13_loading_and_preprocessing_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 13 – Loading and Preprocessing Data with TensorFlow**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This notebook contains all the sample code and solutions to the exercises in chapter 13._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/13_loading_and_preprocessing_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/13_loading_and_preprocessing_data.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project requires Python 3.7 or above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also requires Scikit-Learn ≥ 1.0.1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from packaging import version\n",
    "import sklearn\n",
    "\n",
    "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And TensorFlow ≥ 2.8:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "assert version.parse(tf.__version__) >= version.parse(\"2.8.0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The tf.data API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: (), types: tf.int32>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "X = tf.range(10)  # any data tensor\n",
    "dataset = tf.data.Dataset.from_tensor_slices(X)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(5, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(7, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(9, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': (<tf.Tensor: shape=(), dtype=int32, numpy=1>, <tf.Tensor: shape=(), dtype=int32, numpy=4>), 'b': <tf.Tensor: shape=(), dtype=int32, numpy=7>}\n",
      "{'a': (<tf.Tensor: shape=(), dtype=int32, numpy=2>, <tf.Tensor: shape=(), dtype=int32, numpy=5>), 'b': <tf.Tensor: shape=(), dtype=int32, numpy=8>}\n",
      "{'a': (<tf.Tensor: shape=(), dtype=int32, numpy=3>, <tf.Tensor: shape=(), dtype=int32, numpy=6>), 'b': <tf.Tensor: shape=(), dtype=int32, numpy=9>}\n"
     ]
    }
   ],
   "source": [
    "X_nested = {\"a\": ([1, 2, 3], [4, 5, 6]), \"b\": [7, 8, 9]}\n",
    "dataset = tf.data.Dataset.from_tensor_slices(X_nested)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chaining Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 1 2 3 4 5 6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([7 8 9 0 1 2 3], shape=(7,), dtype=int32)\n",
      "tf.Tensor([4 5 6 7 8 9 0], shape=(7,), dtype=int32)\n",
      "tf.Tensor([1 2 3 4 5 6 7], shape=(7,), dtype=int32)\n",
      "tf.Tensor([8 9], shape=(2,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.from_tensor_slices(tf.range(10))\n",
    "dataset = dataset.repeat(3).batch(7)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([ 0  2  4  6  8 10 12], shape=(7,), dtype=int32)\n",
      "tf.Tensor([14 16 18  0  2  4  6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 8 10 12 14 16 18  0], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 2  4  6  8 10 12 14], shape=(7,), dtype=int32)\n",
      "tf.Tensor([16 18], shape=(2,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.map(lambda x: x * 2)  # x is a batch\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([14 16 18  0  2  4  6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 8 10 12 14 16 18  0], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 2  4  6  8 10 12 14], shape=(7,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.filter(lambda x: tf.reduce_sum(x) > 50)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([14 16 18  0  2  4  6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 8 10 12 14 16 18  0], shape=(7,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset.take(2):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shuffling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([1 4 2 3 5 0 6], shape=(7,), dtype=int64)\n",
      "tf.Tensor([9 8 2 0 3 1 4], shape=(7,), dtype=int64)\n",
      "tf.Tensor([5 7 9 6 7 8], shape=(6,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10).repeat(2)\n",
    "dataset = dataset.shuffle(buffer_size=4, seed=42).batch(7)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interleaving lines from multiple files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by loading and preparing the California housing dataset. We first load it, then split it into a training set, a validation set and a test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra code – fetches, splits and normalizes the California housing dataset\n",
    "\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X_train_full, X_test, y_train_full, y_test = train_test_split(\n",
    "    housing.data, housing.target.reshape(-1, 1), random_state=42)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X_train_full, y_train_full, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a very large dataset that does not fit in memory, you will typically want to split it into many files first, then have TensorFlow read these files in parallel. To demonstrate this, let's start by splitting the housing dataset and saving it to 20 CSV files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra code – split the dataset into 20 parts and save it to CSV files\n",
    "\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "def save_to_csv_files(data, name_prefix, header=None, n_parts=10):\n",
    "    housing_dir = Path() / \"datasets\" / \"housing\"\n",
    "    housing_dir.mkdir(parents=True, exist_ok=True)\n",
    "    filename_format = \"my_{}_{:02d}.csv\"\n",
    "\n",
    "    filepaths = []\n",
    "    m = len(data)\n",
    "    chunks = np.array_split(np.arange(m), n_parts)\n",
    "    for file_idx, row_indices in enumerate(chunks):\n",
    "        part_csv = housing_dir / filename_format.format(name_prefix, file_idx)\n",
    "        filepaths.append(str(part_csv))\n",
    "        with open(part_csv, \"w\") as f:\n",
    "            if header is not None:\n",
    "                f.write(header)\n",
    "                f.write(\"\\n\")\n",
    "            for row_idx in row_indices:\n",
    "                f.write(\",\".join([repr(col) for col in data[row_idx]]))\n",
    "                f.write(\"\\n\")\n",
    "    return filepaths\n",
    "\n",
    "train_data = np.c_[X_train, y_train]\n",
    "valid_data = np.c_[X_valid, y_valid]\n",
    "test_data = np.c_[X_test, y_test]\n",
    "header_cols = housing.feature_names + [\"MedianHouseValue\"]\n",
    "header = \",\".join(header_cols)\n",
    "\n",
    "train_filepaths = save_to_csv_files(train_data, \"train\", header, n_parts=20)\n",
    "valid_filepaths = save_to_csv_files(valid_data, \"valid\", header, n_parts=10)\n",
    "test_filepaths = save_to_csv_files(test_data, \"test\", header, n_parts=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, now let's take a peek at the first few lines of one of these CSV files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,MedianHouseValue\n",
      "3.5214,15.0,3.0499445061043287,1.106548279689234,1447.0,1.6059933407325193,37.63,-122.43,1.442\n",
      "5.3275,5.0,6.490059642147117,0.9910536779324056,3464.0,3.4433399602385686,33.69,-117.39,1.687\n",
      "3.1,29.0,7.5423728813559325,1.5915254237288134,1328.0,2.2508474576271187,38.44,-122.98,1.621\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\".join(open(train_filepaths[0]).readlines()[:4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['datasets/housing/my_train_00.csv',\n",
       " 'datasets/housing/my_train_01.csv',\n",
       " 'datasets/housing/my_train_02.csv',\n",
       " 'datasets/housing/my_train_03.csv',\n",
       " 'datasets/housing/my_train_04.csv',\n",
       " 'datasets/housing/my_train_05.csv',\n",
       " 'datasets/housing/my_train_06.csv',\n",
       " 'datasets/housing/my_train_07.csv',\n",
       " 'datasets/housing/my_train_08.csv',\n",
       " 'datasets/housing/my_train_09.csv',\n",
       " 'datasets/housing/my_train_10.csv',\n",
       " 'datasets/housing/my_train_11.csv',\n",
       " 'datasets/housing/my_train_12.csv',\n",
       " 'datasets/housing/my_train_13.csv',\n",
       " 'datasets/housing/my_train_14.csv',\n",
       " 'datasets/housing/my_train_15.csv',\n",
       " 'datasets/housing/my_train_16.csv',\n",
       " 'datasets/housing/my_train_17.csv',\n",
       " 'datasets/housing/my_train_18.csv',\n",
       " 'datasets/housing/my_train_19.csv']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_filepaths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Building an Input Pipeline**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_dataset = tf.data.Dataset.list_files(train_filepaths, seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'datasets/housing/my_train_05.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_16.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_01.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_17.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_00.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_14.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_10.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_02.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_12.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_19.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_07.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_09.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_13.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_15.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_11.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_18.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_04.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_06.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_03.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_08.csv', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "# extra code – shows that the file paths are shuffled\n",
    "for filepath in filepath_dataset:\n",
    "    print(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_readers = 5\n",
    "dataset = filepath_dataset.interleave(\n",
    "    lambda filepath: tf.data.TextLineDataset(filepath).skip(1),\n",
    "    cycle_length=n_readers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'4.5909,16.0,5.475877192982456,1.0964912280701755,1357.0,2.9758771929824563,33.63,-117.71,2.418', shape=(), dtype=string)\n",
      "tf.Tensor(b'2.4792,24.0,3.4547038327526134,1.1341463414634145,2251.0,3.921602787456446,34.18,-118.38,2.0', shape=(), dtype=string)\n",
      "tf.Tensor(b'4.2708,45.0,5.121387283236994,0.953757225433526,492.0,2.8439306358381504,37.48,-122.19,2.67', shape=(), dtype=string)\n",
      "tf.Tensor(b'2.1856,41.0,3.7189873417721517,1.0658227848101265,803.0,2.0329113924050635,32.76,-117.12,1.205', shape=(), dtype=string)\n",
      "tf.Tensor(b'4.1812,52.0,5.701388888888889,0.9965277777777778,692.0,2.4027777777777777,33.73,-118.31,3.215', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "for line in dataset.take(5):\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – compute the mean and standard deviation of each feature\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mean, X_std = scaler.mean_, scaler.scale_  # extra code\n",
    "n_inputs = 8\n",
    "\n",
    "def parse_csv_line(line):\n",
    "    defs = [0.] * n_inputs + [tf.constant([], dtype=tf.float32)]\n",
    "    fields = tf.io.decode_csv(line, record_defaults=defs)\n",
    "    return tf.stack(fields[:-1]), tf.stack(fields[-1:])\n",
    "\n",
    "def preprocess(line):\n",
    "    x, y = parse_csv_line(line)\n",
    "    return (x - X_mean) / X_std, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(8,), dtype=float32, numpy=\n",
       " array([ 0.16579159,  1.216324  , -0.05204564, -0.39215982, -0.5277444 ,\n",
       "        -0.2633488 ,  0.8543046 , -1.3072058 ], dtype=float32)>,\n",
       " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([2.782], dtype=float32)>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess(b'4.2083,44.0,5.3232,0.9171,846.0,2.3370,37.47,-122.2,2.782')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting Everything Together + Prefetching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def csv_reader_dataset(filepaths, n_readers=5, n_read_threads=None,\n",
    "                       n_parse_threads=5, shuffle_buffer_size=10_000, seed=42,\n",
    "                       batch_size=32):\n",
    "    dataset = tf.data.Dataset.list_files(filepaths, seed=seed)\n",
    "    dataset = dataset.interleave(\n",
    "        lambda filepath: tf.data.TextLineDataset(filepath).skip(1),\n",
    "        cycle_length=n_readers, num_parallel_calls=n_read_threads)\n",
    "    dataset = dataset.map(preprocess, num_parallel_calls=n_parse_threads)\n",
    "    dataset = dataset.shuffle(shuffle_buffer_size, seed=seed)\n",
    "    return dataset.batch(batch_size).prefetch(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = tf.Tensor(\n",
      "[[-1.3957452  -0.04940685 -0.22830808  0.22648273  2.2593622   0.35200632\n",
      "   0.9667386  -1.4121602 ]\n",
      " [ 2.7112627  -1.0778131   0.69413143 -0.14870553  0.51810503  0.3507294\n",
      "  -0.82285154  0.80680597]\n",
      " [-0.13484643 -1.868895    0.01032507 -0.13787179 -0.12893449  0.03143518\n",
      "   0.2687057   0.13212144]], shape=(3, 8), dtype=float32)\n",
      "y = tf.Tensor(\n",
      "[[1.819]\n",
      " [3.674]\n",
      " [0.954]], shape=(3, 1), dtype=float32)\n",
      "\n",
      "X = tf.Tensor(\n",
      "[[ 0.09031774  0.9789995   0.1327582  -0.13753782 -0.23388447  0.10211545\n",
      "   0.97610843 -1.4121602 ]\n",
      " [ 0.05218809 -2.0271113   0.2940109  -0.02403445  0.16218767 -0.02844518\n",
      "   1.4117942  -0.93737936]\n",
      " [-0.672276    0.02970133 -0.76922584 -0.15086786  0.4962024  -0.02741998\n",
      "  -0.7853724   0.77182245]], shape=(3, 8), dtype=float32)\n",
      "y = tf.Tensor(\n",
      "[[2.725]\n",
      " [1.205]\n",
      " [1.625]], shape=(3, 1), dtype=float32)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# extra code – show the first couple of batches produced by the dataset\n",
    "\n",
    "example_set = csv_reader_dataset(train_filepaths, batch_size=3)\n",
    "for X_batch, y_batch in example_set.take(2):\n",
    "    print(\"X =\", X_batch)\n",
    "    print(\"y =\", y_batch)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a short description of each method in the `Dataset` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "● apply()              Applies a transformation function to this dataset.\n",
      "● as_numpy_iterator()  Returns an iterator which converts all elements of the dataset to numpy.\n",
      "● batch()              Combines consecutive elements of this dataset into batches.\n",
      "● bucket_by_sequence_length()A transformation that buckets elements in a `Dataset` by length.\n",
      "● cache()              Caches the elements in this dataset.\n",
      "● cardinality()        Returns the cardinality of the dataset, if known.\n",
      "● choose_from_datasets()Creates a dataset that deterministically chooses elements from `datasets`.\n",
      "● concatenate()        Creates a `Dataset` by concatenating the given dataset with this dataset.\n",
      "● element_spec()       The type specification of an element of this dataset.\n",
      "● enumerate()          Enumerates the elements of this dataset.\n",
      "● filter()             Filters this dataset according to `predicate`.\n",
      "● flat_map()           Maps `map_func` across this dataset and flattens the result.\n",
      "● from_generator()     Creates a `Dataset` whose elements are generated by `generator`. (deprecated arguments)\n",
      "● from_tensor_slices() Creates a `Dataset` whose elements are slices of the given tensors.\n",
      "● from_tensors()       Creates a `Dataset` with a single element, comprising the given tensors.\n",
      "● get_single_element() Returns the single element of the `dataset`.\n",
      "● group_by_window()    Groups windows of elements by key and reduces them.\n",
      "● interleave()         Maps `map_func` across this dataset, and interleaves the results.\n",
      "● list_files()         A dataset of all files matching one or more glob patterns.\n",
      "● map()                Maps `map_func` across the elements of this dataset.\n",
      "● options()            Returns the options for this dataset and its inputs.\n",
      "● padded_batch()       Combines consecutive elements of this dataset into padded batches.\n",
      "● prefetch()           Creates a `Dataset` that prefetches elements from this dataset.\n",
      "● random()             Creates a `Dataset` of pseudorandom values.\n",
      "● range()              Creates a `Dataset` of a step-separated range of values.\n",
      "● reduce()             Reduces the input dataset to a single element.\n",
      "● rejection_resample() A transformation that resamples a dataset to a target distribution.\n",
      "● repeat()             Repeats this dataset so each original value is seen `count` times.\n",
      "● sample_from_datasets()Samples elements at random from the datasets in `datasets`.\n",
      "● scan()               A transformation that scans a function across an input dataset.\n",
      "● shard()              Creates a `Dataset` that includes only 1/`num_shards` of this dataset.\n",
      "● shuffle()            Randomly shuffles the elements of this dataset.\n",
      "● skip()               Creates a `Dataset` that skips `count` elements from this dataset.\n",
      "● snapshot()           API to persist the output of the input dataset.\n",
      "● take()               Creates a `Dataset` with at most `count` elements from this dataset.\n",
      "● take_while()         A transformation that stops dataset iteration based on a `predicate`.\n",
      "● unbatch()            Splits elements of a dataset into multiple elements.\n",
      "● unique()             A transformation that discards duplicate elements of a `Dataset`.\n",
      "● window()             Returns a dataset of \"windows\".\n",
      "● with_options()       Returns a new `tf.data.Dataset` with the given options set.\n",
      "● zip()                Creates a `Dataset` by zipping together the given datasets.\n"
     ]
    }
   ],
   "source": [
    "# extra code – list all methods of the tf.data.Dataset class\n",
    "for m in dir(tf.data.Dataset):\n",
    "    if not (m.startswith(\"_\") or m.endswith(\"_\")):\n",
    "        func = getattr(tf.data.Dataset, m)\n",
    "        if hasattr(func, \"__doc__\"):\n",
    "            print(\"● {:21s}{}\".format(m + \"()\", func.__doc__.split(\"\\n\")[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the Dataset with Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = csv_reader_dataset(train_filepaths)\n",
    "valid_set = csv_reader_dataset(valid_filepaths)\n",
    "test_set = csv_reader_dataset(test_filepaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra code – for reproducibility\n",
    "tf.keras.backend.clear_session()\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 1.3569 - val_loss: 0.5272\n",
      "Epoch 2/5\n",
      "363/363 [==============================] - 0s 965us/step - loss: 0.5132 - val_loss: 63.7862\n",
      "Epoch 3/5\n",
      "363/363 [==============================] - 0s 902us/step - loss: 0.5916 - val_loss: 20.3634\n",
      "Epoch 4/5\n",
      "363/363 [==============================] - 1s 944us/step - loss: 0.5089 - val_loss: 0.3993\n",
      "Epoch 5/5\n",
      "363/363 [==============================] - 1s 905us/step - loss: 0.4200 - val_loss: 0.3639\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f82912a32b0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(30, activation=\"relu\", kernel_initializer=\"he_normal\",\n",
    "                          input_shape=X_train.shape[1:]),\n",
    "    tf.keras.layers.Dense(1),\n",
    "])\n",
    "model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
    "model.fit(train_set, validation_data=valid_set, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162/162 [==============================] - 0s 594us/step - loss: 0.3868\n"
     ]
    }
   ],
   "source": [
    "test_mse = model.evaluate(test_set)\n",
    "new_set = test_set.take(3)  # pretend we have 3 new samples\n",
    "y_pred = model.predict(new_set)  # or you could just pass a NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5"
     ]
    }
   ],
   "source": [
    "# extra code – defines the optimizer and loss function for training\n",
    "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)\n",
    "loss_fn = tf.keras.losses.mean_squared_error\n",
    "\n",
    "n_epochs = 5\n",
    "for epoch in range(n_epochs):\n",
    "    for X_batch, y_batch in train_set:\n",
    "        # extra code – perform one Gradient Descent step\n",
    "        #              as explained in Chapter 12\n",
    "        print(\"\\rEpoch {}/{}\".format(epoch + 1, n_epochs), end=\"\")\n",
    "        with tf.GradientTape() as tape:\n",
    "            y_pred = model(X_batch)\n",
    "            main_loss = tf.reduce_mean(loss_fn(y_batch, y_pred))\n",
    "            loss = tf.add_n([main_loss] + model.losses)\n",
    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5"
     ]
    }
   ],
   "source": [
    "@tf.function\n",
    "def train_one_epoch(model, optimizer, loss_fn, train_set):\n",
    "    for X_batch, y_batch in train_set:\n",
    "        with tf.GradientTape() as tape:\n",
    "            y_pred = model(X_batch)\n",
    "            main_loss = tf.reduce_mean(loss_fn(y_batch, y_pred))\n",
    "            loss = tf.add_n([main_loss] + model.losses)\n",
    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)\n",
    "loss_fn = tf.keras.losses.mean_squared_error\n",
    "for epoch in range(n_epochs):\n",
    "    print(\"\\rEpoch {}/{}\".format(epoch + 1, n_epochs), end=\"\")\n",
    "    train_one_epoch(model, optimizer, loss_fn, train_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The TFRecord Format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A TFRecord file is just a list of binary records. You can create one using a `tf.io.TFRecordWriter`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.io.TFRecordWriter(\"my_data.tfrecord\") as f:\n",
    "    f.write(b\"This is the first record\")\n",
    "    f.write(b\"And this is the second record\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you can read it using a `tf.data.TFRecordDataset`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'This is the first record', shape=(), dtype=string)\n",
      "tf.Tensor(b'And this is the second record', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "filepaths = [\"my_data.tfrecord\"]\n",
    "dataset = tf.data.TFRecordDataset(filepaths)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read multiple TFRecord files with just one `TFRecordDataset`. By default it will read them one at a time, but if you set `num_parallel_reads=3`, it will read 3 at a time in parallel and interleave their records:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'File 0 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 1 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 2 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 0 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 1 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 2 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 0 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 1 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 2 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 3 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 4 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 3 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 4 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 3 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 4 record 2', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "# extra code – shows how to read multiple files in parallel and interleave them\n",
    "\n",
    "filepaths = [\"my_test_{}.tfrecord\".format(i) for i in range(5)]\n",
    "for i, filepath in enumerate(filepaths):\n",
    "    with tf.io.TFRecordWriter(filepath) as f:\n",
    "        for j in range(3):\n",
    "            f.write(\"File {} record {}\".format(i, j).encode(\"utf-8\"))\n",
    "\n",
    "dataset = tf.data.TFRecordDataset(filepaths, num_parallel_reads=3)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compressed TFRecord Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = tf.io.TFRecordOptions(compression_type=\"GZIP\")\n",
    "with tf.io.TFRecordWriter(\"my_compressed.tfrecord\", options) as f:\n",
    "    f.write(b\"Compress, compress, compress!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.TFRecordDataset([\"my_compressed.tfrecord\"],\n",
    "                                  compression_type=\"GZIP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'Compress, compress, compress!', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "# extra code – shows that the data is decompressed correctly\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Brief Introduction to Protocol Buffers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this section you need to [install protobuf](https://developers.google.com/protocol-buffers/docs/downloads). In general you will not have to do so when using TensorFlow, as it comes with functions to create and parse protocol buffers of type `tf.train.Example`, which are generally sufficient. However, in this section we will learn about protocol buffers by creating our own simple protobuf definition, so we need the protobuf compiler (`protoc`): we will use it to compile the protobuf definition to a Python module that we can then use in our code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's write a simple protobuf definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting person.proto\n"
     ]
    }
   ],
   "source": [
    "%%writefile person.proto\n",
    "syntax = \"proto3\";\n",
    "message Person {\n",
    "    string name = 1;\n",
    "    int32 id = 2;\n",
    "    repeated string email = 3;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's compile it (the `--descriptor_set_out` and `--include_imports` options are only required for the `tf.io.decode_proto()` example below):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "!protoc person.proto --python_out=. --descriptor_set_out=person.desc --include_imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "person.desc    person.proto   person_pb2.py\n"
     ]
    }
   ],
   "source": [
    "%ls person*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: \"Al\"\n",
      "id: 123\n",
      "email: \"a@b.com\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from person_pb2 import Person  # import the generated access class\n",
    "\n",
    "person = Person(name=\"Al\", id=123, email=[\"a@b.com\"])  # create a Person\n",
    "print(person)  # display the Person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Al'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person.name  # read a field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "person.name = \"Alice\"  # modify a field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a@b.com'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person.email[0]  # repeated fields can be accessed like arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "person.email.append(\"c@d.com\")  # add an email address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\n\\x05Alice\\x10{\\x1a\\x07a@b.com\\x1a\\x07c@d.com'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serialized = person.SerializeToString()  # serialize person to a byte string\n",
    "serialized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person2 = Person()  # create a new Person\n",
    "person2.ParseFromString(serialized)  # parse the byte string (27 bytes long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person == person2  # now they are equal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom protobuf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In rare cases, you may want to parse a custom protobuf (like the one we just created) in TensorFlow. For this you can use the `tf.io.decode_proto()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Alice'], dtype=object)>,\n",
       " <tf.Tensor: shape=(1,), dtype=int32, numpy=array([123], dtype=int32)>,\n",
       " <tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – shows how to use the tf.io.decode_proto() function\n",
    "\n",
    "person_tf = tf.io.decode_proto(\n",
    "    bytes=serialized,\n",
    "    message_type=\"Person\",\n",
    "    field_names=[\"name\", \"id\", \"email\"],\n",
    "    output_types=[tf.string, tf.int32, tf.string],\n",
    "    descriptor_source=\"person.desc\")\n",
    "\n",
    "person_tf.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more details, see the [`tf.io.decode_proto()`](https://www.tensorflow.org/api_docs/python/tf/io/decode_proto) documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow Protobufs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the definition of the tf.train.Example protobuf:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```proto\n",
    "syntax = \"proto3\";\n",
    "\n",
    "message BytesList { repeated bytes value = 1; }\n",
    "message FloatList { repeated float value = 1 [packed = true]; }\n",
    "message Int64List { repeated int64 value = 1 [packed = true]; }\n",
    "message Feature {\n",
    "    oneof kind {\n",
    "        BytesList bytes_list = 1;\n",
    "        FloatList float_list = 2;\n",
    "        Int64List int64_list = 3;\n",
    "    }\n",
    "};\n",
    "message Features { map<string, Feature> feature = 1; };\n",
    "message Example { Features features = 1; };\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.train import BytesList, FloatList, Int64List\n",
    "from tensorflow.train import Feature, Features, Example\n",
    "\n",
    "person_example = Example(\n",
    "    features=Features(\n",
    "        feature={\n",
    "            \"name\": Feature(bytes_list=BytesList(value=[b\"Alice\"])),\n",
    "            \"id\": Feature(int64_list=Int64List(value=[123])),\n",
    "            \"emails\": Feature(bytes_list=BytesList(value=[b\"a@b.com\",\n",
    "                                                          b\"c@d.com\"]))\n",
    "        }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.io.TFRecordWriter(\"my_contacts.tfrecord\") as f:\n",
    "    for _ in range(5):\n",
    "        f.write(person_example.SerializeToString())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and Parsing Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7f829147c040>, 'id': <tf.Tensor: shape=(), dtype=int64, numpy=123>, 'name': <tf.Tensor: shape=(), dtype=string, numpy=b'Alice'>}\n",
      "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7f82390756a0>, 'id': <tf.Tensor: shape=(), dtype=int64, numpy=123>, 'name': <tf.Tensor: shape=(), dtype=string, numpy=b'Alice'>}\n",
      "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7f8239068a60>, 'id': <tf.Tensor: shape=(), dtype=int64, numpy=123>, 'name': <tf.Tensor: shape=(), dtype=string, numpy=b'Alice'>}\n",
      "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7f829147b310>, 'id': <tf.Tensor: shape=(), dtype=int64, numpy=123>, 'name': <tf.Tensor: shape=(), dtype=string, numpy=b'Alice'>}\n",
      "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7f829155d850>, 'id': <tf.Tensor: shape=(), dtype=int64, numpy=123>, 'name': <tf.Tensor: shape=(), dtype=string, numpy=b'Alice'>}\n"
     ]
    }
   ],
   "source": [
    "feature_description = {\n",
    "    \"name\": tf.io.FixedLenFeature([], tf.string, default_value=\"\"),\n",
    "    \"id\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n",
    "    \"emails\": tf.io.VarLenFeature(tf.string),\n",
    "}\n",
    "\n",
    "def parse(serialized_example):\n",
    "    return tf.io.parse_single_example(serialized_example, feature_description)\n",
    "\n",
    "dataset = tf.data.TFRecordDataset([\"my_contacts.tfrecord\"]).map(parse)\n",
    "for parsed_example in dataset:\n",
    "    print(parsed_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.sparse.to_dense(parsed_example[\"emails\"], default_value=b\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example[\"emails\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7f8281729dc0>, 'id': <tf.Tensor: shape=(2,), dtype=int64, numpy=array([123, 123])>, 'name': <tf.Tensor: shape=(2,), dtype=string, numpy=array([b'Alice', b'Alice'], dtype=object)>}\n",
      "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7f8239068f40>, 'id': <tf.Tensor: shape=(2,), dtype=int64, numpy=array([123, 123])>, 'name': <tf.Tensor: shape=(2,), dtype=string, numpy=array([b'Alice', b'Alice'], dtype=object)>}\n",
      "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7f8239075b50>, 'id': <tf.Tensor: shape=(1,), dtype=int64, numpy=array([123])>, 'name': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Alice'], dtype=object)>}\n"
     ]
    }
   ],
   "source": [
    "def parse(serialized_examples):\n",
    "    return tf.io.parse_example(serialized_examples, feature_description)\n",
    "\n",
    "dataset = tf.data.TFRecordDataset([\"my_contacts.tfrecord\"]).batch(2).map(parse)\n",
    "for parsed_examples in dataset:\n",
    "    print(parsed_examples)  # two examples at a time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7f8239075b50>,\n",
       " 'id': <tf.Tensor: shape=(1,), dtype=int64, numpy=array([123])>,\n",
       " 'name': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Alice'], dtype=object)>}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra Material – Storing Images and Tensors in TFRecords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load and display an example image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PjYp5HbriUHh4yrRoa3JXlD9Mf3anIBiHBPMQjYcL5OLvXGst3i4vmJSUhVEMXRFJYEgWpA4hrdLCsBr8seeOVBc/ynJxQVRJIp7tCr0Nc/KGMCJX8oyxClBSmI1jzes9hNBD8+TpiEUpnZFlJa3RJllNF8DoHNCEAtrnhLrM5gCs2AGv1rC0jkvXTKq5OBqbw1TuYkWGL1YzLSWroBQmry5qqgnNVQxd0U8VzHXcZ93nC12UUiKOLyejTlTS9aT9kdXtrhsnDnljTOFyVofJ1UAb2pLV4Jj7WQ5zSBscDaWqzTZCyG3H4+x5LnOKpUnSAZRUKLsVFCfK8NPVqXoiywg3wWEpN5NdDRkH54z7I1l0cZSSLCxxiI8sVUUiDaQLU7k2WjVmuW+WTPO3IGg9O/YJZap6JKXQCpE4fSJ13A+tXxSlI9a/7W4gcDGgeD3VPq0VLLkgEUmXzAWUcnEgb5s2th9zxc0mrfP8lr+Lu+NsHx6Hb/J0pIaagWVSfa5FZNqpS8GwJjYyGZP4NCqmSl53UmeBllIZFphdC81RkovaagXwFcA5LeBxQpGOA5RV14auD4lrZ3KU2drE5Bq6m9URPlX0HSJd85VzS7OzFbJEj5syraF0vex+OC5bVuUd9XxZRud1uxRVabH2mquhOMsnQgkIH29SVaUMTKvEgzNvsnolH3fJ6LFgaTBGVZifOQJaLbZAg/2kZRdW4bM8uRA8ygIrFJ9s+SpZF8VMZbBy6R7u4dQfSrJV17NqaQqo1ry3/WmhoMgOCtefnOgRduwZmudV9FnBlLfhcG4+1tZTCzQFs9hWWJruj8d8ReaSQ8DsMJOr6rq78pppYiPNnupy3MWTUf7Qai1CsnvHoResxE77I+vbPITM+hBNqk4ICkRjxCNhPkc0Z2VZATMMEi5N1f1ydP/bCsTJU2l+2zEd2XpyDfdQ9EwyHDXXMwtkBC+NoICihm89b6PdH9UC9xDq3HKKekpS1xCZitxt5k76HESGacqrqOrKglvCFpV6lUxO5LFS9nzGr6X7pnjNokmmKDl4kq+JJH+UBX2SXXCSImhe3fBWVa3z38YMz9OMDrERSkVlwZCPQxGIi4LqsPQYPlTS3Rykq2yjtTi3c6x6blXqnBmGFwfyWNNivWXf6yhfpKv5HaU5jqJ91G93lb9cVnmBpZzvsGvFxTWdts8xMSXtb5XWoQXEx6h0F4oxgA8SYYxG7La4kmWRKT4jBNOweZr39Sgf5eO5NdLGZG2VUoB2ek2Gxzh/Mt2eOQpI3O9iV8dHo/MQ7tsuS4Evn+zJX62r+yF73j7ntnVEclQV+3Ta/0UAtfey544SDpXjbL/r4vxwqsp4QKiwlg5vSJUm7NpX5XsZkFrAlTzkzJr0ShLXnWS4QYYnVGJPUqvWo88EsC6QzBqzbpZ03IxJMM7ovGOGMerwdMROKSsLyBogpUlXdAnk17M2ps8bwVmVdbpahDyMQTK1vcpkKdfx+KFIo8H0uGUexw3wOHRkDF4wVjQiKvM1JQtJaQSAL8QqRswAMeCjERNjjIc2isx8dHFHrKZVNnGGXQ/l9DjtGw28edPcSVb2u5aFY1GQVk+S/HrCO9o4e/pH8GVVk6pbaffmVMPjYX3jmoqj+dgU/hZdEsXFyGGQduehKl2SUh7LY0LVTvGiAj68Wl/Im7mGsgsUxyfBiCxiPdNCc+egJ9Yn6kpB2xaT6QIKyf26mdZo/aijzluQQt/bFuZaqZ0LYHRu7Rhj0JmQcfgwpaWgrR6SDgdUrYtDI6larNIWSl58trHGke5ZkLCkhmu5B4wUiM20D3GZP9XjDWC33XHY5D1CgrvVVwDMBymnAAQ6GXBVYCPbjy5gxKn5IuAMmgXSrGyTHOM2UNBnn7de+20O7r2N6u3RCgJOz05x9tQcEwtXGLTOsa+mEaPS/tKlyWI1tvLETCnJdhulC1oUtY3yNJX8SSDdEmiKJnkmdEfyYUnjT6/ZHXJunlH4lfRb4jRxrThjtV3bjhE8U0VK9kyqrZUX7nJaq1LuJsgF+PBzuSFSKte1Wkzew6S6R+LwsX1qigdepRpcduBHASANuky0Ac9I4v90cFzEipLRFmHVb0tPxi8OXZkTJfuetSzLYzXTXNPPu8JmKpjj2ZjmPOLq+bYOC8aueLTgiqS2obNLrtRFaVcbjlpOPRRQy+HcxpjkXEopQENhm5obYO4e41WgrvSjGC9oEXV49gwNnePjPCq5JvtRYHnc+y4gGzuSpKCqnAWezAZxkY3sWklPA/KIBY2mF4BWmp/9n/4m7331l5luxkzXPJo+dOfHUWenGCy+RW3+BcYvfy979YUUUJJTjErbFSra4143GQNZIWB9WYVoBJzxcH3MpXuSlTBCBxTbXkf7ScvE0YRcM87VVvL+txM4HZuUhfKJcIxxzb4Ve6Bcf1ZWhUA+liB2wK5YVy7U8p5zUSX5x24LLvS4KwwEsGfpOnPQZDfTB0wu+CUTQE49kPgtq5HNKTS/JZIc6uJq22KcvfMkHGYAlQ6aa6lY674I4u54uyI+ISKb2zh2XmmqJXybXNSYTJrZxbfhkDMyDLLDVbV0Vk6HAmoB5fNWYaXMUKYPlAzFWEtxwIZsMudaaunpxzRZXT/ncYCzqvzyxClotpZWGJrkbrlHtcHNo9F0O1tcf+WrjHcOEHziyGDadbY2dln2Qvy4y8zuNvXps8jpxcQlkJ1ucTj9h/dBaQ6V+uQ4pTxOdISFCin0oTtzi4tEhYnH0BxPJq6UNKHDqaWqVWVyh+oZ4rNivxWeMun3TIVLgNSuKxSB25af/jY5jOZaoHsIozPtjfvFRue4tBS/ZoLPLeCw6VXC1ux7ckp3QZgl+SXTQkWS8xnEmvBpFk2uZRZ8Jo57ylUAimOQd63T7EwXdgPGMt9pVX+4BTrPH2fKHC9syvmbEWq1QzvgqUZQyG+1Mlfopt/dXUHuBE8kSbHOTH6nz3wQ/+lRAFK1cl9ebBq1aphrVnkDMx9wSTM9jI7hPknwsBVCzSjqdSEMBjAIif2AoFanFgie77HbE9r1PvVBiNGJRmADmd16RwkIm8fScBTvHOWDrhI4VbxUNYwjee4YNCQ3wJrpOId9HGtGHFV2RZ7h9lH4W0ilnYSZmany6xZUBds/Lg8584McjOzhME62Ep0VIqXQp6U5VaEpViVx7OSsHp3TaLI3FEjhMHq3F9x6tY27thqhnUdlhasi5UZiST0taPEVmmhGfzU/pg0d+ZybHmuVH4on9Cd+FjK/TMVULYBl1fGOWTlGF3whgmROY8BxM0gFkxVKy+p1f1sJ4/pfXDpdWqrLdP8OP2d/Vbn0qwTAKA23kjLlUQvajE9Ms7/zAGMMvcGA/iBkulGjATREY4Jx6s1pIqOJ8ImVeewBzhaqRI4FKqPSYeBaLLe67YfXPaq/Skw/NFwO6BQm6QdLxxfq7pwhMz2NgcIxCQU3QCIQMv+0FMtS7iM42iWjgR4DUvIT5tvacyjKY4urwSsHODO06SHZAuq4arJZW3TfGJO8jAcpRVmXh0RygC1IqCrFwLp+HC0+3bFaKDwXRI62Wl2kQ4Ydl8PH/NivQBnFPEJ6qLyLX5D4Ty3wpT1iyuU52JNLdcmeKcOYNjGFRZwSAybPxsNEGsgW0FJmsNVncOu4GLKmpL4dI+niTklKOa1JGyDZnChARmXf2U5ztVqdaRuC4GGI0yDuOGgytXCacOk9TDygFwrbu30aJqQ7NUZ9YPBrPqZ9At8YQs9DJE4kfFI4MCwQh8lyhNghrg035VlcrT1x4xRjeKHUMxmL59/TntG6WHdGj82enPzp5okzoMxpEeyOGYdQp6Ii7fmkKW9L/GCpqI0mjFGe3vkXYwRV8S6vqqIsz1ZGw6Tzzd35bS0/I+V85IakMZk57tLmam7lsa5aosmDDiyxku2WS8qzu6UkD/0dKsT+cZSRwnQZHhyTfZzVe3DanIdrZRqwgyJuke7CWgq7xxK/j62h5mETuRnsVpwwaT5NHBJL0iLNYcExGzApfTeZJlxEWeMwhAuqls4kf75ameezHZifAp9+nPa41+34FTQLt/PFrS8dgMJBJ5U96fwtrlRaKhNIEgSNr+Gppy6w9I2IaBDiSY24H9H3DWtbO3iTTRbMGHFjmsiL8Ul30wz3eOH7cTXlo5Jr5rq+QOvvynmmWJfVXIwzFvZHwRWUVeRmLLcs50UXEQpruyVrKfNzY7c9WvB/PDQ9bv4cVIpAUTjoxUZiuMDmCIuMi1PkS/raZODjelTts5U+ZMmKyGjCpcP5Xu2+cPEgLcQYh+Mkn0SW5kyTOQRMMyUseSafb+kYSxLEb4zJYlTzPkmfcySAMvZ8WSnxhkm3LSdtUJC6TiQTMsPtP3ycj7VJ2QXQ0X6rwySG5YdSGanuP2oi56t3w6r2cPhPGQaT77l7QEr5RjWjeD8nTYY+kp5Ym60QivtcETCLH52Z1lUbBiyEaEkCmIxoTGR45uVP06OFeAGBivAIqQceLQzjrYD64otEfi3RbFWct1g4fOz+FaThmNd8TI4N5N9O8uXQn49X1GP16/B42+v2ZX1W2A+VmmlNJttJZN+gmqrwzjPJeGdv67TgaT8q/WRtcPmkSOvI5qXAmHB0snqugVingGc/GfBaQUL+0ab42yTHarifpL8SelyXncqO4zeJVSfpllRs+FUCkiKC53wXp++UMihld2MZPAFfDEoMnpf0n1LHwL80PbaG6qaqxYbRZmFJMyDXGIw9WMG4PtcSsEkFg42gqRhyUQW6loLRbXLzucsA2bVyPnG3xbmXrQbmLrwV7xdry+A6+W40sSeEnsfY4mnGZ05Qf7TF0+dm2NzYYkwbTtZ8GsEUrXPP0o9r1NB00mPL8ldYHH9l/4OkKn/pqDx5qh6n0WWkagMUtNrqumDUGGe05ErMITlHFH5oOw+9Xc6da/I6u1TQ1N2ULX6m9CealFVXSPjQ0a7sbiBxy02/H0Z/WlsBwKraNLwRTbJJntpeeQEF5cR5v5e9aiR9BXWxOLRDf4Y1hqpFGXeeWusIO/eMs85v56skNFpNPZvD6TF1btuPo6U+NqCWw1SMc/2wCWNzZw1Kr2qru0rGEsndMvYZCiFAh4F3eXV+REuoegVuuZwsEtA1G0SK4F8F2mnRiS+xrJWIvZmPoHsrNXnEJJI3ceBHPLr9Dp/80GnOf3icT3/yCnOnThOGmoONB/RkFjUWsEWPUDUItE7e9X7M2f1BzX27z/l3C9aH+VrzvdSjaZRsk0l+DmgB5B0kKb/gz9aWw4AL7hW4VuK9LDJF8mfcNo3um9zyUpkl5Wio4rqr8vtJ2VbkgpQNTVersxGJmS5QcqMM6wUlqyzPVMUirqHoLmJZH2muATt9IJlCPTzNXcRPu6ccU1zWt8rKWlnIiH3xoFQdKENmDaihcgstPZZGd8TxfVXfTc591idRQPL82rAEyV277iKQW2TyqCmFuVggduDYkTaHpaNW0AsDanIGz2ks5iqY5unIFsqQXKQb22Jj9Vz7YMq4AqDTKh0gTr/Yr14E6+u3ufYb/4CztS4HUQcZM9QnhEZ7kvZiC2/yLP1Bh+hggw09R2A8YmOwKwSVjDRioh8ZKlOgM++Xw4Qc5AtieY/m+jiSj682Ce8Ux1cK9ZA9b+vPc4vLjzabyV+kN0yhyfM6IFJVT1UajlpwIxyGcpe+571R9tsZdMZjBTokzztUZCm52mnaqiIwGuevkOxjzwAyHx+npx3T3S7ySKEooUhf1gcFZcwN9E//lndelmkstqz4U6ebBSR9ybNylTSdEVTAHYcmqSozKRisY+UY/X0sDdXtj+xlaO4eW2e1SVLbpezgNilAmrI2I86pN2kx9r1A2SntWGuoPGmt+XM43aOTXSl2c7vabUq4MZTdzWLtRJNIwKGTrMXRNIDia0RAbB+lq8naceZbOLbP93TItWuvUg+32Nc14p5mZSvEr+/SQhF4AYEZUPd3mWs1OeiM0TfjZL6ltIPL4TTl9gwLi4pUniTHTBYWsn7Li3J2rduWa4a03iEUd7jCJGcfWBnumneFNlszz+QHKA83o8i/mbtESWl4JXlBpVKOVletTbpJHaPfNIAyyXmkxu3rEnDbeg8RYu4i78g8WexnPjDF81nLD9g/+RxI6nJEkxQfqHSrmDSsKWOOymmUAqMplJWFk9mMKYDak/vtd8GkmOXW7r4br9ywYeH4OOnYYVOQadEkGmnOSGWfpw05qQKqkXUZ8jMYbS8OaUFHl/P4yVaYA2n5rv1W1kQSkyyVYKl2m1zL22yHraiXpP1l+dcUdG9y+0aIjGF1f5Wv/vqXaK7dZzeE7iDk6lKXRiPAr8PcZMBEs8Z3ffqjXHimRptJeqaVAIhJ6HMjJj5oGjKTs8laDdB5ngIquB1hDZ08jdR0DweNTCUy1QIDKfPXqGItSJfpLY6iyTQsk+Ut+8irgDVnb0mfLd1zNLu8DFLXkdOYjGOSEo8aVSkX7FbqCIJ8ETcB7LLynZ9+XyInaxfD90vttKIwH/t8E0o5iCMBxnRuQbpRwG4SSIWmXekv6Gnpoi9Cskjj3suoxZ3XFt/yUK2jhVE5Hb71dMgHlOtOBYnsHlqCO3gV8l+cXUcmb5YAkpkbDoO4k2sET9hyD2+LrvBnFSeCU1jWGBc0kv5wXkCb+XN0xpgu4xuTbrNDZYtq1gTJ3Qv56qHnlGul9yCO+Jmf+SnuvvcOfqdHT4RJMawZRVcpYjE0lDBWM+hwjLfeXuHElU8x/9wPYqSGiCp1WelXwXfp7IuuWIkoj+hhGm2lC8AybpbVjrddNCDVJuwL1qo3B+Tf7d/ScYcVfJcfJpJP6LyE4uQsg0GxriINiawo9swwK7rxs87VFLyMcY+oKyLV6AXFvM4qzq+eD1XXqiaUo0YMCWFT1GATlEvuOMCatSUDaZPfK+BJEnutrLUpUtDiE0DNd1mJU0cRl8raV5IrUeCLMcXDstoRTg6oFnvieIrIsTTUJN4rPUyAXBNzw58yrSwj3lROqvKCTk6me9xNqf4STeXwm2zxSCx9eb6iJlClL45gGqf8bEKUtII8q3s9p1OEfNdGYV7aBbGyG8BhOzEIGmMG7Ny+w5TfYguPsZpBeXD+7Gk2tneIpEk4CDk/02B7K2R7+wFR4yQLz+ZhLGX3S6FOFzArVx2OEtHHE+F2JAsKbaaO5JGi2Rwtle5ORDNEl3HIGN4mjPNsYbXXlDihCvVGNDMpJp+ouUXmtNhhkSoVY1hYVyggzl8zqm0FIquBv5CrpBZnsFTSIjP6S/UVaik9n/RLUc0sa+r2eU2ubQopcJLHlhbb4SxxWaB2hRjuPE+ua+PoRiPZ1O3h4oBVC9ZiW8rpCB+q23V2SyLYVcWMFMkrdNozNIHz6WAltnFGQZOv1B5C0dBEScKDjFhPnNWw3PzVg1RuX+GanaOOJlmAvOJ5hfn1QlvTNjpl5jddjSadKFlO+6/QqDd47vJlrq2vUqtDrSlMNgJOnZzn7OIcnW6MpzQnxoQzCxe4ubzEuQvPEYtHGuqdCry8LisQj/KZpiya0SqF/qKynFHJ0XmK7343KRvYPfeA2OMOSwuPUgZRJH8nUpaxmofKmwTE6MpWjfIvpzchpb1C8S6a+nYOOGM+auu1Q2SheWXxPyxI7PVspG1V1WW6xJYu5WCY/s0qLD8qQ2Bjhv5NvzmaXoEuSblbkvhQix9eKpSy6AU7hwXs4pB17xjsWLlKVz7Hsll5PHlPqZdxFzkfJx0JqDmzWJLFvZ3RMmpFtGiymRygxHVUp7NKSdqJhooAtywVtCrJg9SSiVJNR7VUGd62l+vhJntDAYijjTg7xFxUoCDjipqIpc6daAUNmgw87GIHRiHKI4xiaE2yE0X4NWE30kw16ixHwkHcJwg8vNCw3zMstgyXn7lIe3KWOOHM5ESflEJNsgJa1korQdE4bXIQpNxm2xa3Te412wOFcTbJopAYg0rloDKS8Uh5d5dLp5vSJmYWipt3mIZicvUlN+SpKl8OBI+xOcIOt7Hxlk4Zox8panzO3ww2MtAe5rS8HVIN3lUVpw3MsFLyywYHRLNGFWHbCjMDyTkBYi3PortCcBeJ8j3/yjY4nU+F0Cvy8pL3Q5XXAaQgCH53qVxAzu9VwnNU+sCB/YetGGfDPyTmSkzg3jdCMeBsVNmlVALeVG/BDuQI6uEwM0tbzrJqxfCGsvww5mKpiY+1JFTcnK5im37PArUhBdYALYa+Dtns7bGxu8ueNohRxMTMnjnF/EvP8s033yKgThwNGOsJV3uGk74h3lxmfrGHoYmIh2dSQZaFTaT1ldp/HJk8ss+OAC9bdWLkOMCQnq+baBz5uClXN6vUyvJ6kzl/uKacQcKIPHYhouj/z4E5A+mCrHf1x+EKjc2fZrUCIO9k45jIOUDZv0XecoTSiLZZp6bV+IolpkNeej6ztKRCYOVPpnmHm6l0ct+2oLAFRpls7UC56zEpoBbiRrPCddZ3WbuzuZHky95Y6rDHUVDxOKBb9rfmbsqjy3isVX5XE0kqq9ZGy4Puxn65d9xJkXGasZI2ASZXOkvGLHlS5VKdCeturQOHmUw+iHYyFoqVYn5lGV4kOb8RcWgu90N124dT+jpuR6gYY1DiEaMJRbN1sMlP/cO/x8bSA3qDGB+Nigecu3iBTr/P6tYGfhTgmzr3t3a529/mj/+Bz7O0sUq7t4XfFHzVwBiVBi0bjI5JTsMvnltgaU2GwaHHOU3oMNCs2jWX34PiiZ1ONxe0y1TKaHuQjrNxwhQFoH1W22sjTPXRkQ3lhYpqDcXuJc/KO6SMoXqMk48iLxbVMMDuKU/RxMZpZvxveTZtvOV6N7QQsTyaFenUk+8GokSzVM1byUPMbE8IUnCtuDiYKpGAq5FLFsaUxACbrJ1Zy0zu4Epnm0PF0KkECUSkLqzMG1eRMl95SkdRy3QHvtjurFy3ysdQfx//heNpBY9TiRR6vUL6FhjR4I6UM51zAJNcOilj8Ax4Jtltku/lzSVm9p8RFAqVvl6jMDnLNNuPSX7ZfcFKVEVohxQ+5XYV7jvNdMuwQc0xEKPZ6m3ytXe+zr0H7zHY2aa/18EbxEygOD8/w4lGneldzQVpEXT32V5b4d6NDb78ynX2AviFn/9HLC1dR0sfu3lAaRtbmxBw2CLIcdLhAFvSHIqqWSFfXl41BeVdSVX33YWQKm/REKnC0FgVzWj3c7x+KbH5MJ32r9iwIT2kGWY8lzK5yriXfEcPzl87nlYRybWUIt/Zvfsqw11EUs2RZEHIK3zAE1AeeB7pvnbwxOCp5J5Asg9e5fGf9uMBvuT5DLZSkmP20pMLbF/ERoi1EBmIdPIJYyGMIYrtNUMYayJtknNTTSKCqj9JPXoEP2WfDG+cQR5iMeP02eGc8Lvay59X52ooVk0mlarVBGSePStJ0qtJgQYbTpFvE3QC1J1FlkpYd8zuwgk06aS2dRlSwHakf64Rp9qvlGSkaKtHFDWtrDOK/jyrSRVbbuPjUglqDB5C5CkGYUTH7PHau19jafkm7YawufGQSfrUuz2eefEy9ZlJogdLTJsDVu4usaehFgt0Il5/9U2arYCDnT1u/72f4k/9mX+XMyefAgJ8VMKIktOW7UysGCfP84bA7GgfotuDxeu2r3PAs9EO9hlDdrTbCN9utoBXKdCtBuTSYuz/xXIobk2VlE+KbqqcR/NRz8GsfFiSfdYYm8u9Z7K5Ye8mvJEuthjnaEWrDJSlryMExWFYcRymUvqbXc8qdEHXFHjZNl3Sgt1TLAp1Whqd9tikHALc8rTTjCw0zynMpPkcz5e9k2KA5JViOcZtZVlddbDkECsxIzTrV5NX41gYyf9Hi9Vjn9ifIHrxjEprDmqHMS0viSRhULb51q9TKNsdXNz7qa5mzQ4Lqsak70gi27FlHDMsl972dcxS7ExLg9U63Q4qaYxJ+3BMlAQIFCaN4zeZuVLuq6HmFPKk0GI5iCR0BG2I9YDduMt7b/0Oj7ZuE+zvcrZd40SgaZ0eY7Df5+lLiwQenPV7iLfF5JVx9nd9/vmtFfabLTb293n/1fe48vJz3L51n7//0/8rP/Gn/gynTl1AxT5KAqK0PxP3sFSQnHDTYVEARe2OZHI6krQMa0mWkvAU0njGaq2zqt5KUBfIdjcZJzIgA1czLNxTezH3wdo6y+2SfLE0Lxgxgg/5ifuST+mkvDIo53M7N6NV9kRhESnFDqN1ArJllLS86oBkuToHe0rWgincg+L8sQqEKp3mZjJ+cGPEyfrQ7VbjoKmLS3n3lud9TkemkJGcAuUuUZYVmCJ/VgvhUVaYsnQ47ciTdq6rorQ5AlSPbfInmsFwdhuEPvzRmcQZ5dAtm8j2WoFo5xm7iimptBDyGLakk1xOsVyJo6EI6Stbj2inNc9zaajTc8jyTQ0ZLDxWMoCYxGwSEYx4RCj6BnZ7e7z/rV9i7dF11PoGZ2rw/SfHeGG6wZiKadR85k6eoDfocLLeZ9wPeXFxjrPNLh+aa3C22We2FtDd2+O9b7zKldNn2NxY4X/9qf+Fq9feokcPTZxsNRTJ4v6G218GzENaOeLUn2xQ3A9g0hMoUvmYgszwoRVlN0o5MqGY7ATIx2skuTJctn2orIHke9UT60h0CsJD4Jc8l5i6iaBWxh54UuyO4h5/t2OgzE1SDvWzu4nS+nRmujrH3cVm5Jxzp5a9p41JjtDT+Tvt7XVtkmv21dzJcXwGXQDotM+1QcfJ0X1u3Zau2KTuLHsNSd1b9ui/9KNzgrUWjJY88rD8KYxp3i/u2Cpx7+efVMK7net8ikK14C5xB7UiHQqoh67cOpIg5ynnfEYs85jsu/vb1YCOE3ZjfUJ5jxTu5jspnOvFsCRyn0meg/Lo5DQOa2jHDZkp5i0BlkkAKNEGEibq0mO5s8bvvP7rPLp7n/X7yzR0zCU6+IMBHXoMDIwvnmLiynn6A3j3m+8ztnieropY7fb52HPneLYBzfAAbUL05gGrb7/HuQtn6B7s8KV/+rO8/f5rDOJ9NH202HjQJP7XflLkyIAyOfLVfjc5U9lnhkyt6mTI8MAZpLyfj9IqDrtnFwndnX1FgZC2LdvwmGx5yK2mvA2Wl7IyHHmRywar2RT9rkXWtGVQfLgAoCafsBVNNiTy364ZJJ/E/rIgp515ZcSkfZxwuU7BL/top6/tQKQ6RlZP2ktaBC0QCznwSXLdSPodiLVJzj/FuW4qPpDfT8s2blmSvC21qC1abSjhM0XmeU1+i8l9vULi+1XJR9nrDPt3rZBMxsX6R8sCXJUUSMsjI0/FBh5bQ82/FwpxCLaNAQoTx2WaouVVBrPh8t0JN7QgVl79cCT4EOjbyWEoAK9tW3YYb154JT3ldBi9eTl2M0MyIWOBiJgeXe6t3eT167/Bvfs36HZ2OdHyeVrvcm58jKV3r9EXhR4IF7/jZWoCT0WbHNy/wdPz46yvPGBius65+RYXp9p81+UTnGsIQUOxtbXGyo1bXL5wgcDT/Pz/9nf40i/+HN3+Ntr0E1Ah9WNmEz5nZqli7sydYjszXRwoaY1lUZX1f9ZnyVXj5DpqoTMPXynnqwDZiiE7rkDM6HXp1iaTNUOmVkXdrrk9RJflQUe7LWS22p1OND57YLPWDrsDNnLZYDJR4YoGV8PNNLgUmTO9oUJ9yyJYrJWWCY4UgMk1Slek2gNn3Bd3Wn3Pg+LisS4uKHsk7z8bpVEmAMrQJ79v/5YiKiwPO5Pf+suz8RiZrCA2BVoOS4+xKGUq+ahcfmYmiUFnWoCkRA0fUmDNk+T34dQO75IiA8eyP/Z4Wk4JBEo+mVETNc9msjxV9Zn0er6AIVhfY6gNXelx/c5b3Fu5yfrOBs2mYW6/zw+eP8V4Y5yN3USTbBwYps+con1ykvFuSH/lAe1xRdzdYX19wIsvLNJbeYCWfT574RJ7q9u82qgziBWdpTVuRJoLH3me+vYWX/mVL7F10OFz3/uDnJo9Q42AwpviSvNbipyZtt+1NqT4kL1W6Fpn6cNRjpI7w0H51T15dJJyXenVQ9clSvWI87QLqraAokAo0WeGb7qUFK6lfORyoiGTUUk/ScVzZb8hRd4z5W9pZe5iWCGPqbw6RHEWwGTLcZUlnN1mdk47QjpbTCbHBus+dvs6a5PJy7J/lOuvIAdQl978WpEfpVSY5V9xpVs20hWtL4zD4Yx05E6pvOKkMntQQdkBPKylJXIpnzwWVC3iO9GHh8yXQ31nxs0zupBR/sBMIXBcA2KXoiFbhBM8lPIwRpfa606HxB9p0rYaSXxpxsQoYwNidRIWQsz9gxUe3L3K/vp9DrY3mQnqLCjN911ZwK/HDMyAmu6ieyF+s8n0U+doteZQ/T3effUtnj9/idsrK8zMj+OrGveXtzh59hyNzhZTjZiXYuGNjqYbDthbfUT0quHypfOoeJev/8aXWXr4kB//Q3+ci+evoEwNDw9PBKN15ocvxofitNeCqyI5DCYJxs4W+QzFBT8AMcTuGGVfJXtrrp1NXkFhtv0dQ2FClQRZYcE0d+woSUxJy4d5VcMLikqn81xrxFOVruHDUrZ33AGE7LfbdEdIaXtUZ2riWV8lkB2W46ZhXneEegZtZFpu8kzate5ai33U8dEVcMdVTrChVc5cLcy94jV36PMyi4LSaAu6qdWBVT5Sk75CArqXrLVgCmNKxZjldRTLcq3RHJuKlkWZ/49Oxzptyn53q3CvlQk1liLr0xqF/tlAjpYO5bIf73oRrMsrxJlpU9Km3PvWj2ZPkRquK+/0ODVLMGl8nxGU9lPflibSIQe6y52Ve7xx91V695eY1U0man0WBjs8OxYz3VQQw36/w74JGNQUrclZJl94hq1BnwUTMjYxxtREjau3Bnz0+ae4dfsh/uQUs3PjXHvtERfPLfCpumb7tQ5LYw12DzSdh6vc6Bwwe/4sZ+bnWH33Kj/f/yle/J7P8alPfCftoAVSQ4wgxgN0tqqSAGc+W3KmK2o1Nui7wCOOEpDjXS6Uci0tVbdMqYDskWqhmKThN6TalXTjPJNxWcKgBc04IyGZ2aW6j2dBlcs69LZtu7s8n7a/oP1JMSjdfbZckRsGP6zgJDkKQqmkeYnNUkruNuvynMrLdi9UlJFVk2vFKn3Ymuo2FTeTDJeVyRQBFwATV4jJ8mTixZgRZRW1VLdfq/rrOOlQQM3fz22rT/7NJV8RbHM603cZScGYw+rkSX/anTpWxKTlugA3CoddUVqRXE1FlAFTXqiwEglMql2NmizGWC9RmZCy9DJZGzyRdLePIVSarmgG4QGb+2u8f+Mq+4/uMog6tEQ45e3ykYUJ5oxhzjfoKCIOQur9mN7BAUidmZeeoTE7gdrp863f/k0+dvE8S9fe5uTUOPFA2Nvc5OXnL7Gy/IheS/HMbIv337vDdz97jvdXNvnq5hYD5bO1bejre5w6tcCLH3mRqw/ucfMf/0OW793ju7/r85xaPIcvdXxnJ5XbxkoBantClxnWPVkq7W8SbQeXibPyk7tSMatzRh8Gm3y8i8xfHilxbieKkRRYaKhaffhBPdqu+I8ELrfVeTuK7aI4gQvAXVV7HuNaVnZyxeWwWOFhWpNuqKqrlFfnh7wflq+c8tdk5X5IVHGjQtoiwC4IjirNtjfJn1FSGGxXyJtsbudCMWGEokCy14oRPFnZjyFIDwfU/A1vOYlWuih30CXjZ1eal19ibFcgKZSY/pv1bl6uZXrJyrUqkGT+o+TxEuAVJtrwdsHkkWFNKNNGi5RlpsBhW0ztNUnVHC2agUTERDzsbXD72nvsrC2xubvBRPeAsabwFAN+6ESLlreDFxp8z+NgAKrWQJldlje2GD91hsWnLtJfW2NeC+2JOkGny9Z+xMvPz/GtN+5x+uw5Nnd7rO5GPHfxNCv3HlKvNThzcoL+xgbzL53ntaU1Hm5FdDa2uRX2WTnY49zFi6ytb/PK73yFjeUlPvyxT/HpT30XzcCgJEDEy3lJ7IYKR7MTyQLS3XEVOwZl+9DoIrJlY+Rqagzds/fNkMR1xykF28L7mAsUFX5ZCyIvpcg7o2hxJ+dhqcpvWxXJUDQxc2pyITa6Hfk19yQ4l+YK7bFMZ3IOj4sruZDM6LPaa+ovdcp050q5beDs5hKGQvQ8wemk420acesun5rhhjSVi8rcHsaWk7ey/AZlwcWboeZV+2LSdLgPNYsQyCWu7W13n7cpzJGSbyorwaRY6BJZkgSUgDItPAsFdyV6es+6GMrnBBSlkD0TIAfkxNzQ2Xu4k+Lsfhgp0O50AS6o53GKAiqxkA0QRyGhxKx3d1h6dJsbK++zsrLBjBIm6PFCrUU73ODlkx6T9R7G9/ADQyfs8migaO3HxFLj0W7E/IsXWAgC/LlJfv2f/jO++NRJ3nz7PU5cusTe7gZBs8Hk6ZO888pbPHfxHP3dA1Z7Hs8+e57b1+8wf2qS58amWF9eZnxWeGUzJOoPOFhaZX/ngNOXrnDmbIuVh3f4hQcPuXntOl/84hc5d+5Z0IZAeYmlYcjGz+71MloP41s21sXwkmTuOKvFLgAXuN8496pDVMqgZkfeGJOubBsKhx6n45S8g93ZqFGqVWEFbfWeGFdZODIiIf13FKgNhwweDX7VKbfypGIwhvq2DNzufBT3S1mTc4L0Xc3eAm7pmp2T9p7dtl3ot0MEzijNV5y/JmuCMOw8dRQnRzjZ0c9EfQFQnK9HHUo9IslhjPHrry07I5D+dUIxsp1SxRxJweL6u50YwSNMBDJJa8WtlG7nZ5sWgdw2iHRS5VCeFatUagYolAEtutC5PoJRMcZ4yXFyRBhRiJU7VsMygFLJwRzKQ3TqP9WaThCx193i7p33ebSzTGflLtrz0d0a47s7fH4x4LnJOuNBSBxvEYhPHNTw4z02dJPf2vS4oHz6gz1uB/OcOXeWS37IuxvLzNy5QXdtnf0wZuHkHDfffYsrz7zAtbsPmZo/RcAOWysdFhbm2NrZY6fb5/Ll87z31k3a8ydomAH/9N0HbNBiaR8GRlMbn2D6xAlOzo/R3e3SW9/kxPnTPP3pz/CJT34XJydOUNMgEhB6CqIB4iVnIiT/xo4PzKo4eYgYQyOR6TspM6sSQJkCIxfGdshktjxiKu4nVpQVvCrjxFT0lkxLg6Ra63A91TQIZa3KLtrkl1xANdk95byvqWrRVaW7x9wTt0avE5gklAvy/i5ov8PlCyDl1zWXk3XHkYbVmRQg3VAsEiXC9a+6f9NiMiwYAvvhSlOL0NFayf3s9jAh12pKhjJXpoqCyS5GljBkmJRS00vasikWEQSjGeSI06ZyZsj8NdUUOHvuk3wqm2L5NHIpLF51Bt0OSmFJMX+OTPpRNSLDr2GwNKcTL9FCdNZJtiy7/VOMh6RBswYvDcRPHISSncoiECUmeqwjonhA5Ck63T1uvvsWO/vLDJbuc7rZZqfWQm+u89yEMB90uXJiDj86ADVIloWNhl6H0BhWdvu8c6tH8NIlPHwWzj5Dy+vTM4qD6/c4GW5xc2uHD734HO+9+TYnTp+m39snjkNOzgW887V1Lj77FGF8wMr2Ac+99FGuvfM2s2dP06orbt9Y4kc/fYXfeeUWoSfc1j7SD3n43n329iaYXpzn8otXWHl4l2/+ws9x9/p7vPhdn+MTz77MuNeGCPa2V5iYnALj4UuA7/sJIKbMIqn5H6OxykF5tN3Y01zg5UH5R0z1nC8lD9mp4IS8PmcW2tdySymbC5OPk1yQyjzCFixJHSQCyXm6pnDffX6obcdKyUkMZYGVEeTS5wJ31ui0MgeNLHhaLd3GbIqYAijmYD1sqtt7lfHC+b5bZ4SKRJtUqInlqVKWXLtMKXCVzFInGMm3xru1WMFpLWf37rAF4giRI9wSR6zyD5tcmYS3BGfmlG2Nc6+qUCn6QSzDORfSul2NxWH39PrQUA1JIYeRTB6knjE4OosN9UwSLpTsXPLQOkREE0gNMQYtERqFJ34mJLTSRBISSsTK7iPWVpZY23rI6nvX8Xua+ekxOge7PFfv8qkrDRq6ByYgjHpEEuPrHn4c0481ndgj9puYsTGuvv5rPPfC88yfneF6d5NLjYDXvvUNPkTMuw+2uXD2JDtLK3jNCWZaTd699YgLly9z8+pN5s4uEIa73F/f58rzT/PgwTswFjA5Mcb779/n7FPPER1s8tSM4sUXL/AvXr9Pr17nerRNb3uXe7v7bJ/Y59TMCWZPjXH96g1uvXeH6y+/wme/8AUunD7HX/+b/z3/1p/4k8xMz3Bi9iSDKERJgDXxTXrIsHGmS6JIlveQVGibzsgORZdIiXfspHU4wQXlLFbWuigsD9j9/cZZXLUT3dY+ZI4XaXHpdH+VdQDbAwUrV8hpqdLR7ETPUD8rZbjPLC+L69fOK8+ONsSt0wWifFHQJSe7lgoa1xAYmmfZzTzpErIZk+y0wrksbmuc591+yRaI0vs2vM44/5RHpIhYZbB0mMeUeKtMiMO7hRKqzGL3qcNN/iVTcMZrQAnGQlH2zvc0gN+VFOWKUhvQMlRGc+oRL5oJ1Qxt71UtEGTXINOW7DtrAESZImumfW23o2pJjgeLBPY27zJRG2OieRKpgZYYMQERQqxDtKeJoj531h6wub3Kg+V77O9u0t4fEAQezb09Lk81uNDocGUKfC/CV37io1WCSLKfnlAzCOq8stTjdlezoTVv/M5t/vCPfB/GCwnHZoh215heeZfOvQ4TEx5Tk5M8uH2b85cvc+vqVaZPzKMa49xf3uD5K4tce/N9Lj/zHLtbD9nY0Zy9cplrb7/L+QtzRAOfh0vLPPOhC9x98zre/CKv3dzgmzt79JhExyHGKDwF09Ntxheewvc1Eg7YC0POPv8Sb7z2KvPtFp/+xCf5P/yBH0P8JuIH2AW9pIc1dj3TApwFMasUSao9GOOcslQA05JWUbGnr9IFUMUX9uBqyN1BDt+r1BVkX/6Q8UjJdC7zXplf8xXqfMK6ro885XVXajwpAZ7k72dXosh1tURxyQ5OyV4hahA12pa32bN2SH6YUGbeOzUk5wkPWxfi/s5uuxE7WccMIZLr6lTZizNzxcfNn2iP6U6w1ATONglYLc7k45WrcyV+qujeoX45QvPMa4HaISb/EYD6yBQrMpZXUkLSQVD2HUDF512mHPJLFAjNr7smT9WEybUQyAc13yiQdaGAaJO/CCDtfIVKdnClrKHjGB0oIhOzurHGzv46t975Ji+ffY5Lz36SXuABEZ4Resawvb/Bw9W7bG+ucef+HbY6u8xOt/F6feom5OV6wEfnm5wajzCdXYwyNBoBEofoKMJ4HtRqmF6MZyL2mm1+8ldvILOXGXR2mJg9wUwzZGZhmqArRI9uEd+6hXiaU2fmuXlzhXMXTrC73WG/f8CZ6UnevXGfKx97mXdfeYtLV54i3N1je2+XMxcucuPGLc6fOUm0u85u5HHi7BnW7z/EazbxPFhd7TA2NcUvv/uQnufx1n6XyWCCpwPompi9esDY+DTit+j4TR4s36cV9wkQ/up//d8xMbeIX6tjdOLb1ulx6uJ5eMpLetkCZNl8s5O48JaDKh+qGT4oZAR/VOaxWqnVwIrzLQMXtD0cWcDl1/RhcSb8YYBa0DLTesSqegUrrqwI5NqjZGBnyBSRQsvy2Fv3BMTh+SqF9hZ0MLG7l3IN1+rpmdyxVzJhWezjITmX3S4JxCEzNDmysnCwk5jC4Ng6q840TcosNRUrNJJ+ToL+02gOR0YfdV7uUS6Xem00oB6+yi9xieoSJ5L1+iFa6fD3Yp78e7ITqVxXOX9ZyU87zxmL3CRIY0LS/kymbRp3mmrbnk4k/ddvvsHqm6+z4MWEpkeoDGHLo7uzTSfssHqwycHdh7xx9zaDwSYvSJOnpsZ5qjnLRNRlrKZZkD0+dmacuhoQDiJqNQ9jQkx0gIljfL+GEQ9CDyFG1+tcvxvzL1+5yo9/8Wme/+THmG756J01entbmHsbDNbu0/QUcyfGuXv7AecvPs3+2kO6cZ35kxd599o1nn/po9y5eYO5s6eID3Y42Nzl4sUz3Ll5nfPnLhDt7rDV8zl/7jR3HywxMT5OvV7nwd07nD//NOtLy/zQC2e5t7RCzRtjPQ541D2gbwymEzEeB2x3N3ige3hSY0sbnj1/jt+5+iqXLj7D/MQ0NT9A+QEqqCMoaiRHz4kTpO26ARD3Lbe6sLUwHdFMYLv8M1owF9nC5hDS/QmO41TEHs7hsppkyoIB571H9ii71PLJ6hiOUy2bg1aQ5HhXBFM3lQ/tElLjjaLrzD1wPQ+VqkKIXEDlfej2sHtXCvvv7QkniVJYgsx0SDIkyISSU6DNKEXrsVgIxJgCfxS0NZLIkoyuAtV53ZZFRLmL4Fat0oWnDnlNXbZWlJebK2+HH4dSTEdsPXW4TtIKJY8vzc23olmQSfRjkWBS35PJnksaNcx4boB3DqA549iX6lmzRGMgDpODJcTDNxArgxEPpTVRvE/d8/HigGkTcO3OTZ47P4aKfZavv8W9eJ2DXpeVuyts7e7Q8jwa9TqfnT3BvnTZ793noxM1PrnYpKb6aG8cE3tINKAuAmGMFkH5NZQPHgotirAWY8KYSALGZ6e5fO4pJltt1tbX2KwbZuOQsdUdJNpn0GrR8jVLq3ssnL/C7kGfvajN2XMnuX3tPhfPz7Pz4Abz9TGCts+9W7u8ePkMb1+/wdnT5+n3uqzv9Hj60mnev3GTp556mv2tHVY7B5x95jnevXqTp557lr2V2yzOTfDs0zW+efUBamyML6/u0KrXuLW/wayaZFyaDPxkoa7dqvHqP/unvD93kqmTizz3zBVa7TEi5XP71m1+8Pt/gMnpeTTpubN2wUpbMy+NaXU0hxHTpvC1uMpurztgadx7riZYvJIAWBGs7QS2MJ6HZ1rYGZWG6ysL+cSsLGo/mWY8ykQfbmnpbvJwrv2OqKP0XNYXab4C7KQNl9iCbFHIuVfyossNSBuVr1SPmM/O1iGxBJnst/u+zYJaV2qjfSQzCAo+6uPEDR+OVMfDsTTvYZV9+bUlgzjSZ0TJBa2xbC6VHsw7wxQmiEjqzC7UUVxxywegaBbmvrqkM40IooWeCSEKUVpjjFBrNoi7fTbDHvP1Ft3wgLA3YH5ykf64x9Wv/ALc+jqTQcBq1GelGzHfOsF7cURDtYiCmLl4n8+cbHPWN0hvnbkZj1qkUfVpBh6IPsAzMcpoTGRAahD4eFEINWGgQ/qRz5qe4Gdevc2X3rjH+VMLfP8Pfo6vv/4Gpyan8Sfr9B/cZ77ZZMKL2Lx2i3NPn0Gv7xLFIadOtnl48xHT58+w92idoBXQbo/x4OEyFy6e59Z773PuqXPsL21C0GB8YZprN25z7tmLbN95yPhEi1ZrnLsPVjm1sMjdhyssnD6JPthm76DD9OwMDx6uUZub42uv3mFzss61Bx2adY9NBTXdoNmusTA+yQGawKvhBXXaE5PEUczO5iZ/7t/5s3zoY59GvDoeCkHTN5pet0O90cjfx+556etqKlZXxQGxbNO2XUCR4qHKdoKbFPqskFaCp3OTv+AHLSgA7heT3XcBcWjhN/PpOfVXlJM8X9Rci5Pe5AHwbhVVimf5t2TNLyg/7rO2nuwYwhKgWlotMIsBpR0b2s1VETVQvJ63Cch831UxvIUwSguoJd0s8RQZZ34Xx8KVR0lfuNtvh9tp6RtexS/hYCWOJekwk/9QQP2N1x8a23BIebrM9EZA4pSZxWmMDKnYLoOOqlVSQMxzGbIgrMJgFTvE3TCg00HsRX1+82u/zgsLp+n2+5w8e4aVW3epzU8je/sMDvaYnpkg6kUEJyfZ3XjIe7/xdXZm65j9Dm3Po93wGPcjPjY5z8b2XZ6dDJiYCqgHXZqmThg28JWHeBqMRsU62QES1Ii8gDDsExtN32+wZ+p0abDcb/O3fvm3ePftq+w82OQj3/VJGpMN9m7d5vv+wA/w2u3rvPDMS6x0uuiwR11i9lZWODnZZtyvs7+9xvTsPFFnh2h/n+lWg/3dDRZOP8X6/fvMnFigu7ZMveZTryt2dvY5u3ia+w8eMH9ukf5BBy+G8akpNh9uUJseZ3Nth/a4z1jQZHdrl6lT42xdX0HmTvKN63fwpmZZXtumPTPJ168vMbF4krXVdSbG2pg4olZvEBuP3sEB7UbA5z//nfzwH/wJJuYWUChMnMT8/o2f/Gt8/NOf4uMf+yhKKwK/kZjVXrrAmY6hla1uuF5BRCtJwNMFCSlqVZjEpFQ6Z51ieBaZ6V44nNzh7/IOrtyMtzM753nl+HltEUqpIQ3Jauh5PvtOeldLoKSR26QzM9eYnB6wC1ejw5gKrpCSQpL3ISmgUnm4ZwEMK4HZaUAlsKe0VD6SA2p1ZMVxUr6egpC9W65czGHlFoRfSWgD1GqjldYjAHXJGKOdwwryPSbuIlByEpAlxvlSwUg5cWkq4GKuCufmfaX8qNCAc9M/ef+88Ohgg2/89r/kZM2n1m5RC2p0B31qzRZbK6sEEjHehsYgoqcjert7rHR32N3Z5LvnTlOfgPGwxzMnxxhTiliF1PQBMQ1UawyNRhvoaxClCb06PWkQGcW+F7BpFKGq0wk1myLEjSnWN/b4zX/xJdbfvYN35xHxQgDNSc6+9AL7N25SNzAxOcnY6QX2wpD24kn6/ZCpyWkGgz6d7gGnZ2d4tL7MZHOC+YkJrr9/jbPnz7B09x5nL1/m3v3bLEzP0vLrrD1aYeaps6zdvMvEhVP0ljaYajfwWz6DnV3as5N01rdoIxwojYpDWvUWvc0tGosnufdwhdrYOKvbe9RmJ1hb2SBon2TtwQNmF2ZZfbBKe2aWnd1dwl6fqbExahLz6e/4BEubB9Qn53jpQx/mmYsXODG3wF/5q/8V95Yf8YNf/F4+8vyLXD5/mSjW+MpLGTIdxTLAODI2A0TtaCMp/9hDmDMLyAFUN5SupEgVedPRcnMmLWtmzjVrBKvyQSZls1iKbO4kT/LyshKNVACqsyd+CDiLkQj59bRhQ4uCWJzN14OE9FAfjgRUbIuyqV7W8qrbWpWyhSRTesYKV7fby8+WoMYVMiLJltcyzB29qk9psS+/HnxQDfU3X79nbGNEksD24XhPIXmZQVKzC4SWcHEYfJR5VW6ke6SXGDD21Yo23tF9psBEBi0eKhb2Bnv8b3/nJ6n7Ic0goDY5wX7ngHrNo2kUW2Gflc4W+90tXj57ho+dWmSwdoOLcw1OT9WphTFx6NGr16mrJp3AI/IaKH+SB7GhHxqk6dPTQgefg1AnfeQn10Lfx4hGdExnr8vSjXu8+itfYeW995k0PYwXcH9zh/pYk/mzp1hbWWW+Pkbdh7m5KZ5+5jKPOnvUW5OYZou17T1Onz/L3s4GzYlJgkadrb19TszMsXrvAZfPPsX66gon5udZ3l8n0B7jU+M8XF3mxOIiS/eXmJuYRnyPzY01Ti0usPzwAadOnWNjbY1au0U06GEMTIy1WdneY2ZumvUHD5mcnGFrb4d2e5y19Q1mZ6e5+/A+M5Nz3Lt5F6VDGkoR7vcAYeHSeabnTvLuu7dotsY4tTDN5OJp3r/2LgJE3S4vPvsc/9mf/0/xVC0JcVNedjSkyTQgITPzXfArSdki+DqqU6rJuq8bz1a3y9OipHJZLbjoYkpOt606md1UzLPDJm6+6GZ3Rblk5O0WR3sWYw+ZkQwMk7MUrCZW4W6zYCfDGnd2mxRAJH27bwHFnDaWXBVWkNk6TFZQ8tu66ZKYdvvb0T6NGzbHoQtH5M0YmYbcJuRqYJXWW6WNVmvS+TMfGFB/6/W7Jq/ASlfBfblYknQmRty8uZRI3xmUjfpwXTljWBXFiobku5EqaU12KnhhUDSE2kBD+Lv/4G+zdv8mHz57lvFzp7i6+oD+/h7PzC1QnxpjTzQtL6BVUyzUA8JGg17UQwY9Jrwa4tfZQ4EKGAQQhhELk1MsHXTQETSDGibSdPf6zE1MEukBg06PUITdvX26OuLq119j6eYtdu89ZLC8RtBusHGwT3cQMzY+id8I8Fp1fC9gfWmZZlBjeqxF3O8xNzfLmfPnuL20xJWXP8SNe3ep1ccIo4gQw1h7jBgYrzfo9LuM1Vu0ajVWurs8dfY8Dx8ucfapcyytrXKyNcVOFCJKMTkzydK9e5w/d56V+4+YO7XI/sYWsSeMNRv0wz71esD+7jbtdou9rW3GpyZ59GiZIPbY2j/A4DPY3KTePyCIQ/Y7+wQz0zxY3QEV8JlPPEtPD1jfH6B1H1MfR/cMkfH40KXz/Bf/8V+A9hS9fh/PC/BUsg1VOZEZViAbU/K35Syc/l/iOezCaTrxU8GbCWnnXF4bF5uY3s6Rglk9OtM+LQu7Qfwio3ZrDQO9O9/cyAZFMWogoVfnyow12bOV85xA11qsBNSsX6q39RolmbafzO60PBHK2SsB1ZnU1uWQLTi64On0hwgJbJTpLVVoSj+E9MwMVxCIHReTbU91F9AKL+QbKtn1vY9KrsYr+MEHNPkzQE2pthIsa8Vwk7NOLCilzoBnmuzQkxRBN22IOM9UiR+rldsAfiMGow17UY+vX32VV7/5O3zrn/8zvuczn8QoOPMdL1Nvtan7Pt0a+CpARwOem55nshbQaYxzb2ODsaDORKPBbneP3SCg5tUZRF1q/T6D3T20V4dYs722SrPZZHAw4O1XX2VCeRhtuPbeNeozk6zfe0RnfQtPx8Qmwm/U6JvkXeO+KMLBgIiIxfNn8JXHneu3iCKhWW/hScT0dDs5Oi2MOH1igfWNdeYvXWR7dY2FC+fY2dimPTGBiSNCNK2ZKTaW15ifP8GjtVVOzs6zvrpKe36GlvHoKaE5Nsbe/i6z09PsbG4lrpAoJOqH+K0G0cGAWqPGwGimWxPcebREq1Zjb9ClHmn6O7vMTo8T7e/SUD5T7XFWV1fA94lrDdY397myOMPMiTE+89xFattLfOVRn6WOpmug1mjzsY9/io9/92eYHZtk7dEqJ+bnqXke3X6Pudl5lO9jUKCT11l7ykOlgFvJyCVQy1hJ0vjRgvltk8m1O3G2WpIDsqslugFQWTSBBfNsd5h9tiLQvcS8mXaXsbxj30qqjaagm1tieXuH2l/SvNws5V1BBSBLn7W6YzbP7bx1nnWFSE532k+S5nX6qurFnrYtlVJoCBRy3LAlJYAqeRCBpPVT7m47PqZ4p2pYRsOg82zSRv+DBvZ/5bW7hZvFlcpSQVKk0gZUl2or3B9a4DosVfqTcBY2k8G3L/saxAN+8m//D7z/1hvUuh0erj1kzBMmpib54R/7MXpEtBdmqM9MEYYRPUl2Vk0oj0m/zcPdLcxBj9Zkkwc3H6IPIkRg6eZdVrY2WJg6wcOH9wl39wh8xfb6NrrfR7zkFCajJTlABY3vC1G/T6w8dgchGIWKNcQRSoQYIagpJifbxKFmZ+cAtKEfRxglyYIFMD7WxmiN5xlmJseIdUwYGVpj43T3O0gjQCmFLz6eUsRaM95ss7W+ycyZk+ytbNCcnERrw/jkBHEc0t/rMDY9we72LpMn5lh/+IiFc6fZWVljfG6asNMn8hUy0OhwQFMp1lc3WDh3hv2DPUys2eh08WcmaLbGWLnxAN2LmJ9t8B0feYm+jvgj3/MsJ/rrPLr1iOWBzysP97gW+tRmzzAzN8PizDxNL+Cp8+eot8cIaj6Li4usrq5iUJw6dZqpsWk8b/jctGEXlOUH0kPAJDeb7Q3SSeau+FtNNoNFhX1z73BQespxI0CtagW8Km+JkzPiXC0u0/KkMH2GyijUYUBUvvBVRUPxt6SnZzo+VscidA9tgeQtA0mWXEO1tCbuGluHLvRTcdEtB9QMB6wQcdppID0/NXd1ZPGpbp2phukgVNaHHyxVPZgQ9oFNfguo5Q4xpRPS3Xt53dUO9YyhHUAdtaKXm2z20UMA1WEOk77yeWV7ha985cv87N/9O3Q3tgl8TU0MYSCMT03T8BpMLM4zNTvB7sYGOhbG2+OEgx73HtzDDw1Gw87eDkYZagMIjUHHEZ6CWAxKQ035hCKgDXEcMtCaMNZJrKkxQEx7vEG326d/EBL4PmPtJv14QL1Wp9eNmBgfJ6gpwkjo9vv0w4Nkp1EIkYlpNhqE4YAoivBMzHi7Rs3z2OsN6A4MNfEwXuIp9IBQJdc8z0O0EPqGphegtSA1DyUKrWN8DMb3Eh+mNtTqiol6jb1BCF2NtJuofsw+MVONOt1un25k0GLw4h7NRoNe7HH+8tNohO3VDbY21qDf5fOfeIHO3gEXvvOznL40x+lazMn+Fr39fe7tam4Pmry31mVtbZOZ8SkMPhPTU7QbTeq+T7PVYGNjk098/BN84bPfk+5EKoJq0cU0nETSs09VtUDO80kGspaPXDMvZziTa5XH0BIPq6+YhmksRiMeQbtj3hltsmMMyxqppVHrfLFZjMLumEoLLNSZA2pqTttsqrgVVNKzFkyqMUJy7uihgGrHz9YhZuRoZjM81VDzkCznfgWcjRi+yjxVcJivC/1uAfX1O0lQiJXekgdpY02KrDFFp3+mVztmi8YGcx/F2CRM4ZgV9pXLVkrlY1D0nhkx+NoQRrBOB/SAv/x/+g95/4230Sak1aoxNTvF/vYezWYNY3RSV2QIvICeifG8xEyUWOP7AWI0fR0zGMREgGiNiQ1xbIhN8u7y2DJUGOIFAdpA4CsGgySkrNlIlh1iDXEMrUZAveYjyiOMknNY0RHK9+hHgl+r46kIE2k0Bk9DzfPY2t2hUW9iVEwNRT/W9COhXqvhBx5xGNI7OKDWrCPEBH5Avx8xMBF15dFsNBiEEf3BgIbvAQbfb7Df6+Arw9hYi7Ggzt5AczCIiLWmWW/S1SGBQDgIadZbQMzJuRYLJxe5cXeFbveA7n4HgBMLJ+n1+igT8qnnr2D6PeYuXEKdO0t9boLmTBtRAVFfs7+3z276ifoRG4/WCXshk/UGrckWu1vb/Dt/6Cf4xKc+i/K83D+XS+acR0p/Xf4pxjuCe6B5prymFr3BFF7DUbk7SyzAuO4ElxPzb4m1VubzvKCyTMi15UJRwwd5p1t2XZAoR8ckhqLYhpVoSGNOkSxm9zCFTpyPBc1ciEh2LGGipToaN4JnnJ6RYpmkIFoyzPN+snSXfDqjqB2loLllHmUZl5U9d/gPM/kPP77PmhrpiBdevGaq/SNOhkIHFDpmVH1OR7jqexaWgl1USP4ptCqdH8k7xaFbh2997VvI2joz01N4KpHBURxhDAyimN7WNuNjbfq9Psoo9qNuCowQxzHGxLSbTTzfZ3dvj0EMgd9AxzGel/jz/MDHhMnmgUa9hg58tIE4DFGeh+cL2iTvLvd8wcTpGatekL3/HSUMwih577iO0RLQ6/cYbyrEg1BrxtotdBglZq8SPN+nUW/Q396j5nuJeWc09ZpPGApB4BNHhla9Ta+3je/X0gB7lcSMxkKtFmDQNFtjdAcD/JpPrH06EehYCFIANsB0e4yw32eyPUG/H4Ly6XZjwGNmeorVQZ/Y9xhrtjFhTLPZRKTG+3fu8czZM+w9vI+/sYS3sMj25CRmcpzGyXkmpidpNltMTk/Riwwz588Rbe3RGXTwlObiwiyt8QaoRCBLyoflCWj5y/2bn+PpAJ37bqpU67A+0BycXWCs0GxT5iwHs1ctTRVAsXIaFvdhuZZW+ZDu4dJzpLExqS5QpF7MLJ84mmCBvlTRGXqxYhW12cRMPZvGpcHp28weLz6fPWoXBZ2RtAJgqKOqwK8KfUupyqVYFDyjALdCo86pPzQd/goUu9JuHKarILb8vSrZ1cFs9TW5WNK/0wlQupyf9u80zeSM6Eo9AbRSEPW5cfN97r75JutryzQaQeLXrPmsrW8R9vuMtZqJxmiE2EA/1ni+T+D5iGhM6qM0aDzPJ0ChRCXAE0dEWiN4xMl7QOgNojTURhP4XnIOowbfC4h1jIkMvucTRTH7vR468KjXgnSxhZRbFbVane7+PioyoDxMFOErA57QqteIUNTrAYFSTLQa9LSmH0dMtMYhCjHNOrEY6vWAVkPR63tE4hHHmlrDJ45j/EDQYmj5Ab6n8X0Qo1AoavUase4TiCKOB0xMTNIb9BmbGCfWhrC7z3izzcLJ2XRsQuo1D880qNV89nb3wPOpNWuoVoO3HjxkdmqSE2qS8N4jPJaoT7QJTi+wMdZEBwET8yeYbI/R7/R41DI0PZ9Wp8+YgZ/7lX/B6tYe3/3dn8e3889IvmUZF7PcMD3JZ3BpUcY+k7uU0rxS3JWT8Zvzb2EeOLXn3gKrTBTdWm6QuFUMshcgZrjkSIWMSDKFwfK6A7vFZOwqe9YcbOyjlPK7izUqU1BMsVKn5dnz2iE3A6i8V7MnSniVkGHLLwkPh6pRqax5FjcZlITJEJDaIx2LoDq6Lnf8k3447NhFm47WUK354PDo46as4SlxuYCzBeaV5ItbVt8ke9aY9IRwldNl3IUKY/BNEhXbNvDsR15gbesBeuUe9WaNWPsMYkMcwcKJkwz63eQ8T8/HIMnL6SQ5DEEpL9EajUpOqPdq6DAk8BVxFKNEIaKQWFD4ICbZqaJjfF8RhRFQo+H7aKPR6S6auufjK59ebIjDCIOhF0XEOmas0WLQG2B0kACKV2OvH4NJtMX5k3MEmzts7B5gopgTJ2bxBe6vrlBv1liYnsaLIrY7HTa7B8xOjzPmKVr+JMs7+6hWk+nxBr42LOtdYs8w0aojRPSbAaFWtNoNar6hdxChtSIIAjr7+zTbLeK4T+egw3irztxUk0Fvnyga0Gq16PV7GGOIEWrNFoMopD8I2escoHyPjd0+180mjZrh1MIU8zpkbhBDpBmfnSVc2aY/2aI5OUVje4+aFxDtdtnr7LG5t83090zgQWapZ5MiG/t0gqTnsVotqGwWFk8rs+UkfOgaUa7mVQjhS/msAApW0y2ZpnnNJtOGtYNTYgRDDCZ7wWya3YJcsTyMu5Ehn/FZyGBKqguc2XRKTj1BMpdbidgSxWJptoWZFICztlnY1M5vW5Zjubp9YZUGSWhJAC5ZPKsy0w+L6CjntWA3ygfqplF+1HIep4TMd3zUGvoRh6O4FZicecrEFKSn5ANhrEFjySrQ6GgQJWNJHK3UebJ8ekxCjmsWJfGqoTGs9bbZvnWX1tQEB/u7BEroh5pBqInDiI31dZQYxifHCII6vX6I7g/QotGxJtKC8jx0TAa6fq3OIAxpBD5xBJ4yaBNS9zyU5xHFBvBo1nz8VoudvS61mqLVqLHfHxDGITVPgfaoBT56cIDEmnatQbNW4+lzJ4lMjY29kFajTg1Y2wnxaiHjjZiDvQ6YiNMLp/BEE0Uh3c4+7cCjh8L0+piwD2HEeLNNw29Ar09deYzVJzC+x0xjjJoYdg8G9ASmpmZoYQgHG+wYRayFhalplPbYOejjeUJ30CUKfQaDAYtzJ2nWawz6HVrj44jvce/eEohQq9UJQ4P2oFlvEg5C6kEDrQ2DXh8lEb3egEcrMfcebhDUaszNTDE/CGkrj3q7SW9ymx6G5bUNPv2dn+NH/9AfRcUeQauNXYXIfehF36Z13ReYLZfeaR4baF4Os7HupJJaavLnCziUTl7jXh+y0pwFFqNzvS+9757X65I8KmXrFA6+Z9PxEBPW0pJ/qq676G39uhUCiaK1mjfdUZrcY6KGG5GSoFKwT0DVuIqVLSOnJKm7ypeNA46ZUm/S8bWd5Ix/SlrWVwJJvK+ygFJsb0ELrm5SgZbDBuGr6Sp/8nItjSgNRrJdLFlj3X6QwjA7La0mSEQKlhmGVMCl6rnK6zG6xHQi2SneWU2iCE3ML//qP+en/9b/SHt6kv2VR3jasBfF7O8fEPV6+Gg8D+r1OmNj42hjOOj1iaIQJTH9gU/dh36viwR1AjRBIGhTpzc4oEGdZtBgJ9yl6XnUGj7dHhijaQQKT4PympgoJKgb8BsEXqId+7U6jVobHXmMj9WIiJhuwB/4/PPcXNpmqzPAxCETYy02OzFzEw0G0S77B+MY5bG+GzA/6zMYhDR6G3i1gNhrsL/b5+S4oY9idXNAPQgZHIREcZ9mrYbXGKd/sIlXV5hIkJqi6dXp7x6w2e/iN8dRnmKwu402MaY5Rmwi1pY2mZiY4PS5RXa2D9jaeIQnwuZ+yMTsOFvbG8xMTbK3f4AfBGijicKYRi2g3wvxvHqyUKc71FCEoUFqAQe9kHo9QBGhTcT42DizszMoUXzP93yRP/on/210rY6v05NB7WJNCURNxnfV/IXLjc6kyXfQuL8csy7TzMg14AxLShPenQ8V9Wf8WTKjHWxyiQSc8wWM+/aBUpuHQEYKX4utrzK387bkPmSTBc/bK7miKsW2OjsXi7g2HDM8VGNhu22aJzsu0Dj0O/edMq2rRFSeUaf/qqxoyXdhpGOY+IuT+xY/rH6Xd8Vo9Dxslf8IDTU5fM2kJ2vbQNriEWk5EflG7KrOMEPE5lLNUSckqTeRjlZCOu+6dZ81uQS1JWjR+KL4gc9+gZONcbp723zpX/4iS0tL9Hb38HRCX6AUNc9Da8PG+gZjExN4XkDUj/EbisHAJ4z7xFqoqQa1OEaFQiQ+7cYsxnQIasLCzCy1EOq1Gky2aTfqeBIS1CD0PIyOqTcUB2FAmx661mZte8ALF+bo9ro0Gy0m2nUGnWWC8YiTpzWzsc/mbo9Bf5OJCcXzz84T4rGyOQbGULu2xsvPL/Jbb2zxoWdP0JqZ5cHDLcLF05ydgb1IM7nRY7yxx36/hyeC0gPwW/QG5zg3BZs9Qzeq0Qh3wTfsU2f54Tpz8ydY3+pwZkZh4g5RHPKu0SzOjXHmzCV2JjXjHz5Lr9fj2s1tNne2marVqGmDFynGxifZ2kkOTdnv7OMHAaCJwj5KBZigTiPwQGKUpzCi8Y1Hu9ZCR8JUe4Y/8hN/jE999gvEUk9WtsW1TCRbgEmC6XMWHMXl2fuDHJPZBRdTxJNc28yqTDXiFA4VyVkRBSgYQUBxbcGGJ1mO1e60KM6DvIC8HZYUq3VVKEMiVfNqOF8xsF9lmqfBYN8OO/SY9bNWmdWF75JpraMFnQOmReUY61JxBdRIgMsmfiIIPBKQNMSpa1Bl2mp2WDb528btYTri+rHJ+1ANnc9weDocUAf7eEETLR4mewVDGgRcjjIuNbK6+UWuS0y0nHELTc5A29UAHI0iGwTtzASr3Boa9Raf+ez3IKL57Oe/j36/yy//0i/xi//in3Pvzk0kDpMJaZKYvf1Ol34YM1tvMj/WRo+1mD45zXvvXmdueoa5yUlqdZ/97gGNRhuthbYHRmlmGjW63oB+PEajUcNEAxr1AJTPWK1Lo3VAZ9DmZG3Arqqz9946U3UDniBqEvwx+r7hK2/t0m52ERmgo4DVpQMuPnWeSLfxG3VqAUTKsCt9tgc7NJoBXn0S8WMm2m1Cv0Z7vMbubp+JuQbPLjbZ7+1z0O0wNzFPXGvz9h3NmWkIVJu9XcWkaE5dqHPnDuxu3uKFi7O8eS3izIVFfBXQrHksH3TRvQ2evdTBC2rEYYTvn+bk9NN0Ol26B7vce3Cb6Xaflc1t5ifH6B30aAdjhMbg1xSDfp96o0W330OrAU1PcXJuho29Her1Jh/76Cf4wue+lw9/9BN4zRaRBSCdaBs6f6dKblZaFjA57+QaaapJZY61/FCRHEBdM7nIm25olmvO2gUfk2ppVRbeaD+gAcwwHknehuxCOYtdX0jN2cPqyxdgqrTR4eey6ZQtpg07AoptGE55X6ZmZaZbFbf9ZvQUFgzd4quPcz4MnK2gTP4mVJt0PWN7e5OJmVnngJz0GdGEUUgkEPi1VCEeXowsD68NERuVDjX5/+nP/bI5de4CWgVosR2vHIZ29GVMCrTpPv9sVdNhfLdid4WvaKU4eZM67C4o18Vhd1ZkEwzc82zzFTlJDrPQAqIUr73+Lf6H/+7/RXdvm+3tTXrdAUanAfix4RPPnueFZ06wtRcSK1hb7TBWm6DenkjecDrYQSuf6ZZmdiykF47TatbYMTvUzTzzE2OsbOwkh4kcDJB4H681YO/Ax+9us688Nnp1pgLDXneP3W2PaLuLIUKLEIfrfOKl59le22e/00MaQi+OqAXJebFaaphOly/80Gf4na+8gcgEhg6t2jQ02qjeNnFtEpo1GmZApGoogYanMH6dUISzE8JaaJhRdWLVo9n2wLS4uXSdly7Nc+tBhyvnT9OSPs22x9eubrF65z0+9vwC5546w15nj1PzLxCHoPwDxEBnv8vOrmF9K2RtfZ2V1WW29wbsdXuEJiYc9ImIqAWKU4snCQ+6zM/Oc/7CJX7kD/0Rnn7mOUIgFg/Ey9hLYdJJpjLALPgyywydGTGuLZfyZ8FVMMrn6EQJiMG+MBtMFhstaSUC+StfHMvJbpHNAAaTnkyfa9rJxJXCsxQA1SQB90MgMhys77apojl5nZWtzTVZKyzs4pchWWjN8hrBoAsxuuXX4iVFqXSxySU7h+h8fLKXYTntrxYmI9uXlm1SCwKtOeh0qLdb9Pf3+eY3vsnHv/dz1BF8BI9kJWZ3e4d3b7zDQAwvvvhhppsTycs6KQJvVarVK97Hk6ZDNdS5qfnU5E9j/0ycg5ixr7BIO8I1m6zJdARh+bPllUdxXkqWDIS2EsTmyKS6ZVJD8kIvSXacZNeS7YMeyZa5Z597kX//P/hPGW/V+F/+9t9ideURG2vLdPf3MYQc9DuohuDFHvfv3kOpaZ46e4a1Tgieob+znbwJoOlxoAK6tYjuwKPTr7On+7y11aHX6eHdWaYfDpKzR3VIHMdEkSCx4anLT3FveQfdOYDeLjURYlGJfzEWbly9x2y9yYzSxDF4/RjvIMb3hL0oZGdnn2tfv870YIAXbHPQ2cfoLUIljCmNihtoHyJR9CSgpmBfx2jlEUSG236MiRv04w7dwEd7ddb39+ke9Hjh9BnWrt1g/e1bRLqXmE7+OIP+gG+88pCtThLMf+v2gJmFNo1axMLcDONTNeb7AefDBnt7C3TDy2zt7NDtaVbX9rh99yEPVh9ybm6RswuLfOxTn+Lis89y8dJzUG/Qwy4KCMqYXEMsA4Kx8c8613IM2RpIIS7SfTKdcBao3ZVeg6vw5lpdeaXX1Q7tWyuKAGEnvbPIMuJVJG5dWQ0uYJQWdYoAXQU6uSKTCZCsCgeo3Ucz5V3nM8sJDSrHiI6KmhiiPVH30v6TnJZkEDJlqSD4KH9PNc2S0KgSglY7TUY84tbd95k8fYLZ9gxnF06y8ug+0W6H2lgLU/NYnFvk5rtXee/9N5k6t8jW7hZj9SRu2tJpBYoLr4f5VTNaDtNQv/HKTSOiMUolhxEQp1JTjdR6M7eMNblKZpPtLHfxqhDQX154cJ8balCVLJE03sSksapWu029V2IIw5hbt2+yvbnOe+++ztXX36QxPsar3/oGTYn56CevsN/tsvJghXbrBL726YYxrbEG4+MeK+tr7G500P06UjsgiAzaGJSJqOMTGU0kgO+jjMGLYlTTJ5Qmbc8QH+wQR3UaQZMo2kY3m8wtnqG3t8vGyiZNFKemJlAmZO7iWWafukI4CLn6+jfZ2+3S297hzMkT4AutsYBLH/oMomoMCAkQ1h+tsLm2zKWLF+lEIVEYIaJZuXOHoNmgNjUJWhGHEXvRgM7SErE5YGdzjwtnnkK0plHTbEdJRETNGNb2emituXJ+kZnFebbWd9g42Gfh6ZOce/oKraYwOTHO9Mxpar7HwfYmJtxB6wFr2zFr24p3b7zHpz50me/6wo8yPnMOb6yBFoURlQBpOt5iXTF2wjmWBzjaIInFlENMIpzzxRwnv2POFnHF4SnnBCoXxAXJ39BKviBrsvCsAmsX+L4cu+1+P8okz22zMqAmv7UxqY8vpVLSsgzYsKSkC7NJmfWpcUBXUh+j60or90+qR43wJzrzWJznXDO/AEyJdi6qwryviA6oXnTLtX9jXQhGs7myzM/87E+xaTZ58ZmPcm7qArXxGveuvs9ad4v7O8v8wA/8EObWNqu9PdT0FFeevszC9DR+o0UdH08L6bk8xf5L0wdelDIpIyahDSoHO6MLir5lQEk10+zgAspmlSn9tc+6xwEKksbLQS4Rjanqa5ODpesL0/mksMNomUsB4imev3yFweAC87MzvPzsy9xcf8QgithdWSI2Yyw/WIJ9w/LGQ5r1FiYOOdgz9PfG6e0c4PV7NIMITzXwWoLn1/DrCk/VaDWbNCYnaTRa3L93mxefeRatGijlowcdvvE7XyGMQ3x6yUEl7UkWT51neWUJWVlD6ZBIR/gmTsK1JqYYhBE0A9jexaAwUR9jatS8AMba1KYmMZHGxJqmKDwdwqkzjLdq+Ph0wj7tgWZ6dooT58/T7RvGGg3Wd7d4fetXYLcHxkdFXTAe0/MnufLMRxn0u7z1m18iJCaIoS0e0cEALzzAHESM74G+tsxatM8NZRg0bhJMBJxYnOPsVJ2xmkez7XF5+gxbaw/Z271Da2oMGk0MXgKgWiOpmaytDy3DQ4P1b2WjnqqXyV/HQsk4iOLMt4Bhcxl3Wpr0VP30mYq95FYsWy0NcbaAOtqmpKhjMpV3tDZnlQe7uJoDc24qKxk1fyoW2Gy+7OhD46xzmOxPvoib91emymb+Z6cqp52HLc5I+lAut5zys8qdOS6G5O0Djjg04L5qL3uy4CN2qU9cBkbD7tY695bv4KuAWmSYHmvSaCdvzFi/u0ytBv31Tcz+Nr/+a7/IR89/mL2ww6cufYRgEPLrX/olFl+8xEsXP0xNPEeMZIQdS0M9FFB7uxs0p9rEBCjtoSRXgvNwepN1Zqp75w0esQpZ6DBjELExeglq5sqrXee3JkRumhTCYczwJHB11+x9A5Zh01JrtToXrzxLfG4Ad97lN3/lH+PXY95//33kQNPQHpOB0Kx7jJ84SdTvsfbwEZ5SBMqj3qjz8e/8LmKjkKDO5tY6vd0OZ86dpQvo2FAf26EfQnNqnBDN3sE+kREkipMNBKIY6JAoGtCPQuJU2HrG4JuQlgKJDDo0NPHoRwN6scbEScD/5PgERNDtdpmemWX10Qp1D/w4hjCk7bcRLXQOwuSwFVFs7e1z6tRZdjd3MUol4iyOMLGgteCj6euIHsLA94j9BkZ3MRqM+Jx56lne+dZXqRnFzNQUEwunObh1lRN+AP0BzY0uBxtdfmv1IWGgmTt7kVOnfNq1FidPnsJvjqNV7o4RFFrb14rYsXfG0VjzK08K0n3ozr0KfneFbclGclkQy8dZnKrDR4mykBKWIXKpMjNcplSAauGRDATTJhvL50mvuG222mkRRJNXleQKDOSB9k4fuN/KEsXxSeczVxgy4Uck9z1vldntOLrqb3YrYXZTuprnr6rPRiLodJ4o9ra2uX71Nb78ypfwZiZRdWCrx7U33ub8088z4QW8fe1dVtcfMYg0q+v7bD7c4dzZBW5efYX3Xn+HOIq5tX+PZjDG0+eeJjAKZSRZEIVsV15VaJybDgXUd6/+JtPzcyh/kgtnnkV5Xjp46YCXpFhuZZn0DEq3M4tjmTnAU+nqjG+xQy0AmzwIumA6lWh2aSrcSyWskLznJ3kjYJLLqzVojbXZOdhhcfYEu2u7SGTwfEXc7zJ18gQnz51nY32TtaUt0BokxIii0x3Qmpqh0+8T9gYQRXQOukydOMnyygqeB0b3MV6MxAZfInyjiVOBISh0nEjZwPNBG5RR+EaRQJtH4NepGw+Nj8FD9ADPGGKt8Y0QiBC0xtFa0WxN0hsMMAbqQYAyJnnnla8wXsIU9aCOeD7ie3hxgK88+sYgOvn4fkDdryV0SI1APGpxjKeFMArZ3NqiGXj0IoMxPpNPXWZpc52nL13k0foWZnuDmhLaW3XWHq3j+7t0O9e5cfM+3d5ZFq/c4JmXP4IxMYrEF68saDrgZsc605hSqycH26IVk+Bc8XwJd5uiqxkmcTYJkuT+VCmAeVamW48FH1fbdPjaecyiZFHbgfScU2uVuXwsOVYbGzZYBG9nJiX9IaVrJAKqiJkVIJApr8bpTwt6+dmurmDLFKdCbRXFOoesuKFKtq5sY0ZVAaX+HZUEiAVULPT2D/jyl36ZvfAR1997hXprmqcWnuJse4ET7Wk66xsQw87GJrEWdC3gpZc/AuLx5te/xnJ3l6fPPs3cwiKDXgelDb5jNTjeI8wQgxTTYaebsNNb4r23vka8s4FE/VTBlqzB2YDrBBSNthLXpF2YM3N5Z8rwASj2ubQB6TOHhX9k4+t8MppMYn4oa2kYUhB16BRQJsYzmlNnzvPSxz/LnZsPmPTqBCZmEA3wvGRXVISiH4OJYtBRAgbigefj1wIgRinSj8FITBB4EMUEykMZSQ5JNpKYmKnQEQy+EoJ6QNP3E71EIEITC0Q6hriLrzsgIRGSvIDDgGdCuvubSHSAGuzT31tjsikoBhjP0GjX0ETEUcjY1DieH1ALapyYmWVve5tmvY6vvGR/fBpm4imFZ2L8WNGu16nXm2gFRsUIMb6BiUYDYo02UfISQVXD03W0rhEHTbyJOeafe4aeaMCn2aizvHSPWhTz7IVnef7FlxDJjTtlkleAeJBqBZKNnR0/wTnbNLtmeVDn485wWFF1MmSrzFnoXvqsFfrOX+V8z5EgN70LCoAUAXrIatcmb0PFx86nTOUwuugasLxuUsWl4roFxWTXkE4+I/5L+iEG0cVGi07vaUwaG54/Y5ujC5+0AUkecYgq3NNZm4bGyunbauBKazepD1s0Ck2r2aY5PslYZGj2OmwO9vnsF3+Qy08/C8bn2aeep66bjE9OcumZcwQ1CGjS2Thg0O+xtbbO02evcOX8MwRawGiMaLTkGCT6aN46VEPVkYfyW2yubuNf8QnFvqrBaqj2XFSTB/tbiZcBfL7ANBSfp3UmYu3jVmOoStY0K6xTFbq6mDInupXiDgKnOgrKxIBwsLnHzk6fmvHo9/vpkWYxcfqIJ4rA0wREeAKxsWFiQr8XUqu12ff20IGmNT7NfrfP2NQU6tESzfFxDnSffj/ED3ziOO2TOMJIjBn0uXvjXaJuHyQ5q9IQowU6y6vcXv01qPlMhJqO9jkQMCiUgsEg5sZrr+P5QiSawPPxRaO6PZZe/ya9METFiqDZwGxusbO9xt39LYyvQCcMU5MBorz0VZfJCfkqitldus3A86ljkoNS0MSiqZuYQAVoifGCAOoxftNDqyDRfEODCn3aNOjrbSba4+xsNfDNLgzC9DVPku7GSRgl84+XfJh28mamnh3YdPSLx9kl4GN1ykLgiMslAhgb1lfBRxagXMaSXOsa9k0WV6zt3SwmMr+Qa5Q2YoayomCfcRZUybVgd6U91/qKGmwBk8pdkGq/UAxrKpU61Cv5gpQDdBXKmslWckzpeet6KSlFQ35i9/yOYUC1WcVoRBsG/T5X336dnbhPzyhOL5xFBj2e/sgnOdgbcOapKS699CJRp8vi3Fu8u3yNA92hUR/jwslT+KrFuYuLXDl5jqX7SzRqY8zOzTJbH6fb6TCI+kgQMD02RSQGdcRhJocCqtfzWDh7nppM4qsasbEwZPJBxnUju0eR5ShpwdZIck6iq426nWyvV8fTidObqfw1RWbMSsmYhiG1IlMcDKnZpYjFcHJskj/xE3+Cv/r1rzJAo4zG0zGhaDxPUfN8ms0m+YsiYrTu0arFdLobHPT6BLpP1N1l+cY7RMZw0D2gu7PL1dUH7OuQcBBBr4/p9ZPtHL5HOIiJ9vbY299FRVZXUNRjj348YHtzD99EdEUjEoDyUCqG1FWwurZL8opAhQR+Ck6CKOhs3EZ56eq59Tkrj52HqyDgp8LFF42vk5P+jTFoz2dzcxO9s0kUabwoJowNHoIfxowJnF04w/LaJg2axL0OTRNTCzzGJscJOx2azWbiWvEiIh2idaJJRoN+YhLqbBCwsaWZn9AZ/ixI3WprWICREh+6ktOJ1ZR8Cmc+2ux3UlaR3VJIss87eJVP/FSRMK6ykAWXFPQ3jMpPT0v9sMpqeeXQKOe5pN3uwSM5PQl9OfwNzRZLdJUwyaZF1vq0ueIcDF0G+bTtjiDJV/RLlbj9m80xSmBafGZ4g4X9VoUFkr7FNHEXbTxa5Zvf+Bq3Vm8jZsC503N0B1129jv4GrY215iYmuL20jpB0CQIGpw8sYjsx3Qn91jq7vHy+GV6g5ju9Vv84j/7J3zuT/4Rvvv0C6yurnPr/i2eeflDrK8+Yv70acbrTQ6DzUMB9bU3vspH/I9ycvYSsR6AaSaanWik1CmV2qFldZNKdet7LT+ZMRwZ2GV8LU6+DBSLoVa2o5N6TQ6m9roT8e/6WI0xeMaktqVHV/mIBqNj4nRbYOD7hGGX5bs36fUHIMmro4k0/kDzxpd/i9hAFMVERGgdE0WJ1hvHGh9J3kkvKfOKoA14qYIei58cIej5TJ1dJBqEhAa6kdDxfYKJCbzVDRpRhKkrPB+8bnImahgO0GJQYoiNSpshiPIT1wSJCS3pKVrJu5kSIWIwiR8XCHTM3MQYvb0+aEM3HBB4NXwT4KPRXsCpk2dZ31zh8onTNBp1BhFMtMbwe12W33iHcU/RW1smCDxqCgIdUqt54PuIMTSCBvtsE/X7qb9Up8I1CRZ3VNQcSIa0F5wxd5mjNEEpP1fUdUpQkS9KZYBcyjuEWplqWahZD7mm8vJsG5PXsgxTYaspnszmWm7FfEMadUaaIwmyS5L9LSzQWWsyE0K5Nj3kZit1xFEhX2XaXKFZ9eyoWNPy+IukKp0yKK2Zmpphfn6e3s1v0Q37aB1zcnGeCVNjbfkBcW2BqaZGavC9P/xFLlw7w42lO+xuHRC3Vvn0Zz/DD/7ADzPYOsAoTXdtn/X7jziYOs/tWzeIRdPd3cMYqO3tMhm0Dm3voYAatPZZvv8WU60pIvpAUNBGE2lB5ugvDJgjzTPGK50jmWu4JreFUvBLzg5wHfquhNTZfMrjVckH3cY4kzJyYUTsl2TjQDLAyRFqfq2BMckCSWTAM4LuRWysLDNYfYQ2Ci9ONh3EyqPT66J0UrGWZGUebaUCKAmISFaylVEJI4jCmAgPjTZCaGKiWCH1OrtjTertk/Q9YfnRJmEdJqZnmIgCGmFM5CVv32zEHtqv4QU1ojhKXBdaiEj8ZV5aJySvaFGigYhBLAQ6wPd8dKyTF+ApRddTaCXESjAxBJ5igGHg2d1AMT0xvPT9n2V84OO1a8jcFJdbhs5Ohw6amYMuwcE+sTL4qs76jQNOz82z9nCZeBAz0WyzrjUSxWngPukefZuGgbOQtNUac46wbcxlbq5ZutNcuRcy8LST2gEaKYp7q8MNg1/udqoClKEwn4y1k3p16dlh0HBnR3WyO7JGbUM9Khj+OPcq2zLi/nGA9ahtusdJJuWDbCMHwv5+lxMTs7y/co9gfI6XP/0FmruGd77xKrtf22PQPyButfjT/+5/wOcuXeHKgyUevXmV9w+WWLx4AY8Af3KS8VqdZy48w9trS9x+eJeD3h5zC4t4kWbqxDxT7YkKMVhMhwLqxHTAVH2Kmj+OeHWMSkJ1JIPV6sEsbLkrDGzqSLbJmvGZoLTaCjlgFuqxmowbZ+qUl2XLxCwiaeBvKn3t+3YiHRMJ1IzGS7KijaEH1Go+cZCcx+lrINSZRmVEqBkNYUQtnVgm3eVjRNDpMWAGSc1zL4kWALwgIDaCiIcJAiQICAzUgxoSKBYWTqKmZ7n7aBk9NsZufEA/DNk/NcND3yPsDghQ1BYXIPDoDXpoJUSRoVGr0+v2kx7RmmazgdaaKIoIVJC8YUB5RFGEYJKzX3UMWlOvKbQRpk8t0o96DII6/d4AMTGtdo3xVpvJdp37vQ7iNZno92idnCVon2L9/fv8xle/xV/44hd5uQa7+5vsG8Ve3Ev6+MQcU+0ac5cusLe/gdIaHYXg2xcn5xZF7oesZtuqXTpDE9lUm4qFoyeMe8Fqb0W2TBaFshMCquviuIDiusKOTuUyR9VxXKA8qpxR9eYpmadWtz1KiJTrPQ6Nh6VCOZnATGiSQPGp7/wsW/ur7Ohd/CBm3K+xtHwb5pp8Zv4i/+TnfgY5u8Df/Qd/lz/+w3+MhRPzrC6u84UTz6AnW4yPtYlN4o994Ts+we47r/D+m29Sb9RZ3vCZbLS5ce8On5lf4L033+ZDn/r4SFoPBdSaanBi/jLPv/AdiFdHJy/SJj3aJQO7/PCDfMdFbu7bAON0s56Lp45GkXXWEOOXezeZhFqS7bAalR6eYVdOk1VyrcCL099pMXGs8YzQifoMdMw3X/sWD197g3/r//jvI3gMogGDekBrYpqNtQ085VHT4HkK5Slqvo/v+wiKEOgbg+cF4PtoJcmCkO/TC0OMUsSiEA1BENA3EUYlr0P2EMT36Pe7NIKAoFmnt7PG+ttv8dIXvkCr3eZAfJQOGG9OItSot8Y58PrUjLBvemgRdHss2WUkwj6a2kSACpMg+a7R+IFCYkMoin5sUAr8wKMXx3iiqMUaP4C9KEaLx14U02qfoNPXtE76nFuc58ypk+gw4t6jh3QernAj8OjcfMjDr36TMyfmmKuPc3N7m//3r/48/8mPfoHnz82z0NOciPtIT3OiXaenPE6cmqP5+c/xYGOPne4etTEhkIDklfCp1qENvnjZO9cLO+coga71Czmaa26sOGbtKDZymc0FH/fBAiMO82WhzKKztVxA0WTOzO4R6wUj0qhtmFX7/R+nHPcpodxf9srjaZLf3mQpShQuG0q3u7HJr/72b6F7ffB8FudmefbCaa6+9ttsrG2zVfNoejFqZorPvvxJtjZ3uPbK11iZPcHHPvop6kFAEARsLj1id3ebAx3iozDb25x95gLXX3ubixfP02g02Nlb48b7r/FgZYUP8QEB9dlzH+bylc/itybQqo8yQdYwN/bPOumTeZGAppHyQJskKiP95ZrqRii8hXF0cg9qSD6RB74heXke6bbD9HcgQkyMFpUcHK0N1ALeX77Hb/7ql/n1L/8KP3DmEkG7zvbaNqsP7+KPjfFgfRsde0zMzANC5PtEGAael5z3qQ1GdKKdegE6jlCesBeHybUgINaaGIPyhUA0/UFIpAcEtTq9/QMGOsJTinGEP/ij38t2d40oGrDZ2aTn+bQbDWpRTEzMWneL9iDiwBOizR162uCJwRufpoYw0BrxQdVjTCeiO+giykfaAVPtMXa2O5gwRsURE2M+MT6DcIBoaCvDwPfp9UJMHDJ20EDV60zogL07D7h7+wHdMKYhMfVGQDcWrm9ss7OywcPNXSYjxcCAro3TO/8S77TgRBDQ9hWNfp9Gp8fB6ib7gU8QGCYmGvy9v/9TfOdnP8OHnv0UcZSchfqLv/orfNd3f4520EgW0rIDRhKeURmwWkAgPR9XFYS5QadnOTggmQpZl+8yWV5YJS9FmNhDjrPbReA9FhRmTklbkOOWOIYGdxzAHdZGBRx3isFkZeUx4cMarBSeGE3jcbXRKi35gySjNckhRwJiiAYDbl+/zuvf/Drru9us7W0TxhF72wO6nQjpdtnaXmP81AJ37rzLxWcvATVm6lNsbu7SaE2yv7dJp1mj3m+wcvsWd3eW0HFE52Cf7b0tfGmgA2F+/gSr793j5oNX+dLyewSLZ/nRH/2jI2k9FFDvPHyL8099Cl+EyIBIRLJHSjIGpjwwViO0ppejOVR4CYrPVnXmIYOiBZROFlc8DZExDIgJgCCCvoBRAmj0oM94rcWuifnZv/8zfOuVV4jEcHNjjf/P3/jr3Lh1m9tLqzR9RRiF+LUmXUhP4o+IjEbFISbsEQONZoMoihkc9DFxRLNVp98LMTo9GzWK8ZRQazZRgUcNkEGfViAE7TpKB0gAtTDG6A6T8zVm9hrI9Ay/cX+N1t4W+3v7jLf7dPdC5loxywcDzk+Ms73XYXZijKX+LguNFhtRj6nmOGG3Q19iGhNNBpGh5fvsHWxxstZg13SYb41zEA1QHsQSIXVo6oCOComaEQcHfU7W6vjtGjOtBrGnGIt6RMaj2+kSqAFb63tMxBoTtOipAzQetXrAhUvP8C++8jVmT5+gaRTieUxMjzPWDBg/McvU1AztM/PUI6H15jXu3L9FVGty9uRppsYm+O2vfY1rq4/4ke/7fp45cw4T61x0i8sPRZ+nMToDPiT3eBY4xZH9+aLPCE2vxIZVbOm67UvMWpG3WM+RB31UmPWP80zeluo8h/lYP7hL4+hU7Sc+ZrKuQQxoQ293n43VZdZ2VrmztcyECZCax8UXXubs1Axfev19VvY77HXvU5sbozF1wOys8LHnP8Qb711lf32DX/onP83U5Uu0mtMsCFx98ytMnTrN3kHEuXOXqHV7tJ4e4/ziKe5cX+PWtbt0Wx6L7blDST0UUEP/Dt945Wf5galF6uMLRKLtUg6u9T7SuW7AlfqjzIzs+wfw8XgGBjpGG8NAR2ijadeb7Nc0zSjZ0rgZdRgTeO3huzxz7mn+o3/vz/H/vHuH5a0dbuzv8f7Xv0kceAwkZsxr4XkNkID9QUS9VQeVLDgpPLxagPGgpqBZ84mVxkQGz2gadUGUEA86NGoKhaG7uUk9qNNWcLC/S7tRg0ARhQOCsI5fb7Pf6/GpZ58j9hSdMOSZtQ2uvfcGTy1Ms3Z7g8vtOXpbD7jSmKa7s8SCCN6OMBHHnDpxAr22zHh/gp2VbU7OjLO9HzEzNwlLA9otkE6f8YbQjH1MGOP5wnZnn8WJFss7+zw9O8neQZ9TCwv4+xt0Nu9z+exJrl+/y0tXTnHj/gZXplrshiEsb6M6wkFnh/HWGDsmJuqGfP0rv0291WSs4XP29GmmTiwQ9iKu7WyjwxijfS59+Hn29QDTnGB/t8P/92/8TU6fXODKuYts9kPuv/E2N998l//rX/hPOH/uLDrWeEphdPouplQiJ5ZM8TAPyx/5OUG51onzLO51itdtdEGRp/P5XLToc+3gcc32ch1H3T/uM4eB9+PSdhgNVbQ8rg91lP90SKCQWr2iECP0D7rcu3OH7t4+3YMOM9MTtGPF9ftLvPCRj1EfQH2szQtnT3D3+jV2OttMz0/Qaiq2420aps/r19+hMeGx2B3wcOl9rm0ts7qzztyFy5xsjBNow7XXvk7vTJ359xZ58P5V5k9fZPL0LI/WVke2CY4A1Nn5SXaX19hcv8fpsROI+ECcneLkdMPQoBcPbzikM2X4eaCwglklpUWSpbEQQ4RhS3dZvnWPyfYYu8qwpnucqI3zzjtXWbj8FDdfe41t6fPu117lAMP69hbKBITaEEYRQT9iXAl7a48Y0wYJY8aVj+gDAjFINEi2hUYeETG+CakJ6CgJxA88RV3A84Q4jPGVSt50amJqJtl2KnWD74XJuZI1xV6/y2Y/5Ju/+RU+dPIU9TGfttlnfvk+J54+x3g75r5veOZEi197e5mPnz7Db79zn+984RLfePNdvvjMc1y7f4PvOX+ejeU1zHyNQW+dSSOcJeDtziMuTS5w62CFK1OnuXr/Lh86vcD1Oys8szhDd2+b73t6gaaJkKlxzszAW7fW+N7nTvC119/jhz56mpvv3uR7np7m4eYe3sEBFxdb/Na7K3z3uTpX7/U5e2qaN+/fo1Grw37E5oHQXdvE1N4lDmpIs02khEvnn2LRD9nTIVFQp+0J47rL3dvvsbb8iE4vwo8j/vCf/pMsnFvk6o1rnDl9mkZQIxyEtFotPCVEsWYwGBD4yYsVxVOOtmkyRSZnF+vwLB6ikjOf62nN/+YcXjrGL3tf0vHA63ej/Q1vlz26ruNonscxxbP43woaRtXnPnec9h2nDcnF5B9jko0oa6ur9AchY402Pd3l4b27jM3O0dva5Z37D5k5d5qTrXEePXxAGHa4d/U6g9MR773yDaKwx3vvXGfxwy9x5uEav/nlX6ErA0ygOfuMZkJF3L5/je1oi7F4nGC8zff9xB9kY2OT5du3eO/tVw+l//C3nqpZ5hfmkjM9jaJPssBkD9wlXWzKFgjcTsl7OO9cZyAzP1gJW7PlhHRmZC4oJ7841YuC2Pf46ltv8Dv/5Jf4wz/xx/jyP/sFnvv0x3h3ZYv3lx9Su/oW716/gfIVvd19uru7CdgpiHtdGkoxEwT4HnQG+wTKxweiMCIwHp7R+FpTC+rJir/R1BoeojWR0QQ1n0AiFArjCZGn6WmNGcTUPKGuYnwFcawI07eixj1FVxSNsSZf+NTHiduKhztrjG0uE9QjzrXGubuxwuevnOfO21f5Y5/9KNfev8tnn3+Wyf1HfO6ly0wMDtAzTa7MNHi/4/OhK2d447WrvPDyU3zr3Rv82He8xO3r9/jD3/0Sd99f4Yc/8wLdpW0mLiwQeT67KC5NjXPr9m0+/txlvvHaVb7jpSvceusGL148wcMb92lMNok7MZ3dLhfPLnDz5iO++LGLvH53mR//5GnevfOQP/nhy7z2/l3mTy/wldsP6Yqm249oEVBngB7sY6YbTG/d4nnPZ4DPoLPLj56e4+bWHttaeBhFTMzNsvroAb/48z9Ha2qWG3du8/Kzz/JoeYWpqUlOzp1gZX0dUYpTpxdp11vpAUapZurwkuUXsuuSM5mTJ2FRe89k6/oJFyYnn2VC31lQGoqmtkielVehvWUehSI4uotvZSKPCjXKwceCGM696nYeR5M9LBa0Kt9hmuwoheioMvMLScdpMfjNOldeeJ5ms87s1+r8+iu/yXMf/jBXr99mf2uXD33y0zxafcCrv/IrjJ1d5OXJeTa2tllZesjp2TlMX/H0lYvgt9nrdtk82KPb36fWqnPj/m0WW9O8/+ar7Js9Fk2f995+k/Ynxjg9c4Krv/ZVwm5/JN1wBKBO1p5hZ7DLw433uNj/FASNZJeuSd6oY0MurQSxScRu/Ss7ckondIt7qJpl1MS/ZUyc/Xaz5AxpGCiDFwuhMrzxxht0B33+9k//NNJo8O6//DU6a5soiVEB9HoDGlpTHwwITJdx36cThQz6Pdq1gCYxOopp1hS+1qgwoi6aAEFHfTxRmHiQ/CWipiMCnZww3ydERzENr4ZvkkD7rlH0VUCtVkf5oPt7GD3gfHuS8bEx7qlx+ms7BERcu3+fXr3O7sEetaDBiUun+NqNG1yZOcXdpQe05hbxopBaM2Bxwuf9beF751q8/t4jvvChp3jj0RYvfPQZbr/yGp/56CXeef8+Fy6eRe/vU5tuEB8c4LU8pmsea1HEpVPzvPnuDb7nkx/ildev8vyHn+NbV6/z3Edf5P7t20yfOkHEgG7c4ES7ztVb+7z00ileffMuH37xGa69d5tz87MsLy3z9MI4OuzywsXTbO53+O4PP8Pr165zYuEMO3sHeMrj4qkFVM2jEweM1frUvR74cLI5wcsnAva0/P94+88Yy9I0vxP7veaY6+Pe8BHpTWWW991dXd3TPYYcDsdwdocUlyJ2IUCLhSBI+0X6IghaCFhAgnYhaCV9ICCJ5DpwKUrEkDPDmZ2ZNtO+u7rLm6xK78JH3Lj+uNfow7k3Miorq7q6Z8UDFCrjHn/Oe/7vY/7P/2GvaPPerXvc/tEmeVhlO4XG2jqvv/sOtUqFWr2BkoJWp81Bf8C5jRV+8yu/Vg7g6bjws+D9zO2fjkshjoGtf/C/T4CApxRjOA7MxxgFx4HS83EgKf/wD62Ts2E+Bepj9Ckxk7Dj2D4PH/M4CM1kDcWDieJjqaXZHsf3me3y6fHTj93/8eP8ghb1LxLKmP39uS1X4cmThI9u3OD0xfPMryxjjWH96Sf4erPCyOZAg6W5DrJRY2k0jw0srWZE2h9z5+41Fi+cANFkY+8286srfPDGO0xOn+LE8hnu3r1BkRp2N+/QWPCceuZp2kGNIulx79qH1BbaLF78EqefeIK0/dmJ888E1EvrXyGdi7m39To/ee8vefapr6Mr9aMM9ww4f/HlAagK+Fi3QXjEyzkaGDPD9QEVqqwCcly+cJF/8+0fk4WKRCnG6QQlPG4wYkFIzscl3zSdJDhXgNJUcktVgbQGHUakHqQo+aUV7QiUQISWoF7DO4GSGlsYfGEIhSfQGktIFFcweY6TOQaLJMCKkCAMCZXB5znPPvMMDZ+wdvEM37i5yRtvXsUdTGhGgjO/9gXefv2nnOx0KBoNrt4sEPNrpHsJzeop1gPL9c0dLp1+nO+99jNefOllvv3GD7lw6QTXbu0jLy4zUpJ45STdicHXKtTry7x//S7nz6zy3pUbvPTkKd549wZnz5/nrXubPPXcU/z0o3ucOnWSO3sjotOn+GC7i4+a9IlI7g+4/PRFfvbGR7xw+SJvXL1B88wJ3t7u01xb5TBLmVSryKjF7WzCUrPBwXhAfzJk/tLT3Lh1nScff4ydm3fY29+h0mnzo40epy7McUpOcAHUdU5dBiwjWBOO89UVcIruyPDR4YSPNj5kJCJuJB6rI1QY0qjXSMYj1v72bx5Jqj2cPedzjspHurHuUaDxabFMf3T+R0HD8Zjs8SyRP/LAHlzro0Hu2Lpj9/YxYsLDyyN+/2X4np83nvo/FAvgM7fDM+r32bx1k8HhAUbAwfY297o7PL56kklh+N2///f58Ls/xpiCsFPn5RdfYniwxY7LCaoN9u9ucCv7iEZNY3qGx86eZO7JJ+icFXxTKdbXF7l59yNu3rzJ5S++wqVLT5Bv7/P+1oeEgSbutHj2S1/i/f/mg095euXymYD6+od/yuKJx1BCo4IBRTomjOeOulAeL0X+mHfFp73xh0pGZ+Etjjk74pPx1KMH+9DP0oE3hiLybN29y4gc5xTWa8KoQpJNqMUVqnHIJB8TFTmh0jQaNZzJySNLbi1SBRRSMR5PKAqDR9Ks1gi1gAAyr8lc2ZMmSy2VsEriCnSjhZcVDodDQLHSXGU0GTJ2hsbCAifaDU4vd6i3qjTqAWlrgX/87Te59tEGbA+ZbzfpuT7vvfk6zbkWMsl48433+fKv/iq3r95HrywRr6zw0w/e59zZx/ir/QOiS8/yo91NipOXmdQk/W6TWrTC/Q+v8MUnn+ZnP32Lx5/9In/1wQ3aT3+Rb1/7GadeeplvfvA2zSde5Lube6xdfIGfJAP8yQsMlSDKBa4astffZfHCeW5+dJNLz7zCDzbv0Hn+i3z/xlUWLj3DzfEI36nTq0B3a8DaE6d4/YOrrF16km+//jqnn3+C3e++RWshYkkrDj58j7lqFScjbt+7y3g84mLtWdSJOSq6QdMeEEQFzlrqskplrspknJAbx1OdgKcWTrDZL7gxEnxgPakOqFVCOpU6C3MtZrF3JcQjyj6Pj5tPBwD/MWB7REmzeGgoTyv5ZnzI2b4POWmfON/niVv+IlVDn3a8j7EYPhZG+DzHhCOL/tixP28BwcPX8sssnzge5dON45BGFHH9/ff48Xtvc3p+CdGM2Q52mTjD2++9zdLyEpNBn9pim8Uv/CpXfvIjvvXOH/LUc09x//oNhITcSHbv7bJbHGB2bvHqYy+RZwN29nIun36MrFbh3/3t32de1flg+CZ4yckTZ+jMzaNzze/85u985vV/ZguU//B/dtlXRIcXnv1tdg7eJwzXePn5f4fa3DyIUrVfIY/qk4/cmRmgHh16FrtxZUNE/4uZ/J/2kXgh6LmCf/Rf/xPeeftNko0dnAKhY1SlSiFKi0NagxKCTi2m5hxBkZOnY4QxZFmOVBHDIsXHMTkCLSW1KEQqyXg0JrGOSq2B9wItBLHWBHhcGKKqVfb6h1Q6NQIhOLe4yPOXL2LwiIrGS0E3GzHXmOfP/z9/QffefYLDPSpzHW7v74GEpeU2NhSkg4yFVoeKEJBknPuNV9jf3KXRXGTsR9y+s8fl557m/vsfsHjpMe7dvs6lc89x9/5d1tZX6G5vsDa/wOHhgGatyfZgQKAdcQBMcmygcEXAXKvBxsEBq+srXLl+i2cvX+D9D9/jsSee4catq5w9c5mDjfs02y3SdELFC8Yux+aCaKlNf+uA1dPr3PzoBifOneejd95gfmWdw/17VHJNkI7w6YTHnjjPzRu36U9ShpOCr770PFnV8dKrrxBXW6TZkIauUs1zlM0IsFSR9CYpOnXYNONAB9z0isIUTJwm2b3P4yeWEbLKq1/+GhcvXvoYf/mzx9TDQijHx+enAbJHfKqXdzz2+fG46Wddz88b9/829vnkxHIsvPZLXsPn3e4XBV3pPU54kl6fH37rW1y5fY3UZaQu56u/8nWW55coRgX73S4r6yv0khHPnH2Kaz/8CX/4l/+MPPbU6zW6uxtoGdBPE4bDAVQanG8vs7G3xVh6fvvXfo/5pVXOnHmMSlDj3W/9FWlcMH/+DL/y6q9TKUKEsET1yqfewGdaqHWliATc3/gQLxucWb9IJagAolSeUh5hQRzB6bH40VFg63jQfBbX+sUe6KeBvvJQFZrL5y/w9s9eY/XEKmkyZufGbdRcC+cp5eeaNXxhOcxy9i2oSkwhNS4vCGoB3jus9MigSoBgLKFnS5ET1aohvcMFGucEDo/AonFElQifprSimN/6la8xzsdUtKQ/6rO320MEIW+88yZJb4wqHHn/gFaoKBaabPUPqbXbpQyeK+hUW+ylI3a6e2SF4/L6Ot/7479kpVbFnMjpHfZYXl5j6513iaoV7r31LpEP2d35EJNkHI4idvf7VJpNNg72MYFgvL9Lc22Vmx9+QGflBMlBl7WzZ7h1/zaLy2ts3b3HfK3Gxp07nJpbZLC9Q0dUmexukRUZk2zIpGvQcxUOu0POn73A1Rs3aS112Ll6h0qjwa07VwmcYHD9Q1ZUiA8MVkBSlxymKS5ucLjVx2YFaTogkhXuX9tDN3vkrQ6EOTEaayVCBygpqDYFLATk2ZDACeoJDEyK6Q05dfY01mT85Ic/5smnnj82tj6PJfhpFL4H7vUn3GlxzOp7xD4zq+7R53v0vr8sDerh5edl9D+/237cmP3k+tl+v5gr/3GL/5Prjx/jwXkfsTHeOzY2NqhWYxZOrbE67HGwt8393bu89/Yb3G+08F6jgCvv/4RGZ4nUa575wnP8nYrjzatXaM5VeLfXYzweMswy2p1VRBBz5sxZ+uMeYS2gN+7SdPM0Ik1WjDn75CVu7Nzn1PIJJt0etNoEWCIqj75Wfo6F+h/9Ly/6fNtz9vSTvPrFf5/505cASa49DsvOzgZNQuaXVvE+AOHwwvBA0M8ee6gfc+wfeqCfPht+1vXN+tccipy333+H//v/4f/I8LBLPhmzsrjIcDxGRRVSJak4SRIGRPUG1jkyUxBNvx7vDMI7AlVFRwF5kRF6SSEcucmpSlVSq0SINykmS6hVKlgDLs+RFUE1rpFMJgShJkuTEng9uCIncJ4Yj67ETLwntR7rDVlmuLze4cknl3n95i7KaZrScnWjizUxkoIwEKzUq6RZStCo48YZshqS9jJanTb9bEyt1mBSDKjaKokbUa+3ScY9cBrvChq1NoXNGecFzVadZDymFpZWeRRXyLsDOnMNhuMxYbNDno6orswzuLdFpTKHtQlWOgJTIQ1y4kaLottDViqI8T615gKZsYxTyJIupy6tkRhPU4e8/vYd4jAgzzxxYPjy0xdIxpb1C6dwc/PIapU0t6hKjBca5x1SuDJerUBIRYBG5iNElnNhdY0vvvRlkBH1emPai4ojRBBiJtLjHgKZh8fg7IP/5Hj8rHH5yW0+33j+vOs/i8b0ixyz/POTKasH3uLHDZ0H62fP5ZNm+aOP+Ytf6y+2nUBZx4++/S2++YNvcmfc43/1H/3HbL57hW+9+UMuPX6B6x99QJLB0uoJ9u7dIa7VqJ5d5Q9+7bfYuX2Xuxv32N26w/Wr77Ld3yGsV6kETe7f2+GVZ59mc/su7U6TVBsqtRqNRptzl1/k1179HeiOOOgd8MZ7b/PCV77CiXqH5ZMnP/XCPxNQ/71//7Q/3V7hqbMv8+wX/qe4IARh6RYD7mxeJZ0c0kZz8dKzaN3BE+AweK/Lft7CcrzVr5APA6rnUYP502I4D8+SHo+R8Gdv/ICf/Ok3uPvhVe7ubJCPJggK1laWMLZAqRCbpORCIVTZH0h5i5YOYy1yxhklQHpAKyI8uTcI5wicwAhPqDTWOZQsSyGdKev8HQbrJForggAKZ3AenBOEUqEVTGyKdSHKx+jAY+OQeqyoVwKef+YUvY0xpgaHoxGtYJlhHiDkHgebhziZY0cWpwK8c2gtSJzHCoMxkjAthbDnag2MH2J8RKAhtynEBmuqSO8gz6jELSaFRUpDw0u6WoAxdELBxBXIPKSIY5TxTEYD2kozUZZao4XBU5OWuoa82iSUAXFYQ0eS5tw8m9t77O7c5vlnnwav0WRsDwsOdvaZjLv0JgErrZiVuRpxFBNEHuEVTgRIIQh0gAxChNaEKiBqNtFhSCAFrjCsnTrHr/+N30LqoFTYkoJSrAfKWNLx4fSwFfhJa/Wzxt2ncSw/bfm35cZ//uN9sqOomJVvfuw3+Gzgnf39iA6lv8Q1/jLbCQSHt+/yh//ff867+3d5+rkXWK13UIGid7DPwcZtvn3tCi9cfoEXH7/Em2/9DLIx24Md5teXGe4eYPKU7t4Btbm5svqxcOzu7nFypYPWnqJwuLpiaa5Nb38P0V7gV1/5d5gLWyR2SJEMCOfmaboGX/tbv/nLufyRDGnMrdAfCSwZlggrQ4zOyMwu+/euQqgYL56jubAMQk9bZUxJ1B4QftrgDPDyKDAwfV2PNA4eJjIf//1j2wnQznNh6RSvoaitryL7+6gsxxrP7sEBnbkq7UaDcQjZ4ZBYa6pB2UfJC40SnkmRY6wuxayMJS8suiIRUqOMReuAXFowDpygEALpLVYLnMshFPjcMykEygikEFTjiMlkgok1YRgS6RBEhIxiYukIpENUqwQY7ly5w3yzzWNrp7h17yYHTpApictjTix2uD+qYKIAW2SsywNSCUa1ibMRi2JIfTXEOcthWmcSNAmNpGoToqhClzqFBW97nGitsTMROAVajFmPQ7zwZKlgpDNWpWYcBkxUSIRjeSmk3pAIp9n2DpsKvI9YaFe5mghCH2HHBRfmNXcSg4oXePaypmIMJ06tcndgWdQjQrGClj02t7ZZXV0jH6WElYBhMmbiBUIYiizFZAbjDMuLdRYac8hcstPtUYwT/uD3/z7PvPQqTsVYIVDeHHko5TDzjxxLD8bNcYv0s7b7fOt/UbL8L+PGz7b55UIJn379H9+HKXvm+Hk+HkIpDZkHoPrLhBUetfy87WYsIiskjc4il84+xkbvgJ2Dfb748itM9vrEVnDjvXdQUcSXXnyJ+3ducPnZZ7j3k7dxNoKR43BnD69BhtBpRiTjnHGRE1RCJsUYXxh82OC5My9w++ZNDvYTTjVDtu7d4dtvvsFessNT58+yNc7427/x+595T58JqEtrc6ydOkWNi9ze/ZC5E+e5v7XNm6//EYPd+6xV5/FLbepRBYHAeofwCikKnLfTByKnGdGHAvdHL+XjL262fJ6sqPQC7QRPrp7jP/nf/e+5cvca/+l/8r8ltSmZjZBKgpAIJWnNtRn0e7SabeIowOQZmYhQNgEnUZUmoU2JgoCdgwE5VbyGWgBpUhAFEQgYmxwpQ6JAUAkDEl8gNNSqDXYHY9qNmIosqFcDJtWYRGiCmqLhQ4pUoFsNhCioa8Uwjqk6z0ItxIsMLRUr7QsUWqEOPaKiOF3LiAdz9JzCu4TzUjOMNVFWR9kOp9SQsOoIwoDdQYOd3BFbTb3oE7UlNVPDSIUpYpaDOjKr0FYKkj6dekCuQkTuSdWQJRsyCSXaOFxRhaDLUqtClgrmwoCxVMgspV6pcs4ECF0lHg9oNwJSHeNtQCWJaMRjFhcDJmEDBgqVGWqNJYQ3nD+zzAcf3iOcqxDLAHJLq14lSQZIFM1KhKxqKoAUIe3WCi9+5RJPfPFVCiRCWJSXeD9txjObqznWxONTE56P9oj+bSwz7+rzkNofBppfphLrs6hVD4PnZwOfwLkZkD5q////LX7K/8VbVCXkwvPPY6sxSVUShjGN5Sq7gzGpgMuPX2b95AlatRq6VeeVF7/K3t1tMAX/r//n/42Ptq4hQsNSp0My7DNKCvI8Y36+gwxCMh0RyhBrDUZ4Brt9frT7E7RN0GGIFJL+4T7/7b/8p/zeb/+DT73mzwTU7niD3cMrXDz9GFnR40+//Y/p7d4hFAecP7XOYv0UEyV544Pv8OxLv4ZQrdJK9QK8JEmHaA1RUAGvjuWnStX6WTD84VI1+KxZ8Ng2QC7LapYwdzyzfpb/+H/+v+C1b/w5v/G3fofBKOE/+8//T+xs7vHkE4/TOHWBJClQSlCv1RkXglY9om4chYioyZi4IfEEWBPS6dSIlecgGRJUqkQipDJO0VGNqi7rxQppqdcUJvPUOjUaVUU6SSCMSHzK8sIa9UYN58Zkk5wwjogicCaiXa2zl6bcnfRZbHryhuS1dyZ85ZU15KqmFmcMxxHrSmBciBUSUziWZY/HIsegiPngo22WVcbt7ogXzp3govBkNkSFFhWEyKrkoO9wxTmq8TbLXpK5CoGaJxcFTVOnksesCU/fJ5wJYiJt8FJwdb9OFvQ5TAwvn7mIUSNqUtB1IRdUDVtoqK0zTh3PN0eMCrh1Q3Locnx9kWfXVlG6hvIH9HPJ7vYZGmHOE5dXCYI63ckYkzaQQUE+7mE8dPf6tNfW8OMUghiDRAcNhAlRCmDmAU3bvXxKTO/n0oo+ByB8VvLk0y2zR/dEO1659fOu4UHHAnnst08/38eP+enX+Ohvyn8MKB8sR82hjp3n4/fzaffw1+GhHgfr2XmF8BhpmVtZ5ivrq9zZuoc1nvn5ea7/6Mck2vI3XvoCh6MJ5y9fpqkqyDhkobPERz/7GUudDu9se06tnSEI5xiP9tHeUp+bI+smiEgTzlV4993vI2xOPc5w7LJ+8gKpa/Hscy8QHGbs7qRc37jxiWs+vny2yx9o9ndvMjj8R7TaHSpWsrG7QTc8xE4Kas8uk9ucWNe5073C9v0dOtUOZ04+xyQK+e5rf0pl0uNv/8q/i6ss4WSphl8q8s8e4GdeHw8C5Q/FcGbZ2tk6JVBO8JWXvszXX/wyAkdSOP7T/+L/wn/2v/lfE8/VOUxh7cwpwrkAM05YjwQIj8sVIqhzWCQUk118FHD2bJtGPKTwHZZ0h9Q6jIhZ1iHSgFYCIQMm2QRdNRSZYSFqoe2YURLQzwQVI2jXq2jhyXWFfTskGE/wfUcUz9HSgo7I8Y2YbJKyf/2QF8+3aFer7IwG1Jzg3t4B/f4E78pXFUjBk+s1+gNBXR9yqu1IupZXn7pIJfbc281LAZs0I3cpP/jeFYbpBJMHOJMQSktuJMZbrDegApwzLFQq/M7f/BIHB3tYkzJfq/JkI+P9u4d87cmn6PW26ReWt7cOeP3DG4SBJlQSpyAfev6Dv/tV+gdDXjy1yLe/cZOf7h/wK7/zm/zJn/852/tjfvvVF3DScHNwyPMvP87e3oRbBz0a0kLgOL9YJ6qH7O3ssNyuodptdC1EeRj0xliTIWXItL/D0Xh4lGrU0ch5ZEZ79tusau+T23/a8miGwCfXf9oxHzBcPt7d4sHiOB6WmFULfvbxjseIH2pg+Qmr9WHxl0dPAA8A9AHz4dOu46/r1j+8nZDT7WcFFr4UbvdSEQUar6AaRuRaEmpFt9ul1+tz4+5tXr04z0dvvcWbH37AH/z9f8jh9Vts7W0yNz9PrVFnudFka2ebHMk48Zy4cIbBnbt0Mcxnmvs3d1joVIjmIvA5V2++RyjqbG5cpcICJ1cuEN2+85n38dm1/EkNI0P6e8NSHq9Q+HFIpd7B5p57t95FSMOgskAvy+hlCVvXLXONBnmrzlJ7EQEM+3vU4gVwiqP2r0eyaw93AHjYWn0QHC+t2Qc/+4f2KgG7bCosAS8lqhLTaM0hPOgqNKughWQjSznoDikKw0qnTZWC+UrIwDap+j3acYe0qGGV52BS8O67VxEiAm/xHooiKzP5TgATzp86x/qJZXpDz0KnSpGMSPe76IWIcS5wqspbr79PkVkC72jPVak9eRkpoRZJNq7cJXKaJ/7mi2TDXZL9MaNKm59850fs7h+SJRYhoBIqTv/+30AgEaJgXgo+vH2fyuoSoRK4yQE+jqhG8P6Hd7l36w5BZJEqRjqPweEpO7sOR0OkkggZYjzcuHGToFL2zAniNnE9YnLvfbLVMa12h61bu3z43lVM7jB2QtcYPI75uMbdD6+xuLROsxpSSVOeq5+Bgz7b9+6wM4F7d67z+GoHOUnpbY/4y++8xmtvvUtVSwoJf+frr/LEhRPkOwdk58acf+Jxbl77iLVqzI133mf41Eu0F5angwCOg+HDicvZ8tm/8Yn9P0+Y6eHjfp7Y6SfpVp8EqeOdVD8PgH3ynvwjt3vUMR7e59HrZr/PwgefHjN+eP/jz+UX8QiAYy3ep/chHMpBrgU+S5iYlGtXr3HpC8/hJQgCTi2f4vTSae7duMvNK28zbCu+//q3WHYVbmzfoZsOaMVVBt0hQaNDR89x8ek1Bjs7bHa7jAOH1gEiCKm15olCRToaIGzC0oklRtmAr73661xcfpL5yqdTpuDnAGqn5fAi4KCXs7+/xXJ7gVYjoD8Ys7BWJ3blA7j34Qc05jaZX3+ahcde5N2N71J8sMdjl17lysE13tvq8ZSq0VhcwwmPQOFRTJ8IH2sd+4mZ++EB+NCLPfrHtBxVMBUU9ngpyLKEpRPr3Hn3XS6dWIC8R9/Xee/DG+zs7qCF5MzyIk88dpa5KKC/u0OUJgRWM7CSJJ1wb++Q7d0hVTFCaYcVgsI6rPVoLZHecf3aTUbDlNW1FapRjc39LWrKU6+1ufr+Ve5u7pJMJggEYaAZJhN+/PrbzC90+NJzT2KTgnFoyYOAw8Tyb771I+arc5BPaMchqc0wRYFNcv67P/4zMq/4yotP85VXXsD89AP2d7epVE/xzjs36PaG1LTAyZzVRoRTKcI5hDfTLuuOqhJUZUieF+QmwRSev/z291Fe0ktSLp0+za/9xpehn7N5a4tbV6+zdW+XehATSIuSmo2dAyaTlGChRjKyfHR4E1tM0C5gWVd4460P2N/ao9lq8f5777BS+yKPVdv8k3/9x1y5tsXFtTVqYcBm94C//OYPGRw8yYtnTnFh/QwLa2eZ66zx7o++wflTK7TbnfLt+4/blY+qET8+lo7/9mmA8otwRT8tYfrw8tnbzVzZh63NjwP1ZyWoPk947OH7+rxJtY/v88n1P899/6x38PDyifUekOC8wztHv39Ip9li+8Zd3vzZayQYao0GvR9+n0tPP8PlV1/m2fiLbPb6uBD6O3u88db73N+6zYXOOm+8+y4n1k7QCBrsbO+y8Fib5RMLrC4sI1PDl7/0Ff7lj/+CdQ/rp9Zx3tDbOUQqT7NWZ/P6fcZmwp8N/zXbT2xy8uzyI+9jtnwmoLooYOPOAWvLJxFmgq5AZ6XO+N6Q7nCIP8hp1OYY5tDbGbHa6dHtvsXd+7fIh9vcuH2VSmxYfuJFMr2H6SnmWmsgIsDij3T2p5bqI92g4y9t9sQ/+UIe2gjvHdYJbr5/hZvvfUhjlFHTIc89+wpPfPVvsPr97/Ff/6N/RGA9t+/tcHdrh5effRqfplhbQ0YNrn30Jrt7BwxHY0ILSisCIVG+7D6QFzkKjVAVcjdhd2eL/YMtBvsnMeMUYxO6711nd3OPdDRGe4sAIh0xzBKcDOje2SJLCjrWoYIaf/KNN1BRBzG2dJY13VEMuowhxXMNkiwFZSgGKd/7q9fY2+0y3zMcbiT88N4HJLmFTo3dgwmBC2m3F9E1RzaA/uEW86vLSBlgjGWU71NpxiwEgiy3dMdDGqpg1M843N7nxz94i2aacP3OPQ7rDcZpTqNaZ65WZzzu02nUEYmhUYnJnGa/2+P+nZ9yqdHCesPhIOXwcMTLT1zGesW//uYP+YevfpH+QZ+Gglak6Xe7xHlBYTzrZy7x1gdv8dt/9+8xEoJKtcmbH13liTDCWYdUCucsQsqPjYPPAy6fBlIfHzqfJ4b/KKvzF9vueNzzwXU9+n4e/u1R93R8+XnX9XnA9eet/3ng+Khtf15S7cEPM4vYY4qMu1c+4i2XkG/v8M3v/gWdU6vMzS9hdgf0fMGvPPkSo4N9gkCy/Pg5RParJN+Z8Fc/+in3Gx/hQk2n2+Dm3bs8/twTTAYD5NoS7773Fqkp8ElKmEp6xmL7I9aWl5CTFGJBFFXZu7WHqCgG/S5vfPBD9vvr/AePvJNy+UxA3Trs05pboVapkRvDIEnY2CnY3ciJJ55mGrPQXiLMC8Q4QWjFsHuPothHBp56KyY3Q+5v3ub2vf+CU+cus9C6TLN2hsWFywg/NZ9F+SQfBZhHT5lHlxc+6oU5PBIIpeILL32BP/+v/iusCtjdH/L1X/ttTG2Zk088jTeWVhCRekFuHFevXGUhqHKI58pffgNpLCpQhEKitUaHAounVq2BE4yTAa25JqFq4H0FISVeh+zu7RClEEhFt9hESEEoJKDQQlNTIbkyxGFMbkbkSYZQATUPG70uc0tVKoEmVDGLzbIz6V5RUG/WyVwNsjEis9hhQTZMkNYhxQQpNXONCFuto5IQbcbMtRRBI2bkFJPhHs1qBSU1RZ4zCQIajRbVmiN3ArMniH2PSCmiwNOREQ5FJWqCbpLqLvVqA+IQkedUg5xEWOqBxWUpwmZUdETgLXUJWniU1jQrEusDdJojvKGmYpSGII6xgSLyklxALCSDYYaY9pUKbNkRweQaJSTOT1uZPMJtf3g8PGqcfByA/NF/M5f28yavPm9o4OFtHywPLNTPco0/Jq4y3e/nUZb+h6BvfTqAPjB8HrV84jjHaJGfN1FV7uXBOkRhEIXhh9/9NlfefwMjC7pplxPrE5K9Pmq+xZW4Qb61TV5kfPfguzx14hxFIQlcwGg8pFNf4KPbt7j8heeomZzDos87P/0hydjwha9+jfVGh8PNHi+++jc515rj3NlTvPGNv+S7V36IKXZRFYWOqownE4JqzP6t0SPvfbZ8JqDKfUk/GnB4uEutWSXva6qywdyyYpQMWVhsEeqIX/3y3+Xezg18XAERMtroMjw8ZE3EjEcJmB06nQZXr/6I6+Ztzqy8xMIXTyCjGCsFDlf2lBdTpVUPiAfhAO88Ujm8F0jvsZTalGoWRD3+wqRATIVoJZA6Q+osNRxF5tBhTCIcSgdEKiCINFoKpJcESuBNjvaQJSnNRgPpLKFQBIEmUJJCgFABTjoCHVANK+hKQJFWUVIhAoVVGZK8TL5ZiRIKLQRSKqQKUFpBASrQhCgcpRSiFZpIRChyaiiknxDEc6U+AQdElQbOCrA5lUpAf2KoCRBK43JLPQpwlTY1V1BUFa7nqTXmyIohcTNCbUItrmAFqDBEs089NLi4ThPNoRpRJUJpqBGjMRg8YaQp4ohAVgkCgarVGA/6SAlSamKvUVmGcpJmJcLGCm89k8GQqrfYUYIWDm1y4tTRbNRQhcAnGbIoaLUX8d0eYTbGZRkumxA4cMpTjZuYyQCJxYqyJXmp8WxwDh6Q1d2Df09J/t49SDwJKY/oP+XH/HDccdbB9li/NFGaS0eik9Nzl/KS0+KOWfIE8TGE/6RFPNPzPZ5UcxxbC4gjo6JMtqoyVzALK5ZXCX52qhLc3KMUsh6KNT84tzzaR8wy/OLBPQHHJpjZ+WbPw4JgWs1WCsAfccyPTi0R02dzNFmIY9d0jINuEQg/+9tNn4MH68FrvA9pzbXZuH+Xw8mY5eU248mYLB1z6sJjvPjlX+Xpsxf54Xv/jH/2J/8tJ595nLZUvHftGkFQ9k1bPHGRX//VZ4lCQf/ebSr1Bm+//SFnzizRDC3tTp219dNomyOU47C7xfbGffaHCXPNmOWTJ5mL62ztbBPPtajT4LOWz5bvO3ueq/e3qYZzTCYSkdRorZ1gcTVmY2uPZj3i9MnLXHriawTtVeIgwO+8T2/8Ls32KiKWjHaGtOtt8p4gH7ZJ6dGvb3L3znc4d/l3wcUEKLyzCMUUKsEeZThF+fLcbMhNZ72p++xRH08STPeZ/aaVPoqXlT+VsUQtJUpqpFToko+DVuV+Ell2JgXCQGNyiVYSpUqQ11IilCYbjRiNxmWYwFikLbutSjzh9PxBECJUySpQCFSokUoSBJpKHJGrhDAMwXiQgkhrpC2IUDTiKhOvKLQi0iFxECMDTTY5IBCCSGmUL8sslYewzFoRFgKtMhyCMAiAgAKFEqBlyVlNjUOJsvOoDyTCFChhCYUGIVBaYZ1FItA4pChQ3iCdRXqHFg7ny3YkAk8gQUmBCjRBHGGNYWlhnnpcYTTOqLTqhI0GeIGWUJFQlYpYKXyaoYuyKk2GAcZ5rFcY7wkqNbLeXjnhTBNHygNSgXjQxA8k1pWCOTNtUfngG0a4WWO/cgc5TYR6N9Mp9eUEKI61QvEeKSSO8txSluNPS4mdjifn/JH/pI9hi1QlNfCIfeSPkKtsaz6lDs5i/x6PnGKylHIq4u4oL+VY7LQsESvvwx9LZnlxdG7k9Dvw5T0/4OeW4ujHcXfG9RRHAjMP7r/smjH9ZsrDIeVsMpnCvpsC8XQDL+xR8Y58KLwgpUTK8mqsc1jv0VKgZNl1wQuP8xKwCGdJJwkfXfsQqSB3Bdt7uywtznP91g1WT58nnfTZunWTg/wQG+SkoyE/eO8d1k6ssnF4G6ss97dvsL66ynKlxsb2Hayw1DoN2q05Dvdu09u+yZWPbvHGxlVOrXY4vbIARUaSTVgPO7QrHeaWl8F61Ll1vrT4zCOxcrZ8JqBe29gkrDQRec762hy7QcHyimdn/w5PXX6Kx85f4PDeATc33uCgd5dG7TKaZc6eeYXVOcUwv4ZYq1BvrDEcbJKNuxRacNDd52B/h5PGMc4KKuGs6R8oCUIojBdYa3HOIoVECpDeoQR4XT58f+xlHblPvhysCPGAegEl8CAoXDmolCyFoL236OlLltMZW3iPEhAqWb5wIVBMgw5CEIcBXshS0b+hS9fVWCbDEXOdDtYUcDgsAUxIvBQlGPtSuT8QoIUgkCXIKSXBmNIm8QLpfAmYgSYwgiAM6QPVQBHqAKdKxauUHI1Heo9SkkBLjCuoViLkYIz3HptnVGKNSSzKe1yR05qrYifZ9JoEtVDhlSfQgooI0VO3S4gSiBSeaiwIlEVi8SajEgZkskwvUhSsLy4gozEGKEyOs4ZYCUyWYXWIrrWoN1sk1uGTFJFnzFereNFGq5BJb0iRW1AhWWpJM1MK1uiALE2ZJGX7RY9He0DLKZhOJ0EpsQ6ct9gpkMgjabPSojqi5pSYhBDwwGgt69e9KFvaKKWw1iG8QyhZWlMWlBAYV7Ykn6IVJRO6VEQ6AjU3Y8g+YLAciUhPJ35nH4gFlcwXi0CglJgeeqpH4D1SKbwvgUiIBxVLR1a581MAFdOkznTimApml8eZ3fsM6KZBDyGOmqQeCbuL6TGn35icWu6K2TMrZ4oZeY3ZZCfl1KI/Vg3p3HQsgXDTvmvO4rzAeVHKcM4mNA9SlJPX3uE+b33wDkk2Ic8ThpOUcTKgWmnws9deY+3s46iBo9tPyW2FUeqRzvKF3/gV/tU/v0+hY8b7hzhnee/td9nYvEdcD6mvnUKoCjtbBYfb90mynC/97m+yUquiA8G9yXUWmmtoF1KMJ+xsb2BswuNzTfa37/7ygHpns09a9LmwWmWwu0slmuP+3pj9rUOW209x4/4evTv3WIrbDA726R+8xdnzL5OcXOTgxvs02mc58dQFNjfGLK5dZM/9lFMrCoynvfAySW4wXjIxuhwkxoG3U/cqJLfmyM3SspTxirVGKMVMB9A6i3MOrTVKKQrrEAjyPEOpED97mWVYhsEoJ6+GFMaXbjgOLQTeehSU/ANfrsN7FGK6nUC4Umtq5kpJIVBSEccxk/EEIQRRJWY0yEsA9qWFm8zcK6ZNDo0lEJJQa+R0hlaqTLQEQmLTDOklkRZomeGlQvkMRQrCo6VEOod0DlcUeBQqClBhQBgGCCxKCZy3hEpSrwZkRQHWoaUEZwm0wHuDlFBRAqMCEIpIlA0HhTG4JCOYfhhRqEA4pPA055ps93tHYIUp0GYK2FGAMznD4YDV06eJtSKqVJEqIKpVMBJaywt00y1cmhMCoRRIW4A1CO8p0pQ8y/GhQmmNyVJM4cjVLCFVAlgxLQu2zuGcASAIggf6DKK8PCmmw/wYsEBRWkZTUCzxoZxsSxCaAYM7sjK9L9kjQpRvUvLA6ixd2FIj1TqHM6UwkMfOTv0gBCBKcDriXEIZzhL6CGjL8SWPzivsLISgHoQJSgwq/54aDN6W1zsDtPJJlRsLWUptYh/Ebz1+2iPuwXJEqD8G/kKUxoybVqg5Nw1XHBdRmYUnLEcWJ96X4C5nRo8r9S+m1rkRZecC5x1CSrQ1XLv+PpNsgskzcgFbm1tgDQvtNlFYjgeX7vGv/ui/5N/7W38PqRKajQAROdxoxM27N7h48QUOXM5qtcLdKzfZurtJkk9QWUKitznzyjP8T37r7/O9736D/X/xRwz6PTrNBZ585jmWVh7n60HAt/78D4nrMYeHA2iGsLXLrd2dXx5QA6A61yQpRqy353GZoNf3DHuOWNYZS4EVObvbu6jcUNgtNu59xM37r/H42XVW1l9lrzuhVgvJRz2eeO5Vkv37rKy1yKr7XLv9M06sPEmhmoQShHfE0ySQCwxVFF4IpHVYLzHOEGhJfzzGe6hFERaLdR5rLZVqhUkyIdQR3nuK3DAYDKezdwmsFsU480zyqTXhZoPco5VEmnKACQGVSlQCizUlGgcaHQSEUYjUGiklSkpq1SpFluOdJ44iXCUi6/VRQhFXqti8IPEO4RyuyMsJQZYgrRDoQCELNw3ZOVxWwLSXVaAUQVRB2pLUr4IApXU50ztLkaYIHc/CW2ALokoFoTTSgcQRqhDh0vIDsdNPx5YhFucLhBM4KQhUiHQZ2AJf5NgsI/YSKSWJ8WipEEJQGId1FrzDOofPc5L+AekoxXfaxFGI1gHVUKNxCG9RWhGKAGNz4iBESsVoeMhBmtCo1whcCfhKCnyeYa0lUFE5WWUZ1jqywpVhmanhmRcOpC9lFacUPik91jqs8Tgx/ei9mQKfOAY0vnQVjsU5vbVlS3IvpmI5M4vSHmn8uKkf747ZYDMLWcyO5aeAI6c9gv0M4ATOlIDtnEGpspVQecQS6OXUMhQIhBJTA++BpaiknLrWMxyfgfI05ulnxym3O4oIeI8TAidmbvc0/HCkyjWzijkC1zJOOg3tCCiDOx+nOErs0cQwdeaPjjfT8fCAtw/CE9a48t68R3iDEJZiek9Ft8u9WzfZH+yhtGJ7f496s0XFVTFFwomVZRY6TW5v3yJoSL7/7T/j1o2rrJxax+YZmcy5+eEVvvprv8+zQRUhMl777/97dg52aC82kKFB5Bk9m9FNLedfeIEvb+7z+r0PeOWrX2fsPeeefoaVuAmuIF6Z4+B2l7OPX+A7/+KfcpikvzygDg4TGqJS8jnHnu3uAc4FyEBx/+AKYTLPTvc+6jAhqmvCTk5FxcxVLDbIuHH/R1QrJ6nNgwgn3NnYoJJ7KlGF/e5PiFyTx+ZPMZZVCidoxhFZMqDdrGEEFK6gMIp+MqQWdzBZQSgdjapkmEmsLUgzR60aM05ympFDK8Ukd+A8gSpjejN3zODwxuGVxAuFk2VYwRuDcw7jVBkHm8781pfuLpQfqzOWoAL4qaUnPM7bMubkpy6bM9NERuk+2sJMrYjSaXLWlYLcXmBcSbIv0pysyJFYpHB4Z7DOk2cGW2jSiS2jWBYyP403C4+njGcKD8KUo1apiOGkAGGxFoLYcdAbUanGICwyiMmMIZQai5t+MCCdRcsHfY+Md2jrMDi0FwgVIGWA9AKpNcI61PSeFgPNeqvBZj9FWAilRImY6zfu43OHHU5gwWC9Iy8yhNY0pOKJ+Ra3BgEm0gy0QlhHoQRZasicITMpXils7imygrG2VJxDaIk1JRfWGovxHmc9wnkyW+Aoxc+FlKjpJDWLEc48HgQUuTlmeZaq/w5RvgPvS+ARD7pBPdh3hmZTd987hC3fqZy64dZaoGx/PQM5L2bDxGKtQUlVWmvOTa1FcQSEHlC2vAY5yw1MrWbnLHgIpl7N7NqPrGXhEd6V4Dwdz25qVSI8/iiR9CCGzBT4xZSRJuAotOC8ewDSPEgrSSFRQsBR6GJ6vxxvDXOsWSJlwY0UHrxEI3BZj/v3r/H2tY+orS7zpae+gAiqNOcWuffOm6R2QmOug3SGve42wio+unmN2mKLdm2eMxcv0GgtMhnuM86HPPOFr3H9yh1WV9f48Dvf4c3bb5Pt9UE6whjWTp/E7BYc3LnPB/InNJfmeeELL/Otf/IT3nzrNdbXz6InBRtFTr+3x8mFRbSV3Ll/n0JGzJ147JcHVNVqYFTKmZMxJzrLjPoZBB4Zx7z3wUc89dRzbO7usjznaC7WMWHBRzffpuY0tneRW7c3WVie0OhERKGhHo7oxMtoV2Ghep5aVKXXu8n8xTn6vQrKBAgnwQm8MBgrsR4MMYVMyoC1A+s8o4nDaYN3CmcLLA7lCgLp6SU5QQB6Jgc9S1Z4T4hHu5yKsDCNUXlXMgmcK8vchJi6QVPv3nmPdFBmnQymyLDOg/M4B6PxpLTUvCcdJ+RJPgtpMRmPcEFUDnzvwCusLxNbOghBabQMSrHu6YBzrgwz2KIg0FV8pEAYAg2pLmPEkvIjEr78gOU0nlir10nTgjiNyZwhCqpYlxMEAd5lID2xCMmERzuBKiyFKK1IbIG1Bd6Xx9beY3GlNYvHeolzFoedxvLAIWnHAUuVmNAL3PT5KZfRqsY0qzHrnTaxVhQyJLaOTqNCpxpzeb5Cu1FhIgTZ3h4iNWVr8LRfPr9UoojIbIZ3FiugMBbnTQkKrvQcvCg7Ryivyvcgffnmbekil/mbcvJUsny23kxhV8gjQHHeI6TCzBI3tgSJ40mhKQ5NHWI/+6OM2XrKOKyzOOeRqgwZfIwyJCVuOtYcnsJZnLWIGcMFe8RGKBNAHufKGLmkFCDy3k3j/OV1lewRNY2n+iMQnVHBxNT6REqsL8ftLPY7S+xNs1+lJcnRfDG9x1nOyR09Cz+dSMwUSI+YCEfgWU4i5b1Oo8lKI0UZ+iq/Y8Prr32bt975Iboasxhl3N1skRYTFtZXufXhe5y/8ARxa4VOYrm9f4PD7hYmlPS3djGDgrs3brLcOUnSPyCsxuwPBrTbLX7wnb/gxvuvY+yEUZYRhJL5+RVuXN0ktgWme5Pe5m1OPfE8P3vnDslel82PrrC0vMDr3/83eDGmUom5vnETmXiubtxhdX6e1uLaLw+oT5+vMjcfsHl3Hx1kzHcaJKOUcZpQjQLefOM1lE6Ya9ep1kNu7h7SO0xZnD/LQnOJ5gtnuL/7fba396mHNRbn2ySDIa3qSdqLZ+nMd5irNbBBjZFPyYzCTgfQYXqbMF7GUUFYh3WqdHHReBTCWLwuy0yNK4PpTsw6pZbxw8K50nKhTL54D9amNBsV1jt1lFLg3BRsS5d8FqN64BqKIxqIwE2zsAqtA6TQSDRKaaJ4+lmqgFpN0+8O8M6jtQYtEbL8YJUsY23WlgPKUWCsRklJ6AQBgmw66CIdABYtPEJICudQzpRxPk9JWfElNxafI2WD/cND6o0axhqQjskop9GM2T0c8fKXXyJXZaJrIkGL0sYMpCSQCqE0oZo5fSVQC2dRXkCaooVAuALStOz+Ksvgo8Ow3mxx+ZRhM1L4UUbhCtpRg1ooqFc0KoAoDCjsgMDG1KqKWDhOr82R65iP7t8jIWVtYb60ti0UPieuRizNL5AlY1StjpvGR3NjQIY4MbXWvcBgjqwmawwWTzZ797Y4YnsIOJqQShA45rJOYycPXN9SL00cOefiyNIsn5E4Aj+8m7re5cTvncULO7Xjp8ez4L1ECI03tqT5CTkNC0wn+FlUZmo9Ou/xtkxMOV/20PKilLUr3WaHmJVsTq9xlpyd5eVK4JWltckDBQR/7F2Dn66fguPscHL2HZSW8ix7X34gUyCePcvZ1zOL7U6/JOfLxJegnAi0h9s33uejD9+hPzrkxQvPs3nrBt+8doWsa0njmGeef5HTC6cZTVLGW/fITEGt2aAm2gwORiSkDMZjDvfepxJJhvcmiM0e7ajO/ugQkQxAK3LviOohuzsjlufP49wBadqlPb/Es+ef4Cc3dlhcm2cwGnPn7Q+YjPqcPnuSg60DJn6fwXhCkXhy2+HgoPtZkPnZgNpcMGjdxJsq2bjCYiPm6sF1mp2QxeU50kmBzSTrp1bYT4akE8nJtXUCH9LtbfDGh++Qsk27M8dkOGZsdvBpjA5qLPozNGrLyKhJd5CWLqq2pC7jzu5desObzHV6JEVAOzqJty3wFiMFzkIQKIzLcGgKLymQpFaSOE/hHJqSGWCsKUETj/FlUz6MwdgCgZxmZssEk7OmpKT48gNSQuJszkzE2HtPoxZTiJJ2I6TEWQfOUI0kzVYN4yxKlNtrLzHOUq3UGOCmFq9DUYKgsAZJGVYItWat3SAcJIzSAUKK0oU0FhkWKCsJrUJ7x8g7CllKI0LZ6roWawJjkJUYKUq9AGsLhLWYJME7gxZln6wsSTHTpFaR5Yz6I7RzuLxAxuqBmzvNumpnMYf7kAyxuaPQPVwypqI1gS0nr6aUPHtqhXR/h7uHA7pUefb5M1R//CY1rSHPma+FnJLzdBFo4ym8YqE9hxEBq7UGGMtqpDEHO6Tv/5BaFLJejBk16oRakLicWlRjUqRUopgsz9FS4JzF2rLuzliHFqD11D11DudcCXjOobQqJ2Ffeh6lZTkNB8gyJDOllk9Bo3Q1tCq7CTj3oDnfLGZZ8jrFdCKfRl6ndCfvXamXMD2mn1KxBNMkmgxw3mNMccRaKBHdlUwWP010ebCmJHtZMSMXziYC8NZNk1jiaPyWAVgeWKNWPIi1AkeSb/Ax03S2v5gxZcRswpiWhHpfGiMz1/7oSmaJp2PJt2kownrHrC2S9Y40Sbl18waDwYAsNbz79ntEyhPUQ3YO9zi0jvVTJ1isL/PBa9/h9as/IdKSSAakxZhJpug0WwSBZJx0Cett7KSAJOXu3iE+lJxtL7LVP0SGAUoFLC4uc/7sRdCe4XjE81/+Kj/+o29TP7nCSjHhTq/L8vJJxKVn+Pqrv853/7v/Nz69h1WSy199hWxzzJvvvv7LA+pCc4HeeMLyusfZDVInKXTOYOyo6j46d6wtNBiZLqNsgBYhxnSJ2xW6+9dZqDnu7YQMbZ+5+RpFKhEMGRcHjJ3h3rAgG22AzQhVh2FWsNO7zeGd16jZgDt3P0TWLOeXXybieQwxo2GB9wW5DRDSIp0ht57MUQqSSJCE5IVBIOhNJuUgmbpWoyLHjHJ2xiOKwhBMLT1HWTgws14A0jQndOIooG59Qa0SsT/OsV5ivcUJh/Ie7yytWoXUG7LcIpUHWbpmxhXTTKpAiJLlgDck4xG+yEnHAlWRtLQj7DS519/GCYEqHIPxhNBafJ5y0DsgslXIJ6AEVjukt8yHntNRlet37+PXVhChgPEEOTbkhzs0mkvE1vDuT67QbrQ48dg5NJ4D70mHQ86cP4XNx+zj8UVRph2mH3EgPREFl8+c4iPjsId9Hls/ybvjPoHSLLdahGEVFQY0w4DFapX7o03CxTkaUvLrX3qB1cU2QVxlPLGkO9s8fuEc5rHz1OIY3a5RdfBbv7vEhhc0+jlSW6LVZQ6NKSuz6hUmwyFFpULmBMbmpGlGrCSRCEitI8ssqQeDIw4E1SAkn6RIFJHWCF8yAoI4JjUFibEYW7rPWoKzZeLJHUsgzdxgIcCYabLFlyI1Qk4lfUTJJ5255wh5ZGE6ysSMsw9rVbgy7oqnsOkxe5Eja7XMTZWwOQPpGW1PSjHl5T6gRQlKoFJyaiGK6YXP4p0SQJW5A3e85PXB8UWZHZuZtCilpta4Lx+FnyXHStrYx6qyjlnr3k8TXGJmL08TbxKcc4Si7M7gpWKus8xwOKE9t8a9ux+yJFp0uwdM8Hz0+k8RuSD1CcO9LfYpCMMaMg5Y1FXqskpc9Zxdb5JmKYND0K2Y9YV59g+7rJ46wUq+xPZgSKuxSGoNu/ubLJ+9zLmFM+zc3+HmR1cI9+bY39wkCyXP/Ppv0N3aZ/f2PeqdJd750VvMnWlRaXV45uIrzFfjXx5Q58NFpN9jEoQMi5Qb24cMh56liqDTiWk1G4x6Ofe2epgcTs83GA37bG9sko0zdNyiEdTxJgGTMckVXmSMW3sM8hGm2GV78k3kRNGKn8TGTa689zatSJFOxiBS+oNtJv2CF194Hi8smTPkkrKCxwmUz0vXSCpyZ8uYqsiRHpyTpbsK4Bz98ZA/+tM/IlxYxAcak+dlQgNbFmYpiZQBWjuCAupR2R9KSMuUpUoxHVjVMMQHEmUdcRhiRY4QnkAEqCjGMSzdOKmIdIw7ys468IYwCqi222RJhtaCIptA7ug0BFWlSBDkXjCPolaJmcSKRQR1HdANq4ykZ9I2eMMRR/ZMp0UWNxAip1rNWbh8lk4YU0kLgkhTO3GWuThEJglOKJ49vUYkJLowOBRzqytUlKBBBFKRpRlJb4fFWkhbBVQuPUbFOmy9zquPXSJcnGfrfpeqTLhWr7NkLSZqopIci8MEsPryU9zfPySIa6wvxOjxiKt72+RxhN7pUb+Vsd5u4ZXmXjLipKrTGFrS/Ao6joitwOdg04IkzcsuDXHZwmLoDBNncG5qwU29XVMU9IYFeNAioPAWk+fgHWo8KpOBSiCVxjtP4fPyvUwJ5uIoIChLEj4OMCU/dBobt95jXWmpee8wBkqyvj0CF2dLl35qyk7BssyYH/OJj6xKplVMZVJnamMes4Jn3pOdARvgnZxm38swgHFMJe/KpGnp8jskgnocEqngiNXCsQSbF2X1lLUW78pYtTHF0TWWICmOrMzSqzvu4peJJiGOTS5+Bqfl+f2UdZJPhvTGE1bPnmHQ32W1s0ow1+GZ1nPcvfY+ca0CccTJsxdwgwmDbp+l1gr3+jvoRog3GfVmg/Ggi6qEFMKTWYMMQuqVkNVmjUYtYP9gi9XFDtWJpCodh71tRFXxhctnOXjzKpvpPn/7P/z71Nw8ZnTIf/5//T/zw/ff4u999TfYv3qH9RMLNOcryCjCDA/5YPATGpXPhMyfw0Pdt/T6E+pRg8WlOVqXz/OjH79LIBXtTkC/2MI1YmqqRVBo0sEB3d2CxXab1toi21sHLNdjwprAtmts3tmjWanSrmo27vwFrVQj4h55EfDR1k9pNy+yWq1y59ZNmvMp92/tsdSqs3LqArkXjIVDFbp0d1yKt5ArhRAWKS3OghUeLx3WZHggL1KccwRe4oyhf/c+jUBC5pmrNSDPCbTEW1dWetkyblWvNzCFJYxqyKZGGoeuhKQjg/SaJEmZlxHVOGK030MIh6/VyHt9TOyJ/LQzbGEYjiY0qnPYKmjjCSJFUI2JohpqaYlYSOzeLqmzrGnJV55/nq5VVAOQ5+vY/QHnf/XLSCSHd7a4/NIrHHR7PJmkHBYF6f1b6MLy+GKbvfPnuP69n1FbWqDzzAk2/urHqEbMua9/iXf++Hvsbw+49HtfZ/fmbXqv36D18mWal8/x03/2r5lfXebsr3+JH7/5hzRbDV66cJHK24ecrrfIKhVe/+B9FlTI2Wee5drN25xemufaxm30sMfTl58k27pDXVTRqiyWiGNFPhwgJwUqGdLowGg05snFDlmoiXNHLBVykiHIqI8tzuxTizzZYB+rFBNTZu1xCcZaRt6SFWOk1/jCo8rMD9YY/JQQbmBqNUrw5e9lgYYvC31VSbPCOoQXyGmM2/tiankq0qQok4dhhJQWhKEo3LTCaga0lPSqGQB7gbVlHFJJTRllKHmgSkhKHu+URnWMruW8e4Bt06RVGdMs3X2l1NFxgGlmfvrvKc/UI3CyLPss2UllyEALicARBgFzzSra+ykXVE9d+xIf3TRw7NGAwFpXJpSELClisyTX0ZmnjAb/wO33vgRq75jW1pRHdzOmiBMIl/NXP/gBW8mAVtRkkhc8+fLLiFqFez/8AUFrkVOtFRAhp06eZf/+BhbPM1/5Knz3B2wNuhiXcb/YpRk2iHTM2EY8/fTL3L1+i/Fwi8NBj8QFjFIIRyk68NzbvIaRsNKuko+HhHFEsn9Id7TN7vY2i52A1U6d8Z1rfGPQRxvJ7evX2BvssV7RvPPG91lrtdixD7yJXxhQcS10kHFwcIBwPVrLdS6db1CMQ0aTkP6kwdWbt8GEtALFuYstVH9CqGOSkcVnDlnLULWQsU8Iq7Cyfoa+7THKduFggbha5SDfoTdKmOwkKBeTDg3r66uk9i420STjm9ze+CukFKzOXQaa5CpkKMr6fOddGRt3ZeZaesNc6BFa0JIK7SHHkisYZimmPybPDGquyVyjxng0RE6J2mZzCw8srqxxd2+Xhc4iDti9fZtnX3qGD157g8XFZZonl/jge6+x+vwT2P6QUX9A59w58us3WDhR48N3t1AeojBELHZQicNUK+zf2+HiU4/z+g9/yFe/9jjXv/89njp3gcPuHlI6Ygv1hXn++Xe+Rz2u8cXf+x3+6I/+G5549WV0LeKN9z7kN595ijc/eJtRt89zFx4nso6WlKRasNHr8fbGfeLhgCfn57mzN0Ts95m/1WfQGwMF3avXSfo9WKqT7O+zslnjfKtOBajf2uSpxQ7j/pBicxcdRETGonf7PKkqkBu4dpOLcY3oozs822gS1WsUb79HO5bowT6EjtA5JrfvUBPQShXSQrazi8tHNLqGzORYoRl6g7WOIIpJDaWugpU4Y1FBBQeM8bjRPkm9BaqKcTmFtQjviZQiDBRSz2KUrqyuGqXT7LWcup+OKNRoLTDekecWUHhbVrGGgUTJkl9qURS5IVAakyWAJ9AlkDrrEOIB93K2yGlSR0gBxmNtAVohpkkn60o5Oi3VNOjpjuhLfvqfcxacQwcBxhTTRpAlWClV6iMwpSFNiVXTrq9lwYgSUz7sLLsvJCXlWREo8DYHNbVnhaAocgKlS/aDDqbMFDdNPIGQHjllQjBtFX9kVE+v3MtpiekxYD1KYk3pXl4IJJ7ASnp7B7z90x8Qn1zk4rkVWotPcvmFlwgHI36298fULpzk+RdfQo097334HqZZ52vP/C75wZDmr83x3vtvcfPedbzIEHHAcNLFuyqFCdAiZDxMCOp19voJFy48yca197BuTGLKjqbaej585z0aUYtWXMX2dtnfHPCdv3qHSZ7w9je/wc1mndRL8v6EqFWht/0eoRao5RUm0V+Dh3pm5Xl2R5vUTgdcu/FnhL4UDHFhjXHmuLt1j5pqIXRGq14DKajVGmzv79CsL5bZ2DrYOKd7r8t8dR6bKGQtJBQRflBnc/+AtAL3bkoeP1mhd9hjdydnabFBo7qAdB4Ky73rP2OhOY+IFmnHVQajAh0F2NxS+JI2U/IfPU4JlM1w1pNOxlhXfnzKwe7GBgthxOHBIenePvv7XQprMIVBIZmTJa2k1+2SjSfsbe+A9VRyuHflOmFuGe/ugymo6YDBlVtUwgBlLRvvfci89uxsjafC0xY3yRht76ETRxpp/Dhj69pNYgt3PryGLjz9zV0qXhJKRegKmAx5Yv0EdSGo3bzOlx8/zVqa4mzGwuNnWbpxj19pdkjjGt37G0hvsIAbpyxu3+FvnD1RAsPVK3z5zByB81SuvMMr7Qq5qxBt7bHgMqhqgjTFfXSdM7UQn0+Qt27THg8IhSPv7lJTBaGbMO6PqeQTCiGgn6Ek+MGIWAdQeGzg2BeC2JU9rRwh43FOYi2RjMvabJMipAPvSkqWdAQyQCqJCkMi6THOkElNsNghE5p4oUE/6XHy3CrzlSqZUyR9yU4KhZbkNkcqhRKavHDkhQVR4IwD1JRwX0KAt+XkOxPXKV3iMlatvCvLiX2A9RalBSoox0ReeJxVU9qRmyZ4SiqTnVKcSgrTNIGlNca4kis71X+YJceMeECSR8ySVuX2zrspcJojZsERcd97pPJHGhOzrLxSlNoQWhIEAVCGMpTSKDEFxql7rmbJeVV2+ZWqpIxJIUgnY4TS6CAsE2h+RuKbRW9nPIUyyzWNriDdzMqeUrOmFL7ymsttjM1xQWkROy+ZX1hi7D1/8Zf/ivmnn+bMuQuYzS2+8Gtf44N717lz/yaPn7yMKzRf+d3fpNpP2E/vYasR//CVl3ntT/6Mb7z5PSaZJc0muHTC+1e+y9riCc5cepb7G3cx2RajvVtoWxBX64y7h4xHKZv3NsmLbRaXlhjubVFvB2zfTXC6zTgbkWcTktzy2Isv0/Qxb/7kB1TqIcrV2BoaAvPJLgqfG1D3koKucdy7u00QhQhp2dnb4WAvI08dtahK7MHpiMG4wB8oJmlA4TRpOmYhanBqeR3XLFgI5zjcGXHr+gaN+RXSxNE4IeiPJvhJhSDP+OD6PuPuiEun1mnUMlrhPHP1Bv3RgBOtZZrVOXb370IVTiyewUhFHkgGowmRliy3m4SinOGHB5ZJIbg7TNGmJH1rJwi8Z7S3Q2Q9Ok3L2JooYzsC8Lp01cb9LpEXuMHgiNA93tpBes84yUhGI4w3kBcceoeXYKXDGoGJJc1cgBaMM0OWJ1grYAwRgmxrQgPBZGuLOh47PCRUEhUEFHVFfrjLqUAgTcHo+jUWwgC6uwgvqEWSyf59pFMERc5oMKKiAZNSlQG9QQ/lLQMNwikiBGOfY8KYQZahVFma6UKByXMqhSeOKownI2QU0JuMya2gUq/hJhnFtAxThQI1cggREKMYyZyaEKV5R+lqKxFRxEmpx2BLMNKBQjiBbtTQIsbe2cYvdciFK+O0eGwcE3TmsdWQSrtBp9mi0lkkbtYIQ8/TxRiTjEiSIUNbkDYiDsaGQlXx3mGNK0tTjcC4Wblm6W5KV/KNwyDAObBeYL0+imviHfVKhUYUYvKM1EFRGLRXuKIMJZUlzbYMEcipZelK0PGubGLnVXksIcBJh/ezJFKZyFJKlpoUSpXblWZcmWASME7KSjYhIQ7DUn4yriAETCYTgiDE5Y5atUKr2XjAJvAeIRy1KCadJKR5hrGeKI7Js5S5Zp1qJZ5OIYIiN+hgGkedxhmElESValnY4GfltaX2hJ+GCGbMgyMwF8dKV2dxWMqJwwtxlMwqS1BLtoBCUqlXqdQquGKEoU9v4zb3r39Es1rh5s27dBoVRsMhcwttTl86TVhYtm7cgFrAnR+9ze7d6+z1uiy0FhhnPawvyvJtN2K/ew2tFsr29lVJd/8uQVhFKkGrEtBst7h99xqFFUwOtqhUND60tOcXeOrkU/zln+0yCRIWluZYmKuiEkGjEbN4+SSXLr3MQjzHj/7iD395QJXVU4gwwmSW/e0cR8idWzl73SFxEFJRXdbmO3gRMUpTxKTF/lafZDjhuSfOcHa9SlNbuvkQLwQ2Fpx/9ikODxSJbfCdn1znxJmIZhgTNTU/e3+DrGsYVAaYxWXaTYfNC5pRnV7/gJ2t64xTSe3xV5Hr6wjvqCpFo1IlxSJCR5El7A56jEcZ/aTgyo3rpJRVhoW0tNpNtvf3sFlR9lQKBIUzpSSZ91g3ZY8Ij3O2/GgQpL6MAyXW4cuaTcBTaFHG1jx4azFeU6QeP80cGxVQWEuOJ/RlplMLRUFZGhnFEdaJUnNASTJtoFUj3ekhpSdYmEOYgswWzDWa2DzBGse4n5MhMNITOFVaOzhy4YlNQFWUEt4zyTvpPTpQJTlelG1cvA4wwmNEQRAqrPBU6opmo4mQMV07JEjHCAsuhLBex/gQpKQSl9VV1VoD4SWbyZBGVCcLKqTRHolSjOtz9GqSarWCDkOC+Q7zX/wy9ZUVms0q1WZEqBzFJMMUlnH/kMlhFzPZZzC6RzdNMcKTmoTCpAgbImzBxNcwrcexxGXyRyikEARBKbThfBnr00GZUTfWI6RFBxLSknQvvC4/eueZTDLyJCNSAhUohAeTe7wOQBRo6cFMn5mSJZ3OeSphRCAl3pcUKOMtUgjq9QZ5npMmCbr0nUs3WJdJpVK7QSOVBiHZ3TsoLUYpp6CtSLMMoSyFyZlMJghRelxZmjIa9DHGIKUiMwYpPMsLS3T3e2SmrBILA43HMZpknD65irM5URCWnSYojtgs3juGowmF81TrVbTW02qvssKvvGlxlJySYlouO3Xvy/Le2eRE6elpdcQa8NNQWlkQ4pFhwMn1M3zn23+MjCuMDvp8ePMal0+eolqLGXd3ub5xEzvYo7W0zPJBjZ3uJsVEM1+t88HNWzQqMfdv3UJHEqRnfrFDrBWT0SE7g5voMGC+pik6dTY2erSbdagJ9nd2cEowV2sgs4JQQ+A9UeAZHu4xHo8J4ojD/QOuv/MG589eYmF9gdW1Dhcvr1Pc2efU2fYvD6hpboiiDkvrFi0vsL99h8lQoIOI1JXiyzumgMl4Wr4oWF1qMQjUlNoSMEwG0CpIexmqELjhfZSNMMWYS4+fZKd7C58b6mGHdrsFC4okNAy8YWd/h1P1S/Q3BdHpC9ze+RlnGwucXz2HIaDarFGMBizML3H98AAzTJmrNVDNFj967VtM0oLNbhdZrSF7Doll49Y9VBCinETKkNwVSBkchQTsdAYua7GPhMuQasodncaFJCCcx1PKAJYWT4BTntBBhiAUuuSJez21XEqugJOSHIvQgkqrzvigSzuscZg5dAG2BWEWQirIbIGuGKLQMnI7NFbmOLxjCWtVFCFBkjBOU1JjkYEiqCj6sYZAlbxTIfCFpQgczhoKJylcgYgqFJUqBoGpVzBBiPSeEysdVhaWEFLTzSYUWYaLNAvzTWpRSC5CVFhFRx4daMKoBUFAPgWEVqXCKyREwhOoCIRnNBrQ6SyiY0GW9HHjLtnePfq3hxTpkHEvmRYZGKxwmEjhyJmMxigfUtURNecZYIj0lOtpwKGQEnILkSyLIUqBm9LVVdOYZqg0tiioxTEyqOC9xiHJi5yimGXXS9qRtp5mFJI7i/UOhKLRqBPlhsJwlGEPIo1zZhpOKstMAxWA96TDMQhBqIIyDqkDkAKlNFqXurtSKXb2D8lyyyQzIErlJuElvcEY5z1pNiQIJEqHmMIRhjELrTqqSPFao4SgiCKMlGQTQ54LUifwUpRaBs6TFgmT9C4Sg9Zl6WochThjCGRJCUzzotQPPlAEQUCtUWNurkUkgyPqGMCs95ubBX3hAUNh6t4rockzA3iiMJyuL3m+FodWAU8/8xLvvP1jbLtOfX6N0EGOISgcu/sHNALB3Y/eoLa/wuaN+2xs7fLMK68wtAVf/OpXee1Pv8nKiVPcuXcDIR2q1yVPJLZwFEVINFdhdb3Knb1dFJK97RGqFrI/SLBKwWTI4mobR4HPYzb2D+kP9wkqkqwAFyjiSoWbN27zwvPPceP6Db5974/YvXOb9tpfo6dUr7dBs7PCRvcaB9t9AqdoL4X0k5RTaxXqjRYHac72jSEnF1cQmULoglZbkhYjgsoJ9nojbv1sm2ZQpdkJqEWCoCGJ28t0uw6plllbXmWcKk6fPYuf8+xvfsBhL6Wz/hT19iWWlj272nFq7SLrKxfJWiskhaGDY4xGScXN0YRK37IzHCE7VYbJiFqtwYXzZ2iEgrx3iNdM6/cdSihMUWCsKekmxuALQzAlbmfmQdmqmMaitJR4V4LsLDEgkUeamI4p8RuPcKVea0VI6qKkjpSSaJYgUIRhUCbZlhcZ9Xqcas+TJymHVUtrJWRpvcXWZsBcoGh3Qpz1jCYFzeUF5Lka1SDG4jjhDBWpWIoVca2JtQKnK1RqFcIQNIqgWaWuLEJJlFNYkyFEACLA2pSQgmFmEEWZzba2YFIIKqOE8d4+o2TE/m4X5xzpeEhhCmwxIk8TMhEytgXeFSggLDIin+G0xHmBxpKZgpd+9dfx0oPLiaISDEplsZReMmZp+Qy5gGQ8plIIpNGIXOKUIcUzCUKsissKOF8hDqKSK0kpIlKWwZaSjGpag6+VnMbtykmyTBLq8p3i0Qis0iBLabxAilJkXAhCoZgkKcZ5tru9KZhIxFRcxHqLogThQEuKvKAiI5IkoVKpMB5PCKKANM2QSmNtgdK6jB9rjZeSw/6Qws+Uq6Yao9PyV+8cTpYsAiVKhd58UrBX9EuVMxSOEtABMB7nBV77aeVcWYYrhaIYFygFWpUpt9yU5dYSVwr1UFZfWQ+2sEwOuoRxQFhtTDVgZ8UKBicEs9Jn4Mjln9UHOGypchZGGGcp2YLTWjFhEVLTaLZ45omnuNbd5fkLF9m6d52JnnDQ3SVzDu0EyShhr3+D9XMRgYJr1z7kxWee5cP33iITE1Q75tVzX+Pa+2/TatY56O1jvSAZD5hXFSQe6TzzzQqHBwXdvUN0UKWqNarICeKI/iChNx7hXYwKBFEMXlma7TrOO+IoQNqMZDxkd3MDn1vGd5NfHlCVdEwOb6HkNhmbpKOCWsOxXK/TCgsWKi16e7s0OwpnRrSrq7RXQrJ8E2/qvLF5m4PtA86f+RWcOKQ+36d7uMfa6jrjbpede9ucOv04hwcp64+9TL02z6HZZrK5zUKUo11Ckm7QaJ8B36JZ7bG3e5Ms2WRh7VlG4wWG2YS7wz6H4wRjFOvtDoN0xOada5g0IaC0GKNAEipdxrLiECcE1mniOKZaq+GcJVQBkRLkeU6BKFuFFIYoCgm1Rosy2+oAAjXl7jmUDCgKg8cwHo+mZaqeKNQEOiw/VqWRSmGsRUlJHEbUm/WS/5jlPPfskygtUd7hRA85sDxxuoUPU7zN8ZMJMs2x1jFWCSJLMLljPE5JkkOuyIwYSU943DAn8AWJ9aQjT1o4tM1xOiDLCqRJsUqRAWE+JpIWEYa4LC+VloRiVDgim6NrdRIN43FGKEKs1hQSGlqhdQjVGCsyLEVpMbmEzBaYqMl4NKHmHGmRMcxSalFEqVdvkT5AIzFFGbIQBWX3Ai/QoWIsFIkNCQKJ8CGZEThZwakKniqRF4jYk8uS4yi1KMMurqQeIUoXvIwVanSoMUKgpaLwBlsUJQ9VOApbhgFy4wgDdSRzJ4OQSEriqeaoNdM6e+/KpJwrq4t0oMvJNfQI44nrAUnhabZqFMbSaDQZDPvUqlXSNKPILToKEUJjbOmiaw8qKOlRtij9ImcdSjiccGgV4hHkQBCGFBaE98SBwltLpRqTu4I0GzEL4ArAmWKqT+BxFpRQZJl7QOi3Bh0ItHugETs3N0e9Ui0TbVNilYCSB4srrX8lp+Ir5TdgbenS5yYjCKKpsIw+ClVba6dJu7IE9rDXIx32+fDd1xnsbVM/uUAlFBBGpKMxBFW6/W0GN68hRo5ie5Or197h7Noyo3ybhdUzPPnEJYa799Fas3N4QFrktOebzHfmuHljk6DWpkgzzl0+x9btTXIcC3MttjfvcnjQI7UCKQLOnDjNwe4ezhUYX+pdeDnh9InT9A+3SJM+1ZpCBCDVX4PY/9ob/4bYeR57/izWCgJiVupVkAkBLXzuWWq3yQ5SKg1PXE25tnGNi6tzEBgm22NOnHqJ5eUVBmHA/e0D5sMa1kqcyTiz3MIM9qk3T5JMumzubTCnenz5qSUSs8PmxoA1vUSjIUiN5Ts/eI12rQJZjVPnn8WlkHtPMeizrqrkkScd9TiwY/YGh+zcuEYYCGThsFoQSk2o1bRU0JYgOBMedh5vLcIWFLbASgVClW1StMbbUu9TKImxHotESI2QUBiDlJIgkKRJggQipaiEIWhJURTTDDM4qcrZLwxLqTmXUlGS229fokVRWmChRk4yeskE7SI8KVVtqMYOF2icA+UtVR2ROs9YCHRhqWlNzzpqQiNVSiIC0gKqWpGaCV5VyQKDjBSF0tjCoULHOPek2iClpa1iRh6GY4vWKYXNyVSdQGXUlGUsNTIsk3uFVShjqIqyei2xBhmVsbswy1BCkLuMKPBU7QTlLcZbChMjQkFicwgEwVyLzAu01hSBplAh2dDjxrA1GnJrc8yo6PLq3/kD5MI5vAiILYTOIlQpui2VptT9KJWlzDT77mYqNa7UrlVaI6zClXo1SAGVaaZb4IjiSslrdR5jLVopAq0xeYHWZWcHwQwgxJFSlI0CvLdUaxGFyYgrAWk2IYwkhZkQhQrvCuJYk08TUUopAjsVq8ZiclOqSwFBoFFotCzLl60va/kjKTCTBCQEUoIoJ4NJapFaEOsIY2e6qGUYxtjSirWl3H9ZuCBABrIsb7YQaY/EEMVVFucaVGTZtaLfHxDXqoRR2SjRGYfUGm/dVMj7QUmt954gCEpPbtpufRaGZUrpwkOWpSAlyaTPy7/yFaryRa789KdcvXeXk088zXf/8gO6vU2MAj/pgRcU/YJas0bYaLKsFYe3N/nmnX+FjiTGawajCQtLK2gy9va7ZLkjGY8YHw649PKXWG+s8K0ff4+bvQOaC3UCBOODMSIIGA36NCohgexwePsWSZhz7uQJhr0uuxt9LIK4ImlWAibD4pcH1NGoy8raY+QpzDUaZOM+EkM9Crm3s4USFfZtDgby0NNL7hPWPAe9CavLAV+49CSj6BL37/yYSX7I/n6GrFS4eeUeSxcvMTnY49LTz3L/cJO5MGU06TMfG/YHQ/bGffYOLJdOVtgb3CXLAh47Pc/K6lOMXI3uSBHkPTIkwngaUUBakUzGQybWomrVqTK4x6uSOqOOKDTTHk5SUniHEpIgkmTjFI3DKYmVEpwnlx6nKa0b7wlljpNlXbd1pRA02pH78vdcG5TQpRsqy5k7tfZoltZKogJNikVmGdIVJFrRSxNqSxGmKIjiCsZMaIlB2XupEhFFVRQFRhomY4EWRZnsCAOywiA0ZCplXOSoMEBZsKIgH1viSox3BcNhl7quIqQnmxSYNKfarJLj6Q8GzNVqFN7gEcgkw0Y1gkCwP0qJKzX2swLiEAqDkhFWBgQEZDIGIXE6I5VgxAQvSnWpIlZIl3FISOADpKxTVQEBEmcLjLVExGx3B2QuJS3G7I8chB2a83VUewGT9nETydvv3uDyE8sszK8gNVz56G1W1teotOZRriTAG8rqHinK5JRXpfpVoEoVMyFcmbBxnkBpnDUILxj2+0euaxCGFL7U3s1Nigor6LgUI5dSkWUFUaTJ85xqJeJBDX9pyc1EV44qhphWL820RaVikuWkkwEykIgowk01W5UsqUwCgXD+QcsRWapXpa4oNXlVgDcFCEEQB5jcYIwBL3G5J4oCUBqPJNISYw3OG2Id0KrVyLKEpEgJo4BGpJmvV1BacDAako7HxN4RRlWiQBFqifCOJBlTjatTr0xOqVPlNUskxpZhLSk4En2BMvNfhiHKfMQkGZOlGdVGk7vXbtNZXsVZQXWpQ6/bY35+iVEyRCkgk5x56jGS/oCD7j5xNEfaN+x3+9SrAmHKPlkmzcAKrAwRRqBUTDsO8bnl3r3bJFt9nLWkhUHmCQuVKivzqygtWV+f4723XmcysEjKoozRYEJVxOTW4WRAHDeJIkGoPhNPf04b6Uyzs39Afdmg3Zi1s2u4dEgxEgwHhudPrHJrex+ZJOSRQbuIaqXD1s4+1ZogT8ZUTwjCdpt044AKgu1hRqXeotpax9QkrrWEzHuMxj1cfkC0sM71+yOaQZO1Jcl+oSA5xUfv/QnaDalWT8LcKocZyDzBOY23BdeKfXzPlcH/QBHP1fHWooNpRj0v9TMLV+qXCl0KVks5rQwxlNU3vmxMpyipK86CtgKcIgAwBiHK9hhqKrjshARrcblDywBrIXeWYFrF451HSA04nDGlglMUo6MKvpAI5ZHeEuUJ2AJRJOjYoIXGSE0vT1GVGAqPl4JBkrJaCaDQJM6iQhjlCukr6CxlhMcLSezBxIp9YfC6ThhA30ckziKrVVTg6HqLEAWm1iaPajg9rQmPBQMtiExGrV6bNkqEkQnRUqCjEKMrjCmo6qiMCxuBHR1SFxETW+CRxN6Bych7B7h6he6wSn/SxVvB/LymFjWpRho3Nwe6oCYsycAysRV6bp50D8K6JG4r7m3tUT+8yYm1eRrVGnfef5eD7Q2e/9IrVBtNnLfUw7C0hoQCxLRUs0AJhVBQFAVKBkgBWTohjmOsgXGaMxkPWVtdplKtIKb80SAMyNKEuFKZtoyBKA7wQKUaH8US1fRcSZJQrZZ0rizLqMQxD9T5FUyz/FEgMPMNBqMJxtlpu56SAlVqAniQpZCKlKXavqAUYo60JxAWr8qOFdZahJZIJ1DWEUWCKCzHnbH5VLilLG5IszHCpEShplbRaA2RdownI6z1+EDhJaggKCutgqCMdwPCemxRoIJgKiPoj/R9S02DktHg/JQqM1XPElNXQFBa9J2FJcJ6i8WgStEbcyC3+Ol7P8V5ydKlZ/m9f/AP+N6f/DHtc6fo3d2FKORLv/8H/OSb32Dr7l0O79+nvTSPKCYI5zGFZb5Wp7e7TWNxjt/6rT9A5RGvff97jKMue/fuMhmlCCdQXlCMU0wUI50lCiSHw1ucurzIZFeRDiZkdkxv0GUcVFGVCqaA/YMR1fUFrP9rWKi1tuHMqRPMNebY3OwyNIeIccpGYSnyiF4aMdmbsNyKEYEnlDVktERYb/P2tQ9ZbyXUJ3eotSLScZXD+ztceOI5rOkiJ32e/MKLTKKQpfAyo/0hkXb08xQvJTevDrl4uoWxY3Z7iv64R9pPaTR7rLc9xiUECooiwwvBxGRkacqbP3qNr331a8S1OVIrUM5R6KnOeFlGPxV8dhhAeYcmQKNKrVNTYIXACVs2IEOVtJswIDMGjST3BV4E5YfkPcKBc2Uli/Ueaz1RqMitxQlJoRR54XBOEgqFzA1ag9OQe0tsAakZFgU5Hl9MSv6miEkKsKpJf2IJZEDmJYNYIQoHOgCpiISlKi0D4ZhUFbF21IOQNMsIXUBHRCTG0c8g0qqsjiksgclp1WJcWhD5ED9M0KIoyx0LhxgnjAy4IGZY5MRFxlBadODp7x0wpEqhHI4Q5QXaZ6S9LrFUrF84zU5/SBBrVldPcRCugYiIOx1ELAjCFpXVFrlqMLaKw0mCG0OR5DhZo1KvUa3HnGhXaFbmqckaO/0u17bu8uZbP6HenONguMPNjRtcu3GVv/s/+h+zsrxOVuQIVSp1iWk4J5RhCXxSoCNZxs+9I6iEpeRpIJhbmGNhdR4tSwWyI4k6PNVKpQRn56djp0ziCPGg7ckMVOv1WrmXl1TiqLSyjm0jhUQ6EFLRqEQILGlRcEw/5ZjoyIOSzpkKvhIlsHo8QjGt4JqJugikm3Y0VQapHCqYxkClwnmFEAGBlGVnhGmdakLZGSKOI0QYlNQpWZbuxlF0JFdYrVan9+wfXKwuCah2yqEtZTLlEY2q/F/5PL1w0xJXydf/5u+RHtznn/4//jEHKmPp1El2N/bQnSZzcx1OrZxhN8t5/IUXuP/Wh/zJv/iXtAKBNwXNToutjftcOHmSIss47O8hUDhXcNjb5y/+/M9ohy0O93aZjEakNifNcppRHWE8Sc9xb7BPp5ORpprmguXCqSX2koROUEfGDa7u7JJ6hSAnjOqcO3eexx+7gCj+GqWn9blDCrfBnbuH7O3vcmYxRjhDdXmRYJjz9sYucaRoLtUITEht4QTdHGQ0jwkWUVLSXltlb3DIpbMvUa1NMEXGZDihXYnJRcwoEeSjJlZE6FjgYoPd7XLrcMjwsMfzr8aMszvM1VY5++TzqLBNmiqsclhXEqydgG6/z09/+CN2b97l1adfZLGzRLO5QkU7trubOFNQGIfJbNkYTyoSk1OJKzibEyoFrkBbj8WiZVjOvs6gCoVMCoSfZrKFZyINTmqELsHJTQWXS8FiR2GmJGg/Fdh1TDuXBhSurBe3xmCMp8CTZAWmohGpK7OkQYDMJApLmlmiaNp+2kvmRpZakWGExUlPtR4jsxGSiEphMKMxYylwKuDQG3ZGY3pOY0WABZIyc4O2BqkkeVQnchmFh/8faX8SJFmWZmdi3733jTqa2jz4PISHe4THmJFjZRYyC1UooAF0ESgIIGyQ0hThokXYIhRywQW35JbCBRfkprtJotHNBhpDFZBdWTlUZlXOkRmje/g8mrnNpqazvvHey8V9auZRLZ0tklCRCDM3U1N9avb0vP8///nPEUENqafUQp8Ri7xIx1z3Eo6FJYrW6ax4dALJuKzDBM60AgrPUBYxtfaYg+2E4+5Tzq0v8sprl7A2pdZo0Lp4BkGDYiqxZsIg9xgflFh/hA5rRGGblZUz1IOqSpPO8sWUlu3dlOdZn5KMBWr8uz/9Nk0vJC5GBNKwduYiusjZ2ttiuTNPUbpJuu8FFEXhFBqehye8qlK01US9WkQyFj9yEi/XlZ56hsqZV2kl2j+JnDaG4aBHo906iS9xsb2nm0zOgtFVnrPWffakAosfKOoiwivcRdu5kZ1eCGbqkdMcrMrWWjgudLZLjzVuYQCXAuE4fXESxqeU0+nmZYmQAt/zHAiWJQZTra+6Ya3v+QS+Vy1GufPazJp7WfkGC3ESAtgfDGg2W244Zd167Yl71gx7KvWCdc9GqQ1Rvcn+3QHbO7uolRZFaZBRnbffeJtmKuknCbRD1i+cox1EbP37p/z0k4+p12JWV1aIm3Pce75FNp4QxzFpllKIkjzLUewi5nJG0wHHgwlGgZGWSZmDUgQqwvOUUz54rnPQScKZlYgf//gJiQppLjawyZjClHhBRK0Z8Hxni1Z96bcHVN+22XrxhIPumEZjjk9ud7m00cIWXWTUQsXznGmdQYddVoIWhZBok9CZb9NZfI9OLWarX5Lu7lNbXsfWGjz+8AHrGxeZiJBx16ILiRAR1oNILrG1+4CN1XM8vyzYvfOAM4MxzYVLNBeXEPEyaelB6Xaqs6QkVFAoy69+9nMePriPbwX/7nvf5ngw4vXX3+RwfEiNlFhYkiRnaDLqtRpSWOoyw5QgvRoFgmmeI6ZTN1AociJPUaIphcR4BpNrSu1T2BxTaoQwZHmG5/mV67ozxii1JTWGXCm3++35aGtJsoTxxMWlDKfK+aAqQWO+yZOjMY9fpKTTjFpT4asQM9XIwJCLpnOgFxnGKmTYJA4FuQ4oLGzdPeTNSw0OEkMUzzFf20CGOYWMKFSdiysZpY0oPMVq7YgSjbWKWtDAyJC9aZ0Vayl8n4Q6kR6x3NZ0x5Ds91mujxFIlDnL+jmfWGWg5wlMSEOmCF+TTCO8wBLP1RnubfHpoyNeebVNPY6piTkePj1it7fN9YtXmF+Y59LcCn4giMKQqVXowlCi6Zcp43FOMvQxTGnNSaKax/XQw2YTxPEeb61GHCc506lHfa5Ne7HDdDJkOJwy6R/TaLTI8gKDJQgjjIUL586h0UgjTjbfBDOAq4BVVO9+cWqiPPM/VZUf6IlLv5Q0Wq0TcxbkDEhfMp2ufKCc9/TMqcltERlRrYziwhGDk/B6Z2AOuMn8SzvygioVoNJEy2phw1Z+r6XWCE2VAOFAWUoXBok1hIHzw0VUS6WV96sQziDG8xTKU1V4IScXAmevMjP6F5VZiiupm436adEg3O/TzmRU4tR71RhTvRZOHLCmWc47X/kyu4MDMHD+0lVqyuezW3cgCrl09gIvHjzi4b3PyKdjSl2QjQ0Px89YWFpE+CGdpRajwZBJllNv1sgz2N3tkk+mBFGN+twy0+EIS470fW689jogacRtbt3+gDyfUArJ3UnJ+qImI+J4lLF8boEsLZlb8RiNMm7d/ZBme54b1/8DAPVgOyeKWqw0m9TiiOPxkJ3tMefOzmEKQyOosX7mGpu9B6RpQmfZY1pvcvhsk0x3EPEqw2kXacZMii2Ut8oXvvx1+uOAI52TlwF+KZFBibRQlpLpNKFx9jzXrpxlub3KwsIKVswz1pAMM+IwoCwzBCWmNHQnKaXVvPHW2xzvH1Ciebj3nIYN2Osfc5C7AUmuS5RfI2q1ieoNavUQX06ccYUNKKxPmOYk3RfMdSLysqQZRViT4/kxVmpMAYGqUZa5y+yxPtoWCFlSWqerDAIXzzIaT5lvN8hSjR/XKXQB5HiqJDcCoWsYIfB0wtUbF9k9PiRoeJQjD+Yi4oVVVmoxdTmgly0T+gGqmJJkBuIFmvEBiakzLS07uwXLq02SUcHGxmWWGvMEJGSJTxHM0ZBjbKbRURNlVymlIMtLcus5+U3kc5xBmlvmGjH9fsrOkXZbXbUWR0qgpSAZZjzaEwim9K1lWggXxa0sdlqiREox1iSiQ55aGkc9Xr14loWVs5R+ne/84L/j6vo6nctraBVxlEwpx1O6ZUkxGBN7AX4joqNgbX5AXBSIoyEqHdAko2U1djLirasdng6HfHCkGRQ5u0/vc+/5QzpzS7TrDYKojtEC7SnGRcZk0Ocfr/wx9ah2mhVfVagV8jGzmBNVtTkcDqjXGycyuZmnU1mW9AYDFheXUNJHWXFSkdlZvHP1sGa2FmIFs3jm2eKRrVp0hESpWW7V6Zaee3NWiaEnHXYVeSOdeTqi2qWvtuQCX1Iq5egAKU8qbK/Sms7c+JVSlfuVsygX0jn7y+pnrNEU1rlMzcB1FmMCLkpbzvSr1eDNCscvW1lddKoXO4twcWvAEi0r/wQLwveYFDnTPKfTnuP58+d88W98g6BTZylYZuv+PRpRyNbjRzTmG6zqDcQwQTbmWGovsPniCVlZMp3mUFoG3SFxq8VU5yS5ptlp8Hu//3d4+tl95uZqPNl6RuTFaF9w7cZ12q027//8u0wmEw7HhhKPXm+E8ATKU/SnKUGtzlytwURPaNQUQfs/QDZ1uD9mecWn4Usuzrd49+obPOweMDrcZDUOUCLhKDkiyT2yImKp5fPTj+9THwiW18+QFR6R16C2foly8oTFSJNS4nkNpAyJKTHS4kkPD0vgwZVLV+ilHjVbY27jXTyVkBbOyb/3eI9aGBO2I+LI58nTJ6SmYDqccrS9hw0C/DhkyZa0ipI8m5D1pmRZjrSWUEmUJ+lnml6eYa3EkyBsjidLx6pGMbkISWxKOs7xpcAzEiMFQvh4NkCqOkLmaOFj0ARy5HzzC4EUCSqK8UxA0GogAkUQtvBKg+9ZJAmFsBgT08tTetOct1sLjJIJrVKQFYJJapFlzNXFc0gzIDDLSCUosxHaUxRJSGhScuMz1QXNjfNMvQARanoDnxcHh4hiii8CkmDEtDCYQqLFhMGkhwids1CWZgjlbO2CWpu0LPGDHEyJZwNKpZGZ5bAAQo2ONUxHzEURg8KyGMYIUdCq1SlbEIURmS5oJSk1T9EISza7E456t+hsLDHp9fjz7/w5ufwWfqtFFDUIVciF+Sb1dgR5RjJNkduPiWTKEoa2ThHlBEOAFyi8uRAPzateyNkwY1JoCi/i1mafza3HjDzBw74m8Wq0Y0HgRTTbC04za1xGllBu6g6CIAwp8hzHjLo1XYul1mg4K72qSgOnb1XKZ2V5yRkklxptXNierdzvZ6mnMwJRz7RZVcvu+t/KXETMjJ+r1p7T6lZU7by09nPvyVnmvawqaeWJyvzZuVhJz1kLeswyrlyVSlUlU0mdZLUQ4SDUVaoS4VYMpONqYVYVC5SaqRc+fzwu4sz9zoyogoOqShVrq98fYCxSSQrhuFsPS6vd5OJr11guL7PeWuDF0T5zy0vcPHOZX//iZ+zevs39rUOyQPGNm++x+/1/TzeZEMYN/vDLXyH7QcKzrecM04ygFMStNstLK9SDOisXzuAVmo8//JA3rr3C9LjH6toaaDhz/iIWqAcRly7c4PbTT0GkFKWk1ogZJinbW5sIUTLoJWSjAxY2QhotQSn/A4ZS80sBUQPKvCATGZv7e6RljcXFS6TTJ6xvNCiKRlU1HnDcTxHjiHYkOLtuOUpycs8SyDbjkfMjNTajHuREEiJPgvAoSkuRa1q1yO1RBz5jUnQBcRygaoaPfvUxLz56QDg/R641SZaSj0YUeYEII2rteaznY8qMs40FlDIM44h8dx+UoN5o4UmFFMpl8WDItSVXAoWlMBllmYPxycaQa7+K7NVQVHvIQqBNjkRhijHKj51bkJ1iZADGI1CGsszwgpBJd0pZGKRKECKkyEqUECjPoGyCNZr5+Q4XO+tkjSXu3HmKqjUo8zGDNOODB9tooylDSTod4BzkAuek5ZXkpUVZQV6T3DpMKZC0goREQp5r5mPLxCbE7Ta+9PB9SzaNaPkxofQpSosXxQhladQbWFtQr4cII/GCGAKYi2KS8QSvzPHCiMg3zuVnnJNpaFJ5COgSHYCVEXvkUAqOJznjfp/9O0/AywmQPNt6xu1PP+Uf/4O/D8MeNu8Sbo+pTXq0sylBYJEqYJqn1GS1UhpGZKVFlBPayscYgQwDMiR1oxgPR7y3JvmKHzEepfyis85fJYaVyKc3mtJp1QnQGAxK+RUYCSaTyclm04y7dJP2l0ANqiwkV8Wayuxzb7/LwuIi2joOf9ZGoGjbtQABAABJREFUfx5wXAVqT9r2l0jFk5v93Nde9kmdHcPJ1yyOehCOTphh1wywjTXVMVQZaBWVMbManAnxZx9FtXIrTp/MfagmuLLiTR2dWg3WZj387PFwvKrWlS/sS7pUwSnYWusSVx1kG1RR8tmdT6ndvMwfXvsiRnl80TrdbFBovv7lL7P92R0++uQDlt+8QXt5hfOddTb3d7nyxg3e/spXKIYT5upN7vd2iRJNvDjH/MI8q/U55q9f4huvvsE/+y//P/zsL3/Kce+Iy69fp9mos/v8KXJtnZ//5C9Z2DjDYmeN7XKbIIpIsh18FVIkVTClyBAqIB1NmJRTvnxu4zdB5m8G1M5yi2G/oJiW+E1Fnu6xEZ7hwuLb6PQq1MHUFkh1yf3tDyllx+04qx65TBFegaIgTQQiOkPh1/BkBEoQeM6/scwmRFGAF1iMyZBaI0tDveZTJAqTFNhAkUwzjtIJwVZKUhYoo+l4gmbsU8aKiZ5SZE6yMVQeVsbsDkdoT+J5PklakqcThBIEUY0yS7EI/DAgNy6qxHpOWjOdJhgsca2OLgyU2om6BTg6qqDWapIVVCd5hMK5J1nlEUhJXiZIL8bKAuUFWCxhqDDGCcLL3DCRkrr02JqLsTJG7Xj0u2M6nUXiyBmR1OoBRkCjPYfyA2yuaNUjukVBrGJiJciVpqECeqMunbDGGEOr2cGMxgzL0nG4aBrNmOCFj5+mNDxFu9NkkIxJSk0y7tPwJTZJydICjHWa0qVFdoZdVoKAKA4JF1YoijFGKlToEYSKUEiM8hzXZkuacYxKhwyHcPvBY9Jc0zTQakXg12gJyfHHv+aMyogCSxu38iiFphxOqdVDWnWfMjXkXoCVgtIWhKUiKwp3IfTr2NCnoxQNDKMsZZpYntY2uD8xNKI50lyzvKAIZMbDh/c5d+kikVJgnNbTyZtmk/SqURWnnp+zwDxhZ8CGIwCFoF5vIAT0jrs0Wk2XKnuCjad8wsylf8bPui+b6uMp0Jrqx17mYGc3l4klT6vWajDmCIrT45WzoZmdXQyqQMLqcF6uOq115jwgKs2u25oyxlAWpZOOCeEMuKk459NfTMWr2pdeakVD4Phh94qogv0cBeGm/c5j9eDFC37+/k/ZYMpr61dp1Vo0lY8yJXc/+ohxlvBg8yk3v/RFtja3+bM//y6Xzp/jK62A5bMXWF5e5u/9w3/AvbUzXOnvMHz4nJ1xn4X5Bf7quz/iNal5Y+kcawsr7BzssHl8xCAt6R5ssry+BGaBILQ829ki60/xaoG7eCrBZDwiS6dI6eEpn1qjhtElsgwY7gx+e0AttQObWj2kXvOIazU6+gyRVydcWWKn/5ij7Q9pNBbctNM2mWtbluZCSjvnMtilIvSa+IGH9HzSQuMpXUUwSwJZR2eGQIHUhvm4jh/XGQ5yMr/Ei0HVApbm5rgnLNb3UQqWvYAVH4JQkOBW7MDDr9VIJQwHCZaMuinRhSbwY/ya0w/6SlD6yk3elSQpMzAGhIf0FLnOEEaj02rTo9CUhZuM53kG1jINQ/JKXxoFEVk2cu5Qqo4SJXmRg9cg8gTpsWutvdB34YKlRklDpDwuLi2wvDjP2VaNpWadjz+4Td2DxYU1nj8+wtcG5Qn645LC19jII/AjksExRkMaKWIvwAY1kvGYNNOoWNLQEVHglhosAumFzDc7yDWBQiNsQdQImQta1EzANJ/iRxFFXmLzlDwpKHIos5wNX6EnU1TS4/HeC9qBx3A4ZTk0PDscstGJOR6mlCWst0PuPdvlb79+ge/des6CKtlJ+vh1j+OBYXmpzu7OPh+lQ0YLDVaaIbIeMMxL2nHESl1RF7jMKOuRTzMKz6MV+xhdoq2HrRzs60pR0ynaK5mqDi/aq7w/FgiVsZALDkWftUaTR/efsHMw4n975RoSeRIPPhsinUz7Z+Bk+VxVObuvxcWLGwsqDCgMtOcWTgLsZkwrZqaBmvnbn4KcrFQGs5ut2v9TNHbPLaU8+TzPc7IsY25u7qWK0rXUcgbA4tTFn5PXIk4A8OTxK64TcH4BVQs+SwYoy5LJdIIfh6hqqGar459xoqdVdKUyqKpeJdx2layok9PqerZhJiidwInA80myhJ079/h042Pmoxp1XTI1Bb0XBzw7POB4OOIgS6hJhfEUy2c3KH3L21eu8eL5JqGF3sEBuy+e4suSmjU8efiAqB3z+sVL/Bf/7/+Snf194tDn+htv8p/9H/73fPqv/ox/9bO/4P79eyzOx2RjQ2O+QzNUPH36FC+O6EhnVB3X6jRrEUdHPTqdFiKRfPdPf8D/+f/4f/3tALXbnZKlU+K5iN29IyJV53ffu87qK2cYKk366JjGMOX4eIdW2KDZqNFozRFjyUWJLTdRqkmjtoivwAtiVG6w2u2MZ5klUBGL7SWUKomUZLExhxfHlEsFqUkJvIj37z7g4MUBzaiFjkNUkWFMThEqPKUw2qfZbFAmqZu2FhnrdY9sUlCvC4xS5NmYVGuS3KBQeFajjaCcWoLAd27puUYLSaRdRIaZTpyhR5UVJJVX7U4DxhIFEqxB6ZzQ8zDKhfUJWxJ6ARZQylBGmdt3JsRqH1NqrDLMK0m31+PuT3/KG197FzLNYr2J1Amri3OUKBqxotVs0u9PMPgoT9P0PG7MNylyQWIyalrjlVPmVpfwpGSUTPCO9xhkGiKYZJYals+e3mU1Crj3fJOLa6s8eL7JqxfP8OnTHa5eXOSDrUPOLa+iD7eJ5yOaecnzScrXL5/jR3ef8vvXNvjOZ0/55jfe4vt37/NHf+td/u2ju/yTd1/jOz99xhe/+i63f/kBX373AtPNbb6y4nNnd8Qr7QY6kuyOc/aPDjjq9djcr3GnHnHz8hrX11a49XCXxfk6f3TzPKLYoRaAJwyx8RmOJphcYKI6nnAX0QDDNDM8U/NsBvM8JqCLR4wgH/VJGfJqFHP98iv87rf+I8IoRkkPqytN8sut+UuAenITJ//jFFZPh0pOCqSd/EjPKstK4jQD5hmX+Lnb/5iHnCH8LDQP4ewcZ0Bbi2NqcYx9GSThpJK1M2b1RFp1Av+nFfLsdWJPquDZsc4+amPxfY/OfMdZDFaPJKsBmq6ObaaMOK243UswL1fLs1dZVczGWJ4/e0Z7aYGFZsxoPKbZqPHgwT28KObm1asMtzY5NBlXz1/l0mvXSEcJv753h+1+j/bqPLd/9T66HpAXGbE2hAjuPHtEajNuP71H/7BLbgSXrr/Cj779P1AiiOKQp1ubXJ5v8+3/77+kt7vPRnORPVFy1OtRDztkRYoowfcCUp3wysVL7Dzfob08z3I7ol0XdFbqRFikyX8TZP7PuE31RpyZX2BlsY2MoUgmPH70c3ayDh8+/Yx0MuZLV97k3PpFnmx28RSgDcNBSmMu53jnKcurlynyOgUhoe4T+hF56fx1glihizFZWdJQHu3mAv20z+7ePtNxQnuxxcHRFh9sbvJs9wApPGxunB1ee45jDxpRnWEyZTpyUcjBYMJaqJjzLaIpGaeKNM9YiX2QId1Rguf5lKV1+95WYYSmpCD0BJktXd68kEyzjCAMmGY5AudelNoSU5ZIIQiVR5amSC+ktM6JKgx8hNAk6Zhas46elC7aRITo8YSpNLSVj/Jiludi5jtNOqstzomChYUG2a+3uLjW5M5PfsjSnGTnxTFqZYXe7hFxFDAdacy8z/HBiHOX1zneOiRebfJ8a4cz1y6xtXXAuY0VJoOE5ThgufDY0ynrZAyjOsueJaor3mkLHi02+RsdzWdZzLsrhvkefOXaMj/ffsrfunGJn/z0E/72V1/l1z97zH/y3jX2Ht7if/2N8xzvbvLHXzrD9Mlj/vDtc2Tbm1xcimmkYxo1n4uNlL8cH/Heq2cZHAe8cXGB5/uHvLWxwd1HT+mmcJznaNPkBx895JefPcEEPvpFwca1a9woGmxQoGRCJi21+TrTvOAwsQgCdFTnIG6xaxXHVjFRNXIMggKUIerMESYe71x6hbVzl8gqY2S33TNDo1miqeS0n50BlD39zDrer1JiVuhhsegTTaar3ATWSOwsebRqz+1fA89TfK1g2p5KkKoDqx6/AkdBtZEHhS4xVpzwpCfgLXCvAXcMCPm5C8QJJ4w5URCcXgA+TzWccJ/WVL6xnFSbM5C0cCLnsjNKoJr2O4vL6qL1Mh8sJGtnz7j7YegsLXF+7Qz3nz9jMp3iRyEvdvfwF+f45QcfcPnG61y6cJnn0wH/m3/6T/mX//ZfoHVGMk44Pj5Abh3x41/8HLHQIKDGqNsnCwTltGDn+SZ5phFW4Pke585ucO21V/mbX/19/uSf/3fMXdvgvehdPnv0gN7BPnce3cfY0vl8KM3W3haFKVio3t+rKwsgCvaPDuiPJ78JMn8zoC4vnCOs5USNAmMKfL/GUbpJ3J+QJH0WGk2MGPPo3hOa8TJ+GfPwzlOGkyGdTo2oPsfS8jJF6bYthLFkpass3MFbcmMo05RhVnIwGhDV6uRKcahT7myNuPWLTwhbDRbn5xG1GoknaQURZjpllEzZ6XUJkxHLtkQkKYvNJrIYEXo+01TQmV9iPBoSGDdUsSgKo9HKEscRqsiRwjLKDAqPqN4kzxKKMiNcmuewP6QRxDSDCIlmkmcIHzzpIaxFNGukRUmuS6IowlqN74Oo1UCWBGHA8TilMCU2gJYqUbZkdXUdfz7i6s3rhCshXqPGl+UEm495L5qS5Af8/vXX+JPHj/kH187y/7t/xB+9scGf/miTf/rNt/kXPz7mn351gX/7pzt89c1LfHtri//0quK/fbDH7128wXe+/yv+0R+8xve/d5v/7I++xH/7Jz/jj/7e7/Pf/OkP+Ye/9w7f+f4nfOsPfocH3/0zvvA7r/PZB/d558pFDu/f5gs3z3H48CmXNlZpTo4gVqy1BA8yODe/yKefbvE7X1zhB3ce8s1v3eCvvn/M229d5uN7D3ntxlU+vfWA5XMr+HnCfC3j3SurpPuHfHnNI9+G62fm+cvHPdQE9scFRbuNH/n4Cu58+iu+8aWL5LnP0PokZQDGx9QavJhf5F4+RaUCmzXIQkMhBMk0IfQDrAFVJLTCGjdvvsVKZ5HCzlpWg9soP63xZvMVtytfAVAFRDNP5dk+OtYBrT3BPHNSVb4El59r1atHOvn36eCLk2ryZUB9eQA1i6TGws7ONtZaVtdXTwQDMxCcPc5MOTADWfeAtlppnYGqPXnNM7rA1Z6nVewsdbUSIwCVLaJxa7CumK5AF4ER4pTLrRDavPw7sZVcSxjwBEIbytIQBAEiKQmiGkWusTLgypXX2HzxnCcPntAXgvLidf7uH/9DOjbgP/7jf8yV1RUO9g+YCM2tX/wl+2bAty69xp3btzFNn1quST2n85aeoN/r4UWR8/+dpvzpv/yXzJ9ZobZYp16UzNd94rUV7j6+iwKwJaHvUfN9JkWGyaeUvuJoOMYTkl43q4Rw/9O33wiocQhR1GB0OGB1rY4VBlFr4QUeZ1fPu6hmr8SPLZPskP1BjIlaSKnIEawunKEoFFnuYoylJygzS15Y/MBjkkzIC4PKFIEwRL5CqJRGEPJ3v/Jl/u//+tuo2Kfb2wVpMVlGlGj6gyd0hOaS79PtDznbCmmYkqgzzyAt0MInnYzx4wY2ndBQjtYyeUGsBKHnocKQNJ8SC4NAIwOF50UYmSFlSS0OSTNLK2yjPI/Yc3wSxqCNobQGAuXia+sRobV4RuIJRRyHSK0hCEinOZEnCfyAosiZkx7zq8t4TcX6tbMQGs6cXaU1nbDz81u88eo6w8HHfO1Ch+bhEV+6tkrY3eLKxTmaOuHaK8sUL/bYWAgZPnzM9Q0PO+hx4fJZ+k832VitcbTT5ey5FQ6Op8j5FqPRgNXVBcK8z9lOyKXskJYoaBjDgY0JbczBJOYLK2e599lTvvJOh+/9qM83fmeRT95/whdfO8PBvYe8fv0Se/c/48qr53n25JiVc5c52uyhOusUpkWPLl9dafDpR5ZvvnaBD7/7K75w7QKbj57z2vV1et1j3r64TOArJuoMDwYJ7RAGSU6r7hNYw7tf/AoDe0wZxezYGjt+wK4tkTZCZaELcTRjlCnJCgvKEosMs79HkBYsLM1z8eordOYcmL7sZ3vSIgtm+iPAEAQhReHkMEHoDGqcz6jFZajKz03+TzjJk6r2ZaC1J/EfFbdwUqMKYV9iVcVJVUgFaKdazxkYu6HR4tLyCWia2ZQMZ/pyCqCzrwkqm/TP0QMn99OWmYjVVFtNVtiX7nMKvrMlBGFm1bQ5ueacKCCqi8tMxDA7khO9r3XP574GSgl849Of9jg4PibTmuVXr/DOu19msL7HVJe8s9DhzI1Xef3iKyy155nsdzl39hyehuH+MX/xix9z686HiKbkB+MBxlo3s8jdck2WJ1gEYS1CeT6lERz3BmwsLLK8UCcfTvnk/i22u12S0QRTupTe5eVFp8e1ltjzMCYnjOcpEkuvf8jcUhMV/GbZlPxN37x6fpUr567g6RbLjVWCwKMx77O9uU86GnI42OH2k+cUpcKLfVRzlXj+Mp3Fy6yu3UCqOfLcBxlTlBKtIQx9/EAglbP6yjJLUQYkmSTLLXmZMrFjPnhwi7BZpzw6hv4Em7u/1v7hEaUxdObqhHrCK2c6WJ0StOscJUNKZ47OfGeBdmuOMBAEvqUWxlgKpDLEoUeeTqlHAVEUUQiFF0RIaxGJpNVexYgQZMZqqyRgBDrDmIJaFOGFPvP1Gi3lMx83aCBpGmj6glY9pFVr0qzXiVRAXPM5uzrHXCR45/wy/4u/9bv87a++RSsO6Cwu88q1a8ynKWvdXcbd5yy2Uw6zkLWrb/P8+RE3z9TYfJHwxo1L3H005I2LKzzeO+AL1xrcfrTPK6+s8Gx7h9curXPn+TEXL6ywu/WQG+fb3HpwxOVr5/jol8957+oFPv3xbV6/tMaDpy+4fqXNo4cPWXn1Ck9evGDxzBwPN4/xljbYOUpRfp1UKEovZmO+xXY34dWrHT57Meba2hqfPd3j8pUmn97d48K1dT5+tsOrr5zl+cMtNjYWSLYPyRbmaS6dIRkOuXI+4FFvRPz6W/zrvZDlN9/mILGs3ryO3wmZ6zRot1r8yY8/4N/pDt8LV7gdL9Etm5iiQVIKSpVS6BE6MkyLY7y0S9w9Qm0eE45LAgnXrr3CfK0OusIzYfCEPYnzsDjXfeOujWht6fcHGGMpteH+/YcMJ1NOXP+sdfx3teljjGuFHYgYnE2dwVQO/6VxdnrWuM0iXYXzgeMRtbEYbdEaRzsVBqP/x1TrrCWfTeoFL7fjnByHA2eJlM742g2nTodHpwB5+rhOb+rMuTl5TcatWlf/ti993UXpWEph0dY46qS6j0W70EHhEimMrH7PpgJf63wVPAS+lc6dTQqe7b7glTfe5PW3v8Af/p2/i81KtIIv/o1vcOOtt/lbv/s3uXHuMmtexJNPPqXXP6afTJFIer1jwlaL2Phs7rxgb3SMZyVJXjJJEiZJSn8wIksKyrwkV073dvvBbf7k3//3fPtf/it+fes2B70eO3sHeF7A2toZfBUhjGQwGDGapkyTBFumdPf3scbjoHvE7uHBbw+oWWEZjI/otCLqUUi7vkr3WJOkGQ8fbpIetwnD6yy1LtJqniOKz9If+GDa6CKgKANGiWU0LpgmmsmoIE9ypNXoIkdJnzBukltJKXysHzHRlkkKmfbY29phMJ2S+j49qemNJ3jNDlNV5/kQxrU1NgcFOuzQ65XM+U0aWcZ8JPEDyMqMSW4w1sNYCDwPU2RkoyG1wEcZQ1YK2gsbWBlTD+ssNAXYIaGn6DSa1EpDOwqoh5LIE3g+NKIYz4eoplCUKDQr83UaoWWhrmj7CTU54UxsubAQcbMV8n/53/0nfOHKEuVowPt/9TPCVszVi69Q5JrGpGD3179keSkiP3hBYBT17DG7Kkc1I57lOXNzHbayjLWa5nACZ+quBYl1STkasd6Bkaxz8cIi6XDCepgRyglvXfQ5TqbENZ+9bMpSaPhka8xrF9psP3/BzfNLPHx6yLWLy2w+eMj5i0v87NYeV69d4P7tPc5en+fx/ResXJzj4DCldvkiSXebufY6eX+EqTeYb8cMbM7ZuRaf3u+ydv0Nfnz/kOWNK7z/dJu9Szf5f27Oc3f1Lf78IEOvb/Dx9i4rVy7Tf7rDmWaTYNxj+/k2k2nJ4fNDKA0+ltxkJHpCmGfo55uYo03snbtE95+RPn7KZNCjtbLCK2+8wZXVDe59dIvxND3N6xS2UpSAdt7QDuyMrVp9B0Yz0JtfWMDz3LaRxmIQGClOgudM1fKerITa0ypR2FOVgKsWOVkK0EZX4FrtxxtdZZYZtCmx5vNrpk7OdKoKELiBZ1mWzsz55HMHcFpbtHaP6+T05qSStrNNJVzmVpY4sDjxMz15zlMw/RygViBqrKWkMgCqnsUV+xWdYjRojbAGhcU5EemKRpmt4VqM1bz1zjvceP1t/ugf/DE35leJjKbTanJp/RxfeeMdalIhypyDF1t874ff5Ze//CkfP/yMR08eI+KI1994B0/ESOuRlYbRcEKgPEQQYIXEGJCeR61R4+LFc5w/c4YXj58xsBnP8z7Hw4QShYpCkIInT7bZ3jlCqZhSS9JCUxrNaNij1IYXL44YDRRv3/yd3wiov7nlX1umlkxZvXmFYHJAXF+iP3qOiJbYPOoTiJDVxiKLK+fJSjC1iKEe45kSJUoKLcm1cVspFmQBzVoHP/Yx2pKkBUpYMg/ajRhrc7yoTqYtu+MMKXyOjo6YDwSLnXn6nqI3OqLphciwzqPSkAdtmtqwWGsytBkL7YDSuCA8S0bdg3wyxVdOFB2KiLDukSuDUAWdKCbJx1CzNH1DXoQIa4jqPmIyBglBs0U2LVCiIDMFoR9RqzUZjfsEoU97bh5Pp9SbLaSStH2PtdUm6x3JxRtfYTro8mDzCQ/u32et0eLCe9/iX//yp9Sin3PlK2+y208oowZfWYp4eO+A9bPn2T/cobEQc3A4pr6yzOi4R/PMKsPBmLjVYNgfU19r0n1xgPI9prub1ObrJC+OWDu7RHY8Yq7uMd3dor5YZ2f3kMbyHIM0xHbWYTSg1vYJ0pxWu866N4XIcqEVcdtA3ffZOpjy3peu8O2P93jvK2/xs4+2ufTNL/LDP/sRN776Bb790S+4/MY7/Ivbh3gX3+Bf7xckr7zJvz00PG+dY7JwkfcfHXHj2jXu/fgXnHvrbd7/0Y95++tf4db3/4JOrY0dHUIaoPwALSSPHz6lqTMWygn+8hoqiKjlE1Q6ptgfMcoG1OMOWlhee+0twnaHpYUOT5/do97vsRDUCeIG1paIalBYnhgrC4xLjnNAIqocqcCfyUup16MKFF8CmwooZ3SqZMY1Vv8WM3FR1eNXz+FE7q5yPGnKq09eEhGc6kLNqY4U3PvGHYO7o1TilOs9oQw41dBSmZNYgaLSg1aUAhUIguGwP6A3HbG0sMBiq+38AMCJ+F9iHcTJM7lP3FNXAF9V/AKBkc5ARVkQ1pIVE/J0wngyxAsj5hfPIgkpjEFVVbexgqvXrqN9hbQlhS4Yp1PSSUI/m3Lj5ms8v3WHf/5f/zP8+QY2z/jJD77PK2cuEDdrvH3zTV7cfwSRz2Q0ol8ZgOdlii1x+Vk2Z3V1kVYQMe73aNeb7PS6CC0Iax6LrTpHaUEymXLllXM0GjGD3tAFL3oeo0lCqx0jfU2tDWUuuH/n6W8EVPF5Uv3zt//8//SfWtMdU19sc3auJCHhcOsJ7fVFPru9B1mXy5e+xKVzX8GvxUzynME4hayg1vDJtOa4PyQKIoos5dzCHHOtBsL3qDebDEcTsIrhKCU3BeNkRLtRo281P737mJ//4kP86YS20Ax6A0TkI5otUi2I5hdJy5JIuAzyVDk7vbooENOJ87lEY01JFNawWU7g+QSeRxC4aIZIZfjjEWFpqNdbpKZA5RM6CwuM+yOiNMOzBboeEyo4f2GVab9HPVCMtU+3N2SuWSfPSupKc/bCOr3DIb//tXexWY9Gp07aO+bTnUPGo5BdGvSOnvP9h4ecW17mb/3+NzjOp3y8ucmVM2eZ7m3TMAPeXpxj+uQjblx7ha3nz1m4/CqHt+/QvHiV8tYHFGcvUbx4in/lKuWDe8SXrjDZesziK6/x5KMHLL7zBkef3mH5jTc4fnyLuevv8vGvPuXyu2/x6MN7XLrxCs8e3mPx1es8efyMcP0yx3tbNFfP0j0+JIjbRHlKXwqWlM9EwX47JN/us9qcp7u7z5UL5/nk+UNev/wav/jsHm986St8+MGvufHGTW7dusWFV66y++Q+UbPDmTSj3x/Sm2ty8GATdE4oShb9iMLXRM06L7Z3SArwjOCrr5xBlznzi2t49XnGWUKrVicIY1bPNlBhiwe3PuPaG69z4dV38b2AaXLMzq9/zVzcoP3e11DSIPGr4ZKLfn6ZB50Ngv66LvRk0m9nc31zqlE92TCatdCnP+eUROpzQ6rT6m/2cy8NuSq4chVhNeD53ADJ1XSzxzhp3+XpQGp28+zpYEh6XrVIqt0gyM50tm7K5oQ4hkJa58DF56VUVK+al6rvk+OR8uSYlBAYrTm1wwZFwd7hC7rJiDiYx7OSZqtBGNWc6YrnpFjOFlMjfIXG0QGj7jF4kjxN2H2xjWfg7t27dEd9bn38CYf5lMsXLvLqjevYyOettYv8V//Vf8GX/uCb/PTPv8/t+7dQzQi0Jp3m6NzNRV594ybf/NJXiTPBj375MzKT83RzE8+XdFoNdCHpHfeIawFpmqEUSDRGGqRy8rV63GJursHuzoBmK+KjD57+T06mfnOMtPSIl89jrMfE+gwmT+geagoTMTffpOmfo964SXdQUk6OGKdT8olAG4kdTIiFx9raBmcvrKGzKaF0sbOjZMq4P2AwGFBoy7goyCVktmSagVIBb752k8W5VXyrkSbnZ9//C/q726R7B0RhhMhTms0GCQIbRxh8giCmzAwmbDC2yp32nmQkPXQtBikIrECmxqV85gFCxRiZgVFQRCCbNKYBtXqdjeWQIBT8k299keRwl1oUEdQixoeHnJ9fQU+GTLv7rJ+7wuHmU165skooIB1NaS4s0x90mYgacuUyw8trvH/3GQ8fTygmmoPhgAfPtzg67tNotniQGUZjzdrZ17nb3WZx8YvsBCt0i5Ibco67SY2vnLnKn330jNff/AP+/P6f8vbZb/Hpr4745oUv88lnY/5G/Tx/lk24sfomP/+4z9tn3uHDO0d8Y+E6PwsHHMiz3LPb9Ovr/Hy0w1vxBT5Oj3i3tc6vb+3wzutX+MW9Hb7y3kV+/Iuf8va3fo8f/vlfcvPrX+HZT3/G9Xff4Ccff8ir77zBDz+5x9Lb1/nuLz7izNUr/OL9XzJ/ZomnW88o0wKpBf1+zvmNNh/c+wnLXsjx3h4dKYh8Q6sWMY0ES55PriRra2sEIsKk0MsLwjgmXGyQqZILr14lsh3COCAfPEOpHEZDFjrz4HmUNsCP5/n2n/+Q//x/+U/czn5lSKIlCKERVZV2IhOqWmprZwOYGT7KvwYuVFKjGbhVMStCVJN1e4Kr9qT8fFnnWQGvfcmKrwLkGc3g7nEq23oZLGf3+fwxfR7ItbVgNSXw7MkWaZZz/epF51PBzA4QkBZhLIEUzpDFnj7rrMadDZpmwv3ZAIyKJ5UvPb+QuM0/6yMEaFMwzYZYCStrK2SjCVl2yP7BPlHYotVewiBo1tscHewzv7BAWG/gacmkO+Cwf8Svb3/EG6/e4Pvf+QE75YSb773L+t4G094eyvMZZSk3Ll9if2sXawV3PviUpbU19IPbqIra8X2PZhzhhQFJlmM0vP/TX3I0GlDmOZSS85cu4inoHg64+fob3Ln7KUVeUEpYWu5w7foVtjdfYEtLfzJBCo/QU4yS/4CQvqX2efzmPEUyZZyXPN0e8O4X3mY4rZEO28i4xcCkBFajhaAUAc1mi+Uzq7w43Cc96lNrt5lQctjdY9wbIKPQRTMYiSkNmXYO+RhBJEKkVcR+TKcxz9qNVbR0gvrLN9/lwd1b6PGY4+ND7n36MYN+DxM08TT4EaRZji8tqnBu+ioK0AWUhTOINgjyrHS2ajJGA2FhsKLEkxKvrlhvzOPJkr/5lfcIQ0sj8DieDgmaKxwLibaC2sp1dno9Qr/FwtUz3D44Yu3yGzy1JYPhhM7cGqPRCF0E0Jrj8fCY+/f3uf/9n1Ae92nEine//C2eHu6x9dEtvv61r5G92CeuB/QOnzAZFixee5VfPdtFzZ+nO1SMF68wObBsLd8knAqONy6yWUh66+f55DChf/YVvrO5z3jtPB8832PUXuTje1vo9hluPT2gWFvlF0+eEa6t8Vd3nhCvzfPZvccEa8s8evIE5ue49fwZqhXxbHOT+flFDp8/w8wpnnc3IfI46g1QqSA5GJPlFrM9xJceZjDFjlLUIGHvxQtWV9b55Bd/QUPWOP7LH9NoBJjRkIsLqyAidnee0I4sV9bWgTaDgSVultRj0GWNS1dfods/4NyZRZrNGGptmisXqTfb/Jv/119x+cI5Xl1ZZX1+mbFwbSxSoDWQ5W5KH3j42rW9WHMy2DnZjEJUPqCnbf3LN/cle1LZzvbWmYGirZ73BBCBE40nJyvGUgonNzKfl0hZa2m1WkwmE8pSg1CzRzmtCu3n9aEzgDaVckHgEM5IsMJpZc+cP4PWzh/VVtaDQtoZJp4I9qXA2exZd/QnVIPgRGr10m+Dz6+yzrK0TKVoKDAGkjxD1RqstxZQ2kenGdtbT/G8jEl/wM72HvW5eRY7Kd3uAV4gqBnDQq2DzXIe373Dz376Y/Z2tvnVrY/4vX/w97l+8yZvXr3BvU8/RSrF3NI8e/cfMxmNWVpf47Nbn3E47GKAdJoQ+AFx6NGsx/h+SCgtW9sveLz7gm6/RyEMnhCMB116gwHWSHrHx0yTKVJ5xJFHVAvIp4aD7SNa9TqN5hyv37zOZ+VTNo+3f3tAXW5cpL7RZPvJx4x0ztrKGtNkmUwBYZMUD0SJVBbh+cRGsVGLOdvyKL0aW9mAp3tblD0Pm2d4wieUEZ4f4uOztLFCqSzPtp+jjCBAIiOJV6/jCw+hK91eYaiJiHdfew8FZJ7hq7/3hzx79IDv/Zv/huRwDw8f4QVom6OxqCDACInWBuF5qNDFVUgpEVYS2gZ5npMUGhv61LRm2Zb8o7//LY4O9ylHB3TUPDv9LvNzHZTn0e/3aS8vonWKrElyXbB5uEVzbp6xFAx6IzqLC2xOE6znsRA3mKaa+7/8iI9//HO8NKXuKWQuuP3hrzn/5Xc4vhvwkx9+l2a9xfVrrzHJRsQo+g8es5NlXLy5zoN7Dzh3/VU+/vQurZVVPvrlh8RnNrj9g++xdvMmd9//iBtvvsMHv/4R773zDX71wa+4/MX3uPXxh1y9dpkHn93j4vUNXjx7ztKrN+m+2KJ9fpVnTx9z6ZVL7Gw+YencGzx/cJfLF9Y5evKMSxfP8ejXH3Ht9Ssc3nnIcrvD4ae3WFxsMLzzMTVPMry9x3yrxvDTTZqezzjrsiQl+cPPOAeIeUmyXMc7GtFsrJAETTw/4trKOZK0S6zaNJY3MDZh/dI5fL+gNbdCjYJBf5fxcQ89nbJwbpFGECLweLZ1wGef3OJbr1zhTRlV0SMliBIF2Cxzk24xqwhnLbp8Sb5U5cRXg5zPbyBVeqqqYq3SndAnhOdLw6eTxzs1XP4cDFnAOld/l3BinOs/oHXJx598wrmz55Dy1ONh9sSiGqY5OONUplW9CFlFZWPc2oFQ1Ss02mVAaf2S5vXEIprSWie/qipxNbvImJe4ZXfYJ8/rvq9dhPSslnU8BVJbRsMjhqMR8ysrCBOQjce0WjWEMFgtOe5OSfKM9bOXWWgvEYcNlpYkvf6AztIaaZGxc7DLt7/z79kbH7O3u8vZN16jGTcoe2PWLl3gd4Mv8+Of/phbH3zA9KhHP5ly0B8QtJvI40M8IdC+48LDyGc8GtDpLJD0uvxq/5j9wSGy0OArmo06o+MuWVGQZhqJclSK58zlS62xOmNlfoH+cEx/tMuzhQZpmuB5/wH2fdvjAdHzCX5tlbo/xa/FpKmbfMYNgyckATG+76OCCBF7HJYJxcEBI10i4whN1SYEEb7y8KMYzw/QVvHp0y3m2i0atTZGa5AefiBRMkIjUBI8q07MFqy1WCnxjSTyY1bPn6fuV6e8SvEEIHKmGrwiQ0kP0oLM85DZEGslpS1ASYpjhbAaZaBEMFKK3JP8P/5vj4nCGmWpOXN2g0G/j/UD1pc7bkJqSi5cOMvh7j6N+RYXzp7nowcf89aN1wnwefTgMUIFPPnsKU+e3Gc87JF2R7SsxoskaRAyLBXpYZ/0r37BYDBhOiko/JSfvv9LmvUGloJ0Yx1tLQcffsI0K+nvHpKMRsS1iNHeHvO1DtkkRR4dMxylbG09J8lqbG7tYCcFx4+38DNB9vQFNi1JHzxlxRr01lM6pITdfdrKwNZzVkZjyiefcc6OMY/G+KOU0b0utazg+a9/TaAU3eMj7CQhKdw6qC4sqYg4OjiiIUpqQY1uDqksaOGzsFRn3wZIL6TWEZydb3EYtlDUqWtDwpT2QovlM2tcuLgCJuHZ02d0H/+CGzcu4017dJbO8uc/fp8/+uOLWKnwyZFBiJ7mHB71sRhKjNsjNxaJRpc5VkBgLHgWhXKgi+Xl0LiyAsDPtdFCVIMhWYGhrVpuiRWO65TCVhXeqTZ1BrzWzPSuMNutL8uyenT3WM4ez5nJhK0WpZR4Tm568nhaGwQSY/VLAn57IkMSsym2e6iTXXys8zp1Q3WJEAohjfuvEvQYYcgtbhqP262fVbdiVsZWWlcppIt6qcDcbek7cxNhLEpI8jylKCfkRZfu/pjJtCT0I5KDAVt7W2zvbrI818aLItK0SzmF+5vHqLjOtSvXsNMRhohas8VrN99k52c/Iopinj16zNe++jUa0mewc0DD9znc2ubPfvhnZKVb+a3Nz/Glt95lf/MZGOmSjMsS1WpQloYsSbAaegcDdGEw0vmFGKtJdUFhoRa1KPMEPIMfSyLhM+5PqZ+JoVXn3tNttDDs7B6R54VLmv1tAXVhbRkpLGWZEym3umlUTqA1EidLQEqk56E8lzNutWKKRiiPSPkYY/A8VYWNefie7+Jzpc/ZjTUXm1xmeFKhhMKvPsrqj2xOztlTDmo23YzqdZqrq/T6Bc1GRCACzPjYbcx4As9YhnmOkYK6yekVljCQLGcwqhJJfSEQyrX9tvQhnVCmKbkueXb3GJ3laBVwvCmQpQP92+9/gLSKwPf5qZRgDH/15z8m9AISU2BKi80NvrREJqcZBJiaZC8dEoqQ0qRYk2HLKWHDbZukyZBxJhgVmoWWYPjwAV4QkMgQpaeU0xE1afE3ezR7Y2SeEGVjjj8ZsChhuvmEzmBIVh6ySEH/1h71TovpMMMzPnl3QuAbescetUZItnPIQgZTv0mhEmx/yhlf0rUBVgQMtKWhPEKvwSBooDyfJkd45ZhaZxklcqbNDiqYg/EmUVCnM3cBGwqYTKgHliuNOqg5vHzAUjOhpmI8NY85E/P4fY9aLaDTChgdHvPf/Yt/zvbWDt/86heJV1dYODxg+/Emh0+fo8qZjtPgKYUwFp1nSFvFbVhn7IGx6CLHq7i+mcB+VqG9zF++VOy5x7YvcaGzLaeX7o+1J1Z2nxflUwGym5JTPV8URBhjmBZpFZ88MzJxwx1PwHxn3gGz88A/GR65yb0+meC/fHP309W2rMQY92Jllap6SlGUaKNRQjHbZDDWJYRyMqGfmVqfeg+c8K0nL0o42VmlPHD/F84XwVhMmXM0HNDvD4mVz6g3wgrN0X4XEddozi3SG0wYTntIm+O9doPJNOfM8mUG/QG98oCyhO2DZ3z5q18iDzzS4ZSdSQ8vrnH39j0Wz6/x6dYmmTUUCBJdcHFjg8PuIXvbz6mFHmgPYUoEgsl0QpKXBLkmnWQUpaMxvMBzr1lY0lzjxTXW19fY2nyKH9WIg4iV+QWGoyF37j1kY6ldDfUMh70BKLh86cxvD6hWVF2HUCgZEoQeRhUszbWZqzfZOdoj0QWecACphXR8DgbPuhLaVPEMQji9n6c8RCVhkcK5LlrlIyz4QqKEi6Tl5M99enN/3xmZLlBKIn2oRwFnlleRxse0a9RUBGKKX6Z09AKpBZXmNAuPQmnMuI8XhgwPRtTrAZ4qQUjSBOLqipxOE6I4RAlBmiZoZQmlR1BFSIsAYsqX4qY9ZJLQlBZbGvwgIJAaU5T0BRS5xdMClWqacYPlms9yu85Or8/8fJswtQyZJw8E00mCT5/xNEfPxyjRYXCwTcPzKMM6hdbIyTGNUpPYkFJohFE0Sk15lCNaOWfjOfYzjySwRKZBPS3J5yKI6kwDgZRTzjU9ingOqS7iJwmEU2qtDrGoERYD6rKOmWrm2gGJgYN6A3N0j2iljs4ucP6cj2GdUGpMaSjLFvgCG3iUaU4xLJikW4wGR6jVJrYRUNgDIpWyaJ/w6+98yuI/muf+nRek3UMCqwl8yVyjw598eJcztQgPhZaVOY3wCHzfAVCZgy6wpqzMoi1KSbIswzsBxJd296t/w+n65ylozj6egtrL38NC4Ek836sy7kUFqnzucdz+uiFPM14826LdmaPWbACcrKQ6esCtwCrhquWZDZ6ZXSBw2k5ZqWk/J9AXp9pWI4wDVitcsmnFf1phQRoECqOlc8jidCA3k4G58pZTMMeFTlrsCZjPfmenVxt3f2MNo0kfQ0ozrHPQ36b0NYNpysMHd0iTnMwI5peW2Vhe5PyZs2TpgOcvHtHPBoQdOLN8jVZrme5xD2RA73DI0uISBUPa18/z1XfeY7p6jCctIYa68rj89ByHoz6eL7DSMEonXHv9OuN0zP7BLslo5JQDQUhvNMHkBmMFURSjTQYeTLOU0kDoeWhTMLfUZDROKKYF03DMNBtgtYFgDmNKDBY/jPBCj/nFxd8Emf8zU36cwbK0EgNIYTDS4/79J9SDgPmlDh6KQPjMzGpRnstkfIlnEswkF1StknJSvZM/rDc7K4Eq49zZkH9u2sqMDMedfB9/8gm7D1/g91PKRpekULz9hS9y6UvfZFhM+MG//ZccPntGFIS89sWvsfvkCVokHLUs/rhkoRmwstShsDleEGDxSDVYLQnaC2hj8KwlNCWoHIxFlFDkBYFQpNrFTRe6YDIp0cIjUoq0TLDCoihoxhFpKaihaAYxqRWIMGYyHNAvxzTmaqzNL1J2jzFBhyEJba/GUlhi63WeTCWJDfAbUFOKfdWG0FAUJatihJ5vMg1bBBbEuEsyMpy/3OTxkz5LF2/gFQm58WmurlK2FugUJUUjoGMz6nLEol8DTzIXNDFSkto5Jn1B2++Qqph0PCGWU+pGU/MEW8eKV9qrjAZDzKQOdkQpY375y/cZ9W9TmCnaaESpmas1SYuM7vCY4+UFfvebN5EqpDHXoRtGrFuPhVJz99e/QlESWIHujenuH3JwcMQr16+4iBV0tdZuCapYY6tLsKZKXAAhJWEUI6ULpKPaZHJwMAOF0+GOOS1IX6o0T7//8u69kpAlIwK/iScFpba8dFK+9DNVBScFfiMmKXK8osD3vM9JtsxLAOZcmtzjuKp5NkjCcbWfqylmzytOKYITeZc4yb9yt6rTsxpB6SpZAVaXLgHVuPf3yxWwko7ysNacgLaroDnJgcIKJJonD+/SbAbs7m3TaM8RLbYZ7Heh0ORaUWvMk4z6dI93Obfe4d79D0iSKZ6nePeL71FXTXpHL0jTEV5Q58KFdbLhgCc/fchHH33KaytfRU9GrFxcYedXt7l//w6D7hG6SDA2Y//wmEJrNnd3CarI67//d/4e3/v2n7F7fExpQCkPvBJRaYHzwtGGtUYNZZwK5OjokGk55Z133uXNC1d5+vwOz/ZStLYcDSYgAsoiYzwcETd9Llxc+02Q+ZsB9eWWQApQFvB9VtY2kGiU9BG2dKxK9QeYtRQSDS+56Myim91NOrmKcFXqrKx2/ZDgr7c5J0Q9p1dqX/q89fa7PPz5X6KLLRYXF7j05tdZPHeOeruGUnUmxrK5tcWZuQV+95t/wPfbP2brJz9ivdNh6/CAmucjjAIiBAHdwZDRMKUeN/GDgND3CX2FVB6IBKtK5lREWowxtsbeYICQASIoudRZdC5DxYSstOggRORTAquxykOoAK+wiDQj6qzgJ01EMEV5AXGjjU1ixuEqQTNCFjm1Qc4kVlxcuknNauRknwiPOdFEeopxHDJXPmIlhD25iufXSIo17v/8GUXYZuAPubzYZi4RDC00vZC+iJmODzGFpC8M/axgONkhzaGQJeV4yKSvGaV9tM754usXuPLqNRK7hCx2mNOGrrBMd/eYX11kEtR5cv82/fEhB7s9OvU6CANVfHfsFfhhg+PBGG0jfvnzbabplJuvnSVWAVcWIwaPNomlz8SPQUxQpUV5zpc2CgIUoCaJq5xs6SI/tMaW7oLr2WrfHKfkyNOEprAwkwxZN6yZZR6dGoqctv4OaV6CrZNizFIWOSWGPEtID6a05hbcEMlaxIyPeukHtQXpKertVqXXlJ8DaFtVi8wWA6yjG2YaVykBUSWsMispT/lNW/GzAlVVm7iK14qTomOWmmpNhjEjkukQ4bXwVUg6nRDFdVABWs/UCcYdpwaEri5IjsJwjvsSIRwHLYXh+GCPlcUGz57eJWjEzC+0GB30yNKUX//qFwy05tWrN1gMFBbDT//yl2ycXyautanVI/a2d1nMS+pxjDYeVghi32PY7fL8wX12+/usb23y8d2PeO/Nd8jTMfvHexwe7ZHkY7xQseQvsr/fRQUBnvRotxpsPXmBNAHD3hiv5p/kgdWaNYSRWGHJdU5RWhr1iNW1ZcajKZP9BCUUW1t7bD7bwa/HTKeWwTB3G26FZqG9xNJynYOtrd8Emb8ZUI0xWCEIbIb2fISRBGqCoAAiSjwQEilnE0LXDltelqGIkz88wtnP8lJ16naLZ96NlUB5ZpZbXc0Fs1ZIgHX7Hr4KXEWjYZRbEhFz/Y13mQiD0Y4wl8qNI0prKAvNe1/6OoP9Q9LnT7D9EY2lDn7k0T0eULOC7Z0ug/EEa/dRyom0lZRIqdw6nRRcO79B5GskEy4sztHXgkm3x7xvyaVA+XVEYTBeQFxvEArNfBBSiIBynLMwHxDUAsaNGtNyyFqQ4tfneLCZ8c7NFqm3Qlns0jzT4qhsEQYdhJ4gG2uMJwllbjA6J+326ec57fkaukyRqqBVdvH0EUveMl+7uEpQ7NM1NbyogSXlr77/bfb3dp1Tlus3yQuDLyWeEqAMZS4QQlMUJR9+mrKzd4T0Y169tMHV168w3Nsj7x7RvPE63/3OD3j69Ak6K2nFEbUwRGooRYGxEmEhCn18z9KRAmk1E0/y7N4TxtOcr375C0jtuGwx1cQIku4Rmz/5BSadonwfZWF8sIN9/yesbaxjkjHSGDAaY0oHetaiDCjPRxc5DhlU1RkpTpw6Z5N74YZLJxfukwF+xReenI8SpQIkFqWcBEojMNpd3GdV3Clt4Fp4hMATL8utPk8tzJyspHR2fydmJCcFjAP90kkDkKqqEq1FChevTVXBgsATFksJ1ger0cK9AkOOyScMB8e053xMMWU62kGzQrN5BmSJFQEgkKLANwrwMcK6ChmLiwoUCFNiy5w8GfP88QNacw0C1UIVgu0HT+nvHhG3F9k/HhE0fO7cv8WlM5fZPzxCByFe1GDaHzBI+sw36yirudsd8fqbb+AFhnQimGSW+sIc4dE+3e4hRTZAj4cUnuFwd4ujSY9MF4S1Jku1BSajKYXWPH2y6dZ3FZiyxHqWPM8orcALfApb0pmbIzmc4EtFkab4DR9fJnSPDjCl4JMPPkYUltZcxNLSAl9453WuXLvJX/zzP+XXH/8aJSSvv3KdD371o98eUD189vM9du99l/de/yqHeUHN70I+Qak62t8A5rHCq06cAiVmhuUCxF+fiMlTITUvt1ozMv5lkuCUQXWDB4lFYaVB2YKOp7nQabI8P8eL259w3J9U0KuwBmcYGwRIISiFxWqNJ2oMs5Ktp5s0rGQqA6ZTwePNfYTRlMZxu9ZaZxSB24NWsgRrKQ3sd/sszbcJJYiiYHIwJESgy4JUeviex87BIVvbu+4N6SmcP6WkJiRfePUqpfJoeCEimRC0GyS9HksNj3HvKX2doE1KUgT87JNP6PZ+QllmAOR5iS5LpJSUWtMKFO1vfo1ROmC11aReq7EQeDx//xOufOEmIojIBhlPHj7CpEMm/S5GazzlKgcQyKoFlNZVCtaWBEIyRTAcF5jsiJyMF8832Xm+A2nGYhzzz/7Vv6F31KuC5NxQyFeS0ig0OVJKfOExGQ7xlSX2PYQnEQU02wsMxjsoI4ikwPMs6ILAlVvsb25hphN8a4mKkjO+R9AypM8f0d187HbZtSTKS0qrKaRxbyZfoK1zRRGSk7bZ9dcn9lJVVIfEvoyw1fUaJdwkvTonZVXRSs+rioAZFylf4mertt/AbIEA6wDJGOOGWbNJfTWRF7KiCCpOdLY++nJvJoWL3RF29h03dJodE6p0lJy2lBqs9JHW4NmUdNpDZwW+jYjECvm0ZDrapz/eYbE+V5UowsVqSwHCc78GWYAQpHlGHIQIWyKtQZIwHQ95eO9jRtMh3fE+83NL+NOCvYMXeLUm9+/cJi9z9BgyLHtHR7QaLY5fPOdgr8eZcxv4RiObPkEYOBd8L+b+nTu8fvU1BtMJttBsLK+wOt8k729y79MhwxGsLq6yc3xEnpfINGdvtEuaJRRlSeiH6NyQ5gXKl3gqoMwLytJFk4deyfGgi/IleZojhaIeN+gfDkmnBq1hMB4QxnUura5RlBnHB/u894//V9R2x3zy8a/pD/s82nnG/Lmzvz2g5jKB/JCN+Zj+5BP63UO8QGGyI0RUo7HqY8wCpfExQiOlE1WAqk7aU4Pd04W9U8/IGcE9u82iHxwL4AgCCSfSDUOJkQHGWIajCdNsSJpn+BhsodGlxnrSZZYjqDUbKOVjhMSUBb2jLpPuMd3egLjZ4fnmHqrmHs8zBr86qQtAKInV+mTQaa2bxu92exwOhvhS8eW3bxCV0J7vQGOO2x9/QplnmFIjypICF41thUAqNzB4frhFu2izUK8zPT5kb5QSl9tcv3qBspwnRHAwOGDvaMrTZ1tIYdCFe3NlOsf3nGYOYxklCX/ynR9hrGWl2eYP/uibLLWXmBw+wqu3ebDf5Yd/8SMi4SzqStz2m/JUlWIpKXKD8jyCIMR6hrJw7bKfCaTUSAI83GR93O27YMV6Eyr5Tig9cgp8JJ62UDqhnK/Al4JWXKM3HhD6PkJJAk9hS4PEoIsU384xV69TFJZx/xjlebz79a9y62AHjKUe+fQePWbrs5Tly+eIZIDVmhEZB/cfMG51WDh7jma7xkJjDmksQpcIXzmhkJh1QlWFOIOt2UTdIRbgptfOrs7RWwJRDY3MySDHWo1AnXRZxtrPCeE/J8IX7rGVmPVguEFQtVE1C8+bzQoqo6ZTqtS9KaoqlkprOqtoFRgfYS272z9DeQHN+XP4soXNwWb7TAZH1IIFSqMoy4K8GJGkQ+I4qH4HEqGnHHcPieZWCL0AVRisLcimI2wxJlIRFDndg218VeP4cEyhBKvnLhH5Efd+9WOG+YjpcIcsavGl3/kdfvLjH3PhlSukScLjZ4/Ik5z5G6+RT6eMpkPWmqs8e3FIvR7x/OF9VhbmODzcxwiNCgzH3V1WNmKScUIqa/zkZ7/ixWGfTAuyVKPtGKEtMgyZb7cxpWYwHGK0QdoA60mMUY7rNgZLRhi66OwwDDHWcjwccHZ1jfk84OCgi7WSRmOOdJIxmfbZ2z3m3/zX/5yOianXaoySEVs7z2i3Wr89oI7KhO7zT1iua170dti+tcOjUco3fucirVZEkk7wvAlWKdcWEzo+VQgkpTt9/9oO9cl/cJITPvseUMXxOkAV1YlvtQVbAAajLKUQFMIHo9xpaiXDfh+rNVHgkVlNOh6BMUgLZVnw/R98j/Vrr/Ls7n23My0svieYX+gw6B+j7Ix6kJRu6jU751wbJsHRwk5vmOuSX91+wJnYp7+dsP/0GdNkjGcsPm7XuTBgTYnvS7S2lFby+Pk+drPL5QtrnF9dYXhvn7rv0V5aJQlivve9H/JsexORa6znpFmFclWO1BYpXRa6kG69MskzfCk56h/zr/7777AWRFxuhPz7/+EHbE9T8lxTa8YILyAgIE/6+J5PELiJda5y5wjvga98CpXjCYlnU3wEAQXCWjxR4iHwrMUzCl96TqpkXV/gAx6CoMprxxiEhoWlebaPD5CUeLIGJiXNUpQBU+ZEQC3wmK/HJH2LFvDDjz8izwrwfBQQ1wP64wn7798j600ppUKXJfXOHLtKsv3wHq0kp5XlzC3O4UtBgcYIlxumRBXfgduPd2XhqemIO+EsEos2jk8UVTKuw+QZCM8GracyJ6qJuDbGIZ51ulhX+1Zb9VqfTOhfDvw7cbY3TjgvX3qfVG+GqtIW7m8u7InjvUIirUZqTSNYZDg+Zq/3lChsgBRIKxkdS47L5ywsrRLEi2hSsnREmk0IRI4wBaWeIn2DsDnSFG6ZBg8fTTbNOBrs4lnNg/u3QSm0TllZX6c33uPK2UtcuHCevCi4v72HHGqOB0MuXz5PvVXDsyVzl8/ghxGvfek6O598SrPdwOQDbrx2hZWFFp/d/pAiFzzfOuLgeMDuwRZZkfL4+R7Pn8C5s5buKGOSFSSTFBV4SC0plYCioDdInEIo9DFpAUjK0jj/4+qCJ4wCI8h0gVGuM0szy8NHO6BcjJGnfJLJmCePu9SbPoWG7/zZnxMIn+5oyMr6PHXfY3w8/u0BNUv2KYGj/R6pLzAmpj3fZtgfE9ULBnlBs9lG1TVC1hCmWZ2YGVZKZskyn49WeMlwYXbivcQvzUweTLVRgrNVpN1eoFGvM04Tjvs9hPLRHpRCgRAMhsd8/NEvOR6PkWFAnuYEvjsJfd/j7MYGUS1C+U7fqiqeyyCxQrmqVroqQdpZCqU4eXPMdIRWuOrKWkGZW2zkguzyvKiGaxZfKYy2FFi0FE6ra90eNRY0msdbOxTJlBUpMMrnJx/fZf+ox3gwxBYCzyiEdbJ0FAhPUBYQCg9fKkppKEqLtQqFY7rCaY5HiPJC/Cx3aaRWozxFEEXINCHH8cIg8D0fz/PxhSIOncuSDmLQOQhBKSTS81FGo9AEnkJikMYQSImSTvAuhcGThnrkk2clQvro0kBZIowBA56QSO0ucFZrAiWRunT2h6ZksdOmu39ArVHj2pfe4sW9e3haoIH2/BxvnL/I3QdPkcMBokjBCPY+/Ix933DxlcssrDS42HmLyaNnWK0x2qlFlHET86pcPe2Hqg2qGfTN6tdaFIIQJNPUUTXVT82kfzNYdkxCJfq3s1XPlwyfpeNcZ13ZyTBstiorZtpOTodR1p33Ss78TasiV80Gae41CMAWCVnaZTp+RjrVTHsTrB1TTHyixjpR3CRutsmPx2xtPqbR7hOrHFsUTEYDSA+QOseLAvywQ9IbkZtj+oM+QdgmVG4pQZcw6B1ijGD9wgWeP7rH0eE+Ub3N9tNniHSKUSV5OiTLFH7kI+oh+1t7mKykP54wt7bM0e4RG+c3uH/7IxbXl/nk08/wPZ+FhRr1xhxXL83RO/4lC60OZ1cvcnTcY5ANefDkIf1hTmEsge9coNBA6agmawR+4IOsWv1SkxUuPcNMT2NupPLAlFhjkMIjmWaYLAdPYrUhVIpmPSYvJGc2zgCSleUNeodHvNjep7Q5Rkga80u/PaCWg+d4tTMMj3aoFSnpeEqRTOg0a6h+jUnZQ5cf0LCvEzVepVQWIRJUKd2u/ku7ztV5dTK1NLOrewWkM8nHbKhlqjtZ6/wXD59tosuSpU4LP1CuNTfC+SsCw36fF7s7XPvSuyxEDbY3t2FvilCQpil7e7vYYR+/qqqsMZRlQZZMwRgUAiWqYDRh3SRXa/dWE27KaXQ1nRXKJQgIjVIKT1q3eSIUs0whYQxSQGEFxipkVZFYoZHWorVl2B2w0KwjZcTes6fkmSYOQqzQWGGrVVnrZGVBgEg1SgQoL6AsNL605NIFHkpp8aVBKfcmDz2PGgHFRFAPYmxZ4AVhxXVWixhVDrqnFEILdzYID58cHw8hwZPuokOl0jDGOrcoz3cqD5mTOg0cngGhDSYvXQKm0Yx6xwgjENqgfCgpqXfaGAm5EXTiGtfml3k0nXDz8hWCyOfr773Gd/5kjsJTeNowHB7z+HifoO0zv9hg7+iYq2fOgZeyMdfgiorZTjVmnFO3ltKOEGYOi8YKyEXFl9rKA1U402PgpEq1VZX5ZHuLhflFwihygx7huo1ZtJ+o2nhshY2zLCdrXTdjK+qg0kVJUX0RA0K7i67z1XNVqpwNW0HK03QBMJXXgLtgh0GIwVBmSbVBmJMkU5JJRpZpDJrD/R5SCrx+Slybo1mPGXSPSdMxoe8xzUf4KiYuBXk+oNQG0oLllTlCoXn25AlhLUYKj729PZYWFhEWxv19PFGSDnvYMmU6KllZOoM0AYNRn+PuLuc25tG2gbUh21vbqLN12o0GD2/fZ3kxZG2xTjo6ZHGtTS32+PrXvsiz+8+o1+tsbR5RasPxYIQXRFy4+Qr+nUds391DqJA//Obv8mff+y6JFTQ7NfLBBJ0WBGGECpXTBlsojSHPNZ4fsLK6wuryMnfu3GY8HVFW/KHQkiQpET5Iz2nIozAk9n20TQlrPtgUYQIODg55fO8elCVlbjGFYlj0f3tALZIenYUljp4N6e2MuXhliUE+ovAUJRorU6xJOD7YYT16BaxPKQDtAAilT7gmwEXWVu45L12cTxQBJ07jODpAUklJlIcKS5QnycsSz/cqLZ9B+n4lFRFkyZSnDx/ywgge3L1PPXDtmnsTue2rJHWBe0pKTFkyGg4rv1ZT6WSrOkbMhmH2xAdSYE+qkpPjrSpolPvjyNl8o+I5lRSok+gL6wTbRjjOGdBWIK0hAIRU6Gp1UFYbKkqAp3xUVCed5HhSENXqFEWGKN3EWFWNrPU8POXE2QqDUApbrV7KuEGqNUJ6KKkIoxrSlqikQElBLaphfENe5AgzPdl8V1h84YZ1vqh22o0T2QvlIYrqwmgNJs8xgNEuJtgrS9f2WoFvoaEUa+05YgQTLRjnmnE+pUZJbTDAL2Gofe7c+pR8OqE0JdJaLl6/zr4uefBgj3H3EfVWyJlWhxuvvMl2PeRZb0xQZlyajjksSsrCIH2DpXTDReNXF8VTxcnJdP1E6OlAMAgCSl3gaYVS6tSBfXYOVy79s22pWZFwEsgnqqmskK49r6z/3XzAGVfPAu2UlCeDLscIVMdYdXA6HdMfPicrp7RaZwiDOWSZU5gpe91tFAXD/g7jtE+tcYaNi69xsHvM8kqH55v3efFiTJam+IHH7Xu32TizQqNe48nDO5TWp9le4pVXr5EOXzA86DKdjLAyZTA+ot5sE7fr3Lt9mywdYnVBaTI8odEGnj58SBQ1MWWKr2p0jwcsLjaJ4wab5YTzl9bJJjntwOfZnU3S4ZDlpRadekhca/Hwzh28WoT0BWmR89En95lfXqDVaLL7bIvVtXXMp7fJioRbtz8kDn3iVhOhLIUnKY1mnCbMd9pkwxECQVzz8UOJkJLV1Q4mSwilpPRjjMTRZ5MCYyyB5yidMysrDI6HyBBee/cVFB7LrZjB8YSwvcL2803s8YjDwwESy9/8j/7mbw+oUWPE8cEWR3s7zMdLGH/MdDSkHS4xHU3IrCBoCJJ0jC4ThMgRUlCoFGkcsDrQrFr+Kl3SAUYFStXVfrarXJ3bKFHRBQiQUKtFrrIAjJQzFosorLmTD0M2HrPh+UySFN/3CTxAW3SR8umtj0hKQ5JkRL5fATYUZYkfBpAmFTBXLb+tpq9VuzbzDHZ7Mu7IlOcqNyUknpToaqtEVQoHD0thjDOhrKgMK5z574nYwc7eSJJQCrR063EzRyBpDZ6UGCuqFV5O3ownGURCVkel3LEaTSMOCEv3pvfDEOH7BMpDCoknfTylkNYyozxdBpI5Nf6oLnTSWhSSUmi8SgrlS484qiEH4wpMBNJaAiVo12pMUuU4YFOwsniWzYMBkbJcXe4wOO4yF0QclhkaQ80LCbIEa1KsDFmwETeuvkrcbCDLjMIazHTCl966ytVGi8HKPIvtBt7egIPNbfxL87yeTSiOj8nTCb4Xs9Js0DNQYpEiwO3FV/FqJ4PQU4d8UYGf50nm5ubc+adclryjnma/kwpAbbWNxExQr09F9tad28a6SBF1Ut3byqFfoGY8uLAY43b9lY1AuNR6KwoEkjLXHO5OOHv2LI2wybC/RznJ6XYPKEgxhcYmgmEyZjje4uz6Ao1am2fPnuIpj93dIxqNJhZBkYNUMUIGSL8kHw1JJvDw3oDRcZ960KJRlxzsP6WztEh/lDNJxyTpkAAYJ2N2Dl/Qbi7Sai4ghOX5s0c06vMIKUhyQy1O2HrxGVGrRSOYoyngqXzIldeWsYVBegHPNo/ojgy+EETK0s+nrC0v0fj6AuNpQr3u0z8a8OjJLmFYJ1AFL44PaTdaHB8OUIGjctxg2y19WKtJk5K4FiJ8iy5zHj14gCks49GEXAv82KMWeXixjzWGRjPCmoxmMwQdcvmVC9x8/W2SXsK9T+9w/+ETFjbGvPnG6xzt9xChYXX9DEf7/d8eUB89/JQg8Xn1codINThKdphv1lhfWqS7vUdinUlCvbnsBkQzZYqw1Zz9tAIQp71MBZ6G2SD0hFc9wVNRud1UWj6jkUIhhEvbs8YilAPmQEq8amo0TXMOdvfpj8Y8vHefduQ5IDSane1tZFyn0ezQkgapM5QQTtakJEU6rQ7dtWBCSOIwIC9zlBCUZeHeLICsqhJfOoCRAjypsJVrEDjuVVUtnx8ECGNdVfkS7QEShcVD4kvp3OSlxLcCZXGGvBanafQ8psqRgYEfUHg+WhXIwlXhSoAvHK8kjADrEUiLlB5YSZ5kEAZIX1bpnw4spAyQOifwfTJb4Ps+tpiJimxlguFc74UtUSh30pQ5Sjo1hBQSTwhqgY8f+dQin8Fhv7ooGowuWG3WWIzg3PkFpvsDAuU8FKTVeHgERmGVx7Dmcf/BQzAGL8kpbEk+nbAwysj0hGSlw4NByuUiY91LyR91yTMJwuA3ahQYlhs+G/PLHCQZewd9RrqseGMHhp/zFhUVS1oNKiXVdpKZrZhWADy7dlU/a3FOVacDJHfhsZUsRFQtP5XF3eyRpKjymrQD2ePuPq1GnRJATfDVPOOkS6OxjAx8zl8+Qzo+pt89YNQ/4GBnnySbImWMr+r0+wmN+hmkgeeP7tA9GiClYjhM8bwW05EBmxJENT7+8B7nz68Q+JbVlTWSpKB/2Kd/3OXIHhPWfPLpgMgLsMZjWg5Q2iKiiMEkY3FxFSFrHB4NKYsUawQPHz0nrMUsrS6T5wXnz6yyevYCt+/cYr45z9bRkDcuvM7GXIPxeECrvcTSygoHe/s0UKwszXF0OEFkUIt9TFbSG+VMyyl/+Ed/g/1HL9g+PGZ3dxdrBNk0QXk+EvBDjzzP8HwPmxQunysr0dZSioxAhCAUvi84u7GONhOSaYLnt2g0Y8bDY3q9Hp1WTDv2KXsHDLeP2T/cRbYUy+sR3/r6V/nk1mccZ2P6vSOWVjq/PaCORxkX2jXmagHZNMfH0Iw8ekfblFZT66yjRQNRg3H+nHpgEbTwrOfc8sXpbvLLOjx3/pmqTJt9/7T9OZE3CxDWII1m5t5jq5N/Mh6SZlOK8fjE5uzhkyc829pClxopPfI0o9qlq6RBEUIFSFsgdeka5QrgTo5UOJerklPh98xyzVWps+EClXuQ07MYkzJr7N0gyy08WF1+bvNr9gaV9vSN6sxiFCXOgk5aNzRT1f086aGNcIJ8Y1HKQ0mJFiAM1RaLdZZtABUJH6CRtiTyJVHoUQhJHPiEgTzx91TCBzKsMfieh5YQ1GOmg8TxosLFhpjCCek9QXWMbi2XatimrKXmKaampNGok3hu4l3zlLuwlNrdp9Vgf3ufMKg5DaSGIqozqs/hd5pc+sLr1Bbm2ZjvcDaOmP/936W5vIzwYti4yJVRxtX8gGm/TxwKp+JotRm0QoKLq2ysLOPFMbHJOReDH2sejks08ekfQAhH4VSV+UmCJ7PVaHFy0bMVZXQ6FZpVpuCJ0/PZ8aCmKmKrtt7M4lHKk+rWPZ+HEM61aWmhyWQ4JIp9+qMdOo2IIumREUIm2N2/z3DSpxbX0TbneDQlTcbYckhcX0KHIWGzxbB7SL3R4efvf0xca3E4SOnMt/nCW2/TPdxhPDymNVcnjBXtRsDOzhNWltcJa4sUmSKIAnIzxVfz7B8NQLkCKc1KPBXTaq3y6NEmiytLzC3MMR0qjCnwQk1W5kRxwMJCE2UzXjz5FWfnm/z8o89oLzZoNXx6h30e72/TVIpgro0tAsZpweb2NmEYM8xyFheWuXL+NTaWRtw+us2Ni+u0Rpaf/+WvGKYJQRAShgG66qQ8X1GW5UmnoXN9MndQARhd4AWSubkWUpQgXaZVmqaYOEQJRZ4XdLs5neYujUiytbvNweCI9lKdpm94cP8eeTZByILSCp48e/rbA+pSbZlMZTx4uE+z1UJEMbosWZxfoCiaNNZu8nT/Eb3eNunwBc+STzh3+Rs0aueqifjspDw9lzGz0/HUSu3kbD4hH6sfMLZiB70KlJzOtbSazWfPSId9Jt0ewrpQsmQ8oggCoiDG831EqU9OdlmZswirKU1JoctqSmixSrkwtoq/FRYUBq1zZ5IgJLMYTFm9H8Ws4qn6SFmtOkoJ1pRIKZwcSQusNhVFUYGpAM9ajFCUQGE0SkhKLD5UAA9BxfMJ5RHVWiRpimdTgjAi83zKk0iK6oJkDMYWCKEJhcXIAKTzKMCC5zkvyEYcOkmR75OkJYXxCaPQhRnKKbYQOKGqqSbcTkWpjdtrlxR4ZYnMJ0glnC0aIIxhaWmeJMupNVtoUVKvx2hP4gkJ1mfUTTDGR/oehXC+D9Fck5Wz67z29lU2lucQheXc+iqt9gK1xZjtoyMuLS+w9OwAtbNHniTk+ZAiXiBrxhSXluhcu8JCZ41Gew5P5GSjPg/e/xlbSUl87Ysn8jd38a12+6sLo5wxxpXkidnZ+TJWzqgqxKwGOFEKuId120VKvBRlIhXCaCwaY5W7AJ7wpinKCHrdLbK0D9YnGT5ndLTDJC04ey4iGU2o11qk04BBb0S9FWNtjcFwRFlo2mrK5VcXGQ0mDMaazd19brz1ReKwYGdvzNXrNzjc3SaOAubqK3RH+6xurFOMJyg89na3WVq5SFBvUZZT+r0Bw8GE+fYSyWTM7vYuNq5x+fo6L7Z3Wd5YRQC3P72LF4QUxrKwsAhGo/OC7v4hw/4xe90Br1w5x5s3L7C72yXvHTMaZETCY+/omDe//AXGHDApUiZ5gV+POLOxTuQr7nz8KbsHRyxfWuD9H7zP081jvHqDs6vLZOmU0fiYNM3ww5A0zUFVHW8JQRQynU4QnqCzUGfQG1IUhmQ6YTIeoTyIooBOO8JTlqhTQwDt+hwHh0MmyTZb2/uY0pKPNHfvbXN4eI/SWi5dXGZtvY3O8t8eUG+8Pscv7z3BbwSsXrvEixfbJGXBKC/wUXR3PkOn+yhVo8gXOL/+NmHUBKkRBMw2UU41phWYnnCqs3arkpFUYIUQ1YpdZYQywy1tXLVUndjKzFpu99GbyU086WRbavZGsRRFgShLpDEISnSpXUtbAaOnAqzOmVXG0rqpr+X0/XOiE5wNuqRCFwWe7zu7QW0IfI+yyPGsOqlWnCzGeUu60EJ7wsUJIbAKyEEagYep3rzGvRZhsbbA2Jw4ULSaDdI8q4BWVBrxWRvqxl7WWnwpCDzp6FujkZ4gSxPKfMqkzLBWuD3lJKPMc6Z997vPksSxsUJQzmgXqSqtpEV5lpqytJAsz3fojQZYpljrkZcWxgm+Eqw0a1gy7HiCNIZElhDGJM/28IMWtY6iMGNSKShiQ2ehQW/rGWxKziwv83tv3ODo7lPioM3ZcBV1e5PkaIdEamRQowyajNoh3msXWL7+GstrG/h+QJH2ePrBLzjY3mO3t0u5eMZx2bZw3YMUeEhMpQ11/LujQGYO9xJRyf5OMJeZifQpmSVOK1chHCjr2bkqT5YK0vyIyfQpcbxAHF90w6nAsn+wxUJjgV7/gGYUk4y69Pf38FRMnht2tj6jLAI6cys0mhHPt57w9NmA3d0trly5jO/7dObqTHtdorDDxYsdtND0xyNISzw9xqZTKA1PdzdZWZunObfIwU6PSb+PoGQ0nbC4Ktje2ULrhDiKsFLxcHuXpaUlGktrTkc+nnKu0SDJC4zf4NprV+i06vz4L35Oa+0Mg0GPe/e2EJ6l1W7Rml/k07uPWdzv0W7Ok2Ul+0f7JFNL2GyQTjNMmTDsDwmaNYyV5NmYQTdh58UxO0cHRPWA7rhgUpZ4UcBgcozOSxCgpaRINEJZ4nZIlmUIBKPRGKSjvHq9AcK4C2OeF+RFQRAGWJFjraQWxYwnE4wp//+k/UeTJVmapok9R49y1cuvcTdzHu4eJCMjkldVVjWZZoPuBqYhI9IyIsAAK4hggR3+A3bYA4v5A0N6ZqqqZ4pkF+nKqsxIEsyDODM3bpdT5UfPweKaZ2VDBNWC7LuwjZncK3JN9dPvfN/7Pi/pakq6WuLMbYTwsKwCpWtG8xlZYej2+9iWzWw6J1/9J+hQy3XJds/CJA5Cb7PV1+hS4PhH5MmEIpvje4c0+ofYtocbhDiyzQa2VQN/S+5589qMlTa6vg056teOTW+E1mJzs/9qpirejAg0xkgqVVFVJWWVU96QnRBQaYVlLKy6wjY+lvlbqUtdb2J3bSNugLubm0nrm8Hor90sv+qu3yDXzMZ3/Uag/YaTaQmJtCSm3ojihS0QusJU1eZB8aYzl5vu8c389Q3oYtOxm01KpbCofm32Km7eXyCwxcZuq8XGZqd1hdIV8ubmFzddstHixppooaqCfD3HNhmr8RWtThNXwGKdUAhBu9vFNxYeBiIP33UxuiawG1RKsVolvDkrWHLzcLMNuEZw7+CA4cU5KilwjUWvESOUxlIGp6jwUNSmRouaVZZhlwIrqTf20/0tnFaDxWCEmNcU65SjnX3M3h4OCi/sMHYE5fEpzWRJb6jJxsvNbNOxkEGDpN+FRw2ce3vsP3hIx29T52vGr7/g/IvPuZpdksgQrTcdfI26uQ4t6ptN+ptK+etMCfFrRVSbN+eJm9XTzXf8ZjT0BnH3pgl4cx1vploWmxhngWs3mKwaFGmN1R2TZym1kRRZivF7tBs2w6svGU9GLEcaVY/Z2rrPePSS5TIjCLdotvYYjSckWcrb776H59qYquLq9Jz9vT2EpXBCg2P5NAJNYSoef/CIyXJFb2eLbqfDYnKFTivWuiTwAxarlE5/n8lkRV2W1AZKJUkLi4f3H7EcDWn5DZ4+f0Yj9Lg6vWCVVvyDv/dbDIdDciXY2WkzmU958eIl+wcHOK5DWVUgHO48eMjw+prnn3zCzu42jU6f+w93SIsChEOaWrw6HiDsiN/57W/g2EuisMtsnDJdZ/z846/oN1pEccg77z/kT//kj6jrFKVttIaq0lg1ZOti8/gSBqQhjH0830fpnGydoTUbC2q9kSsaVdGII9JMcXE2Jwgd7tzuo4oUy3KYL9eEzWhjZc6hEbl4jo1tOaxWC6T4O0vmfwSOkmua7HIxHZEk53Qdl7rMqW9pklIgrRat9lsE0R0qnWK5HlpVIJ2buF7zH7zfrxdWXb8pr7/aY21UAAbQijekq00DIH7tLyFJlsxXI4RKUSrHsSWu8cjzGl1XoCR1kVMb9avPteQG4bVxOxmklNTa+rUF0RspzQ28Retfffab15su0AJ0rciSAuVHuK5LkWeUWiONwbc2Kx1piZsj842nXymqN93pzVJ4UzA33asxm5mpNH/bqQssdF5SmyVpmpBORsTtJqJQmwBD3jA/N1tpwQaEHfs2sbRo7O9sdKbWplvf29pG1QrLkqiixKprNJIsK9GqwtSbrrnTaLBK1hgskrxAC6iFwUib0HWILRudLrBlRdfdPMSkKRFlieM4WLhQK1I289xpXaFCSaPf4b333ufPfvRj0ukIz3XpdtukDR/tCfKion81p7qYsK7W6GWN8sGKYlaRh72zQ3Swz+G9B2xtHUCdk1y94tlf/yXrdELq+6ja4AqH0o6pZcRkNKPSNTt7+7zZFSH+VsHxpnT+at4Pm07njbrj105T4k2D8OvXDQaosaSiLOsNsT5d4boOjpR0+gFZYTGbrVmMr1guZuzserx+fg16zXSUgegSdz3Ozk+IK0GrdYtWs2a9rliulsSNBv3dPQyGsihxgTwpefblMZZtaPf3qGuPW4cHFKWFWeasr8fk8o13PeKLp5/jhzHz5ZSHT+5i2R5x2GV0vaDZbjNbrZiNM3Z6Fdv7fdJFjpQOeQm+30DYFT/+6AsG42M++OAJ2nLY3d9BoTjcP2Q2npJR8fXXr3jv/Yes1iv2b91ia2eHp59+xnqZ4Pg+buATegFxo8Hb736Dp08/49Hjbc4nx3iNmIbvM1qsmc/nvP3oLb747EuarRazpaHKFaqqMfXGhVklm5Gc1gLbk4QNn7rW+K5DnZfklcLUm0ZJFRVSOGRpzXQywvM92q0Wl5eX3D3aQWuXtDrHCE1dGR6/dYfZcI5wbBaLOWlR4gf/CQXVyApRSIwSuK0hZtlhr9EkLS6pszWx2yawYDI7IVECcyHoBi22bu3dQEr+wyP/TVV6U702BfLNQuBmIyqwMLpCqQ3azpYuWm94rEZojGVIyhXHZ18zuzzFXpRYGkLHI/IcaqFwLYeKTTxtKaxNSNmNC0vcoNxt+wYQciNu582t9Kvl7Bvu6htJ968t1tgc+z3PxpEANVIYfMcmz3KM5WBuFktWbSjWKRsoxpu79WbpcXPk/JWn7D9gwN48kIzGcwBZY4cuAg/bAul75HUNrLGk3Dh1yg1wuNsMsJohX52tKFW9YWDeQDuE3mS3G2FhTH0TtmZu+Jhm0zGzWcw5NyOatCgw0qISkqrW1HnB/vYWn52OodYI20X7UDkOllRYjsS1fUTsIrf6/O6tQ94KoN3q8w92+oyfH7N6cUarMCgpmVJjSxvneoT3+gqTL1nNVtSyxsQ+quujoi7erVvs3LtPv9fHtSXFYsDw84+YPP+Y8XJGEbRwbJvKd9B1hWu7jLOa62SGdG26O2+0xBWw6bz1DR3K2ghBb8AjN8MTbW7wfwJxs5j8W77+my6Wm/+ZYTgcUpeag90D0jQB42P7Lmmxwov6uG6HwHWo6g083LO2uLickmaCqrIw1oz9/UMqteaLz1cYowmimDQvWKcZj995l9l0Qb8TU+RrtncPGAyXmNrQ293BkFOUY6Tn8+itJwxOf0TtGqJOgJSC/t4+pycXCBlR1x7tZodWo8Mry2W2SInjNvfuhdTKMDxfcef+Ie/6Nu12i3Le4aOffYrbbXD7rSc0Wwc0owKdJHTiBlm2BjRlkXL/4T08z+bBg3u4MqTZbDPb2WM4HCPrGqQmqyruPbjNZHTC/k6P+bjEdjy0sNja6tLp93n87jucPHvJV18PKcXmO1Cl3ihfbkA4UlhoBLUxeK6N0DVFluPFPrYQeNLGciyyrMQojVGGZFUQRQGWrEmyCbZ0mExmrJN0o+sW4NqwmgxQZc3l+Qw7sPBCHyP+v8xK//8U1MTU1FZNUZaEqUMrUhTplGycs9ePiVoxP/3yUyo3ZplpZAXff/JdLFWBpUB4myOSFlhi4xcWwt8sSWyFNhnFeoEjNsv4IIyxcMiKGevFGYEdEfdvY4gQGLSwqbQmSVO0qZjNpjRym9hY+I6DbLTZ2d7ienhNr91lPpmw3QgpkjW142LYWEKF2khWhADHbGattS3ZLGMFvIFZGPMrIfibwm/ftJaVpTFaUGqDZ21E76XczN7qmg0e76aIRq6DJS2SPLvJLBJIx9lYXU1NP3CxnYrRWuNibXRY6mYJos3GHlvXm027tDC1Ra1rtDbE7Q4mSW6Ynxs741YY47o+Zr7GGI3CIGuJ0KDExg7rGIOy9CZV9g2I+SZPSVsW0pLYjgBLEjaalBhKBLqokbZNJw7o9bu0I5cnjx8Q+wEt26H2wJ4XpHvbJOkKW9h0tj2u/vonxI8fUpc5ExR5bTA21HVGe5HB7BgWA8r5EuFKLDtgntasWkvS1or7d9/jwdsfEuNjyozRV59x/NWnVCLDLqaYXJPFLsJkZNMV+F3GecIn85p1Ce89ekRZKjxHgtgoJcyvLRMR1o0GV2MZC244q+ZX23mFhcPZ6Tn7Rz2MCTBG3syPC6Tt0Wn0kGaJyr7GVgnprCIRTfxoh8DuglUzm5eEzhZuaDO4GJGmGXHc4XRwRewL0uWS4WiG74VcjxKuRnOCOMSybY5fn5BlFcv1mmK1pNNpEwYhURwxmc6JGg66Lgl8wWI94sPf/oDheMLg7BpVKs7OLhnOpuz2d3l9esV8saDRam4Yp8s1ewe3uDg5Jmi02X30hOHwim6rRzKdErkB66Kik8GDR7sYWxF1b/H1z39GLRRhI+L8csTpyRV3Ht7h668u6XW2eev+Q54/e8X55TU7e/tMZlOqrOb2rS0ir8mfvXxGt9Ok2Whw53YXbTwcI2ns9HGVpsgLArfDZPz6Jhp8c6HKG1uvtASV0tRljcot1nVFoepN2obcNFi1Nli2RFUaKQzvvXvAaLxgvsy4fecellGcvLgAucEY3jrYYXI1QlWasqpxXYe0KKisNb4Mf/OCSmUR+S4fvv8WdiWYXp4R+132ml16h21G1YD2rQLbgf56C5XmaP0VKtvGKBc/6qBkgBIeo/El6+k1W509Gp0+ebbm8uqY+fhqIz9B0N/ewxiXxXxKO6iR3pS1qfAbuwjhkCtBqWF4cYpWBdK2UMZgao1lDPP5DGEs5ssMvwGVseh3uuRlyd6tQ+aTxaaQKQ81m2EwRJ5LJR3cTgddhthlha0rclWBqlFmk3teY9DW5kgOYCwLrWq0s5FNWcaizAuE0dg3pPQ3W+LabEAhmwP8Jj6iriqEtNC2zXYc0fObPBvMkTdkdXFjWa0ti7oSN+9hsOqSXFRoyxAqgWdv/PqWcVFCUomN//3W9hZxs8VsPt10XY5BSYO8CY2rbR/b8QmNwHFdomZE4LuEtk0nbtCIQ6QUuFGIsB3MuqDSNV/87BcoXSOLive++10+/fwzijuHfPnJU9rC5eCHH3Ix/Yq77QZxu0mv2+evfvozllXFh7UhanZYTBY0ywpXlNh5jZrOKJIBjlRoO6BquuRUXDiaKHb5wXf/S44OP0RWGcvjX3L+8x+xSOdUloeId1npNWtP4bpdXl4l5PY20mlyauUoc5vhFz/ntf2S3V4PX0abDrPWWHLDIpBS8Kbx2HSdCmM2SaUCi9qUqCIh8Br89Bc/5h+0f4840pRqQa0SyqKmFXcoFmvmy69YJyd49j5B2CFoVJTViGK8JFsumI8nFArMwmG5WnFycYmwK6TnQxUxHo5RwuLwsMnbW0f89KOf4do+XuCzu7/L65MTjKnYP9zGc236nQ5FlZKtS55+dMH2/h2afspq8AlH9+/QciR1o8Wf/NWPaXY6dGtJo9HB9T0e/NaH7LXbPP3Jz/jek3dZzBc8uH+Pq6sR3SBilqT8+aef0tvuYwtBo9PCGEGRKra7DYrplGbXxfEDhqMpd+4d8M1vPqLMChrvPeJnH3/Mz58+JS0Ubz95xMPbByxHV5ydX3KsS/Lh1zS9bTw0ZZIwubbZOThgOZnT6PawqHjn3fs8++prGlGTJE+p6jdjwE168ZuxmaCmVoZK1ygJaVlCqVBKIR2B49g04wYSRVEsaLUsOt0DhO1xcTIgjENyrQgifxPGF/gs5iVJVmCEi+NZIOtNmOhvWlDVWEPLwm7U2L7g8O09LNPgfDBhpTRlNsRfLXn7rQ/4bPKcZtCDApaLF9RlTp63WJQei8xltZojqpRsfUW4aLFYrjB1TVVlVFaF50kGk2Pq0sEom3Kd42w5lHpBlp6C0yUtYn728WfEbQejc4LIh2IzJ914nB3K+YyW7bO+HiCpmV5cUpUF46sxFha1qvBsUHWFUYpOf4t5qfGiCGkJBifH3Lp7j8F4iG85CGlTpTlaaGqlMLWiqBWFNkhdbRYSWm9shQo2WQUbX/cGHSc2HNNfgTZufmiQZnN8QZf4dgNcm0yCZyRKbBIP8qokrnJsIaisjXDfNg7a3uhDjRDg2Hg36xchNHW6Jqwqtnf6RNtbNG1JI/LwO01cLKIaoihCFAVVtobdDlWhOHv+GjvwePzB+/zoD/8XCl3yw3/+z/jLn/6cV58+5Xe++x2EI3FKhVNqbC9DtFyKiym3t/aQuqZ9OebW0S6N9Qg/Clm9GnB7MUJ0IqrXZ4jQ53Yz4JmlcTQ4FQhVoKVG7MWklQu7bfzDXX5364CDvT6h5VEtL7n65Z8x/PwjllqjLInx4w2LNYio/B1eFA3UvQf4IuH09IQnT97ly+uCUFd8+pOPmAzG/Pbv/R47u3sb44DZAG+0UjcgE4k2m6wiDFjCwaJgOTtnNR/TaDdxvITry1P29/qMJq9wZEjgdXjx8isarYgkr0nXNut8imBOZ+8OW7t71FXFfDJncD0jE4bVMifPcxqdLWbzAVK5FNUmPC6ra8bzFdLy8VwHS0CWJKznC/pxhB94tFse09EI0QrZ7rWwdz083+X4/IzZoqDVirkrJSLVXF9cEwYNtlo7nCZnvLw84x//i/+ct28/JF2sKErN6fkpYejzw+//gD//03/Pf//f/ve8++Qe3/3+d2l32vzJH/0RtmOTVhV/+fNf8Fs/eIdbW228xEUKm93+AV89P2br29/AGMHzV5d88/1v8PzFNbrOyedLPp2uGAzGPH70Fq7r0Xm0zdOnXxDGHV4enyGCiJ6Bnb0+g7Nruv0ATxr+6T/9+/zhn/4FeVFQqJJaaeo3Gugbl6ElbVS92ZU4zs061RJYjrVB91kOQeChCovXr+cb67RdUqicVjNkvijIioJ1aigahmYzoKgztLWxgudpQbcXURTFb15QB+MEJTRBO2A8nxL5Pu3IYu+oz+fnZzSUxVF0F51N6O+sUbVDtp5Qo1CWjZGaZVYxnQh0IbEdybJcMxslLGYFQeDhexJL2NQGbAlu6BCEbZaLCaUMUNmI2DN88eUpn386wHY8LNlFqWIDRZAa5dtQ1dhGY6GwqoSiSHFcSVkqwCLNFa7no616A52tNiqCZLGi0IKyKKirkjwrKKuarNAEnZio1WR8ccXdB/cYXl3jWBZRq8n51SUtKRCrBUJIQidGNBpYZsMFdVWGFoYwsCkwNxrRX/eSb0YCxpF4gctBGLDfaqJXBUhDbUtqRyAqh9p1cR0Xz7axjaHfDPGDENt1aFoecTPCt22WSnHy7IuNkL4u+Zdv7XImPU4/PabRb9Hu7PAnf/BvqaXF3/8//mv+8N/8Pulwyge/+320Ujy7HuEHEZ11jm532Ilj1CTl2ztH3PvARSYVWWaospTtOCJN1vyD3jbV1TnCtejv70G2xi4kuWdYjaesFilSKdrdDttvPyA46DP/6iVaa7QQ1KbCtLtkvkW25dO89ZDW3n22+js4OkfXmunzjxh88u9YrAsqXZEaEG6LSuYosc20bnBOQOetx7zTb1AKhdvf569+9EdMB0tWl5cIYTEdTyjyAl3X5EmGY0u8jT8ZXZcgJEL4YDsIYbCFZjp5RSMscaTi9Pgzus2cJP2U09OAujK0on3mq5ekySkqE2Spj7DamGhFls+xkwvKiymu1+bg8JCf/uILamru3b3HZDym3YxBG0bjCZETYqSmKhWzyZxG2GCZJKzyjN/54e/wk5/+DBvF/v4+t+7usVguObsYIl5doZSi0IrFYkGFxPEEX331mtD4jGYz5vMZz1+/4u13HvNbv/cdQl/yiz/5U8K4yXtvP0F4gtHwkpPXx+RZCq6g9l1m0wmnz1+SLjKUtZGfbXd7vH/nERcnX9HtdhiMrtna2aXXb3F2/oLf/d5vs5hmXJ5fE9oBp9eXNJohjVaTo/s7bO23uBosyFTBo/fv0/dsoqZDFAeMh1OCsImZrlnMV+z0Y6bjS548eouzy3Oefvk1ValvulONEPpXD8OyUriWREpNEPrUutzordmMcKbjCVW1EbYVhcLzFUEIZZGidIUfOChVkiQldQWu56CEJk80aElV1NiW/M0L6sJL8VzN+ShnOVPUakq/HeMELu2eTTmcUwcex1cTnC2frEwJ4htGZmXR8G3EtmA6nWFMQKU8NIYsLVmtaop8ThgLfC+iVjauU2PcFaKs0RhOzme4uiSrJFr4aAGe7+G5Hq7l48iM2pdkRYlwNgL6ymiMZWFqgXAktu1snFPa4NqK0mhUWeDckM/XiznCCdDGYFPTdjwW1wMCKVHrhEWRY6GYTkakVY5tHGS42eYGvRZput5QwxstjCvp9HeYXF/TiLuUVclWoYhdn/VyiXRdiqpEG01VlMgyx5iaLEuww5A7h1tYpWTPe4IbBkSuxLYsAleiZ2uavQ5Jy2Hw+QsOv/UNBqMxVy/Ouf3D7/Cn/+Z/4e7RfaKwia4qMCV28xa/vBpzsc7xplOedA6w9w6IbYG+nPL+0W28t+7RKhUty+bDtx9j6xr16it+y5Nk6Qr91WfoqkAmCfOsQqkKVecElkekC6xGgHzrLWxh45aQLhb4zZjKgCs97vf3yajZO+qTr+d88fVHOKvN+KEUFiOrwG21sHZ2GFVjnrz1Fi23B65gdXLF6c8/Yn39CdSamoqFCBFujI3Fqgq4HLvUvTYH7z4kNhamLLgc5tg2POpJno4VbqdJKSwePnnEaj4hjxuMZzNKVXJ065DR4JzZbECn0+HgziNUuYmEqcyKbF1iMs1XX31Od1vRaTZYLTMWkzG3D+6wnD+n2Q4pizVWGJCzxiQ1zVaJ7dqYukBrODsb8WXynDsPDjk/v+DTTz+myGtMrckzvYn2sJeEjYDyhhf79OmXmxgfAycvjymThA8/eBvbdnn55TGuhHI9YbfTYLfbYbwYYKyKwljk6yXe1j5//uOP6Ha6HN4/5GHjXRq+i9SK4ctjUgST8zl5seLy6pL+TpemHUCuuXfrFhfX13jC4uLla4TtMxyNCTsOF8Mh/82/+QO2d1pMT17huDbjmcP5+ZRbh11Onr3grQfbvDoecnVxRqNpsbUXcv9ulypLkHKJMDVHW0d88skvCd86wjUOq8slSWGwgzV4Gl84fPnsJbHfZDK8IkmSm3m3pNYgLRshNgBw27ZAQa00jiPRlcKRFn4csFr8bZS01ppSWUjXQki4f+82yXqBrnM6vQanZxuwea4lXgi2p7Gp2es3aLRutK6/aUFt7EgKvUZUNrq2yDObq6Ki0SsJvIxeP8JvQ2seMCsXxJFPni5RdYUnO6hsSVkr8mqzffaFZrKGbLnRJN49DDnYirlOlgwnU/rBFkHoMpmvmE5z4jhmObN4/tEllbBxrYDYjRGFxLFiPE9hrAJlWdRKb6xyyuAKC2F51I6g1pulDHWN427scgaoSoWla4xxkMJgS2ujITU1aVniSYuyLOj0AqIw4Or6DMdycZ2A+XDB4U6T3qHLWw8ekCwUr05GlKkimSxI5ws6TheVbyDJdq9HWdc8ePSA45eveHzvPq9PXhOhqC7PUUqDVrwfNxnv7vCjjz7jH/7WP+bf/dlfEAdNvv27P+D//f/6b/jG47fZa93jf/ryFf/5rYdcT3Om65o7lwl+q8nV1QVtI6EscbMceXnBh07Ah48OKeuM4OI1R82AtExxnn7GXrnJX9Kew8KuqaTBshw6wkVWNYG00FZJ3Axp+yENLC6nEyrXRnguTreJ1W+gJjOUgVVRIqoaMZtT+RbBts/x1UtmieGTY4fOjo2IK1QY8eCHH9J0JN6tPvLBA6L2Fg9636XptijWS4Z//kdcn3/JuvaRlkUpBWXpEpqQytrm2dpmFHTZfXyP2zsxZV2jVM3zZEVLlmxfveaJLtnZdfmL85xKWGTrhFxXjC8HxM02OTlGJRRpwevTC559/YLvuZLdg3t4VoCuK2o9INOwWlqoWtDZWTOZjYicXSaDHNuWXF0usGWbbDyk0QiYJRWzRcj0ekmzadHuWtjS49beLhYhn4w/Y7pY40ibZF2A2VDBrgYT9DV0Ok28hss6S4m8mLIsefb8FXEUMp1POT0b4AUxnm/R7zjMszVqWpHnCRcnI7TTYjlLGV0tuXx9hmW7JHlKVzq89/47NB2HUlskgwleEPH1V19xeHjAdDjg5bPXXBxfk+QF9999iygOaXZazNKcb374TZ59/hlbO118J+Cwt8t2c5cyXXI5mvKD73yb88Epn7+44pEXoSzN1n5AYIVcT3NOX08xacZ8NcTubRE0PO7cv831bMXlyQVSSFRd0Oq06G5vU9c1jtfgcjDFsl2UNjSiiOlihRAGaW/MFhiLut4YYf4Wt7k5AeZZgaoFWt38XmgQmrgZk67WvD6+JEtymu0Wo8kYgUWpLLKsoNnpE4SKrmOQosKLBV4Y/+YFtRvHVMpGLTW+E+FGTRzfQYYLHGNzdlLilglOw2XL7KC1zXy1JmoHqCqjNiHN2KLVU9jlint3e/z8uWY1q3BReLYhmWb0trdJtc3F6wXNZocXx2P29/vkZcLT4wsmYwWiQtaGt996zP7uHreFpqRkNB7cxKVI0ixnsVyQJAnFagVFiSkVlusghaIQamMzrS20BbaRaLtGBOD6Lqq0UOsCowss7fCDb79H7l3T2Y3YXTm07Q7pvGY6Tbh16LGWOV4leXE9IBvPEI5PkWYEumY1niKxkNKQmRlSKeanV8gkJbmeUCU5TttHCxthLBwtqPyQ0+mKZaqYTBPiqIVVlhTnA77z3js0KsHeIuO//t43cGeX3Lct1I4Pv/wrPpSSq1XKcjWjDDZhefl8QZ4NsYVL4ZoNAV9YeI6k1AVB28PIG4dZkUPHh3aAniVQbAb5jaCPKkoIDabhkLR9cgNVJLHzhHImuRpNwXapHEnQaVJ1WlgND7fr8yB+n25jGx21WZdDyvKax0++j291sUwGtcZUmiwrWY8GqOtnnP3yJ8xWY3yjcdyK0nYQtYfnhExkjxeFJDja5dH9u+BGLExFsVhQ1jb9asXu+DlyeIE2FvdjuP/OFgUul3nB35ydMZ0npNv7LKm5nkRkSY2xbHzR5PL4gn1fsMyWDBbPKNQQR0bs3/J5dTyguXWEH9SopCYtKpRK8EPITE7Y2uH6Yopvh+RVSW+ri+cV2HZKI+xw/OpLhpOM4XBK3NmhSgscXzKezdFVjeuHWJaF7we4vk8Yhkhp43sutZIUlWYwSyhqTSvyWaYLWFT0uj3OzycEHjRbO8yzilt3j7h6fUXQabJOEoJWQDP06VqSZDTh+uQrLkcTJsM1/W6H88sB/a2YO/f7hHGbH/3o37P65We0owDHhdZWn92dPq55l+vpgFWa8Pzr1zx45yGdnV12d/b4+PNnBK5N1Gnz8c8/44Pvvsvg5ZzMs2hFAYvpkv39Ay4Xp7zXb3H55SdMlkusuE3ca3H31i1++oufsLxYE7Y6/PLrT1FFwXi65MHDR7z3zttcX12z/uwLhLtRZBTlBjRjcYNEtAXG2gR9Oo4D2uHW/SPOTp8hLQctAFFzdHDI82fPmc9yDAa9WqGtmmYjBGGzXuRcXkzYOgx5dK/J4GxBtniT0vAbFtRykpFLSGYKkpxqqdi+62GLFZ1mi+UkpSptRGGRrdcYAZ4J6DgNvE4DVbgYHA46YFcOZVFhURJGmshtEcQevtYUeU6xzpkmOYnOuJzknA6eo6oUlEtQGzxREdg+Z08/Z/bqFWEc4fo+cRzT3d4mCOKNHdTxqISh1DVFVbAuM5brBcPzF6iqpCyhyBRlmaJyxU6vweFtH8fTLNcWL56PkYscXRrc9YSH9/eQbUibHllheHVyzjtv7VDqhEbgoldQqQJhbzSpFgrbFugqpzbgiRq9KvClYHE+x3EdppfnOBash0saQiKNhacUaj7hthtz+/YRsy8/5ZHZdBbmZ5+wrVPcumY0OgHLw5QFTdcnsw2hY1OVS/zIZVnaaKHwpUVobBqRRWkpAmnwjEVl1wShB4kijDeLDEvBZGgoyOl3HDIcspWise0zyDIqTyO9kHavyYGMCOwG5e4tZOzSbnd4sr1DO24TuD5G6002kbRBlzjGoMucsq7JFz3m85qn/9O/o1glzEdjkqzmbLogU4btUPD9xx1Weg61oLBsbBWg7IDS2uJV6bAO4K3v7NNv7pFXNutiztVSII3k9uIZB/NTApWStVxy7VOnOdosCXLBncqw92CHhbGpk5RZ5vDn0zWLJOGgEzJKJnzQ/ZD36ytMZHiVdHk+XLCzE6C8gN7DJ1wOhmw1+4zVmmcnF2xtt8mKBGlbrPI1tnDIqnxD9TLw8vkFW50OzWZNXRtm8xWtZpv5fIFrO/TaMUWtmM0WVLqiTHOmk2vGoxB9A/nZ2dkDXXNydoHn36XdbnJ1/RqsgLeOnjCaDSiV5PWrKzQCL3RxXYX0A3biLrP5gru391hMBvzbP/5TqmzFt+7v83C3zbxQjIYTXr46ZT4XhHGb4TilNprQsVC6IFmWNDp9vvzkKbUqCJsRUbNDlhWovGClCzxL8Lu/802SbMnlcMl733yHxWCOkS6NZowvLIJ2iLYr3nlwiMkFLwcjkuWSoyhmMhuyyFc0Oi4rY3j1+iVxI0D5Dqsi53pwTpFnFFl+42qsN2GNGhxp0+v2mUzHSCnQtQYcSlWjCsXV1QmOY6HKijDwEKLi1YsXOPZGKbCJCYopdYYx0GkF5EmKJWziKKYZBCRWyWSSYYXVb15Qb+33uF6VdP2IwYsRdx/ss3bmyDpkPoEo6PD09ZyGXWO7m61auXKIvRJjJdQqpyhCupGL5cNKabYjjyqoCX1Bu12yHq8JnW32WhGtxwFXVyt2txucnxZ4OqLpC2JZYUmfoNVjNJtzcnq6yf1R4BqJ7djY0sH3AvwopBM1aLXaNDpNOn5AwwuxW2329g5oxl1s2wUnZ50v8P0FRXqB4wlq6dC/E3D+tEnQhiAKGV5P2NM+fmAjQsHjb2/R6nhkSYVThJSV5vsf3ma+dBgvUk5fnyGEtaE4GUVt1xslQL0R7W/YmSCVRuoSLIkymwyctKq4Hl0RYBN2G1RJituU2KuKfhAgTI30XLKkxLg2ndBlYWlc4RIkFrYjMV1DZEpKu4KOpKpLwjCmrgzziwK/YaNjg9uKSF2b6zJlnpckrmCrG7Cz3eOdD96jzATNnRgviOnuHmA8F1d6RL6DU4EjJKpcYZKc1WBIOThnnWQsxxMuz48xpiaZTVC5YL1MGc2vqS2HPLep3JKUCoNN1IxZZQphDJHnkxkXUbrMPQ8nEjh1xLw84FQpWvuHfOf9LoY5WVZznlXUmUcvuWBn9iU7Vo3QK2rL2qgolMKNQgojMVLhVQWOKnBsizWGqSfwooCHe31anmZLNSikzfH1EmXlrNMlD6J96sJjuJpxcjmk2Q0ZX83xOz1qM+B0eMnBTpflYIHrVaiqYLvXw7EltiU2fFVj41qSbsenMSy5df+Q07MxW90mi/mUpSfYf/IIR7r88pc/xw8DelvbWKJmsVhSqpxlumRndxvXtbl39wjXq+ntdViMJmRZTVlrju7e5/mrMxzt8PWXz7i1f0DgOmzdP+K77z7mxesLyqBBvVrQ3dvGtmqmFwOW6yX3H94jK3MGV2OyVYoqN/bpLK9QtWaxmOEKqGpNb6uLVde89+57PP/qa2bDC977xmPOps+J44CzF6dYD25zp7vFXz79lNFyie86JNmSQNi4lse8gn/4w+/x1z/9OfPVnIf399lpb5NlE86sMa9OLhknKaWqWaXrDd/1ckwYhDSaMVWZkacFthA4ts16tSAIfIqqoBXGCFGDJajqHJ2nuLZFXmwWT8KA59ggavKkotVusr3TZzQesttv0m42sYXk9GJCrRWqEISODY0+pTf7zQuqdC2cEuqswjMW69UYOiB1zUF7h7RcM5lDVhocXHq9gPU6I69qonqLdTFnPtZMhxn9fZuSlP2uQ6MJs9GUZKGZrWqs2YJVWTNY5tS5Q7Iu8b0A12T4RoEukZZHtVgjqnIDPb7JLC9ERWlsTJmwVEtYaU4weNJFYuHYkrLe2B8uomMafsjWTszBvYBK1qTzCi0qmpbAFzmHsUf3m1uUqkIIQ+FLrq2EZuxRlSnNwGcxG4NlkQ4MLTek26hpdmLq14LX2qCpUWW9AUS/iT25kVPZ1g0dCja0qdCljm1qu8LXiu0YjDC4bcm0yAhu9VHnM3wnoJYS96bY1jrFix3aUkIK5xeGlc7pPthhcTFi5gasmh6V4xK0IlToEj5q0+oHbG+18JHs9naIGk2CuIXneVhaYdUGz/apViXFasXk/JzV+RdUpWY2HjFbjEmmCyqVUy4Tsqpkli4psSiKnEBIHEfh+g7aKhG2Q4XHlVXiWCXSMWjh4wkfYyqkLgl1Qq03+LVCu8wwxI5HnUc8qwPSQHH3nUMOt2JSLZhXtzgfr3A8xdHihJ31SzwrJ0s0UTOC2uAaC1NnG35r1KCSK+r5EtvY5CuF7XUYZB4qgKKqOVksuXtwxB/95cf87775gJYlkbmhtmGarBllFbv37jJbjQk6Mefn59ze7WG7NTWC42lBmuXcu33Il0+vuXN/Gz8QFLnBOA5JocHKqbOEzz//kiRV2EaTZwnpLOH09ad0trfo7/QpipLJas67d7dxPUXc8ojiNg9u3efLVyccH5+Rlwu6Wz1cV6CWS6rKsH24T7pq0IzbrOcramETtBrcOdphdH1OMxa89fgBL79+yacvnuM6AlVYrFYa26/JKs1wvqZWFtK1ycrihm0hWS2WRIHLbLmi024iMfzhH/wB/f4WT95+RJ4WXF1PuHf7iMCP+Kt//xFfdyPe/+AxVZ4S+R6rhYOxHCbjGaFXMrh8wVv3trEsw7qAFyeXNALJ4GrB4HpBqQu01ihjyPOCIq2oCkXcDrAdiWM7OLaNMRsyXKk2iRjr9RLLFmhrAw/Py4q40aWqa+bz9SZfSgg81+PO3R3GkxF3bh1hG0EzCiiLBXgVB/cadJou88UCIyvs2GM8TH7zgrpI1pQa7u3doWruscznzMohaRnx5aevEU7Nw7sPeH19yv27eyyXI3rbMV4ASXaN21SosY+DxXA0Q7uGwo6oKhcHlzAMyIuU1SJHuj5FVfH6YkJRWGgh2ZIO0iiioIGHQ2JqbN9DLSX6xicvtX2Tfe6ijcJwQ/QWYKTAaYR40mK+XLKeDXCkwd++yyzNGU8WeEFIUZXkazg6aqBUwSzNWJ+lNFsBOgpwvQ6T0Zog9PFdSbPpsVIFXlfiNhxqT9KoFLfuxIzyW1xN1+RKU2cVuqoRxsK+ATWXWoJtIQUEnovb8vG2YowSqLSmcDWi6VKFFqkr6fkQ7HVYpoZE14SehXsQ4joxdsfDxtCKtzloHtJtNYiaLeJGm1bQIK9rsmpBX1a4nsRWLmWeUquMdF2SrVYsz08ZjNesV2NWkyXzaYIqZoznU9ZKIy3FdJnjG0lhCYowILZsLBtKJCQ5DbcEpRGuAApWxkbmBY3IQFGgCo1YGFw/xrUFyzIjCyUYD0uE5HVJoirajT5zERI0Qqqgy2fzhPatHt++t0/odxgXmmXiUOYpbZNwePaUxnqFyHIKx8YVDtWiQMUBkbAJvZo627jFpBMg2z1ylTNrtfnZXHMuFO12izQbs9VsYZyatz/8kEksyROLnZ0mw+yawuR0vAYXkwGrLCVZ5azTFZ5XsbO7x3JtsbNTUJoc4de4DYHlSqbLJUJ7vHp9xt0HXQLbJVlklFZEI/LRqmI4WpKX1YYPVCp6W70NkFqAaykEFaqq+Nbbjzl5cYWkYDRa8eite9y/e8TJixfUJbTjNl989iVBFHN2cYJjC4LAZ3d3m3W6IpuPaPYaTK+P8UTK+x+8DcKQzFNOLsfMVwlSW1BsilKzHfPeOw+5Pr9kPltRC80iWdFsNVmvEkStcGywTM3Z9ZjXr45ZpYrXVzM++N6H/JP7fWrfsHj9ilWSsb3dR4sa17bpdTWrWpAVKfPBGBFKDu4+4B997wd8/cnHhLGP5UjytcL3PSwM62SFLW063RZh7LJaLnFch72dQ66uz+n2elxeXVPXhjj0N2oUS6B0jdYbB2OjEzNfTUHVVErhOjaTyQTfbTAYjNDAxdUlrhMwnM/YudVAV4bFukBKTVYMCNrOb15QcxSe65OLMYmluFrNmauMk9dTGnbN/V6T0ekIy3FZXc8IQ6hkQl3HrLMMXzr0uiG+o/GiHvOsZrFykfYus8EFQSgYDlIQPo6r6LZcptsNrk7n1FJQOpK2aIClKDAbC6u2kVhorXB8h7gVc3i/Q1HVLGcZ4+sZd+9s0em4VKWi1w9YJQWvXsL1+ZQgbPHi+JxF3mA9S9nerXj4qIMWFWfXS2xtSFKLy3HCLMnR7oy6rHl00GG0FDQfNijqGl82NiF2gWCR5PjjJTsHDr/9gxZufB/jtclSn9XCUFaSZbomLVes05zZbEmxTrEci7AtqPcaXClB0w0JIhcaknanw20nYrvj04i7hI0OMvax5SZ22RQWdZXh2+D7fWy/iSoVZDn1PGF29pK0KLi6OuGvvvqMuiop85R8tabMclapTV3nKK2ZaEWz42O5gsCyyeqNJTh0QjIkpW8DNkIIKqsirSqksUhNgcsG1G1Lj4WqN4wEOySvU7RwUFpgXJs8UBQ6xTUO2nFBu2gt0VVFZasN90E10I2HXKiaaVFx/71HhKEkRzDPa2aLksrU9LIR27PP2fUVlZOAIyjKikJVWCgapc/SLujaDq6bUqoFSrvUWOSWy1kQk7ptotmCsljT6W7hpDN6gHSh0DBcwnWhKGqYTlJUMub52TWOF7LTPaCz5TJYDli+llydrXjy8C7ayim0gxdkXF0tCQILakGpJMu1QruC7lafy8kSQYVWglJXGHuTNbVKVnCt6DXbbLeajOYJWgXM5hkn1ohplXL7zhEnp1d89vkLHj++x90HTyjrmOuzS/qtLteja27v3eJyvKBEMx5NoVize3uPstZ8/OULDrY72KPFpiFpOPzrf/3P+em//5g/+5uPqF1BVSgePXjMfr9LMh5zupjR6HZRChaLJf27ffK0ZL/X4PjVGdrU7Oz3edDt0Ot3ELLg5YtL6qrk1lGbe+/dJZ2mTMYJlnQJPAe7VkwWahPU6HqcPTtn/npOs1lw726HIA7443/3YwK/yTJLcX0H33Hp73RJlitqBUmacXZ+im1vsJ0b6K1GmI2qqEwrNAahLZaL5a8AP/LGsZiVGWUhiNs9Pv3iy40VXUK7adjrHhL7ktH4knu3d5iNVpT5ijuP9n7zgloX4HbGZKXAc2P6TYdyltNwJPd2utxqNLECmxeLCctEs3uwRZEM8RtwNi5ZX63ptEr2d46oForA93GbIefDjKATcnp5gms3MXXA7l6HW0FF3CuYXa9JLQ/HA+oSI2pcE1BoRWBLjna38UObR49vsbWbcTWao01EeH+b5TLg4MBGOgFIia4TLBkwSzLSteS9d5v097v4HYvFsE+VpTi+oKodXFtQlgXG1vR3tynLhKwqEG6Ds4spdQWxnbP7sMdyVVM7FTqBxSrBOD5pNseJDEVaUK4viHSXO+0j+v07NNotuttNoriB49ogNFmRMR2e0mq0afkxqs7RRtD2HVzPozIWdZ6TjmYkgxnJ8ZJ0sWK9SFgultRVymox2+jv1hnzVDGparQqWGYzOmGAbRl8qcnyiiDyUJam8Cxmysc2Bl8bWsYlDmwssVmkqXlK4Nl4piApFNqWGHcD8KjyGs/1EdLHtwTGr1hnOZ5doL2QqjQooXG8Jl2R4boFSQFa+3hSYwnDZGWh8wW9gxhjO8TNNmZ3l+6T3+LzuWG33+FbBwF224Uq5jqxWNQFlqy5df2CretXSFGQBhadwCNbpchK03CjzfJrtcAPQ9KGwCodQtsGa81UtHka3udlGYBSRC2XpuNTHo948dEvmcQuWiqQDsKRlHVBr+PixRHDeUb/3jbFWmPJDCMqJsOC2gyYzxNCP6LVDpgmU1RR0YmbPP/qa/YOdvFsl+dPL3h4/wDbDvDdiGWyoKLm3oO7rJOS9lYflaasZnPyvGQ4T1gWCkcJri+nrHXFndtbaKUI3CafX73ik89fsduPyZMFewc7SEvy1nsP8aTkm0GDRbai129w/uqMi5MJlrTQdYbZ3uHrVxdYlkUgNOquJrFKDh4f8vDoHnYmOTu/5k///CccHBxgR00qA1HUpNHwSMuMq+GExWpBUSmwBDtbfba2W3SaTY5fX3N8MiQMQsJQMz49Jl0XFLWi2WuwzkukZbO914MKVtOc0XjJST2k1yzpdLtMJwme6zObzFAYyqrGd2xevjiGWpBlm7C9tx7c5vTshIvza3Z3t1itltiOwFDh6JsocUtSZOVG1K8MwjE0WiG1glWZcXl9hXQtwsDl4b07HL84Idi2ub68pt1uUKSKNEsxpUUnaPzmBdWzag78Q3JXYsol8TwmVDZKLwhxyTLBbDFHSo2mJvZiCrUmrwqChqHbbpMtK9bZjKoIsc2K3p7D/mHA9WSJyGzWs4rb223c0ub87IJ7j/uE//wJf/3LK/wMdFIjpIaqxLJsAscQOBZeZKFNQo3Fk3f20RiWSYZwDJ63oYmXZcFykbDVjPj+D7ZZ3gtBKRqxxNiGsJEQbUcsZyv8yEfagl1vm5cXU3ALdGHRCmJmizXL1HD01h51qeis+yhzwXGSsB9Y7O36LOaKyvHwtI2TaUpVcp5e8vTVNcef/z5NEdIJNlCRZqdLv9um39vi0Vt72HXAYHRNks1ZLwrmlxdcDa/Ji4pFoUiXCcYogtCiXCXYliTXGwusbzs4dk1p2+C71JnClQJpa4y9xo0DclWRWDWVW9OoBKQZonSpLSiNJslLGj0PpEDWFrVts8AwKAyh08DyPVJjqITC9yXCkhubn+3hlIa2FeJrg6NsQmXQ1Yrc8znPK2pjSBEs4i2qdUZcVlxZkjJf8fd6bdxmhOX5WF6Hsih45/3bbMUNfEuQFg6nOeS6pJuv6Q0+ZitZYMqKTFQI6XNZVATCJQxd5lkKnocraoRtURQWodMmKZcUcp+nwS4XhAhbY9lsOsTRgvOvXnN9PSHd9WhvR8RRiOfZjE7OyXNJp99lsVqz1wqxfcH14JoiV6SJJGxJHFcym87I0ozMFFRljW1smq0eaaZI1zntuM/1xYL+tst8MWcwmXHrlsfrk2Peffw2nV6TUZ6gXIevXz4navXp7fSZjidEB1s02z7v3r5HnWd02n2uZxNqo4h8h7fuH9KIG5y8OuPq9ARqSZHntHoNPNllp9dnNi0o1gm9fouiVlzPEnYOOgR7HeqGy2996xtU4zX/3f/8I2ZVTsNvsVhuiPcN3yduWtx9dMjO7hbzaYEvI1bZkntPdvCsNq2Gx/X5FZf1kLwCLGj3mwyHS4SqmaYZsR9QXq8ZLReEOy0Gz+e0g4Beu4tG0dveYjGfcPL0ima7iXAlRbkh0DqWzW9974f88hcfMxgMMcZg25KXL19RVQVh6LC/02UkUlzHYbkyNBoRw+EMVWuEkZtY+hsta1HklGW9iZOvK1wnIM8rhoMRZaUYjWYorcgzuL68ptNxePJOn92d/wSnlCctPn92QlYY7gYdvNGCx90I+1ZMEUjKDFoygNDDkhU6VcSygeUoLDHGUYZ2q0UR+EjPQxYVo+GAqBXj6Bph+eDD1fSSqupimYDh2YxG1+df/Yv3+PEff4lYWwjjUdoay1R4ocudB4e4oYUWCnLIxgZdC6LQRzkuWSFYLqvN4FqGKGFQ6wTPD5nOEvJJQZZX2IGHsAraXXdDrKkNVTmh2TQ8H85xhY/NJv/HdRtU2mK2dHj6xTn+XcPtfgOFoSgt+t0O2tUsJnPqHHLLELQizq4SrmdzUqfieqHQokZwvilGVs33bjW4FWuWZUkqYpbrDF8KHFnhBA2WRuE1QNoS7TqUdY5jFEWeUPsN8moT2bzICnShCS0fqpqsgnbTRggIvJDleo1CsHQslGWjjE1pDKqSiFbEHINvuQhjoVyH0EBMjTaQrDOalkVSKxLHhcWcdtNmurCI/R7ryCIsE05FhL3Vw+MKx/WxjIvrO4TCo9Y9qkGJq0pCpyIrFc/KkC0VYyvB7YM91uWSHd+hUiVzLTkZLYnaPruz5/Suj2muEoyUlJGFnxuk0EjLolqXTJcpYSPaYP1QVKs5Ld9joGwyP+Zza5eLOqYtK/wiJ+w0WR4P+fSvPyHLSpxuzN13exipGVxNsBJJUkgmi5rz6xFBI8AuI55++oykqmgGPlEjoMgzbEKU0gwmS1KT0mr4BI0m42WK7Xh4gYPv+AxHSybPX2Esh29/6zFn5+fkBZyeXfHjH/+Cqsy5d++Q733/+3z18jXXZxfcvneLRifEM5qPP3lKq91kXQ64fWuXe7cPubw6wbElgTPj0cMjvnp9xcefH+NYFoXQSNejWoxI8gInCBgtZlQYqqKAvKKeJ/h+zM9/8WMO7h/xze9/yI/+5C8I9h1++9Z3ePn8hCxJCbw269kalWmoJAe3GjQaPdZ5AkWNjcVyWVHojHsPDjg+PWM2LkjTNU/euUsrL/j6ixNanQbtdo8PPnzM9dWE1XTBeDYjKXKauiYvNG89esDV1SX9bouyzMiTmqP9I16+OCZJEhzHwbZtaq1AaILAQZuai/NTqEvQFmmqqFY5Rmyy37TeYDFtWyAsg2UsPM9G1xUaSLMMYQTzxRJDxWA4JIx9Sp2QJDUHB3tcX87pdq2/q2T+3QV1uMpotnuk4zHnU8WBMYyyBYUTE/d3uPpqzoOjbcZmSpLkiMoiz0p008XRAcXE4vBhk6XwuFxeka4HdKOIfqNPWRc0AovtXoguLSg1VbWmGR1x9XIBZ1d881GfbMvw2S/PaDT22Lm9TZYsSKo5jhtQVDmrTGPmOY3Qp2PHRI0ep8MrilXJzlafIG5QWpsvq6gMkzxDV4a7O12GyznZleDWbpPVakWzHWA7mlYUcPexz/XrJYEdYpoRy9F6M7B3Ui6qFc2FixJNbEdg6YLxVcnWwxZhu4ltS9azNQUefmA4vHPA6GII1mbeKJ0aY5Xk2nC8WhNGTSzfYCxNocxNPIlG6xSFoGmBow2rRU5sCaSxCBotVqlGui4rVYHl4khJbRvySlO1fFKfTYHRCoIAjIXRkqoweNpAJvGNj1ttcIHLYr3hVQYeVa2wXYs5IUsnwxc1QseMTYfFuub9t3e5vlb0mg+xQxtZ5lAJ7GYfr+5TKpekdNBVDlqBFBgPElPi6oqG53Od2VRS84137xAcbbMtA/7wf/2Ib739Nm4o2bZqwi++YLcYUKgcXZVI2ydAkpUCy9fIwKKsIfAaJFmFZzfwHFCuYTK3WHb7fObEvF5kNBou6yqn3+6wOn4OkwQpJO2DJirPmA/n1KXNdLZGk7G7e5eJXuN4HuPJlKdfPictCnrbt5iMzxlMB3hOm/WqoNEMScucnYMuQehxObomLVN8I1G6xlKA7bC3u8vxq9csZ2tacUCr6XB+MqV2HG7fPyRZzQnXS7aiCB171HmGk1oUZUEZuFhhg8XFa24dtfjjP/5Lfu+3v8N2v4FrVQynI3o7Xe4rB0yFMfDRJ1+QrxN2bm3zzq0jfvI3vyA/ELw+G+LFIZWuyalo3dljsVgyGRWUGGbjFeNiRLrOEdJnlVhcD2YIa0m3G/Gtd2+TlRPSSU6jGXJ8csqjd24zGK8YXM7Z7jfZ3+nw9PmMZZqzFfg0Oh5FXSGxcAsYnQyxvIB1qrHdiKvLMY2WzWq+QNQ1o8sRNg6OBYPhFXEjpCgzbMfF1DW2lPi+Q1kWSOExGq/xfJtiubxBftcbN9UN0d3UG45tmmVEkUeabEArwt5EKG3isIuN60pYLOc5UVOgSnj58oqHj7qcnS9/84K6nNWc5EMaocX1YMrAspGJJAws5ifP6MUBPTvG8jWucJBuhF0kJFnObLjm7k6f2TpluBhT1grwidyIvMgorYpWs0lkSTqdiPE4xZfbrBZjjo4arPOC9WRJr9ngOz94gtOIqXXBcpHT7TUImzZngyXDVYatHLr9FtoWWKGDsRRxGCJsl3xd4LUaDGcz5rMJZa5JVookmNLvxBuSvsnodyO0cXAdh1opXKHZ32lSTTXC14wpuRoNefy4h3BcLC04fz1h96DDdFYgCovepKZwFEWuMGmX0oezk1Mml2tsW6BqgWPAMQJH2lR1iS43M2LHMrjakCuNdkOELSh0hjYBI20TiRrZjijrnDJLSbFwfQ9RWdRugCkURtc0hUdh5XhG0EgEbaMpnIpcCYqyxvNiAjQj1qTRPnI9x0Myyl2ioE9gSZK4jxQVjfaKWh0SmhmymJNlBjvuU6U12m8QeSVJneOngvNUo2yHcjJjqCpKk2LbG5ODIzzKukQ7FoHXYlVqlmFFy/fYPjpkMF1SfvaaO/fuc3b8munZFf/o+4/ZyV8TJ3OKykNLid2MsKOItCqJ2j5pUaHLEkc4lMYmlJAu1lhZRdCUnGzd5SeZR6ErOlETrRUdTzJ/9Yo//p//gtVsSlXWBJ0m3a0e4/US214jvYDlPOXr2QmBH3Kr0yaODzg7u6DUEmFBqWp8t0WSFLiBjTKGbqOBVJrlKMXxbWzHYrWc0ombLJYbkfvJ6TnYkq+enRPYFmHg8Na9Bwwmc0SZ8/4773I1uGY4nfDk7UOWq5LxMiNLMlzXwWobHn7jLaQTE1wN+eLrV7x8UXK4t4UwhkZbsN9xub5e0mh4/M533+eXH3/F/sEdCrti606Xt27fod/u8vWrV3S++WADLtEupajpvt3n4FGfltPi57/4mh//zcfUjmLn/h5mJckXCbUjOD6dc3u3T1Fe8PHffMFv//Y3SFYL9np9RnqBRUbciLCFxZeffcV4e4cHd49oRD5/85On/Olf/ZwnT+7z/OUJ2khs4dLuhPih4HD/gD/6X/6CrFJUlUJYBmlsqrK6yYKrsYQgCiOCMODq8hpMjZAC19oEZVaqvnFOSYLQY7XKb/jEmwBM23WwMo2wFJ4n0aKmrBTKldRig8qUQmALG8+r0aYmSWz6u/5vXlBbno2FIbBvkclzdrZ2Gc1nnJwktFohWwddklxhGUMYd1hWBYs0o7+/S74jEG4EXot0nuMFNZ0wQgiHOAw4bEfMJhmL8Yqr1xOkHXI1XdDpNjEofCmwGk2SPMELfTxpqLXh3uFtMpWyWilibxvjr3B8C9sTXJ8OuXf/iFarw+Q6oStcFrpmdjFjOFqx23M42OuySBfoSlFmGWFgWCcudk+SFiXLWY3rWwQhzJYJFS7n51PcIKLb8agVlIlmvdLUWYhUPuvZiqKo8Y9rpG/Rb+zxi6fHjEcL8rpAaoNl+Vh6k9hqa0VRgAKKIGKJYJbkeL6D6u2RrhfYGGwZI2sfrRVQIFcaoSVNbVORbQLuhGC7ysmqgnnUpi5srrKCu02fT5cWpe7QaC3xQ4/UVcj4AR17TKUFpfM+QT0GC/zSwfVdclGTaw+RzTF5Qp5kZMpCqDalFlRlhg48PjlNsN0m42RNKAWW7aFcgeOCK11qY7D9ECfwwPNp+g5t28IXDsJz8bKHfH3+mlmRU82nfP7XP+PH/+4vCKocVSg+eLjHw90Osk5wYpuyNiSFJrQTYplvwkuDGFmtKcYrZGQIfKhNxTTs8pdWyFQLhGMTWhJ0ScvA5NkFP/7Lv8ZxNA8e7pCmBa8uRpR1RTOq2el3SHOL7a0DFrMZjTDk5PUp8+WcbrfD1lbMcj5EK0FhFHEc4LguCNjb2+Xi9IS40eHFqzP62z0cJyIvCw4Odri4GuG4NmleorVma3sX1zK8fvka6bg0ow4/++hT3nrygLAxpxF1efH8KZZ0cByfStWUCkKgXExJpgt0FBE3fc6HyeaecVsMF2PGwynvPXmEEJrHj+6TFymhcPCMoqwqhpMr0LA6n4Pb4NngAstR3G/ZyLLADT0O92L+r/+X/5LXg2tiP6Lr2xwPlvz0p5+h8pJ5NUcaxTe/+y7NdoRrHD7+5ClnVzPefe8+p+cXLNcpYRyws7VRBpy8uiRdZRRK8cknT1ksU3r9HdZ1hlYFUWFxpV/T63hgCa4HC5RSYCkWyw3ZrSw32Ew7yzbH+VoTRi5VtcnLWpYr6rq+kVBaFIXCsti4pWqDtATrRYGqasJQUhuzSQVRGq1rbM8CNJUyJEmCdAW10pyeDFA6+s0LarayyDJBUo0JG4aKFLRPf7tif7+NqmuELwj9kPkyYzCe4wPr4yvSYoHdDWk1O8R+wW7fBzbzjdm4wLiC1Syl0WzRbgo8L8YEkiTJKLKadquHrjSO7zMeDakGirLMiJoJnW4LT/oUiwXN2IfIYq1K0rrm+npAvO9xno8xwuA3AlbrBUEMvqcRqiRdFVxcr3nvnQ7LxJDnFdcvx5Ro1MLi/t0mcaCJWh5GhJRnK8pFgq5TVgufdF3jujb9boCiQDua+3eOiJycStSoPCEXK/qeTVlLagyqMtRCoIzGqg1SKIywqNYFpunjK5dKF/gdh3RdEBmDkIqVKUl0zUQoPLHGs3JqWSItycSOUHKPraoCXzPb2iddVpxOvqLdEYgqxJg9ZDMEbSNdAXGfVSUoshVlsmIqFLUuccqN8WBtK9KiwrP8DeFfTTdknyAiFT5uXSEil7LWdBsBzS1B6EbsdGPiyMN3JA3XxZYethAs5mvSPEPriumgQqicwoKtqM3+vUPW0ykvLkdcD+b4wqIpIbZtfvQnP2b/n37AoePhIvFdi1AusYSNSh2cuiKKCorKwul6VPmGiJb2t/lF0OOV8mnmLrgZgQzxyBGzKbPTV+x1uzihQNQljUbB0aO7XF2mNHyH9TpBEHF+fkm7HVPVFdpAGAeslmu2u1ssZmts2yNJ10SNkLLK2OptsVzOefjwNl88fclufwttNrO7TFUYCQ8e3efickiSjmi1YixjmM0ThGVTVjlJsmQwGrFMStZ5ynpVkhUZ2mgc26URR0RRg1bHQ7YU91f7aL/FVy+/ZL/TJWg0ePH6jKgdcXB3h7/5+JfYSGzXJQw9trduc/9ejKUFe70d0GO6e/t8cXZJssw4OuwijcSVNpdXU7JVzb/92Z/R6nZ5eXxM1OkTex4ffvAuSqVg1WRpzmQ8ol4v2e12mUxSvvXtD/ADmExm9Hpdgijm4npAVSp0rXBDn9j1mMzGWFIyXyzodDosFymu1yQMQx69dcjJxZI0rZktlsBNOrK1ianZdKEFvufgew7tZkyabYpkXYMQEkuyCeHUNbreLKmEgKrUGC3xfb2xsJaKKtcYI3AsidSbJFyxAVjhOGzu1VyT/N0n/r+7oB5frFmlNo1Asb0VkSjoNEO2tyOMqIhcn4PdNkbkGBEznSZYsmCVz6jZfKFaBwhVkS5DjCW5uh7RjppIxxD5XbhxM3nS4vZ+h2TtI1Bg5/z1Xz+j3+9xsO/RbfX59JMzsqIivx5R5zWdZoOqLNGFYDgZ04w9wkaIU9s8uf8OSbJmMhvS7ETMLzJGsxKtx0ynYIuQk9cF7XaP+WLG+VXCo3fv8elXx9gePGo0UcpjvCzJlMCSAi9oMxtm+H5Eq+kj3ZLGlsOT3hZxFZGqFMtYKAm3bveoxzZHB1vgVqyWiqvLCUoXiDwjLzRrY+G6ktlihZXnCAyh5ZI321TGIdIp17pJtLNNrE+JMYxxadpNXFxqt82yDhhPZwQqxxcepayIW21yF4Kww3RmWCwkppaUlqJeXIC0maQrdPkSLEHc6ODbgiwvcYKAsjZoXxI4W/Rjm7qWNJsObtTE9TwCyxBbFlaRUWcF62VBuzasVxkXywRPSmzPJopjTK2ojSHwXfa2GlBrvGrMaDggThKunl/RKwvOywTbbZAIiKTgW+8/Rq7mmLBkaRzaSqOdNdoOkP4WuaXQWiBaBlUWlCJi5vt8ZdmUzjUt9vDtECuIKScTQnGB7S4RJuFquKC71aLfgrJymI9qPNuj2Yopdc1ildPtdWk0NllJ/Z0O8/kSWyr2DrYYjocsljmtThvp2QSRix85LOYJYcMhCD0cx2WZrMirkkopTk6vMOYa23FptxuosuD1+SWLZcqto112b20Rei5XwxFnF1dIx8JyHJQueHT/Pk3PI3A0ZyevWM8k69UKYTcQJudb7zxhvVyxyBOcwObB/SMwmt2tNr1Wi1/84jXPXp9wPfwl3/32N8mLJQ/fvs364wWOXWI7mooMLIdXZ5fURc6HH3yLSwb4nsJ2ag5v7dPc67EVbha7q+mKZnebYlmymqU8v3zF4cFtytrm5ctnNGOX5Txnvkw4uhNS6pyw2aAVx1xdX5BXBUgHx3MwGlbrZKOVHswpyxUHuy3GswVJnm0y026SkN/Ef3u+vxmjqZJG7HN4a49nX5/geA6WlSEdidKaqqrwnU1sUFkUwEZyFQY+63WOZVm4oUdW5lgGWs2YPM0wm/hLfM+l3XbJk4x1UbOcrX7zgnrriUdZNem1DY4OmY0V60lBnpR88O4hkS8wGazqjNm0whQ+8baN37O4Hk/ptdqoYklNRqYk67RkulxhS5fbvT0qS6FqTZ5U6NywSlPQ4IUNnr0eUQqP2vJ59vWU7bbCl5p3Hh8xWq746tkF9dqjrHLisMmt/Uf4QYXlSYpVhWPbOF6D2fVrwsDGiIDLYcr+YZ/1+YTYDZhPUpqRoC4k/WaPk69GuG6T8aQiPCkJGg5pmiPdGildlAFtHFRdkSY1dWG4PllzdLDL6mpK+06PVbJmoTOaW12eLSfY+TVP7vVp6wZ+r+bgYJ/LLy9YzkqOZ2uCXofL0QC/EWJqxa1GjNd6QJVIKJa4OsQTu6x1xCKXLARcVQWoGmOtEHKGUZIrrSmvh1iWoK5DxhND7hZY0iIv7I1iXdYoI+i2AgJ/HxMGuLKm0+jgNzxCO6BhuwSWwvgWRWrjqCXLdQlVhprMGQF7fshE1ixMRVfGmNjG7bZoigo7DAirgmwxxZ+uOXl9wk475PTVOf/kh0/4iz//mP/Tv3ibP/7rT/mv/rMH/P5VxkJLVnOXhSVQVc1MV/yvP/sC+TsPeXG15L39HWInQZQefmxTyBWWjDBlhc4EZdhkGTX4Sd7iepHzINC03RTHd/HWC7SYIuyCq9OMoNnj7uOYO3fazAZjloMc6cN0vCRqRDQ6LUqdcjWbMpsm3Lm3R7sT02h30EoxHE+QjkN/K0RbBW5YU2WGdbZme6fP0y/PcJyA0I9YrlbcOtjj+PUF60TjOpI0W7K93ePJw3d5+sVLHjzcJq9WDKZzfC9AhgF2kBIGLo1GiyyZkS8W3D7aJ19PSMYTmsEWre4uYbODJQ3LyYLpeE6hFHfvHqKrClPldDpNdnstQl/xjffu4sUhw+sBtw/62FbJgzs7jMcLfuvb3+Av/+IXjAZj3n7vHkk6p5AVhSm4e/8+aam5c9hmXRY0mx7brYDPlwtOTo5RuuLt9+9w594WFxdT7jzY4+igz3w8Zr0acuvgkKoo8RyXfJ2zmi4xusC2Q7I0p7/VZDZb4Lk27VaL7lYbB8Xl+ZzlKkFRYyzQqkbaEtu2wUDgB+RZQtSM8FzJ8fExrufSbDQpqpI0TzFAEPg0AofFIruJDBdYlkHVNVpvyP611tguSGNwHI3xbIyuqZUi9D12+11O0xN8z0Lov3vLL8zfwaP6v/3fj0yWOWwfBvz8r1e0ZchynWByye62oBm3iZsdarNmkWmypWa7o/G6JfOlwadilRpG+Yo7B08YXeQE0Qb0alSA4/lcnF2yu92m1fbJqw25/OxizmiUsVqkPH78CD+2GAwGxMLi0Z0ezlbIs/Mpq0GCY0u+/f5jnECTFxmuK/F8H52UdPd6nA9P2enaPD2ZcHEyRViGvKhoNruMricIbOJWRJJosrTmYCfC1DWlUlR1ydaujbBdTk9zwtihyl2kJejv+AyvBxzseWxv2Wz3+kwXCZEXIuyC4Vxgap9kccHhXhMpNFkGaRry1RdTxuMcx3cJw5DlasGde3fJ8hpwsFwbYzkkaUUrDLGsBOnlNJyQ42VCohqUlUALCxxwXAdLyk0Kqmejhc1208VIm8ARm+wof0NEN1mNbEbovMaUKbbSrDKFyjM0hrW2OPINi6KkkgbP3qbhldD2cWvQicaqc6o8YTGaEYmKZDBhZ3eHfHLC/n6b8mqOc9DgcFHROnJJr1fcOmqTDpdY/R56MSdobJNdPGPkt3iepOx/5xaffTXCC3x+8tMJsevz/d/t0YigqQy9ec2j9jZVliEjgSsljqlZy12eug7nVYWO+lS1Ztce0vRq5knC5MWEFy8HPHznDnmy5PBgm/OrAetpTpI7nF9ltLoWUVtQ5oaT1xOQPt1Oh+FgxP27tymzBEtYtFo+J2eX9Lb3qXK4vHrJ/t2Q9dKw3emymGYM1xUd4RD7EV+fXlBUBcYSVLXBsgXf/fYH1GnG65NzonYHYVW0Wi38IODLL57juD5CWkjLImo1WF1fUhYlXhTx5N4e18MZq9Uc2/EhjIh9QycMUCJgOl/jO4LvfPMB48tzVsLmH/7D3+bF01d8/cUrHj+8x49+/Eu2j/b41jfvoVcl/8Mf/Dvu3juiXCWUFjQjn/ce3aa31WU4nlGWHqv5EtexuLge0d1tEvsCKotJppgvFrQ7EdcXV4wGa5Ik5cFb+8zna7b3bpOWG/bG9XBMnuVIo6m0IU1LLOlgOw5KabQqaUUu3/3wbT779EuG4ymZrsh1RV0qfNulKmuEJWi1WhhT43s2q+UKkBTlRriv9Q1izzI4nqTdCLGUZp0X1OZNHpVBGU2tN15/x9toS0PPxbUlUeAzmczJy5r9o31cWbFYTMC4WFrz6ngl/n/VzP9ISJ/BE5IAi6NbLU5PBqxVDZVDW3hU1Zw0yylX0N9xQeacXGXciQPSeUKj3WfvVhOrzji5mJOtBWpSo9KS2C/wXEMoHXb6HWSomQ4G3H37gJ23D7i+NHz2V58wH10TrQI++MYdzq+GvJ7McJOUb3/zHT41X/L4aI/QsxjMN46KrWCLRTLj4quX7JX7+I7NYKhoxx2Oi8kmzEtbDAdzZtMMzzds7/t0Oh6T8ZLFfIqqXFzfZTZLUJXLnYeSnV2FEAF2q83l5YDlPMd3bFAhZSFZZ4ZCWSTZHMdpcH2VYwmLTstDuiXjkWKZdqkqxTe+u9H0hc07fP58QBhscZ0u6TQdeu2QMMhptktWWQV1RSPwcCwbs1wiQpuh7QMRjhtj2RZS1MRhRIyL79tUOfSbAuFoLpYZx6Mpq2FBUpZoT+K2Omg02u3TsTXNMEA0PWylyQqDVybsScHV9YB2c8n4xSnfeu+Iv/rpC/7Jt3b49794yb/8Z9/lJ7/4iv/Df/E2//ZPVvwX33uXP/zDZ/xX74X8D6fH/O+/8xb/3X/7S/7+D77J//jJGb/z3cf84c+H/G+/2eF//Phr/uE/e4ff//QF3/hXd6gHC5ajBYexT1dIrpsOdVZiZxXDpGTuVqzjHbygzXBU8J3bj7GSJVOn4GtiTnKJquY0lyNsO8cSBYtRSpkrynnOOw8fYUmBE5Wk65TY61HFNfFuze13GwxGK6aTBZ5o8fDoNtJ3GM8rdvcOaDZDSilYzDI8J8B3HdLFnGxV0210qZdL7LqiSD2GV1P27z7EA16fnIJrIYxLXSscR/L97/0Wo+EFZVkxmC3pSgvbtUjSDZh7NhhvEneNoFIV/WbM/p0+dtzBFIJqXTIeTDk6PGI4H9L2IqLIpkaxmEyxsOg1W5TTjKtBQnvvgC8+fcpWu83uXpvnV8eEcYgpDRcXA966e4sf/vBDcATrhcNynbPd6qLKgqrKMTKjtxUzW+bMlile7GK70Gw0yLMCV9c4luTq+BytDNs7u8wnU0hgPktobdU8vn8PX9rc27nF69NTlAWXVyNuP7zLeDYGIbl79zarxYKL0xP+6hefI6TDoqjodlpU0wlFYUjKkjgKqMqK+WyJZWnqyKVUFZbQKGWQ0kGpTeaTZcAow2Kx3uRyeQ6qVPieR600VZFtolMESGkQ1gZSbaTD5eUYXWuELUnTFY1uG12H2L5EVf8JxP6t2zv89CfPCcIdXFVzsBcxmcIozVknOW3PJklL1jPob+8QxBI7Eth+gBMZEiV4cL9NRzTZihSqMhy/mjC5LLh1L2S1nuHJBuPpjKby2W1t0ZABTSvB3VIMbnd4+fU5vm/QyRpbGq7nJe/0jrAzwdZWn59+/JrdrS6OJynzNdvNDq7j8fC9J9R1yWKeMp7nNBpdioyNdEJaJKsc33NotXx0aZhMJ9SloRm3yAvJdL7mvQ/ukSQLJsOMXj9AihYX19cYkWHZDlu7HZIV4LisiiVCBmQLBys2RLHLerXElUDu044rtg9Cjs8m5LVHaVlE7orf/Z0DqCXT9QDbz5DVNbZQNIOIyKqII4vFco1WhsgPeXv/Nk+kQ2Vr5uuCy8uMxbRmpUbYQlNGPrd9Hz/xCB2fpuNwGLYp2i56OWMr9lEEGJNy9uxTGr7k5etzvvl4l88+OeYfff8ev/9nX/Ff/+ABf3z2nP/zv/h7/Pnykn91R3NY7vB+NKH54T53EVgPb+NNXvP3HjXIzr7ivQeHnA9qDt96wPTcEB8dcjWsCbu7DAcJou2RTRKyICT3coL7e7z8YsBHz1/yww/ukM3W3JY9GpMEudXkR3/2jId37yCdkFkx5fdPn3O0e4e/+PKv+Cf/8veYaM25tAldH+HaXA026LpcFOxuhVAmhN2A0fqC2bkBLB48uI22CxbzKT16VELR7bSoa5+T19fUlcRog2UJoihmOJijspwkz3BXkqwWdLttmg2bwfgaVXax5ArLdgijJtPxkPVixSor0JZNss7xfR9HWiiVMB5d0e716G/1sL2NzrTSikyVxHt99nb3ePrZ57hByO79ezw5atNt2JyPMuarlKDR4Hw0oNNqspjN0bTY293jk0/+BjvwSfKSs8sBa1UyrWwcd4vdBjzc3WV4ec3p2ZhmN8dzDX/w+V8QNiMOjnbp97YpVkOevzphud/narkkiGC2WNHt7+OHMR/97GM+jB9zdjpiPFtS5AX5qiCvNNPZFMWULC0ZeRLpeTw4uM1u0GV+NeRv/uYjXg4vaHe7/G/+8T/DFzYXl2eM53N+8dEvuHPnDo/eeptPn35GVSd4nkOaJyitsaTECENeljjS3bikpMSyHKAmCEKCQKK1wXE2sCSDIctzGq0AZSo8z8a1bfIspy7BdVygxvEcalOjqk3GWTZb8Saw2BaGMArQxmE4WOCEFjv/EdnU33nk/3/8P79jvj6/Rq8L1DSjli61sSjWHkVec/9hm9kiB+WSLAu2jywuLxbcu7VH2HRwbJft/YhZUuBWEVFTklYaozwGlxPKUnJ5PaKqKlpxg2bQoq5KLJ1z68EWZ4uC569G1MucclriNASldrGURRy62LHPl89Pif2IKLKgTHj7/n0uZ1N27vbIJyMMFv39A66HKy6vh8yWM5Sq0Mqwu9PGc3xWywRpGVzHoduXnJ8uSctqsyk0Ac0YDvZCFjOD7Uia2wFR5BE3LbRwQBhqtWRwtSL2OhzdbTGYLFGqIp8t2Gl2ka5DaS/ROAReA2MqikzRaTWpqgQtKioFLi5CV/x/SPuPJ1vX9MoP+72fN/vb3qV3x5/ryqOAAlCNNuwm0Y0gGWBLCoaokAnNNFRIIQ30P2gkRWggDcQBGewGCYHdBNCN8lW3bl1z7j3+nDzpc3v7efdqsG/0rKsjgNGZZJxBZu4nn/dZa/0WSIaTFfVqgyDZlNMddlqEQmcel2iVLploslopFJhYqoabC1KRoMcJjWrK559fcrxT44unV/y9b27xb370hP/yjx7zL372nP/N79/lb16N+c+/dciTVcGRvCKsHeJOZ9itLtNnX1A9uUP46pLGgwPWqyEP7t/lyd/8kru/8wFPXk249943eP7Lv+L3f/AtfvSXn/F7f/+7/PmPfsMf/P73ePrLL3j8nfd49utfc/jwkJunb+ndPeT6Zkrnm3fJsoBxEHAzL0ndkq4BgS9oLBaspM1t6HOxXrBet0hyuLi+xVSr5KrAsFT+2T//E9JOnzBKQMZUdWjpGvMXnyOLCKllxP6C/b195sFr6s0+Nzc+ZQGGrvHufIo/LzANyc6WgaF1ePZmQrVvUjEtsnXJcLwiiRL63TZZIanWLG4mY/ISgoVPHCsohsS1YyylQ1FktJsuqmpzdjUmjGPm0zluxUU3NJoNj3qlwnSx4t3bS5yqSafV5OpqSGerTyFzWvU66/mSOErJKGmbkvdPdhFWhbPBgDwtOTo64Ue/+BW9bp/BZI7telQ9m5rnEiYhW9s9avUKNxc31FoaRAmnp5dUam3Wi5zTd+fs7G7xweP7rOZzSl3h3v1DbKHz819+wqMPD+m1q1xdzrCsgidPX1FrNmjW2qSLhMFoQS5zhqMpWVIyWW4GX7fbohQFu0c71HQHJS65HQ6Iypx6u8XV9RXtZpumVyVcrGh3WlwNBkzmSyrVGu1WE8s0+fjjX2G6OpksiLMUIQRFnqOpKoZmYmgmrmuzmM9ptVpEYcR4PEMiabarSCS+HyDFpgVVNVQ0Q0AhyMIUQzURQiFJM/IiRwiB0BSkgCzOcQyTKA6p1F3QJYUvWa0jLEdn/9Dh2Wfzf++T/7deWNNJSLgoGM8yStskViwOjntUmya5NFgFKRo1dMvl8PAh4QpqlTYiSyHNiOeSaG6wHAgGNwGDS5Unv7rg9u01ybIkWcS8//ge3e1tWlttXl0PGEcxa8PmR7885/blBD3KaDV77D/exjBdOu0mGCrrJOHly3c8vLNNxdMoFJVcq3B2s0RqNpObNRKd9x7s0+yolEpEq+Nw7/EOj97vcniniabBfLZitY5x3TqWbRAnCppawdSr2JZJnMQ4jgWyZLtXI1gsmFwvGJzdEM3WRIsB0XyBSAUH21uIMiZYT6jogmi4olGtkisplZbHu4uMPNeJ1lO0MkFXM+J0RabAIkhZzQNup1Mm/opVmuD1auBKVklMu9sgjlaIJMBIEpxohLF+TXv9htkvfsrO7Bmf/Ov/hu/LF9z8zf/AH5tz7hpD/mNvyJ9+UOWPrDX/239wwvb8Ff/nf3aEcvOG/9Xvb3H94jl/8rDF5NWM79RzLm8uOdoqWYcqd3uCy9mQLa/g5asl7Waby0jQFpI8WrLfUJmvSkwrJM5j6uUcNVep6hmDyZKuNma6WLFTE5wvfba2TIbTKXsy5Rc/OSMsHN6+vuLIqbJ4G3J3r89TI2coS2Td5Hvf+RaWa5HUHUrTJdAhyRKKvGSRrnG7JjXPQHNqyELDuB3R0jx61W1c1cQSJpPBDFupkwUFtlWyvd1hOc042D2h1rTQNI13FyGp0Pnwm8f0KlXi+YL1akwYRoyHM0zFZrUMidKcLE4YXo4wTAXLLRle3RLMY6IgotWuUZQpb9+9YzxbsPZ9ao0G9UYDVdHp9be5GY0ZDqc0O20kCsv1mnq7wXg2YzqaMhzckKQRaZEiKWi02ySqy+1owB9+4z6PdussFzN2u10e3TtE03TCMMR0LXJ/gRqvOem16RoauipQc7CNCqt5ye3lENMCQ9Mok5Rf/+Jjri8GvDu94C//8me8fv0a1zMIU584y9jb38axHe4/3EGoKbrIEGlEmSYs/Dk7O33sWhXN0Gh0a7ieyT/9h9/nD076HG+7NHsGD9874IcfvkfdLDne6kMU0O806PSr7O806TU8TF0hSJY8ff0VX778knq3Sk5GUeQYigZFTr1ap1JxNpQumTGbzyjLktVySZqmICSqIQiTED/yQQXV0CgViWnomMYGfrTpd4csz1EVDU01KHJBFhZQKiAkeVmAopLlBUGYkRYZQhUURU6wyn/rhvpbB+qrr8YctPoYUlIqGY6pkwY5RjWju2swH0vyuECRKr/+zScE84Q72zs0qiaDyYx56LMcZTh5FdOskpURW7tt1mmOMAWqobJaptxejxndzGnYNjtbdfJ4RbfVRq+bJLnkdnzFwl+RkxGnMbZnspzOUKXk5mpMluWkWcpwOCbPcpahz2zoI6TFYu6jlyW27RBHCfgFj0+2ObrTRlUlrl1BJiXTyznxIkEWKlLJUPUN4abbqzObhaRphh8F5KrGMg1ZZpJIQhCnGy+stLm5WZLLktFoTbhY0XHrFEkCNkyiBaapkqxSSgyGkwASHUtvMJ/nyMQA3aS0VFBMykIjSkKyPMcyFaJ1QhSXCCGw7YQ6MfVwwn/1nsE3KzH/i/2A/+L9BkfRkD/9bofozVv+Zz84YfL2mn9wx+P2bMj339/lellycthhHJbc3dJJshxtfkZSmihRgqIbGLMJ0q2izX2MrS5ZuKLaMolOv6S/32Y2uKDXtRk//yk7hy0mtyG1u/e4fHPN7r7L6OUb9g53WdwMaB9vMx9C/eCYcJZg1WpkkymWtBhfTYgKMMYBearx7vSC+WDJ7sER8Vrn4uaK7/2wT3e7Su/4AKtS5fDhA0xdo5Hn2INL9mxwwjHW+RUXX3zBmxfP2OtU2Wkd8dVvxmizhOPuR9TNE2r2NrPRglrTZDg6x9YMes1d1MLAX8x5+uQtP//ZKy4uE4bDkuurKUGc8G9/+THD5ZLrixHTcUCY54RhgalaVN0quu6yf9ik5ujMpjFhnFOtawhVEGcp17cDxpMpv/n4U25vRoRRzHw1RyJQDZu1H+Cv19iGiWEYmLaOrhcc7u0RLGJM3cTWNT7yHLYUm9fvhtQ8k2A94aPvPKbdrpJFIWmRcf/BHn/5b3/O//Q3H3N7PeKrT99xfTlnugzRTJ3VbI6iSG4mc64XS95eDTg/H9Dvb7G736XISr769C1ZkuFHAz7+zWc8+XxEFAiWo4D5dMlkvuZw9xgtA8tSOfnomP/0n3yfRwc9kmjFIpV8+/19/i9/8g/4zlaHe1se/tSn1qmzd7RLmRWEoWA0TZBahRSTdVSQ5zlRGLJarZBSkJcbsHSt4qELBQ0Fy7Q2XtM8QzdUDFNHsjnj5bLc+Ly1jW2vECXK19l9mZas5hFZXiB0gekoFGWEZap02hUMQ5CEGYaqU246MykyKLMCzVRRxAbFGAbl336g4hoEQYSCyuHBLjtbDWSuksVranWFsnSpeE3iKMa1YadXZTG5RWrQ292ilJIkhSgrWC3XpBEYWhvPbSELiVdpMBhMMG0LEOxt9/Ecj+72AeskIVxLlkFOicnZmxFFIam2dPxwSV4kBGHKbLXC0AT7W016XZvjxy1+9/cecPRoj1jkPDkdM1sU7HVdXEdjr7VHOPHZbu9TJjZV08U1jE21h7ZxIAhVoKgZfpCgmwa2VaVSbRDEJaWiYhjgWBqu7pAtda6vAp4/ecd0sCLwJdVKk1gWpI2SoFTIIqjaJf0dg3GY8vJ8QVoYTMKMT56/pl6pYpeCuuFStx3UTFCGCaY0yRbgKgZV16TWblCqKrksWJYrvLbLcDTkvXsu02XA3/9gn4uriEeP93gzXHFSd5gXDrUsZBTHWLFBoLbJRyl6q8n0IsPbOWA4mNG/1+LmdMDx0Q4vng05OalycTnko4fHvHt7zp2DKi9e3PLo4T5fvh1z1LZ5/WzMowOTd5+e8uiwz+fXSx5+eMjLl6/54KTCZ0/G3D/Z51fPbrh3fMCbz9/w3QdHfP5mzoP37zEaR9w7OWAynXHw4JhloGBXt/jl6WcYOzUsz+D69ozj7YKjb+2gyhnTt6+ptzxSPyM/u2X94g2Tz57w6qc/5t69u0gp+Ld/+VNSX2U2mGFPM5LRFEMVZGHGeDBldBswnWS8uxzz6u0ly3XGZJygKjaL5ZrhYEIQxOzu7rC9t0Oz10FVVTRdYWurRn+rgaKVGLbA9nRU0+DlywFffXHJ9eUEx9CJFgGObhL7AWWWkiUxK38NQkVVVA4P9uh3O2x1ezRrFR7fOca1TdI0oVWr8dGdu0Qrn7eDWwZLn+t5zP/zJ8+4RTCejkgyWAcF8+ECpIqCwTooGQxiilKj1ujy6s0l1+Mlby9v0HQFRdUZL0JM18S0S771nTsYTkmS5nz1/AWffPWSiT9FagqXgwGXt2c02jqqlqHoKUZVYZ37PDzexiXjP/rwHtLPcVSTtBC4XQ9hCD57/oa/+vnnzNcTvtmq8eLymn7foaZkqDqkaoaQMXEaE2Y5WSHRdJMkL4iTlDjOSJINnk8zdGzHJgoC4jgmzb++h+qbipkizzeVQ6qGQEVIlSIvyMscRUgUBVQVKDefbcs2iLMYoUncqoWqQRgEVByXbrtLmUsM1UBIlZ3+Np1qC03oWIaBEAphmPztB+pkGbCKllhuhdlkhFsRaGbJQXsHUaS0duHzLy8YjwMabp0yUUhShZvZmucvzonCnHngkxQlt7dLnn15zuBiTJ5mm9SOntPZatDfaWFaOpdXl6RxQOaPOdjrcDUcc+dwh6pn0Nrr4LkO81nG9dUK162zd7+N0zCwdBWt3DAZ8ww8qXH3/hZHxzv095o8f3HB7VlOzWpjezpvJ2uefX6NKyzi5ZJ2u0KUB9RbdaK4wA9jSqlgWhpXN2P8IOJ2OOHd+ZjZwqfZrFJtWEwWPn4EwVIhilVKoeP7IXmugFBI05REFIxXIYtZjpqptOoOnuswmoScXi0Qisdq4aMKhSzJuD4fkvoKd07ukueCPBaEq4wijRlNhownM+JEEkjIhMoMlc6hzRsE86ggtfaoErDWFcL1hOp+n/EowmjuspoOuX+3y8VXz3l0r8PLi5Djjz7ixfmSx8cdXt0E7O9u8eUo5OD4kDfLkoOOyvVS52CvzldzQavq8aqoUJEaS+HQFjE3y4xtQnwpqCQlsb2LZjYZ4NEpfLLMp+VlPPdLzKrKm0VEd7uBFkne/+jb/M1lzgutxi+up6SOjed5LAdTojij1+xQ1zU+cCM+ONmmbruEwymDiwGMfH70F/+Gy5sbXEPw5KefsrydgKLyi998vIkjB2viIuLt+VvOLyeYdo35PCEIYgyjgmLazPyAMCvIMui1e1iGhWWZhNEa319Rqzj8/rc/oGabNNwqHXvzh1gTKv1Onfcf7fG7v/uA/bu7fPDRA/wgIooKhuMJURyDKtA9C2wDo+IiNJWb6yuyNGU8mmwqO0SJbWtYtsl4MmExXTAeL+jt7rIIFlh2BV9X+OJsiGFobH7FJHm+Zja8Zj6bcnMz5vMvX9Bue7x48ZSCArfukSsC23WYTFYEYUKUZDQaLdI4pdttIktQpI6pGXzzww/wqh5fPn/Ji+cj1kuXxTwnTyVhOOLO3Q6WAFUqPHl9wenVFc+ev+Zv/vKnzIe3eLrK3cNtTi/nPL1eEImMP/rmN9jbbjKZDwiWU0aDAeOVz+nNNU7VpVavkifx18q6RlaWIAQNr45AMp9OUABd29DyiyKHskRTVNJk033lVTzIIE8KVEVFBXQBulBIk5RCFigaCG0DSMlzSSEhSRPyosAPYnb2OuwfbBMGEULIDYAbQR7FZGmKlJLit7/4f7vKf3BvZ4PEylT6uw5pucZfhyhKDUUzWCa3uM0uWqkynq8ZzUJst0INhb2tPS4v52i6wWo8J03g/r1jRuNrjIpDu9VkuUjxTI/z8QWWqXL3bg/HNpCOw3g+5nsf3AVVgFB42G6i6yXTAIKwwYcnHZRayudf5EThmlUQU+Q5y1lAGUYc39+BPKZiWPi2S61qcjqZMlvMOLrTJhcuP3t2iVbkVHWHAo13lwOCROJ6FtW6i6pFRCGQZti2jW4keJ5Lu1XBdeq8eHbJeh5iGRmNhkuWh9i2xWQ6w3V0SAuaVZsoKVhPc8pU8uDhHpfljEmisd/v4BfhpkxMCFIZ0e60UEJYzFbkRU6mFNiWShqXUGg4jkUQRKjoTIoQYRlEUcZQN8jThLgmGY7WbN075OxmwZ2dBv/qL17x8Nsf8fL1ax7/wbf46ZOMf/JdnU/PX/HRhx0+yVwaasbKttAVlaLTR9FqlFaFeHKN2WySTNeIdpX4/IyT431ubkbs7LtMxyu2Hva5Pv2SvaMWb56+Zfv+Qz6/vGDrwQOezBZ4H32Lv37ro773Df78piA/vsf/++WUfPeAP7saMNvZ4fz6Hbrb5vT1kF1Po9/rIZQUTbN48/wUr5A08gYf/ekPWCYhlUjQtTzijz9FyBh9q09RqCRpzFoolGZK77hBXnF5OR4wDTKk0Ll4eYum6qxXEY2jLjfjG5yageXYvHz2inq1yf7RAaPJaNPf3nARqqRW9ViEPomUpBFcXk/otFya7Rr+fM5ylWA7Do2Gzf5+hzhReXtxiVAValWPUgrmyzmh77O31WU2n3N9c4vrOFRrFYLAp11vsU4CcjTGfkyYpMTDMannYCkCu+pQazY5ubODolq8ffscx1bQDYmhgaaX7B9uMxmPkOjkaUDkr+hvdcjyGNM26fYblKVCEMZcX62YTxaUcmNiH9+OGY9GPPrwLts7FdI0ZDJcswgCtsoa753skKkZn52PiAsFP86pbrdoddsUswXNWp+6WmVlDNjpd/gXv3jCwXaX41rEtl5lWO3w869eobgVhKpAIYkjn9VyTJql2NUqe1s76KJgcHNDrVIljkMUWZClBYUCeV5imir37j3i6npAKXJUFGResrvdYzwfI1RBLiUyBxmnaJqGIkqEUDaeVySKVqJ83TWTpCWaWhJHEWmUIcoSoeoMhrfkaYamqsgyRQoF0zD/9hvq+btLbF2n1jCpNSukWQxSYJsmwXqB7emU6oKVP2FRpCylyjyO2Orvkmeg6Q6D0YK374ZcXk14/uwU2/Fw7Aq3t1OSuGA8XuAZOlstizSdkBUJioQ8Uvjpj58wPL/GLCXBQpAtJd2mwvFhhSLOqWp19nZb7N3pU20Z7J9U+U/+8bfpH7YYDK5YrZe8eX1JUeikWUHDKKhWTEzF5ZNfvKWUBdIy0T2HSqNOvdFA0xUsO4cyQNElpiOxLUGl4tDrNFFVhTRMWI1mNCyVXtWk1zAxlZLj/S10VZIJySqHIIV0luLlBraqcXTSIYpjgnXMdneLes1CFCUv397w1ZdnqIqCphhkfsJ6PEcrShI1Ac3ADzcts3G4puaZeIZBmKwYy5CzZcZ6lVC1Cw4PdG5Dk3tHFqenQw49E2om3zyB8bTACwakVMgnM/R6g3g4wOt3GI5mdI5q3Lx+x85Wj6snP+HBwxrvXi14fL/Gy5drHn/0mM++vOXDx/f55GZJ6+4+P7tS2f7ge/zFQKX7zd/nv3sZMu/t8mevZsT3H/H/ehExuvOY/+75AI5O+PhmQe+D93j37oqTB8fo62sOj/ZR0hRdZnhmQf+kxdaehVrdKLzdWpuf//SWelWjqp7xcHuJbqwo4hV37++xf7xLy9N4vHVEf++QwdWIilpQcVMUURL4kuevL1n7OfVag8VkSbVSYTwac7B7yNbWNr1Oj3a7i1etgrrJ7h8cHvD+hw852Gsj0wyRKtzezPDjAsXQuZnMmU1z/MBjcB2xGoSMTse0K02C9YqH90/Y6nb5ez/4Hf73/8s/5Z//8R+ThRkXF9c8evQATVVZLFbcDmZc3045O7/GD1L8OCFTN8/ag+1t/CBl4mfcDpa8fXvJ8GLI5fUUw3CRhUmvs8ViEXJyfIBtWTRbW+Qy5d6DbT58/xDLkBiGxvZWG8fSWC3nhP6KdrvxNSdUIqVkFcRols6rV+e8eTlkOs65vIrQLZVGo06+UnEo+aPvH/PD7zzCUVMUkWMogm6vx49/8mu+enFJ3/D4bqeGRsJiGvMvf/Y5v/ZjRsuch/dPONzr8x//7rfY61Q5O32FCtRrDbxKg9l8ztZWF9s0WC7m2JZOtVbfkMXKDdoyy0oGwymrlU+W5fj+GlWBVr1BnqSkSQoSVEWjKAVhkG+aJLKSPCvRNY0sLynlJiUllI1oH/gBAlCUjQUriXKyrCROc0oUBIIwjP72G2q/0aDbcNBdgShjomGEbXjkWgauRMkVmkaGum9QWE2CZcT3PthmtYrw1zmOqVLZ1lENj1fPb7i6zSmTkuVIpb1VoX1U5+zNGeFgyUn/GxiORZYpVEyHnb6D9fcUKkIlJoM0Iskgnil0alVKFYShUK9WCaKE3pZGRdOZzC7RLQPLk0iR49XqbO8fMnh1RrXqUGQ+wSKg16ux0hRaWzarpSTPFBquzoePqmjWCkUIStng11+dIlc5H9gubsfgqzczbs9LGjXBapEQBxHVho7h6ZwPhtRtk5Zh47VcVtEaGUSYosK7ixFoBYZhY7smqaWwKiLiLOPxyR5itqCtw7tRgBAKjuvi6iVJJBiPl+jColR83LrFbBGh6RZVt46WqdTMCpfTBdMiR6vWuB5c8u0DFb80KedzsCokt1O8nW3WF3N2H21zeX3O1vEOZxcjHr13n7PPn/L4D+7y03/5kt/94zv86s+/4I/+5Hf4sx+f8Y9+uM0vfz7kQ8PjJ7FJITyeKDsM1g0+y2osC5vP9B12Eot3tQ5dv2Du9Xi5CtEcj6urEYZb5e3VKRVN4XIYU/gRq/mI43qBYko+exNw770+nuMwOh2g1BW+e+cDhN3l48Gv+MN/+iEnD7vMxiMIDao9hfNXE9DAn+d42xXuN0x6h9u0tw0ur29ptPdZzxYMJnN0zUEIwWw+5/j+ATdXM9Z+glIKXFUhDVecHOzw/Plr0qRCf6tLVZd0VJ1P3l1g7Gq0Oh36+zar1YLDvSYH9yrMhgGLKWwf7OKvl1wPBlQjqNYbHBzuUa1VOOzVMPOYYD5DyILAT/j5zz5B0TeCiaEbbG3vIaXBYj0nzUJ2t7p8dOeQZzcjGt0qcVBSZhnf/fAek+WKp188x9Ch7TUwdJV2xUGmJYqtcX17g1dV6PU9urUOt9czau0W61VGmpVUqwZaqSHyZHOfTBI8z2O1mlNOVvS2a9xerqg3BB9+bwc/mJOx4jwNmX6+5s6RQeAHnI3WBIUkia64++CY4/vHvLg6o7By/tn9h3zrWOfjNyMuJgsiQ1KkKVXFpdGq8MWXL7ger5CohFLSPTjAsytk4YrJ0scwTVRFYbJYbFR6U0NSUkhBWSpMFjM0VSWLSxzbZh0EPH39mlJVN0moooQiJ5cbKFEZJ6hCoJQFqqmjCIUk2nSj6ZrAqzjkWUSa5GRlgW4YNBpN5vMZqqaSJAmlLPltNtP/4EBt1jU8L2cR5MzXK1RLRbcV5osxutC5urLoVbcJkgEqMe+/5zIZjdGUOtv9PotgTViEREXI8f0d5sMJbrvJF09e07xNOJkrNFs1VnnOy5cTdnerNOs1RusVs8WUs+dD3n/0iDKd0uvUmYyHNNouV68nVNsNbi/X6K5BFJakFCxJmPuCtlsgbBXbUPnm4wZpWbBum7w6O8MzNVqKRlEIwjjBSSDNVK6vxpzs38VrVxkNfbb22rw9u2Jrq8G8TMlihTQp0IVKEkWcz+eg6pSKib8OSKchzaaNaxts2wrvNSuMfI2Z6nI+WVCoOm+v5jjmmv3DbSxTpWt7nC1nHDY0ricKe5U9ZosBl+Gc6HZGt1khLxUM1QKpYpkOVVcDJUYTCoicYLmgVzHYaVeYXK/4wFK5dKuENzHbx0fcTuY8enTMuzef8+C9Rzz5/B3v/d42f/0/LvmPvvFN/uXnF/zTPzzgs8Vr6maXl9aIHUXlq8ohTb/Ns8YeXw4h2D7h7NkA585D/uLZDdqdh7w+m/J73/g+7z5/y8HdO/zkl5+wfe+Iv/ryU/qHR/zm4yfsHewxefGOB4cnvHn+lna7ztkXn3H/js6vfv0r9rdr7Gy/5sGxpF8PuLpdAA612hHRIiIZPKF7YLJcrZlfTPBjiWZraH7McD7g9YshuzsHWHs6K7sg1hK6TkrlUZ/h4JaoCFgnKX4UMHk62dCHMHn0+BFPnjzh5csX1KrVDbqtkNTqHnlZIgTkmYq/WHN82OPOvT3SSOfpy0sGw1vee3DA2bMRdw+PSeIBUs1YjRJae3v8/T/8PovRNWc3Y6aDIVbeR2YZX3z1FE01EGREUYxVGhimRhKnTKdjiqIkiiIUoTK6XhCvYqb+mpPDHc7mV3iex998/ClIBa9WYTIcsZgtURSBZmiASp7n5HlOpaZzfjpiavuopsr5+TkVx0XRNI4Od1DziOubzbBQdI3r6yv2drvYFZ3VMsbxVJIi5t2bAbWazsKf8NGHLXb3jrg9X3P6dsn21jbXwwmVRoUgSDi9vKbbbvP63Ygn1R1++vRTwlwjiGPkdcHJ8SGChLtHJ3y+fkajoQM5uYjx2gpxKKh6HrPbCYKC2XyBaesgcpzKBlai6QZZWnLn5IgkCLm5GfLhhx/y6eef8uDufZ49+wpJiSokopRQSkxdBQFFXpIXGxFOFlBkBbrcOATm682WK4UGOpQyZ71eU8iSPMm/HqQSXf87VKAMrnOSMGUVpJhVaO1rEDoYSgPLrmKLGUEUoxkG3/3mPpcXy6/JRQ7xes6D4z7XywVSUVBzFSWv8vbqilhXGUcxV796zvZ2F1URrKM5aVJQbcYYlouSOXz/e99ivp6zmq6oeDUGNzGpXCLImU1CLN1EyhiZrOnv1Kg2HMpI4Dh1nr98iecIZKJSa1vESoxdNeh0m+iqw/j8DBSDLHORwP2HD/DjEmVZMFkrdAONnd0dFNHg7XpIs3PEx7/+hFQIhCJQTAM0g1IDzzRo6A0eP+yy8ueYtTrDyyEHR0fEaoDiJ4RlSJJLGk2TRksjWCwxTYP73Q4vXwxxTIs3p7cs1gtUR0ekgls/RxWgsCmic0yFWsXDFhqqKlBMDd0QLMoQw7ZpnNTwiowHR1t8/uRLHvz+Ac9eLjlo6fz4TY3v75/w/PMLWuYhn1dGRGGDTytbhJcJV519/h+v12RHu/y3z0bU3r/Pf/P0lLvffMyr11O+9a33+Dd/8SM++sPv8Od/8XO++89+yM9+9lO+8+33+eKv3vLH733Izz7+BT+4+5hg/Bne/gFXFyNKu8rr509xKViNbujaJlUtwckz9hsV1Fjl2SdXVKoKttHE9ipgpQxuzhBFiuE2mV8qGKLD2WLBnZMW6XpJ5vbYf1+jf7jD+sxncrsidEoqnTpBqJCmGcFCsFhBtVFn7gcoqkqRS8bjJVnymmajhWn2mS8XpEVEFIUYRsbgdsLpu3MUqbO9VcOrGhSKRhrpDAYLqlaFJy9OaTk1Fs6Elu7ybjznv/rf/acMX7/kzlabi9zn4VGH6SJiuhb82V/8JaPpZLM5oQCCNM3IspxOu4NtWUgKunUHq1JHCp3T8yv2jvaYXIwwNQ1VkaRFQRInzKdr0jRGGCqOY2HbNvP5gjTN0FSDKNLIspCiKShlSRimqIqJ7cD1zYh2zaa/vcPF7QsMReXBwzvcu3fM9fCSrZ0mX372JRIFRTGpeS16HUjLgldvXnFzAaNBwoPHu1TqB4wmC0Seo9sWk+WS7W6XH331lFUOO70eCIN2u8l8PKLQJM9fPKfVaiKVFccHdzk9v+b1s7dUWx2Ojo5omrs8/erphi4VFxSFQNMUqm6bMPYpKBlcX3F0cMjp2RWffv4phcw4PX1NkRdo6gb1pwgVVRZogFQEuqtTig0YJUtKdFUhLTbiFqogpUQ3BIYwsBWTg70DbgcDZrM5SZIiFLEZ8H/bgdptWUjLRK4DZAFm2eX2OuPZyysctUK/q2JoIFSXL3/1jvk6Zhko/OF373Lnzg7z9Yq6pbH//j5JEhFFa7w4w2loSGkQBQ0Gsxk126W73SGUsJ4v2Dqo4uo6mR0RTQJGI59CWbN/2OXV2RBXqlxP5xR5TsWzuHPnmGw+JvBTnGqdy/UtYSqpORauU8cPIg4P6jQchbK0SQuN9x8+oN4y0Q2VN28uCddLHj4+Yr5c88Odh9xMJuzV+hSFwW11zV/+za948PiI8fKavZ19srhENXVevHpFHmRIU6GOJNIsfv52jBH6tCdf4jYrDFchjXaTt+8G5KXOzdWCwdSnW7HwdBtzW2d0vsZ1qpSGSbRIMAzICsFkHNCoVrEdhVwpGc8CTLXAUlXUqoYwBWWSo2kZqZ7zZSSQYsbooMEyCTkrMkapz2TH5bNwgrh7xJObgM57h/x6PkAc3efj52/Z/cYj3v3sl3z3h3/AJ//qL/nGyQe8ePExe4/f4/L0Y+784QmDxYrlbEkeS1ZXU6pGjV/8659T8Tx++dOfIBPJ1U8/oasr5Gfv2O24+LfneI6JaVXY3Tni2ZdP2N1qUN+qoCxTriZz8iwnQ+WTX15x0PMoWCL2bGLFQb+KuHg7I08iHu0e8d7J93hy9ZbH9x4xnb7kXfqGp6MrLmbwg0dV1lrA9XhB362yShPGYUQyXwIa1arHaDAjCEKKLCMvXFzXZjyZ4DgVklSy9jdNnoZuYxoGWa5xex3y7vQJtUYdUQpqtQo/+M7voXsGYr7iy1cX/PE//iO8OMBqbdFQVd7cTnjyJuHp6SWrPEU1YG9/n6vrG4qiwHWqGLpGFPrM5xOmM4Hn2lT2t4jSmFZLRxUZ16cX7LXrVL0quqZgHh6S5vD66UtKKZFJjp9FKKUgS0uQG+Ely2JUVZJlObZt0m51qNdcHFdHUqDqElM30VWd3b0m9Vqd8WiIqWmMbiYoSonj6kR+ynyc4ocrpFJSrznEScZ0PuSLL1dsbXVpd+p4rst6uSJGcjkasNXrko5W1A0Nq9ngdr3gajbBdHSMieRxu89sPme5jGhVG+w0ulQ7DSgyJtMJC3+9qdcuC0CgqjpFkRMFEYpqkGQ5T5+9JC8LTMcgywqyNEZRIElzNF0AkqIAVVWQZY7MShRdQVM1dCHIiwz5dVmKAGRRAhIhSwpZcju4odfrEoUxeZaj6QpQ/O0Hqj9bMhEJxye7lMmM9XhBr9ZF+SDh81+v6WdNgiBG0zTKUqXX7mBWMgY3S9LYJEgiktintgiodV2aWzaN7hE3pwu2WnXaHZX5OmO5UqhqNvuHHXItIwhSVoMJNXsXVEGvU+FmeYlIGnSrbc5OJ4iqyex2RaPT5uLshm7fZhgkZNMBtUaFMirxtmp4joVWhhhGBaumsIpygmnIerDEEX1uJnOqVpWDvRZbXhdVhVU4IU5CfvXzN9S9CnGYYRgKN+en2J7K7eUlq0VMVkgsz0AqGplj8ePTK/rdLiJXiOwqT2dzFH/JOknY3rIwNJX5bI1drWMaDqVlspIq0WKN3WoyGqyJohy9VKnuthmPp3hOg/l8ieVaVOoO4TqkIl3atRqDaIyiCDBUslDSLARTHObLCeNM0lytqbVbzBcrFDUmGF/g1ls8v3yDe/8ezicv6e54/Jvra77/D3+XjycxceGzDHLC+RK3VuH82Vv2Dl2mP/6UfrPJs998wVGvjXX+gg+22oSTt2yZLabTW9qaSi2dEI5Tzt4OUSyF9WyNrdj0vnvEOvR5c/HXiAK6PZ1ez2O/vcvbxZjRdM5sPaNq2DQ726ipQblMuRlrXI58tl2T9paHGQr+6N6HFImPbXe5Ulbs7dvcRjeYAs4WAcskISgWDG8nZGGKXqkwnyxI1ADd0vAqNnESU8iSyXRGHCUs5gFFKanX6lQqOlmaUfEsXM+k1bMp8y6z+ZJSLbgcDkhWMZ1Og1fDG37/Dz5gv7oiSmzeXU35r//lv6W9s0MiMh587yHT6ZRoFfLy5SVexaRWqxH4ARVb4Xh3G1VVmMxiroZTBuM5URTgWbt0eh0uzm6YriWj63O+9/g9rt6dU6oKhqmS5gIhFVyvgu0adHoVXMditQiZzNYEQUwYZOQ56HpIWWRYTpvhaESn7qDkm+9Dx2wxuLoERaKZKkksiWINP/BRFZWL1QgUidANVsslDa+J5dgEYcRsGmEZFQJ/RllKyrREFjmz0Zg0K9EMDaEVTC+HSB32DvfxDJU3L89ZLUOaXY279+qEC4dPv3zDRVYQJj5RmhHHGTXPwbJUoighSVdkWUGZxqiqRpKmKIZCKUpUXSOOcvJSkrMhTimUoKmkeQmyRBUKomRjf8o2lSmlBCElsgBdUVBKuWn/VcGyDW5vb4miEE1X0DSVMv87DNRRnLBYSmx9xnavhlIWZNacKBhx/H6L1y8nKBmYSkJ7xyXPMoSSEcQZTbVGe7vCu9dT+q09wuScOycmcWpSpiEiC5GpTUXJ8VMVWcDV7QWOViCbLVKjwtOXt9gip9XoY9sOqu3x+vUFFcfGtBxoF6h2gUgEZQCHezv86vkz5lcx6+mK73x4xHKxoNIwyKKMIAxw3SpunDEsE569uECS8+ChS6mkXA7OWK59DAdWtzMMxWMymwAGD+5uMR6fIaSGFBlFGXDn7kNmq4Dtg20urs6o6w5FnhOUAUlREsgCJc15+OAIRWRYepXQD9BLFcsTmI5GHCsUgUNkZuieSigA3WU4j/CcBkEaoJs2k0FMlizwGiZmTWFBQiQl5Spn23GQQUFQh8wWiLSJpSRcDEYc7j9mMp7TaugM5ytqriDKF3SiW3ZOHC6mZ7RbVX76Z/+Klqvz5f/4I7Z7dV791U/Z2a6RTM842qthlgF3erB+vcZWU0ReUrEEszgiWU+IhEQtFcZ+wNlsges1UGRJkaYktomWSSqGSpGX3AxmVC8seu9WnE9e0DzpYVgKItdZpwkMFdLSotBquK5GGhVc394w2GozuTvj5uKCy4sliSzxDgysKpx855ilXMM4wF8GBHlBvdelgsL55YAH9x+yWvnM5hPWUURaJORITE2jyEtUsREzlssVRVngOia2peGaFmohcBwbvYTBbEaZCxalpFMx+T/9l/8Fw/NrfvPZBRWvw//vRz+n2a2z87BCsJzTdGKieczVdIqmGahqQr3msNtvcH56iW0YOJakZjc4OdpmPF/z9Kslv3lyRqVaRdMVDNuirelEsqSz3SKOYyZhTL1ap5QlmqWhGypxsMHjySKh3aqgqIIozDEtncVyTpbbtPIaEg1FOjx/+gZN0/Ack1W8ZhGErIYxtuNgOyqrlU+j0WG2WJHnKaDS77WxTGcjEGUlSVRycTGgVBTyJKdXraLbJuPFkk6jxs1oyM18iLAUDrottCJgHRbUqhZurYVbbfJXf/UpvU6byXzB9t4BZmYzm62QuSTwIzTN/RptWVBKlSzP0RSBVBWkkJRlQZKESAFFWaKqAlVRNrXRSKSQ6KZOXuSQlxumqRQg+RpcDWUm0XRBURQIVWVnu0etUuc3n35JKTfe1izLv95i/5YDtd/vsF5ek0YB41uNxSxCcWN2+h71fYUsd2h6u1ycXbO/7aHaGehVstmCVqMgt0IePWyhazFq4eKUOrYi0La65KXCKhjiuArHdZvT6xJVCpIcgosBqvQo0Li4HnF0p8m7N1MSOSMoSiI14m6jDcGI4/fuc3N1zmQxptXzONrtkOYwVFNSdUEeFdhlkzBc0ujUmM0nIAo++OY+Z6+uCEKTTFHQRYYuSnb3tnn+7B1C0YmzBabZZTyaMLVj3LpDminYVQvVNhlNB5Sa4PzsDYoQXAwnsOfS6ZpIRZC2a8RRyHw6oCwKjo96iJZHIUs0U8FfLBHSZbJa41Z1bNvEq9do97Z58+wUtwZOTceuW1y+fYumuUwXa2x7kxDp1z3iJKEsM6x2jUm2Qslz6l6dlBk1tYIfJpQhJFqKqemkSUy/YhLdvkG1KvR7fVptwfzcx3C2uX5xTbBMqNR08vCKTs/BdUIa1QrFbULea/NmeMXhQZOtjk5fbFFJFByvyr/40afUI5VqqVMWEgVQpQAFcpmhGipCQpnnnL4Y0qp2UAuY3C6wPZXVYEXlsI1T26Za6TGazVFUAbqK2/W402ug5yVS2gSUnA+u2C101GXMrT8mt6BRr3E5DFBUjYvLG9KN2MvV+Ttcx8HWNGzdZDgLkUpBmpbouoUwBOu1j2npdLtdyiJlq1OjyCWTmc/pxQVHhwc8+uAhw+sB4+GYw3aDt7/6iicv3nDjQ56fIjWFu7sttOmIA6/Dxe2M4XTNfOETZ5ttslKRqGWAbeoouoViKQR+Rhr7VGs2h3e2iFNJGIY4toNuqHR7HTTLYTGcEwQhO9s7JEnGYrUgS1IMFSzLYjicUxSSkphKpUISz9FUg7sfHCFliWVp2KbF7WBBhmQ+X7NYh5sXkKYiFZPZbE6l0ocyYDqeYpgWcVSQxDHXWc7V1c2GZWBr1NtNLi/egarzj373O/RchS/fnmO7Esc1OT09RbNUYhEzuA6wNYsyk9zmBQ/fPyBLYhRdYzibk0vB1eAKQYnjOMRxQk5Jmmeo6qYqXojNFq0pChQFlILF3MetmJQiw7KVDee0kGRZAUJBaIKcHE1XyZMSRW6AKIqm0643mE3mUBSUAkpKLMPg6vqaqBaTpTkSSZ4VG4uZ8u/lovyHB2o0Djm818W1cmRUsogERWnS73QYXI6oVVUUe0a74aDYOl7NYT1OqNpN4nWMDEyW4Zp777mobUESpZw9nWErbbyqQX+rxiycUlcEO92UimkzIkP1NeIA1suQWRhy+dmXhPMIqUg61QZlKXjy5g3tvss6GrPMfXq7bYoiR4sF3b5H3VXo9jwSs6SIJHkqyGKoum0ofMok56NvnjCbhViGhVKmGJrOYu0jlQyvpTG/iEjyBfv3dYQesw4kSaxyNQxxbJtC5iiFRuT7tNo27Z6N7RlUPJvBYEq357JEIUs0/CgkTSJksSnyKwsNJVfZ3d1hOj8lTDLiOEdRIbJDLEPDdnTCZLlp1Ox6+GHCYLJGZilJ1WB/ewev2iJIAmxL0LSbJEsQDYvpYMF8vaCilXiuy3hxTaNdYbmMsHUF1dG4WE7oZQotT0GxdApjiWbnPP/knGrd4eDIwjFNkjBjTYJTJrT7FWy3jVer8urZgG6vQm5nhHLNd//eEZMv1owuh2RZjioUkJu0SVaWKJq12QjygjTOkE5JHuSUQpIVOlapUXerLMIAFhFlplDKHMNR6VsONV3l6fmYxk6de9/qU7k1GF3MeDF/ye/c3ePt6ZB14ZMmOVEqyDKVVRBSlFB1XXw/oSxLxuMhtmNQRjmW5TBeTbEcGyEUsjjDcy3a7R6OaXJ6ekmSSmqNOg+PtqEs2bp7yJdFxpPTc9IiYm97lz86POBnv/oVCIlWtXj5bs3RSR0/yrm+OCUvNdIwZG+ri6oUeBWTmbHkZjgllwWlUPAcg8KQuJqBa2o0WhVkKaGQPH35jr2Dg02CzzBZLKaoio4qFHpb26z9BXkuiNOS8utW3e2dDu0tg+koxvcjKAuuT8eAzmy2oN5o4FRczs8v2e632D8+5PPPv0IWkqurG6QsMHSDNA1I4k0tdSlLLMtEVUEgmU5GKJpOKUsaVZ3tioJ7f5+/+vUTyiwEoREXKYoKSSCJ8xTTFLiuRRQlnJ2dE6Ul/e02ZqYRpzGUBXmWIwRfG+9THHNzJ82LElVVUASoxte31bwkDlIkJYZlIPMMSrEx9lOiCIGuqWiqgu2aFFlOKUqECnEUoglIyhIFHaRE5pIwTmi3MizLIAwjJIAisB3nbz9Ql2nB7293mckF77IRtruhtnz5xTWloiDsko65sRgYIqHTqvHmdIyeafS3PK4HU3Qcrl8H3H3QYGWH2A2D3C9YRhmzqwJDszGrVfJ8QkTGYbdOaAWs9YK5ryJ1nfVkQa/dpSxiDk46zIM1lcLgaLvGVtNGNdrMpwGxbjBeLWm26yiZzhcfv0IrKtiqS7Vbx19AFIfEQUHdq1DGChQltiMwFJXbS5/JKqa/V8UPfPYOt7AMjVZX52owRagZbsMENafZMlgsJGWm4TY86g2DLIvI8xxZ6ixnkmqlZDGNMTQLr9Ll+sKn1dIwFW0Dakl14qXCTqtHXIYMB0uyuGByPqTIY4RnUDFsBtM5VadCJCXb2xr1hoJmlazSNf4owI8zTKUNeYGquYzGS8pcwcBDZJI3b89wmxqVXNDu95AkaOjkiYW/ThGpQ7CY0jrMOPqwxdvRnMVyQYHFbLKk33bJ8gyloTNKfBItoV6J6D20McnJkxxNszhwNNpHLQbDGXEU4xgulCWilBRJglKWCEUhLyWCkkyGCEXHNXRkKqgYFkkQsxIZhh2gqxqGJfmH//D7fPKvfgxJyKOHW3i2xpubN0hf56TfZHC9SeO1hce72ZK0UGh3WzRbLZ4+e4sEmq0ewXpN4PvUGi1WqzkqAn81BSGwbZc4XrOztQ2FynQ4Zyp0VpHk4OiAdq3Oy7NTfvDD3+OLX39Gt1+nRFKxqxu+RHRNq1php7tN36kQ10oiXzIZLeh2tzh9d0WaZLx4/oZqzaGiK5QZ9HYbJHlIKTVcU6dpu4RZgqZoXC9mKIoKpYrj2ExnM5r1Kp5d4ya4JvBXqKrOeDxCUQVpvlGidU0lT1NG4wGd/tepJAwcV0UXOncf3OHJy2eoQiXxFYZXMePRjGqjwd3jA8IgYBVElIVgOp8RhZvuJds2aTTrLJcLVNUgSTK8qoYuEyqOx7OvTtn6YB8Rl2hIbm7HxHkMKJilRZnmICRpnlLVbTRdRSKQiqCUGbWqhRlLQj9G6CqapnydTtI3/NIiJ89i8rKgyAos0/x6gxSUBZQSsqTAMmyyJENRNp5RBYUi27Sj5qJAQSBFiYKkzAukLFE0hTTNEbpC7KeUouRmMKXI+XcNABKI4r+Dsf/wsA8eMFdpVhrc+EvSJGMZpnh1D1fziMYqwaRENlTUDD54/5B4HWJJwf73TxjPI86/GhGtIg4euTw47jFbKCA8Xnx1SRFBHqxxLJOsKJlfzbEaCp5W0vZ01lUDQRuvLPnggxN2dzv4RcrZxQhdWpSZRr9eo+NZZFGOedJkth6xmksqTpNVJlllCW6RU6u2WK5WdDotXE3gWiaruWA+SVG09abrPV9TZjrd1jbmtk4crImiNft7+3A7Yzia4tg6SVjQbtYRRNiuxXSxplbTcUzB4GaE41qcnQ9JkoIkSgj9mFbLptNvkeUJbsNjNvY5H79DFDmtlkfT85COQbAuCFJ48eUZH37zGE+XBNMIyBBaRh6qeNst/MmcllVnq9lgscigyCmVkiCWNNwWTJa0WzbenoKzVSNcTZmPpwR5xN3eLvXIILUE03JJZ69KIguqVs73v3vA//SjFU9fDWg3HMJFhmFl3Lt3QLBaU7ddZCop4gRZOFxN5/Qagskoop63qLYcFtcREomCgDzn7PPfsJzPUaWkQAEUVM1AdyR2tUoShCiFQpkWuPUqBTmqWqIKuHjxDqM0CMYjCr/KxTTnztEJxnDEp5/8hJ0wo9Lo8LPpBY5bI81XvD09RRM6cRRiGAZNz2W708IPfDRTZTiacfb2DMtxKIoCRSi0200qdYd6zaHmVhhNV3TR2HIrzOczfvcb32Tw6prlIiWWIXHm8/B4n5pVZx6uuHvniHSRspxHTAcjrJ5JYpRM5mPMhk2lWSHNIhDQqNuky5AsSqnWqkSyJAkSUgsGgylSlEhdpVqvsJwHpGGAMAwmo80H2q3adHYadFvbjEcD/CBCSQtMy+Bgb5fRZES3s8WrV6/YO9wBNWU8mePqJmeX55ieSb3qkPoFfpAQ+zEvXpzS6zUwNKjVXdarkHq9hqGb5EVGve6xXvtUKh5RFJFlOXmRUXNNup02RSb4my8vcWyNqyghptykzvQNaFtRxdcvF5Wb2wWrZUBZQiEV6pU6Mk2J/GgT/aTEtgzWfkyhKcz9FVm64ZsWlEgEeQlpmmKYBkITiFRAJhGa2ECmhURVVWRRIuUmelqU5abcUQpKVSI0yEuJFKBqYJoKWSoppUJeAKpKKTJUNq8tWf4djP2/c3jCRBlSkwnHW30s+4J5ELJ72GR06xPMNDTdouJVWMwy3p4GLEdjCk1yvPOAci6xpcLxg2OCNOZqPMG8qdDp7TLNr7HdFIROnGXYZp/J6C11T8fQ67w7nzC5KWh7Fv1ek08+ecpk3UYZTciKiHazwru3EwyzR9M1MXSN0s7xRclousDQNPwE5suEhqFSNxxGozEVo8Iking7XmFqKtI0sS2Fw519Jusb/ESQRD5H/S6mIxil6uZpEN6wt9UijgPqrkFZpKTpgu0dlziPyHKFuutgWgWLWvD12cKg0AVlDkKV6NYGaFHxHKKoQIiUXr/OZJgymC0QpYlSCGaLGZW2zt2jJpoZUsYKSVqAnqOVgvkootPTsA2PJCuQ5MyzjMVqTRQU5HHKcb+NyOHd1ZKjkxbvXo2ZL1ds91xqjRZBkjFerTDUCjfXPvo+WIZGKAPiqGC3t8XteEqWWIwnGY6rMLyZYHomfl4wW2TkscDPMoJSZ+AnWAi27qh8u9vH+sokGK3JVANFCGaDc5RyU5ImVBVdFZReFdtWaVZrTOMMAWiGialaCDTIMnIpSBUV7JQgMLhX3+PjN2+4eHGxqR0WFmORcHp6wyxJKdSMSrXK1WREsIpxbAcpJMPRGCEEd45PGA5u2O32idcBs/mcdquFLAQVz2Fvu0MW+SRpTH+nx/X1JZNwxnK55M1thfUioOrVyIKUo+MTup0tVKETzubs92ssNJ8nz18wTWOOLYVOpYZuKUwXYyqVClWzyV6/zs3FkNEipuUa3AwG6JbFo+MTkjDk6LDHFy9eURQW4e0GSSl1jaIoyURJreJhavBg/4DxeIlne6xWPp12g/XK5+3bM+I0QgHuHu2TFgWaZlN1Co72DgnDkMV6yezcZzVf4ug2YZlQyIJ6q8VyuSAPM8Iw5OBon+VyzfX17WZDbjRZr5aAoFatkMQpywQm4zfsbO8wX85QdAU/zYnTFK2QaFKQlymmbmE7BmG4ec7HUY4iBKapcXsxZjFfkGSSarWCqhpk/grHNkjTgjyHjR60EZpUdUOV0jQFhU3NM8pm4CVpimmayCRFygJFVyiEglQ3nlyFDeSkzDaoEGUzKzFs8+vbsMJiFlIk5SYkgIIsN1+jiL/DDbUMlgTGgJvU5/adj2KmNFydMsuoGRYHj1XCheT2wsdUKzx/PqBXr3DUdBkvp7w5j6m1GliWimVWEYaLXmaoBaz9kvEywRYFXt3m5e05bddBNUws4XH3TgMhR/R7FX7865e8//5dEApRnFG3TdyKxsnDDjIOKQoYjxfYNZdMWOSxRxBnKIXCYbdBmod8dX7Dcp1hKxlHH+7Sbe9SBAG1rsdyuaTIIlbzGXFYYDgmL1+f0uo0SIuCOMk21cBqgevKr7+pCtvbPQQJtq4h3RKllBQJlKUgjDeVDU7VQUjJYadNuw2KsrnTyVBDK03iIKEsTdodHdvQMIyEwwcV4lLHDyWr1Qqv7aBWbFZ+jloa1PtVrs5HtBsWtlLBkhZ3etssvRWW57Jcrnn97JKjzh56uuLd2xXutkVSSKJUY36RUavkVLsmUa4gNANh2OieytXtjNmtTppreK5FsBIs/AQJzCONiqWhCQMpE8gUdFPByHWywGd/v4NmK0zGK9w9A8urUG3XWI3mhH6MJgSmpbK/tYWDitXtUMiMSRDgryOalomqSlI9p1Q0dGHj2grb7QZ+GREulwSzFTuVNkGS88nTr1gla4SiMFmllKVCJDOW/hykiq7rmKaOzEoWiyXNVos3b0+ZDke49pTuVo/9/QOiKGIxn7LTa0NWMBiNcWyPm8GY/d19nnz2OYbrwvCWYB2xvb3L73zz97hZDFDMClEU09va3uT7R0OciksRasR+wmU0p6mqVFBouybtXpPhYIrpVWltl7x4dUWYqXgVlc+fPiMXkpZbxbUahDKlv9XBsnSmkxWjyRyhCAxVw48SPn3+inUQsbO9S6fbIUszms0GmqZwfRuQlCXS0Dl7eQYo7B9sEfpL4nVEGaWUqURRDSquTeAHlIDnONxcXpKXgqwoePPmAtPScByb1TpgOlsgy5JKpcJ0tkRTNVRVQTcMFus10+kKdIFqbgRIwzURqoapquiqhm3rpGkAUlKWG99nnudMpxEgUHUFTTcIwwCJpCw2T3EpQShQlBIQG6uUqgKCNE43z/KvlXuZ52jaZuCilKArSKAsSzR1A9hOimKzmWYSVREYqk6v2+f29gJNNxAqKOLrnilZUhYAYjOB/7YD9fVgTtSJWBYJt++WPH7UJ4lioqGNiUZzp8dKKCAKCmHRsLt0m3UG4wvKXGP3eBvFWaFoAZa+ifQNbkLWQcbu/S7VrokhNBaDnPFwSpon1Ot9Bq9HRKJgZ6uNNCP+4I/ukkQ6zz+/4uLtnO88OuD+vRZBGZBnFVAFtmZiOBXenF3T77mkisHkbECnbnM6HNPq1DE8A1t3KCOd+WhFmixJ8pj52sdzq7Rb2+xu5YynC8JUcjNbsb3VYrIeg5Ih9ByvorOaxjQbNTRNRwEWi4gkkSRZSKVeYTyMCOJNYuXq3Yid3T472zXqDbg+m1O1Xea3AYqugEjQrAJVkfjrklrNohAZqqGR5TmTVcDBVkmlXiO/NqGEMI6o1z3CwMetOyRJyptnQ+7e3SOOZ6gy5+TOEbkPi+kaPw1oVG1AI84gSmKqjoeQDqVc0u1Z6GaOJMd2XDJZksQpW70WoyKHImGvV2fvoMJoNcbWDExLp3BhMoqwSwlNZ5MmGgZstzrUlTVJW7Kdb2EVj/mzf/UTlCLnw/0dFF3Q6+4xDFMyCUIziPMckSt0DI2hiGlv1zbyfJSxmqyY3IxpmRr/+rNfM1vm5KicDee4jo5um0RBxB/8/g9wWw1++qtfMjvzoZSUeU6eZjR7LYbDEft7e6AKRrMxTt2j3WqQLOfs1GssZwta/TarKAbDpd3q8sVXz1hLgRYm2NWSna0tdloNtnSVw/171Fs9erUGb9694vPXp8zDgLKU1Osey/Wc/Z0+o3fn9BpNwumUlShpNGyevbxlNQ9BNZF5ysOTfZbzOdfBirfXI9yKRxSk5M2crb0+OoLRYEKURSilhq7qrFZLqvU66/kCx7FwXZvRaICum3zw/ntMp2M0Mr79Ox/gRwFlJBncjKg4FVTDZDmZU/VcZosFtWYNmRas5kuiOCFKcjTTIMtLOv0GzabH2bubjdezKAjjGNu1yWWO53kUWUEYxgihIKTAsysUZUqYJ4RRgibBsQzyPMUwNv8iBVGcoQiwbZMsyZFAnARfQ0gUFJV/t0IKKUFsCPyF3EBNSikRAoRQKHMoi3IjgCUJQoCiakhFReY5siw3wJQ0BwSKUNANga6CKBWqTo0RJmmaY1oGWZZT5iWKoiDLTY5f/HaC9G8fqJ+8HdEJSupeFdFyuR4scF0TUxPkYcrVecIq1ZgPblnoc2pOk8HViId3T3BUg3FyzvHhPuNpwHg5Y+ewRtesoGYZ8foW3SopVoKqo7O9o1HJ9xlcztDqdRbnV9w7NFkka9brFX6k0q7YJO2cUJe8fD2gYki8ZpUYCP2MbL7AtSoUZCR5CEbCKoyQ0ub1iwH77Qad/Q01K6ykJKqCq5ZYNZW5f0tUSLIAdNOk12/i1TVMuyQRJiJLsVyB26xRsVNCv0SRKovpkigJqXg10kLBFhW+/fiQ89maNy9juv1tTDtCUWbMJxn9rRbhKqDWkdiuxzJYoisZqmoiKVmuQjq9DkkWUHcFoetgCoMsynF0gyAMSeKYhUzpdeqoVoltFpwNYtSrW0xrTb1hUOYlC7Gi0XTY69lkWkbVdXnx8pqihCgu0I05TtOgSDIq9qbvXFdMinSFa2lsbXUYDt/S3/aYTOacPLLotz0WqyVlLqlUWrQ6JlkomfhzJtdTcD0iQyMWAr3QuVzPmF9MCAtJV7exS43a3h4X6xVloYKhUmQbL2HP0nm83UCtlihOzkeP38MxbL56c0okF7y6vaGrVmnoFl++O8c2LAzT5GjniIoxRdck09trHF2j22owGk2I0hRNSsLAJ4pCXr99zZ2TA5aLgJev3tDv9zi8f5fVekrhZ3zx/DmKoTIeL1gu1piuw87+Fpfn50xHC4IgphQpW10H1ZCspxK5XmHlcNjaZTJacDq8YhZvfJHZWU4lK7HDhFUmCQvYtasc9jOyLvhLn/FsTR4EdOsNnO0Wl/KShb/plb+5ntJotXnz7pqtrV1uhwM0RSfJMhRLJ5c5ZSwJ/IClaaAqGp12F2TG7/zOt3h7+g5D5mzXPbKqzmy6oNqs4IcRtX4LQzdJxlNWSx/dtFgFITIHyzRQTBOEJIojolAhz1JM28AzXPK8JAwDPMclWIfkSU5RlGiahqYpxIGPV3U2Qk4uKNKUSKZ4lQrNZofh8AYJ6Ja1aTZIUrI4o5AbhgJiUzlSCjaR4aIAKVDEZrNVlM3WKRBINmmoLMk3gHhFIMQmKSVLQZEUSLHZOAXqJlWVl3x9K6AoJEoJb968Jkk2AlySFaiqhiLkxoalKEBJqfwdjP1pvCSdtalWa+TqCLV08ZcL7EZJCUh9yXISUG3WCBKVVtMDS+PJ89uvvXAZw8tbdveqdLea2IlHkgQotsZqAgQKIhfEsc/oZkbkKihWzM5eBcfeZhr4GLqLnmccdavkbYNW4hFFS+LQZHC54o60EZZBlhSoqkWcSPwgRIqMnYMGt4MJZW5ScatUmzVOJ0NqmkZv10Hs2ThGwXwV4bXqFMs1dtVjPl/R1EzM0iCPNvUpimOgyJLJaIKmG6zDFF0ROI6+acosEip1hyKKQCb0qib5fZ3lKMerOei6tjkNlCm6lWNaG3bnyk+pujp5oiLVkskoIY4m1BsSr25RdyRW4SJkTrstcMMK6HXmiwWT+ZzAtGg1VCpNwTJcUdVLplHC1XlEHGZU/JCry4DjewfMp2CKKtVGBUUsMRyV5Sqn3dr4ayfjANeps1zfIOICz7tLs21Sb+j4tsJs4bPOImqdKlpZsH6T0HZrrMm4OQ9p9lp8cXXFibONyDMsw6a+2+HV2TtKpSDxbNx+n3eTJZkmqVgmua5RajpCgquBUUTMg5iOaHOn3iZY+4h1zIun5/QrGgfLjNtY4hz20XEoA583V9fMByPa7QYfvvcevUaD4XjKl/Ipg8mUUhQUZNTqVbIsJwxCinJzFHv+1TM+ePSAVrPBbHpFo97GMkwa9Q5X16dMlwvcWpPjB8eIQiFar0lKhYvJgm/VHLy6RaSYLLOcn375BaWqsLN/wM8K2uQAACBmSURBVMuXr2k2qkThJnsuay41s07NaXDxZsAqjdAdk0q9yrurMUu3zvX5BZ3jPrqm0m7Uub4dUmnU+PkvP6NSsaipYNoms8GMk/eO+d3f+zZf/uYpz56+YrUK8CoVut0OgZ9yfn7G7WhGksVMPZtGxWEx9al5LpamcTOZIBWDOM7ZPdgnTCKWixWqrmFgspwt0YTEciyWM58iKbAMC900GU9ngIqmaUR+QsWpIC3JcjnHtAy8ikWRx+zubTMaTzE1g0W2pMgLsizl+vpyc/rKNk/+pMjQNQ1N0aCUqOpGo1BVKCgQigqlALEx7evqRv0vBMgyQ9M1hFDQdEGegmka5PlmCy2LjX9UsxRQIElTKEFIgaKArqkoQJyUINLNIM83OMMkyTBMjVLkKIqC0DTEb4/y//aBemerg2EbzBcrZG6j6BEyU1kscuIsR0nn1Js2Kh6Or6HEJZ3tJiUptlNhMIq5nlxgqjFp4vFVOmV0HtCuNbC7Jqv1jKP9OrptsXt4QiYzVgH8+hdv+eD9O+RZzsXtnF61SplKUKGpawhDpWyYZGWDX311iqob7BzbUMYMJzH7PZfjnS2G8wm1vV38RYTVcCjChD2vxXK6xF8FNCxBtebhqbucDxZMp0tyTyJUHUUtyYucOC3odToMhlMub0d0OhamZiH6NhVHY3rlYzkaZQGepWOZJouppKNVuZhf0e80OLu8JMtULL3OcragWs8xTJsoCjk46GIUCUVRcnXho1o6iqowHCbkqaRiVVjMAxRNYT5dYVke4XJJIQVxJpnMxyyDDNtVqHgOhqgRTSKaDZNFomO7C0zbZbJaEo1VKk2D68EE3YlIM4Nqo0bFtBjdJtxeCGrNNU6lAlrKeHJLpVYyXQ1RjSqnF2tM00YROW7VYRStmMSXHJzscnLSBmFimls4qkBXDJTUJEtdiiTBqzcxui0GIsSpmTRsqAgLoZisyNC/u4+RbHyPf/KHP0Q3BY5QWCYpUZzy3jcfUFnNQGvx/OoNP/zP/jGGbfGNO+/x//1v/wVLP0CpuqQiQ29UMIuSpR+hGzZezSQtYryKDVnBTqdFUSqcHO5yeT3gdjZFsw3+/h/8kL/8q7/i7OyCWWVFu1HhB7/zLXqtFq/fnHI5HXF894BWvcHg+obFqsBSYwxK1ucjHM3FL+d0Oy3SpM1kvmRvv4utGKxXPlFWsBgMKfMc1dS5Ob+l0+mh6SaZApVmk9HliLrXIopDtnd71GtN8jBBKCUn21v4szV7R9s8Oj5ATiKuXp2DUlKt2dw5PCZJEhxTp92s02w3sRyDOApZzBZkeYbr6mR5RJSGLH2fAsH2dp9eu029UiWXJZPxGIEg+rpyxDIsFksf5Iaoj5AoqiRJ4q9LAgNyWXLv+IDffXiXv/71E3JLIUtSptMJUioIUaIISOKMshQURUaeb7yglqNTlhuCvqII8iz72jK1qYQuKdE0gaGZKGxuoMtlgFDVzRbKhvAPAqFAmmQIsVH6VU1D0SWlsnEIlF+3nJqmQV7mFIUkyzeugc0hVmxajAu5uceWm2RUsbEjoGm/dWT+B7L8IiIPE3QpadgVrJrOzbuEPFLQXBWZRuhmiSItdBoolsHCn2OokKwmHJx0yFITqdpc3a7AgVUac/ryNR8Z++x26piFxXQ8was6VCpVbL2KERhc3UwxBRiahb8o2Lnf52Y5wp9O8GdrKtU283lEhkYWJ7hGGxDoacJ6ofDLqwtsB9rbNqpbUoYpnVaLebSk0VdJ0oDri4jlNGG0KBCJSXfnLqW6pFY1idMMVeqYRoVg7KNbNQ5PagxPz1izxDRMLL2KazjYrsfF5QirEuALn9PZgvWLKdfTBdWmT6NlIqTKZBTS324Q+GvMik4ZpATrgFVUkEU5XqVOEGcs/BjbqBNEBTKD5Tpj57DDIg0wXSg1SFPoNdr4b0AxFKJQcv+uQzKOqdgVPn1yRpkqFGlJo93GT3JyCizXxb+dYVglVc0jzVImC4HuuqyDGU+fDmi1Pdptk9FkRLNdQ9ccJrOAq+slMl3y3gdtah2dk/ea2LrCyo/wmuC5daazOaYpoVQpXYOChJNHWxSpjqpJahWBZhvky4JVnLOMBLY0mL4ZcZuVmPcfsGMYXMwHYFXo1mtUg4B0PMZcReh3DonIOSwtarqLf3HL9z74CMO0+Ob+MfePjnj36oxf/uYnmKqGlDmtRo2icNjd2iJar5kMh7Q6TYRS0GtXuTi7YXR1y2rsc3V1g+051Jo1luslb16+5FLXWIUF3/r2R7x4+iWDizMePHjEq6sbPn3xlFUQURgetf42hCmvXj6nX2vgF4J4HRLIkOvLCaalbj7wmYrXUtk5OuL1s7ebio6yQFFKapaNogqqVRdci9vRHLddJwkWfPX6JSHwv/6f/2cE4wlPPvuKWbrCti3uH9/hH/7wD/nv//v/gXfXpziuR5Kk3NxcUat61OoN1qsFYRAxHgdkpU2Wr7EdmzAI8ecr5uMJuQDdNEjybJNwyyR+GmJZJgoqeV6g6BJNFaBkNOt1ijjnajJlOFnyk1/+hma3gWFbDEYDdEMjSTbU+6zc3DE11UCWEl3X0AwVhQJZQrEJxZKmKYqqIkWJqqjIskQISSELCglplGEY+mYgK2xqpotiQ+kHNFVDFsVGubcMpFaQZBl5XkAJqrLx7CqqQpJsggKGqZGl2SZaKgSKIjZ32UJukI8CKKHI/g7R01jkTMagKT5BxaftdolLhySPsRRJr9MijVJEKcmjNZEOg5djlpOAxyeHFHKBZ9eQSky9o+JHOq6TcXDvAXkZcXM1p93pEQkb13C5ms64vByw29mi3WyTZxEmFpObmPOnE1otEKbLMCpoNuts7TgE6YTDgz3eXV2Txgmtep9myyV2BWVR4vsp07WPnjsE6YB2x0EiaTV3WF+NiDKDWsMkW4eEwZhqo8pitqBZqyCLgquLKypNFz9ZUqnq1Fot5qsFw9uAxC/Z6e+ionGnf0hcjlFrOok5x+6ZHNT6SJnQabqUMifRVPIsJQw3f5nrzSpFUrKIcuajGMdNiZKSQip0t9acnS5RS0mr3WAymmGZKqqUtCoubqXKaLRiu+ugKh5uvSAMl5i2iqkrdHf7nL0coGk5q8kYvdVimE4w15L+vo7b8EijlIrn4LgOX35xy+MP7rGz66PqCRfnl3iVBtdXU3TdYT1LqFSqrJcJ43GKZZbU7ITcjTd+yZqLLFIsK0U1DM7P1hweCJIkob9rc/50zcO9JtfDKWsVknFKZ/+YSJoUBVzOQsy6R6SVLKMh6/mEcanw4uMviKZjPNfkOo0ZL67ZObnD29kAe2nyizev+dM/+c/5/gcf8Kuf/5jh4Ip/88VnmJ0q2XIj7k2nC2pOBUOaXI0uSTLBYpaQFZviPD+J0DSdz159gWO5ZHHKzfXlJgk0nZBnGZbtcDOdcufgAPKE+XhGvdag2qjjJSGzyQovDhiNR5QI3l1fQaGhRRqLMMJrNbm9vUYIgWGquLGF7Zrcu3tMlEeMxnPyNCdHoGiCyWKBVxYksyW6a9Gqetiuzp2GR0OkLOYLXp9e4dq1jTWozPnqi19y9/4u9ahDksLrr14RhTEVt04YRihoCGGSlWtUQ6NRq1GWBbEf0O9tsVgsEGVBXhaoug5IyqJAlJB+XU5XyALL00izYmMnzEIaXoNGkuG1msR5TK9a4eLmhsV6TZZJ8nQzEFVV+XfxTVUVaJqOZVtkWYzMC1QKTENFlhpClMhy4wCQikJaFOhaia7p5Gz+L1EUKIqy2SQLIN8Mw5IS1VIRpYKqSUxXQ0sFcZBSio1SX1JsvlZuOA5pmkEp0Q2dstiEAHRDIwqTf+cwUBQFmf12H6r4bQTq/+v/7Y589smSo0c7rNIZg9sl02WBpbnYporITShN9nb2GA1mpNLg4mKIWurkWUF/q0YYhPTqGq4LUaJxOwtQNBvPliyCNYEvqKglx/t9VAuWizWOpdLpVHGaDdI4YLwQvH19ycFuG6kpTKZzDto1HFPFtB0KTKSacnY2piwzPFdHK1V01QbTYR2mlEmO19ARWoSqZKQpBLFGkkNTFzS33E19SangL0KS0MfzbEzLwuu6yDzFtKDe1Xj68hqWNhQ5qi4IgpA4Ceg3qhw/2uNsPCRYxQxvh/R2mtyOSmxL8tWTOY1aBd0BSg3Hi6l6LqqiMR0nZFlO1aswuA0xnBySCkkcIcucrT2Xbq/CarFGTS22t5vMggtcr8vTLxc8fFgBJUJTPDQz5807n5vLjFZNsHuvwfXNjPPLmINei8nshqO7u3TaFSh8BAXLpcJqHWI7GhWvzxefv8UyHW6uRggJq3WOaiicHG1jaDnNbhWFgiJfYlgGlmXgOB6GJVmuVpBX0Y2S9SJAVWE6KGg3TQpdoOWQ0CKbS7JEwz1s8cmf/4i7Ox7v/2Afr17DTATCz7h+d83pfMR8lRGFIWa9g93q8O1vvccnv/qMsmLx3vYR786v8Is1AHf3DhgMp6i2yWQesF4tefzwHmVe8POf/wLLdTk5OGI8GiFVQSoLql6N0F/yx//oH3F+fsV2bxvTtnl+dsbTZ88xdI13l1eUeUHddfEcF01VUbWcrZ02q7nPfLkAQ8Ego2F7pJnCzcxnGcdgqKRJylajuaEXKQpRkHJ0sotjWTx/fYqUgsVqyXa/T5AkHDw+RkXl+vUr9vsd9rd2ON49ZHJzzqvRkFGaIMMQ0hLTUDHVnJrbwGs0WQYZQRTx6sU7/DCk02pQSkGShlQam0y8SF3enV5Sq9fwqjWurm+IkhiEQDcMLNMiWK8p882tEwSqoWDXdKQsKEswbYf9/g7zyZw4S2k0NTI/J84SojgjijLKvEQzdGRZbEAk5cZ6KBSB9nU2PssKbNsgTlNMzUaoBXGeI/OCQgpkKTf4PEXZ3EdLEKVEN1QURSHPSrKsxDBNLMfGrlgkUUJZpqRZimO5CCFYLFeUlJRCIkqgEJuCzHTjg9Z1DeVrEIppGgR+DMgN1U18TarK5b/XO/VbB+r/4f94R64vwXFtLq4HNOseshHj+ypN/YCL8xXdTodXF1fEQcr7j0/49MtnRGswjBIdFdtW8cOELBVY1sYmcbS/TbdbJy5KtvpdkuUCXbFQbKAM0R2BmhtESU6ZB1i9Gq+eXVP1qvT7HfJ8RRhFmNJBRhEIjTAsoSiwHI11kaHkGn64wvGqvDy9ZKft8t3vvM98PqXZtTm9uGIVCmRpU4QlVkVCqmDr0GpWUQwF0xTohiAuIV3N2eo5FHbK29Mx8XRj++juWfg+uDUDNdDQjIJVFuCvl9gVjep2k4urJcEy5u2LgrsnPRbrBUWZUbFVup0KcZQgpInUHa7fjdg/3ubFl9fojoJtqGiKRq2mUKsbBKuSPFbo7Cj4UUIYeNSaBUU8p9lvk6wyWi2deSC4vFmiqIKKlqKaFc6vQ5RMR9OTjXq9p1IxLIKVShitsV2Icri6yhmP1/jrNRQqhmEjdNAUgWdo9Po20shQpEKtalGUUKu6xHGOYSqs1ivSxCYJEqbTgP5hneU6JVwV3Nl30fMStXrIOmmQJin//Nsf8X////zXHL/XxUmXNNu7jM7OiOKIbJWj1zoUeU4cRCiNNlGZc++oR4TC5eAWgcp8OWO8XtOsVdGFBoXERGU68Tm4c0h/q81nnz2h4nr8k3/wB/zi5z/l+M4Jf/3jnxFGm5/lvf1dvv/R/7+982qu6zrP8LPW7nufXtEBdoqkSFtuceJxMo5/QDL+mfFkMrIzkRLHkS0pkiUXFXYRJAjgADi97bN7ycXG+C6OJ9blea7X9TfrK+/73mGRhjRqTcIw4Xg64+mj59y78waPnjzjyy8fYZdMGpstVCRx4lMuWSjSwPVWGJqgWdLQhMYfnvZwmi243Iiblomm6+hCshgOuLG9xenFBV6Ykuk6tmnh+cVs0I9irLLFmwfXaDomcbhk7Lrcf+Mu0yTgP977EH/hIbKUH/3oe0zH50SZZKNSx9FtpKaiOhVMp8ZP//lt3NBH13QsQ+XK1Q55mjCfuEznPr3TIVmeEyfxZfEE3dBJ07TwC81FoXqToJkquilJswRSiaIqGJpKFhfvDFsUMSJxCEK9XPKkly5QhcF3FCXoeuFvqulFWy1loZ6LwgihqRiWhFwlCmPyuJAqq7IoqEEUkiH+eMZk6CpkgjhMEUKQC0Gz08JdLLEcE8VUmA5nKIogFylpCkIK8qQIzkyTvBgFKLI4w7qcxZKLwpLwMsFByuLONUmz/7Wg/smrqs6GQ7WhEK6WNJ0SJUthu2ujqPDVyyGT6RI/TOh2OuiqoD/tU63a7O12karOYpGQKzqRUOiN5+SZQrNZ5/D4lF+//3u+enrM++99yvFRn+FwyuefPWM29fAWHsezc+bxkgjQYoPNSgczj3FyFxn72ImOo5XYv3kTF8kscAlERK6mBCLHS1N64zFfPTvh9sEOuztdLk6H+AuXi+MBURBzZafGQdfGbqpcnI952evz/OUZzw5PuZgumc1XqJrOeOTiLQMGoyVaWEa3Suzf2sdqOOxu72DbEEzmnA4nHJ0P0NFp1DuoloOawP3dDRrVCmmaM5u4SF1hY7tFp9nADxQm5zAfp6gI4kzy+LNzao0a08mSVRgRBJDEglF/hudmaFIjjlROXkXUKhaaopOiEq5igtBjMveYzifYjk4cQH8MkadiqyonFwPGM8FoOsCyHYJIMr0IyTOTOLPQpM33vr/Bd79/gOXYaLrD3v4u33rrBoZl8Pp8wdNnQ/yFxJAlFuOI2Dc4fj1jtYzJ04R2bYNnX1ww6qfMhzH+LGbluRhlh0GYkG1c4+LMxXdjkonHv7z7S7w8Ynja40qtSURA+XqLG99+QKTr3HxwB+VgkyMjp3LzgFfnPT7+/CmjiykvH5+QSh0/SHBynUlvip6pqCiskphF5HPy+pSTw1PGwxH+asHh0xd4bsLHv/mSar1DHKTEXsh8vqDf7+NPZsgwQUYxepRw5+AAW6rUTYc0zdEUhZ1GDRGHuJMZ3sxjNVuyWvloUrLyItxYUu10ESLH0AV5EJGHMbHrkXtF2sT1m1dBKFQqNerlKmEY0mq3WcUBYRyRRTGn4z6/+OQTnl6M2L1+nXqjQUWUqekGRhZz+2qHn/zgO2yUa7w8veDLw1P+/dPfcTo85/T1S4avX2NWik6rU69Rr9UZDnxm85gkTWjWTN56cAtdVcjSorcVQiGOkuKmU4CUOYomyYpLJJIwQckVsjQljRL8IMALQhaLgMkkIEhSsryIDFKkRhpnpFFGEmXFb1W9XFJJQRJLhKagWNDeqnHl1j6bexUqpo1m6JTqdQxTJVMzMiUlkQmKpVFulIsoHNtAsUwsx0EqEkWVSEUQBgGaprKYLamUaliORSqKOW2aFuOEPCsKuUCgSPFHoUFOcXMqpUBRFIQQKFIpRgv8BdLTeOkhlJR6u06W54znE4yVSaNeoups89GvPuW0l6EZDt/+5i0+efqYWW+FIT0SRdLolKi1Kkw8F1MT2E6ZLMnYaLeYuwt6gwkHBy3mfkymzig3q/SGU/bSCroBm90649mMj/77IXqusrmlk600Ot1thpOAMPZ59vACYekYhoFVMZjOfHrnfVqtBp1mh8VoiYnG8UmfIEhptRyMLCOM4PTwgoOdA3JC4jCj3W0wm64YTxOWYkRNz8lFwKujAW3HJpEK2WpCqCYM5gMyJGcvJoRawnASkgmbklphNY+otsts7HQIRisWZyGLWYDt5FQqCqUNlWDpYtgmg0WAH6QE0wDLKhHEGaadk6Y+tUYNRWaoqoLvZdTrLR49HNFpB1Sw+eZ39pkvpnhhArrKYDCnVi6zchOiUEG1cqIkJiNlEfjYVZNWq8Fs7vGN7x6gKgarMOXmGwZn0xGTRUBZt1DzFF3z+au/vsXH7x/z6sVrytYm33hwDU0/YTacMBnHXJz02Nxuc9Q7J4wj9g5qlKs1hEi4961NwkDD0jW8RY6qGuzVGpRKkrKS09Ryth7s8PzpBTuNW5zOR5xNx0xPzmnHCYOqwam+4t6N65h5wpv71xmPpjiKJEXQmy2IUVB1lZJtwsY221st+mc9vnr2FbbdIFcFugE37u6jCp2230DVDH77+DGGbhPGCTe3Njl69ZpcKoxXK4ZBiJLD7MvnRGlMZgpqzSadrU0+fP8jqjWbaq3E5NKoZDAaolsqYRDizl2qpkYSRJQqJtOzC2zHIFcEcehRKlmUSxUmkyG+H3HU66PYJq9O+4SxjyFVNMPgxu1rzCeFdr3WsNjbvctqGmFJg9v7V/jZw58Thi5Oq0yrWuXdf/0FXpjjmDZ5kqJKA8do8eWjh7Q3fO7d3GM4m9K0a/zuD4/wspRqtYKWJlRqZjFzb1RxveAyBVUipMC0dOIoQtc0YjIUTSmMRZKcKMmKJZBIUVKBoRlIkSM0iWYKomVMmiZ4fnT5cyx+hXGcoJtF4J4QFK5jQqHermKUTNqbde5ffYC/yPng4085my4oOSbhKqRcKhGmCVmukKQxdsmGvBASLGcueZaTZmmxTMtiRJYjyRlfDLFsC9dzUZRiCZZFRRRNmqSXLloUC/7L+1VFEUgp0TUd102Rl6qsXP4FS6l5P0BqBpP5FFAIwpgsNajYDX7xyy/Z6pa4ccPkyasVj5+8pNvsUhYpx69P8dwQTeYkiUW5YmIoKq97p5imjiJzut0ueSIJvQSnZDCZezi5YDWZMC85aAHIhsZmbZ/ZjmA5d3FjydKVeLnLwvXpj85pNmsoaU6Sejz84hWG2WR7c5ej3jEVS2Vrt8XrwRndnTqlVKHStBlPhqSZBgqMgxWmptHplqnWTCaTAYZdJgnntPY3sB2dZqvJpD9jY3+HPAvZb7QRSorULcbHE45PR5Q1B3l501arNygbJqPjAce9PjvtDnbZwVI1bLNMFodc2a0hkpgrlSovY5fgJGM+X7K91WDY7xMmOaZhkeZh0V7JLZ4/H9DsmmzfsPBXU2ynRS4tXvd6OLaNUVbxIhep2cyXLltVjWbdQNUsAi9id6eMH7ps7TbJooDxICNYJZxnCtVmm1I9wHdXLJcBpZKBHcOdWw1eHI548WKMfT7l6vUtoq7J+cWQIBLMfJd6t4xhaJTKGqtQsgqWlGuSTc1hZdt88MEjrl7dxnQEz18estdqUA5S1IsBu0Lj1dEhpW6F8y96GBWdTNGRaolWt0VvvuI37/0ntlHh3o3bNAyLne0twijCtHWu3blFc7PK+ck5/VdnCD3l3p3reGEKhsHpy5BnT75is92gZlq0W5uwIdAMnVazwk/+8R/Y3drkn376NqP+GOX+W3z++CE1w+TBN+4xjaZEyznj/oij0YBKzUJB4rkeVauCFBpRlKGrOmW7VPzeyEnjiBvXruK6LsPRiIMrV+md9eldDMFQcKTOyPfR7RKmuSSKQuI4o3fWQysJHrzxJr/77e/pHa+g2+HB3btM+uf81ycfc7QaceXaLpk34e/fvMvD8xnHj75A1y32DrZA07h74zozb0mlrHBlr8Hffvseh08OeawpxXmgH7G7ucFotMDQVHy/iF6GvHBfEhLHcVgkKX4QIRUVqUKUxJRLDsvVCqFKdFVCCmEQYpgGQgFLN4jz6NJfNCe/XEKll5v3OE7QDQVdU4rZpYA0Ubg4n/F3P77H8Mk5L/oLfvzDt/j5e58SLVLyKCNa+eRCEoYRKIW+XlU1Qj9E5kWrH6eg6hqmZbMYT1GEZOW6+GGAhkIcJoUxSgqIy7mwWggECllrXowN8vwy8DAFAc1mA6dsctbv/f8LqiZsxpMpQSJZ+RGVcpWzwwxhj4hCH1OUsPSULM1YuQknZy8xDYMkDwAVUzOwTZsohTyKaLU1TFNnNh3jzueQRwhhMZ0u0So6URBw++A6L/p9rmx2mE6mLKMxe50yStPibDBjMvbwhz5O3aFSKVOtVxmeTVHVjK3tBqfnK4LQpFFRuX93DyURjKdz1AjMqsLKXVAyHRyrjFByev0eJdui3algmxp3bu2S5SqG2WIwnTEduFTqTfavbeHYOnGak+cxqqISRD6Vep3li+eYJQUly5itPPzIo2Lo2E2dTI8xqhF1RyG7puJNF7hBQlkFIWNSBFapihvM2dgoMx6eU6paZCloqkqWqwihM+hP2Ni3MK0YVRGUTBV/5uN7CWW9gqlJ0jRAtTXSPGV7r4FtppCH1BtVAl9hvrqgu2HSbDqs5ksCPyQmZOwqHF0MaTerqFpCp9kqDLvLFba6VZ48u8Cdp6zmGZVKn0o7463vbjGbCoIgRFMyGvU6YZBwfDyi3W5BljMZzRDpZUdjq3RbNknSptus0e4YDOKcXx8+4XZtk7eu3uRau0148hJbq3F//yb9LCLVFHS7SqPbZndvl5sb21iZwsfPn1DvVKlZDnEcoWoaP/jh3/DOuz8jjUOkVWY0GFApV5BqTqlZRld0njx/xvUb12lXW3z64Yfc2tnioNuFOMRpVJlGAZPlkr3OJh/86iN2bu1wf/cKb7/zDqalc/PadR4/fM58OuXoZR+rUuXo1QVl00CIjNZGA1PXeX16wXR5hKppuJ7HeDIjy6BaK7G1vUMyWzCbzjh93SOKIxTDQAiJdum/+c6/vYs0DISqsXJPefbsBeWyQ8su0dotYdka9+7fxSlZfPXFC1TdYDZ3GasTlonHzY0mrd0m37x3FWO6ZHB0wu07Nzg8nxIjOT455dF0QaNiUa9YLBbuZUwyQEaW5UzGE2rVKkEUEQYR6aXZ8nzlkucCERVjKkVRMXSVaqVMLnOC0CfNMnRNJUwTpLxUNOUZOSARBEFKpqfopoaqSjwvQLUUqlaJzw8/4zwM+Pyh5NbNa3z2yRMURSPwU4TM0VSVImlKEIYBhqniBxGWqaML0GyLNC+O8w3TxPdX+CsP9XKDn2dAXshJJSBVhSwvLgQUKYsE3Cy/bPsV8ixnMOhTTUqo+p/Wnv7JpdSaNWvWrPnz+T+k/mvWrFmz5s9lXVDXrFmz5mtiXVDXrFmz5mtiXVDXrFmz5mtiXVDXrFmz5mtiXVDXrFmz5mvifwCN0HNK/B3pHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_sample_images\n",
    "\n",
    "img = load_sample_images()[\"images\"][0]\n",
    "plt.imshow(img)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Original Image\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create an `Example` protobuf containing the image encoded as JPEG:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = tf.io.encode_jpeg(img)\n",
    "example_with_image = Example(features=Features(feature={\n",
    "    \"image\": Feature(bytes_list=BytesList(value=[data.numpy()]))}))\n",
    "serialized_example = example_with_image.SerializeToString()\n",
    "with tf.io.TFRecordWriter(\"my_image.tfrecord\") as f:\n",
    "    f.write(serialized_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's create a tf.data pipeline that will read this TFRecord file, parse each `Example` protobuf (in this case just one), and parse and display the image that the example contains:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_description = { \"image\": tf.io.VarLenFeature(tf.string) }\n",
    "\n",
    "def parse(serialized_example):\n",
    "    example_with_image = tf.io.parse_single_example(serialized_example,\n",
    "                                                    feature_description)\n",
    "    return tf.io.decode_jpeg(example_with_image[\"image\"].values[0])\n",
    "    # or you can use tf.io.decode_image() instead\n",
    "\n",
    "dataset = tf.data.TFRecordDataset(\"my_image.tfrecord\").map(parse)\n",
    "for image in dataset:\n",
    "    plt.imshow(image)\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or use `decode_image()` which supports BMP, GIF, JPEG and PNG formats:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensors can be serialized and parsed easily using `tf.io.serialize_tensor()` and `tf.io.parse_tensor()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=string, numpy=b'\\x08\\x01\\x12\\x08\\x12\\x02\\x08\\x03\\x12\\x02\\x08\\x02\"\\x18\\x00\\x00\\x00\\x00\\x00\\x00\\x80?\\x00\\x00\\x00@\\x00\\x00@@\\x00\\x00\\x80@\\x00\\x00\\xa0@'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor = tf.constant([[0., 1.], [2., 3.], [4., 5.]])\n",
    "serialized = tf.io.serialize_tensor(tensor)\n",
    "serialized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 2), dtype=float32, numpy=\n",
       "array([[0., 1.],\n",
       "       [2., 3.],\n",
       "       [4., 5.]], dtype=float32)>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.io.parse_tensor(serialized, out_type=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=string, numpy=\n",
       "array([b'\\x08\\t\\x12\\x08\\x12\\x02\\x08\\x02\\x12\\x02\\x08\\x01\"\\x10\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n",
       "       b'\\x08\\x07\\x12\\x04\\x12\\x02\\x08\\x02\"\\x10\\x07\\x07a@b.comc@d.com',\n",
       "       b'\\x08\\t\\x12\\x04\\x12\\x02\\x08\\x01\"\\x08\\x02\\x00\\x00\\x00\\x00\\x00\\x00\\x00'],\n",
       "      dtype=object)>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_tensor = parsed_example[\"emails\"]\n",
    "serialized_sparse = tf.io.serialize_sparse(sparse_tensor)\n",
    "serialized_sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value: \"\\010\\t\\022\\010\\022\\002\\010\\002\\022\\002\\010\\001\\\"\\020\\000\\000\\000\\000\\000\\000\\000\\000\\001\\000\\000\\000\\000\\000\\000\\000\"\n",
       "value: \"\\010\\007\\022\\004\\022\\002\\010\\002\\\"\\020\\007\\007a@b.comc@d.com\"\n",
       "value: \"\\010\\t\\022\\004\\022\\002\\010\\001\\\"\\010\\002\\000\\000\\000\\000\\000\\000\\000\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BytesList(value=serialized_sparse.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Lists of Lists Using the `SequenceExample` Protobuf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```proto\n",
    "syntax = \"proto3\";\n",
    "\n",
    "message FeatureList { repeated Feature feature = 1; };\n",
    "message FeatureLists { map<string, FeatureList> feature_list = 1; };\n",
    "message SequenceExample {\n",
    "    Features context = 1;\n",
    "    FeatureLists feature_lists = 2;\n",
    "};\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.train import FeatureList, FeatureLists, SequenceExample\n",
    "\n",
    "context = Features(feature={\n",
    "    \"author_id\": Feature(int64_list=Int64List(value=[123])),\n",
    "    \"title\": Feature(bytes_list=BytesList(value=[b\"A\", b\"desert\", b\"place\", b\".\"])),\n",
    "    \"pub_date\": Feature(int64_list=Int64List(value=[1623, 12, 25]))\n",
    "})\n",
    "\n",
    "content = [[\"When\", \"shall\", \"we\", \"three\", \"meet\", \"again\", \"?\"],\n",
    "           [\"In\", \"thunder\", \",\", \"lightning\", \",\", \"or\", \"in\", \"rain\", \"?\"]]\n",
    "comments = [[\"When\", \"the\", \"hurlyburly\", \"'s\", \"done\", \".\"],\n",
    "            [\"When\", \"the\", \"battle\", \"'s\", \"lost\", \"and\", \"won\", \".\"]]\n",
    "\n",
    "def words_to_feature(words):\n",
    "    return Feature(bytes_list=BytesList(value=[word.encode(\"utf-8\")\n",
    "                                               for word in words]))\n",
    "\n",
    "content_features = [words_to_feature(sentence) for sentence in content]\n",
    "comments_features = [words_to_feature(comment) for comment in comments]\n",
    "            \n",
    "sequence_example = SequenceExample(\n",
    "    context=context,\n",
    "    feature_lists=FeatureLists(feature_list={\n",
    "        \"content\": FeatureList(feature=content_features),\n",
    "        \"comments\": FeatureList(feature=comments_features)\n",
    "    }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "context {\n",
       "  feature {\n",
       "    key: \"author_id\"\n",
       "    value {\n",
       "      int64_list {\n",
       "        value: 123\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  feature {\n",
       "    key: \"pub_date\"\n",
       "    value {\n",
       "      int64_list {\n",
       "        value: 1623\n",
       "        value: 12\n",
       "        value: 25\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  feature {\n",
       "    key: \"title\"\n",
       "    value {\n",
       "      bytes_list {\n",
       "        value: \"A\"\n",
       "        value: \"desert\"\n",
       "        value: \"place\"\n",
       "        value: \".\"\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}\n",
       "feature_lists {\n",
       "  feature_list {\n",
       "    key: \"comments\"\n",
       "    value {\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"When\"\n",
       "          value: \"the\"\n",
       "          value: \"hurlyburly\"\n",
       "          value: \"\\'s\"\n",
       "          value: \"done\"\n",
       "          value: \".\"\n",
       "        }\n",
       "      }\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"When\"\n",
       "          value: \"the\"\n",
       "          value: \"battle\"\n",
       "          value: \"\\'s\"\n",
       "          value: \"lost\"\n",
       "          value: \"and\"\n",
       "          value: \"won\"\n",
       "          value: \".\"\n",
       "        }\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  feature_list {\n",
       "    key: \"content\"\n",
       "    value {\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"When\"\n",
       "          value: \"shall\"\n",
       "          value: \"we\"\n",
       "          value: \"three\"\n",
       "          value: \"meet\"\n",
       "          value: \"again\"\n",
       "          value: \"?\"\n",
       "        }\n",
       "      }\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"In\"\n",
       "          value: \"thunder\"\n",
       "          value: \",\"\n",
       "          value: \"lightning\"\n",
       "          value: \",\"\n",
       "          value: \"or\"\n",
       "          value: \"in\"\n",
       "          value: \"rain\"\n",
       "          value: \"?\"\n",
       "        }\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "serialized_sequence_example = sequence_example.SerializeToString()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "context_feature_descriptions = {\n",
    "    \"author_id\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n",
    "    \"title\": tf.io.VarLenFeature(tf.string),\n",
    "    \"pub_date\": tf.io.FixedLenFeature([3], tf.int64, default_value=[0, 0, 0]),\n",
    "}\n",
    "sequence_feature_descriptions = {\n",
    "    \"content\": tf.io.VarLenFeature(tf.string),\n",
    "    \"comments\": tf.io.VarLenFeature(tf.string),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "parsed_context, parsed_feature_lists = tf.io.parse_single_sequence_example(\n",
    "    serialized_sequence_example, context_feature_descriptions,\n",
    "    sequence_feature_descriptions)\n",
    "parsed_content = tf.RaggedTensor.from_sparse(parsed_feature_lists[\"content\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7f8281d310d0>,\n",
       " 'author_id': <tf.Tensor: shape=(), dtype=int64, numpy=123>,\n",
       " 'pub_date': <tf.Tensor: shape=(3,), dtype=int64, numpy=array([1623,   12,   25])>}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4,), dtype=string, numpy=array([b'A', b'desert', b'place', b'.'], dtype=object)>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_context[\"title\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'comments': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7f8281d31be0>,\n",
       " 'content': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7f8281d31280>}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_feature_lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.RaggedTensor [[b'When', b'shall', b'we', b'three', b'meet', b'again', b'?'], [b'In', b'thunder', b',', b'lightning', b',', b'or', b'in', b'rain', b'?']]>\n"
     ]
    }
   ],
   "source": [
    "print(tf.RaggedTensor.from_sparse(parsed_feature_lists[\"content\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras Preprocessing Layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `Normalization` Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "363/363 [==============================] - 0s 863us/step - loss: 2.6287 - val_loss: 1.2771\n",
      "Epoch 2/5\n",
      "363/363 [==============================] - 0s 691us/step - loss: 0.8460 - val_loss: 1.3751\n",
      "Epoch 3/5\n",
      "363/363 [==============================] - 0s 729us/step - loss: 0.6995 - val_loss: 1.2119\n",
      "Epoch 4/5\n",
      "363/363 [==============================] - 0s 716us/step - loss: 0.6606 - val_loss: 0.8703\n",
      "Epoch 5/5\n",
      "363/363 [==============================] - 0s 696us/step - loss: 0.6374 - val_loss: 0.6106\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f8241cba1f0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)  # extra code – ensures reproducibility\n",
    "norm_layer = tf.keras.layers.Normalization()\n",
    "model = tf.keras.models.Sequential([\n",
    "    norm_layer,\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(learning_rate=2e-3))\n",
    "norm_layer.adapt(X_train)  # computes the mean and variance of every feature\n",
    "model.fit(X_train, y_train, validation_data=(X_valid, y_valid), epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_layer = tf.keras.layers.Normalization()\n",
    "norm_layer.adapt(X_train)\n",
    "X_train_scaled = norm_layer(X_train)\n",
    "X_valid_scaled = norm_layer(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "363/363 [==============================] - 0s 806us/step - loss: 2.6287 - val_loss: 1.2771\n",
      "Epoch 2/5\n",
      "363/363 [==============================] - 0s 642us/step - loss: 0.8460 - val_loss: 1.3751\n",
      "Epoch 3/5\n",
      "363/363 [==============================] - 0s 647us/step - loss: 0.6995 - val_loss: 1.2119\n",
      "Epoch 4/5\n",
      "363/363 [==============================] - 0s 669us/step - loss: 0.6606 - val_loss: 0.8703\n",
      "Epoch 5/5\n",
      "363/363 [==============================] - 0s 651us/step - loss: 0.6374 - val_loss: 0.6106\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f8272695400>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)  # extra code – ensures reproducibility\n",
    "model = tf.keras.models.Sequential([tf.keras.layers.Dense(1)])\n",
    "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(learning_rate=2e-3))\n",
    "model.fit(X_train_scaled, y_train, epochs=5,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = tf.keras.Sequential([norm_layer, model])\n",
    "X_new = X_test[:3]  # pretend we have a few new instances (unscaled)\n",
    "y_pred = final_model(X_new)  # preprocesses the data and makes predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 1), dtype=float32, numpy=\n",
       "array([[1.0205517],\n",
       "       [1.5699625],\n",
       "       [2.460654 ]], dtype=float32)>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra code – creates a dataset to demo applying the norm_layer using map()\n",
    "dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train)).batch(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.map(lambda X, y: (norm_layer(X), y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(<tf.Tensor: shape=(5, 8), dtype=float32, numpy=\n",
       "  array([[-0.1939791 , -1.0778134 , -0.9433871 ,  0.0148516 ,  0.02073434,\n",
       "          -0.572917  ,  0.92925584, -1.4221287 ],\n",
       "         [ 0.7519827 , -1.8688954 ,  0.40547717, -0.23327832,  1.8614666 ,\n",
       "           0.20516507, -0.9165531 ,  1.0966995 ],\n",
       "         [-0.41469136,  0.02970134,  0.8180875 ,  1.0567819 , -0.08786613,\n",
       "          -0.29983336,  1.3087229 , -1.6970023 ],\n",
       "         [ 1.7188951 , -1.315138  ,  0.32664284, -0.21955258, -0.337921  ,\n",
       "          -0.11146677, -0.9821399 ,  0.9417729 ],\n",
       "         [-0.96207225, -1.2360299 , -0.05625898, -0.03124549,  1.709061  ,\n",
       "          -0.30257043, -0.8041173 ,  1.3265921 ]], dtype=float32)>,\n",
       "  <tf.Tensor: shape=(5, 1), dtype=float64, numpy=\n",
       "  array([[1.442],\n",
       "         [1.687],\n",
       "         [1.621],\n",
       "         [2.621],\n",
       "         [0.956]])>)]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dataset.take(1))  # extra code – shows the first batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyNormalization(tf.keras.layers.Layer):\n",
    "    def adapt(self, X):\n",
    "        self.mean_ = np.mean(X, axis=0, keepdims=True)\n",
    "        self.std_ = np.std(X, axis=0, keepdims=True)\n",
    "\n",
    "    def call(self, inputs):\n",
    "        eps = tf.keras.backend.epsilon()  # a small smoothing term\n",
    "        return (inputs - self.mean_) / (self.std_ + eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_norm_layer = MyNormalization()\n",
    "my_norm_layer.adapt(X_train)\n",
    "X_train_scaled = my_norm_layer(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `Discretization` Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(6, 1), dtype=int64, numpy=\n",
       "array([[0],\n",
       "       [2],\n",
       "       [2],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0]])>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = tf.constant([[10.], [93.], [57.], [18.], [37.], [5.]])\n",
    "discretize_layer = tf.keras.layers.Discretization(bin_boundaries=[18., 50.])\n",
    "age_categories = discretize_layer(age)\n",
    "age_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(6, 1), dtype=int64, numpy=\n",
       "array([[1],\n",
       "       [2],\n",
       "       [2],\n",
       "       [1],\n",
       "       [2],\n",
       "       [0]])>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discretize_layer = tf.keras.layers.Discretization(num_bins=3)\n",
    "discretize_layer.adapt(age)\n",
    "age_categories = discretize_layer(age)\n",
    "age_categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `CategoryEncoding` Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(6, 3), dtype=float32, numpy=\n",
       "array([[0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [1., 0., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_layer = tf.keras.layers.CategoryEncoding(num_tokens=3)\n",
    "onehot_layer(age_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 3), dtype=float32, numpy=\n",
       "array([[1., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [1., 0., 1.]], dtype=float32)>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_age_categories = np.array([[1, 0], [2, 2], [2, 0]])\n",
    "onehot_layer(two_age_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 3), dtype=float32, numpy=\n",
       "array([[1., 1., 0.],\n",
       "       [0., 0., 2.],\n",
       "       [1., 0., 1.]], dtype=float32)>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_layer = tf.keras.layers.CategoryEncoding(num_tokens=3, output_mode=\"count\")\n",
    "onehot_layer(two_age_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 6), dtype=float32, numpy=\n",
       "array([[0., 1., 0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 1.],\n",
       "       [0., 0., 1., 1., 0., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_layer = tf.keras.layers.CategoryEncoding(num_tokens=3 + 3)\n",
    "onehot_layer(two_age_categories + [0, 3])  # adds 3 to the second feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 6), dtype=float32, numpy=\n",
       "array([[0., 1., 0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 1.],\n",
       "       [0., 0., 1., 1., 0., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – shows another way to one-hot encode each feature separately\n",
    "onehot_layer = tf.keras.layers.CategoryEncoding(num_tokens=3,\n",
    "                                                output_mode=\"one_hot\")\n",
    "tf.keras.layers.concatenate([onehot_layer(cat)\n",
    "                             for cat in tf.transpose(two_age_categories)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 6), dtype=float32, numpy=\n",
       "array([[0., 1., 0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 1.],\n",
       "       [0., 0., 1., 1., 0., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – shows another way to do this, using tf.one_hot() and Flatten\n",
    "tf.keras.layers.Flatten()(tf.one_hot(two_age_categories, depth=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `StringLookup` Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 1), dtype=int64, numpy=\n",
       "array([[1],\n",
       "       [3],\n",
       "       [3],\n",
       "       [0]])>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = [\"Auckland\", \"Paris\", \"Paris\", \"San Francisco\"]\n",
    "str_lookup_layer = tf.keras.layers.StringLookup()\n",
    "str_lookup_layer.adapt(cities)\n",
    "str_lookup_layer([[\"Paris\"], [\"Auckland\"], [\"Auckland\"], [\"Montreal\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(5, 1), dtype=int64, numpy=\n",
       "array([[5],\n",
       "       [7],\n",
       "       [4],\n",
       "       [3],\n",
       "       [4]])>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str_lookup_layer = tf.keras.layers.StringLookup(num_oov_indices=5)\n",
    "str_lookup_layer.adapt(cities)\n",
    "str_lookup_layer([[\"Paris\"], [\"Auckland\"], [\"Foo\"], [\"Bar\"], [\"Baz\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 367 calls to <function PreprocessingLayer.make_adapt_function.<locals>.adapt_step at 0x7f8239426dc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 4), dtype=float32, numpy=\n",
       "array([[0., 1., 0., 0.],\n",
       "       [0., 0., 0., 1.],\n",
       "       [0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str_lookup_layer = tf.keras.layers.StringLookup(output_mode=\"one_hot\")\n",
    "str_lookup_layer.adapt(cities)\n",
    "str_lookup_layer([[\"Paris\"], [\"Auckland\"], [\"Auckland\"], [\"Montreal\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 368 calls to <function PreprocessingLayer.make_adapt_function.<locals>.adapt_step at 0x7f8239426160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 1), dtype=int64, numpy=\n",
       "array([[3],\n",
       "       [2],\n",
       "       [3],\n",
       "       [0]])>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – an example using the IntegerLookup layer\n",
    "ids = [123, 456, 789]\n",
    "int_lookup_layer = tf.keras.layers.IntegerLookup()\n",
    "int_lookup_layer.adapt(ids)\n",
    "int_lookup_layer([[123], [456], [123], [111]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `Hashing` Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 1), dtype=int64, numpy=\n",
       "array([[0],\n",
       "       [1],\n",
       "       [9],\n",
       "       [1]])>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hashing_layer = tf.keras.layers.Hashing(num_bins=10)\n",
    "hashing_layer([[\"Paris\"], [\"Tokyo\"], [\"Auckland\"], [\"Montreal\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding Categorical Features Using Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 2), dtype=float32, numpy=\n",
       "array([[-0.04663396,  0.01846724],\n",
       "       [-0.02736737, -0.02768031],\n",
       "       [-0.04663396,  0.01846724]], dtype=float32)>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "embedding_layer = tf.keras.layers.Embedding(input_dim=5, output_dim=2)\n",
    "embedding_layer(np.array([2, 4, 2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: there's a bug in Keras 2.8.0 ([issue #16101](https://github.com/keras-team/keras/issues/16101)) which prevents using a `StringLookup` layer as the first layer of a `Sequential` model. Luckily, there's a simple workaround: just add an `InputLayer` as the first layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 2), dtype=float32, numpy=\n",
       "array([[-0.01896119,  0.02223358],\n",
       "       [ 0.02401174,  0.03724445],\n",
       "       [-0.01896119,  0.02223358]], dtype=float32)>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "ocean_prox = [\"<1H OCEAN\", \"INLAND\", \"NEAR OCEAN\", \"NEAR BAY\", \"ISLAND\"]\n",
    "str_lookup_layer = tf.keras.layers.StringLookup()\n",
    "str_lookup_layer.adapt(ocean_prox)\n",
    "lookup_and_embed = tf.keras.Sequential([\n",
    "    tf.keras.layers.InputLayer(input_shape=[], dtype=tf.string),  # WORKAROUND\n",
    "    str_lookup_layer,\n",
    "    tf.keras.layers.Embedding(input_dim=str_lookup_layer.vocabulary_size(),\n",
    "                              output_dim=2)\n",
    "])\n",
    "lookup_and_embed(np.array([\"<1H OCEAN\", \"ISLAND\", \"<1H OCEAN\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "313/313 [==============================] - 0s 903us/step - loss: 0.1491 - val_loss: 0.1188\n",
      "Epoch 2/5\n",
      "313/313 [==============================] - 0s 723us/step - loss: 0.1069 - val_loss: 0.0967\n",
      "Epoch 3/5\n",
      "313/313 [==============================] - 0s 667us/step - loss: 0.0924 - val_loss: 0.0886\n",
      "Epoch 4/5\n",
      "313/313 [==============================] - 0s 677us/step - loss: 0.0870 - val_loss: 0.0856\n",
      "Epoch 5/5\n",
      "313/313 [==============================] - 0s 671us/step - loss: 0.0849 - val_loss: 0.0843\n"
     ]
    }
   ],
   "source": [
    "# extra code – set seeds and generates fake random data\n",
    "# (feel free to load the real dataset if you prefer)\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "X_train_num = np.random.rand(10_000, 8)\n",
    "X_train_cat = np.random.choice(ocean_prox, size=10_000)\n",
    "y_train = np.random.rand(10_000, 1)\n",
    "X_valid_num = np.random.rand(2_000, 8)\n",
    "X_valid_cat = np.random.choice(ocean_prox, size=2_000)\n",
    "y_valid = np.random.rand(2_000, 1)\n",
    "\n",
    "num_input = tf.keras.layers.Input(shape=[8], name=\"num\")\n",
    "cat_input = tf.keras.layers.Input(shape=[], dtype=tf.string, name=\"cat\")\n",
    "cat_embeddings = lookup_and_embed(cat_input) \n",
    "encoded_inputs = tf.keras.layers.concatenate([num_input, cat_embeddings])\n",
    "outputs = tf.keras.layers.Dense(1)(encoded_inputs)\n",
    "model = tf.keras.models.Model(inputs=[num_input, cat_input], outputs=[outputs])\n",
    "model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
    "history = model.fit((X_train_num, X_train_cat), y_train, epochs=5,\n",
    "                    validation_data=((X_valid_num, X_valid_cat), y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "313/313 [==============================] - 1s 1ms/step - loss: 0.0839 - val_loss: 0.0838\n",
      "Epoch 2/5\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.0835 - val_loss: 0.0835\n",
      "Epoch 3/5\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.0832 - val_loss: 0.0833\n",
      "Epoch 4/5\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.0831 - val_loss: 0.0832\n",
      "Epoch 5/5\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.0830 - val_loss: 0.0831\n"
     ]
    }
   ],
   "source": [
    "# extra code – shows that the model can also be trained using a tf.data.Dataset\n",
    "train_set = tf.data.Dataset.from_tensor_slices(\n",
    "    ((X_train_num, X_train_cat), y_train)).batch(32)\n",
    "valid_set = tf.data.Dataset.from_tensor_slices(\n",
    "    ((X_valid_num, X_valid_cat), y_valid)).batch(32)\n",
    "history = model.fit(train_set, epochs=5,\n",
    "                    validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "313/313 [==============================] - 1s 1ms/step - loss: 0.0829 - val_loss: 0.0830\n",
      "Epoch 2/5\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.0829 - val_loss: 0.0830\n",
      "Epoch 3/5\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.0828 - val_loss: 0.0830\n",
      "Epoch 4/5\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.0828 - val_loss: 0.0829\n",
      "Epoch 5/5\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.0828 - val_loss: 0.0829\n"
     ]
    }
   ],
   "source": [
    "# extra code – shows that the dataset can contain dictionaries\n",
    "train_set = tf.data.Dataset.from_tensor_slices(\n",
    "    ({\"num\": X_train_num, \"cat\": X_train_cat}, y_train)).batch(32)\n",
    "valid_set = tf.data.Dataset.from_tensor_slices(\n",
    "    ({\"num\": X_valid_num, \"cat\": X_valid_cat}, y_valid)).batch(32)\n",
    "history = model.fit(train_set, epochs=5, validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 4), dtype=int64, numpy=\n",
       "array([[2, 1, 0, 0],\n",
       "       [6, 2, 1, 2]])>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = [\"To be\", \"!(to be)\", \"That's the question\", \"Be, be, be.\"]\n",
    "text_vec_layer = tf.keras.layers.TextVectorization()\n",
    "text_vec_layer.adapt(train_data)\n",
    "text_vec_layer([\"Be good!\", \"Question: be or be?\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.RaggedTensor [[2, 1], [6, 2, 1, 2]]>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vec_layer = tf.keras.layers.TextVectorization(ragged=True)\n",
    "text_vec_layer.adapt(train_data)\n",
    "text_vec_layer([\"Be good!\", \"Question: be or be?\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 6), dtype=float32, numpy=\n",
       "array([[0.96725637, 0.6931472 , 0.        , 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.96725637, 1.3862944 , 0.        , 0.        , 0.        ,\n",
       "        1.0986123 ]], dtype=float32)>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vec_layer = tf.keras.layers.TextVectorization(output_mode=\"tf_idf\")\n",
    "text_vec_layer.adapt(train_data)\n",
    "text_vec_layer([\"Be good!\", \"Question: be or be?\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3862943611198906"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * np.log(1 + 4 / (1 + 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0986122886681098"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 * np.log(1 + 4 / (1 + 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Using Pretrained Language Model Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.25,  0.28,  0.01,  0.1 ,  0.14,  0.16,  0.25,  0.02,  0.07,\n",
       "         0.13, -0.19,  0.06, -0.04, -0.07,  0.  , -0.08, -0.14, -0.16,\n",
       "         0.02, -0.24,  0.16, -0.16, -0.03,  0.03, -0.14,  0.03, -0.09,\n",
       "        -0.04, -0.14, -0.19,  0.07,  0.15,  0.18, -0.23, -0.07, -0.08,\n",
       "         0.01, -0.01,  0.09,  0.14, -0.03,  0.03,  0.08,  0.1 , -0.01,\n",
       "        -0.03, -0.07, -0.1 ,  0.05,  0.31],\n",
       "       [-0.2 ,  0.2 , -0.08,  0.02,  0.19,  0.05,  0.22, -0.09,  0.02,\n",
       "         0.19, -0.02, -0.14, -0.2 , -0.04,  0.01, -0.07, -0.22, -0.1 ,\n",
       "         0.16, -0.44,  0.31, -0.1 ,  0.23,  0.15, -0.05,  0.15, -0.13,\n",
       "        -0.04, -0.08, -0.16, -0.1 ,  0.13,  0.13, -0.18, -0.04,  0.03,\n",
       "        -0.1 , -0.07,  0.07,  0.03, -0.08,  0.02,  0.05,  0.07, -0.14,\n",
       "        -0.1 , -0.18, -0.13, -0.04,  0.15]], dtype=float32)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow_hub as hub\n",
    "\n",
    "hub_layer = hub.KerasLayer(\"https://tfhub.dev/google/nnlm-en-dim50/2\")\n",
    "sentence_embeddings = hub_layer(tf.constant([\"To be\", \"Not to be\"]))\n",
    "sentence_embeddings.numpy().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Preprocessing Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_sample_images\n",
    "\n",
    "images = load_sample_images()[\"images\"]\n",
    "crop_image_layer = tf.keras.layers.CenterCrop(height=100, width=100)\n",
    "cropped_images = crop_image_layer(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(images[0])\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cropped_images[0] / 255)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "datasets = tfds.load(name=\"mnist\")\n",
    "mnist_train, mnist_test = datasets[\"train\"], datasets[\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "for batch in mnist_train.shuffle(10_000, seed=42).batch(32).prefetch(1):\n",
    "    images = batch[\"image\"]\n",
    "    labels = batch[\"label\"]\n",
    "    # [...] do something with the images and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_train = mnist_train.shuffle(10_000, seed=42).batch(32)\n",
    "mnist_train = mnist_train.map(lambda items: (items[\"image\"], items[\"label\"]))\n",
    "mnist_train = mnist_train.prefetch(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1688/1688 [==============================] - 2s 1ms/step - loss: 9.6765 - accuracy: 0.8348 - val_loss: 5.8894 - val_accuracy: 0.8835\n",
      "Epoch 2/5\n",
      "1688/1688 [==============================] - 1s 796us/step - loss: 5.6335 - accuracy: 0.8785 - val_loss: 5.1325 - val_accuracy: 0.8800\n",
      "Epoch 3/5\n",
      "1688/1688 [==============================] - 1s 793us/step - loss: 5.0494 - accuracy: 0.8832 - val_loss: 5.3470 - val_accuracy: 0.8938\n",
      "Epoch 4/5\n",
      "1688/1688 [==============================] - 1s 767us/step - loss: 4.8245 - accuracy: 0.8867 - val_loss: 5.2491 - val_accuracy: 0.8870\n",
      "Epoch 5/5\n",
      "1688/1688 [==============================] - 1s 765us/step - loss: 4.6808 - accuracy: 0.8871 - val_loss: 5.1136 - val_accuracy: 0.8960\n",
      "313/313 [==============================] - 0s 769us/step - loss: 4.6993 - accuracy: 0.8975\n"
     ]
    }
   ],
   "source": [
    "train_set, valid_set, test_set = tfds.load(\n",
    "    name=\"mnist\",\n",
    "    split=[\"train[:90%]\", \"train[90%:]\", \"test\"],\n",
    "    as_supervised=True\n",
    ")\n",
    "train_set = train_set.shuffle(10_000, seed=42).batch(32).prefetch(1)\n",
    "valid_set = valid_set.batch(32).cache()\n",
    "test_set = test_set.batch(32).cache()\n",
    "tf.random.set_seed(42)\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\",\n",
    "              metrics=[\"accuracy\"])\n",
    "history = model.fit(train_set, validation_data=valid_set, epochs=5)\n",
    "test_loss, test_accuracy = model.evaluate(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Exercises\n",
    "\n",
    "## 1. to 8.\n",
    "1. Ingesting a large dataset and preprocessing it efficiently can be a complex engineering challenge. The Data API makes it fairly simple. It offers many features, including loading data from various sources (such as text or binary files), reading data in parallel from multiple sources, transforming it, interleaving the records, shuffling the data, batching it, and prefetching it.\n",
    "2. Splitting a large dataset into multiple files makes it possible to shuffle it at a coarse level before shuffling it at a finer level using a shuffling buffer. It also makes it possible to handle huge datasets that do not fit on a single machine. It's also simpler to manipulate thousands of small files rather than one huge file; for example, it's easier to split the data into multiple subsets. Lastly, if the data is split across multiple files spread across multiple servers, it is possible to download several files from different servers simultaneously, which improves the bandwidth usage.\n",
    "3. You can use TensorBoard to visualize profiling data: if the GPU is not fully utilized then your input pipeline is likely to be the bottleneck. You can fix it by making sure it reads and preprocesses the data in multiple threads in parallel, and ensuring it prefetches a few batches. If this is insufficient to get your GPU to 100% usage during training, make sure your preprocessing code is optimized. You can also try saving the dataset into multiple TFRecord files, and if necessary perform some of the preprocessing ahead of time so that it does not need to be done on the fly during training (TF Transform can help with this). If necessary, use a machine with more CPU and RAM, and ensure that the GPU bandwidth is large enough.\n",
    "4. A TFRecord file is composed of a sequence of arbitrary binary records: you can store absolutely any binary data you want in each record. However, in practice most TFRecord files contain sequences of serialized protocol buffers. This makes it possible to benefit from the advantages of protocol buffers, such as the fact that they can be read easily across multiple platforms and languages and their definition can be updated later in a backward-compatible way.\n",
    "5. The `Example` protobuf format has the advantage that TensorFlow provides some operations to parse it (the `tf.io.parse`*`example()` functions) without you having to define your own format. It is sufficiently flexible to represent instances in most datasets. However, if it does not cover your use case, you can define your own protocol buffer, compile it using `protoc` (setting the `--descriptor_set_out` and `--include_imports` arguments to export the protobuf descriptor), and use the `tf.io.decode_proto()` function to parse the serialized protobufs (see the \"Custom protobuf\" section of the notebook for an example). It's more complicated, and it requires deploying the descriptor along with the model, but it can be done.\n",
    "6. When using TFRecords, you will generally want to activate compression if the TFRecord files will need to be downloaded by the training script, as compression will make files smaller and thus reduce download time. But if the files are located on the same machine as the training script, it's usually preferable to leave compression off, to avoid wasting CPU for decompression.\n",
    "7. Let's look at the pros and cons of each preprocessing option:\n",
    "    * If you preprocess the data when creating the data files, the training script will run faster, since it will not have to perform preprocessing on the fly. In some cases, the preprocessed data will also be much smaller than the original data, so you can save some space and speed up downloads. It may also be helpful to materialize the preprocessed data, for example to inspect it or archive it. However, this approach has a few cons. First, it's not easy to experiment with various preprocessing logics if you need to generate a preprocessed dataset for each variant. Second, if you want to perform data augmentation, you have to materialize many variants of your dataset, which will use a large amount of disk space and take a lot of time to generate. Lastly, the trained model will expect preprocessed data, so you will have to add preprocessing code in your application before it calls the model. There's a risk of code duplication and preprocessing mismatch in this case.\n",
    "    * If the data is preprocessed with the tf.data pipeline, it's much easier to tweak the preprocessing logic and apply data augmentation. Also, tf.data makes it easy to build highly efficient preprocessing pipelines (e.g., with multithreading and prefetching). However, preprocessing the data this way will slow down training. Moreover, each training instance will be preprocessed once per epoch rather than just once if the data was preprocessed when creating the data files. Well, unless the dataset fits in RAM and you can cache it using the dataset's `cache()` method. Lastly, the trained model will still expect preprocessed data. But if you use preprocessing layers in your tf.data pipeline to handle the preprocessing step, then you can just reuse these layers in your final model (adding them after training), to avoid code duplication and preprocessing mismatch.\n",
    "    * If you add preprocessing layers to your model, you will only have to write the preprocessing code once for both training and inference. If your model needs to be deployed to many different platforms, you will not need to write the preprocessing code multiple times. Plus, you will not run the risk of using the wrong preprocessing logic for your model, since it will be part of the model. On the downside, preprocessing the data on the fly during training will slow things down, and each instance will be preprocessed once per epoch.\n",
    "8. Let's look at how to encode categorical text features and text:\n",
    "    * To encode a categorical feature that has a natural order, such as a movie rating (e.g., \"bad,\" \"average,\" \"good\"), the simplest option is to use ordinal encoding: sort the categories in their natural order and map each category to its rank (e.g., \"bad\" maps to 0, \"average\" maps to 1, and \"good\" maps to 2). However, most categorical features don't have such a natural order. For example, there's no natural order for professions or countries. In this case, you can use one-hot encoding, or embeddings if there are many categories. With Keras, the `StringLookup` layer can be used for ordinal encoding (using the default `output_mode=\"int\"`), or one-hot encoding (using `output_mode=\"one_hot\"`). It can also perform multi-hot encoding (using `output_mode=\"multi_hot\"`) if you want to encode multiple categorical text features together, assuming they share the same categories and it doesn't matter which feature contributed which category. For trainable embeddings, you must first use the `StringLookup` layer to produce an ordinal encoding, then use the `Embedding` layer.\n",
    "    * For text, the `TextVectorization` layer is easy to use and it can work well for simple tasks, or you can use TF Text for more advanced features. However, you'll often want to use pretrained language models, which you can obtain using tools like TF Hub or Hugging Face's Transformers library. These last two options are discussed in Chapter 16."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.\n",
    "### a.\n",
    "_Exercise: Load the Fashion MNIST dataset (introduced in Chapter 10); split it into a training set, a validation set, and a test set; shuffle the training set; and save each dataset to multiple TFRecord files. Each record should be a serialized `Example` protobuf with two features: the serialized image (use `tf.io.serialize_tensor()` to serialize each image), and the label. Note: for large images, you could use `tf.io.encode_jpeg()` instead. This would save a lot of space, but it would lose a bit of image quality._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train_full, y_train_full), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
    "X_valid, X_train = X_train_full[:5000], X_train_full[5000:]\n",
    "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-20 15:27:32.431462: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "train_set = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
    "train_set = train_set.shuffle(len(X_train), seed=42)\n",
    "valid_set = tf.data.Dataset.from_tensor_slices((X_valid, y_valid))\n",
    "test_set = tf.data.Dataset.from_tensor_slices((X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_example(image, label):\n",
    "    image_data = tf.io.serialize_tensor(image)\n",
    "    #image_data = tf.io.encode_jpeg(image[..., np.newaxis])\n",
    "    return Example(\n",
    "        features=Features(\n",
    "            feature={\n",
    "                \"image\": Feature(bytes_list=BytesList(value=[image_data.numpy()])),\n",
    "                \"label\": Feature(int64_list=Int64List(value=[label])),\n",
    "            }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features {\n",
      "  feature {\n",
      "    key: \"image\"\n",
      "    value {\n",
      "      bytes_list {\n",
      "        value: \"\\010\\004\\022\\010\\022\\002\\010\\034\\022\\002\\010\\034\\\"\\220\\006\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\000\\000\\rI\\000\\000\\001\\004\\000\\000\\000\\000\\001\\001\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\003\\000$\\210\\177>6\\000\\000\\000\\001\\003\\004\\000\\000\\003\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\006\\000f\\314\\260\\206\\220{\\027\\000\\000\\000\\000\\014\\n\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\233\\354\\317\\262k\\234\\241m@\\027M\\202H\\017\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\000E\\317\\337\\332\\330\\330\\243\\177yz\\222\\215X\\254B\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\001\\001\\000\\310\\350\\350\\351\\345\\337\\337\\327\\325\\244\\177{\\304\\345\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\267\\341\\330\\337\\344\\353\\343\\340\\336\\340\\335\\337\\365\\255\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\301\\344\\332\\325\\306\\264\\324\\322\\323\\325\\337\\334\\363\\312\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\003\\000\\014\\333\\334\\324\\332\\300\\251\\343\\320\\332\\340\\324\\342\\305\\3214\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\006\\000c\\364\\336\\334\\332\\313\\306\\335\\327\\325\\336\\334\\365w\\2478\\000\\000\\000\\000\\000\\000\\000\\000\\000\\004\\000\\0007\\354\\344\\346\\344\\360\\350\\325\\332\\337\\352\\331\\331\\321\\\\\\000\\000\\000\\001\\004\\006\\007\\002\\000\\000\\000\\000\\000\\355\\342\\331\\337\\336\\333\\336\\335\\330\\337\\345\\327\\332\\377M\\000\\000\\003\\000\\000\\000\\000\\000\\000\\000>\\221\\314\\344\\317\\325\\335\\332\\320\\323\\332\\340\\337\\333\\327\\340\\364\\237\\000\\000\\000\\000\\000\\022,Rk\\275\\344\\334\\336\\331\\342\\310\\315\\323\\346\\340\\352\\260\\274\\372\\370\\351\\356\\327\\000\\0009\\273\\320\\340\\335\\340\\320\\314\\326\\320\\321\\310\\237\\365\\301\\316\\337\\377\\377\\335\\352\\335\\323\\334\\350\\366\\000\\003\\312\\344\\340\\335\\323\\323\\326\\315\\315\\315\\334\\360P\\226\\377\\345\\335\\274\\232\\277\\322\\314\\321\\336\\344\\341\\000b\\351\\306\\322\\336\\345\\345\\352\\371\\334\\302\\327\\331\\361AIju\\250\\333\\335\\327\\331\\337\\337\\340\\345\\035K\\314\\324\\314\\301\\315\\323\\341\\330\\271\\305\\316\\306\\325\\360\\303\\343\\365\\357\\337\\332\\324\\321\\336\\334\\335\\346C0\\313\\267\\302\\325\\305\\271\\276\\302\\300\\312\\326\\333\\335\\334\\354\\341\\330\\307\\316\\272\\265\\261\\254\\265\\315\\316s\\000z\\333\\301\\263\\253\\267\\304\\314\\322\\325\\317\\323\\322\\310\\304\\302\\277\\303\\277\\306\\300\\260\\234\\247\\261\\322\\\\\\000\\000J\\275\\324\\277\\257\\254\\257\\265\\271\\274\\275\\274\\301\\306\\314\\321\\322\\322\\323\\274\\274\\302\\300\\330\\252\\000\\002\\000\\000\\000B\\310\\336\\355\\357\\362\\366\\363\\364\\335\\334\\301\\277\\263\\266\\266\\265\\260\\246\\250c:\\000\\000\\000\\000\\000\\000\\000\\000\\000(=,H)#\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"label\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 9\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for image, label in valid_set.take(1):\n",
    "    print(create_example(image, label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function saves a given dataset to a set of TFRecord files. The examples are written to the files in a round-robin fashion. To do this, we enumerate all the examples using the `dataset.enumerate()` method, and we compute `index % n_shards` to decide which file to write to. We use the standard `contextlib.ExitStack` class to make sure that all writers are properly closed whether or not an I/O error occurs while writing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from contextlib import ExitStack\n",
    "\n",
    "def write_tfrecords(name, dataset, n_shards=10):\n",
    "    paths = [\"{}.tfrecord-{:05d}-of-{:05d}\".format(name, index, n_shards)\n",
    "             for index in range(n_shards)]\n",
    "    with ExitStack() as stack:\n",
    "        writers = [stack.enter_context(tf.io.TFRecordWriter(path))\n",
    "                   for path in paths]\n",
    "        for index, (image, label) in dataset.enumerate():\n",
    "            shard = index % n_shards\n",
    "            example = create_example(image, label)\n",
    "            writers[shard].write(example.SerializeToString())\n",
    "    return paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_filepaths = write_tfrecords(\"my_fashion_mnist.train\", train_set)\n",
    "valid_filepaths = write_tfrecords(\"my_fashion_mnist.valid\", valid_set)\n",
    "test_filepaths = write_tfrecords(\"my_fashion_mnist.test\", test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b.\n",
    "_Exercise: Then use tf.data to create an efficient dataset for each set. Finally, use a Keras model to train these datasets, including a preprocessing layer to standardize each input feature. Try to make the input pipeline as efficient as possible, using TensorBoard to visualize profiling data._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(tfrecord):\n",
    "    feature_descriptions = {\n",
    "        \"image\": tf.io.FixedLenFeature([], tf.string, default_value=\"\"),\n",
    "        \"label\": tf.io.FixedLenFeature([], tf.int64, default_value=-1)\n",
    "    }\n",
    "    example = tf.io.parse_single_example(tfrecord, feature_descriptions)\n",
    "    image = tf.io.parse_tensor(example[\"image\"], out_type=tf.uint8)\n",
    "    #image = tf.io.decode_jpeg(example[\"image\"])\n",
    "    image = tf.reshape(image, shape=[28, 28])\n",
    "    return image, example[\"label\"]\n",
    "\n",
    "def mnist_dataset(filepaths, n_read_threads=5, shuffle_buffer_size=None,\n",
    "                  n_parse_threads=5, batch_size=32, cache=True):\n",
    "    dataset = tf.data.TFRecordDataset(filepaths,\n",
    "                                      num_parallel_reads=n_read_threads)\n",
    "    if cache:\n",
    "        dataset = dataset.cache()\n",
    "    if shuffle_buffer_size:\n",
    "        dataset = dataset.shuffle(shuffle_buffer_size)\n",
    "    dataset = dataset.map(preprocess, num_parallel_calls=n_parse_threads)\n",
    "    dataset = dataset.batch(batch_size)\n",
    "    return dataset.prefetch(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = mnist_dataset(train_filepaths, shuffle_buffer_size=60000)\n",
    "valid_set = mnist_dataset(valid_filepaths)\n",
    "test_set = mnist_dataset(test_filepaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X, y in train_set.take(1):\n",
    "    for i in range(5):\n",
    "        plt.subplot(1, 5, i + 1)\n",
    "        plt.imshow(X[i].numpy(), cmap=\"binary\")\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(str(y[i].numpy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "standardization = tf.keras.layers.Normalization(input_shape=[28, 28])\n",
    "\n",
    "sample_image_batches = train_set.take(100).map(lambda image, label: image)\n",
    "sample_images = np.concatenate(list(sample_image_batches.as_numpy_iterator()),\n",
    "                               axis=0).astype(np.float32)\n",
    "standardization.adapt(sample_images)\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    standardization,\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=\"nadam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-20 15:30:49.689831: I tensorflow/core/profiler/lib/profiler_session.cc:110] Profiler session initializing.\n",
      "2022-02-20 15:30:49.689858: I tensorflow/core/profiler/lib/profiler_session.cc:125] Profiler session started.\n",
      "2022-02-20 15:30:49.691427: I tensorflow/core/profiler/lib/profiler_session.cc:143] Profiler session tear down.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "     59/Unknown - 1s 3ms/step - loss: 0.9230 - accuracy: 0.6817"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-20 15:30:50.428921: I tensorflow/core/profiler/lib/profiler_session.cc:110] Profiler session initializing.\n",
      "2022-02-20 15:30:50.428945: I tensorflow/core/profiler/lib/profiler_session.cc:125] Profiler session started.\n",
      "2022-02-20 15:30:50.433359: I tensorflow/core/profiler/lib/profiler_session.cc:67] Profiler session collecting data.\n",
      "2022-02-20 15:30:50.446608: I tensorflow/core/profiler/lib/profiler_session.cc:143] Profiler session tear down.\n",
      "2022-02-20 15:30:50.461272: I tensorflow/core/profiler/rpc/client/save_profile.cc:136] Creating directory: my_logs/run_/20220220_153049/plugins/profile/2022_02_20_15_30_50\n",
      "\n",
      "2022-02-20 15:30:50.465450: I tensorflow/core/profiler/rpc/client/save_profile.cc:142] Dumped gzipped tool data for trace.json.gz to my_logs/run_/20220220_153049/plugins/profile/2022_02_20_15_30_50/kiwimac.trace.json.gz\n",
      "2022-02-20 15:30:50.480245: I tensorflow/core/profiler/rpc/client/save_profile.cc:136] Creating directory: my_logs/run_/20220220_153049/plugins/profile/2022_02_20_15_30_50\n",
      "\n",
      "2022-02-20 15:30:50.480582: I tensorflow/core/profiler/rpc/client/save_profile.cc:142] Dumped gzipped tool data for memory_profile.json.gz to my_logs/run_/20220220_153049/plugins/profile/2022_02_20_15_30_50/kiwimac.memory_profile.json.gz\n",
      "2022-02-20 15:30:50.482034: I tensorflow/core/profiler/rpc/client/capture_profile.cc:251] Creating directory: my_logs/run_/20220220_153049/plugins/profile/2022_02_20_15_30_50\n",
      "Dumped tool data for xplane.pb to my_logs/run_/20220220_153049/plugins/profile/2022_02_20_15_30_50/kiwimac.xplane.pb\n",
      "Dumped tool data for overview_page.pb to my_logs/run_/20220220_153049/plugins/profile/2022_02_20_15_30_50/kiwimac.overview_page.pb\n",
      "Dumped tool data for input_pipeline.pb to my_logs/run_/20220220_153049/plugins/profile/2022_02_20_15_30_50/kiwimac.input_pipeline.pb\n",
      "Dumped tool data for tensorflow_stats.pb to my_logs/run_/20220220_153049/plugins/profile/2022_02_20_15_30_50/kiwimac.tensorflow_stats.pb\n",
      "Dumped tool data for kernel_stats.pb to my_logs/run_/20220220_153049/plugins/profile/2022_02_20_15_30_50/kiwimac.kernel_stats.pb\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1719/1719 [==============================] - 5s 2ms/step - loss: 0.4437 - accuracy: 0.8402 - val_loss: 0.3649 - val_accuracy: 0.8682\n",
      "Epoch 2/5\n",
      "1719/1719 [==============================] - 4s 2ms/step - loss: 0.3333 - accuracy: 0.8775 - val_loss: 0.3346 - val_accuracy: 0.8790\n",
      "Epoch 3/5\n",
      "1719/1719 [==============================] - 4s 2ms/step - loss: 0.2970 - accuracy: 0.8905 - val_loss: 0.3235 - val_accuracy: 0.8866\n",
      "Epoch 4/5\n",
      "1719/1719 [==============================] - 4s 2ms/step - loss: 0.2723 - accuracy: 0.8995 - val_loss: 0.3308 - val_accuracy: 0.8888\n",
      "Epoch 5/5\n",
      "1719/1719 [==============================] - 4s 2ms/step - loss: 0.2534 - accuracy: 0.9047 - val_loss: 0.3174 - val_accuracy: 0.8916\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa3e08af370>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "logs = Path() / \"my_logs\" / \"run_\" / datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
    "\n",
    "tensorboard_cb = tf.keras.callbacks.TensorBoard(\n",
    "    log_dir=logs, histogram_freq=1, profile_batch=10)\n",
    "\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set,\n",
    "          callbacks=[tensorboard_cb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-a8e8524a8e4cf37d\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-a8e8524a8e4cf37d\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6007;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir=./my_logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10.\n",
    "_Exercise: In this exercise you will download a dataset, split it, create a `tf.data.Dataset` to load it and preprocess it efficiently, then build and train a binary classification model containing an `Embedding` layer._\n",
    "\n",
    "### a.\n",
    "_Exercise: Download the [Large Movie Review Dataset](https://homl.info/imdb), which contains 50,000 movies reviews from the [Internet Movie Database](https://imdb.com/). The data is organized in two directories, `train` and `test`, each containing a `pos` subdirectory with 12,500 positive reviews and a `neg` subdirectory with 12,500 negative reviews. Each review is stored in a separate text file. There are other files and folders (including preprocessed bag-of-words), but we will ignore them in this exercise._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
      "84131840/84125825 [==============================] - 27s 0us/step\n",
      "84140032/84125825 [==============================] - 27s 0us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('datasets/aclImdb')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "root = \"https://ai.stanford.edu/~amaas/data/sentiment/\"\n",
    "filename = \"aclImdb_v1.tar.gz\"\n",
    "filepath = tf.keras.utils.get_file(filename, root + filename, extract=True,\n",
    "                                   cache_dir=\".\")\n",
    "path = Path(filepath).with_name(\"aclImdb\")\n",
    "path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define a `tree()` function to view the structure of the `aclImdb` directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tree(path, level=0, indent=4, max_files=3):\n",
    "    if level == 0:\n",
    "        print(f\"{path}/\")\n",
    "        level += 1\n",
    "    sub_paths = sorted(path.iterdir())\n",
    "    sub_dirs = [sub_path for sub_path in sub_paths if sub_path.is_dir()]\n",
    "    filepaths = [sub_path for sub_path in sub_paths if not sub_path in sub_dirs]\n",
    "    indent_str = \" \" * indent * level\n",
    "    for sub_dir in sub_dirs:\n",
    "        print(f\"{indent_str}{sub_dir.name}/\")\n",
    "        tree(sub_dir,  level + 1, indent)\n",
    "    for filepath in filepaths[:max_files]:\n",
    "        print(f\"{indent_str}{filepath.name}\")\n",
    "    if len(filepaths) > max_files:\n",
    "        print(f\"{indent_str}...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets/aclImdb/\n",
      "    test/\n",
      "        neg/\n",
      "            0_2.txt\n",
      "            10000_4.txt\n",
      "            10001_1.txt\n",
      "            ...\n",
      "        pos/\n",
      "            0_10.txt\n",
      "            10000_7.txt\n",
      "            10001_9.txt\n",
      "            ...\n",
      "        labeledBow.feat\n",
      "        urls_neg.txt\n",
      "        urls_pos.txt\n",
      "    train/\n",
      "        neg/\n",
      "            0_3.txt\n",
      "            10000_4.txt\n",
      "            10001_4.txt\n",
      "            ...\n",
      "        pos/\n",
      "            0_9.txt\n",
      "            10000_8.txt\n",
      "            10001_10.txt\n",
      "            ...\n",
      "        unsup/\n",
      "            0_0.txt\n",
      "            10000_0.txt\n",
      "            10001_0.txt\n",
      "            ...\n",
      "        labeledBow.feat\n",
      "        unsupBow.feat\n",
      "        urls_neg.txt\n",
      "        ...\n",
      "    README\n",
      "    imdb.vocab\n",
      "    imdbEr.txt\n"
     ]
    }
   ],
   "source": [
    "tree(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12500, 12500, 12500, 12500)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def review_paths(dirpath):\n",
    "    return [str(path) for path in dirpath.glob(\"*.txt\")]\n",
    "\n",
    "train_pos = review_paths(path / \"train\" / \"pos\")\n",
    "train_neg = review_paths(path / \"train\" / \"neg\")\n",
    "test_valid_pos = review_paths(path / \"test\" / \"pos\")\n",
    "test_valid_neg = review_paths(path / \"test\" / \"neg\")\n",
    "\n",
    "len(train_pos), len(train_neg), len(test_valid_pos), len(test_valid_neg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b.\n",
    "_Exercise: Split the test set into a validation set (15,000) and a test set (10,000)._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(test_valid_pos)\n",
    "\n",
    "test_pos = test_valid_pos[:5000]\n",
    "test_neg = test_valid_neg[:5000]\n",
    "valid_pos = test_valid_pos[5000:]\n",
    "valid_neg = test_valid_neg[5000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c.\n",
    "_Exercise: Use tf.data to create an efficient dataset for each set._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the dataset fits in memory, we can just load all the data using pure Python code and use `tf.data.Dataset.from_tensor_slices()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imdb_dataset(filepaths_positive, filepaths_negative):\n",
    "    reviews = []\n",
    "    labels = []\n",
    "    for filepaths, label in ((filepaths_negative, 0), (filepaths_positive, 1)):\n",
    "        for filepath in filepaths:\n",
    "            with open(filepath) as review_file:\n",
    "                reviews.append(review_file.read())\n",
    "            labels.append(label)\n",
    "    return tf.data.Dataset.from_tensor_slices(\n",
    "        (tf.constant(reviews), tf.constant(labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b\"Working with one of the best Shakespeare sources, this film manages to be creditable to it's source, whilst still appealing to a wider audience.<br /><br />Branagh steals the film from under Fishburne's nose, and there's a talented cast on good form.\", shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "\n",
      "tf.Tensor(b'Well...tremors I, the original started off in 1990 and i found the movie quite enjoyable to watch. however, they proceeded to make tremors II and III. Trust me, those movies started going downhill right after they finished the first one, i mean, ass blasters??? Now, only God himself is capable of answering the question \"why in Gods name would they create another one of these dumpster dives of a movie?\" Tremors IV cannot be considered a bad movie, in fact it cannot be even considered an epitome of a bad movie, for it lives up to more than that. As i attempted to sit though it, i noticed that my eyes started to bleed, and i hoped profusely that the little girl from the ring would crawl through the TV and kill me. did they really think that dressing the people who had stared in the other movies up as though they we\\'re from the wild west would make the movie (with the exact same occurrences) any better? honestly, i would never suggest buying this movie, i mean, there are cheaper ways to find things that burn well.', shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "\n",
      "tf.Tensor(b\"Ouch! This one was a bit painful to sit through. It has a cute and amusing premise, but it all goes to hell from there. Matthew Modine is almost always pedestrian and annoying, and he does not disappoint in this one. Deborah Kara Unger and John Neville turned in surprisingly decent performances. Alan Bates and Jennifer Tilly, among others, played it way over the top. I know that's the way the parts were written, and it's hard to blame actors, when the script and director have them do such schlock. If you're going to have outrageous characters, that's OK, but you gotta have good material to make it work. It didn't here. Run away screaming from this movie if at all possible.\", shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for X, y in imdb_dataset(train_pos, train_neg).take(3):\n",
    "    print(X)\n",
    "    print(y)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.7 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r1 for X, y in imdb_dataset(train_pos, train_neg).repeat(10): pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It takes about 17 seconds to load the dataset and go through it 10 times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But let's pretend the dataset does not fit in memory, just to make things more interesting. Luckily, each review fits on just one line (they use `<br />` to indicate line breaks), so we can read the reviews using a `TextLineDataset`. If they didn't we would have to preprocess the input files (e.g., converting them to TFRecords). For very large datasets, it would make sense to use a tool like Apache Beam for that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imdb_dataset(filepaths_positive, filepaths_negative, n_read_threads=5):\n",
    "    dataset_neg = tf.data.TextLineDataset(filepaths_negative,\n",
    "                                          num_parallel_reads=n_read_threads)\n",
    "    dataset_neg = dataset_neg.map(lambda review: (review, 0))\n",
    "    dataset_pos = tf.data.TextLineDataset(filepaths_positive,\n",
    "                                          num_parallel_reads=n_read_threads)\n",
    "    dataset_pos = dataset_pos.map(lambda review: (review, 1))\n",
    "    return tf.data.Dataset.concatenate(dataset_pos, dataset_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.7 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r1 for X, y in imdb_dataset(train_pos, train_neg).repeat(10): pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it takes about 33 seconds to go through the dataset 10 times. That's much slower, essentially because the dataset is not cached in RAM, so it must be reloaded at each epoch. If you add `.cache()` just before `.repeat(10)`, you will see that this implementation will be about as fast as the previous one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.6 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r1 for X, y in imdb_dataset(train_pos, train_neg).cache().repeat(10): pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "\n",
    "train_set = imdb_dataset(train_pos, train_neg).shuffle(25000, seed=42)\n",
    "train_set = train_set.batch(batch_size).prefetch(1)\n",
    "valid_set = imdb_dataset(valid_pos, valid_neg).batch(batch_size).prefetch(1)\n",
    "test_set = imdb_dataset(test_pos, test_neg).batch(batch_size).prefetch(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### d.\n",
    "_Exercise: Create a binary classification model, using a `TextVectorization` layer to preprocess each review._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a `TextVectorization` layer and adapt it to the full IMDB training set (if the training set did not fit in RAM, we could just use a smaller sample of the training set by calling `train_set.take(500)`). Let's use TF-IDF for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_tokens = 1000\n",
    "sample_reviews = train_set.map(lambda review, label: review)\n",
    "text_vectorization = tf.keras.layers.TextVectorization(\n",
    "    max_tokens=max_tokens, output_mode=\"tf_idf\")\n",
    "text_vectorization.adapt(sample_reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good! Now let's take a look at the first 10 words in the vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[UNK]', 'the', 'and', 'a', 'of', 'to', 'is', 'in', 'it', 'i']"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vectorization.get_vocabulary()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the most common words in the reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're ready to train the model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "782/782 [==============================] - 4s 4ms/step - loss: 0.4521 - accuracy: 0.8189 - val_loss: 0.3894 - val_accuracy: 0.8419\n",
      "Epoch 2/5\n",
      "782/782 [==============================] - 4s 4ms/step - loss: 0.3608 - accuracy: 0.8537 - val_loss: 0.7081 - val_accuracy: 0.7643\n",
      "Epoch 3/5\n",
      "782/782 [==============================] - 4s 4ms/step - loss: 0.3123 - accuracy: 0.8742 - val_loss: 0.3367 - val_accuracy: 0.8569\n",
      "Epoch 4/5\n",
      "782/782 [==============================] - 4s 4ms/step - loss: 0.2535 - accuracy: 0.8968 - val_loss: 0.5343 - val_accuracy: 0.8040\n",
      "Epoch 5/5\n",
      "782/782 [==============================] - 4s 4ms/step - loss: 0.1879 - accuracy: 0.9274 - val_loss: 0.3888 - val_accuracy: 0.8439\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa401b8f9d0>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "model = tf.keras.Sequential([\n",
    "    text_vectorization,\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(1, activation=\"sigmoid\"),\n",
    "])\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\",\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get about 84.2% accuracy on the validation set after just the first epoch, but after that the model makes no significant progress. We will do better in Chapter 16. For now the point is just to perform efficient preprocessing using `tf.data` and Keras preprocessing layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### e.\n",
    "_Exercise: Add an `Embedding` layer and compute the mean embedding for each review, multiplied by the square root of the number of words (see Chapter 16). This rescaled mean embedding can then be passed to the rest of your model._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compute the mean embedding for each review, and multiply it by the square root of the number of words in that review, we will need a little function. For each sentence, this function needs to compute $M \\times \\sqrt N$, where $M$ is the mean of all the word embeddings in the sentence (excluding padding tokens), and $N$ is the number of words in the sentence (also excluding padding tokens). We can rewrite $M$ as $\\dfrac{S}{N}$, where $S$ is the sum of all word embeddings (it does not matter whether or not we include the padding tokens in this sum, since their representation is a zero vector). So the function must return $M \\times \\sqrt N = \\dfrac{S}{N} \\times \\sqrt N = \\dfrac{S}{\\sqrt N \\times \\sqrt N} \\times \\sqrt N= \\dfrac{S}{\\sqrt N}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[3.535534 , 4.9497476, 2.1213205],\n",
       "       [6.       , 0.       , 0.       ]], dtype=float32)>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_mean_embedding(inputs):\n",
    "    not_pad = tf.math.count_nonzero(inputs, axis=-1)\n",
    "    n_words = tf.math.count_nonzero(not_pad, axis=-1, keepdims=True)    \n",
    "    sqrt_n_words = tf.math.sqrt(tf.cast(n_words, tf.float32))\n",
    "    return tf.reduce_sum(inputs, axis=1) / sqrt_n_words\n",
    "\n",
    "another_example = tf.constant([[[1., 2., 3.], [4., 5., 0.], [0., 0., 0.]],\n",
    "                               [[6., 0., 0.], [0., 0., 0.], [0., 0., 0.]]])\n",
    "compute_mean_embedding(another_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that this is correct. The first review contains 2 words (the last token is a zero vector, which represents the `<pad>` token). Let's compute the mean embedding for these 2 words, and multiply the result by the square root of 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[3.535534 , 4.9497476, 2.1213202]], dtype=float32)>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reduce_mean(another_example[0:1, :2], axis=1) * tf.sqrt(2.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good! Now let's check the second review, which contains just one word (we ignore the two padding tokens):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[6., 0., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reduce_mean(another_example[1:2, :1], axis=1) * tf.sqrt(1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect. Now we're ready to train our final model. It's the same as before, except we replaced TF-IDF with ordinal encoding (`output_mode=\"int\"`) followed by an `Embedding` layer, followed by a `Lambda` layer that calls the `compute_mean_embedding` layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_size = 20\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "text_vectorization = tf.keras.layers.TextVectorization(\n",
    "    max_tokens=max_tokens, output_mode=\"int\")\n",
    "text_vectorization.adapt(sample_reviews)\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    text_vectorization,\n",
    "    tf.keras.layers.Embedding(input_dim=max_tokens,\n",
    "                              output_dim=embedding_size,\n",
    "                              mask_zero=True),  # <pad> tokens => zero vectors\n",
    "    tf.keras.layers.Lambda(compute_mean_embedding),\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(1, activation=\"sigmoid\"),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### f.\n",
    "_Exercise: Train the model and see what accuracy you get. Try to optimize your pipelines to make training as fast as possible._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "782/782 [==============================] - 9s 10ms/step - loss: 0.4758 - accuracy: 0.7675 - val_loss: 0.4153 - val_accuracy: 0.8009\n",
      "Epoch 2/5\n",
      "782/782 [==============================] - 8s 9ms/step - loss: 0.3438 - accuracy: 0.8537 - val_loss: 0.3814 - val_accuracy: 0.8245\n",
      "Epoch 3/5\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.3244 - accuracy: 0.8618 - val_loss: 0.3341 - val_accuracy: 0.8520\n",
      "Epoch 4/5\n",
      "782/782 [==============================] - 10s 11ms/step - loss: 0.3153 - accuracy: 0.8666 - val_loss: 0.3122 - val_accuracy: 0.8655\n",
      "Epoch 5/5\n",
      "782/782 [==============================] - 11s 12ms/step - loss: 0.3135 - accuracy: 0.8676 - val_loss: 0.3119 - val_accuracy: 0.8625\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa3a0bf9460>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\",\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is just marginally better using embeddings (but we will do better in Chapter 16). The pipeline looks fast enough (we optimized it earlier)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### g.\n",
    "_Exercise: Use TFDS to load the same dataset more easily: `tfds.load(\"imdb_reviews\")`._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "datasets = tfds.load(name=\"imdb_reviews\")\n",
    "train_set, test_set = datasets[\"train\"], datasets[\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b\"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.\", shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for example in train_set.take(1):\n",
    "    print(example[\"text\"])\n",
    "    print(example[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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