVRm7m/P2bGSPKMNP2dcj7Po2fwO+/MXA3S1Zz3nnsOV50zJ6yvE4yPXLiAv7/ZRNsMFx//uLeB9587NyxjSE02UZKbSWPnwMwnRwEJDCMsHj7IRRutNbUnrSwKsCLpRItn5XLISzrpiQgUngH3HkNZMRQzi4cUxbK8zJC0rAhk77b7MZfet5Xf76mnIj9zxseEwq2XLOYPe+rp6JMLgkKEw7YDzaxfVh6WolYFQTS5b7faKDZnBLwfb2L2wdQqq0sr8/nPD5/Ldz56Hs1dg2z66XYe+ftBOl0XoUJd32LbgZOcMSsv5Kn3oZSfnc5V58zhFy8e8XrO0RYrHX3DrA3hXtSp5pWYqW+LjX2GYV/7VUp9CTgLOBuoAk5orecG8DyXA/cCK4ER4AXgC1rr+tCNNvw2bajme3/cP6l0eax9kIs27VYbSUpRZE4P+rmWVOTxcu3pjVG11tRHoFUFOFcM+2yj2B0aU1L0bOYW0cedJrVley0dVhtFlgw2baiOqRTF8rzMoFNJ3Xu33SlUvlTqm/oYreFnL9SSnZ4a9vevJDeTK8+ew5ZttXzh6lVhfS0hEo3Wmu0HTvLlD54VlucvyEkLuJdhe99w0AXs3NUuvZlbnMPn3r+SdquN379az7/8v38wtyibIy1WRsed6YzBVjN1tqio5/ZLlwb2TUTQtWvn8fGf7uB4W5/H3rR/2FPPVefMCWtl7LnFOdS3x8Y+w0gkBX8b6AZeB3IDeQKl1DXAU8AbwOcBC86SzC8ppc7RWp8MyUgjYE5RNpmpJjLTUujoG8ackcLtl8pek2A4G9tbQlIRa3FFLv+77fBpx7sHRkhSitys0Far8sSUlERWWjL9tlFys9LC/noits30ISHalbqKz2itA/4d9rZ3+6G/vcWwl/02/7ftsNf93pF4P6+/YD6feGgHh5p6WDIrL+yvJ0SiONTcS3KSYkEY9ouBe8UwwMDQtWIYCcWWDP7lPWdww4ULuOXH208FhW7BzHdvNHShNayaG/pU3VDLSkvhhnUL2LLtMPfdsHrSfT0DI7xypI1/ec8ZYR1DVXEOf9p3IqyvESqRCAzna62PAyilDgB+NYdSSqXg7MHTCFzoLrmslPoLzn47X8fZrycmPLn7ODesW8h158/nNzuPMjA8FtMf6qJB7cnekKSRgjOtbWzcQUefjaIJk3dDRz9zirIjVo7ZnU4qgaGId+aMVJIU9NnGAt53422Pdr9tjCMne73e589zhVpGajKbNizm4ecOsnnT+VFV6l2IWObu5Rqu36mCnHRqTnQH9Ni23qGw7WP2xpyRytDIuMf7Ap3v/rCngaujrEXFdC4/azZPv1rPmye6WDHnnWD2z6+/zYVLyjBnhPeifyylkoZ9j6E7KAzCxUA58LOJfXi01vuBHcD1ruAx6rX2DrHvWAeXnzUbgOryXI6c9FzoRPjuSIs16MIzbkqpU43uJ2po72duBNJI3XKzUumRAjQiQZTlZQW1z9DbHu1iSwZ3XbnC45e3D2eR3O99yYoKHA5n2psQInhjdgf/ONjCxmXhu+BekJNOV4DFZzr6hiMeGEJo61u09AzxVmM3l8TQokZqsolb1lfzvy8cRrv2co3ZHTz72gmuXh36FhVTFZnTGRmzx0RhwVgoPnOu63a3h/teAczAosgNJ3C/f7We966qJCvdGccuLLdQ12o1pPdMvHBozdGW0BSecVtckUuth8Cwqjg8aSme5EmTe5FASnMzaekJvDLppg3VJE/ZjzvT3u1oKNyTpBT/eukZ/O+2wwyPer6iL4Tw3WvHOphVkEVpXvgKSRVkpwWcShpMD8NgeJrvAC5d5X//wT/ta+A9qypD2h8yEtYvK2d03MFLh511JHYdamF2YXZELvorpagqMdMQA/sMYyEwLHfdeiqf5D4W9Zct+oZGef7NZj5w3jtXJswZqeRlp8VMCdto1NQ5gDkjJeAUNE+WzMrj0NTA0JVKGil52dLLUCSO8iArk25cXkFpbiaWzNTTKvVN9xhv1f0iaWllPktn5fHk7mCTa4QQ4ehdOFUweww7guhhGAxP8901a6r4094T7G/o9Pl5bKPj/P2NpqhuUeFNklJ84pLF/OQvB7jpRy/w3d/vp769P+jqrL6qKs7heAwEhrEQ7rsv+3haPhmecs4kSqnbgNtuv/32cIzLL8/sO8EFi0soyJlcOXNRWS5HWnojmqYYT0LVpmKiReUW6lqsjNsdJJuScGjN2x0DEWlV4WbJlF6GInGU5WdysLEn4Md39g3TOzTKY3dfQmry6VfFvYmWwj2fuGQxn/rZLi5dVWnIaoIQ8WBwZIx9xzr4zGXLwvo6ORkpjIw5GB6zk+5hFc4bu8NB98AIBSGooB4IT/Pd2kUlfPOp17nriuWcv7h0xud4/s1mVszOpzQ3Mq19Qs06NELv0OipzgDWodGgqrP6Y16JmUNNgf+di5RYWDF0X0b2VIUjfco5k2itH9FanxOWUflhZMzOn/Y1cO2aeafdV11ukX2GQTjSEnxj+6my0lIoyc04VVq4tWeInIyUUynAkZCXLamkInGU5Qa3YviPQy2cX13iV1AYTUpyM7nq7Dn8n4eKyEII37x0uJWVcwowhzCDyBOlFAU5aXT7uWrY1T+CJTOVFFP0fPReMaeAb31kNT/+ywH+/kbTtOdqrfnjnnreH4E9eeHy6PYjk9rFwTvVWcOtqjhHUklDxL0r31Mo7z4WmXXgAD33RhOLK/KY7WHFaVF5LrVequaJmTlbVeSG/HmXVORxuNl5ZaehIzL9CyfKzZRUUpE4gm1y/+JbJ1m/tHzmE6PYhy6YT82Jbg7GwBVlIaLRCzXNESuI4ixA49/F23arjSKLMauF01lYZuG/blrDL148wu9f9d4a/PX6TpJNSayYkx/B0YWWtyqskahGPbc4hxOdA1FfVyQWAsO9rtu1Hu5bA/QBRyI3HP/YHQ5+98pxPnT+6auFAAtKzZzoGGB03HOvLeHdmN1BfXs/C0pDu2IIsHhWLoeaegFn4Zk5EUwjBcjLllRSkTgKzRlYh0ZP6yvoi9aeIVp6hlhVFf39tKaTkZrMLRuqefhvB3FMvaQthJhWZ98wdS19nLeoOCKvl5+dTleffyuG7VYbJZboTMGcXZjN/R9by7P7TvDzHbWnKndO9Ic9Dbw/hlpUeBLK6qz+ykhNJj87jZPdgRdai4SoCgyVUmVKqcVKqYm/OS8CLcCtSqnsCeeuBNYDT2qtPTekigK7DrWSl5XG0krPV1jSU5Mpz8s8lbYofNfQ3k9pbgaZaaHfKutcMewF4ETHQMT3gLr7GAqRCExJimJLBm29/q8avnjwJBcuKcWUFFV/zgJyyYoKNJrtESqGIES82P5WMxcsjlw6eUFOmt8tK9oNqkjqq2JLBvffspY9R9t56G9vTbpA1dw1SG1zLxvC2AYkEoyuRl1VnBP1n/fD/pdUKXWTUupepdS9QBFgcf9bKXXTlNO/AxwCVrsPuIK+O4FKYKdS6g6l1BeB54AO4Gvh/h4CpbXmyd3Huc7LaqHbonKL1ybMwrsjIWxsP1VlYTY9gyP0DY1S394X+VRSV7sKT1fthIhHZXmZnAwgnXTHWy1cHONppG5JSvGv7zmD/9tWK+0rhPDD9pqTES0kVRhAZVJnq4roSyWdKDcrje/dtIbjbf18/w/7+fsbjdz04DY+/tAOxuwOXna1eohVRlejriqO/kb3kahK+gmcTeonus91+yLwy5meQGv9pFLKBtwL/ABnhdIXgH/XWkftpdU3GrqwjY6zZlHJtOctKnc2VL8qQuOKF879haFPIwXnCsaicgsH3u6mpWeIysLItaoASE8xYUpSDI2Ok5UWuaI3QhglkH2Gb3f0Yx0a8ZqREYuWVuazbHY+T7x8nJvXx0SLXiEM1dDej9U2yoo5kUsnL8hJp67Vvw/47VYbayKU6hqMrPQUvv2R1dy15SV2vHUS95a4oZHxiFXwDCcjq1FXleTw/JtRG7YAEVgx1Fqv11orL1/rp5x7i+v4Dg/P86zWeo3WOlNrnae1vlZrfSzc4w/GE7uPc93aeSTNkI9dXZ4rK4YBqA3jiiE400mff7OJYkuGIdUOc7PS6B2QdFKRGMryMmn1M5X0xYMtXHRGOaak2N3z4sknLlnMn/Y10B6BgghCxLptNc1sWFo+42etUMrP8b/JfbvVRrE5elNJJ0pLMdFvG2NqnZRIVfCMV/OKzdS3R/eKYexvyohSx1r7qG/r8+mqRFVxDq29NmySOuSz4dFxTnYPhjXFc2Tczku1bTR1DXLTg9si1gTVLTcrld4hKUBjFKXUzUqpfyqlbEqpNqXUz5RSRX48/lGllPbydW04xx6L/E0l1Vqz462TrF9aFsZRGaPYksFV58zh2797nZse3MZ779tqyBwkRLRzaM22A5GrRupWkJ3u1x5DrbUzMMyNjcAQnAV9PIlEBc94VZqXiXVwlMHhqC2NEhMN7mPSU7uPcfXqKp9WmpJNSVQV51DXYmV5BFMhYlldax9zinLCtpK3raaZZ/edOPXvdqst4ikUeVlp9PhZDluEhlLqbuCHONPd7wRmAfcAa5VSq7XW/pQVm7qXGmBP8KOML+V5WbT4Ua3teFsfY3ZHWNrVRINSSwaHXAWwwJg5SIhod+DtbrLTU6gqMUf0dQty0unqd9YB8KVKZ//wGMkmFVNbQ4osGR6zFiJRwTNemZIUc4pyaOjoj9otELJiGAZtvUPsPdbBFWfP9vkxi8ot1Eqje585C8+EZ38hwJbttYyOOyYdi3QKRW6W9DI0glKqEPgmzlY5l2itH9FafxW4ATgDZ6DoM631rzx8vR36kce20twM2qw2n1s1vPhWC+vPKI/p0unT+dXOutOOSRqXEJNtq2k25EJJZloyJqUYGvEt06u910ZRjKSRuhldwTNeVZXkcLwteiuTyophGDz9aj3vXVVJdrrvV4YWleWyt649pOPYVtPMlu21dFhtFFky2LShOuavNLu/p3arjZyMFJZW5oflezKyCapbblYqVullaISrgUzgx1rrU431tNbPKKWOAzcC3/b1yZQzcskBBrTWjpnOT1Tpqclkp6fQ1T884wcorTU7Dp7ka9edE6HRRV40zEFCRLPRcTu7Drfy0CcvNOT183PS6OwfJsuHz3rOHoaxFRi6P1vF2+dIo80rzqEhivcZSmAYYn1Dozz/ZjP/718u8utx1eUWfr3zaMjGsa2mmc1ba041jI6HNKSp31O/bSxs31M0pFDkZqXxdkf0XlWKY+e6bnd7uO8V4AalVLbWesDH57PiDAxHlVL/AO7VWr8agnHGHXdl0pkCw9qTvaSakphXEtk2MpEUDXOQENFsz9F25pWYDesN6E4nnVM08zzU3meLyd9dIyt4xquqEjMvHmwxehheSSppiD372gnOry6h0Oxfr5pZhdlYh0bpGwpN6uCW7bWnAii3WE9DiuT3FA0pFHnS5N4o7qZ4nip9NANqwjnTaQUeAG4HPoBzlfEcnP1Y3xWCccYdX1tWuHsXxmsaKUTHHCRENNtWE/miMxP508uwLQZXDEV4zHU1uY/WPtWyYhhCI2N2/rT3BP9103l+PzZJKRaUmqk92cu5C4LvcxOPaUiR/J6iIYUiLyuVHkklDZhSKhe4y4+HPKi17saZRgrOfqlTuT8FZHq4bxKt9RenHPqDUuo3wH7gv4GFnh6nlLoNuO3222/3ZcxxpSx35sDQ7tD84+BJvvtR/+fZWDJxDmq32kgxJUW0EbMQ0azfNsY/G7r4t/etNGwM+dlpdPtYmbTDamNxnBbKEv4xZ6SSmZpMm9VGae6MHyUiTgLDEPr7m01Ul1t8SivwxNnP0BqSwDAe05Ai/T0ZnUJhieUVw1e2wdOPQncH5BfBNbfAmo2RHkUu8DU/zv8V0A24I5M0YOoPnDsVwL+Gey5a66NKqSeAW5RSi7TWRzyc8wjwyB133BGdlxPDqCwvkz11HdOe81ZjN5bMNGYHOM/GEvccNG53sOmnOyjPj74PEUIYYeehFs6eV+jT/r5wKchJ52SPb5WU26w2w1JeRfSpKsmhvq0/KgNDSSUNEbtD89Tu41x3/vyAnyOUje49piElJ8V0GtIN605/b+M5tcqZShqDK4avbINf/Ai62wHtvP3Fj5zHI0hr3aC1Vn58uctAnnTderoqUAHoCecEosF1WxjEc8SlsvysGVcM47V34XSSTUlcu6aKJ146ZvRQhIgKRlUjnci9x9AX7RIYigmqorjRvQSGQdpW08xND27j8m/9mY4+G23WgBYSAGfLiiMt1pDkHW9cXsEt6xehlHNDVFqKiXWLSw2fSINx4O0eVs3Np9iSgcLZBDqeU6uy05MZGbMzOm6f+eRo8vSjMDrlj+XoiPN4bNjrul3r4b7zgFo/Cs944k4hbQviOeKSM5XU+xX4cbuDXYdaufgMX7Z4xpdLz5zNW009UpBKJCz3561L79vKgcZuBgxuEl6Qk0a3D3sMR8bsDA6Pk5edFoFRiVhQVRy9LSsklTQIU6tkjts1P9p6AIUKKFgptmRgd2g6fSjX7gsNXLqqkruvXMGBt7t54Jk3fW7GGm12HmrhcHMvD31yHempifFjq5Qi15VOGlNXGru9tF3pnj5FMIr8EXgQ+LRS6jfulhVKqauA+cBXJp7s6ntYCLRora2uY1mAXWs9POXcM4HrgENaa1n+mSI3K5Uxu4PB4TGPKWL7G7ooy8ukNC/60m/CLT3FxPvOmcuTu48buq9KCCNM/bylNfzkL2+RnJRk2MXhgpx0ugZmXjHs6LNRaE4nKQY/e4nwmFdiDmknglCSFcMghLpKplKK6nILR0LU6P6lw62sW1wKwNLKPFKSk9jf0BWS546krv5hfvqXt/jC1SsTJih0y81KjZ10Uocd/vY78PbHL78osuMJkNa6A2fwtxp4Xil1m1LqP4HHgMPA5ikP+TRwCGflUbeFQL1S6r+VUvcopf5FKfUQzhYYduC2MH8bMUkpRek0BWhefOskFy9NvNVCt6vOncPLtW0e91oLEc+isdJ6fnYaPQMjOGbI8pKKpGKqWQVZtFttDI9FX0aYBIZBCEeVzEXludSGYJ9h98AwJzr6WTm3AHB+4LrynDk8s+9E0M8dSVprHnj2TS47q5LFFXlGDyficrPSYqcy6Z9+Bftfhus+CalTUmZS05wFaGKE1vp+YBOQj3P18HbgCeBiH9NIW4HngQ3A14GfAFcCjwNnaa13hWHYccFby4rRcTsv17Zx0ZLE2l84kTkjlfesnMXvX603eihCRFQ0VlpPTTaRkWqasc2Yu7K5EG7JpiRmFWRH5dYACQyD4O0XPZgJYFGIVgx317ZxzvxiUpPfKUBzyfIK3jzRRUdf7Fxt/vPrb9M7OMpHL/RY2T/uRX0vQ61h19+caaKXXguf/x685xq4+U7ILwaU8/bmO42oShoUrfWjWuuVWut0rXWx1vrjWuvT8mS11l93Fa95dMKxVq31TVrrxVprs9Y6RWs9W2v9Ma314Yh+IzGmLC+Tkx4Cw9eOdVJVnON3j9h4c82aKp57o4k+WxTPC0KEWDg+b4VCgQ+9DGXFUHhS5epnGG0kMAzCpg3VpCVPfguDrZJZXZ7L0ZbeGVMTZjIxjdQtIzWZDcvK+fPrbwf13JHS3D3Iz3cc4QvvX0myKTF/VKM6ldTaDT/+OrzwJ2dxmYwsSHJdiFizEb73C/jZX5y3MRYUCuOU5WV5LECzI8HTSN2KzBmcX13CM3tjK/tDiGB4rLQeBVXJfalM2mEdjq06ASIiqkokMIw7G5dXcP0F8zElqZBVyczNSiMzLYWT3b71xvGk3zbGoaZezllw+p6uK8+ew1//2ciY3RHw80eC3eHg+3/czw3rFiREvzJvcqN1xXB8HL73eaisgi9vhtJZRo9IxImyvExaeievGA6PjrO3rp0Ll5R6eVRiuW7tPP64t4Hh0XGjhyJERGxcXsFdVywn3RUcRktV8oKcNLpmaHLfZh2SwFCcZl6xmfq26GtZkViVPMKg0JzB+qXlfOHqVSF7TncBmlkF2QE9/tWjbayYW0CGh0Itc4pymFWQxcuHW6P66vsTLx8nLcXE+1fPNXoohsrLSqWuNTTFiAK1raaZLdtrnfskctLYVDrExuuvgv/YDFmJG7SL8PC0x/DVo+0srsglN0vKvQPMLsphWWUef9vfyPtXVxk9HCEiYuPyCv60r4FPXLKE5bPzjR4OAAXZ6XT1TR8YSg9D4UlVSQ7H2/qirluArBgG6XhbH1Ulof1wHGwBmpc9pJFO9L5z5vLsa9GbhlTXYuX3r9bzb1etTPjyzkYXn3GXCG+32tBAe/8Im+sU295slKBQhEWJJYPu/pFJWQ2JXo3Ukw9dMJ+nXqlnPMqzP4QIFa01jZ2DVBZkGT2UU/JnaFlhd2i6+kcoSvC90eJ0eVlpKKXo9qHlSSRJYBik+vZ+5hWbQ/qcwRSgGR4d558NXZy3qNjrOWurS2juHqQhCnObR8ft/Ncf9vMv714iV9hwBoZWA1NJPZYI10lsebHOoBGJeJdsSiI/J+1US4bB4TH+2dDFBdNc7EpEiyvyKM3NYMdbJ40eihARYR0aBTSWzFSjh3JK4QzFZ3oGRsjJSJlUCFAIcHYLiMYCNBIYBkFrTX0YVgwXllk43taH3eH/leB9xzpYVG7BnOF94kw2JXHZmbOjctVwy/Za5hRlG75vIFrkZqUaumIYjSXCRfybmE76cm0bK+YUkO2h4X2iu/6CBTzx8rGgi5UJEQuaugapLMiOqrS7gpy0aQPD9j4bRWa5yC08qyqJvn2GEhgGoat/BKUUeSHe95KVlkKROZ2Gdl/apU3mqRqpJ5edOZvtB04yNBI9xQveaOjixbdO8tnLl0fVxG+k3KxU+m1j2B2R/+BndzhOqwLnZnSJcBHfyidUJn3x4EnWL03c3oXTOXteIclJSew5eloXFSHiTmPXQMC1F8Jlpqqk7b2yv1B4JyuGcaa+3blaGI4gZlF5Lkdaev16zJjdwZ66Ds6vnjkwLDSnc2ZVAS/UNAc4wtAaHB7j/j+9wd1XrsAcRWkiRjMlJZGdnkJ/hHuWjdkdfPt3/6Q0NyPkLVmEmElprnPFsG9olLcae1izqMToIUUlpRQfumA+T7x8zOihCBF2TV2DzIqi/YXgvHjbZxv1ute3zWqjJFcCQ+HZvBIzx2XFMH4cb+tnXklo9xe6VQewz/CNhi4qC7MoyPFtk/OV58zhmX0N6BCkIW2raeamB7fx3vu2ctOD29jmQ8A58TEf2fwCpbkZnLvA+97IRGXJTKUngpuTR8ft3Pfka4w7ND++dR13XbmCYtfG+UJzelSUCBfxrdyVSrrrcCtnzyvyWGFZOF24pJTugREOvN1t9FCECKumzgEqC6NrxdCUlDTtlo+OPtupv59CTDW7MJvm7sGoaiEngWEQ6tv7Ql54xm1ReS5H/KxM+tLhVi7wYbXQbeWcAhwOHfQHitMqV1ptbN5aM21wOPUxw2N2Djf3+hRQJpq87DR6hyKzYjg8Zudrj+8jNdnEV649i9RkExuXV/DLOy9hxZx87rlyhQSFIuzcewxffEvSSGdiSkriurXzeFxWDQWglEpSSt2tlDqslBpWSjUqpe5XSvm81KaUulwp9bJSalAp1a2UelIpZXhflMYoXDEEV8sKL+mkbVYbxZbMCI9IxIq0FBMllgyaOv3fOhYuEhgGob6tn6ri8JTsn19qprFr8LSKkN7YHZrdtW2c70flPqUUV50zh2f2BVeExmPlyjE7Dz93kFePtnn8evi5g6c/ZtzBlu21QY0lHuVGaMVwaGSce3+zh7ysNL50zSqSTZOnh2DbqAjhq6MtvdS397O/oYuHnzsoF4xm8O6Vs6hrsUZdEQNhiAeAHwIHgc8ATwKfBZ5RSs34mU8pdQ3wLJABfB74PnAR8JJSyrCeMWN2B+1WG2V50Rdk5U9TmbTDaqPYIiuGwruqEnNU7TOU/JwAjY7bOdkzyOyi8KQ1pCabqCzI4lhbH2fMypvx/ENNPeRmpVKR79/VtHetmMUvXjxK98Aw+dmBTV7eKlRah0Z51kvQafWyAibVLk8XiRXDgeEx7v3NHqpKzHzm8mUe+0cuKrOw/YCUxhfhta2mmf9+7tCpf3f0DbN5aw2ArFZ7kZps4urVc3ly93G+cPUqo4cjDKKUWoozGHxaa/3BCcfrgQeBDwO/mebxKcCPgUbgQq31gOv4X4DXgK8Dt4Vr/NNp6RmiyJIelW0fpqtMKiuGYibRVoAm7CuGwaY1KKV2KKW0l69zwj1+bxo7ByjNzQzrJOVPOulLh1sD6vOVlZ7CRWeU8ZfXG/1+rFtuludiMcWWDO67YbXHL29VuqTa5elys9LoDeOKoXVolH//5StUV+TyWS9BIUB1AAWRhPCXtwwEySaY3pVnz2FPXTutvUNGD0UY5wZAAZunHP8fYAi4cYbHXwyUAz9zB4UAWuv9wA7gelfwGHFNndFXkdTNWy/DgeEx0JCdLmswwrt5JWbq26Mn2yMSqaRBpTW4dAI3efg6HvLR+iichWfcfC1Ao7UOODAE5weKP7/+dkB9E99o6GJ41E6Kyb/KlZs2VJ/WCkGqXXoWzl6GPQMjfOEXr3DWvCL+9T1nTFthtyQ3g3G7prPPe88mIYIlvTMDk5WewmVnzuZ3rxj2Z1EY71zAAeyZeFBrPQzsd90/0+MBdnu47xXADCwKboiBidb9heBqWeHh4m271UaRJV3ab4lpzS3Oob4tQVYMp6Q1XKO1/h+t9T3APcAGnGkNvhjUWv/Kw5dhZdjq2/uYF+LG9lP5uqfrWGsfSUkq4P2O80vNlORm8MoR/3ph7TvWwbd+9zpf//A53HPVCootGSicK4UzVa7cuLyCu65Y7tdjElVeVnhSSTv6bHzu57u58IwyPr6xesY/XkopFpVb/C6KJIQ/vGUNSDbBzK5ePZdtNSfpDdOFJBH1yoFOrbWnH4BmoFApNV0/qPIJ53p6PIAhf6SbugaojNIVw/xsz6mk7VYbJTJviRmUWDIYGh2nL0JFBmcS7vXt6dIavoszrcFrvvtErtXFbKBfh6K/QpDq2/v5wOrwFumaU5RNZ98wg8NjZKV7z95wN7UP5qqUuwiNr6uOu2vbeODZN/nah85maWU+4P/+n43LKyQQ9EFuVmrIUkm31TSzZXst7VYbSUmKCxeXcuNFC31+/KIy58UKf4ocCeGPTRuq2by1ZlI6qWQT+KYgJ535JTls+ukObCPjFFky2LShWubZxJEJePtjMTzhHG+fQN2b4Tw9x/CUc06jlLoNuO3222+fYZj+a+wa4D0rZ4X8eUOhwEsqqXPFUAJDMT2l1Kl9hivnFhg9nLCnkgab1uBWAQwAVmBAKfW0UmpxCMfpt+Ntzub24WRKSmJ+qZmjLdOnk+463Br0B/ULFpfS0N5Pow8lc/9xsIXNW9/kmzeceyooFOGTG6IVw4ktQgAcDs0rR9v9qvi4qNzCkRl+HoUIhmQTBG5bTTOHmnsZGhn3uXWQiCtDQJqX+9InnDPd4/HyHDM+Xmv9iNY6LLUfmroGo66HoZszMPScSiorhsIXzsAwOvYZhnvFcKa0hvOVUqla6+k+9dYDLwFvAnbgPODTwCVKqXVa65pQD3omPQMjjNs1hT42kg+GM53UyqqqQo/3N3YOMDA8xuKK3KBeJzXZxKWrZvHsaye4/dKlXs97/s0m/veFw3z7I+cxvzS8eyyFU25WGj0DI2itg1oVnq6oh68fuqvLczly0hr0WISYjmQTBGbL9lpGxyfvFff3d1zEtJPAGUqpNA+fuypwfh6b7vPWyQnnHppyn/sHKOJXGaxDo2itsWROlwVrHHNGCiNjdkbG7JNqJ7RZbfI5SfikqsTMUR9qikRCuFcMfU1r8EprvUlr/WWt9eNa66e01p8H3oMzrfSH0z1WKXWbUmqfv4OeyXHX/sJIfDBeVDb9nq6Xa51FZ7xVkvTHFWfP4YWaZoZHxz3e/+fX32bLtlr+60YJCiMpPcVEskkxNOL5/4uvQlHUIy87jYxUEyd7pPKhENFGCvckvL04P9etnnhQKZUOrAJm+jy013W71sN9a4A+4EhwQ/Rfo6siabRejFRKkZ+TRveULR/OHoayYihmVlWcw/EoWTEMd2AYbFqDR1rrncA/gA1KKa+/deFKa6iPQEVSt+qK3GlT93YFUY10qmJLBssq89n+1um96v6wp57f7qrjezevYXZReFNoxelys9LoHQwunTRURT38aaMihIgc77/j0mA7QTwOaOCuKcc/ifMi/K/dB5RSZUqpxUqpiRfnXwRagFuVUtkTzl0JrAee1FqPhWfo3kVz4Rm3gux0OqfsM2yTwFD4aG5xDic6BrA7DC+hEvbA8CTOKliegkNf0hqm0wCYgJm7v4dYfXtfwBVA/VWel8nQyDg9Xkoht/QMsXx26Pb5XXXOHJ7Ze4KJ9X0ef+kYf9jTwPdvXkNFfnSWi453oWhZsWlDNaakyVdcAynq4WsbFSFEZHlqA6QUVORn4TC+ZpsIM9fWmp8C17hqMdyqlLofZ3bVi0wu9vcdnOmiqyc8fgy4E6gEdiql7lBKfRF4DugAvhaZ72SypihuVeE2tcn96LidftsY+dlyUUbMLCsthbysVFqjIBsr3IFhsGkN01kIjAMRb1lxvK2fqgitGLpbBHhqW7G7tpU1C0tINoXuf+OZ8wrpGrBxwwMv8N77tnLN9/7GH149zg9uXktJ7rRZvyKM8rLSgi5Bv3F5BXOLsrFkpgRV1MPXNipCiMjyVLjnzsuXMTLm4EdbayQ4TAx3AZ8DluIMEj8M/Bi4Ums9Y7NirfWTwPtwbgP6AfDvwE7gAq21IVWMnKmk0R4YptM9ITDs7BsmPyfttIuxQnhTVWyOinTScBefeRz4D5wT1c4Jxz2mNQAW4G2t9ZDrmAUY0FpPqpihlLoCuAD4i6vCacSM2x00dQ0wJ4LplM59hlbWLCqZdHzX4VauOW9eSF9rx4GTDNjGGXctZw+OjDNmT+LNE11SvMBAuVlp9ASZSgrQMzjKjz+xLqggf2GZhWOtfdgdDkxJ4b62JITwh6fCPRcvreBrj+/lB398g3973wr5vY1jrs9L97u+pjvvFuAWL/c9Czwb6rEFKporkrpNbXIvFUmFv6pKnI3uL1xSZug4wvrXIdi0BmADcFQp9SOl1J1KqU8ppX4O/Ano5PQ8+rBr6hqk2JJB+pR0nXCqLs/lSEvvpGO9gyPUtfZx1jzP1UoDtWV77amg0G103MGW7bUhfR3hn9ys1KBXDLv6hxmzO4Le85CdnkKhOZ0THTO3NhFCGC8zLZn7blhNz+AI//X7/YzbZ1w4EiIqjNsdtFltlOVFd8ZSQXYanX3vrFO099koMktgKHxXVWyOipYVkbhseBeBpzXUAq8BVwLfwhlQrgMeBlZprSNeHet4Wx9VxZGtyLloQosAt1eOtHH2vMLT9pMES6raRadQpJLWtVpZWGYJSWW3ailAI0RMSU8x8Z/Xn4NtdJxv/+51xiQ4FDHgZM8QheZ0UpMjdzE+EAU56XQPTAgMe2XFUPjH3eTeaGEPDLXWdq31/Vrraq11mta6Qmt9j9Z6YMp5t2itldZ6x4Rjh7TW12mt52uts12Pn6+1/pRRue717f3MC3Nj+6kKzemYkhRtE4Kzl2rbQlaNdKJQVa4UoRWKVNKjLX0sLLWEZDzOfa9SgEaIWJKabOIr152NBr7x5GuMjttnfIwQRmrqGoj6NFI4vcl9e59NPjcJv5TnZ9E9MBJ0a7JgyUYDPxmxYgjvrBoCDI6MceBEN6sXFof8dTxVtQukcqUIrbwQpJLWtVhZUBaqwFBWDIWIRanJJr78wbNITzHxtcf3MTwmwaGIXo2d0V+RFNyB4fCpzK422WMo/GRKUswpzKahw9hVQwkM/VTf3kdVhFcMwd0ioBeAvUc7WDo7j6y0lJC/jqeqdoFUrhShFYo+hkddqaShML/ETGPngKw4CBGDkk1JfPEDq8jLSuMrj+3BNmrsFWohvImFHobg3MerFKdWe9qlh6EIgLMAjbH7DMNdlTSuWIdGsY3aDbkKVF2ey29fqgNC29TeE09V7YSxnKmkga8Y9g6OMDw6TmluaH5201JMzCrI5nhbH4srIt5KVAgRJFNSEv/2vpX8aOub3PHITsbsDjr7himyZLBpQ7X8DRBRoalrkPesnGX0MHxSkO1cNcxIS6bDOiyppMJvzgI0smIYM9yN7UNRvMNfC8st1LX0MTw6zuvHO1g7pXWFiG/Z6cmMjTsCXqE72mJlQWloCs+4yT5DIWKbKUmxYm4Bbb02OvqG0ThXOjZvrWFbjSHb+IWYpLFrgFkxsGIIUGB2tqzoHRwhMy05otXrRXyoKsnhuMErhhIY+qG+rZ95EWpsP9W+ug6Gx+28/7/+xui4g9ePdxoyDmEMpRSWrNSA00mPhnB/oZvsMxQi9v18+xHsUxrfj4zZpUWRMJx1aBSHQ5OblWr0UHxSkJ1GV/8w7dZhSSMVAakqNtPQ3j+pC0GkSWDoB/eKYaRtq2lm89YaHK7+gmN2h1zRTUC5makBp5PWtYauIqlbdbmF2ubekD6nECKypEWRiFbuiqRGZGkFwl2ARvYXikBZMlNJTzXRMaEnZqRJYOiH4239VBmwYrhley0jUyrHyRXdxJOXHXgvw7qW0BWecZtTlENH3zCDI2MhfV4hRORIiyIRrRo7B2KiIqlbvqtlRZt1SCqSioBsq2mm3zbGTQ9u46YHtxmyACSBoY/sDgdvd/QbsmIoV3QFQG5mYJVJ+4ZG6R8eoyw/M6TjSTYlMa/EzNEW2WcoRKzy1KIoxZQkLYqE4Zq6BmNmfyFAoWvFUArPiEC4swNHxx2Acfu9JTD0UXP3EPk56WSkRr6Qq1zRFQC5AfYyPNpqZUGpmaQwpOMsKrec6q8phIg9U1sUWTJTyMtKZcOycqOHJhJcY9cglTG0YliQk0bXwLD0MBQBiZbsQGlX4aP6tj7mGbBaCM4rupu31kz6gZGm84knLzstoLzzOldF0nCoLs/l5drWsDy3ECIyJrYo0lrz6Z/t4uXatrC2RRJiJk2dsVORFNztKkYYGXPIHkPht2jJDpQVQx/VtxuzvxCk6bxwys1MpWcggBXDlr6Q7y90kxVDIeKLUoqb1y/i5ztqsTuMq4wnEtu43UGb1UZ5iLdAhFN+Tho9AyO0W4ckMBR+85odaE6P6DhkxdBHx9v6DG2yKk3nRW52Gr1D/geGda1Wbl6/KAwjgvL8LAaGx+gdHCE3Ky0sryGEiKzVC4p5bGcd/zh4kg3L5O+OiLyWniEKzemkJsdOL8DUZBMZqSZGxh2YM1KMHo6IMZ6yA1P0OOvyAutfHShZMfRRfbtxPQyFAFfxmQH/is/025xBW0V+ePZpJCnFQlk1FCKuKKX42IZqfvniUewOh9HDEQmosWsgpvYXgrN4yODIOCNjdm7+8XZpKSb84ik7cNO7l/GyNZl9/7sFGo9HZByyYuiDgeEx+m2jlObFTkqDiD952al+rxgea7Uyr8SMKSl8faCqy3KpPdnL6oXFYXsNIURkrZpbQEFOGs+/2cylqyqNHo5IMLFWkdRdUdKdfu2uKAlItpfwmafswPXLKnj6wcMs/e7nSf/YZ1GrLw7rGGTF0Af17f3MLcoJS1VHIXxlyUyl3zbm176fo2HoXziVc59hb1hfQwgRWUopPra+ml/vPMqYXVYNRWS5m9vHimipKCniT0FOOtffcys/rr6Bvl8+jOOV7WF9PQkMfVDf1mdY4Rkh3ExJSWSnp9A35Hs6aV1rX9gqkrpVV+RSe9KK1lKoQoh4smx2PrMKsvnb/kajhyISTGPnYEw1t4+WipIiPpkzUvnMv17Ng2d+ku/VpzHaehKGBsPyWhIY+uB4W58hje2FmMrfXoaRWDEszEnHlKRolz+AQsSdj61fxGM7605bDREinJq6BmIqMJR+0yLcMlKT+febL2Y0OY0X/u83OL75WWgJ/UU7CQx9IIVnRLTIzUqjZ9C3FcPB4TG6+oepLAzvH1elFIvKpACNEPGoujyXhWUWtr7+ttFDEQnCOjSK3aHJi6FK15s2VJOWMrmCqvSbFqGWmmziyx88k0PL381vc87C8V+fgzf3wCvb4As3w62XOW9f2Rbwa0hgOAOH1jS098uKoYgKeVlpPq8YHmvro6okB1NS+H/NF5U7C9AIIeLPzesX8cRLx7CNjhs9FJEAnKuF2agYqusg/aZFpJiSkrj7yhUMnruR7826mqGDb8AvfgTd7YB23v7iRwEHh1KVdAYtPUNYMlPJSpeeNMJ4/qSSHm2xhn1/oduicgtP7o5MKWUhRGTNKzGzYk4+f9rbwPUXLDB6OCLONXUNhj3TJRyk37SIFKUUt16ymMfTUxj69X+QOTblc+HoCDz9KKzZ6Pdzy4rhDOplf6GIIv6kktZFYH+h26LyXI62WHFIARoh4tKNFy/id6/UMzg8ZvRQRJxr7ByIqVYVQhhBKcWH1y2gYKzP4/26uz2g55XAcAbH2/qlIqmIGnl+rhhGKjC0ZKZizkihqXMgIq8nhIis2YXZrF5QzO9frTd6KCLONXbFVkVSIYzUneo5RvF2fCYSGM6gvl1WDEX0yPVxj+HQyDjtfcPMjmAfKOc+QylAI0S8+uhFC/nj3ga/WuYI4a+mrgEqZcVQCJ/8zLwGnTq5UJNOTeNn5jUBPZ8EhjOQiqQimjgDw5k/lB1v62NuUQ7Jpsj9ii8qt3CkpTdiryeEiKyyvEzWLSnjKdlPLMJk3O6grddGeX6m0UMRIiYcm3s2Jy7/OOQXAwryizlx+cc5NvfsgJ5PAsNpDI2M0z0wQnm+pDSI6JCXlUqPDyuGR1usLCiL7AWNxeW50rJCiDh3w7oF/Pmfb9Mz4Hs/VSF81dIzRKE5ndRk08wnCyG4Yd0Cvlafw/47fsj4/9vK/jt+yNfqc7hhXWCFwsIeGCqlkpRSdyulDiulhpVSjUqp+5VSPkdbSqnLlVIvK6UGlVLdSqknlVJV4Rw3QENHP3MKszElxU7JZBHf3CuGeoYiL3Wtkdtf6LagzEJ9ez9jdkdEX1cIETnFlgwuWV7BEy8fM3ooIg41dQ1SKfsLhfDZhmUV3LKhmof++hZXfecvPPTXt7hlQzUblgVWITcS7SoeAD4L/B64H1ji+veZSql3aa2n/RSplLoGeAp4A/g8YAHuAl5SSp2jtT4ZroEfd/WBEyJapKWYSDElMTQyPm0LlaMtVq5eHfZrJ5NkpCZTmptBQ3t/xINSIUTkXH/BfG57+B98cM08Cs3pRg9HxJHGLqlIKoS/NiyrCDgQnCqsgaFSainwGeBprfUHJxyvBx4EPgz8ZprHpwA/BhqBC7XWA67jfwFeA74O3Bau8TtbVcj+QhFdLK50Um+B4fCYndaeIeYaUDRpUXkuR072SmAoRBzLz05nSUUut/73DoZH7RRZMti0oVp6uImgNXUNUF2ea/QwhEhY4U4lvQFQwOYpx/8HGAJunOHxFwPlwM/cQSGA1no/sAO43hU8hoUUnhHRKG+GAjTH2/qYXZRDSgQLz7hVl1tkn6EQcW5bTTP7G7qwjdrRQLvVxuatNWyraTZ6aCLGOZvby4qhEEYJ9yfHcwEHsGfiQa31MLDfdf9MjwfY7eG+VwAzsCi4IXqmtaa+vV9aVYioM1MBmqMtVhaUGnNBw9myoteQ1xZCRMaW7bWMjk/eBTIyZmfL9lqDRiTihbO5vewxFMIo4Q4My4FOrbWnT7HNQKFSKnWGx7vP9fR4gLDkrrRZbWSmJmPOnG54QkSeZYZehnURbGw/VVVxDie7BxkeHTfk9YUQ4ddhtfl1XAhf9A2NMu7Q5GWlzXyyECIswh0YZgLePsEOTzhnusfj5TlmfLxS6jal1L5pR+iFFJ4R0WqmVFJnqwpjAsPUZBNzinKoa+0z5PWFEOFXZMnw67gQvmh0NbZXSirBC2GUcAeGQ4C3Sz/pE86Z7vF4eY4ZH6+1fkRrfc60I/Sivq1fCs+IqJQ7TSrp6Lidk92DhqZALyq3cETSSYWIW5s2VJOWMrnPXGpyEps2VBs0IhEPmroGJY1UCIOFOzA8iTNd1FNgV4EzzdT70ofz8e5zPT0ePKeZBq2+vY95smIoolBeVhq9XppLH2/rZ1ZBtqHNgasrcqmVAjRCxK2Nyyu464rlFFsyUEBGqomz5xVKVVIRFNlfKITxwh0Y7nW9xuqJB5VS6cAqYKY0z72u27Ue7lsD9AFHghuiZ7JiKKJVbnYavUOer6c400iN/bldVJbLkZZeQ8cghAivjcsr+OVnN/LXr1zBQ5+8kLcaexgakb3FInBSkVQI44U7MHwc0Dgb0k/0SZx7A3/tPqCUKlNKLVZKTdwz+CLQAtyqlMqecO5KYD3wpNZ6LNSDHh6z09FnkytXIirlZnpPJa1rNa7wjFtlYTa9A6P020L+qymEiELl+VmcWVXIn19/2+ihiBjm3mMohDBOWANDrXUN8FPgGqXU00qpW5VS9wM/xBn0TWxu/x3gEBNWF11B351AJbBTKXWHUuqLwHNAB/C1cIy7od2ZjpdsQB84IWaSl+29+Exdi5UFpcYGhqYkxfxSc0yvGiql/kUp9Wul1GGllF0ppQN8nvOUUs8rpfqVUn1Kqb8qpVaFeLhCGO5D58/n96/WMzpuN3ooIgaN2x209dooz5+uHqEQItySI/AadwENwG3AFUAn8GPgq1prh/eHOWmtn1RK2YB7gR/grFD6AvDvWuuw7S+UiqQiWmWlJTM27mB03D5pL+HouJ3GzgHmlRifAr2o3EJtcy9nzysyeiiB+hJQAPwTyAJm+fsESqk1wA6c+6C/6jr8aZwXuc53XTgTIi4sKLMwpziH7QdOcumqSqOHw7aaZrZsr6XDaqPIksGmDdWyBzKKtfYOUWhON3R/vBAi/KmkaK3tWuv7tdbVWus0rXWF1voerfXAlPNu0VorrfUOD8/xrNZ6jdY6U2udp7W+Vmt9LFxjrm/rj4oP10J4opTCkpVKz5QCNCc6BijPzzqtWqARFpXnciS2C9CsByxa64uANwJ8jgeBUeAirfUDWusHgItwptffH5JRChFFrj9/Pk+8fAy7I6AF9pDZVtPM5q01tFttaKDdamPz1hq21YTlWnLUUUrdrJT6p1LKppRqU0r9TCnl81U6pdSjSint5evacIy5sVMqkgoRDSRX0oP69j4pPCOiWl7W6QVojkZBGqlbdXlsF6DRWjf4ktHgjVJqAXAuzn3Qpz6Nuv77SeBdSqnS4EcqRPRYMSef7PQUdte2GjqOLdtrGRmbnNI6MmZny/Zag0YUOUqpu4GfA1acW3H+H/BhYIdSyt/I6yYPX3tCN9p3NHUNMEv2FwphuEikksYUrTXH2/qlVYWIarkeVgyPtlhZaHBFUrdDTT3YRu1c9s2tVBZmc8O6BWxYllBpXOe6bnd7uO8V4OPA2cDWiI1IiDBTSvGh8+fz25fquGBxacgalfuSFmp3OGjsHKSu1Uq71ebxeTq8HI8XSqlC4Js4K7pforW2u47vBf6EM1D8tq/Pp7X+VTjG6UlT1yCLyqPjwqYQiUwCwyk6+oZJMSWRm+Wp9aIQ0SE3Kw3rlBXDuhYr71phfPC1/UAzP99Ry9c+dDbLKvM50NjNA8+8CZBIwWG569ZT7pr7WMK8GSJxrK0u4f+2HeaNhi5WVRUG/XzutFD3CmC71cbmZ9+ktXeIvOw06lqs1LX20dDeT2FOOgvKLGSlJzM4fHrrjKJM49Psw+xqnBXff+wOCgG01s8opY4DN+JHYKickX0OMBBMBoUvGrsGZA+oEFFAAsMppPCMiAW5mZNXDMftDk50DjA/CvbGPrarjruvWsGquc4PhavmFnL3VSt46K9vRTQwVErlcnqrnOk8qLXuDtHLu0vreeorMjzlnEmUUrcBt91+++0hGooQkZPkWjV84uVjIQkMPaaFjjv41T+OsnFZBQvKzGxYVsG8EjOZac6PNFODSYBkBZvOKwdrN/xzN1zwbkhJDXp8UWamTIUblFLZU2s8TMOKMzAcVUr9A7hXa/1qCMZ5mqYu2WMoRDSQwHCCbTXN/OQvBxgcGeemB7dJFTMRtfKy0yalS53o6KfEkkF6qvG/0o2dAyyrzJ90bFllPo2dvn4WCZlc/Gtp8ysgVIHhkOvWU+pB+pRzJtFaPwI8cscddxhbwUOIAG1cXsEvXjziSm8PLj3QW/qnw6H53PtXen194FT6aX5OGiNjdlIKimB4GPbvhj//Fi77EKy7NJ4CxJkyFZTrnCMzPE8r8ADwGjAIrMR5kW2nUupyrfXzIRmtS9/QKGN2B/nZkqklhNGM/xQZJTymq2x1VpOX4FBEm7ystElVP0PxASxUKguzOdDYfWrFEOBAYzeVhZEtLKC1bsD5QcgIJ123niYP97HEKJEoEk6KKYkPnlfFEy8f48sfPCuo58rLTqN74PSF9yJLxrSP27i8YtLf7qMtVr78mz0UffgcFt/1TTh+2BkcrjgPUtMgPQNe2wVPPwrdHZBfBNfcAms2BjX+QASR7RBwpsJEWusvTjn0B6XUb4D9wH8DCz09LtBsh8auAWYVZIVsT6oQInASGLpMV8VMAkMRbSxZqfQOvvO3v661jwVREhjesG4BDzzzJndftWLSHsNbNlQbPbRI2uu6XQv8bMp9a3C2rHgtoiMSIoIuO2s2v33pGM1dg1QEmCLYOzjCuN1Bskkxbn9nAT0txcQmP+eThWUW7rlqBf/5xGs8sOl8Sucthk9/3Xnn83+AP/4SRkfA7tqb2N0Ov/iR878jHxzmEli2w8RMhalLrdNmKsxEa31UKfUEcItSapHW+rRVx0CzHZq6BqmUiqRCRAVpV+HiLV0l3quYidiUl5VG7+A7xWeOtlhZWGr8/kJwFpi5ZUM1D/31La76zl946K9vccuG6rgtPKOUKlRKLVZKnYrMtdZ1wD7gOqVU+YRzy4HrgG1aa2Nr+gsRRhmpyVxx9myeeuV4QI8fGbPz9cf3ceU5c/i3q1ZSbMlAAcWWDO66YnlAF2zXLCrh+gvm85XH9jIwPPbOHe+6GlJT3wkK3UZHnCuIEeZql6P8+KpzPXSmTAU94ZxANLhug988OoHsLxQiesiKoUuRJcNjieuZ0lWEMEJuVio9rhVDu8NBfXs/86OkhyE4g8NYDgSVUlfh3FcDsMB17F7Xv3u11j+ZcPqncV7d3wQ8OuH4ncB2nPtyfuw69hmcF+T+LTwjFyJ6vP/cuXzioRe58aKFFOSkz/wAF4fWfP+Pb1CSm8nNFy9CKRWyzJ2rV1fR3D3IN596nW/ecC7JJtf1cWuv5wd0d4TkdSNkL3AbzkyFuin3nQfU+lF4xhN3CmlbEM9xmsbOAS6RzCwhooKsGLps2lBNWsrkUtaBpKsIEQmWzFQGhsewOxy83TFAkTn9VEU+ERIfBO5zfbknAfe/P+fLE2itXwbW47zK/k3XY+uAi7TWb4R2uEJEn9ysNC5ZXsHvX63363GPbq+le2CYf3vfirDsO/vX95xBSnISP/7LAbR2ZT3mF3k+2dvx6PRHnCmkn1ZKnfpA47rQNR/49cSTPWU7KKWylFKnRfFKqTNxZjsc0lofC+Wgm7oGIr4HXQjhmQSGLhuXV3DXFctDkq4iRLiZkpLITk+hb2iMo61WFkTRamE80FrfMk3a1twp537ddfxRD8+zW2t9idY6W2udo7W+VGv9eqS+DyGM9sE1Vfx1fyODE1M3p/HXf77NzkMtfO1D55CaHJ6+g6akJP7jmjM5etLKk7tdqa7X3OIsQjNRaprzeIzQWncAXwFWA88rpW5TSv0n8BhwGNg85SGfBg4BH5hwbCFQr5T6b6XUPUqpf1FKPYSzBYYd54pkyIzbHbT22ijPn7EmjhAiAmSJYYKpVcyEiGbudNK6lr6oqUgqhBATleRmsnpBMc++doLrL1gw7bn/rO/k0e1H+MHH1mDJDG8LiYzUZP7zw+dw15aXKcvN5EJ3gZkoqEoaDK31/UqpLuBu4EGgD3gC+KKPaaStwPPABuCjQAbQAjwOfEdrfTiU423tHaIgJy1sFwGEEP6RwFCIGOUuQHO0xcr5i0uMHo4QQnh03dp5/Mdv9nD16qrTtmy4nejo57u//yf3fvAsZkWoQmWROYP//NA5/Mdv9lBoTmfJmo0xFwh64speeNSH874OfH3KsVbgpjAMy6OmrkFJIxUiikgqqRAxKjcrja7+YY639UkqqRAialWVmFlYZuHvbzZ5vL9nYISv/HYvn3zXEpbPKYjo2Ba42lh848nXaO0JqJODCEJj50DELgQIIWYmK4ZCxKjcrFQONHaTl51GdnqK0cMRQgivrr9gPt//4xtcdmYlpqR3rkkPj9n52uP7ePeKWbxrxSxDxrZmUQltvUP8289fJjMt5VQxlBvWLYjp6sqxoKlrkIXlcmFTiGghK4ZCxKi8rDT21XXI/kIhRNRbWplPfnYaOw++077ToTXf/8N+KvIzufGihdM8OvzMmamA4lOXLeWZL13GHe9dyqPba9l+oNnQccW7xq4BaW4vRBSRFUMhYtC2mmaeeuU4/bYxXjvewbaaZimcJISIatdfMJ+f/PkA/7vtEB3WYTLSksnLSuXhf7koLG0p/PHYrjo+f/VKVs119m5fNbeQu69awUN/fUtWDcNgW00zW7bX0m618b0/7OcTlyyWv2FCRAFZMRQixmyraWbz1hr6bc7y74PD42zeWsO2GrmyLYSIXgPDY3T0DdNuHUYDQyPjdPQNs+tQ64yPDbfGzgGWVeZPOrasMp/GzmD6wQtP3H/D2q02ADr7h+VvmBBRQgJDIWLMlu21jIzZJx0bGbOzZXutQSMSQoiZPbr9CHrKsdFxR1TMXZWF2Rxo7J507EBjt1TMDAP5GyZE9JLAUIgY0+G6yurrcSGEiAbRPHfdsG4BDzzzJvsbOhm3O9jf0MkDz7zJDeum770o/BfNPwdCJDrZYyhEjCmyZJxKwZl6XAgholU0z13ufYQP/fUtGjudVUlv2VAt+wvDIJp/DoRIdBIYChFjNm2oZvPWmkmpOGkpJjZtqDZwVEIIMb1on7s2LKuQQDACov3nQIhEJoGhEDHGXblty/ZaOqw2iiwZbNpQLRXdhBBRTeYuAfJzIEQ0k8BQiBi0cXmF/BEVQsQcmbsEyM+BENFKis8IIYQQQgghRIKTwFAIIYQQQgghEpwEhkIIIYQQQgiR4BJqj+Edd9xh9BCEEN7phx56SBk9iGgj85YQUU3mLS9k7hIianmdt2TFUAghhBBCCCESnNJaGz2GqKOU2qe1PsfocRhN3gcneR+c5H2IbvL/x0neByd5H5zkfYhu8v/HSd4HJ3kfnIx8H2TFUAghhBBCCCESnASGQgghhBBCCJHgJDD07BGjBxAl5H1wkvfBSd6H6Cb/f5zkfXCS98FJ3ofoJv9/nOR9cJL3wcmw90H2GAohhBBCCCFEgpMVQyGEEEIIIYRIcBIYCiGEEEIIIUSCk8AQUEolKaXuVkodVkoNK6UalVL3K6WyjB5bJCmltJevAaPHFg5KqS8ppZ5USh13fZ8NM5xfrZT6g1KqRyk1qJTaqZTaGKHhho0/74NS6uvT/Jx8LoLDTngyb70jkeYumbfeIXNXbJK5yymR5i2Qucst2uet5HA8aQx6APgs8HvgfmCJ699nKqXepbV2GDm4CNvJ6Ztex4wYSAR8G+gGXgdypztRKTUfeBkYB74HWIFPAn9TSl2mtX4+vEMNK5/fhwnuBjqnHHsthGMSM5N5a7JEmbtk3nqHzF2xSeaudyTKvAUyd7lF9byV8IGhUmop8Bngaa31ByccrwceBD4M/Mag4RnhuNb6V0YPIkLma62PAyilDgDZ05z7HZy/wGdrrfe7HvML4C3gp0qpxTp2Kzn58z64/UFr3RDWUQmvZN7yKFHmLpm33iFzV4yRues0iTJvgcxdblE9b0kqKdwAKGDzlOP/AwwBN0Z6QEZTSqUqpXz5QY1p7l/MmbjSW94H7HBPUK7HDwA/AxYB54ZjjJHg6/swlVLKrJRK+ItLBpF5y4NEmLtk3nqHzF0xSeauKRJh3gKZu9yifd6SwND5w+UA9kw8qLUeBvYTwz98AboW5+Tcr5RqV0r9WCllMXpQBlsBpAG7Pdz3ius20X5O3sSZ2jGslHpZKXWZ0QNKMDJvnU7mrslk3vJM5i5jydw1mcxbp5O563QRm7fkihmUA51a6xEP9zUD5yulUrXWoxEelxH2AE8CdYAZuBz4NHCxUup819WaRFTuum32cJ/7WEWExmK0Xpz7IV4GeoBq4C5gq1Lq41rrRw0bWWKReWsymbtOJ/PWZL3I3BUNZO56h8xbnsnc9Y5eIjxvSWAImYCnCQpgeMI5cT9Jaa3Pm3LoF0qpN4FvAXe6bhNRpuvW08/J8JRz4prWevPUY0qp/wMOAA8opZ5K4D9mkSTz1gQyd3kk89YEMndFDZm7XGTe8krmLhcj5i1JJXUu4ad5uS99wjmJ6vs4J+grjB6Igdz//z39nCT8z4jWugt4GOdG8fONHU3CkHlrZok+d8m8NQOZuwwhc9f0En3eApm7phXueUsCQzgJFCqlPP0AVuBMeYj7K1feaK3HcL1HRo/FQCddt55SF9zHPKU8JJIG120i/5xEksxbM5C5S+YtHzW4bhP15yTSZO6ahsxbgMxdvmhw3Yb850QCQ9iL831YPfGgUiodWAXsM2BMUcP1PswC2owei4FqcKY0rPVw3xrXbUL/nAALXbeJ/HMSSTJvzUDmLpm3fCRzV2TJ3DUNmbcAmbt8EbZ5SwJDeBzQODdzTvRJnDnMv470gIyglCrwctd9OPeiPhPB4UQVV/72M8B6pdRK93FXeelbgaNMqbAWj5RSyZ6qpSmlKoHbgS6cG6RF+Mm85SJzl2cyb71D5q6oInMXMm9NR+YuJ6PmrYQvPqO1rlFK/RT4tFLqaeDPwBLgs8CLJE6j1XuVUmuA7cDbOBtuXg5sAF4Ffmzg2MJCKXUTMMf1zyIgVSl1r+vfJ7TWv5xw+peAS4DnlFIPAH04/5BVAFfEcKNVf96HbKBeKfUH4BDvVMi61XXfDVprW8QGnsBk3pokoeYumbfeIXNX7JG565SEmrdA5i63qJ+3tNYJ/wWYgH8DanEuXzcDPwSyjR5bBN+D9wN/c33vw8Agzp5C/wGkGz2+MH3PO3BeufT0tcPD+UuAP+IsHzwE7ALeZfT3Ean3AedG8J/hTPPoAcaAFuApYLXR30eifcm8dep9SKi5S+Yt/98Lmbui60vmrsSbt1zfs8xdfrwPRs1byvXiQgghhBBCCCESlOwxFEIIIYQQQogEJ4GhEEIIIYQQQiQ4CQyFEEIIIYQQIsFJYCiEEEIIIYQQCU4CQyGEEEIIIYRIcBIYCiGEEEIIIUSCk8BQCCGEEEIIIRKcBIZCCCGEEEIIkeAkMBRCCCGEEEKIBCeBoRBCCCGEEEIkOAkMhRBCCCGEECLBSWAohBBCCCGEEAlOAkMhhBBCCCGESHASGAohhBBCCCFEgpPAUAghhBBCCCESXEIEhnfccYe+4447tNHjEEIIX8m8JYSIRTJ3CRG7ko0eQITJRCVE9FJGDyBKybwlRPSSecs7mbuEiE5e562EWDEUQgghhBBCCOGdBIZCCCGEEEIIkeAkMBRCCCGEEEKIBCeBoRBCCCGEEEIkuEQrPjPJ2NgYTU1NDA8PB/wcw6PjDAyPY3doTEmK7PRk0lMj/7amp6cza9YsUlJSIv7aQsQjpdSXgLOAs4Eq4ITWeq6fz7EDuNjL3edqrff5O65QzFvRTuYzMdG2mma2bK+lw2qjyJLBpg3VbFxeYfSwhJ8SYe6aicxtnsnvePRI6MCwqamJnJwc5s6di1L+FxbrGxqlzWojS79TeEspRYklA3NmaiiHOi2tNV1dXTQ1NVFVVRWx1xUizn0b6AZeB3KDeJ5O4G4Px48H8mTBzlvRTuYzMdG2mmY2b61hZMwOQLvVxuatNQDywTHGxPvcNROZ2zyT3/HoktCppMPDwxQUFAQ8QXX2D6P15GrMWms6+yN7NUwpRUFBQUJfhRMiDOZrrQu01u8GTgbxPINa6195+OoO5MmCnbeincxnYqIt22tPfWB0Gxmzs2V7rUEjEoGK97lrJjK3eSa/49EloQNDIKgJatzu8Ot4OCXqRCtEuGitA1rR80QplaSUMqsQ/aLG++97vH9/wncdVptfx0V0S/Tf7UT//j2R3/HokvCBYTCSTZ7fPm/HhRAJqQIYAKzAgFLqaaXUYoPHJERUGx4d54mXj3ltw1xkyYjsgETC2LlzJ0uXLmXVqlXYbBKchJu332X5HTeGRDBBKMxJP+3qj1KKwpx0g0YkhIgy9cD3gE3AdcBDwGXAq0qp5UYOLNzsdvvMJwkxxfDoOE/uPsYtP9nB0RYrmzZUk5ZimnROWnISmzZUGzRCEQ+01jgcnrO7fv3rX/O5z32O/fv3k5ERXHAi8+DMPP6Op5jkd9wgEhgGwZyZSsmEKxrJpiS/Cs9873vf48EHHwTg7rvvZuPGjQC88MILZGZmcs899wDwox/9iHnz5gFw7Ngx1q1bF8pvQwgRJlrrTVrrL2utH9daP6W1/jzwHiAb+KGnxyilblNK+V2tNJIaGhpYvHgxH/vYx1ixYgXXXnstQ0NDzJ07l2984xusW7eOJ598kueee461a9dy1llncd111zEwMADAF7/4Rc444wxWrFjB5z73OYO/GxENhsfs/O6V42z66Q5qm3v57o3n8eUPnsX1FyzgriuWU2zJQAHpKSaqK3KlKIXwW0NDA0uWLOGOO+7grLPO4hOf+ATnnHMOS5cu5Wtf+xoAP/vZz3jiiSf4xje+wUc/+lF27NjBRRddxAc+8AHOOOMM/vVf//VUQOltfps6D4rpbVxewWcvX3oqOSDFlMRdVyyX33GDJHRV0lDIyUihzarISkumPD/Lr8dedNFF3H///Xz2s59l3759jIyMMDY2xq5du/j3f/93nn32WcCZ1lBQUEBzczO7du3iwgsvDMe3IoSIAK31TqXUP4ANSqkMrbVtyv2PAI/ccccd2vMzRIfa2lr+93//lwsuuICPf/zjPPTQQ4CzHPuuXbvo7Ozkmmuu4fnnnycrK4v/+q//4oc//CGf/vSn+f3vf8/hw4dRStHb22vsNyIiampZ+hsvWsjgyDhPvnyMJbPy+NZHVjOvxDzpMRuXV5z6kDg4MsYdj+xkd20ba6tLjPgWRAyrra1ly5YtPPTQQ3R3d5Ofn4/dbueSSy7hzTff5NZbb2XXrl1ceeWVXHvttezYsYM9e/Zw8OBB5syZw3vf+16efvpp1q9fzze/+c3T5revfvWrwDvzoPDNnCIzlYXZ/PST6/jwD5/nzKpCo4eUsCQwnODS+7aG/Dn/9pUrvN539tln89prr9Hf309aWhpnnXUW+/btY+fOnTz44IP89re/pb+/n8bGRj7ykY/wj3/8g507d3LNNdeEfJxCiIhqANYDeUBQm1giPW+5VVZWcsEFFwBw4403nsp+uP766wF45ZVXOHjw4KlzRkdHWbt2LWazmfT0dG699VauuOIKrrzyypCPX0QnT2Xpf/jMmywsNfPNG85lfqllxufISkvhC1ev4ptPvc7iilzystPCPWwRJkbMXXPmzGHNmjUAPPHEEzzyyCOMj4/T0tLCwYMHWbFixWmPWb169amsrRtuuIFdu3aRnp7ucX5zc8+DwjcHG7tZWplHarKJcxcUs/tIG5efNdvoYSUkCQwn8OXD0FSj43Ya2vtJTTYxtzjHr8empKQwd+5ctmzZwvnnn8+KFSvYvn07x44dY8mSJaxdu5YtW7ZQXV3NhRdeyP/93/+xe/du7r//fr/HKYSIKguBcZx9EoMSyLwVCp72VwNkZTkzJ7TWvPvd7+axxx477bF79uzhhRde4Le//S0/+clP2LZtW/gHLAznqSw9gNU25lNQ6La0Mp/3rJzFD599k29cf45UeoxRRsxd7vmpvr6eH/zgB+zdu5e8vDxuueUWr20kPM11081vE19H+OZgUy9nz3euEl6wuJS/7m+UwNAgsscwSHaHJtmUhN0RWNbXRRddxA9+8AMuuugiLrzwQh5++GFWrVqFUmrSfWeeeSbbt28nLS0Ni8X3P6BCiPBTSpUppRYrpTInHLMopUwezr0CuAD4u9Y6Zhtavf322+zevRuAxx577LS9z2vWrOGll16irq4OgKGhIY4cOcLAwABWq5XLL7+czZs3s3///kgPXRgklGXpb7x4ET0DI/z59beDHZZIQH19fWRlZWGxWGhra+Mvf/mL13P37NlDfX09DoeDxx9/nHXr1nmd30RgDjb1cMasPADOXVDEocYe+m1jBo8qMUlgGCS7Q5OanIRD69Oa3fviwgsvpKWlhbVr11JSUkJ6evqpPYQXXnghjY2NXHTRRZhMJiorK6XwjBARopS6SSl1r1LqXqAIsLj/rZS6acrp3wEOAasnHNsAHFVK/UgpdadS6lNKqZ8DfwI6gbsi8G2EzZIlS/j5z3/OihUr6O7u5vbbb590f1FREY8++ig33HADK1asYM2aNRw+fJj+/n6uvPJKVqxYwcUXX8wDDzxg0HcgIq3AS8XuQMrSp5iS+MLVq/j5jiM0dQ0EOzSRYFauXMmZZ57J0qVL+fjHP34qJdSTtWvX8sUvfpFly5ZRVVXFBz7wAa/zm/BfR5+NkTE7Fa46HRmpyaycW8CrR9sMHlliklTSINkdDpJNSShlx6E1Jj9TWi655BLGxt65KjLxitP8+fMnBZvPPfdc8AMWQvjqE8DFU47d57p9EfjlDI+vBV4DrgRKgBSgCXgY+LbWujl0Q428pKQkHn744UnHGhoaJv1748aN7N2797TH7tmzJ5xDE1FIa01uVio9gyOTMmyCKUs/uzCbGy9ayH/9YT8P3HK+9BAW05o7dy4HDhw49e9HH33U43lTj2dmZvL444+fdp63+W3qPCimd6iplyWz8ial7F6wuJSXDrfyrhWzDBxZYpJZNEh2h8aUpDAlqYDTSYUQ0UdrvV5rrbx8rZ9y7i2u4zsmHDuktb5Oaz1fa52ttU5z/fenYj0oFMJff9vfiEPDXVe+03qi2JIRdFn6q86ZgzkjlV/vPBq6wQohIuatxu5TaaRu5y0qZn99F8Oj4waNKnHJimGQTgWGKvB9hkIIEUumXnkXYjrN3YP87wuH+f7Na5lbnMN7VlaG7LmVUtxz1Qo+9T+7OHdB8WkfMIUIxvr161m/fr3Rw4hrB5t6uO3dZ0w6Zs5IZfGsXPYe6+DCJWUGjSwxyYphkJyBYZKsGAohhBBT2B0Ovv+H/XzkwoV+V+72VUFOOp+5fBnf+8N+hkZkhUGIWDE8ZudExwCLyk4vquhOJxWRlfCBYSAFYyay210rhiZjA8Ngvw8hROyI99/3eP/+Eslvdx0jPTWZ96+eG9bXuWBxKSvm5PP/njsY1tcRwUn03+1E//6nOnqyl7lFOaSlnFbAm/OrS9hb187o+OktbkT4JHRgmJ6eTldXV1C/qHaHY8IeQ0cIR+c7rTVdXV2kp3uu+CaEiB+hmLeimcxn8eNwcy9/2tfA5963kqQI9Br81/cs5Y0TXbwsqwxRKd7nrpnI3Ha6g009LK30nP6dn53OnKIc9td3RXhUiS2h9xjOmjWLpqYmOjo6An6Ozr5h+rJTGR61o4G29JTQDdAP6enpzJol1ZuEiHehmLeincxnsW94dJzv/WE/n3rvMgrNkfkgnJmWzOffv5L7nnydxbNyyc+WD+DRJBHmrpnI3DbZW409vHuayqPrXOmkqxcWR3BUiS2hA8OUlBSqqqqCeo73f/ev/ObuS3jxrRZqm3u5+6olIRqdEEKcLhTzlhDh9sjzh1hckctFZ0S2cMTSynwuO7OS//j1HgZHxuiwDlNkyWDThuqgqp+K4MncJSbSWnOwqYc7r1ju9ZzzF5fy25de4rMOB6akhE5yjBh5l4MwMmbH7tBkpiZjyUzFOjRq9JCEEEIIQ71ypI19xzr41HuXGvL65fmZNLT3024dRgPtVhubt9awrUa6xAgRLZq6BslMTaYgx/vKfmluJkXmdA683RPBkSU2CQyDYB0axZKZilIKc2YqfTYJDIUQQiSu3sERfrS1hs+/fxVZBm2t+MWLR5m6i21kzM6W7bWGjEcIcbqDTT0s8aG9jFQnjSwJDIPgDgwBLBkpsmIohBAiYWmteeCZN3n3ilksn51v2Dg6rDa/jgshIm+6wjMTrVtSxkuHW3EkaNGiSJPAMAh9Q6OYXYGhOTOVPgkMhRBCJKi//LORzv5hblq/yNBxFFky/DouhIi8g409nOHDiuHswmwy05I5crI3/IMSEhgGY+KKYU5GCgPD49LkXgghRMJp7hrk0e21fOHqVaSYjP1osWlD9Wl90dKSk9i0odqgEQkhJuqzjdLRZ6OqJMen8y9YXMquQ5JOGgkSGAahd2iU3CxnYGhKSiIrPZmB4TGDRyWEEEJEjt3h4Ht/3M9HL1zAnCLfPuiF08blFdx1xXKKLRkoIJcRVlWapSqpEFHicFMv1eW5PlcavWBxKS/VtiZsD8xISuh2FcGyDo5gzkg99W9LRuqkVUQhhBAiHm2raWbL9lo6rDYy05IpMqdz1blzjR7WKRuXV5wKBHsGRrjnoec40d7HnGKzwSMTQhxs8i2N1G1BqRm7XdPQ3k9VifwOh5OsGAahzzaGJeudIFD2GQohhIh322qa2by1hnarDQ0MjoxzsmeIHQdOGj00j/Ky0/hh9zP87bfPyoqDEFHgYFMPZ/hQeMZNKSXVSSMkJgJDpdQipdQ3lFKvKKU6lFL9Sqn9SqkvK6WyjBqXdXBk0uqgBIZCCCHi3ZbttYyM2ScdGx13RHU7CMv7r+eS49vYebDF6KEIkdDG7Q6OnOz1qVXFROuWlLJLAsOwi4nAEPg4cDdwDPgG8HmgFvgm8LJSypBSY71Do+ROCAwtmSlYpZehEEKIOBaL7SCSzltPRbqD3b//M7bRcaOHI0TCqm/vp9iSQbaffU6XzMrDOjRKc/dgmEYmIHYCw6eAWVrrj2qtf6y1flhrfT3wLWAF8AkjBjWxXQWAOUNWDIUQQsS3mGwHkWQi/bYvYK6ax2O76oweTdgopb6klHpSKXVcKaWVUg0BPMcO12M9fZ0ThmGLBHKwsduv/YVuSUqxtrpE0knDLCYCQ631Pq211cNdj7tul0VyPG5TC81YMlOlyb0QQoi4tmlDNckmNelYWoop+ttBzFvMh9ct4PDLe2nqGjB6NOHybWAjzgyrniCepxO4ycPX8WAHKBLbwaZellbmB/RY2WcYfjERGE5jluu2LdIvbHdoBobHycmYusdQ2lUIIYSIX+uXlWPOSCU3MxUFFFsyuOuK5THRDiKv822+1P8PHvrrW/FaiGa+1rpAa/1uIJhqQINa6195+OoO1UBFYvK3IulEK+cU0Nw9SEdf9Katx7qYbVehlDIBXwXGgd9E+vX7baNkpydjSnrnqqklM1X2GAohhIhru2vbKDSn8+DHL0ApNfMDosmZ55P77G+oePtNdtfO4fzFpUaPKKS01iFb0VNKJQHZQL+O0yhaRFZHn42RMTvl+ZkBPT7ZlMSahSW8XNvG+6OoPU48ieUVw83AGuCrWmuPpdCUUrcppfaF48U99SuUqqRCCCHimdaa3+6q44YLFsReUAiQlIR6/818bPhNHv77wdOqq4pTKoABwAoMKKWeVkotNnhMIsYdbOxhyay8oOYOSScNr5gMDJVS9wGfBh7RWn/H23la60e01mHZKG0dGsWSlTbpmLvBvRBCCBGPXq/vZHjMzprqEqOHEriV55H9uW+yqCyXx186ZvRoolE98D1gE3Ad8BBwGfCqUmq5tweF82K8iA/BpJG6nTWvkLoWK72DIyEalZgo5gJDpdTXgXuBLcC/GjUO6+AolozJpXZlxVAIIUQ8++2uOj58wXySYnG10E0pSEnlnsGXeHbvcVp6howeUVTRWm/SWn9Za/241voprfXngffgTCv94TSPC9vFeBEfDjb1sNSPxvaepKWYOGteEa8ciXh5kYQQU4GhUuprwNeAXwC3GpnzbrWdvmKYlZ7M8JidcbvDoFEJIYQQ4fFWYzftVhvrl5UbPZTgpaWT2XyMu0p6efhvbxk9mqintd4J/APYYFTvaBHbhsfsnOgYYGGZJejnWifppGETM4GhUuqrwNeBXwKbtNaGRl/WwdP3GCYpRU5GCn1SgEYIIUScefylY1x3/nxMSTHz0cE7peD9N7O29nmaO/t59aisPvigATABwS35iIR05GQvVcU5pKWYgn6ucxcWceDtHgaHpRNAqMXE7K6U+hTwn8DbwPPAR5RSN074enekx+Sp+Ay4m9zLD6oQQoj4cay1j6MtVt6zctbMJ8eKM85Elc/mznPy+O+/HWR0XArRzGAhzkrw0rJC+O1gY/D7C92y0lJYPiefPXXtIXk+8Y6YCAyBc123s4Gf41w1nPj15UgPyFtgKE3uhRBCxJsnXj7GNWuqSE0O/mp/1FAKPvVVlp+3iqqibJ7anVi925VSZUqpxUqpzAnHLK52YFPPvQK4APi71no4kuMU8SEUhWcmkuqk4RETfQy11rcAtxg8jEm8rhhKARohhBBxpLlrkH/Wd3LnFV4LUsa2J3/GXXmlfOylLl6oaeZk9yCVhdncsG4BG5ZVGD06vyilbgLmuP5ZBKQqpe51/fuE1vqXE07/DvAxYAOww3VsA/BDpdQzwHGcK4SrgRuBTuCucI5fxCetNQebekI6h6xZVMLDzx1keMxOegjSU4VTTASG0WjaFUPZYyhEzFNKfQk4CzgbqML5oWpuAM9zOc5KyiuBEeAF4Ata6/rQjVaI8Hli9zGuOmcOmWlx+pFh1Vpe/8UTZBVezGcuX8ayynwONHbzwDNvAsRacPgJ4OIpx+5z3b6IM8tqOrXAa8CVQAmQAjQBDwPf1lo3h26oIlE0dQ2SmZZMQU56yJ7ztWMdjI07eP93/0qxJYNNG6rZuDymflejUpzO8uFnHRrB7HGPYYqsGAoRH76Ncy/N60BuIE+glLoGeAp4A/g8YMF5xf0lpdQ5WuuTIRmpEGHS0Wdj16FWtnxqvdFDCZ9Fy3jMfIzPX72SVXMLAVg1t5C7r1rBQ399K6YCQ631ej/OvYUp2Vha60M4excKETKhTiPdVtPM5q01jLm6ALRbbWzeWgMgwWGQYmWPYVTRWtM3NEZuluwxFCKOzddaF2it3w34HcAppVKAHwONwIVa64e01t8BLsV5Jf7roRysEOHwu1fquXTVLI8XQuNJ42gyyyrzJx1bVplPY+eAQSMSIn6EsvAMwJbttYyMTS4WNTJmZ8v22pC9RqKSwDAAQ6PjJJuUx034ssdQiPigtQ62EsXFQDnwM631qU+XWuv9OPfzXO8KHoWISr2DI/z9jSY+uGae0UMJu8rCbJr+8gx84Wa49TL4ws00/eUZKguzjR6aEDEv1CuGHVabX8eF7yQwDICnHoZuzj2G0q5CCHGqmvJuD/e9ApiBRZEbjhD++eOeBi46oyyk+4Ki1Z2FHZT98X+gux3Q0N1O2R//hzsLO4wemhAxrc82SmffMFUlOSF7ziJLhsfjBeb4n6vCTQLDAPTZRr2m1ciKoRDCpdx166lYg/uYbIYQUWlwZIxnXzvBh86fb/RQImLpnj+RpscnHUvT4yzd8yeDRiREfDjc1MuiCgumpNCFHJs2VJM2pRKpKUmRnmw6LcVU+EcCwwD0Do6S623FMEMCQyEEAO7eYCMe7huecs4pSqnblFL7wjYqIXzw7L63OWd+EWV5p/2IxqduLyuD3o4LIXzyVmN3SNNIwVlg5q4rllNsyUABxZYM7nnfChaUWfju7/+J3eEI6eslEqlKGgBnq4o0j/eZpfiMEMJpyHXrabJIn3LOKVrrR4BH7rjjDh2ugQkxnZExO3/YU893Pnqe0UOJnPwiVxqph+NCxIFtNc1s2V5Lh9VGUQTbOxxs6glL5sHG5RWnjf/iM8r56m/38uCfD3DXFctRSoX8deOdrBgGwDo0ijnTc82IjFQTdoeWpWwhhLuSqae/vO5j0hNMRJ2/7W+kujyXucWh2xMU9a65BVKnXMNJTXMeFyLGuds7tFttaN5p77CtJrx/gsbtDo62WFkS4hVDb1JMSXzl2rM53trHz3ccichrxhsJDAPQN82KoVIKc2YKfdLkXohEt9d1u9bDfWuAPkD+comoMm538OTu43x4XWLsLTxlzUa4+U7ILwaU8/bmO53HhYhxRrV3qG/vp8SSSXZ65ApwZ6Ylc98N57LzYAt/3FMfsdeNF5JKGoDeodFpS1ibXfsMi8yeqyYJIeKLUqoMZ/P6t7XW7vTQF4EW4Fal1APulhVKqZXAemCL1lpKGIuosv3AScrzM1lcEZkr/FFlzUYJBEVcMqq9w8HGbs6ojPxckpuVxrc+upp/+/luLFlprF9aPvODBCCBYUCcewy9N/t1NrmXz3tCxDKl1E3AHNc/i4BUpdS9rn+f0Fr/csLp3wE+BmzA2aMQrfWYUupO4HFgp1Lqf3C2qLgb6AC+FvZvQgg/2B2a375Ux2cuW2b0UIQQIVRkyaDdQxDore1DqLzV2MO5C4rD+hrelOZm8s0Pn8sXf/0qORkpnD1P9gv7QgLDAPQNeW9XAdKyQog48QmcTeonus91+yLwS2agtX5SKWUD7gV+gLNC6QvAv2utZX+hiAruohTtVhvJJkXXwPDMDxIiRhhVdCWabNpQzQ+feZMx+zvVOtNSTGzaUB3W1z3Y1MPH1of3NaZTVWLm3mvP5r4nX+ObN5zLovJcw8YSK2SPYQB8WjGUPYZCxDSt9XqttfLytX7Kube4ju/w8DzPaq3XaK0ztdZ5WutrtdbHIvV9CDGdiUUpAMbtmh9tPRD2ohRCRIJRRVeizcblFVy6soLUZOfH/iJzOnddsTysAXK71cbouIPyfGNb3iyfnc/dV67ga4/vo6lrwNCxxAJZMQyAdZo+hvDOHkMhhBAimk1XlCLRVlVE/JGf73cUmDP4wOoqXq/v5F/fcwbLZueH9fUONfVwxqy8qGgZsba6BOvQCP/xmz18cE0VT+2uT+gV5OnIiqGfRsftjI7byUzzHlNbMlOkl6EQQoioZ1RRCiEiQX6+39HaO0RJbgbLZudz4O3usL/ewaYeQwrPePPeM2dTXWbhv/96MOFXkKcjgaGf+obGMGemTnsFRPYYCiGEiAXeik+EuyiFEJFQaE73eDwRf77bem2U5mayrDKPtxojEBg2OlcMo8nhk73oKcci0bYjlkhg6Cfr0Mi0+wvBGRjKHkMhhBDR7mPrFzH1MmckilIIEQlnzStk6nX8RP35dq8YLq3M52BTD3bH1BApNLbVNHPjj17gSIuV7/7+n1G1Gtdh9VxYKxFXkL2RwNBPvTMUngGwZKTSJ+0qhBBCRLkkpSjLy6DYko4Cii0ZYS9KIUQkjNsd7G/o4iPrFpCXlQYk7s+33eGgs2+YYksGedlp5Gam0dDeH/LXcRf76ehzBmAdfcNRlaopGRIzk+IzfurzITCUVFIhhBDRbtzu4Jf/OMKdV6xgVVWh0cMRIqSef7OJsrxMbl5fzYfXLeDa7z/H/95xManJJqOHFnGdfcNYslJPfe/LZufzVmM380vNIX2daC/2s2lDNZu31kwaY6KuIHsjK4Z+sg6NYsmaYcUwMxXr0Chah2eZXgghhAjW399soticIUGhiDvjdgeP7arjxosWAZCabKI8Pyssq2SxoM3q3F/oFq4CNNFe7Gfj8gruumI5Ra69p5Fo2xFrJDD0k3VwFEtm2rTnpKWYMCUphqdcNRFCCCGiwei4nV//4ygfkyvlIg69UNNMSW4myye0ZFhQZqGutc/AURmntXeIkgnpkksr8zjQ2B3yBYxYSNXcuLyCX915CfNLzPzHB8+SoHAKCQz95NxjmDLjee5VQyGEECLa/OX1t6kqMUdd1UAhgmV3uFcLF046vrDUzNEWq0GjMpa7IqlbWV4mWkNrb2hX8jZtqCbZNLnaT7SmakaqbUeskcDQT849htOvGILsMxRCCBGdhsfs/PalY3zs4kVGD0WIkNtWc5Iiczor5hRMOu5cMUzMwNBdkdRNKRWWwGjj8gqqyy3kpKdEfTGrZbPzeUsCw9NI8Rk/WX0oPgOulhUSGAohhIgyf9rbwNLKPBaUWYweihAhZXc4+M2uo9x5xfLT7ptXYuZEez/jdgfJpsRaF2nrtfHuFZmTji2bnc+Bxm7evXJWyF7HoTXN3UP85NZ1lOZlzvwAAy2tzOPBP9fg0JqkaXqTJ5rE+s0IAV8DQ0tGiqwY2g71/wAAuhhJREFUCiGEiCqDw2M8tfs4N8tqoYhD2w+cJD87nZVTVgsBMlKTKbZk8HbngAEjM5ZzxXBKYFiZF/IVw7oWKznpKVEfFAIU5KSTnZ7C2x2J9/MwHQkM/eTXiqFNehkKIYSIHr9/tZ5z5hcxuyjH6KEIEVJ2h+axnc69hcrLClAippOO2R30DIycqsTpNrfYTM/ACL2DIyF7rT11HZy7sDhkzxduy2bnUyPppJNIYOgHh9YMDI9h9rH4jKwYCiGEiBZ9Q6P8cW/DaUU5hIgHL751EktWKqvmnr5a6Lag1EJdS2JVJu3sGyY/J/209FlTkuKMyjzeauwJ2Wvtq2vn3PmxExgud/VzFO+QwNAP/bYxMtOSMSXN/LbJHkMhhBDR5Mndx1m3pIzy/CyjhyJESNkdml/vPMpHp1ktBFhQak64FcPW3iFKcz23i1hWGboCNNahUU50DrBsduxUOl4ahnTaWCeBoR+sQ6NYMmZOIwWwZMiKoRBCiOjQPTDMn19/m49cuMDooQgRcv84eJKcjBTOqiqc9rz5pRaOt/XhCHH/vmjW1jtEicXznr+lIaxM+tqxDlbOKSA12RSS54uEivwsxuwO2nqHjB5K1JDA0A/WoVEsWb4FhubMVPpsEhgKIYQw3m93HeNdKyooMkdPo2khQsHu0PxmZx03XrRo2tVCgJyMFCyZqTR3DUZodMZr7bV5XTGsLrfwducAttHxoF9nb1075y4oCvp5IkkpxbLK/JCm08Y6CQz9YB0c8anwDEiDeyGEENGh3WrjhZpmPnyBrBaK+LPzUAuZacmcPW/61UK3BaWJVYCmzUNFUrfUZBPzS80cauoN6jXsDs1rxzs5d0Hs7C90kwI0k0lg6Afr0ChmHwNDc2YKfUNSlVQIIYSxfr3zKFecNZu87DSjhyJESDm05jc7j05biXQqZ2XSxClAM92KIYRmn+HRll5ys1IptsReRsKyEKbTxgMJDP3ga6sKAHOGM5VUJ1AeuxBCiOjS3D3Iy4dbufb8eUYPRYiQ23WolbQUE+fM9z2FcUGpmbqWBFoxtHpfMYR3Gt0HY29dB6tjcLUQYF5JDp39w1IXxEUCQz9Yh0bJ9TEwTDYlkZ5iYnAk+LxtIYQQIhC/evEIV6+uwuxj4TQR25RSX1JKPamUOq6U0kqphgCf53Kl1MtKqUGlVLfrOatCPNygOLTm1//wb7UQ3kklTYQL96PjdvqGxijISfd6zhmVeRw52cu43RHw6+ypa+ecGNtf6GZKSmJJRa7sM3SRwNAP/qwYgrSsEEIIYZyG9n5er+/k6vPmGj0UETnfBjYCx4CAPukqpa4BngUygM8D3wcuAl5SSpWHaJxBe+lwK6nJSX6vVOVlp5Gekkxbry1MI4se7VYbheZ0TEneA+fs9BTK8rIC3nfZOzhCc9cgSyvzAx2m4UKxahovJDD0Q58fewxBmtwLIYQwzi9ePMK1a+eRlZZi9FBE5MzXWhdord8NnPT3wUqpFODHQCNwodb6Ia31d4BLgRLg66EcbKDcq4Uz9S30ZkGpmaMJUICmtddGiQ/7/pZW5gVcgGXfsQ5WzS0gxRS7IcXSEPZzjHWx+3/RANahUXKzfN+8LyuGQgghImlbTTM3PbiNS+/byu7aVkkhTTBa6+NBPsXFQDnwM631wITn3Q/sAK53BY+G2l3bhilJcd7CwPa1zS+1JMQ+w7beIUqn2V/o5izAElgq5d66Ds4N8P9DtFhckUt9ez/DIWjbEeskMPRDr5+ppJYM6WUohBAiMrbVNLN5aw3tVmeKnEPDT//6Fttqmg0emYgh57pud3u47xXADCyK3HDe4b7o8d77tvLt373Oijn5Aa0WAiwoM3OsLf4rk7b22iiZpiKp2/LZ+Rxs7Mbh575LZ5uKDr+K/0SjtBQT80pyOHyy1+ihGE4CQx9prbEO+pdKas5MkRVDIYQQEbFley0jY/ZJx0bG7GzZXmvQiEQMcu8h9HQ1wX2sIkJjOWXiRQ8NjDs0W19/O+CLHgtKLRxtif8CNL6uGBbkpJOVnkJj58CM505Ue7KXwpx0isyx16ZiKmfbDilAI4Ghj4bH7CQlKdJTTD4/xrnHUHoZCiGECL8Oq+diGt6OC+GBO4oY8XDf8JRzJlFK3aaU2heOQXm+6OEI+KJHkTkdraF7wNO3GT98XTEE5z5Df/fZ7T3aHpNN7T2RfoZOEhj6yDroe6sKN7MUnxEiZimlkpRSdyulDiulhpVSjUqp+5VSWT4+foerXLynr3PCPX6ReIq8FJnwdlwID4Zct54KKqRPOWcSrfUjWuuwzG2hvuihlHIWoInzfYZtVt9WDCGwwGjvsQ5Wx2ibiqnOqMyjtrkXuyPwth3xIGYCw1D15glUr58VScG5x1BSSYWIWQ8APwQOAp8BngQ+CzyjlPJ17uwEbvLwFWyBCCFOs2lD9WmVAdNSTGzaUG3QiEQMclcy9ZQu6j4W8U2r4bjo4exnGL/7DIdHxxkcHicv27eiicsq8/3q5dc9MExLzyBLZuUFOsSoYs5IpdiSwbE4/pnwRbLRA/DDt4Fu4HUgN9Iv3udn4RlwrRhK8RkhYo5SainOYPBprfUHJxyvBx4EPgz8xoenGtRa/yo8oxRiso3LK3jujUaOnLQyNDJOkSWDTRuq2bg84lvCROza67pdCzw/5b41QB9wJKIjwnnRY/PWmknppMFe9FhQZonrwkxtVmeriiQfC/TMKshieMxOu9VGsQ8B975jHZxZVUhyDLepmGrpbGc67aLyXKOHYphY+r8ZVG+eYPnb3B6kXYUQMewGQAGbpxz/H5xpVDf6+kSulFSzCrR8nhA+0lrT2DnI5k3n89evXMEvP7tRgkLhlVKqTCm1WCk1MdfwRaAFuFUplT3h3JXAeuBJrXXEiydsXF7BXVcsP1XnodiSwV1XLA/q53tBqTngpu6xoLV3yOf9heBMr13mxz7DvXUdcbO/0G2Z9DOMncAwBL15gtI7NIIly89UUtljKESsOhdwAHsmHtRaDwP7eaek+0wqgAHACgwopZ5WSi0O4TiFOOVoi5X0VBOzi3KMHoowiFLqJqXUvUqpe4EiwOL+t1Lqpimnfwc4BKx2H3AFfXcClcBOpdQdSqkvAs8BHcDXIvKNeLBxeQWzi7J5YNP5IbnoUZqXyeDIOL2D8VmApq3XRomP+wvdls3O50DjzIGR3eHg9eOdMd+mYirn998T99VqpxNLqaSGsg6OYvGzUXB2egqDI+PYHRpTkiwWCBFDyoFOrbWnTwzNwPlKqVSt9XRXfuqBl4A3ATtwHvBp4BKl1DqtdU2oBy0S20uHWzm/utToYQhjfQJnk/qJ7nPdvgj8cqYn0Fo/qZSyAfcCP8BZofQF4N+11oblXmqtaeoaZFa+T/W/ZpTkKkBzrLWPs+MswAHnimGpHyuG4AyM/ra/acbzDjX1UmLJoCAnfcZzY0mxJYP0FBNNXYNUFmbP/IA4FDMrhkbrs436vWJoSlJkpSUzMCwtK4SIMZl4LtcOM5Rsd9Nab9Jaf1lr/bjW+imt9eeB9wDZOIvaeBTOku8ivr1c28b51SVGD0MYSGu9XmutvHytn3LuLa7jOzw8z7Na6zVa60ytdZ7W+lqt9bFIfR+e9AyOkJyk/C4EOJ14LkATyIrh/FIz7VbbjPUx9tS1c26cVCOdamllnk+rpvEqrgPDUH7Asg76v8cQZJ+hEDFqCM/l2mGGku3T0VrvBP4BbFBKebyUG86S7yJ+NXUNMDA8RnVFrtFDESIsmroGmVUQ2lWceN5nGMiKoSkpieqKXA7OUJ10XxzuL3RL9H6GcR0YhvIDViDFZ0D2GQoRo04ChUopT8FhBc4000B/sRsAExAfNb5FVNhd28ba6hKfKxAKEWucgWFo0kjdFpRZ4jYwbLPafO5hONFMgVFX/zBtVhtLZuUGMbrotWy2f2074k1cB4ahZLUFuGKYIYGhEDFoL875cfXEg0qpdGAVEEwmwkJgHGf7HSFC4qVa2V8o4ltT10DIVwxnFWTT3T/CYJxt+RkcGWN03BHQ59Zls6dPpdx3rIOz5hViSorPEGJ2YTaDw2N09Q/PfHIcis//q2HgTCX1rUnoRJbMVKzSy1CIWPM4oIG7phz/JM69hb92H/BU8l0pZVFKmaY+qVLqCuAC4O+uCqdCBK2rf5jGzgFWzi0weihChE04VgxNSYqqkhyOtcXXPsO2XmcPw0C6JC2uyON4W/+knpET7Tnazuo4TSMFZ9uOMyrzqUnQdFIJDH0wZncwPGYnO93/Iq5mSSUVIua4Kob+FLjG1WLiVqXU/TiLxrzI5Ob2p5V8BzYAR5VSP1JK3amU+pRS6ufAn4BOTg84hQjYq0fbOWd+MSlx1GhaiKnCERiCqwBNS3ylkwayv9AtPcVEVXEOh5t7T7tv3O5gf0P8tamYatls3/s5xpuYaVfh6r8zx/XPIiDV1acH4ITWesYSzIHqGxrFnJEa0JUXc2YKPQPx2SNHiDh3F879gLcBV+AM6H4MfFVr7ZjhsbXAa8CVQAmQAjQBDwPfNrLku4g/L9e28u4Vs4wehhBhM2Z30G61UZbn/565mSwss/BGQ1fIn9dIgVQknci5z677tCyEg009lOVlkZftfwZdLFk+O59tNQeMHoYhYiYwJAS9eQIVaOEZcKaSnmgfCPGIhBDhprW2A/e7vqY77xbglinHDgHXhWtsQrgNjozx1ts9fOmaM40eihBh09ozRKE5ndTk0zL0gza/xMxTu4+H/HmN1No7REmAK4YAyyrzeWZfw2nH99Z1cG6crxaCcxW5pWeQgeExstNTjB5ORMVM3ok/vXlCzTrkfw9DN3OG7DEUQggRHnvrOlg6O4+stMT68CISS1PXIJVhSCMFmFOcQ1vvEMNe9tTForbewCqSui2tzONQcy92x+TkmL117Zy7MH73F7olm5JYVD5z2454FDOBoZGsg85U0kBIuwohhBDhsru2TaqRirjX1DVARYgrkrqlmJKoLMymPo4K0Dj3GAYeGJozUynMSed4W/+pYx19Nrr6h6kuzw3BCKPfssrE7GcogaEPrLZRcgNdMZQG90IIIcJgdNzOvmPtrFkU/1fwRWILV+EZt3jqZ6i1ps1qCyqVFE7vZ7i3roOz5hVhSkqMXqnLZudP27YjXklg6ANnqwpZMRRCCBE93mjoYnZhDvnZ6UYPRYiwauwaCG9gWGqhriU+Vgz7h8dAQ06Qe+OWVU6uzLmvrp3VC+J/f6Hbklm5HGvtY3Q8flKMfRFLxWcMYx0aYU5RTkCPzUpLZnjMzrjdQXKMlBLfVtPMlu21dFhtFFky2LShmo3LK4weVsTJ+yCEiGYv17ZxfnWJ0cMQIuyauwepDFMqKcDCMjN//efbYXv+SHJWJA2sh+FEy2bn88jzh9BaM+7Q7G/o4rNXLA/RKKNfRmoyswuzqT1pZfnsfKOHEzGxEakYzDo0hjnAFUOlFOaMVPpipADNtppmNm+tod1qQwPtVhubt9awrSaxquvL+yCEiGYOrXnliOwvFPFvYHiMkTE7+WFskVBVbKaxcyAuVoecFUmDb+tRkptJiimJ5u5B3mrsZlZBNrlZ8d2mYqqp6bSJQAJDH1iHRsgNMDAEZy/DvqGxEI4ofLZsr2VkSmWukTE7W7bXGjQiY8j7IISIZrXNveRkpFARxvQ6IaJBU9cAFflZQa+ATSctxURZXhYnOmK/vZizImlw+wvdnP0Me5xtKhIojdTN3c8xkUhg6INg+hiCc59hrBSg6bDa/Doer+R9EEJEs5cOt8pqoUgIjZ2DzApjGqnb/FJzXBSgabOGZsUQYGllPjVvdzvbVCxIvCJXSyvzONjYg92hjR5KxEhg6APr0GjAqaTg7GUYKwVoiiyerzJ5Ox6v5H0QQkQrrbXsLxQJo6lrIGw9DCdaUGahriX2A8PWEK4YDo2M8fybTZzoGOBbv3st4bbT5GalkZedRkN7ZAoTbatp5qYHt/He+7Zy04PbDHm/JTCcgUNr+m1jQa0YmjNjp8n9pg3VJJsmp2ukpZjYtKHaoBEZY9OGalLkfRBCRKHGzgFGxu0sLLMYPRQhwq65OzIrhgtLzdS1xn5l0rbeIUoswa8Ybqtp5tc7j6Jdi2Xt1uGErLXgbFsR/kb30VLbQgLDGQwMj5GRagqqomgstazYuLyCVXMLyExzFqwtMqdz1xXLE64a58blFaxeUExGqglI3PdBCBF9XnKtFoZzz5UQ0SLcPQzd5pWaqW/vx+5whP21wkVrHbIVQ2ethcnvRSLWWohUo/toqW0hgeEMrIPBpZFC7DW5H7NrvvzBs5hXYubea89O2GDIruGeq1YyqyCLb3z43IR9H4QQ0eXlWtlfKBKDQ2tOdg9GpMhSVloKhTnpNHYOhv21wsU6NEqKKYmsIHsYgtRacHNXJtU6vPsMo+X9lsBwBn224ArPAFgyUmJmxVBrzbHWPhaUmqkut1B7stfoIRnmWKuVBaVmFpRaOBYH6SVCiNjX0WejpWcoofpqicTVbrWRk5FKRmpk2m4viPECNKHcXyi1FpxKczNQClp6hsL2GnaHJi3F5PG+IneHkDAHpm4SGM7AOjiKJTO4vi3OPYax0a6izWojLSWJ3Kw0FpXnciRBA0Pr0CiDI+OU5mXG/B8KIUT8eOVIG6sXFAe1vUGIWBGpNFK3BWWWmN5n2BaiHobgrLUwNVhJxFoLSimWVTrbdoTD6Lid7zz9OsWWdNKSJ8/raSkmNl22Cg7sg833QldbWMYwkfxlmUHv0GhQPQwhtvYY1rVYmV/qLGhQXZ5LbXOvsQMySF2rlfklZpKUYn5pbP+hEELEj5cOSzVSkTiauwYiGxiWxnZl0tZeGyUhWjHcuLyCu65YTrElAwUUWzISttbC0jA1uh8aGecrj+1Fa/jpJy/kritXUOxakU0xJb3zfi9eBdXL4b7PwPN/AId92ucNRmTW5mNYsK0qwLliGCuB4bHWPhaUmAGYW5xNR98wg8NjIclXn2pbTTNbttfSYbVRZMlg04bqqJlwjrX2Mb/U+T4sKDVzvK0Ph9YkSbEHIYRB+m1j1Db38vUPnW30UISIiMauyFQkdVtQauZYa+z+vW+zDjGnKCdkz7dxeUXUfC4z0tDIOH9/s4m/7W8M2efV3sER7n1sLwvLLHz6smWYktSp93twZIyPbn6BdUtce8mTk+HyD8NZF8ALf3IeGxuFlODiE09kxXAGfUE2t4fYanBf19bHAlcJdFNSEvNLzRwNw9WzaCnL641zn6XzfTBnppKVlhzW/HIhhJjJ3rp2VswtID1C+62EMFqkU0nNmalkZ6TQ0h2bf+9be22UJNgewHDbVtPMYzuPYnfokH1ebesd4t8e3c2584v47OXOoHCirLQUKvKzTv/8XVoJH/0UoODbd8Mff+kMEF/ZBl+4GW69zHn7yraAxyaB4QysIQgM01NM2B36tDK00ehYq/XUShm40knDsM8wWsryelM35X2YLwVohBAGc1YjlTRSkTiaugYiumIIzlXDozFaV6Ctd4jSEO0xFE5bttcyMh66th0N7f3c8/PdXHXuHD62odpr2yFnNVQv+xqTkuAzX4e36+BLt8DPH4DudkA7b3/xo4CDQwkMZ9AbgsBQKeXcZxjlTe57B0cYGXNMutoUrn2G0VKW1xPb6DgdVhuzC9/5Y7RQCtAIIQw0MmbnteOdnLew2OihCBERw6PjWIdGT+25ipRY3Wfo0Jp2a+j2GAonb59L2602v7MB32rs5t9/9Qqf2LiYq1dXTXuus+DNNPsa84vg01+H8XEYm1LgcnQEnn7Ur7G5SWA4g76hUSxZwefwxsI+wzrXvrqJVy8WlVuoDcMEGc1lkI+39TGnKGdS1T9ZMRRCGOmf9Z3MLzGTmxVclWwhYkVz9xBleZmnpdmF24IyM8faYu/vfc/ACBmpyRFr7ZEovH0uTUsxsekn2/nPJ/ax61ALo+PTZwXurWvnP594jc+9b6VP+xOXzXZWQnVM16ZCKRjo93xfd8eMr+GJ/PTMIBSppADmzBSsQ9HdssJZkdQ86VhZXiYjY3a6+ocpyEkP2Wtt2lDN5mffnLQ8Hy1lkOsmFJ5xm+9aMdRae132F0KIcNld28b5i6WpvUgcRqSRwjsrhrH29761d0hWC8Ng04ZqNm+tmbT9KS3FxF1XLOe8RcXsOtTKH/c28KOtNVx0RhnvWjGLxRW5bD9w8lSBxZyMFMbtDr710fM4Y1aeT6+bl52GJTOVE+39VJWYvZ+YX+RKI/VwPAASGM7AOjgSdLsKAEtGbKwYrl00OU1JKXVqn+H51aH7ULJxeQUNHf387pV6xu0OstKS+fRly6Ki+tWxVisLy3InHSsyp+NwaLoHRkIaIAshxEzsDs3uI23csG6B0UMRImIiXXjGrSAnnWRTEh19wxFPYw1GW69N9heGgftzqbcq+peuquTSVZW09Q7xQk0zP/jjGwyMjNFvG8PucK729dnGSE1OorVnyOfAEGBpZR4HGrunDwyvucW5p3B05J1jqWnO4wGQwHAaw6PjaDitwWcgnE3uozswPNZm5aaLF552fFG5hSMnrSENDAEUcN3aeSyZlcvvXqmPiqAQnBVJLztz9qRj6lQ/Q6sEhkKIiDrY1ENBTjqlefKhTySOpq4BzpxXaMhrLyg1U9dijanAsLV3SCqShokvbTtKcjP5yIULuWHdAm544IVTQaHb6LiDLdtr/fqsu2x2Pq8f7+Sqc+Z6P2nNRuft048600fzi5xBofu4n2SP4TTcaaShSCWI9ib3gyNjdPePeEzbCFdl0jcaulg1t4Bls/Opbe6dMT87EsbsDho7BzxenXH3NxJCiEiSaqQiETVFuIfhRAtKLTFXmbTNapOLR1FAKUXv4IjH+/wtsLhsdj4HGrvR0+0zBGcQ+L1fwM/+4rwNMCgEWTGcVqj2F4JzxbCxcyAkzxUOx9v6mVuc43GTd3V5LkdO9oY0335oZJz69n6WzMojLcVEVXEOB5t6WDXXmKuDbifa+ynJzSTdwyrxglIL/zjUYsCohBCJaFtNM1u2H6bdOkx+dhoV+VlRk1khRDhprWnqNiaVFGBodIw/v97IYzvrQtbQPNxae4dYJ/uQo0KRJYN2D0GgvwUWy/MycTi0M+iPUJqwrBhOI5SBoSUjupvceyo845aXnUZmWgonQ9jw9cDb3Swqt5xK010xt4A36rtC9vyBOtbWxwIv78P8UjPHYuwKohAiNm2raWbz1hrarcMAdA+MBN1UWcQ/pVSSUupupdRhpdSwUqpRKXW/UsqnCEsptUMppb18nRPu8bv1DI6QYkrCnBGaz2D+2FbTzJ9fb2R03BGyhuaRIHsMo8emDdWnbUMLpMCiUsq5z/DtadpWhJgEhtMI9YphNKeSHmvtY0Gpxev91eWWkKaTvnGia9Lq4Kq5hbxxwvjA0NnY3vP7UFGQhXVolH5bdFeXFULEvi3baydVwYPgmiqLhPEA8EPgIPAZ4Engs8AzSilfP/N1Ajd5+Doe8tF6YVThGXD+7o2GsKF5JNgdms6+YalKGiU2Lq/griuWU2zJQAHFlgzuumJ5QKvO7rYVkSKppNOwDo1iCVHPKEtmSnSvGLZaed+5c7ze795nGKpUiv31ndx+6dJT/z6jMo9jrX3YRscN7cFzrLWPC7wU2UlSiqpiM8farIanvAoh4pu3vSj+7lERiUMptRRnMPi01vqDE47XAw8CHwZ+48NTDWqtfxWeUfqmqWuQinxjAsNY/N3r6h8mJyOF1OTgiyWK0PClYI0vllXms/W1t0MwIt/IiuE0rIMhXjGM0qqko+N2TnYPMrc4x+s5i0JYgKbfNsbJ7iGqK3JPHUtPMbGwzBLR5fKpHFpzvK2PeV5SScGdTioFaBJBsClZrue4XCn1slJqUCnVrZR6UilVFc5xi/jgbS+Kv3tUREK5AWfB781Tjv8PMATc6OsTueY/szKokV+jQT0MITZ/99qkh2Hcqiox09k/HLHFJQkMp2G1hTAwzEilb2hs5spCBjjRMUB5fta0V5oWllk43tbPuN3h9Rxf1ZzoYsmsXFJMk3/8Vs0t4I0G49JJm7sGMWemTrunYWGZRQLDxBFUSpZS6hrgWSAD+DzwfeAi4CWlVHm4Bi3iw6YN1acVAwtkj4pIKOcCDmDPxINa62Fgv+t+X1QAA4AVGFBKPa2UWhzCcc6ouWuQSoNSST3tD0sxqaj+3WuV/YVxy5SkWDIrj7citHAigeE0QrlimJZiwpSksI0a35JhqqPTFJ5xy0xLpjQ3g/r2/qBfb39DFys9pGKuNDgwPNbax4LpmogC80vM1EkBmrg3JSXrGq31/2it7wHuATbgTMma7vEpwI+BRuBCrfVDWuvvAJcCJcDXwzl+Efs2Lq+gxJJObmZq0HtURMIoBzq11p5q5TcDhUqpmT7U1APfAzYB1wEPAZcBryqllodysNMxco/h1P1heVlpJCclUWSO3h7GbdLDMK4tczW6jwTZYziNUBafgXd6GWamRdfbfmyagisTLXK1rVhYNvO503mjoYt73rfitOPVFbk0dg0wMDxGdnpKUK8RiOkKz7jNKc6htWeI4TG7x5YWIm5Ml5L1XZwpWdPt1bkY54e0r2qtT/Wp0VrvV0rtAK5XSn1Kay2VjIRHXf3D9NnG+e097zotu0IILzIBzw3UYHjCOV5z0rTWm6Ycekop9SdgB84Mind7e6xS6jbgtttvv93X8Xo0ZnfQbrVRZtAeQzh9f9hrxzu476nX+e6N5zFvhgvIRmi12lhamWf0MESYLJ+dz/88fzgiryV/baZhHRrFHMLA0JyZijUK9xk6K5LOPNGFotF97+AIHX02j6+XmmxicUUeNSeM2Wd4rK1vxpXTFFMSswqyaWiXdNI4F2xKlvv+3R7uewUwA4uCG6KIZy8dbuW8hcUSFAp/DAHeKualTzjHL1rrncA/gA1KKa/LUlrrR7TWQbe0aOkZotCcHlU/+2fPK+KOS5fylcf20toTutZdoeJcMZRU0ni1qDyXho5+hkfHw/5a0fNbF4WsQ6PkhjgwjLaWFXaHpr69n/k+XAFbXJFLbXNwaZRvNHSxfHY+piTPP3qr5hawv6EzqNcIhNZ6xpYdbvNLzdTJPsN4F2xKVvmEcz09Hpz7eITw6KXDrdKsWvjrJM65yVNwWIFzTgv0Q0gDYALCvizV1DVg2P7C6axfVs6Hzp/Hf/xmD72D3hZmjeHsYSippPEqLcXE/BIzh5t7w/5aEhh6MW53YBsdJzsjdCmNlozoa1nR3DVAXnYaWT6kbs4tzqGldwhbEFcsnPsLC7zev6rKmH2Gnf3DKAUFOTO3J1kgBWgSga8pWdM9Hi/PMe3jlVK3KaX2zThCEbesQ6McabFy1vwio4ciYstenJ/rVk88qJRKB1YBwcwrC4FxIOwpPc79hcZUJJ3J+1dXcdEZZdz72F6GRsK/euOLcbuDrv7hqK6aKoK3bHZ+RCr3S2DoRZ9tlOz0FJJCWKk5GlcM63xMIwVnGmVVcQ51LYGvGr7hpfCM28IyC21WW8SvxtW19DG/1IIvlbkXlEoBmgQQbEqW+z5PzzHt40OVjiVi1ytH2jh7XqHsYxb+ehzQwF1Tjn8S54WoX7sPKKXKlFKLlVKZE45ZlFKn/dAppa4ALgD+7kqnD6tmAwvP+OJj6xexoNTMN558jdFx4wsKdvYNk5edFlWptyL0ls3OoyYCBWjkp8iLvqGxkBaeAWfxmWhbMfSl4MpEi8otHA5wn2FHn41+2yhVJd77JZqSklg2Oz/i+wyPtVpnrEjqNq/EzImOgZC07hBRK9iUrJMTzvX0ePCcZioEuw63coGkkQo/aa1rgJ8C17haTNyqlLofZ9GYF5lcMOs7wCEmry5uAI4qpX6klLpTKfUppdTPgT8BnZwecIaFkT0MfaGU4jOXLyMj1cQP/vgGDoPbkLX2DlEirSri3hmz8qlt7g37Z08JDL3oHRohNyu0gaGzyX10FSH0tfCMW3V5LkdOBrZa9kZDFyvmFMy4CrtyTuT3Gda1zlx4xi0jNZminHQaOwdmPlnEqmBTsva6btd6uG8N0AccCW6IIh4Njoxx4EQ3qxcWGz0UEZvuAj4HLMUZJH4YZ+ucK7XWM32irAVeA64EvoUzoFwHPAys0lpHZM4yslWFr0xJSXzxA2fSNTDCw387aGiP6jar7C9MBDkZKZTmZnKsLbxbmSQw9CKUPQzdLBnRtWKotfYrIILgKpO+McP+QjdnAZrI7jM81uZb4Rk3KUAT94JKycJ5db4FuFUplT3h3JXAeuBJaVUhPNlztJ1lc/LJSot8yx4R+7TWdq31/Vrraq11mta6Qmt9z8S2Oa7zbtFaK631jgnHDmmtr9Naz9daZ7seP19r/SmtdUQyHPptY4yNO8jPnnm/v9HSUkz85/Xn8OaJLh7bVWfYOFp7pCJpoojEPkMJDL0IdasKiL49hu1WG6nJSeRn+960taIgiwHbWEB7AN9o6GKVD4HhvFIzvYOjdPWHfSsDAH1DowzYxijL931inV9qCftVG2GcYFOyXEHfnUAlsFMpdYdS6ovAc0AH8LXIfCci1uw6JNVIReJq6hqgoiDLp/3+0SA7PYVvfWQ1f9vfyA+feYObHtzGe+/byk0PbmNbTWR2C7RZbZTmyYphIlhWKYGhYfpC3Nweom+Pob9ppABJ/5+9O4+Pu64TP/56574nTXOnd9KE0qYtyn0ILYgcgivqCq6wsKsoiBzquu6uuu66u+76E0FQ3AW1eK/gIorIIbQg9yWlaaFp09KSJs3dzOSaTI7P74/vTDtJZpKZyXznyLyfj0cfab/zPT4zST/5vr+fz+f9FmF1tSPs6aQdR4YZm5hkaenc6wbSRNiwvCRm2UlbOlysqiwKK9FQXVUR+zQBzUJ3M5FPycIYcz9wKVZm0m8Bfw88A5wRq6fvKrm4xyb489s9nFpfEe+mKBUXyTCNdLrFhTlccuIKHtt+iC7nCAbrwfvtDzfFJDjs0BqGKWPtskXsaj1i69RlDQyD6I9yDUOAorxMXAlU4H5vmIlnfCKZTrr9QA/rly8O+SnghpWlMQsM93U4ww6Q6yqtkhXxXFeg7DWfKVl+r/3eGHOqMSbPGLPIGPNhY8y+mL0JlVRe29dNfZUj6g8llUoWhxI88Uwwv33lwIxto2MTbNnWbPu1tYZh6igryiUnK53W3iHbrqGBYRC2TCXNzWJgZCzuGax89oW5vtAnssCwl40r555G6hPLBDThrrMEa/Q3NzuDjv4Rm1qllEo1z+3u4Mw1Oo1Upa5DvUMsKUmuEUOAbmfge4Fg26PFMz5B/9AopUWhLwlSyc3u6aRJExiKSJqI3CIiu0XELSKtInKriNjSg1hTSaO7+DkjPY2czHSG3IlRFNWaShrZiOGedmfIo2XGGCvxzPLQA8PlZQW4xybo6J+tVFx07OtwUlsR/udQV1E0r5qOSinlMzYxyUt7uzi9QQNDlbqScSopELS4vN1F57udbhYX5ZCeljS382qe7E5Ak0w/SbdhJX54E/gscD9wI/CQiET9fThtWGMIiZOApn9olBHPeETTD0qLcshIFzpDHC1r7R0iIz2NqkWhz4EXETYsX2z7dFK3Z5wu5wjLy8KfulJb6dBC90rNYWtTW1wSMiSb7W/3sKy0gMWF+uRfpaaJSUP7keQMDK/Z1EB2ZvqUbdmZ6VyzqcHW63Y4h6nUGoYpZd3SRey0sdB9UgSGIrIWKxh8wBhzmTHmHmPM54DPYRVkvTza13QOe6JexxC8CWgSYJ2hbxpppJm/6quKQy507xstDPdaG2OwznBfp4tlZYVkpIf/X6Guqkgzkyo1i61Nbdz+cFNcEjIkm+e0qL1Kcd2uEYryssjJyoh3U8K2ubGGmy9upNw7QpiRLtx8cSObG2tsvW5n/wgVNo9KqsSytLQAt2eCbpc905STIjAErgAEuH3a9nuAYeDj0byYMQbnsIfC3OjXkUqUEcOWCKeR+jTUFLMn5MCwJ6T6hdNtWGGNGNqZ4CXSdZZwLAGNUiqwLduaGR2bmLItVgkZksnEpOH55k4tU6FSWrJOI/XZ3FjDT2/czB/+6UKKcrOoq4r8HitUHf06YphqRIS1Sxex650jtpw/WQLDk4BJ4GX/jcYYN7Dd+3rUDI2Ok52ZTlZG+tw7hylRity3dDgjDojAl4Bm7mmUk8aw42BfRIFh9aI8EGjrsy/7UiQlO3zKinIYm5ikbzA29RaVSjbxSsiQbHa19lFWlENlGNPtlVpoDvUOJmXimenS09I4Z101T+44ZPu1OvtHqNCMpCln7dIS26aTJktgWA30GGMCVVVvA0pFJGrzPp1D9qwvBG/JigQIDCNNPONTX+2g5bCTicnZy7kd6BogPyfj6PSKcIgIG1csZruN00lbIizZAVb7aiuLdNRQxVUir+Erzg+cwMvuhAzJ5tm3dBqpUtaIYfKVqgjkvMYatu5stz0LfaeOGKYkOxPQJEtgmIdVJDoQt98+U4jItSLyargX6x8etS0wTIQi98Oj4/QMuFlaGvmTuYKcTEoLc3ine3DW/bYf6GXjitKIr+ObTmqHsYlJWnsGWVVeGPE56iod7NXMpCpOEnkNX8eRYTzj42ROW78bi4QMyWTSGKtMhQaGKsW19g4m9VRSf6sqisjLyrA1eyRAh44YpqS6yiI6+0cYGBmL+rmTJTAcBoLVjsjx22cKY8zdxpgTw72Ya3jM1sAw3kXu93e6WFFWOO/0xg01c9czDLdMxXS+zKR2rDN8p3uAckfuvBa611boiKGKn0RdwzfoHuMr//sKf73pOD53yfqjMwZE4BPnNtiekCGZ7Gl3kpuVzrKyyB9QKbUQtPUOsXSBjBiKCOeur+HJHfY9pBsdm2DQPaaZjFNQRnoa9TUO3jwU/QcPyRIYtmNNFw0UHNZgTTONWrTltHHEsCgvC+dw9CP8cOyb5/pCn/pqx6zrDCcmDTvf6Y1ofaFPRXEeedkZHJxjZDISLR2ueS8Or6tyaGZSFTfB1up1xXEN3/jEJP/+f39m48rFfOCkFUcTMjz2lYv58KmrQlqbnEqefeuwTiNVKc/tGcc57FlQ08w3ravm2d0dMx7eRUtn/zBljhzSIswur5KbVeg++glokiUwfAWrrSf7bxSRHGAjEPZ00dnYVcMQvCOGcZ5KunceCVf8WYXu+4O+vq/DSUlBDosKgg32hmbDisVsP9Azr3MEMp+MpD41Jfn0D40y5I5vsK9SU7CbKBH4xgOv8073QEzbY4zhe4/uIj1N+PT5x894/a/es5rtB3rZZWMNpmRijOG55g7OXFMV76YoFVdtfUNULcojPW3hBDllRbnUVRXx4p5OW87f6RzR9YUpzK51hskSGP4KMMDN07Z/Emtt4c+jebF+GwPDotz4B4ZWQDT/NMq1lUW09gwGfRq2/UAvG1dGPlros3HFYra/Hf11hi0dznkl4AFITxNWlBfqqKGKi2BFlW++uJFVFYV84ScvWgFiT/RH3AP5zUtv89ahI/zDZScEnKqem5XBJ89dw3cf2cXEpL1JGZLB210DTEyaqDyoUyqZtS6gxDP+zmtcwpM2rfnu6B/WGoYpbE1NMfs7XVEfkU6KwNAY0wR8D7hMRB4QkU+IyK3At4GngV9E83quYQ8OG4rbQ/wL3HvGJzjUO8jKeSRc8cnKSGdpaUHQoOiNeSae8Vm/fDFN7/RF9UZy0hje7hyIypTaukorQ6tSseZfVFmAckcuN1/cyAUnLOOjZ9Rx7w2bWFFeyBd+/AL/9ZvXOdRrBYh2ZDJ9obmTX7+4n3+9/CTys4PXgD17bRUFORk8/NrBeV8z2fmK2otOBVMpLtlrGAZz5ppKdr7TR/9QsPyJkevs1xHDVJaTlcHyssKQa4qHKvKsG7F3M3AAuBa4GOgB7gS+aoyZvWZCmOycSpqfk8nw6DgTk5PzTv4SiYPdg1QtypsxyhCphppimtv6OX7JoinbxyYmebP1CH//wY3zvsbiwhxKCrLZ3+lidZQKxrb3DVGQm0lR7vy/z7WVRTQd1KlxKj42N9YETeaSl53BFWfWcelJy/ntywf43L0vUFOSR0uHC8+41W36Mpn6zhWJvYed3Pb7HXz9ipPmLE0jInzmgnV88acv8p7jq4KWtEgFz+3u4LMXrYt3M5SKu7beQd61qizezYi63KwMTlldzlO72vmLk1dG9dwd/cO6PjnFrV22iKZ3+micR5LH6ZJixBDAGDNhjLnVGNNgjMk2xtQYYz5njIn6HCk76ximpwkFOZm2pJgNxb4oTJ/0ZxW675+xfU97P9UleVEJvCD66wxborTOEqwRQ81MqhJZfnYmHztrNVs+cw4HuwePBoU+88lk2uNy87X7XuWzF62jobo4pGNWlBeyubGGLVvjmz01ntp6h3AOe1gz7aGaUqlooY4YApy7fklUs5P6Znw881YH//P4WwlRnkjFR+PSEna2RjcBTdIEhrHkHPHgyLPvKXZRbvyK3EczIAJfApqZ0yjfONDL+nlkI50u2vUMo7XOEmB5WQGHjwzZlnlMqWjxzVgIJFiG09mMeMb551+9wqUnLuesMBOoXPme1bzc0sXutv6wr7sQPLu7g9MbKjSjoEp5xhgO9Q5Rs0ADwxNWLqZnwB2Vtd7+tWsBjgyNJkztWhV7a5eV8NahI1FdaqWBYQB2jhiCt2RFnEYMWzqcUQuIAJaWFtA36J5Rm9EqbB/FwHD5Yna9c4TxiejMGrZGTqMTIGdlpFOzuIADMc4AqVQkgmcyFe5/fl/ID60mJg3/+ZvtrKoo4i9Prw27Hfk5mfzN5uP43iM7UzIRzbO7D3PGcZqNVKm+wVEyM9KiNsMo0aSnpXHOumqe3HFo3udK1Nq1Kj4ceVmUFubwdhQTIGpgOM3o2AQTk4bcrOiswQskXiUrJiajl3DFJz1NWF3lYK/fqKFnfILmtn7WLSuJ2nWK8rKoXJTHnigkeTHG0BKFUhX+aiu10L1KDsEymf7Ve+o40D3ANd/bxq2/e4O9c/xf+8GTbzE8OsaNFzdGnDzlvPU1ZGak8dj21oiOT1ZdzhE6jgyzfnn0+kilktVCnkbqc15jDVt3tjNp5vcQLNjMjkhmfKiFYe3SReyMYgmoZEo+ExO+xDN2ZomzitzHPjBs6xuiOD+LgpzgGQMjUe9dZ/juWmvh+JuHjrCivHDWzISRsMpW9MxIdBOu3gErO1hpYU40mgVAXWXRnDfSSiUCX4KZLdua6XaOUObI5ZpNDUe39w+N8tj2Vv71/tdYXJDNJScu56zjq3j2rY6jxxTkZJKRLtx93dlkpkf+fNFKRLOWf/zFy5y5pnLBjhhM99zuDk6pryBjHp+dUgtFW9/CDwxXVRSRl5XBznf6WD+PRCFljtyj00inb1epad2yEl7c0xm15EYaGE5jZ0ZSH0ecahlGO/GMT0N18ZQ6PW8c6GVDFDMk+WxYsZjfvPw2Hztr9bzOY02nLYpq8F9b6WBrU3vUzqeUnWbLZFqcn81Hz6jjw6fV8tLeTh569SDffWQno+OTR6d8DrjHyMpI49WW7ogzmfrUVjo4a00VP97WzGcvapzXuZLFc7s7+PBpq+LdDKUSQmvv4IKsYehPRDh3fQ1P7Dg0r8DwijNr+c7DO6dsy85M55pNDfNtokpS65aV8MMnd2OMicp9rT6unMZpYw1Dn6I41TLcF+Xpkz4N1Q72tPdjvFMk3jjQy4YoFLafrnFZCbsP9eMZn1+SFysBT3QD5NqKIg50DzAxGdXKKUrFTXqacHpDJd/4q1PIycqYsQ7QMz4ZtXUtf31OA8/t7kyJUfcjg6Ps73TxrlXzr/Gq1EJwqHeIJSULe8QQYPO6Gp7b3TGvRHW7Wo9wwsrFM2rXzvcBnUpeFY5c0kRoPzIclfPpiOE0zqFR26czOfKyeLsr9uvR9nY4+dAp0X9KXe7IZWLS0DPgpiAnk30dLtYujf7amfycTJaVFfDWoX42zCOxzb4OJ2evrY5iy6x6caWFObT2DLGivDCq51Yq3o4MBi7OHK11LYW5mVy9qZ7vPbqTb199+oLO1PnCnk5OrC0jK8O+dexKJZNDvYMLfiopQGlRDqurinlhTyfnRHAP8vzuDna1HuG/rz2LnCy9fVcWEaHMkcOXfvYSPa4RlpYWcMWZdWxaF9nDAh0xnMY5Mkax7SOGsS9XYYyxbcRQRI4Wut/VeoS6Kgc5mfbc9GxcUTrvshX7olyyw8dKQLPwRzxU6gm2fiWa61rO37iUyUl4IgqZ+xLZs7s7ODPM0h5KLVRjE5N0O91UpcCIIcC5jTVTlt6Eqn9olDsf2cnffWCDBoVqim072+hxufn8pet56B8u5PoL1nLvtma27YyshIkGhtM4h0btX2OYl4VzOLblKrpdbjLS0lgcxYQr/qxC9062v90T1TIV022cZ6F714gH14iHaht+CdVVFtESxZTBSiWKYJlMo7muJU2Ez1y4li1bmxl0x6ecj522NrXxV995ktf2dXP3H9/UumNKAYePDFPmyJlXEqtkcuaaSna900f/UOBZGIEYY7jjDzs5t7HGltlYKrn98tkWvvCBDWxcUUpGehobV5RyyyXr+eWzLRGdTx87TOMc9lBXFf0ELf6KcrNm1P2zmy/hil0aqh38+oX9DHvG+eR5a2y7ztqli9jX4cLtGY/oqdm+DherKopsmapWW+ngV89F9h9RqUQ2VybTaGmoLuaU1eX8x//9mdbeobCutbWpzfb2RcpXlNq3tqjb5eb2h5sAEqaNSsXDoRRIPOMvNyuDU1aX89Su9pCzSG5tauNQ7yBf+uBGexunklJrzyDrpj0wWLe0hNaewYjOp4Ghn61NbWxtauOR11v51XP7bLuxcMShXIVd0yd9Op0jbPdO8fzmg9v5m83H2fLZPd/cycSk4QP/9RjlEdz8tdiUmRWsEcP9na6oZYZSKpHMlsk0muqqHDzy+rG6hl3OkTmDqOmBVyjHxNJsRakToX1KxUuqJJ7xd+76Jfx4W3NIgWG3a4T/+eNb/MfHTtZ1ySqgpaUF7GztY+OKYwnNdrb2sbQ0sgcuqTF2H4KjNxbjVlZJ342FHdN98rIz8IxNMDYRuwyWLYftC4i2NrVx9x/fOvpv39PwaH92vu+R73OL5Htk1zpLsNL852Rm0NmvhWZ5cSt88Sr4xIXW1xe3xrtFKkn86rl9M7aNjk3wo627GXKPBfzzo627gwZeiUCLUisVWKoknvF3wsrF9Ay4ead7YNb9jDHc9vsmPnDSCttnsqnkdcWZddz20A62H+hhfGKS7Qd6uO2hHVxxZl1E59MRQ69YPtEVEYryrFqGdq35m66l08WnbAqIYvXZReM6+zpcfMTG+mG1lUW0dDipXJRn2zUS3otb4SffAY93DUVfl/VvgFM3x69dKikEDaJcbj5+R+AHDMOj42GdK5bePHSEtDSZUe4DtCi1Uod6hzi3cUm8mxFT6WlpnLOumiea2vibzccF3e/hP7/DwLCHj55RG8PWqWTjyz5616O7aO0ZZGlpAVdvaog4K6kGhl6xfqJblBteYDif9TPOYQ/Do+O2BSux+uyCna/LOcLAyBiFuZmzHu/2jNPZP8yyMvvKSViZSV2pnXXwgXuPBYU+nlFrexIFhiJyFXALcBzgAh4C/sEY0x3i8fcCfx3k5Y8YY34djXYuNGWOXLoC/F8vd+Ty0xsD//xcecfWgMeUFcXmwVsgnvEJfvLUHp7Y0cYl717GI9sPTXmwpUWpVSrz3dN0OUf45oPb+dtz7Vl+kqjOa1zC1+57las3NQTMedDeN8SPtzVz61+fRkaKJOZRkdu0ribiQHA6/WnzikU6dn9FeZkhF7n3TaHsco5gCH8KZUuHk1qbEq5A7D67YOfLzkzn6u9u5c4/NPHOLItt93cNsLS0wNbsZ3WVjtTOTOoZtUYIA+kLKZ5KCCJyC/BjwAncBPwPcDnwlIiEO+/pygB/Xo5eaxeWSDKgBjpGBDLS02jrHbKlnbPZ3dbPZ+55lsNHhvnvT53FdRes4+aLG7UotVJMvacB6BmwZ/lJIqutLCI/O4Omg30zXpuYNHzrd29w+Zl1tj7IVioQHTH0umZTw5TkBWDfE92tTW00t/Xz9z99adYEKsYYul1uvv/YroBTKO/+45uccVzljBsi/+v4nsjlZqWztanNlhuRWH12wa5z88WNbFixmN+/dpC/+8kL1FY6+ODJK3h3bdnRYHhrUxt3PbqLAfcYV96x1bbEQp3OYV5t6eKCrz9sazbFhMzA2N8L37gFsrJnjhgClJTFvk0REJFS4N+AV4BzjTET3u2vAL/DChT/I9TzGWN+Zkc7F6pIMqAGOuavN9UzODLGzVue42NnreYDJ6+w7eGYj2d8gp/9aS+PbW/luvet5ezjq44moopV8h6V2kQkDauP+hSwAugG7gO+aowJ6SmJiFwEfBnYAIwCTwJfNMa8HY02ajImi1XT8BAbppX4+s1Lb5MmwgdPCS1rqVLRpIGhV6zSsQdLcgNwWkMFew87eetQP81tR3irrZ9JY3CNBK7pdWTIw0du/SPLSgs4rqb46J+akny27WyfEkSNeCZsy9IXq89uruv89TkNXHFmHU/tamfL1mb++7E3+cDJK8jKSOOux960PWPh1qY27t3WjG8pkV3ZFBMuA2Prfujtgg2nwHVfho5DU9cYghUsXnZ17NsWmb8A8oA7fUEhgDHmIRHZD3ycMAJDsSKDQmDQGBO7jFNJLJIgKtgxJ9WWc+tDb/Ds7g4+f8l6W2qYAuw97OT//XY71Yvy+f61Z1FSEL9prCql3QbcCPwGuBVY4/33CSJy3lx9kIhcBvwaeAP4O8AB3Aw8JyInGmPa59tATcZk2bSuhk/9z9N85oJ1Rx/wH+ga4L7n93HH35xh+4MspQLRwNBPLJ7oBntSduvv3iA9PY1V5YUct2QR7zm+mmvPP54KRy5X3bkt6JqbH1x3Ni0dTprb+nlpbxc/eWoPw55xRscm8IxPzriOXU/kYvU0fK7rZGWkc/6Gpbx3/RJ2vtPHb14+wPPNHZhpeR/sS44z8zP/0dbdnFJfHvCYYNkUIzkm5k9bB13w25/Aq8/CRz5hzd1bUW/9AWtNYV+3NVJ42dXJtL7wJO/XFwK89iJwhYgUGGNCLRLkxAoMPSLyJ+DLxpiXotBOFYKaxfn8v6tO48GX3+amHz3HlWfX8/4Tl0d80zV9tP7Ks1dz+Mgwf/jzO3zqvcezaV21lqtRcSEia4HPAg8YYz7kt/1t4A6s6fC/mOX4TOBOoBU4y9fHicgjwGvA14Br59vOYOuIUy0ZU2lRDqurinlhTyfnrK1mfGKSb/3uDa7e1JDaCexUXGlgGGPBnoiNTxp+8/fvDVinZrapmtmZ6axdWsJav+KWfYNurrjtybCuv9CICI3LF9O4fDHv+/rDAfeJVXKcbpebj98eJJuiJ0g2xQiO6XKOcMC7jjI9LfCNaVSnrf7y+5BfCF+/BwqmroPYVtDAL1f9La1FVoasKwrq2DTrVRJKtfdroAUvbYB499kzx3k6sJ7evwYMYU3Luhl4RkQuMsY8EeggEbkWuPa6664Lv+UqoPQ04UOnruKkunJu/Z01evi5S9bzZuuRsP4/BBqt//bvdrCqopC7PnlWzLJMKxXEFVj90+3Ttt8D/CfWbIeggSFwNlbf9lX/B1/GmO0i8hTwURH5jDEm8DSmEMVy6U6iq1qUy62/e4P/fOB18rIzKHfkcuEJS+PdLJXCNDCMsdky7gUrXhruVM2SghzK9YncUbH6LKKZTTGSY3Iy0/nX+1/jyOAo9dUOGmqKWVOziONqillUkB29aasPvgbuETb/7RcgbebP7Lad1pTaWy5Zz7qlJexs7eO2h3YARC1rVihEpBgrEAvVHcaYPqxppGCtrZnO7f065+NcY8yXpm16UER+AWwHvg+sDnLc3cDd119//cz6BmpelpUW8O2rT+f/XtzPp/77acYnDOPeud9z/X8wxvDDAKP1Bhhwj2lQqBLBScAk05JbGWPcIrKdY7MhZjsegs+W2AzUA7vm08hYLT9JdFub2nhyR9vR2V1Do+O09Q2xbWd7yn0WKnFoYBhjkT4pC3eqpj6ROybeyXHmyqYYrWNu8mY5dA17aG7v561D/Tz02kG+9bvt5Odk0j84enRtq8/o2AT3PPEWy8sKAl7rnifemjltlXTuefYAy5cGTibz06f3cMsl69m4ohSAjStKueWS9dz16K6YBoZAMfDPYez/M6APGPb+OxuYHoH77v6HiYAxZq+I3AdcLSL1xpi5Rh1VlKWnCX95ei2/eelt+ganxv6jYxN895Gd7DnspH9oFOewh/4hD87hUVzDY4xNBF6e1e10B9yuVIxVAz3GmEAPtdqA00UkyxgTLCX6XLMlAGqYZ2AImowJvMtPpv1O9oxPplwSHpVYNDCMsURJ1JJKEvkzt+OYorwsTqor56Q6a43ipDG09Q7xie8/HfB8fYOjfOt3O4K+Fu4x7X3DrPOb2gywbmkJrbOUErGDMeYA1rSqcPmSK9QALdNeq8EaJJpPAoYD3q+lzD0dVdnkSJCf7aHRccqKcqirLKI4PxtHXhbF+Vk48rL427ue1pkYKpHlEXimA0yd7RAsMJzXbAmdBh8eTcKjEpEGhnGQKIlaUkkif+Z2H5MmwtLSgqBTassduXz/2rMCHjvbVNdgx1z730+zs7Xv6IghwM7WPpaWBh6VTECvYCVYOI2ZgeEpQHMYiWcC8U0h7ZzHOdQ8zTb1+0Onrgp4jM7EUAluGAictSy02Q7+syXCPl6nwYdHk/CoRKQF7pVKEdEqHD7XMVecWcdtD+1g+4Eexicm2X6gh9se2sEVZ9bN7w3Ezm+xppDeICJH37yIXALUAj/331lESkXkOBFx+G3LF5EZi85E5ATgI8Bbxph9dr0BNbdIfrY3N9ZooXqVyNqBUhEJFNjVYE0zDTZa6Dvet2+g4yHwNFMVgUj6IKXspiOGSqWIWE119a0jvOvRXbT2WFlJr97UEOv1hREzxnSLyFeAbwFPiMgvsW6KPg/sZmbGvxuw1jJeA9zr3bYaeEREHgT2ciwr6d8AE0Qh5buan0inmOtMDJXAXgHOB04GnvFt9D6k2gj8KYTjwZotMT1r8qmAC53+HjW65EclIg0MlUohsZrqumldTdIEgoEYY24VkV7gFqz6Xy7gPuBLIU4j7cC6sdoE/BWQCxwGfgV8wxiz25aGq7BokKcWmF8B/4i3LI7f9k9irQ08OttBRKqwite/Y4zxTQ99Gquf+oSI3OZXx3ADcA6wZb6lKtRU2gepRKOBoVJKBWCMuZdjI4Cz7fc1rMLP/ts6gCttaJZSSgVkjGkSke9hTYN/APgDsAa4ESvo869h+A3gr7EeXj3lPX5MRG7CCjCfEZF7gCKsB2TdhJflWSmVhDQwVEoppZRaGG7Gynx8LXAx0APciVW0PnC9FT/GmPtFZAT4MtZ0+lHgSeDvjTG6vlCpBU4DQ6WUUkqpBcAYMwHc6v0z235XA1cHee33wO+j3TalVOJLqcDw+uuvj3cTlFLBmbvuuiuSuoMLmvZbSiU07beC0L5LqYQVtN/SchVKKaWUUkopleLEGK1DOp2IvGqMOTHe7Yg3/Rws+jlY9HNIbPr9sejnYNHPwaKfQ2LT749FPweLfg6WeH4OOmKolFJKKaWUUilOA0OllFJKKaWUSnEaGAZ2d7wbkCD0c7Do52DRzyGx6ffHop+DRT8Hi34OiU2/Pxb9HCz6OVji9jnoGkOllFJKKaWUSnE6YqiUUkoppZRSKU4DQ6WUUkoppZRKcRoYAiKSJiK3iMhuEXGLSKuI3Coi+fFuWyyJiAnyZzDebbODiPyDiNwvIvu97/PAHPs3iMiDInJERIZE5BkR2Ryj5tomnM9BRL42y8/JF2LY7JSn/dYxqdR3ab91jPZdyUn7Lksq9VugfZdPovdbGXacNAndBtwI/Aa4FVjj/fcJInKeMWYyno2LsWeYueh1LB4NiYH/APqAPwPFs+0oIrXA88A48E3ACXwSeExELjTGPGFvU20V8ufg5xagZ9q216LYJjU37bemSpW+S/utY7TvSk7adx2TKv0WaN/lk9D9VsoHhiKyFvgs8IAx5kN+298G7gAuB34Rp+bFw35jzM/i3YgYqTXG7AcQkZ1AwSz7fgPrP/C7jTHbvcf8BNgFfE9EjjPJm8kpnM/B50FjzAFbW6WC0n4roFTpu7TfOkb7riSjfdcMqdJvgfZdPgndb+lUUrgCEOD2advvAYaBj8e6QfEmIlkiEsoPalLz/ceci3d6y6XAU74Oynv8IPADoB44yY42xkKon8N0IlIkIin/cClOtN8KIBX6Lu23jtG+Kylp3zVNKvRboH2XT6L3WxoYWj9ck8DL/huNMW5gO0n8wxehD2N1zgMi0iUid4qII96NirP1QDbwQoDXXvR+TbWfkx1YUzvcIvK8iFwY7walGO23ZtK+ayrttwLTviu+tO+aSvutmbTvmilm/ZY+MYNqoMcYMxrgtTbgdBHJMsZ4YtyueHgZuB9oAYqAi4AbgLNF5HTv05pUVO392hbgNd+2mhi1Jd76sdZDPA8cARqAm4GHReRvjDH3xq1lqUX7ram075pJ+62p+tG+KxFo33WM9luBad91TD8x7rc0MIQ8IFAHBeD222fBd1LGmFOmbfqJiOwA/h24yfs1FeV5vwb6OXFP22dBM8bcPn2biPwI2AncJiK/TuFfZrGk/ZYf7bsC0n7Lj/ZdCUP7Li/tt4LSvssrHv2WTiW1hvCzg7yW47dPqvp/WB30xfFuSBz5vv+Bfk5S/mfEGNML/DfWQvHT49ualKH91txSve/SfmsO2nfFhfZds0v1fgu075qV3f2WBobQDpSKSKAfwBqsKQ8L/slVMMaYMbyfUbzbEkft3q+Bpi74tgWa8pBKDni/pvLPSSxpvzUH7bu03wrRAe/XVP05iTXtu2ah/RagfVcoDni/Rv3nRANDeAXrczjZf6OI5AAbgVfj0KaE4f0clgCd8W5LHDVhTWk4LcBrp3q/pvTPCbDa+zWVf05iSfutOWjfpf1WiLTvii3tu2ah/RagfVcobOu3NDCEXwEGazGnv09izWH+eawbFA8isjjIS1/HWov6UAybk1C887cfAs4RkQ2+7d700p8A9jItw9pCJCIZgbKlichS4DqgF2uBtLKf9lte2ncFpv3WMdp3JRTtu9B+azbad1ni1W+lfPIZY0yTiHwPuEFEHgD+AKwBbgSeJnUKrX5ZRE4FtgHvYBXcvAjYBLwE3BnHttlCRK4Elnv/WQZkiciXvf8+aIz5qd/u/wCcCzwuIrcBLqxfZDXAxUlcaDWcz6EAeFtEHgTe4liGrE94X7vCGDMSs4anMO23pkipvkv7rWO070o+2ncdlVL9Fmjf5ZPw/ZYxJuX/AOnA54FmrOHrNuDbQEG82xbDz+ADwGPe9+4GhrBqCv0jkBPv9tn0np/CenIZ6M9TAfZfA/wWK33wMPAscF6830esPgesheA/wJrmcQQYAw4DvwZOjvf7SLU/2m8d/RxSqu/Sfiv8z0L7rsT6o31X6vVb3vesfVcYn0O8+i3xXlwppZRSSimlVIrSNYZKKaWUUkopleI0MFRKKaWUUkqpFKeBoVJKKaWUUkqlOA0MlVJKKaWUUirFaWColFJKKaWUUilOA0OllFJKKaWUSnEaGCqllFJKKaVUitPAUCmllFJKKaVSnAaGSimllFJKKZXiNDBUSimllFJKqRSngaFSSimllFJKpTgNDJVSSimllFIqxWlgqJRSSimllFIpTgNDpZRSSimllEpxGhgqpZRSSimlVIpLicDw+uuvN9dff72JdzuUUipU2m8ppcIlImkicouI7BYRt4i0isitIpIfxjkuEpHnRWRIRPpE5H4RWRnq8dp3KZW8MuLdgBjTjkqpxCXxbkCC0n5LqcSVaP3WbcCNwG+AW4E13n+fICLnGWMmZztYRC4Dfg28Afwd4ABuBp4TkRONMe1htEX7LqUSU9B+K9UCQ6WUUkqpBUdE1gKfBR4wxnzIb/vbwB3A5cAvZjk+E7gTaAXOMsYMerc/ArwGfA241q72K6XiLyWmkiqllFJKLXBXYI0E3D5t+z3AMPDxOY4/G6gGfuALCgGMMduBp4CPeoNHpdQCpYGhUkoppVTyOwmYBF7232iMcQPbva/PdTzACwFeexEoAurn10SlVCLTwFAppZRSKvlVAz3GmNEAr7UBpSKSNcfxvn0DHQ9QM4/2KaUSXEqvMRwbG+PQoUO43e54N2VecnJyWLJkCZmZOsMjmrY2tbFlWzPdzhHKHLlcs6mBzY36O1HFl/ZbyUn7ExUDeUCgoBDA7bePZ5bjCXIO97R9ZhCRa4Frr7vuuoCv+/ddbs84g+5xJiYN6WlCQU4GOVmJdUuaan2UUpDigeGhQ4coLCxkxYoViCRaYrHQGGPo7e3l0KFDrFwZcjZpNYetTW3c/nATo2MTAHQ5R7j94SYAvZlTcaX9VvLR/kTFyDBQHuS1HL99ZjseIDuS440xdwN3BytV4eu7Ssqr6XK5yTfHdhMRKhy5FOXNNqAZO6nWRynlk9JTSd1uN4sXL07amyuwOtPFixcn/ehBotmyrfnoTZzP6NgEW7Y1x6lFSlm030o+2p+oGGnHmi4aKLCrwZpmGmy00He8b99Ax0PgaaYh8fVdvYOjGDM1djTG0DOQOP1BqvVRSvmkdGAIJPXNlc9CeA+Jpts5EtZ2pWJpIfyfXwjvIVTan6gYeQXrvu5k/40ikgNsBF4N4XiA0wK8dirgAvbMp4EiwvhE4FKKwbbHSyr1UUr5pHxgmOj+6Z/+iaVLl1JQUBDvpqSUMkduWNuVUsdccMEFbNiwgbVr1/LpT3+aiYmJuQ9awLQ/UTHyK6yi8jdP2/5JrLWBP/dtEJEqETlORPzXDD4NHAY+ISIFfvtuAM4B7jfGjM23kRnpgW89g22Plv7+fu666y5br6FUstPAMIEYY5icnJzy90suuYSXX355jiNVtF2zqYHszPQp27Iz07lmU0OcWqRUYgrUb91333288cYb7Ny5k+7ubu6///44tzK+rtnUQFbG1F+32p+oaDPGNAHfAy4TkQdE5BMicivwbaygz7+4/TeAt/AbXfQGfTcBS4FnROR6EfkS8DjQDfxzNNpZWpgzYzRORCgtzAlyRHRoYKjU3DQwjLMDBw6wZs0arr/+ekpKSqitreX666/nXe96F62trZx66qlUVVXFu5kpZ3NjDTdetPbov8sdudx8caMmilCKufutoqIiAMbHx/F4PCk/JWtzYw0fPOVYAgvtT5SNbga+AKzFChIvB+4E3m+MmXOupjHmfuBSrMyk3wL+HngGOMMYE/H6Qn9FeVlUOHKnjBCWFubYnnjmS1/6Evv27WPjxo383d/9Hd/85jdpbGxkw4YNfOlLX7L12kolCw0ME0BzczNXXXUVr7/+OgcPHjz69+XLl8e7aSntxForuds5a6v56Y2b9SZOKT9z9Vvve9/7KC8vp7CwkA9/+MNxbm38LSst4MTaMvKyMrj3hk3anyhbGGMmjDG3GmMajDHZxpgaY8znjDGD0/a72hgjxpinApzj98aYU40xecaYRcaYDxtj9kWznUV5WayqKKK+uphFBdmMxWB94X/+539SW1vL9u3b2bx5Mw8++CAvvfQSb7zxBl/84hdtv75SySCly1VM976vPxz1cz72lYvn3Gf58uWceuqpHDhw4OjfVfy5RqylFEeGgpWFUir+ErXfeuyxx3C73fzVX/0VW7du5b3vfW/U25lMegfcrCwvpK1viEO9gywvK4x3k5SKq3j1XQBPPPEE11xzDXl51hLLkpKSqLdFqWSkgaGfUDuUaMvPzw/4dxVfrmEPBTkZ9CVQCm2lpkvkfisnJ4dLL72U3/72tykfGPYMuKlelE99lYM97U4NDFXK8++7Dh8ZJjsznZKCQJU2os8Yk/JT3JUKRKeSKhWEa9jD8rJC+gZ1xFCpUA0ODnL48GHAWmP4hz/8geOOOy7OrYq/3oFRSgtzqK8uprm9P97NUSqhLMrPon9oZn3DaCosLGRgYACA888/nx/96EcMDw8D0NfXZ9t1lUomGhgmuC9+8YssWbKE4eFhlixZwte+9rV4NylluEY8VJfk4xmfnFGcWikV2NDQEJdeeinr169nw4YNlJeX8+lPfzrezYq73gE3JYXZNFRbI4ZKqWNysjLITE9j0D3vahhBLV68mDPOOIN169bx5JNPcumll3LiiSeyceNGvvWtb9l2XaWSiU4ljbMVK1awc+fOGX/3+eY3v8k3v/nNeDQt5TmHxyjOy2JRQTZHBkepXJQ390FKpYDZ+q2KigpeeeWVYIemrN4B99HMiwe6XIxNTJJpc902pZLJooJs+gbcFORk2jbN8xe/+MWUf2s2UqWm0t9KSgXhGvFQmJtFSUE2fZqARikVoUljODI4SklhDrlZGVQuyuNA10C8m6VUQsnPzmDSwIhHZ+goFS8aGCoVhGvYgyMvk0X52ZqARikVMeeQh/yczKMjhA3Vxexu649vo5RKMCJCsXetoVIqPjQwVCoI17CHorwsSgqzNQGNUipiPQNuFhfmHP13fXUxezQBjVIzFOVmMeKZwDOuo4ZKxUPKB4Z2ZsCKlYXwHhKRc8RDUW4WJQU5HNHAUCWQhfB/fiG8h1BZ6wuPpeHXBDQqVc31/z4tTXDkZdE/5IlRi4JLpT5KKZ+UDgxzcnLo7e1N6v/8xhh6e3vJycmZe2cVloHhMWvEsEBHDFXi0H4r+fROGzFcWVHE4SNDuD3jcWyVUrEVat9VnJ+Fa8TDxORkjFo2U6r1UUr52J6VVETSgJuATwErgG7gPuCrxpihCM53H/ARYJcxZt182rZkyRIOHTpEd3f3fE4Tdzk5OSxZsiTezVhwnCMeHHmafEYlFu23ks/0qaSZ6WksLy9kb4eLxmUlcWyZUrETTt/lGvbQf1jIz8mMQcsCS6U+SimfWJSruA24EfgNcCuwxvvvE0TkPGNMyI+EROT9wIeAkWg0LDMzk5UrV0bjVGqBmZg0DLnHKcjJPJpCW6lEoP1W8ukbGKWhpnjKtgbvOkMNDFWqCKfv2t/p4su/fJkff3azlnVRKoZs/d8mImuBzwIPGGMuM8bcY4z5HPA5YBNweRjnKgDuAr4HdNnRXqV8Bt1j5OdkkJ4mOpVUKTUv1ohh9pRtVmCo6wyVCmRVRRHLSgt5amd7vJuiVEqx+zHMFYAAt0/bfg8wDHw8jHP9O9YI55ej0jKlZuEc9uDIzQJgUX42zmEPE5PJu6ZLKRU/vuL2/uqrHTRrZlKlgvrQqSt54KW3k3o9tVLJxu7A8CRgEnjZf6Mxxg1s974+JxE5GbgBuNkY44pyG5WawVeqAiAjPY2CnExcw/HPkqaUSj7Tk88ALFlcgHPIo/2KUkGcWFvG+MQkbxzojXdTlEoZdgeG1UCPMSbQPLw2oFREsmY7gYhkYI0wPm6MuS+ci4vItSLyajjHKAXgGvFQlHts0btOJ01+IpImIreIyG4RcYtIq4jcKiL5IRx7joiYOf6cEeL+v7f3napE4hmfYMQzcfRBk096mlBXVcSewzqdVKlARITLTl3J/724P95NUSpl2J18Jg8Idjft9ttntkemfwesBj4Y7sWNMXcDd19//fU6D0GFxX/EELAS0Ay6qaUojq1S8zSfRFhvAVcG2J4N3A30MG1mhNfdwDPTth0Ks90qifUNjFJSkE2ayIzXfIXuT6wti0PLlEp85zbW8ONte3ine4BlZYXxbo5SC57dgeEwUB7ktRy/fQISkTrgq8C/GWP0kZGKGefwGA6/wFBHDJPbtERYH/Lb/jZwB1YirF8EO94Y0wn8LMB5r8CaefETY8xYgENfMMbMOE6ljp4BNyXTEs/41Fc52KrJNZQKKisjnfe/exm/efkAN13cGO/mKLXg2T2VtB1rumig34o1WNNMZxstvBXoA34jInW+P1gBbZb331XRb7ZKda4RD4W5/oFhDkc0MExm0UyE5e8T3q8/CLaDiOSLiFZJTlGBEs/4NNRYI4ZKqeDef+Jy/vTmYfq1nrBStrM7MHzFe42T/Td6b5I2AnOt/1uOtU5xF7DX708N1vTSvVg3dkpFlWvYgyPv2BrDRTpimOyikgjLn4isxCq786wxpjnIbt8BBoEREdkjIjeJBJhTqBasQIlnfCocuUxMGnpcWidVqWCK87M5a00lv3/tnXg3RakFz+7A8FeAAW6etv2TWGsLf+7bICJVInKciOT57fcF4CMB/nQDrd6/f8OuxqvUNX2NoU4lTXrzToQVwN9gjUIGGi0cA34HfBG4FPg00I81YvmjMK+jkljPLIGhiGjZCqVC8MFTVvL7Vw/iGZ+Id1OUWtBsDQyNMU1YBekvE5EHROQTInIr8G3gaaau6fkGVoKHk/2Of8IY8+vpf7Cmfrm8/37OzvegUpNzxENR7vTAUJ/qJ7FQE2GFRETSgasBF3D/9NeNMc8ZYz5gjPkfY8xDxpj/AU4FHgOuFpEzZzm3ZlNeQHoHRllcEHiNIUB9VbEGhkrNYXlZIXVVRWxtaot3U5Ra0OweMQRrtPALwFqsIPFy4E7g/XNkAVQqbgaGx3TEcGEZxsogGsicibACeB+wBPilMSak47z9nW+Gw0Wz7He3MebEMNqiEljvgJvFRcGXmNZXO9jTriUrlJrLqooi7vjDTi74+sNcecdWDRKVsoHdWUkxxkxgJZG5dY79rsZ6Ah/KOVfMt11KzcY54pmWlVSTzyS5duB4EckOMJ00lERY0/2t92vQpDNBHPB+LQ3zOJWkegeDJ58BaKguZu/hfowx6PJTpQLb2tTGgy+/zcSkVX2syznC7Q83AbC5sSaeTVNqQYnFiKFSSWVi0jDkHqcg51jymdysdAwwPDoev4ap+ZhvIiz/Y8qBS4Adxphwp3yu9n7tDPM4lYSMMfS6gq8xBCuxVV52Ju194QxYK5VatmxrZnRs6iSz0bEJtmwLlvdLKRUJDQyVmmbQPUZ+Tgbpacee3osIJQXZOmqYvOabCMvfVUAms5eoWBxgWzbwNe8/Hwq14Sp5DY2Ok56eRm7W7JNz6qs0AY1Ss+l2joS1XSkVGQ0MlZrGOezBkTszQaUmoEle802ENc3fYCWsma1w/aMi8lsR+ar3Wl8FmoAzgDuNMS/PcqxaIHpc7lkTz/jUVxez57CuM1QqmDJHbsDtudkZjE1ougqlokUDQ6WmcQ17KPSrYehTUpBNr44YJrObmWciLBE5HVgDPGCMOTLLrr8GyoHPAt8HPodVFuNjxpgbI30DKrnMlXjGp6HaQXNbv/0NUipJXbOpgezM9CnbsjLSqCrO5eYfPcc73QNxaplSC4vtyWeUSjaukcAjhot0KmlSi0YiLGPM81i1C+e61n8B/xV2I9WC0jvoZnHB3IHh6ioH+ztdTExOkp6mz2uVms6XYGbLtma6nSOUOXK5ZlMDm9ZV84c/v8Pnf/wCH3/Pai49aYUmcVJqHjQwVGqa6cXtfUoKcrRkhVIqZD2u2TOS+uTnZFJalMOBrkFqK4ti0DKlks/mxpqAGUgvfvdyNqxYzDcffIOX9nbx+Us3zJrwSSkVnD6aVGoa57Qahj6afEYpFY6+wdGQppKCVbZiz+F+exuk1AK1ZHEB3776NNYsWcT19zzDM28ejneTlEpKOmKo1DQDIx6KNPmMUmqeelxuNq6YkaA2oAZvofsLT7C5UUotUBnpaVx5dj0n1ZXxXw9u56W9XVx3wfG8tKdrxhRUrX2oVGA6YqjUNM5hD44gyWd0KqlSKlS9g25KQxwxrK8uZo+WrFBq3o6rWcRdnzyLzIw0rr5zG99+aAddzhEM0OUc4faHm9ja1BbvZiqVkDQwVGoa13DgEcNFGhgqpcLQO+CmJITkMwC1lUW09gwyOjZhc6uUWvhyszK46eJGRGRGOYvRsQm2bGuOU8uUSmwaGCo1jWsk8BpDR142g+4xxrVmklJqDhOTk/QPeSgJoY4hQFZGOktLC9jX6bK5ZUqlDtewJ+D2budIjFuiVHLQwFCpaYJlJU1PExx5WfQPBf5Fo5RSPv1D1syDjPTQf83qdFKloqvMkRvWdqVSnQaGSk3jHPFQlDtzjSFoAhqlVGh6BtwsLgxttNDHl4BGKRUd12xqIDszfcq27Mx0rtnUEKcWKZXYNDBUys/EpGHIPU7hrIGhrjNUSs2udyC0Gob+6quLadYRQ6WiZnNjDTdf3Ei5d4QwKyONmy9u1KykSgWh5SqU8jPoHiM/J4P0tMDPTDQBjVIqFL0DbkrCDAyXlxXQ43Iz5B4jPyfwwymlVHg2N9awubGGsYlJrv7uNpYszo93k5RKWDpiqJQfZ5CMpD4lBTkaGCql5tTjCn/EMD0tjdrKIvYe1umkSkVbZnoaHz51Ff/73L54N0WphKWBoVJ+BkY8FAWoYehTUpDNEV1jqJSaQ+/gaNhrDME3nVQDQ6XscOEJS9nV2sfB7oF4N0WphKSBoVJ+nMMeHLOMGOpUUqVUKHoH3CwOc8QQoL7KoZlJlbJJTlYGHzhpBfc9r6OGSgWigaFSflzDHgoDlKrwsUYMNTBUSs0u0sCwoUYT0Chlp0tPWsFLe7vo6B+Od1OUSjgaGCrlxzUyhmPWwFDXGCql5hZJVlKA6kV5jHgm9AGUUjYpyMnkohOW8esX9se7KUolHA0MlfLjmjP5jDWV1BgTw1YppZKJe2yC0bHJoGVvZiMi1Fc7dNRQKRt98JSVbNvZrnWJlZpGA0Ol/DiHZ08+k52ZTlZGGoPu8Ri2SimVTHq9xe1FJKLjrXWGmoBGKbssKshmc2M1D7z4drybolRC0cBQKT+ukbFZk8+ALwGNPmVUSgUW6fpCn4bqYvYc7o9eg5RSM3z41FU8ur2VgZGxeDdFqYShgaFSflzDHopmWWMImoBGKTW7+QaG9dXF7Gl36pR1pWxUUZzHqfUVPPTqgXg3RamEoYGhUn6sNYazrwvSBDRKqdn0RJh4xqe0KIeMdKHTORLFVimlpvvL02t58OUDuD26PEQp0MBQqSlcI6GNGPbqVFKlVBB9A6OURFDc3l99VTHNbf3RaZBSKqBlpQWsX17CH15vjXdTlEoIGhgq5TUxaRh0j8+ZSXCRTiVVSs1iviOGAPXVDvYc1gQ0Stnto2fU8X8v7MczPhHvpigVdxnxboBSiWLQPUZedgbpabM/LykpyGZ/pytGrVJKJZtIaxj6a6gu5n+fa4lSi5RSwayucrCivJAnm9q48IRl8W6ObbY2tbFlWzPdzhHKHLlcs6mBzY018W6WSjC2jxiKSJqI3CIiu0XELSKtInKriOSHcOwiEblJRB73HjciIs0icreILLW77Sq1uIY9sxa39ykpyNERQ6VUUL0DbkrmGRiurnbQctjFxKQmoFHKbpefWcevntvHxORkvJtii61Nbdz+cBNdzhEM0OUc4faHm9ja1BbvpqkEE4uppLcB3wbeBD4L3A/cCDwkInNd/xTgVsAA3wVuAP4AfBxoEpHj7Wq0Sj3W+sK5C1L7ityr5DKfh1Te458SERPkz4kB9neIyJ0i0ua93i4RuU4iLW6nkoIxht6B0XllJQUoys3CkZ/Fod7BKLVMKRVM47ISSgqyeebNjng3xRZbtjUzOjZ1quzo2ARbtjXHqUUqUdk6lVRE1mIFgw8YYz7kt/1t4A7gcuAXs5xiN9BgjNk37bwPA38E/hX4cLTbrVKTc9hD0Rw1DMEXGGrymSR0G9ZDqd9gPXBa4/33CSJynjEmlEfFPcAtAbbv9/+HiGRh9VEnAHcCbwEXAncBFcDXInsLKtENjIyRnZlGTmb6vM/V4C1bsbysMAotU0rN5ooz6/jhk7s5e20VC+35XXeQDMfBtqvUZfeI4RWAALdP234PMIw18heUMebA9KDQu/0JoA9YF51mKmXd0M2VkRSgMDeT0bFJXaieRKY9pLrMGHOPMeZzwOeATVgPqUIxZIz5WYA/fdP2+wRwEvA5Y8znvNe7DHgA+EcRWR6lt6YSTM88axj6a6h20NzeH5VzKaVmd2JtGWkivLS3K95NiboyR27A7fk5GbhGPDFujUpkdgeGJwGTwMv+G40xbmC79/WwiYgDKAQ659k+pY5yhrjGUEQozs/S6aTJZV4Pqfx5p6QWzTEl9GPe894zbfvtQCbw0VCvlyi2NrVx5R1bueDrD3PlHVt1bUoQ8y1u72/APcYjr7+jn7lSMSAiXH5mHf/7bAvGLKy1vReeMDMtR2Z6GstKC7jmu9v49kNvsFezICvsz0paDfQYYwLdQbcBp4tIljEm3McVX8a6ufrxfBuolE8oxe19fAloKovzbG6VipKgD6lEZDuhP6SqAQaBXGBYRB4D/tEYs9u3g3ft9LuAP3sfgvl72duOiB6KxYsvcYFvjYovcQGgWe2miVZguLWpjf97YT/jE9YNqn7mStnvjOMq+fG2ZnYc7GPDisVhHZuoWT8njeHlvV1ceMJSXtvfM6N9/UOjPPp6K/9y36uUFuVw6YkrOHNNJVkZ6Qn7npR97A4M84Bgwypuv31CDgxF5MPA54HHgC1z7HstcO11110X6ulVCnONeKhZHFIeEhZpAppkE42HVG8DzwE7gAms5Fg3AOeKyJnGmCbvfouwAscZwzvGmFER6cUKMJPGbIkL9CZhqp6B0XmXqgDvZz4+ddmrfuZK2Ss9TfjLM2r53+dawgoME/nh2R/fOIQBbry4kbQAE12K87O5/Mw6PnL6Kl7a08XvXj3I3X98i+NqHLy2vwePtx9KpPek7GP3VNJhIDvIazl++4RERC4Cfg68BvylmWOs3xhztzFmRrZApQJxDo/hCCH5DGgCmiQU6kOqoIwx1xhj/skY8ytjzK+NMX8HnA8UYGVe9r8Wc1wv6LVE5FoReXW2tsSaJi4InTViGOzXXuj0M1cqPjY31rC3vZ/Lv/1ESNO4PeMT3PPEWwmZ9XPQPcaWrc185oK1AYNCf+lpaZx+XCX/+fFT+OZVp7L9QO/RoNAnEd6TspfdI4btwPEikh3gSX0N1hP8kEYLReQCrMQNu4DzjTFaYVxFlWvYQ2EIawxBS1YkoWGgPMhrYT+k8jHGPCMifwI2iUiuMWbE7zyzPRQLei1jzN3A3ddff33CLHIpc+TSFSAgCZbQIJX1Drg5qa5s3ufRz1yp+HjmzcOMeCYYnxwHvCNlv99Bl3OEZaUFtB0Zor1vmPYjQxzuG6ZvcJSxicBJreP9IOcnT+3htIYK6quLwzpuWWkBbk/gBHvxfk/KXnaPGL7ivcbJ/htFJAfYCIT0VFxE3oeVYn43cJ4x5kh0mxk5TcgQuUT77FwjHhwhrzHUwDDJtAOlIhIoWAvrIVUAB4B0rCmkAEeAEQJMF/VefzEBppkmsms2NZA9rfxCdmY612xqiFOLEle01hjqZ64iJSJXicjrIjIiIp0i8gMRCflphYjcO0vN1gVfImzLtmbGJ6c+lxsdn+QnT+/hse2t9A2MsqqikL88rZb/uvJUfvul91Ee5IFNPB/k7O908dSudq6OsM8I1nZ9OLWw2T1i+CvgH4GbgWf8tn8SayrVz30bRKQKcADvGGOG/bafDzwI7AHODZAWPm4SeU55okvEz8417AmpXAX4ks8svJTWC9grWNM+T8avL/J7SPWneZx7NTCOVUIHY8ykiPwZqz7i9NkSJ2M9LEuoqaJz8f2f/PZDOxibmKQ4P4tPvfd47ecC6I3SGkPfZ/ujrbvpdrkpK8rhbzYfp5+5mpWI3II1tf1p4CZgCVZZntNE5GRjzFAYp7sywLaXA2xbUIKNiE1OGv7l8sB5w67Z1DDlngbi+yDHGMN3H9nJVefUh5RtPZBEe08qNmwNDI0xTSLyPeAGEXkA+APHiko/zdTi9t8A/hqrpthTACJyIvBbrDTzW4ALp2eIN8b8zM73MBtNyBC5RPvsJiYNg+5xCkMcMdTkM0lnXg+pvCVyBo0xU35oReRi4AzgkWkZSH/p3X4tVoF7n5uxgsj7ovGmYmlzYw33PPEWy8sK2bSuWvu4AMYnJnGNeCjOj+xGbLrNjTVsbqzhph89xyfOPY7G5eFlSVSpRURKgX/DehB2rq+/EpFXgN9hBYr/Eer54nl/FU+RTOP29YdbtjXT5RwhPU246eJ1cesnt+1sZ3RsggtPWBbxOaa/p+yMNG6+uFH7/gXO7hFDsG6EDmDdIF0M9GDdKH3VGBN4UvYx6zi2/ue2IPvErePS5ACRS7TPbsg9Rl52Bulpoc2u1uQzyWW+D6m8f/+2iDwE7McK7k7Gqn/Yg9XP+bsHuMZ7zArgLeAi4IPAvxlj3o7uO7TfoHuMEc84xy9ZREd/2MsxU0Lf4CjF+Vkh9yOhqq920Nzu1MBQzeUvsB503en/EMsY85CI7Mfqr0IODL21WguxHorNdb+2YEQ6UuZ7kGOM4ZrvPcWSxQV2NzWgodExfvDkW3z5w+8mPW32hDNz8b2nIfcYV96xlRNr579+WiU2u9cYYoyZMMbcaoxpMMZkG2NqjDGfM8YMTtvvamOMGGOe8tt2r3db0D92t382Ov86con22YVa3N5nUUE2/UMeJhdYEdwF7mbgC8Ba4HvA5VgPqd4fwk1PM1Y25PcD/441VetM4L+BjcaYPf47e9crngf8D3CF93rHAZ8FvhqdtxNbh3oHWbK4gMpFuXT268OvQKJZ3N5ffVUxe9r7o35eteD45jm+EOC1F4HjRCScaMXp/TMiIn8UkVPm28BksLmxhpsvbqTckYsA5Y7csEbKRITzNyzh8e2t9jY0iJ//aS/vWlXG8UsWzb1ziPJzMjl5dTnbdibV8ngVAdsDw4Xsmk0NZGVM/Qh1/nVoEi2xgmsk9OL2AJnpaeRnZ+AajjRfiYq1eT6kessY8xFjTK0xpsB7fK0x5jPGmIC/KY0x/caYG4wx1d79jzfGfHeuMjuJqrVniKWL86lw5NGpsyIC6h1ws7gg+oFhQ7WDPYedUT+vWnCqvV8D9UltWMtyqgO8Nl0H1iyt67BmOfwHcCLwjIicF4V2JrzNjTX89MbNPPqVi/npjZvDnj553volPP3mYTzjgTN72uVg9wBP7GjjbzcfF/Vzv3fDEh5/41DUz6sSSyymki5Ymxtr2NPez+///A5j45OaHCAMmxtrGB0b5zsP78QAjrwsPn1+/JJZuIbHQk4841NSkOOdOjb/mmVKJbrWnkGWlhZQUZxLp04lDah3wE1pUfQDwyWlBTiHPWElyFLJS0SKmTk9fTZ3eBPzzVZDNaR6rQDGmC9N2/SgiPwC2A58Hyvh1gwici1w7XXXXRdKmxe0ckcutZVFvNDcydlrQ4nF588Yw12P7uJjZ9WxqCD69yUbV5TiHPawv9PFqoqiqJ9fJQYNDOcpNzuDj5y2ipf2dHHjxY0cV1Mc7yYljZrFBaxZsog1S4opys2Ka0DtGgn/hsuXgGZVhU2NUiqBtPYOsnldDaWFOfQPeRifmCQjXSed+OsdGKXEhhuyNBFWV1mjhrrGJyUUA/8cxv4/w8qK7F9DdfqwfsT1WgGMMXtF5D7gahGpnz593rtPwtVgjafz11sjbLEKDJ95qwPnsIdLTlxuy/nT04Tz1i/hj28c4lPnH2/LNVT86W/1eWrpcFFX6aC2soiWDp3qE46Ww05qK4uoq3TE/bMLd40haAIalVp8I4YZ6WksKsimx6U/+9P12DRiCFBf5aC5rd+Wc6vEYow5MFd+hWl/WryHtnu/BnrKWgMYv30iccD7tXQe50gZZ6ypYnfbkZj0lSOece7+45t85oK1UU9+5e+965ewdWcb4xMpk4so5WhgOE/7OnzBTRH7Olzxbk5SsYLqIm9QHd/PzjUc3hpDsALDI1qyQqWA8YlJOp0jVJdYs9AqHLl0OHU66XR2rTEEaKjWBDRqTq94v54W4LVTgObpa6rD5JtC2jmPc6SMnMx0zlxTxZNN9ids+eWzLaxbVmJ75uKaxfnUlOTzSku3rddR8aOB4Tz0D40yOjZJhSOX2gQY9Uo2+zpc1FY6WLK4gL7BUYbcY3FrSyRTSUu0lqFKER39wywuzCErw0oYZa0z1AQ009mVlRSskhV7DjtJ0txFKjZ+izWF9AYROZrdTUQuAWrxq9fq3V4qIsd567T6tuWLyIwfYhE5AfgI8JYxZp9db2ChOX/DEv74Rqut/28P9Q7yyJ/f4ZPnrbHtGv5870ktTBoYzkNLh4vayiJEhFUVRRzsHmRiUofXQzE6NsHhI0MsLysgPU1YWV7I/s74jRo6h8dw5EaWfEaphc6XkdSnwpGngWEAvQOjtk0lLXfkMjFp6BnQKbwqMGNMN/AVrBqrT4jItSLyL8Avgd3A7dMOuQGrxuoH/batBt4Wke+LyOdE5FMichdWCYwJrJrUKkTHL1nEpIHdNk0DN8Zw12Nv8pdn1Nr2UGq6s46vYvuBXvqH9P5nIdLAcB5800gB8rIzKCvMobVnKM6tSg4HugeoWVxwdAQi3tNJB0Y8FEaYfEapha61d5AlpcfKn1UU59KpU0mnGB4dZ2Jykvxse3K6iQgNNcXsadeZKSo4Y8ytwDVACXAHVsmJ+4CzQ5xG2gE8AWwCvgZ8F6t+66+AdxljnrWh2QuWiNha5uGF5k66nSN88OSVtpw/kPzsTE6tr2DbzvksV1WJSgPDedh72MXqyqMzMKitLGKv1poKScthJ3WVx9IdxzsBjXPYg0PXGCoVUGvPIEsXTwsMdcRwit4BN4uLchAR267RUOWgWdcZqjkYY+41xmwwxuQYY8qNMX9jjOkKsN/XvMlr7vXb1mGMudIYc5wxpsgYk2mMWWaM+WtjzO6YvpEF4tzGGp556zCjY9Gpabi1qY0r79jKBV9/mK//+s+c1lAR8wzR1nRSrWm4EGlgOA/7Oo+NGALUVjrYF8fpkMnEl3jGJ97JeyKpD6ZZSVWqaO21MpL6VGqR+xl6B+1LPONTX12sgaFSSabckUt9lYPnmzvmfa6tTW3c/nATXc4RDDBpDA++fICtMUhw42/9isUMuMfYp7k1FhwNDCM0NDpG38AoS/yeolvBjf4nCYUv8YzPivJC2vqG8IxH54laOCYmDYPucQrDHDHMy85gctIw4hm3qWVKxZ8xZsYaw9KiHI4MjmrKcj+9LvsSz/jUVzvY2+5kUhPQKJVU3hulEbYt25pnjDyOjk2wZVvzvM8djjQRzltfY9sUWRU/GhhGaH/nACvKC0lPOzZtqNY76qVZ42Y3MTnJge4BVlUcGzHMykinpiSfA10DMW/PkHuMvOyMsGv/iIiuM1QLnnPYgwhT6nxmpKdRnJ+liVD89AyMsrgw+sXt/RXnZ1OQk0l7n65lVyqZnN5QSXO7k27X/GZadAeZqRFsu53eu34J23a2M6YPCBcUDQwj5J94xqc4P5vc7Aw6dO3NrFp7higtzCFvWpKGeCWgiaS4vY9mJlULXWvvEEsW589YO1dRrJlJ/fUNuimNQVbA+mqHJqBRKslkZ6bznuOreHLH/KZ8ljlyw9pup+qSfJaVFvDK3hnLV1US08AwQtYaOceM7XUVRVrPcA77OqYmnvGpq3TEZSquayT84vY+moBGLXTTE8/4VDg0M6m/nhhMJQVdZ6hUsjrfm510PrPKzllbPWNbdmY612xqmE/TIvbeDUt4TKeTLigaGEZoelZNn9pKBy2amXRWLdPWF/rUxWnE0DU8FnbiGZ9FmoBGLXDTE8/4aGbSqXoHYxMYNlRryQqlktFxNcWIwJuHjkR0vHPYw9amNj5y+irKHbkIVmKbmy9uZHNjTXQbG6Kz1lTRdLBXH5AvIPYUXFrgPOMTtPcNsaK8cMZrdZVF/OH1d+LQquTR0uHk8jPqZmxfVVnE210DTEyaKWs37eYaCT8jqU9JQTZ9A9ohqoXrUM8g65ctnrG9sjiPpoN9cWhRYuodGI3JVNLVVQ72d7oYn5iMeYp6pVTkRORomYe1S0vCOtYYw20P7eDstVV84tw1fOLcNTa1Mjx52Rmc3lDJtp1tXHbqqng3R0WB/laJwMHuQapL8o8WZ/dXG+eyC4nOGMP+TteM9ZlgFU0tKcjmUG8oNXijxzk8v6mkfUMaGKqFq7V3iKWl+TO261TSYyaNoW/ATYnNyWfAuhErd+RysDv2ibqUUvNzbuMSnnmrA3eYNQ3/8Od36HKOcHWcpozO5r1RmCKrEocGhhFoCZB4xqfckcvYxKROLwyis3+EnMwMivMD30DFo56hS5PPKBWQZ3yCHpebyuK8Ga9p8pljXMMe8rIzAj4stEN9tYNmnU6qVNIpLcrhuJpint8dek3Dd7oHuHdbM1+67ISY9THhaFxewrBnPC5LgVT0aWAYgek1+PyJiI4azmK2oBq8azRjnIBmvlNJdW69WqjaeoeoWpQXcMpiaZH1UGRiUlOVxyrxjE99dTF7NAGNUknJN8IWCs/4BP/5m+1cvamBZQHWeieCNBHeuz46dRpV/GlgGIGWw05WzxLc1FU62KsJaAIKls3VJx4JaJzDYxTlavIZpaZr7Z1a2N5fpreWYbdLf/5jlXjGp0FLViiVtE5vqKClw0lXCLUH793WTLkjl4vetSwGLYvce9cv4ald7XjGw5siqxKPBoZhmpg0vN01wKrZRr0qdMQwmGClKnyskhWumM5VH5jHiGFxfjYDI2M6aqIWpEO9gywJUKrCp6I4L6Sbm4UuVolnfFZVFHGob4jRMNcpKaXiLysjnbOPr+KJHbOPsL22v5undx3mlkvWz6gjm2gqF+WxvKyAl7SmYdLTwDBMbb2DLCrIJj87eLKSusoi9nVqYBiIVaoieGC4qCCbrIw0OmN4s+kc9uCIMPlMeppQlJtF/5Anyq1SKv5aewKXqvCpcGjJCoDeGCWe8cnKSGdZaYH+nlEqSb13w9JZE7Y4hz18+3c7+PylGyLOgRBr529YqtNJFwANDMNkTYUMHtgA1Cwu4MjgKEPusRi1KjkcGRzFMz5JuSN31v1inYDGNRz5iCF4M5PqOkO1AAXLSOpjBYaambRnwB3TEUPwJqBp64/pNZVS0dFQ7SAzPY1drTNrGhpj+PZDOzhnXTXvWlUah9ZF5qw1lexq7dPlNfHw4lb44lXwiQutry9ujfhUGhiGaV9n8MQzPulpwsqKQn2aO02LdxrpXFMiYpmAZmLSMOgepzDCEUOAkkJdZ6gWHmNMCFNJc+nQqaT0DsR2jSH4Ct33x/SaSqno8NU0fPyN1hmvPfznd+hxJWZpitnkZFk1DZ9saot3U1LLi1vhJ9+Bvi7AWF9/8p2Ig0MNDMPUcnj2NXI+dZUOWjQBzRQtHS7qqmYPqiG2CWiG3GPkZWeQnhb5f4VF+TpiqBaengE3uVkZFOQEf2hilazQEcPegdGYB4b1VZqARqlktrmxhud2d+D2jB/d9k73AD95ag9//8ETyAyQDTrRnb/Byk6qNQ1j6IF7wTPtHtQzam2PQPL91MWRMWbOrJo+tXHIrpno9nU4qa0ILajeF6MRQ+ewh6K8yEcLwTuVdEADw0QnImkicouI7BYRt4i0isitIhJ8ruSxYxeJyE0i8rj3uBERaRaRu0VkaYD9zxERE+TP7+15h9HV2jPEkiAZSX2sIvc6YmiNGMZujSHAsrICegfdDOqSBaWS0uLCHI5fsohnvTUNPeMTfCPBS1PMZd2yEjzjk5qZP5b6giT86euO6HQZ82hKyulyjpCVkcaigrlvAOoqHTz40gH7G5VEWjpcXHV2/Zz7VRTnMjo2Qf/QKMX59t5suUY8OCIsVeFTUpBNa+9QlFqkbHQbcCPwG+BWYI333yeIyHnGmNlSy57iPeZJ4LtAD7AO+BTwlyJyujHmzQDH3Q08M21bUqzOb+2dPfEMQJkjl74Bq5bhfEbdk5lnfIIh95jtfdV06WlprKooYu9hJyesTJ51SEqpY6oX5XH775v41m/fICcrnSUl+Vx4woxnjUlDRKitKOKLP30Rt2eCMkcu12xqYHNjTbybtnBlZMJ4gAeEJWWRnW6ezUkp+0JIPOOzvKyAw0esdOLZmek2tyzxDbnHODI4Ss0s65V8RIRVFdaI64m1kf1gh8o1PDavxDMAJQU5vHGgN0otUnYQkbXAZ4EHjDEf8tv+NnAHcDnwi1lOsRtoMMbsm3beh4E/Av8KfDjAcS8YY342z+bHxVzrC8GqZejIz6LH5aaiOC9GLUssfYOjLCrIJi0O6eQbaqx1hhoYKpV8tja18cjrrYxNWM8kRzwTHOwZZNvO9qQNpLY2tfFySxeeces9dTlHuP3hJoCkfU8Ja2QIsnPgL6+FX/9g6nTSrGy47OqITmv7I975TN/yO8dFIvK8iAyJSJ+I3C8iK+1sdyBWqYW5p5GClU68ZnEBB7oHbG5Vctjf6WJleSHpaaHdPMVqOqlrxBNxcXufksJs+oZ0KmmCuwIQ4PZp2+8BhoGPz3awMebA9KDQu/0JoA9r9DAgEckXkdguQIuC1p7gxe39pfp00ngknvFpqCrWzKRKJakt25oZHZ86UcUzPsmWbc1xatH8bdnWfDQo9Bkdm0jq95SQRobg1n+Al56CzZfAVTdBSTkg1terboJTN0d06ljM/bkN+DbwJtYT+/uxpm89JCJzXl9ELgN+D+QCfwf8P+A9wHMiUm1XowPZ1+GctQbfdLUxLruQyPaGmHjGp66yiL2H7f/sXFFYY6jJZ5LCScAk8LL/RmOMG9jufT1sIuIACoHOILt8BxgERkRkj3edYmJXKvYKZSopQGVxXkrXMoxH4hmf+moHzbqWR6mk1B3kgVqw7clgIb6nhOMege98BVbUHwv+Tt0M3/wJ/OAR62uEQSHYHBhOm751mTHmHmPM54DPAZuwpm/NdnwmcCfQCpxljLnLGPMN4H1ABfA1O9s/XUuHi9UhjhiCL7jRX9oQSVDtYF+n/Z+dc9gz7+KxvuQzmoUroVUDPcaYQBF8G1AqIpH8IHwZyAR+PG37GPA74IvApcCngX6sEcsfRXCdmBoeHWdg2DNnzVGA8hQfMeyJQ+IZn6pFeYyOTWi5HKWSUFmQ/jXY9mSwEN9TwnnucahcAh+7Hmx4zmz3iOG8pm8BZ2Pd0P3AGDPo22iM2Q48BXzUGzzarn9oFPfYOBXFof9w11Y6dMTQa1+I2Vx9lpbm0zswytCovRn3XCMeCuc5lTQnK4PM9DSGRsfn3lnFSx4QbFjX7bdPyETkw8DngceALf6vGWOeM8Z8wBjzP8aYh4wx/wOc6t33ahE5c5bzXisir4bTlmhr6xuiZnFBSOvmKopTu8h9XxyK2/uIiJatUCpJXbOpYUYOiuzMdK5JsvqF/gK/p7Skfk8JY8wDbQdg86XWVFGbEr7ZHRjOd/qW7/UXArz2IlAEzJ3mMgr2dbhYVTF3cXZ/tRVFHOgeYGJytmSHC59nfIL2viGWl4Wefjk9LY0VZYXs77R3jaZreGzeI4bgGzXUp/YJbBgINqyT47dPSETkIuDnwGvAX5oQhou9WU+/4f3nRbPsd7cx5sRQ22KH1p7BkNYXAlQ48hJixHBrUxtX3rGVC77+MFfesZWtMSqy3DPgpqQgfktI66uLadZC90olnc2NNdx8cSPljlwEa/bFzRc3JnWSlunvKSNNuOiEZUn9nhLC+Bh8/9/h8QesUcI0+5Ja2p2VdK7pW6eLSJYxxjPL8b59Ax0PUAPsml8z5xZq/UJ/edkZlBbm0NozxIryQptalvgOdA1QXZJPVkZ4P8jWGk0njctKbGqZN/lMFALDRQVWApplZan7fU5w7cDxIpIdoD+qweqngvVDU4jIBcADWP3O+caYcKYFHPB+Teg0kq09oa0vhMQYMdza1MbtDzcxOjYBxDYTXu+Am9Ki+AWGDdXF/P61g3G7vlIqcpsbaxZc0OT/nl7c08mWrc1MGhOXzM0LwsQE3P2f1gjhlTfafjm7RwznO33L91qgc8x5fDSnZIW7Rs7HF9ykskiCarDWaLbYPBXXOeyhKHf+s5G1yH3CewWrvzvZf6M3W+hGIKR+QkTeh1UHcTdwnjHmSJjtWO39GixZTUJo7R2cs7i9T1lRDr3eWobxsmVb89Gg0CdWmfDimXwGvAlo2vt1jbNSKuGcsrqcnKx0/vTm4Xg3JXn1dUN2LnzqHyDD/iqDdgeG852+5Xst0DnmPD6aU7ISObhJdC0dTuqqwg+q66octNicvGdgJEpTSQtzNDNpYvsVYICbp23/JNbDpZ/7NohIlYgcJyJTHjqJyPnAg8Ae4FxjTF+wi4nI4gDbsjmWMOuhsN9BDB3qHWJpCDVHwSrN48jLojeOD0bilQnPGBPX5DMAiwtzyM5IpyOFM8MqpRKTiHDV2fX87Ok9TEzqw6u5TFkS8Z0n2Xr/w1ah+r/9AmTO/141FHYHhu1Y2f4C/dYMZfpWu9++gY6HwNNMo2p4dJyeATdLS0MuvXhUbaWDlhQfMdwXRv1HfyvLC2nrG8IzPjH3zhGYmDQMjIxRGIURw0X52RzRWoYJyxjTBHwPuExEHhCRT4jIrVildJ5manH7bwBv4Te6KCInAr/FSqa1BbhQRD7u/2faJR8Vkd+KyFe91/oq0AScAdxpjHmZBDUxaWjrGwp5xBDiP500XpnwhkfHSRPIz45JDrSgfKOGSimVaN61qpSivCye2hmbdd/Jyrckoss5ggG6XG5u3z3B1h3vxLQddgeG852+9Yr362kBXjsVcGE9vbfV/k4XK8oKSY8gA1BdZRH7O10pO81nYtJwoGuAVRXhr73LykinelE+B7sH5945AkPuMfKy0yP6vk6nyWeSws3AF4C1WEHi5VjlcN7vTQwzm3VYsxSysWqz/jTAH3+/BsqxyvV8H6tETxvwMWOM/YsE5qHLOUJxfjY5WaFPWalw5MZ1xOqaTQ1kZ0z9fxyL7H49A24WxzHxjE99dTF7NDBUSiUgEeGqc+r52TN7Uz4Z42wCLokgnS3PvB3TdtgdGM53+tbTwGHgEyJS4LfvBuAc4H5jjL31DIh8fSFg3WBlZqRsAehDvYOUFGZH/ES9trLIthHXaCWeAW9gqCOGCc0YM2GMudUY02CMyTbG1BhjPudfCse739XGGDHGPOW37V7vtqB/pp3jv4wxpxljyowxmcaYYmPMJmPML2P0diMWTkZSn4ri+GYm3dxYw4dPryUjzfo2lBbmxCS7X+/AKIvjmHjGp75aS1YopRLXxhWllBXl8sQOHTUMJtjShy7niG0z5wKxdRWjMaZJRL4H3CAiDwB/ANYANxJ4+tZfYxW+f8p7/JiI3IQVYD4jIvdglai4BegG/tnO9vu0dLg4rqY44uN9wU3lorDKpC0I+zpc1FaEP43Up66yyLZakM5hD4551jD00eQzaqFo7Q09I6lPuSOX3W3h5uGJrsKcTM7fuJQe1wgXnLCMM46rtP2avQNuFhfEb32hT31VMfs6XExMGtLTNPOfUirxXHV2Pd/87XY2N9aQmW73uFTyKXPk0hUgOMxIFz566xM0Li/h3bVlnFhbRk3J1Ie323a28ctnW45mFL/izDo2rYvswaj96W2s0cIDwLXAxUAP1vStr4YwfQtjzP0iMgJ8GfgWVobSJ4G/N8bE5NFDS4eLi9+9POLja70JaM5cUxXFViWHvR1OVkeQeManrsphWzYr1/AYhdEaMdTkM2qBaO0ZDHtNcEVxLk/vap97Rxvtae9nw4rFlBRk09zeH7vAMI4ZSX0KczNZVJBNa89gSpdGUkolrnXLSlhSks/j21vndU+9UF2zqWFK2SWwlkTcfHEjJ9aW8ee3e3htXzf/+2wL2ZnpnOgNEp3DHn7xzF5uuWQ965aWsLO1j9se2gEQUXBoe8g+n+lbfq/93hhzqjEmzxizyBjzYWPMPrvbDlZx9rbeQVbO45dtXaUjZUtWRJp4xqe2ooi3uwZsyWblGoneiGFhbiZuz3hMh/uVskNr71DYU0krE6DI/Z52J/XVxTGdVtkT5xqG/jQBjVIq0V11TgO/eLZF75UC2NxYw9Wb6hGxstyVO3KPLokoysvinLXVfP7SDfzi5nP56kfeTYUjl9+89Db//dgubrlkPRtXlJKRnsbGFaXccsl6fvlsS0Tt0LHcORzsHqRqUT7ZmeEVZ/eXqiUrjDHs63BRF+H6TID8HOtJeFtv9BPQuIY9FOVFJ5tgmgjFBdkc0VFDleQORTCVtMyRQ4/LHbd05IPuMXoG3CwvK6Chupi9h2NT1683QZLPgCagUUolvuNqiqmtKOKR11vj3ZSElJmezuZ1NTz6lYv56Y2bA66TFxFWVRTxkdNr+a8rT8U9NsG6pSVT9lm3tITWnsjumzUwnMN8Es/4lDty8YxPplzQ0OkcITszjeL8+a3Bqa2wJ7C2ittHry5MiZasUEnONeLBMzZJSZjr5rIy0inKy6Q3Tpl59x62+un0NKu/ycvOpL3P/vIZiZJ8BqBBE9AopZLAlWfX86vnWmZk4FTwaksXJ9WVhXXM0tICdrZOLau8s7Uv7Ae8PhoYzqFlniNeYEX3dTZm10xU851G6lNbWcS+zugHhgMjY1HLSgqagEYlv0O9QywpzUck/AQmFXGcTrqnvZ/66uKj/66vis20ykRJPgNWzdyDPYM6RUspldBWVzk4rrqY3792MN5NSSie8Ql2vNPHu1eFFxhecWYdtz20g+0HehifmGT7gR5ue2gHV5xZF1E7NDCcQ1SDmxSbTtpy2EldxfyCarDWaLYcjn5Q7Rz24IhmYFiYQ2+KjQqrhcUqVRHZU8Z4FrlvbnfSUH2sn66vLmaPDX2Gv4lJw5GhUUoSIPkMQE5mOtWL8ni7ayDeTVFKqVldeXY99z+/H7dnPN5NSRg73znC8tKCsAcsNq2r4epNDdz16C4u+cYj3PXoLq7e1JDQWUmT1sSkYX+na95TScEKbp5v7ohCq5JHS6eLCzYunfd56qqsqaTGmIhGMoJxjXgoyo3OGkOARfm6xlAlN1+q60hUOHLjVq+1ub2fvz33uKP/bqhx8ONte2y9Zv/QKAU5mQmVdr2hxlpn2OA3eqqUUolmZUUR65eX8NtXDvLRM2rj3ZyE8Mq+Lk6sK4/o2E3raiIOBKdLnN9oCaitb4hFBdkU5Mw/eEjFBDTRWJ8JUFKQQ1ZGWsD6LvNhJZ+J5ohhNn2D8VljpVQ0RJKR1Mcqch/7EcO+QTduzwTVfnViV1c52N/pYmJyzopIEesdcFOaIKOFPg3VxTS3pdaSBaVUcvr4e1bzfy/uZ2h0LN5NSQiv7O3i5DDXF9pBA8NZ7OtwUhuFqZAANYsLODI4ypA7Nf4D9A+NMjo2SYUjNyrns2MqrmtkLLpTSXXEUCW5Qz2DLIl0KmmcRgytMhWOKbMJ8rMzKSvK4UBX9LMZ+yRS4hmfWK2tVEqp+VpWVsiJtWX89uUD8W5K3HX0DzPgHqOuav5L1+ZLA8NZWOsLoxMYpqcJK8sL2W9DEpVE1OL97KI19TPamUknjWFgZIzCKE4ltUYMNTBUyWlsYpJO5wjVJXlz7xxARXFuXJLPNLf3Ux/gl6m1zrDftuv2JFDiGZ8V5YV0OkcYHtV1O0qpxPdXZ63mwZcPMJgigybBvNLSzbtXlZEWxeVSkdLAcBZWRtLoRe+1KTSdNFrTSH3qKh3si2JW10H3GHnZ6aSnRe+/QElBDn1arkIlqcNHhilz5JCVEVnN1nJHblxqGe5pdwZcU2d3+Ya+BJxKmpGexqrywpTLgK2USk41i/M5ZXU5//fi/ng3Ja4iKVNhFw0MgzDG0BLl4Ka20pEyv7D3HnaxOopBdV2VI6pBdbTXFwIU52fRPzjKZAwKaysVbYfmkZEUrFqGhbmZMV1na4yxkq3UFM94ze6C7z0D7oTJSOqvvrpYp5MqpZLGX521mt+/ehDXsCfeTYkLz/gEOw6GX6bCLhoYBtHtcpOZnsbiKP7iX13lSJmSFfs6oxtUVxbnMuIZpz9KI3LRLm4P1o1xbnZGynZuKrm19kaekdTHKlkRu+mknf0jZGYE7qdrK4to7Rm0rYhy7+Bowo0Ygha6V0oll8pFeawsL+Svv7uNC77+MFfesZWtTW3xblbMNL3Tx/Ly8MtU2EUDwyCiPVoIsLysgPa+oQVfgHhodIy+gdGIk1gEIiJRTUAT7eL2PlqyQiWr1p7IM5L6VDjyYlrLcHd7P/VVxQFfy8pIZ2lpAftsWtfd63KzuDCx1hiCjhgqpZLL1qY23mrrZ3h0HAN0OUe4/eGmlAkOX23p5qTayMpU2EEDwyD2dbiilpHUJysjneqSfA4s8ALE+zsHWFFeSHpadBfRWlNxo3OT5xz24IjyiCFoAhqVvKI2YhjDBDR72vuprw4+Zd2O6aRbm9q48o6tHOge4Kv/+2rC3bw0H+6nq38kJZ+8K6WSz5ZtzXjGp5YWGh2bYMu25ji1KLZeSaD1haCBYVDRTjzjUxfF4CZRRTvxjE9tRVHUEtC4hj0U5kUvI6lPSb4Ghir5GGNonUepCp/K4ryYTiUNlnjGJ9rTKrc2tXH7w01Ha6r2DLgT6sn21qY27nh4JwZS8sm7Uir5dAd5mNjlHOFPbx5mYGThZixNpDIVPhoYBmFbcFMZveAmUbUctiuojl5WV9fImE0jhjkaGKqkc2RolPQ0mXddzwpHLh0xKnI/MWklCFs9x4hhNKdVbtnWPGPNYiI92U709iml1HRlQepdF+Rk8Mc3Wrnqjq3csuV5fvanvexu6z+a+do3eyOZZ0ckUpkKn4x4NyAROYc9DI2OU7kosnpes6mrcvDUrvaonzeRtHQ4ufSk5VE/77KyAnoG3AyPjpOXPb8fXdewhyobvr8lBdlHRxOUShaHeofmPY0UYpt8prVnkOL87FmTSC0vK6DH5WbIPUZ+zvxnCAR7sh1se6wlevuUUmq6azY1cPvDTVMeamVnpvOZC9axubEGz/gETe/08dq+br790BscGRylpiSfvR1OxiesINE3OwJgc2NNXN5HJF5p6WLzusRqr44YBtDS4aSussiWCH5VRSEHugZiXusrVjzjE7T3DbGivDDq505PS2N5WQH7o5BMwspKGv2ppJp8RiWj1nmWqvDx1TKMRcmWPYf7Z51GClafUVtZxN7D0ZmlEezJdrDtsZbo7VNKqek2N9Zw88WNlDtyEazfIzdf3Hg0wMvKSOfdq8q49r3Hc/enz+aua8/iUN/Q0aDQJ9lmR3jGJ2g62Me7akvj3ZQpNDAMYF+Hi1obpkIC5GdnUlKYzaHeQVvOH28HuwepLsmPuEj2XKJV6N414pn3tLlANPmMSkatvUMsKZ1fRlLwq2U4YP//AWt94dz9dDSnk16zqYGsjKm/NrMz07lmU0NUzj9f12xqIDtzat+bSO1TSqlANjfW8NMbN/PoVy7mpzdunnXUr6wol8Eg6w67nCO805Mc99dHy1TYsKxpPjQwDMCOjKT+aisWbj1DO8p8+KuN0jpDOwrcg5V8RkcMVbKJ1oghWOsMO2OwzrC5rZ/6OUYMwUpA0xylBDSbG2vYvK6a7Iy0gE+2483/yTtAZnpaQrVPKaWiIdgsiLzsDL74kxf5+5+9yPO7O5iYnAy4XyJItDIVPrrGMICWDieXn1Fr2/mtJCrOBfnLuuWw07bRVrA+u4dfe2fe53GNjNk0YqjJZ1TyiUapCp8Kb2bStUujcrqAPOMTHOweoC6Eh1D11cX88MndUbv2kSEPN79/fcL235sba9jcWMPo2ASX3/YEJ9YmThp0pZSKhmDrEj974TrOXFPJs291cN8L+7jrsV28/93LueCEpRTnW3Vntza1sWVbM93OEcocuVyzqSEu/fkrLV188S82xvy6c9HAcJoRzzjdzpGo3SQFUlfl4L7n99l2/nja1+HinLXVtp1/ZXkRh3oH8YxPRDxdddIYBkbGKLRhjWF+dgbjk5O4PePkZOl/L5X43GMTHBkcpbI4OuvQKhy5dNhc5P7trgGqS/JD+j9WvSiPEY/1HhcVzK8g/YhnnKaDfQn5y3y67Mx0TlixmJf2dvHeDUvi3RyllIoaXyAXLMDzPSDbe9jJQ68e4G++9xSn1ldQXZLHfc/vY3TMGkmMV9KajiOJV6bCR6eSTrO/08Xy8kIy0u37aOoqi9jX4cLEIEFDLE1MGt7uGmCVjVNJszPTqVqUx8HuyOeQD7rHyMtOJz0t+t9jEWFRga4zTEQikiYit4jIbhFxi0iriNwqIiEvrhORi0TkeREZEpE+EblfRFYG2dchIneKSJv3ertE5DqRBMpLDbT1DlFZnBe1/w+xKHI/V2F7fyJCfbUjKusMX93XTUNNMQVRyHAaC6c1VPLCns54N0MppaIulHWJq6scfO6SDdx7wyZWVhTy8z+1HA0KfeKRtOaVfV2cWJtYZSp8dEjDz9amNr736C4G3WNcecdW24aX/7y/h+HRMS74tz9QHuIwdiRD37EcLt/a1MY9T7yFe2yCT//PM7Zda2tTG539I9zwg2dD/uyms2t9oa99vQNurvneUwn5vY3VtbbtbOOXz7ZYa9dKC7jizDo2xT8l823AjcBvgFuBNd5/nyAi5xljZl2MICKXAb8G3gD+DnAANwPPiciJxph2v32zgD8CJwB3Am8BFwJ3ARXA16L5xuYjmtNIwZpK+sxbHVE7XyDN7U7W1BSHvH99lVXo/tT6inld94XmTk5vmN85YumU1eXc9dguRscmZiSlUUqpVFGUl8VHTqvlh08EXlYQ65I+r7R0J1yZCh8NDL22NrVNma9s1/Cy7zq+ahWhXCeStsXq/cTyWtG6jmtkzJYsUL72hVNXJ5bf21hda9vONu7d1swtl6xn3dISdrb2cdtDOwDiFhyKyFrgs8ADxpgP+W1/G7gDuBz4xSzHZ2IFeK3AWcaYQe/2R4DXsAK9a/0O+QRwEnCjMeZO77Z7ROT/gH8UkS3GmINRenvzcqh3iKWL55+R1CcWyWf2tPfzgZNWhLx/Q3Uxf3h9fmuTxycmebmli6uTKMNnUV4WdZVFvP52z7yDYqWUSnZljtyAtaZjWdLHV6biCx/YELNrhkMDQ68t25qnLGKFY8PL0Qxugl3n9t/v4LndgZ+yv9LSxej4zKHvSI6J9vuB+H924V7HrhHDeH9vZzsmltfa+U4f//ChE9i4wqrNs3FFKbdcsp67Ht0Vz1HDKwABbp+2/R7gP4GPM0tgCJwNVANf9QWFAMaY7SLyFPBREfmMMcaXQ/tjwLD3/P5uBy4DPgp8M5I3Em2tPYOcVBe9BCXljly6nVYtQzumyQyPjtPRP8LKMGql1lcXc9vvd2CMIdKZvDvf6aOqOO9oxs9kcVpDJc83d2hgqJRKecGS1sSypE+ilqnw0cDQK9gwcrSHl4Odb3R8MmjSlmeD3IBHcowdw+Xx/uzCvY5z2IPDhv+Q8f7eznZMLK/1XHMH65aWTNm2bmkJrfGtLXQSMAm87L/RGOMWke3e1+c6HuCFAK+9CGwG6oFdIpIGvAv4szHGPW3fl73tmOt6MXOod5DLTg24TDIi2ZnpFORkcmRwlMWFOVE7r09Lh5OVYa4DLy3KITMjjc7+ESoX5UV03Rf2dHJaEk0j9Tm9voL/fbaFiUlDelrirWdRSqlY8U9a0+UcoSg3k+vetzamiWdebenm5LrEK1Pho4GhV6yGl4Ndp9yRy1nHVwU8pvyJ6B1jx3B5vD+7cK/jGvZQmBf95BHx/t7OdkzUr1WUE/SYZX8qYGdr39ERQ4CdrX22ZvoNQTXQY4wJlBWoDThdRLKMMZ5ZjvftG+h4gBpgF7AIyA20rzFmVER6vfvG3aQxVnH7KE4lBSsBTUf/sC2BYXMYiWf81VdZhe4jCQyNMTzf3MnXL0+YeD5klYvyWFyYw1uHjrBuWcncByil1ALmy1i6bWcbf9zRFvNSFS+3dPGlD54Q02uGQ7OSel2zqWHG4nw7hpcjuU60jsnKSLNluPyaTQ1MfxAds88ugvfkGhmzZcQwnt/bUD7vqF0rQ7hm83Hw6jMwNjOOuuLMOm57aAfbD/QwPjHJ9gM93PbQDq44s27W9tksDwiWKtbtt89sxxPkHNOPn21f3/5BryUi14rIq7O0JWp6XG4KcjLIz47ug5IKRy6d/fYs5t/T7qS+qjjs4+qrHew5HFmh+30dLtLThOVlcX24EbHTGyp4vtnehEBKKZVMTmuopLntCEdimEW+48gwg+4xam3M3j9fMQkMReQqEXldREZEpFNEfiAiIS1qEZEcEfmkiPxWRA54z7FfRH4pImui1cbNjTXcfHEj5Y5cBGsU5eaLG6P+JCGS60TjmOzMdM5aU2nLk5ETa8vISBNKi3Ji+tllpAmXnrwiYbKSxut7G+rnHbVrvX8Dm9dUwEvb4N9uhNb9U47ZtK6Gqzc1cNeju7jkG49w16O7uHpTQ7yzkg4DwYrY5fjtM9vxBDnH9ONn29e3f9BrGWPuNsacOEtboqa1Z5Ali6Mf7FQU59lWsmJPez8NYWQk9WmoLmZPhCUrnvdmI02wSiMhO62+guebOxdciSQ1k4h8SkR+7i3LMyEiEX3TReQUEXlCRAZExCUij4rIxig3V6m4yclM55TVFTy1q33unaMkkctU+Ng+lVREbgG+DTwN3AQsAT4HnCYiJxtjhuY4xQrgbuBZ4IdAO7AKuA64TEQuMMZsi0ZbfcPLdovkOvM9ZvvbPfz342/OK/lCMFt3tnHGmqqYDI37v6ffvXKAne/0hX0O57CHIhuK20N8vrdxu9b1X4EXnoRb/8H6e/26oy9tWlcT70BwunbgeBHJDjCdtAZrmmmwaaS+4337vhXgeDg2dfQIMEKA6aIikg0sxuoP4661dzCqGUl9KopzaYlwdG42zmEPrpGxiKa+rq520HLYFdFauxf2dHL9BWvDvmaiqK0sYmLScLB7kBVhJO1RSekfsPqY14F8rHuusIjIqcBTWH3aV72bbwCeEZHTjTFN0WmqUvF1bmMN9z7VzAdPid46+9kkcpkKH1tHDEWkFPg34BXgXO+T8K9iZQg8HitQnEs3cIIx5ixjzNeNMT80xvwTcKb39f9nR9sXmvUrFjPsGaelwxXV8xpjePT1Vi7YuDSq5w3FpnU1vLqvG9fIbPfzM7lGPDhsqmOYUkTg9PPgq9+FVcfB/mboit2TtzC9gtXfney/UURygI3AXFM3X/F+PS3Aa6cCLmAPgLce4p+x6iNOHzU82duOmEwVncuh3iFb1n5aJSuiP2K4p72f1VWOiJ62FuVm4cjP4lBveEmQOo4M0zvg5vgli8K+ZqIQEZ1OmjrOARzGmPdg1VyNxB2AB3iPMeY2Y8xtwHsAg1UDVqkFYePKxfS43GH/XoiEr0zFu2pL5945juyeSvoXWGtp7jTGHM0Na4x5CNiPlSJ+VsaYXmPM9gDb3wR2AutmHKRmSBPh/A1LeWx7a1TP29LhYtgzzvoVi6N63lAU5mZyUl0523aGF4zYWeA+JZWUQUYGHD4I/3ELPP0HayTxi1fBJy60vr64Nd6t/BXWTc3N07Z/EquP+rlvg4hUichxIuK/DvBp4DDwCREp8Nt3A9aN2P1+pSoAfuk9r39tQ7zXHwfum8d7iZrWnkGW2jWV1IY1htb6wvATz/g0VFsJaMLx/J5OTq0vT/qMnqfVV/BCc2e8m6FsZow54H04FRERqcPKmny/MeZoAi3v3+8HzhORyvm3VKn4S09L4+y11Wxtsv+hdtPBPlaUFyZsmQofuwPDuVK8H+d/kxUOb0r4KkB/04Xo/A1LeGpX+4xae/Px2PZWzt+wNG7zpd+3cSmPvR5esGtXgfuUd8b58MVvwsP/C1u+DX1dgLG+/uQ7cQ0OvVOfvoc1/fwBEfmEiNzKsWnu/jUMv4E1XfRkv+PHsGY4LMWaTnW9iHwJeBxrVsM/T7vkPViF778tIrd6r/cAVg3D/zTGvG3LGw1Ta++gbSOGXc4RJqO8pq25vZ+G6uKIj2+odrCnPbwpri80d3B6Q/LfB69bVsLh/mG6Xfas/VQLxlz3bQK8O3bNUcpem9dVs3Vnm+1rsF/Z1x3VmsF2sTswnCvFu/jtE67rsALDH0d4fMopd+RSX+WYtQh6OEbHJnhqVzvnbwh7CUPUbFy5mEH3WMjrmSaNYWBkjEKb1himvOrlgIHJaQ8fPKPwwL3xaJG/m4EvAGuxgsTLgTuB94fyhN0Ycz9wKVa20W8Bfw88A5zh/2Tdu68HOA/4H6yp898DjgM+y7E1O3GztamNj3/nSXoHRvn8j59na1OgLjpy/rUMo8UYY40YRlCqwqc+zBFD17CHlg4XJ6xM7Kk/ochIT+PkunJe3KPPUtWsQi3No9SCsLrKQXqasLut39brvNLSxUkJXL/QJ6TkMyJSzMwpWLO5wxjTR3gp3kMmIqdjzXPfAfzHLPtdC1x73XXXhXuJBev8jUt55PV3opJk57ndHdRXOSi3oTZiqKwpskt4dHsrN4QwxWzQPUZednpYxbFVmPp6gmzvjm07pvFOZ7+VOdbIGGOuBq4O8trvgd+HeL1+rIQNN4TRTNttbWrj9oebjs4c6HK6uf1hK5dENJNvlTuiW8uw2+XGYObV39RVFnGwe5CxiUkyQ+gDXtrbxQkrFs8o2ZKsTm+o4OE/v8MlJ66Id1PULOZxzxUNEd+36T2XSkYiwuZ1NTzZ1MYam9aSJ0OZCp9Qs5IWM3Oq1Gx+BvQxNW379PkroaSIn0FE3g08jJUl8CJjjDvYvsaYu4G7r7/+es3R7XV6QwXfe2QnHf3DVBaHX+jZ32NvtHLhCcui1LLIvXfDEm74wbNc+941ZGXMfgPnGvZQqNNI7VVS5p1GGmC7irst25pnTCcfHZtgy7bmqAaGFcXWdNK1UcpLtae9n/rq4nllVc7JyqB6UR5vd7qoD2FK6vMLZBqpz7try7j1dzsYdI9RkKOzJhJYMZHdc0VDOKV5ptB7LpWsNjfWcNOPnuPT5x9vy8DBK/u6OKm2PKHLVPiE9O69i5kljD8t3kP9U7xPV4OVDCLkFZ8i8i7gj4AT2DR9+paaW1ZGOpvW1fD49kPzOk9H/zD7O1yc3lARpZZFrqI4j7oqB8/vnnuKlGtkTDOS2u2yqyFr2j1FVra1XcVdd5BsocG2RyraRe73tDtpmEfiGZ/6agfNIawzdI9NsP3tXk5ZnfhTf0KVm5VB4/ISXmkJ8OBGJYx53HNFw1z3bRB4mqlSSatqUR7VJXm8tt+emU2vtHRzYhKsLwT71xjOluL9FKDZGBNSjlgROQErKBzACgoPRqeJqed9G5fy+ButTExG/lDv8e2H2LSuZs4Rulh534alPBpCxlXNSBoDp26Gq26CknJArK9X3WRtV3FXFmQqZrDtkYp2kfvmw/0hjfLNpT7EQvev7++hrqpowfUXVtkKXWeogpqrNI/BSqyl1IJybmONLdlJj5apWJUca9XtDgx/izWF9AYRORpBiMglQC1+KeK920u9aeId07afADwBDGEFhQmR0S9Z1VYW4cjLYvvbQdaCzWFi0vD4G628Lw61C4M5/bgK9nU46eiffWayncXtlZ9TN8M3fwI/eMT6qkFhwrhg48xkUdmZ6VyzqSGq16kszqVzjv+PoZo0hr3zTDzjE2rJioU2jdTnlNUVvLavG8949LJTq+QU6J7LO/r4KvAREan227ca+Aiw1RijBTHVgvOe46t5uaWL4dHxqJ43WcpU+NgaGBpjuoGvYKV9f0JErhWRf8Gq8bUbuH3aITdgpYn/oG+DiCzHGilcBPwQOF1EPj7tT76d72Mhet/GyGsabj/QgyMvK6EW0WZlpHPOumqeeGP2KbKuER0xVKlrYnKS55s7uehdSyl35CJYSWJuvrgxqusLIbpTSdt6hyjIyaQ4P9Cyp/CsLC+ko3+EEU/wX/4Tk4aX9nZxWgJMlY+2RQXZrCgv5I0DvfFuirKBiFwiIl8WkS8Ddd5tX/b+mZ4Ia8Y9l9dNWGsMnxGRm0XkZqwMzGnA5219A0rFiSMvi8ZlJTzfHN3nHslSpsIn1OQzETPG3CoivcAtwB2AC6u485dCnEa6EvBVT//aLPsMzbOpKWXTuhru3dZsBUphPsV47PXEGi30ed+Gpfzr/a/xsfesDrrA1zU8hiNJntooFW2/e+UgedkZ3HhR47ySuISivDiPLpdVy3C+C+59iWeiISM9jZXlhbQcdtK4fHHAfd48dITFhTnzTtCVqE7zTidNhtTpKmwfAv562rave78eBL471wmMMc+LyDnAv3n/GOB54CPGmDei1lKlEszmdTU8/kYr562ffxm2rU1tbNnWTJdzhEUF2VQW50X9AawdYpKz3xhzrzFmgzEmxxhTboz5G2PMjNXvxpiveRdS3+u37akQFl4fiMX7WEgKczM5qa6cbWHWL3ONeHh1Xzeb1iXeD3ddlYPC3Ey2vx38SbiuMVSpqts1wi+e2ctnYxAUAuRkppOXnRGVWoZ7DjtpiMI0Up+5EtBY00gX3mihz+n1lby4p5NJmws6q9gzxlw9y73Simn7zrjn8nvtBWPMucaYAmNMoTHmfcaYP8fqfSgVD6c2VNDc3k/fYNCCByHxlYXq8q6zPzI4yu0PN0W9ZrAdtJhbCrOmk4aXnXTbznZOqitP2ALx588xRdYaIU3Mtitlp+8/uotLTlzBstKCmF2zwhGdBDTNURwxBKivCp6AxhjDC82dCzowrFmcT0FOJs02F3RWSqlkkpOZzqn1FTy96/C8zjNbWahEp4FhCtu4cjGD7jFaDs+dut0nUaeR+mxaV80rLV24RjwBX3cOe7RchUo5LzR3cqB7kMvPrI3pdSuikIBmfGKStzsHWB2FUhU+DdUO9gTp9w50DTA5aVhVkThrqO2g2UmVUmqmzY018x7Zi1VZKDtoYJjC0kQ4f8OSkMo8ALQcdjLoHmPjysDrchJBUW4WJ9WV89TOwCmHtcC9SjUjnnHuemwXN160LublZSocuUen0kTqYPcA5Y5c8rKjtyR+SWkBziEPzuGZD5Be2NPJaQ0VMZluG0+nH1fJC1FOsqCUUslu44pSegbctPaEVE0voJKCwInSol0Wyg4aGKa4925YwtO72kNKXf7o9lbO37Bk3okk7Hb+xiVBp5NqgXuVan769B4al5WwcWXsayhVFOfSMc/MpM1RKlPhL02E1dWOgNNJn2/uXJDZSKdbXeVg2DPOO/O4+VFKqYUmPU04Z201W3dGNmo4PDqOATLSp94r21EWyg4aGKa4iuI86qocPLd79ifHnvEJnt7Vzns3zD9Tk91OWFmKa2TmFNlJYxgYGUvY9ZFKRdu+DidP7Gjj2veuicv1o7HGsLm9n4Yori/0qa9ysGdaApou5wgd/cM0LiuJ+vUSTZoIp9VX8IJOJ1VKqSk2N9awbWc7JswEXcYYbv3dG5y8upzPX7LB9rJQdrC9XIVKfO/bsJRHt7fOmmn0+d2d1FU5qEiC9O2+KbKPv3GIOr91SYPuMXKz0slI1+chauGbmDR85+GdXLO5ISr1/yIRjTWGe9qdXHjCsii16JiG6mKe2DE1+dYLezo5ZXU56Wmp0Uec3lDJT5/ew0fPiO3aU6WUSmR1lUVkpAlvtfVz/JJFIR933/P76Blw8/cf3EhWRnpSBILTpcZvPzWr04+rYF+Hk45ZbuAe3d7K+zYkbtKZ6d67YQnbdrZNmSI7MDympSpUynj4tYNkpEtck0X51hiG+9TVxz02QVvvIKsqCqPcMqtkxZ7Dzilte6G5k9PqF/40Up/1KxbT2jtI78D8UrMrpdRCIiJhJ6F5dV83D758gC9/+F0xX88fTRoYKrIy0jlnXTV/fCNw6YqO/mH2dTg5/bjkuWGqLM6jttIxJeuec0QzkqrU0Dvg5md/2suNFzXGdU1wTlYGuVkZHBmKrJbhvg4ny8oKbfklW+7IZWLS0OMNigZGxmhu6+fE2rKoXytRZaancWJtOS/tnVFWWCmlUtqmdTX86c3DjE9Mzrnv4SPD/L/fbucfLzuBsqLETzAzGw0MFWBNJ338jUMBCx4/8cYhzllXnXRPQN43LQmNa1hrGKrU8P3H3uTCE5ayojz6I23hsqaTRrbOcI8NiWd8RMQqW+FdZ/hKSxfrl5eQk5VaKyxOa6jgec1OqpRSU1QtyqOmJJ/X9nfPup97bIJ/vf81PnZmHY3LEzdrf6g0MFQA1FU5KMrN5PW3e6ZsnzSGx984lFTTSH1Ob6hk72Hn0TVOrhGPTiVVC97Le7to6XDysbNWx7spgDcBTcSBoT2JZ3zqq4uPFnl/vrmD04+rtO1aieqkujJ2vXOE4dHxeDdFKaUSijWdNHD5M7CSzdz++x2sLC/k0pNWxK5hNtLAUB11/salPL596nTS7W/3UpibOSWJS7LIzkznnLXV/HGHNUfcOayBoVrY3GMTfPfRndxw4TqyMxNjhL+yOJdOZ2QJaPa0O6m3se9pqC6m+XA/nvEJ/ry/h1NWl9t2rUSVn53JmqWLeHXf7E/FlVIq1bzn+CpeaekK+uDsNy+9TWvPIDdd3Lhgat9qYKiO2rSumldaunCNHCv6/Nj2Vs6PY/KK+XrfxqU8/kYrk8bgGh6jSIvbqwXs53/ay5qaRQm1Tq7ckRtRyYpB9xi9g26Wldk3Hba+2sHedid/3t/DivLCuGVvjbfTdTqpUkrN4MjLonFZScCSbtsP9HDf8/v5ykfenTAPYqMhtRZTqFkV5WZxUl0523a284GTVjAwMsYrLV185sK18W5axFZXOSjIzuSNA724RjxULUr8chtKhWNrUxtbtjXT5RxBBK5/3/HxbtIUHf3D/PGNQ/zhtXcoc+RyzaaGOVN4b21q438ef5MRzwRXf3dbSMdE4s/7exjxTPDPv3qVgpwMtja1JWV68fmamJzkqZ3tPLWzPazv0ZZtzXQ7R0I+JtLjYnXMtp1t/PLZFlp7BllaWsAVZ9bNWsZJKbXwbWqs4fHtrVPqeHc5R/iv32zni3+xkcokKOMWDg0M1RTnb1zCj57czQdOWsG2nW2cVFee9KNsviQ0o2MTmnxGLShbm9q4/eEmRsessizGwA+ebKYgJyshApytTW089OpBPONWVrcu5wi3P9wEELR9099TKMdE2rbbH246mnBr0D1uy3US3damNn74ZDO+tGN2fo8iOS5Wx2zb2ca925q55ZL1rFtaws7WPm57aAeABodKpbBT6yu48w9N9A26KSnIwTM+wdfvf43LTlnJu1aVxrt5UaeBoZrihJWldDpHuPy2JzgyOEpxXlbSP0XftK6GHzzxFhMGnm/upDyMp9tKJbIt25qP3vz6jI5NsGVbc0L8fG/Z1nw0KPQZHZvgzkd2crB7IOAxv3v1YEzeU6J/drES7HOI5Hs02zGRHherY/644xD/UTfEirs+B33dbCwp41/e8yH+49kWDQyVSmE5memsKCvkk99/miH3ONmZ6SwvK+DDp62Kd9NsoYGhmuKpne0Mj44zMWk9P+4f9iT9U/RX93UzPmnwviXbRiCUirXuIGv3gm2PtWDtGB4dD7omI9gi/2i/p0T/7GIlmt+j2Y6J9LioHuMeIzsznbrdz5A+MYazuApncRUjeQ7Wd+xg+Z6nweOtudnXxfI//IjaorOBs4O+J6XUwra1qY3mdidj3nqG7rEJDnQNsG1n+4K8h9TAUE2xZVvz0aDQJ9mfom/Z1sy0t5T070kpgDJHLl0BbuzLHIlRYDdY+8oduUHLaTzyemtM3lOif3axEs3v0WzHRHqcLcfkHoY9O+HQc/DmEfjXu+n95QvI2OiU/cUzyidcLwZ9P0qphW/LtuajQaHP6Pjkgr2H1KykaoqF+BR9Ib4nFRkRuUpEXheRERHpFJEfiEhIKTxFJEdEPikivxWRA95z7BeRX4rImgD7rxARE+TPzmi8n2s2NcwYNcnOTOeaTQ3ROP28RdK+WL2nRP/sYiWW36NYXSvoMZuPs/5x4nvgY9fDF/4L/vVuAErGAk8xLfG4gl5HKbXwpdo9pI4YqikW4lP0hfieVPhE5Bbg28DTwE3AEuBzwGkicrIxZmiOU6wA7gaeBX4ItAOrgOuAy0TkAmPMtgDH/QZ4YNq2/gjfxhS+p5WRZIeMhUjaF6v3lOifXazE8nsUq2tFcoyUlENfV+DtSqmUlWr3kGKMmXuvJHf99dcbgLvuuiveTUl407O5gfWk9eaLG5P2hmkhvqcFyrbqsCJSChwEdgGnGWMmvNsvAX4H/JMx5j/mOMdiYKkxZvu07ccDrwNNxpgT/bavAN4G/sUY87Vw26z9llIx9OJW+Ml3jq0xBMjKhqtuglM3z3bkwqhqHUXad6mFZIHeQwbtt3TEUE2xEJ+iL8T3pML2F0AecKcvKAQwxjwkIvuBjwOzBobGmF6gN8D2N71TQ9cFO1ZEcoA0Y8xwZM1XStnKF/w9cC/0dUNJGVx29VxBoVJqgUu1e0gNDNUMmxtrFtwP/EJ8TyosJ3m/vhDgtReBK0SkwBgzGO6JRSQNqAI6g+zyeeCr1q5yCNgC/LsxZjTI/kqpeDh1swaCSqkZUukeUgNDpVQqqPZ+bQvwWhvWtIpqYE8E574OKzD8+rTtk8BW4EGsaaxlwF8CX8Fa13iB/+ilUkoppVQ8aWColEoaIlIM3BzGIXcYY/qwppECBBqlc3u/5gV4ba72nA7cCuxg2lRUY8w7wLnTDvmhiNwNfBK4HPh5gHNeC1x73XXXhdscpZRSSqmIaWColEomxcA/h7H/z4A+wLe2LxuYnl4sx/s1rPV/IvJu4GGs7KQXGWPccxzi8+9YgeHFBAgMjTF3A3f7EjgopZRSSsVCSgWG119/fbyboJQKztx1112zZvgzxhwgsiyA7d6vNUDLtNdqAOO3z5xE5F3AHwEnsMkYE2iKajCtwARQGsrO2m8pldDm7LdSlfZdSiWsoP2WFrhXSqWCV7xfTwvw2ilAc6iJZ0TkBKygcAArKDwYZltWAekET1ajlFJKKRVzKVHHMFwi8qp/PbJUpZ+DRT8HSzJ/DiJShpUApgk4PUAdw68YY/7Nb/9SrBG9w8YYp9/2E4AngCHgHGPM/lmuudhb4sJ/WxrwC+CjwEeNMfdF6S0m9fcnmvRzsOjnYNHPIbHp98ein4NFPwdLPD+HlJpKqpRKTcaYbhH5CvAt4AkR+SXWFNLPA7uB26cdcgPWWsZrgHsBRGQ51kjhIuAO4HRv8hl/vzHGDHn/fo+IFAHPY00fLQU+BLwb+C3w6yi+RaWUUkqpedHAUCmVEowxt4pIL3ALVmDnAu4DvhTiNNKVwGLv3782yz6+wPBh4ErgWqAEKyPqLuAzwH8bYyYjeBtKKaWUUrbQwDCwu+PdgAShn4NFPwdL0n8Oxph78Y4AzrHf15gW/BljniKMxDfGmB8CPwyjefOV9N+fKNHPwaKfg0U/h8Sm3x+Lfg4W/RwscfscdI2hUkoppZRSSqU4zUqqlFJKKaWUUilOA0OllFJKKaWUSnEaGGKlkBeRW0Rkt4i4RaRVRG4Vkfx4ty2WRMQE+RNSfbdkIyL/ICL3i8h+7/s8MMf+DSLyoIgcEZEhEXlGRDbHqLm2CedzEJGvzfJz8oUYNjvlab91TCr1XdpvHaN9V3LSvsuSSv0WaN/lk+j9liafsdwG3Aj8BrgVWOP99wkicl6KZQ98hpmLXsfi0ZAY+A+gD/gzUDzbjiJSi1V2YBz4JuAEPgk8JiIXGmOesLeptgr5c/BzC9AzbdtrUWyTmpv2W1OlSt+l/dYx2nclJ+27jkmVfgu07/JJ6H4r5QNDEVkLfBZ4wBjzIb/tb2OltL8cqyB1qthvjPlZvBsRI7W+AuUishMomGXfb2D9B363MWa795ifYJUf+J6IHGeSN5NTOJ+Dz4PGmAO2tkoFpf1WQKnSd2m/dYz2XUlG+64ZUqXfAu27fBK639KppHAFVgr626dtvwcYBj4e6wbFm4hkiUgoP6hJzfcfcy7e6S2XAk/5Oijv8YPAD4B64CQ72hgLoX4O04lIkYik/MOlONF+K4BU6Lu03zpG+66kpH3XNKnQb4H2XT6J3m9pYGj9cE0CL/tvNMa4ge0k8Q9fhD6M1TkPiEiXiNwpIo54NyrO1gPZwAsBXnvR+zXVfk52YE3tcIvI8yJyYbwblGK035pJ+66ptN8KTPuu+NK+ayrtt2bSvmummPVb+sQMqoEeY8xogNfagNNFJMsY44lxu+LhZeB+oAUoAi4CbgDOFpHTvU9rUlG192tbgNd822pi1JZ468daD/E8cARo4P+3d/+gVV1xAMe/Pyw1iJvtZMXJwalOajspdtLB2cGl6FAQFZwUN3EqaKEIHcTB0qEgooiFulShS7GTFoo4GAUVB1EENeBwHM6J7yW9eUmg755nzvcDj0vue5Bzby7fcJL7B44C1yPi2/IAeY2f3ZrLdv2X3ZrrJbZrEtiuAbvVzXYNvKTnbjkxhDVAV6AAZoY+s+IjlVLaNm/VxYi4A5wGjpRli9aUZddxMjPvMytaSumH+esi4gLwD3A2Ii41/MusT3ZriO3qZLeG2K6JYbsKu7Ug21XU6JankuZ/4a9e4L2poc+06ntyoPfUHkhFsz//ruOk+WMkpfQc+Il8ofjXdUfTDLu1uNbbZbcWYbuqsF2jtd4tsF0jjbtbTgzhCfBZRHQdgOvJpzys+L9cLSSl9I6yj2qPpaInZdl16sLsuq5THloyXZYtHyd9sluLsF12a4mmy7LV46RvtmsEuwXYrqWYLsv//ThxYgi3yfth6/DKiJgCtgB/VxjTxCj74QvgWe2xVHSXfErDVx3vbS/Lpo8TYFNZtnyc9MluLcJ22a0lsl39sl0j2C3Adi3F2LrlxBB+BRL5Ys5hB8nnMP/S94BqiIh1C7x1inwt6rUehzNRyvnb14AdEfHl7Ppye+kDwH3m3WFtJYqIT7rulhYRG4DvgOfkC6Q1fnarsF3d7NaA7Zootgu7NYrtymp1q/mbz6SU7kbEOeBQRFwGfgM2A4eBW7TzoNWTEbEd+AN4RH7g5m5gJ/AX8GPFsY1FROwHNpYvPwc+jYiT5euHKaWfhz5+HNgF3IiIs8Ar8i+y9cCej/hBq8vZD2uBBxFxBfiXwR2yDpT39qWU3vY28IbZrTmaapfdGrBdHx/b9UFT3QLbNWviu5VSav4FrAKOAffI/75+DJwB1tYeW4/7YC/we9n2GeA1+ZlCJ4Cp2uMb0zbfJP/lsut1s+Pzm4Gr5NsHvwH+BL6pvR197QfyheDnyad5vADeAU+BS8DW2tvR2stufdgPTbXLbi1/X9iuyXrZrva6VbbZdi1jP9TqVpRvLkmSJElqlNcYSpIkSVLjnBhKkiRJUuOcGEqSJElS45wYSpIkSVLjnBhKkiRJUuOcGEqSJElS45wYSpIkSVLjnBhKkiRJUuOcGEqSJElS45wYSpIkSVLj3gPOkdtRFqI/bwAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 1080x1152 with 12 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s=random.randint(0,len(dataset_test)-sequence_len)\n",
- "\n",
- "sequence = dataset_test[s:s+sequence_len]\n",
- "sequence_true = dataset_test[s:s+sequence_len+1]\n",
- "\n",
- "pred = loaded_model.predict( np.array([sequence]) )\n",
- "\n",
- "# ---- Show result\n",
- "pwk.plot_multivariate_serie(sequence_true, predictions=pred, labels=features, save_as='02-prediction-norm')\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.3 Real prediction\n",
- "We are now going to make a true prediction, with an un-normalized result"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:37:59.129365Z",
- "iopub.status.busy": "2021-03-09T21:37:59.128977Z",
- "iopub.status.idle": "2021-03-09T21:38:01.006348Z",
- "shell.execute_reply": "2021-03-09T21:38:01.006681Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/SYNOP/figs/SYNOP2-03-prediction</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 3024x2304 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Gap between prediction and reality : 1.40 °C\n"
- ]
- }
- ],
- "source": [
- "def denormalize(mean,std,seq):\n",
- " nseq = seq.copy()\n",
- " for i,s in enumerate(nseq):\n",
- " s = s*std + mean\n",
- " nseq[i]=s\n",
- " return nseq\n",
- "\n",
- "\n",
- "# ---- Get a sequence\n",
- "\n",
- "i=random.randint(0,len(dataset_test)-sequence_len)\n",
- "sequence = dataset_test[i:i+sequence_len]\n",
- "sequence_true = dataset_test[i:i+sequence_len+1]\n",
- "\n",
- "# ---- Prediction\n",
- "\n",
- "pred = loaded_model.predict( np.array([sequence]) )\n",
- "\n",
- "# ---- De-normalization\n",
- "\n",
- "sequence_true = denormalize(mean,std, sequence_true)\n",
- "pred = denormalize(mean,std, pred)\n",
- "\n",
- "# ---- Show it\n",
- "feat=11\n",
- "\n",
- "pwk.plot_multivariate_serie(sequence_true, predictions=pred, labels=features, only_features=[feat],width=14, height=8, save_as='03-prediction')\n",
- "\n",
- "delta_deg=abs(sequence_true[-1][feat]-pred[-1][feat])\n",
- "print(f'Gap between prediction and reality : {delta_deg:.2f} °C')\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:38:01.010098Z",
- "iopub.status.busy": "2021-03-09T21:38:01.009596Z",
- "iopub.status.idle": "2021-03-09T21:38:01.012691Z",
- "shell.execute_reply": "2021-03-09T21:38:01.012324Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Tuesday 09 March 2021, 22:38:00\n",
- "Duration is : 00:02:08 328ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/SYNOP/SYNOP3-12h-predictions.ipynb b/SYNOP/SYNOP3-12h-predictions.ipynb
index 749d936..40d9c3f 100644
--- a/SYNOP/SYNOP3-12h-predictions.ipynb
+++ b/SYNOP/SYNOP3-12h-predictions.ipynb
@@ -81,9 +81,7 @@
"\n",
"scale = 1 # Percentage of dataset to be used (1=all)\n",
"train_prop = .8 # Percentage for train (the rest being for the test)\n",
- "sequence_len = 16\n",
- "batch_size = 32\n",
- "epochs = 10"
+ "sequence_len = 16 # Sequence len"
]
},
{
@@ -99,7 +97,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.override('iterations', 'scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs')"
+ "pwk.override('iterations', 'scale', 'train_prop', 'sequence_len')"
]
},
{
@@ -107,7 +105,7 @@
"metadata": {},
"source": [
"## Step 2 - Read and prepare dataset\n",
- "As before, in episode 2... ;-)"
+ "**Note** : The `scale` and `train_prop` parameters must be identical to those used during training (SYNOP2)... ;-)"
]
},
{
diff --git a/SYNOP/SYNOP3-12h-predictions==done==.ipynb b/SYNOP/SYNOP3-12h-predictions==done==.ipynb
deleted file mode 100644
index 1332520..0000000
--- a/SYNOP/SYNOP3-12h-predictions==done==.ipynb
+++ /dev/null
@@ -1,553 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [SYNOP3] - 12h predictions\n",
- "<!-- DESC --> Episode 3: Attempt to predict in a more longer term \n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Prediction at 12:00\n",
- " - Understand the principle of using recurrent neurons... and the limitations of our example !\n",
- "\n",
- "\n",
- "SYNOP meteorological data, available at: https://public.opendatasoft.com\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Read the data\n",
- " - Make a reccurent prediction\n",
- "\n",
- "## Step 1 - Import and init\n",
- "### 1.1 - Python"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:38:02.377959Z",
- "iopub.status.busy": "2021-03-09T21:38:02.377649Z",
- "iopub.status.idle": "2021-03-09T21:38:03.845186Z",
- "shell.execute_reply": "2021-03-09T21:38:03.844775Z"
- }
- },
- "outputs": [
- {
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- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.19\n",
- "Notebook id : SYNOP3\n",
- "Run time : Tuesday 09 March 2021, 22:38:03\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Run dir : ./run/SYNOP\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/SYNOP/figs\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "from tensorflow.keras.callbacks import TensorBoard\n",
- "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
- "\n",
- "import numpy as np\n",
- "import math, random\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import pandas as pd\n",
- "import h5py, json\n",
- "import os,time,sys\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "run_dir = './run/SYNOP'\n",
- "datasets_dir = pwk.init('SYNOP3', run_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:38:03.849727Z",
- "iopub.status.busy": "2021-03-09T21:38:03.849197Z",
- "iopub.status.idle": "2021-03-09T21:38:03.851702Z",
- "shell.execute_reply": "2021-03-09T21:38:03.851969Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- About dataset (no need to change)\n",
- "#\n",
- "dataset_dir = './data' # Enhanced dataset is very small, so ./data in a good choice :-)\n",
- "dataset_filename = 'synop-LYS.csv'\n",
- "schema_filename = 'synop.json'\n",
- "features = ['tend', 'cod_tend', 'dd', 'ff', 'td', 'u', 'ww', 'pres', 'rafper', 'rr1', 'rr3', 'tc']\n",
- "features_len = len(features)\n",
- "\n",
- "# ---- About training\n",
- "#\n",
- "iterations = 4 # number of iterations for prediction (1 iteration = 3h)\n",
- "\n",
- "scale = 1 # Percentage of dataset to be used (1=all)\n",
- "train_prop = .8 # Percentage for train (the rest being for the test)\n",
- "sequence_len = 16\n",
- "batch_size = 32\n",
- "epochs = 10"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Override parameters (batch mode) - Just forget this cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:38:03.855035Z",
- "iopub.status.busy": "2021-03-09T21:38:03.854696Z",
- "iopub.status.idle": "2021-03-09T21:38:03.857210Z",
- "shell.execute_reply": "2021-03-09T21:38:03.856916Z"
- }
- },
- "outputs": [],
- "source": [
- "pwk.override('iterations', 'scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Read and prepare dataset\n",
- "As before, in episode 2... ;-)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:38:03.862282Z",
- "iopub.status.busy": "2021-03-09T21:38:03.861927Z",
- "iopub.status.idle": "2021-03-09T21:38:03.970353Z",
- "shell.execute_reply": "2021-03-09T21:38:03.970041Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset : (29165, 14)\n",
- "Train dataset : (23332, 12)\n",
- "Test dataset : (5833, 12)\n"
- ]
- }
- ],
- "source": [
- "# ---- Read dataset\n",
- "\n",
- "df = pd.read_csv(f'{dataset_dir}/{dataset_filename}', header=0, sep=';')\n",
- "\n",
- "# ---- Scaling\n",
- "\n",
- "df = df[:int(scale*len(df))]\n",
- "train_len=int(train_prop*len(df))\n",
- "\n",
- "# ---- Train / Test\n",
- "dataset_train = df.loc[ :train_len-1, features ]\n",
- "dataset_test = df.loc[train_len:, features ]\n",
- "\n",
- "# ---- Normalize, and convert to numpy array\n",
- "mean = dataset_train.mean()\n",
- "std = dataset_train.std()\n",
- "dataset_train = np.array( (dataset_train - mean) / std )\n",
- "dataset_test = np.array( (dataset_test - mean) / std )\n",
- "\n",
- "print('Dataset : ',df.shape)\n",
- "print('Train dataset : ',dataset_train.shape)\n",
- "print('Test dataset : ',dataset_test.shape)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Predict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.1 - Load model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:38:03.973371Z",
- "iopub.status.busy": "2021-03-09T21:38:03.972783Z",
- "iopub.status.idle": "2021-03-09T21:38:04.085411Z",
- "shell.execute_reply": "2021-03-09T21:38:04.085664Z"
- }
- },
- "outputs": [],
- "source": [
- "loaded_model = tf.keras.models.load_model(f'{run_dir}/best_model.h5')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.2 Make a 12h prediction\n",
- "Note : Our predictions are normalized"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:38:04.090781Z",
- "iopub.status.busy": "2021-03-09T21:38:04.090440Z",
- "iopub.status.idle": "2021-03-09T21:38:06.304104Z",
- "shell.execute_reply": "2021-03-09T21:38:06.303822Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/SYNOP/figs/SYNOP3-01-prediction-norm</div>"
- ],
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- "<IPython.core.display.HTML object>"
- ]
- },
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\n",
- "text/plain": [
- "<Figure size 1080x1152 with 12 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "\n",
- "# ---- Initial sequence\n",
- "\n",
- "s=random.randint(0,len(dataset_test)-sequence_len-iterations)\n",
- "\n",
- "sequence_pred = dataset_test[s:s+sequence_len].copy()\n",
- "sequence_true = dataset_test[s:s+sequence_len+iterations].copy()\n",
- "\n",
- "# ---- Iterate on 4 predictions\n",
- "\n",
- "sequence_pred=list(sequence_pred)\n",
- "\n",
- "for i in range(iterations):\n",
- " sequence=sequence_pred[-sequence_len:]\n",
- " pred = loaded_model.predict( np.array([sequence]) )\n",
- " sequence_pred.append(pred[0])\n",
- "\n",
- "# ---- Extract the predictions \n",
- "\n",
- "pred=np.array(sequence_pred[-iterations:])\n",
- " \n",
- "# ---- Show result\n",
- "\n",
- "pwk.plot_multivariate_serie(sequence_true, predictions=pred, labels=features, save_as='01-prediction-norm')\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.3 Full prediction\n",
- "#### Some cool functions that do the job"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:38:06.309336Z",
- "iopub.status.busy": "2021-03-09T21:38:06.309027Z",
- "iopub.status.idle": "2021-03-09T21:38:06.311219Z",
- "shell.execute_reply": "2021-03-09T21:38:06.310898Z"
- }
- },
- "outputs": [],
- "source": [
- "def denormalize(mean,std,seq):\n",
- " nseq = seq.copy()\n",
- " for i,s in enumerate(nseq):\n",
- " s = s*std + mean\n",
- " nseq[i]=s\n",
- " return nseq\n",
- "\n",
- "\n",
- "def get_prediction(dataset, model, iterations=4,sequence_len=16):\n",
- "\n",
- " # ---- Initial sequence\n",
- "\n",
- " s=random.randint(0,len(dataset)-sequence_len-iterations)\n",
- "\n",
- " sequence_pred = dataset[s:s+sequence_len].copy()\n",
- " sequence_true = dataset[s:s+sequence_len+iterations].copy()\n",
- "\n",
- " # ---- Iterate\n",
- "\n",
- " sequence_pred=list(sequence_pred)\n",
- "\n",
- " for i in range(iterations):\n",
- " sequence=sequence_pred[-sequence_len:]\n",
- " pred = model.predict( np.array([sequence]) )\n",
- " sequence_pred.append(pred[0])\n",
- "\n",
- " # ---- Extract the predictions \n",
- "\n",
- " pred=np.array(sequence_pred[-iterations:])\n",
- "\n",
- " # ---- De-normalization\n",
- "\n",
- " sequence_true = denormalize(mean,std, sequence_true)\n",
- " pred = denormalize(mean,std, pred)\n",
- "\n",
- " return sequence_true,pred"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### And the result is..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:38:06.315042Z",
- "iopub.status.busy": "2021-03-09T21:38:06.314323Z",
- "iopub.status.idle": "2021-03-09T21:38:06.878509Z",
- "shell.execute_reply": "2021-03-09T21:38:06.878172Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/SYNOP/figs/SYNOP3-02-prediction</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 3024x2304 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- " \n",
- "sequence_true, pred = get_prediction(dataset_test, loaded_model,iterations=4)\n",
- "\n",
- "feat=11\n",
- "\n",
- "pwk.plot_multivariate_serie(sequence_true, predictions=pred, labels=features,\n",
- " only_features=[feat],width=14, height=8, save_as='02-prediction')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-09T21:38:06.881718Z",
- "iopub.status.busy": "2021-03-09T21:38:06.881350Z",
- "iopub.status.idle": "2021-03-09T21:38:06.885277Z",
- "shell.execute_reply": "2021-03-09T21:38:06.884396Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Tuesday 09 March 2021, 22:38:06\n",
- "Duration is : 00:00:03 043ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"todo\">\n",
- " What you can do:\n",
- " <ul>\n",
- " <li>Trying to increase the forecasting time</li>\n",
- " <li>What could we do to try to improve our forecasts?</li>\n",
- " </ul>\n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/VAE/01-VAE-with-MNIST.ipynb b/VAE/01-VAE-with-MNIST.ipynb
index 83460de..1c6ad1a 100644
--- a/VAE/01-VAE-with-MNIST.ipynb
+++ b/VAE/01-VAE-with-MNIST.ipynb
@@ -67,7 +67,8 @@
"source": [
"## Step 2 - Parameters\n",
"`scale` : With scale=1, we need 1'30s on a GPU V100 ...and >20' on a CPU !\\\n",
- "`latent_dim` : 2 dimensions is small, but usefull to draw !\n",
+ "`latent_dim` : 2 dimensions is small, but usefull to draw !\\\n",
+ "`fit_verbosity` : verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch\n",
"\n",
"\n",
"`loss_weights` : Our **loss function** is the weighted sum of two loss:\n",
@@ -80,18 +81,19 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"latent_dim = 2\n",
"loss_weights = [1,.001]\n",
"\n",
- "scale = .1\n",
+ "scale = 1\n",
"seed = 123\n",
"\n",
"batch_size = 64\n",
- "epochs = 10"
+ "epochs = 10\n",
+ "fit_verbosity = 1"
]
},
{
@@ -107,7 +109,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.override('latent_dim', 'loss_weights', 'scale', 'seed', 'batch_size', 'epochs')"
+ "pwk.override('latent_dim', 'loss_weights', 'scale', 'seed', 'batch_size', 'epochs', 'fit_verbosity')"
]
},
{
@@ -264,7 +266,7 @@
"source": [
"pwk.chrono_start()\n",
"\n",
- "history = vae.fit(x_data, epochs=epochs, batch_size=batch_size, callbacks=callbacks_list,)\n",
+ "history = vae.fit(x_data, epochs=epochs, batch_size=batch_size, callbacks=callbacks_list, verbose=fit_verbosity)\n",
"\n",
"pwk.chrono_show()"
]
@@ -381,9 +383,11 @@
}
],
"metadata": {
+ "interpreter": {
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
+ },
"kernelspec": {
- "display_name": "Python 3",
- "language": "python",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
@@ -396,7 +400,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.7"
}
},
"nbformat": 4,
diff --git a/VAE/02-VAE-with-MNIST.ipynb b/VAE/02-VAE-with-MNIST.ipynb
index da74204..a109ccc 100644
--- a/VAE/02-VAE-with-MNIST.ipynb
+++ b/VAE/02-VAE-with-MNIST.ipynb
@@ -73,7 +73,8 @@
"source": [
"## Step 2 - Parameters\n",
"`scale` : With scale=1, we need 1'30s on a GPU V100 ...and >20' on a CPU !\\\n",
- "`latent_dim` : 2 dimensions is small, but usefull to draw !\n",
+ "`latent_dim` : 2 dimensions is small, but usefull to draw !\\\n",
+ "`fit_verbosity` : verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch\n",
"\n",
"\n",
"`loss_weights` : Our **loss function** is the weighted sum of two loss:\n",
@@ -93,11 +94,12 @@
"latent_dim = 2\n",
"loss_weights = [1,.01]\n",
"\n",
- "scale = 1\n",
+ "scale = 0.01\n",
"seed = 123\n",
"\n",
"batch_size = 64\n",
- "epochs = 10"
+ "epochs = 10\n",
+ "fit_verbosity = 1"
]
},
{
@@ -113,7 +115,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.override('latent_dim', 'loss_weights', 'scale', 'seed', 'batch_size', 'epochs')"
+ "pwk.override('latent_dim', 'loss_weights', 'scale', 'seed', 'batch_size', 'epochs', 'fit_verbosity')"
]
},
{
@@ -260,7 +262,7 @@
"source": [
"pwk.chrono_start()\n",
"\n",
- "history = vae.fit(x_data, epochs=epochs, batch_size=batch_size, callbacks=callbacks_list,)\n",
+ "history = vae.fit(x_data, epochs=epochs, batch_size=batch_size, callbacks=callbacks_list, verbose=fit_verbosity)\n",
"\n",
"pwk.chrono_show()"
]
@@ -470,9 +472,11 @@
}
],
"metadata": {
+ "interpreter": {
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
+ },
"kernelspec": {
- "display_name": "Python 3",
- "language": "python",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
@@ -485,7 +489,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.7"
}
},
"nbformat": 4,
diff --git a/environments/environment-cpu.yml b/environments/environment-cpu.yml
index c582c77..01ce33b 100644
--- a/environments/environment-cpu.yml
+++ b/environments/environment-cpu.yml
@@ -2,6 +2,7 @@ name: fidle-cpu
channels:
- default
dependencies:
+ - numpy=1.19
- tensorflow
- keras
- scikit-learn
diff --git a/environments/environment-gpu.yml b/environments/environment-gpu.yml
index b9ff07c..0c77ab9 100644
--- a/environments/environment-gpu.yml
+++ b/environments/environment-gpu.yml
@@ -2,6 +2,7 @@ name: fidle-gpu
channels:
- default
dependencies:
+ - numpy=1.19
- tensorflow-gpu
- keras
- scikit-learn
diff --git a/fidle/01-update-index.ipynb b/fidle/01-update-index.ipynb
index 6173420..1cf98df 100644
--- a/fidle/01-update-index.ipynb
+++ b/fidle/01-update-index.ipynb
@@ -27,23 +27,23 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import nbformat\n",
"from nbconvert.preprocessors import ExecutePreprocessor\n",
"from IPython.display import display,Image,Markdown,HTML\n",
- "import re\n",
- "import sys, os, glob\n",
+ "# import re, os, glob, time\n",
+ "import sys\n",
"import datetime, time\n",
"\n",
- "import json\n",
- "from collections import OrderedDict\n",
- "from importlib import reload\n",
+ "# import json\n",
+ "# from collections import OrderedDict\n",
+ "# from importlib import reload\n",
"\n",
"sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
+ "# import fidle.pwk as pwk\n",
"import fidle.config as config\n",
"import fidle.cookindex as cookindex\n"
]
@@ -58,7 +58,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -86,227 +86,28 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Read : LinearReg/01-Linear-Regression.ipynb\n",
- "Read : LinearReg/02-Gradient-descent.ipynb\n",
- "Read : LinearReg/03-Polynomial-Regression.ipynb\n",
- "Read : LinearReg/04-Logistic-Regression.ipynb\n",
- "Read : IRIS/01-Simple-Perceptron.ipynb\n",
- "Read : BHPD/01-DNN-Regression.ipynb\n",
- "Read : BHPD/02-DNN-Regression-Premium.ipynb\n",
- "Read : MNIST/01-DNN-MNIST.ipynb\n",
- "Read : MNIST/02-CNN-MNIST.ipynb\n",
- "Read : GTSRB/01-Preparation-of-data.ipynb\n",
- "Read : GTSRB/02-First-convolutions.ipynb\n",
- "Read : GTSRB/03-Tracking-and-visualizing.ipynb\n",
- "Read : GTSRB/04-Data-augmentation.ipynb\n",
- "Read : GTSRB/05-Full-convolutions.ipynb\n",
- "Read : GTSRB/06-Notebook-as-a-batch.ipynb\n",
- "Read : GTSRB/07-Show-report.ipynb\n",
- "Read : IMDB/01-One-hot-encoding.ipynb\n",
- "Read : IMDB/02-Keras-embedding.ipynb\n",
- "Read : IMDB/03-Prediction.ipynb\n",
- "Read : IMDB/04-Show-vectors.ipynb\n",
- "Read : IMDB/05-LSTM-Keras.ipynb\n",
- "Read : SYNOP/LADYB1-Ladybug.ipynb\n",
- "Read : SYNOP/SYNOP1-Preparation-of-data.ipynb\n",
- "Read : SYNOP/SYNOP2-First-predictions.ipynb\n",
- "Read : SYNOP/SYNOP3-12h-predictions.ipynb\n",
- "Read : AE/01-Prepare-MNIST-dataset.ipynb\n",
- "Read : AE/02-AE-with-MNIST.ipynb\n",
- "Read : AE/03-AE-with-MNIST-post.ipynb\n",
- "Read : AE/04-ExtAE-with-MNIST.ipynb\n",
- "Read : AE/05-ExtAE-with-MNIST.ipynb\n",
- "Read : VAE/01-VAE-with-MNIST.ipynb\n",
- "Read : VAE/02-VAE-with-MNIST.ipynb\n",
- "Read : VAE/03-VAE-with-MNIST-post.ipynb\n",
- "Read : VAE/05-About-CelebA.ipynb\n",
- "Read : VAE/06-Prepare-CelebA-datasets.ipynb\n",
- "Read : VAE/07-Check-CelebA.ipynb\n",
- "Read : VAE/08-VAE-with-CelebA.ipynb\n",
- "Read : VAE/09-VAE-with-CelebA-post.ipynb\n",
- "Read : DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb\n",
- "Read : Misc/Activation-Functions.ipynb\n",
- "Read : Misc/Numpy.ipynb\n",
- "Read : Misc/Scratchbook.ipynb\n",
- "Read : Misc/Using-Tensorboard.ipynb\n",
- "Catalog saved as ../fidle/logs/catalog.json\n",
- "Entries : 46\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "# ---- Get the notebook list\n",
- "#\n",
- "files_list = cookindex.get_files(directories_to_index.keys())\n",
- "\n",
- "# ---- Get a detailled catalog for this list\n",
- "#\n",
- "catalog = cookindex.get_catalog(files_list)\n",
- "\n",
- "with open(config.CATALOG_FILE,'wt') as fp:\n",
- " json.dump(catalog,fp,indent=4)\n",
- " print(f'Catalog saved as {config.CATALOG_FILE}')\n",
- " print('Entries : ',len(catalog))"
+ "cookindex.build_catalog(directories_to_index)\n",
+ "cookindex.build_default_profile()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 3.2 build index"
+ "### 3.3 Buil index"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**Index is :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "\n",
- "### Linear and logistic regression\n",
- "- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) \n",
- "Low-level implementation, using numpy, of a direct resolution for a linear regression\n",
- "- **[GRAD1](LinearReg/02-Gradient-descent.ipynb)** - [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb) \n",
- "Low level implementation of a solution by gradient descent. Basic and stochastic approach.\n",
- "- **[POLR1](LinearReg/03-Polynomial-Regression.ipynb)** - [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb) \n",
- "Illustration of the problem of complexity with the polynomial regression\n",
- "- **[LOGR1](LinearReg/04-Logistic-Regression.ipynb)** - [Logistic regression](LinearReg/04-Logistic-Regression.ipynb) \n",
- "Simple example of logistic regression with a sklearn solution\n",
- "\n",
- "### Perceptron Model 1957\n",
- "- **[PER57](IRIS/01-Simple-Perceptron.ipynb)** - [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb) \n",
- "Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !\n",
- "\n",
- "### Basic regression using DNN\n",
- "- **[BHPD1](BHPD/01-DNN-Regression.ipynb)** - [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb) \n",
- "Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)\n",
- "- **[BHPD2](BHPD/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb) \n",
- "A more advanced implementation of the precedent example\n",
- "\n",
- "### Basic classification using a DNN\n",
- "- **[MNIST1](MNIST/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb) \n",
- "An example of classification using a dense neural network for the famous MNIST dataset\n",
- "- **[MNIST2](MNIST/02-CNN-MNIST.ipynb)** - [Simple classification with CNN](MNIST/02-CNN-MNIST.ipynb) \n",
- "An example of classification using a convolutional neural network for the famous MNIST dataset\n",
- "\n",
- "### Images classification with Convolutional Neural Networks (CNN)\n",
- "- **[GTSRB1](GTSRB/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB/01-Preparation-of-data.ipynb) \n",
- "Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset\n",
- "- **[GTSRB2](GTSRB/02-First-convolutions.ipynb)** - [First convolutions](GTSRB/02-First-convolutions.ipynb) \n",
- "Episode 2 : First convolutions and first classification of our traffic signs\n",
- "- **[GTSRB3](GTSRB/03-Tracking-and-visualizing.ipynb)** - [Training monitoring](GTSRB/03-Tracking-and-visualizing.ipynb) \n",
- "Episode 3 : Monitoring, analysis and check points during a training session\n",
- "- **[GTSRB4](GTSRB/04-Data-augmentation.ipynb)** - [Data augmentation ](GTSRB/04-Data-augmentation.ipynb) \n",
- "Episode 4 : Adding data by data augmentation when we lack it, to improve our results\n",
- "- **[GTSRB5](GTSRB/05-Full-convolutions.ipynb)** - [Full convolutions](GTSRB/05-Full-convolutions.ipynb) \n",
- "Episode 5 : A lot of models, a lot of datasets and a lot of results.\n",
- "- **[GTSRB6](GTSRB/06-Notebook-as-a-batch.ipynb)** - [Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb) \n",
- "Episode 6 : To compute bigger, use your notebook in batch mode\n",
- "- **[GTSRB7](GTSRB/07-Show-report.ipynb)** - [Batch reports](GTSRB/07-Show-report.ipynb) \n",
- "Episode 7 : Displaying our jobs report, and the winner is...\n",
- "- **[GTSRB10](GTSRB/batch_oar.sh)** - [OAR batch script submission](GTSRB/batch_oar.sh) \n",
- "Bash script for an OAR batch submission of an ipython code\n",
- "- **[GTSRB11](GTSRB/batch_slurm.sh)** - [SLURM batch script](GTSRB/batch_slurm.sh) \n",
- "Bash script for a Slurm batch submission of an ipython code\n",
- "\n",
- "### Sentiment analysis with word embedding\n",
- "- **[IMDB1](IMDB/01-One-hot-encoding.ipynb)** - [Sentiment analysis with hot-one encoding](IMDB/01-One-hot-encoding.ipynb) \n",
- "A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB)\n",
- "- **[IMDB2](IMDB/02-Keras-embedding.ipynb)** - [Sentiment analysis with text embedding](IMDB/02-Keras-embedding.ipynb) \n",
- "A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)\n",
- "- **[IMDB3](IMDB/03-Prediction.ipynb)** - [Reload and reuse a saved model](IMDB/03-Prediction.ipynb) \n",
- "Retrieving a saved model to perform a sentiment analysis (movie review)\n",
- "- **[IMDB4](IMDB/04-Show-vectors.ipynb)** - [Reload embedded vectors](IMDB/04-Show-vectors.ipynb) \n",
- "Retrieving embedded vectors from our trained model\n",
- "- **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](IMDB/05-LSTM-Keras.ipynb) \n",
- "Still the same problem, but with a network combining embedding and RNN\n",
- "\n",
- "### Time series with Recurrent Neural Network (RNN)\n",
- "- **[LADYB1](SYNOP/LADYB1-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](SYNOP/LADYB1-Ladybug.ipynb) \n",
- "Artificial dataset generation and prediction attempt via a recurrent network\n",
- "- **[SYNOP1](SYNOP/SYNOP1-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/SYNOP1-Preparation-of-data.ipynb) \n",
- "Episode 1 : Data analysis and preparation of a usuable meteorological dataset (SYNOP)\n",
- "- **[SYNOP2](SYNOP/SYNOP2-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/SYNOP2-First-predictions.ipynb) \n",
- "Episode 2 : RNN training session for weather prediction attempt at 3h\n",
- "- **[SYNOP3](SYNOP/SYNOP3-12h-predictions.ipynb)** - [12h predictions](SYNOP/SYNOP3-12h-predictions.ipynb) \n",
- "Episode 3: Attempt to predict in a more longer term \n",
- "\n",
- "### Unsupervised learning with an autoencoder neural network (AE)\n",
- "- **[AE1](AE/01-Prepare-MNIST-dataset.ipynb)** - [Prepare a noisy MNIST dataset](AE/01-Prepare-MNIST-dataset.ipynb) \n",
- "Episode 1: Preparation of a noisy MNIST dataset\n",
- "- **[AE2](AE/02-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/02-AE-with-MNIST.ipynb) \n",
- "Episode 1 : Construction of a denoising autoencoder and training of it with a noisy MNIST dataset.\n",
- "- **[AE3](AE/03-AE-with-MNIST-post.ipynb)** - [Playing with our denoiser model](AE/03-AE-with-MNIST-post.ipynb) \n",
- "Episode 2 : Using the previously trained autoencoder to denoise data\n",
- "- **[AE4](AE/04-ExtAE-with-MNIST.ipynb)** - [Denoiser and classifier model](AE/04-ExtAE-with-MNIST.ipynb) \n",
- "Episode 4 : Construction of a denoiser and classifier model\n",
- "- **[AE5](AE/05-ExtAE-with-MNIST.ipynb)** - [Advanced denoiser and classifier model](AE/05-ExtAE-with-MNIST.ipynb) \n",
- "Episode 5 : Construction of an advanced denoiser and classifier model\n",
- "\n",
- "### Generative network with Variational Autoencoder (VAE)\n",
- "- **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [First VAE, using functional API (MNIST dataset)](VAE/01-VAE-with-MNIST.ipynb) \n",
- "Construction and training of a VAE, using functional APPI, with a latent space of small dimension.\n",
- "- **[VAE2](VAE/02-VAE-with-MNIST.ipynb)** - [First VAE, using a subclass model (MNIST dataset)](VAE/02-VAE-with-MNIST.ipynb) \n",
- "Construction and training of a VAE, using model subclass, with a latent space of small dimension.\n",
- "- **[VAE3](VAE/03-VAE-with-MNIST-post.ipynb)** - [Analysis of the VAE's latent space of MNIST dataset](VAE/03-VAE-with-MNIST-post.ipynb) \n",
- "Visualization and analysis of the VAE's latent space of the dataset MNIST\n",
- "- **[VAE5](VAE/05-About-CelebA.ipynb)** - [Another game play : About the CelebA dataset](VAE/05-About-CelebA.ipynb) \n",
- "Episode 1 : Presentation of the CelebA dataset and problems related to its size\n",
- "- **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Generation of a clustered dataset](VAE/06-Prepare-CelebA-datasets.ipynb) \n",
- "Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset\n",
- "- **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered dataset](VAE/07-Check-CelebA.ipynb) \n",
- "Episode : 3 Clustered dataset verification and testing of our datagenerator\n",
- "- **[VAE8](VAE/08-VAE-with-CelebA.ipynb)** - [Training session for our VAE](VAE/08-VAE-with-CelebA.ipynb) \n",
- "Episode 4 : Training with our clustered datasets in notebook or batch mode\n",
- "- **[VAE9](VAE/09-VAE-with-CelebA-post.ipynb)** - [Data generation from latent space](VAE/09-VAE-with-CelebA-post.ipynb) \n",
- "Episode 5 : Exploring latent space to generate new data\n",
- "- **[VAE10](VAE/batch_slurm.sh)** - [SLURM batch script](VAE/batch_slurm.sh) \n",
- "Bash script for SLURM batch submission of VAE8 notebooks \n",
- "\n",
- "### Generative Adversarial Networks (GANs)\n",
- "- **[DCGAN01](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb) \n",
- "Episode 1 : Draw me a sheep, revisited with a DCGAN\n",
- "\n",
- "### Miscellaneous\n",
- "- **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb) \n",
- "Some activation functions, with their derivatives.\n",
- "- **[NP1](Misc/Numpy.ipynb)** - [A short introduction to Numpy](Misc/Numpy.ipynb) \n",
- "Numpy is an essential tool for the Scientific Python.\n",
- "- **[SCRATCH1](Misc/Scratchbook.ipynb)** - [Scratchbook](Misc/Scratchbook.ipynb) \n",
- "A scratchbook for small examples\n",
- "- **[TSB1](Misc/Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb) \n",
- "4 ways to use Tensorboard from the Jupyter environment"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
+ "catalog = cookindex. read_catalog()\n",
"styles = open('css/readme.css', \"r\").read()\n",
"\n",
"lines_md=[]\n",
@@ -357,17 +158,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "README.md is updated.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# ---- Load README.md\n",
"#\n",
@@ -432,17 +225,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "README.ipynb built and saved\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# ---- Create Notebook from scratch\n",
"#\n",
@@ -477,17 +262,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Completed on : Friday 22 October 2021, 15:43:20\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"now = datetime.datetime.now()\n",
"print('Completed on : ', now.strftime(\"%A %d %B %Y, %H:%M:%S\"))"
@@ -504,10 +281,10 @@
],
"metadata": {
"interpreter": {
- "hash": "7822d55dc7294a4f6f06b86d8ad2ca65bd6e1ee5d72628c47c30a06bbf89aef6"
+ "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
},
"kernelspec": {
- "display_name": "Python 3.9.7 64-bit ('fidle': conda)",
+ "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
"name": "python3"
},
"language_info": {
diff --git a/fidle/02-running-ci-tests.ipynb b/fidle/02-running-ci-tests.ipynb
index 0231841..cbc1df8 100644
--- a/fidle/02-running-ci-tests.ipynb
+++ b/fidle/02-running-ci-tests.ipynb
@@ -10,7 +10,9 @@
"# Gestion des tests d'intégration continue\n",
"\n",
"**La liste des notebooks a éxécuter** et de leurs paramètres (override) est définie dans un **profile**.\\\n",
- "Un **rapport d'éxécution** est généré durant l'éxécution des tests.\n"
+ "Un **rapport d'éxécution** est généré durant l'éxécution des tests.\n",
+ "\n",
+ "## Step 1 - Init"
]
},
{
@@ -23,23 +25,6 @@
"import os"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Generate default profile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "profile = cookci.get_default_profile()\n",
- "cookci.save_profile(profile, './ci/default.yml')"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -71,7 +56,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# cookci.run_profile(profile_name)\n",
+ "cookci.run_profile(profile_name)\n",
"cookci.build_ci_report(profile_name)"
]
},
diff --git a/fidle/ci/basic_example.yml b/fidle/ci/basic_example.yml
deleted file mode 100644
index f5595b3..0000000
--- a/fidle/ci/basic_example.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-_metadata_:
- version: '1.0'
- output_tag: ==done==
- save_figs: false
- description: A basic profile example
-LINR1:
- notebook_id: LINR1
- notebook_dir: LinearReg
- notebook_src: 01-Linear-Regression.ipynb
- notebook_tag: default
-PER57:
- notebook_id: PER57
- notebook_dir: IRIS
- notebook_src: 01-Simple-Perceptron.ipynb
- notebook_tag: default
-# GTSRB1:
-# notebook_id: GTSRB1
-# notebook_dir: GTSRB
-# notebook_src: 01-Preparation-of-data.ipynb
-# notebook_tag: default
-# overrides:
-# scale: 0.1
-# output_dir: ./data
-# GTSRB2:
-# notebook_id: GTSRB2
-# notebook_dir: GTSRB
-# notebook_src: 02-First-convolutions.ipynb
-# notebook_tag: default
-# overrides:
-# run_dir: ./run/GTSRB2_ci
-# dataset_name: set-24x24-L
-# batch_size: 32
-# epochs: 3
-# scale: 0.1
\ No newline at end of file
diff --git a/fidle/ci/default.yml b/fidle/ci/default.yml
index 1297528..0fc539a 100644
--- a/fidle/ci/default.yml
+++ b/fidle/ci/default.yml
@@ -37,21 +37,29 @@ Nb_BHPD1:
notebook_dir: BHPD
notebook_src: 01-DNN-Regression.ipynb
notebook_tag: default
+ overrides:
+ fit_verbosity: default
Nb_BHPD2:
notebook_id: BHPD2
notebook_dir: BHPD
notebook_src: 02-DNN-Regression-Premium.ipynb
notebook_tag: default
+ overrides:
+ fit_verbosity: default
Nb_MNIST1:
notebook_id: MNIST1
notebook_dir: MNIST
notebook_src: 01-DNN-MNIST.ipynb
notebook_tag: default
+ overrides:
+ fit_verbosity: default
Nb_MNIST2:
notebook_id: MNIST2
notebook_dir: MNIST
notebook_src: 02-CNN-MNIST.ipynb
notebook_tag: default
+ overrides:
+ fit_verbosity: default
Nb_GTSRB1:
notebook_id: GTSRB1
notebook_dir: GTSRB
@@ -60,6 +68,7 @@ Nb_GTSRB1:
overrides:
scale: default
output_dir: default
+ progress_verbosity: default
Nb_GTSRB2:
notebook_id: GTSRB2
notebook_dir: GTSRB
@@ -72,6 +81,7 @@ Nb_GTSRB2:
batch_size: default
epochs: default
scale: default
+ fit_verbosity: default
Nb_GTSRB3:
notebook_id: GTSRB3
notebook_dir: GTSRB
@@ -84,6 +94,7 @@ Nb_GTSRB3:
batch_size: default
epochs: default
scale: default
+ fit_verbosity: default
Nb_GTSRB4:
notebook_id: GTSRB4
notebook_dir: GTSRB
@@ -96,6 +107,7 @@ Nb_GTSRB4:
batch_size: default
epochs: default
scale: default
+ fit_verbosity: default
Nb_GTSRB5:
notebook_id: GTSRB5
notebook_dir: GTSRB
@@ -110,7 +122,7 @@ Nb_GTSRB5:
epochs: default
scale: default
with_datagen: default
- verbose: default
+ fit_verbosity: default
Nb_GTSRB6:
notebook_id: GTSRB6
notebook_dir: GTSRB
@@ -145,6 +157,7 @@ Nb_IMDB1:
hide_most_frequently: default
batch_size: default
epochs: default
+ fit_verbosity: default
Nb_IMDB2:
notebook_id: IMDB2
notebook_dir: IMDB
@@ -159,6 +172,7 @@ Nb_IMDB2:
batch_size: default
epochs: default
output_dir: default
+ fit_verbosity: default
Nb_IMDB3:
notebook_id: IMDB3
notebook_dir: IMDB
@@ -185,13 +199,15 @@ Nb_IMDB5:
notebook_src: 05-LSTM-Keras.ipynb
notebook_tag: default
overrides:
+ run_dir: default
vocab_size: default
hide_most_frequently: default
review_len: default
dense_vector_size: default
batch_size: default
epochs: default
- output_dir: default
+ fit_verbosity: default
+ scale: default
Nb_LADYB1:
notebook_id: LADYB1
notebook_dir: SYNOP
@@ -225,6 +241,7 @@ Nb_SYNOP2:
sequence_len: default
batch_size: default
epochs: default
+ fit_verbosity: default
Nb_SYNOP3:
notebook_id: SYNOP3
notebook_dir: SYNOP
@@ -236,8 +253,6 @@ Nb_SYNOP3:
scale: default
train_prop: default
sequence_len: default
- batch_size: default
- epochs: default
Nb_AE1:
notebook_id: AE1
notebook_dir: AE
@@ -245,6 +260,9 @@ Nb_AE1:
notebook_tag: default
overrides:
run_dir: default
+ prepared_dataset: default
+ scale: default
+ progress_verbosity: default
Nb_AE2:
notebook_id: AE2
notebook_dir: AE
@@ -311,6 +329,7 @@ Nb_VAE1:
seed: default
batch_size: default
epochs: default
+ fit_verbosity: default
Nb_VAE2:
notebook_id: VAE2
notebook_dir: VAE
@@ -324,6 +343,7 @@ Nb_VAE2:
seed: default
batch_size: default
epochs: default
+ fit_verbosity: default
Nb_VAE3:
notebook_id: VAE3
notebook_dir: VAE
diff --git a/fidle/ci/fidle-ad_s04.yml b/fidle/ci/fidle-ad_s04.yml
deleted file mode 100644
index d3deb8c..0000000
--- a/fidle/ci/fidle-ad_s04.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-_metadata_:
- version: '1.0'
- output_tag: ==done==
- save_figs: true
- description: Light profile for S04 with CPU
-#
-# ------ SYNOP -----------------------------------------------------
-#
-Nb_LADYB1:
- notebook_id: LADYB1
- notebook_dir: SYNOP
- notebook_src: LADYB1-Ladybug.ipynb
- notebook_tag: default
- overrides:
- run_dir: default
- scale: default
- train_prop: default
- sequence_len: default
- predict_len: default
- batch_size: default
- epochs: default
-Nb_SYNOP1:
- notebook_id: SYNOP1
- notebook_dir: SYNOP
- notebook_src: SYNOP1-Preparation-of-data.ipynb
- notebook_tag: default
- overrides:
- output_dir: default
-Nb_SYNOP2:
- notebook_id: SYNOP2
- notebook_dir: SYNOP
- notebook_src: SYNOP2-First-predictions.ipynb
- notebook_tag: default
- overrides:
- scale: default
- train_prop: default
- sequence_len: default
- batch_size: default
- epochs: default
-Nb_SYNOP3:
- notebook_id: SYNOP3
- notebook_dir: SYNOP
- notebook_src: SYNOP3-12h-predictions.ipynb
- notebook_tag: default
- overrides:
- iterations: default
- scale: default
- train_prop: default
- sequence_len: default
- batch_size: default
- epochs: default
\ No newline at end of file
diff --git a/fidle/ci/fidle-ad_s05.yml b/fidle/ci/fidle-ad_s05.yml
deleted file mode 100644
index 9016674..0000000
--- a/fidle/ci/fidle-ad_s05.yml
+++ /dev/null
@@ -1,68 +0,0 @@
-_metadata_:
- version: '1.0'
- output_tag: ==done==
- save_figs: true
- description: Heavy profile for S05 with GPU
-#
-# ------ AE --------------------------------------------------------
-#
-Nb_AE1:
- notebook_id: AE1
- notebook_dir: AE
- notebook_src: 01-Prepare-MNIST-dataset.ipynb
- notebook_tag: default
- overrides:
- run_dir: default
-Nb_AE2:
- notebook_id: AE2
- notebook_dir: AE
- notebook_src: 02-AE-with-MNIST.ipynb
- notebook_tag: default
- overrides:
- run_dir: default
- prepared_dataset: default
- dataset_seed: default
- scale: 1.
- latent_dim: default
- train_prop: default
- batch_size: default
- epochs: 30
-Nb_AE3:
- notebook_id: AE3
- notebook_dir: AE
- notebook_src: 03-AE-with-MNIST-post.ipynb
- notebook_tag: default
- overrides:
- run_dir: default
- prepared_dataset: default
- dataset_seed: default
- scale: 1.
- train_prop: default
-Nb_AE4:
- notebook_id: AE4
- notebook_dir: AE
- notebook_src: 04-ExtAE-with-MNIST.ipynb
- notebook_tag: default
- overrides:
- run_dir: default
- prepared_dataset: default
- dataset_seed: 145
- scale: 1.
- latent_dim: default
- train_prop: default
- batch_size: default
- epochs: 30
-Nb_AE5:
- notebook_id: AE5
- notebook_dir: AE
- notebook_src: 05-ExtAE-with-MNIST.ipynb
- notebook_tag: default
- overrides:
- run_dir: default
- prepared_dataset: default
- dataset_seed: 145
- scale: 1.
- latent_dim: default
- train_prop: default
- batch_size: default
- epochs: 30
\ No newline at end of file
diff --git a/fidle/ci/fidle-ad_s06.yml b/fidle/ci/fidle-ad_s06.yml
deleted file mode 100644
index d74b707..0000000
--- a/fidle/ci/fidle-ad_s06.yml
+++ /dev/null
@@ -1,144 +0,0 @@
-_metadata_:
- version: '1.0'
- output_tag: ==done==
- save_figs: true
- description: Heavy profile for S05 with GPU
-#
-# ------ VAE --------------------------------------------------------
-#
-Nb_VAE1:
- notebook_id: VAE1
- notebook_dir: VAE
- notebook_src: 01-VAE-with-MNIST.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/VAE1_done
- latent_dim: 2
- loss_weights: [1,0.0001]
- scale: 1
- seed: 123
- batch_size: 64
- epochs: 10
-
-Nb_VAE2_r0:
- notebook_id: VAE2
- notebook_dir: VAE
- notebook_src: 02-VAE-with-MNIST.ipynb
- notebook_tag: =0==done==
- overrides:
- run_dir: ./run/VAE2_done_0001
- latent_dim: 2
- loss_weights: [1,0.0001]
- scale: 1
- seed: 123
- batch_size: 64
- epochs: 10
-
-Nb_VAE2_r1:
- notebook_id: VAE2
- notebook_dir: VAE
- notebook_src: 02-VAE-with-MNIST.ipynb
- notebook_tag: =1==done==
- overrides:
- run_dir: ./run/VAE2_done_01
- latent_dim: 2
- loss_weights: [1,0.01]
- scale: 1
- seed: 123
- batch_size: 64
- epochs: 10
-
-Nb_VAE2_r2:
- notebook_id: VAE2
- notebook_dir: VAE
- notebook_src: 02-VAE-with-MNIST.ipynb
- notebook_tag: =2==done==
- overrides:
- run_dir: ./run/VAE2_done_001
- latent_dim: 2
- loss_weights: [1,0.001]
- scale: 1
- seed: 123
- batch_size: 64
- epochs: 10
-
-Nb_VAE2_r3:
- notebook_id: VAE2
- notebook_dir: VAE
- notebook_src: 02-VAE-with-MNIST.ipynb
- notebook_tag: =3==done==
- overrides:
- run_dir: ./run/VAE2_done_005
- latent_dim: 2
- loss_weights: [1,0.005]
- scale: 1
- seed: 123
- batch_size: 64
- epochs: 10
-
-Nb_VAE3:
- notebook_id: VAE3
- notebook_dir: VAE
- notebook_src: 03-VAE-with-MNIST-post.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/VAE2_done_0001
- scale: 1
- seed: 123
-
-Nb_VAE5:
- notebook_id: VAE5
- notebook_dir: VAE
- notebook_src: 05-About-CelebA.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/VAE5_done
-
-Nb_VAE6:
- notebook_id: VAE6
- notebook_dir: VAE
- notebook_src: 06-Prepare-CelebA-datasets.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/VAE6_done
- scale: 0.02
- seed: 123
- cluster_size: 10000
- image_size: '(128,128)'
- output_dir: ./data
- exit_if_exist: False
-
-Nb_VAE7:
- notebook_id: VAE7
- notebook_dir: VAE
- notebook_src: 07-Check-CelebA.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/VAE7_done
- image_size: '(128,128)'
- enhanced_dir: ./data
-
-Nb_VAE8:
- notebook_id: VAE8
- notebook_dir: VAE
- notebook_src: 08-VAE-with-CelebA.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/VAE8_done
- scale: 1
- image_size: '(192,160)'
- enhanced_dir: '{datasets_dir}/celeba/enhanced'
- latent_dim: 300
- loss_weights: [0.6,0.4]
- batch_size: 64
- epochs: 15
-
-Nb_VAE9:
- notebook_id: VAE9
- notebook_dir: VAE
- notebook_src: 09-VAE-with-CelebA-post.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/VAE8_done
- image_size: '(192,160)'
- enhanced_dir: '{datasets_dir}/celeba/enhanced'
\ No newline at end of file
diff --git a/fidle/ci/small_cpu.yml b/fidle/ci/small_cpu.yml
index c6d3a77..785a9b1 100644
--- a/fidle/ci/small_cpu.yml
+++ b/fidle/ci/small_cpu.yml
@@ -12,48 +12,48 @@ _metadata_:
#
# ------ LinearReg -------------------------------------------------
#
-Nb_LINR1:
- notebook_id: LINR1
- notebook_dir: LinearReg
- notebook_src: 01-Linear-Regression.ipynb
- notebook_tag: default
-Nb_GRAD1:
- notebook_id: GRAD1
- notebook_dir: LinearReg
- notebook_src: 02-Gradient-descent.ipynb
- notebook_tag: default
-Nb_POLR1:
- notebook_id: POLR1
- notebook_dir: LinearReg
- notebook_src: 03-Polynomial-Regression.ipynb
- notebook_tag: default
-Nb_LOGR1:
- notebook_id: LOGR1
- notebook_dir: LinearReg
- notebook_src: 04-Logistic-Regression.ipynb
- notebook_tag: default
-Nb_PER57:
- notebook_id: PER57
- notebook_dir: IRIS
- notebook_src: 01-Simple-Perceptron.ipynb
- notebook_tag: default
+# Nb_LINR1:
+# notebook_id: LINR1
+# notebook_dir: LinearReg
+# notebook_src: 01-Linear-Regression.ipynb
+# notebook_tag: default
+# Nb_GRAD1:
+# notebook_id: GRAD1
+# notebook_dir: LinearReg
+# notebook_src: 02-Gradient-descent.ipynb
+# notebook_tag: default
+# Nb_POLR1:
+# notebook_id: POLR1
+# notebook_dir: LinearReg
+# notebook_src: 03-Polynomial-Regression.ipynb
+# notebook_tag: default
+# Nb_LOGR1:
+# notebook_id: LOGR1
+# notebook_dir: LinearReg
+# notebook_src: 04-Logistic-Regression.ipynb
+# notebook_tag: default
+# Nb_PER57:
+# notebook_id: PER57
+# notebook_dir: IRIS
+# notebook_src: 01-Simple-Perceptron.ipynb
+# notebook_tag: default
#
# ------ BHPD ------------------------------------------------------
#
-Nb_BHPD1:
- notebook_id: BHPD1
- notebook_dir: BHPD
- notebook_src: 01-DNN-Regression.ipynb
- notebook_tag: default
- overrides:
- fit_verbosity: 2
-Nb_BHPD2:
- notebook_id: BHPD2
- notebook_dir: BHPD
- notebook_src: 02-DNN-Regression-Premium.ipynb
- notebook_tag: default
- overrides:
- fit_verbosity: 2
+# Nb_BHPD1:
+# notebook_id: BHPD1
+# notebook_dir: BHPD
+# notebook_src: 01-DNN-Regression.ipynb
+# notebook_tag: default
+# overrides:
+# fit_verbosity: 2
+# Nb_BHPD2:
+# notebook_id: BHPD2
+# notebook_dir: BHPD
+# notebook_src: 02-DNN-Regression-Premium.ipynb
+# notebook_tag: default
+# overrides:
+# fit_verbosity: 2
#
# ------ MNIST -----------------------------------------------------
#
@@ -62,11 +62,15 @@ Nb_BHPD2:
# notebook_dir: MNIST
# notebook_src: 01-DNN-MNIST.ipynb
# notebook_tag: default
+# overrides:
+# fit_verbosity: 2
# Nb_MNIST2:
# notebook_id: MNIST2
# notebook_dir: MNIST
# notebook_src: 02-CNN-MNIST.ipynb
# notebook_tag: default
+# overrides:
+# fit_verbosity: 2
#
# ------ GTSRB -----------------------------------------------------
#
@@ -78,6 +82,7 @@ Nb_BHPD2:
# overrides:
# scale: 0.01
# output_dir: ./data
+# progress_verbosity: 2
# Nb_GTSRB2:
# notebook_id: GTSRB2
# notebook_dir: GTSRB
@@ -90,6 +95,7 @@ Nb_BHPD2:
# batch_size: 64
# epochs: 5
# scale: 1
+# fit_verbosity: 2
# Nb_GTSRB3:
# notebook_id: GTSRB3
# notebook_dir: GTSRB
@@ -102,6 +108,7 @@ Nb_BHPD2:
# batch_size: 64
# epochs: 5
# scale: 1
+# fit_verbosity: 2
# Nb_GTSRB4:
# notebook_id: GTSRB4
# notebook_dir: GTSRB
@@ -114,11 +121,12 @@ Nb_BHPD2:
# batch_size: 64
# epochs: 5
# scale: 1
+# fit_verbosity: 2
# Nb_GTSRB5_r1:
# notebook_id: GTSRB5
# notebook_dir: GTSRB
# notebook_src: 05-Full-convolutions.ipynb
-# notebook_tag: =1==done==
+# notebook_tag: =1==ci==
# overrides:
# run_dir: ./run/GTSRB5_done
# enhanced_dir: './data'
@@ -128,7 +136,7 @@ Nb_BHPD2:
# epochs: 5
# scale: 1
# with_datagen: False
-# verbose: 0
+# fit_verbosity: 0
# Nb_GTSRB6:
# notebook_id: GTSRB6
# notebook_dir: GTSRB
@@ -156,6 +164,7 @@ Nb_BHPD2:
# hide_most_frequently: default
# batch_size: default
# epochs: default
+# fit_verbosity: 2
# Nb_IMDB2:
# notebook_id: IMDB2
# notebook_dir: IMDB
@@ -195,6 +204,16 @@ Nb_BHPD2:
# notebook_dir: IMDB
# notebook_src: 05-LSTM-Keras.ipynb
# notebook_tag: default
+# overrides:
+# run_dir: default
+# vocab_size: default
+# hide_most_frequently: default
+# review_len: default
+# dense_vector_size: default
+# batch_size: default
+# epochs: default
+# fit_verbosity: 2
+# scale: .1
#
# ------ SYNOP -----------------------------------------------------
#
@@ -205,12 +224,13 @@ Nb_BHPD2:
# notebook_tag: default
# overrides:
# run_dir: default
-# scale: default
+# scale: 0.1
# train_prop: default
# sequence_len: default
# predict_len: default
# batch_size: default
# epochs: default
+# fit_verbosity: 2
# Nb_SYNOP1:
# notebook_id: SYNOP1
# notebook_dir: SYNOP
@@ -224,7 +244,7 @@ Nb_BHPD2:
# notebook_src: SYNOP2-First-predictions.ipynb
# notebook_tag: default
# overrides:
-# scale: default
+# scale: 0.1
# train_prop: default
# sequence_len: default
# batch_size: default
@@ -239,89 +259,103 @@ Nb_BHPD2:
# scale: default
# train_prop: default
# sequence_len: default
-# batch_size: default
-# epochs: default
#
# ------ AE --------------------------------------------------------
#
# Nb_AE1:
# notebook_id: AE1
# notebook_dir: AE
-# notebook_src: 01-AE-with-MNIST.ipynb
+# notebook_src: 01-Prepare-MNIST-dataset.ipynb
# notebook_tag: default
+# overrides:
+# run_dir: default
+# scale: 0.02
+# prepared_dataset: default
+# progress_verbosity: 2
# Nb_AE2:
# notebook_id: AE2
# notebook_dir: AE
-# notebook_src: 02-AE-with-MNIST-post.ipynb
-# notebook_tag: default
-#
-# ------ VAE -------------------------------------------------------
-#
-# Nb_VAE1:
-# notebook_id: VAE1
-# notebook_dir: VAE
-# notebook_src: 01-VAE-with-MNIST.ipynb
+# notebook_src: 02-AE-with-MNIST.ipynb
# notebook_tag: default
# overrides:
-# run_dir: ./run/VAE1_done
-# scale: 1
-# latent_dim: 2
-# r_loss_factor: 0.994
-# batch_size: 64
-# epochs: 10
-# Nb_VAE2:
-# notebook_id: VAE2
-# notebook_dir: VAE
-# notebook_src: 02-VAE-with-MNIST-post.ipynb
-# notebook_tag: default
-# overrides:
-# run_dir: ./run/VAE1_done
-# Nb_VAE5:
-# notebook_id: VAE5
-# notebook_dir: VAE
-# notebook_src: 05-About-CelebA.ipynb
-# notebook_tag: default
-# Nb_VAE6:
-# notebook_id: VAE6
-# notebook_dir: VAE
-# notebook_src: 06-Prepare-CelebA-datasets.ipynb
-# notebook_tag: default
-# overrides:
-# scale: 0.01
-# image_size: '(192,160)'
-# output_dir: ./data
-# exit_if_exist: False
-# Nb_VAE7:
-# notebook_id: VAE7
-# notebook_dir: VAE
-# notebook_src: 07-Check-CelebA.ipynb
+# run_dir: default
+# prepared_dataset: default
+# dataset_seed: default
+# scale: default
+# latent_dim: default
+# train_prop: default
+# batch_size: default
+# epochs: default
+# Nb_AE3:
+# notebook_id: AE3
+# notebook_dir: AE
+# notebook_src: 03-AE-with-MNIST-post.ipynb
# notebook_tag: default
# overrides:
-# image_size: '(192,160)'
-# enhanced_dir: '{datasets_dir}/celeba/enhanced'
-# Nb_VAE8:
-# notebook_id: VAE8
-# notebook_dir: VAE
-# notebook_src: 08-VAE-with-CelebA.ipynb
+# run_dir: default
+# prepared_dataset: default
+# dataset_seed: default
+# scale: default
+# train_prop: default
+# Nb_AE4:
+# notebook_id: AE4
+# notebook_dir: AE
+# notebook_src: 04-ExtAE-with-MNIST.ipynb
# notebook_tag: default
# overrides:
-# run_dir: ./run/VAE8_done
-# scale: 1
-# image_size: '(192,160)'
-# enhanced_dir: '{datasets_dir}/celeba/enhanced'
-# latent_dim: 300
-# r_loss_factor: 0.6
-# batch_size: 64
-# epochs: 15
-# Nb_VAE9:
-# notebook_id: VAE9
-# notebook_dir: VAE
-# notebook_src: 09-VAE-with-CelebA-post.ipynb
+# run_dir: default
+# prepared_dataset: default
+# dataset_seed: default
+# scale: default
+# latent_dim: default
+# train_prop: default
+# batch_size: default
+# epochs: default
+# Nb_AE5:
+# notebook_id: AE5
+# notebook_dir: AE
+# notebook_src: 05-ExtAE-with-MNIST.ipynb
# notebook_tag: default
# overrides:
-# run_dir: ./run/VAE8_done
-# image_size: '(192,160)'
-# enhanced_dir: '{datasets_dir}/celeba/enhanced'
+# run_dir: default
+# prepared_dataset: default
+# dataset_seed: default
+# scale: default
+# latent_dim: default
+# train_prop: default
+# batch_size: default
+# epochs: default
+#
+# ------ VAE -------------------------------------------------------
+#
+Nb_VAE1:
+ notebook_id: VAE1
+ notebook_dir: VAE
+ notebook_src: 01-VAE-with-MNIST.ipynb
+ notebook_tag: default
+ overrides:
+ run_dir: default
+ latent_dim: default
+ loss_weights: default
+ scale: 0.01
+ seed: default
+ batch_size: default
+ epochs: default
+ fit_verbosity: 2
+Nb_VAE2:
+ notebook_id: VAE2
+ notebook_dir: VAE
+ notebook_src: 02-VAE-with-MNIST.ipynb
+ notebook_tag: default
+ overrides:
+ run_dir: default
+ latent_dim: default
+ loss_weights: default
+ scale: 0.01
+ seed: default
+ batch_size: default
+ epochs: default
+ fit_verbosity: 2
#
# ------ Misc ------------------------------------------------------
#
diff --git a/fidle/ci/smart_cpu.yml b/fidle/ci/smart_cpu.yml
deleted file mode 100644
index 0d90a46..0000000
--- a/fidle/ci/smart_cpu.yml
+++ /dev/null
@@ -1,122 +0,0 @@
-_metadata_:
- version: '1.0'
- output_tag: ==ci==
- save_figs: true
- description: Smart profile, for cpu
-LINR1:
- notebook_id: LINR1
- notebook_dir: LinearReg
- notebook_src: 01-Linear-Regression.ipynb
- notebook_tag: default
-GRAD1:
- notebook_id: GRAD1
- notebook_dir: LinearReg
- notebook_src: 02-Gradient-descent.ipynb
- notebook_tag: default
-POLR1:
- notebook_id: POLR1
- notebook_dir: LinearReg
- notebook_src: 03-Polynomial-Regression.ipynb
- notebook_tag: default
-LOGR1:
- notebook_id: LOGR1
- notebook_dir: LinearReg
- notebook_src: 04-Logistic-Regression.ipynb
- notebook_tag: default
-PER57:
- notebook_id: PER57
- notebook_dir: IRIS
- notebook_src: 01-Simple-Perceptron.ipynb
- notebook_tag: default
-BHPD1:
- notebook_id: BHPD1
- notebook_dir: BHPD
- notebook_src: 01-DNN-Regression.ipynb
- notebook_tag: default
-BHPD2:
- notebook_id: BHPH2
- notebook_dir: BHPD
- notebook_src: 02-DNN-Regression-Premium.ipynb
- notebook_tag: default
-MNIST1:
- notebook_id: MNIST1
- notebook_dir: MNIST
- notebook_src: 01-DNN-MNIST.ipynb
- notebook_tag: default
-MNIST2:
- notebook_id: MNIST2
- notebook_dir: MNIST
- notebook_src: 02-CNN-MNIST.ipynb
- notebook_tag: default
-GTSRB1:
- notebook_id: GTSRG1
- notebook_dir: GTSRB
- notebook_src: 01-Preparation-of-data.ipynb
- notebook_tag: default
- overrides:
- scale: 0.05
- output_dir: ./data
-GTSRB2:
- notebook_id: GTSRB2
- notebook_dir: GTSRB
- notebook_src: 02-First-convolutions.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/GTSRB2_ci
- enhanced_dir: ./data
- dataset_name: set-24x24-L
- batch_size: 64
- epochs: 5
- scale: 1
-GTSRB3:
- notebook_id: GTSRB3
- notebook_dir: GTSRB
- notebook_src: 03-Tracking-and-visualizing.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/GTSRB3_ci
- enhanced_dir: ./data
- dataset_name: set-24x24-L
- batch_size: 64
- epochs: 5
- scale: 1
-GTSRB4:
- notebook_id: GTSRB4
- notebook_dir: GTSRB
- notebook_src: 04-Data-augmentation.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/GTSRB4_ci
- enhanced_dir: ./data
- dataset_name: set-24x24-L
- batch_size: 64
- epochs: 5
- scale: 1
-GTSRB5:
- notebook_id: GTSRB5
- notebook_dir: GTSRB
- notebook_src: 05-Full-convolutions.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/GTSRB5_ci
- enhanced_dir: ./data
- datasets: "['set-24x24-L', 'set-24x24-RGB', 'set-48x48-RGB']"
- models: "{'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}"
- batch_size: 64
- epochs: 5
- scale: 0.1
- with_datagen: True
- verbose: 0
-GTSRB6:
- notebook_id: GTSRB6
- notebook_dir: GTSRB
- notebook_src: 06-Notebook-as-a-batch.ipynb
- notebook_tag: default
-GTSRB7:
- notebook_id: GTSRB7
- notebook_dir: GTSRB
- notebook_src: 07-Show-report.ipynb
- notebook_tag: default
- overrides:
- run_dir: ./run/GTSRB7_ci
- report_dir: ./run/GTSRB5_ci
\ No newline at end of file
diff --git a/fidle/config.py b/fidle/config.py
index 71ba96f..354b142 100644
--- a/fidle/config.py
+++ b/fidle/config.py
@@ -14,7 +14,7 @@
# ---- Version -----------------------------------------------------
#
-VERSION = '2.0.25'
+VERSION = '2.0.26'
# ---- Default notebook name ---------------------------------------
#
@@ -33,6 +33,7 @@ SAVE_FIGS = False
# ---- Catalog file, a json description of all notebooks ------------
#
CATALOG_FILE = '../fidle/logs/catalog.json'
+PROFILE_FILE = '../fidle/ci/default.yml'
# ---- CI report files ----------------------------------------------
#
diff --git a/fidle/cookci.py b/fidle/cookci.py
index 459586c..7c80fc3 100644
--- a/fidle/cookci.py
+++ b/fidle/cookci.py
@@ -39,62 +39,7 @@ _report_json = None
_report_error = None
-def get_default_profile(catalog=None, output_tag='==ci==', save_figs=True):
- '''
- Return a default profile for continous integration.
- Ce profile contient une liste des notebooks avec les paramètres modifiables.
- Il peut être modifié et sauvegardé, puis être utilisé pour lancer l'éxécution
- des notebooks.
- params:
- catalog : Notebooks catalog. if None (default), load config.CATALOG_FILE
- output_tag : tag name of generated notebook
- save_figs : save figs or not for generated notebooks (True)
- return:
- profile : dict with run parameters
- '''
-
- if catalog is None:
- catalog = cookindex.read_catalog()
-
- metadata = { 'version' : '1.0',
- 'output_tag' : output_tag,
- 'save_figs' : save_figs,
- 'description' : 'Default generated profile',
- 'output_ipynb' : '<directory for ipynb>',
- 'output_html' : '<directory for html>',
- 'report_json' : '<report json file>',
- 'report_error' : '<error file>'
- }
- profile = { '_metadata_':metadata }
- for id, about in catalog.items():
-
- id = about['id']
- title = about['title']
- dirname = about['dirname']
- basename = about['basename']
- overrides = about.get('overrides',None)
-
- notebook = {}
- notebook['notebook_id'] = id
- notebook['notebook_dir'] = dirname
- notebook['notebook_src'] = basename
- notebook['notebook_tag'] = 'default'
- if len(overrides)>0:
- notebook['overrides']={ name:'default' for name in overrides }
-
- profile[f'Nb_{id}']=notebook
-
- return profile
-
-
-def save_profile(profile, filename):
- '''Save profile in yaml format'''
- with open(filename,'wt') as fp:
- yaml.dump(profile, fp, sort_keys=False)
- print(f'Profile saved as {filename}')
- print('Entries : ',len(profile)-1)
-
-
+
def load_profile(filename):
'''Load yaml profile'''
with open(filename,'r') as fp:
@@ -357,6 +302,11 @@ def init_ci_report(report_json, report_error, metadata, verbose=True):
_report_json = os.path.abspath(report_json)
_report_error = os.path.abspath(report_error)
+
+ # ---- Create directories
+ #
+ report_dir=os.path.dirname(report_json)
+ os.makedirs(report_dir, mode=0o750, exist_ok=True)
# ---- Create json report
#
@@ -484,9 +434,9 @@ def build_ci_report(profile_name, top_dir='..'):
state = entry['state']
cols = []
- cols.append( f'<a href="{dir}">{dir}</a>' )
- cols.append( f'<a href="{dir}/{out}">{id}</a>' )
- cols.append( f'<a href="{dir}/{out}">{src}</a>' )
+ cols.append( f'<a href="{dir}" target="_blank">{dir}</a>' )
+ cols.append( f'<a href="{dir}/{out}" target="_blank">{id}</a>' )
+ cols.append( f'<a href="{dir}/{out}" target="_blank">{src}</a>' )
cols.append( start )
cols.append( dur )
cols.append( state )
@@ -547,7 +497,7 @@ def _get_html_report(html_metadata, html_report):
{logo_header}
- <div class='title'>Notebook performed :</div>
+ <div class='title'>Notebooks performed :</div>
<div class="result">
<p>Here is a "correction" of all the notebooks.</p>
<p>These notebooks have been run on Jean-Zay, on GPU (V100) and the results are proposed here in HTML format.</p>
diff --git a/fidle/cookindex.py b/fidle/cookindex.py
index 5f8629f..df1c048 100644
--- a/fidle/cookindex.py
+++ b/fidle/cookindex.py
@@ -19,7 +19,7 @@ import pandas as pd
from IPython.display import display, Markdown, HTML
import re
-import sys, os, glob
+import sys, os, glob, yaml
import json
from datetime import datetime
from collections import OrderedDict
@@ -31,11 +31,27 @@ import fidle.config as config
# -----------------------------------------------------------------------------
# To built README.md / README.ipynb
# -----------------------------------------------------------------------------
-# get_files : Get files lists
-# get_infos : Get infos about a entry
-# get_catalog : Get a catalog of all entries
+# get_files : Get files lists
+# get_notebook_infos : Get infos about a entry
+# get_catalog : Get a catalog of all entries
# -----------------------------------------------------------------------------
+def build_catalog(directories):
+
+ # ---- Get the notebook list
+ #
+ files_list = get_files(directories.keys())
+
+ # ---- Get a detailled catalog for this list
+ #
+ catalog = get_catalog(files_list)
+
+ with open(config.CATALOG_FILE,'wt') as fp:
+ n=len(catalog)
+ json.dump(catalog,fp,indent=4)
+ print(f'Catalog saved as : {config.CATALOG_FILE} ({n} entries)')
+
+
def get_files(directories, top_dir='..'):
'''
Return a list of files from a given list of directories
@@ -70,7 +86,7 @@ def get_notebook_infos(filename, top_dir='..'):
return:
dict : with infos.
'''
- print('Read : ',filename)
+ # print('Read : ',filename)
about={}
about['id'] = '??'
about['dirname'] = os.path.dirname(filename)
@@ -181,6 +197,15 @@ def get_catalog(files_list=None, top_dir='..'):
def tag(tag, text, document):
+ '''
+ Put a text inside a tag
+ args:
+ tag : tag prefix name
+ txt : text to insert
+ document : document
+ return:
+ updated document
+ '''
debut = f'<!-- {tag}_BEGIN -->'
fin = f'<!-- {tag}_END -->'
@@ -189,7 +214,72 @@ def tag(tag, text, document):
def read_catalog():
+ '''
+ Read json catalog file.
+ args:
+ None
+ return:
+ json catalog
+ '''
with open(config.CATALOG_FILE) as fp:
catalog = json.load(fp)
return catalog
+
+# -----------------------------------------------------------------------------
+# To built default.yml profile
+# -----------------------------------------------------------------------------
+# build_default_profile : Get default profile
+# -----------------------------------------------------------------------------
+
+
+def build_default_profile(output_tag='==ci=='):
+ '''
+ Return a default profile for continous integration.
+ Ce profile contient une liste des notebooks avec les paramètres modifiables.
+ Il peut être modifié et sauvegardé, puis être utilisé pour lancer l'éxécution
+ des notebooks.
+ params:
+ catalog : Notebooks catalog. if None (default), load config.CATALOG_FILE
+ output_tag : tag name of generated notebook
+ profile_filename : Default profile filename
+ return:
+ None
+ '''
+
+ catalog = read_catalog()
+
+ metadata = { 'version' : '1.0',
+ 'output_tag' : output_tag,
+ 'save_figs' : True,
+ 'description' : 'Default generated profile',
+ 'output_ipynb' : '<directory for ipynb>',
+ 'output_html' : '<directory for html>',
+ 'report_json' : '<report json file>',
+ 'report_error' : '<error file>'
+ }
+ profile = { '_metadata_':metadata }
+ for id, about in catalog.items():
+
+ id = about['id']
+ title = about['title']
+ dirname = about['dirname']
+ basename = about['basename']
+ overrides = about.get('overrides',None)
+
+ notebook = {}
+ notebook['notebook_id'] = id
+ notebook['notebook_dir'] = dirname
+ notebook['notebook_src'] = basename
+ notebook['notebook_tag'] = 'default'
+ if len(overrides)>0:
+ notebook['overrides']={ name:'default' for name in overrides }
+
+ profile[f'Nb_{id}']=notebook
+
+ # ---- Save profile
+ #
+ with open(config.PROFILE_FILE,'wt') as fp:
+ n=len(profile)-1
+ yaml.dump(profile, fp, sort_keys=False)
+ print(f'default profile saved as : {config.PROFILE_FILE} ({n} entries)')
\ No newline at end of file
diff --git a/fidle/img/00-Fidle-header-01.svg b/fidle/img/00-Fidle-header-01.svg
index 2951947..f7467f4 100755
--- a/fidle/img/00-Fidle-header-01.svg
+++ b/fidle/img/00-Fidle-header-01.svg
@@ -1 +1 @@
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HcmV?d00001
diff --git a/fidle/logs/catalog.json b/fidle/logs/catalog.json
index a80aa48..7c5e214 100644
--- a/fidle/logs/catalog.json
+++ b/fidle/logs/catalog.json
@@ -45,7 +45,9 @@
"basename": "01-DNN-Regression.ipynb",
"title": "Regression with a Dense Network (DNN)",
"description": "Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)",
- "overrides": []
+ "overrides": [
+ "fit_verbosity"
+ ]
},
"BHPD2": {
"id": "BHPD2",
@@ -53,7 +55,9 @@
"basename": "02-DNN-Regression-Premium.ipynb",
"title": "Regression with a Dense Network (DNN) - Advanced code",
"description": "A more advanced implementation of the precedent example",
- "overrides": []
+ "overrides": [
+ "fit_verbosity"
+ ]
},
"MNIST1": {
"id": "MNIST1",
@@ -61,7 +65,9 @@
"basename": "01-DNN-MNIST.ipynb",
"title": "Simple classification with DNN",
"description": "An example of classification using a dense neural network for the famous MNIST dataset",
- "overrides": []
+ "overrides": [
+ "fit_verbosity"
+ ]
},
"MNIST2": {
"id": "MNIST2",
@@ -69,7 +75,9 @@
"basename": "02-CNN-MNIST.ipynb",
"title": "Simple classification with CNN",
"description": "An example of classification using a convolutional neural network for the famous MNIST dataset",
- "overrides": []
+ "overrides": [
+ "fit_verbosity"
+ ]
},
"GTSRB1": {
"id": "GTSRB1",
@@ -79,7 +87,8 @@
"description": "Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset",
"overrides": [
"scale",
- "output_dir"
+ "output_dir",
+ "progress_verbosity"
]
},
"GTSRB2": {
@@ -94,7 +103,8 @@
"dataset_name",
"batch_size",
"epochs",
- "scale"
+ "scale",
+ "fit_verbosity"
]
},
"GTSRB3": {
@@ -109,7 +119,8 @@
"dataset_name",
"batch_size",
"epochs",
- "scale"
+ "scale",
+ "fit_verbosity"
]
},
"GTSRB4": {
@@ -124,7 +135,8 @@
"dataset_name",
"batch_size",
"epochs",
- "scale"
+ "scale",
+ "fit_verbosity"
]
},
"GTSRB5": {
@@ -142,7 +154,7 @@
"epochs",
"scale",
"with_datagen",
- "verbose"
+ "fit_verbosity"
]
},
"GTSRB6": {
@@ -191,7 +203,8 @@
"vocab_size",
"hide_most_frequently",
"batch_size",
- "epochs"
+ "epochs",
+ "fit_verbosity"
]
},
"IMDB2": {
@@ -208,7 +221,8 @@
"dense_vector_size",
"batch_size",
"epochs",
- "output_dir"
+ "output_dir",
+ "fit_verbosity"
]
},
"IMDB3": {
@@ -244,13 +258,15 @@
"title": "Sentiment analysis with a RNN network",
"description": "Still the same problem, but with a network combining embedding and RNN",
"overrides": [
+ "run_dir",
"vocab_size",
"hide_most_frequently",
"review_len",
"dense_vector_size",
"batch_size",
"epochs",
- "output_dir"
+ "fit_verbosity",
+ "scale"
]
},
"LADYB1": {
@@ -292,7 +308,8 @@
"train_prop",
"sequence_len",
"batch_size",
- "epochs"
+ "epochs",
+ "fit_verbosity"
]
},
"SYNOP3": {
@@ -306,9 +323,7 @@
"iterations",
"scale",
"train_prop",
- "sequence_len",
- "batch_size",
- "epochs"
+ "sequence_len"
]
},
"AE1": {
@@ -318,7 +333,10 @@
"title": "Prepare a noisy MNIST dataset",
"description": "Episode 1: Preparation of a noisy MNIST dataset",
"overrides": [
- "run_dir"
+ "run_dir",
+ "prepared_dataset",
+ "scale",
+ "progress_verbosity"
]
},
"AE2": {
@@ -400,6 +418,7 @@
"seed",
"batch_size",
"epochs",
+ "fit_verbosity",
"run_dir"
]
},
@@ -417,6 +436,7 @@
"seed",
"batch_size",
"epochs",
+ "fit_verbosity",
"run_dir"
]
},
diff --git a/fidle/pwk.py b/fidle/pwk.py
index d2d6a01..89c1315 100644
--- a/fidle/pwk.py
+++ b/fidle/pwk.py
@@ -289,17 +289,20 @@ def pick_dataset(*data,n=5):
out = [ d[ii] for d in data ]
return out[0] if len(out)==1 else out
-def update_progress(what,i,imax, redraw=False):
+def update_progress(what,i,imax, redraw=False, verbosity=1):
"""
Display a text progress bar, as :
My progress bar : ############# 34%
args:
- what : Progress bas name
+ what : Progress bar name
i : Current progress
imax : Max value for i
+ verbosity : progress bar verbosity (0: no bar, 1: progress bar, 2: one line)
return:
nothing
"""
+ if verbosity==0: return
+ if verbosity==2 and i<imax: return
bar_length = min(40,imax)
if (i%int(imax/bar_length))!=0 and i<imax and not redraw:
return
@@ -872,9 +875,13 @@ def np_print(*args, precision=3, linewidth=120):
def end():
global _end_time
_end_time = datetime.datetime.now()
-
- print('End time is :', time.strftime("%A %d %B %Y, %H:%M:%S"))
- print('Duration is :', hdelay_ms(_end_time - _start_time))
- print('This notebook ends here')
+ end_time = time.strftime("%A %d %B %Y, %H:%M:%S")
+ duration = hdelay_ms(_end_time - _start_time)
+ site_url = "https://fidle.cnrs.fr"
+ md = f'**End time :** {end_time} \n'
+ md+= f'**Duration :** {duration} \n'
+ md+= f'This notebook ends here :-) \n'
+ md+= f'[{site_url}]({site_url})'
+ display_md(md)
--
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