DiabetesPredictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ea534570-7038-4fe1-85a9-507799494f48",
   "metadata": {},
   "source": [
    "# Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "39414209-da4a-4464-acc4-0c683fa010df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.optimize as opt\n",
    "from sklearn import preprocessing as pp\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "import pylab as pl\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fb6ae8f-fdd1-4673-860d-d5d7e923ffae",
   "metadata": {},
   "source": [
    "### Reading the CSV File from user for training our model with past inputs and prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2cd8bc75-f672-4bd6-b9a5-0c0101cfb0e5",
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
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       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
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       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
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       "      <td>8</td>\n",
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       "      <td>0</td>\n",
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       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
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       "      <th>8</th>\n",
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       "      <td>70</td>\n",
       "      <td>45</td>\n",
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       "      <th>9</th>\n",
       "      <td>8</td>\n",
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       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "5            5      116             74              0        0  25.6   \n",
       "6            3       78             50             32       88  31.0   \n",
       "7           10      115              0              0        0  35.3   \n",
       "8            2      197             70             45      543  30.5   \n",
       "9            8      125             96              0        0   0.0   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  \n",
       "5                     0.201   30        0  \n",
       "6                     0.248   26        1  \n",
       "7                     0.134   29        0  \n",
       "8                     0.158   53        1  \n",
       "9                     0.232   54        1  "
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     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"DiabetesDataset.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0612187f-a011-4576-84d2-25eb0949b3ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   int64\n",
       "Glucose                       int64\n",
       "BloodPressure                 int64\n",
       "SkinThickness                 int64\n",
       "Insulin                       int64\n",
       "BMI                         float64\n",
       "DiabetesPedigreeFunction    float64\n",
       "Age                           int64\n",
       "Outcome                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "79c7fb9c-c357-4313-9dcd-0b87c9935018",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
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       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
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       "      <td>96</td>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "5            5      116             74              0        0  25.6   \n",
       "6            3       78             50             32       88  31.0   \n",
       "7           10      115              0              0        0  35.3   \n",
       "8            2      197             70             45      543  30.5   \n",
       "9            8      125             96              0        0   0.0   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.627   50  \n",
       "1                     0.351   31  \n",
       "2                     0.672   32  \n",
       "3                     0.167   21  \n",
       "4                     2.288   33  \n",
       "5                     0.201   30  \n",
       "6                     0.248   26  \n",
       "7                     0.134   29  \n",
       "8                     0.158   53  \n",
       "9                     0.232   54  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.loc[:,df.columns!='Outcome']\n",
    "x.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "35b3d508-2fa4-46e1-9f31-b7a82eff2d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0    1\n",
       "1    0\n",
       "2    1\n",
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       "6    1\n",
       "7    0\n",
       "8    1\n",
       "9    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Outcome']\n",
    "y.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2a11fc67-d39d-49ed-a483-516d56394df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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5URTl5ib5s0aFKk7uqKL5Qg9r70+d0RJF7ZCejy3yEQqlgMEgsukr6fLdjGVFy6ZHMnB2U3Hg1XLOnWhm4fUxpC23LbG0pGBvHQdfN9fpr74nedwW8Zridj54qgB3Xw1bv5VlFWMv3FfHvlfKAbjmniSSF49ddutgfDoa+9nzQglN53uISPVj5V2JM/pv7WKZ1QYdcQiE6ddDABBFySPqcZ64ZPFimS7jPRoXDzVD/Torz8HNywmNuxo3L2f+88OD47wb5q2LtLpQ6oYNHHrTXKGi1xutjLnKSSFfJFw81Lh6OaEbNhAQ7sH+V8vH/JzoSYRbGiu6aavtk59nXWutASSXKC4MntDzvVQOv10pV/ko1Qr0OiMr70okMk0Kn1hWtFz39Ux8gt049v55CvbVk7Um3OauZzSF++utvq/U5aHjll2eP9PKrn8V4hPsxpZvZOHqac4pHH67Qr6T2Pz1TLke3sHUMBiMnNlVzYmdVaidlay5N4WE3KArNpk8qw26aOxGUMyMQRdGkqLtrpe16XVaWHJzPCe2X6C7dVA+NtivIz4nCKNRpL97/C5VU1LPROGo+HXB3jo66s1GNjDSU/bgV96VRMnhRty8nDh/1n51kF+oO+31fZPSC8nbbe2dx1k0Qsklin4alt2eMOFel0JNUTtF+6XvQaEUMOiMzFsfSeoys8dtqmhZdptU0ZL3aS0nd1SRvDiExTfFjWsEig7UWzV/+YW6s9SOnIGJsmNNfPrfEgIjPbjua5lWHYgf/qNAbv669f/lWCVHHUyeluoedj9fSnt9H3HzA1l2W4LVRfNKZFbXoYvGrhnbWzBIv7huTStKo/m6Z2R65HlnktAEb4b6zd2YGjc1Rr1IeLKv3bb70Rx845zciKQbNnD0vUr5tdzN0dQWd8gXBUGAvi7Ja/ULdWdOvDc1Re2oNSrqSjtt9nb1dEKhFAiM9JiwmaanbZDzFl2pTholAZGScRJFkX2vlNPfpeXa+1JntERxqF/H+4/nyc+NBpH4nCAWbomRj1lWtKSvDKP0aCMH3zhHzNwAVt6VOK4xLz7UYNWIpXJSsPb+1DHrwwv31/PJf4qZE+/Flm9mWRnzV351TDbmd/9mkcOYXwR6rYHDb1fw5u9OMtinZcND6ax7IO2KN+Yw2w26oWvG9nYblpJ13S6tzK27Vj4+pO4f6y1TItRp7MqPS8FZNYirlzPDA+YcQHSmP0qVgpB4b7lywh7uPs5s/Go6Xc0Dcty6YG+dXC4499oIm+oSr0BXekbuBFbdnUTl6RaMBpGu5gG7nxGfHURrba+VTsxY5I/qYg1N9JETnuXHmjh3opnc66IIjp7ZBo7Xfnvc6vmceG9W35Msh7QaK00VLT4suz2BC3lt7H6+lLAkH9Z+OXXcUrbSI43seaHU6tjy2xPwDXGzu/7MRzXse7mMyDQ/K+0XURR54qu75eqb+/68bEbkWa92Gs518upvjnPmoxqSF4dw5y8WEJMVMPEbrxBmdcjFOIMeut+ApITXo2ljxfnb5OMK4/R01SkuwtN3FvoYFsdvRIkJqGV4wForpaW6h5A4L6pHaprHImtNBNGZAURl+HNyZxUxWQEce/+8/PqCLTEolNaepslwR6b5ERTlyeG37HeDmnDzdgZx4nJF7ZCevE+ki0p0pj8X8trkipju1kH2vVpOSJwX82ZQEhfg5AdV9HWYu2N9gl2tFBF72gf54OmRipYH02iq6OajZ4sIiJhYObHsaCOfPl9idSwhN0iWWLBEFEWOb7/AyR1VxM0PZM29KXJozKaV//EVsu6Og8mhHdRz5J1KCvfX4+mvYcu3sghPmrgC60rD4aFPwOguUY3Bvuc0VboMU9N+ASY05gChgT0M9ZkNelSGP+31/YQn+7LrGbtjYQEpLGPyvpfeEo/RIPLSL47K3rl/uDtKtYJTH1bZff/y2xPo7RiyqoaxR2NlF+6+zviFjv+zlBwy5y5MDThhST6SfspzRQjCzJcotjf0cWyb+YLm4ulkFa8eXdHS0zbIjqfy8QxwYfPXxldOLD8uxcBHDz1YcadteEYURQ69WcHJHZI+zbX3pcrGXDuktzLmDz+5ymHMp0hVQRuv/OoYhQfqyVwdzu0/WzArjTnMdoM+gx46ovRHZVmyWOF3etq27zXMTEXGnDk6q6Sn6dbdw8+6osYn2LoRIuOaMLmE0CvAhdTl1qGVZbfGI4oix967wGhSls3B099lzGYlE1EZ/tQWdxCdETBuTFk0inKsf+19qbRU9eDm7Yx3kCsnd1bRdL6HlXcm4uk3cyEFvc7Aq78yh1pUTgqueyRDDmPIFS2N/ax7IBVRhPf/JtWCT6SceO5EM588V2zV6axQCax7IM3mImA0iux9qYy8T2tJXxXGNV9Iki9i/d3DPPOt/YBUXfTI09dcUTXRVzqDfVo+fq6IHU/ko9aouOn781l6S/yEpbRXMrM65IJoP047nawt+7L8eEjVN87K6SFQdY4W/cWJSnkqG/HwdeE9i2qJvq4hXDzU7HnROk5rmTRVKATSV1gP71CPSsiFxHpblSpasuh6qYvxyNuVdl/XuKsZ6tPRXt+HXmecsFyx5IjZO4+bH8j+V8uJTPejqbKbkzurSFwYTHzOzJUoikZJWtaEIMC6+9OstMItK1p8gt14+4+nZOVEd5+xk70Vp1r4+Lli3HycGerVoR8ZKLL4xjibBKbBYOTT/5Rw7kQz89dHsmBrjHwh7Gzq5+X/OQZIMf0bvjtv2n7+qx2TmNaB18oZ7teTvSmK7PVR44bHZguz26BfBiK6zG3Sg9OUEB0PJ4X9i5S7oo0+4/iGMNSpEFwS5Lh2QIQHdSWdBMd4cSHPrBipVCmsNMnTV4VZeZTDg3pOWTQEOWkk4z5Wd6nGTU17g/2LXVC0J80XpOlEve1DqDVKm6al0ZiShLf/LJe2+j6G+nUERnry8b+L8fDTsPy2mS1R/OQ/xVbPl9+RaBXzL7WoaImbH8Q7fz6NdsjADd+dO65yYuXpFj76VxF+oW5oB/WyMY/K8JenF5nQ6wx89GwRF/LabMbnNVZ08fafpLvFlCUhrLp7GucuXuX0dw2z9+UyqvLbCIz0YMs3kycUSJtNzP5L0gxTGHxAfpzcsnDGP69Tb7+TcCJjDhCiLqFz0Bz7C0uUukItjTlIsWhLstZYT7HJH1X7rR0ykD9OdUx/9zA7n8y3+5pBb8Qn2JXAkXLDiBQ/mzp3S2R9dkEqg6wrkUofz59toa9rmGu/nIqTy8z5IcUHG6ymMc1bF2HV3dlY2c2ekYqWhVtjef/xs/R1DLHpkQz8w8YuETx/ppWPni0iKMoDjZuanjZJQdHdx1mqmBnVyLXjiXwu5LWx/PYEK2NeeaZFNuYLtsQ4jPkkEUWR4oMNvPzLY9SWdLD4pjhu+sH8q8qYg8Ogj4NAm2sdGY0r5CPuWp9x1k8P/XYMt5JhOytt6TKEcfCw2aionOz/ehsruuTHSYuCrdrFhwd0VvK3CpVkaOzVr88fEcHK310rGyhLVt6VSFttH5mrw+ntkF4fr1zRYDDKnZL3/HYxAHVlHQDUl3WRsylqXG2YS6W2pMMqNBWfHcjCrWZRLMuKljVfSuGDfxTQUd/P+q+k28gRWHIhr5VdzxQSGOVBUJSXXJ8vKASuvS/V+u5oQMd7fz1LfVknq7+YTPpKs+eet7uWD/8hJbbXfCmZ7I1R0/STX910tw6y7bGz7HmxFP8wd27/WS5zr434zJURZ4Kr7yeaJgSg1b0GweIr6ld3fSbn4q+uAsBZsC9Ba+Js/2ZqLHKWplCHJX6hbmiHzCWTo9vRR4t5LdwaKxv10SzYGkN4ii+nd9XYvOY7x42aog6c3VQkLAiWQzymNnl7mC4kLp5OePhqMOiM1BRJBj0k1ku+gMwEJulZEyFxXlzzRXOtuWVFy4aH0tn3cikNFV2suTdl3J+pKr+ND/9ZiH+EB8lL5kidryNfZ+510VYXgsE+Le/+5Qwt1T2svT/Nqnzx4JvnZI2XLd/MInHh1KukPm8YjSJnP6nh1V8do6W6h5V3JXL9t8cPi812HAZ9HFrczYaqKOgQjR5TawZyUXRPy3k066Qh2CFOJeOuM2JdWVFvp4TQsgrC01+DT7C5DHN4QMeJ7RZXBEHSWxGwNehbvpGFIAhEpds3ZusfTON8Xitpy0KtEqxjDcnt6xyWhbyu/5akbNlYaT7/NfemzJhH1d89zPt/Oys/d3JRsfGrGXL5n2gU+fjfIxUt96dyelc1VQXtrLg9YdzkbFVBGx/8swD/MHcW3xjLgdfKcXZVIQgCYUk+VjK//V3DvPPnM3Q2DbDhoXQrDZcdT+bLNfm3/TRnUgqVn3fa6/t4+4+nOPRmBaFJPtz5iwVXnDLiTDDrDbooGidedJF0uphjqR2uDfj0T62NeoP376b1fPxUtp7weBh0tt+NaUAFwNr70qxeOztK1TAs0YdzJ5ut5pGaMMnw1pbYtvcvuTmOov0NKASBtBVh9LSZNWX0WvsNVQdel0ItXgEuspb3tsfOArDijoQZ63rUDRvY+WS+Vannrf8vx+rCc3RbJVX5bSy9JY6qwnbKjzezYEsMaaMqgyypKWrnw38U4jfHnWu/nMon/ylGpVaiUApo3FRWNfQ9bYO8/adT9HUMsflrmVYaNy/94ihV+VIO5J5HF48bp3cg5WyOb7/A64+eoLt1kGvvS2HTwxNPerpamPUGHXFy8eWLYVhlrmrpdGnCe3hqWtJVw9nTdi6hTvkMGi8tfuzm5WRl5C2n+gz16zi5owoAn5Ha9fAUX858bHsRMZXI1Zd3ysbGkpQlcyg+3EDs/EDcfZypKjCvsSyXNFFb0iFrtiwbqWBprDTf3YxnOC8Fo1Hk438XWU1TuvlH2VZ64lJFSw2py0MZ6tNRsKeOzDXhzN8wdvintriDnU8V4BPiyqZHMtj9QgmDvTo8/TUM9upY86UU3Lyk0sbOpn7e/tNphgf0bPlWFqEjc15FUeSJh3bLFUv3/9+yK1Ku9UqiuaqH1x89wYntF4idF8idv1hAQk7wFauMOBPM+rJF0dg3Q4qLAl5D5tveTpdmlEzNoJYMrpm2s0nQ7KdoYN0l7aEbNnvHK+9KtHrNUnPc019DT9sgXU0D6IZsPerzZ1oJjvWyUgc0cfvPcik53IhuyCBXz1ga/aF+nZW3ZNCZE6EevhoiUnzRDurlqpn0VTNjzAEOv1lhVQG0+ovJBFnMN7WsaPEKcOHwWxUkLQ5hyTjKibWlHex4Kh/vYFe2fnMuR96tpLGim5i5AZw/08q8dRGylG1bXa8ct7/+O/PkiguDwcjTFt2fDz2+8qqokZ4pdFoDx987T96ntbh6ObPx4YxJSTNfjVwFBr0LmBnxnPVl98uPB9XjJyTtcaketSWxmiPs6fnaJe1hmQy1FNka6tdxcmcVIE0mupDfhn+YO2XHm6QEnkVLY2S6H/l76xBF0Sp8Y8Ldx5n8PbWExHoRGOnJ8KCe+vIuuR59sM/aQz/zSY3shc5bF4GgENj3apnsyc+UMFL+njorad6odD+rJKRlRUt0hj8H3zhHdKY/q8ZRTqwr62TnE/l4B7qw9VtZnDvZTPHBBuJzgqguaCMo2pPcEYXGpvPdbP97HmpnJVu+mSXnMrRDern7E6RW/qs97nsp1Jd1svvFUnpaB0ldNodFN8bhPINlrVc6s/6yP6Pt/xYoP0PV3BjnoziP0XB0MYQmelsZJcupRoFRngz16ehuGZSSoRbG3M3bmTVfTAFRHLMufdczhfS0DZFxjeSd1xS1YzSIsrG01JnpaRvk1MiFxNlVReLCEMqPN1F+TMpdqNQKgmOmf9hzVX4bB1+3Hryx6ZFM+bFlRUvKsjkcequC0EQf1t4/tnJifXknO57IwzPAha3fmkt7XR8HXj9HRKov3S0DCAqBtfelolQqqCvrZNtfz+LspuaG786TjXl/l7mV383b2dHKPw7Dg3r2vFTKu385A8DWb89l5V1Jn2tjDleDhz5TAl0WhqzDpYnoFleLl4xW5YyXigI9xnF+FfEu+zGI06cvYZkMHezTyl2h6SvDaCiXkpxD/TrisgOpOGlu97/j57k4u6pRqBQ2CdeF18dw/kwrtSWduPs6E5Ml3fJW5behcVcTle7PPsqsDPrBN87J3ZIpS+cw2Ktl38tlBMd4MTygw91XM+1CU601vex6thDR4vf7wF+Wy48tK1rmrY3g+HsXCAh3Z+NX08c8F2mmZz4evhq2fmsu2iEDHz5TiHeQKx6+GmqKOlj/YBqe/i5UFUhljJ7+khdviqV3NPTzyq+kVv6wJB+2fuvyzLCdjVzIb2Pfy2UMdA+TdW0EuZujbaQqPq84PPQxMXtGvoPBJDbFW7wy8dfmquiYwieNX6kToi6hdnj6/sAthfrPfmwOO6SvDOX8SEzZSaO0ShYGRXvi7Kqmo7HfbvXM/PVRBMdKISa/Oe4olAqMBiPVhe1Epfnh4iFVjZhCKVUFbXL8WlAIpC0P5ZPnpJb7JTfH0dk0QFji9DZy9XYMsf2JPPRa8/lv+UaWVeepqaIlcUEQ+Xvr8PTXsPnrWWMqJzZWdLH973m4ezuz9dtzUTkp2PlUPoiQtjyUogMNpC0PJXZeIBWnWvjgqQJ8Q9y44btzZWPecK5TNuapy0MdxnwMBnu1fPSvInY+mY+zq4qbfpDNkpviHMbcglnvoRsvg4QuQPjwlydeZMEqz6fY0fWTSa01MP4klGHRndLBVVP6/LFY/UVzq/hgr5bTuyTvPHN1OM0XemRjPXdtpDx5HuD6b89FFEWbUAVIZX4AAyOlf6Yyx8bKboYH9ESNDNhw0igZ6tOh1xo48Fo57j7ODPXriEzzp+xYE42V3ay5N0UenTed9dbaQT07nsiTzxEgaXGIXH4J5oqW0ERvqgra0biqpYlAYygnNp3v5v2/5+Hq5cT1356Lq4cTO58uoLNpgNX3JHHwzQr8Qt1ZckscJYcb2fNCCcExXmz6WqYcGjh3slnWyFl0Q+yEc0c/j4iiyLkTzRx47RzaIT25m6OZty5yXAmJzyuz/xsRp56snCq7416c8ntEO804F0u7LoILwznTslesxUxOy5LEeesiKTvWBEidmqJFTCJ2XiAqJyXVhe12687VGiX93cPyDNH68i76Ooe4kN+GQiXIhlnjrmawT8epXdX0tA0RFO2JXmskKNqTEzuqSMgNInFBMHWlUofpdOlsGAxGdj1TSHu9uQxVqVaw4g6zyJeposUr0IXOxgEEgRHlRPulgs0Xenj/b2dx9XDi+m/Pw83bmWPvn6cqv40lN8VRcqQRvdbAugdSKT7YyO7nSwhL8mHzN7JkY372kxrZmF97X4rDmNuhr3OInU/m8/G/i/EKdOHWn+SQsynaYczHYNZ76NOOwQBK61u4cv8TXFPxhSltoxPHn5c5FfIGtth0gV4M0Zn+stbzQI9Wngo/d20EolGUNUZyNkZZTZ9fc28yBr2R3c/bdqoqFAKH3jiHf4QHRqPIlm9k8d7jZyk60MCFvDbCEn3lcIXGTU3zhW4qz7QQnx1Ia20f3kGuFO6rw93HmeV3JCKK0nmEJfpMS0JQFEX2v1pOTbF1COym78+XY+Kmiha1kxLtoB6Dzsj1352Hd5D9FvGW6h7e+9tZNO5qrv/OXNx9nDl3splTH1STsiQE7ZCe+rIurrknmfNnWzn67nmiM/1Zd3+aXH64/7VyeRzg9d+eK9efO5AQjSJFBxs4/HYFokFkyc1xZFwTPqMDTa4GHAZ9FAqdDqNSiWUMPbB7qp6iEa146XoRUc7HqRrOlVv/LxXLOZVnPjLL485dG0Hp4Sb5uWkQM0h14Cq1kjMf11hJ7gIkLAjCP9SDw29XUFXQTlSGP+EpvkSl+cllkHOvjZDXa9zVtFT34qRREpbsy7mTLSgUAqIocsN35+HsoqKreYC+zmHmb5iecMuZj2ooPtBgdSx3c7SsPS5VtOQz2KdD5aTEoDOy+ZtZBITb78hsrZFqxzVuKq7/zjzcfTS01vSy+78lhMR6EZcdxPt/O0vCgiC6mgc4vaua+JwgVn8pGeVIhcz7j+dRUySNA7z9Z7kTTm/6vNHVMsDeF0upL+8iNNGHVV9Ismr2cjA2jvuWUSh00nBlSz/gmsovTmkPF0UPDdq0iRdOQKrrrkvew5LzeW0YDUb6u4c5O6INMn9DJC7uThx+W5oFeu19Kbz1+1Pye5bcGMdAj9burNBr700l4xpz449JZjbNQiHQUuvFNJ4ud3OMPCzDaBSZvzFKHjNXVyp50qMlfi+GilMtHHnHeuhGYKSHrKFiqmhpr+8HEYw6I+seTBtTObG1ppdtj53BSaNi67fn4uGrYaBHy86n8tG4q1l5VxKf/rcET38XFAqB07uqSVk2R5r/OWLMX/jpYdmYf/F/lziMuQVGg5EzH9Xw6q+P01rTy6ovJLH1W1kOYz4FJuWhC4KwHvgroASeFUXxd6Ne9wJeBCJG9vyTKIrPTfO5XhYEg2j1vMOlCd/BqQ1UcFV0cm5o2SWfi7+qeuJFk8TN25nOxn4K9tXT226Wus1aE0FrrTkPERLrLT/O3iRNcTn6iu0kooQFkiiV5dDotrpeItP8iLBIZppi0NohvVxdEproI8vxBsd4kmMhA1tbKpU9XuofcdP5brlqxoRSrWD1l8zG1VTRAoAAq+9NttJRsaStrpdtfz2DWqPk+u/MxdPPBYPOyAdPFzDUp+PG78/nyDsVDPRo8Qt1o/RIE1lrwlk80lUqiiJPfnWPvN/9f1n+ua+ZtqStro89L5TQUt1LVIY/K+5IHHfykwP7TOihC4KgBJ4ANgApwB2CIKSMWvYIUCyKYiawEvizIAjjl25coQijGoiORm4DUWt/8Ri4KWwThxeDXhw/bh6gsj/yzR7rH0wjIsWXg6+fk9v8c66LRuOm5t0/SwMTVn8pWW7UAMjeEEVLdQ8lhxtt9ltyk1TGWV9m/llP7qyir3PISq/FdLEwhWAAznxsvlCtudfcrGM0itSXdRKe5HtJ+hvdrQPseDLfRlRs0fWxctip9EijlezvitsTSMgJtrtfe30f2/5yFrWTkuu/PQ9PfxdEUWTfK2U0ne/mmi8m03Cui6qCdkSjSFttHznXRcvG3KA3Whnzh/6+0mHMRzDojBx7/zxvPHqC3o4h1t6fysavpjuM+UUymZBLLlAhiuJ5URS1wKvA1lFrRMBDkP4K3YEOQD+tZzoOojh9bZyKUSXWnS5NeHYcnNIeroquaTmX6uH5477upbI1tPbwDHAhKNpTFr4ykbk6HO2gXpYEiM7wp2ekZDBl6RwUSoG9L9nqtYTEecm17Hmf1uLioebO/1mAKMKht6R4uonCvXV0NPTL8q+A3Am65kvJVp54W20vwwN6wpIvPtwy1K9j+9/z5QYmVy8nFAqB0ARvecxbY2U3e14yD7JYsCV6TAGw9oY+tj12BqVawdZvz5XPN39PHSWHG8neGIVXgIvVAJDFN8WRe100giAwPKjn6a/tlV97+KlVjgqNEZrOd/Paoyc4uaOKuJxA7vzFQuKzgz5XYlrTzWTchFDAUle1Dlgwas3fgfeABsADuE20o2srCMKDwIMAERERo1++eMRhEKZHtF4wmv7YRiarqztQTVErpk0fNS3nUjx47biv1w5njvu6iYQc6Y/Ecpp5RKovzi4qtj0meeTpK8OsvPPldyRQcbKF1hrbslCTDkxX8wBVBe1kb4rCJ9iNeesiObH9ArXFHbj7OBOe4kv58Waaq3tRa5Qs3BrDvlek6hm/UDcSFlh7xLUlI/HzxItLiJpCICZtGE9/DSonJbphA9fcIw2rMFW0GPVSaC1zdTjzN0TZ3a+joZ9tfzmDoBCsBiPUlnRw6M0KojP9yVoTzku/OCq/Z+VdiaQuk3IJfZ1D/PfHh+Vzufs3iy/q57ra0A0bOPbeefJ21+Lu7cymRzLGDHU5mBqTcRXsXS7FUc/XAWeBOUAW8HdBEGxEOERR/KcoitmiKGYHBEyf6JIoDk68aNJYfyUhnSL9bvbnfI5Fmz5mWs6kQz9+XfKwODlt7IRcKd59apc51NHVPEB/97Bcqjh/QyRttdKgZ985bhgNIh/9y3YotFKtICZT+t3l76lDoRLkZOi8tRG4ejkxPKAnItWP9JVh6HVG2uv6WHh9LC4e5ijcDd+dZ+OJ1ZV24hfqZtXJOllEUWTPi6Vy4lXjpiYyzZ+Ohn6W3hKPp7+LuaJlpFonaWHwmMqJnU39vPvYGQRhxJiPlDB2tQyw65lCfIJdWXNvCh89WyTvt+beFNmYt9f3ycY8ItXPYcxHqC3t4NVfHyPv01rSloVyx88XOIz5NDIZg14HWE4RDkPyxC25F3hblKgALgBJ03OKEyMap2cyEADCyE3LyCUrtT6JYc3snhDT1zVMX+ewXPccHONJT9sQ//nhIQACIjzkYRIgtcOf3mU/IRuV7o+Ti4rhAR0lRxpJyA6SW9hVTkqiRmRL+zqHrYZSpCwJ4cN/SvMwPQNccHa1zg/odQYaK7sv2js/sf2C3BilVCtYfFMsxYcaiEr3I3lxyIj2ebHcXBSd6c+qu5Ps1rp3NQ9YiT7JSogmWV8BNn41g/w9dXJ9+4aH0kkcueOoK+vk1V8fB6Syz81fn9yd1NXM8ICO3S+U8N5jZxEUAjd8dy4r7kyc0YHfn0cmY9BPAPGCIESPJDpvRwqvWFIDrAYQBCEISATOc5mYVoEuwbqpKGbg4enb+zPivcfOcvrDKvn5lm/OxSfYHKLKXB1OZ6O5i9JgMMrDLkZj8vaLDzaiHzaQsTrc6nWTeawv72SfRZzacvhy1qj3ADRVdmPQGS8qfl56tJETFue7+ovJFO6rR+2kZOUXkhAEgaPvmitaQhO9x1RO7Goe4N3/O41oFLn+W3PlJKppGEZXyyDrH0ynu2WAY9ukf+Kbv54py/yWn2hi28jFYMnNcSy/bWoVUlcj58+28vIvj1F6uJG5ayO4/ae5zIl3NFLNBBNeHkVR1AuC8DVgF1LZ4r9FUSwSBOGhkdefBn4N/EcQhAKkv+kfiqJoO8pmulF4grFnmgW6rs6EVcG+ekAyMmpnpdWItePvm6+9a+9P5fCbtjXn0Zn+NJzrIjLVD6PBSP7eWkITvK0acERRpCq/Dd85bnQ09HPuZAvJS0IoOdRI6RFz49LwgJ2pRaWdKBQCc+K9p/Rz1Zd1sucF88Viyc1xdDUP0FLdy7oH0nDzcqb0SKPcFRsY6WE1L9SS7lbJMzcYRK7/9lx5FB7AsffOU1XQzvLbE3BxV8se+LoH0uSBFad3Vct172vvTyU+e+x5o58HBnq0HHitnIpTLfiFurPp4QwCI6dfDtmBmUnd74iiuBPYOerY0xaPG4C103tqE6NQeGE09kyrhy5VaRoAgQs+BUR3pk/b3pNhIindqRCW5EN8TpCVwUtbHkp7fZ/ViLeeNnNdusZNTeXIODgTKcvmUH68mYScIJRqBRWnWujrGGbZrdbeZ2tNL/3dWhZsjZVlAmKyAig5ZF2NM9RnWwBVV9pJULTnmKqG9uhs6ueDfxRgHOkdyFgVRmiCD2/+7iTxOUHEzQ+ksaKLT/8rnYtXgAvXfT3T7md0tw7y7v+dwaAzsvXbc60afspPNHH6Q6lJKDTBR1ZGXHh9jDzMee/LZRTtly6aN3x37ufaAxVFkfLjzRx4vRzdsIEFW2KYuy5Crv93MHPM6m9YUEhyrdProZs9N52iZxr3nRwBauva8hB18RgrJyYoytNKsTBnUxQqJyVHt9mPhqUsncPuF2z1WsISfNAPG0gYmXCf92ktnv4aOV5u4kJ+G4Igaayb2PFEvtUaDz8Ng/3Wdf3DAzpaq3um1B060KNl+9/zGB6QLg7Rmf4svCGWT/5TjMZDzfLbE0aGL0s19qaGIBd324RrT9sg7/7lNDqtgS3fyrISBWup7mH386WExHmRvChENuZRGf7MXx8FwLbHzsjG/I6fL/hcG/PejiG2/z2fT54rxifIldt+kkv2xiiHMb9MzOqMxIwYdMH8Dy+hfQkKgxaj8vL1SPUbrJOCTpcwqWiwT8epD6rk56VHmghP8bM72NlEX4f10O3rvzOXs5/U4ubtzJx4b5ov9NB0vpult8TbCCVV5bfhHezGmV01hMR54R/uISdiTaidlVZDLkBSZxRFCEuaXEJUrzWw86l8+c4iKNqTa+9L5cT7F+ho6Oe6r2eiUAq8/ugJ+T23/jjHrnJiT/sg7/7lDLohA1u/NdcqhNTfPczOpwpw8VCTtSaCt/4gSSK4eKhZ/xVJ2uE/PzpEf5f0nX3pd0tw8/58NsSIRpHC/fUceacSURRZems86SvDHGJal5nZbdCVMxGPs46tXk5jDtBntC7nbNCmXvRexQeti5F6O4b45DnbUsSx1itUAn5z3KkpbCdjdTiCQiBvdy1OGiXJS0Ks1vZ2DMllj4JCYNENcdafNTKbtKOhH9Wogcd1JR2onJUERU/8+xSNIp88V0zzBenuydNfw8avZtBa3cuZT2pIXTaH8GRf3v7jKdl7v/X/5dhVTuztGGLbX84wPKBn67eyZMEukGraP/xHAcMDOnKvi2HXSIUOwM0/ykYhCDzx0G752AN/Wf65rdjoah5g9wslNFZ0E5YkiWlZVjg5uHzM7vsgwW3iNVPBaJCrXASL8vt2l9FVmpeHWM0hdKL1H4bA5Lpi0y0EshQKgaRFUkldT9sQ7r6T8yK//MdlVJxuwWgUScgJoq9zmMpTLSQvmWMTh7b0+jNWhVF8qIHe9iE5segX6i5X1lhKA4CUEJ0T5z2pDsoj71TKMX5nNxWbv56FyknBp/8txtNPw+Kb4tj/arls8G/47jwrQ22ir3OId/9yhqE+HVu+mWWVrBNFkb0vl9J0vofYuYEcfa8So1GK06//Shpuns48+bBFK/8TKz+XxtxoMHJ6VzWv/vo4HQ39XHNPElu+meUw5p8hs9qgC4L94QMXvZ9ebxVyMaE2fjayNC3aeJtjIhOP23JyUZG2wtwMteKuRBZujZWfjw6r2CNteSjOLirOnWjGJ9gV/3B3CvbVIYqi3EJviWm4hauXE75z3Cg93Mi8dZGII4awo76PkJEKFsskbF/nEF3NA4RPolyxcH+9PJRDqVKw6asZeAe5cvitCnrah1j9xRTKjzfL8exNj2TYrZrp7xrm3b+cYbBXy+ZvZhEUZX1nkL+7jtIjTXgFulB+olnuKk1fEUpYog9Pf32vvPbhp1Z9LuPDbXW9vPn7Uxx5p5LIND/u+MUCkhfPcbTtf8bM7n+JwvTGKwWDHsGOwfQc/mw62XqNgRMvskPcvACraUT+Ye7UlU1NMGzFnYn0dgzRcK6LhNwg9DojRQfqic4KsPHAtIN6ueM0c3U4h9+qICjak+BYLzqbBlh6SzzOrmpaqsxJZpNwlul9EyVEq4va2feKWVdmzb0phMR5U13UTtGBBrLWREiCWS9La1Z/0b5yYn+3ZMwHurVs/noWwdFeVq/XFLdz6E1Jl6W7ZRBPfw1KlQK/MHcyrgnn2e8cAMA7yJVHnr7mc2fA9DoDR7dV8sajJ+nrGmb9g2lseChdbi5z8Nkyq+8ThRkw6KLpGjda3GAWUV/eJc/lBKkGvaF88gY9ckTD/NwJSUQrPieI8mNNDPfrybzGtinI1KEpKASqC9oxGkSu/XIK+189h6unE2nLQ1E5KayEvrqaB/ALdaeutBMXDzV+c8bWBW+r65Vi2CO/k8U3xhE3P5Chfh17ni/Bd44bKUtCePl/pAqUBVuiSVoUYrNPf/cw2/5yhr6uYTZ/PZOQWGtj3tU8wEfPFmGavhee4svwgJ7+bi3ZG6JkzZaoDH82PZwxma/yqqKxsps9L5TQ2TQgySbcEm/Vz+Dgs+eq8dBFcZrEHeVO0dnreVka88QFwZQebqSnbYhNj0zOCDWc66K/e5jy480ERXvi6e9C3qe1BER4EBLnZbPeNK4uPjuQhnNdLLstAaNBpKaonbQVoSjVCpKXzLGKZZ/5qAZRFKkt7SB0nHFz/V3D7HgiH92wlDtIXxFK1rXh8ucO9upYcUeCbMwTcoPI3hhts89Aj5Ztj52lt2OIzV/LsBliMTyoZ+dT+XIiNXZuAIERHrRU9RCbFcCuZ6SkaOaa8M+dMdcO6TnwWjlv/+kUOq2BzV/PZPWXUhzG/ApkVht0q9tdcWjshVPiyvpKFNh2VU4FDz9znmGgZ2Jd95t/mI1Bb+T9x/Nor+8jITeI2uIOOpsGyLwmzCbEYKmHXnGyhbj5gSQtCiZ/tyTcZRKrUigElt9ubkQqO9ZER0M/A91awscoV9QO6dn+RB59nVLMPyrDn6W3JSAIAhWnWjh3opn5G6N4589Sq73vHDfW3Dtaqh8Ge7Vse+wMvW2DXPdIpk2duNEo8vG/iuhskkpEkxYGk7oiVNazMd2BLL01nqU32+Y1rmZqitt59VfHyd9TR/qKMO74+QK5M9bBlceVZb0uAXFaDLow0ikKGC++/nsquCtabI6pBLOHneG646L39gp0sRosYdkxao/kxSEERXsyb20k7XVSCWLc/CDydtfi6ulE3KhWdoPBaCW56+rlxIo7Exke0FN6tJGE3GAr5cTgGC/8w82hFVOs2l783Ggw8tG/iuRSyMBID9bel4pCIdDfPcy+l8sIjPTglIVGze0/zbW54Az2Sca8u3WQTY9k2B3GfPSdSqoLJQ339BWhLLoxjk+fK5ZDLyBVt9gLN12tDPXr+PS/xbz/tzyUagU3fG8ey29PmFInr4PLz9Vj0I39Ey+aaA+FAvkrUUyPvvpE+KrqbI5ZTjxKdf3oovZd/2AaW76RNaX3rLpbEsict84s2zvYq6WmqIP0laE2ZYWjm4bW3CvdhhcfbECvNZJ5jW01zKovmEU4a0s68fTX2CRZRVHk4OvnqB4ZlOHhp2HTI5monZVSSeGLpei0BgZ6tXIFylefWGkTthnq07HtsbN0tQyy6eEMu41LpUca5QTyvHWRLLstgd0vlNDfbb6bufF784ide3EJ6tlI5ZkWXvnlMcqONTNvfSS3/TRnzDmrDq4srprLrdQtOr5++IQolIxuLJpparTzbI71G81e5KDRNmbtq6qeUCs9JiuAQ29VIAhSTXr+HtsLhyWbHsmQvdu2Ee8c4NVfH0epUsihExN9ncMcf/+C/Hz++khCE3wwGowU7K0jNNEb/zDb+m9LwSsAD1/b0tO8T2tlMTFnVxWbv54pe/olhxupKmhHpVbI5ZcPPLbcRjlxqF/Htr+eoatpgI0Pp1tJIJhosNB5ybkumtzrojn7SY18IQG4838WyPK5Vzv93cMceLWcyjOt+Ie7c93XMu3W8Du4crl6DLqhC1G8tNIUUam0yIV+dklRvUUzUfGA7dSi0fIAo/EPd6e3c4iCfXUkLQohLMlnQoNuWeJXfrwJpVqBV4ALHQ39hCX5WA2nADj01jk5UQmQs1lKRJ4/20Zf57BVvNwSlVqJQiXInrVJn9zE+TOtHBpRe1QoBDZ+NUM2qD1tg/KoN71OKnu8+7eLbMIAQ/063vvrWToa+9n41QwiUmxjvt2tA7wzovOSvTGK3Ouiaa7qkT8b4Eu/X/K5KMcTRZGyo00cfOMceq2RhdfHkHWtQ0xrNjL7DbrgCuLAiIc+tWHOo5FCLlcWpUPXWD3PcH2f/IHN474nONqLE9svICCQvTGKN353csLPGerXoXFTYzAYqTjVQnSGP04aJR0N/XJS0kRtaQcVJ82x/63fypL/+PM+rcUzwIXIcabQuHo6yd71UL8O3bABtbOS5qoePvq3WS5gzb0pcmOQaBT59L8l6IbMF5EbvzcPTz/rcM3wgI73/3aW9vo+NjyUTqSdBN5Aj5YXfyaVIGZeE86CLTEMD+h40+J7euCx5Z+LeHFP2yB7Xy6jtriDkFgvVt2d9Lm5I7kaufIs2BSRBboMXSBemkGXQi5XNtHOxydcU3+ui7KjTaSvDKWmqN1GDMsGAbmio66kk6E+HbHzAqkukqbxtNf3UT9Sx27QGdk/MhfUhCk2bRLuyphAlGm04uHpXdX0tA2y48l8DCOe96IbYonPMSdh8/fUyePlAK65J4mQUXFd7aCe9x/Po62uj/VfSbfbWDTYp+W5H0hDv+OzA1l6azx6nUFuGIKRVv6r3JiLRpH8PbW88uvjNFZ2s+y2BG747jyHMZ/lzH6DrjQrLoqXatCFEQWpK5gm3fiT/XyCXels7EftrCR9ZRjHt18Ydz0AolQX3tsxRPnxJpxdVei1Bvq7hln3QBoefhr2v1qOwWDkzCc18hBmkMIVJvJ216LWKElebNvUY4lJnMukJX5yZxXbn8hncKSsMnV5KHPXmoeIdzb1y6EWkLRikhfPsdpTO6Tn/cfP0joy2CI6w36X6L+/JxnzwChP1t6fxlC/jn98fZ+85vPQyt/Z1M87fz7NgdfOMSfWizt+nkvGqrAxewEczB5mvRti8tDBcOkeuuW+lzmGHu50hlrt3AnXHeu7a9zXTcJXc9dGUnyoQR5gbI8lN8dZxYx3PJFPd9sgCTlBFOytwzvIldi5AShVAjufKuDQmxU2lS3RmeYZopWnWkhfGTahUJVc7704hIaKLga6tfIIvMh0P5bfFi8naI0GI9v/nie/Nyjak2Wjxrpph/RsfzyP5qpe1j2QKo+Ds6SnfZAXfnIEkES9bv7BfHraBnnhp0fkNQ8/ueqqbuU3GIyc+aiGEzsuoHZSsvpLySQuCL6qf+bPG7PeFREUlip502DQP6N/28kun07LPiYDHjsvgLOf1I67NmtNBFHp5hhze30f+mEDnv4aWqp7Za8tKsOfyHQ/K2OucVfj5uUk64cX7q/DKIqk2xHuGo3pojMnzttKGMsv1F2qNbfwkI++e14W81IoBW74nnVVkHZIz/a/59F0oYe196XaLS/sah6QjTnAFx9dQltdn5Uxv/cPS69qD7W1ppc3f3eSY9vOE53hz53/s5CkhSEOY36VMfsNumCRFJsGgy5iND24rAQ7lU28aAqc+qBaFsCyR8oyKWSRtkIywJahk6PvnkehEEhcKEnuCoJgVfaXuTocg85IVIY/gkJArzVQtL+B6Ax/vAImL5165uMaLuSZZXf9w92tYteNld1WImP3/mGpVThEN2xgxxP5NFV2c+2XU+QQjiVtdX3y/E+QqmIaK7qshl9s+UaWVQPU1YReZ+DIO5W88buTDHRrWf+VNNY/mH7V/ryfd2a9QbdSXJwOg66YnN74dDNsNHdQbvH5xZTe6+9nG1YpO9Y07kWpKr8Ng8FIRIovnv4aWf7WhNEo0t0idazqtQYOv20OzQiCZExNI+jKjzcz1K8jc/XEnZSWCdoTo+L7ZUeb6O+Wql+0Q3re/uMp+bW7frnQSjtEpzWw48k8Giu6WPPlFLsDmZsv9PDOn07JF7YbvjuP+rJO3n/cHMKZtz6S8JTJTUqabTRUdPHab05welc1iQuDueMXCz5XDVKfR2a9QResBLqmwaALJpGvy3srWjxorjf3VtVP6b3LV5stt2Vr/VhsfDiDgW4tVXltCAqB1OWhdDTYdtq++YeT5O+p5dSH1XLdOMDZT2pRqATCknwQRZG83bX4hbnb1R4fjT0Z3+u/MxdPf6nB6PBbFYhGkWe+tV9+fcs3s6wmDum1BnY+mU99eRerv5RCQk6wzZ715Z1se+wM2pEyx5V3JVJf3snu583yB8ExXizYbCvkNdvRDunZ/0oZ7/zpNAa9kS3fyGL1PckOMa3PAbM+KWqtuHjp+itmg355KR1cKT9+p/3RSb8vQFVBYFQaIMWZXT3Gv5Ve90AakWl+uPs6U7i/nth5gSQvDuHI29bDqVfdncSFs60ceO2c1TEXdzU7nyrAqBdRqZXUlnSMTKtJnlQ8tnC/9cXq2i+nEJrgw9Jb4tn5VAHlx5spP94sv7789gSrcI9eZ2Dn0wXUlXWy+otSUm801UXtfPB0gVwCmb4ilKbKbkqPNslrnF1VrL0/1abDdLZTXdTO3pdK6escJmNVGAu2xlz1JZgOzMz6f81WHrqx+5L3M35GIRedaPZApzLYYoXnPykqMhvxmuKOcdfHzQ9EoRBIXRpKXWknXc0D6LW2sfazH9ew/qF0q2M+wW64W7Tq93cNk7e7FhcPNfE5E5/zYJ/WSp0xMNKDhFzJIEem+6Nxt/YgExcGW43S0+sMfPB0AbUlHVxzdxJJC23LIytPt7DTop49NNGb5upe2ZibksDX3JNsV3ZgtjLUp+OT/xSz/fE81E5KbvzefJbd5hDT+rwx+3/blgbd0ANMHHIYj8l46NXeRUR2Xfzw5unER1XLtk/6Jl4I3P6zXPlx8pIQTmy/QOH+epsE2YavpPPBPwp477Gz8jFnVxXv/Pk0CqXZC9/5VD4t1b3kbIpCpR6/KUuvM/D6b09YHUtZaq4lVygEVE7W/oVJLAxMQ5sLqSnqYNXdSTZ16CAJbe1+vgR3Xw2DPVpcvZyoL+uSX7/mniR2P19K+sowu6WNsxFRFKk83cr+V8sY7teTvTGK7A1RKNWz3ldzcBHMeoNueZsveei2f+hTYTJJUXftxPMvLxd5A5vRDY9dzWKJX6j5Yufm5UzM3ABKjzTaeMbRWf74hbrJnZlbvpFFYLQne18qlVv+05aHyuGT1OXWwl2jEY0iu/9bIksIaNzVDPXprIZFlx9vspp1KgigGzSgdFdg0Bv58JlCqgvbWXlXIilLbH/HBXvr2P9qOSGxXgyNDKmwnF161y8X8tYfTuEf7s7im2Jt3j8b6e8eZv8r5Zw/20pAhAdbvplkVxDNweeHq+syLk6DhO4ESdEmj/P4DVzaRWM6OdN/vZxQHI+NdqbspC0PZXhAL1ezmGiq7LYSzQpP8cXZRcWSm+LkY5ax8NEXhNEce+885yy0X+7+zSJUaoVc8VJV0MbH/y62eo8oSu8z6I3seqaQqvw2VtyRYKP6CJJ0wP5Xy4nK8MfVy4nOxn5ZvAvgocdXsufFUvR6I+vuT5vwbuJKRxRFig818Movj1Fd1M6iG2K5+YfzHcbcwVVm0KcB4wQhl+DemMt0JpNDL2rkOPR4RKTalubNSfC2ep69MQonjZIP/lEgH1M5K2VPumpEVnbt/dbhprxxGpiKDzVw6sNq+Xloog9OGpXspTec67L6vNh5UixeUAgU7q/n5f85yoW8NpbfniDXzJsQRZGj2yo58k4l8dmBBEV7UnnaXH6pVCt4+KlVnPqwioZzXay8I8GqWmY20tM2yHt/PcueF0rxnePG7T/NZd66yKsuuevg4nD8KxiNYnLhiyuFJJ9TFOwdXxo3c3X4pPRJMleHk7ggWO42XXFnInqtgdMjBrkqvw3PABdi5wbg4mH2yo+8U0nTeduEdG1Jh82UJFNVisZdTW1pJzueyJNLIl29nFj/YBqBkR5o3KRoYE/bEEtvibdKjoIUxjn4+jlOfVBNypIQEnKDObbtvPy6f7g7Dz2+kobyLk7urCJpYTCJdpKoswWjUSTv01pe+dUxmi/0sOKOBG74zrxZf4FyML1cFQZdEGf3LfSlEOLdKg82HoukRfY9+KZKayOscVNTcrhRfp62PJSkBcHk76mjo7GfutJOojP8uZDXxmCvjg1fSWfBFumO5a0/nOJCntk7bm/oY+eT+fJzUxOSadzccL+e/q5huU4c4IuPLgYgdVmolQbN6JCO0Siy58VS8vfUkXlNOJlrIthh8Vlx2YHc9pNcBnu1fPTvIrwCXVk2hj77bKCjoZ93/nSKg2+cY068D3f8YgFpKxxiWg5suSoMulKYfsnPmRTnSnHZNW177b6wcdzX/cLcx4ytlp9otnp+/myrHHt293HGaBTJHTHYb/9R6riMzvAnb3ctHn4aojL9yd4YRcICqUtz51MFHHi9nJ72Qbb/PU/ea82XkhFFEe8gVzx8NfR1DtHbYT0D9vaf56JQKjAajPJ8T5BUEQ+/VYF2ULpoGQxGPv53ESWHG6WKjk1RvPLLY/L6+esjWXd/GqJR5JP/lDDcr2fdA6mzsnzPYDBycucFXnv0OF3Ng6y5N4XrvpZxVZVbOpheJmXQBUFYLwhCmSAIFYIg/GiMNSsFQTgrCEKRIAj77K2ZKcyKi5e4j/HyeDyhToWX5XNAmmBvD4PBKE+zN4VPPnhaimWnLJ1DX+cwNYXtePhqSF8ZKt8FKJ0UNFZ0k7HKrHm+6q4k3Lyl8tH83XW88JMjcsXKgi3RxOUE0VDeRViiD4N9Wt7761mrc8m5Lhq/Oe4YR4ZYmGQIBIXAvHURDPRoObGzCr3OwIdPF1BxsoVFN8aSsymKf33XrGO+4s5EFl4vVbCc/bSWmqJ2ltwcNyuThS3VPbzx6EmOvXeBmKwA7vjFAocyooMJmdCgC4KgBJ4ANgApwB2CIKSMWuMNPAlsEUUxFbhl+k91bETl9GhxCMaR0M0MC3N93P3dmf0AC8ZKmNYWd8jTf9bca/51ege5svyOBFy9nOS5npZDowv31qN2VpJsUTqoclKy8q5Em89QOSmYvyGKlgs96IYNBEV7sv3xPFk+F6TpRTkbozAaRXY/X0L58WYWbI3h7t8sQhRF2mr7SF4SwtmPa3jxp0eoKmxnxZ2JzFsbyVOP7JX32fRwBmkj5ZPNF3o4+k4lMXMDSFsxfknllYZJN+fN351ksE/LhofSWXd/mkNMy8GkmMx9aC5QIYrieQBBEF4FtgKWdWZ3Am+LolgDIIpii80uM4nKGyYYyjMZpFj8Z9P6f7EsnVvNwTP2B0b7znEb0xCY2uu9AlysZoMuvjEWpVJB6tI5nNhZRXfrIAM9Zo2csmNNpK8Kw3mU5rm96UB6rZE9L5bKGiJ5u2utBlCDNJwaYM8LJZQdbSJ3czTZG6KkPdP8KDrYwNZvZlFyqJH+bi1rvpRM4sIQnnhot7zHzT/KlmV4hwf1fPSvQty8nVn1haRZ5dHWl3ey54VSulsHSVkSwuKb4nB2deivOJg8kwm5hAKWdWl1I8csSQB8BEHYKwjCKUEQ7rG3kSAIDwqCcFIQhJOtra32llwcwvTEFAXjzMdZV3o+Oa37dQ/ahptMCUh78zRBUko8NxI/T1gQzIf/MIeATPXiKUtDEQSBogP1VOVb/64yVtpqno8eQn3fn5cxf30kJYcbOfORJIHbVttnozWjUivZ81IppUeayNkURc4ms1hW2sowBnu0VvK3ameVlTG/65cLZWMuiiJ7Xyylt2OYtfenzhoxKu2gnr0vl/Hu/51BFEW2fCuLVXcnO4y5gykzGQtmz8UZHZRQAfOB1YALcEQQhKOiKFoNnxRF8Z/APwGys7OnLbBhqedySfuIpq9j5rw6F8Wl681YUlDqbXOsKl/SGA+K9rR5DeCChYGuOCkZdhcPNXHZQRTtr2fglnjcfZyJzvSn5FAjTi5KQuK8aKyQzr2jsd+qXK4qv40Dr1nPGW2t7WXh9bEERnpa1ZmbvH2lWiG18z9TSGdjP9kbo8i5zlr50DfEnOze9EgGR9+ttNrrrl8utDqP4oMNVJxqYdENsQTHTE9eZaapKmhj38tl9HcNk7kmnAWbY1A7f36rthxcGpPx0OsAS6HrMKDBzpoPRVHsF0WxDdgPZE7PKU6CaTLoCnHmPfTCgfUouXiZX3fF5KNZw4P2w0cnd0p15QqlIMezt35rLmnLQzEaREoOS7/etBWhDPXr6GkbsgrLHH23EqNBqmBpremVSwYFhcDNP8rG01/DgVfLMeiNHN1mreIoLYT1D6YB0NnYz7z1keRujrYKj3S3DvLu/52Wn7t6Oll1r97y42wrY95e38eB188RnuLL3GvN80ivVAb7tHz87yJ2PJGPWqPixh/MZ+nN8Q5j7uCSmIxBPwHEC4IQLQiCE3A78N6oNduAZYIgqARBcAUWACXTe6pjM20eutF0iztzWdFa7Vzmub1tdcxbOX5jkCV9U1BibKnutTk22Gee32k0SD9n5upw/ELd8Q1xIzTBm6IDDRiNImGJZs2antZB/ELd2PCVdDqbBig92kRvxxDv/e2svGbz1zMJipJmfnY2DfDv7x+ULxhWwlsi7HlRajjSuKlZuDXGyph3NEp118ODerZ8IwuAN/73pNXPMXrYxa5ni3B2UbHmSylXdH22KIqcO9nMK788RsXJFnI2RXHb/8shOHp23FE4uLKZ0KCLoqgHvgbsQjLSr4uiWCQIwkOCIDw0sqYE+BDIB44Dz4qiePlq86Yt5DJiJGbQHggYiHQ+bXXMU9k8xuqpsegGa9Gpov31VgJYACWHGq2eK1UKci1CHanLQ+ltH6K2uMPKyGqHDGRcE050lj/BMZ4cfOMc2/5yRtZjWf3FZFm33JQg1VrcIZgkek011APd0l1K1rXhVp/TWtPLO38+jVGEG74zz2ZgR9qKUFROCg6+YdZpP/BaOZ1N/ay5N+WKrgbp6xxm51MFfPRsER6+Gm79SQ65m2McyogOpo1J/UsSRXGnKIoJoijGiqL425FjT4ui+LTFmj+KopgiimKaKIqPzdD52mX6Yugzf7sb43wMrWidxK3Rzp/wfX6qC4Q55Y27xhRiSbNQP9z5ZD56nTlccuQd6xDI6i8m42RRsRKTFYCLpxOFdi4GCTlBCILAgq2x6IYMdLdKol4510WTtMjcVl9+ogl7uHk5EZrobXXs6Lvn5YtC0/lu3v3LGVRqBTd+dx5KtYJ/f/+g1frltyeQvTGKC3lt1BS1U36iiZJDjcxfF2k1CONKQhRFig7U88ovj1Jb0sHim+K46QfzrdQvHTiYDq4O12AWxdBTXT9ke+fPpvy+BM1+6rSZ+KvO2309Z1OUPHA5KtOfgAipmaaxsptPnitGNIqyATYRmuhDXLZ1CEepUpCyJITqgjaKDlhPFzLojYiiKCdSQaqoydkUJT+vKmjj0+dK7I6j07g7UXqkiYxVYTz85Cr5+Ku/Oc7x98+z7a9ncXFXc+P35zM0oOOlnx+1er+btzOIkLU6Aq9AF7Y/kc/u/5YSEutF7hU6Sq67dYBtj51h70tl+Id7cPvPcpl7bYRDTMvBjHBV/KuarlpjubFoBtGJLhiZejla5dBi3BVtNvF3ExFpfnJs3MlZiYu7msBID5bcHEfl6VYOvVXBtr+csXrPijsS7H53KUvnICJ5z5aUHm3izMc1FB0w58Q9/TXyHg0VXez6ZyG+oW52B0i01/eRviKUpbfGIygE/MLccfN2pr9rmBM7qtAPG7jhu/Noq+vjrd+bB0S7eDqx6IZY+ruGqS3pQKlWsPjGOESjiEFv5Nr7rrxRckajyNlPanj1V8dpre5l5V2JXP/tuXgHOsS0HMwcs0/gYgYRLoOH/kHXjy/qfS36eFZ7/ZWPur9n9/XORnP35fCgHo2Hms6mATJXh9PbMUTep9YSt/PWR+ITbF8Dx9PPhfBkX2pHxtklLAiiu2WQg6+b49a+c9zwDnSlaH8DWWsiGB7QseOJfNx9NWRvjOKjZ4ts9k1dHsqy280XEY2bmvZRjUb/+dEhq+cKpcCGr6QTGOnB2U9rKdhbR0SqHw3lXeY1V1gStL2+j90vlNJS1UNUuh8r7kzE3cehv+Jg5rmy3JrPmMvhoV8sXsoGvJX1Y75eXdAmP9YNGXBxd2KwT4sgCCy5Od5qrbuvs9yNORaWNeCZ14TjH27WQ9G4qdn89UyW3irt+/G/injvb3k4aSQJgL0vldktFJq/PtLqjsA0XzQkzosH/rIcFzsJzRV3JhIS64VSJXWvVhW2k7+nlrzdtUSm+6FUKTj8dsW4P8vlwqA3cnz7BV5/9AQ9bYNce18KGx/OcBhzB5cNh0G34HLE0C+WeW7v8FbHH8Z8vaqgXR5iMTyox8VDjV5rRKc10F5v7QXHzQucsN7ZcuKPs6uaIosJRVu/nYW7jwYPXw0xcwNorOxmsEfL+q+ks//VckSjiHewObQQluQjGd63zIbX8o5h8zey2P1CKYM9tvX5iRbiYqnL5oAIB147h3+4OxseTGfu2gjKjzfL4/I+K5ov9PD6oyc4sf0CsfMCufMXC0jIcYhpObi8OAy6BZcj5HKxtOvt67WYMOiNZKyS+r+0Q3pc3CVvt69jyCZ2XnK4kY7Gscf1iaJoZcBf/NkRq9dNew/16WQv28XTiRM7LtDZNMDKu5LoaJD2j88JYss3ssjeGEnFqRZqSzo4ufOCVdnhm787SeVpqWHq2i9b6b7xzp9O09MmJXMtSxLXfCkFpVrBvPWRuPs4s/+1cozGGVZVs4NOa+DQm+d46w8nGR7Qs/HhDNbel4qLx5VbPung6sVh0C24kkMu+QPXTbhGNIrScOUhgzwU4pP/lFgNwNjyrSwUKgXbH8+jv3vY7j6WHZmWzN8gXVSKDzWgHdLz/t/zGB7QE5Hqy2CPluqCdpbfFs+uZ8wtCGvulRp9sq6NwDPAhff+epZj710gcUEwi0dmlJqM/20/zeHgmxV4Bbhw35+Xsfb+VDob+3n90RNUnmnhxM4qed/WGqlpSu2kZMnN8bTX9VldhC4HdWWdvPqrY5z9pJaUpXO44xcLiM6wFSlz4OBy4TDoVszu2+NP/luMKJpCLpKH2FLVI7+edW0E4Um+XPdIBoP9Orb/PQ/tkK08wMkdF2yOZa0JZ+HWWCJSfMnfXceOJ/Jprell7f2pVkqLxRaNS9fckywnLJVKBaKFB73q7iSrEMx9f17G7udL0WsNbHw4A42bmvjsIG79SS5eAS58+I9CTu6oIj47EJ9gV6uxe7HzAghN9OHYe+cZ7Lt4WYXJMjyoZ89LpdKdjyBw/bfnsvKuJBsFSgcOLjcOg36VsPnrmXIr/1Cfzsp4mliwRarVDoz0ZP0DabTX97Prn4UYDNZzVCvP2Cph+owkSVOWzWGoXxrufM09SWjc1FbhE5PnDBCRIsX0jQYjnz5fQm+7eUrR01/bKz++4btz2fdKGa21vaz9cqpVQtYrwIUND2XIz1tqeglP9qWlupfmC9LFShAElt+WgG7IwNFt9uv0p4sL+W288j9HKTnYQNa1Edz+s1xCLSQSHDj4LHEY9KuEiFQ/rrk7GYBzJ5ptKj/SVoSiUptDSpFpfqy8K5Ga4g72vlSGKEoXgNZaa/2X0ERvvAJdKNpfjyiKcvMSwJw4bz74RwGe/i5yOAakYc8+wa64eTtj0BnZ9WwRZUebWLAlmpt/mG1z7offrpSmEF0fK0v/mhCNInteLEGpUrDw+hiG+/WyVG/BPrOX7jvHjfRVYRQfbKCluofpZrBXy0f/KmLnk/lo3NXc9MNsltwUh9rpyg3TOfj84TDoFszkHNHpwks5WujSPEQ5br6569PkvZpYeot16SJAypI55GyKovRwIye2S2GW1397Qn7dO8iVDV9JJ2NVOC3VvWz7yxnKjjbJYZSXfnEU0SgSnuzLqQ+q5fcNdGsJS/ZFpzWw8+l8zp9pZekt8cTOC+TN35tFtkxTjpov9BCfE8TctbYqiWc/qaWmqIOlt8Qxf30Ut/00R+5CLTvaRE+7ufs157poXDyc5Eqb6UAURcqPN/Hy/xyj8nQLuZujueXHObIGuwMHVxJXhUE3eZdXE26KNptjc93eptswx+a4pXaLaT6oJaGJUtmgPXKuiyZpcQgndlRZaZqr1Aq2fDMLZ1e1XDpYX95F+sow7nl0MSCpNYYm+sjx7JA4s2Kg3xw3tj+eR01xB6u+kERglCcv/480zDk+JwivABepXn2Ea+62nS7UdKGbo+9WEjs3gNSRn9HdR8PWb2URO1fqRH3hJ0fkKUjOLioW3xBL84UeeV7qpdDbMcSOJ/P5+N/FeAW6cOtPcsjZFD3md+nAwWfNVfIvc5oSYVfQdSHRxXbOdpbraNViCQ8/qXFFrzUw2Gs7iy/3uqgxP0cQBFbelUhAhIfV1KEbvz9fVkY0TTcCyN0czdlPauTn50fi7Uq1gjX3phCa4A3A3pfKaKrsZu2XpclBb//xlPz+tfelWg2zSMgNQjUqdDE8oOOjZ4ukUXKjjL1CqWD9V9Ll52/87wkK99UhiiKJC4IJivbk8NsVY+rBT4RoFCncX88rvzpGfWknS26O48bvz8dvjkNMy8GVzdVh0MWZr2y43Jzuv8nmmEKwb6BM2uCWVSOWhMR6j/tZokG0GmARmuAti3udO9HMvlfKcPOSqma2PXaGs5/U2uyx4o4EPP1c6O0wJz5XfiGJwT6dPGVo9ReTydkUjcFgpOSwuRpmsM/6IiSKInteLKO/UxolN9YotnUPSEMyjAaRfa+Us+ufhWiH9Cy/PYHBPp0cRpoKXc0DvPuXM+x7uYzASE9u//kCstZEXHHyAg4c2OOqMOjiVWjQ7VE4sN7ucY27mqr8Ngr22a/Dtle1YkI0inzyn2K6ms1aMG31ffS0DVJd1M4nzxUTEuvFF369SHqtts9mj6gMf5IWhdBe30dPm9mg736+RA7jbPlGliyxe+jNCurLOmWFRJNmjImiAw1Unm5hwdaYcUfJRWf54+btTHiyD4tujOVCXhuv/fYERqNIytI55O+po73B9nztYTQYOfNRDa/+5jhtdX2sujuJrd/KwivAZVLvd+DgSuCqMOjT56GbvTDxSoq/jHCs7wt2j4sGkd0vlOAX6k58TpDN67tfKKGrZcDOO+HIu5VUnjYbfO8gVxDhhZ8eYfvjefiGurHpkUx5FqiJsCRzqV7K0jl0twzKw5wXbI2xWnvr/8shfKSEsfhgAwV76shcE241ENpk1Nvr+zj4xjkiJjFKTqlUkLpsDrUlncRkBnDD9+aBCO/88TQaVxVOGiUHXjs3YY6lra6Pt/5wisNvVxCR4sudv1hAypI5jrZ9B7OOq8Kgz4iHLhgnXnOFcOitCrRDBtbel2oV75YRYdczhVaDLgCKDtRz5qMaq2M510WRu9lskDc8lI4A8txQE4EWVR4ntl/g7T+bpzCVHDJX4oQmmsM3DRVd7HuljPAUXxaPTFcylSnuf62coX4du54pxNlFxepJjpJLWToHhVKgcF89wTFe3PqTHKIy/Tm9q4bhAT31ZZ1WFyxLDDojx947zxuPnqC3Y4i196ey4aF0SXfdgYNZyFVh0Gcihm4cI159JdJa08uSm+LobLJu2Xf3kQzTkpvjaKvt4+Ab5hh7dVG7VZWJCe9AV05+UCU/P/j6OXY9Wyi355s4/WE1CblBBEZ60FrTi3ZAL9/gmMIuKUtCqC/vor2+j96OIT78RwEefhrWWuiXm4xnV/MA//ruATqbB1jz5cmPknPzciZ2bgAlRxrRDRvQuKlZ/2Aay29PkOPeu54ptMoRgDQd6bVHT3ByZxVxOYHc+YuFxGcHObxyB7Mah0EfjVH6SkTBMMHCK4eodD8SFwZbdWwCZFwjiXUFRXsyd20ERfvrKT/RRFtdH9sfl8bZObua29W9g1z58J+FiAaRO36xgCU3x42MepPCIaGJ3tz0A/O4vLj5gfIg6vAUX6sqoa88voJFN8bh7KLiwOvl7HwqH4POyKaRtn4TGjcVljY0cUEw4UlTGyWXtjIM7aCe8uNSqaIgCKSvDOPmH5ubmP75zX0YjVLy9+Dr53jrj6fQDenZ9EgG196bKtfyO3Awm7kqxCemL+QiIIhKRIyzyqCvujuZkzur6Ou0FtsKipJCHYO9OhZsjaHpfDcf/6vYak3y4hC5aqWreQC1s5LrvzMX3xA3ms53W63d9HAmx983t9Z/+Ewh3kGuDA/oqMo3180//OQqBIWASq1k3rpIeY7ppkcybIZquLg7YRni1l5EqWFIrBd+oe4U7KsnZak59h0Q7sEDjy3nmW/tB+Cph/egVCkw6I2krQhl0fWxVvNUHTiY7Tg89FEoRgZFi4rZE3IZ6tORN6qUMDzFF82IzO1gnxalUsGqLyRZrbn5R9mcz7NuYNr4cAaBkZ40nOtkzwulVq8debuCsxY65ka9yLoH0qxq303G3ISlgY5M87M5d7XGXH+eujxUuiMobp/wZ7ZE8shDaa/ro7HS+iLkpFFxx88XyM8NeiPz1kWy4o5EhzF3cNUxqw26Wivd7ovT1ViEWRN9Nnno+18ts9ECj0jxlbtGB3t1GI2iVUjG1dOJgR4tPRaDozc8lE5Yog/drYO882dJQ913jhtf/uNS/MMlD9gyrOLspuK13xy3+lxLY37+bCunPjRLAlw4a9v9akrKzlsXybJb4vEKdOHAa+cw6KaWlE7IDcbJRUWhhQqj6Ry2/dVaD/70rmoOvXkOg372JL4dOJgMs9qgO+lGaoyNg+MvnAKCyUOfRUnR+vIunN1UVuq/SpUCjasaQZA8+IOvn5Nj4S4eagZ6tOy0qFxx83IiJisA7aBeHmihcVdz0/fn4+LhZFWP7erpRGCkB8P90ndk6g4F5Mai9vo+PnmumMBIKezhE+zKkXcrMVooO17Ia5Xr34NjvVCqFSy/LYGu5gHOfmpdfTMRamclyYtCqDzTSn/3MAM9Wj78ZyEfPF2Ai4cTN/1gPt5Brrj7OJOyRAozvf3HU3S3Tt+/HQcOPmtmtUGXPXTj5JpHJkJAkIdciIrZ46F7BbiACC4WiT3tkB5BIaBxV3NyZ5WstzJvXSRf+v1Sq8QkwPqvpGM0ijzz7f3ysS/8ehFOLiqqCtqsSv8GerRyMlQQIHaeWRSs6EA9g31adj6Vj1qjZMNDGThpVCy8Ppau5gG5Q7S3Y4hPny+Rk7JDI92iEal+xMwN4OSOKquu08mQtiIUo0HknT+f5uVfHuVCfisLtsRwy4+zCY7xYtlt8fR1DuMZ4ML6B9Poahnk9d8ep+JUy5Q+x4GDK5VZbdCddCaD3jlte5o8dOMVHnIRMJ+fu68zwwPSUAuVWvqVagel1y3j23HzA1m4NUby2vvNx1XOSgKjPHn1V8fkY/f/3zKcXVQM9evY8YTkyUdn+hOfbTbeIHnxNUXtePhqiMrwp3B/PR88VUB/l5YND6XLpZPRmf4Ex3hxfPsFtIN6Pv53EUa9yJZvZgFmgw5mZchDo6p2JkI58rN3twzi5e/CbT/JJXtjFMqREsmIFD+iM/05ubOKoGhPbvtJDj4hbux6ppC9L0nDNRw4mM3MboMue+jdE6ycClLcYqohF2/11IzPpZDuuhMRczKxvqwLv1B3Brq1RGf6o3FXox3U02wxrSgw0oM1I806h0ZpvuiHDXz0bBGdTVL4474/L8PZVY0oivzruwcACIjwwN1Xw7mT1t7sYK+OqoJ2wpJ8SFsRynC/nsbKblZ+IZHgaHPbviAILLoxloFuLc9+9wCNFd2yKJhCIVhdYDx8NWRviqLyTCs1RRMnSEWjSMHeOl75pfmClHVthNWgDBNLb4lHFCXdG09/F2743jyppPNAA2/+/uS4s1YdOLjSmdUGXa3rnXjRRTLVkEuXzlZvfKYQ7ei2d7UMMNSvIz43GCeNkvb6Pt78nVl7fPM3slCqFRTur5crYpbdZj5n05DmL/9RCscYjSLPfueA/LqrlxMFI2qMt/00l1V3W1fMhCX7WE0kSloYYnOOc+K8UTsrEY0iURn+JOQGIwhSWGho1Oi4rNUReAW6sP+18nETpF3NA7zzf6fZ/2o5wTGefOHXC/Hw1Yw5X9TT34V5ayM4d7KF+rJOlEoFi2+M47qvS/IGb/zvCUoON1yVkswOrn5mtUF30k5P7NweV3JStHBgg80xg86Is6tKHvs2unxP46bm3Mlm9r0sdYeGjlSzWLLhK+m4eDhhMBh577Ezcsmhp7+G6gLJU77n0cX4h7lLwzEsJHC7WwY58KpZT7293vZ3M9CjlTs2LdvrNe5qhvqtv29TgrS7ZZAzn9gmSI0GI6d3VfPqr4/T0dDPNfcks/kbWXgFuJK2IlTqUB1DmGveukg8/DTsf61cTtJGpvpx209yCYr2ZPfzpXzyXLHdeasOHFzJzG6DPpMe+hUeQ7fH8Ej7vaXiYWSaHwhQVdDGJ8+Zm4rmXhtB/m7rEr/D71Qw2Kvlw38UUl/eJR837Xf//y2TNdIBcjZFyY+Pv38BzwAXvvDrhShV0p2AJaJR5NP/FKNUKQiM9KDkcIM8bUjjprY73Dki1Y/YuQGc2mmdIG2t7eXN35/iyDuVRKb5cccvFpC8OERuKEpeEiKdwxjqkyonJUtvjqejod9KodLN25kt35xL7uZozp1o5vVHT1jNSHXg4EpnVht0tW4mPHTpVvuzqHLJcN1+yXu8/7c8+fHGhzOISPUDEXY8kY/vHDe8AlzwCXa1kstde38qN3x3Lt0tg/z7+wetuj5NPPT4Shtd8tGhkMQFQXgFuBKXHUjZ0SYrD/fMxzXUFHew9NZ41n8lHUEQOPG+pFfu4q62SopasuSWeBDg4Bvn0OsMHH23kjf+9yR9XcOsfzBNEtPyshbTcnF3Mp/DGJ2n0Vn+hCf7cPz9C1ZKkgqFQM6maLZ+ey76YQNv/uEk+XtqHSEYB7OCWW3QlYbhGftD+yxCLvPd3sRXVT3xwnGoLzNX/ERn+NPfbZYDyN4YRXfrIP7hHnKTUeryUOKzg/ALHXsaz8NPrpIrSCwZHdY59t4FWqp7SFsRim7YQPnIGLim890c23ae2HkBpC6bg4evhoxVYZQea6K9vg9nd7VVUtQSD18N2RujOH+mlX98fR+nPqwmMTeIO3+xwKpccjTpK8LQDRvGHEUnCALLbktAP2zg6LZKm9dDE3y47We5hCf7cuC1c3zwdMGY5+jAwZXCpAy6IAjrBUEoEwShQhCEH42zLkcQBIMgCDdP3ymOjcKoZ9rGz43isyhbHDB6M9/tzYt6r7uvtZfq7Kaiu3WA0yOdmqu/mCxL61pK7K68M5HBXi3v/uUMo3Ot7r7OPPL0NWPK2L7317OAFHoxiVtt/3seLu5OBER4ULi/nqF+HR/9qwg3H2dWfcE8Sm7eukicXVQcebcSFzcphm7v4qwd0lslWzd+NZ3VX0qxqaMfTVC0J4GRHhTsrRvzou8T7EbG6nBKDjXaDNUGydPf9HAGS26Oo7qwndd+e9zmIubAwZXEhAZdEAQl8ASwAUgB7hAEIWWMdb8Hdk33SY55bkb9jI2f+yxCLv1GX+I0hy/qvX0d1sJcw/162eCCVF44Whc8Z1MU/V3DvPPn03Q2DdiELm7/2QLGovKMuXwx57pockcSpIO9Ot5//Cxx8wNpr+/n5V8esztKTuOmZt66SKoL2ulo7Ec0ijbhkZridl791XGKDjZIgzeA9obJlxWmrwyjs2nA6q5lNDkbo3D1cmL/q2WIRlvDLwgCWWsiuPH781EoBN7582lOfVhld60DB581k/HQc4EKURTPi5Ks4avAVjvrvg68BVy2tjuFqJ+x8XOfRcil1xBIky7xot/vE+LGnHhv834dw2x8OAOAw2/bzhsNiPDg7T+doq9zmOhMf/q7pIuCf7g7CoXA7udL7Hq3bXV9fPiPQgDmrYtAEAQSFwSjclbiFehCX8cw5SN3AYM9WhZcH2NVk24iY1UYbt7OXBgRCDPNFh3q1/Hpf4t5/295KNUKbvjuPO765UJi50kJUlMydSLisgPRuKnHHM0H4OSiYvGNcbRU91JypHHMdUFRntz6k1xi5wZw9N3zvP/3PJspTg4cfNZMxqCHApZSfnUjx2QEQQgFbgCeHm8jQRAeFAThpCAIJ1tbx55zOVkUM+KhjxgwQUTk8og3KZEMaZsuinc6Hr2oPQSFwI3fncemRzLkY/PXRxKRaqst7jPScLPvlXKGB/QkLQ6hYqRhKOvaCG77SS4Lb4jl/JlW8vdYV8IM9lprwESmSROHnFxUJOYG0d85zPLbE2ivMyesExcE2z1nlZNSnisKkiGvPN3Cy788RtmxZuatj+S2n+YwJ84bgCU3SwnSQ2/YXpzs7q9WkrwkhAt5bfR1ji0jkJAbREicF0ffrRw3Tu7somLt/amsvCuRhnNdvPab49SWdoy53oGDy81kDLq9AOpot+0x4IeiKI4bpxBF8Z+iKGaLopgdEBAwyVMcG4Vx+j10UbB8fHm89FjNUQCKBu0PgZ4Md/1yAWpnJR/+o0A+dnJnlZVhBUmTvHOkG9JoMBKV7i83DMXOC2DJTXEAZK0JJyrDn8NvVcjxZYPByIf/LGSgR0tglCcqZyVB0eZRdGkrQtHrjDaeq0m/xR5JC83G/p0/nubDfxbi5uXELT/KZtH1sajU5o5YOUF6tpXqSXSQAqQtD0UURYoONIy5xpQgHerTcXz7hXH3EwSB1GWh3PKjbJxdVbz317Mc3WYtOubAwWfFZAx6HRBu8TwMGP3XkQ28KghCFXAz8KQgCNdPxwmOx8x46GYuVxx9dJhFJUxdAVDtrOLj54qkgclzzRfLN/7X3C264s5Eq6EVfqHuchWIu48z6x9Ml18TBIHVX0zGzduZXc8UMtSv4+Br52g418Wqu5PQDuoJjfdGqTL/E/IP8yA4xpNj70lDMPzDpcqZY9vO28j7yp+jEOQKG6NRZOH1Mdz8o2x5DulostZE4B3kyoFXx+8gNeHp70JUmh9FBxvGlcsNCPcgdVkohfvq7TZFjcYv1J1bfpxD8qIQTn1Qzbt/OTNlMTEHDqabyRj0E0C8IAjRgiA4AbcD71kuEEUxWhTFKFEUo4A3gYdFUXx3uk92NII4swb9clW69BisQxJ6cepDinc+lU/l6VaW3BxH5jVhdtd4B7ly6gOp6sXTX0NdqTlZePvPcm3Wa9zUrHsgjf7uYf713QMU7q9n7toIQhO86WoeICzJx+Y9ao2knhgS58WtP86RK2Qsm5pM9LQN8v7jeVYGNGNVuCymZQ+laqSDtHWQMx9PTmI3bWUYgz1aq0SuPRZsjcHZRcX+V8snVQ6rdlZyzT3JXPvlFNpq+3jtt8e5kHfpoUQHDi6WCQ26KIp64GtI1SslwOuiKBYJgvCQIAgPzfQJjofAdI6fM2H+Q/4sEqMpLru4mPaA5gs9zN8QSdaaCKsyP0vef/ys/Niym3TzNzJtmoZMBEV5EpXhLz9feH2sfCEIGzX7s6t5gPqR11zcnRAUAvf/3zJAKpWsL5deE40i+XtqeeXXx2mq7GbZbQnyHnm7rScv2SM8xZfYeYGc+qCKnraJ72Yikn3xCnChYM/YyVGQLmALtsbQcK5LzilMhoTcYG79fzl4+GrY+VQBB16f3N2DAwfTzaQshyiKO0VRTBBFMVYUxd+OHHtaFEWbJKgoil8SRfHiiqkvAlE0/0ELyrEbTS5q78+gdDFGc2ziRXZQOSlYsCVGGizxnxK7a4x6W68zbXkoESm2o+FM9LQN0mAhA9B8vpva0g5cPNT4zTGrGRp0RnY9W4jaRUlcdiBV+W30dw/jpFGRsUq6Y3j3/85QebqFd/58mgOvnWNOrBe3/zyXjFVh8nSlM7uqx+watWTJzXFyB+lECAqBtBWhNJ3vprV2/Fb+lKVzCIjw4NBbFVPScvEOcuXmH2STsSqM/N11vPXHU3S1DEz8RgcOppFZ3SkKgNH8B6pQBk3PnqL0tXwWHnqnPtzmmKX2+VjMXx9FZ+MA2x47M+FakHRLPANcWDySBLWHdkjPzqfyEUWRW36cjWeAC7ueLaLiZAthiT5WDUeH366grbaP1V9MYcHmGIxGkZJDUqpl3rpIed2H/yyksbKb1V9K5rqvZ+LpJ01C0rg74RPsim7YwMkPqyY8fw9fDTmbormQ10Z14cQJ0qRFIajUCpsRdaNRKASW355Af9ewHJ6aLEq1gmW3JbDhoXR62gZ5/dETlJ+w36nqwMFMMOsNutFgLhsTlJdeOQOYpxZ9Bt2iJ/putTkW4zyx115f3sm7I8bcw08z7tqUJSEMdA+z5ovJqJ2VdtdIYloldDT0s/b+VAIjPVn/QBr9XcMYDSKhieb4+fmzUnlj5jXhRGf44x3kSniyD0UHGjAaRZuqF1dPJ2IyA+SuUQCNmwoXDyeSFodQsLduUrXmmavD8Q5yZf9r5eh14/+uNG5qEnKDKD/ePGELf3CMF4kLgzn7SY2V5s1kickK4Laf5uI3x52P/1XM7hdK0DmGZzi4DMx6gy4azEJSgtI2SXcxKEyDoi9TyMVVYb4oaUVzGEMtSMakcnjxhHuY4tqBkZ5WMXTVKA2WwEgPig81MndtBCEj9d32OLHjAufPtrL4pjg5JBMQ4SHXsLeMqBD2dgyx+/kSAiI8WHRDrPz+tOVh9HUO8+qvj/OGhS57Qm4QQ306PvhHgVXViYu7E0P9OnKvi0YQBI6/P375IIwkSG9PoKd1kLOTSJCmrQxDrzNSOk4DkYlFN8SiVCs48Pq5i9IL8vDVcP135zJ/fSQlhxt5439Pjinn68DBdDHrDTqYvS1BYduNOHVEBFEB4syFXDyV1rfhA0bb5h8AjTA1A5CQG2QTftCPSs61VPfiF+pG7nUxY+5TcaqFEzuqSFoUTOZq6xCQaVh0ycEGaks7+PhfRRiNImvvT7US8DLNCu1s7CdpYTD3/XkZ3kGudLcOsvILSdSVdrLnxVLZWGrc1Qz26XD3kYS7yo410VY38c8fniwlSE9+UD1hgjQg3IPgGC8K99VP2Lrv5uVM7nXR1BS1U1UwuZr30SiVChZeH8uWr2cx1Kflzf89SdGBeodyo4MZ4yow6GYEhe3IsYvcCUFUzVjZ4ugyxbHoNU4tyWuaRDSakDjrC11CbrBd9USQtMY//W8xwTGerLwzySosYjQYaSjvJD4nCK9AV9577Kw0bu6uRLwDJa0V7ZCe/a+USWJfI8zfEIXGTU3ailCaL/TgH+ZO7uZoyo42yZ64xk3NcJ8OURRl4a6j79qqINpj6S1xCAphUgnS9FWhdLcOUlsycYdn+qowfIJdOfj6xCGd8QhP8eW2n+YSHOvF3pfK+OhfRWPK+jpwcClcXQZdGF+Bb0p7iUpExez/o1tycxwr7rRuXDryTqVdL3GgR8vOp/JxdlWz/ivpNka/pboX7ZCBmCxJBtdE3HwpGV1d2M4rvzxGwf56Mq4J465fLkRQCBQflMoFkxYGo3JSULivjuyNUaQsCeHkziqKDtSjcZfG3mmHDJJw1/pIqgvbxxXWMuHuoyFnYxQX8tqoKrDVcrckdm4gLp5OFEyQHAXJw152ewI9bUOTCumMh5uXM1u+kcWCrTFUnm7ltd8ep6XaVuHRgYNL4aoy6JeKaFGDLhhVV8zUoslUuYxF1poIeXwcSB2hAB//27rRx6A38uE/Cxjs1bHxq7ZDIwDqRnRLfEPcOPOR2cAdeuMcnzxXzPa/56F2VnLT9+ez7NYEvINcic7wp/hQ48iIPDUJOVJiUjuoZ8WdiUSm+bHv5TKaL0gdrKaSxYyVYbj7OHN4jIvPaDJXh+MT7MqB18+N600rVQpSl86hqrB9UjXs4Um+kijYB9WX3AkqKASyN0Rxw3fmYjSIvPWHU+R96hie4WD6cBh0C0TBAIL0xyWIyivGoIvYr0QZD5WTAt85boiiyJF3pNBFeIovX/jVIkBq9DENvxBFkf2vldNY0c019yQRGOlpd8+60k78wtw5+OY5hgf13P6zXJRqBfl76ig71kT2xihu+0kuwTHmEE/a8lCG+nRyl2baClNisgmFUsHa+1PxD/eQpX1NBt0k3NVS1cP5MxN3XypVI95066DVxcYeqcvmIAiCzZi8sVhyszRM+9CbE4d0JkNInDe3/TSXiFQ/Dr5xjp1P5k+q9t6Bg4lwGHQrjJKPLgooZnnIJW5+INpBvZUolcZNjVKtkJpygP/88BAAhfvqKT7QwLx1kSTk2I/v67QGGs93017XR21xB/PWRXLsvfNWHZFpK0JtwjRhST54BbjIxjMgwoOgaE8K90vJQSeNiuu+limvb64yhyESF4bgO8eNo9vOY5iE+FV4ki9x8wM59eH4CVJ3Hw3Rmf6UHGqcVGzcw1fDvPWRVJ5unTZ1RY2bmo1fTWfprfHUlHTw2m+P03Cua1r2dvD5ZVYb9OlurhYF845XUsjFXTF1iXm1k5K+zmH2vVwmHzPVX2etiZCP7XmplAOvnyMq3Y8FW8eufGms6LLqNM37tJaa4g4W3RjLbT/NReWk4KNni2xUBwWFpE7YWNEta7akrQiVZAJG4uOuntJkIIADr5XLA6MVCoGF18fS1TxAyaGJSw1ByhlMJkGaviKUoX7dpFv8566NwNNfI4mCTZOyoiAIZF4Tzk3fn49SpeDd/zvNyZ0XxhQyc+BgIma1Qdeppi5iNR5WBn0GQi5xmgMX9T5P5dQFn8qON1s9nxPvzWCvucHnrl8uBKD4QAPegS5c++VUFGOMmgNsph35h7lz+09zmbc2Ev8wd1beKWmE25OfTV4cglKlkL30uPmBOLupKLQYPBEcaw7T7HgiX27EiUr3IyTOixPbL6Abnvj34e6jIWfTxAnS0EQffIJdJ5UcBUlbfekt8XQ2Dchyw9NFYKQnt/6/HOKygzj23gXe++tZq1mwDhxMlllt0IecXKZ3Q8GISZxLKluc3pBLisvHF/W+Bl0qAGFOZyf9HsuyuIhUPzz9NVZxWlcvJ/mxbtiAk4tqzL2MRpHig+bQzYo7E7n+23PlsXAghUeSl0hSsqO1yjXuauLmB1J2rAntkB6VWknK4jmcz2ujr1MyXM4uKgQBfIJdaa7qkevbBUFg0Q1xDPRoJyXcBZB5zUiCdJwOUkEQSF8ZRkt1r915ovaIyvAnItWPE9svTLvBdXJRce2XU1h1dxLN57t57TfHqZmk5rsDByZmtUHXqsdvcZ8qZg9dkNr/FUarypdLJUh9aUm1dNcPL+p90Zn+uLg7Mdgr1XmLRtFKzravc5juVvst7h0N/Tz18B75+T2PLiZteajdwdHLb0vAL9SNT/5dbDMhKG1FKLohgzygOnX5HERRpHhE70VQCGjc1cyJ92bZrfFcyGvj4GuSjG1IrBfRmf6c2VUth2PGQ06Qtg2NmyBNXBCM2llJwb7JedyCILDs1nj0OiNH35lcjfxUEASBlCVzuPnH2bh4OPH+43kceadi2kI8Dq5+ZrdBV5m9TAT3S97PcuScIJr0XKbPS+81XJoa5AddP7qo90Wl+6HxUGPQG9ENGzi+/QIX8tpYems8q7+UDMCLPztq9R6D3siJHRd45VdmHZkbvz8fD9+xL6IqJyXrHkjDoDfy0bNFVoYoKNoTvzB3ORnqFeBKRIofxQfq5XUaNzVD/ToyVoWTtSacgn31sub5wutj0Q0bJi2YFZ7kS1z2+AlSJxcViQuDqTjZMqkLBUiqillrIig92mQ1LGQ68Zvjzs0/yiZl2RxO76rh3T+fnvQcVQefb2a3QVebDbpCNbnuy3ER7Bn06Yujlw6umra9ALbGPjepde4+Glzcpe+qcF89J3dWkbw4hIxVYSQtDJHXmeLJLdU9vPG/J230VIKi7E8RssQn2I1VX0iisbKbY++el48LgkDa8lDaavvkEEfailD6u7VUjQyJ1rir5bDQ4hvjiJsfyJG3Kyk/0YRviJsk3LWvblL14wBLbopHUAgceH3sO6O0FaEY9MZJJ10B5m+IxM3bmf2vls9YAlPtpGTVXUmsvT+V9oZ+Xv/tiUmVbzr4fDOrDbpOae4MnQ6DLlrE0BXG6RfoKryEmaH2CFWendS6/q5hWW/8yDuVBMd4seKORLmt/4v/uwSA/a+Wc/D1c7z5u5MM9mklPZaRyEpUhj+KcSYJWRKfE0Tq8lDOfFxDVb45MZmQG4TaWSknRyPT/HD3daZgJDlq8tBBCsGs/lIyc+K9+fQ/JdSVdU5JuAukJqqcTVFU5bdZnYclfnPcCU3wpnBf/aSNs5NGxeKbYmmt6ZUlgmeK+OwgbvtJDl4BLnzwjwL2v1J2STIEDq5uZrVB16rNiTxB6T/OyslhiqELzEzIRS9OPuavRMt8N9s5Ib4qc0xYGJq4LR5gz4ulVmO9Nzxk3dbv7uOMxl0y+Hm7a0leMof566MYHtCTtEC6UNobNzceS2+Jwz/cnU/+UyyHC5w0KhIXSCGOoX4dCoXktdeXddLZ1C8LdJlQqZVseChdMmZPFzA8oCfzmjDKjjfRVjf+oAoTcoJ0HD2WtBVh9HYMTUpX3UR8dhBz4r05+u75CeV4LxWvAFdu/P58MldLYai3/nDqomR9HVz9zGqDPmQxoFhQeF/6hlYhlxEP/TOqRRcEA6f6b7Y6tt7798T7FsnPh4Ym51FWF7az48l8QBKccvU0h6q0g3r2vlxmVQETluTDkXcrmRPvLZcTTtWgq9RK1j+YhmgU2fVMkSyVawpxmCRskxfPQaGUujZNHrplK7zGTc11X89E5aRg+9/zSMgNxtlFxZF3ztv93NHIErvjJEijs/xx83aedAkjjCRIb0tgeEAnD8WeSZQqBUtviWfTwxn0dgzx2qMn5AHfDhyYmNUGfVBtYYAVE8d3J0LEOBJiEGQP3fgZjKED0Iu2JZmxmqMMqc0DoIsH1kx5X3dvc+1+VUEbr/zqGMUH6slcE866B9IA+OjZIlQqBdd+OYX68i5cPZ3wDZm6kqVXgCvX3JNMS1UPR96WqkL8Qt0JifWSkqNGEVdPJ2LnBVJ6pAmVWoFRL9rUm3v6uXDd1zIZHtDz8b+LSV8ZRk1RO3WTEO4CafZp/EiCtLvVNv6uVCpIXTaH2uKOKXm+/mHupK0Mo2h//YSj7aaLqAx/bv9pLgHh7nzyXDGf/qd4SqPyHFzdzGqDPqCyrEwYu456shgtWv3NU4uunD8Wg6hkyGIq05n+66e8x2CfjsE+LR//u4gdT+Sj1qi48QfzWXpzPHHzzVU47r7OuHk5U1faQViSj5WM7lSInRdIxqow8nbXykm91OWhdLcMygY5bUUo2kE9tSXSc3u6JgHhHqz/Shqdjf3Ul3Xi4unEkbcrJi1stfimeBTjdJCmLB25U9g3OX0XE7nXRePspubASInl5cDdR8P1355L9sYoSo818ebvTk5KO97B1c+sNugGSwMsCJf8B2VUDCNNtvjsQy722N/7FYZEc0el5WNP/8nF5wv21vHy/xyj4mQLOZuiuO3/5RAcLe1z/qy5iqKtto/mqh4Ge3WEJdkfwDFZFt8UR2CkB58+X0J36yBx8wLRuKvl5GhIrBd+oW5yGeBYMemIFD9W3S1V0Az2aGmp7rXpYB0LKUEaPWaC1M3Lmdi5AZQcaZxUR6oJjZuaRTfE0ljRTfmo7tyZRKGUhoJv/WYWwwN63vzdSbkk1MHnl1lt0G25tAYMg2pQzh3KSdHPUKBrtGxu8cC1DOntyx0suy1hzH1UFnNDDTojnn4abv1JDrmbY+TkqOUouZSlktb5W384BUw9fj4apUrBugfSEATY9UwhIiLJi0K4MNIpKggCaSvMoaTBcZQHkxaFsGBLtPz86LbKSTfeZKwOwyfETUqQ2pnxmbYyDO2gnvLjU4tNJy8KITDSg8NvV1z28EdYkjQ8IzTBm30vl7HrmUKGBxzKjZ9XHAbdAr2qf+SRIP1n/GwldO1JBTQ32v+VGQ1je2b6UR7nTT+Yj1+ouRHLYJAagYxGkXUPpLLqC0lW68drJposnv4urP5iMq01vRx6s0LqFDWKlByWyv4ScoPktRNJyc7fECVfdLpbBiddQ65UmhOkp+0kSKU7BXcK9k3N0xUUAstuT2CgW8vJHVWTft904erpxHVfy2TRDbFcONvGa789MWk5AwdXF1eZQb+0201RaW1IZloTfZH78+O+Hq85KD9e6vEvu2vWRkmljQad0WbU3FicP2sdcjjx/gWaznez6q4kvAIkfZbrvz13UntNhejMALLWhFO4r56W6l4iUnwpOtCA0WDESaMiLluK4U+UmBQEgRV3JBCZJg2v3vdy2aTDJGGJPsRnB3LaToJU0ncJpb2uj8bKqXWBBkd7kbw4hLxPa+ls6p/4DdOMoBCYty6SG743D0R4+4+nOPNRzYSzUx1cXVxdBl2cXs0LyaDP3C10iy5u3NertfPlx6FOhXbXBLlKpXZ6nRFPv8mJle16plCWua0t6eDUrmpSloQQn2P2kgWlOQm656XSSe07GRbeEEtwjCd7XiwlLMmX/q5heQjz/PVRAJMa92YajmHio38VjbPamsU3xaNQChx8vdzmtYTcYJxcVBROoYTRxMLrY1E5Kznw+rnPLJYdHOPFrT/JISrTn8NvV7D9iXwrlU0HVzdXl0GfFiEt6zF0M1m2WDm8eNzXz/TfID92UvQzz3+3zRqlIN1VGIa1U4r9Fh9qZKBHy8fPFeMT7MbSUTH4ulJzSWDxgYZpm6ijVCpYe38aSqWC0qONVslR/zApDKTXGSflWTppVNz7h6UAVOW3TVpbxd3HmZzroqkqaOfCqASp2llJ8qIQKs+0TllR0dXTidzN0dQWd3Ahb/zZpjOJxk3N+gfTWH57AnVlHbz2m+OTms3qYPZzlRn06fLQJe/0ShpDpxH6iPM4a3NcNWLQ60vbmIpTuO/lMj78ZwHaQT3r7k9F7WQ95q6utIPASA9yN0sJyH997+K03O3h4athzb0pdDT0M9Snk+q/W6zDLDUlk5sM5OrpxIaH0gEpiTtZbzTjGilBetBOgjRtRShGg7Vk8GRJXxGK7xw3Dr5xzm7i9XJhkge++YfZqDUqtj12huPvn3cMz7jKuUoMuunHmO6Qi+qKqUNXC4O8fuE7Nse1BqnrsyLP7J0mLwmxWWePxopult0ab5UgBdAO6Wk+30NYki85m8wVJZZljZdKZJof89ZHys9No/ICI6UGsanUg8dkBeAz0vj0yq+Py8MxxkOpVLDClCDdZa3g6B3kSrhFfH8qKJQKlt+WQG+7/cTr5SYg3INbfpxNQm4wJ3ZUse0vZ2QNegdXH1eFQRdUkgETpzuGblRekjiXn2pyIlL2cBKsE2s9hiC76z6ousPmWHjy5OvGY+fZSvo2nOvCaBTlcsXbf5YrfdbTBdMaG16wOZo58d6AFDfX6wy4ekllmdUFbVOSjN3yjSwABnu0csXORIQm+hCfE8TpXTU2evDpK0Lp7xq+qNBJaKIPcdmBnN41/mzTy4WTRsWae1NY/cVkWqp7eO23x8ed5uRg9nJVGHSF0mTspsGgC6JJYFAaFH2RHrqXsgGD6DThulSXXXaPa0U3PJXmmPiLbU/bXdc6GGpzTK81TrrRyJ5yYV1pJ0qVgpARHRe/UHf8QiUPeOdTBZPadzIolArW3mdObJYdbZJFwkCK3U8Wdx9n5q2TPP6q/DYOvDq5zs0lN8WNJEitO0gj0/3x8NVMSd9l9L6CAIferLio988ESYtCuPX/5eDm5cyOJ/I59OY5WWPHwdXBpAy6IAjrBUEoEwShQhAEmykLgiDcJQhC/sj/hwVByLS3z0xhls6dJu9RNMXQpUHRFzO1yFXRTZfB1tiOplEnDZjwUNp2Ga72+tuUPlPlJP06+7uHickKGHets5vUCVuwt47+Lutb8LrSTkLivFBZxNVv+X85gGQsezuspxFdCm7ezmz+hvTPZe9LZbi4qVGqFESm+1N8qAGDbvIGZ966CEnyFyjcXz/utCLLz8/dbJsgVSgE0laEUl/eRXvD1Nvq3X00ZG+M4vzZVmqKr5xRcj7Bbtz8w/mkLQ/l7Ce1vP2n03b1bRzMTiY06IIgKIEngA1ACnCHIAgpo5ZdAFaIopgB/Br453Sf6LjnqPAbeTT9IRdJS3fq+5oM9UR06CMA8FLaVqho7Qh02UMjSPHz2LmBOLuqGOjWEpo4fnenUS/i7iOFNw68Zi7fG+jR0l7fZ9MdqlQqWHFnIgDP/7/DkzqvyRKR4oeTRrp4nPm4BoPeSNKiYAZ7dVSebZn0Ps6uauZviAKQtF7eqZyUImH6qjB857hJM0gt4u/JS0aGW09R38VE1uoIvAJcOPDaleUJq5yUrLgzkfUPptHVPMDrvz1OxanJf88Orlwm46HnAhWiKJ4XRVELvApstVwgiuJhURRNdVFHgTAuI4LSy3Qi07uvSXHxMiRGB42eNsdO991kd61GY/1zmjRdojL8cfVypr97mDlx3uN+nm7YICdDK8+0ys08pvI2e/otacvNdxzF0zzY4Qu/WWT1PCDcA88Alykb0/SVobj7OOPq4cSceG92P19CXen4FTNKi0TmKYsEqYu7E3HZgZQdbbIauj1ZlGoFS2+Np6t5gPzdFxe6mUli5wVy209y8AlxY9czhex9uewzrcxxcOlMxqCHApbj1utGjo3FfcAH9l4QBOFBQRBOCoJwsrV1OsdpmeKu0+EFWdShz7BAV6DKHLdt10fbvN6oG30jBC4eaoaG7CsfRqT64ublRH/XME4u5mlD9lA5Kagr68Q/XDLqn/5XGhpdW9qBs6uKgAj7csR3jxjePS+UTrkCZDxc3J2ISDVfRHo7hkhbHkpjRTft9ZMPeajUShZsiaG9vo/EBcF4B7nywdMFE+5hSpCeGZUgTV8Rhm7YcNHa41Hp/kSl+3FixwWb0NaVgKe/Czd8bx5zr42gaH89b/7+5GfS6epgepiMQbdnFuy6woIgrEIy6D+097ooiv8URTFbFMXsgIDxY7xTwSzteukGxqAw1zHPtECXIBhtBLjGQyloGewdu8HHSaPCzcuZgW7pZ4jOGHuKk15rxKg34u4jJU+bzvfQUt1DXUknoQk+KBT2rwae/i6EJ0vhGJN413SRc535ovbhPwtJXhSCUj31kEfCgmB857hx+qNqNn41A7Wzku1/z6Ovc/zY/5Kb4lCoBA68Zu70DIr2JDDSg4K9dRdd4bPklngMBiOH37lyEqSWKJUKFt8Ux3Vfy6S/W8vrj56g5HCjQ7lxFjIZg14HhFs8DwNs7rcFQcgAngW2iqL4GWWBLt2gS3ouI0lR48x66AMGHxI0+ye93lQ1M540uZu3E/3dw4iiSHTm+BdNtUZFTVG7HG9/439P0tsxNKG64uavZwHQUt1LR+P0eXNBUZ6oRtQfh/p0VJ5pIX5+IGXHphbyUCgEFt0QS3fLILUlHVz39UyGB/Vs/3sew+Ps4+btTO510VQXtltJ7KavDKOzaeCiuy29A12Ze20E5ceaaajouqg9LgeRaX7c/tNcgqI82f18CZ84hmfMOiZj0E8A8YIgRAuC4ATcDrxnuUAQhAjgbeBuURRtBTIuF9PsUSjkuaIzZNCN3sRqjkz5fZax7NG4ejpjNIgM9evwCjAnVU3CV5Zoh/QgSu3ulkxk0AWFIE83euWXx6Zy6uPvKwhkb4qSnx947RxBMV4XFfKITPMjJM6L49sv4OnvwoavpNPZOMAHTxeMm6CUE6Svmzs947ID0bip5WHWF8P89VG4+zhz4LXyK7pb083bmS3fmkvu5mjOHW/m9UdPXLZpTA4unQkNuiiKeuBrwC6gBHhdFMUiQRAeEgThoZFlPwf8gCcFQTgrCMLJGTvj8c512jtFZzbkYsCJdn3kxAtHIwg4KeyXmrmNjJjraRtiv0X1SvbGKNvFolS+WF3YTsxcszfvHeg64SlYTjca3Wl5KaRb6KJr3NWc/bgGr0CXKQ9vEASBxTfGMdijJX93LeHJvlxzTxL1ZZ3sfr5kzL1MEruWCVKVWknyEpN++8WVbKqdlSy5OZ622j6KD1z8heFyoFAI5GyKZuu356IfNvDm70+Sv+fiQ04OLh+TqkMXRXGnKIoJoijGiqL425FjT4ui+PTI4/tFUfQRRTFr5P/smTzpsZnuskUp5GKcQT2XY313XdT7BIXtAAyjwYiblxSW+fjfRbRbjCXz8NXYrU0f6tejUAhWXmvlmcklrE3CWEfeqUSvm57vyMnFPEpw+e0J9LQP0d0ySEdDP40VU5S0jfEiJiuA0x/VMNirJXFhCAu2xlB+vJmj28Ye7Bya4ENCrpQgNWnMpC0PRRRFWaLgYoidF0BoojdHt51nsO/KV0AMTfDhtp/mEp7ky4HXyvnwH4VjTpNycGVwVXSKmpl+cS64MuaKXuv1f/Ljge5hQEDEOlRy8I0KuXW+u2WQzDXm1EdjRTfzN9jeDYhGEY27muoCc9rDUl53PFw9nUhcKDV1vfTzo1P6eSZDV8sAC7fGyM8L912MpG0M+mED/7+9846O6jzz/+e90zWjhlAXkpCQKBK9gyk2jjHBveF4Eye7KZtNsjU5WTvZnJPfObHTNrub7Obn/JI4TnGyhrg7bhgbG7DB9CYBEgKEJEBIoIrqzLy/P+6dphlNkUbAiPs5h8PcO3dm7rxH97nvPO/zfL973zwDwPzbi6hYkcf+t+rDvt+yIQukKRNtFFdmULXj3IhryoUQrNhQzkCfi4/D3FCuJ2zJZtZ/ZRbLH5jCmSOtbHxid9SqljpXn/EV0ONdh44CUrkuFBebB8u8j+sOtKhT9CEceb8xIBAvvafUm0dvOtFGVlGKt5vUn1CzxWMfRecCtOazagNVd1s/F07H50LPKVFr8qu2n2POJwopmqk2jtXuvUhPZ2wz2/QcO9OX53H0gyY6W3sRQrDy4XKKZ2aw7bkaTh8K/WvEnqoukJ6tuuTVc6lcXUBv5wB1B0behJOR52DW6gKqdpzjYn1iuAoJRTDn1kLu+8Z8FEXw4r/vZ99bZ3TzjOuQ8RXQYygBjJbRCnR5mG7bMqrXH+65M2DbJQ0hj/vTd9VFysKKDAxGhSQtBdNUo1Zo3PRgWdBr3E7fhVm5Sl1wff+P0TWZCCG465/mAPDCD+NTxmi1q30FnS29NB6/zK2f9dXjH4jC/GIoC9dPRlEEH7+qzooVTZM9szCZzb+uGvZG5Fkg3bGplsEBF4XTJ5CaaePo+6PLgS+8czI2h4ntG2sSKihmT07hoW8vomROJrtePsVr/3Mo5husztgyDgK6L+eKjE9qRPhdY6MR6PLHYYhnIxU4naEDugfPTNyTk24520V/rzOixkuHny75ofcawhzpY9K0CRiM6ufteL42wtGRsTpM2JLVf0c/aMLqMHH/N1X3poPvnI25ocmRbmHWmknU7G6m5axasWGyGFj/1dkkpZp5/eeHg/TYQZM7+FQ5XZf72P9WPULTdzlf1zGqyg+LzcjSe6dw4VTniBuWrhUWm5G1X6xg1SNTOVfbzsbv7aYhQieuztUj4QO6Yir1PpbEK9ftK/T2CHSNlj3dwTK3I+HO1ZED5sRJDm9Xotmq1dJLOF/bjsVuCvdSGo61MXuNmnvf9fKpqBfB/ubH6gLpoS0NYWu9o8FqNzHY52L6sjzOHFYrS3JKUr2eqa/+9GDM7znvtkIsdiO7Xq7z7ktKMas19RJe++9DIc0x8srUBdL9m+tpv9jDtKW5GE3KiCzq/Jm2JIfsySl89FLdqMfraiOEoHJlPg8+tgBLkpFXf3qQXa/UxbVzWGdkJH5AN/qZOcRphu6PmnIZ/fvOtb9IqfXDUb9PQWrkWXNrQ7fXPs1sM2KyGFCMgsaaNhRFBEjUDsViN9JytgvFqN7U9r5xJqrzMtuMzL1NFRp7+uujczeyOkw4B91MXZyDBKo056C7NePqppr2mBfmLEkmFqwr5mz15QBtl7TsJNZ/dRZX2vv5y88PhzTHWHb/FAxGhe0ba7EkGSlflE3N7uZRVXwIRc3l93YNsOf1kevmX0sy8h08+PhCpi3NZd+b9bz8nwfiqsSpEzvjIKDn+DbGIqBLY1zKFpcl/wGTGP0f+6WW4WdB927wff+ejgGklFhsBlwuNzmTUzlX0w6AzWHyBuyh2FMtnKttZ84aNTgferchag2SZfepptfSLUclGevJoZusBgpnZFC94xwulxuDQWH2Leqvhxd+tC9mn9PKVfk4JljY+VJdQE11Tkkqt32+gpb6TtUcY8hM055qYfGdJd4F0srVBTgH3RzfGd3C8XBkFaUwY3keR95r5PK5xNRPMVkMrHl0Orf+9QxaG7rZ+MTuIJ9WnatHwgd0ofgEnSTxr5GNq6+oDNOz74ddGf6COFEf2rkIINXa7hXV8nSLmqxG3E5JTmkqLQ1d9F0ZxJZsJrs4Jch6DqCztRdbsommmjavScaHL0SvQfLg42oLwms/OzTiRhSbQ13I7esepHJVPj0dA95WfM+vAIAtv62OaVHRI9x1sb6Luv2BaxolczJZsaFcNcfw03LxMHN1Phn56gJpWnYSOSWpHP2gadSLmkvuKcFkNbB9U3SGHNcrUxfn8NC3FpI8wcob//cwOzbVxqRlrxMfEj6gIyy+x3GaofsH8HilXN5s+1dkOPlDP664hxfVqmkOVmX04LrSzR1f83mL9HQMYNEWRTMnJYNU7eVsDhN93YMh69KdA27saRaaT3dSvlj99VO7p9krrxuJrKIUkieoN4Itz1RH9ZqheFJCfVcGKarMwDHB4hXosqdZKJ2nLuzWH70Uc9VL+SJVuGvXy3W4hszEZ64uYN7aQo5uawrqflU8HaTaAunMm/PpaFG1YkaDzWFm8V0lNB5vC7rJJBpp2Uk88M0FzLy5gEPvNfDCj/eFXGzWGTsSPqCLAKWq+AR0t18AH41rkT+n+pdEHdDD0esMnlV7cPZcISnFZ3t35kir1zgiPTcJg0mhqaYNa7KZ3u7BkH6iAO3NPTjSLZw+1EpOiboQ+f6fTkR9jo/8n8UA1Oxu9ubyY8GTcunrHkRRBBU35dN4vM17U6n0kwfY9cqpmASvvMJdLb0c2xHc9bnk7lLKFmaz6+VTQRUoeWXplC9WF0gn5idjSzGP2KLOn4oVeWTkO/jw+dqoDK6vZwwmVVt+3Zdn0tnay6Yn91C7J9iNS2dsSPiAHkCcZuguo29WoUiDVscYj5/DkQN6lrEWm9I+oncfuKKe98qHywG1SsWkVbm4nZKcklSaarQZepgFPc8s/VJjt1ejvOlEW9SNMEaTgWX3q/n03/5r7AvBnhl6r5Yjn748F0URHNU0UPLL00jPSSI9J4mUDCubf3U0ZIXKcBRVZpBXlsbu188EqQkKRbDm0enkT1XNMYaW5C27bwpGo8KHL5yk4qY8zhy9NGojaM/sv7utn/1vxU8X51pSMieTh769kIw8B5ufrmLrH44l/M0qERhXAV3K+OTQ5ZAZ+tB9IyUaIS6L0s1ky8gUDNsuqzeM6ct8lT+e+vD+XicFU9O41NiNUARIwi7EXWrqJmWildo9zd7a9VhSKHM/4ct11+6NbYZm1fxOPTcde6qFkrmZHP/oPM4Bl1o2tyqftgs9zLu9iL4rTt55Jvp8uhDqLL23c4BD7wZXDRlMCuv+diZp2Um89YsjtPpp4thTLSzSFkhtyWaEEBzdNnqxrbyyNNVgY/PZcePxmZJh456vz2Xe7UVUf3Se53+wd0T+rDrRM64CerxSLv4Id/wkdC85iyMeY1Z6opbU/cytHwRst7Wr6RZ/c2eP4uJAr5P8clUWt03TMD+pBdoZN+UFvbdnlt52oce70Np2oSemJpJHvqumXjb/uiqmxUPFoGBJMgZUsVSszKe/x+n1vpy6JBejWeHCqQ5WbCijofoy+946E/Vn5JSkUjI3kwOacNdQLEkm7vjabExWI3/5n0MB5XieBdID79QzaXo6xz48HxdxsuX3T0ExCHb8efTNWdcLBoPC0ntKufPvZ9PbNcDz399L9Y5zCb0AfD0zvgJ6nGbo/lxNX1EAi+gh33w0qmNTsgLz6Ze7fNtp2aoEbqc22xvoc5JVrGq5XNJm5rV7m7HYjdz0UJlXIsCflvou0rKTOPbROWYsV2f9bz51JOqLMT3H7tVleeWnB6J6jQeLPTAt5EmzeGbDFpuR8kU51O5upnReFmULs9n92mkaYzChWHJ3Cc5B97C19skTrNzxtdkM9GnmGD3q+agpkql0X+5noNdF35VBTu4dvcmyPc3Cgk8Wc+ZwK/VHr5FHzBhROCODDf+2iJzSVLY+e5x3nq4akU+rTnjGVUCXY1SHDsRFzyUazOIKhih/DXTJ3IDtyz0+YwpHuiXguYFeFwajQm5pqneG3tnaR8HUdExmQ0CKxINzUJ2ld7b2kZyhinwN9rs4FaW8LsC931Bb9ptOtAd4dUZCrcTxswMUgooV+TSf7vS23Veuysc56ObErgus/quppGUnsfnpqqgXYlXhrlyObmsaNs0xscDBui/PpP2CZo6hleLllaUxdXEOF+s7UQwiLoujALPXTCItO4ntm2rGXdmfPdXCnf8wh8V3l3Byfwsbn9yTMAJlicK4CujI+AsFxTPl4k+Scjmkn+iAjGwu4aHmbBbz7M97t7sGfAHdY3ThfV9tNuSxm/NQME1d9KxYke+tLvHnwqkOMvIdVG1v8gb9t34ZnbwuqFUlaz6nKjI++53oJXatDhN9VwJv0NOW5qht99osPXNSMjklKRzd1oTJYmDtFysZ7HXyzm+qonYFWjREuCsUk6Zp5hg17bz7+2Pe9NHS+0oxmhTcLsnF+i6aT48+OBmMCiseKqPjYi8H341diOx6R1EEC9YVc8+/zMXtdPPCj/Zx6N0GPQUTJ8ZVQJdjEdA9M/Q4p1wGpC1Izxyguvc2ALJMkZ38TpwwY1dCpxjsqWYUg6+qxrMY5cmje/AYPpssBq+Giz+uQdU0o7utH7PNd77Hd0YvKjVtie+XRLQzWavdFCTra0kyUbZQbbv33KAqVxXQ3txD44k2MvIdrPxUOU0n2qNup7enWZi9ZhK1e3zCXaGYuiSXJfeUULunmV2vqHow9lQLi+7y6bUfGYFeeygKKzKYPHsie9+sH7FD0vVO3pQ0Nnx7EYUVGez4cy1vPHUk5s5fnWDGVUBHxl7zHImx8hV1SlvY56ORCWhrdXPFnRGwT7rU80xKVb1Fl96ripd5cryZRckY/TxEUyb6zmPmzQUBjkEemmraySpK5vDWRhbfpTY2bX32eFTyuh4++/3lAGx7riYqgwir1vw0lMpV+Tj9PEZL52VitZu8jUfTl+UxbWkOe984E7X8wNy1RUHCXaGYt7aIipX57H/7rPfGNHNVvrfj9sSuC3FzIrrpwTKkS/JRDF26iYbVYeKTfzeTmx4s42zVJTY+sfu6NtFOBMZXQGcMUy5j5CsaCpc0okSp7X62f3bAds8ldcZu15yLCit8Ad/tlhgMCrnaQiUENmZZbEZmrg42oHY53SSlmOntGsTtF4sPb41+RupIt3jLHzd+b3fE4612E84Bd9BNI6sohczCZK/HaKDfp3pDX/mpqUzItbPlmWrvvnBYbEavcFe4Kh4hBCs3lFE8ayLbNtZw6mCLt4bcw7EPR6fv4iFloo25awup3XvRq2U/HhFCMHvNJO7/5nwUo8LL/3GAvW+cua6NtK9nEjqg+0vnjuGngBwbX1GLCP0T/9CVO1FCfN4Sxx8wEhigWp2BY7Dl92rJm8dbtMdvgXDni+psz2wbXm1x9ppJIV2NzlZfJndKKoffa/CaZOx8qc5b+RENt/9tJaCWP7Y2htcTt/m1/w+lclW+6jFapyouVqxQ/T6rd6izdJNZy6cPuNn8dHT5/pmrCkieYGXni3VhSywVg8Jtn68gqyiFzU9XceFUh7pAqlnx7XypLm7BaN7aIpInWNm+sWbcS9NmFaWw4VsLmTI/i49fPcVrPzs4oi7jG52EDugG84zIB40QT6u/QMRNE30oZbYd5JuPBO3f2f0o3a6M4OOt2ym27gn7no0n1WoNz6LolY5+sorUOvKDW9QmmnALUDaHmYqVwbN0t0tic5jp73EGpBX2vRl9Z6MQgvVfmQXAxu+F/x7e9v8QAb1sQTZmm9GbZknNtFFUofl9aoFvQq6d1Y9M5fzJDj5+NXI+3WBSWHTXZFrOdnFyf/gSRJPFwPqvzMKeZlHNMZp7vEqTAPVH4qM2aDIbWP7gFC41XYlL89L1jtlm5BN/M4ObPzONC3UdbPze7lGpdt6IJHRAD9BCjzfCNyMSMrJAV2P6/pg/IsNYzzTb1pDPXXIGi3Cl5GVSZt0R8X07W3u9deVXOgYCKl4aT7RFXHyae2shBqOidpSCV7Hg9OFWCqalc3hrI6v/aiqgWsJFK68LUDzLJzy2+7XhK0uGtv/7Y7IYmLYkh7oDF71NQZUrVVXG0wd9wXTq4hxm3JTH/rfrORNFkC1flENGvp1dr5wKEu4aimqOMRsEvPbfBwFY/oAa1N94KvgmPVJK5mRSMC2d3a+dviHs3oQQzFiexwOPL8CWbOa1nx1i50vBQmo6oUnogI6wj9lbuxRfkBLuyDN0pzX2GVSPO5VMY/iFuACKV1BoiXzjeOOpwxhNBixJRnra+wMWOl/5zwMB5XWhZuv2NAvTl+Ui3RKhCG+eXbql6ibU76LjYq836HqqPqLl8z9ZAcCe188w2B96XL2Ki8PcfCpW5uN2Sq+ZdWFlBskTrBzdFpjXX/FQGRkFDrb8tjqi+YKiCJbcU0pnSy/V24OFu4aSlqWaY/R0DPD6zw8xfbmv43Y0FnX+CCFYsaGcwT5XzOOcyGTkOXjgsQXeG/LLP9lP56XxIYkwliR0QA9UWowv/gJd0WiiW5TYL+AeVzpJhugXvGTmNIwics76UpPaOGRPs3ClYwCz1YglyRfU/atMLp8Precy97ZCFEWgKALpll5DjLr9FymqyODI+42seEjNpR/feSFqeV1Q0ymetM7vHg8t3uWvuBiKCbl28svTqNquapIriqBiZR5NJ9oDvpPRbOD2L1bidkk2//poxJmeR7hrz+ung4S7QpEzOZXbvlBBy9kutvymitu+UAHApifCp5RiYUKunVm3FHDso/NxqXVPFExmAzd/ehq3fb6CS+eusOmJPZw6mNgSw2NNQgf0scRp9AUFJYqUS27TIzF/Rq87DauIXqyodVBNw6QaIs8ea/ZcICnFzJUOtX58oM/FgvXF3uc9rfx1+0Lni1Mm2ihfnI3b5UYxCoSWd5FSzXW6XJLm051k5Ku/krZvik1/ZPUjasqmv8fJudrgm5o1zKKoh4qV+XS29nG2Wq1Mmb4sD8UoqBqSb07LTuLmT0/jwqlOdr0UfpYrhGDpfaX0dg2GFO4KxeTZmax8uJwzRy4FSA+Eq2uPlYXrJ5OUbGbbcydGbaqRaJQtzOahby0kZaKNN39xhG3P1cRFO2c8ogf0YXCafBdjNCmXkdDjTkWI6C/OuoY0AApCLKQO5Z2nq7UZej9mqxHplgHt/ZO1EsKTwwR0UKssJKrAksvp9tav1+5pZvLsiRzd3sTCO9SbzNmqSzG3cd/3jXkAvPSTYJ0Xg0HBbDWEzfeXzMnElmL2LhgmpZgpnZvF8Z3ng1I5ZQuymbkqn4NbGiLO8nImp1KqCXdFm7euXFXAvLVFVG8/5xUz2/Tknrh1QJptRpbdV8rF+i6OjdL6LhFJy0ri/m/OZ/aaSRx5v5EXfrQvpl+FNwp6QB+GQbMvOMXVhs6PHnd65IP8qKvuQ0pQouxaNVuN9HQMeHPo3Zd96wKeRqO2C8NfFOk5dqbMz8LldGMwKQFq7p7SxrNVl73dplufPR7L1yF3Spo3tbL1j8GvtTpMIRdFPRiMCjOW5VJ/pNWbH5+5Kp+BPhc1u4M7WZc/UEZmYTLv/f5YRA3zxR7hrjfPRP19ltxdQvmi7ICZeSy6N5EoX5xDbmkqu16OrVx0vGAwKtz0YBmf/Mosui73sfHJPUEmJDc6ekAfhqGa6GOhttjlyozp+PbmXi47g9vzh5KapXZ/Hnm/EbdLemuYTx/2BRf/CyHcYuH824txuyRGk8Jgv8vrgFTzcTOlc7M49tF5ZmnGza0N3THJ6wI8+uQyAKq3nwuajVvtJvrDpFwAZqzIQwLVmvtQTmkqGfkOb+ORPwaTwtovViIlvP2ro2HFr9Jz7MxYnktVGOGuoQhFcMuj0wP0ct765dFhF35jxbNA2ts9yO7XopM2GI9MnjWRDd9eROYkB1ueqebd31XHbYwTHT2gR4FwG0Bxj9qGbigSA10h6s2HPxE41b8UAIvZFaSo6GGmn0Ub4P1jP3WgJaDt30O4TsSJBQ6KZ03EOejGaPIrZQQUg8BgENTubaZ8kWpe/c5vqmNKM5gsBhbdqaZtnv7G9oDnrA5z2Bk6qCYKxZUZVGs16B7zi9aG7pALiKmZNtY8Op2L9V18+GL4tvqFd0xGMYQX7hqKwaiw7sszvQbbQEyz/EhkFiZTsSKfIx80canpxjWLSJ5g5Z5/nsuCTxZzfNcF/vz9PQFGJDcqekCPAuHVc4n/LP0vbd8J2LYNI7YFkFuSSl2/qomCe4Ap80N7gg6tC3cOqDPRi/Vd5Jelse7LMwOeb4qgIb5gXTGuQTcmq4H+Hqc3TXJi1wWmzM+iZnczUzVD6d7OgZjTDAvX+2ruTx/yvTaSVZ6HipX59HT6atDLF2Vjshq8jUdDKZmbyexbJnFka2PYNQR7anTCXUOx2Izc+/V53u39b9XTdmF4d6hYWXJXCWabgW3P1dzQKoWKQWHxXSXc9Y9z6O9x8vwP9ob8ZXYjoQf0KPApLsb/Z93lIbZ0A+7h5XMnz8nk0mAhHc5ccDkpW5gd8ri25h7Wf3WWd3uwz3feBdPSvZoqHppOtIc9x+zJKUyans7ggLowKvz/aoQ6y67+8ByzblF/GcQir+vh4e8sAtSmHM8FabWHFugaSmFFYA262Wpk2uIcavc1DyuWtfS+UrKKU3jvD8fCOtPPva0Iq93EzgjCXUNxpFu9UgcA2zfGL/haHSaW3F3Kudr2sDekG4VJ0yaw4d8WkVeexgd/OsHbv6q6IdcYIMqALoS4XQhxQghxUgjxWIjnhRDiZ9rzh4UQ80K9T6KieAW6xj5P5yJ0GgUgtzQVgLMD6vBmFiaTmhms2njmcCvFM30dmYP9vl8WHv3zaX6+o12X+yIuEs5fV4yz34XFaqC3a5CkFLUT9fjOC5QtzKZufwvFlb7PjEVeFyAj38GEPLUE8s1fqFU8VofaxBTJ6MG/Bt0zE65YFdh4NBSDUWHtFytQFMHbvzo6bBmcxWZk/roiGqov03AstvWB0rlZXgnjhmNtcV0gnXFTHhMnOfjw+ZNR1cuPd5JSzNz5tdksvbeUUwdb2PjEnhuqZt9DxIAuhDAAPwfWATOATwkhhoqorAPKtH9fAp6K83mGZeGeJ8f0/ccy5eJPhvE0BoZvo+/pGCArz9cgJIQYdpYupSR3inoDqNYUAG3JJjK0oOmpA/cQSdEvryyN3Cmpvlm6X8mL2yWxJBk5tLXBW8YYq7wuwEPfXgjA6UNq1Uo0tegepi/LQzEIqrapi6MZeQ7yytKo2tY0bN12SoaNWz83g9aGbnb8efh8ule466Xwwl2huP1Lvln6jj/Xxm3xTlEEKx+eypX2fva9Fb2eznhGKIJ5a4u47xvzkFLy4o/3ceCdszdU3b6I9DNQCLEU+K6Ucq22/TiAlPL7fsf8P+B9KeX/atsngNVSymELZhcsWCD37t0b8wk3PuZbONt4+odBz3dNXxDze4bDMJiEVFy4Df2kXK7EMjAhru+vo6Ojkp4bPymPwT6nVzq5YmV+0AQmkRFC7JNShgx0wW4GweQD/i1zjcDiKI7JBwICuhDiS6gzeAoLgz0sr0cMriRwgRhIxTToiPwCHR2dETEhN3r7xWjI1tbas7RGrxuBaAJ6KMGUodP6aI5BSvlL4JegztCj+OwgCn6wwvv466wIc6SOjo7OjUU0i6KNgH83SwEwVEwkmmN0dHR0dMaQaAL6HqBMCDFZCGEGHgZeHXLMq8CjWrXLEqAjXP5cR0dHRyf+REy5SCmdQoivAW8DBuA3UsoqIcSXted/AbwBfBI4CfQAfz12p6yjo6OjE4pocuhIKd9ADdr++37h91gCX43vqeno6OjoxILeKaqjo6MzTtADuo6Ojs44QQ/oOjo6OuMEPaDr6OjojBMitv6P2QcL0QKMVIRiItAax9MZb+jjEx59fMKjj094rvX4FEkpQ7rjXLOAPhqEEHuH0zLQ0ccnEvr4hEcfn/Bcz+Ojp1x0dHR0xgl6QNfR0dEZJyRqQP/ltT6B6xx9fMKjj0949PEJz3U7PgmZQ9fR0dHRCSZRZ+g6Ojo6OkPQA7qOjo7OOCHhAnokw+obASHEJCHEViHEMSFElRDiH7X9E4QQ7wgharX/0/1e87g2ZieEEGuv3dlfHYQQBiHEASHEX7RtfWw0hBBpQojnhRDHtb+hpfr4+BBC/LN2XR0VQvyvEMKaMOMjpUyYf6jyvXVACWAGDgEzrvV5XYNxyAXmaY+TgRpUA+8fAY9p+x8Dfqg9nqGNlQWYrI2h4Vp/jzEeo38B/gT8RdvWx8Y3Nr8DvqA9NgNp+vh4xyYfOA3YtO1NwOcSZXwSbYa+CDgppTwlpRwAngPuvsbndNWRUp6XUu7XHncBx1D/EO9GvVjR/r9He3w38JyUsl9KeRpVt37RVT3pq4gQogBYD/zab7c+NoAQIgVYCTwNIKUckFK2o4+PP0bAJoQwAkmo7msJMT6JFtCHM6O+YRFCFANzgY+BbKk5RWn/Z2mH3Wjj9l/ANwG33z59bFRKgBbgGS0l9WshhB19fACQUjYB/w6cRTW575BSbiZBxifRAnpUZtQ3CkIIB/AC8E9Sys5wh4bYNy7HTQhxB3BRSrkv2peE2Dcux0bDCMwDnpJSzgWuoKYQhuOGGh8tN343avokD7ALIT4d7iUh9l2z8Um0gK6bUWsIIUyowfyPUsoXtd3NQohc7flc4KK2/0Yat+XAXUKIM6gpuVuEEM+ij42HRqBRSvmxtv08aoDXx0flVuC0lLJFSjkIvAgsI0HGJ9ECejSG1eMeIYRAzYEek1L+h99TrwKf1R5/FnjFb//DQgiLEGIyUAbsvlrnezWRUj4upSyQUhaj/n28J6X8NPrYACClvAA0CCGmarvWANXo4+PhLLBECJGkXWdrUNeoEmJ8ovIUvV6QwxhWX+PTuhYsBz4DHBFCHNT2fQv4AbBJCPF51D/MBwGkauq9CfXCdQJflVK6rvpZX1v0sfHx98AftUnRKVRTdwV9fJBSfiyEeB7Yj/p9D6C2+jtIgPHRW/91dHR0xgmJlnLR0dHR0RkGPaDr6OjojBP0gK6jo6MzTtADuo6Ojs44QQ/oOjo6OuMEPaDr6OjojBP0gK6jo6MzTvj/YA6rlT5h0DUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea49dc1-56b7-4ca0-9be2-0f71834a5bc5",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Model -> Support Vector Machine\n",
    "### Transforming out input to normalize our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a5eb0c27-7caa-411f-9e96-765e6e7b7cdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.64  0.85  0.15 ...  0.2   0.47  1.43]\n",
      " [-0.84 -1.12 -0.16 ... -0.68 -0.37 -0.19]\n",
      " [ 1.23  1.94 -0.26 ... -1.1   0.6  -0.11]\n",
      " ...\n",
      " [ 0.34  0.    0.15 ... -0.74 -0.69 -0.28]\n",
      " [-0.84  0.16 -0.47 ... -0.24 -0.37  1.17]\n",
      " [-0.84 -0.87  0.05 ... -0.2  -0.47 -0.87]]\n"
     ]
    }
   ],
   "source": [
    "x = pp.StandardScaler().fit(x).transform(x)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22e05ea4-8322-4aa8-bf97-704acf810410",
   "metadata": {},
   "source": [
    "### Creating a training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ec06843f-60a0-47c9-b54d-094908f32348",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(614, 8) (614,) (154, 8) (154,)\n"
     ]
    }
   ],
   "source": [
    "train_x, test_x, train_y, test_y = tts(x,y,test_size = 0.2, random_state = 5)\n",
    "print(train_x.shape, train_y.shape,test_x.shape,test_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce10e9a-3d9a-40bf-bdaf-7f320935ff29",
   "metadata": {},
   "source": [
    "### We can use any of the 4 types of kernel \n",
    "#### I have used (kernel = linear) for better accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "cd841963-4790-460f-b587-63b9a772a826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "clf = svm.SVC(kernel = 'linear')\n",
    "clf.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a82e7905-96e9-4d8c-ab08-45bd64f2b38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_ = clf.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "22adef91-cd16-4cbf-8520-3fe3f4ad9604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_,test_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5321697c-2a89-420c-ad02-8302cc2cfcb0",
   "metadata": {},
   "source": [
    "### Using confusion matrix for visual representation of accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "4f6a2a80-0e8d-4bec-864b-2d13f0fccd02",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "fbee646c-8f8f-4fba-9526-70da5141409b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "    print(cm)\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True Outcome')\n",
    "    plt.xlabel('Predicted Outcome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "cc8ecfca-ccaa-42ec-a4a5-3c8c8528c4f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[88 12]\n",
      " [19 35]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = confusion_matrix(test_y,y_,labels = [0,1])\n",
    "np.set_printoptions(precision = 2)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(mat,classes = ['Outcome=0','Outcome=1'],normalize = False,title = 'Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b12c5e0-cd14-4c71-929b-1b8e675bf5d0",
   "metadata": {},
   "source": [
    "### Checking the f1_score for accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a7f2cea1-ca74-4f5b-a21e-f69eb729105f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7951292023612299"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(test_y, y_,average = 'weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354640ce-2aae-4719-9a0b-bbb256a5b3f0",
   "metadata": {},
   "source": [
    "### Getting the classification report of our Support Vector Machine Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bf9caa11-ce23-40c3-937e-82dcec021d88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.88      0.85       100\n",
      "           1       0.74      0.65      0.69        54\n",
      "\n",
      "    accuracy                           0.80       154\n",
      "   macro avg       0.78      0.76      0.77       154\n",
      "weighted avg       0.80      0.80      0.80       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_y,y_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c9b01f-5185-45bc-b319-61f855b2d88e",
   "metadata": {},
   "source": [
    "### Making a predictive system by getting input vector from user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ec6cb581-8f7b-4c65-ad7e-fa8da9c81da9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "input_data = (5,166,72,19,175,25.8,0.587,51)\n",
    "input_data = np.array(input_data)\n",
    "input_data = input_data.reshape(1,-1)\n",
    "predict_out = clf.predict(input_data)\n",
    "print(predict_out[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6ab46cee-addf-4355-93b3-aad75ce2a147",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The person is having Diabetes\n"
     ]
    }
   ],
   "source": [
    "if predict_out[0] == 1:\n",
    "    print(\"The person is having Diabetes\")\n",
    "else:\n",
    "    print(\"The person is not having Diabetes\")"
   ]
  },
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   "source": []
  }
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