Simple Linear Regression (ML).ipynb

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    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIVQRyUwF6sp"
      },
      "source": [
        "import sklearn"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LgPZKqBgGBua"
      },
      "source": [
        "import numpy as np;\n",
        "import pandas as pd;\n",
        "import seaborn as sns;"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8QimNtVIFpC"
      },
      "source": [
        "df=pd.read_csv('/content/House_Price.csv')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "d_pe3py9IIrS",
        "outputId": "fa175b49-03d9-4678-a970-4eb1439f7aeb"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>dist1</th>\n",
              "      <th>dist2</th>\n",
              "      <th>dist3</th>\n",
              "      <th>dist4</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>airport</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>waterbody</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>bus_ter</th>\n",
              "      <th>parks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.0</td>\n",
              "      <td>0.00632</td>\n",
              "      <td>32.31</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.35</td>\n",
              "      <td>3.81</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.01</td>\n",
              "      <td>24.7</td>\n",
              "      <td>4.98</td>\n",
              "      <td>YES</td>\n",
              "      <td>5.480</td>\n",
              "      <td>11.1920</td>\n",
              "      <td>River</td>\n",
              "      <td>23</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.049347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21.6</td>\n",
              "      <td>0.02731</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.99</td>\n",
              "      <td>4.70</td>\n",
              "      <td>5.12</td>\n",
              "      <td>5.06</td>\n",
              "      <td>22.2</td>\n",
              "      <td>9.14</td>\n",
              "      <td>NO</td>\n",
              "      <td>7.332</td>\n",
              "      <td>12.1728</td>\n",
              "      <td>Lake</td>\n",
              "      <td>42</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.046146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34.7</td>\n",
              "      <td>0.02729</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>5.03</td>\n",
              "      <td>4.86</td>\n",
              "      <td>5.01</td>\n",
              "      <td>4.97</td>\n",
              "      <td>22.2</td>\n",
              "      <td>4.03</td>\n",
              "      <td>NO</td>\n",
              "      <td>7.394</td>\n",
              "      <td>101.1200</td>\n",
              "      <td>None</td>\n",
              "      <td>38</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.045764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33.4</td>\n",
              "      <td>0.03237</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.21</td>\n",
              "      <td>5.93</td>\n",
              "      <td>6.16</td>\n",
              "      <td>5.96</td>\n",
              "      <td>21.3</td>\n",
              "      <td>2.94</td>\n",
              "      <td>YES</td>\n",
              "      <td>9.268</td>\n",
              "      <td>11.2672</td>\n",
              "      <td>Lake</td>\n",
              "      <td>45</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.047151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.2</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.16</td>\n",
              "      <td>5.86</td>\n",
              "      <td>6.37</td>\n",
              "      <td>5.86</td>\n",
              "      <td>21.3</td>\n",
              "      <td>5.33</td>\n",
              "      <td>NO</td>\n",
              "      <td>8.824</td>\n",
              "      <td>11.2896</td>\n",
              "      <td>Lake</td>\n",
              "      <td>55</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.039474</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   price  crime_rate  resid_area  ...  rainfall  bus_ter     parks\n",
              "0   24.0     0.00632       32.31  ...        23      YES  0.049347\n",
              "1   21.6     0.02731       37.07  ...        42      YES  0.046146\n",
              "2   34.7     0.02729       37.07  ...        38      YES  0.045764\n",
              "3   33.4     0.03237       32.18  ...        45      YES  0.047151\n",
              "4   36.2     0.06905       32.18  ...        55      YES  0.039474\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIaf2AFfIli4"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression;"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sanry8V3Iu6r"
      },
      "source": [
        "#Defien dependent variable\n",
        "y=df.price;"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "dtZ1OmFOI8ue",
        "outputId": "dd70ee4e-91e3-4f84-985f-460c6ec4af56"
      },
      "source": [
        "x=df[[\"room_num\"]];#Note : keep the single variable inside double square bracket--[[]]\n",
        "x"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>room_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>6.593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>6.120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>6.976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>6.794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>505</th>\n",
              "      <td>6.030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     room_num\n",
              "0       6.575\n",
              "1       6.421\n",
              "2       7.185\n",
              "3       6.998\n",
              "4       7.147\n",
              "..        ...\n",
              "501     6.593\n",
              "502     6.120\n",
              "503     6.976\n",
              "504     6.794\n",
              "505     6.030\n",
              "\n",
              "[506 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CskyLxXoJKkg"
      },
      "source": [
        "lm2=LinearRegression()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxoL-p2kKWca",
        "outputId": "94a25646-2ac9-4341-88c4-85bdc1b4d904"
      },
      "source": [
        "lm2.fit(x,y)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhVAhjNPKa_G",
        "outputId": "53fb2942-a171-4b4a-ec16-d4023c705f9e"
      },
      "source": [
        "print(lm2.intercept_,lm2.coef_)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-34.65924312309717 [9.09966966]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mT2kuUeaLQi4",
        "outputId": "f15da552-13e3-49b1-b761-e7da8172de01"
      },
      "source": [
        "lm2.predict(x)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([25.17108491, 23.76973578, 30.72188341, 29.02024518, 30.37609596,\n",
              "       23.85163281, 20.04797089, 21.50391804, 16.58099675, 19.97517353,\n",
              "       23.36935032, 20.02067188, 18.92871152, 19.4746917 , 20.81234314,\n",
              "       18.42822969, 19.34729633, 19.84777816, 14.98855456, 17.45456504,\n",
              "       16.0259169 , 19.62028642, 21.23092795, 18.23713663, 19.24719996,\n",
              "       16.28980732, 18.23713663, 20.36645933, 24.44311134, 26.07195221,\n",
              "       17.32716966, 20.59395107, 19.48379137, 17.21797363, 20.81234314,\n",
              "       19.32909699, 18.49192738, 18.57382441, 19.62938609, 25.3530783 ,\n",
              "       29.25683659, 26.9455205 , 21.47661903, 21.85880515, 20.56665206,\n",
              "       17.0450799 , 17.99144555, 20.21176495, 14.46987339, 16.31710633,\n",
              "       19.60208708, 20.98523687, 24.58870605, 19.92057552, 18.91961185,\n",
              "       31.30426226, 23.42394834, 27.3641053 , 21.25822696, 19.27449897,\n",
              "       17.58196041, 19.62938609, 24.08822422, 26.87272314, 29.98481016,\n",
              "       22.57767906, 18.00054522, 18.82861516, 16.24430897, 18.89231284,\n",
              "       23.7333371 , 19.58388774, 20.53025338, 22.16819392, 22.42298467,\n",
              "       22.54128038, 22.47758269, 21.21272861, 22.04989822, 18.79221648,\n",
              "       26.5542347 , 25.57147038, 22.68687509, 21.45841969, 23.47854635,\n",
              "       25.67156674, 20.0752699 , 21.03983488, 29.10214221, 29.75731842,\n",
              "       23.7333371 , 23.62414107, 23.96082885, 21.85880515, 22.2045926 ,\n",
              "       25.62606839, 21.42202101, 38.76599139, 36.50017364, 32.8239071 ,\n",
              "       26.5542347 , 27.04561686, 23.62414107, 21.1854296 , 21.45841969,\n",
              "       18.58292408, 18.44642903, 21.0944329 , 24.25201828, 22.02259921,\n",
              "       21.71321044, 26.44503866, 19.14710359, 20.77594446, 22.25009095,\n",
              "       19.28359864, 21.54031672, 20.12986792, 18.77401714, 17.49096372,\n",
              "       18.7558178 , 19.97517353, 19.58388774, 18.62842242, 18.83771483,\n",
              "       19.81137948, 16.4172027 , 17.14517627, 23.86073248, 16.63559477,\n",
              "       24.10642356, 22.90526717, 23.32385197, 18.31903366, 17.72755513,\n",
              "       22.98716419, 19.41099401, 24.07002488, 18.63752209, 21.31282497,\n",
              "       21.52211738, 11.01199892, 14.50627207, 15.09775059,  9.95643723,\n",
              "       21.12173191, 16.55369774, 10.16572964, 12.53164375, 16.27160798,\n",
              "       21.04893455, 14.51537174, 10.94830123, 17.29077098, 21.11263224,\n",
              "       21.32192464, 13.31421534, 28.51976335, 20.53935305, 24.57960638,\n",
              "       22.21369227, 33.48818298, 36.33637959, 41.55049031, 18.61022308,\n",
              "       20.85784149, 37.49203764, 18.81951549, 22.84156948, 23.59684206,\n",
              "       18.80131615, 18.8468145 , 16.04411624, 23.72423744, 18.65572143,\n",
              "       24.90719449, 20.12076825, 22.8051708 , 27.76449077, 28.85645113,\n",
              "       35.99969181, 21.24912729, 30.44889332, 25.06188888, 16.33530567,\n",
              "       21.33102431, 36.60027001, 27.05471653, 24.99819119, 30.72188341,\n",
              "       28.5925607 , 26.66343074, 30.65818572, 27.21851059, 25.43497533,\n",
              "       37.00065547, 31.65004971, 30.01210917, 31.53175401, 28.81095278,\n",
              "       30.26689992, 21.41292134, 34.58924301, 36.80046274, 38.44750295,\n",
              "       18.94691086, 22.90526717, 17.96414654, 20.52115371, 13.96939156,\n",
              "       19.57478807, 14.51537174, 18.18253861, 23.35115098, 14.58816909,\n",
              "       21.59491473, 18.91961185, 25.78076278, 19.49289104, 23.33295164,\n",
              "       28.5925607 , 21.43112068, 27.93738449, 25.56237071, 40.55862631,\n",
              "       44.73537469, 38.50210097, 30.52169067, 35.28081791, 24.96179251,\n",
              "       19.76588113, 32.78750842, 41.20470286, 40.38573259, 26.54513503,\n",
              "       20.72134645, 25.68066641, 32.29612626, 24.31571596, 25.45317467,\n",
              "       28.10117854, 20.80324347, 23.19645659, 23.51494503, 16.2352093 ,\n",
              "       16.34440534, 20.92153918, 21.9953002 , 23.87893182, 26.47233767,\n",
              "       24.37031398, 23.92443017, 28.64715872, 40.49492862, 20.92153918,\n",
              "       18.81041582, 33.16969455, 44.54428162, 32.06863452, 27.60069671,\n",
              "       30.88567746, 33.77027274, 41.75978271, 32.0140365 , 30.91297647,\n",
              "       15.9349202 , 29.16583989, 40.84071607, 33.31528926, 19.21080128,\n",
              "       18.62842242, 22.12269557, 24.83439713, 35.32631626, 26.83632446,\n",
              "       27.70989275, 31.46805632, 27.455102  , 24.32481563, 27.32770662,\n",
              "       36.50017364, 28.74725509, 34.90773145, 37.43743962, 29.83921545,\n",
              "       24.06092521, 22.03169888, 21.84060581, 22.8051708 , 25.08008821,\n",
              "       27.77359044, 30.38519563, 25.67156674, 21.0944329 , 20.02067188,\n",
              "       26.10835089, 24.9344935 , 18.02784423, 23.07816089, 29.41153097,\n",
              "       27.86458713, 25.30757996, 24.44311134, 28.87465046, 31.18596656,\n",
              "       25.54417137, 32.86030578, 27.6643944 , 25.71706509, 19.6839841 ,\n",
              "       10.59341411, 21.04893455, 20.14806726, 22.35928699, 25.09828755,\n",
              "       17.2543723 , 19.15620326, 17.95504687, 23.41484867, 20.96703753,\n",
              "       23.81523413, 23.36025065, 20.31186131, 17.28167131, 23.71513777,\n",
              "       23.86073248, 22.77787179, 20.69404744, 18.73761846, 22.96896485,\n",
              "       21.24912729, 17.26347197, 20.22086461, 22.81427047, 22.75967245,\n",
              "       20.27546263, 18.74671813, 18.98330954, 20.47565537, 19.80227981,\n",
              "       19.64758543, 31.23146491, 24.85259647, 26.27214494, 27.89188614,\n",
              "       20.06617023, 19.01060855, 24.6342044 , 25.71706509, 28.48336467,\n",
              "       24.39761299, 25.20748359, 18.88321317, 26.56333437, 16.87218618,\n",
              "       19.356396  , 21.86790482, 23.53314437, 21.0944329 , 20.95793786,\n",
              "       23.56044338, 22.22279194, 14.13318561, 18.14613993, 45.23585652,\n",
              "       -2.25531945, 10.50241741,  0.49278079, 10.5661151 , 26.15384924,\n",
              "       29.18403923, 21.9043035 , 18.80131615,  9.98373624,  2.99518994,\n",
              "       31.88664112, 25.84446047, 27.16391257, 23.39664933, 21.96800119,\n",
              "       28.74725509, 24.89809482, 15.71652813, 15.57093342,  5.08811397,\n",
              "       13.35971369,  7.67242015, 10.83910519,  9.74714483, 14.38797636,\n",
              "       17.32716966, 20.40285801, 11.1666933 , 21.6950111 , 18.91051218,\n",
              "       24.22471927, 23.62414107, 17.63655843, 14.96125555, 18.59202375,\n",
              "       19.82047915, 23.05996155, 23.6150414 , 14.0148899 , 15.67102978,\n",
              "       17.05417957,  2.99518994, 16.37170435, 16.45360137, 27.69169341,\n",
              "       17.72755513, 25.91725782,  7.45402808, 12.24955399,  6.46216408,\n",
              "       23.88803149, 27.05471653, 13.60540477, 19.54748906, 27.43690266,\n",
              "       23.67873909, 19.99337287, 16.73569113, 20.87604083, 15.98041855,\n",
              "       18.99240921, 18.4555287 , 21.77690813, 21.6950111 , 23.39664933,\n",
              "       23.1054599 , 27.51879968, 23.80613446, 23.90623083, 21.83150615,\n",
              "       25.66246707, 24.13372257, 21.32192464, 19.34729633, 16.54459807,\n",
              "       18.28263498, 23.63324074, 21.93160251, 24.35211464, 18.61022308,\n",
              "       24.11552323, 23.04176221, 22.22279194, 21.62221374, 23.7333371 ,\n",
              "       26.75442743, 25.89905848, 22.64137675, 32.6146147 , 26.56333437,\n",
              "       24.71610143, 19.72038278, 19.356396  , 22.67777542, 20.6758481 ,\n",
              "       26.31764329, 23.36025065, 22.82337014, 24.60690539, 21.84060581,\n",
              "       17.74575447, 19.50199071, 19.96607386, 19.2653993 , 17.32716966,\n",
              "       21.45841969, 22.02259921, 23.9153305 , 28.85645113, 14.72466414,\n",
              "       21.41292134, 24.34301497, 13.60540477, 21.62221374, 22.02259921,\n",
              "       22.14089491, 26.7635271 , 29.59352437, 17.77305348, 18.76491747,\n",
              "       22.77787179, 20.9761372 , 19.07430624, 14.97035522, 14.60636843,\n",
              "       11.68537447, 19.78408047, 19.78408047, 17.27257164, 19.2653993 ,\n",
              "       16.93588387, 14.38797636, 18.0642429 , 20.11166858, 16.01681723,\n",
              "       20.18446594, 25.33487897, 21.03073521, 28.82005245, 27.16391257,\n",
              "       20.21176495])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "W9vj7wSlLdqW",
        "outputId": "562484c9-17ca-4eab-baae-f9cfe9d6e813"
      },
      "source": [
        "#Visaulization\n",
        "sns.jointplot(x=df.room_num,y=df.price,data=df,kind='reg')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7ff59a3e0cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lYoRMl49d/n4WlIeYXx7K3IepiwbxH7FE8aVXNlFfGZ7ycQmRi/Hsk5rqYwJ88aLVfOzse8Z8XDAY4uaHXsRxNdYwMzXLdkll7n9193e58ov/NGKA60/ZxJL2kOXxsey++cKcHzvXFEVQ+tbvXgNAOzZoF+06oN3M1949rjP0a+2C64Ie/HXmua5LMBjgrW85G9NQ3k2pw18P+p5hKH5w1y9400WXYiiFUt56unHkPWAoxca7bsFAe6+VGcvg18V10FaSdCrOtbfeg99Q+EwDn6Hwmd5SwpGzm8+cv4qv//zmnN4ruaYj5rLxzNSAzM+6OWoF9Z88/QD/eMHYgc5yXKKVdXzpnj8eFeAsV2M7Lnbml2cxMlUMrZSVUo8ANdPwUjVAxzS8TiGQc5295tL5Fuu5dmitz5/pQRSioghK00UptVlrvXamxzEd5Fxnr7l0vnPpXOcK2RQghBCiYEhQEkIIUTAkKA1110wPYBrJuc5ec+l859K5zglyTUkIIUTBkJmSEEKIgiFBSQghRMGQoCSEEKJgSFASQghRMIoiKJ1//vkar9uC3OQmN7nNhlvOZunn34iKIih1dBRjFREhhJi8ufb5VxRBSQghxNwgQUkIIUTBkKAkhBCiYEhQEkIIUTAkKAkhhCgYEpSEEEIUDAlKQgghCoYEJSGEEAVDgpIQQoiCIUFJCCFEwfDl8+BKqd1ADHAAW2u9VilVBfwEWArsBt6vte7K5zgEbNrRxp2PN9HcFWdRZQlXndPI+pV1Mz2sSZvq85ru90nGP/Q4rx3sxXI0AZ9BTWkApRRtsSQJy8WyXQxDsay6hC++83jWr6yb9Osf+fyzGqt4sunQlP573PLIDpo6+gGGjF0ML6+dZzNBaa3WumPQ974BHNJa36yU+iJQqbX+wmjHWbt2rd68eXPexjnbbdrRxvUbt+E3FWG/ScJysBzNTRtWFfUPx1Sf13S/TzL+ocdJ2w6d/WkAHEejFGgNKHAzH1M+A0BRUeLnw2cu4YHnWyb8+keOv7M/RVssTW0kQE0kOCX/Hp97YCtdcQtDed9zNVSU+OnoS79z980XPpLLcQZ//rmuxna9oF3k1Eh/MRNn9m7g7szXdwPvmYExzCl3Pt6E31SUBHwo5d37TcWdjzfN9NAmZarPa7rfJxn/0OPEkjYGCp9hoDOBKHuv8G6uBlMp+lI2P3jijUm9/pHj703YGApiSXvK/j1iSRvTUJiG4d0yYwc+N5Fjnn/b41xz7wsTGk+xyHdQ0sBvlFLPKaWuzHxvntb6QObrVmDecE9USl2plNqslNrc3t6e52HObs1dccJ+c8j3wn6TfV3xGRrR1Jjq85ru90nGP/Q4acdFZX5/1jrT4yC7kDPo+0qB42r6086kXv/I8acdF0N595M5n8HHt93D5wSHxw4sG+25gz//tmzZglIKpRQvbXme/9n40MCfR7otWrxkQmMuBHm9pgScrbVuUUrVAb9VSu0Y/Jdaa62UGnb9UGt9F3AXeNPXPI9zVltUWUJbLElJ4PA/d8JyaKgsmcFRTd5Un9d0v08y/qHHCZgGdmbZTim8qJRdwst8AmSX9ExDETYNEpYz4dc/cvwB0yDtuATMw7+rT/bfoyOWGgikcHjslqPfGO25gz//lFL61t+8CsADz+0D4ItXvjrqa1933nETGnMhyOtMSWvdkrlvA/4HWAccVEotAMjct+VzDAKuOqcRy9HE0zZae/eWo7nqnMaZHtqkTPV5Tff7JOMfepxoyIeL9mYXGgzFwH02LhkKHK2JBH18/Oxlk3r9I8dfFvbhaoiGfFP27xEN+XBcjeO63i0zduCbEzmmocDNYx5AIchbUFJKlSqlotmvgfOAl4GNwEcyD/sI8GC+xiA861fWcdOGVdRFQ/QkLOqioaJPcoCpP6/pfp9k/EOPs6wmQnnIR9hvUhUJsKIuwnHzo1SV+CkJmPgz12aOqS3l3y45mWvefuykXv/I8S+tjnDtucewrCZCa0+C9liKeNrmzseb2LRj/L87r19ZxzcvOZkVdZGBZbXs2HNNcjiShiHLgbNR3rLvlFKNeLMj8JYJ/1tr/c9KqWrgfmAxsAcvJfzQaMeS7Dsh5o6Z3r4wTVmMOYeW4ZbvLjmtYdTnXHfeceQzs3oKjHj+ebumpLVuAk4e5vudwF/k63WFEMVrcECoCPtpiyW5fuM2boIpCwhjBb3BWXkAJQHfwIxpplcXXK0xjdk9VSr6ZHchxOyR77T2bNBriyWHBL3By3OFnK2atl2Cxb9HaVSz++yEEEUl3wEhl6C3qLKEhOUMeV6hZKsmLIfQEe/PbCNBSQhRMPIdEHIJevnOYoyn7Qk9T2tNynIlKAkhxHTJd0DIJejlM4sxlrQ42Jua0HMtR+NoTcg/uz+281r7bqpI9p0Qc0c2EWFfV5yGKc6+m8k6kN3xNIcytf0aayPjyL4zNGjMaC0Nn/5/dD58G30v/nbU5zQsWkzz3j2TG3B+jXj+EpSEEHNKPoPeSDr6UvQmrIE/jy8oeVVvXtrXzUXffYI7rjiN80+cn4dRTqvpTwkXQohCtH5l3bSldmutaYul6E9N7DrSYD2ZoFZR4p/0sQqZBCUhhMgD19W09iZJHnENa6K64t7SX2VJYEqOV6gkKAkhpsVMV2qYTrbj0tqbJG27Yz84R93xuTFTmt1pHEKIgpDLptXZIm277O+e2oAEXqIEQHlYgpIQQkzKbG00eaSk5XCgJ4HtTm1AAm+mFPabs36fkizfCSHyatOONp7f24WrNQHToCYSpCzsL5jSPVOlP2XTFkvlrRBqV9yicpYv3YEEJSFEHmWX7bLtzG1Hs78nAYDPVAVRumcq9CYtOmIT2xSbq55EmrJZvnQHsnwnhMij7LLd/PIQoLL/52AsOSsaTQJ09afzHpAAepM2ZaHZH5RkpiSEyJvmrjgVYT9KKRZWQHssRcp2UVodVUWhGLPz2mMpYklr7AdOitcf/unnt/L2s07L82vNPJkpCSHyZnCtuWjIT2NthCXVpaxZXHlUQCqm7DytNa09yWkISACaWx/djqP8REOzfx4hQUkIkTe5Flgtpuw8x9Xs70lOqtr3RKhgCdE5sHwnQUkIkTe5Vtye7sZ6m3a0cdldT3H2LY9x2V1P5TwjsxyX/d0JUhOs0pCyHL7x6Kvjfp7WGiNYOidmSrP/DIUQMyqXWnOLKktoiyUHWpBD/hrrTbTlesp2ONiTmvAepNYe73V2tfWN+7m2q1GGKTMlIYSYDvnuozTYRJYKE2mHA93JCQekZ3cf4pP3PMeutj6MnOuDH5atDhEJzu6NsyAzJSFEAVi/so6bYFpaSmQzAgcbbakwlrTo6EtP6FqQqzX3PrOX//vEbjReiaCvXHT8uI/jZF474Jv98wgJSkKIgjBdLSXGs1Q4uDHfePWnbG5+ZAf/u6sTgOPmRfnqhhOYVxYa97Fc1wtKflOCkhBCHKUY9xRlXXVOI9dv3EY8bQ/pPnvkUuGRjfnGY3dnP9c/uI19XV71igtOms81566Y8EzHcWWmJIQQw5pookAux52OQDfWUuFkG/P98bV2bnlkB0nLxW8qrjl3BReuXjCpMWeX7+bCTEnaoQshxuWyu56iLZbEdjQdfSnSjotpKJZWlfDIP7x1QsccHOgGz16GSx/PJ8fVHJxgYz7H1fzgT038ZPM+AGojQb664QSOX1B21GPH1w7d0IEFK1jw4Vs5+NOvUmO10bx3z7jHV2BGPP/ZH3aFEFOquSuO7bjs70lgOxpTKVxXs7O9b8IVGAph86yd2YM0kYDUHU/z+Z+9OBCQTllUwR0fWjNsQBo/zd99+34APvkv32df894pOGbhkuU7IcS4LKos4YXmLgwURia/2XU1rgtX3fMcaxZXjnvpbbwZcVNtMnuQdrT28tWNr9CWKcr6/rUNfOItjZgTyf0eQXb5zlRTd8xCJTMlIcS4ZPcUZf9nOS62qzENLwV6InXrBtfIy8rX5tkjTWYP0q9fOsC1922hLZYi5De4/qIT+ORbl09pQILDiQ5TfdxCJDMlIcS4rF9Zx7F1Ed7o6MdxNa7W+E2FgcKXWYKLp23ufLwp59lSrhlxU2FwQsWC8hB/taaBdcuqxnWMtO3yf/6wi4dePABAQ2WYGzesYllN6ZSPF7xgD3MjKMlMSQgxbl84fyV1ZSEWV5VgZHokuWhqIkFg/EtvudbIm6zB1cijQR+tPUlu+/1Onmk6lPMx2nqT/P1PtgwEpDcvr+Z7l6/JW0CCwzOlORCTZKYkhBi/oWnVCZSCedHQQGfUhOUQCfq47K6nck7xno7Ns9mEioBp4Lh6YFZ237PNrGsce7b0wt4uvvbQdroTFgr46NlLuWzdYow8X+vJJkmrOXBNSYKSEGJCskEkO/vwmQqtNQnLoTdhoYG0407pXqbJ2nuon0jQNzDzAAj5DVp7E6M+T2vN/Zv38f0/NeFqKAv5+KcLj+f0peNb9hNjk+U7IcSkDLf0Vl0aoDzsL6j+SI6rmVcWIp4emlCRtFzml4VHfF48bXPTQ9u583EvIB1TG+E/rlgzIwFp9s+TZKYkhJgCRy69nX3LYzOa4n0k23E50JPk/act4rbHdpKwHEJ+g6TlZQ6euqic636ylQO9CRaUhbn09EWsa6xi76E4N2zcxp5Ob9znnTCPf3j7CoL+6a3WrSn8IgdTRYKSEGLKTWd/pLEM3oO0rrGKa1nBfc8209qbYH5ZmFMXlfPIKwfxGYqykI/O/hS3PbaTt++fx89f2Ed/2sE0FFevX867T1k46es643++4p5//Sy1Gz7Hv370HTQsWjyp1y90EpSEKFKFXBR1OlO8R5NIOxzsTQ6kVAOsa6waktRw3U+24jPUQOfbkM/gYCzFj572SvlUlwa44V0ncGJ9+aTH4zcN6sqC43yWHghkr776Ko21kUmPo5DJNSUhitDg1ObBiQQTLfMz1aYrxXs0/Smb1iMC0nAO9CYI+b2PQsfVtPQk6U16xVhPqi/nzg+dNiUBqSzsp6EyTNA38aU/yb4TQhSkwbXigAltWM236eqPNJzepEVHpuzPWBaUhensT6GA/T1J7ExmXk0kwL+/bzW+SVbm9psGNZEg4cDkr0MVQwHtyZKZkhBFqLkrPrDclDWTiQSFpDuezjkgAVx6+iJ6kzbNXQlsV6OAirCfz/7lcZMOSGVhP/UV4UkHJO16Mzfbnf1BSWZKQhShQkokKCSdfSl6xtGYz3JcnmzqHHiOqeCY2ih/8+alOW2mHclUzo4AtOUF2UR6/BXMi40EJSGKUKEkEhQKrTXtfSn6krk35muPpbjxl6/wyoFeAM5YVsU/XrCSaMg/xjNHFw35qS4NDFRQnwra9lqyT6StRrGRoCREERqre+pcorXmYG+KeDr3gPTivm5u/OUrdMW9GdKHz1rCh89aMqlyQT7DoDY6dbOjwbTtzZSS9vgrmRebvAclpZQJbAZatNYXKaWWAfcB1cBzwIe01ul8j0OI2WYmEwkKheNqWnuTpHKcQWit+fkLLfzHptdxNZQGTf7xncdz1vLqSY0jH7OjwVxLZkpT6VpgO5BtwXgL8C2t9X1KqTuAjwH/MQ3jEEIUmMnstbIcl9aeJJaT2+whYTnc+pvX+H0mbb6xppQbN6yivnLkEkNj8RkGNdHAkGt7+ZCdKc2Fa0p5zb5TSjUAFwI/yPxZAecCD2QecjfwnnyOQQhRmCaz1yple435cg1ILd0J/u7eFwYC0l+srOO7Hzx1UgEpGvL2HeU7IIFCWV7B2K747F9Uyve7+W3g80A08+dqoFtrnV383QfUD/dEpdSVwJUAixfP7rIaQsxFE91rNVyVhtE81dTJv/x6B30pG0PBp9Yv5+JT6ye8EXU6ZkeDP/8ArFgXhoJD/RKUJkwpdRHQprV+Tim1frzP11rfBdwFsHbt2tmfnC/EHNPcFR930da+lE17LJXTJlJXa3705B7uftIrF1RZ4uf6d53AyQ0VEx5zJOSjujSY9w6wgz//lFIaNFWlATr6JChNxpuBDUqpC4AQ3jWl24AKpZQvM1tqAFryOAYhRIEa716rnoRFZ19um2JjSYt/fXgHT2U6yp6wIMoN71pFbXS8dec8pnXEKvgAACAASURBVKGoiQQpDc5cwnJVaYBD/blvCi5WebumpLX+kta6QWu9FLgUeExrfTnwB+CSzMM+AjyYrzEIIQrXVec0YjmaeNpGa+9+pL1Wh/rTOQek19v7+NSPnx8ISBtOXsi3PnDKhANSScBHfUV4RgMSQF00RGvv7A9KM/EufwG4Tyn1deAF4IczMAYhxAzLZa9Vrptin2k6xH3PNtPU2Udf0sbVEPAZ/MPbV/COVfMnND5DKaoiAcomuZl2qiyqCvObbQdnehh5Ny1BSWu9CdiU+boJWDcdryuEKGyj7bVyXU1bbOxNsc80HeLbv3+NvpRNX8pLmTaV4qq3NE44IAX9JnXRIP5J1r6bSouqSujsT9Ofsmd81pZPhfOOCyFEhuNqDvQmc6rS8KOn9tAVtwYCUknAZF5ZkD/t7Bj36yqlqCoNUF8RLqiABLC4yrvW1jzLi+4W1rsuhJjzLMdlf3cipyoN2/b3sL21l1Sm/E5ViZ/68hClQZPW3sS4XtdvGiysCFFREpjQuPMtG5Syrdlnq9k7BxRCHKWQu9WCV0bnYG8SZ4wWDVprNm49wO1/2IWrQQELykNEMstaCcthflnuG2PLw36qSgMF3URveW0EpWDHgdiElyWLgcyUhJgjCr1bbTxt09ozdkBKWQ7fePRVbvv9TmxX4ze9QNLRn6IvZZGwHGxXc+npi8Z8TZ9hsKA8THUkWMABSYEyiIT8pNr3cut/PjD2U4qYzJSEmCMKuVttLGnR0Zcec1Nsa48XSHe19QEQ8hlURwJorenoS9Pam2JpVQlXrz9mzH5IkZCPmtJg3oqoTh3NrY/uAODRba28vHP3zA4nzyQoCTEL5LIs19wVx1TQ1N5H2nEJmAY1kcCMd6vtjqdzKp/z7O5D/POvttOb9MoFzS8LYSgGgmwk6CdhOZSFA6MGpELYCDtRddEgvmg1B3uTzCsLzfRw8kKW74Qocrkuy0UCJi3dSWxHYyqF7WhaupOU5qH/T646+lJjBiRXa3789B6++LOX6E3alIf9fOOS1ThaH9W7KOQ3Rk1wKA36aKgsKcqABFCXCUQv7euZ4ZHkjwQlIYrc4GU5pbx7v6m48/GmIY8buGaiBt0Gf38aaa1p603SO0br8v6UzQ0bt/HDJ3ajgZXzo9x5xRrWLK5kQVmYpDW0SnjScodNcDCUojYaZF5ZKO916/KpNhJEuw4vtUhQEkIUqOauOGH/0BnDcIVNYymb+ooQPkPhuBqfoaivCNGXyr1j61RwM435xnrd3Z39fOrHz/O/uzoBuPCkBXz7A6cMzBYuPX0RtqtJWA4aPWKCQzhg0lAZnnSb80IQ8BlYh/ZJUBJCFK5FlSUkjtjTM1xh00WVJfhMr2V3wDRI2i57DyVo7U1y2V1PTUsWnu247O9JjNms7o+vtfPpHz/Pvq4EflNx3V8ey2fOO5aA7/BH1rrGKq49dwXVpUFiSZvq0iDXnrti4HqSoRQ10SALysP4Cmwj7GSkD+zipZaenCqlF6PiXFgVQgy46pxGrt+4jXjaJuw3SVjOsIVNrzqnkc89sJWuuIXWGifzmeY4mt2dfVy/cRs3Qd4y8dK2y8He0RvzOa7mB39q4ieb9wHectVXN5zA8QvKhn38usaqYZMaQn6T2gIrEzRV0gd30R5LcbA3xfzy2ZfsIEFJzGmFvpk0F7kUNs3qS9nYmX1ACvAZCo3mYG8KQymuue8FvnPpqVP+HuSyKbY7nuamh7azpbkbgFMWVfCVi46nchwVFpRSVJUEKC8p/qW6kaRbdwLwUkuPBCUhZpNs1prfVEOy1vI5W8iX0QqbwuFztRxN0KdI2RqNl9mWnTH5TehP2wPvATAlATuetjnYO3pjvu0Hevnqxldoz7SneP/aBj7xlsZxJSUE/Sa1keCQJb7ZKH3wDQwFL+3r5i9PmDfTw5lyqhjWJdeuXas3b94808MQs8xldz11VJO5eNqmLhri3ivPnMGRTb3subb2eCnhlusyeNJiKK/2m89QzC8PETAN+tMOflMNWRK8acOqcQWmXBrzPfTiAb772E4sRxPyG3z+HStZf1xtzq+hlKKyxF+wNetGkHO0VcrQMOgfSxks+JvvYPe20/7AjQA0LFpM8949Uz7IPBrx/GWmJOasibTjLlbZc62JBNnfk8AABl/ZMQ2F1lAbDRL2m+xs66OhMjyp6g+dfSl6Rkn5Ttsu33lsJ79+qRWAhsowN25YxbKa0pzPa27MjjS3/ubVId/5zSut7O6I88VHd6CU4rrzjpuhsU09CUpizhpvO+5Clct1sey5lmWCcEdfCjft4AKm8voPAbR0JzCVwtU6pzTz4WitaY+lRk35butNcsMvX+HV1hgAb15ezRfeuXKgoOpYinR2NGXmRUNsPxCjL2XPilT3wSQoiTkr16y1mTRWwMn1utjgc42GfPhMheVoLllTP9CPyFDemoqXCKHp7E9REzl8IT2XgO24moO9SZIjtJ14pukQ3/9TE02d/ehMde+Pnr2Uy9YtxshxE+/cmB2Nrq7Ma+3eFkvNuqA0d/9VxZy3fmUdN21YRV00RE/Coi4aGvc1k3zKpXzQnY83YTkOrT1JXj0Yo6UrQWtPgqvueW7I3qMjz9VvKEoDJvc/t4++lFdLTuNdV6qvCFMTCXKo3yKettFaE0/bYwZsO9MHaaSA9PTrnfzzw9t5vcMLSIaCyhI/K2qjOQWkbAO+heWhOR2QwEuVVwoO9iZneihTTmZKYk4bK2ttJuVS1XtnW4yeuIVhKBSQzDS7Mw2OmjVlb5t2tPG5B7YSS9rYrovteo9vqCgZWN7T2oftuNRFQ2OmmQOkbIeDPSlsd/g9SPG0zS2Pvkos6S3pBX0GC8tD2K7mvmebx6zoHfSb1EQCBH0zV6evkPhMg+rSAG2x0ZNIipEEJSEKVC6JGGnbBeVVL0g5LgpvxqO1HjE54ZZHdtAVtzANhc80cLSL48L+7jgdfSZpx8U0FEurSnLKQkykvT1I7giZvHsPxbnhwW10Z5IeykI+6qJBDKXwmXrUAqpKecuSFSX+Au53NDPqoiHe6OifdZUd5vYcWIgClkv5oGyDO9fVuEMTh4HhkxOaOvoxMoFMofAb3seA5XqtyBVgO5rO/vSYpYf6UjatowSkJ3Z28OkfP8+eQ3EU3nLdvExAgpELqIJX521hRYjKAu8IO1PqyoIkLIfYNNcuzDcJSkIUqKvOacRy9KjXdY6dV0Z1aQCf6S3fKQU+xUDmXC7JCWZm6Q8OX1dqqAxTFvYfVWl8sJ64RVtvctjf1B1X88Mn3sgkVzhUlwb45DmNhPwmSdsdtYCql1kXoL4iLMt1o5gX9ZJQ2npn1xKeLN8JUaByKR+UzaqbX+5dA2rp9i5810QCIyYnLKsuYVd7P8rVKAU6M8MKmopj5x+uMae1HjEFfLQ9SD0Ji3/+1XY27+kC4KT6cm541wlUlQZYUl3Kfc8209qbYH5ZmEtPXzTkelLA5xWMlWA0tuqIlw4/1ubkYiMVHYQoctm08X1dcSJBH1pr+tPOiMkJm3a08dkHttITT2O7XkBSQEWJj4bKwxtXh6tuMdYepNcOxvjqxldozWSFXbymnk+e0zhmle45eO1o4hUdBll45fdJH9xF169uxbbG7t5bQKSigxCz1XgzCNevrOPDZy7h9k2vY+ASNA1CfpOepE17LElNJDjsnq2x9iD9Zlsrt/5uJ2nbJegz+Ox5x/IXx49dm01mR2M5uqJD1i+37qe7fgkdD94yzWPKHwlKQswS46l4/mTToSFlhACCfUl6EjZdcW9Zbln14WtRluPS2pPkf3d2cN+zzRzoTbAgs/x26pIKvveH13lw634AFlaEuHHDKpbXRkYd7xycHU256kiANzr7wZw9H+Wz50yEmIVyDTTf+d1r3L7pdRxXE/QZ2I47asXz4dLNbcclnnbwGYqgz6A7YXH9xm182XY5bkGU/3ziDe55Zi+OqwmYCtd1ufV3rxHym+w95F17OmNZFf94wcoxqwz4M80GQ36ZHU1GdWkQrcFf1TDTQ5kyEpSEKFC5lBDatKONmx/ezo6DfQD4DTWQzl1dGhjInrvz8SZeO9iL5WgCPoO07eK47kAZod6ERXufN0PyDTpGZYmf7/3xdS5du4h7ntmL1hqfqXA0HOq3cPESJQA+fNYSPnzWkjGrM0RDfqpLAxjjaEshhpdNdvDXLJnhkUwdCUpCFKixKjoMLkMEh+vW+U2FgSKWtNl5sJfrN24jbTv0ZqopJNIOkZBJW8y7MB4wDZq7EgPHcFyNzzTQjiaWtGntSXDfs824rvb6G2lwNQN9mBTwz+89kTMbq0c9H9NQ1ESClOZYdFWMrbIkgKEgUCtBSQiRZ2NVdMgGLcfVQ1KZbNclYBqkbK8yQ7mp6OyzMVAYhlcBPG1raiMBuuMWaedwbyUNWK5GawfDUKRtb3Prgd4EAZ+BZbu4MKQX08r50TEDUiToozoSHFfTPjE20/D2dPXJTEkIkavxJCAMfmxvwhqyxAZDN8Nmg1bANNDaHWg1rjU42pvVBHwGYX+mdFBmWU0pSDsuNZEgXXGLpdWltPYkSTsuVmb643i1ijAMxaWnL+K+Z5tJWw4J63BtO4VXM+8jZy0d8dxldpR/1aUBWmfRTEkqOgiRR7lU+h7psSUBb4mtoy85bEWHbBmimkzFaNNQXt07vBJCV69fTk1pgF3tfdiOJmW72I6L1t6SXbaEUdhvUhsNgoZs8W2Nlx13xbrFrGus4pRF5XQnDu9NygakD52xZMRiqpGgj4bKEglIeVYdCeKvmE//LCk3JEFJiDwafF1IKe/eb6phy/cMfmxfyiaWtHFdTVsszd5DcdpjKfpTFnc+3sSmHW0DZYh8pmJheQifqTANxfHzo9x5xWmsbqigsz+N7eiB1hSWq7Fsl7KwD8vRNNaUkrAcIkEf88qC+E0DQ0FpwORrG07k8rOW8F9P7ubuJ/eg8ZIgqkr8nNxQwdfffRIfetPSo87DNBR1ZSHqykKyXDcNsskOO9v6ZngkU0N+hREij8bTcj372FjSYn93EqUg4FNYtlehoS4aoLo0eDgLb8MqbtqwaqCaw6mLKocsDV5211OUhf2UBn20x1KkbG+JzzQVS6sjAzOurzz4MpbjEg6Y1Kggtqu59twVnLCwjC//4mWeajoEwAkLyvjqhhOoiQRHPN/SoI8auXY0xUZvd+6rWED9Vd/ntdYYpyyqmMZx5YcEJSHyaDwt17OPbY+lUJkq3pajcQFczcHeFEGfSTTkH8jCu/fKM0e8PpUNckqpgX1DvYk0rb0pmrvi3Pl4E3/zpqX87duO4d5nhtajq44G+OSPn2N/ppbeu09eyOlLK/mXX+0YsnE2u3RnKEV1JDDruqAWhpErOgC4WnPboy/z6sHYNI4pf2T5Tog8Oquxin1dCbYf6KWpvY/2WHLEDq7Z5bik7QAay3Ezrck9rob93UliSWvE2dZgR7a+iCUtWjIzsIqwn9aeBDf8chtaw60fOJn//sSZ3PqBk4mlLP72v19gf3cSn6FYXFnC4zvbufGhV9jX1U9ZyEdnf4rbHtvJM02HKAn4aKgMS0CaIYZSWB3NvCZBSQgxmk072njg+RaqSv0ETEXSduiKW1yypn7Y2U22ZXlpwIfjer8B+wyvbXiW5brs6Yyzq72P0sDo1RCObH3R2uPNeuZFQzha4zcNfIbivmebAa+iw+1/2MU//3oHKdulssQrAaTRJDLH6E5Y9Kccwn4Tv6n42Qv7mF8eGrPgqsgvq2MPr7ZKUBJCjCKbuFATCbG8LsoJC8ppqAzzZOYazXDWr6zjO5eeysKKcCa1e+ieoGybiVya8GWDXF00RE/CQgP1FSHCARMnk/od8hu09iY41J/msw+8yM+ebwFgRV2ElOXQ1Z+moy9FytaZluuKrnjaWxIM+gYCnZhZVsce2mIpuvqLqlL4sCQoCZEnzV3xgWZ74C2fHehO8MzuQ1x211MjBpT1K+u4ZE094AWhI1MGDEVOTfiyx7r3yjP50xfO5dTMRXB3UJRLWi7RoJ+r7nmOF/f1APC242qJJS2StotheAFQA47j9V+yHJeAzyBpu2M2EBTTI92+B2BWLOFJUBIiTwZf08lm1FmuJuQzRt2vBF4V7/nlQQKmQcB3+MdUAYurSoiG/MNeV9q0o43L7nqKs295bEjgc1zN+05rIO14HV813rJeT8KiqaOfzr40JQGTmzasoqvfwm8aBH0GZKpAmCo7a9MEfeaIDQTFzLA6JCgJIcYw+JpOW28SnWnUVhMJDrtfaXBAeX5vFwHTYGFFCF+mXbm3YfVwJt2RWXwjbdT93Sut7O9OcOqSSq49dwXVpUF6Exb9aa8enqM1S6pL+N7lazh7RQ0HehOE/AZVpYFMnTuNaXgfFqZhEPYb1EVD3LRh1bj6OIn8cWKdREO+WZGBJynhQuTJ4HbmuzvjhHwGNZEgZZl9S4NnOkdWBO/oS9HSnaShMkxjbYTehEVLdwKfodBaD9uEb7gCrn0pi3/99Q6iIf9AKvd5J8zj51ta2JXZbHnOsTV8/h3HDTxvQVmYzv4UpQEfdWXQ1Z8m5WgiIR/fufRUCUQF6rh5UXYckKA0IqVUCHgcCGZe5wGt9Q1KqWXAfUA18BzwIa118V+dE2IY2a6wl931FG2xJLajaWrv82rRGYqlVd5M587Hm0jbDp19NmnHxVAK1/Uy5rTWHIylvD5GPoPW3iQr6qJH1dA7cqOu42pSlkNrb4r6ijBlIR8t3XG++dtX0dq7NnXlOY2877SGIU32Lj19Ebc9tpOU7VAR9hPym1iOlplRgVu1sIwHntuH6+qibguSz5lSCjhXa92nlPIDTyilHgauA76ltb5PKXUH8DHgP/I4DiFmTLbA6s62GN3xNFp7S3CKoRl0rx3spTfpVfI2lfISHAxI2y77uhOYShEwFSnbwXE1ZzVWDQkQm3a00ZuwaO1JEvQZVJYEKAmYdPSl8ZmKkM+gq9+iI5OdZRqKW/7qJNYsrjxqzGcsr+afQj5+/PRe9nXFaRijiKzIt9ErOngPMfj3r1xHzQXXEqxpYH7ER/PePdMzvCmWt6CktdZAthiTP3PTwLnABzPfvxv4KhKUxAwaTxXv8R43uyQ3vyxEb8Ii7WqUhqDP67xqGt51pWx17uxvuEqB4SpcA+qiATr7LFBeEz/H1dy+6XVWN3jZdDc/vJ2d7X2ZYKZJ2Q6tvQkqwn5sV1MXDbC/J0l/2ku6CPoMykK+YQNStiPssppSLjp54aTfAzEVRq/okNUWS3LvM818/DsbueNDa6dhXPmR12tKSikTb4nuGOB24HWgW2udLWe7D6gf4blXAlcCLF68OJ/DFHNYLt1dJ8oLNoeX5GzH2wwb9Bk01kYA0FqzrytOwGeQSDu42ku71ply31preuLWQNkhAFN5zfxufng7cculLZYcaEsB3uNsV5OwXBaWhWiNpQYqQ5SHfERDviHtMLLKwl5HWDVG51iRf4M//3JVXRrEUNAWS+VpVNMjr9l3WmtHa30K0ACsA1aO47l3aa3Xaq3X1tbW5m2MYm4bTxXv8drZFqMjlsbOdmxVYLuQsg/3JMpm0K2oi1ITDeDLzIR8hqImGiAS9JFyXAbHCZ2Zab3RGR/S5E+pTPq2oVheW4rfVLT1pQcCUl00QFnYj6O960ZZftNgQXk40wJDAlIhGPz5l+tzTENRGw1yoDuRz6Hl3bRk32mtu5VSfwDOAiqUUr7MbKkBaJmOMQgxnPFU8c7F4KXArriF1hqf8n73U9lqDK7m9bYY5SV+/KY5kEH3uQe24rgarTWOq7EdzcfPXsbtm173qntnZlAummjIT1fcq4HnNxR2ZmOrUpCyXF5v7x+oBFEaMFlYESaWtKguDQ4ppBoNebOjYr4wLg5rqCzhhb1dKN/IldwLXd5mSkqpWqVURebrMPCXwHbgD8AlmYd9BHgwX2MQYixHFi2Fkat4j+XIfUJaa1wNlu1gOw7uoMemHc2h/qF18DSA8mY8ZPofrW6o4Or1yweW5Hymoro0QMBn0lhTSn/apqIkgIvGdTWWrYe0Kw/6DEqDPj725mUDBVfXNVZhGop5ZSFqo0EJSLNIQ0UYV0OwPudFqYKTz5nSAuDuzHUlA7hfa/2QUuoV4D6l1NeBF4Af5nEMQozqqnMauX7jNuJpm7DfHHb/z1iys6Pn93ahgPnlIZRShP0mScsLRto9vPk1mNmvdDCW5PZNr/Nk0yG642nKw34WlIcHjju4PcXqhoqBvknZbLik5XDjQ6/gMxR10SBtfSkGlckjEjSxHZeOvhRf+9UrfOXCE1jXWEVJwDeQZCFml4UVYQwFoWVrZnooE5a3mZLW+kWt9ala69Va6xO11jdlvt+ktV6ntT5Ga/0+rXVxX5UTRe3IoqXjrVQweHbkao2r9UB7idpoEEMpDKXwmQq/6X1dGjDZ35PIzGxcXmjuYntrjH1dCXoT1sCxBy8jrl9Zx1XnNNJQWUJzV5zb/7CL7rg1UKGhL+XgZCZ8SkFViZ+U5eJor2150nK47bGdbN/fy/xy6Qg7WwV8BouqSig97s14CdDFRyo6iDkvu8F1IgYnSgRMA9vxKqi2x1I01kaoiTr0pxxStosCykI+OvrTQyp/25l08JTtsr/Hu0hdFvYPWUYcnCUYCZi09ia57bGdXP3W5dRXhtmyrxsgs0fJT0/CSyH3NuF6HWzDfoN7nt7LhZLqPasdWxdlT+d8XtzXw8lF2IlWgpIQE5Bdsntm96GB8kE1kSD7exIo7QWYeNrGb5p859LVAHz2ga10HhGQwEt88BlqIEuuoy+Fz1RDlhFvfng7B3sTOK7XB6myJABo/uXhHcQz18TevLyavzx+Hnf+qYmU7WIa4Lqg0dRFw5QEfBNO4BDFo7G2FP2yxS+37pegJMR45Wvjaj4NnrUETUXa8WY4C8vDLCwPczCWRGlFXTQ05HxqI0E6+4avqOUFGy+LznIc2mMpPn72MtavrPMqPrT1YWZmPrajae31+hi5mdYWHz17KZetW4yhFCG/ydd+9QpJyyHoU9RGvTYX8bQtrSaKUg4VHY6w+LKv8bPnS/jsO44j5B+9GWShkaAkZkw+N67m0+Alu7qyEPu7vQrgHX0p5peHRrwuFUvZhzfGHkHjfd9nKgKZqgoPPN/C6oYK/uOPr3uFWDOPHZxdZxqKf3nviZy+tGrgWOsaq/jX957EN3/zKgGfQdgvrSaKW24VHbKuO+847v3aJ/ng959m45b9vH/QnrRiUBStK3a0xkZtiiaKUz43rubT4OZ90ZCfhRUh/IYiabujJkosqiwZaNo3XJqB1t7epITl0NqTJG07fG/T6+zp7Kc06BVFTTveHqas9568cEhAyqZ6bzi1nq+9+8QJJ3CI4nZWYzUr50e544+vYzvu2E8oIEUxU/IZqmh+ixa5m+qNq9NlUWUJbbHkQKuHaMiPaXjLdfdeeeaIz7vqnEaefqPzqGtKWa72Sgj5MgkTHX0pUpZDZUmAtt6jk1QV8L9NnaxdWsW6xirCAZPaSBCf6f2uOZkEDlHclFL8/duP5ZP3PMcvtuznktMaZnpIOSuKmRJQNL9Fi9xN5cbV6TS4eZ/WetSlscGN++58vImFZUEC5uGmfUFTsaDc2zPkMxV+nzcDy1b7STua/vTQjbdZPlPhMxT3bW6mujTIgvLwQEAS4h2r5nFifRnf+u1rJNLO2E8oEEX1X3Ax/BYtcjeeD/fpNlJbcch9b9NwnWBTjqYs7KextpRVC8toqCrBb5qU+A3Q4LguGo2jNdr1EiAOjlBg08rMppo7+ygv8Q/7GDF3KaX4yoUn0NKd4P/8YedMDydnRbF8l1UMv0WL3A3uzFpIfXtyScDIZWlsuE6wAAHToKIkMOSc73y8iTc6YvQkbCzHxWcoML3rVODtP1JoLEeT2daEwks9T1oup33tNxw7r6wg3j9ROM5orObiU+u56/Em3nNKPSvmRWd6SGMqmplSIf0WLabO+pV13HvlmfzpC+dy75VnFsQH6lQlYAxOiMgK+03a+7yZz+BLSx85awlKefudKkv8JG090GOpxG8Q9htEQ76BgJR9vpsp8toVt3ijo4/rN26ThCAxxJcuOJ5oyM/f/2QLabvwkx6KIig5rpbsoVlqtGWymTJSMMl16Th7Tu2xFLva+4aUDursTxFL2kOW9L78i5cHSgYlLYf2vsOPN5U3G7Jsl2goMGLmnquhsz9N2nbkuqsYojYa5OaLT2Lb/l5u/e1rMz2cMRXF8t1x86OjZjWJ4lSo+5SOzK6D3JeOh3abDdLSnaSlOwFofKbBoX6LyhL/kKW8tO1y5x93EUs5A+3KgYFaea7WWK6mJhJkXlmItliS1kwnWaUAnelUiyKWtOW6qzjKeavmc9m6Rdzxx9dZu6SSt58wb6aHNKKiCEpidhrumku2MvZMBqXJVA4/8pyUUrT2JGntTbFmcSXd8TQ1kaC3J8n12k2kbYfm2NGVHnQmR9xQXn28Az0JvvbuE7l+47aBWnrZNUCfYXi9lGxXrrvOOuOs6KCMgWaNDYsW07x3DwA3vGsVL7X08A8/2cLGvzubZTWl+RjspElQEjOmUPcpTSYB48hziob8RII+ehIW9155Jpfd9RQHexP4TTOTcehwcFBAGlwDz9HeD6jGqzDeUFkyMLZr7nuB/pTt7W0yFIbhLXObhpLrrrPO+Co6DDY4mIX8JndccRrv+u4TfPQ/n+WnnzyLmkjhNQMsimtKYnYq5H1KE03AGOuc/uZNS0lYLv0pi654mn2Z1tUKWFgeYl5ZEDNz0chLZPAqOERDvoFgs35lHd+59FTqK0uYX+7te7IcF0Mprl6/XK67ihE1VJbw/Q+v5UBPgo/832eIJa2xnzTNJCiJGVMo+5SmMtlitHOKJS1WzI/yFiKkNwAAIABJREFUqXOW059yaM8UZw1krj9Fgj5Kgz4WVoS9DbaZLrQr6iJ885KThwSb7F6ppdURqiNB1i2t5s4rTuOatx876fdDzG5rl1bxH5efxqutMT5+92aSVmFtrFXF0Ahq7dq1evPmzTM9DJEH2SrhM7VPaXBiwuDrR5esqefJpkMTql4+3DmtXlRBdzxNS3eCz/30xYEq35VhPxedNJ/fvdru9UoK+rxsO0dLtunslnOXRaWUnszy3Uif8Q9uaeHa+7bw9uPncccVa6a7GsiI5y9BScw5g9tl9CYsSgImQZ9JR1+KpO0OFDwN+QzmlXm15IYLEpt2tHHLIzto6ugHYFl1CV985/ED7SbufLyJvYf6mV8W5n2nNeCiuemhV0ha3l6R2kiAoM/A0bBh9QK27uuhpTtBJOhDa01f2plwO49ibAkyx8x4UAK4+8+7uWHjNv5qTQPfvGQ1xvR1JJagJAQcPTPa3trrVe5WXj06e1C1VJ/hpWRnO7lqYM3iyoHlxc89sJWuuAVoHNe7BuQz4F2rF/Dc3h58hpcV19mXpCthDxRiVQrqy8OUBExQYNku88vD3HvlmSPO3G7asAogp0Az2jEkMBWMgghKAN/+3Wt8+3c7+djZy/jyhccPZO7l2YgvItl3Yk45MmU75DOJpx201hz5s2i7GtOAtlgKQ3kbVJ/dfYjn93YRDfno7Etz5I+77cL/bDnAvGiAUEmA3oTFobg95HEGXgKDUl5WXcA0BjIOR0qTv/nh7cQtN6c9XYWaai8K07V/sYKu/jQ/fOINqkoDXP22Y2Z0PBKUxKwx1pLVph1tPL+3C1drAqZX0qc2GmR3pxcQhvuF0hmUnq3wgolta1IjdJDNOhhL09mXxh50TAUEfYqUrTnQk6S9L0XA9MoHLauJACOnye9s66OhMpxToCnUVHtRmJRS3PCuVXQnLL756KtUlPi5/IwlMzYeCUpiVhi8ZGUqeG7PIf76PzvxGbCiLso7T5zPj57aQ9p20YDlOMQPxTEMb9kul0XsbHfYXNlHPFYDfsMgiZftZChIOy7tfWk+uM5r1DdSNQkg59JHk6lIIeYmw1D82/tOpidh8eVfvExFOMCFqxfMyFgkKIlZIbtk5bialu7k4Q2oLuxs6+O1x3aiXW+jafbvNAzp4nqk4YJVrgFsJLFBfW1c7ZUZKgv7eLLpENcwcjWJxppSEpaTU6CZTEUKUYjGWdFhyFONcV0jUr4gdR+4iU//yKLtgZtI7n5hXC83uILEREmigyhq2SW7Z3YfImgqXA2pTNaBxgsiAZ8xUJbH9//be/P4yMo63//9nHNqS1LZOkmnV5rQ3TSLIM0uCC0gLiDM4gLqjOPVAUdm0EGvOouCOs4PvF4V5noVdGbU64KKztAqoALTAyog0ILQdNNNp/ct+1Kp/Zzn98epU11JqpJKUpWqSr5vXrzSqVSd85xank893+f7/XwNRWoKIcolkHlcufAZsKKlDq01R0cStIcDrGqp48KuVp7oHhiXUg7MKAHCu62aWoII45iXRIfZkEjZ3Lf1IEPRFH+6cSWdTcGiH1tMYkUGSXQQ5o/5SkfODdkFLYOk7WTbPXjveLcA1f23BuxpPjCmckMZftPA1hrLUPgNiE6MxRXJVCurlAMHB2NorfFZBs0hH3v6ItlEinUd4XHPXT7rI2Bc2PL3+wd537efZl17QzY9XRBmQsBn8kevXsEPnznA5ucP845zV9EUmr8mkkWvlJRSJwDrtNYPK6VCgKW1Hi3r6DLISql2mM905OvveTK7dzISS3F4OJYVpdngJiIYGAriaQet3X0fr29ROTmh1Q3DHR52bYd8hmJZc2ja5857DmxHc3gojlKgtcYwlLR7qW6qdqXkMTCW5IfPHKDeb/G2c1YSnLCnmY9SrJSKKuFVSv0lcB9wd+amlcB/FvNYYXFRqgZ5xeD1PRqJpeiLJLCdyWndM0HjJh6kHY2TaQehdXkFSeF+CBtDPvoiCQwUZibEWMxz5z0HvaMJt32FcmurbEeX7XkXFget9X6uPmMZQ7EkP//DEdLO/DQILDZ8dxNwHvAUgNZ6l1KqZr5+SXX7/FFsOnKh12Qmr9Wqljr29EXoH0ti4IbcUo6T7cY6GxwNSVu7Ybc5HAeOi1ohDMDK9EwCVxBNpdCZBAiYPpXby7RL2g5mphrfe7ykgQtzZWVLHa8/dSm/2HaMx3f18bqTyz9vFmt2lNBaZwszlFKuo34N4IWTcjt9Ssvo8lGM8/ddD+/kxu88y9N7BxgcS2bbeN/18M4ZvVY3XtKVcVQAZbj7RbbjhtwUrtFpMQunfPfRzP0NPl0Uw8EVwIDPoC/ihuDiaYdk2qEh4H5fnC6V2zOANZXCyfRncnAbAkoauFAKNnQ2snF1M384OMzOY+XfsSlWlP5bKfX3QEgp9XrgR8BPyzes0jGf4SRheufvLTt6+MqW3TiZJIK0rbNtvL/x6z0zeq3+cHCIZMa8NJ5yfyrIrjwKrZgmilAh7SiH24rPPH5QhZtYoR1NXyTpdpDFFdWBsSR9kfi0qdyeW/iJbfVuga/htsCwTCVp4ELJeM1JbXQ2Bnlkew8jsfK2uyg2fPcJ4H3AC8CNwAPAN8o1qFIi1e3zy3QN8u5+rJu04+AzDRRuewYcGI2niacdVk9RILplRw+3P7idPf1R0o7OW2OkgVRmPyVdIOmh6BVQCWIBE7PvvDEbyv0/YJkkbbc534bOxuz+WCLtMJawueu6M6YNNW/a0DEu9HlwMEpHOChhaqFkmIbKFqBv2dnLW85YVjaPvGJFKQT8m9b66wBKKTNzW9XP7FLdPv94k2Q+DgxGCZiuM7b3nvbaeNf7zYIFolt29PDR+55nKJrCUFMXvYIbApitpnhCUpL49ARV8obtZBIoWv0mA9Fk9gPeGPLRGPKhtWY4lpqRqEz1vAvCXGkM+biwawmPv9LHK70R1nWEy3KeYkXpEeAKIJL5PQT8EnhNOQZVSqS6vbpY1VKH7Tj0R1I4uNlytnZXCu+/+ETu23oo72t192PdRBJpTKVce327cGMyU7n7NZ6Jqqlc77pi8e7qNxX1AZPBaHrW15u7r2QarsMEZEJ3hmIsaWMZxqSYYixlU+83uf6eJyVBZ9EzB0eHUqMMlr33Lu7fcoDD/3oT6PEZeStXrZ77KYrJKVdKPae1fvV0t5WLudYpVbqRXDVQLRmIXuJJyrYZjqZI2A6WYXDTppO4+Yr12XHu6hklmXZdsTvCQXb1jJLOJDGYampXBjPTMsJxtLt3ZRporUkWqUymgoCp8PkshksQP/dCid5+kjcOnwEatzWGBppCvqwYD8dSKNxvp9J+YuHgvb+f6O7fs/f2q4r6ZlypOqVC7Do2ygMvHuVNp3eyfunx1dIMapSgBI4OY0qpjVrrrQBKqbOBWLFnrzSLPayRW9A6XduDcjNuz8mY/CXB+/mpzdtoCinStsOungie248b9tJu+4cC57AdV1QSmboKJ2PCWiy2hpitiaZnL0jeJ85vGZy4pI69A1G0k3GLQJNyHFIONAZN/tdbzwTG78P5TdehQtpPLBxyP4fAQKXHM1vWdjTQUufjuQND40SpVBQrSh8GfqSUOoz7eesE3lHy0Qhlodr660z3JSF3vN29kWz9je3orLhMJzLRnLT0iff1ZTLTpmIulpB+03WFSDuajatbODAYZWk4wOHhOGRWez5DYWvNXdedNUmQAS6+41FJ0FlgTPwc1ipKuUk5T3T3MxpPEQ6W1oKoqJRwrfXTwAbgr4APAKdorZ8t6UiEsuFV/edSzRNc7niTtoNSmS6whutxB67QeJ2bFeM97qZjLlZExZB2nOw+2Y2XdLGqpQ7LNFjeFMIyXTEyDMW69oYpC4Onq/cSaot8n8NaZd1St/9Xd+9YyY89pSgppS7L/PwT4C3A+sz/b8ncJtQAtTbB5Y7Xbxqus4KGoGWwbmmYk9rrubBrCdee6fZ78focVYvhvaPdWqmbNp3Epg0d2doty1Sc2FbP6tY6OsJBPvGmUwoeY7p6L6H2yPc5rFVa6vzU+U16RhMlP/Z0K6VLMz/fkuf/q0s+GqEs1NoElzvetgY/tqOxtaatwZ8d+4Vdrfz6lf55HZdX2Oq5kmdXaOS4kgONQYu73302N1+xHjhe4NoRDjIcS2WNUsE1VL34jke5/p4nxzlXFHqM7CfVLrnv64XAkno//WOlF6Upg5ta61uVUgbwoNb6hyU/uzAvTFfQWgpKkd2Xe4wGv4lSimTaYV1HA1prxpJ2tij0jod2MDA2dUvyUpPtPOtoWup99I0lXQPUzFc7R0NznY8vvPXMSdc+cR+tmOSTxZ6gs9DI/Rzu7h1rrfR45kpD0OLgYOnz3abdcdNaO0qpjwEiSjVMOSe4UmT3TTyGmwLt8NlrT897jBu/8+yMao9KhVdfNBBNsaI5REPAorvPjauvbasruodRtSWfCPNDzuewOsMUM0ChyhIyLzYN5GGl1EeBHwDZnS2tdc2mNQqloxQTbDHHyF1JJe35sdGfiGUoDBQ2mnq/yYMfvmRWxxH7K6HWcfTcWsUUolhRegdu9OKDE24vqPZKqVXAt4Glmcfeo7W+UynViitua4C9wNu11oMzG7ZQTRSaYHf1jBbtSDDxGJ4H3N7+KNff8yQXdrVy39ZDx1djI/Gii2FLhZHZPLJMRWdDgLFk4U3r6cKZYn8lFE8VOTrksPSdd4B2uOXTb3RvUMa0fngrV63mwP59U96nWFE6FVeQLsYVmMeBr03zmDTwEa31VqVUGHhWKfUr4C+AR7TWtyulPoFr9vrxIschVCHhgMUrPRFsrfGbBu3hAIm0zWg8PakNRaGQXu4k7XWRBbcIdm9/hCe73aQG7z1vlMkMciq0hhXNIcJBH9Fkmo5wMO/9iglniv2VUDyaanJ0ALe78dcf38OJbfW8/r3Fj60YcS22dcW3gFOAu4B/wRWpb031AK31Ec8BItM2fTuwArg257HfAv6oyDEIVciWHT30RhJut1ZHE03a7O2PcnQ4QchnFN2GIjczqS/iZvQoFOGgj/5IKmuQ6hmZpsvdnzwPGjg0GJuypcSWHT3cfO/vOTQU5ehwnEginffaJbtOqGX6x5LEUjbLm/N/MZsLxa6UTtdan5rz+38ppV4q9iRKqTXAWbida5dqrY9k/nQUN7wn1Ch3P9ZNU8iHAvoiyXGOC8OxNL7hGJ1NIWDqPZPczKS9/VECpqKjMZht810tpBzNkeEEK5sCk/7mrZCiSdvtFeVoDg/FWd4MDQFr0rVLdp1Qq+zrd9/Lq1tLH24uVpS2KqUu0Fo/CaCUOh8oyiFVKdUA/Bj4sNZ6JDfmqLXWSqm8X3mVUjcANwCsXj1351mhPHh7QUeH45iZidhDA72RJHV+i8ZMRt3EPZN8ey9D0SS7eyPs649WbXvjo6NJPnrf8+PSv71kjYBlkLZd1wYHTe9oAtNQsl8kFE3u/FdtaK3ZcXSEjnCg5BZDUHz47mzgt0qpvUqpvcATwLlKqReUUn8o9CCllA9XkL6rtf5J5uZjSqllmb8vA/L2utZa36O1PkdrfU57e3uRwxTmG69KPWk7BUNqR4djeQt287Wq/5/3Pc+evghJW1elIHkFtKZSRBLpcSG5ncdGODoczz4fKdsBNPG07BcJMyN3/qv0WCZybCRBXyTJacsby3L8YldKb5zpgZW7JPpXYLvW+os5f9oMvAe4PfPz/pkeW6gevA170xhvcprb2y5h67ydUPOlgR8ajJG0NZaCdDWqEm5bDJVpNJjbFTeSsHEyyR5p28F2NI52Q3eyXyQsFH5/YBCfqTi5s4JN/rTWU+fw5eci4M+AF5RSz2Vu+3tcMfqhUup9wD7g7bM4tlAleHtBtz+4nR3HItnbPT0xFVimwfdvuGDSY/OlkqcdB0e7LR/sTL+KSmiT3zSyY/FQgM80MA2F4+hxIbm7H+umtf54UoZlGShHYyg1zglcEGqZgbEkO49FOPuEFgJWecxly+ahrrX+NYUbOV1ervMK84+3YX/x7Y9waCiedfB2XbwVXW31eR+3qqWOPX0RRuNpkraD3zSy4bG0PbMeSLPB60zr4XnYOZmfplJofTyM6F6Pzq6A6gyDwbEEF9/xKL2jCTobAyxvdpMzkraDz1DUBSwRJGHB8ER3P5ah2Li6uWznKHZPSRCm5Z/+6FW0NfgJWm4/IcswaKnz8fE3bsh7/wu7WumNJEnaDoZy21TYmfbl8xG6swwjK0QKCPgMWhv8NAZMNDrb3dYAWuosDMO1VVFK0dkYwO8zSTma5pAPpeDQUByA9nAAv2mQsB2SaWec0aog1Cr7+sd4pSfCOWtaytoTqqh26JVmru3QhfljJq3nr7/nSfb2RxiJuSslAzdU5+1Nzfc7c8PShmw7iZvv/T1jyTRBy6Q9k2XUOxonmrRpDPkYiaWo85u0ZwpoR2IpDg3FMFTGtDUTI1hS78dvmbKnJEyk6EIHpQxdmSB2DqaP5f/jK6AdDv/7X4M9O6fzHEeHObdDF+aZUrhuV4KZ1N4cGIyypD5AW0Mw6+LgJUhYBtkW6PnITaSYDUbmII52j7WiOchDf3tp9u+NIR+rW+uytikjsRT9Y0kcrVndWseR4RixlJ2Nq/dFEjhak850lg1ZJm0NARpDPjFaFebI/Dg63HLlyRRapHzpVzu585FdfPf953PR128s6zhElKqQUrhul2oc5RTGXGuhvkgCA7c5kaE00/mtzlaQfIbCdjQ+082gS2dChsdGElmPvSe6B+gdTdAXSbA0HKQx5Mu6TAQtt6WGpRRJW7NvIJrZf3LNWpO2mwDhCRIsDKPVWv2SJMydPX1jfHXLbq45czkXrW0r+/lkT6kKyU2VLsaipxzkqyH61OZtJdsf2bKjh4ODUXb3jvHCoWHGkja2dtAa2hsCRYmOqaZ+A1uZfulKuWJ0SmeYxpDFqla3LXl2D8sAjWZPX4Q7H32Fvf0ROhsDpG3NoaEYI7EkibQD2t0vGomlsHMCKhp3D8x2dLZduydiUPtGq+V+LwjVi9aaT93/IgHL4B+vKtwpuZSIKFUhBwajhHzj0y3n+9t2OYVxy44e/ud9z3N4aHyDsLQDLXU+2sMBAubxkHPQUviM8SFoU5H1wytE2tEYuIKkFOzqiTAST5NIO3S1NxDymfhNA9MwCFgmo/E0hoKRWJrGkJ+VLSEsQ3F0JEGd36Qt7CccdFdNplL4csbopYt3NgVBQyLt1ESX32Kohi9JQmX4+QtHeHxXHx+5cj0djaX3ucuHhO+qkGpoa1DOfj93P9bNcGa14U3rnrgcG01gGtAYsugfS9EYtBhL2tlMuMaghaEgnnaIp6bvqeTeQ2EaCqU1WmsGoynqAxZJ23HPrxXt4UA2UcHr1RQO+mgIWAzHUnz22tOzrt7e4xSKoKXw8hpsrQkHfbSFbcYSNsOxVMFkj1oKh0nvp8XJaDzFZ376EqevaOTPLlwzb+eVlVIVkuuYXalv2559UC6lEka3SZ+eJEgeR0cSGErREDAZirnXroC3nb2S/7zpIn7ywYs4tbORgKWwishhSjsZaVJuY7Jw0KIjHERrTdrR2Nr1pzOUcgt3zeMfC++ac129DaUwlGJ5c5CmkI9k2iGedkjZmm2HhhkYS/H+i0/k8Y9fxvdvuCCvINVSOKyc7wWhevnir3bSG0nwuT96FaZRxAetRIgoVSHV0NagnMK4Kmcyyxd+a62z6I0kGYm7E6FpKJY0+Dl7dQu/6x7goz96nucODpFM6ynrmbyPkaMhkbaJp1yHhrZ6Pzde0kVLnR9DKUwFKdshlXatgRpDVt5r3rShg+/fcAF3v/tsOhqDJNI2A2PJcS7mGqjzG9y39VBBkam1cFg1fEkS5pcXDw3zrd/u5V3nr+bMVeUrlM2H1CkJBZlJzdFMj/uX/++ZcV55Hr5MKrj3lzq/ybLGYNYhYTiWYixpE5+QLz4xRdxQXkht8vmbQhbLGoOkHNedwXNgMJWircHPqtb6aa/Z65s0lkhnz2F4g1BuE8I6v5nXYujiOx7NFNyOc8xnOJbi8Y9fNu3zVwnK9V5YxMygTknp+UwJdxzNn3z1txwcjPLILZtoqiu9EzhSpyTMhnL1+9m0oYMbX9vF1x7bPakWKXebqLXOx5J6P0opgobBnr4xvP0hv6kKtkM3FQR9Jo0hiyPDx7PgFO6qayxhs7tvjJOXhlFKZe33PWHI59OX7xr8lkEsqbC1G150ctLxfCaMJdLc+J1naQiYrF/amJ3IJ+4ZjsRSHBuNo7VbUFyNE770flo83Pv0AZ47MMQX335muQRpSkSUhEmUcxPedjR9kQR/cvZKVrbUcc9ju+nuz79hPhBNMRBN4TcVLfU+bMed7A2lQCn86GxSgsZtnV4fcBMjQj6DNUsa6Bt1w2umYeSMwd3/iaXsOSWTJNNOZlWUcXEYd51el1xNPOWMqzXLbYWetp2sPdGK5mDFatKEakYV1UZ8rqxctZqhaJLP/2IH55/Yyh+ftaLs58yHiNIiohixKWfh7ljCbXVuZ5YU53W1cuaqJt529xNEEnbBxyVtTe9okol7raah8OP60Z2zpjUbWvIKYA8MRklrjQlM9DO2Mq02osk0IZ9JLFW451Gh581nKmKp4yauudiOxjIVpnLroer8VtbZ4fs3XJDtsrt1/yCWoehsCmZXbOIAIYynfI4OE10cPnX/i4zEUnz62tPGhZfnE0l0WCQUm/FVjk14J7Nvc2wknhUkgKPDcf7m3ueygtQQsAjmSadTyg3J+S3DXX04Ohv7djSc1F7P92+4gMc/fhk3XtLFfVsPZa/TUu4eVcp2Jj2mmGSSqZ639Usbqfeb45zGx405Y+DqZfPlplF7SRPt4QBrOxrGdfCUdGuhEuw4OsJ3ntzHuy84gQ2d5WngVwwiSouEYsWm1IW7v3zxKH/61d9y9b88zi0/eJ7fdQ8A8PTeAT7wnWd5ped4D6ZIIk08J51OZc7tMxQpxy1Idb0X3DRuZUBznS9rouolHxwainJ0OE4kkWZFSx0Gbhht4mM2bejgxku6WNlSx4HBKHc/1j0jkb7xki5iKQfLVAR9BhkzB9chIuPw4KBpawgA+cODkm4tVANaa27bvI3GkI9bXr++omMRUVokFCs2pZoktdb89LlD3PrTbfRFEjQGLfrHEnz5kZ3c8eAOPvHjFxiJp8e5IkzEZxrYjs4mNIR8rmO3UoqmoMVZq1r4wlvPZNOGjuyKJpq0sQxF2tEczuzVrGoNuW4LjcG8j5lq9TjV87ZpQwfhoJX10wtYJk0BE52xIEo7mnq/SThoFUyjlnRroRr4xbajPNk9wEeuPJnmOn9FxyJ7SouE3Iyv0XiK3tEE8bRNvd9iy46ebNgqdxN+ur2WQiTSNr2jCb70q530jyVwMgaoTSEfw7EUv3jpWGZMIQ4MxqY40nFBsnJMTuv8bvFrbpact6IJWAZpW2MYCgc3bNjZFGTj6pZJWXV3P9ZNyrbpjxxvMtgYssbt50znrrGuI5z9u+d0bhqKoKFoqvMxMJbi6HCMdTnZdzB+n6rB75q8TuUAIQjlwnY0X/jlTtZ1NPDO81ZXejgiSosFT2z6InH6RpPjamlyExm89uazrUkZiiYZjKZ4anc/eweirmmqUqTSDkdHjqdnX33GMvb3R/OKklfuk8rZrFEKDg/H6IskaGvwT1rheVY4bQ0BDg/HMu1jddZpIZ+o7uoZZTiawjDcNPO0o+kbTZKyRyc9bxNF+sKuVq6/50l2HhshkrBprfcxEktnxq/oaHQTF/IJ6MRkEveYDp+99vSqEKNaskAS5s7m5w/xSk+Er75r47w6NxRCRGmR4InNzff+PpM+bRTs9zObmpSU7dAzmiCRCf3d+/QBLMP1hXP0+E6yq1pC3PL69Vz/9SfHHUNlUqs9L7mutnoO9I+RdNzGfz5DZZy746xtH99i3VvReO0i+iIJEmlNvd/iM9ecBrg1QLkT7fGU7uNu4intMBhNcfEdj2bv99aNK/jGr/cwlrQJWAYhy+DOR1/BZyqWhgP4TIOBsRQp2yFoGVlBgvwh0tx9KmBcZl6lJ/9qaZsizBOGyZcf3sVpyxt5w2mdlR4NIKK04Jn4rddvGeOa18Hcs71G4ikGIm4DPI8jIzGW1Ps4NpIcly6tgJs2rQVgWWOIeNJmOJ7OGKMev19LnbvKMAwDHCdreGoYrj34xHTV3BVNOGhhmW7KtydI+SZaR7sjczPynONOElpjKugZjfPR+55H4basaMnUFEWTtutS7igOD8dZ3hRiZYtF72iC9nBg2tqnajY4rWbBFEpPw+mXs68/yr/9xTnuZ6sKkESHBUy+jfzReJrDQzG6eyPsODpCd2+E/rHErLK9bEdzbCRO32giK0i/6x7glh88T38kwdEcQVK4FkInttVzXlcrAO88fxWNIR+tmapxb4VU5zdZ3hxyz6F1tvWE1yXWVLCzJ8L19zyZTUqYyi8wd6Idjac5Ohzn8FCMWMpNRABNKsfaSAGHh+OkbU0kkWY0ns40Ijzuc2drSDlu/6e+SCLTBkMVlbRQKJmk3m9y/T1PcvEdj467tvmkGtqmCPND2nFouug6zlrdzOtOrp4vHLJSWsDk+9Zb5zcYiKbwmSrbpqFnNMn157bO6NjRZJq+0WTWgRtcQbrz0V1uKnROE7x6v0lLnQ9bww2vdSdov2XwJxtX0hEOcvdj3ezqGSWZdvBlJvb+sQRtDUH85vHEBc/KRwNBy5gUWsoXdtyyo4et+wdxtHb3tmwHn2FgGpC2NaMJd9Xj2RZ5fZEUZAt9veLCRMaw1UNrSGuNk7J5pTeC1tDWEJg2aSHfPtVwLIXC3UerZNisGtqmCBMpj6NDeOPVtL7+Azz0+ZswbnqelatWc2D/vpKfZ6aIKC1g8oWJEikHA7eg08vRLUiOAAAgAElEQVQ4Cwctnuge4OYijqm1pn8syUgsNelv9z59gHjKZiiayq56TMMVvraGINedu4rzuloJ+U2WhoMYhspOuJ/avI2mkCLkM+mLJOgZTQLQ1uB3bXj08cZ+7u2BaUNL3kpRZcaSTDuZPS5XoII+N9vuaI4/noaMOSs4GZ89tLs8ciZ6CWVwtCtwK5pdk1cvaQHcLwb/eP+L4xIG8iWTeK9HvrCZd5z5SDwoRfalUGpK7+iQsh2++du9tNT5ufmeH6DU/FgZFYOIUhHUajZSg9/kld4ItqPxZxIbErZDyG/S1d6QvZ/WuqjwTDzlpnqn7MnN9ZJph5ePjRBLeW31XE84n2kQtAy++I4z3TEFrGytEeS4bSfTBC23Dqk97Ha4HEvYWCGDte31KKXY2RMhaB1P0ICpQ0veSrGzKcjhoTg6I2lprfFpRb3fZCSWzts+w85kaDSFfCjclSEcF0WDcVtgrGwJjbMJuv3B7URTzrQJA94xeiMJOid09gz5THb1jM5r4sFcsy+F2uAPB4eJJm3efPqSitkJFUJEaRpqNRtpy44e+seSbugr0y/o0FAMBYSD41/26cIzXrfWoWgy7997RuLc+tOXxgmSqcAw3HBZ2tH8rnuAK05bmnU32LKjh9sf3M6u3ohrtGqQLXhd3uyuhCa2crj+nieLDi3lhu38pkFzyKJvLOlaAmloDlkM5lnt5WJr+PMLTuCMlc2ZSTqG9hzBcVebsaRN0GdMsgna1RNhZUuo4Mpn4ntqNJ7GZ7ohy9xrS6YdmkLzm3ggjuALm2Ta4Zl9A5zQWseKllClhzMJEaVpqNVspLsf66Yx5KM+YGX7BVmGoj0cRClVdHjGK4RNTuwxkWHr/kE++7Pt2T0RLyEhrQHbzWJrqbO4b+tBrjvfLczLTcAwlcJRbqKB3zX/pnc0gWmoSWJTTGgpV+wcx12tpdEMxlK01fsZiKYwFPRE8gvsRJ7oHuDmK9aPc4DwmSp7/oODsUn2/l4SQ6GEgdsf3E7PaHzcCrYlU2gLMBxNkbAdLMPAZxY+jiDMhucODBFPOVxw0pJKDyUvkn03DbWajeSNOxz00dXewIbORtZ2uCG7YrvaDkWTHB6K5xUkrTU/ePoAH7vvDwzHUjQGLd77mjWTWjgABC2TYyPx7O+e0NuOu7djZcIHblhQE0/nF8qJGXY+ww3B/eP9L3L9PU9y18M7+dTmbZmiXYVlKmxNtt/RSCJNfcAkXaAP00QMxbjXOV+G302bTsJnmpMy7rra6vNm2DUErIxgakzl1l0dHo4RsAz8JgyMpUjamqBlUuc3GEs4bDvsZkmOxlPZ40jigTAb4imbZ/cP0tVWPylcXC3ISmkaajUbaapxTxeeSaYdeiPHC2Fz+V33AN99aj87e0YzBqmwtqOBz1xzGp9/6GV8lkI7ZGsetNb0RpKctbolewwvAcPLrLNMA7BJO24fonq/xVs3riiYJDBx1eKFwL6yZTf1fpNEyk1oMDJO3V6CgtKKZY1BEukoidTxPkyFaApZk17n3PPf/Vg3P3z2YNYm6OhIPJtBWOczsskguas6n6HxGYabCKLcVHccODaawG8arGwJZK2gDg/FMTIrz6TtcGgwRlvYxmeaknggzIqt+wdJph0u6KrOVRLISmlaatUwc7bjHo6lODQUKyhI//tXL7P96EhWkEI+g/dccAKdTUGOjMRoq/e7Pt6Ozna+SznOuPN6dTptDQEc3HYSSrm+dcubQ7z/4hPHtZ/IZ5Saz707bTsMRFPZRss60+bCUIoTltSzcXULkaTt9jqaplDQMhThoL9gf6Xj4UfYOxDl5WOjDMdSbvv2phApx02r8JvGuBVpJGmztDHgjk1rvP9StsZvGdlVee9oApVJFDEMhd90hWwsYRdc2QrCVESTaZ47MMT6jgbaw4FKD6cgIkrTMFVRZjUz03GnbYcjwzH6I4lxTb9y+dpju+mLJElnanU6GgIsqffz462HANehwTQMOsJBLFPh4K6Y1rU3jDuvJ5iWqVjeFMTRDklbY2dctR944Qgp2+bocJyXj41ydDhOyrbHtdnIDauOxFJ090bwonLemzqT00Da0YzEUtx4SRerWuowDYWhFD5z/NvfZ7rhQMtwV1iFni9PEN3QWxyd6TLr1lcls8W2TSEfzXV+Hv/4ZXz/hguyrdAt02B5cxDLOB7CXN/RwLqOcDbkl7SdrO1S0DIyIdgwTSFf1b/3hOrk2X2DpG3N+VW8SgIJ3xVFrWYjFTvu0XiK/gk2QbnYjuabv93L3kzbctNwxSTkM9Fojo64pqrXnbuKux7dRdDnTqJeyMrrd5Q7Li/teNexEQzDYFnYx5L6ALGUzZ6+MdDgs4yCRqleeNLbkzE4vvLJt2Xk3XTjJV189L7nGcokPHjmr5ahWNHspnVHk2n8ppE3fAjHw4+7BiOZ/anjJzRQbquOkC/v3qOXrOEzFSdm9p1Stubjb9wAkE3kcHtIucf1MvJqIWwsVCdjiTTPHxxmQ2eY1vrKtqaYDhGlRYztaPoiCcYS6YL3GY6l+NzPt/PMvkEAApbBiqZgZh8I4imHzkY3rfTSDe10NAb4+uN78ta45Kv3uvux7nF7X3V+K5sskWuU6ig9LuHCm9x7RuOuHGUEJrc1uaHc/wOWSVPIl21F/oW3nsntD25nT38U03BDh20NfhoCbmblSCyVLaLNVwawqqWOPX2RbAgzNxDo6OPtNvKJyHR1QN7fhmMpRuNpWup8U/ZjEhYDcy9sbbn8BsIbr+LhT7+Dh4aO5r3PylWVb1sBoAqFaqqJc845Rz/zzDOVHsaCYiyRztroFGLnsVFu2/wSRzOZcxedtITdvRG3INZnEE+5NUgfumwdl53aQXtDoGAhXr50am/Pq7MxOO5x2w4P42jXydwLYTlomoIWz3zyynHHvPE7z2I7DgHLpCFgMRhNkcwU9wYsA61heXOQhoA1qe4p9zi5IjE4liDl6HFJItFkOtuCwjtvokCafL3fpLMpmDWEne0qe+K4pIh1QVF0xapSSs/U0eGWK0/OhuGPDMe49PNb+OOzVnDHW8+Y2SjLR8Hrl5VSEdSqo0M+HEfTN5YgEi+8OgK3E+WXHt5FMu0QsAw+euXJXH5KB7/rHuDepw+wrz9CMrM5f9/Wg3Q2Bad8TgrVeyXTDrGUPU4A/KaBo909p+NWSD5ObGsYd8xNGzrYuLpl3Eor5DfZPxDF0WTqsgLZkFyh0NfEMOfFdzw6pYv3pg0d+E1FoQVmyOfuq831fVKrYWOhuvjKf72CRvPXl62t9FCKQkRpGmrZ0WGikJ7X1TrJRHUiKdvhK/+1m83PHwZgWVOQz1x7GidlbIk8h+87H91Fg6Eyq5PktM/JRB++kViKvkiCaNImkXZorT++pxQOWmigKbMvM1WB78SCWtNQtNb7UZDd15lJ6GvLjh5GYimODMeytkfhoG9SKE4p5bZyz9RB5faBCvrlYyVUBwcHo/zg6QO8/ZxVrGqtjf1I+fRMQy06OkwU0mMjMf7hP1/kb163Nisq+eiLJLht80u8dGQEgAu6Wvm7N20YZ6EDxxv4hYM+TENhmQbRZJo7HtpRcEWZWzfltQ0Hd1XRGPJlmuRp1nWE+eRVpwLF+a/l26OZyePzPW91flcIp6oN8luuxZBhqEwRrNuPyVDU1JcXYWHzfx59BYXiptfVxioJRJSmpZobshUiV0gdrfGZJik7zb1PHygoSs8fHOIzP32JwahrF/TnF57An114QjbZIJejIzFa6/zjWienbYe9/VHWLKnLOynntmM/NpLI9kZqrfPTHg5SH5jcNrzYybxQmGumYuA9b02hIAHLzHSvdRhL2Nx13RnjjreuI8ze/ggjsTRJ28HRriN60DKzdVPV/uVFWNjs6x/jR88e5M8uOCHbn6wWkDqlaSjUkK2aU3MPDEYJWgZp2yGVdtBaE/QZ2dTtXLTW/HjrQT7yw+cZjKZoCFh87o9P5z2vWZNXkCzD4IQl9SQmOIUfG0lMKmb1mSpbW7RpQwdv3biCgbHU8WZ9hmIwmmI0nqoKoc+tfWoM+aasDbrxki58ppvQcPLSMIZSGKhxRYnVcE3C4uWuR17BMhQf3HRSpYcyI0SUpqEWHR1WNIcYTaTHZdblpm57xFI2//zADr7yX7txNHS11fPVd28saEHiyxR9/tWlJ01+ThyHpROqxCdOyk90D7CyJUS938RvGm4zvYwBazUIvQK2HR7hhUPDbDs8TM9IvOC4JhYn1/lN2sL+caFOz+uu0t1khcWH1bKc//i9u0rqqFKPu0JI+G4aaq2/zHA0xR+ftYI7H9mF1va41O3rzl2Vvd+hwRi3bt5Gd98YAJdt6OAjV66fZD7rEfSZLG0MYmYa8018TnKLPT0mTuheKLStIeDuKTmA0sTTzqyEvpRZkXc9vNNtTZH53dGuH108bWf3qCaSGzb09qNyHcynq3cShHLRdMHb8JkGN15aW6skEFEqikqm5hY78aZt10Q1lrQ578RWPnTZOu59+gBHR2J0NoayXV8Bnuzu53MPbGcsYWMo+MClJ/GnG1cUrDFy93vG1yBNfE7yTcoThcZLdvAa9Ll7Npp6vzWjep7c9hQ+w2BpY2DOE/43fr0Ha0I2HbjmtMUcbyqhrqUkGaH2GY2nqD/tdVx37qqq9rgrhBTPVjGFCk4nTuDT2QR5OFrz7Sf28e0n9gHQUufjU285lTNXNhd8TGNmZZNvbBPFEqZeURZ7PcU8Jz2j8ayRq1cgaxpqUrJEsZz09w9gGWCo4xFtR7sZdbv/+c0zPh4cr3fKFXOtdcEi3tmykOroFhEzKJ419NR+9uNpufwvCW+8mt/83RUVD4lPgRTP1iLTpaM7GZugyIQqTq/A9chIjGWZVdIpy8P88wM7eGrPAACnLmvktmtOzSs4Hq31fprrJvtkFazduua0KQWhFKFQ7zlJ2a67uPdhPTocZ21Hw6wTC+ozaeCGcu2X0o6XUafYsqNnVpP8fLQ9qdU6OmEmaAo5OuQ6NwD0RxJcfMd/8eZXLatmQZqSsomSUurfgKuBHq316ZnbWoEfAGuAvcDbtdaD5RpDrTNVOno85XaETU3Igvtd9wB3ProLy1A0Bi36xxJ84VcvozX0j7ndVq89czkffN1Jk1yyPTwvuIn1SR5zqd2aayj0wGAUU7nOFG5PIkBDPO3QP5ZgzZKG6Q6Rl/dffCJ3PvoKSdsm9ykNB8xZT/LFdMqdK7VYRyeUj289sY942uavNlVvItZ0lDP77pvAGyfc9gngEa31OuCRzO9CAQqloy9tDHJ4KDZJkOB4YWvIZ6JQpGyH/kiS/rEkfsvgE288mQ9dsY7f7xvilh88z/Vff5JbfvA8v+t2V1CGUnQ2BgsKElS2G++qljqOjSTw9FTnBDYGxlKznvBvvmI9H7psLV5UwVDQ3uBnVWv9uNT2mTAfbU9qtTOyUHoSaZvvPbWP153cwdqOcKWHM2vKtlLSWj+mlFoz4eZrgU2Zf38L2AJ8vFxjqHUmftOOJtPEUw5/efHKgo85MhKjMWhlO74OZbqfmgr+5bpXs25pOO9q6s5Hd/G3xjquPWsFASt/Bp5HJbvx3nhJF+/79tOYSuEzNGnHFaWAZRAOmHOa8G++Yj0/fPbgpH2gYif5Qns75Vyx1GpnZKH0PPjCUfoiSd7zmjWVHsqcmO86paVa6yOZfx8Flha6o1LqBqXUM0qpZ3p7e+dndFVG7jftwWiSxpCfmy9bN6VV0LLGEGMJm4NDsawgBS2DUzobWbfU/fY0cTUV8pn4TMVPth6aVpCgsrVbmzZ0sK69wW23rhR1fpMTWutY2RJi3dLGOR+/2GLpLTt6xtUf3fXwzmwCRqFuueWgFuvohOnJnf+Kfcy3nthLV1s9r13bVsaRlZ+KFc9qd3euYEqJ1voerfU5Wutz2tvb53Fk1cVr17fz5etezf973/l88e1nTilIABd2tXJ0JE4s5Yb2GoMWzXU+3n3BCdn7HBmJEfQdf+kNQxEOWBwamuz4kI9Kd+P9xJtOoSMcZHVrHSe21WNlEh9KMREXM8nnZgB6AvSVLbtJ2XZBR4tyUenXQigPufNfMff/w8Ehfr9/yLUGM4pO7KtK5jv77phSapnW+ohSahkg5e1TEEu6yQxTuXp7aK3Z/Pxhvv7rPTja3RNpDFqsWdIwrkYJ3NVU/1iCkM/EMBSWoWYc8qlk7VY5C5qLOXa+5ALb0QxHU9kusTB/ezvS4kL43lP7qfOb/OnZhUP7tcJ8i9Jm4D3A7Zmf98/z+eeV2daPaK3pH0sykgm/TUciZfOlh3fxy5eOAXDCkjo+c81pBa3qrzt3FXc+uouk7dBgWWXJCis3U03Ec63bmW6Sz5cVGbAM4una8kgUFgaxpM3P/nCEN79qGY1TJCjVCmUL3ymlvg88AZyslDqolHofrhi9Xim1C7gi8/uCJF+Ip5g9hkTa5uBgrGhBOjIc42/ufS4rSJeub+f/vnPjlL1Tzutq5ZNXncKyptCCC/nM9nmfCfn2ncJBC8swZG9HmHd+se0okUSaP91Y+6skKG/23fUF/nR5uc5ZTcymfmQommQwmqJYl42n9w7wuZ9vZySexlBwwyVdvO3slQXtgsBN+e5oDNDV3sCbz1g+8wubIzNdxcz0/rc/uJ2e0Ti2o/GbBm0NgezeTqlEN1/9kd8yuWnTap7oHihpSFHcGoTp+PHWg6xsCXH+iVPvN9cK4uhQJmbShyllO/SOJohP+PZdCEdrvvfUfv79N3uzHVo/efUpbFzdMuXjTEOxtDFIsIDp6mwpduKcqftAsff3zr/z2Aj9Yyksw22xkbY1h4djLG8KFtzbmc2kP9W+080zeeKmQdwaBBfFLVeenPcvZridx3b2MPybezE/cTkrV63mwP598zy+0iKiVCaKrR8p1rfOI5JIc8eDO/jN7n4ANnSGue0tp05rT+8zDTqbggVdHGb7jXwmE+dMV4/F3D/3/PFMxqHXAdY0FDiu2/dZqyYL9lwm/flILhC3BsGlsM3Q1v2DPL6rjw/9z7+j+dZbC4pXLSH9lMrEdKnFtqPpGYnTO5ooWpD29o/xwe9uzQrS1Wcs48vvePW0ghTwmSxvDk0pSLPdh8mdOKdLhZ6p+0Ax9889f9J28GXSYVOOg878V2hvZyZjrwTi1iBMxys9EdrDgbwelbWKiFKZmKp+JJa0OTQYm2SkOhVbXu7lg9/dysHBGD5T8ZHXr+eW16/Hb039EtYHLJY3Bce1Lp/IXCbnmUycM+3iW8z9c8/vNw0MpfB7LSgcjaEU6zsa8q4sqn3Sr8Wux8L8MRpPcSRjRLyQkPBdGZkY4tFa0x9JMFxkZh24E+s3Hu/mB88cBKAjHOC2a05lQ+f07gVNIR9LJriA5wvTzWT/ayIzsbmZqUFpMffPPX+2eSAQ8hksaw6RsjUff+OGOY99vtmyo4fBsQR7+8eyPaMs05CMPiHLKz0RANaJKAmzIZF2C2GT6ekLYT0Go0k++7PtPHdgCICzVjfzyatOKWqpvqQ+QFPdeKEptIcSDri1SsVOzrnC1uA3s+nr0wnNTItei7l/rnCFgxZL0n4GoynqAhYd4eCUx5+Ni/dUfaRKlSGX+zqtbA5xbDTBwaE46zsa+ORVG2Q/SQBgV0+EtgY/LQsodAciSvPCVKne+XofndfVyvYjI9y2+SV6IwkA3nHOSt7/2q4pw3Dgtp1oDwdoCEx+aQttnGuts/tf003OE4UtlrLRuKGz4ViKlS11XNjVyt2PdfOP9784aZKeaYLAdPefKFwntjVwe5GiMFORzCfqH73veRRuM8RSZchNfJ0aQ36iyTTNdX4RJAGAsUSaI8NxLuxaUumhlBwRpTKSTLstyhMFUr0LuXVfuGcJP33hMClbE/KZfOyNJ3Pp+un9/wzlpnyH/PlTvguF6YZjKT577elFTc75hA2guc7Pgx++pCJpzHPJhJvJY/Nd+6GhGGjobAplb5trhtxcwqnC4mBP3xgAXe31FR5J6RFRKhPD0RQD0eSUhbC5bt3gWtUMDMf5yXOHAFjZEuIz157GmiXTv/FMQ9HZFJzS5XuqPZRiJ+fpJsyFnMac79ptR096jecqINW81yVUB3v7x9xwdf3CCt2BZN+VnJTtcHgoRv9YYlpnhly37pTtcGAwxljSXVVdtHYJX33XxqIEyWcaLG8OTdt2ohRtDqbLCKv2jLa5kO/aTUNhGeM/RnMVEGlHIUxF2nHYPxBlzZL6Kd1bahVZKZWQ4ViKwbHiC2E9t26tNUeG49jauz3Ip685DaOIN1zAZ9LZOHXKt0cp3LWnSw6Yz2/5823Bk+/aGwIWCkra8rycLuhCLTLe0SG45tUsfcc/8cidt/Cz3U+Pu+fKVavne3AlRxXrs1ZJzjnnHP3MM0X3upp3UrZDXyRBLFmcTZDHU7v7+eeHdjAad+uVDAXNIR8fe8OGafsmgRsaW9oYmPdvS54Y5Jswc/eUcifpUhu+ztd58p134rWDCIgwY4r+0CqltOfocMuVJ3Pb5hf53lP7ee5TVxbcP64BCl6/rJTmyEg8xcAMbII8osk0D710LCtIlqFY19HAey5cU5QghYM+2sOBae9XDqbaf5qvb/mV2rsqdO0iQsJ8seXlXi48aUktC9KUiCjNkrTtZtbNdHUEsH8gyq2bt7Gv391necNpS/nw5esIFGmU2lLnp6WKNzjnwxdOMtSExYjVvIw9fWO896I1lR5K2RBRmgUzNVHN5de7+rj9oR1EkzaWofjry9byljOWFRWCU0qxpMG/IBp5zZVi9q6k7YOw0AiecCYAr103fYlIrSLZdzMgbTscHZ6ZiaqH7Wj+9dd7MhvlNksa/HzpHWdyzZnLixIktwYpIIKUYboMtflo9icI803whDNY2hhgzZKFWx4gK6UiiSTS9EcS2M7MV0fDsRT/9PPtPLtvEIAzVjbxqatPpbXIEFy5+iDVMtPtXS3keilhcaK1JrjqVVzYtWRBpoJ7iChNg+24JqozcfTOZeexUW7dvI1jI65d0FvPXsENr+3CKtBGYiI+02BpY3BaN/BqZC49mop53FR7V7LnJCw0BsaSmA0tXHjSwrMWyqX2Zrp5ZDYtJnL5xbaj3HzvcxwbSRC0DP7hzafwwU1rixYkv2WwrKl2BWk24bNShd2k7YOw0Dg46DrgX9jVVuGRlJfam+3mAa01fZEER4ZjpJ3iXb09UrbDlx/exR0PvUwy7bC8Oci/vPMsLj+l+LBRyG+yvClUtIBVG7Pt0VSqxnviiiAsNA4OxkgP97CqNVTpoZQVCd9NIJ5yW0yk7JmLEUDvaIJP/3QbLx0ZBeCCrlb+/k2n0BAs/qluCFq0N8x/UWwpmW34rFRhN3FFEBYKPp+fW648GV9HF0tP3IBS7630kMqKiFIGrTVD0RRDsfwtJorhDweH+PRPX2IwmkIB73nNCbz7ghOKsgvyqPYapGKZrd1QKW2K5qNeShDKzRlnvIpqdrQpNbUZGyoxybTD4eE4g9O4ehdCa82Ptx7klh8+z2A0RX3A5HN/fDp/fuGaGQlSWziwIAQJZh8+k7CbICxuFv1KaTiWYmBsdmIE7rf4L/5yJ49kNuK72ur59DWnsaKl+LivUoqOcID6PI35apXZhs8k7CYIi5uFMwvOkLnYBHkcGopx6/3b6M403Lp8Qwe3XLl+UuuGqVjINUizDZ9J2E0QFi+LUpRma6Kay5Pd/Xzuge2MJWxMQ/GBS7v4k7NWzCg5wTIMOms05VsQBKEcLCpRmm2LiVwcrfn2E/v49hP7AGip83HrW07ljJXNMzqO3zLobAzWbMq3IAhCOVg0ojTTBnz5GI2n+OcHdvDUngEATl3WyG3XnEpbw8xaSIT8JkvDQYwiGvMJgiAsJha8KJVidQSwuzfCp+7fxpHhOADXvno5H9x0Er4ZrnQWQg2SIAhCuVjQojQcTTEwyzTvXB7efoz//cudJNIOfsvgb69YxxtO65zxcZrr/EWbsAqCICxGFqQoJdPu6iiemtvqKG07fO2/u/nJ7w8B0NkY5NPXnMq6peEZH2tJQ4CmkLSdEARBmIoFJ0qlWh0NjCX59E9f4oVDwwCcu6aFv3/zKTMWloVYgyQIglAuFsxMmUy7dUeJOa6OAF48NMynf/oS/WNJAN51/mr+4jVrMGeYmLCQa5AEQRDKwYIQpVKtjrTW3P/cYf7vlt2kHU293+QTb9rARWtnbhVfy32QBEEQKkVNi1IpV0eJlM2XHt7FL186BsAJS+r4zDWnsap15kagAZ9JZ2NwxisrQRCExU7NitJQNMlgdPaO3rkcGY5x6/0v8UpvBIBL17fzsTecTMg/87Bbnd+iIxyQGiRBEIRZUHOilEjb9EWSJVkdATy9d4B/+vl2RuNpDAU3XNLF285eOas6onDQR1uDX2qQBEEQZknNiJLW2nVlKNHqyNGa7z21n3//zV400BTy8cmrT2Hj6pZZHW+h9EESBEGoJDUhSlrD4eF4yVZHkUSaOx7cwW929wNwcmeYT7/lVDoagzM+llKKJQ1+GoNSgyQIgjBXakKUkrZTMkHa0zfGrZu3cXAwBsBVr1rG31y2dlZZcoZSdDQGxnVJFQRBEGbPoppNt7zcw+d/8TLxlIPPVNx82TquOmPZrI4lNUiCIAilZ1GIku1ovv54Nz985iAAHeEAt11zKhs6G2d1PJ/p9kGaqRmrIAiCMDUVESWl1BuBOwET+IbW+vZynWswmuSzP9vOcweGADhrdTOfvOoUmutml5QgNUiCIAjlY95FSSllAl8BXg8cBJ5WSm3WWr9U6nNtPzLCbZtfojeSAOC6c1fxvotPnLWg1AfcGiRJ+RYEQSgPlVgpnQe8orXuBlBK3QtcC5RUlH72hyP8y6O7SNmakM/kY288mUvXt8/6eI0h34yb+QmCIDN5rNsAAAhgSURBVAgzoxKitAI4kPP7QeD8iXdSSt0A3ACwfOWqog+eTDvc9cguHnjxKAArW0J85trTWLOkftYDbq33zzrcJwiCMFNy57/Vq1dXeDTzS9Xu1Gut79Fan6O1Pqd1SXGGqMdG4nzo3ueygnTR2iV89V0bZy1ISinawwERJEEQ5pXc+a+9ffYRnlqkEiulQ0Du0mdl5rY5sXXfIJ/9+XaGYykU8D8uXsP1563GmOX+j6HclO/Z+N8JgiAIs6MSovQ0sE4pdSKuGF0HvHO2B9Na84NnDvKNx7txNDQGLf7hqlM4d03rrAdoGQZLmwIELBEkQRCE+WTeRUlrnVZK/TXwC9yU8H/TWm+bzbGiyTSf/8XLPLazD4C1HQ185prT6GyauV2Qh9QgCYIgVI6K1ClprR8AHpjLMfYPRLn1/m3sG4gC8IbTlvLhy9cRmIPDQtBnslRqkARBECpGTTo6/HpXH7c/tINo0sYyFH992VrecsayOdUPSQ2SIAhC5akpUbIdzb//Zg/f+52bUb6kwc9tbzmV05Y3zem4UoMkCIJQHdSMKA3HUvzTz7fz7L5BAM5Y2cSnrj6V1jn2MFpSH6CpTtpOCIIgVAM1IUqJlM0HvvMsx0Zcu6C3nr2CG17bhTWHZASvBqkhUBNPgSAIwqKgJmbk/QNRkiMJgpbBR648mctP6ZjT8aQGSRAEoTqpCVHSwPLmIJ+55jS62hvmdCypQRIEQaheakKU6v0WX3vX2TQE5zZcqUESBEGobmpClFa0hOYsSFKDJAiCUP3UhCjNFalBEgRBqA0WvChJDZIgCELtsKBFSfogCYIg1BYLUpSUUrQ1+AkHpShWEAShllhwoiQ1SIIgCLXLghIlqUESBEGobRaMKEkNkiAIQu2zIERJapAEQRAWBjUvSlKDJAiCsHCoaVGSGiRBEISFRc2KktQgCYIgLDxqTpSkBkkQBGHhUlOiJDVIgiAIC5uaESWpQRIEQVj41IQoKWBZs9QgCYIgLHRqYpb3W4YIkiAIwiJAZnpBEAShahBREgRBEKoGESVBEAShahBREgRBEKoGESVBEAShahBREgRBEKoGESVBEAShahBREgRBEKoGESVBEAShahBREgRBEKoGESVBEAShahBREgRBEKoGESVBEAShahBREgRBEKoGpbWu9BimRSnVC+ybh1O1AX3zcJ5qQK514bKYrrdWr7VPa/3GYu6olHqo2PsuBGpClOYLpdQzWutzKj2O+UCudeGymK53MV3rYkHCd4IgCELVIKIkCIIgVA0iSuO5p9IDmEfkWhcui+l6F9O1LgpkT0kQBEGoGmSlJAiCIFQNIkqCIAhC1SCilEEpZSqlfq+U+lmlx1JulFJ7lVIvKKWeU0o9U+nxlBOlVLNS6j6l1A6l1Hal1IWVHlM5UEqdnHk9vf9HlFIfrvS4yolS6m+VUtuUUi8qpb6vlApWekzC3JE9pQxKqVuAc4BGrfXVlR5POVFK7QXO0VrXYtHhjFBKfQt4XGv9DaWUH6jTWg9VelzlRCllAoeA87XW81F0Pu8opVYAvwZO1VrHlFI/BB7QWn+zsiMT5oqslACl1ErgKuAblR6LUDqUUk3AJcC/AmitkwtdkDJcDuxeqIKUgwWElFIWUAccrvB4hBIgouTyZeBjgFPpgcwTGvilUupZpdQNlR5MGTkR6AX+PROa/YZSqr7Sg5oHrgO+X+lBlBOt9SHgC8B+4AgwrLX+ZWVHJZSCRS9KSqmrgR6t9bOVHss8crHWeiPwJuAmpdQllR5QmbCAjcBXtdZnAWPAJyo7pPKSCVFeA/yo0mMpJ0qpFuBa3C8ey4F6pdS7KzsqoRQselECLgKuyeyz3AtcppT6TmWHVF4y3zLRWvcA/wGcV9kRlY2DwEGt9VOZ3+/DFamFzJuArVrrY5UeSJm5Atijte7VWqeAnwCvqfCYhBKw6EVJa/13WuuVWus1uGGPR7XWC/Ybl1KqXikV9v4NXAm8WNlRlQet9VHggFLq5MxNlwMvVXBI88H1LPDQXYb9wAVKqTqllMJ9bbdXeExCCbAqPQBh3lkK/If7OcYCvqe1fqiyQyorfwN8NxPW6gbeW+HxlI3Ml4zXAzdWeizlRmv9lFLqPmArkAZ+j1gOLQgkJVwQBEGoGhZ9+E4QBEGoHkSUBEEQhKpBREkQBEGoGkSUBEEQhKpBREkQBEGoGkSUhJpDuch7VxAWIPLBFmoCpdQapdTLSqlv4xb7/mumZcELSql3ZO6jlFL/K8/tm5RS/62Uul8p1a2Uul0p9S6l1O8y9ztpivN+Uyl1l1Lqt5nHvjXnmD/Lud//UUr9Rebfe5VS/5/XGkQptVEp9Qul1G6l1AfK+DQJQs0jxbNCLbEOeA+wAvgAcCbQBjytlHoM12bm1XluJ3PbKcAAbhHtN7TW5ymlPoRbYDtV76FlwMXABmAzrl3RdOzXWr9aKfUl4Ju4dlZBXEH9WrEXLAiLDVkpCbXEPq31k7gC8X2ttZ3xePtv4Nwpbgd4Wmt9RGudAHYDnqP0C8Caac77n1prR2v9Eq4jRjFszjn+U1rrUa11L5BQSjUXeQxBWHSIKAm1xNgcHpvI+beT87vD9BGD3MeqzM804z8/E7ue5h5/4rklQiEIBRBREmqRx4F3ZFrYt+M28vvdFLeXg33AqUqpQGblc3mZziMIiwr5xibUIv8BXAg8j9uw8GNa66NKqUK3byj1ALTWBzItuF8E9uAaggqCMEfEkFUQBEGoGiR8JwiCIFQNEr4TBEAp9Q/A2ybc/COt9ecqMR5BWKxI+E4QBEGoGiR8JwiCIFQNIkqCIAhC1SCiJAiCIFQNIkqCIAhC1SCiJAiCIFQN/z8JfgbOcGd2MgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}