ckd.ipynb

{
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  "metadata": {
    "colab": {
      "name": "ckd.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztBrHERRzYs-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Description: This program classifies patients as having chronic kidney disease (CKD) or not using Artificial Neural Networks (ANN)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3iAKfmdzozI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import libraries\n",
        "import glob\n",
        "from keras.models import Sequential, load_model\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from keras.layers import Dense\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.metrics import classification_report\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import keras as k"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8OItG7V0Akk",
        "colab_type": "code",
        "colab": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "9c943b31-5ac9-41c5-ee4a-b93f364471d9"
      },
      "source": [
        "# Load the data \n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n",
        "df = pd.read_csv('kidney_disease.csv')\n",
        "\n",
        "# Print the first 5 rows\n",
        "df.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "        style=\"border:none\" />\n",
              "     <output id=\"result-38259b18-1aa3-4449-a101-9e500014e312\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kidney_disease.csv to kidney_disease (1).csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
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              "      <th>cad</th>\n",
              "      <th>appet</th>\n",
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              "      <th>classification</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
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              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>normal</td>\n",
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              "      <td>notpresent</td>\n",
              "      <td>121.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.4</td>\n",
              "      <td>44</td>\n",
              "      <td>7800</td>\n",
              "      <td>5.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
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              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
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              "      <td>18.0</td>\n",
              "      <td>0.8</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>11.3</td>\n",
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              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>423.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.6</td>\n",
              "      <td>31</td>\n",
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              "      <td>no</td>\n",
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              "      <td>ckd</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
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              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>117.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>111.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>11.2</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>106.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.6</td>\n",
              "      <td>35</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id   age    bp     sg   al   su  ...   dm cad appet   pe  ane  classification\n",
              "0   0  48.0  80.0  1.020  1.0  0.0  ...  yes  no  good   no   no             ckd\n",
              "1   1   7.0  50.0  1.020  4.0  0.0  ...   no  no  good   no   no             ckd\n",
              "2   2  62.0  80.0  1.010  2.0  3.0  ...  yes  no  poor   no  yes             ckd\n",
              "3   3  48.0  70.0  1.005  4.0  0.0  ...   no  no  poor  yes  yes             ckd\n",
              "4   4  51.0  80.0  1.010  2.0  0.0  ...   no  no  good   no   no             ckd\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1Thqj7J5U2e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data Set Column Identifiers \n",
        "# bp = blood pressure\n",
        "# sg = specific gravity \n",
        "# al = albumin \n",
        "# su = sugar\n",
        "# rbc = red blood cell\n",
        "# bu = blood urea\n",
        "# sc = serum creatinine\n",
        "# sod = sodium \n",
        "# pot = pottasium \n",
        "# hemo = hemoglobin"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLy6C0L_1GiX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2be088eb-0510-46ec-dba4-62179103798e"
      },
      "source": [
        "# Get the shape of the data \n",
        "df.shape\n",
        "# This shows there are 400 patients with 26 data points for each pt"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 26)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPQcQPPi34UP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "86e71da1-78b0-45e3-c358-b6dc5e6ce43b"
      },
      "source": [
        "# Show df's statistics\n",
        "df.describe()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>bgr</th>\n",
              "      <th>bu</th>\n",
              "      <th>sc</th>\n",
              "      <th>sod</th>\n",
              "      <th>pot</th>\n",
              "      <th>hemo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>391.000000</td>\n",
              "      <td>388.000000</td>\n",
              "      <td>353.000000</td>\n",
              "      <td>354.000000</td>\n",
              "      <td>351.000000</td>\n",
              "      <td>356.000000</td>\n",
              "      <td>381.000000</td>\n",
              "      <td>383.000000</td>\n",
              "      <td>313.000000</td>\n",
              "      <td>312.000000</td>\n",
              "      <td>348.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>199.500000</td>\n",
              "      <td>51.483376</td>\n",
              "      <td>76.469072</td>\n",
              "      <td>1.017408</td>\n",
              "      <td>1.016949</td>\n",
              "      <td>0.450142</td>\n",
              "      <td>148.036517</td>\n",
              "      <td>57.425722</td>\n",
              "      <td>3.072454</td>\n",
              "      <td>137.528754</td>\n",
              "      <td>4.627244</td>\n",
              "      <td>12.526437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>115.614301</td>\n",
              "      <td>17.169714</td>\n",
              "      <td>13.683637</td>\n",
              "      <td>0.005717</td>\n",
              "      <td>1.352679</td>\n",
              "      <td>1.099191</td>\n",
              "      <td>79.281714</td>\n",
              "      <td>50.503006</td>\n",
              "      <td>5.741126</td>\n",
              "      <td>10.408752</td>\n",
              "      <td>3.193904</td>\n",
              "      <td>2.912587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>99.750000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>1.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>10.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>199.500000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>12.650000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>299.250000</td>\n",
              "      <td>64.500000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>399.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>1.025000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>490.000000</td>\n",
              "      <td>391.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>17.800000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               id         age          bp  ...         sod         pot        hemo\n",
              "count  400.000000  391.000000  388.000000  ...  313.000000  312.000000  348.000000\n",
              "mean   199.500000   51.483376   76.469072  ...  137.528754    4.627244   12.526437\n",
              "std    115.614301   17.169714   13.683637  ...   10.408752    3.193904    2.912587\n",
              "min      0.000000    2.000000   50.000000  ...    4.500000    2.500000    3.100000\n",
              "25%     99.750000   42.000000   70.000000  ...  135.000000    3.800000   10.300000\n",
              "50%    199.500000   55.000000   80.000000  ...  138.000000    4.400000   12.650000\n",
              "75%    299.250000   64.500000   80.000000  ...  142.000000    4.900000   15.000000\n",
              "max    399.000000   90.000000  180.000000  ...  163.000000   47.000000   17.800000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_i2QsjNI3B_m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "4e7eb97a-e5d4-46f9-e5c8-a96ef5f53bbe"
      },
      "source": [
        "# Visualize the correlation \n",
        "plt.figure(figsize=(10,10))\n",
        "plt.title('Chronic Kidney Disease (CKD) Indentification Correlation Heat Map',fontsize=12)\n",
        "sns.heatmap(df.corr(), annot=True, fmt='.0%')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fda2dfd8b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5hpEVXGv3vuMYncBT5nqDMyzXIfuquayrGJnDFGCvuc38ZMZ7QmsdiZFVHIrRjsOBJ7XWEXewjv0Y2bx55uc9AbyQRfFhGPURi7HPZ/zifY8s2sP8/O0wHjEQgfEYi15mfTrcrY4rOW3jTNZ1VWv9k3mczOhWx4pUX2CcQJ3CuAya42fpiLxFZb5tiP+PlFKeGHdiPKy1/je347lfzCzPH0ALbfdwvXu8zuoYg0Qb3o/1CfH/mVLqDMadeFtyOxZx78gDn4S9V4F9/586MwDmJZ8q93mdhzHukhJCCOEA+fqSk3Ac8wxmIOkf/CaEEELcEaXUYmU8APSvLOYrZfy8zwllPPzyYYesVy45CSGEEMJRlFKPYQzSXqa1rpbJ/DbAGxi38dcHZmutbzXOMVskQyOEEEIIh9Fa7yDzZ5Kl6oDR2dHmI0N8Vfrfubsr93wMTUrEqTyXAnq7ztu5HcJNosnuM/jun1kj8uZzp6qNzuyuzdz1aMGyuR3CTeLSHlibd3gYP9OVp+TFmACmV832TVT3zZI/S96+0H326qjCuR1Cpgr0m31ff6j3fn7XuhUp/wrGHbmpFmqtF97BIoqT/kGH4ea0HN2UIYOChRBCCJFtZuflTjow94VcchJCCCHE/fQf6Z/cXAIHPM1aMjRCCCFEfmez5nYEd2It0N98+np9INoRzwCTDo0QQgghHEYp9SXQFOMHpMOBdzF+iBit9XyMp663wXhi9nWMp4nnmHRohBBCiPwu01+dyR1a6+63ma+B1x29XhlDI4QQQoh8TzI0QgghRH5nyzsZmtwiGRohhBBC5HuSoRFCCCHyOZ2HxtDkFsnQCCGEECLfkwyNEEIIkd/JGBrJ0AghhBAi/5MMjRBCCJHfyRgaydAIIYQQIv+TDo0QQggh8j255CSEEELkd/nrxynvCcnQCCGEECLfkwyNEEIIkd/JoODc69CMnjiTHb/8hr+fL99/Ph+A6JhYho6ZxPmLlwgOCmTGuJEU8vFGa82kD+azc88+PDzcmfD2UKpUrMDps+G8NXYKKRYL7w4fQM1qlbFYrPQbOpq5U97F08MjRzEWKR9Ml2mvULxqWX6cvoodn6xPm/do79bU79YclOK3lVvZtXgDACEjulOpSU3OHz3DqqEfA1CrYyMK+nunlcmJeh0a8US/jiilSIxP4IvRnxB+7CwAE3Z9SFJcIjabDZvFysT2IwB4asSzVG1Si7CjZ1gydB4A9Ts2xsvfm58Wh+Y4ptjEFMZu/ouTkXEoBe+2qkaNYD8Alv1+mlk7/mZrv+b4ebqx5d+LfLz7BIU8XJnZvha+nm6ERV1n3i//MKVtzRzHkqr+o7VZuHwWYWfPA7Bx/VbmTl+YNt/JyYk1W1Zw6eJlXu4xEIBZ8ydQsXIFtm7ayfQJRj29PuRl/jl2gs0btuc4pkc6PkZbu7Zb8vZCzh07A0Drl56kSbeWoCHs+Fk+eXMeKUkpvDp7ECUqluLgT7/z1bQVAHR442nC/z7H75t+y3FMAPVa1af7sGfRNo3VamXx2E85tu9o2nxPL0/m/PQRv238lU/eWYCLmwsjPx1NQLEANiwL5cflxjb06uTX2fj5j5z662SOY6rdqh5dhvbAZtPYrFaWjV3E3/uPATBi6TtUqFWRv/cfZdqLE9Le8/rswZSqWJoDP+1n1bTPAej0xjOE/X2O/Zv25jimBh0a06ZfJ5SCxPgElo1eSNixswSVC+bVeUPSyhUpGch3s1ayefF6nhnxHA81eZhzR0/z6dC5ADTs+Bhe/t5sXrw+q1Vlj6sbhabMAVdXcHYm+ZefSVjxGQCevV7GvVFTtM1G0vo1JK77BrdHHsPzuRfRsbHEjn8bHRuDU1AwBZ7vQ9yUsTmL5RbcCxXgiWl98S1dFEtSChuHfULkP+F4+nvT4ZNBuPsU4JdpX3Ni0+8AdPh0MFve/oz4S1EOjSMvHqeEY+XaJaeObVoxf+b4dNM+Xb6aBnVqErpqEQ3q1GTR56sB2LlnH+fCzxO6ahHvDR/AuOnGl81Xa0IZMfAVPp7+Pku++AaAVd/9wJOPN89xZwbgelQca95bys+f/JBueuCDJajfrTlzO4zmg5C3qNy8FoVLB+Lh7UnxqmWZFfIW1hQLQRVL4uLuSp1nmrB72aYcxwMQEXaZGV3f5f3WQ1k/92uem/RKuvkzur/H+DZvpnVmPLwLULJqOcaFDMOaYiG4Yilc3d145JlmbFu20SExTd1+jEfKBPDdC41Z9dyjlPP3AuBibAK/no0gyPtGW6w8eJbPezSkc/WSbDh+AYAPd//Da4884JBY7O379Q+ebNaNJ5t1S9eZAej9Sg9O/ns67XWlKg+QmJhEmyZdqV6rKt7eXhQJDKBm7WoO6cwAXAm7xIQuYxj1xGC+n/MVL07qB4BfoD+P927LO08OZ+Tjg3BydqJBu0aUrFSa5MRk3m49hHI1KuDpXYBCRf0oX/MBh3VmAA7/cojBTwxgSMhA5g2bw2tT3kg3v8ew5zi690ja61pNHubYvqMMevwNmj7VDIAylcvg5OTkkM4MwF+/HOat1oMY2WYwC96cS58pr6fNW7fwez4a/EG68qXMunqr9SDKm3XlW9SP8jUfdEhnBox9b3LXMYxpPYS1c7/mebP9Lp46z7tthvFum2G89+RwkhOTOLDxNzy9C1C6ajneCRmCNcVCCXPfa/RMM7Yu+zHnAaUkEz1qMNFvvET0Gy/hWrseLhWr4N4yBOeAokS90pPofr1I2vETAB7tniJ68Csk/rgW96YtASjQ62WuL/8057HcQv3XO3Dl6FmWPTGKHwfPp9nYngBU6tCQQ59vZUW7d3n4pdYAlGtZi8tHzjq8MwN59zjlMDbb/fuXR+Vah6ZOzYco5OOdbtq2nXvoEGLsaB1CWrJ1xx5j+q5fad+6BUopalSrTGxsHFciruLi4kxCYhKJiUm4uDgTExvH9l/20j6khUNijI+MIfzwKWyW9IOtilYozrmDJ0hJTMZmtXFq7zGqta6HtmmcXZ0BcPV0x2qx0qTvk+xeuvGmZdytUwf+4XpMPACnD/yLb1DhW5bXNltaTG6e7lgtFlr1bcfWpRscElNsUgoH/rtGp2olAHB1dsLbwxWA6duPM7BxRZS6Ud5JKVKsNhJTrLg4KQ6EXyWggDul/QrmOJbsCipWlGatGrHq8+/SpqWkWPDwcEcphYurC1ablcEjXuWDKfMdtt5/f/87re1OHPgHv2I32s7J2Rk3DzecnJ1w83Tn2qWrWC0W3DzcUErh7OKMzWqj85BufDNrlcNiAki8npj2t0cBd9A67XW5h8pTKMCXgzv+SJtmTbHi7ulubFdm23Yf9hxfzFjhsJiS7GJyL+AB3IjpyC+HSYhPSFfeYrHa1ZULNquNZ4Z05+tZXzosphMHbrTfyQP/4J/Jvlfl0Ye4fPYSkf9duWnfs1istO7bni1LN2B10PGARLMeXFxQzi6AxqNNB65/uTStHXW02TnQGuXqinL3QFssuFStju1aJLbz/zkmliwUfqA453YbGb+rJy9QqEQABQJ8sKVYcfF0w9ndBZvNhnJ24uGXWrPv4x9us8Q7lx+PU+LO3fKSk1LqqVvN11p/68hgIq9FUSTAH4CAwn5EXjN2xEtXIgkqGpBWLrBoAJeuRND9qXaMHD+dlOQU3hk+gAVLvqRPr244Od3bftqlv8NoPawrBXy9SElMplKzmoQfPk1SfCLHtx1kUOgkTvxyhMSY65SqWYGf5n53+4XehUe7NufI9htfNGgYtHw0WsPOLzaz88stJMUn8te2A4wOncbxX/4kIeY6ZWs+QOjcbxwSw/noBPw83Xh305/8cyWWyoGFGN60Er+ei6SolwcVi/ikK/9i3XL0+2YfRQq6Mz6kOsN/OMjktjUcEktGtepUZ/32VVy6eIVJ787k379PATBmwptMHjubgl4F0sqe/Pc0VyOusW7rl3z31XpKly2Jk3LiyOHj9yS2pt1acthsu2uXrhK6cA0f7FlAcmIyf+08xF87DwEQExnDuPXT+eW7nwksE4STk+LsX6ccHk/9Jxrw3FvPUyigEBNeMC4/KKXoPfolPhg4g+qNbqTZD+78gyZPNWPK99P5fsF31G1Vj1N/neTapasOjanOE/XpNrwnhQIKMbX3+FuWPX8inNjIGCaun8mu77YTVKYYysmJM/egrgAe69qCP+33PVP9do+yd+0uABLjEzm87QBjQ6dz9Jc/SYiJp1zNB1g392vHBeLkRKHZC3EuVpzE9d9j+fsYTsWCcX+sGW4NG2OLjiZ+wWxs5/8jYfUKfCbMxBYZSdz08XiNHHtPLzWlunLsHA+0rsN/v/1NUI1y+BQPwKuYP8fW7KbtnNep3qMZOyetomavlhz7ZheWxGSHx5CXj1OOIj9OefsxNO3M/4sCjwBbzdfNgN1Aph0apVRfoC/ARzPG83Kv7nccmFIKZd9lzkSxoKIsmTcVgHPh57l4OYJyZUoy4v1ppKRYeKNPT8qUKnHH676dyyfPs33+Wl5ePpLk60mcP3oWm5mG+3nBOn5esA6Apyf3YdPMr6jXtRkPNK7OhePn2DrPMZ2bBxtW5dGuzZn29Ji0adOeHkPUpat4F/Zh4OdjuHjyP/797RibFqxl04K1APSc3I+1M1fxaNfmVGlcg/+OnyV03t33Sy02zfHLMbzVrDIPFfNl6rZjzN9zggP/XeOjp+rcVL5B6QAalDY6p+uO/kejskU4e+06y/YfwcfDlTebVsbTPKvNiSOHjtO4VhuuxyfQtGUjFiyfRfN6HWj+eGMiI67y16Fj1H+0drr3jBs9Pe3vT1Z8wNtDJ/Da4JeoXPVBdv38K6uWO6btKjesxmNdWzC+8ygACvgUpPbj9RjS6FWux8TzxkfDeKTTY+z+bgcr3l+c9r4hi0ayeOR82vfvTKnKZfhr5yG2r9zikJj2bvyVvRt/pUq9qnQf9hzv9RhD615t+H3bfiIvRqYra7PamDXAqCtnF2feXT6WiS9PoPeYlwgoXoTt32xl3+acXxLbv3Ev+zfupVK9KjwztAcTn333luWXvb8o7e9hi95m0ciP6Nj/aUpXLsufOw+ydeXmHMcEUKlhNRp3bcHEp99ON93Z1YWaLevy9dQbmaoNC9awYcEaAHpPfpXvZ67ksa4tqNq4JuHHz7BuXg5PLGw2ot94GVXQC+/R43EuXRbl6opOTiZ60Cu4PdIYr4EjiHnrDVIO7id64H4A3Jo/Qcr+X3EuXhKPp7qi4+KIXzgHkpJyFk8mfvtoHc3e60nPDROI+DuMy0fOoq02kmMT+K63sR25FypAvdeeZE2fD2g15SU8ChVk/8JQLhw44ZAY8upxSjjWLVMZWuveWuvegCtQRWvdWWvdGahqTsvqfQu11nW01nXupDNT2M+XKxHGWd6ViKv4+xYCILBIYS5ejkgrd+lyBIFFAtK9d/aCpQzo24sVX62hc7snGPr6i3y8+M5T4A17tmJQ6CQGhU7Cp6hfluX2rd7OnHZvM7/r+yRExxNx6kK6+cFVy4BSXD51gYfa1mdF/9kULl2UgDJBdxxT055PMDp0GqNDp1GoqB/FK5Wi1+R+fNRnKvFRcWnloswz5NjIGA5u/I0yNSqkW05JM6ZLp85Tu21DPuk/iyKlgyh6FzGlCvT2oKi3Ow8V8wWg5QOBHL8cw3/RCXT9/BfaLNrO5dgkeqzYTUT8jYNlQoqVdUf/o0uNUszf8y/jWj9EzWA/Nhw/f9ex9HyxCz9sW8kP21ZSwKsA183LEtu37MLFxQU/f19q16tJi9ZN2HFgPXMWTqZho7rM/Dj92X/LkKb8degYBQt6UrpMCd54+S1C2rXEw/POx2W17NWa8aEzGB86A9+ifpSsVJqXprzGBy9PIs5su2qNqnMl7BKxV2OwWqzs+3EvD9SulG45D7eqy+k/T+FR0IOipYOY9/oM6rZpiJuH213VVUivNszcMJuZG2bjF+ifNv3ob0cILBWEt58PFR+uRJvnn2TBL5/ywugXadq5OT1HPH/TcrZ9s42KD1ckPjaeGa9NpUOfTncVU6teIUwKncWk0Fn42e17x387StFSgXj7ed/i3TfUblWP03+exL2gJ4GlgzGtm20AACAASURBVJj9+jTqtXnkruqqec/WjA2dztjQ6fgW9aNEpdL0nvwqc/pMTrfvAVRvWouzf50iJiL6puWUqloWlOLCqfPUbfsIH/efQZHSQQSWKXbHMWVGx8eRcvgPXGvXwxZxheTdOwBI3r0T57Ll0hd2d8ejZWsSf/gOz2d7EzdzEpajh3Fv2sohsQDU7NWSnhsm0HPDBFwLurNx2EKWh7zNhkHz8fT3JvrclXTlGwzoxK9z11KpQ0P+2/cPGwYv4JHBt7xAcEfy0nHqnpExNNm+y6mk1tr+G/sSUMrRwTRt1IA1G7bwcs8urNmwhWaNG6ZN//KbdYS0bMLhI8fx8iqYdmkKYN8fhyka4E/pksVJTEzCSTmhlBMJd3G2sWf5ZvYsv/2ZXMHCPsRHxuAbXJhqresyr9M76eY/MeQZvhn5Kc6uzmmXwLRN4+p55wfV7cs3sn25MYDXLziAfvPfZPHguVw+faNJ3DzdUU6KpPhE3DzdqdK4BuvnpE9ttx/Sjc9HLkgXk82mcfN0v+OYUgUUdCfIy5MzV+Mo4+/Fb2GRVCrqw4Kn66WVabNoOyt6PIKf3Wdftv803WuWxtXZiUSLDVA4KUhMufuxBcsXr2b5YmMgeUDRG+MbqteqipOT4trVKKaNn8u08cbdJvUfrU2f13sx5NXRaWVdXFzo/UoPXuo+gDLlSqUNJ3F2dsbV1SVtyEJ2bVn2I1vMAaCFgwMYuGA4CwbP5qJd20Wej6B8rQdx83AjOTGZqo8+xOk/bwyudXZx5omXnmTGCxMIKhucNjbCydkJFzcXku8iRb9hWSgblhl3JwWVvvGlWq5aeVzdXIm9FsMHA2ekTW/2dAsqVK/A8slL06YVLFSQ2i3q8v5z71K3pTGGTGt9152szcs2sHmZcSdgYOkbnewy1cqZMcXedhnOLs6EvNSOqS+MI6hscFr7GXXlesd1tXX5j2xdbrSff3AA/ee/ySeD53Dp9IWbytZv34i963ZlupxOQ7qxdOR8nF2dUXbHA7e7OB6kUj6FwGpFx8eBmxuuNeuQ8PUXJP+6C9fqD5O0ORSXh2pi+y883fs8n+pGwtpvwGpFubsDGm3TOLnf/XEgo4PLtnBwmZE9dPcpgJOrM7YUKw91b0r4b8dJjruxI/mWCcS7mD/hvx6jSJVSWJLiQGtc7nI7ykxeOk6Jeye7HZqflFIbgdQRdl2BHOW633x3Mvv+OExUVAwtOj7Hay/15OWeXRg6ZiLf/rCR4KCizBhnpOQfa1iXnXv2EdLlRTw9PBg3anDacrTWLFiykhnjRgLwdIcQRoydisVqZcyw/jkJEa8ihRiwdgIeXp5orWn0YggzWr1JUlwCvT4eTAE/L6wWK9+P+YzEmOtp76v6eB3C/zxFzOVrAJw/epbBP07h4vFzXDh2LkcxPTngaQr6edFjfB+AtNuzfQIK0W/hm4Dx5fvbml0c+flg2vtqPF6Xs3+eJNqMKezoGd75cQbhx8+m3fZ9t95qVplRGw5jsdkoXqgAYx9/6JblL8cl8telaF5paGSQutcszXNf7MHb3YWZ7R/OUSypQtq15Nnez2C1WElMTGRAn5HZel/Pl7rw7cp1JCYkcvzIP3gW8GDDjtVs37KL2Ji42y/gFjoO7IKXnzfPj+sLgNVq5d12wzl58F/2he5h3Prp2Kw2zhw5xbYvbtwV17JXCLu+3k5yYjLnjp3BzcOdiRtncWjbAa7bbXd3q2GbR2jauTnWFAvJicnMeH1qtt7XZWB3vp67Gq01f+w4QMjzbflg8zw2fu6AxxOENOSxzs2wpFhJTkpizus3Lge++9VEgssXx6OgB/N+/ZSFw+dxeIexrT/eqw07vt6WVlfuHm5M2Tibg9t+TxvQe7c6DHgGLz9vepr7ntVi5f32bwHGCUXVRjVYOmrBTe+r9Xg9zvx5kqi0fe80436cSdjxs4TlYN9z8i+M15BR4OQESpG8azsp+/ZgOfonXm+OxqPjM+iEBOLm3GhP5V8Yl4qVSfjS6JgmrvuWQrMWoOPjiB33dlaryhH/CsG0nvkKaIj8J5yNwz9JN7/R8C7smmqciBxfs4eOnwyi3mvt2D3DMeP8UuXF45RDyRgalLa7o+GWBY0Bwo3Nlzu01tkaUJAScSp7K7iP3q5zb3bcnIjGktsh3GTWiODcDiFT1UbvyO0QbvJowbK5HcJN4nRKbodwEw+V98Yd5MWYAKZXjbh9oftsyZ8lczuEm7w66tZ3euaWAv1m33oQqIMl/bPrvn3Xuj/Y6L5+tuzK9oP1zDuaHHpXkxBCCCEcQH7L6ba3be/SWjdSSsVi/yAI4+kTWmvtk8VbhRBCCCHum1t2aLTWjcz/s3d7gRBCCCHuPxlDI7+2LYQQQoj8Tzo0QgghhMj3cu3XtoUQQgjhIHn4gXf3i2RohBBCCJHvSYZGCCGEyO9kULBkaIQQQgiR/0mGRgghhMjvZAyNZGiEEEIIkf9JhkYIIYTI57SWnz6QDI0QQggh8j3J0AghhBD5ndzlJBkaIYQQQuR/kqERQggh8ju5y0kyNEIIIYTI/5TW+p6uYHiZ7vd2BXdhwv4JuR3CTQ7VHJLbIdxkpYtnboeQqU6JKbkdwk0K+1zP7RBu4uqW9+56SLjumtsh3CQmwT23Q8jUbte8t/9VTsp729S2vFdNAEw/86W6n+tL/P37+/Zd61G74339bNklGRohhBBC5HsyhkYIIYTI72x5L3t2v0mGRgghhBD5nnRohBBCCJHvySUnIYQQIr+TB+tJhkYIIYQQ+Z9kaIQQQoj8Th6sJxkaIYQQQuR/kqERQggh8jsZQyMZGiGEEELkf5KhEUIIIfI7GUMjGRohhBBC5H+SoRFCCCHyO8nQSIZGCCGEEPmfZGiEEEKIfE5r+XFKydAIIYQQIt+TDo0QQgiR39ls9+/fbSilWiul/lZKnVBKjchkfiml1Dal1B9KqcNKqTaOqALp0AghhBDCIZRSzsCHQAhQBeiulKqSodhoYLXWuhbQDfjIEevOEx2aIuWDef3bsUz8exmP9Wmbbt6jvVszZONUhmyaRqMXQ9Kmh4zozuANU+g649W0abU6NkpX5k6NnjiTx9p2o+Nz/dKmRcfE8vLAUbTp+hIvDxxFdEwsAFprJs76mJAuL9Kp16sc/fsEAKfPhtPlxTfo1OtVDv51DACLxcrLA0eSkJh417EBlJnenxoHl1B1y+x004v2bkvV7fOo+tMcSrz9PABedSpRZfMHVF4/HfeyxQBw9inIAyveA6VyFEdGRcoH0//bsUz+exlNMrRfo96tGbZxKsM2TaOxXdu0HdGdIRum0M2u/R7u2ChdmZyoMOs16v61iJrbZ6ZNK/NOT2rtnE3NrTOotPhNnH0KAOBdtyI1t86gxsYpeJQNAsDZpwBVVo5xWF25BAVQctkkyobOp+z6j/Hr1QGAIsNfpOyPCyiz9kOKfzgaJ++CAHg+XIUyaz+k9DezcS0dDICTd0FKLh7v0PZzDipC8OKplFyzkJLfL6TQcx3TzS/0fGfK/7URJ18fAAq2bETJ7xcSvHQGToW8jc9WshiB00c5LCbXYgGUWTGRChs/osKPH1L4hfZGrIW8KLNsHA9sXUiZZeNw8jHqyqf1I1T48UPKrpqCs68Rk1upIErOGe6wmADKz3yNun8upua2WWnTSg7vRo2fZlJj83SqrByDa6AfAP5tG1Bz+wdU+34cLn5eALiXDuTB+UMcGpN/+WL0+O5dBv37GXX6pj/JLdOkOi9um8ZLO2ZQ77V2adPbzH6V5zdOpNHwLmnTGrzRgQqP13ZobKlcfArw8KeDaLJrOk12Tse3zgNUGt2dxtumUGPujf2/eOdGlOnrmP0/M3nxOOVQ2nb//t1aPeCE1vqU1joZWAl0yBgt4GP+XQg474gqyBMdmutRcax5byk/f/JDuumBD5agfrfmzO0wmg9C3qJy81oULh2Ih7cnxauWZVbIW1hTLARVLImLuyt1nmnC7mWb7jqOjm1aMX/m+HTTPl2+mgZ1ahK6ahEN6tRk0eerAdi5Zx/nws8TumoR7w0fwLjp8wD4ak0oIwa+wsfT32fJF98AsOq7H3jy8eZ4enjcdWwAEV9t5d/n3k83zfuRavg+Xo+jjw/iSIsBXJz/PQCBr3Tg317jCHtvEUWeaw1AsQHPcHHe16B1juLIKMFsv+0Z2i/owRI06Nac2R1GMzOT9puZof3qPtOEX3LQfvYur9rG0e7p2zLq58P80XQwB5sPJeHUBUoMeAqA4v3ac/TZCZwa8xlBzz8OQMnBTxM+51uH1ZW2Wrk8+VNOt+nH2S5D8Hv2SdzKlyT+lz843fZVzrR/neTT/1H4FeNLxv/FToT3eYfLExfg1934oir8Wjci569ybPtZrERMW0hYh76E9xiIT7d2uJYrBRidnQKPPEzK+UtpxQs9257wbm8Q89V6vNo2M2J943muzlnisJC0xcrFiYs48cRrnOo8DP+ebXGvUJKAfs8Qt/sQ/zbvS9zuQxR59RkACvdqx8mOQ7j6xQYKtW8CQNGhPbk083OHxQRwZfV2jvYYl27a+Y/WcKjFEA61GsbVzb9TcogRU7EXQzgcMpyLyzcT0KkxAKXe6sG5KV86NKbEqHi2vruc/QtD001XToqW45/nm+en8lmL4VRq34DCDwQTUKkklsQUlj4xiqAaZXHz9qRgUV+K1SrPiU2/OzS2VFXHP8+VbYf4udEwdjR/i6SL1/CpXpadzd7ClmLBu3JJnDxcKdG9CWcXO2b/z0xePE7lV0qpvkqp/Xb/+trNLg6E2b0ON6fZew94TikVDoQCbzgirjzRoYmPjCH88ClslvSjtItWKM65gydISUzGZrVxau8xqrWuh7ZpnF2dAXD1dMdqsdKk75PsXrrxpmXciTo1H6KQj3e6adt27qFDSEsAOoS0ZOuOPcb0Xb/SvnULlFLUqFaZ2Ng4rkRcxcXFmYTEJBITk3BxcSYmNo7tv+ylfUiLu44rVdzeo1ii4tJNK9IzhAsffoNOtgBgiYwGQKdYcfJwx8nTHW2x4F46CLfgAGL3/JXjOG6KKzKGsCza72yG9nsoi/Zr2vdJduWw/ezF/HrsprqK+vkQWI2zi9jf/8G9WGEAtMWCk6c7zp7u6BQrHqUDcQ8uTMzuIw6JBcB65RpJR08CYItPIOnkOVwCA7j+yx9pMSUcOo5LUIAZkxXl6YHyMNrPtWQQrsWKcP23Px0WE4A14irJx4zsor6eQMqpMFwCjRgChr9C5MxF6TtQNo1yc0V5uIPFisfD1bBGXCPlnENOsACwXLlG4hG7ujoRhktQYXxa1Sfqm58AiPrmJ3xaNTDittlQbi7mtm6lQN2qWK5cI/mM42ICiPn1KJZr6bcpa1xC2t/OBdyN805IqydnMybv+pVJuXKNxNMXHBrT9cgYLmay7wXVLM+1M5eIPncFW4qV4+t+pfzjtbFZrLh4uIJSOLu4oK02Hh3amV9mfuvQuFK5eHvi37ASYSu2AcZxKflaHE4uxv7v7OmOLcVKuVef5MyijWgH7f+ZyYvHqfxKa71Qa13H7t/CO1xEd2CJ1roE0AZYrpTKcX/kjhaglCqQ0xXeiUt/h1G2biUK+Hrh6uFGpWY18S1WmKT4RI5vO8ig0EnEXo4iMeY6pWpW4Mim/Q6PIfJaFEUC/AEIKOxH5LUoI7YrkQQVDUgrF1g0gEtXIuj+VDs+Wb6Kt8fPoE+vbixY8iV9enXDyene9B09ygXjXb8KldZNpeLX4ylQowIAF+Z9Q9nZAwnq35nLS0IpPvxZ/pu24p7EkJWLf4dRLov2O7btIIPvQ/tlJbB7c65tPQBA+JzveHDuG5QY0IkLizdQamQPzk527Jm0PdfiRfGoUp7EQ8fTTfft/DjxO4w6iFywmuCpQyn8SheuLV9HkSHPc2XWsnsWE4BLcCBulcuTePg4BZo1xHI5guS/T6Urc+3TVQR/MpmCTRsQF7oNv349uDb/i3sWk2vxonhULUfCwb9xCfDFcuUaYHR6XAJ8Abgy/yvKLp+Ad4v6RK/7maL9u3Jl7sp7FlNGpUb0oPb+BRR56jHOTTPWGz73W6qufhe/VnWI+G6XkfGb9fV9i8k7yI/Y81fTXsdduIp3oB9XT5wn4WosvULHc3LLAXzLBKKU4vJfZ+5JHAVKFSU5Mobqs/vRaMskHprZB7Tm8k8HafTTJJIuRWGJuY5v7Qpc2nD/9n97efk4dUfyzqDg/4CSdq9LmNPsvQSsBtBa7wE8gAByKFvPoVFKPQJ8CngBpZRSNYBXtNavZVG+L9AX4HH/OtTwrnBXwV0+eZ7t89fy8vKRJF9P4vzRs9jMyvx5wTp+XrAOgKcn92HTzK+o17UZDzSuzoXj59g677u7WuetKKVQtxm/UCyoKEvmTQXgXPh5Ll6OoFyZkox4fxopKRbe6NOTMqVKOC4mZyecfb053m44BWs+QPmP3+TPR14h4ehpjrd/CwCv+lVIuXwNUJT7aBjaYiHs/c+wREQ7LI7MXD55nm3z19I3k/bbvmAd2832e2ZyHzaa7VexcXXOHz/HT/eg/VKVGPgU2mLlyjc7AYg/cobDbY0xID4NKpNy6RooRcUFg7GlWDnz3lJSHFRXqoAHxee+zaWJC7HF3zi7L9yvK9pqJWatcSabdOwUZ7sY4y0861TDcvkqKEXwByPQKRYuT/4Ua2SUQ2ICUJ4eBM0aQ+SU+WC14tenGxf6jrypXMKeA4TvMTqCXu1bcn3HPlzLFMf3haexxcQRMfljdGKSQ2JyKuBBqY9GcXHcJ9jsMiFpzGxI/K6DnNw1CADfTs2J3b4ft7LFCejzFNboOC68v9BhMWXm3OQvODf5C4q/0YlivUMIm76K6B2HObzDGMNT5JkmXPvpAB7lilH81Q5YouM4PWYxtoTkexbTrWwbe+NSXKfFQ9g0cjH1+7enaJXSnNn5J39+ud1h61Iuzvg8VJYjo5YQdeAkVcb3ovwb7flnylec+tDY/x+a2Yd/pnxFyWebEdCkOrHHznFi1r3b/zPKq8epfGwf8IBSqixGR6Yb0CNDmXNAC2CJUqoyRofmSk5XnN20wSzgCSASQGt9CHgsq8L26aisOjMNe7ZiUOgkBoVOwqeoX5Yr3rd6O3Pavc38ru+TEB1PxKn0KdvgqmVAKS6fusBDbeuzov9sCpcuSkCZoGx+tFsr7OfLlQjjTOdKxFX8fQsBEFikMBcvR6SVu3Q5gsAi6TuYsxcsZUDfXqz4ag2d2z3B0Ndf5OPFjs2SJF+MJGqDcRks/uC/aJvGxd8nXZliA7pwfvZqgod0JXzCUq58sZnAF5/M0Xof6dmKwaGTGHyb9vtt9XY+aPc2H92m/a6cukCNtvVZ3n82AQ5sv4yKdm2KX6va/PP67Eznlxj0NGGzvqbU0C6cGbecSyu2UOxlh9xRCC7OFJ/7NtHrthO3aXfa5EKdWuLVrB7nh07L9G0Br3Uj4qMvCejfg8tTFxO1+kf8erV3TExmXEEfjCF2/Vbit/yCa8liuBYPosQ3H1Nq41JcAotQ4qsPcS58o52Vhzs+HVoRvXIt/q/35PLb00g88BdebZs7LKaSH40iau12YjYa27clIgqXIkYMLkX8sGTo0CkPd3w7tyBy+XqKDnqW8GEzub7/CL4dmjomptu48u1OCrdtkG6ak6cbRbs04+JnP1LyzW78O2AuMXuPE/BUlofP26rZqyW9Nkyg14YJFAz0zbRM7MVreAf7p732KuZP7KVr6cqUb/Uwl/48g1sBD3xLB7Lutbk82KYeLh5udx1bRonnI0k8f5WoA8YlxAvr9lLoobJp832qlUEpRfzJCxRrV58/+s6mQOmiFCjrmP0/vx6n7koeGRSstbYA/YGNwDGMu5mOKKXeV0qlHriGAn2UUoeAL4EXtM754MBsPylYax2WITuRo4uIe5ZvZs/yzbctV7CwD/GRMfgGF6Za67rM6/ROuvlPDHmGb0Z+irOrc9plHW3TuHo6Zqds2qgBazZs4eWeXVizYQvNGjdMm/7lN+sIadmEw0eO4+VVMO3SFMC+Pw5TNMCf0iWLk5iYhJNyQiknEpIce6YY9eNevB95iNjdf+FeNhgnNxcsV2PS5hd+uhnRW3/HGhVnjDGwabBpnDzdc7Te3cs3szsb7edV2Ic4s/0eal2XORnar/WQZ/h65Kc4uTqjzPazObD97Pk2q0nx1zvwZ6d3Mz07LtLFOJO2RMXh5OmWVlfOOayrVMUmDiL5ZBjXPrtxVlewcW38+zzNuWeHZ5pF8OnUgrif92GLNtoPmw20xsnDMTEBFH1/CMmnwoheZoyjSP73DGeadE2bX2rjUsK7voEt6sZ25dv7aaJWfA8WK8rdGDuitePqqvjkgSSdDCNy0fdp02K27MW3cwsi5n+Nb+cWxGzem+49AX2fInLpOrBYcfJwMzI4DtjWb8WjbLG0cTH+T9Ql4UT6zHrwqx24sCgUnRqTUVE5qqeDy7ZwcNmWW5a5eOgUfmWDKFSyCLEXr1KpXQPWD7hxZ6yTizO1X2rNty9Mx69sUNo4KSdnJ5zdXLAkOiZ7lHQlmsTzkRQsX4z4kxcIaFyN2H/C0+Y/OOIZ/hz6KcrFGeVsnl9rjbOD9v/8eJz6X6C1DsUY7Gs/7R27v48Cjzp6vdnt0ISZl520UsoVGIjR83IIryKFGLB2Ah5enmitafRiCDNavUlSXAK9Ph5MAT8vrBYr34/5jMSY62nvq/p4HcL/PEXMZePM4/zRswz+cQoXj5/jwrFzdxzHm+9OZt8fh4mKiqFFx+d47aWevNyzC0PHTOTbHzYSHFSUGeOMSxOPNazLzj37COnyIp4eHowbNThtOVprFixZyYxxRsr+6Q4hjBg7FYvVyphh/e+6nsrOG4J3w2q4+PtQfd+nnJ+xkohVP1FmRn+qbpmNLcXC6UE3Mg9OHm4U7tKcf3u8B8ClhWt5YPkYdLKFU/1n3HUcGXkXKcRAu/Zr/GII0+zar6DZft9mo/2G/jiFC3fZfvYe/HgQhR6piou/N3UOLODctFWUGNAJJzdXqq4aA0Dc7/9y8i1jLJuTpxuBXZtxpKtxF8v5BT9QZcUodLKFf17LPJtzJzxrV6FQxxYkHj9NmTVzAbgycymBo/uh3FwpuWQCAAkH/+bSu8Ydc8rDnUKdWhL24mgArn72HSU+eR9SUjg/dGqOYwLwqFUV7/YtSfrnFCW+Nr7wrs7+jOs792X5Huci/rg/VJFrHxvZxugv1lBi5VyssXFcHDA2xzEVqFMFv6eak3j8NOV/mAPApenLiJj/NSXnjcCvy+Ok/HeZsP6T097jUtSfAjUe5MocY+xT5NJ1lP9+JtaYeM71G5/peu7UAx8NTtumav++kLDpq/Br8TCe5YPRNk1S+BVOvbUgrbxroB/etR4gfOZXAFxcFEr1DVOxRMdz/MUpDompQJFC9PxhHG5enmibjdovteazFm+RHJfAT2OW0nn5cJycnfhz1c9E/nOjs1Xz+ZYc+XonlsRkrhw7h4unO89vmsTpbQdJsttHHeHIqCXU/Kg/Tm4uXD97iUMDjToKDKlD9MFTJJmZo5i/ztJ4+xRij54j9mjO9v/M5MXjlEPJj1OispPlUUoFALOBloACNgEDtdaRt3vv8DLdHXuPsANM2D8ht0O4yaGajn0+hSOsdPHM7RAy1SkxJbdDuElhH8d+CTiCq1veuxMj4bprbodwk5iEe5fByYndrnlv/6uclPe2qW15r5oAmH7mS8c+8Os2EjZ9dN++az0ff+2+frbsylaGRmsdATx7j2MRQgghxN24/QPv/udl9y6nOZlMjgb2a63XODYkIYQQQog7k90xNB5AJeAr83Vn4DRQQynVTGs96F4EJ4QQQohskDE02e7QVAce1VpbAZRSHwM7gUaAYx9dKoQQQghxh7LbofHDeKhe6tPFCgL+WmurUurePbFKCCGEELcnGZpsd2imAgeVUtsx7nJ6DJiolCoI3PqBCEIIIYQQ91h273JapJTaAPTEeP7MJiBcax0PvHkP4xNCCCHE7chdTtm+y+lljIfplQAOAg2APYCDnnMuhBBCCHH3svtbTgOBusBZrXUzoBbguF/GE0IIIcTdyzu/tp1rstuhSdRaJwIopdy11seBivcuLCGEEEKI7MvuoOBwpZQv8D2wWSl1DTh778ISQgghhMi+7A4K7mT++Z5SahtQCPjxnkUlhBBCiOyTQcHZztCk0Vr/fC8CEUIIIYS4W3fcoRFCCCFEHpOHB+veL9kdFCyEEEIIkWdJhkYIIYTI72QMjWRohBBCCJH/3fMMTTSWe72KO3ao5pDcDuEmNQ7OzO0QbrKszsjcDiFT7i55b5tSTjq3Q7jJtSjP3A7hJu6u1twO4SbOKu+1HUCKyu0IbuZM3qur/4xHpAkZQyMZGiGEEELkfzKGRgghhMjvJEMjGRohhBBC5H+SoRFCCCHyO533xjfdb5KhEUIIIUS+JxkaIYQQIr+TMTSSoRFCCCFE/icZGiGEECK/kwyNZGiEEEIIkf9JhkYIIYTI7+S3nCRDI4QQQoj8Tzo0QgghhMj35JKTEEIIkd/JoGDJ0AghhBAi/5MMjRBCCJHfyU8fSIZGCCGEEPmfZGiEEEKI/E7G0EiGRgghhBD5X57I0NTr0Ign+nVEKUVifAJfjP6E8GNnAZiw60OS4hKx2WzYLFYmth8BwFMjnqVqk1qEHT3DkqHzAKjfsTFe/t78tDg0xzGVmd6fQi3rYImI5kjLgWnTi/ZuS5HnQ8BqI3rr74RPWIpXnUqUmtQPnWzhVP8ZJJ2+gLNPQcp9/Cb/Pjc2R9c2R0+cyY5ffsPfz5fvP58PQHRMLEPHTOL8xUsEBwUyY9xICvl4o7Vm0gfz2blnHx4e7kx4eyhVKlbg9Nlw3ho7hRSLhXeHD6BmtcpYVcnhtwAAIABJREFULFb6DR3N3Cnv4unhkaO6qtOhES36tUcpRVJ8AqtGL+L8sbP4FitMz5mv4x1QCK01u7/8iZ8/2wBA+xE9qNykJv8dPcvnQz80ltOxEV7+Pmz/H2q/VC5BARSbMgznwn6gNVGrNxC1fA1F3nyJgs3qQ4qF5HMXuDhqJrbYeDxrVSHwvf7olBTOD51CytnzOHkXJPiDUYS/PNph18vzWj0BFJ8yAJ/mdbFERvPv/7F332FNXY8fx983CSSAgMh2gaviqli31boHzloHaqvWUbXuvffee6DWvbVuRXHh3nXvvQeyZAeS3N8fwQjFuogK3995PU8ek5Nzcz+em5uce+65oUZnADT5PMkyuhMKaw3xT4N43H0yhqhYrIvmI/PoP5HjdTzuNon4B89R2NqQfU4/HrQcZrZMOaZ2wqFKMRKCX3O5Undjzl6+uDSrQkJoBACPx63i9YFzZCjuRY5x7TAk6LjTcVpiO1mTZ35vbjQbZda5DplyuVN7cjtcC3hyaPIGTi8w7ju27pmoM60DNon73oXVgZxdEgBAhf6+5KpQmJfXHrKj53wACtT/EWuHDJxZHGC2bG94tK9J1mYVAYi6/ojL3fwoOK09tvmyEbT3PLfHrgUgZ4/6RN14TNCus2bPAFC0agka92qGwSBj0OtZPmIRN89eB6D/sqHkLpKXm2evMan1GNMynWb0IHteD87tP8u6SSsBqN+lEY9vPuLsnlNfJOdnEyM0aWOEJvhxEFN8hzGyRi92zvqb38a1T/b8lKbDGV2zj6kzo7G1JluBnIzy6Y0+QUfmvNmxUFtSplFFApebZ4cM3nCA27+NTFZmW6YgGauV4Fq17lyt3JUXflsAcG1fj9stRvF4+CKcf6sBgHvXRryY/XeqP7x+rlkVv6mjk5X9tWI9pYp5479uEaWKebNo5XoAjpw4w6Mnz/Bft4jhfbsyarKxo7dhqz/9u7Vn3uSRLF29EYB1m3dQu1qlVHdmAEIeBzHTdwTja/Rh96xNNBn3BwAGnZ7No1cwtmovptYfTLnm1XDLnQWNrRVZC+Rggk9f9Ak63PNmw0JtQclGFTj8P7b93pD1eoImLORB7fY8bNIDh19rY5krO9HHz/OgTgce1OtI/IOnZGrnC4BDq1940m4oQWMXkLFJTQAc/2xKyPy1Zv1CTGvtBBC2cT/3fx+erCzLuK68mLiM2z5diAg4gXO7XwBwavszD1qN4Pmov8j0qw8ALl18eTVnvXnbaV0gN34dlaL8+cIdXKnaiytVe/H6wDkA3NvX5WbzMTwcthjX5tWN+bs34unMjWafuBkXHs3eYSs4tTD5QYBBb2D/6NUsrNKP5T8Pp2iLKjjmyYza1gq3gp4sqjEQfYIe57xZUakt+L7RT/yzfJ9ZswGo3RzwaFuDE9UHcqx8H1AoyNa8Mvq4eI5V7Ie9d05UtlaoXTKS8YfcX6wzA3Dl2CX61ejOgJo9mN9nFn9M6GR6bvuCLcztMT1Z/exeHsTHxdOvRndyFc6Nla01GV0cyOX9XdrrzAhAGunQ3Dt3i5iIaADun7tNRjfH99aXDQaUFkoALK3U6HU6qrarw4FluzDo9GbJFHXqGrrwqGRlzs19eD5nI3K8DgBdyGtjngQ9Co0ahZUaWadD7eGGZWYnIk9cSXWOYt6FsLezTVYWeOQE9XyqAFDPpwoHDp8wlh89Sd0alZEkicIF8xEZGcWr4FBUKiWxcVri4rSoVEoiIqM4eOwUdX0qpzofwP1zt4hN3H4Pkmy/iFfhPLl6HwBtdBwv7z7F3i0TskE2bT8LK0v0Oj2V2tXh8LLd/3Pb7w39qzC01+4a1xcdi/buY1SujsQcOwd645FV3MUbWLg5GevodEhWaiQrNXKCHots7qjcnIg9fdlsmSDttRNAzOmr6MMjk5Wpc2Qm+pRxPVFHL2BXo4wxk06PwioxU4Iey+xuWLg7meqaS+Spa+jCIj9c0ZTJEqWpnVwT2+mqWTMBxIRE8PzSPQwJyfeb6KBwXl55AEB8dBzBd55h62rc9xQq48D8m32vZPtanF26x2z73r9JSiVKjSWSUoHSWg2ShFJjCZKEQqVC1hvI3a8RdyZu+CLrf0MbE2e6r7bWAG87l1ePXSI2OjZZfZ1Oj6XGEkmSUKpUGPQGGvVsyt/T1nzRnJ9NNny9WxqVJk45JfWjbyWuHjz/tkCG7isGI8twZPVejqzZhzY6jiuB5xjsP4kbxy4TGxFDDu88+M/a+EWzaXJmxrZkfrL0+w1ZG8/jUUuJuXiH57M3kmNGNwxx8dzvNp1sg3/n6aRVXyxHSFg4zk6ZAHBydCAkLByAl69CcHNxMtVzdXHi5atgmv5ShwGjJ5MQn8DQvl2Zv3QNf7RogkJh/v5sad+KXD94IUV5pqzOZMmfg4cX7qCNjuNa4AX6+k/g1rErxEXE4OGdm4BZm8yeJ6m0sv1UWVzQ5MtF3MWbycrtG1Qj0v8QAKEL1uM+vjeyVsvzvpNx7tuW4BnLv1impNJKOyUVd/sRdlVLEbH3JPY1f8TC3fg+fzV3A9mm9MAQF8/jnlNxH9ial1NWfJVMAG6tfHBuWJ6oS3d5NGIp+tfRPJu1kVwzumGI03K360yyD23J4wmrv1qmf7PP6oRrAQ+eXbhLfHQcdwMv0Np/DA+OX0UbGUtm71wcm7nli6xb+yKMB/N2UP7cHAyx8QQfusTDhbvwGtWCMvvG8WzDEaxzuCEpFERcfvBFMiRVrHpJmvRtjr2TPRNbjX5v3Wd3nhAZEsHYnVM5uvkgbp7uSAoFD67c++I5hc/zUR0aSZJyAjOA0oABOAH0kGX5nVtWkqR2QDuAcpl+IJ9tzo8K813pAvzoW4lJDYeYyiY1HEL4y1BsHe3otnIIL+4+5fbp6+yZv40987cB0Hx8B7ZNXcePvpXIX64wT288xH+2+b8cJaUCZUZbbtTpi413HnLN68PlMu2JvXafG3X7AZChZH4SgsIAiZxzeyPrdDweuQRd8Guz5wGQJAlJkt5bx93NhaWzJwLw6MkzXgQFk9MzG/1HTiIhQUeXP5rjmT1rqrPkKV2AUr6VmN5waLJyS2s1beb1ZNPIZcRFGY+C9s/fxv7E7dd0fHv8p26gtG8lvMp9z9Mbj9jzP7r9JGsNWWYOJmjcfAzRMabyTO2bIOv0RGwPBEB74x6PmvQAwKpYQXSvQkGScJ/aH3TG01f6kHCzZEqRMQ2007897TsT9+HtcOniS8S+U8gJxtGjuOv3uftLHwCsSxRAFxQGkkS2WX2RdTpejFmMLvjLtNPLZbt5Om0DyDJZ+zYl+7Dfud9zDjFXH3C1jvH0uG3J/MS/DEOSILdfL+QEHQ9HLP1i7fRvFtZq6vt1Y9/IlcQn7nun5u/k1PydAPhMaMvhqRsp3KQCOcoVIujGI47P2mq29avsbXCpUZRDxbugex2D91/dcW9QlhtD3nbOf1jRh6u9/yJn95+xze9ByOHLPFl5wGwZkjobcIqzAafwKpGfRr2aMfbXYe+tv3zkItP93osGsWjAXH7u3BCPfDm4fOQCB9bu/SI5P4dsEL9D87GH6KuB9YAbkBnYAPznuJssywtkWS4my3Kx/+rMVGhencH+kxjsPwl7FweyeGWnxfgOzP1jItFJhsDDX4YCEBkSwYWA03gWzp3sdbIV8ARJ4uW9ZxStVZqFnafh7OGGi6fbR/7XPl78ixDCdxlP70RfuI1skFFlsktWx71rY57NWE/mnr48GbOMV6v34tq6tllzODpk5FWwsV1eBYeSKaM9AK7OjrwICjbVexkUjKuzU7JlZ8xfRtd2LVi1YSsN6lSnV6fWzFv86UfZ5ZpXo6//BPr6T8DOxYHMXtlpOr4dC/+YREyS7adQKWnj14uzW45yKeB0itfJWsATJAi69wzvWqVY0nk6Th6uOP8vbj+VkiwzBxOxPZCovcdNxXb1q5ChYgme95n4zsUc/2xKyLzVOHX6lVeTFxO+YTcOzeuZJ9M7fPN2egftvSc8aDGUO3V7EL79MPGPXqSo49LZl6BZa3Ht2pQX45cQtnYPjr/X+WKZdMGvjRMxZZmgVXvJ4J0nRZ0s3RvydPoGsvT05dGo5QSt2otbm1qpWu8PLarQ2n8Mrf3HkMEl43/WU6iU/OLXjatbjnNrd8q5Ka4FPJAkCL37HK+aJdjSaRYO2V1x8HRNVb6kHH8qSOyjVySERCLr9LzceRqH4t+ZnnepUZSIi/dR2miw9nTlYrsZuNUuicLK0izrr9rCh3H+0xjnPw0HFwdT+Y3T13DJ7oqtg+17ln6raNUS3L98F7WNFa4ebszoNIkSNctgqTFPTsE8PrZDYy3L8gpZlnWJt5VAqmaTHlwRwOiafRhdsw8KlZIOfn1Y3GMWQfefm+pYWqlR22hM9/OXK8yzW4+TvU7dnk3YNmUtSgul6RSKwSBjaaVOTbx3Ct99CtsyhQDjOX2FpQpd4hUOAI4NK/L6wD/ow6OM5/QNMhhkFGbOUqFsKbbuMk7g27prHxXLlTaVb9u9H1mWuXjlOhky2JhOTQGcOX8JF6dMeGTLQlycFoWkQJIUxGq1n5zhyIo9TKzZj4k1+6FUKWjj14sVPebwKsn2A2g2oQMv7zwlcNHOd75OzZ6N2TllfbLtJxsMWPwPbj+30d3R3n1M2NLNpjLrskXJ1KYRT/8cgRyXcjvY/VyF6ENnMLyOQtKojV+gBoPx/hfyrdvpXZSOxk47koRLZ19CV+1K9nzGXyoRGXgW/esoJCs1GGRkgwHFF2wniyRfkJl8ShJ781Gy550aVSB8/zlTOyEbzNJO55bvY3HNQSyuOYiooP8efao5sS0hd55x5q9d73z+p14NOTz5bxQWSiRl4r4nm3ffi3sagv0PuU0dFMdyBYm6/RQASaXEo11N7s3ZhlJj8XZKi1KBwsI8syH2Lt/FgJo9GFCzR7LvBM+CObGwtCDyI+ZFKVVKfNrUYbvfJiw1lqZ53QqlApWlhVlymkXiZ8NXuaVRH/uu2SVJ0gCMozIy4Av4S5KUCUCW5dDUhKjdtSE2DhloNvrt1TFj6/bHzsmeDguMw8lKpZLTW49y9dDb+RmFqxXn4eW7vA4KA+DxtQcM3T2FJzcemi77/lw5ZvfEtnRBVJns+P7MXzybspbgdfvxnNKZAvtmYEjQcb/7DFN9hcYSx8aVuN1sOAAvF2wjz4ohpktcP1efYeM5c/4S4eERVP75Nzq2aU7b5o3pNWQsm3YEkNnNhSmjBgLwU+niHDlxBp/GrbHSaBg1sIfpdWRZZv7StUwZNQCAhvV86D9iIjq9niG9O392PoAaiduv0eg2gHH7Ta47kJzF8lKiwU88vf6Qvv4TANgxcQ3XEufYFKpWjMeX7xGRuP2eXHtA/92TeHbjEc/+R7bfG1Y/FMD+5ypob97HerPx6rPgactwGdQBydKCrIuNl4rGXbzBy+HG5yWNGvv6VXjcZhAAYUs3kXXBSOQEHc96T0h1Jkh77QSQbUZvbEoVQuVgh9fxJbycvhqFtQbHFsaRjde7TxC24e0VOZJGjUPDytxvYTzVGbxoC56LhyEn6HjUfbJZMuWa2wO70gVRZbKlyNmFPJmyFrvSBbAukANkGe2TV9zv62eqr7CyxLlxRW40NV5B9mL+NvKuGIycoONOp2lmyQRg42zP79tHoc5ghWwwULx1DRZW6YeLVzYKNShH0PVHtPY3vrcOTVrP3cCLAOSpVpTnl+6bOkRB1x7SJmAcr248Iuj6o/9c36d6fe4OL3ecoszecch6AxGXH/B4xX4AsreuxrN1hzHExhN57RFKK0t+PDiRV/suoIuI+cArf7oSPqX5qUFFdAl64rVaZnZ6+94YtmEsmXNlQWOjYfbJv1jQdzaXDhs/p6q1qMnhvwOJj4vn0fUHqDWWTAiYwYXAf0wXswhpgyR/xGWEkiTdT7z7pnLSSRuyLMv/OUmmvWejNHdi7w9dwreOkELhC1O/dYQUehUb8K0jvFML3aePKH1pthnSXqbIqC83OvG51BZf5kqa1IjRpqGj7CQOWFh/6wgpFInTfesIKSzTxH240jew5uGW909uNLOYeV2+2net9Z+zvur/7WN97AhNfqAjUBZjp+YIME+W5bT5ThIEQRAE4f+Vj+3QLAMigJmJj5sBy4HGXyKUIAiCIAjCp/jYDk1BWZbzJ3kcKEnStS8RSBAEQRCETyQu2/7oq5zOSZJU6s0DSZJKAl/uN6oFQRAEQRA+wXtHaCRJuoxxzowFcFySpEeJjz2AG18+niAIgiAIH5SGL6f+Wj50yunL/VKWIAiCIAiCmby3QyPLcup+DEQQBEEQhC9PjNCkjb+2LQiCIAiCkBqiQyMIgiAI6Z0sf73bB0iSVEOSpJuSJN2RJKn/f9RpLEnSNUmSrkqSZJY/R2+eP5ghCIIgCML/e5IkKYE5QFXgCXBGkqRtsixfS1InDzAA+FGW5TBJklzMsW7RoREEQRCE9C7tzKEpAdyRZfkegCRJa4F6QNLfrvsDmCPLchiALMtB5lixOOUkCIIgCMJHkySpnSRJZ5Pc2iV5OgvwOMnjJ4llSX0HfCdJ0jFJkk5KklTDHLnECI0gCIIgpHdf8ZeCZVleACxIxUuogDxABSArcFiSpEKyLIenJpcYoREEQRAEwVyeAtmSPM6aWJbUE2CbLMsJsizfB25h7OCkiujQCIIgCEJ6Jxu+3u39zgB5JEnKIUmSJdAE2PavOlswjs4gSZITxlNQ91LbBKJDIwiCIAiCWciyrAM6AwHAdWC9LMtXJUkaKUlS3cRqAUBI4h+5DgT6yLIcktp1izk0giAIgpDepaG/ti3Lsj/g/6+yoUnuy0DPxJvZiBEaQRAEQRDSvS8+QjOtf+YvvYpPNnS8WS55N6vlxQZ86wgpTDk77ltHeKeShVp86wgpeMU6f+sIKYRIcd86Qgp2BstvHSGFaFXaayeA1WXCvnWEFFYc/ffVt9/eom5pb98Tvg1xykkQBEEQ0jk57fyw3jcjTjkJgiAIgpDuiREaQRAEQUjv0tCk4G9FjNAIgiAIgpDuiREaQRAEQUjvPvyDd//zxAiNIAiCIAjpnhihEQRBEIT0TsyhESM0giAIgiCkf2KERhAEQRDSO/E7NGKERhAEQRCE9E+M0AiCIAhCeifm0IgRGkEQBEEQ0j8xQiMIgiAI6Z34HRoxQiMIgiAIQvonRmgEQRAEIb0Tc2jECI0gCIIgCOmf6NAIgiAIgpDuiVNOgiAIgpDOyeKH9dJOhyYyLoERe69wNyQKSYJhVQtSOLMDAMv/uc+0wzc50KESDlaW7Lv9gnnH72CvsWBq3SJktLLkcXgMs4/dYkItb7Nlcs6VGd9J7claIAe7Jq/j0MKdpufKtqpBqSaVQJI4tfYARxbvAqBW/6bkLe/Ns2sPWNtrHgA//FwWm0y2pjqpUaxeWSp3qIskSWijY1k3eBHPrj8ko7sjzad2wtbJHlmWOb5mP4eWGNdXt38z8pX35um1h6zsNcf4Oj+XJUMmOw4u9v/kDIPHTuXwsdNkcsjIlpV+ALyOiKTXkHE8e/GSzG6uTBk1AHs7W2RZZtx0P46cOINGo2bMoF7kz5ub+w+f0G/EBBJ0Oob17Yp3wXzodHo69BrMrAnDsNJoUt1WAPkLe7F0hx8DOgxn/86DFCtThF4jupqe98ydnQF/Dufg7iOMnjOUPF45ObLvOLPHLQCgTfeW3L1xj4O7j5glT9GqJWjUqykGg4xBr2fFiMXcPHsdgHINKlK/S0MANs/6myMbA1FZqui1cACZ3J3Yu2IX+1bsBqDtuD/ZtyqAB1fupTpT6WqlaNG7BbLBgF6vx2/4Aq6euUrh0t/Tflg7U71subIxtvN4TgScoN/Mvnh6eXJ6/ymWTFgGQNOuTXhw8yEnAk6kOlOxqiVo0uvXxEwGlo74ixuJ7TRo2TDyFPmOG2evM771aNMyXWf0JHteD/7Zf4Y1k1YC8EuXRjy++Ygze06lOlOpqqVo3rs5BoMBg97A/BHzuXbmGgDOmZ3pNrEbTu5OAAxtOZSgJ0H0mdEnsZ1Os2yisZ2adGnCw5sPObEn9e0EgKQgw1g/5NBgoicNxLLaz6h9GqJ0y8LrdvWQIyMAsCjxE5pGrZCjIoieMgQ5KgKFS2Y0TdoSM3OkebK8Q5H2tchbvwwACpUCh9xZ+Mv7TySFgloLu6O2t+bkpL+5F/APALUW9eDgwCVEvww3bxC1FZZVW6JwygwyxO9ZiiHsBepa7ZHsHJEjQtDumA/aGJR5fsCidD3kuGi02+ZAXDSSvTMWZesTv3OBeXMJZpNmOjQTD16njKcTk+sUIUFvIC5BD8CLyFhOPgzGzfbtF9zaCw9Z2aw0B+68ZNeN5zQt4sGc47foWCaPWTPFhkexdfgyClQrlqzc7buslGpSiRn1BqNP0NF2WX+u7T9HdGgEWQrkYKpPPxqN/wO3vNkIfvCC4o3Ks7DleLNkCnkcxEzfEcRGRJOvgjdNxv3B1J8HY9Dp2Tx6BU+u3kdto6HP9nHcPHKJ8JehZC2Qgwk+fWk6vj3uiZlKNqrAvJbjPivDzzWr0qxBXQaOmmwq+2vFekoV86Zt88b8tWI9i1aup2fHNhw5cYZHT57hv24Rl67eYNTk2axZOJ0NW/3p3609md1dGT99PtPHDmbd5h3UrlbJbJ0ZhUJBt8F/cvLQGVPZ2ePnaVq1FQB2GW3ZenwdJw+dJk++XGhjtfhW/p25a6eRwdYGjZWGQkXys2j6MrPkAbhy7BL/7D0NQDYvD7rN6U3vyl2wsc9Ag+6NGVS7D8gyY3ZO5tze0+QtkZ+bZ6+zdfZGhm8ax74Vu8mezxNJqTBLZwbg/NELnNhzEoAcXp4MmjeQthXbcfHEJTrW6AyAbcYMLDmymHOHzpHDyxNtnJY/q3Vk3KoxWNtao7FS4+XtxZqZa82S6cqxS/RObKfsXh70nNOX7pU7AbB1wWbUGjVVf61uqp/dy4P4uHh61+jGkJUjsLa1xtJKTR7v79g0a4NZMl04doGTe43t5OnlyYC5A2hfqT0Avab1Yt3sdZw/ch6NtQbZIOPp5Ul8XDydqndiTGI7qa3U5C2Sl7WzzNNOAGqfBhiePkKysgZAf+sK0edOkGHo9OT1qtcnclAHLEqUw+LHysQHbEbj25q49YvMluVdzs/fyfn5xoNBzypF8G5bA214NN+3qsaVlQe4u+sMdZb34V7AP3hWKcKrKw/N35kBLCs0Qf/gCvE7/EChBAtLLErURP/oOrozu1EVr4FFCR8SjmxE5V2JuNVjUOYpgsqrJLoLB7D48WcSjm0xey6zEZOC08YcmkhtAueehlG/YFYALJQKbDUWAEw+eINu5fIiSW/rKyTJ1OlRKSTOPQnFyVqNh4ONWXNFhUTw+NI9DDp9snKX3Fl4eOEOCXHxGPQG7p26TqEaJZANMkoLpfH/YKVGr9NToV1tji4LSPEan+v+uVvERkQD8ODcbTK6OQIQ8SqcJ1fvA6CNjuPl3afYu2X6VyZL9Do9ldrV4fCy3Z+dqZh3IeztbJOVBR45QT2fKgDU86nCgcPGo8/AoyepW6MykiRRuGA+IiOjeBUcikqlJDZOS1ycFpVKSURkFAePnaKuT+XPyvQuTdo0YP/OQ4QGh73z+Sq1K3Is8CRxsVp0Oh1qKzWSJKGyUKHXG+jQtw1+k837Ya+NiTPd11hrePMR9H15by4fuUj06yiiI6K5fOQi31cogl6nR61Rm7YhQKNeTdkwebXZMsX9O5Oc8oOxbM1ynAk8izZOiy4xkyRJKC1UGPQGWvRqzoqpK75cJt5munLsErHRscnq63V6LDWWxkwqJQa9Ad+ezVg/bc2Xy5TYTtnyZEOpUnL+yHlTPW2c9p2ZmvdqzsqpK82WScrkhKpIKeID344e6x/cwRD8MkVdWTYgWVggWWpAr0OZtxCG8FAML56aLc+HfFevNLe3Gj8bDDo9KitLlGoVst6ApFTg3aYG5+btMP+KLa1QZP0O/ZWjxscGPWhjUebyRnfNmEd37QTKXIkj/LIMShWoLMGgR5ElD3L0a+TwIPNnE8zmo0ZoJEnaDvz7U+41cBaYL8tyXMqlPt6z17E4WFkybM9lbr2KJJ+rPX0reHHyUQguGTTkdbZLVr918Zx02HgGZxs1o32+p++OC4yvVTg1ET7Ji5uP8enti3XGDCTExeNV0Zsnl+6jjY7jeuAFeviP486xq8RFxJDdOzf7Zm3+IjlK+1bk+sELKcozZXUmS/4cPLxwB210HNcCL9DXfwK3jl0hLiIGD+/cBMzaZNYsIWHhODtlAsDJ0YGQMOMR1stXIbi5OJnqubo48fJVME1/qcOA0ZNJiE9gaN+uzF+6hj9aNEGhME8f29nNiYo+P9GuQVeGeQ94Z53q9Sqzcv46AO7ffkh4SDir9yxm598BZMuRBYWk4MblW2bJk1Sx6iVp0vc37JzsmdRqDACZ3BwJeR5sqhP6IoRMbo6c2nmcsr+UZ+SWCeyYv4UfqhTnwZV7hAe9u5P2ucrUKEPrfr+T0SkjQ1oOTfF8hbo/sWmh8X38+M5jXoe8Zs6uWezfdIDMnpmRFAruXLlr1kwlqpeiWd/m2DvZM67VqPfWfXrnCREhr5m4cyqHNh/EzdMdhULivplGsd4oXb00vye207DfhwGQNUdWoiOiGTR/EG7Z3Dh/9DxLxy81tlPoa2b6z+TAm3aSJO6asZ2sWnQmbvV8JI3VB+tqt67GZuAU5LBgoueMxab7MGJmvr9dzUmlscSjwvccGmIc8by15TjVZ3WiwK8VOT52HYVaVOHGpqPo4uLNvm7J3gk5NhLL6q1QOGfF8PIh8YFrkaztIPq1sVL0a+NjIOG0P5qGPZGjwtHuWoS6dnu0/mn8VJMYofnoU073AGfgzeGOLxAJfAcsBJonrSz/w+f5AAAgAElEQVRJUjugHcCsZpVoXa7ge19cZ5C5ERRBv4r5KOSekYmB1/E7cYdzT8OY+0uxFPVLeThRysP4Jbn92lPK5nDmYVgMy89exU5jQZ8K+bBKckRrbkF3nxHot412KwYQH6Pl2bWHGBInZB2cv52D87cD0Gj8HwRM3UAJ34rkLfc9z248Yv9s83Ru8pQuQCnfSkxvmPzLx9JaTZt5Pdk0chlxUcaj2P3zt7F//jYAmo5vj//UDZT2rYRXue95euMRe2abt3MjSRJS0iG1d3B3c2Hp7IkAPHryjBdBweT0zEb/kZNISNDR5Y/meGbP+tkZeo/sxszRfu8cbQBwcnEkd76cnDj4dm7F5KEzTfenL5vA6L4TadOtBXny5+bU4TNsXrX9s/MkdTbgFGcDTuFVIj+NejVl7K/D/7OuQW9gTtdpAChVSvqvGMaUtmP5bUgrHDM7cWTjQc7tO/Ofy3+s47uPc3z3cQqWLEjL3i3o32yg6blMLg54euXg7KF/TGV+I+ab7o9YPJwZA2bStEsTcubLwbkj59m1ZneqM50OOMnpgJPkK5Ef316/MurXlB2tpJaOfDua1m/RIBYMmMcvnRvhkc+TS0cusH/t3lRnOhFwghMBJyhYoiDNezdnULNBKFQKChQvQJeaXQh6GsSAOQOo0qgKe9btYcGIt1+CwxYPY9aAWfh29iVn/pycO3KOgDUBn51FVaQUckQ4+vu3UOX78AGd7vI/RF02niKzKFcN3YVTKNyzoq7tixwdSeyy2RCv/ew8H5KjahGen7mFNtw4whwfGcv2342nrdX21hTtWBv/P6ZTaUIb1PY2nF/gz4tzd8yybkmhQOGSHe2BNRhe3Meigi8WJXzeUdP4eWF4dJ24Vcb5Wcp8pdHfv4zCwQ1V0WqgjSY+cB3ozN/xElLnYw+Hy8iy3EyW5e2Jt9+A4rIsdwJ++HdlWZYXyLJcTJblYh/qzAC42mpwsVVTyD0jAFXyuHIjKIKnr2PxXXmMmosOEhSppdmq4wRHv93hYhP0bL/2lMaFs+N34jajahTCO7MDu248+8j/1jv+o82r0sN/HD38x2Hn4vCf9U6vP8j0OoOY6zuS2NfRBN97nuz5zAU8QZJ4de85hWuVZEXnGTh5uODk6fbJmco1r0Zf/wn09Z+AnYsDmb2y03R8Oxb+MYmY8ChTPYVKSRu/XpzdcpRLAadTvE7WAp4gQdC9Z3jXKsWSztNx8nDF+TMy/ZujQ0ZeBYcC8Co4lEwZ7QFwdXbkRdDbkYeXQcG4OjslW3bG/GV0bdeCVRu20qBOdXp1as28xas+OUPj339hzd4lrNm7hPyF8zLObzg7Tm+gSu0KDBjfiwo1ypnqVq1bicBdR9C947Rb+epluX7pJtY21mT1yEL/9kOpUrsCGiv1J2cCqNrCh7H+UxnrP5WMSd5TN05fwyW7K7YOtoS+CMHR/W27ZHJzJPRFSPLXae7DkY2B5C6Sl5iIaGZ2mkKtP+p9VqY6LWszd/ds5u6eTSbXTKbyK6eu4JbdDTuHt6OiP9X+ieO7j6N/R1uVrlaK25dvY2VthbuHO2M6jqNsrbKoNZ/eVtVb1GSS/zQm+U/DweVtpuunr+Ga2E4fo1jVEty7fBeNjQZXDzemdZpEqZo/Yqmx/ORMtVvUZtauWczaNSt5O51+207Bz4O5d+0eLx69wKA3cGLPCXIXzJ3sdUpVLcWdy3dM7TSu4zjK1vy8dnpDlbcgFj+UwW7mGqy7DkVVoAjWnQZ+eEFLNZblq6PdswVNw1bEzBuP7uYVLMtW+ews/1aoZRWa7B5Dk91jsHE1fq7nqVuaW9vePRG6eLf6nJ21je/qlebZmVvs7TGfEj1/MVseQ2QYcmQYhhfG0/L62+dQuGRHjokAG+NnFTb2yDGRyRdUWaIqUAbdxYNYlK5L/O7F6J/eQZmvpNmymY1s+Hq3NOpjR2gySJKUXZblRwCSJHkAGRKfS3U31clGjVsGKx6ERuGZKQOnH4fg5WLH/IYlTHVqLjrIqmZlcLB6+6G0/Ox9mnp7YKFUEKczABIKCdOE4s9xfMVejq/48JFcBkc7okIiyJjZkUI1ijOzfvKjxxo9G/H3gL9QWCiREk+jGAwyFlaf/qF6ZMUejqzYA4BDZkfa+PViRY85vLqfvBPVbEIHXt55SuCine96GWr2bMzaAQtRWihNp3ZkgwGLz/yiTqpC2VJs3bWPts0bs3XXPiqWK20qX7NxOz5VynPp6g0yZLAxnZoCOHP+Ei5OmfDIloW4OC0KSYEkKYjVfvqR4vqlm1i/NOVo0/DpAzmy93iyK5Vq/FyFWWP9UtRVqZQ0+6Mx3Zr3IVuObKYRHoVCicrCAmI/Pdfe5bvYu9x4xZmrx9vOo2fBnKgsLYgMi+TSoQv49v0NGzvjPLBCP3mzdsLbuRY2djYUqVyU8c1H8kOV4sZcsvxZX9IA25ftYPsy41yFzJ7upvLcBXNhobYgIizCVFahXgWWjF+S4jWUKiX12/zMkJbDyJIjs6mtlAoFKksV2rhPa6uA5f4ELDdedeeWpJ1yFMyJRWI7fYhSpaRWm7qM+30k7jkyG+dCAAqlApWlBfGfeDpjx/Id7FhubCd3j7ftlKtgLiwsje0U9ToKGzsb7DLZEREaQeEyhbl96XayTPXa1GP478PJnKSdjJk+vZ3eiFv7F3Fr/wJAla8w6tq+xMwZ+8Hl1HV8id+9CfR6JEtLYxsZDGBpnsn4AJeX7ePysn2mx5a2VmQp5cWervNS1LX3dCWDWyaenryOU/7s6LRRIMuoPvO9/U4xEciRYUgOrshhL1Fm98IQ+hxCn6PKX9o4KTh/afR3k5/CVxWrju78fuOcG5VxXieyjKQyYzbBbD62Q9MTOCpJ0puTvzmBjpIk2QBmuQSkX8V8DNx1CZ3BQBZ7a0ZUK/Te+kFRcVx5+Zr2pY1HQk29Pfht9Qls1Sqm1k0xaPRZbJ3t6bZtDJoMVsiyTLnWPkyq2gdtVCwt5vXAxiEDep2eTUOWEBcRY1quQLViPLl8j4jEeQ7Prj2k1+4JPL/xiOfXH6UqU42uDbFxyECj0W0A48S6yXUHkrNYXko0+Imn1x/S138CADsmruFa4hybQtWK8ThJpifXHtB/9ySe3XjEs+sPPylDn2HjOXP+EuHhEVT++Tc6tmlO2+aN6TVkLJt2BJDZzYUpo4xHij+VLs6RE2fwadwaK42GUQN7mF5HlmXmL13LlFHGOS4N6/nQf8REdHo9Q3p3TlU7vY97VjdcM7vwz4mU848at/qFHet3ERer5fa1O2isNKw7sIxj+08SFRH1jlf7NCV8SlOuQQV0CXoStPHM6jQFgOjXUWyeuYFR2ycBsHnGeqJfv11f/W6N2TL7b2RZ5tLh81Rt4cOEPdPZt+rzT1m8UdanLFUaVEan06GNi2dsx7dX5LlmdcE5sxOXTl5OsVzdlnXY+/c+tHFa7l2/j9pKjd/euZw5cJboxInrn6ukTxnKN6iIPkFHvDaeaZ0mmZ4buWEsWXJlRWOjwe/kIub1nc3Fw8YJudVb1OTQ3weIj4vn4fUHWGrUTAmYwbnAf4hJZaYfa/5I5QaV0SXoiI+LZ3wnYzsZDAYWjVnEuDXjkCSJ25dvszvJKbfaLWuz/+/9aOO03E9sp7l75nIm8Eyq2+ldLKv/gqZOE6SMmbCdsIiE86eIXWg8tSM5OKLKlY/ojcsB0AZsxnaMH3J0FNFTBps9yxs5axTj0eHL6N5xQFC6b2NOTFwPwK2tJ6j1V3eKdqzDqSkbzZohPnANlj5tkZQqDK9fER+wFCQJde32qAqWNV62vfPtqVTJxh6lmyfak8ZTzbrzB9A0G4SsjTFeyp3WiDk0SP81xyBZJUlqBAQAOYC6wI/AQFmWz31o2Ri/bmmulYeOT3sz1eNJe8N4U85+3mXdX1rJQi2+dYQUvCydv3WEFEIMqZqr/0XYKdLekW20nPCtI7zT6h9jP1zpK1txNMu3jpBCmx4fnhD9LVj3XPj+iYRmFtWz7lf7rs0wddtX/b99rI+dQzNEluUIwBaoBMwCUo4dCoIgCILw1ckG+avd0qqP7dC8mZRSC1goy/JOIO0dagmCIAiC8P/Sx86heSpJ0nygKjBBkiQ1aeRH+QRBEATh/700PHLytXxsp6Qxxjk01WVZDgcyAX2+WCpBEARBEIRP8FEjNLIsxwCbkjx+Djz/7yUEQRAEQfhqxF/bFqeNBEEQBEFI/0SHRhAEQRCEdO9jJwULgiAIgpBWiUnBYoRGEARBEIT0T4zQCIIgCEJ6J0ZoxAiNIAiCIAjpnxihEQRBEIR07mP+LuP/OjFCIwiCIAhCuidGaARBEAQhvRNzaMQIjSAIgiAI6Z8YoREEQRCE9E6M0IgRGkEQBEEQ0r8vPkJTcPDhL72KT7ZCletbR0hBrdJ96wgplCzU4ltHeKdTl5d/6wgpPKvW7ltHSMHWI+FbR0hB5ZL2/oCePlT7rSO8U5WD+m8dIYWZxH7rCCm4DTn+rSO8U0TPr7s+WYzQiBEaQRAEQRDSPzGHRhAEQRDSOzFCI0ZoBEEQBEFI/8QIjSAIgiCkd2lvetpXJ0ZoBEEQBEFI90SHRhAEQRCEdE+cchIEQRCEdE5cti1GaARBEARB+B8gOjSCIAiCkN4Z5K93+wBJkmpIknRTkqQ7kiT1f0+9BpIkyZIkFTNHE4gOjSAIgiAIZiFJkhKYA/gA+YGmkiTlf0c9W6AbcMpc6xYdGkEQBEFI7wxf8fZ+JYA7sizfk2U5HlgL1HtHvVHABCDuM/637yQ6NIIgCIIgfDRJktpJknQ2yS3pH7PLAjxO8vhJYlnS5X8AssmyvNOcucRVToIgCIKQzn3Nq5xkWV4ALPicZSVJUgBTgd/NmQnECI0gCIIgCObzFMiW5HHWxLI3bIGCwEFJkh4ApYBt5pgYLEZoBEEQBCG9Szt/+uAMkEeSpBwYOzJNgGZvnpRl+TXg9OaxJEkHgd6yLJ9N7YrFCI0gCIIgCGYhy7IO6AwEANeB9bIsX5UkaaQkSXW/5LrFCI0gCIIgpHNp6ZeCZVn2B/z/VTb0P+pWMNd6xQiNIAiCIAjpXpro0JT8sSgX7x1mR+BadgSupUvvdsmeVygUbD+whr9WzzCVTfMbg/+hdfQe1NlU1qlnW6r6VDBLptzTOlL8yiK8D041lXkObU6RIzPwPjAFr8V9UNpZA2BbPC/eB6ZQOGACmhxuACjtrMm/dghIklnyAHhO7kzhC0spsG9GsnKXVrUocHA2BfbPJOuglgBkKOZF/r3TybdzMuoc7omZbMizarhZMyWVv7AXpx8fpHKtCgAUK1OENXuXmG4n7u+nQo1yAIyeM5R1+5fSecDbbd2me0vT859j8Nip/FSrCT//1sFU9joikrbdBlLTtw1tuw3kdUQkALIsM3baPHwat6Z+iz+5dvMOAPcfPqFx6y7Ub/EnF65cB0Cn09O22wBi4z7/5xKUrs64zp+M+9+LcN/wF7ZN6wPgNH4w7mv8cF/jR5YdK3Ff4weAunAB3NctwG3lHFTZjFc8ShlscJkz3rzbz8ISu4l+2E1dhN2MpVg1aQWAqtAP2E1eiN3Uv7AdOwuFmzGDuuYv2M1YQobBE0BlHOBV5SuEdatO5sv0hqTAZtAcrDqNBEDTshcZxizDZvBcbAbPRZE1p3H9RcpiM2wB1r2nINnYGhd1csfqj4Hmz6RQYDtxITb9xxkfurhhO3YudrNWYdNjqKlN1DXqYzdlCRkGjDeVKb0KYdXyC7QTxn3v5ONA074H4JrFhdlrp7Dh8ArWH1qBe1bjZ9OoOUNYs38pHZPtey0on4p9L6lcUztS/PJivAOnmcqy9W1C4f1TKbx3MvnXDsHC1QGATLVK4X1wOgW3jELlkAEAtYcr3/n1NEuWfytbriRHT+zg1Jnd+O9eY1yf2pLAQ5s5dnInp87sZuCg7qb6fy2exvFT/gwd3ttU1qdvJ2rVrvpF8qVK2vkdmm8mTXRoAM6cPE/tik2oXbEJsyYnvxqsVftm3L193/TYK38e4uK01Czvy/dFCmBrmwFnVye8ixZk766DZskTtC6Qa01HJysLP3SJ8xV6cKFSL2LvPSdr118AyNKhLtd+HcO9IUtwa1kNgGw9GvJk5iaQzTcMGLzhALd/G5mszLZMQTJWK8G1at25WrkrL/y2AODavh63W4zi8fBFOP9WAwD3ro14Mftvs2Z6Q6FQ0G3wn5w8dMZUdvb4eZpWbUXTqq1o36grcbFaTh46TZ58udDGavGt/Dv5C+cjg60NTi6OFCqSn4O7j3x2hp9rVsVvavJt9teK9ZQq5o3/ukWUKubNopXrAThy4gyPnjzDf90ihvftyqjJswHYsNWf/t3aM2/ySJau3gjAus07qF2tElYazWdnQ68nbJofzxu24UXLLtg2rodFjuwE9x/N86YdeN60AzH7jxBz4CgAds0bEtRlIGGT52HbsDYA9m1/5fXiNebdfgnxRAztQUTPNkT0bINFkRIov8uPTYeeRE0fTUTPtsQf3o9Vo+YAqH+qQkT31uhuXMHCuwQAVo1aELthufkyJbKs/DOGF4+TlcVtXEj06I5Ej+6I4ck9Y72KdYke24WEIztRlagIgKZeS7Rbl5o9k7pmAwxPH5oeW/3anrgdfxPR5VfkqCgsK9U0ZipXlYjerdHdvIpF4eLGug2aE7fR/O2kUCjoMrgDp5LsewAjZw5mxdw1NPqpOS192hEaEkbufLnQxsbTtPLvFCjshY2tDY4ujhQokp9Dqdj3knq1/iDXmo1KVvZs7lYuVu7Jxaq9Cd37D9l6NgLAvbUPl3z68mLFXpzqGztU2fs149GENWbJkpS9vS1Tp42kSaN2lCxegxbNjQfDWm08tWv+yo+lavFj6dpUqfoTxYt7U6CgF7GxcZQpWZMfin6PnZ0trm7OFCvuzc4de82eT0i9NNOh+S9u7i5UrFqWdSs3m8oSEnRoNGokSUJloUJv0NOj/59Mn+BntvVGnLyOLjwqWVn4oYugN3ZPI/+5hdrdEQBZp0NhpUZppUZO0KPxcEWd2ZGI41fNlgcg6tS1FJmcm/vwfM5G5HgdALqQ18ZMCXoUGjUKKzWyTofaww3LzE5Enrhi1kxvNGnTgP07DxEaHPbO56vUrsixwJPExWrR6XSorZJsP72BDn3b4Dd5UaoyFPMuhL2dbbKywCMnqOdTBYB6PlU4cPiEsfzoSerWqIwkSRQumI/IyCheBYeiUimJjdMSF6dFpVISERnFwWOnqOtTOVXZ9MGhxN8wjgLJMbEk3H+E0sUpWR3rquWJ3h1orKPTI2nUSBo1sk6PKqs7KjcXtP9cTFWOd4qLNf6rVBlvsgyyjGRlHIGUrG0whIYY60gSKFVIag3odViWr0b8uVPIUZFmjSRldEJVqATxR3d9uLIsg4UFWKpBr0eZuyCGiDAMQc/MmymTMxY/lEK7/+1vgakK/kDCyUMAaA/txrJ42cTKGNtSrUbW67H8qSoJ50+bvZ0AfNs04MDOQ4QGh5vKcnzniVKl5NRh44UjsTGxaE37nqVp3zMk7nvzJy82W56Ik9fQhSX/nNJHxZruK63V8KZPbpCRLC2Mn506PbYl85HwKoy4+8/NlueNRo3rsX1bAE+eGN8Xwa9CTM9FR8cAYGGhQmWhQpZlEhISsLLSIEkSFioVer2eQYN7MHbMdLNnMwfZ8PVuadV7OzSSJGV6382cQYoU+56dB9exeO1s8uTNaSofMqYP40fMwGB424p3b98nNDiM7QfWsD/gMB45sqGQFFy9dMOckd7LtWklwg6cA+DJzM18N6sLWbvW5/niXWQf0IyH481/hPEumpyZsS2ZH6/tE8n792isC+cG4PnsjeSY0Q23zg0IWupPlr6/8nTSqi+SwdnNiYo+P7Fh2eb/rFO9XmUCNu8D4P7th4SHhLN6z2IO7zlGthxZUEgKbly+ZfZsIWHhODsZ36pOjg6EhBk/9F++CsEtSYfC1cWJl6+CafpLHRauWMeg0VP4o0UT5i9dwx8tmqBQmK/vr3R3xTJvbrRX3r5f1T8UQh8ahu6x8ecaXi9eg9Oo/ti3akrkui1k7NSa8DlLzJYhGYUCu6l/4bB0CwkXz6K/fZ3oOZOwHTKBjAs3oK5QjdhNxvdOnP9m7CbMQ+HsQsL1y6gr+6Dd9d/b/XNpGncgbuNfKUaj1PV+x2bIPNSN2oPKAgDt7nXYdB+P6vtSJJwORF2rGdqdq82eybpVZ2JXzjf9cT7J1h45JgoMegAMIa9QZHJOzLQZ27FzUTi5ortxGcuKPmgDzN9Ozm5OVPD5ib+XbUlWnj1nNiJfRzFx0WhW7VlE1yEdUSgUPLj9kLCQcFbuWZRk35O4+QX2vX/L3r8ZRc/Ox/mXn3g0aS0AT2ZtosD6YThULUbw5qPGke1pf3+R9efOk4OMGe3ZuWs1h45upWmz+qbnFAoFR0/s4O6DMwQeOMbZsxe5dfMuwcGhHDm+nV279pMzlwcKhYKLF8x7oCqYz4eucvoHY1/6zUn7N58uUuL9nO9aKPFnkNsBONpkxU7j9K5qJlcv3qBckZrERMdSoUpZ5q+YRqUS9ahUrRwhwaFcuXidkj8WTbbMqMGTTfcXrprOoF5j6NijDfkKfMfRQydZt8L8Hx5vZO32C7JOz6uNxiHa6KsPuFTLeL7erlQ+El6GgSSRd34PDAl6HgxfRkLw6y+SRVIqUGa05Uadvth45yHXvD5cLtOe2Gv3uVG3HwAZSuYnISgMkMg5tzeyTsfjkUvQmSlT75HdmDnaD/k/ToU4uTiSO19OThx8+zfIJg+dabo/fdkERvedSJtuLciTPzenDp9h86rtZsmWlCRJSB+Yf+Lu5sLS2RMBePTkGS+CgsnpmY3+IyeRkKCjyx/N8cye9fMzWGlwnjyM0ClzkROPCgFsqlcyjc4AJNy6y4uWXYDEzk5wKEjGOTeyTkfYVD8MoeEpXv+zGAxE9GyLZJ2BDP1Ho8yeA03dRkSO6of+9nU0PzfBulUnYuZOIv7QHuIP7QFA07glcTs2YvFDSdQVqmMIDiJm6dxUnxJTFSqJHBmO4dEdlN99byrXbl6CHBEKKgs0v3XDsnpj4neuQn/9HNHXjQcXFqWqoLtyBqVrFiyrNkSOiSJu3TxI0KYqk8UPpTG8DkN/7xaq/N4frB9/eC/xh42nJTQNW6D134RFkZJYlje2U+zy1LcTQK+RXZk1el6KfU+lVFKk5Pf8WrU1L54GMW7+cOr4+rB1zU6mDp1lqjd12XjG9p1E627NE/e9s2z5AvsewKPxq3k0fjVZutTHvZUPjyev4/XhS1w63BcA50blCdt/Dk1Od7L8WQ/d6yjuD1mMITbeLOtXKZV4FylInVq/obHSsP/ARs6cvsCdO/cxGAyULV0be3tbVq3xI1/+77h+7Rb9+749dbZuw0K6dRlE7z4dKVgoH4EHjrJs6TqzZBPM472HnbIs55BlOacsyzmAokBToBXGnyxu9Z7lFsiyXEyW5WL/1Zlp3rqxaRKwdQZrYqKNQ5IH9x1FpVLhkCkjRUt4U7lGeQ6f28nMBeMpXbY4U+clnyNRxacCVy5ex8bGCg/PrHRp2w+fOlXQWKVivsN7uPhWwKFqUW51mvHO57N2b8jjaX+TvVdjHoxawctV+3BvW/OLZAGIfxFC+C7jaZToC7eRDTKqTHbJ6rh3bcyzGevJ3NOXJ2OW8Wr1Xlxb107Vehv//otpsm/+wnkZ5zecHac3UKV2BQaM75Vscm/VupUI3HUEnU6f4nXKVy/L9Us3sbaxJqtHFvq3H0qV2hXQWKlTle8NR4eMvAoOBeBVcCiZMtoD4OrsyIugYFO9l0HBuDonf6/OmL+Mru1asGrDVhrUqU6vTq2ZtzgVo1wqJc6ThxPtv5/YxLkyACgVWFcqS8yeg+9czL7Nr7xeuJKM7VoQNmMBUZv8sWta/511U0OOiSLhynksfiiJ0jMX+tvGSdHxRw9g4VUwWV3JwRFVHi8STh9FU8+XqCkjMERHofq+6Lte+pMoc+VHVbgUGcYsw6rtAFRehdG07mvszADoEkg4vgelZ97kC1qosShdlfjAbajrNCd26ST0d65gUbJS6jN5FcSy2I/YzVmLTY+hWBQsglWrzkjWGUChBEDh6Iwh9FWy5SQHR5S585Fw5ijqOo2JnjYCOToKVaEfPjtLo9/rs2rvYlbtXUy+wnkZ6zecbafXU7l2efqN70n5GuV4+TyIm1fv8PTRc/R6PQd3HyVvoe+SvU756mW5kWTfG9B+GJVrV0Btpn3vv7zadATHWqWSlSmsLHFpXJEXS3aTrU8TbnedRcT/sXff4U2V7x/H3ydJm7SlhQ7aUkYLiAwRUbZfZK+iDEWWCsgUZO8hyN57b2SoDEFEtICyZMqWXWRD2S3dNG3G+f2REqgFRRJo6+9+XVcvmuc8J/mQ9Jw8uc9zTg6E4/dBJYceq32HFuzZ/xN79v/ErVt32bZ1Nw8eJHE/Kpq9ew9S/PUiafrHxsaze9fv1KiZ9nHrvluDP46dIls2D/IXCObTll1p+H4obi/ofea5yKTgZ7sOjaIo7bB9zXce4A9slyreBzz3xIIVS9awYoltgqafv6+9vcSbr6HRKETfj2HiqJlMHGX7NFHuf6Vo37klvToNfhRep6P1Zx/Rtnk3Qgrks3/g0Wq1uLjo7FMDnCVH1ZLk7tyAk+8PfeKnhpxNbJ8wzDEJaNxcbdcFsKpoX+AOImbzATzffp34fafQ5w9C46rDfD/Ovtz3w6rEbj+CJSbBNp8mNZPGwUxrln7PmqXfp2sfNm0Qu3/dl2Zyb52GNZg5Jv38Jp1Oy0ftm9C9RV/y5s9r/5Sp0WjRubhAkmOfqgGqVCzPhk1badeiCRs2baXqOxXs7SvXbSS0RmVOnA4nW4h6sREAACAASURBVDYP+6EpgEPHTuDv50Nw3twYjcloFA2KoiEp+fkz+X7ZB9Plq8R/sy5Nu6FcKUxXrmF5bID1kMd7NUnaexBrXDyKQW873KFaURyZoPwYxSs7mC22Qyeurri8URrj+m9R3D3QBOXBejMC3RulsURcTbOe+0dtSVppm3ehuOofzbvRO/63nvzDVyT/YDu8pn21BK41P8S4ZAKKl499UKMr+TbWm1fSrOda+0NStv9gOwTkmjpPQ1Vt+Rxk/HYhxm8X2h67WEn09ZvyYMZoPHoNw6V8ZUz7tqOvXAfTob1p1nNr1gbj6r8+T1YU1+d//b5bup7vlqavQA+dNog9v+7jt8270Wg0eHplI4dvDmKiYij9v7c4e/zRIU6tTkvz9o3p3qIf+fLneWzb0+Di4kKyE7a9xxny57LPi/GpXYakCzfSLA/q1IBbi8NQzRY0BlfA9vfk6L5z4YIVLFywAoBXCxdk0pThaLVaXF1dKF3mDWbPWoKvnw9mk4nY2HgMBj1Vq1Vk2pT59vvQ6XR83rk1jRu1peArIY+eq9T7SUpy2pdFCwc964X1ugNlgN9VVa2qKEoRYIyzQoTWq8HHrRtjMVswGo10az/wmdZr0bYJ36/aiDHJSPjpP3FzN7Bp1xp2bt1DfFzCP9/B33h1bg+yv/0aOh9PSh+dz7WJq8nT7X00ri68tnoIAAlHznOxv+2MLI2bKwFNq3K6qa1EeXP+TxT7ZhBqipk/P39yNeffyj+rF54ViqPz8aLEoUXcnLyKyNXbCJnchde2TsdqMnO5x6PH0hhc8W1SjfMfDQPgzoIfKbRiCGqKmUtdJjsl0z/JlSeQgCB/juz/I92yJq0/4Kc1mzAmJXP+zAUMbgZWb1/G3m2/k/Acr1/foeM4dOwEMTFxVG/4CZ+3bUG7Fk3oPWQM3/+0haBAfyaPtB0arFShDLv3HyK0SRvcDAZGDuppvx9VVZm/dBWTR9r+Dj9sEMqA4RMwWywM6dPliY/9T/Qli5PtvZqknL9kPzU7etYSjHsP4lGrSprDTQ8pBj3Z6tXmTmfbocO4b9biP2MMqtlE5CDnbH4ab188ug0CjQY0Cil7d2I6vJ/EOZPI1m8kWK2oifEkzhpvX0ebvxAAlkvnAUjetRWvaV9hjbyLcf2Lmzvm1rY/imd2QMEScRHjN48OWyrZfdCGFCblJ1sFLWX7BjwGzUR9kEDS3OEvLFPS1/Px6Pklbs3bYrl8nuTtj64lpg2xzWezXLY9Tym7t+E1eQnWqHsYN6x6YZkArFYr00fMZu6aaSgKnD3xZ5rDuLZtbzPJScmcP3MRg5uBVduXPve297hCc3ra952ljizg+qTVeFd/C7eCQahWleSIe1zq/2jA4BLgjeebhYiY8h0AtxeHUWLTBMyxiYS3Gf+0h/nX/jx3ka2//sb+A2FYVSvLl67h7Jk/ea14EeYtmIhWq0WjUVi/LozNm7fb1+vwWQu+/fZ7kpKMnDoZjru7G/sPbuKXLTuJjXX+JO/nlZkn674sytPmPaTppCiHVFUtoyjKH0A5VVWTFUU5rarqa/+0bgG/NzPP5QtTrdAVzOgI6eh15oyOkE4HS/qKQWZw4KTzT3111M1aHf6500vmGWzK6Ajp6PwzUYk+leW+c6sRzlJzT/pDtBltBv4ZHSGdOnHHMjrCE8UlXnoxF/x6isjQyi/tvdZv028v9f/2rJ61QhOhKEoO4AfgV0VRooGr/7COEEIIIV4GqdA824BGVdWHMxCHKYqyA8gObH5hqYQQQggh/oV//eWUqqr+9iKCCCGEEOL5yByaLHClYCGEEEKIf/KvKzRCCCGEyFykQiMVGiGEEEL8B0iFRgghhMjipEIjFRohhBBC/AdIhUYIIYTI6tRMea27l0oqNEIIIYTI8qRCI4QQQmRxModGKjRCCCGE+A+QAY0QQgghsjw55CSEEEJkcapVJgVLhUYIIYQQWZ5UaIQQQogsTiYFS4VGCCGEEP8BL7xC8z+P/C/6If41X/2DjI6QjqJRMzpCOkWScmZ0hCe6WatDRkdIJ+iXBRkdIR3zzm8zOkI64b0OZXSEdNzdM+fnugauPhkdIR0ffXRGR0inpb5MRkfIFFS5sJ5UaIQQQgiR9ckcGiGEECKLkzk0UqERQgghxH+AVGiEEEKILE6uQyMVGiGEEEL8B0iFRgghhMji1Mx3ouxLJxUaIYQQQmR5UqERQgghsjiZQyMVGiGEEEL8B0iFRgghhMjipEIjFRohhBBC/AfIgEYIIYQQWZ4cchJCCCGyODltWyo0QgghhPgPkAqNEEIIkcXJpGCp0AghhBDiP0AqNEIIIUQWp6pSoZEKjRBCCCGyvExRoXm7YSXe7dgQRVEwJiax9IsFXDt7BYA6bd+jcrMaoML18Kss7DsLU7KJTtN7kKdwPv7YdoTvJn4DQIOuHxJx7hpHfjnocCZdoB+5JvRG5+cNqkrM6s1EL99Azn5tyFatHGqKGdP1W9waMBVrfCJubxUjYFhnVJOZm73GY7p6E42nB7mnD+R62yFOmYKuC/Qj1/g+aH1TM63ZRMyKDeTs2xaPquXAZCbl2i1uD5piy/RmMQKGdUE1mbjZ+1GmoGmDiGg32GnT4kvVLEvj3s2xWlWsFgsrhi/h3OGzALzTqCrvd/0QgPUz17J73Q50rjp6LxyITy4/fl2xia0rNgPQbmwntn6zhSunLjmURxuQE78R/dGkPk8J3/9M/Mr1+I0bjEtwHgA0ntmwxidwq3lH9G+8hs+g7qgmE5EDx2C+fgMlmwc5xw/hbpeBDj1Pg8dMYdfeg/h45+CHr+cBEBsXT+8hY7l5+w5BgQFMHjmQ7F6eqKrK2Gnz2L3/EAaDntFf9KZY4Ve4fDWC/sPHYzKbGdqvGyWLF8VsttCx92Bmjh+Km8Hw3Pmu3Iul36rd9ts3ohPoVP0N7sY9YFd4BC5aDXl8PBne6G283Fw5dvUuYzYcwEWrYWzTdwj28yIuKYV+q3Yxp1V1NBrHPyXmm9gVr+qlMUfFEl6zGwAhs/uiLxAEgNbLA0tcIudCe+JRugh5R3dCNZm50mUSyVduofXyIGROXy62GO60v/HMuu35FsxF/UmfEfhaCDsmreH3BWEAaPUutFozBJ2rDo1Oy9mwg/w2dR0ADad/jn/hvJzfdowdE9cAULFrQ+6du865X444lCcz7jcfKt2gIjU71re/z6wevJgbZ6+SI5cvLad0xtMvO6gqe1duY+dXmwBoMOAjilUuScSZq6zoPRuAMg0r4uHjxc4lYU7L5gyqNaMTZLxMMaC5d/0Oo5sM4UFcIiWqvEmbsR0Z1nAA3gE+1Gr9Lv2rd8eUnEKX2b0pX68iV05dIsWYwhd1etH/66G4ebrj6qanYMlCbJi51imZVIuFu+MWkXzmIhoPN0K+n0Hi3qMk7j3GvclLwWIlZ5/W+H7WhHuTvsKnzftEtP8SlzwBeDevy91xi/D9vBlR81Y7baNULRbujl9I8pmLKB5uhKybwYN9x0jcd4x7U74CixW/3m3w6dCUyMlL8G79AREdvsQldwA5mtXl3vhF+HZqTtT8VU7dUZzae4Ijv9oGkXmLBNN9dh/6VO+KR/ZsNOrRhC/e6wuqyuifJ3H014MULluMc4fPsmHWOoZ9P5atKzaTr2gIilbj8GAGAIuF6KnzSAm/gOLuRq5v5mL8/QiRA0bZu3j3/AxrQiIAXi0+5G7XQeiCAvH88D2ip84ne7uPiV2y0uHnqWHdmnzUqD6DRk6yty1asYbypUvSrkUTFq1Yw+Kv19Dr87bs3n+IaxE3CVu9mBOnwxk5aRYrF07juw1hDOj+GUG5Ahg3bT7Txgxm9fqfeK9WNYcGMwAhObOzput7AFisVmqNX0e1Ynm5EhlHt1pvotNqmLb5KEt+O0WPOm+xYs8ZZrWqxs3oRNYe/JPedUuzaOdJ2lYu7pTBDEDUd9u4t+xngqf2sLdd6TzR/nvuwa2xxD8AwL99Qy62GoFrXn/8PqnDjVFfEdC1MXdmrXXq33hm3faSYhLZPHQ5RWqXStNuSTaxovloTA+S0ei0fLr2Sy7sPI4pKRmzMYUFdQby8dcD0Hu64eKmJ3fJguyZ+YPDeTLjfvOhqOt3mdZ0OElxiRSrUpLmY9szqeFgrGYL349aQcTpy+g9DPTfOJbw3SeIuXOfvK/lZ2xoPz4a9xlBhfNy78ptyjeuwuxWY52aTThHpjjkdP7IOR7E2d5cLhz9E+9cvvZlGq0WV4MrGq0GVzc90XfuYzGbcTW4oigKWp0Wq8VKo17NWDd1tdMyWe5Fk3zmIgDWxCSSL15DF+DHg73HwGIbCicdD0cX6AeAaraguBlQDHpUsxmXvIG45MrJg4MnX0gmNTGJ5IvX0QX48mDvUXsm4/FwXOyZzChuehQ3ParJgkveXOgC/UhyYiaA5AdG++8GdwMPd0MlKpfk5O7jJMYmkBiXyMndxylR5U0sZgt6gx6ti9a+XuPezflu0rdOyWOJvE9K+AUA1AdJmC5fQ+vvl6aPe83KJG7eYetjtqAY9KmvnQVdnlzoAv1JPnLc4SylS75Odi/PNG07du+nQWgNABqE1mD7rv229j2/U79OdRRF4Y3iRYmPT+Be5H10Oi1JxmSMxmR0Oi1x8Qns3HuA+qHVHc73uAMXb5PHx5Mg72y8XSgInda2eyiR1487qdunTqvBaLKQZDKj02q4HhXP7dhEyhQIdFqOxINnsMQkPHV5jvcqEr1hF2B77TRuejSpr51rcCCuQTlJ+P2U0/JA5t32HkTFcevEJSwmS7plpgfJAGh0WjQuWlRVxWq2oDO4gqKgSd13Vu71ob1646jMuN986PLRP0lK/Tu+fPQ8OQJt7zNx92KIOH0ZgOREI7cv3iBHoA+qVbXvo1zdXLGYLVTvUI/flm3Gak7/fGc0q6q8tJ/MKlNUaB5XpVkNTuw8BkD0nfuELdjAtP3zSTGmcGr3cU7ttr3JxEXFMfLnSexd/xsBIYFoNApXnfHp/glccvtjKFYQ4/HwNO05GtUiLsy2Y42av4agCb2xGpO51XcS/gPacW/q8heSB0CX2x9D0YIYj59L0569US3iw34D4P6CNeQa1wc1OZlb/SaRs187Iqe/mEyla5ejWb9P8PLLzsTWowHwCfQl6lakvc/921H4BPpy4Od9VPygMiN+GM9P83/grRpluHLqEjF3o52eS5srANfCr5B86tFrp3/rdSz3ozFfvwFA7JKV+I0cgGpMJnLIOLx7fkbM7K+cnuWhqOgYcvr5AODn601UdAwAd+5FEfjYwCvA34879yJp/kE9Bo6ahCnFxJf9ujF/6Urat2yGRuPczyNbTlwhtERIuvYfjlygdmp7m0rFGbx2L3qdltGN/8eUTUfoXKOkU3P8HY+yxTBHxpB85RYAd2avJXhqD6zGFK72mEruwa25NfHrF5ohs217T6NoFNr9NBqfkAAOL/+Vm3/YBhoPouJo//NoTq7fg09IIIpG4fapK05//My433zo7aZVObPzj3TtPnlykqdYfq78cYHkRCOnd/zBgLDxnNt7iqS4B4SUfIXNM79/4fnE83mmAY2iKDuAdPU/VVWrPaV/B6ADQDmfkhTKlv+ZwhStUJxKTaszqtEgANy9PChVqyy9KnbiQVwiXef04e33K7Fv/S6+GbHEvl6vxQNZMnAe9bs0Il/REE7tPs7OVVuf6TH/ieJuIPfML7gzZgHWxCR7u2/HpqgWC3E/2j7lJ5+9xNUmvQBwK10c8937oCgETRuAajJzd9wiLFExzss0YzB3x87HmvjA3u7zWTNUs4W4jamZwi9xrVnPR5nu2TLlmjIAzLYSurMyHd5ygMNbDlCkbDEa927OmI+HPbWv1WJldrepAGh1WgasGMrkdmP4ZEhrfIP82L1uJ0e3HnI4k+JmIOekodyfPAf1sefJo3Y1e3UGwPTnRW636gqkDnYi74MCfuMGo5rNRE+Zh/W+c56ndBkVBUX5+088uQL9WTprAgDXIm5y+24kBULyMmDEREwmM13btyAkXx6HcpjMFn4Lj6Bb7TfTtC/ccRKtRkPdN2zbcJEgH1Z0DAXgyOU7+Hm6AdBv1S50Gg2965bCN5ubQ1n+jneDSvbqDEDSmcv82bAfYBvsmO5Gg6IQMrsvqsnMjVFLMEfGOu3xM+O29zSqVWVh3UHovdxpsqAnOV/Nw70/I/hlxKMBX9PFvfl54GIqdmlAQNF8XNp9imOrdvzNvT6bzLjffKhQhdeo0LQaUz/8Mk27q7uednN7sW7EMowJtsxb5//I1vk/AvDRuM/4acp3VGhajaLvlOBG+DW2zMo8gxs5y+nZDzn1Afqm/gwB/gAOP62zqqoLVFUtrapq6acNZmq0rMOosMmMCptMDn9v8hYJpu34z5nWbiwJqeXm4hVLcO/6HeLvx2ExWzi0+QCFShVJcz9v1SzD5ZOXMHgY8A8OZFbnyZSpWwFXg+sz/tf+hk5L7plfELtxJwm/7LM3Z3+/BtmqluVm74lPXM3v82ZEzlmJX5ePuDthCTFrNuPdsr7jeR5mmjGYuI07SPj1USav1Ey3+k544mq+nZoTNfdb/Dp/zL1JS4j5bjPeLRo8d4yaLUMZEzaFMWFTyOHvbW8PP3gG/3wBeHp7cv92FL65HlUcfAJ9uX87Ku39tAhl97odvPJmYR7EJTKj82Tebf/8uex0WnJOGkZi2DaStu951K7V4F6tIg9+2fnE1bK3/ZjYhV+To0NLoqcvIOH7MLyav+94nsf4eufgXuR9AO5F3scnR3YAAnL6cvvuo4rWnbuRBORMe6hs+vxldOvQkm++20CjerXp3bkNc5d843CmPX/epEiQT5rByIajF9l9LoIxTSqmG3SpqsrCnSfpULUE87Yfp0ftt/igTCG+3Rf+17t2Hq2GHHUqEL1xzxMXB3Zrwu3pqwns0YwbY5YStfIXcrau57zHzyTbXumWNWkfNob2YWPI5p/jH/snxz3gyr4zFKxSIk37qzVLcevkZVw9DHgH+7Ou80yK1i1rOyTliEy036zUohYDwsYzIGw82f29CSqSj4/GdWBB+4kkPnZYU6PT0n5ebw7/sIfjW9KfVJLntRBQ4O6lm7z1bnmWdJlGzuAAcoY471CrcNwzVWhUVf3r1Pe9iqI4dCrR1uWb2brcdnaLb5Af3ef3Y37P6dy+fMveJ+pmJAXffBVXgyspxhRe+9/rXD550b5cq9NSu+17TP50NIH5g+yTyDRaDTpXHSnGFEcikmtMD1IuXif6q/X2No93SuHT/kOufdwP1Zicbh2v96uT8NshrLEJaNz0YLWCqqIx6B3K8lDgqB4kX7xO9NJHmdwrlsKnbWOut3hKpoY1SEzNpBhSM1mttt+f06/LN/HrctuZAAHBjzbqkOIF0Lm6EB8dz4nf/qBpv0/w8PIA4PVKJVk1/tGnQw8vD96sXopxLUbwVo0yqKoKquqUwajvl30wXb5K/Ddp5wYYypXCdOUalscGDvY879Ukae9BrHHxqc+TCqoVxcGJt39VpWJ5NmzaSrsWTdiwaStV36lgb1+5biOhNSpz4nQ42bJ52A9NARw6dgJ/Px+C8+bGaExGo2hQFA1Jyelf839r84nL1HnscNPeP2+wbNdpFrWvhZtr+t3ExmOXqPhqbrK76zGmWNAoChoFjCazw1mexrPiGxgvRmD6y6AYwOfDqsTtOILFvt2pqKpq+91JMsu2d3j5rxxe/uvf9nH38cRitpAc9wCd3oUC7xRn39yf7Ms1Oi3l2tZh5acT8c0faJ9/q2g1aF11mB3Yd2am/eauFb+wa8UvAHgH+dJ+Xm+W95zN3cfeZwA+Ht+R2xdusH3xz0+8n/d6NWHlwIVoXbQoqYd6rVYrrk78+3KUXCn42Q85+Tx2UwOUBrI7K0TD7k3I5u1Jq5EdALBYLAyt14+Lf5znUNh+Rv48CavFypXTl9jx7S/29Wq0DGXP2p2kGFO4dvYKrgY9Y7ZM5fiOozyIe/C0h3smbqWKkb1hdYzhlwnZMBOAe1OWETC4I4qrC3mX2uaJJP1xjjtDZwGgGPRkf78G19sMBuD+V+vJs3AEmEzc7P3kT2//KtNbr5G9YQ2Sz13Gfb3tMSOnLsP/C1umPEtsmYzHw7kz7C+Z2n4BQPTS78mzYITtNMk+4x3OBFA2tALvNKqC2WTBlJzCzM6TAUiMTWD9jO8YudH2iWz99DUkxj76VPR+9yb8MGstqqpyYtcxarYMZfwv09j6zRaH8uhLFifbezVJOX+JXCttp0pHz1qCce9BPGpVSXO46SHFoCdbvdrc6dwfgLhv1uI/Ywyq2UTkoDHPnaXv0HEcOnaCmJg4qjf8hM/btqBdiyb0HjKG73/aQlCgP5NH2g6xVqpQht37DxHapA1uBgMjB/W034+qqsxfuorJIwcC8GGDUAYMn4DZYmFIny7PnQ8gKcXE7xduMbhheXvbuI2HSLFY6LjEdui2RF4/+/KkFDM/Hr3I3Na2ic0tKhaly/LttlO5m1R0KAtAyMzeZKtQHJ23F68dWMytKSu5v3or3vXfIfrH3en6KwZXfD6szoVPhgJwd9EGCi4bYjuVu+sUh/NA5t32PHJmp93GUeizuaFarZRrE8rcGv3I5p+DBlM6omg0KBqFMz8d4Pz2Y/b1SresyfG1uzEbU7hz9houBlc+2zKOCzv+INmBfWdm3G8+FNrtQzy8s9F0VFsArGYLE+oPokDpwpRrVIkbZ68yIMz2uvw4YaV9jk2JWqW5dvISsalz/G6cucKgzRO5EX6NG2evOi2fcJyiPsOpcYqiXMY2h0YBTMAVYISqqk+u/T6mRfAHme47QL/QG/+500umaDLd08TwJOdWJpxlrG98RkdIJ+iXBRkdIR3zTuecNeZM4b0cnxvlbO7ujlVyX5Q1ST7/3Okla6x3/sR9R81K8fznThlg1pXVL7VkcrZQ3Zf2JlL0fFimLAc96xya/kBJVVXzAyuARMCxEogQQgghhJM864BmsKqqcYqiVASqAYuAuS8ulhBCCCGyIkVR6iiKck5RlAuKogx4wvJeiqKcURTlhKIo2xRFCXbG4z7rgObhVYTeBRaqqvoz4ITTiIQQQgjhKNWqvLSfv6MoihaYDYQCxYDmiqIU+0u3Y0BpVVVLAGsBp0yWetYBzQ1FUeYDTYEwRVH0/2JdIYQQQvz/UBa4oKrqJVVVU4BVQJrrFKiqukNV1YfTVn4HHLuQVqpnHZQ0AbYAtVVVjQF8sF2TRgghhBAZ7GV+9YGiKB0URTn82E+Hx6LkBq4/djsite1p2gKbnPEcPOt1aB4A3z92+xZw6+lrCCGEEOK/SFXVBYDDp3YqivIJtsvAVHY4FJnwu5yEEEII8e9koq8+uAHkfex2ntS2NBRFqQF8AVRWVdXxq4Mi82CEEEII4TyHgEKKouRXFMUVaAb8+HgHRVHeBOYD9VVVveusB5YKjRBCCJHFPcM1cl8KVVXNiqJ0wTbvVgssUVX1tKIoI4DDqqr+CEwEsgHfpX5P3DVVVR3+wkMZ0AghhBDCaVRVDQPC/tL25WO/13gRjysDGiGEECKLs2aeOTQZRubQCCGEECLLkwqNEEIIkcVlorOcMoxUaIQQQgiR5UmFRgghhMjiMstZThlJKjRCCCGEyPKkQiOEEEJkcXKWk1RohBBCCPEf8MIrNAmq6UU/xL/m4mrJ6AjpRMe4ZXSEdKIUY0ZHeCLP4Mz3N2Xe+W1GR0hHV+WjjI6QTor5aEZHSCc+JltGR3iiS4bMt/1divXK6AjpRGXC5ykjyFlOUqERQgghxH+ADGiEEEIIkeXJpGAhhBAii5NJwVKhEUIIIcR/gFRohBBCiCxOrqsnFRohhBBC/AdIhUYIIYTI4mQOjVRohBBCCPEfIBUaIYQQIouTC+tJhUYIIYQQ/wFSoRFCCCGyOGtGB8gEpEIjhBBCiCxPKjRCCCFEFqcic2ikQiOEEEKILE8qNEIIIUQWZ5VLBUuFRgghhBBZn1RohBBCiCzOKnNopEIjhBBCiKwv01RoytYsR/M+H6NaVSwWC0uGL+LsoTP25W7Z3JixbQ4Ht/zOwi/no3PVMXDRYPxy+bFpeRibV4QB0GlcZ7Z8vZlLpy46lEcbmJOAMX3R+uYAFeLWhhH79Q/25dlbNcKvbwcuV2yMNSYOjxoV8enSEktsPLe7DcMaG48uby58u7fmTp8xDmV5KGRSF7LXKI05MpbTNbrb2/1bv0vOVqFgsRK7/QgRo5eRrXQR8o3tiJpi5lKXySRfvoXWy4MCc/ty/pPhoDrvgGuFWuVp2aclqtWKxWJh3rAFnD50mjcqlOCzoR3s/fIWzMuYLuPYv2U//Wf0I6RICAe3HeCr8csAaN6tGVfOXWX/lv2OBXJxxWv0DNC5gFaLaf9vJK36Ct3rb+HeqhNoFFRjEokzxmG9fQN93Q/Q166H9d5dEsZ9AWYzuqKv41q+Eg++mu1YlsdcuRdLv1W77bdvRCfQqfob3I17wK7wCFy0GvL4eDK80dt4ubly7Opdxmw4gItWw9im7xDs50VcUgr9Vu1iTqvqaDTP94ls8Jgp7Np7EB/vHPzw9TwAYuPi6T1kLDdv3yEoMIDJIweS3csTVVUZO20eu/cfwmDQM/qL3hQr/AqXr0bQf/h4TGYzQ/t1o2TxopjNFjr2HszM8UNxMxie+3nKP6Uz3jVKY4qM5WS1HmmWBX5Wn+Chn3KkeCvM9+PxrluePH2bYY5J4HybcZijE9AHB5B34Cdc6Dj5uTP81atTO+FTsxSmyFiOVOkNgF+98gT3aYJ7odwcCx1IwvFLAHiVKcwr49ujmsyc7TgN4+XbaL3cKbagFyebj3bqtle+wTvU7fg+igLGxCSWD17A9bNXCSwQRKdZvez9cuYNYP3UVfy65GcaD/iE1yu/xbUzykYI3QAAIABJREFUl1nUeyYAFRpWIpuPJ78u+dlp2QA8CubizQWP9lVuwf6cn/Aden9vclZ/g7hTVznRdQ4AQY0q4urryZUFm5ya4aFSNcvSpPdHWK0qVouF5cMXc+7wWQAGLPuSV94szLnDZ5jYZrR9nc7Te5KvcDBHtx1m9cSvAXi/a2Oun7vG4V8OvJCc4vllmgrNib3H6Vm7G71CuzOrzww+H981zfKP+nzCmQOn7bffrPwWZw+doUetrlT5oCoAIUVD0Gg0Dg9mADBbiJy4gOsNOhDxUXe8mtXDpUA+wDbYcX/7LUw379i7Z/+4PhHNuhL33c9ke9eWx6drK+7PWOp4llSR323n/Ccj0rR5vl2cHLXKcqZWD05X78btebZBV8BnDTjfciTXhy0m5yd1AMjVrTG3Z6116g4V4NieP+hU63M+r9OFKb2n0nOCbQd2fP8JPq/Thc/rdKF/swEkG5M5+ttR8hcJIdmYTKdan/NqiVdx93THx9+bIiWLOD6YATClEPdlT+J6tSWuV1tc3iyL9tVieHTsRcK0UcT1akfKrm24NW4BgL5SDeJ6tMEcfgqXkmUBcGvckqTvljue5TEhObOzput7rOn6His718XgoqVasbyUfyUXa7vV47tu9Qj282LJb6cAWLHnDLNaVaPvu2VYe/BPABbtPEnbysWfezAD0LBuTeZNGZWmbdGKNZQvXZKw1YspX7oki79eA8Du/Ye4FnGTsNWLGdavGyMnzQLguw1hDOj+GXMnjWDpt+sAWL3+J96rVc2hwQxA5OodhH88Ml27a5Av2Su/QXLEPXtbYJu6nK7bj7srfsH3/UoA5On/EdfHf+tQhr+6s3onp5qPTtOWGH6dM20mEfv72TTteTrW49THY7g4ZClBrWoBkK9nI67N+N7p217k9buMazqEIXV68ePMtbQa2xGA25duMrRuH4bW7cOw9/qRYkzm6JaDuHm6E/xaAb4M7YXFZCZP4Xy46F2p2Lgq25dvdmo2gMSLt9hTfYDtp+ZArEkp3A47RPYSIeyp2h/VZMazaF40BhfyNK/C1SW/OD3DQ6f2nqB/nR4MrNuT+X1n0n58Z/uyjQt+YE7PaWn65ysSTIoxhf51elDwjVdw83Qnh783BUu+mikHMyrKS/vJrP5xQKMoilZRlG9edBDjA6P9d4O7Ps2GX+D1gmT3y8Efu47Z2ywmC3o3PVoXLQ+f3+Z9PuHbyc6Jaom8T8rZCwCoD5IwXbqOLsAPAL9+nxE1ZXHanZNVRXF1QTHowWzB8FZxLJHRmK7ddEoegIQDZzDHJKRpy9kilFuz16GmmAEwR8XaMpssaAx6NG56VLMZfXAgrkF+xO8/5bQ8D6V97QyoT9hpV6z7Dod2HCbZmIzZbEFv0KMoCloXHVaLlZa9W7Biygonhkqy/avV2X5UFVQVxc0dAMXdA+v9KFsfRQGtDkVvAIsZ18q1SDl6ADUh3nl5/uLAxdvk8fEkyDsbbxcKQqe1bYol8vpxJy4RAJ1Wg9FkIclkRqfVcD0qntuxiZQpEOjQY5cu+TrZvTzTtO3YvZ8GoTUAaBBag+27bAPLHXt+p36d6iiKwhvFixIfn8C9yPvodFqSjMkYjcnodFri4hPYufcA9UOrO5QNIP7AGczR6Z/74GFtuD5qRZrtTk3d7jRuetubY9mimO7GkHz5lsM5Hhf7+1lMf9n2ks7fIOli+u3baragddOjdXPFarJgCA5AH+RH7L4z6fo66sLRczxI/Xu5ePRPfAJ90/Up9r/XuXv1DlE37qFarbZ9JuDqpsdstlCnQ322LtuExWxxer7H+b3zOolX7mCKTkDR2Q4OaN30WE0WCnSqx9XFm1FfYIbkx/ZTencD8Ojv6PTeEyQlJqXpbzZbcDW42vZTOtt+qnGv5qyduvKFZRSO+cdDTqqqWhRFCVYUxVVV1ZQXGaZc7fJ80r8V2f2yM/rT4QAoikLrwW2Z1n0yJSqWtPf9Y/cxKn9QlfE/TOKH+espU7Msl05dJPrOfafn0gUF4Fq0IMYT4bhXrYD5biQp5y6l6RO9aDVBC8dhvhfF3QHjCZgymDt9xjo9y18ZCgThWa4Yuft/gpqcwvWRS3lw/AK3Zq0j//TuWI0pXO4+jbyDP+XGxBc3Ln27ztu06f8pOfxyMKTVl+mWV6lfie8Xrgfg+oXrxEbFMnvTTLZ9v52gkCAUjYYLzqisPaTR4DVpAdrA3Bg3/YDl/FkSZ0/Ec8h4SE5GTXpAbP9OABjD1uM1fi6W65cxnT2J56AxxA/v47wsT7DlxBVCS4Ska//hyAVqp7a3qVScwWv3otdpGd34f0zZdITONUqmW8cZoqJjyOnnA4CfrzdR0TEA3LkXRaC/n71fgL8fd+5F0vyDegwcNQlTiokv+3Vj/tKVtG/ZDI3mxRR9vWuXIeV2FA/OXEnTfnPW9xRdPYyUO/e52GU6hRb04XynKS8kw7O6PmM9hWd2wWpMIbzLTAoMbcmVcS/+TbBS0+qc3HksXXu5ev/jwI97ADAmGjmx4yjDwyZxZu9JkuISKVCyEBtnrn3h+XK9X4Fb6/dhSTRyb9sxKm4bR+TuU5jjHpCj1CtcmPr9C89QunY5mvVrQXa/7ExoPepv+968EEF8VBxjfp7CnvU7CQzJhaLRcOXUpb9dL6PIVx88+xyaS8BeRVF+BBIfNqqq+sQ9h6IoHYAOACW9XyckW/AzPciBLb9zYMvvFCv7Gs37fMKwj4ZQp2Vdjuw4TNTtqDR9rRYrU7tNAkCr0zJ0xXDGtBtN6yFt8cudk53rtnPo14PP+N97OsXNQODUIUSNnwcWC97tm3Grw8B0/ZL2HyVi/1EAstWvwYNdh3AJyU2OTz/EGpdA5Li5qMZkh/Oky6fVoM3hSXi9fniULETBuX05+fZnJJ25THj9/rY85YphuhsNKBSY0wfVbOb6iK8wR8Y6Lce+zfvYt3kfxcsVp1Wflgz4aJB9mY+/NyFF8nP4tyP2tnnD59t/H75kGNMHzqB512YUKJqfo7uPsWmlg+Vvq5W4Xu1Q3LORbcAotPnyY6jfmPiR/bGcP4uhYTPcW3fmwZyJpPz2Cym/2UrdhiatMP60Dpe3yqGvUhtr5F0eLJ3j1EMFJrOF38Ij6Fb7zTTtC3ecRKvRUPeN/AAUCfJhRcdQAI5cvoOfpxsA/VbtQqfR0LtuKXyzuTkt10OKoqAof19WzhXoz9JZEwC4FnGT23cjKRCSlwEjJmIymenavgUh+fI4JY/GzZWgro0Ibz4i3bK4Xcc5tes4AH4fViFm21HcCgSRq2MDzLEJXP1yMdakF/o5LJ3E01f4490vAMhevigpd6JRFIUi83uimsxcGrYckxO3PYAiFYrzTtPqjPnwizTtWhcdJWuUYe2ERx9mNs3fwKb5GwBoPa4TP0xZRaWm1XntnZJEhF9h46x1Ts0GoLhoCahVinOjVwFwafZGLs3eCMDrUzrw5/g15Pm4KjkrlyDu7DUuTl3v9AwAh7cc4PCWAxQpW4zGvT9izMdD/7b/8hGL7b/3WfwFiwfOoWGXDwkump+Tu/9g+6pfX0hO8Xye9ePUReCn1P6ej/08kaqqC1RVLa2qaum/G8yEtqzLlE3TmbJpOt4BPvb2MwdPE5AvEE9vLwq/VYS6rd5j/t5FfDq4DVUaVaPFgFbp7mfHuh0UfqswifGJTP58Ag3av/+M/7W/odMSOG0I8T9vJ3HrXlzy5sIldyB51s0l35Zl6AJykue72Wh9ve2rKAY9Xg1qErvqR3w6t+DuFxMxHj1FtnerOZ7nCVJuRxGzyXZ4IPGP86hWFZ2PV5o+ubo14eb0NQT1akrE6GXc+/ZXAtq859Dj1mv1HnM2z2LO5ln4PPbanTpwisB8gXh5P8pQ6b1K7Nu874kl7Qq1ynP+5Hnc3N3IFZyL0Z+PpeK7FdEb9A7le0h9kIDp1DFc3iqHNqQglvO2+Q4pe7bjUqR4mr6Kty+6QkUwHdyDoUFTEiYPx5qYgK5EKadkeWjPnzcpEuSTZjCy4ehFdp+LYEyTiukGE6qqsnDnSTpULcG87cfpUfstPihTiG/3hTstk693Du5F2qqb9yLv45MjOwABOX25fTfS3u/O3UgCcvqlWXf6/GV069CSb77bQKN6tenduQ1zlzivGqgPDkSfL4DXt06h5IF5uObypfiWSbjkzGHvo3Fzxa9JVe4s3UTuPk252H0G8QfP2ufVZJR8PRpxbeo6gns35vLIFdz+Zhu529V16D6rtajD8LBJDA+bRA5/b/IUCab1uE7MaD+OxL8cFitR5U2unrpE3BMGUPleyw+Kwq1LNynz7tvM7TKZnMGBBITkcijfk+SsXpLYk1dIuZc2h1fxEFAUEi/eIle98hzrMB2P4ADc8zt2WPWhmi1DGRs2lbFhU/H2f7SfDj94Bv98AXh6P/VtLI1SNcty+eRF9B5uBAQHMr3zRMrWfRtXg6tTcjqDzKF5xgqNqqrDX8SDb1oexqbltrOTAoMfbUQFihfExdWF+Og4pnV/dKZC1Q+r80qJV1gxbpm9zSO7B6Wql2HEJ0MpU6MsqlVFVVWn/KH5j+hFyqXrxC63lUJTzl/hSuWm9uX5tiwjomlXrDFx9rYcrT8k5psfwGxB0etBtb0had2c8wb9VzGbD+D59uvE7zuFPn8QGlcd5vuP8vh+WJXY7UewxCTY5hlYVbCqaBzMs3HZT2xc9hMAQY/tAF8pXhAXvQtx0Y8yVGlQha/GfZXuPrQ6Le+3bciQVkPJnT/IPvdGq9Ggc9WR/JwVLcUrO5gtqA8SwNUVlzdKY1z/LYq7B5qgPFhvRqB7ozSWiKtp1nP/qC1JK5fY7sNV/2jejd65r93mE5ep89jhpr1/3mDZrtMsal8LN9f0m+TGY5eo+GpusrvrMaZY0CgKGgWMJrPTMlWpWJ4Nm7bSrkUTNmzaStV3KtjbV67bSGiNypw4HU62bB72Q1MAh46dwN/Ph+C8uTEak9EoGhRFQ1Ky86qRSeHXOFqitf12yQPzOBXaF/P9R/NscnVqyJ0lP6OaLWgMroBz/s4dEdCkMve3HcWcuu1hVVGtVjRuju2btq/YzPYVtgqmT5AfXeb1ZWHPGdx5wryhcvUrcmDjnifez/u9mrFs4Dy0LlqU1EOFqlXF1cF8TxL0/v+4tX5vuvZXBzThZO+FKDotSuo8Mtv+0jkZfl2+iV+X286aCgh+NEgKKV4g9T3mn+fJaXVaQtvWY8KnIwnMH2Qv1mq0GnSuLqQYX24FUDzdMw1oFEXZyOMzqGxigcPAfFVVjenX+ncq1H2bKo2qYTGZSTGmMLnzhGdar0n35qyduQZVVTm26yihrd5l2q+z2PK1Y6f+Gd58Dc/6NUj+8xJ51tpOK7w//Sse7D701HW0OX3Qv16Y6Lm2T6ex324gz6qZWOITuN3N8TFh/lm98KxQHJ2PFyUOLeLm5FVErt5GyOQuvLZ1OlaTmcs9ptv7awyu+DapxvmPhgFwZ8GPFFoxxH4qt7NUDK1IjUbVMZvNJBtTGPP5OPuygDz+5Azy48TvJ9OtV79VPX5du5VkYzKXzl5G76Zn3q9zOLT9MIlxien6PyuNty8e3QaBRgMahZS9OzEd3k/inElk6zcSrFbUxHgSZ423r6PNXwgAy6XzACTv2orXtK+wRt7FuN558x+SUkz8fuEWgxuWt7eN23iIFIuFjku2AraJwQ+XJ6WY+fHoRea2tk3YbVGxKF2Wb7edyt2k4nNl6Dt0HIeOnSAmJo7qDT/h87YtaNeiCb2HjOH7n7YQFOjP5JG2Q4aVKpRh9/5DhDZpg5vBwMhBPe33o6oq85euYvJI2yHYDxuEMmD4BMwWC0P6dHmubAAF5/TEq0JxdD6evHl4IRGTV3Fv5ban9ncJ8MajZCFuTLGdmXVnSRivhU3AEveAP1uPe+p6/0aRud3J/vZruPh4Uu7oPK5OXIMpJoFXRrfBxdeL4l8PJOHUFfuZUBo3VwKaVuFkU9s8jYj5Gyn+zSCsKWbCP5/+dw/1rzTo1phs3p60GNUeAIvZwojUQ82ubnpeq/gGywbNT7fem7XKcuXkRWLuRgNw/cxlRm6ewvXwq1w/ezVdf0do3fX4VXqdU30WpmkPCC1N7B+XSL5jyxB36irv7JxA3JlrxJ+55tQMAGVDK1CpUVXMJgspycnM6DzJvmzod2MIKpgbg4eBWb8vYkG/WZzY9QcAtVrWZdfaHaQYU7h29gp6gyvjt0znjx1H7BOyMwOZQwPKk85ISddJUaYDOYGHe/amQBy2QY6Xqqotnrbu+/nqZbpvmJjklflG1NExzp8L4agvFOfP+XGGlaUfZHSEdNw+rpnREdLRVfkooyOkc7TEi51s/TySrdqMjvBEXxky31tU4ySXjI6QzgqDw5+nX4iVV394qcdmNgc0e2nvtXXurMqUx52edVLw26qqlnns9kZFUQ6pqlpGUZTTT11LCCGEEC9c5hv+vnzPOik4m6Io+R7eUBQlGMiWejPzlTuEEEII8f/Ks1ZoegN7FEV5eKGQAsDniqJ4AMuevpoQQgghXrTMfPbRy/KsFZrtwHwgBtu8mXnANlVVE1VVnfa3awohhBBCvGDPWqFZjm0S8IzU2x8BK4DGLyKUEEIIIZ6dVQo0zzygKa6qarHHbu9QFMX5X0wihBBCCPEcnnVAc1RRlPKqqv4OoChKOWzXoBFCCCFEBrPKHJq/H9AoinIS25wZF2CfoijXUm8HA8677roQQgghhAP+qULj2Bf+CCGEEEK8BH87oFFV1bnXwBZCCCGE02W6S/JngGc9bVsIIYQQItN61knBQgghhMik5KsPpEIjhBBCiP8AqdAIIYQQWZxVkdO2pUIjhBBCiCxPKjRCCCFEFidnOUmFRgghhBD/AS+8QmNQtC/6If61pAcuGR0hHb2LJaMjpONldc3oCE+k88988/nDex3K6AjppJiPZnSEdN46MSmjI6Rj+mpURkd4ohmzIjM6Qjpl38p8mVruvZXRETKFzLdXfPmkQiOEEEKILE8GNEIIIUQWZ1Ve3s8/URSljqIo5xRFuaAoyoAnLNcrirI6dfkBRVFCnPEcyIBGCCGEEE6hKIoWmA2EAsWA5oqiFPtLt7ZAtKqqrwBTgfHOeGwZ0AghhBBZnBXlpf38g7LABVVVL6mqmgKsAhr8pU8DYFnq72uB6ori+IV0ZEAjhBBCiGemKEoHRVEOP/bT4bHFuYHrj92OSG3jSX1UVTUDsYCvo7nkOjRCCCFEFvcyr0OjquoCYMFLfMhnIhUaIYQQQjjLDSDvY7fzpLY9sY+iKDogOxDl6APLgEYIIYQQznIIKKQoSn5FUVyBZsCPf+nzI9Aq9fcPge2qqjpcZJJDTkIIIUQW9yynU78MqqqaFUXpAmwBtMASVVVPK4oyAjisquqPwGJghaIoF4D72AY9DpMBjRBCCCGcRlXVMCDsL21fPva7EWjs7MeVAY0QQgiRxclXH8gcGiGEEEL8B0iFRgghhMjiXuZp25mVVGiEEEIIkeVJhUYIIYTI4jLLWU4ZSSo0QgghhMjypEIjhBBCZHFylpNUaIQQQgjxH5ApKjSlapalSe+PsFpVrBYLy4cv5tzhswAMWPYlr7xZmHOHzzCxzWj7Op2n9yRf4WCObjvM6olfA/B+18ZcP3eNw78ccDiTSy4/ck/qhc4vB6gq0au2ELX0R7TZs5F3Zn9c8gRgirjDtS7jsMYl4lXnbfx7fIwlNoFrn43CEhOPa75AAvq05Hq3CQ7nAcg9vhte1cpgjorlfJ0uABiKhpB7VGc07gZSbtzleo9JWBOScC9VlKBRnVBTzFzvPpGUK7fQeHqQb3Z/rrQaCo5fZdqudM2yNOv9MarVisViZenwRYSnvn5fLBtKoTdfJfzwWca1GWVfp9v0XuQrHMyRbYdYmfr6fZD6+h1ywusHgKLBY9BMrDFRJM3+EkOr3uheLYGalAhA0tJJWCMuoXuzIvr6LVET40maOww1MR7FLxeG91uTtHCMc7IA+SZ2xat6acxRsYTX7AZAyOy+6AsEAaD18sASl8i50J54lC5C3tGdUE1mrnSZRPKVW2i9PAiZ05eLLYY77fXLP6Uz3jVKY4qM5WS1HmmWBX5Wn+Chn3KkeCvM9+PxrluePH2bYY5J4HybcZijE9AHB5B34Cdc6DjZoRyDx0xh196D+Hjn4Iev5wEQGxdP7yFjuXn7DkGBAUweOZDsXp6oqsrYafPYvf8QBoOe0V/0pljhV7h8NYL+w8djMpsZ2q8bJYsXxWy20LH3YGaOH4qbweBQRkOHCZBiBNWKarWSvGIESs68uNZqgeJiQI2NJPnnBZBiRJP7FVxrtkC1WEjZOA815i7o3dDX70Tyd1Nx1jkpZWqWo1nvj7FarVgtFr5K3fZCiuWn/ehOuGdzx2qxsm7WGvb9tAeA7tN7ka9wCEe2HeLbiSsAaNS1CdfOXXXOtufiSvaJM1BcXECrJWXPbzz4+isA3Fu1Q1+xCqrVivHnDRh/XIfr/yrh3qINanw8cSO+QI2PQ5MrCI9W7YkfN9zxPI/5X8WyjBr3BS4uOu5HRVO/7icATJ89hlp1qhJ5L4p3yr9n7//l8D5Ur1mJUyfD6fxZPwAaN62Pj6838+csc2o2R0mFJpNUaE7tPUH/Oj0YWLcn8/vOpP34zvZlGxf8wJye09L0z1ckmBRjCv3r9KDgG6/g5ulODn9vCpZ81SmDGQDVbOH2mMVcqP05lxr1wafFu+hfyYtfx8Yk7DvO+WodSNh3nJydbBc79G1Zj4sNe3H/201kr18ZAP/eLbgz5Wun5AGIXreNy58OS9OWe2w3bk9YxvnQrsRt2U/ODh8A4NeuIVdaD+fWyEX4fBxqy9O1Kfdmr3HqYAZsr1+fOt3pW7cnc/rOoOP4LvZlGxasZ+ZTXr8+dbrzyhuFcE99/QqVfNV5gxnAtXpDrLevp2kzrltI4qjPSRz1OdaIS7Z+VeuTOKYrpt0/oytbFQBDg1Ykb1jqtCwAUd9t42LLtDvoK50nci60J+dCexK7aT+xm38HwL99Qy62GkHE8EX4fVIHgICujbkza61TX7/I1TsI/3hkunbXIF+yV36D5Ih79rbANnU5Xbcfd1f8gu/7lQDI0/8jro//1uEcDevWZN6UUWnaFq1YQ/nSJQlbvZjypUuy+Os1AOzef4hrETcJW72YYf26MXLSLAC+2xDGgO6fMXfSCJZ+uw6A1et/4r1a1RwezDxkXD0B47JhJK8YAYBr7U8x/bYW49IvMZ8/iksZ27amK12b5LXTMG1fia6k7W/KpUI9TL//jDNPsD259zi963Sjb90ezOk7k07juwKQnJTMzJ5T6VmzC6NaDqP10Ha4e3kQXCSEZGMKvet0o+CL2vZMKcQO6ElM57bEdG6LS6my6IoUQ18zFI2fP9EdWhDzWUuSf9sGgFv9D4jp/hnGsB/RV60BgEfLdiQuX+ScPKm8snsyYcowPmnWkYrl3qVNy272Zau++Z6mH7RN09/TKxsl3niNym/Xx5RiomixVzEY9DT/uBGLF3zj1GzCOTLFgCb5gdH+u97dwOMb/Om9J0hKTErT32y24GpwRVEUtDodVouVxr2as3bqSqdlMt+Lxnj6IgDWxCSSL1xHF+iLV81yxKyzbYgx67bhVbM8AKrV+n/s3XV0FNfbwPHvrG+IO5pgRQLF3QsphBb3tgQtpcWKFimlOEWLFHdKgdJSNLhTvLgW9xAhnmyyMu8fGxbyCxKSxfrezzl7kr1zZ+bJjt157p0NkkaFQq9FNplxKBeAKTyKlJv37RZT4tHzmKPj0pRp8+Yg4cg5AOIPnMK5XmVrPCYzCr3WGo/RjCaPL+rsnra69mR4avvpHHTIT22/c8/YfuY020+JxWyhVZ/P+N2O209y9URVvDwpBza/vLIsg1oNGi2YzSgLFMMSG4UlzH7bDiDh6AXM0fHPne76aVWi1u2zhvR4++ms+5PGzxdNDi/iD9t3+8UduYApKi5dud+PHbkzalmaxpNskZE06tR9yoRT+SIYw6JJvvEgy3GULVkcF2enNGW79x+iUZD1AtcoqA679h2ylh84TMN6tZEkiRLFihAXF094xCNUKiVJhmQMhmRUKiWxcfHs+fsIDYNqZzm+51G4+2C5+y8AllvnUX5QxjrBYga1xvqymJFcvZCc3LHcuWzX9RvSnDu1tmPvwY37hN60bpeosEfERMTg7O6MyWRCm3rsqVKPvdZ9PmPVlKw3StMGlnrMq1RIKhXIMrpPGpH42xLbPiXHRFt/WmRrNkenA5MJVcCHWKIisdz/33/QnDXNWjRg44Zt3Ltr/VwiIh7Zph06eJyoqJg09WWLjFpt7cTQO+gwGk1069mJeXOWYTKZ7BqbPcjSm3u9q96JLieAsnUr0HpAW1w8XRjfYdQL696/epe4yFjGbJrMgb/24OufHUmh4Oa5668lNnVOb3QB+Ug6dRmVpyum8CjA2uhReboCED57NXmXjcYY9oi7fSaSZ8ZAu3U1vYjhym2cAysSu/0wLvWroM7uaY1n5mpyT+qNxZDCnT6TyT64Iw8nLXttcZSvW5HPUrff2A7p7/ifdu/qXWIjYxi/aTJ7U7efQiFxw47bT9eyK4Y/5yPpHNKUaxu1R/vJ55gunSL5r4VgMpK8ZRXZvh2HJSaSpAU/4fDV9yTOG2u3WDIiW/mimCKiSU69CD385Q/8pnyLxZDCrW+nkPP7DjyYYL9s34u41S1HSmgkiRdupim/P2MNRVb9SMrDR1zrPpWCc/tx5evJry2OyKhovDzdAfD0cCMyynoBfBgeia+3p62ej7cnD8MjaNO0AYNGTcSYYuSHAT2Zs3gFXwa3RqGw032bLKNr0RdkGePpvZjP7MUScR9lgVKYr55EWagckrM1XuORTWjqdwZTCimb5qMK0+H0AAAgAElEQVSu2RLj/jX2ieN/lK9bkc8HBOPs6cLYDiPSTS9QoiAqjYqHt0KRZZmYyFjGb5rCvqfOnfY89gBQKHCdNhdljpwkbVyL6fJFlNlzoK1RC02lasgxMcTPnorl/j2Sfl+Oy5jJWB5FEjd+FE5DhhM31r5dTQD5C/ijVqtZt2kZjo7ZmDN7Kb+vWPvc+vHxCezYtpfdB9axf+8h4mLjKFO2BJPGz7R7bIJ9ZKhBI0nSbp6RJ5Vl+aPn1O8CdAEo616CAo7+L13H8a1HOL71CIXLF6VF388Y8/mwF9ZfOmKB7fd+C4awYNBMGndvjl+RvJzdf4pdK7e/dJ0ZoXDQkWfmYEJHzsMSn5S+QuqnknDgFNcOWMcguDb5iLg9x9HkzYnnl00xx8TzYMRcZEOyXWJ62r0B08j+Yxe8e7QidscRZKP1zsFw8QbXmvYHwKF8AKawKJAkck8fgGwyETp6IaaIaLvFcXTrYY5uPUyR8kVp1fdzRn7+wwvrL35q+323YAhzB82iafcW+BXx58z+U+zMwvZTFa+AHBeN5fZVlB98aCtP/msRcuwjUKnRfdELTd2WpGxajvniCRIungBAXbEOpnPHUPrkRBPYHDkxHsOqWWC0/7Z7mluj6rbsDEDShRv829jaZ5+tfFGMqdvP/5f+yEYT90YtxBQR87zFZZpCryFHj2ZcapP+whi77zTn9p0GwLN5TaJ3nkCfLwfZuzbCFBPPrR8WYElKsXtMAJIkIUkvvjXM7uvN4hnWm4jbd+8TGhZBPv/cDBwxAaPRRI8v2+KfJ1emY0heMRY5PhocnNC16If86AEpWxaiqf0Z6koNMF07BWbr8SeH3SF5uXXMnyLXB8gJMSBJaBp0BYuZlN2rIDE207E87cmxF0Drvp8z4qljz9XbjR5TejOj71Tk1MzI4hFPunIGLvieOYNm0rR7C/yL5OXM/lPsWLkt60FZLER374yUzRGnoaNQ+uW1ZmFSUojp9RWaytVw6j2QmP49MJ48TvTJ4wBoa9cl5dhhlLlyo2/WCjkunvg50yA568efSqWiRMkAmjZoh06nY8vOVfxz7BTXrt587jzTp85n+lTr5/Xz9NGMGz2VL4JbUPOjKlw4f5nJE2ZlOS57EWNoMt7l1A/on/oaCpwCjj+vsizLc2VZLivLctnnNWYCg4MYGzKFsSFTcPN2s5VfOnoB7zw+OLk5PXO+/1UmsDw3zl5Dm02Pj58vU7tNoHz9ymh0mgz+aS+gUpJ75mCi1+8hdqs13W2KiEblZY1X5eWGKTJto0DSaXFtVpvIZZvw/vZz7vabTOLx87g2qpn1eJ4h+fpdbgb/wNWGvYnesI+U26Hp6nh3b0XY9JX49GxD6LhFRK3chkf7Bllab93g+kwImcKEkCm4ebvbyi8evYDPK2y/soHluX72GrpsOnz8fJnSbQIV61fJ0vZT5i+KqkRFHEcvQd95EKrCJdB1HGBtzACYjBgPbkPpXyjtjGot6kqBpOxej7ZBW5IWT8B89RzqCs9st9uPUoFrvUpEbTjwzMm+PVsSOnUVvt+25t6YxUSu2IZXh6xtv+fR+vmizeND8R2TKXlkNprsHhTbOhG1l6utjkKvwbNlLR4u3kzOfq241msacUcv2sbV2IuHmyvhqd0C4RGPcHd1AcDHy4PQsAhbvYdhEfh4eaaZd+qcJfTsEszy1eto1qAufbt1ZNbCrI17kONTj/XEOMxXTqDInhf5USjJqydjWDYC88UjWKLD0s2nrvQpxkMbUFduiHHvakyn96IuXSfTcdQLrs+EkJ+ZEPLz/xx75/HJ42s79vSOegYv+oEVE3/lysn0XV3lAivYjj1fv+xM7jaeivY6d6aSE+IxnjmJpmx5zBHhJP9tbbSnHNyPMm++tJW1WrR16mHY8BcOX3QgbuJYjBfOoK0VmOn1d/zyc3YfWMfuA+sIfRDG7p0HSExM4tGjKA7+fYyAYoUztJziHxYBCa5euUHDJvXo3P5b/PPmIV9+v0zHJthfhho0siz/89Trb1mW+wA1s7Li7Us3M6h+bwbV741Gr7WV+xfLh1qjJu4Z/fr/S6lSEtSpARtmr0Gj09i6+xVKBSqNOivhAZBzXC+Sr90hcsGTtGTsjiO4NrP2ybs2q03s9rQD6Ty7NCVyyQYwmVHoNNYMjkVG8dTfaE9KD+tJHknCu3srHi1PO2bEtelHxO0+jjkmHkmvBYuMbLGg0GUtnq1LQ+hfvzf96/dGq39yAsz7itvvk04NWZe6/R5vwKxuv+S1i4gf+AXxQ9qRNH8spkunMSwcb+sOAFCVrIzl/s0082nqNidl11rr+AeN1rrtZBlJ83q23WNOVUtguHYXY2hkumnuzWsRu/sfzDHx1n3IIiPLr29/Srp0mxMfduBUha6cqtCVlAeRnKvbD2P4k4Z79q8b83DhJus4H50GkF/LPl6zakXWbd4BwLrNO6hVrZKtfP2WnciyzOlzF3F0zGbrmgI4dvIM3p7u+OXOicGQjEJSIEkKkrJyl6/WgFpn+13hH4Al/B44PG64S9Yszak9aWZTBlTGfP0sGBJArQXZkjpmK/ONhi1LQ+hf/1v61/8W7VOfed5i+VClHnsqtYoBcwez98/dHA45mG4Z1mOvAWtn/5l67rTPsQcgubggZXO0vtFo0JQqi+nObVIOHUBdojQA6uIlMd+7m2Y+fbPWGNb/CWZz6jFn3a8kbeb3q4XzllOraiNqVW3Epg3bqVCxDEqlEr1eR5myJfj38rUMLWfQ998ybtRUVGoVSoUSAIvFgl6vz3Rsgv1ltMvJ/am3CqAs4GKvIMoHVaJ6s1qYjGZSkpOZ1m2ibdqw1WPIkT8numw6Zhyez9wBMziz7xQAHwfXZ98fu0kxpHD74k20Og0/bZ3Kqd3/kBibkKWYHMoWxa3pRxgu3SD/xmkAPJy4lIjZf5B7xkDcWn6M8V4Yd7qPs82j8nbHocQHhE+zDm6NXLKB/GsnY45N4HbXF48LyojcU/uRrWJxVG7OFD64iIc//4bCQYdH8CcAxGw5RNTqHbb6kk6LW/Pa3Ai2pqAjFqzFf+EwZKOJ299OfOY6MqNCUGVqNKuF2WgiJTmFKd0m2KaNWD2GnPlzocumY/bhBcwaMIPT+04C1izP3j92kWJI4dbFm2h0WiZtncoJO2y/Z9F3+g7JyQWQMN+9hmH5NNs0ycUdpX8hUjZa7+JTdq0j2+DpyInxJM2yT3++//S+OFYqhsrNmYAjC3gweQWPVu3ArWE1otbvT1df0mlwb16bq19Yu1/D5q8j/5Kh1ke5e9hn7Er+mb1xrlQMlbsTpY7P4+6klYSv2Pnc+mofN7KVLMi9ydYnjh4uDCEgZDzm2ET+7TDuufO9TP9h4zh28gzR0bHUbvwF33RqS+e2Lek7dAxrNm4lh683k0YOBqB6pXLsP3SMoJYd0et0jBzc27YcWZaZs3glk0YOAqB5oyAGDh+PyWxmaL/uz1x3RkgOLmgbp86vUGC6eATLzXOoStdBVcqawTNfOYH53FNZNpUGVbEqJK+2bivTsa1om/UGs4nkjXMzHcvTKgZVokazjzDZjj1rl1ulT6tSpHwAjq5O1Gxuje+XflO5eeEGAPWCP2HPU8eeVqdl0tZpnLTDsadw88Cp32BQKECSSN6/B+PRQ5jOn8VpwPfoG7dANiQR//OTMYYKdw/UhYqQ9Jv1Ueik9WtwnToHOT6e2JFDshTPY1f+vcauHfvYd2gDFouFX5eu5tLFKwDMXTiZKlXL4+7hxpmL+/hpzDSWL/sDgKBP6nDq5DlCQ63Zt3NnL7Lv0AYunL/M+XOX7BKbPYguJ5DkDDwCKknSDZ6MoTEBN4ERsiw/O0f+lDZ+jd+5fwI6RPkOjlB/B4eOj7C8Ew/BpbOgnuHlld6wa5vt81iwPaWYlG87hHRKn7FfQ9pejIuyfrPxOgTPiHh5pTdsdlH7j9vKqsJ/Z/0pu9chIvbfN3pSn5H7izd2re1+59d374JFxp9yKgp8A1TF2rDZzwvG0AiCIAiC8Oa8c5mDtyCjDZolQCzwOEf/GbAMaPE6ghIEQRAEQXgVGW3QFJNluehT73dLknThdQQkCIIgCMKrsbyTnUBvVkYHSZyQJKni4zeSJFVAdDkJgiAIgvCOeGGGRpKks1i75tTAQUmSbqe+9wPeneHdgiAIgvD/mHjK6eVdTp++ZLogCIIgCMJb98IGjSzLt95UIIIgCIIgZI7I0Lwj/21bEARBEAQhK96Z/7YtCIIgCELmiO+hERkaQRAEQRD+A0SGRhAEQRDec+J7aESGRhAEQRCE/wCRoREEQRCE95x4yklkaARBEARB+A8QDRpBEARBEN57ostJEARBEN5z4rFtkaERBEEQBOE/4LVnaHSS8nWv4pXFJr17MSmld699naAyvO0Qnsn8KPlth5COg8O7d28QF+34tkNIx7ho1NsOIR11h+/fdgjPtHZItbcdQjq/5C34tkNIp7iz39sO4Z1gETkakaERBEEQBOH9J8bQCIIgCMJ7Tjy2LTI0giAIgiD8B4gMjSAIgiC858QIGpGhEQRBEAThP0BkaARBEAThPSfG0IgMjSAIgiAI/wEiQyMIgiAI7zmL9LYjePtEhkYQBEEQhPeeyNAIgiAIwntOfFOwyNAIgiAIgvAfIDI0giAIgvCeE/kZkaERBEEQBOE/QDRoBEEQBEF474kuJ0EQBEF4z4kv1hMZGkEQBEEQ/gNEhkYQBEEQ3nPisW2RoREEQRAE4T/gnWjQVGxUjRGbJzNyy2SG/Dma3EX8APDNl4PhIRNtr5lnlxHY8RMAWgz8ghGbJ9N5Ug/bcio1rm6bnlX5J39DubMLKbl7iq0s94DWlNg5mRLbJ1J05VDUPm4AuH9SkZJ7fqbY2pGo3BwB0Pr58MHsPnaJ5bG8k7tR+swiiu/62VaWs28rSv0zj2LbJ1Fs+yRcPioNgGO5whTfMZmAzePR5s0OgNLZgcIrfgDJvt+RXTGwIr9s/YXpm6czdeNUipYrapvmlcOLUb+OYvbO2czeORvvXN4A9J/an1+2/kK7Ae1sdVv3aE2ljyvZLzCFAqfx88g2cKz1rbcvTmNm4jx9Odl6/wAqa4JSW68JzpMW4ThonK1MWbg4+nbd7BaKyteT3EvG4b9xDv4bZuPathEAXv074R8yF/91M8kxfSgKp2wA6EsVxX/dTPz+mIraL4c1fqds5Fow2q7b74MpX1Px3HzK7JlkK/NsUJEyeydT7f4qHEvks5U7lytE6V0TKbV1HLq8voB1nyq+8nu771O6LuPRtR+Brt2PaNv+AIDklRvt54PRtR+BtklP0OgAUOQsgK79cLRtf0Byte5faPVoW/QBMh/X92MmU/2T1jT+oqutLCY2js69BlO/VSc69xpMTGwcALIsM2bKLIJadqRJ8NdcuHwVgBu37tKyYw+aBH/NqXMXATCZzHTuNYgkgyHTsT2tRvVKHD+2jdOndrFrxx+28h7dO3Hq5E5On9pFzx6dbeVjxwzmxD/bWbRwqq3ss8+apqmTJRoN7rNm4T5/Ph6LFpGtfXsAnPv3x33+fNwXLMBl+HAkvR4AfZMmeCxahOu4J8efunhxHLvZ7/h7WqESH7Dj5haqf1LNVla3eSDL9i9m2f7F1G0eaI1Bo+anX8ewcMdcGgU3sNXt+9O3FCxW4LXElhXyG3y9q96JBk3EnTDGtRrK0Hp9WD/9D9qNtZ5AQq/fZ1j9fgyr348fPx1AiiGZE1uPondywC8gHz8E9cFsNJGrUB7UWg1VW9Ri19Itdokp/Pc9XPhsZJqy+zPXcbp2H04H9uPR9n/I3acFANk7BnEmaAChy7bj2cR6kOT57jNu/7TCLrE8FrFqN5c+H5mu/MG8jZwL7Mu5wL7E7DphjemrhlxuO5pbwxbi07YuADm/bcG9aX+CbN9d8tTfp+hWtxs9gnowpd8Uev3Uyzat75S+/DnnT7rW7sq3Db4lJiIG/8L+pBhS6Fa3Gx+U+AAHJwfcvN0oVKoQh7Ydsltc2vrNsNy7ZXuv//wrDBv/ILbH58jx8Wg+qg+Aplogsf06Yrp8HnWJcta6zdpi+HOp3WKRzWbCfprHzU+/4lbr3rh9/ima/HlIOHiSmw26crPRN6TcvId7l1YAuHVoyt0uPxA2Zi6ura1xenzdhsg5K+26/R6u2sO5NqPTlCVcusOFjhOJOXwxTXmurg049/kYrg1dTI52HwOQp3czbk9bY/d9CsCwajyGJT+SvGwEAJq67THu/QPD4h8wXTmBulwQAKqydUn+42eMu1agKlkLAHWlBhgPbyIrp9/G9QOZPXlUmrL5y36nYtmShKxaQMWyJVnw6+8A7D90jNt37xOyagE/DujJyIkzAFi9LoSBvb5i1sQRLP7tTwBW/bWRTz/+CL1Ol+nYHnNxcWb69DE0adqeEiU/olWbrwAICChEp06fUanyJ5QuE8gn9euQP78/zs5OlCpZnNJlAklJSaFYscLodDraB7di5qzFWY4HgJQUovr04VHnzkR27oy2fHnURYsS98svPOrcmUedOmF++BB9kyYA6AIDiezYEeP582jKWY+/bG3bkrDUfsffYwqFgi6DO3Ns3z+2MidXJ4J7t+WbBj34+tPuBPdui6OLI+VqlOXs0XN0CvyKwGZ1AMhfJB8KpYIr567aPTYh696JBs3VE5dJjE0A4NqJf3H39UhXp2iV4oTdekjkvXBkiwWlWgmARq/FZDJTr0tDdizZjNlktktMsYcvYIqKT1Nmjk+y/a500D45V1pkJI0apV6LbDLjVKEIxvAoDDce2CWWx+KOXMAUFZehurLJjEKvSY3JhNbPB00OT+IOnbdrTACGxCd3mjoHHXLqxS13wdwoVUpO7j9pq5dsSMZsMqPRaZAkCaVKicVsoW3ftvw6+Ve7xSS5e6EuXZHknZtsZapipTEe3gtA8t4taMpVTa0MKFWg1SKbzWiqB2I8eRQ5PmOfdUaYw6NIvnANADkhieRrd1D5eJD49wkwW59PMJy+hNrX01rHZELSa5H0WmSjGXXu7Kh8PUk6etZuMQHEHL6IMTrtfp505R5J1+6nq2sxmVHqtSj1GixGMzo/H7Q5PIk5eMGuMT2Pwt0Hy91/rbHcOo/ygzKpgZlBrbG+LGYkVy8kJ3csdy5naX1lSxbHxdkpTdnu/YdoFGS9uDUKqsOufdYG+O4Dh2lYrzaSJFGiWBHi4uIJj3iESqUkyZCMwZCMSqUkNi6ePX8foWFQ7SzF9lib1k1Yu3Yzd+5Yt1d4eCQAhQsX5OjRkyQlGTCbzezbf5gmjYOwWCyo1dYsiIODHqPRSN8+XZkxcyEmk8kuMQHISannSpUKVCpkWUZOTLRNl7RaWyNYSq0nabVgNqMLDCTl6FHkOPsdf4816dCI/SEHiI6ItpWVq1GWf/b/Q1x0HPEx8fyz/x/K1yyHyWRCp9ehUquQUjOQHfq3Z+GEJXaPyx4sb/CVFZIkuUuStF2SpCupP92eUaekJEmHJEk6L0nSGUmSWmVk2Rlq0EiSlE2SJMVT7xWSJDlk/E/IuOqtanN2z8l05RUaVOHI+gMAGBIMnNl9guEhE4kOiyIpNoF8JQtyctvR1xFSGnkGfkaZ43Pwalqd2xNWAnB3+hoCfh+GW2BZIv46QO7ezbk75Y+XLMl+fDsEUXzHZPJO7obSxdplcX/6n+Sf2osc3ZvycNFmcg/8nDs//fbaYqhUtxJzds1h+OLh/Nzf2iWWK28uEmITGDJnCNNDptNxcEcUCgV3rt4h5lEM00KmcWTHEXL450CSJK6du2a3eBw6dCfp1zlgST1pOrkgJ8ZbL36AJTIchbsXAMlb/sJpzEwUnj6YLp1FUyuI5K1/2S2W/6XK6Y2uSH4Mp9NecF2afUzCvmMAPJr7O9nH9cOjS0uil2/A89t2REy1/x3rq7gz7S8KTe9O7p5NuL9wM/6D2nBznH2zkDayjK5FX3Rtf0D5YQ0ALBH3URYoBYCyUDkkZ3cAjEc2oanfGXWF+phO7ERdtSnG/WteS1iRUdF4eVrX6+nhRmSU9cL4MDwSX29PWz0fb08ehkfQpmkD5i1bxZBRk/gyuDVzFq/gy+DWKBT2uZcsWDAfrq4u7Ny+miOHN/PFF80BOH/+ElWrVsDd3Q29XkdQvY/IlSsH8fEJbN6yi+PHthH6IIyYmDjKlyvF+vVb7RKPjUKB+/z5eK1dS8rx45guWrN9zt99h+eaNajy5CFxjXUbJf71F+4zZ6Lw8cF49iz6oCAS/7L/8efp60G1oKqsW7ohXXnY/XDb+/AHEXj6enB83z/45vbhl/XTWLPwLyoHVuLKuStEPoy0e2z/zwwEdsqyXBDYmfr+fyUCwbIsBwD1gJ8lSXJ92YIz+pTTTqAO8PhWzgHYBlR+VmVJkroAXQAquZeikFPeDK2kcKViVGtVmzHNh6QpV6pVlKxTjj/GL7eVbZ6zjs1z1gHQYdzXrJ28kuqtahNQrSR3L91kw4w/M/invZrb437j9rjfyNmjCdk7BHFn4ipi9p3hzL4BAHi1qEHUzhPo8mUn59eNMMXEc2PoQixJKa8lnodLtnBvymqQZXINaEOeYe250ecXEs/f5HwD637iVKEoKQ+jkCQoMLsvstHEreGLMUXE2C2OQ1sPcWjrIYqVL0bbfm0Z8tkQFCoFAeUC6FG/B2H3whj0yyDqtKjDtlXbmDt8rm3eYQuHMX3QdFp1b0W+ovk4sf8EW1dk/gSrLl0JS0wU5uv/oipa8qX1U/ZtJ2XfdgB0zYNJDlmDulQFNDXqYokII2npTLt1qUgOOnJO+56wsXOwJDy5Y3X/qjWyyUzsht0AJF+6zu3WvQHQly2GKfwRSBLZJw8Ek7X7yhwZ/cx1vC4J529y6hPrselSsUjqPiVReE5vZKOJ6z8uxWinfSp5xVjk+GhwcELXoh/yowekbFmIpvZnqCs1wHTtFJitGQU57A7Jy63dZopcHyAnxIAkoWnQFSxmUnavgsRYu8T1NEmSbHfuz5Pd15vFM8YDcPvufULDIsjnn5uBIyZgNJro8WVb/PPkynQMKpWSMqU/JLBuS/R6HQf2beDIkRNcunSVCRN+YXPIbyQmJHLq9HnMqZnAiZNmMXHSLADmzJ7Aj8Mn0LFDGwIDa3D27EXGjJ36olVmjMXCo86dkRwdcR05EmXevJhv3CD2p5+sY9t69kRXqxaGLVswbN+OYbv1+MsWHEzimjVoK1RAV7cu5rAw4mfa5/jr9uM3zBkz35ZBfumfYLYwqrt1/J1SpWT88rF833EY3/zwFd45vdn2xw4ObrdfF3lWvUdPOTUCaqb+vgTYA3z3dAVZlv996vf7kiSFAV7AC096Gb1N0MmybMtLp/7+3AyNLMtzZVkuK8ty2ec1Zj5qW8822NfV241chf3oMO5rpn05joT/SYF/WLMUt85dJ/YZJ8s8AXlBknhw/T7lPqnMrO6T8PLzxcc/ewb/tMwJX7Mfj08qpilT6DV4t6xF6KIt5O7fmis9pxN75BKeTau/tjhMETFgsYAsE7Z8O44lC6ark/Pb5tz7eTU5+7Ti9silhC3fjm+nrA2e/jT4U6Zvns70zdNx93G3lZ87eg7fPL44uzkT8SCC6xeuE3o7FIvZwqFthyjwP4PpKgZW5OrZq+gd9GT3y87Yb8ZStX5VtDptpmNTFi6GpmwVnH9ZSbbeP6AuVgp9h+5IDo6gsHZVKjy8sDwKTzOf5OaBskARjMcOoG3QkoQpw5ET4lEVL53pWNJQKck57XtiN+wmfvtBW7Fzkzo41irPg/7jnzmbx9dtiJz1G57dPid84kKiV2/BLXVQ8duS59tm3J7yJ359W3Bj5DJCl+8kZ+f6dlu+HJ963kqMw3zlBIrseZEfhZK8ejKGZSMwXzyCJTos3XzqSp9iPLQBdeWGGPeuxnR6L+rSdewWl4ebK+ERjwAIj3iEu6sLAD5eHoSGRdjqPQyLwMfLM828U+csoWeXYJavXkezBnXp260jsxYu51V93bUdx49t4/ixbTx48JBt2/eQmJhEZGQU+w8c5sMPrYPyFy1eSYWKQdSq3Yzo6BiuXLmeZjklSwYgSRKX/71G82af0uazruTL50eBAhm7Ac0IOT6elJMn0ZYv/6TQYsGwaxe6GjXS1FV4eKAuUoTkAwdwaNmSmOHDrWPdSmf++GvcriHzts5m3tbZFPqwID/8MpgVh5ZR45NqfDu6B1XqViYiNBLvHF62ebyyexIRGpluOdv+2EHR0kWIj0tgxNejaflV80zH9b6TJKmLJEnHn3p1eYXZfWRZfjweIxTwecm6ygMa4KUp/IxmaBIkSSoty/KJ1BWUBZJeMs8L7Vq2hV3LrAN43XN40n12f+b1nsbDZ4w7qdCwKkc2HHjmcpr0ac2SQbNRqpVIqWlc2SKj0WuyEt4z6fJmt42Lca9bjqSr99JMz/F1Ix4sCLGOX9FpABlkGaU+8xfnl1F7u2EMi7LGFFSBpMu300z3bFGT6J0nMEfHo9BrQbaARbb+ngUbl25k49KNAGT3e9J4zF8sP2qNmtioWOJj4snmnA1nd2diH8VSonIJrpy5YqurVClp1KkRP7b/kRx5c9junBRKBSqNimRDcqZiM/w2D8Nv8wBQFS2JtmErEqeNJlufH1FXrIHx4C60NephPPZ3mvn0rTtiWLUQAEmT2scvW5A0WR+8CeA76luSr90havGTdLpD1TK4d2rBnbYDkJ/x9zo3rkPC3mNYYuKRdFpr49Visf7+lvi0rMGjnScwPd6nLDKyxYLCXsecWgMowGgAtQaFfwDGg+vBwQkS4wDJmqU5tSfNbMqAypivnwVDAqhT93VZTl2efdSsWpF1m3fQuW1L1m3eQa1qlWzlK/7cQFCdGpw5fwlHx2y2rimAYyfP4O3pjl/unBgMyTfqF74AACAASURBVCgkBZKkICn51ffxWbOXMGu2dRxH4cIFmPbzaJRKJRqNmvLlSzF1mnXf9/LyIDw8kty5c9C4cRBVqjZIs5zhwwbQ9ZsBqNVqlEprQ99iseDgoM/UZ/OY5OICZjNyfDxoNGjKliVhxQqUOXNivmc9Z2qrVMF0O+25yrFjR+IXph5/j8fYWCxIWRg8vXbJetYuWZ+u/LvJ/Tm08zB/bz2Ik6sTnb/rgKOL9QnVstXLMG/cwidxuThSsXYFBnw+iMqBFZEtMrIso9HZ/xqTFW8yPyPL8lxg7vOmS5K0A/B9xqQ03S+yLMuSJD03dEmSsgPLgHayLL90+E5GGzS9gNWSJD0eKZgdyNAgnYxo1LMFjm5OtB31JQBmk5kRDa0ZKI1eS0DVEiwZPCfdfKU+Ls/Ns9eITr2g37lwg5FbJnPn0i3uXLyVrv6rKDizNy6VA1C5O1Hmn7ncmbgKt9ql0efPgWyRSb4bzvXvnsSk9nHDqVRB7k5eDUDoghA+3DweU0wClzr+lKVYHss/szfOlYqhcnei1PF53J20EudKATgE5AXZGtONAbNt9RV6DV4ta3GpjfUpkdA56ym07Htko4mr3aY8bzWvrEr9KtRuVhuT0USKIYVx3cYB1pPjgtELGLtiLJIkceXsFbasePIU2qftPmXnHztJNiRz4+INtHotM7fN5NjuYySkDhK3p6Rf55Ct9w/o23TCfOMKybtCbNOU/tbMkfmGtcGVsn8nzpMWYokMx7BuZZbXrS8dgEvjOiRfvoHDX9YnYCKmLMF7SFckjZpcC61dJobTl3j4o3W6pNPi0qQOdzpZzwFRi9eQa+4IZKOJ+/3ss08VntULl8oBqN2dqHBiNrcm/I4xOp4Cozui9nCm2K+DiD930/YklEKvwadVTc62sj79c3fOBootH4wlxcSlb+zQVQFIDi5oG3e3vlEoMF08guXmOVSl66Aq9REA5isnMJ976iZHpUFVrArJqycDYDq2FW2z3mA2kbzxuefdF+o/bBzHTp4hOjqW2o2/4JtObenctiV9h45hzcat5PD1ZtLIwQBUr1SO/YeOEdSyI3qdjpGDe9uWI8sycxavZNLIQQA0bxTEwOHjMZnNDO3XPVOxPXbp0lW2btvNyRM7sFgsLFy4gvPnrWOzVq+ah7uHG0ajiZ49hxAT86TbrWHDuvxz4jQPHjwE4PTp85w8sYOzZy9y5kzWBnkrPTxwHjQIFAokhQLD7t2kHD6M27RpSNmyIUkSxqtXiZvy5BykKmA9/kxXrMefYedOPBYuxBweTsLKrB9/LxIXHceyqcuZvcl63C39eTlx0U8GJAd/+wW/Tv8NWZY5uvc4jdo1YuGOuaz/deNrjet9Jsvyc9OikiQ9lCQpuyzLD1IbLOlTrdZ6zsAmYIgsy4czsl4pI/2JkiS1ALYCeYCmQAVg6OOMzYt08G/2znXsfZm5G//XSvn8RupbM1xln+/JsLflFRJfXukNCzubtbva1+FhtOPbDiGdsn2dXl7pDVN3+P5th/BM+hzVXl7pDbtXOX2X9tvW+qr6bYfwTLvvbrfvlzO9RC//1m/sIjL15spM/22SJE0AImVZHidJ0kDAXZblAf9TRwNsBjbIsvzzs5bzLBkdQzNUluVYwBWoBcwEZmV0JYIgCIIgCMA4IFCSpCtYHzYaB9ahLJIkzU+t0xKoDrSXJOlU6uulT3hktMvp8Ze7fALMk2V5kyRJo140gyAIgiAIb4b8njzlJMtyJJDui5hkWT4OdE79/Vfglb+YLKMZmnuSJM3BOm4mRJIk7SvMKwiCIAiC8FpltFHSEusYmrqyLEcD7kD/1xaVIAiCIAjCK8hQl5Msy4nAmqfePwDs+73+giAIgiBkSlb/JcF/geg2EgRBEAThvZfRQcGCIAiCILyj3qN/ffDaiAyNIAiCIAjvPZGhEQRBEIT3nMjPiAyNIAiCIAj/ASJDIwiCIAjvOTGGRmRoBEEQBEH4DxAZGkEQBEF4z4nvoREZGkEQBEEQ/gNEhkYQBEEQ3nPvyz+nfJ1EhkYQBEEQhPeeyNAIgiAIwntOjKF5Aw2aiQERr3sVr2zR2dxvO4R0jNLbjiC93ypHve0QnqnOHvPbDiGdRhr3tx1COtd1hrcdQjrTZrx754O1Q6q97RCeKen+/rcdQjr7Awa+7RDS+aPsw7cdgvCOEBkaQRAEQXjPiTE0YgyNIAiCIAj/AaJBIwiCIAjCe090OQmCIAjCe04MChYZGkEQBEEQ/gNEhkYQBEEQ3nMWWQwKFhkaQRAEQRDeeyJDIwiCIAjvOZGfERkaQRAEQRD+A0SGRhAEQRDecxaRoxEZGkEQBEEQ3n8iQyMIgiAI7znxrw9EhkYQBEEQhP8AkaERBEEQhPec+KZgkaERBEEQBOE/QGRoBEEQBOE9J55yEhkaQRAEQRD+A0SGRhAEQRDec+IpJ5GhEQRBEAThP+DdyNCoNbj8NA3UalAqSfl7L0nLFwGgD+6MtmpNZIuF5E3rMGz4E03l6ui/6IgcF0fcqCHIcbEofHPg0O5L4n8abrew3PNnp97ELngX8+fAhNUcnxtim+Zf40M++rEtklLB2ZV7ODpzAwD1p36NV+HcXNt5igPjfwegYo9GRFy+y9Vt/9glpk8ndsEnwJ+9E1dzNDUmp+zuNJjSlWyeLsiyzKnfdnN80VYAag5sRf6aJXh44RYb+8wBIKBJFRzcHDm2cGuWYwJAUuA4ZjbyowgSJgxG83FjtEHNUfrmJKZLI+S4WADU5auja9EBOT6WhElDkeNjUXjnQNe6M4nTRtgnlqcULVGYhRtnMaTrcHZu2gOAT05vhk76Dp8c3sgy9Pq8Pw/uhjLyl6EUKJyf/TsOMnPsXAA6fRvM1Us32Ltlf5Zj8cifnYYTv8I3wJ/dE3/ncOq2U2rVtPt9KCqNCoVKycWQo+yd8icAjad+g3eh3FzZeZLdE6z7U9UejQm/fIfLdtifACo2qkb9rk2QJDAkJLH0+7ncuXgL33w5+HpGH1s9r9w+/DVlJdsXbqLFwC8oXqM0ty/cYH7f6QBUalwdR3cnti/clOWYygVWoHXfz7FYLFjMZhYNn8+l4xfxL5qXL0d/jYOjAxazhT9n/M7BjQcA6DW1D3kK+fPPzmP8NmEZAM16tOT25Vsc23YkyzEB1KheiUmThqNWq4iMeMRHdZoD0KN7Jzp1+gxJkliw4DemTZ8PwNgxg6lbtxanT1+gQ8deAHz2WVM8PdxtdV7V92Mms+/vo7i7ubL219kAxMTG0XfoWO6HPiSHrw+TRg7CxdkJWZYZ+/Ns9h86hk6nZfSQvhQtVIAbt+7y3fCfMJpMDBvQk5LFimAymena93um/zQMvU6X6c+o8M9f4xlYmpSIGI7W6AeAyjUbxeb2RpfbC8OdcM59OQVTTAJen1Qg74CWmKLjOdN+AqaoePR+PuQb0obzXX7OdAzpPOca4/zTdCQHPQAKFzdM/14kbtT3b+waI9jXu5GhMaYQM7g3MT06EdOjE+oy5VEVKoq2ThBKT2+iv2pLTNdgkvftBEDXoCkxvb/CsGU92pp1AHAI7kzissydIJ7HEJ3ArmHL0jRkACSFRJ1R7fiz3XgW1R5A4YYV8SiYA8/CuTEZjCypOxjfEnnROOnJ5u1K9lL57dKYeRzT9mHLODIvbUwWs4Wdo35jXp3vWNr4R8oE18GjYA60Tnp8i/mzoN5gzEYzXoVyodKq+bBFdf5ZusMuMQFog5phuXfb9t787zkSRvfFEh6atl7dJsQN6Uryzg2oq9QGQNeqI4bfF9gtlscUCgU9vu/Kkb3H0pSPmPY9y2auoEX1trQL6sKjyCgKFMlPclIKbWq3J6BEYbI5ZcPD24OAUkXt0pgBSIpOYMuwpRyel/aCb042sqzNaOYGDWZu0GDy1/iQnKUK4F04NyZDCnPrDSJHiXxonfQ4eruSs2R+uzVmACLuhDGu1VCG1uvD+ul/0G5sVwBCr99nWP1+DKvfjx8/HUCKIZkTW4+id3LALyAfPwT1wWw0katQHtRaDVVb1GLX0i12iens36fpW68n/et/y8z+0/n6px4AJCclM733FHoHdmdU8I90GNYZB+ds+BX2J9mQQt96PclfoiAOTg64ertRsOQHdmvMuLg4M336GJo0bU+Jkh/Rqs1XAAQEFKJTp8+oVPkTSpcJ5JP6dcif3x9nZydKlSxO6TKBpKSkUKxYYXQ6He2DWzFz1uJMx9G4fiCzJ49KUzZ/2e9ULFuSkFULqFi2JAt+tTZ+9x86xu279wlZtYAfB/Rk5MQZAKxeF8LAXl8xa+IIFv9mbTyv+msjn378UZYaMwChK/dwqvWYNGV+PRoTtf8shyv1Imr/Wfx6NAYgV6d6HK83iHtLd+DbtCoA+Qa15vrYlVmKIZ3nXGNiv+tBTI/OxPTojPHSeZIPWo/1N3WNsSfLG3y9q96NBg2AIcn6U6VCUqoAGV39RiSuWAKytW9Qjom21pFlJLUaSatDNplQBXyIJSoSy/17dg0pMTKW0DPXsZjMacp9S+Yn6uZDYm6HYzGaubThMPk/LoPFZEalU4MkoVSpkM0WqvRtxt+T19g1pgdnrmMxpo0pISyah+duApCSYCDi6n2cfNyRLTIKlTURp9ZrMJvMVPjqE44v3pbu78osyd0TVamKpOx+cqE237yKJeJhurqybLFuO40OzCaUhYpjiX6EJdS+2w6gVadm7Nq0l0cR0bayvB/4o1QpObLvOABJiUkkJyVjMpnQ6jVIkoRKrcJittB1QCfmTFxot3gebzuzMf3nbkxMBkChUqJQK5FlOXV/0oAkoVApsZgt1OjT3Ja9sZerJy6TGJsAwLUT/+Lu65GuTtEqxQm79ZDIe+HIFgtKtRIAjV6LyWSmXpeG7FiyGbOd9ilDosH2u9ZBaxsf8ODGfUJvPgAgKuwRMRExOLs7W7efLnX7pX5Wrft8xqopv9klHoA2rZuwdu1m7ty5D0B4eCQAhQsX5OjRkyQlGTCbzezbf5gmjYOwWCyo1dZjz8FBj9FopG+frsyYuRCTyZTpOMqWLI6Ls1Oast37D9EoyHrhbRRUh137DlnLDxymYb3aSJJEiWJFiIuLJzziESqVkiRDMgZDMiqVkti4ePb8fYSGQbUzHddj0YcvYoqOT1PmWa8cD1btBeDBqr14BpWzTpBlFBo1SgcNFqMZlwqFSQ6LJulG6P8uNuuecY15TNI7oC5RGuOh/ba43sQ1RrCvFzZoJEnaIEnS+ue97BuJApfp83FfvhbjqeOYLl9EkT0H2uq1cPl5Dk7Dx6PIkROApN+X4zx6MprylUnZuxN962CSViy1azgv4uTrRtz9R7b38Q8e4eTjxqOr90l6FEdwyCiu7TiBq78PkiQRltrQeFNccnniE+DH/VPXSEkwcG33KTqGjCY+LJrkuCRylMzPFTve4euDu2P4bQ5YXt52T173G9kGT0JduhIpf+9C17QtyWuW2S2Wx7x8PakZVJ0/lqxNU54nX27iYuIZv2AUy7ctoOfQb1AoFNy8couoyGh+3baAfdv+JnfenCgkictn/7V7bM8iKSS+DBlD3xOzuLH/HPdPXSPi6n0SI2P5ctNoruw8ibu/L5JCIvQ17k/VW9Xm7J6T6corNKjCkfXWrh1DgoEzu08wPGQi0WFRJMUmkK9kQU5uO2rXWMrXrcjUnTMZtOgHZvaflm56gRIFUWlUPLwVyr2rd4mJjGX8pikc33kMX//sSAoFN85dt1s8BQvmw9XVhZ3bV3Pk8Ga++MLa3XT+/CWqVq2Au7sber2OoHofkStXDuLjE9i8ZRfHj20j9EEYMTFxlC9XivXr7dTN+5TIqGi8PN0B8PRwIzLK2oh/GB6Jr7enrZ6PtycPwyNo07QB85atYsioSXwZ3Jo5i1fwZXBrFIrXc4+r8XIhJcwaU0pYNBovFwBuTv2LkquH4vlxWR7+dYC8fZpxc/IfryWGZ11jHlNXqobx1D/ISYnA27/GZIYsy2/s9a562Riaiak/mwK+wK+p79sA6W+/U0mS1AXoAjCpWEHa5cn+8kgsFmJ6dEbK5ojT96NQ+uVFUquRU1KI+fYrNJWr4dhrILHf9cB46jgxvax32JqP6mI8fhhlztzomrZCjo8nYe40SE5++Tpfg93Df7X93mRhH7YNWkiF7g3xLurHzf1nObtiz2tdv9pBS5PZvdgx4ldS4q13JEfmbOLIHGv2JOinzuyb/CclWtckb7XihF26zcHp6zK9PlWpisix0Zhv/IuqSImX1jed/Yf4s9ZUvbrax5hOHUGRPRfaT1shJ8SRtGQGpGR92/Ud0ZPpo2alO/hUSiWlKnzI54EdCb0Xxtg5P9KgVRDrVmxi8g/TbfUmLxnHmAET6NirLQWLFuDIvuOsXb4hy3E9j2yRmVd/MFpnB1rO7Y3XB7kI//cu20Y82Z9aLejLpkELqNq9ET5F8nB9/zlOrtxttxgKVypGtVa1GdN8SJpypVpFyTrl+GP8clvZ5jnr2DzHut90GPc1ayevpHqr2gRUK8ndSzfZMCPrWaSjWw9zdOthipQPoHXfzxnx+Q+2aa7ebvSY0psZfafatvHiEU+6AwYu+J45g2bStHsL/Ivk5cz+U+xYuS1L8ahUSsqU/pDAui3R63Uc2LeBI0dOcOnSVSZM+IXNIb+RmJDIqdPnMZutjfuJk2YxcdIsAObMnsCPwyfQsUMbAgNrcPbsRcaMnZqlmJ5FkiQkSXphney+3iyeMR6A23fvExoWQT7/3AwcMQGj0USPL9vinyeX3WOzSd1mUfvOcvzjgQD4tqhOxM6TOOTPQZ6vG2CKSeDf7xdhSUqxzzqfcY0x37oBgLZGbZK3brRVfZevMcLzvbA5LsvyXlmW9wJVZFluJcvyhtTXZ0C1F8w3V5blsrIsl81QY+bpeRPiMZ45ibpMeSwR4aQc3AdAysH9KPPmS1tZq0VXpx6GjX+h/7wD8ZPHYrpwBm3NwFda59NKBtchePNogjePJpuP6zPrxIVG4ZTD3fbeMbs7cQ+j0tTJH1iah2dvonHQ4ernw4ZvpvNB/fLWLoRXVDq4Dh1DRtMxZDSO3s+OCazdFU1n9+L82oP8u+V4uuk+AX5IEjy69oDC9cuzttt03PL44Obv88oxPaYqVAx16co4T1uBQ88fUAWUwqHb4JfPqNGiqVGX5G1r0TXvQOKscZgun0NTtU6mY2nRvgnLty9k+faFFClRiDGzf2T90d+p/WkNvhvXhxr1qvHwQRiXz1/l3u0HmM1m9mw5QKHiH6RZTo26Vbl05jIO2RzI5ZeTQV8No/anNdHqta8cU9ngQL4MGcOXIWNeuO0eS45N5ObBC+Sv+WGa8g8Cy/Dg7A002XS4+XnzZ7fpFMnk/gTwUdt6DA+ZyPCQibh6u5GrsB8dxn3NtC/HkfA/3QUf1izFrXPXiY2ISbecPAF5QZJ4cP0+5T6pzKzuk/Dy88XH/9WOe4B6wfWZEPIzE0J+xs37yfF18eh5fPL44uRm7WbRO+oZvOgHVkz8lSsnL6dbTrnAClw/ew1dNh2+ftmZ3G08FetXRpOJz+rrru04fmwbx49t48GDh2zbvofExCQiI6PYf+AwH35YFIBFi1dSoWIQtWo3Izo6hitX0maGSpYMQJIkLv97jebNPqXNZ13Jl8+PAgXyvnJMz+Lh5kp4hDVrHB7xCHdXawbEx8uD0LAIW72HYRH4eHmmmXfqnCX07BLM8tXraNagLn27dWTWwuXYU0p4DJrU/V/j7UpKRGya6Qq9huyta3Jv4Vby9m/BhZ6/EH3kEr7NnnuZybSnrzEAkrMLqg8Kk3LscPrKr+Ea87pYkN/Y612V0aecskmSlE+W5esAkiTlBbLZKwjJ2QXMZuSEeNBoUJcsS9Ifv5Fy+ADqD0uTvD0EVfGSWO7dTTOfvmlrktb/CWYzklYLyNYxI9pXv/A8dmrpDk69ZLBs6OnruOX1xSW3F3GhjyjcoCKbes60TVeolJTpVI817SfiltfXdjeiUCpQalSYDK92x3Fi6Q5OZGAAb/3xnYm8ep9j8zc/c3r1vs3ZPHABCrUSSWlty8qyBXUmLtSPGVbOx7DSemesKlIC7aetSPxlzEvmAm2DVqRsWWPddhqN9TOyWECT+QGJqxf/xerFf6UrH/bzYA5sP8jeLftRKBQ4OTvi6uFKdGQ0ZauU5uLpS7a6SpWSNl+2oFfbAeTJm8t2969QKFCr1SQnvdpd2fGl2zm+dPsL6zi4O2E2mUmOTUSlVZOvWjEOznpyt6hQKanQqR4r2k/AI6/v490JKZP7E8CuZVvYtcw6gNc9hyfdZ/dnXu9pPLzxIF3dCg2rcmTDgWcup0mf1iwZNBulWomU2l0hW2Q0+ldvPGxZGsKWpdbB7r5+TxpEeYvlQ6VRExcVh0qtYsDcwez9czeHQw6mW4ZSpeSTTg0Y034E2fPmeLL9lApUGjUpr/hZzZq9hFmzlwBQuHABpv08GqVSiUajpnz5UkydNg8ALy8PwsMjyZ07B40bB1GlaoM0yxk+bABdvxmAWq1GqbSOP7JYLDikPmGTVTWrVmTd5h10btuSdZt3UKtaJVv5ij83EFSnBmfOX8LRMZutawrg2MkzeHu645c7JwZDMgpJgSQpSLJz9iFi63Gyt6rBrenryN6qBhFb0g7Uz/NNQ+7M34xsMqPUPTkfKDKxHz3L864xAJoqNUg5egiM6feN13GNEV6fjDZoegN7JEm6DkiAH6ldSvagcPfAsc9gUChAkkg5sAfjsUOYLpzFsf/36Bq3QE5KIn7aeNs8krsHqkJFSFphPdkYNqzBZcoc5IR44kYOed6qXomDlwttN45E46hHtlgo06kei2p/R0p8EjuHLqHZsgEolArOrtpL5L9PBouVbFeH83/sx2RIIfzibVR6Le22jeXG7lMkxyZmKaZsXi603zASbWpM5TrWY16d7/AunJvizaoRdvE2Hf+vvfuOb6r6/zj+Okm6oLulFJCNsodSpgwZVQrKUEEcoDJVBEUUmV+/imxFEZCNCKKCCrL3nrJ3kb1XKXTSleT8/kgolBYIkK7f9/PkkUeTm3Nv3tx7kpx77rk3SwYDsH7kHE6s3QfAk89X5dL+U8TZj2NfPXyGjsuHEnHkLFfDz97z9R6V6wsv4/5SW5SvP17Dp5Ky5x8SJtuOYCq/AEwlyxL/l+2YdNLyeXgNnoCOjyP+2wFOz3Inq9XK6K/GMX7O9ygF4fuPMu+OQ0lt3n2ZRXOWkZSQxLHDJ3D3cOf3NdPZvHobcTFx91nyg+XN50OnhV+nbrsaHcIY37g3nkG+tBj1HspgQBkUhxf9w7E1t8exhLQPZZ+9Pl0JP4uLuytdlw/juBPqE0CLHq3x9POi3dedAbCYLXzV/HPANui3fJ3K/NxvYrr5nn6+OqcPnCDqqq138tzhUwxaNopzR85wLvzMY2WqGVaL+q80xJxiJjkpme+62d77tV6sQ9nq5fH09eK5VxsCMO7T0Zw+bDt00KR9M9b9uYbkxGTOhJ/Gzd2Nb5f/wJ61u1IHPj+qI0eOs3zFWvbsXoXVamXatN84dMjWQ/TH7Mn4B/iRkmKmR4/+REff7oFo3vwFdu3ex6VLtqP0+/YdYs/uVRw4EM7+/YcfOsdnXwxjx579REXF0KjlW3zQsR2d2rWh18AhzF20nILBQXw7yNZLWq9WNTZu3UFYmw54uLszqF/P1OVorZk4/Xe+HdQXgFdbhNHnyxGYLRYGfvrhI6+n8hM+wrd2OVz8vai9ZzynRs7hzJi/qTC5JwXeaEjiedtp27e45vfD++lSnP7WNnbm3NRlhCwbijkmngPvjHzkHHe613cMgFu9hqmNmztlxXeMM+Xks4+yinJ0gI9Syg0oY394RGvtUBM+sln9HNc/9dOBwtkdIZ2U+x/yzhbv186ZI/obr3v0M0QySwvXItkdIZ2TKvHBhbJYrE7J7gjp/H3JeQPknSnhonMuF+BMG8v3ye4I6VQOuedwzmwVsHh9ln6qv1TkxSz7rl14dlEO/MZysIdGKeUCdAXq2SetU0pN1DoHfjoJIYQQ/2Pkpw8cP+Q0HnABbg0UaWef1ikzQgkhhBBCPAxHGzTVtNZ3npO7Rim1LzMCCSGEEOLh5OSzj7KKo1dRsiilSt56oJQqATjnkqBCCCGEEI/J0R6aT4G19rOcAIoB72ZKIiGEEEI8lJx8Bd+s4miDJgCogK0h0xKoBaS/ypYQQgghRDZw9JDTQK11DOANNADGYhsULIQQQohsJr+2/RBjaOx/mwGTtdaLAedcwlEIIYQQ4jE5esjpglJqIhAKDLdfZC9zfpZVCCGEEA9FrkPjeKOkDbAceEFrHQX4A59lWiohhBBCiIfgUA+N1vomMPeOx5eA9L9iJ4QQQgiRDeSwkRBCCJHLWdFZdnscSil/pdRKpdQx+1+/+5T1VkqdV0qNdWTZ0qARQgghRFbpA6zWWj8JrLY/vpdBwAZHFywNGiGEECKX01pn2e0xtQB+tt//Gdu17dJRSlUF8gMrHF2wNGiEEEII4TClVBel1M47bl0eYvb89nG4AJexNVruXr4B+BbbrxQ4zNHTtoUQQgiRQ2Xlj1NqrScBk+71vFJqFRCcwVP971qOVkplFPwDYInW+rxSyuFc0qARQgghhNNorRvf6zml1BWlVAGt9SWlVAHgagbFagF1lVIfAJ6Aq1IqTmt9v/E2qMz+Qatvi7yV4672UyYp5/1QuDEHXhTpmGvObO9WNSdkd4R0/L1yXqaT0d7ZHSGd6s/kvKs9WBIc3wPMSofCg7I7Qjp1Dw3L7gjpDKs6MLsjZGjgmVlZWrGee6Jxln2JrDu/6pH/b0qpkUCk1nqYUqoP4K+17n2f8u8AIVrrDx+0bBlDI4QQQoisMgwIVUodAxrbH6OUClFKQxiuEwAAIABJREFUTXmcBefMXXAhhBBCOMyayUdbnEVrHQk0ymD6TqBTBtOnA9MdWbb00AghhBAi15MeGiGEECKXyx39M5lLemiEEEIIketJD40QQgiRy2XldWhyKumhEUIIIUSuJz00QgghRC4nPTTSQyOEEEKI/wekQSOEEEKIXE8OOQkhhBC5XGb/jFFuID00QgghhMj1pIdGCCGEyOVkULD00AghhBDi/wHpoRFCCCFyOS09NNJDI4QQQojcT3pohBBCiFxOznKSHhohhBBC/D+QI3to3Hzy8MLILvgWDcKclMLyTycTefQ8Hv5etJj8MW7eedg88k+Or9gFQIspPVnV/yfir0RlWiaTdx4qjeqCV5knQMO+nhMJbhJCvkZViDl4mn3dxwNQ6JU6uAR4cXrS0kzLckvRrk154o0GAMSFn+XARxOo8F1XvMoW5urKPRwb8jsAJXq2Iu7IOa4u3ZnpmZ7u2ozSrWoDYDAZ8CtViClV3kcZDDSb/DFuPnnYNvJPTi63bbtmU3uyrp/zt13JUR/gHxpCyrVo9jboCUDh3m3xf6E6WK2kREZz7KOxpFy5gX+zmhT5rC3mqFiOvDsc84043Irmp2jfNzn63iin5DEFB1JgRC9MgX6gNVGzl3Fjxnzy9e6AZ8Ma6GQzKecucanPd1hj4/F4phz5/9sNnWLm4ifDSTlzEYNXXgqN7su5jgMhE/bG8pYswNOTPkp97FE0iGMj/sAtyI98jSoTc/AM+7v/CEDBV+rgmln13MUVn5E/oFxcwGgkedN6bv7yEwB53u6EW53n0FYriYvnk7jgL1yfrUeedh3QsbHEfNUfHRuDoUBB8r7dmdhhXzonk6sr/qNHg4sLymgkcf164qdPx/uzzzCVLg1KYTl/nphhw9AJCXi0akWe5s2xXLlC1IABYDbjUrEibvXqETdunHMyAWW+f5/A0GdIvhbN9vqfAmDyzUuFST1xL5yPxHMRHOz8HeboePI1q0Hx3m0wR8Wx/52RmG/E4VE0PyX6v86hLt8/Vo4BQ0axYfN2/P18+fuXCQBEx8TSa+BQLl6+QsHg/Hw7qC8+3l5orRn6/QQ2bt2Bu7sbg/v3olzpUpw6c57PvxxOitnMF717UKVCWcxmC+/1GsCY4V/g4e7+WBkDShag+TddCS5fjLXfzGHbpCUAGN1ceHvOQEyuJgwmI+FLtrP+u78AaDn6A4JKF+bY6j2sHTkHgDrdWxLx7zn+tX//5BRyllMO7aGp0a0FEYfPMOOFfizrOYEGX7YDoEyLWuz7ZQ2zXvqCZzo2AaBE46e5euhMpjZmAMp//TYRa/exvs6nbGj4OUmXb+BdqTgbG3yONcWMV9nCGNxdeOL1+pyZtiJTswC4BftRtFMTtr7Qj831PwODgcLtGmFJTGZzg8/xqVICk5cHbkG++D5TKksaMwB7Ji7m9yb9+b1Jf7YMm8OFbeEkRcXzVItaHPxlDXNe/ILK9m1XrPHTRBzMnG0XMWcdh98YlGbaxR/ns6/RJ+wL/ZTrK3dR+JPWABToEMb+sN5cnrmSwFZ1ASjy+RucHf6b0/Joi4Wrw6Zwqul7nGnzCX5vvohrycLEb97DqWbvc7p5N5JPXSCgaxsA/Du04nzn/3B1yET8Xm8KQMAHbYmcMDtTGjMA8ScusalRH9sttC/WhGQuL9mBT6VibGrwOTpNPX8u8+p5SjLRfXoS1a0jUd064lK1OqYy5XALDcMQGMSNLu2I6tqepPWrAfBo/jJRH3UlcckC3Bo0BiBv+07Ez5jivEzJydz45BOud+pEZKdOuFWvjku5csSOG8f1Tp243rEjlitX8GjVCgD30FAiO3Qg5dAhXKtVs2Vq1474GTOclwm4/Ps69rYdkmZa0e4tubHxANtqfcSNjQco2r0lAE90bMLOJn25MGMVwS/XAaBE37acHPr7Y+do2TSUCaO+TjNtysw51AypwpLZU6kZUoWpv9gaBBu37uDs+YssmT2V//buwaBvxgLwx/wl9PmoK+O/+Yrpv9oaFLPnLeLF5xs+dmMGICEqnmVfzGDb5MVppluSUpj5+mAmhfVjUlg/StavRKGnSxFUpjDmxGQmNelLwcolcPPywDPIl0JVSua4xoywyZENmoAnC3F2y2EArp+4hM8TgeQJ9MaaYsHk4YrRzYTVakUZDTzTsQk7xi/K1DwmLw/8a5Xh3Ky1AOgUC8k34jCYjAAYPdywplgo8f6LnJ66HG22ZGqeW5TRiNHdFWU0YMzjBkphdHcFpTCYTGiLlVKft+b4iD+yJM/dnmpRi2PztwJgNd/edtpi23ZVOjZhdyZtu5hthzHfiEszzRKXkHrfmMeN1B0aq0a5umD0cEObLXjVKEtKxA0ST11yWh5LxA2SDp+wvVx8AkknzmLKH8jNzXvAYgUgYd8RTMGBAGizBeXhjnJ3Q5vNuBQOxqVAPm5uP+C0TPcTWLci8aevkHIjDmWydeTerucvcWbqssyt54n2bWUy2V5fa9ybteDmrz+nNuh0tK0hrK3a1pvj7g5mM6bylbDeiMR68YJTI+mE25kwmdBao2/eTH1eubmlZlO3sru5gcWCe2goydu3o2NjnZopals45qi09TywSTUuzV4PwKXZ6wkMq2b/D2gMri4Y87hiTbHgU6MMSVejSDh1+bFzhFSpiI+3V5ppazdupUWYrYHZIqwxazbYPgvWbtpG8yaNUEpRuUJZYmPjiLh2HZPJSEJiEomJSZhMRmJi41i3+R+ahzV67HwANyNjuLT/JJaU9PU25WYSAAaTEYOLEa217TMr9fPUiNVipf4nr6b23uQ0Wussu+VUDjVolFKrHZnmLBHhZ3mySQgAwZVL4F0oEM8C/oTP30Kp0Kq8OqsP28cuoEr7xoT/tQlzYnJmRQEgT5EgkiNjqDT6PeqsGkrFUZ1Ba66u3kud1UNJuhKFOeYmvlVLcSWLekKSLt/g9PhF1N89jgb7J2COucmZyUtJjoyh9qqhXF2xizzFg1EGAzEHTmdJpjuZ3F0p+lwlji/dAcDRv7dQ4vmqtPi1DzvHLqBi+8YcmZv52+5uRfq8QdWdE8n3cj3OjrTtmZ4fM5fyc77ALzSEa/M2Ubjnq5z/7s9My+BSKAj3ciVJ3HckzXTfV54nfoOt/kROnEPBEb0I6NqGGzMXku+Tt4n4zrl79/dToFUtLs3bgiU+kYjVe6izehiJV25kXT03GPAdO4WA3/4mec9OzP+GYyxQELf6DfAZPRHvr0ZgKFgIgIQ5s/AZMgq3GrVJWreaPG+05+avmbCuDAb8p0wh399/k7xzJ+bwcAC8P/+cwLlzMRUpws25cwG4OW8e/j/+iCF/flIOHMAjLIyb8+Y5P1MGXPP5kHzV1thLvhqFaz4fAE6PnkeVPwYS+HwIV+Ztovgnr3B6VObV88gbUeQL9AcgMMCPyBu2TFciIgkOCkwtlz8okCsR13j95ZeYPHM2/b/+ls7t2zJx+m90bt8WgyHz97uVQdF5yRB67R7PqY0Hubj3BNeOX+RmZAydFw/m2Oo9+BcLRhkUlw+ezvQ84tHcdwyNUsodyAMEKqX8sO94AN5AofvM1wXoAvCqX3Vqej75UKG2/7iQBv9tR7ulg7n27zmuHjqDtlhJjk1g3rvfALZxNtU/eJH5nb8ndHhH3H3ysnPSEi7tPv5Qr+UIZTLiXbE4h/pNJ2r3Ccp93Z6S3ZtzdPgfnBy3EICKozpzdPgfFH6zAYH1KxEbfpbj32XeB5jJJy9BTaqyvlp3zNE3qTLlYwq8UocjA29/kD8z8zMOfTqFEh+3xKtcUSI3HOD8L2syLdOdioc+zaUdR0mKigcgOTaBhe/c3nZVP3iRJZ2/p+Hwjrj55GXPpCVczoRtd7ezw37l7LBfKdS9FQXeDePcN7OJ3rCf/Rt6A5CvdX1urN6Ne4kCFHq/BeboOE4NnIY1wTkNL5XHnUJj+nNlyCSs8bd7jALeew1tsRCzwNYLmBR+kjNtPgHAI6QC5qvXQSkKft8HnWLm6rApWCIz5zCrcjGS//mq/DvY1uA7OW7hHfW8C0eHz+GJNxuQr34lYsLPciIz6rnVStSHnVB5PfEa+DXGosVtvTDJyUR/1BXX2nXx6tmH6M+6k7JnJ1F7bA0st0YvkLxjG8YnCuPxymvo2DjiJv4ASUlOyXS9UyeUpye+gwZhLF4cy6lTxAwfDgYDXj164N6gAYnLlpG4ciWJK1cCkLd9e27OnYtbjRq4v/AClqtXifvxx0w7dJiO/XVubDjAzuf7ABDcuh7XVu8hT8mCFHn/JczR8Rwd8JPT6vndlFIope5bpkBwENPHjgDg7PmLXL56jRLFCtPnq5GkpJjp3rkdxYo8kSn5tFUzuWk/3Lzz0GZST/I99QQRR8+z4qtfUsu8NrUXi/tOpc6HLchftggnNx5kz+9rMyXPo5AxNA/uoekK7ALKALvt93cB84Gx95pJaz1Jax2itQ5xtDFTpX1j2i0dTLulg3HJ68byTycxM6w/Sz+egIe/F9FnI9KUr9mjFdvGLKBMi1pc2HGUpT0nUrvnyw691sNKvBhJ4sXrRO22HTK4tPAffCoWT33eu0IxlFLEn7hEgZdqsKfLaPIUDSJP8eBMyQMQUK8CCWcjSImMRZstXFm8Hb9qT6U+H9SkKjH7TmHM606eYvnZ12U0wS/WwODh6vQsFd9uTNtlg2m7bDB58/sC8GTzWhxdsDXD8tU+asXOMQt4qkUtLu44ysqeE6n+SeZsu3uJmLuRgGY100wzeLgS1KYBl39aRuHP2nKsxxhi/jlC4Mv1nPOiJiOFxvQneuE64lZsSZ3s06oxng2qc7HXyAxnC/ygLdd+/I3AD9/g6ohpRM1Zhl/75s7JlIF8jaoQfeA0yRHRaaZ7VygGqfW8Jnu6jCZv0fyZWs91fBwp+/fgGlIdy7UIkjZvACB5y0aMxUukLezmhlvjJiQunEeet94l9puhpBzej1uDUOdmiosjec8e3KpXvz3RaiVxzRrc69dPU9YQEIBL2bIkbdpEnjZtiP7yS3RcHK7PPOPUTHdKjojGNcj2PnQN8iX5WkzaTB6uFGj7HBemLaf4Z6053GMcUf8cIfiVuk7NEeDnS8S16wBEXLuOv6+tpyh/vgAuX72WWu7K1WvkzxeYZt7RE3+mR5f2zPpjPq+89AK9unVg/LRZD50hpH0onZcMofOSIXja18n9JMXc5PSWw5R8rlKa6U+FVuXSgVO45nXHr2gQf3UbQ9mm1W2HpESOcd8GjdZ6tNa6OPCp1rr4HbfKWut7Nmgexd4Zq5gZ1p+ZYf0xJyRjcLGNT6n4+nOc336E5DvGP/gWy49XAX/ObwvH5OGGtlpB60yrXEkR0SRejCRvyQIABNatQOzR86nPP9WnNf8O+wNlMqKM9lWqNcZMaDzcknghEp9nSqU2UALqViDumG3MgDIZKdqlKSfHLcDo7nJ7rIjRgMHF+Se2Hfh5VepA4PgrUbh6eVCoZhlOLt+drqxPsfx4BvtzIYu23Z3cixdIve//QjUSjqcdY1Hw/RZcmroEbbZgcHcFtH07ujnl9QsM+ZjkE+e48dPtHo28davi3/lVzr/3JToxfS+Cd6tGxK3fgTU6DoOHG9jXl8HdOZkyUrDVs1yatznd9Kf6tOHosDlp6rnOhHqufHxQeT1tD1xdcX06BPO5syRv3YRLZVtDwKViFSwXzqeZz+OVtiQu+AssFpSrG6Bt46PcHn9dKR8flOcdmUJCMJ89i7HQ7Y5qt2efxXz2bJr5PDt0IG7aNNsybo2xsVpRThjkei/Xlu+kwGu2hlWB1+pzbdmONM8X+aA556YsRZsttjF39kzO3tl5rk5N5i9dBcD8patoULdW6vQFy1ajtWbfwXA8PfOmHpoC2LFnP0GB/hQtXIjExCQMyoBSBhIeoZdt54yVTG7aj8lN+xF3NeMezTz+Xrh55wHA5OZCiboViDx+e/ycwWSkRscmbJmwCBd319SONWU0YHTNOScK6yz8l1M5ujUmKqV6ALd2VdcBE7XWKZkRyr9UQZqM6goaIo+eZ3nvyWmer9O7DZtG2EbMH5m/lZaTP6b6By+x5dvMG6x1qN90qvz4IQZXEzfPXGHfRxMByB8WQvTekyRduQFAzMEz1F03nNjDZ4k9fPZ+i3ws0buPc2XRP9ReORRtsRJz4DTnZtqGNRXp8DwXZ2/AmpBM7OGzGD1ceXbdCCJW7cUcc/MBS358JZqEcHbDAcwJ6T+AavVuw1b7tjs6fyvNpnxM1Q9e4h8nb7snf+yJT+3ymPy9qLprEue+mY1fo2fwKFkQbdUknY/g5OcTU8u75PfD6+knOT/KNoD68tQlVFo6AnN0PEc6DH/sPB5Vy+HTshGJR05RbP4YACJG/Uz+Ae+hXF0oPH0wAAl7/+XKF7Z9BeXuhk+rxpzrMACA6z/N44nJX0FKChd7jXjsTBkx5nEjsF5FDn6a9j2XcT0fQUwm1HODXwBen/YDgwGUImnjOlK2b8V86ABevQfg0bI1OjGBuO9vrwODfwAupcuS8OvPACQsmIvv6InouDhiBvV/7EzGgAC8+/YFgwFlMJC4di3J27bh98MPqLx5UUqRcvw4sd99lzqPqVQpAMzHjgGQuHo1AdOmYYmIIP73xz+zCKD8hI/wrV0OF38vau8Zz6mRczgz5m8qTO5JgTcaknjedtr2La75/fB+uhSnv7WNnTk3dRkhy4ZijonnwDsZ9xA64rMvhrFjz36iomJo1PItPujYjk7t2tBr4BDmLlpOweAgvh3UD4B6taqxcesOwtp0wMPdnUH9eqYuR2vNxOm/8+2gvgC82iKMPl+OwGyxMPDTDx85H0DefD50Wvg1bp4eaKuVGh3CGN+4N55BvrQY9R7KYEAZFIcX/cOxNXtS5wtpH8q+PzdiTkzmSvhZXNxd6bp8GMfX7iUpCz5PheOUIyOWlVJTABfgZ/ukdoBFa93pQfN+W+StHNecK5OUNWchPQxjDmz1HstBex93qmpOeHChLObvlfMynYz2zu4I6VR/xnlnjjmLJeH+Yzuyy6HwoOyOkE7dQ8OyO0I6w6oOzO4IGRp4ZlaWVqxKwbWy7Etk/+WtOfJN4+g3VjWtdeU7Hq9RSu3LjEBCCCGEeDjWHHw6dVZx9Hw4i1Kq5K0HSqkSQM7r5hBCCCHE/yRHe2g+A9YqpU7aHxcD3s2UREIIIYR4KDl5sG5WcbSHZjMwEbAC1+33Mz4nVwghhBAiiznaQzMDiAFu/TjOG8BMoHVmhBJCCCGE42QMjeMNmgpa63J3PF6rlDqcGYGEEEIIIR6Wo4ecdiulUi+rqpSqAWTNjxYJIYQQ4r7kwnqO99BUBbYopW5dQasI8K9S6gCgtdaV7j2rEEIIIUTmcrRB0yRTUwghhBDikckYGgcbNFrrM5kdRAghhBDiUeXMa9sLIYQQwmE5eWxLVnF0ULAQQgghRI4lPTRCCCFELidjaKSHRgghhBD/D0gPjRBCCJHLyRga6aERQgghxP8D0qARQgghRK6X6Yec3u8XkNkv8dD+M+xqdkdI54JOzO4I6Uz9KF92R8hQ8MAt2R0hnfZu1bI7QjqR7jmvTrXffCm7I6RT0btodkfI0J8hV7I7QjrDqg7M7gjp9Nk16MGF/gdobc3uCNlOemiEEEIIkevJoGAhhBAil7PKoGDpoRFCCCFE7ic9NEIIIUQup+XCetJDI4QQQojcT3pohBBCiFxOxtBID40QQggh/h+QHhohhBAil5MxNNJDI4QQQoj/B6SHRgghhMjlrNJDIz00QgghhMj9pIdGCCGEyOW0nOUkPTRCCCGEyP2kQSOEEELkclrrLLs9DqWUv1JqpVLqmP2v3z3KFVFKrVBKhSulDiulij1o2dKgEUIIIURW6QOs1lo/Cay2P87IDGCk1rosUB24+qAFS4NGCCGEEFmlBfCz/f7PQMu7CyilygEmrfVKAK11nNb65oMWLA0aIYQQIpezorPsppTqopTaecety0NEza+1vmS/fxnIn0GZp4AopdRcpdQepdRIpZTxQQuWs5yEEEII4TCt9SRg0r2eV0qtAoIzeKr/XcvRSqmMBuWYgLrA08BZYDbwDjD1frlyTIMmNjGFL1ce5ERkHErBF6EVqFzQNlZoxq5TfLfhX9a81xA/D1dWHbvM+C3H8XF3YVTzp/H1cOVc1E3Gbj7K8GZVnJYpX8mCvDayK0+UL87Sb2azfvLi1OfqvNuEmm0bglL88/saNk5bCkCzPq9Tun4VLh4+ze+9xgPwTMs65PX3Si3zOKqGVqdNrzewWjVWi4UZX07l353hAPT5+T+Uero0/+48zMgOg1Pn6Ta6J0VKF2X36p3MHvkLAK26t+bcv2fZueKfx86EmweuoW9jCCwIGpJXTMd64zJuzbqivAPQMZEkLZoISTcxPvkMLrVaoBPjSVowDhLjUT75cKnTiuTF93x/PJI6dWswbMRAXEwmIiNv0LTJ67i5ubJsxWxc3VwxGY3M/3sZQwZ/D8CUad9Rrnxpli1dw1f//QaAz3p34/DhoyxetPKx84S0qEPoe81RSpEYn8DsAVO5EH4G3wIBtB/VDa9AH9Cazb+tZt1PtrrSos8blKtfhfOHzzCz1zgAqrWsQ15/b9ZNW/LYmSCH1ing2TrV+XpYf1xcTFyPvEHzpm8BMHrcEJ5v0oBrEZHUrfliavn/fPkpjULrcfDAEbp17Q1A69ea4x/gx8Qff87wNR5F6cpPMW7+D3zVbTAbFm8E4IVXQ3nrozcB+GX0LJb/uRIXVxe+nvYl+YIDmT9jIfNnLASg1/CPWTBzEccOHn+8IC6u+Az/AVxcwGgkefN6Emb9hPfwMag8HgAYfPwwHw0n9usBuNauh8dbHdCxscR+3R8dG4MhuCB53u5M3PAvHy/LHQJKFqD5N10JLl+Mtd/MYdskWz01urnw9pyBmFxNGExGwpdsZ/13fwHQcvQHBJUuzLHVe1g7cg4Adbq3JOLfc/y7Ytcj5RgwZBQbNm/H38+Xv3+ZAEB0TCy9Bg7l4uUrFAzOz7eD+uLj7YXWmqHfT2Dj1h24u7sxuH8vypUuxakz5/n8y+GkmM180bsHVSqUxWy28F6vAYwZ/gUe7u5OWGPOkZN++kBr3fhezymlriilCmitLymlCpDx2JjzwF6t9Un7PH8DNXlAgybHHHIasS6c2sUCmfdOXWa/9Swl/D0BuBybwLYz1wj2ul1xft97hl/eqMUrlQqz9Iit52rclqN8UPtJp2ZKiIpj/n9/Zt3kRWmmBz/1BDXbNmR0iwGMCvucsg2fJqBofty9PChUvjijwj7HkmImuHRhTG4uVGtdn80zVjgl08HN+/m8ycf0bdqTiZ+NofPwbqnPLZz0Nz/2/D5N+SJlipKcmMznTT6mZOVSeHjlwTfIj5JVnnLaF4/rc22xnD5I4vT/kDjzS6zXL+FSLQzL2XASfxqA5Ww4LtXDADBVaUjir4MxH1iPqUwNAFyebUnK5r+dkuUWHx8vRn33FW1bd6FGtSa0b/chAElJybzY9E2erdmMZ2u9SOPQelSrVoXyFcqQkJBI7RpNeaZqJby9vcgfnI+QalWc0pgBiDx3le9f+5IhTT5j2Zi5vD60MwBWs4W5X89kcGgvvmk1gHrtnie4VCHcvTwoXL44Q8N6Y0kxU7B0YVzcXKjZ+jk2zFjulEyQM+uUt48XI0b9l7favkedGs3o0L5H6nO/z5rLay93TFPey9uTSpXLU792c1KSUyhb7inc3d14/c1XmDppllMyARgMBrr068SODbe/ZL18vWjfsx0fvNSd91/8kPY92+Hp40m1+iEc2H6QjqFdCX3F9vlesmwJDEbD4zdmAFKSie7Xk+juHYnu3hGXqtUxlS5HzOfdie7eiejunUg5coikLbZGl/tLLxPdsyuJyxbg9pwtT572nbg5c8rjZ7lDQlQ8y76YwbY7dgABLEkpzHx9MJPC+jEprB8l61ei0NOlCCpTGHNiMpOa9KVg5RK4eXngGeRLoSolH7kxA9CyaSgTRn2dZtqUmXOoGVKFJbOnUjOkClN/sTWeNm7dwdnzF1kyeyr/7d2DQd+MBeCP+Uvo81FXxn/zFdN/tTW+Zs9bxIvPN8xRjZlcZgHwtv3+28D8DMrsAHyVUvnsjxsChx+04BzRoIlNSmH3hRu0qvAEAC5GA17uLgB8s+4IH9UtjVK3yxuUIsViJTHFgsmg2H3+OoF53Cjql9epueIiYzi3/yRWsyXN9KBShTiz9zgpiclYLVZO/hNOxSbV0VaN0cV2mM/Fww2L2cJzXV5k08/L0y3jUSXdTEy975bHHe64mNKhzftJiE9IU95stuDq7opSCqPJhNVipfUnr/Pnd785JQ+uHhieeArLwU22x1YLJCVgLFkF8+GttgyHt2Isae850xqMJjC5gtWCodCT6PhodNQDB7A/lNZtWrBwwXLOn78IwLWIyNTn4uNtY8tcXEyYXExorUlJScHDwx2lFC4mExaLhf4Deqb23jjDqd1HSYiJt98/hm9wAAAxEVGcP3QKgKT4RC6fuIBvsH+a+uTq4YrFbKFRl5dY//Myp9UnyIF1Cnil9UssWriCC+dtOyzXrl1PfW7rlp3cuBGdpry2alxcbB3OHnncSUkx061HRyZPnInZbHZarlbvtmDjkk1EXYtKnVatfgi7Nu4iNiqWuOg4dm3cRfXnqmE2m3H3cMfkYkLZP8De/ewdpo10Xm8RifZtYzKhjCbu3HbKIw8ulZ8hZautQYPWKBcXlJs72mzGVL4S1huRWC9ecF4e4GZkDJf2n8SSkr6OptxMAsBgMmJwMaK1xmq2YHJ3BaUwmIxYLVbqf/Jqau/NowqpUhEfb68009Zu3EqLMFtjrkVYY9ZssH1Grd20jeZNGqGUonKFssTGxhFx7Tomk5GExCTQsTKpAAAW2UlEQVQSE5MwmYzExMaxbvM/NA9r9FjZMoNV6yy7PaZhQKhS6hjQ2P4YpVSIUmoKgNbaAnwKrFZKHQAUMPlBC3a4QaOUaq6U+sZ+e+kR/hP3dDE6AT8PV75YcYC2v2zmy5UHSUgxs/bEFYI83SmdzztN+Q7VSvDeXzvYcPIqTcoUYPI/J+hcs6QzI93X5X/PUaJaGfL4euLi7kqZBlXwLRBAUnwi4Wv30nPJUGKvRpEYc5MiVUpxaMVOp75+yAs1+Gb1WHr/NICJn429b9mLx88TGxnDkMWj2L16B8HFCqAMBk4fPOmULMonEJ0Qi+sL7+L+1kBcQ9uDyRWVxxvi7V868dG2x0DK9iW4v/oJphKVMR/ZjkuNZqT8s+g+r/BoSj1ZHF9fHxYv/ZX1m+bz+hutUp8zGAxs2rqIE6d3sHbNZnbu3MfRf09w7dp1Nm5ZyNKlqylRsigGg4F9ew85PRtA7dcacHjd3nTT/Z/IxxPlinN673GS4hM5tHYvfZYMJ/pqFAkxNylWpRT7nVyfIGfVKYCSpYrh6+vD/MUzWb1+Lm1eT3ciRBpxcfGsWrGetZvmc+VyBLExsVQNqczSxauclikwOIC6YXVSDx3dOf3qxYjUxxGXrhEYHMDODbsILpyfcQt+YO60edQOrcWxg8eIvBJ596IfncGAz5gp+M/6m5S9OzH/G576lEutuqTs3YVOsDXgE+bMwnvwKFyr1yZ5/Wo82rYn4bcZzsviAGVQdF4yhF67x3Nq40Eu7j3BteMXuRkZQ+fFgzm2eg/+xYJRBsXlg6ed/vqRN6LIF+gPQGCAH5E3bA3TKxGRBAcFppbLHxTIlYhrvP7yS0yeOZv+X39L5/ZtmTj9Nzq3b4vBkCP6AnIlrXWk1rqR1vpJrXVjrfV1+/SdWutOd5RbqbWupLWuqLV+R2ud/KBlOzSGRik1FNt54Lf6bnsopWpprfvdo3wXoAvAmDca0qFuhfsu32zVHLkaw+cNylKxgC8j1oYzYetxdl+4wY8vh6QrX7NoIDWL2irfwsMXqFM8H2du3GTGzkN4u7vw2XNl8XB54IDoR3b1xEXWTlhAl5l9Sb6ZxMXDZ7BarQCsm7iQdRNtH3ith3Vm+ag/qP5aA0rXrcTFI2dZPXbeY7/+zuX/sHP5P5SpXo7Wvd5gyJtf3Lf8jK9uH3b8dGp/pvb9kZYfvkrRssU5sHEva35/9EMqymDAEFSEpDW/Yb18CpfnXks9vJSWrVVvPRtO4ixbN7CxbC0spw5g8AvGVPV5SIonee1sMD+w3j6QyWikytMVeKnZW7h7uLN6zV/s2L6X48dPYbVaqVPrRXx8vJj12wTKlnuK8MNH6dN7UOr8s/+YzEfd+/PpZx9QoWJZ1q7ZxM/TZz92LoAna5Wn1msN+e7V/6SZ7prHjU7jP+Gvr34mMc62571q4gJWTVwAwBvDurJo1B/Ueq0hZetW4sKRsywfO9cpmXJSnQIwmUxUrlKel196G3d3d5atns2uHXs5cfz0PecZM3oKY0bbDp98P2YwwwaP5q32rXmu4bMcPvQvo0aOf6xM3f77AROHTHF4rILVYuXrD4cCYDQZGTFrKAM6fMEH/+lKUKEgVvy5ii0rtz5WJqxWort3QuX1xGvA1xiLFsdyxtbb51a/EUnLb+8spOzdSfRHtsawa8MXSNm5DWOhwri//Bo6Lo74ST9AUtLj5XkAbdVMbtoPN+88tJnUk3xPPUHE0fOs+OqX1DKvTe3F4r5TqfNhC/KXLcLJjQfZ8/tap2dRSqX2nN1LgeAgpo8dAcDZ8xe5fPUaJYoVps9XI0lJMdO9czuKFXnC6dkeRU4aQ5NdHG1mNgNCtdbTtNbTgCbAi/cqrLWepLUO0VqHPKgxA5Dfy50gLzcqFvAFoPGT+TlyNYYL0Qm89stmmk5dx9XYJN6YtYVr8bffcAkpFhYevkCbykWYsPUYg5pUpEpBP5Yeuejgfyu92u1C6blkKD2XDMU7KMMLGAKwfc46vn+pPz++9hUJ0fFcO3kpzfMFyxcDpYg4eYnKzWow88PRBBYNIrBYRgO/7y+0fRhDl3zH0CXf4XdHpiPbDxNUJD9efl73mfu2qqHVOXXgBG55PchfNJjR3UZSvWltXN1dHzrTLdbYG+jYG1gv2z5ELcd2Ywgqgr4ZA3l9bIXy+qBvxqad0eSKqXxtzPvW4VKrOcnLpmG5cBxj2RqPnKVzl3Zs2rqITVsXcenSVVav2sjNmwlcj7zB5s3bqVCxTJry0dGxbNywjcah9dJMb9qsMXv3HMTTMy/FSxTlnfbdadkqDA+Phz9mXq/d8/RZMpw+S4bjE+RHwTJFeGNYFyZ1Hkl8VFxqOYPJSOcJvdj59yb2Ld+ebjlPlC8GCq6evMgzzWoy7cPvyVc0P/keoT5BzqxTHTq/ydpN81m7aT6XL11l7epNtu13/QZbNu+gfIUyD14IULFSWVBw/NgpmrdqQqd3PqZY8SKUKFn0oTO1fLs5k5dPYPLyCZSu9CT/GdeP37bOpH6zunw8uDvPvlCba5cjCSqYL3WefAUCuXY5Mt1yVvy5inLPlCUuNp6v3h9Mm66vPnSee9HxcaTs34NL1eoAKG8fTE+VIXnHtvSF3dxwb9yExEXz8HjzXeJGDcV8eD9uz4U+8uuHtA+l85IhdF4yBM8g3weWT4q5yekthyn5XKU0058KrcqlA6dwzeuOX9Eg/uo2hrJNq9sOSTlBgJ8vEfbDlxHXruPva/uMyp8vgMtXr6WWu3L1GvnzBaaZd/TEn+nRpT2z/pjPKy+9QK9uHRg/zXnjs8Tje5iznHyBWweyfZwZIjCvG8GeHpy+Hkcxf0+2n4ukTJA3E1+tnlqm6dR1zHqjNn4etyv2jJ2neL1KUVyMBhLNVkBhUJCYwbFbR22ZuZItMx+8d+kZ4E1cZAy+BQOo2KQaP7RKu7fd5JPW/Nl3CgYXI8rePWm1alw8Hv6NuXLGUlbOsJ31kr/o7S+wYhVK4OLqQuyN2HvNmspoMhLW8SVGvDOI4OIFudWYNxgNmFxdSE58xF6RmzHo2Bsov/zoG1cwFimD9foluH4JU7lamHcsw1SuFpYTaQ+vmEJewLxntW3Mjck2XgqtUaZH/+CaPGkmkyfNBOCp0iX5ZtSXGI1GXF1dCKlWmXFjpxEQ6I85JYXo6Fjc3d1o0LAO34+aeDuXycQH3d6l9SsdKVmqWOpej8G+nISExAxf+142zFzBhpm2AeF+BQPoPKEXM3qO4+qptA3gN4e/x+XjF1gzdXFGi+HFT9rwW9/JGNPUJyuuHm4PleeWnFinpk2exbTJti+IJ58qyfBv/pO6/aqGVGbCuOkOLafvgI/5pMdATC4mjAZbT63VasXDw+Oh8gD8/fMC/v55Qbrpn4/6jK2rt7F5+Ra8fL3o9Pm7ePrYTmQIqVeVycOmpZb19PGkZqMa9H6zL7VDa6KttsvHP86OBNgaLVgs6Pg4cHXFpUoICX/+CoDrs/VJ3r4VUtJvA4+X25Kw4C+wWFBuboBGWzUGt0erSwA7Z6xk54z7f27m8ffCYraQFHMTk5sLJepWYMv42z1IBpORGh2b8Ns7IwkoHpxan5TRgNHVhPlRP6Pu8FydmsxfuopO7dowf+kqGtStlTr9t78WEta4PvsPHcHTM2/qoSmAHXv2ExToT9HChUhMTMKgDChlICGTe7QehlV+nNLhBs1QYI9Sai22wTn1uPflih/J5w3K0m/pfsxWK4V88vDl8xXvW/5qXCIHr0TTtVYpAF6vUpS3ft2Kl5uJUc2fcUomr3w+fLRgMO6eHmitqdshjJGhn5EUl0D78T3J6+eJxWxh7sCfSIy5fRHD8s+HcP7ASWKu3gDg4uEz9Fo2nEtHznIp/OxjZaoeVot6rzTAnGIhOSmJH7p9k/rcF38MoWDJQrjndWfstilM6j2W/RtsDYnn2zdlw59rSU5M5mz4adzcXRm+fDR71+7ipn2g6qNKXvsbrmGdUEYT1ugIkpdPB6Vwe7Erpgp1bKdtL77daFB5fTAGFyNpm+3QnHnPGtzf6I9Oumk7ldsJjv57glUr17P1nyVYtZUZ0+cQfvgo5SuUYcKkkRiNRgwGxby/lrBs2ZrU+bp0bcevv84lISGRgweOkCePB1u3L2XF8nVERz/4S/5+wnq8Sl4/T1772naGjtVsYUTzfpQIKU2NV+pxIfwMfZYMB2DBiN9Sx9hUej6EswdOEm2vTxcOn6bfspFcOHKWC+FnHisT5Mw6dezoCdas2sCGrQuxWq38MuMPjoQfA2DStFE8W6c6/gF+7A/fwPAhPzBr5p8AhNl71y5ftg0yP3ggnA1bF3L40L8cOnjksTLdS2xULDNHz2LCYtvYoxnfzyI26nZdaf/xW/wy5le01mxfv5MWb7dg2qpJLPjl8caOGfwD8PykHxgMoBTJm9aRssN2CMutXsPUxs2dlH8AptJlSfjNNjA5ceFcfL6biI6PI3ZQ/3TlH0XefD50Wvg1bp4eaKuVGh3CGN+4N55BvrQY9R7KYEAZFIcX/cOxNXtS5wtpH8q+PzdiTkzmSvhZXNxd6bp8GMfX7iUp5oEXiU3nsy+GsWPPfqKiYmjU8i0+6NiOTu3a0GvgEOYuWk7B4CC+HWQbNVGvVjU2bt1BWJsOeLi7M6hfz9TlaK2ZOP13vh3UF4BXW4TR58sRmC0WBn764WOuLeFMytHjbvbzxavZH27XWl92ZL6bEz7Kcc3G/wxz7hk1znBBJzy4UBab+lG+BxfKBsEDV2d3hHTa56v24EJZLFLnnL3HW1beeOCZl1muovfDH4rKCn9WfvweCWf78WDOGC9ypz67Bj24UDZwCSxx/wE6Tuadt0SWfdfGxJ/M0v+box5mqPatbzcTUFsp9XIm5BFCCCGEeGiOnuU0DagEHAKs9skacM4pFkIIIYR4ZE64Pkyu5+gYmppa63KZmkQIIYQQ4hE52qDZqpQqp7XOeQfAhRBCiP9xWs5ycrhBMwNbo+YykITtTCetta50/9mEEEIIITKfow2aqUA74AC3x9AIIYQQQuQIjjZoIrTW6a8uJYQQQohsJ4OCHW/Q7FFK/QosxHbICQCttZzlJIQQQohs52iDxgNbQ+b5O6bJadtCCCFEDiA/Tulgg0Zr/W5mBxFCCCGEeFQOXSlYKfWUUmq1Uuqg/XElpdSAzI0mhBBCCEfoLPyXUzn60weTgb5ACoDWej/QNrNCCSGEEEI8DEfH0OTRWm9XKs3vUZkzIY8QQgghHpKMoXG8h+aaUqoktoHAKKVeBS5lWiohhBBCiIfgaA9NN2ASUEYpdQE4BbyZaamEEEII4TDpoXG8QXMB+AlYC/gDMcDbwFeZlEsIIYQQwmGONmjmA1HAbuBi5sURQgghxMOS/hnHGzRPaK2bZGoSIYQQQohHpBw57qaUmgSM0VofyPxI983RRWs9KTsz3E0yOSYnZoKcmUsyOUYyOS4n5pJMwtnu26BRSh3A1pNlAp4ETmL7CQQFaK11pawIeUeenVrrkKx8zQeRTI7JiZkgZ+aSTI6RTI7Libkkk3C2Bx1yejFLUgghhBBCPIb7Nmi01meyKogQQgghxKNy9MJ6OUVOPLYpmRyTEzNBzswlmRwjmRyXE3NJJuFUDg0KFkIIIYTIyXJbD40QQgghRDrSoBFCCCFErpejGzRKqS33mD7d/gOZ//OUUsWUUgezO0dup5Q6rZQKzO4c2SWn16Ocni83Uko9p5RalN057qSU+lgplcdJy5I68z8mRzdotNa1szuDEOL+lFKOXnFciAf5GHBKg0b878nRDRqlVJz9r1JKjVVK/auUWgUEZWGGv5VSu5RSh5RSXezTOiqljiqltiulJiulxtqn51NK/aWU2mG/PZtFMU1KqVlKqXCl1J9KqTz2HocRSqkD9pylsigLSqm8SqnFSql9SqmDSqnXlFJNlVJH7Ovyh+zcM8xom2ZjlozWVWpvkVIqRCm1LoviZFSPMtxuSqn/KqVmKqU2AzOzMV92rSvsr5nR9qumlNpin7ZdKeWVDRkaKaX22N//05RSbvayTezbczfwcmbmsr9eMfvr3b3d0uVTSvUACgJrlVJrnRTBaP+MPqSUWqGU8lBKlVRKLbPX6Y1KqTL2rNOVUuOVUtuUUieVrQdrmj339Dv+T6/bcx9USg13Uk7hDFrrHHsD4ux/XwZWAkZsFT4KeDWLMvjb/3oAB4FCwGlsvzruAmwExtrL/ArUsd8vAoRnQb5i2K7m/Kz98TTgU3vG/vZp7YFFWbjdXgEm3/HYBzgHFLc//i0r8ziwTQPs6yswG7JktK5SswAhwLpsqkcD7rXdgP8CuwCPLFpP96vnWbquHNh+J4Fq9sfegCkbMpwDnrI/noGt58PdPv1JbFd7n5PZ78MH1Ks0+ez3nfY+tL+2GahifzwHeAtYDTxpn1YDWGO/Px343b5uWgAxQEVsO/67gCrYvn/OAvmwXcdtDdAyK+uc3O59y9E9NHeoB/ymtbZorS9iq0RZpYdSah+wDSgMtAPWa62va61TgD/uKNsYGKuU2gssALyVUp5ZkPGc1nqz/f4vQB37/d/u+FsrC3LccgAIVUoNV0rVBYoDJ7XWp+7KlV3u3qZPZmOWNOtKax2djVnurkch3H+7LdBaJ2RZunvX8+x0d10vAlzSWu8A0FrHaK3NWZyhGHBKa33U/vzP2D5Dy9inH9O2b/BfMjnXLXdvt0b3yJcZTmmt99rv78K2bmoDf9g/pycCBe4ov9C+bg4AV7TWB7TWVuCQfd5q2BrNEfbtOisTs4uHJMe+70Mp9Ry2RkotrfVNe3f2EaDsPWYxADW11olZkzDV3RcT0hlMz7ILDmmtjyqlngGaAl9j2yPKEe6xTd2zK8/d60optRrbXuWtnY2szHZ3HfF5QPn4zApyDxnV8+xaV7YA6et6Vu5s5ZgMD3D3dovC1iuaFZLuuG8B8gNRWusqDyhvvWteK7bvyxSnJxROk1t6aDYArymljEqpAkCDLHpdH+CG/YuvDFATyAvUV0r5KdtgyFfuKL8C6H7rgVLqXm8aZyuilLrVA/MGsMl+/7U7/m7NoiwopQoCN7XWvwAjgWeBEkqpYnflyg4ZbdNsk8G6egZbt3tVe5FX7jFrZri7Hq0i52w3yLienyZ71hWQ4farARRQSlWzP++lMnnQdAYZagHF1O1xc+2A9dh2xooppUrap7+embnucPd223mPfACxQGaOOYoBTimlWkPq+MzKDzH/dmyf/4FKKSO2dbj+AfOILJJbemjmAQ2Bw9iOX2bVl/My4D2lVDjwL7ZDFBeAIdgq9nVsHxK3DhP0AMYppfZjW7cbgPeyIOe/QDel1DRs62g8toaVnz1LEln34QW2484jlVJWbHs072Pr1l2mlIoHdmRhlrtltE2zU0brygOYqpQaBKzLwix316MewH5yxnaDjOv5drJnXd2S0fZTwBillAeQgK1HMC6LM/hgO6xiwrbdJmitk5RtEPxipdRNbOP/MnXAsl1G9Wrb3fnsZSdhq28XtdaZteP6JjBeKTUA2zjI34F9jsyotb6klOoDrMW2nRdrrednUk7xkOSnDx6BUspTax1nfzPOA6Zpredld647KaVOAyFa62vZnQXSrDMFjAOOaa2/y+5c4v5ku4nHYe/dW6S1rpDNUcT/gNxyyCmn+a99QNlB4BTwdzbnyQ0629fZIWx7jxOzOY9wjGw3IUSuID00QgghhMj1pIdGCCGEELmeNGiEEEIIketJg0YIIYQQuZ40aIQQQgiR60mDRgghhBC53v8BDCxb/oW/PJEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jS-hUmiF1RMU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create a list of column names to keep \n",
        "columns_to_retain = ['sg', 'al', 'sc', 'hemo', 'pcv', 'wbcc', 'rbcc', 'htn', 'classification']\n",
        "\n",
        "# Drop the columns that are not in the above parameters\n",
        "df = df.drop( [col for col in df.columns if not col in columns_to_retain] , axis = 1 )\n",
        "\n",
        "# Drop the rows with na/missing values \n",
        "df = df.dropna(axis=0)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "304H2-h72j54",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Transform the non-numeric data in the columns\n",
        "for column in df.columns:\n",
        "  if df[column].dtype == np.number:\n",
        "    continue\n",
        "  df[column] = LabelEncoder().fit_transform( df[column] )"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jX_pKFea3EkC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "7d154756-36d2-402c-e70e-be9ee3f7ce3f"
      },
      "source": [
        "# Print the first 5 rows of the new cleaned data set\n",
        "df.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>sc</th>\n",
              "      <th>hemo</th>\n",
              "      <th>pcv</th>\n",
              "      <th>htn</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>15.4</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>11.3</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>9.6</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>11.2</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>11.6</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      sg   al   sc  hemo  pcv  htn  classification\n",
              "0  1.020  1.0  1.2  15.4   28    1               0\n",
              "1  1.020  4.0  0.8  11.3   22    0               0\n",
              "2  1.010  2.0  1.8   9.6   15    0               0\n",
              "3  1.005  4.0  3.8  11.2   16    1               0\n",
              "4  1.010  2.0  1.4  11.6   19    0               0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opNDml8U39hF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split the data into independent/feature (X) data set and dependent/target (Y) data set\n",
        "X = df.drop(['classification'], axis=1)\n",
        "y = df['classification']"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpeQ-fAN4nXm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Feature Scaling \n",
        "# Min-Max Scaler Method scales the data set to that all the input features lie between 0 and 1\n",
        "x_scaler = MinMaxScaler()\n",
        "x_scaler.fit(X)\n",
        "column_names = X.columns\n",
        "X[column_names] = x_scaler.transform(X)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KH8N823497x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split the data into 80% training and 20% testing & shuffle\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, shuffle=True)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dK8-WQWt5hRv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build the model\n",
        "model = Sequential()\n",
        "model.add( Dense(256,input_dim= len(X.columns),kernel_initializer= k.initializers.random_normal(seed=13),activation='relu') )\n",
        "model.add( Dense(1, activation='hard_sigmoid') )"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3WhRpOO6jjZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Compile the model \n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2bk3Whr6z4M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c9f9d4e5-d037-4581-d4b2-3ed8a02b9489"
      },
      "source": [
        "# Train the model \n",
        "history = model.fit(X_train, y_train, epochs=2000, batch_size= X_train.shape[0])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2000\n",
            "229/229 [==============================] - 0s 401us/step - loss: 0.6928 - accuracy: 0.4716\n",
            "Epoch 2/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.6878 - accuracy: 0.5066\n",
            "Epoch 3/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.6820 - accuracy: 0.6943\n",
            "Epoch 4/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.6754 - accuracy: 0.7860\n",
            "Epoch 5/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.6689 - accuracy: 0.8472\n",
            "Epoch 6/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.6625 - accuracy: 0.8690\n",
            "Epoch 7/2000\n",
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            "Epoch 768/2000\n",
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            "Epoch 777/2000\n",
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            "Epoch 784/2000\n",
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            "Epoch 786/2000\n",
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            "Epoch 791/2000\n",
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            "Epoch 800/2000\n",
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            "Epoch 801/2000\n",
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            "Epoch 803/2000\n",
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            "Epoch 805/2000\n",
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            "Epoch 807/2000\n",
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            "Epoch 808/2000\n",
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            "Epoch 811/2000\n",
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            "Epoch 819/2000\n",
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            "Epoch 820/2000\n",
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            "Epoch 822/2000\n",
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            "Epoch 824/2000\n",
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            "Epoch 832/2000\n",
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            "Epoch 840/2000\n",
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            "Epoch 844/2000\n",
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            "Epoch 850/2000\n",
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            "229/229 [==============================] - 0s 14us/step - loss: 0.0197 - accuracy: 0.9869\n",
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            "Epoch 880/2000\n",
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            "Epoch 884/2000\n",
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            "Epoch 886/2000\n",
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            "Epoch 898/2000\n",
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            "Epoch 900/2000\n",
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            "Epoch 911/2000\n",
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            "Epoch 912/2000\n",
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            "Epoch 914/2000\n",
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            "Epoch 915/2000\n",
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            "Epoch 916/2000\n",
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            "Epoch 918/2000\n",
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            "Epoch 919/2000\n",
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            "Epoch 921/2000\n",
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            "Epoch 922/2000\n",
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            "Epoch 923/2000\n",
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            "Epoch 924/2000\n",
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            "Epoch 926/2000\n",
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            "Epoch 932/2000\n",
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            "Epoch 937/2000\n",
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            "Epoch 938/2000\n",
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            "Epoch 939/2000\n",
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            "Epoch 942/2000\n",
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            "Epoch 943/2000\n",
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            "Epoch 944/2000\n",
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            "Epoch 945/2000\n",
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            "Epoch 946/2000\n",
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            "Epoch 947/2000\n",
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            "Epoch 948/2000\n",
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            "Epoch 949/2000\n",
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            "Epoch 950/2000\n",
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            "Epoch 951/2000\n",
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            "Epoch 952/2000\n",
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            "Epoch 953/2000\n",
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            "Epoch 955/2000\n",
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            "Epoch 956/2000\n",
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            "Epoch 957/2000\n",
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            "Epoch 958/2000\n",
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            "Epoch 959/2000\n",
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            "Epoch 960/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0188 - accuracy: 0.9869\n",
            "Epoch 961/2000\n",
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            "Epoch 962/2000\n",
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            "Epoch 963/2000\n",
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            "Epoch 964/2000\n",
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            "Epoch 965/2000\n",
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            "Epoch 966/2000\n",
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            "Epoch 967/2000\n",
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            "Epoch 968/2000\n",
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            "Epoch 969/2000\n",
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            "Epoch 970/2000\n",
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            "Epoch 971/2000\n",
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            "Epoch 972/2000\n",
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            "Epoch 973/2000\n",
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            "Epoch 974/2000\n",
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            "Epoch 975/2000\n",
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            "Epoch 976/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0187 - accuracy: 0.9869\n",
            "Epoch 977/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0186 - accuracy: 0.9869\n",
            "Epoch 978/2000\n",
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            "Epoch 979/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0186 - accuracy: 0.9869\n",
            "Epoch 980/2000\n",
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            "Epoch 981/2000\n",
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            "Epoch 982/2000\n",
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            "Epoch 983/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0186 - accuracy: 0.9869\n",
            "Epoch 984/2000\n",
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            "Epoch 985/2000\n",
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            "Epoch 986/2000\n",
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            "Epoch 987/2000\n",
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            "Epoch 988/2000\n",
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            "Epoch 989/2000\n",
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            "Epoch 990/2000\n",
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            "Epoch 991/2000\n",
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            "Epoch 992/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0185 - accuracy: 0.9869\n",
            "Epoch 993/2000\n",
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            "Epoch 994/2000\n",
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            "Epoch 995/2000\n",
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            "Epoch 996/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0184 - accuracy: 0.9869\n",
            "Epoch 997/2000\n",
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            "Epoch 998/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0184 - accuracy: 0.9869\n",
            "Epoch 999/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0184 - accuracy: 0.9869\n",
            "Epoch 1000/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0184 - accuracy: 0.9869\n",
            "Epoch 1001/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0184 - accuracy: 0.9869\n",
            "Epoch 1002/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0184 - accuracy: 0.9869\n",
            "Epoch 1003/2000\n",
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            "Epoch 1004/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0184 - accuracy: 0.9869\n",
            "Epoch 1005/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0184 - accuracy: 0.9869\n",
            "Epoch 1006/2000\n",
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            "Epoch 1007/2000\n",
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            "Epoch 1008/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0183 - accuracy: 0.9869\n",
            "Epoch 1009/2000\n",
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            "Epoch 1010/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0183 - accuracy: 0.9869\n",
            "Epoch 1011/2000\n",
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            "Epoch 1012/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0183 - accuracy: 0.9869\n",
            "Epoch 1013/2000\n",
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            "Epoch 1014/2000\n",
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            "Epoch 1015/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0183 - accuracy: 0.9869\n",
            "Epoch 1016/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0182 - accuracy: 0.9869\n",
            "Epoch 1017/2000\n",
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            "Epoch 1018/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0182 - accuracy: 0.9869\n",
            "Epoch 1019/2000\n",
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            "Epoch 1020/2000\n",
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            "Epoch 1021/2000\n",
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            "Epoch 1022/2000\n",
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            "Epoch 1023/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0182 - accuracy: 0.9869\n",
            "Epoch 1024/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0182 - accuracy: 0.9869\n",
            "Epoch 1025/2000\n",
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            "Epoch 1026/2000\n",
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            "Epoch 1027/2000\n",
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            "Epoch 1028/2000\n",
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            "Epoch 1029/2000\n",
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            "Epoch 1030/2000\n",
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            "Epoch 1032/2000\n",
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            "Epoch 1034/2000\n",
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            "Epoch 1035/2000\n",
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            "Epoch 1039/2000\n",
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            "Epoch 1044/2000\n",
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            "Epoch 1045/2000\n",
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            "Epoch 1046/2000\n",
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            "Epoch 1047/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0179 - accuracy: 0.9869\n",
            "Epoch 1048/2000\n",
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            "Epoch 1049/2000\n",
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            "Epoch 1050/2000\n",
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            "Epoch 1051/2000\n",
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            "Epoch 1052/2000\n",
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            "Epoch 1053/2000\n",
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            "Epoch 1054/2000\n",
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            "Epoch 1055/2000\n",
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            "Epoch 1056/2000\n",
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            "Epoch 1057/2000\n",
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            "Epoch 1058/2000\n",
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            "Epoch 1059/2000\n",
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            "Epoch 1060/2000\n",
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            "Epoch 1080/2000\n",
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            "Epoch 1092/2000\n",
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            "Epoch 1099/2000\n",
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            "Epoch 1100/2000\n",
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            "Epoch 1111/2000\n",
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            "Epoch 1119/2000\n",
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            "Epoch 1120/2000\n",
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            "Epoch 1121/2000\n",
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            "Epoch 1123/2000\n",
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            "Epoch 1125/2000\n",
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            "Epoch 1126/2000\n",
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            "Epoch 1128/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0171 - accuracy: 0.9913\n",
            "Epoch 1129/2000\n",
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            "Epoch 1130/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0171 - accuracy: 0.9913\n",
            "Epoch 1131/2000\n",
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            "Epoch 1132/2000\n",
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            "Epoch 1133/2000\n",
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            "Epoch 1134/2000\n",
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            "Epoch 1135/2000\n",
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            "Epoch 1139/2000\n",
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            "Epoch 1140/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0170 - accuracy: 0.9913\n",
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            "229/229 [==============================] - 0s 11us/step - loss: 0.0170 - accuracy: 0.9913\n",
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            "Epoch 1147/2000\n",
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            "Epoch 1149/2000\n",
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            "Epoch 1150/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0169 - accuracy: 0.9913\n",
            "Epoch 1151/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0169 - accuracy: 0.9913\n",
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            "Epoch 1153/2000\n",
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            "Epoch 1154/2000\n",
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            "Epoch 1155/2000\n",
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            "Epoch 1156/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0169 - accuracy: 0.9913\n",
            "Epoch 1157/2000\n",
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            "Epoch 1158/2000\n",
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            "Epoch 1159/2000\n",
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            "Epoch 1160/2000\n",
            "229/229 [==============================] - 0s 17us/step - loss: 0.0168 - accuracy: 0.9913\n",
            "Epoch 1161/2000\n",
            "229/229 [==============================] - 0s 19us/step - loss: 0.0168 - accuracy: 0.9913\n",
            "Epoch 1162/2000\n",
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            "Epoch 1163/2000\n",
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            "Epoch 1165/2000\n",
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            "Epoch 1166/2000\n",
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            "Epoch 1167/2000\n",
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            "Epoch 1168/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0167 - accuracy: 0.9913\n",
            "Epoch 1169/2000\n",
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            "Epoch 1170/2000\n",
            "229/229 [==============================] - 0s 10us/step - loss: 0.0167 - accuracy: 0.9913\n",
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            "Epoch 1172/2000\n",
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            "Epoch 1173/2000\n",
            "229/229 [==============================] - 0s 10us/step - loss: 0.0167 - accuracy: 0.9913\n",
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            "Epoch 1175/2000\n",
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            "Epoch 1176/2000\n",
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            "Epoch 1177/2000\n",
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            "Epoch 1178/2000\n",
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            "Epoch 1179/2000\n",
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            "Epoch 1180/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0166 - accuracy: 0.9913\n",
            "Epoch 1181/2000\n",
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            "Epoch 1182/2000\n",
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            "Epoch 1183/2000\n",
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            "Epoch 1184/2000\n",
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            "Epoch 1185/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0166 - accuracy: 0.9913\n",
            "Epoch 1186/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0166 - accuracy: 0.9913\n",
            "Epoch 1187/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0165 - accuracy: 0.9913\n",
            "Epoch 1188/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0165 - accuracy: 0.9913\n",
            "Epoch 1189/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0165 - accuracy: 0.9913\n",
            "Epoch 1190/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0165 - accuracy: 0.9913\n",
            "Epoch 1191/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0165 - accuracy: 0.9913\n",
            "Epoch 1192/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0165 - accuracy: 0.9913\n",
            "Epoch 1193/2000\n",
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            "229/229 [==============================] - 0s 11us/step - loss: 0.0165 - accuracy: 0.9913\n",
            "Epoch 1196/2000\n",
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            "Epoch 1197/2000\n",
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            "Epoch 1198/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0164 - accuracy: 0.9913\n",
            "Epoch 1199/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0164 - accuracy: 0.9913\n",
            "Epoch 1200/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0164 - accuracy: 0.9913\n",
            "Epoch 1201/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0164 - accuracy: 0.9913\n",
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            "229/229 [==============================] - 0s 14us/step - loss: 0.0164 - accuracy: 0.9913\n",
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            "Epoch 1205/2000\n",
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            "Epoch 1206/2000\n",
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            "Epoch 1207/2000\n",
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            "Epoch 1209/2000\n",
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            "Epoch 1210/2000\n",
            "229/229 [==============================] - 0s 9us/step - loss: 0.0163 - accuracy: 0.9913\n",
            "Epoch 1211/2000\n",
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            "Epoch 1212/2000\n",
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            "Epoch 1213/2000\n",
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            "Epoch 1214/2000\n",
            "229/229 [==============================] - 0s 18us/step - loss: 0.0163 - accuracy: 0.9913\n",
            "Epoch 1215/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0163 - accuracy: 0.9913\n",
            "Epoch 1216/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0162 - accuracy: 0.9913\n",
            "Epoch 1217/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0162 - accuracy: 0.9913\n",
            "Epoch 1218/2000\n",
            "229/229 [==============================] - 0s 18us/step - loss: 0.0162 - accuracy: 0.9913\n",
            "Epoch 1219/2000\n",
            "229/229 [==============================] - 0s 22us/step - loss: 0.0162 - accuracy: 0.9956\n",
            "Epoch 1220/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0162 - accuracy: 0.9956\n",
            "Epoch 1221/2000\n",
            "229/229 [==============================] - 0s 17us/step - loss: 0.0162 - accuracy: 0.9956\n",
            "Epoch 1222/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0162 - accuracy: 0.9956\n",
            "Epoch 1223/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0162 - accuracy: 0.9956\n",
            "Epoch 1224/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0162 - accuracy: 0.9956\n",
            "Epoch 1225/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0161 - accuracy: 0.9956\n",
            "Epoch 1226/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0161 - accuracy: 0.9956\n",
            "Epoch 1227/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0161 - accuracy: 0.9956\n",
            "Epoch 1228/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0161 - accuracy: 0.9956\n",
            "Epoch 1229/2000\n",
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            "Epoch 1230/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0161 - accuracy: 0.9956\n",
            "Epoch 1231/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0161 - accuracy: 0.9956\n",
            "Epoch 1232/2000\n",
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            "Epoch 1233/2000\n",
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            "Epoch 1284/2000\n",
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            "Epoch 1291/2000\n",
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            "Epoch 1293/2000\n",
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            "Epoch 1296/2000\n",
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            "Epoch 1300/2000\n",
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            "229/229 [==============================] - 0s 11us/step - loss: 0.0152 - accuracy: 0.9956\n",
            "Epoch 1311/2000\n",
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            "Epoch 1313/2000\n",
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            "Epoch 1315/2000\n",
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            "Epoch 1316/2000\n",
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            "Epoch 1321/2000\n",
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            "Epoch 1325/2000\n",
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            "Epoch 1326/2000\n",
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            "Epoch 1330/2000\n",
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            "229/229 [==============================] - 0s 11us/step - loss: 0.0150 - accuracy: 0.9956\n",
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            "Epoch 1336/2000\n",
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            "Epoch 1340/2000\n",
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            "Epoch 1341/2000\n",
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            "Epoch 1348/2000\n",
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            "Epoch 1350/2000\n",
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            "Epoch 1353/2000\n",
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            "Epoch 1354/2000\n",
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            "Epoch 1355/2000\n",
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            "Epoch 1356/2000\n",
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            "Epoch 1357/2000\n",
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            "Epoch 1358/2000\n",
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            "Epoch 1359/2000\n",
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            "Epoch 1360/2000\n",
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            "Epoch 1361/2000\n",
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            "Epoch 1363/2000\n",
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            "Epoch 1364/2000\n",
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            "Epoch 1365/2000\n",
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            "Epoch 1366/2000\n",
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            "Epoch 1367/2000\n",
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            "Epoch 1368/2000\n",
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            "Epoch 1369/2000\n",
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            "Epoch 1370/2000\n",
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            "Epoch 1377/2000\n",
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            "Epoch 1379/2000\n",
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            "Epoch 1380/2000\n",
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            "Epoch 1385/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0144 - accuracy: 0.9956\n",
            "Epoch 1386/2000\n",
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            "Epoch 1387/2000\n",
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            "Epoch 1388/2000\n",
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            "Epoch 1389/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0144 - accuracy: 0.9956\n",
            "Epoch 1390/2000\n",
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            "Epoch 1391/2000\n",
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            "Epoch 1392/2000\n",
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            "Epoch 1393/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0143 - accuracy: 0.9956\n",
            "Epoch 1394/2000\n",
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            "Epoch 1395/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0143 - accuracy: 0.9956\n",
            "Epoch 1396/2000\n",
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            "Epoch 1397/2000\n",
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            "Epoch 1398/2000\n",
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            "Epoch 1399/2000\n",
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            "Epoch 1400/2000\n",
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            "Epoch 1401/2000\n",
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            "Epoch 1402/2000\n",
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            "Epoch 1404/2000\n",
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            "Epoch 1405/2000\n",
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            "229/229 [==============================] - 0s 11us/step - loss: 0.0099 - accuracy: 0.9956\n",
            "Epoch 1928/2000\n",
            "229/229 [==============================] - 0s 10us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1929/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1930/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1931/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1932/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1933/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1934/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1935/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1936/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1937/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1938/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1939/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1940/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1941/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1942/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1943/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1944/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1945/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1946/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0098 - accuracy: 0.9956\n",
            "Epoch 1947/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1948/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1949/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1950/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1951/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1952/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1953/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1954/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1955/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1956/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1957/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1958/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1959/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1960/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1961/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1962/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1963/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0097 - accuracy: 0.9956\n",
            "Epoch 1964/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1965/2000\n",
            "229/229 [==============================] - 0s 18us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1966/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1967/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1968/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1969/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1970/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1971/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1972/2000\n",
            "229/229 [==============================] - 0s 10us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1973/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1974/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1975/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1976/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1977/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1978/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1979/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1980/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1981/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1982/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0096 - accuracy: 0.9956\n",
            "Epoch 1983/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1984/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1985/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1986/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1987/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1988/2000\n",
            "229/229 [==============================] - 0s 16us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1989/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1990/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1991/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1992/2000\n",
            "229/229 [==============================] - 0s 15us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1993/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1994/2000\n",
            "229/229 [==============================] - 0s 13us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1995/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1996/2000\n",
            "229/229 [==============================] - 0s 11us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1997/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1998/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 1999/2000\n",
            "229/229 [==============================] - 0s 14us/step - loss: 0.0095 - accuracy: 0.9956\n",
            "Epoch 2000/2000\n",
            "229/229 [==============================] - 0s 12us/step - loss: 0.0095 - accuracy: 0.9956\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xE3P8-je77JY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save the model \n",
        "model.save('ckd.model')"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgSTFg4g8Dco",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "eaac5144-33d4-4563-e20e-6ad490b324e6"
      },
      "source": [
        "# Visualize the models loss and accuracy\n",
        "plt.figure(figsize=(7,5))\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('CKD Model Accuracy & Loss',fontsize=18)\n",
        "plt.ylabel('Accuracy & Loss')\n",
        "plt.xlabel('Epoch')"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Epoch')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zgd29TkY-Law",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a8c75d7d-ad81-404a-b81f-2180375d6247"
      },
      "source": [
        "# Get the shape of the test and training data\n",
        "print('Shape of training data:',X_train.shape)\n",
        "print('Shape of test data:',X_test.shape)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of training data: (229, 6)\n",
            "Shape of test data: (58, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eb8bma2tAg0G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "ae4d39b0-df12-44cf-b1ee-0c681a3aeb6a"
      },
      "source": [
        "# Show the actual and predicted values\n",
        "pred = model.predict(X_test)\n",
        "pred = [1 if y>=0.5 else 0 for y in pred]\n",
        "pred\n",
        "\n",
        "print('Original : {0}'.format(\", \".join(str(x) for x in y_test)))\n",
        "print('Predicted : {0}'.format(\", \".join(str(x) for x in pred)))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original : 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1\n",
            "Predicted : 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mf7SepPKCYJm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "fe30a9fc-36c4-48cd-c1cd-dc63eea5b79e"
      },
      "source": [
        "# Get the model's accuracy, precision, recall, and the f1-score\n",
        "print('Chronic Kidney Disease Prediction Test Results')\n",
        "print()\n",
        "print(classification_report(y_test, pred))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Chronic Kidney Disease Prediction Test Results\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        28\n",
            "           1       1.00      1.00      1.00        30\n",
            "\n",
            "    accuracy                           1.00        58\n",
            "   macro avg       1.00      1.00      1.00        58\n",
            "weighted avg       1.00      1.00      1.00        58\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEYxstptUZYV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "de1f8ca0-eb7d-4b18-a234-40676617ebb8"
      },
      "source": [
        "# Conclusion\n",
        "print('Using the Sequential Model gave a 100% accuracy in predciting CKD.')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using the Sequential Model gave a 100% accuracy in predciting CKD.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}