tensorflow/Image Segmentation using a modified U-Net in Tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Segmentation in Tensorflow 2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Experiment overview\n",
    "> We will train a neural network to output a pixel-wise mask of the given image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image segmentation has many applications in medical imaging, self-driving cars and satellite imaging to name a few."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![ImageSegmentation](https://cdn.analyticsvidhya.com/wp-content/uploads/2019/03/image-segmentation.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The dataset that will be used for this tutorial is the [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The dataset consists of images, their corresponding labels, and pixel-wise masks. The masks are basically labels for each pixel. Each pixel is given one of three categories\n",
    "\n",
    "> Class 1 : Pixel belonging to the pet\n",
    "\n",
    "> Class 2 : Pixel bordering the pet\n",
    "\n",
    "> Class 3 : None of the above/ Surrounding pixel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.7.6\n",
      "Tensorflow version: 2.2.0\n",
      "Keras version: 2.3.0-tf\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow_examples.tensorflow_examples.models.pix2pix import pix2pix # Manually install \n",
    "\n",
    "import tensorflow_datasets as tfds\n",
    "tfds.disable_progress_bar()\n",
    "\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import platform\n",
    "import datetime\n",
    "import os\n",
    "import math\n",
    "import random\n",
    "import pathlib\n",
    "\n",
    "print('Python version:', platform.python_version())\n",
    "print('Tensorflow version:', tf.__version__)\n",
    "print('Keras version:', tf.keras.__version__)\n",
    "\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('C:/Users/bokhy/Desktop/Python/Python-Projects/Tensorflow')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking current directory\n",
    "pathlib.Path.cwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load Data\n",
    "##### https://www.tensorflow.org/datasets/overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfds.core.DatasetInfo(\n",
      "    name='oxford_iiit_pet',\n",
      "    version=3.1.0,\n",
      "    description='The Oxford-IIIT pet dataset is a 37 category pet image dataset with roughly 200\n",
      "images for each class. The images have large variations in scale, pose and\n",
      "lighting. All images have an associated ground truth annotation of breed.',\n",
      "    homepage='http://www.robots.ox.ac.uk/~vgg/data/pets/',\n",
      "    features=FeaturesDict({\n",
      "        'file_name': Text(shape=(), dtype=tf.string),\n",
      "        'image': Image(shape=(None, None, 3), dtype=tf.uint8),\n",
      "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=37),\n",
      "        'segmentation_mask': Image(shape=(None, None, 1), dtype=tf.uint8),\n",
      "        'species': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
      "    }),\n",
      "    total_num_examples=7349,\n",
      "    splits={\n",
      "        'test': 3669,\n",
      "        'train': 3680,\n",
      "    },\n",
      "    supervised_keys=('image', 'label'),\n",
      "    citation=\"\"\"@InProceedings{parkhi12a,\n",
      "      author       = \"Parkhi, O. M. and Vedaldi, A. and Zisserman, A. and Jawahar, C.~V.\",\n",
      "      title        = \"Cats and Dogs\",\n",
      "      booktitle    = \"IEEE Conference on Computer Vision and Pattern Recognition\",\n",
      "      year         = \"2012\",\n",
      "    }\"\"\",\n",
      "    redistribution_info=,\n",
      ")\n",
      "\n"
     ]
    }
   ],
   "source": [
    "builder = tfds.builder('oxford_iiit_pet:3.1.0')\n",
    "info = builder.info\n",
    "print(info)\n",
    "# by setting register_checksums as True to pass the check\n",
    "config = tfds.download.DownloadConfig(register_checksums = True)\n",
    "builder.download_and_prepare(download_config=config)\n",
    "dataset = builder.as_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PrefetchDataset shapes: {file_name: (), image: (None, None, 3), label: (), segmentation_mask: (None, None, 1), species: ()}, types: {file_name: tf.string, image: tf.uint8, label: tf.int64, segmentation_mask: tf.uint8, species: tf.int64}>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_TRAIN_EXAMPLES = info.splits['train'].num_examples\n",
    "NUM_TEST_EXAMPLES = info.splits['test'].num_examples\n",
    "NUM_CLASSES = info.features['label'].num_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Re-size / Augment Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(input_image, input_mask):\n",
    "    # Normalized to [0,1]\n",
    "    input_image = tf.cast(input_image, tf.float32) / 255.0\n",
    "    # pixels in the segmentation mask are labeled either {1, 2, 3}, but let's subtract 1 from the segmentation mask, resulting in labels that are : {0, 1, 2} \n",
    "    input_mask -= 1\n",
    "    return input_image, input_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_image_train(datapoint):\n",
    "    input_image = tf.image.resize(datapoint['image'], (128, 128))\n",
    "    input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))\n",
    "    \n",
    "    # flip image for train-set\n",
    "    if tf.random.uniform(()) > 0.5:\n",
    "        input_image = tf.image.flip_left_right(input_image)\n",
    "        input_mask = tf.image.flip_left_right(input_mask)\n",
    "\n",
    "    input_image, input_mask = normalize(input_image, input_mask)\n",
    "\n",
    "    return input_image, input_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_image_test(datapoint):\n",
    "    input_image = tf.image.resize(datapoint['image'], (128, 128))\n",
    "    input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))\n",
    "\n",
    "    input_image, input_mask = normalize(input_image, input_mask)\n",
    "\n",
    "    return input_image, input_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Train/Test Split and Batch in Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train length: 3680\n",
      "test length: 3669\n",
      "NUM_CLASSES: 3680\n"
     ]
    }
   ],
   "source": [
    "# Train/Test Split\n",
    "NUM_TRAIN_EXAMPLES = info.splits['train'].num_examples\n",
    "NUM_TEST_EXAMPLES = info.splits['test'].num_examples\n",
    "NUM_CLASSES = info.features['label'].num_classes\n",
    "print(\"train length:\", NUM_TRAIN_EXAMPLES)\n",
    "print(\"test length:\", NUM_TEST_EXAMPLES)\n",
    "print(\"NUM_CLASSES:\", NUM_TRAIN_EXAMPLES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "BUFFER_SIZE = NUM_TRAIN_EXAMPLES # Usually use the number of train examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = dataset['train'].map(load_image_train, num_parallel_calls = tf.data.experimental.AUTOTUNE)\n",
    "test = dataset['test'].map(load_image_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<PrefetchDataset shapes: ((None, 128, 128, 3), (None, 128, 128, 1)), types: (tf.float32, tf.float32)>\n",
      "<BatchDataset shapes: ((None, 128, 128, 3), (None, 128, 128, 1)), types: (tf.float32, tf.float32)>\n"
     ]
    }
   ],
   "source": [
    "train_dataset = train.cache().shuffle(\n",
    "    buffer_size = BUFFER_SIZE\n",
    ")\n",
    "train_dataset = train.batch(\n",
    "    batch_size = BATCH_SIZE\n",
    ").repeat()\n",
    "\n",
    "# Prefetch enables the input pipeline to asynchronously fetch batches while your model is training.\n",
    "train_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
    "test_dataset = test.batch(BATCH_SIZE)\n",
    "\n",
    "print(train_dataset)\n",
    "print(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display(display_list):\n",
    "    plt.figure(figsize=(10, 10))\n",
    "\n",
    "    title = ['Input Image', 'True Mask', 'Predicted Mask']\n",
    "\n",
    "    for i in range(len(display_list)):\n",
    "        plt.subplot(1, len(display_list), i+1)\n",
    "        plt.title(title[i])\n",
    "        plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))\n",
    "        #plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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g/uw6X5438rY0Z4/xwBHbHteP69LFACBseodJl8vlujbkJMvlcrlcLpdrE7SlSZYkStIjocozX0j+poQG8QY6L9rC92OPlISECswvkhcGEHFgVVYUObM79V6XZIwVZaInl1JdlYRrk+wMDdb5xvkaxnGIuXSmkhZfI0lxwxXjZfphcsp0Ssal/WeymJJqPu2dc5aQrl5qeVGNvtLJSW9EC3MQzbHHXN6Wn54yUlUq2SU8O/mYrUfidvr0ab5vZOzUKaM0q6Q/mqe5xRXu156Pj++w9/s2viX6pBqs/Kuzkm585y5u13xLiwvLAICZWSNfU1O2/x27jGbmOe4VErVSdRTVAdvXo4/ZsrRrYX7a9tlatW3me7yOeA5yyfVNzxOvs6lpI0ndVpvrG9ULJLAt+v5ouUIv2vZWlkTbjJTt2zVsc0WPV7Vsr09PG2n76r33AgCWm7b9+oCRrtVlm8sSPVjJlaZLQyRrk/PpXC6X61qRkyyXy+VyuVyuTdC2IFlJQnUmL6tFKqIg65BUoCkdnOvn5SuSh8XWK/SMDLR4h16ojXAFVojRP6RmcRFGFIr0tChBO6mI28DwFPIiQrbPHfvMT9Zn7tOaJyt99/+4l5PkcVG589CCJHL8PNV7j3tFWUxMBxf9y2QtJVV8Id2rUKvn0qALffb6izzOJL2cyemRvqFcmXPcM5LUXDYS9MA9X+T2bL1HHjrG57b/5WWjQIMkT9PTRpjm5uZsu/RU5XJGaZpN29/CnD3WajUAwOKCka8Wx1OpGLU5dsLImApGK2Wr0JucnAIAzNPr1WT1YanEtHT1MkQbc0u27EPHjgMAbn6aeZkWllZ5rLaNMq+vbpdzRZIlb5aq93pRpMqupS59a9UqPV/Ldj2D122NeVYz89OcO5KsveY7K9A/Vma1oFBbjl6rwHFVKmnPoR67Ovce+O5yua5TOclyuVwul8vl2gRtaZKVZTFFJWTTv1OiF6WjvnMJ/VHvNvOuJJVxQVVWzB+aMw/Kh9//NwCAid1WVfWs57wQADAwaNRJPd2SbKYC6UuS/G5P8xvQI3mVevKrJMiHQz1PJVYe6eXWUBafhvNVcG1Mxs6fiM65g8haGp3lhWagDDCSraRfnnr/kYBpOzwH55hHNTV1FgCwsmCk6eYj+wAALWafiaB99Z4vAwBOnrRU/07H3j9zzgjX/FI63X/HDst+mp217S6TDtVqRnFq9GwRVCHH8S/MG2UqsHJ1fHzMjo7zlOf9yYmTRrSGBo1yTs/Qc8WMtOkZ22+9bmRsedGej4wF9HMkRLzuWm3SQtiYiiVbp7lsWVvDI+aVypfpBWTi+8ysebi6nKOlFSNho6M25vqQ+fxOnbVjOnjQSNXIyKgdI8daZJbX6IR5xSq8judnjHSVKjaeIslWsctKT5KroWH1LqSHMetJjBtfey6Xy7Vd5STL5XK5XC6XaxO0pUhWzICgkPFi6Q75zCmjI/v2WVp3qWB0I8c77E7DyECe1GJ+3uiDzCNjO+1OPjDD6vSjRk0+/MGPAwAmTxk5ePV3/iMAQLli6wVWZ62umIdLvp4L9ToUQMrRJ9MRBUjynGRoSd/992NIvZz0BhRJyp/Hy4XzJa1fmGTFnqEe5VnJi1UgCeqSOOn1UlFeKlbVLdkcLy7Z3D9wv+VKPfaY+ZGWWMnW6xiF+eLnOV56rJRzde7sdOp4RU0iqVCzZfvT3J89Z+dKVYXLy/Q7tZgt1bdro1yycyfvlao3e33b/8AAzyXntdG29QaHjS4V6V8aGjU6JMLWYhZVZ440asg8Yo1GA0NjRr9uu/1W21fb9j00OGRz2bMxTRw8DACoD9q6q6x0fIxJ7M0W+ybSBFWp2hhqA0bxlAE2NLzTjoXJ7rMc0+yskufteYs9DA/t22/HzKrZbt9eP0vquNxiTleLfjlWG2qO+kn/SFAh8+hyuVzbW06yXC6Xy+VyuTZBW4pknU8iRcpg+pv3fwAA8L2vN9I0OGJUJfLOO64yGZv+GKWsqyLsuc99PgBgbMKykNSnrktK8sm//xQA4IYbngkAeNbzXmDLqVcbKwY7rCyrMqlbREHj7fd7Scmj0uNLTH5XJdlaojqPFeoxaM9VIakEciWkq1Iyl8sQLVagqWefqgKVKabU/A7JVIfEJseKy3NnjWLcc889qfWe8+xnAwDuv/8BAMAKvU9o2/7m6bU6c8Y8TAt83uNx9lgRp4q5KonPyuoKl5NvzhZYYsWnfEeLs0ZhlK7f4rjVz1Lp6CMjRo9E3JrL9n6d/qdZ5lyNjhpNUvVgmVWOXY6zwnlaXbIqxuG6jaN8wHx7X2H1Y0F9Mrm/MglfqVpHjtV/C3NTqTEeOWSp8zleFB1WPn7xizbnIlatrvxjdp3tJ/Hau8/8bDv3GMlVZliL1/H4kJHaQiRt3GXXwAwT3Ls9m5NTpy17bDfJbrNhc37bc28HABRrNtc1JcPXK5wzpvAX6TlzuVyu61ROslwul8vlcrk2QVuKZG2cYQ6AhCiyx+C7//x/AgB2kEbcddfLAAAdVmnd88XPAABWlyw3SISpwfTv6TOW4j1YHOIObJoOH77B1mvZfkRrzpy0O/69h/dzHKxgG7L1Y2+Zj6wgJG3JoYteL90PMRftOWOVEKCKMzvqDn07+YJRA4GqfseWK7LHX5sULdJbxPaNWJmbBABUSGZm6XVKiE/D1hP9kFfq4EEjNF/72tdsDkmyRMJOHD+WWl4p4KIxIm/DwzbuWs18QfLvqLcfWNHWzasyTen6yuEqpMYrchXbRpjKrGhLSFXTiJb6/unxxAnz2U1PzgAA9uwxWtNoGs1ZYM/CRfYD3H9gNwCg1bL5mWG/vrFR8zkN1W3cvY7t74YjNl+TJHfHH3sEANAZ5DiqZezdbdsss9KRkWGYpe9shWRrbtb29epv/zYAwPyivf7Vr9q5uOVWI6pHb3q6TSFpWa1u261XjdKdPmHX6ZHDh22OSDN1zj/6qb+z7c8bZSxwvWOPGJ1cXbW/n6Eh226pyj6TCaFVT9AMbUVWfm/ncrmuD/mnncvlcrlcLtcmaFuQLL3ep78ncIl3vfNdAIBTp4wiFFhZN33GKMYoc32GWCG2tGKUqKo8oRuMDDz99lsAAPUB8wkFJl/v3Gn+nrvf/2cAgBfd+SIAwEOPmsfryBHz1hw8dBjAWgXbypJRmOHREQg0BZKs7rKRkOkZIyy7D9o2jj9wPwBgP/062TkIBTv26bNWcdZj2n2fHqKZGcv8uo85UwrvOnt2kk9tIDVW0RXoOVLeVK9t9OTsGdt+lRWVI6M2dwM1W37PbjvWMXqfKpX0dgqsVGs0V1PjW+vBaNtt0oNVVuo4yZvyp+RBy5FwFbiFgjxmPD55y+SDU5bZzTcZdVwivZSH6/ZnH7V5nBHJMjI3yuNZYeVovW7EbGzciFynRSLYojeM5+MwCdj+vebZ6pGEFasVTEzUODeckxUbQz7YmIs5exwaY8J7sDkbGrE5fdZzbwYA1AaMmE5M2Lm47z67Vsb6ts9a3eZ+/37LdVtZNl9dh5SvQIq4f894aszDTISP7HlYKNtye3bbcj2W9xZJHXWOsllqj68q9Jwsl8t1fchJlsvlcrlcLtcmaEuRLCl7f7yW2WSUY4xJ1qsrRiE+/PHPAQAO7LM79DyXX26aVwrnjE6stoyqnJozYvDIcSNgohw7mR6u/X75HqsyPHPafDf332d3+g8+Yv6kx459HQBw4w3m5ZI/SHTlRS9+MUaZJH7yUVtnYtwowYP3WY5UtWBjmZ40OjY6SHpQZR9FUpD2qh3rZ+irWeHzvfvtmBXVtWtsIDV7e3fZXBVZIVZUr0CqS4qBgs2tvEkiQqqczOXVCzBpEGkPwdbXORJhq3F7/T774YlMqXqyz+yxDAHL5Wx7bXrTeqxeXGQFXkHHQdqoa6XDNPWEaCV0xh4rNTvu1VUjTTfcsI/j1eFkcsu6Kxyf7aeQZ05X0ypSlVQ/UKumxq+A/Ea7AZAg5UgNe22bo5tYoagemp18g/uy5fOlanps/DPu0//2zNutB6J8d23mWO3b8xIAQImeqchzUeJcPe0mo6QDdSNiBR5zIZO51mL1YFR+Fi8RecAUYKeU/qRpqJMsl8t1nclJlsvlcrlcLtcmaEuRrKB49IwhSZVnva7dsb/yW78BANAkqVrmLf9Q3TxYuciea/Qh9XjHXSqplxzJWN4oUqFgXqzBwRr3Y7fuO3bY8yOHjXAtM7vp6Tce4Ho2vWX6cyqDrEDrknh9/csos4ywVLLvu6dPmp9GPp1/+OTfct82hnu+bH6xWs0IVJskR6VptTJ79o0zC4mZXX1miOVKrFJMqhFFnmxOuqR5SlSX54mFkWgsGjEqV5hH1WBCfT5bBWjLd7jdxCtFeqJEe811ZJq4grLKJEYdntMek9MDx9unt0mEqFrQ+0qo53H0eBxcr8sk9j5pS50ES+0EhtiPcnnJzn21rCpO0SJ5xexaKZXtvDSbdhxF+v7Gara9Jj1atSL9fDyeaiWHLjPL2uwBWKzbMqo47ciXRjrXbHEuYY/yzzWZ0F6q2jnpVnhIJE4V4rM+56LN7LGBaoX7YXo9L/9C3uauKAKlaycnmsdzxlu0Pl/P83VOUdJ1s6C+nFGZb35v53Jda4pF/uEOnq8rSFphtnjxhVyX/2kXQjgQQvhICOG+EMK9IYR/zdfHQggfDCE8yMfRqzdcl8vlujryzzCXy7XZuhKS1QXw0zHGL4QQBgF8PoTwQQA/DOBDMcZfCSH8ewD/HsDPXvlQ1zKjFPqkO+JAipPjHfjYOGlKn9+0uVqh0OHy8pKoT6D8PqzII2TpBdEZJsr32ZtN9+i8M+8yeXtwgInXzNEKrBLrNtgPjzQnSWkv5NHpcBvsk1gs0Z/DSskB5k11SDsC/SxNeq4SOtAXSaHnqMEk9ahEdT721JuPcyASdh6/TJ8p9/miUZIKiVAeNlc95nG1ZMwhAcuT4jG2ikuvkaCQkCzORV7nkjlZEBVJE7KOkFqO+Vg83irnLdkO9L6t1yChKw1wO7xmyiJ2HFeViftDo6yUEzxVr0Sel3LOqNNyhz0La3bui+yFWIVlTbUqtt65WaOqI6Q9hUIJ5bpVBS5wjjukcIt5VsnS71a0KUapbNdCct3SZ0Zohi4T2XMcq2idyGvSRYAXYIOVkvLX5fmH0m/a6w3OtaimyGwi5WDpEqJPLurckIQFLpB4KHHN3AE/6Z9hLte1ope82HL2/u2evwEADNI/e6RYv6T1v0Kv8nr9wfRLAQB//bHnXY0hbgtdNsmKMZ6JMX6B/14CcB+AfQBeC+BtXOxtAL7rSgfpcrlcV1v+GeZyuTZbV8WTFUI4DOA5AD4NYFeM8QxgH2IhhJ1XYx+2H3t8fG1STC1QIG0pkork+mkaosfEd5T0EmRPOPp2klR1VWOJECjhOtmf7b7ItPJ+Iaa2J7qj/nUht9aHL0CUTMuI9NhG1Ytw7UhJqpSknp0JVfX11YuQx5xTtV46t0qPa3ORniNRjiLzqSStV6qyeq7T4Xizv+enc6vWAFb6ZEbiwyiKGHtYr+x4deTqtRj4mJjBFHHPc1+jh0y9B4eGrRKuyqT45QVLes91DBuph6Eq6ybGrOIuz1M63zE/VCc3xv3bG1OnLUes0LIsNnUH+NyXrFJ19pS93m528Jq77L/dz3ye9cqcatidYUnHEtPUT+ekzHOhcyZp7tVPUnOpayE5B0kbS9FNEix6uHRStPzadS+XFVKvF5kwnxO2VIUoto6erM8wl+tq67te9plLWu7X93zhPO9ULmu/t5cev95/3vtZAMBf77AuFJgqP26Z601X7EANIdQB/E8APxljXHwC6/1ICOFzIYTPTU1NXekwXC6X67J0NT7Devzp1eVyudbrikhWCKEI+3D64xjj/+LLZ0MIe3gHuAfAuY3WjTG+GcCbAeCOO+64ouCc5Iae/1hhlV+vbT3flAaevfPP0hHd0Xd7RgTy/KYueCJCkFAc+pS0XnLvnkt/d5WXRcspTT1fAPJJwWR6LEnulJ5zm6WSUYN8hhOsEZ60RD+SY82l7yzWCFN6P3rUlHX76fe1/FpPwPQcr80tKWFPvrl0nlZC0DIerV76TmR4AAAgAElEQVQvQ+IySe6ah4TcafUgTxb3z4q6QDLGYkaszhu5Os0eho8+8CAAoE5f3d///YcBADffZJlTR592A8dt2y0fsgpWJfIX+fpj95lXDkt22ZcrRrwmBs3n8MVHjXQtLzfw9re9HQDwAzk7lqO3291f7Ji3aXGJFZSsTFzLGuN1mqGGej0KD4rAZoiUyv/6HXkHRZ6ySe1Iv5+hntn99nsaz9bJwbpan2HlAwe2zkG7trTe+IqPAAB+buL+q7rdz7N6+ReOv/aCy/3F0fcCWFd9vIEefvkfAgBufOePXaXRbV1dSXVhAPBWAPfFGH9j3VvvAfBD/PcPAfjLyx+ey+VybY78M8zlcm22roRkvQTADwC4J4TwJb72cwB+BcC7QghvBPAYgNdf2RAvrrU7bHu+sGjEf3H2DACgUkj3rctSGBECUZoCvVWBRCEkdIVVWKQq7cDMJ1KZKhOv5U1R4nWS6J0EYPN9AIHeoSrJTKkk0sRqQ25LY1irwksf+/kes36afuZ5QoL42Ol0Uu+veZ6KqTnQ8gsLC6n9SToXlbIRnG63w+2JrIH7EfkS4Urnemn5te2mK92SHoVCWbxvUIUdSFcieyUWo53TT378QwCA5VMGKUYGbJy/82d/DwB41atuAgAc2bsHAFDNpelQpWbnuj5o1f3HHjISduaM9YLcVVKSvC1/9LARr4UX3gkA+MiH/w6Tp60/5Vt+7/cBAP/6Z34SwFreVZGp+KtdZYKlj1kkK0tiE7ubqg4z57Sg1HpekMWCUveRUfqc5nkN9DMeL/kJc9yOKOIWQDvXzGeYy3Ux9Uft7/hqEaxbf/dfAAAOvOmTmXfOXHC978D5KwfvPv2l8753veqyv2TFGD+O83tbX36523W5XK4nQ/4Z5nK5NltbKvH9fEo8InwcZLJ7Oc9eg6IZmWpCPapaa833RJ9RtZ7ePiXas9ZXjx4r0qhcIU2y5MHS9vNKLe/HpNKrpKT0zNiyXqQ1kpMlRxtzg6RXHylHq9VKHUPWQ/U48iVKEZUt1ku9L29Um736NAwRuLVxI/WY/W/bmhdNVYGR+0v7jjTXGn/iBwrsWUgSmNQYkjwxAB6P3vcAAGCJxGl2yvKrHviaeaVueYb1Zrz56eaPUqbT2ak5js/mob7b1tvZTZ/jxpL5AfPjtt/VRXteGzbiddszbbsLC8v4zKetEkek6dMfM4p227NuAQCM79nPg66ljjU55oxHS+da56JNeqhemZq7+RWjej1WVorolkuilelrTRQyS4JFH3usxj3E7ZqDbEuQLJdry+tFP3NpvqfRd98DAOizSOMAsgTrynXkr/43AMCxu94CAPjC9/4mAOC57/o3V31fW0Xe38LlcrlcLpdrE7QtSFZYwxYAgGrNspuq5REAQDm3sV9Jd+a6k1/LI1KWFGmMcof4WKkYvSkWRE9IsJRFVcj4iPppv1MuiTkPybfcfj9NKXI59TmU70tkK02cso+SCFjWt5PNWNKYzlfFp80mdK/MqkOkvVHViqoHmVFW0Jxs7CPSfgqFNOXrqYch0rQmmxWV9QV1uqrsJFFaNarSbFn2VIUzvThjRGp51ipPR8aNXB0/aT6Eb/yWV9o4mbE2PW93fSvLVjW4a89uzgeT6TNZUyEo1d/Gs7La5LCM9uTKtt7Tn34Tzp0zT9axRx8FAJw+ZWMYYY/LsQnrP1msbuyFyiawr1WQ2rGKJhYyc13k9d1lNVGzYZ6vHq/f3OPOjfpOpv14kq7hfMbUtZVyslyua13HXvXW1PNXfo/VZgx/6h8uaf3+xRe5Yt30o0bncZc9DOfsv8Xlg0b+W49dWpr8dpKTLJfL5XK5XK5N0LYgWdk7a5Gj5I69k+7XJ2WT3pN+fvKyMP27lFT+0WtF2tJjBlOuw/0rD6vN/n2iSaJFBfVyU/J7LsldUtUgkswijUmVWvLfaN00LTtfhlH2WDvtdNWeaEX2cW178krZHYnoiMiNSFihVEyttzYucD3lYonUaQF7WEvHZ1VjIU3WspV12XNZ5PzxlKBJs5tmQ+OanzOCdfLR0wCA3UfM73TD0RsBAPUhu9PqMP18ZckIVoN9AZEz+rRH1Z39NJ0ssAJvasq231xh/0oYVR3daf0Kd+6cwIFDBwEAO/ZYBePKgqXMz3KMCwvMeavvtWN5nF9u4wrRXiaTTCdBFFG+vNBPz5FeTxLc5blSv8YMBdVjieQ4WwnqcrmuD/3FSUud/+79LwAAvOq+7wAA3H2LZWq9/ugXAQDveOylT8Honlo5yXK5XC6Xy+XaBG0LkpXgEN2xK7Gd9CfxQmW8WAMDlnUkIpClNJUSqwZ5g57j9orl9HKIrDaUf0neFr6QV04XfVfqIxh7HbSa7JOo/KhcuoqvJKog4kXC00239rtAL8I0rdMxZ6sXs1ojVd3U8yzBqrOSM+tzW0sfpyeMXqlSOe3BOl+VY9KHr9/fcLnsYzHKT0SPGL1PNZ6rPj1Vaok4MWo9BydPHwMAvPDFL+TyNq5z5yz/a+ac+aSmz1meVnOXtbGbmjRSNbHHKgG7pEDzM0afmtNWrYg+z3XR9jcwYs8rAxXsO3QAALBrl5GqD3/wfeBk2Rwq6kvkKTM3a70D056tStn8c4WSSK7IrD2Oj4/b86Ztd2jI6Fqvk/bBZelm9lpJfHRd9jw8T9cBl8u1PfXAW+8AANRylo/1wO8byboJp5+yMV1rcpLlcrlcLpfLtQnaIiTrwok7CQWhn0eVZd0cK8t0B873dWffbtOrlVfSuy3XU0VecuOuPn7CR+l8oqQyL5emSI2GbT9b4adU91arhS6r6HoxXftR5jK9KArH/KwuSZbS46EE+HRPwRar6lY5F8mRsFmiKiqrVfPT5DIVaXpMUvBzoiWkJ6ysVCVmNiE+qXwLoiPpLCed02Ix3dcxm8WUzWjK9oFM8r/a6fWaXfWJ5ElsNPlg5+SWZ1he1cc+9XcAgKEBm+/JSSNQ09PmvTrFqsPGslXH5Fn1ubSwzHlgbhfHMT1n3QZWZlhN07DxP3B8hu9bdeKzn/9ijE8Y3RocZhUsU+cLpHB79+8DAJxupSs5NYfNZjP1epKen7l1EsEtl9J9KzVXCcmlr065WvIJFllZKm9WN+mdyL8jkrbEV6ftY2N5fpbLtT20e+9c6vlPvfRuAMC/Gj2eev0dH7r+vFiSkyyXy+VyuVyuTdAWIVkbq5fkArECjffOoa/qKasOzBfMhzQ3NwsAmJiwJPhKxe7s20ysViy4vFfqK6g7dVEf5QUluUCJP4gkrU1qwvwhkYckj6vAqqxcAaHPbbBisVyucVvapr1dKKrPokiUnTpVHapRnehGZYAp3gOl1PbUD2+Nqtn72cyltRR9EigSt3KFtEOp932bO/Ve1OtJ/zz505S1lORlpb1dUrcnctVMHX+SjxXl9bLttNo2xxWeY2Wbqeov8lyVeXwx2nj75CnPe+5zAADtnp3bT93zdQDAw8ctEX55ybY/warDyJ6FhfrT7HnJ/E0T+0geB28AAEyeNm9Wr20EC6xw/fwXHgUA7LvxNozttW32SRcHhiwVfmDY5nJyysaQG7Lk9yqvjVbTjm2gQgqJdEp+q7XKp7acrlsRVZEo5b+1SHQXeZ2LNYm4VqskWaSR8g32I72P9L3lVD3LrajdYxDgUiWp39q5XNtSWYL11oXdT9FIrh35x53L5XK5XC7XJmiLkqysqyOmH1nV1yLVUYZRknMl4tSgd0uVbLzDX0vzTle4tdvpSsBsppSoTJYOyd/UynhXCoXCWgVilN/GDqGxSrrG78H93ir3bdsqibKRQmhfAzX2YUwS0kt8tPdXG+11WwWCspU49mzCeuKNYm5UAu9INTqs5Ox0Nu6JKGNbUpnG7ck3p7lq8FwtL1lVX6XKxPtMNaPS/LuddOZTl3ajYqnG3aTp4eqMVQcqXmx+2fZz223PAgA88IhVwwySVA3TJ7W4xP0OW1Vhsb4LAHDDTc+w/dCrNjRmdPTOl30rAODer1i1za4JqxxcmjoFADh6xHxWhRxQKqUrSjVXgyRaTR5jZEaXrt+8emMyVV6UUNsrMv2+2VrhciKznCNej9m+kMPDg6nX13K1RBXtnIqWLjFDbHTMkulV1Su5J8vlur71y+//rqd6CE+5nGS5XC6Xy+VybYK2KMlKK/Ev8R55gDQiFHknziyqbP6PKImynrQd0ZFarZZaPts3L9tPT3f+a/0H01lQSa5Qby2PqMWMoXySKp9+FA9Q4dba6+nk84Q4KYE9k5CuMahCDElVYPp79lp1H1LH1mmnKzJFM7J+n7U0cuZ0ZXsTZqoH5+dnU89V4ZZUMRbLPB7Sl468a6qOtHPdaMsLJi+b/EdKkLfXyzVb79xxI0uiPPWyHedYjcc7btsdYcVfM9o4vv+N1vF+30HLuCrSF6Wei9/4MquiWWRO1kc+8G4AwMEbLFH+4K4ajwuoltTnkLlsVdvXjl3mwSrXSfNK8puJLtqhqipWafTqm5gvpCsz165TVSWmiWq5vDFpzdJMkVxdg6OjRtySv49MFwKXy3V96o2PfcNTPYRrRk6yXC6Xy+VyuTZB24JkyX8j3NPhHTx45y16MjhonpOk51qmIk7eE1GSte2nK/eyz9c8V83U/mq1dKK8vGBrZKyAwGwtZXRle/OpD6PK7JLcKBEpUo3Er5NXYnx3w0dVIfby9rzNMctPk82jkqdJ48glFZ2aHJEyoyA9VQ+KOKkik/4hEbHEr8ZU8oB0mrkOrNdN9+dLPGnMbkp8cpw/VT92uZ9uk142Us4K++/1o7LPSG1EvpatGnCM567LcT//zpcDAI7e9lwbHvfXYTUkuL7sVa969bcBAM6eskT52cnHOH5Sq0oBXfbGrA/adbj/wCHbJ1itl1SeRh5rIzUHuu5r9Kmpr6WqC2NMl/Xp/UzbxyTDTMpSxyyhzfZIVEVsyG7Y5XJta718zwOp518iDf/oJ5/5VAznmtQ2+ZLFf6iBrn4i4heYWk3/QW5xMXtfX0xUoq4vOvoCsLBg5mh9OdN/VNSGRBENCtQMQV/O0l/i9DOMTOhrP+X1ErO9ogb0ZUOteySZnPWzoX5WazZtDF0eW5H7jPzSpJ+iFMQ6N2vRAjW219GXqKIaMufSX3aaLA7QmLtJ0GqDI1MMQDoYUz9ZFfLpn5oajWW+rrBSniP+7FfmcTUaagWT/qlXX2ibsP3Nz9vxdBh6Wh/kT7/80tfiORzWl1AeX4XnXLEeag1T4rls8Pj3H7Cf+V72ilcBAHJVO/fnZqYAANPTZzkNtv+d48MAgLEhu2a+5VWvAQB86APvsfGsWolzPl/CMgNODxyyMT/tqMVBLLAFUKnKoodc2kK+VsBhzxcWbQ50zioVfRFN/yzeyRQ3ZK/PtS9T6Z8Lsz856xwkPyN20jczLpdr+ys3MIA37fxE6rW7l/3LVVZ+6+lyuVwul8u1CdoWJCsxWWeaDFdpShadEE1Z+zlEPxMalVmLZDCqMzo6nNpeCPrJzrajn8j0etYorPcLbBCtnxNXVtZaxbT5859M9qJk3bZIEH86Cvo5kbShw1YrMr6TnhEcodUUaTKJPoyNjPB5idszSrGytJxaTuyk103Tj2x4qIzxon3ZeIAcCVqxYvurV43WyHydnKuq2vjYORiqK06ARKudpiwaT71qP/81+HNdsUgSyHkbztl8Lk8ZeVJ7HP1s2li1c9Jic+RWYCPrUYsl+KZXfycAYOfegwCAr95/PwDg7r/9KwDAycfs58Aer6Fbn34TAOCVr3gFAODmm+z5C19qz0/f+zHbb6+NyLBQFSGA0QtVFmKgQKLU10+46VZCuo6rjLsIoZR6XT9n65zpXGWbhuucFQq2vv5OyvpptZ9uI5X92VA/W+ZCmrglKR5wuVzbTVPffzuAT1x0uetdTrJcLpfL5XK5NkHbgmSJhoBl/vIrRbZ8aTQV5qgWMzRnM2BT0Q9FthFZpW9ozSifXr65ZD6lxGfUTBOsbIsaEYS1mAL6n/oBtbrRtqzPRRQsaT8jIzeN4JVqur2NSFiWMmQbPYcgEpQ1uqebYK8FsjLWopMmT9quPFlJyyA1Iy5X+XZ6PAlVKaSbaSsCQXNaKKQbQytmQ4RMkQ4AIxy4fofesLKM7R0GZ9KztjJvHq0Sqc0wyd7Uop3TwX0WOvrsl5hxfe/N1nanSfL10Y98GADw/rvfDwBYXjRPV4UI8eGHrC1PjYb/g0fM03XoplsBABNDNk+nTtyDXt/GstJU8QHnXP1oeN31OulYDBnV83mFkdIY38mE5RZJbPtpk3+vJ+qX9v2pjVSjYdtfWbG/GxGttVZPMfV6J1OckGwv8UrC5XJdpm5+zmMbvl48MQ0A6G747uar9vrJp2jPW0tOslwul8vlcrk2QduEZPEfpB4t+X1yoiHpUEZRnWyEQzYoU56WbFud7J18NhA0G+ZYyARyrg8pLYogJdEHrBIspLedI50rl0SKSLIY2VAupivJapW0n2Z5cZFjR+rY9X4+49PpK9JBkRF8nrTBYdVhL2mlksYVCZnrKTzUxlNiFWOvo5NGCkjKEtnYWV40zUubsQSaj24nUzFHqtOgt2xsxMjXDNv0nD1pd4M1jmNu1uajxLiOyHm98bZvBAAcft43AwBarBhdJtU5e/oEAOD0JMNMRRwZZrq8YGTroWOP2OskatVBI2aVIr1mhYDJk/fYMfTobWIVIYEqAq+b1cRLqCgGeqP68kTZ8v2+/HPp61heQGmNOKW9hKKbImG63vV3oP0mFZ7J3wfXTyIjXC7Xlaq43z5z3nfz+1Kvv2fFfrXonjz1pI9pvT522188pfvfKnKS5XK5XC6Xy7UJ2hYkSwpJo1zmUUGtXtIkS3fyulPPkispW6Wl57qTF52RdyVbrZU0RaayIY6FQiGpslNGl0I21+jZaur1fNJKRQ2j09QhaYPT6aQeVaV36tQZAMAIvUgakzxdeszSutZq+pf/8fHx1POsV0u5X7T5oMUgTY1THq5sxVuP52pldTH1fjGhfzxnpDqRnrBq2aoIC3V7fvLRRwEAJ+6717bf5nLMKlvkwCIr4w7ccLM93mJho4vBiNPQsLWOaaxYvlW9ZuurUXWnqWyoPudlDACwc6d5u2hpQzeQ7OVtfgfH96HZsKbV8pHFHB/pJezTPxe7apadpoZJKyWe29lZo2itlq7rNJ2Ux1DKXjONVbsW63WrqtU1oDyvtf0xQ41/F4Vi+pp1uVyXr8+9/jcAAKP8rMjq9267nf9qbvi+69qSkyyXy+VyuVyuTdCWIllJi5mEYjC3Sh4UekKaxAfzrCQrM8cqN0iakHiySJxYhbVMP0+bxKA2YHf+yw0lttsdf63GXCHSkCrv/NXapZO0vmGrFg67xRYvFe63Vq6tVTzS11IpW7WcKMNaI2hRMfrKSDm6XXqCmB9VIElaWeFdDvO1Kqx2O3zjDfY6KcXc3BwAoMHqu3wpXXFWK9rd1MjYGFez9ZokSsvLRvHkO6uSUGluF+csjTxJzef4W2y7U+jLm2Xjb3COIliFGGz/ffqWIo+nR1qTk4+oa/vprdg5PPPwQzYvDdEh288cnz88aZU5h/j6nmEjYU0StHLNtheTBHpbb3yP5WV1ScY6qzbOkojdshG7fXv22LhVrcmcMM13DgHD47bMzLlHbeXIlPoGr1slspN2FsvqFMBm3Xxd7XaKeXYaGFTT6nQ1oGip6OlaCyPTwIDNdaNpc9hJWhZpCVUxqr2PvdrhuAPPYbZNdExu5ZjlBideLldWf/e6XwMAjObrG77/op+x5vTDzX8AAMy+96bU+2Pf8cDj1nmy9dZ7X/xUD+Gak5Msl8vlcrlcrk3QFiFZYd3/AzGTIa2qK90gT00bLZg/a33lJtinT3RnYMie51nZV2IlXkdNhEmwVpqiO0Znqmp2LJrDDCv5m9TPL/LWXz0RA4lCbdBIQZtp7K12Czne9yvRvc3kcDVaFh1QvpXogionuz0bo7KKlCAvUlRjcnqJY18k3Zuhf0feISmXqcBsk3YsLy2l5mCR1Yrylw0P08ejSrWkB6PNQYPkSURLFCWS0In6BZ0Tltj1mQDfIl0s5JVunuNzZo7Re/bAPVaxd+6EVQEOVm3Om0zI73C+p5eNmHVLfJ8kcOWUea9u2bHf9tM2qqPekD2uX6/Z3eYC/UoFXhN7d+wCAOzesTO1Xpt0qN20BtSxv4IO35ucsut0ZEjNrVm1x2y0fiQbIhIqMNV+iedEPTnbXK/RtPXGJmwMui5bJLK5XKY6kJRwoWXXhqRrQD085cNTT0R5Eds65/KMJYWmYf2w17YbvcehyyXFcfts3F9IE6zfmjsMAHj/M+y/L8MwgvXIrxotevC5v5da/uiv/jgA4IZ/96lNG+v51FMG3+mNfWTXs66YZIUQ8iGEL4YQ3svnR0IInw4hPBhC+NOgXh8ul8t1Dco/w1wu12bpapCsfw3gPgBDfP6fAPxmjPGdIYTfB/BGAL93vpWvhnTjrOon0ZXEY8LUb1GMwZLdGYhkyY+0uGJ0ptiQx6XE7Rj5yiZayzelO/rFJVt/8qwl4R46aDSkzHTyDv1Ty4tGBnIAKqW0l2lh3vbdX1eBCADFZVbtqTchs48CqZlytQr08dQH69ynbWeB+1RV3kDd5qaVUBBV/THPitsZILFRZpOWUxViq9XieunE92xFpthjl8dV5vI6V6uke8pqgvLBaBEbHLBxtFaZl0UaUmU+1rEHzYM1fc56FIoeNugZa/ZIyIqcJ57bx46fBgAcvulZAIC/vtt6cT37+ZaTtbho3qwQ7bgnTz/K8bPKcMCOo8I7udtus2T3iir2mJs1xPMxM2vZNvv2jif9GPfsMfrV7XCsq/bYo9dQlaTyd6ljgDLEEo+heg3yIumRZnZJ+WKU74/erl46Qy1XZCUnfX0zM0bddI5VqSpqKZKmYK++qh7t1eRa3QJ6yj/DXNef+jX7+3v+jcdTr//ytFU6/93t1Q3Xe/ANG1+Kev1V/+7ZV2uIl6x8cOfR+XRFMxNC2A/g2wH8AZ8HAN8C4M+5yNsAfNeV7MPlcrk2S/4Z5nK5NlNXSrJ+C8C/A6AAnnEA8zFGlS2dBLDvCvdx6SJoEnkq13nH3jUKkZAkpZWTDATSkGLV7shHhq2Srt9O991TXpD66IkyTU9bpdr4BLOjeAcvuhO76e0oe6rX6WB12QhTe9UeOx35WkhiWNXWJLmSJyrmmJ1EH1pOOVsVe3+U1YC02yR5Ty1uT9V/586dS41pZWUpNdYxbmdswmhLtseiiFU2Q0nK9ruTOgldianjKhTke2MWU4vL5Yx0lWW84zldZUXocVYTllXJSUqUpOszzyrHTKdKucZxkAQyiX14xObp2DGr1BkatnPaWDIi1Vk2slVnTlZosb8l561GqtNYmOP7tn53yajQfV//AgBgsP4shMAss64qIJV/xYpGVg32eGHnSMcWSJ5EE1cbRr7m5+mz27EbALA4P5+eA6bkFwLpqcYqokXqNzJmcyDKqHOl61nnOMlEK6qjQbaucEvo2voMc103Ovadb97w9fMRrK2gb7zTcgk/9slnPMUjuXZ02SQrhPAdAM7FGD+//uUNFt2wPWwI4UdCCJ8LIXxuamrqcofhcrlcl6Wr+RnWo2XA5XK51utKSNZLAHxnCOE1ACowP8NvARgJIRR4J7gfwOmNVo4xvhnAmwHgjjvu2PBD7FKV+H14h51jKnqvw9we+p5CUG4VfUCs5JNPKddlNRQr/XKsSMvSm2yytSrt2syOGmPq91lWNzZXSM76yn6y9ZYW5jE2xOo/JZ6TKijLqEIytcz8palFIyL5miokbWzjO3YAACb4WCwb6VoluVpaYfYSSY68WsOkFqJyq/Q8DQ2ZPUV+nCbnSjRD/90psYeilhOZ0vrKhVLGmGKx5DfSXMoD1mYvwD7pSVFzTs+WqgjBSrrjD3zd9k9/UKlSS21XJC/wfmJhcZkv2PF+7X7rMfiCOVv/xhufBgC4956vAADufJFV8hR67FvZov9u1s7D7KT573I895/99GcAABMj5lu66ehhAMDqvBHDZ9xqz0+feRh5Xo9jI3Yul5fs2JFTtpkdy+LiHI+FVYD0/umxzHNdIV3TuZibtfWkuVm7BoZJNavcvs6NHh97zPo8TkxMAAB27zYypnOpa0WPff59iL4qv27NLIlrVVftM6x84MAVfYa5tr9e8uKvAQB+ds/dfCVNrF7zza/jvx664HaOvsOqCM/nzco96xYAQP/L913eQC+i957SPcnjyfVXp/dsyj63si6bZMUY/48Y4/4Y42EA3wfgwzHGfwrgIwB0tfwQgL+84lG6XC7XVZZ/hrlcrs3WZuRk/SyAd4YQ3gTgiwDeerU2nPQefNwtsj0WiurxZpREVVrVOvvk8c67R7wh2tOg32l0wOiDKuCGq+n09bV+gvZ8dNSIwIEDBwCsebb0fjZnqMSqLpGsZrOJ00u27xsPWRL72cnTPFa7SxgmEbrxiN0hyEO194hVLio/qkuPkiocxypGIfrq7Sc/DT1f8qGJgshbtXP3Dm7PlqsOWCVmj34eebbkodI5yef1KH+OHeMA/Wuakwb3s9ZTkXlZUWng9Ghxe8rnGqKHbG7OCJJq6k8dt8ocVdrpUZWlSwusgGMfsMVlVpByPr527CQA4O8/YRk03/7auwAAf/u2twEA9u/kfDBjrcT57iwZWRuq2vnpw661xoq9vkTq016yc3/mmPWMLO9lvtbCuaQPImFjQvFEsFZ4PYkKdvj+kSNH7H3+RLXK67zNKlq9Lm/V4gKrB+nbW6EnsUFaOsjt54vqaGD7P3nS5ubQoUOcU/t70HUtYqYU/yTDjc+79Jjltl7h0aZ9hrmuP5UP2t/x2w99jK/w14m+/b0+7w//DQDg8P2Xlm+V5GC9YeP33xZYSpQAACAASURBVP/+PwEAvPJ7fggAED715Sc65A0lQlYMX3rce8rJmn1g7KrsazvpqnzJijF+FMBH+e9HALzgamzX5XK5ngz5Z5jL5doMbZHE94vJ7pj7/XS21BATqhv00Sj5ulIzijE4ZFRhfsGqsOZZjbVrp3lQGvRByackiZSJ4kiiNSVW/onOiHSVK6QxM1aNWMgFjA1ZKne1YpTgpqExHoMtO8/efyFvlWA33cKqDdpgls6d5ftMAyeZUi5Wp0+6UCBViDY38kCJTGnOREtEmOQ3q9EbJt9Oix4tZSXJtybPlqifqgNVvSgKInKWpYRjzO8K7GE4xL6SS9P0t5HudEhL5qbmUvOnxPuxYfO6ra7a9lsMGV9l1eHJM3YO+hzPBz74twCAl7zsJQCAG47YOfvjt/03e77Lng+XbD8Hx/facXM+lOJ/601Gme584Z0AgPu++lUAwOGnGXmcXbHzWS3n0O7YMS4u8hwxa2ZxxcaoFPzBQdvn/JKyztjPkue8yGyzat3O0bkz57gdu+7HdxmNGx6zv4dcUOYZzw3XX1q2cznGXpyilfq70TnWY0IzmeqvcyyJbrpZyXU96sde+UEAwM+MPbzh+//4jtcCAA5PXl5C++/M22fST4yc2PD9d//ZWwAA373/6twv/I/3Cug+PtX9ae+xvopbD1pvvnxOXC6Xy+VyuTZB24RkmRLPFr0gK027k0/6+yVeKfVCNCKgNPE6iUGbXhVRCnliRLrUf0/URnf0Sk+XVSzwt3flES0zUV5VW/1uF60GacIASU9I+7hqo8xC4rEU2qQQq6R2pApnJi1JfHjMKIS8R42W0RJ5lAbr5rEqdYxkJRVipG6zc5a1JLIkD1eHvQfVp1FEKskC4/prHivOsQhbKV2FmKc/qFYYSI2vOWuEqc3Mp7l59vrT3NJPF4s2t21WkOYKNo91eq9a9HwFpZizR+GJM1ZxMzljRGyqZePce8Do5Qfufh8A4GUvtqrC2RM2r9OnjaS96Dn2erEwnjrO2Tkb9xFup8YqR+WRNZllNbTHjvfEqSkM8Jx3IysmFQHWtWMcpEdQ52aUHitliIkCxkA/G2+ZDt1o/r5l+sbkseqyv6Wi2Gv59J//8Jid2y4Jl/6e5DVcyzIrpLbb4TUrz6MUr/3qQpfrqquwzz5zswTrB49/IwBg5p/Y33F3Mp30/kT19v/4HQCAn/hPG1cZ1viLyqP/j31mHf75yyNm/Zc9BwAwmn+8F0vKNZzXnE8+My6Xy+VyuVyboG1FslR1KM9Um16SKr1QEyPmd8qzyi8SHYiqdEkIFPUs/5F6tem57vDl1UoymbhfkQUwjysX09uTJysf1tK7mz0bq4iQUuV7wbbdz7NPYmuB25JfxrxNt95qXq2z0+bHET3YuWtnMjsAUCjq2O35QN3IivrfteiVkg8nOx4de9aLJY9XNvk95uThaqXmTJ4szd0wc6Va9DbNnzKfQZuJ+LuYPaYK0kVWZYr61AdJ6Jja3+T+uvTpzTCp/fQ0qQzzyYbo1eowB+tjH7MKoAp9TztI7kq05dVytv2DTPfPk1JOjBqd3LXb7lLHJ2zep2cf4/EZDV0iVa2UywjJOVD6PAkSM8wKJTsnaufY4NwU2HNT15POwTLpZcizVyZsO3NLduydtnp62rEr5UbneoCeqhIJl7YvrWQCN3UuQ6YP5VovUaSeu1zbWrze73/p21Mvq4rw7IsX+coiroZG/sjI1Gv+2WsAAO+7+X0bLnf/G9nT8Ocvr6fhB//kD8/73gu++PrL2ub1JCdZLpfL5XK5XJugLUKyNk6SFjUJMZ3wrqyoNivnJs8YDdm71/wyYzuMQixxudFxoxUN0pkCE+JFuCbPWsaRcoLkhZFqpEGFki3faBotEalYIEVZYW84ecOGB4cwt2jvnVswAlUiqRlcNXJUJiEaIk3LlemnKSmtmxVfJCoV+mZ2lKyirEeaodfbDdt3m1V/Z86Y12iIZGeAy+VqNvaEQNFPptyuei3txZqnl6pctdf3s6JS1X3Kx2rwnPR7tv+drHgr0nO1yAT1U48+yveNDMWukuFt+yus0pNfbgfP6QI9XD1SoVkmvM8vMW0fppE6qzCnjMaM7LRr4L4HbP13vOv9AICf/6mfAAAcu8fGc8Mrze90btqOp1y2c1/nvA0O0OtGgneIie8LK9Y6qkFiCfSTO1/5tuQV7EOZZ6xEZEWjvFAN+rt0bkTJqsx1a5JujrMPY5ddBFaZ4dWnr63PayPP/Wr7+vvSdZ70KMz03tTrdMshX1R6WdqEFZPnYlp+b+faPopFu64f+Z7/mnr9ZNc+e9548Bs2df+9n2XP3HdfZMEX3GaPn7nnkrabVy/e86gVO5i5/8LLuPzTzuVyuVwul2tTtEVIFpXcECvzSS+z9yCpToUel9ExelNWzS9TqRhdmGEF3eTZU3xu9KQ2wLyfvt2pH5+29yu8Q2+zX17iaeGd/+SMUYoQjJZE3sEMkyQ0SGmUZ6TMKxSrOHD4Jo5dfjKlynMd7rtYNrK1usrXoz0uLSzz2ERQROGMaiytGjlaYeaXiJCoh5Lcu23mWvXlmWJSOn1ltIZhcYYVayJeLGkrF1nlRwpz/NiDAIDRIbvTKTEzabhOKtI0o1GdWVEnP2/d26fus/UqsHMRekzxj/a8SQ9VnhRlYnyYx2X7VRVkV8Y6sqsS+wQ+52k2nnPTlq81wXGNVpgfxmtkaIeRrT95318DAH76B3/U9g8e/4jtX8lQOp5yv5Cav8KIzU/M2/zwphcjA4MJiZqfN0pYTWiYneu5to2xS/q1smqPpaLtNelAwBCwYsHWV7Hi6jypIXtythq2P1WkzrCfYuQKnboRsfFx5mqRnia9OZXoToKsRPglTvZL5YNjdWKe13vMdGYIfm/n2gZSkvuHXvD7fKWeev9HX/A9/NfZzR0IydStn7QI+K/d+Y4NF7v73X8EAHjV3kvzZv3+F9RNqr7h+2+ef9oTGOT1K/+0c7lcLpfL5doEbS2SRT0+dkeZTCRVs+arGSjZHXYpbzSkUrFv5LpDr7On4cys3WkoZb1H2rNKctDs22N5wrajyjlVsBVY0ca4IORZwacKvGrFyNcw09xXlm29HTt2JvlKMzNGFVShpRyqPjO/NOYBUo5W06iCPEmq1svT3wOSMfVhrCQUDan1+qzOW2Xl2Dx7AzaarFSjb6dWM/IFjkdVfLPTTE4nXdTjCCs5p06bn63OdP05HmdVSekt29+D91p+VaHPHoi59KWp4+hlqhKLnOt59grsNdXL0d4PfCySvtSZTr5atnlqsOdWk3Tz6ftt3HM8R3P0rC2xynF20Y7nmTdbdkyux0o/euVyRFUd0qflRdtucdDG2W2qv+Bqco6VQ6VzrOsrT19XnsvJN4eYzijLca5W6LkSaZK3cJm9OZuko6M8lzt2WgVks7XMObXNH2c/SM2xPFjyZOWS8dg1NEKvYllVhlxP9NNv5VzbQc9/4QOp5+888mH+K0167viFHwcAjF9mkvvl6sDrrLsETl94ucOfMeL96AsaF1zuYGFjgiX91t2vvuSxXc/yjz+Xy+VyuVyuTdCWJFnnkyrKdIcfYHRmdcXu1JXBNDJq39CVBC9aJIKwY+cuAEB9xB77TXmt7P0BVtAtrtr2c0HVj+D+7U6hVrP9LC2a72iOtKRSNiIxPz+fVHSJDuhRadqiCdlEdeVZyVs1O2skp8+vzWOsmCwSr+nb9DyT5EU/KkxO13ORstExptzPmd9MeV5tzrGOvdMxonbkiFXdNUhNFudtP2M81ippY1OXHKscH7rvfgBAj3McSG/KrF5UVpOOu5ArpudBvqZpm9tqif30WEFX4PGXNA/K5SJ9WaV/bqVhj+MlI1nNhp2zhVnbz4c/Ynetz7j9Wba/VTu+A7utJ2GhxGrMNr1seWVXsfckz5+uzcGBocTjNKyMrwJ7CDZsGyPDo5zLOW7L5nxszDyGWr/XowGtn96HfHO1qp3LWs2OSVWGrbY9LzGPSx0LlIela1PnQI/K1Up6F+ocKedLHQ88J8u1DXTLc43srpGrtN61bAT68yvWt3T8LU8uwcrq5V/7TgDAh259z4bv/9f9Nr5XYWNvVuPuI/zXxgnvH/V09yckny2Xy+VyuVyuTdC2Ilk5UhDlBJXy6lUo0sQkeBKAStU8Vvm83YmsrhrNaNLvpHT03buMaHW43cUFIwsiBZH+p1BgFSJpTmPV6EqgESr25aUxmtJqNdFqK7ncaMToqNGLbIK6qILITYdJ5qpcHGGeVJHrTc+Yl6hPz5GIkxLelXEkUiZP1/KCUZSxMSbOs5qvxeVy3P7IDqtAE3mboidq6qzttxps+8OD9v4sKUyP4509bRWdTVbADZFAJVlonLMeidTqqo2vwr573U4/9f7inJG20ohS+UnmON42SVmlmOPx2oGNsKK0RDIHkqen7zBaVI52XB//1Gfs+JpGf244aHd7M8vmSVsibarXbb9VJsUPsOIPJRmUbHyzc7NJD0v1xLz/6+b5EImanbXrp0afnXKvRAlXOGbNxSCJWI5+s3nSxxIz1cq8XidIak+efNQOmVROPTlFNXVuE2+hKkr5uv6O2rpIKF2rPfr9CB9dri2p9970/gu+/4fPeSYAoJ/piPBUqfAK6zJxMW/WmZ+6EwCw5zc+mXr9Y7f9xQXXe+Nf/chlj+16lJMsl8vlcrlcrk3QtiBZkbRGZYfKblK/vYGyiJXoiFGNao0Vd1xPlWpdZke1SS2mSArKvIMfIaVYZHq51u8yRb1aNXKgasVCPk2NVMU1NDyAVosepHL6VGhZZRSJIqjSq07qIGoQk56BHR4j+9/xmObmjJZ0Rbbadpuzc8JS8AeqzP4ixYisYBMdqQ2br6dH0sQiQIjPqJqwygyw1gL7czEzqUGS1dLdXsvI3TD32yYlzCX+nl7qsU3fUYd9/Todex7otcqrtLOvKsduarlBVkNGbmeAtKfA5XNtXhskioGp6rccNa9ZXVWLTNbfzyT3xRmjTa3A3pILVi25o8NcM26nIE9ag+Nqt5NehbGvNHu7ThvsCahzvkAfXZ5ISNfx3j17AQBnz5pvrk3KqUrIAgnYwoKNoVy263KR1YwdXiNjY+wLSfqn61PX4O7ddo1kOx1IeXoQdQ0m54QZZO7Jcm1l/fSZ5wIAfn3PF1Kv/9zZ2wFcOwQrq5e/4Y0AgA+9460bvv+Vf/u7AIBX/YZ5s4of3cN3NvZiJepf+G1XWk6yXC6Xy+VyuTZB24JkKfFd/iWRpgKrC5skUjOkDsOkMnPMhBIBGxiwO/KBGqlOngncJAq9jpGCHmlKu2VVVrnIVHFmUani7/SpUxyPeWlEBkR9+v0exsfHORbz9MjvIsqhRxEr0Y4+k9hbpB4jI4PrDwWjyT6NnBTzlolUqBjdWGTFY7Usj5Ztd+8+q5aLfZEgEqayvFfmQRLZStK9ScCay7bdYSbUR1KYyOPItZkDxvyqpI+eHqOqKeWns7ntcO46TMSXd0seNJE9ec06JGAiV10tX1T1pvmeChxXsW7neJr+pMj0/k7fHo8+/VYAwN9+5osAgC997WsAgFd96zcDABaU/1Wx/czNWxr64pJtr9gh3cmxurA+iMhjqbOic4D5VfLndUimppjar16bOrYO516Pe/bss+U5FtHGai1dQaoqxUjaNzNrZKvbtucjg3btiJjJMyYyLMImWrqQJMGDj/qHPTjJcm1lLXarG77++edc24yi8OHPAwDeumAk+o3Dkxsud/dpkasLE6xbPvEDV21s15Ou7avE5XK5XC6Xa4tqW5As3ULrjl45PtOTDwMAqky+7vPOu9+3O/IxUqTFRaMNbVKW4SGjI4RFkOUrJgnYLJfi8wIT5evMXor0+ezffwDAGgnQuOSx2bNnN86dM+qgSrIBbkP+F6Vqz8wYdVMVYJs0bfcu+XKsqm+QtCHSd9ZYkR+H+VKssCwUWPHI/CZVQJ5mQvuRw4dseVYlKnequcLEdRGmptGW4R1WsVYrGKmp0o92dt6Oq8/+en32z2ssiQKK3Ik4mWJQdhMJWFHeM3C89o8a7xNED3PyWDHZvpCz5VSFyd2hRKKnu4wCt1ccMGq0yPkb4LXznd/9Ojv+olWiHjtpnrbPftnu/r75G19k+1k10vfYpFLcRzhe8RwODEBUlhePrcmqVFWQ6pyNjRr5bHMu6iStup6OHLJzNTubrhIs0Is4O2METLltTZ4z9clUR4Q2q2p1vSqBXttTMn224rVEn1ouz48TkSyXawsrFuzv8y0HPpF6/dsO3sF/dbEV9KaP3QUAeONdb7ms9f+/Oft8aZ8YuGpjup7kJMvlcrlcLpdrE7QtSJYIU1I9SMojitBcsTtzJVq35TPKjXJ9u/NeZjK8nve6pCQkDePD9D0xo6pE/488KmfOGAUaHzP/k+78ld6e7U3X6/cSiqZlRLLkLcplKhrlgwnskad9DNEj1RZZqnN7HFuHfrIWSVaPxKfVMnoxdc6yVeqsFGs0lN/FDKaK0Y6DBw7afpnvdZpVi8usJizT93Pm+AkbJx05HVKapVlbXj0GI7/nN0myIskT8urdyD54JG+5gipE6UviXEbSH0aWrc0fN1djDlentzb3AFAkWVLFXWGYffhydrx3vPQlAIAdu6zy5sf++U8AAP78r/4YALDC+dN8LDE5foVeudU+ieSAnY+z56wSsFarJWPUuc+TBEWu0xVZrbMCkySrWDRyVOT1Pkn6KK9fvmrbPTF5mnNgx/LVe++1sQzZdVgdYDUj0+2HeY0tzRnNW+UxiKrqOhf9lFQB2+9ujTt7l+tSdP/3/C7/ZX9nN/yvHwUAHO1++ika0eXp6b9rn0m46/LW9x6FVyYnWS6Xy+VyuVyboC1JsiJC5rlJrz52wqjM6KC9kqcfZmKH3enLY5Knp2THTiNPAyvq5WakQJVgE8PmZWk3lIfCyjVRGlIi+Zzkrzpx0irM9uyx7YtKDZGI1WoDyHOdEv0zFeY5KV9p8pR5tgZIOzrMwYokRrVxIy/yaikv6szkJNdTZaTRiCVSPKWAj4/Y2JQaXq3Y6yusEizTk6TehbmqUu2ZNE86pzT85iwpCL1goWPbaS3Z3VSfPqCkgo5Erc3HQNNUoBGuS2KlvpEiWX2RqKL8c1yOfqASlwOpi7xcop4hp2vDXi+xHyVg8/SMZ1s2ztHbLM05x/0UQ4X7sxUP0A/14COPAAB6XasS7QnI8ark4aFaXctma7E6Ncln6zJLi9WwFdLDBVaCylPVIK2Ud4pTkVC9Xjvdw3Bu1s7dLHtn7tljVE4dENRlgE+T61jkqkUKOc9Ue3myRI7bInBIS3lbfivn2gr6/m8x79Wbdt7DV+z6/uMl8+4e/Zdbi2BJ/S/fBwA41rHP4CPF+iWt95Nn7rj4Qq6Lyj/+XC6Xy+VyuTZBW4pkiWD1QyaAh0/lsxknsVpdMk9KbYh0hplINVKMmQW7s68yRb3Iaq6REfM3tZgnNDVjnhdVrLWUqs6codyK0Q91cFslNdp/g1UXqvebqsGKzOOaOTuJKpPec2W77T83Z1WCp88YiVrkGCrMwVpgf8U9O9gbr260QtWHpydtrDWmzj88aVldQ6oUGzBq0YEdayS1KNOfs9o06qFMMGmQ+3vsEavY7PMYD9Kr1GKG09Qpo3c99hos0dfW75DWsGSzSbQje09f/6BnqsJ0867ytXjOuppDppn3VPFWsPcrg3aX1pf3TIRLVZT02zWatp0h0qMSr6lK0c7NngOW9N5n5V2TBLBKOvSso0dtfdKcex6+HwBw4002H8Uc7xbpD1wg3VxaXassXViwMax07RhaJFTy5c0tK2PMtjG3yJ6YDZv7MRKobrTXz7JfZXXQjkFVhGOjdu086/ZbOZc2ZnUkmJlmBWjPtjsyatf/KvO5BkdrnEv7O9G1VqTPrc4keZFhgNlp7F+ZEK5o7+scuFzXktYIlul35u3z+z23jj8Vw7nq+rFD3wBgfS7WhfWeTz4PAOB/rVcmJ1kul8vlcrlcm6AtQrIu/F26n3GDqKfbia7docuboiqoySkmYrOiTd6SGivrTpHGTJ0xMnDjjUY18sU0BSmQfDXooSlV1F+wwke785f/SONQDlIIAR15pUiCkuwibrsaq6nnEzsmAAA5+szmZ80nI59ZjRlcgdV58t+Mjhvdix071iEuv8L+iyUmoS8zv0r9HTWzx7/+VVuPqeRN+teYGIYZVg0yWibJgGqRuojiKcG+T5LVzVSkFTlunfEcCVOOXrMuiZSS5vskRQP0zckvpIq4FXrDVMmX+Im4vWbLjqPHc1OpG8XZtceS78skgo22+gna8fdatt0/epv1BTvydJvnhx+y+dy1Y5jjs3EOkY4WqryGWg0Mcswn6d1T+v/SymLq2NUvUlQvTw/XMqsCe6R/jaYRsclpo5mqED1yxK5fVYqukjLu3rWbx2TH2GKW2cpKuipWXkJ5s/bvt2R5VbbO8++ql/Q2TCe+u1zXst762jdv+Pp7njHBf22vngV3/IcfBwB87pd+b8P3X/fwKwAAoeN/wFdDTrJcLpfL5XK5NkFbhGRdRMpGosfkJEmUqgSn6IcZYT6QegqOjRndUenXKeZcKWuqQsI1u2C0CDlbrk7fT5flU7ML7O1GCqWKN3lXlIyt3m8iWmfPnkWbJEVVbj2Smr17jRaMEQf0eiI/RjPapBFD3GaDvq+jNxi1mJqx5HFRj4bytKpGVKbPnubY7Ht2g9utDRqpabWNPC0tG6XYuZsVkhxHiVRk/pwdY5sErLtic9dp2vZWl9NZS5KIkkiWaCJErDiXeq7qvByXU0I8uNyIqgy5mnohigz2eszRyq0lrq+fnx73U2B1ZZ40UtWfNVZV9rpGsOp1u9b+4dN/DwC481ssP6vZtflqNG25HUxrb62Q5LE/YLlcQrGgSk32aeQYBkjL8qR6IqyRPTIX5u1c6vqenp7k8jane0kvl1QhSqK6wrw4ETN1G1AvxFpVVYI2d4uLS6n1dR1r7uU1jLDxqaOC/iB1zeby24sEuLaXvqnaTz3/tteyR1+8Z4Olt76yBOsfPfStAIBjc+yA8vDIkz6m7awrIlkhhJEQwp+HEL4eQrgvhPDiEMJYCOGDIYQH+Th6tQbrcrlcV1P+GeZyuTZTV0qyfhv4/9l78yi58ru8+7m170tX74vU2kfSzNizeDe2iY1tTAADBpyYNX5DIAQHSAgcEsIhCTkkJrzn5Q3YGAzBecEGbBOMF2zjhfE6ZrzMrmUkdUu9d3VX177Xff94nm/LLTQzmpFao9H8nnPm9FTVXX73d2+V6n7q+T5f/I3v+2/yPC8CIAHglwF8yvf93/A875cA/BKAX7zK/TyhjIJYVpRnNER0BvKUnJ2fBwBEdEe+sUHCVdMd+9FbbgEAlES+CkpR76sizqoJQzHe2Q/AO6CEcq+sh6GRiZ68MlbZZt6b6SlSqkw6g6qWGRkm8diUv2VdyeAp0bfSFr1WuUJef7m8pWwb6SkVuV5P1YEb6ls3qiywgK+efDHOWVueMAR4jBtbXF+tDDE0yrubiOhbp0KK0jf/zgopn9+Wh6stwqS/dk4sud1oSH+bemDHHNnz2yTLXr/kf3qXVA2GRLIsR6wnr1ZAB7Kdsm/XhBSKKHcrzL8FEUTLIQuIJkWCHE+lSAIYT1j2GkliaYtUCEFu3/M5nmTc/nL+ItEhjTe8nY919Air/qzC0giR+fSQVUcDpdBD15356aLK0/Jbdr0re0z00IjV2lpR6/OYrBrXCKuvayMkwtbv89zFoiRr8/NzPHYdSzKpDghB5c6FbXzcvlWEXjx7lyba3RC6IT7DnK6/vvdVO3OvfuDsqwEA/t/fnATr8fSNB/nrh9e+od6XN42eNsnyPC8D4BUA3g0Avu93fN/fAvDdAP5Yi/0xgDde7SCdnJycrrXcZ5iTk9Nu62pI1n4A6wD+yPO85wH4KoB/DWDM9/1lAPB9f9nzvNGrH+YTy2iGES27485l5TfSHfrkJKsOV5ZII8x7NTbBKitLzLaqKk/bS6uirqeu69vJ2v7OLKaOfD855RKNjIwAuNgDLiYPTUV9/iKRCMZHue9Sid6mqKrgCkMjep6kKCta1hYZevQ086ruvpPJ5JYyn8qQvkX7Vk3HuTGSsrLINHxLcB8aJqnqmZlJdCWkpPe6Jc+v0q82rXG1a/QYBeUx2lgiLYl5qnxTwrvRkkurCO2xebOMdHVEsHz9tddNdq47Imf2upEsq/y0Cs5LiZk9tv01PI1D53zPgYMAgESavoSI8riaZRK+epE08uwi5//lr2AqcjrDa+T8ApPfC/uZBL+8TP9fNsHz12hw3LOzs6iKCtq5M8pn9C8lf1yzIb9bj9fR6MjEzmMRYe126Y2qK0ttu6+lrgnrfzkvomsVn9NT7Ee5aT4+0bvhYVZXpZKieEYx5bXydK4TKZExXQsGrIzMXbTB7fTZ3QC6YT7DnJ559QY3zHV5XeUI1u7qaq6qEIA7AbzD9/07ANRBrH5F8jzvJzzPu8/zvPvW19evYhhOTk5OT0vX7DOsX68/+QpOTk7POV0NyVoAsOD7vv2w/X7wA2rV87wJ3QFOAFi73Mq+778LwLsA4O67776q8iOjG5YrND3NjCO7Ay9ukRR0W0opnyFlsKqtrrwrPXmpIhFSn3yed/4h+XXqyiEyktWRn8kyqjrKhPJkkGqI5hi1GRkiNTJS0Wg0UN4iUbI+b0FLJNeHthGZiBLMrSfg2PiIts3X40mOuaaMpfVNUgnz/fQHHFtWeU3WPzGsnonNjioqU6QnDc3VihLkn3eUfrWFE49xeyUeW0OerLAq31pNbscC3O3YLWvJtF1NiJ3LDbYb3u3Mx7r4PNWRl828V1YB1+2SSG1pXm2/F7e/0xfUE+kbyvPc5IfH1SY5xQAAIABJREFU+Kr8TzV54SIdbu+Rr7K/2f47jwIAqm1eW2fOnAIATO+Z1ji4v7KoZUj7y2V43jY3NjAlb15MYzeyFBHNLBZ5Ds0rVa2SZPWUX2PH1mzRm5URTfPkQzt48KDmglWF1vvQ7q3Mq7Woalsbh3mtoD6SG0We42PHbgMAlNQD0c7h2rrGqWt3oMrTQMTO8Q1bXXjNPsOiMzM37EE6XaIRfka9ffzrO56uv8Ld7Dtdez1tkuX7/gqAC57nHdFTrwbwCIAPAfhRPfejAP7qqkbo5OTktAtyn2FOTk67rautLvwZAH+iqpyzAH4c/OL2557nvRXAeQDff5X7uGIZYTJvy2BAYlTIkVIM5PNJxEmwmuoLGBfRyuRIGTptEgXDS+ur8kvFSRjMp2MeE0sfz8r7kpb/xvxU5huytHPLyYrH42gqyyiiii4jQJZI3lGafDhCCjGsvoytCgnL6gZvss13kx8mfevKa1TWcoJxKFVKO4650eEcJVMcc1yVZJ76zI3k6Mtpi8jkolxvucxspp68WZaev+23kRHHKKPp0lwsmxujMhd7GfJ5X/cB3iVEKhDkOUgpU+ri/YJ5vLghS9S/SNK8nftX1WA6K2qp4x+IyqR1vF/8zF8DADolHnenQx9TRL0nyyuc51tSrBQsb/Ka2btnlseh+Umoz99gMEBxlQTIqgktnd+WieqasL6MEfVV1GWEDZGuSJjLJ5OsRLVsM+twYF6sUoljtGtlZoa92TY3SLosw2ujyLEn4rwmzDMWiym/S5QvqDkdynO/1ulgu08kLpF3aZXhDaEb6jPMafd15tV/tOPxoc/+GABgP66sp9+zVcv/5qX6Px7nB2qZZ24wzyFd1Zcs3/e/AeDuy7z06qvZrpOTk9P1kPsMc3Jy2k3dHInvktEJI1bRpHw1qoryREf68qzk1afOcoLsznx9mdWH2Ryfn1SPt0qNd/xhrV+xRG1lIbVVBVZqkggYSTCPmFV7xbMXexuOFli4tKFcrJi8VVbdF1XPwlpTFWMiU56qAQc9kqiTZzjmA4cO8XmPXiWjIN2BaJvytTaVEZaI8xjjohYdVb+Zfy2XIAVJBrifxSLXG8h7NbB8rIFVA+qS0lzaOTFvmT022rjdSzBo5Eteqa6qDM1vt026LCmer09Ocv6s0s6Wu7SHoe0vEuG5MpoYJATC8hK9Z4e73F9BNHL+FHs2PvbA/QCAVzz/AFeQT+rQIT5uW8K8gF42W/jmaUAsqf6ArYs5XVa9Z8e6rvR8I0+pFP9ubimLTNd1QBle4+PT2hKPvVoraww7M8esutWO3Txb5mOzuUgl5M9rcM7KZRK2jQ3u32xxNsc2t0ePs8I1EFRivOHIfxCLZU/s9Nc5OV0PfeZNv6n/42fel1t8f+z/pzc3wTL99596947H7158+TM0kueWnps1q05OTk5OTk5Ou6ybimTZHXZI1VnNJklSaZ134gGr8NorD4mqCHuiC8k0ydbszCwAoLhBulEvW6Ua95OMk0qkMkr7VsZVOsn1K5dUthlFMZJl6euVSgUxjcF8MtFEbMc219bpuYrEdp6q/Qf3AQA2N3lsQaV+L2t5y/pqK+E8p7F6qjacmKYfp16lL+3EoycBAJNj9AV5EHFShZgvv05tkzSvIz+beY0soH0g71fXvGWXeK9M5k+7SKi4gtGSvqiH9cPz5JHqiIi1VA0Z0PF0O/2d41b6PvzmJa/zb1AJ980+X89keNxZ0c1+n/v79Mc/CQAYVr/KrmhRTOOzSrtCgX4+qyY0+nlh/oy2y/Mxlh/bPv6avFhjuh6WlN/WvyTN3vKphgv0RLVESjeV5m+Voz6s6lVzo7m96AVMaLs8Nqs6tA4JVi1rZNdy3sy7FZeX0So9bbxVVdF25U0MirSZPc+/sTxYTs9R7Qmldjz+Ty96vf7PVRU67Z4cyXJycnJycnJy2gU9u0iWspg8uzMWHNn2WvX4d3iIXpdGnT4i6zVoKeTnLjDxupCjPymjO3fzlFjPw44q1KLWG1GVeLV1VphNzJBKlBskAgP1jMukScomJ0kAQtrugX3073SUSl6vlrCldO2kMoyMtKRTqv5r8e/ExJjGoHV1bNtNBmEVaKR45t/pqodgJMjtVOUjyyS43FiBKfiRUFr7JcmJiRz1RVtK86sAgHaF+/c8rt8VPQlue55I4rDds1DVfRplzwjWJTlZVj0Iz7xaIlw61xZI3+1ZXz7O06Yq5hKqbNN0IhTi9i0/rNfXftHVuLidmPxPR29nBlQqzf1/7X7mYQV95YEpfywY5Px43YbGzXmYHGO1YXGTywdF7iaGSak87X+9KuqZSmKzxjvoRpfbTg+RVJk/bCrM68eq9gbqMNDtiThlSKYaDU5OTZWezRbPSVyVl8HtfpGcnHbXOhPoWIKqxBTR7fQ7Wo7bC6mS09M11qrz9aEMfWd16zupa2HbD6j36/b71P7Hd/d2TtdP7/ru39/x+H1V9n/tuxBsp+sg92nn5OTk5OTk5LQLelaRLM8yk0R7jHL0t7OleMfd71uvNfqV+h4fZwqkFgJOiFtCtiiK9SK0Sr72gHfmMcshUgXeVpU+nAtLF7idNClRSP4gS09X4RqaSupOWvWWkr3h97BVVrWe9uXbWhpzSF+D61Ulj6vqMBEV+fLkvxE56YlCWPWdhgafTyNiOU0iQr2WKtY0dzWrkFQ2UlRjr6yQ1vkyW/VEgnwNsKeTEJBfJ6tqukqNRMvIWlD5WV3zWmnuA9uVaJabxeMM+jszxsxvF1JelnnSMnHLiorqOLlcOKaegU0SuV6Hx5OWRyou79TMLLsAnD31AADgka99EQCQS+iaG3C+6yKDY0mRQbVGLCuvrN3igYQ07lSY4xkE+LjYkE9wZQGbWieV4LbGRkkVu3YtBLnOiDxbzSbH7nuci7K8Wlbl52kf42NMks9mSWqbIlR9EaflFSa8byrNvufzHMWVCdbHzn6T5u1aXSPNDInejY2opZ96dpp/bxtYGXC+pFehkWcnp+uhR1qswn11nL9gfKZ8i15pPkMjuraq/uCLAQDZR/WLygMnLrvcLz70vQCA17/wvQCA41kS85PYs9tDfE7LkSwnJycnJycnp13Qs4pkPZ62vR6iIOvqyVYs8jf3TI533MkkyVVIy1VEkUKiK22lq1eVb5XK0wvT6ZAYRAKkElZRZmnf4/JL9UQ5zCfkW1aUprnZlo9HBC4/PIaAmYh0DB2jaVWOodFgFV9DXqx8nn6Cso5tdXVVx6befepluJ0LJQrXVfZRV2c8EuZcnF88BwAIy5c2d34OADCR4OtJVRUOFACVlL8tlCKVK5c4zqaIVbVMz5GnaklfcxtWZVpNx2NeJl/nom90Q70Iw1aVqNkxLGJELBaJ6nVVypV5F9fr8ZxFYty+9fPrK0/M8znuwgh9e/n9s5wXzfs9n/409y8Kk7Mej6I0rR7HbzQnrBTzQMcokipHt3jNzOzhXWJL9LK8yvO91e4hJho5XOBY6jVliYFjDIfkJxNRsupUu44t92pjg3NufSqD20CJ69l13VKm2bgIVGfA/TXapHy+5iAaNzLLx52mugIIi1qu1laNifMt+QIHdo58o6NwcnrG9TP5+R2P517ceYZGcm31ygf4mfLLw+8EcPEz7B9P3XXZ5cff+Cj/h0XB270b3594AQAg0HDMZTfkZtXJycnJycnJaRd0c5AsgyC9nZVre/fSZ9PtyCcj6pCSH6fV4B28kYG6sp8QIAHo9kgO+qqSCoW5o/2zswCAhiroypukOdV6VfvT+vInHdx3GACQUO+39VVWJ5ardbRFnubP826rryo0I1aZNNdpK7dpoAyuoTRznWpbpBSTIig2FxGRnmZDuVKiGGfPMQ+rkGAF5NQw/Tt1eZ7uvv15nEvROm+Lx9Swfo2W1C7Sk0pwPxBtKW/w7mphQX0b5ZXq983L5Wmc+qtcK8vH8i0jSt4yy9m6NKnd6I5lSYU0vpaynury1fl+T/vj/URKeVepHEnbxH7mjYXiPI6yKo5GVWUZUP5YtsDHWzX5oZQXNjbL9VuqKuzrGrTxrcszJosdcjme16FcFoMBvSIDjXmg6r90gvuam5sDAHS6vM4sIb6u69bytJotXbeePFV9zlmjztct3yqmhPdtD6KuiUaloTlQxaiqF43exVJcr6X9rC4tAAASOrdd8Bo1kmb88aInC05O113/4zv/v8u/MOhf34FcYxX/xUsAAL88/I4dz1snksIX+Bmz8bLS9R2Y02XlSJaTk5OTk5OT0y7opiBZJvPnWCL14iKrqPLKrWqILkRFpHJZUo2YaEVUlXvtNu/Yo3HeigdDvMOvy9uSSpEorMn75esOwqoKLbqqIQ/WhrxfHVX81UTAhgs5dOv0b1lPwaAqyow61OXliasCrS16UVcm0uzMPh2ziJWWb9X5GD6PaSjHMeeSpGi5FOek2+JyExOssotEOfjFC6Ql0TbH2u+ZN0v98JQ51pZfx+hdYTinY1X6eIjHkcnw7urCAg0Ba+qdGIsZBRERE/6w9HHrr2cp5HZuw6oytKrDnu5OzSfnqT+e5XIlVQ2oIkrERS+Xt+QhE5m6647nAwAe+xr7mXXCSu0f4/jN8LRV5HkbmVTelq6RtsaxscntBkTuLD29XOHzyXgYdaXHh0WWqkrf7yvHylMqfS5HX5j1/AuHLSWfr+fzPJfm2doqWQ5bReuPaW1VZoaskpTXZ7PO5dvKAouIaLXa3F+xuKY5lN9NhKvRq2q/ymyDTZFV63JuHMhyeib0xmRtx+M33PFa/d/a9R/MNVTlwM7Ha31+bowG+Zn2p/s+AwD4P6f5mfmOQwd3LP/t3/5PAAAf+xirDM9917sAAAfe95O7M+DnuBzJcnJycnJycnLaBd0UJMv8PZYXND7GO/d+j9SkoTv8nNK9q+ovt+1xEZVpKIl9SEnY9RppS1cVb1YutdJlRV9CveDs9a6SsQNReVIGSpBXRVytzjuOkvKJ9h/ch2aXz21uslJr/37edZinZ6CqOyM+VjW4NE8iFdujCkhtp91uan1VholGtFrWv45Ep66MpZZIV2ZoSOur12Ga9KRpPjWYl0o+HatA03i6bc59U3QkocT6yQmmlo9P0H+079ARAMDnPvd5AMBjZ0kbM1nOeSpKqtg2khVXgrzm3rxmRrD6yu2qKJl+Ks1zH9F2Asp86oswBZQ2PhAxLIn2+PK8DSlLDQHOmy+a1GhyHnLqEmC5ZY0S97tS4vlLK3dreJTkSpYw1EX8Wm0Ss2gkgeImj70gf9jkFMdeU6r+Vpn+sESSc5jUnJbVczCmSs+1NT5u6frtycOV13aDuh4tr63V4TkMKhXfEtrHRdvWlN9Vl8cwFOZy6QyviY0SxzXQwaXTe7V/UU31swyaj8/J6Trqw9/7W/o/fj4fefdPAQBmV7/0DI3o2ur0D+30Yp3t8jPyJX9BEvXID/6/AC6SvF/8z/Rwzf4Kj39wP6sM93/yn3H9b/tDAMCvf8efAQD+/Ud+cNfG/lyUI1lOTk5OTk5OTrugm4Jk9eXTiYl6bCitfHiIvdVKsidVNnlnPjTC55tNvlAscvmgfD61qnxNTdKUrAiY0YmlZSbldpSLtaaKtFSev4E3tN1klF4Z8xk1RHkmp0g7Fi/MoaPqwgMH7Id2Uodslut25HUKWi6T/DS3qdfe2qqoQ41E5vRjrB6cmGB6eFnVh1HRu6woXUM9DBvKc2qpKi4S4/fuI1OsXtwQGQvKW2W0EEbtbNSidQlV76WznLOIvFB9Haf1Vrz7bma5nD7DuTQ/Ub9LApWUT64uT1hAVCQh+hJQJVu3QQoTFLmyrCYV8yEStOpF5W9FLAmex1MQvfGtl6GOb3gvz1FVfqSRIClTUFWKXscS7rmfgqoGi6KUEXneqqrubKjf31aP14qHDHrK7lpf30kfR4a47/EJktZQiNfEyZO8A82K+lk/xqEC91Uq+ZpL9R40X1yf57pmeVcZXZeK2delBU9Ea3aG5Gx1jedmc4tVSq0G1w8HlWKvqttchufaKCmst6iT0zOgo5HEky/0LNa+j78VAHDude++7Osnu3wf367Put9/C8nXr//K86/D6JwulSNZTk5OTk5OTk67oJuCZAVVnWWZUObRMjqSStBL0u8SO2ysssLLMpXy8iNFlMHUULXV1AS9JuZdWV4khVhcpB/KerwNi4zFlBuUjnF7Ad3pBz1O8/omyUBbFYWtVm27ciyZ5DqWJD7oKONIVXfRMMe2usJ9Vzx6hMynE9ffg0cOAQAyohWHjshjpO/Ta0oHH0rx+WZaeVKquGz3SD1W1+k7a8rnE+4rn0rkJhQ0/KH8qRz3H5Z/J6HqPc9ytTzLpuHySVHH6Ske95Lmtixy1umZF42EKiRq06zQf2ReMEQs2Z7jaeqcplKc164S5kOxoLbL7VTrIniqKiyJBObUXzIyQiLXr5NyllV5NylaE9FbZ32JpCsyxvk0urShJH6/x+3t3cNrKaQMqng0jEFWvTfbvG4XF7SOaF42SzpW2uIYEqrusx6GlqlmOVpWmdrp8Hmr4MyINCUznOuuGllaCn8yyO1aNtmgrbwueQ57cdFL+eIsWbpWlpcxxXNm3QuC2/TQ+lXCyWnX9aqXPvRMD+GKdOqdL3zC1w//5Fee8PXQemTH4+nQzh6M3/PBnwUAnHkzk+BfIVvrbxzaDwDonz4LAJj8a32Gfxv/PC+6+MQDd3pach9/Tk5OTk5OTk67oJuCZG2nfsuvNK+k7NECn09GeMc+lCNxysjvVFSqeVkUY6usCrUppqBXt3gH7yuuO6IKvwP7SYv64OsdEQC1bMP4MD01Ag3oikrdcpjr9bq881hfX0RI9KEqL08izn2Mj3MMQU89+VS915cXCEl+P+70jMaldUwkK1Elsc+MiLCoym0gKpeRr6aVIhUpN3nsBRE1X/lRbW2/0+I4w0peD4tE2fitp6HnWd86S0eyZHY+6su7FQ7ynMxM0Pu1LroYiqmXocxObVETv0FqElQvxJjH/U9O0Xt2+DA9bfd9mRU0Vn0YlXfL8r30Z9t/ZzlfxVJJx8vt33orqyCt79/WEq+VsRTnJx3lfFeUbZWaprcrKx9e2+d6XVHMkiXni7Q1Qz2E5HPrdnis46PMPIuESdFWVzim4VFeE5YAb1WxwRCvAfP69dVfMprg3CQ1V2a66lrStSom5xbYt3JqD/1mcfV17CiDzeY6FVdGGdfGwnkSN+txuLxMulqt8BizWU/r2z2crenk9NyR/zJ6oKp7+X780m++U6984wnXe9Vf/XMAQPRjf39F+5kOpS77/P5P0Lt19rX0bn307z4IAHjdJMeVfP+9AICvvp2fSXdFSa5nb2dV/twDk1e0f6cnliNZTk5OTk5OTk67oJuCZFk1kz/Qnby8VfUGScD6PH+DDsu/Y2nhB4+wp2AkyuWtijCljKiHvv4AH2eUDK+MqLKylQ4cos/my/cy8yk3QsLw8MOsAts7MwvgYuq3J/JgjfqSyTgC8hbl86RsS4ukBPU66cTYCKmY5WaVSyJe1lNvSFRukwTLeu11hNXKyuZKisLFgtxfs6GKNmUdheIRHTvpREO+sZpITTyo10WskknOSSJheVSWx8Vxmy+ub3lWwnqe/D8tpYUPF+g7mhhn78WqPFlDUyRD5q0aUyK9VU1WlW0Gze3oFNdv9u7hfjTXPfmdrHlgv2c9E6mUfE6zySnNF4na3NJ5vq5qzK0F0hrzdMWU19VXsn6rxuMJp3k3OJzntXRhmddgWXljmfSQxtFAROR1z4FZAMDmBue8rjyrrDxUMVV8Tk5yjOZ1auvYrPfmdvq9bzRQ3QFUFbgwR3IVVap/psC5PXmaFalRWTtycZ4T899ZVltC3sZ0nNdcShWkQXm3wrqWbXw6PPiXVBu62kOnm1n/+8IXAADZAElU1HtqeXGf+oPfAwC8YerOqxqHt3n5/ea/wM+V0stI5//Drd8KAPjYaY77o0c/AAA49sDPXNX+nShHspycnJycnJycdkHPMpIlk5N9N7T0blVL9Qa8FbeMKfT0esaoCb/BG8VoiRZNTpKSGGHyRFPCMU6PWVuMYPVFbTbL9Evl5ZFRwPt2ontwgTTk4PQsAGBilPs5eeoRAECl1UIsSwpxZnEBALB3mt6ihGhBRXlWK4vzHFNQ5ipldEVUwej3yQeiogq+jnJTXqO1FhPJD4iOtMyXU+J+x2bojVpd5+/xSWWteKIYWfl8RkT7LLvJG/Cc1FSt53kcR0+5U3bKWqryi4X5RDDC7YWV1WTZYRHlbO09ykqY2UOHNFfKelIFaU7kr6m+eWVlTGXG1CNQiewp+ZCiGm8kpKR6UZpkniTO13bWKpyvsSkm1E+JqLWUJzZfZIXo4bE9PLy2KuyKJJD5OGmTAuRx6DB9Vg+e5PkL9rj/ffvGsLpGslSTJ9BsbIk0x9Ttcc5Wlzg2S9kPh/W60vy3NqyDAcfaUA/NVJy0bVMVlHF5LsbGeb3Wmnw+KR9gQblyvmhfQNW4s3t4LpqidkM55WuJ7AYTSW3f+lDy3NoloEtom2AFAo5lOd18qv8N3yejwSf2XD2ZLItv5V+/FAAw/v988YrWKxwhhd84Wdjx/L9afBEA4H9O0YP1C5N/AwD4ZbDKcVCv71jeyJsfUu/Rnus+ejVyJMvJycnJycnJaRf07CJZ9oXa3/nN2qqYIvJc9Y1qJOXnycuvIxrii24sr5E+rKySThwSNSkMkdZksrzzN++LpbOPjA7vfF7UxxdZm1L/usN72IewuknSYPlbRhw6G11ELTldpGU4RT/M6jLJU7VE2pCRNykU5N1FKqJEdY9UYyjJx5Z3VRLBqqzw7mY4xzH3NXchy6manQUAhKPKOFL1W7BPQhMI8/moEtV9VftVlYo/kA/OVxWh0ZiBzoGvubG+dnbmovLFGSVMy/cWzdADZZ4v81KVq0rlV4rx+DiPt7im6siSPGyqVlxWSnkiyXPY7yg53tt57fREaxSsj4OHjgO42LOxpD6CR47fDgD4yud4V1lTRZ8X2On9iilLyhNFag74fFjnrdnk9jY2Bhge5nVpSe/9Ppe1npj1htL+VRGallfQqgnzw7xj7at0M61ehV0R1rY8WmnRwbD6V24oK22gc9YQjRvUNcaa8ubA/a4uq4eiSFVPFa6Wv1WYVBWSlZBuMyt3B+z03NHnb//gNd1e5Rjfr+NXuPxX7vgLAMCBkz+54/mPfe4OAED5Bz4LALgrqs4ML7xNKz4IAHjdG38YAPDx//O/AQBn30Rv2IH37dye01OTI1lOTk5OTk5OTrugZxfJehxZNZNVtCVTvHPPZ1UJp8q448eOAQCiUdKQBx9m9eDCIr1TTZGqk2dPc7vyeuXypEv1TdKNrnxI3R7v2PfMskqxKm+NUSfFFmF02O5FlJjdtX6EQbTLJBvxkHKn5GEak+corO/BvT5JjGV2ranfYiymhPZNeYLySnrfS39ARz3zskkew0bdKAefn1ue13ZUGSZvVUalZb6qFGuqnms07Pd7HrslvEciVsliVXzqKejtpBuWKYYu1+vq3AWUvxVPkeLIloATJ+hf23uE3qazC6wU3SzTS9bX9saHSQ1Ds/S0LTzKCk/zhllC/EDetbq6AbRXSAxrGndSPQdzQ6SNNZFECz2b3DvL9c8Xdbg8zoD6VdZE0OryOSw3OL6RYfWCXOL5nptfR76mSsQReQiNdnV4Tq0DweISk5gbomXLKySv08pzi4rWVbTPnuZkcmJMx84xGa0rm2cwoi4ARR77aIFkrRblua5WuL1Glddep6k76zGSq4IqQ7e1nYslmneJF8vJ6abUp6b1P1fnxbpShepPjxAXFRKY1dv0wrfx36kZC5gX0Vru8bNxQvlbg7g+o5uOyTwduVlzcnJycnJyctoF3RQky3wxHXlOOh3eyZfk06l1eS+9pn5yDz/6MADg7hfdDeAiqeqISgSVGVVTIrxf5fMjY6poa1qFHElZIKBEeXm4fKWkl4qkIPUAycDICOlIocDt5AppdOV/2VwjbVhaYHVfo0160NfX4FiS+2i2ue+UKrpSWfpwvDhJ0sYG87JOnDgFABjNcp/FBR7LyQ2Sq7x8Zbb+wtxjAICDyqsaVa7W/etfBgBE80p0F6UwchVUY7qeyJfleQVVQdbtyt8TtEpQLmcZ4J58dOkkaU46a5lPzR3zcF7zcuokK/JGRkX6dO4f1fMTOY7f/HFdJczH5SnrGVFr8hxFt3PFeI43N0mo8sOswtys8rwMqRfkHvX/WqzwODdX65oHebvkuVL7PkQCXK7V4LVQrXM/Q4Uc2kJLc/M8JwFVfDbUm7Ci3oC+XQMiW9axwPpQml9uepp31JsrPNdfuZf+sRe96BUALmasrazyGEPy2yXlhzOvV1xVjFNTM9yers2KUustWX55mURtoGrHto7d0vWh6kNnzXK6Hvr0A0f5P3s+f132V38Tq/Y+f/T3rsv+THt+je/r9/wTfob/SKZ4Reu99h7mXp159R8BAN7+438IAPjtX79lx3Kv/OK/BACcesV7AAD/4VUfAgD814+98WqG/ZzVVZEsz/N+zvO8hz3Pe8jzvPd6nhfzPG+f53n3ep532vO8P/M8L/LkW3JycnK6/nKfYU5OTrupp02yPM+bAvA2AMd83296nvfnAN4M4A0A/m/f99/ned47AbwVwDuuyWgfT6pqMu+H3dm35C8KKvejroqxbfKl9O60fExbFXqqRsbpoQpHeUduVYu1Bu/kN1QtuLBIkhCLkAYZntk7QXp06AC9WkuLpDCzs/wN3DKvCkMpeLrp39gkfdh/kBWOQSWpR5Qgfl6+nLK8UaEh0oaMCFaxSEoxEJ2IKatocZ307rD6LWbB5XrynzXKJDpH9tLzFFQV4NJ5jjmohHhv20+2kxba44gVrN5AAAAgAElEQVQ8VQGZqXyhnL78Z3ZOguol2NM564iAZTOcm/yI/G8dJdvLX7exQUryHW/4IS0vP5BO+pKoSqDH8cdFxgLy1ZlBKBw2jxsnPm3/fKqCr6oK1EqNFXUp5Wh15FN44MQcAGCmwLT/pSVRqCaPs7KlalD1jpwclbdrwP1WkhxPo97dTl63ishqnanyTeVfjek67Fn/Rs1Z0OZWxzDQ8+tF3tEGVOE4NcXttto8psIQ6VxNifJpEbFEmteK3+Xc1ZWmX1aV4vQMvV+bMT4urvG67w2sIaYqRfW+svENbnBP1g31GeZ01QrUgtd1f5//7etLsC7Vn9xCcv0jS1dGsrAe3fHwOxJ8v/+rP+AvOof/r/sAAEN/zX9zQACOu2LzVznS57au1pMVAhD3PC8EIAFgGcA/AvB+vf7HABxjdHJyulHlPsOcnJx2TU+bZPm+v+h53m8COA+gCeATAL4KYMv31RAPWAAwddWjfDLpjjmkO+hqhbQnIl9QS5V0K0XSiX0H6avJyxvVV15Qr29ZT9yg9UCsimDNK8G9oqqr0hbpyp69rOJKRUldCgUShGaL2ysM8/WuUtqtzCvQ6qGpBPNjR/i7+FqJY9xS78BCgDSjqETxeJrUzIiPjS2aInEpK9cpoHymqX301dTlccokuVxJfp5Cir/rj4n8bKracXGO5KxmlWUxbs8oRVveMKui61nFpHK/zD+UEK3xlc9l1ZKRBElczPxEqoa8sDLH7YG0ZWKCNPA1r/sWbj/E466UlfyurKbcEC+zQpb7v/AgK2VOfpn9uBJ55XBpfL6qGoM+j6Ms/92mkuKtYi8hf5J1A8gNc/4jQZI38yOFRfIadc5XyvLBdK3UzCMmz9qgl0JCvrqOCFIyzTtNH9xHOMBzsq7KUSOwDc35kMiUESQbQ0yZa2mN3Qtwu13Rx3xOHkTlclUrOrdCsV29X8yjVSvzGhsd4nU9oT6Sq/J2jSod3/Z/MS/rxjZj3VCfYTeYBnm+L8697t07nj/yuR/Z8bjbCsHbuDF+TR1kek++0DXQXy5YOd7uHve57/x9AMAbfvGVAIC+qoKv+X7e8AcAgNfh+QCA7J/Qh4u388/zo9HLreZ0hXraJMvzvDyA7wawD8AkgCSAb7/Mopf9tcDzvJ/wPO8+z/PuW19ff7rDcHJycnpaupafYf1LWpM4OTk5AVdXXfgaAOd8318HAM/zPgjgpQBynueFdCc4DWDpciv7vv8uAO8CgLvvvvvqbBtau9PZmWg9u4d+mLCSq5+vruZV0QpP3pVqiR6ruu7YH6vzcXqI9OP8BVKdinKw9h5kFtPsAdKOgEeq8bxbeSdw561MB8+KOgV0Z780d4YDrXH/zWIRUdGGXJJUo1FnJdfcWXqMNov06aTTJDY99TLsyZuVV3VgMMBjX16cAwC05AGCiNP4CClE1OPdXlp5WwPlQG2VSVOKSvdeOb+m/XN/s5zKbcphCe59eaqM7g2US7VNaVS911TlZyjK9T3ld73kW18DAPjw33yCO1Bvxr7P7YdOkDCdnef6q+scTyZFimP0cm2Lx/3SFzGx3cjYw19kpVFf+VjmX4ooat4XVRxK6Fzp+aAS2we6uGK6m8srRyvicf9je9nDcOMcs9WGUrrr80OaLz6sNnZWSwYGSVRVWdr1eIwCnsgrf6pV51hGC5z8kHxji0t8S1W3eD3WlPm1naYf0THoHiqjYJyyvFgp+dx6yvZaXpefTR6rKeVvnT9/gXM5MaO54Oth0UDzfA10DjzvxiZXl9E1+wyLzszcqNazp6UTr32n/i+84/mT3/KeHY/bfhfVQeea7PNFf/Fvrmr9h1//u/q/nYTpcz/2mwCAH/6Vl13V9s/+KT/fE4Hrk4dleucDHwEA/PM9L7+u+z0vH+oe5WWN3EJyvX5i+LqO49muq/FknQfwYs/zEh4/XV8N4BEAnwHwJi3zowD+6uqG6OTk5LQrcp9hTk5Ou6qr8WTd63ne+wF8DUAPwNfBu7qPAHif53n/Rc+9+/G3cm0VVuXa7L5ZAMDGGj1U0RhpUVdVWpYmbv39rE+d0Y2MesBtNfhN3qq8Eln6jCJKjI/GSLCmJpk2fujIEQDAqTPMbAoFOJ6yvGBf/hRpTfnCHLebjCKfJxGZf4w5VYURbnNfgc+bh2dIfe4EktAXfVtZoV9ns056d+dxjqEsylHZ4rEtnSNFK2R4jPtG6EerrXK5zQu8Wb9wltSuU+NclYr8W6nIIyYfW7ejJPgqSZjZcAZ90gzzJvU7Kp+sao6VQTY8RQL0pa98DQDw+S/+PQAgXyCxm5kmTQmEefyf/vQ9AIBkmpTnttvkX1JK+i230rv16KPMBxsJqedhmcc3rCrNmErezMfULHP7dVGclsic1+K4V5WbFZeHbCPK4whESIHa8i0lsxyXB67X6/Pc5wrEU/0ET9xWm0n1tc0O4PEYYhmOqVwn0fJq8nVFeN2VVDW4vs6/05qb7RR7EdyVFVLPVILHmkqpD2Scd6IB7a/VI1XLZfl8rcFz0u9a5pm8hLoGm00e89mzvK4nlfjeEp0MRNUzsbOzkvNG1434GfZMKz4rj6cXfpIlsb2c+RyvVr/+HX8GALjQHdrx/C8MnXnC9Yp9Xp+JQPKyr48GRdU/yYrgyLc9tWq5/qv4C8jpV/3hU1rvWslI0pXK/HSB0uXPy4/Ms2zwPXvvecLtfO9//gUAwH2/xsLaXzn8YQDA20782FMaz3NdVxVG6vv+rwL41UuePgvghVezXScnJ6frIfcZ5uTktJu6KRLfLZvJqqEaSp5uyjdklWwVkalYWCRK5Cuix7767UEp4cV1ki6jKfa3G+WdQrVK31Iqzd+ov/TlLwEATj50ksvJkJOT76rV5HgGoiXz5+axsUrKFY5xDJVJbiuZsnRvUoLeBklSUt4htU1EMs67tBjkyTrJu75hpcuPTNI306hxTtpl+XnW5bla4DGuL4rqqTqx1eCxZlTxFovGdEwiNUrHb4mitFrKzWpbljtlc2xJ75kAiU9DlZdf/Ao9U1lVykV1PKVN69Uo35G2H5Dd4qv3f5bjinEcw0WSsUCXFGVohPPoqW9lUNQnKF9TWI97TS4/Pknf0dwKSd43vsaeiQMRrwn1Adzq8hoLp7nf8TTJY1wJ+F3lZDWUm5UMcf6qm7qWRFEr1TKiStWvy5+2PEdfVyRMEpVNKN9K/jYoF6u4ynNn19MREdSkEVtddxn5+EolG5MqRZUllh3i2HvqUJAQAbMk+EBQVbYRnvuA+lua59H8axMjJGs2x07PXjXneE184w6S2utZWfbmdEn/V3rC5S7VcPDyBOtSfeY4f/V96Q/+JAAg/WdfvqL1/vZPr4xg/dci34e3xull/K5k44rWu976wpfYwxeXkKzs5/krRfnlqjz/ff57hl/jH8vVetvuD/Gmkutd6OTk5OTk5OS0C3pW3Xpul+/I8uGZD0gZRuazCYieJOWtyg/z7qy8Rk9LSnfmMRmJUqIRTWU2dUUMpvKkQauq8MsmSFsG6j+XVO9Cr01PwPIyPWBhVcYN+iQETVVxrdV4hzZQSvtWJIpje1mp2KjTC7E6II0oL5E+hJe4zsyoqEaGZCelu7eE6EU2TgqRj8l7pAobKy3fWiPB8mock3m1VldJ97bUiy+YIP2o1+Txegl/NZnRXcycPGDnNJfVdkfLq3pQUTVWZNdXDlVIuVL7j78EAHDvgw8AANryc4xOs2rPYEigpV6IfZK1u4/Tc5VIcsMPPMh04kCUK6Qi9DOFlMuVypI85SdJWVq6aLLKcoqE1CdQXqzF5TnOj8jYC19OH4al+sfjvGY2N0i6EnEuFzefnk9C1wvzONu6Ni3Tau0CCePEMR5HONhFcYXnOB4hbbzrCF975ATvMGttEqupAismjx+/DQBQLpNqmqewVue1Ye+DZEaVqh1eK6VqVcvzXI+OcW7K8iLGEpz7klLuffVhzBX4/mkoOy0UUv9HnZugqnbLWxvav0opPaOZj3cP9+zwbD2X9X1/+a8BAJ9/029e9vWJp+gTup5ak0err8/3S8d627+5HwAw92dPvJ3fP289EJ/4WEt9vv/e/bffuuP5FzzO3D2ehoL8bLuvzffVy2I73z/BMfpy+yLZV6uGqkITgaeY92XDGjzhUk6SI1lOTk5OTk5OTrugZxXJejwFVM1k1VZteVK6A0sh1+uqCrTqwE6bhMkoTk++IV+IzEJSjx5hd/fPfu6zAIC9s6xSqVVJTywNPCZC1tZ+FVeEmjKtrLoxN0wilh4pIKoKsIl99BSV66QGR1/ADu9jQ6QMzQ3evWRFwc6dOq8xqD9jjWMYURL8+TlW0ASj/B69oWyuXqWjIXM7LXmGyh7n4tzZswCA2++4AwCwVCUtMW/VkqoEV0Q3WoZsZGdLWkGL+j2GhLQKw/Q8ratKMZgkgTs6S0pzoUjSMzZKXwDUV3LPDNPEHznBbJqxMdLFu+5+HuelparHYetXqRwqEa2XvIp3l3//d6RDfR1vU70b85PcXkcUZ6vBcxoUNw3Kn+Trtm1sjF6vjhL01+Thmhhhhen0fvoyWsrFikd57Y0Mk1blRFdjER+dOklWV5WMjSr/3nr0Vs7VJitOt4v1dF3m8zzH2+RW3qjVVVLGRlPkaou0cUSeqUaE5764br0HOcfJFM9FKGTVh+p8oGMwX2DU5+tDqvBcWOCxZ0T5LCfL1/tou4zXkatnrV7+/n972eeDE81d2+epV/7xU1r+gQ7f82/60r8AAPTWdT32eN39xLd9CgDwiwV6Hn9p7JMAgJ/E5XOnzv430vY9oSvLw7rzwz8L4B8Si8ebu8eTP6wMwSI/y868+Z07Xn/PfX8JAHjLzNXlfZl+dY3/xrx9/OsAgP8y8yEAwM/gibf/jn/MYtuf+tBbr8k4bnY5kuXk5OTk5OTktAu6KUjWwN8ZttxUdWFD1YTnF1VtpeqpLZGlTkM945SA3eyQbuw9wLTwxBCpSlsp3Xe+4AUAgL5IWa/L7aXTXD8sz0oynNoxrlaL2x0Z52/qLe0nFA6jWiJVaPXkd9Hv5GNaNqRDG5+ZBQBULAF+mllFffnHPNGzNfnDyqJrVaXY+yJMSyWSn3KJPoJKlWMpbsp3oyrHjTbnLiIc94gqyqxXX7ksX45yp1IiU8ksPUnRJO8me0qY74ZJQe756hcBANMHSANLooGZDOlIVwnsWW3P9/p6Panxkqx1OiRQlQppUC7PTKmaqiKb6hM5O7mPr4+qyrLH+emKrhyc5DjaRT4uyoNlvRhD8kkERY36qkAt5HiOK2VeCwP1vZyYJHnLiPbMz88BAPIjnOevf425YIcPHkVC2K/U5Lmoa+y9AedkbJzHOOgEdKw8d7kcSejEBK+BqmjmsDK5QmGl2utcRcI8F+EAr+P8cFZzye3Z9dxXcrtVjLbV+zPk8Vw05TEM62MjP6IcLUuxF8nyPKN/Tjer+svxXdv2gfex+i80xffFi/bMXXa5z93Pfq+Bxk5WcCk3vdDambu1L3x5j1XgVm7v9A+/4ymN81qRCiNYjyerojz92yRQh952LwDgtd/3owCAT3yABND6Tdr4Hk8f/KxI1ptJsg6HuX3LBQt+9muXXe+1ie4TbtdppxzJcnJycnJycnLaBd0UJMvuXKxv3toa/UvVKj0qI/LRDOX51xeliGV557CqRO1hVV0tqIIuGCPFqJRIFhKqPhwf5naC2nNLZMx6xXkh9URUTznLRtoqk8Lk8iQJ6UwaIZXjWaK40YLPf4mVLd/5+u8EAJR0TGuqEKv0eTeR0HqRCP+ui4zFlJEU7pGWDNSnMTPFu5VuVP0aQyRBo2nOXVS5OBOzJDKdDu8mq1v8m0tz/ZEU5yKulOVohsfUDpOGLKtaMqAKtPUW/Tszt4gc+SQ7lm3WUfL6hOjIxibnfHYfqy+tum9pmdtZ3+A5arVIpoKiNZkMx5VTI8C2zlFmnCRr+QI9ZyN5vj6qtP5KnzRmwuP2QpqHxUWOI6D5Gx3jdry+MteUNdVq8nx8+tN/BwD4ttd9FwAgkSFhe+FBes+WdQ3EY3kkE/JSdVj5aYHptxzlMXshnestaz5Menj+PP12W0r9P3KEuTeex+3VK5VvXhxDSun3dS1EVJFaUDbZhQvcXkBzGLQV1e+xrurYdb0vLFduROsHg/RseYGdBGvwZEWGTk5PoN4ifyn4wuKxy75+pZfVxz5HfynefO+O50sfOQQA+Modf6FnrsyDdeDPn5gQPdv0J1V+PrwlzX87zn4v39+HPvvE6/36d/wZ/v1HfnA3h3ZTyH38OTk5OTk5OTntgm4OkhWwqibeQ3eEBOJx8w3w9WXdiW8nr+tWe3SU/ier7GvLX9PcIsUortIHtX+adCegqsO0MqmWL7Af3a3HSCsWtR/rgTg5Se9MIkNSUJcHp1bfQk4kxPq+NdvKPOry1Dzy6KMAgIJ6HC4WlbUlr1RUPrOWvEJj4xzjytoyAGBYfeZK1ntQx7pnL8e2uECK4otaNBpcLqF072X1NNw/xv1X53lskynlcintviny88gG9/uNM7wrGsvQ/1CY4nHG8vzbqSs/S2axEXmmctpuo8pztrnF403Ko5WW5+uxM0zVH1Gy+/33M6H94AFWgp46pz57I7Pcv85dV7Tltuexgi9V4PysP/gVjqvDeRgoQT8on1GnzcfzczzX1fU5AMCBA9zOhcU1HQerC/s+589X/8pojHeLr/lH3w8AiMc9fPyjH9M2L2hbHOuF87zupvdw7kIhXpdV5V2V5bPLZnM7njf/WCzKuQr4vI5XlpXzluUYpvfSp7am1P8hebzWG6qClceqpwrPmjLQavI6Blv8G4vxeh7TNRW4pGeh3pYuTsfphtRFgvXU9Hffy/yrVz7F6sFrpXve+D8AAG992+WrI5+q/uNH+Jn0FlUznn3T7wEAXve25z/hem9Ol/DfDvCXkMqZ3DUZy80oR7KcnJycnJycnHZBNwXJ8uXF6otAtVW911XidSbDb9mBgPKxevIBdUmPHj5xCgAwPMw7/XPz9KhUuvTCWFXisGjS/GP09RzZTyIQUmr50gopTkyel4UFEgpLwg4r4yoXI6For9W3iZOvZoQJUQhPlYpra6RmRi0K6km4vE6iVFyhnywkWlfIc7mxHJeLhuT1AunEhQVSjU5aFY+amwvzzN3KpuixevT+BwEA6YSIzArHMZPk60ElykdFnpa63L4X5rEKmiAEHrOlhddU0bm+SWIWlmcr0OW5SmguPZ2rNVHDPQlmPQ2Pklydm+c5iBi10X6CSi/e3CABGxubBXDRS5VRZtS4aE4bHP/4FCtKLywqTV25ZIkYiV88yvNTU9q/l+a1sKrzI8iDaDyi8ZGkJbM8D70LPM+tOrnOi19yHHffxX2dn+M57CpJfXmBXqykKjQ3SjxnU1OkcVtbj+ov7yLrygZLJlXlqlunQo4+uXpDFZAit8vLS5o7jtVyt3yN3Zevr7TB6z+n7WR0rld0na8WeewT45y7rvK1jCi7Wzinm1HTSpC3HKsP1PgZ8e8+/E+v6/6fTC97Cen+dq/CJ9G/XHwxAOB3p9jTceX/8FeBF/87Pv/l//7Of7BOWJ0znB5f7mPQycnJycnJyWkXdFOQLIvENo9IRr0IIV9Mvcw78sUF3oGn5JUyP9O58yRO3R7v9I0aDdpcPy6/0fIa6dH+GVbIdfpG0EjE2hv00sQ1Dqv829jkHf/0XpKIsrKhQmEPkQ730RJVS2jdkK9qQRGZjQ3L0+JyqTTHnhAhMpI1KtrWUfaRpYH3GlxvQhVhuSz/Foscy4QqJlPKozo/x3GkEvzblxcskLAsJB774iKJzXKbKCeipPe8KjPTImkTo6yy2zPL5PfVNe53fILP9+ska+Egj8fon1ViViukO6US6c3wMNfLZjjuhKora1v0FdXVl+/0WXq3/DBfn5llFk43yPkLBkUOI7w7jMb5NyPSt7XJ7bRapEXBMK+RIXm51oocT0yZVw8+zAqlQpGU6pWvej33r7T/9VU+f/Lkadx+O/1cX/oiq54eup9jzaTUOaDBuewoEd48V7ksydOmKjCrVY1BGWdW3tfWtRLTOdyQv61UmQMAHLmF1VXmAwwJgRXkexvonEb1vHUnMK/V0hKJmHUdMF+hVWL2rczQBb473cT6vhQp/NtvIZFePzF8TbZ7x9+/GQDw9Re877KvB/XLS+9x1v+3E58AAHwBV0ayPn6C5AoiWfe/8L18/MIrWt3pceRIlpOTk5OTk5PTLuimIFnWM60remNZT6Egv0OG+3aHz8eRBGnNufNzAIBbj7MP3maRtKioqqtmkPcIB/bOAgCC8rSkRcKqmyQIPXnC4uqdaB6W0Qn6cer1isZHGtJsWdr6FvLyTmVS8lwlWal18iH2rYvKo2Tp9VFRjqT6JHZFWPJKGK83uZyRr1yedzuWDD88xLFbRtNmgM8vr5K2XVjpaD8kPauqsPRFdHoBZSmpAi0lMjaluSlozg9McLlsgeRq4sBhAMDsMdKbTILH/dlPfRwAcNetpCrFIuc+P8rXj+v5+x/4KsctmuKD47GU9IUznK8X3slMnFiS4+96PCdF0cSRKW4PIltdVTfOzPL5r32V4xkrKM0/KS+WaE1c2y1VeA639DcU4TVV07n++qc+CgAY0t3my17CHorHjrP6sFR9DI+cJBmt1njMjSYp10iB53J5mXfGMyKgDXmrCgXlvalHYEfX1UaJpHZ8XMnvqhBdse3M0IcWKKoP5aq8WSJgIZHclq7PnKphmw1SvY0tji+k9P6YcrV6qmwNq5p30Od2vKBLfne6cdTX+yXo7Q5b+PLz3w8A+K099Cj+zidee1Xbs4q9DxzlZ54RM1PrTr6fw1vty65/u/6N8NVD13sy+9Q639e/sMLPUOtp+ETqDxymfjI5kuXk5OTk5OTktAu6KUiWL4oSUAbSwgKzjOL8Yo4DqjDbULJ7WB4UW/70aXZnP3qIlMFys+ZW5wAAA8uQUrL7aSW533X77QAAT0lAAd3B75tkBVvPJxU6d47EotnlfrtKF4/EQgjIsJIQCanIcxQSnbNMr+3sIo/b7DR4W9IQYUmr6u/0GVZK5uXb6Xkc04joRrVEarJVIaF66FH6gManSJzCqtbz66J+uv05vcBjD4sGjhshU387VOjfSchj1pbHqh0mQZoaZmf3gzMkRsN50pnnHyXZOv0wfQDhkPr06bhLmxXNA4/bMqH2iMpUK+or+fy7AFykkMNTPN6NKud8TYRsQt6ubIFUKJlQr8UY56+j3olnVUFayMrzJYJlPqROW5lsXd0e9knWRsY4Lykl4G+JLp09y0qfrc0TPK7KaSTi6gEW4BjHJ0kFG8qraqszQWSVczIkCplK8xxNTrFislqj12p0PKdjUHR8UNlpk7yeBwFeCxFVEUbkI2vr/RAN85oLif7FlANnnQ0s8Mrmyu/z+aAIrhFlo6bB4E3x8eJ0k+joPT8OADj1yj/e1f38/BA/O37nGm3Pqha/7807q/s+/b/+AADwusknzrN6/h1nAAD333fgivZnPQ1/9Qe+BABI6deLS/WBWgZbp4cu+5rTRTmS5eTk5OTk5OS0C3p23mr6O/6gr1yeQIjoKqpKs6E878Q7uoP3ojzcZscS10kMjAJ12qQcViEXjyjtWynoGfXtS8W5XcspioZJBrKiHZZS3uqQ7lg+F3pWvTW6fSB+x5LBuczsnj0AgMP7SNXmF+a4TdGKUplkp2/+MHmUHjnxkMZKilHa5PLTyi6yPnKDAYlLoUDP0+HD7N1XGGG1XlG+m2ZVY5efLSO6Fxvm3/y48p90rGtF3r2NZkiGenXOcSzAcxE1Uqftbm7xOA4eYrXf/j3c7of+iinMRoga8jyND7Oab3GZlLKQI2GqllkZulWVR6vN4z1zLxPcZ/bRH+EFRQZF8AIDZTqpNies7JlXv+ZNAIDPf/rD3H+b2w2ICnny6WWHSKySOt4N5Wq12py/vHxV8HiNLS0z26rfFSEMd+GJAobj3LYf4rGubPI6HBsj7SuWuM0umvrLuVxdpe9uKMd9tTp8nFC+llWWbsdW6ZjTCV6vxU2S3bo8WH11GRiIwDY6Rto4d4GY+luqb+VQgrSwXtb4dY2FVCHaf7zehc6k5fQM6OD4+jM9hF3VpTlXpg8e/CQAYP+D+ixsX5mP6nl//rPXcHTPXTmS5eTk5OTk5OS0C3pWkazAJQRrIK+IJ++HeUAOqJLNfDLLogwR3YHHlN3U7tHPMyYfTTarXocD3pnHlImUinO7WfXP66mPXSdA39HmOulIWB6Z1eVFAEBLPd5CqmYJKN08GSZ56Pf7OHL4Nh0bXzNPUQAcQ1cZXC3Rt5ho3J5Jeqh6Ij5teYsGqhArrhZ1DDzm5SV6g0bk+0mlzYtEgnP6FElUKklfz2iGc5KJ0VsUTXAu0yml1WvuK0rZb4ierHiqdInymGen6ANYL3Ouy+c5N2fOM2F+TBWYo3lSkQP7ee7OfeIjnKMutzc5SdKWSpDclUXcoqKONVVZeqKZU1MkghurnM/xqVkAQLXEu9nQgPPq9+0vKeSxY9/C405ynj7ztx8AAJS2mAc2NMz5CARIk9JpHvdWmfMelQdueFTz1OS1srnJLgK3HuPd5OlTawhGeG6qdR5LOq9jO0n6leup76EqT8dnRGbbG9oH58TS7rMiTcEw/y7rGhjLk/p5ona+CFVO6f6RqFUbyg9oBLdOSrlV4f5GRnh9VzZ4jPUq/+aSPHcwYmvvy0t6Gdojd2fn9EzoLZP3PtND2FW9pfBF/d/l32Fnv4c9CQ+87yev04icAPd55+Tk5OTk5OS0K3pWkazHF++RBzKf2A10SZ6pDdGLap1el5EJ+n+O30o/UE5UpyYfU0uZVJOT9MQkVWXVVeXZ5Bj9QRcukMocPkz6Mk4ZTngAACAASURBVLCUdavOiio7ST6qsDKvOh3Sp5GRESwukjAV5PEZVvK63+O2MkOkGPkGx7xR4RgXlklopjWWU/OsIDm4n+QoprR585ft20+y0/G53ZAqwtptkqJcjvvpiYT1VVE5MS6v1hluX9YmxBMkRuurqtJTdlinaX43EqKMCNL+W5lFFstze48t8pwENEdL6sHY0NxM7uFxWRL+qXOsAO2L8HXWOO5qzeaW5yibJmnKq4djtUpKZCSvBxKmkvrzFeKqkhwoeV9VgzMzPKf/7K1vAwC8811vBwCsLJP4ZbMcR0J9Ku0aSqZ4nlaX2W9wRMn0W0pP/8pX6BU7dOhWrCnLLBoTYVVK/i238By2CA9Rr3JfC+dF5Sa4XF1+sWaNczE1RfrWFbFKZbhcQ+e4H9DbXbluS+pgEAgr303ErCV66ImI5TIc31aJz8/N8bofVgZaRgAY29WF8kjqYnEWLKcbQf/xI98PAHjLm/9hD75rqd/ZmtnV7T+efv0FrwEAfPTBTz/hcj//ev5K8Ft/8x27Pianm+RLlpWMt/WT0dwcf5oJyCjc79sXC4K7jQ3+Az86wn9UFhb4c19TX4YsViFV4Otra/wCsLGusNI0/+5VSKlFQBy7hW0JEvpJq1LhP/CepvnOO18AAIhZkGivi9Onrdkvl83JyN5Vk2tf/3CdUkPkRIZfEloyuJ9Si5Rbb2UZb0f/mA98C4TkP3EWXFnRMa7rmIaGRjVWfVlR0KSnL4r2JWV0mP+AW8Pm1RUa0EdG+KXw5ClGE9iXxJlJfqm771E+P3n8TgBAQD/FRhVcWa7wi0KnzC9nDzz0sMavRs0KQ23quHxrfhznT64balE0Pcp5O3yEX45Km/wCcfgwv0h//X5GKDTr3N9DDz0AAHjZCKMg+vpyGdLPrj39nFjT8j/4Az8FAHjvn/4RAGCzyKiM5T6/vMW3f0rmetk0x9NqcP2YAnLzBS5XKhXR0892RTWA3reP+x4d4Tn+wqMMWJ2c4Bitbc2qvpDGFMUQ0q907ZaKHBQCekbvgy0FzY4N8dwcPsAYDb/HayuisVmYr7VkCuvLV1jbSyc5vpEh7nBslP+YNDRHPa136c+ETk43kvb99T8HAOw/sHrZ139mL7+kvDFZe0rb7ap11m59eXnzuX8EAHjfvst/ieqrrdtdv8bPqq/+6jsuu9xP51gs9Lt7aUtozaev6Tiddsr9XOjk5OTk5OTktAu6KUhWUHfaFooYVMhoQYGZ4ZQaRbesBQpN5k2Rr5QiHGLaTiLKn54iioKIBNWeQBEMIwVu1xr01hUFcX6eZu5hxQtEI/oJa5zU5cEHSFMCutPfv38/Jib4s9i6CFFdZKeo4NREQj9vqax/Q5EGRw8d1HokKdbeIKDy+XCYtGFDJCivcMx6g+vnNTdl0ba2fpYbn5jSMXG5niIgmlpvYozHtr7paU453mSW5KYhI/m42vKceIw05mOf+BAA4OUvexUAYHWJPz9+XZEHJx9k25wNGcQn9nF7GQVwDhQTUNbPg7GUNXzmT7qVTZ6DB0SoxkcLmh/OY1KNn+fOcTw+OE93voDrRVQMURLFDOhHroDHc2/tjb7rH/8QAOC+e9lC4+QJNoROqDVNoUDiVxfB6ve4nbTiPfpBErmx8VlsqPl3Jifz/BZ/DtyjAoqW2uzA51g31cx7eg+v13abY4+pRVDA4xgWl3iH3mlzzgrDNKZ7A16/1S2ObXqCtNEaSXsxvi8SXY7RmoDXGnyftDt8vKWfWqNhjuOQyNilBMt3DaKdbkAF6rzO5x6YvOzr7wy8EgDwxls+ckXb+5sGSfBPf+jHr8HoHl9/fy8p/Vcm2frrhVG+33/6NKn67xzi68O/xxDR1/0ef91ofpwk/J7b/nLH9r7nAD8r3zv/st0c9nNejmQ5OTk5OTk5Oe2CbgqStd0gWp4Ve2xtczIZ0pBVxSPEzD8jc3lKhuNJUZxahYQgFOTzq2v0zAyrMe+FCyRWdXlRzPxs4aRri6QnIyOMJ6h2SaEsNHJabXc2NzcxPMZl5ua4TfPyxNW6ZFU+sLPzcwCA/bcwpDSjbZlxvVzmmJtqEH3hPH93L8gAvqVQzJbiJyo1jn1Cxzw6JhqoGIy6DOyVGo8poO/jPZmih4e4/6V1UpOePF/m6Tpzlj61qUkSpUCfVOaTH/5Tju8sj+eeFa5/923HeXx7SXHKXVIeM0/n5Ak7+jy24SmVSHXSefoJ/L4aXM9xuzNTNJwvKTJifIzUZl3EqyLCt6SCgaCCO5O+4jVEsrbk3ytkeT4mFIp663FGb5w7S8/Z9NSUxst5aDY5vymFlaayJGn9sLx2/RD27eMd5olHHuQy6gNVkUfL4h7W13ku4mqDU8iLfm5yjqtqrTQ5yu3Va7yWbrnlGABg7tRj2g6PeVQ+vEZTRQ9G33p8Pa4uGpYlmgmTwhkpax1WfAghJ+oN0sVgUJ1oJQNbzvju9GzS6W/w+l4+qBZcgciO1xN6/ExFIfzm4usBAH++/1MAgO9K8vPh536LYaQHf35nGGn8dYyf+ZOT/Cx+izzF/2WUnzvvhSNZu6knJVme5/2h53lrnuc99E3PDXme90nP807rb17Pe57n/bbneY95nveA53l37ubgnZycnJ5M7jPMycnpmdKVkKz/BeB/AnjPNz33SwA+5fv+b3ie90t6/IsAvh3AIf33IgDv0N9dVV/9O2KqWBuoh0xNtAZR3lJPz+zlcoo3WF5ihdzGBinLcIakqlgkRVk+MQcAOH6UFWpGDCqiRnnFK6yJxkRHiQDsjn5xkRTFetpUVdlnVWXBYBDLD3DdtGhbQ42RY/LwZFWNdzTNysVjzyNBWVplo+KKiM7cPI/h9uMkQsfUeNmqFFNptQJSW5st0bqDh0laFhZUlj9MshZUZVlL608qysETHfT0erqR0FyQhlQVf5FRJVpbDZ09kaxunZTvloPcT2uEcxaPc4727iHJOs/DwtCwGl0r9HRTPqYLCyR1SXm/rGJzVOGm8wo7HVL7oIw8Y7ffyvlbVnXlPZ/+KADg6G30tt31kpfzOKwtkCrvli7wbnDQKGn7nK+IKOaGKGY6o8pRkb2oQlrPzrE6dKvBA5uaOIS6tS7SnELVSR2Rz/37eb0+coJevukovVUnHuK5n9nP63ViTMRVwau3HWdcRltNxQ/M0L9XSfMcnD/P9YdGOTdLRd7ZhhM81xE1VreKU4jyTUzQ/zY7y2thWTEiAbXdGfT637z4jUSw/hdu8M8wpxtPL3//v73s8zFV5T1T+upX6IF8eJqtv47LO3xG0RRv+N3vAQD0Hzu3Y733HCGhe8vSxnUZpxP1pCTL9/17AGxe8vR3A7BW5n8M4I3f9Px7fOrLAHKe501cq8E6OTk5PVW5zzAnJ6dnSk/XkzXm+/4yAPi+v+x5nnU8ngJw4ZuWW9Bzy09/iE8u834YEZicpGdl0CMRqMlHZHfWjz2mzCmZTzy1Jem0SR9WlunDsfBGCx09sJ+elzvu4C8IFxZYCXcxyJOEqpBlJV+jyTue8TESiEgoonFywL1+d7sisil/zPFj9NGcV8ZRX+GhQVWG1ZskJnU1rQ6E5T9TBpgnimYtfaanePeyoeDL0XHOjbWlWVrmqRkd4ylcuEC6t7pKwjazh+snklHNoTLEFPppAbDFEsc1kuXv/qEQx1EtkXasF7mf2SkFvIoQLbdISxI53o0l1IQ7VuPjVYWW2pU6PsG5LG1yfwV5w4ryTllYaSHHcVi4aleNo1MibBlVJ2aTPI7HTrC6sdXjePKjOme6Bh7++n0AgOfLE/fIY6wqDEd5/BslEq5QjONRJyUM5Gzqi2yNjvLf643iFgI+95VR2GdIfriuAlmD4DXR64sogXNXq3CfIU/0sCwvlRqXt+skWGfP8Dp/5UtJ55Z9EqyIql6Xizw3GzVeC/1VbqfTNsqpzDE1AZ9v85oc1/trdEy+wLrQ1yVVhDd4deEN9Rnm9OzRjZIr9V0f/DkAwI+/5rMAgP8wTH/oR+/5y8dbZYdu+fwP78q4nHbqWlcXXu7j9LK/Gnie9xOe593ned59Fl/g5OTk9AzraX2G9VUE4+Tk5PTNeroka9XzvAndAU4AWNPzCwC+uafANICly23A9/13AXgXANx9991XZd8wgmWJ79Ua78Q310mgkvILPXaWd/ZRtYRZVi5RWgQg0OUwjhwhTWqIgjSUzbQi71VQn7nxBAmEeVFScVKYvbOsZBsdpWfGGkR7+vzeP7tP21sBAhzLQMs8+igT4GMR0rU9yoHaqJJENTvyA3jcpycqNiSis7DE6R4bVnuXFX6BXVjg86E4tzs1Nannldw+bN4l3qXl8qJxynsyf8+Str9vP30BAbVoCXE4WLvA148ck49Nc9/XP13VNr1OZWuKbEny8s+Nt63pNunJpjxnAZGxaNhyqzj37SYJVVptbYbVnsjriLipwXWfw9+uIsyo6q++yUq+8VHSyLNnmB2z8Q2OZ6/mqawE+bVV7tfmPag8ssoWz0+kwu33BzyO4hlm2JTlx5tJ8NoIh2Pw1BQ8FuUxWTungToUBIKc1IlpHkNVGWnDOZ7buTNKfk+pWXaIYwoHSOnG8jynK4s8xqUl/vXUamlVdNNXblwkTN/a+DArJRtq5xMOyC9Y4/i6Hb4vrIJ1JH9Uc/KsSoS5pp9h0ZmZG8iC5vRc0h/97asAAG99E2n7RCj1hMvXBvww7C4kd3VcTtTT/VT8EIAf1f//KIC/+qbnf0QVOi8GUDYk7+Tk5HQDyX2GOTk57bqelGR5nvdeAK8CMOx53gKAXwXwGwD+3PO8twI4D+D7tfhHAbwBwGMAGgCuaQSu+YH8b3qGf1RdGCFlaJZ5xx3Sy2dPMk8omiAxqNVIBBLy54yP03/TUSVcVPlYjRYfp5S6HrSvpKoCC6ov4MgoKVI6pd/qA/JRqcGuVahtqWrx3DlmM60uL2MgkjWsRsLT06QIgwG3cV75WF2Pj315sKanjbDIT9PlHGxu0Ku0pRKx6UnelIeUX9VS1dxAlO72228HACwukfptlTe0POlGWlV5lnIfUrJ5SKStIwIV1TFm5Ud78GFWy+emlX6vasmCKjIzSc7pvDxgAVXh+TqHqSTndHR0UtsjYaqowtPO4VSOJGzgMSMtJy/WoMlzNH+WtGW0QC/UuipK987OAgCaWyRUxXVSnpxyyiameU20lHSfScl3JJ/U0jLhRirL47IekUX1D5O9CkFRo7D8f488ymvxW1/x7ViY5zbWlIU2o6yt2pauX53DqSkSqS/fo7nyVXmqKr7ZgzyW9TVWE21t8FhefCdzc5blu0uoqnZBuW9jE7zmzi1yjkKihItLyiiT2TGnHp6dbkjbK+l5Xu9bet/45j/TtRtQU8Xt96v/zJizbqTPMCen3dJLP/zzAIBzb3zXEy5320d/BoBLIr9eetIvWb7v/5PHeenVl1nWB/DTVzsoJycnp2sl9xnm5OT0TOlZkfhud8I9ffe2O2xP5Uue7pwh39DxWSZlh/yOnmflWF3LD5SnNaKqv6Eh0pa91oNNffxCMhoNdAe+rn6CwyOkNa0ufTaRBJFCvUdC0KyQJmWTpCwPPspk3flTJAZHVbU1nMtjdYPbTKfp1SmLJIVFiqoVUo0R+bvM91JZIcFKJEg14qqWazc59kpVPjL1NlxcE9HJkl6sr5BGRGOkFxl5sIpVeriWN0h4JuI89sWzHGe7xblOxLjfSlnJ5kqoj4gWRrr822tyHOap8lX9t6LtD0KkHwPRSPMJZeU76qoX4pGjzLeqqp9fVMtDPqR8lPPjN7mfsnpAFizFv8lqy55HD9ep0/S+3X3X3QCAkydPcnml+ldEZyzdebMmT1qA5zYuD10qRNoUlK+qourPlipNkzFVDOpanF/jfHz87/4Oo3nuK6bMseKmqmDVI/Mb9zK5+egxZXdVvwYAyBU4N4Maj7XS0l9d/wNRz4eVrzWzd4+2T8pWk2cxmSe1e94t9FSVRNAW5atLpHhOO6q+bfS4H6/Pa62yzDltqX9lX8cYDOp9p/eNt33PLBLtOfuSk9O1VqDF99mTJdE7gnV95ebbycnJycnJyWkX9KwgWSZzdFwav7Pdu1D0wPKGYqoAm5XP6dEFpoBbjlVKPQwD8pJsqZ9dVr4j2YO2e7wVhrleNkcC4FdIDtY3SV8s8yoiH06vyzv8QIj7ueVWpbArswmhCHqqdgtoLAmtW5fXKT9BL1F2mNRjZYW0bI/yppaU1N5XhaN5isa03nklo1uW1+iwegNWSIS2NrmfVXmT1jbV31FkbKCywFOnSXpGRVFKOuYh5WKdm2fl5vgISdlASfWpKPebU/Vjv8/nt1RJ11CmU0v0EA2Oo6IMqT54Tvfso7csGFVVZY3P15VFVt/k8lPjnJdV9VQ0L5jBEyNpY+Ocz3BoZ9p6Wz0HM8md/ShvV2/Fz37mM1xf/f5adY6jq7/nl3k+0nl6ztKan6JyxwJ9kq9O00exx3MwOcZz0mzwOiwVeR2Oy48WjfAcfMsrXggAOHGKdDKVI4k9P899jo1yXxoKpuTHazTaOlbu+/bn3cExKEU/oTkqrnAOQyFSubU1jqOvjgWWYl9TUn1Pz/teUI9VYmrafsN6lzzh5OTk9NyQI1lOTk5OTk5OTrugZwXJuvT+91Ki1dUdeVCJ6t94UJVtcRGtJJc8sG8WALCodO5Tjz4MAHjVS14KAKit03c04I065jetP56ylvrcTjgm349oU7XKv0H5qGZU+XfioccAAHunDgAADh9gdlRdfp9cKo3RfczMWi9zTCWNbV1jmZmhn2Z1i5ShKStSSsTq7jtIM7qiCFbVd0J5Wy94wQsAAFF5tx74KqvbDisLzA9yg5E0yU1TvQr3qmJtTVV3x48w6bzfVWXlEI81IS+Sp/3ffoy0zvpG9hvEKm1Rv4R6De4ZN8rCya4qzHFUnqjimvK9lHwfTfKsZ3L0CaXUIzAkP1MymNL4OD+WDJ9UdaTNR09Ucvr/b+/cYuO6rjP8r7nxfpVIibq4km3JlnxJZLvxFY17g+XUcFC0DzJcxEACBAUCNC0KNBH8UPQhD0WKNi2QpA2a1EBhOGnTpDUMOInhGOhDYbs2nLiKbclyJOtGiRRJiRTvM7P7sP495AxJ0SF5OIfS/wHEzLmvvc6ZjX1+rrU2M+suDDCrctQVvAbGoHW3tfI6rtwNDLgS1dPrz8KVEbaP4k2Rlfn7ev1+FZrdP+OX/Xo5uL1bN7uyNzI0jHKBT/Am4zF+D/bv84zPy4xPm2EV+jvu8DkI/+fVt9xGZv0FxkpNjfs70+4b/F41Miu2iUrvOH39ASvBx/kpH/yN2+ijk97mRvfZpRFXGzs7XLWLcXUxfq2Jat+2G3yOxRxrl1Xe3ZYMvSovtUEIIa4ppGQJIYQQQiTAhlCy5ohvwNmqtTEGa3zG1YSLQ64GbdnrKtHUjKtCo1RXZih33M3YlNMnTwIA9rBO0RDVjcA4nVnOfXiZmX6XmLnXzdpRp075+XezOvvwOVdhGpiZNj3mtZYGznosTQsz8M6dOYXRop978IrH/hRibSTGkQ0wtmiM6lee6sMHH/j4uLeTsU5UDYy+6e1hvA7rUBmLfJWZXffu+5551rW5i070E2Tzvt8QpzqK8zGOMLOytdXVkZlp90Wmw9WUwX63882J1wEAbaxB1pJ1taORWYw9HX69HAPejl/ymmGdLa5QZTNuxwTnNIwV7xtZqb7Iezdw8QLP4+3taXPFqb3V70lUHxupnN263zPoZianeJz7ucCq6r1b/bi2NsYnMTZt61ZXvCan/dnZtsOX229itmfWuP9Ffg6y/a4G/ZKxaleo1G3tY92w3gLKRb8XfZwnMcvYphznn2xo4lyXlIQ+PHWMvvBrjjKLtb2N8yEOus9s1s+77xb3fYE10mI1/yY+f5lsvNfe1ibW8hq57Ms7droiG+/5kSOukvbwmWniPW1o8OvMi5Ks+YxIwRJCXF9IyRJCCCGESIANpWTVvhfHkI8S619ls6523HW3Z2EVMq5SNPEFeoZxTb09rnqANacuMTPtPLOvTn/ocUAdfa6GTDKOB6yNNDDgSlkrVZN9+z4OANjU7m/8Bao/Y7EWFWPCYrZjK9WosakJnDjhcVvN3X6tWaobI4yFGmZsVgPjvYZHfLm34IpLnhXji1QvhlgBfttOV4DOsjJ5VGSmSq6qjYx75liesU4drJm0/3ZXfN5nNmGGaXkFZkjesNPVwRgzlct4W/ayxliRWY5Z+nZzu583Hyu5c97Iji5XwEolV+yGWcPpwoCrfTE7cpZKX4y9mpmN98LVnv5+90dTrpN2/xwAsIPzR8Y4uajGjF1y/0zmXdHKU9kr830jZso1sA7YxJRfbztj44ZY0b1/IFZ89/Y1sWJ9W8nVoi62r33Et+/Z47XRCo2+3ULAFaqTk7NuSyvn0DzNCuyNVLIYaohScFvvPOBxb6/81FXD6NvNzCAd43yTJ854Bfibb/KYwOFhfx6nZv0ZyPHn33/BlduhS+7LLlZy588KZda/uuVWP0+sIL+5x1W5oSu+vVSMv9BMzWdtFKXqZAkhrg+kZAkhhBBCJMCGUrJipff4HlyOFd+ppsS4pNmy7zcw4LEl+byrER1drmCNjflyqewHdHUxzoZZVFu3uxpTaHMVpJNxT8OXXQWZmXGV6fwFV8AeePAuAMDIOa/DlWctpta8xxltYWXvAu2cGPXjpsbHKzFVTYxtaqPicvasqws37eTcg6zBlbvBP3u7XAH60Ys/AQBs2+bxM+NTnkE2PuNq3ChjgWbLPp6+MsmaXowlKtHWUtlVkgKVnwMHPF7trbd+5ufvcxUjl/E2tcVpGql0lWZdvetgtmFD3s/TQeWq0OBqywxVvjOsJxUzQ6PdDawc39zkCtAIsy4R/PiYjdjc7Crizp2sVzXlcuWeva7yZBr8+j9ntuUWVvfvaPLzn+13JW5TJxvCp2qSVfxjnN/0oN/z7u5u+svtKGf9GZpglfVYCqqR9+/iiJ/n1z9xv7c/78fN8pkbuTiEYWaSnj7mGZ99VBujCjh4zlWzG2/2GQx6trrtHUX3cVO7t23vPs9a3coaaGBV+sFhj+H6kGpmC+PeGhh3NkpVbyuzYUPGfXiKSloL55fczXke47PZx8zNGCc4PM54wVyc37FWwapBFd+FENcJUrKEEEIIIRJgQylZkZhtVakvTRmhzMy6YpwzLetv1t1bXCEYYDzNO0c94+tmziHYyerdrazkvrVvFwDg2C89Xmq26PFIHR0tvKCrH+fO+/neP8YMvmmPTTEXGtDe6cpCoZlqC9Wecpn1uzo2o8g59bb1uWLV3+8qWW+PLxdjXSpzZWYL5+I7ecL32/exuwEAY6OuJpw/4+u3bPM2d/WwajgzwTb3uQJ0mZlp+YZW+srb+OFJj7fJU3mZmWYGWsywHHPlqZVVwscuuRozMenZhh2sLxVYDXw7K9X3be/m8a7wNLPO12isTUaFrJFKWGMj7TzpilP3Jo9Zixl1jc1uX1RnGjv8Xpf4DAwN+XVuvNFrQJVLrOo/5naWSn781m1ee2piwuOUZmb8Ho5d8f2mpry90xdcEazElrXFuSSpCLJg1hQzXNva3f6jrM6+nUpjb6+rqc07NmFoxH3a0ua2DQ77s9DKNkWfjF7hPImdfs5JVlzvYJbfWWagBnPbdux0X7Vw/seLzB6cDpwPk8XWco2MAyyzptesX6fIOL+xSW97/wW/xxeH3SeburroM2/z3r0ex1co8MFXZXchhAAgJUsIIYQQIhE2lJJlnDstZpZFYmxWocHfwB/65CcBAJOjMbvPVZf+C66aPPDw7/n2KVctCqxkvZ21kvKMf7rtzocAAKNXXLHqopoyyEy4OIdbVIOysRp7Y6x35PY0MlMNjCmLNZ+mZ8YrdbEYqlOJx4nxZH3MspumStbA2Kb9t7PKPWsqxTpMkzMeI9TI6uEtra66xZpITY3MbOTchfmc25LhdbOMRWK5KowwbqeDdbdGqAYWGvy6s4xJilXujbXFLpx3+7s7XPFpbnVVZpyZb60M6hq94grcDLMqM1Qh25rcp/fe+5sA5rL+pni9YtGdbbHie6w8z3ifK9wvy9iwMpWmlkbOV0mHT81ENamN7Xc/NPC+TE0x5qpmxswy51qM/o9+62AdsFjV35iRmuMclWXGvrW1t+Oeew8CmItni/FqxWKcwYCZjmzbZNGv2cK5Nx/7g0PuyyZftjJ/Fxk/T0sL68dxPsYYtFigrTFuLsM2PUE7Mrlqn1VCqKhONlMVjWWvso3uq07O0xjf3WZmYpzfhupmhBBizZCSJYQQQgiRANfEK2aMh2ni/HxP/tFnAAChHN/M/U07xmgFqiXT8c2dr+T5nI85KxElgcdl/I28xEkNYyaZMU5qcsK3N1F9KVFxKzPLMVA1yTXkuL7M88wiw30yzDyM6kGWykegSheVlKicWHmK5yqxjVn6okjbrMoWo1IVA9lyrCkWG2tsVGDsksXi3FT1pkqu+DAECXlmJ5apXIF2GA+84wDPX4oZZ9UZZSVeB7mooFGFoW+MfgjR7li0ifuVqWRlolJFRSrDivVRNSzGDFTaXaIiGLP94vYM/R0ryhsdk2X7K9ePfp0JVftFxSoqYZHonjk/x9OVK8/BXJas7xRrgpUrzxFtYttiRui2Hbu4H22F+yJWjo+XjkqVxXsQfRyvG23iveD0j8jyvDm2ObasNMNnlPeiCI9nK8aJHONxuaW6F2UXCiGuD6RkCSGEEEIkwAZTsqh2RFGDb9jZfPUcb7FuVqASEBbU7WFsFLOholpUCTKx6sV4XWO2V6wNFRWxlpamqvNEJaFG1JgTYyoXyFXmvossePuPmZKVc0RlhqoF42fmhJ581eFWHUoEy1nVeRcU4a4U666u0h0rvkelHZGBSgAACVRJREFUqkbYgeXiZeJ2nihTrQ5SuKvU6Yob5trFyut0XqgxLCpulovPAlWWfLViVuvrSi3ymvigHNsZ989nq+P9UFGeQtVyNld9c+e8VVPdPFuzHO2wLIxtyiyoiB7VTbaZc2DG7dFHC+5ZyFTtt/C8PHvNPUFlL19TqOxYfXhFwc1n5q9GZu6IWoNqThRvtt7thBDXB+rthBBCCCESIDVKlpnNU5SW3KtqaeEIsboifMxCrH1jN6tdjitqVIyKYFCthix3nqWqBIVQo5TBKsZa7cnmjlp8Nds219YldlvqtDXrQ+1yzY5zm7NXPW+IPqwVL65uZqX9lWfAau5u5XxL+bj6gstdZ+H66uXaZ3HhcUs9q7Xra5YrsxbM9/DVj5m7Ntu2zCVWSrRjgYcWPOfVF7ba381VruB81P2FEGJjIyVLCCGEECIBUqNkXQ8srVat7TEbmbS0Ny12CCGE2LhIyRJCCCGESAANsoQQQgghEkCDLCGEEEKIBFBM1jqyWJzPchmVS8UGLZ+JubFJS7vXOjbrWr9vQggh5pCSJYQQQgiRAKlRsq6HN/yVtPF68MtipKXdabFDCCHExkNKlhBCCCFEAmiQJYQQQgiRAMsOsszsO2Y2YGZH5q37qpm9Z2Zvm9kPzaxz3rbDZnbczI6a2SNJGS6EEB8F9WFCiHrxUZSsZwAcrFn3EoDbQwh3AjgG4DAAmNl+AIcA3MZjvmFmmqhMCFFPnoH6MCFEHVh2kBVC+G8AwzXrfhJCKHLxVQA7+P3TAL4bQpgOIZwAcBzAJ9bQXiGE+JVQHyaEqBdrEZP1WQAv8vt2AKfnbTvDdUIIkVbUhwkhEmFVgywzexpAEcCzcdUiuy2aA29mnzezN8zsjcHBwdWYIYQQK2Kt+rDS+HhSJgohNjArHmSZ2VMAHgPwZJgrJnQGwM55u+0AcG6x40MI3woh3BNCuKenp2elZgghxIpYyz4s29KSrLFCiA3JigZZZnYQwJcAPB5CmJi36XkAh8yswcx2A9gD4PXVmymEEGuH+jAhxHqwbMV3M3sOwMMANpvZGQB/Cc/EaQDwEud2ezWE8MchhF+Y2b8BeAcuwX8hhFBKynghhFgO9WFCiHqx7CArhPDEIqu/fZX9vwLgK6sxSggh1gr1YUKIeqGK70IIIYQQCaBBlhBCCCFEAmiQJYQQQgiRABpkCSGEEEIkgAZZQgghhBAJoEGWEEIIIUQCaJAlhBBCCJEAGmQJIYQQQiTAssVI1wszw9z0YemGFaIXsFHsvxrr1baN4sOl7FyKtNm/Eq7HNgshRBJIyRJCCCGESABLw1uomQ0CGAdwsd62XIXNSK99abYNkH2rIc22Aauz79dCCD1raUy92AB92LX8HCVNmm0DZN9qSLz/SsUgCwDM7I0Qwj31tmMp0mxfmm0DZN9qSLNtQPrtW0/S7Is02wak27402wbIvtWwHrbp34VCCCGEEAmgQZYQQgghRAKkaZD1rXobsAxpti/NtgGybzWk2TYg/fatJ2n2RZptA9JtX5ptA2TfakjcttTEZAkhhBBCXEukSckSQgghhLhmSMUgy8wOmtlRMztuZl+usy07zewVM3vXzH5hZl/k+m4ze8nM3udnV53tzJrZW2b2Apd3m9lrtO97Zlaoo22dZvZ9M3uPfrw/Lf4zsz/jfT1iZs+ZWWM9fWdm3zGzATM7Mm/dor4y5x/4O3nbzO6qk31f5b1928x+aGad87Ydpn1HzeyRpO1LA2nqv2hP6vsw9V8rtk391+rtW9f+q+6DLDPLAvg6gEcB7AfwhJntr6NJRQB/HkLYB+A+AF+gPV8G8HIIYQ+Al7lcT74I4N15y38N4O9o3wiAz9XFKufvAfwohHArgI/B7ay7/8xsO4A/AXBPCOF2AFkAh1Bf3z0D4GDNuqV89SiAPfz7PIBv1sm+lwDcHkK4E8AxAIcBgL+TQwBu4zHf4O/7miWF/RewMfow9V+/Iuq/1sy+9e2/Qgh1/QNwP4Afz1s+DOBwve2aZ89/AfhdAEcB9HFdH4CjdbRpB/zh/S0ALwAweEG13GI+XWfb2gGcAOP95q2vu/8AbAdwGkA3fEqpFwA8Um/fAdgF4MhyvgLwTwCeWGy/9bSvZtvvA3iW36t+uwB+DOD+ejyH63jvUt1/0aZU9WHqv1Zsm/qvNbCvZlvi/VfdlSzMPTiRM1xXd8xsF4ADAF4DsCWE0A8A/Oytn2X4GoC/AFDm8iYAl0IIRS7X04c3AhgE8C/8d8A/m1kLUuC/EMJZAH8D4BSAfgCXAbyJ9PguspSv0vhb+SyAF/k9jfYlTarbnNI+TP3XClD/lQiJ919pGGQtNhtt3VMezawVwH8A+NMQwmi97YmY2WMABkIIb85fvciu9fJhDsBdAL4ZQjgAn2qk3v9aBQAwNuDTAHYD2AagBS5h11L3528J0nSfYWZPw/819WxctchuafXlWpHaNqexD1P/tXLUf60t69V/pWGQdQbAznnLOwCcq5MtAAAzy8M7p2dDCD/g6gtm1sftfQAG6mTegwAeN7OTAL4Ll9y/BqDTzHLcp54+PAPgTAjhNS5/H95ppcF/vwPgRAhhMIQwC+AHAB5AenwXWcpXqfmtmNlTAB4D8GSgto4U2beOpLLNKe7D1H+tHPVfa8R69l9pGGT9L4A9zJAowAPPnq+XMWZmAL4N4N0Qwt/O2/Q8gKf4/Sl4nMO6E0I4HELYEULYBffVT0MITwJ4BcAfpsC+8wBOm9ktXPXbAN5BOvx3CsB9ZtbM+xxtS4Xv5rGUr54H8Blm6dwH4HKU5dcTMzsI4EsAHg8hTMzb9DyAQ2bWYGa74QGur6+3fetMqvovIN19mPqvVaH+aw1Y9/4r6aCzjxiY9il4lP8HAJ6usy0PwSXCtwH8jH+fgscNvAzgfX52p8BvDwN4gd9v5ANxHMC/A2ioo10fB/AGffifALrS4j8AfwXgPQBHAPwrgIZ6+g7Ac/D4iln4m9TnlvIVXM7+On8n/wfPMqqHfcfhsQvx9/GP8/Z/mvYdBfBovZ7BdX6mUtN/0Z4N0Yep/1qRbeq/Vm/fuvZfqvguhBBCCJEAafh3oRBCCCHENYcGWUIIIYQQCaBBlhBCCCFEAmiQJYQQQgiRABpkCSGEEEIkgAZZQgghhBAJoEGWEEIIIUQCaJAlhBBCCJEA/w/b81+g/PD/owAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check for any sample image by changing number in 'take'\n",
    "for image, mask in train.take(6): \n",
    "    sample_image, sample_mask = image, mask\n",
    "display([sample_image, sample_mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Create a Model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The model being used here is a modified U-Net. A U-Net consists of an encoder (downsampler) and decoder (upsampler). In-order to learn robust features, and reduce the number of trainable parameters, a pretrained model can be used as the encoder. Thus, the encoder for this task will be a pretrained MobileNetV2 model, whose intermediate outputs will be used, and the decoder will be the upsample block already implemented in TensorFlow Examples in the Pix2pix tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RGB has 3 channels (Red, Green, Blue)\n",
    "OUTPUT_CHANNELS = 3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_128_no_top.h5\n",
      "9412608/9406464 [==============================] - 1s 0us/step\n",
      "Model: \"mobilenetv2_1.00_128\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_pad (ZeroPadding2D)       (None, 129, 129, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 64, 64, 32)   864         Conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_Conv1 (BatchNormalization)   (None, 64, 64, 32)   128         Conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_relu (ReLU)               (None, 64, 64, 32)   0           bn_Conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise (Depthw (None, 64, 64, 32)   288         Conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_BN (Bat (None, 64, 64, 32)   128         expanded_conv_depthwise[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_relu (R (None, 64, 64, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project (Conv2D)  (None, 64, 64, 16)   512         expanded_conv_depthwise_relu[0][0\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project_BN (Batch (None, 64, 64, 16)   64          expanded_conv_project[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, 64, 64, 96)   1536        expanded_conv_project_BN[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_BN (BatchNormali (None, 64, 64, 96)   384         block_1_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_relu (ReLU)      (None, 64, 64, 96)   0           block_1_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_pad (ZeroPadding2D)     (None, 65, 65, 96)   0           block_1_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, 32, 32, 96)   864         block_1_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_BN (BatchNorm (None, 32, 32, 96)   384         block_1_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_relu (ReLU)   (None, 32, 32, 96)   0           block_1_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, 32, 32, 24)   2304        block_1_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project_BN (BatchNormal (None, 32, 32, 24)   96          block_1_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, 32, 32, 144)  3456        block_1_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_BN (BatchNormali (None, 32, 32, 144)  576         block_2_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_relu (ReLU)      (None, 32, 32, 144)  0           block_2_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, 32, 32, 144)  1296        block_2_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_BN (BatchNorm (None, 32, 32, 144)  576         block_2_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_relu (ReLU)   (None, 32, 32, 144)  0           block_2_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, 32, 32, 24)   3456        block_2_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project_BN (BatchNormal (None, 32, 32, 24)   96          block_2_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, 32, 32, 24)   0           block_1_project_BN[0][0]         \n",
      "                                                                 block_2_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, 32, 32, 144)  3456        block_2_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_BN (BatchNormali (None, 32, 32, 144)  576         block_3_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_relu (ReLU)      (None, 32, 32, 144)  0           block_3_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_pad (ZeroPadding2D)     (None, 33, 33, 144)  0           block_3_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, 16, 16, 144)  1296        block_3_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_BN (BatchNorm (None, 16, 16, 144)  576         block_3_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_relu (ReLU)   (None, 16, 16, 144)  0           block_3_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, 16, 16, 32)   4608        block_3_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project_BN (BatchNormal (None, 16, 16, 32)   128         block_3_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, 16, 16, 192)  6144        block_3_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_4_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_4_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, 16, 16, 192)  1728        block_4_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_BN (BatchNorm (None, 16, 16, 192)  768         block_4_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_relu (ReLU)   (None, 16, 16, 192)  0           block_4_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, 16, 16, 32)   6144        block_4_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project_BN (BatchNormal (None, 16, 16, 32)   128         block_4_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, 16, 16, 32)   0           block_3_project_BN[0][0]         \n",
      "                                                                 block_4_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, 16, 16, 192)  6144        block_4_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_5_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_5_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, 16, 16, 192)  1728        block_5_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_BN (BatchNorm (None, 16, 16, 192)  768         block_5_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_relu (ReLU)   (None, 16, 16, 192)  0           block_5_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, 16, 16, 32)   6144        block_5_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project_BN (BatchNormal (None, 16, 16, 32)   128         block_5_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, 16, 16, 32)   0           block_4_add[0][0]                \n",
      "                                                                 block_5_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, 16, 16, 192)  6144        block_5_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_6_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_6_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_pad (ZeroPadding2D)     (None, 17, 17, 192)  0           block_6_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, 8, 8, 192)    1728        block_6_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_BN (BatchNorm (None, 8, 8, 192)    768         block_6_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_relu (ReLU)   (None, 8, 8, 192)    0           block_6_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, 8, 8, 64)     12288       block_6_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project_BN (BatchNormal (None, 8, 8, 64)     256         block_6_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, 8, 8, 384)    24576       block_6_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_7_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_7_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_7_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_7_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_7_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, 8, 8, 64)     24576       block_7_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project_BN (BatchNormal (None, 8, 8, 64)     256         block_7_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, 8, 8, 64)     0           block_6_project_BN[0][0]         \n",
      "                                                                 block_7_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, 8, 8, 384)    24576       block_7_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_8_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_8_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_8_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_8_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_8_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, 8, 8, 64)     24576       block_8_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project_BN (BatchNormal (None, 8, 8, 64)     256         block_8_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, 8, 8, 64)     0           block_7_add[0][0]                \n",
      "                                                                 block_8_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, 8, 8, 384)    24576       block_8_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_9_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_9_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_9_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_9_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_9_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, 8, 8, 64)     24576       block_9_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project_BN (BatchNormal (None, 8, 8, 64)     256         block_9_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, 8, 8, 64)     0           block_8_add[0][0]                \n",
      "                                                                 block_9_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, 8, 8, 384)    24576       block_9_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_BN (BatchNormal (None, 8, 8, 384)    1536        block_10_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_relu (ReLU)     (None, 8, 8, 384)    0           block_10_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, 8, 8, 384)    3456        block_10_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_BN (BatchNor (None, 8, 8, 384)    1536        block_10_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_relu (ReLU)  (None, 8, 8, 384)    0           block_10_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, 8, 8, 96)     36864       block_10_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project_BN (BatchNorma (None, 8, 8, 96)     384         block_10_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, 8, 8, 576)    55296       block_10_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_11_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_11_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, 8, 8, 576)    5184        block_11_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_BN (BatchNor (None, 8, 8, 576)    2304        block_11_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_relu (ReLU)  (None, 8, 8, 576)    0           block_11_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, 8, 8, 96)     55296       block_11_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project_BN (BatchNorma (None, 8, 8, 96)     384         block_11_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, 8, 8, 96)     0           block_10_project_BN[0][0]        \n",
      "                                                                 block_11_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, 8, 8, 576)    55296       block_11_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_12_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_12_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, 8, 8, 576)    5184        block_12_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_BN (BatchNor (None, 8, 8, 576)    2304        block_12_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_relu (ReLU)  (None, 8, 8, 576)    0           block_12_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, 8, 8, 96)     55296       block_12_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project_BN (BatchNorma (None, 8, 8, 96)     384         block_12_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, 8, 8, 96)     0           block_11_add[0][0]               \n",
      "                                                                 block_12_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, 8, 8, 576)    55296       block_12_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_13_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_13_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_pad (ZeroPadding2D)    (None, 9, 9, 576)    0           block_13_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise (DepthwiseCo (None, 4, 4, 576)    5184        block_13_pad[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_BN (BatchNor (None, 4, 4, 576)    2304        block_13_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_relu (ReLU)  (None, 4, 4, 576)    0           block_13_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project (Conv2D)       (None, 4, 4, 160)    92160       block_13_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project_BN (BatchNorma (None, 4, 4, 160)    640         block_13_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand (Conv2D)        (None, 4, 4, 960)    153600      block_13_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_14_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_14_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_14_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_14_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_14_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project (Conv2D)       (None, 4, 4, 160)    153600      block_14_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project_BN (BatchNorma (None, 4, 4, 160)    640         block_14_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_add (Add)              (None, 4, 4, 160)    0           block_13_project_BN[0][0]        \n",
      "                                                                 block_14_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand (Conv2D)        (None, 4, 4, 960)    153600      block_14_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_15_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_15_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_15_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_15_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_15_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project (Conv2D)       (None, 4, 4, 160)    153600      block_15_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project_BN (BatchNorma (None, 4, 4, 160)    640         block_15_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_15_add (Add)              (None, 4, 4, 160)    0           block_14_add[0][0]               \n",
      "                                                                 block_15_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand (Conv2D)        (None, 4, 4, 960)    153600      block_15_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_16_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_16_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_16_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_16_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_16_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project (Conv2D)       (None, 4, 4, 320)    307200      block_16_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project_BN (BatchNorma (None, 4, 4, 320)    1280        block_16_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1 (Conv2D)                 (None, 4, 4, 1280)   409600      block_16_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1_bn (BatchNormalization)  (None, 4, 4, 1280)   5120        Conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "out_relu (ReLU)                 (None, 4, 4, 1280)   0           Conv_1_bn[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 1280)         0           out_relu[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 2,257,984\n",
      "Trainable params: 2,223,872\n",
      "Non-trainable params: 34,112\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model = tf.keras.applications.MobileNetV2(\n",
    "    input_shape=[128, 128, 3], \n",
    "    include_top=False,\n",
    "    weights='imagenet', # If None, random initialization\n",
    "    pooling='avg' # If None, output of the model will be the 4D tensor output of the last convolutional block. If max, global max pooling will be applied\n",
    ")\n",
    "\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_pad (ZeroPadding2D)       (None, 129, 129, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 64, 64, 32)   864         Conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_Conv1 (BatchNormalization)   (None, 64, 64, 32)   128         Conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_relu (ReLU)               (None, 64, 64, 32)   0           bn_Conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise (Depthw (None, 64, 64, 32)   288         Conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_BN (Bat (None, 64, 64, 32)   128         expanded_conv_depthwise[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_relu (R (None, 64, 64, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project (Conv2D)  (None, 64, 64, 16)   512         expanded_conv_depthwise_relu[0][0\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project_BN (Batch (None, 64, 64, 16)   64          expanded_conv_project[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, 64, 64, 96)   1536        expanded_conv_project_BN[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_BN (BatchNormali (None, 64, 64, 96)   384         block_1_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_relu (ReLU)      (None, 64, 64, 96)   0           block_1_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_pad (ZeroPadding2D)     (None, 65, 65, 96)   0           block_1_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, 32, 32, 96)   864         block_1_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_BN (BatchNorm (None, 32, 32, 96)   384         block_1_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_relu (ReLU)   (None, 32, 32, 96)   0           block_1_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, 32, 32, 24)   2304        block_1_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project_BN (BatchNormal (None, 32, 32, 24)   96          block_1_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, 32, 32, 144)  3456        block_1_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_BN (BatchNormali (None, 32, 32, 144)  576         block_2_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_relu (ReLU)      (None, 32, 32, 144)  0           block_2_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, 32, 32, 144)  1296        block_2_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_BN (BatchNorm (None, 32, 32, 144)  576         block_2_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_relu (ReLU)   (None, 32, 32, 144)  0           block_2_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, 32, 32, 24)   3456        block_2_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project_BN (BatchNormal (None, 32, 32, 24)   96          block_2_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, 32, 32, 24)   0           block_1_project_BN[0][0]         \n",
      "                                                                 block_2_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, 32, 32, 144)  3456        block_2_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_BN (BatchNormali (None, 32, 32, 144)  576         block_3_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_relu (ReLU)      (None, 32, 32, 144)  0           block_3_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_pad (ZeroPadding2D)     (None, 33, 33, 144)  0           block_3_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, 16, 16, 144)  1296        block_3_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_BN (BatchNorm (None, 16, 16, 144)  576         block_3_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_relu (ReLU)   (None, 16, 16, 144)  0           block_3_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, 16, 16, 32)   4608        block_3_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project_BN (BatchNormal (None, 16, 16, 32)   128         block_3_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, 16, 16, 192)  6144        block_3_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_4_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_4_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, 16, 16, 192)  1728        block_4_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_BN (BatchNorm (None, 16, 16, 192)  768         block_4_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_relu (ReLU)   (None, 16, 16, 192)  0           block_4_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, 16, 16, 32)   6144        block_4_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project_BN (BatchNormal (None, 16, 16, 32)   128         block_4_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, 16, 16, 32)   0           block_3_project_BN[0][0]         \n",
      "                                                                 block_4_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, 16, 16, 192)  6144        block_4_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_5_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_5_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, 16, 16, 192)  1728        block_5_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_BN (BatchNorm (None, 16, 16, 192)  768         block_5_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_relu (ReLU)   (None, 16, 16, 192)  0           block_5_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, 16, 16, 32)   6144        block_5_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project_BN (BatchNormal (None, 16, 16, 32)   128         block_5_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, 16, 16, 32)   0           block_4_add[0][0]                \n",
      "                                                                 block_5_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, 16, 16, 192)  6144        block_5_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_6_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_6_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_pad (ZeroPadding2D)     (None, 17, 17, 192)  0           block_6_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, 8, 8, 192)    1728        block_6_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_BN (BatchNorm (None, 8, 8, 192)    768         block_6_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_relu (ReLU)   (None, 8, 8, 192)    0           block_6_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, 8, 8, 64)     12288       block_6_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project_BN (BatchNormal (None, 8, 8, 64)     256         block_6_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, 8, 8, 384)    24576       block_6_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_7_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_7_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_7_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_7_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_7_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, 8, 8, 64)     24576       block_7_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project_BN (BatchNormal (None, 8, 8, 64)     256         block_7_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, 8, 8, 64)     0           block_6_project_BN[0][0]         \n",
      "                                                                 block_7_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, 8, 8, 384)    24576       block_7_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_8_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_8_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_8_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_8_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_8_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, 8, 8, 64)     24576       block_8_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project_BN (BatchNormal (None, 8, 8, 64)     256         block_8_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, 8, 8, 64)     0           block_7_add[0][0]                \n",
      "                                                                 block_8_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, 8, 8, 384)    24576       block_8_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_9_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_9_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_9_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_9_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_9_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, 8, 8, 64)     24576       block_9_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project_BN (BatchNormal (None, 8, 8, 64)     256         block_9_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, 8, 8, 64)     0           block_8_add[0][0]                \n",
      "                                                                 block_9_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, 8, 8, 384)    24576       block_9_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_BN (BatchNormal (None, 8, 8, 384)    1536        block_10_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_relu (ReLU)     (None, 8, 8, 384)    0           block_10_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, 8, 8, 384)    3456        block_10_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_BN (BatchNor (None, 8, 8, 384)    1536        block_10_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_relu (ReLU)  (None, 8, 8, 384)    0           block_10_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, 8, 8, 96)     36864       block_10_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project_BN (BatchNorma (None, 8, 8, 96)     384         block_10_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, 8, 8, 576)    55296       block_10_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_11_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_11_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, 8, 8, 576)    5184        block_11_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_BN (BatchNor (None, 8, 8, 576)    2304        block_11_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_relu (ReLU)  (None, 8, 8, 576)    0           block_11_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, 8, 8, 96)     55296       block_11_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project_BN (BatchNorma (None, 8, 8, 96)     384         block_11_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, 8, 8, 96)     0           block_10_project_BN[0][0]        \n",
      "                                                                 block_11_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, 8, 8, 576)    55296       block_11_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_12_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_12_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, 8, 8, 576)    5184        block_12_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_BN (BatchNor (None, 8, 8, 576)    2304        block_12_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_relu (ReLU)  (None, 8, 8, 576)    0           block_12_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, 8, 8, 96)     55296       block_12_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project_BN (BatchNorma (None, 8, 8, 96)     384         block_12_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, 8, 8, 96)     0           block_11_add[0][0]               \n",
      "                                                                 block_12_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, 8, 8, 576)    55296       block_12_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_13_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_13_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_pad (ZeroPadding2D)    (None, 9, 9, 576)    0           block_13_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise (DepthwiseCo (None, 4, 4, 576)    5184        block_13_pad[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_BN (BatchNor (None, 4, 4, 576)    2304        block_13_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_relu (ReLU)  (None, 4, 4, 576)    0           block_13_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project (Conv2D)       (None, 4, 4, 160)    92160       block_13_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project_BN (BatchNorma (None, 4, 4, 160)    640         block_13_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand (Conv2D)        (None, 4, 4, 960)    153600      block_13_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_14_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_14_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_14_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_14_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_14_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project (Conv2D)       (None, 4, 4, 160)    153600      block_14_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project_BN (BatchNorma (None, 4, 4, 160)    640         block_14_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_add (Add)              (None, 4, 4, 160)    0           block_13_project_BN[0][0]        \n",
      "                                                                 block_14_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand (Conv2D)        (None, 4, 4, 960)    153600      block_14_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_15_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_15_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_15_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_15_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_15_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project (Conv2D)       (None, 4, 4, 160)    153600      block_15_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project_BN (BatchNorma (None, 4, 4, 160)    640         block_15_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_15_add (Add)              (None, 4, 4, 160)    0           block_14_add[0][0]               \n",
      "                                                                 block_15_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand (Conv2D)        (None, 4, 4, 960)    153600      block_15_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_16_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_16_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_16_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_16_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_16_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project (Conv2D)       (None, 4, 4, 320)    307200      block_16_depthwise_relu[0][0]    \n",
      "==================================================================================================\n",
      "Total params: 1,841,984\n",
      "Trainable params: 0\n",
      "Non-trainable params: 1,841,984\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Use the activations of these layers\n",
    "layer_names = [\n",
    "    'block_1_expand_relu',   # 64x64\n",
    "    'block_3_expand_relu',   # 32x32\n",
    "    'block_6_expand_relu',   # 16x16\n",
    "    'block_13_expand_relu',  # 8x8\n",
    "    'block_16_project',      # 4x4\n",
    "]\n",
    "layers = [base_model.get_layer(name).output for name in layer_names]\n",
    "\n",
    "# The encoder (Down-stack) consists of specific outputs from intermediate layers in the model. Note that the encoder will not be trained during the training process\n",
    "\n",
    "# Create the feature extraction model\n",
    "down_stack = tf.keras.Model(inputs=base_model.input, outputs=layers)\n",
    "\n",
    "down_stack.trainable = False\n",
    "\n",
    "down_stack.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The decoder (Up-stack) is simply a series of upsample blocks\n",
    "up_stack = [\n",
    "    pix2pix.upsample(512, 3),  # 4x4 -> 8x8\n",
    "    pix2pix.upsample(256, 3),  # 8x8 -> 16x16\n",
    "    pix2pix.upsample(128, 3),  # 16x16 -> 32x32\n",
    "    pix2pix.upsample(64, 3),   # 32x32 -> 64x64\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MyModel(output_channels):\n",
    "    inputs = tf.keras.layers.Input(shape=[128, 128, 3])\n",
    "    x = inputs\n",
    "\n",
    "    # Encoder \n",
    "    skips = down_stack(x)\n",
    "    x = skips[-1]\n",
    "    skips = reversed(skips[:-1])\n",
    "\n",
    "    # Decoder and establishing the skip connections\n",
    "    for up, skip in zip(up_stack, skips):\n",
    "        x = up(x)\n",
    "        concat = tf.keras.layers.Concatenate()\n",
    "        x = concat([x, skip])\n",
    "\n",
    "    # This is the last layer of the model\n",
    "    last = tf.keras.layers.Conv2DTranspose(\n",
    "        output_channels, 3, strides=2,\n",
    "        padding='same')  #64x64 -> 128x128\n",
    "\n",
    "    x = last(x)\n",
    "\n",
    "    return tf.keras.Model(inputs=inputs, outputs=x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The reason to use \"SparseCategoricalCrossentropy\" is because the network is trying to assign each pixel a label, just like multi-class prediction. \n",
    "# In the true segmentation mask, each pixel has either a {0,1,2}. The network here is outputting three channels. Essentially, each channel is trying to learn to predict a class\n",
    "model = MyModel(OUTPUT_CHANNELS)\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's try out the model to see what it predicts before training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_mask(pred_mask):\n",
    "    pred_mask = tf.argmax(pred_mask, axis=-1)\n",
    "    pred_mask = pred_mask[..., tf.newaxis]\n",
    "    return pred_mask[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_predictions(dataset=None, num=1):\n",
    "    if dataset:\n",
    "        for image, mask in dataset.take(num):\n",
    "            pred_mask = model.predict(image)\n",
    "            display([image[0], mask[0], create_mask(pred_mask)])\n",
    "        else:\n",
    "            display([sample_image, sample_mask,\n",
    "            create_mask(model.predict(sample_image[tf.newaxis, ...]))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_predictions(train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's observe how the model improves while it is training. To accomplish this task, a callback function is defined below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DisplayCallback(tf.keras.callbacks.Callback):\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        clear_output(wait=True)\n",
    "        show_predictions()\n",
    "        print ('\\nSample Prediction after epoch {}\\n'.format(epoch+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sample Prediction after epoch 20\n",
      "\n",
      "57/57 [==============================] - 254s 4s/step - loss: 0.1846 - accuracy: 0.9183 - val_loss: 0.2893 - val_accuracy: 0.8909\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 20\n",
    "STEPS_PER_EPOCH = NUM_TRAIN_EXAMPLES // BATCH_SIZE\n",
    "VALIDATION_STEPS = NUM_TEST_EXAMPLES // BATCH_SIZE if NUM_TEST_EXAMPLES // BATCH_SIZE else 1\n",
    "\n",
    "model_history = model.fit(train_dataset, epochs=EPOCHS,\n",
    "                          steps_per_epoch=STEPS_PER_EPOCH,\n",
    "                          validation_steps=VALIDATION_STEPS,\n",
    "                          validation_data=test_dataset, # Already included testset as a validation data\n",
    "                          callbacks=[DisplayCallback()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def render_training_history(model_history):\n",
    "    loss = model_history.history['loss']\n",
    "    val_loss = model_history.history['val_loss']\n",
    "\n",
    "    accuracy = model_history.history['accuracy']\n",
    "    val_accuracy = model_history.history['val_accuracy']\n",
    "\n",
    "    plt.figure(figsize=(14, 4))\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.title('Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.plot(loss, label='Training set')\n",
    "    plt.plot(val_loss, label='Test set', linestyle='--')\n",
    "    plt.legend()\n",
    "    plt.grid(linestyle='--', linewidth=1, alpha=0.5)\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.title('Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.ylim([0, 1])\n",
    "    plt.plot(accuracy, label='Training set')\n",
    "    plt.plot(val_accuracy, label='Test set', linestyle='--')\n",
    "    plt.legend()\n",
    "    plt.grid(linestyle='--', linewidth=1, alpha=0.5)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "render_training_history(model_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_predictions(test_dataset, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}