experiments/digits_recognition_cnn/digits_recognition_cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handwritten digits recognition (using Convolutional Neural Network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> - 🤖 See [full list of Machine Learning Experiments](https://github.com/trekhleb/machine-learning-experiments) on **GitHub**<br/><br/>\n",
    "> - ▶️ **Interactive Demo**: [try this model and other machine learning experiments in action](https://trekhleb.github.io/machine-learning-experiments/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this experiment we will build a [Convolutional Neural Network](https://en.wikipedia.org/wiki/Convolutional_neural_network) (CNN) model using [Tensorflow](https://www.tensorflow.org/) to recognize handwritten digits.\n",
    "\n",
    "A **convolutional neural network** (CNN, or ConvNet) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other.\n",
    "\n",
    "![digits_recognition_cnn.png](../../demos/src/images/digits_recognition_cnn.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import dependencies\n",
    "\n",
    "- [tensorflow](https://www.tensorflow.org/) - for developing and training ML models.\n",
    "- [matplotlib](https://matplotlib.org/) - for plotting the data.\n",
    "- [seaborn](https://seaborn.pydata.org/index.html) - for plotting confusion matrix.\n",
    "- [numpy](https://numpy.org/) - for linear algebra operations.\n",
    "- [pandas](https://pandas.pydata.org/) - for displaying training/test data in a table.\n",
    "- [math](https://docs.python.org/3/library/math.html) - for calculating square roots etc.\n",
    "- [datetime](https://docs.python.org/3.8/library/datetime.html) - for generating a logs folder names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting Tensorflow version v2 (the command is relevant for Colab only).\n",
    "%tensorflow_version 2.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.7.6\n",
      "Tensorflow version: 2.1.0\n",
      "Keras version: 2.2.4-tf\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import datetime\n",
    "import platform\n",
    "\n",
    "print('Python version:', platform.python_version())\n",
    "print('Tensorflow version:', tf.__version__)\n",
    "print('Keras version:', tf.keras.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuring Tensorboard\n",
    "\n",
    "We will use [Tensorboard](https://www.tensorflow.org/tensorboard) to debug the model later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension.\n",
    "# %reload_ext tensorboard\n",
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear any logs from previous runs.\n",
    "!rm -rf ./.logs/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data\n",
    "\n",
    "The **training** dataset consists of 60000 28x28px images of hand-written digits from `0` to `9`.\n",
    "\n",
    "The **test** dataset consists of 10000 28x28px images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_dataset = tf.keras.datasets.mnist\n",
    "(x_train, y_train), (x_test, y_test) = mnist_dataset.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: (60000, 28, 28)\n",
      "y_train: (60000,)\n",
      "x_test: (10000, 28, 28)\n",
      "y_test: (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('x_train:', x_train.shape)\n",
    "print('y_train:', y_train.shape)\n",
    "print('x_test:', x_test.shape)\n",
    "print('y_test:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMAGE_WIDTH: 28\n",
      "IMAGE_HEIGHT: 28\n",
      "IMAGE_CHANNELS: 1\n"
     ]
    }
   ],
   "source": [
    "# Save image parameters to the constants that we will use later for data re-shaping and for model traning.\n",
    "(_, IMAGE_WIDTH, IMAGE_HEIGHT) = x_train.shape\n",
    "IMAGE_CHANNELS = 1\n",
    "\n",
    "print('IMAGE_WIDTH:', IMAGE_WIDTH);\n",
    "print('IMAGE_HEIGHT:', IMAGE_HEIGHT);\n",
    "print('IMAGE_CHANNELS:', IMAGE_CHANNELS);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the data\n",
    "\n",
    "Here is how each image in the dataset looks like. It is a 28x28 matrix of integers (from `0` to `255`). Each integer represents a color of a pixel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>253</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>253</td>\n",
       "      <td>249</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>253</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>250</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>171</td>\n",
       "      <td>219</td>\n",
       "      <td>253</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>172</td>\n",
       "      <td>226</td>\n",
       "      <td>253</td>\n",
       "      <td>253</td>\n",
       "      <td>253</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>253</td>\n",
       "      <td>253</td>\n",
       "      <td>253</td>\n",
       "      <td>212</td>\n",
       "      <td>135</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3    4    5    6    7    8    9   ...   18   19   20   21  \\\n",
       "0    0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "1    0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "2    0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "3    0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "4    0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "5    0   0   0   0    0    0    0    0    0    0  ...  175   26  166  255   \n",
       "6    0   0   0   0    0    0    0    0   30   36  ...  225  172  253  242   \n",
       "7    0   0   0   0    0    0    0   49  238  253  ...   93   82   82   56   \n",
       "8    0   0   0   0    0    0    0   18  219  253  ...    0    0    0    0   \n",
       "9    0   0   0   0    0    0    0    0   80  156  ...    0    0    0    0   \n",
       "10   0   0   0   0    0    0    0    0    0   14  ...    0    0    0    0   \n",
       "11   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "12   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "13   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "14   0   0   0   0    0    0    0    0    0    0  ...   25    0    0    0   \n",
       "15   0   0   0   0    0    0    0    0    0    0  ...  150   27    0    0   \n",
       "16   0   0   0   0    0    0    0    0    0    0  ...  253  187    0    0   \n",
       "17   0   0   0   0    0    0    0    0    0    0  ...  253  249   64    0   \n",
       "18   0   0   0   0    0    0    0    0    0    0  ...  253  207    2    0   \n",
       "19   0   0   0   0    0    0    0    0    0    0  ...  250  182    0    0   \n",
       "20   0   0   0   0    0    0    0    0    0    0  ...   78    0    0    0   \n",
       "21   0   0   0   0    0    0    0    0   23   66  ...    0    0    0    0   \n",
       "22   0   0   0   0    0    0   18  171  219  253  ...    0    0    0    0   \n",
       "23   0   0   0   0   55  172  226  253  253  253  ...    0    0    0    0   \n",
       "24   0   0   0   0  136  253  253  253  212  135  ...    0    0    0    0   \n",
       "25   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "26   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "27   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "\n",
       "     22   23  24  25  26  27  \n",
       "0     0    0   0   0   0   0  \n",
       "1     0    0   0   0   0   0  \n",
       "2     0    0   0   0   0   0  \n",
       "3     0    0   0   0   0   0  \n",
       "4     0    0   0   0   0   0  \n",
       "5   247  127   0   0   0   0  \n",
       "6   195   64   0   0   0   0  \n",
       "7    39    0   0   0   0   0  \n",
       "8     0    0   0   0   0   0  \n",
       "9     0    0   0   0   0   0  \n",
       "10    0    0   0   0   0   0  \n",
       "11    0    0   0   0   0   0  \n",
       "12    0    0   0   0   0   0  \n",
       "13    0    0   0   0   0   0  \n",
       "14    0    0   0   0   0   0  \n",
       "15    0    0   0   0   0   0  \n",
       "16    0    0   0   0   0   0  \n",
       "17    0    0   0   0   0   0  \n",
       "18    0    0   0   0   0   0  \n",
       "19    0    0   0   0   0   0  \n",
       "20    0    0   0   0   0   0  \n",
       "21    0    0   0   0   0   0  \n",
       "22    0    0   0   0   0   0  \n",
       "23    0    0   0   0   0   0  \n",
       "24    0    0   0   0   0   0  \n",
       "25    0    0   0   0   0   0  \n",
       "26    0    0   0   0   0   0  \n",
       "27    0    0   0   0   0   0  \n",
       "\n",
       "[28 rows x 28 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(x_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This matrix of numbers may be drawn as follows: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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(x_train[0], cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print some more training examples to get the feeling of how the digits were written."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numbers_to_display = 25\n",
    "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(numbers_to_display):\n",
    "    plt.subplot(num_cells, num_cells, i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(x_train[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(y_train[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping the data\n",
    "\n",
    "In order to use convolution layers we need to reshape our data and add a color channel to it. As you've noticed currently every digit has a shape of `(28, 28)` which means that it is a 28x28 matrix of color values form `0` to `255`. We need to reshape it to `(28, 28, 1)` shape so that each pixel potentially may have multiple channels (like Red, Green and Blue)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_with_chanels = x_train.reshape(\n",
    "    x_train.shape[0],\n",
    "    IMAGE_WIDTH,\n",
    "    IMAGE_HEIGHT,\n",
    "    IMAGE_CHANNELS\n",
    ")\n",
    "\n",
    "x_test_with_chanels = x_test.reshape(\n",
    "    x_test.shape[0],\n",
    "    IMAGE_WIDTH,\n",
    "    IMAGE_HEIGHT,\n",
    "    IMAGE_CHANNELS\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train_with_chanels: (60000, 28, 28, 1)\n",
      "x_test_with_chanels: (10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print('x_train_with_chanels:', x_train_with_chanels.shape)\n",
    "print('x_test_with_chanels:', x_test_with_chanels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize the data\n",
    "\n",
    "Here we're just trying to move from values range of `[0...255]` to `[0...1]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_normalized = x_train_with_chanels / 255\n",
    "x_test_normalized = x_test_with_chanels / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.18039216],\n",
       "       [0.50980392],\n",
       "       [0.71764706],\n",
       "       [0.99215686],\n",
       "       [0.99215686],\n",
       "       [0.81176471],\n",
       "       [0.00784314],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check just one row from the 0th image to see color chanel values after normalization.\n",
    "x_train_normalized[0][18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the model\n",
    "\n",
    "We will use [Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential?version=stable) Keras model.\n",
    "\n",
    "Then we will have two pairs of [Convolution2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D?version=stable) and [MaxPooling2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D?version=stable) layers. The MaxPooling layer acts as a sort of downsampling using max values in a region instead of averaging.\n",
    "\n",
    "After that we will use [Flatten](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten?version=stable) layer to convert multidimensional parameters to vector.\n",
    "\n",
    "The las layer will be a [Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense?version=stable) layer with `10` [Softmax](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax?version=stable) outputs. The output represents the network guess. The 0-th output represents a probability that the input digit is `0`, the 1-st output represents a probability that the input digit is `1` and so on...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential()\n",
    "\n",
    "model.add(tf.keras.layers.Convolution2D(\n",
    "    input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS),\n",
    "    kernel_size=5,\n",
    "    filters=8,\n",
    "    strides=1,\n",
    "    activation=tf.keras.activations.relu,\n",
    "    kernel_initializer=tf.keras.initializers.VarianceScaling()\n",
    "))\n",
    "\n",
    "model.add(tf.keras.layers.MaxPooling2D(\n",
    "    pool_size=(2, 2),\n",
    "    strides=(2, 2)\n",
    "))\n",
    "\n",
    "model.add(tf.keras.layers.Convolution2D(\n",
    "    kernel_size=5,\n",
    "    filters=16,\n",
    "    strides=1,\n",
    "    activation=tf.keras.activations.relu,\n",
    "    kernel_initializer=tf.keras.initializers.VarianceScaling()\n",
    "))\n",
    "\n",
    "model.add(tf.keras.layers.MaxPooling2D(\n",
    "    pool_size=(2, 2),\n",
    "    strides=(2, 2)\n",
    "))\n",
    "\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "\n",
    "model.add(tf.keras.layers.Dense(\n",
    "    units=128,\n",
    "    activation=tf.keras.activations.relu\n",
    "));\n",
    "\n",
    "model.add(tf.keras.layers.Dropout(0.2))\n",
    "\n",
    "model.add(tf.keras.layers.Dense(\n",
    "    units=10,\n",
    "    activation=tf.keras.activations.softmax,\n",
    "    kernel_initializer=tf.keras.initializers.VarianceScaling()\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is our model summary so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 24, 24, 8)         208       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 12, 12, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 8, 8, 16)          3216      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 4, 4, 16)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 37,610\n",
      "Trainable params: 37,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to plot the model the `graphviz` should be installed. For Mac OS it may be installed using `brew` like `brew install graphviz`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R72dqL596xx139BJnzSXLwM1CBDxmb7/99vMPjfThkXgu8vvzn//cg+1Z4WKjwgZDGbGtttoqKzfW7RrQEGALwyYItmeffbaPceCBB/p1YUezcJVAAnLiDUnsTno64jXNM7atXb0d/6zWiAtFo0HidvMy9bR6XAN25xQHSEIGku/jAAPGfIQhiFjlQeZNG3gWwRpBv8UWW2T5e0nQgC0K5w+e+6OPPtrP51nK1zptx6PahYAQqCgCg9Z9txuvrI2WEAHi60zIRdzWsYMahNESi2dTYAOkZBS2U8pGEfdHHxMe3qdIiS86mmHdz7MsL35e+sde5NHUw2l3zK8Jco/1I64wkRnoo71AownEiJ3S8pD62GasT10K/Q67ZBkxi2BlHxPOD/alFB+J/QJqV7IMGxfnEiPYjUwIel/COaBf//rX7qbP+fYhEE19GymRRXykrfqjOZzEQZZds2QL0T6qnAdsh5Reow3Cpgkf3LNuZB9a0VLuub2IayDOFL4hQlHMMcbHsiQM2VCEIhB6YB+K3kbMJTGOJ5xwQrQ0dx6DyvwJb2Ij2eePsnM8j2ybNiUbs9PGeNloO/FUxWPDstFW8VrFU2sERm2jrWwc7ec//3kPBM/HzRGrxgvNVmLRvM38OHUv80TgOy+n0047zZvNTuj7+UBnWxH7izJ/ntkhvR8B+YlsNRoJqG5HtpKJphLNgs0J3GffVt3ZKQhbXsplBS0DWJo15ynVBqWtyPUgqMBgueWWy2IViWsksB7nLZIG2MrJ+yRBy9jJeSm9+GnjYwdhmSfiG3Hy6hSvl/o3C1rauXfwR3C6ZRHyrmluPgCgItdAv/e///0+FtuJuL84rCXio4f5+OjJU1FByz20qjbuNJXOBxez66bdDLv8Rxcfh+aRnvUhMN+Kc2f7PFvwxTOdyErOeZutev05Jz6V570IWYk5P5cx9dcZA7P7CaNJ/pzwPhwVVVJ1bBcf7EUeVllllQa3bZJdo17D3okakDyf9qK1d8pLhOqYEBxsqraCycI1mkt8od5FLZzKghGmg8MMakzUgrfddpvHZzWPn2ZCVUgIi2WEyXKQor4kPRyqykQ4BRHbltSyqb3Ib+It3zeFn3S6nmRzbFWyDJd41JmoW4tSc1/iG3Hy6pXSNaDqT7xSdg1CxQul9k7XUFad2nwdPlGBf+DXVrHOEykKqWxCqATPXyKeNdTp4EvKQWIZMWmgDocoXUaoAnbM9CyguidEjOeZsVAxp3y2ONrRbgI9TdH1F7U1z799fHbtO5k7YI6588473VwymXGYzNdOqlD+r46KKilocV6xleyYShW8SPiDiJ+CeBnliTg3CDtaO2rlAfzRj37UbbxUwqCINYK+nbcvvFHvEdtcyt3KXORkhVJpMN+xf3jB9/qST2N0+m11Pa36J76wXRMXWJSGyXvioZdrSOcW/e3nOvAL2H///f1jbKeddnJP8iQwmR9hjG0bBzlss2bG8NhK8tlC6XnkOE517Sg9z0XxyI/DuXhPY3cXtUcAHwEK0Qun9hhN9CN8+PIhPCpqlFKjmrXLPCkBOUHgZDPJ07PPPhtwkkrZT6677rr84SwLy5xzztnQ3m2HFxsZWkxNGCz/qic433TTTcecxiqEdpymTE3YcBy+IFbeVaSJULKs12vgfpQVtGg80Fhwz3GQw1GJL2EcmNIzmr/P2223na96TQ3uzlkUKmcFC6WAelMF50/xbZ45ku+LhIAQmJgIVFLQoj7DmxdPz7POOitDPgk5PE3NHuXtqInzdOutt/russsum28utI06lBcoq5EPf/jDXnUjfyIqbYQrak48SxPxMcDqFzUnRN3IPKHuJh51vAl1eSpZljQD+Uoo8AnleUU4gXs6lq4hVXlJ+91+wW4QlL8GxkvXkfhhHvhtvgb6Ijjz1I0nPLH5aMIMgZBdddVVs7SAaDWaz08qdTyL8WxGnZwIFTEeyngu41GdCD4333zzzGs5tetXCAiBiYNAJQUt8GLvgsx5xG1OCD9eVksssYSXSfrIRz4SWDGYJ6e/CL2z/cMLkdAO+kNlSnyhHkzqpFZq41122cXLdlF/kjJV/BFKhIAlvyv2OMZglc38qLexAxCCxKrl8MMPL7VyScIjhY6UvZ5OJcssQbeHl2CvohQZKeW+9KUvOWaEEKG+h/joQaCYg1RWsgw7Nis1QqQ6EdgnYZRqSdK/0z2htmaeOl0D/ZLqvlvZNfpSw5MPMUwD8JVWx4kf+vBBwX0zx6lAqFD+gwuzAqnfCHvCho8GA7sr9r5EqIUR/mhUEMyJ+IBDtcxHgDk8uRoaPswhy5/nlMYTIQ3xXIuEgBCYIAjYC2ckVDa8x15IEa9evHUNak/tZi++zIsWpimXZSpcP2bqPPf2xZPVbKV+TUVLfOUBsLqOEW/jZsLLFj7a/eHVC1mh5iwdnamjI2Eh5rjk4Rp41BISVIRGUbKMsBfwNcEQ7cMlWrxmNJV8NBV6NIHkbBJmZKvahpJlZnd0HMxm2fZSTGhnOICZraQ9HMjiWt0TlzazKcYvfvGLHupiNSZ9TLy28XpO1WDwHu637BphNvYx5OPDx+mnn+73OH8vwQHP4nwbXol4HPMsgg/8WnyshwvhFcw+4xJalicTsNGEcb7Jt00wu9cx5zEPuOKlnObgWU7esIQScT/KkMJ7iqGl8J5iOE3kXgrvabq7ppb1cBBeUu2IsBpTKUbiFAdBxEX2S9SXTEJ11Hlfk5AqUrLMVs3RVpF+uWDdCmfwaC5ZNgiMOmFc5hoYhzhdU8P6kPmQsDQHwpLng99eidjuPDXvcwyhbmaP2Cl3MjmfTX3s8eH58frdlqAthqAEbTGcJnKvUQva/7nw2ud1VQk1HCrjToQ9F3XcoAjVc7+UwjQYB3VyItSzhCZ1ItTQV111VacuhY8xFn/tKKUh5Hiydzb3bYVHq7bm8wa13+0amCefqjI5HuXnt1Wkq2jzbWW3m3HM72NrZQ5U7ISEdcqdjK128cUXLzu9+o8jApiNkmMmfgt4j/PeyRMmB0K/MDtAF1xwgZuOqGxEqCIOdYT6lSHMR2QSwxcFh03ec5gnmkP/8CUgUgKTA/4rmLTKhIYlnkY9X5oXvxt8Irg2026l5oBjIRESZATME34wmLgSca2Y8ipLo/pqKas6HhVfo57HQguiebJ2/LM0kX2xlZJRWAhSX+OM58l1ugazb3uGMnuJuhmD1fh40HivaJNGYVjXPqjxe1nRYkYwJ8hINRmKtaP9yZP5ZXjWOrP3e7NFLvg+WpTLL7/ck7OYECic5YtB0DZhCrOY8mgf65lZw4Rtg2aG/yuYI8xx1LOUMTf7zQla8vy22h71fPAAXmSYA1/wueOOOxpYQztFMqG99957TDsmG1u0+HkpG2BDpw47o17R4hQyEpKgHQnMsW4ly1qhUrdrIIWnxdG6vRvb+njReApaVPKWqCWaM9lQLn+Q4/cqaK3KU8tr43nFdk/GtUT4eeRf/haz6QIB+31Rgk/SclpIo+OKUEk2fJ65RPiAIHzzZCtoF875tm7bo54PfsypMFpO9ywdabOgpQ/XTxY2c8JkdwzhY5LHekyHFg2jFrSVVx1XVhVQUcZWWul/Jcvy7KVYznxblbfrdg0kOyEUDdVeO/V7lfEeBG94YmPusHfaIIYbM8awxx8zYcEGPM+33357D+/LZyrDtIJqEy92ngkKkEAkiylC4EimOjAlYxi0zjrreOYvCqPg6c5zRz+yqeE5T2RDMpuwjSmjKI16vsQXRVcgssS1I67f0tAGEsVQ0pIwurpR8TtRtyubpPzyUCJY8391g6KO14DNrI5CltSPhx12mL/ACJkzzZNXOOKZMQ/8LDnLhRde6I8RdjSStfDHcQghaB7+vk04Hi9FbH1k3kE4UEHJPNSD5d728DlbtXjffsZnAM4nhKo5lt4HH9E/hMVRm7rZPoh9lhCw9ExQnQqitnNRogIX9vw8pXA2y4/tzdiLET4IcLAnoQ/hhFToQjCVoVHPV4Y3aluDJdn4akn2JTMSkup4JDBrkkmMQFnVMZ7k2JVRM6Kyw85I+JN9pPk2UBKSZi+20lWeLGY4s7sRukWhjVTwA5tjihDodXx4Q9UJb0WqSNE/0SBVx9htF1pooTR0219zTopWljPz8G/bscsBE57uD5APKSO0MNlwF1hgAQ/RKxsa1m7aUc1nHyp+L1upjhNvVlLTTTTN5gkTwJVXHWtFW8vPIzEtBPpHwASOF5iwSlfuDcuK06pc+eqI5CuoRVOxhzQbK6hmj2m8XPFe5xhqf/bxlE0FOawsoGd4o5YvBQ/IqpVWwL2ODz8khcHTlhX0eBBqYYpLpNVlOx5Q7ZLYhBzYZXKMN4+XtAT2YRGshGJ22IRrlo2OnNpoKbrxlJ3cYWPU83VgxQ+ZY5iv2NGQ1I0kaOt2x8SvEBgAArZKCKgKeSHnQ6NsVez5mhEgVHkqQwjaPKUKTHnBbN6j3iVfkCF/Tqft5vFTysuU97zTucM4RjgPIT/dhBrZ4dZaa62w/vrr98XGvvvu61nomjOyWR6BsMMOO3iWNFT1JMyn6ElS9/c66ajn68anJbPxLimjW7f+VTouQVuluyFehMCIEGCVxQu52WEGO1jKI54qDhVlqVkQtjqPlRhzFnUKyo9RZPx8/2Fv43AEtSowkZ+bNKJpBZ9vL7NNfC6xo9h+k903nU/cLvGz5OQmRh9BTFw3Mal8UPVCo56vCI/J0Yu45LqRBG3d7pj4FQIDQAAVJi9sysWlvNZpWAtV8c2yFbCKCEKEN0LAUkym6Qr/Fhm/8GAD6Mg1wNNTTz3VcTQS1nRKYNLxZDtIMgqcgBC0zREErKjJwU0ii6RBwKGNFTQ50m+++eZuw485Pur5xjDQpoGVO9TJQ7nNqePeLEE77rdADAiB0SPAKiypdJu9dim8QH1djqfVU69VnpqvLGU8w8MZ6nd87IjjRdilWaFTZKQTUbSjHXXj33Kne+jQZZdd5gU+0jgUyEA1jD2WQhzmHJUO+W/yTibsLE9Vmy/PW7ftVJikn4+WbnMM67gE7bCQ1bhCoOIIUD2IFdnRRx/tL2zYZXVrCQQ8zIdVbz9VntLlU/EIwgkKZytLsOChKLT1M37RKlLMMyxafvnl3ZZtCTVaTkE4FKk6qa3dTFacw52jNthgg+ZDvo8N2JKAeBwt9lJCiPhDiPLLMYQ9IUOEOlkxlWwcy0blghmbe6KqzZf44jdVKOukFsY2u8giizSkaMyPUent5Do97F+F9wwbYY0/2REoG94DXmQYomKTrV4joRxk4LGY14YUf71WeaLikb38vMLR0ksvHS2hQ4THlKYw3a9exy9SRSrNkf8dZHgPYUoW9+0pVfNzpG1Cj8CAzFDNdNBBB/kxjlOEJE8U95g6dWp2nD75PzJGJaLAB1mrTDsQ11hjjWi54aPl/o3mcJa6+G+V5kuM3XjjjXG99daLVDrj+kg5aR8V6XD2S5EOjtuHWtaWNrjuqmeGwlg+EpKgHQnMmmQSI9CLoAUu4hIt01C0VVE0e2NLBE3lmMWAkue3SJWnJGjJDUx5RKpstaNexmesXqpIDVLQwgN5ePmQaEXkZzY7eKtD3gY2xOE2x4a2PaHDATAkDtUc3dr2qut85tgVTfvh1bGaL64OglYpGO0zSSQEJjMCpI5EJdeJeq3yxJh4GVtN4U7Duwo0dUh227SffltVjGrVlvoP+redpzTOR6RLJMMWVX7yhGo+OZfl29nGkYlzrf6zp+9sPl52n3vUqTpQXeejIhaq92uvvXaMh7eVpXTnurJYjbq/BO2oEdd8QmCSIJDsbZRAy5eKrOPlY6/GgxebLMKd1IspfpawEwSBqUDd5r3zzjsXukRilS3bkaelLHRCn53qOB+JPrDFW4WihrKE2P2JT8aOTZrKFPrTJ0RDO12CdmjQamAhMDkRwDGIvLt4L0PbbLNNsJSMYYsttqgtIA8//HBH3kmmwKo25XHu2PnFg2TFas6MVeS8XvvUcT5Tq3t4k1UtarhsEnLwVxeSoK3LnRKfQqAmCKCKPvLII/0vsUyhiIlOqIkXW2yxiX6ZI70+PNQnAknQToS7qGsQAhVDoDmxQsXYEztCYKQIKI52pHBrMiEgBISAEJhsCEjQTrY7rusVAkJACAiBkSIwUtUxBZKnTZs20gvUZEJgsiBA5hzS1On/WOc7jsMSRRWEU2ecJvJRZNEoaQrBv6OY8Oyzz/aajKOYS3MIgYmKAKnqKASw7LLLTtRL1HUJgZEggIMeIUKjoJEJ2lFcjOYQAhMdAZIiHHPMMWOSyE/069b1CYE6IyAbbZ3vnngXAkJACAiByiMgQVv5WyQGhYAQEAJCoM4ISNDW+e6JdyEgBISAEKg8AhK0lb9FYlAICAEhIATqjIAEbZ3vnngXAkJACAiByiMgQVv5WyQGhYAQEAJCoM4ISNDW+e6JdyEgBISAEKg8AhK0lb9FYlAICAEhIATqjIAEbZ3vnngXAkJACAiByiMgQVv5WyQGhYAQEAJCoM4ISNDW+e6JdyEgBISAEKg8AhK0lb9FYlAICAEhIATqjIAEbZ3vnngXAkJACAiByiMgQVv5WyQGhYAQEAJCoM4ISNDW+e6JdyEgBISAEKg8AhK0lb9FYlAICAEhIATqjIAEbZ3vnngXAkJACAiByiMgQVv5WyQGhYAQEAJCoM4ISNDW+e6JdyEgBISAEKg8AhK0lb9FYlAICAEhIATqjIAEbZ3vnngXAkJACAiByiMgQVv5WyQGhYAQEAJCoM4ISNDW+e6JdyEgBISAEKg8AhK0lb9FYlAICAEhIATqjIAEbZ3vnngXAkJACAiByiMgQVv5WyQGhYAQEAJCoM4ISNDW+e6JdyEgBISAEKg8AhK0lb9FYlAICAEhIATqjIAEbZ3vnngXAkJACAiByiMgQVv5WyQGhYAQEAJCoM4ISNDW+e6JdyEgBISAEKg8AhK0lb9FYlAICAEhIATqjIAEbZ3vnngXAkJACAiByiMwJRpVnksxKAQmIQI33XRTWGGFFcILL7yQXT3/XfmbYYaXvpGnTJkSPvaxj4XTTz8966cNISAEqoPAjNVhRZwIASGQR2DppZcOs88+e3j44Yfzzb6dF740bLTRRmP6qEEICIFqIPDSZ3E1+BEXQkAIvIgAK9XNN988zDTTTB0xmXnmmcPqq6/esY8OCgEhMH4ISNCOH/aaWQh0RWCTTTYJ//nPf9r2QwhPnz69qzBuO4AOCAEhMHQEJGiHDrEmEAK9I7DooouG+eefv+0ACGGEsUgICIHqIiBBW917I86EgCOw5ZZbhhlnbO1OMcccc7jDlKASAkKgughI0Fb33ogzIeAI4FH8/PPPj0EDtfFmm23W4IE8ppMahIAQGHcEJGjH/RaIASHQGYF55503LLHEEgHnqDxJbZxHQ9tCoLoISNBW996IMyGQIbDFFluMWblOnTrVBXDWSRtCQAhUEgEJ2kreFjElBBoRmDZtWkPiCtTGCF+REBAC1UdAgrb690gcCoEw55xzhpVWWilb1aI2xnYrEgJCoPoISNBW/x6JQyHgCOD4lGiRRRYJ73znO9OufoWAEKgwAhK0Fb45Yk0I5BFYf/31sxUtIT8iISAE6oFA6+C8evA+UC5vueWW8MADDwx0TA0mBAaNwOKLLx5uvfXW8JrXvCacf/75gx5e4wmBgSKw3nrrtY0BH+hEFR9M1XtevEGsEFT9pOJPq9gTAkKgVgj885//DOTinuwk1XHuCfjIRz7iJchSKTL9/q8km3DoDYcbbrjBn66HHnpoYM/Vs88+G84666yBjVeVewtQ55133oS7rqrgO2o+fvCDH+TerNqUoNUzIARqhMArX/lKeRvX6H6JVSEAAhK0eg6EgBAQAkJACAwRAQnaIYKroYWAEBACQkAISNDqGRACQkAICAEhMEQEJGiHCK6GFgJCQAgIASEgQatnQAgIASEgBITAEBGQoB0iuBpaCPSLAGEZq6yySiD0TNQagXvuuSfst99+2cG//vWv4XOf+1z4wAc+EDbYYINwzjnnhKeffjo7XnTjpz/9adh0003D8ssv7wUcSGrTTE899VT4yle+EtZaa63w0Y9+NHzzm98M//jHP5q7Fdof9XwwxfN1wQUXhK9+9asNPD7zzDPh4x//ePjb3/7W0K6dHhEwoEWGgFVCifYyExZCYGAIWBxttP+W0eJoex7TCr7H2WefPb75zW+O//3vf3seZxAnvvDCC4MYpuUY4GRxtC2PdWq87rrr4oc+9KH45z//2buZQI2WAzqacI1/+tOf4h577OH3YMkll4z//ve/Ow3VcOzUU0/1897ylrfEl73sZb7N749+9KOsH3gsu+yycfXVV4+///3vo2XsivZRFOeaa674l7/8JetXZGPU88HTd7/73bjYYov5tVl6zzFsXnzxxfHtb397vP/++8cc69ZgcbQ+riWs6NZ1Uhzni0ZkCEjQ6jEYNAKDELTw9Pjjj0dbJQ2avVLj/eQnP4n77rtvqXPKdO5F0PIyn3vuueMf//jHbCqEwwwzzBDvvPPOrO0d73iHv/Svv/76rK3TBnhbtaR4zTXXeLcHH3wwrrvuuj7Ge9/73uxUhDl8X3XVVVmbrUq97bjjjsvaum2Mej744aPt+9//frzkkkuc31aCln4nnXRSXGCBBWJZgSlBC3ovkVTH9j9FJASqjMCss84aXve6140bi3/4wx/CJptsEuzlPG48NE9sq9Ow/fbbh09/+tNhnnnmyQ7b6t/r9prAzdpMOPr2E088kbV12rjooovCF77whbDCCit4N1vVhn322ce3f/WrX2Wn2qrVt2+88casDZUrNOOMxdPIj3o++LOPkfDhD384vOtd72K3LZGalvt+2GGHte2jAwUQeEnmTu4trWgn9/0fxtUPYkVrL+5oxQN8RYUaGUJFiUr0oIMOirfffnvcdttt48orrxy/9a1vZZfBCvjb3/52XHvttaPZMKPZMF3NueGGG8Zf/vKX3o8VngnQuPHGG0ez03mb2ep8nzaOcy7qU3uVxIUXXjhutdVW8eabb87OR22bVn7Z5D1sMH4Z1fGRRx4Zp0yZ0lKtyfWBWyLUo5ZRK5qgTU0df1H1W6rLMX1mm222+Na3vjVrpx+r59e//vXxiiuuiFYjOJqtNprgj3//+9+zft02Rj1fnp97772344qWvkcccUS0IhaZej5/frttrWgbkZHq+EU8JGgbHwzt9Y9Av4LWnHri5ptvHm115C9DXuS//vWv4zLLLOP72G2tLm20OrXRVr3+0k9CcMcdd3ThggCbd955oxWJj7ZC8/N4ad53331+gV/72te8bf/99/d97I6f+tSnvO2UU07xl6s5+/i+rW5cTZrsobvttpu3m+NR32CVFbS2EosLLbRQ13l/85vfOI98QPRDCGkEO9jkiWuHd+4RPK266qoNqux83zLbo5qviKDlmeIa+bArShK0jUhJdWxPkEgIVBGBOeaYwytKJdUnPKLqs5Wqs2srqfCzn/0s2ErWPV9NSAZzDvJjRx11VHj/+9/v2yeccIIXIrCVZ9h6663dA/eQQw7xY82qQxMmgVJ8icxWGd797nf7rgnssNJKKwXaoF133TWYDS/stddevj+qf2xlH8z5KLzpTW/qOuU3vvGN8J73vCfssssuXft26nD88ce7+t40Aw3dbGUdKAUHT3fddZeXL0TV3y+Ner5O/Jomww/n1ead+uvYWAQkaMdiohYhUCkEXvGKVzTw86pXvcr3sR1SlxZKApPQlkTpWF5w7r333n74F7/4RepW+BchnKc3vOENYZtttgl8EIySCOex1X1XQUufM888M9jKvJTNtPlazBkqHHrooV5dyLQIDYf56DG1cTBHsYCANU/eYF7IIdlqGzoX3Bn1fN3YevWrXx1mmmmmYNqBbl11vA0CErRtgFGzEKgTAhZ6Uojdt73tbe4IU9QxKD9os6DNHxvldhJiL3/5yztOa/ZrF3qsaHsl4m/Nzu1xpquttlrDMDhDmc06HHjggeGAAw4It912W1hwwQWDmQw8drehc8GdUc9XkK3Ax53ZnYt2V78mBCRomwDRrhCYyAj87ne/c6/c+eefv/RlVkXQwju8kCyiE7EyNKeuTl06HsPbloQV06dPD2abHdP37LPPdo/clEwE1ToraMgc2Mb079Yw6vm68ZOO4+FNAXc0KKLeEJCg7Q03nSUEaomAxXw630k4pDCUfDYj7I0QNl8oCdh//etfvp//hxfwqGnmmWcOrMwtfrbj1BYb6hmbWnXqxre5srhwRSW/++67Z0OgjiakCLKEGP77wAMP+C//YE837+RgsbFZGxtVm6+BuS475vzmPSweuUtPHW6HgARtO2TULgQqgkB6SbNCg9KLLy8ciXWFHnvsMf/N//O9733Pd1FLkiIQ9eYnPvEJbzMP5oDgYiVm2YkCTjhf+tKX/Nill14aGDfZJS3BQbDQomCeyn4cRyPskqQgHDWRFhHnnHaxvQg6C8UJFvY0hrWvf/3rAUcy0jO2I5ynwANhbl7d/sfqGMewFNOMSpl4VDBNfKA+RsWaX0lXcb503emZ6qQWTrZZUkyKekSg0Ql58u4pvGfy3vthXXm/4T0m5KIJAw8rsf/e0byIPW3eUkst5eEWxHB+8Ytf9HhXQnjoM8sss0TCciBSitJmwiEuvfTSHt9p+X/HxEMSvmMOLx6iwjknn3xyNAcnj88lZtdWsj4GYxFHmrIumQDx8VNoUD84MnaZOFrCk4iNbXfOI4884sdNzTyGLcJUmI8/0jQ2E9mQ0vFWv7/97W+zU8466yxPjzl16tRoCSAcf0tuEQmTSlS1+RJfhGeBD9doWgsPJTMv9nQ4+7Wcx56K0T4msrZuGwrvaURIcbQv4iFB2/hgaK9/BPoVtP1ykAQtQod4yUcffbTtkLZqjrYa9uPE6zbnBeYli3Brftla0vm2Y5Y5wMu+ndBsN455UPsHRLvjJI3IJ67I9wMT4nBTEpD8sbLbYHL//ffHu+++ewxuaay6zgffr33ta/0DL11LkV8J2kaUpDq2/+EiITCREUC9acnh3XbY7jpRH6NOhbDbEs6RJ8bA2YffPJHycLzIsmMFvK3bpQck/CiFQuV5xM7KuZZRy8/PH+tlG0xsRRusmMEY3BivrvOhfidU6eCDDw7rrLNOL9DonBcRKJ6QU5BVHgFseSQqoNwWL6AVV1zRc7Y2x2GmC7FvrvDjH/84WEWSQG5YkgAkx5fUp+gv9rIf/vCHwdLfBUspFyzZu794TM3pSQ0oVdb88i46dj/9umFCfCT85glBgwAhIQIYImDqSMnuhtPOG9/4xjpeQkeeie+0lZMnjOC53XnnnTv2Twd5ztdcc82w0UYbpaah/tZxPhzfrJCClwckp7SoTwQaF7iTd6/uqmNUf+SiJb2evVQzG5MJijHqvnSXUZthj7Ivfy8FltrL/DKGOc+4fQ9VHHa7q6yaCWW/Uso/e0S9Ak2ZcQfRtwgmpDn87Gc/m+FlgtevgdR6VC2Bd+yb9rIszdJ4qY65JzzPpAWEfyrOmMNOaf5HeQJ8llUdJ/6wh1oCjrSr3wEgwDNkma56Hkmq40boZKN9EY+6C1ocG0gQTzJ0/pNQa9JWkP6iNa/TxrvetEdNTQv+b2ottgtuvCTJ8dpcyg1bn6X88+OtnE6KzdB7r6KYWPUV59HUpw2TgSMfIuBITdhWjiINJzTtjJeghQ1Kr+X/WiXJb2J3XHf7EbTjyrgmb4mABG0jLI0GF3vaRfVDwG6pp4Ej3MI8MV1tjE1ljTXW8IsxL9GOF4WNqdn21vGEFw+Seu70008PZOghzV0Ke0jnooI9/PDDvZ3QklFSGUya+U58on4nxaAlpA/mSOShIub0kg5X+pewm/wfz4VICAiB8UFAgrYP3MmY8n//93+B+DJqO2Ifzeeafe6559xRw0qTBRIE4IDx8MMPZzOSBu+MM84IBNYTr3f00UcH0ryR+D3FSpqnp9uSSFxOP1Ov+fnm4egZa2jHNnruuecG8w7MxmYjpYxrTr5+9dVXB3PZD8stt1wwtakLkWbb7HbbbedCpmHApp0vf/nL3kL2nHZZY3jZk3g9OaVUFZOmS2vYpTaplVoLtioMVgC94Zh2hIAQEAJdEWhc4E7evbKqY9SkSy65ZLQsMRFboDkUufqR+pwQoQWLLrpoXGWVVeIdd9wRraqKxyCa4PFt+hBfh+u83aRoSQQ8ZhI1LvvUtUyE6pc2y4aTmvzXnD+8TmlDY25np512cpWwZa7JWqlZiorUkhF46IPlaPWxiaNM9OSTT7p9z1a5HetqUocTvuwDI53a8beqmFB6jutoVh3nLyaVjqMkXVEaT9VxUR6r0g/8e7XRVuUaxMdLCEh1/BIWbMlG+yIeZQXt5z//eQ+IN5VohihOM7Z6dOcjUzl6EDhJBxJdeeWV/kInSJwkABDB4Lxk8s4qODOZOjOd5r8f/OAHvd9NN92UtVuy9Ih9sRXxIUDSARIaJCKOkoQG1CZNRJwhc+UFLcfMGznyn6UdEV8H3/xhDy5CVcQEvosIWgqNc63zzTdfkUv1PknQJpz0+7/nRThMHhxYhIhiVHiP/a8vS/bgeMiKrVYDIQaJrr/+eq9LiRoWVS4qW9LAJbKsPF7WDHd/wmHIi5pUqqTFS0R+VdS7uNin0BxUvNhEscOaQPdKIeSiTTVH07npl7JdpIvLp8cjCTpp+/Jp6ZjfVt6huWyarczTUC1/Uwk2DqZqKi07vtgIZlXEpBPP+WOWmMF3eykJRz1YYjpF7RGYNm1aMA1MICWkqP4I8D7Jv3vqf0X9XYEEbQ/4kf8V5x7sj3nC+Yc/ymeZ+jXY6jF/2I+9733vC7ayDVRRyRf0zndsVfIMOzB2UOJRyTVLwe2U3Dx/LtvUyCQ+FsEPP4koTA2RvCBPfBg022jzx1ttk+AAfrAt8+HQjbBdVxGTbnyn4+Swhbh/ZcnMAB5XXPa8ydYfITuq2NbJhu2or5f3g+glBOQM9RIWhbdSHUxTrXrWl/yJOMyw2kPAkQgBoZwnEjlAc845Z7656zbCl0w2rB5PPPHEQKJ4nJCaCeGKlyyCtvlDAOctiD6DoJVWWsmHwUHL0tB1HJKsQ1XEpCPTLx7EMc1Ce3wPpzeREBACQqAMAhK0ZdB6sS/VTFIpLEsqno2AsEH4oWZcfPHFvZ0sTXmi+gnq2nQ8f6zbNqEmCPldd93VvZybw1LIysQq97LLLssqrjAmVVcuvPBCVyWzjxDOk8WLZiXRUjtCGdV1J0Kg8+VqNs5w3HHHte2KytocwrJrrhImMJ3KwbW6ALMxOdakoyPzECnpREJACAiBUgjIUP0/BMo6Q6XKJbbSjFtttZU7HeEVbCEvPqAJE3eGotJKShaA96+paOOBBx6YwW4qYXeywWs5kdV99LZWSR5MkPuxm2++OXX3XzIX4QW8xBJLuIMVTlb8kUiCzE94/OKYkLJG4STFORYH6+PZQxMtZ6xXdiGhPNeCM5WFIDXM07yDpyie01yX1e2MVozbuzDGtddeG60cW8QTm7mriAnMmjreMSAxBU5kZBoy9b9nt8JzHGzMhlg6AX1yhjLNRjNs2m9CAIzlddwESo135XXcePPkdfwiHmUFLRU7bEXn3rq8JGyV6gKUl3QiwnIQVrZ6jYTaIED333//LCXiIYcc4udxvsVpRjyKUxgJbaaaHVNxhZc33sZ5QqhZUvNMYHJu/o+MUYls1euhRBznI8FWaL5Pmj6z/bpgxSN6rrnmioQikV2oG91zzz0Rr2iySzGmJVfPUkESwgR/iaqGCdmjyPqU8DL1tt9Ts2N72TNSMZoTWmK/1K8EbXG4wF+CtjheVe8pQdt4h6awaw/5pKctt9zSEzdQ7LoMoXbFDot3cauk+aiTKZyMIxBewHlv3TLz5PuSpajfqikkzoAXHLZwVGpOOp88ifNe1XkeWm1TpcRqdfp4JqiDhTHVCpNW19RPm4VeBYuLzoos9DPWRD8XZzxs/XKGmhh3miQ6+DNgepFjVAiy0fb5XOPgY7GVLQUKQ+PEtMgii3jYwiCELGP2K2QZAztz8opuFrIcR8CWEbKcw4cG10rYEyFKrT486FdVTOBNVD8ETKMS9ttvv4xxPhxNExEIp9tggw3cU7+XFKD4EuBzsfzyy3sVm1tuuSWbo9UGa5ZPfvKTnu2t1fFubaOeD6dIFhhW/COYKSngP5KID22yx6WwttSu394QkKDtDTedJQRqgcCwFVbDHr8byAiLz3zmM4GUoRACAsGBJoG4bWrEWoKWgIc8GpeidNppp3mJRPMzcI9zy6jmH8uXX3552yGOOOKIcPLJJwfSo5alUc9HaUxwwrnvoosuClapykPXzH/EWecjm9hm4vTNt6Ts5ah/MwL2H0VkCJS10Qo0IdANgfG20Vpe5miJS7qx2fPxQY5v76XSNlrsgBYuFy2WO7sGq6scSR1qhTSytuRcaEI5a+u0gV+Chd/Fa665xrs9+OCDnuoUHvFlaEWU6cNHgT577bVXqy5t20Y9H4yQ8pUSmnkiXSzOknmiehXlInFmLEOy0TaipRVt85eH9oXABEAAvwFzgusa39zrpQ57/G58mYNdoCA54WzzzDNP1h2zCuFaJnCztpQYhiIeRYgVHoUkWPFBJGbZZ599fJuMbs1E7DxqVla0vdCo5zMR4H4UhAMmXwz4Zru5iheqZfxMLCKhl0vTOS8i8FLaIEEiBIRAJRAgOQZVnVBB8pKn3OHGG2/svBHLa2FfnsmL+GScz1D38TJEyBxzzDHh3nvv9TSb2CotD7VXjEK9ij8B6lTs8+al7rZL1IJkrjLva7ed9zO+hbK5ChchiAp1mHT88ccHShZakYeGaVAZI0BwxEsERpQJpFpVEUKdSpx8nhDWtLXys8C+SXUu1NO90Kjnw/GMimIkvrHwu3Dqqac6Zhb1EA4++OCGS+CZIf3r3nvv7fe2bKKdhsEm807jAnfy7kl1PHnv/bCuvBfVsSX+cBWkvfwicdeWnMTVkeaN62yanTHaC9/bEt+oTlGXmlD2JstkFS3PrPexFUm86qqroq2aXO1p7zqPtybkzMox+jZtJoj7Gp85i1Z9Snznf+GhTHiPOdvFhRZaKD9Ey23z+HccCMXrh4gnJ1ac8Ls8UQWL2HVC2FJxirKq4/x4aXvY89nHRxZTj2qYMET7OErTN/wSs8/9IVSvKEl13IiUVMf2BImEQBUQYCWKyhI1pCVBCZY0JFistaswzz///GAVnnxVml+twTeq07wnOqsOQsmgeeed11da1C0mRzaERzi5m0855RR39GG1x+qGlKGsYHoZnzmph2wvWM9MNswiCoTUkV+7uc6yX1zTPxThsLjzsMsuuzQdKbfLCppMbHnvZnKas9pD+9DOw77cLC/1HvZ8OD+lDHHkXadOdDs8F154YWesldr8JY611QkBCdpO6OiYEBghAqh5eeE1V2RCdQxhyytLqAkTJbUnQji188LFuxTVcy8v0jROmoOqT4nf1DboX8J58CBuJxjSfPQ588wz/YOCD4heyZyhwqGHHupxvqjdE5lGwFXu+cpb6Vg/v6OYj1j8HXbYIay22mphnXXW8Tj/tdde21O1NvOOBzIfEuQDEPWGQO9PX2/z6SwhIATaIIAAgZodUojjhFh5lKVmQdjq/FTNqaizUH6MIuPn+w9iOznwpOIe7ca8/fbbPXyFFW2vRPwtAoi83gilRHz0ECLDag/7NoTwgkjWgJAn+caSSy7pbUX/GdV8BxxwQLjvvvvcD4APMK7PVN6uUVl//fWzD7HEN/nZLY1r2tVvSQQkaEsCpu5CYFgIpFq31113nTuppHl6rfjE+UUEYVqpNKuM0/ydfouM3+n8Xo7BJ/NaXu2Op7MyTE5kHTu2Ocgqn4QV06dPD2abbeiFpzHZz/JFRVBpQ3wQUfHJbMilBO2o5uMjgHKbOGElLccee+wRyGR2ySWXBLPJNpSDxLmNDE8koxH1hoBUx73hprOEwMARSLVuyRCUp5SxB49aKKlBeflB5nbhqt98FaIkALtVYOL8q6++2lOIWgEFdvsan5dykTl9oh7/IaUf9mtqIXciVmZ4VLeihF2rY7SBKcIVe7YVy8i6IaQIKUIAY9PO/6X7tuOOO3o7oTGJqjQf5onHHntsTCKKtGKnUlWe+GiALB4536ztEghI0JYAS12FwDARIEQEGycqvbw9FlUkzk6UR4TSC5GyiZREREXJy5H81dQpRuAmWyIlEhHUyRGK86+88spsNYiTFU49VnXKSzD2Mz5CCJsvIUlWBYmhhkao07EpswpsReBB/vGVV155zGGrvBWoj0x6xnaE8xRhLwhzQoj4Y3WMfbu5PGW7MVJ71ebjQ8WKgATzig933XVXYjOQ9YrnhtSVeUoaD6s0lm/WdhkEGp2QJ++ewnsm770f1pX3Et5DliBTVXpVJ55JSx8YzQ4Y7YWYsUk4TyrfR0iPeQxHswVGS7DgFZhMhRmpwGRCwcMyTOB4piTL1ev7nEN1omWWWcZDhczDNRubjV7HL1v1KT+pvbNKhffYx0g0b+m25zzyyCN+3NTM+Wl8mzAV5uOvVSlKsiGl461+CY1pRbTTvzm8p2rzwbvlMI4WSxtNOxLNeS1avnZ/hshw1UyU2+R5sY+a5kNt9xXe0wiNyuS9iIcEbeODob3+EehF0KZZzbEmmko3IlDakWVn8tq5HDcnmjHdeDFyfnpBJkFLzCx1dxEM6diYk62h7PiMAR+teGk1fr4NAVUmjpZzLYlCXHrppfPDNGxTg9kcpxra0g6CmDhcPkpGQVWdz1Ta8Y477oiW3KQlDPBNvWnziG95vF2jBG0jMlId2/9wkRCoGgJkIbJctB4H2443VKPJFtuq0hLey8TRNnsxMx7qT9S8rY6l+XoZHz5a8ZLGHOQvDjxUgmqXHpBYXrxlmwkVN+duu+22fn7z8UHvV3k+1MjY5pMjXv7aUb/jMEW2KEKARIRxmwAAQABJREFURL0jIK/j3rHTmUKgVgik8AxSOE4EQqCTIINkHHxwkD6yCJHsYs011xxZ7ds6zodD27rrruvlAckpLeoPAQna/vDT2UKgFgjgULXTTjs5r2QEIvMUWada1SKuxQW9yCQ1la+44opg6s/CbONJzN+oqI7z4dl+wgkneIjSqHCayPNI0E7ku6trEwIvIkDCe+Ik8zTrrLPmd2u7zWp2scUWqy3/VWQclTxxwKLBICBBOxgcNYoQqDQC5DPmTyQEhMDoEZAz1Ogx14xCQAgIASEwiRCQoJ1EN1uXKgSEgBAQAqNHQKrjHOaUDqOSiUgIDAKB5OVLGEmrMJNBzDGRxiB7FZmqRPVHIKVtrP+VDOYKphBWO5ih6j3KscceG6655pp6X4S4n/AIkCif/Lop7/GEv2BdYK0R+Na3viXfALuDErS1fozF/GRDgOQMxxxzzJiE8JMNB12vEKgTArLR1uluiVchIASEgBCoHQIStLW7ZWJYCAgBISAE6oSABG2d7pZ4FQJCQAgIgdohIEFbu1smhoWAEBACQqBOCEjQ1uluiVchIASEgBCoHQIStLW7ZWJYCAgBISAE6oSABG2d7pZ4FQJCQAgIgdohIEFbu1smhoWAEBACQqBOCEjQ1uluiVchIASEgBCoHQIStLW7ZWJYCAgBISAE6oSABG2d7pZ4FQJCQAgIgdohIEFbu1smhoWAEBACQqBOCEjQ1uluiVchIASEgBCoHQIStLW7ZWJYCAgBISAE6oSABG2d7pZ4FQJCQAgIgdohIEFbu1smhoWAEBACQqBOCEjQ1uluiVchIASEgBCoHQIStLW7ZWJYCAgBISAE6oSABG2d7pZ4FQJCQAgIgdohIEFbu1smhoWAEBACQqBOCEjQ1uluiVchIASEgBCoHQIStLW7ZWJYCAgBISAE6oSABG2d7pZ4FQJCQAgIgdohIEFbu1smhoWAEBACQqBOCEjQ1uluiVchIASEgBCoHQIStLW7ZWJYCAgBISAE6oSABG2d7pZ4FQJCQAgIgdohIEFbu1smhoWAEBACQqBOCEjQ1uluiVchIASEgBCoHQIStLW7ZWJYCAgBISAE6oSABG2d7pZ4FQJCQAgIgdohIEFbu1smhoWAEBACQqBOCEjQ1uluiVchIASEgBCoHQJTolHtuBbDQmASIHDTTTeFFVZYIbzwwgvZ1fLflb8ZZnjpG3nKlCnhYx/7WDj99NOzftoQAkKgOgjMWB1WxIkQEAJ5BJZeeukw++yzh4cffjjf7Nt54UvDRhttNKaPGoSAEKgGAi99FleDH3EhBITAiwiwUt18883DTDPN1BGTmWeeOay++uod++igEBAC44eABO34Ya+ZhUBXBDbZZJPwn//8p20/hPD06dO7CuO2A+iAEBACQ0dAgnboEGsCIdA7AosuumiYf/752w6AEEYYi4SAEKguAhK01b034kwIOAJbbrllmHHG1u4Uc8wxhztMCSohIASqi4AEbXXvjTgTAo4AHsXPP//8GDRQG2+22WYNHshjOqlBCAiBcUdAgnbcb4EYEAKdEZh33nnDEkssEXCOypPUxnk0tC0EqouABG117404EwIZAltsscWYlevUqVNdAGedtCEEhEAlEZCgreRtEVNCoBGBadOmNSSuQG2M8BUJASFQfQQkaKt/j8ShEAhzzjlnWGmllbJVLWpjbLciISAEqo+ABG3175E4FAKOAI5PiRZZZJHwzne+M+3qVwgIgQojIEFb4Zsj1oRAHoH1118/W9ES8iMSAkKgHgi0Ds5r4v1nP/tZ+OMf/9jUql0hIARGjcDiiy8ebr311vCa17wmnH/++aOeXvMJASHQhMDaa68dXvGKVzS1Nu4Wqt6DI4b+UzcCpz0hIASEgBAQAn/+85/dh6ITEoVWtAxAdZDzzjuv01g6JgSEwJAReO6558J3vvOdyjhC/fKXvwyssn/3u991TBU5ZFhqMTxaiOOOO07e4rW4W92Z/OlPfxpWXHHF7h2th2y0hWBSJyFQDQRe+cpXVkbIVgMRcSEEqo+ABG3175E4FAJCQAgIgRojIEFb45sn1oWAEBACQqD6CEjQVv8eiUMhIASEgBCoMQIStDW+eWJdCAgBISAEqo+ABG3175E4FAJCQAgIgRojIEFb45sn1oXAREAgxhhWWWWV8JGPfGQiXM5QruGee+4J++23Xzb2X//61/C5z30ufOADHwgbbLBBOOecc8LTTz+dHS+6QYjKpptuGpZffnkPO7rllls6nsq9+uQnPxnOOOOMjv3aHRz1fNdff30gi9oKK6wQvvjFL3qyl8TbM888Ez7+8Y+Hv/3tb6lpaL8StEODVgMLASFQBIEXXngh3HnnneEXv/hFQ4WiIucOug+CpGqEsPjMZz4TtttuO2cNAYHgWHbZZcO5557rOa8pMEHRCYpNFKXTTjvN40CvvfbaQPa/b33rW2GZZZYJl19+edshjjjiiHDyySeHu+++u22fdgdGPd+PfvQjx2n11VcPF110UVhggQXC+973vnDggQc6i69+9asDyZje//73hwceeKAd24NptwerK1myisifSAgIASGQR8CEI5IpWsKKfHPp7ccffzz+4x//KH3eIE/473//G1deeeX4/PPPD3LYbCx7scdvfvOb2X6RjR/84Adx7rnnjpYCN+t+8cUXxxlmmCHax0nW9o53vMPvgwnlrK3TBnhbRah4zTXXeLcHH3wwrrvuuj7Ge9/73pancq9f/vKXe5+99tqrZZ92jaOeDz4WXHDBaAklGlgyzUl84xvf2NB20kknRRPC8Z///GdDe7cdsOPZt8xQ3bpGrWgH872iUYSAEOgDgVlnnTW87nWv62OE/k/dc889w1VXXRXsrdn/YAMY4d///nfYfvvtw6c//ekwzzzzZCPOPvvsvvI3gZu1mXD07SeeeCJr67TBCu8LX/iCr/jo95a3vCXss88+fsqvfvWrMac+++yzrmZlRdsLjXo+7iHqdTKXoQFIxLZ9pKRd/0W1bB9Z4bDDDmtoH+RO44yDHFljCQEhIAQKIMBL/IILLgjrrbeev/A4BYGBHZCKRRQ0Ofroo8Nqq60Wtt5660BuWchWIN5nnXXWCffee2/Yf//9w3LLLefpYu+44w7vc8MNN7gNcvr06eHCCy/0toMPPjiwzx/HIYTsIYcc4tuf+MQngq3YfJvjq666asC2OGo6/vjjw/333x/y5RHhAZUxAgRBmei3v/1tIGsY11+EUKfuuOOODV0R1rPNNluYa665GtrZwb6JDR31dC806vmmTJkSNtxwQ3+OuJ88Y6jHb7rpprDTTjs1XMKMM84YPvvZz4bDDz88/OUvf2k4NrCdrmte6yDVcRGU1EcITD4E+lUd26ojbr755tFedq6GMxujg3jQQQfF1772td6GCtAcfqIJGN9fa621vI8JimjCxdvmnXfeaHbKaLZL37e8wvG+++7zfl/72te8zQSx75tNOH7qU5/ytlNOOcXb7CUcrb6vt9mqNrIP7bbbbt5mjke+388/ZVXH73rXu+JCCy3Udcrf/OY3zuNXv/rVrn07dbCPm2gCyrHJ97v00kvjEkssEW2FHX/961/7XGVVx/nx0vaw57OPD1cTm7B01fAcc8wRzb6cpm/4vfnmm/26eO6KklTHA/sM0UBCQAgMEwF7+YXTTz89JNVnmosVptkMfXf33Xf3FS9OQWZfy1aXRx11lDuy0OmEE04IZ511VrCXn6968cBNK1QTWGlY/2W1QyGEPOEkM/PMMweOsWpjH9p1112D2fCyFW7+nGFum504/P73vw9vetObuk7zjW98I7znPe8Ju+yyS9e+nTqwgkZ9n/duZoXHag/twkwzzdTp9NLHhj0fzk8//vGPnS+KXlCQox2eCy+8sPdrpTYvfWEtTpDquAUoahICQmC0CLSq5/mqV73KmbAVbcYMQhOV8b/+9S9voyIOlBece++9t7fhxVyWELR5esMb3hC22WabwAfBKIlwHjyI2wmGxAt9zjzzzGAr84AKtFcyZ6hw6KGHeoW2N7/5zdkwqOptVR/y9yA72MfGKOZ79NFHww477OAmB8wLTz75ZKB2bDIh5NnHA5kPCdMO5JsHtt37nRkYCxpICAgBIVAMgZe97GVdO77tbW9zh5eijkH5AZsFbf7YKLeTA495+Xac9vbbbw/YP1nR9kqs/hFApnp2oZTGwYGJEBlWewhbCOEF/fCHP/QPAcqnLrnkkt5W9J9RzXfAAQcEMx94KBIfZFwftnds29j+m+81H3Z///vfi15GqX4StKXgUmchIASqjgBqQmJz559//tKsNr98Sw8woBPgHV6eeuqpjiOyMtx444079ul0EG9bElbgGGZ264auOBDhGIVKPhEqbQiMcUozG3IpQTuq+Vjpk8SDj5Ck9dhjjz3CjTfeGC655JJgNtnMPMD14OGNpmSRRRZhd+Ak1fHAIdWAQkAIjCcChOhAKdNUUqlanG7GVhIYCORECDYEQTqW2nkBj5qwF7Myx+O6E7EyM+ewll268W1OPy5cUcdjB0+EkCKkCAH80EMPNfwl72s8ljlGaEyiKs2HPfaxxx4bk4gCz3XI4noT2/6bPNktHrmhfVA7ErSDQlLjCAEh0DMC6SXNCi1RevnlBeQf/vAHP9ys4vve977n7aglLSmE2xQJ64DIdoTgwpZ56qmnBpxwvvSlL/kx86gNaUxskwgf7J30u+uuuwKORsT4fuUrX/H+o/yHtIg45yD8WxHC4q1vfWuwJBtjDn/9618Pr3/96z0945iDLzbgPMV1IswJIeKP1fG73/3u0jHNVZuP+/3BD37Qw7e4j4nIesV9JnVlnpJt9qMf/Wi+eXDbRVyZFd5TBCX1EQKTD4F+w3tMyHnojq0mPbzC0uHFW2+9NZrHcDSbmbcttthi0eIfs5Ace/tF8wyOZi+Mtmr1PiYc4tJLLx0tsUO0l+iYbD0mKCPhNYQRcQ5hHoR7bLvttj4fd84EsYe3wIvZ8SJhQCZAfPwUGtTPHS4b3kN4EuFL5513XstpH3nkET9uauYxxwlTASf+/vSnP405TjakdLzVL6ExrYh2+jeH91RtPni3HMbRYmn9nq+xxhpxvvnmi2ZPjjyzzWQ5j+Pb3/72aB81zYfa7pcJ7+ELritJ0HaFSB2EwKREoF9B2y9oSdAidCxphQvfdmPaqjmaOtEPm3rU40Kb+/JyttVyQzNtg6CygpY5zYPaPyDazQ+v5jjV8jCYEIdrqvCWxwfdWNX5uO+WwCQSs92K4JuY7e9+97utDrdtKyNopTq2zzOREBAC9UaAtHq2IvHMRu2uBHUi6lQIu22ruFDSGxLSkyfaxotw4MHTul16QHhNYVB5HrGzcq6t2P38/LFhbFd5Pu77oosu2jJEC/U7DlNkCyMEaFgkr+NhIds0Lm74uMtjEyLV3L777us9sC3hWHDiiSc2uNY3nd7XLg4M2JqwRZn6xAPQl1pqqbZj4uQAn/yR0o3/sEXo5z//uVcASX2x9SQ7WWrL/xLnSEWPRMTqUaWkX8KuRZKBPOHowkuJnK6mRuoan5g/t8g23qHf+c53HDPsQHlni/G+x8RHck/zhKBBgBCnaYnXg2VWyh+uzXay1Zp61JNZ1IbxgowS32mFBfydwTO88847FzqTZBdrrrmmp6MsdEKfneo4H7HYJEXZYostPKd0nxB0Pr3tujh3QKrjHBg9bpqwc1uU3Y2Yt/cceeSRbvOwzDY9jtz5NHN28PFNwET7MvZtfi0+ru2JJjAjNgt4NaeRtv2aD2DTQn3HXJyLja2T2s0+OLwffVGRNavsmscvuo+qDLyTjY/nF5ytjma0zDdegcS8LIsOV6gfdjDsXvb17NeUP2m87zEqM8vuk2Ftgtdtj6QVpGoJ+GPftJdlnu1C2+OlOuYe2wsyS91IxRlzgirE83h16kV1nHjl/1Yr22I6rt/yCPAMmaNU+RNfPKOM6lg22p5hLn/iZZdd5i+1vKBlFBxChkG2qipdCivxYasy5xUnkrKUhDQvcEvn1vJ0HD04nv4Q0IMm89r08a3OZjY0/7HMi9Tbv/3tb2ftg9owTYGP3TzeeN9jix90vvgQyBMvGz4QTI0abYWb5fjN9+m0PV6CFp54vvN/FvfZidVxP9aPoB135sXAGATKCFrZaO1NPypK8XzN8+GiPwwqW5oqz0MqJZV+88e6bZMvNaVxO+aYYxrKVKVzqcaCaisRdpRBU6sxCbAnly2ESm7QVNV73K4EHfY/UgySNYesP4SK3G8VY+pAhN3k/6heIxICVURgKDZaAoXJyoGdyipzeImru+++26vZW4UNDyIm2wgli7ABEiyN/QHinPPPP9+Tg5N9xFRanhzcvMI8sTWJxSH6o1tHmBCDh0DA7mnFjP14t384B9sZc1l1j2CrG09AjYCgLiP2xUSUVyKpNteQbHzN2VgIkEao0JeMKpxPns0kcNJYzb+MSXwfwtZUm34YWyf5OLlmhBH2VcpgbbXVVg0ls7AxkDgdGyzXD1bYXuGR8TDyU/YqT6kUVsqWko7BMw4B4IuTCMHyULovbG+33XaeQcVCI9jtSFwLSd+xoZ1mdljqaiYi5yixitg0v//976fm7LfTM8Ax7k+KLSTJPPFyZ599tp8PBuDUidI1IWQSjfIekxoQ+zfPLhiZt6Nnq6HmKPGa+TJlg7zH6Vpb/ZKWjv8DlF/7yU9+kj2LrfqqTQgIgZIIjFkPt2goY6OlZFMqZ4WtzpJ9R6v/57/GWtxkk0288j1lqkwIuDrr2GOPzWY14RCJC0slmbCpWSB1dvz//u///BzLv+ltxG+ZR1m0QPesT5GNoiW2jjvuOLfpYet84IEHolXz8PnBJBG2RXhYZZVV3I38uuuuiyboXEXJdiLsomCQVMfE7XEebSkujTJUFmDvbSako6UE8+tH3WnCNFLOKdGHP/xhjwszweWxdiZA/DxblcQrr7wydWv4bVWaCvd3ymBRfswEWeQeElMGX+ANMYet1pyHbrZUE8jRvCSjOVH5GIyFijIRNkueEUsu4MeZJ+963+0ZsIoicerUqX6ufSj5OOBDzGM+Dg58GDuvOsYOaZ6n3m4fM87SIO9xunfpWlvdY57ZbiXg0vmDusepvFmz6jjNw28qHZf//5Y/3mp7PFXHrfipcptUx1W+O+V5K6M6HoqNlhc3LzhqRKaXslWj8DaChlMsm6movM1WIH6VxINZFY9oK5TsqgkwtrRY2T5OAQg0xsepw7wmo60Ks+NlNkyF6OPkHYOsWoW3WQqyCM8EjNOWp1Tz0lZq3myqNw90z9vhEHTwyEeDrUq8X7OgpdFWdN4vCVraElZ8TJg3K00RYcB41NaECOpnP28DtcTi3vbwww97n1b/ULNylllmaQhixxGJsfJB6ikAPQlaxrKSU9HUra2GbWhLgpb4NO4nY5977rneB0HIM4CQayVoizwDDGRp9hxzPlRMczDmHtEnCVqSEphXtzsEmcbDncLSx86g73GzoIWPVvc42bHzDjy2OndnLc6BBnmPiwja5LTF/SlKSdByj/XXGYP0ISycOuNUN3wsg1nX/y5DUR1jG0M9N/fcc2cxaYSVYL9CrZli2WxVErCrEHYCEQ9mLwRPAo1qF1UWbuOoZRMxLqpLe8GGL37xi+Hwww8PVrA5HS71m9SnzSW2SEtmLxBX6TG3ZatpGJfwENS1qP5QX5sQ8TCJvK2VFF+MD/+EmzTX20wDdioPBlaJx1RT01Z+fqp58/ov6vVE1NAkjAjVfavyWu1KU5GSjntF/cZEqGAh8E70oQ99KG0W+kUFiumAUBtCTKZNm+YqUk7GrT5/X9OARZ4B+tpHkscIwjs1M01gpiHG/N55552ev5bwHtTOzM31Qqhtx/Me58uPcY+vvvpqLwHHczHIezwGlBYNab5eSsKhAm/1zLWYZtI22ceV28N5dkX1R4DUjvbBXuhChiJo283cSqggfAl2ToTNDeavuOKKYCpnz1mK0MuTrZTdhmRf4C7E8sf63c6X2Eov72aHIHKQQgg5BB82R1slNkzNdSH4bGXr/doJ2oaTOuzk7Yl0Q5CDJ/Y08rsikLH38vHS6sODPq1KYcE/dtR0TYmFJGDTb2ov+0vcH/ZYW50FW4WGI444Inz+8593m3K7sYo8A5z75S9/2eOP+Siz1bPns201JgnSyePaiibDPW513a3abrvtNm9ORc9b9WnXZtqDnqrltBtvIraTgJ8PWDM7TcTLm3TXxGILWVWEKuV1jIMLqx5qHSKgWC22qsdIsnGcmFhB4mhjKtki11qoD8LT1NP+0khf9mZnbTg3rYZwvGJ1jlAlIUBamafO+X6pbVC/CFYciUiUwBcywgShiYMR/OQJXNuVwqI8FIQDFo43gyaEfsq4goMUq8tOzkpFnwH4RJuBpoQVKQWquW9laTLc4yKY8H8KhzAIoSkSAkJgcAiMVNCaIrsj56hiEbKrrrpqpnJmJdZ8HuEIZBC64IILPL0YL3BWlYMgVl0QJbbSlz1fLnlidQaZQ49/CCTVc6t+qELT8fwYg9imqDFlsvjYMDurq92b5wI76ky2K4WFpysqVbyO8x8U5rzkLOaFF0K5iDBG8HHfEqHihyxW1j8Ikjo8P3a6x0WfAYSC2Te9viQrdbylqSCSpzR+Gjt/LG1PhnvMtSYs0nXnf9EIIFzx6EYDgbe6SAgIgcEhMBRBi82Ml1u+vBUvcnN0aaivyMuYdv6DQynWj9AgQm/23HPPgDqLFzwrNVZD2OSwNZr3r4eyIHAp85QPHykLD3NB8MPLG7sZqQMRttglEWgIgER8DJC+Dh4gwoNQsRIbmuyOrG5R5ZK+MNmkzUnJ+yc7Kzu85KCi5cGwv0LEOrKK5Xw+Dkj7h604fQR4J/unSCmsdB1cMzZCcEYtC2HDRD2Neh8bLnbj/H31Tk3/cN2MkYgyZaim0U4Q8pQIdW+iVMqqyDPA/Nh+wZ2PBPNad1u/OZQ1zJvGz/OS5ku/w7rHyd7JPGXvMap8aJD3GN8HiOcTfvj/yXPIRxr3BvMM2iTCxURCQAgMGAH7D9eVyoT3mOo1mnByD0TCUQgZwOMxhfwY+5HUe2TosZVr5qlIOjU8bKnGwXmmdo1kJTInC9+nDJZ9bXuIib0YImEqUPISZlxb3TWEkXS7MObiPMY220nLEluEu0yfPt3T+cGjxQFHvIHhP08mrL3slq0oPZwJT2nT32fhJoQwUcKL+QhFsY+IaHG3WRgNYUyEDpEhKWUXAgdbDUZbrbkHN+fiMWw2z2jCJjuX9vwfOEBk/Mm3N28nL2NCb5jHBKH3x/vVVM2eThCPcFupu+e0OTc572DSighlSl639uHh26ZS967gA36JuK9glHhibEqj4ZXc6RnApZ6yaWCTyofhnQvPjEVIFLjiuZ7GJqzC4pE9dCnNn/8dxD3mOq2WZTYnnunw1eoeFy0BN6h7vNtuu3nWp4SHmRa8ZBxhV3hmk4rRfCLykBTeTl7H/L8XdUZA4T2d8anb0TLhPVO4OPsP2JH40oXsxdax36AO8sWdVjaM2bw/qHksbjRYWkR3pGG1jUNTc4KHNBerDLyHsQvjjNWKsC9SQBg1Ngkrkoq0Vd9+25iH5B8WI+rqXOZEO8AqiBUqK5h2fLabG80BtmbOAw9W6fZyyLrTBuXbsoMD3mi+5837A57Oh5sM93jQuJHgAnMFvg0Wyjbo4SfUeLwPLEzPfU8m1IVN0ovBVEhBDvwbuiVKavSaqQhgeSELS837ndi0JPiu7uzUh/GSLZZ+tjryEludzkEAA2onwjPYEkx06jKQY6hxV1ttNVdtI9DzxHfTxRdf3PaDId+3eRvVLqXGoFYfCaMQsImn5nvevJ/6DfJ3MtzjQeKlsUaHAN7xhDsecMABPilqf4t1d9MaXvp4MptGpeX/225c8s4gEx2hiK0qdfGRS/gWgoV3HO9BMom1iiLpNhfHRzkfvhuEF2L+wx+DjH6Wm8HZZOGA/wqREMkpsgj/vfSppKDt5ULSOdjc8rGJqT3/i4MSlGxhdSuxxcoZYct/DlafCHf+A9xyyy1uT+U/3SgEUx5TbQ8WAd3j4njy4u43DK3TbMMev9PcHENYmBnK08Cyj4Aw04QLXTMLeOpXtFsIkBtuuKFlnV3Oa0V8lFvSG0+9iWNlM6Epw+nTsuK5EEfAE+XBH5EhLFLK0Cjng0dS2JI+Fwdb/HwQtlwv6XpZOKCtJU8C4aSERg6N7CHqSmVstF0Hq0AHbJLYC7FVGbCxDiW2mmHDbortONkmk12VKi2iiYFAHe7xeNtozVEv2ktzaDd8kOPbi710KT8yseGvYg6f2TWasHIfBXPyy9qSvwP+HEUJfwiyll1yySX+Hky+Hfnzse+TNtfMUu7/Ys6RXumJ9yZ+F2Vo1PPZgiva6ruBRbIK8q7ME74slIu0lXu+uet2GRttuc+RoYn70Q7M6o9kF3iG4vHM10xzkYDRclR+NhJWkEgf+wBfuHh68+XWnMWq/Mg6oyoI6B53vhN49psQyApMdO5d/uiwx+/GET4TRFMQXUAYXiIiHgjXYnWYKCXEoWBFUWI1SlhXyjrXfJ5JGn83UtSEDH68N4mJJzMe1Mmbv3ks9kc5H7yz+saHIPmWwAPbzavwLS2RCBoky89Ol6HQpBS0IJkvr8V2XUtsoTJLqvChPCEadNwRmIj3mDhoK0bv4XMkMCE0LRGxvKatcWc/2qhclRKucA5ETDYfIrxMCUEjYQmmE+yJfHAiEOiDypUKYZhT7rjjDj+3n/EZgCpWxPIPmwhlxLmxOasZqlwECHbSRGDEO4xrHSRxX6gilif8Q6BhpNwc1Hz8n9lwww0DHx6ELeIoyjNHxTgyDuaJBD88VyTAYcEyFOq6PrYOE011XOSa1UcICIHuCPSiOu5WLcn8D7LKXokDVKe2EolUBINMk+Phf/ZSjLYiiebc6G1FqnL1M77ZLAtXsUq8p9+yqmNbaUarn5xOb/tLtS1woGBIL0RIIee3Uh23Go9qbIQCmt221eGubaOaj/DFZFpDNWwOT9Hy5Lfkj6poYEAxlaIk1bEhJhICQqB6CLDKZCVGgn1WsuQWJ0kGzj2kVSVhDCuM5lAhVKeoTBMRTpE87nEIXGmllTzEAu/YZD454YQTAnWv7YXoK14S0jBXP+OzujO7qYcFkixlWERmNryAi6waUe1a5S5PTjMsftK4SWNgsddZzep0bBi//cxHgh2rOOZsEX5GspZ2eFpuBO9HCOcwaNKqjocBpsYUAkKgMwKdqiVxZj4DW+eRXjra7HGcQtPy6UhJUQqRAassNY9Ptrhkpyw7VtH+hPMQWdBOMKRx6GM1j71wBx8Qwya8dfnAsWRCw57Kx+9nvkcffdQz0aHqxpSABzXpWonWaCY8kGeaaSbPg9B8bBD7ErSDQFFjCAEhUAiBItWSCg2U69QsCHOHss18Va6sseBGkfELDlW4W3LgIba9E1EWk9zUrGiHTcTxskIkx/wohHq/8xFznNLn8oHHxwFOZGhUTD08Bi58XVLI55iDfTZI0PYJoE4XAkKgOAIpMUC+gAVn91PpqoggRHXIS7ZZJV2E8yLjFxmnTB/4ZF6qc3UicqSPImKCWF6zAbugxXl02NTvfKz0yZlPco2k4SAZB9kAwcxssg2XgIc3ampyuQ+DJGiHgarGFAJCoCUCRaolcWJaMfHyg1iBEIKRr0KUBGCRilIpExwJbaB+xuelXGROn6jHf2aeeWa3gVIwpRORZALh0YoSdq2OlWnDw5kQI9LVWi7x7FTKdObVsFWaD3ssBVhItJGn5DFNWGeeCJOELB453zywbQnagUGpgYSAEOiGAIKuSEWs9EIkjIYXPOE5vBypgEW1LQRueunzwqdqFdWc8kQ/qLkqF229js9KqWgVK+bph6iqhHMOHxitCDzIvb7yyiuPOUzJSKqGbbDBBmOONTekymGt1Kao+hmf0CHspYQa8Qd+/Ka5qzYfHypWWMQzZaXKYFw3Vc54bggNyxO54yHSWA6FirgyK7ynCErqIwQmHwK9hPeYgOhaEYtwnkUXXdRDLgjpOfHEE6OlGIxURTKB6lmKbFXplbfsxRhN4MSUKYnqT7SZ007bqly9js+c3apYtXsKyob3mH0xmoDLqlQ1j2tlIP24qZmbD3mYChjwZylmxxxPDWR+4nz6mYYgWvnJaPGmfthW7tHSEvqxNFb+l4xRiQiLSceqMp8lJIoWS+vhWOa8Fuebbz5/hnhmm8m84L0aGtmrilKZ8B5UMl1JgrYrROogBCYlAr0I2gSUeYVGq38cESjtyLIzRVu9+mFbmY7pxouR8/MvyCRoEUTEbDJPO+plfPhoxUu7OVJ7WUHLeeYt7R8LaYzmX1uFRnOcam72fa6fOFxSzo6CqjqfqbSjJSuJltykJQzwbWFbkfSSZaiMoJXq2D7DREJACIwegVQtqVM5R1SjyRZLCEYzkU6P85vT6tGPNqpRtSt9SZ9exoePVrww3qAJBx5SH7ZLD0gsb6vMcKi4OXfbbbf18wfNV/N4VZ4PNbJpR1pW6EH9jtf2wQcf7CFAzdc1qP3hB14NilONIwSEgBAogECyNdatKlerS0OgkyBjvfXW8w8O0kcWIZJdULkG2/YoqI7z4dC27rrren1gckoPkyRoh4muxhYCQmBkCOA0RF5bHKMgHKksJWPtC63PMsssntw/5WouAiiFAtoVCyhyftk+dZwPz3Oyh5l6vezllu4vQVsaMp0gBIRAFRFIVbmozJWorsVCEv/pF/X5Yostlnb1OwAEeF5GIWRhVYJ2ADdMQwgBIVANBEaRTKEaVyou6oSAnKHqdLfEqxAQAkJACNQOAQna2t0yMSwEhIAQEAJ1QqCw6vjGG28M06ZNq9O1iVchIASGjACFtSGcjgijELVHAC/XY4891jNdte+lI3VBwBJiFGZ1CgG63XofccQRAUErEgJCYHwRIF3eQw89FJZddtnxZUSzCwEh4AicdNJJAc/wTlRI0HYaQMeEgBAYHQIkLjjmmGPGJEsfHQeaSQgIgbIIyEZbFjH1FwJCQAgIASFQAgEJ2hJgqasQEAJCQAgIgbIISNCWRUz9hYAQEAJCQAiUQECCtgRY6ioEhIAQEAJCoCwCErRlEVN/ISAEhIAQEAIlEJCgLQGWugoBISAEhIAQKIuABG1ZxNRfCAgBISAEhEAJBCRoS4ClrkJACAgBISAEyiIgQVsWMfUXAkJACAgBIVACAQnaEmCpqxAQAkJACAiBsghI0JZFTP2FgBAQAkJACJRAQIK2BFjqKgSEgBAQAkKgLAIStGURU38hIASEgBAQAiUQkKAtAZa6CgEhIASEgBAoi4AEbVnE1F8ICAEhIASEQAkEJGhLgKWuQkAICAEhIATKIiBBWxYx9RcCQkAICAEhUAIBCdoSYKmrEBACQkAICIGyCEjQlkVM/YWAEBACQkAIlEBAgrYEWOoqBISAEBACQqAsAhK0ZRFTfyEgBISAEBACJRCQoC0BlroKASEgBISAECiLgARtWcTUXwgIASEgBIRACQQkaEuApa5CQAgIASEgBMoiIEFbFjH1FwJCQAgIASFQAgEJ2hJgqasQEAJCQAgIgbIISNCWRUz9hYAQEAJCQAiUQECCtgRY6ioEhIAQEAJCoCwCErRlEVN/ISAEhIAQEAIlEJCgLQGWugoBISAEhIAQKIuABG1ZxNRfCAgBISAEhEAJBCRoS4ClrkJACAgBISAEyiIgQVsWMfUXAkJACAgBIVACgSnRqER/dRUCQmBECNx0001hhRVWCC+88EI2I/9d+Zthhpe+kadMmRI+9rGPhdNPPz3rpw0hIASqg8CM1WFFnAgBIZBHYOmllw6zzz57ePjhh/PNvp0XvjRstNFGY/qoQQgIgWog8NJncTX4ERdCQAi8iAAr1c033zzMNNNMHTGZeeaZw+qrr96xjw4KASEwfghI0I4f9ppZCHRFYJNNNgn/+c9/2vZDCE+fPr2rMG47gA4IASEwdAQkaIcOsSYQAr0jsOiii4b555+/7QAIYYSxSAgIgeoiIEFb3XsjzoSAI7DllluGGWds7U4xxxxzuMOUoBICQqC6CEjQVvfeiDMh4AjgUfz888+PQQO18WabbdbggTymkxqEgBAYdwQkaMf9FogBIdAZgXnnnTcsscQSAeeoPEltnEdD20KgughI0Fb33ogzIZAhsMUWW4xZuU6dOtUFcNZJG0JACFQSAQnaSt4WMSUEGhGYNm1aQ+IK1MYIX5EQEALVR0CCtvr3SBwKgTDnnHOGlVZaKVvVojbGdisSAkKg+ghI0Fb/HolDIeAI4PiUaJFFFgnvfOc7065+hYAQqDACErQVvjliTQjkEVh//fWzFS0hPyIhIATqgUDr4Lx68F6Yy4suuij897//LdxfHYVAVRFYfPHFw6233hpe85rXhPPPP7+qbIovIVAYgaWWWirg2DeRaVJU73n1q18dnn322Yl8H3VtQkAICIFaInDKKaeErbfeupa8F2V6UqxoAeOb3/ymvDSLPhXqN1IE5plnnrDLLruEz3/+813nfe6558J3vvOdSekIhec1dN5553XFSR3qgQAFMSYDyUY7Ge6yrnHCIPDKV75yUgrZCXMDdSGTEgEJ2kl523XRQkAICAEhMCoEJGhHhbTmEQJCQAgIgUmJgATtpLztumghIASEgBAYFQIStKNCWvMIASEgBITApERAgnZS3nZdtBAQAkJACIwKAQnaUSGteYTAEBH43ve+50H/l19++RBnqffQX/7yl8Pdd9/tF/HXv/41fO5znwsf+MAHwgYbbBDOOeec8PTTT/d8gX/605/CDjvsEO6///4xY/zzn/8MX/rSl8Kqq64a1lhjjfDVr341/Otf/xrTr0zDqOZ76qmnwle+8pWw1lprhY9+9KMeJvmPf/zDWX3mmWfCxz/+8fC3v/2tDOuTsq8E7aS87broiYYAL/g//OEPLV/0o7zWGOMopys017///e9AysrZZ589LLjgggEBscIKK4Rll102nHvuuZ4zmgINFG2gWEMZ+stf/uLxz+94xzvCMcccExBMeXryySd9nkMOOSTccccd4Uc/+lHYc889w+qrr95QjSl/TqftUc7HvYTPn/70p+HII48M+++/f/j2t7/tGPKhQiIgYpvf//73hwceeKAT25P+mATtpH8EBMBEQIDVGYL205/+9LhdzgsvvBA++MEPVirdKalXP/zhD4e55porbLvtto7NT37yk/D73/8+LLzwwuHNb36zr9gQlKS2vOWWW0rh9/DDD4fPfOYzLmxanXjQQQeFxRZbLDz66KOBvt/97ncDJQ6vueaacNlll7U6pWPbKOcjMcgNN9wQdt999wA+SyyxhAvbP//5z+HCCy90Ptdee+2wxx57uEDmo0LUGgEJ2ta4qFUI1A6Bt771rePKMyu1q666KlRpVUtGOITnvvvum2HDypaPgosvvjhre+973+vbTzzxRNZWZOM973mPr4jf8pa3jOkODldccUX4xje+EUg08rKXvSyss846rj6m85133jnmnG4No5yP1TN04403ZmyhDYBmnPGlpIJoC/igOeyww7J+2mhEQIK2EQ/tCYFaIoDtcZ999gknnXSS84/AOOOMMwIVf/74xz+Go48+Oqy22mqeU5YVSaLbb7/dVyxf//rXA3beTTbZxFWd2OV4ebKi2XTTTcP06dOzVczBBx/s+7RxHELIoh6FPvGJT4S99trLtzmObRL146gJoYCA3XDDDV3NmeZHZfzLX/4yfOELX0hN4be//a0Lw+WWWy5rG8QGqunXvva1DUNxH6A3velNDe2D2BnkfNiuZ5hhhnDooYeGK6+8Mjz//POuHidlKMcSIXQ/+9nPhsMPPzwk4ZyO6fd/CEjQ6kkQAjVH4KyzznJbGc4+qI+h4447zlWa5EXmxY6qEmef0047LXzyk5/0PrRts802LiCxv+23337hVa96lTsMIShx7kEoLbnkkm7L/NWvfuXn7bbbbmGWWWbxtuRcxEot1cfdaqutAipFCOGNqpYKWqMmVLOoWnFAaqZ3v/vdfq20cw0IXq6f6xoUTZkyJbz97W8fM9x9990XXv7yl4dVVlllzLF+GgY939xzz+3PwOOPP+6qYVTgOHGxwn3DG97QwCofKDxfFAgQjUVAgnYsJmoRArVCgFVoWk0mxllhrrvuur6Lje2CCy4I119/fXjjG9+YrS5XXHHF8LWvfc37vOtd7wq33Xabvyh/9rOf+eruxBNPDA899FDgWJ54oVOuL0/ve9/7AgniOYZTEfvQrrvu6qvstMLNnzPs7bvuusun6LZyRLWLSpbCDsMmPJDRNGBTf9vb3jbs6UK/8+EEtd566/lqFjwpzzjrrLOO4Rt7N5Q+xsZ0mOQNErST/AHQ5U8MBF7xileMuRBWpxCetokQmrx8U3gJL06IFR5CElpggQV8hYzquOyLM43hA9k/rHxYNc8xxxypaWS/v/nNb3yuToIWL+MzzzzTPzDydsdhMYkqG6xRzY+C+p0PL2PszIyDgMWZC0/kZKtN14AHMk5eCfPUrt//ISBBqydBCEwiBHDIKUJJ5VnWOahZ0BaZa1h9kjBATduOsFEjOFjRDpsQWj/+8Y9duzAKod7vfNhbMQMceOCB4YADDnCNBx9t2N2JO24mPuz+/ve/Nzdr3xCQoNVjIASEwBgE0spk/vnnH3OsU0OVBC0rc6g5tjXP/4MPPhg23njjfNNQtlHbk6gCQdtK9TroSQcx39lnn+0OcR/5yEecvXnnnddX/+ycf/75DSwTq4ympJX3dUPHSbojQTtJb7wuWwh0QuDqq68OhAstuuiiWShHygjEeXigQoTJJELIom5Ox1I7L+DxIHiH8LpuR3hlk/WoFXEdzz77bKtDpdpwtCK+Gecs4nYTff/73888uWmr2nxkn4LyySgIg5ptttkCDlJ5Sp7sxNuKxiIgQTsWE7UIgdohkIQZK7RE6eWXF5DJK7lZxUf4Rlr5sVpBbYgXM2rXZZZZxh2dsGWeeuqp4fjjj/eUgsxz6aWXZp7OCBFiR/E8pR/OMzgasYIblU0yXTu/8E14yi9+8Yt8c7aNsOBjYuWVV87a0gYCFvU513Tttdem5ra/CfdmXO+55x4fnzha7JybbbaZ/+EJznaau4rz4TkOfsQi8wEF4TDHNTZrAZIGhDSNohYI2H+MCU9mO4j2sEz469QF1hMBC6OIRxxxRM/MW+q/aEKB3Ifxda97XTRP32heyJHnnjYLy4g33XRT/NSnPuX7tJlncLRsRdGSOXibqfx8DBNO0VYs0YRpAz8mKKM5vESzLUZTJcaTTz45moNTtGxL0TIqeV8TxNFWtf5nMarRVrvR4nN9fAsfahivl52NNtoo8leGbCUZzSPWeWk+75FHHokmAKOpx5sPRctyFM2Ry3kHt3ZkoS7RvHKj2b69L3OZQPXupk6NU6dO9XYwb/4zb/Fs2KrOZ6Fj0T42/Dosw1a08Kdo8dpj8LScx/78mEDOrqnIhsUYR/swK9K11n34Ap3wJEE74W9xrS+wX0Hbz8UnQWtpBKOtfKMlbojtXpa2ao6PPfaYT2feuhFB0kyWYD7aiqehmbZBUC+Clrlf//rXR1PTtmQBXs1pquUx2vlAOPbYY1seH3RjVefjebBc2tHijVvecz5YEJjmkVwakskiaF/Ko2WfWyIhIAQmLwK2Gg78tSPiZBO185olvWEztWpr7jOsfeYmW9bOO+/sOYfzNlLmbE68kOcDlTN2VAoBjIKqOh/qY1uZt4QgJbMgWxhJS0StEZCgbY1L5VqxwR111FGebIAQDZINkEKuVfwkzJNoAPsZf2RtIfF3L4RDBP+JmAuvw36ImMwf/vCHnoUH/sg8QzahpZZayl+GhAwQizceRIpA7InYMOebbz5PKQdfEHyn1IZ53hA8c845p8edYmvrFEaSP69K28mmmBxfqsTboHghXSBesWuuuaY/fxQYKEIUGeB5HYWXMPzUbT5isUmKssUWW4Ttt9++CKSTt0/ptX4NT6i76hiVHbYfSy4QLbNPZusxYdtWzffzn/88YjexJztaLczSd80caaJlr3EbFmNYia/SY6QTzJvSecC+t9BCC7ndzpLPR3OYiVauLLse+zpOp4z0Fz64RuyUydbGr61knA/4t2xJroKk3/LLL+82StSKqDO5LmyjveDMBOOlOv7BD34QLS7Sr53/I5YcPpoT1EixLzNZL6rj/Pj33ntvtLjgfJO2+0CA/xfm8NbHCNFVzrLR9gVhdU6uu6C13LIRxwnzTIw83NhCbOXnL0jLJdsWaCsC7n1wZClL5l3oNhmcZvoVtPbF62NYcnm3A+Z5wda39dZb+3FbVeUPjWQb4W6r0mh5f30+8x6N9pXu/FgoQwMPltjA2y15ekO7lVyLtqL1YxbgH7mmMjRegpbnievP/5Xhe9R9+xW0o+ZX83VHYLLYaBXeY1KkymSPas+ltrCtQOm3zHV2KsdVZhzSt51++umuViXso9kGiK2Pqh+0k5R81ESye9TiFAKHCLinCg7UnH6wmXfvZP8QO0guYUJFTrOk/STjrwMRcoJaNP9XB77FoxCoGwKy0ba5YzfffHOwEAav7LHIIot4ubEPfehDWe/nnnvOS0aRgJ0YOPKX8oJNzhamuvVgdPtic9sQ9j9KcZHSjPg5iEwxzAPxEjfvRi+pldqpJMKLu13pq0suuaSh1BZ8YE8lKwy2zpS0fFjZerbbbju3fYFTOyIWE6LUWrusMbzoSV5umodsGHAl+TqVVTiPCiz52L1u+FIhhSozpgHwHL6UdJs2bZqPZypfz/VLEgHS7xGAn6cUlJ/yAOePtdvGqYZrIAEC94VYU5EQEAJCwBHovrivf4+yqmNibq3mYrRAdVfXJvXpdddd52AQEmBZZ6KVuXLbJe22mokmMP6fvTOB92cq//j5ibSpZGvvV0iKVHZKJJFCiH4ktCJ7RaKytKgohcpWIpQtISmRrUJCRUiFStr3vWT+z/v590zznTvf73e+270z936e1+vemTlz5pwznzPfeeY8a8b+9773vQx/RAPYfdCMUWfGXP28rS4zY67eDmLgNdZYw+sZQ82BRieLP6LFYc3Lyjv77rtvZsY3mUVt8VNcs+qqq2YW5cZFgeZAnvtWfuADHyhfXvuYcXMfZR0tfn/oJrmfsjtHsXFcK7jeDLmKxT33LcWb3xu6U+4Pv1DaQHQI1cUXsTrX2QeHXxf/zALVxcNxXN6ixwP/sv8k/dNeWXQc11tKNj9PHfSBdWmmRMd1x9eUehIdN2UmxjeOuSI6lh9t6Zkxa1hX0IfTOafNctcZSvjTWTYSfxGbhWp+tUXW8Zcszu9mjZdZRBg/xojJIu54PRgIL2FLTZZfBzOnDGfwoPPPPz9DH9iN8HckWMB+++2XV3n729/u7eAHGfSe97zHyybBaOnD4rZmGNR0I/zruDf+6vrYgRtBBNDbFimMpuKDpC6+G264ofdPwIYgE4tnBBroRiZRcMf8ss64H6OlPT62uF8L6N6t+Snl5oKS4xR4aTs1wIMwmX2YYHQ4F4yhJDq2X2+RSKJNKLpi/FOCahPGDjGwvSVdlEvqLXRyQc973vM8V6MZxrhuL1wIEHuGCDLyev7qV7+Ky5JZsLp/H756uJYg7kUUaxF38jrlHUK5lVNtIarEXSYCqXONrZb90kmJjoui9PIYOY77Zj8yqbDfi0jDhVh+rbXW6qiG6BgXHHSqZh2bi5n74bvHHnvkOm7wIIQc8XnL7UdnhNI74ogj0llnnZWrAeJcvy3jjjCGg6SFQ0+Ni0QEb+/Xz1w9H7lz0amLZgcCPPdzgcRoS7NsYkkvMZFnxxmYLASTNLFpQn9aJF6WJLsmZuwdd9yRgtEW6+D/WkX4oJmY0n010XsShLzI6IvXROor9LD0CTEm/CFh2kUKBhvb4rnp2MfPFEZIUHc+QOoQsWGhsgFX3BvYdqMqfIm9yhjw0eVFjT8sutkqwhiL+K7oyIlFOyiZeD3XCcdHTp02GDeJ1G3FXKf6nK0TGWOE0+x5BExyNXtupsedyOq4BE4EHWBlVSZWtTBgGBwBFyJAe9RjRQkRxGAQwlAIgyACsZ9wwgm+ugkmWmynW+ornPEhDIQioXfxupncN/22d88KMQKT9xpPrARN191RbVhsYWJIB1hRg63pbd0wq6NxO2BszANGU3z0DEPnnXeeX8YHV/lDbZj2dI0QEAKzAwEx2tI8RmqtU089NRcDUsX8LJPpQd1NhdUHhCizSER2wXI2zhfP9dq3YO1pZxOHmk7TkyybDnhK9V6ptkz/6KHksDouMigsbqFiKrMpDY9QAIPvx9hZHbKyRVJgOuquvZH7EvxgUlAVtpSvs846bAYi8OQDyoyqkunCp7gYoQ6AuSLaP+CAA/K2zR+2Y/XbC0dcmLD4xoKZexEJASEgBAIBMdpA4r9bRBmIGm+++WZ/6eNTifiR1c6hhx7qtRBBIo495phjXJ9IIatbXFEIdchqpleKMgvM/t/e/rexoO7eJoygqPulBuJUQvz1SrUFE4Fe+9rXJnKJMv5wrWF1fumll/r5Qf91S/8FE0IfDFas9LsRK1H8ZxG9W6Qpxyf8ZWmDDwMYIat5wjGip0T3i3sO+tggQuERtzbucxB8LZpWLpatEhu/5S1v8f4RceN6xR+uROjBi76zoVYghRyEThadL6J/7gEmiz9tt7iwcS/aCgEhMMcQmGICOQsLBnXvIUtFuN3Y45ARIYiyIuE6guWvrV4zXG0saIFn+iDTBa4dFifXLUlxf8E62MS+mcUK9jJSTVVZ2pleMMPiuEhkSLEXd1er1Ei1hasQ/eDyw5gJ1WgfB5mtJjOiFdkKsdhs3/1e6b+4GMtq00O7lW2d0IlYCWMBzPhMnJsZU81DSmIdzX0G0Z6JcD3NmxlLZNttt52HoIxwb8Pg+41vfCPD2rhMprPtii04YsWNa5MZT3XUI2UcFsbMM6EuseweNryffYyMlCavfE+z9VjuPbNvZueKe888pm62f1sgmsUqd1ALN0ux5WLXbjpX9HokPMY4itVP0cp2GExZ6bIaHsV4CXEu+mMSAKCXpC3ufxIUlsSDtM8qlsAdGHBhMGbuUF0TCWDgRXQmVvijJjTg/i3/qq+KJ4HFKG2az3ZiVb3PPvuM0sysv5aAIxD6ftHsQAC1EslSZrtRlKyOezyvYZjTrQqGNkSNGhf1StlVtw90kZYE3KsXGT+MDbF0P+KFbyvIftX8/CAMNhokYlVdzBDFkqVoXDST6drGdQ9qZ3wIoJLBij9UQvxGTLri6oBQN6A2Kv6O6vSO0SLW7ag/sDlADbHaaqvVubSjDq5iRx99dDJpjH8wk4XIksxP8XjouKjPQd1sXNhLmM967nJI1DpsLFgI1H0/9BnKnDotRjtHpht3pKKhT7fbHuaF0K0tlbcDAYRao0hR+t3lpNvv13/VeZjhIYcc4mE+OY90hnjXMF1Cdx577LHOUPg9wOjqpm/EH550fIQPtSQaCUt0mO3BBx+c8H+vS2BGeFBWfIwFOwjsE7ABwTCSD4FBCLsCDBMJBYttQTfLeqRqBx54YP4BQljRuHcMNnF/MxVOskxVg3Svujahs54G1dHOekB0g41CYCZ1tNgUkHkIHf8kyIzwsmKUtVH6GJeOlmhmYG7Gb/lwsI3AnsKMCPMy7C6MQ7h9RV7YZ4e0gyaF6ahFqFZsJgYh8/v2vkknGYSdBeMhwtygVCcbl31suM0JOFgykMouyPgELsawK88PWjhXdLSyOta3lhCYwwiwerGXuUc8GzcMWPvVEdQAAEAASURBVOKbsV4t/+lx992tPWwYsBLH+hzdeBBqBdy3jOFGUSK5BGRGbnlZrx1jMm57wIoz7Beoz345AEuvdjgXlu1mlJhXjTarfOzzSl126mTjwt/829/+tmevso+DypbwfDjooIN81V/lPVF5kQqTGK0eAiHQQgTIbkR4SVyhyAhFhqcgRJ0RfOPcc8/1Ynx8CcbBH+chmOz73/9+38ctjBeoJadwsecWW2yRzLrbxavrrruuu0cR+Qqq0z7XEpYUvSfuZRi7XH/99fn1iFXLvtJ+csL/MIq866678gxa0R3+2TDIYnhHjPZgLNx/HUL8/vKXv9wZM3ji18484edungl1msjroI+FORMOlGhz+MQjQubjgHPjJtz/iB2AqBo3PNwDw7Wv3JdZ2XvQHkTRopoIDLrUb2N9iY7bOGtzZ8yDio4RHeIm1S27EciRuMJeAe5yxrGt1jwbEWXhWmZMwN2sKENEyfFee+3lSR0owx0N16pI6GBGQZkZ+NBc3/bNzzl773vf62Mw3Z63TxlkOlAvtxe6H9f9Nw7RsQUlyVZcccW+XZL9CgyMmfStW6yAOxhiYq41P3N3AbTY5cUqtffBh3ZsBZsxbvs46RB3126oULFbNi6zePe+7GPB3fYWXXRRP8Z9Dde8Mpke2t3vRlU5SHRsMywSAkKgWQiwUmTVxaqClSxJKFiVYshDLGBL8egDjgQWMXpWW+WIZRjpsILhHKEyOcbVIhIuHHfccYkkG0RFY0VKoJFYAfdrH5c4XN4gXLNoP9zkMOrBKpcV9HQSq0JibpMQpB9hNIS4FSv8QYggLpbVyi8hLjeGR3X6q+oDQySsjBk3BkhYPxOqdRJEQBgIYzAi1OFax6ocKQTi/3JUNMtK5it2nkdRfwQkOu6PkWoIgcYg0Cu7EYMsRtOqO2gYbZHCnaXImAk/CqHDG5TK7ePGRiStfu5zg/bTrz7uPPhx92N81Dn99NM9otmg+lB8tffcc09PSoH4HR97LHVDhN9vjMXzuB6ZUZJbK8NgmXsskUNXW6w76n6IiWGuEM8Aomp015Y/2+MFFPuIpCrEERD1R0CMtj9GqiEEGoMAzAIqG9fUyW7U7SbKjLCqHitn+qxrGFRso077xfqT2g8Gha95L7rpppucobGiHZRYEUb4UBijic99NYgUwsSvtZvDGAqJxWGHHearTEJ9mkWz68fJRDVuMvWFN1n0jUc/HR9bd999d0eXUY+Vr6g/AmK0/TFSDSHQGARiFVhMHsHg4kUZ4tlBBlyHESIGRXxIJK9BqU77g7Y5TH3GzlgiZ3C3NljdEWRiUGIlDBMkyEpIBfB7JeUlbbIyrEskpiDyXOQoRvzOKhuKdIF126pTL/JYl8cYq/+yyJqVO0Ssc1F/BMRo+2OkGkKgMQigR4XKFrtE8oEiu1GIPIsJH9D1QUV9G4yHF3qc8woV/3ABguLFX6f9YLBVGZ6wbp5uQh/NypzkEb1oq6226poPute40cfi8lJe/UVuY4vh3dFtr7aI4AQV28LdiGhp5XaYOyycRyFWzxAW0kXC+hjcQt8e59DjQuZTG0Xa9kBAjLYHODolBJqGQN3sRmuvvba/IFkFkRkJt5aI5vOFL3whz6X82Mc+1kWaZFiiHkY3QeTuhTCCwsgK0WXo8Oq0T9vQF7/4RU+BSNYrCEMjVkiIVaebELETP5uPiyqCiRFbm2xZZSIcIiEIu7nXwJAscYaLd4s4XnLJJQkscHcK6tcWel1E9eAeY0V8jKi2uNqGwRJylfavvvrqaL7rNnSxZZEv94SrGAZwuGRBiMBhtBitcW9FQjdLKNVxxCAvtjtb98VoZ+vM6r5mLQKIDvGHxfKYsHhYhSLyY5ULI4DQoSG2RExKcIaLLrrIQ/AhekYcGKI/AvWz8iRNI2kAzfUlx400kKygYbDm7uHpFyMcX532sUxmJXTDDTc4c4qVHUEj0Fciap1uQufJCrCb0Rirb5hQrCiL4wNLpAFce++99xZP5ftYacO0VlllFY8tzoqP1R8fGyFOpnK/tmDKhHFEkkAbhHUkiARpO4vJJ2DCfAihO6d+N8Kfl5V6qBzwwSYsZJEuvPDCtMkmmyRz6XF/YBgvYSojNWXUxU6A9ga1yI7r5+S27B81G4/lRzsbZ3X23NOgfrRx58YsM8s9nPu2Rnlxa+LJzMSZXmSMrSMdYdSzLFWZMZc4zGzV7D6UxiA85SP9dKN+7Rsj8PGxLRJ9Dkrj8KOlT7Og9jSY3foHCzOcqjwNJvjhGrOuPB+F4EJ6RVsdRtGUbZ22wM0CbHiazmIqyWJjjNUYYmaB/4vFQ+/Tnq3IM56XKjLDrswkGp4qs+r8IGVzxY924Tn5daGbFgKzAIE62Y2KIr/Qq5ZvvVtWI0SXkQmqfE0c92ufNqrEi936jHYnuWWlj9vMkUceWbkq65ZFixU41xKqkMxdvQhcVl555a5V6rYFfvPnz+/aDidwuWLFTEKDcZAtTDokG8U2UTFYrGgXU/ez3i5eN9f3xWjn+hOg+xcCJQRCf4f4dNAsMaWmGnmI2BtmQTAIxOZvfvOba42TYBeIcG1lXat+r0rjbAtDOAJOlC2De/U/zDkyEaFfpy8+8kT1ERCjrY+VagqBWY0A+j6MncKCmaASFpIx7bTTTrPuvgm4wKo24jfXuUF0zuWIWHWuq6ozzrYIkDEdhP6e9IKhp5+OPmdLH2K0s2UmdR9CYEQEEIcS9o+/IIIWzFZiNYvRkqgeAhjFiYZDQIx2ONx0lRCYlQhMWvw4K0HTTQmBPgjIvacPQDotBISAEBACQmAUBMRoR0FP1woBISAEhIAQ6IPAnBEdm4+ZO+33wUOnhcC0I0DYPiIARUL2aR9ASzq85pprfKQE2RDNDgQIWzkXaB7OxbP9RomgQzQakRBoOwKE0LvnnnvymMZtvx+NXwgQlawq5OVsQmZOMNrZNGG6l7mNAEEWyBNaDDY/txHR3QuB5iMgHW3z50gjFAJCQAgIgRYjIEbb4snT0IWAEBACQqD5CIjRNn+ONEIhIASEgBBoMQJitC2ePA1dCAgBISAEmo+AGG3z50gjFAJCQAgIgRYjIEbb4snT0IWAEBACQqD5CIjRNn+ONEIhIASEgBBoMQJitC2ePA1dCAgBISAEmo+AGG3z50gjFAJCQAgIgRYjIEbb4snT0IWAEBACQqD5CIjRNn+ONEIhIASEgBBoMQJitC2ePA1dCAgBISAEmo+AGG3z50gjFAJCQAgIgRYjIEbb4snT0IWAEBACQqD5CIjRNn+ONEIhIASEgBBoMQJitC2ePA1dCAgBISAEmo+AGG3z50gjFAJCQAgIgRYjIEbb4snT0IWAEBACQqD5CIjRNn+ONEIhIASEgBBoMQJitC2ePA1dCAgBISAEmo+AGG3z50gjFAJCQAgIgRYjIEbb4snT0IWAEBACQqD5CIjRNn+ONEIhIASEgBBoMQJitC2ePA1dCAgBISAEmo+AGG3z50gjFAJCQAgIgRYjIEbb4snT0IWAEBACQqD5CIjRNn+ONEIhIASEgBBoMQJitC2ePA1dCAgBISAEmo+AGG3z50gjFAJCQAgIgRYjIEbb4snT0IWAEBACQqD5CIjRNn+ONEIhIASEgBBoMQJitC2ePA1dCAgBISAEmo+AGG3z50gjFAJCQAgIgRYjMC8zavH4NXQhMGsRuO6669J6662X7r///vwe+bnyt9BC//tGnjdvXtpuu+3SKaecktfTjhAQAs1BYOHmDEUjEQJCoIjAGmuskZZccsl07733Fot9v8h8Kdhmm22m1FGBEBACzUDgf5/FzRiPRiEEhMB/EWCluuOOO6ZFFlmkJyaLLbZY2njjjXvW0UkhIARmDgEx2pnDXj0Lgb4IbL/99unf//5313ow4QULFvRlxl0b0AkhIAQmjoAY7cQhVgdCYHgEVl555bT88st3bQAmDDMWCQEh0FwExGibOzcamRBwBHbeeee08MLV5hRLLbWUG0wJKiEgBJqLgBhtc+dGIxMCjgAWxffdd98UNBAbv+pVr+qwQJ5SSQVCQAjMOAJitDM+BRqAEOiNwJOf/OS06qqrJoyjiiSxcREN7QuB5iIgRtvcudHIhECOwE477TRl5Tp//nxnwHkl7QgBIdBIBMRoGzktGpQQ6ERg22237QhcgdgY5isSAkKg+QiI0TZ/jjRCIZCWWWaZtP766+erWsTG6G5FQkAINB8BMdrmz5FGKAQcAQyfglZaaaW0wgorxKG2QkAINBgBMdoGT46GJgSKCGy11Vb5ihaXH5EQEALtQGCKc94999yTrrnmmnaMXqMUAnMMgWc961npW9/6VnroQx+azj777Dl297pdIdB8BPBtR81TpCnZe/jxYnghEgJCQAgIASEgBAZDgIxbV155ZcdFU1a0cVbZ8wIJbYVAcxD4xz/+kc4777zWG0J95zvfSazO77jjjp4hJpuD/MyNBOnFxz72MVmZz9wU1O55zz33TN/97nen1JeOdgokKhACzUXgQQ96UOuZbHPR1ciEwGQQEKOdDK5qVQgIASEgBISAIyBGqwdBCAgBISAEhMAEERCjnSC4aloICAEhIASEgBitngEhIASEgBAQAhNEQIx2guCqaSEgBISAEBACYrR6BoSAEGglArggvuAFL0gveclLWjn+6Rj0D3/4w3TwwQfnXf3qV79Ke++9d3re856Xtt566/TZz342/fWvf83P1935+te/nohOhs/ofvvt50FU6l5brPeXv/wlvfe9700vfelL02abbZY+9alPpT/+8Y/FKgPv/+xnP0u42dx11109ryXwy+677+59s4Vwo/rMZz7T87phTorRDoOarhECQmDGEbj//vvTzTffnL797W93ZDaaiYE1Me4AzHC33XZLb3zjGx2Sv/3tb84Y11lnnXTmmWd6rGwSUxDFiCQVdenLX/6yt7Pxxhunz33uc+mpT31qWnPNNdNhhx1WtwmvB2a0cdVVV6UPf/jD6ZBDDkmf/vSn09Oe9rTEB8Gg9Mtf/jLts88+abnllkvHHntsgolX0e9+97u06667puc///mJXM/4pX/0ox/1qnw8fOITn0jveMc7qi4dvsxutoPOOuuszFrrKNOBEBACQmCcCBhz9PeMBawYqdnf//73ma2ARmpj1Iv/85//ZBtssEF23333jdpU5fUPechDMlvpVZ7rVnjxxRdnj3vc47Kf/vSneZXzzz8/W2ihhTL7OMnLjCn5PBhTzsv67RgjzIxJdVQzyUK29NJLd5T1O7DVtPd9+eWX51WN6XqZrSzzsro7N954Y3b77bdn9uHgbVjgiCmX2sdGZoFSHIfLLrtsynkK/v73v2fgcvzxx1ee71W4xx57ZLbKn1JFK9rhv1F0pRAQAjOMwCMf+cj08Ic/fEZHceCBByZjFqxOZnQc0fm//vUvF4nusssu6fGPf3wUpyWXXNJX/sZw87LnPOc5vv+HP/whL+u1wz2y2iSyFyvkIPaNicdhrS0rUKgYWz/aXHjhrkELu7b97Gc/21fpT3jCE7rWYSWLBIQVK2qHKiIozEEHHZTe+ta3Jla/46DBkBlHj2pDCAgBITAGBGzlkc4555y05ZZbJltVeoswjNNOOy2R6chWc+mYY45JL3rRi9JrXvOa9Itf/MLr/OlPf/I6W2yxRfrRj37kIst11103bbPNNnn4vG984xvpla98ZVqwYEE699xz/br3ve99fkwZ5yGY7Pvf/37ff+1rX+svaA44v9FGG7lY1E9O47+Pf/zjrp8splWke0TGMMg3velN+Wi+//3vJxgL91+H5s2bl17+8pcncOZ+mYNrr702XXfddWnfffet00ReBx0xzPmII45IX/3qV5NJBFzky8cB58ZNl156aTr11FPTYost5npq1A4/+clPKrvZYYcdEsz+8MMPrzw/cGF5jSvRcRkRHQsBITBuBEYVHduqKttxxx0zexm6mNB0jD7E97znPdnDHvYwL0PEaS/szBiMH5vBjdfZa6+9MmMuXmY6usz0lC7us5dnZnGFszvvvNPrfehDH/I6pjv0Y9MJZ294wxu8zPR4XmZMJrO8wF6GCJRjyFZDXmaGR348yr9BRcdPf/rTsxVXXLFvl7fddpuP0ZhJ37rFCsacXUwMXqafzSxbTXbSSScVq9TeBx/aYR4Zt32cdIi7azdUqGgfGN5mWXRs+lsvt4+F7NGPfnS26KKL+vHqq6+emdFYoYX/3910002zBz/4wQOpBCQ6HvgTRBcIASHQVARIRXbKKaekEH3GOFlhvuxlL/PDAw44wFe8GAWZ/jBfXX7kIx9Ja621ltc57rjj0hlnnOHZVlj1YoEbK1R78UezvmU1RyKEImEExAqJc6YbdKMgzu+///7pxBNPzFe4xWsmuc+q8Ac/+EF6zGMe07cb00EmxK1vectb+tYtVsD46Stf+YoXkRSCRBd1+iu2EfsYQSGRYNy33nqrp39EHTAJ+tKXvuTNHnrooennP/95+u1vf+ur8uuvvz5tv/32UwzqnvGMZ/iKHanHqCTR8agI6nohIARmDAFblUzp21YhXob1ahBME5HxP//5Ty8iIw5UZJxvf/vbvQwd3qAEoy3Sox71qPS6170u8UEwnYQ7DxbE/RgfdU4//XS3sB1UH/qb3/zG3WcQySN+//Of/5w233zzXMQ+yP1iZWxGSemd73xngsF+/vOfd0vk0NUO0la/uiEmRuQN8QxgnYzu+pvf/GayFX5HE494xCP8uFzeUanmgRhtTaBUTQgIgfYi8IAHPKDv4J/0pCe5zrCuYVCxwTKjLZ6bzv1gUA984AN7dnvTTTc5Q2NFOyixIjTxurv2wBjxg8XVCt2vCV1rN4cx1Ktf/Wp3C6JNsxp21x702/j3jpvMCtubNFF83jT66fjYuvvuu/NydqIeK99RSYx2VAR1vRAQArMCAcSgMIzll19+4PtpCqNl7Iylmw9p3Biru1e84hVxWHvLShgmiA9qSAXe9ra3edAH2mRlWJcIDIERWwQcwaeVVTZ09tln122mdj1E3lB5jLH6L4usWblDvayYvUKNf2K0NUBSFSEgBGY/ArjoQPHiD5FqMVIRukQIhhwEY4NhxLkoR1Q93YS+mJU5Fte9CKtsojFVUa9xo4/F5aW8+kOMDJlfc0eTvdoighNUbAud+xJLLDGlHbDFwnkUYvUMYSFdJKyPwe2Zz3xmsdj1uBQQAGNUEqMdFUFdLwSEwIwhEC/y0L8xkHDjKTLIH//4xz7Gshjwggsu8HKMoAj/h143dHhrr722v4BZZX3yk59MuM28613v8vpf+MIXUrT52Mc+1kWmRBSiHkY9GBqxQkKsOt303Oc+N91yyy25y1O5f5jhE5/4xGRBNsqn0tFHH50WX3zxru41MKQNN9zQ3Ze4z6BLLrkkgQOhHYP6tYVeF/cecA/3LMTHzFFxtQ2DfcpTnuLtX3311dF81208C+W5xmXohS98YcIALiJPIQKH0eI3y70VCd3sSiut5NGjiuXD7IvRDoOarhECQmBGEeBlij+nuXD4OPB5veGGG9IHPvCBhL8khOUvYkICN2AkBFGv+ALGzxbLYRgshlVXXHFFWmSRRbwuOjrEoohhaeOiiy5yv0oMnBA3hmhx2223dXEt4Q6/973vJXOtSQSNQF+JqHW6iVCIrAAJj1hFGISBQawoi3W4V1brXHvvvfcWT+X7WGnDtFZZZZX04he/2Fd8WPF+8YtfzMXJVO7XFkwZn2ckCawazZ3Gg0gQTIJQikEwYT6Ewkc6ystb/HlZqX/ta1/zU+Zq0xHnmcILL7wwbbLJJslcevz5gfGa+5Y/K8X2eF5ob1CL7GIbHftl5yH50ZYR0bEQEALjRmBUP9pRx2PiYfehNAaRmftGZkyza5O2as5MXOrnjXFmxkSn1P31r3+dGfPqKKdsHDSoHy19mgV1tsYaa3TtnrGa4VTleTDBD9eYdeX5KAQXfFXxae5GddoyRppZAgAPn1iFLW0zVvyZLSZxt64GKqc9W5FnzGcVmWFXZhKNzD5Kqk53LZMfbcfnhQ6EgBAQAslFl4gl0Qt2I0SKiFMh9Lax4i3Wx0UEl54iUTZTxEocS+sjjzyycgiMNdygihVYgXMtoQr7WWqDy8orr9zVhaluW4iP58+f7+ETq7BlfLhcsWLG33UcxL0jeQg9fLFNVAAWK9pXv/2st4vX9dofPKBkr9Z0zhHAdB7RC3ocnLHxEYPsM8j1G0wyYqhJEDorHPLJiMEPBetAzO6r/A3p/5577vFxMlbCsPEjG4YQQxGijr6wHhyEEPkRGi0Io4TQk0VZccuP7uSTT86LEPshthuV0GsRZKBIGLrwUsLyEJFTWCgW64yyj3iN7CHgj56raEyC/tCiGKUTTjjBwwiO0k+3a3lO0Ceib1x22WUT4jbEahCh8Xg+isSLCQYCDjxbg851sa2Z3A/xMc8twSxmGyH2hlnw/uEZfvOb31zrFgl2gQiXcJSj0jjbIqUdASfKlsGjjrF8Pb9Ffg/01evjq3xd3+PyGlii4zIigx+TgYLQbwa+izuiBUQx9pLKzGggQ1wybkKUY9FMPIwcmTTonz97IXbtz5hcZnE9vZ4Zegw8JDM8yQijFiHtymHP6jRoOiEX3xkz83HYh0jWS+xmL4/83hCRlUV2dfqsqsP8MHf0D272ssksck32+te/PrPA9Zl93WYWbajq0qHL7EWfGXPPbHXgfRYbom/GYcYbxeKx7ZvhjrcP7vZR5vtsLQ2a94FIEFFYPEfGeDMzcPH5JvQe5Ygn7YU68JhmSnTMHO+000556Eazch04M87ANzviBcOIjqNLfltgLaqHAGEpu4mv67TQTXTMKquDxGg74Bj6wFas/iJCr1CkSab1Ir6qiVY8zRMvFHMmz0wU4+Ow1VFxGB37tpLyOmYh2VFe56BOaqo67VAnGD4v8IMPPrjyMuLQxoufLfq1cZNZbXoftsrOm0afY1/TXm7RbPLyce0Qb5X7KZOtNMtFYznmOVxmmWWyK6+80tsz46LMQhf6GGA+QZZZxcv4ECgSzxcfCDxffDxGjN9inV77M8VoGRP3Xvwzq9ZeQ53xc6Mw2hkf/BwbQDdGK6tje7tNgqpk//SD6GMSab3sefZQZog9iHaC2JjwaIg7IUzYu1Gkt4ptt3pV5XVSU1VdV1UGLrgIQIRGiyg3xbpYiSLaCkJPNG6qahN9DrFsIURy46ZuzwtuGJMgVBuI+S13pjePaDySXSNCD+r2rPJ8EWKQ7CZY3+IqYgYtcVmjt/wGi3/8XkRCYJIIjKyj5QVPyCx0bPvtt587A2NCjd4D/RI6HOTe/PEyMTFjRyBwruUcZviWdcOV8Jtttpnrqvjhh38V7eG/RTQRyERWHr6rDjjoLdF3EW3EMnIk4msSFJuXOn0UHZVxyOYlj2k3/luc23PPPXMGEP1xHtN0SzSc6++Kvl9Rr7ilPXSz+OWR3ouXFfpJ0nBx7zAQGCWpq3CuLqe5wtGaINw4eKOXBANM7LkWo4QzzzzT94t94kjOfBR1i4wDfSrB1jE+wMEdQpczCXrjG9/o7g6W4aNv8yamdR0zOrSTTQ+7++6759cQUxVDBZ4XDCPKZKsUn2NbpSWi/IAPAeLBh3OjPk+BD/MWVOc5qPtMRZux5dniWYHZggvuDehyYZLo4Ql/x9ySVgx/TctIEpd6TF/uHR0sH1Bgge4Vhkp7G2+88RQdVAQLiIg/eWM9dmDW/J5Iv4ZbDeMUCQEhUEKgvLIfVHRsL+zMgi+7eInM9ejP7OXoGeztBZAZ88iMcWa2svI6iKtCP2n+Tl7PHJbdjJrr7CWWkYYJsliYmVmj+XXGJDPzpXLxHXqiaKM8/qrjummx0PUZw8osIbCbrTM+eyl5n+wHfexjH3N9HTouY3qZ+ev5GNHpBaHnMqhzHW1VWi/zuXMTcuqhtzXn6IwUT4go7eWY2cdHNJfRv32oZBb+zMVekfoLjEkX1o0sR6SPlXFC6HFXXXXVjJRhiM/QSZjVpY/VfBC7NdO3vFtqKmOOPm7up58u1RhyZlaSmRlk+XgYFyLKIHSW3DfPAZjxV3QtMOaRWQg617GALXpVxhVU93ky30Bvuyg6Rg9plqdebh9G3mSd56DuM4UrAfcTZAzWn0XKzJnei+ukgIvruYfVVlstA39+0/aR4O3byjOz3J9RrWNrjNzrkQouCBwZQ1l0HOfZRuq4ItbF81X7Myk6rhpPk8skOm7y7HSOrZvo+H+/7P/WH5TRcpl91fqP8aijjsp7DX0bTDEIIwR+tKF3shWGH2OQA9nXuh/bSjEuycyZ2X/8MEBbWWaWyio/N8iOif287TD04FraYjzmjO5NmSjM+4rxUchLiTq8wPGpIm8hhj/lcZgIzuvZKszbKjNaL7R/trLweuG/RXu0jxGTWaB6NV7glJEPMwjGyEdIMJ7zzz/f6xRfilE3thYZx3NFmqQhity/jrbjY4YTvMApmwSjpX2THmQmbmW3JwWjxfcuckXaKt2v4cPKrGIzmFwVo8UvjmtM6pH3AaMxR/j8mJ06z1MwWnNxyMzi1w2C+EAE/9C5130O6jxTjKvMaCmzVbvPSzBayuJ3xcdpEIZvfFQEmYWmX1fUc5uI38ssCEFUm7IlLykfzRhoBdVhtGG0xfzUpWC0PHf6641BfCQJp944NQUfS8E45WcwsujYbi5FOiHEl0GWDNl3i2JZxJ0QrgSIr0hLRRguRFaIRQmNBRXjdKIXw6eL8GdkeyBixzAU4rDI1EAb9E/INPvR88HholdErIwtiOglXIupOrore1F7/sXIZxn10IUipkOst/POO0fxlG3ZzQZXHwiRXowx8mDaai2/3qxwXYxObkSCY0f/xB3tRrgVgX8xDBw4ksUiAmxzLWJFKESjfjDGf0RfGYQQgdoq3V1tcDEh8g4iUohco4hiywSOxhQcQ1QFiDOZs3LdQZ4n9Nr2YePuPYid6TsygCC2pe2YhxhP8TmwD8tazxTPfxWVnxXqxPMSvyXKeF6IaETEH67hWYEQnwcR/Qi3M56XohohzhNpCaztQ3uKmiTqdNtGf8OkhEMEXjWebn3NxXL7uHJ9OM+uqNkIwE9w2SvTWBhtuVGOqwwMwhkZR2YIZ2D0TuiOYICE80IHVaZ3v/vd7kvIC9RWO1NiUpbr1z0upsWCqaEDjI+GaAO9Mi8pW9n6iysYfdlwiPiiUPHlFm0Mui3qAONamBUfI7aSdV145EgMY6eoF9vQQ6OHDUMb7hHdZ4w16gaDjW2Uz+QWvz/0sfjP8XFj0hIPy1bGvThG9Pi24nSjMBOZux6bj6gy1X2eCLtn4tDy5X5c5zmo+0x1Y7SVHVcUlp8XPg5huOhMCV3HBxz6XlPDeFCAchPU4YMXw6YIDl+u0+uY+LQQv5NBid/8MNlyBu2nzfX5cOdjeBy+rW3GoQ1jZ7EVYUGL451Rq2MMgmAUWC8SKKD4lV4c5Ac/+EF3pmcFYSLbjswZxXqD7hfTYhH5BYaEgz4r7iLFKsbEh3kUFNOZFqvkKx3qTIIOPPBAdz5nS3AGYoESyxU8ygRz5aWJwRfWlUHEX4UwwGL102RCIoLVNISBDavLyL5RNW6M5lj54mjORxGryW5RXcbxPMXqrddzUPeZqrqfUcpgrBiL8WXNKogPBph+GCQW2wY34v8uWLAgmRqieKrWPgH8MQiDYJoiISAEpiIwMUaLKLYfYRXJDz2sdfmyhorX8iM2fVQyfz7/6oaJkBViHMRKCSItFi/lECvzVVIkVlWI7DgfX+1VdbjGjHWKl45tH0thVqOIpk3h7hbD4FdehWL9yYsV6+ZwlWEQvHhZERPliLaKDALxKFRM/eUFY/oHg6/D2PmQimeArrFihxCXc08hWi+OM54VcIHJbrTRRnkoPNqK896Q/avzPEX75WujDbZ1noO6z1Sx3XHtk5WEAOtYbqMiQawez3f0wf3BXBE9WyCOKPZA+OANBRb5ycIOEiaYKxbdSCCwZBYJASEwFYGxMFrEuVAxLRVMASrqWyPbBT9MKESavAhY3caPHVcfRKS0h64OlxwYhAWUdpE0KY16+YV6413+9UqLRT8wLnw1Q7fH6haxG6EJWaHAlBHj8iLj5R7ES57QdKwyoch8wUqiSLycoEjl1CulV1H/ShYJRBIwUj42cFXCnzNwpk3EmfgzIrZHP4vYkz/EgWw5F+MjxCF6PXBElAqhd4zMJ14wwL+4n+J4uBw1AfpgdNDF56OqaXAuzitpyhBzw7BwsQqK543jSNUV/p4kpWaOWfkj0oTJs5Kj3brPU7RfHEv0Hdu6z0GdZ4o243kJfSdl5WeFsl7PS2CPPyuMkuv5mCS0I65ffDAWiWcKnRK/0XhW+OhFrx94wqAhfg+0B3PmmYaBMzeI5pEk8NEnEgJCoAsC9sPpoEGtjrGMDfceLIOJiBTWi9ZlZi9Yd0kx5uURZCgjfJu9/DIzcPHoNESXsZVIhvUx7jy2enRLZvMRdTcXxgRhTYmFJW3gDoOVcl2KbB32EnHLX9xiTJeVEUKwSERQMrFgZl//Ga4xWK2a3q/DnQi3GBO1+TixpN5uu+3capjoQRAZJmifceKqYy99t7QmLKMxci/HMo3ITRERCPcXrIONiWbme+x1wNX0lN4mYfhor/xH+0T3IWwY2JXPxzERoyCslunHmJfXBU8THbr7holmPQShV6z5j8hBuHRhkUtfWE8bk8+vxlLbjJscB3CrIlyXwuoWfNg3Eb5XZT7AOMiMZ3xO4r5o217yPj/MMTiaqD8jyhV1OWbOwajf82T+0261HG3jVoF7Wrdx93sOYsy9ninuE/e36BPrdZ5zxhJuV1gU40LGffLboC73YhKK3LWGMhMTexYa+6DIr412Y2urXB8WUZ2irGqLVTqRxoj6FOftwzgDE8ZlK1kPxXjZZZfFbQ60DatjU98MdN1crCz3nvbMejf3nnncgv2QcmKlxBdqqTg/P+4dVjz82cPkTSPG5DisK8fVn7nHuDiV1QoRhzB66hY0GnE2xkYYR/F1HyLL8lhYQWCJjJUygTkmSRj3YOCCYQSrHu4BHRwGQ+gLTznllIG6Z6WHPppx0xYr+ZiDgRqqUZn2oUm1XxwCq65YjVFePi7WHdd+neeg7jM1jjHx7NrHnweRQWTPc4y6gJUuEg1WqZN+XvvdB5IZRNnYScgYqjdavH/M5c/tDnrX1NmZRgDJG5JHguYUaWJWx8VOeu1jiRzWyNRDnBwi5V7XcQ4RVh0DjGLyXqxW7Yu8Z9NYcVrwiJ51OAmjtoD9feuNWgGXHCJCwWARTxeNxhBnIxoclBDHBg7FD4lBMOVlXoemg8HGOIpMlrLycdQb57bOc1D3mRp1XHykoipAtM1HYpH4eEYl0+0Ds1hX+7MDAdRJeCAceuihfkP8vs1v3tUqWOljyUwkvuI7YNA7x14FF0LsQHi/Dks8n9gM8E7FpWlYqptJDFUKKhDUg3igoJrkg4Z3at13W90xzjijrTvQqnqsSkOvW3U+yixwQW5A1ca0WKEv5iHEl5OHAh0xxk3oNSMsZdzvKNtBMB2lH107GQRYOcNsCevJqpUPRpj89ddf7/p3XqzT8fExmbsbb6u82JHkTIom3X6/cWPLYWovDxVLXSRLxLaG6ZpawEPNwlB4PxIKt7jg6dd2nMc9E6aInh/DuVEYLS58hGkd1nODOAt4WxDGlndmNyt6bF+w4YgPEBYxce9IDHF1w/bDspnFbY6+tYehgwbV0XZc3MADdJLo+NAvGVqZ+Sw2Pi1WGUZ7aFzvaZaurgs1EbLrkNGPRzSp8jU6nrsI2ArDbQjCniH08OjTm0IzraM1o78OW4Jx4zLO9k0iNPA7i0hs2CoYA8xvjWhy2CyYkV9ehg0K70VsQ4YhM57z62nDPvCGacKv4XkIu5FiJLRBGqyTScw+NvzdCQ7d7AtMzeJ2IMawB+ne63bT0Q6/zh+dx09LC3zNh9jVjFc8mEG4E03LAMbQCbpZvkJxTUEvi76NKD/obUcR+YxhaGqigQgQsAIpBxbKrGL40icBRjmKVQOHPi1DQlRoxoF5wpJxdzrp9vuNF/sLknFgeW5GmXl11E6sOlEfBEWwFFamgxKJZLBPMCPPQS/tqM/7jFUxK9pRqE4mMaIMYilPpDeLaV/ZHV4beLaw6i96flRWrlk46xktOBC0ofhXFbWqJl4zXo2xT1LcNeM3qAGMDQGek3EbFY5tcEM2xMcmfuS42BHABLelIHx5CbwR+jWyYEUwDq6B8MnmQwRdJe5sBHxBrI7RHB8jBEmhDiLXdddd13WYEelnlPbpmyxWBOeZNGHTgeEbLltFwscfIzQyLgWBEe8U7nUQQnVlsbTdCHMUcTF94i+PTYFZzQ8yhIHr4rrIxwFpMMkih/teuCWWG4PxYyuEKHosVF4bzzbRcfn+dCwEhMDMIzCM6LhftiREl2bo5aLMuENEp4gJcTOEcOfD9ctenpnp4zLzM/ayOhm+RmmfLEqorxiLrQJjeLW2g4qOLQBJZvmT+7ZN5i5wwB1zEEIdhwuexT7wy8haRjvDiI5x0SSbGO6JZg3v7QwrOo57sA8Mb8c+kKLItxZNz8vtA9RdDlHBMW5cLM1orKMuB7j24U7H/dalOSs6NiBFQkAItBwBVpmsxFhpsJLFIJAgGRj34JJI9DhWIGVXIUSniEyDMLQJa2yMxVhFUUZygxCtk9zkjDPOcBcNVrxEGKOvUdonJzIBZojYRvCdSRHukSTTqJOoAaMhxK1Fr4w648JIyBj5yCJjVBpIGpAkhDFSnf6HrUNQIQg1HG6eiL0J3INEA1VCOQqaxQRwNR3P3qg0J0THo4Kk64WAEJhZBHplS2JkxShtdUdaVsGEvUMxVCVR6yD0eoNSuX3E3d2SgAzadrf6uPPYyrIvo6WO5Tx2P3w+IOoS1slEX+PDZFTiIwY9aNFdcdQ2e10fYmKYK8R8W2AY/xAjGmEkaok28MCAyuVxfpBtfYQHaVV1hYAQEAJjRAAGApX1gZGJapisWWVGWDVcVs70OYyxUJ32q/ocpSyCw+An34swpiQ2NSvaQYgEH6yWi64vocPG1RLpQR2XSz6MLGd3YtUIs4UiRC8rTz4EcEXD9WhcRHIYVvtFv37003xYob+9++67fTzRX9Rj5TsqidGOiqCuFwJCYOIIFLMlxYqETouZtQYdRB1GWMzwNYn2B22zX31E59wX3gm9iNXdMN4XMCaYFX9BYZnLShexeB1Gi6UxeacR0Qch9obAHIt5xNPjZLTEXGfcrF6L6SBDzI7BbJGC8ROnfVSS6HhUBHW9EBACE0egTrYkBhFiUKyIITNicTeeov4tGGydjFLFDF+0N0r7uN3U6ZN+hiUsalmFE0CiF5HZibC0VRTYVZ0jxSahW4t/BNGBWBHG6jau7dYW1uDFNtiPjGhmmObnCB4RBBOGOY9CkWaTQD9FwvoY3EJ3H+ciuYj5GkfR0Fsx2qGh04VCQAhMFwK4f9TJmhUrFdxoMDxC/Ij/PNmRyOoEw430kYQMJAwfGZaKRD0IIyiMrNAhxip62PYRhdbNYlUcyzD7iNOJwU6UsCoCD+Kzk82rTKQgJQThqL6xtDuutmCwhItl3q6++urykKcchy62LPLlnniGMHbDvQvCTQlGi98szLZI6GaJrDaOuOBitEVktS8EhEBjEcC6GD9ZLI9Z7WApihiQlRDMAWLVYlnE3BJ5t912cz0kQRnwncV6lBUu+XdZvbA64+UbzDNunDSZrKBhsASLucLSSYZV7LDt0y+rWRgu+5Okww47LLEC7GYgxjhgQoSjLRMiZz5GuDZSN5br1D0eV1t8MPDRg54cC+VuhI81K/XItY1FM76+RbKMcW6QZi496eUvf7kzXnymI31o1MUmgPYGtciO66dsbdI7SH60HXDoQAgIgQkgMIwfbQzDdGeZMb/MViNRNGVr0ZkyYxhebi/pKeft5e3Xsw0izaK9IDMTGWbGlD3lYJwrb4dpn3FUjaXcdvl4UD9arjdraU8HWm4rjo3RZoQjrCLuHz9cY9ZVpwcqG1dbjNUYoqcgHWgAXSrTHmlN7cOnsoa5krmvsH2UVJ7vVig/2imfGCoQAkKgjQhEtqReIj1Eo6GLDevR4r1iScz1ZStm6lCGqLJXlqNh2mccVWMpjmtc+29729s8mcSRRx5Z2SRGS1VRw1hxcy2hCglfOwqNsy3cqxD1I8UYB3HvGFuFzr3YJqlH8Xlm9dvPert4Xa99WR33QkfnhIAQmDMIhE6vjRm+ypMEQ4dZbLnllv7BQfjIOoRVrkVEct12nfq96oyzLXTpuP2ULYN79T/MufPOO8+z/9BXrw+tQdsWox0UMdUXAkJgViGADhBjJ17mEIZUWL5a1q9W3ycBFyxDzRRL4F43hf6av3HQONsiofp0EKtc0guGTn5cfYrRjgtJtSMEhEArEYgMX2T5CsJfdDYQ4vNVVlllNtzKtNzDpKJUidFOy/SpEyEgBJqMwKRFkk2+d41t8gjIvWfyGKsHISAEhIAQmMMIiNHO4cnXrQsBISAEhMDkEegqOt52220n37t6EAJCYE4iEEH6MToqR+SZk4D0uGkCTHz0ox/1SFc9qulUAxAgWUNEHisOZx6Ot8UComGUQ5IVz2tfCAiBmUOA8HLEhV1nnXVmbhDqWQgIga4IYLlMztsiTWG0xZPaFwJCoFkIEICAHJoEcBcJASHQDgSko23HPGmUQkAICAEh0FIExGhbOnEathAQAkJACLQDATHadsyTRikEhIAQEAItRUCMtqUTp2ELASEgBIRAOxAQo23HPGmUQkAICAEh0FIExGhbOnEathAQAkJACLQDATHadsyTRikEhIAQEAItRUCMtqUTp2ELASEgBIRAOxAQo23HPGmUQkAICAEh0FIExGhbOnEathAQAkJACLQDATHadsyTRikEhIAQEAItRUCMtqUTp2ELASEgBIRAOxAQo23HPGmUQkAICAEh0FIExGhbOnEathAQAkJACLQDATHadsyTRikEhIAQEAItRUCMtqUTp2ELASEgBIRAOxAQo23HPGmUQkAICAEh0FIExGhbOnEathAQAkJACLQDATHadsyTRikEhIAQEAItRUCMtqUTp2ELASEgBIRAOxAQo23HPGmUQkAICAEh0FIExGhbOnEathAQAkJACLQDATHadsyTRikEhIAQEAItRUCMtqUTp2ELASEgBIRAOxAQo23HPGmUQkAICAEh0FIExGhbOnEathAQAkJACLQDATHadsyTRikEhIAQEAItRUCMtqUTp2ELASEgBIRAOxAQo23HPGmUQkAICAEh0FIExGhbOnEathAQAkJACLQDATHadsyTRikEhIAQEAItRUCMtqUTp2ELASEgBIRAOxAQo23HPGmUQkAICAEh0FIE5mVGLR27hi0EZjUC1113XVpvvfXS/fffn98nP1f+Flrof9/I8+bNS9ttt1065ZRT8nraEQJCoDkILNycoWgkQkAIFBFYY4010pJLLpnuvffeYrHvF5kvBdtss82UOioQAkKgGQj877O4GePRKISAEPgvAqxUd9xxx7TIIov0xGSxxRZLG2+8cc86OikEhMDMISBGO3PYq2ch0BeB7bffPv373//uWg8mvGDBgr7MuGsDOiEEhMDEERCjnTjE6kAIDI/AyiuvnJZffvmuDcCEYcYiISAEmouAGG1z50YjEwKOwM4775wWXrjanGKppZZygylBJQSEQHMREKNt7txoZELAEcCi+L777puCBmLjV73qVR0WyFMqqUAICIEZR0CMdsanQAMQAr0RePKTn5xWXXXVhHFUkSQ2LqKhfSHQXATEaJs7NxqZEMgR2GmnnaasXOfPn+8MOK+kHSEgBBqJgBhtI6dFgxICnQhsu+22HYErEBvDfEVCQAg0HwEx2ubPkUYoBNIyyyyT1l9//XxVi9gY3a1ICAiB5iMgRtv8OdIIhYAjgOFT0EorrZRWWGGFONRWCAiBBiMgRtvgydHQhEARga222ipf0eLyIxICQqAdCFQ757Vj7I0Y5T/+8Y904YUXNmIsGsTsR+BZz3pW+ta3vpUe+tCHprPPPnv237DucMYRWHHFFRMSFNHwCCh7z/DY+ZUEfH/c4x43Yiu6XAgIASHQTATe9ra3pfe+973NHFxLRiXR8Zgm6utf/7qnL4s0Ztr+fzq3uYLDRz/60USUpknf79///vd0xhlnTLyfSd3HTTfd5L+4H/zgB629h0lh08R2n/3sZ4/pDTm3mxGjndvzr7tvGQIPetCDZG3csjnTcIWAGK2eASEgBISAEBACE0RAjHaC4KppISAEhIAQEAJitHoGhIAQEAJCQAhMEAEx2gmCq6aFgBAQAkJACIjR6hkQAkJACAgBITBBBMRoJwiumhYCgyCAe8cLXvCC9JKXvGSQy+ZU3R/+8Ifp4IMPzu/5nHPOSZtvvnlad9110zve8Y5088035+eG2WEOXv/616fTTjttmMs7rhlHW7TBPR5++OEdbVcdEMhk9913Ty996Ut9+7e//S3tsMMO6de//nVVdZVNIwJitNMItroSAr0QuP/++51RfPvb3+7I1NPrmkmd4wXfNMJXfbfddktvfOMbfWgnnnhiOv7449NHPvKRdMghh6TzzjsvPfOZz/TtsGM/6qij0kknnZRuv/32YZvIrxu1rfPPPz/hx7rNNtt4NLC84dLO7373u7Trrrum5z//+YncxeCAX/dDHvKQRNantdZaK919992lq3Q4nQiI0U4n2upLCPRA4AEPeEAikMNtt92WxzTuUX1ipy677DJnXBPrYIiGv/SlL6VXvOIV6eSTT/ZMRjTx8Y9/PC2++OLOXDbaaKNEBKMo950B/33nO9/J2xjw0inVR22Lj64HPvCB6d3vfveUtosFBDDZcMMNEx8dhIJ9y1vekkihGMRqH1w23njj9Oc//zmKtZ1mBMRopxlwdScEeiHwyEc+Mj384Q/vVWWi53784x+n7bffPv3nP/+ZaD+DNP6vf/3LRaG77LJLevzjH59fuuSSS6YvfOEL6b777vOy5zznOb79wx/+kNepuwPDQszKKnRUGkdbCy20UHrxi1+cnv70p/ccDitZJCCIzVE7VBEJKJjPI488suq0yqYBATHaaQBZXQiBOgjwgkYft+WWW+aM7oYbbkgHHnigx5rlhYrolBfqpz/96bzJP/3pT65T3GKLLdKPfvQjX42is0Tk+N3vftfrvfnNb04LFizIo0p9//vfT6985Su9bI899vA6XPu85z0v/epXv0qf//zn02te85p0/fXX+znEta973evyPqdzh5XrXXfdlYppAukfrG688ca08MIL+3C4J4gV3qC03377uW6cnL+j0jjb6jWWSy+9NJ166qlpscUWS3vvvberHX7yk59MuQR8mOMPfvCD6Ze//OWU8yqYBgRMFyMaAYGf/exnKLMy0x+N0IoubTsCphPLLNbx0LdhzC3bcccdM3sp+vNkid2z733ve9naa6/tx4997GMzy6CSGbPJbNWb2Yon++Y3v+n97bXXXpmFZvR6pqPLLCF8tt566/mxZfnJ7rzzzoz2llhiCS+LQf70pz/1dp7whCd40S9+8YvMgsd7HVsFZZdffnlGmYkcfVz0+dvf/jYuH2prsY69fROR177eVnWZZZDpW99Ey9mjH/3ozHSWfesWK9iqOFt11VUzWzk75vyeDzrooGKV2vvjbItO7ePH8bIUiVPGsM8++/i5efPm+X0vuuiifrz66qtnZjTWUZ9nhft6z3ve01He78B0xJmJnvtV0/k+CGhFOw0fM+pCCPRDgIQEp5xySgrxJ/URG8bKFV3ktdde6ysYMqmgw/va177mzWIMhMELdNxxx3nSgSuvvNJXpH/961/T+9//fl/1Lb/88l4n/iGGRfwatMwyy7gxEccY1bC6o+xhD3tYuvjii9NFF12UHvWoR0X1adkiFkZv/ZjHPKZnf6zCwwgIrOoSKzxWe1gZF3Wbda8v1htnW8V2u+2jt4YOPfTQ9POf/zzZR1B67Wtf61IIxP88I0HPeMYzfPeWW26JIm2nEQEx2mkEW10JgX4I2Kqko8qDH/xgP7ZVp+eg5SD0djCXIPLTQuSrDXr729/uu4icByVbJXVc8sIXvjBtsskmHWXTcYA7j63G+zJaRKi4tdjKb6BhIR5/61vfmp72tKcNdF1V5XG2VdV+uSzExDBXiGfg2GOP9Y8nW8G6UV1cgwUyHxIY2ommH4H/V25Mf7/qUQgIgSERwDq5Dj3pSU9y6+VhjIPKjLZOf5Oogy8ohAVuL/rqV7+ajj766F5Vppz73Oc+l7785S8nVnswW+g3v/mNb1ktwuDRc6+22mpe1uvfONvq1U/xHHmwWe3DRIPI7sTHFvpbXHpiJct5PtpY9YqmHwEx2unHXD0KgWlB4I477nDxYVlkXKfzpjBaxs5Y/vKXv/Qc9tJLL52WW265nnXKJzE+M52ui9rjXFgwg53pp5Pphmsx2nG2FWPpt33qU5/qjJbV64te9KK8eojZsWAPwnIboznT80eRttOIgETH0wi2uhIC04mAGTN5dxFpKqxzeeFCZr/h1s1FXV4w2H/+859eJ/7xoi6XxblJbrGoZWVuhls9u/nkJz/Z9Xzcb7kCVtf33HNPx99VV13l1czAzMtxjQmCCcNQq2jQtrqNqartbmWvfvWr/dR1113XUYXoWOBG8I4gPhqgQT9G4nptR0NAjHY0/HS1EBgrAvECDv1bvCD/+Mc/5v3g6woREahMF1xwgRdhBPWpT33KdY+hw4tVD246GDYhFv3973+f7r333sR1MFyzbvbrv/jFL3o0og996EMuQmX1hJ64OI5y35M6fu5zn5sw4unm2/u+973PfY8x2CoT4mSMo7beeuvyqYGOYbBPecpTHJ+rr756oGvLlQcZUzwHVSJf7gndOQZwoa83C3N38zGraWe20XfoZjfbbLMo0nYaERCjnUaw1ZUQ6IYAL9SXv/zlud8rKyRC8BGQAGLVsv/++6dvfOMbHpyAsjPPPDOVV3LHHHNMWnPNNZ3BYlh1xRVX5Na0rIBWXnnldPbZZ7s/LtGCsHLGdxYfWla4GFqxEsJ/lxc5zJlyVrPoLNmfbjrssMM8KAV60CriY4QPi6oPD0TOfEBwLR8UwxJMnj7Qd48aB7numA444ID0hje8wYfMSnunnXby56B4D0SDwkjNXHr8+YHxHnLIIf6sFOsxZj4UBjUWK7ah/REQsB+OaAQE5Ec7Aniz6NJR/WhHhcLEw+4naW4e7ntpRj1dm7QVcWbMx88b85hSz5iK+96yDaJeVd04X3c7jB8tbZsFdbbGGmtUdsO9mAi48hyFYIIfrol+u9apc8IMszJjYhlzPSqNa0wxDsZ26623ur90lMWWvsxFK7MgJFFUeys/2tpQ9ayoFe0IHym6VAg0DQFC97FyseAUXYf2xCc+0Q2MqFC0WI0LaAM/WrZB1KuqG+cnvSVeL9bWVWEE0StjgVtFrMK5FslAXWvtqnYow00KkTo+qqPQOMcU48CiGMOt0MNHOaoBJBeI14kcJpoZBGR1PDO4e6+ElguRFqIf/OAIDkCAgbbSNddckz772c/2HT7MwKIX+f0Tr5awg+985zv7XqcK1QiEDs8kLAkL3NlGMHl0sDwnMFZCStYh3F823XRT10fXqd+rDmnocPspWvP2qt/t3DjH1K0PyhH3v+xlL3ORM+nzRDOHgBjtDGH/4Q9/OH3gAx9IxGeF4RIUHMtOnN4HJZNZ5CuUuLaqLM5NcovP4T/+8Q/X7WGlSZquMMDgGKMNIiDxQYGIVNFVAABAAElEQVR+ECMNVgq8EAalqnusKhu03TbVR3eIsRNMAMLQCYtZ9HmzjR7xiEckMgtF/OY694fOOQJ81Knfq86ee+7Z63Ttc+McU69OWd1iKMVKVzSzCPxPNjSz45hTvWOcgTUn+SMxw8fIBaMVKNwr/KDGv6qUZrRPYHVewtNNRJ/ZYIMNcn89ovWQFYU/XCW4V0Ll8VGBEc4wHxbcU9V9V5VN9/1Pd3+IQ/loI7k3YkIwIJ3cbCV+H6ussspsvb2x3hfPhpjsWCEdujEx2qGhG/5CrCTxCyyKoIaJIdstpRnZXvChZHU3U8QHRDfCVYRQcVBZp9TtmmJ51X1XlRWvmc37PEfFP6IDiYSAEGgOAhIdT/NcEPLtM5/5jPeKPx6JmqEq1wTKcedgBUj0F8StGHUgisUdg5UjolhSmuG6QAo16oaOF5EigeMtYwdNeT3qEvwcsS2rS8RxEO4c5557rveBTuv44493sTYuIcX0ZKRLYzV60kkn+XWD/kOHS8B7XBe6ESszXFCoR4Qeszb1e+L+q+4bPEgTVsYCvTcrPYLuI56G+SNSjZi9uGqgH0ZPTh2wwV0CzAjcT9QgkRAQAkJgVAS0oh0VwQGvRz+DyBQy03n3VQx/xXJTlnrP6xI27Stf+YpbgmIMAvOB6cBYIRiKpVhLWJNiWbjCCit4OUxy88039310wIgYX//613ufRxxxhPtb4ohvbgEJ/dPhhx+ezHXBGSs+g+aK4eLeyEmK/9+JJ56YTj755K4fBt5Z4R+xaglyAFMj9iofARFPtlCtY9fSvLl1KQEX0OfiAwiDh6rumzB9VViQw9TSzHmgBVb5SBJIph2+px/72MfyjxP8RWHs3Df3B04iISAEhMBYEDDxomgEBIbxo7XgA+7zaCvKvOfbb7/dy2wVmpcZc/QyW216GbkubdIzYzwdx5YmK7+GHTNIykyXlZfhu2j6msyCIuRlZvLvbdnK1cvIX0nbFoQ8M4bqZcaIvMz0yfl1xvAzs/7Mj7vt2MeEX0s+VHO98D/26cMsRvPLbIXvZfgnQvgDklfTdMx5He7HQsflx4FD8b6ryuyjJCviaatp78tiweZtme7Yy4yp52VmtZs9/OEPz4/r7OBbafppb4t71J8wmA3PgCVzUD7aOi+APnUkOrZfQ1OJNGesSBHzEhkIC0KoHPe1yoCqWMaKEMMoVsNBrNyWXXbZPKRe3XRsRJ4ZhLCoDtGzPYv5yrRbG4zDEp67qxPhCMnHijsElsxlKt5jnIsyS1buYnR0l5EmDgy4Z/xDaQ9dZtx3MU0aUgeM03CPKKeti36qtrigsOIXdUcAqQYqC8IQSjTfHaemnOml4mnKGNswDjHaBs8SqcEQuSIaJvUVYk90imUK5lIsL5bhQmQrtNwFpFiv1/6oDv7lthkTulRE0r0IP1Bb4boF7b777uvhBINZFq8r3mOURxnidQiR+Zve9KY4XWs77H0zX8QPFnVHIOaRZ1kB7rvj1JQzqJNEoyMgHe3oGE6shXPOOccNd/CNZKVUXHUVOw3m0q2MlRarQxhumdBjTidxD+hguxGrTnxvCQxAjlGMl7rlIu113xHFKPTLxf7QG2MQJhICQkAITAcCYrTTgfKQfWA4BOMJv0jEvRAiWCgYTTl9GeVcR4AIiNUwREaPuJZjLJoj4TXHdahuurToJ7Z12qYOFsAw2Y022iiFyxP3XWyn6r7LZYiIMZzCerm4gsbHGMOxyIpSd1yqJwSEgBAYFgGJjodFboTrIhVahM2jKULnQYiKg8LHFF3t+uuvnw4++GA/hasPmV3I0wkRfxVdLhk+EJOS6gzG9IlPfMIzt0RgDNx3sK7FypnoOlyHZTNUJx0bMVrxgWVFiN40XIO8gdI/C2TuJaQ360WRUSXSfCHihgjj+IIXvCBde+216cYbb/QyXJcQNxZTucV9UxcqYkGEJNx0uH9woW0wWGeddVwcT/1e9838RF/UFQkBISAEhkLAXsiiERAY1OrYVmtuQWuTlWGFawHPM2OcmbnwsEz1srCmNZ/ObJlllnFrVlvhZVgfz58/PzMDnsx8YzNbyWaW0syvM9eezBI++52cfvrpbnVsq7zMGIxnarHVa2ZMw+vSjzHpzESzXt98UzPTA/s5MxTK9ttvv8wYcGaB5b3MGGpmTNv7MwOWzAyMMvN1rUSN68LimH4Yq/m5ZrYSnlIfS13zWfU+aNNccDJbiWdkomEcWCsbo8zMx9WPuVczpqm87yos6BOrY9piLOBhacfcspnBmMTAx8c5izaUYQ3OeY75s4+brFcWnOINcS9LLbVUsUj7FQhgAQ+29qFWcVZFTUNA2XvGMyPuA2IPvmhIBFiRkTmElSErpXETq0j+QueIOJjjsJZFFEpUJFa3WNMG4avKcYhfKbdHxgM+IGYlqH+IW+OaOltWs1CMp841w9Rh1R+rW64vH1fdd1UZ1+L/a+5LbnHcK2IVdYclfHIx4IqV+bDtzPbrMIbCfxyJyFw2huK3CA3zG5zOZwSPBwK8IBkSDY+ARMfDYzctVxI7mL8gxMkhUqYMZmorzzidb5dccsl8P3b4UY/6cps0g42xFpksZeXjqvuuKuNadLWhp+ZYNPcQ4EMLVzGTFvnNY2h46qmnJtQDqB0wwFt55ZUHAoaPPyKKobLBUj1UFGWXMD6OcZPhAzkI1Q0fZ1WxiEkQQdCU+IAmuAwJ4I866qhkUpNoQts2ITCehfHcbWVQ0fHcRWp233kTRMe2op8YyONqeyZEx1/72tcy8//OTB/v+Jxwwgl+fOedd2aXXHKJB2mxd3Zmhni18TMm69eh/iHACdfzZ8zW1R/FhozB5+ejHuqVMhnTz3bZZZfMPmYzY64d6hazychMCpWZl0D5sokeS3Q8Hnj/J2u0J0AkBIRAOxGYZOYiRPIzlQ1q1NnAgh2rfVaIZu/gzRFEZfHFF3dJENbtJIaHKK9LxA8nixAqGtRHxMlG8kQYz4suuqijGZLVE9+cFWr8YbRXJEKhgjFufMTbJgZ6UZKF0R/jJIk7wVhE7UJAjLZd86XRCoEpCPDy3n777SeWFrEJ2aCm3HSNAlzRSHhuq0RPFBGXoFYh8Eu4v6GHhIoW/1G3amtrHA+mQuINooshNibGeCSrMKPE/DJilBM8BbUN1xGPnD/UGUUiWQj6awKshAV98Tz7O1uaSdz2YNyidiEgHW275kujnUUIEAaSdIG4MLGiMatqT+4QLkUWE9rdvnhJsyIi4Mhhhx3mL1uYBddWZTMiwQJ6/DPPPNPdk+bPn+/uUoQ/JD8wvtMwhzrtw2S7ZYMaNZPTpKeSFSoBWSIEaPSHfpZVaNg6RCAXVpR1CWzLzBLXOVajFks7bwamyNwuWLDAy1hdf/CDH3QDyqh06aWXur4YQz0ip8GocZ2DIReJ8e6xxx4Jdz+wjxV6sY72G4rAeCTQc7cV6Wjn7twX73xQHS36ODO+yWz1kplhTIYeERct3JzYh8x4JltiiSVcvxd9WZxrd1d6whOe4EXoHXGBstdLZiuezPIQu67RVmleZiLSzAzBste85jUZ+9QzRuzX1mnfPgIyywbl19E2x5CJLzN78ftYuJe6NJ06WotZnZmxUd+hGfPLcFuzVJV96/aqYOFCM4Lw44IWdNZZZ2Xmz51ZqsfczYw5Lib42GeffRxf+6DycZBUg3nC5Y5kH0XCFZBzuPdNB0lHOx6UEWeIRkBAjHYE8GbRpYMyWgur6X69JvbNUcCvmZeopf1zX2FOrLXWWl6WV7IdjG+C0VJelbnIkiL4deuuu677UVPPVm6ZiTo9kxMMG6rTfjkblF9o/+pmcor6bKeL0fIRYTpO/5Ap9l/et1CczhwtkEn51EDHlgrS/ajxQe9GzEn4jZvONa9mYUl9rkxa4WVkz8L/m2cBBo1veZBFSfNyC2MaRRPditGOB17paO1pFgmB6UTAfrou1kXEWBQPkqfYrFjdx7RfRK2q8SJiDqIdCHF0lBPVCzcW9HyDth9tRPtsyeQUeslieRP2cefBnaYoxq0aFy4+iNO32mqrqtO1y975znc61r38TXH/sQ8N90HHeA0jMyjCgRpz9WPmDrUA6gGiwN12221ezj/c6zCSKpblJ7XTWATEaBs7NRrYbEWAoBZYjuL3WyR0cGuuuaYXRfah4vl++1XMsHwNgUqguoY/cX2dtqNuE7YRWKVbQooYI4krQgcdZYNu8c/F6Andb+h9u7UB80SXS/zuCP9JwBuo6KOOkVX4fqNbLxLBavD/FbUHgc5fenvGrZEKgdYigGsJL+R77rnH3T2KNxIv3WEMXeoww1gJmXi62G3f/Tpt921kGitwf4yZqGC9iJSMowRxISIcqeRgtKZ77dVVfo65JWJbrLaRNECsXosU54vtYklNoAxTHRSrar/hCIjRNnyCNLzZhwCrrFitEFWoSEQFYsUS52OFxMsVQuyM6DfEjpQFEyxnceJcmUxP6OLqiIJUt336DHeYaJOXfp0+o/50brHgJSyp6aJ7dvvJT36y6/nAvFuF73znO+46hN9sWIpTFx9ZkldUETjic4tfbMzbq1/9aq9qsbY7LsH6mPtA/B8Uq+BRPg6iLW2nDwEx2unDWj0JgRyBD33oQ/6iPeaYYxJuPhD+sLfffrsHJmDVCyFmhMhJzAudxPKW0MHdUy644AJnuPGS5wUPo6btIESjsaojZSB5eN/97nfnOX7rtg+DJxsUjOnWW291/ScrMVZWZggU3TVq+9znPtd10TC3Knrf+97noT0vvvjiKaePPvpoD2pBpqsqQge8wQYbuB8t+llciPgDT7acI+UjPrq4+ARGtIuImHCKQfSBvvu4447LY2Vb1Cp38yG1ZTE+d0gkLFFHXK5tGxAYj03V3G1FVsdzd+6Ldz6o1THXGqN0S1Xcb3ANsVVKdsghh3RYmWIdjBuQvUvc0pjwgVgBr7feepkx1MxWmZXZjCzhfX4NofvWXnttdxUy39LisLM67VdlgyJbUr9MTh0d/fdguqyO6Y4Qi1hZ42JTReaz6hiddtppU07jPgPm/PEbLxJZocw3OT8f9WJrwUO8Opm6yF5FuTHLbNVVV3XXKtOPF5vzffO1dRcsXLyM8XrmrHe96125xXhcsMMOO3goRvt4iKKJbmV1PB545d4zIo5itCMCOEsuH4bRcuswStIbksYQt45uhBtQxBvGxaNMvHhhLPECDkaLzyyuJ7j2xLnytRz3a//Xv/51VvaXZRxVY6lqP8qmk9HSpwV3cBeZ6L+4BU/TkxeLOvYtp7L74TJHwxLYm4jZYxTH/PVqy4y4MpMYuA91uR7jsSAZmYmey6cmdixGOx5oJTq2z02REJgpBIjQZLmIk6043bWn2zhwAwqdXtE6NepH5qKyJTPnyXyEmLfqXFzfr32sZYspF7mOcVSNJdpswpb4wGBcFbYQPMP4rDxWXIO4ltCIXD8sgT06VlsB5/PXqy3082T0Cd151EVdQJxjxN2EexS1CwGFYGzXfGm0QqAWAuH+YRKXWvVnayU+BNDBbrnlls7oCDtZh8iXu+mmm7pOvE79SdbB4OxlL3tZ2mmnnTx28yT7UtuTQUCMdjK4qlUhMGMIkLHGdL7eP24nWLXiK4ory1wk4gYTIIIcsHXJwjcm/ppArG4xlKrKXduE8WkM/REQo+2PkWoIgVYhsP7666drrrmmY8xFX8yOE3PkADExae3aSIiuxWTbOHP/G7MY7f+w0J4QmBUIEFWIP5EQEALNQEDGUM2YB41CCAgBISAEZikCYrSzdGJ1W0JACAgBIdAMBCQ6HtM8kIwZFwjR3ESASEGE7CM7jqg7ApHMwHK0Tkmc3v0qnZkpBO66666Z6npW9TsPd9xZdUfTfDP4t+2yyy7T3Ku6m6sIkFKNZATrrLPOXIVA9z3NCOAaZflvp7nX2dWdGO3smk/dzSxHgMAL5Cotp06b5bet2xMCrUZAOtpWT58GLwSEgBAQAk1HQIy26TOk8QkBISAEhECrERCjbfX0afBCQAgIASHQdATEaJs+QxqfEBACQkAItBoBMdpWT58GLwSEgBAQAk1HQIy26TOk8QkBISAEhECrERCjbfX0afBCQAgIASHQdATEaJs+QxqfEBACQkAItBoBMdpWT58GLwSEgBAQAk1HQIy26TOk8QkBISAEhECrERCjbfX0afBCQAgIASHQdATEaJs+QxqfEBACQkAItBoBMdpWT58GLwSEgBAQAk1HQIy26TOk8QkBISAEhECrERCjbfX0afBCQAgIASHQdATEaJs+QxqfEBACQkAItBoBMdpWT58GLwSEgBAQAk1HQIy26TOk8QkBISAEhECrERCjbfX0afBCQAgIASHQdATEaJs+QxqfEBACQkAItBoBMdpWT58GLwSEgBAQAk1HQIy26TOk8QkBISAEhECrERCjbfX0afBCQAgIASHQdATEaJs+QxqfEBACQkAItBoBMdpWT58GLwSEgBAQAk1HQIy26TOk8QkBISAEhECrERCjbfX0afBCQAgIASHQdATEaJs+QxqfEBACQkAItBoBMdpWT58GLwSEgBAQAk1HQIy26TOk8QkBISAEhECrERCjbfX0afBCQAgIASHQdATEaJs+QxqfEBACQkAItBoBMdpWT58GLwSEgBAQAk1HYF5m1PRBanxCYC4icN1116X11lsv3X///fnt83Plb6GF/veNPG/evLTddtulU045Ja+nHSEgBJqDwMLNGYpGIgSEQBGBNdZYIy255JLp3nvvLRb7fpH5UrDNNttMqaMCISAEmoHA/z6LmzEejUIICIH/IsBKdccdd0yLLLJIT0wWW2yxtPHGG/eso5NCQAjMHAJitDOHvXoWAn0R2H777dO///3vrvVgwgsWLOjLjLs2oBNCQAhMHAEx2olDrA6EwPAIrLzyymn55Zfv2gBMGGYsEgJCoLkIiNE2d240MiHgCOy8885p4YWrzSmWWmopN5gSVEJACDQXATHa5s6NRiYEHAEsiu+7774paCA2ftWrXtVhgTylkgqEgBCYcQTEaGd8CjQAIdAbgSc/+clp1VVXTRhHFUli4yIa2hcCzUVAjLa5c6ORCYEcgZ122mnKynX+/PnOgPNK2hECQqCRCIjRNnJaNCgh0InAtttu2xG4ArExzFckBIRA8xEQo23+HGmEQiAts8wyaf31189XtYiN0d2KhIAQaD4CYrTNnyONUAg4Ahg+Ba200kpphRVWiENthYAQaDACYrQNnhwNTQgUEdhqq63yFS0uPyIhIATagUC1c147xj7rRvnDH/4w3XTTTbPuvnRD40PgWc96VvrWt76VHvrQh6azzz57fA2rpVmFAGE5N9lkk1l1T22+GWXvadDsHXPMMWmvvfZq0Ig0FCEgBNqIwFOf+tT0/e9/v41Dn5Vjlui4YdP66Ec/2tOgRTo0bf8/LdxswOEXv/iFP21XXXXV0HP897//PZ1xxhlDX98WHJ/ylKek973vfbP+PicxH+9+97sb9lbTcMRo9QwIgRYh8KAHPUjWxi2aLw1VCICAGK2eAyEgBISAEBACE0RAjHaC4KppISAEhIAQEAJitHoGhIAQEAJCQAhMEAEx2gmCq6aFgBAQAkJACIjR6hkQAkJACEwYgfvvv3/CPaj5JiOggBVNnh2NTQiUEMAdZMMNN0wPfvCD00UXXVQ6q0MQIPDLpz/96XTooYc6IL/61a/Se97znnTjjTempZdeOm2zzTZps80286AfgyCGW9bxxx+ffvzjH6dll1027bHHHmn11Vef0sSXvvSldPHFF+fl//rXv9J//vOfdMIJJ+RlsUPwkZNPPtnbfNKTnpQ++tGPpo997GNp8cUXl3V5gDQbtvbDFTUEgaOPPjozP9qGjEbDGDcC5keb2Tsjsxf20E1bAvhsySWXzB772Mdm9vIeup1xXGirtHE0U9mG+dFm5kdbea5X4de+9rXshS98YQbW0F//+tfMYkJnn/3sZ7Of/exn2dve9jafg9VWWy0zBtirqY5zn/zkJ/26JzzhCdkDHvAA32f75S9/uaMec2LBIvw8cx1/l156aUe93/72t9kuu+ySPeQhD8mOOOKIjrEwZvuYyt7+9rd3XFP3wPxofQx166ve5BGQ6Hg2fC3pHuYMAvZyTz/4wQ/Sbbfdlsc9nombRxTKypqVWlOIleQrXvEKXyGS7QgyBud4PeMZz0j2cZLe+973puWWW87DWF5//fW1hv6HP/whGYNOV155ZfrJT36S7rrrrvSyl73M753yIl1wwQXpkY98pK9QWfnyd8899zhWUY+gI2B34oknpgsvvDC95S1vSaQ9DDLmm77whS8k+zioXAVHPW3bg4AYbXvmSiMVAo4AL/KHP/zhM4rGgQcemC6//HKP3DSjA/lv54hnd99992SrxPT4xz8+H5Kt/j2P7/nnn5+XPec5z/F9GGgd+tznPpfe9KY3pfXWW8+r26o2veMd7/D9W265paMJW52mJz/5yc5gYfZPfOIT0+Me97iOOrvuumv69re/7W284AUv6DgXBwQmOeigg9Jb3/rW9Lvf/S6KtW0pAmK0LZ04DXtuIsBq6JxzzklbbrllvpqEYZx22mmJ7D4//elPEzGzX/SiF6XXvOY1KcI+/ulPf/I6W2yxRfrRj36UDjnkkLTuuuu6vvK73/2ug/mNb3wjvfKVr0wLFixI5557rpcRBpFj/jgPwWTf//73+/5rX/taZwgccH6jjTZK6DKnmz7+8Y/7SrOYSpAxrLPOOuk73/mOM8oYEzGAYWTcfx3aeOONp8Qgh1kvscQSiZCpQddcc41jcOaZZzpThuEyL0VihX3qqacmgv7vvffe6eabb/ZVcrFO7O+www5p4YUXTocffngUadtWBCYvnVYPdRGQjrYuUu2sN6qO1ox6sh133DGzl6/r/iz5uwNhhj7Zwx72MC972tOelm299daZMRg/fulLX+p1LFlFZszFy4wBZJY0PrMVmh9bJqDszjvv9Hof+tCHvMwYsR+jh33DG97gZZ/4xCe87Nprr3W9p73zMlvVZhxDtvryesZA/HiUf4PqaJ/+9KdnK664Yt8uTeTuYzTm1bdurwr2cZPNmzfPsYl69gHj+lb74MnM6Mr7ASNbDUeVbJ999vFyrsUeY9FFF/VjM6rKzIgrrxc7m266aWaGbxm6+bokHW1dpKavHqIfUUMQEKNtyERMaBijMtoY1hprrOEv52C0lNvqx8s+9alPRTV/2ZuIOT9ef/31vU7RgMdWvV6GYQ5kek4/DkZLmVnBelkwWsowJoJZFAkDH9M7ZnwQjEqDMFpwMB1nZmLYvt3C6J797GdnRez6XlRRAUb9iEc8wg2sKk5nf/vb3/IPFHAy62KvxocQzPewww7z47/85S+ZSQW8jHktG7jtt99+fs5W4VXdVJaJ0VbCMqOFEh23VRShcc9ZBGwVNOXecfeB7EWen7NVXkJk/M9//tPLyGELkdM2yCxbfRed4aBkDKTjkkc96lHpda97XVpqqaU6yid9gDuPMc70mMc8pmdX1Dn99NOTfTC4SLZn5R4nMYhCF3vWWWe5gVVVVeYDV6D999/f9diIjCGuhRC5Q8zJsccem9Alf/Ob33QjNz/x33/GzH0P4zdRexEQo23v3GnkQqAnAlgo9yN8NxdaaKFU1zCo2F6Z0RbPTee+rR69uwc+8IE9u73pppsS+lZb0fas1+ukud6kzTff3PWm6MH70fbbb+9V0ItDYRiFZXEQ+uL4+Ln77ruj2LdRz6QFHeU6aBcCYrTtmi+NVgiMFYE77rjDrXKXX375gdttCqNl7IzFxLA974HVJO4/wxKuTGEsZnrrWs2EsRTuRRAJ2SFWr0WK1TgW5UX6zW9+44dYOovai4AYbXvnTiMXAiMjgIsO9JKXvMS3WLlCf/zjH33LPzPE8f1iGEEYG4wnzkVlRNXTTVjwsjLH4roXYZVtxmGVVfqN2xR8CeaKOP6AAw7I20AcjUtRNzr77LMTK+0Xv/jFXuXVr361b6+77rqOS7A+5j6e+cxndpT//Oc/92N8f0XtRUCMtr1zp5HPUQSCKYS+DxjCjafIIAmWAJXFjgRVgBCDmvGU63VDZ7j22mv7Cx9dpkVDSrjNvOtd7/L6BFGINgn+APNB30m9W2+91XWSrMgICjHd9NznPjfh09otgMbvf/9792ndYIMNpgzNjBA95KFZa085FwUEleA+Yea4EPHH6hjGiE8zWFhEKneXsuhUfhlMkpCK6HNjJUsf1DvuuOMSoSEhs/h2Nx/8ZmG2RUI3u9JKK7lvbrFc+y1DYEZNsdR5BwKyOu6AY9YdjGp1bEzOXXdsNemWqGuttZZbs5pPq7uA2KsnW2WVVTJbLeUWr5RhbWwiyMxWrX6dMYcMC1cL7JA973nPy8MVBuDGKD00IG5EXHPSSSdlZuCUWaCF3HrWGLFbHTMW3FdwA+L5pb+ixXK0Oeh2EKtj2sY9CfclM1Cq7MqYnp83MfOU87hHMW7+CNNYJiyp43zVNiyCLVpUXg9XI+YHK+4ymS90hrW3BbPw+cTdyj5mHMNiXYsA5u0VLcmL57vty+q4GzIzV86XmKghCIjRNmQiJjSMURntqMMKRgvTwecT5tuNbNWcWUQiP40rTFVc4F//+tcZLj1FomwcNCijpU9iA/MB0Y0YK243VQQmMMdB/FXL7fCxAa4WICPDbacfMRaTBHR1NeIDxiQMmVmN92uq47wYbQccjTiQ6Ng+UUVCYC4hgJWxMTKPbNTtvhFhkkEGQm9bjMUb1+CSgktPkSibKSLuMJbWRx55ZOUQGGu4QRUroGflWkIj1rHULl5b3EdvDa6Ik8OVqni+vM9YjLlXuhohkicDELGQ+1lTl9vVcfMQ+H/Lh+aNSyMaAgGCl6NH44/wcuWA50M0Oa2XoMviHorES56XN1aZz3/+86WrKoIz4H7oak086uniBry88dVxhYE5EZ4SpvfmN7+51phJ0mARmFy/WuuCCVc677zzXN9NkgTCPIraj4BWtO2fw/wOMK74+te/7nlKuxmF5JUbuLPzzju7qwnGKfzh0jB//vz0y1/+0v0WWS2sueaanm+0gcNv7JB4FsCW3KcQQSVOOeWUxo53lIER4OGyyy5zg6O67WBJTI7aphCrXH7HJCQQzQ4ExGhnxzz6XRDo3GLh+v4oIrCZgoSIQvgpQogusfTcc88904c//GG3aiWtGEEHsIwtu0fM1Jjb0C/PAhia/jRhfQsjGsWftOn3zGrWjMKaPsyu4yO6V5WovusFOtF4BCQ6bvwUDTZA9G9QbAe7euZrd0v/BrNgJYb7CgwYN43vfe97EiXXnLJyIISal6maEBACY0BAK9oxgDiTTZA27eCDD3ZRGU7xOMhDfNUX6fOf/7yLD6mDv17R3/KGG27w1Gf4PxLzdrfddkvkyfz0pz9dbCKRl9OyvrjzPbk/LTh9x/lefYwrhRp5QVmtcN8RP5ZBsFojbi8BCRgj+q2gOmnkqIuPJFiCEaJEGHqRevVRrKd9ISAEhEAHAo2wfdYgHIFB3XtwwVh11VUzUqGZSDAjBRhuETbB2Qc+8IEcVdwezJAoM0d6z65CppMVVljBXR1sVeguBFxjQQgyc47PzBk/sxVQZqvizELFeTvGKDOz2nSXEPwAN9xww4yUakG9+qBO3RRqjIexmOg4mp6yjbRtjBPCh3LZZZfNLAhAZrot9xulDbPc9PN10shREZ/S8AHF/9Gsbv16/vXrI6/YY2em3Xt6DK1xp3iOLRdu48bVhgHJvad5syQ/2gbNyaCMFuYGQwmHeW4lnO+D0ZpOMzOxa2ZRhPI7tcDqfp1lF/Ey8mDSjsVjzf3/IjVaMFOCFZj1rzN0Lrr66qvzF2GdPvBhrJNCrQ6jNX2jjxfmCpmVqaca8wP7Zwm4/bxZKkdR3zRyFszdr+ElFYTfaVCdPqJut20wWrDWX28MCJYhjHpj1AsfgmGImoOAdLT2tLaVCI9HNpAI78Z9mMO+306Ijk877TQPS4fLQxCh94xJ5eLj8C3Eyjf8/7DEhCJMnEW58bBxZD75yEc+kjbbbLM840idPiKFWoxhlC0iXAjjqT//+c8Jdwh0kJHqDStb7g899T/+8Y9EdpS4x3IauSuuuMLTyGHhaRF6XPyMCNnyhabPfOYz3k/dPrxyjX+HHnqo+0/WqDpnq+yxxx6uDrFoS3MWg2FvHBXP9ddfP+zlum4CCIjRTgDU6WgSBohfJDFeixQMNra22vVYrOHaUazba79stYyVKkyJ2LhbbLFFIkUYDPZhD3tYGraPXv33OnfjjTf6aVx9yD4DveMd70jobweh4j2CF7Fs0e+SRxQmS5xaS6g+Uh9V48GQy8TUVadU9l8ECNxP4Icmud20ZXL4TcRvpC1jnu3jlDFUS2fYQuL5yDFkisTeVbeCEz9B6GGGZbrrrrvKRV2PydJy8sknp89+9rO+ir7kkkvyl+C4+ujaeeGEiV/Ttdde6yUYLUW+zqovePKU4oNbh1gFs2q38HmJHKJgttNOO6WvfvWrY+ujzjhURwgIgdmHgBhtS+fUAsJ7+DusbyNbCLcSacsipVkklMbS2DQW+d1iBWwGSvlxvx2sjGFyrGy/+93vutj4yiuvTBYPNxch9+sD5tWPYtxV9bge5oovKFF/SOKNiJhVNdbW+NgG0Q4+xcUMN3GuastHB20uvfTSicw1hMCjDSypx9VHVb8qEwJCYPYjIEbb4jnef//9ffSkOEOsS05LM+bxMhgE7i/k0CT4w7nnnuviXtJzvfGNb/RV21FHHeV1YaBQ0eUn0qHBSCFWfMcee6zvo29FbEsya3SjdfpAHEvdfinU8I2F0K3CWPk4QEzOahoxOXrYbbfdNlnGGq9HHNi99trLx0eIRrMaTmbA5aJZdK6rr7661+t1jxGa8Mwzz8zTzZkhlF9HKMu6ffgF+icEhIAQKCPQHLssjWRQq2Myjey3336ZMQK30LTVWGaRldw1xhJMZ1dddZWDimsOrjs29/5nSbIzE4n6ObKNGDPyctx5aA8XGVJ3Ud9C2rmbjIlTMwsm4Ra+pPiylaXXi1nr1Qd1uDfaC/eZuK64xQUIy+YYJ5anJhp2lyX623vvvTOLalS8xPfJLGMfG+6OxLWmb/U0cZGppU4aOdyYcGnivs0QJ9tkk02yfffdN09d1q+PKYOqKDBm7/cW81JRRUX/RUDuPcM/CnLvGR67SV05j4bt5SRqAALHHHOMr/hIGD0Ioa8lGD8rOPSSGPaE7jLaYZqNqfo5YgaHsVSc77cN6917773XV3hVWVr69WFp2TxBQL++hj1vqck8DjKiXlbxgxBiYsTurNwJug9GVdG1RukDffGjH/3oZIxWxlB9Joc5RFIyiHqjT5Nz5rS5+KVTTz210i5jzoDQsBuV1XHDJmSY4SDahDFA4Z5TbgfGutxyy5WLax/jIgPZyrjrNf36qGLOXRsb4gS62tBJD3o5TBUcoV44jdLHoGNS/eEQML9wj2qGGxWE6gHmgyUuOngsmXFP6/Zb6dUrH5OoYcj4U5UdC3UH7m98TGHVjjoDa/hFF100b5aPNZPwJOwk+M1svfXWnnGIhAiQ+bB7ikIs4EWzAwHpaGfHPOouhEAtBCYtwJp0+/1ukqw3hBDFDgFCwrPeeuulddZZJ6GDt4ho7sK1/vrrJ/LQDkLnn39+wo8cRl3lLoe/Nf1gP4DBICFKDzzwQDfaCyM/8MGID0ZMogdsCgh1in93+KzvbJmWMMbDZU00OxAQo50d86i7EAJ9ESBrDy/2SdGk2+83buJbYxWP4dwyyyzj1TEIZPWJ4R7SGIzxkFjAKKtcwrr1AaNE4hHGhlX1WDUThxsVCSoWDBLJwoN1/kUXXeSXnHXWWb6SxU+YcVgIVZ8TjPVYKUOofcgpjSvdCSec4GX6124ExGjbPX8avRCohQBW5PgHo4OeBE26/X5jxk4BF7Rddtkl4foWhLoCJslqNIh0khDJJuoSqgVcyyJiWvk6Vqp8aGBdj5oFsTGBXcyozqviEQCFX7eFCfVj/rHqhsz4z7f8ow3c5dBRh+V/flI7rUNAjLZ1U6YBzzUECNARIQnNmtxFoIEBej5y+C5YsCBfEVkwfj+mjPMYwRGJCtEkqyyzGvfVHPpEonvBEKjDahd3JkSjiD4hfItpJ/SFBD6J/hgT1K19ziHCJb3hpIlwpPhCW6KJjq4Q5RKEpBg1jHuAkXGv4yRE0+jwi0QENcjibvsWfSxM+4gjjvBgKBjg4TbHxwHnikRUMpjv4YcfXizWfhsRsC8xUUMQGNS9pyHD1jBqIjCMew/JHXDfsvCQGYkPzHfaXYSMGea9kvjB3j2565St4Ny9iTIyGNGviUy9jun/sssvv9zLzP84M4bj5bg1GTPNTJ/px2Yo5BmLTI+ZLbHEEl4WHVosaHelstjYXtStfdNZZrhoGWPJSCoxCA3q3mMrzWzFFVfs2wUZrsDFmFffulUV7KPCr99qq62qTk8pw0WM+WPugnBTYwxgw7g32mijDEyraNNNN80sTneGK19dkntPXaSmr55WtPbEi4RAExFgpchKjJUNK1nzf3ZDG4x7iIRF3GmoLM7EkrVofY2+krjBEC5gGAJRhnUsYSchApmcccYZrk9kxUviCYx6WFEtv/zyXif+sfoqWpB3a5/V3cUXX+z6SYKcTIpYFaKHjVVjr34Q7WLQVM413OuaYc+FxMAYq89dtIMRFEk+GPett97q1s8Ec6kidMtEf+NZELUXATHa9s6dRj7LEUDMi/9yMMO43dD7kaVlUIIJFylcXIqM2dIvepXIhlSs32+/3P4LX/jCXE/Z79phz+POgwVxP0ZLnQivyQfEpOmd73ynf+CUo6FhZYw+l/MwWOYZS+TQ1RbHFS4/thIvFmu/ZQiI0bZswjTcuYMADARCp1ekyNgUmYuK5/rtlxlhVX1WzvQ5iLFQtFOn/ag7rm0wqPCD7tYusbBhaKxoJ00w06985SvpnHPO6TBywhgK6QRpGPHzxbcX1x506VgZlykCz0SY0PJ5HbcDgc5fcDvGrFEKgTmBAPl2oWLSCI7JQQyFC4sf1PxXhxHCwLHULYuM63RRp/067QxSh3HSL4EgehEJJnD/mTThy4sBE4y2LBIm/SKW3xFLG1E+q2wIdUCZcBWCyBUtai8Ck5eftBcbjVwIzCgCJG6ACG5QpAiWgEUtFGLQYlII9H8QDBMKBtgrpaJXtH9mLOW7wQyifXSOFu/aEz3ALKJtKndrH7cbMznpiIzkjY/xH+E2WYWbQVHPVs2AaYp0IC6Ie4vjYbdYOONihN9sMYraF7/4Rde1Et4TMuOoPAIZ7kZmcOZZqcr9RjjWXtHKytfouHkIaEXbvDnRiISAIwCjQ8d55513pqI+lsAMGCNF9qa1117bYzuzMiJ5Pa4u73rXu7wNAh/g4xovfV74MGoyHBXpggsu8EOMoDCyQpxJVigoXFRw04GB4P5DqkKCMnAdDLeqfXSiT33qU301VvwI8EbH/A9x+i233NLVT5jxPvGJT0wbbLDBlJ4Jh7j44otPca+ZUtEKIu1ilSgXUT/t4zqE/hVXI/7Ajy3nNt98c2f2YBw+zYiPaa9qtY1udqWVVnIjtqrxqKwlCEyfgbN66oeA3Hv6IdTu88O49xiDyMyP1V08LBG9u+CYJWpm1qodYOC+Q6YjW31mxqCzk046KTPRc7brrrtmxlgzW8lmZnnsbiXGcDILoODXU9deVX5ujTXWyMyiODOfW3f/iQ5wPVl55ZW9Hi49Fq0oW2211dwVCNciXE+q2qfMkih4ViTuYxAa1L3HPkbcVckiL1V2YytDP29i5innLaKT3xs42IpzyvkoILsU11PPVvCZ5TvOzMfZT5Pdaf78+Xk71Cn+kf0qyKy7PZsW9clKRYYsC7eYZ4qKemZJ7W0YU46iWlu599SCaVorIdYRNQQBMdqGTMSEhjEMo42hmK4uu+KKK9y3NcrKWxN/ZhZFyIvxf+XlXyRbQfn1bIOC0cKI8BGln25kK+OcGdjKd0q1qvapV1V3ysWlgkEZLZebtXTGx0I3wpfXDKcqT3P/+OHy0TAdBFYWYCO7/fbbp8xT9G+uXZlJK/wjJsrqbMVo66A0vXUkOrbPTpEQaDoC6PDIBIPxTDdCV4kIFEKvSpzdImFJzPVlK2bqUEYGKPrpRoheQxcb1rDFulXtU6+qbvG6ce2TTYfQh0ceeWRlk/jyWvCHKecQcXOtrf79+ikVJlAAVrai9SQH5XmiO5IK4IN84YUX5lmlJjAMNTlNCIjRThPQ6kYINBGB0DWGkU4Tx1h3TDB0mBN66A9+8IN1L/NgFxaBKVmkrNrXTLLieeed5zGT0cX3+vCZ5BjU9ngREKMdL55qTQi0AgETXSYLx5ine8PQ6ZRTTmnF2HsNkgAPBIPAiKwuEVkLA6+mkImwEy5CSBBEswMBuffMjnnUXQiBgRBAxEooQP6CsJadDYR4m3R1bSUsvkWzCwEx2tk1n7obIVAbgXIwhdoXqqIQEAIDISDR8UBwqbIQEAJCQAgIgcEQEKMdDC/VFgJCQAgIASEwEAISHQ8E1+QrE8h92223nXxH6mHaESATD0TUoIhjPO2DaEmHBN8n0tUNN9zQkhE3Z5jK9NOcuYiRzMNtNw60nVkEcE04+eSTZ3YQ6r3RCBAC8J577kkR57jRg9XgZgwBUgaSb1jUDATEaJsxDxqFEKiFAMEYjj32WA9KX+sCVRICQmDGEZCOdsanQAMQAkJACAiB2YyAGO1snl3dmxD4v/bOBO62qfzj6xaVEIWuStxLl8xE5rhkTsh4jZeQWRSFzGPFP/MYIbmGa0pIJFPlmiqRocGQkIjKUCL7/3wf1rn7nHefYZ9xn31/z+fzvmeftddea+3vft/znLXWM4iACIjAwAlI0Q78EWgAIiACIiACZSYgRVvmp6t7EwEREAERGDgBKdqBPwINQAREQAREoMwEpGjL/HR1byIgAiIgAgMnIEU78EegAYiACIiACJSZgBRtmZ+u7k0EREAERGDgBKRoB/4INAAREAEREIEyE5CiLfPT1b2JgAiIgAgMnIAU7cAfgQYgAiIgAiJQZgJStGV+uro3ERABERCBgROQoh34I9AAREAEREAEykxAirbMT1f3JgIiIAIiMHACUrQDfwQagAiIgAiIQJkJSNGW+enq3kRABERABAZOQIp24I9AAxABERABESgzASnaMj9d3ZsIiIAIiMDACUjRDvwRaAAiIAIiIAJlJiBFW+anq3sTAREQAREYOAEp2oE/Ag1ABERABESgzASkaMv8dHVvIiACIiACAycgRTvwR6ABiIAIiIAIlJmAFG2Zn67uTQREQAREYOAEpGgH/gg0ABEQAREQgTITkKIt89PVvYmACIiACAycgBTtwB+BBiACIiACIlBmAlK0ZX66ujcREAEREIGBE5CiHfgj0ABEQAREQATKTECKtsxPV/cmAiIgAiIwcAJStAN/BBqACIiACIhAmQlI0Zb56ereREAEREAEBk5Ainbgj0ADEAEREAERKDOBUYlJmW9Q9yYCw0rgrrvuCiuvvHJ46623KrfAvys/73rX1O/Io0aNCltssUW44IILKvV0IAIiUBwC0xVnKBqJCIhAmsAyyywTZp999vDMM8+ki/04rXwp2HTTTUfUUYEIiEAxCEz9WlyM8WgUIiAC7xBgprrtttuG6aefviGTmWeeOay11loN6+ikCIjA4AhI0Q6OvXoWgaYEttxyy/DGG2/UrYcSnjBhQlNlXLcBnRABEeg5ASnaniNWByLQPoFFF100jBs3rm4DKGGUsUQERKC4BKRoi/tsNDIRcALbbbddmG66bHOKOeaYww2mhEoERKC4BKRoi/tsNDIRcAJYFL/55psjaLBsvM0221RZII+opAIREIGBE5CiHfgj0ABEoDGBsWPHhqWWWipgHJUWLRunaehYBIpLQIq2uM9GIxOBCoGJEyeOmLmOGTPGFXClkg5EQAQKSUCKtpCPRYMSgWoCm222WVXgCpaNUb4SERCB4hOQoi3+M9IIRSCMHj06jB8/vjKrZdmYvVuJCIhA8QlI0Rb/GWmEIuAEMHyKssgii4QFFlggvtWrCIhAgQlI0Rb44WhoIpAmsNFGG1VmtLj8SERABIaDQLZz3nCMvZCjvPzyyz3oeyEHp0ENPYElllgi3HvvvWHGGWcMkydPHvr70Q0Uk8CnPvWpMN988xVzcEM4KmXv6fJDI6uKEiJ1GaqaEwER6CuBU089Ney+++597bPMnWnpuAdP99JLL3VlG1Oa6fXt1G7i0BmHAw44ICy22GJh0qRJ+vt6J11g1t/Uj370I/+v/ve//y1ODThlsaOMaGOS7hKQou0uT7UmAj0lwIqJrI17iliNi0DXCUjRdh2pGhQBERABERCBqQSkaKey0JEIiIAIiIAIdJ2AFG3XkapBERABERABEZhKQIp2KgsdiYAIiIAIiEDXCUjRdh2pGhQBERABERCBqQSkaKey0JEIlJ7ANddcE8j6c+ONN5b+Xtu9wT/+8Y/h0EMPrVxOEJr1118/rLjiiuHggw8ODzzwQOVc3oOnn3467LnnnuHxxx/PvPT2228PW221VVhppZU8acQ999wzot4rr7wSjjnmmLDeeuuFz3/+8+H8888P//znP73ea6+9Frbeeuvw/PPPj7hOBYMjIEU7OPbqWQT6ToAP+CeffLLuB30/B4TPZtHkF7/4Rdh1113Dbrvt5kP77ne/G84666xw0kknhcMOOyxcddVV7svMax557rnnwt577x0+8YlPBIJBoCxr5bzzzgurrLJKuOOOO8KUKVPC97///bD88stXfSmC2VprrRVQyCeeeKKP6cILLwyf/OQnw9/+9rfw/ve/P5DpabnllgtPPPFEbRd6PygC9uAkXSRgybkTC1jRxRbVlAi8TcACViQWgrFjHKZoO26j0wZ++tOfJoccckinzWRebwEr0OCJBazIPF+v8Mc//nHysY99LHnqqacqVZZccslk0003rbz/wQ9+4G2vscYalbJWDn71q18ljzzySGIZmPz63/72t1WXvfTSS4llaEpuu+02L//zn/+cbLjhhl7XwiFW6l5yySVedsstt1TKTOl62emnn14psy8Iyfzzz5/861//qpS1emABKxL7MtBqddVrgYBmtIP6hqN+RWBABOaee+4B9fx2t8yot9xyy/C///1voONId/7f//7XQw7uvPPOYa655qqcmn322cO1114b3nzzTS8jBjDyj3/8w19b/WUK27MtffzjH8+85Morrwxf+cpXwsorr+znqccyNfLggw/6K7+YGSN33nmnv/KL5WJkuummhq4n6QR8jz/+eD+nX4MlMPXJDHYc6l0ERKAPBGxWFS666KKAst1pp528x/vuuy9cccUVYaaZZgrrrruuL5U++uijYfvttw8xNd+vf/3rQGjRj370o77HazMrX5pkn/DrX/96ePe73x2++tWvBvYgbVUnXHzxxYE2jjjiCP/AR2GxZPqnP/0prLrqqr7MefXVV4dnnnnGl2o//elP+3ItCu+cc87pA4nqLs444wxfTo/3G8+yP8sYoxLjnpDPfvazsUpXXlkOnm222araQqlTRgKJKBtvvHHYZ599wnHHHReWXXZZV8xw5csB56Iw3j322CMcdNBBzpV8xpIBEmhh1qsqOQho6TgHLFXNRaDTpWNTsMmiiy7qy4zf+MY3vO/f/e53ie0Depkp0cTy3CambJJZZ501sXCPyd13353ceuutCcuX9jGVfPCDH/Tl6y9+8Yt+TJntaXpblow+McXg9eKNsQxLOzZD86K//vWviRnyeB2bdSUsgVL28ssvJ6YcvO7f//73eHlbr+0sHS+00ELJggsu2LS/zTffPJlzzjmTF198sWndrAqwhVnt0nFWXZs1J3yefOlLX6o6/eUvf9nbgBfjZhk7vdwdK/Ps6Ovoo4+ORS29aum4JUy5Kmnp2P4SJSIwLRBgufZb3/pW1a3aB3XAmAYxJVoxwsGq9a233go///nP3UDnO9/5jtehvu03hnPPPdfrvu997wtnn312+Mtf/uKzvnHjxnm9+IuZFrPZKMysSIyAjB07NtieZaCM2bTtkYbrrrsufOhDH4rV+/LKsvAf/vCH8JGPfKRhfxgbYQR12mmnOauGlbtwkln2Bz7wgSoLaJrFCOoLX/iCL2c/9NBDPuO1L0Yjelx44YW9LL30PKKSCvpCQIq2L5jViQgUg8B73/veEQOZYYYZvIx9wbhMiUJFUC5ILEdJsjSMmLGNW7iyF9jOh3lsxxuzX6uvvnpYe+2149u+veLOY7PxpooWK2CWyjfaaKOej82MoXx5+LLLLvPl+nSHfDG6+eabgxmTBRQsS/AsPce92lgXC+Tpp58+PPzww7FIrwMiIEU7IPDqVgSKTIA911Zk3nnn9Wp5jYO4qFbRttJfL+pEBfWe97ynYfM/+9nPRqwINLygzZOvvvqq++0ee+yxYc0116xqBWMo9s7Z+z788MN9dQHXnl/+8peBffNa4UuULcXXFut9nwlI0fYZuLoTgTIRiLOl2iXjVu6xKIqWsTOWLN/W9H18+MMfdj/YdFm3j1kdIGDFhAkTgu3NjmgeIzPqfO5zn/NzLL9j3IZMnjzZX+MvDMvMvSfUs3SO9fTaewJStL1nrB5EoLQEzFDKLZjNyMrvMVrn8gGPmMWIKwb2e6NEBfv666/HIn9FMdSWVVXo0ZuZZ545zDPPPMEMihr28L3vfa/u+Xi/dSu0cAJWKFeW7ffff//KFSxr43aEYNWNpINRROtk88X1c/GXGZn5IUEyJIMlIEU7WP7qXQT6SiAqBPYAo8QP5BjGj3J8XRGzrvXX+Ivl0zjzYwbFUuZRRx0V4rJrXOrccccd3bDJgj0EFAAuMoR/ROHiIoRcf/314d577w0YWqFM2PNl9pUeR+y316+EPGSfmdlilnzzm990wyQMtmrl5JNPduOotHtNbZ34PnLPWs7dd999A8ochY+bET9m5ezGYxhFIYSCNCtuD7sYx4pxGu1RNy1xtYEwjZIBE8hlo6zKTQnYt3VFhmpKSRXaIdCpew/RfmxP1V0+7IM7+drXvpaYX2tiPqxehhvOfvvtl1gYwsSWJL1slllmSczCOLGYu/4eNx3awCUIVx6zjK26FdxMogsRdc0iOVl66aUTC8SQmEJNzMI3sVlrYkZV3p758yYWO9jLcJvBrYgoSZ1IO+49jz32WGIW1IkZH2V2HV1qiAxVK7jP2Me4/9iMs/a0v7cAE4lZCie29+31zCK4KjIWkZxiG1mv5r9baXfSpEkJrlhjxoxJ1llnnYRnZMEtEvsSU6nDgcU89mdlCrmqvNkbufc0I5T/PEs7ki4SkKLtIkw1VUWgU0Vb1VjON1HR4jNrM86ED/5GH+CEeYwf/GbcM6I3rkW5pdugXlbdERc3KWhH0dKkBXdIlllmmczWuRdzYco8R+Gzzz7rfrh8keiHwM3iVntYR1tyH9El4zGXqcQskkeca1YgRduMUP7zWjoe8IqCuheBYSPAMibLvCxh1hMiT8W9WNxMaoVrMeRJt0G9rLq11/bqvX2R8QhXWWELuReLg5zZNcveXLvLLrv49ZmVulwIN5vRelhHXHjSwlI97j4sd2+wwQbpUzoeEAGFYBwQ+Kxu7XtSuOmmm8JPfvKT8MMf/tCd6OOHVVb9spURVo7AB2nBuIaABwQTILMJH86S/hOIe4rRGKf/I+h9jyh59mAJBsH/HSElWxGCXRC6kv3oQQvGZJaMwFPs7b777oMejvp/h0D9r6RC1HcCGIpgLHH+jzQmEwAALjlJREFU+ee7VeEwK1m+NOQVAqHDAOMSfjCM4Vs7Bjf4FOKzSXxXAgwMu7TDZ1D3fMMNN3iKN/rniyB+nDGQxaDG1Kt+bb/Tg0EQPKNVwUq4CEqW8fLF9Mwzz/QEBa2OX/V6T0CKtveMW+6BIAFYa+KA3mrAgJYb72NFlCVB120fKVevtjfkPoRchMsFVpgkySbkHKHmyA1KcHtydN5111252i5S5Xb5DOoexo8f79liovXwCSecEPApLavwBXfxxRcfytvjc8NiNg/l2Ms8aC0dF/Dpsv+S3rsq4BAbDunAAw8MFizefSgbVsw4Gd0Yak/FLyG4fqCAyQBjAfGHcim5Ez61XPrxnnjG/EhEQATaI6AZbXvcunoVTv9mih9WXHFFT231wgsvVAxJ6AifxiOPPNJjn7J/SzzYk046ycfwn//8x3NObrLJJh4tBqMMfBaj4DdpLgluFEGKssMOO8z7YanLMojEak3bIsRbjFhDSjUEYwsi2PDDeQQlEgPX77DDDsGyxHg55y3LSLAk1f6+3V/k7GS2YUm9gyUP92bKwKddHrpOBERgCAjkN1TWFY0I2LJTLj9aC1Se2DJpYsmlE4u5mlj8UvenM8MM78aCh3uKMftTSmxJ2f3izMowMWWTmIGK+yyuttpqnnbLMq0k+CXii8gxstdee7l/INfjG7nFFlu4TyPvLVC8u1hQr5W28IPkOlPWXOLuG6TwogxfS2TKlCnJAgss4GU2q/X3lFvOUi/DH7GRkLaN9mBST2KfpBwrC59695ouH6R7T3ocRT9u172n6PfVr/HJvaf7pOVH22WmeRStzVzd2RzlFwVlSzCBqGgpR2GhfHBSt5mc554kMIDt53q+SnwWo1jkHq9r8Vs9CADltsfmZTYbjtUS8onSpoV287JW2jKjGL8mKlouPP30070sKlrKCFAAh7SgyHHKNyOadPGI41YUre3Zep/zzTefX18GPiNAZBSgaC0Ck987z04/YtCLvwG+yBPcRNI9Atqjtb/UQQkBwtlzZL8xCtk2iBv7m9/8JhZVQtbhG8deGTk+7U8gXHrppe72gs9ilM985jOe0gyXA0LKEQc1pjhbYoklYrVgzvke7o1+Wm2rcnELBxiUpIUcoxh6dUOef/55bwbjKSSG9BtmPn4jLfzi2bNkL6lP4L777vPtC7ZMYmjI+rV1ppbATjvtVFuk9x0SkKLtEGAnl2NJi8RUY7EtlFRaUUXDqLQlMu4VL7/8csAdIS2Y9+MCQ0za3//+965o0+fjMUHUaZf0Zq22RWLwViU9/lavabUesV0R7hOZlvhgLFYUVxKHX8BffFnFToDYwzLiyv+A5H+bn1mzK2QM1YxQD8+TrQSx2LK5e0HpoVQJ8IAxUFpiBJvRo0eni6uOUcK4mZAirNO2qhp+502vFC0B8G0f2HuxOK9ZXXtZp/c0rHzqAtEJERCBgRGQoh0Y+uBZOeieIABpsXiprgTTZbXHLInFpeBaS14yovCtPp6vvZb3tq/pxeS1bLUtFDuSzq7CWBGUdhSULD608Vwsj5lj4vus13Q7tee5HuWKPydRe1gqriet3lO96wfBp95YVC4CIjDkBLq33auWIGBKpmWrY1MciTn+u1ELWVNsXzW54IILKkYuFnM1sRlcYq4xXoZ1cVpMwXp/ZF/BSAqxPJVedsQRR1SqmjL16zFGQizNmQdPx4o5BiRvpS0CvmMNzJgxfsIQCgtF+xdILKaq9037HFNmEWq8HgZOHMOGTCeN5JJLLvFrMcggeL0p3sQiQyWWPqySFWazzTbzLDCxnTLwiffS6FVWx43oTD0nq+OpLNo5ktVxO9QaXyOr48Z8cp/No2hp/P7773e3HRST7cEmNkvz92bE5GnFzGCq6rz5y1ZZ7lqOT1d2NntN9tlnn8SSPLv7Ddk9okRFS2oyspOYQU1iRlOuxGMdXltp65hjjnGLaJvdJrR7zjnneP8WUD2xmbQ3d9FFF7lShYX5vbqytJCKrkDTFsvpvjnGBcjiGns9eNAH1tekZSMdGK5BuPOkhTHzhSHyG1Y+6XuqdyxFW49MdbkUbTWPvO+kaPMSa17ffTDsQ0rSJQIY5tisLNisK1eLBJnAOhjjJoyT8oS4Y5mWJM8YR5kyrVgZxwGst956noTbUmcFcx/yPiyXaDxd9dqsLSrTD8vC7IPyan9moTaDCEE3YIG1cRTKSBDQb2l2T0Xh04wLwUAIek8YSkl9AuaTHkh2TlATGUPV51TvDJ89hx56aJBRVD1C+ctldZyfWU+uiC4qNJ5HyVIfa+RFFlmEw4aC4qu1cK69oJW2iEMcJe7bxvfxNUuhZpXF+r18beWe6H/QfHrJQG23ToCkFRdeeGGw4DF+0eWXXx4ssEwgg5Ft3/iXaFzw2hGyH+GeRYSzrExU2FucddZZbuBofuIeKc62hqq6sq0fT7pBtDVbNXLrajIO8SWdL9IW0CUQjzq6v1VdrDcDISBjqIFg72+nfEAgZU5x1glR8WmNHisXvZRet9/K2PEAsOT2YbfddvPqJLJA8RHy1LY9wlVXXeWrRrzmETJQ7b333sG2doIFgwgoy1o577zzPBXkHXfc4Zb1KHcSaNx4442VqjDCCBCFTLINxsSXAhKRsBJGqj9W05ZbbjnPAFa5UAeDJWAPTtJFAnn3aLvY9YimbFk3mThxou912l9Zwr6vpeAbUW9aLRg2PoPco2XP3wKrVBmhdfPvppvtt7tHa8vyibnGeeS1eG9LLrlkYn7L8W1iQTDcHsDidlfKWjkw3+/kkUceqURpszjjVZeZJX1i7njJbbfd5uWWLjOxvLLeF/+3UaKxoFnFx6IEQ0b+vzFOjILh4/zzz59gcJlXtEebl1jz+prRDvZ7Tk97Z8mUb71EUsIlxgyJwuabb97TPoepcfFp/Wn1OuNQr9tvdqf4tLMnaSFJPfJarM92B3u+2CIgRFpDCPSSR0xhB4sB7jmWs6678sorfTl55ZVX9tPkYj744IP9mAhvUZgZI3feeWcs8uVi3qS3cbaz3M7YJpjnQqWeDgZHQHu0g2Pfl54twUBf+hnWTsrOh+xOLFUS5APjIIzlyPEbbQLwR2ZLgb0+QoI++uijwVzD/EMaJcO1tRmZCANpblpulEUYUNoaM2aMGwGae1nAuMwsyD1n8GmnneZtEc2KSE3sT8bwopbwIqywwgp12+dviiVclKBZt/f0T+yMM84Ijz/+eLBEFVX9sD+LoWJUYvBByLfcTWE5uNZAEaVOWQyhSn8wNO8Cz+RFZDQUM8+IZ8K5KIx3jz328FCrMGwUvCZeo9ceEmg+6VWNPASKtHScZ9yqW3wCeZeOSeRgRjsJ/tcsVWZld3rjjTcS+zD3pcdIgIQVZhjmWaMoy8rIZKkdfSvCPpoSsz5PcC8jUQXHlNk+pzfXScYns273bQ/Gwr20Ku0sHS+00EKJJUxv2oWtCCVzzjln8uKLLzatm1WBjFPwqV06zqprs2Z3kyNbVVpwc6MNU6YJ42YZm2dWK3fffbfXa+a7Xnudlo5riXT+XjNa+4uViEAZCTCrZNmRWWZMPGF79G45u/322/s5ImgRhjMahMGB2VHaQpyZE5bmzHrHjx9fQWVK1N/bh33AgIfz9Em+4LPPPttnqpxLC3VqI5bVa3+mmWZydyaWbdNuYun2unFM+yThICFHI8HYCCMoZv64tvVamGUT2xpXm7SwHWR7uD4W4qXbXmzIWplZeOGF/bL00nO6HR33j4D2aPvHWj2JQN8I2HfwlrI75RkQSjItcUmT5eh4jg99rF7ZH8z7AR/bSPex+uqrh7XXXjtd1PVj3HlsZu+ZsBo1jhUwy+IbbbRRo2pdOYciPe6448Jll11WWeaPDWNljL3FIYcc4gr26quvdktkXHvSggUy/u342EsGS0CKdrD81bsI9IRAzMiEb3Ba2LuLWY9InJBHshRh1vXRVzuvwVCr7Wf12UlZVFDM7hsJGbHICtRrsVCnYf311w/HHntsWHPNNau6wxiK1Qj20fHzJZMVrj341BIop1aIeZ5erag9r/f9IVD9X9ifPtWLCIhAjwmwtIlSbTe7U9bwWlWEcQbFknQeabX9PG22Updx0neWb2v6egLJ4AfbS2ElYKuttgoTJkzwwBO1fbFsTR2SgSAEvbCQp348efJkf42/MCIjEQcWzJLBEpCiHSx/9S4CPSGQJ3sRChnhQxlh2ZkP83QmJRQRZdHNxSvW+WWGUr4nTPSk2Ha7GZ9QFq+//nqdnrpTzP4z+ZnNoKhhg5bYou75yK5uhRZOwJ2oTuxr77///pUrWNbG7QiJQWfYd48SrZNx4UsLKSWRXn85SPep42wCUrTZXFQqAkNPAGMlFOQpp5wScPNByF1sgROCWTBXDHri8uSOO+7oMbFxxeFDG7cWS9rgChcXHhSBZW0KKByMcKKwpBpng8yqWN486qijPP0ikY1QZMy6uA4DnyOPPNIvxT815lLOah8Fw54vM7K0oo79dvN1pZVW8j1lvkxkCW5JGCYRa7pWLGGGs0y719TWie/Ze0WylnP33XdfZ4TCx82IH/ze2QOnb4QlZbYDMGqLY2X5mPZqfeTjygJxnyUDJmD/PJIuErAPtpbT5HWxWzU1DRDI694DklYyMuEaghuQfRS5S49ZDCdLL710Yj6ankHKZrFJVkame+65p3INGZZMqbqrkCnTqqfRbsYnm8m6K41Z1CZETmpV2nHveeyxxxJLQJCY8VFmN9GlhshQtYL7DOz4sRln7Wl/bwEmEotH7Bm6qGcWwYkZM1XqEskptpH1av67lbqTJk1K7ItJMmbMGM9qZTGOEwtu4VmyKpXsYOutt/bMV6aQ08VNj+Xe0xRR7gp8S5V0kYAUbRdhqqkqAu0oWhpAUT7wwAOe19hmnlVtpt/Y7LLyYU3u4VqxCGNV/qxR0eIzS+5glEG9D3VCAUbfU3x3bUm4tvmktn0qMI6ssYy4OFXQjqLl8oMOOsjTSKaaqhySF9n2uyvvaw8sM5b74cK6HwJnC7DhYR2zWDIec49KzCI593CkaHMja3qB/Gjt66NEBMpMoNXsRdHXFha4htRK2re29hxLm3F5s/Yc79vN+JQ1jqz2u1HGcjpuM4QtZBk3LSzBWxzkdFHlmCVurrWczJ5Jq3KihwcsH9uMNrMHlv2JNMVy9wYbbJBZR4X9JSBF21/e6k0ESkMg7jNGA51hvzGUOnuwpJxDsRKeshUh2MW6664b2NsetGA4ZskIgiUTUT7ZQT+MVP9StCkYOhQBEWiNwA033OAxd6l90003uW8nPqZ5cym31lv/apHTlVmthUhsuVOshGsjYLV8cZcrYuV95plnBgsn2eWW1VwnBKRoO6Gna0VgGiUwfvz4qgwyYMgKAziMeJjNEkZyGIVtAinZ4j05KdriPRONSAQKT8AsdAM/EhEQgeYE5EfbnJFqiIAIiIAIiEDbBKRo20anC0VABERABESgOQEtHTdnlLvGCSecEEgYLRGBbhIwX9hAWD2y40jqEyCiFULMYPYsJfkIdCOcZL4ey197FJ625b/N/t0hYdCEtH+8p7WeCOFHooAVVlhhWrt13W8fCeywww7ui9vHLkvdlRRtqR+vbq5sBAimcOqpp3oy97Ldm+5HBMpKQHu0ZX2yui8REAEREIFCEJCiLcRj0CBEQAREQATKSkCKtqxPVvclAiIgAiJQCAJStIV4DBqECIiACIhAWQlI0Zb1yeq+REAEREAECkFAirYQj0GDEAEREAERKCsBKdqyPlndlwiIgAiIQCEISNEW4jFoECIgAiIgAmUlIEVb1ier+xIBERABESgEASnaQjwGDUIEREAERKCsBKRoy/pkdV8iIAIiIAKFICBFW4jHoEGIgAiIgAiUlYAUbVmfrO5LBERABESgEASkaAvxGDQIERABERCBshKQoi3rk9V9iYAIiIAIFIKAFG0hHoMGIQIiIAIiUFYCUrRlfbK6LxEQAREQgUIQkKItxGPQIERABERABMpKQIq2rE9W9yUCIiACIlAIAlK0hXgMGoQIiIAIiEBZCUjRlvXJ6r5EQAREQAQKQUCKthCPQYMQAREQAREoKwEp2rI+Wd2XCIiACIhAIQhI0RbiMWgQIiACIiACZSUgRVvWJ6v7EgEREAERKAQBKdpCPAYNQgREQAREoKwEpGjL+mR1XyIgAiIgAoUgIEVbiMegQYiACIiACJSVgBRtWZ+s7ksEREAERKAQBKRoC/EYNAgREAEREIGyEpCiLeuT1X2JgAiIgAgUgoAUbSEegwYhAiIgAiJQVgJStGV9srovERABERCBQhAYlZgUYiQahAiIQBWBKVOmhFVWWSW89dZblXL+Xfl517umfkceNWpU2GKLLcIFF1xQqacDERCB4hCYrjhD0UhEQATSBJZddtkw++yzh2eeeSZd7Mdp5UvBpptuOqKOCkRABIpBYOrX4mKMR6MQARF4hwAz1W233TZMP/30DZnMPPPMYa211mpYRydFQAQGR0CKdnDs1bMINCWw5ZZbhjfeeKNuPZTwhAkTmirjug3ohAiIQM8JSNH2HLE6EIH2CSy66KJh3LhxdRtACaOMJSIgAsUlIEVb3GejkYmAE9huu+3CdNNlm1PMMcccYeWVVxYpERCBAhOQoi3ww9HQRAACWBS/+eabI2CwbLzNNttUWSCPqKQCERCBgROQoh34I9AARKAxgbFjx4allloqYByVFi0bp2noWASKS0CKtrjPRiMTgQqBiRMnjpi5jhkzxhVwpZIOREAECklAiraQj0WDEoFqAptttllV4AqWjVG+EhEQgeITkKIt/jPSCEUgjB49OowfP74yq2XZmL1biQiIQPEJSNEW/xlphCLgBDB8irLIIouEBRZYIL7VqwiIQIEJSNEW+OFoaCKQJrDRRhtVZrS4/EhEQASGg0C2c95wjH2oRnnrrbeG559/fqjGrMEWj8ASSywR7r333jDjjDOGyZMnF2+AGtFQEVhzzTXDLLPMMlRjHsbBKntPn54aQQXuuOOOPvWmbkRABESgOYHf/OY3YfHFF29eUTU6IqCl447w5bt4zz339BRnMdWZXt9O+SYO1Rw+85nPhD322CPzb+Xf//53mDRpUua5aY3j17/+dXdvmtbuuxv3++ijj+b78FLtjghI0XaETxeLQH8JvO9975O1cX+RqzcR6JiAFG3HCNWACIiACIiACNQnIEVbn43OiIAIiIAIiEDHBKRoO0aoBkRABERABESgPgEp2vpsdEYEREAEREAEOiYgRdsxQjUgAiIgAiIgAvUJSNHWZ6MzIjC0BK655ppAdp8bb7xxaO+h1wM/6qijwiOPPFLp5vLLLw/rr79+WHHFFcPBBx8cHnjggcq5vAdPP/10wJ3v8ccfH3Hpv/71r3DkkUeGNdZYI6y99trh2GOPDa+//vqIer/4xS8CEcDwwd9vv/08UEms9Nprr4Wtt95aQXAikIK/StEW/AFpeCLQDgE+4J988snMD/p22mv3Gnw+iyb//e9/XYHNPvvs4ZOf/KQP77vf/W4466yzwkknnRQOO+ywcNVVV4XFFlvMX/OM/7nnngt77713+MQnPhFOPfXU8Morr1Rd/vLLL4cVVlghfOtb3wq//e1vw09+8pNw4IEHhrXWWqsqOxPlKFjKr7zyyjD//POHZZddNhxxxBHe3vvf//5ARqflllsuPPHEE1V96E3xCEjRFu+ZaEQi0DGBL3/5y65od955547bareBt956K3z2s58N//vf/9ptouvXMZZ11lknzDnnnGGXXXaptH/GGWeED37wg2Hs2LE+0zzggAP8HOV55Jlnngm77rqrK8Cs644++miPxPTCCy8E6l599dWBlIe33XZbuO666yqXoKwJXEKGJr4Q7LTTTp696bTTTqvUYfbNOFHGKHBJcQlI0Rb32WhkItARgbnnnruj6zu9mJnaLbfc4lGsOm2rW9eff/754Z577gmHHHJIVZMos2uvvTa8+eabXv6pT33KX//xj39U1Wv2Zskll/SsSh//+MdHVGV2f/PNN/vMmcAj7373u8MGG2zgy8dUjkvV1Pvb3/4W7r///sAScRSO3/Wu6o9slpb58nD88cfHanotIIHqp1bAAWpIIiAC+Qmw98g+I0uiCArjBz/4QSAD0FNPPRVOOeWUQED5L37xi+Gvf/1rpYNf//rXYf/99w8nn3xyYJ93yy239KXOY445pjIz/epXvxomTJhQiVBFOL+tttrKywgdiaBkWR5Fdthhh/CNb3zDj/m12267hR133LHyvl8HKCoU7CabbBJYek0L+7O/+tWvwnTTvZ1nJYYoZEbeTbn00kvDTDPNVNUkzwH5yEc+4q+jRo3yMfLMYEfYzSlTpoS77ror7LPPPl4n/mK8MP+///u/wLK1pKAE7NuTpA8EbBkoMeOIPvSkLoadAH8r9uHZ9m1cdNFFyaKLLsrmaGIKztuxJcvEPuC9zPYlk4033jixvUJ/v95663kdyzCV2EzOy2wZNbFMQYkp4oRj2rIlUa9nSeeT2WabzcviIE15JzbbSmwm50WmGBLLl+t1bFab8B6xJc7ElIPX/fvf/+5l7f6yWMfJUkst1fLll112mY/HlF3TazbffPPElpeTF198sWndrAqWO9j7sn3YrNNVZaY8k/e85z2J7bVWyk3RJx/+8Ie9DdufTeaYY47knHPOqZxPH9x9991ej2fcqtA+z9SSCrR6iep1QEAz2oJ+AdKwRKBdAsxC42wytsEMc8MNN/S3zFiZwWHVah/m4fbbb/fyVVZZJXznO9/x44UWWshneOeee67PpljqPPvss8Nf/vIXn/WNGzcuNu2vc801l+8lxkIMd2aeeebA7Gz8+PFuyMM5ZnM//vGPfT/yQx/6UKzel9eHHnrI+4kzx3qdsmyLMRT7oezb9lKwQGalgT31eeaZp9IVxk833XSTv//9738f/vOf/1RmvJVK7xwsvPDCfvTggw/WntL7ghCQoi3Ig9AwRKCbBN773veOaG6GGWbwsmhpyxsUKh/20b2EPLcIFrcoSYQPfSxc2QvM+2Ee2/CG3vm1+uqrV/Yl0+W9Pn744Ye9i2aK9vvf/36wWb4vs/d6TCxlw5ql+bRgLIV7EMvK7ONi7ITx0xVXXJGu5scsg2NQFe9vRAUVDJyAFO3AH4EGIAKDI4BBTisy77zzerW8xkFZiraV/npRJxoW2TJtw+Z/9rOfjVgRaHhBmycvvPBCn7WyuhD3hmNThx9+eHjsscfctQfLZBQxVtxf+cpXMo3L+BJlS/Hxcr0WjIAUbcEeiIYjAkUkEGdLtUvGzcZaJEXLzByp9W2tvQeW0/GD7aWwbE+gCpaHZ5111qqubA88XHLJJYGl/LjCgBsPs+w///nPwfZkq+rjF8yqRJalc1VFvRkYASnagaFXxyIwPATMUCrgLmRGVj7oOAPjAx4xOxFfWmbWFQUly3JzdJmJ5SiGuFQdy/rxGseO1XUj+d73vpd5mvvAArhTwW0H/2b8Zj/60Y9Wmrv++ut9aZj9WDPCGhGIIlonv/TSS5VrOIhW473+clDVqd7kIiBFmwuXKovAcBCICpAZUJT4gfzPf/4zFnlQC97ULjuyfBpnfpMnT3bXEUIWxmXX+KGPmw4KY9NNNw0oAIIw4BaEwkWJoIAxqEJ5YYzEbI2ZJbOv9DgqA+rhwfLLL+9+qGZpW7eXb37zm+EDH/iAG2ylK6FgWT7nnu644470qczjyL2W6x//+Mew6qqrBozL2J8162T/gSfHnMOIDLeiX/7yl84sdkA4TfonkEVa4mrD5z//+XSxjotEoAOLZV2ag4Dce3LAmsardureY6H/ElMK7r5hSiP52te+lpgVcmL7eF62+OKLJ+aTmXzpS1/y9/Z5lJhlcGIGOIkFc/Ay3HRow5STu/JYhKSqp4I7T3Qhoq5ZJCdLL710YmEDE7NcTmz2l+BmZLNa/7G9xcSUb2IzWXebseXSxBRzVZt53+R176F9m0kmZqXrY8nqz6x//f7NErjqNG5JZiXt5+BWT+68887kC1/4QmJ7316XvkyhenWbySdjxozxcpjX/pi1eKXZ559/PjF/X3eFsnjIyXzzzed8s9xxLOaxPytbPahc3+xA7j3NCHX3PN84JX0gIEXbB8gl6aJTRdsJhqho8Zm1GWfCB3KjD3CLp1xRWq+++uqIrlEYtf6y1MuqO+LiJgXtKFrGg1+wLdNmts6XAXNhyjxnxlSJxUFOzO0n83wvCm1lIsEX11yOMpt/9tln3T/aDKYyz9crlKKtR6Y35W+HQSnSFFtjEQERKAQBllD5aSTpMI+10Za4jtCGtZJVr7ZOr94zHqJlEd3KZvZVe6T0yb7yxz72sczuWXJmH5WA//0SlpHj3nJtnyzVE+eY5W5cgCTFJSBFW9xnUzUyAgUQi5Uf0njFoOdVlYbgjX1fdIOPP/zhD23dA36cMaxg+nb5QBo9erTv/7HPFfcS03V03JxA3FMkzVtZxaJiBQyy1l133XDDDTd4goFW7vXee+/1+rVWwq1c2+06GJMRgGTixIlh991373bzaq/LBGQM1WWgvWqOaDW4BGB4giXnMMoPf/jDQNB1DGf40GpHFlxwQY+/iw8i8XiJT4vDPw77GKngAmHh6gKGO5J8BFA6ZI1BcDvZfvvtPbh9vlaGozZZcUg/F4N4tDJqAkj0OlJUK+OgDlbfZ555pvvVtnqN6g2OgGa0g2Ofq2eyiWy77bYerq3VIAO5OuhxZaxQmWWiADuxjuTeCe+3zDLL+BKeGZ544PU4fKw6zVjFA+rj8E/YwOiKEuvoNZsAoRLNmKfqZBFmb1UD6uKbGISji032rSn+D/jSKRkOAlK0w/GcfJQxRVZ8HaKhu1sFeUBRft2QenuH+BISaYeZ83nnnRcIRZg3p2g3xjeMbeBywo9EBESguwS0dNxdnl1tDd+9Qw89NBAbFiWFPyOSjrZjVpThoIMO8iVTlsNY/ovSamo06rOMxvX0w55P2uCjUR+xr269diOFGsHqTzzxRB/Sj370o6qhEc5uu+228/skdVv05RxGVlU3pjciIAKFJSBFW9BHQxBxc/PwPUhmaCeccEIlm0dUtI8//njACR/nf7KzEJAARRkj25x++unB3DQ8EwkO8bfddlswtwqf6e20006VO2e5kPfmf+l1zfS/4ijfrI9KI104IEAChk7MRImM04kst9xyfjlGPXEWTX5WFDD3ikHMcccd58vQfKEZNladsNG1IiACfSbQG68htVpLIK8frc1S3aEdf7co5Ju0P4/k29/+thfhGG+JoePpBGd5zlt2kkoZzuyUnX/++ZUy8lwSyCDKLrvskpjbQyWAgBkVJeYy4Kdb6SO208rrn/70Jx+PJSDPrG5GOImlUcs8ly40gypvxxJep4urjgmKwL2b4VRiCc09iIBF7KnUMdcIP3/WWWd5WVFY2R60j4ux66cxAzOCE6MO/k6yAmBU/kF00DUC2qO1T7IiCvuK+PPFQOiMEQMghBktM15yZmKsEkPKYY1sEWR8P5R4qey3RavK2tRoxK7FRYA9TGZ/WDCyr3nSSSe5sZIl/W65Dx9Ul36xTN4N4f5jCEGskMn5CR+Mp6Iwu4dXXD4uCiuMtywaULCk63Goes0gYJGnwgMPPOB+pBmnVdSAAKEyo4V5g2o61SUCUrRdAtnNZnDlwZ9xpZVWqmo2LhnzSjJohOVQUmflkVqr5c033zygeG3W647vLDOjmGK81nb6yDOeXtS1aDoezB5WfEFhWRwDqrxuRYNghbEbRl24QUnqE7jvvvs8Eb041WdU7wyfH1K09eh0v1x7tN1n2nGLONMjfJDUy3ISo+tYyLwR/ZF387nnnhtRXq+ArCTsi5Kai1k0wcv58OpmH/X67lU5s30EVyB8H7kXAu2z/1wr7EO3KmVk1eq9q54IiEB7BKRo2+PW06vmmmuugOUsRjo///nPK33xIY/gk8qS50wzzeSWyLb/WKnDOfxt42y0cqLBAVbGGFIxs2UmyLIxhlMoqG710aD7qlOtplDjPuvJBRdc4MuJs802W7j44ou9GveEYGlsGy9+zC8ypFjM3Mr7ZgdFYtVsrDovAiJQDAJStMV4DiNGYRlXvMyMnXxZl72oGO0IF5Xbb7897LXXXr7vSIJoC3YeLGuKWyqPHTs2fPrTn/brW0mNxt4lS6sICp5ZoGUdCSTAbqUPv7DFX/ELQAz1l74sTwq13/3ud35pnLmzJ0uUKBQhqdtQslhrjxkzxusRxIIwjVdccUVgaZw9aVyJLGOKW3RTqWisfOD6JQIiMPwEumZWpYYaEshrdUyasf322y+xaEpuVYml8FZbbZWYskgsNF5iijYh7RZWx7an53VsP9JTn5FlBGk1NRrpubBCxsLYDHAScxFKLNyjt9GsD6/U4i+yrYwbN64yVpt5J1OmTKlc3UoKNTKZmPGWt2H/ff5qy8IJFsb25SLBchirbPOLrbQbD2z2mlg+z8q188wzT2J5V/10kVgNMntPZDUMr+1k7xmG++rHGJW9px+Up/YxisPh/7pQ/DuwPJ2+JEt83jzCUioJBZilsveKcU/cO43tYF1L6EGWk5m15ZVooYwlImESszKudNpHq2PiHpHae2z1+mb1+HM3FyPnSAg+eOaRfrDib4XMMqecckqeoU1zdffff//w05/+NLeB2zQHKuOGMYZaYIEF3GOBvzVJbwnI6ri3fDtuHcUXY7LOOOOMme2xjxr3IDMrNCmMYfdstle3ZlYfWEcTIKOZ7Lvvvh51qlk9zvdKwca+UaxY9LYr7bJqtz9d1z8CfFklWcXhhx/unfL3bb7rviXBNgoGgsTprvd/2GikfMFj26Je1irc9djqwBCRvzGSY5ivuX8R5MsnWx8ErcFVTTJ8BKRoh++ZFWbEs8wyS2BW0UyWXnrpZlV0vkAEUAp5Z/p5ht/r9vOMJdYlMxZ2Dri1ISg3VhZQuhjLYcNAiFL+ljGgI1tUq0LWKkKp3n///a48a6/DyBH/7hdeeMGjumF3gEEj9Y844gj/8rnZZpu5v/vNN98cxrxjd1Dbjt4Xl4AUbXGfTeFHRrAL+TAW/jHlGiDW3AQNIU1erQ9xrobqVO51+3W6bVhMfHAM6MxewHMaU5klaWafGAWy0nPMMce4hT9+2LjUrbDCCg3bjCe532ZZqw455JCAAr3mmmsCWboQjPpQ8uSeJrn7+uuv7ykLOWYM7WwRxTHptf8EZHXcf+bqUQQKS4CY2bfcckuVC1Q3B9vr9vOOFRsIlNrOO+8ccKuLgp0CSpLZaJSoBElA0aoQfITtlYUWWijzEiz+ie9tYVPD5z73uUodFD+CdXwUkmFQ//jjj49Feh0SAlK0Q/KgNEwRaEQAIy0+gDfZZBP/wD7ggAMCxm0IS51msR4mTJjg+4SUWSxrf08Z5xGUoFlf+zFuZfgcE+SD5dQNNtjAjchYXmWWxUoGPtdIJ+3H69dYYw13WfMG+/iLUKcELNlmm22qemXGytJtOuoawU7YP+X+uyV33323Lxkzc06nv0TpszVz/fXXe4Qz+iM05x577BEsvneugDTdGqvaaZ+AFG377HSlCBSCAJmOCDNpyRh8LxCFOWnSJF/2ZO8RpcHe4qWXXhoefPBBHzP7jnyQU/bII494GcoUS1TEXMh8uZLwm2Q7YlkTZYi1Kh/4GO7QLkqqk/bpi7ZZqiVVY7/l7LPPDsQBz9r3XGyxxSqxwmGE4mWvFW7dkuhXzoy2VjDAYsb97LPPVk6h5InRfe6551bKdFB8AtqjLf4z0ghFoCEBlCYK9Iknnghzzz231yVu9WqrreYKk3O1S5cYO9VaqhOohL0/zo0fP97boYyZ660WC5tlTIJ9IMx4ScfIDJjydtunLYKzYAmOou+nYITEPizpKJuJZXjypBtY0HdTyPWMxIQW6bZjGdbPpMJEmPki8QuTv9GvwhPQjLbwj0gDFIH6BLDgZVbKjCgqWWqjPHBDQZHk/VCutTiO7ixpxWxpHH1QMXNU/RGOPFPbPtHI2JPst+sK7jxEI8uaTaZHTR0yBTGLZDbfTYmzY/aDa4V+EeKPR8H9DYvnhx9+OBbpdQgISNEOwUPSEEWgHgFmO/hgpvf3qItCYDaKxExP/qaFX7WKMOsSi6rlfeYxDIrttNJ+rNvL1xgcBavgRkIscax9SSPZbYlfjmJKx3T7PFfijc8555zpYp/9ZoUwraqkN4UiIEVbqMehwYhAPgJ8EKNUiR725JNPVl0cZ0KjR4+uKm/2phVFiPJmFmYhNZs1N+J8K+2PuKgHBYydsWQpuXR37KOScKMXQr5pviQ99thjVc0zm2VZOS4Vx5Ps2WKgFpeSY7lei01AirbYz0ejE4GGBJiNxSVdEk2kBX9L9vk4H5c8Y5J76qWzQcXrUDy4kMRzsbz2FRcgJLqkdNo+yqPfwn40M/OnnnqqYddEaCJSU5Z0Om6WrTfccEM3tIpJLeiHfXFSZBKoIi2xTifRzdLt6bg/BKRo+8NZvYhAzwiQtQkFSWxk3HwQZrdYyuLmw6x3+eWXd0Mn9hoxYsKt5cgjj/S61157bWU2THAG9n3Zj6TeQw895HX4hXUwgtUrxlZY62IUhXTSPoZGlhTCg0J4Y338tdJKK/keNl8usuSll17yve9VV111xGnilsN24403HnGutiBaF2ct+eJGxawWl6soJ554oi/94+Oblrg3SyhIyfAQkKIdnmelkYpAJgEMnwisgNUxCg/fT6I74YrChziCEQ1Kl2VSgjNcd9114dhjj3UDJGZVhP9DmEGhtHfddddAKsIFF1zQy/mFImffFwVLVDAskWMowk7aZzkU5R6Nfyod9uGAEIfM3uu5FjGrRDk+/fTTI0YDS5bPuTb6LI+oZAWEKSVWMcKqw8SJE8Ndd93l7/lFIAxiHPNM2Adee+21fUzEXa7de8enmdjnzLIlQ0TA/sAlfSBA6rM999yzDz2pi2EnwN+KBSbIfRumMBLLW5xYAInElEDm9bbUmZjfrZ8zxeapFmsr2t5gYsqlUmzLw55a0Pw5E8t8lJhSrpyrPWinfdqgz7zSrTR5ZkGdmB9y3e5hYYZTmedhYl9GEth3Q0yhN+zLknsklo+6466UJq9jhLka6K6t+hB9wdBQRaBsBIhNvMgiizS8rXSM3LivWntBVppE6jC7ipmkaq+J79ttv16fsd1evjLTJ9YwkbWy/GRxP8oSZuBcu8suu3QtLnS9DFosYWP5zPJyv/2Ns+5dZfkIaOk4Hy/VFoFpjkDcV8xaPi0DDJa9iapFuEPCG7Yq+Civu+66Ya+99mr1krbqsXyNwRRLzrV7tm01qIv6TkAz2r4jV4ciMBwEMBDC2AnrZYSgEigVPvDLJgSOYFYb4ze3cn9Ew6qNiNXKdXnrsPJA9K30fnneNlR/sASkaAfLX72LQGEJsBSN9Ss/UWLi+/i+TK8YgS2++OKFuyWeg5Rs4R5LrgFJ0ebCpcoiMG0RwO1GIgIi0BkB7dF2xk9Xi4AIiIAIiEBDAlK0DfHopAiIgAiIgAh0RkBLx53xy3U1lo21IdVyNaDK0wQBov+Qg1R/K40fN4ZLJFUQp8acss6SsEDSPwKj8LrtX3fTbk9E6Ynh06ZdCrpzERCBIhE47rjjPN5zkcZUxrFI0ZbxqeqeREAEREAECkNAe7SFeRQaiAiIgAiIQBkJSNGW8anqnkRABERABApDQIq2MI9CAxEBERABESgjASnaMj5V3ZMIiIAIiEBhCPw/2NqYx18+X0AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(\n",
    "    model,\n",
    "    show_shapes=True,\n",
    "    show_layer_names=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=adam_optimizer,\n",
    "    loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 30s 494us/sample - loss: 0.2223 - accuracy: 0.9326 - val_loss: 0.0550 - val_accuracy: 0.9830\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 32s 538us/sample - loss: 0.0720 - accuracy: 0.9779 - val_loss: 0.0394 - val_accuracy: 0.9871\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 29s 488us/sample - loss: 0.0519 - accuracy: 0.9834 - val_loss: 0.0362 - val_accuracy: 0.9890\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 30s 494us/sample - loss: 0.0422 - accuracy: 0.9867 - val_loss: 0.0315 - val_accuracy: 0.9892\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 29s 484us/sample - loss: 0.0349 - accuracy: 0.9893 - val_loss: 0.0291 - val_accuracy: 0.9891\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 30s 492us/sample - loss: 0.0287 - accuracy: 0.9908 - val_loss: 0.0335 - val_accuracy: 0.9901\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 31s 517us/sample - loss: 0.0257 - accuracy: 0.9917 - val_loss: 0.0337 - val_accuracy: 0.9902\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 32s 531us/sample - loss: 0.0233 - accuracy: 0.9923 - val_loss: 0.0244 - val_accuracy: 0.9908\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 31s 522us/sample - loss: 0.0211 - accuracy: 0.9934 - val_loss: 0.0363 - val_accuracy: 0.9893\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 34s 574us/sample - loss: 0.0192 - accuracy: 0.9941 - val_loss: 0.0286 - val_accuracy: 0.9916\n"
     ]
    }
   ],
   "source": [
    "log_dir=\".logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
    "\n",
    "training_history = model.fit(\n",
    "    x_train_normalized,\n",
    "    y_train,\n",
    "    epochs=10,\n",
    "    validation_data=(x_test_normalized, y_test),\n",
    "    callbacks=[tensorboard_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the loss function was changing during the training. We expect it to get smaller and smaller on every next epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1551d61d0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.xlabel('Epoch Number')\n",
    "plt.ylabel('Loss')\n",
    "plt.plot(training_history.history['loss'], label='training set')\n",
    "plt.plot(training_history.history['val_loss'], label='test set')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1551c77d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.xlabel('Epoch Number')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.plot(training_history.history['accuracy'], label='training set')\n",
    "plt.plot(training_history.history['val_accuracy'], label='test set')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate model accuracy\n",
    "\n",
    "We need to compare the accuracy of our model on **training** set and on **test** set. We expect our model to perform similarly on both sets. If the performance on a test set will be poor comparing to a training set it would be an indicator for us that the model is overfitted and we have a \"high variance\" issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training set accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "train_loss, train_accuracy = model.evaluate(x_train_normalized, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss:  0.00793841718863229\n",
      "Training accuracy:  0.9974\n"
     ]
    }
   ],
   "source": [
    "print('Training loss: ', train_loss)\n",
    "print('Training accuracy: ', train_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test set accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "validation_loss, validation_accuracy = model.evaluate(x_test_normalized, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation loss:  0.028636791965250815\n",
      "Validation accuracy:  0.9916\n"
     ]
    }
   ],
   "source": [
    "print('Validation loss: ', validation_loss)\n",
    "print('Validation accuracy: ', validation_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the model\n",
    "\n",
    "We will save the entire model to a `HDF5` file. The `.h5` extension of the file indicates that the model shuold be saved in Keras format as HDF5 file. To use this model on the front-end we will convert it (later in this notebook) to Javascript understandable format (`tfjs_layers_model` with .json and .bin files) using [tensorflowjs_converter](https://www.tensorflow.org/js/tutorials/conversion/import_saved_model) as it is specified in the [main README](https://github.com/trekhleb/machine-learning-experiments)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'digits_recognition_cnn.h5'\n",
    "model.save(model_name, save_format='h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = tf.keras.models.load_model(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the model (do predictions)\n",
    "\n",
    "To use the model that we've just trained for digits recognition we need to call `predict()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_one_hot = loaded_model.predict([x_test_normalized])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions_one_hot: (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print('predictions_one_hot:', predictions_one_hot.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each prediction consists of 10 probabilities (one for each number from `0` to `9`). We need to pick the digit with the highest probability since this would be a digit that our model most confident with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8.205532e-08</td>\n",
       "      <td>1.508753e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.997569e-01</td>\n",
       "      <td>1.507927e-10</td>\n",
       "      <td>3.036238e-08</td>\n",
       "      <td>7.496398e-11</td>\n",
       "      <td>1.072911e-10</td>\n",
       "      <td>3.716224e-07</td>\n",
       "      <td>2.426345e-04</td>\n",
       "      <td>4.577839e-09</td>\n",
       "      <td>1.174887e-08</td>\n",
       "      <td>9.708089e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.499753e-09</td>\n",
       "      <td>1.776901e-11</td>\n",
       "      <td>7.716882e-12</td>\n",
       "      <td>1.750144e-14</td>\n",
       "      <td>9.988386e-01</td>\n",
       "      <td>1.832140e-09</td>\n",
       "      <td>1.151763e-08</td>\n",
       "      <td>1.740052e-10</td>\n",
       "      <td>1.392591e-09</td>\n",
       "      <td>1.161428e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>1.009779e-14</td>\n",
       "      <td>1.738570e-09</td>\n",
       "      <td>9.999994e-01</td>\n",
       "      <td>1.041259e-10</td>\n",
       "      <td>5.272622e-16</td>\n",
       "      <td>2.269359e-19</td>\n",
       "      <td>2.778265e-16</td>\n",
       "      <td>5.792643e-07</td>\n",
       "      <td>4.054746e-12</td>\n",
       "      <td>5.626435e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>2.263675e-11</td>\n",
       "      <td>6.903200e-08</td>\n",
       "      <td>3.381161e-08</td>\n",
       "      <td>9.999995e-01</td>\n",
       "      <td>8.395995e-13</td>\n",
       "      <td>3.699991e-07</td>\n",
       "      <td>2.905099e-14</td>\n",
       "      <td>5.455035e-10</td>\n",
       "      <td>4.476401e-10</td>\n",
       "      <td>1.262823e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9.689668e-24</td>\n",
       "      <td>5.208208e-12</td>\n",
       "      <td>3.422634e-17</td>\n",
       "      <td>4.813990e-21</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.983922e-16</td>\n",
       "      <td>6.201705e-17</td>\n",
       "      <td>2.069550e-11</td>\n",
       "      <td>2.440485e-11</td>\n",
       "      <td>1.458596e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>1.358465e-08</td>\n",
       "      <td>9.598834e-11</td>\n",
       "      <td>2.364747e-11</td>\n",
       "      <td>5.815844e-09</td>\n",
       "      <td>2.983105e-12</td>\n",
       "      <td>9.995547e-01</td>\n",
       "      <td>2.545367e-05</td>\n",
       "      <td>1.387848e-11</td>\n",
       "      <td>4.197330e-04</td>\n",
       "      <td>1.053049e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>9.229651e-12</td>\n",
       "      <td>5.644470e-12</td>\n",
       "      <td>4.068150e-12</td>\n",
       "      <td>1.636682e-17</td>\n",
       "      <td>2.020714e-12</td>\n",
       "      <td>9.393594e-12</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.027714e-21</td>\n",
       "      <td>7.427696e-13</td>\n",
       "      <td>6.800262e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0             1             2             3             4  \\\n",
       "0     2.174955e-15  2.053761e-08  1.115043e-10  1.815501e-11  4.382045e-09   \n",
       "1     8.536813e-10  1.098458e-06  9.999989e-01  2.133420e-10  1.870166e-11   \n",
       "2     7.979921e-12  9.999999e-01  1.698135e-09  1.035064e-13  3.937335e-08   \n",
       "3     9.997569e-01  1.507927e-10  3.036238e-08  7.496398e-11  1.072911e-10   \n",
       "4     2.499753e-09  1.776901e-11  7.716882e-12  1.750144e-14  9.988386e-01   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "9995  1.009779e-14  1.738570e-09  9.999994e-01  1.041259e-10  5.272622e-16   \n",
       "9996  2.263675e-11  6.903200e-08  3.381161e-08  9.999995e-01  8.395995e-13   \n",
       "9997  9.689668e-24  5.208208e-12  3.422634e-17  4.813990e-21  1.000000e+00   \n",
       "9998  1.358465e-08  9.598834e-11  2.364747e-11  5.815844e-09  2.983105e-12   \n",
       "9999  9.229651e-12  5.644470e-12  4.068150e-12  1.636682e-17  2.020714e-12   \n",
       "\n",
       "                 5             6             7             8             9  \n",
       "0     1.084139e-11  7.955132e-17  1.000000e+00  2.871653e-11  5.481966e-11  \n",
       "1     3.222684e-15  2.280592e-08  2.037868e-10  9.249878e-11  2.877182e-15  \n",
       "2     1.830633e-08  1.814277e-09  3.928236e-08  8.205532e-08  1.508753e-11  \n",
       "3     3.716224e-07  2.426345e-04  4.577839e-09  1.174887e-08  9.708089e-08  \n",
       "4     1.832140e-09  1.151763e-08  1.740052e-10  1.392591e-09  1.161428e-03  \n",
       "...            ...           ...           ...           ...           ...  \n",
       "9995  2.269359e-19  2.778265e-16  5.792643e-07  4.054746e-12  5.626435e-18  \n",
       "9996  3.699991e-07  2.905099e-14  5.455035e-10  4.476401e-10  1.262823e-09  \n",
       "9997  6.983922e-16  6.201705e-17  2.069550e-11  2.440485e-11  1.458596e-10  \n",
       "9998  9.995547e-01  2.545367e-05  1.387848e-11  4.197330e-04  1.053049e-08  \n",
       "9999  9.393594e-12  1.000000e+00  3.027714e-21  7.427696e-13  6.800262e-16  \n",
       "\n",
       "[10000 rows x 10 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predictions in form of one-hot vectors (arrays of probabilities).\n",
    "pd.DataFrame(predictions_one_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0\n",
       "0     7\n",
       "1     2\n",
       "2     1\n",
       "3     0\n",
       "4     4\n",
       "...  ..\n",
       "9995  2\n",
       "9996  3\n",
       "9997  4\n",
       "9998  5\n",
       "9999  6\n",
       "\n",
       "[10000 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's extract predictions with highest probabilites and detect what digits have been actually recognized.\n",
    "predictions = np.argmax(predictions_one_hot, axis=1)\n",
    "pd.DataFrame(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our model is predicting that the first example from the test set is `7`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "print(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print the first image from a test set to see if model's prediction is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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(x_test_normalized[0].reshape((IMAGE_WIDTH, IMAGE_HEIGHT)), cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that our model made a correct prediction and it successfully recognized digit `7`. Let's print some more test examples and correspondent predictions to see how model performs and where it does mistakes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 196 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numbers_to_display = 196\n",
    "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "for plot_index in range(numbers_to_display):    \n",
    "    predicted_label = predictions[plot_index]\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    color_map = 'Greens' if predicted_label == y_test[plot_index] else 'Reds'\n",
    "    plt.subplot(num_cells, num_cells, plot_index + 1)\n",
    "    plt.imshow(x_test_normalized[plot_index].reshape((IMAGE_WIDTH, IMAGE_HEIGHT)), cmap=color_map)\n",
    "    plt.xlabel(predicted_label)\n",
    "\n",
    "plt.subplots_adjust(hspace=1, wspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a confusion matrix\n",
    "\n",
    "[Confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix) shows what numbers are recognized well by the model and what numbers the model usually confuses to recognize correctly. You may see that the model performs really well but sometimes (28 times out of 10000) it may confuse number `5` with `3` or number `2` with `3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = tf.math.confusion_matrix(y_test, predictions)\n",
    "f, ax = plt.subplots(figsize=(9, 7))\n",
    "sn.heatmap(\n",
    "    confusion_matrix,\n",
    "    annot=True,\n",
    "    linewidths=.5,\n",
    "    fmt=\"d\",\n",
    "    square=True,\n",
    "    ax=ax\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Debugging the model with TensorBoard\n",
    "\n",
    "[TensorBoard](https://www.tensorflow.org/tensorboard) is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6008 (pid 48630), started 1 day, 21:00:20 ago. (Use '!kill 48630' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-2f4cd01402f6859b\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-2f4cd01402f6859b\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          url.port = 6008;\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir .logs/fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting the model to web-format\n",
    "\n",
    "To use this model on the web we need to convert it into the format that will be understandable by [tensorflowjs](https://www.tensorflow.org/js). To do so we may use [tfjs-converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter) as following:\n",
    "\n",
    "```\n",
    "tensorflowjs_converter --input_format keras \\\n",
    "  ./experiments/digits_recognition_cnn/digits_recognition_cnn.h5 \\\n",
    "  ./demos/public/models/digits_recognition_cnn\n",
    "```\n",
    "\n",
    "You find this experiment in the [Demo app](https://trekhleb.github.io/machine-learning-experiments) and play around with it right in you browser to see how the model performs in real life."
   ]
  }
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