3_Divisive.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "3_Divisive.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZPCViM7NFFd"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XR_1aDJjWz4G",
        "outputId": "4e501096-77d4-4960-d710-3b4340c1d89b"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "data=pd.read_csv(\"/content/drive/MyDrive/Data-Analytics/football_data.csv\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "ByDYVw8vW1GI",
        "outputId": "cab3ab78-455b-4e83-c28e-6662bad7ae95"
      },
      "source": [
        "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
        "new_data = data.select_dtypes(include=numerics)\n",
        "new_data = new_data.drop(['Unnamed: 0', 'ID','Jersey Number'], axis=1)\n",
        "\n",
        "new_data.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Overall</th>\n",
              "      <th>Potential</th>\n",
              "      <th>Special</th>\n",
              "      <th>International Reputation</th>\n",
              "      <th>Weak Foot</th>\n",
              "      <th>Skill Moves</th>\n",
              "      <th>Crossing</th>\n",
              "      <th>Finishing</th>\n",
              "      <th>HeadingAccuracy</th>\n",
              "      <th>ShortPassing</th>\n",
              "      <th>Volleys</th>\n",
              "      <th>Dribbling</th>\n",
              "      <th>Curve</th>\n",
              "      <th>FKAccuracy</th>\n",
              "      <th>LongPassing</th>\n",
              "      <th>BallControl</th>\n",
              "      <th>Acceleration</th>\n",
              "      <th>SprintSpeed</th>\n",
              "      <th>Agility</th>\n",
              "      <th>Reactions</th>\n",
              "      <th>Balance</th>\n",
              "      <th>ShotPower</th>\n",
              "      <th>Jumping</th>\n",
              "      <th>Stamina</th>\n",
              "      <th>Strength</th>\n",
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              "      <th>Aggression</th>\n",
              "      <th>Interceptions</th>\n",
              "      <th>Positioning</th>\n",
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              "      <th>Penalties</th>\n",
              "      <th>Composure</th>\n",
              "      <th>Marking</th>\n",
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              "      <th>SlidingTackle</th>\n",
              "      <th>GKDiving</th>\n",
              "      <th>GKHandling</th>\n",
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              "      <th>GKPositioning</th>\n",
              "      <th>GKReflexes</th>\n",
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              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>2228</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>89.0</td>\n",
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              "      <td>87.0</td>\n",
              "      <td>88.0</td>\n",
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              "      <td>76.0</td>\n",
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              "      <td>29.0</td>\n",
              "      <td>95.0</td>\n",
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              "      <td>85.0</td>\n",
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              "      <td>31.0</td>\n",
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              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>92</td>\n",
              "      <td>93</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>94.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9.0</td>\n",
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              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>91</td>\n",
              "      <td>93</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>94.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>2281</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
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            ],
            "text/plain": [
              "   Age  Overall  Potential  ...  GKKicking  GKPositioning  GKReflexes\n",
              "0   31       94         94  ...       15.0           14.0         8.0\n",
              "1   33       94         94  ...       15.0           14.0        11.0\n",
              "2   26       92         93  ...       15.0           15.0        11.0\n",
              "3   27       91         93  ...       87.0           88.0        94.0\n",
              "4   27       91         92  ...        5.0           10.0        13.0\n",
              "\n",
              "[5 rows x 41 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "pY1Snve3W2PK",
        "outputId": "1c2d8424-41a6-4bde-e3c7-048e4cfd09eb"
      },
      "source": [
        "# Convert wage and value to float\n",
        "def convert_Wage_Value(i):\n",
        "  num=''\n",
        "  for j in range(1,len(i)):\n",
        "    if (i[j]<='9' and i[j]>='0') or i[j]=='.':\n",
        "      num+=(i[j])\n",
        "    if i[j]=='K':\n",
        "      val=float(num)\n",
        "      val*=1000  \n",
        "      return val\n",
        "    if i[j]=='M':\n",
        "      val=float(num)\n",
        "      val*=1e6  \n",
        "      return val \n",
        "  return float(num)  \n",
        "\n",
        "Value=pd.Series([],dtype=pd.StringDtype()) \n",
        "Wage=pd.Series([],dtype=pd.StringDtype()) \n",
        "\n",
        "for i in range(len(data['Value'])):\n",
        "  Value[i]=convert_Wage_Value(data['Value'][i])\n",
        "  Wage[i]=convert_Wage_Value(data['Wage'][i])\n",
        "# inserting value and wage into dataframe after converting into numberical attributes\n",
        "new_data.insert(2,'Value',Value,True)\n",
        "new_data.insert(2,'Wage',Wage,True)\n",
        "\n",
        "new_data.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Overall</th>\n",
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              "      <th>Potential</th>\n",
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              "      <th>Finishing</th>\n",
              "      <th>HeadingAccuracy</th>\n",
              "      <th>ShortPassing</th>\n",
              "      <th>Volleys</th>\n",
              "      <th>Dribbling</th>\n",
              "      <th>Curve</th>\n",
              "      <th>FKAccuracy</th>\n",
              "      <th>LongPassing</th>\n",
              "      <th>BallControl</th>\n",
              "      <th>Acceleration</th>\n",
              "      <th>SprintSpeed</th>\n",
              "      <th>Agility</th>\n",
              "      <th>Reactions</th>\n",
              "      <th>Balance</th>\n",
              "      <th>ShotPower</th>\n",
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              "      <th>Stamina</th>\n",
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              "      <th>Aggression</th>\n",
              "      <th>Interceptions</th>\n",
              "      <th>Positioning</th>\n",
              "      <th>Vision</th>\n",
              "      <th>Penalties</th>\n",
              "      <th>Composure</th>\n",
              "      <th>Marking</th>\n",
              "      <th>StandingTackle</th>\n",
              "      <th>SlidingTackle</th>\n",
              "      <th>GKDiving</th>\n",
              "      <th>GKHandling</th>\n",
              "      <th>GKKicking</th>\n",
              "      <th>GKPositioning</th>\n",
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              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>94.0</td>\n",
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              "      <td>96.0</td>\n",
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              "      <td>68.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33</td>\n",
              "      <td>94</td>\n",
              "      <td>405000.0</td>\n",
              "      <td>77000000.0</td>\n",
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              "      <td>81.0</td>\n",
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              "      <td>63.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>92</td>\n",
              "      <td>290000.0</td>\n",
              "      <td>118500000.0</td>\n",
              "      <td>93</td>\n",
              "      <td>2143</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>91</td>\n",
              "      <td>260000.0</td>\n",
              "      <td>72000000.0</td>\n",
              "      <td>93</td>\n",
              "      <td>1471</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>94.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>91</td>\n",
              "      <td>355000.0</td>\n",
              "      <td>102000000.0</td>\n",
              "      <td>92</td>\n",
              "      <td>2281</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Overall      Wage  ...  GKKicking  GKPositioning  GKReflexes\n",
              "0   31       94  565000.0  ...       15.0           14.0         8.0\n",
              "1   33       94  405000.0  ...       15.0           14.0        11.0\n",
              "2   26       92  290000.0  ...       15.0           15.0        11.0\n",
              "3   27       91  260000.0  ...       87.0           88.0        94.0\n",
              "4   27       91  355000.0  ...        5.0           10.0        13.0\n",
              "\n",
              "[5 rows x 43 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "XPpBsD6WW5Zb",
        "outputId": "7072c545-9b2b-42b0-9880-5fb3e0fe191c"
      },
      "source": [
        "import re\n",
        "# converting height in numberical and inserting into dataframe\n",
        "def HeightConvert(cell):\n",
        "  cell=str(cell)\n",
        "  if cell[len((cell))-2]==\"'\":\n",
        "    cell=re.sub(\"[']\",\".0\",(cell))\n",
        "  else:\n",
        "    cell=re.sub(\"[']\",\".\",(cell))\n",
        "  return float(cell)\n",
        "  \n",
        "Height=pd.Series([],dtype=pd.StringDtype()) \n",
        "for i in range(len(new_data.index)):\n",
        "  Height[i]=HeightConvert(data['Height'][i])\n",
        "new_data.insert(2,'Height',Height,True)  \n",
        "# new_data.describe()\n",
        "\n",
        "new_data.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Overall</th>\n",
              "      <th>Height</th>\n",
              "      <th>Wage</th>\n",
              "      <th>Value</th>\n",
              "      <th>Potential</th>\n",
              "      <th>Special</th>\n",
              "      <th>International Reputation</th>\n",
              "      <th>Weak Foot</th>\n",
              "      <th>Skill Moves</th>\n",
              "      <th>Crossing</th>\n",
              "      <th>Finishing</th>\n",
              "      <th>HeadingAccuracy</th>\n",
              "      <th>ShortPassing</th>\n",
              "      <th>Volleys</th>\n",
              "      <th>Dribbling</th>\n",
              "      <th>Curve</th>\n",
              "      <th>FKAccuracy</th>\n",
              "      <th>LongPassing</th>\n",
              "      <th>BallControl</th>\n",
              "      <th>Acceleration</th>\n",
              "      <th>SprintSpeed</th>\n",
              "      <th>Agility</th>\n",
              "      <th>Reactions</th>\n",
              "      <th>Balance</th>\n",
              "      <th>ShotPower</th>\n",
              "      <th>Jumping</th>\n",
              "      <th>Stamina</th>\n",
              "      <th>Strength</th>\n",
              "      <th>LongShots</th>\n",
              "      <th>Aggression</th>\n",
              "      <th>Interceptions</th>\n",
              "      <th>Positioning</th>\n",
              "      <th>Vision</th>\n",
              "      <th>Penalties</th>\n",
              "      <th>Composure</th>\n",
              "      <th>Marking</th>\n",
              "      <th>StandingTackle</th>\n",
              "      <th>SlidingTackle</th>\n",
              "      <th>GKDiving</th>\n",
              "      <th>GKHandling</th>\n",
              "      <th>GKKicking</th>\n",
              "      <th>GKPositioning</th>\n",
              "      <th>GKReflexes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31</td>\n",
              "      <td>94</td>\n",
              "      <td>5.07</td>\n",
              "      <td>565000.0</td>\n",
              "      <td>110500000.0</td>\n",
              "      <td>94</td>\n",
              "      <td>2202</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33</td>\n",
              "      <td>94</td>\n",
              "      <td>6.02</td>\n",
              "      <td>405000.0</td>\n",
              "      <td>77000000.0</td>\n",
              "      <td>94</td>\n",
              "      <td>2228</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>92</td>\n",
              "      <td>5.09</td>\n",
              "      <td>290000.0</td>\n",
              "      <td>118500000.0</td>\n",
              "      <td>93</td>\n",
              "      <td>2143</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>91</td>\n",
              "      <td>6.04</td>\n",
              "      <td>260000.0</td>\n",
              "      <td>72000000.0</td>\n",
              "      <td>93</td>\n",
              "      <td>1471</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>94.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>91</td>\n",
              "      <td>5.11</td>\n",
              "      <td>355000.0</td>\n",
              "      <td>102000000.0</td>\n",
              "      <td>92</td>\n",
              "      <td>2281</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Overall  Height  ...  GKKicking  GKPositioning  GKReflexes\n",
              "0   31       94    5.07  ...       15.0           14.0         8.0\n",
              "1   33       94    6.02  ...       15.0           14.0        11.0\n",
              "2   26       92    5.09  ...       15.0           15.0        11.0\n",
              "3   27       91    6.04  ...       87.0           88.0        94.0\n",
              "4   27       91    5.11  ...        5.0           10.0        13.0\n",
              "\n",
              "[5 rows x 44 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "8jBKZ48-W6EO",
        "outputId": "c4c1d367-25b3-403a-880e-da95504c65bf"
      },
      "source": [
        "Weight=pd.Series([],dtype=pd.StringDtype()) \n",
        "for (coln,cold) in data.iteritems():\n",
        "  if coln==\"Weight\":\n",
        "    Weight = data[coln].apply(lambda weight : float(weight[:-3]) if type(weight) is str else float(weight))\n",
        "# converting weight in numerical\n",
        "\n",
        "# new_data.head()\n",
        "new_data.insert(2,'Weight',Weight,True)\n",
        "PositionsList = ['LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW','LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LWB', 'LDM','CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB']\n",
        "for (coln, cold) in data.iteritems():\n",
        "  if coln in PositionsList:\n",
        "    data[coln] = data[coln].apply(lambda position : int(position[:-2]) +  (int(position[3:4])//2) if type(position) is str else float(position))\n",
        "    new_data.insert(2,coln,data[coln],True)\n",
        "\n",
        "# converting all the attributes from list PositonsList in numerical and inserting into dataframe\n",
        "\n",
        "new_data.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Overall</th>\n",
              "      <th>RB</th>\n",
              "      <th>RCB</th>\n",
              "      <th>CB</th>\n",
              "      <th>LCB</th>\n",
              "      <th>LB</th>\n",
              "      <th>RWB</th>\n",
              "      <th>RDM</th>\n",
              "      <th>CDM</th>\n",
              "      <th>LDM</th>\n",
              "      <th>LWB</th>\n",
              "      <th>RM</th>\n",
              "      <th>RCM</th>\n",
              "      <th>CM</th>\n",
              "      <th>LCM</th>\n",
              "      <th>LM</th>\n",
              "      <th>RAM</th>\n",
              "      <th>CAM</th>\n",
              "      <th>LAM</th>\n",
              "      <th>RW</th>\n",
              "      <th>RF</th>\n",
              "      <th>CF</th>\n",
              "      <th>LF</th>\n",
              "      <th>LW</th>\n",
              "      <th>RS</th>\n",
              "      <th>ST</th>\n",
              "      <th>LS</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Wage</th>\n",
              "      <th>Value</th>\n",
              "      <th>Potential</th>\n",
              "      <th>Special</th>\n",
              "      <th>International Reputation</th>\n",
              "      <th>Weak Foot</th>\n",
              "      <th>Skill Moves</th>\n",
              "      <th>Crossing</th>\n",
              "      <th>Finishing</th>\n",
              "      <th>HeadingAccuracy</th>\n",
              "      <th>ShortPassing</th>\n",
              "      <th>Volleys</th>\n",
              "      <th>Dribbling</th>\n",
              "      <th>Curve</th>\n",
              "      <th>FKAccuracy</th>\n",
              "      <th>LongPassing</th>\n",
              "      <th>BallControl</th>\n",
              "      <th>Acceleration</th>\n",
              "      <th>SprintSpeed</th>\n",
              "      <th>Agility</th>\n",
              "      <th>Reactions</th>\n",
              "      <th>Balance</th>\n",
              "      <th>ShotPower</th>\n",
              "      <th>Jumping</th>\n",
              "      <th>Stamina</th>\n",
              "      <th>Strength</th>\n",
              "      <th>LongShots</th>\n",
              "      <th>Aggression</th>\n",
              "      <th>Interceptions</th>\n",
              "      <th>Positioning</th>\n",
              "      <th>Vision</th>\n",
              "      <th>Penalties</th>\n",
              "      <th>Composure</th>\n",
              "      <th>Marking</th>\n",
              "      <th>StandingTackle</th>\n",
              "      <th>SlidingTackle</th>\n",
              "      <th>GKDiving</th>\n",
              "      <th>GKHandling</th>\n",
              "      <th>GKKicking</th>\n",
              "      <th>GKPositioning</th>\n",
              "      <th>GKReflexes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>18207.000000</td>\n",
              "      <td>18207.000000</td>\n",
              "      <td>16122.00000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.00000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>16122.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18207.000000</td>\n",
              "      <td>1.820700e+04</td>\n",
              "      <td>18207.000000</td>\n",
              "      <td>18207.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "      <td>18159.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>25.122206</td>\n",
              "      <td>66.238699</td>\n",
              "      <td>57.80691</td>\n",
              "      <td>56.614750</td>\n",
              "      <td>56.614750</td>\n",
              "      <td>56.614750</td>\n",
              "      <td>57.80691</td>\n",
              "      <td>58.513398</td>\n",
              "      <td>57.825270</td>\n",
              "      <td>57.825270</td>\n",
              "      <td>57.825270</td>\n",
              "      <td>58.513398</td>\n",
              "      <td>60.734834</td>\n",
              "      <td>59.221002</td>\n",
              "      <td>59.221002</td>\n",
              "      <td>59.221002</td>\n",
              "      <td>60.734834</td>\n",
              "      <td>59.983253</td>\n",
              "      <td>59.983253</td>\n",
              "      <td>59.983253</td>\n",
              "      <td>60.037774</td>\n",
              "      <td>59.719514</td>\n",
              "      <td>59.719514</td>\n",
              "      <td>59.719514</td>\n",
              "      <td>60.037774</td>\n",
              "      <td>58.815594</td>\n",
              "      <td>58.815594</td>\n",
              "      <td>58.815594</td>\n",
              "      <td>165.979129</td>\n",
              "      <td>5.545641</td>\n",
              "      <td>9731.312133</td>\n",
              "      <td>2.410696e+06</td>\n",
              "      <td>71.307299</td>\n",
              "      <td>1597.809908</td>\n",
              "      <td>1.113222</td>\n",
              "      <td>2.947299</td>\n",
              "      <td>2.361308</td>\n",
              "      <td>49.734181</td>\n",
              "      <td>45.550911</td>\n",
              "      <td>52.298144</td>\n",
              "      <td>58.686712</td>\n",
              "      <td>42.909026</td>\n",
              "      <td>55.371001</td>\n",
              "      <td>47.170824</td>\n",
              "      <td>42.863153</td>\n",
              "      <td>52.711933</td>\n",
              "      <td>58.369459</td>\n",
              "      <td>64.614076</td>\n",
              "      <td>64.726967</td>\n",
              "      <td>63.503607</td>\n",
              "      <td>61.836610</td>\n",
              "      <td>63.966573</td>\n",
              "      <td>55.460047</td>\n",
              "      <td>65.089432</td>\n",
              "      <td>63.219946</td>\n",
              "      <td>65.311967</td>\n",
              "      <td>47.109973</td>\n",
              "      <td>55.868991</td>\n",
              "      <td>46.698276</td>\n",
              "      <td>49.958478</td>\n",
              "      <td>53.400903</td>\n",
              "      <td>48.548598</td>\n",
              "      <td>58.648274</td>\n",
              "      <td>47.281623</td>\n",
              "      <td>47.697836</td>\n",
              "      <td>45.661435</td>\n",
              "      <td>16.616223</td>\n",
              "      <td>16.391596</td>\n",
              "      <td>16.232061</td>\n",
              "      <td>16.388898</td>\n",
              "      <td>16.710887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.669943</td>\n",
              "      <td>6.908930</td>\n",
              "      <td>9.51627</td>\n",
              "      <td>11.702684</td>\n",
              "      <td>11.702684</td>\n",
              "      <td>11.702684</td>\n",
              "      <td>9.51627</td>\n",
              "      <td>8.956184</td>\n",
              "      <td>10.068153</td>\n",
              "      <td>10.068153</td>\n",
              "      <td>10.068153</td>\n",
              "      <td>8.956184</td>\n",
              "      <td>9.231938</td>\n",
              "      <td>8.780952</td>\n",
              "      <td>8.780952</td>\n",
              "      <td>8.780952</td>\n",
              "      <td>9.231938</td>\n",
              "      <td>9.749195</td>\n",
              "      <td>9.749195</td>\n",
              "      <td>9.749195</td>\n",
              "      <td>9.860040</td>\n",
              "      <td>9.820331</td>\n",
              "      <td>9.820331</td>\n",
              "      <td>9.820331</td>\n",
              "      <td>9.860040</td>\n",
              "      <td>9.108812</td>\n",
              "      <td>9.108812</td>\n",
              "      <td>9.108812</td>\n",
              "      <td>15.593344</td>\n",
              "      <td>0.461820</td>\n",
              "      <td>21999.290406</td>\n",
              "      <td>5.594933e+06</td>\n",
              "      <td>6.136496</td>\n",
              "      <td>272.586016</td>\n",
              "      <td>0.394031</td>\n",
              "      <td>0.660456</td>\n",
              "      <td>0.756164</td>\n",
              "      <td>18.364524</td>\n",
              "      <td>19.525820</td>\n",
              "      <td>17.379909</td>\n",
              "      <td>14.699495</td>\n",
              "      <td>17.694408</td>\n",
              "      <td>18.910371</td>\n",
              "      <td>18.395264</td>\n",
              "      <td>17.478763</td>\n",
              "      <td>15.327870</td>\n",
              "      <td>16.686595</td>\n",
              "      <td>14.927780</td>\n",
              "      <td>14.649953</td>\n",
              "      <td>14.766049</td>\n",
              "      <td>9.010464</td>\n",
              "      <td>14.136166</td>\n",
              "      <td>17.237958</td>\n",
              "      <td>11.820044</td>\n",
              "      <td>15.894741</td>\n",
              "      <td>12.557000</td>\n",
              "      <td>19.260524</td>\n",
              "      <td>17.367967</td>\n",
              "      <td>20.696909</td>\n",
              "      <td>19.529036</td>\n",
              "      <td>14.146881</td>\n",
              "      <td>15.704053</td>\n",
              "      <td>11.436133</td>\n",
              "      <td>19.904397</td>\n",
              "      <td>21.664004</td>\n",
              "      <td>21.289135</td>\n",
              "      <td>17.695349</td>\n",
              "      <td>16.906900</td>\n",
              "      <td>16.502864</td>\n",
              "      <td>17.034669</td>\n",
              "      <td>17.955119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>30.00000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>30.00000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>5.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>51.00000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>51.00000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>154.000000</td>\n",
              "      <td>5.090000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>3.000000e+05</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>1457.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>59.00000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>59.00000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>5.110000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>6.750000e+05</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>1635.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>65.00000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>65.00000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>176.000000</td>\n",
              "      <td>6.010000</td>\n",
              "      <td>9000.000000</td>\n",
              "      <td>2.000000e+06</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>1787.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>85.00000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>85.00000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>243.000000</td>\n",
              "      <td>6.090000</td>\n",
              "      <td>565000.000000</td>\n",
              "      <td>1.185000e+08</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>2346.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>94.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Age       Overall  ...  GKPositioning    GKReflexes\n",
              "count  18207.000000  18207.000000  ...   18159.000000  18159.000000\n",
              "mean      25.122206     66.238699  ...      16.388898     16.710887\n",
              "std        4.669943      6.908930  ...      17.034669     17.955119\n",
              "min       16.000000     46.000000  ...       1.000000      1.000000\n",
              "25%       21.000000     62.000000  ...       8.000000      8.000000\n",
              "50%       25.000000     66.000000  ...      11.000000     11.000000\n",
              "75%       28.000000     71.000000  ...      14.000000     14.000000\n",
              "max       45.000000     94.000000  ...      90.000000     94.000000\n",
              "\n",
              "[8 rows x 71 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HoYRPUhGXv2m"
      },
      "source": [
        "# droping rows and columns which contain Nan values\n",
        "# new_data=new_data.dropna()\n",
        "# new_data.reset_index(drop = True)\n",
        "# print(new_data)\n",
        "# new_data.describe()\n",
        "\n",
        "# Forward fill = Filling the empty NaN values in data\n",
        "new_data.fillna(method ='ffill', inplace = True)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "28-ApQyQXwTw",
        "outputId": "38f98d2b-2e91-45f8-cb36-daa2994b4f29"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler, normalize \n",
        "\n",
        "# Scaling data so that all the features become comparable\n",
        "scaler = StandardScaler()\n",
        "data_scaled = scaler.fit_transform(new_data)\n",
        "\n",
        "# Normalizing data so that it follows gaussian distribution\n",
        "normalized_data = normalize(data_scaled)\n",
        "\n",
        "# Converting numpy array to dataframe\n",
        "normalized_data = pd.DataFrame(normalized_data)\n",
        "\n",
        "normalized_data"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "      <th>64</th>\n",
              "      <th>65</th>\n",
              "      <th>66</th>\n",
              "      <th>67</th>\n",
              "      <th>68</th>\n",
              "      <th>69</th>\n",
              "      <th>70</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.033019</td>\n",
              "      <td>0.105411</td>\n",
              "      <td>0.006563</td>\n",
              "      <td>-0.018863</td>\n",
              "      <td>-0.018863</td>\n",
              "      <td>-0.018863</td>\n",
              "      <td>0.006563</td>\n",
              "      <td>0.019517</td>\n",
              "      <td>0.011433</td>\n",
              "      <td>0.011433</td>\n",
              "      <td>0.011433</td>\n",
              "      <td>0.019517</td>\n",
              "      <td>0.089116</td>\n",
              "      <td>0.077141</td>\n",
              "      <td>0.077141</td>\n",
              "      <td>0.077141</td>\n",
              "      <td>0.089116</td>\n",
              "      <td>0.091690</td>\n",
              "      <td>0.091690</td>\n",
              "      <td>0.091690</td>\n",
              "      <td>0.088005</td>\n",
              "      <td>0.091717</td>\n",
              "      <td>0.091717</td>\n",
              "      <td>0.091717</td>\n",
              "      <td>0.088005</td>\n",
              "      <td>0.086907</td>\n",
              "      <td>0.086907</td>\n",
              "      <td>0.086907</td>\n",
              "      <td>-0.011712</td>\n",
              "      <td>-0.026957</td>\n",
              "      <td>0.662141</td>\n",
              "      <td>0.506809</td>\n",
              "      <td>0.097011</td>\n",
              "      <td>0.058147</td>\n",
              "      <td>0.259105</td>\n",
              "      <td>0.041863</td>\n",
              "      <td>0.056942</td>\n",
              "      <td>0.049019</td>\n",
              "      <td>0.066494</td>\n",
              "      <td>0.026760</td>\n",
              "      <td>0.055971</td>\n",
              "      <td>0.063927</td>\n",
              "      <td>0.057837</td>\n",
              "      <td>0.065358</td>\n",
              "      <td>0.076837</td>\n",
              "      <td>0.058769</td>\n",
              "      <td>0.059254</td>\n",
              "      <td>0.046363</td>\n",
              "      <td>0.038112</td>\n",
              "      <td>0.048928</td>\n",
              "      <td>0.096660</td>\n",
              "      <td>0.057633</td>\n",
              "      <td>0.045029</td>\n",
              "      <td>0.006463</td>\n",
              "      <td>0.014436</td>\n",
              "      <td>-0.013158</td>\n",
              "      <td>0.063872</td>\n",
              "      <td>-0.011934</td>\n",
              "      <td>-0.031375</td>\n",
              "      <td>0.059252</td>\n",
              "      <td>0.075332</td>\n",
              "      <td>0.044263</td>\n",
              "      <td>0.085748</td>\n",
              "      <td>-0.018893</td>\n",
              "      <td>-0.023916</td>\n",
              "      <td>-0.024292</td>\n",
              "      <td>-0.015749</td>\n",
              "      <td>-0.008358</td>\n",
              "      <td>-0.001939</td>\n",
              "      <td>-0.003653</td>\n",
              "      <td>-0.012706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.055514</td>\n",
              "      <td>0.132233</td>\n",
              "      <td>0.015126</td>\n",
              "      <td>-0.006802</td>\n",
              "      <td>-0.006802</td>\n",
              "      <td>-0.006802</td>\n",
              "      <td>0.015126</td>\n",
              "      <td>0.028141</td>\n",
              "      <td>0.014342</td>\n",
              "      <td>0.014342</td>\n",
              "      <td>0.014342</td>\n",
              "      <td>0.028141</td>\n",
              "      <td>0.101139</td>\n",
              "      <td>0.085616</td>\n",
              "      <td>0.085616</td>\n",
              "      <td>0.085616</td>\n",
              "      <td>0.101139</td>\n",
              "      <td>0.098228</td>\n",
              "      <td>0.098228</td>\n",
              "      <td>0.098228</td>\n",
              "      <td>0.100414</td>\n",
              "      <td>0.105050</td>\n",
              "      <td>0.105050</td>\n",
              "      <td>0.105050</td>\n",
              "      <td>0.100414</td>\n",
              "      <td>0.119784</td>\n",
              "      <td>0.119784</td>\n",
              "      <td>0.119784</td>\n",
              "      <td>0.036003</td>\n",
              "      <td>0.033889</td>\n",
              "      <td>0.591280</td>\n",
              "      <td>0.438724</td>\n",
              "      <td>0.121696</td>\n",
              "      <td>0.076081</td>\n",
              "      <td>0.325034</td>\n",
              "      <td>0.052515</td>\n",
              "      <td>0.114996</td>\n",
              "      <td>0.061492</td>\n",
              "      <td>0.081728</td>\n",
              "      <td>0.069592</td>\n",
              "      <td>0.050049</td>\n",
              "      <td>0.082052</td>\n",
              "      <td>0.056881</td>\n",
              "      <td>0.060554</td>\n",
              "      <td>0.062486</td>\n",
              "      <td>0.052241</td>\n",
              "      <td>0.070382</td>\n",
              "      <td>0.053748</td>\n",
              "      <td>0.059054</td>\n",
              "      <td>0.052452</td>\n",
              "      <td>0.124910</td>\n",
              "      <td>0.014031</td>\n",
              "      <td>0.075582</td>\n",
              "      <td>0.083378</td>\n",
              "      <td>0.051247</td>\n",
              "      <td>0.035955</td>\n",
              "      <td>0.078418</td>\n",
              "      <td>0.013479</td>\n",
              "      <td>-0.028222</td>\n",
              "      <td>0.076016</td>\n",
              "      <td>0.066598</td>\n",
              "      <td>0.076492</td>\n",
              "      <td>0.104689</td>\n",
              "      <td>-0.031966</td>\n",
              "      <td>-0.025441</td>\n",
              "      <td>-0.035114</td>\n",
              "      <td>-0.017894</td>\n",
              "      <td>-0.010485</td>\n",
              "      <td>-0.002432</td>\n",
              "      <td>-0.004582</td>\n",
              "      <td>-0.010436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.005976</td>\n",
              "      <td>0.118552</td>\n",
              "      <td>0.011284</td>\n",
              "      <td>-0.022861</td>\n",
              "      <td>-0.022861</td>\n",
              "      <td>-0.022861</td>\n",
              "      <td>0.011284</td>\n",
              "      <td>0.027188</td>\n",
              "      <td>0.010711</td>\n",
              "      <td>0.010711</td>\n",
              "      <td>0.010711</td>\n",
              "      <td>0.027188</td>\n",
              "      <td>0.097715</td>\n",
              "      <td>0.082717</td>\n",
              "      <td>0.082717</td>\n",
              "      <td>0.082717</td>\n",
              "      <td>0.097715</td>\n",
              "      <td>0.098147</td>\n",
              "      <td>0.098147</td>\n",
              "      <td>0.098147</td>\n",
              "      <td>0.097014</td>\n",
              "      <td>0.098271</td>\n",
              "      <td>0.098271</td>\n",
              "      <td>0.098271</td>\n",
              "      <td>0.097014</td>\n",
              "      <td>0.091466</td>\n",
              "      <td>0.091466</td>\n",
              "      <td>0.091466</td>\n",
              "      <td>-0.032561</td>\n",
              "      <td>-0.031295</td>\n",
              "      <td>0.405057</td>\n",
              "      <td>0.659702</td>\n",
              "      <td>0.112394</td>\n",
              "      <td>0.063591</td>\n",
              "      <td>0.314029</td>\n",
              "      <td>0.098940</td>\n",
              "      <td>0.111102</td>\n",
              "      <td>0.050742</td>\n",
              "      <td>0.067566</td>\n",
              "      <td>0.017778</td>\n",
              "      <td>0.054848</td>\n",
              "      <td>0.073887</td>\n",
              "      <td>0.068415</td>\n",
              "      <td>0.070584</td>\n",
              "      <td>0.080387</td>\n",
              "      <td>0.052548</td>\n",
              "      <td>0.069907</td>\n",
              "      <td>0.062586</td>\n",
              "      <td>0.054882</td>\n",
              "      <td>0.070079</td>\n",
              "      <td>0.113619</td>\n",
              "      <td>0.045080</td>\n",
              "      <td>0.045349</td>\n",
              "      <td>-0.011021</td>\n",
              "      <td>0.035505</td>\n",
              "      <td>-0.041289</td>\n",
              "      <td>0.057632</td>\n",
              "      <td>0.000196</td>\n",
              "      <td>-0.016506</td>\n",
              "      <td>0.063662</td>\n",
              "      <td>0.075576</td>\n",
              "      <td>0.065800</td>\n",
              "      <td>0.098365</td>\n",
              "      <td>-0.032481</td>\n",
              "      <td>-0.034861</td>\n",
              "      <td>-0.018979</td>\n",
              "      <td>-0.013690</td>\n",
              "      <td>-0.013896</td>\n",
              "      <td>-0.002350</td>\n",
              "      <td>-0.002559</td>\n",
              "      <td>-0.010082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.015714</td>\n",
              "      <td>0.140061</td>\n",
              "      <td>0.013870</td>\n",
              "      <td>-0.028100</td>\n",
              "      <td>-0.028100</td>\n",
              "      <td>-0.028100</td>\n",
              "      <td>0.013870</td>\n",
              "      <td>0.033419</td>\n",
              "      <td>0.013165</td>\n",
              "      <td>0.013165</td>\n",
              "      <td>0.013165</td>\n",
              "      <td>0.033419</td>\n",
              "      <td>0.120106</td>\n",
              "      <td>0.101672</td>\n",
              "      <td>0.101672</td>\n",
              "      <td>0.101672</td>\n",
              "      <td>0.120106</td>\n",
              "      <td>0.120637</td>\n",
              "      <td>0.120637</td>\n",
              "      <td>0.120637</td>\n",
              "      <td>0.119245</td>\n",
              "      <td>0.120789</td>\n",
              "      <td>0.120789</td>\n",
              "      <td>0.120789</td>\n",
              "      <td>0.119245</td>\n",
              "      <td>0.112425</td>\n",
              "      <td>0.112425</td>\n",
              "      <td>0.112425</td>\n",
              "      <td>0.005129</td>\n",
              "      <td>0.041937</td>\n",
              "      <td>0.444583</td>\n",
              "      <td>0.486074</td>\n",
              "      <td>0.138149</td>\n",
              "      <td>-0.018180</td>\n",
              "      <td>0.286688</td>\n",
              "      <td>0.003114</td>\n",
              "      <td>-0.070378</td>\n",
              "      <td>-0.069740</td>\n",
              "      <td>-0.065017</td>\n",
              "      <td>-0.070461</td>\n",
              "      <td>-0.023044</td>\n",
              "      <td>-0.065884</td>\n",
              "      <td>-0.077209</td>\n",
              "      <td>-0.055361</td>\n",
              "      <td>-0.053287</td>\n",
              "      <td>-0.004288</td>\n",
              "      <td>-0.038333</td>\n",
              "      <td>-0.020014</td>\n",
              "      <td>-0.018003</td>\n",
              "      <td>-0.009260</td>\n",
              "      <td>0.122297</td>\n",
              "      <td>-0.058067</td>\n",
              "      <td>-0.055375</td>\n",
              "      <td>0.006317</td>\n",
              "      <td>-0.049820</td>\n",
              "      <td>-0.004027</td>\n",
              "      <td>-0.070983</td>\n",
              "      <td>-0.040300</td>\n",
              "      <td>-0.031625</td>\n",
              "      <td>-0.076027</td>\n",
              "      <td>0.040431</td>\n",
              "      <td>-0.021201</td>\n",
              "      <td>0.032061</td>\n",
              "      <td>-0.063482</td>\n",
              "      <td>-0.048265</td>\n",
              "      <td>-0.060069</td>\n",
              "      <td>0.162292</td>\n",
              "      <td>0.158809</td>\n",
              "      <td>0.167816</td>\n",
              "      <td>0.164507</td>\n",
              "      <td>0.168443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.013198</td>\n",
              "      <td>0.117633</td>\n",
              "      <td>0.056339</td>\n",
              "      <td>0.029652</td>\n",
              "      <td>0.029652</td>\n",
              "      <td>0.029652</td>\n",
              "      <td>0.056339</td>\n",
              "      <td>0.071853</td>\n",
              "      <td>0.066269</td>\n",
              "      <td>0.066269</td>\n",
              "      <td>0.066269</td>\n",
              "      <td>0.071853</td>\n",
              "      <td>0.100873</td>\n",
              "      <td>0.107640</td>\n",
              "      <td>0.107640</td>\n",
              "      <td>0.107640</td>\n",
              "      <td>0.100873</td>\n",
              "      <td>0.097970</td>\n",
              "      <td>0.097970</td>\n",
              "      <td>0.097970</td>\n",
              "      <td>0.093512</td>\n",
              "      <td>0.094795</td>\n",
              "      <td>0.094795</td>\n",
              "      <td>0.094795</td>\n",
              "      <td>0.093512</td>\n",
              "      <td>0.087267</td>\n",
              "      <td>0.087267</td>\n",
              "      <td>0.087267</td>\n",
              "      <td>-0.025187</td>\n",
              "      <td>-0.030885</td>\n",
              "      <td>0.515129</td>\n",
              "      <td>0.584233</td>\n",
              "      <td>0.110679</td>\n",
              "      <td>0.082263</td>\n",
              "      <td>0.240781</td>\n",
              "      <td>0.102139</td>\n",
              "      <td>0.071243</td>\n",
              "      <td>0.077437</td>\n",
              "      <td>0.061348</td>\n",
              "      <td>0.005116</td>\n",
              "      <td>0.074497</td>\n",
              "      <td>0.072568</td>\n",
              "      <td>0.053258</td>\n",
              "      <td>0.067521</td>\n",
              "      <td>0.075472</td>\n",
              "      <td>0.082099</td>\n",
              "      <td>0.064290</td>\n",
              "      <td>0.029401</td>\n",
              "      <td>0.025254</td>\n",
              "      <td>0.034509</td>\n",
              "      <td>0.106358</td>\n",
              "      <td>0.030266</td>\n",
              "      <td>0.067766</td>\n",
              "      <td>-0.005816</td>\n",
              "      <td>0.055243</td>\n",
              "      <td>0.025396</td>\n",
              "      <td>0.074809</td>\n",
              "      <td>0.038035</td>\n",
              "      <td>0.022630</td>\n",
              "      <td>0.062354</td>\n",
              "      <td>0.094252</td>\n",
              "      <td>0.063745</td>\n",
              "      <td>0.084325</td>\n",
              "      <td>0.034071</td>\n",
              "      <td>0.015563</td>\n",
              "      <td>0.008178</td>\n",
              "      <td>-0.002989</td>\n",
              "      <td>-0.006570</td>\n",
              "      <td>-0.022338</td>\n",
              "      <td>-0.012286</td>\n",
              "      <td>-0.006748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18202</th>\n",
              "      <td>-0.138991</td>\n",
              "      <td>-0.295226</td>\n",
              "      <td>-0.128939</td>\n",
              "      <td>-0.094339</td>\n",
              "      <td>-0.094339</td>\n",
              "      <td>-0.094339</td>\n",
              "      <td>-0.128939</td>\n",
              "      <td>-0.156850</td>\n",
              "      <td>-0.121647</td>\n",
              "      <td>-0.121647</td>\n",
              "      <td>-0.121647</td>\n",
              "      <td>-0.156850</td>\n",
              "      <td>-0.177516</td>\n",
              "      <td>-0.155367</td>\n",
              "      <td>-0.155367</td>\n",
              "      <td>-0.155367</td>\n",
              "      <td>-0.177516</td>\n",
              "      <td>-0.148821</td>\n",
              "      <td>-0.148821</td>\n",
              "      <td>-0.148821</td>\n",
              "      <td>-0.158956</td>\n",
              "      <td>-0.155816</td>\n",
              "      <td>-0.155816</td>\n",
              "      <td>-0.155816</td>\n",
              "      <td>-0.158956</td>\n",
              "      <td>-0.180420</td>\n",
              "      <td>-0.180420</td>\n",
              "      <td>-0.180420</td>\n",
              "      <td>-0.217460</td>\n",
              "      <td>-0.104354</td>\n",
              "      <td>-0.042079</td>\n",
              "      <td>-0.044544</td>\n",
              "      <td>-0.108972</td>\n",
              "      <td>-0.113109</td>\n",
              "      <td>-0.030421</td>\n",
              "      <td>-0.152288</td>\n",
              "      <td>-0.050576</td>\n",
              "      <td>-0.090928</td>\n",
              "      <td>-0.040680</td>\n",
              "      <td>-0.075099</td>\n",
              "      <td>-0.069734</td>\n",
              "      <td>-0.106886</td>\n",
              "      <td>-0.074816</td>\n",
              "      <td>-0.098398</td>\n",
              "      <td>-0.053543</td>\n",
              "      <td>-0.053158</td>\n",
              "      <td>-0.097632</td>\n",
              "      <td>-0.075620</td>\n",
              "      <td>-0.056084</td>\n",
              "      <td>-0.025121</td>\n",
              "      <td>-0.150913</td>\n",
              "      <td>0.090255</td>\n",
              "      <td>-0.076404</td>\n",
              "      <td>-0.090638</td>\n",
              "      <td>-0.155174</td>\n",
              "      <td>-0.154583</td>\n",
              "      <td>-0.049665</td>\n",
              "      <td>-0.060449</td>\n",
              "      <td>-0.003786</td>\n",
              "      <td>-0.059495</td>\n",
              "      <td>-0.010170</td>\n",
              "      <td>-0.037254</td>\n",
              "      <td>-0.126238</td>\n",
              "      <td>-0.039072</td>\n",
              "      <td>0.001295</td>\n",
              "      <td>0.006483</td>\n",
              "      <td>-0.039651</td>\n",
              "      <td>-0.021222</td>\n",
              "      <td>-0.059292</td>\n",
              "      <td>-0.052146</td>\n",
              "      <td>-0.045441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18203</th>\n",
              "      <td>-0.094262</td>\n",
              "      <td>-0.200219</td>\n",
              "      <td>-0.207938</td>\n",
              "      <td>-0.143797</td>\n",
              "      <td>-0.143797</td>\n",
              "      <td>-0.143797</td>\n",
              "      <td>-0.207938</td>\n",
              "      <td>-0.218279</td>\n",
              "      <td>-0.182105</td>\n",
              "      <td>-0.182105</td>\n",
              "      <td>-0.182105</td>\n",
              "      <td>-0.218279</td>\n",
              "      <td>-0.166939</td>\n",
              "      <td>-0.186598</td>\n",
              "      <td>-0.186598</td>\n",
              "      <td>-0.186598</td>\n",
              "      <td>-0.166939</td>\n",
              "      <td>-0.137619</td>\n",
              "      <td>-0.137619</td>\n",
              "      <td>-0.137619</td>\n",
              "      <td>-0.144157</td>\n",
              "      <td>-0.120246</td>\n",
              "      <td>-0.120246</td>\n",
              "      <td>-0.120246</td>\n",
              "      <td>-0.144157</td>\n",
              "      <td>-0.098844</td>\n",
              "      <td>-0.098844</td>\n",
              "      <td>-0.098844</td>\n",
              "      <td>0.018666</td>\n",
              "      <td>0.075601</td>\n",
              "      <td>-0.028537</td>\n",
              "      <td>-0.030209</td>\n",
              "      <td>-0.097337</td>\n",
              "      <td>-0.131838</td>\n",
              "      <td>-0.020631</td>\n",
              "      <td>-0.103280</td>\n",
              "      <td>-0.034300</td>\n",
              "      <td>-0.104790</td>\n",
              "      <td>0.023951</td>\n",
              "      <td>-0.001221</td>\n",
              "      <td>-0.076663</td>\n",
              "      <td>-0.027821</td>\n",
              "      <td>-0.062154</td>\n",
              "      <td>-0.058927</td>\n",
              "      <td>-0.093925</td>\n",
              "      <td>-0.129921</td>\n",
              "      <td>-0.079154</td>\n",
              "      <td>-0.113951</td>\n",
              "      <td>-0.126481</td>\n",
              "      <td>-0.124297</td>\n",
              "      <td>-0.174206</td>\n",
              "      <td>-0.061004</td>\n",
              "      <td>-0.060160</td>\n",
              "      <td>-0.110198</td>\n",
              "      <td>-0.091662</td>\n",
              "      <td>0.009785</td>\n",
              "      <td>-0.018772</td>\n",
              "      <td>-0.036852</td>\n",
              "      <td>-0.106851</td>\n",
              "      <td>-0.014544</td>\n",
              "      <td>-0.103422</td>\n",
              "      <td>-0.025265</td>\n",
              "      <td>-0.104474</td>\n",
              "      <td>-0.091514</td>\n",
              "      <td>-0.108729</td>\n",
              "      <td>-0.090239</td>\n",
              "      <td>-0.026890</td>\n",
              "      <td>-0.031424</td>\n",
              "      <td>-0.031487</td>\n",
              "      <td>-0.048041</td>\n",
              "      <td>-0.018792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18204</th>\n",
              "      <td>-0.155701</td>\n",
              "      <td>-0.221957</td>\n",
              "      <td>-0.197120</td>\n",
              "      <td>-0.186635</td>\n",
              "      <td>-0.186635</td>\n",
              "      <td>-0.186635</td>\n",
              "      <td>-0.197120</td>\n",
              "      <td>-0.206534</td>\n",
              "      <td>-0.209764</td>\n",
              "      <td>-0.209764</td>\n",
              "      <td>-0.209764</td>\n",
              "      <td>-0.206534</td>\n",
              "      <td>-0.133460</td>\n",
              "      <td>-0.179843</td>\n",
              "      <td>-0.179843</td>\n",
              "      <td>-0.179843</td>\n",
              "      <td>-0.133460</td>\n",
              "      <td>-0.120021</td>\n",
              "      <td>-0.120021</td>\n",
              "      <td>-0.120021</td>\n",
              "      <td>-0.111445</td>\n",
              "      <td>-0.100990</td>\n",
              "      <td>-0.100990</td>\n",
              "      <td>-0.100990</td>\n",
              "      <td>-0.111445</td>\n",
              "      <td>-0.109575</td>\n",
              "      <td>-0.109575</td>\n",
              "      <td>-0.109575</td>\n",
              "      <td>-0.091864</td>\n",
              "      <td>-0.080181</td>\n",
              "      <td>-0.031635</td>\n",
              "      <td>-0.033489</td>\n",
              "      <td>-0.055948</td>\n",
              "      <td>-0.119542</td>\n",
              "      <td>-0.022871</td>\n",
              "      <td>0.006352</td>\n",
              "      <td>-0.038024</td>\n",
              "      <td>-0.107475</td>\n",
              "      <td>-0.022422</td>\n",
              "      <td>-0.028907</td>\n",
              "      <td>-0.112119</td>\n",
              "      <td>-0.021838</td>\n",
              "      <td>-0.043595</td>\n",
              "      <td>-0.039367</td>\n",
              "      <td>-0.072188</td>\n",
              "      <td>-0.128418</td>\n",
              "      <td>-0.068620</td>\n",
              "      <td>0.028649</td>\n",
              "      <td>0.023200</td>\n",
              "      <td>-0.072934</td>\n",
              "      <td>-0.131162</td>\n",
              "      <td>-0.033757</td>\n",
              "      <td>-0.048191</td>\n",
              "      <td>-0.034382</td>\n",
              "      <td>-0.041417</td>\n",
              "      <td>-0.211517</td>\n",
              "      <td>-0.008413</td>\n",
              "      <td>-0.109759</td>\n",
              "      <td>-0.122306</td>\n",
              "      <td>-0.007950</td>\n",
              "      <td>-0.058332</td>\n",
              "      <td>0.032929</td>\n",
              "      <td>-0.122786</td>\n",
              "      <td>-0.061408</td>\n",
              "      <td>-0.127898</td>\n",
              "      <td>-0.130011</td>\n",
              "      <td>-0.047851</td>\n",
              "      <td>-0.053718</td>\n",
              "      <td>-0.030070</td>\n",
              "      <td>-0.048572</td>\n",
              "      <td>-0.016388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18205</th>\n",
              "      <td>-0.143039</td>\n",
              "      <td>-0.229011</td>\n",
              "      <td>-0.186157</td>\n",
              "      <td>-0.171499</td>\n",
              "      <td>-0.171499</td>\n",
              "      <td>-0.171499</td>\n",
              "      <td>-0.186157</td>\n",
              "      <td>-0.194812</td>\n",
              "      <td>-0.200155</td>\n",
              "      <td>-0.200155</td>\n",
              "      <td>-0.200155</td>\n",
              "      <td>-0.194812</td>\n",
              "      <td>-0.119953</td>\n",
              "      <td>-0.176267</td>\n",
              "      <td>-0.176267</td>\n",
              "      <td>-0.176267</td>\n",
              "      <td>-0.119953</td>\n",
              "      <td>-0.115442</td>\n",
              "      <td>-0.115442</td>\n",
              "      <td>-0.115442</td>\n",
              "      <td>-0.098354</td>\n",
              "      <td>-0.104199</td>\n",
              "      <td>-0.104199</td>\n",
              "      <td>-0.104199</td>\n",
              "      <td>-0.098354</td>\n",
              "      <td>-0.095127</td>\n",
              "      <td>-0.095127</td>\n",
              "      <td>-0.095127</td>\n",
              "      <td>-0.063111</td>\n",
              "      <td>-0.079168</td>\n",
              "      <td>-0.032641</td>\n",
              "      <td>-0.034554</td>\n",
              "      <td>-0.071129</td>\n",
              "      <td>-0.111575</td>\n",
              "      <td>-0.023598</td>\n",
              "      <td>0.006554</td>\n",
              "      <td>-0.039233</td>\n",
              "      <td>-0.025692</td>\n",
              "      <td>0.018973</td>\n",
              "      <td>-0.062993</td>\n",
              "      <td>-0.093287</td>\n",
              "      <td>-0.013243</td>\n",
              "      <td>-0.018869</td>\n",
              "      <td>-0.058473</td>\n",
              "      <td>-0.050948</td>\n",
              "      <td>-0.111025</td>\n",
              "      <td>-0.031330</td>\n",
              "      <td>-0.020063</td>\n",
              "      <td>-0.026645</td>\n",
              "      <td>-0.064099</td>\n",
              "      <td>-0.372774</td>\n",
              "      <td>0.040889</td>\n",
              "      <td>0.040949</td>\n",
              "      <td>-0.160879</td>\n",
              "      <td>-0.120371</td>\n",
              "      <td>-0.113357</td>\n",
              "      <td>-0.055580</td>\n",
              "      <td>-0.108508</td>\n",
              "      <td>-0.098361</td>\n",
              "      <td>-0.025069</td>\n",
              "      <td>-0.036943</td>\n",
              "      <td>0.007778</td>\n",
              "      <td>-0.090734</td>\n",
              "      <td>-0.112937</td>\n",
              "      <td>-0.086374</td>\n",
              "      <td>-0.072289</td>\n",
              "      <td>-0.012143</td>\n",
              "      <td>-0.050555</td>\n",
              "      <td>-0.011068</td>\n",
              "      <td>-0.040450</td>\n",
              "      <td>-0.035249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18206</th>\n",
              "      <td>-0.227484</td>\n",
              "      <td>-0.341141</td>\n",
              "      <td>-0.129432</td>\n",
              "      <td>-0.083736</td>\n",
              "      <td>-0.083736</td>\n",
              "      <td>-0.083736</td>\n",
              "      <td>-0.129432</td>\n",
              "      <td>-0.146396</td>\n",
              "      <td>-0.122097</td>\n",
              "      <td>-0.122097</td>\n",
              "      <td>-0.122097</td>\n",
              "      <td>-0.146396</td>\n",
              "      <td>-0.169857</td>\n",
              "      <td>-0.170659</td>\n",
              "      <td>-0.170659</td>\n",
              "      <td>-0.170659</td>\n",
              "      <td>-0.169857</td>\n",
              "      <td>-0.163469</td>\n",
              "      <td>-0.163469</td>\n",
              "      <td>-0.163469</td>\n",
              "      <td>-0.162825</td>\n",
              "      <td>-0.171153</td>\n",
              "      <td>-0.171153</td>\n",
              "      <td>-0.171153</td>\n",
              "      <td>-0.162825</td>\n",
              "      <td>-0.185483</td>\n",
              "      <td>-0.185483</td>\n",
              "      <td>-0.185483</td>\n",
              "      <td>0.075082</td>\n",
              "      <td>-0.112103</td>\n",
              "      <td>-0.046220</td>\n",
              "      <td>-0.048929</td>\n",
              "      <td>-0.100720</td>\n",
              "      <td>-0.118261</td>\n",
              "      <td>-0.033415</td>\n",
              "      <td>0.009280</td>\n",
              "      <td>-0.055555</td>\n",
              "      <td>-0.055430</td>\n",
              "      <td>-0.068534</td>\n",
              "      <td>-0.042234</td>\n",
              "      <td>-0.084526</td>\n",
              "      <td>-0.084522</td>\n",
              "      <td>-0.076018</td>\n",
              "      <td>-0.044874</td>\n",
              "      <td>-0.058813</td>\n",
              "      <td>-0.065992</td>\n",
              "      <td>-0.051351</td>\n",
              "      <td>-0.059640</td>\n",
              "      <td>-0.077522</td>\n",
              "      <td>-0.067076</td>\n",
              "      <td>-0.139904</td>\n",
              "      <td>-0.008081</td>\n",
              "      <td>-0.083924</td>\n",
              "      <td>-0.030502</td>\n",
              "      <td>-0.119144</td>\n",
              "      <td>-0.049128</td>\n",
              "      <td>-0.090778</td>\n",
              "      <td>0.000718</td>\n",
              "      <td>-0.026679</td>\n",
              "      <td>-0.095204</td>\n",
              "      <td>-0.035856</td>\n",
              "      <td>-0.115113</td>\n",
              "      <td>-0.159029</td>\n",
              "      <td>-0.042918</td>\n",
              "      <td>-0.025475</td>\n",
              "      <td>0.023543</td>\n",
              "      <td>-0.043553</td>\n",
              "      <td>-0.009517</td>\n",
              "      <td>-0.050998</td>\n",
              "      <td>-0.029903</td>\n",
              "      <td>-0.049914</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>18207 rows × 71 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             0         1         2   ...        68        69        70\n",
              "0      0.033019  0.105411  0.006563  ... -0.001939 -0.003653 -0.012706\n",
              "1      0.055514  0.132233  0.015126  ... -0.002432 -0.004582 -0.010436\n",
              "2      0.005976  0.118552  0.011284  ... -0.002350 -0.002559 -0.010082\n",
              "3      0.015714  0.140061  0.013870  ...  0.167816  0.164507  0.168443\n",
              "4      0.013198  0.117633  0.056339  ... -0.022338 -0.012286 -0.006748\n",
              "...         ...       ...       ...  ...       ...       ...       ...\n",
              "18202 -0.138991 -0.295226 -0.128939  ... -0.059292 -0.052146 -0.045441\n",
              "18203 -0.094262 -0.200219 -0.207938  ... -0.031487 -0.048041 -0.018792\n",
              "18204 -0.155701 -0.221957 -0.197120  ... -0.030070 -0.048572 -0.016388\n",
              "18205 -0.143039 -0.229011 -0.186157  ... -0.011068 -0.040450 -0.035249\n",
              "18206 -0.227484 -0.341141 -0.129432  ... -0.050998 -0.029903 -0.049914\n",
              "\n",
              "[18207 rows x 71 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKlII28xxduj"
      },
      "source": [
        "vector = normalized_data.to_numpy().tolist()\n",
        "# vector[0] # vector[0] = all values of messi (vector of a point)"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1HDO9iBlyq0P",
        "outputId": "03f6341e-8455-4a29-a7b8-d02e64035bb7"
      },
      "source": [
        "def cluster_quality(index_of_cluster, cluster, vector):\n",
        "  if len(cluster[index_of_cluster])==0 or len(cluster[index_of_cluster])==1:\n",
        "    return 0\n",
        "  \n",
        "  pts = []\n",
        "  for i in cluster[index_of_cluster]:\n",
        "    pts.append(vector[i])\n",
        "  pts=np.array(pts)\n",
        "  centroid = np.mean(pts,axis=0)\n",
        "\n",
        "  quality=0\n",
        "  for i in cluster[index_of_cluster]:\n",
        "    quality+=np.linalg.norm(vector[i]-centroid)**2\n",
        "  return quality\n",
        "\n",
        "# testing the func\n",
        "c1 = [[0,1]]\n",
        "v1 = [[2],[2]]\n",
        "print(cluster_quality(0,c1,v1))"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tueul9YOwFvZ"
      },
      "source": [
        "from queue import PriorityQueue\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# cluster[i] = stores index of pts in ith cluster\n",
        "cluster = []\n",
        "\n",
        "# Putting all index of vectors into one 1 cluster initially\n",
        "cluster.append([i for i in range(len(normalized_data[0]))])\n",
        "\n",
        "\n",
        "k=10 # no of clusters\n",
        "q = PriorityQueue()\n",
        "\n",
        "# putting (-quality,index) into priority queue for decreasing queue\n",
        "q.put((-cluster_quality(0,cluster,vector),0))\n",
        "\n",
        "count = 1\n",
        "while count < k and q.empty() == False:\n",
        "  pair = q.get()\n",
        "  err = pair[0]\n",
        "  index = pair[1]\n",
        "\n",
        "  kmean_data = []\n",
        "  for i in cluster[index]:\n",
        "    kmean_data.append(vector[i])\n",
        "  kmean_data=np.array(kmean_data)\n",
        "\n",
        "  # print(kmean_data)\n",
        "\n",
        "  Kmeans=KMeans(n_clusters=2).fit(kmean_data)\n",
        "  label = Kmeans.labels_\n",
        "  # from collections import Counter\n",
        "  # print(Counter(label).values())\n",
        "\n",
        "  arr1 = []\n",
        "  arr2 = []\n",
        "  for i in range(len(kmean_data)):\n",
        "    if int(label[i]) == 0:\n",
        "      arr1.append(cluster[index][i])\n",
        "    else:\n",
        "      arr2.append(cluster[index][i])\n",
        "\n",
        "  # print(len(cluster[0]))\n",
        "\n",
        "  # changing the cluster\n",
        "  cluster[index] = arr1\n",
        "  cluster.append(arr2)\n",
        "  # print(len(cluster[count]))\n",
        "  # print(len(cluster[0]))\n",
        "  \n",
        "  # finding quality and adding to q\n",
        "  q1 = cluster_quality(index, cluster, vector)\n",
        "  q2 = cluster_quality(count, cluster, vector)\n",
        "  q.put((-q1,index))\n",
        "  q.put((-q2,count))\n",
        "\n",
        "  count+=1"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "59FAIhfLKGGn",
        "outputId": "70d26331-4e98-46ad-d3ac-bc135f6a077c"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components = 2)\n",
        "reduced_data = pca.fit_transform(normalized_data)\n",
        "reduced_data = pd.DataFrame(reduced_data)\n",
        "reduced_data.columns = ['P1', 'P2']\n",
        "\n",
        "reduced_data.describe()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.820700e+04</td>\n",
              "      <td>1.820700e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.002433e-15</td>\n",
              "      <td>-4.174998e-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.806985e-01</td>\n",
              "      <td>4.946387e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-9.225924e-01</td>\n",
              "      <td>-8.918885e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-5.599268e-01</td>\n",
              "      <td>-4.292171e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-7.733905e-03</td>\n",
              "      <td>-6.115128e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.401389e-01</td>\n",
              "      <td>4.401620e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.004308e+00</td>\n",
              "      <td>9.546268e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 P1            P2\n",
              "count  1.820700e+04  1.820700e+04\n",
              "mean   1.002433e-15 -4.174998e-17\n",
              "std    5.806985e-01  4.946387e-01\n",
              "min   -9.225924e-01 -8.918885e-01\n",
              "25%   -5.599268e-01 -4.292171e-01\n",
              "50%   -7.733905e-03 -6.115128e-02\n",
              "75%    5.401389e-01  4.401620e-01\n",
              "max    1.004308e+00  9.546268e-01"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "N-SQ9Sg6Jd28",
        "outputId": "e3acf52f-59b3-401c-99f8-7a6350a81419"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "labelle = np.zeros(len(normalized_data[0]))\n",
        "\n",
        "for i in range(k):\n",
        "  for j in cluster[i]:\n",
        "    labelle[j] = i\n",
        "\n",
        "# print(labelle)\n",
        "plt.figure(figsize =(8, 8))\n",
        "plt.scatter(reduced_data['P1'], reduced_data['P2'], c = labelle, cmap ='rainbow')\n",
        "plt.show()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g2URLiBuSLGX1JEu8a8ZoSwOKsy3zuBEApOXAlrvMcmKb+jAUrLfa+SthzWT47Q1LKLIGxc6y5q+E726zxhjVuesICPs0TJiVHbFZhP63mz689kQcpg8tynX+Y8N4HGY9Fc6iIQQ+WwLfdbqBrU16goAuRYvQgA/6PF6niHfZs5B3pvQAwj/BtU0H83GvhwnoLrz2pOo1+4Aexw2LbsQUGs0rt3L6htewG16QJnbpQ9QRr3/uuucj1ut3JcQKL5OUOFsAVnGZgBYyx7tjWGICActZ7tVXoXNnK6OeQrGfKCFXND7y1sDEy+DXF2DVNzBrPHxxtVWPOxoBL8x8GlZ/2/DsaEEfLPowMqTM8FuhZh9fCD/cv295xY80/AYYscYvaVO+DoDlLUZFXa9uW7aWcbPPJLNsg/VgVQshTYQ0Yn5GhmZncufr8NkSyI/LYkGrU9mR1DXGcCS9837muZnH4zLcEaFxP3a4Ar8WGX8uETx37EcMyJvOmz/2ZXdiW4KaA4TGnKxz8UXxtK8a7ZCd33Hh6sdxBt24AuU4gh7mZv4t4hHMq8czudN1+ENOiJqUDN4ZGYO/pMVonj36Pby1z7l5s1UxTpVjVewnyrSuaFz4K2HGk+FhW2DNtGc8Bee+FmmmnPc67FrFvs+C62pvHhGT6gPCZYNEB1REmpdNdLY36Q7ArsSOfNv5Js7Y9BouY28M+Ir04bzZbzy7EjvQJFBMpS0JTRqYmh274UUKHRNBQHeA1MJ+L0KaDNz1I5ll6xm6/b/cMXoONtNPULNHH6sQpHlz2ZXQgaL4VrQuXUczX171bre9SXQTvxBoZpC7F1yGy3CzNm0IMmRiX502lJ/bnM/I7RNxGB4kAik0KmzJJATLsMkAY9c/R2b5BoLCRrfixbRw731YlFhV3/7X5VY+6fkQSInTcHPuuufJrNgUNowJPf7NV91u59TNMYrBlJSw57MfWdb6VFJToF9vga4pc7uiYSghVzQuti2IPav2lUFRNjTrsHeb3w05i//YPOWNFSHgikHw0m9hmyVQ4UhhZdqw6m3v932SpS1Hcff8S2nqy2d96iAeGvZdtRm8zJmGZgbpumc+R+X9yKaUASzIOJWALXLG6wh6eOaX0bQpW4PDcHPp6VsJ6K6oGd1qsjptKDeOWUTXPQt5aM7fCQgbNhlEAFcvv4vFLcbgdUTmsm9XsgKb6ccQGh5bjdA0IXh50OvMaH8JQ3Z8Q0Bz8nOb88lp0p3uhfO4fdFVZFZuZlLn67ln3iWk+MOT9QhAmCZfdLsLU9NJq9zOv38bS5fiJdVtVqQdxzedb2J+1llIodOqYgsuM7IIjM8d5POXtjK9u4kmID4OnnlYJ6OlEnNF/SjTuqJx4a+kzqmwt8wS75Id4PfAtvlRzb6KEIOy4LahkGyJqBSQk9gViaC5JwdXKF7cGaxkQ+ogpnS8Gp/m4uOeD1k1yGtgajY2ND2Kkze/zdKWo/HXWutuV7KSQbt+5II1j9O+dAXxwQrWNz0Gb412zmAlLSu2YDcis+TlJ7QjLlDBI7PPJilYgj0k4gDpnh38+7e/W6b8WmSn9uODXo+xutlQtNqWHCFYkzaUd/r9hw/7PM72lF4ITG5efC07Ezvi0RPQpCTOiJ6NLqg7yazYhCtQwRUrHwgTcb/mpEPpSjz2JshQHfc1aUPCHyaqPjsE65IH4fWC2wN7imHcc+rvVtEw1Ixc0bho3o2YHt9mEGY+Ze3XHaH47sZu/z4E9G4FZ3SHX7MJBnWyRQ/65s/itan9+C3zbNY1O5qMis2sSB/OpE7Xc8am19ia0iusJnkVmjTId7VmxPbPAcH8zDMwhJ1HZ59B27K1GMJGfLAcEfq9+GxxCCnRzCDXLL+Tk7PfxcTK1f5p9/v5stud1Sb5k7Lf58alN2GLklBGAP3zZ5JRvpHcpK5hZnwpNKZ1uJw9cRnYzUDUFLA1MYWNRRmncs6GFzCFzg1LbrCWCoiMW7eZfgrisjg291tGbA/PzOcwfThMHw/NPZd/nr6ZMmcac7PO4cI1j+MIeni/z5PMzzwDTRr0LJjLxqZ702hLCXnbvewcfCEZK6Zb5VUfeACuvFJ5uCsiUEKuaDwUbobpj1G/OEurApmifqSEF+fC2nyk3+DbLrfxeY/7qLQ3oaknjytW3Me1y+8E4Itud1Pqas6LA9+gddk6CuMyI8RcCp1M9xau/v1uQHDN8jvZndCWVhVbsMtICe1R+BuGZuOfKx9kTPb7OI29XvAXrnmcEldzfmp/Ga3L1nL9sltjZoUDMIVOj8K55Me3JVAr+53PlsDiVicT829HSnQZQJcGo7M/pHf+LDQkmgySUbkFAL/mCivL6tXjmdn2Qny2BP6+/vmY5k2BZPj2L5jc+XoCupNbR81FIPHYEjFDPgHW2GodFwzgW59tFWTZuhVuuQWKiqw87wpFDZRpXdE4cBfBjw8qgT7YbC6CdfngN/iy6+183OthKxmL0NkTn8mLg97g5zbnUeRqiYFORtl67p1/MZesehRnrcQozmAlZ2x6lTjDjcvw4DLcOE0vrcvXRxVxAJfh4ZaF13DGptdxGeGhWnGGm/PXPg3AqK0fW/HhdRAUdsqd6RgxHOb8ehyuQEV1yVRg76sQGJoDvx7HjHaX8FP7S3DbEnmv12MUu1qwPH0EDx/3DTsTOiABjx7PN51u4s2+z3HZivvJKl+PWW1nCMdu+PbGvUtLwCvtKdUiXnX+2jgND23K1hDQ7GQn96ZApsLjj4fVi1coQM3IFUcyUkLBBtj8M+xaGTtmW7H/bCiAoImJ4PPu90XEcPtt8fzfoLc5YdsnDMqbwrXL78QmA3QvWsDDc87hjf7Ps71JD5L8RZyz/nnGrvtPg09tIjCFzpDcb6qzqdWmZWU2Kd7dxAXKYxZbqcJni2NTSj/spg9fjVKq1QhBuSut1vZIg7nPlsDU9ldw2YqHaVG5ldtH/sqQnK+4cO0TpHrz8OrWZzQwfzrtS1fQt+AXHKavureo1yq06ocGGRpLXehmgNZl63i1/8vManMeUljpcbuULuGBTfmk9PgD0vQqGi2qHrniyERK+PlZyF16uEfy5+a3bfD+EtyGi7F/y486m3UEPXz2bUvrZ8OLhsn6pkeztOVo4gPlDMv5gmbefauhHhB2FrY6hQ4lK2jm3omD6EK+LakbhfFZfNX1dv499x9hKWLBEsWA5qTCnkJhXCbtylazIXUgD4yYgl+Piyzk0sD1ZWEafPR9B5p6dhHUnOxxtaCpNw+nudciFBB2NGmg10oqE20t3aPF8XXX25jQ+1Fshi8Uyx57LMIMIjVbxJh100+njjZeuKcSUlL27quogG+/tUzvo0ZZtdEVfyrqqkeuTOuKIw/TgF/+o0T8UDAwE2yCuGAFif7iqE2yyteFzOVuNEz+c/T73H3CDCb0fIQ3+z3HZadnM6v1P2KeYnGLE/HqcQRDnts+zUW5sxk/tb2YFF9BVBE3sUqmvnDUWzx57GdU2JJZ3GoMnpDFwETg1eKY1u5S7jnhJ4pcrWhfuhKH6aPXnt947ucT6Lt7JpoZrJ4JRzd8EzWuWwoNQ9jQkDhMLy3d28JEHMAuA9X54WsSzRUzzvRw7vrnSXXn0rp0rSXANc9bawyyqthOLbE3NAebNwfZ0WkYZGTAp5/CZ59BaipcfDHcfDP07QsXXBBeZlXxp0aZ1hVHHks+gh1KxA8JThs8eiJiQyGXlbzJm8Yd4dnOpMn2Jt24cfQCnp8xjCWtxjC79ViC1cVEBIbQeOaYj+mdP4umvvywGakExh/9Hsn+Qs7a8DKtKrewrPkofuh4Da5gZdgM26PFURifRUbFZgRWVbFhO77ig159cTuSeerYiRy1awrDc77Ar8Uxrf1lrEs7lvYlv5NZsZE1aUP4b7c7KYzPon/eT9y+6EqWtBxTXao0yV9EmTMt8jOIMjN2GF5KnenVCWD21U+8ZnsTq2Lb9ibdLH8B00921XlDAp7oL8ZtS8CsZ6YOli/AxI53kOwvpOVjCzhhywSSamaFCwYtcf/mG8vT/YEHlKf7nxwl5Ioji6APNs5EhY0dQponQnoCJ7MWc+unvL7qMgxsoXzlGkE9js1NByCF4Mf2l9cQ8b1IBB/2Hsd1S29Fl0HsofXsgOag1JVOSVxLXjrqzbBjPPYkTARa6HddGN+adE9OtanaYXo5dfPb2Ew/b/d9Fik0FmacxsKM08JMzmmenUxrdynv932qeo0/N7EzM9tdzPVLbkQgkQjshhdnsNJ6UKlH2AQmbcrW1rrGSIGusKfgNDxh3uw1KXK15M4TfqbE1RxT6Jho2EwfvfJ/pdSZRm5SJ07Z9BZXrXyAbU26c/cJM/FHSaITPjjBzLYXkOor4Mrf77WWF4Q90qHQ64WnnoLkZLjpprr7VDRqlGldceSwZwvMfCZ6fW/FH4sQCCHYVN7eiiiLInQeexKVjpSYx69regyz2/yDyZ2ux6vHWyJtGrSoyGb49i84fttEknx7qg/RzYC1jh2iZWU2riglQsdkv49eh6PjxpT+fFir2EpQd1Jpa8I3nW+pTsm6Jz6Lc9Y9HzXZTE3shpcLVz/GjLYXceUpazjvrF280v8l/JqzOk+6V4vDbU/mvhHT+LHDFRgxbqX/OfoDdie0xWNvgs+WwGmb3+LTSW14eO7ZvPTTsbz401B0aTClw5W0qthMumdHg5w6NSQvTT+GETlf0MRfHDMqALcbnnyy3v4UjRs1I1ccGeSvh58eVyJ+mJm762iCMnr41pT2V3Hsjm9ZnXZcpNBLyfbkHjx/9HsA/NrmHxyX8xUtyrN5Y2r/kBOdRDeDvDLwFX5qdwlBYeeTHg9yyepxBDQ7NjMAUQTJRCfZV0C+rW3UcZXEtbQqmtXC0B1sSe0btm1izwdDhVzMqAltAJJ8eyiIa8225GTuXPBPmviLmJt5FhedmcOorR/TtWgx2cm9mNbhcsqcabzR9EXS3Ts4Zud3YXJeaW/CqvTjqsPM+uf9xGWrHrIeVkIPLO1KVjJKTOSlga+yI6kzd827mHHDvqHM0ZSg5qzTclDsSifNuzPm/moKCiK3+f2wcCHExUH//lZJVUWjRQm54vDjd1se6krEDzuaiL2kMbHngzz+y0kk+IupdKRGDe+qYl2zY9iZ2IkPJ3ewzM41TM83Lr2J3fFtWJ0+jC+738X09pfRu+BXxmx5nwG7p1eb2qswhU6Rq+XeDdUz1r3nqy4bWgu/HrfXAzw0Rinqvu0VuVrSzLuLK1Y+UL2Gn+7JxRA2vutS20QtsRk+dse3wRS2sBA5n3CEjfHsDS9GxMrbMOhUvIzxM4/HJgNo0uCjye2Z1vbS0FJEdCF3BSspc0RZ749GbQ/2776DSy6xfjZNy1Fu0iTLSU7RKFGPYYrDS9FW+PJf4K843CNRACMz52DXoj9QaTJIZuUW3pral7alq9ANP3bDQ5y/lN75v0S0H7LjG6qEaI+rFY8f+xlnnlvO2LPyKHM2wwx5sZe6mjOn9d95v88T+PW4MD9wrx7Pxz3/jW74ayRv0ayyqjU9vaPNXKVEMwPVec4bSpNAMedseCHMEc+vu4gqqlJyUvYHnL3pFTQZJChsVNqa4NET8DhScNQQ7m8738DOxI4RXQhMnKYXXRoIQJcmm5sOjJo3Hik5f/WTTJjcnv75Mxt2QYWFcOGFsGABbNliebSXlVn/KiogJ8cKWVOJZhotakauOHxICbNfUDPxI4iLu3zJqg1Z5NDeEtWQCCZ7C7hm+e0081qlQ9+YNoDd8W0pd6TSrnQVuUmdufbkFWF9OQwPQpr4dBe3jp5LkatltZk5p0mPavHVTIOBeT+yJy6Du06YweUrH6Rz0RKK4loxsccD/Jr1d3TTF54/vSrGusrpTZqARJcGhrCHHPUEpu7Y55rxHUp+x6+7qpO8AAzI+wkjSplUV6CSsze8aF0H1sOOB8nLA17l1zZjETXGvKTFiVwzZgVH5f3IGZteo//uGRF11asojM9CRonpH7xzEuetewaX4cYQOjsT2pHoLyLJXxzWz+aUvkzqdB0F8a05atcUTvrvB8R//jkceywEoqyn+/0wZQqcdVYDPyXFkYQScsWhRZqWZ7rNBUFgrnsAACAASURBVO49UFl4uEekCCElZJe25pEfTianSQ+2pPajRcUW+hTMIiFQhgDy41vzY/sryEtoT9+CWRy/fSI2GSTRXxLR36JWp3DFivuZk3UuFbVSkpohU7czWMnzM4bRsjKbMkczbjxpEf8eNik8fSkQ1KJ4codE0hmspGPxMi5d9TBzM85iUpcbCZPHfQy9KnK1tNbra5Du2cHYNc/weY/7qpO5uALl/Gv5bRG1x+0ySFB3IDUbsua5NR0DnfkZZ7C8xUhO2vIe1y6/I6qQ98qfxYJWp0WMfVmL0bjtSczLOIPXB7xIQHfiClRw+qbX0WWQwrgsuu1ZwCuDXiWgOTE1G6vThvJt5xt5efpgEubOZW3TwWRUbCLFv4c1zY7l+47XUBbXnKG/JzLyFInDoULVGhtKyBWHhopCWPA25K0KORoJq2ZmPWk3FYcOgaTtg6/j8pXRt2AWfQtmVe+TwIr0YTw87DsMYSOgu5iXeRZfdLuL8TOGsajVKRFZyHYldeLLbndQ7miK1x5ZJxygX95PZJVvwGH6iA9W8PrUAfx72LdsS+5dvwCHzuezJbAm/ThcwUqmdbyCfY/6Dmd7ck92JnakfenKsJ7OW/cMv7S9ALc9iZ6FvzFy26ccs3NSxNl2J7S3QuRiIQReWyLfd/wX3QoXMHzHl2HZ4SRUW0KiHMzn3e5haocr8NkSGLxzMvfOuxBT6AhpJa/5sNe4MA9+ny2B3fFtuebklfTbPZNm7h1ctPZJXuv3PN93us46lxCsWG/y/UMG48fpSswbGUrIFfuPNMEIgm4Pv+nuXAGrJ0GgEtoNgYR0y4ReM6xGhYkfcUhvkFLRFLvmwRaq4V4Vozyt3SVM6P043hq1tL32RAq01nze/V4cQQ+vThvI1uSevDzwdbx2q93HvR6hZcVm7IaXgO6KOOeInP+GmbDTPTus2XhDZtG12kzpcDUSsX/JT2oUTwHQZTAiblwKnayKjdhMP7cuuoaEYDlbkvvwZr/nWJd2DM6gm6aenRTGZVjXWs84DM3Bm/2fZ+Du6dWx6AGhM7v1WL7oFr3CWUB3sLTlifj0OBL8Jdw778KIkL2L1jzOgozT2J7cc+/YNRvFca2Y1eY8HIYXV7CCSZ1vDBujP6ixfQf8PEcyZqQS8saEEnLFviNNWPEVrP3eMpPHN4VBl0Gbo+G312Dz3pkce7agVLsREDDIXujkvpE/c/bGVxi97WMcQQ+6NLCZfvoUzsUTZVYd0F3MzzyT93/oClhx34YWflvJi+LgVSWcffJnhW326vGWGEejnlzpc1qfyz79rdUS76rEMa3KNtKicluEJ7DD9HHWhpd5+pgJ2E0/OxM7csfIWXht8SA0/Hoc5Y6m+/QgER8o5ZvON9KqMhtnoJIve9xFTlL36geh2ggpCWp2EBrH7JwUdeauG35GbvuUD/o8EbHP1Gx4tUQ+7j0u6jj9AZj2s8mYkcoPujGhhFyx7yybCOum7i0pWlkIc16GfueHizigRLwRICXM3Ezl1AQ+2dMBgWRV2lC6FC0mLuR1nRgorW7evHIrvQtmU+ZMY2mL0biCe727Myo2k+zNpzC+dWxBkxKH4WHgzimUONNI8RegS4NKexNuHj2fgvjW0Y+rSmkaVgjFxGH4MIWwktXsi2ObECT6i7lu6c0M2/EVmjRZkT6CVwe8SGFcJlkVGyMOSfQXU+Zqzhfd7iYvoS0+W1x4PPq+WAOEYFdiJ/7X7Q4rTj6hXUzLRRWmZsOvOdHNADbTb3nv10LDxBElrr72uWOxdiOsWifp1U3NyhsLB0XIhRAnAy8COvCOlPLpWvv/Dzgh9DYeaC6lTAntM4CVoX3bpZRnHowxKf4gDD+s+zHS09zww+9fHp4xKQ6MoAlTN9CryIMICWHvgtlhRUaaevPoULyc47d/xilb3sXQbEgEAd3J8ubHh3Vnk4GYQmEPuLl74aUckzsZHct8HxR2fLY4vupyKwXxresUsup+pQSkNRO2xeEMVtI/93sWZZza8OuWkmd+HhXKf245t/XJ/4XxM4/ng56PcsuyG8Oa+zQXc7LOAeCTXg/hCpTHDm2LeOCIYU3QNHxaAlU+53Vee4iieKuE6eKWJ3OdvDViv0+PZ27W2fX2EwspYdx/DB67T6dLR8I87xVHJgcs5EIIHXgVOBHYASwSQnwnpVxT1UZKeVuN9jcB/Wt04ZFS9jvQcSgOEd4yYjoT1TcLUByZrMiDCj/C3CvctZOyADwy52+4DHcowYu1TQbLGZET/gCXWb6JvIT2UTOn3bT0Bo7K/QEbe2Ok7TLAu72e5pc25zdIyKqp0b/l8DaU9kVLyG4WtdJjBL0LZtOyMhu7ufehVMfEaXgwdDu/pw+nU/FSEoIVePV4CuMymdz5epJ8ezgx+0NWpw1hfbPB0QU6ZD3QZQAhTUxhw6wjEU1Qc6CZfqtoSgPZE5/Jh73Hcemqh7EbfgQmPj2eX9qcx+q0oQ3uJxrl5ZLb7g+QKCto3Uaj/zFJnD5GI6WJEvUjkYMxIz8a2CSl3AIghPgMOAtYE6P9BcDDB+G8isOBKyW2U7DmrE49uV8Im/JiPxzklUMgSvKRWqT490Rsi/ancP7ap1iVflyY5zTSxGb6GbH9cxxExjGfu248Uzpc1fAxRxHPoGbnkjVPMG7Y1w3qIqt8PVot03SJM41xQ//HxtSBzGh3KZo0OGnLe9hkkGntL6e5ezvjZ47AZvrIS+jAdWOWEvMLIQTx/nIemf037jl+WrV3eDTspp8xW96NcECrj6+73say5iM5Yftn2E0fc7POsUT8QGfRQiARlJPEmh2CNV+afPa15I4bNE4YqtbPjzQOhpBnAjk13u8ABkdrKIRoC7QHaqYkcgkhFgNB4Gkp5Tcxjr0GuAagTZs2B2HYigZTkgOlOyElC5IzISkDirMj2x2IiIMS8cNFVjLYdSsCoQYmwqrWRf0iX4UhNHKadKOpZyf5CW2RaIDE1Ow0r9yOHi1bGZDu3cmti65h/OD3MGrPSutxcqtuJjQ6Fy8JP876ASENKzVrjX62JfcIj/MGHhv6FRuaDqweg4mNHzpdW33cLYuvIT5QioakXdlqOpSsYEtqbINiULPz8qDXCOqx86ZrZoBTtrzDtcvvoHXZOt7uPx4TMKrSztY20WNlg5OhdDJbU/vyfuoflF41ZFmIC5bjM+N57hUbRlAyesS+ZctT/LEcame384EvpQz7NreVUuYKIToAM4UQK6WUm2sfKKV8C3gLYNCgQcqD6lAQ8MIvz0LBRtB0MA1o0QN8ZYd7ZIqDSe8W0Czempkb1ldLAoawsSOhE20q1qNhxjTEBIQDu7TM0w8M+4HV6UMJVpnITQNCXux+PQ6N2JW9huZ9xwRbCbvMdGrOcjUZxBTRC7lU4QxWMmrrBLam9AasCmbjZp9Bu+KVBHQnW5N78MTQL8OsBGuaDWF7UnfS3Dv4vtO/mJN1DjuSuiJrZ3ALCakj6KFr0eKwZYerfr+HR4/7X7j1oQopcQTd5DTpGr1AS+gBpUVFNstajOLss4vIKt/Afb+dT5krnc1NejO149WYQkOTBlJotCtZwQO/jWVnYhf2xGXwyqBXY5w7dlGYfUfy6o8DSfEXMLnTtbzz/mOccJwLXVdm9iOFgyHkuUBNN9Os0LZonA/cUHODlDI39LpFCPEL1vp5hJArDgNLPoL8DWAGqJ6U7V4NMUJjFI0UTYMHR8Lnv8P8HPAFQcIvbc5jYo8HaObJ5cHfzsMZdGM3fdWiHhB2DM3G+KPe5YZlt/BZt3v4vcXI8BlkDVE0NBuGsGGLYnmRgOOpx3j39lZccbNBXv7efTbDj7/WbBpp4jLcyJCsjt76EVcuv4fbR88BYOzaZ+m+ZwFOwwMBiDcqqsuZViME9x4/DZsZwGeLr3d9vqqeuF7DQtE/fyY3Lrme1/u/iLvKa75qnEJQGtcyRm9Ut9uV1Ln6501NBzLuuP9hM/1o0qRFZTZnbnyV4rgW/Jr1D5795URchpsWnlzy4ttiEn1mLKQR8mz34dUTLFE/AHP7taf8znXLbmFzSj/0ogIee7YF/7zIQbs2SsyPBA6GkC8COgsh2mMJ+PnAhbUbCSG6AanAvBrbUgG3lNInhEgDhgLPHoQxKQ4UKWHLr5aI18QIgFQFTv50JDjgiqOsGfnsrbzZbzw/drgSny2BXUmduOj0bfQp/JUkXxHxwTJ6FP5GsaslP3a4kiJXS6YXLWZS5+vrLrsZ14odSV1pU7Y6LEY7KGxM6ngtU3Jv4Zb1JsGgNR+vmvf67ZEzTs00OHnzO5y74f9I8hfhNLz4NBcOw4cwDU7Kft8S8arLC5Qxdu0zfNb9foL6XtN9rIxz0QjqDma1Hsvw7Z/jYO/DyLCc/7HHlcEHfZ4MLQ/ZonwOtdPL1CCiipxWbdHIadKdVwe+RIK/jLHrnkUPfR/XNh3MU8d+al1LlKUHKWwI08OQHd9y/PaJfNH9HlalD49oJ6RhLX/UJfJCw2+L58VBb1ntNRuLlpssX2vwxP06PVWY2mFHyH0sKBC1EyFOBV7ACj97T0r5hBBiHLBYSvldqM0jgEtKeW+N44YAb2L5wGrAC1LKd+s736BBg+TixYsPeNyKOpAmfHwhKg78L8ZVX1JKKpecsTVyhhprrVpKy/QLe8uFxqBdyQr+8/ModNNPnOHGbUtkY+oA7h/xY0R+9fpoUZnNB993qX5voPF155v5sM9jfDS5I6m+fHKSuvJGv/GsbD4CV7CS9MptbEntv/+zUylJ9Bdz0epxnLrlHQyhsyl1IA8O/z5UIY064+c1adTp9BYLYRq0Ll/HyVveZUjuN1w35veoCXrCz2fSxFfIY7PPoHPxUiQaK9OH8eqAl8hJtkqbxvuL8enxGHr0MrD1kdES3n1RpSM5FAghlkgpo4ZkHBQhP9QoIT9E/PgQFKw/3KNQHEq2FbPydS8PDf4uZnaxmDTAKU0zA8T7ihm281uaV25jbbPBLMw4fb+EtalnJy/8NJR0zw7AEvKvut7O+32f4tqltzI4dxI3jlmC29YEqVk2AEfQbQnuAa4fO4OV9Mv7iVJXc9Y1OyY8vj3GtQjDj0P68dn2Y2mqRpU33fQjYK8fQh08/fNouu+ZX50G10Tgtjfh6lNWU+JqEer7QNbTJdeVvcSZJV/CRRfB1VeDc/8eChR1U5eQqzgCRWwGX8WBFqBQNDKykgme2RdvNAeq+ohZEzxoiY80sBs+KlxpTOl4NR/2eZyFmWfsd270Ymdzrjp1DZ93uwuAgO5kVpvzAPi418P8t/td+HVXtYgD+G3x1UlvDgSfLYEFmWewLu3Y8PHXcS1Ss+PTo1RxawjV6+4ahuZskIi3K1lB16JFYbnsNSR2w8epm9+u0feByIDg9aQbcS9eCTffDM2awSef7HPpWMWBoYRcERvtwBxkFI0QXaNlX7Pu37uUYQVw2pf8zt3zL+HlaUdx/ZIbaV6xNzTRZnq5/7ex2A0fUuj47IkHx5taCKRmw6/HMaHXI/ycNZYvut1THQpW4UhlSsdrojqwScLHfwCD2I9DtAMXuQZ+J1uXr8eIknnOaXrpWLzswMYQhsYPHa62rquyEq68EsaNO4j9K+pDCbkiOt4yWPrJQbrhKRoXdYm4yeO/nkrPwrkA9Ns9g+dnDGf49i/oVLKcU7a8y+vTBtKm1MoHJXQbHldqROKVg4kh7Dx77EdM7HFf+FBjPDA4TD9d9izEFbCcNoU09k9cG/qQW6s4S7S48P2inmNzkrpFjdv3aS42pQ7Y//NGYX6r02ucwAfPPIMsLaOsXOL1qtn5H43yUlDsxV1k/YtLhan/BnfJ4R6R4hBjSpix47iY+52Gh56Fc7l22R3cdcIMblp8Pa5QYRUAmwyiBSu4YsV9PDXiK4YfZyNuFRGJVzoWL6V3/q+UuprzS+vzImO3a1Lf2rsQgN5gUZRCo3/+TMauH8+8zDOJD5RiM3x83e2O6Oc2g1Ys/P5ap+od+35Sz7FbU3qzttlgehTOs9LqYq2RB3QXP3S8ev/PG4Xs1D5h71cnDeT/bvaxO2gtJRzVT3DbdRpJicrC90egnN0UVuKXOS/BrhXWDSvoAwTEyMKl+PMiJdw4+zG2lLUn2sy8R8Fcxv98PAA5iZ3JqNwSddbntiXyTY/bOe/yNAL3Psj/9XuVxRmn4tUTuGf+RRy9awqaDBLUHHj0BG44aQmlruYA6LpVqMMwJCne3QzKm8rszHMIChtBW1zdg2+gMHYsXsYr04+ufu/XnPz9b7sJVPkGhO6Lib49jNj+OVM7XBG+Ln24l5xqO6jFuHZn0M3lK+7jxK0f4TC8/N78eF7v/wK5Tboe5PFIrl5+B+dsfJm8hHZcN2ZZWO16mw06tIUXn1Rzx/1Fea0r6mb2S7B9YWTMuOIvhzvg5OMN5/LDtlH4TCe1xfyhOWdz7M7J+9xvQNhw25P5sf3lnLnpteryqFXkx7fmstM2gxDY7fDq0xrp6QLXtMlwwQUUBxOY2v5y5mecxoZmRyPRcBhehu74CrvhZ2PTAWxv0r3BYVSDdk3hsdl7Cy26bUlcfHo2HkdyeMPaZvEjASkR0kRqOsIMoiExaofu7cNDzcFCM/x8/l0GE7vfz3edbwyL1wfLmf25R3U6tT+CPstGhPJaV2BIyRy3m4mlpSz1eql+gAt4YfsCJeJHBIf/BqdrJovy++Ezo3tFf9HtbsrsqfX2YwJLWozms+73MLPNBSzMOJV/nraJvgW/Rog4QJKviHalqwCQpmTTVnBNeBfOPx8qK0n15XP+umd4YeZw7pr/T9Lc23ltaj92JXbk1zb/IDulDzYZjFqfOwJpcs76F8I2+TUHHnuTyLZCHFkiDpajX2g2fuqmt3AGIz/PiDEfzAlbjL5Mzc67fZ5kTtY5ESIOoAnYXdD4Jo6NAWXn+AtQEAxyUW4uuw0DA0suOtrtTMzMJC7gblhBCo4Emfmzc/hvcqbU6NF0IzsqM4n2G1/X7BgeHP49T886ifhgBV7hxCaDSE1HInCYPoLYuHPkL2xL7oFfj8NheCwzq6iZry0cKQR6KHVrMCgRs2fDs3eBO1KkTsj5jJaVW5iTdS5bUvrit1nrsD5bQoOcM22mnz75v+w9N4AQ2Ey/VdykoezLrLda/MzQl6mBESGxLAKhYibZKb0INGTMB/NhpI6+prX7J2aMbHMer8TZ8Cqtin1Azcj/AtyTn0+uYRDEuoeYwMZAgOt37WI+TqS9/thWJeJ/DUwpKPXXkTFMCDY2HcSEng9TZm+KQwaQms78Vqfz2JDP8WpxfNzrIbJT+uC1J2FqtrA0qD+1uxRvlFhqnx5PdnKVw5Sg97gLoCS6s+Xv6SPoVLKcGe0urhbxveOrJ7xLSrrsWUJAc1WXgRFAsq8Qe4146wbT0JmuEOhmgN75s+i+Zz5atHNV9RUK79PNAD1L5tfZ5+amA+heOA/tYFvU9nMGb1bNxGOI/YQvVBTMH4ES8j85PtNkodcbdd9Cn48r83ZzS9vT8Wv2I2A+qDicmBKCpp0l+XWXxGxVsZlLVz1Ck0ARGiZ208/gXd9zzoaXmNzpOma0vRh/bae00I19avsrWN/0KDwhRyif7qLEkcZ9I6ZihjzXNTPAHSN/4Z7jpzMv44yI87824EVMNMT+/MUKwaC8KdhNb1gVM1PoVnGRBiHRTX/YdYXvllGF0NAdrEk/jqyyNdFLnUiTrgXzOH/NE9y+8EremdKDB2adW+dyQUBz0rVoATYzUI/4ylqv9SHRjDoeNqJtr3fWL9icbc3MFQcXJeR/ItymyXN79jBi2zaGbd3KE4WFlBpGvV/dGWm9+Ff3S3Frjuoik+qr9tehSnf2eFO5d/79BGXdK25/2/hyxOzVYfrovmceM9pehKdWatf2Jb8zdu0znLnhZU7e/DbplduxGT6KHWm83edpLjgrl20pvarbm5qd3YkdWNH8eJ455iMm9Hyoel9OYme2N+nBnKxzGLl1As5gZa2LqSeZDTC39TkR5ug9rpYx485DHYM0cRgeTtn8Nvf+FlEXai91nN8UNjalDuLMDS+HLwOEvNC3pval0pHCiJz/0rJyK6n+AtqWrYopoBL4pc0FBDVHnTnerUT4ZoO/2Mfl/I8++b9Gnjdk0t9fDBM++a+KhjnYqDXyPwlSSi7ftYsNfj/+0Bftv2VlzPN4iBMCTz1fvj4VO7CKNFooU/pfj1JfIoWeVJzBCnx6QkxhaFu6Omop0qDmoJl3F9tSeoZESnDdsls5Kft9bKYfKQU2gtV/WxqSn9pfHpnprcZ5fbZE/tvtTs7Y9Bpfdb2dbzvdCMCb/Z/nqV9Gs7jVqWxKHYBfd2I3/fj0OsLTQmxOHcCb/cZz7fI7sBvWzHxLSj+rspeIvCUKM8DJ2z7ihOwJ9Cz8DQ2JiSAuWB7dQa76GiI9S9LcO8hO7cc5G1/i8hX380Hvx6xzhj4Dny2eqe2vYHd8W07cOoEvu95BqbM5munD1JyRVc40OwXxbRoYq97wb7UpNIbv+JJV6cMI2mo5PkY7V5XAN2At/pvvAnRf+T+GPv13K3uk4oBRn+KfhAVeL1tqiDhAANgdDHJSfD1r4FJyzu5FuMzIm7PiCKaumOp9oMoxu1NKDp+MuI47tUewGdGXYwDWNjsGvxbpYOUwfGxL7hEqi6nRb/dMTsz+EJfhwSYN7DVEHGBO63NpyBTRbviZNvwhJnW6noAtDoSg3NmMm09cjNNwc9SuKWSVb0CYVa6c9fNjx6u5edTcavN8iq8gLCd5NVIyJPc7bl50Lb0L51ab4zUk98y/BEfQXccMteZYJA7p47ZFV9G8chvjj3qX9aFkLbU/A78tnsWtTubpYz5mfdpgiuNaWSIuDTQzGGm6/wO86n/LOptiZ4t6rBT7h6HZ+XppS3jwwYPe918VJeR/Atb6fDxVWIg7yg3FLSVJQpBcx5fdZfhp7iv7I4eoOOjoEPTU32wfccRJOg6qoC6B/a7zDfj1OIwatw+vHs/s1udQGN+6WliO3z4Rp1EZqxs8tkSCov7SpabQWTPoQvy2eDQzSLuSFbSoyMbUdJa0GsO8rDPJKltv6WaNKmRtStfQpnQNmhEIEz+b4cMVrOCmpTdZUqvrdPWuoomvIKoob07ty0c9H2Fl2nE8cexELjttE/eOmEpcsIL/mzEMLWbipJp9CRJTnfRxbeXlnwbTunw9S1qexNq0IVFzz5vChlEzhEsIEDpmlbf7wRDvGGv5Veeb0PvRqLnaq4+Ncky9bULkuzLZ9P7P+MtiPzAqGo4yrTdylnm9XLVrF94YXxon8EVFBf46+uhWuQubyuJ2+NGdYPhp2ELmH/P7yi5rzZKibhDrBg4Ux7Xi5tHzuHLFvfTfPQOPLYnvOl3Pl93u3KdzDcybzsc9H8YgdkySZgZI9+USXxjg6Nxsbl90FTbTjy4NcpK68syIL7hg2wu4yw0WtxoDQJeiRTzw23kk+YoAic+WQJxZyc/tLmRF6nE0r8ymTfl6dqd0ptktl9LqrssQkybR7nsfBZW1zOFCkJfYiS+63cnEHvdbSwaaTn5CW9Y1G8wdCy6nS9Fi1qUdE2X0tcOv4Le3l9L/1et4a8pANsd355MeD7Iw47Rw0Y6FEBF9HhD1PQyEHh4iCC2bNCx1bhSkpCC+DXcf+wPcoHH1ZQanjK4jRa+iXlRmt0bO+bm5rPTFDpvRgPoCPuL9HuYsfhJnlHVPxSEkIR1GPQDTHwVPceR+ezwEoiT/OAgETZ0nltzMsoLeCNPEJx0hE/l+FgYB+u2eyUNzziGujln5KwNeYka7S6yQNKFhN7yYaDhNL0Fho03ZOh6aew55KZ3pnL8Al7HXCmEInd3xbUnunsmO3Tr3HvU1uhnkw8kdSAiWhw8PEC1bsjPQlLuHTsGjJyLtDqRu4z9rzqfjxulccOImSp3pdV9jrc+jmTuXu+dfzL+H/xDpqV+Lqom0JqBFuqTXogmcu/IZbjlxnuXFXzUzPxKzydXkIGeNczrh0bs1+vZSBuK6qCuzm5qRN3LW1iHiTqAhkbH3b52MQ4n44UUISO8CGOAtjd7mDxJxgG+2jGFZQe9QWlasiZ+UYBqRyUti3chrbVve/AS+6XwDo7Z9QlPvbgxhQ5dBDGHDYXoRwA1Lb2ZI7rf81PYSpG5j5MOj6Fsyl213vURC0Q4yKrcAkObJreGKaaFLg+bu7ZjLcsk6YQxdvatovWtJVFO3V09gie0YPjrlbYrKk6qLtIzaPIGsVdMRhpsUT17dQh7lmkuc6bQvW81js0/j3T5Pk53SJ2rp1KqPTUrrwTo3T5Db+hIcnjKaV25jW3Kvhgn4H5F6NVafdSWjOYj4fJKvJkv69qq/rSI66hGokZOixzZJNUTEkwOVnFrwu/JSP9zoTug0EmbXCks6RPywfdReEa9CiJDzdSBie9j6Zx3i8lHvx7ns9C3887SNvDrgJcYf9V6YL7cABuyewd/XPYeh2/l6VjzTzGG0KV5VLeJV7fQotiW/7uLpoyfw99QvWZlwFFM7XMmzgz8iL6FdWDuH4WVTygByKpLDKq2N2fJedfW289Y9GxnOVg+GZmdLcm/6FMzmxRlD+ezbjBgto1k+BZO63GiJeNV0vT4Rj7X9gP5mYqyV7+ta/H5bdwVLlhv84xIPLz20jfJCtW6+ryghb+RcnpzMPiSVjKBb5S78dZWQVPzx2JxgBOCnJ6B462EZQsCM7nQmpAStnvXbWDf7anHS2BOfxfQOl3PBmidwmVFu1EIwp9XfWLY5nnd+SOX2IdOiesbXxmYGWNzqZBACU+gEdBcLM07j5tHzKHU0q27n112sTj8uYqw1Z+/Hb/+MC1c/jitYgW4GiBDfGGL3Ue/Hqt/qEZYtiYaBzajDS6WhYhlLWIXAZgZI9O2xMrxJE1HvglrN4w+CDBzIw4SUWPwcrAAAIABJREFUmFKjwm9n+pp0bv3nNoI/TD3wMf2FUELeiPGaJklCYDsAU1ehIwmbSv9yeAn6QiVjD1/6yuNaLcQmItN8tqzcwjMzT9jnmWpU0ZOS/IQ2+DUHS1qMZmGrU/CEyoa2KVtbHerk8wt2JndhZodLw4+vJWQePYFPejxohaTVwNRs+PR4pnS4CrA86lenDWFV2nER45re7rLqlLECGLv+OT77thXP/T97Zx0eR9m18d8zshaXSlJ395aWllKglBZqFIcPd3fXl+JOgeIOL1Lc20KBtkDd3VNJKkmTRtdm5vn+mI3ubpIqTd/c15UryezIM7Mzc55zzn3us+hUOrUXOB3Y+uDVeJubEspjwl41hmYFq1CkiUOTnNJ1F6//fhRKxO/2wD13huqkf9aPfPFtQ777MobmBSv3fifVsdhr2k4IqK1DUE00x1Ad5DkaMOvGD2HXrr0fy/8o6nPkdRS/Fxdz586d+Ni/18EGTyPWexrRsSgL/V80JHUWnhQ7p13Ha/DPa/cNs3f2YU9RDD4lBt3wokmDO+ZcQuu8RUyc0pvYYD7FegIv93uNpanHYkUQTylDpN7YZgmLGg3lyaM/QSJAgCk0bp17GV1y/qm0rk+4+avjBYzI+i/oOvj9cPHFAHjf+ZiMuE581/4mpjc7K+LhA5qHBY2H0WfHVKa0uoTJbS4HIWhYuJF8dyMMoWOqDmY2PZ1hGR/QefessnC/06XS8d37eP5Yja2ZkucmmqxdF0RGiUw4TC//N3ozXi2WXjun8fDMsSQlAouXsuKuL9iuN+Xobd8yq8nYKtrwkUVj9gW66aNxcQamojOl1SUUVohG1Ar7k/eubU4/ZMBPXTuBjIRuZCR0Id/ZAFmFGe/V49kQ15XBkybB9dfv+7j+h1DPWq+DyDIMRm/dGrXkbG/hMXy8supD+hVkVNKfrkcEKLqtRtX7/6D9MFg8CVZ8H/Ko6zb8f+3iz19bsjx1EOlFGxi+6X2SfTuAyuZmZ0Ibrhi+lGA1ZWOR4DBLENIuB6u03Chh8NYvmdaqogduMbTjbm4/czds2wa9ekGjRgBkLt3F1ePdGNWpuJWWSFUwMsIyeHTGSNKKN/FL68vI9jSj746pDN4yCUcpDyA5Ge68E0aOhK5dMWbP49qnEtmqt4xosIQ0Ua0gRojgJiyTmGA+Ny+8mhf6vIkUAiQYikbTgtVsTOoVIZRd4epWCVELJFKphb8lLdrlLmBbfHssoYWu8WHQs9CycBmF+DUPaUUbuWHB9fTM/hOAv5uM5bmj3g1Tx3MFC7l68a0Mb5sJP/xg09rrUS1rvd6Q10G8kZfHa3l5HMh+Rx0KM7l30w/0KtyMwr/++B9+UDRIbAGtB0HbE0F3QuEu+Omug8omP6Qo9MN139W4mongh4438l6fZzAMgSWjGAwp0awg3bOn4wnmk+dsyIakXpW6oQEoloEl1DBm/NP/nEy3W06G224L2/Wdd+azLMMVtc7ZbRTRLH8la1P628uEQDe8tNmzmKf/HIoerVuYwwGqSqGeyNtD32aqdmL5uUUQPBHSDDO0mulFCg1Tqcw7UCwDK6JRtrXQddOHqeh2fl6AO1hEgSM5csg6krJbRJb5v2vMGxZv4v2f2rPb2ZhU/45KnxlC48JR68lzpZVPbiyThEAu7//UFpdmwsCBMG3a4VuKdwhRnSGvz5HXQeSb5gE14udsn8Wkpa/Sp3AzKuGPvaS+iQqWAbkbYNGnsPBjWPwFfH/LkWPEAXQFnDXnOf1aDF+1uRFpmrRvCyLKS7ZL7j98+kMT7p91NnfOuYgnpw9n0LZvwtazFC3sRa2Zfsygact45uaGbfPQf+Lp30dFERZIiWIFUa0AijRpn7uA8TNHk5HYvVJePai5yUjoxp/Nz7WPi2BjQncy4ruU39+BAHusGK4+dhZTtWHlpXdhRtzErRu49fB0lKG6MSOkHSyhoEYgvSnSRLP8BFUXlqIR1NwEVTcFztToRLRILPeIZLgaDODBdOSkJM+VxtXDF/NbywuYnT6KYGhys8vTjLuO+5U8V3r5GKVECLhm0U12JYHfD3Pnwl9/HbwxHiGoz5HXQQz2ePi0oKBatbbaIiFYzJ2bfkarJj8eFCoL4lowoGBjvaduBmHt1H97FAcHigIOFfwmQUUn292UtOJNYd+5ZgUYteEN3u/+GGvWQ2IC5FVpHe40Shg/YzSeYGVhlhsWXs/qlP5kxneodiiG6mRyq8vouWohzJwJY8dW+jzGI/jPXRrFJZKSEknqqcdj/TMrVKMeYG7jEWhWMOwZ8emxTG96BkGh816PxwkqDgQQ79/NA3+fQYuCFbzQ903yXI2qVSZz6ILzznbw8RcyXGTPitaBTdgSq5ZhR3gA3fDSIXceRXoCGUlV2scebC/0YNSkV4QQBFUXW+I782G3R1CliZAmCf5sW8o3tE7F9SUq/+18P0O2fmEvCwRgzhwYPPjgjfMIQL1HXgfRRNM4UNSqPgWb+LRxf8a3HsuXjfpSooSXIZkI5ie05o+k6C9fi3qvvW5DQEYeFAaY0vIizhmTxcddHqJEiwtb02EFaJG/ArBtQVExaFVcgvTCdfhFuDCKagU5OvP7ygujlHXNbjKaVQl9ICEh8pBLSojJ3kIDvRAxbx6qtHCE+oR7jEI7P10V0mJl6kBe7vsaRY5k/FosPi2WXTEtuOv4aVwwahPz0k+pPi8tBAFD4eMvJS2agV511Yqa71W2U6RJetEGnEYxsYFcRm18g0dmjsJpHnjd/BpxqMLVQiAVFUN1EFRd5MQ0r7ZGPTOuXVklAS4XNGlyaMZZh1HvkdcRBKTkt+JiMoJBllej5ra3mJHUkX8S2+NTHXgMPxObncjnSyfSMGB7UhJwSoNrt05DQUbNuCnUG/I6DVWH1AYsPuYsXms4Ab8Ww9qUfmhVxWAAv+pidWnuOYRm6bBpS/ndsSWhM/ceN4UJvw0oM64AhY5kfmx3TfjxI3iHAcXF9OZn06mqN2YYcMcd8MYb5W0wzcpucafds3AbRZWlT7HTAF5HHJHuYq8WQ4kaGz2cXQXBILRoCk3SBH/NlpgWNG8C8ZtXsZROEfdjqk52xLbh/OUPMyrjLeJ8uwHon/UTa1L6HxjjejhLvNZiTKo0y/vdOxwwbtxBHlTdR71HXgew0zAYsWULD2VnMzEvj5klJQesUMwQKr5Qw4YSzcluPZZnWp4ClBtmBVDtgqFqQ+uH4WujGtSt0R50mAFIlnzR4+4yVnlmXHsWNBqOrwI73BSKXaPd5oqyZULA1iyoeE1NRWdHTCumNzu70mG+an9LeM/wqIIyIE4bB1XVC++9F958E7xeKC62f6zKT4QqLR6dMZJEfzZuLYjHbUcNFFUQ7bUnUfaqP7ZlwfZdcOcNKp+/o/DRawqvPatx5SPtcVTjYVuKxmed7+HaE+eTH297m6eum4Bm1ZAsq22dd1XyWx2CZvo5fvOnqKEqkMDNd/DOlzpnXmow5nyD+x832JZV987rYKPekNcBjM/JIcc0y9qUHtBq7yovUVNR+SO5U5kRPyLNXYsB0PFkSGodmfVcp7F/31iON6nS/08c/QlfdbiNPGcDvFos/6SP5YZhc2wiVgixMRHCy9j56HlpI7BCY/KpbpY2Oi6sbjgadIIcN6aK9rlhwKuvstzTi+uHzWXkGV7OGZPJpPa32vnnCmiVv5z/Fl/CJ3/35NNv03gh7wb0SKpypdjLS6cLg/YNi3h2osnZl1tccI3FVbcZ+HySx/4ajStYFHXbgOZhjzOVr1pcC4Db9PLwzNFopj/UV51KhrhsWW1bhUYL7x+uCJXddcqZzTWLbi5b/Phvnfn+F5OiYjsCsnAp3HyfSe6eemNeEfXlZ4c5pJT02LTpIDWtjIw4w8vsOeMP4REPIbqMhW3zoXi3Xa8rrTov5lIJqe0hZ+0+b/7qsov4JeP4sD7hLqOIJ/8YxsQ+L7EuqQ+2Drugbw8YdpzCC29YeKs6odKiYfFmLljxMC6jhOnNz2ZW+mikUMLKsDTTj5A20yKoOgEFISStWijceq1Km5aCzdskP37vJePLOaxOPgpDLa8vdhrFjNr8Dpe3ng3LlkGnTuB2w7ffQoldWWAKlXPHbKOwwiSkFComqkMlUK1TXJ46UCwDj1FIi8KVrG0wgKBVPjlRFGhRtJo2O2czs9kZBBUnVgTuCUDz/JW8MSVEctM0soaeyw+db2br/EySvDvJczfCEiqLGp0YPeR/IEhr+7OP/T1+yIirVhAhBEdnfsedcy5iZ0xLrj1pQRURHVsf6MwxggvOOtIm4dWjvo68DkNKSc9Nmw4Yua0mOKwgZ+6Yy72bfrSPzxHmlSs6RKshPhLgTAB/lO5ptUBepspVc16kRIvFDCmZOY0iLlr2IOPWvWyv5HBQcv3tqE88itMhMC3JxdeZ7M617PB0KSK84BXTrpGuaNh000fH3bO5c/YF3HDSfPIdqZUam3jccM3FglfekQSNUBQ9wr4d0s9nH7hxv/UKPPxwxLK1WemjeWrARwQUB1LREZaJKoNc7n2X3POvZdK30a+NikFKSSYlWhy9dkzj5I1v8Z/B3xGIIkyjmT5cRjHddk1ndtNTyyRoK6Lrrhk88+fQ8gUeT9nEoxQSGHmmr9aRjPKtonQ0i+TVlzbCqU3TloPdfc0yOXv1k7TPXchzR71LiSOc7Ni3p+CRe+oNeSnqQ+uHOYQQHOvxHBJWopAW7Yt3cNPmqWQ6E7mp/XlYiEokNguBty7rvx3JRhz2y4gDJDUK8p7jVE7a+B6NizbQJfsv7p51frkRBwgE8CyZg9Nhv3xVRfBU8w9pWrgWp1GMO1iAO1gQUZ/dUnWaFqyhUdEmdNOHZvo5KusnHvr7dDKTuuDXPJWMONjR9JfflvgDFVLhEYyJ6tDIfu59uOeeiEYc4OisH3jxt0GM2PgufbZP5oold/D25C4MSVnLitXRr4tDh0EF02iav5r0wg20zVvInLSTCSiRW5YCGKoLrzOBxHhBJz0DVVROijmNIsatnVB5o5JwXQJTaMiaGpJU0S9Pjpc0b1q7dcuuZQ1GPMmbVf0Y9hVVj6uofNnhdtKL1oWJ6oCdxmnV4uAMpa6inrV+GCMgJe/u2cMqvx8J6NglqwdLEV0ieHrtZ4Dk3O7X0id/I0qI5AawzZnIxV0ux1Q0Ji94BucRIEv6v4lSje8I0BRiTkrnxt/vxFzkZ07aSNYn96bYkcgx277CafpsJnGvXpU2S3/pPt7csYNtce3xqTEk+Hdx2Smrwndv+um3YzKt9izlzR7PUuRIZH7acCZ1u4/Gg9oid3uoGn4KBEGthcthopL64oMRjWFFtCxYwY0Lryfb3YSn+3/Iuz2eQBg6YmO0LSRpWg5zYgfjj7fDvJsSuhBU3TV6pyYa89uMY8JjKg8+YbB1fXFI0tXJmaufZWDW91G3zYppzVs9n2F+o6FQna49ABa6EUCXQQalb+PSx7uhCLj4ehNvRVrAvvYWF4I8d3rk9WvrqVeN/lazvqk4WNh4GB1z57Cy4TEEKTfomgajh9f7oBVRb8gPM6wNBPiqoIA8w2B1MMg2w8AfegBqYo3vN4Tg3G7XcMPW3/CqDgbtWQ/AFlcyU1K68EbToXhVBwjB/PjW9C3YWMmYlyrA1T9ihzMEpLaB3E1gRZmISUlR93bcqn9IjqcpXi0Ot1HE2z2e4vlpg0lTcuCmmypvs9suo2paWJ6fH5rxX/5ocW4ZC15YJk6zhJZ7lvNCv7cwQzluPw6+bH0DQ1qA3KOGGXKnUUJQcwLVh1JPOTaIZ1LljlnRUkOm5uCOodPJdqXbYX6TcGGXCtdjS3FypUhBUPPUmhUeHwdJiYKXn9LJ+GA+eQ88R9sds4gL7om6zR5nA24aNosiLaFWXcV0YXFC/BLmKX2Y6++M8wuLC89WePUZlbc+tJi/WBLY32BUNYY32ZtFrqeaeu+q4fOarp0QfN7xbj74qS1v9HyW31tdQFB10qGt4PrLVRqkHFEJv/1GvSE/jPBOXh6v7NmDIWVEr/uA5cmryYXl6zE83moUlqKxxZXCcy2G836TY8v98tB2t3U8l+dXf0LvggwMRcVhGbzedAiXZc4kJhS+PuLy60cEJHQ9DdK62g1fVv1EmHeuq7wfexvb9TZlhDKvHodfdfN8/3d4pvPXyMaN+Wu2xTc/WuQXQsrwmeSYCRiKg0GZ33Duyse5fuF1NCtczbftb6LYk0rPktlcOv927u/zeZkRL4Wl6EyfDz26wIolvrKQtWoG0KwAfukCEf2OUlUYNVKHe+Nh924sBJ93uINmRWs5avvP2Kpqqi39mZjI4lf/ouDnpliBWuRZRamCQtXlNd/dLiecMbp8atvs/BPYltiZdz9YROL6+ZyU8SFphRvCtvuxzVX4FHetW4MaQueP4IAyYz15mmThUpPXzlnMA+vf5iNjKJOUMRhWTfuToQe39k9ubCCPs1c9yWu9J0Qm5EV639Ri/wXOVFYlH0W+swFtd8/naM9KRt13NS53/VulKurJbocBsg2Da3fsYGX1lNlDDtUK2prR1Tx0Df35pAQK2exKpo03h8+WvVb2WW0Meb2x3wvEp0PRrpAnvT/PrQIOt1165y8M25cEzpr6NkWBcBKXagX5cnIzdnQZwgx/b5YlDmB5g8Ghe8T+JjXTT4o3i9en9MTlkHDrrTb5TAiYMoWR7w/FihK3iXUatN06gy2xHWwPbPc8ljY8lkCVjmlVoWvw0Wsqrg/f4s9XFvJ1i2vIjG+HqThwBYtwG0WUaLHcO+tcjsr9nZ8nrubN6c3wB+xzGrLlc47dOolv2t3EksZDww9QW2Z2aD2XC4wgjBgK116qIoQgEJTcPd5k02bw+UFVJaplcM/Wuxjwz8uVdnP/4B9YkDai5uNVA5cS4Mb513D8xo95sderTGlzWdg6QtjcmNISQftWCD2VtThfp1HMlYtvZ8TGt7nhxLlsTOoZnUy3l4jz5xBQ3bbugFBwGMU0KtnCS+a9uF6fAK1b7/U+6zLqyW6HOa7esYPVh5kRByISTapilzOBVXFNKdE9ZLqSy5bX1kDXG/G9QNEu25h3GM7+PboWBIrBX0BVI25JwfOLrqQoEJ3EhbeEFnO+xmfprEnuF/LCyr9JQ3WSH9OY319aaIu1PPooqCp5BYI3s0+KasQBivwaq5L7M3jbV0z6Lg3d8kdlhZdCNf10zZ+NlrGe69Zdymu9XmBLYpcy1r1PjyXP3Ri/HsvHXR+CQID2954DRhDFMnhs+slcv+B6+m//hW7Z09HNCLXmtTTiqrDo1N4m6DkcMOUPeOwFi0BQMvUPi40ZthEHME1BQOo80+55gg3SKu2qZf6K6KmPWsJnOVgT251FqcexLrl3pfaoZUO2ZCjKHZqICQFCsTuwRSLYSROnHqLASklAcTK15UVcMCqDjMSupTu1f1smulFMkm9H+H5qgMMowavF2WmZkJcf0GLY5WnOlIwW0L8/5O8fsfNIQr0h/5exNhBgczB40Ahs+429mElr0iwL/x/RBlpotpjMoT5Ly4A9W2DNFA4G5bE46OK8X1/mt8whRDo3xTLomv0XLtNLgSOFH9tdY+eKI8An3CwNti27f/YUSK67y+SHKTVHEvxaDL+0uYKsmFasT+wZNVzrNIpwGsW03bOYu34bx5dXfs+uHInfij4B3RHTCoC22XPpuv1PBu/8jvZ583GbNsP+pIwPUWWVJJa0apUPV6TJSZvfZ8MGE8OAEq8tYjJvkeTDzy3++Mtm3kfC2iuerKQql164ttZSsdHgNIrZ7Urj4WO+jtIHHVsHPcJ37bD8DNz2TQSCmoo/GKLACoFUNNak9CfX08TmGpRGZqQEoRBU3cT7cnAYVQiIUVXqJGlFGzhv5WM4Ikyo/FoMs9NG24TGjz6q/cU4wlFvyP9l7DYMlLqW3pASt+HnosyZfLpkIm8tf4eTspcydueC8hBd1U0O8RAPKpyx0P6EUAerf2PKcnCu5tStQ/AaEQyzlDiMEhL9u7hl3uUAZCR0RTeja/7rGqRXcDK/+9miqNj2VGsDxQxy47A55HrSI37uMEu4a/aFTPhtIC9OO4YEfw4zU0YSNKr/PlrmLyv7+6H553J29ju4K5TJNfBm8p+Zp5Lk24FL+HEYJTQpWo/LKIrsoZZCShoWZTA9/UwCZuU8dCAAP06REdXvAPwBeGV5Px4c9C3zGg+nWIvjzV7PR2eIS6uSt17af6SiuqxAosog89NPKSMbRkWE4wQVB8sbHlu7iXyk/Lco9+63JHQixsinYckWkJKUkm2cuerp8AkTEGMW8uqYf+i58/cwpT6wCZOJ/l22IV+6tOax/Y+gnuz2L6Odw0Fd62jtkAafLXmFpr48XCGqb++CDDRpolYwMhblTPsjykPXnLDgoyOuJn2XN5WA5QxbrkiDY7d+wQ0Lri1rgNLAuw0jFLqOBEWBk08ofxEvWCIJRrhcDsNLQHWFGQO/5kEIm6BWFU4R4KpFt3B01g+VlruqtEwN284o5pJlD5T9rxfn02rttLD1emRP5+PfO7N10Jnoa1eS7t/Cxn6n80LD8WzMicWSERJHQpDnaRw1DeAPwJIVkcdlmpIMVzsy0tqxrMFg+mX9ghKltNOpmSDBQMUMzStat4DrLlP44DOrrBa+VTPJGd/dxoROz1d7TYDIwj3SpNCRUvO2tYBUNHwxKdzd7Ve6Pd4DUWRL1/bM/oMnBnyCqWhIFHTLz2XL7sHxwUTa33AVSb6d7FAr6wo4LB9j1r9q5y169jwg4zsSUO+R/8uY6/PVrdmUlDy55nPa+HJwYSKB5bFN+DW1K2ti0pBAAPg7oQ0Boe2zAZeAgYIhFIKHmwBNyW4w9qcD3aGa1uzdcbokr8Wlhjf70C0/oza8YRtxIUDXSS/aQMs9y6KGnMecLGgQF4DJk+H772mQaEYczY0LrsFpVp7KOo1iNGlENOIADt2iMC6dtUm9+bn15cxrPBxTKAzK/Drca5YSIU065czi0Rkj6bR7duXPrchetlJUSIsp75K+aTZkZdF6ylu83OhlJr3ktWvpIyDev5tOObOr99wjovzK+LRY/mk6NmK4GyAoNfyWhmmVf741CxqmCp58UOPzd1Q+fUvlpaccdHjxBgw1+mSrIpQKMsUuo4gTtn5KygEUgJGag52t+iEqdKnrvXMan32fzjFbvyKgOvGrLt7o8RyX3qFSpMTy2IxT7GhIsAhPIB+nUcwVi++wv8NAAJYvP2Djq+uoUzbkSMSskhLqkl93cdZMhuauRADFioOrulzC6pg0hAQpoGPRdl5e+QH98jfh2I88rsAWoPmqYT8u3PE3DappQFH3cJCmJY27QUEWePeAIwYadYasxWBU0yikAmK1YhRhISiXWnUaxXTN/ov2ufNsI3700XbL0KVLOXHzx6xP7o0lwnPSuat32scPGfrTYnuzYPBP+GW5YVFV2Np3DOPnnsWrnZ5ic0IXPEYhYza+xrR2l5BNZMJdYcDF+x0eQLS/D90KoEgLRQZDpXJVDaDkwb9OZ8D2n2q+ADExdijB6w3PAfh88PjjxDgcjNjsYXLzi/BX4Ac4jWJOX/M8bqOYlakDaz5WNRBS4lfDUxyl4fOqcw8p4c9/LFQVvvtZUlgEzZvC2af2pn1nk1VrLUxZjc8mBEJadM7+G49RwIitH3FUYDb3x7Yhx93kgEiyWhLaDkyzW5JW0L+fk3YKfzY/G0vRCYTItb5cyf3H/MiL0wbx5uSubEzsQZGeSIfceXb5YCnefRceeADS0iId8n8K9Yb8X4ZaFzoUSUnH4iwmrvyAZKMELWSInml5CitimhBQy1/kK2Kb8FLL4Ty08bv9PmwLfy63bp2y3/s54DhQTVaEErLpB4K4JqBRR+g8CvK32uz2Rl3gh1trZchn7+jJ3F29iXcUUmLEgLTQrCDnrHiMM9a+YJtHKWHRItuamCZNk304CeClqiGXrJ2fy1rRhvb5CwHoUvAn18++ktd6T0AqGqbTQ8f2gnG3nEnCxA28/shATBQUvxeRnEzMuOF8vL5hVHKYFCpSqPjLKiuia4v/3flCBuyaHNazvBLi4myRm379YOzYyOuYJnz7LZctXECRiGFG8zPRzQCG4mDU+tcYk/kez/V7Z78DLgKJ0yzGJ+JC+xKlpxKRY2AYMP1vyZZM8IcCRavWwn+etmjUQNCiuWDTlur5ek6jhMuW3kPn3bPYEteRq4+ZwvbYNuEiLvv4vmrRFFo1F/Dhh/D66xiPPck6XxpP9/8wjDAppWBLQhe2xHeiRcEq2uxZEnmnLhcsWACjRu3TmI4k1NeR/4sISsmZ27axLlLy8HCBlChS4rH8vLfsLTqVbC97T/UZ8HBZL/OKcJsB5s9+aP8OyxGWV4+E2EY2cS530z6EY6tA0SAmBbz5du5e0cGdCENugxnPQUH0EiApwWu4EEKiCIvJW47n9RUX4jS8XLz0Xk5dPzHidhaCy0auYZe7WVg3M/u+MTln5WNcsPLRssVBobEtsQtxTRJIXfqnbRhME044AebOtT1fwIyJY/y4GcwNdN2/6wL0bV/CI5P6wdpqusIpCmzfDhs2wLHHRraYQsC558Lnn4Npku9IIdvTnLSi9cQofnjoIcbvvpBZ2xrXalyaat/nFecXwjJp4N3GC78N5OrhSyh0hXdqqwpdt730aPOUUk++unmMw/Ty1s+dWdB4GK/3mhCRt1AtqpFpFQKuvEhw6smhVMnVV/PLdJWXur0QlZnvNIoYsmUSLQtWcuyWSaT4toevFBMDM2eGyQUfqTjodeRCiBFCiDVCiPVCiLsjfH6xECJbCLE49HN5hc8uEkKsC/1cdCDGU1fwWE4O6w9nIw4gBJaiUKS5ub7ThZU+ClZ9eYcQqKUaVbWH3e891AHEptrlZAfCiCc2s1uzGj6b0WxwnBJiAAAgAElEQVT4oCgb/nkVCnbWuAuP7sOt+XGqQU5qNp2Bjefj1zzManpq9MMieXjGaBRpRNTRthQNS2iV8r26NGiVt4TUjIW2N2UY8MUXsHBhmRHfkNiDKY3O4eg5r6Gr+3dtnEYxA+e+Bv/3f9WvWDqhaNmyMv27Iho1shuyOG1CYEJgN233LCJGltjh3Wee4difHrAZ7pFQoeRK1yEhAa4604vLsHPALqOIJkXreeLPk4gN5jNu3YRIOwlbomu2glw0SFm9EReWiSXhkpHreKXPqwS0ajTko/Y/F1E/dzhASfVx5adZnPf0eqb9WshmZyuqe8r9agy/tryQ97o+wqWnrObvJlWiJJoGbdvWE95C2O/QuhBCBSYCw4BtwDwhxPdSypVVVv1cSnl9lW2TgYeAvth36ILQtnn7O67DGYt9Ph7NzmbV4W7Eq6BQd5PpSKBpwBZiOGrPBmYntqnUnlFIi+be3cxKaEP/UNOVQw7VAebhJ7AThqSWsHsTmPt5H0hgz9bwkL80bW+/BlR9Z7s1P6NbTmFWVh8SfLsibxTCvLSTiaYClla4jnNWPVmlf14IlgXPPgs//WTnSy0LQ2g8NvBzFjWyldUUaWJZ+37/OI1i0ovWc8KsZ+CSV+xQrC9KmiE2FjZuhEGD7ND6t99SiWYvBEycCM2b2+O+4YZy66jrkJ1NYdBBvhmDO1iIT42JIksqadEUBg0QjB2hEL9oESf9cRbr1LbEBPNpmb+8zLz13Pk7H3Z7pPI+IoSqDDMqZ68WkEhFxVCqF92xVzVJ8u0iz9U4sueNhdsopERLKPtc00CPC/LaCzqQCqbCKz0n4glUI+YSCuFLoZU5C8/0/4A+36XbExbThAED7MhIXUhNHgIcCI/8KGC9lHKjlDIAfAZESTKFYTjwq5QyN2S8fwX2T5fwMMeckhIuzMqqc0YcACnJ12PKXssPbPyOOMOHM2Q0hbSQCLY5k7ix4/mM63kje7RavCAONPbXMB4qtB9+YMYqjQOXtw+hW/JqOsWvskt9qsGCxsPL9NirolHxZtYl9cEUKjs9LXit5/PcesJ0Xu79MllKGnz1FRQVlVmh79tdx6JGQ/FrMfi1GLx6PJYUaJrt1dX6nW2ZJHq3c+nSe3h+2mCcwWK7qcuVV9qWJRL8fmjf3v77wgttwx7C1kY9eGjI95zx0bGceglc9f1A/tvhbvY4G9gr+Hysd7Tn4pHrea/7Y+S5qydfNW4kuOBMlfg4AenpOH0FdM35m1YVjLiFYE76yFp1DNNUSbN95qTVciMpcZpezl71JEqUskuJwhlbnqHl2F2kNYKkJIlhWRTlaGAq9g82M7/AmRJ1PxFb1EqDxcddb3M0MjLgzz/tCEk9gANjyJsAWyv8vy20rCpOF0IsFUJ8KYRotpfbHhEwpeTGnTujNlk63OGyDDoW27kqCTT37ebnhc9x7ZZppHtzEaGZdFDVKdFcZLhTeaLV6LL1D51vHuVI6b3Bc2BqY/cbigZCQueRELEeu+LL7NBXiaqK5OHuT9E15+/QEJRKL9iM+C48c9R7rE/qGTU1sLjRUO484XfGnZrLlSOW8nObq1iVOpAprS/lupPmsya+cm7zl9ZXhImXlLLn77xB4a4bFV5/TiE1meiQEo9RwFuTuzFm/Wu4TK/tMbdqBS++CK+/btPlK8LjgQsvJFdL5dfXVzDzug/xFdieuyF0JnR6jrkNhlPsSMSvedgS15mPuzzEhaM2MrWlnW56asBHlOhx5eOPalUFa9ZLZsyyuOthg1veb0lOq95YWmXCYEB180fLGtIBIfhLTC49D/r3qdXqew8paVSSwfPThtBv+2SUaORMIZjW6EJ6ifdofVwReXmAVVm+txSG6kKXQbQopXzh+1bgtlvtyVbj2nEQ/pdwqN4QPwAtpZTdsb3uD/Z2B0KIK4UQ84UQ87Ozsw/4AA8Ffi8poaiukQulRLMM3GaA59Z8ghrKeJb+JBolXJ41g92OOKwquXFD0Zia2q3MrEZ6tUns7pGSg2zsFR2Smts14LXCQQ7ZWaF4aM9z4OgrIaEpaC5wxkF6Txj+MJz/GZz/KYx83E4XHGLEJks4fgC/97+Vy8/N5LRzi7njnBX81P0Wbj7xH/5sfg6FzlTCrlUF4pMUCkHdQ0B1l9U0m4oDnxbLxN4vlW3i1WLwRZF7NU3bkR4yUKFFU4WxJ1fz3QhBkm8XscFQ6FZVISUFhg+3jetll8H8+XDccbab37Ah3HcfX4yYyMU3mLz6W1Ne6PM6543ZxtIGgwmqDtYl96tsmEOqZUHVxYt932RKiwvZFdOi1pKqlgUvvGaxdCWsXgfXtPuaRanHEVCceLUYilzJrHvgHXo0K0TUVNEgJVJKxj9tkZos+OhVQdPIYnh7DynRZYBhDVfx9l8DaJ2/lBI9gaYFa6PmynOcTZj322j+/sJDTc9QWtF6+u2cUiuOiBUTS88TGu7LWfxP4ECUn2UCzSr83zS0rAxSyopvz7eBpytse1yVbf+MdBAp5ZvAm2Cz1vdnwP8WphTVwVpoITBQGbh7DV0Lt4V/HPptRfFAzNDyaI+0hSCAgvtgxyksE1b9WMuV7VzmwYWEDX9C7/Og9WD7Jz8TZr8J25fA9mXQ/Cjofzkkt4I+F8C89/afGLc3EIKf48fxZvLV+IO2kV1Oe1Z0errye1yIMuEVKdTI3miEZeuTeuNV3Lza52WmNz8bU6gRS5ykhPHPWKQ3toiPg5FtM+gRr7CkoBmR7qwUrdD2wgEGD7ZLnip44UZKQ9bF90aPK6B1zmLWfbGQ/3YyCJoaQTWmrO35w8d8w+N/jkCTQaIxLqRQeLnfa1E+Jex8FCtIcaHArPDqLXImc/+xP5Pg20VsII/dSW3oqGiszLcq8U8iQggsoWOZ8Nt0SddOCmeNFUx8xwor3SutZKutL5GSLLjrRhfdmrfG+6HKPcf/zrqk3jb3Isq5+jUP24o7U+NEWEoCmpuL1jzGwsYn4RdVU3DSpl7oFgqCu2/WcDn3bnLtlybz5W5y8dOKWDqLRJQjNKd+IAz5PKCdEKIVtmE+Bziv4gpCiDQpZWn9wBhgVejvKcDjQoik0P8nAfccgDEdlqi5l9hhCiGYmdqJwk0uPFFy+8fkrWF6csdKalyKZXLMnrWVHumKXB2f0FCkdfCNOADWXjCCDtE8cdNM25AD+Itg8gN2VzIATNg61ybDtRoEqgZD74MZz1dY52BCIDfn8o5yWZg4SURDIASSvatWcJg+JvR7nVlNTiWoVtNtDfur2xYSGlu/ohGNizcQ69xNkTOlkqF0OuGMu4+Cj3bZbnyFXDfAgvkBnnzcQZL/Yh4v/hS/cDNVOY5AUITFJyWCzLh2BKuRokUITOGwmfuRmGgRDIe0rIix0HxXQ/JdDcGExcthbwOmPj/8ONXisXtVvvgOdmSX8/VcTojxwO5qacSVY2e78yR3jjeJaajQ4MylbMuPxaj4PclQDE0oFSYstTSUQpAV05amOUtouWc565N7VZrcKMLAc+Iu1KYB4vvvoSC+CVD7UEOWLOFJcxkGFgEsHCik4+EOtSvOKIqBdRn7HVqXUhrA9dhGeRUwSUq5QggxXggxJrTajUKIFUKIJcCNwMWhbXOBR7AnA/OA8aFlRxT8lsXlWVl8X3woXsAHBxLB1NToNb33bfyB5GAxnpB0qdv0k2SUcP+GcGGYEqGxzt2QRXEtUKsJHZaG2kslWv2Hm1RrNAgF3Ek1rxcsgckPwqQr4Od7w2VfLROKdsCyr2Hpl/DHk9DlVLvU7EBAdUBsY+zXgN3gAkWzQ/zFBrlvrLf70dcWpY0yagMpaZ6/gn+ankYgSkg9GgKamy3xXShyJCOkhSJNNM2uyz59lKB9G0G2kYCMqZxvz86RPPK8RZGeyNWLbiHRvwu3WYy/ip53RSjSZMiWSehmuHRtRdgT2JrP3VL0iEp4BwpeLzgcghceUzlzjB1mb9MKrr5YYXifArSosQUoT5hV+F8Kinc6yChKqWzEofz73kehGEUarE7uz5g1L9E5+29004fTKCbRt5O75p7PsxNPImngFsy4AL/I8GhgReRJP4ut3WyWRRRbQV40V1KMgR8r9O6w2EYxv1iZ1e6nrqJeEOYQ4MqsLP6OVvZSVyAlJ2cvZkjeGj5PG4BP0Tk5eynn7ZiFO8Q+LVF0fkntwZqYxrQv2cEp2UvwVGGmlhrmoKLhsmwTHe0VEEDhpo7/R4EeQ5fCLdyd8XPdaMAiVDj/E/j8Mggc4HSKqkPDLrB98b7vQ48Bdzx0Hg1tTyh/Ge/eADnrADd0PxG/pXP22J2VpEj3CVFe9C3zlrAzthVePf4A7B9URWDZf6JpkJQIt1+n0rWTfexPvzb5ZFIQYRh8/XUSWqj71py0U3hywMf49LhKu9VNLx//0BJhGdx04hy2x7WxP9jf8GxFTzb6SuzLnd6oIbz5vIpDr7Lt9u3s6X08FwxaYDe7+bdDzFKimz40aSCwMBQH41Y/z0mbP6JR8SZUaeF3O/n6kSv57aazAbhJ6UR3pTLTUZomnxjrmaHuRkchiIVRzXQ/BSdPaxE1VQ57HHRBmHpEx7t79tR9Ix5CljORh9uOY1F8S1bFNmFi86Gc3+3qssfGYwU5fdd87t30I2fsnB9mxEvhwCLGCpQR56pCAj5UnmgzmhkpndnsTuGujF+IzH89yBAa6J5aE5kASO9u/+5a2yrMvRmPAnr1YehqoWhw1psw9kVoN7T8hS4EpLaFjifD859A0MRp+jhlw+s4jX2MJEmJagUQEdpVIiXbY9tgRPROJcrefNGhkK4ZahtuSQgEYWc2PPCEya4c+w7dkw+G1Ozqigrot/0Xeu/8FVdIz18REqfl48q1DxHfuz3qY49yTuYrXL3yTk7c9jHqfna9c5glxAby0Ex/WSo/wklRbYonSj/vvD12y9gwPPccesFue4+H2ohH4XUEVSdePY4SPYGA6ubbDjez09McVVpsSOzBXw3PoOE3O8tO81NrE15pYPr9BGfMIOuMkRgxbs71dOKOY64kZemaSkY8fmcuYx5+h5tG3cYZ97xK0tadh78TsI+o98gPIjKDQUZu3VqnmqJERRQJRrfp59f5T5NkhDdjldgq4nubkbKAX1K6c2fHcwG4bdvvXLL513/pIQyVXUUj+FSF6oTRz0JcQ/sF9sczkLnwwA1Hc0K3M2DRf/d+W0WDZkfB4Bth1yoozoGU1jZjviLi4uz6bsAUCh92Hc/37a4joLjsHtHVTWoqeN8f/NCSZN9OTj29wA7RR6oPtgIIaZWFbYVl4vKoDB4A02caBIJEJ9DVAppmh9svPldl9gKLpyZY+Pzw1B9D6ZLzF2rIyFgIFjY5mb+PvwP3sMEMO06xtcFLYZowdy7Skryz4Sh+mGrv2+urgTxWJRqhmv5QTtnCUjQUVcWtByny1xRur72H3jQdnn9eslEWEi90WhKL6NuX33M68Ez/D/bDkMtQ8KZ0klHzfj56VXDXVdvIUtJrPq60OCrzR/x6LKtT+qNIE1PVkG0tUu5ejRJj0veL37nomqdwFnlRTKtC3T3442O4f9l/2ZOeSsN1W7l/4JXoXj8Of4CgQ8d06sz6/QuO73vKPp7/v4vqPPL6pikHEVOLi+tGTrc2iPIQelUnU5M7c9au+RH6Tu1byEcBhuWuYPE/97E6tgkd4hr8izNpq/bct9bHQt+LbP10gD3boLBmedRKUB2UKaWZgXBvxjKh7RBY/GntGOyKDooKSNtg9zgDvr8VSkJUFGlBk54w+ObQelTSGVelxSXL7ufC5Q/xY5ureK/7Y/i12LDDqDKIw/Dxfyse5vcW57M5oQvb4jqwIbGHvUK0qgbFQcPiDOICueR4mpKWrjH2/1IZ4p/OjbePIRCQ/NbifF7t/fI+GSDDgKyQzHy/XoK2rWHdBsnz/d7mhWnH4DKKcZvFKDEe+sZuoO9LPSApfOq5M1dhrehPagPBZQPh3DNgxy5YvtLkrY8jS6C6dBOH5aOBdxvblKbE+3PY7U7DUsqNtmlBkU9DSAMZRfJ4b1Ekg9xuLkJHwUKShIP7WrXkzabP7sU1DC8a1XVB6pDd7M5WCCxNrPG5aNUCUlNUerX3krXOslNO1UEoLG8wmBJHYuVxbrHI/6AF3QdO4dLLH8NZEt5CWAFUf4Dj3viGbx++gnNuewl3fhFKqSRuIIgeCDLkuv/AnLppyKtDvSE/iDCkPHIMeTWYm9CasdmLcVUJoSrYM+W9ff1KwBHygLsVbsUq3n7wm6gIzdY+L8ndN3lXdxIMuq78/4IdNgs9aucxYb/YKl4z1Ql9L4T2J9r/L/0KlnxBpTemlLBhhu1d1zhOAUPvsduaBopg8xz46a5wNbnMxbDqJ+gS4qaOHGlLlFawTqo0GZD1I+/0eDLsKLomOX3F85y9/HFcZgmnr52ABCZ1uJ3tsW1qJMwVOFLov/1nJqdexZYSlQlvWvy3uCVPyHhSjSxGb3iDX1pfxuaErpWMYHWNOkrhckL3LqFzUASP36/y6x8W02a04vle6/g/8TUdxEZEz54wejRVY92WJXn5LYtpMyW6Zh8yJRmefEDFsuCDSZXnUwLQdBh+vKBnV52j+jjQ1c7wzDP88uFWXu34eDi9s5Txb1nRdd5refdruoRjd2AgMULVIDvx8coFFxH4MIrKYlSyWuV6k27dIWvwDoJPdLTFjGTFdcoNv6KArkvGXebnJyOPP9MSYEPNcTnVCoQbcQBDwTs7hRMWfoXmix7fdPiDNFu6HoBOfywoM+IVocxfYFP5o+c06iTqDflBxBCPh1fyjmjZeAD+TupApjOJNr7KQj2ywu+9SnlWXSCtg2zIBWCCr9DWP89Zx16VoKkOmzRmGrB5FmydB3lbamgfKm3P3fCGXqQqdBtn561LkdzSJrdVNNjShMWfQ0wqFEboCFVpXDo07gLrp8OCj6MbfjMAa38rN+QTJsA//8CuXZWMeaOSLZy5+lm+6nArAc2DROByQo92AS746lGUCipdAjhrzbNcd+LcGr3A+EAuv7a8iKCpEgyRw7NEGo8OnMSL044B4NGZYxg/6Cs2JXRFlQamojN004dktB/KKqttxP0qip0l6N5JUOKVeNwCXROcMkzllGEA8YQKaKLi1z8t/vhbEgyWl3Jl7YDHXzApLLZZ4hUhFBg2BK67rIrhuvNORG8T6+3IER5VGrTLnc/m+M549bgI6YtaPAFSIlwG7hGV7wsJrOjdBPNzN5EqPTXLjyJNAlpMBANddmYsWyYxV3dA+iIZZYnWtATVJUltbmCN3MLXjQMEsJCe5pHHX4H05zSK8Wvu6OdoCRIyc1CrKSENuJxs7NvJ/tvjwuGLcL/reriy3xGAerLbAcaPhYWM2LKF7hs3clpmJgdWAfswQNVZrpQUam5O63UTp/W4gSxHQtlHAg4IQU2RFiWK8yBGN0LEoWDx3htxsPPMnUfB1P/AnLdgy5yajSyAr8COBCS1gBPutMlxlUKKcyMbX0WF9B62B18dmve3Q/ELPqjZe6/4eZMmdsvPCC/NC1aM55EZozjhaJNjBghuv07hgXvdKOnh+uICiDEKqz2swyhBN334qkizWorGpsTuZLttxeZk3w5enDaI16f05MlZo/liSgtuPM9H6xPbRdmzJL2x3Y/lpvsszrnC5NmJJoHA3n2330+WZT2+y8ZmwdqNsD1C1sSyYPaCyPvq31fBktGEk1QuXP4gX3+byri1E3CaFTknEl23u5xVCyEwisLr4QHUxj6sRkHbUFeA0yjmmkU3c+Gq/+DstAf3wBzQo5DTAmAVRzOCCsZOD0l3rUZethbR2EcgFHvwLYiiqSsEMYF8em7/jRGb3iT6m0Kity1i5Yh++D3OCkvLYSqCoNvB9Cttgun0K8YScFd5PpxOuwte1KhH3cWRd0b/IiYVFPBQTg5bDaNMdvSIRKkxLw3JCYGhqKyNaczFXa/AQGBGeCj3VYLVRBB0xByiPPm+jFDYxjtvS3gteLWwyicPvz4CO5ZX/liP5qEIaNwV+l4AjtgI3puwt+15tt3atKaGKkKBZv0qL3O7o7K4uuX8xe0DFnPfLSqD+iuoqgLvvmtrlldpSnLi5o+j5/KlxaVL74n6YrUQ5HoqiIB4PKTdfSntZ36AY/cOuO02enQRuCKQ+HXTz65MP8UltlBKMAgzZ/iZcNrPcNttFK7NZNK3Jo88a/LxFya5eZHP1RslqKIo0UlujihR26REgRbVGRRMa3E+Z43dwU9tryYtzktKsv019Oul8PITGvfeUvMTIKRAGuHrCQHJt6xFiQ8iXCa600IVAY7J/IoRG9/llK1v0OCuFSRduxHhqo7YGX0MQrUQeuWLYhapWLsdUbfz6TFsS+jAiuRjou9bQOLlm/jr0tHkN0rB79SZesOZ/Hr9GRQnxRFwOVh28tE8+s9bFDWw9Ru+e/BSlg87ioDLgTc+BsvtZtvRPbjlufO42ZjL12YGwUOpkniQUR9aP0CwpGRCbi6+OlgFsNeoWLJUAVIoZLpTeLDd6dy34XtirMpeoARytRiSjeKoM8hIuuwKkp8TO3DWzjno1Tx8exd+r6G8Z2+wa5Xt+Zp7Y8SrQFow6w0Y93L5sjbHwbpp4d60FYT0bnZIP7EF+PbYvcfXTgV/ITTqAr3PBXeiLftq1cC4d8ZBjzMrL1NVSEqCSKkhRbE7TxUU2I1IJk2yFdTGj4dly+Cjj8q8+RMyPuLFvq9HObBgoGclucMS+WYeBKvMNwzVycNDf+alecNJTVHsXuCnnVZpnaP7CpqmwZaMIAFpW1CnUYTT8FLgalBp3QAOZriGMO7Dh7hv4534PCYBQ2HeYvjmJ5NnH1Zp1ULA4sX2uSxezMAeT/Kd61QMq/IdG+uBtMawqkrgwuGAESdEvgsDQYlRzVcxvfk5ZZ3kNnvdxCrw3ksqCfECy5IUpRfSoJtCznIXUkZ+gpL1TBRX5GdES/fR6KXFBJbHo+9x07xwFtdfeD0KEneRl/RVGWzr3tZmpkfcQy2eLrXylr5FidWubioOChyp7HZF6xgnSbh6A3pTLz48jJ/7Dm3+Wcaa43sTdLv4/IWbI+/XoTPxqydouG4rTVZuYme7ZmR1bhX6NMhUuZ0tVgk3q51rPqc6gHpDfoBQIiVF+94UuO6gFqzXGUkdeDCCwfUrOrd2uoCx2YsYuWshimUiBChS4hU6TmkLxERyWkZlLywrFSpFVcNd+6KY0q33AqGSoajIWbd3+4uEoiq9v5NbQrfTYPFnVQcDf020jxn0AtImsDliIbWdnevePBeWf2N/VpMhbz8MXBFEWZ57Di69NHx5nz52B6reve2WkqU6CcuXw7hxdh6yNB4tqpswSVIvGsMZlzdm+nqTnWG9kAT5SiLv3jKXO2+I7MpqmuCZh1W+H/wEfySNQrf8jNzwJv/tfH/E9Q3FyQ3Hz7LHZNjGsDT/PeFNkxdHz2f9uFuYknYuG1vcgL/IjaL6UTUnplRQVTvocOu1Cs2bCu54yKSgsNyYd2wHCfHw598WR/UWeNzld+PyVdXcc4JK7WCltKMBn39rsTFDsmSlBMWDs2ceIkZHz/cRUD1lUTHV9KPLIEmXr6eY8jSFDAiMHS6UhCBqgoHQJM6e+WgUsE22JLNzK5otWY/DH+CSK5/g6V9fRmvkJVCwt016JMJphT98QSWUd4/+VAY0N6oViCzU7LCIOaa8VYc3MY7lpwzEKlYp/qEBgVUJqI18xJ60Ey0tPHyyq10zdrULV0IMYrFa5pMlS0gX+yl4dBig3pAfIHiEwKUo/xvGvAbk6bHM6HoOJ674HEtadp9yVcfRbigvdzuWpf7+FC79lNT10zClJCgUNCxWxabTvShcQlEA8aY/Av0mHAct/F5jGO4AePeR6rPNgM1Qrxget4K2DntV+Asga3EoRC/s9WqD4igd4S65BNatg6eeKmdU9+2L9+ufWf3y73gKGtDet7r8mhcXwyefVHJRdStIk8L1ZMZ3CN+/lLzlvowrYwXjRsLr74evYlkwZ0H119blFJy1+hnOKnywbNnihsczw52OVbWkq2wiGn6nrNsgefuhNXw3aBpGRXa8EGBZqBqcMBjOGaeS3tje/p2XVBYvlezMgcztFj9MgTXrJUJILAkP3KrQp6fC5N9NXn8v+nkIIcJC9YYB3/0skYQMoSnwL05CTfeSdsxy2ry/lhIS8aoxtChcQeF1OmuOL28NW/RrQwo/s4lm0lRwdskn6boNqB6TWDT2iCDPTp3AqQ+9zcD/TqbBhkzGPPIun/Vsy8518ZWvUU0VAi6TlHtXh33s7FpgC0lUl92REs0MoFpBm3BXCs3EM2xH2OrmHp3s+7pilagQUEG18P7ZgOTb1trHqyVMLDZbRaSrdd+Q1wvCHEBcv2MHf5SEC6PUVSjYz6BTCAwp8VN7c9VO1/k2yWmzuI0gNOtjd/ECyN8GP94dZmgMwELBUVPrxn1CDaF0oYAjxg5N/1to0AFGjK+8bOrDsHPlwT1ux1Og30Xhy//+227/6fPZ7HVdZ3K7K3i91wuoAR/SMIkL7ObRGaNoVrgm6u4XtBjFw/0+IyjCpUGdDrj5GoUXXwvv1lWKhHj47K0IPofXS+FtD/HjP24WJR1L46JNnLruZVrnL2VbbDtuHDYbnxZjC8rUBjJULBnFWAkh6dtTYfzdlfdnWZLJ0yxefU+G1ZM7nfD+ywqX3GgRTeDR7QJ/YC96+ghJ0i1rcPfew9iF2ZR4C/ijX3MMZ7kX7VuSQO7z7cuiDgCoFs7u+aTevhb/2lhKZqUA4Bm4G0c7WwBISth5Qy+svCoeeUU7EeH6NH1uCVbjyCeY/3lTiic3hkAp9TVC3MwySPJnU+BKxlR1sASoEqFJ4s7aSuyIcmbhnndaUjK9AZiVJ75qip+GExbvldzACNI5VlvA3kkAACAASURBVG2MiiBV7Idi4iFAvSDMIUCOYfB31VqUOg4LcALtHA7uSknhiZwclgRqV2O9NRhkvaMRbbtUkSkt3GkTuyJ4iyocvLYoVWu2Q7AABQXanQgbpx+cY9cWR10Wviw+DXatrp34y75A0aBpr8ifXXml7WWHsD6mK693fBx/UAHhAR18agz3DpnMBz+2jvzdORz0ubAXQ5s4mfx7+OemBV9+ZxGIEDzQTR8pxi4GaNspbncNMWYRXHQR3HEHeDxsHHcT97jG420bQ1B1syJ1EDOan8nds89nQPYUXvpnKB9ePoXF63SKLXe4d14BwjJsweBqGM1SChYulSxYYrFkuSQhHgb0+3/2zjpOrvL6/+/nyti6b5JNsjGixIOluFuBFNfSL1BaoFjxIgWKFKctlLZQCi0QpBSCEzRCEuLum+xG1nV87r3P749nVmZnZndD+f4K/ebzegV2Zq48V89zzvmczxE88LhDRWVqURhNwHuz00vOCtPhfx5r5I9XFe5Rc77WF8vxTVnBx1P6EaY4aerb8nx5ohEHsDUiq3Joerac4NxCFfZGEvysCPekJjKPrMMYEMJpS3GeEqxjV0Ms0XXYdcu+ZF1cQcYh9Umr5py1A8++LQQ/LyJW58LamCKNoxk0e0pwD2/C3pantm8LpA1tMwdiFEfwTG4GILwsL8mIA9itJk6jC72g7zoQs9nNp3Y1EijCwxX6SEq/h6H2vYb8W8K8UGiPpUi/DwgByyIRLti1iwJd7zNFzAJuq61lmtfLeTk59DMMsC2iH9yOEW5JSXYTgIWIK8LJHnPe7VUBfes5haqpthINuYXglhFn8JPqBYyO+lUd+H8KQoM1byn51K4YfSJsnZNIeBN63LB/C5MeKVUTlu4IBFQJWhe8O/wyYlpiSY/UNAJmNmsKpxMyMnl2wgPsyhxOYWgHF666k8N3vgZuN2X9NUzDSSK06Ro0NqdmgOt2DD0SYlb9JN6ZuICRDYv45ZOXk/3B59x/+JssyXoyQb7V0QwimsHj+/+Ffwx6kLKrr+LWIcXUVgW59Honue+XlOhOBENaKt/cBz19KeHeR5V3bZrw15dV3bWdxghbttJuTzVRAZCW4PWaWnxDfLRu6qsBEdhNJrFqN7I0mWApYwK7Lk1poiMIflkIVvvbSkAMIgsLsBbl4Bhaylrz7vvvfDKFmsDYgpZnyzGKw7hHJTcKco9pw9rlJbRgcNqtSiEIV+Qrb7zr91Ed/6x+HYZc89o4qeQ5JAj3nrVEtrrU0uwmyAP2ah7Sp2LuSW+F7wD2GvJvCaYQRL6HaYq+IgZUp3I50sACVkejrI9GebW1lb/174+n6mtKokF6otGYOMzLHsaSnCGc0LyeYf5dSd6ojeCjgrGY0uGoxr6EnQXkDEA2bO6mVSUYE9jFJeMuYc62N9D6qqfeF2SVKia54e4bo106qoStu8pWzgA4/CZY8EynrOqAyUonvXFr6m3tiaEXAqJtiuHeFS6XYq53mfw0u4tTerUR3csdB88ions72oFWZw7jiWnP0JZVSkQ/hblfOSkZ246EljRpzbCZyS5jeMc21xUcwHXTP2afluWs2OpJK2kaMTKovukRBvRT57F4oI/9D7JZuNgmGou/oKWDxw5y8YpbGdS6jjsOmdVrT3QhlOlqD5HH+kBBiMVg1oeQlQFNLalEUcC/IpuMGVWEHtuHWLSPcWFD4rSZEDfk0QofgfdLsevdmEP86QlmDqm/FwIbM57Lbjdue8g4sTQafjMG94QmXCPbiFVkYg4M4ju8juiGLFr+MSilJ500vhSwmzvfGr5jqml9aZDKj7dDd3CPbkXL/ObPsESR4FbKJqaIgm+8nf8Evl/Tju8wwrb9v5LZ/b7DAgJScnd9PZ/VVWL2xqAGdAF/H3wUzvG/QZSOSzRsQqO+dAL3Dz8dW4g++qQCmquSXksmDufvms8FO+ewI7N/XOf824CA0SfBYTfQ0WylLzP8dOHzfuPg1CdhxlNw5rNQMlrxDHpC0UjoP1FJx5aMhsySNEPVFTegO0wTzj5bJXnjOGDXLDxWsrflCIOwmZnU0ztiZPDM2Ad5cfUoNm7p9LoNA3xeidslmXGiwJ3mtAuchG06mkHIyGRx8ZHEZHofxNYMMro5tzdcqXHaiTpZmWDokgnN83ls3lH8cMvTTGiZT040FeFPdvzTNJXLTud594SmFlTNciq7aEo0n40+vpmBF+ykr1EW4QiMgYqPE1yQT8PdYwjNLyC6IZvAx6VJXm0HjL6IJovelxGpxinAEUSW5dE2cyDhhQW0vdmfmuvH0/pqWaLh7XHf3aA7uMe1dHzMOLIW70ENYDoIr4Vw2xhlIXJ/vqUP2+8ZNpLmHnu2fzex1yP/FvCR38+9DWmYv3sBwMpIBM3bj3OF1mst+LTWCj4KrsX98TPIpoqE34V0KK5Zw4d169Ecq9dXknrdOIg0HrGJw0+qPkUXmgq/OzZ97nTW016XvBgPh3d74ekupQRXu5EE90MI6Dc+PStYCPDGVfNW/6tnpTZpQ1MFHHQFDN5ffVe1GOY8kbieZsCwQ9Rxp8If/gC7d8OcOeBycXj1P5ll/oodZqYipvVB69wRWhIdQlgxrlxwGQfseodVq05h1ringW5jkBKZws+I6t4eIyeGE2XsKMjNSRyTaagOaD8+J/5FyyR47jyYOxgxciRXHuLh/n9ANNruizoIx2K/mg8Z1ryCgePb+FP57QTXJTeM6c17lRJCQanetrFuywmJ90D17ggdvhPxUikylOq13L4PiXA5ZJ1TieZxkDa0PDcE2dVIpvV6Jdiix1KwtOupwXb+rdFDCL7LPmwdbIld/Q2NuHAQHpvMU3Z1fqVB3qUVZJ22k9i2DPSCCGZ58FvpziqAYSKr1+W+a9hryL8FPNLYyL8hBfJ/BsuzBrE8exD7tWzFEjo7PPnkR9vIscMdj7BAddzKXvp3JE7K16OQMbzdG3+Q+nXal2fbJePGWzeg/EComMee5Z/b28N0QbpQuh2DjCLwVCuddSuiwu+6G/ZPQXZLhXAfSmysCGyb12nIB06F/S5RE4yon47j2/y52t4PrkyOSGRkwIcfEl23ieXzWoj0G8xdBxawYLFkzlvVrKnNw9Z6i2IkXwEzFqSotQJvuJmJq17BHvaE6jTSBUr/2yFqJLrWLjuklATplgOWEg2boWUON9/QGWFYtVbyzAs22yohOxNO/6HgtBM1RE4OXHut+gfsDzw4XPLqmzY7v9rK6MpPOGv9bykKVrK05GjWrdqfMuMrmtxHICPdjJKGiqZIjXRG3RUOkn3OTmpnjej0Zh1B7s+3oOepe1kIyDxlF/43ByTuw2Xj3rcFu8GFnhcj8/hq3GPVPWDt9irjnAZC2uiOhRRCXauuRrwPE7H4Aol/Gw7msDasikxkBxO9r+t3R9cwfvJyRlmQghs2pSSwGYVR9JwY/vf60fTECKQt8B7UQNYpu9B8fZuMd+X8uNAYI3IpF6kma99t7DXk/wZ2xGL8samJHdZ/naL6/w6E4NIxP+HgxvUsylONLqLCYFiwhsfX/4PBkcbORXH+V/I+vWb+DE9yo5L4iJTkVYpoQskYaNxCR8eP3kag6XDqE7BtPjRtg9xBMOQHYMZztG01sPlTlRPvPwEGHaAmGYEGWDEzPo4+TDS6e9rDD4VQI6z6pzq+9tr0nUth8YspJxKr10vu/O0Q2uXorZcdfnqh4MF7fVx+7ma2Z/eijJWis5YtDLKiiq0U0b1kh+upi8fCDSeKhsMJm/7I4v7HszNzREd4XUibTAKcdxo8/UFmB4FM08BlCm7/pYvJ4zvz3Bu3SG6/3+4oa2tqgRdelbT5HS46W2fBYoe/vuxQXQv9SuDiczRuD98Pb6p69JCRwTVHfsXOrBGEDR+utiDSI0B3EFLJrVqGje/IGgIf9Ivnl1PfXboT48SNf+SDP5zNwL9sY+xHixkR/prF6w5n4cSjceLStpkn7QYb/LP6Iy0N4bbJOmMHmUfXJm3zUEqo9MWoS2PIhWPz0qwypITzTkmRionfz0JK5J6QuywNGTDJv2EDTX8citPgTnvcvUMobffukQrAtILkTNmRloUuJTQ8NJLopsyOsH3gg1Iiy3Ipum81wuj9GWl/H+Tj5ljRn8O0dApz323sNeTfENtjMc7csYPQfzHBzYCOnuJ9lBbpFY6m80VhIkt6Y2Z/Tp58HZ8ufoDCWHIOdk/QF4qOKjlLtbKE1l2pw9ZCSx9yzyxUhrwvMNww+EBltEcckfz7zmXwxaNqX46tCHBrZsFhv4T3blXedF9K0YQG1evgnRuhdF8YdRxkFsH6D5KPz46picO0i1S4PY5IVHLnAzbBbvOTP78oGTMyl3MPWcajiwNEjBQ59g4kXhHNsejn38Lg1rU0ePpx1dGLaPYUdxh7S3fjibUxf+Bp1HkHJuTIpdBp8Zby4hxFkmtH/xL49c0a/UsTr+rfX3PoXi0ZicCb70oGDrB58hmHSNyAbK+C+x53uOnrDRwUX/bl0bewPXs0MUO1/4wYWWDJjqOyhARdqqYg3Uu94tCdKIYT4/qFP8FjNTNg7WYm/+sLPAHFmBu9cCnTXv+UJ//1WxACISDr1N1knrwbGdIRPjstvcIjdH5VPJxrh8VYv8lJCqmPbliAyw7jtfy47HDK6+QmwKHbX+WjwRejJqvQF6OsZVjouTE8E1pUTbcj4xGJb4BYaq/eFgajZi9jXdZkjOl+9KxEhym2OZPY5szE3LulYTe4CS/Ow3tAI32BBJqJ0kQU/duIz/8HsJfs9g3xZGMjQSl7r9T4HsNCpcG+LSMOpPUkbaFxyZifsM1TCMDqjAH8fNQFHD71Zi4adykLcob1smHR5b89L5X2pje9SqdcS5UzFqnJcIYHyqamWacbNEMt239C6t8dG+b+Pu4tx+8sK6ImF189ozz+VEY8K07UMzyKvAZquVADNG2Hde/ArOtVPXo6wRvHgo/vTVCQW7xcpkwwRGPw1vs2h1w5jYtX/wpftCXlddWcKGM9lbhc4POCxyUp82/glE2/556DZnLdEV/Q4i5M8gbDZha1GeVJ5DlQeuyNzYmVhDt2w813O4TCiWPYVpl6/JoGf/1zW4cR7ziuKDw79LaOz58OPq/DiHdCdPyTtob0G9i1qUu9NMfiqIoX+MNHU5nS8BH+/GymvPl5hxEHcAfDjPxiOSO/WJa4Fx20zPRGHKBCqknvjdcKzEFBhMtGeC3aw9XlLavQpGpfNH3HP5PvHeHAQWE+HnGRmviJ9mPrLMlKDYleHqD2tnGdwiyCXtbZcziayfzW02l+cTC1V00k9HVewu/RLRkpHwcZ1olu3rPwuI1kttxFneyp9fB3F3sN+TfE1+HwXpb6N0EPZK5NGSWcOeEK3i8YzwXjL+eL/FHUunNYnDOUK0ZfyIcF4zoW7/rKkKia8H9rLq2bcPiNsM9RySFpzYCCIeDJ6TSUoELkvnwomwJH3UavEJqqC//6eWWwt86F7QuVF27HoLkydacyOwrVq1JLrppemHYhnPFnmJpCna0dVgTmPdVzPvSTOXDQVBg1Cq6+mnB1c8p3s5Tw8Rfwrzk+TjE/Y+ZbpRxV8UKiMZcqhXDd/eW89IzOHb/UeOQenSH9LJ6Z9Cjzy2ZQmzmkR5GWPWkdXdcAsz5QT6TtSJqaJWX9Uy9rWw714dRRhN2ZQ/u2ww4o4RLFBk9Efriaq5f8jAH+zdimQdOAYjQreTl3IMSY2V/v4X6hH2qS4cmxKb5+E+bINpWzNh0wHJYVH9nxTGzOm5Ji6ILYlkxkrPs16OVJ0iXhz4uVJ9weBXD6akq63VCGndRopev4HM3ExoOM6TQ9NSyhjapeGCWlaJ/LRi/ac9aSQLBGNu/xet8F7DXk3wBSSjzf0xDMdxpCI6i7uGmfM4hpRkLJVlh38cCQk1J2RxOAJUQaalyfd67C1t5cOO4eKB6tvtMMGHKwMtTH3wuDD4h7v24on66W1TTIKaPXx8m2VM/yDR9CxRyY9zuY87hik792GTRX9S333RWODRmFKlTvzQWjB/JZTy1NpYSBGXBSMeQE4OmnmfSzg7Gs1ONxHCWGsuvhv7OjeDyflp/XrUxQoBs6i1YIMnyCCeM0IlHBQmsCYaM3b0nyi0sFt12r4dqDisAvv5K8/4nNOZfaXHSlzYp1DmiJhtPthpPsD8gPp+4XX2AGVDtW4Ijt/8C0+qbWqGXFoF2MRLcxZYhrll6CZRrsHF3OIx88Rt2wAdgp+pjGPC78hT13CUuF4zTVq90XNam5YyzRNdnKsMZ0cAS7c4bz4dCLqMgeQ3VGeXIJpCNw6vdQllS38R5YD1qa8rO0SFWbLjGHBOLNTnp3i4QG4eWd58kzsRnhtVVkocs2hS7xTU9WmOsNGuD9nsp67c2R7yGklNxRX0/dXoLb/wqk0LDTxBObzAzadA/ZdnL4yyN7q+PX6UmyyrGj2Os/wOw3HnLL4Ni7lLWK91sHlPc7/UrAScgls3sVfPkYvYcWneT8tHQ6SXIL/gK+PEV260uYUjMgrxxy492dXL6eJwLSIpGn2w25XvXvkv3grTXkf7SJ833v80LghJTyo7YNnwf25Y2j5+NEk69ZzIL3PpaceoL6vGRFej31duh2hCn1n3L81t1YP7kUlwl9VT6OxuCZv8mOxmuggWEjvDFkwMDwxDhrw5OctfROigZfyl8mPIjdpeOY2wpw/thVMH8+3Hsv56x+nWX6Gex0DSYU0+KnLbWx8k6vR8uPEtuYjV4QxgkY3O59BxnRMYf5ySnZRsmGStzB5HtXahoLzz4q+XsHwkvyCH+dh/DaZBxeh1muascF8IVTzeminHmLJCJoJHrFjiLK/fWK2xm3YiGyrYe68rT13Z0lb2qnEt8xtXgmNxFenJ96lZRCNKmm3+qzXe+h8M411N89GqexF9KcIiZ0rm1ICu9YS9PvhxHbriIswmvjntCEVe3BNTyQZkPpIJgo0hzXdxx7DfkeYlUkwnt+//dQMuA/iHRlLjI+S+9uuKWTUkDFkA5eJ/2ZlwhkXNo1AUKDKRdCzRrYsThlnlkD1rU1MlpKtPZxdtXdDrfCwr+oemzpQMEwVbLlyoDPH1Kh657QbvjTecSgzs8+x8DatyEWUcum7WAmlODLQT/r/KpoHzUeq6c8X5pYeddr4zHg1LHw8WbOXPgrGq87kVkfyASCWftq78+WBHsQ+qjtIq+Q4RMYhkxWRJMSlx1Gw6asdQPXf/VjdmwqZ8eEi7n+5zpP/smhvhfekjBtasI20Ug3F97SQYd+zy7kkVEzyN1Vz5acCTw//jfxcqzOgzph25859omzoawMXnsNL/CEI1m6UrJpC2zY4rBwScq9E/i4FM1rU3T3GlpeHEx4eY7yjIHY5iwa7hnN0e+9kXRvSmDWrRfRWpqoJCYdaHxoJNENWaoUTXMIfllE9jmVZB5TiwQ+ZBdZtskr2zRi4eKkUUlL4CoPsfNnxdi/0KC7rKlp4z2ggfCCgsQ6dCG7TVrU/43yALnnV6rH0yVJSie7bHxH1RCeV4jTYnZZtwedgZCGMCVFD6+k9ueTkeH0Jkk6AvdEFfpuv2WN4ghFd6+l9c1++N8agPQbhOcWEZ5bhD4gRMEvN2IU9x5m14Gr9dG4+9pg5zuGvaH1PcTnweB/tRTrt4fOc2RKm5PrluLtUlvtsmOUh+o5c/fChLU8dhQzBTvcY0c5q3oBpnTSUnH0VN9qJhx6HYw+DqZfAQMmp1w3JAzeKxjHV6ncP+mokHjlojhzXULDZnj7OtjwUe/hcFeWCsOnIG8l7keqEPmMp+CQa2D8j1KT6ISueogffgO4M1XkYMNH8M5NatLSa2139+2lkvKUUJIJAwZwyvEaRor3q5SKeNYTIhGwbXV+Dj1IpGwe4rJDXLXk5zz06eE8/Nlh3H7wLC6bPp+7H4O7futgmpAiIh0fuwTdwXt4HbFw6teZdASD52zE0xoAKbl7+uuKwd0ebYn/e2f4FYSLBiSsq2mCqRM1fnCAYOWapC13OVAdp9mk6ZkhKvwb6zbgqODtYVcmjw3ot2F70vfhr/M7jTgobzuq0/rSYBy/3rHu62wnWtaWUmNcmA5G/7ASULlyi1rGVJNY4bExBoTJuWg7eVdtRi8OAxKR0U6WS75QVkVGxxw7/4YNCJ+F8Fgd2804tprc86ooeXK5Cnn3Ra4polNzzUTqrp6E78hahMtWefOOI5QqPWLaZJ+/DT1HTYRlVGCH1fbtBhf+t8rUOZedZER7p5e6m/fF2tV7+kBDI6u7KNH3CHs98j3E97Ht638GnQ/xWP9Obtn6Dgc0b+WlfgcS0k2OrV/Fj3fNpcpbRK4VYlLbdip8RRRE/NwxfAaX7Pyck+uW4qDxVvEkYkLnmm0fAiCz+9MaaiEnFuhdACa7Pwycpv42PTD1AuydyzG6dEKTQFQz+CB/X/apr4B+wzprugGq14C/lqTpg3Rg/ftpmq1oMPEMGPNDWPw32PxZH/qDO4rRrukwYKL6t3sV1G1MXFc3YewPOz/PeRx2Lu9dz31PYGgQEXDDDfQrEVx4luCFVxPbdNp273MYt6szsJGfJ7jlGo0HnnQ6DXrAz6/m/ohJtZ8CcOOhH7Mxf1q8xlltf3dN6m3rhsQ7vR7faTswiqJYO7xE1+YkLad5bHIijSAEuzKH0+hNXSscEwYLlzgcepAylI4j+eRLyXuzHSp3QCgp0JF891krfegeC7tbRwEpdbZmp6hWEAInBaMvtCgvWXhGbYm2t/qTNWMnmldNat37NSJeGYiMaZ3SrLqDlhPDPUHNtNyj2ih+dAXBLwuxG1y4x7ThmdKEMCSeyc14JjerOaoG1ZdNQQZTT4oaH96HvKs24xoaoPQPSwmvzEUGdFxjWzEKVbTMaTF7EKmJTxLavX4pICZwYhrBj0vIuWQrdr0bu8WFURjGanQR3ZCFVemj9fkhhL4sIvcn2zAGBzt0dcIrckg9tRfIiEbrq2XkX7M5zXjaRyVZ6zTT73vam3yvId9DfB4M/m812vyvRVgzMaTDqXVLObVuacJvowK7GR7YjSlt9m/Zgo1g7IqdlEab8cbD0FdWfcIWbxFPlR3O1f0Go406FrNmA/bsuzF6ClUDNG+Hhi0qFA6wZhZat2y6ADLtCO8tfhDdMJWBHnU8TDpHGZS26vSd0aJ+RX7rnvvWTSW5uvJ12PJ570ZcM8GVCe/dAvlDYeJZSsr18Bth0bNKPEY6SjzmgEtVTThAU2Wc9f4Nkj2a3lnm1hVS0lhpsvTnb+D2Hca0sORHJ+vsP0Uyd6Fk526Hz+f1XrHvMuHEYwSii8e//xSNV/4sWLVWIgTsWzEP13sLAIhqblYVH9oHpbH28Uu8J+zGKFLHnn12FfX3Znbpew2YNu7JTSzlWJq0p+KcqjTbl7DG7+dQcrCl5Lon/Wxa6kpjUFPDkBYyLLC7r6I7DGtZnrR8zOdm/gXHJR9aO4mre212TCPwQSmB2SXk/2IznknNaG6HorvX0PxcOZGVuSAknilN5Fy8DaGB1WDS/MdhRDdlIQwH32F1eKY1JmWv2qPKviNqCXxYkhxVQBBZk0PzH4eRf+0mhEvinZrchkzLiqV9R4rcKGZZSE24uuXSZVQn+EkJhXes6/iu/t5RWFU+sHS07Bi+w+rQ8qPgdI5X6LKHW1EQXZ+ibWo3WEhmym0IW3CE/v0ThdlryPcAW6NRKveS3PYY6zP60WRm4I1EE3I5TlxkpT2g5Y6H1AeFGxLC5F4nxtBQHYZ0oHoeDD+cjOJ9lPHrzZCDKvNqN+SNFWgpc+QSNzYdLbrWf6DKzYpHxpfoYfqWQi4WUDHI9e+nNrKuDJW337Ucgk0qVB+KvxR3LYeadXDsnWrc06+AAy9XRrc7K71uY/K2+wJNV1KxKYh1r285iRc2nYVu6oin1bm660aN8WM1zv6hwy9+vBPLGpBio6A5McbWz6MxexCjDhnGxecke3Zul2DqxPibd8KJ8OijcNNNbPf1ohLXBR43DJ4aIVwW7tA5cA0LUPirdfhnDkRuzyTmieE0uwgtKCAsC7js2HVMqp6Nx/ITcOUlb1STrByzFUdO5NGKbWxa0j8xd5wCmZFGxtbPI2hmsyF/KodWvkrQzGbhwJO6yMhKhClpu8ZF5FJF6NIsC0s3ee78u1m46nTEFhvfYbW4higym++wOoLzCkkm4wjldUeh8YkRlP5hKVqGjV4QpeCGjUl0FLtVp/baiXEPWSBjGoH3S4msz6L43qRcAQDZZ+zArvYQXhzvC94VlkZ4SR7RbV5c5alZiMIlyTiqlsDs4gQinXDZ5P20AqM4TN2t+6acIFm7PTQ8MkJpqOdFiW3LAFtDeGyKfrMKLTuG6Ga1PJOb4a8phwKAtAXRLRm4hvVMfrORvCq3MVrm0O971pNcfB9DxVOnTpWLFy/+/77fRaEQV1VX4/8enrP/NEa17eDZtc+REy/nsdGUrnofq/E71NhMLxx8jQo7L/gTbPqk95Unng37nqb+XvAnFebuizoaxHPO5jcLWxtepaeeCpoB5/1DjeP1n6bWTy8dB0ff3vM+diyBOb9Lv5900HQ44jb47IGEicbG5iHc+NXtROxEkROvB17+s477rtuYsf4GQmZyYwnNsfBYfv7wyf6UUI9YugSGD+/beGIx6lfv4sIH+yN7aeqRnwsXna2x3yEWt8olRLrdQz50ogGNyisnpGBkS3Qi2NKdqGImJL6jaij88Xb+pzaHRdd8wsfOT4jq3QVhOrHfzlnc9NX5SE1HIIlpHkwnjOlEePTEZ/ii4BxkWMc1oo2cC7djlgfJb2hj3D8/xfRH+FfjDfh3FimDJhyEKck6u4rMY1Uuwf9OKa2vD4wrMqUgjQlJ7qVb8R2avtSq8XfDCC8oSF4XSeG9qzsmDqlQfcUknOZUZ4C2kgAAIABJREFUfAsJXpuS365Ez089iZUOtL1WRuDDUqQl0LIssk6vwndIPUhB9U9TENtEPCcuux6rivz4jq4m++wqNE/q5za4KI/mJ0ekqSxwEC5J1plVZB6fJk8ThwacIMo4TR/c43L/CQghlkgpp6b6bS/ZbQ8w2u3+dlXO/i9ASvKjbRzdsAZbQkxozM8ZxiHTbuGwabcw7qD7OGHydXyeN6pzlRSbSXg025Ouo07oQ+tRAcMP7/w45uQkwZcep2XS+TdyzxI8aeqD84eo/0f8EE3zMm1I02+8K/pPAPMbaF3rLrDDMPYU9bfQQTP4eMdhxJxk0o8QsGSJBU8+SWmgc1zCsRnUsoa8UDUH73id380+gNK2rYiAn8idv+Jdu4qZdgWrnSacnibApknhpMHsM6z34zAMOOZwjVzdxTX6GHIwcaPhQqMID4eKUgJLc9NE6AU2HiURkB9FLwlh7tNK3rUbybloO0bUYvT04xm7aD56ynRIJ9VyZfHh3Dd9Jh47gM/ykx2tx2f5kQaMmjiHfn9eQv8XF1F4xzrM8iAmghMKJ/DlpafwzkGXdxpxAKkhozqtLwym9bUByKgg86RqSp5Yljjh6DYUu9XAbjHSzksja7NTrwsE5xZiN5lp13WPb0lfLx7RaHsnfQhaaJB91g5K/7yY7HO348QELc8PofqyKbS+1Q9zVCuJT178b9ldrlX97dqnLaURb7+lfPs1UfLEMswhgfiYu25bnVv/zEE4bT0HoR1UT/LvG/Ya8j1AlqZxeU4Orr1iMH2HEDS6svjTwCO4cPzPmL7fHVw27hJaXJk0ujKRQrDdW8T1I89hfs5wImhY3ZJ3DtBiePkwf6x6QxTHQ7C5ZTDlfGWYu8qTduxbg2kXK6EUKVVXszlPqGXdcY/S8CDMnrTCu0AzlFBMXrlSc+sNdlRFDhImG0J1OmtXYTN9KUvtAKUal267FfNg9VtQsx6OvguySxOXMbww4qj0EwkpVepgwulw4gMqJz/pbML9D8ZJoZkdCsHjf5L8eeSvOXPtg7gtFaYsDlby+OyDeGnWQG5ecAH9/XHNecch+MWnzLK3Ix97lP7lo4nlZuGcfDKsX596TMA9t+iMGRUPDacx/F25YfuIHB7Wp3GrPp479Ancr09Wp6hX7WSB0+yi+KGVFN25Du+UZoSACbPm4m1oYnrVWxhOLEXkptNbDJuZrCmczoL+J3f8IgHLbfLZ5TOS9iiB3TKIBoQW5qfJvQv87/Sj4cFRSAl6joXooZNX2yuDqLliMrsvm4L/4+QSNC0rRrqpavCjEmqunUjNlZMILky+17Jm7FAs8lTrOxrRdb3nnsOL8mmdOQgCpmq2EjYI/GsA0VW5JBjsePVBakis3V5kNPm9KyMaoRVqHHpBjKJ71+Ce2EiqyYvLEDhrc3s0ei40JmsFPSzx3cTeHPkeQEpJhWXtZa5/A0R0k22+orS/h3UXjw0+lpsr3uGPg47m7s1vkBtvoLLLncu1o85jpzuPQRmS0XqX23bksao72K7lyqBnFcOuVcroDj6gkxS24lVY+26ndy00ZcxPflgRxj5/qHfCmGNB4XA1eQCYdaMi06WDbsKww1Sp2Mo3oGUH5JfD+NMhLx660w1VO77xo8T96261XHe0VsOHd6i6dTum9pFTBuFuzWasEGz9UpXeffFo4raFAG9eJ28gZwDkDEBKyfg6hzmLZZJwiwTaghqzhlxOSfGx3Dj/XF4bczOV2aMTKgC6orm0gLN+8QgHvfhBhxCKfPdd+PJLWLUKBg1KWicrU/DIrw3q/jaLX80cTGXGyISyPZcV5NiDTLq+ujQhKKNzMjZS5JA1YQvNTu8T7q5LmAhGrN+J7g9iSMmDnx/F3dPfoNlTjI1OzPDQ3UCEzSzmls1g+s63ALANnZkP/4K24uQcvIXkE6rVmNOR2QAsnVhFBtF1WbjHtOE7pobAv/p3W7YL2VACIYPWfwxCz7TwHthZdJ935k7qHxvRbQfx95ejgQNOi4vmp4Zh17twlQcRXovAB6VYu724JjQRWVSYwpbLPsmgtrxelpzeSCXnKjUQaSYsQhL8sBTXyFY84zp7BUgL7Do3TY+PgJ9vxTtNcUz0XCvluRXA+b7BbBOwUarttBDFQWnyu9HYXxQxjL39yP+r8Y7fzyy/fy9rHRXK8QjBCNNkfTT6rfRjX5/Zn9snX8nZ2dmcnDuMolA9ttDIjQU4vGEtDa5sXs3Ynzu7r+jNgWGHdn4u6JaXjQZUB7GuodJ2RbV178Lk8+CYO2HF62pCkO4KGx7ot2/n5/1+DB/9Ov0BFY2E4rh7ecRN6ZebfK6qT2/P92uG8pDLD0xedu6T8Xx6fIyWDY0VqbcrHcXYP+AyxXwHRZjLLoXDbuxkREmHdRscHnoKGhqVIlu6Tqkx3U29dwBRM4vHPj1EmRNNA5eLrq3GIj4Pn/7sNC648hFc4c7vhZTKvX/4YXjyybSnpOjt57nry2X88ojPCRmZWJoLXVqMbv6aGZ4IMXkCFg7+Bp0333NYv0lSPlAw4ySNsf1yGZLnInBOFY2vlKkyuvY2tHFoToyBJbuYphexgiYMBPuQw6aRAzgww4PHH2JIy2qee28kldmj+Xrgsfxtwr1YViJ3QHMsMmItHZ8dQ2fJjEPpDb7DawnOL0hBZlOQMY3olkzcY9rIPnUXVqWPyPJcFZ5KyCF3QUyn9Y2yBEPundqEnhfFbnJ1WSfFupag7eVBqs48Gm+CIgVsbZ8gJVYpCJdD1kmpZW7bEZhdjFO9BxKwukRodrdIhdqvE9BpvH+0ik5IMPqF8UxrJPB+P4gatL1W1mHIfYfVEZybTBSMajaHjnNxtD6y47tK6ecrpw4LyVStgH3ITqiy+L5gryHvI5psm9vr6vYa8TgcICgl66JRDvX5+DiYnjTTV4x1e3ilrIwvAgEsoMqdx0MbZ3JY03pcjqUIRVvfUka3qLuXoRBxHGptm0Jdx9ueS2+uUp5r95ynY0HVEph0rvK0j7wZls1Uxr17Xlwz1D67GvKSMdB/EuxK7FwFgDsb9p0Bs+9VHn9WCUw4E/qPT15W02Haj9U4Im1qYpKqmUi4VXUzS6pnT+PJOJbqaT7+dBWdaNquQvk58W4iVgQW/43GtSu4dfYDhO1OYpcQYBpgO0pvJmEYZhZrCg/ksKqZ6tWelQVHHgnvvgsuF450mHXn/1A/dACW20ww5ADEYrBgQeoxt8PjoV+ggt9+djjPj7uXuoxBTN31AWfsepqPXA+w5ss/YVfozPvsArB0bEuwYbPk07k2992m88t9xvHZcbtZZb+P954KPh1wPjHdRVT34bX8eK02zttwLX/nZg6giMNFKU/Kdfh/OJ0ZN+dghKMYluocNiC4EVcgwHOu++M9xzthOFGOrXgeRwhiXjev3/czQrm9e3Su4QFyTt9B68xB8cvXzXiYDnatm5aXB2KWhci/cjN2g5vgV/n43+kPaUriundisyyB3dLViKeDiAsqtOfsu3zf8YUE00HzOGRftA3XPulbDgfnFdD6j0F92G8nzKEBPONalEKbEnfoHFf7KILquYhtzSS2tVOz367vPG7XsADZZ1bROnMQor2ZjSYpuGkDlcZQhtOZEhgkMhmk71mntO8i9hryPuJ3jY17iW4pEAXmBoP4hCD4b6QcDOAX+SpPtzwSwbQi3LH1bY5qXIPZJU/pdmKKaX3GnxNyy1JKnm5q4rkW5R05wGDDoETXOVrqzHBiKeUxZetu7Levxzz6dqVzPuEMZcQ3fKSWaG9Ksu9pyuvvns8uGpHai48G4JPfdJamRVpV+H76lTB4/zQnwQXGt5ifM9zQLy5CoptqsgKKXLf9K1jzNgTq+bDiRGyZaBjaJTBNg6Qwu8sK0t+/ha0542nw9mN4dCN5Z5wBzzwD1dXIoUOZ41qFq3IHRiTFU6PrMHp0z2O/5BKWzmvh7in/wBYGlu5me/ZY5g2ewW1X/ogj6qq5Z9rrOIUaMu5BOY5SkvvdX2yefsjgGH0Ax+RXENt4AZctuZEF/U9mW+44BrRtYvqON9n6g9FYSOZSy1xZGz84k9/MfYbzrn6MibPmArDshwfz0hPXUtC6gYbfjuywadLSGD3qc/xRL/Pzj+Pzy06lYv+xfb48GSdW45vaxO6b902sfUdCVCM0rxAZ0RFum9aZAym6ezW+HzTgfztNWzcAW9Dy4iByLqhUn4WMs8H/XS9Tra8XRCl+aGWP7VUB2v45oJfSvW5jctnknLED1z5+Mo+vJjC7hNbXy9JOWLrD6J9YtZF5fA3eHzQQXZuN8Ni4x7YiDEmtE2a43ntu//uGvYa8D5BS8nZbmj7Oe0EIepf46gWPFhdzkM+HlJLPa7fxwbLfk2sFU8uuRkOK0V3YGUJ/qbWV51paCHUZx8ZYjI2xGHPROUTzUGgnGnMVoHSwW3ezcfaDFJ/4G3J1HaZeqELboRZl3Lu3Ne2KhgpShuKlk8y4sqOw5AUYtF/fRU+6wpOt8tlNPeTl26G7VO58YLdqlcYK+Ohu5a3H8+a7giXEnGT2vxCqlWjM6uKVSwddWnw05CL+tu896I5FTHcz8v2lOCdUMm10LidqJlcxmscGSdYftR+jZi9K8Mott4tXrjuNInsnB2sl+LoXBgP2oYfx24MmEbE7c99hM5Nd2nDmes7gbP9vWZs3PamXOUDlDohGJZopWTV1GKOFwCMtfrDzTX6w8021LZ+Hr85PFmIBaC0t4OmZ99K9KNtV4qf0qaVE1mQjIzquMa3UZRXwex5MfQ36AFESoeiutTT+fjh2nfIqheEgw3pHiFlGdGRMo+X5cvKv24RrH7/qdJbSOAsCn5TgO6Qec3AQoYNnUjPhZbmdLUf/Ddi1bmRYx+tzCPcQn7QbUvdo7zrO9ufGGBQk+7xKjLJQh467nhdtF/frHcIBAcEvCvFOb0AYai09y0K4HPzv9qP15YF4JjejnWhCsvhfBxwpqSKARDKIzM6+C99x7GWt9wGVlvV/3hvfw2aHe9QMsEjTODJThbcs4LpNb6Y34qCMZDc28Z+amxOMeFfkxAJkx4JpfRITh8GtVdxT2UVcxXAr4lxPRhwUcz5lT+00Yw82pheQ6Qt+8AslJpMO7mylDDfpbDjmrkR9dykV8S0WTCC/jS9Yj0dP3Wjl5qs1Rg5XJV+mZjPIv4Gy1g1UZY0mYmQQdOUQ0z2sCe3P2p+N471VQX5tL6c/Ph7RpxF++e/UnTsD6XYjDYOaEQN5/K0H+WxsEf+SldxhL6NNJp+PyiqImMkhz6ju5cuBZwLgi6WovUfpsjfrEW6yl/AXYxvPvHgHEY+HaFxMJ+TzsnX/sWkNOajby2pw44QSr60wJZ6JLXj3b0TPSi9GFF6TReMzQ2j6cznhVdk9znPNwUFKHlpJ8cMrKHlkheop3r2W3hGEl+ciJeRfswmR2YMQki0ILemsVsj5nwqMkgjCY4PLjvdO/4YTbw2EmWjEnYig+a/l7P7ZJHb/fBItr5Sh9+uLroE6Ru/BdTQ9OYLqyydTfflk/B+W4Brf0pfOprTXnce2ZtL8t3Ia7h/VkWlqe6sfTb8bTnRNDlZVBv53+/H7WzJp86c+9m2yjRvtxTxor+Ihew3X2otY73w/+pPv9cj7AJcQ/7UzHoOktF9KHOD18kUotEePf+ecu2eUd2k6bQIHNW9Ob8SBqNBY4O3HIfHPbY5DfQ/1RoPCDUR1A3cPy9hCY2uwjRrLoiRVh5B0GHmMUm/rqjDXU6czw62Y6t8EVgTqN0HJWCUEkyo37s6CE+9PvX5bDYSSX0yH9P+KVzadQm2oAEuqiYvbBVMmCKZM0JgyQaO1Tar5xwnXc1HZP7G71e9LoYMF9Y+NwPvUcr701HCcPoD9MgfDsy/jPB3lV4F51GR1rhfFwSbGO04V5+hDE7bncqXX7HHHxW9O3vwUM0ffrBqgtK9nwpGHCJ6TG2kligPMaTiLJcefyhGbXyY3XMvSsqPYcPlYMs0anIhGbFMmmA6uEX6EBqHFebQ8V44M6UhH4JnYTO5Pt6L1UAbWDrvFoP7XY7BrOqe+oS+LcI9vJv+Xm3oMxLRrlaORuuNunK8nfDa5l1bQ9OTw1F62ppTk2qFnWxQ9uJLo2mysag/GwCCxHV78b5QpXXSjnTjZvq00oXgh8e7fkLBtJ6hTc/VEZFDvWCcwqz8iOwamnULmNXmb/tcGdoThZUCjdeZAhMsh98rNNP1uuNqsFVe069bPPOFzRLH9w4vzcY1spe31geCoGnRjYBBrt4emzZnM+tDh3B/pHdVHQggi0uZhew2hbif+SWcdD4gpZIs9bET0/xl7DXkf0M8wGOZysS7aS3nS9xB9MeImcHRGBotCIfpKacvQNGwpCfQScjeA8e7EMJzQtGSGFZ2TgtuH/4jZdfU8oxtM9Xp5o7W1x0nDDnc+rlSa4l0Q0k12eQtotO09M+S+fEW+++qP0LxDvWnLpqh682UvJ5LmdDeMPil93Xg7Im0qj51R1Cl+46+Dd29W3nRaVTqhxrP5M5XXLx2buK80RsStx3j8B7fzyqZTmVN9AK7sXI49ysA0JFfdrO6Qow8TnHCUTvVL/0K/FWJpxbQlbSuzWLdfM8fRKePaYDo0ZXno7mbZSJbJRs4h0ZAP6CcoKYaqnYlZG7cV4MQtzwBw5vqH2JU5jC8GnY0wHdB9TNxXcMFFkpvwK3GPbT4CnxQTMHVmjr6lc0NvO0ifxP/qICUgIgXCbZN1biWtzw5JyO+Gl+fS+PgICm9NX/+eg8nV2hiuv0dg13QjlzmCyPI8mp8aSt4VvYv8ePdrILSgINFI6w6eaZ1sdM+UJnxHVxP8oB9JF1ZIvAeo/rFWvYvA+6VEt2ZgDgqRefxujNII7pF+Mo6og5hA6pLI1/kE5xYiQxrRrZkpe5QLt03OxdsSvmt9uSzBiMeXRLaaaEVhHL8JoXTPkwMGybn0iE7rzDI1YRCALRA5MTzjmwkvKlDNYXRHnR8r8dhlRCe8NBf/+6UIl03BretV/lyT4AjsejefvTuYWruGNbIJgWCKUGx1J8UbxEHylVPHsXpqSeLvCvYa8j7ixvx8Lq6u/k8P438dqTz0AzweTsjK4r6Ghj7nwv2O06fImCkEZ2d3IZ8IgTb4QKxt8zG6eJwSaNPcXDX6QhbnDgUp+X1TE897vSwK95Stg0ZXJu8X7ssJDatxdWOuq2MVVLnzObJuJcN8NuxaovqW2zEYfCCMOwVcPWgvFwyFk34LsbAKZeumOk+OBave6GxMUjxKfbd9EQyYoLzzroj4VXlZ9RplgE0PHPBTZZDfuUGVy/UIqTTa6zcCQgm+HHtXp7BMZon6uy35Ps5yR7j06LVcOnUqMtvFr+6zWbO+k+hWtVOyYLHN3bd4cGc5KRVl24cgYhpF3VpCutFSvigBvGkSMXf8UufGu2zawg6WlEhHUFK2kYPfmYkEdGlz/deXcuHqO9m8zyEMeu1pBozIxS9jHR5taHEexFLVLUPbS4MTjKUM67T8aWhnB7F2WBrRjVlYte60va11BKEtGUTr0k0YBaEFBWSeWI1ZHkRKiG3OxK53YQ4JYJR2bjfngkpiFZnYjS5kTCBMiZYXJeeiTm6EEJB7QRVG/zCtL5QrL1yAdAQ5F23DKIoS2+Gl/q4xKlRva8S2ZBKaU0jBbetwDQuo6IBLIgDvAY14D2jECehUXzE5xQE6+I6sBSkIfFaE02riHt1KaFEq+Vd1vE5de1QiHdlOkC5nKdtMZJd1ZLNJZFUuJU8vgYhOZGMWzX8YhrS6X1sJhiRWkUH2+dsxBgbRXLLjN1Eawr5kPWtk+5RSsljWs5LGlIpuMSSt34PE6l5D3ke82NLS+0L/BUjloc8Jh7m9tpbpPh+zA4EeDbSGekD6YsQF8LuSEvqb3fLQ036M3biNSFsNGg4OGtu9hVw87lL8RmfIsiKmHrDBhtFriuCREadzYmEZbPoYaYWpd2XjSEmBFcCQDhP9VUzY+Aqiew+Sde9A1UI48bfJDUu6o2vrUyFg7Mkw6jj4+q+w+XPYvUL9A2XsD74WBnZRiPvsQVX33W747ahSois/qA9GPA5pQXtjH38E3r4ekMqAjz8DDrkWPr5b7cOKqMlEwVA48raOkP+qtZK1GxLZ6pEorNsIazcIrrlc475HHWIpTri0NTL2beUIbVzC99nCxTCy2ERrwr3hQuMokVrqc0A/wQtP6SxYAc82bcPZpwWnKIj9rBsR7Xy5FoV2kbv+n3zyNx/Vdz5EbGUu2s6BBAY2gy6VN9a9rWa6NHFHnXYihOFgN5lpDXkWJnX1EtkTQ9wRBD4vxHdQA83PDlXkNgHSFnimNFLy863EdImWpULhkZU5WDu9GANCuMe3pAzkZB5Zh2+/JkVmk+Ce2NzRs7vlhcHIUBdv2daQNjQ/O4Ti+1anHKKWYZNx/G6CH5Z21nNrEuG1cY9upeYXE5GOgJjAb/ZHWmmOVZ21Ln+nOC+GRLhtZKAPfcClhgzqRNfm4JnQgmd8S2dKoNsujf4h0CW+6Q1djHj8Z1MNpes96ADhNG8sNxqjRQ/suO8I9hryXrA9FuPK3bvZ+n+869k7gZ47B8GeF7h4STMhd2fy94Nv46uKxQwK1bHFV8zi7CFJTO/h8QnAOTk5vNbWhpUmWmACN5WUYg47H6ach0CiNe8i972bVUe1nsbfXou9fb5SadtT7F4JW+ck57PtGHz5GMz4vZKQbdkJjduS24o6Mdj21Z7vFwCpQvGgtj//aZhyHsx4SpWfhZqhaB9VD9/l3K5Zl6zsBsqYr1knOedHGo/fB/c87FBTJ1WQRkiECcXnV3FVzgj6p+ge9VN9JI/Ya2iM+RmycDUxJLUHTGCOUYtwBAeJ4kSW8KJF6I8/zvQdO5h03NG8PvF4dqyrQrOTX7pmJMqgt1dyU7MLo80mZpXiGMUY+dE0F1akNNiqlMFJUh+TloZZlqbbFxDGZt3gKixShLq7IPhJCcHZpXRv7hFZmkfjByVknaiiJUID95hWnKBBbKcX4bNVDr/Lpttvdy3LUs1ISOSlRDdmpRyLVelD2iQpGrcj+8wd+PpH4N2BNPolrn1byDxtBw13j0V2CZPLCOpcpfW4uyK1CI0x2k9sQ3aKUH4KOVYpsBtcBOcUEppfgDkoSGxbhppMRHU1DpdN26sDVchdTxOn6+OLSgMGksEYkUbm+DuEvYa8B7TYNmft2EHbXknWPiFeXttnaEIQS3FuA7bNU83NhHOH8VXusG47UQXOBnBVvO58kGnyVGkpt9bWsrsboU0DsnWdo31xwyIUYaagdm3fR2tFYPfqb2bIN87uQfpVKiM9+ng1WdCM5GWlTN0z/JvAjsCyV5TyXe5AGHpIIqsdCEqLDTkN4MpNquF1uSAv/k4bOljjuScFK9ZI5i+SaB6H/Q+xmDBgaNqSnRzh4tefN+CceQYxK4ZEYpsGf3jtN/zjkCmsEk38TI83z3nxRbj8cqUCJyW+r7/mwmf+Am+9BXbq6/bc4AewGlxY7aFxS8ep9VAyPELtFhdoSooTR+A7qprQpyXJeueaUi2TYdERYhduG9+x1WgZiddBR0XwJVBDmJriHXj3dxH6qiB9qVcqeVJUnjg4u9OQR6u8NPx6DDKsd9ymIitG0f2rEELQ8tfyuBcucE9oJvcnFej5sQRDLjy2yid3h+H0WK8kBNx7aH+KDzW5ylnYwTVQ3n33gWvs2VPfDVGd7HMraX2+nF4trC0IflGEVeXrvG5G12si4ves2k5kWa7qu961cMMGu9qDVhBN20mtK3YQZDchBvDdbmu615D3gLfb2tKWNO1FerS/4HpDDNjfm9gmcl0kwo937SKc7rzHjUR3Yu9+Xi8fDxrE0nCYhxoaOoiJ491u7isuxq11eXNJR+mQ96WXOSgDm5nckCIJUipWeVOlkkEtGQvBhvTLO7bymP114M7soa+5BPT0Cm57glgQPo7LykoH+k2EsSdB0Ugc4AF7Fbv3i8LfJyStqmtw8IHq/K9aK3n2HzbbqqAwH847XWNSWfqWnwDU1yNOPRU9EEjIil/9wxu5oeINVubBNumnPGrCFVdAV7XAcBhqauCNN2DMGOzly9C7eOb+zEzWZvwgyVDalqCt0k3pE0sJLstDCIlncjMiw8LalqGIXXGjINw2vqNryDiqhrY3yoisykHLipF5YjXeHyS3Ck11NXJ/uhVzaIC2f/VHtprKpnSE9ns2VCKg7vnodi/1t49LWke2mdTeMB4tw8ZpcnVMFiLLcqm5ZiJ5P9+M94CmjuUzjqrB/26/RG/XdPAdVterjEGlDLBJtPatAuwbQxDbkknWybsQXjvB208JTRKr8iVOMK24J95lm+1o+ftgXCPbEF4bzePghDVkTKPh8RHouTEKb0tPXoT2kLvNS/ZWbjDG9bjsfxp7DXkP2BCN9onVvRcKAhhpmiAEG6LRtPN0A9CF4NeFhWR2MbBSSn5RU9Onfu9R4C9NTUxpXqYUyiJtiPxypky9iFfK9iEQZ71naClcj8qvobmy7wem6TDiiJ6XiYWVHGtzZTzSKBRzPEW5VweEDps/hdX/QpF03GDJ5AmGYwNCiby0/42Ms9c7z1Wf9busLnneHV+rvP3QQ1m93wzqCeP4HApuWU/jEyOQfvWKyM4U/Po6kwyfYPV6ye332x3h95274YlnJMGgzYnH9FBuNHNmymoEpGTq658x/9JT2SBbKF9dnfpYIhF46y0a33mD2JFHkFPdgCMERtRi7o9PQtZrKZ3DqCMRORYZh9W17w4hoODmDYTmFRD8qgDd5TDliAhbJlQhBORd3ocWsikgNMg8tobMY2uwm0zCi/MIfFGEVdFzhz3djjK5+WOqYqXU3zs6jeEXEDJw4uS1hO9tQdPTw3HCFWQcpiYdWafuwq7xEPo6H2E6yJiGe99mcs6owMw0AAAgAElEQVTt/d7/p9xOS5f6fmNQUHn44W7X14jfj0m58o64QJfPqe9OvTScgrSWYp20SnGpt+s0uai9fgLeg+oxy1XJXWhuITJk4NTbxKq8mAN7555sIh2z87uDb8WQCyGOA55AOWN/kVI+0O3364BLUHykOuAnUsrt8d9sYFV80Uop5Q+/jTF9GxjrdvO2398n7/L/InRU/jmMaqBiCsENBQU81dSU1ogX6TqnZ2VxclYWg7uR3DbFYjT13n+yA1XNu1SouL3Eq34zfHwPHHcPGfnliQvbFuxcAv5axRq3+tLmRVP564OvUka5Jyx/OZ7j7uJVt+yiR9qfAAJdPL32bmYph6IrBn1miSLg2ZGOJKkEnLhx3xMhns79RmHrl7QNHU0sTnB3DQtQ8sRyrJ3Ky55RVshIQ3Ure/5lOymHHonC32ZKjjtKomtpphONjcoYd4MRjZHR1IqOIAeTZTtzGRO0SKUNtjUzk8qyTF5f+yr9v1pBTk0jW/cbQ/OAIly/aSW6Lov/x955x8lVl/v//T1t2vZekmzKphcSCCGEEiD00DtYUFEQwcsVVK5yr1yvFxUrP1EpiiKCoEiR3gOEhIQkpPeySTbZvrM7O33mnPP9/XFmd2d2ZjaLgOWaz+vFi+yZ0+fMeb7f5/k8n0+G65Vqox/tZEXC75QRfGw0do8LpSxB4WXN+BZ24V3YxQIqaRAF7Jcja8kcCdTSJN6TO+l7LI/meGpEoZsRihI9XL/s37l53UupoJjnHkryp+1NheBjY/Ce2IVQQGiS0ht2U9TdTLLFjVYTQ6t0vrhDaTwEcrBX9IYw8d6051OAPimI0CTJ7UWD6W7DwmgMOTPopgKUwiRKgeloow/pCFBLE2g1cQovOkDo6fq0lLmdY3DwwSHjKpEl1dkfqBKz1T2iQG78E6iIfOhALoRQgV8ApwEHgFVCiGeklFvSVlsLzJVSRoQQ1wM/AC5PfRaVUs7+sOfxceDcwkJ+1N19OJDngEcIbi4tpUBV2RiPM1bXOcPn4/KDB2kfJhh7hODGstw+25aUWa+v+mg3B91lWUQ3zTY5r3VltrmJlXBY4kd+wqkFK4qTun7pW05a2U7mnhWCk0IvrHGU08bMg/o5UFTnHNu2B3u6c2HP0mxTluGCuNrfazwkbEhAaA77PGNXqb/thHONaXVzAShIkigoqZCeAaGS6k3K34NuJRi/fwtG2YwBBq8QoI+K4kKhWhlk5O/NM6ELheGLt1hMbhRccaHCqLohZ7JoEdx5J36rgCcn3cSGqpOpCu/j3H2/YOvJcxEIlgX8vP6n8fykeAaNPWszLFKjqpdHJ3+JZrsJW8DuBTMzdl/yhSa6bp+OHZeQ0ihXipIUXbmfwOP1hJ+upz9A2n4XgQfGAeA7oZtVdNMhYwPfhrAsZr68kvErNtFTX8V7ly8akRnKUNgBPf9jkPpObEXjys3fwasGnfVzkfDStxkmBNtRFTusZajOqeUJ1PLMkdcHLRiGX6omvnWILKxioxZYlN64i+i75USWViBUiffkTtxzezJ+sla3QcdtM5wZfVIBxUZokpIvNCEEuI/sJfx6FTLu/MaMmX4Sa/9a34H+mfzQrEDaGpZAy0Fe7O+66YeOwokix0DgHwwfxYx8HrBLSrkHQAjxGHA+MBDIpZRL0tZfAXzyIzjux4pdiQT39PTgVhRi+V78/6IwgBO9Xi4vLkYVgvMKnRfcK6EQQTtft7DzsJ3gzU8amWQYeBSFSGogUJ4I8uzan/LHmmP4WcMZRBUNhILLSlCRDHJ+x/u5d9SxDV67w5ndLvyqY+EZ9edeNx2qAYvvHFRek7aT9t7yrGOCUljjuJTVz8nedqT19n74yqEvhw2kbWYNWoCUAcpM2PpCzmMJwEh7BUnA1AvQPSVOvb64Hjb/Jf/5CIUqrRg3GomUIlr/fl2oHCUGX6rVVbBnb/YupIQDLdDSJlm20uKH31ZpHOdciylt3p43Fv30s7lb/oyoWoiputhZehTLRl+E8USU6oZdrN6kIJH8z3FP8J2l51AX3IUlNHQ7wZ+m3Mq7BRdQzXo8qFkqXFpVnKqfriO2opxkixu9IYJnnh+rx8gI4gNIqvT9cTS+E7qxkQMzLz0a5+uLbqRu617coShxr5tLvnkPP3z1Z+w/cjIfBEphcvigKRQsYfCrOT8hNknFmBIcoSJi7lS1UKXjdf4RI/xKTXZq21KIvV+KNBW8x3ejj4kQfrWayJIqrC4D70mdKB7nSVLLE1T9YAPhV6tJbCtEq43hO6PNEcvZXkD3jyZBRBu4psTG3IP9QyP9vuQZEOkWrml96HXZssTluOklgYbARDJdlHCh0vBXnsvfDh9FIK8HmtP+PgDksXcC4BrgxbS/3UKI1ThTk+9LKZ/+CM7pQ2FbPM6nWlqISnnYtjQNCnCM283XKyqYZGT3VDebJvFh6tseReELJflbOVQh+HFVFde3tWEDplARwKdblzM50sYjtQvo1n2c0r2VE/3bKLWGSYuZMTBjtL35UyoSvSN70BfekimfuvaPjvxq/6w/2OZolS/6htOylY76I5H7VyLyqq4NQbA993LN5bSEdWwfPK6qO2S7hvmwfyX4R1a/1cwI9IWg74AjcDMcFBV13PHcppbzW2sn21J1wUkU8ll1IkYa9fdTlyl8/y47Z4saOMmLWBzuf8jiB7dr7LND/NjeRBiL3tN+QvStUmS/21pq0JJo9tB863T08SGwBT2eWm48fQ1jezdSEu9gV+mRhIwSXKMdMlcUKyvgGZEYjcs3YukqOy+bhZ1S6Au/lp+oKHtcSAmWkPSSQEVw+k8fY9Sm3RhR5wJdEeeF/8VP3M43tzyae6CVfv0hFctvoFbFUdw2vkUdRF6vGt4NTLV5+vwb0OuDiAIT6VfyH0eVaBOCmDuGzJANC+9pbQOmIf1QpM1xvbuYFTqILi1ajGLeKJtCl5GZYdDiCRb8/kXm/fF1YoVe3rzuAjadMd+5plyM9X4kFKLri+m5Z0JKSlUhvrWQ8Ms1VN6xaYDtrxaZFF18EIBkswf/jyZjB/SBvvSMa8lZMx8Jct2zwfshvBbekzoouvRA1louFM5RRjFRFNEmo9QKD1XiEATOfxD8TcluQohPAnOBhWmLG6SUB4UQ44E3hBAbpZS7c2x7LXAtwJgxYz7W8/yJ3/+hLDn/GaEx2EozFMWKQqNhcEWRI6LyTiRC0LI40u1GpF42fZZFn2UNW01K2jYRKUlIiZHnJXW0x8PLY8bwXDBIp1XEgeIGxvQ2cUxgD8cE9rDLU8XawjH8avRJXJVoZ/aBd/O2d7UaxXx33GJ+tv2Rkd2EmjQLSiuRGcTTl69/3JFlTcPaGZcxqnUTPjOG104SVXRUBIadJ9rlCviK5jicjT/JEYHZ8QqYCeffUxc7AX3aOdCyPuOa8+pmjXRQoepw1KeguI4y4BZtBsnUtnoOFZL5Ryn827WSBx6R9AbyVyq27pT8p/k+rQwOuOKb0oJ45tlCQiG5rZj0p3BvSVr6XLMovNAJBAaCdDrlUU8s4XPXfBdbVRBSYho6dz91J7uPnZHSPc/9vAlfciBetqTOc8EjLw0E8XSUHOykYm8rXeNy24hKU9D7wFii71Y4DmaWoOCsNgqv3A+qTeTVmpQhClnnE9e8JB+cgPp8HDuoHXKwUHh2O/aJXfQ9NsaxOlUk3tPaMCYH6bxtOmanG31UhMLLm7m4bDnjol3oqTxLfSLAFe2reah2Pn2aE6jUpMnXFn2Z0Rt3Dwxcpr6xmte+fClPfec6XLMCxFaUZ9e4yxPgMen99fjMGXtCxeoVhJ6voeiyg5n3KSnoumMqMjg4A/9okJ9Qp5TH8Z7cQeEFrXlvrYmkyQ7RqBZxhPLXZgT+PvgoAvlBYHTa36NSyzIghDgVuA1YKKUceDtKKQ+m/r9HCPEmMAfICuRSyvuB+wHmzp37sUbZNbHcTlD/V6HhmKLsSCTotqyBhKUB1Ok6T9TX81Ykwq0dHQM1RJcQzHK5uK+2liXhMN/s7EQhr+Ii4JDizm1uxiUE15SUcF1JycBAIB3lqsrVqZn70vnX41lyBx4zzrcaL+Kd0kkAxBWd14Tgm3oRF+x9Fc3M/s5+X7uA7+36c94xesZyT2nGy9OK9JKXPBZoybwu2+a6QBx55C0s7ljH9NBBdvmq2FrUwIMb70MZaR+4bTpkvGU/c4L66f/t+J2no2oKzPs8rH7QGQzYVirVPkTBamRHRAJLpi3gnQlVLLBaOEmpQRNKzgCejlNOUDn5eEkkKvnEF+1cHDZkQTIjiIOTara68llcpqdEh1yPz6TsKzsxxkUwUGikkC04aosVTS18/rP/ixHNPImvnHMLN+9/GmNaH7F1JTlmeZKC87NeVdh5uBBCSmw1/33pe3S0o5GeVAb6t0Mv1qCUJSi+8gBFlx4ktq6Y3l82ZvevWwK7x+W0lR0KlkL41WoqvrkN78Iu7JCG7rUIrSql9+6JAzP/xPYi7HtqGfeVLnSRKXykSps5fft5q8wpFRz15JuM2rRnIIgDuMMxTv/pH3nzugsxLzeIbyh2zjupoKqOI96Us/rY9tNJyGiO+5JUiK0uywrksXUl2TPwESFXzduRZMUSw3ALBHa3gff47rxBXODo/r9NG8utDm5UpjJd+ccXgunHR0HHWwVMFEKME0IYwBXAM+krCCHmAPcB50kpO9KWlwohXKl/VwDHkVZb/3vg/p6eYdPD/xdhAu9Eo3SkatMKjlHMZ4qLeay+nvdjMW7p6CDJoOhLTErWxGKcvHcvt3R0EJOSyAhKERYQkZJf9/bySN+h2zqOrRjNDcd8g0/Mup43yqcRVw3iqgFCEAdur17A/GNu54l5N2VplxdYcdQcs1IJmOkvA9UFR1w68GdCSq7pjRLPdzElaeNWM8HezS/ywy2/45a9L6JiMznSyrXNb3LD3pew/9pHyTbhlW/n1rZvXAiX3A9zroTqqYji+hShTSElTTbiwwhgwdblHLQCvBLZwqPtL2NHRyZHLITA51U4e5FgqMquYtj4zsrmABQsbgV9hAK+hkXNyT2c/c0uxty3juKpETQEx4pKxjJocXrswy8hchEsbcns55bhPbETtTgJSvpxJfqkIIWLs0scSz+7mJjHRZenjh6Xk5a3haBzfD3RMXXZL00paVi5lVN/+TjH734aPT2Lk1AJP+vM4IUm8cztde6LbiNcltNj3n+9A/8/9PfX3wYmFCdlbamSvkfGZKXvK0t6MRPZ8zUVSXWaDeys55fhDmeXqixdZdLSdWgVCap+uIFJ5/UwbYZN7Sl+9MXNrP9DOfH1JXmFbqyIit2pM6evmc+0LOO6A28zuaUzW89+RMi8N4piMX3qDm7/zk846qoVQ8Rh0iHxnNCFWpEnO8bgEMHGceX7rb1zwB3tnwEfekYupTSFEDcCL+N0JP1GSrlZCPE/wGop5TPAD4EC4PHUDKy/zWwqcJ8QwsaJH98fwnb/myJk29wzTOvUvwL6fwpVqspN5eVIKflWZ2fOe2IBvX/lwx6Vkl/19vLJ4uF1jDUh+GltHWfnkOUEQEqiQnCHXsPiqmkc7GlhZfE4PHaS+lgP7iwmOVgI4oqOZicQriKYfRlMXDTw+Z/6+tho2jxQfyLXHHwLb9o+pGogZjt+2CRj8OJtNIY6mGIlsHAe4v5XTWlgDxKFbC7sCGEn4ZmbU6Q46bDpZ18Ok06HtX+AXa8PttGphpNVaFgAvlJY80hGWSA/f9fp37/+nReY3tZMUlXBethRsZt3zfBM/dS2Yy/pQC4vgd40+U5LoI3NlvV1z/dT0OIh9FR9zhRzxrrFNr+8rgwFQbssxAKqhRuv0Fhn+9HtFpJIfP4+9EQ2AVCxLDy9IRSPTcUdmwg9VUd0dRmK28J7aju+UzuyDwq8eM5neH7lzfSKKkDQENzMTZuv5aFHv8aFNPAYe+m/o2rS5IaL/4Mpb61DxCQhvRhvspdXx33GsXYFrL7M12zRpQfxndJJfHMRvQ+MBTtX3id/mrhuTAvHnL8Stz/IDl81ra4SZELBDmTP5js7y9GU7ABnIejQB82K+qpKMTUVzRyyrhCEy4qwIyrBJ+sJvltK1LZxl1hE36zPbUiTdg2yx6DzliNIntqHd34Try05jk3vz3JKDB8Stq2yfecElLDCBVOWsr9mAp0HKsnoXXdbFJ7Tiu+stkOK4KQjhEk3cSpwH3rlfwB8JDVyKeULwAtDln0r7d+n5tluOTAz12d/D+wYRsTkXw0b4nFMKYlLOWw72YeBf4T7/WMwmPczQ5p8d/vj3DnxYm6ZdjXvRMIIaaNIybzALk71b8Y3pE5tCRWPncCsnIx+xrez6pHPBoPEpOTe0acQ0D184cBblCVD7PbW4D76asZWOul9drwCoQ60VL166OvYeVV9yI6HvrQ0fiIM7/0WuvfA3mWZSnBWAmIBqJoEo450Plv3GCgalm2xxyhBIGmMdmYdwrBMprU1o9sWen8ZYM/bDrN+5kUD69lS0kUMFyrFKX/m5+xmHnpMwQxk1julBT0/m0jl9zeglQ4GWSGg6OKDeBd2EniwgfjmYicYDA3quoXrxA4et0yW45RtJHC6qOMCZQyzRCl1eDlAhE1nzueE3z6fNaMUUrJ10VykDfFNRSR2FiDDKrbEURHL8WO3gxodP5iJTLOy3VUyh38/ezljJm0iLmxcUhlgzJ98z5NMeWvdQEr6zvkPs7V8/kAQBwmmwApmt4R5T+yi7/FR2P4cgVwAipUl/HLKwnc4ddEyNM2EIMwMtLIuNI63Gsc5BiRD1NF6eovZvncCkyfuGqiRA1hC4f2iQa7R0s+dy0n3/yUjkNtA0m2w5aS5dH17KuYBT+p8FKLL8rmeDb0IsCyVJW8cx5pVs+gNFGFZ/ed4KAmjQ0scqYpFMOSjxtfFl6/7HfesPZ+2V8dijIvgXdQ+YK6SXimKby4i9EwtZpcL1+QgBRe0ZJnhSCSuv06V4e+Cw8puaZBS/hMY1v1toAvhSK1+jOmlCUPzsXmwPJLfBb0mHuBk/xbam0v4f2PPxhQK/b/ad0ons91Xy9RwC57UrDouHEnHsF5I0fE35iQV6f3LhODR2gU8WrsAcHrgH61O8yXem59o9/FBwu63nRr6UJgxOLDGCeTTzoHGU8C/h+8F4zyqlHBsYBd3b/39wL0ASAgFFZnt124lkFtfRKQC+Ua7h9/YO4lhYSOZQCGL9k/kd8/rhN+pIGevbkij46Y56OPDlH15F3p5gnJchDBJViTRvrrTWa/dTfu3pyLjTm1ZaBJ9bJiCc1p4B6d22Y9XZAsFts5pah23qjN5yT7IitNOYM8Jsxm/dC3usBNQYz43b19zHm21DXTeOg2rpZ99LJAxjeBTdZh7vVTdtJv0BrHIO+XIoU5pKNhxFc9Gm4b6V/l8/CBRRWNN4RiO/+2zA0F8T/EstpfPI6mlM50FKJLIkkoKz8tRajinleAfR2fWzA0L97FdxFdUZJxLWWkvpy1ahq4PDggM1WS2p4kVPz4G76J2Iq/UZKTXhWHxjDGL0wtjzEyx1ltTrPW+tPPsmjae393/H3z6i3diqyrClkSLfdz17I+IbC93hIHSBxUfUGM9mVTp6S3CttOf2/wMc6U0gd2nZ7vW5UBFudPJoOsmM8/YBBdl9rD3v8KkDeHfjiL0Vg225dyjSIeL6HtlVN6xCa3aCeYK0EgRhWJk76d/BBwO5Cn0Wda/jFXpoaAD0wyDz7S0sDEe/1iyFC7g1vLB3uSobaOl1OGGokrT2JmyLHVZCeKqgWEl0bD53s7HcUuLPe6KrEGYFArXzLiGq1uWc17nWnQrQZO3Cv/o+Zw9fVG2H3gKlxYVsa2rK0tnv0xVBxzXSEYhkN3C8jeBkuclqqgO670fhhdqZrDOPACJBO+WTOQXoxfxb/tfdbgDAnZW1jGtI/d1WMkQD1o7OE3U8Ut720B3ubTg3Z9Vs2S9AsncQdyBAEuQ3O3Df8dUvvjjXs7U6gmR5CX7IBtlDyUYlNYYLP9/64i8X+r4c48PY0wJIkW2nnkCmxflAU6jDpdQOV8dw/nqGJY/8wS/uO8ZDrwxhVatEa0uirg2RPh31VitOVjrSZXk2jIu6TB4r2o/uwkBYHa4c0qBlhX38omSN5BRE12AblkcF9hN/Oqj4GtNAOwvmoqSSw/fVEjmkWj1nd6O3aMTeqnWYbqbClpdlNiyyixls6lTduakTOi6yYyxO3nDfwS+xa2EX6hF2gKh2xRecgD3Cb28zSTeThFFh0IDxlHI6itO5/3zT2TCik3EvW72Hj0VqSgkHi/Io7LWT0ocSc5aDAni/UjfXqK6TeruWItVI+l9sIHo25Vpg5x+hk6q319PcNYZS9BStXErodK1v8wp2GZcn0VjqJOy9yQvvDtrIIgDYCvIOPT9eRRlN+zGQKEKN9eque/VPyoOB3IgbNtcdvAgrf/iVqX9SALrctGQ0zAy0Yr8+GxJCSEpWRWN8r3ubnYlEijA6T4f36qszNBg/1xJCWtiMWJS8qX9r7PTV8OYWDeXtK+iOuEQdixFy3k+imrQOOcSxlmnkWjdQJ3hQx0zL28QBzi3oIBlkQivRyJInB+JJgR3V1cPsuxfu+NjmI2n7qpQwFcFoXby3mVFzVaSE2pOh7YFHg/tkQBT+pq5rnkJSImKxJSCRn87nQXFVIWyB7F7yqtZLbvYLfsw09Ky4deriG0oPkR9NA22gh3Qqd9ehzJDUITBZeo4LsNRVtsnQ7ynd+E5Jr9oj9nmwuxwo4+OECrN/p16dlfy+srPIPUUS6FVwndth1Qlc5+nqoG7uZgp1SXsk2FMJK7JQSd4DNEUP+X4d9GwMmZ6hrSJnj+d6A9fwdPRx6jgDuxcbH/dQm/IzirFtxcQeroes92Fe64fz4IuhCHp+fGknH3UpqmRrX3oYPKEJl789cmUPbCawgtasMMqSqGZ08N8KCxgpihhvwwT87jYdvJRWEGN0B/qiK0uHaaHXIKe0rQdTo1uGCiqha5bWKZKw9hmFp/7OnWJTlb2jmXl1dBwZIyit0fTbAVxzzhAyVqbPU0NFBcFOWXhcqZPc7I6li3o6y1kT6wOrxg0jikwY1zZvgrDNAmVFvCsmeNabIXk1kLq8HCNOpEGCnJ20/wj43AgB54OBumyrMMGKSOEVwg+X1LCfb29fxXDXwC/DwSwpcxoTrKAV8JhWkyTh+sHU9jzPR6+XlbGj/x+Ks0IV+/8c0a9D2Bx73Zerj4qaxYtpeS0TY8impbiktIJgO/9xhF1qZqS8/wUIfhBdTU7YlEiG59m8t63cNsJRN0RjvSrUByXs7wXqDoXmbftTHFMWPYsHSSkCSUzBxjJdtsaQNVUmHMFLPmBQ3YTwtl2wfVQOERO0oxzw6aHuLF5Naq0UNJCgYZEM5OUSR1bNZB2ElVKLCFIqhqPHrmQJJJOMrMykTeqhzGwyA0b6MoTp3tkHCXP0NCOKvh/OonEjgKEJpGmQtVxAazrMjXdH3yIIYxtAcmhzliZsGxJTaVgmlLDa1YLJhL33B7Up+KYbe7BYKpbjBnViprL31q4ME86ivgLK2nsXcv4wAZ2lRxJUu0nSUmELvGenEmsi7xXSuCeCSnSl8DqdBFfW4prjh+ZzB1ENm2ZxPnnvJJ9CgKqqrow3M6zJDSJWvzB3mZ7ZHDgubCjCl23zcAK6GkDitz30XNKO1pxkuCTo4YMPoaunzuN7nHF+a9v3I2qWRlrHB3cS7urCHViN4vK/4zbSiKwUSbm3lNfXwH33P8pmBdEHN2DNMDq0Vm4dxve0gSqIrF9Mew8gzpvic3FSgO1eP/pgjh8NO1n//RYFo0etiv9AEhISa9lcXFBwQcaCaZTXMJDgng/ksDWRILtQzIClxcX805DA9PmfQLV8KbViAWoBsdOOZUzfD48Qjh9sqlPj/Vvxd6zNKVPnhxQfGPJDw8pqzpp1f3M3vkCnlgPIhF2auLP/we0rGPYdKKnBBb/MP/nE0+B+V+ARf/hqLWV93uupz2DOXrDARg1FxbeDBWNcPG9zoBk4S1w6a+cfQ3F8ntwHVyDIU3UPPM5LeJHOeO/Wdcwlf0lFSwfN5X/OeMK9pc5rVcKoKUT2f4KMwthCSY3Zm+3U/Zxr7mD4E4viV2+LJ2c3t+MI7G9EJKqQ+RKKviXF/OXFzJX3LMv/7ENPca8ueu46PwXOe7YVbjdMRCS6nqbxvGCInS+qEymDg+aBtW3b6Hu7C7UijhqTRT30X7au8qxc7RMSWnheehJXA89TOzmm7j+zBVUHdfrtNgJiXdakOpvb8kIrNKGvt+NTQ08Uvu0FWRcIbGjMO/sNhz28eifziOR1IjFdeJxfXDsh0LpiS05tzsUJLCWHsJhiCytoPeBsQ7TPiMw5zgnAYm1JVgdLnyntTktdR4TdBulIo4xsxf38Z3OrD0PTFOjt7coa++GtJkXaOLsrvUUWHE07LzUM8sSrF0/A683yr8f/yglMozdqtP59Zk0lrSgplr8PO44M6ZtR9MyM1m6nsBz/kF+be/kK9Z7LLOclsSINNkp++iUmRoVAZngEWs3XzdXc7u5lnfs9r97q9rhGTlOz/RIPbQPw8mk/bGvD7eioHJot6hjDQNdVYlKybpY7JCEQg1H7nWyKzP97VIUJpbWwbk/gq3PQ9tmKKyCaeciKhr5Xym5uKiIe/1+VqREfS5sX40nl7qatKBzR7bUaj+C7dC8aohHuHRq4xtTtqP5kAg79XNVz+0x3rXL+X/1NOe/Ha/B6t8dOlWvGM4AwEjp1StK3qwCAPEQNK/OYeYyBEKF0rGsXXAlK2VnFs9eReBFJ0gSC4lnQRehZ3K1HuWrl0rKK+WAiUowJGnvgJpqeKipi+a7Zg/MQoUuKf33nbimBJEJQey9sqw0s5lQeOI5yUXnDCmm3zoAACAASURBVC4rKgR/D1koLA9w07UP4XHFcLmSxBM6p5/6Ng+8cTHf/8xYnrOaeVEexMJGRWEhVbR4Y+y4vInqlK1TdHUpS56ez7RJuzCMwac9kdDo8k+iZ0KM9vNmM/b84zlSlPNroWDZjnlNTBTxG9vDWjk4bLWDGnY4x6tXCmREQ7jsbMGYFLZsm8j3fng9UyfvZuaMrUyd7NTnE0kdcd4I/ATyILKylN57JziJoYTI0xcuUTQTieLYjkqwOjxEutwIw6bqto2MC/mZ6jqIqzLChoJRNIlK2lbmcw4UJJI6L7x8Ep/+xFNZn5YlI1k6ELmeLstSWbVmFp+68ikKXVGu6niPXzx6FTKWfQ2XXvw8lqWwdftEVNXC64lyxWefYkx1G7IZdnqq+HNpgt0yyHI6BzTXJ1DIDeoUJPA/1vqB3wLAI/Ye9oswV6njh7/JHyMOB3LgyqIing4GP1aG9v81xHEkV6caBkHbptk084a2nZbFfZWVvBmJjEg1LwkUKArrYjEmGka2p7i3FI7K9t0RQnCk283OZHJgUKYPN+se7rOefc6sf2ggts1U7XoYCOE4reVLeEnb8Slf9VtoXgPYIyMcCAlNy2Da4hGsDMT6ctfS06HqMO4EUFTOlaNZa/mJp/HEDRQuEGNYoFTxnN3MOumn9uwAzatr6WtXiMVwZp+KdK4hZ8pd0NOusv+AzVPPS15fKkGzMU2Q1qiMoCFj4P/BZKrvXussyMNa9gcklpSoqTTo4vNsHn5MZrG/L/zMCxQVhVBSV+QykuhGkq9e/DarfB6elwcGSHwJ2+LFnWFkWMOYpKIUOE+Ra2aAlnsm8NuHLuOi81+irKwHy1JZvfYI1swejWI3EcdpS3uK/dymzqJAcUiRXhRuVKdyo7lioGVtOFMTpTSB0RgmtrLMcQJTZdo9ELjdMU48bhUzpm+jssLxuk8kNJ5bdzxiSu6HqMiMMSt0gIpkjLirhs2GzsRICy7bZKe3iu3JWnrvnQAJddjH0DCSHL9gFT5fmNVrjqC1LVXGsQVK0uaq8EqqKroxpA1RGB/tYpengkeOaSC+shIrR8lASoVde8ZmLU+iYAoxqJeTsU1qHUVBWIInnz+DWMxFZWU3igIuaeEKSK773B8QQg74z4Pz/X/6E0/SF/TSGyiirr4DRbHpr5pMibRTl+jjwdr56NKmJBkirLrYpUl+Ze+gkSLCaUEcHALmW7KNs+UoSsQIlPk+BhwO5ECjYfDDqipu7ej4l9NY/zCwcdLga8eOxW/bnNPcTDjH/euyLD7X2sovamr4VW8vsWHusQvH2/xLbW1oQFJKTvR6+XRxcYa2ez5IKekwTc7veJ9rDr5FVbwPE0EWFU7K4WezBVX5RcQPBduCMfNhw58hPIQ0qBow/kR48TaI9DiZgZHCSjrBeaQoqMqv2S1UZ0ZfPd1xdAOqhYf/UmfxpL2fXbKPYgzOVkZxtOLMqK5Ux3Ml46EAzO9KHnmvj+c3h6AijvfETgIPjnVm0LlcuRS490GbLdshmSRtNp/rTQ3RFeV4T+nIP8Cx4Vu7dnLHRIddvPBMk2cCnfS9UAOKM1byLepgSuV+lCHPmwIk4gd4yd5PIiVdara46f7+FOywRlVlJ8fP20nNlFZa63ysKxxN2Vd2sOsnk/jBL7+AoSdJJnTqzulGTtw7cIpxbLqJ85S9n0+pEzKOWYOHphQzXhgSz3FdRJeXZwx8hMui4LxWvMd3kTipg9jqUswWD/HNRal6P4TDBax470h8vggg8PtLeP3N49gXr6L6ig1UNLUwauNuOsfXc3DGeOpjvVzcuRZVSgQ2RDuYgj3A/R4f7WJGuJ17ldlYw/RNK4pFaUmAs05/0/npVPp54MErBj6fPWsrlb5eJ4inoCKZFO3k5jMe5q7YJwmtThdrGcSo+pYMwaJUNZyiPAPQ/ke6wyjk2bKZtFVUowwxivnCZx/LyJ5I6fyXSOpoqgVSEGk0UeKZMswqkgIrxhndW5gY7cBGoCBpNYp5sfIIYopFLj87HYV9MkSJ+PtotB8O5Cmc4vPhE+JwIP+AkDjkMEf2ND9MKek0Tc4tKOC5UIhoynvcAKo1jS7Lwqso6EBninjYHwJfi0R4OxJhqsvFr2tr8Q6jNiaE4KvNr3H5waUDimw2aY0rKStUjv9yyhM8D8rGQukY8DeNwKI0rQ2n34DE8MCJX4HX/tcZEFhx0NxQPt6pocdDOYK4cGbQkPuYmgtqZxziXNKgajDnKljzcJrKm3D2c/TnoHrKIDkuEQGhUKN7+ZKaf4Dj75U8+ZzNuo2SeAW4FndhTHICVNGVzY6OdiJd386BtGHzNkhkvZtzzNKSCmpIRxcCRU1iW7m/p/U/r2XfXSEaRAG1wkPxpQfxnNeC7TdQShMobhu7OX9rXBATUJASuu+cjNVtMLGxic9+6s+oqomqSkYFFI4IHeCxKfPRfx4mvrYEGVcon9WHXZHd2WEhWSW7+BSZgTw5pGBRcvVeZEIhtqoMXQXTlhSc24L3+C6EAO/kEEdMVljzvSLiycwA2+0v5U9PnDvwt6JYTFnQxRWf/DZH/OUtTENHNS32zZ5I1yNXoWU8Z3aG0KkhLRo8nSw+7Q2efe50ZFp9XggbTTORtsLo0S186sqnEMIJpLU1meS96VN34HJlB14BFGlRPn/ln3giuZiWTaOwrP7+bMmlFz3P3KM2pH6fzrH1ERXsoD4eoCHeQ/LKJpJnG/j9BVTKAIrAEcxJPw8Btq0QDnkpLw9QXBzGF48MZGrSoUmbyZF20vMTdfFezuzaSHPVqTlpmRaSMpG/E+bjxuFAnoKUks7DvuN/FZ4KBmnQdRLDDIIsoMe2ub2igjMLCng26LBkzyss5OjUTLvFNDmnuTnnTziBM/v/RU8PX0vrPycecjzDm1eB7qV53IlceeBt3HJwLwqOtnqkaBRFE46HcceDL1/dLg2LvgEr7nfqzNJO5fSGXKNQoXaWE3g9xTD5DMf7e83DsHc5GAVQ2gAlY6BmKtTMdNzTcpi8IASMXwj73k0x3tOOpWhOBqEmO5DbUrI8GmVpJEKxonB+YSH1/f3uk093rnXTUxDxO4z3Iy5xvNUBevbD8l9AT8qJuGY6HHcDPW4Pj9h72Ch7EAiOFuWc2juOr98qiETBNIG9XsSGKRR/fg/e4/xoVXHKb9tK9+3Ts87RtGSKhHJoopxuwDenj2Haq1v5t66N7Co5MkdmwWF6r2n301BTQESaWEgUl0SpHby3m321HBE6iJbh1a5Q5J1EpfDSTozkHh92yLlfl138PIYxGJA0YaPYNscF9rCnfD6tx/Wl6qP5oQy5Rr+M0z6E2ikMSdkNu6nuWseM5i7GlbTT5XOzMjmBVqMQCewhhPD2kx5z3zchbHTd5Av7vseEZ5aixxIYMYdrMXr/ARLxCOjDdxgoQnLcMWvwuWM8+vj5g9fuSXDT9Q/ic8UoLMyU2+3oypx5hqJeLFsMEMsy74fjuPbFix7nkfgF7Ng1Hk01mTplJ0fN2Zi2Tb9XQ/YbINcdEMDZ/s20B/fT7C4lUKRRHhUIZE5lYSEkZWWDbZYa+b0hhhYZNCT1sR5esNqQqj5kXSfjMlrk1gr4W+BwIE/hcOvZXwcJ3N7VNSChORzmpgL2fI+H+Z5sn98ey0JjcCY+FAkpeSYYHAzkyajDIo/2DMxgq3v25zwTDYlXSJhxwQivDEfb/MSvOCS0aB88e/Ogtnk/FBXmfWYwMFpJeO7rjotZ/6w6FnDq1HNS7Kniemd2PjSYq4ajq56MZV+DtEHRoXUj1M0aWGxJyQ1tbayJxYhIiQ48EAjwvcpKTve4HeKdqjlZgopGMrUqg/Dy7al6fgptm7Bf/hZ3LP4kAdE/j5SsSHTzyr3lBEPFaaIkAplQCfxuHJ75fidb77aH1HUH1xVuExnL8coRcoCp7XbBkUcoTJssEBd/lXHer7Cr9KjsbVJ4jRZG2SoP2rtyPn/LShqpTQSoSIYRUiKFQFELqKs8h8tFnJ/ZW7EjKghJUWGIAl92v7cCjIp2sjOyi1NCB9CkyTZvDZsL6rGGNGprCBaIyoxl0Txhf0Kkg7Ojm9DLnbtcGAsxurWHP3qOoaPKS1zaRBO5rspGUSQed4zx45o564wlNF7wCFo083nSgzHECDOMqiqZOWMbby+fx8GWGoRhox/fS/M4H3OCmSQ6CeyNVTss9ZSIzTptDEer61HzHE/g1Kc/d/Xj9AYKCQQKGVWf3dI3jLRQ1jn0L6tOBqlOBpE4GvL5ZGqUHIOMD9J/YQuB204QTQvkGoKJFHGdOvkD7Omjx+FAnoIuBIVCEDycWv/AcH5A+eERgjN9PiYYwxNBGnX9kIOBjAHXriWpIJkmWSlzk+5sIOyrYniLljxQDSiogJO+Dm/9aHC5lXQC8l++4sx6Z18BSGfmm54atxLQvg26dzutZmPmwfuPOMv7a4pCBXdxSls9R2ZI2nBgtRPIpy1m77QLeTIYZEM8zvpYjH6+exJwJaPIpT9Ddm1ApKf8XQVwyjeckgHArreyU/jSJh71U9u2m55aZ73ELh/dP5iCDOeZ2ZkCq92NVhfDDqsIw85pa6mUJrC6BSQEA0YyuqTwnFasbcVMEIWceYrCwuOEw4XYvp1jap5nScOVmOrQtKVELY8Tr4jyK3tHXkV7U1F5rPpo6sIBSnvihH0erq44AU3xcQQ+dFvBbgwhLUHcNhAi9xOoSpvTuzdhpNLU1Ykg08Jt/Kn6qAwRmAI0zlfGZGwrpcSXDHFSzw5Gx3tICJX1BfXMCLeip9WUBaALi+O27+f+ey+k4KxWEluLyDUX/dIXHmLsWMce1EJAJLvjQQtE8a5pJnjsuBGphiu6xfTFG+jZK/Ae240xKURnqCD1DKUfHRZNX4W8M8ja0CS0mhgxn8VroSmc7t+aoVWQCyXFQUqK8/snjAgSLCEGZs79gbufC/NhOsHzDQRsBAEtcwIykxK+pE5FScsYxaXFTtmHimCiKEIbiSrPh8ThQJ6Gb1dWcnNHbkekfxV8lG14Ck5w/kJpKWf5fDQlEqyIRilWVU72evEMyX+5FIWvlZdzZ3d3TkKcBpzqS0tftW7I2bJloiAFGcSbuKKjzbzww11Q7Qy49H6n7a11E2x/GeIp8lmoA969Fyon506bI6ErFchVHc76X0eY5sD7zsej5zpuY699B2K9+c/BimNtfoZr3VNoNQpzBrB7tjzIzGBz2gtYOvcp4ndq9hff42QS+g7mvH+KlFSEneuSpkgF8fyvCmkKIqtKSW4vxI6oOdt+UGysTlfKvjL1qlQABLVjbP7zcg+VQuNgq+R3j9r0BODG0mrmtzzLxJ41bCs7BtnPH5ASdEnZzY4oz6EKYqGXaljz+NEgQJgCZa7KLV+SuF2C6aKE9R4/xZ/YR98jDWzbMZ7Jk/agp1liJlHQpZkRDHVpU5EMMSHayU7voAhPGJMkdobhxivJnVzVtgpDmiipbY/u20ewp5An3jqZPU1jKCvr4ZSF7zJu7AHqa9tJ7iqg94FxDnN9CAwjQbe/lLFjDzrGJkJl42lHM+uppahDyoPaL9+j94TpFFqOqI8q7YFAmxWoFAWOClOycN/AfT6pd2fOQYCK5PT4VuwqQaEZo8Afp9ldxq9rF3B5xxp8VmKgnJEvqA51C/wgsCyV9XsbGTW5BUsRVCeDWenwfqQT6Yainz8z9DzSg7mNYzLzRunkLOW+tfTwVWsVN6vTGSV8rLa7+I29M5UXcFo3v6xOZaIo4uPE4UCewu5Egu92DaOm9S8AjxCcV1BAl2WxNBLhwwqQakLwi9paalWV73R18XTIIUVpwLeBX9XWMsudaRN4WVER43SdX/b0sDoWQ+D84L1CUKKqfKXMqc3tTyaJGyVMEArKkF7TpFBZUzyOeYE9mAiWlE9lxbjT+ERxw1AZ5gxYUrIlHkcRgqmGkTHKHoBqQP0cJwgPZdVaCWfWrRrZAVKojptYP7xlcNJXB3tp+o819Rxn31Z+idwYCrMDuzlYOTvrs0nhVqaGW7KU7zLOsW2zk56vnOS4qA0tFwD7S530cHxT0fCRUrFBgdCAslf/6y/t9anZjlb30N5kW4ANe+8ZgzpN5Z3tNj/6uY1pgWWBb/w3+GzgVu5881ReGXs1zzVeT1zzUDyhlZ47PKieQ3NaoqtLCf5p9IDqmwRWrpHcdZ/Nf/ybymXKWHZYAdRTu9DHRXhmyQKuqgxSX96JqlipWZ6dM0QY0mJ8tCsjkGso7JJBZqfYy1LaeAPr0aWV0YwY6C7mrp9fQyKhY9sq7R0V7No9lssveZbRo9rAUpzBU1rZoR+WohB0GQRVF/tdZbxbMh79zilMXLIWIxLDiCVI6hqWofP779/E7trp1MUDFFox2o1CkkLlmpZl2Z0cwE5v1cC/vXYi20wnDQI4s2cLVqorZFq4Db/m5fc1x9AQ76UhEWRGX1NWgLWBkOpim7eahlgPpWYYQ9rDBtyh0DSLsmkd/KH6aFy2ybUHlw67fm51A2eAv9tdxvRIdkupAMJCp8VdwuqiBlpdJTn3HSDJT60t3KrM4Nf2zixi413WFn6sHo1bjCQv8tfhcCAH2pNJLjxw4F9GECa3GCbEpOS60lLejUZ5I4/j2BhNG7ZnPB1Hu1y4heCNSIRnQqEBOdf+sHFDWxtvNjQM9AIPbOfx8FuPh6Bt81wwSFMyyQyXizN8PgzgyS1LKGt6i5JkBBNBesLeRBDzlfOnuV/iPyNB+nDc0BQheLGlhbluN3fX1GAMOeZ70Sg3t7eTkA4Bxqco/Ky6OmugQTIG/j0QyrYDdT6PwJD0G0I4LPa67MCbReKasNBJr299AZA52eu2yE7x9WN0zO84wOWDlJBwBlSMXeC0yNnmgJxsQlXZXVHD3nInOOUTJkk7mdR59h9zsOcZYaPWR7C6DTCHd5J68x2bh/8siaeNf/7ScB1aMsSV2+/gtJaHOLnrUV74+id58eufRBX5/eknR9o5KrgPt5VkS8dkXjXqCCYKBlZJJiU9fdvZc2AdQjH5RsE0lnuqWD6hi+CEg7xkT+MzB99GlcMHFQtBWMksFyWweMDegWaanNSznfHhFmakmOLpeOnVhcTjBlIO3rdk0uCpZ85k2pQdziLddhzHhjwCtq2wemEl73tPGFzY4OG2zX/gpPueonH5RlqmjuONGy6ma1wdAC3uzCD0dOVszunaOJC1sYXg+YrZJJTB7ymex78gHenpbENalCfDzA4241G9NMZDRBUDl50gvWhmCYVnKo+gwyhimbT5dOsKSs1IxkAnP73PQVjRWVk8Hokgphq0G0XUJQI5t8m3Hwm8UjKZ4wK7c36eECrbCmoYHevhvM4NtLiKWVbSiF/PJrVFMbnb3poVxJ3jSNZJP/OHcCc+ShwO5MCP/f5/mSAO+UlpEniop4cl0WjOdVTgyuJieiyLBwMBNBi2XW9dPM6iffsoTqm6DUXAtrm+rY0vlZYye2jABAoVhSuLM6vaLcvv58ympXhTam0xoZIQKkmhokqbrQV13Drlk/x7YSF9UrI2FnO+29TxV8diPNjby7WlpQP79FsWX2pryzjHiGXx+dZWljQ0DArSbH/ZYaMrw2h4GwVw6m3wzt2pYC+hbByccNNga1k6In7Y/ZYjEFM7E+qPhCOvgunnOZakK36VMfOXQFJorChuzHn4Hd4a9GF6023bQulXs9NccOz1sOJ+7HAHCUVl6YTp/Hn2cYOXM63PUfEaDjlVwACpoJUlsVpzDzr6YVlwoCXHC1cInpj4VZ5f/Dkarn+PcFkhtpb9ytIQmNKmyIoxp28/M8MHB8oq86ZsZEbDLn5017VEIo4i3vnnvsK8ueuJpnrhovGDjDLKiFXNwhYwNtKGgswILLnqprYQbCpIs7XFyR5FsLik833q4r0525sAdu9pSAvig4hG3axakxrw2QoFi1uIvFCHJhSEkCSkTdmXd6F4s7/jUHkx799yIVu/spgWVwnRPO2VHitBVSLIdm81EdWg2V1Ki6uEaaIMlQD9w3RLqDS5y5kY6xxx+lvH5ti+poH713/1Ns6982s+3iibTIfhpJq9VpJiM5YlnZRvBi2AHtXDI7XzSAp1YCD8fMVMPteyHDXHoGk4LPZvzild3F+GmBU8MDAImRDtpCHm55GaefQMCeZxbFpyik6DjST6MdOpDwdyYN0I1Mb+VfBgMD8JxQLu8vv5Znk5L4wezcpolDu7u+nL07bXLw7TZeUOLBawPBplTSzGTWVlfHpI0JZS0mlZeBSFQkWBvlYqmt7GSAtsbmkRFjp3jjubpaVT6HA5+7jb76fVNLMGaDEp+XMwOBDIV0Sj3NnVlbMmL4FXw2EuKCyE9q2pfuzE8CSCZBQ6tsP5P3UEX4Zai6ajZT28+WOnn9w2YfebTv/6qf/JAcXFYyUzqJ56OVdu+xOqUBHSRCJ4sWIG5ckQ7a5s6l6zp5wtvjqOCO7PqG1KIKboPFR/IlOlwYkAB9fCWz8BK4mCRJU2C5q28fqkI+gsdGZwWqHJZ68SPPyHlJBLFg7x2tRttNoY5gFv3lVUFWZOE7y5LE+N0ysIVpXm/AygNtbNmd2bcVsJhvqDaZqNxx1jwfw1vPbGCZSX+5l/9LoMT28pk7gSXdRHO9jtriSyrpSXNp5EYWGIuXM2ZbRemYgBpvrLZdPo1bOvqzQZpjYRyJm67keBL0IwWJi13Ladb024LArObaHiwjYuXVRMeH0xhg4T55os8RrslG5KMdhNEBNJSTLCxR3v4079NlQpWVk0lpUlmbKhNfEAl3S8j5ASHZuEUJkY6eDxmnk0ieBAEC9JRtCkRZO7nAmxzhGR5fqRHhjTv4utrkp2FVTT7Br8Lg1pYgtxyJaXfkY6wNulE0kINaMDI6S56dY8VJvhPHvIhgJ5B1oi9Zk6ZH1NWhwb2MMLFTNHfByAaSJ3Wv6jwuFADozSdQ7mCTaHkYm4lHy3u5tXvF4uKCzkD4EAmxPDV9OH+41KnOB6l9/P+QUFFKvOT2dFNMp/dnbityxsKZnv8fCTwIacoqc+mWRypJ0nao4ZWNY+jCVtf7/7PX4/DwQCeQ1zkilzGABr6wsoVuLQo31pwfo/wqRTHSnZfLAtWPqzzFq4GYPuJpq2vMylBdNJSolZMp0nZt3IgxvupdgyUZBc0raaCzve58apV7OyJFN45JMH32FKuHXgBdTf+b66cBz3jTmFFSWNlHZ08NaYMagrfpVRy9dtG0UmuGjDu9x33FmAM9utPrOb74yp4hv/a+fww5aOPncOQxHhsvCd3ImuQvdPJ2EmRMZ2zjo2RSf5mXdsJb5HYEgHlbOPRfkJqMXJCBd0rstgfw+FrltMmtjEa2+cQOP4fTk9vQ1pMTro570fn0h7kxc7rqFpSV597UQ+95k/0jh+P0mhsrSkkS69gDZXcVbrWT9KzAh2Pys/D445YTV/eebMzNKFYjttXw0RfGe3UTI3wExRysKqEpTT+++dh3FMHNjkfbubX1nbuahzLYVW5sz26OBe2lzF7POkuBlScnYa877/ukvMKEf1NfFOSSMlyQjnda6n2IriaMEN5awPD5vcwsQCmBrvpETG2eepICmc0NOreTGFknFOuRBH5YBRRLkVZ1Kkm7O6NyOQ7PZU8mbpJCKqi8JcngpDcKiU/dBzHgoFqItnW/4Oh+OpoloMn5X6sDjsfgac4s0/WziMbCjAa+Ew2+JxziksHPEPwzOMvKoGrIo6qammRIIb29poNU3iUpLECez3R5I5/Z7jQqVHy/wOR+k6o/XsuqwOnOHz0WWa3D9MEAdQhWCux8OeRILNgfaRp+xsC8KHIE76m3LbnFpxIrvfIiolJlAX6+EP6++m2IoMvFJdWHjtJHfueAyRFsBcVpJ/2//qgKIdkCILKuzzVrCixEnHx6Vkf6jbad0bes1SMrW9efB0kAQSJm0dUFlBltCGoQuOmCbQB6YEqaGDZuM9qQPP7F5KZ4W5/VbB5EkgvCZqbRRjVgDvSZ2UfW073k81sZR2/uNWiVZoItwmwm2BbuM9pQPXnEwWv5A2cwN7+fzBd7i6dUWWscZQ2EBAd6FWxUiW28gc5XoTwZZ3p5LY48OOOxdjmjqJpMHDf7iIhK3i17ysLxjFQXdp3iAO0K0XDHtO8bhGR3EBBee2gGENOIa5Z/dy1z0mV98e5Jyj3dygTOVMUcd+Qthpz6mUkkSyh6QZ5EilnP8y6ymyklkvc0PaHBEa/C4LrRi+HCRKDZtJ4TaElFzWsYYyM4wubQxp4U6x9Q8VzCUMMOjzQQClyYgzA+/fTgheKZuGOcyAYXDfGqVWhImRFgxpoUubiZF2rmp7D1VadOiFw55nUuSbf+c/bi4EtOwyYD5oCK5UPn4zlcMzcmBLPD9D+DCyEZWS73d3o+HUCUfy4/ACc1wuluUpY4Sl5NaODq5NJukwzSyVuCTweNFErs0xGLCFwl+qMkVDvlFejiIEn29tzZgXuYXgiyUlrIrF0CEvM98jBCd6vUw3DM45cIDr9WJmMsLRvJVwTFeKavOvo2jke1VE0l6GtzY9h8dO5jyu10rQGOlgp88RoxkT60bmuD86NnMDTYOnB7iN/DOEsD74olJiGn/57yq6O2yG/kxG18H1n1OYM1PhQIvk3e1xVnYG6S4Kwgw/Wk2cGaKUTyjjqZihUTCtl5/b27IEUkzgddlK1ageqn/eR3RDMXZIw5jah1aZ/Q3162APNwtPhyUUNh1VRvWx6/8/e+8dJsdV5f1/boXOMz05a0ZxpFG2kpPkhLMBE22DYQGbBda7YMKaNbAsuyy8S1gwYc2SzJpowIlkYVu2heUkWTmnkTRKk1OH6VBdVff3R/X0dJyRhM2+7w9/n2ceW90VblV13XPPOd/zPQxIC3mKQr0dITi4dW7Rpi+GqbE+vJiDCytKa9dnmowmMQAAIABJREFUIax5OeKtZebYILriXOt4445kUqO3v44DSiNlb+7Gf10vZo8HtTJFWYXNbPV82kWAThnmXusABhYgqDSTvHP0JDJ2jImMs8BQNCxp45bFI1De9KKuDA0pFEqUyWMJhdbEMLptlvSoS2HcyxWAW07u9XqlyYJoN3v9TVhpzshRbw39eoDGVPG0ngA8WFSbYwV9E1TAY5u0x/p5oWI2zf1bcn4XNmAKlR5XOS8HZ7A8fJzpiaGca5y4m1Nfc0oobC2fhRuFFdSwmcFM051iWEQQdRJJ6VcKrxlyQP8L3Oj/vyGV/isapyyCgKKwdYoFUwL4wegoNapaNA1tam7WX/BRLtz47YzwiyIld8+5iW6PE8YWwAcqKrjQ5+OOnp6CAGcKeC4ep64IaWocNarKJ6qqeH0gwPFUimEjzuUjByZVlyrAC/dCoDar1ziOnnky6pShVbY5Ai15NedJodGcGGbrS5+l211Jc2K45DkUaWNkEeiG9ACukrrwE0IZc3SdRk/AEaY5sTmHTGcDwcQY79u4jkcXX4i1fhHH+hTysydeD9z7VRVdc0Y33DDCM3UHMdNBZR1BOS7er7TjT4dR/UIvUcgFwyQZJgkaeJaVrqMvM+O0x/pzJFdLwRIqSaHwVOU8BtLkKkuoPFS/jDf178AlJ3pYPV29CJemUWyZmUJjR0ULuhJHsy0WRLuZG+sF4Ii3luOeahpTYeKKzjFvNZZQ+WPNAlYqXSzu78XjMoiOeRkcqGLLjsXs7Gyn5p7dgKOE55oRw4XC9WIaihCctMf4T3tPpjmHyzZ5Y++LWLaRZYCcyIduG5SSWbIQJNCoT4bpc5fjVb0MuvzUGZEcQ5YSCrsDzfis5Fmzvgu/t3LC68X2vWLkIEmhcdDfAELQEeulNjU26WJBALVW8Uoal7SoMSLs9zfySO1SrhncR9B2SlfDipvf1J3HsMupWghrHt7Ruxk97dGnhIIqz4wgJ4FnKucR8M5gJvaURhxgDyHi0sQrXl1T+5ohBy73+fhtJDJln+zXcO4Ytu0z4m3GpaTXNHELkSlXG4chJcunLeL56nv4/dFt6NJkW1krft3DhbqOKgRHUym+PzrKg+EwIdsuSna7PxTiweZmfIrCWB43wiME321ooCPdC90A5kdP54QDxyEA/LUwVqQUzTJg/VehZYXT7ezAWji5GSkUTEXnN/PeSmTp3/Kezd9CsS2SloWCjSotmgwn5D0zPjBptCPpraLPO1HSEnOX0e+pojleyDJuSo7it5JUu/18o6GBiEyxceXrmZccoLH/GNK20aRjhL1migu7DrC4u4t/2rW0wIiD4112HoWOdkfr/T77cM6klkISIsUfzdMoT7Tw2z9KYnE3+sI5uG/pQqk7tyhYrRHFFAJtkhsjhE6Zt53HK6axSU0VRCn6XeV8v3kNjUYYVdr0uIMoQmX1FSZ/PG7lldxJlDITbVocj5Xi1t5NmVy0xNEQJ9SJhZPCsBWFh+qWM+AqY1P1TF5IdTBy72xSJ5zUj9YSo/KzB1E8NgEzwUWho8xIDKErXlrLXdzvj/EigzltMueN9Zyxp5xdxS+QzEgO0do/wkFfPU9WzeexmkXc1LcVl206XeEEnHBXsSvQzJLIqbwGK7nHzIet+lGtBPnsTxU44KtjTmygqEiLAK4b3svisdM8Ub2ApZGTpXUPSlxnNlIoDKcXsVcP78dvJzP3oMJOEs4q1QxpPv6n6SIWRLupMyKEVTfLIydKkt6yERM6ewNNwCSCTQXjFuyXIZaJ6qk3/jPwmiEH1vh8LHC72fFaiP2c4RGCekXheBHSoE+ISRuq5MMGyoTASueJwQl13xAI0KBpvLE8yLVLLuNAMklQVWnTdTbG4/x9b2+GfT48SQOckGWhCsEPGxv5YE8PYdtON1aBz1RX06HiNGI59gJzVBcXBmYWKGZl4K1w6rJTRUpP4iPQ+TR0PpUW9XBW/rpl8Pq9D3BHx3u4b/kn+Xj8BJ2RIW45tYG2ZK6udSm5SBSdiivv5l5XFQ+EQoRtm6v9fpo0teikZyk697mTLJw2j27ifNraRUqzSV12HXMGevjHZx7OCbuqUuIxU1xW+wz3H3574QGFhccTQ8oAvSSK1s+aSB79voexjRP14eLlIMqehTR9dTdWcGKFUGYmWBU+RktilJDmYXP5dE57CsmCIc1b1DiM3yOXVkt1cBWV5cuZbvewUR4r2NYZiKAnzfrXEFwqGrjyEj9/2jFKfFsF0gahOepztR87TLsI0BTelkMoy2HHkxaOsW1u6d3MjxsvIKz70BqS1P77XqyIM9WqZc4v2msZvKt3E2475RATrSQ9w4+jG01Yle05Q603IrjOIAIBE2HibKUyRdq0x/qJnIhz5Yf+m9bdnQxfPZ89N13CE2+5ngF3OW/t30aDEcosUIotEMYV0MaVzjb7G1gR6cKV9zhMBP16OTPEEGqRhcF4GLs5Oco7ejeTVM7dDI2P9aCvnhXhLsrMZIFMa4UZY9A1USGQVHS2lbcBDllyefTklJFFCbilydxoDwcDk6TM8mBgp9Mjry5eM+Q4pKZ2XX/NkOehQggu8/tZG41OqfJmScmPmpsJWRYf7uujO80an6XrfKu+nvf29NB/hpUBFYrCg83N3Ds6yoZYDL+i8K7ycm4unyjjcgmRI9byjeHhSfucj0PDWbiB04d+XWsre5JJxmybJR4PPmynV3ioG9Is9feNniQuBJYUOUZEam5Ex3XIF/+7tMeQydfljs1jp/jB3vtQkBz11rFu1o00GpOwYRXdYZrZlhOyv+bz0H+AC3Z9iwtiw1AzC5bcVDw6APikyaKg05/8frOTWNbk4k/GMFQNLY/167JMOlp2I9xvyXipQkiuft2zXLLmZaKmzf4ujUDlxdh+pWD2t4Z1Rl6qRGaFuqQUKIZGx5ML2Pf27dhAuRnnXT2bMhKmVeYYrYkRnqju4KA/d9IccgUY1P00GJECSU0hNKqDq6gKrgCgWrhRpcjxbouhDJ1blBkIIbjmI6NsPNbD2IEASrmJd/kIfrfgfcoSumJPTMkOHleCe3fvJtZXzmVVuIugGWdU8/J8xWyOUIdum1w/uKuA+yCkycLIKV4qn55TAx5SPSXZ4MVGkELgzjP8Lmlx1eaXaNt2EICax3YT61jE4Nt8zIoP0GCEM7X3pXLFSaGRUHT6XQE2BmcyqvlYFjleMAIpBPsDjdSkosyN9ZYsXXPkai16tHLKzfik5XqlrxZiio6pqMyJF0+5rBnt5Pc1izGL6DiENC9R1U2FGc+57vy0wbjwzdUj+wnrvswi8EwwZCdfdVr5a4Y8jQPFi2T/aqEAF/l8fKG2ln2GwWHDmPQ1q1FVGjSNBk3jydbWTNlWRbqc7FPV1dzV3z9leN0jBB+rqqJe1/l87eRKSBHbxpCSKkXh+CTPb9yrdQEeReFynw9TSjQhUPIWBHRthHBvTlmWaiXxKzoxVxma5Ui4araFmHsdTL+Y472HqDvydA5bfCpkK2LNjvdzz777Gdb8NKTCRbdFUaHjBkeTvXomHHoStmT1GT+9HXr3pGtriyyYVB2CTaSkTRe5pKJBf3nRiIMpFPqbA/iu7mXs8UaQkstXv8Qla17GnW71KaVFdOQ5LpILeK6sJmcalScCuDRI5t2WVAp6D7loI0AXUS4e7cQtzbxJ0+aq4f0c8jUUhMZ3BFq5enhvgYGQ0iQa78wY8lbhP6PcZ5gUKWxcqNyutNM44xTrp/eQwKJDVHCTMp164SWk+ElZxUU/siFw6o2vGt6fWfhVmzFuHNyNJRSEtIqSq8C557WpCCdUJxSr2yZLo6fOOIfbrweoMmNFeZQi/U4m/F6+9Zsvc3j1EmxNZWZkYMryL3CM7neaL8v57MH65dw4sAOP7XBWbKHwh5pFjKluNlTOoTU5QsAyKSW+oEuLYd1HtTlGwEqeMYEx57qQvKV/G/5UcSLtjMQQrx/cxYaKOYzoPmc+kE69e8BKYqbL7Gwh0NNSsaf1IA2pUIGBVKXNytAxfldXRKWxBEbFq29bXjPkaSx1u9n11+yRp2DxlyuZ9WAQNS4YWpHg5u9qiHrBfY2NfLyvj82TCOfke9vjBnwcF/t8vK2sjF9FIgVzjAq4haBR0/iHykquDgRyvo/YNhHbpkFVUYRgyLL4VH8/L6fL1Zo0jRpVLSpM4wWu8PvpTKU4nUph2DZ39fejC8FX6+q4KK/0cOTUTiqLND0RQsF/3s0OEz0ZcZqjpOvEP9VyFXMtN7d1P0djYhTtLNWlANy2yaCvnKAVzzCNc2AmYP9jjuKbbcLWXxTqsVupHJGMHFQ4oUTHgIwvbRycqqzldEU1rSMDaFn30FZUnp67hOCKU7g7wgx/vZ3LL9mYMeLjkDLFqsgxDpa1MIKBQGBis7zOzxNW4XhUBaY1C96utvMf1i5mxIeK3i9d2txilLHTrbKPiWhFSPNiCbVI2FagaxOeUo3wsFLUsFUOlSQlabbFzPgAv5Z/4G2+1Xhc1bxBncYbmFawrbvsPJLDT51RPnVC2T0bsmioOXc/m4g6sbCcN9aDK2uRUwrjJVov1azkxt4NhRvEU1Q+shOAn3/zYxw5fwG25owyruhYmTGXRrEQ+ICrjB82raY2FUWRNv2uMmT6NxhT3fys8WI+FtfoCW8imCqUULVxWP4/r1/FvLEeLh09zNQ9EHOv22cblCWGJ92rLTHEtUMG1enSuqjqwkahzEpgCoWfNqxkaeQ0S8dOkxAq3a4gtWa0gDOgALMSg1w6cohnK+ZMWcWgAO2vcsMUeM2QZ/DuYJCfhAu9of/bICyoe8mLe0hlcHmCWMsrI/235CtVzPpFOVq621LNJg/rLoX2fVDVrHK938/2RKKkR20Bz8VimbB1Ng4kk7y3pwczHfoWFjQ/5aPlST+uKrjtDg/Ll+iMHAWPB0jb8aht85n+fjbEYihC4FcU/qW6mm+PjNCVSmXGctw0KabkXXNY52aCvGdNgKv6TxAdD71LCVLy4b4+1k6bRr2wIdLPac3P7y2N9wkVd/6EKxTwVkLDgoLzRGybBxsv4MHGCzh/tJP/2v+TSb3zYjlIBUmHsNiy+J0s2/UAml1CfOb0NjANMEt4hsU8Gs3tCNQAmlBYIirZKUdyQs7fvvRN/OOmDTT3OB3F8FXx0vlvZrjMA0gSW6tQsXC7iydZbGuML6jLOEKEUWkwU5RR1eqma7bJgUOQyvrhaDq86XqFeuHly+oKDvFM0WMKYIUtqFQaOWpHSYvt0u0OElNcaFY8V0ZVqFSVr8g5xm3KHKbZfp6RPURIYWTx5huTo7ylfzvg3P/Okd3UlK+iOngxoehOkqlBfO4WgoFFDAmL//QLLovXMTfelxlfKZyN8Ej2Pv2ushz5z+yQ92RwohgqtyZS/KpmEdcNOkZbkTa2ULD3j+B97ii2ovDyzVdieiZC9/2u8inHmkJhe1nh4sY5uWDAVahSB+BVvGhl8/i5T/L23peoTUVzzqUAl4wc4oS7knIrMWV4fTxXb6XZ5goTC5DJ2PVh1cP6ynbaY/0siZ5Ctx16oikU1lV2EFZ9zI31IXHUIleOnSg5BgEsip4irLpw246+fLc7yN5AM0beYsePxnnpBjqvJl4z5Gk06Tqv9/t5bGzsHDI1fxkEjmlcdmsTelQBKVEswdGbImz/3FDOr1gAWODr1jCCNqlyO0OAKeoP2FC7yZMx4s4xBHHF4p51UcQNBuvHxqYMi391aIjVXq/TSzoNKSUf7+sjkvb0hAlrbmugZrsHLaYgVMkffypY53IGbqdg+uXw1l/ARxN9bInHM2VuCcviH7sHmP+1Ctq9kqPviJCoda4o22z6Tmusub2B8lMaYV3wdRvq/9VH6M3R3MuWkqM7HqH+8GMA1NsmcyraC/xpCwGaC7V5SdHrvsTv51jI8Rg3Vczmh82X8sFTf8JQVLxCOP2Ip62CkS6isRG8yXAhYUsI1OoZnL/oeoj1OKHzAkgInYa9vyv9EMqbIDbkLFYswzHiDYtg+oR++nuU2XzF2sMgicwomrUq6mZeBRXtDhN/1qUsViUPmdtoSQzgbYjSM7uFULiMyorCBa9br0UIwWzKM79FKS0+/ME/MRTagqoanDrdyPPPX8Mtb55GS5OzkVuoBFyNxJKFuVbHwy6nSXhz89xC8GD9Mm4c2EWNGUPDketsrr0Rtys3HaMIwTVqM9fgaKL/wDrERjmAIm3eNLCzYME2HN7McHgLIJHSJBTdQ//Is2xqfB1xxWZt7SJeNGayZvQw05PDKFJmPPT8nP25YG31wpx/D2n+M86PSyx6I9vobLqQHzatpj3Wh8u26PJWo3SMMN/zIzTDxNJVhLRx2yYtiRGuHt6XGe/4XTYRDgFUKKhSss/fyMvlM0qe22clUaQkoXmxkegoKAjuUOcxED/KdaEdiHRDovz75ELwlsF9+K3opPfNRHDKXcnvapdQZsb5m95NFM0h5EEAFVaCm/u34lBOndr1455qOr21VJox/qbnJbx5tfjji4ZiY3JJm8tGOzMEwJmJQVaFj/PzhlVE04IxCvAxZcFr/cj/0vi32lp6LYstf6b2uhoXYEisyfgQ478/G9Q4WH6mXOKv/mADnn4VJaut4YyHyhhYmeDUDRMaww1Pe1n5T7VoCQVhCbqvGGPzVwboqNDYXSSXrMYF/uO5P4VYg8lTj5wGtyQRtkuOTYsIvP0asSaTYzilXx/MakhyyjTpywq7tzzupzptxAGkJcDK1fE+9gz85EabbT9OFJQEpoRkeIHByk/WMve+Cp75VTeheVleooRL/6YB/wkdYQuSacd14T/XMDDbYGTRxLaXDe5meefvM/lwDbho5BBbgzOYN9aD1zJQkPS5Kwhf8nEWl2DXfriigl+FQpka5O+1vo6HGlZxR8/z3Nz9knPvTmwCaeNZ8/ec6n6GpiO70bPTEYoLFr7ZMcBFFNcAsG3HkJeqFVc0WP4uqJnjtCdNRqFhIdTNywkBBoTOv6lLOUSYfpmgLWnS+viXIBF2QviaB/Y8Svk1/8wdI1sxzBAssDDnbMMwNIyUhkvPHoNGQ/XVBcM5PfA7IvF9GV3z1mnd3PrOnzGz+QNATWa7+uqrONb9I/IlTVXFh8/Til8ozKOc/YxmFpNRzcsjjRfxSWsaikxR52pBPwP28yJRyQ45RG1yuKD9LTh59tx/pzAti+DIZmT1PABGXX5+X7cUpEzLggouHjlEe6wvs0A7F0NuIchv3+Gypw6rZ0OzUwgpiasudmZ70HPL+Oq6b3PzP36TNx7eRKsvnmlskm9YJXDCU8WT1QsyRL1EiQYswVSMG4Z2U2NEAcGY5mWs9kq87kaWi2rikZ2MDT3OjHR6oLjZtQma4Snr1VNCYW31AkxFdbgGZ+FyZXMSVKAj1kdHkdal+fuc6TF1aaNKg9WjnTxesxABvFVMp00JlD7AK4jXDHkWPIrCdxoaOL+r68x/IukNRQoUU4CABV+v5NTVERSpMLLQwPLJiW0FVG11UbvZg2dAY+ZD5UTaDJ79aS+pgE2OSHbWL6nsqI7vtJZjxAG0uMLsn5ZPGHIb+i9K8PTD3Vx4Zz1Vu900PePjgo/WIX8WRssKSY/DPazmeOMAeljhutdNQzUER98eZvu/DSGzfi3ChPM+X82Mh8qwNRA2HPjbUe67c5TbKyrQ0oZjfFU7jmlr/eixyVeotgH9mwXlJzQGpuWZchWirSm0pIJMSlZ8poanH+7OfF21y42nX0PJ0/5WDMHsn5Sz+asT0qkfOrUeV17fcK80WRk6xupVn6E5OUpS0ej11vB4VWvJ8XpVlbWtrdzd38+WhFOedJnXy809GxF2RjoHgPXm83QtbWWpN8nCg0dxGQZDlRVoK99PVWUr9OyC0zuKn2jeDTB0qHj4HKD9amhJK9zNuy73u7y+50II5hJkrgjClm/B2JCjEw+OMbcMeo/+CDsgHSawAM1toGsmJ042oSg2NTUjGHY1y9qvwufJvT8pM0J4bC8yz+M1ZYrfjvwGWXMFb1OmowkFn6eZxupr6Bl6Mu0Hgqp6aGt8F0IojKSGmDP8MhfFe0koGtvKWukOzKROePiCehwVgWr3crOcwWq1nsmwTFTxCxSnhvqMYdMa64O0Ic9ACKKqGwk8XrOQJ+V8bhjYzZxEYeVAPgu6mKc3prqJqu6snSTLoicnNXD537ltg2uH9vDHIk09xpY0E/3ZzcyUsUmNlMAhiEkEve4gSMnS8AmWRU/isk2Oeap5ITgLFZtb+rbgtcflYSVBc4zK3seZ0XQbmmrQNfQk5BEZi12HTfEcffZ3urSZFR+g3EqwKtw1yRX870ABZsad+UUCa+VJVshqasSZS7qeK14z5HnwKwpNqnrGTVREEi6+o57ojBR6RKX5SR9qXDD3R0GkkHRfFafrLRGkInGNqBx/U5TVH2rAMzxx6yv3umn9rZ+j74ggs4WNs95ULSxKe8XZhlEByysZazP500+7ef0lrbjCKvUveDl6KsyKZg87EokcBSthg7DyFggxkSZFAUKgpARWlgrHov+sZPrDZahJBTXNuZr3gwqSNRahT1lUp5XTpmkaOhM9yFM+iS1kwYIkH6oOWp8KeYZcMaD2ZUfgQSCo3OVGSYKdnv/cwwpSLZykFVvgH5i45x4haDCKS0LaQlBmJTnsb8AtBFf4fNRPogQHUK9p/E+T0/u5T/ZzdGgDG8+bx6xjJ6kbGgEg5nFzuqkWVYXd8+ewe/5E84sGRlhi99DY+QxKET1shAa1c0B3wcDhHDU25wJ1WHhj4X6JMLz8Izi52THmzefB+beDLytvd3LzhBEfh7QJew3yp1dVtWlr7eZ36+5i+RUGc9oFHgq9DiM1jCOmWSgWUpMK86DsI2yn+IA6F4Cq4CqCZYuJJU6iKG587mkIITCtGMdO/4BpdhIV8NsGV/Xswb/214w9cZLEv76fY+c7vIWfy6PU2G7mKaU7TbmEyt3qIr7gNhCmKLAe41Kq+dCFjo7IqK2NX0sTPvpIYODkog/762hLDhewwC0E/a4yGoxxsqfMpLvG67LXVc/POfm43nkxFJkiAGdCnx0foNqIMuSaeC6qtHh737ZC7kcJCODC0U7WV3dw5fB+OmK9GUZ5R6yXjlgvEoFSEEMASyY5cvq76as70wVTaX99/BE55V8HMltn41w4CWeD7IXYZOcys1RCx7B40O7i79R5JbZ+5fCaIc/C1nicfx0cpCdtxIUFMj/2lIeG53zUbvHS9Gxuf1qJRJEKLU/6aXnS+c5y27T80Yd7OHf26Fsdp+ut0YlmDnkFjYrheORqvHAgptvm5A3Rgs8BpAonr49Svd3DgfePcioQpz6moakik68CmPnrMuc6sxw9kTWIupdzV5TCgtk/D6Ilcj1rLa4w93sVPHtnjLeka76FEKSkzLRFOnZLmGmP+1GKXEs2UhHB9cu9/EIkJxqbmM6iZe59EzkLqTp/4xhamkQ1Co+t+STL3qAQ9XgwpOSNgQCBhg446eRDc86tuhh0leETgpvKy7mz6szIKoaU3B97EfQTqEELUd7KoRktzD90jJU79xPzeVAtG1st9D166WfIfo5LzOO0Fb0hBnzjLrj1s+DyOWFzaXF0WiNbl8xnzO8joGxghb2U6UraO7YtePyzTl/08Qn89DZYexTe/E0oES4dR+EU7UAokp73b+f3QoAlcaFyp9rBdDFBeHLpVcgirAoL6NfLSGGzVQ7ygKWzXQ6DhOn7W9B2ziBYJrhitdOkpTv0Eqpt5Npbj0bshvnM/sqT3HX1R/jGH77GoTVLMbBZa5+e1JADNAkflVaAXz18Aze/6TEUxUZRbFIpHSOl4fclUJSJ34RAo7rsPNoI0JlVuieBdsoZwSCFjZSSqOomrugolo2G846lhMLL5dN5OTgTzbaQAjRpszDaTUtihFHdSzQwH7/uRyfq9FdHsiYWmdQ4ndbLaSlSroiUNCdHGdL9tMf6OS9ygjIrgV5Cj70YBLB07DRSCOaP9eSQ0HJlYovvm6aknZGBtRCcdlfQlAwVKLyVqmkvfs5XF5NFFcC54tOu3HzqLjnyag4pg9cMeRpHDIMPpJXBqna4WfYvNVTuc/HML7sZWlG6LK3ygAstVvhoRbHHbUPDBl/Bd8feGsHyFnkpJAT361xxSzP6WGE42haS6IwUne8uzrZveNZH26NlSBVWfLaWxV+vYsN9vUTnSlwDgtkPBJECggdcqGbpV6H8iItpj/k58footscJUyvJ4tt7hlW+NzqaMeT7H4XX3TmNwEmdRJXFvjtGOPjeEPO/W5EzDxTcLwH1d1Zw55c1Hg6G6BuxqFrnZcHXKvH2Oz9bS7c5de0YUgNvr8qsn5VTsd/N4HkJqnd4MgsNzQvBFsG77/Bwu79p4hznvQN6djs58vFwteqifNX72DljVg5p70xw18Bh5lecQFfS+lpCYCkK+9pnMvvYSYLhKPYkuv4pUhxpa6Kppy83fz6O3iH45t3w9YfhyJMcsY7x/LJ2rHQZUZgIz9ovIoEZSqsToo+P5nrb0oZUDI5vgplrnM9aV8Hxl3K7sQmV8oROyD8e3Mx8wXFPFXEx8VkCmy9au7hdmcP5wiG96VoZhm8WxDpzaoMtobKlfLrz/8CfZC8pWzJ8Tzt79pYjkxJNgwcetrj7ToXKpuNFmcwiaWLMb6Ds2U5uuuu/+MLGHwIwWFQtvRBLhhu5f18lJ7uaWbFsF15vgv0HZ5NIuPjQ+3+ByzVu9AQedyPRimWckAdzjmEDG+jnTqWDR62jLO1/nkYjjCItJAILGNDL2BVoZk/AIduNi5JYQmVreRtby9tYThUfUOei4Mh5RknRZusMDd9bdOyOiRRFNQfAYeCPqS7WjB5mSfRUhvV+tiReASyOdqfJn+dGAZ6aDS8Ia16Gdaed6cxE8VLE/y1HyqgAAAAgAElEQVTkp0Qmg8AJrXusFAnV8coMbELSICgmXzT/uXjNkKdx/+gohi0pO6Zz2a2NGSPg69YYspJFEzh6n8A9pGK5JFoJw5YNJVV8G6lTkpYamWUWfYdsIem8NcSuTw9nwsrZCBzTueATtahZuW+1R/C6tzXzm21doAnmf6sCIQVSldiqw4IvhRV31+AeUjn4wRCWRxJrNAmcKiz6Gl6U5LRpMmxZDK9TefRdEIg523mGVRZ/rYp4tXNNRRc7mZsCnWsF2voyPv3VMpbdBr+8C46PgFIG0pak2k22fX6Q4H4XV9zchGKAmlIw3TZSkUxb7ZRad7wVVt4BLn/eOYLNcMN/wK6Hof8glNVxMnErL31yFqETMOtqOP9Oh8Q9FfYmk8SVPhRRhEAl4GRTPbMPnUScjGO0leNS7Mx1Zt+G4y2NzGo4xbTTfQ4Zy04//J3dYEnQBex8DuKH2LJmTsaIj8PCYou93THk4dNObXk+zASMnpr494r3wGCnY/TNpMN0d5fRMON9xIYfxLLi2NJAES4MofJMVUfBIW3gR3Yne8Uot6uOxOhL1UtwK0bamJj0uspZXzmPUd2XuXQTSWJTFcbe8ox6nGk6crlf/rbFZz43hIfCiVTqCvpphxTYsvsI4LxC88SZKW5dU1lB96XPsXzpHoSw2b5jIX19NXz8I/ehaRMLGhuI2DEO2qOkijxbkOySI9SG9+d5lI43XpeKcNnoIc6LnuSRumWMZeXA3ShcL1q4QWlhixzkZ/ZRDGwaEsPcOLgLvUSdiFNqJkuaVgXoc5Vxw+CeHKWzcymX07DPUBz27OEwviXVZozKqNPD/dUOkZ8tznYsUijMSAyyP61IqAAv2v1cp7a84mPLxmuGHBjYB0M/dzNLUWhY70VNTDy+ed8PUrXLTcVBFyPzDTrfEyLWaFGz2c0l720EAWpSIJEZw5T9/9koZbjafhug55IYlj/v1TTBdkt2/tMQS79UjRZ3jLKlSUyvzZ5PjBQ14gAzHygr8JoFAi0G7fcFOfy+EKlyiTvkMNulmNyYCynovzA+fiB2fHKQC+6qR006uXRbSGyPZOenHK3wB0Ih9M9UkcprWKTFFQKn9MmN+DgkmDFY9wlYeDPc8hvY/B3o2wWzrhYsuEXn8ZcVln+2Bm1sIqevJRVsIdFccNtzU5yjvAlWfxiAXT+FP3woLZsunfNs+yF8aCcEJudQsS+ZxJIKUgrye0VKBAd8TXyz4zI2KjOZPzLIqrJuyjQDt2LnTMlSETy9ZiXX/eI5GruOQMqC0yGIpQ2ybUHvOqSMM+ZfXHQsUdLEx2CLQzYw87x7zQMVWROLpxze+HU4tQ1Cp5x7Mm05vb0q+/bcQVXVAaZP76fcX8sfPV5GRXEZWAvJZjnEVTJKqwjgFTrPV87mhcrZBdtm+3ix52vyGpWMHy9F9/EqZrXl/YgsG/eBPuLLpjH65iVYvWMo0kYIhWvF1BOmlJKBkZ9z+SU9aKpzb9Zc/DLLz9uNrqdywuoKEtMc5UR8N1fH+5gZH8BCYa+/iY0VMxBCZ48c4eqx7qJhYYFTqlSdGuONAzt5oGEVAC14+YyyBJei0inD/MjuxMDGaxm8cWA7+hnksku9QWHVQ50RxZqiuUzmfpDJfBU9ptPOR05a4+0sypSzEkMSTPhHCqTP8pdHqcXDOS0qpERkESltYGRKges/H3/1hvypT8FLX4c6o5y69GfZRqZyv4fgQTeKLajZ7GXWA+Vs/kI/F3yivoAZPT4hn5GRykLTUz4a1/vouTyG5ZWZ3LwrpGDU2Bx9V4Sx6SZzvxekco8bfUxBsQXTHw3QeWvEeYoS6p/zMv2RAFKBwHGt5DimrQ1w5F1hpx59/JqlQNiy5CLErLEYXWyMXyiNz/kZWBlH2IKy4zrDC5Ps+8hophTsTycTLNpZ/HrP9v4oOuz8MWz4ouNh2ybsexAO/k5weVk1/h3ugmMqUnDihTM/h5WCtR8hZ+FhJSE+LLnnc3Ge+/QgAUXh3cEgbwoECsLuzZrGkdFqVgWPFxxbKhozZt3K8b4wFTLBLE+Ici2JLopPXJrQMJZfC0//B5n6uXFUeEBYCFvii8WJ+Qr7ivtJi/I0LXXU56L9E2FzoTihibbzc3dSVGhdCawE4Ac/tfjDExZSKqjqfKScz2c+rrBgySh/sgcxS0y5Fjb7ZYhWEeASpYFNVmGrRwEspYp9jJLEhiLkRHBa1MoiynAAxoxqej57LdLnwjIl7+l5iQfqV/FdDvAvSqF8pi1lWoZVoS9+mKjRg65OGEtdtygri1Is86FKmyuGdqNLKxM4WxnpotkY5Tf1qxjDnFLtTcHp3FZmJohoHk4R5wnZzRuYxlrrVOYezRvryTEEUyHf2Izn42Oq66yMkyFUVCkLmrOYCHaUtVBhxpkZHyhq7CUwpPl4rGYx7+h9Gf0clA3HkUcPetW981d64aAiOeqdKK10o7ym7PZq49RG2HiPU+40mXEZN9hqSqCk4KKPNRTd7mwN1LjRFFJw4Z11DK5McPB9IXquiIEAo2bipepbHadqp4ua7R4US6CMCeb8JMixm6JYmmT5Z2po+30gw2C3VLuoURYIhClpvy9Y6H3L0ldw8ccFWzSNIcti7hcraftDgLFmk6d+c7ogv6+YYDyuFxChzxkCnv08JLPKqy3g4O/hstsCbBay6BupF4rMAY5NO/4sRPug9WIItsLQwULiNoCdEkSe1Dj9SSfM+cXBQXYnEvxLng78BV4vHnw8NTyLK6uOZKYyFbhAuYDFnkq+1nKarfYu0nmFolBQcMf9bP3mNdiRF2n1PItqx52b4HbDBZeDcFSnztt9gE3LF2FmMeo1VJaJJWCZDhu9ZYXDch864py3ZTmsfF9RoltKmnTJ43T2R9kXdXPJJd3U1Qxx/EQLW7Yt5v/c4+bn36+gSnPTXyIXLRD409PKTFHGW0QbD8kutLQJ1BB8XF1AHR4+aW0BwH/ZAMaeYIFXrig2M9pOUQBFYJd5HK1XQNUEZWaCi0KdPFPVQacdZrZSji0lwzLJ87KPp2QPSSzKcbEqcZyOIiV8pegLChJXWh994jOne9eHjHIe1cYIKR6CxCedAWwhcNspIjjk0d/Lk0STIbzR7dxgROl1l1NmJqZs6ZkPJ2fuDH5noIXd6Xx8VHUTNONFu8VlQwBuadHlrqItOZwx1jYCW/HSVd7O7NBBbBSUImOzETxRvYAhV4ATnipmJQYLthkf59kY6r8Uge2VMuYSOOSty9Tc6wjq8bL0NWW3Vxd7flkoVz0VpjLW+V55KQ83P8MlENRs9vDit/ty6rUBMEFPCub8OJgJrwOUdbm46O/rOXjbKG2/DeSwyFVLKZpFsxWJUWGz4NuFLSJLQaiw+HUqa6dN4/ddY2z9mQ/VUAh2uqjY52JkURI7yy4IQzDrh2feHWgq2KniiqRmDHo2Q9tqwYk8eWnNA8ven/uZlPD8l2D9Zx2jLZS0hsqH4OJPFk8nAySqJyx8XEoejUb5QGUlDVkGVBGCnzQ18ekBnR91VzDdM0qtpvHBsnYWaWUct0+yQ+4pCLuDM5n4cRL4Y2acqCuMdfdjPPiJf6Bt4xtYOmcts1+vo155I7S2wu8+DsDcoycB2La4g7jHjc9SWaavZLZRCX+802G2jwu8eINw3RedtqtFEJJhHrOexMQiVW2y5HYbV8pkQf8pFsw/zBWXvsj37ruNnbuDfGB5O//H3lXU3CiInN7LV6lNXChrOSTDuFGZJ4IoQCR2iH+IHOG4jLBzbh2bVwcZe64O6XSzAODKv12HUHPPIgGEQKh55ZJIOsZ6WV85j2/Y+zBsZyGbr2MwikGXKpgjlCl1z7OfT6nPqyOHudQ4RmWe9GgxI2ULwVCW/GqFEaG9bz2atNCQzEwMYiEwhHJGsqy5Y5P8tnYpx70T9/6humW8pX8blWYsE+YvdS0K0JYcydlGAi5XLe8NDxMa687Jt48T7kCwMTiDvnQ3sEajtLBLttG0ceRczqXj2auBYs/rXBYSs+ID1CdD4K7nIlHHVUrTa8purzbOShPiTI5XJLQ+meEv9t2SL1Yz7Qk/wnRy0uFZBjMeKUeNC0SReafxWR/1GzyIInXZEpBCItLEMkuxUWxB/aYSrmoJmB6buwb6uTHspX+7guWWqOko+5r3N/DyVwfovSQGUuAZVlhxdy3BzleApSnAXe70++jbVXwTIwrvfwoeeAOc2uSkhK0UzLgCLv/3ie1sE35+AxzNUj6VtkNY3/p9xzOfdiGceD63RNv02hy8PVdpzYWTE2/Iqy2v0zR+2NjImF1PSsqcxjF77P1YJTpAOTlKm6Q0kJrjy2ttMap+/BLH3nIpnc9/iSuuhQvnp3eYeRkc2wBmkrlHTzK3qxdDq2X3yJdhmgvp/zoiNpIn8JJy6skv/XjRMWywXiKRrvYXwulildQ1TgUrmT46hKZaXHnFn7DtG2kjQPVjs9nzeAA7puGaF6b81hN4m5J8TJmPT+Tel4DQc4z76YHfEYruQcoUzUBLYpi3vtEgfPW1/GrLAer1EaqX9nCkqob4kAuXNNGlTSrtD+pIiql6uaTFu3s28quGFVhKMfV9B0d8tVw+cignVH6uSKT6qTDHJi2bcp6vYKeviQWR02hIuj0VXDF8MKfrmy5tVJx2oWaJnHQpFrWCpD3Wl2PI640wQSuBYGqJV8eQyZztVCRm4gSxxCm0vN+uwPH4H6xfTkRz0juVqTE89uT54PFxK0gMoYAc7zpmnZPhPFNMxj5/pc4rABc21w/t4byWq/BP8ht8pfFXbchLhV7PFWcTWi+1bevaQCbkXf+Cl/oXvFMeV5HFX1PbIzn29gj+Uxqefg1ft4pn5OwfueG12V+XYORhKPcL2lMTXoUrrLL6gw0kyy1C7QZ1291QIq9ZDEItHtIG5/n8Yy98f3np/TUf6H54z3ro3+uEyGsXQM3c3O1+c1uuEc+GlYDN98JND8Mvb4Se7U7kOZmUHPi7UbqvziVbWThG25AGR2UXERmlVtTQKlpQhIK/SIw2PkVZVJwEMs9bV/wWwc/tov/SRvY/DBeO2+Dzb4e6uXDwcaSRYPcLF/LkfdeTjLtQNPjkN7eiank3VVpwamvRcxsyxSBDBZ9LRWHIX8b00SFU1WZe+yHmzxTce5/NgeeqM9Gs5I4KQgeDfOkrKk11k/fQSiT7CEV350ihSpkiET+Ki+9yyzLDkZGxYMnASaKKm6TiZkx1c9xbxZ5AM9cP7qElOVqQlxZAhRlj9WgnT+cx6z2WI6WaUHUsofKr+hVcN7SHOiNSVNTkTNErk1SegWevIFkVnWjEMd5trNAgOy1QnwvO5pJQZ0FYvFSXMoXxa3QgpOSq4f1n1Ra02Fs7WbZbk3bGiFelxnhnz6YzXhgJQJOStdULUJH4TYOLQp1of8azmOp8fykEzCS6HQPllYtKToW/WkNuJmDTN/+3R5ELQa7XfbY59wJI2P93I/hP6Zz37zWTGvF81j04CnFSQPfrYrxhTSuWSxKelSLUblCx342aVU6nGoLyoxooolTr4RxoHljyN3DBx+E7C4obc9XlbNd2KQyWyGEP7IHDa6H9Bqhb4PzlI9INe389+XiSEfDVwG0vwNBhiPZCfF6K30VDObFZDWjRNJr0GL+21mFjY2KhSY0Afl6vXo2rSM1oi2hiX+oQxWjEwlQQWmG6BUCb7QiQeLPSbCY2vdNnIaffQdc369nwX1pO6uFsI01nmqd0u3VSKVj3rCRXsl9gG4LfPgYfeq/EtpMoios4NifkGBVCp0E4q+Zo/CiyiHGxpXPA8V/ouLEK2s5qocxOctRXS0x1s6Gyg3f1vlh0vBqSuWN9GUNelRrjusE91KQc0aQ+Vxl/rF5IRPfzSMMFqGacD3ZPVdpQHAYK+71BVqZGJ81rFy/rKp2btRSV7cE2TnmreGv/NlRpg3Q89JDqoapIT3QJmWYd4CxotDMw4uNjOO6qoMUI54TPx2EI0PMGawN9rnJqjCiNydG0aMzZkdwUJD47ldGEP+2u4Kb+zQVG6Wxquf9vgJNwmKop7CuLv1pD3reL/3d+GecILanwxoumn9O+tirZd8co0VaD8++qQzUcKdaaHSq77xwiFbCp3eJFqhLTZ9N3UZymdX5scWadmmoXwHm3Q8V00ANgFOkT0rjM+e/qu2HXz6CYoqoZd0rS2m8ofp6ETHKoexR3u4/47uKtFhGw4OaJf1bPcf7AxTf89Xx2YICIbWNLyWKPh6/V1bHBfhIjS0PdxCREmEettVSJCtrFLFpFS4bdPndsAbsjxxFBA8VjZ4yteSiA+I+L8d7/dEHFrrQhtbsC3Q+rPuJ81m338rS9gfFgqHG7RP/ThZi/m9A6P7B1JR0rXs71yoUK01YWvXwLGz++ibK18V1sm+qx8Zuu0VCzjBOnwaVDfu8d04K9B8c4ePz7WHYcWyhsLmtlW3A2loAW/HxE7UBVSutOT/Y66tLmotAR9gVauCal4pj6yVeMum1yc98W3BktcKct6C19W/if5tV8WF3Eb7UThFQvFUWMY2kI4pqfR6s7CGleVkROnBNjajxnnH3dJgq706JFA64yvte8hmuH9tIe68dGUGEVj+xIHMW8cSQVraSefFquKPNnA01GGFuInOswEYR0H7v9TawJTQj7SNJtRG2Ld/ZuApwwfKnnNxmpLVtbPqx5SAgdv0zl5OmdevOzO+7/FmxAuqrRtb9Ms5Rx/NUack8FJXtP/LUhn5DnMOmdtqnz761ANXJfo0XfrGZoaYK9/zCMFhc0bPDT+vsAIwuS6JoguN/NFKkyerbBT650wufL3g+b/6uQeBg+6ZSDlbfA3DfC7p8XP1ayyCJg74OSJ/49zthpFfdym7LvvADfmkf8wTbyX/+KNlj198WPvcbn45nWVk6ZJj4hqNE0EjLBqFV4UolkjDHG5Bg9so/ZYjoXqefTvw9+uMqL5buBso/sx31VL9YJH5FvdGC8WEdZE1woFrFd7sLMNk4JlegXl7Lm0zDrKkjKJE/Zz2JmCYUIH1T99EV6O2qwuh2v948/fR9NM49S2RhCWAZoLqdWfOX7CsY8JEdYa60ryN8rtsRrpmgNRxFCw++ZQU3FGrDAKEIKVBSbyqojWLazGFCkzbJIFxaSjRWzOE6E71oH+bBnGvldzih4IsWhovLPVhOa7KOvhOW0gUM+p5B0bqwPNS8PPh66bo8N4i5XuVQ0sL5qLq8f2Dklu3scptD4XsOqjErfI3XnccPgbtx2Ck3ak5LK8mGhYAvBuDhKt6uCFytmZb5vToaYHR9ARU46PhuFo76JSoqY6qbbHaQ5OZqzXwoFNc9zVnCEX064q/BZBtUp5xl2eWt4sqqDhOoiprk5P3SMgJWg11XOkOZnWfTkGS3aJ7sXK8JdlJkJBJJLRw8XkO0sBCHFQ7V9NgutqTGVl3+uiwQBtJadP+V2rzT+ag15dTvUzoPenbzyxYT/D2Gc2VuQq7MFLev8OeI42aje4aF6R653NTbdZO4/p6j8qJuerU7XzZIRR+l42EYEtn3PcRjzEToJm//bCY3vf6T4YXQfLLwl97OX74UnPymxYo5hi69rIPFCLTVPrgMB8V9Pz2y78Fa48YdOCL8UFCFo1SeIK2eS8jAxOSyP0WF2cN/55aTGgDEPoc+eB5+d2E6o0HYZzBWzOSSPECItuymhKtLALY9W40/zl47Lk0XPJRSJ7+YuIvc4bLhYpJwHfvMFrvnoPrzhHoLzmgksX+ZQ9POwwXqRVF6zWIGgWWnmImUGqeoQHncDHpdjHOtqYNliwfZdMsega6rFZWtyw926tFkROcGm4AwsoXCAEOuNEG3oyIIGtVNDRVKpVmK6XRR7aSVOB7GXK+am8+WJouxvTVr4rBjbrSGeoBvVXcGI5qXanLwr2MSJTFzSJIGTQul2V/CDptVUmjHaoz1cEOk6o8CqiWBTeRtd3hoqzRiDeoBBV27UaEG02wmtF7lW588hi62vaCeeV1L4WM0i3ty/nWpzDBuBKh2ueLFrVIBqI8pD9csZU1zYQsnIyQIc8tVzyDehinTHyfV/FlFw/Ok1G2HqjEjR3Ph4os8rjVeMVZ59/lej9E3g9LSvCk5C7HkV8IoYciHEtcA3ceJdP5RSfinvezfwE2A5MATcLKXsSn/3KeB2nDjZR6SUT7wSY5oKVgrmXA+96bLev1aIScySHlHOOE9vC0nlcZ3kZX4G3aC6wY5NvR84jPJiUUAzDrt/BoMHHE5DwdhVqJnnhOjHYaXgmc+AldMqVSDjCpH/WETNL5/j9BPNVDfpvP770Lb6zMaYDbdwU0MVAwwVzWtnY/ehHoxoaUEIVwAu/zd4Ub5MhKzmNwLC9b10iYMswOme5LTmKLIyctlodU4IxHvpABXffxG1McazChibaxi9aSEr3qZx5VfI6eyVkImJhUMWJJJBhvB7LoMiC5y771T47v02zzwnsS1obIA33vAIDfWF9cOKtHHbFol0zfef1BjvFNYZKY7lHQmPqx63q5qhgU1Fa5JDqof7Gy+kVSnnLqWd024Vi2MFRlUBDKGwltMgJTf3baXCnLwGPBuWUEhmLYqaEqMsi5zAbyWpN8IF5yvm/UlgWPPT465gVbgLv2Vw1FtDWPNiZB1bxS5qMFNC5ainmgF3OQd99YS1QmGguOriF43nU2NE8VtJLgwdoWGS8jC/bfCu3k3EFReP1Sykx128VFGRdskuauMLjFJRiWL3Qp/kLdJwOre90iH0s4manC0Mc/hVOnJp/NkFbkIIFbgXuA6YD7xDCDE/b7PbgREp5WzgHuDL6X3nA7cAC4Brge+kj/eq46Gb4KV7KO0x/pVDIou2cywFRUJgjwsrKUiGcTzQM5ysrVRuv45sCLV0ky5fDdz2IuhZc1i0xykpK4BUMLZUo3kEH+lJ8ff7zs2Ij+NS9WLclNDHTcOKCeJdk2/zwe1QPsuiS54sMNIWFnvlgcy/m0Vj0YWVLjSWn9fE0rtj1K59Bm1mFOG1EW4b1/kDVD32FJu/Kzm6Lnc/McnrH8PiV9Yx+mRhSNPjFnz0gyqP3K/y4P+o/OAejQUdxQUZDEUjkWWYBnUf3a5yzCmmUV2rQBEuFOFCCA2vu5nWhndg2ymGotsKys8E4LcMLggfY8Aa5fP2Dn7hSpa8wulxh6XfmhimyowVlHrJrL9spBBsLp+OTNcGL4qc4q0D25gd76fZCBUNfwscYlxYdRMXGjG9iqaaGzHKOrhxcCez4wM0GSHODx/j3T0bcWfVPx701TtlWnlQpU1LYoRVoWOsHu2kLG+lm313bQEVqRgNRrjk/RjPQ+vSptxK8Nb+7XiLvkgULf3Lxv0NF7IhOItUkbOV2vPV8I4nw6uZW1f+Av3HC875ChxjFdAppTwqpTSAXwL5jZFvBH6c/v+HgNcJhwV0I/BLKWVSSnkM6Ewf71VF/17ofKK4yMhrANUl+Zt1gnlvFoXRWAHN54O/ziGpTSy7/ow1rizOSNf9TrMTs9h8Ipy6by3PTgq1uPcOoLVFceMi6Cn0Xs4WZSLACrEUdZJXyE4JjnyuhVJ6ELULoXIGWCWaYwA5hLpKUcEcMQstK5CmoTFNaeaiq2uZ9oXDkCegInSJ2hBHOa+fbT/IPbZbuKilpmBx4OQmU3TK3fy7tZWddnEPQ9MEXo+zb0P1lYi8+vHU/8fee8fJddV3/+9zy5Tdme19Je2qd8lWt+TejQ0mxhiMIYAJ5CGUJDxA8kpI8sCTkB/Jk5CEEgihGDuAewHj3i1ZzZIlWV3aXWl739npM/ee8/vjzpbZubO7steyjfjopdfu3rlz7pk7M+d7vuXz+QqNl4rnM3FH+EjleZz0VUxiDgQNNR9hccNXaKz7OAtmfY65dZ9kl4jyz6ldmeaYuTCRrAmd5gMtu9j9YhWd7T7SLn6BwKlmB6epiVvoWkz4Cc59ieledma6txnS5pKhY5hqzGvO9w3o9hbz3/UX8Z+zL2Vr7RXohQuZPbQXU43lq00lKbBTnBceS6Gc9FfS4ivHFvpozljibLQDKo1X2SyKdXNb1w584xSNFIBSXNV/kNu6dnLx0LEzygcLFEuina7n20KnywzmvAsK6DSLGPIUsj84m6jhxRr3/UihTemp/64gbbl3pnurMBOGvB4Yn7xryxxzPUc5BNIQUD7N5844OnY70tK/hzsMv2DelXD99yBYD54gIJyfJY3w4YfhSx1w1T85OuhvCnomrD7hmyx0h54WqMuxA4DjhV/0tdzjW//dovD245T9eBvBvzyAVj3S6EVR8rVDbNY2nHF70nzwCBPNzetQIOMave+5nKFDBvUX5D5XaHDzLzPj4BnTR5+AOpHdreUCbR2XaxcxV8yhQczmEm0zl2pbEEIwmA6jdJcQkwB9TtRpBjMBl+pbKKAAM7M5GFnUDaCaMEvo4MfyGPYUnLYC3xwaaj+G3zsbTXgxzAqayi/gUDC3iUlKM3iiYsUki7fGNsPiUTpp8wRIW2G2dv+cSOcvKYmcZDIZF4+QBPUkl2rHeXr7Qu48vS7nHAn0ZHLRIcOP5bLTypeTDdqJUY+0Kh2eFus5LTR2FzWM/n2QQX4VejSLSz8CE8nSaBel6SgasCwZY3Eqia6cKyU1R8l8/JbJ8aRtVkSy5WwXx7pZHOvGVDIjopNrMO08/rWpJME8spd+O0WTvxJLjAm2joxRkx7mox2v0Jjo5xfV69lZ1ECvGaDVW8LjFStI57lfE4++mw27LcM0d/zMUSk8S3jXFLsJIT4DfAZgzpw5U5w9OUoapj7nXEagZuznF47BkYeh7zBULoPFNzrqaQDxASe//aaQJ6Sumw4/fPd/ujzmgVt/DXWZehKlYOAE2P4EbZ99nJLSJFrAQsY1ir56iN6rryT9WinXr13FHG360rRTYZaom2TBURR/Y8/Oh5EAACAASURBVB+hj1/MqltNFl4PO/8NUlGoWwc3/tTxxgGEEGzRNvKUfD6nejyuEljKxsh4lkIIZok6ZlE38YKc+k418tNtaIEJN1VXyEPlrPxy7iwLRQEf1N/HfnmI3Wp/lsiKBniwCBKhnShzCGBLRWtUUeIRlHizl99C3xzm1d8++vcCKXlMvuJ6jyyhcSpZyp0tG9gzPJtSM8ZHZ+3iiorjhHQP98gW0kiORzq5fPAwxUpSClSmhrEQpIWWV+zEq9lsLmvhx62bubNtAzfWHqDCGCvYsIXG9uJ56EpiZHI6I92/Rn7PZ57Hl4slNDNvsxSJk8cWKF4uXkDLuEYaUinWhE64Pk8BxVacj3btwDZK8FthlBrztAtkKq/hrUtle4ErI+05xX4TIwxxzcQj03gmjJoSOm3eUnQlqU8OgoJ2XwkLYj1cPXA44/HLnMiFAKqsKNf0HaTTW8wDVeezvXgeS2NdbBhuybQqnTrn/U6jlZ0pLDtCNNFCwD/3rFxvJgx5OzB73N+zMsfczmkTTvytGKfobTrPBUAp9V/AfwGsW7fuTW11Gi6GYB0MNIHLpvicR/8x+Pd5cM23YcmNsPyDY49FupzWniceh/Ydb939sxIQOuX+mJ2CEabNqZfggdsg3g/Bf95PwcfjCK+zeGl+p2y+7Cfb6F33Xiq8M2fEwelSdqV2iUMJsyRKlwjhRBCEX+HZ3EPgWzt5/NNbCNTAbY+NceMnok6roU5W00pH1vE+Btgj97NBP3/SufQdga5vzaXi/QcR5tg9kFGdxON11FcVs/yWsfOVUnSoLtpUB168WKRdfTMDhY8kXnQebEnx5Z0JEpbCUnBlvcF/bi6gyOO+7LaJGAYCN9MTi3n49GsfIWU55Vx9qQD/ePxqWhMl1KyKkkKiK8mlg0eyDLYTihY0+SsoT0cpc6k0lwr6Uo76oEdYPJZcxo3eA/hlmm4zyAuli0hoJrd3bMUrLUxlj+bDJYJuT5B+s5Al0e4soRcLwdGCmtEQ0YBZyKBRQHk6mk3xEhpPli1jyPDTbwayqr8BKtNhV+EVcAyYjkJXCjM94Gq03e62haDfKMw65pYymDiOR6YJG35KrNhokV4ajX6zEEsI/lfbC2NPyIjSTIem50EyKznI59qeRyinRenI+O9mb3u6UMrCslx4sW8RZiK0vgtYKISYK4Tw4BSvPTLhnEeAj2d+vxl4Vjlxh0eADwshvEKIucBCYOcMzGlSCA0+/jw0XjwDoeG3GRO+uzMDBUPNcP+tsPVbY4c7XoXvLIIX/x5at86AN/4mcPy3MNwO/3PdGN/c997WUQM2HsbcCHVXJygozx0nrhLssPdwn/UIv7aeoEmeonOvYvu/ZURoMhopUknaZDv77NfpsDtpkx28Jl8nRpwPajfi0YycFIDmlxR84DRS2Ay3wh1XjI03EVJJ2unKOW5jc1y5e2/jMdgMmmXQvek6Ij9YiNXmJ30sSOhvV2N860Ju++0Y+0wqyZP2czwjX+SgOsJr6gCvqyOuaQILgZcCTvWZfG5bnIGkImZDSsLT7RaffDE/NSGirExWNBdHD1WQtI2smuyENLmjdRP7fU52rTwdcX2ugaLUinFH3Wa6PEU5QZ2kNLi7w9kxKSVYaXfws9rNfGf25dxTs55ubzFXDRyiwE6NdjXTcIJDRwqqubtmAydL1xL3VZEWGkmhkxYaPZ4iXipbknWthyrPY8AsICU0ksIgLTR2FDXiURaNiX6qU8OOAZQ2FakIPjs9rXD85MglkUmhsW9CGuNwYY1rodx4mChKrDgaGQ9dGLxS1MgjFat4X99+vMoe+5/xp6cLHacXuznOiDuzf3txdjYSCl1/KxZnd7xpj1wpZQkhPg88gfPe/UQpdVAI8Q1gt1LqEeDHwJ1CiBPAAI6xJ3PePcAhwAI+p9SMNb+cFMFa+NhTcNe10PQ079ptopXHMMzI2HF4/uuw7k/AG4SHPu6urvZ2oGol7P1J9mZCJfIUPmjw3v/MXdCSKsnD9m9JkEQiUTLM0Y8q4r+WCFtHN+G3n4ebn4+wY8VjJBlXdZf5vOjoTu5Ry/MB0hTCb6NSOsqCIw/Cqo/mnqZGGf25yNdsZQRSSez17RT83y6sVj/D/7KMoS87uWHdC+d/hayCuybVQje9o8IyrpS20Zco+Ki2jq8eTJKYMI2UhG09Fm1RyazC3Ps7VwTy9i3v6ylEuTX60SE87KW0LOGErvPkGeOaQ2V4uHI17+vdT2UqTMrS0YTiey0XsSc0G4Gk1IyxMthBU7JqNLytKUlDYjDHKBnAgngvTyP4hLmU+rq19CfbaUmeImYU0ecpIEZ2AVjE8HFnzSYq0lH8MgUKbuzb54ynbCw0IrqXoExmuNyKY/5K0kLHM42lzp3vrJH01WImnOBlv1nIU2XLsiRaAV4P1LMk1k1VKpz3WiMRgBHoKLp8JcyLu7cifSdi/Lv4dm8SxiAw3k2GHEAp9VvgtxOO/e243xPAByc+L/PYPwD/MBPzOFOceBzaXuFda8TPBnQTeg9C2UJHg3wmIXRHR711m9O4ZLrwFMHCa+HIA9lqcJEfLaToawfQCsYtWpagxqikclYuh+2wPEYyY8QBYr9qJPabelTM2RCMjP1M6iUMlXItussysi6rrtUcQIWca9spiPbkjqGUYpAhigjm8LoFgnqRmw8fHV/ZPG4/zUDJEIHPWqiERtFfHaTvpktIvVCDpxA2fD77OSdkc5Y63AgMDIIEGCKEAoIUc5V2IaVakFORsOvXxKNBV8zdkBcIgxuZw/3k5kgKAynCw14m3jBpC/x+Z27Dhp8+T4CqCdzslNDYE3TqZGK6l1/VrKc2MUjDkT6+efxq0srAp6Wp9ob5p2UPoZ3h6m4giGfuT7m3HuUp5xv2PpIunHsAhKDPEwCl+EzHS1lG04Ok1M7mqC+M9xLRTQpyXr3L0GR/rNJCo8VfQXfFFvaqPlA2aRehH3C89Hur1jI33sfyaDvz4n1Z99Ftk2AomyWxboYMP/pZLNZ6s3jnGHAHAhOvp3rqE2cI75pit7cCR3/ttMH8PfIjOZzh08qZz4cvvA5uvhee/N/w2k8dr1FojpedjxoYqHUamwgNGi91espnmESEv70U7wW9eK/ocsizEmSPn8i/bslUV2SjQ3Vhj/NGIz9egIpm51q0mjjGsqHpceqFU3gnBKg0KFugUhqVjz9N5EcLsR6bQ8PF2QOF1DBP2s9ldUcbWbx1dAwMNmh5EutKcfTw3fQ3prE9zldZ+CQCScUvXqb4Szdx1Tc1AhPWE01orptXC0WJWMg1ogFDaHjE2L24sNrgaChFeoLznpawqDg/BeQ9xizarRg76c3y+5cv62ewO0hy3D5I0yTVtRH8fgsTjRSSRypX8wc9eym1YqMe7a6ixiw5UnDoXTeX7+X+4p9wNFpFQE8xt6DfackKtPrG6iOk0DjlK6UhMZCVTLAQHCuoJo2igQDdKs7LspvtqpfoJBTBEVSmI3hcBBEmfnRMJCV2Mi8Ny61a3lGt87Az2Mi+4GwU/ZlijHFLuFLUpkLMj/WSFjpHC2sYMgtoKqikqaCSpZFOLhl0WqdOFnCXwClfOReIphxjPp2vwVTypzOJyQoT3y4IYVJXcQPa2ZFEAc5xQ+4vdfKGb2eu992AF7/hFLnlg2Y6hl7oTKmxPoJL/g9c+nfO79d/z6GyxXqdIsS+o45gz1DGkSueDZd/06lSL2kcG2PZB+Hlf4SBkxnv2dLou+lSzJWDeNYOYLUUknyhml6vIPx1J50yHoWiMNug2blLgvDaKDn9PnRCClIHitGqE2jFaTwrQ0AIz8Z+PNu7qFs3psOslOJx+xmiTMwzC2qpol7UslhbgFfkEZW541uc3GJie2pyHjIqUlT8fCsJfRFKVWVR7haLBXSpnhyvPIXiF6qHh9Qgf6GvpI4xQ/6F5V7ubkoxnAY7c88MXbJ5WYR+U6OIPA1pgE/pC6mUPp5S7Vgo/Bh8sqqWNRst/n23wLY1lILa+mHWbmpHAddSh6FpJIXkkdoAntQgfpmixxMk6VLYIoXG9mAjm4ebWVWUHf7uLFmLnVlU6xODrIq0A4qUMBAoTGVjCZ2I7uWlkoWA4iXZxb3qFGnkWxKwm4zTPSLOMvH8Fl85rxXNIWAlWBbtoMhOctpbxomCSiSCqwYOszjWhakkEsGGcAvPli7mYMCpOTgcqKUyFWZ1pA09TzrFFhqHC2vp9QQ5UlDN4lj3aOX7+Mr+N4IRHvxI3/OZMMBvZIy32vDXVbyXkuDKt/gq2TinDfl5n8iou/3ekE+KE485muZuRbDlS2D9Zx1amhDwow0Q7Z58PM1DjkiKp9D5D1C9Ej53OGPIFRQ3uHPJDS98ajts+2fHMx9udard0wdKSR8Y88B0Lwy0SPqq2xhQgxSJIHPFHJZrS2ixT4+Gxws/1kR6bxkqNva1sE8VIvu9aAXT05s1dZ1FlXM5VnoAfGPemRawUFc2E1JLKRaOZGs3PaRw3/kERYBVuktP1hEMD8LT96Ktv8X1YYmimdO02h3MF41Z/Pk5YhYNzOEYzYy0zAE4TBUSQQSL79tH+HtjLBJQV6Dx9LXw9T19vNRXgNdr0bB8kOI5Yf7JbuNT2kLWaRUuM3FKsy4Mt7NseDdSpbGNxfzz8Xk81qVheCzmL+5j/sIBPOMKFZ+nm7WqnKu0Ov6AOXzPc4S9TC59uat4LkndZHOoCb9MY6NhFi7laKAeiLFxqIkN4Rb0jIBLCo1hw8/Rgmr6PAGa/BUooeFF427ZzOpwC2uHT+OTFh3eYp4vXZSjhT4evWaAlKbjsd9YmY+a8HM8LDRChp9ZiQHe37sPTSkMJEuiXWwY9vNy8YIsoztS+X7FwFFO+itJ6B5QilXRNteWqwqHErgnMHtUmvWpsmWc9FexPNqBwGkBuyHUjDmNrU0+Y3mkoIatxfP5eNd2fDMQ4ptpo+xek3BmCEVf/70hP5soXwRL/wBe/+XbPZN3PtJ57Fj/MXjqq44X3boNYtOokZEphy41FabD9/cG4bJvOP9/88ew5ye5KQBZkGTHmidIyDgWFoYy2MVe3qtfw8XaBWyVO7GwKPzDJmL3NpDaXoGKGhg+gdAEs3sX0Dl7/9STIVP8Vh8m3+6wU3WPGvKESrouiQqVFWp3RdsJML0sfvY1+hursb3u9AsLi5OqmQXMoxonHC2EwKct4IhM4yOKhcYABVkV5H0k6VUJKjNyk9F4M4mBX/LlBpuvNkhSQmfAKOAetY6UgDvlSdaIcjSXHVdr931E4idG+dDp9B5uqzrG4+0fIxb1cuxQFbals+K8sR3gMGleUF1stXv4kr4cKdTUtSxC0OYrRRtSWGiYSFLRQ2yJHSFauoyNwy1ZtC8PjhxpvyfAyUzHNOeeKS4bPMrSaPso9W12cpAPd+/mrpqNDJnu4j0Iwa8rVnNL9668HudkYecR1TOHBpft/UrgYEEtH+nelZ2DVzal6RgXDJ/EdClos4WgMTHAkcIadGTeHuUSwZ01G/HLNNf37idoJ2n2l/NacHZWGqPXDPC+vv2Teub5DKEAlsS6GDL8PFW+lBv6DrzzwuIzMEYi2TH1STOMmaCfvauRfIdUYb+jMUVCzU7C3h9Dz+vuUqsTYRbC7M0zNrtRbPmLbN11cCIJc3+1l5geHQ0lW1gkSfKyvZ25WgNrWOUsoqai8rfPUnHvixR9+TDn/f0gX2yCa9evYCHzssYtIkCAwlHK1kg++3L9IgqE35XKJRB4MuHqdtnJDvlqnqIznQaRq4iWhYo6OudVsucDF2IbGXk8KV27z1jYtMjTWccUEMekiyAhfC60IsVJNcywSqGUorXnfpRKM6Ll5VE25ekIV/Uf4qOd2/lI+3M09T5AMtWLPS6/kkj1EokfzxI1MTVJqRnnqkpnN2fbGieOlZNOZ98zCaSQ3GGfYKOoxDuN5eqq/sN4lDXqdZooPMrm+oEDaC6eqEfZLIhlVyB67CTLIu1Z/HWBUwi2frh50ut3eov5dcVqV8kTBXR4ithfWE96ElrYeIM+8ryQ4cen0q7V5yaSknQ87z5nRLZloh77ePSaAbYMneCW7ldZGO9xtN9DLfxh53Z84zTXB803V4ltoJidHOK4v4peI/A7Wmd89s3qOe2RA5TNn/qcdzJG65beyuYv0xg7j5qjK8wCR37VDUrBofscRbd0DFZ+BNZ8OtdAu6F0HnxqGzzxJSc64CuBC/4cTm4+nUOxUii66CEtLfZyYLToTWjgu6oL31VdJGkiYNwACC4yLmCTWkeYCIUU4BVelFJ0qR46VTc+4WWeaMQnvAS0Qvbbh3LmZ2HxktxOu+ykiZasQrsR6OgUUcQ8Mbki1HBlEU/97w9gecYV1Cg1Vm03DgKRpc8OsEKU4CfBMnrwZTYTYbwco5IUBmkUd8km0kiuSJuskLltR00Ui+Pdo8tWMvI6JyKvAwK/t576qveTSLbjtrD59TSfbXiZOu8w93SeTwQvkbCH0rJcY9NNguWUUEcBzeM6xNm24OjBSk6fLEHZsLCmF29NGm0CQWGiYRwPiaPQNh5VVgJbaDneqwbUTIN/2eSvoN1bQm0qlJVfTgudbjPI7qJGEprBmkjraP/yiRATfi+x48yP9eYRVIWY7sGwJVrOnBXN/nIEcNFQfjW58nSY6nQ467oGEr+dYk34NNtKFgBQYsWw0PDkCc+7zX/iOaXpKDf17uVAoI6Lhk5gZrYaZ7NI7q3E9CtqZg7ntCGP9UPLC1Of907G6PdWh0w3hXc8Gi7Jb5gf/RPYf+dYJXr3AUeY5fatY9Kwk6FqBXzsyexjJ638XyyJnTdPndVWFDCFSRljuXchBLWimlqyy8IDIsDl2kW8ILdiY2cZbBub4zTlnc8SFrJWXz0qyZoPh+UxbNMg6w3X3KvRBYL5WmPWMQ2LlXQjx9HngiRZSRevUg8I4pnH9hJiaR6Fc3ffQxFPttPc/hPqK9/vakSVgmIzyYfq9nBD9UFu338r/gL3HuUa4BU6vgkNQl95YQ6DvX7S0lnGDrbW8OnuW/nFmjvw67mRDrdPgS00Xg9kt3dYaMxCV7tzzpVAXDO4aPAYUd3LkcIaYrpLIaIQPFh1PivD7awNn6LITiAAr7JZHe1gabybu2o2saN4Hh/p2pHTB90tT2sqSWNigJDup8yKZt33lNDYE5hDoZ1gQ/i0UzAnnDF+U7FqlJ5WnxzK6yvm+2oZKObH+9hWPB+hJMXpeN4c+3TNV5FMUpRIMjsxMLrJYtzPt9Kgn43Ngq6/+aZMZ4pzOrR+/60OR/p3AQLwl535k/JQUN8wtMm7dgJw+D74x6BjtMdH+wZOwr6fjRlxACsGXXth388nHzMZds555d/gub+D/1gA/1gEP78SKl5bmhPqFkAdNXiEBy/ufVKDBKZ+MXkwW6vnI/rNmHmXSHekSGGIqd+UEMMokWu1NZeQrUAwrCJZTRyOy5M53p0GGNiUEKeEOJVE8JGmx/AR0r05y/fke0aFVGnSdhhDD+QokY0EDby6TcBI8OnF2/D5csPGBoI1lHNQDdIybmM1NOBjoK9g1IgD2OiELS9P9CzJGWfivJNopIXGsyWLHQ54BjqC9WYDJwuqXNtw1iaHWR8+zZahk9zesY1ZCfcCPCk0DgTr8UkrK1+uo/BIi42hJixN57cVK0kJffRabtccnZuyeaRyFXHNM6o2ZyGQCK4cOsL6yGkOF1bzUskCnitZzGNlywnYCaoyGuyxiaGKaSJgJfiz1mf4s7bnuNSli9qZGPHx5+m4G6DxBv1sMgZmDmffPz5nPfJoD5x6EVwihu9KKBvig2f6JKhdC4kQDJ6YGRpeQQVEXNXys2El4LWfOY1Xbv6Vc+zUi+DmiErLCZef93H3jcfprY5CH8oZd3wasfkZReuly2l8sZ/Uoi6kbiMsHa/u4UL/JoQQrBGr2alexRrnnWpo2Njcad1DMUHWaKuZpeUKs6RUml1yDydVMxJFvajlAm0dARFAE1pWG9LxEFIS6A2RLPSRCozt4POpu01ELdV00u2q+qajZx23sXlaPo8fH1u0jczRZjFM2DW0L4BF9GZ5Sb2ikIcqV/Gh7lczFd9OCF+fQmxUqTRpawi79iY6eu6nJjmE5vIcjya5OniENtuprhYITBxBgc2xAUqTx9hhelGFNaPN6YcGfehKYk3w0hPSw+vhWt5X+3qWtzceltDYUTSXE/4qNg83cdnQMWyhcaiwhotK34+F4qny5Vygm6yKtKMrSUzz4JPpcbl3CQqu7zvAf9VfjHIp8iu24q6SpjrQkNkA9HmC/KRuC8sjHZRaUTrNIjaFWwhO4Jk74e8IV/cfotlXTkrTSQudNeFWfJkPvKkkS6Ld+PwWlakIfpliRMy1zVvKrqIGrpygXW8jJpVdVZDFO58qDTBTmGrMN1pdfjaC3qn0mS7Ebx7nrCFPDDlG4Uxyu+94nGGeXPfCBV9y6GD33jy95xh+hw7Wn6fqPNJO/oTkBFhxOPowRLohUA2Fle4Ut5Fzj/0Glrx/7FgqAk9+BV79wWRXEVgxxYl1F+G7thNz+RBWU4Dk07U03aVYfoOkXqtlnX0+BzhEjDg+fCRJjobW+xjgGfkil3Ehc7SxIjSHB/40AwyN5uDbVAeP2I9zs34jHmFSSjH9ZH+xG7cfZvPPnkRPWWi2pG3VXF787A1YBX4atak7+x28F1757gK0/zmCVpEAw7nZGhoePCTyVLzHSfCcfJnrxdVUiyqa1emcYrsRQzveU6okyrDp47/qL2JuvI+yVIS47mFZtJO6VCivDymFgd9by4MizK7qtZSlIny0a7vromOguL1rF3NmfQ40g3arD6PzbmwZRyiLWqGxLtTE3dXr6PcEKAykXSmJHpGmsWAYs2gD9vAu3D6ITmOUIm7peZUCmXLmr2zWRDoYSN3Df1WtxBKCF0oX80LJInQUt3TvJpDKTcGYSlKZDtPjKcq935NIzEb1Me84rnvYXdw4+neXr4QPdb+KR1lZGyoTqE+FmJUKkRI6KIkx4fWZSBbEe3O46LMSg3R4itgZbGRDuGVUXKfNW0J9cjCHUjbSQAYUZ0/WxB0jnvnEEPw7FeMLO88WztnQeun86RVQ/S5DWjDcBg98JH+1ue4BTwC8xWD44OKvwcYvMPm36UziYRq8+H8dlb25V+byy0fnmnby5SMYOgXfnjOVER+ZjwClkXisnvD/W078gQbksIeHP2vzC+t+HrR/w072UCHK+aj+QTyYOcVxNjY75Z6sYz30MsRw1rkKRYIk+20nZ7NBW4s+bimsPN7OxT98FF84jplMo1s29fubufzfH6KaSmZNIscKThObhz8BnS966Fx3HZGfzMfu9mHYRubak9PWLNtmZ88h5okG/PiyUg56psXJxLdAR1HLMJpSLI92cMFwCxcPnaA2NZz3Y2AhGNI9NPnKR/nf3aKIrkSRW2G9Y6jsJAOxgzSpMKcGniBtRxAZLqGpJF5lcc2Ac18rq6IU+VMYWREJhalJbqg+wqKyq6ituMHl1YAldErSUTwTFM6EstFSfVSkxnWtEgJbaLiXpDnI91hC99DiL8Oa8HhKaOwqasw7Xp8nyN7ALNev0chIHmWP9hh3w8RXbSJZEe1kR8k8flB/MfdUr+VH9RfyQPUadhU1YiOQOB66heDVwGxO+CtmxIhPtRxMySqc8H/k2Du1HEjXfVOfNMM4Zw25psP1P3AqqM9VKAVP/2V+OVSz0LlHf/gsfOCX8KV2uOivYOkHnPs3E7BisPuHzmbih+fB5q/iuknwBKFi8djfj/4JJN5kBMvu96JOxtnyvQf56Ce+xaUf/yL93/4UIeWuqT1MOCvPPKSG84bCD3CIXtVPrVbNdfoVFGeUz1Y+ugMtle0FG5ZN7dF2rh1Y6prjHkE6Bi98Y4zTL7v9DH5uI6nXSrFsOa2wvNCh9XQYGTV4n34tS8VCCvATJMCCCRQ7AMvWiKcMNBQXDx2jMT6AgcSnrLyhdYVToV1ixbFa/5u1oZMIpWg7VczjPcsmmaVke/wgv0zuZVa0PaddpgAqUxFMaWEIwQev7GN5eSeGsDGEzaLCXr638gFWzroOTTMoDZ5PafB8hDAQwkAKk6QweLhyNTXpcE6v7pHZV7p0XTsQqHftJJbQTPrM/LUUj5evoNVXisVYF7XtxfOyeOsAJekoFw4d56r+QyyI9ZDW9Ek3DyP340wwUoWf1gx6PUXEdQ9emWZRrGeULqcyr+lQYS0NCfc2qmcCBYQ0Lzb5c97TeR1nK6Q/EygJnHfWr3nOhtYBln3Akfx84DboP/p2z+ZtgHSox3kflrDjO7DqNscLjw84rUzLF8ENP4JHPjkz01CWEybvPwanX4RgPYQ7yEoV2CkngqAy4sonn3jz1xVYvO97/02gP4RuOxer3L0P//BFxItzd3h+fFlSp8UimJdqolC8aG9DQxAlNpp/D/S6h6I1wwtDfVCVnz/efzy3hkBvjOC9uAc8E+h1uSw053hSI/VyNYePwOqPedmor2Mj61BK8bA91vcokTa4d+dKDrTVgIK5pf38aOmv8vbRngh/xpP2yzQbhlsIWEn2Ry7lsZ5lfGLWDjSXQr00Gh6Z4mNd2ye9jkJgoejyh5l/leKiSDNLIl3Mk/2UKEF/aDtK8zDgqyBQcSXzijbQljiGrXkp9S3EFicYTIZIi+6sfLEDwaCRHaorT0WoTwySFjq6crZLtnCSEA9XrsJQkg2hZpZHOxFKcbSgmldK5pPSDFKawYNVawhYCQpkigGjMKc/+fJwG1cOHmVEunRxtIs+zxvnWLvWBSA4PmHzAHDh0AlKrNhoiF5D4Zcpburdi6nc2PBnjhZfOatjHe9YwzvTKA5Oosj4FuGcNuTgGHK3jlS/B9hx6N4LT+2F574GUoHpBTsNi26AgsqMkpsCbYfSHwAAIABJREFU3XcGHcx0cO3MqaD5GXjP9518+Ilx/fTsJDz0CXjmr2D9552IQH4lTAWachqn5Fs+vBYrb3wA/3B01IgD6LZk9cOvsPu2K7DGrbcGOqvFiqwhqqkiSIBBhlwvEVLDOZfvXDaH0tY+9AmTt60ET1acYI7tY7G2MId+lo5D6yu50ROjMQJJDfzZ4wkBMqKDKdG8ziKtLFBRg/C/LyH+Z9nj9DPIEGMc6R+/sJ5T/SVsLmnmy/OfpVBPOB7dNFZjN+rU8mgns8qG2HaigbQSObldlXni0lhX3jChDZzylY0aQpV5oaYhWW51ZjTGIZZoIdR1mscqzqfFXwY6aIU6GjYaR/gjbRFFRQ2Eh1uQKjV6PQvBsOGjzTtGMWyM9/Hevv1oyumpbQG20HmpeAGHAnVYQuND3bupSoVHC+FWR9poTPRzZ+0mZMaLjxg+IuSGXOfFerh68EjWPfMgqUiF6fQUMTsVmpSTPR3jmBIaMc3D9uLciMviaHfOe6EDhTI9I4bXQrAs3nXOGHEAla/Q5y3EORtaH8HeH2dToH4Pd1gJkEmnG5oVh0P3Ok1ORtaAM/nsViyenPb2wjdg0586of3xkGkInYLn/gYQ2Y2fRiFgyUfSzI08QPHXDkAwSXZAT4Em8V3Rxaz3b8NI5RamLHliF2sOxfFgZgrITM4Xq1iqLcq+lBBcp12ZXwDC5fDr79lI2m8ix/XWTHtMXnvfJjqLkuxO7uA3kQeQ425o9wH49ix4+iu5zIL0oWKEP3dHoxTYfV6SL1YhwwZy2CB2/xy6NlwHvQU0XpZ9/mHZTSpjiLpCAVoHillc0MPfLXqcck8Mny4n7QA3lfdoCcHiyh5Wl7djq9w3TgB6HnGUkWYbIaOAJ8uX5Tx+6eCxHM/aVJLNQ0ewUFgoUkgS2MSw+YE8SlAP8mT1Fjo9xaO54WZ/BfdUrR0LZSjF1f2HMDNGHBzPx1A2VVYES9OZlRyiMh3J4lYbKIJ2kvnx3knvSWk6mlem1INi1vhc/YT7Icf9PhkUsLV4AT+vvYCYPn362XQN72TXH5ljbtRj6ue/VdSzs4F4su2sX/Oc98i79uXPEb8RmIW5FKhzAdPtegYwcAzmXwPHH3V/PNoN99+WzScfDzsBCKcIT9mZ+y2dwryr/wXWf85Dq9rIS3+7g+DfHEDFdeJ3zmfwr1ejz4lQ8T/bmLPMT3RXJZbHxJPInrzmK2BFrJ5l+jWkSWNi5s1d+zQvK9RSDtmHsbWxpUdPpihv6aZn8eys8+OlAR7+5u2cd//L1L/eQiLo58ANm2je5HCfbY/BgEryaO+dXPf9p9EtxYGnbiIx+D6UiwGUPX6so8WYKwezVl8hwGiIYdTFEB6QUR3vxn5022TJB6BmdfY4z6hhRsqrBiIF6JritvrdeLSpOYkWTFkUZShF2ONn9fou9D73hT1f/EQBTb5yfl252jVfUGa5f1DK8nyALCQP26c5YuocrlmPnvHk1YT3OGgn8bo09tCBuXGnqUBVajhHTQ2cYrSa5DDHC9x7Uo+E0yejfk12P9wey+ehVyaHsYpy2RC6sunxBJmdHHzDHrPbHCQio4xnT0pRHC9BO5Fq5zb2uwM66m1Y/M95Q163Fg7dM3OtTO00VK+Grj1Tn/tmcUbh7HcQpO3kw4WR2+AEAAXxqZqvKGecWx92GrCULYDGy8HwwKAa4nm5FUvYTsvmQpvgH51kwa0Jri27ELgeIQThdWuxi57DTg+Mhdc1HQqLOL1uMfvtp4gRp0ZUc762kqBwL2paay8h+fK9nNi0GN2ykYbG0qf3cv79L/Ho332MgYbsxTxaXsTWz7wHzbLRLBvLN1FTVNBbrPHQ7etY9uQeNi7+NrPM57h763/gtrzJQdN11RMCRrRutEIbzYyz9L6DbPKtQdrZBYttWHgJUEGUmpIwlq1R7w+hTWM1neoUW+gcLagmrnuIF5rYIS0ntTJZmFiisat47qgR15XNhUMnWBHpGG3ZObEwDhz6l/t4sFP1UpMaQleSTm9JjhEHSGk6Ig99LJkJKQ0b/lGjlfVcoRMy3GkxBXaSKwaPuc55PPLdjzM5LoBl8S4OJ+po9Y0pRq0Mt3HJ4NFJK9/fCARjofwiO5G3U9r493vE+04jZnw+b5Rv/kYhhCBYsHjqE2cY57whP++T8Oxfz5whlylHKfNs4N1oxAFQMNgENedDZ64S5rRh+iBQ47x/Ha861LXFN0L5949hl2Z7SdKw6SluI06CAuEssEGzBP7+PvjxP8Crzzvx6DWXcPQTt7BD3zlaoHZSNXHabuVG/T2uxlzb9woX/vxZ1t/1FJHyIoK9Q3jiKaQQLHxhPzv+8CrnZSswhZ4JFdsU9IWIl+SpeNY1husq2PnhS3n1gxdz9dfvZ9aRfbT151bEpvaU493SB8YU+Q2PpKeslZ+uWYNZ6DAR5mZC7EWYnKCcCB5qC4dZMbuLA+FaGvwDmFruuCk0lHDERPqNQmrS7hrkCo29gdm8XJLJzwrBwxXncXPPHkChKxuFIKUZFOZRZ9ofaKDLWzz69w29+5mTGBwNZ0/kGUM2xUtXktJ0jLhuEtW91CRD3Ni7L8v4Pl6+PKeSPKmZnPaV0ZDoz4o4pBDsCTgebpO/grRmYNpjErYSxys9UpjbJx5gXrzPKd94g7HjM82ZC2BjqInzwq2UpWOEDS/1iYEz1BycHhTgVzZ+O38Tl5E5Tfx9ukb8bBvn6UOjsuRSPGbJWb/yOW/I/aVw3Xfg159hxpIyHW/COJ0rUPabM+KGD1Z8GH5yISRDY2MefRg8xxZTsft4ThRWJXVe+EWYJfP9NF6WcfCKy+FL/8oIudlCssO+L0vlzfEWLPbJ17lQ35Q7mWQclMIbTeCNju2uNKUwwylUXEOGTRpOrGbZhYUckAfppIdIVYkziXwl5oD0eZBS8fIXrqHhsVddDXniJ4sp+cJxV6W2nPFCBqkIpCKKB39yinmbjpA2k1xACS+i0UURXRTRuGmQnUcbuEoeRSONnkkbCGESCyzhJY8ihaDZX86KSDvlQ1EXDW6NX9VdTKeRvcx0e4u4p+5SbujbR1GyD4XAVDYW2UVwlhR0pGooN4f54ulnSWmOZz8n0Z9lhEZsosJRbVMIdgfnsCc4hxXhNi4ZOg6ArhTt3hJqkiG8E0IC7+l/nZ95LiCc8aIrU2FKrBiDegGN9I+ep3BC66EMV1gKjV9Vr+Pa/tepTTq0xT4zwOPly0nlKQRRo7M+OxDA7OTQqEhMmRWdkau7hcDz/T4dvBkjfibG/a3aCAhhUFHisj6cBZzzhhzg/NvhyS+PGYR3KjRPRlL23VoFMgPQDCcXPmszFNbkKvPJNCSOBki+VInv4uxiI1vZ7P6rIl5LOG1Ub/3NuEYsGUMaVuE8C4XT6cwVKzaCnRvSSUkfr/3wE4Ren0fVMo0tX6jltGqlZ8QwjIRu8nQtG3vRgnB1CVogOHbMtAl87iiBTzZh+BSLjfn00EffOKMzETKqE/muk4sv+vo+gn96hAFzxKBFOB/BYeoI40FqULU0wd3pdWwJnWR2YpCEZmIXr6EicB4n1DESGWN4MFDP2nArwk6OGmIhTIoCq+ky3JeYdYOvE0j2o+NIvaKcXHtS6qSljqnZ7B+uZXmgG18qjSbAkJKVkXbXfPyIMZcK7qrdQMgsZHZigMuGsgvhZiXdtdGVDUUHY3TVFPMptZXK1DAio2o20ThpKG7q20uLr4JfV65i2PBzT/V6vDKNUIrEFEVlTf5KrhiYeb6rnUkx5PPKx4upvFlMN48900ZzsrEmXiuf/OxbtYVSShJPdlDgmz31yTOMc75qHZyQ7Owtb/cspsa70Yg3XOrUDMwEdK8jGHP7NvjDpxzuvyvjIKWRPlKcVUkvozrRO+ch+3ykInD6ZXjtp7lP9eHL69kWMsYtH1QhnrSf407rHu4JvEz3zR9CerxOyBRIe026z6sn+moPZd/djfzCLu7jIfao/bn66CMG3J7EoxbQ+ekSiv5hL/41Q1Q/+RwlX9+PuSyEmDfMYesEfYeBVB5eu4LYXXOJ3jEPrTRJ0Z8fQSucWJSjWE4nf8pc7Iy8TMgs4LflK3imbAmDZgHR2DHmJHrwK210QUxpBnfVbGB/sAHdKMHnqaWu4nrqyq+j0MVX8EiLhbGeHA/eAI5GKvn86zdzy+7baYpWYGp2Vp5+IlVqwi3CQHF+2KkaXjfcklMxna9Rh0eTaHHJ5vYTVCZDmCgM8oeqdWBOcoD1wy1oCIoxSWvmlEYcHEnWJ8uWYuE0PZmqQtvtMZn5byFII0ig88ua9ewJzD4rTRAnIXbmnHc24MzH2WaBQNcKOBJoOMvLpcVw5O0RJPm9R55B6NTbPYPJoZnv0AYvAiqXQd/hXAqaWQjXfhuivXDXNby51UXAZd+ALV8dO1S/EY48OKZ0plXFCfzJMbwX9GJ3+kntLMezehAZ8hD+ziJi9zSOPjcdcwz52s9kX8YvfMwSdbSpjizpVQOdVZoj9BBWEX5jP046o1OeJs1jN8ymasmtzHt+L2YiRcvGJZxeuxClCdS4rYFNHnF/ISjTyxhgMNc7lxKRVvhu78Sb6kL82REnOjNeGtawkbNCqISO7smODigL4g/NZvDzGwEwVw+ikgLhUoslkTzPq0Ap6ZTG/j01dJ4qQqplbCxp4c/nP0974gE+W7iUu8rm0kkMgcCvB9hQ8X4Wi7Fc9m7Zh+6ylBfYTv2A2+ehyhPlRNTJVS8N9Ljm5yczDjqK2gxtK3gGjRRitsmuoTn8w5JHMadpfUwlOS/SwR+V3oaWia78vfUazURBKTzKdjTRXSItRwK1tPrKWBjvIWAlKEtHqExHSQuNUiuGxhjt7oSvggWJvtHCMSeFoPPripUUWQkSukmTvwJb6LxQVsRpXynv6zswZTHdVHjn5qLzwZlxZcklJEvX8by9n9p4F6WZNrJnAwPD2yktWo3XU3mWrujgnDfk8QFHZKT30Ns9k0kgoLAqo3b2Vm8xNabffEWD9/7AKVr76SWO3OoIhO6I7Qw2O/3FR2OfU8AoyB5ndDwBB34BwTpYcStEOh21N3QJOuizY1RvfwxRYKH5JSolUGmNnuuuILW1ChFIU/rD7UR/vIDkszUEPnsM8deHuMNKUkEZG/S1VIpyAC7RNvOifIVW1Y6GhoZgvVhDvVYLwH55MCuHDo4B7FpQQ9eC66Z583Ix2hd9JG+uABRKCOwCJwcgPI7/5vYWiUIL7OwlSylQcYOhr64dPWZ3+cE72ZscQalSXnp2LsMhL1I6RuqVwbkc2lfD3Wt+BtFD/EXxZmKeEtJIqiao3r1gd/Er1TzKTR+PYcPn+lGwFRyMjFX4n4iVszTYialN/0NvI0YlU095yyhJx3K8eInARuDJzC1mG7w+XMue0GxXxbnJoCmbNhEDBY/b7TSrCGvCp9kUasZUNinNYFvxPPYFc8OtUcPLay7HNSWZlRhkTmKAqtQwJor9gXrmxfsokGk6vMVsLVlEj8e9ULLTW/KmlwlnE0FOamGq57xRgzlzmwZJ79CLeAvnMCfVT1CmzupmRCEZDO+jpvzKs3jVc9yQ22n48QWOsXlHh6zVGzfiQnNyydGePFSviTgDYRdNd6r+NcMx6I9+DhBO5KBymdPS9MGP5eeDuyFfJb6S0L0PHvkjePiPHHEaoSmUKUFpFP9/e9CKU6MiMcKjEB6bsv/cQdf696DPiVJwUyvmkmHij9YT/OIRKHSC3N308pj9FDfo11AmSjGFyRX6xSRVkgRJggSyeOQ9qo/pthudLnT0MUMOjjEXMBIwnC40M3teQoA9ZGK3ZtR1BNgnivH0F5OuH3RNy0sE/b1+ImHPqBF3jmvEbZMne5fw/tpDROInKfdsQkygbkmluF+dcjXi4BSIbSuexyVDx7PC3JoAJQRCKJQS3N2xluuqDmOO69A2MmL+jmuC3UUNAOwubmRZrAshrVHvNCU0thbPJ2QUUH9qAJUSPNm7hKd6l2CjcTJawaLA5EIuI3BEZKo4bffxFJ2kkJwXbmVz6OSojrtfprl46DhpoXMoMHlDHAAPGmkUG4ZbqEmFRsdJCY39gVlsK13MIoqYhU4PY/l+TUmCdoKY5iGhe2j1ldKQGHjDudORdjLiDBaEN2swZ9KYq57fcr4mJxWjeWugkPLst9Q8pw350YcdA/mODFlPxBu0G0pmZFTfgs+zTMN3l8AXjsOqj8GyW6DnAPhKHXrZ3TedmREfme9kGB8tVVJA0vkI+6/oclV6MxaECX7xEEV/cRhhKMwlQ5gLwoiCbI/awuY39pN4MJkl6lijraJAFODFmzNmiShiULnLsk4FHQ2nvUnuG1pFJW10vKFxRzToXa9ZP07xSDmFXa33rKD8S1sxXVpzeLEoCiVRKnfAhPRwLFqJAHoGX6B74CmUHqCy7Eqqg04xRJg0KVcN3jFEDB8SDW3cB1MAl5SfZO280+w7XU+/KOSniY18IrATv0qjgOP+SkrScUrtOKZyUhYjYehOWcw99hoS0oMXm6ju5ec1m1g/3ExjYoCo7mF3USPN/goAXilpZPtLDdj2mLn71skr+d6Ke/BqdtYmx85kO+wM1zklNJKaydaSeVh0jW5aNg035zRjMZVkc6hpSkOuAVdTx+H4HmpSw1njeJTkvHAbHcEFnDKiBDEw0UgjOX/4NJtDJ50MsVIcLKzlibLl3Na1k4BMTmkc3Qyokadly4iqnObynOlgMorcTCJtDVGvn/2OWEKYFBUuOevXPacNeecep1nH7zrORHXtTDF4Ev5jvmPI6zc4uedZ1XDkIXBpIjV9nCHPVoZNtJLcHZkwFSXf3D/6t5H2gW9iYNyBlfl3XDXRardzk34DXjFmyENqmC7VQwXlnKY9q2hNR6OCcvpSnc5iZ+oIy0ZpmmMBNIGBQR01Thhe9WAJC5UWkNYY+pu1LLmqkvZrO6fl7RsYFOAnKmOkE2C3+xGFFkZdbkjDbp2woCnoLk7SwiwW0keFiqJZNnraYvmjO4hWFHNiaTmakNgTfDqflmZBYR8SG5HhYQs7QmffI7zGENcEL6EQIxNHyP86GuP9ro1RlIDrlh1hzoYwFakwH+7ejZ5p3iFxONgPVp6PiaOcFjZ8PHhiOcePV5GSBkIoeFWwYUsrtfVhErqJFBoBO0mpFcMINRHVPfR4iqiujbJsVTeH9lcjNIVtCY5GqvjInk/wlQVPs77kNDKzmYlJk0fqVzM/0UdZOka7t5imwnrWarXsIKNepBT+CV7BUNpHW6KEOl8IHUEwHWFL6CT1iSFiuoedRY0cK6xBKEmJleQpvZmL4n14XNTBJApvopNooJ5oJkqxNNrNhaETWZ7nslgnthDcX72G2zpfmZQv7mZYJ/eMBd3+WopSIQrtN0ZjOxu5d6UsTCmZIXmQaUEIk4B/PoX+XE37txrntCEvW+AUZJ2p1/h7ZGOoGV78RuYPHXQdqlc5Ifc3IrSj+6ByKXTtnf5zwj9YSMn/2Y8YF1qeWDNmoLOmcCmvsm/SsRSKOAl+aT9AOWWsFato5jSnI4do2HUUTzxFYN0S0pUVJEgiEMwXc6lvDfNymY1t6Gi2xBuOs+mOJ+leMZ/0ui00lq9nlqgjoqLc/9R+UpWDpI8UEf7mCtIHS3nu4TjVx4TT8MXtvmQy9iC4Rr+MSioI6xFOPguP3xLAd3MLwX/bgTYu2qBiOqG/yeWem0f8eKw0RFPc/Lc/QOmCYG8ITSrSXpOSy/p4qGI5vcOB0fC6hsSnpbm68kjOQmwqiX9wF1/3F/NVfQWXi1qeVZ1Z4XUDQQEGcSziuneULpUNQVxzKr8vGzyaZdB0nGK2m3v3sK14Hq8Uz6Ovr5BDx+qyvGqAnVtnc/0fHOGGof3MjfeNVsjXpob5UNdufl63iZBRwMIl/TTOHyQ06EPTJdteaKQnVchXDv0BJUaUP537PJvKTuPxSJbGuthePA+pGcwmwE1aNctFCdvsDC1RCCK6l6CdxFKCfzl5OU/0LMXUbNJSZ233MF+f9Qt8mcB1QKa4euAQ82M9zE0OOI1ZlBqN1+TcY1SO0tz6UJOrzvzKaAcvlS5ia/ECLgmdyGs4z9w7VlTFO2j3VVJo518489HT0kInKXQCZyF3bcmz66UF/POZXX1LVq3I2cI5bciXf2isH/fb0LDmHQuhvwmteNvpStZ9wMnPvxEUz4IrvumE5qergx/5zmKK//IgGNao8RYACoStoymNlWIFy71LCckwJ1RzLg1sAiSSXvp4Uj1P3YFmbv7X+0CAZkvOu+c5Tl66loZPfBdTM4kS48Gah7GNMQ8+XlLI9k9ew4e++H206Dy4uZ4WeZoX7G3YWxSmR2LMiyBMSf+HLkZWxFBxHQrz5JZRFBHgau1SCoWT8y4iyPnXwsoeaNs+l95OaJ6/jwhRfKlCOv5kNbFfzs0aR2+IsOgLu9D0OOueeIGCoQiG5dyL4cpiWtcsRCjFH298iZ8e3Uz76WKQsK7kFF+e/xwBwz3EU2THaSXKHfIkn9EWISQ8qzqxUfjQ+SANrLFsQjKKJ9BAd/gOlMr2YG2h0eJ3ig5r8wg7aMDG4RYUgh82b8G2cxdOIRTJTo159OV4/gaS9/Qe4Je1mSp+U1JR5VRYXnb1SfbvqaWvs4DvrryPen8IU0iQsDZ8mtWRdl4pXowtwAg/SZud4oOeEp4pnU+3J8gpbynLY138vHUDT/YuIaUMUrazzL7aGuROez1/3PDK6Fw8SrIk3jNto1aTGub1rHvuXlSiKYlHWuwpbmRjqBkfM9OS1KH4QVlyCCuTZsiHfNK591et4baevejKRlPWu7A63h2WHX1bjDic44bcUwif2gb33frmVMZ+ZyCcXuMLr4dd339zErB2EkoXQLjdEV1R0gm7T2fDlAzDw7efSVc6ReD/Z++8w+SorrT/u1XVeXLOM9Io5yyBkBAIhABhcjDYBq9xwHFtr73exZ/X2d5d73pts4vjOmAwLDaYnEFCEiCUcxiFyTn1TOeuqvv9UT2hp7tnRgEksF4eHk1XV906VV1133vPPee8Hz+Kkj5i+i+sAD/vP88j9LspNEcVsp6ABavmEpRBGmmJS+FKhozmLrIbO1lx/9PYRiilTdywg8DC9TjnreWwcTRO0QwARUG322icP4Wy7HxMqfO6+SaGMBAxRUuRpuO8vBXX9Q2EXi5CqsnXJq2rlHjp4zVzM+uUNQCY0iREGLvDTtUqlSomsJgJSBN+PFnCVTUU1z6GWhhCP5JO71cWkvmdXWglQYSQlByoGyTx3dcsY9cNywdORobN5MoLDtOzxMVnGtejjRHR3au5kcBO2UUUkxuVSubKHLpliEm6QVfbI9QaVrqaxCQrbT5e/+7B6HxVcUHh1ZjCmuGGFQ0tRQCLP2KnsqOLMtHNjRN3kmML0B5JY1Z6C7n2ALv7S+ggHQOR0MkJoDDaT3bUT4/NE3e/S9JgzcoQczpfwxmIFxNRsMRQLuo9MJixDFAY7uTWth4eLVyK32bpiD/aMp+wGe/Ujpg2HmuZF0fkJwMBVIXiC9qkqgWvAHP76tmSVc3+tBIW+hpO6Zyp4I4NwEYjYTFskUhiDdKeyZuFz55BVtndOL276ex7a5xRuOc+3u2Us+H4myZysNzreVPPE/kAvPVQux4WfhJ2/CKWv24kTwkbC6oNrv8DbL3fcrMvuBseu31sd7u/7WTPJMj6t+QqNUIDURAg0iNQckP89S/15KzYgSKUQVe1Gxc+fJjDZg9aKMLqHz9G4ZFGpAAtkmi0Fo5if/05mLeWIEFkEg+EFAJ/pos/L4kwV9YmjUFX0nTcd5wg/FQFnk1TiVx2OCG9bQAmJl1045V9tJsdvC13osdWAieLiWQ+uZA37vUxy/EL7l75LKZLcmjvfA4WLcQ2o4/cP29AKCA061r7inLIP9pMb1keu65fjmEfIh8BTKKLfRSMOWWKItiYNWnwc63p45fyCAGiaFJwR+sG0oxw3Mp5r28HVcV3ASZCaDjtRVYgldGOCXhVF+4RutgdYQ//cvhKDvqKEEKSa/Nz66QdzM9qjltKudRWEyv5mnqgVh3sYJvNM2iPAkwU6dwcFtQGalNe8sjKcgKrnvuH+joJZ8xG8Z7ArycvDBMw7AlLPiczhwsOi+jUTAM1xfUJYEl/HU6pM8fXfMZnvAOP+mgu+5HftWkeHMLDZzuO4A++gj/2jL8fZuMAeZlnpzwrnCdyAGpfO9sWnCOQliu7dYeVV3/Dg9ZL9uI/WOvgJ4vOg/DozbEPAmpftQYF7wSkX0NkJJLtQPXTosNPopUFwGZiCOLc6gECFFFIK+2DM/Qlf3yFwsMNaFFrzXvvuqUcWTUHqShUb9zHnGe2oEV0nDG3aZkooVY2DJLqAAybyt6rFuNzSfaY+1NfgC6wp8HamfNoFWnslQfol76kvZyCQp3ZwE65N+46jkSPE2wNcduEH5DuarNms22w8JENFBxpYv3nr0NxGrg6fRhuGxGPk31XLWbCloMcv2A6ppZcjNStROnXnGSnWOeQgF91UhuLBlcR/JsccgAXhLuwG9GES5HSoLd/JyX56wa3qcBckYPw7qQ42hd3jCnhs/tuoiWUiYkCElrCWXzl4HU8tOAPFDiG1kRVRSKQhIWGU+oJ5zYRCXHZJrBb9tDtrRtVXjQZBBIzeBxb4BgSybS0Ng74ihP2m+xpjyPx8ZY6HdhXj0nW2U2dO1q3jHqchmSerwlxEtcyHjf3ydg8AAGURvspa38jYfv7ATY166zOyM+XaMUqtvKexTv0JhgheOaTMO166D0TVe9kbCb+DuXr+3452YoAH35KCTKgkP6ZI9gm+hB2M0U5c8FkZSIfVG9kPrNRTcGkTfvQogYSeOGrt7DzhovoK86lvzCbvR9YxrNfvwPd6UKsvAaAKlFBJhmow+drUiI1FV9nEJd4AAAgAElEQVSRJR/pI3nwjQwrZEwwWXO4Bk+ZzjR1MjMev5b+f5+JGUp8RU0MamV9whq/qRnMKn8aj7MLTR1ySdsiOhU7jlK9cS83f/Hn3PyVn/PBT/+MK77/J4KZHl79wvVEXA6SZJshpMQmTV7MmUFUJKc3AXjMMLmxFJDwsFlifqSfy7sPDhZfGXHl6EbiPblTmcQF3sQZ8S5vGT0Rj0Xiw2BIhafaZia0owCRUaqPJ9cKl5jmqa0pSakjMQCTv5+4HqcSHUyvU4REVU0unHci7h4OzFzH81oIoCDaT364j8s795OpB8d8/UWKqx/1OsZhx6ngZArLnMtlPZKhIGfVWT3/eSIHlv+jpab1nsQ7+MQHOqHu9aQVJs8skvnhThLeb8wl9GQ5UrcakhKMVifBZ8sQttHXwXV0mmQLNjTmqbOZJ2eg6tYxbdPK6awuxnAMuZwNu43e0lxO3LCW5+foPKY/zVZzB5coK1gkhkWIj7hxErhIWYoW+2+gWrnikOjzm9ibvZ1HjSfo8vfzxF3Q9+NpSK8dOax+uqJrzBLT8ZN8dlx8uB67lvidFHDRr58nvcOLFjVQdYOiQw2s/f6fqJtbza7LF2MmiU5UMflA826m+1v4Y+ES/Epyl7GJwDUizzFND3FL2zayUhCOEDbSPfHazVFpEsbAkWTdtC2SnvRxj0qNxtCQdGTI0AgY1u/ltbt5Nm0GEVMlaGgEDY2wqfKKuRT/iJdeANPJwuaZNib9jfXazUhv49dzH+Lq4mZmZiksr9S57IrjFOQFrLKtp9AmWEFs13TuYUqoY1yd98m+VgNeCsnohHqS2aHjxnuNwAGc9hIy0+acVRvOu9axote7j8Jr/+9sW3LuYe9DUH6BJTKSDOnl4G85PT13RzrMvdMKsDvlaPmoSudtK7DN66Jo6/MA9H5pIemfPxSXkpYKJ2Q9fUY/KiqdajflVcXknGimY2IxRhKXs+60s+mqqUhhLej3yX6OyVquU69mp7GHCMmDtMpFKbepN1AnGzhgHqKbnsGQIB0DA5MNvi0o2mVEO520LrqKjH/ah+vKJowuBxmbprPgy5X0mF7qZWNC+325OeiGLW5GDlakPSMCo1TDJK2jD3HUy4HJ0ynARzH9KNJySwspqejpxGVGmR5oJaxo6Omz0L27EtaeFSTt9oy4bXP7G1BlquA9FYctl0zPbACklDxlNvC8bEICH1adZBrxA5Lpaa1Jy5Q4lQjzMproirj5fs3lbPdWIIHJng4WLW7gyS2z+FXgApZnW+tDm7onEjQ9rEmvx5XrG0yRk1JC4BAH+hsoTkHlEiuFqs6RzYRQN2qKwikAVe4ePj71BU6UfIAuGWYfIRy+aFJXt8DSeBfCSh8bqO8zsm0FyBxn7XCd1Gpoya5LQFyU+VjFX870+P5UXPbnAqpKPnLWotUHcH5GjjVxWvl1KL/obFty7kEIuPnPYEtSJMnmwaoUdpqpe9EQzP6yjwk3+zndMXl0XzZg2Z370GbUolCCyz0ZDAza6aSFNqJE2fixNUSdNlx9AdRo8lHKcFe0iUmEKHvN/aTqigSC7cYujstaKkUZXvoSrlYi6c1pI/2/3iT/pZfwfKyGvm/PpmXKdbRfcCWO7VUIIVigzEEbMQ4XUZVtW+/AlPHbTSEwNQXVTHJvBVR1tTCXFrIIcZB88vu9lHm7mdPaQJHf0ti2SZO5viZIn0NQtRMd1nVEhMIbmdUJ+tv5UV9StTIT2OMu5L6C2aw3O5BS8qrZynOyiTAmEUyez5qecGSlu4dFOfU4lKFBiiZ0sm0BVucd5tN7b2FbbwW6VDGkyiFfIQ9vXICvz05XJI0n22bzZNtsuqMeQgZwuCrul7q05xCXdB+gNOodrBY3EgaCPxUu4pmCeXg156ikoyNosqfh7HiVZU1P88HWt1GkTFomICJUXsyawZOR2Wz1V/BaYNJgmdThGMt5ZcZs9Cl2dnvKRtkzsd1k206m1vpYGI/LPllxmnMZGekLUZXE6o/vNs7PyIdh9u3Qsn38ucvvdyg2mPMhSCuELzXD5n+z1MZUR0xCNAz9A5PC0/C1SdXgudCraA/4KP0VtM5bh3EsnZSEqFqDiEhf4neO2UO5x0KAVhVbg5Wc1BpB14QiHvvh3cx4ZRfClFak1cj0shEwMWmSLZSKImplQ0KFNolkP4dRTZWt7EgplwrgvO04wgb2pV2k31ND6+IrUfvczP876/sckc06dQ3bzF10yC5cpou2f5xNx68q+GPGL7h+6T+R5uxE0UzUohJEZi7y6F6EnjhT75lQiIrETYQJ9FDh7Upao1qTBlFFoaT079jgfZHKYBs+xc6O9ArqY7nfw9Fmz6A81JMgV2oIhW2ZlfQpkgc5znajizqsmXEgoLFjSykdbTN5I7OSf578Evl2HzoKrfYMvjXlGZ5smcNfW+cQMjRW5R7lrvIt7OsvoTPqwYiLKReYZvJQLwlsDvSzMmZbhh5klq8lztOQdEZuy6bUUcEsqZE7SiqHgaVQNtnfjj1WHy/DCJEX9dHsyIgrwRoyNepkNr/csIL+oJ2oYdWbX5hVzw+mP4VTJAbrJYME9npKeCV3BgCT/K0s8J/ZtLN3G+f67DwYPH62TQDOE/kgpISumvMkPhwZZVAR81I4M2H192DylfDgVUnu02kMnWVIIbgtm7TqfhQnFG19jqaSGyGU/PGsWA4rvg4PX2elxYm0KO7barFN78W1xspBFrEsVmGYyBTR2GPBn5/JtttWpSyZ6u7uZ8r63aS399I6vYLjF0zHbXezUJlHk9GKjp40T90YqxSNABFbkldcBlIzyfrmXqYcWsrEYaJKOSKbNeolALz5n3Dw1yCj0NA1n58++xwZrjakkKz+7+9Ss2oKV/6wBmefgRrTPY/aNeoWT6G/0PJiKIATnQ57GiXhxFFSn+ZilppPnnBSnvNB3pQdPGUew0hxf3allTGvrxF1WHhaFIV6Rw69Ns/gfoewBl+mCRtemkgwaGOSqxOHMPjE7lsxRBofuaaOD7Q9jVOY3FKyi1tKdsWdqyWUSbJoPasqXaJ9impSWNI/+Lkk3IshBNoYz3GmksGnZTmamsZBYUsoaAPWrHi/pwSbaTAl2BY3r7ZJk6JwH78OXcjsQDMOofNixzRe7pqMbg4poEsp2NZTybVvf5ynF/8c+zgV4Kb5W0k3wmzLqOCi3qPjOuY8Th260T/2Tu8CzhN5DAf/AlvvO9tWnFsYGfvkrYcHrji1nPJRIQW9H78A16p21MIQSrpO/pPr6bxxJdJnS+ig04pBXd3A5JY9+Ew/0qGDKRAjpDmFFKdM4oOmpSCpgsONXPGvjyBMEy1qUPX2YeY88SaHvvNtXBlOblDXsd88RJvsoJuehLS0k4GwSXI/0sQVowRkHnxs5OBK0BcsIvc/32TnTUsxnSpPfO8u5j22icrtNegOGwcvX8DBNQvj2pHA1qwqrmrfhyrNQVVbQygEclaQF6tkowmFFaKQ9WYr9fgShitdHS62bJrKZrWSz1W+zvzMJhRhElI1XsmZSjK0NKXjNiP8bPafqXR3oQqJTZhETJW+5jk4RWoPxiRPR1KHiUs1WFGisalZEoiNnhTFZGpmG9eW7MMfctDgyCagJg/iG4lAuI6ahp9Sknct2ekL6e7bBsN+26hQeDNjItsyq/ho8+Y4/0BrOJ3XuybRb9h5rGkObzsq6Ym66Yp6Es4zAB2VI4FCZqW1xm1PliYmAAcmE0JdTAh1nXMV084lW84U7LZET9TZwHkij2HLT98jKmjvIlw58Z+3/eIdIPEBCAj+tZy0T9YA4LykjeKDT9B54yoiW4byMzUnlHynhtfN7eju4QUlEt3YY57SMKw1/hRkr6BQRjFNtMbPoaXk4vufwhYeluIVjpLW1Yf7iQd57PZerlWvZLE6H4BH9SfoT5F6Nl44tVGIxjQpLjiGP81Ot6+CgTsissK47q7FdFrXF8q0Ssa+9dErUjYlgDpHLg8VLmFp3wkKI31EbZmUZ1/CPOeUhP0/q07jx8YBOgmhIIhiEgwrbF5fha6r1JLHlw/cAECaGuLRxb/h2o49PBgrjzocfp+deye+SLWnI06D3KEa5MrdjOb2mZreyoS0To725RGJxQioGGRofv5jTjN7qxfwpYMdhEPwrSnPMtveguwH2S8IqHYezV9ARNGwGSNlYkZCIqXOhtpNfOvoLdxZ2svKnBoipopD09nvLmZ7TEa1Tx3KvX+idRY/ObEKJFxbtIenFv8SUwo0xWBHbwX/cuRKAoYDm9C5rmgvdkXnje6JtERyqc3MY6rZjhKTuBsrpUyM+PedwskMFN6rgWyjQQiNopw1Z9sM4DyRDyJ0aqqU72mkFVspZqkGMN76eOGRuvXvnC3SsPKph0PLMpF6TBdZsdbsV//I4MiEHSkrnw2HIpQxSrAK5j32Ojtvvjjl+vlKZTk75R4OySODa9qezj5c3kTBCFU3qNpykO23X8pe/1YWP7cH3nieKZfPZtfqGRhjOAc0NNJJw0tfnN0aKjNFCmnE/Vvhp1/lCleA1WtM+gJFPLL5J3T2T8Q2yQsRAa5RTjrsBzaADtw40Omye3g2z4ootyH4kMikWsqE6Nxs4eBb6jzq8dMno0wQaXyjriepBKohFTZ1TeKygkMs9p6g1ZFJgyMbhLCK9mT0MS/aFEfiA1DE8Mrdw0RhsLSzazwF/GT6o/yuYRnPts9AlwoX5xzlk5Vv0O81WVu1iIPFPTi9u5jd12LFAMROY9ODXNm9n/8rWMgNHTvIitUGTkU6UsIPai7hhF/hm4fXkm1bQYmjjxY9nWVXNuAUBtKEZyMzWeytpcjex0+OryIiNZZln+ATlZtxqUPXsCy7lofm/44XO6fTE3Fxd8VbKELy0fItPNsziyOlRXRHPHygYzdpRjihstypYiQRO2yFGKY/aW5/MoyncMy7Nah4t6EqbsoKbyTtLCidJcN5Io9h2vWWlvbfElw51gAmFZH722Dj92HBx2Hvg9DwRvL9zggEuNY1DX6UUUGmks6db+fQutOys2iJwWuuV8btpq6knHqzHkOYSYk6rdNLzap5KUlcQXBc1uLDHxeYZtg1K/gtCQy7hohEmPKNb0C7F6IRZj1YT2NFBu2TS5Bq6vmeisoaZRWvmpvopiem1m0wWVQzRalOPKC7Hf71MxAOWnXANchJr+OuSz/Kfa++jLs/guqKYI7s+qUE00SLWNKlLmknnJmFHz95BMgnQASVAxQSwkZUmmwMvo094KUEN1npc/G4Jg6SuhCCStIGe+vcSDqGkSisYpiCGWmtaEiWeU8gheBoJI9v1F5Fa7uVumbmwr1TXsChJJE4BTyuCfSH6kDqBBUb29Ir2JlRwe2tb+NSDe6p2sw9VZvjjotKhYcj+1hqK0L4mxIC+RSgJOylJNJPRpIKdMns+OLE9Xxq720A9EQ99EQ9KKpJQ20WeQV+3l5fjhkV/FXMJmIOJXXdUbotjsSt+we59iC3luxAMBRTacNkXd5e/hJewLy++nGnnY1mt/W/GEyjs0sdRXGQl7aQnKwl1NSfufXF9xt5D4emus8ZEofzRD6IC74IO39jiXz8rcCdDwWzYf/DqffZ+D3Y8C0rEOkdg5C41jWilgSsVDFTEN5UQM7+5ShfEpTElnF3GwfplN2jtxWDDY0L1cXMUKeyxbeBTkcIkKAoYErUqM6y37/EK1+6MWUbBiZbh9UyH0Ao00N3VSF5x1tQhhF61K5x8LL5THjrIO5Oi8TBmqlf+OtneeIHf5eUyAWCXHK4WL0Qj/BwjXIFPdKLX/rJEdm4RfIpdXDDI9iNaBxNK0Licgf4+P3ryb5qPhv21HFifiVSjSdzV1+AFfc/gyIU2j73JXbTjIIcdCs70ZlFK9so47Lug0wLtGGXBl6gL3CIrLQ5caVVh+PCAg2PFsE/Yrx1Y8kuyl2WEIkNk/6og6/suI4+3cFAt/969yQmNnZxZ/nbCeMrRajkZiyit2AtvzUO4hMMDsKUFOIhYKnGrRdeMP18MsV+EsHqnqOo4yhkoAiYltaOS4kQNIeWPExDwe/XOPxqFZHoyK7VOm++PXlwlCkFdcFsJnrin28bBje3bT3tNLCBq7bywiUmgkZnNs/nzOTWnlqUvi109b3JaMsX5zEETcs82ybE4XweeQzOLPjMQShJXLp730IPQf6MMfYJxmbsZ7BGumqH/FlQsRJypgBSEPxzFY3pt9Ey5Vqay2+gY+1qjjzojKthckQeG1N6FCwSv16swyEcFIp8PpB+E+n/eDHl246R1tFL6d4TrP3Bw5TvPk5mc9eobRkY1nq7lJRvP0J+TRNKVGfDPevw52YQcdqJOmzodo3GedUcumwBpfvr0CIRmmZVcWDNQppnVNI8e0JKpSoHdjJEGj7pp85sYJu+izeMLRyWNXhlvJSnLg3CMkzUjFLfuQNVT/ROCCNEzivfRBhR5i66J3FFVQgiLgcb71nHy1+5hV1pTQlLEFYOscmESBfTA61xuuBSRun17SEUjg/AGsDKIpUpuRKbOtSmU4lyc8muuIC059unETa1YcMH0KXK461zMBJc8wJVdZHmnsxsJQe74kAZxvSHPEUkS9KSwJ60MnRFRUey35WXVNDTpWViN8NJrycVRt5XlxLBrlmSp6lQ48sfWZcHsGrDa4rJWz2VCd8NxbKfOgZytDWsTt+GpDLUxa3t28gO1MdKy54n8fFBkJtx9gRSkuH8jHwYHOlw58vwg/Szbcm7g4H0MpsHoolLvqcOxZoopZrcGBFLde62xy1xll8uGoi4FhiNQxG8Hfth5//Cgo9Zn8eSHB2ARNJAE9MZCs5qfbqcJbuPU5b7OOowt+2CR1/n1S9cnxDwJhAoiEGXujBMcuraWfiXTfTnZ9JTkktfQSZH7ryKtL4wzdX5eMvyAIM371zDhk9dbWmhmyZq1MDuD1n56AnGSkIizHGzjuOibuSF0CRbmCfnMF2ZzGZzC7XSygu2Y6d4RjkTN+6OC7oDkIrA74TDe39F64plqCgJwx/DaSfosMUYIt6unLo2Jm/YgxqKcmLlHFR3Eje3NOgPHkVohagCtGEM/Qd5jBkr2pizJ8BbzVUoSK4p3Eu+Pf4hO+7PIzRC6hOgX3fyh8bF3FKyC48WRSDwuKoozb/OslVG+Joym9+YNRylHwPJjvQKJgXayYsVoRm4ooOuQl7PnjzY9luZE6kOduIyI9iliUBDCsGL2TNY0fFG0g4xWVBXXTA77rNDiTLJ08EUpY2q3E5KHH0cD+SxsXsihrTm0womv21cwsV5xxLaM0xBjS+fFzqmsyx76Dl4J93TdmmSH/G98yWY34fo7nubdM+ksXd8l3CeyEfAngaDOTfvc8y9E6pWQdkF0PiGpRd+RmACmlW4JRWZO2OlsfNnwPQbrDX4kTDC8Ma/DxH5RFHFfnloTELXMdgl98UReeE8ePy5H/LRSz6C09aPoujgktiCiWu5dmw4cdLHkBtUair7r1pC/vFWKnYeJb3Di+6wk+efjP3Sm2inkz3GfpppIeKOuVsFSEVF11QMTbHc+iMx0Ium6Ex1DHbKPTQaTbTTOXjtIULULpzErJJcspo6scVkVqMOG93l+Tz/zx/EVBUkHYm6m0CClmYMtkCIdd98ACVqoEhJlozQefkM0OJtlyjcd0By3/E+NAEfrAqzPK+HLgTbSrq4yreX6vwOPp6fWnt7Slo7zs4IITM+Il8VJnMymvn2kav55aopFLstImzpeg6vbw9Smti0DD6dt44u5wR+YO4hqqj8qWgx1cFOykLdBBU7e9NKCIyopx5S7fy++AKmBVqZFfIzyz6Bn3gEnapGgbuQGYHWpGldI6Ouq9xd/P3E13iqdTa6VFhbcIAPFO1FERD1qLjUKAHDxqeibj655zYipsqnJmwiZNOsNWop425/VCr8oXEJHjXxeRwPIqbAJuQ5RMoqZ9SNd05B4g+dIBztwvF+SD8TQuQAjwBVQC1wi5SyZ8Q+84D7gQysX/Z7UspHYt/9DrgYGPAf3iWljK/0cB7vCDwFMOVqqy9fcS/8340QDXLGvGvSgIqLoX596vMPYM2PYP8jyeu1+4Z5b+cqs2gwmvDhR0ePBYMlJ/XgCFGRlV+H/322hJ8+8xyTijeR5W6me2Yhka90ILXYrBuBhsalykpeMhMN15129l25mIqdVqENLRxB27UFVt1MtsyimdaU1dqkbViA3IBvdZy9rkDEkfhgm5rKs//vDma8uJ2Jm/dj2lSOrJzNsYtmxom8JG80RQX0sB6nvZ674wTdlyWWSw0b8If6SZgSLsvfx+P11Txamw0C9K25FFR2MrW0fVQTLs8/xG8blhExtcGSMTahU+XuZmFmIxWeMPtcubwsg8zueBNnoB4Zi1eI6r3UtT4ICD6q2tiSXsnu9HKOugs46i6I3bfk0BWVfWmlHE4TpCuTSA9sZW37QfLGcEv1qi7SjDA2TBQB6woPsK7wQNw+ErDFniePFsWu9PP5Cet5sm02E6q7mBFoQzHjCVdK2NdfTGMoi09VphA1GAOnQuIS6NcdPNc+naOBfKZ62lhbcIg07dQGE/E40ySuMlT9/exDCJVIpPP9QeTA14BXpJQ/FEJ8Lfb5H0fsEwA+IqWsEUKUANuFEC9IKQcSvr4ipfzzadpxxtBbx7nyrLyjmPUhizj97fDwNRA5vTTnBAgFQqPEpTW/bf2rh6H2dcu9H/aO3EsS6YcXvyxY8x9gFzbWKVewXVplSbPIoJV2fCR2wJnEC3gUzYUPvwTPf8HG0V2X4MyGC5bBNGc3++QBevVO0kwXpc4JSGTMrZ6IcPpQ4JmpKBzM7Gab/jAlFKY8ZhAD7mchrOjBcfa8JmbKQYvhsLH3mmXsvWYZSiRKWocXMcpgTEFgphqtSUnhofq4TTZfmLI/vUXj7RcibE4O9OfzSNNstvRW0Ke7mO5pYXP3RPr0+IC839ZfwNz0FmZltCQ/FdDncvHiFU6+9MZx3uqpQhMmlxcc4jOVGxECcmxd3C/rsRtB5gVqkQnXb8VgpxlhVvXWkG6E2JRteWHyogHKpZ09NhUjxX2WQH+4ias7diQtSTsc7bZ0HixaQlWoi2n+VqYHkscHjDyTTTG5NL+GRYUNyBA4pJ7wswsB09PaKHf1cE3h/kHbkrWXKnd7rJzukb+4ABqDWXxyz62ETY2waeM1ZTK/a1zGr+f8iSLnuVGxDKx87dzMC9CUNFq7nzvb5gBgmjr2s6g/PhKnS+TXAqtif/8eWM8IIpdSHhn2d7MQoh3IB87JzO32fWfbgncHW38Gu35jKZsZ70AhHGlA+57U3zdvh95a+M2F1iAi6pfgMCE8XGtJIE3Y9gvJlA8I8lcGeMp8nghRdHR68aKhoqLGBcGpqFSIcl42NqCgMEVUUyqKKb9Q8PGtIww51sKsx37BC3dfTL+m0BRqxbBpSXtFJaJTvmOo7KWpKRxaPQ8Dg0ZaRi9CM8KV7ez1o7sd6M6xK4q5cBIkUSNbQSGPHHzSR0j34ewLULm9BlsgTNP8atqmlCUZLIjYylFyW23BxIAvz6FW7JtDbFz6Uf55X6YlOBK7QfXB7LhgtQGETZUn22alJHKALDNKmUfwrzNfwTATayP7VAdRTHL1EPoY5VNVJIv76/FpHub46snUg0gEFwjBCzkzOeZO7HQXmh5KOjYgk5D4EDEKTKGyOWc6NqHQ6ipkgXsheuOjaEbfuNawNWGSKwNgpHZ4acLkF3MewaEMFTkaLiM6PB87aiqowvIKGBIMU8GWJF1vOAaOH074Pzp2Kf26Y/D3C5l2IqbGf524mB9Of3ocV/buIDttEYU5l3K86Tdn25RBeJwTcdhyxt7xXcLpEnmhlHLgTW0FCkfbWQixBLADx4Zt/p4Q4hvAK8DXpJQnFzp6hlE0b+x93g8wo9YM+OjzZ+f8ehAe/7CVqy5NsC/uwrmukb7vzAE9vnuMBuD537WSO2UvgdzgYK6FHhP+LCAfAx0v/WSSjkDhoDw8mDbWKJuYIiaxTF0Ub4TPi/H9T/DCv98VN9MGhnpQaYmlqOEoTl+QWc+9beXguhxs/MRVeEtyY7tLBNZMW46oFaqGowhTorss0haGyYI/b6KvOJuDVyxC6Dq6y2Gdc9ixCgppeFijXsIe8wDH5InBAYswTLRQmJXPbufpGydTtvMYq+5/ykqt0w1mvridYxfO5I2PXRFH5iamZWcyCEHtBTO46LcvxqXV6TaNHXOK+fquNEJG/LFB04aaxAUgUfDpjkEZzJEzRgG4jSBHG+8jwzOb/sCBuLrlUaHwZuYEAHpsbrRR0suGt3lJz0HrNg4ZwlWde/hT8TK6bB4koCGYGuhmeeerDClwj7RfYNeycDlKyc++iH+2FeAnigcbAvhq8RKuaX2DXD0w5kx45HUnmz071OS+nGRSpC93TCFdC1Pl7uZYIJeFmY3Yxfjc4QPtmBJ2eMsSBmEmClt6qgZ/t3MB3f1b6PFtTzrgOhtQFQ8VRbecbTPiMCaRCyFeBoqSfHXv8A9SSilEaqeeEKIYeAC4Uw79Iv+ENQCwA7/Ems1/O8XxnwA+AVBRUTGW2aeMjFJwF0Kg7R07xXkApcugcQsUZe5ncfXDZExoZLfvEva5ZiAcJvYF3RhtTqK7s0FCv+HDlteeMMGUSDrp4k7NKs7RbLbysrkhLvdbx+CwPMp0OYVMMeRyP/Gtp1HKCzGTlWgVQ3/k1XZQuf0I017Zg8OWzpF7PsQby7ITjhPApK3HOT63HN2m4ewPUnC0kYodxyjfUcMjP/sMpk1DqgpdlfkseXgDM1/eTV9xNmpOKQ13fZg2u480PBSJAnKVHHLIRgjBcmUJWWYGB7xvElElJXtrWfTwa7RNr0T253Hxz5+OW9u2haPY/cGkojGjeQ50hx1/TjqaPwJSohkGrTC5S0YAACAASURBVJfPwrC5sNY9R0ikolo63iPgVCJckleDUJw4tByihg8j6QzWpM+/F5ejhFCkFSmtgUaP6kIiSI8EKI70Ue/Ioizcg30cQRwjCUhDcnFfK+68y2kkwATdJKvzZUYLCFGFyqSyT6EoQx6TDKy/vTJCvyL4U9ESPt24IU7DewBjWTme8qZWvn1iSxfk1HLr9o/iN+yW5nrmo2O0lLxtTUiiSQy1KckS9E62dYUzt04uk4rTnA0IoVFZfDuKcm7FiY9pjZTyslTfCSHahBDFUsqWGFEnjW4RQmQAzwD3SinfGtb2wGw+LIT4LfAPo9jxSyyyZ9GiRe9YwmO4DwId71Tr5zGAUDfkuWu4admXyfK0oGBSUHeUhq/OJO0LR5BhFTSJfjyNzptX4LmjdlxLyg2yOWnlNxOT54yXEQhKRTGZr87B/1oLxWMMrFWhUFh1Ebnpy1EvzICyKZjmURS5A3PEeQQKS3//Isv7vBiahjZMxzxqt+Hy+vHnWYUkDl+xiJpL5pFf38n8zJUUFC+mgNQQQjCrxcGsf/ofiAw5rY6uzCD/eEuCFwAgvdOLYsok3akgikiQFwUrde1H//YVCvY04giFKS4VVGj9ZAeDGDL5HC3P1k+f4SJqWjpnLiXCtLQ2cu0+vnvoYoR9Jl+o+itmNMKjzfN4s2cCeXY/t5buYEFmI2ASDDfCMKd/vu7jyu79cecxgQgKr3ZM5oGGJXRFPExLa+dTVZuYlpY6sE4AOaF2lqnFmNKgrvOPBEahWiFs5GQsRjf89PVtRSLJcE/DYc8DwI2GQBBVNJpiA4yRd2a4K3skolbS2ymTZZYtxH2zH+X7NWvQTQVltKCIFBACLsk7wqudk9GH6dfbRZTrCnefFpG7HOVEoh0YZoT3W+R6VfFduBwlZ9uMBJzusOJJ4E7gh7F/nxi5gxDCDjwO/GFkUNuwQYAArgPO+gr1sRf5mwh2O9voOACqUsmvX3mYT1x+K1meZhpXTyDjQweQdgXhsn4E23Qv+c++ijbRCmgbmTWlIKgSQx4aB7akgWESSSAWyV4jjyMXNOLS5zBj75OYWmonooHJfg5zJNeGRLJCOqlWqthp7BkqFoMVWW5g0FWUQZHXG0fiAxYEM+JVrky7RtukIrbThUv24pV9ZIssMjv6rGC4wvL4iz1xAJT42XVOXTvd5cmDbvKOt+LwBQjkxAf+mcAJsqmmO242KYFO3DQ68mhcbLW59tAeXusppsrdRaWrh2P+3BGa3+A30/jRjL/wcsdU+nQnq/JqaApm8NUD1xE2VSQmLmMCG7svpS/qJCI1Dvsl273lfKbqda4v3jvMMmL3Mx79uoPfNyzhqdaZ+M2hSnDbvOV8du/N3D/nESZ7OpPeBwA3At3wc7Thfgxz9Oj0/OyLUdA42vg/g+7cjp4N5GetID97JTahsFIUslG28XzuTO5uHn+k+YDb/7gzjwmhrqSz+bFgApM8nfx23kP4dBsO5dSU9b44cT0nArk0BrOQwOV5h7inajPp2umtbgbDTYyHwE8yeeOcwHAPzbmE0yXyHwL/J4T4GFAH3AIghFgEfEpKeXds20ogVwhxV+y4gTSzB4UQ+Vhv5S7gU6dpz2mjq+ZsW/A+xvCEXMAw7YQiCq/u/RyTvv042z64KiHXWtgktur4jtfs1xAOAxlWyUh3UU4pB8zD5IgsJooqdsv4mdxISCTSGaXtllz86wtZ8MBGdnx4RSzIbdgadTiKtKlIRSGK5dp73XyTG9R1XKOu5U1zK42yOe6ydtx4EWt+9Gicmzvq0DiwZhGmPfnr1kEXTxrPo5hg6mFK6uq49H+eRfVkwt//CCZZ4iXkJ84EKnbUsPOG5Unb1R02Mlp6BolcQaCisZ8sOvAQxkYV3biJEkalgSw6SbOO1QVvbKjkmc6pqLGV9RU5xyi097LdW4EQAl06WFumcVu1ndmOChZkvY6UOj0RF9878rFBFTKAJ1tnIIQcNvsThEwb/127gisLDuJUU5NRxFT55J5baQ5moCd0WYKwqfLLugv5txlPpozozrCX0tzx9JgknpU2n8y0WRxtuA8ph2yS0qSjdyMZnulEjX6Wda9nht5Jo+YedfYqsChtIIRzwOlcFO2nV3WSbkawydFd2ZqagSkj6NLAwCSkOIi4KsjzHyNNO3WXc7oW5n/nPsSe/hLChsairPqkUrAnA4ucjfcUOZ8M7OdQgNtwnBaRSym7gNVJtm8D7o79/UfgjymOv/R0zv9OIH86CYRzHqcPT5HlTo/X0pA4r2um7R8EvYsvHXd0jYwKfD+aQ1m1B/mRPWyWb2FKE4FCFpksF0t4Q25FYDlqk5V1FQ4T5yUd/PYbv+eCpt9z0b6X2Xf3fLqmF5LZ3MklP/srWc3dmKpKzcrZvP2h1Rh2q2bYUfM489U5XK6uwmf62SX3clSeQCJpnVnJa5+7lqUPvEJ6Ry8Rl4N9Vy1h97UXjnpNBgZGTPmkeUYFOz6wiMUPb4Dvfhx+9jykZ8HU+cjcImitsyRYgVC6m4lbj/Ladz7D7N/8hbzjLaiGFXBXs3I2rTMsb4VAMJlq5ov57JKbyMdHABt7SO4mrNmWS2+nC91UqXR188PpT5Jn96EKiSJM6iMzuWji9WQ7rB/NNC8hHGkgFGlnZ18FmmISGXbbDdSk75QuVY75c5mZkTooZX3nJDrCaUlI3IJEYZ+vGFOCmoRABOAP1jCWq01RXBTnXUlP/47k55EGbd3r8QVrkDKKDZhgJEbbx9s2ROKD50HiMnXezqymx+bhku7DZBqBQT9HSyiDplAm5e5ushxhKgtvxW0vIBRpQ1E0HDZrEabbsY22rhcxk6S0JcfAMGIoH1sImJvRPJ6Dx4WTIfD3Gtnb1GwUcaa0584szq0V+3MAk9Za1d0i504a5XseqgPmfRTe/PcRX9hNQi8XE3y6DOc1DeT9cRMi5rkKvVxE71cXED2UgVoSJOPre0m76zhCgJIdxVVoIO/YO0Ln26SbHjrp5nb1RlpkGyEZ4g25NZHMJcimNAzVzesH7oED9+CpbaH6Px5l3Xd+P1gpTTF1Jr++F2d/gNe+cD2maRDe9GcI7GTn2jns4VCsWxwiiYYFk2lYMBlFNzBVq15tFpn0kpAonxSGw8bhS+ZZRG7osPlZWHs7LbKNDfeu46L7n6T4QD2N86pZ/9kPYGo2pIC2ez9ESYfEF2yjpywvTqBFQWGaMpkXzZeYQd9g+lk/Dg5QgA2NbKKk00OWHuDpumnopooqDH42689k2QJxs7Vq5yE8aj+QSUvApMYrmJB5F4VqI6V6P4oYX9eiS4XGUNaoRL6nvyROnCQZfFEHf7frDn4883Fy7MlKFI5O4jYtm0ll96AoNlKHoclBEh8vUrWkSoOKcC+7Mip4pGgRN7bvwBmO8u1Da9nhLUMTJhGpMaO0lZ8oj2IzelEVN3lZy3FkFiCEIDtjAX3+/fiDdSnOMvz6ckl3VxMMtxIM14+5/3iQojjgybXB2Pnv5wrflxbccLZNSInzRD4CmhPueA7+sNoqEXoepw8jDJt/MHKrhIiKjFgj3NBzpXR/Zgm5v3qb0PpCOm+4GBm0Hk+jLo3eLyxC+jTSP3sEIcD+6f30J5nimZgclSdYJhZRLkoBqNGPJ1RGU4XK1aumc+hzcOwlyCyDpV8pgBfeQo0Okb6hKtQvnERneT7VG/ZQfKiB8p1HCQqFE7M+iFGWl/K6h0e1Dy/3OvI2JOupdHusMlskDF2thGSYF831GJluXvzabWiBkJWyNqwn1TGpz5MgChLavEgsZbfcRy99iGHCphmEqKKXUrIQNGNIA1SISmuPZVl1OBQ9weUqpaTLu4NvHlzKE3VRHCqETbi8JI/7l5fj2OnDp4/8fZJf7K/rL+C/a1eSa/fz4bKtXJoXv75V6uzFIaKEZepqdRKF2kAO3zh8FffNPrn6UqYELe2aGIlDhmcabd0vJT/PGYueVujR3AAEVTt/LFrKwTcLOOLNwzBVBhxXh5sKeERM4kPl2zDMAO096zHNKAU5F9PZu4lguCmhSlwycjUMH0W5V3CobuRo+tRxJmbU44ncPxdg03LwuMrOthkpca6kCp5TqFgOq78PyhhVLs/jdDDiFQ1pBP40EdOr4f363EESH4AM2PB+aw7SENZsegSJC9Mkq6EdpCRKlJeM9RwwDhOVUVarF1MqilFQ0FBx4mClciEVebms+RHcsxtufwaqV6qUNQUHJTGDGW7+8qNPsPHjV3H48oXULZnKlg+v5v9++mlqls/g6m//EVfv+Eripa4PL4eifgZ3NinZXwtA1GnHN2Uqx2VtXBu625m8J1VEwq3VYuP1epmocqYAlQRRaMKMrW0KARPzuwBJtj2AIpLZbrCjo5en6qOETeiLWmVbX27W+e6uMI9d5iHPIfCoOh41jCZ0lBT3oCWcSXfUQ42/gO/XrOFPTQvivr+q8ASqMvZal4HK/v4iuqOZjNa1yWG33JSwsauay17KpiUQC7DUMsjNHH0pJBnESYiNCgRTMpZjQyHD0Lms4yBH63MxzHjXbdi08efWoeIWUkbp8r6BKQ16+rbFreNDanKVGOiGH01xj2LVuek2PhdQVnjuzsbhPJGnxJwPgXqeyN9VCM3E7HISPZRc61cGNcxeW9K+UgtFKd9xFDWqI6WkQTbxttzB48YzAFyuruKD6g1cp17NbeoNTFCS1yJwTFowqN399u2X4stJx7RpRF0OdJeDqNuJYbex66YVdFcUMP2FbfHXgCCPHCpFOeo4Xi8trKNGdJRYlLsa0bEHwix94BV0m0ZPaS5PzPURkqHRK8eNAh2dE7I+5WAiShRlxE29cfFenDad3X0lSakpZNh4pKGMoDFyO/yhJsLsbIX9N7j4+wmv8pGyLTww/w/YU1YfGzpDyLTxv/XLCBsqQmhkpS/gwilf4JkrcpiUbmJXpPV/CsKyKRqutJUpzgNg44X26RzyFbDDW8rXDl7DvYfX4dMF/3NwyAXndpYiOMkIZaHgtBcx8gGVEiKmEjde87iqudAxiR8qc/lo23Ym+9tTpvf59Hg7JCaGEcA4CdlVKQ3qWx8mKvIJmyMGyWikuadyOuk646jX856FIpy4HaVn24xRcd61ngKeArj1r/DQOjDPhIbAeYwNRaIUB9Am+IjuSowOFQ4DJTO1a/Pg5Qsw7EOjLxMTH342GW9xkboMFZUMYWnUGtLgsFlDo2xBxyCbTEqUIso/cBfKxmfACFC3eApoKhgmI33Lul3jyMVzmLJ+d9x2iaSLbkIynJx2h/k+hW7g6u3nyu89xKHVCzh+4XScvX4WP7wBU1XYecNyDqxdhKno2IUdVapJA/dStT8cjTSjoaGjU3CkkXmPbSKzpYfuygJ23ngR3sr4oLfCDD9fvOJ1fvD0JbzcOYXVeUdwxSLLQ4ZGfTCblzuqk5oQMqDBb3L7a0GO9l2CJkx+17CMdYX7eKptFgKJKUUsqj3RViGgNVLMorwp5GctxzQjZIUf5vdzG+mKeFCFzp+aL+GRpmqiI7jHqYQoc3rpCieXMIzg5r/rLmNmei0XZJ1gRnortcFcmkNZbG4bmt067UUgzJMKepUyihBarBab1VZXxM23j1zB3v4S0tUQy7Jr+eyETbhFGs83hOkKNDNJQJqmU+HqoTYYL8IhMJmf2TRim4KmulGEDWPcrn5JKNJM2FAJmZrluZIqNkWnI+ShjBpOJ8L3vRa4djKw27LOtgljQiSrynSuY9GiRXLbtm1j73gGEPbBD/9G9MnfeUgcq1tx31JL75cWIQMayPgq0EqRH1Qwm9wM7+SFO0rGP+0j42sHkjWMFoogpcRwOZKddrApF06WsoBNbEFPQop55HBVSzXaH/6DBz6/jKjLbuV0J5EgLdt5jLJdR3nro1eM/xaYJkJKhISig/VMf3E7EY+TnLp2dl97AbXLpic97EKxhHoaaZatgzNroRsgBGpUx9AUFMPyGRsOW8qetWzXMS79yeOoEd2K6hdg2DReuPcO2icVx5sq4ZuPXU4gYufy/ENcW7QHh2LwUsdUakLz8NjsbGxLvIdzcxRCBhztMzGGdS9OJcr3pj1FWziDkKnxSscU9vsSo+YdChy4KZ2cWER8c8fT9Pp2IYdp4vZG0/jY7g/TG1UJmxoKJjbF4DtTn2F5TkNMYCVZDIUNw9RRkKgKRE2BIVW+e+QKcjJm8JsVQ67npvYn8Pr3n9S6uNtRRTDSOOjyNiWETY3P7buJQ74ibEKn0tVNazgzVhJGIWrCZye8TrWnky/vv55IrLCOJnQcisEv5zxMpXtIVNKmZlFd/mkO1f4rp1JwRTfhhbapZDvCLM2uHfQbvZ/J+HSQk7GU4ry1Z9sMhBDbpZSLkn53nsjHxrfOP+BnBg6D/JdexnlBJ/oJD95vzyb8eiFGu5PsaQblSxVOeFtQp3sJv5VLeHcWZosLJTtC+tf2kf73h+I6G2EYKFGLzJb/7/Ns/tjauBn5qUBBYYGYwxx1Js/qL9GavFghWijCkgdfZd/VS+grSp1bKhBDRWMMk+L9tay67wmiTgcvf/km+guyAIlUBOltvXhLchNKqgIUUUAmmdRSj4GODRtV/kzmfv371M8uI+KyU7r3BG995HLapiYTS7Fw0xd/TkZ7ol5R95QJPPsvHyLCMNIyTd6sqeSpXTMIm0P31aXCA6vcFLoUrnzeR9iAqARNgF2F/1zq4stbgvhHpIYLTFbnHeGbU59HoLLdW85XDlwd17ZTgavKNX61wo0Ra/PAie+RjLD8uocn22awtbeUYmcfNxbvYqK7m1ORvAwYNmzZX2Ze3tBAUEqTupaH8IeOjXJkPFTFjWEmRs3v8Jbx+X03DbTMSE+EQ4nyizmPYBcGDzfP53ggj5npLdxSspNCx8g4DJXs9IX09u8YnPmfLM52MZYzEfH+bmFy+eex27LPthmjEvl51/oYaPlbVUcfrcbkuJDYWQm3jmNxFwDaBD+5v33LCjwKKmS/Oo++dbvIwnJnpmORoKlbRWGSIfdEK9WbD1D9xn6cvhAH1iykq7IwKRGOFwNR75PkRNoZVilsIEJKUdBCEdLbejm6YuaoJA7gxomOSZgwtmCYS3/8GPaIzqaPX423NAdTG3oF+4uykSl6t1baaaMjbp1c8+Sw5d57KP3rk1RtOYS3IHtUElcjOmmdyVPgsk40cbt6E7WynhNmPREZIN3bwBLXa3x+2Wv0RD38vmEJ2/rn8o35LlaXWOS7+Zp07jsQZleXwaxslc/MsFPnM5Pmc0sUmiPlVBXfTSjSyNV5Klq6na/vgKAuMSVcVaFhE1D8UB+6CfNyFT5Xnp20aptH83N72W4+WDpS0s6IEWqQ8T7AThUmepqBCXHbj3q9PNe2lH7dwYU5J1iU2TAqASUjcYBpaaOLN0RNlWfbZvD5ia/z1UmvjmGtQX/gEEIop7w2/W6R6HuJsJNDOydIfCycJ/IxsO1/zrYFZwmn5aiJHWw3IJZehmaS8/O3ECO0KIUA4TbxXj2sCIcYaEUibMSi1BM7hJ6KAkp+9RxqLOd79X89znP3fhBfbgam7dQfbYGgTjbEq4QNhHObJoVKEa2VrrHXq4F88qijEYBImoujF83C3eenYUH1YFDdAAy7zfLFpsBwEtcx2MchlDyFE3ePz+1naCqGXUMJJbqKZUYWqlCYKKqYqFQRCDVQ69s+eN9z7X7uqdrIA43/n73zjpPjKPP+tzpM3Jy1q43KOUuWJQfJlnPG2JZxAGN4wSTDAXcvB8dxvISDIxwmHmCwccbZcpCTbCtbOWtX0mq1OefJ3V3vHzM7u7Mzs7uS5bNs9OMjs9PTXV1d01VPPen3BLhn02I2tRr8ZJGTkhSFnyyOrRyXbVcIJBmao/1uVryq8PTFixjvVlidBjdVSBp9kgybYPWbHra1mwQjyvTODosvdN/Ew/MeINcey8oWZn9OPF6JyqKOBBWJGJb7/tRxH1/ceTOmFBhSZU3LTBZmnOCHU188aeHUGRwpWjxcdazfTOAWSgIpJULoIE9vAE/QUtFFpMLeaRLAw4X5h8kILD4ku5CzUeujwJO8FsM/Bk7pPQ6TUSpZAbAZqGV9uO8+guPi5lO7T0SGDl+zTZvOC9+7g50fP5/WiYV0FueS2twVsapGTk62aiQ5LkImU4KFWFgJo8SFopBrL8KFM8HVsVBQmCWmxzzalrsuZd2XrkMm8LmHrwnHWI0VY9lMDDYuOHjJAkLDqGJDdp2dVy/EGJLK1Nr5Ftaw1CananDb+G1IafDI0RD/fSBx1HSGXfDPc+y4EhhGghYc67NYvW5QKKuKoMglqepsZmeHERXi0f5ZCs80zY5rS1NduBzJKiGenLSQmHj8dXT37yNk9NEfknx5i0HA0iO0sgKfZWNbdyk7uotPqm2fqfFQfdgiahdBlAR9cypBLsgO17rX1NGDckzLEynecXp1sV7Dzqd2rQ4HxJ2W9mzU+TJiUv4G9sQngw9K+IezEM58nNXIR8GUa6BqDciPVhGfseM9TCDnVfVk/nIH0q+AkIiUEQZxDBROQgjSSUMi6aMfCwvDYePAFYs5cMXi8EmWZO7TG+gtyMRSFHqKsukqyYtdOYZHoUdWCREyKDpQy9QHHqL/vx5iuxbfIcUSlOrFTKCMl803CBEEBAYGKbjoY1BAWVjskHsopiiqlSNEUtO/QFCsFNNIE6FT9H2Ohp03no/uCzL5rb1YqoKwLPZfvohDF88jU9YxUYRNy/5QS8KfQyDJ0r20BNP43eEgX5vlSHifr850MCtT5fa3vfiH/eyWDAfCHe01mZim0tN/kMb259naVo7CCiBWMw1JjSPe4YVhFFJckyjIvoRDx3+Q5GklFhrKmMZS0tr5GqAhBOz3X4amTIxzzfstG0+3zGZhZl10RJJNkpClYEiFrb1LeLd3JuWubm4p3ElXSOevdedECsqEq8XNTa9naeZxAAzTw0AluOSw8PhrcNjy8AdPH8Vqjs3Ln+c9Gg6ElHHJGicNm62Uw10aRY6eMXEBJMMHoxgLCnOu/CBufNI4K8hHwaxb4bVvgq/jg+7Jhwv6vE4yfrYTYbcQ9vCCJK3wPxLsyMdCxaigcKm6Ejs2XjRfpYOu2BMsyUW/eIrCAyfQA6Fou/uvWMS2W1eCDBPHjN99jHPWnWDDF66jv7cO3Rtg/N5qSnccIbe6GcNuo2bdrym86HwajQYsVUEKUEMm09/YTXaqA1bcwM3qdTTTSo/Vg18G2cmeuD430sxiFtBGJ14S+08BVFRs6JSJEupl7MKcqJqbgiCNVPrxJIy+H4oU3KxSLuSQrKJWrWfLJy9h+80X4O7qpz87LRzljkmf7KNX9qGjYddz8JqJC4x0hcJm4t7gyAvzghw1qYk9YMKa2iCfnthPQ9szSGlQ7mzBSJBLbVcsZqQOrS2soCp2cjOWowgNTU3FMOOZ81QlBY+Vj0seG5MgCJuADaQEj+8gUlYQb7SU2MTgQwlUEMQRs3hNjd8cP4+1bTO4rNhB5cfdSJlKn9eLP9jCJWUdPHTET2/Qz8qcIyzPOjZEaFoJ7puovyF8gcbTLuQSxTecKlyyhlV54Q3ghwkCO8UFN+Cw53/QXRkTzgryUaA5YNk/wxvfAvn+KEkfSaR8vhLhiF3FhZI4+EVKwn5wkyi5VKLFyYkDF06EEFylXsqL5qu00xn9fvyeYzFCHMKbg+lrd5BX1YCQkpSOPpzd/XTNm8P4XUeYfv8TqH5/zH20QJCUw4fYtWIyaU2dFO09BkJQvvUwudXNYN8E515Ojy3IenMz/YxcUWsHu1kulrBBbk1oCnfjYpaYzkSlgtfNt+LOsbBQEGjoWFhY0mRydYCFrS6OFbrYUtqflCxGIDhXWUymksG5LKbL6MaLD8Npp2dYqt5+eZh95kEkkqI0N1leLaZEps/UeKJxXrSq2YKckYMK93dZ6ApxpnIIb7C+vyvI7w5Ifj3LTaGjhxJXFwvS69jeXRK9hwAcmsIXZk9A+powzD7czgpyM5aja+GqbrmZF9LU/iLDNVi3oww7djwj/zyDYzXknZuXVoOI1i0bhEMxuDJ/sLqeojix6Zl4A42IiOYfMFUafBmsaZmFicK+zoECJSpp7mmkuaeRlwnnFrRztP53cf0OY2z+lQ+Ki3ysQWyS0BlDszpWaGoqk0vuRYgPj+f5rCAfA+bfDRt+BP6u0c/9h4dmgW5hW9RBonmQaPKLsEs9nFJugtWjI9wGik1G11GBYImyACEEPbKXHtnLfGUO66wN0RKjZe9WxgjxAXQXZbPzxvPIO9bIgr+vx7DpbL18OpZmMnUI7/gADE2lNz8LUxP05WWQd7SJsu1VgycoKsaxfbw4+QQBRg82ChefTJzXrKGxWJlPuVIKMKwIzJBbonKlsgrF5yPlh19DrQtvLqZZFiUTJ/LMNy4naIsXrBMpx4OX/dYhisS4ETcdoSGpZ7X2ENXOMkq62xjv6KHPtPNo/QIealgEgFuDHy0cOU5gnEuMuIhbQGtQ598rL+N/5jwOwOdKN3B392oGRJTAxIaPHa29vNhyI/Uek8UZ9Vyd+zcy9ADpKbNIcU5K2H6vdz8wdrPsUOGkKxa/mPMuX9mzLFyKVIIlJdeP28+ijIibBIXMtPnkpC+jsWsTJ9q3kqIFMBHU+DJxaQH6DSfTMhILBF3LQAg1WvN8KMzIqz9ihLwUGBbY1bFru5qahml6kKeQfz4UQnwUItITIyN13odKiMNZQT4mODPhro3w2+kfdE/OXKhl/aR8tgptch9mix0lLXjSE10IQAMlIxQv8ySs793JW0eOYM5oRVMVpGqRS3Y0LSvktGEJAcqgIW/dl66lYU4FlqrQNLOM44umsODxt1AMk6apxXiyUklr7kQZEi0uFUH10qkAGE4bDbPKYwW5ZdLs9IxJiA+gkx7mizns4L7DmQAAIABJREFUtPaEC5MoAjVkkmW5KXUVI6WkUTZHedETIU2kov71p1BTCcag0HVVVbLgcTebb18Rc76CQjUnOG6F6Vl3sAfbWGlHhcTMUvnGruto7ErHGqKZZtvh5UtTmJQ+skY+MU1lXrbKtraRQvIUDvfncaA3nxlpLfysemWkYEv4xbFQ6Qi6+Oy7pUiCSBT2dOTz6Inr+evchzHMLZGyoydvuh3uAzalQBOReAmhs7J4HIcnpfFSXYi+kOS8vABmz1asqNy1wrznlg8VyNDDkfK6YnFxzhFWZh+lPegmz+Wis3cJmanzYqKgFUUjM3Uhnb2bY/rlN1Wea57F/XVLualwJ58sfhdVJAi8RKInsXIlg2H2DmvDTnrqTDy+Y4SMHk5mHD+KQhzAYR83+klnGM4SwpwEHrkSjrz0v37bMx7281rIeX4dQrcQdonlUUGV4c+RtV7KISbAMfsrhx0LinAqtz3xO5vS2k3IYSOQ6gQJjp5+gi4Hln0ISYyUYEncHb2ohknIoXPB718kv7I+bJ4WAsWUSAU6S/JY/5krKN9WybynN0b6rsC4Uvb914/ZJneNaXwUFBaJecxoddLyp3s5vGwKhsNG6bZKinceperrX6Fu2jja6cRIEJilojKf2WQoaVS9+yc0r4+lD76O7gtGh9ICHn3gWwS0Qe1uKBnN0L6Ezx/ddOsPafzhzcXUdsbmys/KENw60c51pToFrpE1l1afxUUv9VLvDfcoMSQqknlpdezsLY7ZNCSDJkxuKNjDlyveGfXcRDCl4J32CpZk1WJXDFoDKaRrPlyagUDFbsujvOjTKEJlYI1s6XyNjp53iSenSRyYNvQdFkInI2U2hblXxZzT3P4aHb2b4q4NWCof2/ZpfJbO1fn7ubfi7aTP8t7IXQRp7ukU599IZ+8OmjteifP3/6MhP2sVORknXzTn/cZZQpjThMvvOyvI4yHJ+vNmlCER6YrbjBPEQoAMCELNdvQSf/RYMiT8TpcjRtH25w3hRBbgz0iJb0gIUAWeyLnCtHjlW6txdPdz/b/8GXu/P1z9zAoTzlzxg0cJ2TUMTUU1TER2PvzLb8hT7GNmx1RRmKCUw8PfJHf/UfL3H435PnhoM63Tzk8qXE1MDlKF3/JjLpzENf/6F7RArO9Rqgrjth+IoXhN5De3sMghmz76IkFykkwy6aQr7v4hQ6O2M54MY1+35N93+vneLj9/WObkmtLkWv6PdnXw0ymP8Mk9n4hhcIuFwESwvbdkzP5UQ6ps6irny4wsyJsDqWzvLsGpBDk3swaHFsKUOn+oWcSjjQtRkcxKbeSfJq5jnNbHQFiWP9hKfevTmKYPr78GEJE880Q/emJBHpsoEaKrbze5medHffsA3iS1wUOWSrm7g109xTzfMovPlGzGrSW2AA3cZ8x+65jzJL2eg0gpyUpbAEBT+5rRGzlJHO7L5T+PraLBn06ps5P/U7qJhRl1o1/4AcBu+3AEuA3FWUF+EsisgPQS6Ek89/4hoRb5UAr8cccTLSgyqOB/thT9nqqkbG2nFWNY1aQatk2OO1SL7g9GS5gCKBJsXj+OfknN/IkoCFru+Sp2Vy8TZDk2bARHMK+rqLhwcqG6HIfUkbvWJ9Q16+ZNHFVD9kR826nNXWQ0dsS4AgBU02LqG7uScrUPRSddnMsiCtVx2LEjkTxjrsGHP9oPDZXm+pnYFUEgQdcGjt31jo/Xr1CYmx2/lDR5Labqaylw9LIso5o3OyczGmGAhEiM/tCRShzSNWDKToY/nTiHRxrCCkwwUl/dpQQJWHo0LlxTLM7JqqHcNRA0KRkQ1n2eobz+EpmUfCX5jq7Wl8F/Hb2IXb1FaEJyVa2HHy8yMLyb8QdbME0/iVLYNGHSEgjnk2tYtAZTKNc6428wBIYUWDJcx86QCk4llHQK9IRspOsDzyPp6d9HespMstIW0Oetot9blfjCU4DfVPnygY/hNcOpiof6x/HPh67hh1PXsCTzxGm7z+mBwO0oH/20MwwfLo/+GYBPvELCIK5/VEivihhjfqjQLdTsACQIzhmLh+f99MmltvWgGPESS7EkpiqoWTqdA5cuYJ+zll1yL09ZzzOb6QwP5xII7Nhx4WQceVysXECuyIaHfx7j1465ZgQ2t+Gw+QJYSchk7P3xG6pEsLDYwFaOmtXoQsMmdK5VL2eamEwKbjLJYIlYSIFZGlP0JBFM4LJXPOzrjBdmVd0h3KqfnT3F3Fy0M/7iJBBInEoQXRg4lSBOJYQ6TFg6lBC3FCZvc3dPEY81LiAotSFV1gRey46JgkTBQiFg6dxfu5Qmf1rStkZG8sWgN2Tnc3tvZldvERKFkFRZU+/g6rUddPbuxBeoI2i0M1yIByyVfX2FNPrDViNDCvLt8el1wyEQPN88k4fqF/CzYyvZ3VOYdF5Ve3JiPje0vUBdy9+RUlKQFc8UKGVYIPcbNvoNPXpsLPP2y/sHhfjgM+r8pua80S9O0I/30xsshIbXf/z9u8H7hLMa+Ukidxos/xas/38fdE/ODFhddkKVaeizukcXtCr4XinEddMJsI1tNp6WyNjRGhGC9rKCMH3psKh3CQTcTk7MrQinngFmRGvdxT6KKaKZlkhcejgyPUCY8cyLj2ZrLVcFzyfr1ccT6qISmPzWHrYVZmEkqtw2DF3FuQmVWkNXw2VXTwK72McMayq6ouMQDpaoC1jCguj3ngIDVQQwRvmpAhZ8Z4ePpy5ys67J4Hi/RYom+PedfvpD1yKQGFKhwtHGcX82Mi5PIBYmKuPsnVyUW8WstCbKne38y6FrqfLkYkgFicAmQiMu6GtaZhCwxsq5L9nYWc514/bSF3KQqvujQW+jQdPSMIzE/PVVnlyydA+9xmB0f0gq1Pky2NeXz+y0ptheSLAQbOis4MdHVwHhDcsNBbtxqSNXYAu/4hZX5+9HAH+uXcre3iLmpicmi2kKpDGPod8Z9PuO4Q3UYRi9gA6RLIa2gJt7D9xAayAVRUiClsoNBXv5Yvk7SafVwJQzpOBQf+LgsVrfyXOYD0TLv1+Q0sIfbCXFlbhE75mKs7rlKSB7EugjUyf/w0Cf0Y02oXdUYSsl+NflE9iYiwwqcd8REkgjdpJm1Lcz7+kNzHtyPRl1bZwyBmb/CNv5xplldBdmY+iDi78Egi47z/3oLqSmYmqx/TYxqaUeA5NCCqLXDIWBwf6O9SNuJCZsOsDqL/ya8btHr7JlaSob77qMkE0LR+gDIZuGNzOVg5cmjINJConkb9YTPG48Q7V1gl7ZR7V1glbZxpPVAa55zYM1kOQf/ZcYW1tNFj7Xx6fe8fKd7X7u2eSj1Q9e04bHtBOwdBqCmRQ7ukdsZwDVvlxqvFnMT68n0+bntvHbIj1WAEGv6eL/Hb2U19rCmxchYv3vAUuLnDs6LBRebpnOZVs+z8e2f5ortn6Ol1umjER7H4USrbQWjzlpjfx5zqPcOX5LzHFJYiEmRNga8WDtYnymDYHkk+O38LmyjaP2IxL6gUM1sasmd5Vs5oKcyoQEO35L46hnOFMeWDJEZ381puVHDNnI/Ovhq6j3ZeCzwr9lSGo81zKT19unjNgnKUETkjQtsaUox9Z/SkL5/bTMKULFro9cCOlMxFlBfgqYfiMo761a5kcG7s9VIcaoXdvPbaPw0AsI1xAzqZSILkHjxOvwPFQenaSzn9vMNd/+K3Of3cic5zZxzb89wJynN5x6RwfDhxN/rwhe/vatHLx0Ef4UJ75UJwcuXciTP/kMAZcDhKBtSmKObQuLOhqSkrI0ZCaPAhZA9/hcNnzmcgJuB0owieZlmgjTQg2EOL50Gi/9220cPW8mDTPL2HHTBTz7w08RdCemSx0NHry8bW3kaXMNG60t/LVzK/ds8eAxICQjSf7R3iaG34K6fkm/QUKfOkDQUqj1Z43YzgDsSojJKYObt9/WLCckhwtrnd/XLEcInYKsSxhqYLw4txKnMrb0QEOqVHrz8Fs2glLDa9r5efVKukKuEV0LQugjVsbSFQu7anLb+O1MccdWPxv0ycdCEXDfrCeBsMA/P+fYKdGk2hST8Y5e1ndU4DO16KbEa+rs6hnP/ASBZpYU/HCPYHvX+Oix5kAqRz25mMOsKH7LxpONc6OfBwSyYQkMS8QUObp9/Ls4lNj32qGE+FTx1jMuhU1RnKS4EvMSnMk4K8hPAboLbl3DB0OpdIZBK/YkDFwbqgAPmNmUFBPhsGJjDITAvy0Pq8lFaG8mllchtbmLuc9sRAsZKJZEsSRa0GDO81tIb3wPXLmjrBqGw8b21Rdi/st9OLBTdKCWCZsPIKQMV1Mbcr3mD6J7x+aT1mxurCvvAHs8gUrdnApe/tZqji+ZStukIiw1OQ/7ZT98hMUPvYGzq5/O4lw2/J8rWft/b+Hg5YuiZnl7n49Jb+1h2qs7SE1QdzwZJBILixAGG48VYlrDx2r0l320IP6RTepDggyx4hjUBvzFw9ESTOX2HTfz73tzCOGIvm/nZtWwOKsbdcwVaGKfz2fZuXvPatZ3TCCY0ESvkpm2gJzM86LWgK6gE58Z7620KQaX5B6O/G0y0d3B9JTkBYQsCWmajy+VvY1bDWLJEYviJX0aTYHFmbX8tW4Jb7ZPYn1HOT89upI6XwbLs2oSXvdy6xTu3ODkROg8mvxZeA096RgOrdYmBDT6U1nXPgkLEbP5uLlwF58s3oJbDaALg1TNz+dLN3Bl/sEErX6wSE+Z9aEjg4GzPvJTRslymHMH7Hngg+7JBwv/K4XYL2xBcQ+vjAGBLdnoM7tR05Mv8cJvYWzNBCT61B6E06JkXRXCir9GWBal26rYe+3S0/wUsWioyOHYb/4VeWQP7WV5ES7yMNIaO7jwN8+TVduGFNA4vYTXvnnziNUl+ujn4RtyWZF6M0XPv4TobgdpYSqCt754bUz7qIkXEQm89cVrCbns4aIvDNkhRVC84wgrfv0cUgiEJVn0yJtsunMVR1fMTdhmMjj1EJpqETTejwUt8Thl6x58po2gVFmUcYJ7K94iTQsACmnu6RS6FeqSkNJV+7LpbvCyMt3BpJQwM56OyVfLnmF9+92n3NO2YCrfrrwamzB4ZMEDFNj7iPCtkeaeTH7WRShCoyj3OhrbX+ahhnl8unhLXDuCsDBP0eDmCjt3j9+HGUhebEWxFXP/3EfI1j3oSliInqwgH0CKFuTzZRtpC7jpDLn45oQ3cGrxFiIp4f7aJfRE/Pm3bp0DzKbC2YWmyLjsOl0YnJ89kEapkuaeyu9rMnmro4LzsmNdRELAbeN3sLpoJx7DhlsLJiS4ORNgybFtzs80nBXk7wHX/DmsnW//3Qfdkw8OngcnkPLlShjvQXFGiqOEBAiJfWEX2EbQiAyD4u3VpL5+gqZ799PxCRtCQHpTJ0pCm6bESiLoYk+TFO0+RsPcCQm1cCEFqgWmNLB5/ATcDhhSkWyj3AqqRE4dH3Pd9JfeZcnDb4bbiBzLr2pAMUws28hTKSQM3rykiAWX/oYZh/vgR5+nP8uFMMaajK7gS3PHCnprkGVH9wa48NfPowVjF+mlD75Ow5wKfFljj8peOeMYF06r5oVd09h09PSk4qgk09glujD5yfTnmJISGwchhI2ygttoCBZy2fgAf64MJcrWBqAz5OKrB27g2cV/jAaqOdUQmpCMUttlVChYeEw3ZYU3gjSx6VnoWnr0+/SU6ezrK+Gd9k4+WxJP7mJIhRvHn+Bfz02LMLt9nNrmJ+jzHkp4vyKXTh9+hkrPkzWvDwSaDYxFrt1Djs2TsM6BJaGyP48nGuYNb4XGQCqfKHqXhxoWE7JULBTsSohM3cvqwh0ApDgrKMq9gdVlT3Fe9hHsSvwvLSWoQpKmJy59O/S8D8rcrgjbhzL1DM4K8vcERYUrfws7/wjWPygZkvRqtJxzGSlfOozrhjqsPg3b/E4Upwzzrie9UHLFDx4l+0QrenGI5+++A+EuBCkp2XU0cQlNC04sHjnAZgBt5UloFi3J0j+/TEqPF3dLB750N6994yZMbWiQW/zKn19Zx8LH347rly0QomLTQaqXz8QaGgyXYEUyMKmtfpUZ9z2LtAzSWrv4+D/9gbe+GKaRHRXDNzFDVvcJe+qRCVZ7NWQy+4WtbL1z1ejtR6ApEhTJ1fMO09yTRnVbNpqwkFJiRr1xI6+2gjCV66JcjYApyXUIXqg18CaQ5rcVvctkdxsbOip4qmkOfaadFdlHuCLvIKtflDT4+sfAvSMISpWd3cUszgwTPbg1g6WZx9nSXRZDRiOwkAz1/ScYA2FGAsUEqrCY4PbishdHKVY7vN1sqV2PYtbTZWTRFCymx5zIL6sv5N6Kt1CERBEWQUtjU1cZz7ddQs4xL7dMsHFNiYbdlk1fkmJ4/mA7MHKU+miQqKzvmMCC9BNoioVDSVy8RIiwjWFaaivPLf4Tn91zCzX+7MG+WDp7eov545xHeapxLi2BVBZnnuCKvAM41BDPNs3GnX4ZN9n3ks4hUlNkws3CmPnuI//53xbmAg2bnkWae2zry5mGs4L8NGDSFVC5hrEWLPrIQfba6PvBLDx/mEz2Y+ujmnnMOcMmZ8n2KrJrWtAjGmTIHWYHc/R6k+ZDmzaNvrzEvtIYCEEw3ZVwNcivrGPC5oPR4io7P3Ye5ijaNMDU13aiJtGel/1lLX0FWbRMHQ9SooaM8MZg2P3VQIiVP3wQvIHoomr3Blj5y6d56r8+izf7VHOZIWT6SGiqlRLdF6Dg4AlmvLyNlI5emqYU05ebTu2iKXgzU3B3e+nPcsOw/HRdNbl+dg2HKvP55GQbUsJtb3sJjeE9VwXcUmHjsmKdB48E6Q5a6CoRS0i0c4DgiCeP39Qs59nmOfgjAve4J5tnm2fTFEhPdouE6Bvit/WaOhdkV5Gm+1nbOg1NWFgI7EqQHsOdpAVJuauDz5eu57X2qbzeNoVvTnqb0oLrokK809tOdcMfKdIMdJtFkdXGOL2OoDWVF1pnsbO3mItyKrErBps6yzjQP46wuDRY32Kw9oSHLxZtSrqPCBkdJCKJOe7N4i+1Szjcn8/stAZuLNzN1JTE2RxSShxKgI1d5SxJr8FpS04OM3DcqYb4n7mPcvmWe4Zs2KDRn0aFq5NvTHwz7tpVuYe4ccf53H8okwfnCfT3UHMcItn+/0tCXBFObHoGUhqkp8wmO30JQow1ZfHMwllBfhpw+a+hduM/cM1yu4E+s5u8DWsRcUFSYQyvljTnuc3oQYOA28H6z15Bz7hMkJKQIzndpzczZWz9GSGnpb1iUFPvKM2ndtGUMa0cmXVtyZnCBaQ1d4QFuQjzwbs6+/DmxAqhkp1H4xjZIEwIM3HDfvZee+r8zvVzKhK6I0y7TvHOI1RsPYwaNBBA1olWAJZG3ARNU4t5/Z9uDPvfh/ZLwJy8EN8uDAu9F2pD2BXGJMgNCY8fD3F/VRCfGRZJDhWy7YK+kMRnDrK1beiaiNplxQiPgNRpCpzcxiZg6jzVOJtNneVcnb8fXTGZk97IJXlHuKdsAx1BFwX2Pq7fltxvPtnVwv3zHgMgQ/dz5XiN1VNWRaPTpZQcbniaVCUYNYioiuShhkWR107S4M/gwfolKAk0/wytmzsLHsXCYnNnOQ/WLaI1mMrstEY+XbyFElcXYY0g9m2r6s/lC/s+jj+SVtfYls66jsl8b8pLLM8KE5hYEoKWhq4YSClZmnUi6n0Zi3AUApyKwcU5h1nbHq4QpWAya1i++1DYFIO/L/gfeg170qC4k9HQE5Y4HmP/TxbFBTeT4iw9/Q1/APjwheedgUgvhltf5B9wNCX6/A4yfr6D/PWvhgukjLGkohUxZa/955uon1MBaliDNe06x5dMjcnnhnCu9L4rFo+1W0lnvmJaNMwOm7E3fvqyMEVrTMcsinYd4847f8rKXz6Ns6sPJWTg7E5cXjSca+6geulgaTzLriMIa+DhA+HANGe/HzWBUq8ZJo7eJHbWMSKQ6mLznavoGpdNZ1E2loCQXadm0WTs3gBaRIgDcUblnJqWhLEHKioloij6+dw8Ff9JVL9s90u85qBe6TfBY0i+O8+GJmIbMhNOnpNZvcOFOff2FbO2bSpfOfAxXm+bSr49PK6pWoAyVxcO1aA0SeoXSD5RPFiMaXamyZ0zF8YI8frWJ0lTmmJ81of68nmpdUYkRWvwC5cS4KsVb/D0wj/xlzkPcWnuIb5Q9g4uJcjzLTP5buUVHOgvpC0Yjva+e+9qan1hi5NNz0bX0iP87vCbmvPwWfqQ3HhBwNL5RfUKpISOoIN/OnAde3sLItSz4VFXxMkJQSFgbnp95A4WDtXkk8Vbk56vCUmKFqTQMTrz3NB7nMy5Pks95WC/ZHA7J35khDic1chPG4oWgz0NAmPP+PmQQ+K4pg7HpU0E3s4nuD2blLuOYjQ5cV3ZAHq8rwwLpCUQuqRyxRyQku6inHBq1xBsuutSbN4AhfuPY2oaqmFy+OL5VK0MR1/b+n2Ubq9CNUzq5kzAkxvRfKWM8JZIXB19eDNT46OEBFiqwFIV2ssTFEdQFBrmVrD1totY+Og6rvnOg7RNKMDmS5yTLAW8fc/VsZHngC/NzRXffxgtZJBZ34ap6wQWLENJsIqFbBqNs957kM2RFXM4cuFshGmhB0K4OntRQyYTNxwY8TrdH2TRY+vYtvoizEhNcxUVJ06mKYNscdkOBZcGvWNw39qUsEgbnlPuMeC+gyEMeXpMmKoYMNUP3Z4omBLWtM7ic6UbsasGirBjyXCg1WdLNvH1Q9cRtIa+d5LZqQ2sjERiC6GSkTId05Ls7DAxJUxz17K1pYcXW1YQsDRW5hzhnIwa1ndWxLHIZeke/jbvb7jVAJoiybP38/UJb6AKE4ngdzXnRd0IECal8Zsaf65dyn9MeZU01zTyslYQDHVytP7XHOwrINHGpj3oxmva+M+jq2gPulmQ0fCetFcpwy6JDM3L7LRGPlu6kWJnd0LNeLhmrYjBKTjw91iIokY6x5KgC2vM7Y0F4Up0s957Q2cQzgry0wQh4PJfwrOf/KB78r8Bib64jYwf7UYr9pBy+3FCx1LovGcxqV8+ROhoClq5B7TwrO5/YALeR8qwenVSP1eF6/ZqjiyfTc3iKVHNfCgMh43Xv34j7o5e3B29dBflRMlOirdXseI3z4dTrKRk8UNvsPv6ZWGzdJgai9wjjcx+dhNvf+lajGGmektVKNp7HCwLxbQS85YLwZELZtGfk8aKXz1L8a5jqGaSymQ2G8v+/DI2b5DGGaXsuPkC+vIzya+qJ6960CSpBUNo2zdDajrS04cI+CIjCVrIoGLjAZqnjB8TTeuIEAKpqQQ1laDLjr2zD6mIUfncp687QGb5uRy4YCo+6aNEjGeaMhmbGDZ+o2hGqggL8RtKdZ6vDcUJchVo9g6a1d8LVODiQpW1DYnNBAFT8HzLDD5euAdrSMGT+Rn1/GDKGn51/Hzq/Jm41SA3jdvJXSXbwq+Q0NHVVGpCi1j+ZB/+iMtibmoP23quI2ipSBTeap/E0szjlDo7UCIWgQHcVLgTpxqMasYATtVASmjyp2DJ+Oe3UNjXW4iiOMjOWIIQArstG13NJFP34gvEu500YWFXQ+zvK+S6gr2RsjOnDiHg5qI93Fy0J+44DAp6u2LEPNsAzCHCdqzmfFOKpOloigAlWiN+8PipmtyF0LDr2aSlzDi5C89wnBXkpxGz74AXPgfmhzMVccxQU7xc+sXvMfVXO9GCBnXzJrDtpgvR1/UggyIcsQ6YfQptF11C6FA6SloI180nsC3sQCAQqgwLrRH82Z7sNDxDAsB0j58Vv4lPsZr77CYaZlfQUR6mSZ339AYK9x6nfPNBqs+djqlrKKaFsCTn/vllWicVsuWOVeFNRJJtvmnTaZpRStBlx9GfvMqWalqktYa5tsu2HqZobzXP/79Pcu79a+NPDgUw+7rYdd255FY3oIZMxh2sRTVMyt6tJKWzj5e+84nkA3+yEIJAdhr1s8oYv/c4SeOQhAJ2B+MW3Mg4NTvJSWEsy9d4tcFIKi5UAd+cbeNz0xysqYtX3RXBqIVYxgwBrzcmt/VbKPyuZjkSwU2FuxmaBLc0q4alWTXRn1/XMpHSBahkpMzG7l7Oymd89A951TZ1lcVUZfNZNjZ3lTM/rQ5r2MZkfno9NiV+8xeUKmm6HzMBdSpAgcNPRdFn0dRwXIJlGUgsPlG0jftqLozR4u1KiKvz9xGyFL5avo4ce/97FOOjQxIOfusMuViYXheXSKGepCk/3ObJ99qIWGGElGhjcGkKoWO35ZHuDld4Uz6kQW3JcFaQn0YIAfccgPs+XHz7JwnJLVd9kdK1u9FD4VWuYtNBCvfV8ORPP4MxhCY08FIxRlUa9uVt5Dz5NigyzOw2dKIPj4IbASU7jiSc8krIpGLjgagg7yrKIaWtB1PXyKtsIKu2BYfHT/nmQ+y+YTnVS6fFmfMTQQmZdBbnUXTwRPJzjPAYBFx2egsycbf3supXa0gLJubwPbpsGvuvXIQamg+AkJKLf/4UBYfryKluIutYI50V405rdM+bX7mey/7zCfIr6xGqFo5HmHUO1B0Fbz/MWQarvwLpiYW4lJLqvrCo+t58O5taDXwGCYupBC14qsbg3pmCJy9yc9ObXozIZi1kgVOFrrExpw7cPfL/g+OhYmKiRjcE8WVPiV4TlDp/OLGMq/IO4NJCKIoDywpE2xUi/FfI6Ipe09G7hc3NE5HEBisOF9YAPkvnbw2LIkFtg2jwpzPF3Yo6bPdkEyZ2zWRFzhHeap9IIEI7m6l7+HrFm5yXXc2ROgkoZKUu5O32bCZqXq4pOEBbKIXHGhaiCAvDUil3dvDZks3owuLi3PdWdlSgMz7vY9S1PgFYdAZdvNk+kQUZ9ZQ6O6MBc4qAvb1FvNw6jcVzHo9v5xRe21NTbrwGAAAgAElEQVQRqboCB3pzOdBXwLKs4xQ5R/bRK4qNCUWnTg50puOsID/NyCwHVy5430ONjzMZaa4mSuXeqBCHcKlP3Rdg8jv7OHj5ouhx35oiZFAh59H18cxvQ5GopFEk+puQQDf9GHYdxTQTrxRSYgzxUe+86Xx23HwBMmJmzmjuZNozm2ivKOD44imJhXiCzYSlq7i6+zE0FVsS07oU8O7qlRxeNR/FMDA1jZKdR8iu62DWms6ooAfozc9gy52rsHQtpg+vfv1GVn/h1whLkt7cFRbkyYbKsrjgvuco23EEb2YKO69fTtv5S+lRktCeAZbdxkvfuY2rmieR57NDySSk3Um9bKRBNuHAzkQljYGcAEMadNGNAwdHOx3cus5LRyAsqsa7BX8538Xj1UGePJ5YM1cjw7gwV6Py46k8UR3kseoQx3pNWkYuIR7fFiZlri7q/RlowsKQCp8av4WD/eNY3zmRldmHWd85kWASDRfC5ucqTx5z0xuwrGA0gMyUCshIKmD0p5dIGeJE1yGC1jnDWop/9xQsWoJpcd891rCA5VnVOBn8/QfcEgL45sTXAcm69snoSog/zn6MPHvfkJAOi47ed8mRqTgjlc/uLtnKJ4p20BxII0PzkaH7Rqw+NuDAGJNwFRYu53hSnBPY3trHvQeu5S9zHyHX1h/t08AU7Qy5cGpGkirxY8fAlDsV4W9JmJraxgR3J3ZldBIPpy35nPoo4KwgP82oXQ+h5Gvqhx59vjy8vgzSHa0xx/WgQV5VQ4wgV/L92Je3Rmd74Z5jtEwtiQsMAxIzsIVCCKEw4D6rmz+JpQ+8Fndee8U4dn1sefSzaYu0H1l5Dl2ygMoVc9D8ocT3Hrj/EGGuBkIU7akme4SqaxI4uGoBlRfNxbRp0Xz0unkTaZpazLS129AMI6orVl0wO2F0uABq50+kdHsVHRPGJbVSCNOiYuN+Kt6tBCClvZelD7xKXVOAw6uvpJnWuLYHoAhBf1EeOaKEGlnLFnMHAQKRemIKe8wDrFCW0y89bJe7EQh6fBrffXlljD+3uk9y1zteDnwslXfb+jnRHyvKnSrcMXHQl3u42+Kft/nxmydrUpdowuKbE14nQ/fz65rzaPan4lKDPNU8l3TNjwD29BZFUqwirIIJiF5MqZCuD+wgLKQUaPZp/PWoizvGv5tQkMxLr0ETS0hMozIIK0LZOhyVnny+V3U535jwBm41iCos3u0uodmfxkW5VejCZGlmDVfmHaDc3UemHp8VIQTk2fswLBH1RztVg3JXZ4ygToRIuEgUIxm9pARFcdHnqUTX0tndm8a5WcdJ0/wxfvCB19KUAq9pi4x1/I96sgFvpwJB2HeuqmNh4lLIy1px+jtxBuGsID/N6KjidMTxnLmQgsq6lSye9FjMYUNT6S6KNc2mfPooXV8N17eu2HiAZX96mRe/eztdRdnI0UzbESGsBUPhFDFFwZ/uZuttF7HkoTcRloViWXgyUnjp27cmXi2GHLN0jeCo9wybujVTogYMZr2wOeliadod+F0a+69cHBdQZ9p1TF3j+e/dztIHX6fgUC2GXad5SjEyQXCdpQgCqU7q51TQW5AV13eAlNYuZr64jWmv74w5rgcNil97B3X11+ikmyDJ7dY5ZPOK+TotVms4AM4wmbR+Hwsfe4vuwhy23tFIV1kBMrJzevTdOQmD2/pDsKbO4G8XuLn6tX4MC4Im6Cosz9e4Y9LgeHxnhw/PKbIeutQAGhbfqbwyyswWNMJttwVTAWgPpUbPVzAHDOZDWpGkqT7KXZ2YUtBv2HFrAaR/P7cUJjfqTna3sCxf4fXG0XYfySf7hs4JbOysoMjRhdfQ6TTCff3F8ZWRKy1W5lTxvSmvkox33bQUpBBoQ0LpTAl+Mzwebm1sDHCJ9oZDNWLT7KOx/QUAri9QowFmidrJsvmo7M+jI+giNwHtqyEF+3oLmJnWjH6aONWtyObAsBRsipXMMBft48BnQypMKLwVp73wtPTjTMVZQX6akTeTZHPyIwGJQpenCMPU0Ibshi00Ki8Kp4dFA4im9eK+6xjSFCx8/G30oMGlP36Mdz5/NU3TS8La6Ui2NV0jraaFnpK86KHKi+dTP2cC+YdrSWvu5siFs7CSadljfqhIzq1pUbH1MIv+9jq6PxgOkEtwujc9hed/dDdTX9lCICW+ohkAAnqLcnjj3uvJqmvDl5GSnJVOKPTmZPDuJy5K2kVbv5/Jb+9J2B81ZHDQu4srU1fxkvkagWHCXEEwWUyk1V9Ls9YaTcmTmsrxpdPpLsyhsyw/bFEYsvAea80mkaCygJo+i1sqbBy4IY01dSFafJKleSoLc1RerDP4wW4/9R4L75iEePx2yaZI+gwHv6s9L4ZedSRYiAQ6oqDbdPFo/TwebFiC39TQFZNbC7dzZ/G2hK9eyBI8WH8Bb48qxJP3f/A5DDJ1H12heCY5iUJ3yAVYSVtQhMWPjlzCjYW7ub/uHPb2FuFUgyAlf537cMy5loQGfwYOJUSuPd4sOPxZh34e+rc9QnaQKA7Va+pU9ucBgnsP3MAfZz+GSx3cTBhS8O9Vl1PtyeYvcx9FjGD2HjNtq4SQpQCS9qCbPLsHPQH5zMBmZUCAb+ys4F3PCh6Y+NEW4nBWkJ92FC0J+8h7ksdHfcgh2HX8eioWrKdC7gAkne5xPLf9e4iMo3E+OfdNtVh+gbujFwBnn49Lf/IE7WV5rPm325ML4cis7EgQ+OXJTac6Z2Zic7xlMfvZzcx4ZRs2b4DO0jy23LGK1inj484FYtQUS1epm12O7dqlnPPwmzECQYqwjKs6fxabPnkJll1n/1XnkF3TQsvk8Qny1QUz12xl/lPrsVQFxbBoqyjglW+tRg5LuTNtGgevHJnsprO8gL7cDDITlHENOWx0O4Jkmm5utq7guNbCcauGHvpw4GC6MpkKUcbzPfdDbmzNcsNho21yUcKxTKaVAczNDlsW3Lrg5opBDfyxY0G+ttWH7ySIYxIhZIXN5G3BMbL5AaAk3kNLyf/ULiMkw8tdyNR4qGExumLxifE74k7/2bGVvN4+8yTYzmMtAAKJIhTcmsUNRXV8qrSOS9fHCxO7EuK8rHDeeshS0YWZULhNT23mM3tXR+/lidDQ3rH7dh6Z/yACye7eIv7z6Cq8pg1LCia62/j+1BfJt/fjNzUeaVjAK63TwmmyuQdZXbQjKrCTwZQCS4po9H3IUugKOlnXHuYWqPVlc/nWz3FuVjUL0us54cvi9bYp9JsOih1dY9ZnBqZgMjN8v6ljFyY2VY5KPCMEBEyVm3Z8iiApvHnFybw/H14IOUL6z5mKhQsXyu3bt49+4geEd34I677DR5R7XZLxsx24P30UXfejGBZBmx3p0RBpBoplkRPJn26vGBdlTrvpS78hpbMv0gIcvmgu7966EnMEStbBW449afScv77KpLf3RjncIUy4suZ7d9BVnBuNDvdkpVG7YFLUr+3o8XDeH16kcH8NQFQbP3bOVLavXoEnJx1Hdz9Bpz1m86H6g1iaGi5YoijR1SjnaAOX//CxKKc7gKkqNE8ez9pv35psaEd0yxTvOMKK+55FCw0uwCGbxp5rllJU28O4HQeR0qKvrJi9n7mZ7NIlTFImoEVSbZ5o/C39eQm4y5OsoE9vm8HGo2VxnXJp8P15dv52LIQl4WNlOh0Bi6dqDJq88jS99jLy72ToEuMj3MOfrCGMaINwKkGWZhwnhMoVeQcocnSxr6+I/z52IXcUv8s1BfuxKSabu8r4z6MX47fG8K4ChU6LAzdmsrXV4Id7/FT2WLg1aPBIAlb4R7YrIcbZe/njnEcB+PXx5dxVspWsBAFsvzh2IU81z4l7LgC7CLEgo46tXWUx7HgKJuMcvTw87wE+v+9mjnlyCEYi5O1KiMnuNn4764kRp5TH0NjYWcGCjHpsisFb7RP53Ynz6DWc6MLglqIdXJl3EEVIXmubwkP1i/BFx0jy5IL7KRgj45thQUsglXx7X1w6WTBiTh+KZEJfSvi3ysvJtXn44fILyHZ8dOg2hRA7pJQLE353VpCffnRVw29ngPERzCcXuV6Kjj2HGFYYRQYFBcdOcNEvn0GJFBexNJU3772epqklTFq/j6V/eRVfuou1//cWfOnucKT5SCvJSUbK2Dx+bvnCfTGCDsL7qbo55dgCBjnHm1EME1NXMW06L373NnrzM7num38ivaUrhvjFUhT+/rPP4hlqEh9jny7//sOMO1wXd9zUVE4smIRimgRSXHiyUmmYXU7nxGJyRDZtdCAj/0uEsq2HWPTIOlLae/Gnudh97VImv7OPrPpOhBnevEgg5LTx7M8+jz29kCvVS9GEylsb/o3qJZPj+5/kmbwBnV+sXU6v14khFQThtJ+5WQr7uqz3rHWPjJOPiQ6zyUkCcXz/ydoaFPxh/7qCrgiQBmWuDn4542nS9ACGJWgPuLh5510RGtbR8feVLu542xszRjYF5mVJgqEWzsmo4tLcw6TqAZ5vnsGKnKNkaL6EJCt3776Fw56CMd13KJxKkDvHb+WB+iVDBOzgdz+a9gILM+Lf0QGELIW1bVOZ4m6j0pPLL6svjGxmJL+a+RTTU5pwRLT6gKlywpfFZ/asjm4oZqc18OuZf4+Jek82dTyGzu9PLONzpRtxqqHoNX5TI2QppOpJmBWHaPNBS6U5kEqpqxtVcTG17BtjH6wPAUYS5GdN6+8DMivg4h/D6/8CxmDK6kcCtoWdyKAaVpQUidDDD2cPernkp3+P0UABVv307zz+q3s4ev4sJJLWKcX05aTHl+Q8DUhp6wmTvAwT5ApQsuc4lqpEBbVqWliBEBfe9xzPf/9OPLnpZA0zW1uqYMLGA+y9ftngwTFuLOxJSGQUw6Rs62HCHr8wZr24lb6Zs8gsng8+Dyy8gIenNxBMYNytWTKNE0umg2UiFYW8qnrSnlgfFeIQieg1TCrW7eLgdSlUy+NMNkqYv6ubE/OM2Mh900pa7NplD/FvV27FXn8lW9tMylMFmoDv7Ai8z8amkxfiqoD/PsdJZY/J7w4HCZlDc8uTxnZH/7IiAjpoAWgc9+bw3crLmZTSxhttU2gJpmJXTKamCg70jDyhBXDvlnj3QtCC7pDguWUtPHLUw9NNc3ihdQZfKNuQVIgDFDu7OOzJT/Aco4/TWx0T8SWIMQhYGof6C1iQXheNMI9pWYbbvyr/IAAZuhcrkuI3O7WRqSktUSEOYb96kaObc7OqWd85EZsIkar5E7abaAppisWGzgr29BTyg6kvUuDow5AKliSpEIfYtmyKSamrm5ClkZs5xroMHxF8dOwOZxiWfAXuORjmYP9wY8jioptYfTpt11xIffrN1KffQtvHzsdss1O+5VDi6BgpKd9yGIBj58+mLz9z7EL8JPNW+nPTo9aAuKYgjmZVkZBR346j10tDAq5zLWSS0ZSswEZyqMEQDTPKMJOkmg0VLwLQAyEyd+xEPH8/Yu2jiJ99lYrd9Qk3gALBNcplLNXOYZqYTHpLb0yA2tC+Z9a3YWBivvIQ3H0eaRvWcdEvnsbR40HzB1GDITIaO1CCoaQMe7qAmn6LVxtC/GJf4IwU4gOXrW0I8d35TrZcncqnJtuxjRgxPbIwDkmVbT2lPNKwMJonHrA0qvsleY4RL0UCDd7E7Vf2SOo6NnJxTiW3FW/naxVvcW7m8aRC3LAEl+YdijuuCZMSZxdLMo4zK7UBJcGv4rN0av1ZOBIEnNkVg1xbH9XeLK7f9un4+0piSpLm2j3cVbIZuxJiRmpTXNEbCEfQT09twqkEuSZ/P/8x5cWY7wfiWgcC0qL9NDWebpqNz7RxV/Fm8h396IqFUzVwjSm9bHCpCFmCN9onk5O+fOQLPmI4q5G/j8gsD2vmj1z1Yc0tl4icALLDHl6dUkIYu7OQHhWkAib4Xy6kdcUq5v/Hm6ih+Mmthgwcfe+tsldsl5Lb54JuB1UXzGbqG7tRxuoyEiAMM6EGHbLptE4qSnDRyDBtOq2Ti5i46QA2XxDVCKdFSSGS9ksAfodOd3Eu/bnpTHjhTaon3UDQHc+9fkLWM1+dzTPGi4iS7ISxGCGbhi/dzeXff5iCyvroyjl+33FWf+E+uguz0QMhtt18Id3jc5KO6a/ensmJtsBJVT07NUhUwmlFp1JQxQRerjeo81j8eI+fN5oMnJrANMAmQGIRssJR5OOdveTa+tnUVcrIm4b47wZKsv5hmYP/3h/gYALt3BUpgpYoYr/Q0c0b7RNZlnWcTJuPpZk1cQVXApaKlAIhJEf6c/nJ0VUxfRFIVmRX8c2Jb+BUDXymxp27bqMpkDYsFkDgNe0R/vXBDZLAQldMLsg6iqpYfLFsfez9TYW3OybSGXKxu3c8xY5urh+3l9vG72BhRh3H+nOQCbjiAW4r2sHtCQIIhyKaj24JApYaJutJa+CWJb8fMbJ+NASlypf3fYxD/YV8bck/lo56VpC/zyi9AObcCXv+CqHTKM/+tyB7dKJBR112cJphIT6AkIrZ4OKEdx6zbVtQhpnWBzjLAVSPH8umJc0hH7/7GAueeJu05i568zPZ9KlL6CwrQIpwHrjmCyAVJZImlXiGb1u9gvyqerJq4+uHD9f1JNCbn0kw1cWk9ftjzrUEBFMcHD1v5qhjNBxKyKB+3kSemVLMjFe3U7T3OJ7sNCovmMWqnz8NJBYfrZPHs+6L14RTqBSFjPr2MEFMTJ8lHbKTPdYBuuiG0nxapxSR1thJ/bwJGDadoj3HcHf0MfXN3dEa5EMhJGQ2hN0IzdNKko5lY3cqx9uyCb7vQhxAhP3P78ENJYALX+ynJzBYwMShwtwsg2tz1rI4oxpdmOzsGc9rbVOY7GqlypugAt4oULAo5A1+Pi+Dq9+ZEVefXQK+BEJ8oquN385+AsMSdIXc2JQQbs2gz3DiN42oqdqumKxvL+O/j19IWzCNqwr2cW3+fjRhsqZ1Bi82T+czpZtxRrRVp2rwp7mP8vuaZTzfMpsULVxlTkb7I6J/acKi1NnJ96a8hFMLX39hzlFCluCPJ5aiKTDJ1cz3j1yGXTHxWzqaMHimeQ7/Nf1Z5qY3MDUlnngoYKps6KqgyZ/OlJQWFqTXJfPYABCS4QA2FUmRo4dMm/c9E8cELI1KTz7n5KmI94OF5gzGewp2E0JkAY8DZUANcJOUsivBeSawL/KxVkp5TeR4OfAYkA3sAG6XUo7KxHymB7slQtNOqHwB9j8OHfGWsjMUsaJPZPqRXf+fvfMOj6s6t/5vn3OmqvdmyZLc5N4L7jammRpqAgGSQNpNz5dAeiM3N/Wm3AQSCJBGiakGG2MHbAMG9yZXuUiWZFmS1du0U/b3x5FGGs3IBTAYo/U8IGt0yj57Zva737ZWdFxRcYYYe/tTzCh8kqxDNeE8ue5yUDO+kLVfvR4ALagjhcBShE2MovZWeeftOsLFv38+QhDFUgS+lAQOLRhPV2oCeXsrGbLrKMt/eicd2anR7BZS4vCHyNl3jAV/XhmVr7eEvbFwBHV0lwNLVVh9z81MWLGZwm2RXNX1w3N59es3EEiK7v89xRTZ86EbCMPE7KdkVrRxP/Pvf5GOrFSOTyxG1Q0Kt5bhafehuxy8/rkrqZxR0uc6JlY/XXYFBdm9NFs99Ce6CcKutJeKHbAfvWYbMx9fd8phmw4VxbTwJcez6yOzObRoUsScbivP57lt4wmYHwzvJt8rOBmQUYprTsVk2dSHSXP4+PmRJbzaOJKA5UBBYiFwCIHenRMm3Ik+sCFQsXhu2kN8cvdttOjeAXjeo/HbMU+zoWUYL9aPR8XCQnBTzi4+M3QrG1uGMDWpClMKhIBqfzKf2X0LPy5ZxczkyrDRDpgaO9ryeKJmGtX+FOK1IJdn7EcTFiEU1jWUMCVd47njyYSiiv7gu8NXc1nmgSgjG7JUrt92F526C4vYjHU5rjaWTX00yuDW+BP5/J5bCJgOgpaKUzEp9Dbzh3FPh8fdH0FT5SeHLmNTq53S0jCZl3aUqcnHuSilgmRHb6VwrCBc/0YWv6nxh/JFrG8Zy+rL4xmdfGGJosC5LXb7FvCqlPLnQohvdf9+b4zj/FLKSTFe/wXwWynlk0KIPwN3AQ+8wzGdl8iZYv+XNx0ev/L9Hs2ZIvLbI7s0hFdH+iKLZ1RLZ0rlarID1ZTPGm23mUnJ4QUTKJ89Jvxt62FAU0M6mfsrQdpa2FUzRjHrn69GqZopliSuqZ3Jz75p319VODR3bLQR70bhloNUTR1J1dQRHFo4gVHrdiNMiaUqCCTrvnAN3sZ24ts66UxP4mRxDpf/z7/xdEWG1XWnxppv3YJ+GklRT2sn7vYuWoZkICyJapg4/EEKN5dxaPHEqONVw2Tn9XPZe9VMe3YtyZaPL2b2Q6tAUaicPiri+GgjLrDCVKS9kN3HmX360w8umULh1kNkHa4ZcPw91f3xzR3M/NerOAI6+5b2FnVMy/GxMbGLYy0JA10iCi4BWV5BVdd7X+FZPUBeWsPgSFcGJ9RQtxG3P4c9dD8WkObw0aR7u884lTcnmZB4nA0txcxNPcqahtFRFeEDYVf7EFbWj4vQQX+6dhIpDh+js2bz40OVJKgBakNJ5LlbmZFcGWHEAfymg58evpxOw4WFQqMez5+r5rIg9TD3lbzE7XnbMaXCjRlxfPvANRzxZUSMYXRCXUxPOWSpZDg7uwlqYqMpFEdDMI50Vxe6VDEsewNz9+6P0Wm6wmF9v6VytCudv1XN4PNFb0XPoIStrfm81jyCnrkO4uDlhjG80liCguSe4f/h8kybirgnFG8T/kiU7ly7bgmagvHUBlPY65vB6Kxh/Gq+kyzPB2Pj+W7inRrya4GF3f/+O7Ce2IY8CsKOfSwGeppq/w78iAvUkPcgZ8r7PYJ3gJCKSPXbVeuG/WVRlSBZSYcZmrIToUPxpgM89bvP40uKHzBUZjodmE4HV/3kX/iS4qiaMYrE+qhATu/xCnSlJrHqh7fhT4i90KghncxDNYx+ZRervvMxNt9xCWWLJjGktBzd5aRyynCS6pqpWTwRy9m7EWkZmoHjcE3YqFmAqSnop+hvV0M68x9YQf7OI5iahmqY7Fk6nfxdR0mrOklzQSaHF06IOq96QjGG2xkl2vLG568esHJcQ8ODGwODRBJooClszE8F06FydM7YmIZcAg3Dcji0aCIhr4vCLWUUbilj0nNvsv+yaeHef+Fp5rNLNnD/K3OobkmMuk7EnACLchS+PclDpy659pXzJ4/ks5z8rXomI+PqY7LEmVLSpA/A0Ict3JLh6qTTcPKTUasYl1iLQxgsST/EZwvf4st7bowymLHwVO3kCBlSgIDl4LGaaTyT82e+O0LFoZhho/Vs7cQoipunaifhNx39ogCCjS3FtBkeUhx+NGGR7ergD+Oe5rptn47YOJS25zLE3RpVXKcJi+P+AZgHu2EhWF4/jjeahzEh4QRHfBns68iJ2aMfkhovN4yJacgBflS2lOgNkwjXR/zyyCVMSaoh02Vz0AsB9f54XKpBmtPeeActB78tX8TO9mLum+rmU6NOvfG+kPFOty5ZUsra7n/XAQMlnNxCiG1CiE1CiOu6X0sDWqWUPdvN48DZVxZ9wBCfDY7TRGvPXwhcl9fg+UgVmtuPx9nK9GH/5vYFn+nlN1YE+TuPgjlgITQAXWndXp6UFL2xl87URALx0WF7qQg0C17/wjX4k+Lt/HqMHYJUFSyHRnp5LbndsqOteensv2Qqx2aOYvibe7nk108x6bk3SaxtwtvNNPefb9xE2aJJhDxOTFXB0lRUQ+JpG7g6ceY/XiF/51E03cTlD6LpBuNWbePkiDx0t5Okk+0MKT2G1i+0H0yKCxPQRKBHXSIGnNgbimyRRSYZZ2TEw3MSa3OgaBy4eDKrvnsrh+ZP4NjM0Wz49FJW33sLwows+pNInKrJjVP3n/ZeXgfcXeJmSrrG/BwHi3NO7SMkanBr0XsV/hTs68hi1ckxA6Tge/oHonFFxl5enPEg/5z8T1bMeJApSdV4VR2HIvFqOvFqkPtKVnImyX2fGXtz2G64cSgm8VoIl2LiUky8qkGwm5HtR2WX82TNZDoMFzva8sMMdX3hUAyOdqX3PpGwUwrzUo9GHPev4zMIWA7MPmF3v6nxz+PT+kUW+j+PxJLw9+MXUe7L5Pn6SeztyOs24rGfvb+0K9g0svs6sgnI00cx1jeNiPg90REMG3EAh2JysDMLnwlrat4mof8FgtN65EKIV4BYbATf7fuLlFIKMWC/x1ApZY0QohhYK4TYA7SdzUCFEJ8BPgNQUFBwNqeed0grgbpTF3aeh7DfWv/fh+NcWMtVv/gK49/aFKMKW6B2GAS+NBz3n44MwMMhySo7jqmpHJs2kpOj83n2V59GKoKsg9UsvP9FPO0++45SEoj30FSUFfYUY0GYkiG7y9GCOlkHqzkxrpBhG/cz56+rIkL241duYezL25BC0JqfzvovXsvmOy9h1/VzufmrD+AIhACTKcteZ/OdSzBc/RYc02L4hr1ouonuclC2aCKV00bi7vAxdMshlv32c1z3+AEWJi6lqraFzXHldGX0YVMbKEzR73Wlm4/Mh+3ZdsqubibxM4MWMih+K9oAS8tg5OulpNQ0svar1xNM8Ng0rcNzqZw2imBcv82UgKyUgaMlPVCFYFEf471ssZc71nfxUk10pVyuV/DyZXFcsTpa8eudwq2CZUGo38dSouK3YiuVDYRpSZV8fdj6AfO8YO/B0p2d5LlaqQmmvK0xj4iLVNgTAsp9qfy7dgqGpRCSDt5oHsa/jk9nYmJNTP11Q6pkuiJZ1DRhkuqIjIycCCbx6dKP8pmCt5iUWEOL7uFfNdNZ01AScZxDGAgEmmJiSYFbMWg1Bgq7R8+pQxhcknEw4rUesZdfHR1YU6D3WEFjKI5HqmQ7ys4AACAASURBVGbQZniYkVzJpIRe8hq/qfFC3Xia9DhUIC/uwxdO74vTGnIp5ZKB/iaEqBdC5Egpa4UQORBbR1FKWdP9s1wIsR6YDDwDJAshtG6vfAgwYEJPSvkg8CDYxW6nG/f5jIKLoH4XyPekGvjdg/dzB/E9OpLQuhy0QBxiiIz6DmtBnZlP/of84UdYI26JfSEJnqZ2nvjTFwnFuSOMWN3oAl7+1ke57juP0FN/FFP60zSRqgqWRAvpDH99D6nVDehODV9yHKpUmPn0pqi8e8jr4s27rqB68jCbSrW8ls60RIo37qcvC+So10oBW9vcnxQXHqO7w4cwLXSXgxfuu5PO9CSbYMWSHJ84jNFrtqPt2oa15XVykdxgWjQNzWTt165HAsEE7yk3JD2w+nk5p2J7gx6f0l7iVUswfMM+smMwywns3Hjm4Rou/t2zvPT92wC7fqH0mouieOAB/KHThyyfvtiLU+19H1VFcLgj9ngb/JLvbPVTcw6i76Z1Kt84Vh9DrNdt3DFk64BGfGtrPstqptCse5mefCym93km93cpBl8qei3qyF8eWYLfdITD1kHLgW6ptOieqOfThEFJfD0FntaI100UdrRFawxU+1P5ftlVA4yxd07itQBXZe1jTmoFHiXEHbvuOM3z2UWCHiVEjrudT+VvjvhrS8jLXaW30nQG/PlCwFMnJiIRGFJjZf1Yslwd/HfJCnymk6dqJ/OfBrumxKnCXSPPrE7hQsU7zZG/ANwJ/Lz75/L+BwghUgCflDIohEgH5gC/7Pbg1wE3Yleuxzz/QsTMr8DOR8+33vJTL2oAvkdHQNBe6N2OTgIJbtxdQZC9y5jA/hLuum0Op6LG3H/lzJgGTWoqHVnJNBZnk1zThBbU8bZ1kVjfQmtuejiXLEyLoVsPoegmIzbsDXOkS1Wh4qIxmJaBKxC5CFtCsPKHt9ORkRQ2WA3FOSiWJLPLhRKKbJgY9VopwzfsZftN82kcnstFj6wmqbYZISWtmSn4kuJ6WdIUgelycOCyaYxbtQUtEAxfJ728jo/c81e0kM7mj1/MkXnjMR1qL0NG1CT0K8k9IwhUVMZ0ZFD8m9+SdrjqlKZFNS3Sj9YS19BmRwwsi7Z+MrT2WBT2Vww/7d29avTdWvq7xd3QJaw4fm52sYY8mw62ngr12Mh2t8d8fVnNJB6smhPOd5f70tDfRu97ohbgt2OfZVR8pEduWAr7OrKjcs8WCvs7c1CFhRFuAZVMTarmhyNfjjhWSnimZiJHzyB3Hw2BLjWa9XiWnZjCpRkH+9xvYDiFyZzUo1ySUcbs1Aq0PgFaKeFEMPGMjDjQfb/eOQ1YTir9aXx8552R91TggTkexqRceFXqZ4N3ash/DiwTQtwFVAI3AwghpgGfk1LeDYwG/iKEsLBz8j+XUvbE/O4FnhRC/BTYCTz8DsfzgUDqcPj4y/DC3dBaAeZpG+7eCwiUgk6sqjgGNOZ6dxGU12DTffMwps4hvr6Fi3//HMl1dvhVAjuvn8vJARS1AFAGUKnqGYkl6UxNJK65I9xCtuCPyym9+iJMh0rNpGEI08ISgulPv4G7w4fhchCM97D2K9cRinOjBXUCXhfOTgXVsl3tE+MK8SfHRXqdqoI0TfaNT6PgRUd0y5qm4kuO55JfLosQYkmubWL23/7Da1+8JnLspkVTQSbePcd6b2FZKF0BBDDn0TWUrN1NxfQRlF4z2+YWjTVFhhlVFHcqSCQmBvqRXaQdrsJ0aJTPHsOJsUOZ8dhavDFy/pam4Gnvsg15DJ10gHa/xlOlAyjH9cGjh4NsPmmyp8WOJaS5BCMTBY2B9zZ49m7erbQ9l0xnR0RhmM9w8JeqORFFc31z1qMSBWXtZzaKIm8To+Ibwvu2vR05rGkowbQEipCYMS7jUXRSnD6q/Cn0fE9PBJNo1j3Ea8HwRrop5OH+qnln/cz9v/shS+WJmqkx2eH6w6kYfG/kGlxK9CZNCMh1xd4Ynck4BsKma+IoShikQ3lHMyClbAKiEh5Sym3A3d3/fgsYP8D55cAHnsT07aBgLnzxIPhbwAjB/+bw/nKyC0ncRyvo+L8S8MeQFnWaELI/Lsm/20pgsgPhsmgryKQjOyVsyI/NGMWeK2ecNb1qX1iaiqu9i5rRQxmxcT8teWlsvXUxdSUFOIIhxr60lex9x8g4WstLP7gNBEhVozUvDYRACRkMe3M/tUVpFJ6sD1+3PTsFK4bBkqpKWvkJtKCO2YePXXdqdKYnMuHFTah65EKmGiZDt5Xhae3En9zrZUhF4OmIZonrOxtplfWkVdaTc6CaNffchNQiv4ZaIMSI9aWULZ4UU+ZV6Q6hR80bkqqiBKZ7XLxw3534UuIx3E5SK+oY+5/tqEbkOcKSdutc9+hihe4TPCG+fvkG7n91FgF94PDlI2U6fZfvpqBkY8MHIQNmk6TEYpN7tHoW81KPIjBQu4353o7sAYs44zTYdG0iX3qrk38dPXXEQSBZmHaYlpCbl0+WUOFL59WmUYS6Wd4EMioXbuedD1Dangd9YmANwXiq/akUentD678tX3RGz346g2mhUNaZSYU/dcBreNUQDmHxqzHP4xTmgOSLTsXEpehIKQhJDU2YmFLgEGZYme1MoQn4WLE2aMS7MTgL7zM8KVCxDpwJEDqbDeu7CbeBmu/DfdVxfI8XYx7v96Vy66T8ZgfuS0/Q9XghcbdVIJy9q1nZoklkH6jGEdTZu3TGmUmTDgAtEKJw80GSTraRUtNEV2oCK350B7rbAYpC0Kmx74rpdGQls/W2xWSWHaelKJvmglS0QAiEILWynhn/eoXW/CwsVUWxbCOcUt2AsKINoOYPkllRZ3szlkV7ZhLCknibO0g60YSILgUAwNI04hvaeg25lDj8QZKrG2IcHY2cfZXMeXg1Gz7bSyyghHTmPPwyhVvKaCzOpmHkkKhV8VSV64phUXrVTDrTErG6K+T3Xj2L4W/tx9XlDxtzSwiQcPn/PMn2m+ZT182+1x9CQFZiBylxfmpbY7+vmrBD2ucKCudWEXhiwnG6LBf1gQQMqdBh2q1oNYFk7t59K3cXbGRCYg2NoTger5k6oNHJ8wr2tpinNeJgm9DVJ0v4S+VcApZGFGdD+P8SBYkQkkQtwEsnx8aQUxW06N4IA3oikBR1zf5QMVFE34hCtGEXWBzxZQ54jUJPE18rXs/EpBo0IQmYGrWBBAq8LRHBppClsOrkaG7M2YlbMTjQmcWhzkwa9ThCMarwT4cvj3XynYmnIb3/EGHQkJ8HEMrZ1NK+i/dNDqLm+/DefIyELxxC35dE6j/epPG6hTYNWkAyoXglU8YvQz3Zzob4K1G/HV2hVDV1BEfmjGXEG3sJJMaobO2X8xW6iUBGho6lxNvUzsQXNlKydhdI0N1Odi+ZbOeT+3jSpstB5dSRFGw/ROvQTKY9sQ5NN2nNSyO5pon0ijpMVUHx+SMMd/bBapJPNNOcnxE2csIwcfmCFG206fYUCYkn285IukMxDNqzIz2VcS9sIhTvga4AWreAy0DVBwpQ/OZe6kfmMeY/O3C3+zA1lcQGu6HDETIGjGyoKFj9CuBUVBxBg4pZJeHnAwgkxfH8/3yK8Ss2U7jlIHFNHShSooR0sg7XcOmvn+L1zyylYUQe/qQ4pBA4grpdiAhoquRk+8CkMF4N2qOF2t41WJwum/1OoLCrfQjLZzzIyyfH8Mdj8yP+Wh1I4YeHloZ/dwqdgTzZ412S3+6JrXoXDcHBrpzTHmOzxCsgoUmPnV+2gJL4On5xZDF7OvK4PGM/RZ4GDvsyY46z5yyvqjMmoY4qfyqGVPAoIU4EkjD6mIVYPeI9cCk69w5/lfGJdgey39R44NhctrUV8MD4ZTgUE6+q02U4qA8m8Ej1RXSZLrxKEJ/l4O10P3tU+PQoJ9+fPHDf/4cRg3rk5wFMHX6TDf6zF9p6R8h46VXcl9RFjqVZBU0QeDafy7c8RGFXKQ4jxMbbl3Bo8URM58AhsKQTTSi6QcuQjAiFs9zdR9GCOm256cQ3tjFhxWY6MhLZdf1cfCkJJNS3MuWp1ynaWhY5FlXh5LBcjiwYT8Ws0WFmOACHL0DOvkqqpo5E1Q0u/t2zDCmtAOxl9tCC8bx11+Vc9vNlZJVV94bLXQ4237qII/MnoFgWBdsPx8whS0EsUbEwDKfGoQUT2PSJS3tftCzGrdhCU1EmufuryN9xhGCCl9TKely+YMzrWIDp1CLy7z1Y98VrqZhZEpMsJq7LZPyanaQcOEJ7Thr7L5tGSu4kqqmBrk70/m1k3bj+mw+SfCLygxaMc7Psd59Dd7t672VZqLoZLub73tOX4h8gtO6293xnBU3Y7UhncppD2EVNXea5MugSl9AJnkFv86mKQuM0yPEKjpxhjvzto3cj4VZCLEg9gt9y8HrzcKJnaACiIWGiCoug5cCjhPCqIf4y4Uk2thTym/KLBzyv59yS+Ho+P/QNJnQb8Vbdw8NVs3i+fgI9leuL0w+R427nUGcmbzYXh3XK3w6GJQiyPAqfLXFxdYH2oeNSh1NTtA4a8vMEx16Dxy4HI3D6Y98tZG1biXNia9TrUkJG+Qmu+O8ncAR1JPDPR74e3VM9EKREWNKuSpeSuQ+tYmR3O1df2NznGu1ZKSTVNofZ1fpDdzkwnRorfng77Tm2B6yGdLwtnbYsKpBY28R133mU8ovGcHjuGBqH52E6HbjbfVz68ydJqmtBNSXCNKmaUcLrn72Km778f7g7Y0+44VAHHI8Edl03m503zIsysiKoU7zpgO0Vd8/Xdd96mNQBwu2n8vxrS/JZc89NmP3mXZgWt37pfpy+EEIP2bz1mpOmb/6YlWPaMLFiE1QDn/z4z6M2KPsuncq2jy6Iug+WHdq1hMKyTRPYeiw2f8M7CX27Vc5KWc0hINEJbSF7o3UOAwGcSQ65LxwK3FSo8Xj5e0VOInEJg6CMDs2fDv3z7woWc1LL+WnJSha99aUz4I+XJGkB/KYDQyqMiDtpy6b6U896LKfDjHSV1VecWbX7hYxTGfIPdxf9eYTCBfD1EzB0ISgaqO9B+se3rIDmL0zjxMhrqJt1OV1PDkU/Go9+MJHsfVW92t4CjAE88Vj7QCWoM2rtTlIr6lCDOscnFqHHKNqyNJXlP/0EhxZMsI3RAHAEdVwdfi7/nydwdgVQQjoJ9S1hIw7Qnp3Kmv93I50pCXSlJ4cjB4FELy/89ydZ+b1bKb1yOkJK8rccZM5fXuTQvPHoMZjWQm4Hp2zDS4ln543RRhxAuhxUTRtJekV9eHL2XT4No18Fuuz+LybTWzdyDlYz5ekNqCHdZjnpxuSnX8fR0YXQ7XYHYVmIUAD3X36G2UNOMIDH4k+MphXsyEqONuJgT4EhCRkaNa1J0X/vRk/o++3gbOVRdQmdOnx/kuu8W72SnfB0xTs14mfjWAmC8tSf1YHQ31BbKGxsKWJrS8EZisAI2gwPIanZBXFd2VT7UzgX8ZIbis6uEO7DiMEc+XkETwp8Yh00HbIL4F79NgROT6r1ttHxq3GgWmComBXQ/JmZIAVaUSf+n2+06UpNCyEhvbyWxmG5kRewJClVjSh+i+aRmUgpMMrjuPhvLzO8chcAa/7fjVROG8XYVdtIrawPh5B1l4OyhRNoz02nMy2Roi1lpJfXoemxF0IBxDV18JF7HmLP0hmMW7mZV+69heahWTi7Aiz59VOkV9SjGCYTVmyibPEkNt+xJNyr3VycQzAxjknLN6JISfHmg+guBztumk/RpgN2RCCgY6kKlqKy6fZFzH50DWr3ZqYnYGk4Nd761GWnrMrX49w0FOeEHbrDCyaQWnWSkld3gZQoloXudGApApf/1L2H41/awojXSqkbXUDDsBz2XDWLUetLUazoBdPb0sElv3yKtpwUdtwwDyNGeL30yhlMX/Z6+LkAMg7XIJZMiSaDEQJd0fj5iwvpCJx6Z/lexvUcCvxuXzBKPvTdhlvRYxSWwUCeesM7jqbJPj/f+9CxIRV+WHbF2z7/VPn0d4JjHef4jb4AMGjIz0OkjbT/C7TCq986hzeSAow+i7ff1h43DiSz6a7/x6yLX6EneDn70TW89N1bMR0qUlNRdAPVMFn0wHLij3Ww9djNvHL0qzh9QRxzn4NuHoqRr5VSN6aAVd/9GDP/8QoZR0/gT46nbPEkqqbaXMqWy8ma/3cjM55Yx5Dd5XhbOlBiWAYBeFu7mPX4OiSw5DfPsOz3n2fen1eQUV4brshWDJORr5XSXJDB4UW9onvxDa20ZybTlRpPzsHjqLqBqpus+OHH7etbkoTGdoLxHoIJHk5MKGbkut3k7q3A1RmgdUgGpVfPpKn4dEVKIPt62kKw+Y5L2H3tbNIq6wnEuZmwYjNFW8owHBqKYcR83h64uwIUbjtE3p4KdLeLkNeFqhscuGQKldNG4erwMWbNdobsLie/tJzcfQqZR2pY8aM7o6IG+5fOQAsZTFixGdUwMTUVw6EOyDYXMLTTGvH3GkETOt+D6HV/gRMbEhEWkj0XeDvXfbcMv6DLOr/ea5cCWZ4PXz78bDFoyM9jTPscvPVr8De+l3e1vzSBxjSeK/kG15z4La4uP2kVdVz37YfZd8V0modmkl5ex9iXtxHf1A4aTC54nv/suAdDlWQk9go1FGw/TM6+SmrHDqUtL53d183GlxatpCUdKgcumcJbd1/B9MdeZcyaHRFeY+To7J+uTj+ZZccZUloR1SPtCOqMW7U1bMjVoM6UZzeQeLIVb2snJ0YNIe1YXbj3HEVBqoRz8AC+1AR23TCXXTfMPbspNC3CWot9EEj0UjOuiMItBynYdoi1X76OhuJs5v95JZlHTiAVgRoyBlySHUGdMWu2cWjOODTTJJDopTMjicZhOdSX5DNh+UYmvbAR1bRQTIkW1DE8/TxKISj9yBz2XD0LT2snSIkvIxlPc7vdRtcnxaGbgu3HTk8G805xtjl2/T1z/2O9E+I8M+I9552emTE23h/v/0yhCril+MNNv3omGDTk5zHcSfCZbfCH4SDfM3Gfni+2pPNrFk96vsDNX/4T8S2dJDa0cdE/Xok42lIVKmaMonJKCclLNiPbVBrUDIbusFuoFClZ8ttnqBlfRHN+BlkHqzk2q8TmSe97V0WhZWgWqZX1ODsDmA4VxTBPrQwtBE5fADlAmNvVU8gmJbP+toac/VWALSji7grw2ENfR/QsgJY1ILvZaSFluMobIZCKwFJjfLWEAGkx74EVHJk/nuMTbVnTVd+/DXdbFw5fEKEb3PDtRwZ8bm9zB5Nf2AhSIhXB9CfXs/6/rqZq+ih2XT+H0a/uxNUVILXqJKphMtDHRmoqvvQkRPdmyZ+a2P0cEhMF3VRoaI9n9Z5RA1zh3YNHg7HJClsaB0Oobx9nZ8hTtC4+kb8FE0G2q51jvlQerr4I821QzZ4rCODxRXFke8+zYojzEIOG/DxH8lDInwVVG96Lu0m7kdoSoEqEx4zpWVrdxgoBu66bw74rpmO4ncTPstXOdh+eRcGOI73es4QhpRUMKa2gPSuZ45OHY7hEOJyrBkMk1DaTWtXA3EdXoxgminUqiZDucWgKNROKCcZ70FoilZ8sRXB8YrF9fd0gr5uLvQeNw3JsetYznBkXToLEyGdLSf7WMkav3U1XWgIZR2t5/XNX0VwYW9E38UQTClC2eFJEO10gKY5AUhxaIER7dgpJddHFEaYi0HQjKj++8P4XeeJPQ7E0lZqxQ2kdkk7t2KGkVNbTMCof2b9nv+/w+4bUhf2eOiyFtQeG8dbRfELGuV0iNOC/Spw8Xn5u688vfAjO1IgLJP879llGxDeFX5uRXEWmq5OfHr78HI3v7FGSpLDgNHK4g7AxuNX5AKAtWsTqHUNJC6DkdNG/59Q5tx5tvG1E9P12pfKxmSUY3YVQpqaCsEU3VMNiwoubGLPa1mQVim0LWgqzMGJUqQN4m9q59juPMGzDXuJPtpJcdZJRr+xk1LrdzPnbGrRQr6HqCRj2N7amItCdGuu/cC1SU9nw6SvQnRpWdz7Y0FSCcW67shzwtnQS1xxp6A/2yZ2fCYKEQEqG7DrKlGWvUfLKDpxdAZAw84n1DNlTwaj1paRWN5B1sCpmOb9imMQ1d2Bqtu75QLC689a6U8PsNrSGU7PPibHzsBSF/F3lgGDjpy5jz9WzqC8poH50AdKycLd2kXS8ASVWIWGMaIalmCwed5jPXbwJRZxbL9mlQapb0PAec7K/d3gvnktyNsmJG3J2UuSN5BJwqwaL0g+T6jg/lJxUAV8bf3rVvUHYGNzufAAQnwVtle/OtUS8Turf3sJz+QlQJFZApfnumQSeLQQg9HoOrkV1pD21mtlbn2PniHnsvmYW+TuPougGcS0dER6hI6gz8fk32X/Z1LCHqRgmJ0fmkddHOCQc+JMSV6efeQ++hKUI6kfkknHsJEGvy/by+48XCLodSFXBlxRPW14aTUXZHJk7Lpxrr5lYzIof38HYVVtJrG+hrqSA/ZdNJZAUB1KStT9SCUx32AprZwvFMJn35xV4OvzoTo1pT65n9b23IPrth12dgag+7hGvlTLzn68iLAstqLP4d8+y4oe3E0iO7I91+oIk1zRSPWkYO66fy9iXt5F5pIaOjGR0t4Oh2w7HHJswLdSQTtDjhG7udqkqSFUhmORFNFs4fUFCXoHl6N5EDESKDaiKpOxEBufaEHUZcN/OIKELNqr+XuWfz9wnuyLzQIQQTA9ClsoQdyvNenSL4vuBGwsH287OFIOG/AOAOffCc7eD3ocdVXVBfA60HTu7a6X9awPuJXUIl71yqg6DtEc20djsJrg+G4DgumxmdW5gTPFO8g+Us+eqmbz2+StZ+McXYrY9SVUhsa4lHE6WioIWjA6VCkAxJU5/yA4ESklOWQ0Ce0Ng9vFS/Yle9i6dwfEJRSimFV0pLmWEIWopyGTDZ5ZGGyYhSKuqjzBHiiVJK6+jdkIRimFialrMnvCo8UsZDkX3tNEtuP9FOtIT8Ta30pqTxtE5Y9l75YyIfHvOvmPM+tuaCPa2hJNtXPzbZ1n9rY9ieJyoIR1hSRb+cTlC2pzpQ3aXU7SlDGFZxDe22/XSqmIX0/V9HtOioTALNRCC+GjqSmFJFv1pOSu/dxvZByrpyE7F6QsQSPDGLDzsC+sM5CvfKXznRtH0Q4Sz2yyUdWYyzNsYZcydikl14Ow3uOcCXo0PJXvb28WgIf8AYPT10HwYXvuJTRZjhqBwEXScgLazuI6S7cd9ca8RD78eZ5LwzX1hQ64InXEFq9CkTkpNI/P/shKAupF5JDS0RXOGGya+lF7hEDVkkH70RMQxot9PsNPxEQbWMJHY1d3P/fwuQl5Xb263n/eo6CYj1+3i6PzxWIpih6BjtFClVtSRV1qB4eqVKFVNi2lPrif110+FWc6OTyjitS9cY/OkDwCHL4inH5Wrt7WT1d+6ha6UeCxNI6wj2QfjV2yOomAVQMbRWqb8ez1H5o+nNS+dhfe/SNoxW60t82AV2QerotjlLCHCLWtSUZCKwlufupSuzOQohbbwOapCW24aefsqWfTH5eF5ODF2KC/fezOoscP8o3IbbHHhQVxQeKJmGksyDqH14cULmBprG0fQch544y4Fbi4arFQ/Gwwa8g8I5twL078IjQchIQcScuGxpVC/+8yvoWb5kUEF4Y6hAJbf6+4rihEzN9qSn0H6sXq0PkbJcKgcn1BMMN7d7SXDpb9ahmLKM2psETH+XXr1rEgjDiAEWiCEp6WTjpxUnP4gF/3zFWY9tpaWvDRe/NEdtnft0CIKuNpy01j+s08hFUHxxv3MfmQ1im6SVt0Qce8hpRUsve8xVn7vVvQeYy5l2LNWQzqz/vlKNP+6tKVLrVNw0Mc1xZa1M9wOjiyYQHORvYFa+5WPULDjMPk7jpBQ30LWoZqoc6Qi2HT7xXau3emg/KLRdGbaXpSlKgjdQPYTo1EMi4qZJQzdcjAiUpK7r5LFf1jOui9da89Zvw1IVmIXyV4/rb4YQjiD+MCiOpDCl/beyNeK1jE6oZ4u08mztRN5tGrWezaGRBWeXuIlaME9W/xUdUksaQfGSpJVfjTl/OpnP98xaMg/QHDGQe7U3t9nfQ2OrQfjDAWXjLJEiOF8SV0QfDOjW3NcYFhuGtqLyUqOzMcO3XqIzbcuZurTb6B1h4KrJw+nIy2B+fev4MjcsdSV5NOenUpGeV30jQZAf4NfM6EoZpW1sCTz73+R9V++lnkPrkJ3OfEnevEnxnHDPQ8R19yJpSkcXDyJrR9bhNTUsOgHQMXM0UghmP/nlVEbDAEk1TaTWV5LV1IcRVvKqJw2gubCHFAE+TuOMHRLpKiLBXRkJNGZcepwZO3ooSTWtYSFW8L3NC2kUMITIFWFyumjqJo8nHkPvgQxDDnAifFFYePdF6puYDj7RDCkfWHD4+TE+GIaRuRRsLscT3vvpq1w2yFu+toDbLjrCiyHSv2ofDtKoqkIw+Sjk3by8Jsz0MVgvvJcy6m+lyjrzOJzez76vt0/P0Fheqb9mXrzao03600Ot5uMSVaZkaEOhtXPEoNV6x9gDLsELv5vm5ddnME7KQMabT+agAz2O1iTaONaSF+5liEd/2aI/3FW/eFGGrOyI4qvve0+pjy7gT1LZ/DKV69n3X9djbMrwIRV2xi26QAX//55NN3A16eAS9KtfQ0EPc5w9XsPbI++KOI1T0tnzPFbmkpcayc3fv1BMg4fR7Eskk62kru/ioTGdhTLQgsZjH15Gwv/b3nU+abLQcXM0QS9sathhSXxtHTSOjSLnTfNp7koJ7zDSK06GeWNC+DAkskxr9UXpdfMwnA7I9IIPXSvgTiX/ZvEFpvRDZy+ACdG5SFiVL5LRaC7HOH+74jnc2p2Pr2+hfjGNjua0J37txwqwTgP225eGBqGOgAAIABJREFUEHWeuzNA8cZ9XPGzJ7n+Ww8z4o09pJXXYm5v58UdJSimyXtLwnp+4kIx4ucDvjym9zsohGButsYnR7qYmfnhVDZ7pxg05B9wzPoafPMk3LkePrcb0see+vjO34+m8WNzMU64kRbIoAAdHCM7cV/UgHBbCBXMYskLP/sEfm9kzszT7mP6ste4/JfLWPKH58nb11tOb2kKufsqyTp0POIcRUpCbierv3kTz//PJ1nxg9s4NN8WLKkvyee1L1xDe0p82FSMf2kLWiCyZ1sxDDIO1xDf3IFiWQgp0UIGhqZGaI6DbWALdh3F2RkdqlAsK7ZmOraRTKluQDGtMEc7QiB0e3Og9DOsAhi3auspZtuGLy2Rw3PGRRDXCMDlCzL/r6vC/dsIgXRoeNp9zHzyNUzVPr5nM2SpCg3FOVz2i39Hs95JiRQCT2sXV/z08YjNVO/DC8pnjw4L2JiqguHU2HD3FRy4bDqWIkisbWbeX18m5Xd7+MGhG6kJpBHU3JzP7F+D+ODBGvw4vasYDK1fAHAlwNB58Myt0HL09McHXsyn9sV8RG4XqX/ajHdCLcqQUIRXLwRIF+xdOIsZq149s4FIiK9vJehxsfWWBRRsP0zmkRPUlgxhzT232EpfQtAONAzPY8ttiwnFe3B0BejMSiW+pROBna+esuw1dtyyAGFaWJpKxtFaFv/hOcAmezGdGigWgQSvTRPbfyhCEN/QRnO/4jVTU2nLsQlX+q4lEjAdGuVzx0b1eLv8oajNQg/69qcnHW9k/MrN5OyvxJ8Uz56rZ1I53WZGK9pyMGojoFiS7ANVaIFQBDnM/D8ux+ELhnfZAmxv3ZTklB1HdzqY8djaSPGW7p9X/eQxHKcQYjGdDl7+9i0M27CPULyHw/PG05GVzJy/rmLdF69l5uNriWvq4E/Ft+DXBi78G8Qg3gmqOwfjG+8mBg35BYLOOjj4HJhno8AUUHEvaICEAVilBVRcMoLJ/3kdh9FbJNUjWxlVvW6aTH3mDSxV4c27L+fApVPJ23mU6mkjonLeUlXCFeKmQyO5tgl/Uhzeti7b0129nVHrS2nNS8fd7iOhsS3i3Gd++WkS61sZs2Y73uaOKCOp6gb+5Di7VatP8ZvUVNZ99QamPfYqo1/dZXvf3c/iDIToSo1uxwrGuzGdjpj65Hq3AU4vO86VP3scYVooEhIa25n/wAp21rey96qZ3eHp2FD65M4LN+4nrboxZg7f1FROjsilfNYYDi8YH1WclnmoBleHD4dukn2gitoxQyMUzZSQgaUppFQ34O7ws+eai+hKS0QNhHD4Q6TUNLL59iXM/Ocr1LvTBhzv+Q4BXDFE5XuT3Nz1hp+DbdZgYuA8gleDyWnnDxXshYDB0PoFgpYKu7f8bCCbXViNLuQAm2PLr9CRlMLWmxcR9LowHBq6y8Geq2ZxYPGkKNY1Te+mVhWCgl1HSa+oQzpUrAHamwCEblC8aT/eti5bwrSPbXIEdTLKayOMuO7Q2Lt0BoGUBE6OGsLOG+baler9rwtc8d9PMGR3OZ6WTtxtXdBtTC1NZcudl/L3f9yDPykuwmhmHDkRof0Ndl/8zmsvirqHBPZdMR2AMa/sQDGsCBUzR1Bn8jNvoAZ1KqePCrO09T2/JT+DULfcqLPTz/wHXxowiB3yunj527dStmRyzGJAb2tnWNd9wQMrSK5tRvOHcPiDqCHdrp5XFHS3k8KtZVzzvb/h8AVACFJqGpn8zAYW/uF5QnEerk7eToqjI+oe5ztUAdkewV/meqnxSQ61v/9GfDCK3AuHAiMTFRbnDvqQ7yYGZ/MCQdoIMINne5ag4bqFZK55BSUtCGqfSK1p4Qzq+K6cyO6PDMF/l5ec/ZV4WjuJb2oj54DNGxuzxUyAEjS45FdP8cJ9nxiYbEVKCreWMfehVRgOlRNjhjJkdzmOfv3QPQtxyONENUzGvrSF+IY2tnz8Ytry0ln5w9u47Of/xt3hjxhLXFM7liKY9+BK4praeeG+T2D221Q0D83EU1oRPm/qU69TO3aoXf3dQ+oiJYEEb8RYpBDUleSz+9rZAKRX1MVcsKWqkHyiie03zydvTwXudh+OoG5Tymoqb3z2yvA9hm47NKAAjE1L60AoCgOx0DcU56B0b0Lc7T6u/dbDNA7PpS07BXeHn7Vf+QhaIETRlkMolq2ONmLdbloKMkk53kgg3s3qe2+hLTeNDEuSuclHS308HyRTZEpoDEgK/92B2v37+43zYAjvKxId4FZAUwU3Fzn4xng3ymBB27uKQUN+gcCbDhM/AaX/iGSAOx2sNgf1iy7BMbaV5M/uwzOnHktTyNtTwax/vII3v4vON9PwlLbijLFTsDQV+hVeCQs03UBYktSqk7Rlp0STtUiJu93Hgj+vxNJUfMnxbLpjCTd/9YGozUEP57ojEAp7vEWbD5J16DjP/PozNBdm89TvPs/cB1+iYMdhpGJzmW/++MWcmDQM0+3kyvseY/pjr7L59iW9PdNCsP3mBWQdrA4TtqRWN3DLF//I65+7iuppIwEYvXo70/69PjwmC+jITGb1PTeTu7eCvNIKtGAo5qZGCxmMXLuLujEFPP/jOxi6u5yMwzW05aRydN54ggketECIcSs3E4pzx8zFSyAY7+G5X3yKU/mX/pQ4js4ey4g39qBYkrasFCxFobEwm0OLJyGkJOPICQq2HwLsiEF6RR31Ywpoy0xm8+1LaCnIwNI0jp5Moaw+M8YTnf/okTkdJIx7f6EJcCrw2MI45mYPmppzicHZvYCw9I+2Wtrm30OgFZILobGMAV0CbVwLWa+vQYYUTmTfSPGBV1k49n4cWh+DrYLH0YZiWDHX9OPjCsnbd8yuuMZWOnvj01eQUtOIqhtMXP4WVVOGR4WVhSVZ8MflWIqgJTeVDZ++kry9x7A0FTUUzVAmuq8dHpZp4er0U7j1EOUXjcZwO1n/5etwdgVwdfrpSE8Kbx460xJoy05h+0cXhrXHe/rqmoqyOXjx5IiqdKc/xML7X2TFj25H6AbT/70+ggRHARJOtvCxL/2xVyo1BiSAlIxeu4thb+3DnxzPiz+6ndoxBQTj3AgJashg/IpNjF+5hZ3Xzo5JgQv2hsl0OWO+BymV9aQdqyeupZOcfcc4Nm0UifXNOII6R+eM4fD8CUhVMOOxtYz+z47wPQyHRktBJk1Ds3jxJ3dguF3hQr9lWyYO+FyDGMRAyPXAlflODrSZjExS+VyJkxFJg/nwc41BQ34BQVFh7rfs/wAOrYBnb4NgbGIxUh/chJJg2OynSTrpCRWRRrwHAto6c0hJrI4qKss4Vs8LP7mDnAPVDNlxmLfuXkowwYPucWG4HKRWN5BaWU/DiLyI4iw7vzyDvP1Pk1bVwFU//idHZo+JMJingzOgk1J1Ese4oegJXhCCUJw7nHNGSoQlUQ2L2lFDcLX70bO7/9Y9FhEyGLVud8RzCUAN6kxZ9jpdaQkxNxaKtPuv+6O/Ge4xms6AjtrQxpyHV5O7t4K2nFRUwySxvgVH0L7+1KffsHXY+xPHAO4OHwknW+nISom650V/W0N2N3lMyOPijc9fidmPaU6YJo3F2eHxWIDpUDm8YAIo3YWH3X9r87lp7fLwXnvjIxIFh9s/7IHoDx7iNEh2Cv69yMvY1EGT8n5gsNjtAsbQ+WAOJPOsWTin2FKGQkDiN/ZR0zmWkB7dcqQpIbYcvplAICHKUHlbO1nyv89iOFRyDx7nuu8+yqx/vMKQ3UcR3ZrijcNyosVMVIWabiIYxbJ7wkvWlw5oO2It77rbQXt2ChNf2BT7JGGLjHRkJFExeyzXf/thhuw8EnFIfHM7qhHDUAMZ5ScYtW73WZkzAbSlJ2IpIuo81bTI3XcMpz9EZnkdaVUNYSMOdr+9NgBfuhSiT4Je9v6UkoSG3mLA9qxkO3rS/3xVpXb0UExVwVIEjcNzWfmDj9sKcdAdqeiWgbUEIoqL9twjYJzdPQcXr/cXLgXuneDi4Xledn8kYdCIv48YnPkLGK5EWPoneOkLttCKNOlNOJsCdAVUe9FPuGc/5b4R+PcmoKohVMXOMNp04xZXTPn1gPdJbGhj3sOrAVANk5GvlUb8XZgSGSO61tfzDBs9GVlAJwGEIOhx4vQHwzlyS9hh6Zn/eIWWIRlMe+xVyi6eQkd2StSmQWoq9aOGYDgdLHjgRR6//8vhtqxgnDum4QPbu9UCehS16ung6Qp0jyEGMxun9nOFjH2MPzk+LL3qaekAIXB1+MnZd4yOjGQ8rZ0osrvAbwCtc19aIv949BsIw8ByRYpSKCEdV1eAYJyH1Dg/8e4QLV3v7fJQfRa1HTDItPZ+ojBe4aG5HqZlDJqQ8wGDm9oLHJM/CZ/eCjO/DBPvgFuehe8b8ENLMMJdiDDsj4BQIP4nh3njB0uwnCLs9PV3pGOwhobRkZFEXUk+wbhewQNDU/G0RVOuKiGD4o37Y16nxwQG4lwcmT2GJ3/3ebbd0kstKrv/p1gSZ1An8+gJxq/axvX3PIS3OXbLlLAkutuJMCXp3QpjakinYOcRYplcU1U4OnfcGcmb9ocjoNNUmIXV71zDoWLFUGjrDylAdzmQgO7UCHlcrPvSteE3I5AUjz8lgfacVA4vmoRi9ba9eTr8XH/PQ9zxiV9zw9f/QvGb+yKurehGVOuaYphMXfY6useF5dQQAj4+ewdOzUBTPpglY4ML27nDf412suO6+EEjfh5h8J34ECBzLFz2v9GvX6ROo5MuGmQjMmAhTZ1L/vQMmmFEGfC+6KsoKiXoXhevfvU6To4YgmKYWA6Vsau2MWH5W/hT4gl6I5WM1JBOUk0TM/+1dsB7COyK7+S6Fq77/t8ihD4gcqHuGapqSfJ3HqVs0cSoKnlXV4D4pjZMVSXpeAPNBRnkllYw4bmNmKoSw+uW1IwvOiMK1lh4685LWPK753D6gqi6iaUp6C4H3vbTu51CwsnibFoLMunISObw/PHoce7wLqpH3c1yaFgO2HD35Vz/rUfC5/eE2pPqW5jz11U4/CHKlkxGDel427roTI5Hunrnp3DzQdqzUyPY5QrTW/nOVWvZcnQIm8qH0tz1/stbnikEMC1DYXuDNVi5fg7w5THOQT708wyDhvxDDIdwsLR5NC0Pfo22eI2UvcdxdgWjvXD66IkLmyxNoZeO/PXPLqV+ZD6WUwurje2/bCrCMDjUY1RNM9yXLaVk2Bt7OTp7DMUb9+PyxW6A13STjPLaM36ekNtByONEMW1dc6kqCMNENUzmPviSXSVumMz968vMeXQ1qmHZOWNNjaFMJineuJ8137yJS3/xbxwBHSHlGebLJVfd9zj1w3NIq6ijtTCTpvwMhg0QgegPAeQeqCb3QDWWIhizZjtv3nU5teMKYx7flpuG7nbiCERTszpCBtOfWEtjYRZFW8toy0m1C9z6IH/nEToykqO8dUWR7K7OpdX3waJqlcCWhsHA+7nCcZ9FlnewEv18wmAE6sOO//0aKXvLKNy4h6SOlpgJ3KiirT6fmpDHxfFJw7CckXtCw+2kckYJt3zlAa77ziOkVTWELb/lcrL144vZcttinv/ZJ7GU6I+hxCZBMTQVw9G7aOhODTlAuFsxLOY8spobvvEgk555g7TyWka8VspVP/g7eXsqws+iSLuSHWyhlFi92wLIKy2nNTeVNz67FET0PPRntguPQ9p99HkHqqkbW8jL3/kYG++6An9KQlS4fSD0UOCqliTxZCtLfvN0TMWznqOX/+QOdHdsqVFnQOfaH/6DcSu30JGR1E2k3ztyf1Icw97ch+jT+qboBs+vHkFdazyWfPvLhEOBuME1/4KBAIbGD76h5xsGPfIPMxpr4Xg5A3K0ngI9XnvI44wwAH0RjHNjqSpxje205GdE/lFRMF0KXRnJvPqV67j4d88hpMR0qJgOjfVfuJqmohyCXhfD39pP0cb9GC4nZYsmUrT5IMUb90dxn2uGCYaJMxBi8vKNjF+1lZfvuZnEk60DetJSypi92wJIrm1m6X2Pk1zTiBSCyqnD8aXEk3n4BGmV9UhBmMxm5/VzqZ46AsUwGfFaKZOffRNNN0ivqAuHrFd991YW/nE5GUdrwbJQrDP18EHVTQq2HaJy1ujIP1gWSEl7XjrtGUmkVTdGPyM9ffiSkld2EYx3k3yimcppI1FMiyNzxlKydhdLfvsM679wDalVJ5n5f6tYM+pelpf/huxAI6+lT+M3I+7khCfzDEdsY0KKwtfGu/nKRh+dIQgOdpeFoQk4y0L99x2X5mmkuwf9v/MNg4b8w4ygv5eGtA8sIaL6xQdCXEsHTn8Qv6t/37JF3p5jSCGoHVuIFgiFRVL64/jk4Wy/cR5SUWgclkP9qCFITUWYFpOee5Mxq7fh8IdoLM7G09pBatVJVN2Mya8e+SCSzCMnYup6Q7c8qKaiBPWYleJCQlp1A21ZKbz0g9swXI7u6IEkr/QY8+9/gdbcVF795s0E4j3hSvj9l0+jYVguS3/2BO6OXilVX2oCL/3g47jbfTi7Alz1w3/g6gqE79uRkcTWjy2iZlwhjkCIMWu2M+6lLSiWRJGS6U+so2rqCPs+fbxqqSoMe2MvnjZf7OfALrTTdBNvaycL//QiKTVN3VKSAiElIadG1sFqbvj6n3EGdHy4eGjnT4jrVuHJr65jeGcVH7no91jizBfy7U0W397ipzU4yLTWHx80I35NgcaDc2NLAA/i/cXg1urDjJxC8EQWMQnAUs+8kEVImPPwy2hB3U6eA0iJwx9kyrNv4Ajp5JeWM3H5WygD9kiD6XJwclQedWN7FbtmP/Iy41duxuULokhJ5tFa5j30MunH6sOhZ9Oh0ZKbFjPcrhomTl+AhuLc6Htiq4lphoUqT90StvarH8Gf6EX3uHD6Akx69i3GvbSZ9uxU9l0+PcKIgy0V2jgsh4bibBqLsqKuF0j00p6dwup7b6Y9OwVLgD/Ry/KffoLKaSPR49z40hLZef0c3vjM0vB4ExvbufOu/9/emcdHVZ3//33uvbNmDwlJgIQlLGFH9kVEFAVFheKGW63WttrVavsTq1bb2rp0sf2237bf1lp33KrizqpQlX2TTSAgO4GwZF9m5s75/XEnYZKZSSYkkJlw3q/XvDI599w758yZmc89z3nO8/yBlIOBWbew3oUh8z5n/L/nN+lIZ9oMqpNcFBV0I+WQFT9Ak9YygwAcHh9eu41dEwYhgQR/Tb2IB16Mb4x8tEUiXsf+KqlEPI7RgKHpGs9NSsDRgt8GxdlDCfm5jKbB934NDicYAeOMw4WuO1qU6CFvXSGX//JFeqz8ksQjJwHo/9EqEo+dCik36IPV9FuyIez+NcPjo+eqL8n/bCt6wGHLWVZF/mdbmo30Znh9pBw5Gfa6PoeNE92zSCwuaVAusQKsCCnRI647W1R0SrZixWsams/EVVKJq6yCrB0HST9QzPn/XmCZyhshgeN5Way+YXLoRaVk4Psruerh51nyg5nsGj+ArZcMx7Qb9R7pAKbDzp4x/anolFx/o6GZfsa8uAS91nqfDI+XofOWY6uNFPnHwlbjYceFQykc25/yzil8dtulvPfwzSy/ZYoVzhZwVtZQsGRDWJP/0oyR+IRaGz3XcGgwIE1j7uT42bVwLqJM6+c6Q8bD796CRa9D8SEYMh7RtRf86g7wNJ3c3K+J+vXl9D1HmPivD5n36G0gBMV9coHl9XUFMO75RSQdLWHN9RciNYHUBIbXR/5/N5G54yCddhdReP5ATuZ1JunISSuwSaN18LDzgYDXmRSn4rGbhk5JTjqD3l1OwsmKBlXBmonKKFJj1SY48AducvyGzome2ay49VKOd89m3AuL0Gq9jH/mI95+4o6GJ2qCjTPGUdk5tUGxXusl+UAxgz5YjZAw/dGX2XT5KI70yw0Jqwqg+Xwc65nF7rEFHCnIJWv7AfotXs8lv3sDW3WttcsgyOM++D0IxrQZbJg1AUd5NfN+czumoSMNneKe2RROGsz0X7xI2v5iNNMf9j0uN9xEv6Kv6CjkJ2n894qk9m6GohmUkCugcze48ccNyx56Gl74HezeCnYHIKG2BsxTM+TK9ESk0HBU1nC0T1dW3zCZspx0hGmSeDx8gPdBH62h28bd7Bo/AL+h033tTjoXHgKswCTTf/USe0f25Ujfrpa5vhHh1oA1f0B8gpOq+EySj5ZwvHtD03bjrGrNcbh/XkhFn9PO9ouGMeS9FSScrCD10HGM6lp8rlMJ4U277ZSIS4nwmox5cRH9F29ocC17dS0ph0+we9wAhM9sYKIHMA2Dpd+90sroZjM4NLAHrpPl5H++rd6PIVi3axOcOCpqGpjafIbOrnH9cZVU4nE7GuwXlzYDr6Gz5Acz8LkcVKUlklhcyhWPPI+79JSpfsKJDXi08F7xio7Ll2V+ak2pTOoxjhJyRXj6DoVfvdCwbMcGePgb4LdmyYnHynnzyTsoy0prIECaz8+ABWsjXjr18AlG/OfTsMc0v6Tnqu30XLUdCBXucFvAImHUeMjevr9ZwW4qbOqhwT1Dw9sBus/HsV45JKy10qaatqa/Sn2WfUG/pZsavI4EfHYDr9PO2BcWseRHX8MMfh+9PsuTPyicqru00son3ijJS10f6hK51B31a4LjPbPYPnkoVclu/I4wYiwEZV061fezonMqb//6dmb+7BlcZVXUaHZcZg0DygrZkDagyX4qOhaGsB6K2EYJuSJ6+gy1Zu9FewFLOKY9/iqLfjyLkq4ZaH4/CcWlTPzH+6TvL26Tlwz3G9IgQE0T5/o1EQhd2rQJvalrJBaXIkwTqTecKUuh4SqpwGfT2T1uQMhMuuELCPou2xSyHl8Xva7fkg1owNTHX+GzOy7Dr2kMeW8FXb/4iuPdO/PFjPFW9jggc+dBK8xrI2NF3TJHyI2OobNnRF80n5/LHn+VjK+KqE1wsuWyUWyaPuZUCNpGNys1aYl89MCNzLrvaX48+Ke81eUiPLoDxbmDXYMZ3W3opxGmWHF2UUKuiB4h4Me/g5/dUG9iTzhRzoyHnqMsMwWj1ourrKrNVlJrEp1snTqSA0PzcZ8oY9CHq8nefqD5GbaAw/27c7JbBqn7j9JlW/Oz8kgMWLiOnZOGYAYJuTBN3CfLSdtXTFFBHituvaThSX5/yLY+d0lovPk66mpm7zjI1f/v6frbDgG4T5TTdctelt51JXtH96M6paHTUVVqIp9+6zISjpUy+qUl2Bo5B+peH+kHihnxxn+tffZYs/phb36Kq6SCVbdMidiu8sDSwM92PM3SzFGU2B3UKPfzDo0uwKVbCWkGpOr8dnR8RfU7V1FCrmgZPQrglp/As483KE4OSqXZFtQkunj7sdupTXRh2g1caYnsGtufzJ0H0cMEcKmbpVcnu3n/oZuoTktECoEUkLX9AJf8/j/NeqiHI/XQcSb/+W0+/dbl1j5yQwcpcZ8sZ97jt1OWnR5yTsLxcjwuO96gffNFBbn0Wr4tbPCZcNR7qWMlmBn37AL2juxL0YA8PG4HthoPUhO898jNVKYn0fvTLWGXAKQQZOwuQmvUd5vHR8HiDWyYdf6p/O2NSDpq7UDIqz7CWyvv5i+3v8pLu5r2jq9re5xtkT5n6GSHNKegMEzed7uAW/rYGJ9lkJ+kM7ST2qUQL6jtZ4qWc9lNcNevQD9z94Gbpo+mJskS8YL5a7j2nr8z5qUlEYWwTsKWfWc65Z1T8bospy7TYedIv1w2XjUu4muZhs6h/nmYETKT5a3fxezv/YWrHnqOmXP+hd/QKeqfR1lWWsOKUiJ8Juf/8wP6fLrZCqkaWMtef/XEQECZU2IrgeKeWbz21Hd45oX7ePWpO8PmMQewV9WQXHQCzWey6YrRAOw/rze1iS6krrNveJ+wgW/8ho7u8YX9ovsNLWRrXh16rZeRry6l1Ejg6e4z+e6Qn1HhjS4CoBLx2EQA52cbLL08kYwwqyR2HX4y2MmsHnYl4nGGmpErTo8LroSP34avtlje7G3MgWH5+G0GKQePMXruJyHhWBsjsfaNHx7UI9Tz22Fjx+ShDH8z1MHOZzd47+FbKMtOI2fLXkbN/ZikIydD1ps1KUk9dJySnLRT2WLAEmoJQvpxllYy7bFXST10nKSjJThPVrBx1gRMh53yrDTm/eZ2hr75GTnb9uI+Uc7R/C4suH92faKZ/ovXRwx3q3tNRr38MWkHj5F81BLfsqw0zEAc+tokF0vvupJJf3sXKUR9m9ZcN4nsbftIOlYa6u3vM6lISwK/H2H6MbwmHqcd57EKxry8mMK9qXx9yl+p0eyYQmf9Ph/R+PprqFzhsYgEFhz04bZprJyRxPc/r2bRIR9SQr8Ujb+Md5PtVnO7eEQJueL00HR46J+wdB4sexcKN4GvebNrtDhLKiCvM70+24JmRmcSbyrXtxnBGe3Li4ZRmpOO6bCxf0Qf9o/oA8DYZ+ZbwhpU12fT2TBzQsMLCIFmmkz633fosWp7vVk5ubiUYe+tRDf9rLvuAjSfSVVKAp996zKEadJpzxFqktz1Io5f0n/huogyKYDu6wsblKXvO4rmNev3ue8d3Y9XBuSRt3Ynuuln/7B8qpNdlOak033tzobvlSbwuOz0/nwrQkp6Ld9G5u7DrE7pz+UT/kpG6sUcHdMJv9b4fWvKx99ykPIoFY9Z6gxC6Q6NlycnUO2TeP2QbFcObfGMEnLF6WPY4OJrrMexw3DftVDRNmvlQ+ctp7hvNwyvGXGWCkEJQQB7VS2pB45xIq/zqV8sQPh8dF+zI+z5X43tf0pMg1g9+0LcpZXkrS+0sqNJ2HbxcHafPyikru71kXykYWIWP9bMdPCHq+m79AsKJw5i9ezJSF1D6hpel4PKQEQ1sLaz6RFC2Eaiy5Y9JB8poaRrp/r0oz6nnX0j+qDXevEbOn2WbWbE68tC1q0Z3sSYAAAfD0lEQVQ1v8RdVs3YFxc3uObA8t3cWLOI5ZlDKaoMd2PU9A++EvHYxabBzO4NP+suQ6Dc2eKfVgm5ECIdeBXoAewBrpNSnmxUZzLwVFBRATBbSvm2EOJZYBJQ9+v/DSllw4gZivggIwf+uQxWLoS1n1j5x1csqN9z3lK6fLmf8974L/vPy6dg0boQb2zZ6G+dmE/8x/t88OBN+HUN02HDqPHgqKhmxOvLwr6OrTo0h7d1Pcn6q89n5Y2TsXm8jH12EV63A83jC0nZKvyStAPFQec2lDtHVS1H+nbDb5wSxpJuGSQeLaEisM5u2m1UZKTUm82jQUi4/NGXWHPdJHaPH4DUBD1WbWfU3E9wVlQ3qCuFZYpPKjrZpGOMkH5+u/RJvjn8EXZ2zokuao6i3dEC3uaVEe4F3TrkJmo8OkLJdkektTPyOcBiKeXjQog5gf/vC64gpfwYGAb1wl8ILAiq8lMp5RutbIciFtA0GDfVeqxYAOuWQk3kRB7NMfjD1RQs2YDX5cDw+MJm9Wpc1mnvUa655+/snDSE0px0MgsPkf/51oixyPsvWsfRvl1PRTuTkhGvfMLA+WvxG1Z89V0TBvLZHVO58ufPs+v8gVSlJGA67QjTRPP5Gf/0h2hmw6mox+VA83oxAnnPy7PSGm5JEwK/rqN7vPWhWVfcegkX/ekt9DB9jYS92sOI15fRY+WXZO08iG6GnxILCSlFJ8MeC8YpvQgTTuhJYb3gFbFJpkPw8/Ps3L2iFm8jA5ZNgx8NdHDvYIfaE95Baa2QzwAuDDx/DviERkLeiGuAD6WUp//rrogPCoY3COdaj6Zb0wdfdGZkW603rAg39XPkKq9myHsro7p+3tqdFCxax7ZLR6KZJn0++YIBC9dheH31QVd6fb4Vj8vB4rtn0XfJBjS/n0ODemJqAr+h10ehA6hMT2LpXVdwtG83EopL+NqcZzB8Jllf7udkbkb9ejZAVUYyWq3HmplnpHAyN5PD/fPI3bg7qraDtdb9+lPfYcaDz0UU8ZYgAK/Q2Zha0OprKc4eF+QYHKwKnxrV57fuyZSId1xaK+RZUsq61E9FQGjOxobMBv7QqOzXQoifA4uBOVLK2la2SRELpGbAzDvgnX9bec8B7E7o0tMytx/Zf6q8HRHA6LmfMHDBeg4P7E7OF7tDbhxsHh8FSzaQt3o7C+Zcj63Wx5UPP8+hgd05npuJ125g9/jwA/PvvZqUIyUM+mAVJ7tlsPCn13DB399n0AerKJw4CI9Lg4BTnlHjoWDROvot3oC7tLLZDGbh8OsautePo7zt3sv1KQURc7i3FA3onWztW46l5fOOtNc9wYDpuTaqfRK3EWpedxuWWV3RcWlWyIUQi4DsMIceCP5HSimFCJd3qf46OcBgYH5Q8f1YNwB24B9Ys/lfRjj/28C3AfLy8pprtiIWuOYu6DsMFrwKVWUwbhpMmmFND5bPh78/ZK2ln2XC+V27j5dysH8uPVZsDXuO4fGSdNzLsLeXs/y2S9k9tj99Pt1Mt+DZsxDMfPA5fHYDw+vDZ7dRnZrIu4/cwvA3/suMB59l7bUXcHhgd+zl1aQeOsaXU4bz5ZThSKDvxxsZ83LkvfJhEQJPgpNjvXLosnVvS9+KsNikr+3WxgXsLJMxJ5oZTkFxTay1quW4dBjWSWd6roHHDw+uraHKJxv4jjh1wVV5KuFNR6bZ2zQp5RQp5aAwj3nAkYBA1wn10SYudR3wlpSyftohpTwsLWqBfwOjm2jHP6SUI6WUIzMzM6Ptn6K9GTIOfvJH+PkzcMl1ViY1m93ah+5wN31uZtc2bYpfE6y59gJMe+j9q89hY8/ofhzrlRP2XAloEnI37MLntHNgWC+g4Tq9JiVCSuy1XjS/xF7jIbG4hJGvLaM4P4ek4lIu/Ou73PC9vzD4/ZUcHJqPZvoZ/8x8bv7Onxj70hIqMpKj7o8Etl84FNNusPrGyXgdNvyBde3WSNTQ0h0k+tpm9csvY3Pm69JjsVXRYwi4OEfnD2NdvDUlAUMTuA3Bh1MTGJKuYdesrYBD03U+nJqAW2U+6dC01t7yDnBr4PmtwLwm6t4AzA0uCLoJEMBMYHMr26OIJyZcZm1hC4fNEX6NvY5I5zXB5stHs+WyUay5bhI+m47HZcfjtB4L77ka02ln5c0X47UbIV7xWuCJ12lH+EzcJ8rDvkbjn0vd9NNj9fb6VK1119w4Yzw+h41pj71CzxXb0H0mQkoSi0ujFj6BJeT4/Rzvmc27v/g6e0f1pTwjuUU3BI3RkLy8eg7J3nISfJUYmklsyvHps68yPh3yjYB3+kuT3bwxJZHZvezYgta++6TofDI9ia1XW4+PpyfSJ0VFaevoCNmKtTAhRCfgNSAP2Iu1/eyEEGIkcKeU8o5AvR7AZ0CulNIfdP4SIBPrO7UhcE7k7BIBRo4cKdesWXPa7VbECFXlVlrUfeH3eKMbkcW8W2/IyIYNnxGtyLz2xzupyLQSgTjKq8jZug+v005R324YXh+1yZaFIPVAMVMfewV3SWWDH3uv3WDdNRP5cspwZvzsGVKj8AKHQDAaKRs4o73w9I9J21/M1MdfDVkbbzrkSkNOds3gg4duxDR0TJuO7vOTufMgl/729RY7vzV+3VJnAj+feDdvJF1ElcfeglbFB/GyTp5gwL2D7Kw95ic3UeP2vnYlzucgQoi1UsqR4Y61ytlNSnkcuDhM+RrgjqD/9wAhdlIp5UWteX1FnONOgideh5tHghnG0aupPegHCq2HbpyqV5eyVIj6GOfB+OynZvG1SW72jLE8szWvjx6ffknhpMGYdhsl3TIpzu9C97U7kYDXaUMz/ewf3pudkwZzwd/eiyjijcXQL0BKidFIVDt9VURShEQzLZHLtIPHuP4Hf2XvqL5UpSWSufMQWTuazxAXjor0JLxuB4nHSqlKS+LJ/Ft52XUpPk/HFI1YEPIuLjhcHbkd07rqPDHaTZ5yVlM0gYrspmhfNA36j4DNK0KPRRDkBpg+0G2QP9B6jL8MUjvBwa/gie/DKQMQuesLKTx/UEgs9qSjJYye+zHHe2ZT0rUTEkHuhl31Ymir8VKak07isTJu+O5fmpzp1onDqXVzQvaYA4x++WNW3tQ297GG10f+5+Gd9FpC0olyDmanseSHM1lj9mHu50PxmVpHm4jXowlrDb89MREIQp0BDQG/He3kG31VDnhF87TKtN5eKNN6B2PfDnjoFvB6LGHWNCund0vo0gOeerdh2ZbV8NhdVgx46acqPYV5v7oFj8vKiqZ5fWimn2mPvULnwkNIoKh/Hie7daL/wvVh9atOpMOZv01NhE2xGonjeZlopp+kIyX1ucJjAQmUGwm4fdVUGi7+lH8Tf+x9M1KoWeHZItkGe2enNF9Rcc7QlGldCbkiNjiyH959Fgo3g8MBu7eBpwVZ1brlw+/fDi0/tAc+fBEO7IbUDDyblrFjQn+O9Msl5fAJChavJ/F4WYuaWpKVirusGqPWU79VzGs3WHHLFMY+vwhblDHTvQ4bG68ah/tkuZWgpQ2/ihI4mZuJBNL3F7dqUl2pO/mf/Bt5su/tbdQ6RXOkOwS7rjt9h0VFx+OMrZErFG1GVi7c8ZD1/IMXYdeW8PVciVBdSYNVRbsTJn8tfP0uPWDMpfD5PbDzC+zeWgZ9tIZBH53+jeCXl4xg33m9GfL+SrK37aMiI5kvrhzH8Z7ZjH75Y4hCyD0OG5/fdilFA7pz5YPPtulibXGvbBbfPQtPghN7eTXX3vt/rYr6lmDWcM2BBTzZ5zYVtrUJ3Ia1DcgrYWQnjc+P+psdVk3UZ8Ktx6ah9n0rWoQSckXsMWAUhDPjOlxwy09g7h8tM7zXC4YO6VmwfYMV133KtVZUuTrKTsCT32/TKHKm3aCicyqff3Nag/KEoyXYa8InYQnGa7exdvaF7J44GL3Wy6qvX8LkvzS1czN6PC47H90/G6/bCYDURJuI7/ysCc1XOocpSIFHhrup9EkmZhkk2gQT3ytnV3lkKXfrcFd/O//a4aHWhGrT8lDPdAoeOk+tjSuiRwm5Ivbo0Q/GXgIrF50SYIcL+p1nzbwnTofVH8P+Qpg/F44VweG9sH4ZvPc8/OJZ6N7POu+zDxs4vLUYISzP+KBc6/0XruVo7y5UZKaS/9kWUg8Uc7xnNkUFuc0655m65TyW/99NuE+Ws2XaKPaN6EOty44jQia2lrBnVD9kkHCbdht7R/Qhb+3OVq3DVxoqa1ZT7C6HHLfG4DSNVcUmb+7xsCeMiGsCUmzQPVHj3sFOrsiz8YOBTl7b7aGwzM/wDJ0Z3W04dWX5UESPEnJFbHLXo3DeBbDkP1aClUlXWdHgNM0ypU+4DP5wj2VmrxNqr8d6/OMX8OuXrbKKUvC0QCBtDiuxC1jX7dwVOuXAxk/rq6TtP8aMh57DD0hNw+b14Q3kNG/q57c6yYWjqhbd46Pz7iI67Sum35KNLLj36rAz+ZbsJ6+jJsmFaWv4tf7sjmm4SyrI2n5629IqdSe7E7rFvFldg3aL5+6T8NwOK/PYf77yUhXhnklIuLm3nV8GpRNNsQu+VaBm4IrTRwm5IjbRNBg/zXpEYuNn4Wfbu7eApxYqy2DpO4RdgI64tU3Cnz+AE0ctK0CXHvDDyxueCgjTX/8XiCrhiWb6G2xF030moqqGsS8ubrM18rx1hWyYdT6+oC12XreTD35+MyNfWsLABWvRo5iZV2kOBJLDzgye7HMbr3VrYhxihPZMyuKXsLPMz9rjZkQRB6uNvljKHqPoECghV8QvNnv4fOdCs9bGf3h55FSqg8fAl+ugNsgz3u60Zv0pnaxHHa7EsC/f0vmpvSo0sZ/ml3TaeyTstU5n/pty+AQ5W/ZycHAP/PaGDlNrr59E8pGT5G7c3ayYf/u8n/NB1vlIrflgMO05E44VEgzQhaCqGT9Hlw4zuitHNkXbojaGKuKXybMsU3gwhg3GXGI5uEWM1S7hnqfggX9Cz/7W7DwhGa66Db75QGj1yTPbpLmRhFlvwymaAC7+45uMeWFRvbWgDmnoLPvulRwuyG32OluSe0cl4hDzFveosAvITxJknIaF263DiAyd/GQR8QdVYIn4jfl2xnRW8ydF26I+UYr45drvwt7tsG2tZYqXErr1gmvuhJ9e3cSJAooPQb9h8Phrzb/OyIvgpadatq+9HdH8kv5LNpJypIRF91yNFOA3DHSvj9z1hXTdvKfJ82uFQX7lPva4u0Sl0mb8haKoRwMcOiTZBEXVMiSXdzBOHWqCDBka0MUt+MUIJ1fl2dh4wuTlXV6qGxk7DAHfLrAzq4eNERnqJ1fR9qiAMIr4Z98O2LcTsvMgfxAc3gP3Xde08KZ0gu88AiMujFzH9MGf7oM1S6wbBW/rvcrPNjWJLr4aW0BtgpMum/eQuetwsyb7Pa4sxkx6CY/hPCttbE+6uAW39Lbxpy2eBiLdGJuAq3vaeGuPF4cOHj+cn2Xw7AVuEmyn3tEnNtbwxy3WEooeCAH7zAVupnVT5nRF61CR3RTnFn4TvjMZyprJTmZ3WlvVeg0MPXa8COZcb621nymEBsjm48mfZSRwyJnJdaN+x9aU/PZuzhlFD5jUd5RFHgMBDE7XWDo9iaPVfraX+slL0OieFN6Qvrfcz8JDXly64PJcgzSHWsFUtJ6mhFx9whQdD02HO39lCXVTeGutsLDh+ONPWy/ihh3y+kU+3qMfON2te40zgAC61hQz//M7ya063N7NOaP4JVR4I4u4S7fCpT59vjVOnV0aE7ONiCIO0D1J445+Dm7qbVcirjgrqE+ZomMyYhL8Zi5cfI1lbjfCrE1KCYf3hZaXl8COjW3Tjl+/CLf9zAoqE4zdCV//fzDnb6HHYgSb38tte99q72aE0NIfrcFpWsRzJHC02pqZByOwIqz9aZyLTbOSVP5vRUyjhFzRccntDd9+GH72dxBhfoh1GwwIY6natZm22dgtoaYapt0AP3wCuvS0tsyldIJLr4deA6DgPLjgKmvPejA2O7jDb3s7Wzikj4POzu3ahnDYo/zVEsCQNI3FlycyoIlEYj6sezqHbs3AEw3o7BR8MDWBa3vacRkdwC1f0aGJzamAQtGWJKbAZTdZ4VzrQr4KDZwuuOLrofVLTzQRMEZAQpIVbKY50jIhKdV6XjDcem3NgNLjsPA1WPYuPPqi5XQ38kL4ZB5I0xL2URdbznq/vxu+WH6aHW8dVbqTt7pcfEau7RBQe5r3SjVN7NZLtkFegsBlaNzez851PW1oQtDZrUNpZG+2BBs8OMyJXRNkuQRTuhrYNCXgivhACbni3ODGu60Z8XvPWqbzwWPg+h9YCVcak5tvrW97QwO4MG22FTXuk3mWU104hGbNqL/18KntW8//FkqOndrbXlttXeefv4QH/wkjJ1uPYJxuuP9v8MqfYd6/Trvrp4MfKLUlcsIeeSprCCs06ekQrYjrArq6BQeqJM2lenfq8MbFCYzKDP1Zu76XnRXF1REDtpgSxnU2GJyuTOiK+EMJueLcQAgrsEs0wV16DYT8gVC46VSylLqgMdd+D6oqYMVCK6pcnZjbndB7CHiqrbCuV9x6KnELwJqPQwPUSD9sWWWVh1snlxKe/CFsam5GLmjLPKgSWJ4+lNtH/DJ8FrrAK84Zauf3mzwYGnj90C9ZY9NJf5tGeTMllHjCi3iiARd1MThYKRmUpvH9gQ56J4cX4qt72Hhzj5dlRb6Qfd42Dfql6ErEFXGLEnKFIhz3/w1efsqK1e7zwuBxcNscy0yfmAJPvA5v/h9sXgXpnWHGNy0Hu0hEipImmkgzumUVbFvdIPNa+GsLsLusGX4kK0Hw6zWx3U0C3xj+KPOzx1Or2cO/HHBDvo17B7u4s8DJ1hKTTKdGjySNmQsrWFoU2oYJWTq7y/wcrm7ZDYdLhz7JGhtO+EMCz3j98IcxLjo5m1801zXB3Mlu/ltk8tzOWhYfOiXoF+YY/G2Cyu6miF+UkCsU4XC64fYHrEc4OneFO38Z/fUmXAafvN1QlHUDhk+KLPJfLLec5ZrDsMPN91rPs/OsPfDP/CY0B7vDBe4ka1tdnXVA0y1xN32AYEH2BBbljKdWhBdxgClddZ4aawlfgk00MGW/clECD6yp4cVCD16/ZRb/0zgXF3Wx0ef1yH4FGtDJISj3yfrALLqwIq79foyL6QsqG0Rdc+lwfS9bVCJehxCCC3IMLsgxkFJSVC1xG4IUu1oLV8Q3SsgVirPBTfdYpvqifZaYGzZIzYBvPRT5nKRUa629uYhyAhg4yvIBqCM7Dx77LiDBNK06F82Ca+6CN/8Byz+yvPYnf82KMW/YoPwkc9fZqNof2Tju1uC6nvaIjmBO3RLe348JneF2dgqO1YSfkafYLS/x/+zx8uxOD2VeSe8kjTv72xmUpvPB1ETuX13NmmMmyXbBnQV27h54+qk/hRDkuJWAKzoGKrKbQnG2kBI2r4T9hZDTHYaOjzwbByuV6o+mNx/jPbML/PmjUBN9TRWsXmKt6Q8ea63dN8N9q6r51w5PxPjpDh02z0oiowUz4Tre+MrDj5ZXN0jzqQFD0jXenJJAmkPjeI2fafMrKaryU2NaDmzZbo2Ppia0aPatUHQ0VGQ3hSIWEMIS1MtvhvMmNi3iYK293/uUZQ53JVjmfl0He2Amanda5Xf/Pvw6u9MNE6+AqbOjEnGA2/raI+7TdmrwyHnO0xJxgGt62vnpEAduw3JUc2gwq4eND6cl1kdAu291DXsr/FT4LI/4Ch/srfAzZ3V8JKxRKNoDNSNXKGIdn9eKNKfpljf9ltWwfb0l9OOnWd70bcibezz8aEU1AvCYllf3tG42vj/AwdBOrffsrvZJvir3k+0WpDcKYZr1UimeMJZ9uwZHbmoiqotC0cFpakau1sgViljHaBSBbtgE63GGmNXDzvRcG+uPmyQYgkFpGqINk467DMGAtPA3BJHmFXE431AozhrKtK5QKEJw6IKxgQApbSnizXFpNyMk7rkuYGo3NedQKCKhhFyhUMQMvx3torNLkBDQ7QQDOrsET45W+7wVikio21yFQhEz5Lg11s1MYt5eL9tLTPql6szobsPZeJquUCjqUUKuUChiCqcuuL5X5IA0CoWiIcq0rlAoFApFHKOEXKFQKBSKOEYJuUKhUCgUcYwScoVCoVAo4hgl5AqFQqFQxDFKyBUKhUKhiGOUkCsUCoVCEccoIVcoFAqFIo5RQq5QKBQKRRyjhFyhUCgUijhGCblCoVAoFHGMEnKFQqFQKOIYJeQKhUKhUMQxSsgVCoVCoYhjlJArFAqFQhHHCClle7ehxQghioG9QUUZwLF2as6ZoKP1B1Sf4oGO1h9QfYoHOlp/4Mz0qbuUMjPcgbgU8sYIIdZIKUe2dzvaio7WH1B9igc6Wn9A9Ske6Gj9gbPfJ2VaVygUCoUijlFCrlAoFApFHNNRhPwf7d2ANqaj9QdUn+KBjtYfUH2KBzpaf+As96lDrJErFAqFQnGu0lFm5AqFQqFQnJPEjZALIa4VQmwRQviFEBG9AYUQ04QQ24UQhUKIOUHlPYUQKwPlrwoh7Gen5RHbmS6EWCiE2Bn4mxamzmQhxIagR40QYmbg2LNCiK+Cjg07+70IaW+zfQrUM4Pa/U5QeUyNUaBN0YzTMCHE8sDn8wshxPVBx2JinCJ9L4KOOwLveWFgDHoEHbs/UL5dCDH1bLa7KaLo0z1CiK2BMVkshOgedCzsZ7A9iaI/3xBCFAe1+46gY7cGPqM7hRC3nt2WRyaKPj0V1J8dQoiSoGOxOEbPCCGOCiE2RzguhBD/E+jvF0KI4UHHztwYSSnj4gH0B/oBnwAjI9TRgV1AL8AObAQGBI69BswOPP87cFc79+dJYE7g+RzgiWbqpwMnAHfg/2eBa9p7XE6nT0BFhPKYGqNo+wT0BfoEnncBDgOpsTJOTX0vgup8F/h74Pls4NXA8wGB+g6gZ+A6egyMSzR9mhz0fbmrrk9NfQZjvD/fAP4S5tx0YHfgb1rgeVo89KlR/R8Az8TqGAXadAEwHNgc4fjlwIeAAMYCK8/GGMXNjFxKuU1Kub2ZaqOBQinlbimlB3gFmCGEEMBFwBuBes8BM89ca6NiRqAdEF17rgE+lFJWndFWtY6W9qmeGB0jiKJPUsodUsqdgeeHgKNA2MAN7UTY70WjOsH9fAO4ODAmM4BXpJS1UsqvgMLA9dqbZvskpfw46PuyAuh2ltvYEqIZo0hMBRZKKU9IKU8CC4FpZ6idLaGlfboBmHtWWnaaSCmXYU2oIjEDeF5arABShRA5nOExihshj5KuwP6g/w8EyjoBJVJKX6Py9iRLSnk48LwIyGqm/mxCP+S/DphvnhJCONq8hS0n2j45hRBrhBAr6pYKiM0xghaOkxBiNNbsY1dQcXuPU6TvRdg6gTEoxRqTaM5tD1rarm9izZTqCPcZbE+i7c/Vgc/SG0KI3Baee7aJul2BZY+ewJKg4lgbo2iI1OczOkZGW12oLRBCLAKywxx6QEo572y3p7U01Z/gf6SUUggRcftA4I5uMDA/qPh+LGGxY211uA/4ZWvb3Bxt1KfuUsqDQohewBIhxCYs4WgX2nicXgBulVL6A8XtMk6KUwghbgZGApOCikM+g1LKXeGvEDO8C8yVUtYKIb6DZUG5qJ3b1FbMBt6QUppBZfE4Ru1CTAm5lHJKKy9xEMgN+r9boOw4lonDCMw26srPKE31RwhxRAiRI6U8HBCAo01c6jrgLSmlN+jadbPEWiHEv4GftEmjm6Et+iSlPBj4u1sI8QlwHvAf2mGMAu1odZ+EEMnA+1g3nSuCrt0u49SISN+LcHUOCCEMIAXrexPNue1BVO0SQkzBuiGbJKWsrSuP8BlsT5Fotj9SyuNB/z6N5b9Rd+6Fjc79pM1b2HJa8tmZDXwvuCAGxygaIvX5jI5RRzOtrwb6CMv72Y714XhHWt4GH2OtMwPcCrT3DP+dQDug+faErB0FRKVubXkmENaL8izTbJ+EEGl15mUhRAYwAdgao2ME0fXJDryFtTb2RqNjsTBOYb8XjeoE9/MaYElgTN4BZgvLq70n0AdYdZba3RTN9kkIcR7wf8BVUsqjQeVhP4NnreXhiaY/OUH/XgVsCzyfD1wa6FcacCkNrXftRTSfO4QQBVgOYMuDymJxjKLhHeDrAe/1sUBp4Gb+zI5RW3nNnekH8DWsdYVa4AgwP1DeBfggqN7lwA6sO7cHgsp7Yf0AFQKvA4527k8nYDGwE1gEpAfKRwJPB9XrgXU3pzU6fwmwCUsYXgQSY2CMmu0TMD7Q7o2Bv9+M1TFqQZ9uBrzAhqDHsFgap3DfCywT/1WB587Ae14YGINeQec+EDhvO3BZe49JC/q0KPBbUTcm7zT3GYzx/jwGbAm0+2OgIOjc2wNjVwjc1t59ibZPgf8fAR5vdF6sjtFcrF0pXiw9+iZwJ3Bn4LgA/jfQ300E7bA6k2OkIrspFAqFQhHHdDTTukKhUCgU5xRKyBUKhUKhiGOUkCsUCoVCEccoIVcoFAqFIo5RQq5QKBQKRRyjhFyhUCgUijhGCblCoVAoFHGMEnKFQqFQKOKY/w+Vovy0q6OftAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}