{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0251f440-865b-4c30-b8f1-b1b1854f0e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd \n",
    "data = pd.read_csv(r\"D:\\Data Mining(405)\\Data1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "267ccbff-a746-468e-a10e-40bbbebfecb2",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>TEM</th>\n",
       "      <th>MXT</th>\n",
       "      <th>MNT</th>\n",
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       "      <th>RIN</th>\n",
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       "       Year  Month  Day   TEM   MXT   MNT  HUM     SLP   WIS  RIN  SSH  CLD\n",
       "0      1980      1    1  12.5  26.0  12.2   69  1013.5   3.3  0.0  7.9    0\n",
       "1      1980      1    2  14.4  21.2   9.5   85  1014.0   8.0  0.0  0.8    6\n",
       "2      1980      1    3  15.3  22.2  14.7   84  1015.0   5.0  8.0  4.1    3\n",
       "3      1980      1    4  12.4  24.0  10.0   73  1016.8   4.0  0.0  8.4    1\n",
       "4      1980      1    5  12.0  23.9  12.0   72  1016.1   8.0  0.0  9.0    2\n",
       "...     ...    ...  ...   ...   ...   ...  ...     ...   ...  ...  ...  ...\n",
       "15426  2022      3   27  25.3  35.2  25.8   79  1008.5   4.5  0.0  6.8    2\n",
       "15427  2022      3   28  24.9  36.0  25.2   76  1006.7   7.0  0.0  7.3    1\n",
       "15428  2022      3   29  23.5  35.4  26.2   75  1005.7   4.5  0.0  6.8    3\n",
       "15429  2022      3   30  24.9  34.4  25.6   78  1006.8  10.3  0.0  6.7    2\n",
       "15430  2022      3   31  24.6  34.6  25.6   77  1006.9   8.3  0.0  7.1    2\n",
       "\n",
       "[15431 rows x 12 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f3f0581-ed6b-4dad-a081-0198c05fe061",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Year      0\n",
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       "Day       0\n",
       "TEM       1\n",
       "MXT      55\n",
       "MNT      71\n",
       "HUM       0\n",
       "SLP       2\n",
       "WIS       0\n",
       "RIN       2\n",
       "SSH       0\n",
       "CLD       0\n",
       "dtype: int64"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 77,
   "id": "5e928416-f194-4886-999e-ebc1c893f4e2",
   "metadata": {},
   "outputs": [
    {
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      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_25240\\1959609314.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  data[\"MXT\"].fillna(data[\"MXT\"].mean(),inplace=True)\n",
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_25240\\1959609314.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  data[\"MNT\"].fillna(data[\"MNT\"].mean(),inplace=True)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "data[\"MXT\"].fillna(data[\"MXT\"].mean(),inplace=True)\n",
    "data[\"MNT\"].fillna(data[\"MNT\"].mean(),inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "62bb7a60-d467-4875-a274-a75fc713a590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year     0\n",
       "Month    0\n",
       "Day      0\n",
       "TEM      1\n",
       "MXT      0\n",
       "MNT      0\n",
       "HUM      0\n",
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       "RIN      2\n",
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       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a27789b7-2483-47c3-af69-ba6784b1bca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "36eb43f0-5bb9-42a6-b9b7-b8a2370dc202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year     0\n",
       "Month    0\n",
       "Day      0\n",
       "TEM      0\n",
       "MXT      0\n",
       "MNT      0\n",
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       "SLP      0\n",
       "WIS      0\n",
       "RIN      0\n",
       "SSH      0\n",
       "CLD      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
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   "execution_count": 81,
   "id": "442aa6e3-2d64-4558-b467-efe7ba5384a9",
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       "      <td>1006.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15431 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year  Month  Day   TEM   MXT   MNT  HUM     SLP   WIS  RIN  SSH  CLD\n",
       "0      1980      1    1  12.5  26.0  12.2   69  1013.5   3.3  0.0  7.9    0\n",
       "1      1980      1    2  14.4  21.2   9.5   85  1014.0   8.0  0.0  0.8    6\n",
       "2      1980      1    3  15.3  22.2  14.7   84  1015.0   5.0  8.0  4.1    3\n",
       "3      1980      1    4  12.4  24.0  10.0   73  1016.8   4.0  0.0  8.4    1\n",
       "4      1980      1    5  12.0  23.9  12.0   72  1016.1   8.0  0.0  9.0    2\n",
       "...     ...    ...  ...   ...   ...   ...  ...     ...   ...  ...  ...  ...\n",
       "15426  2022      3   27  25.3  35.2  25.8   79  1008.5   4.5  0.0  6.8    2\n",
       "15427  2022      3   28  24.9  36.0  25.2   76  1006.7   7.0  0.0  7.3    1\n",
       "15428  2022      3   29  23.5  35.4  26.2   75  1005.7   4.5  0.0  6.8    3\n",
       "15429  2022      3   30  24.9  34.4  25.6   78  1006.8  10.3  0.0  6.7    2\n",
       "15430  2022      3   31  24.6  34.6  25.6   77  1006.9   8.3  0.0  7.1    2\n",
       "\n",
       "[15431 rows x 12 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "445de4b3-c4f0-42e7-9d9e-232e4d880709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "plt.scatter(data[\"Year\"],data[\"TEM\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "17da942f-ed4c-4397-907c-651014bfe128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "plt.plot(data[\"Year\"],data[\"HUM\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b6697d15-6dda-4f9a-ae5b-4ebd956aec3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CrdvIsrhu+gkWPh1ApVClMH3ijsDHAwI/oHmKbUhON+b2igVxbrDnNB/JyaET7DxOEAYhvustmsiiKOK+2zbxF2/8Gz73d/9OYaZE32Avb/jjVyNJEtf95CZuvfauJfc3n9kZWTHMaFwe2fbA4WDH83y+/5Wf8k9/+hlc10OWJM67+CyiKKLpWGx7cBcf++vPE/gBz3/Fs3jzu14PQDlfoVlp0dPXzciKYXoHe/D9EKNl0D/Yy4UXn8vzXvR0UqqG5blM1Is4iRBZkbF9n+lqnR9+9sfMHpzlmitu4Av/8FXmposkkgkApg4eW+PHcz3G90zSqDfx/ADrN2jJPhKLy1iP3UQTRRFzE/OZnaPLWACSJPGnf/c2VE1lyz3buOfGkzNrDYMQzztxy7nv+3z3Xw6vzvZvP4ilW9gn6LQCSCQ1PNdbsovNczx81yeZThIE4ZOSh+I67jEzVr7r0SjXsU2H0HGRJAnP9ZcsZYWhKF8pCY3AD2l1RDiPizAMMZometN4QljR/DY810438vvEWL1s/dhpPpKTQyfYeZwgDALCICQKRbCzfdMuPvi2f+Jj7/0ch/ZP0ZXL8o6/+gO+de2XeNcH3sqb/1QEH//6sW8sGSDM80BGVw6zYp14GLdv3EUYhMwcmuPv3/HPXPnta4miiHQiSV+6i/f8459w8bMuFN1b4wepVxqccdYaPviJ9yBJEqZuUZgpkcwkkWSZMIRauU5xtoQkyaSyopOoN9vFc9aexUCmiyiKmJidpam6DCzrF8c2W+Rf/uGr3HT17URRxCXPegp/+5n3ggSNWpN6pXHU+fiez+SBGYozRfoHewEwmqduQlhMUD7xfn3PP2lRveOhWqrj2C6yIjOyfOiY2y1fs4w3/MmrAfj2539wUit//ySzKTf99DZmD82RTCXIdKWxLYeJvdMnPYlkutJUi/WjOo08xyWKIhRNIfTDJ9VAP4/ADwmO0VavNwwapQaKIhFGYZy18fCWKH3NdxdKsiTKf03zSXm9ThXmuyYtw8I4Tmmwg98OAj+gMB7PMeuXn+ajOTl0gp3HCUI/IApC9u8c5x/f8xn+7x9/jJ1b9pJMJXjTO3+Hb13/Zd70rt8hFbcm/+H/90aectl52JbDpz7wpaNW4bPjooy1bNUIQ2ODpLMp9IbBD7/6cz70hx/l4K5DdHVnee/H3kWXlkSWJPqGevng5/4cJ3AJohBZkvjT9/8R6UyKMAwpTBdxLJdMNg1E1Ct1aqUGRBGKKiNLgsDqt2wSqsqawWGymjje2Zk8m/fuwfAt5lpVvDBAlmSGc70sHxhE01R6ekXr+8SBxTo/gR8weWCGwkyRbDpNIqmJrrGWecpadK3WyXN2oihi9lCe/EmI6p0I81mdkeVDqNrxu9Ne98evZPmaZdQrTb7/bz854b4DLyA4Qcdas9bip1+/BoBzLj6L8552NgD7doyftKJ1KpPCNi1aRyhqO7bb7tyKiJ6Uk3fg+0tmrMIwpDRdwnM8EslEW5cnQloys+M5Ho1Kk0a1geO42KbdMQ49DhxLKHgnUknq5UanVf8xRnmiSOD6JLMp+pYNnO7DOSl0gp3HCWYm8nz9367gr97y92y8czOyIvPKN72Yb177Jd7xvj8g19O1aHtFkfnQZ95L30APh/ZP8dVPf7v9me/7FKYFkXV05TCyIrPu3DUEUcjPv30tju1ywWXn8i8//DhnXXgmAKlsikQqwdVX/ArLE4FTRkvz1Y9/m9mJPPVyg3K+QndfjiiKaNV1auU6sizju367DAUQGmIwbzou2USaoe4+JCRsx2GmXiEIQ0a6e3jGpU8loybZeud2vvux/0GVxOM4vmeiva8gCJkan6UwU6K7JweArCokU0mMpnHKOrIWrg5PRHrUmwbVYhW9oZ+QdGs0TfYdx65j9jjk5COhJTTe/fdvB+DGn9/Ozs3H1k4CMbmeKMD4ydeuwmiZjK4cZvWZKzjvaWcjyRKF6SKFyeJJdQXJsoSW0KgWaouuh9WyUBYEcE+mYMd3PPS5qtDRWSLY0ast6vkaqqohKzKSJHSMFFXGOoLnE/gBs4fmmJsqYFvCHd2zn5zt+qcKth2PUV1p9IZxQnHLDk4t5uIS1uiZY0hHdGk+XtEJdh4H0JsGf/+Xn+WBe7cC8LxXPJOvX/0vvPcj72JguP+Yv9c/2MuHPvNeZFni11fexk3X3AFAYbpEEAQk00n0lsGBXYfYs+0AEYL/8bb3/T4f+fcPMDDS3xYU7O7v5sF7Hubrn/8eAIOZHoYG+2lUm3zs//ssu7bsQ0tqaAkVQzdpVBokkkk81yWKRPAFQgA5MsWK1LBtoigikU6gygoS4qUIooCmZbHh7NW86f1vZOyMZWLSiH/vzuvubfspzcSTQM9AN5I0n60QVhOWaeOcgnp9ECzm/xwvsxOGIaXZMo7tCnPHEwQD3/709/j0n/0Lu48h6jjvibVU2/lSOPfis3jR7zwHgH/+i3/hp/91zTHbmQNfGFCW5ipLZngm9k5x85XimXn2y5+OllTp6s6y9qxVAOzasu+keDsAmVwGvaFjNOcNZX1820VNqNTyVQLPf1K1n2+75h4OXP8A9QP5o9ShPcejPFPGD0OQQVEVIiR810NLaJitwxkz27SZ3DfN1P4ZEgmNKAzFc23ZuPZjn9mxmwbmaVARf6QwmiaKqpJIJnAd97QQuvWG0c7MPplQmC5SK9WPu808X+eJUsKCTrDzuEAyleDCi85m3Zkr+Zdv/xN/+9m/ZPnqZSf1u0+9/Hze8mdvBOArn/wmEwemD5OTlw9x9Xd/xfVX3IQVBwWaovDSN76g3U7ciHVv1JTKJ9//BcIw5JLLLqAv3cW6tStZvmYZlUKN7/zLD9rfGfghISAhBmttQVbHt11kP8TxPWzXpeWalMpVJEliwznrWD4mJvWGbfCt7/6M8X0T/M5fvJYLXnQh3XH26sCuQ/zjH3ySX3z7eqbHZ+juzZFIaARuAFGE53kiQDHtk/LSOh70psH+bQfbXCk4frDTqunUyw36BnvxXA/nBN1OM3E5ce+W/Ut/PnHymZ15vO2v38y5l5yFY7v88KtX8te/9/fcd8uDR6XyfS/AtT18xzsqqxJFEd/9lyuIwohLX3AxPX3dJNNJgHYp6+CuQ0dZiRwLqqoQAfV4kPQcIVCoKBJOy8KoNLFPwhvqiYLmbAUAI19dlNmJoojqXBVLt5Bk8Y7IioyEhO/6qFosLuh41CsNxndOUC3WSKYSpLIpAj/EdUVnpH0KCfgnC6dp4D3OCb+BH2C2zHY2WU1oNCrNx7yUZZs2zVrrSSep0GrozE0WjstJnO/EWrahE+x08AhQLdU5Y/1KnnrROaw5c+Uj/v03/+nruejpF+DE/J2JfZMAOK7LQ3eLbNGLXvccRseGiMJoUZahWWkSRiGbd+6i1dQ5+4L1vOfDf4IkSRQni/zVP7+bnv5uauUG//WZ/1nUYmwZFlEIqnr4MfJbDkEYsrM4h+5ahFFEIqHxwlc+m+e97Jm86k0v5dLLn4osSXhhwA++fSX//k//xdDYEL//3t8FwIsCGpUGv/rur7n6P37Bwa0iGPFdD0mWRLAVhri2d1zTyuPB1C0m90+zb9sBZscXr86OVcYKg5DSXAkJUbYBFpmbLoVGWQQLE3sml/x8XmNn7BidWEshm8vwsa/9De/71J/RP9xHcbbM5//vv/Px93yeqQOHyeqe6xFFIcESg/H9N29i10N70ZIar/2jl2PoBsmUCHZWnbGCXG8XruOxY+Puk+6iynSlqVcabb6J74dEkcjyOC1TKF8/SbgVVtyxZ1Wai0ooRsOgXqiSzCRxHXHd0qpEd0Ii8AMURcG2XKb2T3No1wSe59E72EPoh0iSCJZCP8AxHdGmfoom0pPZT+gH+JZD4HiP6/vk2A6e55FIindwno94KqUoTgauI0jSJ/JHeyIhiiI8R3RXVo+R3bF1i1oc7I+e+cToxIJOsHPa4bkekwemuf7qO/jFz29h345H7oGkKDIf+n9/Qf9QH5MHprnhqtsIw5DZKVEiufT5F/GHf/FGLrz8PGBxC3qj0iDfqtFotOgf7OVj//YhVp6xHDWh4lgO5ZkSf/Ce15Pr7SI/VeRbn/s+ruMReD6mYaElF5NqK7Nl7p3YT1kX5bGEqrFhw1rGFmQuzrrgTF75omeT1ZJEwP6DE3z/P39KJV9FVmTCMOT8551POpemWW5y3bd+xfc+9X0Obh9HUmTRCeX4hEGIqT+y9LVtOcwcmmPftgMUpoptwjfQrj1burUkv6ReadCoNMn1igyUpqnH5fc4ltPuaJrYO3XU577nt0UCj6WxcyxIksSzX3Y5//rzT/OGd74GLaGy7YGdfOAP/pFvfe776E1DlNgkieiIic61Xb7/5R8D8Nq3vRxVUwiDEFVV2tdhPruza/Pek+ZDpNJJHNujUW3hxa3WQSC6sGRZPikO0RMBURhhxffdrhuYcQkl8AMqsxVRLlYUEZwDyxWHtb0yku/jmA6lmRLTB2ZJZVN09+UWdaqFYYSkyOhNHdt0TglJ2WyZjO+coHUC4r3veISeeK+iU6hOfqrh2C6+F7QJ/YlkAsdxMVqPbSnLNh18LzglXZmPFwRBgO8HKJpKcaYkzJmPwLzBc89IH5me7GN9iI8anWDnNKNSFGrE82WmHScgnR4LvQM9/E3M35menMMJxKC1bOUwK9aKktgFl58LLBYXvPuOB9FdC1mW+ei/fojB4X4UVWEs/p1Du6ZYvX4V/+dv/4hMV5rJAzNc/0NRFgv9kNAPqRRrHNwzwa3X38ODu/ZgeR4SEkk1gSarTOyfxtIPZ0C0hEbP6iGed855rOwRTP6WZfDNf7sCVRETbvdwD+/6xDt41mufSSKVoDRd5uYf387NP7mdwlSRMBSTQ6t2cgOc63jkpwrs3bqfmfE5tITKwGg/iaRGtVAFIJlNIcUdZUcKC/q+T3GmjKqpKHFQoCUSWMaxfYwWttBXC7WjnOeLs2XCICSZStA31HtS53EkUukkf/Ce3+VLP/0Ul73gYsIg5Lof3sR7X/e33PGrewmDgCBYvEr/xfduoJyvMjDSzyvf8lJq5QaSIrfPC+CcizYgyxKluQqT+6eP/NpjH08mQbVYQ68Zcct5QBRGyLJEEDw5gh2raRwOIKOI5kyFwA+oF2votRbZ3i4RkHs+WRUUKRIaO4bB9PgsRtMglU6QisuGge/jx9clCiNkJCzTxWgapyTY8VyfRrnBxO5JKvnqMbM2geMS+CFREBA8jjWRlsrgaAmNWnlpJe/fBqIowrEcfFfc5ycLAl9IoOR6u7ANm0q+etQ2h/k6T5ysDnSCndMKx3KYmyqy6e6H2z/bv/PEmZ35B/JIXHjpufzRn79J7DsQYmdjy4ZpVVr4fsD5l56DJElMHZihVq5zz60b2fKwCHxe+4aXcN5Tz2rva16IsFVtoigyoyuGeef//UOSqQQzh+bYuXEvG2/fzO3X3cvGOzazbfNuGo2Y/yMrpLUkmqygKDJhGLFj02GbCFmRaNZ1Bs4e48KxlVx2xno0TcUPA3TLJIxCdjy0By2p8fRXXsa7PvkOLn7hU1EUmeJ0mfuu28ju+/fSqutYhnXcrpWZh3az4/p72fvwfib3zSBLEgMjfe2MjmXY1Ct1cdyaSjIteABHCgvWSw1aDZ2uBV1xiVQC1/GOmfmYL2HNY/KI7M7sAjHBMAi59ns38PNv/IJqsXbM8zkWRpYP8aHPv5d//I8PsmLdGK2GzlX/fT1f++L3+PY3f8TWTeI+V/JVrvnu9QD84V++kSgMMVsmiqIwNTHL7TfeRxhGZHMZ1p2zBoAH73iYMFw8iYRBSBgcPbGI7pgWtUodVVMJ/YAwFKKSYXBsTZpHim13b+eX37zutJRbzCME/1r5GnpdpzpXJd2VbmexwiAipx4+Pk838F2f3qGeRZpEvudDFBGGQBQhazKu7WAbx/fIOln/LN/zicIIRZGZ2jfN3Hi+HVwt2p9ho2gqYSgCnlOJU9lZpjd1tIQmguf4ONPZNEbTXDIQ8j2f679xHePbxk/ZMfie0EUKggDvFJaxPNtl0w9vo3xw7sQbPwKEJynqOX9OiqKQyWUpz1WPogrM7ROZnWVPIHIydIKd3yp8z6daqh3zISsVKuzcvIfSXKX9s327xk84gDeLdYzq0tYSL/6d56LELdyBHDK1Y4LJ7RO4jk+ut4s1Z4tOm1t/cRef/tCXAehJZXnhK5/T3kcYhuT6xKReK9bbP195xnLe8cG3oCgyruPi2C6O72L7LhERqqpwxsAwG4ZH0WSFdCpJ70AvgAiwSmLlpbdMMl1p1l66HjmhMpTIsmbF4RfHDwMefnAH//PFH9Oqt0h3pXnGKy7jlW97KWc+ZR1I4ri23LaVO666u03IPhJRGLL/mjsp37sNp9pgYLSPTOzaDWJ1Vq80FlkqJFLzwc7hCc11PIozJVKpxCKfKCUuuR2rY+lIccSJPYuDnXm3896BXj7+zs/w43+/kqu/eS0ffP3f85//+E3Gd03wSHHh5efx+R98jD/6qzeRSicplirMzRX48ie+Tjlf4fv/9lNcx+Xsi9bz9JdcimXYOLaHIkv88uc3ccsNd7FnhyBTtzV3th1AP0JDx6wb2EuUDWRZhlCiVqijaAp+HJhLkhDLC07gEn6y+OEXfsKv/vvXTOxemgv124R5xLunF6qUpkpCLToOon3PJwwCsguCnXRSIZVNoWoqbkzgnt8WRPbQ90Tpy/N8TMs+5rPluR6T+2dOyhvKrOtUp4sk00my3RnyUwWm9k4tCgxCP8C3HZSEihRxSq09PNdn9uDcKVE69mMLjkRSo1lp0IzfsXkl76W6snbds5N7fn4XN/zX9b/x9y88Dt8XpbRT0RE6j6nN+9l900M8eMWtp2yfIBatxckT64LNv6+yIpPOprAdh9Jcuf15FEWHMztPIHIydIKd3yoCP6BWbiyZujd1k+JMic33bANgxepRZFmiUWtRnC0ftf1CCGuJpSeNW6++i6SaQAJc16NkNDEbRruufMFl5xJGId/96o8wDYuudIbhbC/d/bn2PmqlOslsGoDS9GLjybUbVvGsl11GtjdLoIT4cTlpzRkree4lT2XdwBB2KCa3RFIjoSnkukVdd8s922jUm2S70gwvG0JvmnSvFS7r9XIdACUOJrzQ5/77NvOFD32VLXdvw3N9Ml1pLn/pJVz8gqfQN9wLwPTeGT7/Z1/kR1/66VGWBU7LhDgjYc+UgMV6EJZh06g1aftkyhJqQvAAFgY7tVINo2WSyR1dn5YleUlfKIBG+Yhg54jMzsyEWL3tenA3E3unyHSlWb1hJUEQcu+vN/LRd3yaf37353nw1s2PSE9I1VRe8vrn8Rf/8E7OP19k62Zm8rzndz7EXTfcB8DbPvAHSJIUawX5SLIsrgUwvl8EECvWjtEz0I3n+my+Z/viL4miY7aSJ5IaekPHcz18x2ehD+mpyOyEYUgrDjjycev+YwkzLkfK8bNiFBvUCnWyvYezfq7jIQUBafnwyacUmcgPUTWVwPfbGQHH8pAXcHwUSYZQmNPqNX3JxY9ruxgN/aRarl3bxTMc4VOWTNA32Eu93ODQ7sl2adV3PALXF7pIknRKy1hRFOK53ikhENuWg2eLFn7HctskcABN06hXji5lzR0QmYjWKWyp91yf0A9IphKPukliKZhVMe5UJ4v4p5AL5Doe5kmocgvhS9oLwlx3F5VCtf2cNQp1rKaJrMoMr3lkPMPTjU6wc5pQnKswM1lg73YhOLd67RhDo4K/snvrvhP+/lIvgud6XP+jm5EkieHBATGZuTbVRhMz5qCcd+nZNB0DXTcZGh1gJNuLJEl09wv1Ysd2yE8VGFklgpBmtbXoZTZ1iwN7JigWhdZMOpPimc9/Gk+99Dxk2xeCg5aY/NWERhhFdPd2Icsy5XyVernB8LJBMtkU1WKd3jOGkBMqpuO0XzBVFtwRL/SZq5a44qs/4ydfuxrTtAiCkEQywVkXruXMC9bQN9xLEITc9vM7+cibP8HV37i2rZljVQ4Pbk6+gnEEQbNeaQi5fz8gCEOKpSqNOFsxz9mxLYfiTJlMVxp5CfEsLZ7Yl+p2qcffvzzmP00uyOyUZsvcf+ODgOBpPOWZ5/Puj/0Jr3vXq/jAF9/LM19+OYois/fh/fzbh7/Gh37vH/n1j24+6YHV9wJS6QSvesNLGBoSz1XT1AmjCDWlUZwpxfokpuiYCgLc+Jk6dFBwdCRZ4rxLRHZn050PH/UdgeOzVBJSEJMD9Kax6DmVkE6JP5bRNNvXu3Aag53sSC8AvmET+sEizpNjO3TJiyeWpCIRBqHICAZh3C0Hnu0gKxKe4yNLkiDKSxGOZcdB49HXzHU8bNulXqqfMBD2PJ9IktrZGlmR6RvqxbMdJvZMUp6r4MVdhZIsI8kywSkm3XqOh/UbykSAKP0HgbjWruMuIgen4lLWke9IPu62NBqnTnF9/jlWNRXPPVra4dHCia9RFIRUDp26Z9u13GNalSxE4AeLloTJdJLADyjPlRdldYbWjLYXhk8UdIKd04BWQ6dcqPDwfSKrM7ZqhL6BHlbEHUu7Hz5xsBMsMQD+6se30IzLDRdedh4bzlwDQNls8vB9YmV+752bcAPxu3/y529GQUaSJXK9wseqOF3GaFoMjPbRMyACoIXZnbtvup+9uwSvaPUZK3jhK57N8OggYRSB7eP4MWFPEqTBIDYZ7Y8JuLsf2oeiKIRhiN7USWZSdK8dwvLESxhGEaqioskqg8P9RETUbZ3t2/bww69fza7NewmCAElRSHelOPvi9bz2z17NmnNW49ouv/rer/nImz/OjVfcQmXmcNpWcVyqU4W2S7VpWDRqLbK5tAjCLAPTtCiWqoRR1ObsVPIVbNMmHWe6QKRyTd0SgonJxDHFBRtxiv2CZ4ouuOJMCaNpcMcv7+Ef3vpJ9Diweu3bX857/9+7xYo/CNESKn/8t3/Iv1z1KV7z9peT7c5Smi3z/S/+hL9+7d9yxZd/SukE2b/A9wkC0aJ/6WVPAcDyxCRgmTaf+79f4RN//i9MHZhBIlo0ERXypTZh/pyLNiArMqXZ8lGZKcHbWYL7YTmks2ka5caiMoyiKscUQHwkKCxIxxePyDw+FpgvISeyKbTYD85bkGEJghDXcsnFc0E1VhRPKBL+gjLefFej7wUimxIEItgQsQ6e7WG0liYpO5aDhHiOj5VZnIfneBAtvlfzCxxNVZnaN83UnmmCOAsqK/Ipbz+PEG35v+k+bcsRwVgQxL5kgu8EIqPoOt5RXlnzHk6B5x/Xpf6RwHd9IkDVFHz31HVkOQvuZWnfsU2RH/F+LdHZdyKhyqWCtlxvjmqhTquut5WTn2h8HegEO485oiiiOFumWWvxUFzCWn3Gcvr6exhbKYKdvVuXFqBbiHnS4TyatRY/+cbVACiSxIq1y1g2OEAmbu/+6f9cxw1X3sqPvyW26U5mKU8LrlCuN4esyDRrLYpzZbr7u5AkiaGVwpiyOCUmlMkDM1z705sBGB0b4oKLzkGL2z/9lo0UQdMRk2QimRCKx2GE53rkejIkUwladZ2H79uBbdhCL8cPeXj/QaIFtQ5FkZEkiWc9/zKe9cJLATA8m2Kjxq3X3sND927Hth0S6SSOYZHr6+J9X/5z3v3P72R09QhG0+TnX72a//z0DzkwW2uTa4NKk2q+Jjpnyg18z2f7A7vYs/Ng+9ujKML1fZrVFqZuUs5X6erOtrNOIDJCsxN5LMNGS6jHFBecL2MtXzvG4LIBoijiix/8D775yf8WGkXxdq9668tEqcFx6R3qxWhZFKaK9A728Mb/73V88ZpP88d/8xaWrR7FMmx+dcVN/N83foSv/N3X2b9taUK77wWEoSAHn3nGamRJJogCnvOaZ/CGd74aLaGya/NevvWFH3DXrx+gVluQ4o/g0EER2KSzKc44dy0A99384KLvCMOjifJhGImMXzaNZdgYC4QEZUXGO4EI48mguMCTrDJXXRRAPBaYJyhHsoSSER1VrcLhrpXA8/Fcly5F3OFDRbF9QoGwnRFQcExbEF2DQHCaQqG1AyLDEvg+esPCXaL9V28aNKstXNs9qnNwIQRPKkCKpCV5OOmuNNlchsJknsJcWQQTqkIYBKe8/dyx3JMmVS+FeZsaLaG1fd/CMFqkDp5IaG1uIIhSYHNBhvfI7O6jhW3ZKKqMoqqxKfCpeQYXCm8WT1Gw0yZT+8EJZSRc20OSF4cFWkIjQmSj58UEn2idWNAJdh5zNGpNKoUqOzbvwfd8Bkb6GVs1QjqdYnksLHdwz+RxV8CuaeNazqIo/MdfvwpTt1AUYcswNDaIpdsMZ3tQZZlGo8Xn//E/ALjk6ReSUhPsjMUFuwdymLrFTVfdTqPWbCuTDq8QwU5puoRtO3zryz/A932WrRhhaGRw0Sot0MVL1IozNMmUMD90HeHo3dfby8q14gXZeNtDVEo1UpkUD2/cyYE9hxafnydWH5VilTe+7TW89U/fiKapeKFP1WoyN1fkzps2Mjk+h+v61OYq2KbDU59zIR/5zt/ytg+/hf6RPgzdYuPeWa7fuJ/JYgPVstAbOvlDecrFGvff9CD3xaUkVZZpOgamZ2N5Llvu2sqP/vXnPHzHVqb3zwrNCctBbxoUdk/iPrSX6v5p0WEUBJi6KQimC/7U4mCnqzdLd3+OMIrYt/UAiqrw4t97gbj2vV3kerpwbJcg1rrp7u+iNFdpS7YnUwle8Prn8qkr/pH3f+EvOO+yc4jCiI23PMQn/s9n+fg7P8N9N25c1GEjJhWJ/ESBX/3wVjKqyEAUyiX+4D1v4Is//iTnXXIWURixfdMefv3zxYTIvQu6As+/VJSydjy4e1GmJgpCwiNWgoEn2pbVhIqsKDTrrXawKSunRmunNHM4q6XXderFxnG2fmRwdOuEZaF5gnKvbJPsEu+KVT6s4Ov7AZLjosoQRNCbVPFdcc5SIEp/qqbi2A6u4xEFIVEYEviC60Ykgh3P9XFsu52tnUcYhOh1Q5j1Kgr1cuOYxxz4AZ7j4Vg2vrf0ql6WJLpyGQzdZvuVd7L9Z3cQuP6SWbvfBJ7j/kYeVp7j4VouiaQmut38sJ3hmUe6K43RMtqlrPz44q4mvXaKgh3DQVVVZFkiio7NoXwkCPwAdwHZuXxg9rhikFEY4pwEZyvwAgLfJ5FKYDWP7xDvOA7NausoU99cX45KoUopzpI9ETM7T6yi2xMcQRBSmCnhB357lbzmrJWMjAwSOD7ZdJLuni6aDZ0Du8Y596KzltyPXm5hNQ0CX0wqUwdn+PXPbhMfhkLTY2jZIFbLRJYkBjPdTDZEdmb1uhX8fx/6Y/7ujz7B5P4pMnKC7v5u9mzdx/aNOzn34rNZfeYKwfuJMzuFqSI//MZVlPIVsl0Znv/yZ7Jn6z5MwyaVSpBIJQh1R6y87NiWIqlhuy6OK4KfTC5DpidLfqaE3jTYvnE3685dw4N3bTk6tR0BEpSLVWzL5ZKnX0BWS3DlT2+gWKhQt3W8IGDvjoNM7J+mpRucd+m59PR3Iysyz3jF5TzthRfz3Q/+O9t3TtKyXO7ZOc2uqTJPeellTB2cYeMdW2jWWsiKTH9Pjnq9iRf6eKFPWk1SLta459r7jrr2WlIjndTIKDKJ/bN0b96Hqinse/gg685dTaY70+7YqhZrRFHENd++vp2ByeQyfPg/3s/U+AxcActiMUFLt9uihlpCeJDlJwtkc5m2jYMsyzzlmefzlGeez9T+GX79o5u594YHOLBjnK9+5Jv0j/ycl/zeC3je7zw7zgZE7Nt2AMu2ySbS6J7J3bc+QLlYpW+wlzf+yWs4/5Kzuf4nt1AsiQBivmtq4uA0esugK5dlbPUovQPd1CtNNt+1lWe8RGTbQj84KtjxPZ/ADUhmUqSSGmXLwU7bJCQNeYHI4EJ+yyNBGIaUF3Qv2qZNvVRncOw3d14OfJ+5gzMoqnbMbaIwamd2utIqvizTAOyqju/5aAkN3/dJhy7I0LADVvZnYtM4UIkEb0dVsE0H27DjElYIEu1gR1FkQi/AdXwapQZsOHwMruPiWDaEEYmkhmlYGLp5lFmwOKeAZl2nGStYL4XQ9ZBlme7uDNPFGg5gVJr0rh6F5G9wQY+A54pOqu5j2/2JoM92UFLJozIMju3ieh6Z7gxmU0hUSIG0KNDTEhq+52M0DTJdafIHj1BHPwWZncAP8DwfRYufYUk6JWUs3/HwF2Q+PculMVOhLx6Hj9re9bHqOmo6uchw96jtPJ/AD0llUjiWUHw+Ft/GsV3G90/QbOpc8LRz2j9XVQWrZhD4AamuNL3LxE0M/ACraZLuzjzqd/qxQiez8xiiXmlQK9c5uHuCVkMnm8uwdsMqMtk0hm5SmCqxeq2ImI/F2wmDEM9xsJoGdlOkPL/3pR8TBiHrz1+HLElku7OkMynq9RZTjQp5/bBuS3GmjBf4dPfn8FyfIBQCUgf3TFCpNNi340BbJnwozuwcGp9m452bkSSJ17755WSzGVEm8QLqpQaTu6fIz1WYqFbbAlt6tYVbt0ii0aroGLpJtivNurNXAzC5f5q7bryPMAwXqRgD7aapuakCRtPAd3yGhwf4s/e9lfUb1gBgeBa6b2E7Dg8/uJvPffAr3HntPYdXQhKsX97Pqy9fz4rVQ6iKTK1lc+OPb+GWa+6iWWuRzqZ57R+/grSWxAsPr8yCKEBWZc697GzWP/VMRlYOk4rLFZ7j0Wya5Gs6kzNltt+9gy23beX2n97Btz/+Pf7jQ1/nu5/8H372lauwdGGXMR/oSEDfQDer1q9gdt4AdNWoaMdvGouc47t6spgti/xUsX1OYSi0MmzLYWC0j3f87Vv5wlWf4nXvejXdfTmqhRo/+srP+evXfphffu8G6pUmc9Oi5JNSk5x51loCP+Dan92IZdi4jsfQsgGe/ryL6B3oAUCLyeGVco0DuydEWUM6rKh8/82b2sFpFB7NHQs8nwgRcMdbYekmoecjKQiRw9+gI8s2HRoL5BAcy2H2UP43tlWIoojiTImpg7M0a8fu2rFbZltQUEso5DIySBKh62HFQVDgBWQQ59gwvAVSB6BGodC8URXh8aSbsSp4AJEgEzuxLUoYiYxPs9paROx2bBfbckXZC1E6NI7Rgu66Ho1aE8/1MVvieTwSnuUiyRLegmDIrreOCmR/U6iaeszjnEcUhviWfZTqNwjSdxSGyLKM7/tIkrhGQbj4OLVEglq5QRRFbb7OPE5FsON7PoEXtBXHVVX5jT36IPaTi4MmOd53cd/xBT3D4OgFx1LHSxShxe35xyolBn5Aq97C1C3K+TK2uficnLrICg2sGmo/02EQYtT0R9QterrQyew8RvA9n/xMEUVRuDNu/1171mpGVwwjSxKtpkHoeYyMDLAN2H0M3o7nCKn0wA1oluuMj8/w0N1bURSF9eev49DOCYaWDWBbDi3Hwglc/DBAQuL8i85mz9b9fPqDX+bCp57DA7dswgsDFE1hdiKPIkmU8lU237uVy593CT0D3UQKlGuiTPD0517M2vUr+elXr6JeapBncYp43oNJAljw7Id+yPjOQ/QP99KVy9LTl6NcruEZPql0Ei2p0miJ34sQ3VhB7P48PT5LYt1yZFVGURSe87zLGOzt5YFNW7Fch4CQ3lSOerXJf3z0W1z7/Rv5/T9/PevPX0dku2iqwuiaETasHuaBB/cwXRGDrSLLLBsZIKkJUqOzgPfhhQG26/Kc1z6TgdEBwjCiMFOiMFlAVmTKG3fjtCxajouVTuI5HnrdwDZswjCkURG+MuFCHlLsCpmfLPKXL/sgZlzuK04Wufa7N+BYNrmBbrr7ciQzSYjAD3x2b95LtVQj05XG83yRug/FhNk/3MvyNct4/btezav+6GXc9+sHuOGHNzN9YJYHb9vCA7c+JJy3EUHMUHcf+xnnFz+5gRe99NmYpsXsRAFJlttiiv39vRTLFcIo4sarbqc4XeLZL7uMcy4+i/tuepDCTImpAzP09wry+pFdga7ttlfk851zpmHhZoRGUeh6v5HWjtE0aC3gqERhRGWuTKuu0xN3FD4aVIs1psfzuLbXniiXQiMmhmsJBUmSSCqQ7Mng1A0asxW6R/rwHJu0JK67abmQizNFUYSKuH+qpBBFwsohmU7RqDaoVxs0mzqe7ZAOM8iBhCSJjkjbtNuClp7jEQaBGAdiBe5auc7w8iFkZfH6tVFp4pgOkiSI6ZEfwIIsQBiEBK6LrKrYC7yQrIZ+ystYWiqBFfumacljZ8+OBVO3kONg3Pd8JCQioqMm+3Q2FZeyLPKxBleqK42tW6ekjOW5PkHgo6gZQARxtiky2wu5fY94v9Zhn62hM8co7J6itG+Ws1540TF/J/RDwQNLHzsF53uCTC0rMlEYxh1ZmaO2C/yARqVF4PlYpk2lWGX5msPlqvqMyKim+rvwXK/tD/hEQSfYeYxQK9dp1lpUi1XmJguomsqas1bSO9BLdaKIbTq4lk06Kx7CPVv3Lfny+LaHbdk0TYt6ocF3vio8jl70+ue2X+ShsQGslknTNto6ON3JNL/3R6/mG1/6PvnpIt09OaIowg8CbNtBbxlIkkQmm2JmMs/WTbtYc+YKCnqdCBhdNsTlz7uE/dsOUi+J4CeVSQrF2Agiw6Xh2liOSyqbEk7PUUS3lKRqGni2x8N3bWfNeatRE2qbkDw0MsDcrMhyZDMZdDNOT8fllJt/eQfDf/w75HIZ/CAkDEI2nLmaZcuHuOnme2k2dcp+jQ3r16GXWkzsm+Kz7/tXzjh3Ded3p1nWl8PyfXbummA27o7q7esmsn2qczVu+O8bkSKw/cOrHT/w8YOA6QNz9I30USvXqRar9Az24LkeA9kUyUyKMIqY6c9y9lM20Ky3GF01QjVf5dr/voF6zNeRZZlVZ4xhmy7lfEV0crUsWq5YRR/aOcHsnsVEREmWSGdTpLvSJNJJMrkMY2tH6R/po2egh97BbrRMgsJ0Cc/xWHnGclKZFM99zbN4zqufyZa7tnL1N69lT+yzJktiUigdKNKVy5CfKXLLr+6iO9OFbTt0dXeh62LVtnLlMtSEyvRMHtf32L5pN+N7J3n+K5/J6rNWcXDnIe6/eROveMMLQCLmdkTIinhOHdNBiY1hA89HSyaEHswCuxD/N8jslKdLR5VjmuUWtXztUQc7jWqTyQMzJJIauZ4uqqUapm6RWGICmdu0CwAtqeCHEaoskeoWwU5rrgoXQdhoIklg+xHqgtc3AjQinHgV7LketWKNVDpJuVATBrOqih2KEhWqCEYsy6ZZO6zebZm26LSL29e7+3I06q2jSllRFFGZqyLJMlpCxWyZcfnl8LAfeB6BF5DIJPAWkGPtuol/CqwqFiKR1LB0C8dyHnGwE4YhRtNom3+6toesznPAFmcV5ktZrZpOMW7fHls/xsHNB9CPIcb6SOB7PmEYtcvViiY0kubLmI8Wjm61M6XLzltNYfcUxX3Txw2iTiaz41ru4SBYkrBNh56ltvNEoC+pciwVUmNk+Ujbg6wWdz6mejNUS3VGli9dXnu8ohPsPAbwXI/8dIlkKrEgq7OK4WWDJBMa9UqDkAg/DFAUGVmRqZUblOYqDI8NLtqXYzlMHZqjXKgwMTnH9PgcXT1ZXvy65/Ltz/0AgKFlgzx03zaseALXZBU/ipjYNcmHP/8+PvhH/8jeHQdIqwkkSaLZbBG4gsCmaCrJZIJqqco9Nz+AbTsoksyZa1ejJTQevOUh0R6eUTnvOeezbMUwxc2HsPMNKnkh3jU4NkApXyYtayRklYFMF2VLx9Itdm/aS80QA44iydTLDexY+2GgvwfdNPHDkJ5kGt11KOerPLxxJ8996eUEpkcUkzgHB/v53de/lJtvuoepmTy79xzgvPPXc/aGM7jvtoc4sPMQB4BVw72UDBvLsJElieect5rll51Hw3LZu2kfpakSXhgQRIcHTD8KiKKIHQ/uZGzdKJViLebNROSnCvSHEUiSyMhVm7QaBkQR993wAJtu20IYhKTSSVzbpX+4lxe98fn0Lx/gyv+8hvGdE7zibS/lyv+5nsByeN5rn43VNJibLFIr17F1GzkEs2VhLph8Djx8YNFzECgR/YN99PZ10zfcy4ozlzO2epT+0T5Glg+zbO1YO9iZV9SOIkiEYjC+8Re384Y3v4pUKoGiyu00fKPeYtnYMNMzedK5FGklidEyufZHNzG8TJDSt96/k+e+7HIsyxZaSn6ArKj4XoDv+SiqGFYCL0CRxWqy2dCxTFsYZD5KrR3f8ylMlhZZLYAo/RRnSqw5d/WSv1ct1dE0tW3guhBGy2Ry/wxEEV09XTiVFr7t0qy36D3Cr8yst2hOimdcTiWx3IhcSiLTlaABGKW6mAQNkT2s2SHZxAIeQxShyRFV3aTVaFHKV2mUGixbM0oylRTlkEoTQiEZICEJ0rLroddasGa0HSzPl+3E9VaIQtGptDDYMZomzVoTNaFiBSGO42G3rEVl49Cdb92OcBe0Pbu6SeCeOosHEEF8GIY4lkPXEvfieHDjtmmR9RQdnqqiEPlL2yBoiQSTuydFAJLUGFo9zMHNB2idILMThiGlqRLDq4aPGWAc6YU1n9nx3Ecf7PiuL/g0jocfhAycsQxZkbHqBka5SdfQUuEJEEYnFB90LGeBn5+G1bKWDKD0uo7eMkgmEyiqSqveRG/o9A724poORkWM24PrllGcKdE70I0sPXGYME+cI30Cwvd9SjNlCrNF9IaOqZvs3LwXJFi9YRUDI/0YLQPTsLFMC8cWEfjgsCB/LSUu2CjWmTg0w/j4DHfetgmA3/3jV2FbbjubkM6luerHvwIgqSUYiUXl7r5lIxvOW8e7PvhWACxflLhq5QahJNo2552qp8ZnObR/SpCdu3rQqy32bBJ+VC3XolJrsOW+bdQrTbyG2dbXkWWJXF8XsqqQiImemqKQ7cuCIlE3WkRRRG9vN4lEQgwScVkj1y1UnMMoJKWJskpExP13b8EwLIJABCHzBYZEQuO5z34al15yPrIss2P7Pu646wH+9CNv58KLNiBJMFmsYxk2qUySF156FuesHEYzbbp6u7j4RRex9oI1eIEYLFRZERyIKCKMQg7snmAyrpknEhrVUl1MHgsGCdUPmdw/xS0/u4ONNz9EGIScc8kGLn7eU0WmrEvo86QzKfqG+wAozrf4ShJ/+P438eo/eSUvevPzmWmWqYQ6v/Oe1/Dyt7+E57zuWVz8wqey/qIzGVk9Qv9oP8l0Et21mCjn2bZ3LxO7J9lyx1Z++a3r+fo/fZv/9+4v8JG3fpLbfnmXOG5FRZUVRuNVWFYRE934+CQP3rqZVrVFBFixqNzU5ByhJybSRqPFy97wAs556gZkWaY4V8aPQhzH5ZZr7uLQvhlqlXq73OG7osVV0WSiSHTKlAoV7r15M/t2T9GoNEGSj6m8fCK0qk1qxWo71Z/JiWsbRRHVYg1jic6USqHKB9/4D/z5Kz/I/3zpx4ucv23LYWr/NI7t0NPfTRiGVMp1Ikmikq8u6hqLoojCpu24tvjuUFVpxPo52bQYRs1SQ5RoYj2jUsshl1IX7ENwdopTgqSvahqJhCbMb6MIJMHHCcIoJiwLJWNZkakURAnBc31sy4ZQTLLzE10qlaBRWazWXivV8V1fEHYNC8/1juKWeJaLpMgEnk+woFPK1S1CLzglpazAD5ibLtCoNZEVGf0EHUGWZR/VsedYDm5cOpm3NJg9lGfiwMySnVDpbIq5uBNrZM0o6ZzImBv142d2bvvx7XzmHZ/jvmvvP+Y2ru0uEhhVY/7Vb6J47DsunuVgOx6z1Rb3X3Mf/bE/YXH/0i3opm6yZ+uBYxLPYV5t32sHO2pSxbXdJa9Zs6ZjmzaB7YIvREaFaGVAbVqUb7MDOXqHerENm8JUid0bd5+UWKTverQKtdPiZTePTrDzW4RtuRzaO8nebQdId6W56wbxAq1cu5yRZYNkMmlqxTq2ZVOrNpGQAImBeEW5a8tiB/TA85k5lKdUrFMu1bFth8HhPp5y8Tno9VZbX+K6q27GcVwkSWLdqhVccJEgl05NzTG+Z5LXvuXlPOsllwFguDbVch0FCVlRkGVhIbArJkife+EGUmqCZqXJptu3YPkuYZwFKReq/PJHv2bX5DSVeDXbO9SL0TTRFKVNdgXR2m0FLmEUIUsSaiAxPDZIFP8HkEmn2iuF+WyElhCkuhuvuQPPPVqxV01orD9jNb//h6+mr7+HUrHKJz/0RdJZlTc963zOXDXM4Eg/lz7vEuRYAE6L2zt93yeZTrZFFjPJFElVBFleGGAZNof2TJJKJ2NyeQNlwfdHUUSt3OCBGzZSyVfRkhqvfsfLecmbX9Ru3czEg6wkSSyL5dUP7jgEwOBoP5qmYjYNDu6bQG8Z6C0Dx3EZXjnM2vPXcP4zz+MZr7qc577+2bz4D17In3/u3SR6RXnFDwOe/bpn8IxXXMaGi85kdM2IKLXFZGtZklAkoVlUmauiSDIpNUEqPseHt+zg7uvu5yffvKZ9TglNQ0Emm80QRRHlcpWLnnE+L3vD8xldIVS1QyIeemA7B/ZNUCrWaMQy/L7nCV8dWSH0Q6Ig5FDsXdWo6zTquuioeZTlkepsFbNhtlfyPf1iteu7HpZuUisd3YJ+x7X3CJ6I53Pt93/NX73uw/z8m7+kVW8xtX+GZl1vk7PNlikIrEGIfoQVQ7NQxZ6ewYtbyG0/oFwXg3w2JSEpMqEfoM8UUKOQMIJiVSezKLMDKVXwo7LdWTRNIQxCIS4YhjiGg227+EGA73lEYUgUilJooyyUzD3HxbVcJBm0hEoQiO6uVCaFqR8WGLRNYXCrJTUcS+zX84NFlioL+Tq+4xEu0PMJHA/Pck+JR5ZliTHm0IEp8rMlirOlY2b3wjBk4sAM5eJit+35lnVJkggD8W7OTOQ5tHuCyhLGuVpCoz4nfj66bpRk/O4fafB7JGb2i8zd9HGIwbZpL9l59Jto7XiOj+e42PF12ffAHgbWifHiWOKC9XKTcqlOs9JcpLm2EPOdWO3MjrY0STkMQ6qFGmEU4FsudsskRKJea2G2rHaw07diUCziclkm900xMz53Uqrurm6hF2qEj7Em1kJ0gp3fIlzHozBbYvZQHsu02HjnFgDWnrOKvsEePM+jVqrTqLViB+kITdPoGxQZgIVO4SAG4wN7DmGaNo24q+GyZz+F4kyJarEqVnC+zUQs959SE6xctYzVZ64kHU9wP/z6lUiSxP/5wFtRJJmIiNnZArKqIEtiAt+z86BwTF85woo1y0AC23axLBsnzoL0DfbQ099NFEXMtRrsqxRwA490VxrPFV4/C5OktXoT2xZEyd5sjsAL0ItNZEW8hLIkVFETcQkkijkg8w/ozq37GN83KcJB6chVVcjIsiH+7H1v5eLLzsfzfH5xw93cunM/T7/0HNafv44gCLA0IY6leh6S7+O7vtAg8cU5ja4YYnks7Dgf0O14YBc77t1JrVxHViTkmG9hOh637xhn/MAcYRjRP9rHi9/0fFauW04tX20PJuns4ZLB6rNWApCPlZ2XrRrFsV0M3Wb/7kPt7SrlowfvdDaFawveTz5/WGem3Gjw/Dc8lzf8+et481//Hs99w3PwYp7WeRefhSRJKIrM+gvXMjDajyzJ9KZEBq3pGDQcA8t14nsg0a2mKY4XkFxxnvfduJH7r3+AqZ1TjA4PsuaMFYJIHsHevZPc+uv72XLfDmzTxnf9tiheEIRYhkUhFqS0LUcEA40Wnu094u4Nz3GZG59r831kWW77uZktC1mSKR+hKh1FEbf/4h4ALn7m+azesBLLsPjJf17F+9/wD9xy9R3keruQZYkwjGhUm9iWI1Rmw4hapR5/t8fM/Q8jhSG2E7tsd2cxnJAoitCkiGSP8E1rxH5nTSfAt2wkScJfcK6SJHg7IFr3kUSwZhs2hm6iqDJRGOE6nnAfJySKBE+nVW+JYNH1iEIR6EeB6EZql7Ji8naj2sKxhAqy63rIslBv1utGO9ic5+vIqoJnOURHcKnshn5KOrKEhlRAd3cXpmWzb/c4OzfvoVlvHbXSty0H07BFFnFBQGS0zPaE7boejWods2lgtgzmDs0uSXpvxWKCw2tG2hpUJxIVnA8G6ws6/hYiDENc223zWOYhSRLOEuKPJwvXcghdHy8+Ts/xiOKS2LHEBfWGjtk0MXTzmEHEfOfY/LWTZEk8X0cEO2bLEt54koSEhK3bwr/N82hUG9Ti97hvhaBVpLMpaoUaheniSfmouYaN0zJOuVDlI0En2PktwnVcGg0dz/e587p7Bbl1pJ9lK0aEaulUSXhP2TaZXJoojFA1id647j55YGaRc/bsoTz5mVJ7kOjty6EmFWbGZ6kW6li+i+mKATapJtBklaGRAboHu+lJiQzDfbc8yPjeSarFOkNZsaJ1PI9D+ybRWyZ7dhzEsR2SqQQXPeMCAJSEghf4mJ54mbu6syQzSVaeMcZTzzqDnpQoJ3hhwP59h6iWa0hE7Y4s03Oot8QgMzDUx7rz15BMJ8SE1+7gkqjVmiTiF9yPxEtvOS4rV4h07l23PIgXt+XOQ5IkwjBACiMUReF9f/tOXvv6FyNJEtun5vjONbdQLFUJggA1lcSKQ7BEPDkHXtAO4FKpFGvWrojPJTZpdFy2372DrbdupTxZIiFJTJRqXP/QXvJ1HVmWWLFhOWc9bQOO41GI1X3nFZUX2kz0DfWSyiRFiQIYWz2CYzvozVY7QAWWXKkiQbYnw+6H9+EvKC08vHFH+++pTJI7r7sHIhhbPdq+TolUgoHRfl76lheSSCXo0jKoqkoQhTiB1yYXq7JCSIQXBmiKGMxbpkk1X2PmwCwTOydozNRIKkIzBwTH54Yrb+NzH/x3rv/BTezevJ+DOw5RnC4xvne6zS2JImK/LItmvfmIeTvVfI1mtUUUt3QnM8l2GctsGqRzaUqz5UUTzp6H91OYLqIlVJ76tHP4y0+9m7/45P9hYKQfvWlw29V38W9/9zU2370Vo2mg1w1S6SS2ZaOqiuiqK9bYcc82qIhAyo2DQDWtISsK5nzw0yOC2lYseDhT0snGOiwNyzsc8MRaO0QiE6DICo1qC0sXatyKIqPIMq7r4fkBUkSbo9KoNHBtVwjqhaL1eZ6oDJBKJ2mUGziWQ7VQJZ1JYuqi/JtKJdq8HjsuWc7zdUI/WMTXmYfdME7JSty2bJCEXcjAYC+ZdIr8dJFdD+9lfM/EogyaZdq4rofeNKnHgUcYhJgtq01OblVbtKp6nPmKKOcPZxcXohmLTXb1d2OaonTmHcPaZR7zBrP1JbKEIMj1vu8fldlRNeVRG4JGUYSjW8IwdUEwUI0DruZcdZGy8vzvNGstbNcVmkPHCEoDLyCKwjaZGkBWZewjSk9G08CMZRVkJKIgQI6/R28YVOPMTn+s+RNFIZZu06obGCcoY0VRJGQNTl8FC+gEO79VBEEIgUhFP3SvsIZYf/46cj1d2IbD3EQeN+a5aIpCGEVEkUR3TxeZbJowDNl018OEoeA/7Hl4D9VyjVbs/XLWuWvRGxaFQpndW/eiu+LnG849A1VWSKkaiWQCLZUgl8uSiXkw//OVn3BgxzgpLUFXUkwYk4dmOXRgilpVvOQrVo1RmatgNk0838cJPIJImBiOrRnFd33qlSa9aoqzhkbbE2AYhFQqDWZmS9T0Fl4QMN0QKelkMkEqnaTZ1Old3oekyu12dUWWadV0tDiz43o+Wjyg9GlpkqkEestg5/Z9izJGkiyJ1bMv/Glc1+f5L34Gf/iCy8mlk1TrTe6/fzNzc0UUVWG+Yq+aNo7t4th2u4zV1dVFUomDrTAQrZpAQIjVshh/eJz77t7BvXum8IKA/q40z7poPUMrhpEQPlr1apNMLoMZdzeluw5ndiRJYmTVSDtrtGzVKI7pUClUKS7I1lRKi1P481BUhe0PL8725WdLYjBxfe799UamD8yiKAqDYwNtTRNZFbokSKIrpeEYqMQZtaTMhZeeG9+fZLtrIxFfBzfwWPuUtay/+EyGVw/T1ddFJpNCkWRUSSYRlyobjRabNm7n7ls2ctNPbuX679/Y1heKoogoirj/ls3semgPD966mW337iA/UTgpryzf9SlPFQmCoM2ZSaZFJyCIVWk2l8VqmYvUlG+7WvCW1p+7lmw2SXGywNiaZbz1r3+PV/7hS8j1dlErN/jp167h65/8LlMHZ0imEgRBgK2bzBycY/OdD2ONTyJL4KuacHEH5JjfZjjzz6/4/7z2Vanp0JeLAyDLw4p5UBGgSlHsKxbihwH1ahNFlQkCwYeSFClWPnaJwgjfFxNPrVhHbxptvoisyoTR4W6kVDaNaVjC3y4mIhtN0WUpyzKyqmAapiA7A57lCL6O6xMs0L+Zz86Z1dYpKWMZLRNVPZwJ0RIa2WyGTFeGuZkiu7bu5dD+SSxTlOEkWUJTFUpzZcIwFNk210VLJPA9j0q+iueLLIiW0DANk+JUaRFnxjFszPicXN/D80Ok+Nk+Xvv5vLXEvNXLkZgvC6lHBTuCC/No9J5818d3/DY5eR7jDx+kOxbvK8fO7fNwHQ+9rgvl97p+zMXDfNv5QmiaICm39buCkHqlgR9zIpGEmrcsSbiui1HX8eKOru5RUXUw6gaOaSPLnNBvLPSCU6Iw/Zui0431W8TMtkMkmi6TjQlMwyKZTjK2ahRVUZg6MCuIsGEgXL7jAcYPArpzaQZHB5g8MM2Oh3Zz8bMuxGiZTOybYS5O1Q8M9JDryuL4PvW6xf0bhSv1qjXLcQ3x8KVUjX1b9pDqStA/2o/eMrB8j413bCalicmsN5fD8Ty80KcVG1MODfWTSGjoDRPCCNO0251dQ8sGhUy6qiBFENk+uuOgygo9fTm6B7sZ3zdFEATkW3VmGpV2pimZStBqGbheQDKlkRnIUDXiFm1JEt0M6TjYcT26urPUak1s3eLMM1axY8d+9u+fZGCoj9FlA6QzGeGjJcu4jks6TGPbDqZhsXqwjz998TP54cbtzORLPLjxYWq1Ohedt54RTUGzHXxkmvE5a6pKOiN0QLKpFIZtoyQUQitEy6XIJJOU5yrtgaMnk+LclSN0qQqmDHrNwHWE908QBFjxSmxhZgeEA/qOrYKLNbZ6lGqpxoF9k4u2qS5RxprH+AHhWTWQ7aJi6ARBwH23baK3t5tbrroTgA0XrmNs5Qg7Zqv4YUChWuIC9WzuvnkjWzZuJ4wiUkoS23dotlrUq3VAqPbaePT1dhP5ISWzQRiFbH1gB6Njw/SN9rHynJVYusWWu7fhOh5rzljBwEgvO7cfpFFvYfseARFdyVS7c24ehalSu6y18ZbN7Z939XbRP9LHwEg/g2MDvOB3n8vQ8sNdiGZdp1lpIqtye1WeyiRJxSVCS7dQEyphEFGeqzCyahjbctoq5Rc/83ySySSVfJWZiTwr16/gWS+7nEuffzH33biR239xN9Vijdt+cTe9PV0sHxtCy6aFfg4Sy9KxUKaSbZtOSqqCZdmUWzDckyCTEudpGSKwKTdMzhiI3eZtj2w6QQ4gikjJUPd8QfZvGpi6KVbftoPjuMgRSIqCbTliwRSGSJpCs9ZCSajtjJ3v+BAdNm+cd1OfGZ8h3ZXGDwKMlomWSOBaLql0gmZNp1yoMrx8WJj0qqooVxkiOJc4rHdlN/TfuCMr8ANMw1pU9tESKqZuMTg2wNDogNDTOjRHKV/BLFbpCn3S3cM06k2atZbQnPICtIRKKVZgj8KIwPNJZYQRb7VUpVbqZ2hsECSJ6pxYMKS7M8xOFUhlkqgJFc9y0es6/cuOlnF2HVFOBOGp5douiVRi0TZ+7HC+VGbHsVw81yd5xO+cCL7jEXgerbjbaV6QszZb4ewL1tKcq1LcN82Kp57R/h3btDFb4rq6llDiTi+hoO3F3M0oFAsOWZFRkyqOKYxBk+lkm+vl+z6yLBMFgVAMDUX20TXF85Ub6W2fd6vWwnM88f0n4OD5jkdp9wRmuUF2qJuhs5bumvxto5PZ+S2iuG8W1fCZGBc11+WrRrBNm1qphl5vkelK49iiwyDwA2zDaT84w8vEYD9zaI6ZQ3l2b97L9MRcLKwls2zZIL7v4VoOmzZtxfdF0PScF1xOsc1dkNi9b4q7f3EP/aN9aIrKipiTsn3zHgCRRdKS7bJErruLgcFeXNvFaBrUC/V2+SqhqKiaQq1YF87WiorEYfNPLZ1AkmV6esTqP4hEicj2XdLJBOlUAlVWAZGWT6QSbe3BeTKzbYrB1QsDkilBxDUcmyElxcCQWFXs3L6f4kyZRr1FGIaoiozv+EKLo6HjWA5yGJJNJrjw3LNYtWIMSYID+yf49W33k9cNJCDhuJiWCEoy6TTNmigp5roE/8KLuTy1Up3ibFnopKgKKU3D8yMeOjDHHVsOcmjzOIWJAooqvKD05mFvnoWcHYDhlUNtFduB4V5qpQaTE2LVNjwiJsdqub7k81Qp1zDijNGZg0Mk4lLTXTc9wLb7dmAZFrneLtadtwYkIbg32yoxWS1yxU9+yaZ7thKGEd25LoayPfR1izLm1LQQXmvqOtVGg9lSkeXrlrFi1TIAnMCjVqxzcOs4W27fSjVfozvmqMzNFFm3YRVveMeruPjpF5DKJPF8n5qho3s2/aN9jMap7/7hXs6+eAPDK4boG+ptTwp6XWdyzxSb73iYG394C//4h5/k6m/8UnA9/IB6oYrrekiS3HZtTqaT7Wvrez5uPHCXZ8t4rsd9N27EsRx6B3sYXTHIvl0HKc2V0Vtmu8SYSGo899XP5G3vfzPnPe0cFEWm3tDZsWucB+7cgmO5dAcWMhGBlmiXrOSESn6qQH6uQqUh3o3+7iSyKhNFUKxY+F5Ad0acX8v2sWM+jFg4B0wfnKU0V8a1XNHxJBGXaSQc12/z5BzbAVkIELZqLYyGSRRE+J7P7u37aNWNRfynKILCTEkYseomjuORTImFjaKoSLIgtpotva1kHbg+wbwn04K0qdOy2pmsRwvHcfFdD1WRcQ2hgKzGOjjzY106k2J42SBhENLfrNFt6SiWhYREqVDBMkXx2WgZ1MsN0bTgeGLhEUmEQYRjuKIjL1681GZF91q2PyeCyTBEiS0SmsfI2rQqi0thS5Wyjmw7b1SFOrWqxYagj4J879kuUSS1eULJlEbvMjHWOXH2pbRvcWZHb4iFjqIqOK6HdQyPLMdyRUa7oVOJzWpVTRU+XPHCwWgauLbwLVMlBc9xCYOQyPMJwgCrIjJhmcHDOlbVfDW2nJAwm8ZxM7SB61GfLFDZP4OeP/ZC7reNTmbnt4hMX5aGbaGbFrIis+KM5aL7SnfoH+rFMAwgRJJlVCegJxKrt/5BYZr54F1bmD40x+S+aQ7sOMBU3Eo5OjoQK9M6bNuzj3q9hSxJ9CSzbY5POpFsl4gaLbNdEurt6mJalqhVGyiZboa6M8iKTFcijZZNMLhsAFXRCHwb1/XRW2ab8Nqbyor2eFkiCkFzIyI1ohn7YfUMduPZQv9CgjbxF6DQqDKWHiahaeIlU8UgNx9QJNSEON55M8XQb3N+dNchMjxWLx+lXm3SahmUK3W0VAJNU9FUBS+Wj6/XWgQtixQxT8QPGR0cZNXaFTy48WGajRbfvuleXnPJeSxbNoIR8xfSySSO5dOdy5BJZ4AKumXRncgQhhFhFDG2ZoSnjAyQDCO2TpewTBfddkUWp2XRKjZREiqNwmEF3tQRwU6mO9v+u+/61EpVpuNgZ9nIMMVCBV03cR13kX0EwM6HRUZIkiSW9fQwXa8x06gzMzVHa07c98tecAlyJONHAaVKFd0VwZzlOmiyxiVPu5DV61bywPUPkCFJDag3GmiSRoRQ7LUch4e27ODsC85kcmIGrSvJyNgI5Zkylm4xuWdK+DghJuSDeybZ8NQNrNuwkpVnLGf31n3s2XYAPww5ODHD6OiAKN9oKs95zTNpVVsoSZVzLj2Lru4uqoUqlXyVgzsPsenWzRSnSvzyO7/inuvv59Vvfxl93TlAaLR47uHMjqqpqAkV3/UxGwaZniytSoNmpcWtV4ss1yXPeSqTB2fZumUfyWyasTVjTOybREsl6O7JCn8q2+H8y86hJ5I5sG+SYrNFpVTn+h/exLlnjvCiy9eTWjeKMy6yUgFCPiAKQxw3wPNDNFUm06Wh1x1qNQspCtudWHXdpjetQV9acHaiEL2mI6sy6UwK13XbnIogEHYRolsACvky6e6MOFdNpV5t0N2XQ2+ZQuk5jFgbHF4pu46HY3t4jlisREGAoijYtkvS80kmk+gNnWa5SUZVCH2f0LLbbuyiAQCI5tvPfdFhpzy6dbFjOXheIEoilk2qK42qKZhxmW5euRsgZRqkYv5YYFp0DQ1SKzewWzaSIlPN1whCUf4zDLHoC8NIZJ91E98PqBSEwfB8ZifZlSKZSgqdrXgMPBb5uHmE4GC9VG/7A87Dc712QOg6QhRSkiS6+3JtO5dHCtdykFWpXUJLphKMrh+jPlulNFNBAqoTBXzHQ415S7VyE0mS0DSNIAiXtOGYJ1MrqoLVNNrHNi/YOt8oUCs3kBRZ+BgGPpEfEEWCQK9GCcw446RmkyKgsx1adZ10l2iaMA1byEn0ZI86BgDXdPDiBUay+2jl5scKnczObxGZ3i6mGyKS7R/sxTGd2NzPAYQdghpr0fT7CsOJNIFuYxsWy1aOIMkSrYbOzFSePdsO4DoeqXRSdHL5Prv3jTM7WxQvWzKLIivs3SHE55Ky0g5STMfFiV8ks2owMiZe4KZjYtkuiqYgSzKKJDIkwknYxzHcw6TkZBpNUfFMB8/zUDWZlKRg+8JfC0l0yPiej+06lBp1AIazPfSnu4iA2UKJIArxgxDfCbBMqx2QpTQNVZLbredhFBGPe7RcBwlIOxFnnCEIxAfHpwnCkGbTwHWFmJ0sSZiGiTHfCi1LhFEgRMzSaV7+yuczPDyA5wf8/P6t3Lt1Ny1drFpSiSSyIngQuVjF2vFc5rUGlYRC/0g/SVnG8jymahWswETRAlrYNHyTmqVTadTZt3scw7UxPYerv3Mdt117Nw/esZmDeyeYnczHnJ2Ih+7ayv49k+gtQ+jYTJVj+QGYHJ85auDcvlmo9yYVle5UinWDIvtnGCae67F87TJWnjlGGAki6nhBZBTn2/h116Cnv5tUNkmuP0dWTZFMJAjDUKhWc7jTzTAt9sTPUr3eZN35a7j0pU9j7flrRGo/Orzttof2MrFvCkmW6e7NMTjQS08yQ0rTRLfeXJmGbZDPCw6GosooikR5TqwOV65fwaqzVzK0Yohzn3Eur3v3q+kf6aNaqPHfn/0hP/vWLynnKxCyKLMDImAGsdJNJjV8L2Dv1v3s3XoAJDjrojPZt3sC3/VIagrlQoW92w+w6Y7NlObKFGaKVPJV9FqLRCRxxuAgT125gv5YjXnn/gL/fsXdXPfL+8hPCvK5FwaosUyD5/o0TXFMuZw4JrPlkEsJTo/jBczOlNm+e0ZMzFFEJhlbTSSTRGEkykZBhG05+IHgSqiqjCSDbViUizXKxSr52SIzB+eoFGqUC2V816dZa9GKOTiO7WC2TFLpBHrTpFnTkRQFx3HQdUOMNwkh/pifFjYhnuUSGlZb0iFcUHYMXQ/PdkRZ41HCjg1pjxSwkxCdV21EEYna4VV/aDskkgkcxyE/WxTdai2DTDaNazu4jpjEoyggAvSWiaLIGE1DEMtn5nVhukhl0pi63Ta/rB8jc9o8guTcKB29nW06QhcJUUoydAvXmc9qPHJD0DAMsVsWiqa2fbuSmRRjcefm7L5pkt1pwYWMfb5cx8NoGiiqjKopIEk0a62j/MSEurTI/thHkKcVVcFsmZi6iWVYRMQdWrG0QhSKTkMZqa2sreUEB6xZbYnFWEqMHYHvH1tKIAjxLBs/zvwkc6cv2HncZ3ZarRYf+chHuPLKKykWi1x00UV8+ctf5tJLhfNyFEV87GMf4+tf/zq1Wo3LL7+cf//3f+e88847zUcOlu9RMUXE3TPQjdEyGRzqw/cDquWmcD+WJWzPZ6Zao26ajK0apVHXGV67jMGRfkpzFQ7sONguTa05cwWu7VKq1piYFhmBlWOj2HUbLamRnxWrT01WcXwxmEQAjbgbwXJZeeEK8tNF3MDHME2RgnVEZ1IQiCDENUWJJyJCUWS6UimIQFM0bNdDVRUystbW10mkE+hNg2ZDp1gV4lG5ZJqVPUKXwa/M0rRNpmfzDPT2AVGbaC3Fq3bTt5Elpc0Z8OOygeN5+EGA6iqMDveT7y7TbBoU5sqsXDNGq6WTzghuTKVUR2uaDMgQyDKeL0pmelMnnU3yopc+mwfu3syB8UkeOjjVnuBTCZElatZb1Ip1FEkmiELUlIpne7iOR2muzEAuw7Wbdy7y0joeGhMG+yemuPfezUd99s1/u6Kt3BxFEZXG4cH+6h/eQC6XJZlMkEwJ4nBhTjwDKS3B9tnZRROI6TmMnTnG+L4pDu2dYMvmnbhxl9kFy1ZRMhrMNGrcdvs9vPxlL2BgbIBWtUUukcFxXYIoQEONJQokLN+hFVuIhGFIqVhhdNkwy9eNkevpYuemXYRWSBhFOI7LA7c8RN9wL2dftIHSVBlFltmwYS1j65Zx1y0b0VsmDcPgu//2Y5770qezZv0KLMOmWqiSSCWY3D8jzB0jodX08R98hGu+8Utu/untTB+cY2Y8z5qzVmLH5Zb5YCeVTdGq6xgNUxBbkxp3XCvazdedvYZysUYpX0GSZQI/pHewC8f2mJ2Yo1puEPo+qUSS4bFB/HjyTWsaZ65axoUvPJfbH9jPvskyOzbvZZcssXagnxVjQyQySWTdwvc8mqbHQHeSbE5kKSQ3oLtb/L2q2zSrLRoRHJyucObqQbIpDeT5FXbYbluOiFAVhVAKAUksQBQFRZJIJBPYhuhkcyybYrFCMpmi1dIpzJZYvWEVrbqO7/pkujKipKOb8YLJIAgibNshlUmiqTKVfI0Vy4eJHJdQN9rBjiFDdzT/BoJdE4agyqNUBjb1xeTkeahJFVu3xNdIQL2BGix4p+IMXjKRYGJqilxPF7lcRowTphAdFF1rEYomuFx206J7qJdKvkItzuwMLB8kmU7g2k6boNwoHl2egsPk5HksVcZyLKftdm60TDzHa5esFUXGOgFZ90j4juAAJdIJjMbhpoaekV5SuTR2yyLR143TtCjun2HkbPEO2IaNoqqEio8aKRhNE8e0SXUdDiYCzyf0AyJVEYrTicUkcduwY9kTITzo6BYpJGRVhdBGkiBoiS4qJaGS7s3SqDTQq3pbIsHSbZJxWXIpBK6Hb3uHg53TmNl53Ac773rXu9i+fTvf+973GBsb43/+53948YtfzM6dO1m+fDmf/exn+cIXvsB3vvMdNmzYwCc/+Ule8pKXsGfPHnK53Gk99oce3EEYhgxku1AV4QcVhhHpbIpKoUZXbxdB6GMZFttnZvDDkEQujdyTxrIslq0YoTRXYeLANGEYkcmm6OrKsHfqIFN5EeUvGxlkeHiAyfoMalIl1EOhhHzEsYiuL1mUAiyP7myWpmFQKlXpH+wRugpugCwr+L6P3bLaLdlaMkEogRIJbo0vh6QkBUWWadgi6lc1FUu3KBRKos1bVjizfxQ/DEiqGmcOjLKrMoNlO1TqNXpz3e3uCU1RaNomtu+iSCLLFEQhpmmhqiq+71O2TUazOaKWw4qxEXY2D5LPlznzrDXoDQNDN6hXW1QKVZYnEiDLuIHInkUIfxijYaEoKmvXrOC80QF+/eA2bM8niiJszyWstNqtpwlFFaRsVUysjuVQLdf45f5xXD8gqWk8ZdUYCVVhzjAJEhqe68YrJgu9oaMlNSElbzn4vuh0CKNwkfP0fLlLmBoehmlYi7ocDpusSpiuy/a5uQW/F+EELjf+4vb2PluuCEKTSoJD1WqcuZGoN5pcfeUNdKUygvsRTzDiuMRqLohCNFnFi7x2MLZtyy4a1SaJVIJGtUnDNIUooyyyN0RQKdS454b7kZBQZJl0T5ZMNs3FF5/Dpnu3Y7oOpXyFn/33tZx5zhpe8aYXs3frfhLpJNmuuFST1KgUajimw7Ne8jTWnrmC6390C5P7phnfPSl0gySpXf6Y5+0Ysb5MMpVgz8PCRHfdeWvYu20/URCR687gWA5GQyfXJ9SSC1PCN2lgdIDibBF5AT9ltDvF8uFufuflF/HLB8aZOThHq2lwoFRholpj+YoRMpqC74fUdIe1dLWDHQ2JVFzCKtbMdiCxefcsZ64aRJUlNEkikmV8TzwPrukITzlZ1AeFOKN4JhzbRUuJcoWWSuC4rvDRSiq4jYj8TIlGvUWz2iKZSZBIaNRKdUwjJp46HqlUQnQseh6aotJqtqhVW+QUiXBB63Ddj8gpC4Lo2qPX2gnDEL1lLmmhIIitfmwoqRLN5ZEAV5JJRCHEY4MkSVRLNWRJYnC4H0s30RsG81TqKApRZA3XF4TvnuFe6qVGu/TW1ZeLbSoipNizrVVr4Tpeu5V9Hq0lylgLITo+fVRVWJ40Kk2hY+QIbpmqxQHcI4Dv+IIzpSlt3ku6O4skSYxuWM6hTfsx4p/PiwvaloPvCQkOWZKRVbANG1O3FgU7vis69dQoOsqPTktoGE0dc9YmmU5QLlRxHY9MJoW0QDnVjz3tEj0Z0l1paoUa9XyNbHdGSHdEARLgHcPvznc9XNNu0xMSRzRsPJZ4XAc7lmXxs5/9jKuvvprnPve5AHz0ox/lqquu4qtf/Sqf+MQn+NKXvsTf//3f87u/+7sAfPe732VkZIQf/OAHvPvd715yv47j4DiHJ5Jm82iNhlOBfbsP4oYeEpDSVBw/AAShzo3JbKHr0ajrbXfqmUKZ3lGRwu+Ka6CmbqEpKstXjTI1lWdyLk9ERCqZ4Iy1q2jE6c954azu7iy+vjjSNlyXVELFtF1swyKXzNA0hFpvEAiFzXnSWuj5mPNCc7KMooCkSeBA5AuDwq5ESmg9xHyddFea2XwRN+bgLO8ZQJUVLN/DCUSJafnAEFPlIo7jUm+1SCXE6lyRZKw4CxVEgShlRaIzTY7fu7Kt05dIIwUBub4UkgS6bmIYlhC4qjQY3zOB5x8mNutxK+j84GQZBhERiUSCwb4cLzjvTK7fIlq5D0xN0ZVI05PM0tWdJYgCLN9FN0yWDQ5hGCaFsujGGu7uQo4S5JIpzls5wq5ilaamggzdvTkqsxXm3DzL1o4ytHoIo2myasNKUprGjVfeQbVSJ6VqaJLCjF4miqA7mYUIbN/BDlyyXRkuufwCFFXF8zy2bNxOoyG4WWsHBlBlmbplU2g18MMgzsApoj078NrBTVoV10KWZNJaCtOzsDwbRVJQZUW4IUsyYRQSRAG27y3IGB3uzalWG6IsckQ5wuHYafvCLfegKArplIYcyfSkMiT7MhTmyuzfdYj/+Odvs3LtGBdefi5rz15NfiIvdKeSCaYPzJBEone4l+e86unMjM+x8dbNGE0TP4p48I6HuehZFyxqPweYmcpjmUKzxvMD9IZBs9bCs10GRgYwDRvbPjyRqqqK57goboDsB0xUawznulghC/L23rzO0EAPXZpKWDHZN1ugZTtMTMyhqgrDg330xC3mmqYQEKEgkY27j4pVnbqt4wU+0WxEtWEyNNRFSpOxInBdV7jZc5gXI0sSISBHiNIXkeDheD6SIlOvNkmmEqiKgqJI1Mo1pg5OI0cyfYPdgIRlWjRrTSzTIZNJYdcNtKSEa3uoScEHKufLZLuzhAvmKcNx8FMJFEkiikCvNI4Z7Pi2gxx3Zi4F1xEZ0fLkLFd97zqe/qyn0h03GYjAwMZ1XDTXRrZtwghMy8cxbBJ9QnqjVqoR+oEw5/V8nPmONXle+FMSwYwvDI1tw8aIW8szvdl255ok0c7sWC1Rujky2Jnn7PQO91Iv1mkckdmZ78RKphPYpo3e0AX3xfFjg1UFz/NFs8gxrsmR8BwXpMPkZEWWSHaJd3bFeas5tGk/pekS/apC6cCcCCBrOmEgxk0vDJAkGc/3sY4ItES2JRLCqbZD0j7Mj1I0BaNp4fo+I6uGqRUqyFGELCt4ro3nB4QhBHo8D2QTQmzQsGnUmvSP9GG0DpvFHku7yLfcdsZUTWqPmvt1KvC45uz4vh8LYi0meabTae666y7Gx8fJ5/O89KUvbX+WTCZ53vOexz333HPM/X7605+mp6en/WflypWn/NijKMKLV81zrTp6Q3QAhWGAadqkUgkcy6FZ12ku8GvRLZtCscrBfRNtvZWIiFQ6QSTD1NQsEaLOn1aTuI6HFUffVhx4dHfn2nydZDzoGo5LV7zCspsWBCFdCXFd67UmkiZjeQ6VYoVaNZ5AowhZlQhDMGOxNimM8N2AjKRg+57QBiKi1qzTiIPGbCpNVyJFEIaUjCb5Vh3dtUn5Cmt7hkmqGmEYYjl2O5OwEPPTqRcGKJIYNEzDpmpaVHST+lSdTDyJ735wL3M7ZzDnmpQPFLEKLaYmyxycrZMvNYmcgMD2kCIwdRvLEJ0dLaBpOciShCaLa6S7FnWnxYq+brLJOGNgWUSy+CwCRntyrBsYgihiJk57pzSRffIcj2qpjhmX5xzHpTJXRa/pBL5PKpduCwLKkoQb+m37DDnOhsz7iTmmjduwGVs5wrr1q2jGoowpLcHla9Zw4fIVEEl0aWKyDyNB1Ozu7sJHPHcjXX0s7x7kgpGVPGvtmbzp4qcx1Ccmm0AJyKYFD2v+GvthwIqeAVJqAk1RyWUz9PYczo5KsiS0khIaw91d9GczyLHi6pLvACJgbRk2blz2W7t2Ba984wvp6+8m8AMO7Zvixivv4MHbNzN1cJbZiTkqhQr7tggPOddxiaKIodEBnv3yy1FiIm+j0uS2a+5mdqogzDGbJkEQsmuzIHH39HfTqrdwLRezaVEp1HEdh9APadVbmLrgeARx9q9VaVAxDHTHodTSsQyPctOiarioEnheyFBXhuesX8u5Z60mmRSZktl8mQcf3s+2fXNEUYQviWdZix/pct2kYeuYno3hWmzZMwsRZBIqUii6ZaIoEmrmseS/JAzaxH0KAuEn5npCEM9xMXWR8ayUauimhWO6HNgxThCKxZTnelimTaOmi6yJ76PXdWzbRTctLMMinU5QKzUIWgZB/F1eFFEu1TEdr31HjUrzmO7nVq2FZ9pLfgaCQ+Q5Lg/8+l4OzZS48/ZNh5+luL3atT3ICy5UNZKRdUtYjVgeZtMgP1Ui292F5wUYuolpOni2ixRJBF6IY9miqywEx/VpVBrU444fNZNsc2gUVSGKT8oxnLYO1kLMl7GG4rb02hFEZqGxIwjfpi467sIgwvO8dhv2I+3IcnQbWZXbZTdNUVBjWZCRM8eQFBmjboCm4NsulUN5jFiqgCiKy2CCb2ccoR8kzl3CdcUccaQJqm3Z4h6ZNq1aC01TCaNIeAA2dELfJ4i7Y6WMiusITy3PFcGdbbt4tpA/cKyjy3dRFOGaNsF8Bj+TagecpwOP68xOLpfjGc94Bp/4xCc455xzGBkZ4YorruD+++9n/fr15ONSzsjIyKLfGxkZYWJi4pj7/fCHP8z73//+9r+bzeYpD3gkSeJVb3gJ3/7KFURE7N0/zroNa1DrKpqqksyksJoGpmFSbyxOn9aqTXJ93TQrh3+eTCcwDZsoiEgqIkJ3XI9yuYat223uBEA2lWQuzhSN9fUwXqzQdF36MyJTFPkhRNCVSGN4Do7t4vvC+ftILqLn+AReKAZgTZQ3IiUiqySoxW3bXuBTytcB4S+VSiRRZJm6bVK3RTmlbhskFJXuZJqV3UNMNoptMT/X95BlmaSitcULQUy+XckUlu9gui524JFRE4SR0HExPIemZdCfOtwF4DoW043FK5wUClFDXBuz5mDNNolkib35OSRJIpdMk1BUSkYDw3W4fedOlvcMtHk74xNC3ViVFVYPDDBdaWJ6LoEek6tVBVyPVCaF57rtAdZ1XBzXJZlKotcN9IbZDmwVRcFwxUTR1ZVF8iVkWaJLS9F0DLwgoHCgQOlgEUcJ2kTWke4ckiSxv1jG9vxFgWImlwYNPN9HUzX6Ut3IshCUc/2QpKpy4dozuFN/GMuyqQcyaTXZJoVHREhSxEA2R90yUWWN5WtGae3YI56PIMDzfZ6yagXPXi86gK7buotS3URGJp1NoXiADKvPXYnr+lSmi9TqgvvjBQF7tx1ggyKx/tx12I7D3u0HsQyLu2/cSFcuSy6XxtJt5DDijHPWCqkFRcZ3RVeQKsskEho9A93kZ0qU40miUqpRL9SYPih4bGpSRVakRcTMWrlBV4/wpKrXW4SRCDod12FZOocevz9u4KPrDtv2l7EjCTWXxXd9FCUhygujA/QO9HDowAyFcg3Ldvn5jdu4+6FDrB7uZ1BN48dig7UFLcEt12T7/gLPvXQdmaTMdM3A83y0hCpEBmMvLKnNWhNyJ5EfICUUoXniuwSBTxSFtJq6KG9ms5TzVZatFOOg0TLRdZPQ85ClLIEXZx5MGz+KsJEYHOrF0i2irkQ7yLJ8H8t2qeom3TEJ3W4aeEtMZOLYomN6MoEIdhTXYWpG8AhnClVmp/KsXLdcvAOail9vIunCjLauJhiK9xcGIYVDc1i6Sa63G9/3qdeaOIYt2qIBz3VwvQDZcUim0ziOTa3cwIr9r7SMRqPWYnC4X2gWxdfUMR0alSajK0cWZSrnCcpdWbGQOlJYUHjzCb2aVkNkicNAkIA916OrR1zrk+3ICoMQ1xLE6Xawo8rIMSdIS2oMrRmmeCAP6SS0LGZ3TuJkEiiyRBB37EGIoohO3oWdc67lCrXkuoHv+tim3XY7F3YuNoqmUi/VxL1SFVzbYWa2giQJeyJ1nrCcUtFbJkZTR0sKnSTXcgnCED8IcByRRV+o1Bx6gRBKjIO/RC7dtgc6HXhcZ3YAvve97xFFEcuXLyeZTPKv//qvvOUtb0FZcNGOTK0vZV+/EMlkku7u7kV/fhs445y1qPGq2QsCxg9MMjudp9FoEQYBrh/gxCaAQFuiv9loCSO/UrO9anZ9n/ysWAGlkgmScZamVhM6D0Fsr5DOpGjFRDdVlumJ2e+645KJ07ZRrOaqKDK5WIhqXphsqVW6UHAOadomFbNJq6WzaXqCumUQRiFNRwwuqqKQTaUFZwOJkikGi5SqISHhBj5ls8VUs9xW6AXxviqSTFdCHOs8jyWMQsJ5r6UooGlZRFLIaHcXQ33injmBT9SjkhrMIucShEmJlSM5xga7BM9BEpmjw17pYDsOxVqt3Rqf0ZKMdvVy9tAKMloSPwyYqBXbA1sYhWS0JFktxb65Srt05/oB+2er6LqL17IJHE9MLLFaqKZpBIaHXdSZ2jfNgR1CUVhRFWFLEAs1zj8jyUyKXKxlERHhRuL4qrU6IMpNjhuydTrPvlIRO3AJiVBijSLdMJiZEwuA0Z4BoZwbBzJuzBkaSmdYvUxMNoZrEkiBKOfE2a3ZVpXuOKvlxQabmZj8LcX+SrtnZmnFmb7eTBo/EudrGYIf1dXTRRBEpDJpXvaip/Bnb34mZ8SeOo7tsu2+nex5aB9Ww+Lip1/A6jOWI0kSestgbrbM9NQcxUKZ/bvGRdeJIhSg54PIZFLj3Keu5+kvvITuPvH8GrrFVd+9niAISSQ1UpmU8IFakF5v1Q1My6IR64LYpo1hWYBESlZo2Tam66A7Lrbts/tAnkqlQXGujGWYcTYiwooD2lQywYqxIS67YBXJhEqh0uKBXRPcd3CCqUId2/XRF/Bh3MCnZVns2F8gnVBxTKGWLMkSyFL8ngllYD8IY0VboZ4uSZIgQ9d0FFnBNIULuO95cdu1TzFfxtQtCtMlfMdHSWjt6xYEooMslUzguR7NpkEqCoidK8T9i0VOSy2zPQpEjkez1jyq0wdoe4kdC5Zpk2g2mV1QDtp039b237WESsYUwX9DUlFUtX0wYRDSKtdQE4L3lkonMXWTaqkGMU/Nc3xkENfL8zAaYvHoxVy33GAPel1HbxnYltNeDPqOsKOwj8hKNcsi2OkbEO/gvLDgPOaDGNu0sU1B4PU9wVmxDLs955xsR5YXZ0oUTaWWP5zZkRaUwJafswoAPRaKLe+fFarIEcjy4bFS1VSslrkow+JYjnivai2Mpo5ebbU9xGzTFgsY20WvNAkkMb7UKi08z8d1fWqFOgByUpQqayXh+deVy2K0DDxHXNN5PawjVZJ91xMBT3wNFe3RkdxPFR73wc4ZZ5zB7bffjq7rTE1N8cADD+B5HmvXrmV0VAjkzWd45lEsFo/K9pwOJBIamWQyJt2KdGK5WKFWqVOYKWLqJnprPjBRxIOOqJXPThXilKm4RbbpYMUvZ0JT2x0OjuMSLCC9JhJaW1grm0wgzHXAdty2B1IUS8BHEm3peRCrXCQRPGa0JNlEinQmRSKVQE2oJFXtsPif71EzDBq20S7FZBJJgjAgigKajoUbiIFgVe8g5w2voCebbX+fGxzxYoQhqqy0s1Yggp154bMwDjoKzRZV06RfS5NNi0m5oZuoaRVH8pGzKk85Y4SLN4wSZGTcpIQu++hqgJuWaHgWDVu0vM9fs+5UmmwySXcqzYaRFQx3Cb6GFwZxC7zE6t4hZElqc6tABCR7pktMFRqEuocxXcfNGwQx0TWo20heiOf72EUdq3o4CI3CsE0Al+fb2xWp3cIPoDsOpuahe+K+y7IMkcSBcrFdpswlU/R1iQm/ZZkEYUgmlaY7znYtDF39MCQVSUReRCYufzVMwTuYDz4btkEYW2UANOt6O2BSVVVkpByXnz6whaZl05dJ44dR2wLDC3y0tEarYVCcK0HgMzKY4+XPO5uEoqLIssjUeD6z43Nsv28nSVVj/blr2xwKy3TYv2eK+2/fwtxkQfg/hSF2POAnkhqKKtM32MMLXvts1Ph6GbEKsKaqSGHULmO1n6cgxHN9HMcRCwxFWI1IskToCb7S/8/enwVbtpz3ndgvM9e0pzNXnRpu3XnERAAEwUFoiiI1U9F6kIMOszs6Wn6Uww75QYrQG1/EBz84om0/OByO7rBbD5Kl7mh3k5IINsUBIDERIHBxx7o1D2c+e17zykw/ZK6196kqkJTDF5QVNxEA7q06Z+81Zn75//6D9edQNZpQOaL4cpl310dby2KZc3Y+43w6I44C/uZX3uB/959/hZ/9/AsoJRmnOV/78C7/4qvfJ38iHHJRZvzJ+wf0IoVQ7p0TQiCRmMagjY/XME5FWJUVWZY7Z27rlEhKSZbzBViLQFAWhTMdnKbc+fA+4/Mpg40BgZTUPr0cXJ4ROEPCbJmzP3R5fG2BMZ67zcv5WotHGOdK/SRvpyprTg7OyOY/OnphcT5l/PAAbUy3iXr/nY86Y8xEwsC4d+CogZ5dfYc1lsg7+rboZKMbZlPn6m21KwKlkI53VTVMPbLRvn+DnRFSSQ7uHnL7WzeZHk06Ds9yMr+QZWXWvGpGo17nnLyuyCrzEqkEeZqjm8bHLEw780djDNa3Xp8cWhtmk/mF4NOmqDu+5MQntK8jOwDX3rrhj2OKMZbzu0dYHx8kEM5dWwikEtS+xQmrlls6Tzk/moKxrvXk36FskYOAcpFR1g1Ga6w2LNaQyFOPbKl+iEIwOZ12JPmqrDk5POfh3UOOHp+6YueJZ0SX9SoXC7qC/i9q/Adf7LRjMBhw9epVJpMJv/Vbv8Xf/bt/tyt4fvu3f7v7uaqq+P3f/31+7ud+7i/waN0o84oocH4bG0nfSUyrmtlswXy6JF2m3Yu/Nxiw1et1nARnIe92x9Za71fhKniLQAqXNQXOvK9V1URxSOV3IKMkpqj9LsPCSZkjxUr1Y7CUa/3ldvGPoxAppUNotFe/xDG9JGF/tN2Rkxd13gVmJmGMEJJGa4w2nKRul6SkdAWDlCQqpJckbGyOSJLoAvpmrOFoed4VU/6QO9QHVpk9j6ZTHownDD3BeTadM50uqcqGvucolY2mNtqdhy/G5ufz1fn6r4lUQBwGjJKY7UEPGwu2drc6Hgs49OjR4qwrQpz6yF2ryjTsjHpYCUIJDHaltjKWw8WEg8WYeZai/c4nQHY74kAq2jl+w0b0Mkj8wlrXNc2y6iZIKSTTYtm1rjbjARvxgJHqYaxxRozAtZ1LXXtBrl3jstZMFs7deTMZEgYBxhoqXZGoqOP/HC2nKL8olEVN7DPVqrrm2u42m72EWZbzr771fUa9pHtmwSGYWVGStTlHxjA+TxkkgUeQFFdeuMLOlR0XitpoHt855PHNx0Qohv2eiz0who/ev8tv/+bX+eDt2+jGsPQtwLgfd98X9ZxlgBKiu+62MpzeOyU9d27axtpukcsWOWVZowJFFAaOp6AN0zRf/T4wL3J6KiQvS9I0J4ndc9VYSxQ5zhnWcGmr51p0jeUXfuoV/re/+hVu7G655/R41sn/A/9cp3XB8fmCNCuRa9OvaAm3HpV2ha8jiJc+4qDICppaUxU1aVqgkMjWQdh7z9y7dZ+6qoiiAKEUTaPJ85JASKqqIZu5xa2pavaGCcasMM9ZXtDUhrSoLryb06Nz0vlFjstiumA5z34kP6WqauzJKY+O3SJ+aThkbzhAN5rvf+cdAEal4zGmMqBCEqwhA1ZbNsMnUGYryNPSE4Wb7rq5v3I+RcXCv1dJ6DZo/YTxwbkrdLOqI1PXee1VXf7cT6ZdRyCKAjZ23AZi3WunyEuklCymS6SS5GlJWTaURd1xWYJAdRzK9ZEuUk6Pzi+gSVVRIREUy5x80Rbqykm//RjtbTLYGWKNpdSaOi0wHinpOF5KuEwt69yQwRmWzk6nnD06wzaa3qjnQo/zAq0Ni9kCqZ2xYFaUCOOUYXledqrMrKxYliU2UlhjmE0X3SZISMHp4TlFXfLo0RFG66cQrSorkUpSeXRThX+xrJn/oDk7AL/1W7+FtZY33niDW7du8Y/+0T/ijTfe4O///b+PEIJ/+A//Ib/+67/Oa6+9xmuvvcav//qv0+/3+dVf/dW/6EPnD/77r5HICEjJq5qNjSGz2YI0zVx/U0iKpsJiuT89QwpBL3BohcWpB4LwYj2axBFCgLGCYT9xBE5WC1vjA/IAtno9x6vxFIAH4yl7UcKifVmsvQDTuraHQxdqYQi0RFgQWmCEoTIaI0KmxZLGox7gCgYhhHtJjO1aVko4JEJ7FEJKQRAqlJLUvg0pPEHYKYgsMz8BthOwXpOKGGvphwFl0zArVotTUVaMjyYopZhUmvfCECtgOUtdO6CoEcbvsAUEUUCWe2l2ECH9rlMAYaV5/+AAJSXDIGHmW3Tn6YJ+mJCoqCM6Cuu8lD7/yj7/7vYjKgyoAPy6MG5W7tOTPGVz5CZQFUgWufuh2LcuW3RCG0MShGR1ibaGRZn7e+uPUgiUkOz0hlwejdgZ9rgzndL47wllyMA4N2rjr5k2zqhukuV8eOLk1nEScTne4fHZCY3RNKZB++t5uBhzfdsho0Yb6toRVq21JEqxu3eJP75/j0me8cP7j7HGEIdJpwo7PxsT92LiKOLkYM5vvfcB2945t6mdP0p/o8fG7oA4Tjh+cMLUuwFjLIkM0cpS1jWLecpv/8bXuHbtMrv++rVqO3CtkiAMWLaxH6MegyghX+RUqUNw5mXaGW9SG6Je2CGmAkiCgJPpRYRinOXshgOkgdo20GjHhzKGstRcTmK+9OariJF7jh+fLhj1Qm5c2eRnP/UCLx/v8IPHhyw8mheqgH4cMc9zllXGR3dPMWa1mLv8ImfmJnydY3GLirUWIyyN1Uhhmc+XSASSECMtSIfupIuAsqwJwojd9p0xhq1GMBxt8rBM0bohjgJ2+jFKiC6OAKAyhihQ1FWLYHoUdpExOZuwsbfZfeb58cS5Nf+INlaxzAmnUx76YmdnMGCz1+NseZfvffOH/KWvfIGef7bvLxqCjT4qXxVU1sJArTZmbq4qsQiKokIY6128rTPFNBalBMUiI8BF17SIc7v3sI6k5c6h0cyni85079zzinqDBGs1w60hZwdjph7dMNpQFTVNo10EgwqYns9YzpcMFwOqatN52QQBZVE9RaWYnU6ZjqcUecmm/7NymSNDxcQTqpV08++T6rZrbz7PR3/0Ho1/ZsvzBWKQoGvXNg+ky0SLw4D5eI5uNMf3jzk/OEMGAWHi4zWspVgWFEMXkmq1RobOiNEhhCV5UXGydFL/3f4mR/M5Nk3IDy2T8YysyHnv1m0ePz7i9PwcYy1b1QiLuMBVas0EVRh0ie3q3zMz7P/X4z/4Ymc2m/FP/sk/4dGjR+zs7PD3/t7f45/+039K6Pt///gf/2PyPOcf/IN/0JkKfvWrX/0L99gBnCGddqS2ytQX5OB5/nT1b7wyqSXGGkyHagBIJRFKYn0WTqACojCirD33I1QYbTskYLufcP947F56BItlxqWtlc9BY7VLwwaSIKI2Db3AcVYMmhpNaAMnhW0spalYIi+0gOIgXO3svQ15Zdt4iT550zAtckZRD4Xs4Ox16f9WMqTRDePC7dzXKY/LKkN6lGVWpqhYuQwibZG4HXNjNEVdkdiI2SLnmx88unBd2ylHBpLGaKqmJvffn6xxhw7mE9498jlVww1GUR87dwWHBbK6oNI1g6iHFNL529QNWhsCLIu86lox1loW6Wry1tZ0/JBcN8z9RN9yZSKluDM+pvYFAzglU4ucKSHdrlO6duLl0YgkDNnfGPLu6WGH6oQyuEBa1tayrCoEcLScU9SNV3wobGOdn45pmBYL+qHjW2lrKOsSKVwmk20sYRBSNTVH40WnrAL44OiYzWTgcrqEoNbOnLJQFVEU8vDxFIBFVhKGkqbWLGcL+lsDyqIBWdPfHrBYptSlU80JBMoKeiqktppaaw4OTjgUpwzCCBlI6rphkeZoaxBKUHpkc/+5fXqDhNNHpyzOF+RN6RBCa0mrnGHUc+/PGn3AlA21Z+ZHKqDSDfOi4OrmBk3dEMaRW1xxBUGWFzy3MXSFeu2e+VsPTrm02ePGlU02NmLSWcX+xoiH4zN/XxRfuHGd37/5EYsy49a9My5d210tiMK1mdr1WAjRqbKMsTRFjWkcAbSqqk6NaN3eBN24FkTSS0gXS9fOsIaktowCt8iMgtC3lgVXNlwMSvtuNMbQWJ92DRRNQy8IHTek0pwenHP9lecIwsARoGdLb9b3bMnx4v5DpDE88gZ+2/0+z23t8PbRY2aTOQ9/+AFXX9gjE4rj6YzndjaQT3A+Iik6BLX2KqdAKrJy6cJSXRena2klgx74z5CxM0qNkrBrwVltML6tr8uGIi/J05z+sM+ZD6jtDxNOzxddMdxKwl1bqKGpa4zWFEXNZDwF7dqrZVFRlzX9Yc8p56rmgrR9fHhGOknJPMqh68bZAEQrvk4UKISSHQrZjmtv3eCjP3qP5SJnuNEnO5oyeOMa1cKZBmK9L5MSZPOMx7cfc/rgxPmjGbsiDQtBOssIejHlsiCOFDJUfvPdUBUlWVlgsTTGcJKOmRVLbs0PyYqc+hlGqs4Q1qkh11E+XdWYuiHsJdStJF7g3LifYTL54xj/wRc7v/Irv8Kv/Mqv/Mi/F0Lwa7/2a/zar/3aj++g/pzj/Q9vcZxOun/X+mnlgkJyeWOTYRzy0ekJxhoCFaG1oa5qFtla/1yIzsa7naUCIWnLBqMNxvNxAimJw5CsKGht66yxpNWqyGg/O5SKJIhIWKu8JViEU/b4gickYJqt2igSgRLK2eG1LQDr2jiBlCipsLaibGqKpnaFm7Foo6nqFbwvhUCpgK14RNrkjqjZOgtDR77OKk1xfkYURmz3+nzq8lUO5nMeTM+pjWYUuHLK+GMwnmAsEEShwkqHPpVN0yEhceDCFx9Nx9w8c9yvveGQG9s7zLKSOAhJ69LZphtNYzTzImUY9TqjyFtH5ygBTV11k3/e1N13tMVr3WhCFdBo3SFWSigHGddlhwK1xWptmq7wEULQD2KUVHzuxhV2N3oI44rAo+l591kIOMvnbMdDsJD52Ip+lFC0hOwgRlpJWuaEKqSxjrxcaXePGquZ5Uu2+1vuvhpLolyxk1cFlVnbwVlLVpUEIuiCSSUudiSVOYvat3+MYdiLyLOaPCuxgUIpqOqKs+OxWwQ8fyVS7rNMY4hEgBKSSjuZ/rIq+d4PPuB8NiOMIuJ+RFk7VFAIwdbuBovZkqgfoSeWym823PV03kmqVITezULXDbn3EgmlYqvX42S5oGwaQimptUZqTeTbUFlVMZ8vGOw6eXJdaqbjnA/uH1Pu7zjn8FHMIQuKquk2BaEMmC4r4sAhk2ldsFWUhP3V5sNa/z+iRRp9a85oSq0dtwiH1CnfAjPCtejKonJZRVVNGCrOz6YMkpjNtVokQgKOD3Rlc+CQJP93WVWjS3fOKoxIy5p+ELpCsaxZzObMpwt2Lm0zP587Tk0cPdMx2FpLdvcBxhgenXhkp+84RD/x2vN884e3+NY33+HLL/wCYxE5vosF6ottEGkg9sVJXTpUBWOc+slYelHoo220KxIEeOU/DZY8y4mS0OU2+SGUxALpLMVoTZYWBEJ07aowClikeUfwaI0Fa69oyxYFQRhy/vCYIi+Jw5C6LJy8Oy/Z2B6Rp4ULOvbFTllUjmuFi6qwrz9PXdboqiHq9S+Qk1X09JJ8+aV95+FTNdTakJ/NGH7qhuPZGLeRNZ57dfLwGDT0+jEqVOSzrEsqF1KQpTn1oxqMIYh7FFVDluWcn4w5fHzMyfy86zaA2+Ctj16SsLu9hTQSUTiunxCC2fn0AlndtRoNstGr6++Lsr+o8R98sfP/z+PajSscPDhC+sk6DAIGw77b9S9SGt2gMTy/s83Ex0o0RhMrOrfjau0Bssa6l1tblBRUdXUBBmka3T1MSgh+8PgxWeECMpV0Rn2zqmQgXUho6SeXSIVuR24NkQq6tk6r1GiEJrAKrKXQq4mjJa62iI5dK1Iu9TeYFhmLKkMgGOcxoyTBYKnKZq0FFnbE30ApEhtRUjsugbWESiFx1896PkxRlRxWJYezCRtxj6qp0VKTmpK/8qmX2e73ePv+CZN5jrSehOoXkIGKqIOGcep2nHEQcp4uOFi0BMGArG64e36GRFL43YwFntvc5WjhWkaLKqMXxMRBxAePT7i6t0lRVI6DYyylV1qFUrHTH3G8nHa98Hrt74RHQ9rr2iJG7jtXXB3h/Wy2ej201fzL73yfS8Mhn7p6hVm+apWAS2sXCUzyZVdAzYqUQCqGcY8oCNDGkOZOYRSrmKIpqE3DIExotKZoKpqm8QUrHUG39QYSOD5WYwy1acibkkq7nbeSypMn7YVYjX4cMSZHGEuta7R1vLayrHyB7AiMha4hdmR7ayyhle67tKbSbtG59dFD4ijk0tVdFlmbXB+j65q6cpD9PF16HycXRIl1beOgUC5kNYQiLTtiZT+K6EdRF5S4KCuHampL5Hf6y6KgKesLxP6jR3N0Y7l/cA4Wer3QBapWVVfwBkoxzyv6YULZLFmUGct5zlY/6dDOtlXY7mRcVId1RbbWhGHguBms0MqydjwUay2msVjTECcR89mcrWoDZSVFXZPXtXv/tGGrFxMHirpaVTtpWVHXms9euowSgkmac2nYd+aeWYHQhvOTMYNhn/HplCiJOb13QByFXSuoHcXJOXaZcjxZUlWuzTL0Cr/P3rjGt354iw/uHfF4mjHbdLwhUzUIs66ZdIaKQ2Go8T4uxhunCoFuaoxyRqhWCZQI3MLvW4NGOULtYDTo0tXbuQpgdu7S02fnM8Jak/pWCxKSJIZdd79PPOLT1A1F5nhRQRRweniG1g2TNEcGkjJzmYZSSUfWXWvpZGneqffSuVNMNWWNNU4mfoGcHAXOdPV8zqV+7ObuMODyK1c5/OAReVkTLQuHYGnHGxPCCVVmZzVpVhK/esPxv4xT2bYxEVIJFuM58+WS6XLO8emYu/cfcXR8+kzisMCpOUMVsL+zy6VLO2gsgyRhfrakaFaF0HyyvGDz0BQ1QkB6Pus2wp9wdv4jHn/5b/wcx+8coI3m0fwcrY1ryUhLJEI0rmXxzsGj7oEw1rCo0o6jIRDdQuceSIvVDdpKjH7akC8vSpSQlLpi7hOvrXVoipSSsqkZRDFz738jOxdO317SDYlyE5i2To1hlaBq6q44Ak8abmsiC+C8G9xnCuIgYFG5RdhimeZLhpFj8edrLaxAqgvxCZFHGhrj8HljLRtxn0XlCNuBUq4wM05O3raDagPFtOLrH9zizev7zIuUk2wB1rWKYuVyn0KpOrPHQCrmZc7Yy1+3BwPKqsEYS2k1sDI1tFhe2t7DGME4m1PoirwpaYxGLOGtq1e4NtpkkmUsmqJb5LZ6QyKl3Pca52XUukyHypl4lb7QaTlTAtXJztvrCbCoc7aI+dZd5yF1slgwzt2x90Kn+msRrdPl7MLCAa6Q3h30uLo54vdvfYRhxbnaSnY5Wp6T1aXjAGApdclIDZ3/knW8n9Ymwan1epykUxd30NQkYeyLK4GSghtbGywXq0k/9AWDNY5nMTmf0fiMo0CtAmCtNdS1Q6qiSGGsy387f3xOWVXkTU1tNGVV8+j+keOv+HdkPF6ANcznS48Gme5zYyXRvp0VL53flfE2DKFU9MKIQCoCIamtZpylJCogbRpCX9jnZcXIcw9EIEBbTG3ZiCKOZzPmy4JBHBFEkmVZd6ikFBKhBIOwxyRfUjQVWVnyohWkQqCt7Qj57RDCc1W8eaeWYLR2RbJvN+ZVRSJipBTopiFMIow2hBqGjXuHvvfoEXlV85mrV5EG9jd6HY9FG4eIZWUN1rVTASbzDLHvmD+i1uhaMzmbE8en5MucutE8undItij47F/5AntX9rrjHr/njB3vzwv/DvS7Z7gnFJ965Rrv3j7gd9++yxtf7BOGIWWWd8VOWtYMYlfobgSCw9IVxNoYTNOgEBTWUjVORRSEMRaDMB09kagfMz2foyJFvib/b5+HpTfYm5/PSRpL2hKE4xCFU2QBjI/GTM9nntxbOiVbljOfOY+joixZzFLSRU6RFc52QogLLZ2WNKyEoFik5HmJzSvwfKwO2QkUKgzI04K8Kult9BhtOTpGW+wU2rAJVNPU2YVY5ze0mGQM+gm9QUJRlCjrCuX5fMlkMePWR/d4fHDMyZl7h54crW1I6bk9L+/s88L2Hl+796HbOCyWXL68R6gEaVZ0CHbLTZr5d7mpG+fXk+YIKUk950lGwVPtuR/3+KTY+RjHxo57UJVU9ILQTdK1y7WxeL6EbUirqlNrtMPa1f5+fQbUqfdFUQrdGLfYCifN1L49YoWTIw6iiFmRu9YEmtAKmkZTiJrUp5m3qIG1lsa4TKvKNEihaKxbLGxtqNYM7CIZ4uivq4e9RV0ANuM+96duR9Sea6Er5mVxoYUDrg3Xyrm1dcVFrCKPNNmuPSEcG5EwCOgnMXWtsRie29jmaDFjmqdY4PF4xuPxSi7aIg2hVCilfDtqtQC3hU4vjCgrZ72+fieUkG53by2nqYtr2EqGpHXBosqoTcOsXPJoPOHF3T2youK8U+BI+mFE3lT0w5hZmTnbe88PGQTOGLG9vWptR79O0m4LXwvcH49XnB6jWWQFgZSea2UwVlPrBqmkI44DaV06ybeQ3Dw95oOTgwsFphSC5zb3mBVL8qbsXLEXZcZWMkJrV9DGQUDmJ0ptLYsy7zhTtdEEpiHwHKAARVk84VDp63KjDcvxnKKqUVJQN6Y726qpu4UxaGpk4M59OpmjcPYGW1e3KLOK8WRO3TSdAi7Ncqq7R8hIMV7MO4+kdlicKlAbw7RI2UlGPo7BtRCSMEAIh1jVRjPJci6N+tS6JvY8g7yueWFnC4Ct7R5YmJxlvHn1EiezOVleMYgjGuF8ctrrLITABpLPPH+V8Q/nZHXJosw4Hs/56dee5/ZyuWpH+0OWOHJ/02ikFFSNQzcD4RE87Rb7umk63lxoXLH4fLyBEIKTzIkjAA7nM7ZHQ65ur867qBt6Uei8o7RmnjuD0roxnfpRCcHB/UNe2B5y7+YD6rxmMVsynS45Pxrz7d/5Ll/+pS+xu79Dk2akDx3v7dHUFRCtmSlAltb8/Bde5d3bB3zvTz7kxusvk/RiFkdn2KFDf+ZVySAOMcay3Qu4PXMqNO3l1I6MLJ0SzsA4rcCKzsLBSlguU8qi5P6tlDqvWFYFkQyomoYIpxTK04I6zRkFAWMvMR9t9kFrkp7z/MrmGQ9vP2Yw6nvXbcXxvQMqbwughKAqKyZnUy5d26WuHOpXrhUDs8nCqWSFoMkrlvMlqjQEYeC8fLLSzW3Kyc7zLEMlEScH50RJTJxE7Dx/GaDzZasmC0wkXGvrbEFtXEFxcHTC9z+4ycHjI+7fP+g8utaHlJLr1/Z57voVBr0eTVZTLXLSvOLe4SG1adgbjtjs9bm6vcvh+Iyyqbn/8DEvPH/dRQrp1ZwdiIDZ+azzGBPWeRk1dUPpVXxBHHIRt/vxj0+KnY9x9IcJxjrV1WavT76YUVUNoVQuu0gINnsD0iKn9p40FudsXGuD9pyNVeGzCoRstKauGxcEJ10vvv19ay1XRlsIpTlN50ghSDzx2IV3ul2NwJHijMETOS21aYhU6Lg0gJWCslq1p6QQ9MKYrM79TtR25nsumM9SNJVDi3DZTIWuMNYtMNrzQ9qhPLJjsWg0CpfZ1AVxAo2pfcvEtelsqHxgYogBvvTcS/zhvVtYLD/16nMcTed8dHjqWnCeZ1P6Yw9kgG0DL70iLlRBR4JeHwLY6g1AWKbFkkmeEkq3+++FMYFUzMolxlq+9/g+8zJnmq5Qnd2+czu+NBxyMHOTqfX/EbjFqrEtd8dJcEKpHA/DF6AtspeomBd3t3jHE6gBSu2Wx9cuXeEsLbC2ocEtsJEK2Yh7HCzOXXGn6bKwBG5xb0m5gVQsyoIrw13uTQ87FMfSRp64YynXoPm2hRnJsFNh1dpZxwcqoDGax5Mpe4ONrnhpauPP3aGUgZRetu2K6qxystdYOR5V1dTIShJGAWmaMRA+aDMKmZ3M6Xm+QMv/qoymqQxUtktyX59eK60Z9iJM6Vy6p+WSWEWdmrD182mPVxuDRJIEoVf1WNKyZNP7O/UGIQ0ae2q5sjlie9DvEJNSN11RL3A+K9ZYru9v8Mujz/Avf/+7Lp4ky7l1eMab1y5xH/hwMveFiwLp4mW01oC7X8bYzi+rse74rLXOgBBLUDVcG23SUwG10dz06juAcZpxfXuTUS+iqi7yw8aLjH4Q8P2HhxhruLI9dPOKX9Qe336MGPTI04KdnR3yPHW+NMJ5gt1+7y7ZIieeOEJ2Fcc89gqn7X4fg+OW5GnNZ7/0PNvbIyaTBbdv3uWzn/8U5fkZ1lsKvPHGVeYHC6xvH6o6c6TZsnJeRNYpj371b38RJQX/9f/wPWZpgbUaREBpNDbN6Q/6FFnGNMvJ65qChlEzJPKcsunpFFHWTOOwc0/uJRFWaxIfNFtmJYvpgsMHRxSpC808PxmDdfmGSRwjjWA+WzA9nztFVhh0LZ3ct7eiOEIEjt84PjhnezgkiALO7jqeYNxzLSuk5OzBGc996nnKvODs8Jwrz+9DKEk2+xSzjKJqyM/mTFTNrVsPOU8XnE6mnE2mF+bWduzt7fDC5oiX9ncpRMKNF5/n2ktXQAju3XvE448ekTWGutbd3DWMnLoy6UVs9zcYZ3OWWcajx4cMe4PWvo2y8STrM4/sNA3SWnTVUOcV2s8ZMgwpl8Uzj+/HNT4pdj7GIb3HjBSwESUcMXM8iLVU4SQICRPFabpqOyjplK5qDe1Rgey4BS2Bt/2vsILQurTwtjCKlOQ4dahFa8gnvQ9OW7jsDoY8v7fNH9+7/8SiUKONdr4x1nRtH4Eg8AGSbTJ5uyC3vEqLZeHbZ70wdiZzQlJa03mOtEP4RRScMkwbgxYaJRWJirtiJ60KEu9sbKxFGOc6rIXleDnnxuY2270+0yIHK0iChM1khDZOzdTykYwn4bbDWE0cSOIgwBhLFCgubw54vJZ+LAQMophpsWRW5Oz1I6QUxF5FtZVssChTatNw6+zEmUPKgEC5gshYQ6Qcp0JJ1SkalFRkHU9HIIRkuzfg+sY2d2enLHLbXqTuZ+56ZY/jgFWeOyOY5YXjK6wp9wzG8W7WULSmW3xX1x3vfQPuPC8NtjhJJ17BB6fphMvD3QsxHnAR0WsJ2I0xCLTniLm23Wk6Y3+wCUJQlO4+BMJdo5qme35cq8YbE5qGQPpirCwwxAjrTDKtgDTNXSvMWqw2BEKShCFp5dqKeVN2RWR7CS2uhZEVBcOoT1YXDo1SmlC5RXaa5w4Bk5LA85EqrQk9alI2mrJp2Oq5FkevH/L27VNOz5e8uLfD61cu0fOk1GVW0fjzUVJRmhpRCmbLgv/kCy/zu9//gLNZSloV3D2dsMhLLm84HxqNJunFgKXWGmtdBEjrGxX4zdKsSJ2dgxFIQGMRxnLZn8+dyZi8rDo/H4sjIrcp9dZaIo9YzfOCa6MNtHEtl7JyC59DZiGy8P1vvUMUhog3nK2DUpJhv89ymlIWNYcPDtk6euTQxN6Qh3ddSvd2f0AdCBLteDhF0fDiay8w+fY73Hr/Lr/4M2/x3FvXSY/dd0d9V2hYz9vrGc1MuBBTi/uMy3tDQs8T+uKbV/g337rJSISgwCoXx6JChVQBhUe2LK4I7suEuqjIFikmq1wUjS9O4igAa5DKWVQ0VUMQBJw8PnfhoUlA6h2rsf57AkmVl0zOp8ynKXv725Qe/chTR16Oo9B7+IRMTyYkImBrsNW1sGJfXGltqIrSRcdsDJmO54RxyNGjY8YU3D1+wPx+xvg7y26zsj6Ggz5Xr17mxnPXuHblMpf3dpFW8Hzp5rSb5xlWOpm4FYIyc1wtbZzFRLut7kcRy9o5I/eShG1jmRQLZoslTaXZioYg6NzTZ5OF8wWrGpQ01EVFnZedcliFgS9Wnx0q++MYnxQ7H/PQGAIUvci/wNb6nRo+pdpSNppB2COtXZGwLMtOktzu8NcXM6kkyhpXHPiCo9KND3V0BdbRYt4574Jb6CIlO56GFIJRkjxV6HTHbQ22XoWZrn7HyZNDFaA9a79FH4wxXXsrViv/Gm1dUdeqgdpCKZCy+27XjLDUpkZJdSFjZd0h2rmnCuIgpDI1tbU8mE7Y9MXO9+4cdERdKSU9GdMPEoq6pLJObZI3q4KnbOouNmJ3OODB+QSsa5tJKTnPlt551i3CxhqUCIi8qiYQgkHUo9QVee0WW20MA5kwL3OyquR46Q0Wfbq4ew7oChmBk5RLIVmUBWKtcylYpRvnHbE5YO4nrzgIWZYVoQx4aXePg9mEaZFR1BV1s1JztQtkh6K17RV/EFdHbscfBQHLKierfTvDNMyL9OlIlrX7HkjlkAv/n8Y0jOIeWV3RGMPhcsp2b0hSBi62IPAEZinQlSM4rztqtxYMEkljDaKpSbx82lgLrUO1L976ox5946IGDtNxh366a6XYTjY4z2e+INMsypQ4iFxhVFf0gohBlDi1iEcpW/L1sqzY8ItvXlfEKiDyi2yUBNx8cMLh0ZwX93a4sbtF7Avb8TRbKe6883XVNIwXKVIIfv4nXuW//4MfsCgzBmHC8XzB0XxVZPeDgGXtXHm10a6QFBB4n6WT5YSiLl2avQUtBQrFK5s7KCGZlTkPzs4RXpseKkmtDaeLZdfCOl9mbPX7GGtZFCVNfzUTCBxpuR/GYGEYRSyPzugNe9x89w69XkIiV0KK40fHfPaVfYTW1Ejun7ooCaUkm72EOlQksSRPa+bLhlc+9TJvf/c9ZtMFqlgi1MC/6xYVrRmLWtgMJYepC9s0uPnu8vaqNfa516/yh28/IPLqRC3BNhpjTJf5146iqKGfuLymRjOezZHLgNK7TIeRQjcCYQWDjT6zsznpeAkYFvMFy4PcFSRl6Yj4QhBGgYvgmCw4uH/I/vU9ytxFiiznKcLlOgCQ9BLG53OKjRFCii4TK45DqJ1KtSpr7t17xNnb73LvzkMePnjMYrEyQGxHoBSbgyHXLu/y3PVrvPjSc1y7comsqTG1JfBu+tnxGHYdGtkPJUvrTAetEhR5jvbRRe1GLPAF9cKrzIIwoO/FJbNiSVo64ccgSbpnSWvD/Gzmug3G0hSlE9R4N2kZKkylnxk78uManxQ7H/NYvWiCUZywKAuHxgiBUrJrDXzm2lXeOXhEWjtbeGE0URAhsDTWdg9Vu+i4F02ivNpEG4fA1LhiIltrPYErdkIRdIufEopbZyfPPOZAKhqtu/aE+3mniOkFEUWjSYKIovHogidStoWOi30IKZoSa52x19XhNnen7vukEGi7aoVI4YiebsMpul11ixiAQ5taKa42mry23uVXcLxcdLCqxXJla8Cj8cxxHvzxh0FIiCKrK7/4WK6OdpgUKVntjvN8eXFCiVS44vt4EmmtnWy9MqILvguERAQxAkle51gsyyrv2kkXnaIv3sdAKFecCcGiLFiUBcau+1n41qBeOS4jdWeYFkqHJFzZ2OTl3cs8nk26+228DrfNyIpUSD+MKXXVmRVa61p0dyfHvBlfYXMQc724xJ3JY58pBpWuOpSxdeB2MRquKHOcIkHrz7E32GQQJ2jjrpexlvNs4a6hf0aEV+8Zrcnr9txci9EVt8bL8l32UezXP200TVZ7jpnfNUYBj07OWFYXZbIS6AUJy9ohNiuegSGri05hNs1TBlHM/mjEo+kUy8q1u6hrdoVDSrKqZrvvFo2kF2Ct5c7jc8qy4WAy49r2JuOzjP4wYp6VXdEVqwCBQ/YmXvXz02+9xP/0R+9QN07JFgcXDdeuDvqkOuRsMnebCO+/hb+mLXpqrKHRjpx7dbTBXn+AMYa3Hx/QmIZIRs5uoW6QMmCWF5zPM7b6PU5mS7b6fafEajRp6ZDAFh2epAWDrRgLhNoheVVZ09Qa21ga6SJEru7vcH48xgzcNT81ktvv3wVgb2cLKSSDfsQgcMVOmVV86a19jr/4Kl/7zk2++rV3+E+//BkiBI/Pptz+9pw3Nrew2mK1ZX+jx/ceTFwUROPen0s+Ew0gCBRfePMad993hYMBhDEuX+sJs8i8KLH9EQLIpinZsiCO4+7ZiOOIzNRYY+kPE2ZncyanU8rcEbjHp2OXMm4lSjnjxSAMKDNXmJ48PqGqKsdXyStmkzlJL8Z45+QgDinSjNL7rJ0fnDHNU8YHGafn5xwtF0yWF4Oh23f4ypVLxCVsJQNevXyJ7UvbnGUZvUHCxt4Ww75z6RdWkC4dgXk5XTIyDeBbr4Eik4KqqGgwVHlFVTeuMPSb8HZjsSgLyqpx+WQ9RV8nrjAuUxZVhsa4jaCHTqcnU8o0x1QNTVET9GKafK3Y+VNCY38c45Ni52Mc6wTJWmt6QcjCmzYJBDjhCq/s7dKLwgtk2MZqIutt7v3DtOY/dmHRHEQJxkLhYXxjTbfIt+TWdlcrhPfqWJOQCxxXxCEruvuM9thbQztXbLjfSdYmZ2NWQZtSCPpBQhyE5E3h3WBFR4iGFe+oRRXac5G4c1RC0MCFYqdd/GDFe2m8W3N7HVrFU+kh5tbp2Jk6erI17XWTjGJnehjKsOM81KbpisdK16BrWi2HgE79JIRgEMbEMr5wf5Igdm1Av6BKAdc3djlezi+00FrEJlLOJFCAS1K3hnwNkbNYGt105nHWWs48UtQLEqR0rcuTxYx+FHWoVVt4uOsqSFTkFFZN7Xb60kVFtNf3eDnl+NaUjaRHLGM24xHjYua4OroiJu6KprYYESJYWeyrgFI7GHySLRiGSceHqr1c/TSdM/RKJomgrCqKuupaWUkQOUm6di2xUK5sZ1qcr/EIUlv0pnXB6aPphWcZrM93SzrelvscZ1KnPKm60jWBUFghOFxMXbvDv2RSiK7AnaQV58uMvKrYGThEIemHPDiaUNYGa+HdR0dc295kPilIs4pFUXZKrGGSUPjE7Lk3WJNScmV7k4enY9KqIA6cM3pb0P/w0RE/99oLbLz6Il/76Da6ci7XUdjvnsHu/bOGWAW8edllBd4enzMrMgLaEOKGpmm4vjNimuV8+Oicn379ORZ+IVoWpSOBZwXTYokFNqqEeV7A9iZYGISB804pXeHbS2KaysVWXGo0+6MegW6wUrJUcZextOWv194oJvI3sc5qLvUD/srPvsXXvnOT771zn7/22dcIw5j/6RvvMVnm/K/+6hd55fKOMwtMQmyeO/WVNVijubLrxB/ff/8Rn3/rOV68vM3d98eOrC4sChfI2RY7XatVOw1sgOD0eEJve0DmidRhFBBGCvIKhHNTBreIF7omzVwGWZpmhJ7H1c4FIlBOsTZZcPz4lO2dLRfonJdsbA3JfOL42dmU9967xR9+5wecT6fc/ODuBRJ9O0bDAS+/9iLPv3Cd7a0NbGG5+sI+j799i/RkRj+MCbQlDAJAkC1z4kHPvRtVzfhoTJkXDIcDemtZW/1QcmIsutFM5jOq0rWqtDEdsjPyNgFZ7ZLMy6qm33Pz3DDqgbUsqoysKgjWWueTkwnLWUqvdopWFQXUPuZIrPG//qLGJ8XOxzi++bvfZZ5l6MhgbY+ydfL0fy+EIAkDrm1t8kd37lBp4wyi/E/kTUk/cnlZWhuPhLrZv+UhtK0ECQyjPtYa11LxLQ9jDT0Vk+uS2jYoVIcqOAJx6CFy4YseQdlUnWLJtdM6XRiTfOlbACuFUFt8APQClxWU+JdwI+6D39m3w6wVHEII1/aIIt64fJX743OWXmbaLjxtx6ddWFseVDvc4mfYH25wMJ9yPFu6Ag2HYOW67CaUURwzLwsSbxa47gQthGAgEqQQLKvMqdiM7go1yxpSZ2FR5l4Z4hCz9vhGUZ/GapZVzrIquDM+JpBB53K8Cn90rbLaNFRN07Ukn7QTML44VlKR1YVHHhShRyaEcITY22cnnQT7YmHnLlasAoqmXrUmhQugjGTgZfGaWZEBGcKuFgjXzqo7MrKxhobG85HcMag1dEdbw3k+IwkST3aWWOvQqEVeonqRux56Ze4YqeCJaBCD8TJ2kB0KqK1GIjnNphdaX/0wptGujWo8wTpREVnzhOmd/3OjDFntuD3CttL+glHU89/fcpFc8fzOo2NCJXhpfxtwxc67d46QuLysw9mMympCq8jnJbU23blVdcPexojT2ZzpQnPvYMy//sMPKbPVe461WCnYiBNmeU5ZN/zRzQe8ee0Sf/uzn+IPb97m8WyBCGHpUZ316/3W/lWSIGRZltw6O8YYTeA3JLWu0Riub28wzXLunU747AuXu83TLMvZGw44X+bd3HSWLthPB0jhkJJ+FKK9IWhZlChtUI1rOz26d8gXbrzhPkuEVI3m/NShLJuJu57DXkAQ+Ny/tGK6rPjh4zlxElEWFX/y0SPeurbPxBeDHz0+45XLOzSNIQK2+yF3sgJaHy/fxvrad+9wbX+LdOnFDLh2v7GW5SzrEBu3iXTv8LIs2Ip7ZMuMvecvMfcuyXEScnA85rd/74/5/BdeZ6PvFFmzsxl1CIv5AikFVluMcGT4JmsYDHqoQDmH5rrh4MExcRxz8OCIH/7J+5yenvONr36T8WLR2U6sj1Apru3ssN8fsbGzxYiI7Rf2ee0rnyGKA47uH1MWNfPxHDXwLdWqZtMk/OSn97lznDFflqSLlFE/YXwwZjqZ0x/0QAqSYDVZ9kKFmddUQjA+njBfpJzPpy4Hq85oTMNZNuf377xHHPWQUmAaTVE2CAU0MIz7GGtI64LGGib5gu1kxOR8RjpZoKuGuO/Wgcb7xM0fnVLnFR999bv8xP/yF5+6Bj+O8Umx8zGOH3z7Xe+4m7nIgTUmeotI7Iz6/OHt21TamWW1Bu6ukLDkdU4v6tHpdtc+AeyqJeQ/ehDFXAo3uD057BbyXK8ZE7ZcC1xvtrFtobNSctVri7JtW25e9WWsJW9KatPQ5mG1I1ZRp7DBOvQnb6qOIOxCPsUq7Vu4toS2hkHY54cHT0qifZsLceHYHerkigXPigbgaL6SnAvcMVdmRawNZcDSS6d3+oPOTbo1tLPWkoSRc2OOB1jfOqj0qhBZB2INLgyV2h2rays5cnIiQ/b6Ix7MTrtr1R6HKzFcUdvopjP+A9Z+Rqyc41hJ4Au/eF8eblI2vqDxRbD2artAKiq9Uj65YzXU2rjrYUyH9oHLflovjsChYspzccC3j8Qq7gNaIrFXMrHyQLI4p+6iaZ7i+gDOVdzSPQehb8marpAXHcoYCOme7/aaG8NpPrtQ6LTuxm3RC5AECS0pGejgfYtDTYdxDykUyyrrED1bt8ikV8a0qixP0jdWcvPgnKaxXH95k5t3T/yz6H5GRoKDkxl3Ts5ZlFlnqJjVNc1swagXU1aGf/5bPwDg0taQwhbMs5xlXbAdDHl5d4c/efTYFTHG8N7jEy4tBvz8G6/xwdER7zw+6p6HUAW+lWy4trkFwA8eP3Suz8ju/bTARtLj9au7vH9wwqIoef/xCaOea8/N85JRnPB4unp/srricDJzyLL2Qb5BwLJo2O8PQVsezKcAnL2/5P/y0UNefX6P5PIuV1+9zvGBU2LtemQnjBTDbVf4ZGnND++NuX3vmCBQlMA33rtLXa7mklsPT+ELr3ft6Wu7I75e5FgD1y5toJSkKGum85xvfP8e+8MN93xIC1JiLR2q020ufPG+KFyxY7ShLKrOjVoGkv/uN/+AxTLj8b894/lr+wx0yHKypOwJmqpxvlDKtbCbzGXGFWmBDATTxZLD8Rkf3rvLZDpjOlldz3YopbhyaZf9/T3efPUlTn7wiKvXL7PbjzF5xVRrglLTnMy9jN0Vy3HivM8qP9HXjWvT7QwSil1DmpXMTmcEBmazJdposiLnbDwhjS3fL0umac40zTiZZ84c8xnxD+7elxRNxd5abmBT14Ag8JE/sQpJPQKdVQWb0YDZ+Zzl6RQZhgx3Nlyr2Uvw66LCGkv9I7LUfhzjk2LnYxrWWudsykVUoiWquQm05v6ps/qPg4C8rrviBVaQdlEVXjGzWjguLExaI0QAgo44a54orNqfN14NE6gVtBl6LxqE4SxdkWkDqS5wjkKh0LgMnfUg0NWxGkpdE0rFWb5wfA5YQzQksQq7Ra7z0UFw7qMr2u9M1nb6reqnRU+0MQ7twrrUZ1ZkWUnbFnPePu3uehT3Lyh+pFAYW/sWHT4I06BLzfPbOxzMZiyrfC1gM+ikzEoodHf+tltUjaVTA/XDmGUliFToihAEdYd+RV0rZr3QkWvFo3NSNh458/LwyqtVVMiLO3vcOT8lrSrPl7Lu2TKe0N6hcW4hr6tnT2ywUmk9OVryseNuuXNMVNy1sdbvPVgX8eCJ88ZapG1J9ReLxPWC2t03eeEnpJT++hqQzn1Ien+cs2zeXTPlf6+xhqZZtWWVkMS+Tbce19A6WDemodGGQCpG0YCszn2bt2FRunu+EfUuoFWNMd0Rfnh4ys3/7tTljIkQGRiMlfzz3/+TC9fPthw2335d5I4MDfD516/xSz/7Gv/id77Lt967y7LM2IwHWFwwadE0ZHVBP0w4mS353UVKEkkyjzK2iFp7fIfzKdbCWbp0GxlP0tfGsNMb0Y8SPjoaszsYcLpccuvonL/0xosAzLKCujJPPQePxzM+ODjh5b0957Qt4LmdSwyCkK/dugNAEodIKcjyivfvHMOdY777zXfIyty1df0EGEWKOz46wmiLyEtmkznKz0WLrOR7t1eZdtNlziwt2N506Mrz+1sIBFo3XNp2f3Z0tsBaeO/2CfI5V7BcvTziwTylrlYhpS3K227C8qrqlHzLZYbxvMn7pycslpnjd1nLg4NjpBCkVGxe20EIH3cgHLE3LTLyuuT+/JC8Kp8pq75y9TIjGVGlLlPvb//dn2e0M2KxSAkaQRaf0dscYP2xNnVDgABtyNPcbQgbjerFjtivFCJWpIuMe+cVsz9OWTQl7945IStKyroiL8tn5lg9awTKKWsDqSjrCguehhCBtgiPxnXn5gv7eZV3G5/9jS0kksl4zvxsyujqJYSUNGXVKeqsX9R2X7n25zquj2N8Uux8TEMIwc//9Z/lgx+snGrVE8VKx7vBESHdP69aNy6U0CMTxi2ynf/KWitqfSihOC+mF/79yZ6wFAKFpNXPaOs8QFpDOYFwbrh67ZgE1Fa7IkgoxyNZX6CEU1a1vjbtaHeWwv/Meq5S3VREHhmQQvDC1i6bvR43z44IZUhsNMs6v7A7b6+dNbbLCgI6abdDYFzkQHt8O72hy4BaQwPGaerNGYVLtW6PyWg+PDm80EoahAn9IGZWpR0vRUmFaBEjXwS2CeMW2y1K7fXTwnRkbYEzNlwVKYBwBO2W1+POeXWFy7Ls2k8Wy/ce3+OZ4xkTbnvPO8n5GnoiEJ4ztHqSWu8mYwzLKu/OqTHesHCtCDBW0wtifz7wiz/5Br/9nfc7lCVAEQdx9/3Xtzf58Piwmzz7YQRCdPyi9lg1Djkbxn1nlGkt92cnXaEjfRswCROKprxwv5RUbufv74FrhAmPpkEbjGs8MtoLE68+M2irvcrJtWB3+n3Os5Q4DKgb7Qt040zuzEU0rPt+0aIqbYs0Wssfa/iZt57nb/7cG0gl+PTL1/j2+/c6jtetkzNX9BqDMa7l0ioTj1qXX6/UbInUjdW8f3zk4mKsRVgIwjaTTHfqxIdn887FepHXDH16/CwtWBbPyLgC/u2ffMgLe6d85Y2X+PTeFUIp+b2bt909UILRqMf/5ld+lrNpytc/POHmhw+YzVofL8nvvPsRwzjm0Cw4nud8fm8fGosqa4rUcWBeuLrD7UenLPKcWEWd6OHe8ZitYUJVN/TikJ2NHieTlKs+ff3gZOrue1MznTmU4fNvXuXht2+RLQv/LMjOdNWsSbXTqmSjiinSnCotyeuSh2OHRl3d3WZjc8jR6ZjxbMG90yOCySlJPyHNMrI8f2pOBSeC2BgO2Nvb5fVPv0IcRvzS3/45/s3/8X/gSLg2/vR4yublLQIlOX/gQ2LjELtcBYSm2pCWBUfffhsknB2fU5Ql08mcxWJJsX6vPnzqMFbPoVT045hrOyM2egnWCl65ukulJXMN09nSqaim7v8PZu54+j7o2FjXRjY+1qLRmlBIJvmc1spE4gtrDekiZ3E2Q7x43T17nhMmpMQ2DULAcH/rRx/wxzw+KXY+xvHWT7xGr5c4BYO5KIEEwNquNdQOKSXCiK49EauIUlfd77buqdDCs4BHVYQVZFXZ8XVWaIe80KawrPw18qbyE23VtQZ64ZrMl1U7qbXyD9ZaMt2xtMvlmpy6252zUuxc8GoRdCaGxlqWdclWv89Wr8eyqC9M6oFYFWdtsRCKYK1NYvnsC/u8ffeQam0R2koGbCYDsrrqflYisMJ9VqFXTtBPFmpKKE8cFeReDdb+bCBVhzw1aKd2kooGt2Cuy7xXrSYnw5+U86cflj9j6DU0I5QhSgr6YUxaOQfmdoFs23qt/4USygdrOgfTVmYvEDRWE6uAvk/FbOF+bVxmUm5dGyqSAaVxv9fer/b6VrohVk3XBv3WD+/TD2Myz4dqjEY0lVMWCngwHnfIXhJEWFYRFG0Bsl7Az4olW70Bj+fnnW+NwJ2jEZasLp4q+Ctdc57NOtSoLRbiIKaps+48+kHs+DJYekFEqd172lhDrTXzMmPDkzXTqgLPp3oSAZEeAXtub5Pnt7b46PCc2dqCFEiJtKLzKnr71hFffPMG+5dG7Gz0CaXb1CzKzCnQ2s9ds19w7U53TwdhTNE4nLBFKxZlzijsO0sBLwpoi7fWR0kIqJrWO8eyyCuGSUyt9VNKtnZYLA/OJ/y/vjnjJ557jqKuWZTO8VcpyWde3ScKAwbDHp/90mv87V94g9/5g7f57a+/y9aojy6dlcb33nsIwNnxlP3RiFfCXfLUiTU+9+pz3H506pBOrTvC/zc/uM/nXrpKEjlbgRv7mxyezbh6ybWsHp/MXEyJiMCClIJr+xvcuDTk0GdaybX5QfpNlbHOHLJf1mRZQT5b8GjuEPZ+EiGAw7Nziqogrz26W8FsjXcohDNMTYKYfhRz46VrhGGEFKACxdUrlzk5OufR9++QFq0yz3J6eEp8ecD5+Yxb795isUz5IDshnc5ZlgVpuXJU5+Yzb0l3XkkQsT3o8+ILu8wWJfNlze72FrFQDIYDmkpzfW/ET76wyzQtufl4zJdfu8rpPOfrHx2iG0OWOosJ04lGVhtogcR6opO1FqMNk3rR8fA24yHzcoEVhiTpURYV82narQEtOdlYv3YYOLn5mEuvv/CjT+xjHJ8UOx/jWEwWxCroCAdPTZJS8OkrV3nv6IjW+O+5rS2OFzOWVYnBEMmQiKDjjQijfTijJZCB+zOse6CwFFm5ahGsqQUkslOHlLpG1KIrYmq98jm5PBo5FUYrdceFiL5xeZ/3jg/dgrkmSW9/pg3blL43HivFpFiXcguP6vgCyr8RlXZZXY2xnKVLJlnqiwTf+vO/oaTE+O9doRMQy5DaagyGP7n72Jmw+e8IlQu8dMGWdXeO6+Z77dBeneOOFIRQGKvJm5Xrp+kWOmdO+Kxd/f83o0W93HfYTkGHP/c270og+MnnXuV47lqEm0mPsVyS1S4PTQtXUDe28aWnu0ZKKq6ONjnwnKZQqg5JeXF3l81ow8nei4zaGJRS5HXleGRApCJ3jZ9oX7njsyyqlFhFTuWk6WRwLbpjkF1IaEuuDWWAEqoz/2tRJ220M2pr1TNWc57NuystuvsjGEUJi6roLA8ARmGP3LcvXZGpkcYSRBEC6VqQVndFTvu5gVQooZj7VmGjXQTDw9nYFyCGi21bNwZhjPTFxNk052zquHmdEacQhEqx0x+S6pzT+ZK0KPlv/sfv8Le+8iYvPrdNqBSVril1veJB+c9PKx+xgisC+oHLH1tdjxVKpzH0w8QhYR6JU7hWn0Twl966wTc+eESgXJr7raMzdoa9ddrbU8PxfRLmRcF3HzzwqsfAOwxbfupTNwCoEXzx+gAhBMfnrpj/0mdeYiOLOE2XPMiWjM/nlHXDg/GEB+OJu/oCTKM7R29t3TEjBI/PZ/yrr/+AX/3lLxGqgM+9do1vvH2PK3uu2Dk8mWGMIfQteRkIpBRc3ey7RdqfgMG5HTvem0MsllXJZlUxPphzenTGOF9grGFRLjmejZ+6DoHPI5NSEQch2/0N+t68Ebx3WNPQYJmNp0y/nVLVNR9+9x2Ozqe++De8fXwHvvedix/+xNcJoB/FhHHE1u4Wu7vbbG6NaPIKXWhevbLB/FFKVRkubfT52V94ibuHY/7dd+4QRhE0lqZyMSOjxJtcFjUzj3YNk5DFbIEVdL5XbV6hUyo2tIIIbRxSbg3kVenXG8FmMkB5tLaqavZ2dl26e152RU6TV12R1L46UT/mL2p8Uux8jOPf/N9+E6Ed2qGEdEqRtWmlsZYPT0787lmQN5p+HPHW4CrfeXCvW1gCGXS75FY9IoX0ZFC8AmXVzmlHu9g5xEVc8MJptCYJXIur8EjQKE74yiuv8ps//GGHwCjp5NFZpekHCelaW8n5oFxEL7Q15KYk9aID5f1dmq4V4nfxUrqXyC+I1za2OJjPOrO7dmHtTBWfMRtXRhMqSyRdPEHmIy/a77XWkjcV+dKZ7nUOwmukWWMNZVM/o3j5s3reFw9oXQK/0o+t/rcjC1tLqAIsPLMNIj0ZvFXHOcM9d39eu7TPizt7WAvHiwWzIu/uu7HGKZya8sKibHGk3kledPctUiG1cRPfoiiZpmfkdUnpW5O9IPIcJs9XCgISInKvBKt0zUY8JPepxxZPNvYFTRs30tkgGIOQomsVOZl57Fyg3bYRY21Hng5N4FovfkfZIRisCutQKoqm8aTTtU2EEGzFQ+ZlRtWiUdawqDL2R1s8f/0Kb9953B1XqBSRipBCsN3vU01q5zxtDZFHSVqUTCC4sjni829e550PD10rIl2yLAv2R9vklTt+w4qTpIQrLM6zJZ976SonswUGKJuG3/iD9/mJN64iwOU2mYa8Lrk86CGk8A65wr2z/pHdSAZM1zYRK+KtpmpqwihA+cU/VO46tmG5y6zip16+zjc+ekCtNXdOJlzZHHK2XHnR3Nja5uF0cuGZ7McRb1y9xB/ffehbcTVKSz73ylW2fGDmjee2EEJw/2TBzbvOT+vFvU0Wjyueu7SLUiOiXkBiJDprOFkuKGqXo/Q7377lXZ51Nx+0c94P7x3xf/oXf8B/+cs/zQtXdvjy556nF4ekacX1/i7XX9jlwdShMpNFSlHV3H7o/t3g7BGMMd6rqqIxNXlTUjY1d6ePn8mziYKAnc1Nx+kSPTajAWOdMkkXZEVBrRtOFuNONNAYzZ3Jowvv3Z82hoM+G6MhJq3ohTHXX71Gf14TKcVHx6fcn5zz4vY+V65eZnB1m9c+/TLL6YJHNx9RBTVfePMaN80Jd++OOZqm3Hz/hBff2CUKA+q6oa4bVFESRDEDb9A4z0rGM+9sHwUoKShqjbEgsZ3HjsC9p611gxKKvCmp/CZSIBjGCcZqsqoga0qKRcXrr77E+cmUvKi64NUmL5+atyf3n+3t9uMYnxQ7H+PQ4ML7/I5VCIFyM3en7imams0koWo0EkteVVzbdD3p9ZiHUAadLLk2Tcff+dOGEg7Gb4cL+fTFjtWEQjHxO21XHAj+5/c/JFABer3dYTTTLHUEva4tJdiMBsyrjMY2FwwI3d+7cMp2cVLCLQANq0UA4WTX0yLl+a1dPnf1GkXdcLKcc5Ytu88x1nEl2h3susooqwt6gYuWaL8/ELJT0rTjSVSiaConyX1G7709byHWUCtfNrYmWg59kZ1abf3Y1q9BS9zussV8onggFUkQdUWsMf4c1w47lIpiTTYvrHsG3rx2iaMP3e55q9fjaFF5JMx0k3d73IA3Q6y7BXh9Uj5eLLoQyXbkazET7TkkKupMIl2BXHbnHEjVFbptK3A9ukRbDcauPlMKsia/8J2IVcFY+0y4JxE4uXYvrAUrnl5cllVOpWpCpfwu1D3rxi92dx+vuYpbzUj1uzTmfhSQyIAC9yy1Lbt2jJKEv/rp1zmp50yyjFlRcHk4pNIZP/HyFX5w54CztMSYi+3CUjeUuuF7tx4T+twwd8kt3//ggEHYJ5A1VbFw6iossY/LCKSk9OTOjbhP2VRPLdCtqKHxIbBt6OuTir7v3z/k5994gc9cu8I3791HG8P//N5HF1CzrKg7U8+2bH88nfLCpW1CFXZcszwrOTqfczZdcuXqNqfjlG+9f8jRdMUp2dsYsXh8jogUi1kOQtAf9Xlxe4BU1/jO4QNqbZhNUxq9tgHBtVUCFSAkHJ7N+a/++e/xn/3Nn+J/8de+CMZy7+apD5CFS6MRVjvV29e/d5f3bh05Dyadk5YZWV1S+ny+J0eLekcqZKPfJ46cy7exgvkyZWaW3NWHz5wnnrUdCpQiUK7gvLK1Sc8GPJpMKJoaIQSX+hv85//FL5MtSz765odEg5grr1zn4J3bfP32LZYeYbk/OeO1y1c4O50wO50yPZ6g64bLewO2N3pc3h9y546DhA6PFnzuJ68zGsRMF05lVVQNfaXoRz4OZFmgddMlyg/igEVWonVDY1beZ+0MZq0lrRxBvt2oCmC3v0GpK8qmJvNqrEBKBgOvtMtLirTEaEPdIjtrfLztF/efcdV+POOTYudjHK9/5iXefv8WjWm6CWW3P6KxhnHuFnOLZVYUDKOEJEwoa81Wr9f1lu1aDz5SoX9prYd7VScbd5+1AuZbvsL6EMIpqmpPlhyXC1on3kiGHZRcNauCpF0w2wmj/fxh2KM0qwlk/Xv7Qex2Zu2f+Z14IJXna1gvRQ+6BfO9k8e8snuZk8WCsiXi2hXXxbLyPXEL4crJN10z4YtV0KFD7bDWUpqaxhsLPiujq22/bcVDll6d0w6Jj0SwFuPly9K392qzikloRygDkiDqFryWbxN7bpK2unOq7QWJ/xLXusm9tLxtF7TtkF4QMy1y7k5O+cVPvYyUjlfy1z/zGf7ZN//YFTBi3cNo/fzNBan2es5VW9A5hCNECMu8yLtjdoUjWJxbc21qVzg0JUkQ+/MzbMQD58HieS/rC4s2Bo258Kyurj2OV4MgCp3rdvsZgVy19zo1Tfv/whWexqM6EukXfXeulSdSB0ohrWujTdKUJIi6Fpk2jsjeE04dd/d8jGbNgM5oIhl2BcO8yDlezDmcLJgV7pk7WS4JZMhXf3CRYNGhn1IRKUWl3bXpBxGpcY7Ge5tDsrym0dZZFng0bF5k7PSHq/vov38zHnCWzde+w3bIbdfK8sivsdqTuJ2PUmM10zznYOw4UFdGI26dn1x4zkOpWNRFtxtff4u+c+cBoQzZHvZ5682r/PEP7nH/YMx/+xvf4fOffZ4/fueA/tagi2zf3xkhW1dP5TiDSkmypoJggNGWF65s81Off4H/8//z9zwWulKNNlajrEIaSagUWVnzf/9//xF/6yuf4j/5zMtMpmskXQPTfMnB/Izbv39A4WNbnjXadqVTeHrUUQga2zBO5/B0KkM3WnViFAYIH//Rtpd7Ycy1q/tsbW5QVRXLNOPnrt/gZLLg0WTWOZAvqoKqrFn4Vl+D4bf/4Js8PHKIR6wCGuPe7dP5gr3NIQd3HtPv95FhwKvXtwBIy9Uctkwd8nt1d8j5JEPg7BqKsmIQuyV+lpaAZJ5VDOKQzUHMw7MZjWlQKujayWZN/GKsYVll3ffs9Da8fUdGY1YbuCRIqHwsxHKZ01Qu2qPO3bO0Ph3vf+r5H32BP+bxSbHzMQ1rLb/5r792AWIXwKeuXOWd44Nu1xRID3PnXvIt3aK73XMqEGMt63ncbeSf+w5DL0g6dKDjs7Amt6QNZXQ+CVIqlPG+Iv4z+2HPJWJbx9l5MHEwcLfQCLFSheGktGldPLXbCWTQ7b67E2SFtAjW22yiI7q1i//9ybgjFDoU62IR0Q9i10bDdpPU+nCL1IoTVJvGO0JfVI6Baxt0R+I/RwlFoWt3HrYlGjvFVLUmlQZXVKinlm5PCJaOY9W24FZ5ZJJQSYxVlI27nss6cwsqdCRgd/yStFm1nvre7O7hZMK37jzg7/3kT3D3bMzvfnCr+x1tVuhVq46zvkW0HgK6PiSSXhCShDHDKGJWPIm4rFRFo6jPpJy7qAegbCofdeFJ2ypwz5rW1LruJtDu2ltPwEd0yINDIQRbyYB5kdH4Bb8tNtqCxxX9FoVaqe7W7uoo7vngzqazOjDWUDeWUIXd5qHWDTu9IafZHItzh1a+cF4ZHIadMlEKSS8MO2PJ3/7hTQbxRd5BuyFpicrrfki9IGIQJmjjCmihIDLObfpstnQFv1cLJUHEssqZlUvKpnJqTNN0n5OtmWDWuqGxuosbcYWe7dyoW7Wn8W7gLXr30ckZr18SnKTzC++vwCnVJCukyP25e41rrdnq9fnf/5e/xN61TX7hS6/x6//Xf8Oj4ylns5woChBKUvrrdGN/m+ncFYTN2mo3nsyx17cQRvDq1T0OjmZoY1FKdvLkdjSmQaoQhcIIhxD+5tfe5fe+fZNhGLMsC6ZFyrLKn3i7/bPt7+sKnfXEbUw3N623Rx0qowikRAUBRhuGQcJObwMtLLl11hV/86ff5N/90S2stVzaTfj+vUdUuuHB4wPKumJrNGQ7Tghwlhrr17nSDZPpnOX5gqP5lJt3j7rN5Ys7e9zY3OPtgwfMyozzdMFm0idZ1qhNRVBYXrjinKM/unvW3Zuq0ZRFw5XdIX/y3iOMtkRKkcSKKHBUg7Ygmi0Lrm4P2B72qJravYd2tSFrx35/i+N00l3XWIVY8DErEWWzqgpjFfLhhw8QCObzlKZpqIuScn7RWw4BQRTyFzU+KXY+pmGMoSxb6at7LJMgotINy7LsDK6ubW5hsdw5O8VimRYZy7JgdzjkPEtXiIkVnTldgPN8MViypiTyKMq6v4oztwu67w9ViDFu52fNapFoOSItl+JgNgVWMvd1lUr78+uJ0sEaybQtdFpfltZoz0LHy+hUUnGfRbWKzrC4hcgphwKXV8VKbQKOgNm2tQwGdaEMbAskg/ZFzpMFjvAITS9IuliIdkghuzyk9t9bz5/W+NAdxeoz67XiRODQMY2hNE13LaRUbW3aya8TFROriNQrPSpTd9egLQRcmKNTf0QqZBBG1Ma1kD44PEUKyfky5Xi+XGv1XYzhaCfDlgfhjkf4YNHVMVVaM4oVxjoEry0W2qDD9jN3eiPm1RIrVuaOjSfUFroiIVoRpKUgsOoCL8mzb9aucfvPLhes8ByeQCqsWRHCA+l25BYXrJuokFEQM8lT/6xKYhURKVfYpGXWFUMG67K9hMLQeORnzcTRGCrTdMng4CbvtrhP65zPXrnOwWLKaeqQ0FnuCsJ1XtJuMmRvMOJkubhQOG32elhriVVA1lSkZUVVV+xtjZgu3f1PgqhDFtvRWN0ZOoIrNmY67d6H9p3T1qCs8Zsgdy79MOHFvR2O5nPmhXvH2lbCabrgcDF9il+yBiBdaMuuP0dhILC15f6DM3biiBev7XL38TlYS900FFlOXrXFzg6H5wsihNtwaENVVCyXOaVpSAjZ7Pf4+rffdTygJ3xhrLVerm+4cXmT40XJbLagMZrxE/X46hxaocTFNm77jCiPnrbCDtfyU7yxew0lFVOTU9UVGEdQ18o6tVUYU5qatKl48coOV31UxUY/5u986VMcnqaM8wW1aTg8PmUynfPF5xxx+zxNn2o7/sn3P+T45Jwzn4HVjyL+yutvUJWWcZZ1z8F5vuRl9imyBnk65/kXN1FKcjZJOZtmDFkVDocHM25c3SJLCxCSOArZHTk/okXevtOGs1kKN3bZGw264k9r80R4bsDS8/PaUZkG1VRdoHBbhEuPioP39Go0aVZSZSVlml1AdaR6Whjy4xyfFDsf01BK8fN/42f4nd/4ercIxUHIhyfHnd+KtnC6XPDlF17k8WRC4YuK33r/fZ7b2gLchC+RND7tynr+hXPk1QicH4ryaoF2hL4AuriIQm0MdaeAoYNz+0EIQnbJ6+1EsdMfcGdy3H1uuzuWwrloCs/ob+WLnbzbT5hqjT/TLo4C4f1btO8H+0gCXAHSKnaUdO2NdvEF3MS6JvmGNdXPMwiCDilTFxRVaZ099bPO20V3M74Ugue3d3g0nXYFT3v9fVebFQXcjfV25YoY7hY+gaBoKgIhqUTd+cQIuNBiar+7aAna/vNPs7knDjsJ63fv37/wO41Px3YRDtFTSJ/2SN9I9Rx3yl/TthkwyZ05n4uTaIs4e2HH93jpioYGvXY9ParonbLXRyCVTw9fqW1ccbQqUpVXCVa26nhsrtCExqxQqVZJY7Esq6KTSish6XlvltZjCc+p2khiJlnmig+riYOQsqmZl2mXo2Zx3jet31MoHVLVC2P3jFrD7fNTntveYpJmVHatBWpXhUAUuDT09v6vrAIEi2oV4toYTT9KyPJVhtmlrT6zRdl5GLX3fb3gWI8pWV88WwRnfWRNyc2TZxNBW8PHJ0erpmyRQCUFzRPBjQfTOX/4rZvIgeInXr/O+dQr1xqHMFVN4512YW9nxOO7SyIZUjYN2XjBsiyovXQ9CCS3Hpzwwb1DhwSapnt+120bACYPnhGMuXbMCon0bfLAu3F/5uWrPDqYgxVda6YfxWSVW/hnZYqSis14QBxEVKahrmsfNmr8xkp0GyKJRAnB3/nKWzw8nAIQR4pbR2fsjgbO9LPJWeQpRVnyzTu3mWcZyyJfKxrd57175153zS9vb/H6pX024h4nhXPdbnMH50WOVhqlFdm0QC6cG/X7t04dWr52ew4ezXnx5V12twccnS2QEoa+hTWe5yzyAqzg0dkUeJ69zT7aWKzQaH0RQXftKzcHxyp085815E3Zeb+1z+DlzQ0CJJ/+9Eu8++5dLJZ3fnibl1++RrNmZKq1wSAo5hn97c1nPpsf9/ik2PkYxy/+p7/Ah197h/sTRyQrm5rauF66CBRNqcnrmnmRc2PrErfOnbS7MZr7Y/c76yoscC2hcfXsxnJbWCiv1HKQuuz4AKFSVFUb1eCcbQHSumCz12NRrJAo6Xk29yYnT31H5nkb+ZO5Q/8eo1wLIv3z/DnwFNfmzzPcJv7P5yb65Hj3OPuzf+jfY/xp57Y+nlzMXDuu6Vojf9ZoniT/rh9DfvEY2mvaFrnrozZ/ynVba1Ma66zrmyfaiuUaMm5s4BU2xhcvdpUBge0KnVaOLjyPrPLPWqlr9uJBpx5ctxeQaqW0c6o6/6nWeZG0asOmMV0B0bY72xaUts7mQeDeudDLvy1wni86hGodEXUIlyOpHy9mXPJuv7AigU+yonMr7wUhi8r5+EhkV3AdjufdotKirLVpupZYL3BeOMZzTNYVne05SCm7OIxaNwyjmL3hgHFaOIPEXsTDyXQN/VutlS0KYq31iKql7/lYFoh8oWqB7995zF/7S2/yX/2z36Oq/abDy5d1o0lzR2I/mGVMlgtOq5pb41OOJlOXK2Ut//rt73cOv50X1Z+iZBJCsDMaMAgi9vrODfrWyRmlbp5CcNq566P7512L1FqLsorAt0PbhdypBgMaX2wZn+Jdt9LrKOi8nSSCr3z+BXa3Bnz77YcYa3nn4CF/fP9Od496ccIg6SMxzIuCdw4PVpseALEqTLeSPm9dfY7TKiOUsgtlbbSzFgmlC9B99+iQLz73PE1puHX7nCQJuHXvjJ3NhGyymg/Pztya8NzVTR4eTzCloR97S4RFyiLPPHLr/LiSKCSJFMusvLBhE6xQyWHUw1pDKIIO3S+b+sLms67h8s6QL//kGzx8eMJ8nnL/wTG/89Vv86mNDVeQKkFdOnf6O994n50Xrv7Ie/1xjk+KnY9xVEXReUAAHfT3wvYe9+fn9KOItKo4X6b0gz4bSZ9ZkfoJb71d0tDmU4VSMQh7aKOprb6w89bW9aI1+k8tDNp2zgqFkdyfnHb+LoEIKJuKcm3tCqSirxKscDyH9gXpuAHGXFDUPDkB4ReYVr3UcjaEU2NfQGZac7v2d1uUIhCrYMqL57PmneOvE6xIou6fnz2Ztt8T+jaJRHQ+JOskx9UCap49MbecgLXr8uQIZLDWuqH7uSd32y0JGy6SxFsLgW5X6/9USZffZVoidBB1BNz2yH/UUrJOAHbfbZ76c3dtRMdhan1c1pGH9vhat972GkVBQFoV1KahFzgPmKZtuRkwYoUGCu8GDL4lKgWxiLriZpzP2OttsRkPfevPxX60x73+DAVSdtlUbavVYDrH4XVivbWuRVo2FZV/NiMVkHjTwcYTr9vPVkKyO+xznmbdla2N5ubRCZFyBnftdZHSm2tisbXtktStNQyjHrMyozZ6zedqg9Plgko7fkisXMaXlBKJxNqmI2WvP0OClZuyEz3kVKYhlhEbSZ9XLm9zz2+g2ud0vU3l+HSr427dpY0vpJRxyN/jyYz/9l9/x6vvdNfGy+qMIFzFWfzLf/v11fG1haVeFTY/6nlseVxKOG5Wu2Hri4TXL+3zxtXLpHlDRI/b58edXUL7rraFrFOtXvzsMq0R/u8Egr3eBqVuyJsaKwxlWSEsWGORSnZtcXAo88985iUAPrh3wqxYdq73rUgi9QaXL+zs8sL2Du8etiquFWcN3Pz2yu5l4jCE3KKQ5B4FaZ/ZzV6fs3TOMsu5t5jxuf3LjCc577x7zHOXR7z+6mX+6A/vdedWVA1aG16+scfXv3cHi2Wz71Pb5y7/TVhotGC8yNnbHHB5Y8AiXdl1uPN06GMoA/aH25ykE7SxxF6VuL6utFmAdaMJyoobz+3z3nsO3Xnvwwcsdjb58ss3kAK052Nd/dQLP+LOf/zjk2LnYxy60ZwuV2F07QiVxBrY6vdIq4pJniF6oYskIKXSNUkQOzMmVlLrNmTy2sYlrLXMy2yVQm1cf9vgHsxaN2wNYqIwYLxMO54BrNCCP2u4VpQLHLQqYGE0jdadgZySK2l1qd3iE65l9kh5cWEvvKS5dfRdH06G7xY1JVSXIVSZhrRaQcFKOKZOu6OTXsmz2RugtZMXCxzs3K7g1trOc2V9DKMeRVMTq5Cro22mRYY2hlgFDKOYZZ0zyTIfBWGomhrrC8Lnty7TaM3RcnIxqkAod7/8/Q6EYhhHzEs3EW4lw45w28q0i8bxk0IVeOdeB9tvJSMEYo0cfbGIbL8vVApsifatnu1kRFYXTxn2JUFMEiomefZsGS5uQpAIwsDdn1iFFwoYYzWzMnUok4BIRh3Cp4QilOGFQtoRZt2EGkhBICOkaTpOjLCiI89eVBL6olUKQuty5Iy1jIsZNzYudzwoV1jgHaxWO+d24e7UJevcAY+edM8eF9u9znvJRZnk/tiLpu6KcCUU+6MtNpMe98ZjrEc90qqiVpYLj71/9qwFLfRFQmwIiY7I6qJDn7biPoui9MXOquAALloYiDbwtc17c0UBa6Twom4oaFhUOQ/nZ6tnCLGG6qzaQW0shzvfCulbsFmdEweKrFohas8aLZLXFjehUt7f5tlzTbvpki2fRkoiGYGwT2w0LFlV8/ajA94/OubKcINQhoziPiNwkR75knme04tDlkXZIYXr7bBqzX6hF8QM4ogyayiaCqUkxljv/eWsDRptOq6hFIK8qPna92/x+PychVcpKaFIwtAJCryP1L3xGffwTu2sE6RF9wwdLmbEcYTwRUh7M2rPnXnj0mXO0jlFXVEXFb/wS6/wtd+9w+l5xuQ05zCZ+edA+A2IZT7JefnGnltfcCHTAKfzzCt7QTc1p7Mle5sDdjeGfHRw1j1f6yhZ7N//SIaU1kU9RNJtglfopqTWNW889zy9umBra+BQxM0h09mCh+MZjbX8pI+PkEqy8UlcxH+coyxK5mvqlnZCvXl6jBCC7V6fx7MZi7LEGrfAtQ+v89dZKSwQbuEXQmCl7eTDQgj6QUK5FnrZD3uIWPDXPvMqy6JmnC759u37WGsZRD0EgrKpqDs5tu0Izk+jIRrd6IstlLW6YYWqiDU1ShsuJy8URS1bLeiIgu6lNMaRel1KeYM2FQhDXl9subQTvJN2xz5005vjlTk7ycgTj33P3QriIFrjoKyGErIz0hrFCUpIntvc4v5kTKkbQq0o6pp+FDMrU8q1CI7t3pCt3pBxuiDxuVDtjqft8UcioI0qGMY9lmWJwTIvU5//Yzo/kRXPw1D48wllwEY0ACGYFs6ifRT36YUBRVOzKIvu+5p6RbYWAhY+yfvJa5cEEQKXhZXXJXEQYr3/zbq9YVt8RDKgH8QuPsE0TtJrdUc8bxeSQAY0pqE2NYFVqI6N5BbS0Pf5s7pkEPXpBTGRCryKxna+PJ1ixn93qAKvjlN0AZ7G8Hhxxk5vs1P9WY/qrBP0V88vXfHVFlihCKjWXJdXaNxKdm5x71uLCjVWE/moFiUlx7MlgZS8srNP1hQ88K3q9n1y19x57DTdO+V4H+67bLcRWo9yWRR196zBKln+yYX7retXmC4LHk/HtIToQKycp9v8PCH8dfA0s5YT02a5daag7TV86k1xo6me5JXJLii1RdRW3Dbrr8VFBGoz6fuwY8nOYEhdOf7gepHr5pDgqd8VwnJpa8DpNO1MD2MVEqmAw0VN0dQgBHnlSMftU9iahjZ2hdS6ln7Aaequf9FYAuPiL7RmNQ96UKahZrxM+T/8s6+yzFcE/vZ4a63ZjJ1qq59IFw46Pl/l9vmKXHovM4HgbLng6tY2CElbC9rWcFUINnv9rs25WGY8OprxMz//In/4u3cZT3PuP3DXoBcHpIWbe+7ePueLP/081/a3ODlbsOGRnckyIxABUgnSpuBkuuSt5/fZ23DFUOccv7Yp6IV+sxOEfvMmyUzeIXPtWNYF7z444Oc+/QL7ffc7TVnzl157kW/cus/hZM4fVTVfuHGdzVHyZ3rDfZzjL5Ye/R/5uP3B/dUCIiS7vRH9MHYLvLXcPZt07YMWDWjzoC6+7KudXdGU5HXVwcWhdHLf7V6v++msLjDG8M6DE/7ow3t8+7Yjs/ajhH6Y0AtjRvGAJHTePv2oxzDqM4oHXBntshEPGUYDNuMh28kmW/HI/1mfQAbEXsrbHpn2Mu3aNORNSVpnLKolk2LOWTbheHnO8fKcWbkgqzLSKiOrc0IlGEUuMymtcrK66Iz+2kInUZH3RlH0A6diUn6iTYKIaxvb3bVZVnl3/QBvGCae2dKTyG4RGkYJQrg/2+s7EuCyKomVk/u2i48LjYyptOZoMWFSpN3L3y7W7aiMM1rUxnCyXHSeR9oa5+S6tkBAS15ceQANoh6FrrrPBj9Jy5hLgy1+6sZL7CYbjKL+BVPAJ1tLq2fLE6jtSra/GQ+c5DzuMwgTIhV4KbT7vL3+yF+LzHlrtI7KMmAY9rvrvt5qK5sKLCRB6EjFiI5wWRuNwEUsND72pB1tsdLJ0aVcxQD4zw+8L1OlG86yaYdErLcwunPuCg6HTLbHAK5Fs/6caKtJPNLYWAfht893+/8tqrKR9Ghzp6z/rJ968Xn2RyP/c+sbhovKxdZb6s1L+xfa2+1zqITkvJj5FpK7B5VeBcbqtZ/7xS+/AXp1340xzoHak8ArXZHXOcsyJa1S5uWCebVgUaekTU7RlFR6teHRTxQ6rbowUhGxiumHPUbRgH6QeG6ToNYNaZ07R2JdXbB4CGTAizuXeG3vCv2wx6X+Dl+8/hr/xd/4CkkYOfTKXvRjErg2yiBMeHJJrI3my597gb/62TfYTNxcV+qaRZV3c6HA+dRs9/r89OdecKR403Q9uvbYYhVdeFcd0m2R1m3Ugs7h3BW5N08POcvmLPOSNroF8JYG7h7MywytHWp9sljSyg/W+TBtVlljGkpTc/v0iGjtOeuEF9ZQ6Iar3ly2aCp+43feJYpDvvLXXyWJVadyavO+AB4fOvuSl2/sMvKxDFlRkVeaQZQwiHsoqTiZuo315a0hIFaeWnIVGp1WBafplLTKabTBGENRXzS0bM99lrl8rUtJ0H3npdGAv/zmS0SBYpzmfPveQwgvqmd/3OMTZOdjHF//6rcAN3FcHm3QaOvMz3CBh7Vt/K63IQlChlHiWgItauOl1RYYRT2WXqpd6HbxlV6SazB29dIYaymbiqPpvINbIxUwCJOu5fVkUjTA55+7wQ8ePUIIQRIEPLexx1m6cjKOVUCta17eucztyan7LK2xGJZlvvJV6SYE23EpXFHgzsYRBHPG+bMDMVs4tRdEfsfpFuqstsRByJXRFkXVYIC80ozipJMuK6/caReHLpJgbfS99Nz9c+Thf1xLKwhJgoCiaVhWeWeqJT2C1irLlmVxgTjeOrGCRUnpkTeN1hppnENra+jYFgihDMj9RHN5uMnR0qEDI49+1D6vq51ULm/2wQQsi4pHkzmBCpBSsShXadjKT1hPFjzO0yfv7g84pVqLAIziHrMiw3hMfafXZ1IsWVQrZDKQilHYcwWfaVhUTuLdD5wpX9tSyeqczV7fk2npLAK0dZPuwqucFALlZd7gFoMkCAm9kV/pVX+GlbKtJRw3RjPJF2zEA6e+YqUealszbYvEnaskVmF3X0IRXGhnDTw/p9ANtahd2KpQF1yajbVs9hLOvWQ8VAHLwrW5dgZDjhcLf32fCMn1LQwXuFuyLCte2t7j9vmZQ/bWdvStoi1SgXNT9gskHqWw1tKLQv7NN97hOB1TNuVT9/pPGx3KilcYtegrktxz8dpCIJIhjW2omtrNF8+wc3DHLTv0TAnFZjJ0bWrjTPOUVPSimFlesBX0+fJnXuYbb9/urn2bBCeE5PJwiBSKSameQne++s33+euvf5rXdq/wwckhqXf0rnVDrEI2ewNCFfDm1T2WWcmyyjv0ar2gbIxT34FzPG75Ui13sFrjZ7X3ZRj1EMCkWPrPUfTCiBv7m9x8dIKxhuNswmHq3mGnRFTdPXv18iVmZcbjydQ/S4bzbMGsyCh2Kt7c32eS1y7HT1fcPD7h+tYWDyZjKtMwmeX86999j4/uHzGbFXzmynXKuuHxZMbesE/VQFE2WGN59flLPHrkjuN8kaFYFSYSOJ655/TS5rBrA4NTRpZebVg2DZVYayVae6FwF6x8jEKlkErRC5yvT9VolmXJpY0Bv/SFN/mdP/mARVHyb7/1Qy79xvP85f/1L//pD+nHND4pdj6mYa0lnS0RCAZRRF7VFC1hUjqfnHXyWlaX9KOomxjXuTotehB5d82uVaICLx23ZB4J2e2NOM2cYdiiyrDWMkpiApxfTjuBtsPlW7mWyg8ePfQ9ZnhtZ59JvooEaH9npz9iVq4WTYd0NPRCxwNqJz5wMPNmMqDSNXldcZpNuiJo1XZYl3P7a+fPd90ZuR1pDeN81pHjwJFUjdXgC45YhY5oZzW1tRd2cf0gcX5HVbuouB19oKQnNlq2kgEPZqdMy1Whl6iI3cEG/SDi/uz0guqnH8YMwx6TYukX7ITYL8ptAVA0ZYcYmLZ144HVJIiI1oqWy8NtQhlw7u9je/Tny4y/9tabvP3wmHHqftY9R+552BuMmBZ5x9EJZcCl/gaFbsjqwpGY13bwx8sp0i9Q0reQrF8cTtJVIRoIlx8VKsVWb0htND0ZsZkMuxZbW1w0xiF8s3zJRjKkqCvf8nABnIsqdTk71rW/qi76pOWHlIwi5+bctnJbkmwr/x9EPdIqozYNpa6IvJKpc49GspUMvIqr8W0el7fUJq5bj+6UHj07zqdd0dWgu58LxGrDoa3m0WTiiMu1C5YdxX1OFylHs0VXPLVctY5Ebi/ygu5Pz3llZ59eGLEsc7Rvqa1zs0IZUAt3b6umwQrbOYCXWclZNnvq3XC/2+70HUIWisCT2gX9KOJLL73A1z+67Z5rnJmhErJ7Now3pnwy/mX1+aC8ujNSIZ997jp7uwP+8J27XsXkniElQRuYZH6z5d/VD++ecOvwpCNqg7fX8K3Rl3Z2OVmmXd7d+kjzig+OjhiGfRr/fLdtw1LX1Lrh+f0tAiX44/fv07obt6Rtd21c67zUdbfRhNajS1/gpLQbksCH1lbNym8sVAEvXd4hqyo2+j0my5VC9sbWDrO8cAo6/2585fWX2Rwk/Paf3OLdk0fdZqsxmptnB9w6P2QU9TDWzcnzIudTsYtWKP33fv+HD8kaFx8TKMHZsqRsGhZFRRyENNoymxa8cG3HFTLAeJ51PEqArd6Ao+WURmvCQF3wGlsv8F68tMc8dXYStXaZbe46ub+X0qO51hIIhWkabBAwSiLOlznLouLS5oDECL784g2+c+8RWVXzja/94JNi5z+2IYTg9Tdf5offv4k2UPgwwTgIeGVnj0mRMq8Kzn3fPm8qTpfzNQ8eN9W4vm1DqWtnnhaEaOMWB7eICGpryb0yQAnFMOo5PoS1XN3a4K3r+/zRzXs+WNCNWEXs9oYdglGZ1eL9xv4VNuMBh4slSojOAbXUNaYwKznmGvQaqaDLzWlH2VTMS9+iakq/8/PqJxGynQx9pIKgH7k2SlFXZE1BXhfdZAXOC6VTivgWUNm1py62qVoeT3cvaOMglMvM8XEQUkim+ZJhFJOogDZLepwvmBbriFZIP4q5NBjyaD7pvlcKyeX+JkkQkTcVoVSU2nkJ9cPY/25EqasL5Mi27VMaV6BuJn0GcY9PXXqBaZES+TbhpcEGJ+l8Tcac8eHjMbox3axjvEIKoNau/dHC0u7+GIfIRH0CJcmrinGx8i2xuNbNonpaZh+1E71UhFISqnDlDA1sxQOmxYJSV8QqwgqLEq4omJcZvSChNc+MVEDeuD9PgphYhRT+Om71hggEiyqjbCoWVUbPxgzCnleHFO5zaFGxsCsm87rqOC3t2OsNkcrFNAwsTIsUgVPZlY0rgrXRxGFIqS/6I7UZdO0udn03C7CsjfcZwUnWjea9g4airjvUYD0Xy5X2T6NsdyYnvLC9y1l6sWhpW7Slrn3m3NPkXiUliYrdYt04J+VYuigSxMXvS1RE7mNetvt93n5w0C3sxrRZZs2PFCw475r2GXDtQCWl48gA90+nfP6F6931F54kH4UBNza3+eZ9VzRX2hWHB+dzJ09nJX9v2/qxDBgmMQ+mU5IgInvCaqHlO24nw67tH0v3HFkskzzl9rHle1mONq5QtmbdQsF2G4c2jiLwLeDaK+7a44pkSBSG4BHzWmtm3vIjkAGBUIwXGUez2QWEvOXwrJtpJkHI5sDxVbYHA7aTEbMyRRtDoKTfgFpmfrMjEEQq4L3jQy5vjDiZL6htTUDiWutVAYiOc1g2TUcovnXzhC/9zAs874nAZ4uUQRSjPeoZShewezZL2d0crM2hq2ddAMfTBaO4T6xC8rpamx9WPLheEPP81g4bvcStcbllmMS+2HGoW5lX9MKQT1+9wuF8zq/8/b/1zOfsxzE+KXY+ziFbGaXk6uYGs6IklAqlJMMo5lPXrvD127e7VlEbWNguEG7H425RbRrisO9iCtY4GlJINqKYZZkTSEWhHaHxjav7fHh4zMl8ycl8pQiLVNj1rAvvWBwo1clxlZB89rlrvPfwFIA4DDF11fWqK+8TtNGLvTOne9FHcb9TI1Q+NVpbzaK+uIiGMmCvv8lOb0jR1JykMwSuZaWEwAZhx4kw1imzlJBd6+650SXe2r9EEEhmRUZWVbxz9Jiyrj0PRl8gXsITctTqaf7OeTF1u2nlOCGN0V7ppby5nVsc3z15SNm4xX6nN+TaaJdKu4VOe6RilevksrSQ0JeJW9ybqiNQY1v3atV5mmwmAwZhwrJ2E8Uwcu2288ybtxnNw+l5x3WRQjAvVjtn6a0JWkL2ektCSdcieDCZdOfdDxKiIKBs6u76gkOatpMBy2pFyv6JG9f44NApNySumI+CyIewlt3i0w8TlpXjMp3lMy4NtsA6ZCxvSrQxRDLoCp1+mHQQ+yjqd9k7ee0CNbeSIT1fTK48cgyRv56t/HkllRcs65LEhkiPqkaDDYq65jxfuCDOxj0jC48OtUMhuzbSupqsfZIErpArOp4VjIuGRZm7d3KNx7b+GU+ONjn8w9OD1XdYSyAlRVM+s/BwXCbFRjRgezBk2XliSUzjyO+9MCZ7otDPfKunsZpbZ0ddcfOs0X5HIBRCukytQdSjje5ohzama8UXTU1ZNSglaJoV2XueFTSj1lhRIoRkWeZUSq3cd9d4bBbLII75xr17LMuSQRh3ZOsL5orGMC9zYp9xhoBERF1m4NgjLG0u2FKvCUSEQ3lcjpqk0TWhTAilM1Nt0etAOjRs0I/4z/7WT/Jf//ffIq/r1d/7TdNjT5RWUnJlc4PD6QwhBOfp0n2GH1v9Ho9Plw6NVpJeGLniQUquDDa5tr3Jdx8+8Gix+0+pa+5PT4mU86aapEvikbsmoXJhpUXTOKm6tWx4HtPBkdvI7G447uHJdOnMA7vNkUN9T2ZLbh+fX7z/olUx0qkD2/fsScuAXpB0atjd/pCiqEnnGcPEceOWRYUxYIylagxxEPDFl68xurT1zGfvxzE+KXY+xvHpn3yDh+89YG8wYrOfMPfxEY0xJC3bXUUd/N0WDkIIYhVQ6pWyo3UnDhAdstL2Tdvip5s8jOFvfv5Nrm2P+N33bjGIIy4NRxQlVE3dwf3GOjfi3d6A46XGCkk/jAhQXVRF6dN611scn7q+zywrmKRuIql1TVbTvazrQwnFRtSjNs6Bcxg5zocQsgtD7YURgyimH4YcLeeeXxH5fCJzIcbBWMOirKgyzaPJOXEUEsqIXpJQtcqyjiNkaeXHrq3j+uyTfNlNXJ3TME84GVsATeXn+Da6ARyKNc41izL17Rln1+9QFdO1ZAIhqf0xRCqkHyZYHJG6zTkytuFwOeb5zUsoJUnCkKX/u7yuGUQJk2wFkddaE6ug86SpO1g99ATNcOWCDQzDuAtWrZqVdFn4FobEEb0b31a8vrHtOBP56juHUczZvOgCLdcLnkuDTR7MTrwztsRg2Ew2mBZzGtMwyRdsJcNuAbe4iJMW9VonDo/iHtu9IfMy5ng5odQV43zOVuIg+daNWRtNEESd8Vqlqy6UtF0g07okbyq07ZGEEdMy7e5dYxrSuuie1ZZGmus/n2ljJx4X7v9rGo+0CpQIuudvEEYIobq2YqmrTrHU5VbZVZ4ZayBS1w62omuFDIKEUIVrhY4rlqFNnHfPv4s3cddpHWG4eA5u47PdG5BWVUeKbnlnxv+3Vce1KAwWhBRdC8Ni+ebd+x1JNgkDjHGo6QfHR4Br8165tMHR6Zxpka6p1Zz7e/vZh8tpx7PK6nL1bq2EnLTcsFAFhFK5DZGUbAR9pkXa3R+J2yC1576ZDKh1c7GNLyRFXZKyKuYCseJ5TecZv/G1d6k8Edv9ffAUEtgLY5ZZQ+RT4Vu+oGvna3YGAx6czVBCusyqsJ3zNafZkr/y1us8msw4Tecdgt3OXe2cVNuGg8UpG/HAhQJnBUXjuEVp7WwCjIWiaKirmjhwS3ueNUDL4wNhHNJ0Ml3yg3uPn3gH3J0Q/joXTeWNJvPuZ8D6FqYLKb51fkxtGp6/soESklHPvYeLoqSqXbu6bnybfbdP2L+YK/fjHJ8UOx/j2Ngc0XjYv6ibduNHo92L8N37j8iqikgqirUcnNDv3Evd0KZjG+EJnCqiaQnKfud+6jNWnM+JIokkvSjkS688z8F4zt/50qf51geP+PDwDCklrLmwOhtwB4NWuiFQkvvn0w6mbl/qQRhRNDWNMdw8OiWva89JcRM4aypxiZMbB9KpezbiAef5vON1AKR15nfkgkEYs+tVUG4Clxd2FC4yw+0kp0XKRt5zULpuWKQlUgjevHKVD46PyZu1RUy03hZtTpckDmKGsfMi6gVxF1uhre7cY4dRQl635NCnZc1tqvafNubl8ok8HtcOeHlvF5aWvC7R1k0s8zLj/dOHXBvtcGmw0ZkMNsZA45VZuSskK1MjRI9AKWZZ2k3cSRB1nK52GI+2hErRGENRN4RdYSxIgpBa665dEKmQrV6Ph9NVdtIoTFDSRQGsK0vaezWM+itejfdH2R9sk9U5lXYy6mWVu3sv2haSIQ5DBuFq4muvEcAw7FH3HAG5Ng3jfO5QN/8CtdLqOIioK3cvIo9W7vZHVJ5jYKzLmqNo2wNux7pcc4vuqRgpYOL5WYFQPLexx6TIXJipz0Vb5611eKFd/duqnljxbk6zmbvvnpPklFWm4zg9eS3XW0arjcvKIPTJ33GqxYKqce/gslxl6a2PNnerLaCcolExiCLmZc66m/pGPEBKw9gXu9oYhA8nbnlRAa0zsXsnD8azjvsRSEUcK6radsnwSRjxy7/waf4f/+ob3bl09gUC57Xki/c2C63Bz4OmTXe/qDKsmppQKBBOTbSs1n3EXMu9LbBDqYhk6O+18BsNdy81rpBoj0kJwes3LnPz4QkWuHcwptQrMUdbeCop6am4yx+UQhARdvOFthq8kV5VN9w9e4QSkp+4dsOh+8JFADVG8/bjAxZl6XywgoiahqJykQxyjXOkrWFSLJiwYFzM6QUx/TChMe7ZCmSAMZb3v3/M1csDGmMIZUipdXflDJZh3OPx+YTxMlszOlwTZncIpXPJ79p0viqNfExQu+m+PxmzzCo2BwmjxBc7eUVZNjTarTRKCTZGMSr5pNj5j3JMx3PS2pPaGrqU7KyueDxLqf2uRkkFa5NAoiIMtksWV8JlAmmjqUXjzaosoVLem6VtCUQMoojNDYca3T+ashdv8+7tc7JilWfSml21O4jGGJRQjKKAsm547+joqXMZhG6imBY5R8vJBZKz+27n6dDanbcLV5ur0qxNhsZaxh6t6IcxVzY2aM22kiAg92m8jpdRoq1GSYkxLjTzaDFlGCcdqvETzz1H0/jzsk9P9uB2YNqaLlNpM+kjcaZY06bwUmjJtdEOed1gfTtr/fxiGbI96PHmtT0OpnMOJnPG2bLL9ZFS+JbWKqRxPcwR4LuPfrQC7aNxzp3JYTfphSpgFPW4trHJpFigrfW7zJIhse/duyI3DnzUwZp/jLVuce+FLlPL8RJWbZrd/pC7k1N/fdwEfNe77Lb3aiPpkdaVc5S2q114W9xoA4Owz6JK3RcK6Ich+4MdHs1P/A6xZBD2XKHtSZtbychznNYIosbQD0PmTU2kQnb7m0xyR9I2Td05ULd+M+0xu1DHmq3ekH7oij7HCaoo/e63VSC2C2Aglbv/puk4Xso/c4FS7CQjlnXuW6kuPDKtMkqzWoQCqTq/q5bk3117nCqr0oaKp1unHcfF4osbh5+0yFX7SY3RGI8+lk3Vnf+zFHftcMR90SmttpIBEsm8WnFCtNWd0ED6d0sK6cjVVnXfU+iKvkxos/y0NQRWdQu+XTtWgLyq+Rufe4PD0yW/f+dmd10vb/UpzIr863y36J7FQEqaxp1nG3XT/pzFdqieu7bW57vpjuwP3qIAlwjfmogKnIpyXjq367aobM1Y2/dFAMOgx0v7O7y5e5mPfLFj1tytJdKj7pHPWasoPEF+M+nx0tYOHxweUVq3MWmJ5w+n065YHGdLVyiFzp/LWMuHR8c+uV4yiCO0tgSiYFm6NHfnnG29+skVPUVTUTQVk2Lh/LPONa/sXkFJydlZSj8KMKGheSL3qvVae3B61F1LoMuua69Fi5q213a92LTQCWLAuZUbbQhDyYgIgeteZFXTOSdvjvz68eE9Rvu7z3xuP+7xSbHzMQ1rLV//7W8jpeTRbMLlwYhQKfK64XTpHuJeGJBVLbHYvQw7vaFz8sT60LUVguDkrmGHWMRBwKRY9ai3kwH7myOGQ3dbj88zx2HIKs4XbqILpMQK0D5XBgH9KCSrahprUWv+Hy6ewXY8CGMtSRAy92h/L4gwxsnpX9zaZ5wtvDRzJQG2uMlnFYwoybyEXgnJldEmoyRhnKYMooSNJCFf1igpHLcDR2ZUQlKjwROTy8xdt8ubA964dok/unmfbA3Vcd8vuglif7jJosxZ+AIh8KTbw0XevdCDsEfZaL/IuYmq5RgFUpLVFbO8ZLwoeW3/EotUo42X6aqA/Y0hWVl3GWdb8YBKNx0faqPnlGvH81m3YHfPiy88jTXUVeOKBz8+PL94PmmVeXKqI1QGgWKYREw9MXP1mZA3NVtJn7JxE1frXxRIxUk6p1WpxDLqJuf2Pg3CGKUk1Fxosa4KSrc4DyNX7Bgswjr0bSvZYFYMmFfLzim63T0aa7oWRHevvOx6ucYPSYKQq8NdTrIJlXamhV3bxmjCMCAJY9Iqp9JNJ2F2ZpWWREREMmCcrywYpBBsxiMGYczh8nzN/VmSBDFCCKZFynY86o5PSdXtcKXnibSZc6zxy4zVNJ2J4GphWIE/zy5OGq2f+efPGvqJj3DEa4VAMoz6XB5s0otCHkzOaTxisawKRuEqt0vjeDBJEKCEyx9reWb+Q9c4US52RErRvc9CgrDSRX3ggoTbYwE4mi5IolV7MqtL3r75mKVv47e+SevOvcb/ebtRcM/ASpkaSNm15NrvyZuy46+1QbHaaKxYPceJWhGY23vgavIno04kmS559+CQR5MJcRCS1dUFkrT0AhHLxSy5UlecpjUbw4goCGlqzUWZ9gqRuz8ZuzY+omtl1aYhlMplhxn3LgzDHgrBoirQPivOAomKub61xdF83s1TRVPxYHbG48WYy4NNlvUOl3cGHB0tO67lOt+mrCryJ1Lm23myHYFQ1HYlCmlJ8+0/twXRRpLwqStXKRuDkK5w7icRaVGxLMtOMbuz5bzMgkGPv6jxSbHzMQ0hBF/5qz/Fv/xvfgOk5Gg5Jwlcm0EIwTCKuDwa8v7xMcY6Tse13RHTmVMt7Q0GVE1NpZed/NgtEpLav/T5mhSyF8RI6XrCfZlQdlW14xa0svenX3LBy/u7fHhwSq11Jwl1OyCJ1i4JfDXxS7aSEVJItpI+h/NzBMLnxKxInUC3m1nvcUshyD35rR/G9COHQp3nC5SU9D2Ho/Ul6Xbu7Y7ck4gtrvB67coeWhseTccOYmbVURhF/W6Bvbqxyfx0NUEdLSYYq7t4iTiIsVhma62AWIVc7m9111VKwbIsuXM2RinhWiT+nEKlyKsaJRX9KCb3kuuNZIjLOAr4/7D3Z7G6bulZKPaM5mv/dvZzdbutvavsctlgC3wOwsfoHEESOIiIRAqgKBZIkSXfYCHRiUYWwnbgwrLEDXcHS8gEiSRKlEYHDiEQgwOU47bKtatqt6uf3d9//RgjF+/o/rnWtutIrF0O2qO0a6015/9/zfjGN8Y7nvd5n+cPfeEN/PuPPkTVKmQiwUE+Qat67PraaoLQ53Z9a2Xrjbc9ACKiter2TEX7rsevPdnuPVNnt7DrKgADukGjVQPZLoBYiA7lKhNa5FNG5b5O2I9sKOiYDrVIBEentEViyLU+ERLjNMe2I4L6qq1xPj7EQTlBNTRWI6lGLvMImdHRYmfvw47lOMhIhcQx5rip156QDFDlWSZTzLORtxMZNN1jZvkcWyuM5jWVkhyTdATBHaJj3cgZVau1uofW5KruyMIAUPU1ejV4Dos3Bb0VsP6naPH49Yski3+/n8py5dGDJnmDdVuh6jkk4xg0BSKMsb3UHUDzxfFkgmVVE9kD2HOjd8EOALRDi3FeQGlbWZcI9L0BNxyCGwxDVL4Mg28/u8L5dOaPaQzwv//nvwIgaA4BYZ4AI05iXAYNUJVcjBBLW5nqycpGgxvuNW0maY4nmysfsgvGfYVlIgQSlhCR2X9/35LCaU4tK6rWHKIKUNJlYnvkcWdV49JM335+6dPw8TybSYnz2Qyf3NzA8SDNrWfpCPBaayjbT6lMUGriKLlAo1EdOjPYYDXzMgHa8rOebhZ4ulngN54+xDQbIWGJzQwE78TLxulBxWM3XAtjjEQiI7sZl8ry6vdC4PfffYBZQSKbXa9xs2wwHWUYj3Lsmg6busXhSCKRHKOC5vXR3RN8t9rnwc4rbPOjGZRWGOcZqkhnJ5MJ3jw8wtcvnvmJ/XhSQvcMYOQ2vqprsFsvTW9hW8FJQG3QEbHOTgrNMAAGeP+TJdwA7pXyg5UMOy1sbHfrz5db5CJBal2pm2GA5BydDhBvKjm+//5dfPPplUcYBq0xSkvydIp2hgThh4WgVQFmriJkIRMJnm92yGSCTVODGeD+7BgJJ6EvpZUnKbp+UlrjdDKHMRqjNMdhPsJvPny654TtzkWuxxzHozHKNPGLO2fMo0ucMZyUc2y6eq8MU3KBSTYiGwtDxzoqR5CcYVk3+MbTS//ZzObt635AkVhydU8w8zgrMGiGIk3QDoOXui+SjJR8hcRMjInXZdMUbx/cw7rdYd3ufF86lM8FMb5c2ADHkzGut1sfLOyhRHrAh4uX6BV1lYf02wjdII6RsJVgrhw39OvxdISnC5osY3TqfHLgEa126MABz9daNGs7sRoUIsNuaLBud8jli7u8IRpzDIAABdiHxRTX1co/I213+MoY6yk2YNNWKNMcreqxqDZYtVuLClLqQWmF62qJXvd+IQfIOPe2G/zuJaX430nzgn2M7SlEl0mGduj3COKMMRRJjjLJfCo64dJrMzmyambfLyp5D4Gv6wfXJ0opPN+FNGR8TS5N7BEFYXC52dDmRruFLPA24gWb0pZB72ldN1RBaCueYo4cAFxut75oYpzmnvcE0NzXK+21h+zN+Aqx3/fOfbz/yTU2NsXmeH1EzE8CumSvxiFSuZRUDWqvkVuOmBu5uZAe1XRVdXFz74zn0qjBp7g4OBgPfmJONyoVEgzEl3Eq8gbmBbmCXdfgcsv9tU+yDE3fo2fMB/+DVpBCorObDceTEpwjY+leHz5akPI+t/pYCZfggoKaIk1wU21QdR2qLsy1rr9iRC1u3bCfao2J85JLdJGQLWMM33d2F7OiQDcopJLSmotNCyk5JkWO51hj23Y4HI1Q5gmE4DBgEGnywrk/q/Z5sPOqm63lExaGBUgk6qPFjYd1EyFw/2CObz69RiIFBBjWLRkPlklmK0dgF5IhlJ4b+GM6c8lnmyVeO57jahXg13UTFnkXAI2SzOfsrzY75DKxE6xbMOFLIBrVo61bTPMsBDTGYDAGqTOME5L4Osx5C1E1h2Acre79+d3EfFxOrEdTj6erNSSXOBnPMWiNTCbYdXTPrlzeWTYMViE5S1JIzvHoaovffBqqCgArBMfIVmCWl0iEwLPN2v6OW+NNY/uhsIiAvUYwiygkkCwoMW+7Bu3Q43vuneC3Hj7zhn2ZTCCFsBwWWN5JhmWzRaN6zC3nZZxm+OUPPrQlxsLaMpDukIFLoVnzRw4cllNM8xFWzRYb64ztFqtxWpDDMuM4LMb4r959B//hg0dUqdbtUPetF0wzxqBMUuy6xqeTYqSo1wP67kWy9WW01scE61VXoOk0yjT1yKHkAgKJ50IYGNw0lGqbZiMMesC63aEeWlI8BrBua+SygGSUVg2lsdojmUM3oGGEwjQ2UBj04N3cL6x2k0NaOtVhfb199FrPCQABAABJREFU4V56YC+4+d1aLMwX0ZER6rDo/xiYrQqSYAiWGZ3qfaWZKwU/KefolBNVDEFPY0vDC5mis4KMlCqENcaUKJLcfw4gMvosHZMat+3vZbOFBpXkK629BYS7duVqim27HdwRh0aD2QVWMAHJGVqlKMXTdXbRhJ+L4jQTAC+SaQA8XS+p75XaQ9Cc3osfg8bAWVVMshxMSaQ8hWBtdP0AbDpGRGPMtXpoKYUdLeIJT2yARsYNldUhizWUOGMoRI5atdYaIugNxcd38y0DBRZ3ZjMMA3EkNTSWdYVmIFFDF/TErdcKy2qHzKLWu66Fq35yqSyyckkBm04iekDQGZNcQqmOuIs2JaWMgrYpOc0YNNNQrcZRPoWUHIt6R4bAOk6pwW+iY4wwVGVR8+rWjKMUmUc5mS2UWbWk9fPR4gbjNMX33jkHAFwuaiT2KHVP5tBFKiFEEJH8brXPg51X2BbXK6RJik7R7mRejLBqiV9wYSuoGIAvPzjHR89px//m2Ry/9clzAMAopdSUUxI2xmCSpaj6IFBoWoLWaaHROJqP8OB8gmfXWzuRGKzbQMB0UX38wmtLfI2rPU7KMZ5tKUBgRqNXCh9dLZBJaW0AtM/HGmgc5AUuqy1pQEQLyzQrcVOv0SMsaAkXOBtPMcoTfHB1g04pjNMCvVJgApAMPvjIJZG1XbDjqmIMCAX4jSef+EnN8Zym6QjLdkd8GaVwvdth1VLlUmV5CG7xblXv8/KFTH2Om1Ih3JpgEkTcqR7/8cNP/E4zlylySU7lNCEzDFAo08QHfdu+BofE49UK167UXmaQ1v+JCOoUCDuYPhUcg6Znej4+xFtHp/hkcYWbeguXanOtTAp869k1DIJStOACB/nYB7OpSJAWGWCATV95deUiSX3wNZjBB0QABZW9RRYc0bpVPXZLmw55CfDhngEDwyer55hlpGI8TgtfMbO2z6HWDZ5vyYlb2Qqt2zym76h9ysdDICLsbpTQMLfwjtMC56NDKKNxuVtTAYqBNciMS7b300YJl7bEWFkdn8JXFm27wP8SnGM+ylGtmr1d9TgtsW534CaoFLfWoyoXhJbEhrxlkqFRnVcnJx2rAVtWW66HxCQrKUWpBxwWExQygzIaVddi1zfoLamagflKodvoq4F5QcRvvz9pkXSCmIPuMU4L0nmxzfE+nKQFQD593roDYf5xMhbumZNGU4HHz1dIJKGqyzjdYjldmUwxwHHscoyzDNe7rU1vhebNOg2DYQzM2ADWpswA4KiYgoGjrrp9tCPqF3e/wpsbC8zyEuu6Q6sGdAMZj5aSY2spCsJwT5B2jQI3DYD7vmG2Eo36nwjtPEKjXjDnVbSZSpiAE8xwgSc3dHRtFBbtDvC8ytyiwEFiw9mZuEIXAdrctYrMVGFCKnmeBdkICv8plff+9YXn3dV9i117iJPZCFUzwPT03brvkWcU6DRNj77TWDy6wOho/qnj7FW2z4OdV9SMMfjv/w//Ctw6e795eEiOugCuq0DidYTLbiDy7beeEhs1FRLGUMm32zEDpGPxfffu4muPn/sgyJXldmrA6WwE2J3coDW2XRvKyE14gUdpjl3XRTvqoC46zXLkCe1CBGcQPMX9gxLGvpg3zRqDVtbYNIdhIWpPuEQT1aH3esA4y9FU/d5i3GsFzlKcTSZ4sl5BGWBRV0isOqvXxhFUsZMI6ZVpt11DlgTNFrvInI4BSBLpDUUNgOta+RRR2K0QouLL5m3bdmHHeFuF+WUtVyRsxu3OTwGAJkXTVCSohxbPNmtMs5FPTTAwjNIcJ+MRtGE+UOKMYdNJW2pd4SCf+PRd32ucjA6w7do9U1NXor+sg66OJ1un2Z6tB1W6Jdj0tOi+Pj9GqxQ6a144SjMMiipPHhzMsKqpAqu1ZdyDHsCMQWsVggVn2LQ1nNlpmHqdRkfrd4Nxi9Mw9e+wuDpXbZeKDOk77YMBH4rYgastajBOCxwUU6rKkqQyfFWvsaupzPq4mOLB7BSDMaj6BoKTgvntCkPY50LVN6m1tbDBiJUpGLTC1Io/8oi8zRmHkMz/ve47IjPbRccFAGQGS7o71dAQemIYNAjduqnXtmpHQ3Jy1DYA1h0FAgmnceYCzbpvfYojExKdIu8811KeeVG/aUrjctvXKGRKFUJqQKd6KzMRrEjcIun6qFM9th31Z5nkNBZtX8fIhuAc47QIzy1OtUZjZpqWxGcEcJxPcKG2KJPMm3w6+Q0Voce9HvB952/h33zwTR8UuAXZjzeYPUE9l5oaJwVykeL5jtKa8fNWCCiYtGKdzpan0wM+ubmxuj0RYsKZBwIdAusCf3ePVd9BMg4ZGSmzKJXVa4U8yXA2GePxcgUpuLWcCFwnh5hzi/75NCOoOICEFsM4HKyNTiKkR9HcM/TcJSg0g3egAxccRtO1d1abyPWF8/sarDWIyyg8Wa3x1vkcaRaqurpBIUuo76rdAK0Nvv6L/wPu/8C7L7xnn0X7PNh5RY0xhvtv3MG3v/4RlFY4m07wZLFB1YcF1imA/tYnz+C8iVRPZZaJNdZ75+wYi2aLZUP8jUVd4Zk1cnOL/yQj7sOgFU5mY1wsKvtv7Xde7uUC4KFQIBDj3E6Pjpf7hWpeFPiDbzxArxTee3aFp5ulV5m9qtaYZgqZlLhUCk3f+dJZqlxh6K0+THCoHlD3DX796Q5FmtidQWcXsIDcuLbtX4QQXsZLcG0RWSFQH7yYoqGS2peUA38KTOAmGhesud1NM3ReKBH2N5xRWlIZUhbuVA8Y4yfUMs0xyXLcmU9xuamIo2JI0C0TCaq+wbarMc1KmsyYsYR0SpmR8SaRqcdpYb2MwgLr/p7yFLlMbOCokYsEWxscMxBiANajswHOjOfoFQXOAEeRSgy1ilzLU2RCeP7VNM9wsd3aKqYEmRRIOMOvP/vITo6UbnMESrcAVFbMzwV97xyfY1E1dqEjxWOAE2ppQHpH0XNphw710EFyjl3kK0UBXXi+ropRaY1Vu8VNvQJAFhezosAHiyfeWDduzKYxXbA1SgqfqtU2qFPQSKyB5KAVXQNjyETm30kOhsc3Sz9+XICh7H067aZY/M+rf3/KOOz2syMeUYrHbtW3PkDwgqPGcZwIMXXjuBlaOMX2aTbCJCsxSkl76braYdA69Fs+wqrZeY0jCtDdaHLVaQK1edF7r7fIducCHYvouLE6TUdIZeoD2MYiioXM93hOANCZAaldtjo14FcefbS/AbDPLuFiT/065uAIEELzcH0Z5mKbumMAervRSTl57LkqP3esbd8AjKw4GCO75roP6JCbaQXjpC3lxghIm2YYWq+/5PpOG0JScylprVitkUriIt3UO1/qP1hBSsGcZhnJCLhU4GA0UiZJ/8dO7K4IQzIJA0IIc5nQfG0DU20xIm0AZQnnLtXu5tDcCnm2qocGBZuZSIhq0XV4utrie944xmrd+kKXqusxKbOQgp2NXhzYn1H7PNh5he2tL72Gb3/9IzBQie6mrfdIsueTOTZtjSp6UWgnSYPsbDrG4ajEvencq4Mu6x0ynlnGfNDXaQcarqngeHxFn123pInTq8HmWi18zOwuzS6ivizRTg6LZkVwt9Z4vrvG1y8efuo9LpqX68a8bL5WRvmfD0qhqb9zHkXc2Ev+xRnHOM3tZEwTUyziBhBfZ5KN0Ayd77ujYgKlYwifYZoVKJPMo24Mlhxun1EmJOqhwePNNW6aNfKE+BlEuhY4n05RDx1uKvK10tCemD1KMozS1JbS2t2XDUoLmWGBDbQx2HWdJUAyOJ7rNM9R9cRTOR0dgAEetYtLwoXVSykkEV+ZJcv22qrAcoltT2KWXk9lCNV0VdNhXKZY1/uO2o7PlQrheWCJoKCcMY5EJnjn+A6+ZsfLJB1hlOZ7u+6beo1ru4AOWuO0nEOpjRV0JESCMYZUCGzb1gc6DnInj63OVrSEa4tVvuuh9QGWMpEoHxhW7c7rzbi+KpMcWtOO2aEt7thE1KYgh1vHcGYROkdM7RWlMkZJilVrd8tmsDtcA2aATvekk2U5R99Jus5VxWUixWE5IQ5Q15LXF2PIRYq5rejrVL9XRu9QQR2hD/6MdpzUOnr/NKxQY4JESKzbFtr0SLjAYNMrD+YneLKmjYZL6Wir4s0Zx7qt91AdZ7rJGccky9FWIS0Vi2HmSWrTWBm2XYtVU/sBM8/HuK7X/nve8oGRoOGi3vn7cpsSaYNAw4ivU8gM182KSNGGnrM7puQC83yMcZri6Xbl0+WuwssJQmpjKOXE4Dl8HScj2EEH41jaFIX7F0KAc27HSuibQSsMUHT8KJhSWmFe0OZp0zSBt2SRMfc5DipmAYgLJQz3qapOW780q+gMAOO0pPGhrMyCSNENA2AUcpHuFSK4/kysVYW7hi+e3MHxeIz3Lp5Zzz5KbW/7BgdC4ptPr3D3cAzBgDxJsOs6PFvscDgpICWh3+/+qR95+WD/DNrnwc4rbIencwA0ET9ZrvB0s/L/nmQ5EiEwK0owDuxaeslymfiJZJySENObR8f41tVzb4cwaIU8SfyE0fbaRwC/+sFT5MhgDLBpK9zUnxKM/A7tthDe7cZAg7mzhncA7QJc2qGxzsHCkjyDbkqKTCS2imwAQIFdkWSeP7JodqQnxAXKNAfAIEA7401bRb46Af7ljOOknGFejLDrWss1Aa52K7S2ukFygXeO7uLj5aWfnO+MD1AkOU0i9QaN9V/KhfXdMcb6bYUd2/GITCtLnWLVVth2NS52C9ybnFAKgnMMSlu0g8wqb+pNuH+ZEM9nMBTw9b2vQCJEz4l5DVa0jZA+xhi+cHaMZ5uVF0QjhICubZZlWNTWJ8tWh5VJikVDE+uqrfxk6TVLbGl3M/Sout6OPdI8MZpE/mLSpwGRE6te+WsupERvPwsA03zi4fZtX1sEiaZzyQWm2QjbrvILtN+1ssCPorLx1iMBmU1H7TraFPSROi4DkX/HWYIPbi4xuB20Hjw3BdEdcEaI0ygtUCS5XcwYxlmC9y6fvlCpkicCfav3juHQH3IL77Fud0iFxEL1qK0Q4TbSSfq05tA80pmJXbnteLP9Nk5LTNIxBOPYmGYvHXw+mWLXdaj6Ds3OBtRp6Xf+0yLFx4vrSGvl5UHWRb3ETbvGqh1hnFBK07mRw1aGvXlwl8ZzRXYrvTIeLXFSCPF4cTYvreox1AHV6aN3eJwU9l443j09QzJm+Pff+Dgcw6qwe+KwTWfdPhedzwXeoXIKDFi2WxtgumCHruWomFo1YIYHBwd4vF5EStDSzzFlkpI+FwMmtjig1wqdHnBTbwhds7yiMklRDft9we2zMDoEeQ5Nj8u7jQ3e/l/f/BY0QpBxXI782uGajbvo73YspZwCQRfokp8c7DXAp5eoei3FTb2Bq2h06SiqIDSeo5QIDrT0nXXT4nhMQfc4KbDtKuuPRudp+gHffHSDWVEgT+h9rfseFzc17p4IJJKjOJy8dPx9Fu3zYOcVtjwv/ATz4TVxcVyNh/MEEsygH0hkrUxSKqWVAoVM0QwDjAGyROKgHOP5dgkD4sHoPugegAVC2UeXS3/+duhQWKE0Zy3h9HNo4TIwBuCc+RxwwgVmZUGLTa/wI+++iVVV4WpV47cvHwOM4X/2lS/jzmQGDY1/9tVfs2gVw2ExxSQr8Gy7QDN0yETi4VWAJhennbJsdsSjsZ1ydzr3i43jATiNHGPoXhyxz/Wju+dxUmBWlJ4XYECBnpuAndnmo/UVwfgADospUpHaPuQ4Lia4abY+fehJx7YlXKBIU1vZ1KFTCnfGR/ho+RS9Vriqlrg7OfaTGIxBboMdd52jJMcoTZFIsk3YtK3/nXsmqd+NBS5VPXQYJRnOZuQRRagFMAxBB6RIElzuggM9/Sz1u3yAgoZq6GyqyolUSl/ppC1HCgDWTeurPuh2zB66xezxM5mg7zofGDEbTGy6Cs3QUFDAnV0Bw643OC7neLyh0v33b57hweyMqmyU9ikol17NJFV5vXt2hPeeX+Db11HqgQnkgipYyCyVoHhjDOLkpTNc5bYKpVMaXb1D3fcoZIpJVqIUFIi7HfrpeIrnmxWuqw2MYWhUh0FR1cygFXrdv7SE93ZLuEQmU3z5wRkeXq2xrmskXOJsfIRWkVP6JM1wWW2w7ap99IXRM9p0FTrVY5QWhOzZhbEZWmRSotcag6E5pLc8G8ETDEZj0/Q4KifYtI1V6WX4yr0zfOv55QsL6KA1rqsNrrF54T7qvsEHN1c4LidYVjvrnbW/KSKyvYwC0YDBuvRXrFNTygLMBvKFTHF+NkZlOu8LaAzZo8i9Y1LTxqBIEqvvFYjE7v11zXGQbj+rhEuyK2EkoSHS4I7uxopgAncOp+hb2JQnVfWdjGa43K08kuL4isyOf1fA7+bbu9MZHq+WUGAwCByelCcYzLCHJDpDXscDnBclEslDKtgEEnhEE/LIYwIGrgP53d31rg8obS5S/524uaDMVdxyG8wDtF4pY/DB9TUGayCdiRS1ar3IoAHw8fUC33MnxSTPcL2r0A4DlDZ4erXF3dMxhBT4brXPg51X2LTd8brJ2dk7OPIvELQpJOf+pXnzdI6LRY2mH/yO+WQ0wfPtEgCVWrpAQHL5qZNuJolYyW2wE8O9HEBruQK0280wWP6HUoBWtMCfz8jg7etPLwDG8OBwji+cHmFXD+DgeH1+gm9dP0WniASaJ6ldQDvaYemQWnG6I1QOHXxqdl2Lb10/w8lhiUeXa0gucDwa47qi0klHyvSBRPRnmeR0n0JiMzTE0Wi23itHWt2Yxpb7MjAcl1PaLaoBMAJJkgCceAtDZL7qmqvi6NWAVdP4yrVUStyfnuDD5TPs+gbLZosyPaRn1A17JpeSCySCKqA2bYPr6yos6EkCDkoTkaggTbwTUXrjzV3Xeu6AMhr9oEKKxwaqDkFzwYyTLth0NRIuSEdl6Lz32qA1UsH9ItlrhbGg/DpZZtjSYLuYujZObTUZ454TZkCcFs45jsopNl1lK50WSCWVoLtunWQl5v0Iy2aHZbPFcTlHJoL+hgG9K6nlDGij8dWHH+PJeuEDKkoND1ipFqsuLM6xUiwAnI8OcTo+wM5KB3BOqeBeK9SW33JVrfDhApbk30Ewjk8WFeqhw/J34ak7NCaXqV8cuNNgYQzn40Mcj8b4/rvneHz5G0hFijLJUaYZ2tryURjDLCux62qrnURu76lIUA0tdlYDqq174jXJHLuhwWAUrps1RrJAZ0n8vSZh0Jj0SjpNiVXmzfDFO6dYVwOeb9cUYHOJREjyE4N5ATFxY+7h6uol9x8WdRZxxhgi6MEdw14LYMn11uJkajmHv/HhUzxbrlD3LUZJjtamCRmD15kyCCnNuu/9NXDGMU5yj/wKUOWamzvcNZGmEWlyXVZrjNIMgkl848mFR3VSq+fDGcMf/OID/PJvPraVksrqiXFPng7WHTRuV23lq71csOcQ74ylXhjTgKxdjospbuqdV7sGCAlLkSDhEtfbzb5LPHcWIhpcSBQytYHMPpJEm5ywYXMcNskFoa7tywe244BxGziubOpxYnmJShO9oVEdekM6aNOshGGkyN0MA56tNrh7NMZH1wvaUEmOftB4ernD8298gvHJ4UvP/arb58HOK2wXjy6iXSizjti0i/ELSawlAaqEckGB0gaD0p6P4Ralzor+AcAkzXFvPsPNtvbKxBPLM3BE4pjPkUuJL5wcY9u2eP8qTF5E0OOoh55yuQBm4wJScFxvK1zbQf8jX3wbCiQfr7XBJE9xNjrAs+0Neq3wZH2FaUYIRAybO7QBgCcvZzKFI63WfY9HlzR5pVyiHlrc1Gs/eSZcIBGJl/13RpCpSJAKAa0pLXRTr32VGuXiC1xWSxvUcRzmE0guwySgFYZOU8rI7FON/ETOXHm+8rwVJ7qWygRHxRTX9RqX1RKzrMBBUaIaetJJsZP0KM2RSYlVXYdjcI5cSo8kgZGmEEDkc8k4IJhX7/2lb37id/Wkx8J937RKhWAnErM7KscwMDgqJ55vkUTPwlmA9J3ylVWMO7+pYQ9Nczu/WV5gWVfodUvVGn1HPAFbru6CSgODdbeDGAQ2XY3DfGaRnwxno0PPQ3u0vsA7Rw88CpUKga+8fgfvPbrCrqvxdHsNZ4gLBh9AuyaZwCgtcG92gGebFa6qlb1ejlVL+ia9Ggi1MRq97tEOlEb7TrgziQ0GJJdeDHOWjWAYCcZtuhqZIARq3e72KvSaocM0z7HY1p63lXIJyTkKmfj3jTFa5Fyaw3FKXMUdIU70Pu0iW5RvXT3DNCsxSkorBdGit5WSO1sMof19CEzyDFerClVHRFVlNKTgmOdjTNISzdBZ1Wvly8NdD6XWXy4mDIdF1uz9nNkSb5ei7CPdHye4KBjD6XiC49EIHy8WWGx6XOxogZznBapBe5uZUuYY9M6n4zjjgXDMyUPOBQWJRQljblYhU8zzMTKZYFFvbSpqQN1zcDZg3ZEsxCgpPFeKMYYPn91gXCTYtPSOAMCmpVSl53cZ69vFsMfHUkaDaYUn6xXOx3MKoteXaAbjifvbrkEiBLhm3laG0CIyEnX35PqRmvK8HUpJUSGCT4HaXRRpJxn/nN07QQKVL64NYAwZTyAEGUo7fhJgyQSG/l0NnecKno8PMM5INLdMEjTDgKvdDl+4dwAA2LYd7p6UeHSxQz9o/Na//A28/SO/73d+4V5R+zzYeUXNGINf+ldfBRAWTQA4KIKFgTYaayssSGJxGco0xWZLaQHBOdpBIRUc66ZFmeSkFwPjReYykUIygUxKSzIjMu0sJ+2NqqPdhxvUs0mGp9s1VjtyovZiXXaCbiLi58mshBDA154+AQDcP5jjfDbFza5CmaRoWoWz+RiPl2scFTNc1SvLD9laCHrAyWiMp5sVJA9DzYCQrEmSYFEbjFmBemh96mjTVVhaF2pXJl8mOUg0jyawUVIgEdKXvA5a4/luicoGOkflDCmXuKpWJFQoErx7dAeccyyqak+V+TaRE4AteXbSXwEq9krORvvgcmwXiV3f4OPVBe7N537XOc/HQR9EayjLGzmelNjWnYX2Qx5fRqRhV3LvHOmVDoaIZNPg0D2a3IOatutrhkwmuDulndQQLQZOQdtZVMR6S01PSFJvBl9ZZyyfZtAKHy2f/q7j3403NykPWqEaGhzkYzI4zYJbejN0uKoWOMxnyBKBd8+P8Ssff4LHq+s9VWs3eFw5eCoSHOYTHI3GuK62+OD6OTZdFSZ8GLRDi3X7otBg3BLvNM58wOucwp3AYCZTSBv0OM2s3iKhQX17vzIKADZdjfOD0itnu/dMcJICqIceVd9h0awjbZ3c8pKUHWf7JfraKDqT5fSsmh22XY15NoYxZEdAhP0MO1f8YIigz8Hx/sXSlrzT+eq+w0FO47JIyIS204OVDwhpWAODt49PsWx2uNpufbr2ZeEiIR5xOiugDq5aapxmePv0EN98duXHgRvDi2YHrQPKQNIJma/mI/SKgvr7swNc76xHIKjMP+6zSVpgXkxs8Gb71ypSOw0i56w+z0aec8MYw9c/ucTZfLz3XpIgJo2HXCZYNltLqk/AJazpKDUKDlp8srqi9JBmnh9jQIURhcjBGMNIEgoLBAKzm4ESLiNUk3qTyu+15905Mc4YUXcebhJ078Ke2421WHuNg4ELqvCa5gWebWjMpiIBGJXODyaUop+OZjgoRpiWCaqbHlU/YFbkWNUNPrS6cYMig9w7RyMsty2+8t98/0tGy2fTPg92XlFjjOHBvVMsrpY0oECln/fmMzxf76C0xq7rPGksTyTAqHxWaQMXxHeDwtWWDN3GabEnKCdtfrqz6E+Zpdi1JHbVDoMX2nKKrIDBfFTg64+eg4NbafRg8CgYmQp6Z2wAn9wssKjpnP/Vl74AANhULeZHBZqWYExSNiWS8MVuGXZ41mnbXytCqbuwpb2pkOgAzPMSz7ervZTf3ckBdt2+Po8nJXMG55bc9B0e7i79BHdUTiEZx2W1ggGlzN49uoMiSbFuG0ghICEgQMjObX6OSwO45nbpDto+n49xsd6hG5QPOo/KGdoNLVBfffQhjsu5R0K8bYAB0kTgD3/pNXzwdIl1vV+uKjjHwLn3JWpV7xfXUZKhHjovZ9/rwaIN3CNDru9SEarI4vtyzyIRAq6aaNADKgDbnsrkL3bqO6oWYiAjWleFxsBRJCnGaQaA4fH62lddZTJFbdMxd0a04+OMYZKVWFl05+nmxmrRaPz6sw/2zmUs+jTJCkzzAhfbDdqhQ9U3uKoW0Ne/OzpDStwpyiRDJlIcFCNryEhBbW81XuZFgVVTY91WWDU7G/w7BeZQqWOMwiQtPTHfqTvH/eN+/usPn2BjvakSQXILknFo+04s261fQObZBPNiDMaojFxptYdw+j6hjvFPSSuFq2oFEv4jYn0mJFJrpWEY4QGdGgDNvT2AG+V132GWl2gGStGelhN0eoRd1+C6WlHJtNF4//qCvmcXXVcRxMFwVE7wzKbapbV5ibV0Yl+sRCR44/AAX39y4UVXY3PNZbPDOCk9Eq4BJCYE5b0ekMsMx6Ox3dBR4L4b6n1emcxwkI99n2lDsgGjtMDQUNm/EwAsk9wHOgBwWJRYNg2eL7dI7cZKR0alB3mOTCa4qe35WHjHOBimaYGd1W9ywpyOsxmbnna6g2AC7UBmtvrWu+fEJE3071gvR2njeU6CCy+R4D7r/mQMOJmN8Wy5Dr8H94RtpxRtYHBVBSpAKTMYrdHpwSNNo4SKRwatcO/gGI9u1miHAffmU6ybBs9XO2SJRNsPWO0aHE/GOD8aoZyP8d1q/Hf/yHevDcOAv/k3/ybefPNNFEWBt956C3/n7/wdnxMFaCL8qZ/6Kdy9exdFUeCP/JE/gq997WvfxasOTfcas6z0A+toRBofDLBkxwDrdsOATdv64CdLgtfVszWlkM6nE1vhRC9uFqU8nH6Ha5uu3ku5OKhyVmZetTNOLRlDBDcZffaTixX+7bc+BADcmx3gbEoDdblrwDiQSI7FLripJ0LidDQPyBXMXomynxhdagjAQVGg7ls82Sx8+uN0NMX3nt7H64fHnjDrXvTgeh1EEJ9vF1ZYjRAdwQSua3qhZ0WBB9NjMAvx1p7QyJBIadOG3FdeeG0S29zk5K6BAjRK5UzzAsJO+imXOC1pId/1TfC1io4zyTLcO5hg0BrP1tu9FF0qhE0lMv9cHTnQPY9MJHvkYvqutPpFQRGb20qyXivsugZVX2PZrtHb4OaDxTO8d/0Qj9YXeLa9wZPtDdbtzu+s3XgVlotyWExwXM5wUs5xd3KELxzew/eevIbvPXkNXzq5j7cOz3FQTDBKcszyEZE/00AwbweazA0Mlu0GBsC2q20BC/PpkkW9waLa+XRYrwYMlmC6bis8Xl/jty8e4bpaYdvVLxBPGRgmaYl70xMcFTOkIsUsm+B8fIwyKSG5RCpSHBZTvHF4isNybJGy8B42w4BRSgvk2ejQ7+hJioD7vr+pt/h4dYEnmys44TqXNgZIm2iakdP4J4sbXG12e+9cKkn8jXhD9I7kMsUsH4GBvIrqvsWi2XiNlVymeH12ioN8ilxmuN3cYt7rAd++foyPV8+xbDZkaGkXxlXbWBTApoesCey2r3FYlD7Q7yzhvEhSFEnux/DLmgCR6xdRZd28GGOU5ihk5lG4lCcB4c5H+GixJJ6alHjr+ND7YbmNxmAUEbytdQsRkJ1qOz2HZVNb4dTOc2jcMVKZYJKPwKL3mVBl4avyXDWUZAKn45l/9wXj2LY9vnTnFAAsTy9YgmhDyHssawADPwYSIcE4VdtlIvHzlpsTnCozAI8yO4mI203e+plLU/dK0XwNkrSA7ROP8NrqNA2DSVrgZDTDrul8oEzXE96flEuLItEa4FJdnAu0UaCT8sTzrLZdC8GBUUbnb/oeDw5ndF/2vVpVIYBMJ5+7nr+0/b2/9/fwD//hP8Qv/MIv4Mtf/jK++tWv4s//+T+P2WyGv/gX/yIA4O///b+Pn/u5n8M/+kf/CO+++y7+7t/9u/ijf/SP4r333sNk8t0rcwOA1965h4++/cgPp3lRYFAaTd9HuW6yfXATbjP0Xua+x4CbXWWJpLQbT0Ti7RicceemDSqjrhljIAUhJ2R1QAN1ua09IsJYsIuIBfPcUaquxaLZgTOGH3zwwB97sa2gtUGaCqxqK8Bl1VNdwPN8ewODfWKcTwfZEyilcFNvfGoqFQmmWYn7s0OCabXGJM+xqCpf2uksFWhXw/B8u/DqtYfFDNpoXFvtn1ymOMhmGLTGuqmje3T8E9K6yWTifalCH7jJYL/1WmG5a6NdEDxZmXOOUz3HRbXEotmgSFJf8TXPC4yyDFU34P/zzcf+eKnNj7uqDnGLt+POEVSQU2y72ge3FBj02FqrBw6Gb9888SaSL2tuMqTxJDwnxVWh5DIEVU6FuOr2xTDdAiw495OIMsYqvhqqArTLY6d7jJMSbd/i8foKj9eXXlTupdf4kh85ZGaWFwAcKphg19dY1FtwxvD6/Bwzu4vv1YD3rh6iVT3OxgdIhMSi3nhLh3uzA8vTClfg3oVMCuza1vKLcmy6GkWSYpSU5CWmBx+MUYpof+EgCw4KTmJPvFRQusw9y8vdyotgCi5wb3qEQma4qkiLKEYojooZyiRHakv9iyRD0zc+3WuB22jcGhs8BLSErC7I7sIFaNO8RLPtfeAwzQusWyLhO6JrJhJs/DkMZvkI7UBO3KXMUCQ5ls0mVDNxQcEJOBgzkIz59xmguUDbVOMky/D28RE+WZLlCQWDjPzDtLKfpUB/MNpXZjl0BwOiINMe2wYiuUh9YCA496nqeJ70peCMQXKJNw+O8eb5DP/xg8eouh7XmwrH4xGutjv/LCiopLHu7tnpTYXgm/k5XVqhQ2OERQCDija3ROrBDGCakFLn/eZwMccHAmyqiXPA8s2kYOh7s+f9FbhMHKkgVHXdVdDQWLeNvYcXX7IsIQ7l9949w9efPIMT/4z9xBIuIYXAuq3tvCHw9SeXmBUZtm2LVdPih968i2fLrd9YrquwPsk8feG8n1X7PR3s/PIv/zL+1J/6U/gTf+JPAADeeOMN/JN/8k/w1a8SF8YYg5//+Z/H3/gbfwN/+k//aQDAL/zCL+Ds7Ay/+Iu/iB//8R9/6XHbtkXbRkaZ6//xWjTfSZN55nkdDAzbtsP19jLs9m/nGUBIziSjaihjDNZWYOvObIqr7Q6ZTXEAjqYWGjn/Og8pKtvNhMS2c/dq8O1nRFIdpznBpvYFlcKhMUGRdmNzz2fjOc7mJL4nBcNiW0NZOHhdWzNTLjAwbQmmVH0Tp9wEI1Xcuu+9VtDT3dIT3aZZiUykKNMUeUIGoUprpFFQAYRdjjYKN5YjJLnA6WiOeuh8vryQKQ7zqX3591M6zF8TQ28saTxJAWPQKgXOuZe1dzlwbgyUPUbV9Rhn2d6k6VChaTZCNbTYdjWuqxV+3523CFljwLJpIuNWShsKDzNbvQsh/O5/0ArdQArCypaTEvpCCM3jzYsVFRqEGrqWcIkyoUV32VRI7KKacAmDoIEkol2rZAyND1L5XkUh9T3ddyoEnPK34wC1w2D5RLSo7jrSxGmH70xA0ilVO64MYyQSpyz6s2pqv+jG0D4Dw8PVBR6uLvxzccHUh4uniH2bmqHFv/rg12kXHQU7ccny7QVx21b29/EbF8idtxePeuPUqp1tAB3f6V49Wj/bQ6UGi8bE9+QaZxzPttf2PLf7C7bKz1krxNcEG4gFUnuvB0/gB4BnmwUFzUbhYrfEmwfnmKQ5Nl1jr28/PTfJCpyPD8ABMM7QDgO2XYtZPsa1JYYbQ4bCs7ywfm6+VwFQ6TNA4+et40NIIfDhDUkRkMovAPR+bGqjAS6QcYmO9bYEm3rcBTqlTAHG0arOcxkLK1ZI151hUVEFpAbxyFY2UJQ2yH+2ucFrh29iXozwg2/ewy+99xFudhUyWy7tihXcubdd44Ol1NrZeN8p63tHGmIJrqutHcu0uZmXOZ6sV56TY2DQmwFMMUyyAru+BWyqShkFbtOtnHOkXKBVwiKMO48EC8bRR1Vdxth52Ra2rNuQlnJoons4DIAT76nqAcuG5tFMJj7QSS1J333NpbXaRY8vnp4BIL04rQ2+cH6E33z4DACwqhrff7/9f/w3+OGf+F/gu9F+T6ex/vAf/sP4l//yX+Kb3/wmAODXf/3X8Uu/9Ev443/8jwMAPvzwQzx79gx/7I/9Mf+dLMvwoz/6o/h3/+7ffepxf/Znfxaz2cz/9yBCLf5TtsXlYg9WvakIpREeVXA517CQuJ0xANQWbcikwEFZou57JCLxi3U7OBlwjlGS4u50Ti99dCwAe7sNpQ1y6QZsQHRIBZbBFbp0urfKqQz35geQgkMrWjy6QUEpA6VDWiix0LBroyTzpdKZLXtN7YvSqg5P1te2goDhB+68hh94cB+MMRyUBVIZSqNdoOZ2otLuxsmviQKd12anqPsQ6IySHAf5JARw8YIGADawaJTy1R6JEBjnGVVhRM/Qf9OmmOj6B1+mHze3OB+XMyurPuBb10/QKYVl7dAYYGpLtxmjCr16IFXcy90KT9fXeL67sZVQAx5trvBkc43nuwWuo0qz+JyZoDJVkv0v8frsDN9z/ADff/4Wvu/sdfy+O2/ibDy3qE1GXkZc+IqTvfu0/QPA/z5GGBzyx/24UcTzsF91PA2A/LlcxZg/tOWynI0OcDo6QMITn16aZiMv9++E4bQx6CxR1+2KPWk6Oq773RB9NkY5YkI6/cxC/o5PEX0/PoY7DwVK2iNSoeT404X64n519+K4JS+Ti7h9T67p6Fr0rf8oVchwb3KI09E8BIuW68I5qWkLxjHPRzguZxinuT+2szzp1YCrao1fffptfLh8hm1XYdc1WDf7itPGUDB1OhtbCYJgrRGu16BRHUZpGkqZESqCcplgmuUokww3uxrvPX9q/QJD1VFcdegrsDjxwlx6xqWYp9kIR+XE62plIvVGv671SkHalLjWGp3ufQrqoJh6DtvFlgK2o3KEg5IoCG58CxZ82gCqnos5OvHz41Z7xnEhD4sRyiS1JPAU756c+g0Gj/qOglGa81y5txtjjvH0YH7kZS2GCNUqROoDXWf10KoeZRKed5lke/NbkK8IaO26aYJIpP3cOM0xTgtkQuKHHtxDtldda/DJYoHc9u/lZoc78wnKjIKwxa6G1gZ1M8D8LoK1r7L9nkZ2/upf/atYrVb40pe+BCEElFL46Z/+afzZP/tnAQDPnlHkeHZ2tve9s7MzfPzxx5963L/+1/86/tJf+kv+3+v1+pUEPN/+xkcAwq5ZG4NxlgKGobGVUwB8GilPJGnrWP2C2qI0Z5Mp6o52OpwxvHtyB+9dPvUIj99xMIYiSTzJbtADOM/9pOzaQT7CpmtpArFDv+57jJLci8O5yH+SlthZFExpg4wxHIwL4hjVwYPHXYdrwpKnuSFLBoB2clXf+KAk4RJ/4MEbKGy++X/6+9/F5VXlbRSUNtYKI/CbVFRJkHCJ8/EBltGEPM9GKJPcp3wYg0eTpJ143Ovmjfxs+qhX+zIArsWTg0PCdl1IZcVIUWuo1POd43N84/IxbuotGJ6T87fRKDOBR9s1FlXlvZV+tyZ5SDWlXGDVURrx/uTY+zY93y7QqA6zbIRJVpA0gR1ToyyB2dFduQnfH5tx9NaTibP9fskEpR6DgSr3C4/SGtsuoDXxWHZ9MkpyLPgWhRzjjfkZBJd4vLnCtqutS7eC4ALKEG/j7YN7vqLwoMyxbTqkNgg9GOX4xrMraKPxdHONqm/AGcP3nj1AwiwSaFNKTq8KxuDx5orIpMUE04z4MK3qcF1taMfMGI7LOSZp7tNazUDE/iJJYIzB5W6FXd+iTDK7gL4s9cZw09BCOUkLTLMROCOz1Hpo/aLobFgACrAdb2mSllh35AhP6AbxZd46OMOqIZRFW8I/+aOlmOYFnm8XWDQ7NKrD6WiOUub4ePUcBgat6j0SSuKEVCJ/VEw9KbhMcrKa6Fsb8Gmsmh1WCKgskefJjbvqa2gzwxfuHqHpenz1/SfE8bkVqHWqx5P10pPtVaQUPMkKpJLj6XqN928q1HZxl1xYPSAi79a2GiyzaTeAkKVtV2MwFCTMshEMgKeOGG1F8eKACACqvkcmpN9MLizCNk4LpLY6cN3u8M2LC/z+d+/h/uEB3lkd4d9/QP0gOMckybDu6Bk6dAcg9KMaYkNiQiPBKBWdCoFRmqHqCXUaZzm6nkjFzdBDM/h0lguqD4oxmr73QZ82GoyR2Cwh2TNKZ9vAmVCdMM87KQ03v0xTCtzm+chLUMQcT88LMsBVtfWImeQCbxwdYVvTHJonCfIkxdl4hnbocV0R97DqqRxdcoHnqy3uH87wxTvH+JUPn1CRzWqHUZZBpS9yzT6r9ns62Pmn//Sf4h//43+MX/zFX8SXv/xl/Nqv/Rp+8id/Enfv3sWP/diP+c/FiywQ+A2f1rIsQ5a9+k5/6wsP8PzpFbkP20F3NhnjaltD9XZQ2kAoFQLH4xEeLVbE6TG91ZNgGGcpFnXjJ/HT0RSPVjfYdS12fYNpSkRIYwxSGR5ppxQ434fkEy7AuCsLDn3XDAN2XWtTJUT0k1xglOTY1K0vhQeAt89O8PDZFhABwnUkU9eY5deQ2qyG4AZPtgsf6JRJhpPRDKM8gyEvTKzWrd1dhLTTfFSg0wqrmsQFl5H9xenoAKu28mjHJC1xUIzRKSrNHWeZFb2z5owWBnZKp05CHqCAyF/9LQQg4Ry95QxxmxYbtPYEcANEf6fvdoPBSTn3iMz172DbQSm+BLlM4PgopMFBi9PpmIjPjvfUG4VdRwJwLogO3IEAMzNQgNkOyivNOg6DTwsK4Rdi9/y84rKUGFQIHgRn0Mp9xkAYE3g7jGPTOWNJ2iGWKQWdRDynsTNKcmy72p/zsJjgslr60lnSXgKmeQGlGPKEUrFKEYL14fIp6oFUW+/PzqCVQAvybnLBWpAnJNXui4okCU7KuedmZJMMV9USrepxVa2QCIEvHZ3jelfDoAVjwGvzORY1caGcKOVEZh7W33uGCJUvYBx5kmGWFbgzmeGmbqC0wqrZ4ape2d08AwOhLgC8N1tIa3L84N03UPcKuXSIkgaGHsIYlGmOMs1xNJpg0eywrHc4yKdIZYJcZmhVt0dcz2WK1lb4Pdleg4GhkBSUKaNRJC02bQUpBE6KKa7rDTZt7XErd5xKK/zWxcd4uruCAKnochaquxJr79CqzoqfBkIvQHPFTb3F482NH3NOUG+cFJ6ge1RO8GjdolUdtCn9nD7LC1xsBQBrMGwMqp6ukzOGlCceYfWK3lmKbRvK2jddZS1jGMZpCaosTFAoMu/9v371a/jf/rf/JX7zUZBYMMZgnJF+WWfNVN37Miji1LjZRHKSAukUEQ22XUObKa8ZlGLTNJBCgqkB3NCYCbpkBoVIsGqqvaotDY2jcozeCtO6+VVpBSkF+kimwcltuNT3JC3wYD7HxXYX2XUEaxJp5w3GgI1NYVGxyAwSEto4NXqOjfU0PBmP8YXTI3z78gpXu52Xrni0XCJ9xPHF86Ao/7XHF/gvvvAazl7/7ggKAr/Hg52//Jf/Mv7aX/tr+DN/5s8AAL7yla/g448/xs/+7M/ix37sx3B+fg6AEJ47d+74711cXLyA9nzWzRiDy8fX3hGb2XLA59stBITf3bmYzBjj3cx3Vn4foKCgHRRWtVMEJl2Z88kM719fYNc1mKSl1dcY9iqJAHjui2t5kviSdHc8gFJpvSJDO1fCfVLOvEP6J9dLvHlyiG3dY7ujl+q5rRJLhfCcirilQvqXbdtVnqw8TgscFWNwxrFtWhyPx6jaHlXdI5ECR4clbnZUTcZB4mvP+tWezxdnHItm43VYptkIoyTzqFAmJRiCUSb1dUCgXPl12HGHnZpwHBH7E2U5FwaAYQwp4zZ4DSiGe4b+XCD+TjP0WLdbq9ESITQiQSETq2tC5GYYYNlQ6Ww39F74zE3a7k9uA4deDyhAZaEqWkwcfA5QcOOQKHreYi+Yc+k0N9G63Z4LVrVW0C63GT1eY8eSE0U02mDT7RPlHRdCG41N36Dq6r2USCok7owPsetrVD0tbCUvUKYpqtZJ9zPUw4BlU+Oj5VOP6DyYnhFh1/KGBpsidPysQkq0WuNoRPpPnRrQa7JcoOs3OB0dEGm5r/F0c0M7aktwJlE1hlme+0DReZLdbgzMly8DlBpyFaPGEJrYGY0bS5zPRIJZOsaqC3yWwIuhhfpkNMU4y7G05fvuPO7R2QwtyiRUxbjdeCbpHS/THIIDi5pK2wXjkDYQIY2XGqWiaqlMpNgxSrVWQ4fBLqi5zFDIBFXXWuSJLsDp2uz1g0VSXACstMaub7z4nutDx2MskhSSM2w3FaEhnMJUzhghEJtrOPE9gwSSc0hrrOnGVavayA4l9WkhDbJZ4YxhbFIoraAZx6AUVg2lzKbpyJdkk85ZgTwTWOwq/Hf/9/8AaBHRDZhHzFOrEP9pzVm/lEnidY4WjgMjEqri0oNVd+dQIKTIBTbaGDzdLvxYECD0s9dk7+J96NICN82WlJKN8cFOIVMyr436nCozO9xUmz2NHRe4C5BS+bbr/L0dFCMcFONgzAqaOhyin6cSaSLxxbNT6GfPsWlpU6yNwQeXN/jwaoFEcGgNXG9rrJoGoiw/td9edfs9zdmpqsprnLgmhPATyZtvvonz83P8i3/xL/zvu67Dv/7X/xp/6A/9oc/0Wm83xhjG0xHB4n3ng5q1rV5yA85Ndr3WPlXj+CCO1Ft1PbqBBmWR0IRwPp4DsCZ7WlmIuUPXD3sKutsmOEc7rYimG/yLUCSJD3gcedHYz07Sgoi7AN5/fg2tDS4WNMkZYzzxjET9gs6Law4arXqq7nBaPBQAUocs6wZSut0fdcibb86pNBekM3RdbX2gUybZXj4aAA7ysS8NdgHKOMu8l5Nb+GNjS0pnGZ9TT6P0TpbYXY57NhYpcs0wYJSkSCMPGXGLvHo8LnBvNsMfuP8m/ssHX8QP3HkTXzq+j3uzYxwWU4zTAoLT7m/Ttth1necnMYTyWANEOzFmAzVpr4t+HswE4T3QqP9pt1h1ve8rp83k78UYPwnQgmRz+O54LKBVrkTffc+hWYld3FxzBqWdUp5b8Gx7HVKNNqDoFI03V6LdqRan4xHmeeEn9HYY0A493rt8iF3fgDOO16bnKJMcb58c4ct3z3BQlkglVYk4baNESo8wufORmrLEKE09t+WonOIgpzTr8+0SD1dXMIY8wuq+RyoFypT0eega+z343/Wz2yC43zmkRoMq+B6uLnyV2mExIyKp5W/cnYTdrqt+ujc9wKCpDz0SFI0xJ71A6Ctd26arobWGZG4sa3z/+Wt4bXaKxKoft6rz16iNwcVugZt6hVyShxdAongGBuM0x/3JEabZCMejOcqkQGrTYG8fnuN0NMc4zf3xKAXYYdVssWkpPdUOneXXWbd3Q8KqD+aHmGY5lvUOTh3e8Wg4SKW4kFQEsOtrNH0LpYlT42wnlNaoeyvAymgDSak+kivoVI9m6PBwdYNVW2HZbHFZLbyCO0CeX9uuQT20qPsWs4JS+TfbHQjDJUStUz0utmtb3h64YzDw1XnKGiMzxux9dwCMv5ZO9TBGoVVkptoOtC445XJtNGDgq2cHax1ijPHWQx8tLzyXqlVUldoOPRqbKqU+IPd0J8cxaIVNW+Hh8gbV0PrgKOZ+KaOx62o0Q+vnk7ePTl8QICRdN5oPcylwtSGJjbdPjmwaUiITErkktL8biAPXDj1+5YPHuHj/Gb5b7fc0svMn/+SfxE//9E/jtddew5e//GX86q/+Kn7u534Of+Ev/AUAFFD85E/+JH7mZ34G77zzDt555x38zM/8DMqyxJ/7c3/uu3z1wOHJHE1PolKJSCyfgAZyzG2IW5wOmmSUBnA8HoCcrBloVz0vSixrSuOUCSn0Cs69jwsQRK4A4CAv0FgBPbeA5UmCrJcYOno5XVR/VEyJtyATUiTtBnxyvYRDSjWCmrAAI+O+W/cS6/gkXOCLx3ftziGoNBtjkJQMScPRWyNIpgxGowTbtscniyt8Yj15yiTHcTHFVb22hp4MJ6NZJJwVyLSSC1+SLxjDYIwnh7vFIyaPOxXjTEg/eTsn7kGTySIHLV5OfdZzQwhK8eRuSlAIz5sRQqAUAuMsQyo5PrpeWO5AIKX2UQWVtGia4zt0Q+/L0V1fAkA9OAXm28rJ1HIpcV1VVEo8hBLZuIVi/JCeAujdGuziFG7RLrh2jLbDgE4If70e/YJBa4PzURIqPxIucTo6wCihsvRFs8HFbonz8RzPsMBltcH3WU8pF+x0asA3rx+h6ltILvBgeopcZhhnKWYFES+bobcpG0uATaQPYo0xOCynuKk3WNRb3JuSiaEU3PY5w1uHp3i8lni2XWDXN9h0O0zy3Evgl4nEKMkt+tQjlymGiJ+RS+k9whKLcm67GoYBverx0fIZlNHIZYp702PsutajG7NsHMTbkhxjm5IukwxVFyo5OWNQESKrjMG0yLCqW4yzEru+xaatIXLpn3E7DLjeVRilBd49uodn24XXnwJsVc7QWuXv5zgdz1H3lEpOuMRROUMiBJV8M0YChZq4VoXMMM1G0MZg3VR4tKEgsUwy1H1n0WNKM8WboFZ12C4CuudaPXS+Txa3flf1DY2h38FIvlP9p//yJU0r9dLU8kUVzv7Mptr+x7a2+nTU5xrAx6uL7/hYDfaPdV31uK5evO6+C0TluC+q/tPN3ZQKx3YSCHE7n87w4dWNnyeD5plBlkismhZPVxssqwZfvnuCg7LAoqrt+OH4oTfu4zcfPfWmwje7Cv/n/8cv44f//B//Du/+P237PR3s/IN/8A/wt/7W38JP/MRP4OLiAnfv3sWP//iP42//7b/tP/NX/spfQV3X+Imf+AksFgv88A//MP75P//n33WNHQD45MMnSGyE65rSRIB0LS6N3LatJ89yxjBKU7S2YshN5EWa+NTS+XhOwU5X46Sc2uNrIqHGts+2lUmKetifaHIpkScJdl3npcpTIVEmmZ8UiyQls85nV3j94BBScF/plAqBbR+UgAFCF4h8Scq3ggnMi7GHtwEKGITtj/P5GH21Qb8j+PyTh2u8eWeG//jBx3hqJcun2YgQIUZKrTf1Fg+mR7TguvJpLqAs2lF3ne+zTErkALZdh8EYpNjL5PgAhsp4hS8flUJAK7NXDeJLXiNzTKdFESvF9lqDKwVhORlZIuwiHojCjp9hYDU7LGk0FQK9UiHYiSounKS/YBydGrxUPeBM/rCHZG0tEd1dq7TpGdj7dciXc1Om+6F72nXdC+RzDiBmrGy7jry0hsD78RwfxnA2PoAygzUMpHdSGYODfIJVu0Orek+WbYceq6a2AoSEXH178QR13yLhAvenZ0Q6h1PjhS13V1GQG9AnwRgUiCMxSnLs+gbXuxXOJ4eemO/InyejGSQXeLS+wqqtcA9HMACud7X3OANoFx9MRKgNVhcFIG7MrifPsJtqg6cb8vXKRIIHs1P73KxGCidNoyfWAf6wmHp9G6UNaseDEda3TYVBOyiF8/kIGgbTlojK267BLBv7wFgZQj7IcJPh3pR238939E5VQ+vTKlXf4tlm4cm9k2xEoo4OJWbEmaLUqsKu75BYpJSumd7t43JOFXqqJ4kENXjyswtAE0HPN9a2ctVL9Ox8YtgH8oJRCssFwcEjK+iCxRvHkMYNPmW1naec99VtjmFcsk8pY+xtpAL1ICBjrgkW6+rwvY2fAfyE4wVVo83ubb0p+lW4K7fRCMgr8/0UF5/Q5yIBxeideJl0BPNHC70nOFl5PJgfokwTX20rrXeiAc3ZqeB4vqZ04MEoh+Dk7r6qiUw/2Hnue++d4Vc+fAzGiNf47pfewHer/Z4OdiaTCX7+538eP//zP/+pn2GM4ad+6qfwUz/1U5/ZdX2nrSjzPb4FQJOrl2q3L00mJI5GIzT9gOEWx8QtHm6AFkmKPBVoe43T8RTvXT6xbrwDBCcYmgMvyI4zMNzU1pE3up48SZD0RIiN00J+wTTaG801Pbl+v3VygMerwCVwvBj30iRCILG2E0flDABNVq1SPmUWcvgaTTNgcJVQBlisW/yr3/7IBzp3J4cYpQUqO1nNixLnkzmM1pHzcZhARITUCMZwPpmgUwpby4Uy0cvPEHRpEo8qOM4Jh+acnMdhfGrH92o8gfl+puZSre5YoyJF3xHiEU+ETuBRadItOhmPUHU96mEICtlOSdmSMF01xtPtAotm61MsiQ12XHNVaPG9plx4VCJ+DvEYlUJYZ+lAvH5ZxR1AgUvVdX7xiY83SjMkQuBH3/4efHh1g24Y0NrjMcZwmE9wVa9wWa28dcTz7RJH5QydGvCt60fkAC0kXpueedkFxhiqrsegFLZdv3dexyECgDJL0NfE5Tkdz/Hh4hmuqjURviN0r+4HZJIqchjIeLUZ6GcGBoMaoA0hU70e0GlyNgdocdj1jQ+eEy5RSlLR/mR1AaXp/bk/PbU2E8YjGNOsxLLZWn5HhklWoFeDR8rc4ukW5lj0kjGGJ8sNzqZjXG8ruKq5QSsynbXmoa3qkVu9mfB+CGseS79niuGgGGPTVL70nrUMMCMbGFB6MLeVf9qQSawLULqIHO+ugYL2BLlIbcqWtG+c/kzVd7iqluj1gElaIpMZyiSkC10BxSfL52hUh0lS4o3DUyzqijy/ugbbroWrVnIk/7DBSSAYx7tnxxilGS62K/zSh9+EYAx3x8c+AHLnyaVEryi9NSsKfOvqCbQxOCgmyGWK43KM59uVD8xzmWBhuT9UzVXg0eoKnHGcjeZ7G6E9g1QwTNIUW1tpW1ofMme0zGCNksHRasttkqT6vWlrrDuCt2bZCEflFBfbpdczy2WCSTryTvetDaoPixHWVp+Kg/ljcFAlCGUDJKZpgXkxxmFZ4kuvH2OrW79xKZIEMMxzMzctvfNFInE2GQOMIZUCZ5MJnq4JPXy+2uL1k7lNvXP86Bffwh/7X/4Ivlvt9zRn5//f2xtfuG9zqGpvkdiLrI3BJMvRD8qTDl0zLJT5AoSiSM4xyhJIzqzHC0H5xNzfJ5fGjTOGeiDtHB1VIWVSQgju4c5UJNb3hFqvyCtnYqvXrnc7jPIE11tCiGKpfbfUSzuh3U7R9VGwY0ywe7hYVNAa5KRuNP7tB9/Cty4J6n3j4BSH5dT3n4FB3fXYtC2qPt4ZsqhEOkzss6JAIsnt2fWPivgmdEz4/i2SJCwKgvvqkF6piCC830R0vswutlTNFj45KSgQddwrLxcAp79C3z8cFxhnmeURSXu95P7sKp0AYGRJ3gBQ963VIArIDgPzgaDyi6bY6xs3/tzf/f3sIXCh7Nz1Lew1S/v3QWviK0RBVG6JpJkkcmom6d9xn0yzEYmeWeVnALipN2iH3gc6mUjw5vyuD3Sc9o8B8Hi5xWJX+/MmXPhFzDlCu+d7WE6QCIleK9zYhUX6dGDvRSFHtjS2HYgITTIMdmGyC3EzdL5/CpnYNJTjNQmq2lHEUZFc4M35uedZtZFVQy4yLO2C6XhDmZCWVxVSm8LtqqNgHgCutztUbY9d23i+jasUSqPz+UCcMTQDlT9P8xHuT0+IFwODRb1FIiTOJ4QQV32Di+oGq3bnOTKCBzSytalDh9BQ/0vPMXKNxiPHUTlFmaYebSQTThckJZYgTP0bbyicRkyjOmxaa6uRJJ5L6FCNTMY1eD6zjNzKB/zG04cAqOgiidLrwdJE+YCSg2OWjwCQqz09Z+m/54oZXDp5khZRNZrwvzPRRiOX0gdmDtl3yCRn3POuDMjk2OkJAZTi61QPwQUyK8hY9Y1FzoOu2jQrqNrWjmV3fTf1DtOs8PMDnZv5l54q8zLkSUaBfj9gPs3w4aMb37+SS4/mE7+H3om78ymSJKw1p5Ox5z8+W228pltv/RuzcVhbPuv2ebDzmTQyrgzQuf2pfbnaoUc7KFRdQHyYhfWd2BkQyMlpIpFKepkcuXPdVnvQuktFuP+ViduVxTo15L683FZ+4ill5ndf7loHpcAMvUC9UvjgYoFtE/K9scEfYF24jfGQqvsNTf6RzYL9+WJrPXEE8B8evo8n6wUYgPvTY+Qyt5U2UbAI50WkPHoV71z9og9gnKbgnCGV3L/8pAi8H0gWUmKWU3XPEKFCsRdZfHwePUdXXg3Av9yDRWruHoxwPC1wMi/AGSJ+TZQG8cgSlazOy8xXnDgvIMfzMiZUTJ2N5v5eDayGjv2McykHgDINCqu3a4lijs5hWaJMUpyORjgaFXSs6Bm7Z+kDLhsAD0p5xMZ91qOWMkE/aL8YuaDCnfOoIOSvslor67bCNyNE553D+17wzFXiuDHUWcPbgFKGRUzwoJvknuWZJfVfWc5DYlOJcYm22zz0pscoTdEr5QmZxyUFJBSECasoG1sEOPHQjV8kTkcHENHi6nbhmUhR9Y1XHJ9k+1Uq3TAgRipd0AgEY9p10+Ibzy+gDBGfAXjOnevvxgY7U6v47SuhRErBzfgQ9ybHSLhAPXR4tlnjwcEhMmtYelOv8XRzjXqggNqlyHpLzuVge6XMcT9Qo2vetg2Z9tqgoPWVTQk44zgoyjBfuHcuWsx7PVChByjAjPmA06zwKFPcnN7NR4tLrJsakgvcmRzufc4T7mGin5uINN+DMZvSjuavpu8xsyKNqbWbifs9tt1hILR4Xuah7+xv3HknWewZ5SrJwpx1XW9sfxDfrdfEOYo5mYMywVuLMRxYnzUA2HTtXql53EqZW04iXfOgNX7jm89xsaSxmjgB0mj9MsbgaFSiSBJkqQ2wOL3bX7x7DIDmlutNhcK+l7v2O1NRf1Xt82DnFbbmeoMffO0BJlnhoWdq+wNu2dR4vl37XblbYNc+/2kX5CSF4AxV16HIJIwh3RJuF1unN0MWCfvnyEQSrBbszzjneHizxMVuZT+TWl+b6FqNVac12n///ctrf1ynKho3zplXC+aME6na7sbjIMdd4bPlFu89v8D/8I2vkZkgGO7PTjC2Ew4DoUW3U2B71V9x/tx+rkxTa25KVh1O08QFDfE1jLPM5ta131F7uwIedjR0fLbXuzEyE7gddP+TPMPpbASjifMz3EoLkfdNCD43TYs8TZDZ3ZHfoftUVpALMIzhbDT31+Hc2/Xe4p36l1xw7idHlw6K7+N8MsGbR+RLtqxb319x/zIWPQetkUU8CgCeAK7hdJ/os16126JjPtWU5EjtwupQJyck94XDez4Ym+W5/87IBm9N33vidSZClZnjZMSN2WCHAV6sjrSAAkLAGcPYlnI/XVPKwgl7ZjJBJsM71CkyDF02O49wGGNwXZHKNfmOhRJlBkJJ3TuaicQHPufjgz1ZBACoB+cz5XzAhF+onFknQEhhJiXuTELQGAclreqRSSJNO3SQAUhl4oPITKb4gfM3cDqiYzxc0I5+lORgIFTl0foSi2bjKxAHPUBTmUIUlAX0L/DRYIOsHhfbkKpx1WuZSOn6p9PwTkZ/kk+gtH1C/KNOaaQiQcIpXT7Nc3uuEJBrYyA4GSx//Rl50b19cPoCuhFv1MLmheYtF2jtrKmyAaW2nfUIeQFaMriX1aCgM56b3DsxywN6Y4zxPyddLeP7zFsMMYajfGpT9Bq1RRudnMmmrT1SBxAHi7HIKNRo/0wHK2HhJA7ibHQqEzsOHIo94MkylKiPnSZdNMcmguN4PELdD9jZja8bk6eTkT/Wty6uUWap76PrbwdfwM+6fR7svKJmjEH9ZIGrzQ6Ncrv5fVRD2AU8tlnIRIBLlTEYVCjppjJxhqfLNQRnUDbH7KDeRb2133u5JHc8CQFAP5C3itttUVm3QCqTPURjnJNvlIjfEOynYmLOitNacIFCXLJcd71/Od0i3ymFR6trv5t55/guvnzvDu7PphglCRKbXiqSwBmIq28cedE1VxkzzjLUfY8nqy2uHHrkUllRGTpNWE6ePvJbsovK7SDLndPdbzzZu08orQEGtING2ymstj0EgyfuuQk2DhSqvseHVwu0g/KLsOPtuDJzbYwPQgal9iD5RbMhTY1h8CmsaZb7iVgwjn4IZezx5OVIsZumxbJ2C7IrwWd+dmQsBJPdoND0kWaTdW73fcUo8BU8BB+e6MycGS1xd1iUBs1lircO7sIY+vdBUewFL7ktK1fG+KDKBVMAMMmSPeIqAMAYjBKypACARb0hMqYdN+umRSaF53poY/B4deM3IM77q7QIys6mEZzfkCvRdUJ6Xzl7AM6IzzPYNLYT1HSKwI7sfGgVxuO3y1fAWLsHp7YMfz103ZkU+P7755jmhZdeqIfWBzvaaFxXFS63W6y831Hqx7brt0Vd4wfvv4bvOXmATCReFsC5lQOESi3bLXF2tEJnfbGAIGngNgjjNHsh4DRw74i2QpPMlrKPodSLOl2Bh0XvQjO0yIW0C22Jr5zfwzvH554X51OyLPB4fvviCVo1YJRkuD879pwY19+O5EzXR993HDRnq7Fuax/0plL6lFNry82JpB3m0LhC0h1fG41V3e49Y7cxaa2Seib2U3GCcbRaYWQD8NaWr+ci9c+apASotbY6043pZuhxdzLz7wKZBqtwcoT5kLPwjlOQ3/t1isx3sYcKH5YlbaqUxmrngh333IAvnB/Zvuz9urHYNPjw3/wmvlvt82DnFbZHuw2e293MJMv2/ESAMGkdFSOM0wwj65kkWEjaxGXquZRohgFtP6BuBxJQY8xzbDYdKYkqHfRjAMth8blt6SuElNG4tOZ9hBCFyT8ObCZ5ik4rdLe4R9zujOJJjTMWleVSa1Ug5dIksD+5cMbw7vEdzLISP3D+Gt46PMY0y72TsPtMIEga+2cgcO4FYvbeq7YjImsUUMSck5hn467FE5s595O3g4ZjEq7rQyrRDmkoh1wANNE54rXWBoaFa1aWOxVfm2tXmy3GdsF2ixZpdFjZeAReQ2/TFC7outgtqWQcxEHKpPTBLFkzhCqWeHsnGMembbGw4pXzIodxOX0W7j1OWG66YIMgON+bTFw/kWKr9qJ/ccts0FImOTmEM45ZXuKLR/fhxsisyDHN81AJAyLCnozH4do5h4lQncNJCSk4VJRamxQZjqcjr7mzbndenyqzC343KBRJ4lMKW8vXyAVdZyaEXwDrnkqllX0ejsfBALxzdA9FUmBqj1N1DQCDjdUZmqal58jN81FIxQL7wSJCqg0scNIYGFIpMc1y/KEvvYZpQalet+mputaOHbovhy45xCAEsXSsXJL55gdXC5yMpvjRd74Hr81osWott6aUufd/0pZD1ukhkhUQXoeFgeG4HKNwKZ3o2RQy8VWfmUytQN+ARV35MebT/HDzXuav5Y3jAzw4mGFeFijtOxJkG/aDpWbo8MENcf++dHoPnLM9iQdgP+XqUSgrxEq+bfROX+wo9UlaMiGor/rOV3kBwLalSjyltd/01UOPdhhwtdsRv83+1ytFm1m78SqS4CXmzgUAs7z0YpdV36DTw57flUOnDAjZi9NfN03lPbYGE6yj3T3vGRErbecV43llmQyUiXhjxjlH3fe43u28v6AvalEarx3NPW1jZUUIq67DW3/k+/Hdap8HO6+oMcaQWfjuwcHMIxzRB/wA7bX2dgFukfX8EocacKoFulxvQYJ+nZ/8chF2o5u2jtItoWIk+E1p//edE/uzJoFArI3DPC/m/csbvyiPkrD74NHOeI+/Enn/hIkO/nOOr8KiuenubIIfuPsGRlkBZQx2de91dwC3ew/njhWPnSu2O7822i8QgpFcfC5fDOJc3zg9Ftc/8ecc8uYmBaevIyI2ediFCwt1B+RKKeOJ58rejzEGba/28u2pEJhbOP56RzLxrvIqFhd09+0XaBuAZra8uBk6LJ2bc7TDc+dw95BG4pz0rLj3upqXOeZF4RGyvVw9XrRiccidQ0Fcn0jO0A0Kz9YbXO6CGKU/J+deAfmoIBJ63feR2rObtM1eUJinCU5mhR9Tg9Ze5VgwhqYbPPLjmtHA0+UGRZJ5PsrCEpWnNr2w7VqM0sx7uTVDi0xInw7lnGOSBQTlulr7akk3Gl+fn+F4NIEBVcwAxEPatKSMnVjhNWe0OM3GnrQO7Ff2UR8IJLZaL6RfbD8kCTprO9MMA0qbQqmGkCoDCIGYF0UIyrn06SxHZnYq1Ou2whffPsRBOcW9yQlSi0JVA9kbuI2VgcGiWXs0IZeZv7BxlsKw/aBKWn7goJUPkHIZqq8courQjrj6zImhAkClaj/GitShns5OIfQhADzfLaGNwXE5wemI0kEO5XS0gtvGv0AY52Dw97uot5akTeOg9FwihU0bUOPmJXpjL2sG9B72Vt24t1VwLmXnquDmeYFRmuG4nHpEcNfX1j2dPjtKC78pcgFtaQnc19XOpyOBOEVI//YcH0PjxAV3Maq2bdq98QdQCnnXduiVRuUqYu1JlDKQguN0Ntp7Ps0wIEu/ewXgnwc7r6gZYzBUHcokxc2ufqHkGCZU63Rq8NVJ/tXzlJmwkA5ao+4H5DLZc0cXnHsZ/JX3NYHX8Shl+kJqSxuDVUML0CQrkacpDqwzL8Hx5Ogbqh0kpllB1U1CUr78VvmmO6+K0BFHSB1HQVLiuUPGX4syBvNxyHe7lyvm1rSRu7bjsDCQ31W88wcofTLJMhyPy/3JLN7J2Wfg+URRaizodNC1BlsNS2y+tUNkgK3eCbtzb16qCAmqrNrrbV0NQjcSVP1goXUKeFya0PMvhj6UxfOQXwdoYTuyWkvLZkewuiEdHEeedOkApTX5XsVpAxPpcjCBopCeD7OHNpiwG6TzSr9AxyRxAHjr9ADTInjQZRZBcP1lQNyITErkMvMLy1W9BgPxjQyAXWR5wgCkguPRTfCYAmgibYcBiRSorcBavBNVRmPbNMSDsP10uVvZe2ZeJyVNGO4e0u87NSBPEx8kdkpBCO6Na3tN6QeXwshlipPxDKM0heSEUjEw1EPnSdHTbORLf1NORNu4z5KIm+bGFOdBAoABhPJYtOzZ1Ra7pqPUp+0/GicqIrdTtZl7Z1KRhCDNjvlJZlM2TYt//h/ex6ZpkckEb8zOKM1oj+NSW64Nhiw00kjQsrC/9yakdvPBGMPS8pQEE5hlI8zzfA/NcM2jOx59pbH70fW1R1U5ZzYtZdyH6eeMox06H1R96eQuvc8MEcrJ/FccGhWf120E8iSzgSBJc/hNkBB+7LtdG/mChXTe2WTiuYajNLWVdjy8L7fuubeVWalIkMl0b7MIkHwCAHstHU6s+vcsG/nqtHboIBkhaI4Dpyx3J+UBwXZ/prbK0TCnNRbmJnrXpBdkpb61dIS+R20Lal47o/cl6HvR9+8fTRGH7lXXoVn/DsqQr7h9Huy8osYYw5tfvO8FwQD7YvkPwLPzlc3xdlp5PoYj/ca6JYPWyBIy+tM6RN+cc4ztRLfrG6vZQn5L0qZWXHOD1VWCSC4wSQv0g0IihPVyaiIiK8MoSXE6mdhKkxokbJf4tE0clMTncekSzhikDNBvmMjC9XRKYT6iexhUCHTi3Xnd9y/0iztPrEEC0C4llcISrMNxXKoh/nv87xjZSQSnYyAs+MryhfzxXIBgJzknWoboWNumx/vPb/BstfX3Ek90qRA+Jel2Wrsu+CuFVBap0joBQ8G5362751jYReGmpoqgqgsGiHmURo1RJQB7CFynFJ4tN/45xSIGyqbpJlmGSZaHz9jrdhwuAGgGhXfODnF/PsUbhzNKjWG/uTRCJgQOi1D2PBhCHBkoBRgjnNu2w7p2/kqJR7nqntIFBoCUfO9Zb9ugGXJ3cmgtHjqs2wqDNl6N+fl6i0fXm2B10pEBbyxU6CuEItd6ClgzcHsd45SqnWZ5aftN+9J2R0wuk3xvUaeKM+lRw1C5xl7Q5nIISKcUbqwMhJPrB6hk2v29UwMWNflaCcb3SqhbW/XVK4WJ5Yn1A1WfOdL3QT7B6ejII0VdtNGgazG4qBZQWnnExP3O9b9DknY9oSC5TCn1JQQmafrCwh+jjgCQ25Lrh4sbby9jtPGowe0Nl/O/mmUjTO0z6IYhoN4sbLgc6ThubpPDGfOVbs6XLXD9SHzVI20yJT0ui8j2SmGSZ2CM4QfeOsfrx3MfCDmkMOHkZD622kipkJgXJf7A2/cguUATVRumIsHEKmxXQwulDcZpgTJJfZpLGY112yCT0qcRm6FHkWQvFJK4fog5S7soJTcrLFeoD2PPBXtu855KjpNpCSHCxsPNe+fz8T5HyQBt8jmy859le/8bD335q9sduNefM+YnaSDsTlQcQUeLuNIK/aBwfz4HIiTCfZbMJel4Vd8gsWkKpTV6ExZwbtNMTt/jpJz5yXXXtR7mlZwjs+KAAPClu4fWCJEmrWmeBR2TONDxiMi+iqg2oYrGfTzWjdm1HX770aXdlWrvD+aaW8RuT0pUXfEi56FX5PTtUjHa5sVvB2Uxl2SP9Mw5ijRBJoXfYccpGj/J+tsOk6C7xE4NuNzu8N6zS1xtg3J1IkSAj0FpONeOJ4WfUN2xby8yDglLIlsQyckA9LCYglt15Zt64+0o6J6k7791E2TkGQLKliUSnDMfTHDGvOcUELhGbuJ8WYWQa6tdjboZMMkzFGkKdntFQ3henHMclmM/aV/slp5TEj8xwRhWlkDtnksupa9eu6kqVF0Hxva5CV2ErHLGcGzFLi9tJaKrUl81tAlwC9ymrfcQpF4bZCKxqubBUdzJBDg+xqA1iiTBYRG4RdOsxLarfdBBQWxAN2GDf6/FBJqgtYnFHffR1Kbv8XwTdsuuhL3qW7KoAfHEHOLrUuXuP4eYKq2R5ZRKTy1qoYxFJxl5eB0WM8zzyQuEXuqXARe7G+y6CvVACIiyaZ9BDbjYLfB8exOCQ3BsuhrbpkJjVaPj5tPX9hkmPKTaPrq+sr9jwU8uCtaroUVnyd9lkvvF2un0SB4R6a1ERHxPxoQiAAbm5Qg6NXiJhLjFCucAyV0wEDn3oKSA4f3HN3i+2vlrdI3ZdYDeMzJ5FuCodoNHUEnU1FY38lCddtNQGvV4PEKZpn4+blWHVdtEZftEJneeV7F/HqmDJ5ikoVLMcxmtSKTXsYqQJm2Iw7Nte/z7bzyhp+HSWJr+nqcJ5mWxhwT/2v/z117ov8+qfR7svKKmtcauCpMyY6SNEPMMbnYh/+xIahoRQz7i+LDoxXa7nlgcDgj55WpoqUogvh5jkEoSVVu3tMsrkxSzvEQmpA+8hBW4GiUhmHFVLy5wIOE87ssm4+ZTB7cmL6U1DkeW1e9NUI2PfBZVjarr7X+dJ+655ib7OEgAgNSqzQJh5+F2lTfVDr03rAqpq3iHMmi9p6YaB5uZFMgz6XlUDhlxaba4xcKCrm2aDpcbq5CbJsiE9BV4rg8k5+CCe2fxcZHheDLyk7yDkuPzumo0mvytMJt1lhac49hyTi53a+wsGZQk7E2opLJVJK6583PG8NrxzKfHHBQeP82XBeZ7KJnt223T7Y3BGKVjdjGMYXoG4HvP7oGBoepbVKrdCyLdMbbW84zb58IYQ5mkKOwYvtrtcL3Zr76LkUAwhhNbsr9qdiS8d2ssO02ddVtFCAsAQ75XboGbZiP/PfKk09ajjvyBzkbEteCMYZ6NfMXkOClfuDdmc5hedC7qmxDUBnRUa41OqT3S7emY0DFnAulQQVcJ5gIgQm7k3nhYbRukQtggWu893zKhtEqZ5DgdHb4UJTAAlu0W37h6iH/38Bv4YPEUy3qDdbslk08dgoJtX+Fit8BvXz3Gbz3/GN+4eogPF0/xcHWBJ+srXNcrrJotdn2NduigYbyg3tefPsPT1RqbpvH+d3GwtLCeVwf5GIILXFU7CM6wbp1wahzchD6NW91TQUAmBXKZ+g3H5W6zV8npeI3hAQIPDg4C2mhTquum9Sk0sDhtZjBO01DwYH//ZLVFIlw63FV8DWCco0zIfLXXCtu+9udw6Fc39JQyN9ojVLu+DWiZW1/cuLWVfjLSKpKcQzLh5/zbVcN0XXS8TdPh208XWNet3VAS6maMweGo2NssfOO9Ry/09WfVPg92XlHjnOOLX34LieOEmH02u4uMX7LZ9c3Ngy5Yaiyr/2WllkBQeG2H3vvqxE1pjWbofTXEH/7C23YgcozTDAdFgUmWI5XCk/fc937r4XMY0KJOEG3gtsQLmk9j+PROQFrGmdM0Cc31CJVMhol3Udc+wPE8GgTyL2BL6YXYW8hGWerLaau+94uSMY4rQ81NRs4pGXD6NCEIcYKMbjFzKJeKUohA0NFwKS53Eke0fP14hu+5exx4WCYSKBQCJClvg508xfF4tMfH2hcX7P1n+4gMSYKB9K8yLTw59om13AikVPbCztyV6gKUQswT8WIgw1hEvLau05EbsiMuJ0L4Mb2zVg6MAWnC0fQ2gLK/7wZydY4DfmnVdgHgo8UFxmniAzH3GfevUZp4/oMUAgdl6cUzb6ravl/7wU6RJMgTiYOy9KT8yx3xf/IkcE2mGXG9mqH3pfOCMayaHZ5tySByno9wf3ocEdSd0zOlhoqUKlneOb6Dtw7uoFMDek0ohqv40lFqu49QWNfcvxzaEyuQZ4n0pF6AjlPYBc/ZPmRRoAxgr7zZbb64f66kjFtHgoZFkqBMiIPkAidS/C1sCoQW30Jme4shpdh6NKp7QfML2EeFjP1frwfUQ4ttX2Pb1Vi1OyzqDa6qFa6qpdfmua43+LUnH+GXP34fi3qDbVej7lv0asCmq6AMuaDfmcwBAIu6QlFKL8eQWluYwN3TewsyEBAOhxz7Sre+sb5f2l93QDcpLfV8vfVzhavGvI1Ux+k9wPj5dJRmgdOog7nuoJSfyxIu/fUsmy1WVuspt7ydzmrq1H3nn3+vg4SJm5sFC5xKCvrc80xxZzzzVbQMYdzdDngEYzgel9AGuNk2WFQN2R5pjWHQmJeENHIwvHVyiP/V//q/fmEsfFbt82DnFbbJyRjzosQ4y3z0H3MabgusDQ7d8buGsOA4JOJis4lKrwOyQ7t24aFL56QcD8267/3O8mQ8wZsnRzicFP5Yksu9fHu8073eunLRxPOHfLuFMCVC+N1DTPzrhuA67j4bB4APDuY4mxAZkiDS1qcFXD/waIfhKpb2DPoMMMlD9cG2bSE525tsqDJE+D51Am7xtTh/oq4PqqmBc7SvQxpr3Wiz7/J8Mh3hdDreg4e9zod/ruGZj/MUeSKie6Q/Y58st/i7yc9VnrnGAByVU5+zd8/EBy23ypt98GNItqDuQvrPpdhIk8YKA4Imz9uKvu5aHKFX24U/TTgGZXywE/NOGNtXFlfG4KScQjCObdfgo+VVVEGDaFzFmwHXRwKn43GoVLMaVXFKcJplRKw3BueTAwDAVbWCNhq5lDgZ0TsrhPDibYt6i6NxgV3X4MPFcwDAJC3x2uzEPk8biAiyeTAAlnWNi80WV1UFYyite9MEknLi+0jvpQY2Nr3o3hOX9okXOvesahtMmug51MOwp0uT3tJucQq/JEFhKw0j6YZYcLNIEuLxsDBWA2Ff+I3SLBvjZDTH69NzzLOQtpOMe8uD/Roz2Od9+00KjbHAWXoZgXnT1Vg1O6zbHZbNBpfVEjfNynOCAIOPlpdY1Bvc1Bv86/e+iapvrI3HvvVKrw2h1Y5nFM1PdU8oc2ppAgZU2u+lKqJ30FXRdkr594d0Zm4JXILZ4NHZOZBkCLPHSaX0Y92nsvSwN3853zEAeLi+RqeURxABK1UBl6Z3z9Yh6vb58EB9cPdN90Sq4aGKU+4heX79AvDgaIY/8KU7ePN8BmHVxLdth28+XuD5skImBW1KOMckzzD8Do7wr7p9Huy8wpakiXcxjtEJX60TiVsB+9ov9Puw83YppW3X+iqZl+2YnODVtqt9tZFrrpoCAL7vzl10vUKeBnsDX6EAv4oA0UJR2Hx/3F42XbmJ0+V43ctWdT0yS1CLK6LoOPT3SZ5iWuT+GD7QQQiS3A7qBZVcMFTDYEXngg5Glgi0kfgdTTLGl2bXfbdHOnaIkdOrcZ4wsQBZ3PKoNNZxZNynllWLbzy5wrYNZplORVlaOYFQYmvTWQwvpMmSiGzqJi1XXRPzlnwwyRgezE789cZeafHC46oA4/bJ1TpMiCJUo0kRFsXYUsN93/GaXP4foIm8H+i+61skW+DFUvaqJw8gV3ny0eJiL3h0TzyMrZDOEkL4Kjx3322EUjgl4WYYoIzBYTFGJhMorXFTbaEBFGmKSUZ6J2OLvlzvNqi7Fu9dPbZphwJn1kx02FuEhB/Hg9Y+iE4FWTE43Z6DfAJnjeAWXX67X90coTRo52/5QUkIOLVFQ92zyoVANwwRobbd84ySXITUtNY+eEmFQCrlvm9ZnqFMU3Sxtkqk35JaP6vcChSOrEr6JBvhpDygOc9ozxNx5z0tZ/jBO2/jjfkpDospptkI46RAIQOi4fqFZB60faf2Zxl3Le6eXkinG02p0L7BrqtxsV1hUW9wsVvgG1eP8M2rh/h4+RxPN9d4uLrE1W6JTbtD1TVWCJLes9aOFcaY5185QUkAexpWe75/0XzwMvS+tBIIABVTGEPzkSM4u+oqdmseTKNnmMuMqqW0wvPtgtDJiDwP0BoREOn9ogTJxR5vNE75bixlwmkKjbOEzKVvIdrLXYObVYPT+QivHc8wzoiv1A0Kj683eLba4sDSFy43O6yfr17SG59N+zzYeYVN23z6qmn8Di3W0HHkWPpZmPzcbk3pQEp0Gh0AwbKcUUmz+45rRRK8U1rV+yqQhHPc1KQrUiYZ1MDw5HqH3S6QXrOEtDD84UJGBpwxm94K9+f4HP7st15Md0/uO3VHVSpxiz1p1jWpgWZSYpLneztyp87p0joOofCIGZh/qXddZ3dqsP1V37I0cBwLblEks8dRcNob/RCEtQBCHW7n913wejsGCpMvoSXX69r/LqSemIepASL0td2AxbbeS00AsZJy74NiV6ETBzI+624I6Xvn6IwWbqvCSoTX0OfxDhegsXizI35H/HMZ9XvcpBCIxe4AUpl1u9bLDUHsfaTvxKL7cmku11y65iAfewL2wo5bVx7ursedMz4eA7M78aBPFIJYHrliS5xMxnh9Tj4+V5aorLVBmaaYl7lPNV1VG/zrD74BZTSlrmYnvvIuHn+CkQq1f32MwTwnU1f37o0Tkm6IU53u3vb61c8R+0T4WCkaoKoY/3ebdiid8OHQeiSIvhv4Ou76NWI9G4HM/pcIjoNRvoceJyLw9ATnOBnNvZlrYYMdBiJsn4+O9saP8886ncyRCIF5PsY4KTDNRpgXE5yUc7w5v4PvOXqAO+MjnJRzzPMJJmmJWVYi5QF5oX7T6DSlBXsdyLfUlxyZSFDIFJOsIKsPkdjS8HAcUoKm1NlNvcWq3WHVbrGo13i6vcGTzRWebC7xdHON59tgitoOHZbNFs3QobE6QyQGG1LJ8TyxZ7uCkBaKeTA+ZWb7O7fzF2MOt7fHiuY+xhhOyhkYiKdZD60XEKyHLpiR+ozB/gZKcuGRW0dbcPOWS1VzMO9pyBilWh1ZWmmNdlD47YdXRJ6WApM8xcGowMEo90FP29N5b7Y13v6vPxcV/M+uGWPwtX/726ht6aCKJukHBzMA8DLywL6WiYn+BOjlGKw2CkAuzVRKq/cJcgAmWe5VXsmxl15teqmtaFw2xpPVGm3fWddduwvu9b6dRERydohBPGB8sGNuLWKMwantus/RdQ+eFxGnt1yQ0iuFdiDYdb+cHBGpjvnJQ/Lg9eQIxXTfnSdRA7Tg9tZQVRuNqu9I6l1rIufBoO0DF8Z5g2lDz7GIFpjbbvLCoiq3VZrjag66puBtRZ9x5Nr9BW+xbaKUofBpuxfFBXVUORelCqIU0aA1DooJvv/sdV9R4lpIJUVy+QaYltkeahMHF4KzF7gNgrG9gNhNws7ao1ca17sKdeeMH8Ve6jYu1d/rV8bxxsEpADLuVFp5FIMB3rjQoTrxrrrT2u5yk71r9bvUNMXhqMA4l7g/o0V51zfYdY3fqedS4u5sDgaGTg3olMJBMcIffO3t4A+lgjCkMyl19+R2DNdVjXXb+PTxQVSdRZ91HnJsD9lIvMr5PhLonOvdvcaimy51Ms/KkIa1hqpA0KpxqavbgasBfEl03Sucz0fROxiebYzmUb9Kqg60tivuaLcJ++M0xyTN6T1jgSPlzt2qAfXQQ3KJTKYYpwWOyhneOryDB7MznI2OcFTM/Xem2QijpHjBZkEZjVb1qIYW67aioER16HQPDYU8STDOCkyzEuO0wCQtcFRMMEpyZJKqmuL5SRnaOK6ayiNNy2aLp5tr7PoavR6waSs83y5wVa1wU6+xaSsorei/yJDWleY3VhlfeIRP+/eb5vSARKuoeABw8gdhHj2xlYWrZucRIW2RrfgZvAyBdz9zm56gJ0b8zkVTYdlUeP/6Gle7LZq+QzcQF7IZelzvtljWFbQ2kJKBWTPQcZ7i3uEUkyJFIrhfK/67/90/w3erffeK3v8zb4wxTKcltk27R+KdZBkKC/PHaSuHGjg58bAAOtE7WhRSIdGpAdfVjoQGjdnzLBGMSiU3XY1NV+O4nCIRAjc17VzHaYFUEln18WqNcUYWFZ3N07qXiBaNEEQ5AbI8oZ1R1fWAMS+kW1zawwVo9HcL0SvlUQO7YdlLDQ1a23ST8aXmACFE7iyD1ki5QGdIUbmyi6jbWUluPD/HBV/xS66N8UFC3OqhpyALBgoGu661Ox9gOi0g1iRceHt3IBgL1TMI6QU35XcWZXBonQsMRYRMOIK0MbDcFlpwZ0WOtfXTIS2aFPXQeguR7lawwxl7YUJrhoECN4R0YXgmMYeI/p0I7hHFEERZS4Zo0nULZhzoAkCeCKSC73EBnq42vhIvTxL0UWpp0BqpeHHPdTAqcTo9wqP1Deq+xXW1xigljgxJ2Ms9gn+WBCHHWJKhTFPUPbmUH44KKB3K5ItMYpxlOB/P8WSzwOV2iVGaY9t1VL3T1J6Pk3CBNw/v4GYXUDcVjX+3i3a9Gqcyr60LOqEN9O7HwZ4xBlwISDCrfcXBeFggGpuudORv4qaZvXP4TQnnOBwXeB0HeP/yGlXfYpaNsG4rX60pBe3onR2IQ9dc0YRbcL/++NLzCt2osRfvUYwiIZuPXilkQqCmS8O6rcDAMEkLHJRjPN8u8Mb8xAfhAKn8tlb7xvjgKJDl3bUNStkUKY3JXKZohg6pECiyMZqhw2W1AACcjcjVXFnUhoFsI1qLiA5aY9Avlo+75tJiqUj2nL4ZGDQ0BqWCuSsLFh7K+qLhxanFb1YEE9a8VEIZBckCmV8Zeu/JXT14a2kbXAGhECCTCYlkdlThdViUJCSqBzzdLpBwSS7xQxfZSoQ+dVyr3NpTxFWSuUw8Hy8OaF/OrLIbDylssMM9Aj8MGmkicTod4fd/+Qz/l//3e6i7HrPjyaf2/atunwc7r7CNigyHReGl8hmAPEkxWKKuMgZGazC7aMTlkENUapoKIr0NmoKRTg32uwqchQUHoMWNKiO43f13OJiOcX21AwMwy0r/AitjsGlbzPLck+rO5yPUi37PkytOeTgPnF68WH4NEFToJvteKUvaZH6n29mJyxXhxzuPuNLGSZDTbpIDEQTLGMMsL/Z2tokV5htnGZZ1jXYYkAqBXu/r30hOIo/KBOuGkLWjv7VWjRcALgB8uGAeXbqdegqcGErzEBoWFohBKeSJxKZp9wINr9vDwrESS3ANRqY0VjrLG8iERD20pEWktE8NuJ3oCzt1i/pJmwb1ApC2D5mx7s2WgGkALLZ1SPvAVmnYZ5hIDtExKBB5NRXCl/76EvA0xThL0ETplW5QWOwIrSoSuadnNGiNXIY+dX+WqUQ3GNyfHuFb109wU69xVJKGUCIE8kRiUNp7mRVpgrod/PW4a8okx9lkjDKV2DQ9YJ+NNgarHZE4H8yO8WSzwE29xT01YN0QkfW9q0d+fOU2PWywn96LOSmMMYyzDOuGhAgLSSrUN64UupgEmYP4WTlEDMzL/DuTX22MryJKhMCg3fsTChziILqUCd46P0D1qAZwjapvcFSe4s3DY1ztgro6GIlFKuOeHSFfZSZwZcv2XUmzAMBcmb/7H2M4yksclCM826w9Gd2l3x038Gw8x0ExwhsHxwCYlw2gTVmGVVNDm1i3d39Z1cZg2dSECNrRkQsKdrZtjVzkWFji9ygpSDwvehdOyhESKVH1PQY1oFWDdZ9vKQgaeq+ETeMx6Cfdbhwk+sk5s6krAaVJM+i1+bHnCW27FoM9pk85qwHAAJ/MDlltWyzBsRESuUWWEiGRchmQa3tPNP6IXAzQHL5oahRJjr7dYtAKAi7wHzDPSwxG48YqeLsmbdXVYVHgeWzlYvWuYJ8FCXqG98zpp7k56sHRDF+8cwxtKOgJwoJ2XpYckzFxi5Jc4k/++T/20r79LNrnwc4rbLLMMMoTPF6HKpTjcUmaBlxAqQEaxivUxkiEi+Ypt+uCHY1EwHMZtDFgt8h7vdIQnGOS5li1FbZdg689poE+yUoITg7iDo3QNuCZlzk2TYe67ykf7giRNrhyn5VC+HLK234pvgrLo1Ua3LzoPVPIxC92e8GOveeq6wPpk/GobJb0P7QhkmmcvhOcQ2vtTRu1hdV7HapduIV/jQnk7nGaYtt1XnAMAEZJSuTKWygbjEE8DbpdNnn3MGh7HYMOGkExTBxQndAjQY2V4/veOsZy1+BblySalicJikR5AcAkEheMq6FocuTQ++uEf0a3OQOuuV3doDUSTgunI7/7G0TgHwhBXA2lFBGpRSR/YD+cJ7QYzEc5LrYb/5xrW4lVphK9Cl5W/jQRWgGQuGHVDhgnOYokQ923eL5d4M7kCKmznYiQiXYgblzMoeEMuDOdQGNfLiAWiBQMmOYlplmBdVtjWW9xPpnjvZunaAdKASmt0fQtjDYo0gRFkePjRWf7NwQ7BjSeOjX48ttOd+i1gmAc02wE55FX932EBGpwkHij0z9yCCEAbFuLXjLukTrXEvsd9/dxnuGT52v0A32mGchDb4jETV015bzIsGut35oh3Z4syXwA5ccQ/dIKETIw4kx7jzX3vrkF0UlbFEmGcVpAaYNBGZDbuR3vXEAKgVwmaKw1ze2WcoFBGx+Q+5/LFGiBRvVYthsfAI6tujCPeIcKQO6rKQVKLiCzAuVA5qmupJsB0CBCsgt4OCN9Gpf+1jDQkVaQUq6ySOHbi6fIRGKrVYk87FLPDhsKQRVtQlyBgdIKCmQ74qpoXaP0IQU/DnVSesA0K6NNMIl8jpMSm77yAbgrjomtPNzIEVwgTxIkUoaA2Y7FIstQt4PfFLrnKjkPc6odu5erCieTGkeTAnGgSuOHNvDPLncAGKRg4C+bpD6j9nmw8wrb/P4R0rpG/zRMRokQ2LYtEiE8b8QJ5RnQYtnbAQxYwqWdCHqtoLtbEKwddW7CAWigjdMCq7bCsnFeNBzTrITg5EVEu42gY+NEtC5XlV8wBGPIBEevbACmNRLO0dqFSxkd6n8RVWFxKoWm42gMeh8JyaT0AolAEEl0KQgn/uUUXt13iV9D0HEf8SVCqs9ybJIEu8gmwZ3X/RfDttzuxrcRInAyGdvJ3O6Wc4nHN2s6nzF+AnaVLEVCwZu2/KfbCMu27fYWD27FuxjgA5QiJeVilzrME6rw4pyjTFO0db0nLuigdHfvkzTDqt0fG27BjBG62zwKhzAqYyAZi5R693k5zP5JHAMFzqlKLX6uiaDrNQBO5wU+vpYYun1hwUlOC2yM9rmKlfhzQnB0g4IyBqejA3y8fIZVu8PZeO7lBsgfjiboG4scxQKToyyFkBx62A/KJadAqMwkesWxawc8mB3jaxcPcVmt0OgGW2u38CNvfhH/8dEHqDryWpJCQPKAugROk3MYH3AyHuHhYoVeKTy1OkfH5dQG20TervveLzJKU2qVynNTNN3guXQxCVrbMRmTWmMT0WmWUbBtNGBIVb23qr8uPZ0IgVmRYVW3WNYtjsoCS+tKbQyhOWQcbN9td41GQyuzV11JyG2Qf+htUOCELA/zMW1CjLWlQNjAECGWUrXtltSCb/v3jdLMV0MNWkEzgkEZyMKhHlofHIyTck8Lxo39QQ1g2C+KIFucIJoJO/ZKmYGzwJt6fT7DR4ulDTjI46wZegxaYdfXpMoebTVb1fsA5nbjjFMpPhcYZTlOxzl2bQ8nvVANrZ+vlFYWvafjOxK2azdRPOS0cRIuKXjhwld3AcTbHGVFNP8w/z3ayCri5rnNGGMkSGv2ER2X+p1kGRZ1HY1djY8vV4AKpHfXn8Ogsd51eHhFm+1UClx9fIk3/ouXdtErb58HO6+wdbsGj66CLs4ozbDcNVb7JVRbJIZ2qtrly+MIGQESdE1wDm6YFYqi36VCemgRoIqIIklR97Qono5n/oVzBNJYe2bbdH4X4qYc2qXwvZ0eY1agylbXvIxEnEoiLPZKebGs+H7cPTiYpLc2GoPW2LStzxcTgS7sBOlaDCQTkIKMNelYzEP6yhjkUqLqOmhDC4BPtSBUN8T/lswZcYbUmTbGV7tVTU/aF4rOI3h4Hk7q3bMaGFW+dUr5BTxGSxzRG8zxAOzYyFMYGFytKNUwzoK9QpmmWNY1OONIuUSng2y9g8ATmQC3gx1jYBiVAN+2yXDXmgrp1VYlwoITkxdhJzqw2I5D+/SK60dHSlaa5PbP5iN8cBECPck5yjxBsg1cILcoxcEPADQtHXvQGmWSYZIW2HQ1LnZLnI6nexuC0Lch+GL2fP3g+FBRUG6rigYFrHY0hs4nc3zz+gnqvkPdUyXLH7j/BRyUI5xPZvjg+hJMBpTK8dwcZ8OR7Ku+x/F4hKNJgUc3S2y7BgwMx+UUyhBal0aLMqEqhD42Q4+DUQajAT30SBjJHwRdJnv9EVbqAv6EC3z5jRN8+HSFbWMd1dMC1/UGdd+is1U6oyzB22eHeP/5Aqu6waJuLHpKb9qu6cNziNJrsS9brMdSW5NWV+q86xoYUIonEwkeHE3w8dV6T5IhVJ5S8HuxDZ5xfsiB3qPBaiJxth+opyLxIoOpICPZmF8jOAXu/a0x4vrdBTnxtiRPJJqYz8fdfEbu45tWIhW9RUIlNh29q+fjGQCB2qJABoQK91ZEklLmGp2tIKuG1tuUuCYYkecTLlCmOcaMeZSd5mQDGOPFAY3tTxcEtarD9iVx1sP1JVIh90ydKZBReLq58cH0oDUYo765qsgMeGJFN12gkwiBcZZSsGOPLwXDg4OpHxO3MWQhGBRoI7ht2hc/8Bm2z6uxXmHbPF1iXYUFaJymFCSYUEbuSp6pDHQ/uImJoO7ziRCYZDmObrl5D2ofBs6TBK/NjwDA5mYpD+tJp4xhnAYTO8ejcNo+ia/6CBPMoDUutlvsOlvNdItHA7gSVTLgjHVwHAIB0E4+Vto1CKkSh/i4apE4zbO3GPb7ru8OwRiU8twJgCrAXMUOgD013hi9mOZE5HOwfFzx0g5qr68dP8WVa75YWsqiwOpWZV2sB8LC9UyKFLvdgEsb7EyLzLs6u9QHEETh3EQvLbr0sjkk5lp9WnNGp34M+j4NO9xBOWXoUK0xaB08nOx9vnF/htQShdtO4XQ+2jsXyRsEPZJYQ+R2Rdau7UPwbwyOyzkAcnRf1Nu9QEcwcnmO6dmkjUTjJ02CHQcDBccO5XJCh4kQeG127P/+Q/ffxjQvkAiBu7MZAOBqu/Eu7j5NEgUbAHEZBqXx1ukBFg2Vm5+MJh7BG2Wp199xWlBuHAHA09X+vSF+JpHA4229pzxJwMFRpMIjpE4nyLl/A8Abp3PAAPcOJsgtwhsHw8bsvWZRWboTHA3jShmDq+0WTqOK9Fns+M1GUIbe7djtPpfB6NSlwV7WXLCdWD6jsmk05o8T0Jp5Ntmr8KM5M4yt22TbPaHCeKMSfYohzKkJFz5F7ebFXKb+881Ac1EuU9ybHeLtw3Pcmx7j/uwUr83O8WB66svpj4opTkdzlEm2VwavjPI6aFfVChe7JTbdDru+xravUfUNqqH1lZjfaTLIwFhydrDoMaDn9NHyAp+sLvF0e4PLaomL3RLPtgs82y7w1KqEM+yb9TLOAg8TND6m4xTTcYrDaYY8C88zzyRevz8jnpSmgOr/9s/+zXd45f/p2+fIzitsySgNi7fdRbt0UZxioRLEsLANKqRnXLBDmhBB/2BQzvX6xZ0rA+3gjkYzDFrh7mSGZ2vaPcmo8oUidSJUum+73K37VCxn7xaHULb8IlH3aFYig0TdEzQ9KzM0vbKVK8qnTBJL6HQkYxndi9tJxORhabkT9ir3UKzYkLQaekwEx0FRkHN4hDxx7pALG5gx5s9X5pTDL1IydTTQfkF0yIO/xui6KLVzK3XFGMZZiqU1rHQporifGABmQv+VqYTWBhubUpjluQ/StNW5oMqlBDG7kfL5wpcEK2NISwf4lAlxP4DV2vi0hSNU0nG5L0c1ADqlkSX7FW7ueblKmR/6ofv4D//2Ibpeo2oHnBwUe2c2AFZ1C27tF7RLoxqNJEofMFjtoyiAz2SCeT7Gstnik+UVvvf0AZyCtrRITRfxdYIdiEGZpdjW+yhUmgjUbaTMDOCtozMkkuPB/BCSpdYmguG1o0PgA2BRVZiXKaq2R5Gk4EPvhUFjFe2q75HUwMb6UY1T8jpLpQBneMH7ijHisVGKhKpsMsulcBfnrjuXEuMstbv+EGyOswzXqxorO34SQW7aAHE3ejUgFRIHowy7SkFyjvPZGB/frODL/82+wKMfIzCY5wV2bYuO7aMlg00n0n3XtppMoJAZDAweLVZ7Wj+9UkEmQBsMNhBx1+B4eZKTACRzGlYsXB/JQaS4Pz0irpGx/m0+fiH3eFjeVEzgdr/3aujRMZ3umQEFpZs2IMf76CwHODDLR+jV4E1KD8oRMsHRDMrPF8byaahCkVzNyzTBpi1RyMRW1/aohw7LukKvCUFTe+8W88rKsW0EZyT02VkelwucCNXqPfLtODsO3WGgit2gimwiMG9/nnIyHCQqKtANVF3qwuduUNhVPeaTHGAM2rA90vzjm7VHqzlj+J//b/6bF8bXZ9U+D3ZeYUvGmX9JRlmKXddi07Y+9eTy1E7rhPgsiQ+QuIWXuXEAcPBY6fvg6QTQ4uZeVDexDkrjfDxHKiVav0uJxK7sJFuLfi94MAB6o8GVgrbaKu5tSISwQVsHFS1GrsrksCzQteFF/eJrx/jWwxts2o6CDaVsVRf3V+w8YFwrksQTkYFgQtfbf3cq/M6Rjl31mNIam6bBOEsxzTIsLbl3v3zW7P0skbRYHo1GdiJkYAYYFPf95gQIB0t4jiX6qZRzP4yYFTlWTUuBkX/e+0FRHDAlgiq2tCFxriKT2DUDGKNzugUiJhvSdYUKCEcejpuOCNpx8Og+T7tn4Z26w3FtKo653euAUidW92efX8HAMBll2K1JrXrX9BYNIi6Sq+phAJ6vdihESikK5xxvKPDzx2OU0rydgnh9foLNRYVNV2Pd1jiyZp2OK+Y+73ah7p4ZIi0o++yyVGBVORSRUcqGCfzQgzexazu0vfKpqcNpjqPRCNe7Ha7rHY4mBS7XFXSEiCROPRlA1XV49PgSBkAhM2/ZMC0yLOvGl8zHkgvMolOt1fRph8EHnEDQZRpnGSlZRxpTDJQOWlUt8UDssxVcoEwyVH2Lum8xLworDUHPlXOGeZFjUdVwhexhG7PfBGdUYjwAgik4mYZeK48MObPRg3zsx81i16BMU4/KkIM78ZEGbdD1+7XanHFIHmQNKPDd17ByG5CjcoZMSny8uPbj0PVHJgQ2CKjq3jtrx4zf2Nljdkp5wvcoS3C53vkx49B2bf+dMAEmM+t4T+fdtQ20FVdMuUCnFSZFjkVVRf3IsbNyGdMiR9srqxPEwErhaQfhPhJIITBKU8xzmlOqrkVjSdWc07U56Y043fTVRx9YUVmOB7MTfLh8DmMMEi7xldMHaFSgI8R6ZT7gsffLjfFisO0wIE8ENk0YKU2v0HYDUrth44yq/J6vtnj0UagC+/KDU9x797UXxtZn1T5PY73CdvFs4QObph+wtCWpQIDTgbC7SISgahVf4RGgwhg+7m0ZerwU3LZtWFYtnq63WDUtnqysAq0NXOL00eG4eEEV2MHGXaS34L7jfIVup0aY3VmVSVQuyRk+eb4OFhFwqaQQDLlrdy2JINKQwgoLF5Ed+4gHQtU/MTStjMGT1XpPlfZlA93Ds5FooDEGmeSkFotIDVqIF/opXrDcRBP/1gWWcU/FO0z3t8QqU9eW3Hg8J2M9pZ2zufY70VhUju4/8FR82vAlu/Pb103XTH+LXaDdMWObDte2bbc3bt2xGGO4czpBteuIGFzQ4r6tOkzLfTHDq00FGL0HhQPwmi/x9Wm9zzX6wukhfuj1BwCAj5cX/neH0wI6Co4LKYPAH4wlMYfg2IACXIfsSBHukny83DOnY1wvOpyOKZV1uVthnKeYFRmODgm5SixJ1B1j17X4ZEkL8Mlo5q9fa+N5bo6YCsAjI5yTIa8joscBn7BjPJdU+Zcn0j+3VEocTXOsakL8ZkUQhjyyZcRV3xBaYRXTjSH0LY3GtZ9D2P7zBawhapRGcx9x115bgq3kArN87PsVILmJSZZ6r7beLrLaGOLWuRQjYx4pc8/JWba4a4lT4SQREVX1sZA+jpWmYyUyNwfGKXZ3zE4Nfq7Jk8TP1bEoYLzJKZMEP3D3Ln747fv0zhjS5zLGYJQ5Q1a9N29oK3mRCuEDCEdhiEUl6Vqt5IN9xu56nOFnTHlwSLkLinulvRFqrxWe7xZ7G1bGnYRDgmkW7F3KhIQ4edQv2pCprdvzzqf53nzRDgPaXqPrw0brarPFo0UIdA7GBUZFOM93o30e7LzC9uiTC//3WE04lxIHeeF9rOJUSxwMOWXQWO4esLB0lAbbD1/gIUQG6wfk00P7j9tNiI3XdQkkP2PP2yu1Z/TYKeL1uOon1zjnGKUpjGHB3ZoxVG0fFkfHC7DfiQ0jGQPmRYFpnu+VaQNEeHaBTtN3eyRuyblNETFLuBXIpLD6HA0ErOpzlD4K/UYts+kF12inElfEhdRa3GIOwW3ejtIao9gawwS/HLqcQL5OraeT43edHYz8QmzgAq6gbxIrxiZcejn4+Pneth8Awvhxi5BHtmw607U8mlgneRZcyqMy1/g+AODtNw9R13TNxydUArxres9xcTpE2hjUQ1hU3FWqiFdRZskequG+fzAu8D137kFygapv8Xy3JOSr19DRgjbOg3LyoJz1in2vLJm9U2ovAHJjdF21YQERHFLSvZ5PKGj5+OoGzFDqr7FE4FQmewvaTb1FZ9NGh3ngLXW9q3LZRxZDuTz9bJKlGKWJvf6Q0s5tamucp74MmarOEmwaQowFY5jluf/e6WQKgHyyxlmGfqCxUtl3NE/l3kbhRUsBuq+6HwJBOkqZALCoDqEgZ+NZ4HVZrtegFO4ejHH/aLLH+eqVwrJuQoBj+2Ccpj6N7dL9jvPn3jg3p7W3qqpup2EA+OpRuj966xyyQ+cNi/oozXAyHvv+l7ay1N2na4Sq0O/qRuPebOYDtF1HMgXcIkLzIqRzfWDLOMkxGOP9x+jdjtTa7QZKg9JJLggXnO2Z/Dr9J2Bfjf7dk1P/mboPVZGFTKMAklBN11+p1crhnO/Nlc/WWx8g1lW/x3Vqh4GEEHt63k+Xayxqh6jThvTsaF85/LvRPg92XmFrbM6XAZiVBcokQZmkOCgLcEE5WCDwcGgxD06zzvQPcGXp+6ksILwQ7jNud3Y8KvHgYIbT0WiPp+N0PFzbdR1psdho3u1mXfrFydXHE7OyZdb+hbG/q7oeu6bzwc4os2Raa5UAFtzbHU+JTmXQa+N3FTGh11WDEG9k8ErOMeoTv5gGwL35NAQabB/Cjpux97OoamzaFqumQdP3UNr4VAoQ0j1xnt/93E2EsZAZPSODUZoGNE6T/Hrdd6i6Ftu2xaZpUXUdrnc7/H8/fIInNxv7TIVfcGlshL4Absn+R9VyMVIYj4n9O8YLCJUUAtMsIDAeRWKUqx/ngche9f3eQuJ4M92iw3bd2usTVJZugGlO1zrNM4ztbnfbdp7D5JrS2velL2eOntkkT1GWCa5XNR5YIvEny0sYo9H0g9fx4WDWFiKMs7YPtifc7k53tau8s5wEO+6r1hGWg50HY8Dd+QyCUYXMsqKJfLWjP/MkCfYLBl7A7Ww8RTFKMUpTGtucRyjnftnz4NyzOQkzHo9HSEXYqHDOUUiJSZFiXO4Hg9Miw8Prlf17gTZK806zgtKbRuPZ9gbXuy26QVPVFYBRQQFUkcg9xM5dW69JANWliN21xGO9Vb3XdHEl9q7v3d8/uFxCaYNcEoF617VY1NUeL8shLql1ZHfIFkOYB2K+oCtpj693f2wye31RateE38WIaBDD0yiT1Gsb+U0q9tNpAX3WUMpg3bQorZq0AfB8t9njOt5uzdDj6XqNVdvQvKY1mDHe4iQgNXSdq6bZ46TFGyn3PhP6HPontZyg6LYBgIjrfv7k3ugUIB6VD2Ps+Rlozbne7miz0ikk0Rho+sFXdb1/cU3pe9AYP52T/EAiBPpO4el7j1/oi8+qfR7svMLmXg6nNut2xILRQhDgY/jAwqEsifXxyT3RUnteQ5BWt9DtLdTBgAT1DCWrww7GvohdlB5wyrZuB5En0r9g8YTnPu9KHzXgXxgHeRpj8NH10lfpuFLkQe+TB4EgUOjuX1nTVBf0ufO5FFM79P5lL6LqDpdyccfXxnhvqU+r9HDfdTop66ZD3ZNI4bbrABhM8sSfL6SQbgUJ8e6H7aNGvVJIpNh7xto+Yyfk53x2mmHA9bbGYAUhN0uCwlMpaKG+1ReFoIkuEwHBcLn5+P727jlKPd6eeyWnAMGNNXdX7vlwRtyO+N6i20aRSnStxmAN/7p6wLi0tiOa4Q++dQ/n0wlmFuVpIn5YPB7CDpt+Fy8u0zxDpxSW2xZ3JgfIrG3K083CB610ze7dCk9jE1VECqs4frMOWk7ue3GjYAfQmn53eljiwdEBAGDZbcEAn0IZ51mEgDRoFemn/E9++Cv4b//olyEsuVMKHgk07qcOnVhgJiWShGNaZjielHtSAM0wYDpKkdpg0j3Pph8sETtBmSQ+8KN3A7g3mwMAfv3xI/z33/gt/J9+41fwq48/wrPtAtfbnU3dSpxNgpR/FqGucdDg0mkxWrtqqPhhno/hEFb3XEc5jYNlVePDqyWeb7fYde0LoqLOd0vpoP8V0vlBnC9Ow+uIZxaXnQP7pr23AyrJ9wNpFo0Dx/1b2kCWc+ZTxbc3d44Uv6grHzBMs8y/R1VPWl9NP2CcZT5FP0pTzPJir+JTaY3WIqe5SCxyx33VqgsQHQqeCOnP44YRWREFxGfbdjgeTfxzcm2eF5FkifD6YICtXr21mUmtKSnNlU0wBrWfqfsevVL49vMrHyQCwP3DGZr/H3v/Favbll6Foq2nkf4888pr7VR7l7ddLlzGxj52meCSARnpWuIiISSQ4YEksISEhCyQEZIJIj3wiiyCLF2ki4hCgDHHHK4B1zmOZbvizivO+Od/hN77ffh6GnPt4lCmdu1r7u7S0lprzj+M0UcPX29f+1qrHS+vs+g6g3pzTSfuG9g+CnY+wPaxj93BtCwxq6qg90GVVVFbxOdpvX2BNyL0qI+P4NPJ5htDRBmGWT8fernZ4mq7w9VmG/RQvJMuQAFMp3XQoJGcY5hnuL83w/6AoHfagOCur59aSku4GWOYlDmGReY2dAPBGXIlcWt/iFnCC0oneC+XDVpMtenzNPxk9KJ/iovA7/AQs762ezfOwFS6nHTmK1uuNSIGC0zKAkfjAUYOhVjsaixCv7CANvn3UH9nGKisF8SlG2ZnDDjnmBYlMicmWUqJXNCfUkoUUqGQCoMsC3+GWYZnqw0ez5d49/yq5xHmy8QzqXBrdIBZOe6hTWkwdv00qYRwRM2Yvgz9wEkM8MZ4grvTWS/N6PstV7JXMq64CKfPcZWHtAnnDFkhcXiTFtm2M4FXNciowonIoP0F0yN9SgjA9AMdgNCTr7x35Z4Bx90pQfRvX51hXUcrDvL26VcV6aQrOEcgZVK/uGCH8x6ykZpaTkcZhGC4d0BSDpf1Gnt7VQx2yjykNM+dNcQ0H2C3aMBqgyonVd29QRkDPTf/YyBBxM+bR0PcvjFJxPoiirhuGjy+WgJgGBUqjD2PMN0/nCJXArWf7w5Fev3GbfyOj7+C+/v7TgFX49lqgV9+9C7+/ed/Db/85C186ewxztYL7JUFpmUZ1h//bABHpHZ9kkvlPJ5MCO5m5ShJmft5kodnPt9uQ8VTIRUGKgup3ODJB3Ln9oUdgvGAIgTfvSTA92m3NH3FQCa+wdQySZFSNRLCmujeHJ6DP6j59YYqqUQ4rADxUMMYw8Vmm6BAJB2mhAwHLe3QqXQNKZXCuMhDEKx43/3cus+Sbl6OiyKgy7uOdI0EA6ZFhWFWhGdyMCwxSlDYdd3i7nTm7ttdMxhKR5/wh9rewVb3hWKZe88ozwOHcVnXAZkUjGFWFXjj9LxnEXNjMqKqLVecQKKDwM3X7uDDah9VY32A7fB4DydM4ouPLmBslMunKgAKHqRbfChajrCsn2AWQMY5GkNoSjppOItplOPxENuLNpximq4LpDb/eiUEBGMUVFzjLVRZhlGWo+4MxkWBs9UqlIRzJFox1kKA9bQeOGPYH1aYVjk+996p02Dp0HQdWs3QtKS83GqPPugQ7DF3j8bl8G1CuPWL3Hy3CROyUBLrpi+oJxiDTSqRcknqw+MqQ5VlyEuGX3jjaQ958X1c5hmGeYYqk7hzOMQvvPGMEAS3gQyyDE0itS8cEZE5KMejL+ZasEEVEgyMU+VMoSS4ZQFSp8dGJOa9YQmlSDF4uWl6vlF+3WEJckQER0nEZRZTm6Xqe0z55gPLRmvkQvaMSxkocLXcBji/TfrR33enDUopsWyiCaJv+2My2AQomFjMa9QNqet22oBx4oZs6w6zqsTZaoNt1wa18LQRr6dPWpecY7Gt0eqIAtycTPFwQUaXb1w8w+FgSmNGiEAoTSt3fBOch+owej6MoHtG1Ua6IW4KEVmBUaWgFAU+d/do43jr9ALf/23fHPpxXBVgNZl4equEWTnC+dUW89M1PvXyDbRW473Hq5Cm9qlGf42tm4tdo6G1Rd10uFxtQz94VPbd8wWspVRhQDUYw57b6Np6G75jlOdotMGm1vgtL9/Gg+khLlc7vHc5x+V2hflujdMV8YseLi7xcHEJgNzJx0WFQmTIZQbm0AXa8COPjTMWtIRujqehXPx6scCkKHGx2UBwjnGRY6AUWm2xrHfh2YzyDKJl2LQtWm2CkrgX7fRrURh77rk+H4BEpCMTApu2pYOZuwHrJoc/VEb5iYQP6VSbAYdOJwc0/x5GP+gpWIeUNmMYFyXqrnUiqSYYFgvOUXeayP6M+FldR9V4rdEhyPPFEwwU6DPGMN+SYOOmbVEoSSl85g8f5FRvXKWbV50esgK+co76RMYAU8lwaPXHDgsLIeJa6vcgxhlOJmM8mS+gjcHWIZGCMTx1VWu+3dufBpkVgOa+4ByTSQG72+HDah8hOx9g49pVTiUL+qQsesiFR3Y8IgJELgYQK7XCZ74fQsE5zlabXuXMtutQKhng/FSzR3EBgT4nRQmBddtiXdfYNk0guvlqgpRfUCcmoQBNtv1hhTJXoQrBgtyu1zvyufIk53jC8vfj1VRtsAAIfBwpsazraPaoFKyNvCYlKIiQQvQqqhgYyozk008OBoEYnGIdHhL3jtukn8NwNCFUSzu4eFjE0lJrY1UUldvSpugRHZlC7Dbqd/jnliJQ1tKzVVJgNihwMh1iUpY4Gg/x2u0DfNtLJ8gc0RqIXkLXuTjCnyBd4JxWZPnX3tufhtennBHPkaD0QRJgudd6JIkx77HFUar++YgzIsx6HlWnLeqGxvvQVWVtdi1yRZ+VprKul+L7b/al175lUoZAh7ug5OU7e/jkHSpjfby8RKO751KGkvc3qZS0DJDomXdyl24sAQhE4CIXqEp/vxazsiJZhE6jlrESaOoc3c8dV2eYFchlhlVdY1drFHl0aa+TzTGteCH/rQ6LTYPttsXVOqKLHgHy1//exQKXm76H0rQsyDRWx426UIrSXcbibL5F2xrsmg7TssK33LyF3/7ya/jkzRfwysFNfOu9Oxg7EcJVs8OjxQXeuHyCXz99B188f4inq0usm13C+WPYdQ02LZ3y78/Ikb53EAOwa1sM8wxHwyHuTCc4GA6wNxygzFSPb2PBeqmk8LysjYP92nPs+db51BKYC2psMh76KGeXpMr86EuRFW9Xwxlz9IO+bEOYO8nYVQ6hUZyjVIRa7ZWDQCT27x1kmTMLpqB9WpRhDEhncuspDH5+Sk7PsHCBHwBcbLYwNpoxSy5cUMdi9SqooMQ/VwAY5LFSz+8B6R4ThFnd6wOqpw0KJXFrOg4Iz/u1u/sT7Ln54LMZHuXa2yuxO7143/d9I9pHwc4H2Eqm8c7pIpzOlRAoMhkWdQv00lh+AOVJ2mXXdT29kaDOmwQ2yp+QWaySaHSHq+0unD49X8CnxXRvonBsWuLELJsGy6aBcsGDPz150UMAz+mfFFKSci5jaDoyPRScyh+vtltkkgeysoGNJdk25soN+lwd/wKvSUHEPY6NqyqgRSAS8vrkYI3ClUreujsOUGoaJsb0jAvqtMGu1piWeVhQPIwcT6kx7eFPRGSNQJ/plXnj5UdBAHIPfp5NYwydpDa7Fk2rIQXD0ayC4LGqDQCqTKFUKrFkoGdQqEhkNylvJ+FadCYihn6BTrV56N6o75sEWUweQ1is9wZF7x7HwxywvhIDMNoiLyUOjwbYm5SUNtPxWgopoQR3feM3KIR78v2birgVybWQ/xZB59/1sXs4HI5gYfF0dYkqi6mdutXPBYacM7RdorzNWBIECrIFqPJQZntwOIj8DE6z7u7eHgDgFz7/Tgh2JsMSndE4c8HOQTUBAKzrBrumg8oEjEB4nj7Ik0KEQ43n623qDlfrGnXXhU3fb9rGWtzeo/Tg6TI6mFdZhk3d4dHlMogKkv8dQmr2ycUK26ZD56qE7t+a0lzlHAejMW5VB/jfHryK3/nS6/i2W/dxPJwGcuuy3uHx8hJfPn+Ezz19C1+5eIyL7QILx9W5v3+AKoucrnTskbEmBeLcbdyMMXK1TxSKCQntc+wUJ2Nhn7ZmwVPOj+s+dwyIqZld2/aqSFNNMr+mpJoyxOXxvKvoMu/1iK4f7vzzAIBpVeDWdEypHo9CCY7WGGRSheBDck6BWHKtaVDPkBSIuO8ZJiXsjJGtjb/O882mV9m3PypQKNkjL7dGhzEL9OkOxsY+TOe0QVwnfAbCglJ847LAvaMZ3q8dj4fYq6qg/O6fb+akNS7nO0xeuf++7/1GtI+CnQ+wnb53iabTYQEulXSnEfcCG0mgniwLIJQfNl0XJkOq2ZILGfPYnGOU5/Gkn2z6a1f1A9BE87LtOwfT+slauA207iJik7uTCuDI0ez50mvfRgVZG3SdQavptbemI3iNmNPVOgR11kY+EnnoxMDH2qiIwUAVCAAC50Wb6MdUKo+4xGsKOj7WqwJbLJcNdvXzTt5h8w2BAlxpZ0RjWm2w3tW9U18onXUoXJ3YVgB9m4nLTUwppKWxaS92RsMag83OlW3PKgjB8exqEz435c5cJ10fjMuwILaOQEp9G3vGixUCkb8Sys55JM1qE93uB4WKvCwW0/hKSgyLuGCeHA7RtHEjObk3wad+14t49bvu4uD2OKA7Hu7XBtgfVuHe06atDUTp/uYSlynOGHIlAgn27pQqsy62SwCRaNlq4wKU2DiAOnDnojM2AAyHCkpSgN50lFZ+8mQZypaN67j7h7RxvPfsPBBoTQs8W82d/YFEJR161XbY7FoIxdBpG8ZuDKZF2OCNq9bz973VbdR44tEOZFIVuHcwTfReGO4djMP9bFzlYy5F4HFxxrBrNJauVP7BCzOUhYSPLZijaeSKeHt3Zvv42OFNfNutF/HK/i3cHO3joBoFW5ZFvcHDxTnmTkTw9Rs3A0IGOMKvu8d1YoLLEIPrTuuAZAcJChODpJEzNfXvo3GA90U3fZVW+joL9A4LTaKV41NKz+vaxKCS+jCioOk9hGforuPu/gR7wyIcnKylFGndkfAfGQZL5FKFNPYoI/HBReJnJ6XAJNGlYqB5GBF4FgjOVZbKT9B8OF/tXMo8/q7TJhDUqV8JdfGka+9dmK7v1lqUWXYt4KH9iDOG7/7U3TCHfTscDXAwHDhNMLpOP+cLKSFzibqJAqcfRvso2PkAW910JHbmFoIyU2i7WDrteShRe8LlqQXJgntUJpfSaeC4xlgICgZZhjJTvc3Ut20bK5gyIZArgYvNBhY25GQpCBIYO2JbqRT2ytIJTsVTBeDJf9TS04n/bq8TIzlHrhRmJVUc7NoOT5bLgMKkm2s8yURkx+ug+P7x/jieq5NLGaDevroq/Z1ulE8eLZK8et+wM5PCIURxg1zu6kDkBYCrbY1UUYglCwKl3myAa1tDatidq1i72u5COixdWNNJZ6zFxYrsOjhnKBSVCT+9XIfNOJUgSImjADAqc4xcaqh2qJpvfoFeOM0LyZ9XWJZCoMqjnpPn0IyKGEBfJyxOyjxwNvQm8orKSuGV33ITu12LJw/nMDnHwU3Sedk2HaSgvpu5Bb3uukDU9dfrF8t0fO26rsdjytymYmBhjcAkp4X3S+dPeidjo5+H2r1WSZHFij7BGbIsagvRKZYcm997usK7Txc4v9piW3e4f0Qk5WdOJoAzhsXVDo+WBM+fjKYhELMAzhcblHsltusmVEmFvue8pzVlLZ3E81JitWvC9aRp5MW2xv6oDOjw8WSIUVlgWhUolUg4f0Qe3jYdZiPX3+77X3hpH21rcLGM42JQSNy/MwkCi35e51JhrxrhlcNb+I7bL+Plg1u4MdrDKKO5fXuyh0FWBuIxcE0YFAi/85/JgGBfQ6lkGVCWKqNqsyrZsP049srr1w8Msew8pn78Z/vmAytrbbKuxnQP0BfQBBKKgelXv/o+8y2TAlmWptJt8O5jjAXOTRR6rLA/GISUrvdOF4zhcDgIz5sxBoHIa0tTW/tVFdbsvaFLP+6aIEGRHnq3jcaD/QOM8yKs6YWUPb6c4rz3nmGehbXGOIS67jTG4wxFJfH6S0chqFJC4NZ05PrXc3zivlEoCZU77pP88GjCHxGUP8DWlQqb0xYMrJdvvZ5DzoVEZ2LJXqqcGsvWWdCeSKtpRnmOKlMJVM+Ba75bACljnq7WaLTu8UeIS2AxyAQOhwMwUNrBS5B7T6ZO60RLxIYNkLnU2TvPFpHYqgRV4QiBG+MRHi+W2LWdC1qSnLu1qLIM892ulxPXyb8zKWEZQ6sjrO/z4Kl6MBB5MZ2OHjd1mxhWXnttntHwl4KjMxptZ4KGyqjIsa4baGOxc3A/qUlHUqk2BuAcddOhdqXzvecqBcZFgUoprJsW8C7hnFNNs3s+67pFrhQk43hyRv5Cq8SXRwke0MDU7gNwQWxFizIRkKPaNDgH0r4UCanV/cynJ5UggnQoCU5uJZMC2ybKyY+HGS7WCgwMVaYgJIfRBirn+H//v34e7bYlq4JcYVM3yJiEtaRa3GmNUqno48XJPwjuebVa9xbdYAURuCIWSnCs6xrzLZFPT0Z7mNcbPFnOMd9tUKoCxj23VE/F2jgfjmYVzi6d7IIT3/EIYJEJZEpgsyNTz6Y1aFrPnwEmZRnUinOp8HixwK5rIDjHKyfHeHKxhmBkb3K12cF2BnWtg96UcZpCKtHa8sTf6UGO4SgHHvseiVpSWmusdg1WblwOcoXXbh5gtWlDgBVRiVgNtHX2LdpazEqFN79whoePVlEPq6BUy7bRmAwLrDYNBGfoDB0Iaod0DfMME1OhUjmOhzMXvABni3Wo6gSAPJMQmmPlSqU7ralowGkXcc4CL0YyDiEo6Ku7DpIJlLkKCBxAFjTnjqOUkrLttQDEH3b8eunXOmOjJY03Opbcmyv3FZjTlqZ4zbXXeJsGX8kIC0xHOR7NlzSnHXqUSwnOWZKOpzQTYwyHgwqrXRsc4Y0l+YC7+1O8czGnKjZjQ4Dum7cLmVUVbkxGmA4K/NrDMyoM0Rq5dEi4q+RcbGt814MXYGHxhccUlFd5hrmrHPSSB6smrjlFJjHQynmwWVhjYbhFNSRE/fbJGL/65VMUUuJkOkRRKKDu0HbG7R+xKk9wBjqDcbDs/eVAvhHtI2TnA2yL5TYQj9dtgzfPL/B4vgiKkwANrD65NnJilBAYZCqcElMCLEAbXyYFLGzISZMDdn/SMgCrpqGTP2c4Gg3ColEFLRwioApXUWFhARZdttvrIoJ+0oM2wG3TYeFg8ipTYSEdVzlujEfhvnyQ4K+wSE5Xnh+UBmjDLAMsgmdMJiQFC/7GEFNDqUWA3+KbTqP1yE7SLQxALr3GCi3ArTFY176SJcPEOaHvuq53uvSnunXb4HK7wappQqDjy0gF4yQON6rAOEOqTJsGaJR6aJz3ECFYm7oNJcejIsL5cPeYphOlq/Ty6atUAfs6YKyE7J1QPX+iMwaTKu+pd3cB3o5ppGA3Yhju7o9x/3iCV7/1mHL0jKGuO2htMRrmGI1yjCYl9vYHGE/oBBuFEm1PsTltrTEh9QfQZpGq7tadxuVyi+3a4K1HVwDIbfrmmHgEv/7sYRAs9KlR30IgoAROjqKiK2cMRpuA0I0HBW7dHOOVF/dwMCkxqjIUmQipIu/JBRCX7N2rMwDAyXCKA0dw95viumlx/nSN1TZqy/grkizqaFlQEPT2ozl+7QtPe9ebIiVXmx0eXRKq9MrJAba1huOlhgC5VArTQYHCIWBaJ95zHHj73QWaVocN6eYB3c/isnaVaXGj94T2zhhkUmAUpDAiv+5isw2cEn/vVR7R5tZQKp/GGzDfRoE8wXkQxvSBX0it+DGgVNDr8T9N5wAL60CUt8idzEOagqb7cGnaLAsUgtA1yWemiHWTzP+YOHdpZelTX4jaUojl7rkQyDg950JKjPI8aBetm64nB1Jr0uRZu+c4zLMevSEIAfIoTtlqg85Y7Dn0btO07vDi78264KvFYusDMIHFNpo/F0G3LM4VJUTP6sUgomKMMRSlwsn+AIJzbOoWmeShUMPTGah/KPDsdn1U88NoHwU7H2B76dW7KJ2UvFc8XdY1tm2LddsEzYQskQj3i4yfHJ2x4ZSW5qsBBOZ+SrqslAokvkhGJWSAAbizN4mkZZ6cLI0nDseoPHe+OeGEkwQpPY+Y3C2IOgZpHsERnKPIFI7HDuaExbaNlTP9CqWEtyQlpmWJaVn0xMPGZelOZTykbLj7iLRvfNVLm0w8+v74Wr8gCM4xqgiFoO8gKF0lQns+4EmDndSZeJhnmJUlDoZVgNN3bYc8E4BFD13y1Q8+QKHg0qDI6Xp82glwfKjkvqy1yUJGjTEWdUqSCMdbhvimOOudjH0KpdMGg0KFFJcSIuw0kvFeYA4AQnAc7FV49eUDlKWC7jy3hOP/8YOv4zM/8Bp+52dewW/7nnv49G9/Ca9/643wPf5vD+FfR5rS9C1di8SkzHF3fwKANq3T5QYX6y02dRue52uHtyAYx8V2jbPN4rlAD4jB1qjMnDie6ydYrDYxwKoKgaJS6BqD0SBDmUuMqhz74wLf/PEjvH7/JLyWM47zDaVkboz2ML8ihMOjVY3WuFzusNw4noiIXLsikz1SroVFXXfY1VSp1oYUtBP6tDagQzemQwyLLARooyqq/lYZKTpPB0Xge/k5fDHfwVqgKmRIE984GmL/iII0T+wOBHaPwhqDXEmMy7zXt3liLRICC2MwyFUgy7faBES0yASeLpbhmfvvCpWiTh4joMigFNR0SBtvKEhIti7eCz8o+PFFHim5298HQKrT1+eVL+EGCG1nDn1r3icd6gOPIpPBKobBu33TS7y+F2MMB8MBHfoc0iQ4Q9NGZXrfvvLsEgunQLw/KkNQmKLxMlmXtLHY1i1mFc2nbdP2KrJ8ezZfY7H1ysasJ/6XS4m6a5PKQBvoFb760q/3T8/X2NUdfulXHyPj0h0y6bCYqpaHillHTu7q7n3Tyt/I9lGw8wG29mIR8tCfvHsDrxztY5hYCGhjsG6aAHECUe+hdBPFWBsCEk829H/IgBCodURdPJcFIAKkSHgBSghwMCx3kfviaBTOXJTE1oJqq/tlnhgTAv3TsmCEiJRZRA1qFxgUyc+mwwI3XV5XJxo9TZcEUO5vzhgmeU6TsO2wcimEYZajchVJwzwP+f9UUMy3XeLlo5MNNS1D9v3EOcNokFGqCcDBuArvHeX0vNITVpFRarFUCtOiwLQoUbpqsURsnRSRGem1dCaenfx9+n61FjhdbLDckHXHpo6icNJVH/l+t4inNsk5tjuCjiPR2iaLfrppcRS56umSZMmC6eF+oE+CTitRaPwBt+6McHQygO4MTh+5jYsBurO4eLbG4nyLxRn9mZ9uYDuLIk9E6ozFMM96KaaU49Emz4szhkmVY1xmmDiOggXpzYRrZAyjIsfLB8cAgM+fPo595l6TKkiPBzmauoPfHo2xWPlgRFJaIi8ldGfc2PAndoblssEnX74V+nXX0fsOqjEKmcXPRJzLX3zrLBCH/eiQnJztGYsyE+nGN3Cnev88/Bik58/x4vFeKPHPlIAQkXg7zDO6fwaMq8w9u7gRHe2XGI/o58NSYXo8wCuvH4KLiMwo6bcG+llnTEAUPFfDIFb5penSRmtURSS4tkYHVLDuukRzJpZYk3YUBTtZIKjHax45ZMePzZR7HsdOGE2BLJyWnxtEwb7JtWCHPpNBuTmppF8zeW/O+OAFybzyXDRj+ia5ZYLYe8kEfz+UOjeuci2yhcgzzB34eFRx90EyQGgS5/GwUncaHDwID26aNhyQ6dFQqtxzetZJoOPT8/U1SyCfKfAIrNdVe3a2wrtfucJQ5ZCM4+beGCfTIax1aLHrC69llQkeqlXbTfOhBjwfBTsfYLs6o41gWhWkhFkVGBU5BlmGQioXrPRPJZ4Ut9M66JxEAh4Lp5XIp7EBkfDVTylBr1JZOAkwxvBsuQ7IQS5k4IJoNwnqrgvBwa6NJOb0hJc2wUnwLaZHCDb37uFe4G08LXAyG0W+jvucuuvCCdAfiaosQ5Fl0JYqmjyiNCursFmkMvE+jZVuFpu6Cf3nT/SePOf7RoVgB3h4vgjfU2VZgmZwTJxVgvckE4xhb1DhYFQF9+7Qj9r0KqKaTruNqt9vHmr2/bGuWyp/b6NOysSd1sS1jdsHNlJwNJ3BttbRmkPrns6Gb4QCxuoWT9D27WyxCdVCuZLhzWmwwxmDlBz1qsXyssZ61eDqbBN+BwBP377C07f6f07fmWNYqn7FjOAhhZpeZ59w7ty9pSTrDSEwLvNelQgQdXE+fnwTmRBYNTs8XV3Sa66lsiRnqHKJutbh/cYStwsASheUqSwxZFQikKLXywbLeYObexNYRNL8nT2qCjuYlkFmwW+0y13TExMEEMjxDGkVUAzy/LPx8gfCeRgBREqGZW6DASbDDPM18XgYEAoWhOCohhTUpIFvZwxWLqUxKDLkA4U8l/jY69E4Uqn+nARoLEvBw1gDYmCabtjbpoNkLAScndakFCwZnl6tQ4Dk7RDo3i2GLqC5rlbeah0DLHc56VxPQ5brVVMpcublDJQQyLL3UVW3lF7dKyuM8n4pffqdKV/oYrUNSGSnozK3F6X0zT8bf3/GrSWpGSlDHDN+TPsUfKpAPygUOIvrQKcNNk0XzEvXydrXS5nDCYgirim5FEFgMaR/EQ1Wh0WWvJfub7VtsG1aWOaCOBXR38KJ5tZtLIwJtkDG4ov/+6/gw2ofBTsfYBuUGapcBcg+C6qnhCqUmcJeVWGvGmBaVhhmGY5Go3ACSgXvcil6CxYAgDPUXSTG+tO5XyQEY8iFwKQocDCiipVV7XRqHLJhLfqT0NprmyR3BLZYsZP+nlRwTYDX82Sj3TZdmPyL5QYvvDANiqi++fw1AIAhuJYDFDhc7YiYWKoMwyLvnej8Pfu+TX+1bbqYJrqWKgGieq6xFu+cL/DuKQWmk7LArumC8BwhcyoECnTqoWvenxTPLZgWfQ+tbe3L/MMthn6j9FNEZBrT9aTnvc+NcNVogVw7GeDuwRgno5H7mQ0Lnz/5A1HvxJMi9TVpfc8XWNU13jq/ggX1//6wipVKjPUCPyUFRC5RjjJIxaPtQubQxEygmuSopjkG0xzVJMdgkmM4IGuNMHaMQZVYofTqwBH7isEb0tK1Twakg5NWXZWZQiapAvC1o5sAgC+ePUk2qfiZw4q0ULqur74b0sBuw+3SyilOulEenVouG7y8fwPjvIKFxSgvMRBOgp8znOwRH8iP1W0TK7GigS0PBqU+6G46jVlV4nA0BETk6xCPK44LBgRUZ1RlUErg3OnuRFNSYDTMcLnchmfn58OT83UIdoTg+PlffgwAOL45wvSohBQ8mccxFbRuGrcWRM83f03Xiy6+8OgiHGIIMTbQlgwzUwQ3RSC0tphUeXhefkw3nQ68Ro84+INcJaPCcTDtdMiL4Kx3nanjeJ49r94NtyZOUpfyhCIQAnWkQRULVahni02Y5xysx4ED4qELAHYdPXsf2pOZMelH7Q1IXNQYG1CemCoDDvcrh6LwsEc0rQ7rRe0OWD6Q9IGPD3S8FYlff3ywViXl5v6ecmfKCsRAs5MW717Mw7rAeDJ/SoXWZRp8IG+thcoEwBmOX72FD6t9FOx8gG3veILXbh2G0mCveBk3Eh9ISNwYjfFg/wBlYsTpF/guOdmkG6lkDLA2VBikGhUABSZlRumC1a6F4jycbnxOm7NIDJSC9U7WuVP3BfrojgmnuUiuDuaDCZlyXbchnXTv5hSDQYZcSVpsZDwd+teTGCEPMubz3dYZXDIMshzaRm+e9L2MIVR6pL+9bjYoEmQjV1Te/+7FHJerHRgD7uyNMSxybOsu0Z+ghcbrShhLZfGSc+xq7VCS+J3W9oXBVrsmoFtpk4IHXpB/++V6F1KMwyIL93rdgLQsFF66sYepIxDmSgTDwuuvZ4w5f6IYmPgTJGMWV9ttSKMOsgwv39wP35sGwQDdl+AMjTYYnAwwGufoHGdkdkD9o0qFu998jAefuIH7n7iBB996Aw8+eRPjvRLDMnqJ+cAqrbx6v3a63uArpxc4X24ILcwk9kdVz/W7UBKDkgKnF/aPUMoMu67Fo+U59bUL6gHyufLcsPeJr4IY5eoqVl9t144crziGlULddhBWotEU4N+bHkTTyc7gcEb8F78hpppWITWVpDh86qTRFOyMixwXLkhRnKqUUp7d0hNNM4GqVMhzgbmrIqwyOvUzRunl5YpeO6mysGE2rcbCpe1ODgd4/HSFxYru9/VvvQFwBFFRII6nbaJc7tEdBmBalqHCKDzfa3yxZ8sV3nx2ha0ba54cr3gcA9uuQyZF1HpywXCjE0mF5MB3NBphWJRIm0VEXqjyi/d+57+bc4Zl3bcu8M/LJs+pToOdBNlJ5S3mGzLufXzpy+nhI0AAMVBpE2Xi+lrwm5qW3t2fYFxk2CS6TP7rGGOwJoohjqq41gvGQsHHumlDEO2J6r49OJ6GtDq4dc+KAp/wrDsdUGmPwPr96OGTBV66sQftD4RgQWcpz0XQIPPEeqpaY5CFwvTWPj6s9jWVnv+n//Sf/ode973f+72/oYv5X65lfShRa9sjy4okWoeDBK2LtjdtG6DAVhtMS4k5HEfGvb+UVJmUBh+zqgwBjQ9c0lN6SnoMkLor++wL5JGGSzjNMIZSKayappcKGuYZrra7eAJBJBs2rrpgUCowzaC1gWSeT8QDEuUXSD+ZjAYYDC63dFqtFBGGuftO5tJRaXovEwJrtD2Iede2YJb17r+Dzy1zvHU2dyqyDC+d7EF3TljRPaI0NVYoidyV4TaO37SrqcrEExORfI9vu04jf59qS+WI35kkjkLddVjvIkn2wAVXnp+Vni6bRkNOc2RKYNfQ9TBGiwvZXvS/S1sDDh4+nzEGyRjeu1yEjWWc57i1N4FiEjtdh+9OI7nxmAKwZtvhYH+A9043sJa4XcNJjotnG5w9WeL8Z2qYTmOzqQFOi/3BdIBhqbBY02cb50dVZmSr8D5xBwZFjrqjMfT22RxKCAwHCnvDEk/na3cvtMEXmcB210FygY8d3sAvPn4bb1w8w9FginSZ61oNmyEEaWkTnmgpOJptfBZ+M5AZB7cM67rFs9Ucu65FJiSOhxN36rVojcHBlIKd1hhkIm7eknFYWHjn8CKTwBpJKseg7jqUSmHrUwuch7Lu3Pnl0cHCYjzIUA4zCB4rsQa5Cpvr2RXNnzIjeYmsUOi0oXJ6lx453hui0Qw//8tP8H3fdQ9ScBwcDXB1Gu0o/Hj2FZZK8CD8qFwqyq81VaawrlunlcSDxICv3qqvqUIv6yaQqD1XzVr6/WyU48lyiU4bnF5twjzwPm9VJntIaAhy4HWL4iEw8J1ckPXkch30f2z6x9rege+6ae77tavVDpVSxJ1JIjwD5yNIYH5Qrybfqv6IL5R0fCbiO3LOArdGCt6zEmp2HfFgtOPEMEJdtLUY5Bl23RbLXUOoWdui1bTGWgvcPRiHQgglRBCaLJQMqB2tb5psXqTAqMicxxel3za7FnUdjX8ZInKtlAiBXEojYACY9U/qw2lfU7Dzfd/3fZEM9n7HItCAuy5c9v+vrbOAAu3M5OnUlwbv550ZBAOMCyo2bUuGb5IUNLU1wfDSv1d4ozvP8ShILjycKI2hU57jtdRGh5MPd8JtxsbqnrQySnLaHFMYuJAK6yTYEYxhkGeYJ9VDwomSTcoMjTWYr2ucL7YoczKgi7lkCw4Gg5jGSrkhV9tNSMnlklKBdw8neO9siU1NZEMfWDHGnMko6y1sq12LG3uD554LYwwX623QEvr4nQMMCoXFmngznTNtTassOOi5NA5+XtY1prxwBFP0qjDS5+pPR775vstd2k0KgaNJ9Rzhtswktq0OWhtpgLnetqhEho3U2DWarBoQN5DrJc6t1lBC9qThN20bAsapE5GscoG2s0EZmPOYwuKM4eR4iNOnGzS7Dl/89afYOfSjrFTghgjOcXhzhNG0QDlQKCoFKQWevXWJh188R55L7HZd6KtKKVL5ZgzM9u9zr6qQK4HlrsbZaoNWa7zx9BKXgy3KXGBVk+bSdJTHZ8WA25M9fOXiKZb1Du/Mz/Di3om7B2C+bTGq8lAZ5seMde8tRwqZYFgumvB5Humazio8frTActPg7ctTAMA33byJTMlwMOkMVbYVmSSrCB6DHa/NQifeOCZS3krddhjleUADqaSbk12IlFg6ReJyIHF0Z4zROMeXvnCa8HUIYem0wXxBz6fKaN5WoxzNpkVjox5WpgTuHg3xf33lDMtNg1GV4fB4iMX5LgQCflw1HRFXlUupe+SFAWHMlC7YGVc5rtY7cMahLQXjxkYvK5GUifsgw1gb7C4GucL+KKI2l+tdCHKEMxOWTqrBj5d0zDeJECVVo2rABUrWWjx0801yEa7dt9RGZ53sZR45Tm1GPHLx2MkBDPIMm6aNXDHmCMmMhGCZjzrQT7EeTkrMtzvH29OYDfNAapc8ptwk59jUXUSPHTK/a0nAtnCZgc4YDB163ugOt2djSMVxe3+EN0/n7rMYll5bSioYS4j/sm7QuVTUfFMHDlrav+88m+NoPITlcX4AdK8pORlIUn4cz6mafyPb15TGms1muHPnDv7iX/yL+NKXvoTLy8vn/lxcXHxQ1/qbrrWbFlfrqMFytanDIp9KoRPpOKoiR/l/E9jtm6YNKaJhnuN4OERnyE3XD0CSGk8N8GLVEWMsVBsN3YJoYV1A9LwVxMhZAqQeRcwFN755ODiVVfdVB4xzTKockwF9znunS7z13jygWUH7BugF0MKdfi8dV6fK8gBHc85wMhvAV7CksLmvQoqBhsVy2/SsDHw/0emNSvrvH04wqqg/xgOFshA9dMvfmM91+3Ri0+lQ3i6vTeA0N59WV6QtDSKnw6JHFh6XebB1kIL3lHeJLwBsd20gkRIx0jwXtPpFxi/6fhz6pTaTJCTp31c3LkhOTDdjJRalr4qCXnv6dIXW9W1RSEgloHIK4A6PBzg8GWI4ykl7w1pkjqA8HVPqjVKo/VTWddJpJkmV9uZsjKPhMPT95XqH00VELfanFdrOhMCEMYbXjogb8O78LFRMcc6wqzustm3ijB6fSactWM4xnBZJistdGweqocKT8xWuthtc7Qhp+KYbNzEqYoVlp6mC62BShX7rf5Inbca0YrRMIUdrziMC65/lg4NZSEkDhEDuHw9gjcUjVwhROLFGBmDtTuxlThV9jau22ZtEI2LBOQVkkuPO4RBfeesKTDAMJ57UDHd9LAQ+pITd3zakE6QEiPAMUAl0ys/bNm2PmOw//Gg0wP6kCtftRehGZYaiiFw5bUz47DDnHUoGxODJ91nTmWStjc9AcIHO6IAQXbdfScVNfcUUfWcco+Hfyfs6QwezKhDbCe0IaarnvqcvtTEZ5OE6V7sGy03TQ0jCAZMTAnZwEg9x1ysP/drteYetNpgNC9zcGyHLReBxhQA10emKkiAmrJPbpm+yC1DxymrXYJtoggnBsFzVUcU7VJ8ROfnOrRG6dd/A9hvZvqZg5/Hjx/jrf/2v47/8l/+Cb/7mb8Yf/aN/FD/7sz+L8XiMyWQS/nzUqNWLLTHl6w6rXUM+Me53MbhwqZjkFD0qVZiE8XQeq6wsAMsYVnUdFkMPjUvHXfEbGHlV+fJDOjVNqzIsIp3jEfX0ezjHrb0hDsYlnbjdz401PTG4XErkmehxRMhXxW+eGjcPhxhXJKT1NNF6SMOJ1PlccYadptOr95QBAN0Zx9kQIQftxeyUMzm9HrQ1HWmc+Jbm3geZwt39CYpMYTLJIYSTq8+fBzs9LO7L+AOJb7uDNiZUxqXvCCGXtT3Xe7pfxyVwL9psO9zdH4ffZ0IkEvEskL8ZgMyRNjc1LUB+E+ocspUatqYnZm1icMZAfIiDwaDHGfLIT9APSiqxMiXwwqtHpJQK4PhwBF9glmWky1ONaGxsljWMBXa7DufP1vjlz76LL/8qCeWpNKBxG4cPoH2wC9Cm51E7H8ROigLfdPswBBIAcDgbkvxCo3vB79FgjOPxGMZavHnxDIwBkyFd3+VqF/RpfCDr2/xyh6x63qqgKBUWqxpXqxrvXBGq84n7t1AVGTIVkZrtrgPjDPvuGtO5FcnJhKh0AUGIc33tVIdTR/BZRf5nlRMYBYCrVY12R95Lp5eU0isTAVJ/uqaqHuAzP/hxvPjSQRAQ9E3OCD25tVfh9v09gFOaOFRjAchUNLNc1TUdNpIAgiMG0uPSBztdTzZBG4PO+sNB3PhzKfHCvf1QmTUuctzZmyAXEvNlHfljnIwu6ZG5tFpShNCf913vwJKupdZEdGmUF+H6fEvHQpYGO+5nDGmQ03/vIM9CgHV9nQ+aTgkviOanQxQzFWxb2s7gfBWd19ODseefzR0Xq+s0tI5FKamG1aqOvn4alMpqO41N3fUyDVWWoQxBWnxePqhttQmBi1/LtBNgXe/qsG9JwTBf1iH4CR6IlgI/ZoH1Ww/xYbWvKdjJsgx/4A/8Afzbf/tv8YUvfAHf8i3fgj/9p/807ty5gx/90R9Fl6AAHzVgdHOMIqNqisWmfq6sFkhOKQnV4mBahiodH91rV5GVLjp+MwOAzC8g7kN8NYUUDLNBgVZr59NE8PM4CVpWdU3mom5wVzkt4IfjivKsIc1EhMlcSjJsVBIHkyLkk8EQNHfASNvhjYdXuFhte4GGB4E9QTpdMiZVgSdzOqmO8ljtZEALKKE7w8DhAeBk9wUGuXouI+xz0r4PAdpI7x1NyRdLUlnv4Y0BJrMCTMVTWyBO8/g3Y5F459NZFkR4TsGxFFa/jhQFJWL3/6YzGJUFbkyHqJRC01GADNBi2CYpFyU9MkaKtV4PxadlPPoXr4W5fkhSjYxhUlLfjopIXDXWYljJ3kIdAvBJgaxSKEsalxdPVmicKmqRS3StQe3u8+m7K7z7pSXe+cIcT95eorlqQ1mrkiJwBnwLZb6MwTIao1F2IVb6MEYbyrc8OMad2QQ3JyPc3B9iu/Pk+H4g9a237gIAHq8uAWZwdDgEYySc5wM/r8Lo33l1ucV2HdcxD9LlhcSX3jjHrmvxZElpgO9+7QUcHwxQ5bGMuW6IT7M/pmCn1Ro3J2O8dLwXDiZKCOLRJAhaHAsaLMZaEIzjzsEYxlKA6teNy+UWxh0A5mt6tgPn+r5tKEVZOWRkNCuxtzfAWAqqNnNf1nUGB3emKCbU/wMLZGWGttbIchE27lzxUOmzaVrAoodE+iJ+hlgc0Wrt0swxtRo4P27z9pWe7boNyEejNUaDDIKTGa6/XyViUYMfDz7N48dG6EOtw7X74oaoi0Tv8ebC1xHtlJ5RFpES0Cs7T74zjZXI6Dm+P/2sdAkIAY/72wepHrnqjA5SBVLwXgWaEBFZNdYGRC21tpDcV7nFw9+u7ZApEf3QhH8G1A+DQjlkjWN/QN5dAEIKS7CIGvsDlLUWuotItpAcK8d1GzrhToB+b63FfL7D5OMv4cNqv+FqrDt37uAv/aW/hJ/6qZ/CK6+8gr/21/4aFovF//0bv8Z2//79cAJM//ypP/WnANCA+bEf+zHcvHkTZVni+77v+/Crv/qrX/fr+I00Ljgmg9wJ0tleKWKEQl1EbiKsXhVZKLvctp0rvbXOGiAuMiI5efvN1tdAeDlxMBLB875SlVJucWVBebQ1BnkWJ34oYXSEZT9IUk6N4OSiXhUqpKQYgFZ3OFtu8fhqhavNLtgv5FKgkMKJ7CUVZS7F5r/77ctLWJCYlQ/s/O9Or7ZoO032DoyFIMxv+EomukXub3/iTJe0FM3wJ1jOOQ5vDAPPweeW6XPi6ZH5e3cLpbXA6WoNwfupyZ46K/rNo0Ax0DWYTXOczIY4dnYDu6ZDqzWW1wiY/lp8BZ6/97CR+oqswIlw3+G+m4Hk9xljKJQgET13IU2ne1L9aRrr6NYY+aRE4YKdzqWwVEaVYMv5DmeXtJB2dQcwgEuOcpjhxgszPPiWkyBat3cw6D2TTESNFr8p+JRVurlITnoedavdAp2jymVwjM9UFF4DgElR4d4eVX/8+rOHGFQK40F0JE8voihiWfn8IpKffZVJZzSePF3h3aszWFjc3pviaDSCbg32JhEptZaCiL1xSXwOa3ubvkcfi0JGSQTGwmbfaYNlQhAfF0VEu0REbFdbqvI7vYhE2zJTEIKFQHk6ohRwMcqwWzdYnW97qRPGGP7bz7yBpXao0tkau0WNetW4tYSaUgLTgTeb7RzPLynp7qI1QJZIIDAWyc2pMGWo/nQHNG4QkAVjLQbjDAYUHPk51WodXuPHyK7popwCYnrpur0D59SPgLO9SXiKAXVz95LOVa1TmUKEPkuDmJTAbNn1mZ4UpwT1+fRERH95NLlUcQzEwFDEKkrmlLeLqK/jY3aeoG27TmPmFKf996+29EwvV+RD6NfFgdNk4ow5hJJhnBcY5Xn4vkwKIna7z/L1xKQVhFDZ17YmIJL7B8OYiXBGqpt1ByZ+wyHH/3T7DX1zXdf4yZ/8Sfyu3/W78Prrr+Pg4AD/+l//a+zt7X29rw+f/exn8fjx4/Dn3//7fw8A+P2///cDAP7G3/gb+Nt/+2/j7/29v4fPfvazODk5wfd///djuVx+3a/la22Vtq7Mk0oTQyliCuW7v42LfoXjpnizRIAmincD99ooHt4Oqr6STnH+FOAXBhLti6rJJJhH9gvpiXK5q8Npd5AraG2xrTt4517fUnRqUCjUre7JqbfaOKVO2pyGeYbDUYW9QYlJVVI+2aaTV7h+iCcQABhn0XXbT5rOGDw6W2Nd9xHEdOG6njJ4TksDziTRfXiWpMSausPSlRl7BVaGeP/+bx/0eE6CNhanq03wyaHXxO+7vgR6WD08e4ug3TMosuADtNo0oXopBFLuvrYuiPSltYFUmRqBJtccAywWJPmzID8Q4er1NvJb6NpcMF1l+NIXzqCBwNsBCPHwfXL3VVIw1m2HWy+O8PIn9vHStxzizqsHmB0PQ3rIB+xxXLGeszuAgGxSwYO/Nyo3v7qivP/JjRG0oTkGuIAfMYhsWo3f9soLYAAeLa7w8PwqlOtr72TtHs6wysjaA8DZ6SZch0df3n28hDYGj5xY4bc/uIfdxm1ImcDx7Wl4z8PHSwjOsDemFFHddYEn4fWyctXnhgWHbVh8/q1n7pkRiqq7mKr287ozBotFjXceXrk+VRCMODheodeH9O++eY5f/yylD2oTUQ8G4J03zvDovSWWDfXx5cMFjLZY79owPoXiIXXoveb8YYozFrgfSgjYYDvjyqGTTdLfp+ARVfB+Y4NEUPDx02UovffjoOm0qxiK42bXdWG9SJFSi9QwNH5vnG8xiOGsvwX61zOGnqVCugbGAIjmjv/NzqVS0wAq2FRY21v3gSiF4dNXfh3btm1AbFLeEAeNgejHlVSOsTiGmk4H3qdfB9Y1aSRdrXawsPCWFbmUKDKJTketLir2gLtuQur9uPXfC8Q1YrmtqUpr24bnMdsbISsS13QLWK3D4eHDaF9TsPNzP/dz+BN/4k/g5OQEf/Nv/k38vt/3+/Duu+/in/yTf4If+IEf+EAu8PDwECcnJ+HPv/pX/wovvvgiPv3pT8Nai7/7d/8ufvRHfxQ/9EM/hNdffx3/4B/8A2w2G/zkT/7kB3I9/6PNWgveaTy6WIVBn27e18Z9Qp6llJYnZwLx1K4tOYkfjgZUmmyjfkcQWnOfG3gAEmDKBp2OUkrUHZnHMYfwECLjtRE4BU2gDdXD0+l1+gXMWOAXvkxcjBTNyJXACydTvHhjinFJCE04JUreQ7iCZUPSITemI4DFSotMRPSh1QZXq7pXVurRDYu4gPgUX5qS8f8uE5f4gOwIjsdPVrCWEAJ/ADEWGFVxYb+O2oxygmubTmPV1IALDtn1B5w0mVyjf5XWJmy2o1K50t24aPsNJIwhJcgRuTMwiWZMHtJYETkc5UXYTHywk3rnZAnR2aMknPV9fl567Qgf+6YjzI5HKMuYZ9k/IQE9YYGXX5yAC9Lc6Jou9gFnYFKgGDiNm52zD3CfYWF7xHegLxLZJMEOBeGOH1KpkMJSTkKfcRYCZ2uBmwcTPNg7BAD8x899AVIwHO4TskQbhZs/hcTIWStcLaPGDkCp5KenKzxeXmLXtJiNKnzsJKoNl+MMRwfRYmS1bnB+scX+JAY7QfbfpX+yLJZrcxaDUWNtCO68iq6fb17qwffL6cUKj5wYZpUpABY7x9U5nJVoXABz7/YEAxckrXypsVMQ1tpgvd6h2h+SyKZDac4TrlvXEcGVud7atZROPhiXyGWsFMqEwKZO0RjjLEbiNkOpq/hvjyZ49WRrgavFDsbSmubHrSdYlyqmqgmhiIFArwpSJyRZa7Hr2jDmG62DRcr1ACSg20oGkncMyeP3ps2jhWmA4l4Z9ZUSBWX/dv+cB4XqBd4eyfEIeooACs5R15GHmSJPgkd0Z9to4ni5b13tGjx6tsR6F6t3K0WFGR72Td/v+7LTtJ7tjcpES0oHhHA2IkQajKHTNtAuxpMBhk6CIazFFtherfBhta8p2PnO7/xO/Jt/82/wZ/7Mn8GP/diP4f79+/jP//k/41/8i3/R+/NBtaZp8I//8T/GD//wD4MxhjfffBNPnjzBZz7zmfCaPM/x6U9/Gj/7sz/7VT+nrmssFoven693Y4zh0ZNlIAqmcyqkWuB1CqhZS787u9rQSUbGU5y1FkIy575MGi11UtGUCQGeVHj4NMBqU+PhuefAZGCM99JRjLFeRULp8v7GRAfwVJjLGItJUWBaVVhum1By69NaAG1eVa6gpMB0lOPunXFYAJukzNMr/Ipkc+aM4XjUJ1F6jRv/nnSSU9/GHHh2HSZ1L0xJ1INcRbKojCmlc2d9MBnmSGVYtLEhELkeqHLGcTweQgoqCd0ZgtC/WrDjSeQABSL+31khUVTe8sGSTLs/ZYLFIM7dz2bTYLMjXaEUWaMgWfQCCeHKVemzaCGPFXC00fu262KZdMpNKAsJ22qoQgQYHQDGew4paQ2stqhctdX8fIv1usPVZY2n78zx5K2rkPoyrj/TaxhmMdhhcH5QjBZgnyZVMlb9lLkE0xabXRt+54mzLN48VCbw8RMyCX10Oceb5+fYn5b+1yHwLnOBgQvi6kY7tIg+6GJJ0L9HdX7nb30VQkZEsBop7O8nVibaYLNpkfOIrsbSYXpfrkTQWuGc9RC568KDUrDgL1TlMszHJ2erSE5WKui3MCAYZ+a5IL0sxlBrg4UL5KaDImxsF2cL3LhRYXgQid/rRGeobnTQhAKIK9NpAw667nhQEljvmiQlp4loHiqmmCsDt+H1HkFNK7c86nD7cIypIyV7faAyi2KoYbwk/04PRQCtg9pGJDsIGLYNGVimiE0SxBSZClWQ4fPfZ04rwXDDKWa3RmOQq166zSTr1XVQw4/rKlfY1S2a1vS+InPq2dEAlAce3futLinXrW67gO4AtIadLba94MjbiviUveDcBc0AEwypcrmSHKPUOsJa1C5d5q9JGxPWompYYXQwDd/faSKGr55cvs+Vf2Pa16SzAwDvvPMO/spf+Stf9fd0WvhgdHb+2T/7Z7i6usIf+SN/BADw5MkTAMDx8XHvdcfHx3j77be/6uf81b/6V/GX//Jf/kCuMW1bdzLIpMByFytqnk+LANpNDM4ZTudbx4CPMKCxFp22KHOJnZOKD6rFjvcCawOh1JdArrctHmkK5lJPGzK4M+gMbaalUti2bSCuam3DadRv8DYJHIy1TsAMaLbEoRiVOXbLDottg6Yjby8hOGAioe1sEVMEcOiB4NyVxwKv3jokGf0koCkzIkTv2g7WXbuHSzkDwl5vYzrM9+/1UxgDpRLCvfiKg7oLG8GgUrh03B0A2NZEmqwbEg3zi61/LpkQePFohq88uyR9CtN+VWVgWnCTgMy9rG1NIERq40TAPDEXEToOsLim8aBtFIhjjALTXEqsWdN7fYDCOevpvBhrSYHZ3c91ITaA0IhnTzZgHFCc+C1KceSDDEJxcMFgtMXppYEoCgBbXD1ekj8SLKQ1UEqgKQQWrt9Hoyw4gVtYFC5As4h+UMxtlJ4TokQURhxWGXRnQ/l7ML8NvB3fTwbjqsDHb9zArzx6iP/6xht46cZh+K5OG1QFmbhKYTGoFNabFqtNgzwTaFqN+brGxXaNy/UamZL4ztdfwPLRBu1OIyskLi92WINMeY0FVOlE21hEEvxo8KdjJXkglae2BhYWQjC0BuE5TcYFVuuGgjchSagPwNtP5ui05+vIcPgpcxml/AVDvaW5sq67YAMwGeQYTUu8994V1qsdjDaQVY7GrGCNRV2nwU6HYqAwyDPU3Ra7tqXP98GoR604x7bpemksKQQUp+rCTEpkMlYaKiGC2TAYrZO7ltJwtw5GyDOBu7MRfvntpzDWYrluSIjx2vjMOAdz6TD/887ocDDyROZSZZBCY1VTebTW5rmgwc+LcRl5ju9Xau5/vjeswFxfa2MxKFUQTrxuoRHoCOijMkoKPJtv3NomEoSon+oU7qBEgQNxuEwX09WcMzAbxVozQYdijyDVuoN1wcvQGaEGlNlaTEY5qlZSSfmuCylGYy2qSmG8LXC52bn0mYXgwHxdY5hn6DoTxl+RCWRlAVnGHuucX+L6/MOjl3xNyI4x5v/2zwcpKPj3//7fx+/+3b8bN2/e7P38+in6v3eyBoC/8Bf+Aubzefjz7rvvfiDXe/fmhCS4GYuGeWnJZjgwxFO7dZsXc6eQPEF31ruIpADRrM3L+PtNn3OGe3en4dTiJ3smfCoDbqGK0Pkwz3H/YIaT/QEEZ0G7BSBbCg9Fa0vCVfcOxri1P44Tk5PNg88/Xyy3Mae7amAMlTru2s4hSnD3TDl+yQWmRYmXbuyFBdm3KleO5+Bic94PGP2Gt2lbLHZ1WJQIGrf+i8LreYL++Aqe5ZL6liFxPHYbZ93q8H5/qo6nUAPGgTJXePXWHhhoke9sf6HzjU6T7j8MYVOqWw2VBGFpRYn/vbUI0usAjQlvHeCvibNoBQLQAltlkQybCwl2rf8553gOzgcLStJZJnF1vsOThws8encOxhnu3Bnj4KQCtIVwaTAxv8KwcAt/3aEqOIpCoCgVhORQuQjB1GhY9NJNZCFCFXZFwgfxnCj/7Kx1kD1DQHUyR5IGiEOUolpNa6Fyht/64n3kUuJys8FXrs561SuECtF7fNpkuaEN/WxB3JHHK9IPe+XmCd55c038mYGCypyPFOdQiTHjbFqQh1SaskzSVWkQTlYfNOZS7hJZKXDs6i7h+8VKKM/bKpQCLAvB06DIsHPpPc+REJIjyxgWjpM2KjO8+k0nFPRZ4EtfeIan5w300THOeRnGJUAE5HwgMXbWBB79ey6Qdpusv+e0gnScF8iERClVRIK4iLITxgZEhIGhyqnyr8gid3HTtKHqtCeVIUQk5IdnT9d3td2SY7twVaSch9de7TbPoUTWAp+4d4xhkffsVfzf/rAaglfGcb6owzXWrQ73/37k5/Tf/jUXV9v47PLrB9J4DYyxIA/BGUNRRssO6k/WQ3daY3AwKXu8TP+9Oad5EgxGHSKaXr8//BhjMZkVOD4chDnrEZ/5ehdET/3heDIswDMFLqN+j7EWmnE8+J5vwofVPjxq9NfY3n77bfzUT/0U/tgf+2PhZycnpIzqER7fnj179hzak7Y8zzEej3t/Poh2vu0gBIe1icAVe77LWcLi3zrUxuuM+FSWNhQ5b+vOEcuiMqs3a+t0PAEr1id95kIGKJq5DSRU6qSnB8HpRNvpALsKEXVk/AJQ5gramOCMnrtKKF+1cb7aJkRRuq6FI0nPBkVY3A2AkXMVNwB+9c3THpeFAeE054l5njjtf98ag1Xd4GJNMG1YvPq5LsD9zj8B6VIfYMD5BSFOw0EW0guFcxcGgLoxvRO4/45U9p1D4OZ09Fyfpo10cNy1ICFStxpSxcXNB5FKiF6Zb6dNWFjXdUM6SuEWI3Ha/0y76CCt2gNSjgh1fBqEM4ZgkgoAo4HC7cMML94a4v6tUUBPRk4ITbgUn24tCldyvVm3WK405guN996a460vX+H0dBc3yM5CyvidxtoQpAUCN2MwiIeEkM5SAnWjA7+ocN9fjTLkRd9odjTN8T2/82UMixyfuncPAPAzv/xFpDYlu4RfNCgojdtpg8WqxmJTY9PUeHhF8Purt2+BuQCxc4gloX1Ung8Aq02L0TjHeJwHgTcgBqGZEmi6FNWJQdGmaRNbBgri1psWeRmJ/H5N8K3KVCAJe5Ru53hNfj4WlcSgygKnZzjIobsMgwGlJn7tc4+hpQKXAvWm7xkFAKudxp5TNG66LhRCpNWOkT/ovJScoq9/lkAM1Jh7btxBxtrYgEZ3xuDtJwt86Z1LfP7Ni5AWI9TnuUvrlZDzhBDddF1Yc169vQ9fuu1T/PPtDp3RvfHSag3OWSjnT1ufi9cnCvvnt9o1YY1/X5cBa3u/yyQPSH0meeCNefuG6xWwTavRdOTdt1xGlW86QNK9RyFGi/1RGa7ZB5njksQ+B1WUMuAMeHa5xXzTxCoqC3fAMui2GjcPR71Uo7EWi20TPtcfqKbjElwIwFhIr99jge2mwed/6pee75NvUPuagp1/+A//4fv++ef//J/j85///Ad1jQCAn/iJn8DR0RF+7+/9veFnDx48wMnJSajQAojX8zM/8zP4ru/6rg/0ev5HmnbQ3S6VHO+9IqITfg5pkt2kFFDCg/H6Bk2nUWSSYHBfiaWkk6J3SIzk0J3FsIw520GeuXRQUh1h48nDp09WDoXxPjUMdALzSsieWa8UT/hIDKUrn/QEyk3dYlO3UBlH1xFKtNrV4Vp8xG+t7emjeJ4QksXL5/U5i/8GonLxxWobLCsGuUpM9aLpalCNTXLU/nsZA+YuhTUeZpEXUsiwiO2ajlCDgMbFp7irdTgFDfIMUxe8vV9LSeRlERGYptWORE25b4/geQ2MyFmKn+U3TI9ExYWLhQUdAEaJSJ7XXxpUccPUXX/B90TsEOyMcrTaYrPTuLjaoXYVSqenK7SdgXTIju4MqpMDAIBxYpZFJbB/PMDJnRFu3BmF6795f4LJJPaTBTAuSUl66AKEdLGXPG4KueIwNvpb+cV5vFeQ/EBQcAOaWmN5tUPXGnzi7m3MRhXWdY23Lp4Fn7blton2EZwFdOeZq/p6sqFA56WTQ9y9eQDuRtV616BzD/Pmfo79PQoGtlvSoplOijBvgCgImbtgzV+74CxU0gQtKMfL8/PK9xtjDOOqP74KJcOGM8hVLPMWDFkmULcaQnHsEnKylAJZzvDx128AAFarBpOsgUCLhbM+KHIZOG1vv7fA3jiWM/uDVp2kXELaWanwOuU4ZL75Q44SIrjI+1SyD5KM46nsao3lqg1jc9d2eL/EQa5kSBlpY+KccgHgqMjBDEcuyVlcur61oIAnRWD8mnq5oWfvERXf0vBFsOhZ5dfp5bYOjvL2fd4V1Y590OM+i5NX2t6wwLBURPpFXwiVw1VuuvcMBtH0VYi+hYXv8822b/kDxGrQvUlJRGNtsNx2aDoT7je1MOm0QdsZSl8nfEDtBBrP5mTS64P06ZSu3RqNLPB8aH0cHU3wYbWvibPzZ//sn33fn69WKxhj8Ht+z+/BT/7kT2J0jWD6P9uMMfiJn/gJ/OE//Ichk1MNYww/8iM/gh//8R/Hyy+/jJdffhk//uM/jqqq8Af/4B/8ul7Db6SNK4XVhX2OU+FbGvh43g7gBzSRd706b+fKxbU1UIJj2ySseql6cL8SHHXbhfQOAByPhyFY0I5cZpJJSJ9F6b+2i3luzhgOZyVarfHMmS9qp4DoKzukK2GkShKL6aDA5XqH88UG+zOS8l84CNWrwHp/G5//VU41mCo4PFHTQILharPDpmlgLPWTT99BCSw3dVh4y0yhEAIbRlydRne93D4QK8mAuMjqzoaS8+kox+NzQnlGwwzrZYOmI8uHw2mJzaYNnAn/2bXusNmS7L6xFmWmsGlabE2/RJ4qx2KqalhmWDs+xa6hE6W1SAIdSi/lSmJQSDw8p/5vtQmp2n65exxRpZKY76gfWhFRMM8DK3OFlSudrhtNpqKBBxVTCwAwOxigKCWKEhiNM+xWDVZnG0yGOZT3J2KA1Qb26hxFpbDbtIDVqKoMgNvIEyJ1ngvs7Q/w8MkyBNujPCOrDO+mzKJYor+1XAmUuQrBQllG4be6NhCSJRsKcPZ0jcunK+RSQimF7371Ffyrz/4ivnz+FHdn+44wCzy7WGN/XBJZusqw2DTYtR06rfHW+RkA4LteuY9JmaMLwTQgMgV0HYQx2HNVXm2nSUqAMxwcDPHWsysACKfcTImE2M9cxRLrccz8ZjUaZDANsFq3IU1R5jIYbPpURGcIXcikIDTMUnk7YwynVxu0zGDpZAxGZQaAYf8gx9HxMf6vz74DbSx+5Rce4tO/82V89soVNAzIn6ldNbCtRssJZWw6jW3ToVAq8m9UDHYyKcKaZSzZPNSrLVRiaKmECEG6B7vzTCDPKBA8nBU4Ohjiar7Fstli27ZBjdpXkfomGccmSXUKxmA0rS2CMRwMB9jWHXIpsW0JxSulwqqpsWrqHjmeut9i4SuxkjnleZFwh1NC090anCksdjU2TYdCeDSL3ufXLT8m4/fEVG2VK2RKUHBrgP1RicW66UtI9K4F2G7acH1KiJ6HnnKmxetdh3GZ42xFa5oUlMYrcgljDOqkEqNQAtNhjgunxWMtQ2eo6qptNYazEsNCoW67YCcBABoGUrJQvTY9GFH6lHPkoxLLq3UvWP+w2teE7LyfF9bl5SXqusZ//a//Fe+8884HQvz9qZ/6Kbzzzjv44R/+4ed+9+f//J/Hj/zIj+BP/sk/iU996lN4+PAh/t2/+3df94Dra23WWlyeLh1ZLir3+uYj6EAMBU2gFFr3Qzfl7XTu5LJOCISDQgXDSIDQoc+/fRGQm0GuwgZWZrInTOivKEDQJbnWpkaJoypDqpbRaYNt3WHprB8yp5dBv7NB1v1suUVTd2hbjYVDXo4mFZpOxw3JEtHWp6pao7HrWtSajPw2bYtffe8Ub1/M8e7lHG9fzHGx2eBis8Hj+RKrpkGjyfl7vt3hvfkymFw22vQCHX+tvvlrmC/Js0xJjtE4x9pr7eQCMqnYkBl3Aav7f7hn6iuCmAklUUmfhO9zC7G/oiKXofR9V3fISo7OOcXT53s9Eu4EExGer7+tSVUknJaIYHnNGQB47EiB/sTmF9g0KMizeGLzZee+7/b2Y5WOtRbcl6rXGtopOQv3/DQkcodmzC92uLps8OzRCo/fWeLdr8zD5zx7tEa9i+kjfz8pVyTwpZLfD0rVS+tVhQy/ywoJKfv8o7bVIZUiGXC7mmKcl2iNxlfOn6FyFStPztcwbiOuEsG/Z5s5Wq2xN6zw4skhWNsi80ik5JhMXdnxtsNqF0/rXq/o2FXqAHHsZYoHbaAsCG32qyI9Mjce5VCKB2E2/x6f2knT117JmbtlPXdu51fLHd5+eIm5KycflhlkLlAUAkIKHJ3MAADvvH0JWIu1syLYmxRQDm2xncamMwF92bYtMplYXqTooo3l160xgcc3KvKEnMzDmuKfnxQ86oM1GpNRhts3xiEVZ2FxPKtCytw3kVR4Af1D5LQsAg+GAUFqQwoRNXyuwUXaWGzqr1J2nnyLv4ZMcGTJuLPJ56Rqwx49B/pp7tI9N2/1MyqzcDAIbueubyn1FytpfZMO2QkpcsbCOp/ygEYFlZtLyfEk0ZMqc4HZqECeyWD0Onb2KtYCl8saQnIMqzx8r68SBoCsEESa5gyD2QhGa3AhMJjF8Q8AT7/wm8Qu4qt+COf49m//dvytv/W38C//5b/8enxkr33mM5+BtRavvPLKc79jjOHHfuzH8PjxY+x2O/zMz/wMXn/99a/7NfxGWlWokI8G+ifvVJMGcGXoPrBwG6InzKa8HR89b7voiKucN48XmXp6tUbbGRxPh7h1MMadvSkACorKUiJTzysNa0NQpTXA5XIXyh1zJbDbdeEEA5CfVmdMSGMN8swFbv6+6R5qV8myabtgaro3KtC0uufz1WmNaZWHTTjoisBPWunKz2UoQ/epFsYQDP0yIRwhMg7rKlPYG5S9jSJ9BtbaXgoLgirROGeQLuXlP+/82Rqc8RDceZSk1QaDSro8djxdimvwMZGT488EZxi7HL3WFsh4CNAoD++Iv4JI2NcNGEeFcoaSMTD1jcifPpijTS5zgY4QhCCl6UOtY+pUeBKKa4f3psinFfJZhXJ/iNHJGJnjemyu6BRYTlyas2kwOYrB0cmLM9z++BHufNMhRvuU5mEMGB1UeOkTJ5Eo/VztCj1/j+D4Z13mlLKdTXNMpnnoEyEY8pyq/3rBThOJ3kIw7HYtPu5MQt+4eAbGDTJJkg3n8zo8T4+evXlBPlifvH+PEAVj4Jnbr378CMXAjYFaB5VqC2DpSPaDTGIyqVCVWTi0ZEkaK8v6Inu+5QkCVw08B8al9ATHuMjDGFs1DQCLUsmgZwJQsN4aDQ2LpjWYryKyUw29/YPAq5980Y0Bg1/5tYsQPJ0cjpAN6Zl12oBJibFLS3itHZ/GEskmq030OOuMwZ39MfaGJY4nox4XzcsE+I1dCB5I0Ou6RVZlsMaG1FejNQaF6EknAP05DbgCFfdvbShNPigVuE/hu18GU1+te/waOnBdq8Tyf7vXMADHs4pUvS1QtyZyZWxES+LByKFuSeATXuPRGXeIGFV5rEhNEJFBrnDv1iQgYmkzxgYyup8Tnp9Xtwal8zfzFYLbjT9QOXTYfU/ddQEt9tWnAHC52qJrOwqo3f1b97fg3J+rCTWUGYzWULlCMfAyD+7aksDrG92+rgTll156Ce+9997X8yN/0zbGGG4/2MOoytOfhn/5ScCFtyAgdVQOyt1y5isKnPqn+3/daYDFCitvfUBBAxGGraVT3p2DMV49PoRAhMRLR+AMWi9uopFOgsWjs1VPdbjMpKvMigqgrUMf/Klu6KoCQoWLS2UBwNPLdUB1jmfD4AEmWNS86LShRV1lqFSG49EASkgoITEpCozzAg8OZnjhYA93ZxMyIJUSwyLHqyeHuD2d4NZ0jLt7Uzw42MMLh7MQOD04nGGScGhyGctyOaf79pvAdFQEPspoUqDrrDtx0XWuVw2EIkSkzBSlCXw/jbMegidTbpR/5iyKdnl/rNEgQujvPVoFVMcv6FIwWOOhcNocJs5wc+F0jsLmlslr39Wf3oGc7AOEJODUmsqEAzHSV/FwhquFxfllh2dPNnj83gKnZzt0FekA6dZgu2iQOWJys26C1s564cwMOYNUAs3G8cBcOmAyrRLuFp5radk5pW9UeH1ZKNy5Mwkpr+GswGhaBENX31qPoBQSBy/uoW40DgYj7FdDGGvxK4/fC8Tb06sNcRCu6MR7vlliVe+QSYlvunsLxgeq1oJLjqKSYJJTWgPAOFnIF6to+XB4OMHR0SxM/zyLaawie95x2r8PAIpcoCilS/UhpGYyKbE/HEBwjtbZC3juBvUXIZVS0iHAGoRS/2GZgTOg08DiqsPBnUMM3fP7pZ97I1zDZJyjcvYlnTawrcbU9VWtO5cGcanPJC1D6Sb6f2cMqjzDJx/cQCFlKMvO3TjLcxG4V1Iw0pcC2RtIxbFcNgHx6rSBkALDMg8HIW8ADPTRFz8G1m2LVluc7FfEXUrmJWfkEwj0leGX2/r58fg+41MyChJcZgt7LjBsE/NXHwDxZFym6C5nDJfrHVbbBqeXG1wstoBluFjueuiPcmjebtdhVKmYzmN0GLM2XmKV6GBVjn82LUscDgeBRjGZFMEuhrhxNLYuV7ugKk9ot+97iy998QyC9e19ikzhaDIIPKXxIId1wanIBDJnSgoLjGdDfOr/+b3Pd+Q3qH1dg52vfOUruH379tfzI3/TNmstxtOiZ0TZE8LyEG6Sa2aMVCg5o0nvX84Y6wkMNl0XROJySbny1baJdg+Fwr3jCfJchEFY5tJ519DpV4m+GWWsAorCUJwxjIcxTRKN3QzO/UbGWCC9+oW2aTX23aJ4vtwE2fXDcRUW+UyJsPkaAE1ygg+nGUQp9fMlkZDfu1qideJiH7uxj0kVodbId4iTfbVtghI0QEhVJAVybLZdOGXv7Ze4uqL7Gs1KtA2V/FLZe1qiGU97fmF47/EilkVzFqDgtPnf+c8BvFAY/f6tL58n6Q26ByV4VIl1L5yMMowG5LB8udrCOvi4l55kDOV1C4Yk2GEsXgttHP3rTDV2Li47nJ03ePj2Eo/e2+DLby/x87/4GO+6CrZm04KNiZjc7TrowR4AoF63eONz5/jiZx/ijV86xfljn06jFFiz63oLa7rBuHgoqUYBBoWE39K0saj2q7BRjg4GsA49TLvdn6SP7k2D1gdjDK85dOeLT5+itW3QLzm92gSzxHeuiKvznd/8Ir79+1/G7Y8fhs9VOc07XuRQjgA+zWNgPHf+UgDwwr19PHjxIKY9lOhp4oSDT7o+uKNynklYxgKvJejnwJHX3Wa9bTs8mUd12jwTkIojVzyI53mxvjKT0E2HdSPBlQATAh//jlfp+bnryjMBkSkUQ+/ebsGaDmOXlrAWWDdtX3oiixVEXk6jS3ha2tgopOfSYVLyUJafZTyQw+tW4/TJCqt1i8K9ljiLNkhRTIoSLx0cBhQy5Tz5nmy6Dptdi+Wmgbe+SVOn4yIWXng0+3z5PDmZ8f5cloLjYkk8RMEZTvYGOPCK2a3urZW+heAlCc6II0lcxfWuw3zdYLPrro19FkRNN9sOg0HWGyukxh3X6OkkD/NnMowEYf95t45HuHUypHGR3JcxFOzwZF1INbm+9MZFOKCGYLJpMCgUrpxL+3hYwGgDLgSElMjKKETI3y9i/Aa2r0uwY63Fz//8z+PP/bk/hx/8wR/8enzk/xLtnc+fhQok2dskU/JaHKSBtMYJGRgn1VQZj8HOpmkD+lIoieWmxdXa82c4jicl9g8q3Lw9Ckqo01EMCibjnCoSkomfkpV1ouVwcFiFieThYmMsmi4GW/5m/CRpO4NRmRFJ0RP4coVCyLA4ZlIkSqM2BBxFJkM5N2MMB6MSozJDqzUuN2RkWGUKr946oFOx4D0tE+rLOKy3TYsmWXBS6wrOWTiBDyuF4biAx7TG0xJap8RYutZdrXuBge+ThdsgfZCTXfMEiiexSP6NysBe9VSHxRNhXYx96xfQpjH4zk/dctpKPijhIefvmz8l+1aF6hd3IlQJWf5aYOY31cwtsqYzVKk0HdDpXRvU2iJ3KZaLX/4iuOO/CGvBXRDT1hptrZEPFIyOKE29abF4ukJV9p2s0+sJMvmgAEFJEV5Xbzv80v/5MMyZ1SWl07jrg/R2tDbYbBu8/cWz8PpZOcCtyQwWwH95403sOZ7Zs8sNrAVa0+J0TcHZndEeOCdDTW9maq1FNsyRFwLFiGwwTKMDurbdtWGjf+XFfXznb7sXrBgyFVWHq1JGQUT+fNVSkUncf+UQL71OMhvaWYR4cn0mRdiwL9c7PLlah+/gnDZlpXgwqRyWGbxeUbtpUIyGKEYVXv32V8JnAkROloMKZZLG4tqgc+liALhax6DAc0Sk4AG5BYBd04Yx2modAgrPIwkHlEwgz6n60fP33n04d9cSLSK2dRdK1I0xyKToBRQpYuL/v6xrPLuga02lHyjI4shFpAkACDpf/z0yrf8MwRmOZwNkSmDo0vfG2qjMngTn0fYB7r1RvX06zFDlEkUmUOVENUjHvw8Cd3WHQeWLPAjVmY6jJlCWCeSZDJVuzLKeQjrnDK++fIhTx9fhLOEaudOap0r4Q5T/fdMZLNaNs3Jxz7ftsNFtRMfHJXSnIZQEE7yXtrJdB9P1+VHfyPY1BTuz2Qx7e3vP/cnzHN/+7d+OW7du4cd+7Mc+oEv9zdVoQYk6NNfzyr75TSUtqebJZChklFMHKDi6WG5jkMR4rIoSHIMigwXDbtvinXfmrvyTh8GvO4MiFy51xsMAME61dz+ZOIIxyDySNf2Es4iaDVUymD1h15/SZ8M8XOfxuAo8JN8fqdNxLgXGZd4jMfp0m2XR20tyjpmTug9Ccm5DppWuj0LVnY4VPUi8uNz7fRXWZJijGCksHbJT5CKQbwEKEDhn4Wde0MtXzXi9m5CScEFPCpnz5P+MMVwtGnTaBm+qOjn1Xud5lUXcFOtGQ0mJY+c9459fysEB+hYM9H/a0EMay/WbBSDTlYDFILxwAVPXacg8QzUbUQWToVN5McogcgXTdmT0Zy261SbA10ICH/vu+7jxyj5MZ5x3FbC+2kFvG4z8yfPaoY8hGoAyxjB03KZUIzJj8VkKSSReeW2TAYio/u5XzrE43/RSA5+6dx+cMbx5doZ1u4tqvgAeLs8BADfHM2wuWnz+v72L3dUu7FTNpsXFaYP53EAzFeaxHxfWxrTR1fkGz54s0fkqO8mD6J/XOmGsn8aSggIHKTlmBwO8+smbIdBqdd9UcloVuHdjBgA4W25wvtpgdjRE5vrsd/ze13DzPv1+5A5QnDPwzRLTSYuKr6B2F7h1dy985t60hBqWqHyw493FpQgI4XwbTWpp8xXOjNiGdAkZBdO42CYVnrmzpfHIXFHKYCrrfbJW2xZCMOzNinAgWO8ajNzY0o6j6Mf9dRucECA1LVa1T/tzpNPEy0X4fxsbK1HT2dT/Nx08lKDqVuVK2j252D9/AEFLyqM3QFyvPKrZGYvj/QEmwxzDIkPl7GzSsvOB98uzhDL5kvMs5zAJn7Ioicc3TKxP7tyIOnLHBwOiQbiUskiRUObSaisKDP2nqoQQvtw00czW7UlX6y1aV7Y+GlUwbQfh9oU60SvqWo3TLz/Ch9W+ptLzv/N3/s77Rrvj8RivvvoqXnvtta/bhf2v0GzSVWmZIuBPNMxZAzgV4BTGZkTMbTtD1QJJCfplIvqlnVFboTz51BHmGo3HzwjWHlVZnBBuURWCzC5TkagHJ2OcOtlygHgRF0/WQdchTYv4CoZBnoVZwTkPaZdO66CLAgDPFhsoKYKoWa4iPGodsXdS5cEDx10qni3WOHOwcpUpcDAsNg2k2OD20QjbrQ558ZTQRwZ6OvBo/PX575SSIO+Vm/R7+yW4Eli59JzipEgbTz3AuMpwtapJFr6Q2G1bpMugh8irQmCz6xyCR9/DORmWpidPYy2ulnVw8w5BZa6wcpuhdzmWMiJYTavRNh1pmLgy5ro1WG1Jk8Q4/KNIvHFSSF46YujerMSZ8wOjDUuHVFwQISyIJAprIYsMxbACZ+ew1jjdIYbR7QMs3juHcenKbrnGYFZht9hACKCaFHj2FikQCyVIXt89q3FAHG3oZ+rLGFALTptI53STuIPoBVygJjmyXPSCnVjlSFYc3P1cKgG0pI5zsjfBt714F5/98tv4L298BZ957Zvw5GIDIYEvnxIx+Vvu3QYsYJgFYMM8ssZic7FFtpdB7xDngMoAkO7Icl1jOizw5OEc733uSbgXxmIgVw4yAGsIxqEcwjBwCIHfFN9+ZwEGi6JUWC9rqtRMEGHBOYZ5hqNxhWeLDU6XGzydr/DKdB/drkOzaXF2SmuB194SksO0Gs3FEhIaAsB3fM99vPMmpe5uHA4h8wyTPY960PeNC1JJX+yi5IMveeecYTgtsXhYh+DeWItqlkMvuojqCqpynBwOMHfXNagySEX2EVTxtcW2aTGb5FBKIleECq+3LW7uj3pSFYzHdbVU0TaDMyrP33UdRqG/OKQQqJM1JnO+XMZatClZ+X3S0H4OFUo6tLGPJE6qHPNNHbhkdEAxAdFCHCoolETTxsPYaKAwXzbQmgxO0+CtLBQ0o/VqvW6ptLzVGA9zzOfRuJZntK4OBxlOLykIOZyVeOMdStPeOh7h0ulHFc5WhDMGDQtjKK26WO0wLmXgAZWlROuqTNNqSSkY6g5BZXxQKMgqR2sisrt4fBE4pQbAl3/6F/Hg21/Fh9G+pmDnh37oh/67v/eGmh+UIvFvpmatDZs0g9PCSBCLdJMUnMr8vJS7MRaDnCaSX8yMNsiFRGea6FnlyGJVLl1lkU+HWSxWNTpNcGqVeuWAXpcrjrrppz06awNfgYEm4zZxweb8eXSqzGRfaEswNJ3Fu+cLXLqoXgoyLX3j2RW8KnQq/uY3sDKXWKybEKBYxD68tTdCpRQWmwabpsXFcoe8lBgI1UtjBYNPwZ0KdKzMEIyFbpeSY7MjJWYpOfYOKlycE/9FZQJ61zqCsuubzmA2yUOw07i8urU2BKFN12E8yFCWCptdB8FcBYYhUilp7Lh0QqXQtiYILvqxITnvbWRlIQNKkboO1zUtyukJ/9l8g2mVx1IJawMXRUkR+EDCpXrG4wJFIbHbdbBgmI0KrHct2laH8VJWGYkOcoasUKTnkknkRRZQHzkocfydn8Djn/ksAECv1zi6t4fzt8+wutjg6Vcu8M7nHtNzKXPoDbnDG20wnlXh2QGEOPgF2Kf5hlUGIRg67UrfOXeCne65ShLPM+7g0DutWkrBSB/oFQrY1AADqkGO3/H6K/jFN9/D4/kCV80a929O8SvvvQdtDI7GI3zrq7fxhV8/xeP3Fjg+GlHwyhhgDPR6C+xN0C0iF6lKOAqLVQMcO/TPxhSWT9lywVFUeeBP5VLixeMDKClgGgQ09snTLXjXYrttQwAovRihEOCSQ7cG+0NKOZ+vtvjFzz2C1RZ394Y4f7LEudNo8qnNwfEI29MV1mdrDE+mAAz2j8f43u//GC4fLzAZF2B6jRtO+FG7zVevd9ifDnv8IG8VAwAHxyM8PV0CNf280RrbXYO7Lx3gl996Sn3gkIHBOMPZI7fRT3JYbZDlMlSl7dqOVM4dz2i5rbFtyFS1yiTmWw3ABl4iAMyqCmerNbS1zheKFIc9V0sKQsvXDr0ZOU0lWmP1Vy1hT1uKwqVGwgB5juEsVniFIMF9rkx8/UoX7BhrsWs0ikygyAR2tYZOroUxWgd8sLNcNchzd8jZ6aBQLzlH4a118ujVtt60+NaPH6NuNWaTEp//CqGW42GGi0Ud9oDOGAxyhflqFzg/1gLTSYnlqoHkDLXRIZCDZRiXGWbDAo93a/LbyjM62K9rPP3KGzh9+zQc+JiUePUz3/ZVevWDb19TGms6nWI2m33VP/73HzVHPPOgAuvnf2PZN/0/k4JsIAIcGwXvfMDDGJ4jnErOcDQtUaioNeL5FHOnlXE4K3u5eFqnLTKVaDu43y3XdRSW49wR3xzMnImeDoh1ZEefugIIZRKc43KzC9H+0WiA1+8c4mQ6pO+2Fpu2xZvPLrGt2/DdnTXIFG1YqeklA/DC8QzHkwGkECgzhYMZpW8eP1vhyeW6p1/klyqfNkwN99J7lZIHLZTxIEM5ynH2jDaE8aTAetG4fmC95xT0QxodkBu/8O3ajgTfHHQtBQupuLRqAgAObwwxDC7nJqTu8sRKQHKG6dhB9p3B0P270wbNzntlmXBt1lKFlv+STke/oSqp1PJ5eCFZWKytJa0jf8r0fVYNFHSnIZVE0xqITOLkwQlUrpC5+xSZQjUtMLlHJGVrbFhY1ldbPP7CKTpPqB9W4NKLDFpMDsr47KztnRw9d+vwaJCggOiNZ4CChizjsfRW9cnh2ljo1Mbe39sww6gs8B0v3wcA/B9f+DL2JwV++V3SAvm2F+/h5u0JAODyfIOnD2mD5y7NYpoW7XIdEC3GGcoijrvFqnYcM4GX7tDnkKCgs7koMyjh5zjd0ze9fgevvEref0UmoWExGwucPbpCnTjXV4XEa998E4JzHNyImmIHwwpHEwogf/nzj/H4fAUG4noAEdmZ3Z2CKwHdaGzOV+BSgCuJ17/9Hl5+sE/9WOZQZRHSZ1obrK92mO33tVMk52Ec1es6oGt+XiwXO5w+nodDWiDKu+IKxoB8VEBri6v5LozZtiMlcS5YqCqq2w5Xmybo/TS6w6Kuw+dSpVB/fFjA6ZPR2PefBUSkzSNRvqUoDq79G2BO4iAGO75NBlQp5pGwztgehSGXPHiaDZzaPABcrXfwVVFFLpAkAcAAlJkI3Ju60WGcLVYx0BMZTwopeEj9Xc0bjIc5jvcH0NpgMadDaF70pRrazmA0yMER+XJUNekCXseR8mO10wb3jibhMDaqMmjGsXxygbf+P5/D8slloEcAQHkwweErH14B09cU7PzH//gf8dM//dP46Z/+afyH//AfkOc5/tE/+kfhZ/73HzVqB7MBCUChf0rwg8tPtEzy8Br/ul3ToSj6mji+zNy3QZ7h5CRK8PvPa10qiHOGg70KWZ4K6bl/WK/lEknKF8ttjyuiWxP+PygjguJbmakemx8AzhYbXDri4tF4gHFZgHOOW3sj3Nkfh4BptWvw5ulVuOFdq5ErgW0bxRI5Y3jxZA/7o5Kq1IKqHvBbv+0WGKPPIVKmC5Dc9VaJ9HzQ2EmCNcFZSGFNpwVULoOh5XhaYrt2TskDhcwTe3MZqhu6BFnwi3vT6VCJBeC5/iL9JIfsjMheoHCVFCFIEyKkO5XkgSCstcXsoPCPDk1DDvDeIf1oWgbC8salObQxOJnQxnTsJNylIydz3q8Os9ZCa+NUb/2JkmQFrIP3y2GJo3vH2L91BCFFIDiLbg07f4K9e9Ngsnr19gVVsFhAax2CjXJSQqioqFy4zQFADyHUCYx/fDLoHxaSbuWcggypeHj2qbCghQ0frHKJxm00gjEMnWXG97z2IgqlcLHe4J/811/Aqq5RZRm+/9MfR1FI7B9Q8DCfO/+0/QFyV3mjrwjNzoYktjYaRF5b15H4pmAMrQ/2MhGCuPE4g9UGKjEyne4NUFaeJ2Xw87/4GF/4hYfoWo3xrESuKJ1pDbBwSt9CpocZhpdu7OHmAa0LP/+FJ/i1z1MKrXIEYC4FTp+0yI8pOF2dbrD3yU8Bhw9wtirQOV+t4s6L6IYngX/VcY8AsJ4mUKGiaOnyYgswj666DXlTB6QVoHWDMeDyyhVVKIF2p3FxsSM9qcQjar4i5GHoJBqaTmO1aQJ/8Gy1CfOwUuTXlxYohPW0bYN4X1X0Cc/Kla/33vc+h1ObfKbkHJmSQdwxpMdFvHYLJ6uRxb6SQrhSdcf5cWvLlSP4euFBk5CTCa0UyHOX+reAvSYrwRilnIPNImOBB7p0QTdjDPMFldVnioPLvimwMZaEBFki7skApuOdD3LVm6/GMMx9cQznePK5d7B2iuGDoykMIn+ua/qK8t/o9jUFO5/+9KfDn+/7vu+DEALf+Z3f2fv5pz/96Q/qWn9TNWstri7IVTdXMgQNQCId7n5kDAA32bwXDvFdbEAF/KBPy6r3xyUE0jLhSAoESAGVM9Z7yEpRmqRpDbyKrp/Yq13byxMH915O2j9VqXqCfam4F+cM8+0Oj5zU/GxQBhNEj7xoTQvjzdkIk0R/ASBTy1Xd4CrhI+2PKkwHRdTF8f1mLI5OxviWjx2BMWDbdKg73SP1pZVeMbWVIlOxn2azAlwKLBw5uZoUoRR+MCIuAQAwwTBOPMJ8GySLW9114Tlc17nxmhwAKJDNBEYDFbRKgiO2f1ZCQOXJ896LYn2rdQOTBCa5FGT6BwqAmo6Qn7uHE3z69Xu4dzSla0qe6XrdhgXRgjgyaV5eCo4yB4zRmOyNsH9rH8WoRDHMcffBDMfHFEAxZgHGwcf7GN6nkm5mLJRXVe5sqMTKhgNk7oRqATx+dxWuKRVaSytsBsOid1pID9lCcGTe+ytJa6Wv8aHkzRf20TpURQiOIqfvHZQ5vuPBfQDAl58SV+ebb9/CdFaBcYabtyktf362hrUW0xtDHL5I6Id2Suezu4TcSD9/3D15AvzKBc+ZEkHLKVcCurNQKqptv/XlU3z51yk4Wa4atK1BXmW499oNDAa5KwUmBOD8lJDI04fzHl8tzyU+dmsPw4oKBL783iUARM0vzrHdWgzu3oEaVeg2O1x8/m1kVYm8LIJooihzCCVRuLlq/ZzSpmdG6iurKOhiIfUdgp11Dd1F9HKQK4gyQ+36JM8Frk5XWHk0+rDCnkMTFusa2qVfABL8E4KjfB9xOiFE4NkFiQePWhgdUC3PswGoZF+FCr6o/dVDeTwSn3yXdOTk8Lpw4OTYH5fhZ50xGJTxWv2SkLlr2JvRa9db8iQEo4NUmsKSnOPpUxp7Ht3Z7drnDtBFLgIKxyQFR4BDyFwAezmng+h4mKNjPByQ07SvYKz32Wht7+B2fDCMZsTbJlT8Sk5p+sHhBDc++TKm907o87xWz2+mYOej9j/eGGNBaatQsk+UZT5dZXt/KyeXHtI92zYGHO49aTrieDpwHJwY7Ggd0Zh9l29P0RcpBdrOBKVfzuPATjUxUuGr0ulnTMZZD5Its2hJsNzVeNNF9LOqwMyZFVLVAL0m6ADlGfaGFW5MRuH+tLH4tXfOkioz5vQ0kvJ8wUOqbzmvMRsWeOnWDIzR+2vnSWStDTo15lqw4+/JE6XJskGi0zbAu93WhLx0MSpQeAVbraGu2REAQFFlIVBZ120Ijq7LCqbptqKSkIoIy9tUIPIagsFYJFULxAq01aYNCJDgJP+eSYHZkPrdo3vWOPTumnAdZwzttg0VId6jjIEl41FAKYHjwwEmh1MqI90uIVZP8U3ffBwWDz6cgh3eBxvuY3DzKFy/T3NpH2AogeGEI5PWPXOgmhYoqljB5FtQ8M4lqnERKpYAhDQYQCjOrVcOol4VZ+Ge6UPj597/+GGoiMqUgEoqvL7jtQcYFUV4Tp966S6YFIDgOD4ZgXOGXd1hu+tQN0DTsRB9yzxDA1fp5lEA96UrV8Z86VCYTImgkVVkRKpWioexcfFkiZ3b9DPJcXJjjDuv3YHKM2zmlOq4bkOSJ0G8dCiR4By3jiY4nMUA2SMOTHBwwVHtj3DzOz8BAHjyc5+DEBzWIasiz9BuakgpkLsxBR8YN104TDDEtNRo7NSV3f15JHW+rrFe1SHYKTMFNcjRuipSwTkuXGn4eJyhqhTGwyTY6UxQVu40KYmzZPONYzopnMgy+IpTgOwtmtoXTCSpG20ghQiKz4Jz5NcQ9OuNxAyTFHAinCAEsD/xqsHU0tJvf+bNpURZSuxN6bXGEPdICt5DVzmj72lbg2enm0BarxvznHRElityGwfABKWoPM1gvqxJNNAd6CajDNo5k/vvAYBmR8rYRRHnpBQ8HHJbbfDaKwdh3faaRJkkX7M7v/VVvPoDvxXj4xlW54R6+vHTNhG1/zDaR8HOB9SsC1YA0mhJuQi+pT4mNKgFsoyHcu7ltn1OhK5SWSihngwKmCSdkkLFuYr8kl4Km7NAaFOSFtnrMC1nzBFrXerBCZ8Nh1nIlTMGjEuC7le7Gu+ezQEAR+MKN2fDBPHh6DoLqVggyDLQ5C4yicNxLJ+mz4194su6fQWMn/gAcPGU+BOzSYHDcRUqiHYdVX2I5J5ScqC/Sf9shqVC1xm0GmReCUD7ElnOMD2oMD2kDaNrDebLOpSK0+kM2Kya0NfLTRO5LNeQnfT/WUanwvm6joHnNc6AMQAEC2mKtjVhYffEZgAoS4XcnR4HuQqlu7u2w3rbYr7dRRKjiLYeDIRWAY6MCNurxMokh5Ac4xIYVR2G/Ar68glMU0N3MTVluxr69B10j78C2V6EZ6gyd91u8xvtlyjYLiywlgG3Xz3EKGwOcY746TKeFpC57KnigrNQ0kspRNuD3btWh9da97OiUrh69yrMlbLIsNzG4H40KfE9L70EAHj91i1MRhU+/38+wtnDJZQSmLkT+Pl8h/l5jatHy5Ay6BoKgJirjvPPx1obiw4SjZ3o3u5O3klFTspb+tiL+5gcDFEMC+hOw/p5cC3YYUhQnSonAjWA0TDH93zHg4A0HDiVZCY4VJEhHw6w//EXkU9H6LY7zL/0Vgh2sskQYGSoWTrJf/igqtWYVgXpv+RZSOXcOB7AchZI1J5jWLcdrpwAH0Cqu6JSaCNUgEEAAPlKSURBVHb0Xbumo/VgWGDfzbXcrYFzZ4bpZQaMtYCx2GqDXIpgOgpQwYbvh7FHjl2zFiFF7osKAFqHBI99SgfAfuHG9SYER1Wq5DWJOrLk2Jv0eWhMkPQHceLcs5cCs/0SXevmAoDFukHueHMpsjMeZuBu3fYBm+lsSIH5a1e5inNbCAghQop9vqixXFKVmBAMMpOwECEA8Ndftxp3b88wGvURbL8Wd1pjOitxMKXn5A+wozLD7GCI2d0jMM6RDwpsnOZT5cZdu2v+u/36Qbf/6WDnw7z4/19ujDG8eP8Q9w6mGBVZL9+bNtKL4S7godO0T2W1nQknGZ5MrE/cO8H3vnaHxP0aHTYJKp10Wg5KYLvr0FmLto2byNXFFmu3qSvJe8iObz6F5fePIqNgR0pB/lGgxaQsJJbbBu9dLGABnMwGeHA8hVJ9MqwFcDGvw/99QHa4X+FgGq0cXrtzEE7vzOWcffkjcwhRUdJnrz0EXlDe3GtvWAty7U34qP7upSOqpCXnw1Kh3elw6sxyCe35FbnAYFqg8PyBqxpNS+rNt4+GuHlIgZrROiyei01D5f2OWI7w3Qz+6WdKwGq6R0/k9lB6knJHqzWKgQoLVteZwOEhCw+ndVQp5N52grn0pvvuJ5drvPrySUCyfCWWf+jjg0g2jW7n9P9ckQov2hbC1vQLV6LBgBCRMNMAbQ3oFkKSNhOAYBvhW14q6FqH8dZsO5w/WiZprOSZJQjf4mILo2NVXVt3dPof0yawPtsGZEe3xmkhxc/KlMBLL85webqMzyMTqLc65BXyUuLl4yP80e/+bnzvyy9hvSJZAb+5HDgz1MsrErbUq03oQ9tp8PUSKoscK9/WmwbamJAyzZUgZW5Qeu/zXz7DO48W8B/GBIf1B5tSwUiBbFBg59IPjLOwVgCUlk4AWeTDIgQ7N2+MUA0y/LbXb+H7P/UAU/c8uJRQRY5sWIIJjhsO3Tn/3Jdgtq4seTbG8HgG03QoBrEiixWEtpZZhtduHuH28Sz2qeTIRlHugLg8dC1nC0K2lFvLGI+BSdcaMA7cefUGbJnj9HwD6XV4tMGmodRwCFCshQHHJ1+4gW97cDMKorJ4qPF2NUBcc5e7mj6zM7GAAQCuHTTCz9+nMVBaaTSIwVQae3LBoZyHn2/bXYfv/98e4OXbez3dtdle2VunrlY7CMmwrpuAbDKQ5cPxYQXO0AuKtWUoMh7oCKqKMiAQHDw58C5XNc4vkhQWOEzXxfXWva1pDU6OR6h8at46bl3mRW0tLAMe3Jr2+mU8yCHyDMIrYxcKuyU988GMCPRt/eEiO/9Tpee73Q5//I//cQwG/dP5P/2n//R//sr+V2h113OcBVw1VBL5eI8oKSjw0IYqmopMYtd0WG1bSMHQdjH1ZTXAwdF1pkeU9SdGL7intcV60fT4JU1COi5zieWmeW6ipznvqqBAh0v6MxsXwGOadJ3ReOPZJSzITff1B4dYrdoer0c5/oQvI5auIokz4OhggOVaAG/Sa9fbpkeQJtdmF7zlEkZbVKXC1VWNuu5QjnNoV7opBccgU9g0xDs6XW56CsMABTv+vK8NnXCKXKKpu6ivkyvUW1caWkkwKTBf0TW0raZT/qhAlomgOAqfKgKdFJfbloKdJkFynEkjQJuC6UyQsQci5J96QbXaUOCkBLDtoDtDFiAbBM0Z/xxLdxIjwTNCB1c76s9f/fWn4E70SQruhP0YhBRom1ga27ggL7WKyMcFbN2hWdYoDivwogK4wHq5DdGJOLgFrhQY4wDjKI426N59ClZWAK5CH1QDhXrVhODGdAYPP3+GznE8+sGOe2ZSYuMq4+hgRYaHLEbFRE52qbL1qgFDn3PRaYM8k7i8nLs+Ih4B45QOsSYGkUPHM+OSxqkPdsbD3Bmyalw9ucLAOi+qYYFutUVztUQxzNBsGLJMommoutIYYLVpog2IEmhcsPPeu3NXho1gvqhN5DcVmYTOcywvV1ifu0owJaDrLshRjKosoJQAUI5LmMUaMK560JFyi1yi6Wz4DDUokLnU0N6r9/Hk534Fu4s5Nu+S6Fs+GWK4P0a3bVA4rkuza6H2R4TIMCIpW3okYAxYzGtMJ1kYZ0VOFZy7rgsChLmUkKUCd0E+HWIYbtwcIcs42oYE+qpKUVn4usHFqsaozJArga42aDuNwxsDLE/X2BuUSeUQPfNc0fcKxqBB6LI2BlebHY4mQzSt7qXjdaKnRWOvH+pcPwwqwTGoFJa9cUmNM5LwGJYZdi3JU6x3LbrOgFmESrxhlaGsFMpKQSmBttXYtRqbbYusiGsFZ4ysfjKB4+MBnjxdh7LwqMhuXIGFgnX9zIUAz2QIhIyxOHWp1MkoA8tzoI78SH8PTacxHRU4P9+G323rLpD+jbWYz3fYn0b0CnCmxEUe+DlgHDuX4hruU7BjtEGzqZFX/UPQN6p9TcjOZDLp/flDf+gP4ebNm8/9/KNGE2Z/r0SZy54LrheQAxyrX3g1Y4JC/SAfuDzvpu7CadwHIK2OjuNdUlJbtx7VkbCGUiVHN4eohvG05TdTMPJBWWzq504xnLGAJgzKuHhxwQPJr+k0fuXtUxINyxTuHUwD4z9tVN7OIPPo7g6Q54+UDONRkVSDJZPPXUessHELWRZVhItRBlWIcKpXkiNXZKKqr6UN/aIKxJLm8TAPFUMXrux8NClCemYwyrDRCs0qXtftO6OwMTLGMJqWgRvg01SX823wn/LdUZYq9LO1Fl2n8egZIQ15Us4ZnwEtaOdn60BSNtoGYqZXjwVocV87HRX/fXkWlW7PL7dBzj2ksRhQW+Dq6SrwoLQxPZJ3kQvkzghyN9/BjE4gZicQk0M0Jo4pMZyCF0OwvALLChSHRN7l1kIlZb4cwHZFmj2eQ8Q4D0hg3zLCweeFCs/D31uaEmaCQ8o4TpbzOpBNfWtbjabpcLVwGwEnGJ8LAa6iTkpa1aQU8dHa1lBlWGcCSfb0PRJIVMMckz3HU1k1aEUe+h6gwBkALq7ixvHkdIXVOpqE7s9K/JbvegkDp3+jvSCe42EdPziB8ahIqWDcJXLO8ODWuGf6KJSAyChAB4Bm22K+iKXJfvZxJVGOqkCiZpzj5nd9K/W6J5KPh2CcY3Qyw+SQ1vSmbsJ4CLGmX04YpUrqdVRz92KDALBp6OeFksgLFZR1GWOY7g+ovNnosInnuSTNGhDfJK9UQBfW2xbf8W23ISoZqpeAJOXiCic8QdrPrUZrfPHxGfHqkvlWa53IgTy/fqX/ZozuIU0lJnE3SDDSBj0jC1pvzi+3PRXl40NK9WeZ6ImiLtZNjzsJICD9ZaFwsF+Fa+2aNuwXWa6Iy+b3GimCintaHs8ZBVrV4QRMkxo8S8vPnWBpvYuoz2ITnykAnJ9vkDmvRd9GVQ45LENnrC+WsMZCKAFVZCEIWl9FfaZvdPuakJ2f+Imf+KCu43+5xhhD50hi6eLsxf98U25T5JwhC5A0aTN4dVyvtioFqQK3ncau1ihzGSaPb1LwkDoysJgeVHTa9VH5eofltum9z1d7WcRS9E5rCC5QlRJWg8qy3UQBorptmUnc3htDCI6moZ+lZOzOGGRMBG0J4VJ2knM8erTC8fEAHIAGgmEoBUgkguf7itAAA8lZEMtqW3JB9puklBy8M8glEcI5SBkUcAKM7rN9SmEyzKEUR9sYzN2pZzxWmK8acM5QjXMsn1zg4r2rcD/TWYGLdgfdamSDDLNcYHm5BWMI5MKLxRbH46FbaOg+JGeBcwEDXJ1v8dAFO1miJk33T6fyVhucPl1h4jgXWhuUjsyb6hGVpYLpImcFoABoV+swhpqOdIHSoGpTm9C3bUcqr+nYzIsMOzYC+BLMaJj1CnDGkHBpPyblcxtEsT+la6x3yIclWs/NmG+JcA1A5grttgGMRV70CcoE0dN/9g5GsLZvhNoj0jubCFgKBtdLEgy8nl1fr1vUu8jFUkWGasAhOo7Vht47mORYnG9pHggn7mgs6g0Zwu5NC5xdbHB5ucHdmyNM9gsIyaFKhXbbot16vziOJYAsk6i3Lc4uo7jo2eU23OfHXznE0c0ZsqM9FMXDfh9mEmAMk+N9XD76RQDAwZ19nJ/OA/JobHJ4AVAMCzBjMN4vsXrYoNl2eHa5w42BFx11cyFXKKZV7/umL99FeTjD9vSS7sFJFshM4fA++XI12xrKBzs2SfW4rOh61UBzxwcUHJxzVz1aJ1WSCiW3eLb1dgUctz92DLNcwWodBgGz1lU+LrFY1RCSrHDOl1vUbQfTGXzPdz3Ar/zcu+Ee/KHHB++5kth1HZjj6GhDavRvXVzi5iRqE9VtF4MdkNLvV29Op+d6XM7i+NTG9mx0uk5jflVj6++ZMxy6SsYsowIJuLG5WNfBGd4H7WWoyKRqrMFAYbFsgvgmAGQlGQOzUA1IBRAAIdq1c/0bDTOoXIAPK2i3JkslgmeVscB23aKuOyp+gcVq12JS5JCOE3pxucW9mxMMCoVmRXIbRSaRDeKYWp5e0fOeDKDbDqrIoFdbbC4/vGDnI4LyB9SstbBuAKebv+idCAgRSIX5olQ5MHOpCU8u9QPbn+jX2y64FPs2GWSRbLaj3y2WNZ5crPClhxe4WO1CoDId5tgflc66gfW+o+2oSiSWQ1O1UlHEU814kOP+4ZSsBhAXXp0oYnmn6dXaV14wFBl933bb4e23572KLIAGpeAsfF5RyFB5xIBAzLs4XYM0Iei7fGURYwzTQdE7eXikhQw43f2PMihF6ajlwldiuTJwybBYd/jCf3uXSIveqqExodRZlRJjR1xlLFaFXM6jz1Lq1eR7hXOGN9+6JA0Wznr6P4DzDXP3uFjUwUdJa4uhQ+lSe4nprAwiklGTibu/BY5cRU4Memh38lVS06OBq2LrG9MKJSHyAmxIpdft2Vm8SO+VpJ4vAc6mI6qW0QajiXNvz5x5oLWwjEEmJqUq65tC0r/p7/GkCrovPoDRXaywE06IEgBWq4ZSpNfLaQFsHNEVcJV+A4XZnkrKYg0qJ9qYUJrAOQ/BxXhSBofpTavBOMe6ERCzKfXPYgVe5MEM1Evme74UYwyFR2IYMB3nKI/2IAelcwyP15tnAhACZ28/Q1e3yAcFju8cYpb4obWtQduZKGcwKEhb69gR6huNptZoE4sLgMQC043JX5tHdwBCdnw7uENu7/WmATiDcKrPvs0OBhhOClhL6A5AaIQUfakMwFWm6ljteHRnH5lLa7AUjTU2yDws16QT40mzjdZ4/OYldKNDSpSCFJ+ep/FUyihx4NfaKiPdqIdXi6Cs3God+GoW/XHoD0jp/4dFRrwwj06638VgXEMmPKC67bBrdBBszZXE2FWu5ZkI65a1Fsttg40Pyt1n+vXOd/okMXUOQeSohE32AiZ58AxL95zxMEc+zCBMi85Xw2Wqh4aulk0P2SF0U0O59e1qvgOXUYbDu9Bno5ieWp5Synh0OEHXdsH9/MNEdj4Kdj6g5smmjEU9F6DPJQjqnW4wZlkk7THQoE7TXv6XjTZOK0cHtV3fqkI65MPifL7B//5/vIFff/MMl6udqzxgGFc5PvnKMT52bw/jQUacoWvCWo3WGFVZQiK0qOsOnHO8fHsPR7MKn/62u2GRYIwFBDU9eZM6cFyQlSRlTyVJ0tza56uW/MIU7AKG0TGbc4bJhCbZw3cXaJ3lA0CBhe9dYyymw5hXVlxEHozrJ5UJyIxE/LzB5zbk4YF3vkiy6oPDCYTbwJpGYziQKMY5jl/cx3hWhOfly6N9GTwA7E8HZKqY2kBIjgvHWZiNKY2XpSkUyZFnUcPDczy0MRgMXRrLRPg+zxMdJ9cBvcqvhCz9zrMFkd4LBW6IN/TsyfJ9ZRBkrsCkABtQsNNcXMA4TSBfmsrl8+Aw4xz5jFIf43GG4bTA8HAcFmvGeTAKtDb6WfmNxptIFoUElwK6jamd8B2CkMZylIdNfOU22uuO0wClQoJ/FecYj7gbD/SzrjXYvzlCVkqITATJAiY4OseFU5nAnpNzOLvYYrHh6IyAHA3AhKDNhvOweV2d9xf2YaVwx6kde1n/4nAGNSihMh7Kn+n3EkwpvP1LRGi7960PMNwfoxzFMV23XW9tyUqFvf0cWS7CRiyYxbImn7qQ8qiqwNdJ2+SF29j7+IuYvnwP+TQiH5ODKQByGa/XO+TTvsjj3vEAo1tOoNAdagYlkZPLrD8+BrnC1TymhQ/uHoI5w1prNLjlAa0bOvNLY0iNeuzuvdUaza7DxaMlvIMNT7wHCydZIV2lltfPYYxhr6qw5wK91mjsOnKnZ8n4/+rKydTKXKLrDAajjIY089fgDqOOR+nTyL4gwwc7g6SSK8t4CI5p/COs6V4AkfM4PwAipfOkihIAivEgkkE5EcClI2wzxnAwKzCoFCajHPkgg91Q2jsbFCEo92PGc8D8nXvvwuDN12g0rca9GxNUmcKJFyyt4thcuWBnfDQF5wJ5lSMfFD3k+BvdPgp2PsB2/NI+PvaJw3CySxsDHM+DJqGUDMNhXIAyRRyQScK38cPPGIui8ITWuNgJzrCpO1xtdnh2tcLZYouLyy0YSGPjzskEh+MBZsMCmaKcbqaiLkfuDPEYIgHOL5BNq0NV17e8cozPfM/LKLLEc4tFAb8UWjfGYr3zolOMJNIZVaBNxhkODipk76M0LDgL3JrRNA8nQc4Z7tyfgnOG1arB5eUm6Z14OtHG9D7Xqxr7qqvxKCcNkVIF8mg5IMNUYy1Wa0pdjI6nmN09gPTBTmehcoG733wMKwWWS3dqhgvS3Ibl73mQK/z2734BNiL0qGYDLN2pKgidJa73mRQuKKTP2myaUJLsXbU9qsOYS/H55xDWu4hsbLZd4Bm0ncGbj65QG4NHT5a4XO7Q7KINQapCLB0PwEgFqAwwBo0zyJSep/A+yA4ASDc+TWdw/No9DI9moXTZdh38ozGtDs8GAKoiC9ddVTkYGDq3SchExK0cKKjxECqjNLExFmuHbvl+SyUV5knpM+cMKhNYLjo02vOhDPJS4f7rR5C5SoJFEx6cbjX2fQn66Rp10wFuzKnJCNZaPH14gQuXttKd6Z2qB6VCG+w96PPLwz0SWkyEBQFKY9WNwfzJJZjguP36fagqQzkqwqbaGERneM5QDhUmY2eZ4MZTXuRYtgbvXTVhjBTTQU+ryDfGGB78wHfjxR/8dG+TLwYFMu+DxoGbN6sesrOUFSYPyOJi60juZSZR5DI4iqf35ceYkhz5eADukD10GoyDxCgdCX3iKsgu5ztUrsKq0wa7ugsCmgChdSmHzSPm1xWOt22L49EQrz84pqdrLdZN0zPq/O+1tEp0PM4wHKjngp3OpZR9cYonJ/t9YH9ShtR7pgTyLPYRzfMk2ClVgiDFNfC6ZUo1HcbyeRcBCha5jvuzCi/epXUzH2TYzZ1X2uEk8Or8+70oZIpcBdFFr9uzqHF8MMDv/t9exL0TOtgIR+432mB1Tin60dEUMpd48K0v4vv+0O/Aq9/z+v9QP38Q7aNg5wNq1lqUuYCQIpQb9kBPFs38AGcQt41BkYcuh2XWI8/51AxVi9Dp3lqLThus6wbvPFtgvWthQcHUax87xMu393BrfwTb2UBYoxM0egiCb/6kvdjUkW9kTJigjNHC3zT9FJqxcNdDgUcQwNt6kTTRm6RNY7C3XwQekG9+UnlflWqYR4VSxSEZw607hDY8ekSTljMWcucApc9SxWRvsNmFFFYOLjnygQzGjEJQSssHVoO9IQ5fOEE1yLB3TOkDvygunm3w5q+dY2/UJ//5048Xk1stalw5gqrvv84hXZkU0C5FWSTBjreJCEJeToLAaBtImr4VhQpu0fQdvg9jWq1xqNXesABjpN/0K7/yOFiK3H75JATdUWOHiIUMDDAWYjoDAOyekJmj6RzU/j7ITje/pFJ1APVGo3Vl9kwn48WVOMNaDGdVGCs+cAGAqspgjQkaNTITQTOqbQ2UAASn6qXNunFVWq68nvdVYZ/jzakCm5pDeskCp4vjg0lwjrxUuHl3HLhWujUoC4VqmMNai4uncydiaLFsND7/xgXeePMifFc+KHB0GNNO5IsVveZ4rpCNB1DDCirrk9SLXOLcOYLfePkm8iqnknFPRAXNQf/A8yrH4SGR5TdbDeGCkzKjYgWekEzycfW+wc5Xa0KJgCgdVNrZd4SuRXU4xeBgAlXlYX7luURZSLrPhA8jEzNhpQTycQUmZUT93O88kjtz3KKL+RZDp9fSGYNzl3b2yBZnDGm4IgQRlH3qxQdFG6ehde/mDIejQeAqXmw34bD2fmag/u/Ux2y7bHvoYUR26Nong5hyhrWJ5MYAndc6cyhNim6mgoJVGbVz/GVxxsATJJiI6XFuQHDotgODDeutvyYLAFJi5dC10fEM5agKnwsg8CuDVpV1ekEsqmJfOYHLkImQIqT615dLWEM+elmVIysLyFKBXVeU/wa3j4KdD6gxxjCvGR6+Pe+dwj1SwuDLzen1xlq0bXydcoRggPQ2fPOn1pXT72g6jW3bodE6+ljlCnvDEkfjCkeT0rn/0iZOJFVGpw1GxoUsOVECsQz6Yr4Ni9dokCVEOcr/+7JcT2q2NqawpGBhs/bwsje+9PfYNBpCEYx7PS+eLvw6SYsVzqvpxs0RGIuwOXOrVlgsbTSgA2jjN9a6IJBhWCkKdoZZFDs0Jlxrngvc+pb7mO1L3L2Xh3TVrnHPr2tx/4UhurpzKtEswOYAVTBYawELLBfe94au5dwJMI6rHLUjdRe5xMGkwLjKqIrF8Zp8H3qieqb6m9SgVERsT50DAQzHeVhAfTPGBqG3xjmZv/pbHuDOKzehMgmLxEdMJoGAsVD7VGHVLRboNhtYh7ZcR3bMbovdO29AuTFr2w6268CMC3Q4ByuKEOTrpoPKs4DupFykcpAH1VXGGVTWrxrJFEgTyQKrRVIFVKioJ+RaysPgnAHlEEYbmGTs686SArHgmMwy3HkwhriWxeBFhoMbFPidPpljfrnCL/6nX8Ov/rcvY+vUZw+9rQcDxgmPIVMCG/e880yiPNyjcZMrZEXWQ3aU5Lh8Rgq0dz/xAn23YJBSBoVunaSHB+McwyFVJ626DMWIgizhxmYQj5MC2aAMG9P/SDNti8rpW+22DToukQ2oFN8qjsKJi05uHwR+YuGESKsyqq5njszux2WmOLIRBbrcIUA+ZPGpycMDOtRczrcY74/D/F7vWrz1ZI4nFxQQBlE/Tp5+nLOgT5Y+ws6QbUXdtBhkCqXKesrmaXuuMguUmvLE367VSG06/OPTDtmZjSMvalN3IbU9GeUh2CGUm6QpAMDaKCnBQGt/FP6MY7hIDohZSanc1BfLdIR8xWDH9auU2HYCy0sKdoaHUwxn3vaF3h4y4j0uKV2QP8xdXG7BRayCDOgcYgprdDCBtRaqzFAO+xyxD6N9FOx8gK1brPClL53H6Dc5OYMRQTZNTflNVwjST/Cpi1ES7PgF8fRyg7PlNiwu3mriaFzh5v4QuaLTEomjRfKwJwd7VMYkAYJvUjDkmYCxwNU6lusWmQwaJ8bYANd6l28AIWCTgqOqFGyyeaVCWx5BOX26BrNxIHrI1Ad1TauxcoaBQpDWDwBwY3FwGCeQ1xZiyeLGbLwuqsag652Mc3BO1hOtUeHE5REEzhlu35/i1k2JvT3lNiRaXLYbDevk5IW1WC3rnrGrT8EZEwUeiTjr0AFjcDWn1OL+tIwEw0Jib1wESX8lBaQSPb+zpqEANU15VZXCfNk85+o9ujXp6R0BFIgqKTByqbOr9Q7WvSYf5D0EJFcCygc7AGRZQO3t0bU8eRqRnfSZao3tW18GjPn/svffwZYl+Xkg9qU99rrnTfmqNtV+Znr8AJjBENhdACIF7IBLFyQVEhcMMMgFIyAusYyVEOACWIIiBJGIgAKUxCCFGBlSAggtQIIkAJJj0TODMd09Pe27uuyrqufuu+7Y1B+/zDznvveqzXTXTO9uZ0R3Vb13zbnnnpP5y+/3GaheDyKywaV5gcqGw4o0wcL956CthXxVlCRP1U2xU3pkJ0Rp24FKCygt/QRc5BWUZH7XOTqw14jkiPodiFbLa17STotS1E3AJUedld5YsMgqmNrg5NkuFlcicM4wOWik24wTKrK0Qgvwwd4E3/j8cxhuH4ALjlP3beChe5awtkSFRjbNKdfLDq0Eps4eQgtEy1Q0ccERdMK5Ar/Ia5jaoLvSQ98WV4wLCCWhoibmwo11i3SOZoAIQn9+hTFgvNlUCS0htHzDyM7s1i1sfeZziG2O2O4EuFmnCJd60FIgFBzDl29g68mXveoOAPKM3Lslb0jKgRTonlryc0QQagghwJjwiyW3PXRX7KyuU4tk7yCD0JrmNVCx+/KNfewdNDJ+gO7zPCeFkMsVdPYebiMyzQsc7GUkrGAMoVRzWV/A0TnR/TRQTQCu3cv4wa0K1tkHRGnT7htOZ/5eT2MNZhqSPSFgzpl+vnWURE3YcrtsC8OmvRXE1GI2fkPBwCy3ztkpuGKnlhLjjKPISZ2Z9ALEC+mR13efx71HbQyqFm9nb29mxSFHi502OdlUNVSokC5273RSv2Pj3WLnLo2yLPH8UzfmUAnJ2RxE2W+5B8M0UKuyBD13gerWonc4dkJwhlBJrA0SRFqhMq10cMC3xrxU3BZOZUWF1GhS0O6ivWBzjrVFmrB3RzNCYcqaeuQW7q+K2qM4bR8Hj+xIjsXFcG5CaC++bpEa7mXWlIz58wLMm+xdvXKAujbUrmm1cdZXW+6/vOWt4nZYdXMupOD+2Fz4Hpcc05L7ws8Vkt1UobMQQUqGojA4yDTqmCbdfDLzDsH5tMDt7eIIofEwP2g6Lfw5cN/D8kKMpZXEf2Yu5i0JhGBQ1hTM+fqMbTswCOaLndu3Gx8XgCa4ix86PVcUAcSXOLnawdn1LtJQwRjga3/0AvKsgA6UR74A2oUrv9sGOBcI14jnkG1twTjERTZE49mrL8FkMzClEJ4+Dz2gBbjKMrDKpsgvdhEtdnD6E483O8nJFNql1NfG3zNJL0bp5LGadtQe2cmdQothOi7IYdmilslqD8qSVN3wfB2LwI1fvY6oQwWPg9fzWYFyViAIycDy5vUJxgctw75UI4gVok6I7sC2pxiwdmoZH/1ffhgXP/Ygwm7s0bcsKxBE2rcYQy38hsYhO+4zqCTyjwOAqXX4PvXoueb6st47TtnSHktrCeoa2BnWUJpDuZ10XUPKhrsiAu2NBl9r1FWF3ae/idtf+grqLENs7Q/GuSX/n6ZrQRhg97mr2PrGy9h+mQJMBWc4GJWYTEtMswqx/W4jpZBuLvo5TCs7H3JA6AbZaR9aZ6EDJYmXtbc3QWh5MFlBaqEmVoEeLwW3UR3NvKR8EWlbWUWBsmgZCTIqyNreO8cxeBizrfh2JlbrgWTkWvvPFy2kvjgYTpuEd8fvcwVdECtoO6+5OcN9P3HUEJTbaLXW0j8nTEMqnBx/S0jAyted15PbcOaGIbfzSNINIIspAkvYZmb+U7fFM3VtUDMbEmrP+2ha+CgfcUyxky73AGMgpEDcTyG1PFJQfSfHm/LZeXe88SGEwPJyiumru83PWuiCssjHNghONP5/gA6afB2AbqIkUtg7yFDXFABa1TXG08K3OvKi9sRiAN6LJrMVvOdBBBJTlCjLGnleYTjKrJKIHu92l4NuiEvXh5hmlDWV6NC/fpETV6BqFTYwzGb8uImMY2kpwfWtMfanGbQU4Jyjrgw4I8PDoqBWSqBb+TacQueU5Cgq8n5xPA6elzD2puWSzl8QCGRZhcq+LgDvD1FWNR7YXMbOwQySc8zsBNDvhUBNMO3e7YO5hTDUxCsK0wC3b+WY5RL9Ex3EVkJdzApIRc6sxawEN0WDIDhVWEvRloBkzZ68bSezE2vdJsZCMMxmTYyCa8HoUGE6ztBJA2zvTDC1O+c4Uj4hOo61J44Lm6QsBMNslCOKNbDfFELLizG09f3ZXOrg0u0DzCYZvvbF5xBw3irEraldSMgcGBmPqcVFMClR5znyXbquHbJT3LyOargHMAZ96jyyaQ5h1RkKtZfFyjSBTiLEC10ESYjZaIbZzhBB2ApuBS0saS/Bzg4RHWUgibNjT1JR1DCcg3ODA4fqWK6OThMEcXgkkdrdS0IwzG7tIlQKOgxQSg5TAiTNIqXNlVcOyMRRNEhs3AtgmAHXAe55+CRuXt3ByplVRIHyyFS0toRiPG18q7jBow+sYn9vhihUPnU9DBVCWwwCANPamxECpOyXkcTGfZvNwRsy/QvSQ6TfhNp2w0zQDVrW3vzPlCSDlkoCMJCBhtRHi6X2yPf2sfP1r6McE/k/PXMag5sM+MqryKYZycJPLGNruYtqPENnuQ+pFbZv7QO3yUpCyoZfeP+FTbCXruGB91xA5gwSrUJwdnsXMg58O5Qx652Uk7RbKIFBL8LN7TFu3xoiiQPsjchrRweqoQVYzp7jlORl5TdXUgigKOZ4O/ujDKO8Qe0MSLFZ1jWUEP4+nR/MzmN2A2VtK5DBH4N3sRcMuhMjUBJF2TjDh0phNCnR72jUVQ0uSL0XhBK1aVScbg6JIwUWCLAkAFhTJEkI9E8OkE0LLJ5bgZECiAQ4C8B7EXieAYZDywC6qMEjhVmooXoJprMMuh+ju5HCVFOIOEK4mBBqX1aNX5EWkEWFsqiozSYZlGHoxmQFMuUcYSIhpITox6hYDVMZFHWJYBAjXeuhgg26TTTi5S6yIkPF5lHo1xtKKQjxxjlmdxrvFjt3aTDGoHsptB76n0nGUcEl/0rMdx6aijeKNaqs4eAIzpDaYgewnjFgYMylJ0tfhVc1GbLRzU+tMXdzhjYROYwkRgc5JrMS05mT9XKgrqiFBmA2qzDoBNg9yLBzMMOD71vH3o0Ryrz2i4crbJSkRSbLGlUPZ5QrlHQ0cJMIuEIwlBVJjReXQtzcooU4DhXajqdkjNggWYHmyPIa2azC889sY2Ojg9gmkQdaIsvImbQ21nnYrjRVbTBIQ5iqidJQkkNZgzHDgJuXyDtGciqwlOaQimN/wjArC/TWO1ChQl1X1KOuDK2JjIEbg3ySHyEmum+S7OKp2U1rEAX8KcmxttrB5eu0kAvBkWVVyxeDCidtEaTQFb+Vwf7erPHdACE7y+dW8MT1Ie04rez1lW/cQnQYAWDMtkbpPR/90D348me+hZ2bQxuoaN/ffQ+aJP8uJZtxjmB1BbOr17y5EZMS5XAP+Q0yxeOL68hzg7ATYOXBcxi9cAnGmkWqbgKlpV9s4+U+ZqMbqMoazn7GtRSjOIBOtJedS60I2XHnuqTjqstyjq8TxIFtC0UNwbL1nXDGEFhLgmxrG/FqD4EGpjN6Ta4ETE0tLaGbfahMQms0WEFqhSgNcPredeh+DzCEmFSzDKqTgktKi8+LCqausbyYILZtktwiVd3l/lxyNw+0lyC749y8eApCScDUKLMCdV6ABwrpYg/Adf/Y/kKEGhxTptHf6IAXJS2kSqIuSjLidMabkfbKwsPD1DUOXnwJwxdeBIwBDwIsPPIwwuUldF+m3fps1Dgfx+uLKEczrD9yDkESYPrHzwNPv0L8uNogTRQYGBYunka62Ud3dQHbLxH644i546s3MbjvNLhFIJihTUsFa+xYVVjohbi5Pcbu9gE63Ri4uYeiqhHO2XLQHz5ctaxpzuEMqGkT5UI4SYFVHEEZmOM3sQYlbv/JQK04p5aq68MydSDLmnkGSiHUygs0AJrrCt44ohtjiLsUal9g9eIAeVFDSg51bhVicxEdw+baQKkBFuzfhZQAA4JzfToRQoA51R8Yei1DUa4EyqLCYnUKSglMJIdhHPd96nFfODa0izY5md6fNrw0xwjBfPwPkwKFMjB1jfs/9T4wUJSKMQYlB7Z2tmBqg90ro9dFFY8b/X4fa2tr39Zz3Xi32LmLI1ntYfyNS/7f7S8qjbXnAxz+/nQoMJ6WTT+UszmDvOF4PuIhTTTGk8JfpJT1QhNGVTXqlsiuKElHYXSQe3Kv1gJRbSAFvQ8Dw8Eox9Igxu5Bht3hzOYTUQK344e0kR3WQq2kDRjd38vI3wRUlHjUSTAEofQ3kxTz3VTvKm3RJh0I1DUpobJZhZdf2sPiUoSV1WSOjzHLS3uOjX/tWVZat2FLtE4Da6BncOWlPYz2aKcphbUAEFTsZAVBwFE3ARiDmu4hjBQmoxzDXKIf1SgnOabjwvbX6Vg5ZzAVcY6KkhxbA0nkXzeZba52AKk8cdk5srri0XF+XPuqtjlHeVnhypX9OQSg0w092bGuDDhc24lD29BGY+g1lRaobdEnA4XBcg8PPX4eX/+j55GXFSpbSPgdcaBg6npO/RGurlGx44ZtX9GF1YNeWka63EfUS2iS5NyjceFCHzwMyI0bQGjRhxrNIuVajUknghDS8x+EluB8vsVRFQWycemDP4VgCLsUbqmTeeM7xhlQUVszXuyBiwJ1UaIej31bouYKQgmEnKGuawQgNAQA1KAHYAYYW/xJDVOTP7dKQoS9FFWWoRjPoAddBLbYmc0KpLoxjXMuzv2NpblrXoRBY0Ngkb3Tj55FOcuAqoYINcJ+CgMg3J+gbQuxuNZBJmNgViHtpxDMoAJxrrKiJAVUbdsaUXgsX6cYj7H79W8g36OiJlpfw+DBB8BtYdpdpDbudDQFeUNaZWcgfRtwMnT3EicjurKGlKJJuQeQueBbi5DMtvdQ16fAVavY4bbYkRwwxsd07O0cYOMELfFFVUPLxl/KHY9TNRZFRR41jFzU3QaHwaGHBgtJhOEs88931YQXlBw5S0Qkdk7xVVn7B7k2umuJay2RTzPwQ/2wOA6gkhhVPaNNTW0QaIHtncbrqhNpbBcz3P+J+6FOr2JpaQmaSV9wwNA9Uxu6t5W216mzIVGy+TtnyDOao5QUgOA2A88gSiNw1DBgpGa0WXzeToTTNWZqWwhyZrlGtJFknHyUjDHgoYbUCmVWIBtPwaVE2IlIlaWk3ThVPpPujQ5jDCaTCW7evAkAWF9ff8PPPTzeLXbu4mCYNxQ0aG6iOFDeGbfNseGcYXpQ+ElfSEJsOG9iEg7nPsWBRFXVGE8JdSlKA2kX/fZjo0CCC0ZoC8beEyMJlUWArDtyTV4LK4uUnl1UNa5d3cfSICZVTEB5VI6g7FyI2xwexhjGoxz7VuIYKDFXDDEwaM2RZfWRal0J8v9RpUCWVygs92Z5lUL8bt8YY/v2lJw+s+b8ZjbUksiI5CmTFdWcAd/SKsG1e/sZ9oe5L9jc5Ms5g1QCVU0LrgwksHcTPJ8giKnYwXQEvZpgNpx5M0ItKZZBaYl6mqMTa+wMZ8hLKnaqFmH55FoXTAcY27yqQS/AriVhu/NTVwbKUrqKvEIcKeQHFba2RljfbAzfokTjpefI+6Y2xidiG5AhnrRREEJwSCkws0ieiwsoswKBFMjKBkl0CJMMNUHbkjfeHWkCkSSoxiT5z65fBqsqsDDC4JGHEC90mzBAAMGgi9n2Hv19qY8gjXx5Gtlih4ch9KFE5LQToq4qn9UkAgHGKlJLTchIssgqHNjry7X+wk4MoSQhPI6HJgXifoKD20PiQAUc3UGEvWsHmO5nSNb6AGYU6l4bQoYCAWmPtGYMTGuYKiNOSV2CKQFW0W5fKCrcZRiAayJ1a/UtANa52fpnlS1Z/eD0mv+sdVlBpTG6qcZaj3geaTeEDohIrAcdqDgEGIPYGRLKIDmMnVu6yx1kpYBQFIoqlUK60MOtfopsOCbXZxvFIEM1V+wYYzC+fBn7zzwLU1VgUmLw4AOINtbn7kuXjzUbTcFsMXh4TG3wY9xNAGMwzSt0pICL+6iqCpl9jLYeO/VshumtHeheSsaMrZwqLgRMVWPBFjsH+xNE95GfT1lViLT0BUJlDCSjOIpZbtvphmIiiqpGrBVFR9hjzcvSk50PD584fmhech47hT3vVWXmih0A/ncy0rj1/DUqXNF0UmMrwc4rhghkm1BVNa7f3ANA/KKDaQEZSJx47AyWFhfRT1JvUeFezDAGA4ba1NBhAFPXMLCigSCAYRZNEgKwjiZa2rabIPFK2u2gmllPqByoUIFZ3hGdf9qo1My1yYW1IAG4V91ShpwMAgoizWsooaCjAFprascGEjJQqIsKQr25YgcAooi+/5s3b2JlZeXbbmm9S1C+i2Og5x1OjWkgUSmaXUCbSCoFg1TM83W8G2yL+NoeLi29k2gPyxZlhcPXk7ReDkqTmaAzmANIvuxs7Pu9ELGViE9nFfpW1XH1xoHfSY4mBW5vT1vIDpsz+Aq0AOe0S3cFlTPicp/JmOZzMzOPbknJoVQTIpj53ZLAvY+u4dSZHoRkmM1Kfw6D1u7ZnReA2iJt3t3qWhf7w8wHJDpZJwUX2sXRQr1RPwEb7QITajcpqy7KJjlqHWJmPxsX3L+/W+hD22pyPXiXdhxoiW4aoNLKngNO5mtxW3FH58dNxXlWIgxIXVRVxrcLw1BiOi4xGTaOtIDt6xtSUniUhjPkswYt1HGIyXCCqy/cQKAEur1G2eafoyXqqiaYvPXajqgMAChzMKWw9pEPorMymCt0ACBatSRczhEOugi7qefvhDb6oCoqRAudueclnRjFxCU4c4BRI4ZQRHpMllXeNVlaR2XdSyGVRJA0AbNVWaGwJo6cM4h8giCSCK1h3cyie1Ve+F17nGrPAasZIZfGth9YXYFzbgM3zVzxwDkQLfa8w7SLsACArGyuzU4L2SnzEmE/hdICcaCgpcDiWhfRYhfxygA6iUGZYgzMmkMqF0khGAqVoihKIkNzDqEVpJJIVgf+/TyBt4XsVFmG7S//Mfae+iZMVSFYXMDq93wU8ebGkYW+u0j8omySESeuPFrsTA4sx8fKxd1mrp5mtMAfTFEXjRt1eoquo+mNbcDAy8/dCeOSFtsolAgtf8zYL7+oa0i0iy463kA3nJqiqv08oKyC0hVHY0uwb3uMHUbYDyM7nNG9URa1n2ccQdfJsN28XdZAPprZMM7mNWJrvFewppX1x09eQV0bKGuGmBcVgjSEtIaD7LiDsT9jtrfkZeKMEFnA/ax5okEjKxcW5XGFhw/Bbb/P4fPBmpawuz68YMZ+Ly5ji0vh5eqM82MP/82MOLau10XxOo+883i32LmLIz+YebSDAT79XEnurcEZa5K8AUvOqxqUpJ1Z0tjgNzsJrWgiU1IgCJt+dVuCDTQqLGWl565/LQUVUYO+NQ1bjNG12SuTWYGBLXa296fI8hJFUeHGtRH29zN/Y0slkBeNCkFLuiuu3LJBlzbV2xciLhXatQ8M5go5JUgWrm27wwfo2ccvria4cM8COt2W47T3aaF/Oy+PWV41511wTMaZR1FOPXTCF0tScP/6IlBIFzrg0yEwJlgfcYq0Y1VR4woHJoFrxatWvo2b2JyTMhVbBpmVgi7ZlPTM+vV0uuRRkiYKQlo3XuUmjtq2DGhycgWVsxMY9ELaZbdywwAn36X2kzsuJTnyWeHNwZLlDl588jJMbdBb7OCDn3zIfwdxoCg9235norUDLvMClY7JL4cMhrDywcehO40yrj36Z08AoKJHRiG0hbYBILJhk/k4Q2rDQ91IexFyW+zIQJKxH+NzLsoHuzOSMdsWVhBK8DgClxw6jeYWmayVNK4Cie69F9B/4B6Kg7BfZJ0VJD83Bigag8+aC5R5gdqiW84zyFDfcs7gDaCCO7Ek4tm08Nl2rvANQwXR8ieq8gJRv4O4S1LlIBBYOLEEnSbgfL54FJKKnYG1XYjTABUE6qpGlIbWzM3y2axZnAsHBgBlOU3TG1vY+sxnMbt1C+AcvYv3Y+kD74eMIhw34k7ckv0XvtXTHg7Z6a1RkeXMMIvxFFxJzHwLiyMadBFvrAKMoRhNUE1nELbYaRQ+EsYmcy+tELJU2ELf2FaU22Ax0IZKtjKhiqL29xIAj+S4omeSF0eciI8brmRw3Ei6HxtFFkBtYu/XxYCDHUI+V841bZdACiqCa4OaUyTJ1a0hXr1Gc8z6cq/FEbIqzTdaJviagzdCDUFmg3PDV/AkGmB2I+N+fNh/id3hH8y/jPH/NjBN29luBBwP6q2Ot+M13i127uKY5PUc78bt8EItvKxcSu5TnwEqPoq8aqpi+3PGm4Jg0Auxap1Z21yeTkulUZl5foNbKKWmHbAj7AZKQEiGUyd6ePjBFSwtxv51prMSoRZIrEx569aY/BVgfXJaUvnJpOEfMc5xaWuIaU5pwusLVhliP79W3C66DeTSNlTTdkFzny23uT5SkmFYZ72L/kYH6ydaaMBhdEg0O4+q1V579fldAECvF6Bv+//ckrt9+yYKsXYiha5op1qrALyYkroJwGRSAWCwYAG04p5z4pxfi6Ju4h7ywnvHrC0lMABGB9bBtBuCC7J/v3BxCfc/vu4n0nxWeTIyYw2vZTIu8JH3n8Tj79sEP67Y4YxgbUYxIauDGKnNOTOGitO92yOM9sZgnOHEvetIujG+5wcexCOPnUYcKr87hDHgSqAqSkx2D1DMcsSpQpBq6DTAwsMPIrSGg8eNzskVrH/v41h85H6EvZi8e+yFENpMnaooPRnbP6+beImsCgNqaTHiU7lF16E62i5CQaKhY03tKLtINi6wzX248ZH3YvDgfdCdGMl6g7DUZYVsVmJ2kKMuGsdZnYYQSqKwyAw3dsGta/KJOQSrm9GBD2x1/khAE+gbd+YLiro2iLoxwl6Cey4s4OyZPlQS4rjBlYQSBhcfW8WDH9jE0plVVFVFLUulAMb9AuZMIausUQzKSGH/m89g+4+/ijovoDodrH70w+icPfOaCwoXHLFzMC4K3170n9kY4vMAiLsxAvsZZ1mJcjpDGAXILblZSY54qYegEyO0Re5sZ+i9WtycKbT0KODKBt2ro+HEozWzrGxJz1mr2Gk8uhyHp6oNIkVCCCd+GM9yf88fotYcM8hnzNkDHD5VnKPVUmcUP9KJsHrvpiU9A/esL0JoidrUYJxjktX4ytNEND9zZgW9/vyGYe77ONxSc2729l5wGwgmmgRzLji4bEKePTQDS7EYjgEuaJ1xL28seuoXnhYyZOYJ24B1zLePq6va7+C5EPa92NtSqLwd491i5y6O3e2pv4lEi1AYh6rJh1F8DtkJoybhW7f9MExjtFdVxrd2qBXkWjHC3+gkOW9uTGVvUqUFRUtYtERbR1DOmSfvRhERW53r8rr1g7l+e4zhsFEWuME5807AnDFcvjnEaEYT7KmVDqJAzrl/SkVtmrpufBzahmpxID3bX3niqoGQHNk0x/atCbglQQJAFCukHT3nLlybpoBy7+xMxpJYYWUtwXCnIVSGkbSLIxB1A5h9CgHlQQCJkpyXO7S7dIhDW+HlisnSwuOzrETfuuc6jw3JOZJIA0JgeJtUet1u0HC2DJAk2pp52WNuoV+CcySW/3EwzK3xIPcFVluGa+pmsQ+0RGR9UowtXl786ssAgI1zqwis220Yh+jaAEjZ2mVn4xny8QzJYhcLmwuYXXoJjDOkp0+ic/bMkeuhPZgQUJ0YjHPoJPaTLzmrBg3kXrcS4hlDlIYoXMyIiwvhnDg7hyZP9+8gCVDmFcqsQM8GUx7XmuisL5MEOwqguzF0y+k2m5QY3mwFeBLzGelSD9JC6ahKS9w0YErO7X6NMah2biOxUQF5UXnuT26vjXTQFOl1RRJrHQcQCaWqS8khwtBLgA99WESyAOccQbcDA4Yyr6CcWs0ucAB8wVROM9TO8Xr3BiZXSTnXOXcWKx/5MFSnc/R9jhmJReLKvJzzD6PzlvnrUMcBuhuE7syyilRudYV8bIsdJZCsDCDDAPE6JarPtve8/Nyns4fav+b6Kfo+d7eHiGxRNJw07VuHvIhI+zmwqo3366oNFTvu7wAwyjLvPwS8NnpAmw3p5xT/1dg/OeN2U2aLNcawevE0wiDwr82ZNSetaPP21Au3MJkVSCKFi/euIjmERh4h2B8+Js6bAsYV84L7YodxPsePMWgMD7nkMFWN/GACxpvWnxMVODXv3CbSmCPH0fbmci3KdiubzXfSvqvj3WLnLo52SCXnzaKbRnqOGMsYI04GB7r90HNpDn85yrdmSownLm+qqcI7vQBdmwheVrX/uZYcnNsMlkBgb2fmFTpSMLQR6dmMmPsuomKWVzh9agDGgPG0QF5WiFPlURohGLJZZfOeDEZZjoNpAcaA8yf6SGPt22tuCEELtDGmyQazf3LGfCwF4wzdxch/HtfiMxXlJTkyYBhJnDk/QL/ThEjOZuWcyosxKmrSjsbSYgghBYY2lVoKhk6f1DthrBBKOyFoDVMVAGMw3RXU2kqWJxl55tiCUQrmuVPOYdoYoJc2DsT0PZAkGUphf4sQps4g9m0QU8Er3aSbMBw3wL5If0AL7vUbBzCGUKXat0qb4ta1fdwIWi7ct3cmKPISaT/GiXs3kA46yEYz2p3VTq0lMRtNUMwyxL0ES+fX0dtYwP6TT6LOc+heDwuPPvy6uzYu6NoTATn/MiEAKTzy4hCAqqygbeETxRQW63g2lOXEYWCRnUNvKawETccB4kGK3loPbEiWAu02gFPMRF1qyQTdBKYGBvec8I+pJhmKadlM0FKCSwkG45PAOQzy6cy6P88jUmY6hslmPmOrzdlz6fVdS/YFnIO0ggw0dKeVo9WN/aLVHtX+DjgzKCtg74B+XxYVoUW1AVcSXHBUeQmdRERsbn8fqCCiCMsf+gB69983J39/veFiBbJJ3lrtaTi+TpiEAOdYuUD+QHlRoSxqyCJDOWmCWpPlHoI0gh70wRUhOOWsCbwFiHtS2/bv+hkidA/3xojCxljQDYfsMMGhWq7D3sLCGASSCmW3KZjkhZ9/DsdKHB3UEnZtL7/B8O+PucDntYunEFnjSeW8qKoKMqTg0P3dEV55la7R9z24gSQQUIFAEDT3qVdfgcjac8M0j2kXxUxw1M6YUAqvFF06u47+5jKWzq5j6ew6uhvL6J5YQbq6gKDXxU/89b8GAFg6vYrFUytYObeOlbPrWDm/jrULm/it/+Ffwhjgc1/8PFbOb+De91zELJ8BpomYeeJLT2Dl/AYWT63As3vYOwfZeVeNdReHafXyOeOe5d9NAmzbZGQp6GLt9wIEMcGk3vrf9rIc+U212zr2ddtohikNuv0QOztTv8MIFLdtIXjjs+1tOzFZ5Kgsa3DFURc1ptMScaCQJgrjSYEsL9EZhFjohdjem2E0zfHAI6t48ZnbQEGFy3iSoyhqTPLC8xJOr3QRKYm1jQ4ggJds+4hzmlicMuXwpKQk8TKqsoZUAt1EYvvGiIodq5aKegGqSdGQk0MJoQSZ7+3PMLVy87azs+DkvruwEIIZIiHvX9m33wFHfyGCXKaAQy6pFWDqElxKpOfvAY8TsDDGU6AWSTkrkE9bZnYgNGI8nEIFAvmsQNxqzTAQsqMVR24YyrwElxydhRiFJRhXZeVbHVIzlCXzuT3eFDJU5OGSV7h9e4zNM4OmpcAaY8G6rsFaC3FhJ+KsqDC0LbR733cOOtTorXQxub2P8f7E+9owLiADjbCbYPHsOlSksf3VryPf3wfXCssfeN/RCfiYwSVlr6k4hNBWys5pQuZSIOzGGN8eoioruyhkiJMAxtSeSxN2IxQjUjOKlqkbffe0WQgiSe7MEcPBc8+hynPLF2gdi/1HaAusICWZutAaIlCosoJcaO15rIoaTArE3Qj5NEfUjVELAVQVwkhhOsxQZjlUHPr3qXYIEUyWFwE873kcAGxKOsg6344qLxF0IuJk9VIc2J+H3RR1Wc45htdFgfwWBbEeTOE3DGCAjjRMVUEEtMC644oWOigsAsIFQ7i6iqX3PDoX8/FGR2eRir3ZeHrkd46vEyYhRCCxsLEApQh9nU4LJOOJv06pjdWHigIILREuL2By7Sby/dGcmEAo5dtYnaUeOr0EB/tjKIvsOB6cR/aUBNcaKgbk/gx5XR2uyRBrhYMZRbzUxjT8IEZGoQzHt7QYqGhxbayqMmjXiYyhMfdUAifedwGTnQNURYUg0iiKkgjTUYBymuHpJ18BAJza6GFjpQODGhwMUaQxmxV2E9y8PleSrs+5g2v1pNxxcO7PMxfc389PP/F1/+l++1//Dn7x//Df48k//ipyaxeQtDh3v/oP/iE+/j0fJz5STWhPx6F/9qDSJMHv/dvfw5/64T/l6Qy/8enfwImNTVy5drWhYTB2ZHPy3RrvIjt3cRzsN8hOuwXqEEKAkJHa9kK1lsiLBhZsnDq5RSZYu+3qc5jcxVRkxLEBGk6NM4UqyhpSUwzFzo4z83PhfgWkFlCRxPI6XfRJa2dqKoNuTCjFOCugFZ8LmRwd5Bi3Cp21QeJRjZIBS/euNHAvZzCMJhky5TL+swBEInZrmQoEYsuTKasalVWzCcbmUteDUNqQxMZnw4AKTDck54hCiXxa+VbO3i1qJelAIu669hEDVwJADREE6Nx3ETLtgHOOwcaSzyUabe/7NovbHQaWI+F3f61WmrItRiG4X/T6iwl0KBufl7LGtZf2/Gsk/eBoanFdY32NvqPnnr2NG9eGc2aGDkI2Ze0DSgEgH9HiP7RE3c0La4jTCEGsoQOJdLEDHQUoLZqSLKTorvStlFtg9PIljC9fAQAsve+9TUvndQYXHCJQ3siPcW6JpxaV67rFufQuynESoLBkajAGHcdQcQhj2FwOGwAfihnGCtyU2H/6WdRFAd3rIuzEc7tKzmy7yC70MgwhA40qLxB0G1TFoCHJMikRDxJS6OUFmKLvP1AM8UIKGQWYDSeo8hL1bAozIWJqsk5Ko6JseHuu9Zz0mveqigpBYouvnl1QhEDY7xxBdqbXrhCxlAnMStoAuU0BqbOYT6Ev8xJmvAdWNq0eEWh07r3v2yp0gEaRNR3NjvzOIztxgCCKEMURUtvGnWUlsnGD6jDGEC/1oEINFWtEK8THyfdHHmUmLyOnEGKoiwLLNiPMkWgdsuO+4SgJIJSATkPPxSltzA1A6E6s9RyXK7cF0xETwWN+xm2waKvr7Md02qA66/euk7dMQKacUeLQ6QoiULhy5TaGB1NIKfDoQ4QqsorMbBJbiDt1rBueOHykcGD0u0MVmmthmYoMBVeXV7C6vIzV5RUMBn0wxrBx8iROnDuD1ZVVpFGTZN/v9bC2uoq15VV63soKwiCcoyL8Fz/2p/H/+Bf/TzoPtcF0MsH/97d/C3/6x34c7bPjRAzvhPFusXMXh1uMgYZPQUose/NwKxEGXRRSc+w6HomdFDh31Y1V6rT6oaTEai6kqjIQaHgqFE9As8d4UuD2rQlu3ZwQksObdlFVGWTTEjqUqC0JMwiEL8q2t8dARYtFVRtceXUf3BGAqxq39yeY2YlnsRMhCRoYebSfYf/maK5AM9Y51BhnjGWQBApScAzSCEI5GTdH0mnacsaiP6V1iPUGXoEEjIFoOS8DZA4mLaIjOGVMmZoKrcowr+xYWErI7Eow6ETTdxEn6Nz3AETYkEkZpxYIffAmyM9l13Rs1plbTGdZhdVBAsk5AtfCApBN6DvuL6dgSjfnsm52i4wx9BYjP2k7n42qqHDiRA9KC0ynJf74iauYZGWTSaadpJU8M3yQX1XjYEK29YRwJTAGCJMApqohA410sesn1bBHv+eCI9/fx85TT9MxP3gR0coy3uhgnCNZ7HllEEASY280aBf+YpZjda0PpQTW1ge+haXjAExxKIvCMEbmiEpx6ED4NlUYK1QjakvGpzZx8j/7foTdeI7fwzn58Ph/Sw4Vh6jLCiJsyP0qIX8hMOIc6ShE2I3perFZYHWeI+ql6J9YRrLURVWUKG8S6sK7PYSd1F8XRVUDnPmWVtJ1hH16D2nbMsn6ElgcIdhYIT+i1uJSTSfIb5GxWh10ITndnyJQSDr0OZnl6xTTKczONUwuvQIdNYumjII3HAB63OguWEn5eOoXWGm9uxyyo+MAUS9GEGl0bcDkLK+8glBJOsawl0JoCRVHkAn9BwAuNEVITon2oM/IOMfmBfLYcfNnZeZX+LCTkLGm4D4VvJ2PVxuD1HHRaqeQs2Rz+xrNpvToAu2KL7cBaRyHDfaGTWDywOaGcSlhTO0J6RVnmE0KPPvMZQDAvfdtIojaQbEcS8s9SCmwttwiK9vvdu5AAUtNcD83c39ya95H7bb5z9JWXMkwgLRy+ObDu7/7XtncWwDAj//of44/+tITuHKN+F+/9S9/CydPnMAjDz3iH+sKxndKG+vdYucujnOPnfV/d9dJGCjULbmiJy13NUbTsoFVneeLcDwQen67NeMKnzasf9izhtyD6TVn0xKvvLRD7xdKcM69NHM8zJDnVWNehUbBdeMqwctdO4FcvXHgL+DRrMCe7cV3Qo1uHFjo2L7nKMfWC9toV/pVVfsbxx1fqCUurA2w0Amh7K5Gakk7QXs8uYWJ64LMExuPHQqY45zNIU7GWEm+9SZJ+7ZwKg0mlmQsOENvQBLudCVFtBBD9fro3Hu/d3VtD2eEN7aZTUEa+S/XpdO7hXw6K/DgPSt49N5VcjS2RPShbWH2N1bAgo6ffExtPHeJcQahBRIrr6+shL0qa4ShxKn71rG83iU1ljGY5iUVM97/owYY94VUUVYYWY5RN9HYv7ZLaF6gqP2hyT3YF+VRYHeFNba//MeAMYg3N9A9f+7IOXm9EXSTuUVWKOk5H77YmWZY21zAex45gcFiB4ULTowDMHDoOAK3qIpQHIGWWFiM/b0RhBIyjtB/8CIG998HLgS1qQ61scIWGZlxbjktjby+th5HAKBCBSboe4i6MaQWqJn9HFUB3U0gtUK61EPcj4GZRXLTPhhnxF8BUBryUPHIjj2GKqc2lbKFlooDpI8/jM5D99qfuU2BweTyq/SY/gAsImNGJjjCJKT2WlmBS4nyYA+zl56FmU3AhMDg3jP+88rwrRU7qfVCmh3YYkdwqDAgZZFthwRRQOGdUYC4G3lkc9de80oJRIvdJrG7E4ExjtAmwPsCRysoO9/oXgdhP8Xph+izzGaHRBL2S466CcDp2o87DfHft4KNQaDknII1c/eVUzO1kJ35t2AQgq4RNze764Qk9vSPKBDgVr7v7muH7EAKPPmV51BVNfr9BCc2F1AJ5e/RSDNEaYCHHtjEyfWm1YlDROO5z+0LkTb1gYodMHhkpz3m5OUMUGkE3iLa/2/+2k/g9P1ncfrB8zjz8Hmcfeg8Lr16aa7aWVpcwic//gn8i9/651CBwj/7jf87/tyn/sx88cWOJ1Z/t8a7xc5dHNtXiafS/rqTNPD+NC4WAKA+7/bWyFp5NzddYn1Y3Ggnh2tld3eCe3i/ro03tHPX2XRGpnSdnvacEAaGWVY2idqjHAcHWeNjAVI3AMDQ7lo2bftkx3ruTPMS+7ZoCJXEQqfhLtQ1fCFlWrsLBqAqjY9sqFqussISSN1nCSKJbFr6AmZs4XPXh3YEZWERDHao2GkPBmCwmvhzNNyjdoMUHN1BCDCAKwHV7UOvbKCY5SinGaq8QG0nRACIrRHe3nUqGqNu7MmKibaqG7tQT6albyEClozOGYa79DkGm8vgWjVtr9rAHb4IyExssDLfLvIqGMER9mK87wOb/pogp+td3NgeYzYtyJNFkMx/z/J04lAiijTySYZiStwFMOZbG5VDy6IAVVkiu/wyqiyD6nSw+Ngjb8sujQk7ERuDqO+8dma0e6X+Iwobq6CtOaAINHioyFjQXlcTG4aqIwkmOJY//DhkJ7VtSCpEjyA7c8UOgwgV5X5JZ3BpUNp7QMeS2ppSQkgqeEpnDFpXUEmD+lXb5GLNO13UTCAfzRBbj6osJw6Z4++4NlaZF9Ch9oWWkAJcCCitLNGYiNzl/j7K4T7AGKITpyCVtF5K9BwdKpiigDm4jezKJaCuIZIUKx/7CBbuP++PUUb6rRU7lqA8G8+O+LE4ZCfqRugOOuBSQobat2N8AWll526oUIMHCsFC37ar6NzLUPtrMugm4Epi7SQpsqajxm0coHs70BIioIIdVY04Ul6g4FPDbWHcCbVXRwHwfj3GmCNFjnt9unYojLNtk1HXBrOWi3sYKRjX9pIcXAiEtr196/YQN67eBuMMDz16FowxVKxBZ0JWAzBQrVatR0eOOy4LjbdBGM/XcarHqp57LhcCRxxnGZ1jd2///H/7c/j3//oP8Af/w7/DH/z//h3+/b/+A2ysb9g3aAqeP/fjfxb/r//P/xuvXHoFT3zlS/jP/9SP+XPtXvidguoA7xY7d3Xs3twHMM/RWViMvR8DF9wv9Pv7GZJuQDkkoEKFC4Z0IYQKGq+ENrLjTPdcwQMQ18AhMtw6V9YGuLkzxWRKvhQMpNAqyxoHBznq2mAyypH2wjmo0supK8qr2djsoRNrGABbO2MvqQ6VQKQkwlB6X5iqNr7wKIvanwBmJ2nfxmpld7m4BncMUguM9jM/aTk0xthdVGMI2EDfSvHWJNYajCFJm93e9pblGIQSQURFQbi0hP5DDyJeHkCnMZiktleV5ShGU+TDsYfHHV8nTBsYOgwFhH0OY/ALnCvKtBYoqtrnI/VW+oBU4NLtzpoj9q2NhcSfR2OsRb2UYILZNiBHqCU6SSO9H08LXLqyi1dfuoGqNpjMCuQ2P2q5H6NrDdp2Xr0NU9XgQniycVW4DKUA1fYWqtEIXCksf+Bxzwd5q4NLYaMB6pbXTpO2DMY8OVnFAaErgYYMAlvsOE8jiw7FCjKOICTtTt1nCVthoIBV+h0qdrgQEEo1ZGvTet1E00bCLrphJ4aKLapijN+RV7MM2Ra1mDoXzqK7vggRKminGsor38JinCGy/KWqqKDTpmCSSkIIyjQTShBSkxeYXLkEAAhW1iDCEFwpu6lnFClR58DwJuoxoY28t4iF970XKkkQL7YKiyg8UqS8mdEZOILy7IhZnXdPXuggiIk7I7TyrXJ/DJIjWeo3nznUUFEIJgWCfsffA0IK79KsOylEFEJJjsFKr+my2MEYQxwHVBwKcgYOFIO2942bD2qr/uza4sO9hkO+leBQQkAwdmTRVlJAB5pM+1rFjnNRdyPsxh7ZpfuK++vAJd7f88AZ9AZdb9rpdogcFPNzOCuQcTKhbbskN6MF5XM2p8Ry9gjtxwt1h2KXMwirBFteWsY9587h3OmzOHfmLC6cOw+l9ZGn/ImPfxKzLMN/9dM/hf/kkz+IhcFC6/qyXNJ3i53/eYzC+R0w5hfeXjdEZm3UhVvYBTH5lb3YhA2lJPlqI78G5p2Gta3eCWK1CyZrogoMqPBxRdGt2zQhRZFEHKm5HUpVGdx8degneidNd/d8EEjoWHszw4m9yWMtvVlXGAjowIVXGvKr4t6uAfag/OtXlSNQ089cG8oFW3LJMZs0EvL9nSaE0BjTCt0jxERY4nTbXdoNIVzuFf1ubAmTXesczaVAvrOD3a/+MYqd24gGKXqn1tA9tYruqTV0Tqwg3VjG4FQrKgFAELvWigDnJG0nnxiaUKez0iM7WnHvBt1Z7EIFyqMWAGDqlseIPY8y7SCyni3kUEoKIc64DSW0j+ccnUjjvodPeuL5rWt7uHx1F/t2J7zQDSElR3eVFsC9azuYDscQWnuY3BU7yMYwoz0AwNL7HoNKG1LtWx3cIhimqqBC7Qu72pNF0XB2koA+rxIQjPhGUs1PW2GsoJK48Rmy18uRNtYRZId23lwLcK96MrRjBiFGOpCe9M0Fp4BTu3rUM0LLpleuAMZA9XtQ3S50EqK3voiO5bhkeeVJ6bGVvRt7U6hWMj0XNupBE0dFhAr57VuoZzMwKRHa3TW3ijQpOfodBuzdBEwNpjXU5lkEqxvQVnIuQ+0LKpWGb2mn7fyBypyCJN1oGwoOVhcQRAGZHAYKWrcEB6ohJzefWdgCAQgWuv76J3WcLXjTGCohDtP6GUq+lq15kIEh7SWQNrFeSgHBGpKv3/wYugZSy1FxRU7DpySn99hFNMAhKxZBU9LzxqQN+nXtK3ofIF7sAGDWr4aK6cBxYkB5e/c9fIact5W07WbmC+dAGO+J5j+fhfEOt7JcoeRrHUtIdufV+d60Czfnrn3cmIt6aTkjtxVV7Q0Z5xx/+kc/hc9+4XP4s5/6L+hn7diJdxBfB3i32Lmrw3mWzMGI7k/eGOqVJaBDiWxS+N8BQBhLFLmBslwQxuYjI5rcrOY5dW3m0BXGKB/p9KmeP5Asq3AwyRHHCuubqX/uzs0xtrZGGI1y1DXdyI7wWxki+q1bzx2A7M97ceCLLSmFV9RUtQEMOwJju1vF9ckdqkMp6BQyWtoipigNokT6Yme4NyWI2MAbCgK22JECwrqnusk1buVNBYEkczM7cbiWwpJtbYUrK+BBgDrLMHzueVz/w/+Ana8/iXI0tvyBELoTY/Hsxtx33LX5Q8ou2HE4n5E1y0pf9GolMLbf8WB90BQ2cVPM+sLPuvSKJPZZQzBWgs6J7BwGyhd8bhJKuzHWl1JsLKfo9CJfaGol0EsDksDHETrLXcAAWy9ugTkFmzG+jVXvElKRnj2LaHW+wHurg7Umesa5V2o5xK6ua5Q2GFRHARjjKG5uwezeBOOYX+iY5eukCe2oOfeOxkEa+mub20DGNkGZMVLecaGg4ma3zwDP1VEBm+MdCMU9SbkaT1AXBWbXbwAAopMn/eO4FITcgRSNrthx5OSqqOzrN8UOYwxhEnjkSkiB4jaRnsONEx5Z41JAS44TGxqBtAVi1EF84SIMVwisiaMbkUV3wt7xkR5vdARx4K/zfFL4hSybNoaCy6dWLNmfCjfOmN98uY2aO9f+dTtkNik6iZeKm7pqFTsRZBCAa4X100SObwsRGIDuQgdBqIG6husCBSGZALpNXVnXYKBw0LRFSDega79NEZinPsNLwV27k3PmUR0n5lCKYjCEdRwHiMAdtjyuHvvAReu6TZscU9WogblWljykOHRFwxFPpBYqZA/KuxwzwZvNg2hcx+WdkJ3WMewf7OPG1ha2bt3Ezdu3cPPWFqY2MPTwefk7P/1f47mvP4NPfM/H3ULUOmx2jHrsuzfeLXbu4uj2E2pHtXu8dkJ3NwgAGMbRX4kx2msSnAHq/9aEX1p5MhlbrSzEWFtKfMXtqm8AKPMKabdlLAiD2gA725OmXcQYJtMSW7fHKCuDxeXIv05dG+zvz7C7M0OWN6jKaJQDpsbyehen1jpIAoVeHDQO0ZwKHkfC9Z8zmndPdn+vKoO6hicGJrFCkiisne76YmewmqAqrb+OoFiEkfWk8Q7SWoAJSefI2sV7ngZnUIKME6NYkm8KY3MqDlfs9O+/B+uf+D4sPPoIVK8H1DUmV6/i5uc+j5tf+CNMrt+AqWt0VppdKQDwzBoThgJcci+Vd7v/WV4is4WZahU7/RV6D4DiKdihO1EImjhkGiOxIZ3usBmr0O3FCEPlowi8lYFVu0SBxMXHzuDcfWtIIoXNzb6dfCgbav1+krzefGnLXzvetwXW7iDuoHPhPO7GoIneEsxtAeLcXSvb5uNKgLMa1dYVFFaJBCHmkB0dK3IgTiPbkuN+UQjTCIJzrJxcxtqpZThDwfbgUlIBFUV+IQOojUg5ZYdm68Ig6FqEYzTG9Oo14sikKdSgP/dQF6+ANIFYpkgN911WeQGp1ZyPDgAsbS57BGV69QpQV+BhhGB5BQDdP/XBLjpJBSUZDDhMMoBcXvOLy+GoibPf/14s3HcKS/edOva7eDMjtcaK09EUQlDrY2adkXWkMVih4p9xshwAo5DihbU+Urv5KPYP5l6TWlkB+ezYOcoUJUrrD6TTmMwCkxhrtthpzyn0PUmrzuAQpnHylYIjtKn0tTEY26KzF4dHeDtzrav264NsQQRjYLZwm0wLX2u4NrqSArFFbEvHJ1QSURzinvtP4eIDp7G8vuBfUwSauItoihHBgNiiun6BYOyIG7I96JZat/Vwi/h7zywhEEQBtPW6utNw981f/+mfwv3vfwQPf+hRPPiBR3DfYw/i//rP/m/0lvX8mwVaY3GwaLmWYq62eScpsYB3TQXv6nj8+x/GF37ry5jkueW9UPI0YJEMa9gE2y5yXA/GaLERkiMvOS16miPP6B5c6ke2WDBwJDBmQ9eMAQarHbBnbpK0OFSYZaWPh1hYihEyht39DGVl8OKLu1hYoMmRMYovODjIUZakmHI38iwjX5tOKLB5ZgGzyW37HHuTcsp3cvJqT/hjc/OGJyYXZe15OwAVO93FyCMwAFAMZ8jHORhjSFKNg/0MB3szJGvKk7y1FmBBiLouCdmRzU68KOqmcAyl3326FpySHKY04Am1QRjniDc3EG9uINvbw+jlS5jeuIF8dxc7u7sQYYj45Alw2cqfsQhM0E0RbSwg2aLixy3cs1k5d6yu2Oktpg0x23IpvCLEtjF5EEJwjsgWA86BqSpKdDoB8gPmJ1WGBi1sLOMNFpa7qCYZtJYoS4oQ0ZFGdzHFK1/WyKc5br20hbX7TqC0RQbjjDgSayeOOAS/XUMo6S8Mp8iqClKOOJK8DhXEdI+OSUpUcQ+6LlFnt/zrhHYBlQkFjFLx0rSxAKDXT1BmBWbAXBsLaGB9qQWEonMEwLcW276JdVnBwCBaHqDY2Ua+vw9s2/bUqRNHJnanuipqgFvTNo/s5CWihc6R5zjrgGI0xuQK+RqFmyfBGEOdZZhefhnVZEznqebg0QBcKDr2rIDUeq41BgC9kyvY/NAD0Ol8EfTtjM5CB7s3djAbU6K3DBRGluwfd2LPYeOCQ4aEJArBESUK+QE5U4+v3pj/zIGCjAKMrhHpX0iO4mAClBYRSuk8qijE2ulVHHENBrC0NgAZpFbgrKJtvJJQikMUHN0wwP40w+5ohlgr9NIIV3eGcJUCcRkb7txhBEMHyntGTfIhJtZXJwqaeB+lOIJeikpIzKyYwqGMp8+uUZ+6NYgvJlHxCsrUxMOriFfUfn8qYvkRZMe1sczh13WhyBZd4kIA0kbvMOAv/cW/iL/0F/8iDg8uOG6+eJ3EGla2riTNJ2UN5FmBj37wI7j96hbZh9S1R8Xc+/7JH/mTGO+NPF/nnVTsvIvs3MVxcG2bdmL2yo0i5RUkRKSln0vJMfaoTpuv45QrVEQ0fdP5tFn3c21bRjVrWlm1MUgi5X1Y6qKGkgLLCzEWbZGzszPDwThHURpoLbF5ojOH2LiC5+aNMYwxWLAeEG2lEed2V+kUWPYYq7Ke24UB5NVTVU2QaKAFOosR4kHgPxcYwGBQOLmuPR+jUQ7GWUP6DQQgBHGMLCfAZ+O08nuCuMkjcwWHVgLD7SlUmvrwRP/4fh+L73kU65/4PnQunAfXGtVshoPnX0BovUu44FCRTZ9eXUbn7EmfieQIzONp4RdQGQaYHOIKgTFwHfiePV0DllgZEb8jsUGq7kvZvz1BHAJRIix/gs6XjhREqMGcb0/ZmgQdn4VTq4QxjtULZMF/5Rsv0+MnZLIoFYfePAOh9RtySf52BpPCTtTGFztlXtKintuYCE3Lj+gPIM9cQLi4ANUhRFNqZyZobQriCKY2VOy4XactdrLRFDMrjT6M7DAlAEPcCt626rfFDkdz/VZZAaEE4jVSBZnZDKYsIaIIemkJh4dDdibDMcbWYNQVQAYGOgqOPMeN3aefIe+otAMexch3bmP0/DdRTcYA55iWGtNceeIqUxJlXiLoRsd+Z1zIt+W7dIqs6WjqURFHTk76KQLXDuQcMtK+jZFbtZYQHONrN1G0XJgZ55CBxHTHmnzae8i1VFVC54wHClEvweJaf34RZQwrJxaxuLGA7kICaY1KDeNeZh5rUmeVVY3hJEMSBkSCtvcU8XcOlzjNCENNJoFKYtcH0HII0cxFQayp5R1ozyvkglki8uGUeNqsyjgEOF13/DBy0zo/pDY9po2FFtriWuCWnOzaWMSjufNnc6NN0vfBolbkwt05dRvU1lrkeVZzETfz7d93wni32LmLY3x7iPY9lCQakzEttE5mDQAwNfYtedhd8GFMEkauBMAFpZW3DAP9sC0sY5o4iHxSer6KKyjc7qPKa4wmBTgHTp3u4cKFBWgtUBtamK9vjXzcAgD0+oHnBl2/OsTzX9/CjUu7dKyMzbXGiqKy+UN0aGVJEG2rfqFjqOoWMgV0BxGWzi4g7EbgzmNHCQgtPCriiojRKAfYvMcOz0YQpmg4MM6RuLX7ixKNuqQJaNralY12Z+BRfEdJLg8ChMuLCFcXIBMiXkaJzXBKFDK7q1VxiPTUOk14ooGzHAkbAApDx6WUoAnZtiG5VnMqGVf4iCQG5wKJNWdzu6iD21OY2mBhSTVQNag9pUIFxpzkv2Vq6VEjDhUqmLrC6oUNksJv7WH3lSuopnQN6iQCkySJvlsTFre8DlPV3ruonOXoLgYwhd0QRAp1OkB44hS4VIgHKZTlNy1vdtE/seg3BTIKCdlpcRJcLMR0OEFmWy1HkB0hrMsrpau7wZT2UmRTNsGfMtQIB/3G1wFAdPIoqgM0KM5kOMF43zor9xJUZUUS8+D4lsLs1m1Mr98AGEO4sYHsyiuYtSTl0fn7UYGMD+u6BrNqOlObI1lY/vMI9pZk5250nNfOeAZm7zPnsdNd6kLYBZMyqsIjlA2d0Dnee/Hy3M/3XrpGJO849E7SdW0gWt5AjDFqZZ1aPmIpEKchOGNIrHK1AlCDNo9url2wqNP+JENVVVhZW5g7hllBbfvDqApjDGGsoSOFva19FNadfHOzg/5Co2CNutQKlQEhbeR9JI51OAYYUFs1ouNiCXZsScK8wezh5doVO66V3ajA6rKiXzPWtGdfp95hrAkg9m1xW5C3pwFC7FsomFsDDvGB3kmoDvBusXNXR82YZ/wDRJI1NVX7osVwzyY5xvuNyR1AXBfi6tAOWLaQnaq1Y2+TNYVFVYppgSR1DqI1prYtkKS0EJdljcm0BBccnY7G/fcvom8N9ybTAq+8so+qqiElx/Jq4uMJirLG3q0xbl+nVo1blKVFo7KMbjBXbFQVITMe2bHHXxRUdLhi5PSja/6GdaaGjHPcuF157k/HOimPRjkMgMy3OoR/ae575+6ybp37SKIsaxRl7QnDaRrA1AbjvemxSodsdxt7T30dw2e/iWo8gogCdO87j+4JIuxGqUY+ool+du0K8q0b0AsDJDbIUrdkt1JwzGxh0kk1ytGEVDkAmFJeTUfnlVMApVLgSiBZ6M1NHGVRYXdrCs4ZqrKF7CiKYmB+B2+wsB7jgY+d8d+JU43VVYWwG2HFutJe+dqLXgWnU/IOEsHd63K7RcBUlffaKaYZev3At3pVfwAWJiiyAmE3QZBEkHby1aFAMqBiRlhjOxgzJ493yE6VN95VQcsbB7A7V8GJ2OqQFiLaoXQmltkMdVGCC5vxFQbQlrfDlILoD479jA7ZyaY5htuEWsS92PJ1pJf6tocxBrtPfRMAEK0sI7vyKurJCGAMwfom4nP3Qoaht0UwVQ2uFOq8hAwV1B3QIiHF21LszHnt8PliZ3Fj0T/uMLLjRrJKBcbe85f8z+qqws1vvAAACAYpRBRQAWpw5BzJMMDm+fW5gNckCVHaeBHhpOtRhMHmkuXz2A2kkghDhdoYbO9PsHlmdU5pNCtKhGqeY2i7MYiTEHXNsHeZWm29TgDVCgZVkkOnMbgQkFpCaomyqDwJfm5Y1IdJARVH4JYszRjzHj3+dmcMjLsC8tDJNHR8zlvHb2ikaFpY9n73T3itwebRJcaprVrb7D93yttHQW9JG7f2ho06b+8WO/+zGXlWoDbGcy2cpDCKFXwUBIDMIg0N58L+zuYIgR+V27rR7uN6fs24QM8uBFle+WInDiS6lryc5RWuXT2gCUJwnDzdRxopcEtSzssaeVGhqgw61o3UGCDpBw0HxzH87THMZoQIeX+dqkZZVC1irT0veeULnTBWSPrNBF04gy7OKfjOjjRVEJJbE6/SIztREoCFAWpBOzoumJecl2VNBGZOSdpVSaiOQ7tcDtjtF274Cc4Yg2znNvae/BqGzz6DcjwCOEe0vonFxx5H58x5LN9DRM/Fe88AwrYbUWH47HNQLEdikZ+2lFMpjoltn6WpRjGaEGxsTep4izgoJAPXoZ0wBGQc+tRyJ9XfvX6APK88v4UB0AFHmjAM1mmRlbzGwnqK3nIjG5da0pxXG3CtsPkgfZbb1w4wndi2QURBnPIYb423azBOYatVWaEe7QMgUnuZ18hnrn1BSj8DhnSpC64kZGiRC2O88MOp2RjmFSuk5Gom3KATHZmAGRlaEb+t3xQwpq5R2OiUOs9QZyW4Jm4JVxLRCrWtkrOnkU/mTe78+0XaXwPb1oQy7SUobSL5cW2L8atXkO8PAc6Q7WzDlJTHlVy4H4ElIRMZ1LpQ1zVEoFBmBRFQj+FYcYs2vBWPHTcceTobz6ACBSGER60WN5tWHmNUGB4udvpWzXhw5QbKGZ23289cQn4wgQgUVBKBCYFwgYQAZStyBwC4kjhx/+m573Ww1ENndYDO6gJYSfeY0RqBRXKc/1RZGqyv9gEAw/EMAyeBt/NTXlaItTrSdpdWHn/zhS0YYxAEAnEkUde0OQRsgLElPVNRHTSKLK3mz4MxqKuaEKBQg7ciI9x35Asb1rSD7uSifFwby3EKPTr7BusONocwW2sGLlC3yNyMtcsm4x9LzFHjuRVzb8nsvfldRHveLXbu4lhe73s/DQCeAKctCbEtFwcaZ1hH8gUnQzPODKQWOAwLMjYPLzpUKJsUSLtNCJ+TSEZWUZDYFtfezgzXr45Q5JXPjgqDJtk3yys8++w2RuMcqUWKgkhhYJGeyjsDW0SpqJHnpd9JVVWNqmzaOO5mqapGcr58qo/aTWgMuPHqvv9szu9ECIawn6BnU5enpuEghaGAZDW0lWtyyb0ZYlUZPPbhTTz2wRMwjCaAkS04pBIYrMRgDBjf3MP+q1vItm9j98mvYvjct1BOxmBcINo4gcX3vB/p6bPgdvG/+APvxQ/+9I/jA3/+k6htsdK/7wJUJ4UKJWLbcmuvLVoLjC1vIU01ioOxNf6i6AsZzPutIKBih1snWSfpN6B4jXKaY7iXz5G/lZaQKBDaYivPSgy3p3jhq9d8ESkCBWMqMEHGfgEbI+4SwrV1aY9eJ9LkSXIHN+q3a5iyxP5TT2N65YpHJW/dzj2RXAcaZVkh7MYIujHB+RbpBABhYXthW1iwviZukEqrZfp4qIUF0ORO7QODdHMZqt+D7Kcwdd0gO7MZwEiKLiJa0Jbe+zBO/OD3YunRixCB8vEWc6/NmEd3HGE97iYwVQ2dHG031WWJ3ae/6Z/LGEN88iTU+hnwYP7xXEuytrCLiDHGI1mHhwo1ehtLb0tbod3GinsJVKi8x87i5uLcY4VSc9c1AHRPriJc6gO1wf6LRMC++keUu7Z08Sw9J9QI7fvkBxPPPXFj8+JJCCkQK41YaQxW+1ChBmNAtm8FAmgM9gIlPMfLzEoESsAAuP7CNaStUNayrrHcidFrfTeMWfQ8rzDePgAYsLxCxHLDJVxyhVIc0NpPyDoMfC9ISOFby4Al9BpDbT7GIOLIqzeV4pjxpkhmrMk/9MVO655nhxQg3PoAuUKLS+oQMLDXBXaAeWTHmSIyQXmO/jgM5tqI7jO2j80pP9vHKeTRNew7Od7xxc7Vq1fxF/7CX8Di4iLiOMZjjz2Gr3zlK/73xhj87M/+LDY2NhBFET7+8Y/j6aef/i4ecTMEDKoWs9/YnaLrzDJO5Y/vedrHOe4NEwQ9M+AOyE6jwnLDtXK0TUp3tZa0kRLG0E5nYNGU4TDD7VsTZGOKZTCGzAq7iYYUDEVR49XLQ78A7WxP4Iz9nNeNktwXSJNxARU0xQbQsPV9G65FTl5YTbyB1Wg/QzFzExtDIKw0WwmINEV3kYqsg92JR3aYClAa4Y9PSO6LSTI2FOgOIuQlqX2cqV93gUzIOis0qb74rz6D4fPfQjWhTKF48yQW3vM40lNnjmRkCSlw4pGzUIFGblUX3bMnsfKxj6L36GPoWkJx3SJIKyUwtvyeTpcWZ1MUcG7SsuU9wgUD00QiFpL4NdpJoA3JrQFgtj91p8q2zQSySYlZZhG+UYGbrx7g5iu7HuKWWlEiupCo9ndgZlOsne4DwJy3DR3H3SEnm9pg95nncesLT6AcU9HnycQ2lV1FduFgDJ3lvg09tJsEi4KZwu6qk5hs8flRxUrYKgAOk5MBR/5s/Em4dcmtK4OqtpuRLAMPJITUtEsHqbjitRXoKEC62EM+mR1CBGgkh94zSkPisxxqz5i6xs0vPoE6t8V4HGHpg+9H7/77wLU6koDOpaCoAEHHzpVo2nB3cThkZzaaoqqoRe2k50527o9RS1K7teaneLGLwYXTAIC9Fy5hvLWDvZevgXGG9fdfhAjJSNUhVKaqsfvcq3OvG6YJVjYXITgF//ZsYZTtj7zXkmGckD1Oi3+ySPdknpUY2GLmyks3sHZ63kNqVjR8RXfYUgkML5H6tLcxQNKzKeazHFN3vSpybnZXgNQKTAjiZ9mQYTfqsiRfMOtZxKUi52eQTY1u2ZK0r+cjPjto8fLs410bt93GYgwAZziqMTs65rmDpN7yhZfbnBsXa9NqvcumdU6tv3eWEgt4hxc7u7u7+OhHPwqlFP7Vv/pX+OY3v4l/8A/+Afr9vn/ML/3SL+GXf/mX8au/+qv40pe+hLW1NfzAD/wADg4O7vzC36GxvzuemwAdJCvbfdlWwe2Qnrlix+bgMMas2qh5fcasXJ07slgjXc3yClHLzCoKJSrLleGceCNrG1Q87O9n2B/OmswuQUZg62spVmw208wWIbvbU8zGeXNTW8did8yTCe2cgIYc7R/r04LtvxVHd6HZRV169nZzvFHjAKoiCcOER3b2tw+8C7WUCkzJOZ+ZQDeXdWH5GlVlkLXSwV2bb+EkQdk7r2yjqowtct6P5OTpY4NA28PUNYqp9QJJKJogXl1G/wK1hvK82ZFKRVJOqQQ61iTQlAWdGzNPLJVhACUrCDPB9OpLGL/0nM8wMwbQ1stn9xrxQDxiGAhUeQGhbHhoMb9AAiTzNXUNVuco96m1sv7oPZCtxVcGmoqLu4Ds5AcjXP39/4jtrz4JU9dQ3S6W3v+4byEVE+ecHKLMSgS9xCMyzhjQycKdlFdZ2TkTvMVXotFGO9qGgm4wbpGRel4tY8oatSWUmyKHDDQlfB/jUxIvpJBhiOKYdlbSQg50qMHhEI/mdYrRCFuf+Ryy2/R9hCtLWPvejyFcXCSzPS2PFDuOy8eFRF1U0HE495p3a3RanJ2yqDAbZT6SoLPYnXssF4ISy92/lYRKIvRtG3h46Rouf+FJAMDS/WfQ2ViCjgNwyVHNMr9xu/XkC3OvywTHxj2NuWd30EFd1RjfoPlDdxOki10w+50BhM4kS3SNCXDEtp2bH8zmFuWd8dQnoQN0b4WCTAJVpLF0cgGhJZ7n09xbP4SRRhAGc2aCUklURUXtoBayU5cVZNBkojHBYRzXzACa101Z0oLuWaul5V+rOhRgKgVtpNzmT0m8GQl4u9gRLm7DoaktUjID5rzBXEFkLCn6SKbHO2C8o4udv/f3/h5OnjyJf/JP/gk+8IEP4MyZM/jkJz+J8+fJ6MwYg1/5lV/B3/k7fwc/9mM/hoceegj/9J/+U0wmE3z605/+Lh89MKkP7YztfOraPHO5PcL55dDCaEDFjlAKMBUaWff8FcRbqq66rn32SVXUcw7CvW5w5DmrG11vqre7O8PUFhCckSRYSoHNzS7OnenPOSHv3Jr44iKMSOob2feaTAofyWAsanG4TedI2wvrHVj/L9Q1MB1mjVW/Yh4Z0lFAZOoFWqz2dyYNmVZLKGWam1sTIc+d2oO9DEVeoTQcWVb5YidN6DylSzGiXghTG8zKlIqcN5gBVUwyX8k5V1ipJRbOnbSKrNaD7QF1+yFQ0e692N0DqgooM6iwFQOSSIRBBVbnMGWBKiu9lYCBQVbQjq0hqltOWCDBTQ0dWZ+NsrLeMP4hRDo2Nco98qqRvQGC/gDrFxv3Xxkoau+8DYRWN4wx2Hv2RVz+3d/H7NY2mJRYfO8j6F4k5CL0ZnuWnBwHqOoa3dVBo8axELsI51s6Ko0bj51Dk/pcsfMayA4jqZv/eVUba/tgX68sSDV3zLUhtUK63EU+zY+gO3GrwEo8Xyfw8uDRK5ew9dnPE08HgOqkWPnwB32hzRiDDIOjxY6SMGDUzqpqb8x4t0diyeSmNpiNpzjYpU1l2k/n4wZgvXZayE7YT0nZtNhH0O+gLipsfe15AMDmBx+EVBJhrwMmOMrJzBc7+y9fRTYcz732qQfO+L/HkUY5naGwYoFkZQHJoIOyKBsTvdqgu9LzHjadiEI9XaCvu2oOppm303BD22tg4fwa4k4CZVV+7nFSMOhYI+xGyKfOBZ8jiKn4ofBNh8wa27aeL0xr2GIB8NJ5AHPtL+Aob6etuASoMHEFFzknc1skHacIOzra6JErxhyXtFln6KXmFHGH087fRXbe3Pjt3/5tPP744/jxH/9xrKys4D3veQ/+8T/+x/73L7/8Mm7cuIEf/MEf9D8LggDf933fh89//vN3fN0syzAcDuf+uxvjYG/UHJfl3OhD3Bvj4EeP9jCqqC0hR2oBZpp2zjyyY5UE7jprJY3n09ITZaXknphsjPXysUTo/mKChVb2lpOOGwPf9opjhUce30DaoR21MwZ0nwsA0h6RmOvaoKqaNYIeS39XhyThg9XUt/Qm4xpSNOdGSO6lrSSFFkjSGEIKr9DinEFHHLVtv3DJG9WRnRSuvrKHl57dBgOzxY5Vd1leTbi2jtMffxwAcOWJbx3birjTyK1XiIyCuZZPsjJA2gnnlCAOZFnYGPgd0/TaNbDRFjDZgZLNbjLohCgKgMVdRCfOQnSXfOaYMTZAshvPSesBePVXwAkd8WodxhtnZMmByR4RfMMYaoGceTcfPuNfR4YKQooji9e3O4rRGNf+4LO4/ZWvk/pqdRmnfuiT6N1zzid7R4eiDMivRCEeNGiBS0UXh3ggykZF8GPIuU5+DtyhjcUYhZLC+HYqAMBYGbuNhjD5DDK6sylfPOggSENktq3pf95rFzsx6rJGkISoZjPc/tKXsffNZ2DKJgR18X2PHVkkVHiYNEs7eGG5OkJ+Z1pYAO3gHQ9pOpphZIudbivvyh8j5xBBkzMV2O+YMYb+PaeRZ1SMx8t99M8RUhMOOhBBgGIypfbTGvGAbj/94txrn3rwjP/7YL2PZHXg+TrRQg86UORSbJFuBkCgue8F41gYdMAZg26hdeO8QFHPFxBKCCydX4fuhIg6EXRMjvN5yxldxwGiXgzANJyzKPCol5PkV2Xl5elu5NMMPDz0/Tnuy2FC/WFjwWOKHZ+J1bof/Ou8zvQ218Zqta94a242xqBuq7OOIUC/05RYwDu82HnppZfwa7/2a7jnnnvwe7/3e/irf/Wv4m/8jb+Bf/bP/hkA4MYNcuJcPZTds7q66n933PjFX/xF9Ho9/9/JVqbN2zmySdPucaRjOScxZqjKQ5MYs8WIsNbbxlheDoPU820seny74jZQgVVGjXMsrXYgBMPKUoyghRy4IolLDsOA5ZXYS88ji9TUhlRVbhJWWuDMBZKNti9kT6YG8zEV00nR5H61gvIoFLT5d+IKMACTKQNvJSnzQPlzp9PY7lDg+TAAmehFqUThMsWU8MiW4xBVVY2VMwvodKjYcahS2g3AlEK8cQIb77sPQitMbu1h98VreKPD8XUOk01VqJFaSD+OlC0u6Xe9QQBp21BVUQO1QZ2VMFUNFUpILVDHC5jNFHRvGTJJURxMPLIDkMOwTkO4U8vAKGXaqpK4qZrWJpjnUAGARA7UFZiUCFY3/LUT9xIsn1uF0BLpco94BncwOXujwxiD/Rdexqu/+/uYbt0CEwJL73sUG9//Mag0IXhfSZiq8q0BN7gUiHqJDwkF7K6Ws7lih0JCSZl0XLETvB5BmTsEix0pKLgUYNJ+V7PZsS0sN4QUSJf7qIoCdasllrQQl7gTAwyohnu48ZnPIbu9Ta2ogO6D5OQJBK0WfXMcDlVtEVGlANOKWteR/o60sNxIHUdmPMP0gO6Bvs0Baw8mOJlcWlSgf3bd/653/iRyK5zYePyivw4764vonVxBZZm/Sw9fAADc/sbzc59/5fQKVk+vYOXEIlbPbcCUlKXFhEDQT6FsG8kt2FJwMBhoJRBYh+nQcoNYq8itjWkDfHbDIrD53guQUiLuJ2TvwJi3sFCSQycRok6CsBMjt2R1Gdi5tK6hYm2T3CsSnTiX46pGMSsRLdrzd6gYOaoePGwsaP9wvEgpPKG7rczj1nrAVLbFdYeix6m3hBKt92Zz+VoA5vK8xCHUyHF23mnjHV3s1HWN9773vfiFX/gFvOc978FP/MRP4K/8lb+CX/u1X5t73OET2zY9Om78zM/8DPb39/1/ly9fvuNj38r4T/7KD+De87QzcXwVpYSvzg1j3uXXX1auLyvJmKqybrIGLsSt3cNt2qMA7eRd8ZFPS3T7IT7w/k2sraTH3kRccCyd7JK6ZCnB+fMDaC9xJgWYjzTQEgtLttXQMspztI50OUF3gRbbyaTwyjD3+ThnqOoG1RGCgTlvnYrad8vLkS+uwkHiCdA6DiyJ1KC30CweYaTA6tJDpzwIPM/EITwylDj98DqGN0e+hZXY9G8RUFaMDDXW33sPAODyF984ub2YELKjrXdLlWWY3bqJfOsKFizXKYkVfuy//CBm1jl5sNpBMKBdMJMaLB0AXIAzhlOPrePUe9Yh8xEEr3xAZz4c+cRoACjzgvLAHPLCaBdZS1s8VqZR9oUasjXpSWUAMASrJ+g1WuPUe87h3k88BBU0RNxvd5STCa7/+8/h1hNfhSlLhMuLOPlDn0T/vvNz96YMFOqyRjiYR3aCNETcT+dJ0haS5y1JvGtpGRxnuvZG2lgMXBIXqLEfgI3f4P71qyyjlvJrjKhHi1120LgDt5GdOAnBhjdx8K1nYIoCqtdF9957UI6JFN9/4L5jX1doOScnpuPmkIH2Lazv5OLSsSTlfJojy2hh7y51jzyOcQ4VanDOsHT/CSze22wq83HmEdq4tYEJuik6qzRnMimw9OAFcCUx2x1idPWmf5wQAn/9//xf4b/8xf8V6qLAeGubXmt5QIW/4Ahi7SWRnDHA3v+D9T69SG1w8vw6JOfHnj/3k3RjgKquECYhgjSmNi9rwoi14gh6MW0UFrsobKHmss+qoqTCy6KIVEjYTelohqgXI1w4ev6A129jtY+VcboHGiVWa/MoBGSoqVDjDKaqji16GGdI+gnCTtRsrJlF2qmKAWC5ovazcC0t95TW3bKq8N/+7/93OHv2LKIowrlz5/BzP/dzc5uA78Z4Rxc76+vreOCBB+Z+dvHiRbz6KrHz19bI7v4winPz5s0jaE97BEGAbrc7999dGWXj6eLasEpxaBs34AjBjLmCzdDEyxjxD5RAZQmwzLoozymvJO2YvNrLojGu4q6q2iMsedZMlJXN6WKCIeqELbUUw/r5gX0t+LwugLgUOhDerBAgvo5zQY+7AXq22JnNyjnlFUCy+KKoPbIiOMPeFvXhZzmDqHMETnHGgCRp0s9VbNtEjDUxC7BGgbZHLrQCwi6pHhh8gnE2KVBkJXa3mmKnY2X5Io58T//khx4EANx86mVk1v7+tYYxNabbe/YfBW498QXcfuIL2P/WN5HdvIHBMh3neFJg+/IQ2ZRSoi/80J/A4mOPAgDK6Qy8M4BJliAX11AZ4htxU0DVE4xffI6Kp+GIWqAWESqLCkIJGDvxMdAu0qGERFS3O1rVEAsBanWppdUjUmagQQnr2hwJqHyjwxiD4UuX8Orv/D4m12+CcY7F9zyMzU9+L3TnaOo2VwowjYsy0Ey4Kg7mJmxHJhatlo2IQrgMtsPkZOD11VjuGAzQcqKlghAwUNY8sJrNjlXDzL2O4EiXeqir2hcmbTVWUE+A8RBgDJ0L57H8gfdj+OLLAIDuPecho+Ol41weX+zQe4o7GgnereGMBfNZjsLmqfWOaWO5fCyAojbayMzVJ2hToQOBg0vX557njDp1GkOGGgv3nQFwlKgshEA4IBRydI04aMnaAvkmwSBMQn/9mLr2CqXOYtcj7PnNIWKLzB4udxgjtVd3fRH5OENvtU/t3VCjNo2iVCkBFZHHUdylVntZlBBSQIfKz2PG3ldMULFTZAUYZ+gudr0zODCP4OFIG+vORS2XZIh4XBuLPg+zhHcFocnrzVS2hdpGZjw/p7WxtpEVPjjVco/maiX7j3/wf/xl/Pqv/zp+9Vd/Fc888wx+6Zd+CX//7/99/KN/9I/ueOzfifGOLnY++tGP4tlnn5372XPPPYfTp0m6ePbsWaytreHf/tt/63+f5zn+w3/4D/jIRz7yHT3W44bJC2865YmsSjSxDvk8kx5gvv1Sg0HoAMplZElpkR1L5+GskVg7ibcNEnUKhKKsPcmvLOqGa1nWHtmBadRcTDB0+s0i6HYuAKlTTGWwuNJM3p1u4B8jpaAcLStfdzJHdzMwzqybMv1bcI69rRHq2qDIaogq84u1kIKcnl3+VUx5TwxAr9cUW4EWPj+Hp13oiHyJhOSeK5NNC3zzP76M8TBryMkpnVOVJr5F0NlYQu/0Kkxd4+qXnjnyXdZFgWxnG6NXXsLON76Gm5//LIYvvkTvjQq13eHKJEW0von++XMAqKV308Zr9NcXoMIA2rZs6qIEA0HnPEowq0Ls7pQoDZFP69kMo5de9nLk2PJPqrKC4S0nbQagrFBLbStU49sa0hQIo1Ybq9OF6vaPfL75YXw78M2McjrD9f/4Bdz84ldQFwWCxQFO/mefxODiPXfs4bvFSGjpW1YqChDEAbhScztZamXyuTaWisI7ys6B11dj0TFw12OlHxgDwEAqiaBPG6E6y1AXxbHPb4+wmyDqJ5hZdCduvX8cSfAgwPKHPojevffg4OVLqCZTiDBE98K5O74m4wwy1PPFjoV1hZZHvGzu9nBeO/k0Q57ZrLc7cHakbRlVWe43TrO9EW4/cwkA5fntv3TZe+IA8ERjFwC6bFtZ28+87FELN4RW0L0OJjfpHktWF+maqQ102FK9VQa1Jb9Hg7RBKuoay0v9I74xAM2zUglo21JPl/pEJ1BN+CdlB1K7jksJHQfUyrLKvCAJvXiCceYdoQ0M8kmGzkIXQRKSgeVxx8DvgOwcqczg21Qe6b4D584XPYHyBVFd1Z6z5u+lNj+UMzDWtLLMIb4g7KcCA/7oiT/Cn/yTfxI//MM/jDNnzuBTn/oUfvAHfxBf/vKXjz2e79R4Rxc7f/Nv/k188YtfxC/8wi/ghRdewKc//Wn8+q//Ov7aX/trAOhL+6mf+in8wi/8An7zN38TTz31FP7yX/7LiOMYf+7P/bnv8tEDKAvvmis5b3JaBPEDZnZX5G8004RA6k6EztrAozQ1E5Au6NKSE5VTSDm7cKt+cgvVbJhDxxpC8jluUFW5nXBDigYIqpzsTD0aNJ00UQRkVFX7VhYAdKyKCaA1djbOEMfO4+bwyWh2QgzEVyqyCnu3Z+CTERgaN1KHSjhTNx2FZJnODJJENl5CjuPDGZJFjSAkNZqQ3Lum1rVBPi1QFi0llkWnwoWFuSN06M6VP/omitEI0xvXMXz+Wdz+yhO49cXPYe/pJzG+/CqK/T2grlHacxotLWDw8KNY/sjHsPjex9G9cA8G509DKgkDYPc2ESeXThMZmEsJaVtfpipgWmhyVQJ5rcEXNxGfPAkDJ//kiKLmOjGmMQ5jYKjzAiqJIVPadfsddVGi13d29ICwhOTXG29GiWWMwcErl/Hq7/w7TK7eADjDwqMP4sQPfB+0zTl6rfdxiIy28vugE0Ec4xvDGAMkn+MiUABobc0Bjx5zMIfsJEd+DwBcyPlapzJUMNpoCGbbV8XB6Njnzx0jZ0iXegADioMD8J2rzfsP+ui/930IBn1Uswz7zxFS0X/g/tdVAKoomJPHO4GBTsLvOBnUee3kswK5Dbw9DtlhnHlSd5nl/gRf+9I3AWPQP7OOoBujygocXG7Q+cPFTufUGoJeijovsPvspWOPqRhPAUZtLC5cVqCCtirJNrIT9RIECbWymeAwB1PoO7QopRRgYNBxgHjQoYmOc5RwtAQqmkSovaFfuthBlZeWCtDQArgU1hqEIZ/mCJIQ6WIHdV0jn2YQzn7CcfEOKZqcGaGpq2P+qwEGlLMMpq48alOX5R3/M1VFG2fLKayrEnVWEOenrm2rq/KPozDSFrJzqAfmZOcf/chH8Ad/8Ad47rnnAABf//rX8dnPfhY/9EM/dOw5/k6Nuxd+8zaM97///fjN3/xN/MzP/Ax+7ud+DmfPnsWv/Mqv4M//+T/vH/O3/tbfwnQ6xU/+5E9id3cXH/zgB/Fv/s2/Qafz2pPsd2QUZbOAc05EtkjCuCDMsrYoTfMUIWnHFvY7kEHg21iwN5JU3MuuVSBRGTPvp2Il4wAsTySG4WyuZWYMWadzweeSsRlnmI0yCviEwWSUI42VL34AoJtSbo2pDeJE4+DWBJwzTPdnYCDl1nCY+/ZV67Aasy7OoCOJKquwd3UfSysxDBgOhtbUzhJqnUeOjjVQ5RAmg+FAtxdib2fq1V3hIIEQDHUNVDWR63wYqDHkBWIMytq1sWgBc5OLqSoUowOkCwoikJjtjvDKv/5DdFfnryERRVDdHlSnC93tYevy5wFsI9lYgz6Uj6TDAMliB/s3dnF7mybv5XMNQTPodVCOp4TamGZX5cNMowDh6gKyUQ5c3oGMAsRp5s9lNzFzpoXMGIy3dpGe6qEcDj1MX+Y1GLMGjJxDvp6s3iJ9b9RQsJpluPmlr2J8mYjdwaCHlQ8/jqB/dPE7bjAbCAoD6DTEZOcAsW2THEe65UKAcQEmJUxZQlqPHS7lsciOa121kaOjr0mIkYGNGqhrcNCGw5QVZBShKArkwyGChcGxr9EeOgmhyjHyq9cgDfHoiqJC5+QJaNuu2PvWszBlCd3vITm5+bqveaQlwYncrcLvLKoDNG2sYpb7XKze8vHft3b5ZLaNVZcVrn2ZkNPNDz+E2Y2buP2N57D3/KvonaHz4NpYLu2cMYalhy/g6me/hltPPo+lh87PvYdrYYX9ji0QuOewxH0qcE1dw9i5LkgjBAkp59YvnsC1py6hFwWY5fPWAQw2kxAGUT9FlIQYH4zBOEfuPMCsK7AMtN8gRGkEFWkUs9yGggpkVU2tI85RVSWkkegs9SCkwHhvhKibgHc7mIynTQlxmItaVbj1+c++wW8J2H/Dj3z9sfI930v3SavYOYLtGAMGhp/+6f8tRpMx7r//fgghUFUVfv7nfx5/9s/+2bfxiN78eEcXOwDwIz/yI/iRH/mRO/6eMYaf/dmfxc/+7M9+5w7qDY6KMR9YKQSx+sNUo5iV3qNBBxLMGNS1bWcIDq5sT1VwlFOCw3kYox4dzBU7UpNiq13smMp4xCefVRBaeDiS/BIoiDPLSipaDqkPyqzy91ieVaiqGkI26decM9xz3xIm4xxpR2ML5J8zO5hBaReVMEZdO2dn25ZrRURwTtbhVVZhvD/DwlKEUicwZg8AFXGlLQbdv/Obt8AqUrfd8+AKrryyhyWLMqk4QJYJVCWDASE7jhBeGyC3mV3GWD5IqiG0Qrl/G9vbV1GORn7XubDZxa2XdrB9aQ+L95yA6nahuj3oTneOGAsA+fh4NRZAC3V3uYf9G7tNNMaZNf973U0xvnYTJs8Bw+cnWc48l6g4IF5TtL6KXjIAntyCgYGpSh9B4Nqaw0s3cPLRU5hcvgLO6NwVtUA1tgiQ9ZR5zWHgnbtfb4wuX8WtJ76GKssAxjB48D4sPHT/m1JxcWucWecF1h46g3KaY+XeDYCzY9VPZHAGxJvryIcHCAb9O8rOAaC7NsDFH3gvuquDO5J4GZ/nNZmqBo8C6DiCKUuobgfFcIhi//WNSsvJBDtffxLF7i4tuGkXH/vRj+D21W2snFqB0BL5/hCjV4h3OHjogTdELhZK0uakqryNf9RPIb8LxY4jKI/2RqirmtCMheM3l+7eqHIqdm4/8wqK8QxBN8HS/WcwSkPc/sZz2H/xMswnP0hclhZnx43lh6jYGV66jmx/5GXsAHBwmYjL6eYKIRtoeIwOzTPt8ORQkUpvC+ivDXBwcx/Z1QJbcEytpoOjFAW2Jv3URlJQinhmUflAC4hAUhSCve5loJAMUuxv7SJd6EIFiuTmglsUJ0d3fQlRJ0KZFwAMBhsL2LmRYHKdkN8aeMfJt7ngjQjgKLDjxz//F/8cv/Ebv4FPf/rTePDBB/G1r30NP/VTP4WNjQ38pb/0l75jx3t4vOOLnf8xj8KqY8gyhwIqw1RhNspR2ALFycoB471tmKJATmPhRDAGmcTIt8l3B7a9pAIBxiVyi1wAhJ64bKSqrMl0zB6PkByCA0VRIZs2pDlHMq7KyhYpDEJwVFWNLKsQRApMUDZQOS2xsUkT28SSnpUWlADMyEk5CASyrLJhFjSMnUaceqyuaghBxoGTgs+Z6qlAoLKXJuMMxe0bqLOpb9ctr3WwvN5Ftj8FkxLD2zni5dR9SCp2Dnn6FK0WFucMUjMUu41jM9caqtvDyY+t4tZL/x7DmyNEpy4gGtwZISxssaOOK3a0RH9tAZeffMX/bOnUsv+77tLxltMZTJRQRpYdTDahjflw5B/fS9yuChiOmOdqAVRMZ3sjTG0oI7eM+Kps2m1vNAiSi9d2T66yHLe+/HWMLpGKUfe6WPnw+xC+AdTjuCG1wmyaobexBP7eCjoKYYBj1U/cWjJEa8tQPSKnVll1xzYQYwwf+guffM33d+RLN6j9ID2ypHs9TK5cRf4aflzGGEyuXvW+OUwI6I2TmOUc7/nkBUx2Dry83qWaxxvrCJcW7/iac59bCZtmXXnUTVqS6Xd6OGRnanlJST+dU/y1hy9YDFDnhUd1Nt5/kYxCT6xBhGQKOLp6E52Ta0faWAAQ9DvonlrD8NUbuP3UC9j86GP+dwdXtgAAvbObEKFGPhz72jXuzpO+qThkXkGZjaZ45H/xIXzm//Kv4JK922dUawmVhOgsdj2XpZrWyCxiH8XSqrP4HJk47iXY39oltVwSesVHMcuhgsaaYjqcoL++gKgbQ6UNlw9CHUF2mBBY+8T3e6fo9uCCIVpZxPTmLuqqQrTQO5IY/2ZGXRviqBkQYso5GDfzyM5hTzJLav6Z/+Zn8Lf/9t/Gn/kzfwYA8PDDD+PSpUv4xV/8xe9qsfOO5uz8j32Md/YAwEsbg1AiiBWqsvJtJRjMXUBCcHJqFcLLzkUUQuoATAhPUgbIbIwfIifWlYGWjfS4yGtURSP3dpygIq8sYbhpgzE0ZGdlIxeyWUlmgUWNnRvjufvPEWSl4tBh47bZdm52w90YyqrNsmnpfXbG+1Q0+BaOFqigm79PJ+QPYe8troRXYfEwmpdnCg6u2sgO3ZRVq4UFADzQCJZX0b3vIpbe/yEsfeDD6F98EEsPXcTChU3AEHfntYYzFXQT5+HhODoAEAQCZtKovLTdmZbjKTlWt1p8XDS7xNw6x+pO4t1rAWA8nifLumTza198mrgBLow1Kzz5k0vuC8bXGvwO/BcAGF+9jld/999RocOAwQP34uR/+olvu9ABQAiOqakYY0BdVVa1cnR6cueFChSHyrw5jtFxr0mv26CX0sYcMCG9VUAxPDjWdLLKcmz/8dew+42nYMoKejDA6vd8FP17zkGEGsU0g4GBijSmWzcxu3Ub4Bz9B+9/w8fIhYA8JiPruzEOozh3amEBRCBm9rsZXb+Ngys3wQTH+uMXAdBC2jtHkvS9F4iP45GdQ4TypUfIHuLWky/476Gc5ZjcJBfk7ul1BP0ODOomBPTQvakSUu+FHRuUvD/G4ukVnH383iMkZcYYwlBTEG0UkNcMYxhvU9Hrycla2WuxuV7DNEIQh8inGVTgzFhrlHmFpJ9ARwFmoymCJETPoo7t4s69/+F/CyXBpKRWbus/rijihdBrAREGVsX37f0ntYIMA8unI+4Q5/P2DnP3grVrAAMmk8kRGwghxLvS8/8pj/EOwd7CGuKlCxEME8hmlS306QLyfWUDSM2JFCkYqplV+MQxwAnxka3cJ6ElhIOx7X1R1waoa198jPdnyKzHixDMvycAjHanTeK4HY5rE1oVWJZVnqNTlTW1xezwSiwliNQJWlBdtIQrNNqREc4HqCwqxM6EcG+Muqo9d0ko4dUOSvFGGuluLsZ8rIDsxAgSAckzaDlD0sEcsgPQU53kvdOjSU4trCM9cw7RyipEGM5NLCcsUfnqE8/MqUTawxiD3KY96zukTS+fbdpWaaoxfLnxc9JW0lxYDoDn31jiOBMMVV74a0B307nwyMl+UzgxxtC/QAvG8BadS0dSL7PcFzgy0ChnR/ObDo/juDJVXmDri1/B9f/wBVTTGVQ3xYkf+DgWH3voWMn3mxlcClv021DOsqJMuGMKGGYVhNTOot00M+YtHQMRLzm1B40BEzbA0hhycrb8o7povg83pjdvYeszn8VsawtgDN377sXyhz4AGcfWXLKHbDSlYkUJj+p0z5+FSo4nTN9pyCi44/X4nRxRGs25ax/nseMGc5ERAG5+naIhlh84OxdvMbiH1LV7L1yGqWvk46PIDgAs3HsaXCtkewc4uExozugatbCCfoek6nEEnXZQF9Q2ayugANtWq2sE9rUdEvrAD74P6jhTyjhA1E0o18wiO3s39+i1nL1DqI+gg1xwpIudhrcjBXnqpAGiToy6qlHmJSk0HYJ46PMep84CcNRPyhb7rhAmsv5bX9q5EE0gq0X82yTltoN7277kR37kR/DzP//z+J3f+R288sor+M3f/E388i//Mn70R3/0LR/TWxnvFjt3cYynTXaKUgJJP0ReGBQuEFQRua27vuCLlao0ZLEuBKoZLaYqjm0wo5xDdpgOPELgRm0RkMCiK9k4x8yiAM7oT0r6c7w78x4QTnHlqu9kYFUUZe1VTDok8yihOYJe6D+H0o2UpSwqL/s+bijZtGJ4p0fBlMZgNhx7+FiGCvkOQbVSU7pzexfhJKQiVIgTg7gnIWUNbmFWoUkG70VutWliIroBmQ/adOvjxsqDZ6A7MfLRFDeffuXYx1RZ4YuI49pYANBbW/BFR5oGGL58xX8Oj+xMZ1Yy6vg33C/6hW1hiSi0LsFNETMd0rXBQAhfsLSAeKkHUxtMpsxHUpRZCWYbmSIgtOSwfHd+sCPw9+T6Fi7/7r/DwUu08+7ffw9O/qefRLi0cNwLvOnhScqga9DUNd0Dx3w/zKphnHyWGQCWw/Jtv7/Lx/LHwyG1hDE1RBRASAlpWwyFbWXVZYndp57G9pe/gjrPIdMUKx/5MLrnz80VzvFCChVHkIHE9Pp1lKMxuNbo3XvhTR+n0PJNxZncrXGYo3OcEssNbmXZAJDZaInNDz0095jOqXVwrVCMJth/+aon67e9ZwBCiRbvPwMAuPUkFU6uhdU5seqPTfdSavnZGJk2iVslIUwNn8U225/AGELdeq0gUwaaP8JOhDCJoEPts6aGW3sAmmJHBIoQlkPFSdSJfbEgFM3dSTcBFwyzgwk6ix2vbAMA1Zkvfu/MMTt6rXMpW2aCbx87hUtb8LSKGff+x7axGPAP/0//EJ/61Kfwkz/5k7h48SJ++qd/Gj/xEz+Bv/t3/+7bdlzfzni32LmLw8nOBefkstmNUJSs8a+xF0vv5CqilFCO668OMZsU4EKgtEosmcbWVVl6jx6hOHiceonxnG24MYit+d7+TcvhEE1F7lCPg90ZChf+eahaHw8LfzNnU3pM3CEnYy44hOQopm4ykZ6bUxQ1TD0fi+GGVtzK5ul3FRiiAd3g0/2xf28dcBSW2O2KO7WwZD+mQW6RqqBD3I6yBLIZkBcak7GDdplvZVXGePVEpxNAhKElXR9/+XMhcOIDBLNfuYOjsiMncyXv6Das4wBdO4H2BxGKgzFm1ohQhoH3izFVSTwPRrlPQkpwzpBbcrLuUvYTOIOyviWZfX8w2q1ywbH2CHm17F7d8+cYaNqNvMxQD2+hPNifU3LNf3hA2MmyLgrcfOKruPaHn0M5mUKlCTb/xPdi6b0Pv60hoe22nXPKVnfyjvHBhuQxY+ramg2+RWTHFVuCyPNMEXLkiOLaGo/mwwNke3u4+dnPY/wqIXXpmdNY/eiHoXtHEQ6pFTorPehIY/gsLdD9i/f5oM83M4RS1Kr4LrcDgCb9HHjtNhY5PTefNVldQO/UvOErlwK9s6TEuvVV4vTIODq2jbn8MLWydr71Cqq88OTkzsnmNWWgodMEVZnD1DV01FxLOg5hTO2Jy1VRorDy+XShQ7wd0LotpUAQhYjSyBfTXArs22In8MiO8oVQe+g4QNSJkE8zhEmIuBdDBQpFVkIGCv21hTk06DCSdSdk5/B5YYDncwHU5n87B3eRLIBvZwGgorRlTUW1DkO328Wv/Mqv4NKlS5hOp3jxxRfx3/13/x20/s6T6dvj3WLnLo7z7z2HpW6MSEuEkURWchjOvRJLWLOwdH0Rpx87RUqrssbz//FpXPrSCyi9BDOhm0lISMWxdn6AzYsrEGFgofamkqd5kCG27RrHwQkT1QRyWifmMq8wG9GN7gipjswqBENgJeCuaAsiCW5RG1PXPttGB03lX1Y1yrL2BRVveVMpJSAUh7Kmh2VWIlmgSTM7mHr1ldQCZd78PdzYaHYTNbyJHO8MwKJF5IVCNgFqI4CaeBbtVlZofXWEYAgjCR6G4Fbtdqex+YGLAGPYeeEqxtawrD0avs6dwyE553joT7wH5x45ixMPElQ/38qyC0ZVUesp0JBxSEgH5w05uZOiLisIKYnsCHjnWgYGHQUQkmP5wdOQoUZ2MMVsWnk/Im/WqAVMNgPGuyhuvIJyfwemmkd5OCfEZLJ1C6/+7u9j+MLLAIDevedw8oc+iWhl6Y6f99sdbjJljEGnEWV93UFlxDh5NcgoQLTQJSUWF6/5Xb7eaPKxKIJBSGFJ7NIrwpyT8vjKVdz6wh+hnEwgwhBLH3g/+g9cfM1iK1nswuxvoy4KqE4H6elvL4tPaEnKtXcCb6eFSBxnKOgGE/MmkCc+9NCxiEX/At0fzm/nSEvHve+JFQSDDuqixO2nX8ToOsnOHbIDALD+PiIIUGW53yAADtmpIUPt792DW3sY3tpHnEbgjBHtAORnppPIm3kCtNgf3CJRt0d2tEN9jnJskoUO6rJCMkiQ9BJUdYUiy9FfX/D3sj9sKWlOd8+/gxrL/9yJCC3K6XyExB3I4m9lkGhBzHn/HDIXgWd3v7NEZH68W+zcxbF+bhWdSCNQEp1+hPG49qiENUBFutyDigJE/Q5OnOmhY9tHV77+El746lVk0xIyjmi3bbOMwiRAZRQ4F6iKCiqJ5lo2BMvO7xyjTnMTcc49X2Y8dKF1LpjOcW8EAsu9mYzIf8LUZi6Hq5hZh+OwKaTCVKMq64Yg3DLMU9bZ2LXYpKxx+uElpIvNxEbmVcIv5uGgC90foJqMyCvHIlF6cQFg0mZbkeGaMQaGAUxKiBZJeTKhc54kJB2VcewX1zuNaNDB0v2n6Lv4o6OOyq+lxGqP/voCFtYHSE9RqvPBy1f871wry5Rli7PT9MUbJVaCuighwqCR0nr+EqBiDS5I/rp8Py2kB7u5R8Xcayfr6wjWNigTq65RDXeQX3sFxfYW6jyzb8+w/81v4drvfwbleAKZxNj4/o9h+fHH3lZ4/PDgWgNVRUWzDQg9bjBu5a91bY3TKJ38raiSHGfHpzjbwkVEoSdqu6KnGA4JOd1Yx+r3fPQNqanK8RgHL70CABg8/MCbkua3BxWF4h1S7LSQndcqdmw+FkCo5eqjx7fvumc3PJEZANQdeHCMMY/uXPnMV8kHKQ7nsqUoyZ5DJoSIt5ElZU0GueA+K228PcTCxiJWz6zRcy0hVyoJHWnoVuF9sH0AU5NDuba+TdKFnR7zvUYd4m6VeQnGGGYHU8S9FJ07nLO5VtYbaWPZx5BnmkXp70Kx00RNNDYkx7VUDxshvpPGu8XOXRzT/YlvzXSXEnAuMNm1PAzL7O+sL0JoRTJxwbF5boB7P/4AhJaYjQu89NQtXH/qEpjkze7RVhDG1GB1BRUeYsgbICvEHOITd3Wr7wqkNhZiZlGbdDnF4ETPX8CCc79zKYualFst+DyzrsQAwCTzFc3q6QGq2mZvta55JTk4Zwg72meDMVOBc4beajNxSi3ATe29hKKFPqmpplOYyhBPhjHPF2GCHHWNJWZzbn2KWsjOwQEt5GmqAcYgougNQb3OUfnql791hOfyekosN6JuAt2J0T29CTCG2fZeq4hxuUsZ2vskN3EWVomluilMVZOxYH+eo8UA8vFACVMUOPHBiwADxtsjj+y4obsJwtV1pBcfglxaJXkrgHpygGLrMsRsF2qyg9HLxM3pXjiDUz/0ScRrb8x1+a0MaVtSxhgiRt5BNutclJ167bU8dt7o8ORSa6PsnHClzd0aXXoVw2+RGywMMHj0ESw89ugbbkXtPvUMYAyi1RVEK8uv/4TXGCoK3iHFTgvZeY02Fhcc0SL9fuP99/u24OEhlEL3TGOueKSl0xrOVLCc0Iajc2JlboGlxZjakTKJ53iOOglRFjVGe2MoayoaRgGWT69gsNqfex+lJMI0nCt29q5R4GhvtY/Q5rkFvZSuoWMWeaklkkFKLu55CRigtzaYI3i3h0O07vR6AOb4bQCdY7IQMW9ZmfhagzFqHTtF7zy0w/xxv1PHO/fI/icwdl4hSFYpDhGFEKHCeNdyaGx13D+5Sl4hU8cBEVhY6+C9n/ow4q6GqQ2++btP4Ll/8xVPFIapAWaAcoY4yNBG0F2xXdd8Dt2JOkFzbXKG1CJITn6e5RUguEd2kj750bhW1niUW2THcnu8IzObu7nKqkJV1vO9XTSQb5gGCG2g5WSYYf/6AfpLTcHgoiIqQ89VkUY1m8KUFU0WAESa+gWKCddPNkBNBGWm1byxoF0Y004AETSSzNcbS/edRDjooJxm2PrGi3O/ez0llhthN0ZvfREyiZCsU9EwtOiOQ3aq6Wxu4nCQtOdsJcRf4FohPax8YYBUEmGvh2hlCf1zJ7B0H7UE6kMu1jql75wLgWh1HWJpHXywChYlKKY5yoMJmOXA9O47h8XHHvq2uCXfzuCSyO+oazAlXvN9uRBzvkRvB+LkIHq0pOyMGdz+8lew9/Q36f3s5azfRHDw7NZtTG+QUmvw0MW3fJxE6H/ncHYYYz4r605j+cGzuOeHP4Izn3jfaz6uf+GU//trFTtBN0X3zEZzLCfmOUAOHTXGQCVRc49yhtk0x2w6Q2epi0VrDZFZukBncf57lUoi7iRzKsjdq7bYWR9g7X334dT3PIKgm9K8eIe2U9xLwBiQjTMk/XkLicPDfW4Z3hkxPpKXJUWD6si3hnK+3qCCp4nA8GiobV+9Q0EdAO8WO3d17L1MxU4YSuSlQRQKj6QITinWsV28ygktnkIJlKMDSAGcvn8RJx4+AcYZtl++gef/8CmMhxkYgM5AQdUTb9LnhzN7MsbDx3Tjo5W5QhweYUM7awCogZuvWG4KAyIblhnaltPEFjvuzbwSKxDQrZ31y9/Y8hldbd8KR1gOE+XTu6fDDIEmU8LUJqYrLVCrEE5hqyONYm+HVGZ28Q4WBqjLqiFL2zZGWZXUwtIaXAmvOnOj2w0gosg+7/V3P4xzQkoAXD5EVPZtrPi121jkOmrf/+wJAA1vx3F2isnURybQcwRym8MkrGsrVwpCSXTX5v1sGBjixR6S1QXyxWAMJz9CiNRcjAhoosoOJsgOJiimGRgXyIYTzG7vocoaCFynIbKtG7j+B/8eu08+hXz4+s7Bb3UwKcAYR11TAOeddr6A3bk6ZAd4S+Rk/5pCgFmrHWYAySrcfuJLyKwnzuDBB6B7hFC8lrlgexhjsPMkSc07Z05BvQ0RNlTYs2ZX810aDtl5LUNBN4TWiBZ7r7sI986e8Au5Tl9blr9sPXeAo8UOYy7kmO7h0BYXQitEnQjrp1exfn4Dg3VqQY636fruHSp2dKDQWUzmjnv3KqlE++uLEFJSqC+D9+A5boRpBB1pBHGAZJC+JvLijAXvxFkDmmLH+45J4e/1u9lqBmyx0z7+uXXlnV3tvFvs3MUx3iEUJ+0GqA1Htk+TpLP3T3qhr5IdJMu1gCkL5MMDMMZw9sMX8bG/8WOIBinKrMCVF/ewdWUIBpKYzzIOtLw3jLHAT10hsrsvEQgUWSsxGXRhRtZpuShqRF2N2lYyUgmUljMT28eMRwUqaw3POGsIxJKjsMfOWFMcsZafj1bcQ59BqMADK2vPCwroqwyWT9BEE3QiIO75gEGpOKrxyKebyy5lhtVlZbNauPdkMWVNvIaAwk/VIdJqpx9CJLF/3hsZm++/CMY59i9t4eBa41zqoyLS1y52VKh9GGXHFjuTG7dRTmYIbEhmOZ4CvJH9c8lbZoIp6qKEjEgJd7jYAYhf1OYlLN17CtFi98h6GC12EfQSBL2Ecp/GQ1TbWzBFASYl9Oo69JnzWHjsEahuF6hrjC9fwc3Pfg63vvgEpje27pr0mUthkcXS+5bc8bG2uPHeH2+Hp4gUMCBkMUlqsOkeTFFAdbtY/ehHkJ45TecEZC74Rsb41csohkMwKdG7/763fIyAjY2wniqMccgk+K60Djbu2YTUEmcePvO6jxVvUB0kQ42FB86BCY5k87XbfQv3nEI46CJc6CFePcqbcqo+ABicWgUYQ29jASsnlxF1Y3ApkNiN5siaBHYX59txOlBIuk2BWtc1dq8QIbq3NrDcMUK7X6uA4YKjtzJAb6VHwaKvEQMRry9Tm375zrYOjc2CEx5oT06+G3yd9iBOJfPHQK7SDU/nncrXAd6Ni7ir471/4eO4/dmvQccKhVIYXSOnT+d3E3WUvzhXP/I+7D7/MlRHw8wm3s9DdztIT23gA3/x+/HNf/kZ3HplF3u3pph+/jI2Hz2DgOUQh3xt6roGqhJBGmHpzCLyvESZNZwTt1wFkcRoP0M+KxFECoONDm6/uk+uxY4zEyswRvLl2aRAMqDnb98g5EEGwvMnwJgP54ShhSgKJTqpQlkaisLgDKGmx+ezEtOJQRgA3cUYF95/ApNSgUnu5aC8zlBlpW+bhavLtChmObgtIshgjsGUpb35SPUlWsaCSnEEoSRlmzUHeyMj6MRYeegstr7xIi5/8Wk88GPfR8f+Bjk7MlCeZ6M7CcKlAWa3dzG8dBWD+2liN1UNVlX0JyPUyXnsKEtIdhlI0REvDiDqpXMKDMYZTn7oQTzz25+be2z/5CrCXopsZw9bX/wy8j26xoLFRXTvOUeFY6CQrK8g3txAvruH0aVLmN7YQrazg2xnByKKkJ4+heTkibe1xcWs2gMGEK8B4dPnaxSBVOy8dWSHCQ4UOXpJ7aM2OufPoXvPhQZt6HYwBt4Q0lUXJfa++SwAoH//vXOKpLcyuJREUs4rMEEeXG9HsfdmR3+lj//60//NnKz7TuM4WfadxqlPfggnP/HB1+WdcCXx8P/6T5ES6TinbdFI9JOlHh770Q8jWe4DMP54Ett+c47IaT/xBqoACUGSXnO/DW/sospLcCmQLnWR7x80UvXX+Q7iXgxWlrb18xrFzsoiLv7lH0NeVxi/8sqxj/HFkgHilQG4FMicJ9fbLDs/+uassWqo6iYvyxKT38G1zrvIzt0cwtToLcUIIokoEhjuUG/YoRxRJOYY9FwHUD0iCZdTItWKKMDOk09i/OKzOP3QCk7etwghObJJgZe/+AJuX6KdRjsQsK4N5WpxTnLlUPkWEGDl28Z44l42LVEW1ZznTm2LHaE4YivdHg9z1MZAadkYI2rpF1rG4BGkykU/2FYZYJ2WawNnt1AVNaSg96kNsLtbQghJwZguPqGcopg0sRlBmvjixkG2XJD0mFypCU4WgZ5DdqKAVAQyTShB/k0sEI6ofP2Pn0c5o2MpbLHzemqsw8O1sg5evkxSa9vKMlXlHV9Zy2NHpbENy6TCIuhERyaUsB8fWRw233//kZ8JrbDz5DO4/Ht/iHxvCB5orH3sg1j+wHu8GaUbjDEECwMsvucxrH38+9A5fw5cKVTTKfa/9Sy1uJ56GoVtt73VwRgDD8hH5jgH57nBiUhs6po4W29xsTdVjcnlV2H2boAzCuWNzl5A77575xZpJz8v3kAba//5F1BlGWQSo3P29Fs6vvZgnEGF7wyScpiER918jxnHybLv+FhvA/D6g0t5x3Z0uy0MECLmDCs5o/s/tYqo2XCCMi8gtURo29JCcERxhKglO995lTx9eqt9v8gz8DdJCj5etdUeItCviZAwW1zQMD5NHvhOIDsUGeG+d0KjmefsvJOrnXeLnbs4jM22qipAIMfkoIltCGIKe6xsXpIxNaTWRH5sRSPsPvkNTK9fBwDwtI/eiWWcuX8RcS+CMQa3ro5w7dXRfLFTGZiysggS+YUc7mnUFRF6nfncwfYEUytDDzvaJ2pzwZHYYmcyylE7zxZbKAkpvGkdGBkmGmMavx7OPEFYKo4yL1FlhVcKOWPCWWWdRrXyBaCQHKyqfAsrWl+2EwtxddoFi3f5dOZfSnkDRgCII0XkZCHnXEDfyBic30C83EeVF7j+VTKGaxLPXxvZOTy6Z0kaPrpyA1VRtLx2LOHbKrGcYktG5AnkJrEgieYmQhUHCA/9jH4eYuHcxtzPrv/hZ7HzJCmDkpMbOPXDfwLpqU0EXXL5PRyF4IaMQvTuuxfr3/9xDB5+CKrTgakqjF+9jK3PfBa3nvgSpls333KLSypFbcg7KHbccF47FLj5xlGD40YxPMDNz38Bs2tXAQClkRgXIcKlo35Cjphcjif+Gj1ulJMpDl54CQAwePC1PXi+nSFDDVN/94udNzqa7LHv3KAi6Oj1aGoDZjl+Og48r3G8fQDOOSK7eRFSIEzCOeRq+xVya+5vLNLrSGlJyW+mlWrelnPB7PxZV4ZaWKZloXCXB+NNxmJdNcT9d7LsHHi32Lmro57RgliDYbJNixdJshmSBYJHS5sDY2oDHipwHYCphvdiyhIyTbH4+Pshl9bBtYZUHBvn+1g92QFjwGhngq1Xd5DboqCuapoMHWGsqmAO7QTzaYG6rH1BMLzdZC0pLX2MhFQcSddOCKMcmUVu3E6tt97xzp2MAdmkQKurBe7I0YA3E2Rg/n0nwxnySQ6UBbQN+nWqK6kFsgPLBxLMS6C5ELQotiYYroSVa9r3lhJB1OxykkSBB4EnL74ZNIAxhpMfegAAOSobYzxB+bVMBY8bwUIP+v/f3plHyVGe5/75vq+23nt69mG0gcUqdrAwkUGJsQzGGEwcO4Yj29c5MbmW8KJrm2s7icmJL9hchwMJ1xv3BNvJITg3bPL1OQLlCiRjloBAQdhYgNCChKSRZkYzvXct3/3jq6runl0z07Pp/Z0zB1RdXV3d1V311rs8jz9Kntt/CIbft+PZdvBikJCqjwe+CWy06t1VWy8HACsWqRNNq2XR+86peQ9A5fgAuKGj/fJL0bFqZTjxwQSH2ZQEHyejwoRAbFE32lZdjtaV70WkXTWGlo/1onf7yzi89dfI7tlbfS8nCNPUyPlo48nhen72znMccGNyd7JSSmTf3oMjzz4LO5sF03XwTAdsFgU3jRHv1IVlKj0gYMyMVv/vXof0PJjNGUQ6O0Zdb7JwXQNmOHiYCifSIzdtjHLRla4XHlvGGGJ+U3JQygoyOZomEEtFIWoC1b79frBzSrOyNDH0sCReZ0Y8CipzO/Es11jUlnJrszozEWyoCdzqpCurWT6XmT+/mPlIUV2wYkk9LGHxMEhQDWiOn9nxfJ2dSu/RcOSYCY7k8uVou+wyWJkmv69BXQiE9JBuieLUixYh3pqC50oUCg4KBRuu4yk9GikhpQsIVmcPwBhg+xYQQUCQPVoI+3rKRTu8KRK6QCSqq9KWK1EquZCoWhAYEb2qeSIl7LJbzeRo9V8vM6ZDmEolNDAqtStKSE5zK0gnOEz7uHLUhgq0bF+EUYuYMOMxVRsO1DxrLvpD72q4rsM0a4KduFE1VJ3EnWbXxWeAawLZQ73of/tQWM7STzCzwxgLszuDew6E4+deuRw2f7t+oMMNHcIwoA2RWQ9GYRlUg/RoiqmZ06qZHcYZol0dWPzhq5BYumjYiUmLmP4I7QTKEozBbM6g+eIL0bH6CsRPXQamaXALBQy8/ntV4vrt72Dn8hP7UHxUVscYP9gRyh9LGZ6eeN+QUyzi2AsvYuD3uwBPwmprRfOll0LEEsqSw9RHvSAZ45Syyn39KBx4FwCQOfechlwAhDH2tNpcY7ou8Cf2mqMEO55X93uJN9c3Kcd8g1JNE4il43U+UL37VBmreXGbyuzwoDGZTez9+f060/FZ8JpgpxGeWGPCWHVIwNc9A6qf+bZt23Ddddehq6sLjDE89thj4VNt28Ztt92Gc889F7FYDF1dXfj0pz+Nd999t+G7TcFOA2GaAIQS3Bvo9e/U/S9ELOmXcY73w8kNwC0MghX7UD52JGysUxeoqlUC1wQw5OSeWnYKLvjjy9HcpU7ClYqH3mNFlAoVJbgXsaBbFtwhXkhBf07EL1FVSg6KfpmtVp/FdVSjcdC341QcCEMLvav63xkI9zUoYVUdzuu/XlZUV3LsQgVJAOBJBle34AoDjqvuEkLfqfCDBJiuwSkVAUg1Hp2I1p2IVPAjwqCO6zo0XaC9JYpMykI8YUJPJdTzJ3Gh0KMWOi5Q6q9vb9kevmc9Yo71NF+pNVaXjQmmsrL7Doajpm6pHKaBg2yfnoj5vVz1x9yMWyprJhjMeGTUYEePWOGJyExE0Xnl+6BFR1emNZuSMJsmriEDAFo0ivSZZ6Dzj1YjveJsaPG4KnHt248j236NYy++hNLRoxMqcenxKJLdbdDG+UyDiyfn/IRKRFJK5A8exJFf/wblvj4wIdB07jlovvgiNbbPBbSICWGMPuGk13hkjbT9wNU8tngRjPToYntTgWtandrwXEcYOiKZZMP7SWphjI9UxQKAuj6fcCLrmAp2zrj0dJimjlQqhkjMCi/g+d5BVPIlMMGR7m6F9CSEZcJMxqBKU+MHMEGANJEs0HgEN3rS9WomsWbmO8EYC9sYApmTsIoAIJ/P4/zzz8d999037LmFQgEvv/wy/uqv/govv/wyHnnkEbzxxhv46Ec/2vD9pmmsBhJZfgbe+eWv0dbBUA70dQSDZojwy+IUiygd3AtZ0/fChA7ABuccpZ6jiC1SF0ehC1UnDjAs6NEo4No45fRmlAdLyOdteK7Eu/sG0ar3IdYUh647KDj1v/xAl8GI6IgmTRQGyyj6SsMq+6S+wI4noQGIJQ3kBssY6MmhdXEqLHMFgRnXBZyiHZasGFMj55VK8L6UYSgASMb9i3wW5ZIDl1uQgmHgaAlN3RmwCAD0hdNUXHC4+SJ6nn8ZTBMwkkkY6SSMVBLCNJW6sGmAGyIcSeemAa5znNKugkBNE9CTSVUunORJt/uyc/DuS7vQ96YSBdSj1rgnOS4EIkNExKLtzRARC26xBM/vk3EKJRgZFVjagSdaNAIxQknFikVQ0NTF1kpE65Sya2FcBUPlbAGRzPg6J1yICekPjfhcTUN88WLEFi1CubcPub17Ueo5itLRYygdPQYtFkN8yWJEu08Z9Q6Ucw4rM36AEPRcBTpLE8GtVHD8td+heNj3XkqnkTn/PGixqmIt5wxcaODa6JmIapPy8GCncPBdlPv6wYRA+qzpGTUfCS6Uh5c9Rt/QXGM8ParphnE2YqwjZX1/Tcy3mcj3qWCna1kn2jsyiMQjiCSrNwa9+1QJq+mUltD4lwsOyRkYq0woUxxKJTSojNUIT6yRX9wff2dK78mT6hoRnF6uueYaXHPNNSM+NZVKYfPmzXXL/uEf/gHvfe97sX//fixevHjE500HFOw0mETKwGCfyupoQWNczIK1+FQUenZAOh482w31QrihoeTr8zDBMbBrF5xCFno8AbgsLGNJACwWBxMcTj4PzdCgaRyJhIFS2UWl7OLoW4eQS0Ww+MxMOB2lVC5ZmL0RukC8yUJhsFwVqeLqS2zEDCTb4ygcySKWUJmdbF8RR/YeD9c1DR5up1Lx4PkPRPwGbF+yEEIox3MA8GLNMEz1HsslV20r6LURAp5b7RdSqWIVHNn5AqTjotzXj3KfEkAUlgktHocWi4FrHNJ1AeihZUQA1zg0Q4dru/UB4wmQWtSGRFdLqLdzopNYAYxzJJeegv7Xd6NwpFf16bgumH/nZw+oz0aLRUbMHNX2CUVSsTFLGkZCBTuaNTNKyIwxWC3NsFqa4eQLyO3bh/yBg3DyeRz/3esYeONNxLpPQXzJkjDQmAxc1+BW7AlldkpHj6Lv1dfglcsAY0gufw8Spy4b5jGkLpASrMaFfShBj9XQzI7nuuj/7e8BAMnlp0GLNPbirllGOBFIjIA/Bi19CYzq8voG4fiQzE5gFWKYGqyaLGifX8LKLGlTPTpBJBVkayZSFpcyDNQng5Qy7L2UrgfPceAUy3Ardrh9z25MAMxqlJmDDLTy43J9sdnJCwoODAyAMYZ0Oj2NezwcCnYaiJQSiaSOvQePAwCCe41IKo7KsR51gZcAuAaRalHpe2nD268kyZlg8Co2ij1HUDqqfmwaADMTh+tIcEvAK5Ugi3nVIW8IoOIiFtORykTQ25NHcaCIN196F5GoBs3vomc1Aqxc50hkIjiy93i434nmKIrHcjDjRqhUa1oahGBwXYlCYB6qc0QTJuC44BqHXan268QSeji+DqiMFhMMLtchwWGGWjuu6lcyqk2DFT+zITQO4WfBWi85H9zQMbD7Hdi5HJxsDnYuD7dUhlsqo3zM/8w0HV4irvyvaoIdoQtwoalgZ5InG9WofA5+98hWACc+iVVLclk3+l/fjez+g9DjUdjZPDzXBRNVzQw9ERtxMsmIVgOgaPPYZSczHkUWvdCssUtDjUCLRZE++ywkT1+OwoGDyO3bpwKgvfuQ27sPVlsr4kuXwmzOnHBvC/ed4ceagvFcFwO/34X8vv3+/sSQueC8UAm5Fsa5P9JelS8YiUAF2SuX4ZbLEKb6XLO798AtFiEiFpLvOe2E3stk0Ex9XpWyZpqwXFSjayOlhGD135lYc73WTjC8YJhGXeN/kNlpXtLu68r4wUfQhzOB76/E1BqUpePitZ/8YlLPnSorPv9JsFBiRL1f4Qc7wUTWZPrTSqUS/vt//++46aabkDwBG5bJQMFOA/Gyg+CCYbCmX4cxQBc2vHwWXPhll2QzeDQOzRTwbP8LJZSJnVcswUg3gwkGe3AAcBxEm6tCV9IZgFv2gw/LgFspAlIinjSQaI2g50AOub4i8lkbus4RTxrQTK2qqaMJWBENXDC4fqkr9KdyZZ35ZzRuIDtQRjmvXk83NQgOuFBBU7lQLWPFEgZy/dVR5iCrxXQDbrkUem7ZZcd/DfVvQ/dQ8Ut+uumXFISAiKiJJD2ZgB6PQSxZBM9xYWezKB7rg1cswikUIR0bdn8/7P7+usyOMHVA436vy+Rr5h0XLseuXz0Lt2yf8CRWLbHuDnBdg5MvwmrJwM7mAc8F4wjLWGY6OWKzrlnjxxUfJ9gJfIFGm9iaCbimIb50CWJLFqN87Bhye/ep8lbPUZR6jkKLxxFfuhjRrq4JN1kGE3mjBa6V4wPo+89X4eR9FfMli5E684xRM0FqYijo/xr9gsQ1DVo0CqdQQGUwi0irCbdUwsAbSpIgffZwfaNGwMex1DjZYawakISKNJ4E/F6vgLivmpzvz8Lz1JCIEBy6acCoyaoOy+xw33xTnlhpimuTP/fMJUJLDiitHYYTD3Rs28af/umfwvM8/OAHP5juXRwGBTsNxB3oR6lgq2Zefwybc6bUfaMx6DBRPnoMbqEIM50GGA8DFz0ZR7SjA7k9eyFdD5lzz0cpW0D/7nfg5gbh5LMwIjo4c8M0jW5ylOFbRkhAFxxLV56Kw6/ux7GDWdi2h4H+MvSgpMEAYSj58mjCRLa/pATtcqpR2am4dSeGINjJD/j7aIiaKS+GYl4FKYYpYJo6CrwSPjcYNddME16hGE5jOWUbnutCQO2Tzmw1DQbASsfBmBICDB1+NQ63HPjACBjpFJhuINbejNzhYxjcexCCS7iFAriU6gLmqYu++kFOTYtCM3V0XXwG3nn2NVg1zs8nChcC8cVdGNy9v9qQ7rlgwf9rAmY6MeK+mjUWFcOMQYcQWIaYicmXjKYLxhis1lZYra2wcznk9u1H4cBBOLkcjr/2OwzsehOxRd2IL1kMLTJ21owJMaL4mvQ8ZHe/jcG3disHc9NE5rxzYbUO180ZCtcEGPwG4DG+I3oyAaegVM4jrS04/vouSMeFkU4h1n3KqM+bToSumpRnespp3uCLc8KTwX2UEqEckg2MpGOhinnxuGoHEILDjBpKnwxAcSCPwvEcwIDMojY/C6gEClUZi09YCmAqmktME1jx+U8CAFzbQeFof/iYEY/6zdKNYWgWsTqJpqROGD8xjR3btvGJT3wCe/bswZYtWxqe1QEo2GkoZns79r+8F4Aaww6cwK3WVkSWLod850AY7GiWCWnbcH1tHiMRR6SjHbk9qtFTeh6EEGCaAR5JwC04cLQ4YgkNTnYfuKHXuZxLT8KzPcSiDJmWiN+87MDzJHoOZhGN6oinjFDcz4wZyPaXACnDzI0R0ULxKgaE4oIBuilCZWYlJKgu1PGk6ful1K8LANwyoBUHwUwRlvGcigM9BkQTOrxSJbSqUOUaNxz3BQDGBaRXDaKk6ykFZU1Aj0XBzQis5iSklCi9ewjpTgee68FMJSClp+7Kpqi4u/zDlyHWmkb7+VMrVySXdWNw9/7QBJZzwPC9xfR4dNS+j6DZk3GGaHrsgOvUKy+AGY+i+5LGNcxOBj0eR9M5ZyN1+nLk/RKXWygi9/Ye5N7eg0h7O+JLl8DINI14EtWi1rCpLTufR/9/vorKcTUhGOnsQNM5Z4faOOMhdB1mMup7c41+4jaSSRQPH4E9mEVlYDCUSmhq0Kj5SHBNINHZMmX16IUK46qHpHYKUHpBGamml49zxJoSyB0bQL53EJFYBC1tTbCiVUmHYOQ82Z6BbhnwXA+c+3YUUgLsBBSip3C8VGY8yPzzuiyoFrVmdNoNTInVWnFrzLLvSASBzptvvomnnnoKzc3Dvc0aAQU7DYQBKPiBQ/BV4JzBam5WGQu/QdMtFSEMDY5tw/U1doxkAkY6DW4Y8CoVlHv7oCVT4ZSBlAA4g1tQ/R2e64Ru4oBKLUrXg+d6sMuO37ysw/aAQraCQsFGpeKiabENLWbA8PV2pFQBBIMaFWesOtVgmAJC46HGTq1CcalYCft1EmkTjNdrXQSj5pwBHBLSb9Su5EqhiGAybaCcLYZBk+D+69Q4ICsZ+JoTmOuFujvCVyUMNTAsC1ZMfSbcMGqmIaZ2gdAMHYv/4NwpbQMAEouVrEBgDeIWy6qcBfWeR5uuMP3ymRGzoJlj/4TNRBSnrr5gyvvaKLiuI7FsKeJLl6DUcxS5vftQ7u1F8cgRFI8cgZ5IIL50CaJdnXV3xbUOy1JK5N95BwOv71KN3pqGpnPORqSr84ROwlWn7LG/I3pNk3L/zt8CAKKndMJqzkzmI5g0FOiMDgv8C+qCHc8vV9Z/J2LNSeSODSDXO4hUdwu4LqBpPHRz7wv6dZYqEc1AvT0YpODaxLMa01biZKza84kZnMSqeXlN12BYBpyyXdecnMvl8NZbb4X/3rNnD3bs2IFMJoOuri58/OMfx8svv4z/+3//L1zXxWF/QjKTycCY4I3JZKBfSwOxj/ci2+s32/pTRYwx6H6DqYiqYMcrl8OIX2nJAHpClXAi7Uo1uHjkSKhEKj3VS8Olg0q/MhdljCF1WnVsz/OkEv8r2X5tWa3T3JVAS5dSXnYcD2+8+C6OHqgXSAv8rTSjagURXFxqszuBtxYTDP2HfQVmppzSuSaq2RxRVUz2yr6qtGaFF2234kIwDxoH3IoTvqbGvPCzCOBC1DlfeK4LbqhyBtc1f0LA70eq6XcRphEGRrNhnDgSwjQQO6U9/LdTLIUGoGYqMaptQqK9CQAQb0nNSH/ITBB811tXXor2969CbPEiMCFgZ7Po3/kaDm15GgO73ggFNwPcchm9L72M46/9DtJ1YTZn0P7+P0D0lK4TzrKwoIw1zvcjyDRW+o+jdKwX4BxNZ591Qq9FNJagEbgusyPliIrbQyeyGDiEoYc9UWFz8uK28DlcCEjP8wOoCf4Gp+FGq7qpmm2xE7GrmCb8YCv4fGt/ai+99BIuvPBCXHjhhQCADRs24MILL8Rf//Vf48CBA9i4cSMOHDiACy64AJ2dneHfs88+29BdpsxOA3H0JJyKG5avuP+NCNLvwrJU7cLz4Pk+WkFJIzihWu3tyL9zAMUjPUiddRaYUGPKGrfB8kdD9eKmc8+DlkoDv3oJgBIDhATsmowLoO4ATNNBpiWC7EAFlYqLg2/01jXO2RVXZXF0DmHq0EwNbtkJfbIGj5f8bfl9NIIj36/22zRF2K2vGRqSaVO9d02t6xYLYABEIhk2zTqOC9OQqAyUqj5bqJbB6zI7nKE696nu1oLshjC0sOlbGHpd+UKYFiA9MK7PKVnz5LJuZPcr7zOvYqPUp0owVkt61P1sObUTF/7J+9GyrGvEx+c7eiKOphXnIHXG6ci/c0CVuIolZHe/jezbexDpaEd8yRJ4lQr6X3sNXsUGOEfqjNMRX7pk0sc36L0Y74KkxWLh7xYAkqctm9IYPdEYuODDxFRHyoAM1dphHNDNaikzyOxkllZvTLguIAuq+XmigcZ0K0kHvUZihmwi6l6bMTD4wSRDXbSzevXqMUVEp+qhN1ko2Gkg/QfUOLQQvi6B/30ItFMYYxCWBbdQgFssQkoGr+zbEPgGkVZzM5gm4JXLsLODYNIFL/bC1JywX4abJiId7UpjwU9ten7QYBecOsdzpgm4tgchOFJNJqAJHDuUC9dnDKjYLhJBj5Gupl7csgOuVR3QAUALlJ0FQ9G3dQgUmZn/A9D1ILvjnxBcBxAC0fZW6NY+tci2IaSNSsWplrAMAc5Vqa+ufMFZ2PkfjH0G2Q1lN6CFVg61NW1uqTLWZL2UGkViaTfYthfDbJXrl7QiraOXRLjgaD3tlHBsdqHCdR2JU5chvmwpSkd6VImrrw/FQ4dRPHQ4XE9PJJC54LxwLHyyBI7z4+kwMc6hJ+KwB5RzfOr090zpdYnGwIK+muDfYCNmYYLMTt7P7CjLENWkXs6XkD2qbkBqMztC13x9GUw4szPdRp2hsv4MKSfXvXZQRnar1425ztzI5y9Q+vaoEzJngViVf0HWay/CqpTj5AthoCMsE8LPSjDBYbW2AgBKh1UpC54a8WaGP1acTIaihMEPIJiScorlOvsHzqtNxYwxxJMGTr+0C1ZNEOM6EuCqFNbzTgFOjV6OYQpEEwaExsPSlOPIcJ2EbxoqAXWL5CM0HupRGOk0rEQ0HN2Wjgs75/c2WeoOWfeDktoSlv8G1HOkX8rjPLw4McagWWZ1QoxzSMYBMIiIpdafKf+YCaLHIoi0108KMSFgpkefTmCMIZKOjWtVsVBgjCHS0Y7Wy96LtlV/gGh3d/g9SJy6DG2Xv2/KgQ6A0GB0IuPBEX+6q+mcs8An4c9FNB6m8bosAuMjl3tq/bGEYNA0ASuqGm/79qvm5Fhzsk7ygQmhvAe9iffhTJd6ckCQpRLjGPg2CuabPHMh5lS2fDTm1pl/gXH+x9+PHT9/AvneLKItKTgDWWgRs+6LIQwTNpSLsvDHbYeeuCPt7epu9kgPYmecg6yWRDlXQoz5lg9NqoeDcQ6ha3DcSljeAlSzckCgOsGFUlG2yy5ScQNnXNKF/GAZ+18/CqfswrE9FPM2GNcgXX9//RPH0jNbYMQM2NkyPE8iO+AblzLA8huRpSeBWlfymgtIpL0VesRA1B+LtksVOCVbqUJHVckq0PoZGuyoOwrVpBw2J9fcWWkRIywvMMYg0i3QI4Yqb0l70nYIjSS5rBuFw8fCfxvJ2LhmmInWdIP3am5iJBPInLcC6TNPh+c4ytx1mgguhnwCd9/ps89EYtlSKl/NYRiv98cazQA4FgY7AwDnsOJRcF2N9QfBTvOStrrnMKF0fNgEfbHU65/YePZ4GIko9Kg5pXH2qRC871lxtZ8Ec38P5zGaqUO6LjhnodLt0LvxILNTyebC5ksjWX+Bt1pbAcbg5POQTgUeN9XUlN/sawbBDmMQfv9KGOtI1AU+AYGfUtBTxDiDFTNg+ho85ZKLQtYBwKriZf7vVPhy6kHpKzA55ZxB02vMOYPfNQt+GFKdTHzF3ER7Wr33gspoudBCYcPARFSPDwl2uC/+5kn12Rr1DtXC0NV7Dhrnas0iGebkjzLpG4MGmE2NMZBcSHDDmNZAB0DotTWR7wjjnAKdOY66sanJ7IiRMztBOdgp2bCLFTDBIDQ1yNC7t6qcXLdtriwj1OT5BM8pbHqDApWFnPl+ndrXr57b5z5z78y/gPAcF+WsCkiivhlkrTaIlDLM5tjZfFVjZ0iww3UNZovSInCO9wNSQiAQn9Og+dkP6XnQ9Jp+lmA/anr0pP//gSmn53hVp3DOYFgqMCjlbbgeoOsedAN12/RcD72Hsv72JHK+yKBlVX94POilYYAZDcTfVAkrCD7ireqiXim7kFLChoZKQW0rOCfpiXqhrKCvQkoJz/WgDUnhCl1T3+oh8V0Y/MyRSaxazHQSeo31xETMMInpJwiM58vJmxgHxuomN8FGztpphg4rqQLXwvEcOGMQmlaX2ckMCXaqU51ywsEGm6M3W5OF+RYR86GEBVCw01AGDx6DlBJaxAgvsnqNr5HnuNB8sznpOKgcVw1yenJ4/0Ewgl7p64OEBPc1aLRYPKzdFo8cDoMYSF9rR6JG5ViNdgMq2AlKS5WSUixmXPXkAIDreIhEBWIxEcYNwRCU63ioFFUJrZC3w8yRGfV7Z/yLhRAMVtpCqssP3pgqYQVYZuDcK+FAwJUszPJohgA39NB7KCQoY3leXXNyADc0MKHB9Q3zQgLxrzkY7ADKPiLAammaxT05eWGczZuUPDE+jNcLm47WswNUS1nF/pzq/dM1eLaLgXfVkMnQzE44uXcCiuzTPY01+7BQvHE+QL/qBtK/VzUoJ7ta4PoTQrWZHc9RZZigLyUYPzeGNuVC9e0AgJPLgksJAXUx11MpJfznusjt2RMq8AJ+ozFk3ei5U/J9tIyqDk7gkwUwMCnDElI5V4J0vFAQMXA0ZwCaWlT5LedbS6iskIZwBQCAhNDr3XJrhddKhw9B+CPpZU+H9IBK8DnpfHhzsr8NzpUBnWpOHhLs+Fo70q13/w2EBufqhSy9fEn4/+Y4qshEY6gGO/Pj5E2MzdCeHT7G7z9oUi4cz6m+LV1D/4GjkFLCSkYRSQ/JMPu9XYF1xIT2Z5oblGebIKtDmR0CkBJMcKQXt8H2R4r1GgsA6brQDB1GqubixtiwPhUAEKYJI50GAHCvBO4HO2aTWpY/eABepRxaCQAqo+N5sq5nxymoUplmamETsFNyg5eGa3vhlFX2mBIKZFz9u3Y7wf/ns36ww1AdM/czRgxSTWEFKp/R6hi5Wy4jd7AnfC1VyvLCzJOmixGDHUCNz3uO6zcn1/fYC02DqOknqu6vN2qD4lwg1t0BFkzqzZOTx0KDCwGrKQnNmj3TVGL6qFV/BzBmiTIIdsrZItLtTbBiZp3T+dALelBODz24JrI/c/hmazIwrvo550sAt3A++TnI6desxDnXXoLm93RVg4yaMpZ0PWiWWef9pMdjo6ZaIx0qu8NKeZU84Rx6KgXPtpHfrzRrol2d4fqu44U9OoBvdOiPqHONhWJ8QWYnCIwMPwDJHy/B4xqii5V4Ha/5wXuOh0rFRSVwT69pTg6CHe43+gWYNVmdwsF3UcmVwgCpUrTBwODafrBj8BGDPgChZQQ3tGE/NMYZhKFDjhTsCD5nJfY55zjz0zfgnD//BCxqUJ41jHhkQV2QTmr8rEPQrzfWiHhYxhrIo6mjCbpp1DmdD9+0ag4+IVXkOTgJOlWCa8p8gH7VDYb7vk3VzE5ND4pUU1FGqqqpMlo2AwAsv2+HSZWJEZEIhK4hv38fpONAi8UQ7az2fnhufVYn7BuKqIbhINhxyo5q+PX7XDRdObN7nkS5IqBZVrU05X+xPcdDfrAcLmKMhUFSIHLFOULnYACwmlUvinRdDO49AEiEfl6e64JpXDnEY+zMDheqXq6ZI9+Ba5YxTKVTerI6VTZH0ayTQzeHIGaCcNQ7CHbG8I8Kgp18r+qb5Jqoy+yMBNfFCZVx5uqN1skCffozhDMk2JGeB3Dl51RbxjJGaE4O0GMxaDXZDhGNQ7oO8gcOAAASp54GI14dh3Udtz7Y8X+Uui/mF5SxpCfhOV7oNi50DZGkapzOHhsE51WH3dpMSi5b7dcRGgcXKqUbTDxIV9bqCip7DADFIz2oDKpx9WR3m7+vSvDP9nt2dFOEzdtDYVwFkKPdqWmmCUhviOOxN64yLkEQCwfGlRZOVel49MtdUMbK92UBqACm/4DSvhopswP4WY0TKI3PRY2vRrBt2zZcd9116OpS/nSPPfbYsHVef/11fPSjH0UqlUIikcBll12G/fv3N3S/KNiZAaTnhRYGQYOy53rhBbs2wBk6dj6UYCpLrZtU5SvpQU+lYWSaodeofEqvvjk5fF5CBURMMIiwZ6ZqwCkBRJtUQ162Z1AFMEOCHc+TKATNyUyNrKsSWb3TcG3RXBgq4zK4e6/KtJg6UovU+/FsF3osEvbsWE3J0U8igb7EKMEONwTAeF0Jj8mx7+wIglhYhA3BUtYbZ45AzLeMKA0W4HkuskePw3Nc6BFzVAFPdf6eeEP7RJS5FwL5fB7nn38+7rvvvhEf3717N1atWoUzzzwTTz/9NP7zP/8Tf/VXfwXLskZcf7qgs/8MEAQ6YNVShee4YbDDmPKAcvKFupLWSEQ62pHd/TYkAB4xUXhb9eokTj1NiQoKtU3PcdXYuVef3YBgMJNxoNQPQLmRuxUXdtEFD2IHxpFsS6HnrR7k+/OwyzaYLoAiwPz580KuAimVDQRjgO6XxLhvSqcMS5UWjhC+fongKPf1o9innL1Tp5+Ksuv3DZUr1SEuBkSaRv8cuODKs0uM/PUVuq4mtrzq+LnEwtK4IAhiHPyxaCllmA0eDTNmQTN1OGUb5VwJlZzKPGcWt41apuJCjKt0HsCEOGnKWNdccw2uueaaUR//1re+hQ9/+MO46667wmWnnnpqw/fr5Pj0Z5kwq2OZ1cyI40IzjfCH1LbyIjSfdzas1uYxt2WkUogsWgy9qRV2r2qgM1taYaRUUyvjvO4H6No1ejO+uqBZE0hoZtAg7KDkZ2qYYLBSUZhxE5DAwLv9Vbdg/3cflLAMX0gwmKrS/Gmw4H0GZbTAgXzgzbeVzYMmkFy2CFZKZZkqhQoMX71Z0/mY5Txu6LAyyVHvqLiusj6y9r1LCT5PpgYIgpg6qp9GDTOYqXid7MdI68ZqJrL6D/r6OktHLmEBytcu2pKe0L5Y6QTMZGz8FcdASgmnbM/K33Q5lXueh1/96lc4/fTT8aEPfQhtbW1YuXLliKWu6YYyOzOAU/T7UCJDJ7GqDbaxznbEOkduhBuK1d4Oz3kX9sBRAAyJmqiYcQZhGbD96S/H9uqqSoxzmKk4KodU3BKOn1eq7uhMCAhdQ6I1iXLuKI4f6EVLp/qhMqnulPJZ1YPEGeAB0A0VNxuJGJyBgTCIc20Xmi4gTB12oYDCkT4AQOrUReCaQCSltuuUbdhlJW44VnMy4J/Exqh/c01AGMqpHVAnifGeQxDEwqK2Z0cZMY99sxNvTmLg3V6UsgUcP6j6dZoXj35OVqPkEzunTEdWx604ePKbP5nydibDmjs+P0ytfjL09PQgl8vhu9/9Lr7zne/ge9/7HjZt2oQbb7wRTz31FK688spp2NuRoWBnBgiF/GrGzsEm7pY7FCklvMIAACDS2QktWn/HoEdMlIJ1h/TsGIkYuBnURmU4keXZTjhpxXUNQuNItiZxbM9R9B/oRdtilTmSTGni2BVP6fL4fT6aX4/W4lE4g4PVzE5gRWEYGHzzbXiOB8YZUqef6u+rEZbdcsd8BWldjDp2PhG44OCmAdu3noCc24KCBEE0Bi44nKFq6qNQO34eBDuZpRO7ASUmRmBKff311+MrX/kKAOCCCy7As88+ix/96EcU7Mx3qpkdFWR4rgfG+KQbZp3cIKRdAThHfOmyYY8HDchS1uvTMQYYqZjKcDAOwIPQ1d0PJMLGYs3UwQRHvCUOMKA0WIRtB92+MhQSjMYNOGVfydlQPTleuQzUOQH76slCILvnIAAgtqgzLLUxxmAlIij055A7pgI43dKn3EysmQbKnvoApKcMSJmgMhZBnEwoFeWJlWCCiay+fUfglG0IXUOqIzPOs2YOYWhYc8fnZ+21p4OWlhZomoazzz67bvlZZ52FZ555ZlpeYzQo2JkBAo2doGYsXRdCE9U+mBNAeh4qRw4BAKKndA/3joLK3owMg5lKKEEsQ4frKMdzzRBh0AIAmqUDTH3BE60pZHsGkDuWhQ4AEsj5JaxIwkC2XIRuCjV+buqw+4/7Qlt+YCGlMvnM5tWkFQOazl5et1dWKopCfw4Fv3HZiE+9K18zdXieBy6qDYpUxiKIkwumiQn3mwSZnWDkvGlR65xqKlbaaFMvJc0mhmHg0ksvxa5du+qWv/HGG1iyZMkoz5oeKNiZAYIG5aBnx3Nc5ao7iR9Spb8fXqUMJgRiixaPuE6gozMMhrBJjusGXKaKXbqp1QU7esQCg/pxpU/JINszgMGeLJozOlxXopBXvTWmpSELhI3FwR2UiFiQ+WL1dSVQ7D0OAIi2t9RZZgBAxHccLvoO8WaNVtBEkVJCuh6k5/r/9VSgBRXgsAnU7AmCWFgotfWJrRtvqZ8AbR5FX4cYm1wuh7feeiv89549e7Bjxw5kMhksXrwYX/va1/DJT34SV1xxBf7wD/8QmzZtwi9/+Us8/fTTDd0vCnZmgLCMFa1q7BgJfVIXX7O5GZkLLoJXqUAYIysIG6MEOwyAkVK9MEw3wsmqYCIrWEmPGr4QF0NTdwbvvLIH2WM5ZJrSYIzh/KvOQe/+HnjlQAAw0OBR5SqrpRlF5ygYL0N6Eq7ths3CmXPPGLZfVqo+E2Wlx55akGqmHtLzIF0X0vOrcn72RlgRWEKDONwPzjgYR6gTRBDEyQPnHEpwa/xzbZDZCRhNOZkYm5deegl/+Id/GP57w4YNAIDPfOYz+OlPf4qPfexj+NGPfoQ777wTX/ziF3HGGWfg4YcfxqpVqxq6X3MnRzcCt99+e52zKmMMHR1VOwQpJW6//XZ0dXUhEolg9erV+O1vfzuLezwyYYNyjXqyNkL5aaIY6TSstrFHIkeE+Ro7UOPbAcFEFqBq3Jqh+aUfhkR7CsLQ4NouyiUHDEC8JYGOU5tR8Q1Edd1vbBYcZmszpJTgESssZTl+oGNmkiM7ug8JdiJN9WPn0vPg2TbcchlOoQi3VFbWFoxBRCIw0kkYTU0wMxmYTU0wEnGYyQQ004QnVYYnsLAgCOLkgXEOOYFABwCi6XidnEWGgp1JsXr1apVpH/L305/+NFznc5/7HN58800Ui0Xs2LED119/fcP3a04HOwBwzjnn4NChQ+Hfzp07w8fuuusu3H333bjvvvvw4osvoqOjAx/84AeRzWZncY+HM3T0nAEQDbz41gY7tfVqLWKEjb9cr2aFgoksoMYZOBDiEgKpzjQAoJhT5SvpuGBwUSn6o+KaOkGYvuoxA4ORSg3TwcmsGJ7VAYDIkEyOmYjBrVTgFItwikV4FRsSSoHZSCVhNqVhZlRwYyST0CIRCMMAF1VTOi4EhKlBOq4KdqhfhyBOPhjDRBPoXHBE/RstJjjSp7Q0cMeImWbO5/Y1TavL5gRIKXHPPffgW9/6Fm688UYAwM9+9jO0t7fjwQcfxC233DLTuzoiUsqa0XNLTWKNo+Y5VYJprKGYNVYUtcFO4GklPalUnQWH57phc16qPYm+fcdQyNtoagVc24Z0qo7noaBgIgHGBIRlAiLwjPGzP4kIIi31kw1BOcqM1pfjjKgBLjSwSMTvbRJ+EDbx2JwJDmEY8FwP0E/suQRBLAzUjdvE1483J5HvHUS6q7nOxJiY/8z5K8Cbb76Jrq4uLFu2DH/6p3+Kt99+G4Bqejp8+DDWrFkTrmuaJq688ko8++yzY26zXC5jcHCw7q9ReDVmnHrErLGJaNwPyYyPXMYK+nUAgOnVnqFaB3Rh+I3T/rg2AMSbVPBULtjwPAm3VEKl6KjRds6g6RzC1CF0DSJigfkWGKh5j01nnlYtR5XKcIpFuKUSPMcdltlJdLXDzDTBiMchLAtc1084WGGM1bmIT6YZnCCI+Q1jDPIEWiODvh3q11l4zOkrwMqVK/Hzn/8cTzzxBO6//34cPnwYl19+OXp7e3H48GEAQHt7/Zeyvb09fGw07rzzTqRSqfBv0aJFDXsPgbEl1zVwTUC6bvj/jUIYRvXiXpPDrS1v8SHNzZGUCTCVFaqWgnyhQOFBNwWkBEp51TtT9ktYhqkCGxExISyr+rqeB2ZGlKKzocFqycCr2KrPxrJgJJMwm5pgNmeQXHxK3b5MVVY9/Bwsw58qm7gzMUEQCwjGwMAmrLWz+KL3wIxHsPS9ZzZ4x4iZZk7n6WrNxM4991y8733vw2mnnYaf/exnuOyyywBg2ERTYA0wFt/4xjfCDnEAGBwcbFjA4/oWCOEkluPCmqaL+WioAEOHUyxDs5SSMGOAHouieDwLI2rVlbEAIJqOoOyK0KsKYFXLh3IZsbSF40fyKOQrSNsOygX/ffmTXHo0Aik95Uflqxky00CpKNF8VjfMdJPK+NT01QRIKaGbAnZZlc6mS0tCt1QfDzijzA5BnIQwriQnJlrJWnrpGVhyyekkU7EAmVdXgFgshnPPPRdvvvlm2MczNIvT09MzLNszFNM0kUwm6/4aheNndkITOinrJqEaReC75dmOv4Sh5ayliDan4JRtlAbzvpJy8AwJJgFRI8LFGIN0XXjlCuJpNc5ezClTuDDY8ft1uGWoXiTDhBaNQY/FYCXi4KYJPRGDsExwTRvxJOIWSzD8oMmIR6btRMMNwy+p+SOoBEGcVDDOJjJ1Xv8cCnQWJPPqClAul/H666+js7MTy5YtQ0dHBzZv3hw+XqlUsHXrVlx++eWzuJf1hJmdiKWCCIlJKSefKHq0ak0BqB99tDWNRGcz0ks6EMkkAKEhtHMAwJgE03wRLs4AzmAXldBfokVloyplF3bZQbnoNyfr3H+9KLRoDFrQYyM0cF2f0ImmMjAQavWYowkiTgLN1NTIOefq/RAEcVLBatXciZOaOR3sfPWrX8XWrVuxZ88evPDCC/j4xz+OwcFBfOYznwFjDF/+8pdxxx134NFHH8Vrr72Gz372s4hGo7jppptme9dDnEoQ7JiQrgem8RnRfKn6Y/mqxkY1q6JHTSQ6W6DHY2EwIiWgCRUUuI4L6bjwHAdOvgAAsFJxWAmVncoPlKvBjp/ZYXz4HZHQBPgE/KgqxwdqMjvTF+wwIfyRdEZ3awRxMsKZrzhKnOzM6Z6dAwcO4FOf+hSOHTuG1tZWXHbZZXj++edDD42vf/3rKBaL+MIXvoD+/n6sXLkSTz75JBKJxDhbnjmCBmUtGkxiaTOi5hs2I/vFamHV9+gwxqDHYygdPRYu4zqH4AwcDLAM6LEYKn19/vMtxOI6StkyssfLoVCgbqpSmKxUhu+E4AAX4yZ3KgODMCz1mVijjM1PBi7UlBic8dclCGLhEYjREsScDnYeeuihMR9njOH222/H7bffPjM7NAmqZSwTnuv6TbONv9MY6j8VlLVqEZYZGp4DQLQpBj0ZD8fOhWXCKSiPq8rxAcSbLPQeyiE/oHSDhC4gBAcXHG6lMqw5nAsBzdDARrG1CCgfH0BzZxyOiOI9q8+f/JseAuMcZiIKzx4hECMIYsHDOD/hnh1iYUL5vQbj1jQoS8er035pJPqQcpAxgrmmZll1DcpccDDGlbig4PAqthoXByBdB7G0VadGakRUozUTTKkcu27d9rngyq9qjJq55zhwcnkYpoZzrn0vmha1nvibHQMtYlJzMkGcxJB6+sxy55134tJLL0UikUBbWxtuuOGGYS7ns2H1RFeBBhP27EQtSEiIGZjEAgAjWi8saKaGe1IJy0K1aQe+YR58ewUOO58P12WcQ2gcVrS6/4bfVMw5h1epQLr19SKucfBxmgMrA0rQkZvmqMamU0EYeiiOSBDEyQebQN8gMX1s3boV69atw/PPP4/NmzfDcRysWbMG+ZrryWxYPc3pMtZCIHD71nyBu5kypNQTQ4Kd9GjBThXpqWBHSoAJDaWjR9UDjEGLRmDn84jEdRTzvieWbwDKBINn25CuV1/KmsAUVOX4AADASCXrBBCnCyMZAwuCOIIgTjrYAsnsSCnh+G0RM41m6hPufdq0aVPdvx944AG0tbVh+/btuOKKK2bN6omCnQbiOa5y5wYgTB1MYkaak4HhZauRe3as+np2GOxIuOUSKgMqENETcTiFAhhjiKci6DuiJrSCuI0bOiA9eI4D7nnhyYX7RqJjEWR2jFSyISOiNHpKECc3C0VQ1Cnb+MHau2bltb/wT1+Hbk0u8z7gX0cyGeWNOJ7VU6OCnYXxLZijOCXVGMs4U0qemmio23ktxhB/LG2EL6oY2j8kPUhIeHYF5aNHVaMyALNZfUlFxIKVjkLTVdOfYWlgmoDuB1ae4wBD+nY0yxhTVyjM7KRTyrV8gdyFEQQxN+B8uGo7MTNIKbFhwwasWrUKK1asAIApWT1NBcrsNBCn6Lud+xo7WsyaMY8mPWKpspCvszNSYzRjTE1c5dTElfQ8uKUyUCnDLRSCleD4/2+1tKCSc9C1NAXoBnTmwkjEoMeicLI5SMcJS2EBsdYUIpmRFaqllGFmx2xKw0jOHckAgiAWBswXSJ3vaKaOL/zT12fttSfD+vXr8eqrr+KZZ54Z9thkrJ6mAgU7DaSSV0GEZhmQrgvNnP4G3NFgnEGzjDDg0iMjv7awrDDYAQA4NrxCLgyS9Hgc9vHjAACrpRn5Q33QDQGmAdJRJS4torJIbrkMxhmk9MAmIOTl5PKQrgsmBPTE8J4igiCIKROYgc5zGGOTLiXNBrfeeis2btyIbdu2obu7O1xea/XU2dkZLp+I1dNUoDJWA7HzympBi5iQUCrGM0noxwVVghp5nfpyl3O8319fLeeGDul5EKYJLRaD8AM26fciGYkYNH/yyy2VwYQIHxuPsCcomaA0M0EQDUG1Ecz2Xpw8SCmxfv16PPLII9iyZQuWLVtW9/hsWT1RZqeBBMGObpmqZDQDnli16BETJQBgbNRAa+hEljIqNcLemWCc3GxpBmMMfEh2Sk/EwsDILRTBDcMve42f9iz7/TpmOjXxN0UQBHECMOUEPNu7cdKwbt06PPjgg3j88ceRSCTCPpxUKoVIJFJn9bR8+XIsX74cd9xxR8OtnijYaSCVggp2hKmDCw6uzWzzreZPYAldjKrarNUGO74BqNl5CvJ73gaAUEHZamlW2xqihaMyO77pqG2H4+OBJ1dVn3k4lePVSSyCIIhGoPokKXM8U/zwhz8EAKxevbpu+QMPPIDPfvazAGbH6omCnQbiBGUsUwfTxIyNnQcE4+Zc10YtE4Xj5xIAA/RMqxLh8jwwziE9F8I0oftfQlHbqMYY9HgUjHNwXVdaO7YNMA63WAIYwDUdXBs5yxOUsQzK7BAE0SjIH2tGqd7ojs5sWD1RsNNATr9+FTJnLFaifIY+43ovYbCjaaNOgdX28ujNLdBiMXglFaQxXQM8F5ZfwgIQ9uwAymw02K4WjaAyYMPO5cB0ExISZioF6XngI5Tv3HJZBUSgzA5BEI1DTWPN9l4Qsw19BRoIFwJGzILQxIx5YtVSm9kZDaW14wdhngcpVe8NgNDrympurq5fG+zEYzXL1ftzimVEWppgJOLKaHSUO6qghKXFYuD6zFhoEARx8kHO5wRAwc6MwDDzk1gA0PSebugxC8nullHXqVVR9hwHYBJOwfcwkRJc16HXZF7qgp1kDJ7twM4XwYOpLukpgUBdH6a5U0u1hEVZHYIgGgjnE5LCIBY2VMaaAVS/zswrA0db0jjlD86HPoYgFNeV54mE9EUBZZjZAVPqybV3RbUNysIw4Hke9GQCEc9Fft9+OPkCmBDguqYalkeh6olF/ToEQTQONXpOmZ2THQp2ZgCuiTFLSQ17XcHH/aEzxsB0HdItq2CnUvYfUI9ZLUOzQhJMU1o6kbYMIi0ZVYbyzTbtXM5XZrbglkoYbQoi9MSizA5BEA2EMaamRKmUdVJDwc4MwGcps8M4h5WOgXljd8cLw4BXKkO6LrxKpfp8TQubh5WVRAVgQOas96CSLyC5bHHYoBz073gVG26l4k+AqWmuoUjXRWUwC6DxGjuMc4jIzNl0EAQx94hkUuAz5EtIzE0o2JkBNNOYtQY5zTThlctjriMsE/ZgFtLz4JX9SSzGYPkGoE6xBEgJEbGgx2KIdnUMez9c08Jsjp3Nwcw0gWkavPLwYKeSzYX9QGKIgvN0wzgfphJNEMTJhZiktxOxcKDb3QbDGBvRcXwmX3+8QCscP/c8v/QEpaGTTMIplsBNA2ZzBmamacwJqyC74+TyocnoSFWsWqdzmpIgCIIgGg1ldhrMbDUnh6/P2bglHD2cpJJKFBAAExxmJgMjmYCwrAk1+GmJOHCsF3ZOTXMJQwcXw997tTmZ+nUIgiCIxkPBToPhYnaakwMYw7iNeVo0OmxZpL0NkbaWE+p1CTI7di4HQE16jaQaXW1OpkksgiAIovFQGauBMK78sGbaALRuH4QGM50YM2jRasQB1ZMY4ou6T7ipV4/HASDM7DAhwA2jLtiSUlJmhyAIYoFy55134tJLL0UikUBbWxtuuOEG7Nq1a9T1b7nlFjDGcM899zR0vyjYaSBC15DobJm1MhbjDImuVsTaMmMHO7H6zA4THFZb2wm/XrVnJxf6o+jJhK/SrHCLpdAw1Eg2zvSNIAiCmHm2bt2KdevW4fnnn8fmzZvhOA7WrFmDfD4/bN3HHnsML7zwArq6uhq+X1TGajCzLWY1kdcXtc7nAKzmzKQCtCBD5NkOvHJlxGbmIKujJxNgI/TzEARBEMORUqJSqoy/YgMwrIlPFG/atKnu3w888ADa2tqwfft2XHHFFeHygwcPYv369XjiiSdw7bXXTuv+jgQFO4RqImYM8LMx8SWLJr0dEY3ALRRh53J1GZ2A8gCVsAiCIE6USqmCWz/41Vl57X/Y/H2Ykcn5Ow745/xMJhMu8zwPa9euxde+9jWcc84507KP40FlLAIAqmUuxhDt7Jj0dqpNysNTlkA1s9NoMUGCIAhidpFSYsOGDVi1ahVWrFgRLv/e974HTdPwxS9+ccb2hTI7BAA1Ii9dF1o0MiUXcj0eR6nnWDiRNZRwEosyOwRBEBPGsAz8w+bvz9prT4b169fj1VdfxTPPPBMu2759O+699168/PLLM6qzRsEOAQAwkkmUjh6bdAkroFZYcCiebYfLaeycIAhi4jDGJl1Kmg1uvfVWbNy4Edu2bUN3d3e4/Ne//jV6enqwePHicJnruvhv/+2/4Z577sHevXsbsj8U7BAAgPb3vw9OLj/lCamh4+e1VAaUH5awLAhz/vxoCYIgiIkhpcStt96KRx99FE8//TSWLVtW9/jatWtx1VVX1S370Ic+hLVr1+K//Jf/0rD9omCHAABwzqdlFDyYyLKzavy8Nk1ZGajaRBAEQRALj3Xr1uHBBx/E448/jkQigcOHDwMAUqkUIpEImpub0dzcXPccXdfR0dGBM844o2H7RQ3KxLQSlLGk68It1RuQkpggQRDEwuaHP/whBgYGsHr1anR2doZ/v/jFL2Z1vyizQ0wrjHNosSicfAF2LgctUtXwqTUAJQiCIBYegaDsidCoPp1aKLNDTDtB305tk7KUEpVB1bNjpCmzQxAEQcwcFOwQ045e07cTYGdzkK4LJkQYDBEEQRDETEDBDjHtaCMIC9bq68yktgJBEARBULBDTDvV8fNqZof6dQiCIIjZgoIdYtrRE1VhwaBZrUKeWARBEMQsQcEOMe1o0SjAGKTnwS2WAACV40EZizI7BEEQxMxCwQ4x7TDOoceiAFQpyy2X4ZZU0GOkpi5cSBAEQRAnAunsEA1Bi8dh5/KqSdkvZWnx2JRMRgmCIAhiMlCwQzQEPR5DEf7Iue0AoBIWQRAEMTtQsEM0BD1RFRZ0y8o2gsQECYIgiNmAenaIhhAKC+Zy4di5SZkdgiCIBc2dd96JSy+9FIlEAm1tbbjhhhuwa9euunVyuRzWr1+P7u5uRCIRnHXWWfjhD3/Y0P2iYIdoCFqotZMPlZRJY4cgCGJhs3XrVqxbtw7PP/88Nm/eDMdxsGbNGuTzVZHZr3zlK9i0aRP++Z//Ga+//jq+8pWv4NZbb8Xjjz/esP2iMhbRELRoBOAc8DwAADd0iBpTUIIgCGLiSClRKpZn5bWtiDlh5ftNmzbV/fuBBx5AW1sbtm/fjiuuuAIA8Nxzz+Ezn/kMVq9eDQD4/Oc/jx//+Md46aWXcP3110/rvgdQsEM0BMYY9Fi0mtVJpcgmgiAIYpKUimV84II/npXX/n87HkYkOrmb1QFfUDaTyYTLVq1ahY0bN+Jzn/scurq68PTTT+ONN97AvffeOy37OxLzqox15513gjGGL3/5y+EyKSVuv/12dHV1IRKJYPXq1fjtb387eztJhARNygCVsAiCIE42pJTYsGEDVq1ahRUrVoTL//7v/x5nn302uru7YRgGrr76avzgBz/AqlWrGrYv8yaz8+KLL+InP/kJzjvvvLrld911F+6++2789Kc/xemnn47vfOc7+OAHP4hdu3YhkSABu9mk1t2cbCIIgiAmjxUx8f92PDxrrz0Z1q9fj1dffRXPPPNM3fK///u/x/PPP4+NGzdiyZIl2LZtG77whS+gs7MTV1111XTs8jDmRbCTy+Vw88034/7778d3vvOdcLmUEvfccw++9a1v4cYbbwQA/OxnP0N7ezsefPBB3HLLLbO1ywSq7ucAZXYIgiCmAmNs0qWk2eDWW2/Fxo0bsW3bNnR3d4fLi8UivvnNb+LRRx/FtddeCwA477zzsGPHDnz/+99vWLAzL8pY69atw7XXXjvsQ9izZw8OHz6MNWvWhMtM08SVV16JZ599dtTtlctlDA4O1v0R008wfg7GYCQpy0YQBLHQkVJi/fr1eOSRR7BlyxYsW7as7nHbtmHbNjivDz+EEPD8gZZGMOczOw899BBefvllvPjii8MeO3z4MACgvb29bnl7ezv27ds36jbvvPNO/M3f/M307igxDDPTBCOdgplpAuPzIq4mCIIgpsC6devw4IMP4vHHH0cikQiv06lUCpFIBMlkEldeeSW+9rWvIRKJYMmSJdi6dSt+/vOf4+67727Yfs3pYOedd97Bl770JTz55JOwrNHTd0OnfKSUY07+fOMb38CGDRvCfw8ODmLRokVT32GiDq5pOOWq1bO9GwRBEMQMEYgDBmPlAQ888AA++9nPAlBJjG984xu4+eab0dfXhyVLluB//I//gb/4i79o2H7N6WBn+/bt6OnpwcUXXxwuc10X27Ztw3333ReqMh4+fBidnZ3hOj09PcOyPbWYpgnTnFzDFUEQBEEQIyN94+ex6OjowAMPPDADe1NlTtcWPvCBD2Dnzp3YsWNH+HfJJZfg5ptvxo4dO3Dqqaeio6MDmzdvDp9TqVSwdetWXH755bO45wRBEARBzBXmdGYnkUjUzeYDQCwWQ3Nzc7j8y1/+Mu644w4sX74cy5cvxx133IFoNIqbbrppNnaZIAiCIIg5xpwOdibC17/+dRSLRXzhC19Af38/Vq5ciSeffJI0dgiCIAiCAAAwOZEC2wJncHAQqVQKAwMDSCZJ/I4gCIKYPUqlEvbs2YNly5aNOZxzsjDW5zHR6/ec7tkhCIIgiJMVykUopuNzoGCHIAiCIOYQuq4DAAqFwizvydwg+ByCz2UyzPueHYIgCIJYSAghkE6n0dPTAwCIRqNjasctVKSUKBQK6OnpQTqdhhBi0tuiYIcgCIIg5hgdHR0AEAY8JzPpdDr8PCYLBTsEQRAEMcdgjKGzsxNtbW2wbXu2d2fW0HV9ShmdAAp2CIIgCGKOIoSYlov9yQ41KBMEQRAEsaChYIcgCIIgiAUNBTsEQRAEQSxoqGcHVcGiwcHBWd4TgiAIgiAmSnDdHk94kIIdANlsFgCwaNGiWd4TgiAIgiBOlGw2i1QqNerj5I0FwPM8vPvuu0gkEtMq3DQ4OIhFixbhnXfeIc+teQAdr/kDHav5BR2v+cN8O1ZSSmSzWXR1dYHz0TtzKLMDgHOO7u7uhm0/mUzOiy8NoaDjNX+gYzW/oOM1f5hPx2qsjE4ANSgTBEEQBLGgoWCHIAiCIIgFDQU7DcQ0TXz729+GaZqzvSvEBKDjNX+gYzW/oOM1f1iox4oalAmCIAiCWNBQZocgCIIgiAUNBTsEQRAEQSxoKNghCIIgCGJBQ8EOQRAEQRALGgp2xmHbtm247rrr0NXVBcYYHnvssbrHjxw5gs9+9rPo6upCNBrF1VdfjTfffLNuncOHD2Pt2rXo6OhALBbDRRddhH/7t3+rW6e/vx9r165FKpVCKpXC2rVrcfz48Qa/u4XHdByv3bt342Mf+xhaW1uRTCbxiU98AkeOHKlbh47X1Ljzzjtx6aWXIpFIoK2tDTfccAN27dpVt46UErfffju6uroQiUSwevVq/Pa3v61bp1wu49Zbb0VLSwtisRg++tGP4sCBA3Xr0LGaOtN1vH7yk59g9erVSCaTYIyNeBzoeE2N6ThWfX19uPXWW3HGGWcgGo1i8eLF+OIXv4iBgYG67cynY0XBzjjk83mcf/75uO+++4Y9JqXEDTfcgLfffhuPP/44XnnlFSxZsgRXXXUV8vl8uN7atWuxa9cubNy4ETt37sSNN96IT37yk3jllVfCdW666Sbs2LEDmzZtwqZNm7Bjxw6sXbt2Rt7jQmKqxyufz2PNmjVgjGHLli34zW9+g0qlguuuuw6e54XbouM1NbZu3Yp169bh+eefx+bNm+E4DtasWVP3u7nrrrtw991347777sOLL76Ijo4OfPCDHwy97ADgy1/+Mh599FE89NBDeOaZZ5DL5fCRj3wEruuG69CxmjrTdbwKhQKuvvpqfPOb3xz1teh4TY3pOFbvvvsu3n33XXz/+9/Hzp078dOf/hSbNm3Cn/3Zn9W91rw6VpKYMADko48+Gv57165dEoB87bXXwmWO48hMJiPvv//+cFksFpM///nP67aVyWTk//7f/1tKKeXvfvc7CUA+//zz4ePPPfecBCB///vfN+jdLHwmc7yeeOIJyTmXAwMD4Tp9fX0SgNy8ebOUko5XI+jp6ZEA5NatW6WUUnqeJzs6OuR3v/vdcJ1SqSRTqZT80Y9+JKWU8vjx41LXdfnQQw+F6xw8eFByzuWmTZuklHSsGsVkjlctTz31lAQg+/v765bT8Zp+pnqsAv71X/9VGoYhbduWUs6/Y0WZnSlQLpcBAJZlhcuEEDAMA88880y4bNWqVfjFL36Bvr4+eJ6Hhx56COVyGatXrwYAPPfcc0ilUli5cmX4nMsuuwypVArPPvvszLyZk4CJHK9yuQzGWJ2glmVZ4JyH69Dxmn6C9HgmkwEA7NmzB4cPH8aaNWvCdUzTxJVXXhl+xtu3b4dt23XrdHV1YcWKFeE6dKwaw2SO10Sg4zX9TNexGhgYQDKZhKYpS835dqwo2JkCZ555JpYsWYJvfOMb6O/vR6VSwXe/+10cPnwYhw4dCtf7xS9+Acdx0NzcDNM0ccstt+DRRx/FaaedBkD19LS1tQ3bfltbGw4fPjxj72ehM5HjddlllyEWi+G2225DoVBAPp/H1772NXieF65Dx2t6kVJiw4YNWLVqFVasWAEA4efY3t5et257e3v42OHDh2EYBpqamsZch47V9DLZ4zUR6HhNL9N1rHp7e/G3f/u3uOWWW8Jl8+1YUbAzBXRdx8MPP4w33ngDmUwG0WgUTz/9NK655hoIIcL1/vIv/xL9/f3493//d7z00kvYsGED/uRP/gQ7d+4M12GMDdu+lHLE5cTkmMjxam1txf/5P/8Hv/zlLxGPx5FKpTAwMICLLrqo7pjS8Zo+1q9fj1dffRX/8i//MuyxoZ/nRD7joevQsZpepvt4jbeNyW6HmJ5jNTg4iGuvvRZnn302vv3tb4+5jbG2M9tos70D852LL74YO3bswMDAACqVClpbW7Fy5UpccsklANRkz3333YfXXnsN55xzDgDg/PPPx69//Wv8r//1v/CjH/0IHR0dw6Z9AODo0aPDom9iaox3vABgzZo12L17N44dOwZN05BOp9HR0YFly5YBAB2vaeTWW2/Fxo0bsW3bNnR3d4fLOzo6AKi7x87OznB5T09P+Bl3dHSgUqmgv7+/LrvT09ODyy+/PFyHjtX0MZXjNRHoeE0f03Gsstksrr76asTjcTz66KPQdb1uO/PpWFFmZ5pIpVJobW3Fm2++iZdeegnXX389ADV9AACc13/UQohwuud973sfBgYG8B//8R/h4y+88AIGBgbCkzYxvYx2vGppaWlBOp3Gli1b0NPTg49+9KMA6HhNB1JKrF+/Ho888gi2bNkSBpIBy5YtQ0dHBzZv3hwuq1Qq2Lp1a/gZX3zxxdB1vW6dQ4cO4bXXXgvXoWM1PUzH8ZoIdLymznQdq8HBQaxZswaGYWDjxo11vY7APDxWs9AUPa/IZrPylVdeka+88ooEIO+++275yiuvyH379kkpVYf6U089JXfv3i0fe+wxuWTJEnnjjTeGz69UKvI973mPfP/73y9feOEF+dZbb8nvf//7kjEmf/WrX4XrXX311fK8886Tzz33nHzuuefkueeeKz/ykY/M+Pud70z1eEkp5T/+4z/K5557Tr711lvyn/7pn2Qmk5EbNmyoW4eO19T4r//1v8pUKiWffvppeejQofCvUCiE63z3u9+VqVRKPvLII3Lnzp3yU5/6lOzs7JSDg4PhOn/xF38hu7u75b//+7/Ll19+Wf7RH/2RPP/886XjOOE6dKymznQdr0OHDslXXnlF3n///RKA3LZtm3zllVdkb29vuA4dr6kxHcdqcHBQrly5Up577rnyrbfeqtvOfP1tUbAzDsGI5NC/z3zmM1JKKe+9917Z3d0tdV2Xixcvln/5l38py+Vy3TbeeOMNeeONN8q2tjYZjUbleeedN2wUvbe3V958880ykUjIRCIhb7755mFjmcT4TMfxuu2222R7e7vUdV0uX75c/t3f/Z30PK9uHTpeU2OkYwRAPvDAA+E6nufJb3/727Kjo0OapimvuOIKuXPnzrrtFItFuX79epnJZGQkEpEf+chH5P79++vWoWM1dabreH37298edzt0vKbGdByr0c6jAOSePXvC9ebTsWJSStmorBFBEARBEMRsQz07BEEQBEEsaCjYIQiCIAhiQUPBDkEQBEEQCxoKdgiCIAiCWNBQsEMQBEEQxIKGgh2CIAiCIBY0FOwQBEEQBLGgoWCHIAiCIIgFDQU7BEEQBEEsaCjYIQhiXiClxFVXXYUPfehDwx77wQ9+gFQqhf3798/CnhEEMdehYIcgiHkBYwwPPPAAXnjhBfz4xz8Ol+/Zswe33XYb7r33XixevHhaX9O27WndHkEQswMFOwRBzBsWLVqEe++9F1/96lexZ88eSCnxZ3/2Z/jABz6A9773vfjwhz+MeDyO9vZ2rF27FseOHQufu2nTJqxatQrpdBrNzc34yEc+gt27d4eP7927F4wx/Ou//itWr14Ny7Lwz//8z7PxNgmCmGbICJQgiHnHDTfcgOPHj+OP//iP8bd/+7d48cUXcckll+DP//zP8elPfxrFYhG33XYbHMfBli1bAAAPP/wwGGM499xzkc/n8dd//dfYu3cvduzYAc459u7di2XLlmHp0qX4u7/7O1x44YUwTRNdXV2z/G4JgpgqFOwQBDHv6OnpwYoVK9Db24t/+7d/wyuvvIIXXngBTzzxRLjOgQMHsGjRIuzatQunn376sG0cPXoUbW1t2LlzJ1asWBEGO/fccw++9KUvzeTbIQiiwVAZiyCIeUdbWxs+//nP46yzzsLHPvYxbN++HU899RTi8Xj4d+aZZwJAWKravXs3brrpJpx66qlIJpNYtmwZAAxrar7kkktm9s0QBNFwtNneAYIgiMmgaRo0TZ3CPM/Dddddh+9973vD1uvs7AQAXHfddVi0aBHuv/9+dHV1wfM8rFixApVKpW79WCzW+J0nCGJGoWCHIIh5z0UXXYSHH34YS5cuDQOgWnp7e/H666/jxz/+Md7//vcDAJ555pmZ3k2CIGYJKmMRBDHvWbduHfr6+vCpT30K//Ef/4G3334bTz75JD73uc/BdV00NTWhubkZP/nJT/DWW29hy5Yt2LBhw2zvNkEQMwQFOwRBzHu6urrwm9/8Bq7r4kMf+hBWrFiBL33pS0ilUuCcg3OOhx56CNu3b8eKFSvwla98Bf/zf/7P2d5tgiBmCJrGIgiCIAhiQUOZHYIgCIIgFjQU7BAEQRAEsaChYIcgCIIgiAUNBTsEQRAEQSxoKNghCIIgCGJBQ8EOQRAEQRALGgp2CIIgCIJY0FCwQxAEQRDEgoaCHYIgCIIgFjQU7BAEQRAEsaChYIcgCIIgiAXN/wfenicPLl1ZxAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "sns.lineplot(x=\"Year\",y=\"HUM\",hue=\"TEM\",data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1156d852-038b-4051-aa45-0db984c6091d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "plt.bar(data[\"Year\"],data[\"HUM\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "13662008-1835-4531-945f-f552cca07db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.metrics import r2_score, mean_squared_error,mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "615ebf55-51b0-4ee0-98ab-d8679decf310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Month', 'Day', 'TEM', 'MXT', 'MNT', 'HUM', 'SLP', 'WIS', 'RIN',\n",
       "       'SSH', 'CLD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "678c8111-cd2d-4a50-b7c2-6a6b4c5b9f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[[\"TEM\",\"MNT\",\"SLP\",\"WIS\",\"RIN\",\"SSH\",\"CLD\"]]\n",
    "y = data[\"MXT\"]"
   ]
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   "execution_count": 38,
   "id": "e2dc6928-5b21-4d98-8be5-9b8e2c7a3557",
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   "execution_count": 42,
   "id": "9c177778-2b5a-4483-9572-96f70d9f56d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
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   "execution_count": 46,
   "id": "651b1cfa-b5c5-45f7-9c59-db8e102cbe9b",
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      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_22732\\2528753792.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x[col] = 0.1 + 0.8 * (x[col] - min_val) / (max_val - min_val)\n",
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_22732\\2528753792.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x[col] = 0.1 + 0.8 * (x[col] - min_val) / (max_val - min_val)\n"
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    "\n",
    "for col in x:\n",
    "    min_val = x[col].min()\n",
    "    max_val = x[col].max()\n",
    "    \n",
    "    x[col] = 0.1 + 0.8 * (x[col] - min_val) / (max_val - min_val)"
   ]
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   "execution_count": 48,
   "id": "b18cf5b4-60d2-4d8c-81bb-41bcc56cacea",
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     "metadata": {},
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   "execution_count": 50,
   "id": "9a54573a-0138-4447-8814-befd8db3f0c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_val = y.min()\n",
    "max_val = y.max()\n",
    "\n",
    "y = 0.1 + 0.8 * (y - min_val) / (max_val - min_val)"
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  {
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   "execution_count": 52,
   "id": "dabe45a6-f1ae-4cd3-bc53-a5e375bef3b5",
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  {
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   "execution_count": 54,
   "id": "166f3513-8192-4e72-8070-bf16870b02bd",
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   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.20,random_state=210450)"
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   "cell_type": "code",
   "execution_count": 56,
   "id": "4e6dd168-edbb-4864-aedd-66395471d286",
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       "      <td>0.160</td>\n",
       "      <td>0.103137</td>\n",
       "      <td>0.147458</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>0.792887</td>\n",
       "      <td>0.725714</td>\n",
       "      <td>0.767857</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.109412</td>\n",
       "      <td>0.472881</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5653</th>\n",
       "      <td>0.802929</td>\n",
       "      <td>0.737143</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.112549</td>\n",
       "      <td>0.479661</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12588</th>\n",
       "      <td>0.799582</td>\n",
       "      <td>0.751429</td>\n",
       "      <td>0.750595</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.103137</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14487</th>\n",
       "      <td>0.802929</td>\n",
       "      <td>0.757143</td>\n",
       "      <td>0.776190</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13125</th>\n",
       "      <td>0.498326</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.861310</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.418644</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12340 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TEM       MNT       SLP    WIS       RIN       SSH  CLD\n",
       "4290   0.786192  0.714286  0.803571  0.190  0.100000  0.649153  0.6\n",
       "2440   0.789540  0.677143  0.772619  0.340  0.115686  0.249153  0.6\n",
       "7414   0.759414  0.688571  0.813690  0.206  0.100000  0.628814  0.5\n",
       "8147   0.642259  0.722857  0.811310  0.320  0.100000  0.594915  0.6\n",
       "14433  0.816318  0.774286  0.766071  0.160  0.103137  0.147458  0.8\n",
       "...         ...       ...       ...    ...       ...       ...  ...\n",
       "963    0.792887  0.725714  0.767857  0.234  0.109412  0.472881  0.8\n",
       "5653   0.802929  0.737143  0.775000  0.266  0.112549  0.479661  0.8\n",
       "12588  0.799582  0.751429  0.750595  0.160  0.103137  0.100000  0.8\n",
       "14487  0.802929  0.757143  0.776190  0.100  0.100000  0.100000  0.6\n",
       "13125  0.498326  0.460000  0.861310  0.100  0.100000  0.418644  0.2\n",
       "\n",
       "[12340 rows x 7 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2851e61a-4d99-4f3d-a1d5-4c32909c60f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>TEM</th>\n",
       "      <th>MNT</th>\n",
       "      <th>SLP</th>\n",
       "      <th>WIS</th>\n",
       "      <th>RIN</th>\n",
       "      <th>SSH</th>\n",
       "      <th>CLD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8719</th>\n",
       "      <td>0.518410</td>\n",
       "      <td>0.425714</td>\n",
       "      <td>0.857738</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.683051</td>\n",
       "      <td>0.1</td>\n",
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       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>0.752720</td>\n",
       "      <td>0.717143</td>\n",
       "      <td>0.827381</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.113559</td>\n",
       "      <td>0.7</td>\n",
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       "    <tr>\n",
       "      <th>11650</th>\n",
       "      <td>0.474895</td>\n",
       "      <td>0.397143</td>\n",
       "      <td>0.858929</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.662712</td>\n",
       "      <td>0.1</td>\n",
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       "    <tr>\n",
       "      <th>9849</th>\n",
       "      <td>0.471548</td>\n",
       "      <td>0.351429</td>\n",
       "      <td>0.866071</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.235593</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>12561</th>\n",
       "      <td>0.739331</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.792262</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.716949</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>13598</th>\n",
       "      <td>0.655649</td>\n",
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       "      <td>0.160</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.540678</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>14384</th>\n",
       "      <td>0.789540</td>\n",
       "      <td>0.745714</td>\n",
       "      <td>0.811310</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.520339</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13904</th>\n",
       "      <td>0.367782</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.835714</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.479661</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8930</th>\n",
       "      <td>0.782845</td>\n",
       "      <td>0.677143</td>\n",
       "      <td>0.779167</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.167797</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3522</th>\n",
       "      <td>0.769456</td>\n",
       "      <td>0.745714</td>\n",
       "      <td>0.781548</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.506780</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3086 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TEM       MNT       SLP    WIS       RIN       SSH  CLD\n",
       "8719   0.518410  0.425714  0.857738  0.100  0.100000  0.683051  0.1\n",
       "621    0.752720  0.717143  0.827381  0.174  0.100000  0.113559  0.7\n",
       "11650  0.474895  0.397143  0.858929  0.100  0.100000  0.662712  0.1\n",
       "9849   0.471548  0.351429  0.866071  0.100  0.100000  0.235593  0.2\n",
       "12561  0.739331  0.780000  0.792262  0.236  0.100000  0.716949  0.3\n",
       "...         ...       ...       ...    ...       ...       ...  ...\n",
       "13598  0.655649  0.591429  0.830357  0.160  0.100000  0.540678  0.5\n",
       "14384  0.789540  0.745714  0.811310  0.160  0.100000  0.520339  0.3\n",
       "13904  0.367782  0.357143  0.835714  0.210  0.100000  0.479661  0.1\n",
       "8930   0.782845  0.677143  0.779167  0.400  0.147059  0.167797  0.8\n",
       "3522   0.769456  0.745714  0.781548  0.264  0.100000  0.506780  0.8\n",
       "\n",
       "[3086 rows x 7 columns]"
      ]
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     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ceefcfd1-ae3a-41bb-84af-9b07c4a79920",
   "metadata": {},
   "outputs": [],
   "source": [
    "MLP = MLPRegressor(hidden_layer_sizes = (3,2),activation = \"relu\",learning_rate = \"constant\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1879a094-e16a-404e-976b-3084c7c5965c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPRegressor(hidden_layer_sizes=(3, 2))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neural_network.MLPRegressor.html\">?<span>Documentation for MLPRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPRegressor(hidden_layer_sizes=(3, 2))</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPRegressor(hidden_layer_sizes=(3, 2))"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLP.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4aa436d1-29c8-41c5-b53a-9433760bcdb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7340206962820989"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLP.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a710808e-f056-4a47-a10d-5898b6eebd1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.61347267, -0.66351808,  0.22654204]),\n",
       " array([ 0.38690624, -0.10576964]),\n",
       " array([0.67731755])]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLP.intercepts_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "686aff62-c1c4-4c57-8cd8-6c9564b17d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 8.30631475e-02,  3.10494191e-46,  3.51470969e-01],\n",
       "        [-1.09681878e+00, -2.15979938e-05,  7.97237247e-01],\n",
       "        [ 6.01931090e-01, -5.81975289e-62,  3.37127742e-01],\n",
       "        [-2.86904132e-01,  6.83645418e-05, -2.68552629e-01],\n",
       "        [ 1.04426177e+00, -1.70050711e-54,  2.67780893e-01],\n",
       "        [-4.38008613e-01, -6.12500634e-18,  8.50424951e-02],\n",
       "        [-2.91795715e-01, -1.47247763e-11, -5.91085331e-01]]),\n",
       " array([[ 2.39785251e-01, -7.43950409e-05],\n",
       "        [ 9.07449336e-07, -1.10863143e-03],\n",
       "        [-3.93529532e-01, -1.23470810e-05]]),\n",
       " array([[-1.07362146e+00],\n",
       "        [ 6.32805073e-04]])]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLP.coefs_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "bc1113a3-3b2f-40a9-b9c7-8f70c83f80cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = MLP.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e538f287-ba71-4a53-acff-d840b05f5534",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = MLP.predict(x_train)\n",
    "y_test_pred = MLP.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0bf31bff-f604-4d74-ac5c-c3a83607a2fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#training_accuracy\n",
    "r2_train = r2_score(y_train, y_train_pred)\n",
    "mse_train = mean_squared_error(y_train, y_train_pred)\n",
    "\n",
    "# Testing accuracy\n",
    "r2_test = r2_score(y_test, y_test_pred)\n",
    "mse_test = mean_squared_error(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1d510947-f3cc-4b7d-8e46-4904bde99584",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score , confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ff9fdf52-a5f6-4867-a264-a52d492c7499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7352093487408982"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f64671da-2c8e-495e-9cb0-10af8841ffd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7340206962820989"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ad795553-951a-4304-9253-8aaba41215bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.67731755, 0.6104808 , 0.67731755, ..., 0.61001245, 0.65144337,\n",
       "       0.54489371])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "62a8cd53-d4b1-4adc-9a9a-68440174ca82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.58303811, 0.60127609, 0.55677632, ..., 0.48710922, 0.55526713,\n",
       "       0.63418701])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "2fa47535-2b41-431d-a7da-3f9ed41d44af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEM</th>\n",
       "      <th>MNT</th>\n",
       "      <th>SLP</th>\n",
       "      <th>WIS</th>\n",
       "      <th>RIN</th>\n",
       "      <th>SSH</th>\n",
       "      <th>CLD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8719</th>\n",
       "      <td>0.518410</td>\n",
       "      <td>0.425714</td>\n",
       "      <td>0.857738</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.683051</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>0.752720</td>\n",
       "      <td>0.717143</td>\n",
       "      <td>0.827381</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.113559</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11650</th>\n",
       "      <td>0.474895</td>\n",
       "      <td>0.397143</td>\n",
       "      <td>0.858929</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.662712</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9849</th>\n",
       "      <td>0.471548</td>\n",
       "      <td>0.351429</td>\n",
       "      <td>0.866071</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.235593</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12561</th>\n",
       "      <td>0.739331</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.792262</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.716949</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13598</th>\n",
       "      <td>0.655649</td>\n",
       "      <td>0.591429</td>\n",
       "      <td>0.830357</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.540678</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14384</th>\n",
       "      <td>0.789540</td>\n",
       "      <td>0.745714</td>\n",
       "      <td>0.811310</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.520339</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13904</th>\n",
       "      <td>0.367782</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.835714</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.479661</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8930</th>\n",
       "      <td>0.782845</td>\n",
       "      <td>0.677143</td>\n",
       "      <td>0.779167</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.167797</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3522</th>\n",
       "      <td>0.769456</td>\n",
       "      <td>0.745714</td>\n",
       "      <td>0.781548</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.506780</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3086 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TEM       MNT       SLP    WIS       RIN       SSH  CLD\n",
       "8719   0.518410  0.425714  0.857738  0.100  0.100000  0.683051  0.1\n",
       "621    0.752720  0.717143  0.827381  0.174  0.100000  0.113559  0.7\n",
       "11650  0.474895  0.397143  0.858929  0.100  0.100000  0.662712  0.1\n",
       "9849   0.471548  0.351429  0.866071  0.100  0.100000  0.235593  0.2\n",
       "12561  0.739331  0.780000  0.792262  0.236  0.100000  0.716949  0.3\n",
       "...         ...       ...       ...    ...       ...       ...  ...\n",
       "13598  0.655649  0.591429  0.830357  0.160  0.100000  0.540678  0.5\n",
       "14384  0.789540  0.745714  0.811310  0.160  0.100000  0.520339  0.3\n",
       "13904  0.367782  0.357143  0.835714  0.210  0.100000  0.479661  0.1\n",
       "8930   0.782845  0.677143  0.779167  0.400  0.147059  0.167797  0.8\n",
       "3522   0.769456  0.745714  0.781548  0.264  0.100000  0.506780  0.8\n",
       "\n",
       "[3086 rows x 7 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "050dc8dd-4efa-4191-920b-3eaf2d9550f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEM</th>\n",
       "      <th>MNT</th>\n",
       "      <th>SLP</th>\n",
       "      <th>WIS</th>\n",
       "      <th>RIN</th>\n",
       "      <th>SSH</th>\n",
       "      <th>CLD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4290</th>\n",
       "      <td>0.786192</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.803571</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.649153</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>2440</th>\n",
       "      <td>0.789540</td>\n",
       "      <td>0.677143</td>\n",
       "      <td>0.772619</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.115686</td>\n",
       "      <td>0.249153</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>7414</th>\n",
       "      <td>0.759414</td>\n",
       "      <td>0.688571</td>\n",
       "      <td>0.813690</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.628814</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>8147</th>\n",
       "      <td>0.642259</td>\n",
       "      <td>0.722857</td>\n",
       "      <td>0.811310</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.594915</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14433</th>\n",
       "      <td>0.816318</td>\n",
       "      <td>0.774286</td>\n",
       "      <td>0.766071</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.103137</td>\n",
       "      <td>0.147458</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>0.792887</td>\n",
       "      <td>0.725714</td>\n",
       "      <td>0.767857</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.109412</td>\n",
       "      <td>0.472881</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5653</th>\n",
       "      <td>0.802929</td>\n",
       "      <td>0.737143</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.112549</td>\n",
       "      <td>0.479661</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12588</th>\n",
       "      <td>0.799582</td>\n",
       "      <td>0.751429</td>\n",
       "      <td>0.750595</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.103137</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14487</th>\n",
       "      <td>0.802929</td>\n",
       "      <td>0.757143</td>\n",
       "      <td>0.776190</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13125</th>\n",
       "      <td>0.498326</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.861310</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.418644</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12340 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TEM       MNT       SLP    WIS       RIN       SSH  CLD\n",
       "4290   0.786192  0.714286  0.803571  0.190  0.100000  0.649153  0.6\n",
       "2440   0.789540  0.677143  0.772619  0.340  0.115686  0.249153  0.6\n",
       "7414   0.759414  0.688571  0.813690  0.206  0.100000  0.628814  0.5\n",
       "8147   0.642259  0.722857  0.811310  0.320  0.100000  0.594915  0.6\n",
       "14433  0.816318  0.774286  0.766071  0.160  0.103137  0.147458  0.8\n",
       "...         ...       ...       ...    ...       ...       ...  ...\n",
       "963    0.792887  0.725714  0.767857  0.234  0.109412  0.472881  0.8\n",
       "5653   0.802929  0.737143  0.775000  0.266  0.112549  0.479661  0.8\n",
       "12588  0.799582  0.751429  0.750595  0.160  0.103137  0.100000  0.8\n",
       "14487  0.802929  0.757143  0.776190  0.100  0.100000  0.100000  0.6\n",
       "13125  0.498326  0.460000  0.861310  0.100  0.100000  0.418644  0.2\n",
       "\n",
       "[12340 rows x 7 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ac1a4aa4-44bf-4ed4-86fb-2373a41d49e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8719     0.575676\n",
       "621      0.564865\n",
       "11650    0.610811\n",
       "9849     0.494595\n",
       "12561    0.829730\n",
       "           ...   \n",
       "13598    0.651351\n",
       "14384    0.748649\n",
       "13904    0.494595\n",
       "8930     0.543243\n",
       "3522     0.627027\n",
       "Name: MXT, Length: 3086, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "ea0d2362-4a7b-4b1e-b3af-e627e9044729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4290     0.651351\n",
       "2440     0.635135\n",
       "7414     0.683784\n",
       "8147     0.629730\n",
       "14433    0.645946\n",
       "           ...   \n",
       "963      0.581081\n",
       "5653     0.610811\n",
       "12588    0.564865\n",
       "14487    0.645946\n",
       "13125    0.483784\n",
       "Name: MXT, Length: 12340, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "42104544-31dd-421d-bf94-4a41c74f61fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = pd.DataFrame(y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "01a252cc-f7d1-404a-99cf-9f86b06b78af",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = pd.DataFrame(y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d5d2ea39-3d2d-4d94-9d52-e019474a6e57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c1673350a0>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "b5c5cdc8-18b8-4e26-9895-27909e47a371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c166b306e0>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train,y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d91dc607-630d-480d-bd53-a30e15b4633a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>TEM</th>\n",
       "      <th>MXT</th>\n",
       "      <th>MNT</th>\n",
       "      <th>HUM</th>\n",
       "      <th>SLP</th>\n",
       "      <th>WIS</th>\n",
       "      <th>RIN</th>\n",
       "      <th>SSH</th>\n",
       "      <th>CLD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>69</td>\n",
       "      <td>1013.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14.4</td>\n",
       "      <td>21.2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>85</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>15.3</td>\n",
       "      <td>22.2</td>\n",
       "      <td>14.7</td>\n",
       "      <td>84</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>73</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>72</td>\n",
       "      <td>1016.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15426</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>25.3</td>\n",
       "      <td>35.2</td>\n",
       "      <td>25.8</td>\n",
       "      <td>79</td>\n",
       "      <td>1008.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15427</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>24.9</td>\n",
       "      <td>36.0</td>\n",
       "      <td>25.2</td>\n",
       "      <td>76</td>\n",
       "      <td>1006.7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15428</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>23.5</td>\n",
       "      <td>35.4</td>\n",
       "      <td>26.2</td>\n",
       "      <td>75</td>\n",
       "      <td>1005.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15429</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>24.9</td>\n",
       "      <td>34.4</td>\n",
       "      <td>25.6</td>\n",
       "      <td>78</td>\n",
       "      <td>1006.8</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>15430</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>24.6</td>\n",
       "      <td>34.6</td>\n",
       "      <td>25.6</td>\n",
       "      <td>77</td>\n",
       "      <td>1006.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15426 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year  Month  Day   TEM   MXT   MNT  HUM     SLP   WIS  RIN  SSH  CLD\n",
       "0      1980      1    1  12.5  26.0  12.2   69  1013.5   3.3  0.0  7.9    0\n",
       "1      1980      1    2  14.4  21.2   9.5   85  1014.0   8.0  0.0  0.8    6\n",
       "2      1980      1    3  15.3  22.2  14.7   84  1015.0   5.0  8.0  4.1    3\n",
       "3      1980      1    4  12.4  24.0  10.0   73  1016.8   4.0  0.0  8.4    1\n",
       "4      1980      1    5  12.0  23.9  12.0   72  1016.1   8.0  0.0  9.0    2\n",
       "...     ...    ...  ...   ...   ...   ...  ...     ...   ...  ...  ...  ...\n",
       "15426  2022      3   27  25.3  35.2  25.8   79  1008.5   4.5  0.0  6.8    2\n",
       "15427  2022      3   28  24.9  36.0  25.2   76  1006.7   7.0  0.0  7.3    1\n",
       "15428  2022      3   29  23.5  35.4  26.2   75  1005.7   4.5  0.0  6.8    3\n",
       "15429  2022      3   30  24.9  34.4  25.6   78  1006.8  10.3  0.0  6.7    2\n",
       "15430  2022      3   31  24.6  34.6  25.6   77  1006.9   8.3  0.0  7.1    2\n",
       "\n",
       "[15426 rows x 12 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a003c70f-a11b-4647-9ae3-67d2d492eba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3305150f-1866-4dc9-94ea-d30faa8e873a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop([\"Year\",\"Month\",\"Day\",\"MXT\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "86d1afab-bd37-4685-88d6-26ee73481fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>TEM</th>\n",
       "      <th>MNT</th>\n",
       "      <th>HUM</th>\n",
       "      <th>SLP</th>\n",
       "      <th>WIS</th>\n",
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       "      <td>12.5</td>\n",
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       "      <td>69</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>12.0</td>\n",
       "      <td>72</td>\n",
       "      <td>1016.1</td>\n",
       "      <td>8.0</td>\n",
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       "      <th>15427</th>\n",
       "      <td>24.9</td>\n",
       "      <td>25.2</td>\n",
       "      <td>76</td>\n",
       "      <td>1006.7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15428</th>\n",
       "      <td>23.5</td>\n",
       "      <td>26.2</td>\n",
       "      <td>75</td>\n",
       "      <td>1005.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>15429</th>\n",
       "      <td>24.9</td>\n",
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       "      <td>78</td>\n",
       "      <td>1006.8</td>\n",
       "      <td>10.3</td>\n",
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      "text/plain": [
       "        TEM   MNT  HUM     SLP   WIS  RIN  SSH  CLD\n",
       "0      12.5  12.2   69  1013.5   3.3  0.0  7.9    0\n",
       "1      14.4   9.5   85  1014.0   8.0  0.0  0.8    6\n",
       "2      15.3  14.7   84  1015.0   5.0  8.0  4.1    3\n",
       "3      12.4  10.0   73  1016.8   4.0  0.0  8.4    1\n",
       "4      12.0  12.0   72  1016.1   8.0  0.0  9.0    2\n",
       "...     ...   ...  ...     ...   ...  ...  ...  ...\n",
       "15426  25.3  25.8   79  1008.5   4.5  0.0  6.8    2\n",
       "15427  24.9  25.2   76  1006.7   7.0  0.0  7.3    1\n",
       "15428  23.5  26.2   75  1005.7   4.5  0.0  6.8    3\n",
       "15429  24.9  25.6   78  1006.8  10.3  0.0  6.7    2\n",
       "15430  24.6  25.6   77  1006.9   8.3  0.0  7.1    2\n",
       "\n",
       "[15426 rows x 8 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "x"
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  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "05bb84e9-79fd-420a-9093-f1267b3ae6dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"MXT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9a40e344-f4cb-4d3b-bae6-22c184fbae64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        26.0\n",
       "1        21.2\n",
       "2        22.2\n",
       "3        24.0\n",
       "4        23.9\n",
       "         ... \n",
       "15426    35.2\n",
       "15427    36.0\n",
       "15428    35.4\n",
       "15429    34.4\n",
       "15430    34.6\n",
       "Name: MXT, Length: 15426, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
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  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5372112b-dfb0-438a-aaca-1c488703c8f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in x:\n",
    "    min_val = x[col].min()\n",
    "    max_val = x[col].max()\n",
    "    x[col] = 0.1 + 0.8*((x[col]-min_val)/(max_val-min_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "df3fdd06-9d70-48fe-8374-06dc40ddfdd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.862500</td>\n",
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       "      <td>0.190</td>\n",
       "      <td>0.100000</td>\n",
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       "      <td>0.3</td>\n",
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       "      <th>15427</th>\n",
       "      <td>0.752720</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.588136</td>\n",
       "      <td>0.813095</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.594915</td>\n",
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       "      <td>0.711429</td>\n",
       "      <td>0.615254</td>\n",
       "      <td>0.813690</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.554237</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15430</th>\n",
       "      <td>0.742678</td>\n",
       "      <td>0.711429</td>\n",
       "      <td>0.601695</td>\n",
       "      <td>0.814286</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.581356</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15426 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TEM       MNT       HUM       SLP    WIS       RIN       SSH  CLD\n",
       "0      0.337657  0.328571  0.493220  0.853571  0.166  0.100000  0.635593  0.1\n",
       "1      0.401255  0.251429  0.710169  0.856548  0.260  0.100000  0.154237  0.7\n",
       "2      0.431381  0.400000  0.696610  0.862500  0.200  0.125098  0.377966  0.4\n",
       "3      0.334310  0.265714  0.547458  0.873214  0.180  0.100000  0.669492  0.2\n",
       "4      0.320921  0.322857  0.533898  0.869048  0.260  0.100000  0.710169  0.3\n",
       "...         ...       ...       ...       ...    ...       ...       ...  ...\n",
       "15426  0.766109  0.717143  0.628814  0.823810  0.190  0.100000  0.561017  0.3\n",
       "15427  0.752720  0.700000  0.588136  0.813095  0.240  0.100000  0.594915  0.2\n",
       "15428  0.705858  0.728571  0.574576  0.807143  0.190  0.100000  0.561017  0.4\n",
       "15429  0.752720  0.711429  0.615254  0.813690  0.306  0.100000  0.554237  0.3\n",
       "15430  0.742678  0.711429  0.601695  0.814286  0.266  0.100000  0.581356  0.3\n",
       "\n",
       "[15426 rows x 8 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f5100f0a-c166-479b-bea2-2480fdcfb7ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 0.1+0.8*((y-y.min())/(y.max()-y.min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "6aee2477-f6ca-48fc-a2e7-2905f8d9ac3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.435135\n",
       "1        0.305405\n",
       "2        0.332432\n",
       "3        0.381081\n",
       "4        0.378378\n",
       "           ...   \n",
       "15426    0.683784\n",
       "15427    0.705405\n",
       "15428    0.689189\n",
       "15429    0.662162\n",
       "15430    0.667568\n",
       "Name: MXT, Length: 15426, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e7d809e1-3971-4316-bbe4-9a83d4a2726f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state = 210450)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "7a425d32-468f-4cc9-bd43-c7690dd9bac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4290     0.651351\n",
       "2440     0.635135\n",
       "7414     0.683784\n",
       "8147     0.629730\n",
       "14433    0.645946\n",
       "           ...   \n",
       "963      0.581081\n",
       "5653     0.610811\n",
       "12588    0.564865\n",
       "14487    0.645946\n",
       "13125    0.483784\n",
       "Name: MXT, Length: 12340, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "adc8d658-65b1-4908-92d9-77d16b32ea6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8719     0.575676\n",
       "621      0.564865\n",
       "11650    0.610811\n",
       "9849     0.494595\n",
       "12561    0.829730\n",
       "           ...   \n",
       "13598    0.651351\n",
       "14384    0.748649\n",
       "13904    0.494595\n",
       "8930     0.543243\n",
       "3522     0.627027\n",
       "Name: MXT, Length: 3086, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "849eb9d8-afd0-4c4a-b646-4d7d68462434",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f699ad2b-1943-405d-9e3a-3f2555eef65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Model = MLPRegressor(hidden_layer_sizes=(4,3,2),activation=\"relu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "59f036f5-45a5-429b-b300-1fa49a3a002e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPRegressor(hidden_layer_sizes=(4, 3, 2))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neural_network.MLPRegressor.html\">?<span>Documentation for MLPRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPRegressor(hidden_layer_sizes=(4, 3, 2))</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPRegressor(hidden_layer_sizes=(4, 3, 2))"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "ba6e0192-f6d2-4b82-a54c-818f6f8f7eba",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = Model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "675b421f-4359-4bda-8259-ba82318e23bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.59932163, 0.59932163, 0.59932163, ..., 0.48232191, 0.50898172,\n",
       "       0.59932163])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f4cd4bd9-3a6d-4ab6-a4be-68ae827bfafc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = Model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "2fbe3d2c-7140-400d-80e1-360bb1c8b894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.59932163, 0.5401687 , 0.59932163, ..., 0.59932163, 0.59932163,\n",
       "       0.59932163])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "598a458e-d7ba-4afb-bfc0-78598329f2e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "feecb35e-3f0b-429c-af7e-527e32925e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "sd = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c13d544b-11c5-49b4-a573-311f6d15b93d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.023093142214010487"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_train = r2_score(y_train,y_train_pred)\n",
    "r2_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "342705d8-74fe-4ede-8dbc-a94d0318354d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02108488850981105"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_test = r2_score(y_test,y_test_pred)\n",
    "r2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "b2de8ab8-ed86-4498-bc51-bbf6d32df691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20fd298eb10>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W5SESxWwt8gpXNUO0LlHvydk5ehKpDeLv5Bu62lokSscSqRmU3yfZfL7ai9oq/DpSY2ry2fMucsNvldr2k/2/wd082ygVRL54/HS2Ufh4InV5iEQxqCGv8FThu0BY+sAbBewCkdrCa554DX3xvB+raxDa7/OKazOVhvdJ1tRboyc1XzzSk+JUH9/RFw/LwozFlXUh9zdC6nH4ibzGE4XvAmHpg0AaIvM2NcNaN2ckITUhWvOaTLbUPO965kKJPmrP0VpsLzNibFbbshAv/nKQqpo5elL7xWNaXk/8adt3wvV0APug1d+GaykwMKghr9I7NyMQ1o8JlCEyXxHNUwkPM2DRPQNc3tg7xEWivvGKcE6Jq+dd75vr4O4dhSoKA9a5JmOz0vDXqUPw3x99gzMXnecNuWP0DZ1RcuK8VeE9tV88oiLC8MgIsQU3HX3x8OZ6YxRcOPxEXqdnbgbg/+vHBMIQWaCQb+wd4iLtftcxLhJ/nToEf75voKpjOnvelXKhjPWXMWPtV3h5x1HVQyJpHWKF97XNNRmblYaXJg1RdT5R8p/h37//F842XEFSfBR+k98T6x/Jxb4nRqn+O5p/ZyZ+OzIDzv68HX3x4HAtuYM9NRQU/Hnpg0AYIgsk8mt9sLJOsaLwiqlDsGhLudN1m1w97yJVj1/aUYH1h05g0T3ivRjy+0G0IJ5tT5KeCdOWbGOEcw3NeGv/cbf+jubfmYk/FNyAd4qPY09FLb6qOocLl69NyXfUA8ThWnIHgxoKGv669EEgDJEFmvAwA/L7JCO/T7LD38uBz6s7K/DSjgq734s876IrYdeY2nptHh/TBzNH9XH5OsrvB9H8GNueJG/16Ok13VpezHP6iOuFcpM4XEvu4PATkRf4+xBZMAoPM2DWmL7469QhSNPwvKu9ab60owL5f94pNE18bFYaCqcMdjk042i2kdzTIyIp3n6YTg29p1uLDD1zuJbcwZ4aIi/x5yGyYOGoJ0Dr854cH636/DUm8UTWO2+8Dq/CgN+/a99jY9mTBLRVYLZs+z2D0lwm4T4+pg8eHdkLAxZuVzULyZH9P/zLa+9ZDteSOxjUEHmRvw6RBQNXs5RUP+9u3LdFh2zuvDENfw2zL3OQFB+F8dnX4UjNBSza8q3VNPbUhGhcvtrqsg3rD1VhaM8ktwMaAHh1V6X5/z09rZrDteQOrv1ERAHPE4uaall005LtGknO8knk3+0or8GHpdVW06ndMfO23nh11w+6HEvmrfWaWKeGLHHtJyIKCa6mAGtNdk1up374yZJlTo6rG3R4mAH1l5rx5v7juq7ZVPmvCzoerY231mvicC1pwURhIgpoaqYAq+JmdCEnsoqs+yUyfVyLT8pOIzFW/++u8nN68Mc63Y9tSe+aVhT8GNQQUUDz1BTg2gZt9WAsZy2JFpI7+GOdcN0atZquuM6/sTTztt6YeVtvoX0fWxcYi7FS6GBQQ0QBzVNTgLVMGbZNZBXtRSqu9FyPh0hSsaU+Ke2Ek6rPX7qiebVzLlZJnsCcGiIKaJ6aAuzquEDb0gKW92LbCrlF5TWCZ1N3QzcASIyLRMPlK1DZEeNSl/YxaFUZYKjNr2ESMHkKe2qIKKDJU4AB+1nY7kwBdnVcA4BXJw/B+kdy8fKkbLs1klpaJXxUekroXHnXJyMtMUZoFrm8z009O0K0E6ZddLjQseVhMzVDb2pzlkRyjIi0YlBDFKDYfX+Npyo2uzrunTemKSayHjp2Fmcbml2eIyk+Erm9OikGULZSE2MwJrMLisrPCPfv/GZ4hst9DLgW/GkZehPJWeJileRpHH4iCkDOuu9DdRqsp6YAOzuus9ozoonJwzI64f/+eQpd2sfgtSlD8F8ffYPzjdZ1amIiwzAsIwkj+3TGxJt64MbFn6q6hvcOn8SYzC74+3dnHBbjsx36yUnvaDe05opIIMTFKsnTGNQQBRilQnM19W0LK3aIi7S6KYZSroK7FZuVghRHx3WVFyJa5+aTshp8UtaWe2P72skuX2nF50dr8fnRWiz/e4XqKsE1psuoKVcOJhbcZf3+OPzTOeFzqMlZ4mKV5GkMaogCiEj3ve1NUc5V4MKZzqlJXnUWWMrPdfto9YtJOgpobF24fFX1cZ0xAHh6aznuyEpV3cskE81Z4mKV5GnMqSEKIK667x1hroJrapJXRfNCAqW3wVGir2hQ0Sk+SlWwLM8oUwp/lFYmJxKlKagpLCxERkYGYmJikJOTg7179yruu3v3bhgMBruf77//3rzPv/3bvznc56677jLvs2jRIrvfp6amamk+UcDSeqPUXFU3BKhNXhXNCxFJEvYnlu8tV8EH0JbgXDx/tKreP0/NVCOSqQ5qNm7ciNmzZ+PJJ59ESUkJRowYgXHjxqGqqsrp444cOQKj0Wj+6dOnj/l3H3zwgdXvysrKEB4ejn//93+3OsaAAQOs9vvmm2/UNp8ooLnbLR8ovQdquTMTTO0yC6LPYVK7aOFp2v7A8r0lMp392QkDERWh/nuxp2aqEQEacmpefPFFTJ8+HQ8//DAAYPny5fj000+xYsUKLF26VPFxXbp0QYcOHRz+LinJuqtxw4YNiIuLswtqIiIi2DtDIU2kIJwzwZirIJILo8csJXk/0ecwNaGtDb9b+xUMcHspKY8KMwDnbGrTyMGH7XNrW2BQCy5WSZ6iKqhpbm7G4cOHMW/ePKvtBQUFOHDggNPHDh48GJcvX0ZmZiaeeuop3HbbbYr7rl69GpMmTUJ8fLzV9oqKClx33XWIjo7GsGHD8Oyzz+L6669XPE5TUxOamq79oZpMJqdtJPJ38jdotTdKrVV1/Z1Iwi4Ap0GPaJBSe6EJm0urkdwuGqkJ0ThtanJZwTg8zIAVU4dg0ZZvUWNqstrHn4KcVgl47N0SrAgzWAUrngw+3J2pRuSIqr7D2tpatLS0ICUlxWp7SkoKamoclwNPS0vDypUrsWnTJnzwwQfo168fRo8ejT179jjc/9ChQygrKzP3BMmGDRuGNWvW4NNPP8WqVatQU1OD4cOHo65Oec2UpUuXIjEx0fzTvXt3NZdL5JeUuu87xLXNtgmVXAWRXJj5H3yDGS4SgEXyR8IMwNNbv8OsDaV44I0vcPlqKySoea6t90x08VrJr6Uj8vDPb0dmIM3mPZCWGKO4/ZERGXD28ku4ljtkOZx36NhZ3JyRxJWyKSAYJEkS/sJw6tQpdO3aFQcOHEBeXp55+5/+9Ce88847Vsm/ztx9990wGAzYsmWL3e9++9vf4sCBAy7zZRoaGtCrVy/853/+J+bMmeNwH0c9Nd27d0d9fT0SEhKE2kqBz9nQQyBzdF1F5TUhs6ZOcWUdJq86qPnxco/KvidGoai8Br9b+xUAsR4UuafFVU0gpZ4kV4+Xe0eKymvwUekpq6Rjy3MovbcdbX915w94acdRl9f2+Jg+2PCPEyHxHqLAYTKZkJiY6PL+rWr4KTk5GeHh4Xa9MmfOnLHrvXEmNzcXa9eutdve2NiIDRs2YMmSJS6PER8fj4EDB6KiokJxn+joaERHixXAouCk98J5/hQgOeq+D6VcBXeTni0TgJXyR5Sq6sq9NLGR4Xht+hDUNjTZPdeuepJcPT6vVyfk9eqEJ+/KVHw9lYZwbLdvLzMKBTQA8NIO+89U1jqiQKEqqImKikJOTg6KioowYcIE8/aioiKMHz9e+DglJSVIS7P/w3jvvffQ1NSEqVOnujxGU1MTvvvuO4wYMUL4vBRaRPIt1HxAB8rKwqGSq6BX0rMcHNkGhLUXmvD01u8UHycHRWFhBozP7mr3e9FZVUqPl+lRJXnxx+WaHw9cC8LUrsZN5G2qZz/NmTMH06ZNw9ChQ5GXl4eVK1eiqqoKM2bMAADMnz8f1dXVWLNmDYC22VE9e/bEgAED0NzcjLVr12LTpk3YtGmT3bFXr16Ne++9F5062f8Bz507F3fffTd69OiBM2fO4JlnnoHJZMJDDz2k9hLIT+nZCyLyLVnNB7TeARK5z92ZYDLbqcxyALG5tFro8Uo9Rv6yJICWgo2OcF0mCgSqg5qJEyeirq4OS5YsgdFoRFZWFrZt24b09HQAgNFotKpZ09zcjLlz56K6uhqxsbEYMGAAtm7dijvvvNPquEePHsW+ffvw2WefOTzvyZMnMXnyZNTW1qJz587Izc3FwYMHzeelwKZ3L4ieC+fpHSCRPpzNBJNfhcS4SNQ3XnE5S8kRtSX9bYNy0bWfPD3NXu+gKVhrHVFwUJUoHOhEE43Iu5wlUwLQ1AuyubQaszaUutzv5UnZTrv+AfGE1PWP5IbMN1h/yi1yFhADcJgALPLeammVcMuynYo9QbaJxnb1XBKicflqq2JQBbQtM1A8f7SqIna2z31Oekcc/umc4mvhbkK1rVB6n5P/8EiiMJHePNULoufCef4yjOAv/C23yFVytNYCciI9QQvvzjTPnLJ9D1vWsVGqS1PX0Ixbn99l1xaloNHRc2+bzGz7Wug1TOfJWkf+FCRTYGNQQz6l5zCRJVcf5Go+oLmy8DX+mlvkLJnWnRlhrqrq3p6ZiluW7XQalCfGRSImIhw1Jsfvc9vnTilovGdQGlbuOWZ3LtvZWbbHswzOtLKtv6NnEOJvQTIFNgY15FOe6gUR/ZYt8kGsZ4AUyAI5t8gy6HE2fJMcHw0YgNqL16ZYOwuKiivrXAbl5xuv4J1fD8as90pwtuGKw33k5661FXjsXfug0Vh/Ga/vOSZ0rY5eC6XgzJL8ij06MgNbvjYq9mzpGYT4a5BMgYtBDfmUJ3tB9Fq7Rs8AKZB5qlfNU0SLEyrVogGsb9aOrkk02P7ieJ3DgEYmP3dPfPBPXZZPcPRaWAZnjor6Wf5d/OfY/orDX3oFIYEcJJP/YlBDPuXpXhC9itF5cnE/W/6aXxBIuUWOehNsq/fKnC3o7epmLR5si71+Fy5fFTyeGNvXQu6x0lLUT+8gJNCCZAoMDGrIp7zRC6JXMTpvVOv15/yCQMktUupNcBTQuOLqZi0alOf16oRXd/2g+vzuSo5XnlZu+3chr/ekNAyndxASSEEyBQ4GNeRz3uwFcZcnq/X6e35BIOQWOetN0MrZzVo0KM+9vpMuM5BUE4y3HQXTltISYzAuK1XoWKJBSKAEyRRYGNSQXwilNYscCYT8gkDILdKreq4jSjdrZ0H5grv6IzE2Cv/3z1OYdFN3vLSjQnF6tyfUXmxyuY9SMG2ppv4y3tx/XOicokFIIATJFHgY1JCu3MkHCZU1ixzxdX6B6Ovm77lFnhyqUKocrDRD6lxDM57eap/XAwk4f0n9UJg7bVYi2rMl/95ZUrXaICQQgmQKPAxqSDf+nA/i73yZX6D2dXPVq6ZHorPW95Inhiosb9au2iUHnNvLjA6nZmvJ63G3zc6o7dmSAxq9gpBAGnqmwMBlEkgXnljqIJR4cykGy6DjeG0DXtpRYbeP1tdNj8DWnfeSvLSBXkNQlucE4HSYZvboPrgpIwlnLjTh6f/71ukUbk9S89qJLidiKTYyDNGR4VYBmrtfXvx1xh/5Dy6TQF4TCPkg/s5b+QWuEkJlvlrJ3N33UniYAfcMSnNaqC4uKhyNzS3mfzvLcRGpHCxb/nf74NAX1PRyaOnZunSlFZeutOLxMX3QMzlelyAklIeeSV8Mashtvs4HCQbeyC8QSQi1JL9ub+8/huT20U5vXnoFtu6+l1paJWz52uj0uhJjI7Fq2lDUNjTheG0j1h+qslrCICk+CvdmX4fbM1OFKwf7gw6xkXjtgSHIvb6T8PtE67pQBgAb/nEC+54YZXUu9riQrzGoIbex3oQ+PJlf4M5U56e3fmf+f6VhBr0CW3ffSyI5Isb6ywgLMyA6IgzLdxy1e07ONTTjrf3HrW7I/vzelUOGP/+/gcjvnazqsVrXhXL0ejKnjvwBgxpyG+tN6MdTU9v1muqsNJSkV2Dr7ntJtB019Zfw3KdHhHuW/Om9mxgTgXqLysOugl5XvSdyML1oy7eoMbmeAm5Jfr79vcYShQ4GNeQ21pvQlyfyC/TqaVAaStIrsHX3vSTajrMNzap6luR2eWoIygCgY3ykUHJx4dQchBkMQkGvaO+JHEy/uvMHvLTjqHC7u7SPYU4d+ZUwXzeAAp/chQ3YFzBlvQn/oGdPg+UNXybf9JVeYQPabqauAlt330ui7Uhqp7x8gCU5GJQTkD3pmfFZbXVsnEiKj8RNPZOQ16sTxmd3RV4v5fwZuffENhCTe0+2l1nnHoWHGTBrTB/8deoQpCY4f79Yvp5qhh6JPI1BDelC7sJOTbT+MExNjGHXsx9wdbPXYv8P/8Lm0moUV9YBgG6BrTvvJdGgyNVNW2ZZcM9VArJWaT9fV1iYwWUdm7MNV3Dr87vsAhK5jcWVddhcWo39FbVYtEW59wRo6z1pcVBJb2xWGvbPG4XHx/Rx2Abb15M5deRPWKeGdMXZD/5L/uYOOJ5dNXtMX/RMjkPthSar5GAR8pAGAN2SRd15L7kadpHr2bga5pJn94jWEbI9BgA8OjIDm0tPWeWrJMVF4t7BXc0zrAAI19dxVIdGdKq+LVd1j0SGr7xZY4lCF+vUkE+w3oT/Ep1d1dIq4Y19x1RN87VMCN33xChdAlt33kuuEq7VTqG3nPItKtUi0Ntcat2zEhURjpszkszXp2bKuG2eSlF5jaqp+pZcXZdI4jpz6sifsKeGKMSI9IAo9eo4Y9u7EQhEeiK2lxnxXx9+o6pCcFJ8JJ4Zn4WwMINQdWQtlX0BYN3DwzD3f7/WnMCcFB+JZycMdHt42FUvIIegyV2i928GNUTkkKeGNPyNsyBPbcFCmdz70yEuUjFPxjIIPPhjHR544wvVbZ95W2+8uusH1Y+zbYceQQfr1JAncfiJiNxiO/RQcfqi0A000BJClYa53ClYKD/GWeKvPCvo1Z0VWH+oSsNZLM+knQTgvz78BpeutCI1QftwoadqLBGpwaCGiBRZ3vCLK+uEghp/KlTniGgCsl4FC11xtKCoK3IvT971yXh1V6XbbTjbcAWPbywF4F7vCnPqyNcY1BCRkGBICFUzROKvPU6Wicy5vTohSbBonyh3qwBzBiT5EuvUEJGQQC+yqLYYnT/3OD06MgNjs9IQHmbAhOyuuh7bVR0bZ7aXGXHLsp2YvOogZm0oxeRVB3HLsp0O6+oQeQKDGiIS5k5hPMvicMWVdapvmO5wVcofsL+JixQsDDPYB3gyA9oShQ1Q3kcLCcDre46ZA4Uxmak6Hv3aOdRWAVYbNBJ5AoefiEiRo6EELQmhvp4Zo2UVcZFaNo+MyMDKPccUf//n+wYCsC9IqIc5732N9jFtyyY4GxaU2yNZ/FeU6BAc138if8GghogcchWIiCaE+sMKzlpL+YsULBzco6PLgoZti0VWaEoKVtLY3IIH3vgCaYkxuGdQGlbuOaa4r/zcJ8RGoP7SVcX9bDkbgrMMeGsvNKkOGok8gUENEdnRKxDxl2/w7qwi7qxnqqVVQmJsFP5z7A04e7EJSfFRSE2MddhzteEfJ3S5Fls19Zexcs8xPDwiA6v3HYPSqJ4BQJhB/Dl2tgCp1hpGvky+ZgJzaGBQQ0RW9AxEtAz7aG2zJ0v5O5qq7Kwny/Z58eT0cPk1+eCrasWARt7vnIsFMy0pJX1rLUgI+C752tfDn+Q9TBQmIitqAhElclLwJ4LJoe58gxeZcaP3zC21SbFa1o5SQwJQ19Cs2/EeH9PX4c1ea0FCA5z3/HgSE5hDi6agprCwEBkZGYiJiUFOTg727t2ruO/u3bthMBjsfr7//nvzPm+//bbDfS5ftn4TqjkvBT5fzpYJZVrzT2SWQcaa4p+EjpXcLlq4fbbnEr1h6TVza39FLRZtUTeT6uzFJgd7+0ZSfJTT2VipCdGYOaq3w99p6XHy5XR/LbPeKLCpHn7auHEjZs+ejcLCQuTn5+P111/HuHHjUF5ejh49eig+7siRI1brNXTu3Nnq9wkJCThy5IjVtpiYax8+Ws9LgYndxb7jTv6J1qGJP7xXikX3DFD12moZJtNr5pYzjobUkuKjhK/LHUnxkTjXcMXpENvEod2x/O/2CcvyM7DongGKz4c7q5X74u/WW8Of5D9U99S8+OKLmD59Oh5++GH0798fy5cvR/fu3bFixQqnj+vSpQtSU1PNP+Hh4Va/NxgMVr9PTbWuvaD1vBR42F3sW67qsygNJbizVtJpU5Pq11brMJmcHzM+uyvyenVyGdA4ei+KsOzJSk2MVf14tTrGta0MDjgeYpMAXLrS4jCgAcR6rER7nKbl9sDLk7Kx/pFc7HtilM++iLjb60iBR1VQ09zcjMOHD6OgoMBqe0FBAQ4cOOD0sYMHD0ZaWhpGjx6NXbt22f3+4sWLSE9PR7du3fCLX/wCJSUlbp+3qakJJpPJ6of8G7uLfU9r/ok7ybBaXltP37DcCdIAIDk+2jxk1SpJSE3QNsQm6lzjFYSFGRwOsXWIiwSgvMDm42P6KAYflkNvdQ1iQc2QHh2FgkZPc6fXkQKTquGn2tpatLS0ICUlxWp7SkoKampqHD4mLS0NK1euRE5ODpqamvDOO+9g9OjR2L17N0aOHAkAuOGGG/D2229j4MCBMJlMePnll5Gfn4+vv/4affr00XReAFi6dCkWL16s5hLJx9hd7B9E6rPYcvfbrtrX1tM3LK1BmgFAYlwk/vC/X1sN18iBhRZyWBAbFY7G5hbFfRZ/XI59T4yyGmJLbheNP7xX6vTYG/5xAjNH9bH7ndap297omRIRDOuVkTqapnQbbGodSJJkt03Wr18/9OvXz/zvvLw8nDhxAi+88II5qMnNzUVubq55n/z8fAwZMgR/+ctf8Morr2g6LwDMnz8fc+bMMf/bZDKhe/fuAldIvsLuYv+hNv9Er2+7oq+tp29YWt5j8jBPW4+Ida9I/c+9JB1iI3H+kroFKFMTYzDpph54acdRxX1sg0LL1dVrTMo9LPLjDlbWIb9Psnm71vwoX81yckSkKrQ/r1dG6qkafkpOTkZ4eLhd78iZM2fselGcyc3NRUWFcmXNsLAw3HTTTeZ9tJ43OjoaCQkJVj/k39hd7F/U5J+IrJUkQvS19fQCm1reYykJ0Yo9MnLycmxUOOKjwh3uI+sYF4l104dZ5aX0TI4TaoNtMCYanD327rWcJneG3hbc1d+vggR3Zr1R4FHVUxMVFYWcnBwUFRVhwoQJ5u1FRUUYP3688HFKSkqQlqb8RpIkCaWlpRg4cKCu5yX/x+7iwOXqW7GEtiGY+kbns3PUvLZahslEibwXUxKi8T+/zEbtxSZ0aR+DVknCA298oXhMuVfEFTk/Znx2V3NhwYrTF4XabRuMiQZn5y9dMVeLToyN0pwf1THes7lDWmiZ9UaBSfXw05w5czBt2jQMHToUeXl5WLlyJaqqqjBjxgwAbUM+1dXVWLNmDQBg+fLl6NmzJwYMGIDm5masXbsWmzZtwqZNm8zHXLx4MXJzc9GnTx+YTCa88sorKC0txWuvvSZ8XgoO7C4ObK6CDAC6v7aeumGJvBcX3TMA+b2vDdlsLq1265yWzly4rCqnRSkodBWc2Vr8cTn+c+wN2hoN/x0adlQVmoKP6qBm4sSJqKurw5IlS2A0GpGVlYVt27YhPT0dAGA0GlFVVWXev7m5GXPnzkV1dTViY2MxYMAAbN26FXfeead5n/Pnz+PRRx9FTU0NEhMTMXjwYOzZswc333yz8HkpeHjy2zd5nqsgwxOvrZoblu2SCjnpHXH4p3MO26r2vajnsOjx2gYs31EhFIjYBoW217jgrv547N0Sp8cArvUkuVMskEPD5EsGSZJCZm6syWRCYmIi6uvrmV8TANQuQMcF6wKHr14rRz0fYQZYrZmUmhCDyTf3QM/kOHPbWlolvFN8HD+dbUR6Uhym5fVEVIR9SmJLq4ScZ4oUp04DbUNwMRFhOG1qcjoMJ0mS0wRfS5aFKZUKV94zKA0b/3FSKEn5pV8OwnOfHhHu3bFs974nRvHvjnQnev9mUENBIZgrEDNYa+Pu86B1No+jGi+O3lstrRIOVtbhkXe+VJx2DbQlAf/p3ixzz4mjYa3ZY/o6nekkm3lbL+T37mx+LpSuUT7urNF9FIvvWVr/SC7qLzXjd2u/smujI/LxmXhLniJ6/+Yq3RTwlD7I5QrEgfxBG8zBmhruPg/uzOZx1Oti+95Sk/tyrvEKjPWX8ev8nvio9BTOWixEKQ9rNV1tFWpbn5T25mE3kcKVa4qPIzUhBqdNrhPxw38u5Gd7XR3jIi2mrVu3O5Tek+SfGNRQQNOy/k+gCOZgTQ09ngd3qh07Yvne+t54Qaj3w9LTW78z/39SfCQmZHfFmMxUczBRXFkndBzL/BWRazzbeAXxUa3m9rtK1lbKj5LPF+q9h+R/GNRQQAvWCsTBHKypodfz4IkZOfJ7S21AY+tcwxW8uf84brIIDLSUNhC9xoafh8YS4yKFeluUkrAD6e+JQgeDGgpowVqBOFiDNbX0eh78eUaOo+BMS2kD1dcoSVj38DBzjR2l3hbmdFEgYVBDAS1YKxAHa7Cmll7Pg9paLd7mKDiTp5Mv2lJutYaUUo+K2ms8f+kqAGB8dlfFfZjTRYFG1TIJRP7GVWl+A/xrLRpRwRqsqaXX8+BsSQV/4jg4sw5RlCasWl6jKGe5O3Iuk21PmZzLJC+pQORPGNRQQPP0+j++EqzBmlp6Pg9KawD5E8vgTA4qbGvVnDY1Ycbar7Dk429RXFmHFosiO/I1tot2vrbUNY4DJJGZVIs/Lrc6N5E/YFATAlpaJRRX1mFzabXdh2AwCMYF64I1WFNL7+dhbFYaFtzVX78G6sQ2OBMJKt7cfxyTVx3ELct2WvWajM1Kw4opOULnzbs+2eH2g5V1wrlMRP6EOTVBLlTGxINxwTouF9FGz+ehpVWymk6txoK7+iO5fTSS20XjD++VKlYE1soyOFMzBd3R1PbhfZLRwWZ2k60OcZHIdZBcvb3MiHmbvhE6d7DndFHgYVATxEKtzkkwLlgXjMGaFno9D1rr1XSMi8Sv8jPM51t0zwBztV13JcVF4tn7Blr9LaoJFpRmT/35voGY4aSNf75voN3zp7bqcrDndFHgYVATpFjnJHgEY7CmhR7Pg9aeBcu/o5ZWCYmxUQ4rAqvVLjocB/9rjN06UmqDBaXZU3+dOgSLtnxrlZeTmhCNRfcMwO2ZqSiurLNa2FO06rLSiuBEvsagJkixzolnsGZHYNPas3C+8QoOHTuL+kvNdsNgSfGRGNy9A0pO1KsOcF7490EOF8bUOgV9/w//snpvKvVwFZXX4JZlO22uI0pV+0Mhp4sCD4OaICPfdD8RnG7JMXFxoZKfFMzkYEHLENSO8hq8uf+4XZBxruEKdn7/L7w2ZTA6xkdjR3kNVu8/7vRYcm+J0vvGWfE9Z17dVWn+f8v3puUXF6UhJtGApkNsJP78/wbyPU9+iat0BxE1i+rJ1j+Sy54aAa5WPw62/CRP82WP1/Yyo9NcEyVJ8ZE42+A48VYejtn3xCjzatmOenRs13gSaavav2nLNgHW782WVsmuh0atdQ8PQ35vx7OmiDyFq3SHGLUJfhwTF8f8JH35usdLzjWZ98E3TmcHyQxoG5qpc9KTYTucq1dis+VxRHqAbNtk+950Z2FP+TMj93p+CSL/xTo1QcDZTdeRUKpzogc1+UnknL9UqR2blYbDT92Ox8f0RYfYSKf7SgDGZ18ndFzL4Vw5sXl8dlfk9eqk+W9NPs6Cuwfgr1OHIE1F8UDb96bW4WZ+ZlCgYE9NEFD77SvU6py4i+sw6UPPHi89hq/CwwyYNaYPZo7qjVd3VuClHcqrbSfGRgkdU48pzs6uzbYHqOL0Rby66weXx5Tfm8ntooXaYDvUxs8MChQMaoKA6M30wbx0jMtK44wdlbgOkz70mpHnieGrDf84ofg7A4AN/6hCakK0YsE9vYZzRa7Ncmp7cWWdUFDTpX0MtpcZsWjLt073k6/j8z/ehsM/neMsPwo4DGqCgOjNdJzNLAgS42p6LfOTlFn2OlScviD0GGdBupqCkqK9OaLB1uNj+mD5jgq72UhKQzNK51farqVYpuh781xDEx57t8TpELXldURFhJk/K1jGgAIJg5ogwJuuZzmbXqs21yCUbhBaZ+4oBelqhq+KymuEe3NEezp7JscLL9eg1ONyz6A0bPnaaLd9wV398fTW71QPzYm8NxfclYmnt7rOuVNzHRyKIn/FKd1BQv6WBzj+YOOUY/e5+wGv9w3CnwMktbPxAPtp0baKK+swedVBl8eRe1REp9+LHlcuf+DqedcyE1F0X6USDM7eW4mxUULXt276MOT3uTZVm2UMyJ9wSneI4eKHnufONF291+Hy52/QamfjySQAk27qrvh70R6VtxwUyJOP76jH41xDE8IMgNLi9Wp6OrVcu7qKwbUO33PO3pubS6uFjl3bcG0pBZYxoEDFoCaIcPFDz9Oy/pDeNwh/X6jUnVooL+2owIZ/nHAYnInmjp2/pFx7xjYZeXuZ0WWuCXBteNFVUb3WVsmtwnauvLrrB2z66qTD50fpvakl0Z3LrFCgYp2aIKNXbQzSj5obREurhOLKOmwurUZxZR1abLoPXAVIQFuAZPs4bxLtURmXlepwu1LNGjl3TOkdbQDQIc55zRnLNor0qoQZgNemtAWJSjV2zjZcwer9xzF51UH8du2XQud3h9qaPiLPW5pNTxTLGFCgYlBD5GGiH/zyIoOTVx3ErA2lmLzqIG5ZttPq5hUIhQBFewa+UGijUnAmJ8UCsLtBy//+9fAMoXMnx0cL9Si1SkDH+CjhYaWLTS1C53eH2uBV5HmzTXRnGQMKVAxqiDxM9IP/zf3HXVbaDYRv0CI9A23F3cSWHbA0NisNj47MgMHm4AYD8OjIDMwc1dvpuWV/+N+vUVRe4+pSALQ9l+4MqXmC2uBVzrlLtalGnJoY43SquJreHSJ/wKCGyMNc3SCAtmEOR2y/lXvqG7SrYS81RHoGJmR3FTqWbXC2vcyIlXuO2SX1tkrAyj3HUFReo3huS6dNl/Gm4DpKXdrHeD1IjI8KF9pPTbvGZqVh3xOjsP6RXLw8KRvrH8nFvidGOcy/0tK7Q+QPGNQQeZjIDcJZDGH5rdwT36C3lxldDnup5apnYEym43waWxWnL5iDLJEhIDnhesXUIUhJUF4SQE7QdnZPtnwu9RhmSU2Ixm9HZiAp3nXeT0Oz2DCW2napyblT27tD5A84+4nIC5xNub8zK1Vo9eUzFy7rWggQ8OxMKmez8VpaJacFI2Wv7qrEq7sqkZYYg0k39RDOJxqblYb2MZF44I0vnO4vV+ly9VzenJGEDnGRQqt6KzlzoW3K9IJfDMDjG0td7t8hNhL1l674tKAmZ1RSoGFQQ+QlSjeIQ8fOCgU18rdyvWoSeaMWidI0Y2fBmSM19Zfx0o6jQueUh2RqLza52LPN9Pye2PqNETWma/unJERj0T0DdO2NaJWA1/ccwy8GivVS/Wp4T7z8d/FlGTxFSxkDIl9hUEPkRY5uEFqWudDjG7Sva5EoBWdKbRElB3+iQzPtYyKhPDDY5tCxs2710ljaWiaWoHxTzyQW1CRSiUENkY9pHVJy9xu0P8yksgzO9v9QK7TitBLb4E90yGj53yvstp02WQ+/6fkciC5MU9vQhPHZXTn8Q6SCpkThwsJCZGRkICYmBjk5Odi7d6/ivrt374bBYLD7+f777837rFq1CiNGjEDHjh3RsWNHjBkzBocOHbI6zqJFi+yOkZoq1o1L5O98kZTpL7VI5OCsT0o7t4+l15CM1llneqq90ITNpdXmBHEW1CRyTXVPzcaNGzF79mwUFhYiPz8fr7/+OsaNG4fy8nL06NFD8XFHjhyxWoSqc+fO5v/fvXs3Jk+ejOHDhyMmJgbPPfccCgoK8O2336Jr12tTPwcMGIAdO3aY/x0eLjbtkSgQeDsp099WdxcNHO6+MRVbv6mxmjEWZgAeGZFhFfy5O2TkaNaZq8RmUe1jInDx8lXFY4UZgKe3fmf+tz+t68VeI/JnqntqXnzxRUyfPh0PP/ww+vfvj+XLl6N79+5YsWKF08d16dIFqamp5h/LgGTdunX4/e9/j+zsbNxwww1YtWoVWltb8fe//93qGBEREVbHsAyMiIKBN5e58LdaJCLT1TvGReLjf9Y4rFPz+p5jVtPQ9Royspx1JrfDtl2A+BINYQZg6b0DHR5LZnt9Sksj6FlfyBVPTP0n0puqoKa5uRmHDx9GQUGB1faCggIcOHDA6WMHDx6MtLQ0jB49Grt27XK6b2NjI65cuYKkJOtviBUVFbjuuuuQkZGBSZMm4ccff3R6nKamJphMJqsfIrrGn2qRiAQOTVdbnR5j/gffmG/seg0Z2c46s32ukuKj8Jv8nnhtyhCsmz4Mo25w/mXrkREZ+EX2dQ6PJVqEEfBukKG07pXadaiIPM0gSaJpa8CpU6fQtWtX7N+/H8OHDzdvf/bZZ/G3v/0NR44csXvMkSNHsGfPHuTk5KCpqQnvvPMO/vrXv2L37t0YOXKkw/M89thj+PTTT1FWVoaYmLY/+k8++QSNjY3o27cvTp8+jWeeeQbff/89vv32W3Tq5DhZctGiRVi8eLHd9vr6equhMKJQ50/DCo5Wwk5LjMHEod0dJvXaWvfwMOT3TkZLq4Rblu3UPGQkD7/te2KU1XMhP1c7ymvwYWk1zjZcG+KSh4lKqs5h1d5jDofI5t+ZaXesMxcuo/ZCk9WQk5L1j+Si/lKzw/pCcistA1Itr63lY5LbReMP75VaTXkXeZ6I9GQymZCYmOjy/q1p9pPBZuEVSZLstsn69euHfv36mf+dl5eHEydO4IUXXnAY1Dz33HNYv349du/ebQ5oAGDcuHHm/x84cCDy8vLQq1cv/O1vf8OcOXMcnnv+/PlWvzOZTOjevbvYRRKFEH+qRaKUW/RSkVidmuLKOuT3TlZdC8eSq1ln9Zea8eb+406LFn67uB+e3VaO43WN6NkpDv91ZyZibZY/sHzeN5dWC7Wtpv4Snvv0iFB9oaLyGocBorP8HEdBpTOenvpPpIaqoCY5ORnh4eGoqbGus3DmzBmkpKQIHyc3Nxdr16612/7CCy/g2WefxY4dO3DjjTc6PUZ8fDwGDhyIigrlb27R0dGIjlYulU5E/slxkCUallzbT00tHEvOasGIFC2c/8E3iI4IM/du7K0Adnx3xmkwITpcdrahWai+0Ks7K7B8R4WqatFKFaZF+HIRVSKZqpyaqKgo5OTkoKioyGp7UVGR1XCUKyUlJUhLs/5jev755/H0009j+/btGDp0qMtjNDU14bvvvrM7DhEFp7zrkzXtZ7mQ4/T8nnZrL3WMi0RirPX3O2ej8iJFC881XrEbrnGVfyK6rleHuCjFc1t6y0FPktw+wDo/B3AerInwxbR3Iluqh5/mzJmDadOmYejQocjLy8PKlStRVVWFGTNmAGgb8qmursaaNWsAAMuXL0fPnj0xYMAANDc3Y+3atdi0aRM2bdpkPuZzzz2HBQsW4N1330XPnj3NPUHt2rVDu3ZttSvmzp2Lu+++Gz169MCZM2fwzDPPwGQy4aGHHnL7SSAi/5fbq5PLYnod4iKRq7AsQ16vTsjr1Qn/dVemeWjreG0DXtrhqPhek2JvhtYeCVdLT4gUYbxnUBr+tM113g0AnL+k/Dw5GjJyFawp8fbUfyJnVE/pnjhxIpYvX44lS5YgOzsbe/bswbZt25Ceng4AMBqNqKqqMu/f3NyMuXPn4sYbb8SIESOwb98+bN26Fffdd595n8LCQjQ3N+P+++9HWlqa+eeFF14w73Py5ElMnjwZ/fr1w3333YeoqCgcPHjQfF4iCm7hYQb8+b6BTvf5830DXSarygHOL268Dm8dOO5wH6XeDMC9HgnLYMIRZ7PRHh2ZgZV7juFsQ7PTcxggPr28xnQtiHFn+MibU/+JnFE1+ynQiWZPE5H/2l5mxMLNZTh94drNPaV9FBaPz1I1Bf3lHRVCi2SufyTXKr/H3VlVAPDypGyMz+6q+HvbGUs56R1x6/O7hHpSDABmj+krdG1J8ZF4dsJAjM1KQ3FlHSavOqjmMtAhLhJ/vm+gz4sCUvATvX9rWiaBiMiTXBWVMxjCnP5b5Phv7T8mtK9tD4azejqiXPX22BZhPPzTOaGAJik+EiumDsHMUb2Rlui6R+lswxVzno+rnB5HXpvs3VpGRK5wQUsisuPLujVKdWrkQMLR7BzbBShdOXTsrNOcE0uOAhClWVUiSymEGYDs7h1QXFkn/PyKDg0t+MUA8/XfMygNr+8RC9zkPB81U+CT4iNxE/NoyM8wqCEiK86CCk9/K1eaUizPHEqMixSqz+IqABMNEjrERuLmjCSHQZ6jejqtrRIeWP2F02O2SkD+sp1WuTFpiTFYcFd/dIyPdhjoiObxpCa07dfSKmHL12JVfi3zfNRMgT/bcAW3Pr/LL9akIpIxqCEiM1dBhSeXTnBV/wWA05lPaorAiQYJv87v6bKAneW5RAvo2Sb7Gusv4/fvllhtszyH2sVHtcxkkgM9y2DNUdVkS954XxCpwZwaIgIgFlQ4mg2kF61Tim2J9MKI5I90jItEny7tVa15pGetFstzqF18VMtMJsu2yzk9C+4egIPzxyAp3nFtHG+8L4jUYFBDRADEiso5m47sLr0q0ooEFq6SfQ0A/nRvFp7e6jrI219Ra05ozknvqDrZVoltwOBq8dHbM1PNydW1Fxyv0+SIXNRPqc7M4Z/OOZ1G7un3BZEaHH4iIgDiQYWnyuG728uhtgics2TfhXdnIjE2SijIs8yhSUuMwT2D0rByzzHV6005O4c8pKa0LlZReQ1uWbbTqr1hBkC088RZnRlfvy+I1GBQQ0QAxIMKT5XDF8kb6RAXiXONVxQr7qotAqcUJISHGYTzYyzV1F/Gyj3H8OjIDGz52mgVZCTFRyrmprhiGTDYroullAclEtCIJID7+n1BpAaDGiICIBZUeLIcvsgyAUt/rihs27vibAFKJbYzmn5x43VWAZGWm7Q8C2vL10Z8/sfbcPinc3YF9LQU7VNqi5b1mhJiIjC4RweM7NMZ0/J6IirCeRaCr98XRGowqCEiAGJBhafL4SsNCdkGLUq9K6JEpq27upkrkYeMDv90zm4Wlpo6MJbtUgoY1CZXt4uOgOnyVXx+tBafH63FG/uOuQwG/eF9QSSKyyQQkRVv16lxVAMGgMeK/ykN18hHt5yeLO8LqM+PeemXg5CaGGt3DY6eX2ceH9MXs8b0cfi7zaXVmLWhVGXLrnF0zUp8Wb+ISPT+zaCGApIvK96GAm89v74IoGwTai3JQyn7nhhlvt6l28qxau8x4aRbWVJ8lF2BPfm6WlolvFR0BK/uqnR5HGfrRGlZr8mWo2tW4o33Bf+2yRHR+zeHnyjg8Buj59kmo3qCLwr9qZm2nterE7aXGbFyzzFNs5hsp0HbXld+785CQY2z3B6tQ2SW1BQt9PT7gn/b5C7WqaGAIt8IRYuhkX/yVaE/NdOTtSThOmN7Xa4KALqqHwNY19txl6+nZPNvm/TAoIYChtYboasVn8n7fFXoT830ZL0qHFuyvC61VYKVmIvyJbg3pdqXU7J9Xc2aggeHnyhgqB06ANid7a98VdBNZHpyUnwUauovofJfDbqe25LlOksis71ckevtvLqzAi/tqFDdnjADkJPeUfXj9KLlb5vIEQY1FDDU3gh9uTgjOeergm7Opifj53/XNTTj8fe+1vW8tiyvy1kBQEA8cTY8zIBZY/qiX2p7VbOrgLZCfY6moGuhJdGXVYtJLwxqKGCouRG66s42oK07+/bMVM6s8AGtBd30mBmj1DviDUrXpZSAu73MiEVbylFjsujFSYjBonuuzaKyfT5sg6SK0xfx6q4fXLZNj4BBa88oqxaTXhjUUMBQcyNkd7Z63pxKq6Wgm6MbZlJ8JCZkd8WYzFRV7bW88deYLuPp//tW1RIGWtZ1UpMn09IqKQ4l1ZguY8bar/BbB0sxWAYQ8vu6uLJOKKhxN2Bwp2eUVYtJL0wUpoChJrGS3dnqbC8z4pZlOzF51UHM2lCKyasO4pZlOz0648TVqtOWN0ClmTFnG65g9f7jmtor946kJsSoXpMpNTEG0/N7qn6MaJG7/D/vdJkb8/qeY0IzhfSYZeWKu4m+eiVNE7GnhgKKaGIlu7PF+TL3aGxWGkbdkIJ3io/jp7ONSE+Ks1uPSHRqtdb2iga2M2/rhT4p7ZHcLhqQgOIfawUf17ut10QCahuaUFxZp9irpPRaiHI0tOqNZQ706BnVK2maQhuDGgo4rhIrAXZni/J17pGjISXb9YhEp1Zrba9oYJvfuzPqLzVj7v9+LdQe+T2Wmdbe7jGO8kz0qovjKIDwdMCgV8+oyN82kTMMaigguapsykX4xPgy90ipV8JY35Yzcv+Qrnj2vhtVDRFqaa9oAHyuoRmPvSvWiyK/q+4ZlIbH3i0R6gXTuy6O7fPmyYBBz55Rb1SzpuDFnBoKWs5yNmaP6Yumq60hX4zPV7lHIr0S739VjX5PfYId5TWqj6/mMSL5HAvu6o+nt4r3oqQmxuC1KUOw5WujcJ6J3s+xowBCDhjGZ3dFXq9OugX13sjbIRLBnhoKarbfTo/XNmD9oSq8tOOoeZ9QLsbnq9wjNUNKH/+zBvFR4WhsbhEOKlbvP46bfp7eLMLV8ExibJRQe2fe1hv5vZM1zcDT6zn2xdAqe0bJXzCooaAnfzvdXmbE8h0VLMZnwVe5R2p7JRqbW1SfQ21ujbPhmc2l1ULH6JPSzjx0orYXLCe9I8IMULUauD8FEEz0JX/AoIZCgq8TYv2Vr75hq+2VkADcP6Qr9lfWCeedGOsv46Wio+aeE5EKvUr5HMntooXOaXldanvBDv90Tiig6RgXiaX3DQQA+8J8Pg4gRGazEXkSgxoKCSzGp8wX37Bd9RA5EhcdgX1PjMKhY2fx188r8fnRf7l8zKu7fsCru34wDzECUF3xtq2q77dOz+OoR0ukFywlIRqtkoTNpdU4evqCy+sBgAV3tbW1rRaN9ZElybf5YSKz2Yg8ySD5+q/Ai0wmExITE1FfX4+EhARfN4e8aHNpNWZtKHW538uTsjE+u6vnG+SHvFlRGFBfk2XBXf0xfcT1ANqq5E5edVD4XM4qAMtX6Gj4UaSNIo8H7HvBJAAd4iJxvlFd4b8Fd/VH146xDtvlrC2epvRc+bJNFDxE79/sE6SQwGJ8rnlqZowS8+y0BNfPeZgBmJbX0/xvV7NtbDkLSpQq3orWjXFWKVhpBl5iXCQAqA5oAKBDXJSq6r0trRKKK+uwubTaY7P93K0oTKQXDj9RSGAxPv8kJ+fO3vAVPv6n8jTsR0ZkWOVluFptWy15+PHgj3UIM7Qts1F7oUkof+eF+wchv0+y4u9tE5CT46Pxh//9GoD6gAYAzjc2Cw+l1l9q1rTApFoc3iV/waCGQgKnnPqv8DAD/jIlB9d1KMeqvceskmXDDG0Bzfw7M+0eJ/eCLNryLWpMTbq05bF1X+H8JXXBRvGPtahtaBIesvu+xmSV3KtGWmIMkuKjhPYtKq/BW/uPe2W2H9daI3+hafipsLAQGRkZiImJQU5ODvbu3au47+7du2EwGOx+vv/+e6v9Nm3ahMzMTERHRyMzMxMffvihW+clsqVmAUU1vNG9Hwrm35mJbxePxbTcHhjRJxnTcnvg28VjHQY0srFZafifX2br1ga1AQ0AvLqr0ukioLaLhT699TtNbTOgLfBOTYwV2v+j0lNeGw7i8C75C9U9NRs3bsTs2bNRWFiI/Px8vP766xg3bhzKy8vRo0cPxccdOXLEKrmnc+fO5v8vLi7GxIkT8fTTT2PChAn48MMP8ctf/hL79u3DsGHD3DovkSW9S8U7mu0RysX83GH7XO6tAHZ8d8blc1l7UZ9eGj3Y9oK4u0ClzPI91dIquRxK7RgfibMNzYrH03s4iMO75C9Uz34aNmwYhgwZghUrVpi39e/fH/feey+WLl1qt//u3btx22234dy5c+jQoYPDY06cOBEmkwmffPKJedvYsWPRsWNHrF+/XtN5HeHsJ9ITZ3vox53nUu1MKE+Tb+Cf//E23Pr8Ls3rOT32b73QN7W9w8Db2awqAPhNfk+s3n/c5Tn0nO3nqk38eyB3eGT2U3NzMw4fPoyCggKr7QUFBThw4IDTxw4ePBhpaWkYPXo0du3aZfW74uJiu2Pecccd5mNqPW9TUxNMJpPVD5EeONtDP+4+l3IvgVYdfp6JpBe5F+Sd4uNuLVB5S5/OijPRXA2ljslMFTqHnsNBnhreJVJD1fBTbW0tWlpakJKSYrU9JSUFNTWOZy6kpaVh5cqVyMnJQVNTE9555x2MHj0au3fvxsiRIwEANTU1To+p5bwAsHTpUixevFjNJRIJ8dVsD2/XkvEGd5/L8DAD7hmUhtf3HFN97pm39ULe9cl4YPUXLvddcFd/JLePRsXpi3h11w8u9//pbKPq9shEFn90NpTa0iq5rIHTIS5S9+EgT64ETiRC0+wng8H6DSpJkt02Wb9+/dCvXz/zv/Py8nDixAm88MIL5qBG9JhqzgsA8+fPx5w5c8z/NplM6N69u+L+RKJ8MdsjWPN33H0uW1olbPna6PB3ruT37oybM5KEAoBf5WcgPMyA4so6oaAmPSlOdXvUzsRTWtZBzbn05k6biNylavgpOTkZ4eHhdr0jZ86csetFcSY3NxcVFRXmf6empjo9ptbzRkdHIyEhweqHSA/enu0h5yvY9mjIiam2M24CibvPpeiK35YMEOsNsdxf5qrwn3zsaXk9XRYItI1b9BqqOXTsrMvCfucar+DQsbNunYfI36gKaqKiopCTk4OioiKr7UVFRRg+fLjwcUpKSpCWdu2PNi8vz+6Yn332mfmYep2XSC+iNzY9uveDPX/H3edSbW+YbW+I2gBArnlkeSxHx46KCHO6nwHAq5MHY/0juXh5UjbWP5KLfU+M0qXXjXVjKFSpHn6aM2cOpk2bhqFDhyIvLw8rV65EVVUVZsyYAaBtyKe6uhpr1qwBACxfvhw9e/bEgAED0NzcjLVr12LTpk3YtGmT+ZizZs3CyJEjsWzZMowfPx6bN2/Gjh07sG/fPuHzEnmTN4v5BXu1VnefS7W9YbYLdWoJAEQXAfXFYqEA68ZQ6FId1EycOBF1dXVYsmQJjEYjsrKysG3bNqSnpwMAjEYjqqqqzPs3Nzdj7ty5qK6uRmxsLAYMGICtW7fizjvvNO8zfPhwbNiwAU899RQWLFiAXr16YePGjeYaNSLnJfI2b92wQuFbtzvPpUiNlKT4KDx1V3+kJsbaJa4er20QaqNtACCaFOuL5FnWjaFQxVW6idzk6RlJonVY1j+S6xc9Ne48H1ofq7VGyvYyI2b8/DglcgCw74lRATWLh3VjKJiI3r+59hORmzw92yOQvnW7O0NL63OppadHzlVyRYL31wXTI1D21dAXkS8xqCHyc4GyGKdSVWBPLKDoiNphnoOVdW4Vx/MUPafus24MhRoOPxEFCH+uU9PSKuGWZTsVgwR/G8LZXmbEvE3fCC9gmealtnPpDSLHOPxEFGT8+Vt3IM3Q0rLIpDfa7mrqvgFtU/dvz0z1i9ecyB8xqCEKIP5ardWXM7TU5J84Cxxc8fTsskAKDIn8FYMaInKbr+qiqB2S01J9WKZX25WCsFCYuk/kaQxqiMhtvpihpSUxWUtAoGfbnQVhLJhH5D5VyyQQETkiunSAXrkgWpeOUBsQ6Nl2V+t3nWto1rRcREurhOLKOmwurUZxZV3ALpdBpAf21BAFEE8X+nOHN+uiaM0/cdWjZEu07Y5eF7mdZy5cRnK7aCza8q3TJOCnt5ZjwV398di7JcJT9/1lRpw/vy8ptDCoIQoQ/nIDc8ZbM7S05p+4qvkjAXh8TB/0TI4Xbruj16VDXCQAuFwoUyYHYR3jo4UDQ1/XBbJsh7+/Lyl0MKghCgD+cgMT4Y0ZWmrzT2x7El6bMgRPb3W/R0npdRENZmyduXAZ47O7ugwM/WX6dyC9Lyk0MKgh8nP+cgPzJznpHRFmAJylj4QZ2vZT6klYcFd/dIyP1tyj5M70cCVyEOYqMPSH6d98X5I/YqIwkZ9TcwMLFYd/Ouc0oAHaAp4Vu39QTM597N0S1F9qxvjsrsjr1Un1jded6eG2lJKAlfjD9G++L8kfMagh8nP+cAPzN6LX+tb+46pnSOndBle0zLDyh+nffF+SP2JQQ+Tn/OEG5m9Er9XZ2k7u9iTo9XynJsaozj2RZ3Gpnf6tJ74vyR8xp4bIz/misJ2/E3lOEuMihRJ2RXoSHE1ZltugZghKfq1euH8QahuaNM8O84eV2/m+JH/EnhoiP+ftwnaBQOQ5+fXwDKFjuepJ2F5mxC3LdmLyqoOYtaEUk1cdxC3LdqKovAb3DBLvXbF8rfL7JGvO5ZHJdYFSE63br6XnRwu+L8kfGSRJCpnyk6JLlxP5I9YDsefsObk9MxW3LNvpsidh3xOjFG+8SlOW5d6RDoK9QZbt0vu18nXhO74vyRtE798MaogCiK9vYP7I2XMiByWA4yEaRz0a8vFqTJfx9P99i7MN2mrOyGbe1hv5vZOD+rXi+5I8jUGNAwxqiEKPmp4ER/u66+VJ2Rif3VW34xGFItH7NxOFiSioiS7doDTU5C7O/iHyHgY1RBT0XFXo9UR1YADoGBfJ2T9EXsTZT0QU8rRUBzbg2sKVSs41XkFReY0bLdOmpVVCcWUdNpdWo7iyTnOBQaJAw54aIgp5aqveygNXz96bhf/6qExxBpSa9Y/0SrblbCQKZQxqiEg1LTdgf54hozbvRV7ROzE2yumUbtGFJfUKRLhqNoU6BjVEIUprkKHlBuzvvQci1XGT4qPw1F39kZoYa36uNpdWCx3fWU+QXoEIV80mYk4NUUhSqpK7vczo8nFKq17/bu1XDh+v5THeJlId908TsjBhSDerKsDurn/kKhABxBfd5KrZRAxqiEKO1iBDyw1Yz5u2p2lZdsDdhSX1DES4ajYRh5+IQoo7QxRqbsBy/oiWx/iSaE0bmeXCkkqcrX+kZyDCVbOJ2FNDFFLc6RnQcgMOxN4DuaaN6IKTY7PS8OjIDNjuFmYAHh2Z4TQfRs9AxN1eI6JgwKCGKIS4E2RouQGHQu/B9jIjVu45BtsRNEkCVu455jRnSM9AhKtmEzGoIQop7gQZWm7Awd574G7OkN6BiJa8IKJgwpwaohAiMnU5VSHIsMwfMcDxqte2N2Atj1HLcmp6crtoQAJqG5q8UgtHj5whORCxnfKeqnHKu9q8IKJgoimoKSwsxPPPPw+j0YgBAwZg+fLlGDFihMvH7d+/H7feeiuysrJQWlpq3v5v//Zv+Pzzz+32v/POO7F161YAwKJFi7B48WKr36ekpKCmxvslyIkClbtBhpYbsN43beBaIFNUXoOPSk/hbEOzw/08XQtHr5whvQMRV2tdEQUr1UHNxo0bMXv2bBQWFiI/Px+vv/46xo0bh/LycvTo0UPxcfX19XjwwQcxevRonD592up3H3zwAZqbr30o1dXVYdCgQfj3f/93q/0GDBiAHTt2mP8dHh6utvlEIc/dIEPLDVjPm7ajQn5KPF1JV8+cIQYiRO5THdS8+OKLmD59Oh5++GEAwPLly/Hpp59ixYoVWLp0qeLjfvvb32LKlCkIDw/HRx99ZPW7pCTrru4NGzYgLi7OLqiJiIhAamqq2iYTkQ13gwwtN2A9btpK1XeVeLqSrjvDeUSkP1WJws3NzTh8+DAKCgqsthcUFODAgQOKj3vrrbdQWVmJhQsXCp1n9erVmDRpEuLj4622V1RU4LrrrkNGRgYmTZqEH3/80elxmpqaYDKZrH6IqI3aqcu+5iwp1xlPV9KddFN3xYAG4IwjIm9S1VNTW1uLlpYWpKSkWG13lttSUVGBefPmYe/evYiIcH26Q4cOoaysDKtXr7baPmzYMKxZswZ9+/bF6dOn8cwzz2D48OH49ttv0amT429/S5cutcvDIaLA5Cop1xW9a+G4GgZzJ2eIiLTRlChsMFh/65AkyW4bALS0tGDKlClYvHgx+vbtK3Ts1atXIysrCzfffLPV9nHjxpn/f+DAgcjLy0OvXr3wt7/9DXPmzHF4rPnz51v9zmQyoXv37kLtICL/4m5QomctHFfDYI+P6YuZo3qzh4bIy1QFNcnJyQgPD7frlTlz5oxd7w0AXLhwAV9++SVKSkowc+ZMAEBrayskSUJERAQ+++wzjBo1yrx/Y2MjNmzYgCVLlrhsS3x8PAYOHIiKigrFfaKjoxEdHS16eUTkx7QGJXrntbgaBjMA2PCPKswc1VuX84nSuuo6UTBRFdRERUUhJycHRUVFmDBhgnl7UVERxo8fb7d/QkICvvnmG6tthYWF2LlzJ95//31kZGRY/e69995DU1MTpk6d6rItTU1N+O6774SmkhOR/xK9GbtKynXEE3kt/rielaOhME9PZyfyR6qHn+bMmYNp06Zh6NChyMvLw8qVK1FVVYUZM2YAaBvyqa6uxpo1axAWFoasrCyrx3fp0gUxMTF224G2oad7773XYY7M3Llzcffdd6NHjx44c+YMnnnmGZhMJjz00ENqL4GI/ISam7GzGjtKRPJa1PZw+Nt6VkpDYZ6ezk7kj1QHNRMnTkRdXR2WLFkCo9GIrKwsbNu2Denp6QAAo9GIqqoq1Q05evQo9u3bh88++8zh70+ePInJkyejtrYWnTt3Rm5uLg4ePGg+LxH5N9vg4VxDMx57V93NWKnGTlJ8JCZkd8Wo/imqKgpr6eFIbic2pC26nzvcWXWdw1UUjAySJKmdIRmwTCYTEhMTUV9fj4SEBF83hyhkOAoewgywWwRSJufB7HtilMMbbfPVVrxTfBw/nW1EelIcpuX1RFSEuqXslHo45LMp9XDsr6jFA6u/cHn8ddOHIb9Psqo2qVVcWYfJqw663G/9I7lWQ2EcrqJAI3r/5tpPFPT4jdS3lIIHpYAGcJ6X4uiG/Ma+Y6puyO70cNQ2NAmdQ3Q/d2gZCuNwFQUzBjUU1PiN1Le0FsyT2d609bohu5Psq+fSCO5S2xZ3gjmiQKCuv5YogMg3QNubl3wD3F5m9FHLQoe7BfMsb9qubshA2w25xVkX0M/cSfaVZ2Ep3fINaAucvbE0gtq2qAnmiAIRgxoKSnreAEk7rTOAHAUGet6Q3eltkWdhye205O2lEdS2xd9mbhHpjUENBSV+I/UPWoZglAIDPW/I7va2yLOwUhOtry81McbrOSlq2uJPQ2dEnsCcGgpK/EbqH0QK5tnOglKqLaPnDdlZzRvR3hZ3VzrXk2hbuKo4BTsGNRSU+I3UP4gED69OHoyO8dFuVxRWe0NWqnmjZiFKeaVzfyDSFj2COSJ/xjo1FJRaWiXcsmynyxugUh0U0pdes9Dk5G/A8Q1Zy9BPKE7556xACjSi928GNRS0PHEDJO30Ch54Q9ZHKAZzFLgY1DjAoCb08AYYnHhDJgotDGocYFATmngDJCIKbFwmgehn/pTMSfoLtKA10NpLFEgY1BBRwBIdXvSXQILDoUSexeEnIgpIoqts+0sgoXVVcCISv3+zojARBRzRZTC2/dM/1v9qvtqK//rwGy7bQeRhDGqISFctrRKKK+uwubQaxZV1HrlRiy6D8dTmMp8HEtvLjMhd+necbbiiuA+X7SDSB3NqiEg33hrqEV3e4mxDs+LvLAMJTyWSKw05KeGyHUTuYU8NEelCvoF7Y6hHz+UtPBVIOBsiU8JlO4jcw6CGiNwmmuOi11CPyCrbSfGRQsfyVCDhaojMkqtVwYlIDIMaInKbaI6LOzkjlrk6h46dxYK7+gOAXWAj//uZ8VkuAx9PBhJqe4C4kCSR+5hTQ0RuE72Bax3qUcrVeXRkBrZ8bVRcZTsszIAZP6//ZUuCZwMJ0R6gpPhIPDthIKdzE+mAQQ0RuU30Bq5lqEcp2bam/jJW7jmG16YMQcf4KJ8X1rMlD5EprRQPAJ3io1A8fzSiIthpTqQH/iURkdtEcly0DPWI5Oo8vbUcN2ckYXx2V+T16mQOaOTHKjHAs1O6w8MMWHh3pvlctuc2APjThCwGNEQ64l8TEbnN1Q0c0DbU406ujqfzfETq8YzNSsOKqUOQmmjdQ9UxPhK/ye+JxNgoFtwj0hGHn4hIF/IN3Db3JdWNOjXu5Op4Ms9HTT2esVlpuD0zFYeOnUVReQ0+Kj2Fsw3NWL3/OFbvP861n4h0xKCGiHRjeQPXI8fFnVwdT+X5OMvx+d3arxyu4RQeZkD9pWa8tf+4qscRkTocfiIiXYWHGZDXq5NdjosW7uTqeCLPR2s9Hm/X8SEKVQxqiMhvuZOr44k8H615Ot6o40NEDGqIyM8pJdumJsa4HLJx57GOaM3T8XQdHyJqw5waIvJ77uTq6JnnozVPx5N1fIjoGgY1RBQQ5FwdX3JVUM+Atl4g2zwdrY8jInUY1BCR32hplXSbOSVTM/3aFTlP53drv4IBsApQnOXpaH0cEamjKaemsLAQGRkZiImJQU5ODvbu3Sv0uP379yMiIgLZ2dlW299++20YDAa7n8uXrceXtZ6XiPzf9jIjblm2E5NXHcSsDaWYvOogblm2E9vLjMLHsC2It+2fp/C7tV/ZJenK06jVHFumNU9H7/weIrKnuqdm48aNmD17NgoLC5Gfn4/XX38d48aNQ3l5OXr06KH4uPr6ejz44IMYPXo0Tp8+bff7hIQEHDlyxGpbTMy1P36t5yUi/6e29oujHp2i8hq7HpkwAxSnUcvLJNyemaq6h0Rrno7edXyIyJpBkiRVhRGGDRuGIUOGYMWKFeZt/fv3x7333oulS5cqPm7SpEno06cPwsPD8dFHH6G0tNT8u7fffhuzZ8/G+fPndT+vJZPJhMTERNTX1yMhIUHoMUTkWS2tEm5ZtlNxyrOcb7LviVEIDzM4HE7qEBeJ841XNJ1//SO5Ps/VISLnRO/fqoafmpubcfjwYRQUFFhtLygowIEDBxQf99Zbb6GyshILFy5U3OfixYtIT09Ht27d8Itf/AIlJSVun7epqQkmk8nqh4j8i5oaLnKPju3+WgMagNOoiYKJqqCmtrYWLS0tSElJsdqekpKCmpoah4+pqKjAvHnzsG7dOkREOB7tuuGGG/D2229jy5YtWL9+PWJiYpCfn4+KigrN5wWApUuXIjEx0fzTvXt3NZdLRF4gGlTU1F9SrMrrDk6jJgoemhKFDQbr8V9Jkuy2AUBLSwumTJmCxYsXo2/fvorHy83NxdSpUzFo0CCMGDEC7733Hvr27Yu//OUvms4rmz9/Purr680/J06cELk8IvIi0aDibEOz0x4dtbQsk0BE/k1VonBycjLCw8PtekfOnDlj14sCABcuXMCXX36JkpISzJw5EwDQ2toKSZIQERGBzz77DKNGjbJ7XFhYGG666SZzT43a88qio6MRHR2t5hKJyMtEa7gktdPvb5nTqImCk6qemqioKOTk5KCoqMhqe1FREYYPH263f0JCAr755huUlpaaf2bMmIF+/fqhtLQUw4YNc3geSZJQWlqKtLQ0TeclosAhukZTaoL2YSLbuIXTqImCk+op3XPmzMG0adMwdOhQ5OXlYeXKlaiqqsKMGTMAtA35VFdXY82aNQgLC0NWVpbV47t06YKYmBir7YsXL0Zubi769OkDk8mEV155BaWlpXjttdeEz0tEgUuu4WI7qynVokheS6vktEfHETmWeXXyEHSMj/LqNGpPFBIkIudUBzUTJ05EXV0dlixZAqPRiKysLGzbtg3p6ekAAKPRiKqqKlXHPH/+PB599FHU1NQgMTERgwcPxp49e3DzzTcLn5eIApurGi6uqvJKsJ/anaqxcrC79KxiTETiVNepCWSsU0MU+JwFDP5Q2E6pkKDcCg57Eaknev9mUENEAaf5aiveKT6On842Ij0pDtPyeiIqQtNkTl2pLSRIRGJE799c0JKIAoqjnpo39h3zi6EdNYUEWcWYSH++/2pDRCRIqaKwOwtU6km0kCCrGBN5BoMaIgoILa2SYkVhedvij8vR0uq7EXXRQoKsYkzkGQxqiCggqBna8RW5kKBStgyrGBN5FoMaoiDT0iqhuLIOm0urUVxZ59OeCz0FwtCOaCFBJgkTeQYThYmCSDDXRwmUoR2RQoJE5BkMaoiChFJ9FDmJNtDro4iuEeUPQzuuCgkSkWdw+IkoCARCEq27Am1oJzzMgLxenTA+uyvyenXym3YRBTMGNURBIBCSaPUgD+2kJloPMXGBSiICOPxEFBS8nUTry8Uax2alYdQNKX5ZUZiIfItBDVEQ8GYSra+TkbVUFOaK2UShgUENURDwVhKt2mRkvYMJLcnQvg7CiMh72F9LFAS8kUSrNhl5e5kRtyzbicmrDmLWhlJMXnUQtyzbqXkpAy3J0P6+rAIR6YtBDVGQ8HQSrZpkZE8EE2qToUNhRhgRWePwE1EQ8WR9FNEk4xrTZTy3/XvFYMKAtmDi9sxUVe1SmwzNFbOJQg+DGqIgI9dH0ZtokvHZi00eCSbUJkMHwrIKRKQvDj8RkRDRxRqT4qOEjqc2mFC7WGSgLKtARPphUENEQkSTkVMTY4WOpzaYUJsMzRWziUIPgxoiEiaSjOzJYEJNMnSgLatARO4zSJIUMqn/JpMJiYmJqK+vR0JCgq+bQxSwXNWfkWc/AbBKGJb3cHc2lpr6N6xTQxT4RO/fDGqIyCO2lxmxaMu3qDE1mbelJkRj0T0DvB5MsKIwUWATvX9z9hMReZDSwI93eWpGGBH5F+bUEJGuWlolvLyjAjPWfoUak/UMp9MmVvIlIs9hTw0R6cbRkJMld4rvERG5wp4aItKFnBysFNDIbJczICLSC4MaInKbs3WWlLCSLxHpjUENEbnN1TpLjrCSLxHpjTk1ROQ2Nb0uBrQVy1NbfI/TsonIFQY1ROQ2tb0uaiv5soAeEYng8BMRoaVVQnFlHTaXVqO4sg4trepqcrpaGkGW5mA5A1fkBGTb4a2aek4PJyJr7KkhCnF69ILI6yz9bu1XMAAOE4YfH9MHM0f1UdVD4ywBWc/p4RzaIgoODGqIQpjcC2IbNMi9IGp6VeTFJvUcJnKVgGw5PVxrxWAObREFD03DT4WFhcjIyEBMTAxycnKwd+9eocft378fERERyM7Ottq+atUqjBgxAh07dkTHjh0xZswYHDp0yGqfRYsWwWAwWP2kpqZqaT4RwXUvCNDWC6JmKGpsVhr2PTEK6x/JxcuTsrH+kVzse2KU5uBANAFZ6/RwDm0RBRfVQc3GjRsxe/ZsPPnkkygpKcGIESMwbtw4VFVVOX1cfX09HnzwQYwePdrud7t378bkyZOxa9cuFBcXo0ePHigoKEB1dbXVfgMGDIDRaDT/fPPNN2qbT0Q/U9MLooa8ztL47K7I69XJrWEc0QRkLdPDPRHUEZFvqQ5qXnzxRUyfPh0PP/ww+vfvj+XLl6N79+5YsWKF08f99re/xZQpU5CXl2f3u3Xr1uH3v/89srOzccMNN2DVqlVobW3F3//+d6v9IiIikJqaav7p3Lmz2uYT0c883QuiB1cJyAa0DRWpnR4OeC6oIyLfURXUNDc34/DhwygoKLDaXlBQgAMHDig+7q233kJlZSUWLlwodJ7GxkZcuXIFSUnWH1QVFRW47rrrkJGRgUmTJuHHH390epympiaYTCarHyJq48leEL3ICciA8nrfaqeHywIhqCMidVQFNbW1tWhpaUFKSorV9pSUFNTU1Dh8TEVFBebNm4d169YhIkIsL3nevHno2rUrxowZY942bNgwrFmzBp9++ilWrVqFmpoaDB8+HHV1dYrHWbp0KRITE80/3bt3Fzo/USjwZC+InuQE5NRE6+AqVcP0cEuBENQRkTqaZj8ZDNYfg5Ik2W0DgJaWFkyZMgWLFy9G3759hY793HPPYf369di9ezdiYq59mIwbN878/wMHDkReXh569eqFv/3tb5gzZ47DY82fP9/qdyaTiYEN0c+cTcN2txdEb2Oz0nB7Zqqu067loK6m/rLDvBqtlY+JyHdUBTXJyckIDw+365U5c+aMXe8NAFy4cAFffvklSkpKMHPmTABAa2srJElCREQEPvvsM4waNcq8/wsvvIBnn30WO3bswI033ui0LfHx8Rg4cCAqKioU94mOjkZ0dLSaSyQKKUrTsFP9cEqznICs5/ECJagjIjGqgpqoqCjk5OSgqKgIEyZMMG8vKirC+PHj7fZPSEiwm6FUWFiInTt34v3330dGRoZ5+/PPP49nnnkGn376KYYOHeqyLU1NTfjuu+8wYsQINZdARDY80QsSKAIpqCMi11QPP82ZMwfTpk3D0KFDkZeXh5UrV6KqqgozZswA0DbkU11djTVr1iAsLAxZWVlWj+/SpQtiYmKstj/33HNYsGAB3n33XfTs2dPcE9SuXTu0a9cOADB37lzcfffd6NGjB86cOYNnnnkGJpMJDz30kOaLJ6I2eveCBJJQDuqIgo3qoGbixImoq6vDkiVLYDQakZWVhW3btiE9PR0AYDQaXdassVVYWIjm5mbcf//9VtsXLlyIRYsWAQBOnjyJyZMno7a2Fp07d0Zubi4OHjxoPi8RkVaeCOq49AKR9xkkSQqZylImkwmJiYmor69HQkKCr5tDREGKSy8Q6Uv0/s1VuomIdMSlF4h8h0ENEZFOuPQCkW8xqCEi0gmXXiDyLQY1REQ64dILRL7FoIaISCdceoHItxjUEBHpJFDW0yIKVgxqiIh04slVxYnINQY1REQ68tSq4kTkmqZVuomISBmXXiDyDQY1REQeEMrraRH5CoefiIiIKCgwqCEiIqKgwKCGiIiIggKDGiIiIgoKTBQm8gMtrRJnyhARuYlBDZGPbS8zYvHH5VYLIaYlxmDh3ZmsaUJEpAKHn4h8aHuZEb9b+5Xdys419Zfxu7VfYXuZ0UctIyIKPAxqiHykpVXC4o/LITn4nbxt8cflaGl1tAcREdliUEPkI4eOnbXrobEkATDWX8ahY2e91ygiogDGoIbIR85cUA5otOxHRBTqmChM5CNd2se43knFfoGKM7+ISC8Maoh85OaMJKQlxqCm/rLDvBoD2lZ2vjkjydtN8xrO/CIiPXH4ichHwsMMWHh3JoC2AMaS/O+Fd2cGba8FZ34Rkd4Y1BD50NisNKyYOgSpidZDTKmJMVgxdUjQ9lZw5hcReQKHn4h8bGxWGm7PTA2pvBI1M7/yenXyXsOIKKAxqCHyA+FhhpC6eXPmFxF5AoefiMjrOPOLiDyBQQ0ReZ0880tpgM2AtllQwTzzi4j0x6CGiLwu1Gd+EZFnMKghIp8I1ZlfROQ5TBQmIp8JxZlfROQ5DGqIyKdCbeYXEXmOpuGnwsJCZGRkICYmBjk5Odi7d6/Q4/bv34+IiAhkZ2fb/W7Tpk3IzMxEdHQ0MjMz8eGHH+p2XiIiIgp+qoOajRs3Yvbs2XjyySdRUlKCESNGYNy4caiqqnL6uPr6ejz44IMYPXq03e+Ki4sxceJETJs2DV9//TWmTZuGX/7yl/jiiy/cPi8RERGFBoMkSarqkA8bNgxDhgzBihUrzNv69++Pe++9F0uXLlV83KRJk9CnTx+Eh4fjo48+Qmlpqfl3EydOhMlkwieffGLeNnbsWHTs2BHr169367yWTCYTEhMTUV9fj4SEBNFLJiIiIh8SvX+r6qlpbm7G4cOHUVBQYLW9oKAABw4cUHzcW2+9hcrKSixcuNDh74uLi+2Oeccdd5iPqfW8TU1NMJlMVj9EREQUnFQFNbW1tWhpaUFKSorV9pSUFNTU1Dh8TEVFBebNm4d169YhIsJxXnJNTY3TY2o5LwAsXboUiYmJ5p/u3bu7vEYiIiIKTJoShQ0G6+mWkiTZbQOAlpYWTJkyBYsXL0bfvn3dPqboeWXz589HfX29+efEiRNO20BERESBS9WU7uTkZISHh9v1jpw5c8auFwUALly4gC+//BIlJSWYOXMmAKC1tRWSJCEiIgKfffYZRo0ahdTUVKfHVHteWXR0NKKjo9VcIhEREQUoVT01UVFRyMnJQVFRkdX2oqIiDB8+3G7/hIQEfPPNNygtLTX/zJgxA/369UNpaSmGDRsGAMjLy7M75meffWY+ptrzEhERUehRXXxvzpw5mDZtGoYOHYq8vDysXLkSVVVVmDFjBoC2IZ/q6mqsWbMGYWFhyMrKsnp8ly5dEBMTY7V91qxZGDlyJJYtW4bx48dj8+bN2LFjB/bt2yd8XiIiIgptqoOaiRMnoq6uDkuWLIHRaERWVha2bduG9PR0AIDRaFRdO2b48OHYsGEDnnrqKSxYsAC9evXCxo0bzT05IucVIc9e5ywoIiKiwCHft11VoVFdpyaQnTx5kjOgiIiIAtSJEyfQrVs3xd+HVFDT2tqKU6dOoX379k5nTdkymUzo3r07Tpw4EdRF+0LlOoHQuVZeZ3DhdQYXXqc4SZJw4cIFXHfddQgLU04HDqkFLcPCwpxGeK4kJCQE9RtPFirXCYTOtfI6gwuvM7jwOsUkJia63EdTnRoiIiIif8OghoiIiIICgxoB0dHRWLhwYdAX8guV6wRC51p5ncGF1xlceJ36C6lEYSIiIgpe7KkhIiKioMCghoiIiIICgxoiIiIKCgxqiIiIKCgwqPlZYWEhMjIyEBMTg5ycHOzdu1dxX6PRiClTpqBfv34ICwvD7NmzvddQN6m5zg8++AC33347OnfujISEBOTl5eHTTz/1Ymu1U3Od+/btQ35+Pjp16oTY2FjccMMNeOmll7zYWu3UXKel/fv3IyIiAtnZ2Z5toI7UXOvu3bthMBjsfr7//nsvtlgbta9pU1MTnnzySaSnpyM6Ohq9evXCm2++6aXWaqfmOn/1q185fD0HDBjgxRZro/b1XLduHQYNGoS4uDikpaXh17/+Nerq6rzUWu3UXudrr72G/v37IzY2Fv369cOaNWv0aYhE0oYNG6TIyEhp1apVUnl5uTRr1iwpPj5e+umnnxzuf+zYMek//uM/pL/97W9Sdna2NGvWLO82WCO11zlr1ixp2bJl0qFDh6SjR49K8+fPlyIjI6WvvvrKyy1XR+11fvXVV9K7774rlZWVSceOHZPeeecdKS4uTnr99de93HJ11F6n7Pz589L1118vFRQUSIMGDfJOY92k9lp37dolAZCOHDkiGY1G88/Vq1e93HJ1tLym99xzjzRs2DCpqKhIOnbsmPTFF19I+/fv92Kr1VN7nefPn7d6HU+cOCElJSVJCxcu9G7DVVJ7nXv37pXCwsKkl19+Wfrxxx+lvXv3SgMGDJDuvfdeL7dcHbXXWVhYKLVv317asGGDVFlZKa1fv15q166dtGXLFrfbwqBGkqSbb75ZmjFjhtW2G264QZo3b57Lx956660BE9S4c52yzMxMafHixXo3TVd6XOeECROkqVOn6t00XWm9zokTJ0pPPfWUtHDhwoAJatReqxzUnDt3zgut04/a6/zkk0+kxMREqa6uzhvN0427f6MffvihZDAYpOPHj3uiebpRe53PP/+8dP3111tte+WVV6Ru3bp5rI16UHudeXl50ty5c622zZo1S8rPz3e7LSE//NTc3IzDhw+joKDAantBQQEOHDjgo1bpT4/rbG1txYULF5CUlOSJJupCj+ssKSnBgQMHcOutt3qiibrQep1vvfUWKisrsXDhQk83UTfuvKaDBw9GWloaRo8ejV27dnmymW7Tcp1btmzB0KFD8dxzz6Fr167o27cv5s6di0uXLnmjyZro8Te6evVqjBkzBunp6Z5ooi60XOfw4cNx8uRJbNu2DZIk4fTp03j//fdx1113eaPJmmi5zqamJsTExFhti42NxaFDh3DlyhW32hPyQU1tbS1aWlqQkpJitT0lJQU1NTU+apX+9LjO//mf/0FDQwN++ctfeqKJunDnOrt164bo6GgMHToUjz32GB5++GFPNtUtWq6zoqIC8+bNw7p16xAREThr2Wq51rS0NKxcuRKbNm3CBx98gH79+mH06NHYs2ePN5qsiZbr/PHHH7Fv3z6UlZXhww8/xPLly/H+++/jscce80aTNXH3s8hoNOKTTz7x679PQNt1Dh8+HOvWrcPEiRMRFRWF1NRUdOjQAX/5y1+80WRNtFznHXfcgTfeeAOHDx+GJEn48ssv8eabb+LKlSuora11qz2B88nmYQaDwerfkiTZbQsGWq9z/fr1WLRoETZv3owuXbp4qnm60XKde/fuxcWLF3Hw4EHMmzcPvXv3xuTJkz3ZTLeJXmdLSwumTJmCxYsXo2/fvt5qnq7UvKb9+vVDv379zP/Oy8vDiRMn8MILL2DkyJEebae71Fxna2srDAYD1q1bZ17B+MUXX8T999+P1157DbGxsR5vr1ZaP4vefvttdOjQAffee6+HWqYvNddZXl6O//iP/8B///d/44477oDRaMQf//hHzJgxA6tXr/ZGczVTc50LFixATU0NcnNzIUkSUlJS8Ktf/QrPPfccwsPD3WpHyPfUJCcnIzw83C6iPHPmjF3kGcjcuc6NGzdi+vTpeO+99zBmzBhPNtNt7lxnRkYGBg4ciEceeQSPP/44Fi1a5MGWukftdV64cAFffvklZs6ciYiICERERGDJkiX4+uuvERERgZ07d3qr6arp9Team5uLiooKvZunGy3XmZaWhq5du5oDGgDo378/JEnCyZMnPdperdx5PSVJwptvvolp06YhKirKk810m5brXLp0KfLz8/HHP/4RN954I+644w4UFhbizTffhNFo9EazVdNynbGxsXjzzTfR2NiI48ePo6qqCj179kT79u2RnJzsVntCPqiJiopCTk4OioqKrLYXFRVh+PDhPmqV/rRe5/r16/GrX/0K7777rl+P68r0ej0lSUJTU5PezdON2utMSEjAN998g9LSUvPPjBkz0K9fP5SWlmLYsGHearpqer2mJSUlSEtL07t5utFynfn5+Th16hQuXrxo3nb06FGEhYWhW7duHm2vVu68np9//jl++OEHTJ8+3ZNN1IWW62xsbERYmPVtWe65kPx0mUZ3Xs/IyEh069YN4eHh2LBhA37xi1/YXb9qbqcaBwF5Otrq1aul8vJyafbs2VJ8fLw5s37evHnStGnTrB5TUlIilZSUSDk5OdKUKVOkkpIS6dtvv/VF84Wpvc53331XioiIkF577TWr6ZTnz5/31SUIUXudr776qrRlyxbp6NGj0tGjR6U333xTSkhIkJ588klfXYIQLe9bS4E0+0nttb700kvShx9+KB09elQqKyuT5s2bJwGQNm3a5KtLEKL2Oi9cuCB169ZNuv/++6Vvv/1W+vzzz6U+ffpIDz/8sK8uQYjW9+7UqVOlYcOGebu5mqm9zrfeekuKiIiQCgsLpcrKSmnfvn3S0KFDpZtvvtlXlyBE7XUeOXJEeuedd6SjR49KX3zxhTRx4kQpKSlJOnbsmNttYVDzs9dee01KT0+XoqKipCFDhkiff/65+XcPPfSQdOutt1rtD8DuJz093buN1kDNdd56660Or/Ohhx7yfsNVUnOdr7zyijRgwAApLi5OSkhIkAYPHiwVFhZKLS0tPmi5Omrft5YCKaiRJHXXumzZMqlXr15STEyM1LFjR+mWW26Rtm7d6oNWq6f2Nf3uu++kMWPGSLGxsVK3bt2kOXPmSI2NjV5utXpqr/P8+fNSbGystHLlSi+31D1qr/OVV16RMjMzpdjYWCktLU164IEHpJMnT3q51eqpuc7y8nIpOztbio2NlRISEqTx48dL33//vS7tMEiSn/ZpEREREakQ8jk1REREFBwY1BAREVFQYFBDREREQYFBDREREQUFBjVEREQUFBjUEBERUVBgUENERERBgUENERERBQUGNURERBQUGNQQERFRUGBQQ0REREGBQQ0REREFhf8f+j/8tOQcSC4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test,y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "5dbc94e8-e2da-4bbd-a877-8aff804dd697",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,KFold\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix,roc_curve,auc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "60392baf-be10-4d24-a4bc-d9918897105b",
   "metadata": {},
   "outputs": [],
   "source": [
    "D = pd.read_csv(r\"D:\\Data Mining(405)\\mm.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "c3a94c30-c1b9-4cda-bf43-7f0e2f0f55eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Station</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>TEM</th>\n",
       "      <th>DPT</th>\n",
       "      <th>WIS</th>\n",
       "      <th>HUM</th>\n",
       "      <th>SLP</th>\n",
       "      <th>T_RAN</th>\n",
       "      <th>A_RAIN</th>\n",
       "      <th>RAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mymensingh</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>73.39</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Mymensingh</td>\n",
       "      <td>1960</td>\n",
       "      <td>2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>66.34</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mymensingh</td>\n",
       "      <td>1960</td>\n",
       "      <td>3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>64.13</td>\n",
       "      <td>1011.4</td>\n",
       "      <td>69</td>\n",
       "      <td>2.23</td>\n",
       "      <td>LTR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Mymensingh</td>\n",
       "      <td>1960</td>\n",
       "      <td>4</td>\n",
       "      <td>29.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>59.03</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>27</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Mymensingh</td>\n",
       "      <td>1960</td>\n",
       "      <td>5</td>\n",
       "      <td>29.6</td>\n",
       "      <td>23.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>73.45</td>\n",
       "      <td>1003.4</td>\n",
       "      <td>187</td>\n",
       "      <td>6.03</td>\n",
       "      <td>LTR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>668</td>\n",
       "      <td>Mymensingh</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>28.7</td>\n",
       "      <td>26.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>87.10</td>\n",
       "      <td>1003.3</td>\n",
       "      <td>349</td>\n",
       "      <td>11.26</td>\n",
       "      <td>MHR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>669</td>\n",
       "      <td>Mymensingh</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>28.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>85.63</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>263</td>\n",
       "      <td>8.77</td>\n",
       "      <td>LTR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>670</td>\n",
       "      <td>Mymensingh</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82.48</td>\n",
       "      <td>1011.3</td>\n",
       "      <td>180</td>\n",
       "      <td>5.81</td>\n",
       "      <td>LTR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>671</td>\n",
       "      <td>Mymensingh</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>23.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>81.73</td>\n",
       "      <td>1013.7</td>\n",
       "      <td>13</td>\n",
       "      <td>0.43</td>\n",
       "      <td>NRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>672</td>\n",
       "      <td>Mymensingh</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>18.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>82.68</td>\n",
       "      <td>1015.9</td>\n",
       "      <td>5</td>\n",
       "      <td>0.16</td>\n",
       "      <td>NRT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>672 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID     Station  Year  Month   TEM   DPT  WIS    HUM     SLP  T_RAN  \\\n",
       "0      1  Mymensingh  1960      1  16.9  11.3  2.0  73.39  1016.0     15   \n",
       "1      2  Mymensingh  1960      2  21.4  12.6  1.7  66.34  1013.0      0   \n",
       "2      3  Mymensingh  1960      3  24.1  14.9  2.3  64.13  1011.4     69   \n",
       "3      4  Mymensingh  1960      4  29.9  17.6  2.2  59.03  1007.1     27   \n",
       "4      5  Mymensingh  1960      5  29.6  23.2  2.4  73.45  1003.4    187   \n",
       "..   ...         ...   ...    ...   ...   ...  ...    ...     ...    ...   \n",
       "667  668  Mymensingh  2015      8  28.7  26.2  2.5  87.10  1003.3    349   \n",
       "668  669  Mymensingh  2015      9  28.8  25.2  2.0  85.63  1006.0    263   \n",
       "669  670  Mymensingh  2015     10  27.0  23.5  2.0  82.48  1011.3    180   \n",
       "670  671  Mymensingh  2015     11  23.1  18.7  1.7  81.73  1013.7     13   \n",
       "671  672  Mymensingh  2015     12  18.3  14.9  1.8  82.68  1015.9      5   \n",
       "\n",
       "     A_RAIN  RAN  \n",
       "0      0.48  NRT  \n",
       "1      0.00  NRT  \n",
       "2      2.23  LTR  \n",
       "3      0.90  NRT  \n",
       "4      6.03  LTR  \n",
       "..      ...  ...  \n",
       "667   11.26  MHR  \n",
       "668    8.77  LTR  \n",
       "669    5.81  LTR  \n",
       "670    0.43  NRT  \n",
       "671    0.16  NRT  \n",
       "\n",
       "[672 rows x 12 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "8a5e31fb-deac-4ebc-8076-7d529a7a299f",
   "metadata": {},
   "outputs": [],
   "source": [
    "D.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "8feaac94-0d9b-43a0-a394-e9ebf1673e77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID         0\n",
       "Station    0\n",
       "Year       0\n",
       "Month      0\n",
       "TEM        0\n",
       "DPT        0\n",
       "WIS        0\n",
       "HUM        0\n",
       "SLP        0\n",
       "T_RAN      0\n",
       "A_RAIN     0\n",
       "RAN        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "1976cc8d-1544-41ab-a9f2-6919f8ce2ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = D[\"RAN\"]\n",
    "X = D[[\"TEM\",\"DPT\",\"WIS\",\"HUM\",\"SLP\",\"T_RAN\",\"A_RAIN\"]]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "025338a6-c3ed-4a4d-a582-1b178eb7e872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9878048780487805\n",
      "Confusion Matrix:\n",
      " [[64  1  0]\n",
      " [ 1 33  0]\n",
      " [ 0  0 65]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         LTR       0.98      0.98      0.98        65\n",
      "         MHR       0.97      0.97      0.97        34\n",
      "         NRT       1.00      1.00      1.00        65\n",
      "\n",
      "    accuracy                           0.99       164\n",
      "   macro avg       0.99      0.99      0.99       164\n",
      "weighted avg       0.99      0.99      0.99       164\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9918367346938775"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN = KNeighborsClassifier(n_neighbors=8, metric='euclidean')\n",
    "KNN.fit(X_train, Y_train)\n",
    "\n",
    "# Predict on test data\n",
    "P = KNN.predict(X_test)\n",
    "\n",
    "# Evaluate accuracy\n",
    "print(\"Accuracy:\", accuracy_score(Y_test, P))\n",
    "print(\"Confusion Matrix:\\n\", confusion_matrix(Y_test, P))\n",
    "print(\"Classification Report:\\n\", classification_report(Y_test, P))\n",
    "kf = KFold(n_splits = 10,shuffle = True)\n",
    "cv = cross_val_score(KNN,X_train,Y_train,cv=kf)\n",
    "cv\n",
    "cv_a = np.mean(cv)\n",
    "cv_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "323bf39f-70cb-4547-a819-7e3f34e23e8a",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'D:\\\\4th Year\\\\STAT – 405 Data Mining\\\\2. Python Code\\\\kNN Algorithm\\\\Error_Rate_for_k_values.jpg'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[190], line 19\u001b[0m\n\u001b[0;32m     17\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mValue of k\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     18\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mError Rate\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 19\u001b[0m plt\u001b[38;5;241m.\u001b[39msavefig(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mD:/4th Year/STAT – 405 Data Mining/2. Python Code/kNN Algorithm/Error_Rate_for_k_values.jpg\u001b[39m\u001b[38;5;124m\"\u001b[39m, dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500\u001b[39m)\n\u001b[0;32m     20\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[1;32mE:\\Conda\\Lib\\site-packages\\matplotlib\\pyplot.py:1228\u001b[0m, in \u001b[0;36msavefig\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1225\u001b[0m fig \u001b[38;5;241m=\u001b[39m gcf()\n\u001b[0;32m   1226\u001b[0m \u001b[38;5;66;03m# savefig default implementation has no return, so mypy is unhappy\u001b[39;00m\n\u001b[0;32m   1227\u001b[0m \u001b[38;5;66;03m# presumably this is here because subclasses can return?\u001b[39;00m\n\u001b[1;32m-> 1228\u001b[0m res \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39msavefig(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[func-returns-value]\u001b[39;00m\n\u001b[0;32m   1229\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mdraw_idle()  \u001b[38;5;66;03m# Need this if 'transparent=True', to reset colors.\u001b[39;00m\n\u001b[0;32m   1230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n",
      "File \u001b[1;32mE:\\Conda\\Lib\\site-packages\\matplotlib\\figure.py:3395\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[1;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[0;32m   3393\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[0;32m   3394\u001b[0m         _recursively_make_axes_transparent(stack, ax)\n\u001b[1;32m-> 3395\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(fname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mE:\\Conda\\Lib\\site-packages\\matplotlib\\backend_bases.py:2204\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   2201\u001b[0m     \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[0;32m   2202\u001b[0m     \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[0;32m   2203\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[1;32m-> 2204\u001b[0m         result \u001b[38;5;241m=\u001b[39m print_method(\n\u001b[0;32m   2205\u001b[0m             filename,\n\u001b[0;32m   2206\u001b[0m             facecolor\u001b[38;5;241m=\u001b[39mfacecolor,\n\u001b[0;32m   2207\u001b[0m             edgecolor\u001b[38;5;241m=\u001b[39medgecolor,\n\u001b[0;32m   2208\u001b[0m             orientation\u001b[38;5;241m=\u001b[39morientation,\n\u001b[0;32m   2209\u001b[0m             bbox_inches_restore\u001b[38;5;241m=\u001b[39m_bbox_inches_restore,\n\u001b[0;32m   2210\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   2211\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m   2212\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n",
      "File \u001b[1;32mE:\\Conda\\Lib\\site-packages\\matplotlib\\backend_bases.py:2054\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   2050\u001b[0m     optional_kws \u001b[38;5;241m=\u001b[39m {  \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[0;32m   2051\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   2052\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m   2053\u001b[0m     skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[1;32m-> 2054\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: meth(\n\u001b[0;32m   2055\u001b[0m         \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m skip}))\n\u001b[0;32m   2056\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[0;32m   2057\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m meth\n",
      "File \u001b[1;32mE:\\Conda\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:513\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_jpg\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m    508\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_jpg\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    509\u001b[0m     \u001b[38;5;66;03m# savefig() has already applied savefig.facecolor; we now set it to\u001b[39;00m\n\u001b[0;32m    510\u001b[0m     \u001b[38;5;66;03m# white to make imsave() blend semi-transparent figures against an\u001b[39;00m\n\u001b[0;32m    511\u001b[0m     \u001b[38;5;66;03m# assumed white background.\u001b[39;00m\n\u001b[0;32m    512\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrc_context({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msavefig.facecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhite\u001b[39m\u001b[38;5;124m\"\u001b[39m}):\n\u001b[1;32m--> 513\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_print_pil(filename_or_obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjpeg\u001b[39m\u001b[38;5;124m\"\u001b[39m, pil_kwargs, metadata)\n",
      "File \u001b[1;32mE:\\Conda\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:445\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[1;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[0;32m    440\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    441\u001b[0m \u001b[38;5;124;03mDraw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[0;32m    442\u001b[0m \u001b[38;5;124;03m*pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[0;32m    443\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    444\u001b[0m FigureCanvasAgg\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 445\u001b[0m mpl\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimsave(\n\u001b[0;32m    446\u001b[0m     filename_or_obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer_rgba(), \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39mfmt, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    447\u001b[0m     dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdpi, metadata\u001b[38;5;241m=\u001b[39mmetadata, pil_kwargs\u001b[38;5;241m=\u001b[39mpil_kwargs)\n",
      "File \u001b[1;32mE:\\Conda\\Lib\\site-packages\\matplotlib\\image.py:1676\u001b[0m, in \u001b[0;36mimsave\u001b[1;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m   1674\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m)\n\u001b[0;32m   1675\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, (dpi, dpi))\n\u001b[1;32m-> 1676\u001b[0m image\u001b[38;5;241m.\u001b[39msave(fname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpil_kwargs)\n",
      "File \u001b[1;32mE:\\Conda\\Lib\\site-packages\\PIL\\Image.py:2563\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m   2561\u001b[0m         fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   2562\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2563\u001b[0m         fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   2564\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   2565\u001b[0m     fp \u001b[38;5;241m=\u001b[39m cast(IO[\u001b[38;5;28mbytes\u001b[39m], fp)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'D:\\\\4th Year\\\\STAT – 405 Data Mining\\\\2. Python Code\\\\kNN Algorithm\\\\Error_Rate_for_k_values.jpg'"
     ]
    },
    {
     "data": {
      "image/png": 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D1KRJEzVr1kzJycmaPXu2LrroIvXt21d/+MMflJycrPz8fP3yyy/6f//v/+mLL77wONbbbrtNL774okaNGqVdu3bp7LPP1tdff60nnnhCV1xxhS655JIanYMuXbpoxIgRSktLU3h4uC655BJt3bpVs2bNqtCtqLaSk5P12GOPadq0afr111912WWXqXHjxjpw4IC+++47NWzY0DnJX1VuuukmvfHGG7riiis0ceJEnX/++QoPD9fevXv15Zdf6uqrr3ZJtNzRrVs3SdJLL72k2NhYRUVFKSUlxa2uXS1bttSaNWs0ZMgQ9evXT6tWrXKWV5VGjRrp2muv1SuvvKIjR47ovvvuc+nWFRcXp379+umZZ55x1qk1a9ZowYIFbs0QPnLkSP3jH//QiBEjNHbsWOXk5Ojpp5+u8H26ey7nz5+vL774QkOHDlW7du104sQJ5xC6Na13AHzA6qe9AaA61Y3aJMm8/PLLzm0lmXvuuafKMk4d5ccYY0pLS81TTz1lzjzzTBMeHm6aNWtmRowYYfbs2eOyXWWjGlWnshGEHA4cOGBiYmLMxRdfbIwxprCw0Nx3332mdevWJioqyvTo0cN88MEHFUa8McaYzz77zHTv3t1ERkZWGOFo586dZvTo0aZ169YmPDzcNG/e3PTp08c8/vjjp423quPLyckx48ePNy1btjRhYWEmKSnJTJ061Zw4ccJlu6rOfVUKCwvNn/70J9OiRQsTFRVlfve735n169ebpKQkr47a5PDBBx+Yiy++2MTFxZnIyEiTlJRkrr/+evPZZ585txk1apRp2LBhpfEWFxebWbNmmXPPPddERUWZmJgY07lzZzNu3DizY8cO53ZJSUlm6NChFfavbFSjtLQ0k5KSYkJDQ40ks2jRoirPV2XHduTIEXPhhReaJk2aVFq3T7Vy5Urnv5uff/65wvt79+41w4YNM40bNzaxsbHmsssuM1u3bnXrOzHGmMWLF5suXbqYqKgo07VrV7N06dJK67A753L9+vXm2muvNUlJSSYyMtI0bdrU9O/f33z00UenPU4A/mMzxhj/pi4AAAAA6jqGfwUAAADgMRIJAAAAAB4jkQAAAADgMRIJAAAAAB4jkQAAAADgMRIJAAAAAB5jQroaKisr0759+xQbGyubzWZ1OAAAAIBXGGOUn5+vVq1auUxeeSoSiRrat2+f2rZta3UYAAAAgE/s2bNHbdq0qfJ9Eokaio2NlWQ/wXFxcRZHg2DQubOUmSm1bCn95z9WRwMAAOqrvLw8tW3b1nm9WxUSiRpydGeKi4sjkYBXXHyxlJ0tNWsmUaUAAIDVTtd9n0QCCBBvvGF1BAAAAO5j1CYAAAAAHiORAAAAAOAxEgkAAAAAHiORAALEwIHSWWfZfwIAAAQ6HrYGAsTPP0sZGVJurtWRAAAAnB6JBAB4mzFSTo509KgUEyM1bSqdZgi9oC0jEGIIpuOorUA5jmD5PjgO75URCDEE03H4i0GN5ObmGkkmNzfX6lAQJFq3Nkay/0QddfiwMWlppqhDJ/uX+b9XUYdOxqSl2d+vL2UEQgzBdBy1FSjHESzfB8fhvTICIYZgOg4vcfc6l0Sihkgk4G0kEnXcihWmuGGcKbaFmSW24eZ6vW0GaZW5Xm+bJbbhptgWZoobxhmzYkXwlxEIMQTTcdRWoBxHsHwfHAfnIhBj8DISCR8jkYC3kUjUYStWmNLQMPNJyFCToMzyN5KcrwRlmk9ChprS0LDK/yMIljICIYZgOo7aCpTjCJbvg+PgXATqcXgZiYSPkUjA20gk6qjDh01xwzjzSchQE6riSv8DcLxCVWw+CRlqv6tUvok6WMoIhBiC6ThqK1COI1i+D46DcxGox+EDJBI+RiIBbyORqKPS0kyxLazKu0invhK1zxTbwoyZPTv4ygiEGILpOKibgRMDx8G5COTj8AESCR8jkYC3kUjUQWVlpqhDJ7PENtyt/wAcr6W2G01R2xRjtmwxZssWU9Q2pe6X8e9/Wx9DMBxHh07GlJVRNwPl++A4OBeBehze+ltRBRIJHyORgLeRSNRBBw8aI5nr9bZH/wncoKUVVgZLGYEQQ50/juxs6mYgfR8cB+ciUI/DG38rquDudS7zSAAB4q9/PTlkNOqIo0clSYfV2KPdKts+WMoIhBi8UYalMeTn28eNr40gq5uBEIM3ygiEGAKljECIwRtl1Pm/FbUUYumnA3C66y5pyhT7T9QR/8v6GuuwR7s5t7/1VvsrGMoYNsz6GILlOGJjPdqvUsFSNwPh++A4OBeVlREIx+GNvxW15bM2kSBH1yYAteqH7ujfGixllJZaH0MwHQd1M7C+D46DcxGox+EjPCPhYyQSAIwxgTNqRyCUEQgxBNNxUDcDJwaOg3MRyMfhAyQSPkYiAW/bt8+YPXvsP1GHBMo44oFQRiDEEEzHUVuBchzB8n1wHJyLQD0OH6gzicSLL75okpOTTWRkpOnRo4f56quvqt1+9erVpkePHiYyMtKkpKSYefPmuby/detWc91115mkpCQjyfz973+vUEZxcbGZNm2aSU5ONlFRUSYlJcU8+uijprS01O24SSTgbYzaVIeVm5U0Ufsq/Q8gUftOzkr66afBW0YgxBBMx1FbgXIcwfJ9cByci0A9Di+rE4nEkiVLTHh4uHn55ZfNtm3bzMSJE03Dhg3Nb7/9Vun2v/76q4mOjjYTJ04027ZtMy+//LIJDw837777rnOb7777ztx3333mrbfeMomJiZUmEo8//rhp2rSp+fjjj83OnTvNO++8Y2JiYkxaWprbsZNIwNtIJOq4FStMccM4U2wLM0t1g7lBS80lWmlu0FKz1HajKbaF2e8iVfcfQPkybDfW3TICIYZgOo7aCpa6GQgxcByci0CNwcvqRCJx/vnnm/Hjx7us69y5s3nggQcq3f7+++83nTt3dlk3btw487vf/a7S7ZOSkipNJIYOHWpGjx7tsu66664zI0aMcDt2Egl4G4lEEDh82JjZs01RbGOXW0lFHTrZ+7MeOeJ+GR061e0yAiGGQD+OlDPcL6O2HDFExfB9BEIZgRBDoJQRCDEE03F4ibvXuTZjjLFitKiioiJFR0frnXfe0bXXXutcP3HiRKWnp2vNmjUV9unXr5+6d++u2bNnO9e9//77uvHGG1VQUKDw8HCX7ZOTkzVp0iRNmjTJZf2TTz6p+fPna+XKlTrzzDP1ww8/aPDgwUpLS9PNN99cabyFhYUqLCx0/p6Xl6e2bdsqNzdXcXFxNTkFgIs2baSMDKl1a2nvXqujQa0MGSKtXGlf/vlnqWNHyWbzrAxjpEOH7OOEx8ZKTZrUzTICIYZAOo7rr5fee8/++08/SWee6VkZtXXGGdIvv9iHh921i+/D6jICIYZAKSMQYgim46ilvLw8xcfHn/Y617IJ6bKzs1VaWqqEhASX9QkJCdq/f3+l++zfv7/S7UtKSpSdna2WLVu69dl/+ctflJubq86dOys0NFSlpaWaMWNGlUmEJM2cOVOPPvqoW+UDqOcOHLD/DA+XOnSo2X8ANpt9oqHaTDYUCGUEQgzeKMNbMSQnn/z9wAH/JxKOutm6tfXnIhC+D6vLCIQYAqWMQIjBG2UEQgx+ZPmEdLZT/oM1xlRYd7rtK1tfnaVLl+r111/Xm2++qU2bNmnx4sWaNWuWFi9eXOU+U6dOVW5urvO1Z88etz8PQD2TmWn/mZAghVj+ZxaBJDHx5LKjnvjLsWP2O5ynxgEANWRZi0SzZs0UGhpaofUhKyurQquDQ2JiYqXbh4WFqakHWduf//xnPfDAA7rpppskSWeffbZ+++03zZw5U6NGjap0n8jISEVGRrr9GQDqqZIS6eBB+7KbraSoR8rXiSpa332m/OdRNwF4gWW3yiIiIpSamqpVq1a5rF+1apX69OlT6T69e/eusP3KlSvVs2fPCs9HVKegoEAhp9wlDA0NVVlZmdtlAEClsrLs/Vsl7vqiIitbJMp/HnUTgBdY1iIhSVOmTNHIkSPVs2dP9e7dWy+99JJ2796t8ePHS7J3J8rIyNCrr74qSRo/frzmzJmjKVOmaOzYsVq/fr0WLFigt956y1lmUVGRtm3b5lzOyMhQenq6YmJi1LFjR0nSVVddpRkzZqhdu3Y666yztHnzZj333HMaPXq0n88AgKDDXV9UhxYJAEHE0kRi+PDhysnJ0WOPPabMzEx169ZNy5cvV1JSkiQpMzNTu3fvdm6fkpKi5cuXa/LkyXrxxRfVqlUrPf/88xo2bJhzm3379ql79+7O32fNmqVZs2apf//+Wr16tSTphRde0MMPP6y7775bWVlZatWqlcaNG6e//vWv/jlwAMGr/MUad31xqvJ1wspEgroJwAssG/61rnN3WCzAXT/9ZO9eHxYmdepkdTSosQULpDvvtC/PnSv94Q/WxoPAYowUGSkVF0vnniulp/vvs6dNk554wr786afS4MH++2wAdUrAD/8KwBXJQ5Cg+wiqY7PZWwP27KFrE4A6j3EJAcCbeKAVp+OoF1lZ9mZIf6FuAvAyEgkA8Cbu+uJ0HPXCmJNDBfuDo26GhdWJia4ABD66NgEB4s03pYICKTpauuUWq6NBjZW/61vFnDio504dAtZfCScTJQLwMhIJIEDcf7+UkSG1bk0iUac57vo2bixFRVkbCwKTFSM3lZbau1Kd+vkAUAvckgAAbzHm5IUhF2uoihVzSWRnS45JV+lyB8BLSCQAwFvy8+390yQSCVTNitmtedAagA+QSACAt/CgNdxhRYsEdROAD5BIAIC3cNcX7qBFAkCQIJEAAG/hri/cUX40LytaJEgkAHgJiQQAeAsXa3BHVJR9VC+Jrk0A6jQSCQDwFrqPwF2O+pGZaR/ty9eomwB8gEQCALyFu75wl6N+FBRIR4/6/vNoLQPgA0xIBwQIx//t/B9fh3HXF+469YHr2Fjffp6jbsbHSw0a+PazANQbJBJAgNiwweoIUGuOu77h4VKTJtbGgsB26uzWZ57p289jokQAPkDXJgDwlvIXazabtbEgsPlzLomjR092n6LLHQAvIpEAAG8oKZEOHrQvc9cXp+PPuSR4PgKAj5BIAIA3ZGWdHH2Hu744HX+2SDAIAAAf4RkJIECMGycdOmTvWv+Pf1gdDTzGg9bwhD9bJKibAHyERAIIEJ98ImVkSK1bWx0JaoS7vvAELRIAggBdmwDAG+iHDk80bmwf3UuiRQJAnUUiAQDeUP5ijbu+OB2b7eRFvT9bJEgkAHgRiQQAeAMXa/CUI+E8eNA+6pev0LUJgI+QSACAN9B9BJ5y1BNj7KN++YqjboaFMVEiAK8ikQAAb6BFAp7y1wPX5SdKDOG/fQDew18UAPAGx13fxo2lyEhrY0Hd4I8hYEtLT7Z2kOAC8DISCQCoLWNO3vWlDzrcVf7C3lctEgcPSmVlFT8PALyARAIAais/Xzp+3L7MxRrc5Y+uTTxoDcCHmJAOCBA33ywdPmzvGYM6hgetURP+6NpE3QTgQyQSQIB45hmrI0CNcdcXNUGLBIA6jq5NAFBb3PVFTSQknFymRQJAHUQiAQC1xV1f1ERk5Mm+jP5okSCRAOBlJBIAUFtcrKGmHInn/v320b+8jSQXgA+RSAABonNnKS7O/hN1TPnuI1yswROOxLOgwD76l7fRtQmAD5FIAAHi6FH7dcTRo1ZHAo/RIoGa8vUD144yGzWSoqK8Xz6Aeo1EAgBqy3HXNyKC8XvhGV8PAesokwQXgA+QSABAbTnu+iYmSjabtbGgbvHl7NZHj0rHjlX8HADwEhIJAKiN4mIpO9u+zMUaPFW+a5O3WyR4dgeAj5FIAEBtZGWdHG2HizV4ypctEjy7A8DHSCQAoDa4WENt+PJha4Z+BeBjJBIAUBsMr4na8OXD1tRNAD5GIgEAtcFdX9RG48b20b4kWiQA1DkkEgBQG3RtQm3YbCfrDS0SAOqYMKsDAGA3f750/LjUoIHVkcAjjIyD2kpMlHbvto/+VVwshYd7p1ySXAA+RiIBBIgrr7Q6AtQIF2uoLUcCaox08KDUqpV3ynXUzfBwqUkT75QJAOXQtQkAaqN8i0RCgnVxoO7y1QPXjrISEqQQ/rsH4H2W/2WZO3euUlJSFBUVpdTUVK1du7ba7desWaPU1FRFRUWpffv2mj9/vsv7P/74o4YNG6bk5GTZbDalpaVVWk5GRoZGjBihpk2bKjo6Wuedd542btzorcMCUF847vo2aSJFRlobC+omXwwBW1pqb904tXwA8CJLE4mlS5dq0qRJmjZtmjZv3qy+ffvq8ssv1+7duyvdfufOnbriiivUt29fbd68WQ8++KAmTJigZcuWObcpKChQ+/bt9eSTTyqxim4Ghw8f1oUXXqjw8HD961//0rZt2/Tss8+qUaNGvjhMwC0bN0rr19t/oo4w5uRdX7o1oaZ80SKRlSWVlVUsHwC8yNJnJJ577jmNGTNGd955pyQpLS1Nn376qebNm6eZM2dW2H7+/Plq166ds5WhS5cu2rBhg2bNmqVhw4ZJknr16qVevXpJkh544IFKP/epp55S27ZttWjRIue65ORkLx4Z4Lmrr5YyMqTWraW9e62OBm7Jy5NOnLAvc9cXNeWL2a15dgeAH1jWIlFUVKSNGzdq8ODBLusHDx6sdevWVbrP+vXrK2w/ZMgQbdiwQcXFxW5/9kcffaSePXvqhhtuUIsWLdS9e3e9/PLL1e5TWFiovLw8lxeAeo6LNXiDL7o2MYcEAD+wLJHIzs5WaWmpEk55ODEhIUH7q/hDun///kq3LykpUXZ2ttuf/euvv2revHk644wz9Omnn2r8+PGaMGGCXn311Sr3mTlzpuLj452vtm3buv15AIIUQ7/CG3zRtYk5JAD4geUPW9tsNpffjTEV1p1u+8rWV6esrEw9evTQE088oe7du2vcuHEaO3as5s2bV+U+U6dOVW5urvO1Z88etz8PQJCiRQLeUP4GGS0SAOoQyxKJZs2aKTQ0tELrQ1ZWVoVWB4fExMRKtw8LC1PTpk3d/uyWLVuqa9euLuu6dOlS5UPekhQZGam4uDiXF4B6jru+8IbIyJPzPNAiAaAOsSyRiIiIUGpqqlatWuWyftWqVerTp0+l+/Tu3bvC9itXrlTPnj0V7sFMoBdeeKF++uknl3U///yzkpKS3C4DALjrC69xXOzv328fDay2aC0D4AeWdm2aMmWK/vnPf2rhwoXavn27Jk+erN27d2v8+PGS7N2JbrvtNuf248eP12+//aYpU6Zo+/btWrhwoRYsWKD77rvPuU1RUZHS09OVnp6uoqIiZWRkKD09Xb/88otzm8mTJ+ubb77RE088oV9++UVvvvmmXnrpJd1zzz3+O3gAdR8Xa/AWRyJ6/Lh9NLDaokUCgB9YOvzr8OHDlZOTo8cee0yZmZnq1q2bli9f7mwZyMzMdOlulJKSouXLl2vy5Ml68cUX1apVKz3//PPOoV8lad++ferevbvz91mzZmnWrFnq37+/Vq9eLck+ROz777+vqVOn6rHHHlNKSorS0tJ06623+ufAAQQHHraGt5w6BGx8fO3KcyS5jRpJUVG1KwsAqmAzxhttqPVPXl6e4uPjlZuby/MS8Io2bZhHos455xxpyxYpIsI+n4QHgz4ALv78Z2nWLPvy6tVS//61Ky8mRjp2TOrSRdq2rdbhAahf3L3OtXzUJgCos8rPak0Sgdrw5hCw+fn2JOLUcgHAyyzt2gTgpO3b7c9Ycj1aRxQXS475a+jWhNry5uzWPLsDwE9IJIAAERtrdQTwSFbWyWUu1lBb5ZPR2rZI8OwOAD+haxMA1AQXa/AmWiQA1EEkEgBQE1yswZvKJ6PeTCRIcgH4EF2bgADx3HP24ePj4qQpU6yOBqdFiwS8qVEj+wzXhYXe7dpEkgvAh0gkgADx3HMnh38lkagDaJGAN9ls9nr022+0SACoM+jaBAA1QSIBb3PUo4MH7aOC1RQtEgD8hEQCAGqCrk3wtvIX/eVHBfOUI8kND5eaNKldTABQDRIJAKiJ8i0SCQnWxYHg4a0Hrh37MlEiAB8jkQCAmnC0SDRtKkVEWBsLgoM3ZrcuKTnZmkG3JgA+RiIBAJ4yxvWuL+AN3miROHjQXj9PLQ8AfIBEAgA8lZsrnThhXyaRgLd4o0WCB60B+BGJBAB4iuE14QvemN2a0cQA+BGJBAB4ios1+II3ujaR5ALwIyakAwJEjx5S27ZS8+ZWR4LTYuhX+EL50b/o2gSgDiCRAALERx9ZHQHcRosEfCEiwj4KWE4OLRIA6gS6NgGAp2iRgK84EtPMzJOjL3mCFgkAfkQiAQCeokUCvuKoTydOSHl5nu/PRIkA/IhEAgA8RSIBXynfwlWT5yQc+zRuLEVFeScmAKgCiQQQIH7/e6l3b/tPBDjHxVpkpNSokaWhIMjUZghYJkoE4Gc8bA0EiE2bpIwMqXVrqyPBaZW/WLPZrI0FwaU2Q8AePSoVFFQsBwB8hBYJAPBEUZGUnW1f5mIN3lab2a150BqAn5FIAIAnsrJOLnOxBm+rTdcmnt0B4GckEgDgCS7W4Eu1ediaYYkB+BmJBAB4gos1+BItEgDqEBIJAPAEF2vwpUaN7KOBSbVLJEhyAfgBiQQAeIIWCfiSzeY6u7UneNgagJ+RSACAJ2iRgK85EtTsbKm42P39aJEA4GckEgDgCVok4GvlE9QDB9zfz1E3w8PtM1sDgI8xIR0QIKZMkfLypLg4qyNBtcrf9W3Rwro4ELxOfeC6TRv39mOiRAB+RiIBBIgpU6yOAG5xXKw1bSpFRFgbC4JTTWa3LimRDh6suD8A+BBdmwDAXcac7D7CxRp8pSazW2dl2evnqfsDgA+RSACAu3JzpcJC+zIXa/CVmrRI8KA1AAvQtQkIEPn59huKNpsUG2t1NKgUD1rDH2rSIsHQrwAsQIsEECC6dJHi4+0/EaAY+hX+UJPZrambACxAIgEA7uJiDf6QkHByuSYtErSWAfATEgkAcBcXa/CHiAj7qGASLRIAAhqJBAC4i4s1+IsjUd2//+RoTNXhYWsAFiCRAAB30SIBf3EkqidO2EcLO53ydbN81ygA8CESCQBwFy0S8BdPH7h2bNO4sRQZ6ZuYAOAUJBIA4C7HxVpUlH2ILcBXyrd4ne6BayZKBGAREgkAcJfjYi0x0T7hB+ArnrRI5OdLx49X3A8AfIxEAgDcUVQk5eTYl7lYg695Mrs1D1oDsAiJBAC448CBk8tcrMHXPJndmlmtAVgkzOoAANh9+KH9pndEhNWRoFI8aA1/8qRrE3UTgEVIJIAAkZpqdQSoFt1H4E+ePGzNsMQALGJ516a5c+cqJSVFUVFRSk1N1dq1a6vdfs2aNUpNTVVUVJTat2+v+fPnu7z/448/atiwYUpOTpbNZlNaWlq15c2cOVM2m02TJk2q5ZEACGp0H4E/xcefHMaVFgkAAcrSRGLp0qWaNGmSpk2bps2bN6tv3766/PLLtXv37kq337lzp6644gr17dtXmzdv1oMPPqgJEyZo2bJlzm0KCgrUvn17Pfnkk0o8zR/U77//Xi+99JLOOeccrx4XgCDExRr8yWZznd26OrSWAbCIpYnEc889pzFjxujOO+9Uly5dlJaWprZt22revHmVbj9//ny1a9dOaWlp6tKli+68806NHj1as2bNcm7Tq1cvPfPMM7rpppsUWc2kPEePHtWtt96ql19+WY0bNz5trIWFhcrLy3N5Ad708cfSO+/YfyIA0X0E/uZIWLOz7Q9QVYXWMgAWsSyRKCoq0saNGzV48GCX9YMHD9a6desq3Wf9+vUVth8yZIg2bNig4uJijz7/nnvu0dChQ3XJJZe4tf3MmTMVHx/vfLVt29ajzwNOZ/x46cYb7T8RgGiRgL+VT1izsqrezlE3IyLsM1sDgJ9YlkhkZ2ertLRUCQkJLusTEhK0v4pm3P3791e6fUlJibKzs93+7CVLlmjTpk2aOXOm2/tMnTpVubm5zteePXvc3hdAECh/1/eUv0OAT7g7BCwTJQKwiOWjNtlO+aNnjKmw7nTbV7a+Knv27NHEiRO1cuVKRUVFuR1nZGRktV2lAAQ5xw2OZs2k8HBrY0H94M4QsMXF9q5Pp24PAH5gWSLRrFkzhYaGVmh9yMrKqtDq4JCYmFjp9mFhYWratKlbn7tx40ZlZWUptdxYm6Wlpfrqq680Z84cFRYWKjQ01MOjARDUjDl5IcfFGvzFnSFgs7Ls9fPU7QHADyzr2hQREaHU1FStWrXKZf2qVavUp0+fSvfp3bt3he1Xrlypnj17KtzNO4SDBg3Sli1blJ6e7nz17NlTt956q9LT00kiAFR05IhUWGhf5mIN/uJOiwTP7gCwkKVdm6ZMmaKRI0eqZ8+e6t27t1566SXt3r1b4//3tOnUqVOVkZGhV199VZI0fvx4zZkzR1OmTNHYsWO1fv16LViwQG+99ZazzKKiIm3bts25nJGRofT0dMXExKhjx46KjY1Vt27dXOJo2LChmjZtWmE9AEjiYg3WKJ+0upNIkOQC8DNLE4nhw4crJydHjz32mDIzM9WtWzctX75cSUlJkqTMzEyXOSVSUlK0fPlyTZ48WS+++KJatWql559/XsOGDXNus2/fPnXv3t35+6xZszRr1iz1799fq1ev9tuxAQgiDP0KK7jzsDVDvwKwkOUPW9999926++67K33vlVdeqbCuf//+2rRpU5XlJScnOx/AdhcJBoBq0SIBK7RocXKZrk0AApClE9IBQJ1A9xFYISLCPkqY5F6LBHUTgJ+RSAABIiZGio21/0SAofsIrOKob/v3nxydqTxaJABYiEQCCBD/+Y+Ul2f/iQDDxRqs4mhlKCyUcnMrvl++bjJRIgA/I5EAgNOh+wiscroHrh3rmjSRmDQVgJ+RSADA6Tju+kZFSXFx1saC+qW6uSSYKBGAxUgkAOB0HBdrLVtKNpu1saB+qW5267w86fjxitsBgJ9YPvwrALs//1k6fFhq3Fh65hmro4FTUZGUk2Nf5q4v/K26Fgme3QFgMRIJIEC89ZaUkSG1bk0iEVAOHDi5zMUa/K262a0ZlhiAxejaBADV4UFrWKm6h60ZlhiAxUgkAKA6dB+BlWiRABDASCQAoDpcrMFKcXH20cIkWiQABJwaJRJr167ViBEj1Lt3b2VkZEiSXnvtNX399ddeDQ4ALMfFGqxks7nObl0erWUALOZxIrFs2TINGTJEDRo00ObNm1VYWChJys/P1xNPPOH1AAHAUrRIwGqOepeTYx9FzIHndwBYzONE4vHHH9f8+fP18ssvKzw83Lm+T58+2rRpk1eDAwDL0SIBq5Wvd+VHEXMkuZGRUqNGfg0JAKQaJBI//fST+vXrV2F9XFycjhw54o2YACBwOC7WbDapRQtrY0H9VNUD1+VntWaiRAAW8DiRaNmypX755ZcK67/++mu1b9/eK0EBQMBwtEg0ayaVa4UF/KayIWCLi6WDByu+DwB+5PGEdOPGjdPEiRO1cOFC2Ww27du3T+vXr9d9992nv/71r76IEagXhg6VDh2SmjSxOhI4GeN61xewQmWzW2dlVf4+APiRx4nE/fffr9zcXF188cU6ceKE+vXrp8jISN1333364x//6IsYgXrhH/+wOgJUcOTIyYdbeZgVVilf9xwtEjxoDSAAeJxISNKMGTM0bdo0bdu2TWVlZeratatiYmK8HRsAWIsHrREIKmuRYOhXAAHA42ckRo8erfz8fEVHR6tnz546//zzFRMTo2PHjmn06NG+iBEArMHFGgJBZQ9bMywxgADgcSKxePFiHT9+vML648eP69VXX/VKUAAQEOg+gkBQfrSwyro2keQCsIjbXZvy8vJkjJExRvn5+YqKinK+V1paquXLl6sFQyMCNdazp/0mY2KitGGD1dFAEi0SCAzh4fZRw7Kz6doEIKC4nUg0atRINptNNptNZ555ZoX3bTabHn30Ua8GB9Qn+/dLGRlWRwEXdB9BoGjZ0p5IZGbaRxOjtQxAAHA7kfjyyy9ljNHAgQO1bNkyNSk3RmVERISSkpLUqlUrnwQJAJag+wgCRWKitGWLfRSxI0dck9yEBMvCAlC/uZ1I9O/fX5K0c+dOtW3bViEhHj9eAQB1Cy0SCBSnPnDtqJtNm0oREdbEBKDe83j416SkJElSQUGBdu/erSLHGOv/c84553gnMgCwmqNFokEDKTbW2lhQv506u7WjbtJSBsBCHicSBw8e1B133KF//etflb5fWlpa66AAICCUn9XaZrM2FtRv5ROGn36STpyouB4A/Mzj/kmTJk3S4cOH9c0336hBgwZasWKFFi9erDPOOEMfffSRL2IEAP8rLJQOHbIv060JVitfBzdvrnw9APiZxy0SX3zxhT788EP16tVLISEhSkpK0qWXXqq4uDjNnDlTQ4cO9UWcAOBfBw6cXOauL6xWvg6WTySomwAs5HGLxLFjx5zzRTRp0kQHDx6UJJ199tnatGmTd6MDAKvwoDUCSfk6uGVL5esBwM88TiQ6deqkn376SZJ03nnn6R//+IcyMjI0f/58teQPGoBgwdCvCCTl62BhYeXrAcDPPO7aNGnSJGX+7z/Y6dOna8iQIXrjjTcUERGhV155xdvxAfXG009LBQVSdLTVkUASMwcjsMTF2UcPO37cdT038ABYyONE4tZbb3Uud+/eXbt27dJ//vMftWvXTs2aNfNqcEB9csstVkcAF8wcjEBis9kT2p07XdeT5AKwUK1nlYuOjlaPHj0UExOjWbNmeSMmALAeLRIINJXVQ+omAAt5lEhkZ2frk08+0cqVK53zRRQXF2v27NlKTk7Wk08+6ZMgAcDveNgagebUehgZKTVqZEkoACB50LVp3bp1Gjp0qHJzc2Wz2dSzZ08tWrRI11xzjcrKyvTQQw9p9OjRvowVCGo//SSVlEhhYVKnTlZHA2fXJptN+t9IdYClTm19YKJEABZzO5F4+OGHNWTIED300ENauHCh0tLSdOWVV+qRRx7RyJEjZeOPmf8YI+XkSEePSjExUtOmnv9nUtsyvBFDsPDS9zHo4jJlZIaqdctS7c0I8f/3EQhlBEIMjjIyMuzLTZtKoaGe7Q/4wqktEs2a2etqff3bC8Bybndt+uGHH/Twww+rW7duevzxx2Wz2fTUU0/ptttuI4nwlyNHpNmzVXxGF6l5cyklRWre3P777Nn2931dhjdiCBbe/j4cd8AzM/37fQRCGYEQw6llOBKJ7Oz6Wb8RWI4ckbZuVXH5+38bN1I3AVjLuMlms5kDBw44f4+JiTE7duxwd/egk5ubaySZ3Nxc/3zgihWmuGGcKbaFmSW24eZ6vW0GaZW5Xm+bJbbhptgWZoobxhmzYoXvyvBGDMHCB99HAx0zkjENdMx/30cglBEIMXirDMAXytdN3UDdBOBz7l7nup1IhISEmF9++cXk5uaaI0eOmNjYWPPDDz+Y3Nxcl1d94ddEYsUKUxoaZj4JGWoSlGnsbdmurwRlmk9ChprS0LDK/zOpbRneiCFYBMv3EQhlBEIM3ioD8AXqJgALeD2RsNlsJiQkxPmq6vf6wm+JxOHDprhhnPkkZKgJVXGl/4k4XqEqNp+EDLXfmTp82HtleCOGYBEs30cglBEIMXirDMAXqJsALOL1RGL16tVuveoLvyUSaWmm2BZW5Z2oU1+J2meKbWHGzJ7tvTK8EUOwCJbvIxDKCIQYvFUG4AvUTQAW8XoiAVd+SSTKykxRh05miW24W/+JOF5LbTeaorYpxmzZYsyWLaaobUrNy/j3v2u+f4dOxpSV+e78+Ftd/z68FQPHEZz1G4GlNn9vqJsAaolEwsf8kkgcPGiMZK7X2x79R3KDllZYWdsyarx/drbvzo+/BcP3EWBlBEIM1G8EpNr+vaFuAqgFd69zPZrZGn529Kgk6bAae7RbZdvXtowa75+f79F+AS0Yvo8AKyMQYqhVGcFUvxFYavv3hroJwA9IJAJZTIwkqbEOe7Sbc/tbb7W/alPGsGG12z821qP9AlowfB/eiIHjCM76jcBS27831E0AfmB5IjF37lylpKQoKipKqampWrt2bbXbr1mzRqmpqYqKilL79u01f/58l/d//PFHDRs2TMnJybLZbEpLS6tQxsyZM9WrVy/FxsaqRYsWuuaaa/TTTz9587C8o2lTFXfopOttyzza7XrbMhV36CS99pr02mu1K+Ptt2u3f5MmHu0X0ILh+/BGDBxHcNZvBJba/r2hbgLwB0/6SxUXF5vQ0FCzZcuWWvW7cliyZIkJDw83L7/8stm2bZuZOHGiadiwofntt98q3f7XX3810dHRZuLEiWbbtm3m5ZdfNuHh4ebdd991bvPdd9+Z++67z7z11lsmMTHR/P3vf69QzpAhQ8yiRYvM1q1bTXp6uhk6dKhp166dOXr0qNuxM2qThzEEi2D5PgKhjECIwVtlAL5A3QRgEZ89bN2+fXuTnp5e48DKO//888348eNd1nXu3Nk88MADlW5///33m86dO7usGzdunPnd735X6fZJSUmVJhKnysrKMpLMmjVr3AvcMI+ExzEEi2D5PgKhjECIwVtlAL5A3QRgEZ8lEgsXLjSXX365ycnJqXFwxhhTWFhoQkNDzXvvveeyfsKECaZfv36V7tO3b18zYcIEl3XvvfeeCQsLM0VFRRW2dzeR2LFjh5FUbUvLiRMnXGbw3rNnj38SCWNcZjZN1L4q70Q5Zzb99FPvl+GNGIJFsHwfgVBGIMTgrTIAX6BuArCAzxKJ8847z8TExJjIyEhz5plnmu7du7u83JWRkWEkmf/7v/9zWT9jxgxz5plnVrrPGWecYWbMmOGy7v/+7/+MJLNv374K27uTSJSVlZmrrrrKXHTRRdVuN336dCOpwssviYQxxqxYYYobxpliW5hZarvR3KCl5hKtNDdoqVlqu9EU28Lsd6Kq+0+ktmVUu/8N7sUQLMqfC13vle8jVd+bM/Qfk6rvvfB9+KlOeKOMQIjBW2UAvkDdBOBnPkskHnnkkWpf7nIkEuvWrXNZ//jjj5tOnTpVus8ZZ5xhnnjiCZd1X3/9tZFkMjMzK2zvTiJx9913m6SkJLNnz55qt7O0RcLh8GFjZs+2TzZU7nZUUYdO9j6xR474voyq9k9o434MweLwYWMeecQUKcwr30dr7TGSMa21p/bfhz/rhDfKCIQYvFUG4AvUTQB+5G4iYTPGGP8+3m1XVFSk6OhovfPOO7r22mud6ydOnKj09HStWbOmwj79+vVT9+7dNXv2bOe6999/XzfeeKMKCgoUHh7usn1ycrImTZqkSZMmVRrDvffeqw8++EBfffWVUlJSPIo/Ly9P8fHxys3NVVxcnEf71pox0qFD9nHCY2Pto3PYbP4twxhpzhxpwgT773PmSPfc41kMweC776QLLrAvjxghpaXV+Pto07pMGZmhat2yVHszQjz/PqyuE94oIxBi8FYZgC9QNwH4gbvXuWE1/YCNGzdq+/btstls6tq1q7p37+7R/hEREUpNTdWqVatcEolVq1bp6quvrnSf3r176//9v//nsm7lypXq2bNnhSSiOsYY3XvvvXr//fe1evVqj5MIy9lsUtOm9pdVZdhs0hlnnPz9wIGax1KX7d9/crlTp9qdz5BQ+3JIqOTpdUEg1AlvlBEIMXirDMAXqJsAAojHiURWVpZuuukmrV69Wo0aNZIxRrm5ubr44ou1ZMkSNW/e3O2ypkyZopEjR6pnz57q3bu3XnrpJe3evVvjx4+XJE2dOlUZGRl69dVXJUnjx4/XnDlzNGXKFI0dO1br16/XggUL9NZbbznLLCoq0rZt25zLGRkZSk9PV0xMjDp27ChJuueee/Tmm2/qww8/VGxsrPb/72IwPj5eDRo08PSU1F+JiSeXMzOti8NK5Y+7/PkAAAAIch5PSHfvvfcqLy9PP/74ow4dOqTDhw9r69atysvL0wRHNxc3DR8+XGlpaXrsscd03nnn6auvvtLy5cuVlJQkScrMzNTu3bud26ekpGj58uVavXq1zjvvPP3tb3/T888/r2H/myVXkvbt26fu3bure/fuyszM1KxZs9S9e3fdeeedzm3mzZun3NxcDRgwQC1btnS+li5d6unpqN9atjy5XP7OfH1S/rjLnw8AAIAg5/EzEvHx8frss8/Uq1cvl/XfffedBg8erCNHjngzvoBl6TMSgaK0VIqIkMrKpNRUacMGqyPyv/HjpX/8w768YYP9PNRQmzZSRobUurW0d6+X4gMAAPCQu9e5HrdIlJWVVfo8Qnh4uMrKyjwtDnVZaKiUkGBfpkWCFgkAAFCveJxIDBw4UBMnTtS+ffuc6zIyMjR58mQNGjTIq8GhDnA8F3DggL1lor5xJBI2m+TB80EAAAB1nceJxJw5c5Sfn6/k5GR16NBBHTt2VEpKivLz8/XCCy/4IkYEMkciUVIi5eRYG4sVHA9bN2smeTByGAAAQF3n8ahNbdu21aZNm7Rq1Sr95z//kTFGXbt21SWXXOKL+BDoTn3guj7dlTfmZIuEF7o1nXmmFB9/srcYAABAIPMokSgpKVFUVJTS09N16aWX6tJLL/VVXKgrTh0C9uyzrYvF3w4floqK7MteGPr1iy9qXQQAAIDfeNS1KSwsTElJSSotLfVVPKhr6vMQsDxoDQAA6jGPn5F46KGHNHXqVB06dMgX8aCuqc+T0jEZHQAAqMc8fkbi+eef1y+//KJWrVopKSlJDRs2dHl/06ZNXgsOdUD5C+j63CJBIgEAAOoZjxOJa665xgdhoM6ia5OdF7o23XqrlJ1tHwDqjTdqXRwAAIBPefywtSSNHj1abdu29UlAqGPo2mTnhRaJNWtOzmwNAAAQ6Dx+2HrWrFk8bI2TGjaUYmPty7RIAAAA1BseP2w9aNAgrV692gehoM5y3I2nRQIAAKDe8PgZicsvv1xTp07V1q1blZqaWuFh69///vdeCw51RGKitGOHlJcnFRRI0dFWR+QfjhaJBg1OtsoAAADUEx4nEn/4wx8kSc8991yF92w2G92e6qPy3XoOHJBSUqyLxZ/Kz2pts1kbCwAAgJ953LWprKysyhdJRD1VHx+4LiyUHHOp0K0JAADUQx4nEkAF9XEI2AMHTi7zoDUAAKiH3E4krrjiCuXm5jp/nzFjho4cOeL8PScnR127dvVqcKgj6mOLBA9aAwCAes7tROLTTz9VYWGh8/ennnpKhxxdO2SfY+Knn37ybnSoG+rj7NbMag0AAOo5tx+2NsZU+zvqsfrYtckHc0iMHSvl5krx8V4pDgAAwKc8HrUJqICuTV4pcvp0rxQDAADgF253bbLZbLKdMsTlqb+jnmrWTAoNtS/TIgEAAFAveNS16fbbb1dkZKQk6cSJExo/frxzQrryz0+gngkNlVq0sN+lp0UCAACgXnA7kRg1apTL7yNGjKiwzW233Vb7iFA3tWxpv7g+cEAqK5NCgnxkYUeLhM1mT6IAAADqGbcTiUWLFvkyDtR1jrvypaVSTo7UvLm18fiaI5Fo3lwK886jRm3aSBkZUuvW0t69XikSAADAZ4L8tjH8pj49cG3MyUSCbk0AAKCeIpGAd9SnIWAPH5aKiuzLPGgNAADqKRIJeEd9apHgQWsAAAASCXhJfWqRYOhXAAAAEgl4CS0SAAAA9QqJBLyj/AV1fWqRIJEAAAD1FIkEvKO+JhJ0bQIAAPUUiQS8o2FDKTbWvkzXJgAAgKBHIgHvcdydp0UCAAAg6HlnSl5Ast+d//lnKS9PKiiQoqOtjsg3HC0S0dFSTIzXin39damwUIqM9FqRAAAAPkMiAe859TmJ9u2ti8WXys9qbbN5rdgBA7xWFAAAgM/RtQneUx/mkigstM9sLdGtCQAA1GskEvCe+jCXBEO/AgAASKJrE7ypPrRI+PBB69WrTz4jQTcnAAAQ6Egk4D31oUXCh0O/jhghZWRIrVtLe/d6tWgAAACvo2sTvKc+TEpH1yYAAABJJBLwJro2AQAA1BskEvCeZs2k0FD7Ml2bAAAAghqJBLwnJERKSLAv0yIBAAAQ1Egk4F2Ou/QHDkilpdbG4guOFgmbTWre3NpYAAAALEQiAe9y3KUvLZVycqyNxRccLRItWkhhDHoGAADqLxIJeFcwj9xkzMlj4vkIAABQz1meSMydO1cpKSmKiopSamqq1q5dW+32a9asUWpqqqKiotS+fXvNnz/f5f0ff/xRw4YNU3Jysmw2m9LS0rzyuXBTMM8lceiQVFxsXyaRAAAA9ZylicTSpUs1adIkTZs2TZs3b1bfvn11+eWXa/fu3ZVuv3PnTl1xxRXq27evNm/erAcffFATJkzQsmXLnNsUFBSoffv2evLJJ5VYxcWep58LDwTzELA8aA0AAOBkaSLx3HPPacyYMbrzzjvVpUsXpaWlqW3btpo3b16l28+fP1/t2rVTWlqaunTpojvvvFOjR4/WrFmznNv06tVLzzzzjG666SZFRkZ65XPhgWBukfDx0K9799p7TzGrNQAAqAssSySKioq0ceNGDR482GX94MGDtW7dukr3Wb9+fYXthwwZog0bNqjY0eXEB58rSYWFhcrLy3N5oRK0SAAAANQLliUS2dnZKi0tVYJj3oH/SUhI0P4qLkD3799f6fYlJSXKzs722edK0syZMxUfH+98tW3b1q3Pq3dokQAAAKgXLH/Y2mazufxujKmw7nTbV7be2587depU5ebmOl979uzx6PPqjWAetan88ZBIAACAes6ygfCbNWum0NDQCq0AWVlZFVoLHBITEyvdPiwsTE2bNvXZ50pSZGRklc9coJzoaCkuTsrLC+5Ewgddmx59VMrNleLjpenTvV48AACAV1nWIhEREaHU1FStWrXKZf2qVavUp0+fSvfp3bt3he1Xrlypnj17Kjw83GefCw857tbTtckjL78s/f3v9p8AAACBztKpeadMmaKRI0eqZ8+e6t27t1566SXt3r1b48ePl2TvTpSRkaFXX31VkjR+/HjNmTNHU6ZM0dixY7V+/XotWLBAb731lrPMoqIibdu2zbmckZGh9PR0xcTEqGPHjm59LmqpZUvp55+l/Hzp2DGpYUOrI/IOR4tEw4ZSbKy1sQAAAFjM0kRi+PDhysnJ0WOPPabMzEx169ZNy5cvV1JSkiQpMzPTZW6HlJQULV++XJMnT9aLL76oVq1a6fnnn9ewYcOc2+zbt0/du3d3/j5r1izNmjVL/fv31+rVq936XNTSqc9JdOhgXSze5GiR4PkIAAAA2YzjaWV4JC8vT/Hx8crNzVVcXJzV4QSWSZOk2bPty19/LV14oaXheMWJE1KDBvblCy+0H5eXtWkjZWRIrVszlwQAALCOu9e5lo/ahCAUjHNJHDhwcpk5JAAAAEgk4APBOJcEc0gAAAC4IJGA9wVjiwSzWgMAALggkYD30SIBAAAQ9Egk4H3BOLs1s1oDAAC4sHT4VwSpZs2k0FCptDQ4EwkfdW3q31/KzrafPgAAgEBHIgHvCwmREhKkffvo2uSBN97wSbEAAAA+Qdcm+Ibjrn1Wlr1loq5ztEiEhEgtWlgbCwAAQAAgkYBvOO7al5ba++vUdY4WiebN7d22AAAA6jkSCfhGMA0BW1Z2ckI6hn4FAACQRCIBXwmmkZsOH5aKi+3LPhyxaeBA6ayz7D8BAAACHQ9bwzeCaS4JP80h8fPPUkaGlJvrs48AAADwGlok4BvB1LWJWa0BAAAqIJGAb9AiAQAAENRIJOAbtEgAAAAENRIJ+EZCwsllWiQAAACCDokEfCM6WoqLsy8HU4sEiQQAAIAkEgn4kqMbUDAlEnRtAgAAkEQiAV9y3L3Pz5eOHbM2ltpwdG1q2FCKibE2FgAAgABBIgHfCZYHrh2x0xoBAADgxIR08J1Th4Dt0MG6WGrq+HHpyBH7so+fj/jrX6WjR2n0AAAAdQOJBHyn/IV3XW2ROHDg5LKPE4m77vJp8QAAAF5F1yb4TjB0beJBawAAgEqRSMB3gmF2a+aQAAAAqBRdm+A7tEh4JDNTKi2VQkNp/AAAAIGPFgn4Di0SHunVS2rb1v4TAAAg0JFIwHeaNpXC/tfoFQwtEnRtAgAAcCKRgO+EhEgJCfblYEgk6G8EAADgRCIB33LcxT9wwP4AQF3j6NoUEiI1b25tLAAAAAGERAK+5biLX1YmZWdbG0tNOFokWrSwPwUNAAAASSQS8LW6/MB1WdnJRILnIwAAAFyQSMC36vIQsIcOSSUl9mWejwAAAHBBIgHfqsstEkxGBwAAUCUSCfhW+QvwutYiwdCvAAAAVSKRgG/V5a5NDP0KAABQpTCrA0CQo2uT2z7/3P5IRhj/KgEAQB3AJQt8K1i6NvmhRaJTJ59/BAAAgNfQtQm+1aCBFB9vX6ZFAgAAIGiQSMD3HBfhdblFgkQCAADABV2b4HstW0o//SQdPWp/xcRYHZF7HIlETIxfYn7zTamgQIqOlm65xecfBwAAUCskEvC9U5+T6NjRulg84eja5KfWiPvvlzIypNatSSQAAEDgo2sTfK8uDgF7/LiUm2tfZuhXAACACkgk4Ht1cQhYno8AAACoFokEfK8uDgFLIgEAAFAtEgn4Xl3s2sSs1gAAANUikYDv1cWuTcwhAQAAUC0SCfgeLRIAAABBx/JEYu7cuUpJSVFUVJRSU1O1du3aardfs2aNUlNTFRUVpfbt22v+/PkVtlm2bJm6du2qyMhIde3aVe+//77L+yUlJXrooYeUkpKiBg0aqH379nrsscdUVlbm1WPD/zRpIoX9b6RhWiQAAACCgqWJxNKlSzVp0iRNmzZNmzdvVt++fXX55Zdr9+7dlW6/c+dOXXHFFerbt682b96sBx98UBMmTNCyZcuc26xfv17Dhw/XyJEj9cMPP2jkyJG68cYb9e233zq3eeqppzR//nzNmTNH27dv19NPP61nnnlGL7zwgs+PuV4KCZESEuzLdbFFgkQCAACgApsxxlj14RdccIF69OihefPmOdd16dJF11xzjWbOnFlh+7/85S/66KOPtH37due68ePH64cfftD69eslScOHD1deXp7+9a9/Obe57LLL1LhxY7311luSpCuvvFIJCQlasGCBc5thw4YpOjpar732mlux5+XlKT4+Xrm5uYqLi/PswOujXr2kDRvsSUVRkRQaanVE1bMg3p497flLYqL9owEAAKzg7nWuZS0SRUVF2rhxowYPHuyyfvDgwVq3bl2l+6xfv77C9kOGDNGGDRtUXFxc7Tbly7zooov0+eef6+eff5Yk/fDDD/r66691xRVXVBlvYWGh8vLyXF7wgOOuflmZdPCgtbG4w9G1qUULvyU9GzZIe/eSRAAAgLohzKoPzs7OVmlpqRIcXV7+JyEhQfur6P6yf//+SrcvKSlRdna2WrZsWeU25cv8y1/+otzcXHXu3FmhoaEqLS3VjBkzdPPNN1cZ78yZM/Xoo496ephwOPWB60DuLlRWJh04YF/mQWsAAIBKWf6wtc1mc/ndGFNh3em2P3X96cpcunSpXn/9db355pvatGmTFi9erFmzZmnx4sVVfu7UqVOVm5vrfO3Zs+f0B4eT6tIQsDk5UkmJfTmQEx4AAAALWdYi0axZM4WGhlZofcjKyqrQouCQmJhY6fZhYWFq2rRptduUL/PPf/6zHnjgAd10002SpLPPPlu//fabZs6cqVGjRlX62ZGRkYqMjPTsIHFSXRoClqFfAQAATsuyFomIiAilpqZq1apVLutXrVqlPn36VLpP7969K2y/cuVK9ezZU+Hh4dVuU77MgoIChYS4HnpoaCjDv/pSXWqRsGjo13HjpBtusP8EAAAIdJa1SEjSlClTNHLkSPXs2VO9e/fWSy+9pN27d2v8+PGS7N2JMjIy9Oqrr0qyj9A0Z84cTZkyRWPHjtX69eu1YMEC52hMkjRx4kT169dPTz31lK6++mp9+OGH+uyzz/T11187t7nqqqs0Y8YMtWvXTmeddZY2b96s5557TqNHj/bvCahPyl+Q16UWCT8mEp98ImVkSK1b++0jAQAAaszSRGL48OHKycnRY489pszMTHXr1k3Lly9XUlKSJCkzM9NlTomUlBQtX75ckydP1osvvqhWrVrp+eef17Bhw5zb9OnTR0uWLNFDDz2khx9+WB06dNDSpUt1wQUXOLd54YUX9PDDD+vuu+9WVlaWWrVqpXHjxumvf/2r/w6+vqFrEwAAQFCxdB6Juox5JDx04oTUoIF9+aKLpNPMYG6pyZOltDT78tq19nj9oE2bky0Se/f65SMBAAAqCPh5JFDPREVJjRrZl2mRAAAAqPNIJOA/jucN6tLD1lWMIAYAAFDfkUjAfxyJxLFj0tGj1sZSHUeLREyM/QUAAIAKSCTgP3XlgWtHbHRrAgAAqBKJBPynLswlcfy4lJtrX2ZWawAAgCqRSMB/6kKLBA9aAwAAuMXSeSRQz9SFFgmLZrWWpJtvlg4flho39uvHAgAA1AiJBPynLsxubdGs1pL0zDN+/TgAAIBaoWsT/IeuTQAAAEGDRAL+Q9cmAACAoEEiAf9p0kQKD7cv0yIBAABQp5FIwH9CQk7OFE2LRAWdO0txcfafAAAAgY5EAv7luMt/8KBUWmptLJVxtEiEhkrNmvn1o48elfLzA3vSbwAAAAcSCfiX4y5/WZk9mQg0jkSiRQt7MgEAAIBKkUjAvwL5geuyMunAAfsyD1oDAABUi0QC/hXIQ8Dm5EglJfZlHrQGAACoFokE/CuQWyQY+hUAAMBtJBLwr0BukWDoVwAAALeRSMC/aJEAAAAICiQS8K/yF+iB3CJBIgEAAFAtEgn4V11JJOjaBAAAUK0wqwNAPRMVJTVqJB05QtemU8yfLx0/LjVo4PePBgAA8BiJBPyvZUt7IhHILRIWJBJXXun3jwQAAKgxujbB/xwX6ceOSfn51sZSnqNFIjZWatjQ2lgAAAACHIkE/C9Qn5NwxMKD1gAAAKdF1yb436lzSZxxhnWxOBQUSHl59mWLHrTeuFEqKpIiIqTUVEtCAAAAcBuJBPwvEOeSCIChX6++WsrIkFq3lvbutSQEAAAAt9G1Cf4XiLNbM/QrAACAR0gk4H+B2CLBrNYAAAAeIZGA/wXiw9YB0LUJAACgLiGRgP/RtQkAAKDOI5GA/zVpIoWH25fp2gQAAFAnkUjA/2y2kxfrtEgAAADUSSQSsIYjkcjKkkpKrI1FOtkiERoqNWtmbSwAAAB1AIkErOG462+MdPCgtbFIJ1skEhKkEP5ZAAAAnA5XTLBGIA0BW1oqHThgX+b5CAAAALcwszWsEUhDwObk2JMJydJEYvt2ewONzWZZCAAAAG4jkYA1AmkI2AB50Do21rKPBgAA8Bhdm2CNQOraxNCvAAAAHiORgDXK3/n/+WcpO9ver8dTxtj33bWrZmUYI+3YcfJ3EgkAAAC3kEjA/44ckZYvV7GjZ92rr0rNm6v4jC7S7Nn2990pY/Zs+z7Nm0spKZ6VUX7/e+91ri5+/Cn3Y/Cy556THnnE/hMAACDQ2YypyW1g5OXlKT4+Xrm5uYqLi7M6nLrj009VMuxGqaBAy8y1elc36LAaq7EO63rbMg3TMik6WmHL3paGDDl9GRqmd80wz8qodv93NUzvnT4GH2jTRsrIkFq3lvbu9dvHAgAAuHD3OpdEooZIJGrg009VNvRKrTBDNLrsnzqgit2IErRfC0Pu1GW2TxXyyccVL+RrW4Y3YvAREgkAABAISCR8jETCQ0eOqKRNklYe76vfl32g0moGDAtViT4KuUaDG6xV2N7fpEaNvFOGN2LwIRIJAAAQCNy9zuUZCfjH4sVSQYFGl/2z2gt4SSpVmMaUvSwVFNifn/BWGd6IAQAAAJJIJOAPxqj4hXlapmGVdiWqzH611Hu6TsWz0qStW6WtW1X87Oyal7FlS833f35uzUaUAgAACGKWJxJz585VSkqKoqKilJqaqrVr11a7/Zo1a5SamqqoqCi1b99e8+fPr7DNsmXL1LVrV0VGRqpr1656//33K2yTkZGhESNGqGnTpoqOjtZ5552njRs3eu24UE5OjsL/+5PeNcM82u1dM0zhe3ZKZ58tnX22wvfsrHkZ55xT8/3/+5N06JBH+wEAAAQ7SxOJpUuXatKkSZo2bZo2b96svn376vLLL9fu3bsr3X7nzp264oor1LdvX23evFkPPvigJkyYoGXLljm3Wb9+vYYPH66RI0fqhx9+0MiRI3XjjTfq22+/dW5z+PBhXXjhhQoPD9e//vUvbdu2Tc8++6wa+aEffL109Kgk6bAae7RbZdvXtowa75+f79F+AAAAwc7SROK5557TmDFjdOedd6pLly5KS0tT27ZtNW/evEq3nz9/vtq1a6e0tDR16dJFd955p0aPHq1Zs2Y5t0lLS9Oll16qqVOnqnPnzpo6daoGDRqktLQ05zZPPfWU2rZtq0WLFun8889XcnKyBg0apA4dOvj6kOunmBhJUmMd9mg35/a33mp/1aaMYcNqt39srEf7AQAABDvLEomioiJt3LhRgwcPdlk/ePBgrVu3rtJ91q9fX2H7IUOGaMOGDSouLq52m/JlfvTRR+rZs6duuOEGtWjRQt27d9fLL79cbbyFhYXKy8tzecFNTZuquEMnXW9bdvpty7netkzFHTpJr70mvfZa7cp4++3a7d+kiUf7AQAABDvLEons7GyVlpYqISHBZX1CQoL2799f6T779++vdPuSkhJlZ2dXu035Mn/99VfNmzdPZ5xxhj799FONHz9eEyZM0KvVjM4zc+ZMxcfHO19t27b16HjrNZtN4ff+QcO0TAmq/Ls9VaIydZ3eU/iEuyWbrfZlhITUPgYf69FD+t3v7D8BAAACneUPW9tOuUAzxlRYd7rtT11/ujLLysrUo0cPPfHEE+revbvGjRunsWPHVtmlSpKmTp2q3Nxc52vPnj2nPzicNGqUFB2thSF3KlQl1W4aqhItCBkrRUdLt93mvTK8EYMPffSRtH69/ScAAECgsyyRaNasmUJDQyu0PmRlZVVoUXBITEysdPuwsDA1bdq02m3Kl9myZUt17drVZZsuXbpU+ZC3JEVGRiouLs7lBQ80aqSwZW/rMtun+ijkGiUqs9LNEpWpj0Ku0WW2TxX23juuE8HVtgxvxAAAAABJFiYSERERSk1N1apVq1zWr1q1Sn369Kl0n969e1fYfuXKlerZs6fCw8Or3aZ8mRdeeKF++uknl21+/vlnJSUl1fh44IYhQxTyycca3GCt9tjaaaltuG7Q27pEq3SD3tZS23DtsbXT4AZrFbL8E+mUZ128UoY3YgAAAIBkLLRkyRITHh5uFixYYLZt22YmTZpkGjZsaHbt2mWMMeaBBx4wI0eOdG7/66+/mujoaDN58mSzbds2s2DBAhMeHm7effdd5zb/93//Z0JDQ82TTz5ptm/fbp588kkTFhZmvvnmG+c23333nQkLCzMzZswwO3bsMG+88YaJjo42r7/+utux5+bmGkkmNzfXC2einjl82JjZs01Rh07G2Kd6M0ay/z57tjFHjvi+DG/EAAAAEITcvc61GWPtlL1z587V008/rczMTHXr1k1///vf1a9fP0nS7bffrl27dmn16tXO7desWaPJkyfrxx9/VKtWrfSXv/xF48ePdynz3Xff1UMPPaRff/1VHTp00IwZM3Tddde5bPPxxx9r6tSp2rFjh1JSUjRlyhSNHTvW7bjz8vIUHx+v3NxcujnVlDH2id7y8+3DqzZp4vlDzbUtwxsxeMnvfy8dPCg1b85zEgAAwDruXudankjUVSQS8LY2baSMDKl1a2nvXqujAQAA9ZW717mWj9oEAAAAoO4hkQAAAADgMRIJAAAAAB4jkQAAAADgMRIJAAAAAB4jkQAAAADgMRIJAAAAAB4LszqAusox/UZeXp7FkSBYlJWd/Em1AgAAVnFc355uujkSiRrKz8+XJLVt29biSBBsMjOl+HirowAAAPVdfn6+4qu5KGFm6xoqKyvTvn37FBsbK5vN5tG+eXl5atu2rfbs2cOs2F7A+fQezqX3cC69i/PpPZxL7+J8eg/n0rtqcz6NMcrPz1erVq0UElL1kxC0SNRQSEiI2rRpU6sy4uLi+IfiRZxP7+Fceg/n0rs4n97DufQuzqf3cC69q6bns7qWCAcetgYAAADgMRIJAAAAAB4jkbBAZGSkpk+frsjISKtDCQqcT+/hXHoP59K7OJ/ew7n0Ls6n93Auvcsf55OHrQEAAAB4jBYJAAAAAB4jkQAAAADgMRIJAAAAAB4jkQAAAADgMRIJC8ydO1cpKSmKiopSamqq1q5da3VIdc4jjzwim83m8kpMTLQ6rDrjq6++0lVXXaVWrVrJZrPpgw8+cHnfGKNHHnlErVq1UoMGDTRgwAD9+OOP1gQb4E53Lm+//fYKdfV3v/udNcEGuJkzZ6pXr16KjY1VixYtdM011+inn35y2Ya66R53ziV1033z5s3TOeec45zYq3fv3vrXv/7lfJ966b7TnUvqZc3NnDlTNptNkyZNcq7zdd0kkfCzpUuXatKkSZo2bZo2b96svn376vLLL9fu3butDq3OOeuss5SZmel8bdmyxeqQ6oxjx47p3HPP1Zw5cyp9/+mnn9Zzzz2nOXPm6Pvvv1diYqIuvfRS5efn+znSwHe6cylJl112mUtdXb58uR8jrDvWrFmje+65R998841WrVqlkpISDR48WMeOHXNuQ910jzvnUqJuuqtNmzZ68skntWHDBm3YsEEDBw7U1Vdf7bwgo16673TnUqJe1sT333+vl156Seecc47Lep/XTQO/Ov/888348eNd1nXu3Nk88MADFkVUN02fPt2ce+65VocRFCSZ999/3/l7WVmZSUxMNE8++aRz3YkTJ0x8fLyZP3++BRHWHaeeS2OMGTVqlLn66qstiaeuy8rKMpLMmjVrjDHUzdo49VwaQ92srcaNG5t//vOf1EsvcJxLY6iXNZGfn2/OOOMMs2rVKtO/f38zceJEY4x//mbSIuFHRUVF2rhxowYPHuyyfvDgwVq3bp1FUdVdO3bsUKtWrZSSkqKbbrpJv/76q9UhBYWdO3dq//79LvU0MjJS/fv3p57W0OrVq9WiRQudeeaZGjt2rLKysqwOqU7Izc2VJDVp0kQSdbM2Tj2XDtRNz5WWlmrJkiU6duyYevfuTb2shVPPpQP10jP33HOPhg4dqksuucRlvT/qZphXSoFbsrOzVVpaqoSEBJf1CQkJ2r9/v0VR1U0XXHCBXn31VZ155pk6cOCAHn/8cfXp00c//vijmjZtanV4dZqjLlZWT3/77TcrQqrTLr/8ct1www1KSkrSzp079fDDD2vgwIHauHEjs7dWwxijKVOm6KKLLlK3bt0kUTdrqrJzKVE3PbVlyxb17t1bJ06cUExMjN5//3117drVeUFGvXRfVedSol56asmSJdq0aZO+//77Cu/5428miYQFbDaby+/GmArrUL3LL7/cuXz22Werd+/e6tChgxYvXqwpU6ZYGFnwoJ56x/Dhw53L3bp1U8+ePZWUlKRPPvlE1113nYWRBbY//vGP+ve//62vv/66wnvUTc9UdS6pm57p1KmT0tPTdeTIES1btkyjRo3SmjVrnO9TL91X1bns2rUr9dIDe/bs0cSJE7Vy5UpFRUVVuZ0v6yZdm/yoWbNmCg0NrdD6kJWVVSFbhGcaNmyos88+Wzt27LA6lDrPMfoV9dQ3WrZsqaSkJOpqNe6991599NFH+vLLL9WmTRvneuqm56o6l5WhblYvIiJCHTt2VM+ePTVz5kyde+65mj17NvWyBqo6l5WhXlZt48aNysrKUmpqqsLCwhQWFqY1a9bo+eefV1hYmLP++bJukkj4UUREhFJTU7Vq1SqX9atWrVKfPn0siio4FBYWavv27WrZsqXVodR5KSkpSkxMdKmnRUVFWrNmDfXUC3JycrRnzx7qaiWMMfrjH/+o9957T1988YVSUlJc3qduuu9057Iy1E3PGGNUWFhIvfQCx7msDPWyaoMGDdKWLVuUnp7ufPXs2VO33nqr0tPT1b59e9/XTa88sg23LVmyxISHh5sFCxaYbdu2mUmTJpmGDRuaXbt2WR1anfKnP/3JrF692vz666/mm2++MVdeeaWJjY3lPLopPz/fbN682WzevNlIMs8995zZvHmz+e2334wxxjz55JMmPj7evPfee2bLli3m5ptvNi1btjR5eXkWRx54qjuX+fn55k9/+pNZt26d2blzp/nyyy9N7969TevWrTmXlfjDH/5g4uPjzerVq01mZqbzVVBQ4NyGuume051L6qZnpk6dar766iuzc+dO8+9//9s8+OCDJiQkxKxcudIYQ730RHXnknpZe+VHbTLG93WTRMICL774oklKSjIRERGmR48eLsPxwT3Dhw83LVu2NOHh4aZVq1bmuuuuMz/++KPVYdUZX375pZFU4TVq1ChjjH3IuOnTp5vExEQTGRlp+vXrZ7Zs2WJt0AGqunNZUFBgBg8ebJo3b27Cw8NNu3btzKhRo8zu3butDjsgVXYeJZlFixY5t6Fuuud055K66ZnRo0c7/99u3ry5GTRokDOJMIZ66YnqziX1svZOTSR8XTdtxhjjnbYNAAAAAPUFz0gAAAAA8BiJBAAAAACPkUgAAAAA8BiJBAAAAACPkUgAAAAA8BiJBAAAAACPkUgAAAAA8BiJBAAAAACPkUgAAPxiwIABmjRpktVhyBiju+66S02aNJHNZlN6enqFbV555RU1atTI77EBQF1CIgEAqNZVV12lSy65pNL31q9fL5vNpk2bNvk5qppbsWKFXnnlFX388cfKzMxUt27drA4JAOokEgkAQLXGjBmjL774Qr/99luF9xYuXKjzzjtPPXr0sCCymvnvf/+rli1bqk+fPkpMTFRYWJjVIQFAnUQiAQCo1pVXXqkWLVrolVdecVlfUFCgpUuXasyYMcrJydHNN9+sNm3aKDo6Wmeffbbeeuutasu12Wz64IMPXNY1atTI5XMyMjI0fPhwNW7cWE2bNtXVV1+tXbt2VVvumjVrdP755ysyMlItW7bUAw88oJKSEknS7bffrnvvvVe7d++WzWZTcnKyW+cgJydH559/vn7/+9/rxIkTbu0DAMGORAIAUK2wsDDddttteuWVV2SMca5/5513VFRUpFtvvVUnTpxQamqqPv74Y23dulV33XWXRo4cqW+//bbGn1tQUKCLL75YMTEx+uqrr/T1118rJiZGl112mYqKiirdJyMjQ1dccYV69eqlH374QfPmzdOCBQv0+OOPS5Jmz56txx57TG3atFFmZqa+//7708axd+9e9e3bV507d9Z7772nqKioGh8TAAQTEgkAwGmNHj1au3bt0urVq53rFi5cqOuuu06NGzdW69atdd999+m8885T+/btde+992rIkCF65513avyZS5YsUUhIiP75z3/q7LPPVpcuXbRo0SLt3r3bJY7y5s6dq7Zt22rOnDnq3LmzrrnmGj366KN69tlnVVZWpvj4eMXGxio0NFSJiYlq3rx5tTH8/PPPuvDCC3XJJZdo8eLFdIMCgHL4iwgAOK3OnTurT58+WrhwoS6++GL997//1dq1a7Vy5UpJUmlpqZ588kktXbpUGRkZKiwsVGFhoRo2bFjjz9y4caN++eUXxcbGuqw/ceKE/vvf/1a6z/bt29W7d2/ZbDbnugsvvFBHjx7V3r171a5dO7c///jx47rooot08803a/bs2TU7CAAIYrRIAADcMmbMGC1btkx5eXlatGiRkpKSNGjQIEnSs88+q7///e+6//779cUXXyg9PV1DhgypsguSZH9GonxXKUkqLi52LpeVlSk1NVXp6ekur59//lm33HJLpWUaY1ySCMc6x+d5IjIyUpdccok++eQT7d2716N9AaA+IJEAALjlxhtvVGhoqN58800tXrxYd9xxh/PifO3atbr66qs1YsQInXvuuWrfvr127NhRbXnNmzdXZmam8/cdO3aooKDA+XuPHj20Y8cOtWjRQh07dnR5xcfHV1pm165dtW7dOpcEZd26dYqNjVXr1q09Ot6QkBC99tprSk1N1cCBA7Vv3z6P9geAYEciAQBwS0xMjIYPH64HH3xQ+/bt0+233+58r2PHjlq1apXWrVun7du3a9y4cdq/f3+15Q0cOFBz5szRpk2btGHDBo0fP17h4eHO92+99VY1a9ZMV199tdauXaudO3dqzZo1mjhxYpUtBHfffbf27Nmje++9V//5z3/04Ycfavr06ZoyZYpCQjz/Ly80NFRvvPGGzj33XA0cOPC0xwQA9QmJBADAbWPGjNHhw4d1ySWXuDxv8PDDD6tHjx4aMmSIBgwYoMTERF1zzTXVlvXss8+qbdu26tevn2655Rbdd999io6Odr4fHR2tr776Su3atdN1112nLl26aPTo0Tp+/Lji4uIqLbN169Zavny5vvvuO5177rkaP368xowZo4ceeqjGxxwWFqa33npLZ511lgYOHKisrKwalwUAwcRmTu2gCgAAAACnQYsEAAAAAI+RSAAAAADwGIkEAAAAAI+RSAAAAADwGIkEAAAAAI+RSAAAAADwGIkEAAAAAI+RSAAAAADwGIkEAAAAAI+RSAAAAADwGIkEAAAAAI/9f/tNdWrk6Wb2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "As = []\n",
    "Es = []\n",
    "\n",
    "# Calculating error for K values between 1 and 40\n",
    "for i in range(1, 40):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i, metric='euclidean')\n",
    "    knn.fit(X_train, Y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    As.append(accuracy_score(Y_test, pred_i))\n",
    "    Es.append(1 - accuracy_score(Y_test, pred_i))\n",
    "\n",
    "# Plot error rate for different k values\n",
    "plt.figure(figsize=(9, 5))\n",
    "plt.plot(range(1, 40), Es, color='red', linestyle='solid', marker='o', linewidth=2, markerfacecolor='blue', markersize=10)\n",
    "plt.axvline(x=8, color='b', ls='--', linewidth=2)\n",
    "plt.title('Error Rate for different k values')\n",
    "plt.xlabel('Value of k')\n",
    "plt.ylabel('Error Rate')\n",
    "plt.savefig(\"D:/4th Year/STAT – 405 Data Mining/2. Python Code/kNN Algorithm/Error_Rate_for_k_values.jpg\", dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "61ea43b3-0c47-4516-8606-bb8f3fdee6d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.012195121951219523,\n",
       " 0.012195121951219523,\n",
       " 0.012195121951219523,\n",
       " 0.0060975609756097615,\n",
       " 0.0060975609756097615,\n",
       " 0.0060975609756097615,\n",
       " 0.012195121951219523,\n",
       " 0.012195121951219523,\n",
       " 0.012195121951219523,\n",
       " 0.012195121951219523,\n",
       " 0.012195121951219523,\n",
       " 0.012195121951219523,\n",
       " 0.012195121951219523,\n",
       " 0.012195121951219523,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.012195121951219523,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285,\n",
       " 0.018292682926829285]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = []\n",
    "err = []\n",
    "for i in range(1,40):\n",
    "    KNN = KNeighborsClassifier(n_neighbors = i,metric=\"euclidean\")\n",
    "    KNN.fit(X_train,Y_train)\n",
    "    pred_i = KNN.predict(X_test)\n",
    "    acc.append(accuracy_score(Y_test,pred_i))\n",
    "    err.append(1-accuracy_score(Y_test,pred_i))\n",
    "acc\n",
    "err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "876b0cf1-7527-4638-9a04-9898accd9b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'value of k')"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,40),err)\n",
    "plt.axvline(x=15)\n",
    "plt.xlabel(\"value of k\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0e52012-1271-40ad-b0b7-9cdd2bc05d3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a17abddd-e035-45bc-9355-567ab204f982",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13ece706-5129-4b7f-8504-76cf8f3fa950",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "5d52cb77-bbfa-4283-a44b-3576e8156e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris ,make_moons\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "e26f0ec2-4793-4b4f-aa1b-c3007b57d74e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function sklearn.datasets._base.load_iris(*, return_X_y=False, as_frame=False)>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D1 = load_iris\n",
    "D1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "1c16d0a2-01ec-4905-9963-0ff1b8c58c7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal length  sepal width  petal length  petal width\n",
      "0             5.1          3.5           1.4          0.2\n",
      "1             4.9          3.0           1.4          0.2\n",
      "2             4.7          3.2           1.3          0.2\n",
      "3             4.6          3.1           1.5          0.2\n",
      "4             5.0          3.6           1.4          0.2\n",
      "..            ...          ...           ...          ...\n",
      "145           6.7          3.0           5.2          2.3\n",
      "146           6.3          2.5           5.0          1.9\n",
      "147           6.5          3.0           5.2          2.0\n",
      "148           6.2          3.4           5.4          2.3\n",
      "149           5.9          3.0           5.1          1.8\n",
      "\n",
      "[150 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "D = load_iris()\n",
    "X = pd.DataFrame(D.data, columns = [\"sepal length\", \"sepal width\", \"petal length\", \"petal width\"])\n",
    "print(X)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "af7d92c0-cf21-44f3-b590-d5cc2ab411df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[681.3706, 152.34795176035792, 78.85144142614601, 57.25600931571816, 49.84981451052336, 39.03998724608725, 34.830916305916304, 30.186555194805194, 28.57798109243698, 27.007497899159667]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "wss = []\n",
    "n = 10\n",
    "for i in range(1, n+1):\n",
    "    KM = KMeans(n_clusters = i, init = \"k-means++\", max_iter = 10000, random_state = 17)\n",
    "    KM.fit(X)\n",
    "    wss.append(KM.inertia_)\n",
    "\n",
    "print(wss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "093dae15-044c-489e-a4b7-4bf75e4c9c5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_clusters = range(1, n+1)\n",
    "plt.figure(figsize = (6,4))\n",
    "plt.plot(number_clusters, wss, color = \"red\", linestyle = \"solid\", linewidth = 2, markerfacecolor = \"blue\", markersize = 5)\n",
    "plt.axvline(x = 3, color = \"blue\", ls = \"--\", linewidth = 2)\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"WSS\")\n",
    "plt.title(\"Elbow Method for Optimal value of k\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "83fda40e-0ccc-4ad1-8888-d8984029a57f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but KMeans was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(201681)\n",
    "K = KMeans(n_clusters = 3, init = \"k-means++\", max_iter = 10000, random_state = 17)\n",
    "K.fit(X)\n",
    "K.fit_predict(X)\n",
    "\n",
    "# Accessing KMeans Attributes and Methods\n",
    "K.cluster_centers_\n",
    "K.labels_\n",
    "K.n_features_in_\n",
    "K.n_iter_\n",
    "K.inertia_\n",
    "K.predict([[2,5,3,1.5]])\n",
    "X.insert(0, \"Cluster\", K.labels_, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "c2d1aec4-5363-4ea7-8f3b-23984089d1e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "662dcae5-24a8-4fb8-96a4-086baa08a84b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal length  sepal width  petal length  petal width\n",
      "0             5.1          3.5           1.4          0.2\n",
      "1             4.9          3.0           1.4          0.2\n",
      "2             4.7          3.2           1.3          0.2\n",
      "3             4.6          3.1           1.5          0.2\n",
      "4             5.0          3.6           1.4          0.2\n",
      "..            ...          ...           ...          ...\n",
      "145           6.7          3.0           5.2          2.3\n",
      "146           6.3          2.5           5.0          1.9\n",
      "147           6.5          3.0           5.2          2.0\n",
      "148           6.2          3.4           5.4          2.3\n",
      "149           5.9          3.0           5.1          1.8\n",
      "\n",
      "[150 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "D = load_iris()\n",
    "X = pd.DataFrame(D.data, columns = [\"sepal length\", \"sepal width\", \"petal length\", \"petal width\"])\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "1736f4ee-b4e3-4a51-a255-88936015168a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "E:\\Conda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[681.3706,\n",
       " 152.34795176035792,\n",
       " 78.85144142614601,\n",
       " 57.25600931571816,\n",
       " 49.97767846030011,\n",
       " 42.63021258361205,\n",
       " 40.741967320261445,\n",
       " 35.605430735930746,\n",
       " 29.333648109243697,\n",
       " 26.58990229361282]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wss = []\n",
    "n = 10\n",
    "for i in range(1,n+1):\n",
    "    KM = KMeans(n_clusters = i,init = \"k-means++\" ,max_iter = 1000)\n",
    "    CLs = KM.fit(X)\n",
    "    wss.append(KM.inertia_)\n",
    "wss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "851de744-d3ea-423e-89be-c3b5929a1e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x20fdeced9d0>"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,n+1),wss)\n",
    "plt.axvline(x=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f408544-4ee7-438e-bbf6-6ce39285b5f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32803a06-8a72-4ffe-99fe-cc614471fcde",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a8fc09b-ecba-4d0b-a96b-9b40275574ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef44c101-2d10-446a-b869-846e519391b1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}