{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FhGuhbZ6M5tl"
   },
   "source": [
    "##### Copyright 2022 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:40.131803Z",
     "iopub.status.busy": "2024-08-15T02:45:40.131377Z",
     "iopub.status.idle": "2024-08-15T02:45:40.135291Z",
     "shell.execute_reply": "2024-08-15T02:45:40.134690Z"
    },
    "id": "AwOEIRJC6Une"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EIdT9iu_Z4Rb"
   },
   "source": [
    "# Logistic regression for binary classification with Core APIs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bBIlTPscrIT9"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/guide/core/logistic_regression_core\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/core/logistic_regression_core.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/guide/core/logistic_regression_core.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/guide/core/logistic_regression_core.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DauaqJ7WhIhO"
   },
   "source": [
    "This guide demonstrates how to use the [TensorFlow Core low-level APIs](https://www.tensorflow.org/guide/core) to perform [binary classification](https://developers.google.com/machine-learning/glossary#binary_classification) with [logistic regression](https://developers.google.com/machine-learning/crash-course/logistic-regression/). It uses the [Wisconsin Breast Cancer Dataset](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)) for tumor classification.\n",
    "\n",
    "[Logistic regression](https://developers.google.com/machine-learning/crash-course/logistic-regression/) is one of the most popular algorithms for binary classification. Given a set of examples with features, the goal of logistic regression is to output values between 0 and 1, which can be interpreted as the probabilities of each example belonging to a particular  class. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nchsZfwEVtVs"
   },
   "source": [
    "## Setup\n",
    "\n",
    "This tutorial uses [pandas](https://pandas.pydata.org) for reading a CSV file into a [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), [seaborn](https://seaborn.pydata.org) for plotting a pairwise relationship in a dataset, [Scikit-learn](https://scikit-learn.org/) for computing a confusion matrix, and [matplotlib](https://matplotlib.org/) for creating visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:40.138633Z",
     "iopub.status.busy": "2024-08-15T02:45:40.138409Z",
     "iopub.status.idle": "2024-08-15T02:45:41.207734Z",
     "shell.execute_reply": "2024-08-15T02:45:41.206760Z"
    },
    "id": "5lZoUK6AVTos"
   },
   "outputs": [],
   "source": [
    "!pip install -q seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:41.211958Z",
     "iopub.status.busy": "2024-08-15T02:45:41.211695Z",
     "iopub.status.idle": "2024-08-15T02:45:44.230352Z",
     "shell.execute_reply": "2024-08-15T02:45:44.229597Z"
    },
    "id": "1rRo8oNqZ-Rj"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-15 02:45:41.468739: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-08-15 02:45:41.489749: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-08-15 02:45:41.496228: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.17.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn.metrics as sk_metrics\n",
    "import tempfile\n",
    "import os\n",
    "\n",
    "# Preset matplotlib figure sizes.\n",
    "matplotlib.rcParams['figure.figsize'] = [9, 6]\n",
    "\n",
    "print(tf.__version__)\n",
    "# To make the results reproducible, set the random seed value.\n",
    "tf.random.set_seed(22)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gFh9ne3FZ-On"
   },
   "source": [
    "## Load the data\n",
    "\n",
    "Next, load the [Wisconsin Breast Cancer Dataset](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)) from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/). This dataset contains various features such as a tumor's radius, texture, and concavity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:44.234123Z",
     "iopub.status.busy": "2024-08-15T02:45:44.233715Z",
     "iopub.status.idle": "2024-08-15T02:45:44.238050Z",
     "shell.execute_reply": "2024-08-15T02:45:44.237455Z"
    },
    "id": "CiX2FI4gZtTt"
   },
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data'\n",
    "\n",
    "features = ['radius', 'texture', 'perimeter', 'area', 'smoothness', 'compactness',\n",
    "            'concavity', 'concave_poinits', 'symmetry', 'fractal_dimension']\n",
    "column_names = ['id', 'diagnosis']\n",
    "\n",
    "for attr in ['mean', 'ste', 'largest']:\n",
    "  for feature in features:\n",
    "    column_names.append(feature + \"_\" + attr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A3VR1aTP92nV"
   },
   "source": [
    "Read the dataset into a pandas [DataFrame]() using [`pandas.read_csv`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:44.240950Z",
     "iopub.status.busy": "2024-08-15T02:45:44.240703Z",
     "iopub.status.idle": "2024-08-15T02:45:44.653730Z",
     "shell.execute_reply": "2024-08-15T02:45:44.652998Z"
    },
    "id": "uvR2Bzb691lJ"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(url, names=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:44.657423Z",
     "iopub.status.busy": "2024-08-15T02:45:44.657188Z",
     "iopub.status.idle": "2024-08-15T02:45:44.667589Z",
     "shell.execute_reply": "2024-08-15T02:45:44.666969Z"
    },
    "id": "YB9eq6Zq-IZ4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 32 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   id                         569 non-null    int64  \n",
      " 1   diagnosis                  569 non-null    object \n",
      " 2   radius_mean                569 non-null    float64\n",
      " 3   texture_mean               569 non-null    float64\n",
      " 4   perimeter_mean             569 non-null    float64\n",
      " 5   area_mean                  569 non-null    float64\n",
      " 6   smoothness_mean            569 non-null    float64\n",
      " 7   compactness_mean           569 non-null    float64\n",
      " 8   concavity_mean             569 non-null    float64\n",
      " 9   concave_poinits_mean       569 non-null    float64\n",
      " 10  symmetry_mean              569 non-null    float64\n",
      " 11  fractal_dimension_mean     569 non-null    float64\n",
      " 12  radius_ste                 569 non-null    float64\n",
      " 13  texture_ste                569 non-null    float64\n",
      " 14  perimeter_ste              569 non-null    float64\n",
      " 15  area_ste                   569 non-null    float64\n",
      " 16  smoothness_ste             569 non-null    float64\n",
      " 17  compactness_ste            569 non-null    float64\n",
      " 18  concavity_ste              569 non-null    float64\n",
      " 19  concave_poinits_ste        569 non-null    float64\n",
      " 20  symmetry_ste               569 non-null    float64\n",
      " 21  fractal_dimension_ste      569 non-null    float64\n",
      " 22  radius_largest             569 non-null    float64\n",
      " 23  texture_largest            569 non-null    float64\n",
      " 24  perimeter_largest          569 non-null    float64\n",
      " 25  area_largest               569 non-null    float64\n",
      " 26  smoothness_largest         569 non-null    float64\n",
      " 27  compactness_largest        569 non-null    float64\n",
      " 28  concavity_largest          569 non-null    float64\n",
      " 29  concave_poinits_largest    569 non-null    float64\n",
      " 30  symmetry_largest           569 non-null    float64\n",
      " 31  fractal_dimension_largest  569 non-null    float64\n",
      "dtypes: float64(30), int64(1), object(1)\n",
      "memory usage: 142.4+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0_Z1V6Dg-La_"
   },
   "source": [
    "Display the first five rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:44.670861Z",
     "iopub.status.busy": "2024-08-15T02:45:44.670582Z",
     "iopub.status.idle": "2024-08-15T02:45:44.692635Z",
     "shell.execute_reply": "2024-08-15T02:45:44.692082Z"
    },
    "id": "hWxktwbv-KPp"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave_poinits_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_largest</th>\n",
       "      <th>texture_largest</th>\n",
       "      <th>perimeter_largest</th>\n",
       "      <th>area_largest</th>\n",
       "      <th>smoothness_largest</th>\n",
       "      <th>compactness_largest</th>\n",
       "      <th>concavity_largest</th>\n",
       "      <th>concave_poinits_largest</th>\n",
       "      <th>symmetry_largest</th>\n",
       "      <th>fractal_dimension_largest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave_poinits_mean  \\\n",
       "0          0.11840           0.27760          0.3001               0.14710   \n",
       "1          0.08474           0.07864          0.0869               0.07017   \n",
       "2          0.10960           0.15990          0.1974               0.12790   \n",
       "3          0.14250           0.28390          0.2414               0.10520   \n",
       "4          0.10030           0.13280          0.1980               0.10430   \n",
       "\n",
       "   ...  radius_largest  texture_largest  perimeter_largest  area_largest  \\\n",
       "0  ...           25.38            17.33             184.60        2019.0   \n",
       "1  ...           24.99            23.41             158.80        1956.0   \n",
       "2  ...           23.57            25.53             152.50        1709.0   \n",
       "3  ...           14.91            26.50              98.87         567.7   \n",
       "4  ...           22.54            16.67             152.20        1575.0   \n",
       "\n",
       "   smoothness_largest  compactness_largest  concavity_largest  \\\n",
       "0              0.1622               0.6656             0.7119   \n",
       "1              0.1238               0.1866             0.2416   \n",
       "2              0.1444               0.4245             0.4504   \n",
       "3              0.2098               0.8663             0.6869   \n",
       "4              0.1374               0.2050             0.4000   \n",
       "\n",
       "   concave_poinits_largest  symmetry_largest  fractal_dimension_largest  \n",
       "0                   0.2654            0.4601                    0.11890  \n",
       "1                   0.1860            0.2750                    0.08902  \n",
       "2                   0.2430            0.3613                    0.08758  \n",
       "3                   0.2575            0.6638                    0.17300  \n",
       "4                   0.1625            0.2364                    0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s4-Wn2jzVC1W"
   },
   "source": [
    "Split the dataset into training and test sets using [`pandas.DataFrame.sample`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sample.html), [`pandas.DataFrame.drop`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html) and [`pandas.DataFrame.iloc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html). Make sure to split the features from the target labels. The test set is used to evaluate your model's generalizability to unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:44.695762Z",
     "iopub.status.busy": "2024-08-15T02:45:44.695306Z",
     "iopub.status.idle": "2024-08-15T02:45:44.699284Z",
     "shell.execute_reply": "2024-08-15T02:45:44.698699Z"
    },
    "id": "m2O60B-IVG9Q"
   },
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.75, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:44.702271Z",
     "iopub.status.busy": "2024-08-15T02:45:44.702021Z",
     "iopub.status.idle": "2024-08-15T02:45:44.706087Z",
     "shell.execute_reply": "2024-08-15T02:45:44.705491Z"
    },
    "id": "i06vHFv_QB24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "427"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:44.709220Z",
     "iopub.status.busy": "2024-08-15T02:45:44.708650Z",
     "iopub.status.idle": "2024-08-15T02:45:44.712230Z",
     "shell.execute_reply": "2024-08-15T02:45:44.711666Z"
    },
    "id": "19JaochhaQ3m"
   },
   "outputs": [],
   "source": [
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:44.715272Z",
     "iopub.status.busy": "2024-08-15T02:45:44.714781Z",
     "iopub.status.idle": "2024-08-15T02:45:44.718518Z",
     "shell.execute_reply": "2024-08-15T02:45:44.717968Z"
    },
    "id": "LmHRcbAfaSag"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:44.721330Z",
     "iopub.status.busy": "2024-08-15T02:45:44.721075Z",
     "iopub.status.idle": "2024-08-15T02:45:44.725060Z",
     "shell.execute_reply": "2024-08-15T02:45:44.724503Z"
    },
    "id": "w6JxBhBc_wwO"
   },
   "outputs": [],
   "source": [
    "# The `id` column can be dropped since each row is unique\n",
    "x_train, y_train = train_dataset.iloc[:, 2:], train_dataset.iloc[:, 1]\n",
    "x_test, y_test = test_dataset.iloc[:, 2:], test_dataset.iloc[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3MWuJTKEDM-f"
   },
   "source": [
    "## Preprocess the data\n",
    "\n",
    "This dataset contains the mean, standard error, and largest values for each of the 10 tumor measurements collected per example. The `\"diagnosis\"` target column is a categorical variable with `'M'` indicating a malignant tumor and `'B'` indicating a benign tumor diagnosis. This column needs to be converted into a numerical binary format for model training.\n",
    "\n",
    "The [`pandas.Series.map`](https://pandas.pydata.org/docs/reference/api/pandas.Series.map.html) function is useful for mapping binary values to the categories.\n",
    "\n",
    "The dataset should also be converted to a tensor with the `tf.convert_to_tensor` function after the preprocessing is complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:44.728191Z",
     "iopub.status.busy": "2024-08-15T02:45:44.727639Z",
     "iopub.status.idle": "2024-08-15T02:45:46.967237Z",
     "shell.execute_reply": "2024-08-15T02:45:46.966185Z"
    },
    "id": "JEJHhN65a2VV"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723689945.265757  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.269593  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.273290  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.276976  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.288712  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.292180  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.295550  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.299093  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.302584  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.306098  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.309484  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689945.312921  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.538105  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.540233  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.542239  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.544278  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.546323  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.548257  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.550168  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.552143  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.554591  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.556540  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.558447  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.560412  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.599852  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.601910  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.604061  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.606104  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.608094  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.610074  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.611985  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.613947  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.615903  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.618356  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.620668  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723689946.623031  132290 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
     ]
    }
   ],
   "source": [
    "y_train, y_test = y_train.map({'B': 0, 'M': 1}), y_test.map({'B': 0, 'M': 1})\n",
    "x_train, y_train = tf.convert_to_tensor(x_train, dtype=tf.float32), tf.convert_to_tensor(y_train, dtype=tf.float32)\n",
    "x_test, y_test = tf.convert_to_tensor(x_test, dtype=tf.float32), tf.convert_to_tensor(y_test, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J4ubs136WLNp"
   },
   "source": [
    "Use [`seaborn.pairplot`](https://seaborn.pydata.org/generated/seaborn.pairplot.html) to review the joint distribution of a few pairs of mean-based features from the training set and observe how they relate to the target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:46.970686Z",
     "iopub.status.busy": "2024-08-15T02:45:46.970356Z",
     "iopub.status.idle": "2024-08-15T02:45:50.979183Z",
     "shell.execute_reply": "2024-08-15T02:45:50.978413Z"
    },
    "id": "oRKO_x8gWKv-"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1076.74x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset.iloc[:, 1:6], hue = 'diagnosis', diag_kind='kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5YOG5iKYKW_3"
   },
   "source": [
    "This pairplot demonstrates that certain features such as radius, perimeter and area are highly correlated. This is expected since the tumor radius is directly involved in the computation of both perimeter and area. Additionally, note that malignant diagnoses seem to be more right-skewed for many of the features.\n",
    "\n",
    "Make sure to also check the overall statistics. Note how each feature covers a vastly different range of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:50.985629Z",
     "iopub.status.busy": "2024-08-15T02:45:50.985361Z",
     "iopub.status.idle": "2024-08-15T02:45:51.040986Z",
     "shell.execute_reply": "2024-08-15T02:45:51.040315Z"
    },
    "id": "yi2FzC3T21jR"
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>427.0</td>\n",
       "      <td>2.756014e+07</td>\n",
       "      <td>1.162735e+08</td>\n",
       "      <td>8670.00000</td>\n",
       "      <td>865427.500000</td>\n",
       "      <td>905539.00000</td>\n",
       "      <td>8.810829e+06</td>\n",
       "      <td>9.113205e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_mean</th>\n",
       "      <td>427.0</td>\n",
       "      <td>1.414331e+01</td>\n",
       "      <td>3.528717e+00</td>\n",
       "      <td>6.98100</td>\n",
       "      <td>11.695000</td>\n",
       "      <td>13.43000</td>\n",
       "      <td>1.594000e+01</td>\n",
       "      <td>2.811000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_mean</th>\n",
       "      <td>427.0</td>\n",
       "      <td>1.924468e+01</td>\n",
       "      <td>4.113131e+00</td>\n",
       "      <td>10.38000</td>\n",
       "      <td>16.330000</td>\n",
       "      <td>18.84000</td>\n",
       "      <td>2.168000e+01</td>\n",
       "      <td>3.381000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_mean</th>\n",
       "      <td>427.0</td>\n",
       "      <td>9.206759e+01</td>\n",
       "      <td>2.431431e+01</td>\n",
       "      <td>43.79000</td>\n",
       "      <td>75.235000</td>\n",
       "      <td>86.87000</td>\n",
       "      <td>1.060000e+02</td>\n",
       "      <td>1.885000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_mean</th>\n",
       "      <td>427.0</td>\n",
       "      <td>6.563190e+02</td>\n",
       "      <td>3.489106e+02</td>\n",
       "      <td>143.50000</td>\n",
       "      <td>420.050000</td>\n",
       "      <td>553.50000</td>\n",
       "      <td>7.908500e+02</td>\n",
       "      <td>2.499000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_mean</th>\n",
       "      <td>427.0</td>\n",
       "      <td>9.633618e-02</td>\n",
       "      <td>1.436820e-02</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.085850</td>\n",
       "      <td>0.09566</td>\n",
       "      <td>1.050000e-01</td>\n",
       "      <td>1.634000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_mean</th>\n",
       "      <td>427.0</td>\n",
       "      <td>1.036597e-01</td>\n",
       "      <td>5.351893e-02</td>\n",
       "      <td>0.02344</td>\n",
       "      <td>0.063515</td>\n",
       "      <td>0.09182</td>\n",
       "      <td>1.296500e-01</td>\n",
       "      <td>3.454000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_mean</th>\n",
       "      <td>427.0</td>\n",
       "      <td>8.833008e-02</td>\n",
       "      <td>7.965884e-02</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.029570</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>1.297500e-01</td>\n",
       "      <td>4.268000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave_poinits_mean</th>\n",
       "      <td>427.0</td>\n",
       "      <td>4.872688e-02</td>\n",
       "      <td>3.853594e-02</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.019650</td>\n",
       "      <td>0.03390</td>\n",
       "      <td>7.409500e-02</td>\n",
       "      <td>2.012000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_mean</th>\n",
       "      <td>427.0</td>\n",
       "      <td>1.804597e-01</td>\n",
       "      <td>2.637837e-02</td>\n",
       "      <td>0.12030</td>\n",
       "      <td>0.161700</td>\n",
       "      <td>0.17840</td>\n",
       "      <td>1.947000e-01</td>\n",
       "      <td>2.906000e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count          mean           std         min  \\\n",
       "id                    427.0  2.756014e+07  1.162735e+08  8670.00000   \n",
       "radius_mean           427.0  1.414331e+01  3.528717e+00     6.98100   \n",
       "texture_mean          427.0  1.924468e+01  4.113131e+00    10.38000   \n",
       "perimeter_mean        427.0  9.206759e+01  2.431431e+01    43.79000   \n",
       "area_mean             427.0  6.563190e+02  3.489106e+02   143.50000   \n",
       "smoothness_mean       427.0  9.633618e-02  1.436820e-02     0.05263   \n",
       "compactness_mean      427.0  1.036597e-01  5.351893e-02     0.02344   \n",
       "concavity_mean        427.0  8.833008e-02  7.965884e-02     0.00000   \n",
       "concave_poinits_mean  427.0  4.872688e-02  3.853594e-02     0.00000   \n",
       "symmetry_mean         427.0  1.804597e-01  2.637837e-02     0.12030   \n",
       "\n",
       "                                25%           50%           75%           max  \n",
       "id                    865427.500000  905539.00000  8.810829e+06  9.113205e+08  \n",
       "radius_mean               11.695000      13.43000  1.594000e+01  2.811000e+01  \n",
       "texture_mean              16.330000      18.84000  2.168000e+01  3.381000e+01  \n",
       "perimeter_mean            75.235000      86.87000  1.060000e+02  1.885000e+02  \n",
       "area_mean                420.050000     553.50000  7.908500e+02  2.499000e+03  \n",
       "smoothness_mean            0.085850       0.09566  1.050000e-01  1.634000e-01  \n",
       "compactness_mean           0.063515       0.09182  1.296500e-01  3.454000e-01  \n",
       "concavity_mean             0.029570       0.05999  1.297500e-01  4.268000e-01  \n",
       "concave_poinits_mean       0.019650       0.03390  7.409500e-02  2.012000e-01  \n",
       "symmetry_mean              0.161700       0.17840  1.947000e-01  2.906000e-01  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.describe().transpose()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_8pDCIFjMla8"
   },
   "source": [
    "Given the inconsistent ranges, it is beneficial to standardize the data such that each feature has a zero mean and unit variance. This process is called [normalization](https://developers.google.com/machine-learning/glossary#normalization)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:51.044178Z",
     "iopub.status.busy": "2024-08-15T02:45:51.043926Z",
     "iopub.status.idle": "2024-08-15T02:45:51.204836Z",
     "shell.execute_reply": "2024-08-15T02:45:51.204031Z"
    },
    "id": "FrzKNFNjLQDl"
   },
   "outputs": [],
   "source": [
    "class Normalize(tf.Module):\n",
    "  def __init__(self, x):\n",
    "    # Initialize the mean and standard deviation for normalization\n",
    "    self.mean = tf.Variable(tf.math.reduce_mean(x, axis=0))\n",
    "    self.std = tf.Variable(tf.math.reduce_std(x, axis=0))\n",
    "\n",
    "  def norm(self, x):\n",
    "    # Normalize the input\n",
    "    return (x - self.mean)/self.std\n",
    "\n",
    "  def unnorm(self, x):\n",
    "    # Unnormalize the input\n",
    "    return (x * self.std) + self.mean\n",
    "\n",
    "norm_x = Normalize(x_train)\n",
    "x_train_norm, x_test_norm = norm_x.norm(x_train), norm_x.norm(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6o3CrycBXA2s"
   },
   "source": [
    "## Logistic regression\n",
    "\n",
    "Before building a logistic regression model, it is crucial to understand the method's differences compared to traditional linear regression.\n",
    "\n",
    "### Logistic regression fundamentals\n",
    "\n",
    "Linear regression returns a linear combination of its inputs; this output is unbounded. The output of a [logistic regression](https://developers.google.com/machine-learning/glossary#logistic_regression) is in the `(0, 1)` range. For each example, it represents the probability that the example belongs to the _positive_ class.\n",
    "\n",
    "Logistic regression maps the continuous outputs of traditional linear regression, `(-∞, ∞)`, to probabilities, `(0, 1)`. This transformation is also symmetric so that flipping the sign of the linear output results in the inverse of the original probability.\n",
    "\n",
    "Let $Y$ denote the probability of being in class `1` (the tumor is malignant). The desired mapping can be achieved by interpreting the linear regression output as the [log odds](https://developers.google.com/machine-learning/glossary#log-odds) ratio of being in class `1` as opposed to class `0`:\n",
    "\n",
    "$$\\ln(\\frac{Y}{1-Y}) = wX + b$$\n",
    "\n",
    "By setting $wX + b = z$, this equation can then be solved for $Y$:\n",
    "\n",
    "$$Y = \\frac{e^{z}}{1 + e^{z}} = \\frac{1}{1 + e^{-z}}$$\n",
    "\n",
    "The expression $\\frac{1}{1 + e^{-z}}$ is known as the [sigmoid function](https://developers.google.com/machine-learning/glossary#sigmoid_function) $\\sigma(z)$. Hence, the equation for logistic regression can be written as $Y = \\sigma(wX + b)$.\n",
    "\n",
    "The dataset in this tutorial deals with a high-dimensional feature matrix. Therefore, the above equation must be rewritten in a matrix vector form as follows:\n",
    "\n",
    "$${\\mathrm{Y}} = \\sigma({\\mathrm{X}}w + b)$$\n",
    "\n",
    "where:\n",
    "\n",
    "* $\\underset{m\\times 1}{\\mathrm{Y}}$: a target vector\n",
    "* $\\underset{m\\times n}{\\mathrm{X}}$: a feature matrix\n",
    "* $\\underset{n\\times 1}w$: a weight vector\n",
    "* $b$: a bias\n",
    "* $\\sigma$: a sigmoid function applied to each element of the output vector\n",
    "\n",
    "Start by visualizing the sigmoid function, which transforms the linear output, `(-∞, ∞)`, to fall between `0` and `1`. The sigmoid function is available in `tf.math.sigmoid`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:51.208819Z",
     "iopub.status.busy": "2024-08-15T02:45:51.208543Z",
     "iopub.status.idle": "2024-08-15T02:45:52.159110Z",
     "shell.execute_reply": "2024-08-15T02:45:52.158383Z"
    },
    "id": "ThHaV_RmucZl"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = tf.linspace(-10, 10, 500)\n",
    "x = tf.cast(x, tf.float32)\n",
    "f = lambda x : (1/20)*x + 0.6\n",
    "plt.plot(x, tf.math.sigmoid(x))\n",
    "plt.ylim((-0.1,1.1))\n",
    "plt.title(\"Sigmoid function\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VMXEhrZuKECV"
   },
   "source": [
    "### The log loss function\n",
    "\n",
    "The  [log loss](https://developers.google.com/machine-learning/glossary#Log_Loss), or binary cross-entropy loss, is the ideal loss function for a binary classification problem with logistic regression. For each example, the log loss quantifies the similarity between a predicted probability and the example's true value. It is determined by the following equation:\n",
    "\n",
    "$$L = -\\frac{1}{m}\\sum_{i=1}^{m}y_i\\cdot\\log(\\hat{y}_i) + (1- y_i)\\cdot\\log(1 - \\hat{y}_i)$$\n",
    "\n",
    "where:\n",
    "\n",
    "* $\\hat{y}$: a vector of predicted probabilities\n",
    "* $y$: a vector of true targets\n",
    "\n",
    "You can use the `tf.nn.sigmoid_cross_entropy_with_logits` function to compute the log loss. This function automatically applies the sigmoid activation to the regression output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:52.162622Z",
     "iopub.status.busy": "2024-08-15T02:45:52.162348Z",
     "iopub.status.idle": "2024-08-15T02:45:52.166270Z",
     "shell.execute_reply": "2024-08-15T02:45:52.165625Z"
    },
    "id": "JVBInnSqS36W"
   },
   "outputs": [],
   "source": [
    "def log_loss(y_pred, y):\n",
    "  # Compute the log loss function\n",
    "  ce = tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=y_pred)\n",
    "  return tf.reduce_mean(ce)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q_mutLj0KNUb"
   },
   "source": [
    "### The gradient descent update rule\n",
    "\n",
    "The TensorFlow Core APIs support automatic differentiation with `tf.GradientTape`. If you are curious about the mathematics behind the logistic regression [gradient updates](https://developers.google.com/machine-learning/glossary#gradient_descent), here is a short explanation:\n",
    "\n",
    "In the above equation for the log loss, recall that each $\\hat{y}_i$ can be rewritten in terms of the inputs as $\\sigma({\\mathrm{X_i}}w + b)$.\n",
    "\n",
    "The goal is to find a $w^*$ and $b^*$ that minimize the log loss:\n",
    "\n",
    "$$L = -\\frac{1}{m}\\sum_{i=1}^{m}y_i\\cdot\\log(\\sigma({\\mathrm{X_i}}w + b)) + (1- y_i)\\cdot\\log(1 - \\sigma({\\mathrm{X_i}}w + b))$$\n",
    "\n",
    "By taking the gradient $L$ with respect to $w$, you get the following:\n",
    "\n",
    "$$\\frac{\\partial L}{\\partial w} = \\frac{1}{m}(\\sigma({\\mathrm{X}}w + b) - y)X$$\n",
    "\n",
    "By taking the gradient $L$ with respect to $b$, you get the following:\n",
    "\n",
    "$$\\frac{\\partial L}{\\partial b} = \\frac{1}{m}\\sum_{i=1}^{m}\\sigma({\\mathrm{X_i}}w + b) - y_i$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uTCndUecKZho"
   },
   "source": [
    "Now, build the logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:52.169910Z",
     "iopub.status.busy": "2024-08-15T02:45:52.169272Z",
     "iopub.status.idle": "2024-08-15T02:45:52.175137Z",
     "shell.execute_reply": "2024-08-15T02:45:52.174456Z"
    },
    "id": "c0sXM7qLlKfZ"
   },
   "outputs": [],
   "source": [
    "class LogisticRegression(tf.Module):\n",
    "\n",
    "  def __init__(self):\n",
    "    self.built = False\n",
    "    \n",
    "  def __call__(self, x, train=True):\n",
    "    # Initialize the model parameters on the first call\n",
    "    if not self.built:\n",
    "      # Randomly generate the weights and the bias term\n",
    "      rand_w = tf.random.uniform(shape=[x.shape[-1], 1], seed=22)\n",
    "      rand_b = tf.random.uniform(shape=[], seed=22)\n",
    "      self.w = tf.Variable(rand_w)\n",
    "      self.b = tf.Variable(rand_b)\n",
    "      self.built = True\n",
    "    # Compute the model output\n",
    "    z = tf.add(tf.matmul(x, self.w), self.b)\n",
    "    z = tf.squeeze(z, axis=1)\n",
    "    if train:\n",
    "      return z\n",
    "    return tf.sigmoid(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eObQu9fDnXGL"
   },
   "source": [
    "To validate, make sure the untrained model outputs values in the range of `(0, 1)` for a small subset of the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:52.178501Z",
     "iopub.status.busy": "2024-08-15T02:45:52.177947Z",
     "iopub.status.idle": "2024-08-15T02:45:52.181252Z",
     "shell.execute_reply": "2024-08-15T02:45:52.180594Z"
    },
    "id": "5bIovC0Z4QHJ"
   },
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:52.184391Z",
     "iopub.status.busy": "2024-08-15T02:45:52.183947Z",
     "iopub.status.idle": "2024-08-15T02:45:52.243142Z",
     "shell.execute_reply": "2024-08-15T02:45:52.242534Z"
    },
    "id": "QJ2ievISyf0p"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9994985 , 0.9978607 , 0.29620072, 0.01979049, 0.3314926 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = log_reg(x_train_norm[:5], train=False)\n",
    "y_pred.numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PribnwDHUksC"
   },
   "source": [
    "Next, write an accuracy function to calculate the proportion of correct classifications during training. In order to retrieve the classifications from the predicted probabilities, set a threshold for which all probabilities higher than the threshold belong to class `1`. This is a configurable hyperparameter that can be set to `0.5` as a default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:52.246552Z",
     "iopub.status.busy": "2024-08-15T02:45:52.246028Z",
     "iopub.status.idle": "2024-08-15T02:45:52.250333Z",
     "shell.execute_reply": "2024-08-15T02:45:52.249711Z"
    },
    "id": "ssnVcKg7oMe6"
   },
   "outputs": [],
   "source": [
    "def predict_class(y_pred, thresh=0.5):\n",
    "  # Return a tensor with  `1` if `y_pred` > `0.5`, and `0` otherwise\n",
    "  return tf.cast(y_pred > thresh, tf.float32)\n",
    "\n",
    "def accuracy(y_pred, y):\n",
    "  # Return the proportion of matches between `y_pred` and `y`\n",
    "  y_pred = tf.math.sigmoid(y_pred)\n",
    "  y_pred_class = predict_class(y_pred)\n",
    "  check_equal = tf.cast(y_pred_class == y,tf.float32)\n",
    "  acc_val = tf.reduce_mean(check_equal)\n",
    "  return acc_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J_0KHQ25_2dF"
   },
   "source": [
    "### Train the model\n",
    "\n",
    "Using mini-batches for training provides both memory efficiency and faster convergence. The `tf.data.Dataset` API has useful functions for batching and shuffling. The API enables you to build complex input pipelines from simple, reusable pieces. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:52.253908Z",
     "iopub.status.busy": "2024-08-15T02:45:52.253345Z",
     "iopub.status.idle": "2024-08-15T02:45:53.274754Z",
     "shell.execute_reply": "2024-08-15T02:45:53.273976Z"
    },
    "id": "vJD7-4U0etqa"
   },
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((x_train_norm, y_train))\n",
    "train_dataset = train_dataset.shuffle(buffer_size=x_train.shape[0]).batch(batch_size)\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((x_test_norm, y_test))\n",
    "test_dataset = test_dataset.shuffle(buffer_size=x_test.shape[0]).batch(batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sLiWZZPBSDip"
   },
   "source": [
    "Now write a training loop for the logistic regression model. The loop utilizes the log loss function and its gradients with respect to the input in order to iteratively update the model's parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:45:53.279173Z",
     "iopub.status.busy": "2024-08-15T02:45:53.278874Z",
     "iopub.status.idle": "2024-08-15T02:46:05.279427Z",
     "shell.execute_reply": "2024-08-15T02:46:05.278527Z"
    },
    "id": "jNC3D1DGsGgK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Training log loss: 0.661\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20, Training log loss: 0.418\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 40, Training log loss: 0.269\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 60, Training log loss: 0.178\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 80, Training log loss: 0.137\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 100, Training log loss: 0.116\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 120, Training log loss: 0.106\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 140, Training log loss: 0.096\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 160, Training log loss: 0.094\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 180, Training log loss: 0.089\n"
     ]
    }
   ],
   "source": [
    "# Set training parameters\n",
    "epochs = 200\n",
    "learning_rate = 0.01\n",
    "train_losses, test_losses = [], []\n",
    "train_accs, test_accs = [], []\n",
    "\n",
    "# Set up the training loop and begin training\n",
    "for epoch in range(epochs):\n",
    "  batch_losses_train, batch_accs_train = [], []\n",
    "  batch_losses_test, batch_accs_test = [], []\n",
    "\n",
    "  # Iterate over the training data\n",
    "  for x_batch, y_batch in train_dataset:\n",
    "    with tf.GradientTape() as tape:\n",
    "      y_pred_batch = log_reg(x_batch)\n",
    "      batch_loss = log_loss(y_pred_batch, y_batch)\n",
    "    batch_acc = accuracy(y_pred_batch, y_batch)\n",
    "    # Update the parameters with respect to the gradient calculations\n",
    "    grads = tape.gradient(batch_loss, log_reg.variables)\n",
    "    for g,v in zip(grads, log_reg.variables):\n",
    "      v.assign_sub(learning_rate * g)\n",
    "    # Keep track of batch-level training performance\n",
    "    batch_losses_train.append(batch_loss)\n",
    "    batch_accs_train.append(batch_acc)\n",
    "\n",
    "  # Iterate over the testing data\n",
    "  for x_batch, y_batch in test_dataset:\n",
    "    y_pred_batch = log_reg(x_batch)\n",
    "    batch_loss = log_loss(y_pred_batch, y_batch)\n",
    "    batch_acc = accuracy(y_pred_batch, y_batch)\n",
    "    # Keep track of batch-level testing performance\n",
    "    batch_losses_test.append(batch_loss)\n",
    "    batch_accs_test.append(batch_acc)\n",
    "\n",
    "  # Keep track of epoch-level model performance\n",
    "  train_loss, train_acc = tf.reduce_mean(batch_losses_train), tf.reduce_mean(batch_accs_train)\n",
    "  test_loss, test_acc = tf.reduce_mean(batch_losses_test), tf.reduce_mean(batch_accs_test)\n",
    "  train_losses.append(train_loss)\n",
    "  train_accs.append(train_acc)\n",
    "  test_losses.append(test_loss)\n",
    "  test_accs.append(test_acc)\n",
    "  if epoch % 20 == 0:\n",
    "    print(f\"Epoch: {epoch}, Training log loss: {train_loss:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NoLiAg7fYft7"
   },
   "source": [
    "### Performance evaluation\n",
    "\n",
    "Observe the changes in your model's loss and accuracy over time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:05.283885Z",
     "iopub.status.busy": "2024-08-15T02:46:05.283186Z",
     "iopub.status.idle": "2024-08-15T02:46:05.489845Z",
     "shell.execute_reply": "2024-08-15T02:46:05.489185Z"
    },
    "id": "mv3oCQPvWhr0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P5557Lo8//ji33XYbPXr0KPeaYcOG0axZMwYOHEhWVhZLly7liSee4OSTTyYlJSXg+7Zv35709HSeeeYZUlJSSEpKol+/fn7r1C0DBgygUaNGjBkzhmuvvRabzcarr74akRKcQCZPnszXX3/NwIEDueqqqygrK+OJJ56ge/fuVdr1OCEhgW7duvH222/TqVMnMjIy6N69O927d+fJJ59k0KBB9OjRgyuuuIJ27dqxfft25s2bx6ZNm/j9998rff9TTjmFV199lbS0NLp168a8efP45ptvXMuaWnr37o3dbue+++4jLy+PuLg4TjjhBDIzM8u955VXXsmzzz7LJZdcwoIFC2jTpg3vvfcec+fOZerUqRX+XvizYsUKTjzxRM455xy6detGdHQ0H374Idu3b3ctRysi9UTE1h8SETHcSyMG+tm4caNhGIaxcOFCY/jw4UZycrKRmJhoHH/88cZPP/1U7v0WLVpkHHPMMUZcXJzRsmVLY8qUKcZjjz1mAMa2bdsqHIu/ZSdLSkqM22+/3Wjbtq0RExNj5OTkGBMnTvRa8nHhwoXG+eefb7Rq1cqIi4szMjMzjVNOOcX47bffXOe89957xrBhw4zMzEwjNjbWaNWqlfHPf/7T2Lp1a1B/Tg6Hw8jJyTEA46677ir3/LPPPmsce+yxRuPGjY24uDijffv2xn/+8x8jLy+v0vf++OOPjW7durmWyLSWBD3uuOMCLi06d+5c4+ijjzYSEhKM5s2bGzfddJNryVLPJSoDLQPqbylJfJbZDLQM6DXXXFPuta1btzbGjBnjdWzWrFlGnz59jNjYWKN9+/bG888/b9xwww1GfHx8xX8gfsZtGIbx008/GX379jViY2PLjXX16tXGxRdfbDRr1syIiYkxWrRoYZxyyinGe++95zqnoiVv9+7da4wdO9Zo0qSJkZycbAwfPtxYtmyZ3+81bdo0o127dobdbvf68/ZdBtQwDGP79u2u942NjTV69OhRbsnXYP9Odu3aZVxzzTVGly5djKSkJCMtLc3o16+f8c4771T4ZykidY/NMOrQJRsRkRpw/fXX8+yzz7J///6AjY7SMIwePZq///6blStXRnooIiIRox4AETmkHDhwwOvx7t27efXVVxk0aJAm/w2M7+/CypUrmTFjBoMHD47MgERE6ghlAETkkNK7d28GDx5M165d2b59Oy+88AJbtmxh1qxZXivWyKEvOzubSy65hHbt2rF+/XqefvppioqKWLRoER07doz08EREIkZNwCJySDnppJN47733eO6557DZbBx++OG88MILmvw3QCNGjODNN99k27ZtxMXF0b9/f+655x5N/kWkwVMGQERERESkAVEPgIiIiIhIA6IAQERERESkAWlwPQAOh4MtW7aQkpIS8qZAIiIiIiJ1lWEY7Nu3j+bNmxMVFfg6f4MLALZs2UJOTk6khyEiIiIiUiM2btxIy5YtAz7f4AIAa+vzjRs3kpqaGuHRiIiIiIiER35+Pjk5Oa75biANLgCwyn5SU1MVAIiIiIjIIaeyMnc1AYuIiIiINCAKAEREREREGhAFACIiIiIiDUiD6wEQERERqQ8Mw6C0tJSysrJID0XqCLvdTnR0dLWXslcAICIiIlLHFBcXs3XrVgoLCyM9FKljEhMTyc7OJjY2tsrvoQBAREREpA5xOBysXbsWu91O8+bNiY2N1ealgmEYFBcXs3PnTtauXUvHjh0r3OyrIgoAREREROqQ4uJiHA4HOTk5JCYmRno4UockJCQQExPD+vXrKS4uJj4+vkrvoyZgERERkTqoqld35dAWjt8L/WaJiIiIiDQgCgBERERERBoQBQAiIiIiUie1adOGqVOnBn3+7Nmzsdls5Obm1tiYAKZPn056enqNfkZNUgAgIiIiItVis9kq/Jk8eXKV3vfXX3/lyiuvDPr8AQMGsHXrVtLS0qr0eQ2FVgESERERkWrZunWr6/7bb7/NpEmTWL58uetYcnKy675hGJSVlREdXfk0tGnTpiGNIzY2lmbNmoX0moZIGQARERGROswwDAqLSyPyYxhGUGNs1qyZ6yctLQ2bzeZ6vGzZMlJSUvjiiy/o27cvcXFxzJkzh9WrV3PaaaeRlZVFcnIyRx55JN98843X+/qWANlsNp5//nlOP/10EhMT6dixI5988onred8SIKtU56uvvqJr164kJyczYsQIr4CltLSUa6+9lvT0dBo3bszNN9/MmDFjGD16dEh/T08//TTt27cnNjaWzp078+qrr3r9HU6ePJlWrVoRFxdH8+bNufbaa13PP/XUU3Ts2JH4+HiysrI466yzQvrsUCkDICIiIlKHHSgpo9ukryLy2UvuGE5ibHimi7fccgsPPvgg7dq1o1GjRmzcuJGTTjqJu+++m7i4OF555RVGjRrF8uXLadWqVcD3uf3227n//vt54IEHePzxx7nwwgtZv349GRkZfs8vLCzkwQcf5NVXXyUqKop//OMf3Hjjjbz++usA3Hfffbz++uu89NJLdO3alUcffZSPPvqI448/Pujv9uGHH3LdddcxdepUhgwZwmeffcbYsWNp2bIlxx9/PO+//z6PPPIIb731Focddhjbtm3j999/B+C3337j2muv5dVXX2XAgAHs2bOHH3/8MYQ/2dApABARERGRGnfHHXcwdOhQ1+OMjAx69erlenznnXfy4Ycf8sknnzBu3LiA73PJJZdw/vnnA3DPPffw2GOPMX/+fEaMGOH3/JKSEp555hnat28PwLhx47jjjjtczz/++ONMnDiR008/HYAnnniCGTNmhPTdHnzwQS655BKuvvpqACZMmMDPP//Mgw8+yPHHH8+GDRto1qwZQ4YMISYmhlatWnHUUUcBsGHDBpKSkjjllFNISUmhdevW9OnTJ6TPD5UCgPpm10qITYbU7EiPRERERGpBQoydJXcMj9hnh8sRRxzh9Xj//v1MnjyZzz//nK1bt1JaWsqBAwfYsGFDhe/Ts2dP1/2kpCRSU1PZsWNHwPMTExNdk3+A7Oxs1/l5eXls377dNRkHsNvt9O3bF4fDEfR3W7p0ablm5YEDB/Loo48CcPbZZzN16lTatWvHiBEjOOmkkxg1ahTR0dEMHTqU1q1bu54bMWKEq8SppqgHoD45mAdPD4TpJ0d6JCIiIlJLbDYbibHREfmx2Wxh+x5JSUlej2+88UY+/PBD7rnnHn788UcWL15Mjx49KC4urvB9YmJiyv35VDRZ93d+sL0N4ZKTk8Py5ct56qmnSEhI4Oqrr+bYY4+lpKSElJQUFi5cyJtvvkl2djaTJk2iV69eNbqUqQKA+qRgF5QVQd6mSI9EREREpFrmzp3LJZdcwumnn06PHj1o1qwZ69atq9UxpKWlkZWVxa+//uo6VlZWxsKFC0N6n65duzJ37lyvY3PnzqVbt26uxwkJCYwaNYrHHnuM2bNnM2/ePP78808AoqOjGTJkCPfffz9//PEH69at49tvv63GN6uYSoDqE0ep87YksuMQERERqaaOHTvywQcfMGrUKGw2G7feemtIZTfh8u9//5spU6bQoUMHunTpwuOPP87evXtDyn785z//4ZxzzqFPnz4MGTKETz/9lA8++MC1qtH06dMpKyujX79+JCYm8tprr5GQkEDr1q357LPPWLNmDcceeyyNGjVixowZOBwOOnfuXFNfWQFAveIoM28NBzgcEKUEjoiIiNRPDz/8MJdeeikDBgygSZMm3HzzzeTn59f6OG6++Wa2bdvGxRdfjN1u58orr2T48OHY7cH3P4wePZpHH32UBx98kOuuu462bdvy0ksvMXjwYADS09O59957mTBhAmVlZfTo0YNPP/2Uxo0bk56ezgcffMDkyZM5ePAgHTt25M033+Swww6roW8MNqO2i6AiLD8/n7S0NPLy8khNTY30cEKz9Q949hjz/v92QnRsZMcjIiIiYXfw4EHWrl1L27ZtiY+Pj/RwGhyHw0HXrl0555xzuPPOOyM9nHIq+v0Idp4b0UvIP/zwA6NGjaJ58+bYbDY++uijoF87d+5coqOj6d27d42Nr86xSoBAZUAiIiIiYbB+/XqmTZvGihUr+PPPP7nqqqtYu3YtF1xwQaSHVmMiGgAUFBTQq1cvnnzyyZBel5uby8UXX8yJJ55YQyOro6wSIPAOBkRERESkSqKiopg+fTpHHnkkAwcO5M8//+Sbb76ha9eukR5ajYloD8DIkSMZOXJkyK/717/+xQUXXIDdbg8pa1DveU76yxQAiIiIiFRXTk5OuRV8DnX1rov0pZdeYs2aNdx2221BnV9UVER+fr7XT72lEiARERERqaZ6FQCsXLmSW265hddee43o6OCSF1OmTCEtLc31k5OTU8OjrEFeAYAyACIiIiISunoTAJSVlXHBBRdw++2306lTp6BfN3HiRPLy8lw/GzdurMFR1jDPHoAyZQBEREREJHT1Zh+Affv28dtvv7Fo0SLGjRsHmMs0GYZBdHQ0X3/9NSeccEK518XFxREXF1fbw60ZXhmAssDniYiIiIgEUG8CgNTUVNd2yZannnqKb7/9lvfee4+2bdtGaGS1SD0AIiIiIlJNEQ0A9u/fz6pVq1yP165dy+LFi8nIyKBVq1ZMnDiRzZs388orrxAVFUX37t29Xp+ZmUl8fHy544csr1WAFACIiIiISOgi2gPw22+/0adPH/r06QPAhAkT6NOnD5MmTQJg69atbNiwIZJDrFsM7QMgIiIiMnny5FrZDPaSSy5h9OjRNf45tS2iAcDgwYMxDKPcz/Tp0wGYPn06s2fPDvj6yZMns3jx4loZa52gjcBERESkDrLZbBX+TJ48uVrv7bvv04033sisWbOqN+gGrN70AAgqARIREZE6aevWra77b7/9NpMmTWL58uWuY8nJyWH9vOTk5LC/Z0NSb5YBFbQPgIiISENkGFBcEJkfwwhqiM2aNXP9pKWlYbPZvI699dZbdO3alfj4eLp06cJTTz3lem1xcTHjxo0jOzub+Ph4WrduzZQpUwBo06YNAKeffjo2m8312LcEyCrVefDBB8nOzqZx48Zcc801lJS4L5hu3bqVk08+mYSEBNq2bcsbb7xBmzZtmDp1atB/FUVFRVx77bWuPtRBgwbx66+/up7fu3cvF154IU2bNiUhIYGOHTvy0ksvVfo9a5syAPWJVgESERFpeEoK4Z7mkfns/9sCsUnVeovXX3+dSZMm8cQTT9CnTx8WLVrEFVdcQVJSEmPGjOGxxx7jk08+4Z133qFVq1Zs3LjRtW/Tr7/+SmZmJi+99BIjRozAbrcH/JzvvvuO7OxsvvvuO1atWsW5555L7969ueKKKwC4+OKL2bVrF7NnzyYmJoYJEyawY8eOkL7LTTfdxPvvv8/LL79M69atuf/++xk+fDirVq0iIyODW2+9lSVLlvDFF1/QpEkTVq1axYEDBwAq/J61TQFAfeLVA6B9AERERKTuu+2223jooYc444wzAGjbti1Llizh2WefZcyYMWzYsIGOHTsyaNAgbDYbrVu3dr22adOmAKSnp9OsWbMKP6dRo0Y88cQT2O12unTpwsknn8ysWbO44oorWLZsGd988w2//vorRxxxBADPP/88HTt2DPp7FBQU8PTTTzN9+nRGjhwJwLRp05g5cyYvvPAC//nPf9iwYQN9+vRxfYaVsQAq/J61TQFAfaIeABERkYYnJtG8Eh+pz66GgoICVq9ezWWXXea6Eg9QWlpKWloaYJbvDB06lM6dOzNixAhOOeUUhg0bFvJnHXbYYV4ZguzsbNceUsuXLyc6OprDDz/c9XyHDh1o1KhR0O+/evVqSkpKGDhwoOtYTEwMRx11FEuXLgXgqquu4swzz2ThwoUMGzaM0aNHM2DAgLB+z3BQAFCfqARIRESk4bHZql2GEyn79+8HzCvl/fr183rOmqwffvjhrF27li+++IJvvvmGc845hyFDhvDee++F9FkxMTFej202Gw6HoxqjD93IkSNZv349M2bMYObMmZx44olcc801PPjgg2H7nuGgJuD6xCsAUAmQiIiI1G1ZWVk0b96cNWvW0KFDB6+ftm3bus5LTU3l3HPPZdq0abz99tu8//777NmzBzAn9mVl1Zv3dO7cmdLSUhYtWuQ6tmrVKvbu3Rv0e7Rv357Y2Fjmzp3rOlZSUsKvv/5Kt27dXMeaNm3KmDFjeO2115g6dSrPPfec67mKvmdtUgagPlEJkIiIiNQzt99+O9deey1paWmMGDGCoqIifvvtN/bu3cuECRN4+OGHyc7Opk+fPkRFRfHuu+/SrFkz0tPTAbOOftasWQwcOJC4uLiQynYsXbp0YciQIVx55ZU8/fTTxMTEcMMNN5CQkIDNZgvqPZKSkrjqqqv4z3/+Q0ZGBq1ateL++++nsLCQyy67DIBJkybRt29fDjvsMIqKivjss8/o2rUrQKXfszYpAKhPtBGYiIiI1DOXX345iYmJPPDAA/znP/8hKSmJHj16cP311wOQkpLC/fffz8qVK7Hb7Rx55JHMmDGDqCizUOWhhx5iwoQJTJs2jRYtWrBu3boqjeOVV17hsssu49hjj6VZs2ZMmTKFv//+m/j4+KDf495778XhcHDRRRexb98+jjjiCL766itXUBIbG8vEiRNZt24dCQkJHHPMMbz11ltBfc/aZDOMIBd4PUTk5+eTlpZGXl4eqampkR5OaL69G36437x/yiNwxKWRHY+IiIiE3cGDB1m7di1t27YNaXIqodm0aRM5OTl88803nHjiiZEeTtAq+v0Idp6rDEB94lUCpAyAiIiISLC+/fZb9u/fT48ePdi6dSs33XQTbdq04dhjj4300GqdAoD6RDsBi4iIiFRJSUkJ//d//8eaNWtISUlhwIABvP766+VWD2oIFADUJ149AGoCFhEREQnW8OHDGT58eKSHUSdoGdD6RBkAEREREakmBQD1iXoAREREGowGtk6LBCkcvxcKAOoT7QQsIiJyyLNq0gsLCyM8EqmLrN+L6vQuqAegPtE+ACIiIoc8u91Oeno6O3bsACAxMTHozark0GUYBoWFhezYsYP09HTsdnuV30sBQH2inYBFREQahGbNmgG4ggARS3p6uuv3o6oUANQnXiVAZYHPExERkXrNZrORnZ1NZmYmJSW66CemmJiYal35tygAqE/UAyAiItKg2O32sEz4RDypCbg+MTyu+qsESERERESqQAFAfeLVBKwSIBEREREJnQKA+kQlQCIiIiJSTQoA6hPtBCwiIiIi1aQAoD7RMqAiIiIiUk0KAOoTbQQmIiIiItWkAKA+UQmQiIiIiFSTAoD6RCVAIiIiIlJNCgDqE2UARERERKSaFADUJ+oBEBEREZFqUgBQn6gESERERESqSQFAfaIMgIiIiIhUkwKA+kQ9ACIiIiJSTQoA6hNlAERERESkmhQA1CfqARARERGRalIAUJ94lQApABARERGR0CkAqE+8AoCywOeJiIiIiASgAKA+8Zz0qwRIRERERKpAAUB9olWARERERKSaFADUJwoARERERKSaFADUJ1oFSERERESqSQFAfWEYYGgfABERERGpHgUA9YXh8H7sKDGDAhERERGRECgAqC/8XfH3DQpERERERCqhAKC+8BcAqA9AREREREKkAKC+8BcAaDdgEREREQmRAoD6wt/Ov2oEFhEREZEQKQCoL/yWACkAEBEREZHQKACoL6wAICra/PE8JiIiIiISJAUA9YXfAEA9ACIiIiISGgUA9YVXABBj3tcqQCIiIiISoogGAD/88AOjRo2iefPm2Gw2PvroowrP/+CDDxg6dChNmzYlNTWV/v3789VXX9XOYCPNagKOsps/nsdERERERIIU0QCgoKCAXr168eSTTwZ1/g8//MDQoUOZMWMGCxYs4Pjjj2fUqFEsWrSohkdaB3hmAOzODIBKgEREREQkRNGR/PCRI0cycuTIoM+fOnWq1+N77rmHjz/+mE8//ZQ+ffqEeXR1jCsDEA02KwOgJmARERERCU1EA4Dqcjgc7Nu3j4yMjIDnFBUVUVRU5Hqcn59fG0MLP2uyb7O7m4C1DKiIiIiIhKheNwE/+OCD7N+/n3POOSfgOVOmTCEtLc31k5OTU4sjDCPPDIBdqwCJiIiISNXU2wDgjTfe4Pbbb+edd94hMzMz4HkTJ04kLy/P9bNx48ZaHGUYuXoA7NoHQERERESqrF6WAL311ltcfvnlvPvuuwwZMqTCc+Pi4oiLi6ulkdUgLQMqIiIiImFQ7zIAb775JmPHjuXNN9/k5JNPjvRwao/XKkBWBkDLgIqIiIhIaCKaAdi/fz+rVq1yPV67di2LFy8mIyODVq1aMXHiRDZv3swrr7wCmGU/Y8aM4dFHH6Vfv35s27YNgISEBNLS0iLyHWqNdgIWERERkTCIaAbgt99+o0+fPq4lPCdMmECfPn2YNGkSAFu3bmXDhg2u85977jlKS0u55ppryM7Odv1cd911ERl/rfLaCEwlQCIiIiJSNRHNAAwePBjDMAI+P336dK/Hs2fPrtkB1WV+MwBqAhYRERGR0NS7HoAGy28PgAIAEREREQmNAoD6QqsAiYiIiEgYKACoL7x6AJQBEBEREZGqUQBQXxjaCVhEREREqk8BQH3hdydg7QMgIiIiIqFRAFBfqAdARERERMJAAUB94ZkBsDsDAJUAiYiIiEiIFADUFw6PHoAou/OYmoBFREREJDQKAOoLvyVACgBEREREJDQKAOoL7QQsIiIiImGgAKC+UA+AiIiIiISBAoD6wqsHwJkBUAmQiIiIiIRIAUB9EUoJ0J418MUtkLepdsYmIiIiIvWGAoD6wjMAqKwE6NcX4JenYdFrtTM2EREREak3FADUF6EsA1q83/tWRERERMRJAUB94dkEXNkyoKXFzufVJCwiIiIi3hQA1BdWBsAWxCpAZcXetyIiIiIiTgoA6otQmoDLipy3CgBERERExJsCgPrCXwAQqMTHOl6qAEBEREREvCkAqC+8egCsDECZ/3NVAiQiIiIiASgAqC88VwGqrAdATcAiIiIiEoACgPrCqwTIWgVITcAiIiIiEhoFAPWFVwBg7QMQqASoyPtWRERERMRJAUB94dkDUOkyoCXetyIiIiIiTgoA6guvnYArWQa0VMuAioiIiIh/CgDqC8MzAKisB8DKACgAEBERERFvCgDqC88eAHuQG4FpHwARERER8aEAoL7wuw9AoABAqwCJiIiIiH8KAOoLVw+AvfISIO0DICIiIiIBKACoL7yWAdVOwCIiIiJSNQoA6gu/PQB+rvAbhvu49gEQERERER8KAOqLYHcC9rzqrxIgEREREfGhAKC+CLYJ2CsAUAmQiIiIiHhTAFBfeG4EVtEyoJ5LfzpKweGo+bGJiIiISL2hAKC+8NsEXEkGAPz3CYiIiIhIg6UAoL4IugfAp/G3VI3AIiIiIuKmAKC+8CoBcgYARpm56o8n36BAjcAiIiIi4kEBQH3htRGY3eO4TxmQ7xV/NQKLiIiIiAcFAPWFvxIg8HPFv7jixyIiIiLSoCkAqC+sAMDmsQyo53GLAgARERERqYACgPrCayfgmPLHLQoARERERKQCCgDqi3I9ADbzsUqARERERCQECgDqC88MgOdtuSZg3wBAqwCJiIiIiJsCgPrCNwCwyoB8N/ryveKvfQBERERExIMCgPoiYAagzPs8lQCJiIiISAUUANQHDgfg3PDLNwCotAdAJUAiIiIi4qYAoD7wrPO3NgELVAKkjcBEREREpAIKAOoDw6PMp7Im4MoyAiIiIiLSoCkAqA+8MgC+JUC+AYAyACIiIiISmAKA+qCiAEAbgYmIiIhICBQA1AeeK/1U2gOgJmARERERCSyiAcAPP/zAqFGjaN68OTabjY8++qjS18yePZvDDz+cuLg4OnTowPTp02t8nBFnXeW3RYHNuQNwlDMA0E7AIiIiIhKCiAYABQUF9OrViyeffDKo89euXcvJJ5/M8ccfz+LFi7n++uu5/PLL+eqrr2p4pBHmuwcAuDMB5fYBqGRVIBERERFp0KIrP6XmjBw5kpEjRwZ9/jPPPEPbtm156KGHAOjatStz5szhkUceYfjw4TU1zMjzFwAE3AnYtwlYJUAiIiIi4lavegDmzZvHkCFDvI4NHz6cefPmBXxNUVER+fn5Xj/1jnWV3ysDoCZgEREREQldvQoAtm3bRlZWltexrKws8vPzOXDggN/XTJkyhbS0NNdPTk5ObQw1vFwZALv7WKCdgF1NwM5eAQUAIiIiIuKhXgUAVTFx4kTy8vJcPxs3boz0kEJXYQlQgAxAbJL3YxERERERItwDEKpmzZqxfft2r2Pbt28nNTWVhIQEv6+Ji4sjLi6uNoZXc/w2AVdSAhSbDMX7FQCIiIiIiJd6lQHo378/s2bN8jo2c+ZM+vfvH6ER1RK/PQCVLAMal+z9WERERESECAcA+/fvZ/HixSxevBgwl/lcvHgxGzZsAMzynYsvvth1/r/+9S/WrFnDTTfdxLJly3jqqad45513GD9+fCSGX3tcAYBnD4C1DKhPBsBa9jPWCgC0CpCIiIiIuEU0APjtt9/o06cPffr0AWDChAn06dOHSZMmAbB161ZXMADQtm1bPv/8c2bOnEmvXr146KGHeP755w/tJUAhxB4A54TfCgC0D4CIiIiIeIhoD8DgwYMxDCPg8/52+R08eDCLFi2qwVHVQX57AAKVAFkZgCT/z4uIiIhIg1avegAaLCsAsPlZBjRQE7B6AERERETEDwUA9YG/HgB7gACg1GMVIFAAICIiIiJeFADUB1VZBjQuxfuxiIiIiAgKAOqHkHoArCZgbQQmIiIiIuUpAKgP/K4CZGUAAjUBqwRIRERERMpTAFAfuAIAf03AZd7nlmsC1ipAIiIiIuKmAKA+CGUnYFcTsLMHQPsAiIiIiIgHBQD1geEvAAhUAmQFANoHQERERETKUwBQH1TYA+CxCpCjzB0saB8AEREREfFDAUB94LcHwCoB8ggAPCf7agIWERERET8UANQHfnsA/GQAPOv9Y9UELCIiIiLlKQCIpOJCyNtc+Xl+S4CcGQDPHgDPyb6rB0BNwCIiIiLipgAgkt44Bx7tCflbKz7PbwmQ875XCZBzsh8VA9Fx7tc6HOEZr4iIiIjUewoAImnHUnOCvmdNxedVtBOww08PQHScO0MA5VcKEhEREZEGSwFAJBXtM29LCis+z18GoKISIHsM2OM8jqsRWERERERMCgAipbTIXbJTvL/ic0NtArb7ZABKFQCIiIiIiEkBQKQUeUz6i4PNAPgJALx6AKwMQKyZLbBZfQIKAERERETEpAAgUory3feDLgGqZCdgK6MQHWve2p23CgBERERExEkBQKR4lv0UF1R8rqsEyF8PgJ8mYGvibwUC2gtARERERJwUAESK1QAMQWQAKugB8CwBKvUJAFwZAO0FICIiIiImBQCR4hkAVJoBqKgEqIIMgEqARERERMSHAoBIqW4A4HcZUKsHIM77HJUAiYiIiIiTAoBIqVITsOdOwH4m9577AIB7LwBlAERERETESQFApISUAXD2ANg8AwC793PgsQ+ATwlQqXoARERERMSkACBSQmoCDrYEyLcHQCVAIiIiIuJNAUCkeGUAqrMPQICNwDxvVQIkIiIiIk4KACKlKJR9APwFANbVfc8AIFATsAIAERERETEpAIgUrybgYAMAz43A/O0E7NMEbAUCKgESEREREScFAJESUglQBRuBeZYAuZqArQyANgITEREREW8KACIllCZgw18A4G8ZUKsJOMb7VhkAEREREXFSABApvsuAGkbgcytqAsYAh8O8awUA0b4ZAPUAiIiIiIhJAUCkeAYARlnFk/SKegDA3QdQbhlQbQQmIiIiIt4UAESKZwAAFa8E5LcHIMZ93yrxKQ2wD0CpAgARERERMSkAiASHA4pDCQAqKgHyeL5cBkAlQCIiIiLiTQFAJBR77AEQnWDeVtQIHHQAYK0C5LsTsAIAERERETEpAIgEKwCIiobExs5jwWQAPHoAoqLAFuX9vFUKFO0MALQPgIiIiIj4UAAQCVb9f1wKxCaa9yvMAFg9AHbv475LgQYsAdI+ACIiIiJiUgAQCZ4BQIwzAAi1BwDcJT7WKkClKgESERERkYopAIiEonzzNi4VYpPM+1UJAOJSzNuDeeatlQkolwFwHnc4YOZt8PeHVR+7iIiIiNRrCgAiwV8GINQmYIDEJuZt4W7z1ir1CbQR2LY/YO5U+HpSlYcuIiIiIvWbAoBIsAKA2GSPDEBFAYBzp1/fHoDEDPO2wAoArAyAs/THCgCsfQAKdjo/y2cJUhERERFpMKIrP0XCzisDYC0DWoUSIGsFISsD4OoBCJABKNzj/KyDVRu3iIiIiNR7CgAiwTMAsCbpFWYAAgQASb4lQJVsBHbAGQCUHgTDAJutauMXERERkXpLJUCR4G8Z0Ko0AbsyALvMW2uiH+27CpCzNMgKFDC0MpCIiIhIA1XtAKCsrIzFixezd+/ecIynYXAFAKkQ4+wBqLAEyNoHoJISIN8MgGsjMJ8SIDCzACIiIiLS4IQcAFx//fW88MILgDn5P+644zj88MPJyclh9uzZ4R7foclvBiCYEiDfJmBnAGA1AZf6lgBZGQBnb8ABjwBAfQAiIiIiDVLIAcB7771Hr169APj0009Zu3Yty5YtY/z48fz3v/8N+wAPSV4BgJUBCCIAsPkEAEH3AFglQJ4ZgAOhj1tERERE6r2QA4Bdu3bRrFkzAGbMmMHZZ59Np06duPTSS/nzzz/DPsBDktcqQNXYCMyzBMgwgm8CBveKQSIiIiLSoIQcAGRlZbFkyRLKysr48ssvGTp0KACFhYXY7fZKXi2A/xKgQBkAwwCjkh6AA3uck3zDfBwdKAPg0adRogyAiIiISEMU8jKgY8eO5ZxzziE7OxubzcaQIUMA+OWXX+jSpUvYB3hIKso3b+NS3ccCZQCsBmAI3ANgOGD/Dvdx3wxAqZ8eAGUARERERBqkkDMAkydP5vnnn+fKK69k7ty5xMWZK83Y7XZuueWWkAfw5JNP0qZNG+Lj4+nXrx/z58+v8PypU6fSuXNnEhISyMnJYfz48Rw8WM8aWl0ZAM+dgAMFAKXu+74ZAHsMxKWZ9/dt9TjuZyOwkoPeWQb1AIiIiIg0SFXaCOyss87yepybm8uYMWNCfp+3336bCRMm8Mwzz9CvXz+mTp3K8OHDWb58OZmZmeXOf+ONN7jlllt48cUXGTBgACtWrOCSSy7BZrPx8MMPV+WrREbxfvM2LsU9wQ9YAuSZAfDz15XUGIryIH+L84DNnSnw3AfA8+o/aBUgERERkQYq5AzAfffdx9tvv+16fM4559C4cWNatmzJH3/8EdJ7Pfzww1xxxRWMHTuWbt268cwzz5CYmMiLL77o9/yffvqJgQMHcsEFF9CmTRuGDRvG+eefX2nWoE4pLXI35calQIyfZUB3r4b3LoNtf1acAQB3GZCVAYiOc+/w65kBKPQJALQPgIiIiEiDFHIA8Mwzz5CTkwPAzJkzmTlzJl988QUjRozgxhtvDPp9iouLWbBggauHACAqKoohQ4Ywb948v68ZMGAACxYscE3416xZw4wZMzjppJMCfk5RURH5+flePxFllf8AxHqUAJUUmA2/AItfh7/eg7mP+fQAVBAAWBkAa9IP7o3AHCUeuwA7KQAQERERaZBCLgHatm2bKwD47LPPOOeccxg2bBht2rShX79+Qb/Prl27KCsrIysry+t4VlYWy5Yt8/uaCy64gF27djFo0CAMw6C0tJR//etf/N///V/Az5kyZQq333570OOqcVYDcGyyWapjZQAMhzkpj0lwT+a3LPLIANggyk+8lujcC8DKAHgGAFYJEMD+7d6vUwAgIiIi0iCFnAFo1KgRGzduBODLL790XcE3DIOysrKKXlpts2fP5p577uGpp55i4cKFfPDBB3z++efceeedAV8zceJE8vLyXD/W2CPGcwlQcGcAwF0GtG+bebt7FRzINe/7u/oPkJjh/RqvAMDjvvW8RT0AIiIiIg1SyBmAM844gwsuuICOHTuye/duRo4cCcCiRYvo0KFD0O/TpEkT7HY727d7X5nevn27a6MxX7feeisXXXQRl19+OQA9evSgoKCAK6+8kv/+979E+blCHhcX51qpqE7wDQCi7OaqPWVFZhkQjT2u1huwZaHzvAB/VdZuwK4SII+r/p4BQLkMgFYBEhEREWmIQs4APPLII4wbN45u3boxc+ZMkpOTAdi6dStXX3110O8TGxtL3759mTVrluuYw+Fg1qxZ9O/f3+9rCgsLy03yrc3HDKt+vq7zDQDAYylQnwwAwKZfzduAGQCfHoBoj2Anyg42e/n3BO0DICIiItJAhZwBiImJ8dvsO378+JA/fMKECYwZM4YjjjiCo446iqlTp1JQUMDYsWMBuPjii2nRogVTpkwBYNSoUTz88MP06dOHfv36sWrVKm699VZGjRpVf3YhDhQAHNhjZgBKi7yX7Nz0m3nrr/4f3AGAdUXf86q/9bj0gJ8SIGUARERERBqiKu0DsHr1aqZOncrSpUsB6NatG9dffz3t2rUL6X3OPfdcdu7cyaRJk9i2bRu9e/fmyy+/dDUGb9iwweuK///+9z9sNhv/+9//2Lx5M02bNmXUqFHcfffdVfkakeHaBdgjAPBcCtS3VGf7X+ZtwAxAE+/HAQMAZ5NwUlMo2KkMgIiIiEgDFXIA8NVXX3HqqafSu3dvBg4cCMDcuXPp1q0bn376KUOHDg3p/caNG8e4ceP8Pjd79mzvwUZHc9ttt3HbbbeFOuy6o8i5CVisZwbACgAKYJ8zAIhLNYMFaxWgypqALeUCAGdPgBVYpDZ3BgDKAIiIiIg0RCEHALfccgvjx4/n3nvvLXf85ptvDjkAaHD8lQDFeOwFsL/EvN+kk1m2k7/JfFxZE7Al2k8GANy7D6c0h62/axUgERERkQYq5CbgpUuXctlll5U7fumll7JkyZKwDOqQ5rcHwKMEyKrVT2kGzXu7z4kK0OMQl+odHPhmAHwDgtTm5q32ARARERFpkEIOAJo2bcrixYvLHV+8eDGZmZnhGNOhraJVgEo8egCSs3wCgAAZAJvN3QgM5pKinnwDgtRs81YBgIiIiEiDFHIJ0BVXXMGVV17JmjVrGDBgAGD2ANx3331MmDAh7AM85PhtAraWAS1wN+umNIPsPu5zAgUAYDYCW4GD5z4AUD4ASFEGQERERKQhCzkAuPXWW0lJSeGhhx5i4sSJADRv3pzJkydz7bXXhn2AhxxXBiDVfcxfE3C5EqCKAgCPRuBo3wyAR0AQm+IOPNQDICIiItIghRwA2Gw2xo8fz/jx49m3z5zMpqSkVPIqcfHbBOwMAEoKYb+zByC5mdngm5YDeRsD9wCAdyNwuQyAR0CQ2AhiEsz7WgVIREREpEEKuQfAU0pKiib/oapwJ2DPDIC5FwLZvczbCjMAnj0AAZYBBUjIcGcItA+AiIiISIMUVAagT58+2Gy2oN5w4cKF1RrQIc9ajtNfBqBon7lGP5gZADDLgJZ9FkIAUEETcGIGRDszANoJWERERKRBCioAGD16dA0PowGpKAOwdx1ggC3KXdbT7gT49i5Ibx34PRMrKgHyCACUARARERFp8IIKAOr1zrt1iaPMfwbACgD2rDZvk7PcNf8t+8K/F0Jay8DvW1ETcLRPBkA9ACIiIiINWshNwFIN1uQf/JcAHcwzb5OzvF/XuH3F7+vVBBxgJ2BwZgDizftaBUhERESkQapWE7CEygZHXQm9L/S+Um8tA2pJaRba21bYBBzrfZ4VAJQVgWGE9jm1ZcF0mH4KFOyO9EhEREREDjnKANSm+FQ46YHyx62NwCy+GYDKBLsKUGIGxMS7H5cedJcE1RUOh9nzULAT1s+FbqdGekQiIiIihxRlAOqCWJ8AoDoZgOiKSoAauVcBgrq5EtCWRe6VkOri+ERERETqOQUAdUF1S4Ci48xdfqGSEqAMsEeDzdlgHOmVgJZ+Chvnex9b+ZX7fklh7Y5HREREpAEIuQRowoQJfo/bbDbi4+Pp0KEDp512GhkZGX7PEz/KlQCFGAAAJDWG4n0V7wOQ4Pw7iUkwG5IjuRJQ/hZ4+yJzLOP/dq9ktOJL9zkKAERERETCLuQAYNGiRSxcuJCysjI6d+4MwIoVK7Db7XTp0oWnnnqKG264gTlz5tCtW7ewD/iQVC4DEGIPAJhlQHvXVVwCZE2yo+OcAUAEMwCFuwHDnOQveg0GXgv5W2Hr7+5zFACIiIiIhF3IJUCnnXYaQ4YMYcuWLSxYsIAFCxawadMmhg4dyvnnn8/mzZs59thjGT9+fE2M99AU4xMAVCUDcOQV0OYY88eT1QQcFQOxyeb9UHYDLiuBVd+4NzALF8/P/nWauUfCyq8DnyMiIiIiYRFyAPDAAw9w5513kpqa6jqWlpbG5MmTuf/++0lMTGTSpEksWLAgrAM9pEXZ3ctzYoPkzNDfo/f5cMln3nsCgHu50cQMsNnM+9ZKQKVB7AXw57vw2pkw+97Qx1QRz8l97gZY8ZX5A+4yJgUAIiIiImEXcgCQl5fHjh07yh3fuXMn+fn5AKSnp1NcXFz90TUk1kpASU28l+6sLqsEKMFzt+AQAoA9a83b3A3hG5O/z573BKyZbd7vONS8VQmQiIiISNhVqQTo0ksv5cMPP2TTpk1s2rSJDz/8kMsuu4zRo0cDMH/+fDp16hTusR7arEbgqpT/VMQKJhL9BADB7AZs7U5cXBDecVlX99NbgS3KXPO/pABSsiGnn/c5IiIiIhI2ITcBP/vss4wfP57zzjuP0tJS802ioxkzZgyPPPIIAF26dOH5558P70gPdVYjcFUagCvStIt5m93LfSyUDEBNBQDWZzfuAM16wrLPzMcdh7n/LJQBEBEREQm7kAOA5ORkpk2bxiOPPMKaNWsAaNeuHcnJya5zevfuHbYBNhhWI3C4MwCtB8CEZd67C4fSA2AFACXhzgA4J/fRCdDvn+4AoNNwOJjvPEcZABEREZFwq/JGYMnJyWRkZJCRkeE1+ZcqsnoAQt0ELBip2RDl8VftKgEKYoJdYyVAzuAjJt5cuajzSZDdG9odb+4NAFCsDICIiIhIuIUcADgcDu644w7S0tJo3bo1rVu3Jj09nTvvvBOHw1ETY2wY4pyrKqVm1/xnuUqAgtgHoMZKgJzBR0yCuTrR+W/CP783y39iVAIkIiIiUlNCLgH673//ywsvvMC9997LwIEDAZgzZw6TJ0/m4MGD3H333WEfZIMwYBzEp0K302v+s1wlQHUgA2DtSeApJoR9CkREREQkJCEHAC+//DLPP/88p556qutYz549adGiBVdffbUCgKpqPcD8qQ1VzQA4HN6lRNXhygDEl3/OlQFQACAiIiISbiHP5vbs2UOXLl3KHe/SpQt79uwJy6CkhgXbA1BWCsXWDsBGcBmDYFmfXWEGQCVAIiIiIuEWcgDQq1cvnnjiiXLHn3jiCXr16uXnFVLnWBPsylYBKsr3fhzOplzPJmBfKgESERERqTEhlwDdf//9nHzyyXzzzTf0798fgHnz5rFx40ZmzJgR9gFKDYiOM28rCwCs8h9L8X6gaXjG4CoBSiz/nGcTsGGYTcIiIiIiEhYhZwCOO+44VqxYwemnn05ubi65ubmcccYZLF++nGOOOaYmxijhZpXdVLYTcLkMQBgbgV1NwH4yANZGYBjB9SmIiIiISNBCzgAANG/evFyz76ZNm7jyyit57rnnwjIwqUFVzgCEMQDwXAbUl2dfQEmh/zIhEREREamSMC3pArt37+aFF14I19tJTQq2B8BvCVCYuJqA/Uzu7dFgj3Wep0ZgERERkXAKWwAg9UiwqwD5BgDhnIyXVNADAGoEFhEREakhCgAi4GBJWblj7y/YxMhHf2TVjn1+XhFmwe4DUKMlQBWsAgTaDVhERESkhigAqEXrdxcw9qX5nP3MPAzDcB0vcxg88NVylm7N58GvVtT8QILdCbhWSoD89ACAMgAiIiIiNSToJuAzzjijwudzc3OrO5ZDXmp8DPPW7OZgiYNf1+3lqLYZAPy4cifb8s0r4l8t2caanftp1zS55gZiTbqVARARERFpcILOAKSlpVX407p1ay6++OKaHGu91ygpltP7tATgpblrXcffXbAJgCibuez9tB/X+n192Lh6ACK4CpAyACIiIiIREXQG4KWXXqrJcTQYYwe24c35G/jq721s3FNISnw0M//eDsBtow7jtk/+5v2Fmxg/tCOZKTW0/GWoJUDxaeb9mggA/C0DCh4ZAAUAIiIiIuGkHoBa1ikrhWM6NsFhwKs/r+eT37dQXOaga3YqF/dvzeGt0ikudTB97rqaG0SoTcApzc3bcAUAjjJwlJj3KwsAwhl0iIiIiIgCgEgYO7ANAG/O38Abv2wA4Oy+LbHZbPzzuPaAGRzsLyqtmQGEugxoapgDAM/P9bcPAKgESERERKSGKACIgMGdMmnbJIl9B0tZtm0fMXYbo/u0AGBo1yzaNTWfe/vXjTUzAGty7Sgxr8YHUlMBgOcGZAEDADUBi4iIiNQEBQAREBVl45IBbVyPT+ySRUZSbLnnPv9jS80MIDrOfb+i3YBdAYAZnIRtGVBrUm+Pg6gAv4LKAIiIiIjUCAUAEXJm35akxJs92Gf1ben13NBuWQAs2pjLrv2V1OlXhedV90ArATnKoCjfvB/2EiBrCdAA9f+ezykDICIiIhJWCgAiJDkumucvPoI7R3fnxK6ZXs9lpyVwWPNUDAO+W7Yj/B8eZYeoGPN+oAyANfkHdwAQrsl4aSUrAIFWARIRERGpIQoAIqhfu8ZcdHRrbDZbuedO7GpmAWYtrYEAANyT70ABgFX+E50ACY3M+2ErAXJ+ZqD6f8/xKQAQERERCSsFAHXUEGdW4MeVOykqraBRt6pcS4FWEgDEp0Fsknk/bCVAzkxCRRkA6zNVAiQiIiISVgoA6qjuzdPISo2joLiMn9fsCf8HVLYbcE0GAKXqARARERGJFAUAdVRUlI0TuphlQN8s2R7+D6hsN2CvACDZee7BipcNDZZV1hMdTACgEiARERGRcFIAUIdZZUCzlm7HMIzwvrm1FGgoJUAQniyAKwNQUQ9ALewDsOFnKNhVc+8vIiIiUgcpAKjDBnZoQnxMFFvyDrJ0677wvrl19T2YEiB7LNjs5uNwBACuDEAEm4C3LIYXh8OH/6qZ9xcRERGpoyIeADz55JO0adOG+Ph4+vXrx/z58ys8Pzc3l2uuuYbs7Gzi4uLo1KkTM2bMqKXR1q74GDuDOjQBzCxAWMWE0ARss7nLgMIZAAS1DGgNZQD2rDZv82pot2URERGROiqiAcDbb7/NhAkTuO2221i4cCG9evVi+PDh7Njhf+nL4uJihg4dyrp163jvvfdYvnw506ZNo0WLFrU88trjWg403PsBhLIKEHg0AodhKdCg9gGo4QzAgVzn+6vJWERERBqW6Eh++MMPP8wVV1zB2LFjAXjmmWf4/PPPefHFF7nlllvKnf/iiy+yZ88efvrpJ2JizI2s2rRpU5tDrnXHdWoKwB+bcsktLCY9MTY8b+xaBSiIJmAI70pArn0AIrgR2MFc77GIiIiINBARywAUFxezYMEChgwZ4h5MVBRDhgxh3rx5fl/zySef0L9/f6655hqysrLo3r0799xzD2VlgVemKSoqIj8/3+unPmmenkCHzGQcBvy0enf43ti1EViR/+cDBQDhuGJeF5qAXRkArTIkIiIiDUvEAoBdu3ZRVlZGVlaW1/GsrCy2bdvm9zVr1qzhvffeo6ysjBkzZnDrrbfy0EMPcddddwX8nClTppCWlub6ycnJCev3qA3HdjSzAD+u3Bm+N3WtAhQoA+AMlGqiBCiUZUAdpVBaXP3P9OXKAKgESERERBqWiDcBh8LhcJCZmclzzz1H3759Offcc/nvf//LM888E/A1EydOJC8vz/WzcWP9a/o8ppPZCPzDil3hWw40OtgMQKp5G9YSIKsHIIgMANTMJN3KABhlUFYS/vcXERERqaMi1gPQpEkT7HY727d7r26zfft2mjVr5vc12dnZxMTEYLfbXce6du3Ktm3bKC4uJja2fH18XFwccXFx4R18LevXNoNYexSbcw+wZlcB7ZsmV/9NrQxApT0A6eZtOAMAVxNwYuBz7DHm0qNGmTnGhPTqf64nKwMAZoBhTwvv+4uIiIjUURHLAMTGxtK3b19mzZrlOuZwOJg1axb9+/f3+5qBAweyatUqHA6H69iKFSvIzs72O/k/VCTGRnNk20YA/LgiTGVArh6ACKwC5GoCriADYLPVbB+AlQEA9QGIiIhIgxLREqAJEyYwbdo0Xn75ZZYuXcpVV11FQUGBa1Wgiy++mIkTJ7rOv+qqq9izZw/XXXcdK1as4PPPP+eee+7hmmuuidRXqDXHOPsAflgZpp1rK1oG1OGAIt8egDDuAxDMMqCez9fEBN0rA6AAQERERBqOiC4Deu6557Jz504mTZrEtm3b6N27N19++aWrMXjDhg1ERbljlJycHL766ivGjx9Pz549adGiBddddx0333xzpL5CrTmmYxPu/QLmrd5NUWkZcdH2yl9UEdcyoH4CgKJ8wNlrEOfsAbCuxhcHcTU+dwOsmQ09z4NoP5mZYDIAULMBwIE8j/EoABAREZGGI6IBAMC4ceMYN26c3+dmz55d7lj//v35+eefa3hUdU/XZqk0SY5j1/4iFq7PpX/7xtV7w4p2ArbKf6Lj3eeFUgL05URY9pmZNeh+RvnnrZKeinoAPJ8PdwmQowyKFACIiIhIwxTxAECCExVl45iOTfhw0WY+WrSZBev38PHiLcTFRPHGFUeTGh8T2htWVALkW/8PoZUA7Vhi3uZu8P98MPsAAMTW0GZgB/O8HwdaClVERETkEKQAoB6xAoC3f/NeyvTFOWu5fkin0N6sohIgvwFAkKsAlZW4J/6FAfoVgtkJGDwyAGHoO/B0YK/PeBQAiIiISMNRr/YBaOiO69SUlPhobDYY0L4xlwxoA8ALP64lrzDEtewrWgWoOgFA3kZz8y6AggA7F5cGsQ+A5xjDngHI9X6szcBERESkAVEGoB5pnBzHrBuOAwMyU+NxOAx+XrObZdv28fycNdwwrHPwb+baCdgZAOzfaV65b9m34hKgyq7G717jvl9pBiBCAYDnEqCe4xERERFpAJQBqGcyU+LJTDUnzlFRNq4f0hGAl+auY29BcfBvFO0xuXaUwSunwfMnwHdT3FfIvQIAaxWgSgKAPR4BQEGgACDCTcDKAIiIiEgDpgCgnhvWrRndslPZX1TKtB/XVP4CiysDUARLPoIdf5uPv78X5j1l3q9KCdCeSjIAZSXm7r4QuRKgchkA9QCIiIhIw6EAoJ6LirIxfqjZADz9p3Vs3BPk1WzX5LoAvn/AvN/yKPM2f5N563cVoEqWAd2z2n3fXw+A52Q76CbgGs4AaBUgERERaUAUABwChnTNpFfLNAqLyzjtybn8tCqI3YKt+vsDe2HnUohLgwvfhVOmAjbzufh09/lVyQCUFJS/uu5qOra5sxCBKAMgIiIiEnYKAA4BNpuNp/7Rl+4tUtlTUMw/XviF535YjWEYgV/k24B79FWQkA5HjIWzXoT2J0K3U93PWwGAoxRKA/QalJXC3vXex3z7AKzJdnQ82GwVf7Fa6wFQACAiIiINhwKAQ0SL9ATe+9cAzjy8JQ4D7pmxjHu/WBb4BZ7193GpcPS/3I+7nwEXfQAZ7TzOT3LfD1QGlLcRHCVgj4PkLPOYbx+ANdmOqaT8B9wBQHGYAwArA5DYxHtMIiIiIg2AAoBDSHyMnQfP7slto7oB8OwPa3j15/X+T/bMAPT7FyQ0qvjN7dHmxB4ClwFZ5T8ZbSEp07zv2wdQGkoAUMP7AKRk18z7i4iIiNRhCgAOMTabjbED23KDszH4to//4rtlO8qfGB0Pmd0gLccs/wlGZX0ArgCgPSQ1Nu+XywAEuQcA1FwJkJUBSLUCAC0DKiIiIg2HAoBD1LgTOnB2X7Mc6Jo3FvLHplzvE2w2+OcPcPXPkJgR3Ju6VgIKIgNgldf49gDUxQyAv92QRURERA5RCgAOUTabjXvO6MGgDk0oLC7jnGfn8fHizd4n2WMgLjn4N3VlAAL0ALgCgHaQWEkGIJIBwAHnTscpygCIiIhIw6MA4BAWY4/iqX8czrGdmnKwxMF1by1m8id/U1zqqNobWgFAoAnzbuceAI3bQ1KADID12kiVADnKoMgZAKSqB0BEREQaHgUAh7jU+BheuuRIrj2hA2BuFjbske95bNbK4DcNs8Raq/L4KQFylMHedeZ9rwyAbxNwCBkA6/PCOUE/mOe+78oAqARIREREGg4FAA2APcrGhGGdef7iI0iNj2bd7kIenrmCY+7/juveWkSZo4L9AjxVtBtw3ibnEqCxkNqiggyAxz4AlamJDIBV/x+TZC5/Gu73FxEREanjFAA0IEO6ZfHTxBN56OxeDOzQGJsNPl68hR9W7gzuDSpaBWiPs/ynUVuIsrubgH17AELJALh6AMI4QbdWAEpIr7keAxEREZE6TAFAA5McF82ZfVvy+uVHM3ZAWwBe/3lDcC+uMADwaAAGjwyATwlQVTYCKys2dxkOBysDEJ/uHkNpBAKA/C3wzsWwbm7tf7aIiIg0aAoAGrAL+rUC4Ntl29mcG8QkuKJlQPesNW+tAMDKABTlQWmx+zxXCVAIGQAI3yS9rmQAln4GSz6G+c/W/meLiIhIg6YAoAHrkJlM/3aNcRjw9vwgsgAxFTQBu1YAcgYACY3A5vz18mwEdpUABdED4NknEK5JupUBSGjk/j6lB8FRxZWRqqoo33m7r3Y/V0RERBo8BQAN3IVHm1mAt37dSElZJZPgQCVAhgE7l5r3rQxAVBQkODcY8+wDCCUDYLOFvxHYygDEp3sHGLW9GZj1fQJtqiYiIiJSQxQANHDDujWjSXIcO/YV8c2S7RWfHGgjsL8/MJcAjUmC7N7u4/5WAgolAwDhL9NxZQDSvUuMarsMqFgBgIiIiESGAoAGLjY6inOPbAnA679UUgbkrweg5CB8M9m8P+h6SMxwP+daCcijBMi68m1d2a9MTCWbj4XKMwMQZQd7XHjfP1glzj9DBQAiIiJSyxQACOcd2QqbDeas2sWqHX7W+Lf42wl4/nOQu8HcVKv/Nd7nJzk3A/PMAFibbgWzDwDUbAYA3JmI2i4BUgZAREREIkQBgJCTkciQrlkAPPndqsAnxqWYt1sWwdzHYP8O+OFB89gJ/3MHCBZ/ewGUhrAMqOd5xeHKAOw1b+PTne9fA5uNBUM9ACIiIhIhCgAEgGtP6AjAx4s3s3pngCxAzlHQ5hjzavnMW+HxvuYyn1k9oNf55c/31wMQcgaghpqAXRmACC0Fak38SwpqfwUiERERadAUAAgAPVqmMbRbFg4DHpu10v9JsUlw8Sdw6hPmFXRrKcthd5r19L7CmQEIdwmQlQGIjlAA4BnQ1Hb2QURERBo0BQDicv0QMwvwye9bWLUjwPr0UVFw+EUw7jc4+mo48TZof7z/c109AJ5NwFUNAMKVAcgzbyOeAfD4PioDEhERkVqkAEBcDmuexvDDsjAMeHRWBb0AAMlNYcQUOGZC4HP8ZQCqXAIUhgm6o8wsWQKPHoAwBxjBKvGY9PsuqyoiIiJSgxQAiJfrh3QC4LM/tvDbuj3VezO/+wBUtQk4DFfJD+a57/tmACK1ChAoAyAiIiK1SgGAeOmancrJPbIxDDh/2s888/1qHA6jam9mZQAO7DWvvkPoGYDkTPN2fyWblAXDqv+PSQJ7jPN+HegBUAAgIiIitSg60gOQuueeM3rgMAy++Gsb936xjB9X7mRM/za0a5pEq4wkYqODjBtdm4IZULjHzAi4MgBBbgSWZm5SRt7GkL6DX74rAHmOozZLgAzDe9KvAEBERERqkQIAKSctIYanLjycd37byORPljB31W7mrjIbee1RNi4f1JaJJ3Wt/I3sMWat/cFcsw8gPhUM55KXMUFmANJyzNu8TSF/j3J8VwACdyaipBZLgEoPAh5ZFfUAiIiISC1SCZD4ZbPZOPfIVnx27SDOPLwl3VukkhRrp8xh8OwPa/hwUZATcs8+AM8ym+ggewCsACB3o3nlvDrqSgbAd1MzLQMqIiIitUgZAKlQ+6bJPHROLwAMw+DRWSuZ+s1K/vfhXxzeqhGtGydV/AaJTWD3KjMDYDXa2qLcNfiVsUqAiveZTbyek/dQ+csARKIHoMSn5EclQCIiIlKLlAGQoNlsNv59QkeOapNBQXEZ1761mJKySnax9ZcBiE4Amy24D41NhETnfgLV7QModO5HkNDIfcwqRSqtxQDANwOgEiARERGpRQoAJCT2KBuPnNeb1Phoft+Yy31fLMOoqDTHmrwX7nZnAIJdAtQSrj6APevM20at3cfCuc9AsJQBEBERkQhSACAha5GewH1n9gTg+TlrGTv9V7bnB2iiTW9l3i58FfasMe+HHAA4y4Byq5kB2LPavM1o5z4WiRKgchkABQAiIiJSexQASJWM7JHN7aceRmx0FLOX72TYIz/w6s/rWbB+D5v2FlJc6iwNOuJSaNwR8jfBR1ebx4LdA8BiBRHVLQHa7QwAGrd3H4uORA+ASoBEREQkctQELFU2ZkAbBrRvzPh3FvPX5nxu/egv13PJcdHccdphnHF4S7jwXXh+iNkIDMEvAWoJx14AB/OhYId5P8MjAIhIBkAlQCIiIhI5ygBItXTMSuHDqwdy04jOHNG6ES0bJRBrj2J/USkT3vmdR2auwGjUBi54x321PdhNwCzh6AGwyo+Smpr7EVgisQxouQyAAgARERGpPcoASLXF2KO4enAHrh7cAQCHw+CBr5fz9OzVPDprJRv2FHLvmb2JO+sFeP9yaHV0aB+Q7rEXQFW56v/bex93rQJUixuBuXoAbIChAEBERERqlQIACbuoKBs3j+hC64xE/vvRX3y4aDPJcdHcOfpkuGltFUqAnAHA/m1QWgTRcaEParczA+DZAAwRygA4J/yJGebqSOoBEBERkVqkEiCpMecd1Ypn/9EXgNd+Wc+fm/JCn/yDuZSoVT6Uv7lqg7EyAI19A4AIrgKUlOn9WERERKQWKACQGjWkWxajezfHMOB/H/+Fw1HBngGB2GwejcBV7APYHaAEyFqRqKQWS4CsbIO1SZpKgERERKQWKQCQGvd/J3UlOc7cOOzt36pYx1/dPoA9fpYABe8SoIo2NAsna8KfbGUAVAIkIiIitUcBgNS4zNR4xg/tBMB9Xy5jT0Fx6G9SnQzAgVyz1h789AA4S4CMMigrCf29q6LEtwRIGQARERGpPQoApFaM6d+aLs1SyC0s4bZP/sYI9Wp7mrUZ2IbQP9xaAjQ5C+JSvJ/z3JW4tJb6AFwZgKbmraMESqsQFImIiIhUgQIAqRXR9ijuGt0de5SNT3/fwn1fLg/tDaqTAbACAN/6fwB7LNic/wxqqxHYNwMAKgMSERGRWqMAQGrNEW0ymHJGDwCe+X41L85ZG/yLQ+kB+PwGeKSHO1jYHWAFIDAbjGt7KVBr1Z/4VLA7lzRVGZCIiIjUkjoRADz55JO0adOG+Ph4+vXrx/z584N63VtvvYXNZmP06NE1O0AJm3OOyOGmEZ0BuOOzJXy8OMhlPT0zABWVDxUXwsJXzFKh+c+Zx1ybgPkJAKD2VwKy9gGISYLYJPO+AgARERGpJREPAN5++20mTJjAbbfdxsKFC+nVqxfDhw9nx44dFb5u3bp13HjjjRxzzDG1NFIJl6uOa88lA9oAMP7txbw5P4i6/tQWgA3KiqBgZ+DzNsyDMmc9/cJXzUl9oCVALa4MQG31ADgzALGJEJvsPKYAQERERGpHxAOAhx9+mCuuuIKxY8fSrVs3nnnmGRITE3nxxRcDvqasrIwLL7yQ22+/nXbtAlzVlTrLZrMx6ZRunHtEDg4DJn7wJ49+s7LixmB7DKRkm/fzKigDWjPbff/AHvj7w8BLgFpcm4H5KQFa+yN8ci3s2xb4M0NlfU5MokcGQD0AIiIiUjsiGgAUFxezYMEChgwZ4joWFRXFkCFDmDdvXsDX3XHHHWRmZnLZZZdV+hlFRUXk5+d7/UjkRUXZuPfMHvz7hA4APPLNCia88zsL1u8NvFlYMH0Aa783b5uZvQbMeQQO7DXvByoBsnYnLvVTAjT7Xlj4Mrx1QfhKhKyr/bFJZhbA85iIiIhIDYtoALBr1y7KysrIysryOp6VlcW2bf6vuM6ZM4cXXniBadOmBfUZU6ZMIS0tzfWTk5NT7XFLeNhsNm4Y1pk7TzsMmw0+XLSZM5/+iaPumcWdny2hqLTM+wVpzr+7QCsBFe6BrX+Y90c/A1ExsMu52lBKtvtqu6+KmoCtFYQ2L4BPr63+ZmGG4RMAJAX+bBEREZEaEPESoFDs27ePiy66iGnTptGkSZOgXjNx4kTy8vJcPxs3VnEnWakxF/Vvw+uX9WNUr+akxEWza38RL8xZyys/rfc+0dUIHODvcO0PgAGZ3aBZdzhstPu5QPX/4FEC5NMDUHIA9m0x79ui4I+34afHgv1a/pUVm5uOgbMEyOoBUAmQiIiI1I7oSH54kyZNsNvtbN++3ev49u3badasWbnzV69ezbp16xg1apTrmMPhACA6Oprly5fTvr33RC8uLo64uLgaGL2E04AOTRjQoQnFpQ5emLOW+75cxotz13LJwDbE2J1xqlXD//ubcNjp0Opo7zex6v/bHmfeHnkF/Pmu87UV9IpEBwgA9joDkLhUOOF/8MVNMPM2yO4F7QZX5Wt6l/rEahUgERERqX0RzQDExsbSt29fZs2a5TrmcDiYNWsW/fv3L3d+ly5d+PPPP1m8eLHr59RTT+X4449n8eLFKu85BMRGR3HpoDY0SY5ja95BPv9jq/vJ7mdCTj84mAevnAbLv/B+sRUAWJPznKMgy9kL0Lhj4A8NlAHYu868bdQGjroSel8IGDA/uPIzv6xSn6gYs7FZAYCIiIjUsoiXAE2YMIFp06bx8ssvs3TpUq666ioKCgoYO3YsABdffDETJ04EID4+nu7du3v9pKenk5KSQvfu3YmNjY3kV5EwiYu2M3ZgGwCe+2GNe3Wg2CS46CPoONxs2H3rQljwsvnc3vWwdy3Y7NB6gHnMZoPRT0LfS+DwiwJ/YKBVgPY6NyrLaGu+V79/mY9Xf1v1JUM9lwCF0EqA/voAnj0Odq2q2meLiIiIUAcCgHPPPZcHH3yQSZMm0bt3bxYvXsyXX37pagzesGEDW7dureRd5FBzYb9WJMTYWbI1n59W73Y/EZsI571uXo03yszG3K/+a07KAVoeYe6wa8nuBaMehYRGgT/MCgB8VwHyzACAubJQakszUFjzfdW+mOcmYBBaBmD+NNi6GFZ+VbXPFhERESHCPQCWcePGMW7cOL/PzZ49u8LXTp8+PfwDkohLT4zl3CNzmP7TOp77YQ0DO3g0fdtj4LQnzVWBvr8X5j0BdmefR1Vq8wOVAO1xZgAatTVvbTboPBJ+nQbLP4fOI9znrv0BkjIhs0vFn1UuAxBkAGAYsP0v837hnorPFREREalAxDMAIoFcOrAtUTb4fsVO3vhlAzvyPa7Q22xw/EQ46yWzibesyDxuNQCHItAyoFYJkJUBAOhyknm7/EtwNqCzbg68PAqmn1z5XgGem4BB8BuB5a6HIuceFgcUAIiIiEjVKQCQOqtV40RG9jB3//2/D//kqHtmMeyR73lhzloOljiX0ux+Blz6BUZaS4yM9tDyyNA/KNq5EZjn5N3hcK8ClNHWfbz1IIhNgYId5t4AhgHfTDafK9wFy2dU/FmeewCARw9AJRmAbX+67ysDICIiItVQJ0qARAK578yetM5I5MeVu/hrSx4rtu/nzs+WMO2HNYw7oQNJcXZmLnEwZ+/9ZKcl8Al2Ql701V8GYN9WM6sQFW3W/VuiY6HjEPj7Q3OyX7ADNv3qfn7Ra2ZQEohvBiAmyJ2APQMAZQBERESkGhQASJ2WHBfNTSO6cNMI2FtQzIy/tvLkt6vYkneQ/330l9e5+TsP8P3ynQw7rPweEhXy1wNglf+k5YDd559J55PNAGDZ5+6lSLufCX+9bzYj521yb1rmy5UBCLEHwCsDsLfic0VEREQqoBIgqTcaJcVyYb/WfHvjYCaP6kabxol0aZbCv0/owKm9mgPwye9bAr6+uNTB+ws2sbeg2PsJ1ypAngHAOvPWs/zH0nGIudzoruWwcynEp8PJD0ObYwADFr8Z+Eu4MgDVKAFSBkBERESqQQGA1DvxMXYuGdiW2f85ni+vP5YbhnXmimPMnX6/Wbqd/UWlfl9335fLuOHd35nyxVLvJ/xlAPb4aQC2JDSCNgPdjweNh4R06PMP8/Hi19wNwr6qsgpQ4R7I2+j9WERERKSKFADIIaF7i1TaNUniYImDmUu2lXt+R/5BXvvZbOr9dtlO9+ZiEKAEaJ1528hPBgCgs3M1oJRsc5dggK6nmg3Ce9fB+rn+X+faByCEAGD73+ZtYmPztvRA1TciExERkQZPAYAcEmw2G6OcZUAfLy5fBvTU7NUUlZpX5XftL2LZtn3uJ6Mr6AHwlwEAc3fhgdfBOa96XM1PhB5nmvcXveb/da4MgE8JUElB4KyBVf6Tc7TZlAzKAoiIiEiVKQCQQ8apvc0A4MeVu9i9v8h1fFveQd6YvwGAZqnxznN2ul9YUQmQvx4A6zVD74Acn2VH+1xk3i752P9V/UD7AHg+58sKALJ7QkKGeb9wt/9zRURERCqhAEAOGe2bJtOjRRplDoMZf251HX969iqKSx0c2aYRVxxr9gr8uHKX+4W+y4AezHM32gbKAATSoi8kZ5llOjuWlX/edx+AmATA5v2cLysAaNYDEp0BgBqBRUREpIoUAMgh5bTe7jKg/IMlLN+2jzfnmw2044d04rhOTQD4Ze0e92ZiMdZGYIVQVuKu/09sAnEpoQ3AZoOmnc37u5aXf943A2CzeawE5Gc34NJi2OkMJJr18MgAKAAQERGRqlEAIIeUU3o2x2aD39bvpefkrxk+9QeKyxwc1TaD/u0b075pMtlp8RSXOpi/1jmJTs4yJ9ZlxTD30YqXAA1GE2cAsNNfBsBnFSCouBF45zJwlEB8mrkngTIAIiIiUk0KAOSQ0iwtnpN7ZLse26NsZKbE8X8ndcVms2Gz2Timo5kFcPUBRMfBiCnm/e/vg+VfmvdDLf+xWBmAnSvKP+daBcij9j+2gt2Atzs3O2vW08wWJDQyHwe7GVjJAWULRERExIt2ApZDzuPn9+Hu0T2Ii4kiLjoKm83m9fwxHZvyzm+bvPsAep4Lf30AK7+C398wjwVaArQyTTqZt/5KgELNAFj1/1ndzdtQMwDTT4ady+H6P92vFRERkQZNGQA55NhsNtISY4iPsZeb/AMM7NAEmw2WbdvHjvyD7Mg/yG2f/M2zqeMwPGv+q1oCZGUA9q6DkoPez/n2AEDFPQCeDcAQWg+Aowy2LDLf19pLQERERBo8ZQCkwclIiqVHizT+2JTHfz/6i3mrd7t2Dy5uMpZ/Fz1mntioDQdLysg/UEKmc/nQoCRnQVwaFOXBntWQdZj7Od9VgDzv+y4DahgeJUBVyAAU7ATDubdA7nrgmOC/g4iIiByylAGQBsnqA5i5ZDv7i0rp3iKVtIQYHtrVjw84gU0pvbh0Zhm9bv+aflNm8dOqXZW8owebDZo6y4B2+pQB+c0ABCgB2r8dDuwFW5S7sTiUDMD+7e77VmOziIiINHgKAKRBGtk9mxi7jYykWO47swefXDOIz68dRK+cRkw4eDmDdt7Mt6vyKSp1YBjw3I9rQvsAa8K+y6MRuKzUXGkIfDIAAUqAdiw1bzPauZcqDSUDsM8zAFgf3Lhry4KX4dFe5QMkERERqXEqAZIGqXuLNL7/z/GkJcSQFGf+M2jZKJF3/9mfJ79bxdKt+RzVNoMOmclc8tKvfL9iJxv3FJKTkVjJOzu5VgLymOCWeFzhDyYDYAUATbu4j4WUAdjmvp9bxwKAv94zsxJrvnf/WYmIiEitUAAgDVbz9IRyx2Kjoxg/tJPXsWM6NuHHlbt4/ZcN3DKyS7nX+NXUTwbAWgHIFmUuPer60AABwE5nAJDZzX3MygAczDObfKPsgcdQlzMA1tgO5kZ0GCIiIg2RSoBEKnFhv9YAvPPbRopKy4J7kWsp0JXmRB086v+TzD4BiysACFAClOmZAXDuA4ABB3IrHoNnBmD/NnNPgLrCGltl30FERETCTgGASCWGdM2kWWo8ewqK+fKvbZW/ACC9FUTHQ1mRuwHXtQKQTxmRqwfAIwNgGLDDuZOwZwbAHgNxqeb9yvoAPJuAAXI3BDf2mlZy0MxggNnkLCIiIrVKAYBIJaLtUZx3VA4Ar/8c5CQ6yg6NO5r3rTIgfysAeT72DADyNkHxPoiKhoz23ue7dgOuJADY5xMA1JUyIM/ARCVAIiIitU4BgEgQzjuyFfYoG/PX7WH5tn3Bvch3KVB/ewB4PvYMAKzyn8YdITrW+/xgVwKyymySMs3butII7BkAqARIRESk1ikAEAlCs7R4hnbNAuDKV39j6db8yl/kuxRooAyAv2VAXQ3AXcu/bzArARmGOwOQc5R5W1f2AtjnUUalDICIiEitUwAgEqSbR3ahRXoC63cXcvpTc/lw0Sb+2pzHE9+u5IJpP/PgVz5r2pfLADgDgHI9AM4MQOFuc+IOHg3AfgKAYDIAB3PN/gOoewGAMgAiIiIRpWVARYLUtkkSn/17ENe9vZgfVuxk/Nu/ez3/0+rddG+RxojuzcwD1vr9u1aYE3trH4AYnxKgrMPAHmc26W5dDM37VBwABJMB2L/DvI1Pc2ci6mIJkDIAIiIitU4ZAJEQNEqK5aVLjuTaEztis0FSrJ2h3bIY6Zz0/++jv8gtdO72m9EebHYoyjfLXgJlABIzoOso8/7CV8DhcGcNmlYxA2CV2SQ3g0ZtzPt768gqQJ4lQCWFUFocubGIiIg0QMoAiITIHmVjwtBOXHR0a9ISYoiNjuJgSRkrd8xh1Y793PHZEh4+pzeOqBgKk1qRvH8tpd/dS3Sq2UNQrgcA4PCLzd1x/3gXjrgMSg+YWYGMtuXPDSoD4LzKnpxpLkkKUJRnLrvp2ksgQnyXJz2Ya47zUFe4B2ISzB8REZEIUgZApIqapsQRG23+E4qPsXP/WT2x2eCDhZt58KvlDJ/6A3fsHQJA9KLpGHOmmi/0XQUIoM0x0KitufTn7CnOD+jkf6dfVwaggjX0ravsKc3MjIO1ElBd6APY57OXQkPoAziwF6b2hBeHR3okIiIiCgBEwuXwVo24bKB5xf6J71axcsd+vogZyr/KbmSfkYDNasr1lwGIioLDLzLvL/vMvPXcAMxTMPsAuDIAzqxDI3M34zqxF4C/DEBtWPIx/PFO+ePb/oTPJrj7JmrCrpVmcLf1d21+JiIiEacAQCSMbhjWmcOap5ISH811J3Zkzs0nMPrcyzmj5HbWOpyT8bSW/l/c+0KzZ8BiNRH7CqUHIMXZkGz1AUS6EdhRBgU7zfsp2eZtbWQASovh/cvhw3+Wn4DPmQq/veA/OAiXfVvd960GbxERkQhRD4BIGCXE2vn4moFE2WxERdkAGNE9m52njmDURxkcHrWS/fPaMHDPcgZ3bkqfnEau80hpBp1GwPLPzccBMwAePQCGATZb+XNcGQBnAJBeQQagrAR+eQY6jYQmHarwrUNQsAsMB9iioHEHc2JcGxmAwl1Q5mw2zt/q3QdhTc4LajAD4Fn2tP1vaD2g5j5LRESkEsoAiIRZtD3KPal3uujo1vxrWG9+NHqxcHMhj3+7ijOfnsfpT83lz0157hMPv9h9398SoODOAJQVuTcX82UFACm+JUDryp/757vw9f/g42sq/mLhYO1OnNjEI5ORW/OfW7Cr/Bgs1uS8opKq6srf4r6vDICIiESYAgCRWjLuhI78MvFEHjirJyf3zCYx1s7vm/I49ck53PrRX+QVlkCHIdBxGHQ5xbV6j2FtDmaJTYaoGPN+oEnrPp8eACsD4K8EaMti83bjL94T5ara8Ev5Rl/fcaVkQXy6eb82MgBW2RGUr/W3HtdkAOD557FjSc19joiISBAUAIjUoszUeM4+IocnLzic2TcO5rTezTEMePXn9Qx95Hu+XbkbLnwXznud3zflcdqTczl6yixW79zvfhObreI+gJID5pKf4NEE3Ma8zd1g7jPgyTUhNWDl19X7glsWwYvD4KWTzNIiX/s99idISDfv10YGoHC3xxg8mpCLC8zmXN9zws2zB2D7EveOzyIiIhGgAEAkQjJT43n0vD68cUU/2jVNYse+Ii6d/hs3vvs7t338F6OfmsvvG3PZnl/EhHd+p7TMY+Je0V4A1tXm6HhzJ2CA1BZmg3FZsXcJjGF4X5Fe8WX1vtTKb8zbPath8evln99fxzIAnvcraqquLs8MQFGed0mQiIhILVMAIBJhA9o3Yca1x3DFMW2x2eC9BZt4ed56DANG9WpOanw0v2/M5cnvVrtf5JkBKC2GP98zr+6De1KbnOVuELZHu1cf8uwD2L/D+8r3qm+rtzPv+rnu+98/AKVF3s/v82hOrs0MgFcPgEcGwDMAqNEMgBWUOTcBq0tlQAf2wp61kR6FiIjUIgUAInVAfIyd/57cjXf/2Z+Omcl0yEzmtcv68fj5fbhzdHcAHvt2JX9syjVfYK1is+1PeGkkvH8ZvHGueUV/v88SoJaMdubtzmXuYzv+dj7X3twsrHif9yQ+FGUlsHG+eT82GfI3wYKXvc9xlQBFMAPgeTXeMxg4sLd8eVQ4FBe4S7LaDDJvt/8d/s+pqldOgyeOrNl9EEREpE5RACBShxzRJoOvxx/LzPHHMqhjEwBO7dWck3tmU+YwGP/2Yr78ayu7HcnmC+Y8Apt/M+/vWGJOwH0bgC05R5m36+e5j213XonOOgw6DTPve5YB7V1nbmIVjK1/QEmBGZwMmWwe+/FBKPZYqcizCThiPQCeJUAeAYDhqJlgxAo4YpIgp595v66sBGQYsGMZOEpgz5pIj0ZERGqJAgCROsZms2HzWNvfZrNx12ndyUyJY/XOAv712kLeWVLgen5zQmd2ZA8GYNvs51izziwVKkvK9H7jVv3N2w0eAcAOzwBgpHl/+RfmxHDDL/BUf3j22MCr+nhaP8f5OQPg8DHmKkb7t8Ovz7vP8WwCtjIAtbEzbsASIJ9diWtiJSDPTdmynHs77KgjGYCSA+ZyslCzJVAiIlKnKAAQqQcaJcXy8qVHcWqv5vRplc6S+D4UGHG8VnoiJ+ydyFXrjgMgdfWnLPtrIQCPzd/H+c/9zOu/rDeXEs05CqKiIW+ju1/AKkXJ7AbtBoM91lwq9K/34Y2zzX0GSgph8RuVD3L9T+Zt6wEQHQvH3Ww+njvV7AUwDPfVd88MQG2XAB3Mdfcm+AYANdEIbK0AlJLt3txt5wooKw3/Z4XK888+HEvAiohIvaCdgEXqia7ZqTx2fh/no4HsOzCOrLW5nLtyJz+vbsT6fS1pzSaG282SoC1lacxbs5t5a3aTnhDLyT2zIbu3WTK0/idzZSCrHyDrMIhLhrbHwqpvzJ4CgMTG5pXhRa/BoPH+dx0GcJS5S4vaDDRve54H395lToBXzTIDg9KD5nPJWebVZzADjNJiM2ioKb5Xt/dvd2YodlR8Xjh4ZgDSW5ulQCUFZslN007h/7xQeGZflAEQEWkwlAEQqadSEuIY2i2LO07rztcTBtN66L8AsGM2so4fPYjzj8oB4J4ZSzlYUgatnWVA638yV34pPWiuTGPtE9BphPsDmvWAf80xG3r3rHZf4fdnxxKz0TU2BbJ6mMfs0dD9TPP+n++6r7bHpUFMgnuJUqjZLEBpERTlOz871by1Jv7WmGx287ZGAgArA9AMoqIgs4v5uC6UASkAEBFpkBQAiBwqep7n3iEYaN6yLZNOOYzstHg25x7g+R/XQGvn1fn1P7knoE07Q5RzAtx1lDlJbtIZ/vEBpDaH7meYzy18JfBnr3OuHNSqnznxt1gBwPIvYLdzGdMUZ3NylN0MBqBmG4Gt0paoaGjcwbxvTfytQMBaIalGewCyzVurDGh7HVgK1CsAqMF9EEREpE5RACByqEhuCl1O8nicRUKsnVtGmlecn5q9mh2N+gA22L0S1nxvnpd1mPs1Kc1g/N/mlf9kZxNxn4vN2yUfw8E8/59tLR3aeoD38eZ9zCVGSw/Aby+6xuWS4AwAajIDYNX/JzZxL426f7u55KcVAGR2NW9rugQI3AFAXdgLwDPwKlQPgIhIQ6EAQORQcvgY8zYqBpLcy4j2aZVOYXEZ987e5p6A/vE2AJtj23LqE3N46OvllDkMiE/1rsdveQQ07WpO4v98r/xnGoZHA/Ag7+dsNuhxlnl/1Uzz1nN/Ams/g0AZgF+fh2WfB/HFK2BNbJOauIOPfdvNoMNRYj5u6izLqdESIGcGIKsuBQBhKAFaNweWzQjPeEREpFYoABA5lLQ7Ho65AUZMcZX12Gw2bhtlXuX/YOFmVif2NM8t3g/AHfPhj015PP7tKi55aT65hT47AdtscPhF5n1/ZUC7VpqT7Oh484q/r+5neT/2zABUtBnY7tXw+Q3w/hXVWzGnwDmxTWzs/uz9291X5hMauYOScC9Jahh+MgDOjMueteYmYZFU3QDAMODN8+HtC7WRmIhIPaIAQORQEhUFJ06Co67wOtw7J53zj2oFwMMrmno9t/BgczpnpZAQY+fHlbsY9cQclmzJ935fq79g62L3br+WZZ+aty2P9L+ST9NO0Kyn+7FXCVC6eesvA2AtUWqtmFNVVglQUlN3WdP+He4+gOQsMziA8GcAivaZ4wd3BiC5KSRkAEbkN98KtgfA2hfCtwTsYK7ZYG043D0eIiJS5ykAEGkg7hrdnX+f0IH5js6uY7uNFBpnteSdf/bng6sH0CojkY17DnDuc/P4fWOu+8VJjaHnOeb9zyZAmbN0Jm8T/PCQeb/X+YE/vMfZ7vueJUAVZQB2Lnffr86KOf5KgPZvd1+xrskAwLr6H58GsYnu4xltzds9a8P7eaHy/HMvynfvj+Br7ffw4jD4/Ebv4wUef16568M+PBERqRl1IgB48sknadOmDfHx8fTr14/58+cHPHfatGkcc8wxNGrUiEaNGjFkyJAKzxcRkz3Kxg3DOnPPxUNZb5iT8PX2NrxyeT/SEmPomp3KJ+MGclSbDPYdLOWiF37hz00eV3yH3G6Wy2z/E35+yjz25S3mFe6cfhUHAN3PBJx7CASbAbD2KIDgVswpK4HcjbDtT3eAAh4ZAJ8mYK8MQIZ5P9wr4ezbYt5aV/8tjZwBwN4IBwC+JU+Bvr8VjO1a7n3cM2DaqwBARKS+iHgA8PbbbzNhwgRuu+02Fi5cSK9evRg+fDg7dvivJ509ezbnn38+3333HfPmzSMnJ4dhw4axefPmWh65SP00tFsWGYcdD0DnXv3JTIl3PZeeGMtLY4/kyDaNyD9Yyj9e+IV5q3ezJfcAO4wUSk68wzzxuykwfxos/dRcQ//kh83yo0DSWsARYyGrO7Q43H28ogyA52SzoobZ1d/BI93hzqYwtTs8Mwi++j/3864egCYeJUCeAUCmOwNwYI+5OlC4+Nb/W+pKBqBcABAgA2IFUb67BXuuHKQMgIhIvRHxnYAffvhhrrjiCsaOHQvAM888w+eff86LL77ILbfcUu78119/3evx888/z/vvv8+sWbO4+OKLa2XMIvVdyohJkJpB0sDryj2XFBfNS2OPYsyL81mwfi/nT/vZ9VxqfCY/ZPYjfccvMMNZDnL0VdCse+Ufesoj5Y8FygA4yszmYsv2CkqAFrwEeRvN+7Yosx59zWz3854ZgCRnAFBWDLtWmPeTs5w1+ZivPZjrzghUl+8KQJY6mwEIFAA4J/oFO81+AGtHaM+AQBkAkdplGOZPRRdfRAKI6G9NcXExCxYsYMiQIa5jUVFRDBkyhHnz5gX1HoWFhZSUlJCR4f9/2EVFReTn53v9iDR4qc1hxD3uTbl8JMdFM33skQzpmkVirJ1Yu/mfivyDZZyz5VwcUc5m35TmMHhi1ccRKAOQu97cpdjaoXfvusAr5mxeZN6e/5a5hwHA7lVQcsC87+oBaAox8e4diLf9ad4mZ5nNy7Ep5uNwrgRU5zMAzhIvqyyrsgxAWbF7V2VQBkAkkt65CJ7oC8WFkR6J1EMRDQB27dpFWVkZWVnek5CsrCy2bdsW1HvcfPPNNG/e3CuI8DRlyhTS0tJcPzk5OdUet0hDkBIfw/NjjmDJHSNYcfdIVtw1kqHdslhR2oy7Si6gNCYFTn0M4pJdrzlYUsaLc9ayYH2QtfSBMgBWzXlmN3PijuHdE2Ap2AV5G8z7rQeaV9oTG5tX8q338CwBAkh2Tsatq/NWWZCrDyCMjcCVZQDyNnn3K9SmslIocgYA1g7JlWUAyt33OD9/c+S+i0hDYxiw/EtzJTErmykSgnqdN7r33nt56623+PDDD4mPj/d7zsSJE8nLy3P9bNy4sZZHKXJoiI2O4skLDmdotyxeLBlGtwPTmL6jA6VlZs38pr2FnP3MPO74bAnnT/uFhRuCuJIeKANgTfabdnZvXOavEXiL8+p/447mBmY2m3tn4+1/Q8lBKN5nPk6yAoBM7/ewrs7XRCNwoAxASjOITgCjDHI3hO/zQuG5pKeVkQgUAHhe6fdc79/zuOFwl2KV+6x8mH2vd1mXiFRdUb57I8Oa2MBQDnkRDQCaNGmC3W5n+/btXse3b99Os2bNArzK9OCDD3Lvvffy9ddf07Nnz4DnxcXFkZqa6vUjIlXjGQQUlzqY/OkSTnrsR17+aR2nPjGXPzebk8riUgdXvPwbG/dUkpquLAPQtIt7Qu+vEXjzQvPWs7E4y9mPsP1v9wQ1KsZd+pPsU/ZkPa6JpUADZQBsNmjUxrwfqT4Aq9QpNsWdFamsBMj3vu/5gYKZP96G2VPMHxGpPs9MXLhXL5MGIaIBQGxsLH379mXWrFmuYw6Hg1mzZtG/f/+Ar7v//vu58847+fLLLzniiCNqY6gi4hQbHcUz/+jLXaO7k54Yw4rt+7ntk7/ZU1BM9xapfD3+WA5rnsrugmIunf4reQcqKAuxMgAlBd7lI64AwDMD4KcReIszAGjuEQC4zv/LPVlNbOxuXPUMAKJi3GMIdwDgbxdgT5HuA7CyLgmNKv7upcXe2QKvYMAjwILAjcDWhmdqFBYJD89/q4W7Ap8nEkDES4AmTJjAtGnTePnll1m6dClXXXUVBQUFrlWBLr74YiZOdDcZ3nfffdx66628+OKLtGnThm3btrFt2zb2798fqa8g0uDYo2z84+jWzL5xMJcMaENsdBRn923Je/8aQKesFF4YcyTNUuNZuWM/l03/lVU7Avz7tK7KgzsLYBg+GQDnhN43A2AY7hKg5n3cx10lQH+5J6hJHrsfezY+J2e6V9CwVgI6EKaraQf2mk2zUD7rAB4rAa0Lz+eFysoAJKS7y6N8l/mE8pMLryuPzklIsx7mbaBGYCszkL+lSkMVER9eAYBKgCR0EQ8Azj33XB588EEmTZpE7969Wbx4MV9++aWrMXjDhg1s3brVdf7TTz9NcXExZ511FtnZ2a6fBx98MFJfQaTBSk+MZfKph7HsjhE8cHYv4mPMVXuapcXzwiVHkBRr57f1exk+9QcmffwXu/d77zRbWGpQEmOuvrNtu3NymLfJzAhERbMvsSUH0jsBNvPK836Pq8/5W8y1/G129wQUzKDBFmX+T9HKGiQ1dj+f7BMAWMKdAbDKfxIbQ3Rc+ecjnQHwDAAq6n/wDQr8ZQCsEqxAV/jzNpm3+7eZzcf1wZZF8PMz4d0XQuqWon2w6TfzYkJdVVYCs+8zx+nJXyAuEoKI7wMAMG7cOMaNG+f3udmzZ3s9XrduXc0PSERCEhVlK3fssOZpfHbtMdwzYykzl2znlXnreWXeejJT4mjRKIGYqCgWb8xllj2enKh93PzaD/zrHy3p7zCv/ucmtKLflO/JTotnVnob7LlrYcffkDzY/ACr/CezG8Qmuj84NhEy2sPulbD2e/OYZwbAc9Kf7FGaE+4m4ED1/5ZI7wXgCgAqKQHynPB7Pi4ugFLnUqtWCVagDIDVHGw4zKAtrUXVx11bPhtvBgFZ3aDtsZEejViWfgrr5sCwu8FezSnM5zeY/SkXfwztBodleGG36huYfQ+sngWXfe0+7vlv1V/mTqQSEc8AiMihq22TJKZdfARvXnE0PVqY5T479hWxaEMu89ftobjMQaHdzADYivK46IVfmPfLXADm5jehqNTBut2F/F7snDB6rgTkKv/pXf6DrTKg9c79RKwlQKGCDECYA4Ddzrr3tABLD2d4lABF4gqkVXLlGwD4jqVcBmCX9609zl2m5S8DUFzgPVmpL2VAVmYm0ns11CZHWaRHULmv/gu/PAMbf6783MpY/z3xt8JYbSsrgR3Lyv/7s/pn8jZ7Hy9UE7BUjwIAEalx/ds35pNxA1nwvyF8Mm4gT194OA+c1ZPvbhxMpzYtATihdSylDoP1y8wr+2ttOVw1uD2x0VH8mO+cqO/waAT2twKQxVoJyLpC7VUC5HHV3zMYCHcJkDVWa3LsKy3HLFUqKTSvitc2fxmAsiIo9unXsCYa1p+VlQGw/pySmkB6a+dzO8pvSuQ7cdlXDwKA4gJ3k3R9CViqK28zPNAeZvwn0iMJzFHmLicLx9+LlaWrid/JUPfE+Pp/8FQ/WPm193FX+dx27+CgQE3AARlG4I0jxUUBgIjUCpvNRuPkOHq2TGdkj2zOPiKHtk2SsDlX4bmodxo3jehMhyjzf8ZnDDuRm0d0YdIp3VjmMK+iF2x07t4bqAHYYmUALJ4lQAmNIMpZOlCTPQDWVUXfsViiYyHNDH4icpXZCgDi0yE2ydyXAMp/f2vCn9nV+7F1XmJj8880zrnEsu9SoHk+j+vDhDp/q8f9TZEbR21aN8f8nVj+RaRHEtj+7ebeGeCevFdVabF74pxfzffytX4e3NMCfnoi+Nds/cO83bzA+7hVPuco8b7SX1tNwAfz4IcH61cm7P3L4IGO7uBJ/FIAICKR5dwLwHYwj6uPa8/hCebV8OYdewNwYb9WZHcyr/JH7VrGDW8vYMb3P8HBXAx7LGSaE+xNewt5b8Em7v58Cb8eaO79GYlNMAwDh8MwV/1Jck78PTMArlWA9la/8dPhcK9alBkgAIDyfQAH82Dhq1ByoHqfHwzPDAAEDoCsCX9TZwBwYI95ddO1wlITc4lVKwvg2weQ67M5WL5PRqAu8hxjfQhYwmHPavM2f0vdLQXynNDtq2bWrMBjQztrud5wWfu9mU1b9U3wr9nvHINvGZ3nd/bMFPqWANVUs/qi1+HbO+H7+2vm/WvCqlnmQhJWllj8qhNNwCLSgFnr8P/2IuzfTlRRnlka07gDYGYOxp8zgsL7E0jkAOf8fTVz/+jOSTHwe0kOF931HQkxdnbsc68wNA2DZYmJxDvMcpQZa0u5++PviLHbeO7iI+jU6mhY9rl3/4DVA2CUQVGee2JcFXkbzFIae6zre/iV0dacLFhX1z74J6z4wvyf/vETA78uHDz3AQDz++dvKl9PbE30m3Q0/14MhxkkWBMQq7+iUWvY/mfgCYzNbv7Z1ocJtefV5fow3nDY7QwAjDJzQlwXG7U9d5qubgbAc9If7hIgK4AM9gq0554hvssCewbQ+7e5Swo9A3WjzPz3bP03LJx2O3fvDtTgX9cU7nH/t626vyOHOGUARCSyOgwxy0/2bYVfnzePNWrrtXRmSmI80aMfoyw6kX5Ry5gQ8x4Afzjase9gKTv2FWGPstGnVTon98gGbPxZ2tL1+vt/3MXm3AOs213ImU//xE+974P/rIT0Vu5xRMdBbLJ5v7pNddbyo007V7xSiWcGYN1cc/IPsPzz6n1+MILOADgn+inN3OcU7HQft44FygBYk7Zmzr6M+jCh9swA+PYwHKqsDADU3dIJz7+L6l6193x9/tbwNuJb48zfHNz7Fu0ze4HA+99PyQHvK/2eWY8Cn3+nNdUIbAX09WUy7bmqWn3INkaQMgAiElntjjMn46tmmVfl1/8EfS8pd1ps73OgzdHw6fXmknjA2aedxoDWx5F/sITOWSkkxZn/SbtozW42vdGOI0tXmK9NyWTycd34/M+t/LpuL2Om/8aUM3py5uGp2KwdgsG8gla83/yfaeP2Vf9O24Mo/wGPvQDWwMxb3ce3/WlOlFOb+39dOHjuAwAek/sA6/4nNjF7KQp2mj/WxMRqsG7kDAB8r2Bak8mco2Hr7/Xjf8qeQUrxPjiYD/GpkRtPTTMM96pV4Aza+kVsOAF5lcNUNwDwmNCWHjDL76x/C9Vl/Y6XFJr/ziq7Mu+VjdgKJQchJr588GmVAJUWmb+XYGZQD+Y6A/cKso1VZQUkVpDk+d/LusizVyHcvR2HGGUARCTy4lLgsNFw5jSY8DcMvNb/eemt4B/vwxnPw1H/JKHXmXTITObwVo1ck3+Ao9s15qQThwBQZovms/+czCUD2/LqZf04pWc2JWUGN777O6c/9RNf/rWVModBSZmDgzHm1fDc3dX8H8f2v8zbQA3AFisDsHmB+ROTBE06mcdWzgz9cw/kmq+rrB7YMMpnAKzdgANlAJKaeO8YbF2BtEqAKusByDnKvM3fWvc31/LNUtSHoKU6CnaZZW+WOpsB8OwB2Fa9q/a+GYRwXuH2nLgH82fp+9lW1izPp3/GCgCsf6NR0ZDRznmsBlYCcjjcTf1WkFTXeQUA9SDbGEEKAESkfrHZoOfZcNL93huA+YjLMVcHsqc1J9a5Q3F8jJ3HzuvDtSd2JDba3IjsX68t5Ki7v6HbpC/5ZZs5objr3Tmc88w8Xpyzlp37igJ+RkBWA3CgJUAtVgbAMuDf0P0s877vcoDB+Pq/8PpZ8O0dFZ9XXAAO5468FZUAFReazXRgXv23VlMq2Om9DCi4MwCeqwCVlbonzy2PBGzO1Uzq+M6lvhP+Qz0A8Cz/gbobAHiuyFRSCEX5VX8v3wxCuCaLB/PdV+chuN8d32WArbKbQAGAZ/ldoMA9HPZthbJij8dhbpauCXs8Mln1YcnhCFIAICKHphZ9YfgUOPVxr8NRUTYmDO3E3JtPYNzxHUiNj2Z3QTElZQb5UWaZRzr7mb9uD3d8toSB937LDe/8zl+b/V/9MgyDolKPVVNKDsDuVeZ9az+CQOJS3FfQk5rCgHHQaZj5eM1sM9UfLMMwy6gA5j4GWxYHPte6+m+PhRhnEOXaCM3P+uL2OHOsXgGATxOw1U9xMM+9ydj+bWaDYlSMue+BtexqXZ9QW5NBa8+IQ70PYHc9CQB8x1WdlYDKZQDCNLn1/d2uSgYgd533a+PSnOf5ZAASG7v//dXEbsC+2bz6MKHe65MBiMQmi/WEegBE5NBks0H/qwM+3TQljhuHd+aqwe35e0s+LRol0HzeD/DLHK7rn0Hz9G58/PsWft+Yy/sLN/H+wk10zU6ld04a3VukEWOP4qdVu5izaje7C4oY3KkpF/RrzfGpW4g2HJTENeKtPw/QsdlujmqTQVRUgNrZrMPMlYCOu9mcZDfrZS5TWrCDv3/+krZHnUxibBD/qd67zj2RMMrg43Fw5XdgjzGP5W81a5xjErz3ALBqev1lAKz6f2upT1cJ0E53CZB1LDbJ3SOQu978LKv8J7W5ufxqanPzKmb+Fv87ONcFpUXu751zJCz99NAvJbAyAGmtzBWs6uLeByUH3L+byVnm79G+rdC0U9Xez5rwp2Sb7xOuya1vsBhUAOCbAVjn/doWh8Oa79xZC68AwE/gHi6+K3rVtwxA6cHgejAaKAUAItKgJcVFc1Rb5/8gnJPglI2zuXTQlVw6aCCLNuzlpbnrmPHnVpZuzWfp1nxgY7n3+W75Tr5bvpN/xM/hLuC3wmxu/cQsBWqRnsAZh7dgUIcmtGiUQFZqPDF2ZwL2VOfV+m6nUeYw+HNzPmWxR9C3YAbzvnyT2//K5I0r+hFtryRhu/4n87ZpV3OisP1PmPsodB0F30yG5TOg22g45+XyS4B6fHfvAMCj/h/c+yfkb3HXjFuvA3PJ04Kd5vfJ7uUuYbCyA6ktzA3cQs0AGAZs+wMyu7kDmppiTXLscdCspzMAaCAZgHbHwqLX6mYGwJpYxyabq2vt3169Can12uZ9YPnW8DWMVicDkN7KLKHzLQFqeYQzAHDuXeC3BKgGVgHybej3DYS//p/Zt1TTSxYHq7jAXSYVnWD2LeRvUQAQgAIAERFL11NhzlRz8vz0QDjpQfr0PIc+5/fhf6d0ZcG6vfyxOY+/NudxsKSMfm0bM7BDE5okx/Lugk28t2ATrYvWQTRsiG3LMW2asHhDLptzD/D4t6t4/FuzNCjKBpkp8TRPj6d5egKx0W1Y9d1cVmzfx8ESByOjOtI3Fo6PWsxd6/bwxHeruH5IJVc6NzgDgE7DzYnyh1fC7Cnw3d3m2v1grrJUXFC+ARjcpQR+A4Cm3rc7l5u3Nrt7HweA9ifAhnlm/0LfMe4JTJq5k7NrVaNQr6gveg0+GQfH/w+O+09orw2VNbbUbDNggUM/ALAyAG0Hm3/WB/ZC0X6IS47kqLxZv0upLcyr9lD1lYA8dwFu3scMjsNdAmStzhNKD0DO0WYAYJXeWBm0FkeYt0X5Zl+OZ/+NK3CvwRIge5y5sZnnn1HeJvjJWV551JXu1cAiyWoATmgEqS3N/47v2+pegli8qAdARMSS2QX+9aPZsFqUb06i370ECveQmRLPyB7Z3DyiC69e1o93/zWAG4d3pn9OAh1tm/i/k7oyb+IJnNvKbEw896ThvHpZP3793xAeP78PQ7pm0bpxIrH2KBwGbMs/yMINuXz2x1Y+WLiZPzblcbDEQUKMnbTDhmLY7LSP2kor23Yem7WSX9a4J+ZLt+azYvs+77FbGYDWA6HnOdBxmNnoazig88mQ0txswF3/U4AAwPk/8AN73TvBukqAfAIAa1KTmGGW9lg6DTdvV39nLmVoTWDSnHsyVDUAsHZUXf1t+ef+fA9WVKFhOhDru6W2qPp46xPPJUCb93bXm9e1oMcaT1pLc08KqPqk3doFOCrGvVJXuEuAcpzLqIaSAbBWytq73lyBx/rOmV3NK9pgBj2e/TeB9u8IBysT0aKv9zjB3ecEZnauLrDKfzLamQE8HNr/dqtJGQAREU+N28PYL2HOwzD7XljyEWz8BUY/ZV7h9rThF/jgCvNK2aDxxJ14G3F55t4DVgNwfIydUb2aM6qXOZl0OAx2FRSxNfcgW3IPsDn3AAeKy+iQmUznZim0bpyEPcoGL/WH9XMY33od49dlcf3bi/nP8M68/ssGFqzfi80G5x2Zw03Du9DIscf5Pz+bOYmw2eD0Z2H+c9D2OGjdHz75Nyx8xWwutsoGPNc9d+2E7DAbeRMzPPYAcE4yrNe5XuPzuFlPd031ujkeJUBWBqCKV9S3LDJvt/1pToysoGPPGnj/MvN7X/RB+b+fqnBlAJq7A5dDuQl43zZzpSdblLmUa1pL2JFn/t017Rzp0blZE+m0lu4MQFWX7nTV/zdzB3nhzgDkHAUrvzJ/nxxlEGX3f77nLsBW0HAw18zKlBWbfy+pzc0G+tz1ZhmQvybgmggArAxAq35mhtErAPBoHN/2J7Q/PvyfHyqrAbhRW3f2SgFAQAoARER82aPhuJugw4nwwT9h90p49XQ47HRzQt3ySFj2GXx/v9lwCzDnEfOqd8EOwGZmE/yIirKRmRJPZko8vXLSA4+h0zBYP4dT7T/xWJMTWLurgAnv/A5AdJSNUofBm/M38sVf27i740pOBrbEt+fej9aSlhBDRlIsqQnnsndZMdvm/07bba24Btj1+5fkthxMB2D1/hh++WUDmSlxnNAlk6i4NLO2v2CXMwAwrzSuL0riP8/MI9VexPOeY/QNCGw2MwuwYDqs+NJj0uZTAhTKxK1wj3siUrzP/J+8tUnbxvnOkwx47zL45w/uYKOqPAMAa7yH8mZgVvlPeiuIjnUGAH/XvT6API9sUnKWeb+qqwBZv38pzTzKibaby9ZWtHM3mM3IjlKzYd8fKwBocbhZImeUme8daFM/z12AG7c3J/WFu80AGszx2WPMseauN4MFVwN+Y48N/MIcAJQWuf8ttOoPPOIdJHk22277M7yfXVWeGYAo599jfVi5KEIUAIiIBNKirzmpnHkr/Po8/P2h+eOpxznmSiTf3gW/PG0ey2hrropTHT3PhVl3Yt/8K8+fajD642jiYqK4sF9rLuzXivV7Crn1o79Ytm0fO/+eDdHw1f72fPK7///hNaIl18RDk8JVLFqaSgc7fLSskMf/Nv/n3a9tBq/FNSKmKM91NdEo3IUNeHJ+HvNL9wAGB+JiSbCZa4PnR6VRbkrcaSQsmE7Z8i+hcDd28N8DYBhmtuHti8wJzlkveZcTWbYu9nn8uzsA2PSb+/iBPfDOxXDplxAdV/mfbyCeJUCxSd613KEEAJ9NgA0/w9gZwe0w6yiDzyeYE9xja7jPwZN1JTfD+WdaV7MeeZ4lQGHKACRnmWVt1kS9YEfFu28bBkw70fxdu/rn8n+vhuEeZ3pr873yNprBVKD3tcYSl2r+vqW39g4ArL8PK+jZv8OjBKixu/a+eJ85aa/O776n3I2AYS4TbC1nvG+bOwPnmwGoC6wegIy27jJGZQACUgAgIlKR2EQ4+SFzQr7iK9g0HzYtMP9HO/I+s94ewAC+u8u8n1nJBmDBSGkGPc6G39+g/eqX+eW/zxNrj3KtBpSZGs9n/x7Em/M3cMLs1VAEmd0H878WXck7UMKegmLyDpTQKDGWZmnxNE2OY+f3XWi6fxmD7WbNbqMmWQzJyGTuqt38snYPf8XF0McGP/6+lM3bWzBg43paATscKZzepwWHt0qncFYjEkrNK68zVhfReMl2hnbLYm9BMTOXbOeXFWncQyxx+e6Vkh5feICz+x2kmTVxKymEg7kY63/Ctvxz89iOG6FZj/J/Dr77GWz9HbqfYd7f7AwAhtwOc6fCloXwxc0wamrV/9w9MwBgBgJWAJDZNbj3KNpvZkGMMnNn5p5nV/6ajfPN1wD0PK/6mYxgWRmAxr4BQF3LAHiWAHn0ABiGeynbYHkuARplN98vf7MZUFQUAOxZY2ZHwGyo73Oh9/MH89wb56U4m8itAMCq7/e136McCaBRG/P3eP1c87EVPLsCgG0eJUBNzJ4NK4Ap3F3x+ENh7UXQqI352bYoZ5C0E1KyvDeP27XCzIzEJITns6vKFQC0g+L95v3qru608Vf4810Yclv1L+rUMQoARESCkXOU+3/ijjLA5n3F+rj/mE22PzwAnU8Kz2f2vwZ+fwOWfEzikNvdu+06RdujuKh3Onxlpr5PPvlM83/OgeQOh7nLiMHcBfjSE/twac8j2binkP9+9Bdr1jajj30l6+d/xv9Ks/gpbjfY4JxjezNyeC9sNhv82QK2mAHA9rIUJr76G4e3asTijbmUOcxNd06KOYwT7Wbd/k4jjYe+3cDU2Rvp27oRL0enkVCax/uz59Pqt/s50jm0H758h4EXdzf7Hzwc2LCABGADzWjFNvLWLiANzAmH88pjadfRRDfrDq+dBQtegqOucDd3hso3AEhrYU76QrmSuPEXd2nY+jnBBQDWhA/g7w9g4HXBf151BMwAlF/qNmIMwx0ApLZwT5ZLD5jN+vFpob2fKwBw/luxAoD8rdCigtd5Zpz+/qB8AGBljxIyzAsHaS3NFYMrCqY8sxHg/jduZTesvw9rrPlb3Ut+JjUx/xuU2NjMXoQzALAagP+/vfuOj6pKHz/+uZPMTHonDUjovbcQERCJFBEBsbGo4NpQQBT1x8quou5+hV13xe+6LpZV9LvYwBVUxEJHIXRCJ9IDISEQSO+Z8/vjZFoaCJjEzfN+vfJKcufOzLk3N8l57nnOc4JidVqUb7gOPnLT9PvaS4SazPrvXsYB52Th+lBW7Fy/IqSVs4LZ1U5mX/G0HoUMbQNxD1/dazUwUgVICCF+LpNH9ekqQ2bD7DNVOwZXKrILtLpBp8psfbv6fU5tAZTuwNXW+Qf9Wq4qqgA1D/Hhg/v7EjnofgBuM2/m5vYBNDHpSkM39++mO//grAQEtI6NRSnYcfIi5TZFp6gAnkhoS2z/2xz7eAbH0K9lCOU2xdbjFzheEgTA3h+X07c8ydmWo2uZ9N5W0rOLOHWhgE1Hz/PiV/s5/9MWAD4q1W0vS03izjc38dcPFoOtjPMqgI6v7mfoF54k+cQDkL+7UpqWPTXDZVXQotJyjmTksi45gzNZhXpjeZmzJKN9wnJFh+r0icOOAOeSXDvz9lSOS0lJdH69d8nlPedasOdNN+QRgMKLurMP+udi9nZ2+q9k8m6eywiA6+dLpRSlugQAx9ZVrb3vSFNq6v65tk5obqW2BLkH+c4UoIqg5/xPzuDS2339kms6EdjewbcHJI5RlzQdHJaX6JXEY/XvXL2nAWWl6L+T5ooFCe2BUFGWLp16JfLOOVMQT2+7Fq1sUGQEQAghrqVrPQweP013NnZ8oFcL9grQ+bn5GeDppSfbAsRed+nXiol31vQGtzKghmEwYOg4OPASPhdP8M9OB+FkqX7QtdqPSwBwS3xXVL+epGUXclOnSFqGVQyRZ/vAtucACI5uxeI740nJLGD94XOUbYiEgpPMsHwJCmyh7TBl/kQ/0yEeOnKG/nOd9cyDyGWOl65E1P+2aZR/tYRQI5eUE0fo6rEVzLDL1pbScjh6Lp9FHl3pYd5EysbF/F/eGCb0a47V04OAXW8RteWPrG37ez4uv5G9qdmkZRc53sfiYeKxIa2Z0tMbL1UOhgdny/35fvNJvA7buAP4cecePs/YzPy7e9A06BI/4xMuAUDmEd3Js3egqlNepitK2aXvhYxDNU4k/7nyi8tIyy6iVZiv+4rUNlvNAUBOqnvFpfpkH43wDQezl/7aL1Kn3OSm/fxqRbmV0m4ud3J66g792TDpicAHv4Tek52P57iMUoAzfedyRgD8K40A2FVOAco4qD9bA/WkbXCZCHwN1wKwT7wPbqE/B0TrznBumu74g662E9UDjm+AtHouBeo6AdgwdIBo9tHphrlpzuv75zi21vm1BABCCCHqVOuhENYezifDxxN0VQvXChx2lxMAmL0hpj8cX6+/rzyJ0WSCnvfoCc2b/q63Wfx0OoOda+UfnzBGt6om5SCwGUR01QvxVHRgYkJ9uDc0Fs51gh1bCFZZ+i2H/w989TheuWmMDU3h08w2WDxNNAvy5hbfFDgLKqQVN/TuClvaQ8YBnutdRodzGZAB/QcNY2PfGzmakcep09GUb3iHjsZJfti2nY+3puBJGRutr4MBYckf8n2JMzXI3+pJsK+FlAsFvLbqMD/tOM0/gQyCiZu3DoDbPby5wwxNTRfYeuICI1/bwNzbutEhyp9j5/I5fj6P4+fzOXoun+Pn8wk2l7GicDueQIlXGJai8yz7YjHrzYPwMBmOFaDTsgs5daGA9OwixkWe408luShrAEZMf72Q2r7P4MY/XPpnegnf7kvj90v3kZlfQmSAF8M7RzCiSxT9W4Vg5KRCWZGumBJYsVqzf5Tu4JaXOPO965sj/98lP8c/Uv9OXEklIHtH335X3R4I1JYvXlbsvMvd8x5dUnf/0koBgD19rKn759oCgMqjEZVHAOxzQew/B/scA9fVbe0Tga/lasCuKUDgPu/CPsE2tLVe8RvqfwTAkf/fQn82DB20ZB7RP5crCQCOrHZ+ffG4DrAqVz77FZMAQAghGjKTCeIfg69m6Hxy0JP+/KP0nfyyYv11uxGX93qth7gEAMFVH+8xEda+7Lzrar+7aOcyAlDrP8P+U2D5k1XbFeDSiQtuAW0SdP3+pA+Z1/08swY8SpC3Wd+p/mEHnAUjuqfeP6o7ZBxgVJMMSD0EgH/r/vgHeeu78u2aoFIGwIkfeDzqEH+6EM1w004ibFkAdDWdYN4NfrRq35V2EX4EepsB+HpvGi99dQBb9hmwwKnyYAwDujcL4oboHrAb4sKK6O4RxO5TWUz9aGeNh93GtB9PSxlpKoQVeX15wPMb8g6tY2lZzR0QS+oWMMO28nbkmm9gKN9TnLSY7c0fYf3h84Ts+Rd9C3/kpH8vilomENnpOjw8PCkps1FSZqO0XH8uKbfhZ/Uk3N9KsK+FtzccY+kunX5iGHrxuQ8ST/JB4kk6RgXwQpdzxNl/Dvbylx5m3THOPaPvaPtHUFxWzv4zORxKy+VgWg55xWWM79WMAW1CnalhvyTXCkB2V1gJSJUVY9hTZRwpQPYRgFrmeaTv00GRTyhc/6QOAI5v0Gkifk0qtfMKUoDsd/gDmwMGuqoAVasA2bkF4nWRAuSysFaRXuyQkFbOiftn99e+3sEvzXUEwM4/yhkA/Fw2m3PhQfs8h9Pbof1l/p39FZAAQAghGrruv4FzyfofbOsherXfK61Jb58HYJiqnzwZEK1XEbanFrl2+Ct/X3khMFc979HBROUOouskxT4P6ACn1RBI+hDj2FpChv3R+bh9AbCoHvpzZDfY/bG+M5eVAhgQ3cvt5Y2Oo+HED9zpm8Sdj/8Z/u+fcAzsnaq7fXfotRxc3NItmkHtmrD9k41wAiKbtWLHxJsI8bXA+XDYDZa8ND6bFc+rK3/i3R+O42EyaBnmS6smvrRq4kerMF9ahvkSuGUT7IPD3t04pnpA8TcM8ztKfnwHbApKy22U2xSRgV7EhPgQ6G3GY8lbkAVrCtvwwc5Idlit+OSc5JX3PmKIxy6meC4FA3rnHYK9H3F2TxCPlcxgh7p06ovJgCeuC+OBbhY2F0Tz7b50VuxN42BaDssz1hNnhj2FYbz14U5MJoOYEG8eNEcQzBkOJR/kvURPvtmXTm5RmdvrLt2VyrioTGb7LCN45B/wbNarhhZUTynFicwCUi4U0DHKn3B/L8dj6dlFbDmeSbNgH3rFBGE41gBojlKKlAsFlBX50RpYu2MPa87vI8zPSniAlcHtmhBdQ4rWrpSL/G3JahYBZYYne84b9PRRGPZVY2ubT2DP/2/aW3cyo3vq6/PgF9D3QS7ml2BLO0YoQEAzR3sBPZJSWqTTl5I+0h1K+8Rwx5oEFW3wtOggOee0Lg3qFUhuUSneXqF4Giad5w7uv3uOxcCuUQpQUbbOnQeXEQCXc2T/nQ5trSfHenrrkYkLxyGszbVpw8/lugiYnf1mw5WsBXB2n06zNPtCh5v1vJxUCQCEEELUJU8LjJh7bV4rqgf0f0ynENR0t67nvbUEAK4djxBqVd3dYftdUU8vHSSAMyhJ3+t+R/WMXviM6B4Vbe+mP5+uWACsSfuqgVCHUfDN/9OTo08m6vkTGDBwJvzwN72y88CZVZoV4GXmxuhSOAFNY9qAb0Wes73jU5KLuTSPWSM68GRCO8weRvV3v1clATAoYSyDOo2Bv8wjvOgEj/T216u5VqYUlOwDoH3ccBLyW7L9RH8GFa9ngfV1otBzIDI7TyYvM43wjB+JsGXxuvfbzAhZgGH2wuJhwuJpwuxhkFtURkZuMWdziogJ8eGPt3ag14oxsOMAQ3/7HUPviGP2zR1ZuOkE8Rv1NfV9dnO+vuC8k97BbGW0ByxZncjicj1KFOproXPTQDpG+lNYWs7X25KZmfkSTS6e48Q7B3ih2dt0axGJn9WDwhIbhaXlFLl8lNoUBnquSVZBCXtOZ5NdWOp4z1ZhvnRvHsTBtBwOpec6tndvFsg/rYdpCnx32pMX563hTHYR93sUM8cM+edT+XfaScf+JgOGtA9nQr8YesQE4WPxQCmYv/In3tt4nG6kghXSbUHctiCRtuF+PNKpjNsBctLIKy7j+/3prDmUgbfZg9hQH2JCfel/aBPhQGZQVzwLSwnsfBuc2YXa9zkflt/EX79P5j9lJwk1we4cH7qDHmHz9IayQk6e+IkTKacY/MOjAOxU7ejSqQsWewqT6xyR4BaQc5oLnuE8+M+N7EzJIsTXwjqPIALKKtJ8XEfmrvUIgD39xyfMuaKu64hLWcX8mZDW+m9IRGfdOU7fXX8BQHUjAPbA7kpKgR6tSP9pOVDPndq75L9uHoAEAEII0ZgYxqWDiXbD9YTL/IyqaT72joBPqE4X+bliB+iOf0y8M4Dwa6JTCdL36g57tzv0yqbZKfpxe55x5XUCmvap+vqBzZx3Z//zoN7W9iboPxV+fE2vI3DhmLOjkHlUj4T4hlUtAQq6A+QVqO+K5qRC8jdYtv1Ll2jtPNb9vUuLnJ2EFtfr44vooudCnNyoV5Ku7PxPuuPm6cW4m29hnKcFkh+Fj9c7Ov+M+DOh/afou8tFOfCPvkTnpbGk2w4Y9HStp5ud/3bWrt/6NsTEEexrYWY/H9RGHXi0Hno/L3o1pbTcxpGMPIp/ioZiaGG+yITeMYztEU3fFiHOCcRKMbvgFbySdftaGGn0PLmQ+Udvr70tlVg8TTQN8uZEZj7HzusP0Jdox8gAjpzLY/fpbNIsR2lqgmXHDM7YirB4mPAKaQq50CekmOmd23A+r5jDZ/PYfvIiqw9lsPpQRrXveWtrA1KhzCccL5uJwxl5vJBRwO1eQHE2A//0FRdLLVWet8aymXATzNzoyfofvqdPUCSfAerkJl5P3kAWwURbdQd8+opzdDu1kzA/Kw+pUJpymt8t/JZHPb6Eipj7u8ULeMRjGNtM+phf25KNyXqYg2k53HLawiggKcePnZlZAFzIL+GUxZ/OJh0AbEqHsLO5RAR4cficQR8gKfko73+yi67NgugcHYC/lycWDxNmDxMhfhb8LSaMbf/Sd+7bJABgsyk2Hc3kPztPczIzn14xwdzqtZtu4D4h2d6Zzj4FJRXzEOy/Q5FdKwKAvfzUZBivrfqJrccvcEef5jw0sJUeSavO6R36d6uWoCGvuIz9qdl0iApwpOxVsX+p+yJgjjZXTcEqLisnu6CU7MJScovLiA3xIdSvmsXT7Pn/rYfqld8BUnc2nInx14AEAEIIIdx5mKHvg7Du5aqd7vCOMPBpaHKFFWo8zDDmjarbWw2pCADW6gAgrSL9J6S1M1XJK1AP8duH+5vVUHe842gdANirsvR5QE+UbDlQBxj7l+lRgIPLYfG9+i7t0OcqVj+lai31gGY6APj0Hp1TDLB0jx6BcF0cLHWHnpfhG65TIwBaDNABwIkfqw8A7CVDm/V1VnVpPVTnfOedhVvmQ5/fOvf3CoBhf4TPH4INf9UL1NW0aFhpEayb5/z+wBeQ/2cd7OxZjIGC2AGMu3GA+/O2xMM3n3NPBxPGbdUszpb0EV7Jy8DwQMVPx9j0GtPMX1HUYRxnzTF4WTzw8vTA22LC2+yBl9kDT5OBAmwKvM0edG0aSPtIfyyeJrILStl24gJ7UrNp3cSXgW2bEOJr4XxeMYs2n6TZj7pj3b5dR27r14eBbcPwOhMEC+cRabrIU8OcqVBHz+XxydYUlu46w/m8Ysf26EAv/mdcV4bknIFUaBHbiq1jE/gy6QyLt58i75wXfkYRgWWZBIe14Zbu0XiaDFIuFJB57iytMnR60DFLOyiC7Vm+bLF0IM50iNleSyi84SW81+kVss8SzPI9+q7zUHMQTT1OM868mUGGc6LsWPMWVhb3BivkKm9e25AG6Oc09YhilBny/FvzwoBO3NQ5kuPn8vH8Mgpy9d35dafKeXv+BkwGXGdcZJEFrCUXWZZ0hmVJ1ae8TLBsYK7pTYqx8nTUQkp9Itmbmk2qvQwusDMlCzwS6WaGFacsvPjyKoK8LXQKKmU+6N8DoMxk5X/WZ2Ejm8G5EdwIHNi1kVGr+zqq7S5Yd5QPNp3g3v6x9IwJIsTXSoC3J6cuFHLup83cnTSZQpMfnw9cTseWzWkf6Y+f1dMxSvT+phMs3HiC7MJSPE0G8a1DGdY5kg6R/oT7Wwn3MWFe8zye23SJ5JzmQ1l3woMTmYfJKy6jf4kHNwK5507xzsqf+OHwOXafysKmwI8CnvNcxELVhVPRN3Njh3AGtWtCl+gAPMsK9CreAG2GQlAsNk9vTMU53PXyB/g07cyobtHc1CnCLSjJKSrl1IUCTl0o5PTFAn47oKV71a0GRgIAIYQQVQ16Btom6Lx7V4ahO8vXWushuvLQ0TV6xeV9/9Hb7ROA7aK6uQQAfalWh9Gw+iX9dWCMHgEA6DRWBwAHlkHLQXqEQNl0/vK3v3M+33WiMuiAIGO/7vx7WPWdz3MHYclkeGits0qSvTMfe50z/Sl2AGx50700qKuTm5z72Xla4Lff6TzsyscPeoXo7QshZRN8/3u48/+qf+3t7+ogKKCpHo1I3wtJH8J1j8PuT/Q+3e+u+ryKNC3DHkC5yjwKK57RXw+ZjTHwKTh3AI/D3zOr9C2YuLz2lXltNl2a0Z5aAgT6mElo40dC4bfQcqgj/SrMz8oTcQGw8SIoeGL8EPfFu0BXAXJZDbh1Ez9+P6oTvx/VCZtNUVRWTkFJOcE+Fr3I3Gpn1Z0ALzP39I/lnv6xFM+PhuxjvDeuGS37DnZP7zqyChYBIa344fHbySooYV9qDqkHfofadT9jWA9eFes4+ISx+IEh/HvzSYJ9zMScbQcp+7jTqLij3PFWOPQ1HdUxFiWUwg96NOK+XrHkF5fTPtKPbuHdKMgfyK2dRjgm6jcN8oY2bWCX7phGRzfDM9WgzKbwDw2HPGjpU8TMwe3YczqLn87m6dSrchvFZTYoyedJQ//MrRQTf+odZpc9pE+Flye3do+mR/MgdqZcpOeBdCiD47YmnM0p5mxOMclnFfOsZqyGTts6WtaEhYl6hG6P4c2NVgjL+wmlYGSXSIZ2jGDhxuPsP5PDWxuqVi37wPxXTB42fG05ZK5+jdvL9OiRl9lEZ59sYgsPsrSkDwoTAV6e5BSV8cPh8/xwWM9z8KaIRZa59DYdBmBB2Wj+evhOyg8nOd4j0cjnRivkn0/h76sPO7YbBsywfs1drONmtYX+p3qSdCqLV1f+hL/VkwcjkplhKyXLGs0720o4kLaTR0pa0N90kJjCAyxJDmdt8jk8TQY+Fg/KbYpSm6KkzOZ2jKO7RxMR4EVDJQGAEEKIqkymul3ZMyZezwvITYOP7nRurxIAdNd3ss0+0KQj1WrSDsLa6fSa3pOccx06joavn9JpQIvG68Wl2g7TlYpWvaBXlYWqIwDRPeHISmgxEEb/r56c+eYAOHdIBw63VpRMtS/61eJ653PtHftzB3WVGNdSlkq5BADx7u/pmspQmWHAza/AWwP1uTjwBXQa475PUY4eIQC4oSK4+XI67HgfYq/XJTQ9vXRQVJm98szFE/p17PMsUnfqUZDSfH0urn+yoi1/hTfidJWqTa/DddOrBgGFF3U60tZ39MjGmDecE2FLi+Dju3VVnaAYeHi9DliU0tWvlE3PXXGthOO6GnBRdtWStoDJZOBj8cTH4tLVqVx3v4I1uClkH6OVV27VtqdWVH2q+H0I8rFwfdswaDsOPDbA9vdg5fMV564p3ZsH0b15RXvWtoMUe4PMMPxlHQAdWUX0wfcBCI6I4aUxXSq1fkKV43E9/skJfRjb/EYKSsqJNi7AfPAqyeLxG9tUG4CVrnwJ88YsSr1CMRdlMsFzPb6DH8cS2ZEhHcLxMuvfkTti8mHfBv31bXczKPx6LhSUcOxcHvnrw7CW6FGKksCWPNq5NSYDcnKaYNtvItzI4rsH2tC+rR6RGd+rKTvWLydq03N85HU3K2z9uVhQwlCfowzOc64b8LD5W76wjuF4vhlLaQ7/W/R7mpnO0y74IZqNnMnILlGkXCjgu/3prE8+R2pWIffkfUpv02GylQ8zSx9lrepNiJ+FFqG+tAjzxd/Lk7xznpACTYxsRncNZ2C7SAa0DSPKlI3p9QehFPyNQj7odYS3ixJIPJZJblEZQWc2gCcsz+/IG+t08BLn2Yb+poM82T6bZtHtWLE3jeSzueRUmhwf6muhWbA3zUJ8KC13DwgaGgkAhBBC1D+zN1w/U1f58QrUcwyCY6uuqtzqBlj9R1061KOWf2Gj/w6HlkP/R53bfMOcaUBFWTqYuH2hvhvd/ma9/oHJ5F5uEnQHuusdENbW2bm67W34v7Gw8wM9dyAnVQcE4H433zcUwjtBxgGY3wm8gnSA4R2i3zcnVdfhr2k0oyaRXaDvQ7D1LVh8nx7RGDxLBys5Z3RHu/AChLbVVaTKi+G73+v5D18/qV+jwy3VV5MKbqmDnMKL8I++MOJlXYHqy+l6AmhYO3389sAqOBaGPKs7wSuf0wtkDZ+rc7uProXDK/WoS6nLiqyfP6QDiR73wH8e0J1/0NWdPrsfJv5HXws/fasXnhq7wL1ja18NuChbd+pdAwCl9HZrQNV87cpVd+zsQZ+94pCr0/YKQNXMORk6R6eS5VfMOQiodO24Bny9J+l0rS7j9ajC+eSKttSySJwrP5f9fMMI8rEQ5AOUVkwCtpXqILZyda+sFMxbdNqdecz/wu5PMA4tZ0zmu3DTh879lNKle22l0P5mwnuNwj5tfXC7JnAoFk7p89e1a0+6DnNJA7zYHc7son3yW9D2VQCM0kL6JM2GkhSeKfsrz9zzH2h5Eyx8FfKAXpPg9HZ8MvazdsB+Cgb8P1g8CZ+j+i7/I7ZPMVo+AxUVt6YMbs2Uwa3h/GHUP1eADTzHv8mCTqOrn5Rv6wB/8sTDVsbrt0Q7fxYr5uhrsWKCdp+zS+gz9f9RjsGhU2eJ/ehJKAbPdglMDmpBgJcn4wLHwjdfEZ2/nxkJbZmR0JbUrEKKSsvxNBl4mAyCfSz4Wj31eSwvdab0NVASAAghhGgYbpilP2rTtDdM3VK1A1dZbHzVu+oAXW7XAUBgc/jNYmcqSkAUjK1mbgLojm6Tdu7bWt2gJ+BueEWPDjja16fq/Ij+j8F3s3XnrCjLWWLRrllfsPjWfjzVSXhB16bftUh3oO2daFdDn9OBkocndLsTtv1Lj4AAdK/mLjPooODuj+Crx3XA8JnLHIS2w2H8O1U7mfHT9eq4G/6mJ0K/m6DXq1Dlzn3CO0HcFP3+29/Vd/d3vK/na3hYYfj/6CDi2Dr92IEv9POG/B4iOlVtp3+U7uif+EGPPqTu1OVyMw/r7UGxEPeInnRuDdDBhT19rHKnO6yt/vzja3p0o3lFQKaUswRos2oCAO8gPan+Pw/o7yuPHgVVLLDmYdUBLuhKVR4W/bOrri01ca0i5VoFyOylF+wrydMTyiv/bFa9oAO32Ot10BfWDpJX6AA5ZQvExOn9kj7UaWVmHxj556rvH+DyO1d5Ya2b/ggf3KJHQ3pM1PNzNrziLNdrK9WjR4OegZREfT5u+J2+VhbfB5sX4GP2hqNf65GS4FiMzCOwag6Me9P5PkrBN/8Pw1YKbW7Ct+utNaecmTx00JRzWgd+gU11e7a/px+//V1YOkWn9h1bg0ebBDon/wOKz4J/NHffPcn5e5kTAN+gA/niXLD6V78iuM2mRwUvHoe7PmzQQYAEAEIIIX5dmly6/n2NekzUqS8tBlx+x6smg3+nO2K2Mr1ac5P2OrCofNe51736oyhH353PPaPvrhde1BVVOtxyZe9v8YHRr+kJzT++Brv+rTuVFj+d999qsM45t+t9vw4AQKeT2MuvVqflQHg0EX6cDz++ql934FO6M15d+ViTST/eY6IeoUn6UHf+w9rrORjtR+qREcPQnTirP2x8TXf+DQ+4431db90nVI8AJC3Sr9usn04pqo5fhB51WVFDJaSskzrwWvuyHnlxndNgr9FvFzdFV35JSYR/j4WJS/Qozbq5ulNtMuuKTtXpMl4f79E1EF4p+Iu9Xo/UxPR33oH2CoQ2N0Hy1xXHcZnXoev1WnmBPp8QHQDkZzqr85w/AlsWVMynMfRIjmHo67TnvXr0avmTuqJVRCf4vmJuzw3POgMXt/d3CQBCKgUALQdCt7thzyew/Andad/0un7s9vf0dXdyox4hAuj3kA6WOoyuqJS1D1a/qB9LmAMx18G/hupRoN6T9fkDHbQcXaMDqJF/rn2+Cej3yDmtJ+hH9YB1f9bBSMvBOhDrMVGfoy1v6fkWiRU3AUa/5h6UB0Tp0Z2c0/qabTmo6nuVl+lRst0f6e9PbHBUW2qIDKXs87Ubh5ycHAIDA8nOziYg4AoX0hFCCCEamuI8nS9f2yJx/7pJr6MQP03fcb8c2ad1J9hejvVy5JzRaUM1VSgC2Pi/OlUp4QXo6lJCdOXz+jGzD0z5serdZrt183QH3eKv784376dHGcLa6s7qwS9h8wJnapbJU6dIdbgFBsyo2nksyXfORfCwVtyhr+giDf6dTnOqSXGerh3fbgR4VlNWsrK9nzlHDca/6378Nck+DfM768WpZqe6t//tG3TH1C9C3+E3DPcRobhHYaRLRaicNHi9t07DchXRBR5eV32J341/d3bgZx6sOtqRdw7+0btiTkawDnDbjYTffKK/fne4Tnuy+MGM3c4SwweXw6cVqX5tEuA3S3RA+eV0veJyRFc95yX3DKyco9O0Bj59ecUIPvuts6CA2VfPGVE2eHC1vmYyj+rzgNLrL1w8AV3v1KNclS2epFPZmvbRwW674c5guKxYFxU4+KUOaMcugO53Xbp9v4DL7edKACCEEEI0FhmHdKdq8DOOCjMNjq1cBwYRnaq/02qnlM7/9wuveVE7pfSicGXFusN3qVSr0kL49F5nWlfH0fqOeETnKzuWmpTkwyttdC76/d/oylGXY+s7+ngrT/peOxfWz6u0s6EDkv6P6vNYOeDJOKirQaUk6vQpkwdM/rr6VCeAPUvg8wd1YDb7TPV337cv1CMAoHPsp25xrieQlQLfzNITz107x0rBxxN053vSV86FAPMz4fVeVVPmApvD1K3O6lu1Sd+n5/akbHKUMKXDLXC3y9yHD++Aw9/rr33C9Gv7hlZ9rf3LdOUve1AY2FwHW6BTjDIO6JGJ2xdCxysc1bsGJACogQQAQgghhKhRWbG+Qx/Z1bn69C9h96d6fsGIeTUHMD9H/nk9Z+PCcT1i0254zaMnlZUW6nkCtQWFabvhrUHQvD888F31+9hs8N5wPcqU8IKuFHU19n6mV/a2BuhRncBmOuXNdf2Ny2Gz6Q76uUM6Jc11nsSRVboqGOh0pS7ja36dC8d0kLPr33pUw5XZR8+daT3k57XtGpMAoAYSAAghhBBCXIFj6/Qid5UrZbkqvKgrJ7VJuHSOfkNgs+nJxmYfPTH5ctpcWqjnIhTlOLfFxus0onomAUANJAAQQgghhBD/jS63n2uq8REhhBBCCCHEfx0JAIQQQgghhGhEJAAQQgghhBCiEZEAQAghhBBCiEZEAgAhhBBCCCEaEQkAhBBCCCGEaEQkABBCCCGEEKIRkQBACCGEEEKIRkQCACGEEEIIIRoRCQCEEEIIIYRoRCQAEEIIIYQQohGRAEAIIYQQQohGRAIAIYQQQgghGhEJAIQQQgghhGhEGkQA8MYbb9CiRQu8vLyIi4tj69atte6/ZMkSOnTogJeXF127dmXFihV11FIhhBBCCCF+3eo9APj000+ZOXMmc+bMYefOnXTv3p3hw4eTkZFR7f6bNm1iwoQJPPDAA+zatYuxY8cyduxY9u3bV8ctF0IIIYQQ4tfHUEqp+mxAXFwcffv25R//+AcANpuN5s2bM336dH73u99V2f+uu+4iPz+f5cuXO7b179+fHj168Oabb17y/XJycggMDCQ7O5uAgIBrdyBCCCGEEELUo8vt59brCEBJSQk7duwgISHBsc1kMpGQkEBiYmK1z0lMTHTbH2D48OE17l9cXExOTo7bhxBCCCGEEI2VZ32++fnz5ykvLyciIsJte0REBIcOHar2Oenp6dXun56eXu3+c+fO5cUXX6yyXQIBIYQQQgjx38Tev71Ugk+9BgB14dlnn2XmzJmO71NTU+nUqRPNmzevx1YJIYQQQgjxy8jNzSUwMLDGx+s1AAgLC8PDw4OzZ8+6bT979iyRkZHVPicyMvJn7W+1WrFarY7v/fz8OHXqFP7+/hiGcZVH8PPl5OTQvHlzTp06JXMQroCcvysn5+7qyPm7OnL+rpycu6sj5+/qyPm7cvVx7pRS5ObmEh0dXet+9RoAWCwWevfuzerVqxk7diygJwGvXr2aadOmVfuc+Ph4Vq9ezRNPPOHYtnLlSuLj4y/rPU0mE82aNbvapl+1gIAA+UW6CnL+rpycu6sj5+/qyPm7cnLuro6cv6sj5+/K1fW5q+3Ov129pwDNnDmTSZMm0adPH/r168drr71Gfn4+999/PwD33XcfTZs2Ze7cuQDMmDGDwYMH87e//Y1Ro0bxySefsH37dt5+++36PAwhhBBCCCF+Feo9ALjrrrs4d+4czz//POnp6fTo0YNvv/3WMdE3JSUFk8lZrOi6667jo48+4g9/+AOzZ8+mbdu2LFu2jC5dutTXIQghhBBCCPGrUe8BAMC0adNqTPlZt25dlW133HEHd9xxxy/cql+G1Wplzpw5bvMSxOWT83fl5NxdHTl/V0fO35WTc3d15PxdHTl/V64hn7t6XwhMCCGEEEIIUXfqdSEwIYQQQgghRN2SAEAIIYQQQohGRAIAIYQQQgghGhEJAIQQQgghhGhEJACoQ2+88QYtWrTAy8uLuLg4tm7dWt9NapDmzp1L37598ff3Jzw8nLFjx5KcnOy2zw033IBhGG4fU6ZMqacWNywvvPBClXPToUMHx+NFRUVMnTqV0NBQ/Pz8GD9+fJXVtRurFi1aVDl3hmEwdepUQK67yjZs2MDo0aOJjo7GMAyWLVvm9rhSiueff56oqCi8vb1JSEjg8OHDbvtcuHCBiRMnEhAQQFBQEA888AB5eXl1eBT1p7bzV1payqxZs+jatSu+vr5ER0dz3333cebMGbfXqO6anTdvXh0fSd271LU3efLkKudlxIgRbvvItVfz+avu76BhGLzyyiuOfRrrtXc5fZTL+T+bkpLCqFGj8PHxITw8nGeeeYaysrI6Ow4JAOrIp59+ysyZM5kzZw47d+6ke/fuDB8+nIyMjPpuWoOzfv16pk6dyubNm1m5ciWlpaUMGzaM/Px8t/0eeugh0tLSHB9/+ctf6qnFDU/nzp3dzs2PP/7oeOzJJ5/kq6++YsmSJaxfv54zZ85w22231WNrG45t27a5nbeVK1cCuJUdluvOKT8/n+7du/PGG29U+/hf/vIX/v73v/Pmm2+yZcsWfH19GT58OEVFRY59Jk6cyP79+1m5ciXLly9nw4YNPPzww3V1CPWqtvNXUFDAzp07ee6559i5cyeff/45ycnJ3HrrrVX2femll9yuyenTp9dF8+vVpa49gBEjRridl48//tjtcbn2aj5/ructLS2N9957D8MwGD9+vNt+jfHau5w+yqX+z5aXlzNq1ChKSkrYtGkTH3zwAe+//z7PP/983R2IEnWiX79+aurUqY7vy8vLVXR0tJo7d249turXISMjQwFq/fr1jm2DBw9WM2bMqL9GNWBz5sxR3bt3r/axrKwsZTab1ZIlSxzbDh48qACVmJhYRy389ZgxY4Zq3bq1stlsSim57moDqKVLlzq+t9lsKjIyUr3yyiuObVlZWcpqtaqPP/5YKaXUgQMHFKC2bdvm2Oebb75RhmGo1NTUOmt7Q1D5/FVn69atClAnT550bIuNjVXz58//ZRvXwFV37iZNmqTGjBlT43Pk2nO6nGtvzJgx6sYbb3TbJteeVrmPcjn/Z1esWKFMJpNKT0937LNgwQIVEBCgiouL66TdMgJQB0pKStixYwcJCQmObSaTiYSEBBITE+uxZb8O2dnZAISEhLht//DDDwkLC6NLly48++yzFBQU1EfzGqTDhw8THR1Nq1atmDhxIikpKQDs2LGD0tJSt2uxQ4cOxMTEyLVYSUlJCYsWLeK3v/0thmE4tst1d3mOHz9Oenq627UWGBhIXFyc41pLTEwkKCiIPn36OPZJSEjAZDKxZcuWOm9zQ5ednY1hGAQFBbltnzdvHqGhofTs2ZNXXnmlTtMIGrJ169YRHh5O+/btefTRR8nMzHQ8Jtfe5Tt79ixff/01DzzwQJXH5Nqr2ke5nP+ziYmJdO3alYiICMc+w4cPJycnh/3799dJuxvESsD/7c6fP095ebnbDxogIiKCQ4cO1VOrfh1sNhtPPPEEAwYMoEuXLo7tv/nNb4iNjSU6Opo9e/Ywa9YskpOT+fzzz+uxtQ1DXFwc77//Pu3btyctLY0XX3yRgQMHsm/fPtLT07FYLFU6EBEREaSnp9dPgxuoZcuWkZWVxeTJkx3b5Lq7fPbrqbq/e/bH0tPTCQ8Pd3vc09OTkJAQuR4rKSoqYtasWUyYMIGAgADH9scff5xevXoREhLCpk2bePbZZ0lLS+PVV1+tx9bWvxEjRnDbbbfRsmVLjh49yuzZsxk5ciSJiYl4eHjItfczfPDBB/j7+1dJFZVrr/o+yuX8n01PT6/2b6P9sbogAYBo0KZOncq+ffvcctgBtzzNrl27EhUVxdChQzl69CitW7eu62Y2KCNHjnR83a1bN+Li4oiNjWXx4sV4e3vXY8t+Xd59911GjhxJdHS0Y5tcd6I+lJaWcuedd6KUYsGCBW6PzZw50/F1t27dsFgsPPLII8ydOxer1VrXTW0w7r77bsfXXbt2pVu3brRu3Zp169YxdOjQemzZr897773HxIkT8fLyctsu117NfZRfA0kBqgNhYWF4eHhUmQF+9uxZIiMj66lVDd+0adNYvnw5a9eupVmzZrXuGxcXB8CRI0fqomm/KkFBQbRr144jR44QGRlJSUkJWVlZbvvIteju5MmTrFq1igcffLDW/eS6q5n9eqrt715kZGSVQghlZWVcuHBBrscK9s7/yZMnWblypdvd/+rExcVRVlbGiRMn6qaBvxKtWrUiLCzM8bsq197l+eGHH0hOTr7k30JofNdeTX2Uy/k/GxkZWe3fRvtjdUECgDpgsVjo3bs3q1evdmyz2WysXr2a+Pj4emxZw6SUYtq0aSxdupQ1a9bQsmXLSz4nKSkJgKioqF+4db8+eXl5HD16lKioKHr37o3ZbHa7FpOTk0lJSZFr0cXChQsJDw9n1KhRte4n113NWrZsSWRkpNu1lpOTw5YtWxzXWnx8PFlZWezYscOxz5o1a7DZbI7gqjGzd/4PHz7MqlWrCA0NveRzkpKSMJlMVdJbGrvTp0+TmZnp+F2Va+/yvPvuu/Tu3Zvu3btfct/Gcu1dqo9yOf9n4+Pj2bt3r1sQag/wO3XqVGcHIurAJ598oqxWq3r//ffVgQMH1MMPP6yCgoLcZoAL7dFHH1WBgYFq3bp1Ki0tzfFRUFCglFLqyJEj6qWXXlLbt29Xx48fV1988YVq1aqVGjRoUD23vGF46qmn1Lp169Tx48fVxo0bVUJCggoLC1MZGRlKKaWmTJmiYmJi1Jo1a9T27dtVfHy8io+Pr+dWNxzl5eUqJiZGzZo1y227XHdV5ebmql27dqldu3YpQL366qtq165djio18+bNU0FBQeqLL75Qe/bsUWPGjFEtW7ZUhYWFjtcYMWKE6tmzp9qyZYv68ccfVdu2bdWECRPq65DqVG3nr6SkRN16662qWbNmKikpye1vob1KyKZNm9T8+fNVUlKSOnr0qFq0aJFq0qSJuu++++r5yH55tZ273Nxc9fTTT6vExER1/PhxtWrVKtWrVy/Vtm1bVVRU5HgNufZq/t1VSqns7Gzl4+OjFixYUOX5jfnau1QfRalL/58tKytTXbp0UcOGDVNJSUnq22+/VU2aNFHPPvtsnR2HBAB16PXXX1cxMTHKYrGofv36qc2bN9d3kxokoNqPhQsXKqWUSklJUYMGDVIhISHKarWqNm3aqGeeeUZlZ2fXb8MbiLvuuktFRUUpi8WimjZtqu666y515MgRx+OFhYXqscceU8HBwcrHx0eNGzdOpaWl1WOLG5bvvvtOASo5Odltu1x3Va1du7ba39VJkyYppXQp0Oeee05FREQoq9Wqhg4dWuW8ZmZmqgkTJig/Pz8VEBCg7r//fpWbm1sPR1P3ajt/x48fr/Fv4dq1a5VSSu3YsUPFxcWpwMBA5eXlpTp27Khefvllt07uf6vazl1BQYEaNmyYatKkiTKbzSo2NlY99NBDVW64ybVX8++uUkq99dZbytvbW2VlZVV5fmO+9i7VR1Hq8v7PnjhxQo0cOVJ5e3ursLAw9dRTT6nS0tI6Ow6j4mCEEEIIIYQQjYDMARBCCCGEEKIRkQBACCGEEEKIRkQCACGEEEIIIRoRCQCEEEIIIYRoRCQAEEIIIYQQohGRAEAIIYQQQohGRAIAIYQQQgghGhEJAIQQQgghhGhEJAAQQgjR4BiGwbJly+q7GUII8V9JAgAhhBBuJk+ejGEYVT5GjBhR300TQghxDXjWdwOEEEI0PCNGjGDhwoVu26xWaz21RgghxLUkIwBCCCGqsFqtREZGun0EBwcDOj1nwYIFjBw5Em9vb1q1asVnn33m9vy9e/dy44034u3tTWhoKA8//DB5eXlu+7z33nt07twZq9VKVFQU06ZNc3v8/PnzjBs3Dh8fH9q2bcuXX375yx60EEI0EhIACCGE+Nmee+45xo8fz+7du5k4cSJ33303Bw8eBCA/P5/hw4cTHBzMtm3bWLJkCatWrXLr4C9YsICpU6fy8MMPs3fvXr788kvatGnj9h4vvvgid955J3v27OHmm29m4sSJXLhwoU6PUwgh/hsZSilV340QQgjRcEyePJlFixbh5eXltn327NnMnj0bwzCYMmUKCxYscDzWv39/evXqxT//+U/eeecdZs2axalTp/D19QVgxYoVjB49mjNnzhAREUHTpk25//77+dOf/lRtGwzD4A9/+AN//OMfAR1U+Pn58c0338hcBCGEuEoyB0AIIUQVQ4YMcevgA4SEhDi+jo+Pd3ssPj6epKQkAA4ePEj37t0dnX+AAQMGYLPZSE5OxjAMzpw5w9ChQ2ttQ7du3Rxf+/r6EhAQQEZGxpUekhBCiAoSAAghhKjC19e3SkrOteLt7X1Z+5nNZrfvDcPAZrP9Ek0SQohGReYACCGE+Nk2b95c5fuOHTsC0LFjR3bv3k1+fr7j8Y0bN2IymWjfvj3+/v60aNGC1atX12mbhRBCaDICIIQQoori4mLS09Pdtnl6ehIWFgbAkiVL6NOnD9dffz0ffvghW7du5d133wVg4sSJzJkzh0mTJvHCCy9w7tw5pk+fzr333ktERAQAL7zwAlOmTCE8PJyRI0eSm5vLxo0bmT59et0eqBBCNEISAAghhKji22+/JSoqym1b+/btOXToEKAr9HzyySc89thjREVF8fHHH9OpUycAfHx8+O6775gxYwZ9+/bFx8eH8ePH8+qrrzpea9KkSRQVFTF//nyefvppwsLCuP322+vuAIUQohGTKkBCCCF+FsMwWLp0KWPHjq3vpgghhLgCMgdACCGEEEKIRkQCACGEEEIIIRoRmQMghBDiZ5HMUSGE+HWTEQAhhBBCCCEaEQkAhBBCCCGEaEQkABBCCCGEEKIRkQBACCGEEEKIRkQCACGEEEIIIRoRCQCEEEIIIYRoRCQAEEIIIYQQohGRAEAIIYQQQohG5P8DulFgZ2diI/wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(epochs), train_losses, label = \"Training loss\")\n",
    "plt.plot(range(epochs), test_losses, label = \"Testing loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Log loss\")\n",
    "plt.legend()\n",
    "plt.title(\"Log loss vs training iterations\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:05.493382Z",
     "iopub.status.busy": "2024-08-15T02:46:05.493089Z",
     "iopub.status.idle": "2024-08-15T02:46:05.696786Z",
     "shell.execute_reply": "2024-08-15T02:46:05.696097Z"
    },
    "id": "D2HDVGLPODIE"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(epochs), train_accs, label = \"Training accuracy\")\n",
    "plt.plot(range(epochs), test_accs, label = \"Testing accuracy\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy (%)\")\n",
    "plt.legend()\n",
    "plt.title(\"Accuracy vs training iterations\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:05.700392Z",
     "iopub.status.busy": "2024-08-15T02:46:05.700135Z",
     "iopub.status.idle": "2024-08-15T02:46:05.704140Z",
     "shell.execute_reply": "2024-08-15T02:46:05.703546Z"
    },
    "id": "jonKhUzuPyfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training log loss: 0.089\n",
      "Final testing log Loss: 0.077\n"
     ]
    }
   ],
   "source": [
    "print(f\"Final training log loss: {train_losses[-1]:.3f}\")\n",
    "print(f\"Final testing log Loss: {test_losses[-1]:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:05.707333Z",
     "iopub.status.busy": "2024-08-15T02:46:05.707089Z",
     "iopub.status.idle": "2024-08-15T02:46:05.710863Z",
     "shell.execute_reply": "2024-08-15T02:46:05.710197Z"
    },
    "id": "d3DF4qyrPyke"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training accuracy: 0.968\n",
      "Final testing accuracy: 0.979\n"
     ]
    }
   ],
   "source": [
    "print(f\"Final training accuracy: {train_accs[-1]:.3f}\")\n",
    "print(f\"Final testing accuracy: {test_accs[-1]:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yrj1TbOJasjA"
   },
   "source": [
    "The model demonstrates a high accuracy and a low loss when it comes to classifying tumors in the training dataset and also generalizes well to the unseen test data. To go one step further, you can explore error rates that give more insight beyond the overall accuracy score. The two most popular error rates for binary classification problems are the false positive rate (FPR) and the false negative rate (FNR).\n",
    "\n",
    "For this problem, the FPR is the proportion of malignant tumor predictions amongst tumors that are actually benign. Conversely, the FNR is the proportion of benign tumor predictions among tumors that are actually malignant.\n",
    "\n",
    "Compute a confusion matrix using [`sklearn.metrics.confusion_matrix`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix), which evaluates the accuracy of the classification, and use matplotlib to display the matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:05.714332Z",
     "iopub.status.busy": "2024-08-15T02:46:05.714088Z",
     "iopub.status.idle": "2024-08-15T02:46:05.721292Z",
     "shell.execute_reply": "2024-08-15T02:46:05.720659Z"
    },
    "id": "OJO7YkA8ZDMU"
   },
   "outputs": [],
   "source": [
    "def show_confusion_matrix(y, y_classes, typ):\n",
    "  # Compute the confusion matrix and normalize it\n",
    "  plt.figure(figsize=(10,10))\n",
    "  confusion = sk_metrics.confusion_matrix(y.numpy(), y_classes.numpy())\n",
    "  confusion_normalized = confusion / confusion.sum(axis=1, keepdims=True)\n",
    "  axis_labels = range(2)\n",
    "  ax = sns.heatmap(\n",
    "      confusion_normalized, xticklabels=axis_labels, yticklabels=axis_labels,\n",
    "      cmap='Blues', annot=True, fmt='.4f', square=True)\n",
    "  plt.title(f\"Confusion matrix: {typ}\")\n",
    "  plt.ylabel(\"True label\")\n",
    "  plt.xlabel(\"Predicted label\")\n",
    "\n",
    "y_pred_train, y_pred_test = log_reg(x_train_norm, train=False), log_reg(x_test_norm, train=False)\n",
    "train_classes, test_classes = predict_class(y_pred_train), predict_class(y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:05.724776Z",
     "iopub.status.busy": "2024-08-15T02:46:05.724333Z",
     "iopub.status.idle": "2024-08-15T02:46:05.891252Z",
     "shell.execute_reply": "2024-08-15T02:46:05.890520Z"
    },
    "id": "OQ5DFcleiDFm"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_confusion_matrix(y_train, train_classes, 'Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:05.894315Z",
     "iopub.status.busy": "2024-08-15T02:46:05.894070Z",
     "iopub.status.idle": "2024-08-15T02:46:06.059676Z",
     "shell.execute_reply": "2024-08-15T02:46:06.059064Z"
    },
    "id": "gtfcsAp_iCNR"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ibK/t2bNnksw1PrPkkkumZ8+eufLKK+v8ov7vf/87f/vb32rf5/ygSZMms32aft555802yfmyIZpTU1ZfAwcOTE1NTS677LJcfPHFadq0afbaa6/Z6ihimdrtttsuM2bMyMknnzzbc1988UXt+/nwww9nO////jkvtNBCSYq5Bl9q0qRJ+vXrl5tvvjlvv/127fZx48blr3/9a2HnAVgQmGAASWb9In/NNddk++23z0orrVTnm7wfeuih/PGPf6xd0ahHjx4ZMGBALr744kyaNCl9+/bNo48+miuvvDJbbbVVnRWkKrXDDjvk6KOPztZbb51DDjkkn332WUaOHJkVVlihzs3NJ510Uu6///787Gc/S+fOnfPee+/lwgsvzA9+8IP85Cc/mevxTz/99Gy66abp06dP9tprr9plalu3bl373Qnzg8033zy/+93v0rp166y88soZM2ZM7rrrrtolVL/Us2fPNGnSJKeddlo++uijVFdXZ8MNN0z79u3rdb4rrrgit956a0aPHp0f/OAHSWY1NLvssktGjhyZAw44oHbfIpap7du3b/bdd9+MGDEiTz75ZDbeeOM0a9YsL774Yv74xz/mnHPOybbbbpsrr7wyF154Ybbeeusst9xy+eSTT3LJJZekVatWtQ1hy5Yts/LKK+e6667LCiuskMUWWyyrrrpqVl111W9cXzLrOzT+9re/ZZ111sn++++fGTNm5Pzzz8+qq66aJ598sqJjAyxINBhArZ///Od5+umnc/rpp+f//u//MnLkyFRXV6d79+4588wzM3DgwNp9L7300nTt2jWjR4/OTTfdlI4dO2bIkCEZNmxYoTUtvvjiuemmmzJ48OAcddRRtd9B8eKLL9ZpMH7+85/n1VdfzeWXX54JEyakXbt26du3b4YPH1570/Sc9OvXL7fffnuGDRuWoUOHplmzZunbt29OO+20et8Q/W0655xz0qRJk1x99dX5/PPPs8466+Suu+6abUWojh07ZtSoURkxYkT22muvzJgxI/fee2+9Gow333wzgwYNyhZbbJEBAwbUbt95553zpz/9KUcddVQ23XTTwq/PqFGj0qtXr1x00UU59thj07Rp03Tp0iW77LJL1llnnSSpbWavvfbajB8/Pq1bt86aa66Zq6++uk49l156aQ4++OAMGjQo06ZNy7BhwypuMHr16pW//vWvOeKII3LCCSekU6dOOemkkzJ27Ng8//zzFR0bmM80ktWa5lelme5gA4BvbKuttsqzzz6bF198saFLAQrScpOzGrqEuZpy++Cv36mBac8AYB5NmTKlzs8vvvhibrvttjl+qSHA95WIFADMo65du2b33XdP165d89prr2XkyJFp3rx5jjrqqIYuDSiSVaQqosEAgHm0ySab5A9/+EPefffdVFdXp0+fPjn11FPzwx/+sKFLA5hvaDAAYB5dccUVDV0CwHxPgwEAAOWsIlURVw8AACiMBgMAACjMAhmRarn6QQ1dAkChPvzn+Q1dAkChWszPv4VaRaoiJhgAAEBhNBgAAEBh5ufhFAAAfPesIlURVw8AACiMBgMAACiMiBQAAJQTkaqIqwcAABRGgwEAABRGRAoAAMr5or2KmGAAAACF0WAAAACFEZECAIByVpGqiKsHAAAURoMBAAAURkQKAADKWUWqIiYYAABAYTQYAABAYUSkAACgnFWkKuLqAQAAhdFgAAAAhRGRAgCAclaRqogJBgAAUBgNBgAAUBgRKQAAKFMSkaqICQYAAFAYDQYAAFAYESkAACgjIlUZEwwAAKAwGgwAAKAwIlIAAFBOQqoiJhgAAEBhNBgAAEBhRKQAAKCMVaQqY4IBAAAURoMBAAAURkQKAADKiEhVxgQDAAAojAYDAAAojIgUAACUEZGqjAkGAABQGA0GAABQGBEpAAAoIyJVGRMMAACgMBoMAACgMCJSAABQTkKqIiYYAABAYTQYAABAYUSkAACgjFWkKmOCAQAAFEaDAQAAFEZECgAAyohIVcYEAwAAKIwGAwAAKIyIFAAAlBGRqowJBgAAUBgNBgAAUBgRKQAAKCMiVRkTDAAAoDAaDAAAoDAiUgAAUE5CqiImGAAAQGE0GAAAQGE0GAAAUKZUKs23j/q64IIL0qVLl7Ro0SK9e/fOo48++pX7n3322enWrVtatmyZTp06ZdCgQfn888/rdU4NBgAALICuu+66DB48OMOGDcsTTzyRHj16pH///nnvvffmuP8111yTY445JsOGDcvYsWNz2WWX5brrrsuxxx5br/NqMAAAYAF01llnZeDAgdljjz2y8sorZ9SoUVlooYVy+eWXz3H/hx56KOuss0522mmndOnSJRtvvHF23HHHr516/C8NBgAAlGnoGFQREalp06bl8ccfT79+/Wq3VVVVpV+/fhkzZswcX7P22mvn8ccfr20oXn755dx2223ZbLPN6nX9LFMLAACNxNSpUzN16tQ626qrq1NdXV1n24QJEzJjxox06NChzvYOHTrk+eefn+Oxd9ppp0yYMCE/+clPMnPmzHzxxRfZb7/9RKQAAGBBNWLEiLRu3brOY8SIEYUc+7777supp56aCy+8ME888URuvPHG3HrrrTn55JPrdRwTDAAAKPNNVmv6rgwZMiSDBw+us+1/pxdJ0q5duzRp0iTjx4+vs338+PHp2LHjHI99wgknZNddd83ee++dJFlttdUyefLk7LPPPjnuuONSVTVvswkTDAAAaCSqq6vTqlWrOo85NRjNmzdPr169cvfdd9duq6mpyd13350+ffrM8difffbZbE1EkyZNkiQzZ86c5xpNMAAAYAE0ePDgDBgwIGussUbWXHPNnH322Zk8eXL22GOPJMluu+2WpZdeujZitcUWW+Sss87K6quvnt69e2fcuHE54YQTssUWW9Q2GvNCgwEAAOXm34RUvWy//fZ5//33M3To0Lz77rvp2bNnbr/99tobv19//fU6E4vjjz8+pVIpxx9/fN56660sscQS2WKLLfKrX/2qXuctzazPvKORaLn6QQ1dAkChPvzn+Q1dAkChWszHH3O33+v6hi5hrt67bLuGLuFruQcDAAAozHzcOwIAwHdvfl5FqjEwwQAAAAqjwQAAAAojIgUAAGVEpCpjggEAABRGgwEAABRGRAoAAMqISFXGBAMAACiMBgMAACiMiBQAAJQRkaqMCQYAAFAYDQYAAFAYESkAACgnIVUREwwAAKAwGgwAAKAwIlIAAFDGKlKVMcEAAAAKo8EAAAAKIyIFAABlRKQqY4IBAAAURoMBAAAURkQKAADKiEhVxgQDAAAojAYDAAAojIgUAACUk5CqiAkGAABQGA0GAABQGBEpAAAoYxWpyphgAAAAhdFgAAAAhRGRAgCAMiJSlTHBAAAACqPBAAAACiMiBQAAZUSkKmOCAQAAFEaDAQAAFEZECgAAyohIVcYEAwAAKIwGAwAAKIyIFAAAlJOQqogJBgAAUBgNBgAAUBgNBgAAUBj3YAAAQBnL1FbGBAMAACiMBgMAACiMiBQAAJQRkaqMCQYAAFAYDQYAAFAYESkAACgjIVUZEwwAAKAwGgwAAKAwIlIAAFDGKlKVMcEAAAAKo8EAAAAKIyIFAABlJKQqY4IBAAAURoMBAAAURkQKAADKWEWqMiYYAABAYTQYAABAYUSkAACgjIRUZUwwAACAwmgwAACAwohIAQBAmaoqGalKmGAAAACF0WAAAACFEZECAIAyVpGqjAkGAABQGA0GAABQGBEpAAAoU5KRqogJBgAAUBgNBgAAUBgRKQAAKCMhVRkNBt8b+263XgYN+Gk6LN4qz/znrQw+7Y957NnX5rhv06ZVOXLPjbPL5r2zVPs2+c9r43P8Of+XOx8aW7vP87cOT+elFp/ttaOuuz+Dfn19lllysbxw20lzPP7OR16WG+/6V5KkU8e2OefY7dN3jRXy6ZSpufqWR3LCeX/OjBk1BbxrYEF27TVX58orLsuECe9nhW4r5phjT8hq3bvPdf+/3fHXXHDeOXn7rbeyTOcuOWzwEVl3vb5JkunTp+f8c8/Ogw/cnzfffCOLLrJIevdZO4cOOjzt23eoPcYhB+6XF55/PhMnfpBWrVqnd58+OWzwEXX2+c8Lz+fUU07Ks/9+Jm0XWyw77rRL9thr4Ld3IYD5iogU3wvbbvyjnHb41vnVRX9Nn51Oy9P/eSt/vvDALNF2kTnuf+IBW2TvX/wkg3/zx6z+i1Ny6Q0P5rozB6ZHtx/U7vOTXU5Pl35Dah+b7XdekuTGO2c1Dm+O/7DO8136DclJI/+STyZ/njv+8WySpKqqlBvP3T/NmzXNBrufmYFDf5ddft47Q/f/2bd8RYDG7va/3pYzfjMi+x5wYK79403p1m3F7L/vXvnggw/muP+T/3oixxx5eLbeZttcd8PN2WDDn+awgw/Miy/+J0ny+eef5/mxz2Wf/fbPdX+8MWedc35efeWVHHrQ/nWO8+M118rpZ52d/7v19px59rl58403csSgQ2uf//TTT7PfwL2y5FJL5Q9/vDGDDj8qoy48Pzdcf923dzGA+Upp5syZMxu6iKK1XP2ghi6B+cz9Vx2Rx599LYNO+2OSWatDjLv95Iy89u8544o7Z9v/5b/9Kqddekcuuv7+2m1/OGPvTPl8WvY8/qo5nuP0I36RTdddNatuOXyudYz5w9F58vk3sv/wa5IkG6+zcm48Z7903fi4vDfxkyTJ3tv+JKccsmU6bXhMpn8x4xu/ZxYsH/7z/IYugfnMzjv8MqusulqOPX5okqSmpiYb/7Rvdtxp1+w1cJ/Z9j/y8MMyZcqUnH/hRbXbdtlxu3RbccWcMGzO09Z/P/N0dt7hl7n9znuz5FJLzXGf++65O4cdcmD++a9n0qxZs1x/7TU575yzc8/fH0yz5s2TJGefdUbuveeu/N9fbq/0bbMAaTEf52i6D72roUuYq6dP6tfQJXytBp1gTJgwIb/5zW+y9dZbp0+fPunTp0+23nrrnH766Xn//fcbsjQWIM2aNsnqK3XKPY+8ULtt5syZueeRF7Jm92Xn+JrmzZrm82nT62yb8vm0rL36cnM9xw6b/ThX/t+Yudax+kqd0nPFTrny5v/u07v7svn3uLdrm4skufOhsWm9aMusvNyS8/T+gO+f6dOmZexzz2atPmvXbquqqspaa62dp5/61xxf8/STT2attfrU2bb2Oj/J008+OdfzfPrppymVSlm0Vas5Pv/RpEm59dZb0qPn6mnWrFmS5KmnnkyvNdaobS6+PM+rr7ySjz/6aF7fItCINViD8c9//jMrrLBCzj333LRu3Trrrbde1ltvvbRu3TrnnntuVlxxxTz22GMNVR4LkHZtF0nTpk3q/BKfJO998HE6Lj7nfzTvGjM2h+yyYZZbZomUSqVs2HvFbLlhz3RsN+f9f75B97RZtGV+f8sjc61jwFZ9Mvbld/LwU6/UbuuweKu898H/1DXx41nPzeVcAB9O+jAzZszI4ovXvQ9s8cUXz4QJE+b4mgkTJmTxxdvNvv8Hc95/6tSpOfusM7LpZj/LIovUjZP+9szT03uNnllvnd559513cs75F9Y5z2Kznadd7XPAgq/BhlMHH3xwfvnLX2bUqFGzfZnJzJkzs99+++Xggw/OmDFz/0Q4mfUX4NSpU+u+vmZGSlVNCq+Z748jTr8hF56wY5668YTMnDkzL785IVf9+eEM2HKtOe4/YKu1c8c/nss778/507kW1c2y/aZr5NeXiAcA87/p06fnyMGHZubMmTlu6Oyxz9333Ctb/2LbvPP22xl14fk5fsjROe/Ci3w5GQsM/y9XpsEajKeeeiqjR4+e4x9gqVTKoEGDsvrqq3/tcUaMGJHhw+v+5dekw4/TbMk1C6uVxm3Ch5/miy9mpP1ii9bZ3n7xVnn3g4/n+prtBl+S6uZNs3jrhfP2+x/llEO2zCtvzX7z5DJLts2GvbtlhyMumWsNW/frmYVaNM/Vf3m0zvbxH3ycNVbtXLeuxWZNLsZPmHNtAG3btE2TJk1mu6H7gw8+SLt27eb4mnbt2uWD/5lWfPDBB2n3P9OG6dOn58jDD8s7b7+dS664crbpRZK0bbtY2rZdLF26LJuuXZfLxj/tm6efejI9eq6edu3aZeJs55lQWwOw4GuwiFTHjh3z6KOPzvX5Rx99NB06dJjr818aMmRIPvroozqPph16FVkqjdz0L2bkX2PfyAa9u9VuK5VK2WDNFfLo0698xSuTqdO+yNvvf5SmTauy1U975i/3PT3bPrv+vE/em/hJ/vrAs3M9zu5brZ1b//5MJnz4aZ3tjzz9SlZdfqk6q1n9dK0V89EnUzL25Xfn9S0C3zPNmjfPSiuvkkce/u+Uv6amJo88Mibde8z5w7nuPXvmkYcfrrPt4TEPpXvPnrU/f9lcvP7aa7nostFp06bt19ZSUzNrSe1p06YlSXr06JnHH3ss06f/9z62h8c8lC7LLptWrVvP83sEGq8Gm2AcccQR2WefffL444/npz/9aW0zMX78+Nx999255JJLcsYZZ3ztcaqrq1NdXV1nm3gU/+vc39+TS07aNY8/93oe+/erOWinDbJQy+pc9X+z/rG99ORd8/Z7H2XoeX9Okvx41c5Zqn2bPPXCm1m6fZsct+9mqaoq5azRdVeVKJVK2W3LtXL1Xx6Z6/dWdO3ULj/50XLZ6uCRsz1315ixGfvyu7nslAE57pyb02HxVhl24Oa56Pr7M236FwVfBWBBsuuAPXLCsUdnlVVWzaqrdc/vf3dlpkyZkq223iZJctyQo9K+fYccOujwJMnOu+yWvXbfNVeOvjzrrdc3t//1tjz773/nhBNnrSA1ffr0HDHokIwd+1zOu+Ci1MyYkQn/f8GV1q1bp1nz5nn66afy7DPPZPUf9Uqr1q3yxuuv58LzzkmnTsukR89Zjc2mP9sioy68ICcOPS577DUw4158MVf//qocedSQBrhK8M1ISFWmwRqMAw88MO3atctvf/vbXHjhhZkxY9ZynE2aNEmvXr0yevTobLfddg1VHguYG/72RNq1XSRD9/9ZOiy+aJ5+4a1seeAFtTd+d+q4WGpq/rtic3V1sww7cPMsu3S7fPrZ1Nzxj2ez1wlX5aNPp9Q57oa9u2WZJRfLlTfX/VSw3IAt++St8ZNy15jnZ3uupmZmfnHoyJxz7A65b/Thmfz51Fx9y6M5aeStBb1zYEG1yaab5cOJE3Ph+edmwoT3023FlXLhRZdm8f8fQ3r3nXdSVfpvUKHn6j/KiN+ckfPPPTvnnX1WluncJWefd0F++MMVkiTvvTc+9917T5Jku19sWedcl15xVX68Zu+0bNEid9/1t4y84LxMmfJZ2i2xRNb5ybr5zb4HpPn/XzVq0UUXzahLLsupp5yUHX+5Tdq0bZt99zsg2263/XdxWYD5wHzxPRjTp0+vXVmiXbt2tUvdfVO+BwNY0PgeDGBBMz9/D0bPE+9u6BLm6skTf9rQJXyt+eKPtlmzZllySWv+AwDQ8KwiVZkG/aI9AABgwaLBAAAACjNfRKQAAGB+ISFVGRMMAACgMBoMAACgMCJSAABQxipSlTHBAAAACqPBAAAACiMiBQAAZSSkKmOCAQAAFEaDAQAAFEZECgAAylhFqjImGAAAQGE0GAAAQGFEpAAAoIyEVGVMMAAAgMJoMAAAgMKISAEAQBmrSFXGBAMAACiMBgMAACiMiBQAAJSRkKqMCQYAAFAYDQYAAFAYESkAAChjFanKmGAAAACF0WAAAACFEZECAIAyElKVMcEAAAAKo8EAAAAKIyIFAABlrCJVGRMMAACgMBoMAACgMCJSAABQRkKqMiYYAABAYTQYAABAYUSkAACgjFWkKmOCAQAAFEaDAQAAFEZECgAAyohIVcYEAwAAKIwGAwAAKIyIFAAAlJGQqowJBgAAUBgNBgAAUBgRKQAAKGMVqcqYYAAAAIXRYAAAAIURkQIAgDISUpUxwQAAAAqjwQAAAAojIgUAAGWsIlUZEwwAAKAwGgwAAKAwIlIAAFBGQqoyJhgAAEBhNBgAAEBhRKQAAKBMlYxURUwwAACAwmgwAACAwohIAQBAGQmpyphgAAAAhdFgAAAAhRGRAgCAMiUZqYqYYAAAAIXRYAAAwALqggsuSJcuXdKiRYv07t07jz766FfuP2nSpBx44IFZcsklU11dnRVWWCG33XZbvc4pIgUAAGWqFpCE1HXXXZfBgwdn1KhR6d27d84+++z0798/L7zwQtq3bz/b/tOmTctGG22U9u3b54YbbsjSSy+d1157LW3atKnXeTUYAACwADrrrLMycODA7LHHHkmSUaNG5dZbb83ll1+eY445Zrb9L7/88kycODEPPfRQmjVrliTp0qVLvc8rIgUAAI3E1KlT8/HHH9d5TJ06dbb9pk2blscffzz9+vWr3VZVVZV+/fplzJgxczz2n//85/Tp0ycHHnhgOnTokFVXXTWnnnpqZsyYUa8aNRgAAFCmVCrNt48RI0akdevWdR4jRoyY7T1MmDAhM2bMSIcOHeps79ChQ9599905vu+XX345N9xwQ2bMmJHbbrstJ5xwQs4888yccsop9bp+IlIAANBIDBkyJIMHD66zrbq6upBj19TUpH379rn44ovTpEmT9OrVK2+99VZOP/30DBs2bJ6Po8EAAIBGorq6ep4ainbt2qVJkyYZP358ne3jx49Px44d5/iaJZdcMs2aNUuTJk1qt6200kp59913M23atDRv3nyeahSRAgCAMqXS/PuYV82bN0+vXr1y9913126rqanJ3XffnT59+szxNeuss07GjRuXmpqa2m3/+c9/suSSS85zc5FoMAAAYIE0ePDgXHLJJbnyyiszduzY7L///pk8eXLtqlK77bZbhgwZUrv//vvvn4kTJ+bQQw/Nf/7zn9x666059dRTc+CBB9brvCJSAACwANp+++3z/vvvZ+jQoXn33XfTs2fP3H777bU3fr/++uupqvrvvKFTp0654447MmjQoHTv3j1LL710Dj300Bx99NH1Om9p5syZMwt9J/OBlqsf1NAlABTqw3+e39AlABSqxXz8MffmF/2zoUuYq7/s++OGLuFriUgBAACF0WAAAACFmY+HUwAA8N2rqsdqTczOBAMAACiMBgMAACiMiBQAAJQp1ecb7ZiNCQYAAFAYDQYAAFAYESkAACgjIVUZEwwAAKAwGgwAAKAwIlIAAFCmSkaqIiYYAABAYTQYAABAYUSkAACgjIRUZUwwAACAwmgwAACAwohIAQBAmZKMVEVMMAAAgMJoMAAAgMKISAEAQBkJqcqYYAAAAIXRYAAAAIURkQIAgDJVMlIVMcEAAAAKo8EAAAAKIyIFAABlBKQqY4IBAAAURoMBAAAURkQKAADKlKwiVRETDAAAoDAaDAAAoDAiUgAAUKZKQqoiJhgAAEBhNBgAAEBhRKQAAKCMVaQqY4IBAAAURoMBAAAURkQKAADKSEhVxgQDAAAojAYDAAAojIgUAACUsYpUZeapwfjzn/88zwf8+c9//o2LAQAAGrd5ajC22mqreTpYqVTKjBkzKqkHAABoxOapwaipqfm26wAAgPlClYRURSq6yfvzzz8vqg4AAGABUO8GY8aMGTn55JOz9NJLZ5FFFsnLL7+cJDnhhBNy2WWXFV4gAADQeNS7wfjVr36V0aNH5ze/+U2aN29eu33VVVfNpZdeWmhxAADwXSuVSvPtozGod4Nx1VVX5eKLL87OO++cJk2a1G7v0aNHnn/++UKLAwAAGpd6NxhvvfVWll9++dm219TUZPr06YUUBQAANE71bjBWXnnlPPDAA7Ntv+GGG7L66qsXUhQAADSU0nz8aAzq/U3eQ4cOzYABA/LWW2+lpqYmN954Y1544YVcddVV+ctf/vJt1AgAADQS9Z5gbLnllrnlllty1113ZeGFF87QoUMzduzY3HLLLdloo42+jRoBAIBGot4TjCRZd911c+eddxZdCwAANLiqRrJa0/zqGzUYSfLYY49l7NixSWbdl9GrV6/CigIAABqnejcYb775Znbcccf84x//SJs2bZIkkyZNytprr51rr702P/jBD4quEQAAaCTqfQ/G3nvvnenTp2fs2LGZOHFiJk6cmLFjx6ampiZ77733t1EjAAB8Z0ql+ffRGNR7gvH3v/89Dz30ULp161a7rVu3bjnvvPOy7rrrFlocAADQuNR7gtGpU6c5fqHejBkzstRSSxVSFAAA0DjVu8E4/fTTc/DBB+exxx6r3fbYY4/l0EMPzRlnnFFocQAA8F0rlUrz7aMxmKeIVNu2beu8ocmTJ6d3795p2nTWy7/44os0bdo0e+65Z7baaqtvpVAAAGD+N08Nxtlnn/0tlwEAACwI5qnBGDBgwLddBwAAzBcaSRJpvvWNv2gvST7//PNMmzatzrZWrVpVVBAAANB41fsm78mTJ+eggw5K+/bts/DCC6dt27Z1HgAAwPdXvRuMo446Kvfcc09GjhyZ6urqXHrppRk+fHiWWmqpXHXVVd9GjQAA8J2pKpXm20djUO+I1C233JKrrroq66+/fvbYY4+su+66WX755dO5c+dcffXV2Xnnnb+NOgEAgEag3hOMiRMnpmvXrklm3W8xceLEJMlPfvKT3H///cVWBwAANCr1bjC6du2aV155JUmy4oor5vrrr08ya7LRpk2bQosDAIDvWqk0/z4ag3o3GHvssUeeeuqpJMkxxxyTCy64IC1atMigQYNy5JFHFl4gAADQeNT7HoxBgwbV/ne/fv3y/PPP5/HHH8/yyy+f7t27F1ocAADQuFT0PRhJ0rlz53Tu3LmIWgAAoMGVGksWaT41Tw3GueeeO88HPOSQQ75xMQAAQOM2Tw3Gb3/723k6WKlU0mAAAMD32Dw1GF+uGtVYfPDIeQ1dAkCh2q416Ot3AmhEpjw2bx9gN4R6r4JEHa4fAABQGA0GAABQmIpXkQIAgAWJVaQqY4IBAAAURoMBAAAU5hs1GA888EB22WWX9OnTJ2+99VaS5He/+10efPDBQosDAIDvWlVp/n00BvVuMP70pz+lf//+admyZf71r39l6tSpSZKPPvoop556auEFAgAAjUe9G4xTTjklo0aNyiWXXJJmzZrVbl9nnXXyxBNPFFocAADQuNR7FakXXngh66233mzbW7dunUmTJhVREwAANJjGEkWaX9V7gtGxY8eMGzdutu0PPvhgunbtWkhRAABA41TvBmPgwIE59NBD88gjj6RUKuXtt9/O1VdfnSOOOCL777//t1EjAADQSNQ7InXMMcekpqYmP/3pT/PZZ59lvfXWS3V1dY444ogcfPDB30aNAADwnfFFe5Wpd4NRKpVy3HHH5cgjj8y4cePy6aefZuWVV84iiyzybdQHAAA0IvVuML7UvHnzrLzyykXWAgAANHL1bjA22GCDrxwb3XPPPRUVBAAADckqUpWpd4PRs2fPOj9Pnz49Tz75ZP79739nwIABRdUFAAA0QvVuMH7729/OcfuJJ56YTz/9tOKCAACAxqvey9TOzS677JLLL7+8qMMBAECDKJXm30djUFiDMWbMmLRo0aKowwEAAI1QvSNS22yzTZ2fZ86cmXfeeSePPfZYTjjhhMIKAwAAGp96NxitW7eu83NVVVW6deuWk046KRtvvHFhhQEAQEOoaixZpPlUvRqMGTNmZI899shqq62Wtm3bfls1AQAAjVS97sFo0qRJNt5440yaNOlbKgcAAGjM6n2T96qrrpqXX37526gFAAAaXNV8/GgM6l3nKaeckiOOOCJ/+ctf8s477+Tjjz+u8wAAAL6/5vkejJNOOimHH354NttssyTJz3/+85TKboCZOXNmSqVSZsyYUXyVAABAozDPDcbw4cOz33775d577/026wEAgAZlEanKzHODMXPmzCRJ3759v7ViAACAxq1e92CUtHMAAMBXqNf3YKywwgpf22RMnDixooIAAKAh+aK9ytSrwRg+fPhs3+QNAADwpXo1GDvssEPat2//bdUCAAA0cvPcYLj/AgCA7wO/9lZmnm/y/nIVKQAAgLmZ5wlGTU3Nt1kHAACwAKjXPRgAALCgqxKRqki9vgcDAADgq2gwAACAwohIAQBAGV+0VxkTDAAAoDAaDAAAoDAiUgAAUEZCqjImGAAAQGE0GAAAQGFEpAAAoIwv2quMCQYAAFAYDQYAAFAYESkAAChTioxUJUwwAACAwmgwAACAwohIAQBAGatIVcYEAwAAKIwGAwAAKIyIFAAAlBGRqowJBgAAUBgNBgAAUBgRKQAAKFMqyUhVwgQDAAAojAYDAAAojIgUAACUsYpUZUwwAACAwmgwAACAwohIAQBAGYtIVcYEAwAAKIwGAwAAKIyIFAAAlKmSkaqICQYAAFAYDQYAAFAYESkAACjji/YqY4IBAAAURoMBAAAURkQKAADKWESqMiYYAABAYTQYAABAYUSkAACgTFVkpCphggEAAAuoCy64IF26dEmLFi3Su3fvPProo/P0umuvvTalUilbbbVVvc+pwQAAgAXQddddl8GDB2fYsGF54okn0qNHj/Tv3z/vvffeV77u1VdfzRFHHJF11133G51XgwEAAGVKpfn3UR9nnXVWBg4cmD322CMrr7xyRo0alYUWWiiXX375XF8zY8aM7Lzzzhk+fHi6du36ja6fBgMAABYw06ZNy+OPP55+/frVbquqqkq/fv0yZsyYub7upJNOSvv27bPXXnt943O7yRsAABqJqVOnZurUqXW2VVdXp7q6us62CRMmZMaMGenQoUOd7R06dMjzzz8/x2M/+OCDueyyy/Lkk09WVKMJBgAAlKkqzb+PESNGpHXr1nUeI0aMqPg9f/LJJ9l1111zySWXpF27dhUdywQDAAAaiSFDhmTw4MF1tv3v9CJJ2rVrlyZNmmT8+PF1to8fPz4dO3acbf+XXnopr776arbYYovabTU1NUmSpk2b5oUXXshyyy03TzVqMAAAoJGYUxxqTpo3b55evXrl7rvvrl1qtqamJnfffXcOOuig2fZfccUV88wzz9TZdvzxx+eTTz7JOeeck06dOs1zjRoMAAAoU1Xf5ZrmU4MHD86AAQOyxhprZM0118zZZ5+dyZMnZ4899kiS7Lbbbll66aUzYsSItGjRIquuumqd17dp0yZJZtv+dTQYAACwANp+++3z/vvvZ+jQoXn33XfTs2fP3H777bU3fr/++uupqir+luzSzJkzZxZ+1Ab22bQF7i0B33OLrz3463cCaESmPPbbhi5hri5++LWGLmGu9lmrc0OX8LVMMAAAoMwCkpBqMJapBQAACqPBAAAACiMiBQAAZRaUVaQaigkGAABQGA0GAABQGBEpAAAoIyFVGRMMAACgMBoMAACgMCJSAABQxifwlXH9AACAwmgwAACAwohIAQBAmZJlpCpiggEAABRGgwEAABRGRAoAAMoISFXGBAMAACiMBgMAACiMiBQAAJSpsopURUwwAACAwmgwAACAwohIAQBAGQGpyphgAAAAhdFgAAAAhRGRAgCAMhaRqowJBgAAUBgNBgAAUBgRKQAAKFOSkaqICQYAAFAYDQYAAFAYESkAACjjE/jKuH4AAEBhNBgAAEBhRKQAAKCMVaQqY4IBAAAURoMBAAAURkQKAADKCEhVxgQDAAAojAYDAAAojIgUAACUsYpUZUwwAACAwmgwAACAwohIAQBAGZ/AV8b1AwAACqPBAAAACiMiBQAAZawiVRkTDAAAoDAaDAAAoDAiUgAAUEZAqjImGAAAQGE0GAAAQGFEpAAAoIxFpCpjggEAABRGgwEAABRGRAoAAMpUWUeqIiYYAABAYTQYAABAYUSkAACgjFWkKmOCAQAAFEaDAQAAFEZECgAAypSsIlUREwwAAKAwGgy+N677w9XZrP+G6d2re3bdabv8+5mnv3L/O++4PVtvsWl69+qeX269RR64/+91nh914XnZeotN02fN1bPe2mtm3733yDNPPzXbcR64/77sutN2WWuNHllv7TUz6JAD53i+SZM+TP+f9s3qq62YTz7++Ju/UeB7Y99frpPn/3xCPvzHb3L/6MOyxirLzHXfpk2qMmTvjfPszcflw3/8Jo9cc0Q26rPibPsttUTrXH7SznnzrlMy8cHT8s9rj8yPVupU+/xx+/TPkzcckwkP/Dpv3/Or3HrB/vnxHM67yTor5/7Rh2Xig6fl7Xt+levP2LOYNw3M90Sk+F644/bbcubpv85xJ5yYVbv3yDW/uzIH7Lt3br7lr1ls8cVn2//JJ5/IkKMPz8GHDs66fdfPX2/9SwYfelD+cP2fsvwPV0iSdO7cJUcfe0J+8INOmTr18/z+d1fmgH33yv/d+rcstthiSZK77rwjJ584NAcdOihrrtk7X8yYkZdefHGONQ4fenx+uEK3vPfe+G/vQgALjG036pnTBm2Vg0f8Mf/892s5aMe++fN5+6bHL0bk/Q8/nW3/Ew/YLDtu2isH/Or6vPDqe9lorW657vQ9ssFe5+apF95KkrRZtGXuueyQ/P2xF7PVoRfn/Q8/zfKdlsiHH39We5xxr72fQb+5Ma+89UFaVjfLwTv1zS0X7JdVt/pVJkyanCTZasPuueC47TLswtty3z9fTNMmVVlluSW/mwsDBbCKVGVKM2fOnNnQRRTts2kL3FuiQrvutF1WWWXVHHPc0CRJTU1NNtlo/eyw4y7Zc+99Ztv/6CMGZcqUz3LuBRfVbttt5+2zQrcVc/zQ4XM8x6effpp1+6yRUZdckd5r9ckXX3yRn/X/afY78OBsvc22X1nf9df9IX+7/bbss9+B2Xfv3XP/Px7Noq1aVfCOWdAsvvbghi6B+cz9ow/L48+9nkG/uTFJUiqVMu7WoRl53YM548q7Z9v/5b+emNMuvzMX/fEftdv+8JvdM+Xz6dlz6NVJkpMP2jx9eiybfgPPm+c6Fl24Ou/9/dfZdP8Lc98/X0yTJlV54c8n5OSLb8+V//dIhe+SBdmUx37b0CXM1W3PvtfQJczVZqu0b+gSvpaIFAu86dOnZexzz6b3WmvXbquqqkrvtfrk6aeenONrnn7qyTr7J0mftdeZ6/7Tp0/LjTdcl0UWXTQrdJsVOXh+7HN5773xqSqVssMvt85GG6ybA/cbmHEv/qfOa196aVwuGXVhTj71tFRV+cgE+HrNmjbJ6iv+IPc88t+/T2bOnJl7Hn0xa3bvPMfXNG/WNJ9P+6LOtimfT8/aPbvW/vyz9VbJE2PfyNW/HpDX/nZSxlx9ePbYaq2vrGOvrftk0idT8sx/3k6SrL7iD7J0hzapqZmZMVcfnpdvH56bz9knKy/XsZK3DDQi83WD8cYbb2TPPWU2qcyHH36YGTNmzBaFWnzxdvnggwlzfM2ECRPmvP+Euvvf//d7s/aaP0rvXj3y+99dmVEXX562bdsmSd58840kyaiRF2TvffbLOeePTKtWrTJwz93y0UeTkiTTpk3LkKMOz2GDj8ySSy5VxNsFvgfatVk4TZs2yXsTP6mz/b2Jn6Tj4nOeft718PM5ZKf1s1yndimVStmw9wrZcsPu6djuv/svu/TiGfiLtTPu9ffz84MvyiU3PJQzj9g6O//sx3WOtelPVs779/86kx76TQ7eqW82P3BkPvhocu0xkuT4ffrntMvuzC8OuySTPvksd1x0YNq2WqjIywDfmqqU5ttHYzBfNxgTJ07MlVde+ZX7TJ06NR9//HGdx9SpU7+jCvm++/GPe+faG27K6N/9IWuvs26OOuKwTPzggyTJzJqaJMneA/dNv436Z+VVVs3wU0YkpVLuvOP2JMm5Z5+ZZbsul59t8fMGew/A98MRZ9yUl954P0/dMCQfjzk9vz3qF7nqz4+m5v//XZUkVVWlPPn8mxl24W156oW3cvlNY3LFzQ9n4C/qTnT//ti49N7pjGyw57n525jn8/sRA7JE20VmHeP/h9dPu/yu3HzP0/nX829mn+F/yMyZyTb9enx3bxhoMA16k/ef//znr3z+5Zdf/tpjjBgxIsOH183EH3v80Bx3womVlMYCpG3btmnSpEntL/5f+uCDCVl88XZzfE27du3mvH+7uvu3XGihLLNM5yyzTOd079EzP/9Z/9x00w3Za+99026JJZIkXZdbvnb/5s2b5wc/6JR3330nSfLPRx/JuBf/kzXuvCPJrIhDkmywXp/sNXDf7H/gIRW8c2BBNWHS5HzxxYy0X2zROtvbL7Zo3v1gzqvQTZg0OdsdcXmqmzfN4q0Xztvvf5RTDt48r7w1sXafdyd8nLGv1F1o4vlXxmerDbvX2fbZ59Py8psT8vKbE/Lov1/LMzcemwFb9s4Zo+/OOxNmnf/5l9+t3X/a9Bl59a0P0qlj24reN9A4NGiDsdVWW6VUKuWr7jMvfc1t/EOGDMngwXVvfpxRal5IfSwYmjVrnpVWXiWPPDImG/y0X5JZN3k/+vDD2X7Hnef4mu49eubRR8Zk510H1G57eMxD6d6j51eea2ZNTaZPm5YkWWnlVdO8efO8+uorWf1HvZIk06dPz9tvvVUbhzrjt+dm6uef177+2X8/kxOHHpfLRv8+nTrNfblJ4Ptt+hcz8q/n38wGa66QW/7+7ySz/r3c4Mc/zKjrH/zK106d9kXefv+jNG1Sla027J4/3flk7XNjnnolK3SuewPpDzu3z+vvfPiVx6yqKqW6+axfKf71/Bv5fOr0/LBL+zz01CtJZi2Ru8ySi33tcWB+YRWpyjRog7HkkkvmwgsvzJZbbjnH55988sn06tXrK49RXV2d6urqOtusIsX/2mW33TP0uGOy8iqrZtXVuuea312ZKVOmZMuttkmSHH/s0Wnfvn0OOezwJMmOu+yagXvslquuvDzrrrt+7rj91jz37LM5YdhJSZIpn32WSy8Zlb7rb5h2SyyRSR9+mOuvvSbvvTc+G228SZJkkUUWybbb7ZBRF5yXjh07Zskll8qVoy9Pktp9/reJmDRp1j++XbsuZxUp4Cude/V9ueTEnfL4c2/ksWdfy0E79c1CLZvnqltmrdx06fCd8vZ7H2XoBbcmSX68yjJZqn3rPPWft7P0Eq1z3D79U1WqyllX3VN7zPOu+XvuvfzQHLlHv/zpzifz41WWyZ5br5WDfnV9kmShFs1z9J79cuv9z+bdCR9n8TYLZ9/tfpKllmidG++a9T1An0yemkv/9FBO2GeTvPnupLz+7sQM2nXDJMmNdz35HV4hoKE0aIPRq1evPP7443NtML5uugHzqv8mm+XDiRMz8oLz8sGE99NtxZVywahLaiNP777zdm1uOEl69vxRTv31Gbng/LNz/jm/zTKdu+Ssc86v/Q6MqiZN8uorr+SWPx+SSR9+mNZt2mSVVVbL5VdeneWW/2HtcQ4bfGSaNGmS44ccnalTP8+qq/XIxZeNTqvWrb/bCwAscG6488m0a7tIhu63STos3ipP/+etbHnwRXlv4qzvwOjUsW1qav77b2h1dbMM23+zLLv04vl0ytTc8Y+x2Wvo1fno0/9OUR9/7o1sf8TlOemgn+XYvTfOq29PzJFn3pxrb38iSTKjpibdunTILpv/OIu3WSQTP5qcx557Pf0GnpexZZGoIef8OV/MqMllJ+2cltXN8s9nX8um+1+YSZ9M+Y6uDtCQGvR7MB544IFMnjw5m2yyyRyfnzx5ch577LH07du3Xsc1wQAWNL4HA1jQzM/fg/G3se83dAlztfFKSzR0CV+rQScY66677lc+v/DCC9e7uQAAABrOfL1MLQAA0Lg06AQDAADmN6VG8oV28ysTDAAAoDAaDAAAoDAiUgAAUKZKQqoiJhgAAEBhNBgAAEBhRKQAAKCMVaQqY4IBAAAURoMBAAAURkQKAADKlCSkKmKCAQAAFEaDAQAAFEZECgAAylhFqjImGAAAQGE0GAAAQGFEpAAAoEyVhFRFTDAAAIDCaDAAAIDCiEgBAEAZq0hVxgQDAAAojAYDAAAojIgUAACUKUlIVcQEAwAAKIwGAwAAKIyIFAAAlJGQqowJBgAAUBgNBgAAUBgRKQAAKFNlGamKmGAAAACF0WAAAACF0WAAAACFcQ8GAACUcQdGZUwwAACAwmgwAACAwohIAQBAORmpiphgAAAAhdFgAAAAhRGRAgCAMiUZqYqYYAAAAIXRYAAAAIURkQIAgDIlCamKmGAAAACF0WAAAACFEZECAIAyElKVMcEAAAAKo8EAAAAKIyIFAADlZKQqYoIBAAAURoMBAAAURkQKAADKlGSkKmKCAQAAFEaDAQAAFEZECgAAypQkpCpiggEAABRGgwEAABRGRAoAAMpISFXGBAMAACiMBgMAACiMiBQAAJSTkaqICQYAAFAYDQYAAFAYESkAAChTkpGqiAkGAABQGA0GAABQGBEpAAAoU5KQqogJBgAAUBgNBgAAUBgRKQAAKCMhVRkTDAAAoDAaDAAAoDAiUgAAUE5GqiImGAAAQGE0GAAAQGFEpAAAoExJRqoiJhgAAEBhNBgAAEBhNBgAAFCmVJp/H/V1wQUXpEuXLmnRokV69+6dRx99dK77XnLJJVl33XXTtm3btG3bNv369fvK/edGgwEAAAug6667LoMHD86wYcPyxBNPpEePHunfv3/ee++9Oe5/3333Zccdd8y9996bMWPGpFOnTtl4443z1ltv1eu8pZkzZ84s4g3MTz6btsC9JeB7bvG1Bzd0CQCFmvLYbxu6hLl65s1PG7qEuVrtB4vM8769e/fOj3/845x//vlJkpqamnTq1CkHH3xwjjnmmK99/YwZM9K2bducf/752W233eb5vFaRAgCAMvPzGlJTp07N1KlT62yrrq5OdXV1nW3Tpk3L448/niFDhtRuq6qqSr9+/TJmzJh5Otdnn32W6dOnZ7HFFqtXjSJSAADQSIwYMSKtW7eu8xgxYsRs+02YMCEzZsxIhw4d6mzv0KFD3n333Xk619FHH52llloq/fr1q1eNJhgAANBIDBkyJIMH143N/u/0ogi//vWvc+211+a+++5LixYt6vVaDQYAAJSbjzNSc4pDzUm7du3SpEmTjB8/vs728ePHp2PHjl/52jPOOCO//vWvc9ddd6V79+71rlFECgAAFjDNmzdPr169cvfdd9duq6mpyd13350+ffrM9XW/+c1vcvLJJ+f222/PGmus8Y3ObYIBAAALoMGDB2fAgAFZY401suaaa+bss8/O5MmTs8ceeyRJdttttyy99NK193CcdtppGTp0aK655pp06dKl9l6NRRZZJIssMu+rV2kwAACgTGl+zkjVw/bbb5/3338/Q4cOzbvvvpuePXvm9ttvr73x+/XXX09V1X8DTSNHjsy0adOy7bbb1jnOsGHDcuKJJ87zeX0PBkAj4HswgAXN/Pw9GM++NbmhS5irVZZeuKFL+FruwQAAAAojIgUAAGVKC0ZCqsGYYAAAAIXRYAAAAIURkQIAgDISUpUxwQAAAAqjwQAAAAojIgUAAOVkpCpiggEAABRGgwEAABRGRAoAAMqUZKQqYoIBAAAURoMBAAAURkQKAADKlCSkKmKCAQAAFEaDAQAAFEZECgAAykhIVcYEAwAAKIwGAwAAKIyIFAAAlJORqogJBgAAUBgNBgAAUBgRKQAAKFOSkaqICQYAAFAYDQYAAFAYESkAAChTkpCqiAkGAABQGA0GAABQGBEpAAAoIyFVGRMMAACgMBoMAACgMCJSAABQTkaqIiYYAABAYTQYAABAYUSkAACgTElGqiImGAAAQGE0GAAAQGFEpAAAoExJQqoiJhgAAEBhNBgAAEBhRKQAAKCMhFRlTDAAAIDCaDAAAIDCiEgBAEA5GamKmGAAAACF0WAAAACFEZECAIAyJRmpiphgAAAAhdFgAAAAhRGRAgCAMiUJqYqYYAAAAIXRYAAAAIURkQIAgDISUpUxwQAAAAqjwQAAAAojIgUAAGWsIlUZEwwAAKAwGgwAAKAwIlIAAFCHjFQlTDAAAIDCaDAAAIDClGbOnDmzoYuAxmjq1KkZMWJEhgwZkurq6oYuB6Bi/l6DWd6aNK2hS5irpds0b+gSvpYGA76hjz/+OK1bt85HH32UVq1aNXQ5ABXz9xrMosGojIgUAABQGKtIAQBAGWtIVcYEAwAAKIwGA76h6urqDBs2zI2QwALD32tAEdzkDQAAZd75aP69yXvJ1m7yBgAAvkc0GAAAQGGsIgUAAGVK1pGqiAkGAABQGA0GfEMXXHBBunTpkhYtWqR379559NFHG7okgG/k/vvvzxZbbJGllloqpVIpN998c0OXBDRiGgz4Bq677roMHjw4w4YNyxNPPJEePXqkf//+ee+99xq6NIB6mzx5cnr06JELLrigoUuB+UNpPn40ApaphW+gd+/e+fGPf5zzzz8/SVJTU5NOnTrl4IMPzjHHHNPA1QF8c6VSKTfddFO22mqrhi4FGsy7H09v6BLmqmOrZg1dwtcywYB6mjZtWh5//PH069evdltVVVX69euXMWPGNGBlAAANT4MB9TRhwoTMmDEjHTp0qLO9Q4cOeffddxuoKgCgKA2dgmrkCSkNBgAAUBwNBtRTu3bt0qRJk4wfP77O9vHjx6djx44NVBUAwPxBgwH11Lx58/Tq1St333137baamprcfffd6dOnTwNWBgAUoVSafx+NgW/yhm9g8ODBGTBgQNZYY42sueaaOfvsszN58uTsscceDV0aQL19+umnGTduXO3Pr7zySp588skstthiWWaZZRqwMqAxskwtfEPnn39+Tj/99Lz77rvp2bNnzj333PTu3buhywKot/vuuy8bbLDBbNsHDBiQ0aNHf/cFQQN775P5d5na9ovO/8vUajAAAKDM+5980dAlzNUSi87/AST3YAAAAIXRYAAAAIWZ/2csAADwXWokqzXNr0wwAACAwmgwAACAwohIAQBAGQmpyphgAAAAhdFgABRk9913z1ZbbVX78/rrr5/DDjvsO6/jvvvuS6lUyqRJk+a6T6lUys033zzPxzzxxBPTs2fPiup69dVXUyqV8uSTT1Z0HADmbxoMYIG2++67p1QqpVQqpXnz5ll++eVz0kkn5Ysvvv0vUbrxxhtz8sknz9O+89IUAPDdKJXm30dj4B4MYIG3ySab5IorrsjUqVNz22235cADD0yzZs0yZMiQ2fadNm1amjdvXsh5F1tssUKOAwCNiQkGsMCrrq5Ox44d07lz5+y///7p169f/vznPyf5b6zpV7/6VZZaaql069YtSfLGG29ku+22S5s2bbLYYotlyy23zKuvvlp7zBkzZmTw4MFp06ZNFl988Rx11FGZOXNmnfP+b0Rq6tSpOfroo9OpU6dUV1dn+eWXz2WXXZZXX301G2ywQZKkbdu2KZVK2X333ZMkNTU1GTFiRJZddtm0bNkyPXr0yA033FDnPLfddltWWGGFtGzZMhtssEGdOufV0UcfnRVWWCELLbRQunbtmhNOOCHTp0+fbb+LLroonTp1ykILLZTtttsuH330UZ3nL7300qy00kpp0aJFVlxxxVx44YX1rgWAxs0EA/jeadmyZT744IPan+++++60atUqd955Z5Jk+vTp6d+/f/r06ZMHHnggTZs2zSmnnJJNNtkkTz/9dJo3b54zzzwzo0ePzuWXX56VVlopZ555Zm666aZsuOGGcz3vbrvtljFjxuTcc89Njx498sorr2TChAnp1KlT/vSnP+UXv/hFXnjhhbRq1SotW7ZMkowYMSK///3vM2rUqPzwhz/M/fffn1122SVLLLFE+vbtmzfeeCPbbLNNDjzwwOyzzz557LHHcvjhh9f7miy66KIZPXp0llpqqTzzzDMZOHBgFl100Rx11FG1+4wbNy7XX399brnllnz88cfZa6+9csABB+Tqq69Oklx99dUZOnRozj///Ky++ur517/+lYEDB2bhhRfOgAED6l0TQEMpWUeqIhoM4Htj5syZufvuu3PHHXfk4IMPrt2+8MIL59JLL62NRv3+979PTU1NLr300pT+f+D1iiuuSJs2bXLfffdl4403ztlnn50hQ4Zkm222SZKMGjUqd9xxx1zP/Z///CfXX3997rzzzvTr1y9J0rVr19rnv4xTtW/fPm3atEkya+Jx6qmn5q677kqfPn1qX/Pggw/moosuSt++fTNy5Mgst9xyOfPMM5Mk3bp1yzPPPJPTTjutXtfm+OOPr/3vLl265Igjjsi1115bp8H4/PPPc9VVV2XppZdOkpx33nn52c9+ljPPPDMdO3bMsGHDcuaZZ9Zek2WXXTbPPfdcLrroIg0GwPeIBgNY4P3lL3/JIosskunTp6empiY77bRTTjzxxNrnV1tttTr3XTz11FMZN25cFl100TrH+fzzz/PSSy/lo48+yjvvvJPevXvXPte0adOsscYas8WkvvTkk0+mSZMm6du37zzXPW7cuHz22WfZaKON6myfNm1aVl999STJ2LFj69SRpLYZqY/rrrsu5557bl566aV8+umn+eKLL9KqVas6+yyzzDK1zcWX56mpqckLL7yQRRddNC+99FL22muvDBw4sHafL774Iq1bt653PQA0XhoMYIG3wQYbZOTIkWnevHmWWmqpNG1a96++hRdeuM7Pn376aXr16lUb/Sm3xBJLfKMavow81cenn36aJLn11lvr/GKfzLqvpChjxozJzjvvnOHDh6d///5p3bp1rr322tqpSH1qveSSS2ZreJo0aVJYrQDfhcayWtP8SoMBLPAWXnjhLL/88vO8/49+9KNcd911ad++/Wyf4n9pySWXzCOPPJL11lsvyaxP6h9//PH86Ec/muP+q622WmpqavL3v/+9NiJV7ssJyowZM2q3rbzyyqmurs7rr78+18nHSiutVHvD+pcefvjhr3+TZR566KF07tw5xx13XO221157bbb9Xn/99bz99ttZaqmlas9TVVWVbt26pUOHDllqqaXy8ssvZ+edd67X+QFYsFhFCuB/7LzzzmnXrl223HLLPPDAA3nllVdy33335ZBDDsmbb76ZJDn00EPz61//OjfffHOef/75HHDAAV/5HRZdunTJgAEDsueee+bmm2+uPeb111+fJOncuXNKpVL+8pe/5P3338+nn36aRRddNEcccUQGDRqUK6+8Mi+99FKeeOKJnHfeebnyyiuTJPvtt19efPHFHHnkkXnhhRdyzTXXZPTo0fV6vz/84Q/z+uuv59prr81LL72Uc889NzfddNNs+7Vo0SIDBgzIU089lQceeCCHHHJItttuu3Ts2DFJMnz48IwYMSLnnntu/vOf/+SZZ57JFVdckbPOOqte9QDQuGkwAP7HQgstlPvvvz/LLLNMttlmm6y00krZa6+98vnnn9dONA4//PDsuuuuGTBgQPr06ZNFF100W2+99Vced+TIkdl2221zwAEHZMUVV8zAgQMzefLkJMnSSy+d4cOH55hjjkmHDh1y0EEHJUlOPvnknHDCCRkxYkRWWmmlbLLJJrn11luz7LLLJpl1X8Sf/vSn3HzzzenRo0dGjRqVU089tV7v9+c//3kGDRqUgw46KD179sxDDz2UE044Ybb9ll9++WyzzTbZbLPNsvHGG6d79+51lqHde++9c+mll+aKK67Iaqutlr59+2b06NG1tQLw/VCaObc7EgEA4Hvow89mfP1ODaTtQvP/fW0mGAAAQGHc5A0AAGWsIlUZEwwAAKAwGgwAAKAwIlIAAFCmFBmpSphgAAAAhdFgAAAAhRGRAgCAMlaRqowJBgAAUBgNBgAAUBgRKQAAKCMhVRkTDAAAoDAaDAAAoDAiUgAAUE5GqiImGAAAQGE0GAAAQGFEpAAAoExJRqoiJhgAAEBhNBgAAEBhRKQAAKBMSUKqIiYYAABAYTQYAABAYUSkAACgjIRUZUwwAACAwmgwAACAwohIAQBAORmpiphgAAAAhdFgAAAAhRGRAgCAMiUZqYqYYAAAAIXRYAAAAIURkQIAgDIlCamKmGAAAACF0WAAAACFKc2cOXNmQxcBAAAsGEwwAACAwmgwAACAwmgwAACAwmgwAACAwmgwAACAwmgwAACAwmgwAACAwmgwAACAwmgwAACAwvw/BrJ6NOc4qggAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_confusion_matrix(y_test, test_classes, 'Testing')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DlivxaDmTnGq"
   },
   "source": [
    "Observe the error rate measurements and interpret their significance in the context of this example. In many medical testing studies such as cancer detection, having a high false positive rate to ensure a low false negative rate is perfectly acceptable and in fact encouraged since the risk of missing a malignant tumor diagnosis (false negative) is a lot worse than misclassifying a benign tumor as malignant (false positive).\n",
    "\n",
    "In order to control for the FPR and FNR, try changing the threshold hyperparameter before classifying the probability predictions. A lower threshold increases the model's overall chances of making a malignant tumor classification. This inevitably increases the number of false positives and the FPR but it also helps to decrease the number of false negatives and the FNR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7ADEN2rb4Nhj"
   },
   "source": [
    "## Save the model\n",
    "\n",
    "Start by making an export module that takes in raw data and performs the following operations:\n",
    "- Normalization\n",
    "- Probability prediction\n",
    "- Class prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:06.063081Z",
     "iopub.status.busy": "2024-08-15T02:46:06.062536Z",
     "iopub.status.idle": "2024-08-15T02:46:06.068112Z",
     "shell.execute_reply": "2024-08-15T02:46:06.067445Z"
    },
    "id": "6KPRHCzg4ZxH"
   },
   "outputs": [],
   "source": [
    "class ExportModule(tf.Module):\n",
    "  def __init__(self, model, norm_x, class_pred):\n",
    "    # Initialize pre- and post-processing functions\n",
    "    self.model = model\n",
    "    self.norm_x = norm_x\n",
    "    self.class_pred = class_pred\n",
    "\n",
    "  @tf.function(input_signature=[tf.TensorSpec(shape=[None, None], dtype=tf.float32)])\n",
    "  def __call__(self, x):\n",
    "    # Run the `ExportModule` for new data points\n",
    "    x = self.norm_x.norm(x)\n",
    "    y = self.model(x, train=False)\n",
    "    y = self.class_pred(y)\n",
    "    return y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:06.071189Z",
     "iopub.status.busy": "2024-08-15T02:46:06.070681Z",
     "iopub.status.idle": "2024-08-15T02:46:06.074007Z",
     "shell.execute_reply": "2024-08-15T02:46:06.073433Z"
    },
    "id": "2YzRclo5-yjO"
   },
   "outputs": [],
   "source": [
    "log_reg_export = ExportModule(model=log_reg,\n",
    "                              norm_x=norm_x,\n",
    "                              class_pred=predict_class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gtofGIBN_qFd"
   },
   "source": [
    "If you want to save the model at its current state, you can do so with the `tf.saved_model.save` function. To load a saved model and make predictions, use the `tf.saved_model.load` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:06.076953Z",
     "iopub.status.busy": "2024-08-15T02:46:06.076720Z",
     "iopub.status.idle": "2024-08-15T02:46:06.349906Z",
     "shell.execute_reply": "2024-08-15T02:46:06.349200Z"
    },
    "id": "a4Qum1Ts_pmF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmpfs/tmp/tmp9k_sar52/log_reg_export/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmpfs/tmp/tmp9k_sar52/log_reg_export/assets\n"
     ]
    }
   ],
   "source": [
    "models = tempfile.mkdtemp()\n",
    "save_path = os.path.join(models, 'log_reg_export')\n",
    "tf.saved_model.save(log_reg_export, save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-15T02:46:06.353556Z",
     "iopub.status.busy": "2024-08-15T02:46:06.353293Z",
     "iopub.status.idle": "2024-08-15T02:46:06.415284Z",
     "shell.execute_reply": "2024-08-15T02:46:06.414693Z"
    },
    "id": "3KPILr1i_M_c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 0., 1., 1., 1., 1., 1.], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg_loaded = tf.saved_model.load(save_path)\n",
    "test_preds = log_reg_loaded(x_test)\n",
    "test_preds[:10].numpy()"
   ]
  },
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   "cell_type": "markdown",
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    "id": "vgGQuV-yqYZH"
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   "source": [
    "## Conclusion\n",
    "\n",
    "This notebook introduced a few techniques to handle a logistic regression problem. Here are a few more tips that may help:\n",
    "\n",
    "- The [TensorFlow Core APIs](https://www.tensorflow.org/guide/core) can be used to build machine learning workflows with high levels of configurability\n",
    "- Analyzing error rates is a great way to gain more insight about a classification model's performance beyond its overall accuracy score.\n",
    "- Overfitting is another common problem for logistic regression models, though it wasn't a problem for this tutorial. Visit the [Overfit and underfit](../../tutorials/keras/overfit_and_underfit.ipynb) tutorial for more help with this.\n",
    "\n",
    "For more examples of using the TensorFlow Core APIs, check out the [guide](https://www.tensorflow.org/guide/core). If you want to learn more about loading and preparing data, see the tutorials on [image data loading](../../tutorials/load_data/images.ipynb) or [CSV data loading](../../tutorials/load_data/csv.ipynb)."
   ]
  }
 ],
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