Logistic_Regression_class.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MJanbandhu/Machine-Learning/blob/main/Logistic_Regression_class.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "65aef7dd",
      "metadata": {
        "id": "65aef7dd"
      },
      "source": [
        "### Business Case: To predict whether a person will have diabetes or not."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "284e6dbc",
      "metadata": {
        "id": "284e6dbc"
      },
      "outputs": [],
      "source": [
        "# importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "928ffa32",
      "metadata": {
        "id": "928ffa32"
      },
      "outputs": [],
      "source": [
        "## Load the data\n",
        "data=pd.read_csv('diabetes1.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "99d63fc4",
      "metadata": {
        "id": "99d63fc4",
        "outputId": "deae492f-86f7-4b72-ef96-e0d3ba1fa70d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6      148             72             35        0  33.6   \n",
              "1              1       85             66             29        0  26.6   \n",
              "2              8      183             64              0        0  23.3   \n",
              "3              1       89             66             23       94  28.1   \n",
              "4              0      137             40             35      168  43.1   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
              "763           10      101             76             48      180  32.9   \n",
              "764            2      122             70             27        0  36.8   \n",
              "765            5      121             72             23      112  26.2   \n",
              "766            1      126             60              0        0  30.1   \n",
              "767            1       93             70             31        0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                       0.627   50        1  \n",
              "1                       0.351   31        0  \n",
              "2                       0.672   32        1  \n",
              "3                       0.167   21        0  \n",
              "4                       2.288   33        1  \n",
              "..                        ...  ...      ...  \n",
              "763                     0.171   63        0  \n",
              "764                     0.340   27        0  \n",
              "765                     0.245   30        0  \n",
              "766                     0.349   47        1  \n",
              "767                     0.315   23        0  \n",
              "\n",
              "[768 rows x 9 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04062c24",
      "metadata": {
        "id": "04062c24"
      },
      "source": [
        "### Basic checks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "028f287d",
      "metadata": {
        "id": "028f287d",
        "outputId": "3af1898d-4103-4237-fa84-db5e78c26aa1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "723fe12f",
      "metadata": {
        "id": "723fe12f"
      },
      "outputs": [],
      "source": [
        "data.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ec06e5c",
      "metadata": {
        "id": "2ec06e5c"
      },
      "outputs": [],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fad9234c",
      "metadata": {
        "id": "fad9234c"
      },
      "outputs": [],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "04fed73c",
      "metadata": {
        "id": "04fed73c"
      },
      "outputs": [],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "264b2ac8",
      "metadata": {
        "id": "264b2ac8"
      },
      "outputs": [],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "f3825585",
      "metadata": {
        "id": "f3825585"
      },
      "source": [
        "* 0 value not possible for glucose,BP,skin thickness,BMI and insulin.This is called as data corruption"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5a40b267",
      "metadata": {
        "id": "5a40b267"
      },
      "outputs": [],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "be3817c9",
      "metadata": {
        "id": "be3817c9"
      },
      "source": [
        "### Domain Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ff8d35dd",
      "metadata": {
        "id": "ff8d35dd"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "84770aa5",
      "metadata": {
        "id": "84770aa5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "3eaa00f0",
      "metadata": {
        "id": "3eaa00f0"
      },
      "source": [
        "### Exploratory data analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fd1993fd",
      "metadata": {
        "id": "fd1993fd"
      },
      "source": [
        "**Step 1 Univariate Analysis**\n",
        "- histplot is used to check distribution of the data\n",
        "- count plot is used to check whether data is balanced or not."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8213b955",
      "metadata": {
        "id": "8213b955"
      },
      "outputs": [],
      "source": [
        "# Check whether data is balanced or not\n",
        "# means equal no. of records related to diabetic and non diabetic patients\n",
        "plt.figure(figsize=(6,2))\n",
        "sns.countplot(x=data.Outcome)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "262f0e9d",
      "metadata": {
        "id": "262f0e9d"
      },
      "source": [
        "**Insights**\n",
        "- clearly data is imbalanced since we have more records related to non diabetic and less records related to diabetic"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0014a268",
      "metadata": {
        "id": "0014a268"
      },
      "outputs": [],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "37152b6c",
      "metadata": {
        "id": "37152b6c"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(20,25),facecolor='white')\n",
        "plotnumber=1\n",
        "\n",
        "for column in data:\n",
        "    if plotnumber<=9:\n",
        "        ax=plt.subplot(3,3,plotnumber)\n",
        "        sns.histplot(data[column],kde=True)\n",
        "        plt.xlabel(column,fontsize=20)\n",
        "        plt.ylabel('Count',fontsize=20)\n",
        "    plotnumber+=1\n",
        "plt.tight_layout()\n"
      ]
    },
    {
      "cell_type": "raw",
      "id": "804f9cb8",
      "metadata": {
        "id": "804f9cb8"
      },
      "source": [
        "* Insights\n",
        "#glucose ,BP,and BMI are somehow representing normal distribution.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cccdf4a7",
      "metadata": {
        "id": "cccdf4a7"
      },
      "source": [
        "#### Bivariate Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe744f6e",
      "metadata": {
        "id": "fe744f6e"
      },
      "outputs": [],
      "source": [
        "##Bivariate Analysis refers to plots between input variables and target variable.\n",
        "\n",
        "## Analyzing how preganancies impact the patient with diabetes.\n",
        "plt.figure(figsize=(7,2))\n",
        "sns.countplot(x='Pregnancies',data=data,hue='Outcome')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "e4d7555f",
      "metadata": {
        "id": "e4d7555f"
      },
      "source": [
        "1) after analyzing this data,we found as the no of pregnenacy increases chances of getting diabetes also increases.\n",
        "2) from 14 to 17 pregnancies all women are diabetic.\n",
        "3) Maximum patients conceived 0 or 1 times\n",
        "4) As the no. of pregencies is less the probability of being diabetic is less"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ea215db3",
      "metadata": {
        "id": "ea215db3"
      },
      "outputs": [],
      "source": [
        "## Aanlyzing the relationship between diabetes and Glucose\n",
        "plt.figure(figsize=(6,2))\n",
        "sns.histplot(x='Glucose',hue='Outcome',data=data)"
      ]
    },
    {
      "cell_type": "raw",
      "id": "577e7aac",
      "metadata": {
        "id": "577e7aac"
      },
      "source": [
        "1)In the given data,a person having glucose range till 100 can be considered as they have lesser chance of getting diabetes.\n",
        "2)If a person have glucose more than 150,higher is the chnace of getting diabetes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "93ff5150",
      "metadata": {
        "id": "93ff5150"
      },
      "outputs": [],
      "source": [
        "## Analyze relationship between BloodPressure and Outcome\n",
        "plt.figure(figsize=(6,2))\n",
        "sns.histplot(x='BloodPressure',hue='Outcome',data=data)"
      ]
    },
    {
      "cell_type": "raw",
      "id": "b7ba845a",
      "metadata": {
        "id": "b7ba845a"
      },
      "source": [
        "# Insights\n",
        "* The chance of being diabetic is less if bp is low or normal\n",
        "* The chance of being diabetic is high if bp is very high."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e14b9a77",
      "metadata": {
        "id": "e14b9a77"
      },
      "outputs": [],
      "source": [
        "## Analyzing Insulin with target\n",
        "plt.figure(figsize=(6,2))\n",
        "sns.histplot(x='Insulin',hue='Outcome',data=data)"
      ]
    },
    {
      "cell_type": "raw",
      "id": "fd1bed54",
      "metadata": {
        "id": "fd1bed54"
      },
      "source": [
        "# The chance of being diabetic is less if Insulin level is less.\n",
        "# The chance of being diabetic is high if insulin level is high."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6b47a4a0",
      "metadata": {
        "id": "6b47a4a0"
      },
      "outputs": [],
      "source": [
        "## Analyzing Age with target\n",
        "plt.figure(figsize=(6,2))\n",
        "sns.histplot(x='Age',hue='Outcome',data=data)"
      ]
    },
    {
      "cell_type": "raw",
      "id": "83e407f4",
      "metadata": {
        "id": "83e407f4"
      },
      "source": [
        "# if age is high chances of being diabetic is also high"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3dbf7df0",
      "metadata": {
        "id": "3dbf7df0"
      },
      "outputs": [],
      "source": [
        "## Analyzing BMI with target\n",
        "plt.figure(figsize=(6,2))\n",
        "sns.histplot(x='BMI',hue='Outcome',data=data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "d29704ec",
      "metadata": {
        "id": "d29704ec"
      },
      "source": [
        "BMI increase chances of getting diabetics also increses.\n",
        "more than 40 diabetes yes\n",
        "else less than 40 then no"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "34e47715",
      "metadata": {
        "id": "34e47715"
      },
      "outputs": [],
      "source": [
        "## Analyze Glucose with blood pressure\n",
        "\n",
        "sns.relplot(x='Glucose',y='BloodPressure',hue='Outcome',data=data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "c3b2e1a4",
      "metadata": {
        "id": "c3b2e1a4"
      },
      "source": [
        "#Insights\n",
        "The patients with less glucose level bloodpressure will have less chance of being diabetic.\n",
        "The patients with high glucose level and bloodpressure will have high chance of being diabetic."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f64caee",
      "metadata": {
        "id": "5f64caee"
      },
      "outputs": [],
      "source": [
        "## Analyze Glucose with SkinThickness\n",
        "\n",
        "sns.relplot(x='Glucose',y='SkinThickness',hue='Outcome',data=data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "ea2a5011",
      "metadata": {
        "id": "ea2a5011"
      },
      "source": [
        "There seem to be no defined relationship between glucose and skin thickness as far as diabetes is concered from the given data. Thus we need other features to get the relation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "44b92e86",
      "metadata": {
        "id": "44b92e86"
      },
      "outputs": [],
      "source": [
        "## Analyze BP with SkinThickness\n",
        "\n",
        "sns.relplot(x='BloodPressure',y='SkinThickness',hue='Outcome',data=data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "214219fd",
      "metadata": {
        "id": "214219fd"
      },
      "outputs": [],
      "source": [
        "## Analyze BP with Insulin\n",
        "\n",
        "sns.relplot(x='BloodPressure',y='Insulin',hue='Outcome',data=data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "059132af",
      "metadata": {
        "id": "059132af"
      },
      "source": [
        "#### Multivariate Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "21b7855e",
      "metadata": {
        "id": "21b7855e"
      },
      "outputs": [],
      "source": [
        "sns.pairplot(data,hue='Outcome')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4835b0a6",
      "metadata": {
        "id": "4835b0a6"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "91882584",
      "metadata": {
        "id": "91882584",
        "outputId": "3f0849ec-ae78-498d-f909-d436e52510b5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check for null values\n",
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9d7a1fe5",
      "metadata": {
        "id": "9d7a1fe5",
        "outputId": "46f0e612-c7c5-481c-d2fb-eda1c6c2536e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check for duplicates\n",
        "data.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "01796e71",
      "metadata": {
        "id": "01796e71"
      },
      "outputs": [],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7846b19b",
      "metadata": {
        "id": "7846b19b",
        "outputId": "26aef5af-67dc-44c5-815d-26af1b7e86b1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>48</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>24.7</td>\n",
              "      <td>0.140</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>74</td>\n",
              "      <td>20</td>\n",
              "      <td>23</td>\n",
              "      <td>27.7</td>\n",
              "      <td>0.299</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.389</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>80</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.346</td>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0.727</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "75             1        0             48             20        0  24.7   \n",
              "182            1        0             74             20       23  27.7   \n",
              "342            1        0             68             35        0  32.0   \n",
              "349            5        0             80             32        0  41.0   \n",
              "502            6        0             68             41        0  39.0   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "75                      0.140   22        0  \n",
              "182                     0.299   21        0  \n",
              "342                     0.389   22        0  \n",
              "349                     0.346   37        1  \n",
              "502                     0.727   41        1  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#checking the records where i have 0 values\n",
        "data.loc[data['Glucose']==0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bdd7b4cb",
      "metadata": {
        "id": "bdd7b4cb",
        "outputId": "88607811-111d-47ce-f5db-7ef1b7c21da7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "117.0"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.median(data.Glucose)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cf62dd25",
      "metadata": {
        "id": "cf62dd25"
      },
      "outputs": [],
      "source": [
        "data.Glucose.replace(0,np.median(data.Glucose),inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7de7b95c",
      "metadata": {
        "id": "7de7b95c",
        "outputId": "b1a7329c-3fec-46af-bf90-b6b30411680e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome]\n",
              "Index: []"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.loc[data['Glucose']==0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a2b53cff",
      "metadata": {
        "id": "a2b53cff"
      },
      "outputs": [],
      "source": [
        "data.BloodPressure.replace(0,np.median(data.BloodPressure),inplace=True)\n",
        "data.SkinThickness.replace(0,np.median(data.SkinThickness),inplace=True)\n",
        "data.Insulin.replace(0,np.median(data.Insulin),inplace=True)\n",
        "data.BMI.replace(0,np.median(data.BMI),inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c892f69",
      "metadata": {
        "id": "9c892f69",
        "outputId": "041a57c3-2e73-411b-a230-5f09440ace7e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>121.656250</td>\n",
              "      <td>72.386719</td>\n",
              "      <td>27.334635</td>\n",
              "      <td>94.652344</td>\n",
              "      <td>32.450911</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>30.438286</td>\n",
              "      <td>12.096642</td>\n",
              "      <td>9.229014</td>\n",
              "      <td>105.547598</td>\n",
              "      <td>6.875366</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>18.200000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.750000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>31.250000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  121.656250      72.386719      27.334635   94.652344   \n",
              "std       3.369578   30.438286      12.096642       9.229014  105.547598   \n",
              "min       0.000000   44.000000      24.000000       7.000000   14.000000   \n",
              "25%       1.000000   99.750000      64.000000      23.000000   30.500000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   31.250000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    32.450911                  0.471876   33.240885    0.348958  \n",
              "std      6.875366                  0.331329   11.760232    0.476951  \n",
              "min     18.200000                  0.078000   21.000000    0.000000  \n",
              "25%     27.500000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "5a7ba976",
      "metadata": {
        "id": "5a7ba976"
      },
      "source": [
        "## Numerical representation of string data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce433d6f",
      "metadata": {
        "collapsed": true,
        "id": "ce433d6f",
        "outputId": "23f1a088-857e-4958-eca6-c7f6ab6cca4e"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8YAAAm6CAYAAABJ2GK3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdf5hWdZ0//ufNrxl0YRTIGSdBbT+4ZRCRKYaVmAhRYGmGhqu0+S133ShEtNjSyN0ksZCC7Md+XLEQbT+XYZZhwhaaH8xEw7Jaf2wkWiDl4owgDAj39w8/zjrI8GtmuJnD43Fd57o457zf534daOTV/eR9TqlcLpcDAAAAAAAAAAXVpdIFAAAAAAAAAEBHEowDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABRat0oXUCTbtm3Ln/70p/Tq1SulUqnS5QAAdLhyuZznn38+9fX16dLFv7lsK/0kAHCg0U+2Pz0lAHAg2ZN+UjDejv70pz+lf//+lS4DAGCfe+qpp3LEEUdUuoxOTz8JAByo9JPtR08JAByIdqefFIy3o169eiV56Te+d+/eFa4GAKDjNTY2pn///s19EG2jnwQADjT6yfanpwQADiR70k8KxtvRy48m6t27t6YTADigeERj+9BPAgAHKv1k+9FTAgAHot3pJ724BwAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0DplMH7PPfdk3Lhxqa+vT6lUym233dbifKlU2uF2zTXXtHrNefPm7XDOpk2bOvhuAAAAAAAAAOhInTIY37BhQ4YMGZK5c+fu8Pzq1atbbP/2b/+WUqmUD3zgAzu9bu/evV81t7q6uiNuAQAAAAAAAIB9pFulC9gbY8aMyZgxY1o9X1dX12L/+9//fk455ZS87nWv2+l1S6XSq+YCAAAAAAAA0Ll1yhXje+KZZ57JHXfckQsuuGCXY9evX58jjzwyRxxxRMaOHZtf/vKXOx3f1NSUxsbGFhsAAAAAAAAA+5fCB+M33nhjevXqlTPPPHOn417/+tdn3rx5uf3223PzzTenuro6J510Uh5//PFW58yYMSM1NTXNW//+/du7fAAAAAAAAADaqPDB+L/927/l3HPP3eW7wk888cT87d/+bYYMGZJ3vOMd+fd///ccc8wxmTNnTqtzpk2bloaGhubtqaeeau/yAQAAAAAAAGijTvmO8d31s5/9LI8++mi++93v7vHcLl265Pjjj9/pivGqqqpUVVW1pUQAAAAAAAAAOlihV4xff/31Oe644zJkyJA9nlsul7NixYocfvjhHVAZAAAAAAAAAPtKp1wxvn79+jzxxBPN+ytXrsyKFSvSp0+fDBgwIEnS2NiY//N//k++/OUv7/Aa559/fl772tdmxowZSZLPf/7zOfHEEzNw4MA0Njbmq1/9alasWJGvfe1rHX9DAAAAAAAAAHSYThmML1++PKecckrz/pQpU5IkEydOzLx585Ikt9xyS8rlcj70oQ/t8BqrVq1Kly7/s2D+ueeey8c+9rGsWbMmNTU1GTp0aO65556ccMIJHXcjAAAAAAAAAHS4UrlcLle6iKJobGxMTU1NGhoa0rt370qXAwDQ4fQ/7cvvJwBwoNH/tD+/pwDAgWRPep9Cv2McAAAAAAAAAATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAA54M2bMyPHHH59evXrlsMMOy/vf//48+uijLcaUy+VMnz499fX16dmzZ0aMGJHf/OY3LcY0NTVl0qRJ6devXw4++OCcfvrpefrpp/flrQAAAAAAOyAYBwDggHf33XfnH//xH/Pzn/88ixcvzosvvphRo0Zlw4YNzWNmzpyZWbNmZe7cuXnggQdSV1eX0047Lc8//3zzmMmTJ2fhwoW55ZZbcu+992b9+vUZO3Zstm7dWonbAgAAAAD+n1K5XC5XuoiiaGxsTE1NTRoaGtK7d+9KlwMA0OGK2v/8+c9/zmGHHZa7774773znO1Mul1NfX5/JkyfnU5/6VJKXVofX1tbm6quvzoUXXpiGhoa85jWvyXe+852cffbZSZI//elP6d+/f370ox9l9OjRu/zcov5+AgC0Rv/T/vyeAgAHkj3pfawYBwCA7TQ0NCRJ+vTpkyRZuXJl1qxZk1GjRjWPqaqqysknn5xly5YlSR588MFs2bKlxZj6+voMGjSoeQwAAAAAUBmCcYACWLZsWc4++2zBC0A7KJfLmTJlSt7+9rdn0KBBSZI1a9YkSWpra1uMra2tbT63Zs2a9OjRI4ceemirY7bX1NSUxsbGFhsAAAB0Nr6fBDoDwThAJ7dp06bMmjUrzzzzTGbNmpVNmzZVuiSATu3jH/94fvWrX+Xmm29+1blSqdRiv1wuv+rY9nY2ZsaMGampqWne+vfvv/eFAwAAQAX4fhLoLATjAJ3cTTfdlGeffTZJ8uyzz2bBggUVrgig85o0aVJuv/32/PSnP80RRxzRfLyuri5JXrXye+3atc2ryOvq6rJ58+asW7eu1THbmzZtWhoaGpq3p556qj1vBwAAADqc7yeBzqJbpQsAYO89/fTTWbBgQcrlcpKXViUuWLAgo0aNahHoALBz5XI5kyZNysKFC7N06dIcffTRLc4fffTRqaury+LFizN06NAkyebNm3P33Xfn6quvTpIcd9xx6d69exYvXpzx48cnSVavXp1HHnkkM2fO3OHnVlVVpaqqqgPvDDpOuVy2EqQgyuVympqakrz036VdPQmDzqG6utqfJQDQ4Xw/CXQmgnGATqpcLucrX/lKq8dnzpzpizCA3fSP//iPWbBgQb7//e+nV69ezSvDa2pq0rNnz5RKpUyePDlXXXVVBg4cmIEDB+aqq67KQQcdlAkTJjSPveCCC3LJJZekb9++6dOnT6ZOnZrBgwdn5MiRlbw96BCbNm3KmDFjKl0G0IpFixalZ8+elS4DACgw308CnY1gHKCTWrVqVR544IFXHd+6dWseeOCBrFq1KkceeWQFKgPofL7+9a8nSUaMGNHi+A033JAPf/jDSZLLLrssGzduzEUXXZR169Zl2LBhueuuu9KrV6/m8ddee226deuW8ePHZ+PGjTn11FMzb968dO3adV/dCgAAAOwTvp8EOhvBOEAnNWDAgBx//PF56KGHsnXr1ubjXbt2zXHHHZcBAwZUsDqAzuXlR77tTKlUyvTp0zN9+vRWx1RXV2fOnDmZM2dOO1YH+6fq6uosWrSo0mXQDjZt2pQzzjgjSbJw4cJUV1dXuCLagz9HAKCj+X4S6GwE4wCdVKlUyic/+clMnDhxh8c9pggA6EilUsljmguourranysAALvF95NAZ9Ol0gUAsPeOOOKITJgwobnJLJVKmTBhQl772tdWuDIAAAAAoOh8Pwl0JoJxgE7u3HPPTd++fZMk/fr1y4QJEypcEQAAAABwoPD9JNBZCMYBOrnq6upMmTIltbW1ufjii71LEAAAAADYZ3w/CXQW3jEOUADDhw/P8OHDK10GAAAAAHAA8v0k0BlYMQ4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAABoo3vuuSfjxo1LfX19SqVSbrvttuZzW7Zsyac+9akMHjw4Bx98cOrr63P++efnT3/6U4trNDU1ZdKkSenXr18OPvjgnH766Xn66af38Z0AABSTYBwAAAAAoI02bNiQIUOGZO7cua8698ILL+Shhx7K5Zdfnoceeijf+9738thjj+X0009vMW7y5MlZuHBhbrnlltx7771Zv359xo4dm61bt+6r2wAAKKxulS4AAAAAAKCzGzNmTMaMGbPDczU1NVm8eHGLY3PmzMkJJ5yQVatWZcCAAWloaMj111+f73znOxk5cmSSZP78+enfv3+WLFmS0aNHd/g9AAAUmRXjAAAAAAD7WENDQ0qlUg455JAkyYMPPpgtW7Zk1KhRzWPq6+szaNCgLFu2rNXrNDU1pbGxscUGAMCrCcYBAAAAAPahTZs25dOf/nQmTJiQ3r17J0nWrFmTHj165NBDD20xtra2NmvWrGn1WjNmzEhNTU3z1r9//w6tHQCgsxKMAwAAAADsI1u2bMk555yTbdu25brrrtvl+HK5nFKp1Or5adOmpaGhoXl76qmn2rNcAIDCEIwDAAAAAOwDW7Zsyfjx47Ny5cosXry4ebV4ktTV1WXz5s1Zt25dizlr165NbW1tq9esqqpK7969W2wAALyaYBwAAAAAoIO9HIo//vjjWbJkSfr27dvi/HHHHZfu3btn8eLFzcdWr16dRx55JMOHD9/X5QIAFE63ShcAAAAAANDZrV+/Pk888UTz/sqVK7NixYr06dMn9fX1Oeuss/LQQw/lhz/8YbZu3dr83vA+ffqkR48eqampyQUXXJBLLrkkffv2TZ8+fTJ16tQMHjw4I0eOrNRtAQAUhmAcAAAAAKCNli9fnlNOOaV5f8qUKUmSiRMnZvr06bn99tuTJG9+85tbzPvpT3+aESNGJEmuvfbadOvWLePHj8/GjRtz6qmnZt68eenates+uQcAgCITjAMAAAAAtNGIESNSLpdbPb+zcy+rrq7OnDlzMmfOnPYsDQCAeMc4AAAAAAAAAAUnGAcAAAAAAACg0ATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAAEChCcYBAAAAAAAAKDTBOAAAAAAAAACFJhgHAAAAAAAAoNAE4wAAAAAAAAAUmmAcAAAAAAAAgEITjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAwF5btmxZzj777CxbtqzSpQC0SjAOAAAAAADAXtm0aVNmzZqVZ555JrNmzcqmTZsqXRLADgnGAQAAAAAA2Cs33XRTnn322STJs88+mwULFlS4IoAdE4wDAAAAAACwx55++uksWLAg5XI5SVIul7NgwYI8/fTTFa4M4NUE4wAAAAAAAOyRcrmcr3zlK60efzksB9hfCMYBAAAAAADYI6tWrcoDDzyQrVu3tji+devWPPDAA1m1alWFKgPYMcE4AAAAAAAAe2TAgAE5/vjj07Vr1xbHu3btmhNOOCEDBgyoUGUAOyYYBwAAAAAAYI+USqV88pOfbPV4qVSqQFUArROMAwAAAAAAsMeOOOKITJgwoTkEL5VKmTBhQl772tdWuDKAVxOMAwAAAAAAsFfOPffc9O3bN0nSr1+/TJgwocIVAeyYYBwAAAAAAIC9Ul1dnSlTpqS2tjYXX3xxqqurK10SwA51q3QBAAAAAAAAdF7Dhw/P8OHDK10GwE5ZMQ4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABSaYBygAJYtW5azzz47y5Ytq3QpAAAAAAAA+51OGYzfc889GTduXOrr61MqlXLbbbe1OP/hD384pVKpxXbiiSfu8rq33nprjj322FRVVeXYY4/NwoULO+gOANrPpk2bMmvWrDzzzDOZNWtWNm3aVOmSAAAAAAAA9iudMhjfsGFDhgwZkrlz57Y65t3vfndWr17dvP3oRz/a6TXvu+++nH322TnvvPPy8MMP57zzzsv48eNz//33t3f5AO3qpptuyrPPPpskefbZZ7NgwYIKVwQAAAAAALB/6VbpAvbGmDFjMmbMmJ2OqaqqSl1d3W5fc/bs2TnttNMybdq0JMm0adNy9913Z/bs2bn55pvbVC9AR3n66aezYMGClMvlJEm5XM6CBQsyatSoHHHEERWuDgAAAAAAYP/QKVeM746lS5fmsMMOyzHHHJOPfvSjWbt27U7H33fffRk1alSLY6NHj97p+3qbmprS2NjYYgPYV8rlcr7yla+0evzlsByA3bOr1/WsX78+H//4x3PEEUekZ8+eecMb3pCvf/3rLcY0NTVl0qRJ6devXw4++OCcfvrpefrpp/fhXQAAAMC+N3369IwYMSLTp0+vdCkArSpkMD5mzJjcdNNN+clPfpIvf/nLeeCBB/Kud70rTU1Nrc5Zs2ZNamtrWxyrra3NmjVrWp0zY8aM1NTUNG/9+/dvt3sA2JVVq1blgQceyNatW1sc37p1ax544IGsWrWqQpUBdE67el3PxRdfnDvvvDPz58/P7373u1x88cWZNGlSvv/97zePmTx5chYuXJhbbrkl9957b9avX5+xY8e+6r/VAAAAUBTPPPNMli5dmuSlRYvPPPNMZQsCaEUhg/Gzzz47733vezNo0KCMGzcuixYtymOPPZY77rhjp/NKpVKL/XK5/KpjrzRt2rQ0NDQ0b0899VS71A+wOwYMGJDjjz8+Xbt2bXG8a9euOeGEEzJgwIAKVQbQOY0ZMyb/8i//kjPPPHOH5++7775MnDgxI0aMyFFHHZWPfexjGTJkSJYvX54kaWhoyPXXX58vf/nLGTlyZIYOHZr58+fn17/+dZYsWbIvbwUAAAD2mY9//OMt9idNmlShSgB2rpDB+PYOP/zwHHnkkXn88cdbHVNXV/eq1eFr16591SryV6qqqkrv3r1bbAD7SqlUyic/+clWj+/sH/YAsOfe/va35/bbb88f//jHlMvl/PSnP81jjz2W0aNHJ0kefPDBbNmypcXreerr6zNo0KCdvp4HAAAAOqs777wzf/7zn1scW7t2be68884KVQTQugMiGH/22Wfz1FNP5fDDD291zNve9rYsXry4xbG77rorw4cP7+jyAPbaEUcckQkTJjSH4KVSKRMmTMhrX/vaClcGUDxf/epXc+yxx+aII45Ijx498u53vzvXXXdd3v72tyd56dU8PXr0yKGHHtpi3s5ez9PU1JTGxsYWGwAAAHQGW7duzTXXXLPDc9dcc43XigH7nU4ZjK9fvz4rVqzIihUrkiQrV67MihUrsmrVqqxfvz5Tp07Nfffdlz/84Q9ZunRpxo0bl379+uWMM85ovsb555+fadOmNe9/8pOfzF133ZWrr746//mf/5mrr746S5YsyeTJk/fx3QHsmXPPPTd9+/ZNkvTr1y8TJkyocEUAxfTVr341P//5z3P77bfnwQcfzJe//OVcdNFFu3xM+s5ezzNjxozU1NQ0b/379++I0gEAAKDd/fCHP2w1/N66dWt++MMf7uOKAHauUwbjy5cvz9ChQzN06NAkyZQpUzJ06NBcccUV6dq1a37961/nfe97X4455phMnDgxxxxzTO6777706tWr+RqrVq3K6tWrm/eHDx+eW265JTfccEPe9KY3Zd68efnud7+bYcOG7fP7A9gT1dXVmTJlSmpra3PxxRenurq60iUBFM7GjRvzT//0T5k1a1bGjRuXN73pTfn4xz+es88+O1/60peSvPRqns2bN2fdunUt5u7s9TzTpk1LQ0ND8/bUU091+L0AAABAexg7dmy6du26w3PdunXL2LFj93FFADvXrdIF7I0RI0akXC63ev7HP/7xLq+xdOnSVx0766yzctZZZ7WlNICKGD58uFc/AHSgLVu2ZMuWLenSpeW/K+3atWu2bduWJDnuuOPSvXv3LF68OOPHj0+SrF69Oo888khmzpy5w+tWVVWlqqqqY4sHAACADtC1a9dceuml+eIXv/iqc5dddlmroTlApXTKYBwAANrb+vXr88QTTzTvv/y6nj59+mTAgAE5+eSTc+mll6Znz5458sgjc/fdd+fb3/52Zs2alSSpqanJBRdckEsuuSR9+/ZNnz59MnXq1AwePDgjR46s1G0BAABAh3n3u9+d66+/Pn/+85+bjx122GEZNWpUBasC2DHBOAAA5KXX9ZxyyinN+1OmTEmSTJw4MfPmzcstt9ySadOm5dxzz81///d/58gjj8wXvvCF/P3f/33znGuvvTbdunXL+PHjs3Hjxpx66qmZN2+efyUPAABAYc2dOzdnn3128/6cOXMqWA1A6wTjAACQXb+up66uLjfccMNOr1FdXZ05c+b4EgAAAIADRm1tbUaMGJGlS5dmxIgRqa2trXRJADskGAcAAAAAAGCvTZ8+vdIlAOxSl0oXAAAAAAAAAAAdSTAOAAAAAAAAQKEJxgEAAAAAANhr119/fd71rnfl+uuvr3QpAK0SjAMAAAAAALBXnnvuudx0003Ztm1bbrrppjz33HOVLglghwTjAAAAAAAA7JXLL78827ZtS5Js27YtV1xxRYUrAtgxwTgAAAAAAAB7bPny5fn1r3/d4tivfvWrLF++vEIVAbROMA4AAAAAAMAe2bZtW6688sodnrvyyiubV5ED7C8E4wAAAAAAAOyR+++/P42NjTs819jYmPvvv38fVwSwc4JxAAAAAAAA9siwYcPSu3fvHZ6rqanJsGHD9nFFADsnGAcAAAAAAGCPdOnSJVdcccUOz33uc59Lly4iKGD/4r9KAAAAAAAA7LG3vvWtGTx4cItjb3rTm/KWt7ylQhUBtE4wDgAAAAAAwF7553/+5+bV4V26dMmVV15Z4YoAdkwwDgAAAAAAwF455JBDcu6556ZLly4599xzc8ghh1S6JIAd6lbpAgAAAAAAAOi8LrjgglxwwQWVLgNgp6wYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAgL02ffr0jBgxItOnT690KQCtEowDAAAAAACwV5555pksXbo0SbJ06dI888wzlS0IoBWCcQAAAAAAAPbKxz/+8Rb7kyZNqlAlADsnGAcAAAAAAGCP3Xnnnfnzn//c4tjatWtz5513VqgigNYJxgEAAAAAANgjW7duzTXXXLPDc9dcc022bt26jysC2DnBOAAAAAAAAHvkhz/8Yavh99atW/PDH/5wH1cEsHOCcQAAAAAAAPbI2LFj07Vr1x2e69atW8aOHbuPKwLYOcE4AAAAAAAAe6Rr16659NJLd3jusssuazU0B6gUwTgAAAAAAAB77N3vfnde85rXtDh22GGHZdSoURWqCKB1gnGAArj++uvzrne9K9dff32lSwEAAAAADiBz585tsT9nzpwKVQKwc4JxgE7uueeey0033ZRt27blpptuynPPPVfpkgAAAACAA0RtbW1GjBiRJBkxYkRqa2srWxBAK7pVugAA2ubyyy/Ptm3bkiTbtm3LFVdcka9+9asVrgoAAAAAOFBMnz690iUA7JIV4wCd2PLly/PrX/+6xbFf/epXWb58eYUqAgAAAAAA2P8IxgE6qW3btuXKK6/c4bkrr7yyeRU5AAAAAADAgU4wDtBJ3X///WlsbNzhucbGxtx///37uCIAAAAAAID9k2AcoJMaNmxYevfuvcNzNTU1GTZs2D6uCAAAAAAAYP8kGAfopLp06ZIrrrhih+c+97nPpUsX/4kHAAAAAABIkm6VLgCAvffWt741gwcPzq9//evmY29605vylre8pYJVAQAAAMDOlcvlbNq0qdJl0A7K5XKampqSJFVVVSmVShWuiPZQXV3tz5LCEYwDdHL//M//nDPPPDPbtm1Lly5dcuWVV1a6JAAAAADYqU2bNmXMmDGVLgNoxaJFi9KzZ89KlwHtynN2ATq5Qw45JOeee266dOmSc889N4ccckilSwIAAAAAANivWDEOUAAXXHBBLrjggkqXAQAAAAC7pbq6OosWLap0GbSDTZs25YwzzkiSLFy4MNXV1RWuiPbgz5EiEowDAAAAAAD7VKlU8pjmAqqurvbnCuy3PEodAAAAAAAAgEITjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAAEChCcYBAAAAANronnvuybhx41JfX59SqZTbbrutxflyuZzp06envr4+PXv2zIgRI/Kb3/ymxZimpqZMmjQp/fr1y8EHH5zTTz89Tz/99D68CwCA4hKMAwAAAAC00YYNGzJkyJDMnTt3h+dnzpyZWbNmZe7cuXnggQdSV1eX0047Lc8//3zzmMmTJ2fhwoW55ZZbcu+992b9+vUZO3Zstm7duq9uAwCgsLpVugAAAAAAgM5uzJgxGTNmzA7PlcvlzJ49O5/5zGdy5plnJkluvPHG1NbWZsGCBbnwwgvT0NCQ66+/Pt/5zncycuTIJMn8+fPTv3//LFmyJKNHj95n9wIAUERWjAMAAAAAdKCVK1dmzZo1GTVqVPOxqqqqnHzyyVm2bFmS5MEHH8yWLVtajKmvr8+gQYOaxwAAsPesGAcAAAAA6EBr1qxJktTW1rY4XltbmyeffLJ5TI8ePXLooYe+aszL83ekqakpTU1NzfuNjY3tVTYAQKFYMQ4AAAAAsA+USqUW++Vy+VXHtrerMTNmzEhNTU3z1r9//3apFQCgaATjAAAAAAAdqK6uLkletfJ77dq1zavI6+rqsnnz5qxbt67VMTsybdq0NDQ0NG9PPfVUO1cPAFAMgnEAAAAAgA509NFHp66uLosXL24+tnnz5tx9990ZPnx4kuS4445L9+7dW4xZvXp1HnnkkeYxO1JVVZXevXu32AAAeDXvGAcAAAAAaKP169fniSeeaN5fuXJlVqxYkT59+mTAgAGZPHlyrrrqqgwcODADBw7MVVddlYMOOigTJkxIktTU1OSCCy7IJZdckr59+6ZPnz6ZOnVqBg8enJEjR1bqtgAACkMwDgAAAADQRsuXL88pp5zSvD9lypQkycSJEzNv3rxcdtll2bhxYy666KKsW7cuw4YNy1133ZVevXo1z7n22mvTrVu3jB8/Phs3bsypp56aefPmpWvXrvv8fgAAikYwDgeocrmcTZs2VboM2kG5XE5TU1OSlx6fViqVKlwR7aG6utqfJQAAQCcyYsSIlMvlVs+XSqVMnz4906dPb3VMdXV15syZkzlz5nRAhQAABzbBOBygNm3alDFjxlS6DKAVixYtSs+ePStdBgAAAAAAFEKXShcAAAAAAAAAAB3JinE4QFVXV2fRokWVLoN2sGnTppxxxhlJkoULF6a6urrCFdEe/DkCAAAAAED7EYzDAapUKnlMcwFVV1f7cwUAAAAAANiOR6kDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAkueeeezJu3LjU19enVCrltttue9WY3/3udzn99NNTU1OTXr165cQTT8yqVauazzc1NWXSpEnp169fDj744Jx++ul5+umn9+FdAAAAAAA7IhgHAIAkGzZsyJAhQzJ37twdnv+v//qvvP3tb8/rX//6LF26NA8//HAuv/zyVFdXN4+ZPHlyFi5cmFtuuSX33ntv1q9fn7Fjx2br1q376jYAAAAAgB3oVukCAABgfzBmzJiMGTOm1fOf+cxn8p73vCczZ85sPva6172u+dcNDQ25/vrr853vfCcjR45MksyfPz/9+/fPkiVLMnr06I4rHgAAAADYKSvGAQBgF7Zt25Y77rgjxxxzTEaPHp3DDjssw4YNa/G49QcffDBbtmzJqFGjmo/V19dn0KBBWbZs2Q6v29TUlMbGxhYbAAAAAND+BOMAALALa9euzfr16/PFL34x7373u3PXXXfljDPOyJlnnpm77747SbJmzZr06NEjhx56aIu5tbW1WbNmzQ6vO2PGjNTU1DRv/fv37/B7AQAAAIADkWAcAAB2Ydu2bUmS973vfbn44ovz5je/OZ/+9KczduzYfOMb39jp3HK5nFKptMNz06ZNS0NDQ/P21FNPtXvtAAAAAIBgHAAAdqlfv37p1q1bjj322BbH3/CGN2TVqlVJkrq6umzevDnr1q1rMWbt2rWpra3d4XWrqqrSu3fvFhsAAAAA0P4E4wAAsAs9evTI8ccfn0cffbTF8cceeyxHHnlkkuS4445L9+7ds3jx4ubzq1evziOPPJLhw4fv03oBAAAAgJa6VboAAADYH6xfvz5PPPFE8/7KlSuzYsWK9OnTJwMGDMill16as88+O+985ztzyimn5M4778wPfvCDLF26NElSU1OTCy64IJdcckn69u2bPn36ZOrUqRk8eHBGjhxZobsCAAAAAJJOumL8nnvuybhx41JfX59SqZTbbrut+dyWLVvyqU99KoMHD87BBx+c+vr6nH/++fnTn/6002vOmzcvpVLpVdumTZs6+G4AANgfLF++PEOHDs3QoUOTJFOmTMnQoUNzxRVXJEnOOOOMfOMb38jMmTMzePDg/O///b9z66235u1vf3vzNa699tq8//3vz/jx43PSSSfloIMOyg9+8IN07dq1IvcEAAAAALykU64Y37BhQ4YMGZK/+7u/ywc+8IEW51544YU89NBDufzyyzNkyJCsW7cukydPzumnn57ly5fv9Lq9e/d+1eMxq6ur271+AAD2PyNGjEi5XN7pmI985CP5yEc+0ur56urqzJkzJ3PmzGnv8gAAAACANuiUwfiYMWMyZsyYHZ6rqalp8V7HJJkzZ05OOOGErFq1KgMGDGj1uqVSKXV1de1aKwAAAAAAAACV1Skfpb6nGhoaUiqVcsghh+x03Pr163PkkUfmiCOOyNixY/PLX/5yp+ObmprS2NjYYgMAAAAAAABg/1L4YHzTpk359Kc/nQkTJqR3796tjnv961+fefPm5fbbb8/NN9+c6urqnHTSSXn88cdbnTNjxozU1NQ0b/379++IWwAAAAAAAACgDQodjG/ZsiXnnHNOtm3bluuuu26nY0888cT87d/+bYYMGZJ3vOMd+fd///ccc8wxO30/5LRp09LQ0NC8PfXUU+19CwAAAAAAAAC0Uad8x/ju2LJlS8aPH5+VK1fmJz/5yU5Xi+9Ily5dcvzxx+90xXhVVVWqqqraWioAAAAAAAAAHaiQK8ZfDsUff/zxLFmyJH379t3ja5TL5axYsSKHH354B1QIAAAAAAAAwL7SKVeMr1+/Pk888UTz/sqVK7NixYr06dMn9fX1Oeuss/LQQw/lhz/8YbZu3Zo1a9YkSfr06ZMePXokSc4///y89rWvzYwZM5Ikn//853PiiSdm4MCBaWxszFe/+tWsWLEiX/va1/b9DQIAAAAAAADQbjplML58+fKccsopzftTpkxJkkycODHTp0/P7bffniR585vf3GLeT3/604wYMSJJsmrVqnTp8j8L5p977rl87GMfy5o1a1JTU5OhQ4fmnnvuyQknnNCxNwMAAAAAAABAh+qUwfiIESNSLpdbPb+zcy9bunRpi/1rr7021157bVtLAwAAAAAAAGA/U8h3jAMAAAAAAADAywTjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAAEChCcYBAAAAAAAAKDTBOAAAAAAAAACFJhgHAAAAAAAAoNAE4wAAAAAAAAAUmmAcAAAAAAAAgEITjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAAEChCcYBAAAAAAAAKDTBOAAAAAAAAACFJhgHAAAAAAAAoNAE4wAAAAAAAAAUmmAcAAAAAAAAgEITjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAAEChCcYBAAAAAAAAKDTBOAAAAAAAAACFJhgHAAAAAAAAoNAE4wAAAAAAAAAUmmAcAAAAAAAAgEITjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAASHLPPfdk3Lhxqa+vT6lUym233dbq2AsvvDClUimzZ89ucbypqSmTJk1Kv379cvDBB+f000/P008/3bGFAwAAAAC7JBgHAIAkGzZsyJAhQzJ37tydjrvtttty//33p76+/lXnJk+enIULF+aWW27Jvffem/Xr12fs2LHZunVrR5UNAAAAAOyGbpUuAAAA9gdjxozJmDFjdjrmj3/8Yz7+8Y/nxz/+cd773ve2ONfQ0JDrr78+3/nOdzJy5Mgkyfz589O/f/8sWbIko0eP7rDaO5NyuZxNmzZVugzgFV75M+nnE/Y/1dXVKZVKlS4DAAA6PcE4AADshm3btuW8887LpZdemje+8Y2vOv/ggw9my5YtGTVqVPOx+vr6DBo0KMuWLROM/z+bNm3a5T9AACrnjDPOqHQJwHYWLVqUnj17VroMAADo9ATjAACwG66++up069Ytn/jEJ3Z4fs2aNenRo0cOPfTQFsdra2uzZs2aHc5pampKU1NT835jY2P7FQwAAAAANBOMAwDALjz44IP5yle+koceemiPH2VaLpdbnTNjxox8/vOfb48SO6X1b/5Qyl38XxKouHI52fbiS7/u0i3xyGaouNK2F/NXK26udBkAAFAovoUCAIBd+NnPfpa1a9dmwIABzce2bt2aSy65JLNnz84f/vCH1NXVZfPmzVm3bl2LVeNr167N8OHDd3jdadOmZcqUKc37jY2N6d+/f8fdyH6m3KVb0rV7pcsAkiQ9Kl0A8ArlShcAAAAF1KXSBQAAwP7uvPPOy69+9ausWLGieauvr8+ll16aH//4x0mS4447Lt27d8/ixYub561evTqPPPJIq8F4VVVVevfu3WIDAAAAANqfFeMAAJBk/fr1eeKJJ5r3V65cmRUrVqRPnz4ZMGBA+vbt22J89+7dU1dXl7/5m79JktTU1OSCCy7IJZdckr59+6ZPnz6ZOnVqBg8enJEjR+7TewEAAAAAWhKMAwBAkuXLl+eUU05p3n/5EecTJ07MvHnzdusa1157bbp165bx48dn48aNOfXUUzNv3rx07dq1I0oGAAAAAHaTYBwAAJKMGDEi5fLuv9HzD3/4w6uOVVdXZ86cOZkzZ047VgYAAAAAtJV3jAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKF1ymD8nnvuybhx41JfX59SqZTbbrutxflyuZzp06envr4+PXv2zIgRI/Kb3/xml9e99dZbc+yxx6aqqirHHntsFi5c2EF3AAAAAAAAAMC+0imD8Q0bNmTIkCGZO3fuDs/PnDkzs2bNyty5c/PAAw+krq4up512Wp5//vlWr3nffffl7LPPznnnnZeHH3445513XsaPH5/777+/o24DAAAAAAAAgH2gW6UL2BtjxozJmDFjdniuXC5n9uzZ+cxnPpMzzzwzSXLjjTemtrY2CxYsyIUXXrjDebNnz85pp52WadOmJUmmTZuWu+++O7Nnz87NN9/cMTcCAAAAAAAAQIfrlCvGd2blypVZs2ZNRo0a1XysqqoqJ598cpYtW9bqvPvuu6/FnCQZPXr0Tuc0NTWlsbGxxQYAAAAAAADA/qVwwfiaNWuSJLW1tS2O19bWNp9rbd6ezpkxY0Zqamqat/79+7ehcgAAAAAAAAA6QuGC8ZeVSqUW++Vy+VXH2jpn2rRpaWhoaN6eeuqpvS8YAAAAAAAAgA7RKd8xvjN1dXVJXloBfvjhhzcfX7t27atWhG8/b/vV4buaU1VVlaqqqjZWDAAAAAAAAEBHKtyK8aOPPjp1dXVZvHhx87HNmzfn7rvvzvDhw1ud97a3va3FnCS56667djoHAAAAAAAAgP1fp1wxvn79+jzxxBPN+ytXrsyKFSvSp0+fDBgwIJMnT85VV12VgQMHZuDAgbnqqqty0EEHZcKECc1zzj///Lz2ta/NjBkzkiSf/OQn8853vjNXX3113ve+9+X73/9+lixZknvvvXef3x8AAAAAAAAA7adTBuPLly/PKaec0rw/ZcqUJMnEiRMzb968XHbZZdm4cWMuuuiirFu3LsOGDctdd92VXr16Nc9ZtWpVunT5nwXzw4cPzy233JLPfvazufzyy/PXf/3X+e53v5thw4btuxsDAAAAAAAAoN11ymB8xIgRKZfLrZ4vlUqZPn16pk+f3uqYpUuXvurYWWedlbPOOqsdKgQAAAAAAABgf1G4d4wDAAAAAOxvXnzxxXz2s5/N0UcfnZ49e+Z1r3tdrrzyymzbtq15TLlczvTp01NfX5+ePXtmxIgR+c1vflPBqgEAikMwDgAAAADQwa6++up84xvfyNy5c/O73/0uM2fOzDXXXJM5c+Y0j5k5c2ZmzZqVuXPn5oEHHkhdXV1OO+20PP/88xWsHACgGATjAAAAAAAd7L777sv73ve+vPe9781RRx2Vs846K6NGjcry5cuTvLRafPbs2fnMZz6TM888M4MGDcqNN96YF154IQsWLKhw9QAAnV+nfMc4AAAAAEBn8va3vz3f+MY38thjj+WYY47Jww8/nHvvvTezZ89OkqxcuTJr1qzJqFGjmudUVVXl5JNPzrJly3LhhRdWqPL9S7lczqZNmypdBvAKr/yZ9PMJ+5/q6uqUSqVKl7FfEIwDAAAAAHSwT33qU2loaMjrX//6dO3aNVu3bs0XvvCFfOhDH0qSrFmzJklSW1vbYl5tbW2efPLJVq/b1NSUpqam5v3GxsYOqH7/sWnTpowZM6bSZQCtOOOMMypdArCdRYsWpWfPnpUuY7/gUeoAAAAAAB3su9/9bubPn58FCxbkoYceyo033pgvfelLufHGG1uM235FV7lc3ukqrxkzZqSmpqZ569+/f4fUDwDQ2VkxDgAAAADQwS699NJ8+tOfzjnnnJMkGTx4cJ588snMmDEjEydOTF1dXZKXVo4ffvjhzfPWrl37qlXkrzRt2rRMmTKleb+xsfGACcfXv/lDKXfxFTdUXLmcbHvxpV936ZZ4ZDNUXGnbi/mrFTdXuoz9jq4BAAAAAKCDvfDCC+nSpeUDPLt27Zpt27YlSY4++ujU1dVl8eLFGTp0aJJk8+bNufvuu3P11Ve3et2qqqpUVVV1XOH7sXKXbknX7pUuA0iS9Kh0AcArlCtdwH5KMA4AAAAA0MHGjRuXL3zhCxkwYEDe+MY35pe//GVmzZqVj3zkI0leeoT65MmTc9VVV2XgwIEZOHBgrrrqqhx00EGZMGFChasHAOj8BOMAAAAAAB1szpw5ufzyy3PRRRdl7dq1qa+vz4UXXpgrrriiecxll12WjRs35qKLLsq6desybNiw3HXXXenVq1cFKwcAKAbBOAAAAABAB+vVq1dmz56d2bNntzqmVCpl+vTpmT59+j6rCwDgQNFl10MAAAAAAAAAoPMSjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACi0dgnG3/Wud+XUU0/Nk08+udtz/vSnPzXPAwCAV9JfAgDQFvpJAAC21609LrJ06dKUSqVs2LBht+ds3LixeR4AALyS/hIAgLbQTwIAsD2PUgcAAAAAAACg0CoWjL/8rzWrq6srVQIAAAWivwQAoC30kwAAxVaxYHzRokVJkiOOOKJSJQAAUCD6SwAA2kI/CQBQbHv1jvGPfOQjOzz+2c9+NocccshO5zY1NeW//uu/8sADD6RUKuXkk0/emxIAACgQ/SUAAG2hnwQAYFf2KhifN29eSqVSi2Plcjnf//73d2t+uVxOkvTp0yfTpk3bmxIAACgQ/SUAAG2hnwQAYFf2KhgfMGBAi0bzySefTKlUyuGHH57u3bu3Oq9UKqW6ujqHH354hg8fnn/4h39IfX393pQAAECB6C8BAGgL/SQAALuyV8H4H/7whxb7Xbq89Kryu+66K8cee2ybiwIA4MCivwQAoC30kwAA7MpeBePbe+c735lSqZSDDz64PS4HAMABTn8JAEBb6CcBANheuwTjS5cubY/LAABAEv0lAABto58EAGB7XSpdAAAAAAAAAAB0pHZZMb4jjY2Nef7557N169Zdjh0wYEBHlQEAQEHoLwEAaAv9JADAga1dg/HFixfnuuuuy89+9rOsW7dut+aUSqW8+OKL7VkGAAAFob8EAKAt9JMAALys3YLxT3ziE/na176WJCmXy+11WQAADlD6SwAA2kI/CQDAK7VLML5gwYLMnTs3SVJdXZ33v//9Oe6449KnT5906eI15gAA7Bn9JQAAbaGfBABge+0SjH/zm99MkvTv3z8/+clP8td//dftcVkAAA5Q+ksAANpCPwkAwPba5Z9H/upXv0qpVMrnPvc5TSYAAG2mvwQAoC30kwAAbK9dgvEtW7YkSYYOHdoelwMA4ACnvwQAoC30kwAAbK9dgvGjjjoqSbJ+/fr2uBwAAAc4/SUAAG2hnwQAYHvtEoyfeeaZSZL/+I//aI/LAQBwgNNfAgDQFvpJAAC21y7B+CWXXJIBAwZk9uzZ+c///M/2uCQAAAcw/SUAAG2hnwQAYHvtEozX1NTkzjvvTG1tbU466aRcd911WbduXXtcGgCAA5D+EgCAttBPAgCwvW7tcZHXve51SZIXXngh69aty6RJk/KJT3wi/fr1y0EHHbTTuaVSKf/1X//VHmUAAFAQ+ksAANpCPwkAwPbaJRj/wx/+0GK/XC6nXC5n7dq1u5xbKpXaowQAAApEfwkAQFvoJwEA2F67BOMTJ05sj8sAAEAS/SUAAG2jnwQAYHvtEozfcMMN7XEZAABIor8EAKBt9JMAAGyvS6ULAAAAAAAAAICOJBgHAAAAAAAAoNAE4wAAAAAAAAAUWru8Y/zb3/52m+aff/757VEGAAAFob8EAKAt9JMAAGyvXYLxD3/4wymVSns1t1QqaTQBAGhBfwkAQFvoJwEA2F67BONJUi6X2+tSAACgvwQAoE30kwAAvFK7BOMrV67c5ZgNGzbk0UcfzU033ZTvfe97GT58eL71rW/l4IMPbo8SAAAoEP0lAABtoZ8EAGB77RKMH3nkkbs17thjj80ZZ5yRm2++Oeedd14mTZqUJUuWtEcJAAAUiP4SAIC20E8CALC9LpX40A996EM5//zzs3Tp0nzzm9+sRAkAABSI/hIAgLbQTwIAFF9FgvEkGT9+fMrlcubNm1epEgAAKBD9JQAAbaGfBAAotooF43V1dUmSRx99tFIlAABQIPpLAADaQj8JAFBsFQvGV61alSTZsmVLpUoAAKBA9JcAALSFfhIAoNgqEoxv2bIlM2fOTJL8r//1vypRAgAABaK/BACgLfSTAADF1609LvLyv6bcmW3btmXdunVZvnx55s6dm0ceeSSlUinnnHNOe5QAAECBVKK/vOeee3LNNdfkwQcfzOrVq7Nw4cK8//3vT/LSF6Wf/exn86Mf/Si///3vU1NTk5EjR+aLX/xi6uvrm6/R1NSUqVOn5uabb87GjRtz6qmn5rrrrssRRxyxVzUBALB3fF8JAMD22iUYP/roo/d4Trlcztve9rZcfPHF7VECAAAFUon+csOGDRkyZEj+7u/+Lh/4wAdanHvhhRfy0EMP5fLLL8+QIUOybt26TJ48OaeffnqWL1/ePG7y5Mn5wQ9+kFtuuSV9+/bNJZdckrFjx+bBBx9M165d96ouAAD2nO8rAQDYXrsE4+VyeY/G9+nTJx/72Mdy+eWXp6qqqj1KAACgQCrRX44ZMyZjxozZ4bmamposXry4xbE5c+bkhBNOyKpVqzJgwIA0NDTk+uuvz3e+852MHDkySTJ//vz0798/S5YsyejRo/eqLgAA9pzvKwEA2F67BOM33HDDLsd06dIlvXr1ytFHH51BgwZZMQMAQKs6Q3/Z0NCQUqmUQw45JEny4IMPZsuWLRk1alTzmPr6+gwaNCjLli0TjAMA7EOdoZ9k77T4Rw9bt1SuEADYn73i78g9/QeDRdYuwfjEiRPb4zIAAJBk/+8vN23alE9/+tOZMGFCevfunSRZs2ZNevTokUMPPbTF2Nra2qxZs2aH12lqakpTU1PzfmNjY8cVDQBwANnf+0n23iv7514P31LBSgCgc2hqaspBBx1U6TL2C10qXQAAAHQmW7ZsyTnnnJNt27bluuuu2+X4crmcUqm0w3MzZsxITU1N89a/f//2LhcAAAAASDutGAcAgAPBli1bMn78+KxcuTI/+clPmleLJ0ldXV02b96cdevWtVg1vnbt2gwfPnyH15s2bVqmTJnSvN/Y2CgcBwCAnXjlO+CfH3JO0rV7BasBgP3U1i3NT1Z55d+dB7oOCcYffPDBLFmyJI888kj++7//O0nSp0+fDBo0KCNHjsxxxx3XER8LAEBB7Q/95cuh+OOPP56f/vSn6du3b4vzxx13XLp3757Fixdn/PjxSZLVq1fnkUceycyZM3d4zaqqKv/nBABgH9gf+knaR4unMXXtLhgHgF1o7UmGB6J2DcZ//etf52Mf+1h+8YtftDrmn/7pnzJs2LB885vfzODBg9vz4wEAKJh92V+uX78+TzzxRPP+ypUrs2LFivTp0yf19fU566yz8tBDD+WHP/xhtm7d2vze8D59+qRHjx6pqanJBRdckEsuuSR9+/ZNnz59MnXq1AwePDgjR47c67oAANh7vq8EAOBl7faO8SVLluSEE07IL37xi5TL5ZTL5XTr1i21tbU57LDD0q1bt+bjP//5z3PCCSfkP/7jP9rr4wEAKJh93V8uX748Q4cOzdChQ5MkU6ZMydChQ3PFFVfk6aefzu23356nn346b37zm3P44Yc3b8uWLWu+xrXXXpv3v//9GT9+fE466aQcdNBB+cEPfpCuXbu2+fcDAIA94/tKAABeqV2C8b/85S/54Ac/mKamppRKpfx//9//l/vvvz8bNmzIn/70p6xevTovvPBCfvGLX+SjH/1ounbtmqampnzwgx/Ms88+2x4lAABQIJXoL0eMGNH8xegrt3nz5uWoo47a4blyuZwRI0Y0X6O6ujpz5szJs88+mxdeeCE/+MEPvDMcAKACfF8JAMD22iUY/8pXvpKGhob06NEjd9xxR771rW/l+OOPT7du//Ok9q5du+atb31rvvnNb+aOO+5I9+7d09DQkK985SvtUQIAAAWivwQAoC30kwAAbK9dgvE77rgjpVIpH//4xzN69Ohdjh81alQmTZqUcrmcO+64oz1KAACgQPSXAAC0hX4SAIDttUswvnLlyiTJ6aefvttzXh77+9//vj1KAACgQPSXAAC0hX4SAIDttUswvmnTpiTJwQcfvNtzDjrooCRJU1NTe5QAAECB6C8BAGgL/SQAANtrl2C8rq4uSfLLX/5yt+e8PLa2trY9SgAAoED0lwAAtIV+EgCA7XVrj4u84x3vyPz58/PFL34x48ePT+/evXc6vrGxMVdffXVKpVLe8Y53tEcJAAAUiP6yuMrl8v/sbN1SuUIAYH/2ir8jW/zdyW7TTwIAsL12CcYvvPDCzJ8/PytXrsw73/nO/Ou//muOP/74HY79xS9+kY997GP5/e9/n1KplAsvvLA9SgAAoED0l8X1ykeT9nr4lgpWAgCdQ1NTU/Mjvtl9+kkAALbXLsH4SSedlIsuuijXXXddfv3rX+fEE0/Msccem2HDhqW2tjalUilr1qzJ/fffn9/+9rfN8y666KKcdNJJ7VECAAAFor8EAKAt9JMAAGyvXYLxJJkzZ04OOuigzJo1K9u2bctvfvObFk1l8j+PfurSpUumTp2aL37xi+318QAAFIz+spiqqqqaf/38kHOSrt0rWA0A7Ke2bml+ssor/+5kz+gnAQB4pXYLxkulUmbOnJnzzz8/X//617NkyZI8/vjjLcYMHDgwI0eOzD/8wz9k0KBB7fXRAAAUkP6ymEql0v/sdO0uGAeAXWjxdyd7RD8JAMArtVsw/rJBgwbla1/7WpJk8+bNWbduXZLk0EMPTY8ePdr74wAAKDj9JQAAbaGfBAAg6YBg/JV69OiR2trajvwIAAAOIPpLAADaQj8JAHDg6rI3kxYtWpS3vOUtectb3pIFCxbs0dybbrqpee6SJUv25uMBACgY/SUAAG2hnwQAYFf2OBgvl8u5+OKL8/DDD6dv376ZMGHCHs2fMGFC+vbtmxUrVuSSSy7Z048HAKBg9JcAALSFfhIAgN2xx8H4T37ykzz22GPp0qVLZs+evccfWCqV8pWvfCVdu3bNI488kqVLl+7xNQAAKA79JQAAbaGfBABgd+xxMH7rrbcmSU477bS88Y1v3KsPPfbYYzN69OgW1wMA4MCkvwQAoC30kwAA7I49DsZ/8YtfpFQqZdy4cW364LFjx6ZcLufnP/95m64DAEDnpr8EAKAt9JMAAOyOPQ7Gn3zyySTJ3/zN37Tpg4855pgkyR/+8Ic2XQcAgM5NfwkAQFvoJwEA2B17HIw3NDQkSfr06dOmD355fmNjY5uuAwBA56a/BACgLfSTAADsjj0Oxnv37p0kee6559r0wS/P79WrV5uuAwBA56a/BACgLfSTAADsjj0Oxg877LAkyW9/+9s2ffDvfve7FtcDAODApL8EAKAt9JMAAOyOPQ7GTzjhhJTL5dx+++1t+uDvf//7KZVKOf7449t0HQAAOjf9JQAAbaGfBABgd+xxMD5mzJgkyeLFi3PPPffs1Yfec889ueuuu1pcDwCAA5P+EgCAttBPAgCwO/Y4GP/ABz6Q173udSmXyxk/fnweffTRPZr/2GOPZfz48SmVSjnqqKNy1lln7WkJu+Woo45KqVR61faP//iPOxy/dOnSHY7/z//8zw6pDwCAl3SW/hIAgP2TfhIAgN2xx8F4t27d8uUvfzmlUil//vOf89a3vjXXXntt1q9fv9N569evz+zZs/PWt741a9euTZJ8+ctfTrdu3fau8l144IEHsnr16uZt8eLFSZIPfvCDO5336KOPtpg3cODADqkPAICXdJb+EgCA/ZN+EgCA3bFXXd773ve+/Mu//Es+85nP5IUXXsjUqVPzuc99Lu94xzvylre8JbW1tTn44IOzYcOGPPPMM3nooYfys5/9LBs2bEi5XE6SfP7zn8/73//+9ryXFl7zmte02P/iF7+Yv/7rv87JJ5+803mHHXZYDjnkkA6rCwCAV+sM/SUAAPsv/SQAALuy1//8cdq0aTniiCNy0UUXZcOGDVm/fn3uvPPO3HnnnTsc/3KDedBBB2Xu3Ln58Ic/vLcfvcc2b96c+fPnZ8qUKSmVSjsdO3To0GzatCnHHntsPvvZz+aUU05pdWxTU1Oampqa9xsbG9utZgCAA01n6i8BANj/6CcBANiZPX6U+iudd955eeyxx3LJJZfkNa95Tcrlcqtbv379MnXq1Dz22GP7vMm87bbb8txzz+30cw8//PB861vfyq233prvfe97+Zu/+Zuceuqpueeee1qdM2PGjNTU1DRv/fv374DqAQAOHJ2lvwQAYP+knwQAoDWl8sv/NLId/Pa3v83DDz+cv/zlL3n++efTq1ev9OvXL0OGDMmxxx7bXh+zx0aPHp0ePXrkBz/4wR7NGzduXEqlUm6//fYdnt/RivH+/funoaEhvXv3blPNALtr48aNGTNmTJJk0aJF6dmzZ4UrAg4kjY2Nqamp6bD+Z3/tLztKR/9+7g9e+ffW8285L+navcIVAcB+aOuW9HroO0mK///z9JPtr+g9pX4SAHaDfnKH9vpR6jty7LHH7ncN5ZNPPpklS5bke9/73h7PPfHEEzN//vxWz1dVVaWqqqot5QEAsBP7Y38JAEDnoZ8EAOBlbXqUemdwww035LDDDst73/vePZ77y1/+MocffngHVAUAAAAAAADAvtKuK8b3N9u2bcsNN9yQiRMnplu3lrc6bdq0/PGPf8y3v/3tJMns2bNz1FFH5Y1vfGM2b96c+fPn59Zbb82tt95aidIBAAAAAAAAaCeFDsaXLFmSVatW5SMf+cirzq1evTqrVq1q3t+8eXOmTp2aP/7xj+nZs2fe+MY35o477sh73vOefVkyAAAAAAAAAO2s0MH4qFGjUi6Xd3hu3rx5LfYvu+yyXHbZZfugKgAAAAAAAAD2pcK/YxwAAAAAAACAA5tgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAAEChCcYBAAAAAAAAKLRulS4AAAAAAAD2VGnbiylXugggKZeTbS++9Osu3ZJSqbL1ACm9/DNJC4JxAAAAAAA6nb9acXOlSwAAOhGPUgcAAAAAAACg0KwYBwAAAACgU6iurs6iRYsqXQbwCps2bcoZZ5yRJFm4cGGqq6srXBHwSn4m/4dgHAAAAACATqFUKqVnz56VLgNoRXV1tZ9RYL/lUeoAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAAEChCcYBAAAAAAAAKDTBOAAAAAAAAACFJhgHAAAAAAAAoNAE4wAAAAAAAAAUmmAcAAAAAAAAgEITjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAD2gT/+8Y/527/92/Tt2zcHHXRQ3vzmN+fBBx9sPl8ulzN9+vTU19enZ8+eGTFiRH7zm99UsGIAgOIQjAMAAAAAdLB169blpJNOSvfu3bNo0aL89re/zZe//OUccsghzWNmzpyZWbNmZe7cuXnggQdSV1eX0047Lc8//3zlCgcAKIhulS4AAAAAAKDorr766vTv3z833HBD87Gjjjqq+dflcjmzZ8/OZz7zmZx55plJkhtvvDG1tbVZsGBBLrzwwn1dMgBAoVgxDgAAAADQwW6//fa89a1vzQc/+MEcdthhGTp0aP71X/+1+fzKlSuzZs2ajBo1qvlYVVVVTj755CxbtqzV6zY1NaWxsbHFBgDAqwnGAQAgyT333JNx48alvr4+pVIpt912W4vzu/O+x6ampkyaNCn9+vXLwQcfnNNPPz1PP/30PrwLAAD2V7///e/z9a9/PQMHDsyPf/zj/P3f/30+8YlP5Nvf/naSZM2aNUmS2traFvNqa2ubz+3IjBkzUlNT07z179+/424CAKATE4wDAECSDRs2ZMiQIZk7d+4Oz+/O+x4nT56chQsX5pZbbsm9996b9evXZ+zYsdm6deu+ug0AAPZT27Zty1ve8pZcddVVGTp0aC688MJ89KMfzde//vUW40qlUov9crn8qmOvNG3atDQ0NDRvTz31VIfUDwDQ2XnHOAAAJBkzZkzGjBmzw3O7877HhoaGXH/99fnOd76TkSNHJknmz5+f/v37Z8mSJRk9evQ+uxcAAPY/hx9+eI499tgWx97whjfk1ltvTZLU1dUleWnl+OGHH948Zu3ata9aRf5KVVVVqaqq6oCKAQCKRTAOAAC7sKv3PV544YV58MEHs2XLlhZj6uvrM2jQoCxbtmyHwXhTU1Oampqa9w+090GWtr2YcqWLAJJyOdn24ku/7tIt2cmqRGDfKL38M0mhnHTSSXn00UdbHHvsscdy5JFHJkmOPvro1NXVZfHixRk6dGiSZPPmzbn77rtz9dVX7/N6AQCKRjAOAAC7sLP3PT755JPNY3r06JFDDz30VWNaeyfkjBkz8vnPf74DKu4c/mrFzZUuAQBgn7n44oszfPjwXHXVVRk/fnx+8Ytf5Fvf+la+9a1vJXnpEeqTJ0/OVVddlYEDB2bgwIG56qqrctBBB2XChAkVrh4AoPMTjAMAwG7a0/c97mrMtGnTMmXKlOb9xsbG9O/fv+2FAgCw3zn++OOzcOHCTJs2LVdeeWWOPvrozJ49O+eee27zmMsuuywbN27MRRddlHXr1mXYsGG566670qtXrwpWDgBQDIJxAADYhd1532NdXV02b96cdevWtVg1vnbt2gwfPnyH1z0Q3wdZXV2dRYsWVboM4BU2bdqUM844I0mycOHCVFdXV7gi4JX8TBbL2LFjM3bs2FbPl0qlTJ8+PdOnT993RQEAHCAE4wAAsAu7877H4447Lt27d8/ixYszfvz4JMnq1avzyCOPZObMmRWrfX9TKpXSs2fPSpcBtKK6utrPKAAAAIUkGGePlMvlbNq0qdJlAK/wyp9JP5+w/6murt7lo7bZP6xfvz5PPPFE8/7KlSuzYsWK9OnTJwMGDNjl+x5rampywQUX5JJLLknfvn3Tp0+fTJ06NYMHD87IkSMrdVsAAAAAQATj7KFNmzZlzJgxlS4DaMXLj8AE9h+LFi2y8q6TWL58eU455ZTm/Zff/T1x4sTMmzdvt973eO2116Zbt24ZP358Nm7cmFNPPTXz5s1L165d9/n9AAAAAAD/QzAOAABJRowYkXK53Or53XnfY3V1debMmZM5c+Z0QIUAAAAAwN4SjLPX1r/5Qyl38T8hqLhyOdn24ku/7tIt8chmqLjSthfzVyturnQZAAAAAAD8P1JN9lq5S7eka/dKlwEkSXpUugDgFVpfcwwAAAAAQCV0qXQBAAAAAAAAANCRBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAAEChCcYBAAAAAAAAKDTBOAAAAAAAAACFJhgHAAAAAAAAoNAE4wAAAAAAAAAUmmAcAAAAAAAAgEITjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0AoZjE+fPj2lUqnFVldXt9M5d999d4477rhUV1fnda97Xb7xjW/so2oBAAAAAAAA6EjdKl1AR3njG9+YJUuWNO937dq11bErV67Me97znnz0ox/N/Pnz83//7//NRRddlNe85jX5wAc+sC/KBQAAAAAAAKCDFDYY79at2y5Xib/sG9/4RgYMGJDZs2cnSd7whjdk+fLl+dKXviQYBwAAAAAAAOjkCvko9SR5/PHHU19fn6OPPjrnnHNOfv/737c69r777suoUaNaHBs9enSWL1+eLVu2dHSpAAAAAAAAAHSgQgbjw4YNy7e//e38+Mc/zr/+679mzZo1GT58eJ599tkdjl+zZk1qa2tbHKutrc2LL76Yv/zlL61+TlNTUxobG1tsAAAAAAAAAOxfChmMjxkzJh/4wAcyePDgjBw5MnfccUeS5MYbb2x1TqlUarFfLpd3ePyVZsyYkZqamuatf//+7VA9AAAAAAAAAO2pkMH49g4++OAMHjw4jz/++A7P19XVZc2aNS2OrV27Nt26dUvfvn1bve60adPS0NDQvD311FPtWjcAAAAAAAAAbdet0gXsC01NTfnd736Xd7zjHTs8/7a3vS0/+MEPWhy766678ta3vjXdu3dv9bpVVVWpqqpq11oBAAAAAAAAaF+FXDE+derU3H333Vm5cmXuv//+nHXWWWlsbMzEiROTvLTS+/zzz28e//d///d58sknM2XKlPzud7/Lv/3bv+X666/P1KlTK3ULAAAAAAAAALSTQq4Yf/rpp/OhD30of/nLX/Ka17wmJ554Yn7+85/nyCOPTJKsXr06q1atah5/9NFH50c/+lEuvvjifO1rX0t9fX2++tWv5gMf+EClbgEAAAAAAACAdlLIYPyWW27Z6fl58+a96tjJJ5+chx56qIMqAgAAAAAAAKBSCvkodQAAAAAAAAB4mWAcAAAAAAAAgEITjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAAEChCcYBAAAAAAAAKDTBOAAAAAAAAACFJhgHAAAAAAAAoNAE4wAAAAAAAAAUmmAcAAAAAAAAgEITjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAALAbXnzxxXz2s5/N0UcfnZ49e+Z1r3tdrrzyymzbtq15TLlczvTp01NfX5+ePXtmxIgR+c1vflPBqgEAAACARDAOAAC75eqrr843vvGNzJ07N7/73e8yc+bMXHPNNZkzZ07zmJkzZ2bWrFmZO3duHnjggdTV1eW0007L888/X8HKAQAAAADBOAAA7Ib77rsv73vf+/Le9743Rx11VM4666yMGjUqy5cvT/LSavHZs2fnM5/5TM4888wMGjQoN954Y1544YUsWLCgwtUDAAAAwIFNMA4AALvh7W9/e/7jP/4jjz32WJLk4Ycfzr333pv3vOc9SZKVK1dmzZo1GTVqVPOcqqqqnHzyyVm2bNkOr9nU1JTGxsYWGwAAAADQ/rpVugAAAOgMPvWpT6WhoSGvf/3r07Vr12zdujVf+MIX8qEPfShJsmbNmiRJbW1ti3m1tbV58sknd3jNGTNm5POf/3zHFg4AAAAAWDEOAAC747vf/W7mz5+fBQsW5KGHHsqNN96YL33pS7nxxhtbjCuVSi32y+Xyq469bNq0aWloaGjennrqqQ6rHwAAAAAOZFaMAwDAbrj00kvz6U9/Ouecc06SZPDgwXnyySczY8aMTJw4MXV1dUleWjl++OGHN89bu3btq1aRv6yqqipVVVUdXzwAAAAAHOCsGAcAgN3wwgsvpEuXlu1z165ds23btiTJ0Ucfnbq6uixevLj5/ObNm3P33Xdn+PDh+7RWAAAAAKAlK8YBAGA3jBs3Ll/4whcyYMCAvPGNb8wvf/nLzJo1Kx/5yEeSvPQI9cmTJ+eqq67KwIEDM3DgwFx11VU56KCDMmHChApXDwAAAAAHNsE4AADshjlz5uTyyy/PRRddlLVr16a+vj4XXnhhrrjiiuYxl112WTZu3JiLLroo69aty7Bhw3LXXXelV69eFawcAAAAABCMAwDAbujVq1dmz56d2bNntzqmVCpl+vTpmT59+j6rCwAAAADYNe8YBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAAAAAAKTTAOAAAAAAAAQKEJxgEAAAAAAAAoNME4AAAAAAAAAIUmGAcAAAAAAACg0ATjAAAAAAAAABSaYBwAAAAAAACAQhOMAwAAAAAAAFBognEAAAAAAAAACk0wDgAAAAAAAEChCcYBAAAAAAAAKDTBOAAAAAAAAACFJhgHAAAAAAAAoNAE4wAAAAAAAAAUmmAcAAAAAAAAgEITjAMAAAAAAABQaIJxAAAAAAAAAApNMA4AAAAAAABAoQnGAQAAAAAAACg0wTgAAAAAAAAAhSYYBwAAAAAAAKDQBOMAAAAAAAAAFJpgHAAAAAAAAIBCE4wDAAAAAAAAUGiCcQAAAAD4/9m79zCryrp//O8NAgPGQSFmRECxh0oFSxEtzMRUKvOQPhY2VJpmnhPNLLIUsSAtEb9SmuXxq5N+y+jR8oQnzLBE1MRDdCIRFTFEQOXksH9/+GM/jMwgh4Ht7Hm9rmtf1+x73WvNZ++t8GG9970WAABQ0bYodwG0LMVi8X+f1K8oXyEA8G622t+RDf7uBAAAAACgLATjrJdly5aVfu78lxvLWAkAtAzLli1Lp06dyl0GAAAAAECr5lLqAAAAAAAAAFQ0K8ZZLx06dCj9vPhDRyZt25WxGgB4l6pfUbqyyup/dwIAAAAAUB6CcdZLoVD43ydt2wnGAeAdNPi7EwAAAACAsnApdQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAA2MzGjRuXQqGQkSNHlsaKxWJGjx6dXr16pWPHjhk6dGieeuqp8hUJAFBBBOMAAAAAAJvRtGnTcsUVV2SXXXZpMH7hhRdm/PjxmThxYqZNm5aampoccMABWbx4cZkqBQCoHFuUuwAAAAAAgNbitddey4gRI/Lzn/883//+90vjxWIxEyZMyNlnn53DDz88SXLttdemuro6dXV1Of7448tVMmwSxWIxS5cuLXcZNIPVP0efaeWoqqpKoVAodxnQrATjAAAAAACbycknn5zPfOYz2X///RsE47NmzcrcuXMzbNiw0liHDh2yzz77ZOrUqU0G48uWLcuyZctKzxctWrTpiodmtHTp0nz6058udxk0s8MOO6zcJdBMbr/99nTs2LHcZUCzEowDAAAAAGwGN954Yx599NFMmzZtjW1z585NklRXVzcYr66uzrPPPtvkMceNG5fzzjuveQsFAKhAgnEAAAAAgE3sueeey2mnnZa77rorVVVVTc57+2Vri8XiWi9lO2rUqJxxxhml54sWLUqfPn02vmDYxKqqqnL77beXuwyaQbFYLF25okOHDi6/XSHW9ncVtFSCcQAAAACATWz69OmZN29eBg0aVBqrr6/PAw88kIkTJ2bmzJlJ3lo5vs0225TmzJs3b41V5Kvr0KFDOnTosOkKh02kUCi4THMF6dSpU7lLAHhHbcpdAAAAAABApdtvv/0yY8aMPP7446XH7rvvnhEjRuTxxx/PDjvskJqamkyePLm0z/LlyzNlypQMGTKkjJUDAFQGK8YBAAAAADaxzp07Z8CAAQ3Gttxyy3Tv3r00PnLkyIwdOzb9+/dP//79M3bs2HTq1Cm1tbXlKBkAoKIIxgEAAAAA3gXOOuusLFmyJCeddFIWLFiQPffcM3fddVc6d+5c7tIAAFo8wTgAAAAAQBncf//9DZ4XCoWMHj06o0ePLks9AACVrCLvMT5u3LgMHjw4nTt3Ts+ePfPZz342M2fOXOs+999/fwqFwhqPv/71r5upagAAAAAAAAA2hYoMxqdMmZKTTz45f/rTnzJ58uS8+eabGTZsWF5//fV33HfmzJl58cUXS4/+/ftvhooBAAAAAAAA2FQq8lLqd9xxR4PnV199dXr27Jnp06fn4x//+Fr37dmzZ7p167YJqwMAAAAAAABgc6rIFeNvt3DhwiTJ1ltv/Y5zd91112yzzTbZb7/9ct99923q0gAAAAAAAFq0qVOnZvjw4Zk6dWq5SwFoUsUH48ViMWeccUY+9rGPZcCAAU3O22abbXLFFVfk5ptvzm9+85t84AMfyH777ZcHHnigyX2WLVuWRYsWNXgAAAAAAAC0FkuXLs348ePz0ksvZfz48Vm6dGm5SwJoVEVeSn11p5xySp544ok8+OCDa533gQ98IB/4wAdKzz/60Y/mueeey49//OMmL78+bty4nHfeec1aLwAAAAAAQEtxww03ZP78+UmS+fPnp66uLsccc0yZqwJYU0WvGD/11FNzyy235L777kvv3r3Xe/+PfOQj+fvf/97k9lGjRmXhwoWlx3PPPbcx5QIAAAAAALQYc+bMSV1dXYrFYpK3ruJbV1eXOXPmlLkygDVVZDBeLBZzyimn5De/+U3uvffe9OvXb4OO89hjj2WbbbZpcnuHDh3SpUuXBg8AAAAAAIBKVywWc8kllzQ5viosB3i3qMhLqZ988smpq6vL//zP/6Rz586ZO3dukqRr167p2LFjkrdWez///PO57rrrkiQTJkzI9ttvn5133jnLly/P9ddfn5tvvjk333xz2V4HAAAAAADAu9Hs2bMzbdq0Ncbr6+szbdq0zJ49O9ttt10ZKgNoXEUG45dddlmSZOjQoQ3Gr7766hx99NFJkhdffDGzZ88ubVu+fHnOPPPMPP/88+nYsWN23nnn/P73v8+BBx64ucoGAAAAAABoEfr27ZvBgwfn0UcfTX19fWm8bdu2GTRoUPr27VvG6gDWVJHB+LpcnuOaa65p8Pyss87KWWedtYkqAgAAAAAAqByFQiGnnXZajjrqqEbHC4VCmSoDaFxF3mMcAAAAAACATat3796pra0theCFQiG1tbXZdttty1wZwJoE4wAAAAAAAGyQESNGpHv37kmSHj16pLa2tswVATROMA4AAAAAAMAGqaqqyhlnnJHq6uqcfvrpqaqqKndJAI2qyHuMAwAAAAAAsHkMGTIkQ4YMKXcZAGtlxTgAAAAAAAAAFU0wDgAAAAAAAEBFE4wDAAAAAAAAUNEE4wAAAAAAAABUNME4AAAAAAAAABVNMA4AAAAAAABARROMAwAAAAAAAFDRBOMAAAAAAAAAVDTBOAAAAAAAAAAVTTAOAAAAAAAAQEUTjAMAAAAAAABQ0QTjAAAAAAAAAFQ0wTgAAAAAAAAAFU0wDgAAAAAAAEBFE4wDAAAAAAAAUNEE4wAAsI6ef/75fPGLX0z37t3TqVOnfPjDH8706dNL24vFYkaPHp1evXqlY8eOGTp0aJ566qkyVgwAAAAAJIJxAABYJwsWLMhee+2Vdu3a5fbbb8/TTz+diy66KN26dSvNufDCCzN+/PhMnDgx06ZNS01NTQ444IAsXry4fIUDAAAAANmi3AUAAEBLcMEFF6RPnz65+uqrS2Pbb7996edisZgJEybk7LPPzuGHH54kufbaa1NdXZ26urocf/zxm7tkAAAAAOD/Z8U4AACsg1tuuSW77757Pve5z6Vnz57Zdddd8/Of/7y0fdasWZk7d26GDRtWGuvQoUP22WefTJ06tRwlAwAAAAD/P8E4AACsg3/961+57LLL0r9//9x555054YQT8vWvfz3XXXddkmTu3LlJkurq6gb7VVdXl7a93bJly7Jo0aIGDwAAAACg+bmUOgAArIOVK1dm9913z9ixY5Mku+66a5566qlcdtll+fKXv1yaVygUGuxXLBbXGFtl3LhxOe+88zZd0QAAAABAEivGAQBgnWyzzTbZaaedGoztuOOOmT17dpKkpqYmSdZYHT5v3rw1VpGvMmrUqCxcuLD0eO655zZB5QAAAACAYBwAANbBXnvtlZkzZzYY+9vf/pbtttsuSdKvX7/U1NRk8uTJpe3Lly/PlClTMmTIkEaP2aFDh3Tp0qXBAwAAAABofi6lDgAA6+D000/PkCFDMnbs2Hz+85/Pww8/nCuuuCJXXHFFkrcuoT5y5MiMHTs2/fv3T//+/TN27Nh06tQptbW1Za4eAAAAAFo3wTgAAKyDwYMHZ9KkSRk1alTGjBmTfv36ZcKECRkxYkRpzllnnZUlS5bkpJNOyoIFC7LnnnvmrrvuSufOnctYOQAAAAAgGAcAgHV00EEH5aCDDmpye6FQyOjRozN69OjNVxQAAAAA8I7cYxwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAA2GBXXnllPvGJT+TKK68sdykATRKMAwAAAAAAsEFeffXV3HDDDVm5cmVuuOGGvPrqq+UuCaBRgnEAAAAAAAA2yPe+972sXLkySbJy5cqcc845Za4IoHGCcQAAAAAAANbbI488khkzZjQYe+KJJ/LII4+UqSKApm1R7gJouQor30yx3EUASbGYrHzzrZ/bbJEUCuWtB0hh1f+TAAAAABVq5cqVGTNmTKPbxowZk9/+9rdp08b6TODdQzDOBnvP478sdwkAAAAAAEAZ/PnPf86iRYsa3bZo0aL8+c9/zkc/+tHNXBVA03xVBwAAAAAAgPWy5557pkuXLo1u69q1a/bcc8/NXBHA2lkxznqpqqrK7bffXu4ygNUsXbo0hx12WJJk0qRJqaqqKnNFwOr8PwkAAABUojZt2uScc87JmWeeuca2c88912XUgXcdwTjrpVAopGPHjuUuA2hCVVWV/0cBAAAAgM1i9913z8CBAzNjxozS2C677JLddtutjFUBNM7XdQAAAAAAANgg559/fml1eJs2bTJmzJgyVwTQOME4AAAAAAAAG6Rbt24ZMWJE2rRpkxEjRqRbt27lLgmgUS6lDgAAAAAAwAY79thjc+yxx5a7DIC1smIcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKJVdDD+05/+NP369UtVVVUGDRqUP/zhD2udP2XKlAwaNChVVVXZYYcdcvnll2+mSgEAAAAAAFqmqVOnZvjw4Zk6dWq5SwFoUsUG4zfddFNGjhyZs88+O4899lj23nvvfPrTn87s2bMbnT9r1qwceOCB2XvvvfPYY4/lO9/5Tr7+9a/n5ptv3syVAwAAAAAAtAxLly7N+PHj89JLL2X8+PFZunRpuUsCaFTFBuPjx4/Psccem69+9avZcccdM2HChPTp0yeXXXZZo/Mvv/zy9O3bNxMmTMiOO+6Yr371qznmmGPy4x//eDNXDgAAAAAA0DLccMMNmT9/fpJk/vz5qaurK3NFAI3botwFbArLly/P9OnT8+1vf7vB+LBhw5q8jMdDDz2UYcOGNRj75Cc/mSuvvDIrVqxIu3btNlm9UA7FYtE39yrE6p+jz7RyVFVVpVAolLsMAGiSfrJy6Ccrk34SANgc5syZk7q6uhSLxSRv/Tuhrq4uw4YNS+/evctcHUBDFRmM/+c//0l9fX2qq6sbjFdXV2fu3LmN7jN37txG57/55pv5z3/+k2222WaNfZYtW5Zly5aVni9atKgZqofNY+nSpfn0pz9d7jJoZocddli5S6CZ3H777enYsWO5ywCAJuknK5N+snLoJwGATa1YLOaSSy5pcvzCCy/0RT3gXaViL6WeZI0/cIvF4lr/EG5sfmPjq4wbNy5du3YtPfr06bORFQMAAAAAALz7zZ49O9OmTUt9fX2D8fr6+kybNi2zZ88uU2UAjavIFeM9evRI27Zt11gdPm/evDVWha9SU1PT6Pwtttgi3bt3b3SfUaNG5Ywzzig9X7RokXCcFqOqqiq33357ucugGRSLxdLVKzp06OBbmBWiqqqq3CUAwFrpJyuHfrIy6ScBgE2tb9++GTx4cB599NEG4Xjbtm0zaNCg9O3bt4zVAaypIoPx9u3bZ9CgQZk8eXKDy8BNnjw5hx56aKP7fPSjH82tt97aYOyuu+7K7rvv3uT9xTt06JAOHTo0X+GwGRUKBZfVqyCdOnUqdwkAQCujn6ws+kkAANZXoVDIaaedlqOOOqrRcV+4BN5tKvZS6meccUZ+8Ytf5KqrrsozzzyT008/PbNnz84JJ5yQ5K3V3l/+8pdL80844YQ8++yzOeOMM/LMM8/kqquuypVXXpkzzzyzXC8BAAAAAADgXat3796pra0theCFQiG1tbXZdttty1wZwJoqcsV4kgwfPjzz58/PmDFj8uKLL2bAgAG57bbbst122yVJXnzxxQb3t+jXr19uu+22nH766fnJT36SXr165f/8n/+T//7v/y7XSwAAAAAAAHhXGzFiRG6//fb85z//SY8ePVJbW1vukgAaVSgWi8VyF1EpFi1alK5du2bhwoXp0qVLucsBANjk9D/Ny/sJALQ2+p/m5z0FymHq1Km55JJLctppp2XIkCHlLgdoRdan96nYFeMAAAAAAABsekOGDBGIA+96FXuPcQAAAAAAAABIBOMAAAAAAAAAVDjBOAAAAAAAAAAVTTAOAAAAAAAAQEUTjAMAAAAAAABQ0QTjAAAAAAAAAFQ0wTgAAGyAcePGpVAoZOTIkaWxYrGY0aNHp1evXunYsWOGDh2ap556qnxFAgAAAABJBOMAALDepk2bliuuuCK77LJLg/ELL7ww48ePz8SJEzNt2rTU1NTkgAMOyOLFi8tUKQAAAACQCMYBAGC9vPbaaxkxYkR+/vOfZ6uttiqNF4vFTJgwIWeffXYOP/zwDBgwINdee23eeOON1NXVlbFiAAAAAEAwDgAA6+Hkk0/OZz7zmey///4NxmfNmpW5c+dm2LBhpbEOHTpkn332ydSpUxs91rJly7Jo0aIGDwAAAACg+W1R7gIAAKCluPHGG/Poo49m2rRpa2ybO3dukqS6urrBeHV1dZ599tlGjzdu3Licd955zV8oAAAAANCAFeMAALAOnnvuuZx22mm5/vrrU1VV1eS8QqHQ4HmxWFxjbJVRo0Zl4cKFpcdzzz3XrDUDAAAAAG+xYhwAANbB9OnTM2/evAwaNKg0Vl9fnwceeCATJ07MzJkzk7y1cnybbbYpzZk3b94aq8hX6dChQzp06LBpCwcAAAAArBgHAIB1sd9++2XGjBl5/PHHS4/dd989I0aMyOOPP54ddtghNTU1mTx5cmmf5cuXZ8qUKRkyZEgZKwcAAAAArBgHAIB10Llz5wwYMKDB2JZbbpnu3buXxkeOHJmxY8emf//+6d+/f8aOHZtOnTqltra2HCUDAAAAAP8/wTgAADSTs846K0uWLMlJJ52UBQsWZM8998xdd92Vzp07l7s0AAAAAGjVXEodAAA20P33358JEyaUnhcKhYwePTovvvhili5dmilTpqyxyhwAgNZp3LhxGTx4cDp37pyePXvms5/9bGbOnNlgTrFYzOjRo9OrV6907NgxQ4cOzVNPPVWmigEAKotgHAAAAABgE5syZUpOPvnk/OlPf8rkyZPz5ptvZtiwYXn99ddLcy688MKMHz8+EydOzLRp01JTU5MDDjggixcvLmPlAACVwaXUAQAAAAA2sTvuuKPB86uvvjo9e/bM9OnT8/GPfzzFYjETJkzI2WefncMPPzxJcu2116a6ujp1dXU5/vjjy1E2AEDFsGIcAAAAAGAzW7hwYZJk6623TpLMmjUrc+fOzbBhw0pzOnTokH322SdTp04tS40AAJXEinEAAAAAgM2oWCzmjDPOyMc+9rEMGDAgSTJ37twkSXV1dYO51dXVefbZZ5s81rJly7Js2bLS80WLFm2CigEAWj4rxgEAAAAANqNTTjklTzzxRH75y1+usa1QKDR4XiwW1xhb3bhx49K1a9fSo0+fPs1eLwBAJRCMAwAAAABsJqeeempuueWW3Hfffendu3dpvKamJsn/rhxfZd68eWusIl/dqFGjsnDhwtLjueee2zSFAwC0cIJxAAAAAIBNrFgs5pRTTslvfvOb3HvvvenXr1+D7f369UtNTU0mT55cGlu+fHmmTJmSIUOGNHncDh06pEuXLg0eAACsyT3GAQAAAAA2sZNPPjl1dXX5n//5n3Tu3Lm0Mrxr167p2LFjCoVCRo4cmbFjx6Z///7p379/xo4dm06dOqW2trbM1QMAtHyCcQAAAACATeyyyy5LkgwdOrTB+NVXX52jjz46SXLWWWdlyZIlOemkk7JgwYLsueeeueuuu9K5c+fNXC0AQOURjDejYrGYJFm0aFGZKwEA2DxW9T2r+iA2jn4SAGhtWlM/uS6vsVAoZPTo0Rk9evRG/x49JQDQGqxPPykYb0aLFy9OkvTp06fMlQAAbF6LFy9O165dy11Gi6efBABaK/1k89FTAgCt0br0k4Via/g65maycuXKvPDCC+ncuXMKhUK5ywFakUWLFqVPnz557rnn0qVLl3KXA7QixWIxixcvTq9evdKmTZtyl9Pi6SeBctFPAuWin2x+ekqgHPSTQLmsTz8pGAeoAIsWLUrXrl2zcOFCjScAAOtNPwkAwMbQTwItga9hAgAAAAAAAFDRBOMAAAAAAAAAVDTBOEAF6NChQ84999x06NCh3KUAANAC6ScBANgY+kmgJXCPcQAAAAAAAAAqmhXjAAAAAAAAAFQ0wTgAAAAAAAAAFU0wDgAAAAAAAEBFE4wDAAAAAAAAUNEE4wAt3E9/+tP069cvVVVVGTRoUP7whz+UuyQAAFoYPSUAABtDPwm0BIJxgBbspptuysiRI3P22Wfnsccey957751Pf/rTmT17drlLAwCghdBTAgCwMfSTQEtRKBaLxXIXAcCG2XPPPbPbbrvlsssuK43tuOOO+exnP5tx48aVsTIAAFoKPSUAABtDPwm0FFaMA7RQy5cvz/Tp0zNs2LAG48OGDcvUqVPLVBUAAC2JnhIAgI2hnwRaEsE4QAv1n//8J/X19amurm4wXl1dnblz55apKgAAWhI9JQAAG0M/CbQkgnGAFq5QKDR4XiwW1xgDAIC10VMCALAx9JNASyAYB2ihevTokbZt267xzct58+at8Q1NAABojJ4SAICNoZ8EWhLBOEAL1b59+wwaNCiTJ09uMD558uQMGTKkTFUBANCS6CkBANgY+kmgJdmi3AUAsOHOOOOMfOlLX8ruu++ej370o7niiisye/bsnHDCCeUuDQCAFkJPCQDAxtBPAi2FYBygBRs+fHjmz5+fMWPG5MUXX8yAAQNy2223Zbvttit3aQAAtBB6SgAANoZ+EmgpCsVisVjuIgAAAAAAAABgU3GPcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEou+233z6FQiFHH310uUsBACgbPREAwLvD/fffn0KhkEKhkPvvv7/c5Wy0oUOHplAoZOjQoeUuBaCsBONA2a3eaL790bFjx/Tp0ycHHXRQfvGLX2Tp0qXlLhcAANawcuXK/Pa3v82JJ56YD33oQ6murk779u3TpUuX7LDDDjn00EPzwx/+MH/729/KXSoAQMVa23nGQqGQ97znPXn/+9+fo446qiIC7+bS1PvVvn379OzZM/vss0/OP//8zJ07t9ylAmwUwTjwrrZ06dLMmTMnv//973Pcccflwx/+sJOJAAC8q9x2223Zeeedc9hhh+Xyyy/PE088kXnz5mXFihVZvHhxZs2alVtuuSWjRo3KBz7wgQwdOjRTp04td9kAAK3O66+/nr///e+57rrrsu++++bYY49NfX19uct611qxYkVefvnlPPDAAznnnHPywQ9+MLfccku5ywLYYFuUuwCA1Z144ok56aSTSs/feOONPP7445kwYUKeeeaZzJw5M5/61Kfy1FNPpWPHjmWslOb073//u9wlAABskAsuuCCjRo1KsVhMkuy11145+OCDs+uuu6Z79+5ZunRpXnrppfzxj3/M73//+8ycOTNTpkzJmDFjcscdd5S5egCAyvX284zFYjGvvPJKHnrooVx88cWZN29errrqqvTu3TvnnXdeGSt999h9991z9dVXl56vWLEi//znP3PllVfmjjvuyMKFC/P5z38+f/7zn/OhD32ojJUCbBjBOPCu0rNnzwwYMKDB2B577JEvfelLGTp0aB5++OHMmjUrV155ZU455ZQyVQkAAMl1112Xb3/720mSHj165IYbbsiwYcManXv44Yfnxz/+cW699daMGjVqc5YJANAqNXaeMUn22WefHHLIIdl9992zZMmSTJgwId/97nfTrl27MlT57rLllluu8Z7tuuuuOeKII/L1r389l156aZYtW5bzzz8/v/71r8tUJcCGcyl1oEXo2LFjfvCDH5Se33777WWsBgCA1u7555/PCSeckOStE4gPPPBAk6H4KoVCIYccckimT5+eY489dnOUCQBAI3baaad85jOfSZIsWrQozzzzTJkrevcbO3ZsqqqqkiR33XVXVq5cWeaKANafYBxoMT7ykY+Ufn722WeTvHUJ7kKhkEKhkGuuuSZJ8pvf/CYHHnhgevXqlS222CJDhw5d41hz5szJqFGjsttuu2WrrbZKVVVV+vbtm+HDh+e+++57x1qKxWKuvfbafPzjH89WW22V97znPRk4cGDGjBmTRYsWJUmprtGjR6+x/+jRo0vbk7fupf6jH/0ou+22Wzp37pzOnTtnjz32yMSJE/Pmm282Wcfy5ctz66235pRTTsngwYOz1VZbpV27dunevXv23HPPjB49Ov/5z3/W+lq23377FAqFHH300UmSv/71rznuuOOy/fbbp0OHDqmurs5hhx2WP/3pT+/4viRvfSbf+ta3MmjQoHTv3j1VVVXp169f9t1331x00UWZPXv2O9bQlI353Orr63PNNdfkk5/8ZGpqatK+fft069Yt/fv3z3777ZexY8fm6aefXqfXCAAwfvz4LFmyJEny/e9/PzvuuOM671tVVZXPfe5z6/X7rrnmmlL/uLbb0DTWHzfltttuyxe/+MXssMMO2XLLLdO1a9fsvPPOOfLII3PzzTeXXt/brVy5Mtdff30OPPDAUl/13ve+N/vuu29++tOfZvny5Wv9vS+88EK+/e1vZ7fddkvXrl3Tvn371NTUZODAgfnCF76Qa665ptRTN+aNN97IhAkTsu+++6a6ujrt27dPz549M2zYsFx99dXuEwoArJPtt9++9PPSpUs36BivvfZafvjDH+ajH/1ott5663To0CG9e/fOEUcckd/97nfrdIyXX3453/3ud7PrrrumW7duqaqqyvbbb58vfelLefDBB9fpGA899FCOOOKI1NTUlM7Ffe1rX8vMmTM36HU15j3veU923nnnJMnixYvzyiuvlLa9/dze9OnTc/TRR6dfv37p0KFD6Rzo6ja2p7v33nvzhS98If369UvHjh3TqVOnbL/99vnIRz6SM888M/fee2+j+7366qv5wQ9+kI9+9KOlc6nvfe97s9NOO+Wwww7LZZddlnnz5q2x39rO9a5u6NChKRQKjZ6Tvv/++0vHuf/++7Ny5cpcddVVpfegTZs2jZ4fnTlzZr7+9a9n5513TteuXdOxY8fssMMO+cpXvpJHH310rfUAb1MEKLP77ruvmKSYpHjuuec2OW/JkiWleR/84AeLxWKxOGvWrNLYVVddVfzSl75Uer7qsc8++zQ4zi9+8Ytix44d15i3+uPYY48trlixotE6li1bVjzooIOa3Ld///7Ff//732t9Teeee25p+9y5c4sf+tCHmjzewQcfXKyvr2+0lqOOOmqtryNJsXv37sUHH3ywyfd1u+22KyYpHnXUUcWbb7652KlTp0aP07Zt2+KNN97Y5HGKxWLxRz/6UbFdu3Zrreftn8fba2jKxnxuixcvLu69997v+F7993//91pfHwBAsVgsrly5stijR49ikuJ73vOe4qJFi5rluGvria6++upSzzJr1qwmj7F6f3z11Vc3Ouc///lPcb/99nvH3qix/efPn1/ca6+91rrfjjvuWPz3v//d6O9+4IEHil26dHnH333rrbc2uv/DDz9c3Hbbbde67x577FGcO3duk+8RAFC51vU8Y7FYLB5xxBGluS+++GKTx7nvvvsa3f/RRx8t9urVa619yeGHH15csmRJkzXceeed79gbnXzyyU2eGywWi8Uf//jHxTZt2jS675Zbblm87bbbivvss0+T5+WKxeJaz9utbs8992xwTnOV1fvYyy67rLjFFlusUcvqNranO/3009fpnOjbPf300+/4mSUpXnrppU2+R+/039Xa3uvV/7u6/fbbi/vvv/8av/vt/xYYM2ZMo+/nqkehUCiec845a60J+F/uMQ60GE888UTp5169eq2xfcKECXniiSey995758QTT8z73//+vPrqqw1W1Fx11VX56le/miQZMGBAjj/++Oy6667p1KlT6d7lt912W6688sp07do1F1100Rq/59RTTy1943OnnXbKmWeemYEDB2bRokWZNGlSLrvsshx55JHr/LoOP/zwPPPMM/n617+egw8+OFtvvXVmzpyZ888/P88880xuvfXW/PznP8/xxx+/xr5vvvlmdthhhxx22GHZY4890rdv32yxxRZ59tlnc/fdd+eqq67K/Pnzc9hhh+XJJ59Mz5491/r+3nTTTdlmm23yjW98I7vvvnuKxWLuvPPO/PCHP8zSpUvzta99LZ/4xCfy3ve+d439zz///JxzzjlJkm7duuWkk07Kvvvum+7du+fVV1/No48+mkmTJjX6DdF3srGf2+jRo/OHP/whSXLQQQdlxIgR6du3b6qqqvLyyy/nL3/5S373u99tUG0AQOvz1FNPla7Ks/fee6dz585lrmjdvfHGG9l3330zY8aMJMmgQYPyta99LQMGDEiHDh3y3HPP5YEHHshNN920xr719fU56KCD8tBDDyV56/6cp5xySvr165cXXnghV111VX7729/mmWeeyX777ZfHH38873nPe0r7L1u2LEceeWQWLVqUzp0758QTT8y+++6bnj17ZsWKFXn22Wfz0EMP5eabb2609hkzZmTffffN66+/np49e+bEE0/M3nvvne7du2fevHm55ZZb8rOf/SwPP/xwDj300PzhD39wr1AAoFF//etf8/vf/z5JMnjw4NTU1KzX/s8//3z222+/LFiwoLRS+sgjj0z37t3z9NNP56KLLspf/vKX/OY3v8lRRx3VaG/1+OOP5+CDD87y5cvTrl27nHzyyTn00EOz5ZZb5rHHHssPf/jDzJo1Kz/5yU+y5ZZb5oILLljjGDfffHPOPPPMJEnXrl3zrW99q7RS+d57782FF16Y2traRs/lra8VK1bkr3/9a5Kkffv26d69+xpzpk2bluuvvz59+vTJmWeemUGDBqW+vr50Xi7Z+J7ud7/7XS6++OIkyS677JITTzwxO+64Y7p27ZqFCxfmr3/9ayZPnlzqWVf3pS99KS+88ELatWuX4447Lp/+9KdTU1OTlStX5oUXXsjDDz/cZC/a3L71rW/liSeeyCGHHJKjjz462223XV566aUGV04655xzcv755ydJhgwZkmOOOSY777xz2rVrl5kzZ2bixIl56KGHMmbMmPTo0SOnnnrqZqkdWrRyJ/MA6/pNzkMPPbQ0b8yYMcViseGKmCTFL3/5y8WVK1c2uv/s2bNLq6GPOuqoJleEf+c73ykmKbZp06Y4c+bMBtumT59eLBQKpW8tvv7662vs/6tf/apBTe+0Yrxdu3aNfvN0/vz5xerq6mKS4i677NJorf/4xz+afL3FYrH4xBNPFN/znvcUkxS/+93vNjpn1Tc6kxQHDRpUfPXVV9eYc/3115fmjB8/fo3t06dPL30z9f3vf3/xueeea7KmxratbXVUc3xuffr0KSYpHnHEEU3WVSy+9Z4DALyTG264odQbnX322c123M2xYnzkyJENVh811UsuW7ZsjRU6EydOfMe+e1VPlqR41llnNdh2zz33vOOK8GKxWFyxYkVx4cKFDcZWrlxZ3GWXXYpJih/60IeKL7/8cqP73n777aW+9Be/+EWTvwMAqEyrn2c88cQTizNmzCg9nnjiieIDDzxQvOCCC4o1NTXFJMUuXboU//jHP671OI2dt1t9tXljPcfSpUuL++67b2nObbfdtsacwYMHF5O3rtJ45513rrH9lVdeKe60006l811PPvlkg+3Lli0rbrPNNsUkxa5duxaffvrpNY4xY8aMBivSN2bF+Pjx40vzPvGJTzTYtvr5xYEDBxYXLFjQ6DGao6dbdcXQ7bbbrrh48eIm6337eb5//vOfa10RvnqNr7zyyhrj63L+ulhc9xXjSYrf+973mjzOww8/XHoPmjqvW19fX/ziF79YTFLs3Llzk+878L/cYxx4V1uyZEkeeuihHHLIIfmf//mfJEmXLl1ywgknrDG3W7dumThxYpOrfi+55JK88cYb6dWrVy6//PJssUXjF80477zzsu2222blypW57rrrGmy74oorUiwWkyQ///nP06lTpzX2P+KII3LYYYet82s89dRTG73nzNZbb52vfOUrSd5azb1w4cI15rzvfe9b6yrngQMHllZa//a3v33HWq666qp07dp1jfHa2trSKv3Vv+G5yo9+9KOsXLkyhUIhN954Y3r37t3k71jbtsY0x+c2d+7cJG+t6Fqbrbfeer1qAwBap1WrxZO84+qbp556Kk8++WSjj9dff31Tl9rAggULcsUVVyRJdtttt1xyySVN9pLt27dPdXV1g7Gf/OQnSZIePXo02XePGTMmH/zgB5O81S8vW7astG1VT5YkH//4x5usc4sttkiXLl0ajP3+978vXUHquuuuS48ePRrd91Of+lSOOOKIJMnVV1/d5O8AACrfZZddloEDB5Yeu+yySz7+8Y/nW9/6VubNm5fjjz8+f/7znzNkyJD1Ou6LL76YSZMmJUk++clP5thjj11jTocOHXLVVVeVzmNNnDixwfaHH34406ZNS5J89atfzbBhw9Y4xlZbbVXq3VauXJmf/vSnDbb/9re/zYsvvpgk+d73vpcdd9xxjWMMGDAgZ5999nq9vtWtWLEiM2fOzDe/+c3SyvQkOeuss5rc5yc/+Um6devW6Lbm6OlW9ZS77bZbg6sTvd3bz/Otay9aKBSy1VZbNbm9ubz//e/Pueee2+T2Cy64ICtXrsygQYMyZsyYRue0adMml156aTp06JDFixfn17/+9aYqFyqGYBx4VznvvPNSKBRKj06dOmXIkCG59dZbk7wVit98882NnoA8+OCD13oZy1XB+sEHH5yqqqom522xxRb56Ec/miRrXHLnnnvuSZJ8+MMfzi677NLkMb785S83ue3tRowY0eS2QYMGlX6eNWvWOx5rwYIF+ec//9ngBOyqRvTpp5/OihUrmtx31T8QGlMoFLLrrrsmSf71r3812LZy5crccccdSd66nOaqec2lOT63bbbZJkly00035Y033mjW+gCA1mfx4sWln9d2Mi5JPvShDzU4Ibv6Y9XJ0M3lvvvuK/VCX//619O2bdt13veFF17IM888kyT5/Oc/32Tf3bZt29KXOxcsWJBHH320tG1VT5asf2i9qif8wAc+sNY+PPnfE53Tpk1LfX39ev0eAKB1WLlyZf7f//t/+cUvfpHly5ev17733XdfqcdoLBRfZfvtt88BBxyQJLn//vsb9CV333136ee1HWOvvfYqBd6r77P680KhkKOOOqrJY3zlK19Z59sHTpkypcG52fbt2+eDH/xgfvzjH5cWxYwbNy6f/OQnG92/T58+a12Y0hw93aqe8oEHHsg///nPdXpdq++XJNdcc80677epDB8+vMl+fMWKFbn99tuTvLUIa22fX7du3TJw4MAka54TBdYkGAdahD59+uTUU0/NjBkzsv/++zc6Z23N1MKFC/OPf/wjSfKzn/2sQYPX2GPVt+tW/ybh0qVLS8dYPbBuzO67777Or23ViprGrP7NxtVPwK5uxowZOeaYY7LNNttk6623zn/9139lwIABpROuo0ePTvJWw79gwYINqmP1Wt5ex6xZs/Lqq68mWfu3LTdEc3xuSUr/OJg6dWr69euXU045JZMmTcrLL7/crPUCAK3D6qHw5l71vTEee+yx0s/r27c9+eSTpZ/33HPPtc5dffvq+33sYx/LDjvskCQZOXJk9thjj4wbNy5Tp059xxPSjzzySJJk5syZ79gTnnLKKUmS5cuX55VXXlmv1wkAVI5zzz03xWKxweONN97IE088kW9+85tZvHhxLrroogwbNixLlixZ5+NuSF/0xhtvNFhssuoY7du3f8dFJquO8fe//71BzzRjxowkSb9+/ZpceZ28dYWj7bfffq2/451stdVWOeKII/LHP/4x3/72t5uc905hd3P0dKsWJM2fPz8DBgzIkUcemauvvrp0DrEp/fr1K4X2F198cXbeeeecc845uffee8uykGZt79XTTz9dqmnUqFHv+F6tel/ffk4UWJNgHHhXOfHEEzNjxozS4+9//3teeeWVzJ49O//n//yf9O3bt8l913aJm3nz5m1QPas3RavC3yTp2bPnWvd7p0tqrq6xy7Gv0qbN//4x3dhqlyuvvDK77bZbrr766nVqfNbW5K+tjtVreXsdq19KdPVvXjaH5vjckrcuJ3XMMcekUChk3rx5+clPfpLDDz881dXVGThwYM4999y89NJLzVEyANAKdO/evfTzO33R7s0332xwMnZtl0vc1Damb1v9ZOTbL7H+djU1NY3u165du9x6662lVU/Tpk3Ld77zney1117p1q1bPv3pT6eurq7Rvre5+kIAoHXr2LFjBg4cmAsvvLB0afIpU6Zk3Lhx63yM5uiLVv289dZbN3nbwLcfo1gsNlj0surndzpPuS51rrL77rs3ODf717/+NS+++GJeeeWV/OpXvypdrbEp73QJ8ubo6fbbb79MnDgxHTt2zNKlS3PTTTflmGOOSf/+/dO7d++ccMIJ+ctf/tLocX75y1+WXsPTTz+d888/P/vtt1+6deuWffbZJ5dffnmWLl26QTWur019Lhto3Nr/xAXYzHr27JkBAwZs0L5ruxTk6ifXRo4cudZLFK2uffv2G1TLul6eaGP89a9/zQknnJA333wzPXv2zDe/+c184hOfyPbbb5/OnTunXbt2Sd66b/iq17vq/uibSnO/7ub63Nq1a5crr7wy3/jGN/LLX/4y9957bx555JEsX768dMn58ePH5/rrr8+hhx7arK8BAKg8H/rQh0o/r36p8NbinXq+tfWcO+20U2bMmJFbb701t956a6ZMmZJ//vOfWbJkSe64447ccccdGT9+fG677bYGJ3lX9YV77bVXLr/88nWutVevXus8FwBoXY499th8+9vfziuvvJIrr7yyyfs4b4x3Ohe3LufSmjrGqvGNOcbbbbnllht8bjZZ+/nZpPl6upNPPjmf+9znUldXl8mTJ+ePf/xjFi5cmOeffz4/+9nPcsUVV+Q73/lOvv/97zfYb9ttt83UqVNzzz335De/+U2mTJlSuv3kAw88kAceeCA//vGPc9ttt+X973//Ote3Idb1XPaPfvSjfOpTn1qnY2655ZYbXRdUOsE40CqsvqrnjTfe2KAGb9W9upN3/tbehn6rb31cc801efPNN9O2bdvcf//9pZU3b7e2y6c3h9Uv1fTCCy8067Gb43Nb3U477ZTzzz8/559/fpYsWZI//vGPqaury3XXXZfXXnstX/jCF/LPf/6z2Ve+AwCVZeedd0737t0zf/78/OEPf8jrr7++yU9CrX4loZUrVzY5b22Xdl+9b3vxxRfTr1+/df79q9/i552uVLT6lXhW32+Vtm3b5rOf/Ww++9nPlmq5/fbb89Of/jTTp0/P9OnTc/zxx2fSpEmlfbp3756XXnopL7/88kb3hAAAyVv9Vf/+/fPnP/85L7zwQl555ZVGe5e3W33OSy+9tNYrXDbVF636ef78+XnzzTfXump81TEKhUKDVcarjrEuV0HcHOcq10Vz9nQ9e/bMyJEjM3LkyKxcuTKPP/54fvOb3+QnP/lJXn311fzgBz/I4MGDG10Es99++2W//fZL8tZncPfdd+eKK67Ivffem3/+858ZPnx4g9sQJW+9/8Vica29eNI8t1pa/ZzoihUr9L/QjFxKHWgV3vve92bbbbdNktx9990btHK6qqoq73vf+5L87/1wmvJO25vDU089leStFUtNheKbo5Z+/fqVmvIHHnigWY/dHJ9bUzp27Jj9998/V111VX70ox8leetS87/73e+a7XcAAJWpUCiU7m24ePHiXHPNNZv8d65+X/O1ffFx5syZTW7bbbfdSj+vb9+2+sm4P//5z2ud+/DDDze6X1O22WabHHPMMXnooYdKNf7ud79rcBugVffe/Nvf/pZnn312vWoHAGjKm2++Wfp5xYoV67TPhvRFnTp1avClxFXHWL58+RoBbFPH6N+/f4OrJA4cODBJMmvWrMyfP7/J/V9++eX8+9//Xuvv2Fw2VU/Xpk2b7Lbbbvn+97+fe+65pzT+//7f/3vHfbt3757hw4fnnnvuySGHHJIkefzxx/P3v/+9wbxV/fjaevGVK1eusd+G2HnnnUuf9V133bXRxwP+l2AcaDVWNTb/+te/8utf/3qDjrHqm4R/+ctf8sQTTzQ577rrrtug46+PVY372u4dM3fu3PzP//zPJq2jTZs2OfDAA5O8dU+md2rm11dzfG7vZNXnmjS89yYAQFPOOOOMdOzYMUnyne98J//4xz826e9b/UTq2r74WFdX1+S2fffdt7Sy/dJLL230Xt5N6dWrV+nLmL/61a+yePHiRufV19eXviiw1VZbNQjj30m7du2yzz77JHmr13311VdL21b1hEly4YUXrvMxAQCa8sYbb+Tpp59O8taCmNWvrrM2Q4cOLV0G+8orr2xy3uzZszN58uTSPquvCt9///1LP6/tGA899FCpxtX3Wf15sVhc67nIa665ZpPfXnFdbY6ebrfddist4lnf83xrO0e4qh9fWy9+2223ZeHChev1OxvTqVOnUi33339/gy+eAhtHMA60Gt/85jfToUOHJMkJJ5zwjiupb7vttjXC76997Wul+/Ycd9xxjYbSN998c4PLPm4q/fv3T/LWNyz/9Kc/rbH9jTfeSG1tbYOVNpvKmWeemTZt2qRYLObII4/MnDlzmpy7tm2N2djP7ZVXXsktt9yy1n8ArP7Ny/W5pCgA0Hr17t07P/nJT5IkixYtyt57753777//Hffb0NvcDBgwoHS5zIkTJ2bZsmVrzPnlL3+Zm2++ucljdOvWLccff3ySZPr06Rk5cmSTPdKKFSvWuOTmySefnOStVUennnpqo/ued955pZO3xx13XKmPS5I//OEPa/0CwfLlyzNlypQkyXve8568973vLW377//+71Iwf9lll631BHKSPPnkk7n11lvXOgcAaN3OPffc0nmzT37yk+94f+xVevXqlcMOOyxJcuedd+aqq65aY87y5ctzzDHHlFahn3LKKQ2277HHHhk8eHCS5Be/+EUpQF/dwoULS71bmzZtcuKJJzbY/tnPfrZ0O8Dzzz+/0SsHPf300/nBD36wTq9rc2iOnu6mm25a6/nORx55pNRzr36e7/HHH8/jjz/e5H7FYjF33313kreuELX99ts32L7qC5x//vOf88c//nGN/V988cV8/etfX+vrWR9nn3126Tz0kUcemX/+859Nzq2vr09dXd16n3eF1sg9xoFWo1+/frn88svzla98Ja+88kr22muvfOlLX8pBBx2Uvn375s0338ycOXPy8MMP59e//nX++c9/5tZbb80uu+xSOsagQYNy3HHH5YorrsjDDz+cwYMH55vf/GYGDhyYRYsWZdKkSfnpT3+aPfbYo/RNvlUNTHP70pe+lEsvvTQrV67MgQcemLPOOitDhgxJVVVVpk+fnosvvjh///vfs9deezXarDWnD3/4wznvvPPyve99L3/7298ycODAnHzyydl3333TvXv3vPrqq6X7/LRt2zb33XffOh97Yz+3RYsW5dBDD83222+fww8/PHvuuWe22267bLHFFnnxxRdz66235he/+EWSt05wH3zwwZvkPQIAKs9XvvKVPP/88znnnHMyd+7c7Lvvvvn4xz+eQw45JLvssku6d++eYrGYefPm5S9/+UsmTZrUYLXHqhXn62KLLbbI1772tfzwhz/Mk08+mU984hM566yz0rdv38ydOze/+tWvcu211+ajH/1oHnrooSaPc/7552fy5MmZMWNGJk6cmIceeijHH398Bg4cmPbt22fOnDl58MEHU1dXl+9///s5+uijS/uecMIJueGGG/LQQw/l2muvzbPPPpuTTz45O+ywQ1588cVcddVV+c1vfpMked/73pfvfe97DX73Pffck/PPPz977713PvOZz2SXXXbJe9/73ixZsiR/+9vfcvnll+fRRx9Nknz1q19tsKqqbdu2uemmmzJkyJC89tpr+epXv5pf/epXqa2tzQc+8IG0a9cu8+bNy2OPPZbf/e53mTp1ar7xjW/o7QCgFZs3b16efPLJBmNLly7N3//+91x33XW54447kry1Wvz8889fr2NffPHFueeee7JgwYJ89atfzR//+McceeSR2XrrrfPXv/41P/7xj0sh7Oc///l8+tOfXuMYV1xxRfbcc88sX748n/nMZ3Lqqafm4IMPznve85489thj+eEPf5h//etfSd5alPL2W9S0b98+l156aY444ogsWLAgH/nIR/Ktb30rQ4cOTbFYzP33358LLrggyVsLbJrjEt8bqzl6um9961s54YQTcuihh+bjH/943v/+92fLLbfM/Pnz8+CDD+bSSy8t/a7jjjuutN/jjz+er3zlKxk8eHAOPvjg7LbbbqmpqcmKFSsya9asXH311aUvKBx66KGlLx2s8rWvfS0//elP8+abb+bggw/OOeeck4997GNZvnx5/vjHP+aiiy7Km2++2Wzv9V577ZVzzjkn5513XmbNmpUPf/jDOfbYYzNs2LBss802WbZsWf7973/noYceyq9//eu88MILmTFjRnr37r3RvxsqWhGgzO67775ikmKS4rnnnrte+86aNau079VXX71O+9x4443FLl26lPZr6tGmTZvivffeu8b+y5YtKx500EFN7tevX7/iP/7xj9LzH/7wh2sc49xzzy1tX5vV35v77rtvje3nnXfeWl/DN77xjeLVV19dej5r1qw1jrHddtsVkxSPOuqotdZy1FFHFZMUt9tuuybnjB07trjFFlustaZ99tlng2rY0M9t9f9G1vbYdttti48++uha3wMAgMbccsstxf79+69Tz5GkuNdeexUffPDBNY7zTj3R66+/XvzIRz6y1j5rxowZ79gfv/zyy8WPf/zj71hnY/vPnz+/uNdee611vx133LH473//e419V++B1/Y4/PDDi0uWLGm09r/85S/r/F6fd955TX5mAEBlWv1c2ro83vve9xbvvPPOtR6nsXNyxWKx+OijjxZ79eq1wX1NsVgs3nnnne94vuvkk08u1tfXN3mMH/3oR8U2bdo0um+nTp2Kv//974v77LNPk+flisXiWs/brYt1Pb+4ysb0dKt+19oeVVVVxWuvvbbBfqufI13b42Mf+1hx/vz5jdY9fvz4JvfbaqutilOmTFnre70u/1293cUXX1zs0KHDO9bdvn374t///vd1Oia0ZlaMA63O8OHDM2zYsFxxxRW544478vTTT2fBggVp165dampqsvPOO2fffffNEUcckT59+qyxf/v27XPLLbfk2muvzZVXXpkZM2ZkxYoV2W677XLYYYflzDPPbLBKvGvXrpvstZxzzjnZfffdc8kll2TatGl5/fXX07Nnz+yxxx454YQTcsABB5Tu8bg5jBo1KkcccUR++tOf5u67787s2bOzfPny9OrVK/369cvBBx+c4cOHb9CxN/Rz22677fL4449n8uTJuffee/Ovf/0rL730Ul577bV069YtO++8cw4++OB87WtfS+fOnZvrrQAAWpGDDz44Bx54YG655ZbccccdeeihhzJ37twsWLAgHTt2zNZbb52dd945e+yxRz73uc9lp5122qDf06lTp9x77725+OKLc+ONN+Yf//hH2rVrlw984AM56qijcsIJJ+S55557x+P06NEjU6ZMyaRJk1JXV5c//elPefnll9OpU6dsu+22+dCHPpTPf/7z+dSnPrXGvltvvXUeeOCB1NXV5YYbbshjjz2WV155JV26dMnAgQNzxBFH5Ljjjkv79u3X2Pess87KnnvumcmTJ+ehhx7KCy+8ULpce01NTfbcc898+ctfzoEHHthk7bvsskuefvrp1NXVZdKkSZk+fXpefvnlrFy5Mt27d88HPvCBfOxjH8thhx22Xvc3BwBah/bt25d6swMPPDBf+cpXSvejXl+77rprZs6cmYkTJ+a3v/1tZs6cmTfeeCM9evTIRz7ykRx99NHvePWaYcOG5R//+EcmTJiQ2267Lf/617+ybNmyVFdXZ++9984JJ5yQj33sY2s9xplnnpkhQ4bkoosuyoMPPpiFCxempqYm++23X84888zsuOOOm+x+3htqY3q6Bx54IJMnT87kyZPz9NNPl/ruTp065b/+67+y33775cQTT1zjdom1tbXZfvvtM3ny5PzhD3/InDlz8tJLL+XNN99Mz549s9tuu+XII4/M8OHD06ZN43chPv3007PTTjvl4osvzsMPP5w33ngjvXr1Kl3Ns2/fvs3+Xo0cOTKf+9zn8rOf/SyTJ0/OP/7xj7z66qvp0KFDtt122wwcODAHHHBA/vu//zs9evRo9t8PlaZQLK7lpqsAbJAHH3wwe++9d5Lk7rvvzn777VfmigAAAAAAAFqvxr/2AsBG+eUvf5kkadeuXQYNGlTmagAAAAAAAFo3wTjAevrPf/6TV199tcntd955Z372s58lSQ455JB069Zt8xQGAAAAAABAo1xKHWA93X///Tn00EPzuc99Lvvvv3/e9773pU2bNnn22Wdzyy235Prrr099fX06duyYxx9/PO9///vLXTIAAAAAAECrJhgHWE/3339/9t1337XO6dKlS371q19l2LBhm6kqAAAAAAAAmiIYB1hPr732Wm6++ebcfvvteeKJJ/Lyyy/n1VdfTZcuXfJf//Vf+dSnPpVTTjkl733ve8tdKgAAAAAAABGMAwAAAAAAAFDh2pS7AAAAAAAAAADYlATjAAAAAAAAAFQ0wTgAAAAAAAAAFU0wDgAAAAAAAEBFE4wDAAAAAAAAUNEE4wAAAAAAAABUNME4AAAAAAAAABVNMA4AAAAAAABARROMAwAAAAAAAFDRBOMAAAAAAAAAVDTBOAAAAAAAAAAVTTAOAAAAAAAAQEUTjAMAAAAAAABQ0bYodwGVZOXKlXnhhRfSuXPnFAqFcpcDALDJFYvFLF68OL169UqbNr5zubH0kwBAa6OfbH56SgCgNVmfflIw3oxeeOGF9OnTp9xlAABsds8991x69+5d7jJaPP0kANBa6Sebj54SAGiN1qWfFIw3o86dOyd5643v0qVLmasBANj0Fi1alD59+pT6IDaOfhIAaG30k81PTwkAtCbr008KxpvRqksTdenSRdMJALQqLtHYPPSTAEBrpZ9sPnpKAKA1Wpd+0o17AAAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiVUQw/sADD+Tggw9Or169UigU8tvf/rbB9mKxmNGjR6dXr17p2LFjhg4dmqeeeqrBnGXLluXUU09Njx49suWWW+aQQw7JnDlzNuOrAAAAAAAAAGBTqIhg/PXXX8+HPvShTJw4sdHtF154YcaPH5+JEydm2rRpqampyQEHHJDFixeX5owcOTKTJk3KjTfemAcffDCvvfZaDjrooNTX12+ulwEAAAAAAADAJrBFuQtoDp/+9Kfz6U9/utFtxWIxEyZMyNlnn53DDz88SXLttdemuro6dXV1Of7447Nw4cJceeWV+b//9/9m//33T5Jcf/316dOnT+6+++588pOf3GyvBQAAAAAAAIDmVRErxtdm1qxZmTt3boYNG1Ya69ChQ/bZZ59MnTo1STJ9+vSsWLGiwZxevXplwIABpTmNWbZsWRYtWtTgAQAAAAAAAMC7S8UH43Pnzk2SVFdXNxivrq4ubZs7d27at2+frbbaqsk5jRk3bly6du1aevTp06eZqwcAAAAAAABgY1V8ML5KoVBo8LxYLK4x9nbvNGfUqFFZuHBh6fHcc881S60AAAAAAAAANJ+KD8ZramqSZI2V3/PmzSutIq+pqcny5cuzYMGCJuc0pkOHDunSpUuDBwAAAAAAAADvLhUfjPfr1y81NTWZPHlyaWz58uWZMmVKhgwZkiQZNGhQ2rVr12DOiy++mCeffLI0B+DdbOrUqRk+fHimTp1a7lIAAGiB9JMAAGwM/STQElREMP7aa6/l8ccfz+OPP54kmTVrVh5//PHMnj07hUIhI0eOzNixYzNp0qQ8+eSTOfroo9OpU6fU1tYmSbp27Zpjjz023/jGN3LPPffkscceyxe/+MUMHDgw+++/fxlfGcA7W7p0acaPH5+XXnop48ePz9KlS8tdEgAALYh+EgCAjaGfBFqKigjGH3nkkey6667ZddddkyRnnHFGdt1115xzzjlJkrPOOisjR47MSSedlN133z3PP/987rrrrnTu3Ll0jIsvvjif/exn8/nPfz577bVXOnXqlFtvvTVt27Yty2sCWFc33HBD5s+fnySZP39+6urqylwRAAAtiX4SAICNoZ8EWopCsVgslruISrFo0aJ07do1CxcudL9xYLOYM2dOjjrqqNTX15fGtthii1xzzTXp3bt3GSsDWgv9T/PyfgKbm34SKDf9T/PzngKbk34SKLf16X0qYsU4QGtULBZzySWXNDnue08AAKyNfhIAgI2hnwRaGsE4QAs1e/bsTJs2rcG3MZOkvr4+06ZNy+zZs8tUGQAALYF+EgCAjaGfBFoawThAC9W3b98MHjw4bdu2bTDetm3b7LHHHunbt2+ZKgMAoCXQTwIAsDH0k0BLIxgHaKEKhUJOO+20JscLhUIZqgIAoKXQTwIAsDH0k0BLIxgHaMF69+6d2traUpNZKBRSW1ubbbfdtsyVAQDQEugnAQDYGPpJoCURjAO0cCNGjEj37t2TJD169EhtbW2ZKwIAoCXRTwIAsDH0k0BLIRgHaOGqqqpyxhlnpLq6OqeffnqqqqrKXRIAAC2IfhIAgI2hnwRaii3KXQAAG2/IkCEZMmRIucsAAKCF0k8CALAx9JNAS2DFOAAAAAAAAAAVTTAOAAAAAAAAQEUTjAMAAAAAAABQ0QTjAAAAAAAAAFQ0wTgAAAAAAAAAFU0wDgAAAAAAAEBFE4wDAAAAAAAAUNEE4wAAAAAAAABUNME4AAAAAAAAABVNMA4AAAAAAABARROMAwAAAAAAAFDRBOMAAAAAAAAAVDTBOAAAAAAAAAAVTTAOAAAAAAAAQEUTjAMAAAAAAABQ0QTjAAAAAAAAAFQ0wTgAAAAAAAAAFU0wDgAAAAAAAEBFE4wDAAAAAAAAUNEE4wAAAAAAAABUNME4AAAAAAAAABVNMA4AAAAAAABARROMAwAAAAAAAFDRBOMAAAAAAAAAVDTBOAAAAAAAAAAVTTAOAAAAAAAAQEUTjAMA0Oq9+eab+e53v5t+/fqlY8eO2WGHHTJmzJisXLmyNKdYLGb06NHp1atXOnbsmKFDh+app55qcJxly5bl1FNPTY8ePbLlllvmkEMOyZw5czb3ywEAAAAA3kYwDgBAq3fBBRfk8ssvz8SJE/PMM8/kwgsvzI9+9KNceumlpTkXXnhhxo8fn4kTJ2batGmpqanJAQcckMWLF5fmjBw5MpMmTcqNN96YBx98MK+99loOOuig1NfXl+NlAQAAAAD/vy3KXQAAAJTbQw89lEMPPTSf+cxnkiTbb799fvnLX+aRRx5J8tZq8QkTJuTss8/O4YcfniS59tprU11dnbq6uhx//PFZuHBhrrzyyvzf//t/s//++ydJrr/++vTp0yd33313PvnJT5bnxQEAAAAAVowDAMDHPvax3HPPPfnb3/6WJPnLX/6SBx98MAceeGCSZNasWZk7d26GDRtW2qdDhw7ZZ599MnXq1CTJ9OnTs2LFigZzevXqlQEDBpTmAAAAAADlYcU4AACt3re+9a0sXLgwH/zgB9O2bdvU19fnBz/4Qb7whS8kSebOnZskqa6ubrBfdXV1nn322dKc9u3bZ6uttlpjzqr9327ZsmVZtmxZ6fmiRYua7TUBAAAAAP/LinEAAFq9m266Kddff33q6ury6KOP5tprr82Pf/zjXHvttQ3mFQqFBs+LxeIaY2+3tjnjxo1L165dS48+ffps3AsBAAAAABolGAcAoNX75je/mW9/+9s58sgjM3DgwHzpS1/K6aefnnHjxiVJampqkmSNld/z5s0rrSKvqanJ8uXLs2DBgibnvN2oUaOycOHC0uO5555r7pcGAAAAAEQwDgAAeeONN9KmTcPWuG3btlm5cmWSpF+/fqmpqcnkyZNL25cvX54pU6ZkyJAhSZJBgwalXbt2Dea8+OKLefLJJ0tz3q5Dhw7p0qVLgwcAAAAA0PzcYxwAgFbv4IMPzg9+8IP07ds3O++8cx577LGMHz8+xxxzTJK3LqE+cuTIjB07Nv3790///v0zduzYdOrUKbW1tUmSrl275thjj803vvGNdO/ePVtvvXXOPPPMDBw4MPvvv385Xx4AAAAAtHqCcQAAWr1LL7003/ve93LSSSdl3rx56dWrV44//vicc845pTlnnXVWlixZkpNOOikLFizInnvumbvuuiudO3cuzbn44ouzxRZb5POf/3yWLFmS/fbbL9dcc03atm1bjpcFAAAAAPz/CsVisVjuIirFokWL0rVr1yxcuNBlMAGAVkH/07y8nwBAa6P/aX7eUwCgNVmf3sc9xgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAACAVm7q1KkZPnx4pk6dWu5SAAAAYJMQjAMAAEArtnTp0owfPz4vvfRSxo8fn6VLl5a7JAAAAGh2gnEAAABoxW644YbMnz8/STJ//vzU1dWVuSIAAABofoJxAAAAaKXmzJmTurq6FIvFJEmxWExdXV3mzJlT5soAAACgeQnGAQAAoBUqFou55JJLmhxfFZYDAABAJRCMAwAAQCs0e/bsTJs2LfX19Q3G6+vrM23atMyePbtMlQEAAEDzE4wDAABAK9S3b98MHjw4bdu2bTDetm3b7LHHHunbt2+ZKgMAAIDmJxgHAACAVqhQKOS0005rcrxQKJShKgAAANg0BOMAAADQSvXu3Tu1tbWlELxQKKS2tjbbbrttmSsDAACA5iUYBwAAgFZsxIgR6d69e5KkR48eqa2tLXNFAAAA0PwE4wAAANCKVVVV5Ywzzkh1dXVOP/30VFVVlbskAAAAaHZblLsAAAAAoLyGDBmSIUOGlLsMAAAA2GSsGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAANgMnn/++Xzxi19M9+7d06lTp3z4wx/O9OnTS9uLxWJGjx6dXr16pWPHjhk6dGieeuqpMlYMAFA5BOMAAAAAAJvYggULstdee6Vdu3a5/fbb8/TTT+eiiy5Kt27dSnMuvPDCjB8/PhMnTsy0adNSU1OTAw44IIsXLy5f4QAAFWKLchcAAAAAAFDpLrjggvTp0ydXX311aWz77bcv/VwsFjNhwoScffbZOfzww5Mk1157baqrq1NXV5fjjz9+c5cMAFBRrBgHAAAAANjEbrnlluy+++753Oc+l549e2bXXXfNz3/+89L2WbNmZe7cuRk2bFhprEOHDtlnn30yderUJo+7bNmyLFq0qMEDAIA1CcYBAAAAADaxf/3rX7nsssvSv3//3HnnnTnhhBPy9a9/Pdddd12SZO7cuUmS6urqBvtVV1eXtjVm3Lhx6dq1a+nRp0+fTfciAABaMME4AAAAAMAmtnLlyuy2224ZO3Zsdt111xx//PE57rjjctlllzWYVygUGjwvFotrjK1u1KhRWbhwYenx3HPPbZL6AQBaOsE4AAAAAMAmts0222SnnXZqMLbjjjtm9uzZSZKampokWWN1+Lx589ZYRb66Dh06pEuXLg0eAACsSTAOAAAAALCJ7bXXXpk5c2aDsb/97W/ZbrvtkiT9+vVLTU1NJk+eXNq+fPnyTJkyJUOGDNmstQIAVCLBOAAAAADAJnb66afnT3/6U8aOHZt//OMfqauryxVXXJGTTz45yVuXUB85cmTGjh2bSZMm5cknn8zRRx+dTp06pba2tszVA6zd1KlTM3z48EydOrXcpQA0aYtyFwAAAAAAUOkGDx6cSZMmZdSoURkzZkz69euXCRMmZMSIEaU5Z511VpYsWZKTTjopCxYsyJ577pm77rornTt3LmPlAGu3dOnSjB8/Pv/5z38yfvz47Lbbbqmqqip3WQBrEIwDAAAAAGwGBx10UA466KAmtxcKhYwePTqjR4/efEUBbKQbbrgh8+fPT5LMnz8/dXV1OeaYY8pcFcCaXEodAAAAAACA9TZnzpzU1dWlWCwmSYrFYurq6jJnzpwyVwawJsE4AAAAAAAA66VYLOaSSy5pcnxVWA7wbiEYBwAAAAAAYL3Mnj0706ZNS319fYPx+vr6TJs2LbNnzy5TZQCNE4wDAAAAAACwXvr27ZvBgwenbdu2Dcbbtm2bPfbYI3379i1TZQCNE4wDAAAAAACwXgqFQk477bQmxwuFQhmqAmiaYBwAAAAAAID11rt379TW1pZC8EKhkNra2my77bZlrgxgTYJxAAAAAAAANsiIESPSvXv3JEmPHj1SW1tb5ooAGicYBwAAAAAAYINUVVXljDPOSHV1dU4//fRUVVWVuySARm1R7gIAAAAAAABouYYMGZIhQ4aUuwyAtbJiHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAgFZu6tSpGT58eKZOnVruUgAAaIH0k0BLIBgHAACAVmzp0qUZP358XnrppYwfPz5Lly4td0kAALQg+kmgpRCMAwAAQCt2ww03ZP78+UmS+fPnp66urswVAQDQkugngZZCMA4AAACt1Jw5c1JXV5disZgkKRaLqaury5w5c8pcGQAALYF+EmhJBOMAAADQChWLxVxyySVNjq86uQkAAI3RTwItTasJxt98881897vfTb9+/dKxY8fssMMOGTNmTFauXFmaUywWM3r06PTq1SsdO3bM0KFD89RTT5WxagAAANg0Zs+enWnTpqW+vr7BeH19faZNm5bZs2eXqTIAAFoC/STQ0rSaYPyCCy7I5ZdfnokTJ+aZZ57JhRdemB/96Ee59NJLS3MuvPDCjB8/PhMnTsy0adNSU1OTAw44IIsXLy5j5QAAAND8+vbtm8GDB6dt27YNxtu2bZs99tgjffv2LVNlAAC0BPpJoKVpNcH4Qw89lEMPPTSf+cxnsv322+eII47IsGHD8sgjjyR5a7X4hAkTcvbZZ+fwww/PgAEDcu211+aNN95IXV1dmasHAACA5lUoFHLaaac1OV4oFMpQFQAALYV+EmhpWk0w/rGPfSz33HNP/va3vyVJ/vKXv+TBBx/MgQcemCSZNWtW5s6dm2HDhpX26dChQ/bZZ59MnTq1LDUDAADAptS7d+/U1taWTloWCoXU1tZm2223LXNlAAC0BPpJoCVpNcH4t771rXzhC1/IBz/4wbRr1y677rprRo4cmS984QtJkrlz5yZJqqurG+xXXV1d2vZ2y5Yty6JFixo8AAAAoCUZMWJEunfvniTp0aNHamtry1wRAAAtiX4SaClaTTB+00035frrr09dXV0effTRXHvttfnxj3+ca6+9tsG8t1/ao1gsNnm5j3HjxqVr166lR58+fTZZ/QAAALApVFVV5Ywzzkh1dXVOP/30VFVVlbskAABaEP0k0FJsUe4CNpdvfvOb+fa3v50jjzwySTJw4MA8++yzGTduXI466qjU1NQkeWvl+DbbbFPab968eWusIl9l1KhROeOMM0rPFy1aJBwHAACgxRkyZEiGDBlS7jIAAGih9JNAS9BqVoy/8cYbadOm4ctt27ZtVq5cmSTp169fampqMnny5NL25cuXZ8qUKU3+Yd6hQ4d06dKlwQMAAAAAAACAd5dWs2L84IMPzg9+8IP07ds3O++8cx577LGMHz8+xxxzTJK3LqE+cuTIjB07Nv3790///v0zduzYdOrUyf0wAAAAAAAAAFqwVhOMX3rppfne976Xk046KfPmzUuvXr1y/PHH55xzzinNOeuss7JkyZKcdNJJWbBgQfbcc8/cdddd6dy5cxkrBwAAAAAAAGBjFIrFYrHcRVSKRYsWpWvXrlm4cKHLqgMArYL+p3l5PwGA1kb/0/y8pwBAa7I+vU+rucc4AAAAAAAAAK2TYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAACADTZ16tQMHz48U6dOLXcpAE0SjAMAAAAAALBBli5dmvHjx+ell17K+PHjs3Tp0nKXBNAowTgAAAAAAAAb5IYbbsj8+fOTJPPnz09dXV2ZKwJonGAcAAAAAACA9TZnzpzU1dWlWCwmSYrFYurq6jJnzpwyVwawJsE4AAAAAAAA66VYLOaSSy5pcnxVWA7wbiEYBwAAAAAAYL3Mnj0706ZNS319fYPx+vr6TJs2LbNnzy5TZQCNE4wDAAAAAACwXvr27ZvBgwenbdu2Dcbbtm2bPfbYI3379i1TZQCNE4wDAAAAAACwXgqFQk477bQmxwuFQhmqAmiaYBwAAAAAAID11rt379TW1pZC8EKhkNra2my77bZlrgxgTYJxAAAAAAAANsiIESPSvXv3JEmPHj1SW1tb5ooAGicYBwAAAAAAYINUVVXljDPOSHV1dU4//fRUVVWVuySARm1R7gIAAAAAAABouYYMGZIhQ4aUuwyAtbJiHAAAAAAAAICKJhgHAAAAAAAAoKIJxgEAAAAAAACoaIJxAAAAAAAAACqaYBwAAAAAAACAiiYYBwAAAAAAAKCiCcYBAAAAAAAAqGiCcQAAAAAAAAAqmmAcAAAAAAAAgIomGAcAAAAAAACgognGAQAAAAAAAKhognEAAAAAAAAAKppgHAAAAAAAAICKJhgHAAAAAABgg02dOjXDhw/P1KlTy10KQJME4wAAAAAAAGyQpUuXZvz48XnppZcyfvz4LF26tNwlATRKMA4AAAAAAMAGueGGGzJ//vwkyfz581NXV1fmigAaJxgHAAAAAABgvc2ZMyd1dXUpFotJkmKxmLq6usyZM6fMlQGsSTAOAAAAAADAeikWi7nkkkuaHF8VlgO8WwjGAQAAAAAAWC+zZ8/OtGnTUl9f32C8vr4+06ZNy+zZs8tUGUDjBOMAAAAAAACsl759+2bw4MEpFAoNxguFQvbYY4/07du3TJUBNE4wDgAAAAAAwHopFAoZPnz4GpdMLxaLGT58+BqBOUC5CcYBACDJ888/ny9+8Yvp3r17OnXqlA9/+MOZPn16aXuxWMzo0aPTq1evdOzYMUOHDs1TTz3V4BjLli3Lqaeemh49emTLLbfMIYcckjlz5mzulwKw3qZOnZrhw4dn6tSp5S4FAIAWolgs5qabbmp0xfiNN97oHuPAu45gHACAVm/BggXZa6+90q5du9x+++15+umnc9FFF6Vbt26lORdeeGHGjx+fiRMnZtq0aampqckBBxyQxYsXl+aMHDkykyZNyo033pgHH3wwr732Wg466KA17rcG8G6ydOnSjB8/Pi+99FLGjx+fpUuXlrskAABagFX3GG9sxbh7jAPvRoJxAABavQsuuCB9+vTJ1VdfnT322CPbb7999ttvv7zvfe9L8tY/6idMmJCzzz47hx9+eAYMGJBrr702b7zxRurq6pIkCxcuzJVXXpmLLroo+++/f3bddddcf/31mTFjRu6+++5yvjyAtbrhhhsyf/78JMn8+fNLf64BAMDarLrHeNu2bRuMt23b1j3GgXclwTgAAK3eLbfckt133z2f+9zn0rNnz+y66675+c9/Xto+a9aszJ07N8OGDSuNdejQIfvss0/pssPTp0/PihUrGszp1atXBgwY4NLEwLvWnDlzUldXV1rlUywWU1dX5zYQAAC8o0KhkNNOO63JcfcYB95tBOMAALR6//rXv3LZZZelf//+ufPOO3PCCSfk61//eq677rokydy5c5Mk1dXVDfarrq4ubZs7d27at2+frbbaqsk5b7ds2bIsWrSowQNgcykWi7nkkkuaHHdPSAAA3knv3r1TW1tbCsELhUJqa2uz7bbblrkygDUJxgEAaPVWrlyZ3XbbLWPHjs2uu+6a448/Pscdd1wuu+yyBvPe/m33YrH4jt+AX9uccePGpWvXrqVHnz59Nu6FAKyHVfeErK+vbzBeX1/vnpAAAKyzESNGpKqqKknSsWPH1NbWlrkigMYJxgEAaPW22Wab7LTTTg3Gdtxxx1IoVFNTkyRrrPyeN29eaRV5TU1Nli9fngULFjQ55+1GjRqVhQsXlh7PPfdcs7wegHXhnpAAADSHpUuXZunSpUmSJUuWlH4GeLcRjAMA0OrttddemTlzZoOxv/3tb9luu+2SJP369UtNTU0mT55c2r58+fJMmTIlQ4YMSZIMGjQo7dq1azDnxRdfzJNPPlma83YdOnRIly5dGjwANhf3hAQAoDl873vfK92Gp1gs5pxzzilzRQCNE4wDANDqnX766fnTn/6UsWPH5h//+Efq6upyxRVX5OSTT07yVkg0cuTIjB07NpMmTcqTTz6Zo48+Op06dSpdIq5r16459thj841vfCP33HNPHnvssXzxi1/MwIEDs//++5fz5QE0yT0hAQDYGI888khmzJjRYOyJJ57II488UqaKAJomGAcAoNUbPHhwJk2alF/+8pcZMGBAzj///EyYMCEjRowozTnrrLMycuTInHTSSdl9993z/PPP56677krnzp1Lcy6++OJ89rOfzec///nstdde6dSpU2699dY1LlMM8G4yYsSIdO/ePUnSo0cP94QEAGCdrFy5MmPGjGl025gxY7Jy5crNXBHA2hWKq65vwUZbtGhRunbtmoULF7oMJgDQKuh/mpf3EyiXqVOn5pJLLslpp53W5O0fADYF/U/z854Cm8tDDz2UUaNGNbl93Lhx+ehHP7oZKwJao/XpfbbYTDUBAAAA71JDhgwRiAMAsF723HPPdOnSJYsWLVpjW9euXbPnnnuWoSqAprmUOgAAAAAAAOulTZs2Oeeccxrddu6556ZNGxEU8O7iTyUAAAAAAADW2+67756BAwc2GNtll12y2267lakigKYJxgEAAAAAANgg559/fml1eJs2bTJmzJgyVwTQOME4AAAAAAAAG6Rbt24ZMWJE2rRpkxEjRqRbt27lLgmgUVuUuwAAAAAAAABarmOPPTbHHntsucsAWCsrxgEAAKCVmzp1aoYPH56pU6eWuxQAAADYJATjAAAA0IotXbo048ePz0svvZTx48dn6dKl5S4JAAAAmp1gHAAAAFqxG264IfPnz0+SzJ8/P3V1dWWuCAAAAJqfYBwAAABaqTlz5qSuri7FYjFJUiwWU1dXlzlz5pS5MgAAAGhegnEAAABohYrFYi655JImx1eF5QAAAFAJBOMAAADQCs2ePTvTpk1LfX19g/H6+vpMmzYts2fPLlNlAAAA0PwE4wAAANAK9e3bN4MHD07btm0bjLdt2zZ77LFH+vbtW6bKAAAAoPkJxgEAAKAVKhQKOe2005ocLxQKZagKAAAANg3BOAAAALRSvXv3Tm1tbSkELxQKqa2tzbbbblvmygAAAKB5CcYBAACgFRsxYkS6d++eJOnRo0dqa2vLXBEAAAA0P8E4AAAAtGJVVVU544wzUl1dndNPPz1VVVXlLgkAAACa3RblLgAAAAAoryFDhmTIkCHlLgMAAAA2GSvGAQAAAAAAAKhognEAAACA/4+9+w+vu67vxv88SSVpoQ3QSgq0gaowhcIm/SVVgXtAtRMZMGdHKoJw70ZALL+GQxS6urudZVaqCDc4RJQGcJPOH1ik6A26by2kIFLAoZtgyKRUamkKNAGS8/2DuxmhafnRnJzmnMfjus515bw/n3P6TGvLy/PM5/0BAACgoinGAQAAAAAAAKhoinEAAAAAAAAAKppiHAAAAAAAAICKphgHAAAAAAAAoKIpxgEAAAAAAACoaIpxAAAAAAAAACqaYhwAAAAAAACAiqYYBwAAAAAosblz56ZQKPR5jB07tvd4sVjM3Llzs9dee2X48OE54ogj8tBDD5UxMQBAZVGMAwAAAAAMggMPPDBPPPFE72P16tW9xxYuXJhFixbliiuuSGtra8aOHZujjz46GzduLGNiAIDKMazcAQAAAAAAqsGwYcP6XCW+WbFYzOWXX56LL744J5xwQpLk+uuvT2NjY1paWnL66acPdlQouWKxmM7OznLHYAAUi8V0dXUlSerq6lIoFMqciIFQX1/vz5KKoxgHAAAAABgEv/71r7PXXnulrq4u06ZNy/z58/OWt7wljz76aNasWZMZM2b0nltXV5fDDz88K1as2GYx3tXV1VtIJUlHR0dJvwcYKJ2dnZk5c2a5YwBbsWzZsgwfPrzcMWBA2UodAAAAAKDEpk2blm984xv54Q9/mK9+9atZs2ZNpk+fnnXr1mXNmjVJksbGxj6vaWxs7D22NQsWLEhDQ0PvY/z48SX7HgAAhjJXjAMAAAAAlNjLr4w96KCDcuihh+atb31rrr/++rzrXe9Kki22rC0Wi6+6je1FF12U8847r/d5R0eHcpwhob6+PsuWLSt3DAZAZ2dnjj/++CTJ0qVLU19fX+ZEDAR/jlQixTgAAAAAwCDbeeedc9BBB+XXv/51jjvuuCTJmjVrsueee/aes3bt2i2uIn+lurq61NXVlTIqlEShULBNcwWqr6/35wrssGylDgAAAFVuxYoVmTVrVlasWFHuKABVo6urK7/85S+z5557ZsKECRk7dmyWL1/ee/z555/PXXfdlenTp5cxJQBA5VCMAwAAQBXr7OzMokWL8uSTT2bRokXp7OwsdySAinTBBRfkrrvuyqOPPpq77747H/rQh9LR0ZGTTz45hUIh55xzTubPn5+lS5fmwQcfzCmnnJIRI0akubm53NEBACqCrdQBAACgii1ZsiTr1q1Lkqxbty4tLS059dRTy5wKoPK0t7fnxBNPzFNPPZU3v/nNede73pWVK1dmn332SZJceOGF2bRpU84888ysX78+06ZNy+23356RI0eWOTkAQGVQjAMAAECVam9vT0tLS4rFYpKkWCympaUlM2bMyLhx48qcDqCy3HTTTds8XigUMnfu3MydO3dwAgEAVBlbqQMAAEAVKhaLWbx4cXp6evqsd3d3Z/Hixb1lOQAAAFQCxTgAAABUoba2trS2tm5RgBeLxbS2tqatra1MyQAAAGDgKcYBAACgCo0fPz6jRo3q99ioUaMyfvz4QU4EAAAApaMYBwAAgCr0+OOPp6Ojo99jHR0defzxxwc5EQAAAJSOYhwAAACqkCvGAQAAqCaKcQAAAKhCrhgHAACgmijGAQAAoAo1NTVlypQpKRQKfdYLhUKmTp2apqamMiUDAACAgacYBwAAgCpUKBQyZ86c1NT0/WigtrY2c+bM2aIwBwAAgKFMMQ4AAABVaty4cWlubu4twQuFQpqbm7P33nuXORkAAAAMLMU4AAAAVLHZs2dn9OjRSZIxY8akubm5zIkAAABg4CnGAQAAoIrV19fnvPPOS2NjY84999zU19eXOxIAAAAMuGHlDgAAAACU1/Tp0zN9+vRyxwAAAICSccU4AAAAAAAAABVNMQ4AAAAAAABARVOMAwAAAAAAAFDRFOMAAAAAAAAAVDTFOAAAAAAAAAAVTTEOUAFWrFiRWbNmZcWKFeWOAgAAAAAAsMNRjAMMcZ2dnVm0aFGefPLJLFq0KJ2dneWOBAAAAAAAsENRjAMMcUuWLMm6deuSJOvWrUtLS0uZEwEAAAAAAOxYFOMAQ1h7e3taWlpSLBaTJMViMS0tLWlvby9zMgAAAAAAgB2HYhxgiCoWi1m8ePFW1zeX5QAAAAAAANVOMQ4wRLW1taW1tTXd3d191ru7u9Pa2pq2trYyJQMAAAAAANixKMYBhqimpqZMmTIltbW1fdZra2szderUNDU1lSkZAAAAAADAjkUxDjBEFQqFzJkzZ6vrhUKhDKkAAAAAAAB2PIpxgCFs3LhxaW5u7i3BC4VCmpubs/fee5c5GQAAQ8mKFSsya9asrFixotxRAAAAoCQU4wBD3OzZszN69OgkyZgxY9Lc3FzmRAAADCWdnZ1ZtGhRnnzyySxatCidnZ3ljgQAAAADTjEOMMTV19fnvPPOS2NjY84999zU19eXOxIAAEPIkiVLsm7duiTJunXr0tLSUuZEAAAAMPCGlTsAANtv+vTpmT59erljAAAwxLS3t6elpSXFYjFJUiwW09LSkhkzZmTcuHFlTgcAAAADxxXjAAAAUIWKxWIWL1681fXNZTkAAABUAsU4AAAAVKG2tra0tramu7u7z3p3d3daW1vT1tZWpmQAAAAw8BTjAAAAUIWampoyZcqU1NbW9lmvra3N1KlT09TUVKZkAAAAMPAU4wAAAFCFCoVC5syZs9X1QqFQhlQAAABQGopxAAAAqFLjxo1Lc3NzbwleKBTS3Nycvffeu8zJAAAAYGApxgEAAKCKzZ49O6NHj06SjBkzJs3NzWVOBAAAAANPMQ4AAABVrL6+Puedd14aGxtz7rnnpr6+vtyRAAAAYMANK3cAAAAAoLymT5+e6dOnlzsGAAAAlIwrxgEAAAAAAACoaIpxAAAAqHLXXntt/vRP/zTXXnttuaMAAABASSjGASrAihUrMmvWrKxYsaLcUQAAGGKefvrpLFmyJD09PVmyZEmefvrpckcCAACAAVdVxfh//dd/5SMf+UhGjx6dESNG5E/+5E9y77339h4vFouZO3du9tprrwwfPjxHHHFEHnrooTImBnh1nZ2dWbRoUZ588sksWrQonZ2d5Y4EAMAQ8tnPfjY9PT1Jkp6enlxyySVlTgQAAAADr2qK8fXr1+fd73533vSmN2XZsmV5+OGH84UvfCG77rpr7zkLFy7MokWLcsUVV6S1tTVjx47N0UcfnY0bN5YvOMCrWLJkSdatW5ckWbduXVpaWsqcCACAoWLVqlVZvXp1n7UHHnggq1atKlMiAAAAKI2qKcY///nPZ/z48bnuuusyderU7LvvvjnyyCPz1re+NclLV4tffvnlufjii3PCCSdk4sSJuf766/Pcc88pmYAdVnt7e1paWlIsFpO89G9ZS0tL2tvby5wMAIAdXU9PT+bNm9fvsXnz5vVeRQ4AAACVoGqK8e9+97uZPHly/vIv/zJ77LFH3vnOd+arX/1q7/FHH300a9asyYwZM3rX6urqcvjhh2/1nr1dXV3p6Ojo8wAYLMViMYsXL97q+uayHAAA+nP33Xdv9f/HdnR05O677x7kRAAAAFA6VVOM/+Y3v8lVV12V/fbbLz/84Q/z8Y9/PJ/85CfzjW98I0myZs2aJEljY2Of1zU2NvYee6UFCxakoaGh9zF+/PjSfhMAL9PW1pbW1tZ0d3f3We/u7k5ra2va2trKlAwAgKFg2rRpGTVqVL/HGhoaMm3atEFOBAAAAKVTNcV4T09PDjnkkMyfPz/vfOc7c/rpp+ev//qvc9VVV/U5r1Ao9HleLBa3WNvsoosuyoYNG3ofjz/+eMnyA7xSU1NTpkyZktra2j7rtbW1mTp1apqamsqUDACAoaCmpiaXXHJJv8cuvfTS1NRUzUcGAAAAVIGq+X+5e+65Zw444IA+a+94xzt6r6gcO3ZskmxxdfjatWu3uIp8s7q6uowaNarPA2CwFAqFzJkzZ6vrW/uhHgAA2Gzy5Mk56KCD+qwdfPDBOeSQQ8qUCAAAAEqjaorxd7/73XnkkUf6rP3qV7/KPvvskySZMGFCxo4dm+XLl/cef/7553PXXXdl+vTpg5oV4LUaN25cmpube0vwQqGQ5ubm7L333mVOBgDAUPGZz3ymz/OLL764TEkAAACgdKqmGD/33HOzcuXKzJ8/P//xH/+RlpaWXHPNNTnrrLOSvFQmnXPOOZk/f36WLl2aBx98MKecckpGjBiR5ubmMqcH2LrZs2dn9OjRSZIxY8b4NwsAgNfl+9//fp/nt956a5mSAAAAQOlUTTE+ZcqULF26NDfeeGMmTpyYz33uc7n88ssze/bs3nMuvPDCnHPOOTnzzDMzefLk/Nd//Vduv/32jBw5sozJAbatvr4+5513XhobG3Puueemvr6+3JEAABgi2tvb09LS0metpaUl7e3tZUoEAAAApVEoFovFcoeoFB0dHWloaMiGDRvcbxwAqArmn4Hl9xMYTMViMRdeeGHuu+++dHd3967X1tbmkEMOycKFC3tv2QNQKuafgef3FBhsmzZtysyZM5Mky5Yty/Dhw8ucCKgmr2f2qZorxgEAAID/1tbWltbW1j6leJJ0d3entbU1bW1tZUoGAAAAA08xDgAAAFWoqakpU6ZM6ffY1KlT09TUNMiJAAAAoHQU4wAAAFCFCoVCjjzyyH6PHXnkkbZRBwAAoKIoxgEAAKAK9fT05Morr+z32Fe+8pX09PQMciIAAAAoHcU4AAAAVKG77747HR0d/R7r6OjI3XffPciJAAAAoHQU4wAAAFCFpk6dmtra2n6P1dbWZurUqYOcCAAAAEpHMQ4AAABVqL29Pd3d3f0e6+7uTnt7+yAnAgAAgNJRjAMAAEAVampqykEHHdTvsYMPPjhNTU2DnAgAAABKpyTF+J/+6Z/myCOPzG9/+9vX/Jrf/e53va8DAKC6mScByqtYLJY7AkDJmTkBAKrLsFK86Z133plCoZBnn332Nb9m06ZNva8DAKC6mScBSq+trS2rV6/u99jq1avT1taWffbZZ5BTAQweMycAQHWxlToAAABUoaampkyZMiU1NX0/Gqitrc3UqVNtpQ4AAEBF2WGK8c0/mVlfX1/mJAAADEXmSYDXp1AoZM6cOVtc9bi1dQDMnAAAQ9kOU4wvW7YsSTJu3LgyJwEAYCgyTwK8fuPGjcuHP/zhPmsf/vCHs/fee5cpEcCOzcwJADB0Dcg9xk899dR+1z/zmc9k11133eZru7q68p//+Z9pbW1NoVDI4YcfPhCRAAAYQsyTAACUmpkTAKC6FYrFYnF736SmpqbPFmub3/K1bru2+fzdd989ra2tmTBhwvZGKouOjo40NDRkw4YNGTVqVLnjAACU3EDNP+bJl5gngcHW3t6ek08+Od3d3b1rw4YNy9e//nVXQwKDYjDnn2qZOc2UwGDbtGlTZs6cmeSlnTWGDx9e5kRANXk9s8+AXDHe1NTUZ4D87W9/m0KhkD333DNvetObtvq6QqGQ+vr67Lnnnpk+fXrOOOOM7LXXXgMRCQCAIcQ8CTD4isViFi9evNX1hQsXus84UFHMnAAA1W1AivHHHnusz/OampduXX777bfngAMOGIhfAgCACmaeBBh8bW1taW1t3WK9u7s7ra2taWtryz777FOGZAClYeYEAKhuA1KMv9Jhhx2WQqGQnXfeuRRvDwBAhTNPApReU1NTpkyZkvvuu6/PVuq1tbWZNGlSmpqaypgOoPTMnAAA1aUkxfidd95ZircFYCtWrFiRxYsXZ86cOZk+fXq54wBsN/MkQOkVCoXMmTMnJ598cr/rtlEHKp2ZEwCgutSUOwAA26ezszOLFi3Kk08+mUWLFqWzs7PckQAAGCLGjRuX5ubm3hK8UCikubk5e++9d5mTAQAAwMAqyRXj/eno6MjGjRv7bM+2NbZrA3jtlixZknXr1iVJ1q1bl5aWlpx66qllTgUw8MyTAKUxe/bsLFu2LE899VTGjBmT5ubmckcCKBszJwBA5SppMb58+fJceeWV+elPf5r169e/ptcUCoW8+OKLpYwFUDHa29vT0tKSYrGYJCkWi2lpacmMGTMybty4MqcD2H7mSYDSq6+vz3nnndd7a576+vpyRwIYVGZOAIDqULJi/JOf/GS+8pWvJElvYQPAwCkWi1m8ePFW1xcuXOi+kMCQZp4EGDzTp0/P9OnTyx0DYNCZOQEAqkdJivGWlpZcccUVSV76yfPjjjsukyZNyu67756aGrc1BxgIbW1taW1t3WK9u7s7ra2taWtryz777FOGZADbzzwJAECpmTkBAKpLSYrxq6++Okkyfvz4/PjHP85b3/rWUvwyAFWtqakpU6ZMyX333dfn3me1tbWZNGmSe50BQ5p5EgCAUjNzAgBUl5L86OMDDzyQQqGQSy+91EAJUCKFQiFz5szZ6rpt1IGhzDwJAECpmTkBAKpLSYrxF154IUnyzne+sxRvD8D/M27cuDQ3N/eW4IVCIc3Nzdl7773LnAxg+5gnAQAoNTMnAEB1KUkxvu+++yZJnnnmmVK8PQAvM3v27IwePTpJMmbMmDQ3N5c5EcD2M08CAFBqZk4AgOpSkmL8hBNOSJL86Ec/KsXbA/Ay9fX1Oe+889LY2Jhzzz039fX15Y4EsN3MkwAAlJqZEwCguhSKxWJxoN90w4YN+ZM/+ZOsX78+K1euzNvf/vaB/iV2SB0dHWloaMiGDRsyatSocscBACi5Us0/5knzJABQHco5/1TqzGmmBAbbpk2bMnPmzCTJsmXLMnz48DInAqrJ65l9SnLFeENDQ2677bY0Njbm3e9+d6688sqsX7++FL8UAAAVyDwJAECpmTkBAKpLSa4Yf8tb3pIkee6557J27doUCoUUCoWMGTMmI0aM2HagQiH/+Z//OdCRBoWfxgQAqk2p5h/zpHkSAKgO5Zx/KnXmNFMCg80V40A5vZ7ZZ1gpAjz22GN9nheLxRSLxaxdu/ZVX1soFEoRCQCAIcQ8CTC4VqxYkcWLF2fOnDmZPn16ueMADAozJwBAdSlJMX7yySeX4m0B2Iprr702S5YsyezZs3PaaaeVOw7AdjNPAgyezs7OLFq0KE899VQWLVqUQw45JPX19eWOBVByZk4AgOpSkmL8uuuuK8XbAtCPp59+OkuWLElPT0+WLFmSv/iLv8iuu+5a7lgA28U8CTB4lixZknXr1iVJ1q1bl5aWlpx66qllTgVQemZOAIDqUlPuAABsn89+9rPp6elJkvT09OSSSy4pcyIAAIaK9vb2tLS0pFgsJnlpG+GWlpa0t7eXORkAAAAMLMU4wBC2atWqrF69us/aAw88kFWrVpUpEQAAQ0WxWMzixYu3ur65LAcAAIBKoBgHGKJ6enoyb968fo/Nmzev9ypyAADoT1tbW1pbW9Pd3d1nvbu7O62trWlraytTMgAAABh4JbnH+De+8Y3tev1HP/rRAUoCULnuvvvudHR09Huso6Mjd999dw499NBBTgUwMMyTAKXX1NSUKVOm5L777utTjtfW1mbSpElpamoqYzqA0jNzAgBUl0KxBHuj1dTUpFAovKHXFgqFvPjiiwOcaHB0dHSkoaEhGzZsyKhRo8odB6hwPT09Oe644/otxxsaGrJ06dLU1NgYBCitUs0/5knzJDA42tvbc/LJJ/cpxocNG5brr78+e++9dxmTAdWinPNPpc6cZkpgsG3atCkzZ85MkixbtizDhw8vcyKgmrye2adkjUmxWHzDDwBeXU1NTS655JJ+j1166aVKcWDIM08ClN64cePS3NzcWwwVCoU0NzcrxYGqYeYEAKgeJdlK/dFHH33Vc5599tk88sgjWbJkSW655ZZMnz4911xzTXbeeedSRAKoSJMnT85BBx2U1atX964dfPDBOeSQQ8qYCmD7mScBBs/s2bOzbNmyPPXUUxkzZkyam5vLHQlgUJg5AQCqS0m2Un+9brzxxpx00kk5/PDDc8cdd7zhLYzKzTZFQDk8/fTTOeGEE9LT05Oamprccsst2XXXXcsdC6gSO8r8Y54E2D4rVqzI4sWLM2fOnEyfPr3ccYAqMpTmn6Eycw6l31OgMthKHSinHWIr9dfjxBNPzEc/+tHceeedufrqq8sdB2BI2XXXXTN79uzU1NRk9uzZSnGgKpknAbbP9OnTc/PNNyvFAbbBzAkAMLTtEMV4knz4wx9OsVjM17/+9XJHARhyTjvttPz4xz/OaaedVu4oAGVjngQAoNTMnAAAQ9cOU4yPHTs2SfLII4+UOQkAAEOReRIAgFIzcwIADF07TDHe1taWJHnhhRfKnAQAgKHIPAkAQKmZOQEAhq4dohh/4YUXsnDhwiTJ2972tjKnAQBgqDFPAgBQamZOAIChbVgp3nTzT05uS09PT9avX59Vq1bliiuuyIMPPphCoZC/+qu/KkUkgIp27bXXZsmSJZk9e7b7jAMVwTwJAECpmTkBAKpLSYrxCRMmvO7XFIvFHHrooTn33HNLkAigcj399NNZsmRJenp6smTJkvzFX/xFdt1113LHAtgu5kkAAErNzAkAUF1KspV6sVh8XY/ddtstf/u3f5s77rgjdXV1pYgEULE++9nPpqenJ8lLP8l+ySWXlDkRwPYzTwIAUGpmTgCA6lKSK8avu+66Vz2npqYmI0eOzIQJEzJx4sTU1taWIgpARVu1alVWr17dZ+2BBx7IqlWrMnny5DKlAth+5kkAAErNzAkAUF1KUoyffPLJpXhbAF6mp6cn8+bN6/fYvHnz8q//+q+pqSnJxiAAJWeeBACg1MycAADVRWMCMETdfffd6ejo6PdYR0dH7r777kFOBADAULVixYrMmjUrK1asKHcUAAAAKAnFOMAQNW3atIwaNarfYw0NDZk2bdogJwIAYCjq7OzMokWL8uSTT2bRokXp7OwsdyQAAAAYcCXZSv2V7r333txxxx158MEH84c//CFJsvvuu2fixIk56qijMmnSpMGIAVBRampqcskll+SCCy7Y4till15qG3WgopgnAUpnyZIlWbduXZJk3bp1aWlpyamnnlrmVACDz8wJAFDZSlqMr169Ov/rf/2v3HPPPVs959Of/nSmTZuWq6++OgcddFAp4wBUnMmTJ+fAAw/MQw891Ls2ceLEHHLIIWVMBTBwzJMApdXe3p6WlpYUi8UkSbFYTEtLS2bMmJFx48aVOR3A4DBzAgBUh5JdTnjHHXdk6tSpueeee1IsFlMsFjNs2LA0NjZmjz32yLBhw3rXV65cmalTp+ZHP/pRqeIAVKwDDjhgm88BhirzJEBpFYvFLF68eKvrm8tygEpm5gQAqB4lKcafeuqp/OVf/mW6urpSKBTyP//n/8zdd9+dZ599Nr/73e/yxBNP5Lnnnss999yTv/7rv05tbW26urryl3/5l73btwHw6trb23PLLbf0WbvlllvS3t5epkQAA8M8CVB6bW1taW1tTXd3d5/17u7utLa2pq2trUzJAAaHmRMAoLqUpBhfvHhxNmzYkJ122im33nprrrnmmkyZMiXDhv33zu21tbWZPHlyrr766tx6661505velA0bNvT70+oAbMkVPkAlM08ClF5TU1OmTJmS2traPuu1tbWZOnVqmpqaypQMYHCYOQEAqktJivFbb701hUIhn/jEJ/K+973vVc+fMWNGzj777BSLxdx6662liARQcVzhA1Qy8yRA6RUKhcyZM2er64VCoQypAAaPmRMAoLqUpBh/9NFHkyTHHnvsa37N5nN/85vflCISQMVxhQ9QycyTAINj3LhxaW5u7i3BC4VCmpubs/fee5c5GUDpmTkBAKpLSYrxzs7OJMnOO+/8ml8zYsSIJElXV1cpIgFUHFf4AJXMPAkweGbPnp3Ro0cnScaMGZPm5uYyJwIYHGZOAIDqUpJifOzYsUmSn//856/5NZvPbWxsLEUkgIq0+Qqfl3OFD1AJzJMAg6e+vj4zZ85MTU1N3v/+96e+vr7ckQAGhZkTAKC6lKQYf+9735tisZh/+Id/SEdHx6ue39HRkc9//vMpFAp573vfW4pIABXr6KOP7vP8qKOOKlMSgIFjngQYPJ2dnVm2bFl6enqybNmy3isoASqdmRMAoLqUpBg//fTTk7x0n57DDjssra2tWz33nnvuyWGHHdZ7X57NrwXgtTn//PP7PL/gggvKlARg4JgnAQbPkiVLsm7duiTJunXr0tLSUuZEAIPDzAkAUF2GleJN3/3ud+fMM8/MlVdemdWrV+dd73pXDjjggEybNi2NjY0pFApZs2ZN7r777jz88MO9rzvzzDPz7ne/uxSRACrSbbfdlt///vd91tauXZvbbrst73//+8uUCmD7lXueXLBgQT796U9nzpw5ufzyy5MkxWIxf/d3f5drrrkm69evz7Rp0/KVr3wlBx54YO/rurq6csEFF+TGG2/Mpk2bcuSRR+bKK6/MuHHjtjsTQCm0t7enpaUlxWIxyUv/1rW0tGTGjBn+7QIqXrlnTgAABldJivEk+fKXv5wRI0Zk0aJF6enpyUMPPdRngEzS+3+8a2pqcsEFF+Qf/uEfShUHoOJ0d3fnsssu6/fYZZddlqOPPjq1tbWDnApg4JRrnmxtbc0111yTgw8+uM/6woULs2jRonz961/P/vvvn7//+7/P0UcfnUceeSQjR45Mkpxzzjn53ve+l5tuuimjR4/O+eefn2OOOSb33nuvf5OBHU6xWMzixYu3ur5w4cIUCoUyJAMYPD7DBACoHiXZSj1JCoVCFi5cmPvvvz9nnHFG9ttvvxSLxT6P/fbbL2eccUbuv//+3vvzAPDafP/73093d3e/x7q7u/P9739/kBMBDKxyzJPPPPNMZs+ena9+9avZbbfdeteLxWIuv/zyXHzxxTnhhBMyceLEXH/99Xnuued6txzesGFDrr322nzhC1/IUUcdlXe+85254YYbsnr16txxxx3blQugFNra2tLa2rrFTNnd3Z3W1ta0tbWVKRnA4PEZJgBA9SjZFeObTZw4MV/5yleSJM8//3zWr1+fJNltt92y0047lfqXB6hYxxxzTL70pS/1W44PGzYsxxxzTBlSAQy8wZwnzzrrrHzgAx/IUUcdlb//+7/vXX/00UezZs2azJgxo3etrq4uhx9+eFasWJHTTz899957b1544YU+5+y1116ZOHFiVqxYkfe9731b/HpdXV3p6urqfd7R0TGg3w/AtjQ1NWXKlCm57777+syUtbW1mTRpUpqamsqYDmBw+QwTAKDyleyK8f7stNNOaWxsTGNjo4ESYDvV1tbmb/7mb/o9duGFF9qyF6hIpZwnb7rpptx3331ZsGDBFsfWrFmTJGlsbOyz3tjY2HtszZo12Wmnnfpcaf7Kc15pwYIFaWho6H2MHz9+IL4VgNekUChkzpw5W113RSRQrXyGCQBQmQakGF+2bFkOOeSQHHLIIb1bSb5WS5Ys6X2tLSYBXp/3v//92X333fusjR49us/VigBDQbnnyccffzxz5szJDTfckPr6+q2e98qSqFgsvmpxtK1zLrroomzYsKH38fjjj7/+8ADbYdy4cWlubu79d6pQKKS5uTl77713mZMBDLxyz5wAAJTXdhfjxWIx5557bn7xi19k9OjRaW5ufl2vb25uzujRo3P//ffn/PPP3944AFXHlTzAULcjzJP33ntv1q5dm0mTJmXYsGEZNmxY7rrrrnzpS1/KsGHDeq8Uf+WV32vXru09Nnbs2D7bbvZ3zivV1dVl1KhRfR4Ag2327NnZZZddkiQjR4583f8OAwwFO8LMCQBAeW13Mf7jH/84v/rVr1JTU5PLL7/8db++UChk8eLFqa2tzYMPPpg777xzeyMBVI3bbrst69at67O2bt263HbbbWVKBPD67Qjz5JFHHpnVq1fn/vvv731Mnjw5s2fPzv3335+3vOUtGTt2bJYvX977mueffz533XVXpk+fniSZNGlS3vSmN/U554knnsiDDz7Yew7AjsoPWwKVbkeYOQEAKK/tLsa//e1vJ0mOPvroHHjggW/oPQ444IC8733v6/N+AGxbd3d3Lrvssn6PXXbZZenu7h7kRABvzI4wT44cOTITJ07s89h5550zevToTJw4MYVCIeecc07mz5+fpUuX5sEHH8wpp5ySESNG9F5t1NDQkNNOOy3nn39+fvSjH+XnP/95PvKRj+Sggw7KUUcd9Ya+L4DBsGTJkmzcuDFJsnHjxte9vTDAULAjzJwAAJTXdhfj99xzTwqFQj74wQ9u1/scc8wxKRaLWbly5fZGAqgK3//+97dafnd3d+f73//+ICcCeGOGyjx54YUX5pxzzsmZZ56ZyZMn57/+679y++23Z+TIkb3nfPGLX8xxxx2XD3/4w3n3u9+dESNG5Hvf+15qa2tLkglge7W3t6elpSXFYjHJS1sNt7S0pL29vczJAAbWUJk5AQAone0uxn/7298mSf7oj/5ou95n//33T5I89thj2xsJoCocc8wxWy1ahg0blmOOOWaQEwG8MTvqPHnnnXf22WazUChk7ty5eeKJJ9LZ2Zm77rorEydO7POa+vr6fPnLX866devy3HPP5Xvf+17Gjx8/IHkABlqxWMzixYu3ur65LAeoBDvqzAkAwODZ7mJ8w4YNSZLdd999u95n8+s7Ojq2NxJAVaitrc3f/M3f9HvswgsvdHUiMGSYJwHKo62tLa2trVvsQtTd3Z3W1ta0tbWVKRnAwDNzAgCw3cX4qFGjkiRPP/30dr3P5te/fCtKALbt/e9/f9785jf3Wdtjjz0yY8aMMiUCeP3MkwDl0dTUlClTpqRQKPRZLxQKmTp1apqamsqUDGDg7Ygz54IFC1IoFHLOOef0rhWLxcydOzd77bVXhg8fniOOOCIPPfTQdv9aAAAMQDG+xx57JEkefvjh7XqfX/7yl33eD4DX5qKLLurz/G//9m/LlATgjTFPApRHoVDIrFmzttgyvVgsZtasWVsU5gBD2Y42c7a2tuaaa67JwQcf3Gd94cKFWbRoUa644oq0trZm7NixOfroo7Nx48bt+vUAABiAYnzq1KkpFov57ne/u13v853vfCeFQiFTpkzZ3kgAVeUzn/nMNp8D7OjMkwDlUSwWc/PNN/d7xfhNN93kHuNARdmRZs5nnnkms2fPzle/+tXstttuvevFYjGXX355Lr744pxwwgmZOHFirr/++jz33HNpaWnZrtwAAAxAMT5z5swkyfLly/OTn/zkDb3HT37yk9x+++193g+AV3fjjTfmueee67P23HPP5cYbbyxTIoDXzzwJUB6b7zHe3xXj7jEOVJodaeY866yz8oEPfCBHHXVUn/VHH300a9as6XN7tLq6uhx++OFZsWLFG/71AAB4yXYX43/xF3+Rt7zlLSkWi/nwhz+cRx555HW9/le/+lU+/OEPp1AoZN99982HPvSh7Y0EUBVefPHFXH311f0eu/rqq/Piiy8OciKAN8Y8CVAem+8xXltb22e9trbWPcaBirOjzJw33XRT7rvvvixYsGCLY2vWrEmSNDY29llvbGzsPdafrq6udHR09HkAALCl7S7Ghw0bli984QspFAr5/e9/n8mTJ+eLX/xinnnmmW2+7plnnsnll1+eyZMnZ+3atUmSL3zhCxk2bNj2RgKoCt/4xje26zjAjsI8CVAehUIhc+bM2eq6e4wDlWRHmDkff/zxzJkzJzfccEPq6+u3et4r//0tFovb/Dd5wYIFaWho6H2MHz/+dWcDAKgGheIA3TRswYIFufjii3uHtJ133jnvfe97c8ghh6SxsTE777xznn322Tz55JO577778tOf/jTPPvts75Zt8+bNG/L3xe3o6EhDQ0M2bNiQUaNGlTsOUOFefPHFLbZde7k77rhDOQSU3EDOP+ZJ8yRQHtdee21uuOGG3uLlpJNOyqmnnlruWECVGOz5p5wz57/+67/m+OOP77NTR3d3dwqFQmpqavLII4/kbW97W+677768853v7D3nz//8z7Prrrvm+uuv7/d9u7q60tXV1fu8o6Mj48ePN1MCg2bTpk29t5hYtmxZhg8fXuZEQDV5PfPkgBXjSfLNb34zZ555Zp599tmX3nwbP8m4+ZcdMWJErrjiipxyyikDFaNsfJAJDLYbb7yx3+3UzzjjjMyaNasMiYBqM9Dzj3nSPAkMvs7OznzkIx/JU089lTe/+c355je/uc0rGQEGUjnmn3LNnBs3bsxvf/vbPmsf+9jH8va3vz2f+tSncuCBB2avvfbKueeemwsvvDBJ8vzzz2ePPfbI5z//+Zx++umv6dcxUwKDTTEOlNPrmX22eyv1lzvppJPyq1/9Kueff37e/OY3p1gsbvUxZsyYXHDBBfnVr35VER9iApTDiSeeuMWHlvX19UpxYMgyTwIMvvr6+sycOTM1NTV5//vfrxQHKl65Zs6RI0dm4sSJfR4777xzRo8enYkTJ6ZQKOScc87J/Pnzs3Tp0jz44IM55ZRTMmLEiDQ3Nw/MNw8AUMUGfI/dPffcM5dddlkuu+yyPPzww/nFL36Rp556Khs3bszIkSMzZsyY/PEf/3EOOOCAgf6lAarSKzf+GMCNQADKwjwJMLg6OzuzbNmy9PT0ZNmyZZk9e7ZyHKh4O+rMeeGFF2bTpk0588wzs379+kybNi233357Ro4cOag5AAAqUUlvPnvAAQf4wBKghG688cY+9xFLXrq32I033pgTTzyxTKkABo55EqD0lixZknXr1iVJ1q1bl5aWFvcYB6pKOWfOO++8s8/zQqGQuXPnZu7cuWXJAwBQyQZ0K3UABs+LL77Y7/3Fk+Tqq6/Oiy++OMiJAAAYatrb29PS0tK761CxWExLS0va29vLnAwAAAAGlmIcYIj6xje+sV3HAQCobsViMYsXL97qulv0AAAAUEkU4wBD1Ec/+tHtOg4AQHVra2tLa2truru7+6x3d3entbU1bW1tZUoGAAAAA08xDjBEDRs2LKeffnq/x84444wMGzZskBMBADCUNDU1ZcqUKSkUCn3WC4VCpk6dmqampjIlAwAAgIGnGAcYwk488cSMGDGiz9qIESMya9asMiUCAGCoKBQKmTVr1hZbpheLxcyaNWuLwhwAAACGMsU4wBD38Y9/fJvPAQCgP8ViMTfffHO/V4zfdNNN7jEOAABARVGMAwxxixYt2uZzAADoz+Z7jPd3xbh7jAMAAFBpFOMAQ9iFF174utYBAGCzzfcYr62t7bNeW1vrHuMAAABUHMU4wBDV2dmZe+65p99j99xzTzo7Owc5EQAAQ0mhUMicOXO2uu4e4wAAAFQSxTjAEPU3f/M323UcAADGjRuX5ubmPmvNzc3Ze++9y5QIAAAASkMxDjBEXXbZZdt1HAAAkuQv/uIvUlPz0scDNTU1OeGEE8qcCAAAAAaeYhxgiKqvr8/UqVP7Pfaud70r9fX1g5wIAICh6Nvf/naKxWKSpFgs5pZbbilzIgAAABh4inGAIWzhwoX9rv/DP/zDICcBAGAoam9vT0tLS59ivKWlJe3t7WVOBgAAAANLMQ4wxL2yBFeKAwDwWhSLxSxevHir65vLcgAAAKgEw8odACiPYrGYzs7OcsdgABx88MEZPnx4Nm3alOHDh+eP//iPs2nTpnLHYjvV19enUCiUOwYAUMHa2trS2tq6xXp3d3daW1vT1taWffbZpwzJAAAAYOApxqFKdXZ2ZubMmeWOwQDbtGmTP9cKsWzZsgwfPrzcMQCACtbU1JQpU6bkvvvuS3d3d+96bW1tJk2alKampjKmAwAAgIFlK3UAAACoQoVCIXPmzNnqut1rAAAAqCSuGIcqVV9fn2XLlpU7BgOgs7Mzxx9/fJJk6dKlqa+vL3MiBoI/RwBgMIwbNy7Nzc254YYbUiwWUygU0tzcnL333rvc0QAAAGBAKcahShUKBds0V6D6+np/rgAAvC6zZ8/OsmXL8tRTT2XMmDFpbm4udyQAAAAYcLZSBwAAgCpWX1+f8847L42NjTn33HPtXAMAAEBFcsU4AAAAVLnp06dn+vTp5Y4BAAAAJeOKcQAAAAAAAAAqmmIcAAAAAAAAgIqmGAcAAAAAAACgoinGAQAAoMqtWLEis2bNyooVK8odBQAAAEpCMQ4AAABVrLOzM4sWLcqTTz6ZRYsWpbOzs9yRAAAAYMApxgEAAKCKLVmyJOvWrUuSrFu3Li0tLWVOBAAAAANPMQ4AAABVqr29PS0tLSkWi0mSYrGYlpaWtLe3lzkZAAAADCzFOAAAAFShYrGYxYsXb3V9c1kOAAAAlUAxDgAAAFWora0tra2t6e7u7rPe3d2d1tbWtLW1lSkZAAAADDzFOAAAAFShpqamTJkyJbW1tX3Wa2trM3Xq1DQ1NZUpGQAAAAw8xTgAAABUoUKhkDlz5vS7ZfqcOXNSKBTKkAoAAABKQzEOAAAAVWrcuHE58MAD+6wdeOCB2XvvvcuUCAAAAEpDMQ4AAABVqr29PQ8//HCftYcffjjt7e1lSgQAAACloRgHAACAKlQsFrN48eKtrve3xToAAAAMVYpxAAAAqEJtbW1pbW1Nd3d3n/Xu7u60tramra2tTMkAAABg4CnGAQAAoAo1NTVlypQpKRQKfdYLhUKmTp2apqamMiUDAACAgacYBwAAgCpUKBQya9asLbZMLxaLmTVr1haFOQAAAAxlinEAAACoQsViMTfffHO/x2666Sb3GAcAAKCiDCt3AAAAAGDwbb7HeH8232N8n332GeRUALBtxWIxnZ2d5Y4BvMzL/076+wk7nvr6ejuC/T+KcQAAAKhC48ePzy677JJnnnlmi2O77LJLxo8fX4ZUALBtnZ2dmTlzZrljAFtx/PHHlzsC8ArLli3L8OHDyx1jh2ArdQAAAKhCbW1t/ZbiSfLMM8+kra1tkBMBAABA6bhiHAAAAKpQd3f3dh0HgHJ75k9OTLHGR9xQdsVi0vPiS1/XDEts2QxlV+h5Mbvcf2O5Y+xwTA0AAABQhVavXv2qx9/61rcOUhoAeP2KNcOS2jeVOwaQJNmp3AGAlymWO8AOylbqAAAAUIU++MEPprCVq3lqamrywQ9+cJATAQAAQOkoxgEAAKAK1dTUZN999+332L777puaGh8ZAAAAUDn8v1wAAACoQm1tbXn00Uf7Pfab3/wmbW1tg5wIAAAASkcxDgAAAFWoqakpBx10UL/HDj744DQ1NQ1yIgAAACgdxTgAAADQR7FYLHcEAAAAGFBVWYwvWLAghUIh55xzTu9asVjM3Llzs9dee2X48OE54ogj8tBDD5UvJAAAAJRQW1tbVq9e3e+x1atX20odAACAilJ1xXhra2uuueaaHHzwwX3WFy5cmEWLFuWKK65Ia2trxo4dm6OPPjobN24sU1IAAAAonaampkyZMiU1NX0/GqipqcnUqVNtpQ4AAEBFqapi/Jlnnsns2bPz1a9+NbvttlvverFYzOWXX56LL744J5xwQiZOnJjrr78+zz33XFpaWsqYGAAAAEqjUChkzpw5W10vFAplSAUAAAClUVXF+FlnnZUPfOADOeqoo/qsP/roo1mzZk1mzJjRu1ZXV5fDDz88K1as2Or7dXV1paOjo88DAAAAhopx48blwAMP7LN24IEHZu+99y5TIgAAACiNqinGb7rpptx3331ZsGDBFsfWrFmTJGlsbOyz3tjY2HusPwsWLEhDQ0PvY/z48QMbGgAAAEqovb09Dz/8cJ+1hx9+OO3t7WVKBAAAAKVRFcX4448/njlz5uSGG25IfX39Vs975TZxxWJxm1vHXXTRRdmwYUPv4/HHHx+wzAAAAFBKxWIxixcv3up6sVgsQyoAAAAojaooxu+9996sXbs2kyZNyrBhwzJs2LDcdddd+dKXvpRhw4b1Xin+yqvD165du8VV5C9XV1eXUaNG9XkAAADAUNDW1pbW1tZ0d3f3We/u7k5ra2va2trKlAwAAAAGXlUU40ceeWRWr16d+++/v/cxefLkzJ49O/fff3/e8pa3ZOzYsVm+fHnva55//vncddddmT59ehmTAwAAQGk0NTVlypQpqanp+9FAbW1tpk6dmqampjIlAwAAgIE3rNwBBsPIkSMzceLEPms777xzRo8e3bt+zjnnZP78+dlvv/2y3377Zf78+RkxYkSam5vLERkAAABKqlAoZM6cOTnppJP6rBeLxcyZM2ebtxYDAACAoaYqivHX4sILL8ymTZty5plnZv369Zk2bVpuv/32jBw5stzRAAAAYNAUi0X3FwcAAKDiVG0xfuedd/Z5XigUMnfu3MydO7cseQAAAGAwFYvFLF68OIVCoU8RXigUsnjx4ixcuNBV4wAAAFSMqrjHOAAAANBXW1tbWltb09PT02e9p6cnra2taWtrK1MyAAAAGHiKcQAAAKhCTU1NmTJlyhZXhRcKhUydOjVNTU1lSgYAAAADTzEOAAAAVahQKGTWrFlb3E+8WCxm1qxZtlEHAACgoijGAQAAoAoVi8XcfPPN/V4xftNNN21RmAMAAMBQphgHAACAKrT5HuP9XTHuHuMAAABUGsU4AAAAVKHN9xivra3ts15bW+se4wAAAFQcxTgAAABUoUKhkDlz5qSnp6fPerFYzJw5c9xjHAAAgIqiGAcAAIAqNW7cuIwePbrP2ujRo7P33nuXKREAAACUhmIcAAAAqtSqVavy1FNP9Vn7/e9/n1WrVpUpEQAAAJSGYhwAAACqUE9PT+bOndvvsblz526xxToAAAAMZYpxAAAAqEIrV67MM8880++xZ555JitXrhzkRAAAAFA6inEAAACoQnvuued2HQcAAIChRDEOAAAAVWjffffN/vvv3++xt7/97dl3330HNxAAAACUkGIcAAAAqlChUMgll1zS77HPfvazKRQKg5wIAAAASkcxDgAAAFVqzZo1/a4/8cQTg5wEAAAASksxDgAAAFWop6cnc+fO7ffY3Llz09PTM7iBAAAAoIQU4wAAAFCFVq5cmWeeeabfY88880xWrlw5yIkAAACgdBTjAAAAUIX23HPP7ToOAAAAQ4liHAAAAKrQvvvum3333bffYxMmTNjqMQAAABiKFOMAAABQpUaOHNnv+i677DLISQAAAKC0FOMAAABQhdra2rJ69ep+j61evTptbW2DnAgAAABKRzEOAAAAVWj8+PFbvTJ8l112yfjx4wc5EQAAAJSOYhwAAACqUFtbW5555pl+jz3zzDOuGAcAAKCiKMYBAACgChWLxe06DgAAAEOJYhwAAAAAAACAijas3AEAAAAYeorFYjo7O8sdg+3Q1dX1qsc3bdo0SGkYaPX19SkUCuWOAQAAsMNQjAMAAPC6dXZ2ZubMmeWOQQl9/OMfL3cEtsOyZcsyfPjwcscAAADYYdhKHQAAAAAAAICK5opxAAAAXrf6+vosW7as3DEYANdcc02WLl3a+/wv//Ivc+qpp5YxEQOhvr6+3BEAAAB2KIpxAAAAXrdCoWCb5grx0Y9+tLcYHzVqVE477TSlKgAAABXHVuoAAABQxV5egp977rlKcQAAACqSYhwAAABIkrzrXe8qdwQAAAAoCcU4AAAAAAAAABVNMQ4AAAAAAABARVOMAwAAAAAAAFDRFOMAAAAAAAAAVDTFOAAAAAAAAAAVTTEOAAAAAAAAQEVTjAMAAAAAAABQ0RTjAAAAAAAAAFQ0xTgAAAAAAAAAFU0xDgAAAAAAAEBFU4wDAAAAAAAAUNEU4wAAAAAAAABUtGHlDgAAAAAAAK9FsVj87yfdL5QvCADsyF7238g+/+2scopxAAAAAACGhK6urt6vR/7ipjImAYChoaurKyNGjCh3jB2CrdQBAAAAAAAAqGiuGAcAAAAAYEioq6vr/XrjH/9VUvumMqYBgB1U9wu9O6u8/L+d1U4xDgAAAADAkFAoFP77Se2bFOMA8Cr6/LezytlKHQAAAAAAAICKphgHAAAAAAAAoKLZSp3XpVgsprOzs9wxgJd5+d9Jfz9hx1NfX2+7IgAAAACAMlOM87p0dnZm5syZ5Y4BbMXxxx9f7gjAKyxbtizDhw8vdwwAAAAAgKpmK3UAAAAAAAAAKporxnnDnvmTE1Os8T8hKLtiMel58aWva4YltmyGsiv0vJhd7r+x3DEAAAAAAPh/tJq8YcWaYUntm8odA0iS7FTuAMDLFMsdAAAAAACAPmylDgAAAAAAAEBFU4wDAAAAAAAAUNEU4wAAAAAAAABUNMU4AAAAAAAAABVNMQ4AAAAAAABARVOMAwAAAAAAAFDRFOMAAAAAAAAAVDTFOAAAAAAAAAAVTTEOAAAAAAAAQEVTjAMAAAAAAABQ0RTjAAAAAAAAAFQ0xTgAAAAAAAAAFU0xDgAAAAAAAEBFU4wDAAAAAAAAUNEU4wAAAAAAJXbVVVfl4IMPzqhRozJq1KgceuihWbZsWe/xYrGYuXPnZq+99srw4cNzxBFH5KGHHipjYgCAyqIYBwAAAAAosXHjxuUf/uEfsmrVqqxatSp/+qd/mj//8z/vLb8XLlyYRYsW5Yorrkhra2vGjh2bo48+Ohs3bixzcgCAyqAYBwAAAAAosQ9+8IP5sz/7s+y///7Zf//987//9//OLrvskpUrV6ZYLObyyy/PxRdfnBNOOCETJ07M9ddfn+eeey4tLS3ljg4AUBEU4wAAAAAAg6i7uzs33XRTnn322Rx66KF59NFHs2bNmsyYMaP3nLq6uhx++OFZsWLFNt+rq6srHR0dfR4AAGxJMQ4AAAAAMAhWr16dXXbZJXV1dfn4xz+epUuX5oADDsiaNWuSJI2NjX3Ob2xs7D22NQsWLEhDQ0PvY/z48SXLDwAwlCnGAQAAAAAGwR/90R/l/vvvz8qVK3PGGWfk5JNPzsMPP9x7vFAo9Dm/WCxusfZKF110UTZs2ND7ePzxx0uSHQBgqBtW7gAAAAAAANVgp512ytve9rYkyeTJk9Pa2prFixfnU5/6VJJkzZo12XPPPXvPX7t27RZXkb9SXV1d6urqShcaAKBCuGIcAAAAAKAMisViurq6MmHChIwdOzbLly/vPfb888/nrrvuyvTp08uYEACgcrhiHAAAAACgxD796U9n5syZGT9+fDZu3Jibbropd955Z2677bYUCoWcc845mT9/fvbbb7/st99+mT9/fkaMGJHm5uZyRwcAqAiKcQAAAACAEnvyySdz0kkn5YknnkhDQ0MOPvjg3HbbbTn66KOTJBdeeGE2bdqUM888M+vXr8+0oWBlYwAAtoRJREFUadNy++23Z+TIkWVODgBQGRTjAAAAAAAldu21127zeKFQyNy5czN37tzBCQQAUGXcYxwAAAAAAACAiqYYBwCg6i1YsCBTpkzJyJEjs8cee+S4447LI4880uecYrGYuXPnZq+99srw4cNzxBFH5KGHHupzTldXV84+++yMGTMmO++8c4499ti0t7cP5rcCAAAAAPRDMQ4AQNW76667ctZZZ2XlypVZvnx5XnzxxcyYMSPPPvts7zkLFy7MokWLcsUVV6S1tTVjx47N0UcfnY0bN/aec84552Tp0qW56aab8m//9m955plncswxx6S7u7sc3xYAAAAA8P+4xzgAAFXvtttu6/P8uuuuyx577JF77703hx12WIrFYi6//PJcfPHFOeGEE5Ik119/fRobG9PS0pLTTz89GzZsyLXXXptvfvObOeqoo5IkN9xwQ8aPH5877rgj73vf+wb9+wIAAAAAXuKKcQAAeIUNGzYkSXbfffckyaOPPpo1a9ZkxowZvefU1dXl8MMPz4oVK5Ik9957b1544YU+5+y1116ZOHFi7zmv1NXVlY6Ojj4PAAAAAGDgKcYBAOBlisVizjvvvLznPe/JxIkTkyRr1qxJkjQ2NvY5t7GxsffYmjVrstNOO2W33Xbb6jmvtGDBgjQ0NPQ+xo8fP9DfDgAAAAAQxTgAAPTxiU98Ig888EBuvPHGLY4VCoU+z4vF4hZrr7Stcy666KJs2LCh9/H444+/8eAAAAAAwFYpxgEA4P85++yz893vfjf/9//+34wbN653fezYsUmyxZXfa9eu7b2KfOzYsXn++eezfv36rZ7zSnV1dRk1alSfBwAAAAAw8BTjAABUvWKxmE984hO55ZZb8uMf/zgTJkzoc3zChAkZO3Zsli9f3rv2/PPP56677sr06dOTJJMmTcqb3vSmPuc88cQTefDBB3vPAQAAAADKY1i5AwAAQLmdddZZaWlpyXe+852MHDmy98rwhoaGDB8+PIVCIeecc07mz5+f/fbbL/vtt1/mz5+fESNGpLm5uffc0047Leeff35Gjx6d3XffPRdccEEOOuigHHXUUeX89gAAAACg6inGAQCoeldddVWS5Igjjuizft111+WUU05Jklx44YXZtGlTzjzzzKxfvz7Tpk3L7bffnpEjR/ae/8UvfjHDhg3Lhz/84WzatClHHnlkvv71r6e2tnawvhUAAAAAoB+KcQAAql6xWHzVcwqFQubOnZu5c+du9Zz6+vp8+ctfzpe//OUBTAcAAAAAbC/3GAcAAAAAAACgoinGAQAAAAAAAKhoinEAAAAAAAAAKppiHAAAAAAAAICKphgHAAAAAAAAoKIpxgEAAAAAAACoaIpxAAAAAAAAACqaYhwAAAAAAACAiqYYBwAAAAAAAKCiKcYBAAAAAAAAqGiKcQAAAAAAAAAqmmIcAAAAAAAAgIqmGAcAAAAAAACgoinGAQAAAAAAAKhow8odAAAAAAAAXq9Cz4spljsEkBSLSc+LL31dMywpFMqbB0hh899J+lCMAwAAAAAw5Oxy/43ljgAADCG2UgcAAAAAAACgorliHAAAAACAIaG+vj7Lli0rdwzgZTo7O3P88ccnSZYuXZr6+voyJwJezt/J/6YYBwAAAABgSCgUChk+fHi5YwBbUV9f7+8osMOylToAAAAAAAAAFU0xDgAAAAAAAEBFU4wDAAAAAAAAUNEU4wAAAAAAAABUNMU4AAAAAAAAABVNMQ4AAAAAAABARRtW7gAMLcVisffrwgudKXa/UMY0QJKkmKTnxZe+rhmWFMqaBkhS6Onu/frl/+0EAAAAAKA8FOO8Ll1dXb1f77L6n8uYBACGhq6urowYMaLcMQAAAAAAqpqt1AEAAAAAAACoaK4Y53VpaGjI0qVLyx0DeJnOzs6ceOKJSZIbb7wx9fX1ZU4EvFxDQ0O5IwAAAAAAVD3FOK9LTU1Ndtttt3LHAF5m06ZNvV/vuuuuGT58eBnTAAAAAAAA7HhspQ4AAAAAAABARVOMAwAAAAAAAFDRFOMAAAAAAAAAVDTFOAAAAAAAAAAVTTEOAAAAAAAAQEVTjAMAAAAAAABQ0RTjAAAAAAAAAFQ0xTgAAAAAAAAAFU0xDgAAAAAAAEBFU4wDAAAAAAAAUNEU4wAAAAAAAABUNMU4AAAAAAAAABVNMQ4AAAAAAABARVOMAwAAAAAAAFDRFOMAAAAAAAAAVLSqKcYXLFiQKVOmZOTIkdljjz1y3HHH5ZFHHulzTrFYzNy5c7PXXntl+PDhOeKII/LQQw+VKTEAAAAAAAAAA6FqivG77rorZ511VlauXJnly5fnxRdfzIwZM/Lss8/2nrNw4cIsWrQoV1xxRVpbWzN27NgcffTR2bhxYxmTAwAAAAAAALA9hpU7wGC57bbb+jy/7rrrsscee+Tee+/NYYcdlmKxmMsvvzwXX3xxTjjhhCTJ9ddfn8bGxrS0tOT0008vR2wAAAAAAAAAtlPVXDH+Shs2bEiS7L777kmSRx99NGvWrMmMGTN6z6mrq8vhhx+eFStWlCUjAAAAAAAAANuvaq4Yf7lisZjzzjsv73nPezJx4sQkyZo1a5IkjY2Nfc5tbGzMb3/7237fp6urK11dXb3POzo6SpQYAAAAAAAAgDeqKq8Y/8QnPpEHHnggN9544xbHCoVCn+fFYnGLtc0WLFiQhoaG3sf48eNLkhcAAAAAAACAN67qrhg/++yz893vfjc/+clPMm7cuN71sWPHJnnpyvE999yzd33t2rVbXEW+2UUXXZTzzjuv93lHR4dyHAAAtqFYLKazs7PcMYCXefnfSX8/YcdTX1+/1Ys2AACA165qivFisZizzz47S5cuzZ133pkJEyb0OT5hwoSMHTs2y5cvzzvf+c4kyfPPP5+77rorn//85/t9z7q6utTV1ZU8OwAAVIrOzs7MnDmz3DGArTj++OPLHQF4hWXLlmX48OHljgEAAENe1RTjZ511VlpaWvKd73wnI0eO7L2neENDQ4YPH55CoZBzzjkn8+fPz3777Zf99tsv8+fPz4gRI9Lc3Fzm9AAAAAAAAAC8UVVTjF911VVJkiOOOKLP+nXXXZdTTjklSXLhhRdm06ZNOfPMM7N+/fpMmzYtt99+e0aOHDnIaQEAoPI98ycnplhTNf+XBHZcxWLS8+JLX9cMS2zZDGVX6Hkxu9x/Y7ljAABARamaT6GKxeKrnlMoFDJ37tzMnTu39IEAAKDKFWuGJbVvKncMIEmyU7kDAC/z6p9iAQAAr1dNuQMAAAAAAAAAQCkpxgEAAAAAAACoaIpxAAAAAAAAACqaYhwAAAAAAACAiqYYBwAAAAAAAKCiKcYBAAAAAAAAqGiKcQAAAAAAAAAqmmIcAAAAAAAAgIqmGAcAAAAAAACgoinGAQAAAAAAAKhoinEAAAAAAAAAKppiHAAAAAAAAICKphgHAAAAAAAAoKIpxgEAAAAAAACoaIpxAAAAAAAAACqaYhwAAAAAAACAiqYYBwAAAAAAAKCiKcYBAAAAAAAAqGiKcQAAAAAAAAAqmmIcAAAAAAAAgIqmGAcAAAAAAACgoinGAQAAAAAAAKhoinEAAAAAAAAAKppiHAAAAAAAAICKphgHAAAAAAAAoKIpxgEAAAAAAACoaIpxAAAAAAAAACqaYhwAAAAAAACAiqYYBwAAAAAAAKCiKcYBAAAAAAAAqGiKcQAAAAAAAAAqmmIcAAAAAAAAgIqmGAcAAAAAAACgoinGAQAAAAAAAKhoinEAAAAAAAAAKppiHAAAAAAAAICKphgHAAAAAAAAoKIpxgEAAAAAAACoaIpxAAAAAAAAACqaYhwAAAAAAACAiqYYBwAAAAAAAKCiKcYBAAAAAAAAqGiKcQAAAAAAAAAqmmIcAAAAAAAAgIqmGAcAAAAAAACgoinGAQAAAAAAAKhoinEAAAAAAAAAKppiHAAAAAAAAICKphgHAAAAAAAAoKIpxgEAAAAAAACoaIpxAAAAAAAAACqaYhwAAAAAAACAiqYYBwAAAAAAAKCiKcYBAAAAAAAAqGiKcQAAAAAAAAAqmmIcAAAAAAAAgIqmGAcAAAAAAACgoinGAQAAAAAAAKhoinEAAAAAAAAAKppiHAAAAAAAAICKphgHAAAAAAAAoKIpxgEAAAAASmzBggWZMmVKRo4cmT322CPHHXdcHnnkkT7nFIvFzJ07N3vttVeGDx+eI444Ig899FCZEgMAVBbFOAAAAABAid11110566yzsnLlyixfvjwvvvhiZsyYkWeffbb3nIULF2bRokW54oor0tramrFjx+boo4/Oxo0by5gcAKAyDCt3AAAAAACASnfbbbf1eX7ddddljz32yL333pvDDjssxWIxl19+eS6++OKccMIJSZLrr78+jY2NaWlpyemnn16O2AAAFcMV4wAAAAAAg2zDhg1Jkt133z1J8uijj2bNmjWZMWNG7zl1dXU5/PDDs2LFirJkBACoJK4YBwAAAAAYRMViMeedd17e8573ZOLEiUmSNWvWJEkaGxv7nNvY2Jjf/va3W32vrq6udHV19T7v6OgoQWIAgKHPFeMAAAAAAIPoE5/4RB544IHceOONWxwrFAp9nheLxS3WXm7BggVpaGjofYwfP37A8wIAVAJXjEOVKhaL6ezsLHcMBsDL/xz9mVaO+vr6bX7wAQAAwNB09tln57vf/W5+8pOfZNy4cb3rY8eOTfLSleN77rln7/ratWu3uIr85S666KKcd955vc87OjqU4wAA/VCMQ5Xq7OzMzJkzyx2DAXb88ceXOwIDZNmyZRk+fHi5YwAAADBAisVizj777CxdujR33nlnJkyY0Of4hAkTMnbs2CxfvjzvfOc7kyTPP/987rrrrnz+85/f6vvW1dWlrq6upNkBACqBYhwAAAAAoMTOOuustLS05Dvf+U5GjhzZe0/xhoaGDB8+PIVCIeecc07mz5+f/fbbL/vtt1/mz5+fESNGpLm5uczpAQCGPsU4VKn6+vosW7as3DEYAMViMV1dXUle+ilx229Xhvr6+nJHAAAAYABdddVVSZIjjjiiz/p1112XU045JUly4YUXZtOmTTnzzDOzfv36TJs2LbfffntGjhw5yGkBACqPYhyqVKFQsE1zBRkxYkS5IwAAAADbUCwWX/WcQqGQuXPnZu7cuaUPBABQZWrKHQAAAAAAAAAASkkxDgAAAAAAAEBFU4wDAAAAAAAAUNHcYxwAABg0L7+3ZuGFzhS7XyhjGiBJUkzS8+JLX9cMSwplTQMkKfR09379Wu5LDQAAvDrFOAAAMGi6urp6v95l9T+XMQkADA1dXV0ZMWJEuWMAAMCQZyt1AAAAAAAAACqaK8YBAIBB09DQkKVLl5Y7BvAynZ2dOfHEE5MkN954Y+rr68ucCHi5hoaGckcAAICKoBgHqADXXnttlixZktmzZ+e0004rdxwA2Kqamprstttu5Y4BvMymTZt6v951110zfPjwMqYBAACA0rCVOsAQ9/TTT2fJkiXp6enJkiVL8vTTT5c7EgAAAAAAwA5FMQ4wxH32s59NT09PkqSnpyeXXHJJmRMBAAAAAADsWBTjAEPYqlWrsnr16j5rDzzwQFatWlWmRAAAAAAAADsexTjAENXT05N58+b1e2zevHm9V5EDAAAAAABUO8U4wBB19913p6Ojo99jHR0dufvuuwc5EQAAAAAAwI5JMQ4wRE2bNi2jRo3q91hDQ0OmTZs2yIkAAAAAAAB2TIpxgCGqpqYml1xySb/HLr300tTU+CceAAAAAAAgUYwDDGmTJ0/OO97xjj5rBxxwQA455JAyJQIAAAAAANjxKMYBAAAAAAAAqGjDyh0AgDdu1apV+eUvf9ln7eGHH86qVasyefLkMqUCAKpBsVhMZ2dnuWMwAF7+5+jPtHLU19enUCiUOwYAbJV5snKYJyuTeZJKpBgHGKJ6enoyb968fo/Nmzcv//qv/+o+4wBAyXR2dmbmzJnljsEAO/7448sdgQGybNmyDB8+vNwxAGCrzJOVyTxZOcyTVCKNCcAQdffdd6ejo6PfYx0dHbn77rsHOREAAAAAAMCOyRXjAEPUtGnTMmrUqH7L8YaGhkybNq0MqQCAalFfX59ly5aVOwYDoFgspqurK0lSV1dnu8QKUV9fX+4IALBN5snKYZ6sTOZJKpFiHGCIqqmpySWXXJILLrhgi2OXXnqpbdQBgJIqFAq21asgI0aMKHcEAKDKmCcri3kSGAq0JgBD2OTJk3PQQQf1WTv44INzyCGHlCkRAAAAAADAjkcxDjDEfe5zn+u9Orympibz5s0rcyIAAAAAAIAdi2IcYIjbddddM3v27NTU1GT27NnZddddyx0JAAAAAABgh+Ie4wAV4LTTTstpp51W7hgAAAAAAAA7JFeMAwDAALvyyiszYcKE1NfXZ9KkSfnpT39a7kgAAAAAUNUU4wAAMIBuvvnmnHPOObn44ovz85//PO9973szc+bMtLW1lTsaAAAAAFQtxTgAAAygRYsW5bTTTsv//J//M+94xzty+eWXZ/z48bnqqqvKHQ0AAAAAqpZiHAAABsjzzz+fe++9NzNmzOizPmPGjKxYsaJMqQAAAACAYeUOAAAAleKpp55Kd3d3Ghsb+6w3NjZmzZo1W5zf1dWVrq6u3ucdHR0lzwgAAAAA1cgV4wAAMMAKhUKf58VicYu1JFmwYEEaGhp6H+PHjx+siAAAAABQVRTjAAAwQMaMGZPa2totrg5fu3btFleRJ8lFF12UDRs29D4ef/zxwYoKAAAAAFVFMQ4AAANkp512yqRJk7J8+fI+68uXL8/06dO3OL+uri6jRo3q8wAAAAAABp57jAMAwAA677zzctJJJ2Xy5Mk59NBDc80116StrS0f//jHyx0NAAAAAKqWYhwAAAbQrFmzsm7dusybNy9PPPFEJk6cmB/84AfZZ599yh0NAAAAAKqWYhwAAAbYmWeemTPPPLPcMQAAAACA/8c9xgEAAAAAAACoaIpxAAAAAAAAACqaYhwAAAAAAACAiqYYBwAAAAAAAKCiKcYBAAAAAAAAqGiKcQAAAAAAAAAqmmIcAAAAAAAAgIqmGAcAAAAAAACgoinGAQAAAAAAAKhoinEAAAAAAAAAKppiHAAAAAAAAICKphgHAAAAAAAAoKIpxgEAAAAAAACoaIpxAAAAAAAAACqaYhwAAAAAAACAijas3AEqSbFYTJJ0dHSUOQkAwODYPPdsnoPYPuZJAKDamCcHnpkSAKgmr2eeVIwPoI0bNyZJxo8fX+YkAACDa+PGjWloaCh3jCHPPAkAVCvz5MAxUwIA1ei1zJOFoh/HHDA9PT353e9+l5EjR6ZQKJQ7DlBFOjo6Mn78+Dz++OMZNWpUueMAVaRYLGbjxo3Za6+9UlPjLj3byzwJlIt5EigX8+TAM1MC5WCeBMrl9cyTinGACtDR0ZGGhoZs2LDB4AkAwOtmngQAYHuYJ4GhwI9hAgAAAAAAAFDRFOMAAAAAAAAAVDTFOEAFqKury6WXXpq6urpyRwEAYAgyTwIAsD3Mk8BQ4B7jAAAAAAAAAFQ0V4wDAAAAAAAAUNEU4wAAAAAAAABUNMU4AAAAAAAAABVNMQ4AAAAAAABARVOMAwxxV155ZSZMmJD6+vpMmjQpP/3pT8sdCQCAIcZMCQDA9jBPAkOBYhxgCLv55ptzzjnn5OKLL87Pf/7zvPe9783MmTPT1tZW7mgAAAwRZkoAALaHeRIYKgrFYrFY7hAAvDHTpk3LIYcckquuuqp37R3veEeOO+64LFiwoIzJAAAYKsyUAABsD/MkMFS4YhxgiHr++edz7733ZsaMGX3WZ8yYkRUrVpQpFQAAQ4mZEgCA7WGeBIYSxTjAEPXUU0+lu7s7jY2NfdYbGxuzZs2aMqUCAGAoMVMCALA9zJPAUKIYBxjiCoVCn+fFYnGLNQAA2BYzJQAA28M8CQwFinGAIWrMmDGpra3d4icv165du8VPaAIAQH/MlAAAbA/zJDCUKMYBhqiddtopkyZNyvLly/usL1++PNOnTy9TKgAAhhIzJQAA28M8CQwlw8odAIA37rzzzstJJ52UyZMn59BDD80111yTtra2fPzjHy93NAAAhggzJQAA28M8CQwVinGAIWzWrFlZt25d5s2blyeeeCITJ07MD37wg+yzzz7ljgYAwBBhpgQAYHuYJ4GholAsFovlDgEAAAAAAAAApeIe4wAAAAAAAABUNMU4AAAAAAAAABVNMQ4AAAAAAABARVOMAwAAAAAAAFDRFOMAAAAAAAAAVDTFOAAAAAAAAAAVTTEOAAAAAAAAQEVTjAMAAAAAAABQ0RTjwA5n3333TaFQyCmnnPKGXn/EEUekUCjkiCOOGNBcA+2UU05JoVDIvvvuu13vs72/X9tSKBRSKBQyd+7cAX9vAAAqw9e//vXeufGxxx7b4vhAzb0AAACwPRTjQEk8++yzueaaa/KBD3wg48aNS319fXbZZZe85S1vyaGHHpqPf/zjuemmm/LEE0+UO+pr9thjj/V+4Lc9DwAAqsedd97phw0BANhhvHw+7e+xyy67ZP/998/JJ5+cO++8c6vv88rPSmtqavLb3/72NWXYf//9+7z261//+jbfvxQXBAHVaVi5AwCV55577smsWbO2uFqkq6srjz76aB599NGsXLkyV199dRobG7NmzZryBAUAAAAAoNezzz6bX//61/n1r3+db3zjGzn11FNzzTXXpLa2dpuvKxaLWbJkST796U9v87yVK1fm17/+9UBGBnjNFOPAgPqP//iPHH300eno6EiSHHvssfnQhz6U/fffPzvttFOeeuqp/OIXv8jy5cvzf//v/y1Jhm39JOP22HvvvbN69eqtHn/f+96X3/3ud9lrr73ywx/+sCQZ+tPfdpUAALCj+PrXv97vVUAAAJTXGWeckTPPPLP3ebFYzB/+8If87Gc/yxe/+MWsXbs2X/va1zJu3Lj83d/93Vbfp76+Pp2dnfnmN7/5qsX4N7/5zT6vARhMinFgQF188cW9pfjXvva1fOxjH9vinKOPPjoXXHBBfv/73+db3/rWYEd8w970pjdl4sSJ2zz+Ws4DAAAAACi3PfbYo9/PMQ8//PAce+yxmTx5cjZt2pTLL788n/nMZ3o//3ylY489Nt/61rfy7//+71m1alUmT57c73kvvPBCbr755iTJn//5n/d+DTBY3GMcGDDd3d35/ve/nySZPHlyv6X4y735zW/OWWedNRjRAAAAAAB4jQ444IB84AMfSJJ0dHTkl7/85VbPfcc73tFbhm++Irw/P/jBD7Ju3brstNNO+fCHPzywgQFeA8U4MGB+//vf57nnnkuSvO1tbyvpr7Vw4cIUCoUUCoUce+yxfbbdOeKII1IoFHLEEUds8brHHnus93Wbt3Ncvnx5PvjBD2bs2LGpq6vLhAkTcsYZZ6S9vb2k38MrPf3007nkkkty4IEHZuedd86uu+6aww47LEuWLNnm6/bdd98UCoWccsop2zzvsccey6c+9alMmjQpo0ePTn19fSZMmJD/8T/+R77whS+kra3tdWcuFov5xCc+0ft7esYZZ6Snp6f3+Ob1uXPnJklaW1tz4oknZty4camrq8vee++dk046aZuD9cs98sgj+eQnP5kDDzwwDQ0NGT58eN7ylrfkYx/7WO67775tvrazszNf+tKXcsQRR2TMmDF505velN133z1vf/vb82d/9mf54he/uNVt6e+9996cdtpp2X///bPzzjunvr4+48ePz6RJk3LWWWflu9/9borF4mv6HgAANrvzzjt756XNtwP61re+lSOPPDJvfvObM3z48PzRH/1RLrzwwvzhD3/Y5nv96le/ytlnn52JEydml112yU477ZS99torf/Inf5JTTz01N998c7q6ul7119+aV851r8cpp5ySQqGQfffd9zW99/bOjAAADIyXz2+vtu35SSedlCS58cYb8+KLL/Z7zje+8Y0kyQc+8IHsvvvuAxMS4HWwlTowYHbaaafer0v5odWnPvWpLFy4MMlLA9fXvva1DBv2xv45+9u//dt8/vOf77P22GOP5f/8n/+Tb3/727nrrrvyjne8Y7szv5p///d/z8yZM7coZn/605/mpz/9aX72s5/liiuueMPv/4//+I/59Kc/nRdeeKHP+mOPPZbHHnssd955Z773ve+9rvuzv/jiizn55JPT0tKSJLnooosyf/78rZ5/xRVX5Nxzz+0zGP/ud7/LDTfckFtuuSXLli3LYYcdttXXf+5zn8u8efO2GKwfffTRPProo7n++uvz2c9+tt/7HT3xxBM56qij8vDDD/dZX79+fdavX59HHnkky5Yty3/913/lH//xH/uc88UvfjEXXHBBn8I/Sdrb29Pe3p777rsvV155ZTZu3Jhddtllq/kBALalu7s7s2fP7p2tNvvVr36Vyy67LEuXLs1Pf/rTjB07dovX/vM//3M+8pGP5Pnnn++z/sQTT+SJJ57IL37xi1x33XVZvXr1Dn/Ln+2dGQEAGDgv/6yyqalpm+eeeOKJOf/88/P73/8+P/zhD3uvNt9s/fr1ufXWW5P8d4kOMNhcMQ4MmN133z377LNPkuQXv/hFPv/5z29RJm6Pnp6e/PVf/3VvKf7JT34y119//Rsuxb/61a/m85//fA4//PC0tLRk1apVueOOO/LRj340yUtXwJ966qkDln9rnnvuuRx77LFZt25dPvOZz+TOO+/MqlWr8tWvfjXjxo1LknzlK1/JD3/4wzf0/p/73OfyN3/zN3nhhRey66675tOf/nSWL1+e++67Lz/+8Y/zj//4j3n3u9+dQqHwmt9z06ZN+fM///PeD27/8R//cZul+A9/+MPeK72/9rWvpbW1NT/5yU9y7rnnpqamJs8991xOOumkLT7M3eySSy7JJZdckhdffDHTp0/PP/3TP+VnP/tZVq1alSVLluTQQw9NsVjMvHnz8uUvf3mL15999tm9pfhHPvKR3HLLLVm5cmVaW1vz/e9/P3/3d3+Xd77znVu87oEHHugtxSdMmJAvfOEL+dGPfpSf//zn+elPf5qvfe1rOemkkxTiAMB2u+SSS9LS0pLjjjsut9xyS+6999784Ac/6P1A8T/+4z9y7rnnbvG6J598Mh/72Mfy/PPPZ4899si8efNy++2357777suKFStyww035H/9r/+VMWPGDPa39Lpt78wIAMDA+fd///feInvKlCn9/oDmy735zW/O+973viT9b6f+rW99K11dXdltt922KM0BBosrxoEBdfbZZ+eCCy5I8tLV2FdddVU++MEP5tBDD820adPy1re+9Q297/PPP5/m5uZ8+9vfTpLMnTs3l1566XZlXbFiRf76r/86V199dZ9S+Mgjj8xOO+2Uf/qnf8rKlSvz85//vN/SdKD8/ve/zwsvvJCf/exnOfDAA3vXJ02alCOOOCIHHXRQOjs7c+WVV/YOl6/Vfffd17sl5f77758f/ehHvWX7Zv/jf/yPnH/++a956/gNGzbkmGOOyb/927+ltrY2X/3qV1/1fvIrV67Mn/3Zn2Xp0qV9dhZ473vfm9GjR+czn/lM2tracuutt+b444/v89rW1tb87//9v5Mkn/nMZ/K5z32uz/FJkyblr/7qr3LyySfnhhtuyMUXX5yTTjopu+66a5KXtnn67ne/myQ5//zzt7giPHlp+6ZLLrlkiy1K/+Vf/iU9PT3Zeeed87Of/SyNjY19jr/nPe/Jxz72sWzYsCEjRozY5u8BAMC2rFixIn//93+fiy++uM/6+9///rz//e/P7bffnn/5l3/Jl770pbz5zW/uPX7rrbfm2WefTZL86Ec/2uKK8EMPPTSzZ8/O4sWLd/hbv2zPzAgAwOu3du3aPPjgg73Pi8Vinn766fzsZz/LF7/4xWzatCmjRo3K5Zdf/pre76Mf/WhuvfXWfPe7301HR0dGjRrVe2xzWT5r1qw+sx7AYHLFODCgzj333D5XWf/2t7/NFVdckdmzZ+dtb3tbxo4dm7/6q7/K9773vdf8wdwzzzyTD3zgA/n2t7+dQqGQL3/5y9tdiifJnnvumS9/+cv9Xim9udxPXtrOvNTmzZvXpxTf7G1ve1uOO+64N5zjsssuS09PTwqFQm666aYtSvGX29axzZ588skcfvjh+bd/+7fU1dXln//5n1+1FE+S+vr6XHfddf0OvZ/85Cd71/v7HjfvPDBp0qTMmzev3/evqanJl7/85dTV1WXjxo35l3/5l95jf/jDH3q3kH+1bTdfeW+jNWvWJHnphwpeWYq/XENDQ2pq/CcVAHjjJk2alE9/+tNbrBcKhZx33nlJXrqVzc9+9rM+xzfPK7vttts2t0mvr6/P8OHDBzDxwNuemREAgNfvqquuykEHHdT7OPjgg3PYYYflU5/6VNauXZvTTz89d999d6ZPn/6a3u/YY49NQ0NDNm3a1HuBU5L85je/yf/3//1/SWyjDpSXT/GBAVVTU5Nrr702y5Yty9FHH71FWfjkk0/m5ptvzrHHHpupU6fmP//zP7f5fn/4wx9y1FFH5Y477siwYcPyzW9+M5/4xCcGJOuHPvSh1NXV9Xvsj/7oj3q3x/7Nb34zIL/e1hQKhTQ3N2/1+KRJk5K8dB+ep59++jW/b09PT2677bYkyeGHH77dV70/9thjec973pNf/OIX2WWXXV7XlTpHH3109thjj36PjRw5Mvvtt1+SLX+vX3jhhSxbtizJS39e29rufdddd81BBx2UJH0+MB49enTvh6jf/OY3t7hH+bbsueeeSZKHH34499xzz2t+HQDA69Xc3LzVWWfzPJhsOS9tnlfWr1+f73znO6ULOAje6MwIAMDA6+npybe+9a380z/902u+lU19fX0+9KEPJem7nfoNN9yQJHnrW9/6mkt2gFJQjAMlsXm7x6eeeirf+973cumll+aYY45JQ0ND7zmrVq3Ke9/73jzxxBP9vscTTzyRww47LHfffXeGDx+epUuXZvbs2QOW8e1vf/s2j++2225Jko0bNw7Yr9mfMWPGZPTo0Vs9/vKrmF9PlkcffbS3SH+1K6VfzS9/+cu85z3vyX/8x39k9OjR+dGPfpQjjzzyNb/+1X6vN3+Pr/z+Hn744Tz33HNJkosuuiiFQmGbj1WrViX57yunkqSuri6zZs1K8tLW6G9729ty4YUX5gc/+EE2/P/s3Xt0XWWBPv7nNJWmZdoiLaTXQHHQAQoK9IJlEBApFkVFRqvpKHgZLyAWKvKVQaHgSEfUTKsoXpYCDo06M6DDgCBFLcysCKYIw1XwUkmDvUgsDVBSID2/P5jmR2wLhSY5OTufz1p7rZ53733Ok7Z0veTJ++4NG54317vf/e687GUvy6ZNm3L44YfnhBNOyNe//vXce++9A34rUgCgujzffOn55oNvectbuh8hc+KJJ+b1r399/uVf/iW33357urq6+iRrX3mpc0YAAF6a888/P+VyucexcePG3HXXXfnkJz+Zxx57LF/60pcye/bsPPnkkzv0nu9973uTJMuXL8+qVauS/P/F+N///d/3zRcCsIMU40CfevnLX543v/nNWbhwYf7rv/4ra9euzXe+853u0nn16tX5zGc+s817b7zxxtx7771JkgsuuCBvfvObezXbCz0Testq977+huKO5nixWR555JHuX29ZSfRS/du//VsefvjhJM9usTRjxowXdf9L/b1et27di/qcLbaU6VtccsklOeGEE5I8u73/F77whbzpTW/KmDFjMmPGjHzxi19MR0fHVu/zN3/zN/ne976Xl7/85XnmmWdy7bXX5qMf/WimTp2aPffcM+95z3ts5QkA9Irnmy8933xwzJgxueaaazJx4sSUy+X8/Oc/z4IFCzJt2rTsvvvuOemkk3Lttdf2We7eNFDm5wAAg9nw4cNz4IEH5uKLL87Xvva1JMnNN9+cRYsW7dD9RxxxRPbee++Uy+UsXbo0v/jFL/Kb3/wmiWIcqDzFONCvhg0blve973353ve+1z129dVXZ/PmzVtde/jhh3c/13nhwoW55ZZb+i1n0TzfFuQ74rjjjsuuu+6aJPnYxz6W++67rzdivaDnftPzC1/4Qu6+++4dOr7zne/0eJ9Ro0blmmuuyW233ZZPfOITOeSQQ1JTU5Ourq60tLTkk5/8ZPbdd9+tntmZJCeddFJWrlyZb3zjG3n729+ePfbYI8mzP3hw5ZVX5nWve11OOeWUbf4dBgDoD0cccUR++9vf5sorr0xDQ0MmTZqUJOno6MjVV1+dE044IW984xu3+uFBAAB4Ph/4wAe6d+359re/vUP3lEql7l0///Vf/7V7S/XXvva1+eu//uu+CQqwgxTjQEUcd9xxmTx5cpJnn4fY3t6+1TV//dd/nZ/97GfZc889s3HjxrzpTW/K//zP//R31Ko1duzY7l//8Y9/3Kn3Ouyww3LddddlxIgRWbduXY455pg88MADOxvxBT13i/mnn346U6dO3aFjypQp23y/LavDb7/99qxfvz7XXHNN93PS161bl5NOOmmb20KNHj06H/rQh3LVVVdl3bp1uffee7No0aJMmDAhSXLFFVfkK1/5Sh/8DgAA7Jja2trMmzcvS5cuzapVq/K73/0uX/7yl/PKV74ySfKTn/wk5557bo97nrsS/fl+yO+JJ57om9AAAAxoQ4YMyb777pvk2e8v/vnPf96h+7Zsp37fffflsssuS5K85z3v6ZuQAC+CYhyomC2lYtLzm3LPtf/+++enP/1pxo4dm8cffzzHH3/8Nlf1srUpU6Z0b1nfG6vtjzzyyFx77bUZPnx41qxZk6OPPrp7G6S+csABB2SXXXZJ8uzW+r1p5MiROeGEE3L11Vfn4x//eJJnt/bfkR++2H///fOpT30qt956a/dK+n/7t3/r1XwAADtjn332yemnn56WlpbuFeR/OV8ZOXJk96/Xr1+/3ffqjx+IBABgYHrmmWe6f/3000/v0D2vfOUrux/F2NnZmV122SVz587tk3wAL4ZiHKiIjRs3dm/HPWrUqO4tebZl6tSp+elPf5oxY8bkscceyxvf+Mbcdttt/RW1ag0ZMiTHH398kmefA3THHXfs9HseffTR+a//+q8MHz48q1evztFHH53f/va3O/2+2zNixIgcc8wxSZLly5fnl7/8ZZ98zpbPSHo+m/2FTJ48uXsV1ou5DwCgv4waNSrTp09PsvV85bm77KxYsWK779HU1NQ34QAAGNCe+z3c2traHjtUvpCTTz45w4YNy7Bhw/KWt7zleb//C9BfFONAr3n88cczc+bMXHvttc+7FePmzZtz+umn57HHHkuSvOUtb3nBZ2AfdNBBuemmm7L77runo6Mjxx133PN+845nnXXWWRkyZEjK5XLe9a53pa2tbbvXPt+55zrmmGPyn//5n6mtrc3DDz+c17/+9fn973/fW5G3cu6553b//XjXu96V3/3ud9u9tqurK01NTT2+lt///ve5+eabn/cznrsa/bnfIP7Rj36URx99dLv3rVq1Kr/+9a+3ug8AoL/85Cc/yerVq7d7fsOGDd0/XPiX85XddtstBx10UJLksssu2+bWmLfccku+/OUv92JiAACqxfnnn9/92MHjjjsuNTU1O3zvqaeems7OznR2dubf//3f+yoiwIsytNIBgGL55S9/mRNOOCETJ07M2972trz2ta/NXnvtlZEjR+bRRx/NHXfcke985zu5++67kzz77ObPfvazO/Ter3nNa3LTTTflmGOOyfr16zN79uz89Kc/zcEHH9yXX1JVe81rXpMLLrggn/nMZ/Lggw/mwAMPzGmnnZajjz46Y8aMyaOPPpo777wzV199dWpqavLzn/98h9732GOPzQ9/+MO87W1vy6pVq/L6178+N998c/baa69e/xoOP/zwnHfeebnggguycuXKvOY1r8kHPvCBzJ49O+PHj8+mTZvyhz/8Ib/4xS/yH//xH/njH/+Yu+++u3vL0NbW1hx99NHZf//9c+KJJ2batGmZOHFikmeL7R/84Afd24oefPDBmTlzZvdnL168OPPmzcub3vSmvP71r89+++2X0aNHZ/369VmxYkW+8pWvdP/PwUc/+tFe/9oBAF7I9773vZxwwgk59thjM3v27EydOjW77757Hnvssdxzzz255JJL8vDDDyfZ9nzl1FNPzUc+8pGsXbs2RxxxRD7zmc/kVa96Vf785z/n2muvzaWXXppp06Z5nBEAQAGtW7cu99xzT4+xzs7O/OY3v8l3v/vd3HDDDUmeXS2+o9/DBRjIFONArxk6dGjGjRuXNWvW5OGHH85Xv/rVfPWrX93u9fvuu2++973vZe+9997hzzj44IOzbNmyvOENb8j69evzhje8IT/72c/y6le/uhe+gmL69Kc/nZqampx33nl59NFH87nPfS6f+9zntrruyCOPfFHv+8Y3vjFXX311TjzxxDz00EM5+uijs3z58tTX1/dW9G4LFy7Mbrvtlk996lN5/PHHs2TJkixZsmSb1+6yyy6pra3davy+++7r3vppW/bbb79cffXVW+1esHHjxvz7v//7dn+ytaamJp/97Gfz1re+9UV8RQAAvefpp5/Oj3/84/z4xz/e7jWnnXZaTj/99K3G/+Ef/iE33HBDfvSjH+W+++7Lu9/97h7np06dmquuuioTJkzo9dwAAFTWpZdemksvvfR5r9ljjz1y5ZVX5sADD+ynVAB9RzEO9JotW2vfeuutuemmm3LrrbfmgQceyNq1a9PZ2Zldd901EyZMyKtf/eq89a1vzUknnZRddtnlRX/OoYcemhtvvDHHHnts/vznP3eX4yZn23fOOefk7/7u7/K1r30tN910U1pbW/PUU09lwoQJmTJlSk444YTMnTv3Rb/v8ccfn6uuuionnXRSVq5cmde//vVZvnx592rt3nTGGWfkHe94R77xjW9k2bJl+e1vf5tHH300w4YNy8SJE3PggQfm2GOPzUknndTjeUdHHHFEfvGLX2TZsmVZvnx5Wltbu/9O7r777nn1q1+dk046KaeccspWfx//7d/+LTfddFOWLVuWO++8M2vWrMkjjzyS2tra7L333nnd616Xj3zkI/7uAQAVs3jx4rzlLW/JsmXLsmLFiqxevTp/+tOfUlNTk8mTJ2fWrFn54Ac/mMMPP3yb9w8ZMiT/8R//kW984xu5/PLLu3+Q8BWveEXmzp2bM844IyNGjOjPLwkAgAraZZddsvvuu+eAAw7I8ccfn/e97315+ctfXulYAL2iVC6Xy5UOAQAAAAAAAAB9ZUilAwAAAAAAAABAX1KMAwAAAAAAAFBoinEAAAAAAAAACk0xDgAAAAAAAEChKcYBAAAAAAAAKDTFOAAAAAAAAACFphgHAAAAAAAAoNAU4wAAAAAAAAAUmmIcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKbWilAxTJ5s2b88c//jEjR45MqVSqdBwAgD5XLpfz2GOPZcKECRkyxM9c7izzSQBgsDGfBACgvyjGe9Ef//jHTJ48udIxAAD63apVqzJp0qRKx6h65pMAwGBlPgkAQF9TjPeikSNHJnl2Ij9q1KgKpwEA6HsdHR2ZPHly9zyInWM+CQAMNuaTAAD0F8V4L9qy3eWoUaN8IxMAGFRs+907zCcBgMHKfBIAgL7mwT0AAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiFLMYXLVqU6dOnZ+TIkdlzzz3ztre9LQ888MDz3rN8+fKUSqWtjl//+tf9lBoAAAAAAACAvlDIYvzmm2/OaaedlltvvTXLli3LM888k9mzZ+eJJ554wXsfeOCBrF69uvvYd999+yExAAAAAAAAAH1laKUD9IUbbrihx+vLLrsse+65Z26//fa87nWve95799xzz+y22259mA4AAAAAAACA/lTIFeN/acOGDUmS3Xff/QWvPfjggzN+/Pgcc8wx+fnPf97X0QAAAAAAAADoY4VcMf5c5XI5CxYsyN/+7d9m6tSp271u/Pjx+eY3v5lDDz00mzZtyr/+67/mmGOOyfLly7e7ynzTpk3ZtGlT9+uOjo5ezw8AAAAAAADAzil8Mf6xj30sd911V/7nf/7nea971atelVe96lXdr1/72tdm1apV+eIXv7jdYnzRokW54IILejUvAAAAAAAAAL2r0Fupn3766bnmmmvy85//PJMmTXrR9x922GH5zW9+s93z55xzTjZs2NB9rFq1amfiAgAAAAAAANAHCrlivFwu5/TTT88Pf/jDLF++PFOmTHlJ73PHHXdk/Pjx2z0/bNiwDBs27KXGBAAAAAAAAKAfFLIYP+2009LU1JT//M//zMiRI7NmzZokyejRozN8+PAkz672fvjhh/Pd7343SbJ48eLsvffeOeCAA/LUU0/lyiuvzFVXXZWrrrqqYl8HAAAAAAAAADuvkMX4pZdemiQ56qijeoxfdtllOeWUU5Ikq1evTmtra/e5p556KmeddVYefvjhDB8+PAcccECuu+66HH/88f0VGwAAAAAAAIA+UCqXy+VKhyiKjo6OjB49Ohs2bMioUaMqHQcAoM+Z//Quv58AwGBj/gMAQH8ZUukAAAAAAAAAANCXFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEAAAAAAAAoNMU4AAAAAAAAAIWmGAcAAAAAAACg0BTjAAAAAAAAABSaYhwAAAAAAACAQlOMAwAAAAAAAFBoinGAAmhubs7cuXPT3Nxc6SgAAFQh80kAAACKTjEOUOU6OzvT2NiYtWvXprGxMZ2dnZWOBABAFTGfBAAAYDBQjANUuaVLl6a9vT1J0t7enqampgonAgCgmphPAgAAMBgoxgGqWFtbW5qamlIul5Mk5XI5TU1NaWtrq3AyAACqgfkkAAAAg4ViHKBKlcvlLFmyZLvjW765CQAA22I+CQAAwGCiGAeoUq2trWlpaUlXV1eP8a6urrS0tKS1tbVCyQAAqAbmkwAAAAwminGAKlVfX5/p06enpqamx3hNTU1mzJiR+vr6CiUDAKAamE8CAAAwmCjGAapUqVTK/PnztzteKpUqkAoAgGphPgkAAMBgohgHqGKTJk1KQ0ND9zctS6VSGhoaMnHixAonAwCgGphPAgAAMFgoxgGq3Lx58zJmzJgkydixY9PQ0FDhRAAAVBPzSQAAAAYDxThAlautrc2CBQtSV1eXM888M7W1tZWOBABAFTGfBAAAYDAYWukAAOy8WbNmZdasWZWOAQBAlTKfBAAAoOisGAcAgB3wzDPP5NOf/nSmTJmS4cOHZ5999smFF16YzZs3d19TLpezcOHCTJgwIcOHD89RRx2Ve++9t4KpAQAAAIBEMQ4AADvk85//fL7+9a/nkksuyf3335+LL744X/jCF/KVr3yl+5qLL744jY2NueSSS9LS0pJx48bl2GOPzWOPPVbB5AAAAACAYhwAAHbAL37xi7z1rW/Nm970puy99975u7/7u8yePTsrVqxI8uxq8cWLF+fcc8/N29/+9kydOjVXXHFFNm7cmKampgqnBwAAAIDBTTEOAAA74G//9m/z05/+NA8++GCS5H//93/zP//zPzn++OOTJCtXrsyaNWsye/bs7nuGDRuWI488Ms3Nzdt8z02bNqWjo6PHAQAAAAD0vqGVDgAAANXg//2//5cNGzbkb/7mb1JTU5Ourq587nOfy7vf/e4kyZo1a5IkdXV1Pe6rq6vLQw89tM33XLRoUS644IK+DQ4AAAAAWDEOAAA74gc/+EGuvPLKNDU15Ve/+lWuuOKKfPGLX8wVV1zR47pSqdTjdblc3mpsi3POOScbNmzoPlatWtVn+QEAAABgMLNiHAAAdsAnP/nJfOpTn8q73vWuJMmBBx6Yhx56KIsWLcrJJ5+ccePGJXl25fj48eO771u3bt1Wq8i3GDZsWIYNG9b34QEAAABgkLNiHAAAdsDGjRszZEjP6XNNTU02b96cJJkyZUrGjRuXZcuWdZ9/6qmncvPNN2fWrFn9mhUAAAAA6MmKcQAA2AEnnHBCPve5z6W+vj4HHHBA7rjjjjQ2Nub9739/kme3UD/jjDNy0UUXZd99982+++6biy66KCNGjEhDQ0OF0wMAAADA4KYYBwCAHfCVr3wln/nMZ3Lqqadm3bp1mTBhQj784Q/nvPPO677m7LPPzpNPPplTTz0169evz8yZM3PjjTdm5MiRFUwOAAAAAJTK5XK50iGKoqOjI6NHj86GDRsyatSoSscBAOhz5j+9y+8nADDYmP8AANBfPGMcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQqgubk5c+fOTXNzc6WjAAAAAAAADDiKcYAq19nZmcbGxqxduzaNjY3p7OysdCQAAAAAAIABRTEOUOWWLl2a9vb2JEl7e3uampoqnAgAAAAAAGBgUYwDVLG2trY0NTWlXC4nScrlcpqamtLW1lbhZAAAAAAAAAOHYhygSpXL5SxZsmS741vKcgAAAAAAgMFOMQ5QpVpbW9PS0pKurq4e411dXWlpaUlra2uFkgEAAAAAAAwsinGAKlVfX5/p06enpqamx3hNTU1mzJiR+vr6CiUDAAAAAAAYWBTjAFWqVCpl/vz52x0vlUoVSAUAAAAAADDwKMYBqtikSZPS0NDQXYKXSqU0NDRk4sSJFU4GAAAAAAAwcCjGAarcvHnzMmbMmCTJ2LFj09DQUOFEAAAAAAAAA4tiHKDK1dbWZsGCBamrq8uZZ56Z2traSkcCAAAAAAAYUIZWOgAAO2/WrFmZNWtWpWMAAAAAAAAMSFaMAxRAc3Nz5s6dm+bm5kpHAQAAAAAAGHAU4wBVrrOzM42NjVm7dm0aGxvT2dlZ6UgAAAAAAAADimIcoMotXbo07e3tSZL29vY0NTVVOBEAAAAAAMDAohgHqGJtbW1pampKuVxOkpTL5TQ1NaWtra3CyQAAAAAAAAYOxThAlSqXy1myZMl2x7eU5QAAAAAAAIOdYhygSrW2tqalpSVdXV09xru6utLS0pLW1tYKJQMAAAAAABhYFOMAVaq+vj7Tp09PTU1Nj/GamprMmDEj9fX1FUoGAAAAAAAwsCjGAapUqVTK/PnztzteKpUqkAoAAAAAAGDgUYwDVLFJkyaloaGhuwQvlUppaGjIxIkTK5wMAAAAAABg4FCMA1S5efPmZcyYMUmSsWPHpqGhocKJAAAAAAAABhbFOECVq62tzYIFC1JXV5czzzwztbW1lY4EAAAAAAAwoAytdAAAdt6sWbMya9asSscAAAAAAAAYkKwYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEAAAAAAAAoNMU4AAAAAAAAAIWmGAcAAAAAAACg0BTjAAAAAAAAABSaYhwAAAAAAACAQlOMAwAAAAAAAFBoinEAAAAAAAAACk0xDgAAAAAAAEChKcYBAAAAAAAAKDTFOAAAAAAAAACFphgHAAAAAAAAoNAU4wAAAAAAAAAUmmIcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEAAAAAAAAoNMU4AAAAAAAAAIWmGAcogObm5sydOzfNzc2VjgIAAAAAADDgKMYBqlxnZ2caGxuzdu3aNDY2prOzs9KRAAAAAAAABhTFOECVW7p0adrb25Mk7e3taWpqqnAiAAAAAACAgUUxDlDF2tra0tTUlHK5nCQpl8tpampKW1tbhZMBAAAAAAAMHIpxgCpVLpezZMmS7Y5vKcsBAAAAAAAGO8U4QJVqbW1NS0tLurq6eox3dXWlpaUlra2tFUoGAAAAAAAwsCjGAapUfX19pk+fnpqamh7jNTU1mTFjRurr6yuUDAAAAAAAYGBRjANUqVKplPnz5293vFQqVSAVAAAAAADAwKMYB6hikyZNSkNDQ3cJXiqV0tDQkIkTJ1Y4GQAAAAAAwMChGAeocvPmzcuYMWOSJGPHjk1DQ0OFEwEAAAAAAAwsinGAKldbW5sFCxakrq4uZ555ZmpraysdCQAAAAAAYEAZWukAAOy8WbNmZdasWZWOAQAAAAAAMCBZMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAg1xzc3Pmzp2b5ubmSkcBAACAPqEYBwAAgEGss7MzjY2NWbt2bRobG9PZ2VnpSAAAANDrFOMAAAAwiC1dujTt7e1Jkvb29jQ1NVU4EQAAAPQ+xTgAAAAMUm1tbWlqakq5XE6SlMvlNDU1pa2trcLJAAAAoHcpxgEAAGAQKpfLWbJkyXbHt5TlAAAAUASKcQAAABiEWltb09LSkq6urh7jXV1daWlpSWtra4WSAQAAQO9TjAMAAMAgVF9fn+nTp6empqbHeE1NTWbMmJH6+voKJQMAAIDepxgHAACAQahUKmX+/PnbHS+VShVIBQAAAH1DMQ4AAACD1KRJk9LQ0NBdgpdKpTQ0NGTixIkVTgYAAAC9SzEOAAAAg9i8efMyZsyYJMnYsWPT0NBQ4UQAAADQ+xTjAAAAMIjV1tZmwYIFqaury5lnnpna2tpKRwIAAIBeN7TSAQAAAIDKmjVrVmbNmlXpGAAAANBnrBgHAAAAAAAAoNAU4wAAAAAAAAAUmmIcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQClmML1q0KNOnT8/IkSOz55575m1ve1seeOCBF7zv5ptvzqGHHpra2trss88++frXv94PaQEAAAAAAADoS4Usxm+++eacdtppufXWW7Ns2bI888wzmT17dp544ont3rNy5cocf/zxOeKII3LHHXfkH//xH/Pxj388V111VT8mBwAAAAAAAKC3Da10gL5www039Hh92WWXZc8998ztt9+e173uddu85+tf/3rq6+uzePHiJMl+++2XFStW5Itf/GJOOumkvo4MAAAAFdPc3JwlS5Zk/vz5mTVrVqXjAAAAQK8r5Irxv7Rhw4Ykye67777da37xi19k9uzZPcaOO+64rFixIk8//fQ279m0aVM6Ojp6HAAAFNPee++dUqm01XHaaaclScrlchYuXJgJEyZk+PDhOeqoo3LvvfdWODXAC+vs7ExjY2PWrl2bxsbGdHZ2VjoSAAAA9LrCF+PlcjkLFizI3/7t32bq1KnbvW7NmjWpq6vrMVZXV5dnnnkmjzzyyDbvWbRoUUaPHt19TJ48uVezAwAwcLS0tGT16tXdx7Jly5Ik73jHO5IkF198cRobG3PJJZekpaUl48aNy7HHHpvHHnuskrEBXtDSpUvT3t6eJGlvb09TU1OFEwEAAEDvK3wx/rGPfSx33XVXvve9773gtaVSqcfrcrm8zfEtzjnnnGzYsKH7WLVq1c4HBgBgQNpjjz0ybty47uPaa6/NK17xihx55JEpl8tZvHhxzj333Lz97W/P1KlTc8UVV2Tjxo0KJmBAa2trS1NTU/f//5bL5TQ1NaWtra3CyQAAAKB3FboYP/3003PNNdfk5z//eSZNmvS8144bNy5r1qzpMbZu3boMHTo0Y8aM2eY9w4YNy6hRo3ocAAAU31NPPZUrr7wy73//+1MqlbJy5cqsWbOmx6N5hg0bliOPPDLNzc3bfR+P5gEqqVwuZ8mSJdsd31KWAwAAQBEUshgvl8v52Mc+lquvvjo/+9nPMmXKlBe857WvfW33dphb3HjjjZk2bVpe9rKX9VVUAACq0I9+9KM8+uijOeWUU5Kk+wcst/Vonr/84cvn8mgeoJJaW1vT0tKSrq6uHuNdXV1paWlJa2trhZIBAABA7ytkMX7aaaflyiuvTFNTU0aOHJk1a9ZkzZo1efLJJ7uvOeecc/Le9763+/VHPvKRPPTQQ1mwYEHuv//+fOc738m3v/3tnHXWWZX4EgAAGMC+/e1vZ86cOZkwYUKP8W09mmd7j+VJPJoHqKz6+vpMnz49NTU1PcZramoyY8aM1NfXVygZAAAA9L5CFuOXXnppNmzYkKOOOirjx4/vPn7wgx90X7N69eoeP/0+ZcqU/PjHP87y5cvzmte8Jp/97Gfz5S9/OSeddFIlvgQAAAaohx56KDfddFM++MEPdo+NGzcuSbb5aJ6/XEX+XB7NA1RSqVTK/Pnztzv+fD/YAwAAANVmaKUD9IUdeQ7a5ZdfvtXYkUcemV/96ld9kAgAgKK47LLLsueee+ZNb3pT99iUKVMybty4LFu2LAcffHCSZ59DfvPNN+fzn/98paICvKBJkyaloaEhV155ZfcuFw0NDZk4cWKlowEAAECvKuSKcQAA6AubN2/OZZddlpNPPjlDh/7/P2NaKpVyxhln5KKLLsoPf/jD3HPPPTnllFMyYsSINDQ0VDAxwAubN29exowZkyQZO3asf7cAAAAopEKuGAcAgL5w0003pbW1Ne9///u3Onf22WfnySefzKmnnpr169dn5syZufHGGzNy5MgKJAXYcbW1tVmwYEGWLFmS+fPnp7a2ttKRAAAAoNdZMQ4AADto9uzZKZfLeeUrX7nVuVKplIULF2b16tXp7OzMzTffnKlTp1YgJcCLd//99+dPf/pT7r///kpHAQAAgD6hGAcAAIBB7NFHH83SpUuzefPmLF26NI8++milIwEAAECvU4wDAADAIPaZz3wmmzdvTpJs3rw55513XoUTAQAAQO9TjAMAAMAgtWLFitx99909xu66666sWLGiQokAAACgbyjGAQAAYBDavHlzLrzwwm2eu/DCC7tXkQMAAEARKMYBAABgELrtttvS0dGxzXMdHR257bbb+jkRAAAA9B3FOAAAAAxCM2fOzKhRo7Z5bvTo0Zk5c2Y/JwIAAIC+oxgHAACAQWjIkCE577zztnnu/PPPz5AhvmUAAABAcfi/XAAAABikpk2blgMPPLDH2EEHHZRDDjmkQokAAACgbyjGAQAAYBD77Gc/2706fMiQIbnwwgsrnAgAAAB6n2IcAAAABrHddtst8+bNy5AhQzJv3rzstttulY4EAAAAvW5opQMAAAAAlfWBD3wgH/jAByodAwAAAPqMFeMAAAAwyDU3N2fu3Llpbm6udBQAAADoE4pxAAAAGMQ6OzvT2NiYtWvXprGxMZ2dnZWOBAAAAL1OMQ4AAACD2NKlS9Pe3p4kaW9vT1NTU4UTAQAAQO9TjAMAAMAg1dbWlqamppTL5SRJuVxOU1NT2traKpwMAAAAepdiHAAAAAahcrmcJUuWbHd8S1kOAAAARaAYBwAAgEGotbU1LS0t6erq6jHe1dWVlpaWtLa2VigZAAAA9D7FOEABNDc3Z+7cuWlubq50FAAAqkR9fX2mT5+empqaHuM1NTWZMWNG6uvrK5QMAAAAep9iHKDKdXZ2prGxMWvXrk1jY2M6OzsrHQkAgCpQKpUyf/787Y6XSqUKpAIAAIC+oRgHqHJLly5Ne3t7kqS9vT1NTU0VTgQAQLWYNGlSGhoaukvwUqmUhoaGTJw4scLJAAAAoHcpxgGqWFtbW5qamlIul5Mk5XI5TU1NaWtrq3AyAACqxbx58zJmzJgkydixY9PQ0FDhRAAAAND7FOMAVapcLmfJkiXbHd9SlgMAwPOpra3NggULUldXlzPPPDO1tbWVjgQAAAC9TjEOUKVaW1vT0tKSrq6uHuNdXV1paWlJa2trhZIBAFBtbrzxxqxduzY33nhjpaMAAABAn1CMA1Sp+vr6TJ8+PTU1NT3Ga2pqMmPGjNTX11coGQAA1WTt2rVZvnx5kmT58uVZu3ZtZQMBAABAH1CMA1SpUqmU+fPnb3e8VCpVIBUAANXmYx/7WI/Xp59+eoWSAAAAQN9RjANUsUmTJqWhoaG7BC+VSmloaMjEiRMrnAwAgGpwww035E9/+lOPsXXr1uWGG26oUCIAAADoG4pxgCo3b968jBkzJkkyduzYNDQ0VDgRAADVoKurK1/4whe2ee4LX/hCurq6+jkRAAAA9B3FOECVq62tzYIFC1JXV5czzzwztbW1lY4EAEAVuPbaa7dbfnd1deXaa6/t50QAAADQdxTjAAUwa9as/OAHP8isWbMqHQUAgCrx5je/OTU1Nds8N3To0Lz5zW/u50QAAADQdxTjAAAAMAjV1NTkk5/85DbPnX322dstzQEAAKAaKcYBAABgkHrjG9+YPfbYo8fYnnvumdmzZ1coEQAAAPQNxTgAAAAMYpdcckmP11/5ylcqlAQAAAD6jmIcAAAABrG6urocddRRSZKjjjoqdXV1lQ0EAAAAfWBopQMAAAAAlbVw4cJKRwAAAIA+ZcU4AAAAAAAAAIWmGAcAAAAAAACg0BTjAAAAAAAAABSaZ4wDAADwopXL5XR2dlY6Br2gXC5n06ZNSZJhw4alVCpVOBG9oba21p8lAADAcyjGAQAAeNE6OzszZ86cSscAtuP666/P8OHDKx0DAABgwLCVOgAAAAAAAACFZsU4AAAAL1ptbW2uv/76SsegF3R2dubEE09Mkvzwhz9MbW1thRPRG/w5AgAA9KQYBwAA4EUrlUq2aS6g2tpaf64AAAAUkq3UAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEKoLm5OXPnzk1zc3OlowAAAAAAAAw4inGAKtfZ2ZnGxsasXbs2jY2N6ezsrHQkAAAAAACAAUUxDlDlli5dmvb29iRJe3t7mpqaKpwIAAAAAABgYFGMA1Sxtra2NDU1pVwuJ0nK5XKamprS1tZW4WQAAAAAAAADh2IcoEqVy+UsWbJku+NbynIAAAAAAIDBTjEOUKVaW1vT0tKSrq6uHuNdXV1paWlJa2trhZIBAAAAAAAMLIpxgCpVX1+f6dOnp6ampsd4TU1NZsyYkfr6+golAwAAAAAAGFgU4wBVqlQqZf78+dsdL5VKFUgFAAAAAAAw8CjGAarYpEmT0tDQ0F2Cl0qlNDQ0ZOLEiRVOBgAAAAAAMHAoxgGq3Lx58zJmzJgkydixY9PQ0FDhRAAAAAAAAAOLYhygytXW1mbBggWpq6vLmWeemdra2kpHAgAAAAAAGFCGVjoAADtv1qxZmTVrVqVjAAAAAAAADEhWjAMAAAAAAABQaIpxAAAAAAAAAApNMQ5QAM3NzZk7d26am5srHQUAAAAAAGDAUYwDVLnOzs40NjZm7dq1aWxsTGdnZ6UjAQAAAAAADCiKcYAqt3Tp0rS3tydJ2tvb09TUVOFEAAAAAAAAA4tiHKCKtbW1pampKeVyOUlSLpfT1NSUtra2CicDAAAAAAAYOBTjAFWqXC5nyZIl2x3fUpYDAAAAAAAMdopxgCrV2tqalpaWdHV19Rjv6upKS0tLWltbK5QMAAAAAABgYFGMA1Sp+vr6TJ8+PTU1NT3Ga2pqMmPGjNTX11coGQAAAAAAwMCiGAeoUqVSKfPnz9/ueKlUqkAqAAAAAACAgUcxDlDFJk2alIaGhu4SvFQqpaGhIRMnTqxwMgAAAAAAgIFDMQ5Q5ebNm5cxY8YkScaOHZuGhoYKJwIAAAAAABhYFOMAVa62tjZTp05NkhxwwAGpra2tcCIAAAAAAICBRTEOUOUeffTR3HLLLUmSW265JY8++mhlAwEAAAAAAAwwinGAKveZz3wmmzdvTpJs3rw55513XoUTAQAAAAAADCyKcYAqtmLFitx99909xu66666sWLGiQokAAAAAAAAGHsU4QJXavHlzLrzwwm2eu/DCC7tXkQMAAAAAAAx2inGAKnXbbbelo6Njm+c6Ojpy22239XMiAAAAAACAgUkxDlClZs6cmVGjRm3z3OjRozNz5sx+TgQAAAAAADAwKcYBqtSQIUNy3nnnbfPc+eefnyFD/BMPAAAAAACQKMYBqtq0adNy4IEH9hg76KCDcsghh1QoEQAAAAAAwMCjGAeocp/97Ge7V4cPGTIkF154YYUTAQAAAAAADCyKcYAqt9tuu2XevHkZMmRI5s2bl912263SkQAAAAAAAAaUoZUOAMDO+8AHPpAPfOADlY4BAAAAAAAwIFkxDgAAAAAAAEChKcYBAAAAAAAAKDTFOAAAAAAAAACFphgHAAAAAAAAoNAU4wAAAAAAAAAUmmIcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEKoLm5OXPnzk1zc3OlowAU2sMPP5y///u/z5gxYzJixIi85jWvye233959vlwuZ+HChZkwYUKGDx+eo446Kvfee28FEwMAAAAAiWIcoOp1dnamsbExa9euTWNjYzo7OysdCaCQ1q9fn8MPPzwve9nLcv311+e+++7Ll770pey2227d11x88cVpbGzMJZdckpaWlowbNy7HHntsHnvsscoFBwAAAAAU4wDVbunSpWlvb0+StLe3p6mpqcKJAIrp85//fCZPnpzLLrssM2bMyN57751jjjkmr3jFK5I8u1p88eLFOffcc/P2t789U6dOzRVXXJGNGzf6txkAAAAAKkwxDlDF2tra0tTUlHK5nOTZUqapqSltbW0VTgZQPNdcc02mTZuWd7zjHdlzzz1z8MEH51vf+lb3+ZUrV2bNmjWZPXt299iwYcNy5JFHetQFAAAAAFSYYhygSpXL5SxZsmS741vKcgB6x+9///tceuml2XffffOTn/wkH/nIR/Lxj3883/3ud5Mka9asSZLU1dX1uK+urq773F/atGlTOjo6ehwAAAAAQO9TjANUqdbW1rS0tKSrq6vHeFdXV1paWtLa2lqhZADFtHnz5hxyyCG56KKLcvDBB+fDH/5w/uEf/iGXXnppj+tKpVKP1+VyeauxLRYtWpTRo0d3H5MnT+6z/AAAAAAwmCnGAapUfX19pk+fnpqamh7jNTU1mTFjRurr6yuUDKCYxo8fn/3337/H2H777df9g0jjxo1Lkq1Wh69bt26rVeRbnHPOOdmwYUP3sWrVqj5IDgAAAAAoxgGqVKlUyvz587c7vr3ViQC8NIcffngeeOCBHmMPPvhg9tprryTJlClTMm7cuCxbtqz7/FNPPZWbb745s2bN2uZ7Dhs2LKNGjepxAAAAAAC9TzEOUMUmTZqUhoaG7hK8VCqloaEhEydOrHAygOI588wzc+utt+aiiy7Kb3/72zQ1NeWb3/xmTjvttCTP/ht8xhln5KKLLsoPf/jD3HPPPTnllFMyYsSINDQ0VDg9AAAAAAxuQysdAICdM2/evFx//fV55JFHMnbsWOULQB+ZPn16fvjDH+acc87JhRdemClTpmTx4sWZN29e9zVnn312nnzyyZx66qlZv359Zs6cmRtvvDEjR46sYHIAAAAAoFQul8uVDlEUHR0dGT16dDZs2GAbTKBfNTc3Z8mSJZk/f/52t+sF6AvmP73L7ydQCU8++WTmzJmTJLn++uszfPjwCicCBhPzHwAA+osV4wAFMGvWLIU4AAAAAADAdnjGOAAAAAAAAACFphgHAAAAAAAAoNAU4wAAAAAAAAAUmmIcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEAAAAAAAAoNMU4AAAAAAAAAIWmGAcAAAAAAACg0ApbjN9yyy054YQTMmHChJRKpfzoRz963uuXL1+eUqm01fHrX/+6fwIDAAAAAAAA0CeGVjpAX3niiSfy6le/Ou973/ty0kkn7fB9DzzwQEaNGtX9eo899uiLeAAAAAAAAAD0k8IW43PmzMmcOXNe9H177rlndtttt94PBAAAAAAAAEBFFHYr9Zfq4IMPzvjx43PMMcfk5z//+fNeu2nTpnR0dPQ4AAAAAAAAABhYFOP/Z/z48fnmN7+Zq666KldffXVe9apX5Zhjjsktt9yy3XsWLVqU0aNHdx+TJ0/ux8QAAAAAAAAA7IjCbqX+Yr3qVa/Kq171qu7Xr33ta7Nq1ap88YtfzOte97pt3nPOOedkwYIF3a87OjqU4wAAAAAAAAADjBXjz+Owww7Lb37zm+2eHzZsWEaNGtXjAKiE5ubmzJ07N83NzZWOAgAAAAAAMOAoxp/HHXfckfHjx1c6BsDz6uzsTGNjY9auXZvGxsZ0dnZWOhIAAAAAAMCAUtit1B9//PH89re/7X69cuXK3Hnnndl9991TX1+fc845Jw8//HC++93vJkkWL16cvffeOwcccECeeuqpXHnllbnqqqty1VVXVepLANghS5cuTXt7e5Kkvb09TU1Nef/731/hVAAAAAAAAANHYYvxFStW5Oijj+5+veVZ4CeffHIuv/zyrF69Oq2trd3nn3rqqZx11ll5+OGHM3z48BxwwAG57rrrcvzxx/d7doAd1dbWlqamppTL5SRJuVxOU1NTZs+enUmTJlU4HQAAAAAAwMBQ2GL8qKOO6i6KtuXyyy/v8frss8/O2Wef3cepAHpPuVzOkiVLtjt+8cUXp1QqVSAZAAAAAADAwOIZ4wBVqrW1NS0tLenq6uox3tXVlZaWlh67YgAAAAAAAAxminGAKlVfX5/p06dvtSq8VCplxowZqa+vr1AyAAAAAACAgUUxDlClSqVS5s6du9VjI8rlcubOnWsbdQAAAAAAgP+jGAeoUuVyOT/4wQ+2uWL8+9///laFOQAAAAAAwGClGAeoUlueMb6tFeOeMQ4AAAAAAPD/U4wDVKktzxivqanpMV5TU+MZ4wAAAAAAAM+hGAeoUqVSKfPnz9/uuGeMAwAAAAAAPEsxDlDFJk2alP3337/H2P7775+JEydWKBEAAAAAAMDAoxgHqGJtbW259957e4zde++9aWtrq1AiAAAAAACAgUcxDlClyuVylixZss0t05csWZJyuVyBVAAAAAAAAAOPYhygSrW2tqalpSVdXV09xru6utLS0pLW1tYKJQMAAAAAABhYFOMAVaq+vj7Tp09PTU1Nj/GamprMmDEj9fX1FUoGAAAAAAAwsCjGAapUqVTK/Pnztzu+rS3WAQAAAAAABiPFOEAVmzRpUhoaGrpL8FKplIaGhkycOLHCyQAAAAAAAAYOxThAlZs3b17GjBmTJBk7dmwaGhoqnAgAAAAAAGBgUYwDVLna2tosWLAgdXV1OfPMM1NbW1vpSAAAAAAAAAPK0EoHAGDnzZo1K7Nmzap0DAAAAAAAgAHJinGAAmhubs7cuXPT3Nxc6SgAAAAAAAADjmIcoMp1dnamsbExa9euTWNjYzo7OysdCQAAAAAAYEBRjANUuaVLl+aRRx5JkjzyyCNpamqqcCIAAAAAAICBRTEOUMXa2tqydOnSHmNLly5NW1tbhRIBAAAAAAAMPIpxgCpVLpezZMmSbN68ucd4V1dXlixZknK5XKFkAAAAAAAAA4tiHKBKtba2pqWlZZvnWlpa0tra2s+JAAAAAAAABibFOECVmjx5ckaMGLHNcyNGjMjkyZP7OREAAAAAAMDApBgHqFIPPfRQNm7cuM1zGzduzEMPPdTPiQAAAAAAAAYmxThAlVq9evVOnQcAAAAAABgsFOMAVeqwww7LX/3VX23z3F/91V/lsMMO6+dEAAAAAAAAA5NiHKBKDRkyJAsXLtzmuQsvvDBDhvgnHgAAAAAAIFGMA1S1adOm5YADDugxNnXq1BxyyCEVSgQAAAAAADDw9Esx/vrXvz7HHHNMHnrooR2+549//GP3fQBs3+c+97mUSqUkSalUyj/90z9VOBFA/zDHBAAAAAB21ND++JDly5enVCrliSee2OF7nnzyye77ANi+3XbbLX//93+fpUuXZt68edltt90qHQmgX5hjAgAAAAA7ylbqAAWw3377ZY899sh+++1X6SgAAAAAAAADzoAtxres/Kmtra1wEoCBrbOzM42NjVm7dm0aGxvT2dlZ6UgAA5Y5JgAAAAAMTgO2GL/++uuTJJMmTapwEoCBbenSpWlvb0+StLe3p6mpqcKJAAYuc0wAAAAAGJz65Bnj73//+7c5/ulPf/oFn327adOm/O53v0tLS0tKpVKOPPLIPkgIUAxtbW1pampKuVxOkpTL5TQ1NWX27NlKH6BwzDEBAAAAgJeqVN7SpvSiIUOGpFQqdb/e8hHPHXs+W67ffffd09LSkilTpvR2xD7R0dGR0aNHZ8OGDRk1alSl4wAFVy6Xc/bZZ+dXv/pVurq6usdrampyyCGH5OKLL97hf3cBXqr+nP8Mhjmm+SRQCU8++WTmzJmT5NmdNYYPH17hRMBgYv4DAEB/6ZMV4/X19T2+QfnQQw+lVCpl/PjxednLXrbd+0qlUmprazN+/PjMmjUrH/3oRzNhwoS+iAhQ9VpbW9PS0rLVeFdXV1paWtLa2pq99tqrAskA+oY5JgAAAADwUvVJMf6HP/yhx+shQ559lPmNN96Y/fffvy8+EmDQqa+vz/Tp07e5YvzQQw9NfX19BdMB9D5zTAAAAADgpeqTYvwvve51r0upVMquu+7aHx8HMCiUSqXMnz8/J5988jbHbaMOFJ05JgAAAACwo/qlGF++fHl/fAzAoDNp0qQ0NDTkyiuvTLlcTqlUSkNDQyZOnFjpaAB9zhwTAAAAANhRQyodAICdM2/evIwZMyZJMnbs2DQ0NFQ4EQAAAAAAwMDSLyvGt6WjoyOPPfZYj+fibo/n5AJsX21tbaZOnZrly5fngAMOSG1tbaUjAVSMOSYAAAAAsC39WowvW7YsX/va1/Lf//3fWb9+/Q7dUyqV8swzz/RxMoDq9eijj+aWW25Jktxyyy159NFHs9tuu1U2FEA/MscEAAAAAF5IvxXjH//4x/PVr341SVIul/vrYwEK7zOf+Uw2b96cJNm8eXPOO++8fPnLX65wKoD+YY4JAAAAAOyIfinGm5qacskllyR5dsvft73tbTn00EOz++67Z8gQjzkHeKlWrFiRu+++u8fYXXfdlRUrVmTatGkVSgXQP8wxAQAAAIAd1S/F+De+8Y0kyeTJk/Ozn/0sr3jFK/rjYwEKbfPmzbnwwgu3ee7CCy/Mj370I8UQUGjmmAAAAADAjuqXxuSuu+5KqVTK+eef7xuWAL3ktttuS0dHxzbPdXR05LbbbuvnRAD9yxwTAAAAANhR/VKMP/3000mSgw8+uD8+DmBQmDlzZkaNGrXNc6NHj87MmTP7ORFA/zLHBAAAAAB2VL8U43vvvXeS5PHHH++PjwMYFIYMGZLzzjtvm+fOP/9826gDhWeOCQAAAADsqH5pTd7+9rcnSX7605/2x8cBDBrTpk3LgQce2GPsoIMOyiGHHFKhRAD9xxwTAAAAANhR/VKMf+ITn0h9fX0WL16cX//61/3xkQCDxqc//eker88999wKJQHoX+aYAAAAAMCO6pdifPTo0bnhhhtSV1eXww8/PF/72teyfv36/vhogMK79tpre7y+7rrrKpQEoH+ZYwIAAAAAO6pULpfLff0h++yzT5Jk48aNWbduXUqlUkqlUsaOHZsRI0Y8f8BSKb/73e/6OmKv6OjoyOjRo7Nhw4aMGjWq0nGAQaCtrS0nn3xyurq6useGDh2ayy+/PJMmTapgMmCwqOT8p4hzTPNJoBKefPLJzJkzJ0ly/fXXZ/jw4RVOBAwm5j8AAPSXof3xIX/4wx96vC6XyymXy1m3bt0L3lsqlfooFUB1K5fLWbJkyXbHL774Yv+GAoVmjgkAAAAA7Kh+KcZPPvnk/vgYgEGltbU1LS0tW413dXWlpaUlra2t2WuvvSqQDKB/mGMCAAAAADuqX4rxyy67rD8+BmBQqa+vz4EHHpi77757q3MHHXRQ6uvrK5AKoP+YYwIAAAAAO2pIpQMA0PvK5XKlIwAAAAAAAAwYinGAKtXa2rrN1eJJcvfdd6e1tbWfEwEAAAAAAAxMinGAKlVfX5/p06dv89yMGTNspQ4AAAAAAPB/+uUZ49/97nd36v73vve9vZQEoDhKpVLmzp2blpaWrc7NnTs3pVKpAqkA+o85JgAAAACwo/qlGD/llFNeckFTKpV80xJgG8rlcn7wgx9s89z3v//9HHLIIcpxoNDMMQEAAACAHdVvW6mXy+WXfACwtdbW1m2uFk+SlpYWzxgHBgVzTAAAAABgR/TLivGVK1e+4DVPPPFEHnjggSxdujRXX311Zs2alW9+85vZdddd+yEhQPWpr6/PgQcemLvvvnurcwcddJBnjAOFZ44JAAAAAOyofinG99prrx26bv/998+JJ56Y733ve3nPe96T008/PTfddFMfpwMoHishgcHAHBMAAAAA2FH9tpX6i/Hud787733ve7N8+fJ84xvfqHQcgAGptbV1m6vFk+Tuu++2lTrAXzDHBAAAAIDBa0AW40nyzne+M+VyOZdffnmlowAMSJMnT86oUaO2eW7UqFGZPHlyPycCGPjMMQEAAABgcBqwxfi4ceOSJA888ECFkwAMTKtWrUpHR8c2z3V0dGTVqlX9nAhg4DPHBAAAAIDBacAW41u2AH766acrnARgYKqvr8/06dNTKpV6jJdKpcyYMSP19fUVSgYwcJljAgAAAMDgNCCL8aeffjoXX3xxkuSv//qvK5wGYGAqlUqZP3/+VsX4kCFDtjkOMNiZYwIAAADA4DW0Pz5ky8qc57N58+asX78+K1asyCWXXJJ77rknpVIp73rXu/ohIUB1mjRpUubNm5d//dd/7R6bN29eJk6cWMFUAP3DHBMAAAAA2FH9UoxPmTLlRd9TLpfz2te+NmeeeWYfJAIojje/+c09ivE3velNFUwD0H/MMQEAAACAHdUvW6mXy+UXdbz85S/Ppz71qdx0000ZNmxYf0QEqFoXXnhhj9ef/exnK5QEoH+ZYwIAAAAAO6pfVoxfdtllL3jNkCFDMnLkyEyZMiVTp05NTU1NPyQDqG4rVqzIvffe22PsnnvuyYoVKzJt2rQKpQLoH+aYAAAAAMCO6pdi/OSTT+6PjwEYVDZv3pyFCxdu89zChQtzzTXXZMiQftkYBKAizDEBAAAAgB2lMQGoUrfeemsef/zxbZ57/PHHc+utt/ZzIoBiW7hwYUqlUo9j3Lhx3efL5XIWLlyYCRMmZPjw4TnqqKO22tUDAAAAAKgMxThAlRo/fvxOnQfgxTvggAOyevXq7uPuu+/uPnfxxRensbExl1xySVpaWjJu3Lgce+yxeeyxxyqYGAAAAABI+mkr9b90++2356abbso999yTP//5z0mS3XffPVOnTs0b3vCGHHrooZWIBVBV9tprr4wYMSIbN27c6tyIESOy1157VSAVQOX0xxxz6NChPVaJb1Eul7N48eKce+65efvb354kueKKK1JXV5empqZ8+MMf3unPBgAAAABeun4txu++++586EMfyi9/+cvtXvOP//iPmTlzZr7xjW/kwAMP7Md0ANVl1apV2yzFk2Tjxo1ZtWqVchwYFPpzjvmb3/wmEyZMyLBhwzJz5sxcdNFF2WeffbJy5cqsWbMms2fP7r522LBhOfLII9Pc3LzdYnzTpk3ZtGlT9+uOjo6XnA0AAAAA2L5+20r9pptuyowZM/LLX/4y5XI55XI5Q4cOTV1dXfbcc88MHTq0e/zWW2/NjBkz8tOf/rS/4gFUnfr6+kyfPn2b52bMmJH6+vp+TgTQ//pzjjlz5sx897vfzU9+8pN861vfypo1azJr1qy0t7dnzZo1SZK6uroe99TV1XWf25ZFixZl9OjR3cfkyZNfUjYAAAAA4Pn1SzH+yCOP5B3veEc2bdqUUqmUD37wg7ntttvyxBNP5I9//GNWr16djRs35pe//GX+4R/+ITU1Ndm0aVPe8Y53pL29vT8iAlSdUqmU+fPnp6ampsf40KFDM3/+/JRKpQolA+gf/T3HnDNnTk466aQceOCBecMb3pDrrrsuybNbpm/xl//2lsvl5/33+JxzzsmGDRu6j1WrVr3oXAAAAADAC+uXYnzJkiXZsGFDdtlll1x33XX55je/menTp2fo0P9/J/eamppMmzYt3/jGN3LdddflZS97WTZs2JAlS5b0R0SAqjRp0qQ0NDR0ly6lUikNDQ2ZOHFihZMB9L1KzzF33XXXHHjggfnNb37T/dzxv1wdvm7duq1WkT/XsGHDMmrUqB4HAAAAAND7+qUYv+6661IqlfKxj30sxx133AteP3v27Jx++ukpl8vdK3EA2LZ58+ZlzJgxSZKxY8emoaGhwokA+kel55ibNm3K/fffn/Hjx2fKlCkZN25cli1b1n3+qaeeys0335xZs2bt9GcBAAAAADtn6AtfsvNWrlyZJHnLW96yw/e85S1vSWNjY37/+9/3VSwY1Mrlcjo7Oysdg15QLpfzoQ99KN/61rdy6qmnplwu58knn6x0LHZSbW2t7fDhBfT3HPOss87KCSeckPr6+qxbty7/9E//lI6Ojpx88skplUo544wzctFFF2XffffNvvvum4suuigjRozwA0sAAAAAMAD0SzG+pXzbddddd/ieESNGJHl2JQ7Q+zo7OzNnzpxKx6CXXXDBBZWOQC+5/vrrM3z48ErHgAGtv+eYbW1tefe7351HHnkke+yxRw477LDceuut2WuvvZIkZ599dp588smceuqpWb9+fWbOnJkbb7wxI0eOfNGfBQAAAAD0rn4pxseNG5fW1tbccccdOfTQQ3fonjvuuCNJnveZjAAADF79Pcf8/ve//7znS6VSFi5cmIULF77o9wYAAAAA+la/FONHHHFErrzyyvzzP/9z3vnOd2bUqFHPe31HR0c+//nPp1Qq5YgjjuiPiDDo1NbW5vrrr690DHpBZ2dnTjzxxCTJD3/4w9TW1lY4Eb3BnyO8MHNMAAAAAGBH9Usx/uEPfzhXXnllVq5cmde97nX51re+lenTp2/z2l/+8pf50Ic+lN///vcplUr58Ic/3B8RYdAplUq2aS6g2tpaf67AoGGOCQAAAADsqH4pxg8//PCceuqp+drXvpa77747hx12WPbff//MnDkzdXV1KZVKWbNmTW677bbcd9993fedeuqpOfzww/sjIgAAVcYcEwAAAADYUf1SjCfJV77ylYwYMSKNjY3ZvHlz7r333h7foEyScrmcJBkyZEjOOuus/PM//3N/xQMAoAqZYwIAAAAAO2JIf31QqVTKxRdfnDvvvDMf/ehHs++++6ZcLvc49t1333z0ox/NnXfe2f38RwAA2B5zTAAAAABgR/TbivEtpk6dmq9+9atJkqeeeirr169Pkrz85S/PLrvs0t9xAAAoAHNMAAAAAOD59Hsx/ly77LJL6urqKhkBAICCMccEAAAAAP5Sn2ylfv311+eQQw7JIYcckqamphd179KlS7vvvemmm/oiHgAAVcgcEwAAAAB4qXq9GC+XyznzzDPzv//7vxkzZkwaGhpe1P0NDQ0ZM2ZM7rzzznziE5/o7XgAAFQhc0wAAAAAYGf0ejH+s5/9LA8++GCGDBmSxYsXv+j7S6VSlixZkpqamtxzzz1Zvnx5b0cEAKDKmGMCAAAAADuj14vxq666Kkly7LHH5oADDnhJ77H//vvnuOOO6/F+AAAMXuaYAAAAAMDO6PVi/Je//GVKpVJOOOGEnXqfN7/5zSmXy7n11lt7KRkAANXKHBMAAAAA2Bm9Xow/9NBDSZJXvepVO/U+r3zlK5Mkf/jDH3Y2EgAAVc4cEwAAAADYGb1ejG/YsCFJsvvuu+/U+2y5v6OjY6czAQBQ3cwxAQAAAICd0evF+KhRo5Ikjz766E69z5b7R44cuZOJAACoduaYAAAAAMDO6PVifM8990yS3HfffTv1Pvfff3+P9wMAYPAyxwQAAAAAdkavF+MzZsxIuVzONddcs1Pv85//+Z8plUqZPn16LyUDAKBamWMCAAAAADuj14vxOXPmJEmWLVuWW2655SW9xy233JIbb7yxx/sBADB4mWMCAAAAADuj14vxk046Kfvss0/K5XLe+c535oEHHnhR9z/44IN55zvfmVKplL333jt/93d/19sRAQCoMuaYAAAAAMDO6PVifOjQofnSl76UUqmUP/3pT5k2bVr+5V/+JY8//vjz3vf4449n8eLFmTZtWtatW5ck+dKXvpShQ4f2dkQAAKqMOSYAAAAAsDP65DuCb33rW/NP//RPOffcc7Nx48acddZZOf/883PEEUfkkEMOSV1dXXbdddc88cQTWbt2bX71q1/lv//7v/PEE0+kXC4nSS644IK87W1v64t4AABUIXNMAAAAAOCl6rOlMuecc04mTZqUU089NU888UQef/zx3HDDDbnhhhu2ef2Wb1aOGDEil1xySU455ZS+igYAQJUyxwQAAAAAXope30r9ud7znvfkwQcfzCc+8YnsscceKZfL2z3Gjh2bs846Kw8++KBvWAIAsF3mmAAAAADAi9XnD1ccP358vvCFL+QLX/hC7rvvvvzv//5vHnnkkTz22GMZOXJkxo4dm1e/+tXZf//9+zoKAAAFYY4JAAAAALwYfV6MP9f+++/vm5MAAPQqc0wAAAAA4IX06VbqAAAAAAAAAFBpinEAAAAAAAAACk0xDgAAAAAAAEChKcYBAAAAAAAAKDTFOAAAAAAAAACFphgHAAAAAAAAoNAU4wAAAAAAAAAUmmIcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEAAAAAAAAoNMU4AAAAAAAAAIWmGAcAAAAAAACg0BTjAAAAAAAAABSaYhwAAAAAAACAQlOMAwAAAAAAAFBohS3Gb7nllpxwwgmZMGFCSqVSfvSjH73gPTfffHMOPfTQ1NbWZp999snXv/71vg8KAAAAAAAAQJ8qbDH+xBNP5NWvfnUuueSSHbp+5cqVOf7443PEEUfkjjvuyD/+4z/m4x//eK666qo+TgoAAAAAAABAXxpa6QB9Zc6cOZkzZ84OX//1r3899fX1Wbx4cZJkv/32y4oVK/LFL34xJ510Uh+lBAAAAAAAAKCvFXbF+Iv1i1/8IrNnz+4xdtxxx2XFihV5+umnK5QKAAAAAAAAgJ1V2BXjL9aaNWtSV1fXY6yuri7PPPNMHnnkkYwfP36rezZt2pRNmzZ1v+7o6OjznAAAAAAAAAC8OFaMP0epVOrxulwub3N8i0WLFmX06NHdx+TJk/s8IwAAAAAAAAAvjmL8/4wbNy5r1qzpMbZu3boMHTo0Y8aM2eY955xzTjZs2NB9rFq1qj+iAgAAAAAAAPAi2Er9/7z2ta/Nf/3Xf/UYu/HGGzNt2rS87GUv2+Y9w4YNy7Bhw/ojHgAAAAAAAAAvUWFXjD/++OO58847c+eddyZJVq5cmTvvvDOtra1Jnl3t/d73vrf7+o985CN56KGHsmDBgtx///35zne+k29/+9s566yzKhEfAAAAAAAAgF5S2BXjK1asyNFHH939esGCBUmSk08+OZdffnlWr17dXZInyZQpU/LjH/84Z555Zr761a9mwoQJ+fKXv5yTTjqp37MDAAAAAAAA0HsKW4wfddRRKZfL2z1/+eWXbzV25JFH5le/+lUfpgIAAAAAAACgvxV2K3UAAAAAAAAASBTjAAAAAAAAABScYhwAAAAAAACAQlOMAwAAAAAAAFBoinEAAAAAAAAACk0xDgAAAAAAAEChKcYBAAAAAAAAKDTFOAAAAAAAAACFphgHAAAAAAAAoNAU4wAAAAAAAAAUmmIcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEAAAAAAAAoNMU4AAAAAAAAAIWmGAcAAAAAAACg0BTjAAAAAAAAABSaYhwAAAAAAACAQlOMAwAAAAAAAFBoinEAAAAAAAAACk0xDgAAAAAAAEChKcYBAAAAAAAAKDTFOAAAAAAAAACFphgHAAAAAAAAoNCGVjoAAAAweJTL5XR2dlY6BvAcz/1v0n+fMPDU1tamVCpVOgYAAFQ9xTgAANBvOjs7M2fOnErHALbjxBNPrHQE4C9cf/31GT58eKVjAABA1bOVOgAAAAAAAACFZsU4AABQEY+/5t0pD/G/JFBx5XKy+Zlnfz1kaGLLZqi40uZn8ld3fq/SMQAAoFB8FwoAAKiI8pChSc3LKh0DSJLsUukAwHOUKx0AAAAKyFbqAAAAAAAAABSaYhwAAAAAAACAQlOMAwAAAAAAAFBoinEAAAAAAAAACk0xDgAAL8GiRYtSKpVyxhlndI+Vy+UsXLgwEyZMyPDhw3PUUUfl3nvvrVxIAAAAACCJYhwAAF60lpaWfPOb38xBBx3UY/ziiy9OY2NjLrnkkrS0tGTcuHE59thj89hjj1UoKQAAAACQKMYBAOBFefzxxzNv3rx861vfystf/vLu8XK5nMWLF+fcc8/N29/+9kydOjVXXHFFNm7cmKampgomBgAAAAAU4wAA8CKcdtppedOb3pQ3vOENPcZXrlyZNWvWZPbs2d1jw4YNy5FHHpnm5ub+jgkAAAAAPMfQSgcAAIBq8f3vfz+/+tWv0tLSstW5NWvWJEnq6up6jNfV1eWhhx7a5vtt2rQpmzZt6n7d0dHRi2kBAAAAgC2sGAcAgB2watWqzJ8/P1deeWVqa2u3e12pVOrxulwubzW2xaJFizJ69OjuY/Lkyb2aGQAAAAB4lmIcAAB2wO23355169bl0EMPzdChQzN06NDcfPPN+fKXv5yhQ4d2rxTfsnJ8i3Xr1m21inyLc845Jxs2bOg+Vq1a1edfBwAAAAAMRrZSBwCAHXDMMcfk7rvv7jH2vve9L3/zN3+T//f//l/22WefjBs3LsuWLcvBBx+cJHnqqady88035/Of//w233PYsGEZNmxYn2cHAAAAgMFOMQ4AADtg5MiRmTp1ao+xXXfdNWPGjOkeP+OMM3LRRRdl3333zb777puLLrooI0aMSENDQyUiAwAAAAD/RzEOAAC95Oyzz86TTz6ZU089NevXr8/MmTNz4403ZuTIkZWOBgAAAACDmmIcAABeouXLl/d4XSqVsnDhwixcuLAieQAAAACAbRtS6QAAAAAAAAAA0JcU4wAAAAAAAAAUmmIcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEAAAAAAAAoNMU4AAAAAAAAAIWmGAcAAAAAAACg0BTjAAAAAAAAABSaYhwAAAAAAACAQlOMAwAAAAAAAFBoinEAAAAAAAAACk0xDgAAAAAAAEChKcYBAAAAAAAAKDTFOAAAAAAAAACFphgHAAAAAAAAoNAU4wAAAAAAAAAUmmIcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEAAAAAAAAoNMU4AAAAAAAAAIWmGAcAAAAAAACg0BTjAAAAAAAAABSaYhwAAAAAAACAQlOMAwAAAAAAAFBoinEAAAAAAAAACk0xDgAAAAAAAEChKcYBAAAAAAAAKDTFOAAAAAAAAACFNrTSAagu5XI5nZ2dlY4BPMdz/5v03ycMPLW1tSmVSpWOAQAAAAAwqCnGeVE6OzszZ86cSscAtuPEE0+sdATgL1x//fUZPnx4pWMAAAAAAAxqtlIHAAAAAAAAoNCsGOcle/w17055iL9CUHHlcrL5mWd/PWRoYstmqLjS5mfyV3d+r9IxAAAAAAD4P1pNXrLykKFJzcsqHQNIkuxS6QDAc5QrHQAAAAAAgB5spQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCU4wDAAAAAAAAUGiKcQAAAAAAAAAKTTEOAAAAAAAAQKEpxgEAAAAAAAAoNMU4AAAAAAAAAIWmGAcAAAAAAACg0BTjAAAAAAAAABSaYhwAAAAAAACAQlOMAwAAAAAAAFBoinEAAAAAAAAACk0xDgAAAAAAAEChKcYBAAAAAAAAKDTFOAAAAAAAAACFphgHAAAAAAAAoNAU4wAAAAAAAAAUmmIcAAAAAAAAgEJTjAMAAAAAAABQaIpxAAAAAAAAAApNMQ4AAAAAAABAoSnGAQAAAAAAACg0xTgAAAAAAAAAhaYYBwAAAAAAAKDQFOMAAAAAAAAAFJpiHAAAAAAAAIBCK3Qx/rWvfS1TpkxJbW1tDj300Pz3f//3dq9dvnx5SqXSVsevf/3rfkwMAAAAAAAAQG8rbDH+gx/8IGeccUbOPffc3HHHHTniiCMyZ86ctLa2Pu99DzzwQFavXt197Lvvvv2UGAAAAAAAAIC+UNhivLGxMR/4wAfywQ9+MPvtt18WL16cyZMn59JLL33e+/bcc8+MGzeu+6ipqemnxAAAAAAAAAD0hUIW40899VRuv/32zJ49u8f47Nmz09zc/Lz3HnzwwRk/fnyOOeaY/PznP+/LmAAA/H/t3Xm8XeO9P/DPyXRO0iSHRAYhImlRhCIxxBTaClGKurdxFUGLkOpFlR+un6Gt9Lal/Bqq5qmGEi6tGELVWDVEGtQ1JhJkMGQy5CQ52b8/8jq75zhDTuIkh533+/Var9faaz3Ps757rb3Pfs7+7udZAAAAAACrQbvWDmBVeO+991JdXZ1evXrV2d6rV6/MnDmzwTrrrrtuLrvssgwaNChVVVW5/vrr841vfCN//etfs+uuuzZYp6qqKlVVVcXH8+fPb7knAQAAAAAAAECLKMnEeI2ysrI6jwuFQr1tNTbZZJNssskmxcdDhgzJ9OnT8+tf/7rRxPiYMWNyzjnntFzAAAAAAAAAALS4kpxKfZ111knbtm3rjQ6fPXt2vVHkTdlhhx3y6quvNrr/tNNOy7x584rL9OnTVzpmAAAAAAAAAFaNkkyMd+jQIYMGDcqECRPqbJ8wYUJ23HHHZrfz3HPPZd111210f3l5ebp27VpnAQAAAAAAAODzpWSnUj/ppJNy6KGHZvDgwRkyZEguu+yyTJs2LaNGjUqybLT322+/neuuuy5JcuGFF2bDDTfM5ptvnkWLFuWGG27IuHHjMm7cuNZ8GgAAAAAAAAB8RiWbGB8xYkTef//9nHvuuZkxY0YGDhyY8ePHp1+/fkmSGTNmZNq0acXyixYtysknn5y33347HTt2zOabb5677747e++9d2s9BQAAAAAAAABaQMkmxpPkuOOOy3HHHdfgvmuuuabO41NOOSWnnHLKaogKAAAAAAAAgNWpJO8xDgAAAAAAAAA1JMYBAAAAAAAAKGkS4wAAAAAAAACUNIlxAAAAAAAAAEqaxDgAAAAAAAAAJU1iHAAAAAAAAICSJjEOAAAAAAAAQEmTGAcAAAAAAACgpEmMAwAAAAAAAFDSJMYBAAAAAAAAKGkS4wAA0Ay/+93vsuWWW6Zr167p2rVrhgwZknvuuae4v1Ao5Oyzz06fPn3SsWPH7LbbbnnxxRdbMWIAAAAAoIbEOAAANMP666+fX/ziF3nmmWfyzDPP5Otf/3r222+/YvL7l7/8ZS644IKMHTs2Tz/9dHr37p099tgjCxYsaOXIAQAAAACJcQAAaIZ99903e++9dzbeeONsvPHG+fnPf57OnTvnySefTKFQyIUXXpgzzjgj3/nOdzJw4MBce+21+fjjj3PjjTe2dugAAAAAsMaTGAcAgBVUXV2dm2++OR999FGGDBmSKVOmZObMmRk2bFixTHl5eYYOHZonnnii0Xaqqqoyf/78OgsAAAAA0PIkxgEAoJmef/75dO7cOeXl5Rk1alTuuOOObLbZZpk5c2aSpFevXnXK9+rVq7ivIWPGjEllZWVx6du37yqNHwAAAADWVBLjAADQTJtsskkmTZqUJ598Mscee2xGjhyZf/7zn8X9ZWVldcoXCoV622o77bTTMm/evOIyffr0VRY7AAAAAKzJ2rV2AAAA8EXRoUOHfOUrX0mSDB48OE8//XQuuuiinHrqqUmSmTNnZt111y2Wnz17dr1R5LWVl5envLx81QYNAAAAABgxDgAAK6tQKKSqqir9+/dP7969M2HChOK+RYsW5eGHH86OO+7YihECAAAAAIkR4wAA0Cynn356hg8fnr59+2bBggW5+eab89e//jX33ntvysrKcsIJJ+S8887LRhttlI022ijnnXdeOnXqlIMPPri1QwcAAACANZ7EOAAANMOsWbNy6KGHZsaMGamsrMyWW26Ze++9N3vssUeS5JRTTsknn3yS4447LnPmzMn222+f+++/P126dGnlyAEAAAAAiXEAAGiGK6+8ssn9ZWVlOfvss3P22WevnoAAAAAAgGZzj3EAAAAAAAAASprEOAAAAAAAAAAlTWIcAAAAAAAAgJLmHuMAAMBqUygU/vWgenHrBQIAn2e1PiPrfHYCAAArTWIcAABYbaqqqorrXf5xcytGAgBfDFVVVenUqVNrhwEAAF94plIHAAAAAAAAoKQZMQ4AAKw25eXlxfUFXzsoadu+FaMBgM+p6sXFmVVqf3YCAAArT2IcAABYbcrKyv71oG17iXEAWI46n50AAMBKkxhnhRQKhX89qF7ceoEAwOdZrc/IOp+dAAAAAAC0ColxVkhVVVVxvWZKLwCgcVVVVenUqVNrhwEAAAAAsEZr09oBAAAAAAAAAMCqZMQ4K6S8vLy4vuBrB7knJAA0pHpxcWaV2p+dAAAAAAC0DolxVkhZWdm/HrRtLzEOAMtR57MTAAAAAIBWYSp1AAAAAAAAAEqaxDgAAAAAAAAAJU1iHAAAAAAAAICSJjEOAAAAAAAAQEmTGAcAAAAAAACgpEmMAwAAAAAAAFDSJMYBAAAAAAAAKGkS4wAAAAAAAACUNIlxAAAAAAAAAEqaxDgAAAAAAAAAJU1iHAAAAAAAAICSJjEOAAAAAAAAQEmTGAcAAAAAAACgpEmMAwAAAAAAAFDSJMYBAAAAAAAAKGkS4wAAAAAAAACUNIlxAAAAAAAAAEqaxDgAAAAAAAAAJU1iHAAAAAAAAICSJjEOAAAAAAAAQEmTGAcAAAAAAACgpEmMAwAAAAAAAFDSJMYBAAAAAAAAKGkS4wAAAAAAAACUNIlxAAAAAAAAAEqaxDgAAAAAAAAAJU1iHAAAAAAAAICSJjEOAAAAAAAAQEmTGAcAAAAAAACgpEmMAwAAAAAAAFDSJMYBAAAAAAAAKGkS4wAAAAAAAACUNIlxAAAAAAAAAEpau9YOgC+usqVLUmjtIICkUEiWLlm23qZdUlbWuvEAKat5TwIAAAAA8LkgMc5K6zzpptYOAQAAAAAAAGC5TKUOAAAAAAAAQEkzYpwVUlFRkXvuuae1wwBqWbhwYQ444IAkyR133JGKiopWjgiozXsSAAAAAKD1SYyzQsrKytKxY8fWDgNoREVFhfcoAAAAAADAp5hKHQAAAAAAAICSJjEOAAAAAAAAQEmTGAcAAAAAAACgpEmMAwAAAAAAAFDSJMYBAAAAAAAAKGkS4wAAAAAAAACUNIlxAAAAAAAAAEqaxDgAAAAAAAAAJU1iHAAAAAAAAICS1q61AwAAANZMZUuXpNDaQQBJoZAsXbJsvU27pKysdeMBUlbzngQAAFqMxDgAANAqOk+6qbVDAAAAAGANYSp1AAAAAAAAAEqaEeMAAMBqU1FRkXvuuae1wwBqWbhwYQ444IAkyR133JGKiopWjgiozXsSAABahsQ4AACw2pSVlaVjx46tHQbQiIqKCu9RAAAASpKp1AEAAAAAAAAoaRLjAAAAAAAAAJQ0iXEAAAAAAAAASprEOAAAAAAAAAAlTWIcAAAAAAAAgJImMQ4AAAAAAABASZMYBwAAAAAAAKCkSYwDAAAAAAAAUNIkxgEAAAAAAAAoaRLjAAAAAAAAAJQ0iXEAAAAAAAAASprEOAAAAAAAAAAlTWIcAAAAAAAAgJImMQ4AAAAAAABASZMYBwAAAAAAAKCkSYwDAAAAAAAAUNIkxgEAAAAAAAAoaRLjAAAAAAAAAJQ0iXEAAAAAAAAASprEOAAAAAAAAAAlTWIcAAAAAAAAgJImMQ4AAAAAAABASZMYBwAAAAAAAKCkSYwDAAAAAAAAUNIkxgEAAAAAAAAoaRLjAAAAAAAAAJQ0iXEAAAAAAAAASprEOAAAAAAAAAAlTWIcAAAAAAAAgJLWrrUDAFpHoVDIwoULWzsMWkDt6+ialo6KioqUlZW1dhgA0Cj9ydKhP1ma9CcBAADqkhiHNdTChQszfPjw1g6DFnbAAQe0dgi0kHvuuScdO3Zs7TAAoFH6k6VJf7J06E8CAADUZSp1AAAAAAAAAEqaEeOwhqqoqMg999zT2mHQAgqFQqqqqpIk5eXlpkssERUVFa0dAgA0SX+ydOhPlib9SQAAgLokxmENVVZWZlq9EtKpU6fWDgEAWMPoT5YW/UkAAABKnanUAQAAAAAAAChpEuMAAAAAAAAAlDSJcQAAAAAAAABKmsQ4AAAAAAAAACVNYhwAAJphzJgx2XbbbdOlS5f07Nkz+++/f15++eU6ZQqFQs4+++z06dMnHTt2zG677ZYXX3yxlSIGAAAAAGpIjAMAQDM8/PDDGT16dJ588slMmDAhS5YsybBhw/LRRx8Vy/zyl7/MBRdckLFjx+bpp59O7969s8cee2TBggWtGDkAAAAAUFYoFAqtHUSpmD9/fiorKzNv3rx07dq1tcMBAFjl1uT+z7vvvpuePXvm4Ycfzq677ppCoZA+ffrkhBNOyKmnnpokqaqqSq9evfLf//3fOeaYY5bb5pp8PgGANZP+DwAAq4sR4wAAsBLmzZuXJOnWrVuSZMqUKZk5c2aGDRtWLFNeXp6hQ4fmiSeeaJUYAQAAAIBl2rV2AAAA8EVTKBRy0kknZeedd87AgQOTJDNnzkyS9OrVq07ZXr165c0332ywnaqqqlRVVRUfz58/fxVFDAAAAABrNiPGAQBgBf3whz/M5MmTc9NNN9XbV1ZWVudxoVCot63GmDFjUllZWVz69u27SuIFAAAAgDWdxDgAAKyA448/PnfddVceeuihrL/++sXtvXv3TvKvkeM1Zs+eXW8UeY3TTjst8+bNKy7Tp09fdYEDAAAAwBpMYhwAAJqhUCjkhz/8YW6//fb85S9/Sf/+/evs79+/f3r37p0JEyYUty1atCgPP/xwdtxxxwbbLC8vT9euXessAAAAAEDLc49xAABohtGjR+fGG2/MnXfemS5duhRHhldWVqZjx44pKyvLCSeckPPOOy8bbbRRNtpoo5x33nnp1KlTDj744FaOHgAAAADWbBLjAADQDL/73e+SJLvttlud7VdffXUOP/zwJMkpp5ySTz75JMcdd1zmzJmT7bffPvfff3+6dOmymqMFAAAAAGorKxQKhdYOolTMnz8/lZWVmTdvnmkwAYA1gv5Py3I+AYA1jf4PAACri3uMAwAAAAAAAFDSSjoxfskll6R///6pqKjIoEGD8uijjzZZ/uGHH86gQYNSUVGRAQMG5NJLL11NkQIAAAAAAACwqpRsYvyWW27JCSeckDPOOCPPPfdcdtlllwwfPjzTpk1rsPyUKVOy9957Z5dddslzzz2X008/PT/60Y8ybty41Rw5AAAAAAAAAC2pZO8xvv3222ebbbbJ7373u+K2TTfdNPvvv3/GjBlTr/ypp56au+66Ky+99FJx26hRo/KPf/wjf/vb35p1TPdEAgDWNPo/Lcv5BADWNPo/AACsLiU5YnzRokV59tlnM2zYsDrbhw0blieeeKLBOn/729/qld9zzz3zzDPPZPHixQ3Wqaqqyvz58+ssAAAAAAAAAHy+lGRi/L333kt1dXV69epVZ3uvXr0yc+bMBuvMnDmzwfJLlizJe++912CdMWPGpLKysrj07du3ZZ4AAAAAAAAAAC2mJBPjNcrKyuo8LhQK9bYtr3xD22ucdtppmTdvXnGZPn36Z4wYAAAAAAAAgJbWrrUDWBXWWWedtG3btt7o8NmzZ9cbFV6jd+/eDZZv165dunfv3mCd8vLylJeXt0zQAAAAAAAAAKwSJTlivEOHDhk0aFAmTJhQZ/uECROy4447NlhnyJAh9crff//9GTx4cNq3b7/KYgUAAAAAAABg1SrJxHiSnHTSSbniiity1VVX5aWXXsqJJ56YadOmZdSoUUmWTYN+2GGHFcuPGjUqb775Zk466aS89NJLueqqq3LllVfm5JNPbq2nAAAAAAAAAEALKMmp1JNkxIgRef/993PuuedmxowZGThwYMaPH59+/folSWbMmJFp06YVy/fv3z/jx4/PiSeemIsvvjh9+vTJ//t//y8HHnhgaz0FAAAAAAAAAFpAWaFQKLR2EKVi/vz5qayszLx589K1a9fWDgcAYJXT/2lZzicAsKbR/wEAYHUp2anUAQAAAAAAACCRGAcAAAAAAACgxEmMAwAAAAAAAFDSJMYBAAAAAAAAKGntWjuAUlIoFJIk8+fPb+VIAABWj5p+T00/iM9GfxIAWNPoTwIAsLpIjLegBQsWJEn69u3bypEAAKxeCxYsSGVlZWuH8YWnPwkArKn0JwEAWNXKCn6O2WKWLl2ad955J126dElZWVlrhwOsQebPn5++fftm+vTp6dq1a2uHA6xBCoVCFixYkD59+qRNG3fp+az0J4HWoj8JtBb9SQAAVheJcYASMH/+/FRWVmbevHm+yAQAYIXpTwIAAFDq/AwTAAAAAAAAgJImMQ4AAAAAAABASZMYBygB5eXlOeuss1JeXt7aoQAA8AWkPwkAAECpc49xAAAAAAAAAEqaEeMAAAAAAAAAlDSJcQAAAAAAAABKmsQ4AAAAAAAAACVNYhzgC+6SSy5J//79U1FRkUGDBuXRRx9t7ZAAAPiC0acEAACg1EmMA3yB3XLLLTnhhBNyxhln5Lnnnssuu+yS4cOHZ9q0aa0dGgAAXxD6lAAAAKwJygqFQqG1gwBg5Wy//fbZZptt8rvf/a64bdNNN83++++fMWPGtGJkAAB8UehTAgAAsCYwYhzgC2rRokV59tlnM2zYsDrbhw0blieeeKKVogIA4ItEnxIAAIA1hcQ4wBfUe++9l+rq6vTq1avO9l69emXmzJmtFBUAAF8k+pQAAACsKSTGAb7gysrK6jwuFAr1tgEAQFP0KQEAACh1EuMAX1DrrLNO2rZtW28kz+zZs+uN+AEAgIboUwIAALCmkBgH+ILq0KFDBg0alAkTJtTZPmHChOy4446tFBUAAF8k+pQAAACsKdq1dgAArLyTTjophx56aAYPHpwhQ4bksssuy7Rp0zJq1KjWDg0AgC8IfUoAAADWBBLjAF9gI0aMyPvvv59zzz03M2bMyMCBAzN+/Pj069evtUMDAOALQp8SAACANUFZoVAotHYQAAAAAAAAALCquMc4AAAAAAAAACVNYhwAAAAAAACAkiYxDgAAAAAAAEBJkxgHAAAAAAAAoKRJjAMAAAAAAABQ0iTGAQAAAAAAAChpEuMAAAAAAAAAlDSJcQAAAAAAAABKmsQ4rAJlZWUpKyvL2WefvUrav+aaa4rHmDp16io5Bq2rqdeQ609LWdV/qwAAAAAA4PNCYpw13l//+tdicqj20q5du3Tr1i39+/fPrrvumhNPPDHjxo3LokWLWjtkVtKGG27Y6LXu3r17dthhh/yf//N/8sYbb7R2qKyk3XbbrcFr3NCy4YYbtna4AACfyWOPPVanf/PII4+0dkgAAADwuSUxDo2orq7OnDlzMnXq1Dz66KO58MIL82//9m9Zf/3187Of/SxLlixp7RA/F84+++ziF3FfVNXV1fnggw/y97//Pf/93/+dzTbbLL///e9bOyxYKTU/ADn88MNbOxQAYBW77rrrmnwMAAAA/Eu71g4APk+OPfbYHHfcccXHH374YebMmZPJkyfnwQcfzAMPPJB33303Z555Zv70pz/lz3/+c3r06FGvnUKhsDrDZgX16dMn9913X/HxkiVLMn369Nx888258cYbU1VVlWOPPTYbbLBBhg8f3oqRNuzwww+X9GyG559/vsn9HTp0WE2RfH75WwUAX1xVVVW59dZbkySdO3fOhx9+mFtvvTW//e1v07Fjx1aODgAAAD5/JMahlp49e2bgwIH1tg8fPjynnnpqXnzxxRx66KF57rnn8tRTT+U73/lOHnzwQQm2L5j27dvXu85bbbVV9t133wwaNCg//vGPUygUcuaZZ34uE+M0T0PvZQCAUnHnnXdm7ty5SZKLLroo3//+9zN//vzceeedOeigg1o3OAAAAPgcMpU6rIDNN988jz/+eLbeeusky+7pd8kll7RyVLSkE044IRtssEGS5Nlnn83s2bNbOSIAAKjv2muvTZJsttlmOfLII7PZZpslMZ06AAAANEZiHFZQx44dc/311xfvqf3rX/86ixcvrlOm5p7bZ599doNtvPHGGzn//POz7777ZsMNN0zHjh3TsWPH9OvXLyNGjMi99967QjFVVVXl17/+dbbZZptUVlama9eu2X777XPxxRenurp6ufULhUJuu+22HHjggenbt28qKiqy9tprZ7vttstPf/rT4kiU2q655pqUlZXlnHPOqfe8ay9Tp05tkePV9sorr+T444/PwIED07lz53To0CF9+vTJVlttlSOPPDK33HJLqqqqlvu8G9KmTZtsu+22xcfTpk2rV+att97Kaaedlm222SZrr712KioqssEGG2TEiBF56KGHmnWcP/zhD9ltt92y9tprp3Pnzhk4cGDOOuus5T735F/nvrHzW2Py5Mk59NBDs9566xVjPOSQQzJx4sQky6ZkLysry4Ybbliv7tSpU4vHuOaaa5Ikt99+e/bee+/06dMn7dq1y2677VavXkucmzlz5uRnP/tZhgwZknXWWSfl5eXp06dP9ttvv9x+++3NamNFNXUualveuf/0/b3/93//N0cddVQ23HDDlJeXp1evXjnggAPy5JNPNiuuqVOn5tRTT82gQYPSvXv3VFRUpH///tl9991z/vnn13l97rbbbikrK8ubb76ZZNmX5Z9+P376mi3vb1WSLF26NDfccEP23nvv9O7dOx06dEiPHj2y++6755JLLsmiRYsarXv22WcXj5EkCxcuzK9+9atss8026dKlS7p06ZLtttsuY8eOzZIlS5p1TgCAZPbs2bn//vuTJIccckiS5Hvf+16S5P7778+sWbOW28Z7772Xn/zkJ9l4443TsWPH9OrVK3vssUfuuOOOJM3vcy5ZsiRXXnllsZ9YXl6eddZZJ7vuumsuvPDCLFy48DM+WwAAAGghBVjDPfTQQ4UkhSSFs846q9n1hg0bVqz3+OOP19nXVHtvvPFGcX9TyyGHHFJYvHhxg8e++uqri+UmTpxYGDRoUKPt7LzzzoX58+c3+jxmz55d2GmnnZqMpVevXoUnn3yy0RiaWqZMmdIix6vxxz/+sdChQ4flHvf555+vV7dfv36FJIV+/fo1ej4KhUJhxIgRxXY+HccVV1xR6NixY5PH/v73v9/otVu8eHHhwAMPbLTul7/85TqvkYZeQ7XP/afPb41rrrmm0L59+waP0b59+8I111xTGDlyZKPnY8qUKcXyV111VeHQQw+t187QoUNb9NwUCoXC3XffXVhrrbWabONb3/pWYcGCBQ3WHzp0aLHcimjqXNS2vHNf8xobOXJkYdy4cYVOnTo1+Bzatm1buPnmm5s81q9+9atGr2FD16D2c29O+UKh6b9VhUKh8P777y/3/brpppsWpk6d2mD9s846q1hu5syZha997WuNtrPvvvsWqqurmzwnAMAyF1xwQSFJoaysrPDmm28WCoVCYerUqYWysrJCksL555/fZP1JkyYVevTo0ejn8tFHH92sPudrr71W2GyzzZrsK2y00UaFV155paVPAQAAAKww9xiHlfTNb36zOErj0UcfzY477tisetXV1enQoUP23HPP7LHHHtlss83SrVu3fPDBB3nllVdy8cUX58UXX8wNN9yQAQMG1BmR3ZBjjjkmzz77bEaMGJGRI0emZ8+eeeWVV/Kb3/wmTz/9dB577LF873vfy1133VWv7kcffZShQ4fmpZdeSocOHXLEEUdk7733Tt++ffPRRx/lkUceyQUXXJBZs2Zl+PDhee6559KvX78kyf7775/Bgwfnkksuye9+97skyfPPP1/vGOutt16LHC9JZs2alSOOOCKLFi1Kz54988Mf/jA77LBD1llnnSxcuDBvvPFGHnnkkc88qnjy5MnF9T59+hTXr7rqqvzgBz9Isuz+1cccc0y23nrrdOrUKVOmTMmVV16Z8ePH58orr0xlZWXOP//8em2feOKJGTduXJJkk002ySmnnJItt9wy8+bNy6233prLL788I0aM+EzxP/bYYznyyCOzdOnSdOzYMSeeeGL22muvlJeX55lnnsmYMWNy9NFHZ/PNN29WexdeeGEmT56cXXbZJccee2w23njjzJ07t87IoZY4NxMmTMi3v/3tVFdXZ8MNN8yxxx6b7bffPl27ds3bb7+dW265JTfccEPuvvvujBw5sngeP48mT56cW265Jeuuu25+/OMfZ/DgwSkUCrnvvvvyi1/8IgsXLszRRx+dr3/96+nRo0e9+j/96U/zf//v/02SrLXWWjnuuOOy++67p3v37pk7d24mTpyYO+64ozgSO0muvvrqfPTRR9lzzz3zzjvvZL/99svPfvazOu1+6UtfavZzqK6uzj777JO//e1vSZKhQ4fmhz/8Yfr375933nknV111Vf7nf/4nL730Ur7xjW9k0qRJ6dy5c6Ptfec738lLL72UH/3oR9l3333TrVu3vPzyy/npT3+al156KX/6059y+eWX55hjjml2jACwpqqZRn2XXXYp3gaoX79+2XnnnfPoo4/muuuuy0knndRg3Tlz5mSvvfbKu+++m2TZSPNDDjkkPXr0yGuvvZaLLrool112Wf7xj380GcOMGTOy0047ZdasWenSpUuOPvrofPOb30yvXr0yb9683H///bnooovy6quvZq+99srEiRNTWVnZgmcBAAAAVlBrZ+ahta3siPEHHnigWO/II4+ss6+p9j788MPCO++802i7S5cuLRx++OGFJIUvfelLhblz59Yr8+nR2uedd169MosXLy7sueeexTJ//vOf65X54Q9/WEhSqKysLDz99NMNxjN16tTCuuuuW0iWjWL/tNojQpfnsx7vyiuvLB6roRHhNT755JPCxx9/XG97c0aM33777cVjDBgwoLh92rRpxdG/I0eObHTU8+mnn15IUmjTpk3h5ZdfrrPvH//4R6FNmzaFJIVtttmmwVHP1157bZ1ruzIjxmtG5Xbo0KHebAaFQqEwa9aswoABA4ptLG/EeJLCYYcdVli6dGmDz7klzs2HH35Y6NWrVyFJYdiwYYWPPvqowTYuu+yyYkwPPPBAvf21R00///zzTS61tfSI8SSFQYMGNfj+veGGG4plLrjggnr7n3322eLrZOONNy5Mnz690Xga2ld71PryNPU6Gzt27HKvf801TVI45ZRT6u2v/fehffv2hYceeqhemffff7947bfccsvlxgwAa7rJkycXP18vu+yyOvt+//vfF/dNnjy5wfo/+tGPimV+/etf19u/ZMmSwn777VenL9hQv2efffYpJCn07du38Prrrzd4rIkTJxa+9KUvFZIU/uu//mvFnywAAAC0IPcYh5XUvXv34vqcOXOaXe9LX/pS1l133Ub3l5WV5fzzz0/btm3z0Ucf5YEHHmiyvS233DKnnnpqve3t2rXLFVdckfbt2ydJLrnkkjr733vvvVxxxRVJknPPPTeDBw9usP1+/frlzDPPTJLccsst+fjjj5uMpzEtcbyZM2cmSdZee+0MHDiw0WNVVFSkY8eOzY6turo6U6dOzZgxY3LwwQcXt59yyinF9Ysuuigff/xx+vTpk0svvTTt2jU84cY555yT9dZbL0uXLs11111XZ9+ll16apUuXJkkuu+yyBkfXHnbYYRk+fHizY/+0J598sji6Z/To0Q3OZNCzZ8/85je/aXaba621VsaOHVtndHJtLXFurr766syaNSsVFRW5/vrr06lTpwbbOOqoo7LddtsV6zRliy22aHJZ1a666qoGR0UdfPDBxZkIHn300Xr7f/WrX2Xp0qUpKyvLzTffnPXXX7/RYzS177O6+OKLkyTrrLNOo9f/3HPPzVe/+tUkyeWXX56qqqpG2zv++OMbvC99t27dcsQRRyRZNtJ+3rx5LRA9AJSumtHi5eXl+fd///c6+7773e+mvLy8TrnaFi5cWNy+zTbbNDiqvG3btvn973+fioqKRmN44YUX8uc//zlJMnbs2AwYMKDBcltvvXVGjx6dZFnfCAAAAFqTxDispNpJzQULFqx0O4sXL85bb72Vl156KS+88EJeeOGFvPPOO8XE+/KmMBw5cmTatGn4rbz++utn2LBhSZK//vWvqa6uLu677777snDhwiTLvkBryq677lqM9dlnn23eE/uUljhezQ8K5syZkzvvvHOl4kiSN998M2VlZcWlXbt26d+/f04//fRijMcee2ydKZ1rjrfvvvs2+SVhu3btMmTIkCQpTkFdo+ZHDltssUUGDRrUaBtHHnnkyj2xJA8++GBxfeTIkY2W+9a3vlXnxx1N2XfffdOlS5dG97fEualpY+jQoenZs2eT8dS8Pj7dxufJFltskS233LLBfWVlZdl6662TJG+88UadfUuXLs29996bZNm5qCm3ur3zzjt56aWXkix7vzZ2/du2bVtMas+ZMycTJ05stM3vfe97je6r/X6YMmXKyoQMAGuE6urq3HjjjUmW9efWWmutOvvXWmut7L333kmSG2+8sU7/P0meffbZ4o/QDjvssEZ/+NirV6/sueeejcZR03fr1KlTvvWtbzUZc03f7Z133sn06dObLAsAAACrksQ4rKTayfCuXbuuUN3Fixfn4osvzg477JDOnTunb9++2WyzzeqMZp09e3aSZSOtm7Lttts2ub9mdO3HH39cJwn3zDPPFNfXXXfdOoniTy+1R2fXjNpeUS1xvG9/+9vFL/8OOOCAfP3rX89vfvObPPvss/W+9FsZnTt3zvDhwzN+/Pg6I+znzZuX1157LUny+9//vsnYy8rKctttt9WLfeHChcU2mnvNVsYLL7yQZNkIoqZG1bdt2zZbbbVVs9psLMGbtMy5Sf71+rjvvvuW28avf/3rBtv4tEKh0OSyKtWMom5Mt27dktT/Uc2UKVMyd+7cJP/6Erk11LyOkmT77bdvsmzt/bXrfVpT56TmfCSf7YdGAFDq7r///syYMSNJcsghhzRYpmb7jBkz6s0+VfuzuqkfaiZpdIan5F99t48//jjt2rVrsu+2zz77FOut7P8SAAAA0BIanu8WWK7aCevaSZ3l+eCDDzJs2LBmj7z+5JNPmty/vNG1vXr1qnPsGjWJ9xW1slOpt8Txunfvnrvuuiv/8R//kbfffjsPPfRQHnrooSTLfpzwzW9+M0cccUSdL98a0qdPn9x3333Fx+3atUvXrl3Tu3fvBkfft0Tsc+fOLSZjV+Saraiaaf27deuWtm3bNlm2R48ezWpz7bXXbnRfS5ybxYsXF5PBK9vG501jU8HXqHmdffoHHbX/rjR1y4VVrfbfiuW9Hnv37t1gvU9r6pzUft+1xI9cAKBU1dyOZq211mp0pHbNSPK5c+fmuuuuqzPyu/YtoJbXJ22qr7i6/5cAAACAliAxDivpueeeK65vsskmza73n//5n8Wk+P77758jjzwyW265ZXr27JmKioridIYbbLBBpk+fvtyRrY1Nf1ijsfo1yacOHTqs0PToK3tP45Y63i677JLXXnst48aNy/jx4/PII4/krbfeyvz583P77bfn9ttvz5577pnbb7+90URc+/btmxxN3VjsSXLCCSfk+9//frPqdejQobhe+zos75p93jSVYG+Jc1O7je9+97vFe8yv6T4vr5OV/RsDALSs+fPnF6cwnzt3bvFe4k35n//5nyxYsKDJ2+KsjJr+W//+/XPXXXc1u17//v1bNA4AAABYERLjsJImTJhQXN95552bVWf+/Pm55ZZbkiQHH3xw/vCHPzRatvZojqbMmjUrG2+8caP7a4/mqD2yveb+0osWLUr37t1X+ejUljxeRUVFvve97xXvWfzGG2/k7rvvztixY/PKK6/kvvvuyxlnnJHf/OY3LRp7smyUy4ok1WvUHnU9a9asJssub39zjvPBBx+kurq6yaT2u+++u9LHqdES56aioiKdOnXKxx9/nLlz565UG59VzYjlpUuXNlnuo48+WqVxrLPOOsX1d955Z5Ueqym1/1Ysb8rT2q/XFZk9AwBYMX/84x+XO5vUp3388ce57bbbcsQRRySp2yedPXt2k/9HNNVXrOkDzpo1K1/96lfTrp2vFgAAAPj8c49xWAkvvPBCHnzwwSRJ3759m7z/Xm2vvvpqFi9enCQ56KCDGi338ssv58MPP2xWm08//XSz9nfq1CkDBgwobt96662L6/fff3+zjtWQ5o5qbanjNWTAgAE5/vjj8/TTTxdHmP/xj39ssfZ79OiR9dZbL0nywAMPrNQI2YqKimy00UZJmn/NVsbmm2+eJKmqqsrzzz/faLnq6upMmjRppY9ToyXOTfKv18fjjz/eKlNs1oyiWt6U7i+//PIqjaN///7FL6wfeeSRlWqjJUaa1/5xwt///vcmyz711FMN1gMAWlbNNOrrrrtubrrppuUuG2ywQZ16yb/6ism/7hPemKb21/TdPv744zz++OMr/ZwAAABgdZIYhxX0ySef5LDDDismAE8++eRmj5BYsmRJcb2p5N+ll17a7Hiuv/76RpORb7/9djEJvdtuu9UZPTx8+PC0b98+SfKb3/ymTmwroqKiorheVVXVaLmWOl5Tunbtmm233TZJ3Xs1t4Rvf/vbSZaNTr/ttttWqo1vfvObSZLnn3++zlT8n3bVVVetVPtJ8o1vfKO4XvtL0E+7++678/7776/0cWpriXNT08ZHH32Uiy++uEXiWhE103ouWLCg0eT3okWLMm7cuFUaR5s2bbL33nsnSR5++OEmXyeNqXlPNvV+XJ4+ffpk0003TZLceuutWbBgQYPlqqurc8011yRZNgJtm222WeljAgCNmzJlSh577LEkyYEHHpiDDjpoucu///u/J1nWp5g2bVqSZPDgwamsrEzS9P8Rs2bNyn333ddoPPvtt19x/Ze//GWLPEcAAABY1STGYQX885//zM4771xMVg0dOjTHHntss+t/5StfKY7mbCxp+ec//zm//e1vm93mpEmT8qtf/are9iVLluSoo47KokWLkqRenOutt15xSsV//OMfOeaYY5pMVs+ePTtXXHFFve21p0R//fXXG63fEse77777MmPGjEbrzJs3rzh6taXvX/iTn/ykeB/HUaNGLXeEzfjx4zN58uQ624455pji9T/66KMbnJb7D3/4Q8aPH7/ScQ4ZMiRbbrllkuTiiy/OE088Ua/Mu+++mxNPPHGlj/FpLXFuRo0aVZxG/Mwzz8w999zTZBuPP/74So+obsjQoUOL6+eff369/YVCIf/5n/+5WqY3P/nkk9OmTZsUCoUcdNBBeeuttxot29C+mvdkU+/H5hg9enSSZa+X448/vsEvzs8555z885//TJIcddRRzbrXKQCw4monsf/t3/6tWXVqyhUKhVx//fVJlv2A7rDDDkuSTJw4MRdccEG9ekuXLs0xxxyThQsXNtr2tttum2HDhiVZ1rc766yzmoxl6tSpuemmm5oVNwAAAKwqbgQGtcyePTsvvPBC8fFHH32UOXPmZPLkyXnwwQczYcKE4hdSO+ywQ2677bbiKOjm6N69e/bee+/cfffdGT9+fPbaa68cc8wx2WCDDTJ79uyMGzcu11xzTQYMGJC5c+c26x7QgwcPzqmnnppJkyblsMMOS8+ePfPqq6/mggsuKCaJ99133+yzzz716p5//vl54okn8sILL+Sqq67Kk08+maOPPjqDBg1K586dM3fu3Lz44ot54IEHMn78+GyxxRb5wQ9+UKeNHXfcsbh+4okn5owzzsi6665bTABvuOGGxRH1n/V4N910U/bdd9/sscceGTZsWAYOHJhu3bplwYIFeeGFFzJ27Ni8/fbbSer/EOCz6t+/fy699NIcccQR+eCDD7LTTjvl0EMPzT777JMNNtggS5YsyVtvvZWnnnoqt912W15//fX86U9/Kiapk+RrX/taRo8enbFjx+aZZ54pXrstttgi8+bNy6233prLLrssgwcPXm5yuSkXX3xxhg4dmkWLFuWb3/xmTjrppOy1114pLy/PM888kzFjxmTmzJnZaqutMmnSpM889XZLnJuuXbvmpptuyvDhw1NVVZV99tknBx54YA488MB8+ctfTpLMmDEjzz77bO64445Mnjw5v/3tb7Prrrt+pthrbL311tlhhx3y5JNP5vLLL8+iRYsycuTIVFZW5tVXX82ll16av/71rxkyZEj+9re/tcgxG7PVVlvlnHPOyZlnnplXXnklW2yxRUaPHp3dd9893bt3z9y5czNp0qTcfvvtadu2bR566KE69Xfcccc89NBDefrpp/OLX/wiw4cPz5e+9KUkSceOHYtT3y/PqFGj8oc//CF/+9vfcu211+bNN9/M6NGjM2DAgMyYMSNXXXVVbr/99iTJl7/85Zx55pkteyIAgKKaxHbPnj2zyy67NKvO9ttvn/XXXz9vvfVWrr/++pxxxhlJkrPPPju33nprZs6cmZNPPjnPPfdcDj300PTo0SOvvfZaLrroojzxxBPZbrvtiv9PNNRfvPrqqzN48ODMmDEj5557bu67774ceeSR2WKLLVJRUZH3338/kydPzr333pu//OUv2X///fMf//EfLXRGAAAAYCUUYA330EMPFZI0e+nRo0fh5z//eWHx4sWNtllT9qyzzqq3b9q0aYUNNtig0fY32GCDwosvvljo169fIUlh5MiR9dq4+uqri+UnTpxY2HrrrRttb6eddirMnz+/0Vjff//9wl577dWs57777rs32MZ3v/vdRutMmTKlxY43cuTIZtUbPXp0obq6ul6cNee0X79+jZ6P5bn55psLXbt2XW4Mbdq0KfzlL3+pV3/RokWF73znO43W69+/f+GNN95o8jVU+/p/+vzWuOaaawrt27dv8Bjt2rUrXH755YVDDz20kKTw1a9+tV79KVOmFMtfffXVq+XcFAqFwoMPPljo3bt3s67ztddeW6/+0KFDi/tX1EsvvVTo2bNno8c76aSTlnvum3rf1lbzWm7qtXjeeecV2rVr1+Q5GDp0aL16b731VqFbt27NKt/U66xQWPZ+3WmnnZqMYdNNNy1MnTq1wfpnnXVWs65H7b/DDz30UJNlAWBN89hjjxU/J4855pgVqvujH/2oWPfJJ58sbp80aVKhR48ejX6+H3744YUrr7yy+HjmzJkNtj916tTCtttu26y+2xFHHPGZzgMAAAB8VqZSh0a0adMmlZWV2WCDDbLLLrvkhBNOyLhx4/LWW2/l9NNPb/Z9xT+tb9++mThxYn7yk59k4403Tnl5eSorK/O1r30tZ511ViZNmpTNNtus2e2tvfbaeeKJJzJmzJhstdVW6dKlSzp37pxtt902v/3tb/Pwww+nS5cujdbv1q1b7rnnnjz44IM54ogjstFGG6Vz585p165dunXrlm233TajR4/O+PHjM2HChAbbuOGGG/LLX/4y2223XSorK9OmTeN/Wj7L8S688MKMGzcuo0aNyuDBg7PeeuulQ4cO6dixYzbeeOMcfvjheeyxxzJ27NgmY/gsRowYkalTp+YXv/hFdtttt/Ts2TPt27dPp06dMmDAgOy777654IILMnXq1Oy+++716rdv3z7jxo3L9ddfn1122SWVlZXp1KlTNt1005x++ul59tlnW2Qa+JEjR+aZZ57J9773vfTp0ycdOnTIeuutl+9+97t57LHH8oMf/CDz589PkuJ9Jj+rz3pukuTrX/96Xn/99YwdOzZ77bVX1l133XTo0CEVFRXp27dvhg0blp///Of53//93+I0oC3lq1/9aiZOnJhjjz02/fr1S4cOHdKjR4/stddeufvuuxucYn1VOu200/LPf/4zJ5xwQgYOHJiuXbumoqIiAwYMyDe+8Y1ceOGFufnmm+vVW2+99fLUU0/l+9//fr7yla8U7zm+Mrp165ZHHnkk119/ffbaa6/06tUr7du3T/fu3bPbbrtl7NixmTRpUvr16/dZnioA0ITat2A68MADV6hu7fK12/na176Wf/7zn/nxj3+cjTbaKOXl5VlnnXWy++6758Ybb8zVV19d7CsmjfcX+/Xrl7///e+54447ctBBB6V///7p1KlT2rdvnx49emTHHXfMj3/84zz88MO58sorVyh2AAAAaGllhUIDNw0FoOR95Stfyeuvv55DDjmkOD0nAAAkyQ9+8INceeWVWX/99TN9+vTWDgcAAAA+MyPGAdZATz/9dF5//fUkyQ477NDK0QAA8HnyySef5M4770yirwgAAEDpkBgHKEGvvfZao/vef//9HHXUUUmS8vLyjBgxYnWFBQDA58Drr7+exiaPq66uzrHHHpv33nsvybJb9AAAAEApWLmbJAPwubbHHnukf//+OeCAA7LlllumsrIyc+bMyeOPP55LLrkkM2bMSJL813/9V9ZZZ51WjhYAgNXppz/9aZ566qkcdNBB2X777dOzZ8988sknmTx5ci6//PJMnDgxSfKNb3wj3/rWt1o5WgAAAGgZ7jEOUII23HDDvPnmm02WOe644/Lb3/42bdqYPAQAYE1y+OGH59prr22yzE477ZQ777wz3bt3X01RAQAAwKolMQ5Qgh5++OH86U9/ysMPP5wZM2bkvffeS7t27dK7d+/svPPOOfroo7Pjjju2dpgAALSCl19+OePGjcuECRPy5ptv5t13383ixYvTvXv3DB48OCNGjMhBBx3kB5QAAACUFIlxAAAAAAAAAEqan38DAAAAAAAAUNIkxgEAAAAAAAAoaRLjAAAAAAAAAJQ0iXEAAAAAAAAASprEOAAAAAAAAAAlTWIcAAAAAAAAgJImMQ4AAAAAAABASZMYBwAAAAAAAKCkSYwDAAAAAAAAUNL+P11hqcZMsMNMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 2000x2500 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Checking the outliers\n",
        "plt.figure(figsize=(20,25),facecolor='white')\n",
        "plotnumber=1\n",
        "\n",
        "for column in data.drop('Outcome',axis=1):\n",
        "    if plotnumber<=9:\n",
        "        ax=plt.subplot(3,3,plotnumber)\n",
        "        sns.boxplot(data[column])\n",
        "        plt.xlabel(column,fontsize=20)\n",
        "        plt.ylabel('Count',fontsize=20)\n",
        "    plotnumber+=1\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b92b076c",
      "metadata": {
        "id": "b92b076c",
        "outputId": "5df3568a-24c4-4c57-beff-8e892463e3a4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>30.5</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>30.5</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>23</td>\n",
              "      <td>30.5</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94.0</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168.0</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35     30.5  33.6   \n",
              "1            1       85             66             29     30.5  26.6   \n",
              "2            8      183             64             23     30.5  23.3   \n",
              "3            1       89             66             23     94.0  28.1   \n",
              "4            0      137             40             35    168.0  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "93a51e19",
      "metadata": {
        "id": "93a51e19"
      },
      "outputs": [],
      "source": [
        "# Scale the continuous data\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc=MinMaxScaler() # creating object\n",
        "dl=['Pregnancies','Outcome']\n",
        "data1=sc.fit_transform(data.drop(dl,axis=1))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa6cbec2",
      "metadata": {
        "id": "aa6cbec2"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxscaler\n",
        "sc=MinMaxScaler()\n",
        "d1=['Pregnancies','Outcome']\n",
        "data1=sc.fit_transform(data.drop(dl,axis=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "468df29b",
      "metadata": {
        "collapsed": true,
        "id": "468df29b",
        "outputId": "077b122e-8dd8-4a90-89f2-0e15b2580aa3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.67096774, 0.48979592, 0.30434783, ..., 0.31492843, 0.23441503,\n",
              "        0.48333333],\n",
              "       [0.26451613, 0.42857143, 0.23913043, ..., 0.17177914, 0.11656704,\n",
              "        0.16666667],\n",
              "       [0.89677419, 0.40816327, 0.17391304, ..., 0.10429448, 0.25362938,\n",
              "        0.18333333],\n",
              "       ...,\n",
              "       [0.49677419, 0.48979592, 0.17391304, ..., 0.16359918, 0.07130658,\n",
              "        0.15      ],\n",
              "       [0.52903226, 0.36734694, 0.17391304, ..., 0.24335378, 0.11571307,\n",
              "        0.43333333],\n",
              "       [0.31612903, 0.46938776, 0.26086957, ..., 0.24948875, 0.10119556,\n",
              "        0.03333333]])"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bf979d13",
      "metadata": {
        "collapsed": true,
        "id": "bf979d13",
        "outputId": "2f16f9c2-406b-4af1-c167-6cc1e4a7660a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
              "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "472f3e06",
      "metadata": {
        "id": "472f3e06"
      },
      "outputs": [],
      "source": [
        "data2=pd.DataFrame(data1,columns=['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
        "       'BMI', 'DiabetesPedigreeFunction', 'Age'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3d827ea4",
      "metadata": {
        "collapsed": true,
        "id": "3d827ea4",
        "outputId": "1cc90a6c-7bb9-47e8-d8fa-473dd6586667"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.670968</td>\n",
              "      <td>0.489796</td>\n",
              "      <td>0.304348</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.314928</td>\n",
              "      <td>0.234415</td>\n",
              "      <td>0.483333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.264516</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.239130</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.171779</td>\n",
              "      <td>0.116567</td>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.896774</td>\n",
              "      <td>0.408163</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.104294</td>\n",
              "      <td>0.253629</td>\n",
              "      <td>0.183333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.290323</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.096154</td>\n",
              "      <td>0.202454</td>\n",
              "      <td>0.038002</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.163265</td>\n",
              "      <td>0.304348</td>\n",
              "      <td>0.185096</td>\n",
              "      <td>0.509202</td>\n",
              "      <td>0.943638</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>0.367742</td>\n",
              "      <td>0.530612</td>\n",
              "      <td>0.445652</td>\n",
              "      <td>0.199519</td>\n",
              "      <td>0.300613</td>\n",
              "      <td>0.039710</td>\n",
              "      <td>0.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>0.503226</td>\n",
              "      <td>0.469388</td>\n",
              "      <td>0.217391</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.380368</td>\n",
              "      <td>0.111870</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>0.496774</td>\n",
              "      <td>0.489796</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.117788</td>\n",
              "      <td>0.163599</td>\n",
              "      <td>0.071307</td>\n",
              "      <td>0.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>0.529032</td>\n",
              "      <td>0.367347</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.243354</td>\n",
              "      <td>0.115713</td>\n",
              "      <td>0.433333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>0.316129</td>\n",
              "      <td>0.469388</td>\n",
              "      <td>0.260870</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.249489</td>\n",
              "      <td>0.101196</td>\n",
              "      <td>0.033333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
              "0    0.670968       0.489796       0.304348  0.019832  0.314928   \n",
              "1    0.264516       0.428571       0.239130  0.019832  0.171779   \n",
              "2    0.896774       0.408163       0.173913  0.019832  0.104294   \n",
              "3    0.290323       0.428571       0.173913  0.096154  0.202454   \n",
              "4    0.600000       0.163265       0.304348  0.185096  0.509202   \n",
              "..        ...            ...            ...       ...       ...   \n",
              "763  0.367742       0.530612       0.445652  0.199519  0.300613   \n",
              "764  0.503226       0.469388       0.217391  0.019832  0.380368   \n",
              "765  0.496774       0.489796       0.173913  0.117788  0.163599   \n",
              "766  0.529032       0.367347       0.173913  0.019832  0.243354   \n",
              "767  0.316129       0.469388       0.260870  0.019832  0.249489   \n",
              "\n",
              "     DiabetesPedigreeFunction       Age  \n",
              "0                    0.234415  0.483333  \n",
              "1                    0.116567  0.166667  \n",
              "2                    0.253629  0.183333  \n",
              "3                    0.038002  0.000000  \n",
              "4                    0.943638  0.200000  \n",
              "..                        ...       ...  \n",
              "763                  0.039710  0.700000  \n",
              "764                  0.111870  0.100000  \n",
              "765                  0.071307  0.150000  \n",
              "766                  0.115713  0.433333  \n",
              "767                  0.101196  0.033333  \n",
              "\n",
              "[768 rows x 7 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fea85671",
      "metadata": {
        "id": "fea85671"
      },
      "outputs": [],
      "source": [
        "con_data=data[['Pregnancies','Outcome']]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "597931de",
      "metadata": {
        "collapsed": true,
        "id": "597931de",
        "outputId": "847fdce5-a5c3-42d3-ba09-4586d4c854b8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Outcome\n",
              "0              6        1\n",
              "1              1        0\n",
              "2              8        1\n",
              "3              1        0\n",
              "4              0        1\n",
              "..           ...      ...\n",
              "763           10        0\n",
              "764            2        0\n",
              "765            5        0\n",
              "766            1        1\n",
              "767            1        0\n",
              "\n",
              "[768 rows x 2 columns]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "con_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "22dac0f3",
      "metadata": {
        "id": "22dac0f3"
      },
      "outputs": [],
      "source": [
        "final_df=pd.concat([data2,con_data],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "513a0aca",
      "metadata": {
        "id": "513a0aca",
        "outputId": "85bf8af1-9fd2-45a0-cdc2-c57c8099ba59"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.670968</td>\n",
              "      <td>0.489796</td>\n",
              "      <td>0.304348</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.314928</td>\n",
              "      <td>0.234415</td>\n",
              "      <td>0.483333</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.264516</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.239130</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.171779</td>\n",
              "      <td>0.116567</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.896774</td>\n",
              "      <td>0.408163</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.104294</td>\n",
              "      <td>0.253629</td>\n",
              "      <td>0.183333</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.290323</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.096154</td>\n",
              "      <td>0.202454</td>\n",
              "      <td>0.038002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.163265</td>\n",
              "      <td>0.304348</td>\n",
              "      <td>0.185096</td>\n",
              "      <td>0.509202</td>\n",
              "      <td>0.943638</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>0.367742</td>\n",
              "      <td>0.530612</td>\n",
              "      <td>0.445652</td>\n",
              "      <td>0.199519</td>\n",
              "      <td>0.300613</td>\n",
              "      <td>0.039710</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>0.503226</td>\n",
              "      <td>0.469388</td>\n",
              "      <td>0.217391</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.380368</td>\n",
              "      <td>0.111870</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>0.496774</td>\n",
              "      <td>0.489796</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.117788</td>\n",
              "      <td>0.163599</td>\n",
              "      <td>0.071307</td>\n",
              "      <td>0.150000</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>0.529032</td>\n",
              "      <td>0.367347</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.243354</td>\n",
              "      <td>0.115713</td>\n",
              "      <td>0.433333</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>0.316129</td>\n",
              "      <td>0.469388</td>\n",
              "      <td>0.260870</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.249489</td>\n",
              "      <td>0.101196</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
              "0    0.670968       0.489796       0.304348  0.019832  0.314928   \n",
              "1    0.264516       0.428571       0.239130  0.019832  0.171779   \n",
              "2    0.896774       0.408163       0.173913  0.019832  0.104294   \n",
              "3    0.290323       0.428571       0.173913  0.096154  0.202454   \n",
              "4    0.600000       0.163265       0.304348  0.185096  0.509202   \n",
              "..        ...            ...            ...       ...       ...   \n",
              "763  0.367742       0.530612       0.445652  0.199519  0.300613   \n",
              "764  0.503226       0.469388       0.217391  0.019832  0.380368   \n",
              "765  0.496774       0.489796       0.173913  0.117788  0.163599   \n",
              "766  0.529032       0.367347       0.173913  0.019832  0.243354   \n",
              "767  0.316129       0.469388       0.260870  0.019832  0.249489   \n",
              "\n",
              "     DiabetesPedigreeFunction       Age  Pregnancies  Outcome  \n",
              "0                    0.234415  0.483333            6        1  \n",
              "1                    0.116567  0.166667            1        0  \n",
              "2                    0.253629  0.183333            8        1  \n",
              "3                    0.038002  0.000000            1        0  \n",
              "4                    0.943638  0.200000            0        1  \n",
              "..                        ...       ...          ...      ...  \n",
              "763                  0.039710  0.700000           10        0  \n",
              "764                  0.111870  0.100000            2        0  \n",
              "765                  0.071307  0.150000            5        0  \n",
              "766                  0.115713  0.433333            1        1  \n",
              "767                  0.101196  0.033333            1        0  \n",
              "\n",
              "[768 rows x 9 columns]"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e8c8473",
      "metadata": {
        "id": "8e8c8473"
      },
      "source": [
        "### Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3777b93e",
      "metadata": {
        "collapsed": true,
        "id": "3777b93e",
        "outputId": "21e0d379-86a9-492d-b663-b7e7f7d65862"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# checking the correlation\n",
        "sns.heatmap(data2.corr(),annot=True)\n",
        "# No correlation between the features so we are not dropping any columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9fa097c9",
      "metadata": {
        "id": "9fa097c9"
      },
      "source": [
        "### Model Creation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "20c8b495",
      "metadata": {
        "id": "20c8b495"
      },
      "outputs": [],
      "source": [
        "# create input and output variable\n",
        "X=final_df.iloc[:,:-1]\n",
        "y=final_df.Outcome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "32b4148f",
      "metadata": {
        "collapsed": true,
        "id": "32b4148f",
        "outputId": "318eaca6-1b9f-43c7-a173-472e0a4b6f95"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Pregnancies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.670968</td>\n",
              "      <td>0.489796</td>\n",
              "      <td>0.304348</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.314928</td>\n",
              "      <td>0.234415</td>\n",
              "      <td>0.483333</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.264516</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.239130</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.171779</td>\n",
              "      <td>0.116567</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.896774</td>\n",
              "      <td>0.408163</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.104294</td>\n",
              "      <td>0.253629</td>\n",
              "      <td>0.183333</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.290323</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.096154</td>\n",
              "      <td>0.202454</td>\n",
              "      <td>0.038002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.163265</td>\n",
              "      <td>0.304348</td>\n",
              "      <td>0.185096</td>\n",
              "      <td>0.509202</td>\n",
              "      <td>0.943638</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>0.367742</td>\n",
              "      <td>0.530612</td>\n",
              "      <td>0.445652</td>\n",
              "      <td>0.199519</td>\n",
              "      <td>0.300613</td>\n",
              "      <td>0.039710</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>0.503226</td>\n",
              "      <td>0.469388</td>\n",
              "      <td>0.217391</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.380368</td>\n",
              "      <td>0.111870</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>0.496774</td>\n",
              "      <td>0.489796</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.117788</td>\n",
              "      <td>0.163599</td>\n",
              "      <td>0.071307</td>\n",
              "      <td>0.150000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>0.529032</td>\n",
              "      <td>0.367347</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.243354</td>\n",
              "      <td>0.115713</td>\n",
              "      <td>0.433333</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>0.316129</td>\n",
              "      <td>0.469388</td>\n",
              "      <td>0.260870</td>\n",
              "      <td>0.019832</td>\n",
              "      <td>0.249489</td>\n",
              "      <td>0.101196</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
              "0    0.670968       0.489796       0.304348  0.019832  0.314928   \n",
              "1    0.264516       0.428571       0.239130  0.019832  0.171779   \n",
              "2    0.896774       0.408163       0.173913  0.019832  0.104294   \n",
              "3    0.290323       0.428571       0.173913  0.096154  0.202454   \n",
              "4    0.600000       0.163265       0.304348  0.185096  0.509202   \n",
              "..        ...            ...            ...       ...       ...   \n",
              "763  0.367742       0.530612       0.445652  0.199519  0.300613   \n",
              "764  0.503226       0.469388       0.217391  0.019832  0.380368   \n",
              "765  0.496774       0.489796       0.173913  0.117788  0.163599   \n",
              "766  0.529032       0.367347       0.173913  0.019832  0.243354   \n",
              "767  0.316129       0.469388       0.260870  0.019832  0.249489   \n",
              "\n",
              "     DiabetesPedigreeFunction       Age  Pregnancies  \n",
              "0                    0.234415  0.483333            6  \n",
              "1                    0.116567  0.166667            1  \n",
              "2                    0.253629  0.183333            8  \n",
              "3                    0.038002  0.000000            1  \n",
              "4                    0.943638  0.200000            0  \n",
              "..                        ...       ...          ...  \n",
              "763                  0.039710  0.700000           10  \n",
              "764                  0.111870  0.100000            2  \n",
              "765                  0.071307  0.150000            5  \n",
              "766                  0.115713  0.433333            1  \n",
              "767                  0.101196  0.033333            1  \n",
              "\n",
              "[768 rows x 8 columns]"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adff8c77",
      "metadata": {
        "collapsed": true,
        "id": "adff8c77",
        "outputId": "97d33fa2-2079-42fd-9294-51806ff4dd16"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0      1\n",
              "1      0\n",
              "2      1\n",
              "3      0\n",
              "4      1\n",
              "      ..\n",
              "763    0\n",
              "764    0\n",
              "765    0\n",
              "766    1\n",
              "767    0\n",
              "Name: Outcome, Length: 768, dtype: int64"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6d44f272",
      "metadata": {
        "id": "6d44f272"
      },
      "outputs": [],
      "source": [
        "## Step 2 Creating training and testing data.\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20,random_state=45)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "80bd54fd",
      "metadata": {
        "id": "80bd54fd",
        "outputId": "55b8c32a-edef-488d-da64-298d06e86ec3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(614, 8)"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "84d7bd79",
      "metadata": {
        "id": "84d7bd79",
        "outputId": "40c5ff41-f4bc-4e7b-c05e-83cca29fc458"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(154, 8)"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9bde1293",
      "metadata": {
        "id": "9bde1293",
        "outputId": "c91ef8a6-3dc9-441b-f64c-8b6db365033a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(614,)"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c0c20c9",
      "metadata": {
        "id": "1c0c20c9",
        "outputId": "ab3dae2b-3819-424f-dab7-99051039e8ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(154,)"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "95da794a",
      "metadata": {
        "collapsed": true,
        "id": "95da794a",
        "outputId": "c835c5d8-b4c9-4920-9fd5-986ecbcfd309"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## Step 3 Model creation\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "clf=LogisticRegression()\n",
        "clf.fit(X_train,y_train)  ## training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8b7b1d40",
      "metadata": {
        "id": "8b7b1d40"
      },
      "outputs": [],
      "source": [
        "## Step 4 Prediction\n",
        "y_pred=clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ead4c50",
      "metadata": {
        "collapsed": true,
        "id": "1ead4c50",
        "outputId": "d0f93c77-57b4-4312-9fd5-71c5429f1263"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
              "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n",
              "       1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
              "      dtype=int64)"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "deaf521c",
      "metadata": {
        "collapsed": true,
        "id": "deaf521c",
        "outputId": "de1ce9ad-ed4f-4309-d6c5-3e854d288970"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.33663888, 0.66336112],\n",
              "       [0.90120845, 0.09879155],\n",
              "       [0.74778048, 0.25221952],\n",
              "       [0.54684365, 0.45315635],\n",
              "       [0.80474717, 0.19525283],\n",
              "       [0.85889198, 0.14110802],\n",
              "       [0.52870232, 0.47129768],\n",
              "       [0.92249035, 0.07750965],\n",
              "       [0.37432263, 0.62567737],\n",
              "       [0.91524759, 0.08475241],\n",
              "       [0.24175399, 0.75824601],\n",
              "       [0.54821873, 0.45178127],\n",
              "       [0.94364227, 0.05635773],\n",
              "       [0.43773737, 0.56226263],\n",
              "       [0.73748528, 0.26251472],\n",
              "       [0.9002487 , 0.0997513 ],\n",
              "       [0.07218234, 0.92781766],\n",
              "       [0.60661705, 0.39338295],\n",
              "       [0.39147734, 0.60852266],\n",
              "       [0.91445218, 0.08554782],\n",
              "       [0.6624536 , 0.3375464 ],\n",
              "       [0.71800741, 0.28199259],\n",
              "       [0.57672536, 0.42327464],\n",
              "       [0.82045709, 0.17954291],\n",
              "       [0.79528562, 0.20471438],\n",
              "       [0.37223823, 0.62776177],\n",
              "       [0.85560895, 0.14439105],\n",
              "       [0.34452647, 0.65547353],\n",
              "       [0.22571232, 0.77428768],\n",
              "       [0.83282826, 0.16717174],\n",
              "       [0.84377208, 0.15622792],\n",
              "       [0.36725394, 0.63274606],\n",
              "       [0.59844117, 0.40155883],\n",
              "       [0.85570599, 0.14429401],\n",
              "       [0.77656962, 0.22343038],\n",
              "       [0.90950944, 0.09049056],\n",
              "       [0.70482659, 0.29517341],\n",
              "       [0.77841538, 0.22158462],\n",
              "       [0.53260556, 0.46739444],\n",
              "       [0.86041039, 0.13958961],\n",
              "       [0.79986727, 0.20013273],\n",
              "       [0.56845795, 0.43154205],\n",
              "       [0.48018865, 0.51981135],\n",
              "       [0.25947411, 0.74052589],\n",
              "       [0.69697627, 0.30302373],\n",
              "       [0.52816744, 0.47183256],\n",
              "       [0.53476495, 0.46523505],\n",
              "       [0.36743602, 0.63256398],\n",
              "       [0.79846822, 0.20153178],\n",
              "       [0.74208376, 0.25791624],\n",
              "       [0.82207373, 0.17792627],\n",
              "       [0.22740468, 0.77259532],\n",
              "       [0.53149653, 0.46850347],\n",
              "       [0.68381359, 0.31618641],\n",
              "       [0.89824412, 0.10175588],\n",
              "       [0.84031275, 0.15968725],\n",
              "       [0.96191525, 0.03808475],\n",
              "       [0.44620955, 0.55379045],\n",
              "       [0.38529875, 0.61470125],\n",
              "       [0.20081859, 0.79918141],\n",
              "       [0.34518842, 0.65481158],\n",
              "       [0.36616222, 0.63383778],\n",
              "       [0.89143169, 0.10856831],\n",
              "       [0.86460891, 0.13539109],\n",
              "       [0.68615265, 0.31384735],\n",
              "       [0.80827108, 0.19172892],\n",
              "       [0.93718933, 0.06281067],\n",
              "       [0.70446884, 0.29553116],\n",
              "       [0.47750713, 0.52249287],\n",
              "       [0.93411974, 0.06588026],\n",
              "       [0.64083867, 0.35916133],\n",
              "       [0.68255491, 0.31744509],\n",
              "       [0.9142242 , 0.0857758 ],\n",
              "       [0.76184908, 0.23815092],\n",
              "       [0.9158947 , 0.0841053 ],\n",
              "       [0.85276458, 0.14723542],\n",
              "       [0.59898813, 0.40101187],\n",
              "       [0.71234728, 0.28765272],\n",
              "       [0.82095341, 0.17904659],\n",
              "       [0.92311618, 0.07688382],\n",
              "       [0.81992196, 0.18007804],\n",
              "       [0.74370855, 0.25629145],\n",
              "       [0.18265885, 0.81734115],\n",
              "       [0.8788681 , 0.1211319 ],\n",
              "       [0.07935615, 0.92064385],\n",
              "       [0.79838434, 0.20161566],\n",
              "       [0.44501242, 0.55498758],\n",
              "       [0.8111681 , 0.1888319 ],\n",
              "       [0.86357608, 0.13642392],\n",
              "       [0.91692333, 0.08307667],\n",
              "       [0.43691075, 0.56308925],\n",
              "       [0.74076921, 0.25923079],\n",
              "       [0.80222178, 0.19777822],\n",
              "       [0.764566  , 0.235434  ],\n",
              "       [0.6840745 , 0.3159255 ],\n",
              "       [0.68476142, 0.31523858],\n",
              "       [0.08585843, 0.91414157],\n",
              "       [0.58394366, 0.41605634],\n",
              "       [0.36958673, 0.63041327],\n",
              "       [0.78252551, 0.21747449],\n",
              "       [0.09160919, 0.90839081],\n",
              "       [0.34440171, 0.65559829],\n",
              "       [0.2541107 , 0.7458893 ],\n",
              "       [0.25254642, 0.74745358],\n",
              "       [0.82497922, 0.17502078],\n",
              "       [0.96024544, 0.03975456],\n",
              "       [0.72072971, 0.27927029],\n",
              "       [0.60227463, 0.39772537],\n",
              "       [0.36942649, 0.63057351],\n",
              "       [0.2279912 , 0.7720088 ],\n",
              "       [0.36269605, 0.63730395],\n",
              "       [0.7867244 , 0.2132756 ],\n",
              "       [0.82080739, 0.17919261],\n",
              "       [0.32177956, 0.67822044],\n",
              "       [0.91118696, 0.08881304],\n",
              "       [0.55026703, 0.44973297],\n",
              "       [0.84942453, 0.15057547],\n",
              "       [0.12092668, 0.87907332],\n",
              "       [0.85965461, 0.14034539],\n",
              "       [0.84769748, 0.15230252],\n",
              "       [0.83119747, 0.16880253],\n",
              "       [0.86639185, 0.13360815],\n",
              "       [0.97163441, 0.02836559],\n",
              "       [0.74434907, 0.25565093],\n",
              "       [0.83683401, 0.16316599],\n",
              "       [0.86821448, 0.13178552],\n",
              "       [0.67331082, 0.32668918],\n",
              "       [0.83293355, 0.16706645],\n",
              "       [0.67082288, 0.32917712],\n",
              "       [0.90704105, 0.09295895],\n",
              "       [0.66360782, 0.33639218],\n",
              "       [0.88665223, 0.11334777],\n",
              "       [0.83355439, 0.16644561],\n",
              "       [0.7626183 , 0.2373817 ],\n",
              "       [0.84916429, 0.15083571],\n",
              "       [0.4609156 , 0.5390844 ],\n",
              "       [0.91166978, 0.08833022],\n",
              "       [0.87583117, 0.12416883],\n",
              "       [0.79433969, 0.20566031],\n",
              "       [0.17646657, 0.82353343],\n",
              "       [0.82929904, 0.17070096],\n",
              "       [0.18139875, 0.81860125],\n",
              "       [0.90089274, 0.09910726],\n",
              "       [0.9045041 , 0.0954959 ],\n",
              "       [0.30625311, 0.69374689],\n",
              "       [0.75823574, 0.24176426],\n",
              "       [0.93306307, 0.06693693],\n",
              "       [0.75928858, 0.24071142],\n",
              "       [0.86097236, 0.13902764],\n",
              "       [0.56897708, 0.43102292],\n",
              "       [0.82515764, 0.17484236],\n",
              "       [0.52499623, 0.47500377],\n",
              "       [0.17096444, 0.82903556],\n",
              "       [0.89777422, 0.10222578]])"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred_prob=clf.predict_proba(X_test)\n",
        "y_pred_prob"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be4a46bb",
      "metadata": {
        "id": "be4a46bb"
      },
      "outputs": [],
      "source": [
        "#Evaluation of model\n",
        "from sklearn.metrics import confusion_matrix,accuracy_score,recall_score,precision_score,classification_report,f1_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "33439f99",
      "metadata": {
        "collapsed": true,
        "id": "33439f99",
        "outputId": "4e4a0e06-2095-4de2-d80a-f470831c96e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[90 14]\n",
            " [24 26]]\n"
          ]
        }
      ],
      "source": [
        "cm=confusion_matrix(y_test,y_pred)\n",
        "print(cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "703ceb87",
      "metadata": {
        "collapsed": true,
        "id": "703ceb87",
        "outputId": "9076df6a-c05a-4e23-e7b4-070a966029c8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.52"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "recall=recall_score(y_test,y_pred)\n",
        "recall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5469f011",
      "metadata": {
        "collapsed": true,
        "id": "5469f011",
        "outputId": "5a2f896f-ffc1-43ac-f2a1-3949d79cedb2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.52"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#recall = TP/TP+FN\n",
        "26/50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "051d4f94",
      "metadata": {
        "collapsed": true,
        "id": "051d4f94",
        "outputId": "7cf983a1-38cf-44cc-8462-f0e7d933581d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.65"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "precision=precision_score(y_test,y_pred)\n",
        "precision"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "72747b05",
      "metadata": {
        "collapsed": true,
        "id": "72747b05",
        "outputId": "829376f7-ba56-44d9-a780-f78dd373e3b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5777777777777778"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "f1score=f1_score(y_test,y_pred)\n",
        "f1score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8829a3c1",
      "metadata": {
        "collapsed": true,
        "id": "8829a3c1",
        "outputId": "3091d416-5dbd-49ae-c37f-9d47c1663cf4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.87      0.83       104\n",
            "           1       0.65      0.52      0.58        50\n",
            "\n",
            "    accuracy                           0.75       154\n",
            "   macro avg       0.72      0.69      0.70       154\n",
            "weighted avg       0.74      0.75      0.75       154\n",
            "\n"
          ]
        }
      ],
      "source": [
        "cr=classification_report(y_test,y_pred)\n",
        "print(cr)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5c4e554f",
      "metadata": {
        "id": "5c4e554f"
      },
      "source": [
        "### Multiclass Classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a90656a0",
      "metadata": {
        "id": "a90656a0"
      },
      "outputs": [],
      "source": [
        "# loading dataset\n",
        "df1= sns.load_dataset(\"iris\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "54f7cea4",
      "metadata": {
        "collapsed": true,
        "id": "54f7cea4",
        "outputId": "07e78aad-58eb-462f-a55a-39fc366ca9e0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "0             5.1          3.5           1.4          0.2     setosa\n",
              "1             4.9          3.0           1.4          0.2     setosa\n",
              "2             4.7          3.2           1.3          0.2     setosa\n",
              "3             4.6          3.1           1.5          0.2     setosa\n",
              "4             5.0          3.6           1.4          0.2     setosa\n",
              "..            ...          ...           ...          ...        ...\n",
              "145           6.7          3.0           5.2          2.3  virginica\n",
              "146           6.3          2.5           5.0          1.9  virginica\n",
              "147           6.5          3.0           5.2          2.0  virginica\n",
              "148           6.2          3.4           5.4          2.3  virginica\n",
              "149           5.9          3.0           5.1          1.8  virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ab0c589d",
      "metadata": {
        "id": "ab0c589d"
      },
      "outputs": [],
      "source": [
        "X=df1.iloc[:,:-1]\n",
        "y=df1.species"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f44deb6f",
      "metadata": {
        "collapsed": true,
        "id": "f44deb6f",
        "outputId": "58c11961-0b9b-43e4-fd50-ab1e520d8a16"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "0             5.1          3.5           1.4          0.2\n",
              "1             4.9          3.0           1.4          0.2\n",
              "2             4.7          3.2           1.3          0.2\n",
              "3             4.6          3.1           1.5          0.2\n",
              "4             5.0          3.6           1.4          0.2\n",
              "..            ...          ...           ...          ...\n",
              "145           6.7          3.0           5.2          2.3\n",
              "146           6.3          2.5           5.0          1.9\n",
              "147           6.5          3.0           5.2          2.0\n",
              "148           6.2          3.4           5.4          2.3\n",
              "149           5.9          3.0           5.1          1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a1ac91a7",
      "metadata": {
        "collapsed": true,
        "id": "a1ac91a7",
        "outputId": "45a7d1eb-72b3-4698-b8a3-9979e6a63d03"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         setosa\n",
              "1         setosa\n",
              "2         setosa\n",
              "3         setosa\n",
              "4         setosa\n",
              "         ...    \n",
              "145    virginica\n",
              "146    virginica\n",
              "147    virginica\n",
              "148    virginica\n",
              "149    virginica\n",
              "Name: species, Length: 150, dtype: object"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "34280140",
      "metadata": {
        "id": "34280140"
      },
      "outputs": [],
      "source": [
        "## Training and testing data\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train1,X_test1,y_train1,y_test1=train_test_split(X,y,random_state=25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "483c42be",
      "metadata": {
        "id": "483c42be"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr_multi=LogisticRegression()\n",
        "lr_multi.fit(X_train1,y_train1)\n",
        "y_pred=lr_multi.predict(X_test1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f682e62f",
      "metadata": {
        "collapsed": true,
        "id": "f682e62f",
        "outputId": "06db10df-c769-4a08-ac7b-7864ff2b0a4e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['setosa', 'versicolor', 'virginica', 'versicolor', 'virginica',\n",
              "       'versicolor', 'virginica', 'setosa', 'versicolor', 'versicolor',\n",
              "       'setosa', 'setosa', 'setosa', 'virginica', 'setosa', 'versicolor',\n",
              "       'virginica', 'virginica', 'versicolor', 'versicolor', 'versicolor',\n",
              "       'versicolor', 'versicolor', 'setosa', 'setosa', 'virginica',\n",
              "       'versicolor', 'virginica', 'virginica', 'setosa', 'versicolor',\n",
              "       'virginica', 'virginica', 'setosa', 'virginica', 'versicolor',\n",
              "       'versicolor', 'setosa'], dtype=object)"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6adfaf2e",
      "metadata": {
        "id": "6adfaf2e",
        "outputId": "e6869683-9e5f-47e1-d981-900f4649896b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.973831844688861"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "f1score=f1_score(y_test1,y_pred,average='weighted')\n",
        "f1score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c99d4671",
      "metadata": {
        "collapsed": true,
        "id": "c99d4671",
        "outputId": "8a3c5222-5291-4558-c2e7-42c38966b0d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        11\n",
            "  versicolor       1.00      0.94      0.97        16\n",
            "   virginica       0.92      1.00      0.96        11\n",
            "\n",
            "    accuracy                           0.97        38\n",
            "   macro avg       0.97      0.98      0.97        38\n",
            "weighted avg       0.98      0.97      0.97        38\n",
            "\n"
          ]
        }
      ],
      "source": [
        "cr=classification_report(y_test1,y_pred)\n",
        "print(cr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "09125304",
      "metadata": {
        "id": "09125304"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}