Classifier: Light_GBM.ipynb

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  "metadata": {
    "colab": {
      "name": "Light_GBM_Classifier.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOYefCkLJLRLpm8LFXJCVpg",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/ML_Algos_Python/blob/main/Light_GBM_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlpi6hT1Oj9g",
        "outputId": "6c199591-1ebe-4f15-c087-de2e0184727c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "!pip install lightgbm"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.6/dist-packages (2.2.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from lightgbm) (0.22.2.post1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.18.5)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->lightgbm) (0.17.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQ5_TYCk8A6W"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AMlQV3cj8NC2"
      },
      "source": [
        "df = pd.read_csv('/content/golf.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlBv11K98Uam",
        "outputId": "f35db05e-a0e3-4822-abfb-868b160a1cb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        }
      },
      "source": [
        "df.head(20)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Outlook</th>\n",
              "      <th>Temp</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Windy</th>\n",
              "      <th>Play Golf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rainy</td>\n",
              "      <td>Hot</td>\n",
              "      <td>High</td>\n",
              "      <td>False</td>\n",
              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Rainy</td>\n",
              "      <td>Hot</td>\n",
              "      <td>High</td>\n",
              "      <td>True</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Overcast</td>\n",
              "      <td>Hot</td>\n",
              "      <td>High</td>\n",
              "      <td>False</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Mild</td>\n",
              "      <td>High</td>\n",
              "      <td>False</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cool</td>\n",
              "      <td>Normal</td>\n",
              "      <td>False</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cool</td>\n",
              "      <td>Normal</td>\n",
              "      <td>True</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Overcast</td>\n",
              "      <td>Cool</td>\n",
              "      <td>Normal</td>\n",
              "      <td>True</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Rainy</td>\n",
              "      <td>Mild</td>\n",
              "      <td>High</td>\n",
              "      <td>False</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Rainy</td>\n",
              "      <td>Cool</td>\n",
              "      <td>Normal</td>\n",
              "      <td>False</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Normal</td>\n",
              "      <td>False</td>\n",
              "      <td>Yes</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Rainy</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Normal</td>\n",
              "      <td>True</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Overcast</td>\n",
              "      <td>Mild</td>\n",
              "      <td>High</td>\n",
              "      <td>True</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Overcast</td>\n",
              "      <td>Hot</td>\n",
              "      <td>Normal</td>\n",
              "      <td>False</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Mild</td>\n",
              "      <td>High</td>\n",
              "      <td>True</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "     Outlook  Temp Humidity  Windy Play Golf\n",
              "0      Rainy   Hot     High  False        No\n",
              "1      Rainy   Hot     High   True        No\n",
              "2   Overcast   Hot     High  False       Yes\n",
              "3      Sunny  Mild     High  False       Yes\n",
              "4      Sunny  Cool   Normal  False       Yes\n",
              "5      Sunny  Cool   Normal   True        No\n",
              "6   Overcast  Cool   Normal   True       Yes\n",
              "7      Rainy  Mild     High  False        No\n",
              "8      Rainy  Cool   Normal  False       Yes\n",
              "9      Sunny  Mild   Normal  False       Yes\n",
              "10     Rainy  Mild   Normal   True       Yes\n",
              "11  Overcast  Mild     High   True       Yes\n",
              "12  Overcast   Hot   Normal  False       Yes\n",
              "13     Sunny  Mild     High   True        No"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BVoOmIUlARob",
        "outputId": "20a6472f-7422-4900-ab18-7ac24f295633",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Outlook', 'Temp', 'Humidity', 'Windy', 'Play Golf'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "1c76da02-db33-42ea-87da-d6c06f9a0d64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14 entries, 0 to 13\n",
            "Data columns (total 5 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   Outlook    14 non-null     object\n",
            " 1   Temp       14 non-null     object\n",
            " 2   Humidity   14 non-null     object\n",
            " 3   Windy      14 non-null     bool  \n",
            " 4   Play Golf  14 non-null     object\n",
            "dtypes: bool(1), object(4)\n",
            "memory usage: 590.0+ bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWEHN4bFAgn9"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn import preprocessing"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3KKgr3uBMCN"
      },
      "source": [
        "le = preprocessing.LabelEncoder()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X42jOMoSB9-3"
      },
      "source": [
        "for column in df.columns:\n",
        "  if df[column].dtypes == 'object':\n",
        "    le.fit(df[column])\n",
        "    encoded_feature = le.transform(df[column])\n",
        "    df[column] = pd.DataFrame(encoded_feature)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_4KL6a-NaWG"
      },
      "source": [
        "le.fit(df['Windy'])\n",
        "encoded_feature = le.transform(df['Windy'])\n",
        "df['Windy'] = pd.DataFrame(encoded_feature)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uD2k_eoQD9Vs",
        "outputId": "ddc4b36e-6afc-4ef2-f31d-97690af90ab6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "df.sample(10)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Outlook</th>\n",
              "      <th>Temp</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Windy</th>\n",
              "      <th>Play Golf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>11</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <th>13</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "    Outlook  Temp  Humidity  Windy  Play Golf\n",
              "11        0     2         0      1          1\n",
              "9         2     2         1      0          1\n",
              "7         1     2         0      0          0\n",
              "12        0     1         1      0          1\n",
              "13        2     2         0      1          0\n",
              "3         2     2         0      0          1\n",
              "8         1     0         1      0          1\n",
              "5         2     0         1      1          0\n",
              "10        1     2         1      1          1\n",
              "1         1     1         0      1          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEWVBsczEAMz"
      },
      "source": [
        "X = df.drop(['Play Golf'], axis = 1)\n",
        "y = df['Play Golf']"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LSiW9WKF9xC",
        "outputId": "1bce87a1-b9a9-459c-eb3d-767a21cd99d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14 entries, 0 to 13\n",
            "Data columns (total 5 columns):\n",
            " #   Column     Non-Null Count  Dtype\n",
            "---  ------     --------------  -----\n",
            " 0   Outlook    14 non-null     int64\n",
            " 1   Temp       14 non-null     int64\n",
            " 2   Humidity   14 non-null     int64\n",
            " 3   Windy      14 non-null     int64\n",
            " 4   Play Golf  14 non-null     int64\n",
            "dtypes: int64(5)\n",
            "memory usage: 688.0 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oELoRhjKOdb_"
      },
      "source": [
        "import lightgbm as gbm"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yt3XXZ0NO1GD"
      },
      "source": [
        "#convert X,y in gbm form\n",
        "gbm_train = gbm.Dataset(X,y,feature_name = ['Outlook','Temp','Humidity','Windy']\n",
        ",categorical_feature = ['Outlook','Windy'])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGZDhafJQ1Ye",
        "outputId": "3bdf55e8-1397-4167-900a-c7a3fdd88da7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['Play Golf'].unique()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_6MFzr7P1xT"
      },
      "source": [
        "params = {\n",
        "    'boosting_type': 'gbdt',\n",
        "    'objective': 'multiclass',\n",
        "    #'objective': 'regression', #for regression\n",
        "    #'metric':'rmse\n",
        "       'num_class': len(df['Play Golf'].unique()),\n",
        "       'min_data':1\n",
        "}"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnvKUYyRRUyf",
        "outputId": "c80683d9-98ff-4767-bf85-ec93032647a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "gbm_model = gbm.train(params, gbm_train, num_boost_round=50)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/lightgbm/basic.py:1205: UserWarning: Using categorical_feature in Dataset.\n",
            "  warnings.warn('Using categorical_feature in Dataset.')\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6h7TGRyLRqgU"
      },
      "source": [
        "predictions = gbm_model.predict(X)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-D0waueDSzDt",
        "outputId": "77432482-452e-43e6-f413-3161a29439f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "predictions"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.99589453, 0.00410547],\n",
              "       [0.99589453, 0.00410547],\n",
              "       [0.00235277, 0.99764723],\n",
              "       [0.00235277, 0.99764723],\n",
              "       [0.00235277, 0.99764723],\n",
              "       [0.99589453, 0.00410547],\n",
              "       [0.00235277, 0.99764723],\n",
              "       [0.99589453, 0.00410547],\n",
              "       [0.00235277, 0.99764723],\n",
              "       [0.00235277, 0.99764723],\n",
              "       [0.00235277, 0.99764723],\n",
              "       [0.00235277, 0.99764723],\n",
              "       [0.00235277, 0.99764723],\n",
              "       [0.99589453, 0.00410547]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_h4IxB42Szql",
        "outputId": "adfed976-8e84-4cbf-98d1-79c407c612ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "index = 0\n",
        "for pred in predictions:\n",
        "  if np.argmax(pred) == 0:\n",
        "    p = \"No\"\n",
        "  else:\n",
        "    p = \"Yes\"\n",
        "    #-----------------------------\n",
        "  a = y[index]\n",
        "  if a == 0:\n",
        "    a = \"No\"\n",
        "  else:\n",
        "    a = \"Yes\"\n",
        "    #------------------------------\n",
        "  print(\"predictions is: \", p, \"   Whereas actual is :\", a)\n",
        "  index +=1"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predictions is:  No    Whereas actual is : No\n",
            "predictions is:  No    Whereas actual is : No\n",
            "predictions is:  Yes    Whereas actual is : Yes\n",
            "predictions is:  Yes    Whereas actual is : Yes\n",
            "predictions is:  Yes    Whereas actual is : Yes\n",
            "predictions is:  No    Whereas actual is : No\n",
            "predictions is:  Yes    Whereas actual is : Yes\n",
            "predictions is:  No    Whereas actual is : No\n",
            "predictions is:  Yes    Whereas actual is : Yes\n",
            "predictions is:  Yes    Whereas actual is : Yes\n",
            "predictions is:  Yes    Whereas actual is : Yes\n",
            "predictions is:  Yes    Whereas actual is : Yes\n",
            "predictions is:  Yes    Whereas actual is : Yes\n",
            "predictions is:  No    Whereas actual is : No\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVq0bNA3Uu_h"
      },
      "source": [
        "##Interpretability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rCFQQGaVNm4"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGf5SPYAUPVF",
        "outputId": "365deaf6-af8d-46de-f8bb-c581bb608b71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "gbm.plot_importance(gbm_model, max_num_features=10)\n",
        "#plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f26ef0714a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nAYuwHhVe2T",
        "outputId": "61b6bb47-63c7-4207-ce1d-588e994d3e1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "fig_size = [20,20]\n",
        "plt.rcParams['figure.figsize'] = fig_size\n",
        "gbm.plot_tree(gbm_model)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f26eeb9c198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b1Melhjed7LmTyaRanwZQixEf77VS26Ioyse+9+HMYDBgtVoJBoMS1AghhBDnAekfK4QQQpwhRUVFVFVVnfHCr1qtdkhh3dMlmUySSCRYtmwZL7744qd6bCQS4Q9/+APbt28/4f4Jh8M89NBDvPbaa+rftm7dym9/+1tisdgx63d2dvLggw/ypz/9ifLy8vO24K7f78fn80lPJCGEEOI8IUGNEEIIcQZoNBoWLVrEli1b1Bop55NkMsnGjRspLS1l9OjRdHd309PTQyAQIBqN0tPTo9Z48Xq99PT0EI/HCYVCdHZ2Eo/HGTFihPq4SCSCoigEAgE6Ozvx+/2YzWbGjx9Pb28vyWQSj8dDdnY2Ho+HZDKJ1+tVAxtFUVi3bh1Tp07lq1/9KitWrCAajZ7lvXTqJZNJNmzYwIwZM0hLSzvbmyOEEEKIU0CGPgkhhBBnSF5eHtdeey2PPfYYP/vZz3A4HOfNcJxYLMYbb7xBcXExkydPprOzk9dee43W1la+//3vc88997Bo0SKysrLYs2cPTqeToqIi2tvbsdlsFBQUoCgKDQ0N1NXVkZWVxc0338wDDzxAUVERdXV1/PKXv1Rfb9++faxZs4bi4mKCwSDxeJxly5Zx4403MnbsWBKJBPX19Vx22WUUFhbS0dFBKBTCZDKdxb10aiUSCTZt2kRNTQ333HPPeXMuCSGEEBc66VEjhBBCnCEajYaFCxdy2WWXcf/993PkyBGSyeR50bvGZDJRUFDAokWLSE9Pp6CggJtvvpnu7m7S0tLIyMjg9ttvJx6PE4vFGDt2LBs3bqSjo4NgMMi0adPQaDRMnDiRG264gcbGRg4cOIDRaFSn0N6zZw/wUS+SzZs3c/XVV3PLLbdgs9kwGAzcddddjBkzBhhazyb1+/mwnwG1p9Hy5cvZv38/d911F1ar9WxvlhBCCCFOEelRI4QQQpxBOp2OxYsXU1hYyIoVKyguLuamm27C5XIN+x4RGo3muIWEFUVRCwAbDAZMJhNFRUX86le/wmaz8a9//YsHH3yQsWPHqo9LFQBOBVmKomAwGNShTfF4XF0vtSQSCfXxer2ekSNH0tvbi9vtPiNTZZ9uiqIQi8UoLy9nzZo1zJkzh2984xsYjcZhf+4IIYQQ4t90995778nuP+mdQgghhPj0NBoNTqeT+fPn43a7WblyJV1dXeTm5mKxWE570d/Tpbm5mfLycvr7+2lpaaGwsJB33nmHyZMn88477zBhwgQmTpzIzp070Wg0ZGdn89ZbbxEOh7Hb7fT09BCLxdBqtezbt49bbrmFsrIyKioq6O3t5ctf/jLvvfceHR0dLFq0iDfeeIOuri4qKyu57LLLWL58Obm5uTidTjQaDVlZWWzYsIFDhw5x1VVXMWHChGEZaCiKQjgcZu/evSxbtoyenh6+9a1vMX/+fPR6/bB8T0IIIYTgf090h+ZjugGfH32EhRBCiHOUoij4fD5KS0vZunUrWVlZLFq0iJKSEtLS0oZVIzwWixEIBNBqtSiKgslkIhQKYbFYCIVCmM1mzGYzwWCQaDSK1WolFosRi8WwWq1EIhE0Gg06nY5oNIrdbicajarPYTQa8fv9KIpCeno6gUAA+GgfpqWlEY1GMRqN6PV69e+BQIBkMkl6evqwC8Di8Tjt7e3s2rWLDz/8kPz8fK655hqKi4vP6+nGhRBCiAvECT/IJagRQgghzgGpYS2VlZVs27aN5uZmCgoKuPzyy5kwYYI6bEca5+en1PexZDJJV1cXZWVl7N27l1gsxiWXXML8+fPJzMwcdmGTEEIIIU5IghohhBBiOEh9Lvv9fg4dOsSOHTvUGivTp09n2rRpZGVlqb1GJLgZvlIFjyORCHV1dZSVlVFVVYVOp2PGjBlccskljBw5Uj3GcqyFEEKI84oENUIIIcRwlBq+09rayt69e6mpqSEejzNy5EhKSkooKSkhMzMTi8UypICvOPekek0FAgGampqorKyktraWQCBAfn4+M2fOZMKECbhcLqk9I4QQQpz/JKgRQgghhjtFUYjH4/j9fpqamqiqqqKhoQG/34/NZmPkyJGMGzeOkSNH4nA4hmVdlvNF6lgNDAzgdrupqamhpaWF1tZWYrEYhYWFTJ48mYkTJw4J2oQQQghxwZCgRgghhDjfpIbORKNRuru7aWlpoaamhsbGRoLBIGazGZfLRVFREaNHjyYrK4usrKxjpqmWgODTOfq7UyKRoL+/H7fbTXd3N01NTfT09NDb24tGo8HhcDB27FiKioooKirCbrej0+kkRBNCCCEubBLUCCGEEOe7wZ/p0WgUt9uN2+2mpaWF5uZm3G43gUAAvV5PWloaLpeLkSNHqgGOy+XCarWqMwqlbi+0IGdwYd9UGBaPx/F4POo+7ezspKOjA7/fTygUQq/Xk5mZSV5eHmPGjCEnJ4fs7GxsNpvUmBFCCCHE8UhQI4QQQlzoFEUhHA7j9/sJBAL09/fT1tZGd3c3brcbr9dLJBJBq9Wi1+ux2Wzk5OTgcrmw2+3YbDbsdjsZGRmYTCZ0Op26aLVadTnXAglFUdTAJZlMkkgk1CUej6v7wufzMTAwgNfrpb+/n/7+fiKRiLqexWIhIyODrKws8vLyyM/Px263k5aWRnp6Ojqd7my/VSGEEEIMHxLUCCGEEOLEUjVVYrEYsViMaDSK1+vF7Xbj8XgYGBjA5/Ph8/no6enh4MGDFBYW4nA4UBQFvV6PyWTCZDJhNpuxWCyYzWZMJhMGg0FdUn8zm80YjUYMBoMa7KSGAh3diyeZTKrbmPrektrWSCRCOBwmEokQiUSGbH8kEiEUChEOh4esk1JTU4PVamXSpElqTR+bzYbD4cDlcuFyuTCbzUO2/1wMooQQQggxLElQI4QQQojPR1EU6urqWLFiBXl5edx+++1YrVa1Tk4wGCQYDBIOh9WA5HjhServ0WiUWCwG/HuYUernwQbXchk8LMtoNKqhj8lkUoMfg8GA0WgcEhhZrVYsFgsWiwWdTodGo6GhoYEXXniBsWPH8rWvfW3IMCUhhBBCiNNMghohhBBCfDaKohAMBvnnP//JoUOHWLp0KVOmTPnU9Ws+5jvHKfFptycajbJx40ZKS0tZsmQJs2fPll4zQgghhDgTJKgRQgghxKeTquty8OBBVq9ezYwZM7jxxhtJS0s725t2SimKQltbGytXrsRoNLJ06VJycnIkrBFCCCHE6SRBjRBCCCE+OUVR6Ovr4x//+Ae9vb3ccccdFBUVnddTSkejUXbu3Mnrr7/O4sWLufLKKzGZTGd7s4QQQghxfpKgRgghhBAfT1EUEokEpaWlvP7661x11VVcccUVGI3GC6KHiaIoeDweVq9eTXd3N3feeSfFxcUXxHsXQgghxBklQY0QQgghTk5RFDo6OlixYgU6nY5vfetbZGdnX3AhRWp2qYMHD/LKK68wbdo0brrpJqxW6wW3L4QQQghx2khQI4QQQojjUxSFSCTCu+++y/vvv8/NN9/MvHnz0Ov1Z3vTzrpAIMBrr73GwYMH+drXvsbUqVPR6XRne7OEEEIIMfxJUCOEEEKIYyWTSWpra1m1ahX5+fksWbIEh8MhPUcGSSaTNDY2snLlSlwuF1/96ldxuVyyj4QQQgjxeUhQI4QQQoh/S025vX79eiorK1m6dCkTJ06UqalPIDWV95YtW9i8eTM33ngjCxYswGAwnO1NE0IIIcTwJEGNEEIIIf495faBAwd45ZVXmDVrFjfeeCMWi0UCmk9AURTcbjcvvfQSkUiEO++8k/z8fADZf0IIIYT4NCSoEUIIIS50qSm3V69eTW9vL3feeSdFRUUSMHxKqbBrz549rFu3jvnz53PNNddgNptlXwohhBDik5KgRgghhLiQxWIxSktL2bBhA/PmzePaa68lLS1NgoXPQVEUBgYGWLduHbW1tdxxxx3q8LFP8lifz4fZbMZoNJ5wvVgsRiAQICMjQ46VEEIIcX454Qf7x3+TEEIIIcSwpSgKra2tPPjgg+zdu5e7774bj8eD2+2Whv/npNFoyMjI4Fvf+hbf+MY3eOWVV1i+fDkDAwN8zH+EoSgK69evp7Gx8aTrNTc38+STTxIIBIhEIqdw64UQQghxrpIeNUIIIcR5KFX89u2332b79u3ceuutzJkzh1gsxv79+5k0aRLhcJjq6moKCwtlCNTnlJri/K233qK0tJQlS5Ywe/bsExZnjkQi7N+/nwkTJpBIJKiqqiI3N5dwOIxOp8Pr9TJu3Di0Wi1lZWXs3r2bCRMmcNNNN520B44QQgghhg3pUSOEEEJcKFJTbt933310dXXx61//mnnz5qHT6QBYt24dlZWV/O1vf8Pn89HW1naWt3j402g0mM1mbrrpJv7rv/6LLVu28Oijj9Ld3X3c3jUajYYNGzbw4Ycf8sQTT+DxeGhra6O0tJSNGzfS1NTEk08+SWZmJpdffjlNTU1S8FkIIYS4QEhQI4QQQpwnUjVTVq5cyQsvvMBXv/pVvve97+FwONQGvtFoJDMzE61WS05ODps3b/5ENVXEJ6PRaMjPz+fnP/85s2bN4i9/+QtvvfXWMcOWDAYDTqcTjUZDXl4emzdvRqPR4HA4yM/PZ+7cufh8PpLJJAaDAYfDwfjx42U6cCGEEOICIN/MhBBCiPNAIpGgrKyM++67D4vFwq9//WsmT558whAmmUxyww038O1vf5sXX3yRcDh8hrf4/KXRaDAYDCxcuJBf/epXNDQ08Oc//5m6urpjetcoisJVV13Fj370I15++WUCgcAJnzeRSHxs7RshhBBCDH/6s70BQgghhPjsUlNuv/zyy/T39/OTn/yEwsLCEw6Rcbvd9PX1cfjwYcrLy/H7/Vx99dVS9+Q00Gg0uFwufvSjH3Ho0CGeffZZpk2bxk033UQkEqG7u5vKykpqamro7+9n0aJF9PX1MTAwQH19PbFYjIGBATIzM7n00kt5++23KSgowG63n+23JoQQQojTSIoJCyGEEMOQoijE43G2b9/Ohg0buPrqq7niiivQ6/UnrWNyss99qX9y+iiKQjAY5LXXXuPDDz/k9ttvZ9q0aZ9o2JlGoxly3OQ4CSGEEOeFE36gS1AjhBBCDDOKotDW1sZLL72EyWRi6dKl5OTkSAN+GEgmkzQ2NvLSSy+RlZXF1772NbVWjRBCCCEuKBLUCCGEEMOdoiiEw2E2bdpEaWkpX/nKV7j44ovR62Uk83CSmjp9y5YtvPfee1x//fUsWLBACgULIYQQFxYJaoQQQojhTFEUqqureemllyguLmbJkiXYbDbpiTGMKYpCT08PK1euJBwOc+eddzJy5Eg5pkIIIcSFQYIaIYQQYjhSFAW/38/atWupqanhjjvuYOLEiWg0GmnQnwcURSGZTLJ3717Wrl3L/PnzufbaazGZTHJ8hRBCiPObBDVCCCHEcKIoColEgv3797NmzRrmzp3Lddddh8ViOdubJk4DRVEYGBhg3bp11NbW8o1vfIOSkpJPVGxYCCGEEMOSBDVCCCHEcKEoCm63m1WrVjEwMMCdd97JqFGjpIfFBSCZTFJVVcWqVasYM2YMt956K3a7XY69EEIIcf6RoEYIIYQ4FyUSCfx+P3a7HYB4PM7WrVt58803ue6661i4cCEGg0Ea6hcQRVGIRCK89dZb7Nixg1tvvZU5c+ag1WpJJBL09vbKLF9CCCHE8CdBjRBCCHGuURSFzZs38/zzz/Pkk08SCAR48cUXsVgsfOMb38Dlcklj/AKWmob9xRdfxGQycccdd1BXV8f/+3//j6effpqSkhI5P4QQQojhS4IaIYQQ4lySmsVpyZIlHDlyhG9/+9tkZmaqU27rdLqzvYniHBGLxdi+fTtr165l79697N69my9+8YusXLmSrKyss715QgghhPhsThjUSIU6IYQQ4izweDz8/Oc/5/Dhw8RiMdauXcuXv/xl5syZIyGNGMJgMHD55ZczatQo9u3bh6IovPvuu/zhD38gFAqd7c0TQgghxCkmQY0QQojzkqIoZ2X5JCKRCPfddx9vvfWW+hiPx8MzzzxDNBo9nbtFDFOdnZ2sWrWKRCIBfFTb6JlnnuGFF15Q/3YyZ+t6+DTXhRBCCM9sytUAACAASURBVCE+IkOfhBBCnFNSn0uDG3iDG3zxeJxwOEw4HCYSiQxZotEosViMeDxOPB4nGo2qf0+tE4vFAIY0bpPJ5JCfFUVRe7UM/hlAq9Wi0WjQ6XSYTCaMRiMmk0n92WAwoNfr0ev1Q+4bvM769eu555570Gg0jB8/nilTpjB//nwuueQSpk2bhl6vl9ojF7DjXQOhUIgjR45w6NAhtm/fTllZGbW1tVgsFp588kkmT55MNBpVl8HXQSKRIBaLqfelroNIJKKe74OvgaPDlUQigUajUacKVxRFvQ5SUtdI6rw/ejEYDOh0uiHXxtHL4OskdQ2kXuPonwffCiGEEMOU1KgRQghxdimKQiKRUBuOiUSCaDRKf38//f39DAwM4PP5CAQC+P1+BgYGCAaD6rqJRIJkMolWq8VgMKjL0SHI0UHJ0Q1Ag8EAMKShObgRmvp9cGN5cCN2cI+GwQFR6jYej6uN5FTDOBwOqz8HAgFaW1vp7+/HaDSSl5dHZmYmer0ek8mEzWbDZrORlpZGeno6drudjIwMMjMzsVqt6HQ6dTm6MSvOTYNDxmQyqZ7PsVgMn8+H1+vF5/Ph9/sJBAIEg0H1OggGg+pjUrepWaH8fj8ajQaXy0V2drZ6DRx9Heh0OgwGw5DrIfV7KmA5Ong5+voY/H3xeMFO6vfB5/3RwVHq+k9dI4MDo1gspl43sVhMvda1Wi06nQ6tVoter1evi7S0NKxWq/q7zWbD4XCQlpamvufUY1M/CyGEEOcYCWqEEEKcXqnGYygUIhwOEwwG6enpwe1209/fr96Gw2ESiYQauNjtdpxOJ3a7XW2A2Ww2MjIyhjS6UreDG2DDKaRIfd6mGuuDG+ypxms4HGZgYICBgQECgQCBQICBgQE1uAqHw2qjWKPRqEGO0+nE5XKRm5tLZmYmZrMZi8WC2WyWejdnQCKRGNLDKxwO09fXR29vLx6Ph/7+fjweDz6fj1gshlarRVEU9Hr9kDAuPT0dq9WK1WpVwwer1Trk3Nfr9ccEGOdDD5PUeT14GXx9pIItv99/3EArdb34/X71+RKJxJB9nApzsrOzyc7OxmKxYDKZMJvNGI1GCXOEEEKcaRLUCCGE+OwGf1bE43G1F0xvby8tLS10d3fT19dHIBBAo9FgNpuxWq3k5uaSlZWF0+lUF4vFMqSRmWpoDudG5ul2vEZsNBrF4/HQ29tLX18ffX19dHV14fV61R4KJpOJzMxMXC4XBQUF5OXlqb1zLBaL+vyy74919PejaDSK1+tlYGAAr9dLe3s73d3deDweNURL9YqyWCxkZWXhcrlwOBxDFoPBMGT43ODeXHIcPrtUr6Wjr5NwOIzH41EDs1Ro7PF41HBNURSsVqt6bYwYMYK8vDzsdru6DA485TgJIYQ4RSSoEUII8ckMbvB4vV46Ojpob2+noaGB7u5u/H4/er2ejIwMnE4no0aNIicnB5fLhdPpxGw2HxO8SMPm9Bs8VAs+CtRSQU4qUEsFOcFgEJPJhNPpZPTo0RQWFpKXl0d2drbaYwMunOM2+JwPBoN0d3fT3d1NW1ubOkwtFAqpPcAyMjLIz88nJyeHzMxMMjMzcTgc6PV6ADn3z2FHD+FK9QRM9XryeDx0dHTQ2dmp9mZLJpOkp6fjdDopKioiLy+PnJwcsrKyMJlM6r93cqyFEEJ8ShLUCCGEOL5U7wy/309jYyMNDQ00Nzfj9XpRFAWn08mIESMYO3Ysubm5OBwO0tPTZZjAMDW4YdrX10drayutra10dHTQ39+PyWSiqKiI8ePHM2bMGFwuFyaTSe39MdylasVEIhE8Hg/Nzc20tLTQ2trKwMAAiUSC9PR0cnJyyM/Pp6ioCIfDQUZGBlar9bzYB+KTSyQS+P1+vF4v/f396rXS09ODx+NBq9XidDoZN24chYWFjBo1ivT0dEwm0zF1f4QQQoijSFAjhBDiI4qiEAwGcbvdVFRUUFVVRVdXF0ajkZEjR1JcXMy4ceNwOp1qjRhpbJz/Ur1JfD4fjY2N1NXV0dTUhM/nw2azMW7cOKZMmUJ+fj52u13tPXKuUxSFUCiEx+Ohrq6OmpoampubicVipKWlUVBQwKhRoxg9ejQOhwOr1YrRaJRzXpxUMpkkEokQCARwu900NDTQ0tJCe3s7kUgEm81GcXExEyZMoKCgALvdrvY2FEIIIf5/EtQIIcSFKlVUs6Ojg8OHD3Pw4EE8Hg9paWlMmDCBiRMnMnr0aMxms4QyQpUaCpSqhVNZWcnBgwdpa2vDarUyYcIEZs2aRVFRkVrv5myfO6nvNMFgkLa2Ng4dOkR1dTUDAwOYzWZGjx7N2LFjGT9+PHa7fUi9GCE+r9S/tdFolL6+Purr66msrKSpqYl4PE5+fj6TJ09m4sSJ5OXlqTPQyfknhBAXLAlqhBDiQpJqMDQ3N7Nr1y4OHTqEXq9n8uTJzJw5k6KioiG1Fc6m9vZ2mpubueiii2hubmbkyJFYrdYTrh8Khfjwww8ZM2YMOTk5Z3BLT52mpiZqa2vVKY01Gg3jxo2jqKjorB+PE0l9X0gkEnR1dVFRUUFZWRm9vb0UFBQwf/58Jk+ejMViOaPvIVVnpLe3lwMHDrBv3z7cbjdOp5OpU6cyceJECgoKhvSSOdv7WFEUGhoa8Hq9TJkyhbq6OsaOHYvRaDzh+j6fj8OHDzN58mQyMjLO8BafGu3t7VRUVJCfn09xcTHl5eXE43Euvvhi0tLSPtFzKIpCfX092dnZ2O12kskkhw4dwmw2U1xcTG1tLePHjz8nenylrplkMkkgEKCpqYlDhw5x5MgRwuEwo0aNYvbs2UyaNIm0tLRz4t9jIYQQZ9QJ/9E/+59iQgghTplkMonH46G0tJSdO3diNpu59NJL+dnPfkZOTs452RDQaDQ88cQTPPjggxw4cAC73Y7X6yUtLQ273X7M+uvXryccDuNwOE4Y1CQSCRoaGhg3btzp3vzPpLy8nMzMTD744AOCwSBXXXUVBw4coKio6Gxv2gmlzhu9Xs/IkSPJz8/nqquuwu/3U11dzY4dO1izZg3FxcVcccUVFBcXn7YeWqnePv39/ezZs4edO3cSi8WYNGkSN9xwA0VFRWrYd66d7ymxWIwnnniCv/zlL5SVlVFQUEBHRwe5ubmYzeZj1l+2bBl5eXkUFBScMKiJRCJ0dHQwevTo07z1n43RaORPf/oT999/P1qtll27dtHT08PcuXM/8XMoikJFRQUzZszAbrej0Whob29nz5493H333ezbt4/Ro0fT1NREUVHRWQ1sUueeTqfDbrczbdo0pk6dCqAOxduzZw/r16/H6XRy6aWXMmPGDNLT08/Z81YIIcSZIUGNEEKcB5LJJK2trbz55ps0NDQwe/Zs7rrrLnJycs56EVhFUdi3bx+HDh1i0qRJNDQ0oNPpCAaDLF68mOzsbKxWK4FAQF0eeOABxo8fzw9/+MMhvWvcbjevv/46kyZNwmaz8eqrr+L3+/niF79IW1sb+/fvZ/To0SiKwt///neWLl3KwMAAN9xwA+vWreO6665j9+7d2O12EokEeXl5lJWVMWbMGBYsWDCkQPLAwABr1qyhqKiItrY2brzxRgA2btxIMplk0aJFbNy4EZPJhM/nIyMjg0gkwuzZs5kyZQrbt2+nqamJSy+9FK1WS3t7O1/4whfQaDRcc801mM1mGhoaCAQCLF68mGAwyObNm+ns7OSSSy5h7969RCIRIpEIZrMZRVEoLi5m4sSJvP766zgcDrRaLdddd91JeyCdLqlzymazcfHFFzNr1ix8Ph/79+/nlVdeUd/njBkzMJlMp+x1o9EoVVVVbN68md7eXqZPn853v/td8vLyzqnaMolEgu3bt1NfX8/FF1/Mvn37sNlsRCIRrr32WrKzszEajXi9XmKxGD09Pfzud7/jmmuu4etf//qQgKG1tZWNGzfypS99Cb1ez+rVq9FoNFx99dUcPHiQmpoapk2bRn19PZs2beLLX/4ysViMyy67jA0bNnDzzTezceNGNQg0Go1UVlYyY8YMZs6cOWSfdXZ2smHDBkaNGoXb7eamm27C4/Hw7rvvYrFYmDt3Lm+88YYaqGZmZhIOh1m8eDEjR47knXfeobe3l8WLF9Pd3Q3ARRddBEBmZiZ2u528vDxMJhN5eXkAdHR0UFpayvXXX8/69eu5/vrr2bVrF319fcTjcXWIWl5eHl/4whcYGBgAoKuri3feeYeGhgaSySS9vb0kk0mam5v5zW9+w80338zAwAAlJSXk5+dTVlbGLbfcMmRq+jMtta+dTidOp5PZs2cTCoVoaGhg27ZtvP7660ycOJHFixdTUFAwZFpwIYQQFw6ZskMIIYax1JCPZ599lmeeeYbJkyfz+9//niVLlpCfn39O1JyJxWIsW7aMadOmkZOTQ319PVarlcLCQh577DHi8TgAVquV2tpavF4vZrOZyy+//JieBZmZmRQXF3PllVeyZ88eamtrcTgcLFu2DIfDwWWXXcaLL76IxWKhsLCQuXPnsmXLFsxmM7W1tfT29tLX10dZWRk2m41HHnmEmTNn8sorr9DW1jbktSwWC3v27CEnJ4fOzk527dqFVqtl3rx5tLW1cfDgQQ4cOMDkyZOJx+MEAgGmTp3KqlWrqKqqYs2aNcycOZPHHnsMgPT09CHPPZhWq1XDh5KSEp5++mkaGxtxOByMHDmS6upqLrvsMl5++WUASktLmTdvHkeOHGHTpk2n/Jh9FhqNBrvdzhe+8AV+9atf8fWvf51t27bxxz/+kYqKCnWY12cVj8fZu3cv9913H2+99RZXXHEFv/vd7/ja1742ZCjfucLn87F69WouvvhiXC4X5eXljB49Gp1Ox/PPP68Oi8nIyGDv3r3AR+fFwoULj2mc5+XlMWrUKK666irWr19PLBbD7/fz6quvkpeXx+zZs3n++edJT09n0qRJTJo0iXfffZeMjAz27dtHNBqlrq5ODTSeeeYZ5s2bx1NPPYXX6x3yWlarla1bt1JSUsK+ffuoqanBaDSycOFCdu7cSWdnJ3v37mXBggW0trZis9nIyclh3bp17Nixg3/961+MGjWKp556CrPZfEyIGA6HWbZsGY8++ihvv/02AHa7nffeew+r1Up1dTV9fX14PB4AJk2aRHl5OZdffjlr164lHA7T1tZGQ0MDjz76KA6HgwULFqDT6XC5XGzbtg2TyUR6ejpXX301AwMDtLe3o9frsVgspzQ0PBU0Gg1Wq5UpU6bwgx/8gN/85jeMGTOGp59+mocffpiGhgY+pkyBEEKI85D0qBFCiGEqmUxSVlbGqlWruOqqq/jmN795zjVWAQwGAwsXLuTpp5/mmmuuQa/X43A4mDx5MsuXLyeRSAAfhRWp/zk3GAxYrdZjpgDX6XQYjUbS0tLYs2cPhw8fJhAIYDQa2bFjB01NTXR2dqLVajEajepzpJ4ztT1FRUWMGTOGpqYmNm7ciKIoRKPRIa+VekxmZia5ublqjYmNGzdSVlbGhAkTMBgMuFwu8vPziUaj6m1bWxv19fW8/fbbGAwG8vPzP3YYVktLC7W1tWzZsgWj0ajuJ7vdTnp6Onl5eWi1WhRFwWg0kpWVxcyZM6msrDyFR+vz02g0GAwGxo4dy9133011dTUrV65k/PjxfPWrX/3U56iiKPT09PDCCy+QSCS48847KS4uPieH8Q2WlpbG9OnTefzxx1m6dKl6Ll188cV88MEH6no6nU5d9Hr9cacA1+v16vlcV1dHMpnEarUyatQo3nvvPTo7O+nt7UWn02E2m7FYLGi1WvU5U8dk/PjxZGRk0NjYyBtvvIFer1eD0sHbYzQacblcuFwugsEglZWV7Ny5kyNHjhCLxdTzLy8vD7vdjs1mY//+/TQ2NnLkyBF1OveSkpJjrmGTycSdd95JQUEBr7zyCj09Peh0uiHXqUajQa/Xk5aWRn5+vjpdutFoVHvYRCIRurq6mDlzJl1dXepjUvvSYDCQnp7OLbfcwsMPP4zFYmH+/Pnn9Dmj1Wqx2WwsXLiQ+fPnU1ZWxt///nemTJnCkiVLZNYoIYS4gEhQI4QQw1AymeTdd9/lgw8+4Oc//zkjRow4Z7/AR6NRRo8ezU9/+lOWL19OVlYWfr+f8vJydShGPB4nGo0Si8WIRqNotVq8Xi+JRGJI74JkMqmuW1RURDKZ5Lvf/S6hUIjf/OY33HPPPVRVVaEoCuFwmEgkovY6CofDxONx4vE4sViM9PR0ioqKuPnmm8nPzz+mmGkymSSRSBCLxdTHbNq0iZKSEvR6PbFYTL1/8BKPx8nNzWXMmDF897vfxWw24/F4qK2tZdq0aepxSs2oFI1GSSQSFBQUMG7cOL7//e9jMpl44oknhjxn6jaRSJBIJOjv76e8vJxZs2aduYP5KaSClJKSEn7961+zevVqHn/8cX7yk5984qEniqJQXV3N008/zU033aQOTztXz/XBwuEwc+bMYeLEibz//vtqQeDKykomTZqknouRSEQ9vhqNhoGBAVwu15CAI3Xex+NxiouLyczM5IYbbqCzs5NHH32UH/7whzQ2NqLRaAgEAiQSCSKRCB6Ph2g0qp43sVgMp9PJ6NGjWbp0KXa7/WPP+0gkwsaNG7nppptwu93q86VuB5+jhYWFlJSU8KMf/QiTyURdXZ1aKBtQtyOZTKLT6dTXSb1u6jodfM4fff6n/gYfDburrKxUr6PUOvF4nGQyycDAAMXFxWi1WlpaWsjOzh4W504qWJs7dy7Tp09n/fr1/PnPf+auu+7C6XQOi/cghBDi89Hde++9J7v/pHcKIYQ48xRFoaqqitdff51f/vKXZGVlndNf3BVFYc+ePdTX1/OlL32J+vp6qqur0el03H777fT29qq9AcLhMCaTidmzZ3PkyBHGjh07ZKiCz+ejvr4ejUbD5ZdfTk9PDxUVFYwcORKHw0F7e7s6o0wymcRkMjFmzBgqKyvV6XBTDb3JkyczduxYdu7cSSAQYNSoUUNCIb/fT3t7O3a7nWAwiMlk4tJLL6W6upoRI0YQCoWwWq1YLBaCwSDwUQMrGo1y6aWXYrfb2bNnj1pfpqurS+0JAh/V20nNxlVUVERxcTHxeJyysjKMRiOBQACdTqc2Ti0WC4FAgMLCQt555x3C4TDFxcV88YtfPCdmuDmRVKNz6tSpNDY2cvDgQWbMmPGJztnOzk7+9re/8eMf/5iZM2cOm5AG/l2jpre3lxtvvJFdu3bR3d1Neno6t912Gw0NDfh8PrRaLZFIBIfDQUlJCY2NjYwbN27IMXW73bS1tWEymVi8eDE1NTXU1dWpAUgq3Jk+fTput5u8vDwcDgdNTU1qDahgMIhGo1GHIO7cuZNkMklBQcGQUKivr4+enh6cTicDAwM4HA5mzpxJVVUVRUVFBAIBLBYL6enpDAwMqNeUoihceeWVhMNhDhw4gMPhYGBggHA4TH5+PgB1dXX4/X7S0tLIycnh8OHD6PV6pkyZgsPhoKqqSq01FIvFSCaTaDQawuEwNpsNn8+Hw+HA4/FgMBj48pe/THl5ORqNRh3WNDAwoBZd7ujoYNy4cSQSCUpKShg1atSwOX/g39fOlClTUBSFdevWMW/evHP6ehdCCPGp/O+J7pDpuYUQYphJJBI88MAD3HjjjUyaNGlYNTwSiQS///3vmT17NjfeeONJC2X6/X5Wr16thiBarZbFixczadKkU75d8XictWvXqsVPAWbNmsWCBQtO+Wt9Xm63m//8z//kgQceYOTIkcPq+IfDYe69917uueeej51aPVUQetKkSSxcuHBYvc+jRaNRfvrTn/If//EfzJkz55jhQIP19PSwZs0adUiSwWDghhtuoKCg4JRvVygUYvXq1fh8PuCja2zhwoVMmzbtlL/W2eDxePjwww9pb2/n5ptvPitFt0+VRCLB008/zfTp08/5IVxCCCE+MZmeWwghzheJRAKv10thYeGw+7KeSCS45pprMBqNH1sgMy0tjW9+85tD1jtdM6DodDpuvfXWM/Jan5dGo+E73/nOMTV1hoPUTD9ut/tjg5p4PE5rayu33377sDvPjxaPx7n99ts/0ZAvl8vF9773vSF/O109KMxmM3fcccewOO8/C7PZjFar5corrzyrMz2dCjqdjlmzZlFRUcH8+fPP9uYIIYQ4zWTokxBCDDOKorBt2zZmzJiBzWY7K9uQTCYpLS0lFArhcrk+cUM6Ho9TU1NDSUkJdrv9uOuEw2Hef/99dRYXnU5HRUUF9fX1xx26oCgKXq+XxsZGsrKyhmzjjh072L59O5WVleTn52M0Gtm/fz8HDhwgKysLk8lET08P27dvJxaLkZubi6IolJeXc+TIEXUK5fb2drZv346iKDidTqLRKLt27aKuro68vDz0ej1NTU3s2LFDLQIcDocpLS2ltbWV3NxcdDodtbW17N69G6vVis1mIxAIsG3bNrq7u9WpiisqKigrK8Nut2O1WvF6vWzdupX+/n5yc3Mxm80EAgGam5txuVyYzWZ6e3vZunUrwWCQ7OxsEokE+/bto6KigpycHDQaDTt27ODAgQMkk8lPdcxOJUVR2LRpE3PnzsXhcJx0XY1Gw549eygsLDxr23u0lpYW/vWvfzF69OiT9oo5Wk1NDfF4nNmzZx/3canhjBUVFRQWFmIwGPD5fLz//vuMGDHiuCFDPB7n8OHDZGRkDAlyUsPnDh8+rJ6/XV1dbNu2jXg8jsvlIhaLsXv3bmpqahgxYgRms5mOjg527dqFTqcjMzOTSCTCjh07aGxsJC8vD51OR0NDAzt37sRkMmG32wmFQmzfvp2Ojg612HUsFuPw4cPqMfN4PGzfvp3u7m5yc3PRaDQcOnSI8vJyHA4HFosFj8fD1q1b8fl85Obmqtvb0NCA0+nEYDCwZcsWdu3aRXV1NUVFRer5UVVVRU5ODgaDgSNHjrBr1y7go+mvGxoaqKqqorGxEZPJhMvlOu7xSSQS7N27l7KyMsLhMNnZ2XR2dvLmm29y4MABtVD42ZQaQpqWlkZJSck5cT0IIYT43E449Emm5xZCiGFGp9OxYMEC/vnPf6pFNc+klpYWent7aWtr47333vtUj9Xr9ezevZuKioqTrlNXV0dpaan6N51Ox+rVq4+ZoQY+Ggr08MMPs2LFiiF/TyQSvP322+zbt48DBw4QCoXYt28fb7/9NolEgkcffZRAIMAjjzxCeno6K1asoLq6mvfff59du3bR39/Ps88+i9fr5ZFHHiEzM5OnnnqK1tZW3njjDSorK2lpaeHll1+mt7eXxx57jMzMTB599FF6e3tZtWoV7e3tHDhwgNdee4329naeeeYZ0tPT+etf/4rf7+e5557D7/ezbds23nvvPerq6njllVcwGo08+OCDhEIhnnjiCQBef/119u7dy4cffsgbb7yBoig8/PDDBINBHn30UaxWKytXrqSyspLt27ezdetW/H6/OgVzQ0MDhYWF3H///cdMRX4mKIpCWVkZWq32EzV6tVotV199Na+88oo6/O1sSYUPBoOBl1566bjn4ckkEglWrVp10l5kFouFlStXqj2lTCYT77777nGPVTweZ9OmTfzP//zPkOm1FUVh586dbNmyhWAwyBNPPIHf7+fhhx8mIyODZ599lsbGRjZv3kxZWRldXV0sX74cj8ejnuOPP/44nZ2drFu3jvr6empqalizZg3d3d088cQTZGRk8NBDD+H1elmxYgVut5s9e/bw1ltvEY1GWbduHb/73e/UQt4PPfQQ0WiUd999l/fee4/KykrWrl2LTqfjr3/9K6FQiMceewyDwcCaNWv48MMPOXToEOnp6fT19fHAAw8QCoV48803KSsr49ChQ0SjUd577z12796Nx+Phueeeo76+nkceeYTs7GweffRR2tvb2bNnD6Wlpezfvx+3233S41tRUUFRURGPPfYYlZWVVFVVsWXLFvbv309ra+unOt6nmqIotLa2snPnThn2JIQQFwgZ+iSEEMOMRqNh0aJFVFZW8o9//IPbbrsNo9F4Rl47GAxy//33M2LECCZOnEjb/8fefQfHdd2HHv9uBRbYxWIXvfdeSLADBEkREiWaz5QVFevJcZI3ihN54vKsTOyxnOd5bzJu44kiO7E1jy6xnNi0JFMSVSgzlMVOgCQAAkQlSIAA0bFLYBfbF1vu+0PvXgEssgo7z2dmZ0EQWNzde8655/zuOb8zMcEvf/lLSkpKKCws5JVXXqGkpISsrCz6+/txuVxs3bqVCxcu0NvbS11dHSaTib6+Ps6cOcOmTZsoLS1l3759jIyMUFhYyJYtW0hMTCQcDjM7O8sbb7yB3W5Xdsd57bXX2Lx5s7J0xmKxcP/99/P2229fdryFhYWsXr1a2WL7pZdeoq6ujs2bN/Piiy9y9uxZZmdnWbt2LWNjYxw+fJiJiQm2bdtGRUUFX/7yl2loaCAUCrF27Vra29s5fvw4zc3NPPXUU1gsFp555hmKioqIi4tj7dq17N+/n46ODo4fP863v/1tgsEgzz77LAaDgfT0dBoaGnj99dfp7e2ls7OTP//zPycnJ4edO3dSV1dHcXExGzdu5KWXXmJgYIDBwUG+8pWvoNFo2LdvHxaLhWXLltHU1MTvfvc7zp49y/T0NGvXrsVms3Hw4EEcDgf33HMPdXV1vPrqq2g0Gp544gnUajUxMTE3fNmUJEmcPn2aF198kaefflrZKv1PWb58OSMjI/zoRz/ib/7mb27arj379u3j3//93/nqV7+KJEn84Q9/wG6389hjj/HGG29gtVqJRqOkpKTQ0dFBcXExVVVV7N27F61WS319PV6vl5dffhmVSsWjjz7K0NAQBw4cQKvVsn37dmVmTCQS4Y9//CMDAwOMjo4C0NXVhc1m47777gPeC1xu2rSJXbt2LTlOSZI4duwYfP1G0QAAIABJREFU69atY82aNezatYuBgQE8Hg9r1qxhYGBAmWH2+OOPk5uby9NPP60kal67di3Nzc20tbVx7Ngx/v7v/x69Xs8//dM/kZaWhsViob6+nr1799Ld3U1rayvf+c53cDgc/N//+3/Ztm0b9913H3/4wx+U47Hb7VRUVDA7O8vMzAyTk5NUVFRwzz33KHXwwoUL/MM//AOBQIB3332X//k//ydarZa+vj6OHj0KQFVVFQ0NDVgsFkwmE0ePHuUzn/kMJSUlfOUrX6GgoIDU1FRWr17N0aNH6ezsxGQy8ed//udkZmaSmZlJKBTi7bffZmJigg0bNlBdXY1KpSImJobPfe5zACQkJChJzR955BGKioqU2W43gyRJDAwM8O///u888cQTV50VJAiCINxZxIwaQRCE25Ber+epp57C4/Hw7LPPMjk5iSRJfzLvyydlMBiwWCxs27YNo9FIUlISGzduZNeuXRiNRk6dOkV6ejpdXV2MjY1RWVnJ888/z86dO1m3bh25ubkA5ObmUlFRwVtvvUVXVxf79+/n0UcfZe/evcpsG0mS2LlzJ1qtlj/7sz/DYDCgUqkoLS1dshREq9Uu2RlKptFoKCws5Ny5czz77LNKACMuLg69Xk84HMZut6PT6VCpVJhMJmw2Gy6XC4PBQGxsLH6/H7vdrry+0WjEZrPh9XqJiYkhLi4Ot9uN3W5Xjs9gMGCz2QgGg+j1euLj43E6nVy8eJH4+HjUajV6vR6bzUYkEkGr1SqzB2ZnZ4mLi0Oj0aBWq5XkxhqNhoSEBC5evKi8B51ORyQSwW63o9Vqlfdgt9uZn59XdsEJBoPK3zl37hwlJSXXJTHtlUiShN/vZ9euXbz88st89atf/UhLSNRqNQ899BCNjY388Ic/ZP/+/SwsLFz3cn6p9PR0Kisrqa2tRavV0tjYyLlz5xgfHycQCDA2NkZ2djY/+9nP2LJlC6+++io7d+4kEomwfv16NBoNsbGxNDU1sX//fmZnZ/nxj39MfX09ycnJ7NixQ3lPo6Oj/O53v+Ozn/0sJSUlACQnJyt1B94L1sr5VxaLRqM4nU7l3C8sLGC329Hr9ahUKoxGI3a7HbfbTWxsrLJj2eLyGxcXh81mw+/3ExMTQ3x8PC6Xi4sXLxIXF6cENmZmZlhYWECn02E0GnE4HMpxycE0tVrNunXr+NGPfsQ777zD6tWrlTKu1WqRJAmbzYZarUatVmMymZidnVXK9okTJ3j00UeJjY0lJyeHM2fO8J3vfIf29nZcLhexsbFKPc3Pz8dut3PgwAHGx8dxuVwUFxdjs9nYtWsXP/vZz+ju7ubAgQNs3ryZHTt2EAwGiUajwHsJn3/0ox8RiUTIz88nJycHr9fLO++8ww9+8AMCgcCNKGoASlvucrn47W9/y69//Wv+5m/+huXLl4vZNIIgCHcJMaNGEAThNiQHBL7whS/Q2trKj370I2pra9m2bRsWi+W6deZVKhUajUaZEWEwGJRcI5IkERMTQ0VFBW1tbfT09BCNRsnIyGDDhg38/Oc/p76+HkmSMBqNWCwWQqEQ4+PjZGVlkZqaSmZmJtPT08B7g87x8XEaGxuJi4tDrVaj0Wioq6v70Me6fv161Go1CQkJ/PGPf8RqtRIIBAiFQmg0GqxWq7K1sNfrxWq1EgwGCQQCLCwsEBMTo3wP3ptRlJSUhMFgYGFhgUAgQFxcHFarFb/fjyRJBAIBkpKSlG2LFxYWMJlMWCwWzp8/TzQaJRQKkZSUhFqtJhwO4/P5MJvNJCYm4vf7iUQiSi4ZSZKIRqN4vV4sFgtmsxm/3084HEatVmO1WgmHw8p7sFgsqNVq5T3o9Xo0Gg3nz5+nvb2dp5566rrPwJIkiWAwSHt7O2+++Sbl5eV861vfIj4+/iOXTXknooqKCl599VX27dvH1q1bWbNmjRI4uN7ksic/jEYjCQkJBINBNBoN6enppKenMz4+zmuvvYbJZGLDhg289dZbPP/883z+859Hr9djMpmIjY3F5XIpW63Hx8ezZ88e5W9NTU2RlZWF1WpVztOHDW7JwQ753Ot0OiWnkiRJ+Hw+rFYrMzMzBINBgsEgsbGxSr2QA2vp6elKoEelUhEfH4/FYmF4eBhJklhYWFDKuFx+r5Rzyuv10tLSwv/5P/+HM2fOsGvXLmVr+0gkAryXPDkajSplPDExkYWFBQ4cOEBhYaGy41dTU5NSrpubmzEajQSDQaWe5ufn84//+I9MTU2h1+tJSUmhrKyMiooKZae03NxchoaG2LNnD1arld27dzM1NUVjYyMrVqzga1/7Gr/97W/5/e9/z9/+7d+Sk5NDKBTi85//PPPz88TGxl6D0vTB5GDbgQMHOHHiBKtXr+bb3/62EkgTBEEQ7g4iUCMIgnAb02g0rF27lsrKSt59911+8IMfUF5ezr333ktmZqYy0+Ja0mq1zMzM4PV6CQQCBAIBJbgRDAbx+/0UFRUxPT3NX/7lX6LT6ejp6eGv//qvefHFF5VghPw7BQUFHDhwgHPnzjExMcHDDz/MxMQE4XCYrKwsWltbqaqqwuPxsLCwwJEjR1ixYgUWiwV4L7+Ey+XC7/fj9XpRqVQcPnyY9evXs2fPHhobG+no6GDZsmVkZGRw8uRJDAYDOTk5lJaWYjQa6e/vp6Ojg8997nNcuHCBU6dOMTc3R2VlJWVlZUiSRH9/P+fOneMrX/kKoVCI1tZWDAYDq1evpqqqijfffJP+/n6mp6epq6tjcHCQ9vZ25ubmaGxsZNmyZRw9epSenh6CwSBVVVWUlZXR2dlJX18fmzdvpqioiJdeeonOzk4MBgPl5eVkZ2fT3d3NsWPH2Lx5M3q9nkOHDmGxWMjIyKC0tJTExET6+vpoa2vjscceY2Zmho6ODvx+P2VlZXg8Hr797W9TVVXFCy+8wPbt2yksLLym5QLeH2SeOHGCI0eOkJGRwd/93d+RnZ39kZLvXkqlUpGamsrf/u3fMj4+zt69e9m7dy81NTU0NjaSmZmpzIy6HuTkvnNzc0vKulz+/X4/CQkJFBQUsHXrVjIzM7lw4QKf+9zn2LlzJxcuXFACI8FgEJ1OR1JSEj09PYyMjFBbW6u8ptVqZXR0lKGhIaanpwkGgwwODuJ0Olm1ahXwXiDM7XYTCARwuVwkJyfT3t5OcnIya9asobOzk0gkQnFxMWVlZej1es6cOUN3dzdf+MIXMJlMtLW1MTo6yrJly6ioqOCVV17hzJkzjI6O8thjjzE7O0tbWxuRSIT6+npqamrYt28fvb29OJ1Oamtrqa6u5tSpU4yNjbFp0yYA3G43wWAQl8ulBG9sNhvT09MYDAZWrVrFW2+9RXt7OxaLhbKyMtLS0ujt7aWlpYWtW7fyyiuv8Mc//pHKykomJia4//77OXToEOvWraOvr48NGzbg8/lob2/HZrNRU1ODJEnKbJ1wOExdXR27d+9m5cqV9Pf3U1JSQn5+Pnl5eTz++OMYjUYSExNRqVS43W527drFhg0bmJ2dJTU1lf3795OVlYXH4yElJQWj0XhdypZ8Pv1+P4ODgxw6dIjx8XHWrFnDN7/5zesaeBcEQRBuXWLXJ0EQhNucvBShvLyc+vp6/H4/u3fv5ujRowQCAUwmEwaD4RMNlBdLT09neHiY2NhYZWaBSqUiKSlJ2fGorq5O2eEpOTkZu93O+Pg4DzzwgHKHXqfTodfrqa+vJzU1ld7eXiVYMT4+TmxsLNu2bcNutxMOh8nMzCQvL4+ZmZklO+HMzc3R29tLQkKCsrPL6OgoBQUFzM7O0tvbS3FxMffffz+5ublEIhFsNhuPP/44VquV8vJyOjs7Wbt2LXV1dRQUFCgzHh599FGsVivFxcV0dnbS1NREeXk5paWlysyfhx56CKvVSm5uLt3d3XzqU5+ioKCAyspKhoeHSUhIYNu2bSQlJZGSksLAwAAPPfQQmZmZVFVVMTAwQGZmJvfeey+pqanEx8czPDzMY489RkpKClVVVfT09FBWVqYEJVQqFdPT03z2s58lKSmJiooKTp8+zcqVK1m5ciV5eXn4fD7m5+d57LHH0Ol0zM/Po9FoUKlUlJWVKYGuT0oOGvT09LBr1y727t2L2Wzms5/9LE1NTcpg+FpQqVSYzWbq6upYvXo1brebPXv2sH//fux2u7IU5loHKI1GI4FAAI/Hg8ViISkpiUgkgtlsRqfTodVqKS0tVc5DKBTCbDbT0dFBdXU1WVlZaDQakpOTkSSJrKwsNm3aRF9fH0ajkc985jNMTEygVqspLy+nrKyMc+fOUVxcTGJiIrGxsQQCAWVmjbxLkcFgIBQKkZ2djc1mIz4+Xgn6OBwOHnvsMaxWK2VlZXR0dNDY2EhNTQ2FhYXMzc2xsLDAI488gtVqpbCwkNOnT7NlyxaKi4spLy9nfHwcvV7P9u3bSUpKIjMzk76+Pj796U+Tk5NDVVUVQ0NDJCcn88ADDxCNRmltbSUhIYFwOEx+fr5SfmNiYnj44YfJyclBr9czNjamlN/Kykq6u7upqalh3bp1uFwuwuGw0lZUVVUxOTnJwMAAK1asoLGxkcLCQpxOJz6fj0cffRStVsvx48ex2Ww88sgjpKens7CwoCSwlnPymM1menp6iI+PV3IeqdVqRkdHOXv2LEVFRWzduhWtVktHRwcul4vPfe5zWK3Wa1qm5KVNAwMDvPHGG+zevRubzUZDQwOPPfYYtbW1N2zGmCAIgnDTXHXXJ9WfWOd9YxeBC4IgCJ+YJElEIhEmJiY4duwY3d3dxMbGsnz5cpYtW0ZWVpayna8YBAgfhdxnkCSJubk5zpw5Q2trK1NTU2RnZ9PY2EhFRcWSPCXX+3gikQizs7N0dHTQ1taG2+0mKyuL5cuXU1JSQmpqqhKkFOVduBnkehMKhZiYmKC/v5+uri7m5ubIyMhg7dq1VFdXYzQaUalUopwKgiDcPa7a4ItAjSAIwh1MHsiOj4/T0dFBd3c3Xq+X9PR0ampqlGUH8gwEMUAQFpP7CJFIBJfLxeDgID09PQwPDxOJRCgqKmL16tUUFxcrM5xuVhmSj9Xj8TA4OEhnZyfDw8OEQiFSU1OpqKigsLCQ7OxsYmJiUKvVorwL19ziOuP1erlw4QLnzp3j7NmzOJ1OTCYT5eXl1NbWkpeXp+QhEmVREAThriQCNYIgCML7OUQmJibo7u5meHgYr9dLUlISeXl5FBYWkp+fT1xcHDExMWLwcJeRJIlwOKwsnTl//jxnz55lZGQESZKUpSyFhYWkpKRclxxI11IkEsHhcDA9Pc2ZM2cYHh7G6XQSExNDZmYm2dnZ5Ofnk56eTmxsLHq9/potERTuDpFIRMlXNDU1xejoKOfPn2d8fFxZ5lZUVERpaSkpKSnKUlFBEARBQARqBEEQhCuR7/pOTU1x7tw5zp8/z8zMDNFolNTUVHJycpSBrMlkwmg0ipkIdwh5FyCPx8Pc3BwjIyOMjY0xPj5OKBQiMTGRgoICCgsLycvLIyEhQdnm+XYVjUaVRLcjIyNcuHCB0dFRHA4HkiQpeViysrLIyspSyvyVtsIW7h7yzms+nw+v14vT6WR0dJTJyUkmJydxu91otVrS09PJzc0lLy+PzMxMjEajCHgLgiAIH0QEagRBEIQ/LRqNEolECAQCjI+PMzo6ysjICNPT0/h8PmJiYkhOTiYtLY2cnBySkpJITEwkMTHxsi2fxeDk5rj0ui4n+3U6nTgcDsbGxhgbG8Nut+P1eomJicFisZCfn68E5hISEtBqtXd8UE6SJGUWUTAYZGpqiomJCeXhdrsJh8PK9tTyFvLyNukmkwmTyXTZZ3Qnf2Z3kkvrSjQaxePx4Ha7mZ+fx2azMTY2hs1mw+FwEAqFMBgMmM1mcnNzycjIICcnh+TkZCWpNIjzLwiCIHxoIlAjCIIgfHSLrxHysim73c7k5CQjIyNcvHgRl8tFKBQiNjYWk8lEUlISaWlpSgDHarWSmJiIRqNRBv7yQEYMaD6axcl85Yc8S2Rubg6Hw4HT6WR6epqpqSlcLhc+nw+NRkNCQoKypXdmZiapqamkpqYumSUjzsd7Fn/O4XAYh8OB3W5nZmaGsbExLl68qGyPrdFoiIuLw2QyYbVaSU5OJiEhgYSEBMxmMxaLRUmuvPgB4vO+Xj6onszPzysPeVncxYsX8Xg8+P1+dDodJpMJs9lMUlISGRkZJCcnk5ycjNVqVXZOA3H+BEEQhE9MBGoEQRCE60OSJPx+v3In2m63MzU1xdzcHE6nE5fLpQxo5TvOiYmJZGZmYjablwxo4+LiUKvVSlBn8fOdmux48UAyEokseQ4GgzgcDhwOBy6Xa8kA0+PxEA6HCYfD6PV6jEYjiYmJWCwWZdZTQkICRqMRo9G4ZIApfHJyom6v14vH48Hj8WCz2bDb7cr5crvd+Hw+ALRaLVqtlri4OBITEzGbzRiNRmVWjhzc0el0Srlf/LiT68AHWVw/FtcN+REIBJR2Rp4JI88W8/v9BINBwuEwkUgEnU5HfHz8kkBaamoqFosFo9FIXFycqCuCIAjCjSQCNYIgCMKNJw+w3G43g4ODvPvuu3R3d1NVVcW6devwer3Mz8/jdDpxOp243W4ikYjyuyqVitjYWGVwZTAYiI2NJSYmRnmOi4sjPj6euLg4JRnsBz2uNKvhSl/De7OIrvb1pQPIKz3C4TB+vx+v14vX6yUQCCiJR+Vnv99PKBTC5/MRiUQuOx45T4o8mE9MTCQpKQmz2Yxer1ceOp1ODC5vIXL5CIVCLCwsEAqFCIVC+P1+JeAmz3iSAw0ul4uFhQXgvXMfDoeVOmAwGIiLi1MSfcfExKDX65WvY2NjiYuLU+rI4hlsl5b/xf8GluTfubQOXJqbZ3E9kN/j4v+7Wr249HuRSAS/34/P58Pv9yt1YmFhgWAwqDzk+uH3+5XPZvGSPI1Go9QRuQ04deoU58+fp6KigqamJoqLizGbzeh0OlFPBEEQhFuJCNQIgiAIN448WLPZbLS0tNDW1kZMTAwbNmxgxYoVmEwmZQAoX4euNKiLRCL4fD7cbveSO+SLAx1erxefz4fP5yMUCl0xYLJ4kCh//UHBmbm5OVJSUi5bQrHYhxkEazQaDAYD8fHxxMfHXxZkkgff8qwXnU53xde6G2dS3E0WBzcufY5Go0qwTw5qLA5kyI9AILAk6PFBZf/S50tdGpyx2+2kpqYqxyq79HcXB0EvLceXfk+uG4uDS5cGoOTgkxyMlRPzXq1+qFSqJcHhrq4uWlpacDgclJSU0NDQQGFhodgSWxAEQbhViECNIAiCcH3Jg02v18upU6c4cuQIPp+PlStX0tDQQFpa2jUPOFzpGna169rVvh+JRJT/i0QiTE9P88tf/pJnnnlmyfbTV1sO8UHv56P+vCB8HB+lHvyp/1tcH+T6/L3vfY9vfetbxMbGKssXgSWzci71p8r5jagbcpvkdrs5ffo0zc3NOBwOSktLaWhooKioSMywEQRBEG6mq16AtFf7D0EQBEH4MOQcNWfPnuXIkSNMTExQWlrK448/Tn5+Pjqd7rr97Wsx2NNoNEv+HRcXh06nU/LlCMKt7loGPS6tD9FoFL1er8wIu53IgWGz2czGjRvZsGEDDoeDnp4e3nzzTebm5pSgTV5enpL0WRAEQRBuNhGoEQRBED4yOffGxMQER44cobe3l/T0dDZu3EhZWRnx8fG37YBHXj4hCML7uaLuBCqVCqvVysaNG2lsbMTpdCpBm9nZWcrKyqivryc3NxeDwXDHvG9BEATh9iMCNYIgCMKHJkkSdrudkydPcvLkSVQqFY2NjWzfvp2EhITL7sbfjkSgRhAud6cFLdRqNVarlQ0bNrB+/XpledTrr7++ZHlUQUHBki3sBUEQBOFGEIEaQRAE4QNJkoTH4+H06dMcPXoUl8tFXV0dX/ziF0lLS1uyA4sgCHeWOz1oKSc2TkxMVJZHuVwuTp8+ze7du6+Y00b+PUEQBEG4XkSgRhAEQbiMJEkEg0EGBwc5dOgQIyMjFBcX89BDD1FYWHhHJ+AUM2oE4X130tKnP0XOaSMHbTZu3IjD4aC7u1vJaSMvj8rPz1d2oRIEQRCEa00EagRBEARFKBRiamqKY8eO0dXVpSwN+Mu//EuMRuNdMSgRgRpBeJ9cF+6Gur+Y/H6tViubNm1akoj4jTfewOFwLMlpIxIRC4IgCNeSCNQIgiDc5eS8M+3t7Zw8eRJJkqivr+frX/86ZrP5rlvadDe9V0H4U+6mGTUfRK1Wk5SUpCQidrlcdHV1Kcuj5KCNyGkjCIIgXAsiUCMIgnCXke+Q+3w+urq6OHjwIE6nk+XLl/Pkk0+SmZl51wVnLiVm1AjCUndze7CYnNPGYrEsyWnT2dnJa6+9xvz8/JKcNlqtVvk9QRAEQfiwRKBGEAThLiFJEgsLC5w/f55Dhw4xNDREQUEBDz74ICUlJSJJ5v8nlj4JwvvEjJqrW5zTZtOmTWzatAmHw6HMtHE6nZSXl1NfX09eXp7IaSMIgiB8aCJQIwiCcAeTJIlwOMz09DQtLS2cPn2axMREGhsbeeKJJ0hISBADh0uIQI0gvE/UhQ9ncU6be+65Z0kiYnl5VEVFBfX19eTk5IicNoIgCMIHEoEaQRCEO5AkSczOztLe3k5LSwuRSIT6+nqefvppLBYLGo3mZh+iIAi3gbs1mfAnJee0kRMRz8/P09XVxauvvsr8/PyS3aNEThtBEAThUiJQIwiCcAeQB1OBQIDu7m4OHTqE3W6npqaG//E//gfZ2dlKcEaSJIaGhhgZGWHjxo2XBW3k6fx3K5VKRTQa5cCBA2RnZ1NcXHzX5+wR7l6Llz5JkoTL5aKjo4Pi4mIyMzOX/Ozd3nZciZzTRt49auPGjczPz3P69OklQZuGhgYKCws/dk6bUCjE/v37qaioIDs7W5wLQRCE25wI1AiCINzGJEkiFAoxPDzMkSNHOHfuHLm5uWzdupXS0lL0ej3wfvDh3LlzyvKnlStXMjs7y0svvUQ0GgUgLy+Phx566K7u4Mvvva6ujs7OTjo7O6murqa8vFzMRBLuOnKgZn5+nvb2dpxOJ8uWLSMtLY1du3YxMTEBgE6n49FHHyU9Pf0mH/GtSw6eWCyWy3LayEEbOafNR51po9VqWb58Oe3t7bS2tlJXV0d+fj5qtfo6vytBEAThelD9ibXHYmGyIAjCLUaSJCKRCDabjebmZtrb2zGbzTQ2NlJdXY3ZbL5i597tdrNjxw7q6uqor6/HYDAQDodxOBzKjBy9Xk9iYuJdHahxuVx897vf5fvf/z7BYJC2tjZaWlp48sknSU5OvtmHJwg31PT0ND//+c9Zvnw5ExMTfOYznyEtLQ2VSoXT6WRhYQFACUDIScmFjyYajTI3N0d3dzcnTpxgfn6eiooK1q1b96Fz2kiShMfj4fDhwwwNDfGFL3yBuLi4G/QOBEEQhI/hqg27CNQIgiDcJiRJwuFw0NnZybFjxwgEAqxZs4Z169aRlJSkTJn/oN8PBAL09fVx9uxZSkpKyMvLY9++fUqgJiMjg82bN9/Vd2FdLhff+c53eOKJJzhz5gyFhYXU1NRgMBju6gCWcHcaHx/nP/7jP/j617+OzWajtbUVtVrNpk2bOHHiBDabDXhvRseWLVtISkq6yUd8+4tEIsryKDloI8+0yc3NveLuUZFIhJaWFsbHx6msrKSsrEzkvhEEQbj1iUCNIAjC7UgOrvT393Po0CGmp6eprKxkw4YN5OTkoNVqP3JHXF4uNTExQW5urljOcwm3280//dM/8cUvfpHs7Gwx2BHuaiMjI7z88sv8wz/8g7Ijmt1uR6/XY7FYbvbh3dEkSSIajSp5gY4fP47L5aK8vJyGhgYKCgqUAH00GmV0dJTs7OyPdV0QBEEQboqrNtYiR40gCMItRg6kjI6OcujQIc6cOUN2djabN2+moqLiEwcOVCoVer2egoKCa3jUdw75sy0oKLirZxYJAkA4HF6S4FalUpGWlnaTj+ruICcitlgsbN68mc2bN+NwODh9+jS7du3C5XIpW37n5+eTn58vAjSCIAh3CBGoEQRBuEWEw2EuXrzI8ePHaW1txWAwsH79eh5++OG7Pm/MjbR4hxtBuNuFw2E0Go1of24y+fO3Wq1s3ryZTZs2MTc3tyQRcWVlJevWrSM7O/tD5bQRBEEQbl0iUCMIgnATSZKk5CJoaWnB4/FQV1fHl7/8ZVJSUsQA6SYSgRpBeD9QI9xa1Go1ycnJNDU1sWnTJpxOJ6dPn+aVV15REhHX19eTk5NzxZw2giAIwq1NBGoEQRBuIHnwHwwGOXPmDAcPHmRqaoqysjIeffRR8vLyRH6Bm0xe3iECNYLwXpLaP5WoXLi5NBoNSUlJykwbOafN73//e9xut5KIeHFOG3GNEQRBuLWJK68gCMINIG+pPTY2xqFDh+jr6yMtLY177rmHqqoqYmJiANF5vhXI5yAajd7kIxGEm0/MqLl9XC2nTWdnJ7t27cLtditbfufn56PT6cQ1RxAE4RYlAjWCIAjXUSQSYXZ2lpMnT3Ly5El0Oh3r1q1j+/btWK1W0Um+RYkZNYLwnkgkIpZg3oYW57RpamrinnvuYXZ2lq6uLl555RVcLhdVVVVKThuxPEoQBOHWIrbnFgRBuMbkvDPd3d00Nzfjcrmoq6ujoaGB1NRUsYzgFhWJRGhtbWVubo6dO3fy6KOPYjKZaGxsVGY8CcLdYnJykpMnTzIyMsLMzAz33XcfhYWFYre4O0AkElF2jzp58iROp5OqqioaGhpE0EYQBOHGumpjKwI1giAIn5Dcji4sLHD27FkOHjzI6OgoJSUlbNy4kcLCQjHF/DYQDod58skneemll4hGo2g0GjZu3Mhrr71GfHz8zT48Qbihurq62LJlC06nk2g0ilar5YV2FcKXAAAgAElEQVQXXuDxxx+/2YcmXCOSJBGNRnE4HLS1tdHS0oLf76eyspL169eTn58vctoIgiBcXyJQIwiCcK3JeWcmJyc5cuQI3d3dWCwW7rnnHmpqajAYDIDo4N4uJEni5Zdf5q/+6q8IBoOo1Wq+973v8Y1vfEOcQ+Gu4/f7eeCBBzhy5AgABQUFHDlyhKysrJt8ZML1IEkSkiQxOztLe3s7LS0teL1eampqaGhoUII2oi0UBEG4pkSgRhAE4VpYfAdSzjujVqtZt24dK1asICkpSXRkb2PT09Ns2LCBwcFBzGYzhw4dYtmyZTf7sAThhpMkiR/+8Id885vfBOCpp57i+eefR61W3+QjE26EaDSKzWajo6ODkydP4na7Wb58OevWrSMnJwe9Xi+udYIgCJ/cVRtSkShBEARhEUmS8Pl8aLXaJXlJJEnC7XbT29vLkSNHmJubY/ny5fz1X/816enp6HS6m3jUwrWSnJzMli1bGBwcZOXKlZSUlNzsQxKEm+bee+/FYrGwsLDAww8/LAbmdxG1Wk16ejqf+tSn2LJlC7Ozs5w6dYqdO3cSDAapqalh7dq1ZGZmXjFoI98IFmVGEATh4xEzagRBEP4/SZK4cOECzzzzDJ///OfZtm0boVCIwcFBDh8+zNDQEAUFBdxzzz0UFhaKhIt3IEmSeOedd3jooYf47ne/y9e+9jVxjoW7ls/nY8uWLQQCAfbv34/ZbL7ZhyTcRPJyX4fDQXt7OydPniQYDFJdXa3MtJG3co9Go7z44ovce++9pKWliXZUEAThysSMGkEQPhw5eHulZ0mSCIfDBINBQqEQCwsLhEKhJY9IJEIkElF+/tKv1Wq1MnV+8Xp3rVaLWq1Gp9Nd9tDr9cpD/nmVSrXk68XPH0c0GqWtrY2/+7u/49SpU0SjUVwuF93d3ZjNZjZt2sRf/MVfEBcXJzqcN8HVyuXir6PRqFImw+Gw8nylh1weZZFIhGg0CkAwGCQ3Nxej0Uhzc7PyM4vLq0qlQqPRoFar0Wq1V33odDpiYmKWbG/8QeVWlC3ho/qgNhveK9vBYHBJey3XD/nraDRKOBxWfk9ut6PRKEVFRUiSRHd395JyDyjlWqfTKeV98UOr1RIbG3tZ3bmWbbdw46hUKrRaLSkpKTzwwAPcf//9XLx4kVOnTvEf//EfykybdevWEYlEeOaZZ8jPz+e5556jrq5uybn/JK7WP5G/DoVCSj9F7qssLvN/qp8il2u5vC/+WqPRXLWfEhMTo5T1D9PeC4IgfBAxo0YQ7hKLOyGRSIRwOMzCwgLz8/PMz8/jdruVh9PpxOl0srCwoPy8/JA7apd2WOROuvz9SzvycicnGo0qA+JLO0fycV0p8CMPJuTXXvz3ExISSExMxGQykZCQoDwnJiZiMBiUn720AyULhUK89tprfOMb3+DChQsAJCYm8txzz/HpT39a5J25DuRBYDQaXVK+/H4/brcbj8eD1+vF5/Ph9Xrxer1K+QwGg0pgZfGzJEnKuV5cTq4UQFncAQeWBBBVKhWTk5NkZWUpZRVQBrLwftmVy2Y4HCYSiSwps/JxyYEh+Zjk45KDPHFxcSQkJGA0GomPjyc+Pp64uDji4+MxmUwYjUb0ev2Scq9Wq6/ZoEe4Ncl1RC5bkUgEn8+3pL32eDxKm+1yua7YZkuSpJT7xe3m4sCKXLYWB87hvbrgcrlQq9XKzmfyawJK2b408LP4mEOh0GV1U66XRqNRabvlsm4ymTCbzRiNxiXXFLH70K1Nzmlz6tQp2traaGtrY8+ePUSjUfLy8vjud7/LY489hl6vv+prLO4PyGUoEAhc1k9xuVxLyvyl1xG5bV1cdhYHVeTvX6m8azSay/om8vuT6+Ol5X3xdeBK1yG9Xk9iYiJms1npoyzup8jBfPmYRdsuCHcVkUxYEO4Gcsfe7/cTCATweDxMTk5is9mYm5vD4XAwPz+vDDg1Go3SYbi04yB3HuSOw6XP8oDzenYmFg/mFw9YFj+HQiHm5+dxOBy4XK4lASe3283CwgLw3kDcZDJhsViwWCykpKSQlZWFwWBg586dfP/738fpdCp/W6PR8Itf/IK/+qu/Eh2mj0ju3AaDQeXhdruZmZlhbm4Ol8ulPLxerxIMUalUxMTEXBaskJ8TEhJISEhQOrWXBmQW/1sumx838Wk0Gv3YnWW5g7+47F46eF4cxPF6vUrZXRycWvy8eHCs1+uVupqQkIDZbCYlJYWUlBQMBgMxMTHExMQoA3Dh1iSXkUAgQCAQwOfzYbPZmJ6exuFwKAFzn88HvF8/5GDG4meLxYLZbEav1y8ZoF6tzf4o5frj1IUrBWIXB2/kQe3iIJMcdJKffT6f0jbodDrMZrNybUpJSSEzMxOTyURsbCyxsbHodDrRVt8iAoEAjzzyCG+//bbyPZPJxJe+9CW+/vWvYzKZCAQCSmB+YmICu92Ow+FQruVygESn012xn7K4zF+pj3Iz+imXlnX5ORgMKvX5Sv2UxX0ys9ms9FNSU1OVcm4wGIiNjV0SYBIE4Y4gAjWCcKeJRqO43W4cDgczMzOcP39e6eAHg0H0ej3x8fFkZGSQlpaGxWIhKSkJq9V62UD3Trt7s3igvPiursPhYG5ujrm5Oex2O5OTk5w9e5aenh7m5uYIhULo9XosFgsZGRncf//9PPnkk8rdNuF98l10OeDidDoZGxtjZmYGh8OhBMm0Wq0SWEhLS8NqtSoBhsTERBISEi4bSIrZIu+5NNgjl2e/3690+OXP/uLFi9jtdgKBAAsLC6hUKgwGA2azmeTkZLKyskhLS1Pu6MbFxYkgzg0k1xW5vR4eHsZmsynttU6nIzY2ltTUVNLT05WBWlJSEkajcUkdkXOA3Kn140oD30AgoLTdTqcTu93O1NSUMsNOo9GQkJBAcnIyOTk55OTkYLValRsOd+pndauamJjg4YcfZnJyEr/fj9frJRAIEBsby9atWykvLycajRITE4PJZCIzM5PU1FSlzFssFiUAc6fOILxSPyUYDCrlfG5uDpvNxuTkJF6vV+nXJSYmkpaWRmFhIenp6co1VbTngnDbEoEaQbgdLc694Xa7GRsb4/z58wwODjI7O0skEiEhIUG5aGdkZJCcnExiYuJlHZs7qYNzLSz+bCVJwuPxcPHiRaanpxkeHmZiYoL5+XkkScJsNpOfn09JSQk5OTkkJSXdkR3HxRZ/PuFwmNnZWaanpxkfH2dkZIS5uTn8fv+SmUo5OTlKMEZeznBpIPBO/bxupkvzNEiStOQO7uzsLOPj40xPT+NyufD7/cpsuvT0dKXDn5aWhslkUs6XOFcf3uJzEA6HmZmZYXx8nMHBQcbGxnC5XKhUKsxmM2lpaRQUFJCWlkZSUhKJiYminnxEi3OSRKNRvF4vs7Oz2O12xsbGGBsbw+FwEAgEiIuLIyUlhbKyMnJycpSZlIuXOgof3+KAgxyw7+3tpbm5GZfLRVxcHOnp6eTn55Ofn09ycjIZGRlYLJYlbY04D5db3KbLefPkfsr58+eZmppS2haLxUJRURHFxcXk5ORgNptFGReE24MI1AjC7UJOiDo7O0t/fz99fX1MT08DkJqaSmFhISUlJUpAZvFsD3Ex/uQuTVIrd4xGRkYYHBxkcnKShYUFLBYLlZWVlJeXk5mZeVnC2NuNPMAMBAJMTEwwPDzM4OAgdrudYDCoDHays7PJz8/HarViNpuJj48XyXBvcZde50OhEC6Xi/n5eaXDLy9LkyQJo9FIfn4+hYWF5OfnL1leIM7v++SkpR6Ph6GhIc6cOcP58+fx+XzEx8eTmZmpBHflu96irlx/i8u73+9XZjGdPXuWsbExZmdn0Wq1pKenU1FRQWlpKcnJyUoeKHFePtjiYPD09DR9fX309/dz8eJFtFotGRkZFBUVUVRURFJSkggYXAeLy3gkEmF+fp6LFy8yNDTE0NAQ09PTRKNRUlJSqKqqory8nNTUVFHGBeHWJAI1gnArC4VCzM3N0dvbS3d3N3a7nZiYGPLz86mtrSUrKwuTybRk1yPhxpNzr3g8HmZnZzlz5gx9fX3MzMyQmJhIdXU1K1asIDU19Zafbi9JEl6vl7m5Ofr7+xkaGsJmsxEOh7FYLGRmZlJaWkp2draSJ0ZMrb5zyUEHOWnzhQsXOH/+PCMjI7hcLoxGI8XFxVRWVpKfn4/JZLrrcoLI9d/pdHLu3Dm6uroYHx8HICMjg9LSUsrLy5cEMO+mz+d2IAcY3G4309PT9Pf3c/bsWVwuF8nJyVRWVrJs2TKSk5OJjY0V5+//kySJhYUF7HY7XV1d9PX1MTc3h8lkoqioiJqaGmVG3uIdvoQbb3EA2Waz0dvby5kzZ3A6naSmplJbW0ttbS1JSUl3XRsuCLcoEagRhFtNOBxmfHycEydO0NvbSyQSobKykuXLl5Obm6vsViQuorcu+c5iIBDAZrPR1dVFZ2cngUCAoqIiVq9eTUlJCQaD4WYfqjJAmZiYoLu7m4GBAaWjXVZWpiwLiIuLUzpvouzd3RYnhXY6nQwODtLX18fY2BgajYbc3Fyqq6spLCwkOTlZyZ1ypwmFQoyNjdHa2kp/fz/BYJCioiKWL19OUVER8fHxYsBzm1pcxqempuju7qa7u5tAIEB+fj51dXWUl5djNBrvuvMrD/jPnz/P8ePHOXfuHFqtltraWpYtW0ZGRgaxsbFihsYtbvEmExMTE3R2dtLT0wNAWVkZa9asIT8//wN34xIE4boSgRpBuNnkunbx4kWOHz/OyZMn0el0rF69mtraWtLS0q5ZYGZiYoLp6WlqamoYHR1V1uRfjc/no6enR5mqfDsaGhpibGyMkpISMjMzle/PzMxw7tw5UlNTMRqNZGZmXvEzXlhYoKOjg8LCQlJSUj7WMcgdIo/HQ29vL8eOHcNms1FTU8OGDRvIzc29YZ1aubx5PB76+/s5fvw4k5OTmEwmqqurKS0tJT8/X5n5czt2tKPRKAMDA6jVakpLSz/wPXi9Xnp6eigtLcVisSx5jfPnz39gHZmenmZ0dJQVK1Z85MTSFy9epLu7W9k5Z/Xq1ZhMpo/0GrcSeWA7Pz/P0NAQp0+fZnBwEL1eT21tLatWrSInJ+e2DjLLAdiJiQlaWlo4ffo0sbGxrFq1iurqajIyMq7Z+5ufn6e/v5+amhocDgcGg+ED2+BwOExfXx8JCQnk5eXdtp9xJBJheHiYqakpjEYjZWVlGAyGq74fp9PJmTNnqKmpUbYJvxqfz0dXV9eH+tlLyW24z+djYGCA5uZmxsbGyMvLY8OGDZSVlRETEwPcmct45LI/OTnJ0aNHOX36NGazmbVr11JTU4PFYrkm17BQKERXVxfp6ekkJCRw8eJF8vPzr/q6kiQxOjqKw+Ggurr6tkzwHwwGaWtrQ6fTsWzZMiUwEolE6OnpIRQKYTKZKCgoUMrYpSYmJpiamqKuru5jB8blMj43N0dXVxfNzc34fD5WrVpFQ0MDaWlpt22fQBBuU1etbLdfSycItxl5YDM0NMTevXuZmpqitraWp556SgkaXOsLYjQa5cc//jHPPfccbW1tJCYm4nQ6la1cL7Vr1y7C4TCJiYlXHSREIhFGR0cpKCi4psd6rbjdbn7wgx+wY8cOIpEIv//973nooYcA2LdvH5s2bUKtVi8J4iymUqnYv38/MzMzPPjggx/rGFQqlbK9Zn19PfX19TgcDjo6OvjNb35DOBzmU5/6FHV1dddtGZs8c6a3t5ejR48yNTVFRkYG69evp7S0VAkS3CmdsJmZGf7whz/wve997wM7rr/73e/Q6/UkJydfFqhpa2vDbDZfNVATDof56U9/yr/+679iNps/0vF1dXXx85//HK1Wi91uZ8eOHbd1oEalUqHVapUd5FavXk0kEmFqaoquri5++9vf4vF4WLFiBRs2bCA1NfW2ueMuz47r6OjgwIEDBINBVq5cyVe+8hVSU1OvS1utUqn4z//8T770pS8xNTWl5JHwer2kp6df9vPt7e0cOXKExsZG8vLyrvo+hoeHyc/PvyWXK0ajUV566SWGhoZoamqiv7+fl19+mX/8x3+87PoUiUS4cOECSUlJ/PrXv+ZrX/saZWVlf/Jv/Pa3v+WLX/wiVVVVH+nY5DbcZDKxcuVKVq5cidfr5cyZMxw8eJDf/OY3rF69mqamJqxW6y35+X4c8uyZnp4e3nnnHTweD6tXr+bpp59Wblxcy7KvUqk4deoUkiTxwAMPcPr0aXJycrhw4QJFRUWX/bzD4eBnP/sZGzdupKys7IqBGkmSmJmZIT4+/pZsY9VqNXv27MFsNrN8+XJGR0c5e/YsTU1NDA8P43Q60Wq1pKWlXTVQs7CwwL/927/x/PPPf+QgpEwu4ykpKTQ1NbF582ZsNhsnT57kpz/9KXFxcWzfvp3y8nKxjE0QbjIRqBGE60i+W//KK68QCATYtm0b1dXVn3jtuyRJtLW10d/fT1VVFefOnUOn0xEIBGhqaiIpKQmDwYDH4yEcDuPxePj+97/PsmXLePLJJ4mNjVVey2az8eabb7Jy5UqMRiOvv/46gUCA++67j5GREWWmjd/v54UXXuC///f/jtfr5f777+f1119n27ZtNDc3k5ycTDgcJjk5mc7OTkpKSlizZs2SjqzT6WT37t3k5uYyPT3Npz/9aUKhEPv27UOlUtHY2MjevXsxGAy43W7MZjOBQIB169ZRUlLC0aNHGR8fp6GhQdmJqL6+HoDq6moKCgrweDy43W6OHDlCeXk5GRkZ1NTUoFKpiImJ4cSJE0xOThKNRsnMzKS+vp6zZ89y8uRJLly4QEVFBW+99RZOp5P777+f//qv/2Lz5s1kZ2d/pHMkn1+r1cq9997Lpk2bGB4eZu/evezZs4dt27axcuXKa7ZkQpIk5ubmOHLkCC0tLWRlZbFp0yaKi4svS/h7p1Cr1aSnp6NWqwmHw+zfvx+bzcbGjRsJBAI0NzeTlpZGdXU1b775Jhs3brxstpTX61WSKP/mN78hMzOTyclJtmzZgsVi4eDBg4yOjuLz+QA4d+4cx48fV5LsHjlyhM2bN9PR0cHWrVs5d+4cSUlJlJSUANDQ0MDGjRvxeDzs27ePnJycG/45XS9ymdJqtcqWyFu3bsVut9Pa2sqOHTswGAzcd999VFdXX3XwcbPJAZqWlhbeeecdcnJyePzxx8nLy0On033i1x4eHqa5uVkJvFy8eJGFhQVWrlxJWVkZiYmJhMNh5ufnSU1N5Re/+AWTk5N8/etfJzU1VXktv9/PG2+8gcPh4MEHH+Tw4cOMjo7S0NCAVqvl0KFDJCYmUlRUxP/+3/+bJ554Ao/Hw3/7b/+NgwcPUlVVhcPhwOFwoNVqKS0tpaWlBYvFQlNT05Lzs7CwwOuvv47VamVqaooNGzaQnp7O3r17mZ+fp7Gxkfb2dnw+HwsLC8TExCBJEnl5eWzcuJGenh46OzupqKigsLCQ1tZW7rvvPrRaLT6fjxdffJEdO3aQkZHBmjVrOHHiBAcPHsTpdNLU1MTp06exWq0Eg0F++tOf8o1vfIPExEQAxsbGOHLkCHq9nvvuu4+4uDgOHjzI9PQ0tbW1yswPSZJ45513iI+Pp6Gh4SOfO7l8G41GVq1axYoVK5ibm+PYsWP88z//M8XFxWzfvl0J4t2uwuEwPT09vPrqq5hMJrZv305xcfEnvja53W727dtHOBymsrKS1tZW4uPjSUpKYtOmTaSnpzM1NYXdbic2Npbm5mZ++tOf8s1vfpO6ujrldaLRKM3NzbS3t3PvvfcyOjrKyZMnKSgoYNmyZezbtw+/309jYyP//M//TGFhIVlZWVRXVyu7OdbU1HDo0CFl168zZ87g9Xq57777SEpKUt6nJElL+ggZGRnU19cryx4rKioA6OzsJDY2loWFBYxGI2q1mgcffBC32827775LQkICmzZt4vDhwzQ0NJCYmIhOp+Ozn/0sv/zlL9FoNPT19XHgwAE2b96MJEmsXr2ajo4OvF4vb775JtnZ2UxOTvKpT30Ko9HIu+++y/j4uLLU+u2332b9+vWEQiGmp6fZunXrR26v5OBzeno627dv54EHHmBgYIA//OEP7N69m4ceeojKyso7dlmrINzq7oxbAYJwi5EkCbfbza9//WteeOEF7r//fp555hlWrVr1gdO7P6xgMMgLL7xAdXU1VquVvr4+ZYvk559/nmg0CkB8fDw9PT14PB70ej3r16+/bB2y1WolNzeXzZs3c/ToUUZGRtBqtfznf/4nZrOZNWvW8Ktf/YrY2FhycnKUjlFcXBw9PT04HA6mpqY4deoUOp2OH//4x9TW1vLrX/8am8225G8ZDAaOHTtGeno6g4ODtLe3o1arWbVqFQMDAwwMDNDW1kZlZSVOpxO/309RURG/+93v6O3t5bXXXqOsrIyf/OQnAEsGFlqtls2bN3Ps2DHGxsb4zGc+Q0tLC4ODg9TV1aHT6Th8+DDRaJTW1laqqqr41a9+xfz8PD/84Q9ZtmwZFRUVynbTra2tGAwGvF7vNVkOptVqKSkp4Utf+hJf+MIXaG5u5l/+5V+Ympq6bFeej0KSJJxOJzt37uSHP/wh0WiUb3zjG3z5y19m+fLld01uhVOnTvHuu++Sl5fH888/j06no7GxkVdeeQW1Wq2U8UvvQsbFxdHb28v8/DynT59Gq9USCAR45513aG5u5sCBAzQ1NWE2m1lYWOBf/uVfqKio4Pe//z0qlYqhoSFOnjxJamqqsjvS4ru9sbGxaDQaWltbqa6uvuPPhUajIT09nU9/+tN861vfYvv27ezfv5/vfe97tLe3Ew6Hb/YhLhGNRunr6+O73/0uZ8+e5atf/Spf/OIXlYHqJyVJEr/5zW9IT08nMzMTl8vF1NQUK1as4LnnnsPtdgPvtQ8ul4u+vj4MBgOrVq3CarUuea2YmBiKi4tZvXo1Pp+PV155hcrKSqU9lAPdHo8Hk8nEPffcw/Hjx/H5fMzPz9Pb24tKpWLv3r0kJCTwk5/8hJSUFFpbW2lvb1/yt3Q6HUNDQ7jdbuLi4ti9ezeSJFFaWkp8fDx79uxhdHSUmJgYMjMz6enpob6+np07dzI1NcW//du/UVdXxy9+8Qvm5uaWXPdcLpeyzEN+78nJydjtdnp7e5mZmUGSJFpbWzGbzWRmZiqzaCKRCM8++ywFBQWEQiF++ctfcvToUU6cOMHatWv5yU9+wszMDICyk1l5efknPo/wXmA4OTmZBx98kG9/+9tkZ2fz7LPP8uabbxIMBq/J37iRJEnCbrfzk5/8hD179vAXf/EXPP3001RWVl6TGZ8tLS0MDw9TUVFBbGysMhPsrbfeUnKkACQkJPDHP/4Rk8lERkbGZedLpVJRUlJCQUEBtbW1PPfcc1RWVvLiiy8yMTFBVVUVgUCAo0ePotPplMB4b28vCQkJHDhwAIPBwNtvv41Wq+Xw4cMMDAwQFxfHr371qyt+LidPnlT6CKFQCKvVysqVK/nFL34BwMDAAOvWrePdd99lzZo1nDp1iv7+fp5//nnMZjNdXV20tLQs2QYeoLS0VMkVZzKZiI+PZ2xsjIWFBQoKCjh16hR+v5+2tjZMJhOzs7McPnyYd999l7a2NpqamjCZTJjNZgYGBvB4PIRCIcxm8ydeDibfzKqtreXv//7veeSRR3j11VfZsWMHTqfzE/VTBEH4eESgRhCuMXl99/e//30SEhL4X//rf7F8+fJrutRFr9ezdu1aduzYQV9fH1qtFovFwqpVqxgbG1MCNWq1Gp1OpzzLd34W02q1xMTEYDQaOX/+vLLzVCgU4vjx47z66qvMzMyg0WgwGAzKa2g0GuU96fV6CgsLKSwsZGRkhL179ypLcBZTq9Xo9XqSkpJIT0/H6/Vy4cIFdu/eTVdXF4FAAL1er2wDnZCQQHZ2NoFAgLGxMc6fP8/+/ftRq9Xk5eWxYsWKJa/f0NCgBI9Wr17NhQsXsNvtZGVlKblY9Ho9RqNRmYlhs9mUu8vywGHVqlX4/X5a/h97Zx4dV3Xl668mVZVKQ2me58karMmDJFuS8YCZYxsbTHiPkDwImegknZBOHgm9CP1COuR1spLQ6XSS5oUQBmMbbIgDBhxjrFnYGmzZsiRLsmaV5lJJpZrfH6x7uzwBTmxr8PnWqiVbVaU6de++++7zO/vsXV1NVlbWeRlIfy+SaPCNb3yDm266iX/7t3/j2LFjf1MQ5Ha7qa2t5amnnkKv1/PEE0+wbds2QkJClkxK/qfl3LlzdHR0cPToUQCamprYs2cPPT09uN1ufHx8CAgIuGhl0Psa8fHxITg4mKioKLlGRV5eHhEREfj4+MhtmN99910cDgcajYb777+fvXv3snz5clQqFYWFhRdtD5yamqK7u/sT6+gsJaRrLTMzk29961s88MADHDx4kF/96leMjY3Ne9AvbfXYs2cPL730Eg8++CCPPPIIUVFRV/XaUSgUrFu3jj179nD48GEUCgX+/v5kZWXJnVkkvH2UwWC4aOKlVCrR6XQYDAZMJhOdnZ0cOnQIhULByZMn2bNnD52dnbK9e3fgkURtjUZDZGSkvPWioqKCiYkJrFbrRePWaDQEBQURExOD1WplfHycv/zlLxw5coTp6Wk0Gg3BwcHExsbi7+9PdHQ0SqWS0dFRurq6eOutt1AoFPj6+rJu3Tr52vP19ZWPP3zkx2ZmZggODpbFMe/x6nQ6eVvizMwMk5OTLFu2jNWrV3PmzBna2tpIT0+XRaSRkREcDgcvvPAC4+PjcibO1TynBoOBW265he9///sMDQ3xzDPPMDIyMu92/WnxeDycPn2an/zkJ+Tm5vLd736X1NTUq1r7JScnh6GhIV588UVmZ2fR6XRER0eTmppKb2+v/DrvOML7XEtI9qvT6fB4PJw9e5Z33nlH3o795z//WZohn3AAACAASURBVBYkJbuX7Ee6ptRqNQaDgVWrVsmCaGNj40XnS7J7g8FAZGQkKpWKubk5Kisr2b9/PyMjI6hUKvz8/IiKiiIoKIiwsDBCQkIwm820t7dTVVXF6OgoTqeT9evXExAQIP99vV5Pbm4uhw4dIjIykpUrV7J//36SkpLQ6XRyFpN0bUVFRTEzM0NbWxsFBQWEhYWh0WjQarVs2bKFQ4cO0d3dTUFBwVW9t6jVajIzM/nf//t/k5SUxI9+9CPOnj27aOxbIFgq3FiRvEBwjZFEmp/97Gfcc8893H333dekxafdbicrK4svf/nLvP/++3Lxw6amJhITE+W6OA6HA6fTidPpRKlUMj09LYs4Em63W35NQkICeXl5fOtb3+L+++/n/fffZ9u2bfLKrtVqxeFw4HK5mJiYwGazye91Op34+/uTkJDAPffcw09/+tOLtnlI45Je73A4eOutt8jJyWHlypU4nc7znpceLpeLyMhIUlJS+OIXv8gPf/hDRkZGOHXq1Hl/PyoqCq1WK7eYjoiIYHp6Gr1eLx8L6afL5cLlcuHn58fs7Cx9fX2YTCZcLhe+vr6UlZXx8ssvX5MsCClwLCoq4rHHHmPPnj3U1tZeURDkcDjYtWsX7777Lo899hjbtm27YbJnJDwej2wjUnvkr3zlKzz22GMcOHCAO++8k/j4ePl8OxyOi46xZP92u122Pck2oqOjaW1txWQyyXaUkJDA1q1beeaZZwgLC6Orq4v777+f3/3ud9jtdk6fPk1/f/95Y6yqqiIvL++GTB+X6iGkpKTwT//0T+Tl5fHjH/+Yrq6ueQ36XS4Xzz//PCMjIzz++OOkpqZeE3HT7Xbj7+/P97//fU6ePInZbMZms9HR0SEL5JJtSr5J8tUXZh9J9u5wOIiIiCA5OZmHH36YJ554grfffpubb76ZtLQ02Y5nZmZQq9WMjo5itVplu3Y6najVahITE9m0aRNPPfUU5eXlF33WhddDfX09Ho+HzZs3A1zkp6XX+vn5kZCQwM6dO/npT3+Kn58f9fX1uFwu4KOtROXl5bzxxhtMTk5y4sQJTCYTxcXFaLVaRkdHmZ2dlY/F3NycvEVREtq7u7tpamoiIyODpKQkOjs76enpwWq1EhYWhkKh4Atf+AInTpy45IT8aqBQKAgMDOThhx9m3bp1/OxnP8NkMi34yazH46GlpYXnnnuOr33ta6xfv/6a1CKxWq189atfxWg0cubMGZxOJ+Pj43R2dpKYmHheHCDZ+tzcHFar9bxj6G2LOp2OhIQEtm3bxjPPPENvby/BwcGsW7cOj8eDQqHAbDaj0WgYHx+X7UayYZfLRUJCAsuXL+eb3/wm//AP/3DRsfF+rcvlYmRkhOrqarZu3SpnVnpfG94xVmJiIuvXr+fJJ5/kpptuoq6u7jwxFpAz3SIjIyksLKSxsZHExET5b0kxlve4o6KiOH36NCMjI1gsFtxuN6tWreLkyZPyItzVRhLPbr31Vh566CF+85vf0NHRseDtWyBYSqiefPLJj3v+Y58UCATnY7PZ+MUvfsGOHTuu+gqHN9Ke7a6uLu68805aW1s5e/YsSqWSz372swwNDTE6OopSqZRXsvLz82ltbSUtLe287U9S9xaFQsH69evp6+vj1KlTcqvmoaEhwsPDSUtLY3Z2Fj8/P2JjY2ltbSUsLAwfHx9sNht2u52cnBzi4+OprKxkdnaW+Pj48yanZrOZvr4+AgMD5Ynv6tWraW1tlVeOpBU1aUuAx+PBbrdTWlqKj48PH374Ib6+vthsNkZGRkhMTJT/vkKhICoqSu4gIo3b19eXpqYm5ubmZCFHKrCcnZ1NRkYGtbW1GI1GdDod6enpBAQEMDc3R1lZ2TU7jwqFAj8/P5YvX85zzz1Hbm7upwq43G43b775Jv39/Xz9618nKCjohhJoJNxuN01NTXJ9AvhoC1RQUBCxsbF0dHQQExMjtyZVqVQkJSWdZ5MjIyN0dnYCH60i6vV6bDabXORyfHxcLmaamJhIfn4+lZWVTE1NMTAwgEKhICcnh9bWVqKjoxkaGkKtVsu1cNxuN0NDQ3I9ohsVhUIhT2Ti4+P53e9+R15e3jWZYHwSUu2SwcFBHnnkkauyHfXjOH36NLW1tZSXl6NUKvnggw+w2+3ce++9aDQauru78Xg8WK1W3G43JSUltLa2EhcXd15RVKfTSUtLCzabjVWrVqHVauW6HykpKfJ7AgMDiYyMZHx8nFWrVtHU1CQX0Far1UxPT5Oenk5+fj61tbUMDg6SkJBwUY2arq4u1Go1brcbh8NBWVkZPT09+Pr64na70Wq1qNVqXC4Xc3Nz+Pr6YjabyczMJDMzk8rKSmw2G0ajkZaWFjIyMlAqlSiVSgoKChgZGaG2tpbJyUkeeOABYmJiiIiIoKGhQbaLlStXMjY2xujoKDabDYPBwG233UZ1dTUul4udO3eSlpYmb1287bbbiI2Npbu7m4SEBMLDw+nr65MLo14LFAoF8fHx6PV69u7dS0lJyYIWZScnJ3n22Wd59NFHSUpKuma2PzY2xnvvvUd0dDSFhYXs27cPp9PJihUryM/PlzN3HQ4Hdrud5cuXY7VaUSgUF3VnPHPmDJOTk8TFxVFQUEBlZSVms5nCwkLOnj2L0WhEqVRSXFwsb0vy9uvS/T40NJSVK1cyMDDAyZMnCQsLw2g0nvdZnZ2dWCwWgoOD5S5TGo2GsbExgoODZVHJz8+PqakpAgICmJiYwGg0csstt/Dhhx/S19dHTEwMTU1Nsm3AR7ZiNBplodVgMBAeHk5GRobcXRA+yp6TFpFUKhW33norAwMD9Pf3ExwcTGJiIqGhoUxOTrJ27dprGgMoFApCQkJISEjg+eefp7i4WLTyFgiuLj+83BOiPbdAcBWpr6+nurqaRx999LptPXE6nTz++ONs2LCBzZs3f+znms1mdu/ezdzcHPBRPYmNGzfKhU+vJg6Hg7179zI2Nib/buXKlRQVFV31z7qauFwuefJSWFj4sS1DrxYej4e//vWv9Pb28uCDD37i55lMJn72s5/x+OOPn5dWLfh4rFYru3fvlkVAqYB1bm7uPI/sxsLj8XDkyBFOnz7Nl770peu+TW92dpYnn3yS7373u1el/tSnxePx8PLLL9PX18c//uM/fqxw53Q62bdvn1xvBSAvL08WJK8mbrebw4cP09raKv8uPj6e22+/fUELDgsNl8vFs88+y5o1a1i5cuWCFc9ff/11XC4X27dvv25jbG9v58knn+Q3v/nNx3ZkkmoT1dfXy78LCwtjy5Yt16QYudSNU8LX15e77777irv7XW/6+vro7OzEZDKxdevW69Ku3O1288ILLxAdHc2mTZsWrH0LBIsQ0Z5bILgetLS0XPcAzeVyceutt2IwGHC73R876fH39+d//s//ed7vrtUNXq1Wc/fdd5+XJrsYgn5pP/yyZctISEi4LudSoVCQm5vLkSNHcLvdn3icTp8+zfLlyxdkC9KFjE6n47777jvPJq9HgCs4H4VCwcqVK3nnnXew2+1XtQbUp2FgYEBuL349kYrxRkREyDWOLodKpeIzn/nMdfGfCoWC8vLy80QgKfNF8OlRqVSUlJTQ2NjIypUr53s4l8TtdnPmzBnuu+++6xqnaLVauQvZJ923CgsLycvLk/8v1Wy5FiQmJvLwww+f97vFkC3i5+eH2+1m06ZN1y2ukjKWDhw4wMaNG4VQIxBcB0SEKhBcRRQKxXXfv6vVatmwYcOneq13UckroaOjg8HBQUpLS6/o5nzy5ElUKhW5ubmXfJ/b7ZbTnwsLC1EqlQwPD9PU1ERZWdlFRQWlApTt7e1kZGRck0m2UqlkxYoVV/3vXk3mw84k7HY71dXVxMfHX1Q01xupXpOUhu7r6/upP6Onp4eOjg7Wrl37d62iejwehoaGcLvdxMTEyHvu4SOBs729nfj4eHkr3bFjx+S9/zabjb/+9a9ywdOioiKsVitnz54lNDSU/Px8VCoVzc3NmEwmuehwZWUlg4ODAERGRlJaWorNZuPs2bNkZmaiUCjktPvIyEiWL18ub+GanJwkPz+fwMBAKisrMZlM57UgPnv2LO3t7cTGxpKdnS23JT958iTZ2dmoVCpaW1vp7u4mJSVFLl5ss9lobW0lJyeH6elpPvzwQwwGAytWrJi3Scl81jmYr+tH6nD3afC21b+VlpYWZmZmWLVq1cf6be+JsNvtpq6ujqCgINLT0y/5epfLxbFjx9Dr9XINr97eXtra2igvL79oUi0Vlj979ixZWVlLeoL3SYslC4H5sP/4+Hji4+M/8XVS/ba/5d7e3t7O8PAwa9eu/dQ2plQqaWlpQa1Ws3z58svGKc3NzbhcLgoKCuQ4pbm5mbKysouEZu84ZdmyZddESDEajdx0001X/e9+ElIdoKV8DQsEC4mFfTcRCBYZOTk5V1wUdiFjtVrp6OjA7Xbz/PPPX/H3mpiYYN++fZd9XqFQ4HK5eOmll+Qix1qtlr179zI5OXnR6+12O3v37uUHP/gBdrv9yr7MAsbj8XD8+HHS0tI+VZCfmZnJiRMnMJvN193WVCoVXV1d/PWvf/3Y1zmdTn7729/icrmuuHWtr68vf/zjHy8qwngleDweOjo6+Od//meOHDly3nMul4sjR47wj//4j3Knltdff53Tp0/T2trK7t27GRoa4uDBg1RVVfHqq68yPDxMXV0dERER/PnPf+btt9+mpqaGV155BY1Gw7/+678yOzsrv+fgwYMcPXoUq9XKSy+9xFNPPYXL5cJisfD000+j1Wp58cUXaWxspKKiggMHDjA3N8f//b//l5mZGfbv309lZSX19fXYbDZOnjzJK6+8QkxMjHzePR6P3PVrbm6O2tpaDhw4QExMDFNTU/Jr/vrXv/LTn/5U/vtms5nq6mrefPPNefFVHo+HmpoakpKS5kUoio6OZnx8nJGRkev+2dcDs9lMd3c3c3NzvPTSS1f0XoVCQV9fH++8887HvsZisbBnzx75dz4+PnJ3nwuxWq386U9/4v/8n/8jFxReijidTqqqqsjPz5/voVwWpVJJZmbmkopTZmdn/644ZXx8nP3791/2eYVCgdPpPC9O8fHxYffu3ZeNU/bs2bPk4hSpNqJ3tpNAILi2iIwageAqkpOTw4EDB6irq6OoqGhRrzp4PB527drF+++/z8MPP4zdbmffvn3YbDbuuOMOXnnlFZKSkuQU+TNnzpCfn094eDiHDx8mMDCQhIQExsbGePHFFwkNDWXz5s2cPHmSmpoaDAYDn/nMZwgKCpIzA95++225pTZAVVUVWq1WznDRaDRs3LjxvD3lix0p8+TQoUM89thjn8pmQkNDKS8v57e//S1f/epX8fX1vW62plKpCAoKYnR0lMnJSd58802cTid33nknp06doqWlhWXLlhESEsL7778vd8Hw5r333uPYsWM89NBDHD58mKioKAwGAzU1NURFRbF582Z0Oh39/f3s2rWL++67j1deeYU77rgDf39/WWC48847qa2tJTs7+7yi0hLx8fGUlJRc9HsphTs+Pl7u8lFRUcE3v/lNdDodP/zhD9m+fTu//OUvcblc7Nu3j9zcXHJzc1Gr1Rw7dozp6Wn6+/vJzc2lrKyM1157jb6+Pn7wgx/g4+PD4cOHiYuLQ6fTcfPNN8ttw6XOYiUlJUxOTnLkyBFiYmKIjo5m+fLl8vHMzc1l7dq1GI1GjEYj//Vf/0VaWhr+/v4kJyejVCoZGRmhp6cHf39/PB4Pb775JmVlZQQGBhIeHi5PusfGxtDpdMzOztLT08M3v/lNMjIy+M1vfsPWrVuv65ZEj8dDc3Mz7733Ht/5znfmJftAp9Nxyy238Mc//pGvfe1rF2XuLWZcLhfPPfccbW1t7Ny5E4vFwquvvoqPjw8bNmzgT3/6ExkZGeh0OiwWC93d3ZSUlKDRaKisrCQqKoqAgACOHz/OH/7wB5KTkykrK6O+vp7GxkaCgoK48847CQ4OlrO1/vKXv9Df3y+Lg4cOHSI6OprMzEwA+Rqoqqqa56Nz7fB4PFRUVMhFcRfyvb+8vJynn36alStXkpKSsqDH+km43W5eeeUVjh49ykMPPYTNZuP111/H4XBw++238/LLL5OcnCz7OKnNdWhoKIcPH5YLz4+NjfGnP/2J8PBwbr75Zpqbm6mrq8PPz4+77rqL4OBgOU5566236OnpYXR0FIDKykr0ej2FhYXAf8cpBw8enLfjcrXxeDycOnWKzs5Odu7cuahtRiBYTIiMGoHgKqLVavnSl77Enj17qKurw+12L+pVq/DwcFauXEliYiIajYaysjKOHj2KxWJhYGCAiYkJOTNg8+bN/P73v+ePf/wjwcHB8qqiv78/ZWVl7N27l4mJCX71q1+xceNGZmZm2LVrl/xZTU1NvPvuu+zYsYPo6Gj580NDQ+XXKJXK6ypKXGs8Hg99fX384he/YOfOnXKnoE9CoVBwyy23kJKSwk9+8hN6e3uvu515PB727NmDy+XC39+fXbt2kZSUxM0338xLL71EUlISKSkp3HXXXRcJAWlpaTQ3N6NSqbDZbKSkpBAREcFdd93Fm2++KRf7NRqNHDt2DI1Gw8TEBAMDA+zatQuNRoOPjw+vvfYacXFxcocYKYNEOhZarfaStQ0UCgV6vV4WCRwOB7Ozs2i1WgwGA2azWU7BP3HiBPHx8fj4+KDRaHj++edpaGhg5cqVFBQU0NjYSFVVFSaTiZmZGbRardzdTBJUvLsKRURE4OPjw6FDh+jq6mJ8fFzuYvLTn/6UvLw8/P39iY6Oprm5mX/5l3/h2LFjDAwM0N3dzfvvv8/TTz+NxWLh6NGjrFmzBpVKhdPpZGBggI6ODt5++21+/vOfMzs7S1VVFSUlJfK1k5SUxMGDB2lpaWFsbOy62Y3Uve2tt97ixRdf5Gtf+9p1LeTrjUKh4KabbiI8PJzf/OY3TE9PL2o/7Y1SqSQ0NJS1a9cSFRWFXq+nvLycv/zlL7jdbjo6OnA4HMzNzfHGG2+wYcMGfv3rX/P888+TlJREVlYWAEFBQaxdu5ZXX32VoaEhfv/733PHHXfQ3d19nlBeUVFBQ0MD27dvJzQ0VO7YYzQazxvTte6sNV9IQu+hQ4d49913efjhhxd8d7fAwEAeeughfv3rX9PW1raobV+hUMhxSkJCAj4+PpSXl3PkyBFmZmZkAVGj0fDSSy+xefNmfve73/HHP/6RsLAw8vLyUCgU+Pv7U15ezt69exkfH+ff//3f2bRpE2azmVdffVX+vMbGRg4dOsSOHTuIiooCPvLp3r5sKcYpJ0+e5P/9v//HQw89dEXbmAUCwd+HyKgRCK4y4eHhfOc73+Hf//3f6ejoYNu2bYsySFUoFKhUKtRqNSqVCo1GQ2BgIAaDAafTKbc61uv1nDt3jtdee42goCA2bdrE66+/TmNjI5s2bUKn02E0GlGr1YyPj8uBfG5uLm+88QYbN24EoLu7m7S0NIxGoxzopqamzuchuGZ4PB45Tf6NN97gwQcfvOJVWLVazdatW0lMTOTZZ5+loKCA22+/nYCAgOtiax6Ph56eHiYmJggLCyMyMpI33niD8fFxRkdHZfvx8fG5aDzx8fGkp6dz4MABNBoNoaGhvPLKK/T29tLf3y+3P5WytaS/5Xa76e7ullv+pqWlndexyePxcPjwYZqamsjNzZVt65NQq9VotVrsdrvcZlipVGK322loaOD++++X//4DDzxAamoqv//97/nxj39McHAwY2Nj+Pr6EhgYKG/ryc3NvWS2iMFg4IknnqCrq4u2tjYiIiI4ePAgd999N2vXruU73/kO99xzj9xVw+l0UlFRQWBgIMXFxZSUlPC5z32Oo0ePyl16+vr6OHv2LIGBgZSXl5OUlMQXv/hF3nvvPXp6enC73fT29tLT08Njjz3GmTNnOHfuHBEREdelo5lUwHT37t2EhITw/e9//7rZ6eVQKpXcf//9HDhwgB/96Ec88MADZGZmLvj6Ip+Et99WKpVotVoCAwPR6XS4XC7UajWpqakMDQ3R1dXFG2+8QUhICLfccgt79+6V6x/p9Xq55e/IyAi+vr5ERESQnZ1Na2srycnJwEetjLOysggMDJQFWSmTZqnj8XgYHx/n5ZdfxmKx8Nhjj13U6nkholAoSE9P5ytf+Qq//e1v2bBhAxs3bkSj0Sz4sV+IZO8ajUb+GRAQcFGcotFozotTbr75Zl577TUaGhrYsGGDHKeoVCrGx8dRKpVynHLgwAE5K7Srq4uMjIxPjFMWs/glIdWWeuutt6itreXrX/868fHxi85GBILFjBBqBIKrjLTC893vfpf9+/fzL//yL2zdupX8/PxLTloXMhqNhqmpKcxmM3a7HZvNJk9m7XY7VquV2NhYEhIS2L59OwEBAbS3t/PlL3+ZZ599lqGhIfk9drsdPz8/VCoV7e3t1NbWkpeXx9zcHDabjejoaI4ePcq5c+cYGxvDbrdz4sQJfHx8yMjIAD5Kc56ensZut2OxWNDpdItuYuVyuejq6mL37t1otVq++93vEhYW9jfZhVKppKCggLS0NN555x1+9KMfkZ+fz4YNGwgLC7smW1rcbrd8zlJSUrBarWzfvp3p6WmeeeYZvvzlL9PW1sbs7KxsK/7+/hd9vx07dvDtb3+bX/3qV9jtdo4cOcKjjz7KmTNnsFgsst0olUr6+/uZmZnB6XTKreS3bNmCr68vVVVVJCUlERUVhVKpZOPGjWzcuFHO4JidnZUzd8xmM6dPn2bt2rVYrVZsNhsWiwWlUklhYSHHjx/H7XZTVFSEWq2mtraWuLg4uaPaq6++yk033cTo6Cg6nY6RkRGUSiUej4eYmBji4uKYnp6mvb2ddevWoVAoZJuVPsvPz4+BgQEiIiIYGhriwQcf5MiRI0xOTjIyMoLb7WZ2dpYPPviA4uJiTp8+LWfE1NTUEBISQlBQEHl5eaSkpDA7O4u/vz8hISGUlpbK2y+ioqIoKioiIyODiYkJAgICMBqNdHV1ydfabbfdds2uH4/Hw9zcHK2trbz11lu43W62b99OZmbmguj+JmVM3XXXXWRlZfHSSy9hNBrZtm0bMTExskC4GNFoNIyNjcl2J/lfb/8dFRVFQkIC9913H3q9ntbWVr7+9a/z85//nMjISPm1NpuN4OBg5ubm6Ozs5Pjx43Jh7bm5OWJjY6moqCAvL4/JyUkcDgcffvghoaGh8nZEl8slj2VmZmbeRbq/F4/Hw/T0NIcPH6ayspKNGzeyfv36RdEtSEKhUJCamsrjjz/Oyy+/zNNPP829995Lenr6ouuEp1armZyclOMUyW4lG7ZarURFRZGYmHhenPLVr36VX/7ylwwPD5/3en9/f5RKpRyn5Ofny/eL6Ohoqqur5TjF4XDQ3NyMTqeTi2+73W4sFgt2u53p6Wm0Wu2ii1McDgctLS3s3buXxMREHn/88UvexwUCwbVF9eSTT37c8x/7pEAguDRSx47s7GzS09N55513ePfdd9FoNISEhCyalSuj0SgHMX5+foSFhcnZDEqlErVaTXZ2NrGxsRw/fhylUolKpaKxsZHi4mICAwNRKpUEBwfjdDpJTU1l1apVNDQ0EBUVxa233kpXVxcKhYLVq1djNBo5d+4cSUlJhIaGYrfbZeELwGazUVtbi8FgwOFwEBcXt2iCSrvdTmtrKy+88AL19fXceeedfOYzn/m7gx+pk1dmZiarV6/GZDKxZ88empqaUCqV+Pv7o9Vqr5q92e12zp49i0aj4dZbb2VsbIyuri6SkpIICAhgenpaXnVTq9UEBAQQFRV13ucrFAqCg4NJTk4mOzsbnU5HYGAgAwMDpKWlyYJGREQEy5cvp62tjfj4ePR6PRs2bGBwcJDu7m6ioqIwmUwEBQUREBBw0Vi7u7vp7+9HqVQSFBSEj48Pg4ODxMXFyfZqt9uJjY0lNzeXnp4eVCoVW7ZsQavVMjY2Rm5uLjqdTs4saG5uRqvVsnPnTlwuF0ePHsVms7Fz5078/f2ZmpoiKiqKyMhIFAoFVquVuro6/P39cblcxMTEcOzYMTo6Oti0aRNZWVmkpaXJGQ7btm0jISGB7u5uTp8+TV5enpwl43K56OjoYMeOHSQkJBAaGnqecJORkcH09DR9fX3ce++9xMTEEBYWRkhICCEhIeTk5NDa2kpTUxM5OTmUlpZeddHE6XTS39/PO++8w65duzCZTLKtS2LaQkKyRamW0a5duzhx4gQBAQEEBgYuGv/iTXBwMH19fbjdbnx9fQkNDcXhcBAUFAR8VDNm+fLlhISE0NjYiE6nw+FwcOLECW666Sb0ej0qlYrAwEBcLhc5OTnk5ORw/PhxMjIyKCsr4+zZs6hUKtatW4dSqWRoaIiEhASioqIwm834+vrKn+d9DbjdbmJjYxeEWHeluN1uhoaG+Mtf/sKuXbsIDg7m85//PDk5OYvSTqQtoIWFhURGRvLaa69RXV2Nn58fQUFBqFSqRRGnBAUFMTw8LC8GhYWFYbfb5ewmjUZDdnY2MTExNDQ0oFKpUCqVNDY2smbNGgICAs6LU9LS0lixYgUNDQ3ExMSwefNmOjs7UalUFBUVERgYKMcp4eHh2Gw2lEqlvHV5scYpkrje0NDA888/T1dXF5/97GdZv379oswKFwgWET+83BOKT0jPW/y5ewLBPCOl/vf29vL222/T1dXFsmXLWLduHQkJCfLERdwElxaSbzWZTNTX11NbW4tWq+WOO+4gOzv7mol13q1BKysrOXv2LGFhYRQXF5OZmUlwcDBw/exteHiYXbt2yd1eNBoNW7duJTY29rp8vuDaIdm4w+Ggt7eXxsZGTpw4gd1up6SkhJKSErlY+GLwb1IWVktLC++99x5TU1Pk5eVRXFy86LNsBFeOZN9TWL8DegAAIABJREFUU1OcPHmSyspKJiYmWLNmDeXl5QQGBi4Ze5Bq7Zw5c4aDBw8yOjoqF0qXhPal8l0F/41U062/v5+amhqOHz9OeHg4d9xxh1yEWZx3geCac9mLTAg1AsF1wjvoq6+vp6qqCpfLRX5+Pvn5+eetuogb4+JEEuUmJiZobm6mqqqKmZkZCgsLWbNmzXUNeCV7m5ubo62tjdraWs6dO4dWqyU9PZ1ly5aRnJyMwWC4phNQ7+K+EiLoX5xI59LlcjE2NkZ7ezsNDQ309PQQGBhIQUEBBQUFREZGLmpRQ/qekshaX18PQH5+Prm5ucTHx8s1YARLC8mHT01N0dLSQm1tLSaTifT0dEpLS0lJSVnS92nJ9kdGRqipqaG+vh4fHx9WrVpFbm4uERERYvK+yJF8uMlkoqGhgZqaGjweDyUlJRQVFcmFkcU5FgiuG0KoEQgWGm63m8HBQY4fP05jYyPT09OkpaVRWFhISkoKBoMBtVotbpYLGCngsVqt9Pf309zczKlTp3C73aSkpFBUVERycvKCqV3gcDgYHh6mpaWF06dPMzQ0hFarJTU1lYyMDJKSkvDz88PHx2dRT7QFVwcpO8tms2EymWhvb+f06dMMDw/LHaRycnKIj4+XM2eWIi6Xi8HBQRobGzl58iSjo6PExcWxYsUK0tPTCQgIQKvVAmJys9iQCrtbrVZ6eno4ceIEra2teDweEhMTWbFiBWlpaTfs1g+n00lvby/Hjh2jubkZh8NBVlYWBQUFxMfH4+vrK4SbBY5k47Ozs3R3d9PU1ERbW5tc/2/VqlWy+CwQCOYFIdQIBAsZl8vF5OQkra2tNDc3MzAwgFKpJD4+nuzsbBISEggICFhSLR8XI1LAMz09jclk4sSJE5w5c4apqSkiIiLIyckhLy9vUdQhkvajT05O0tbWRltbG4ODg9hsNrk9dEpKComJifj5+WEwGERAvoSRtv1YLBampqbo7Oyks7OT3t5e5ubmMBqNpKenk5WVRUxMjFwY/EazB6kwbmdnJ83NzfT09GC32wkPDyc1NZXMzEyCgoJu2OOz0PF4PFitVsxmM319fZw6dYqOjg6sVivR0dEsX76c7OxsgoODF13x/2uJdO8bHx/n5MmTtLS0YDKZ0Ol0JCUlkZ2dTXR0tCxaiuM2f0i+3Gw2Mzg4yMmTJzlz5gyzs7PExsaSn59PVlYWQUFBQpwRCBYGQqgRCBYL0ir21NQU7e3tnDhxgq6uLhwOB+Hh4SQmJpKRkUF4eDjBwcHySi6I1dyrgbdPdLlcTExMMDo6SkdHB52dnYyMjAAQGhrKsmXLWLZsGREREXJwuljPgZQdZLfbGRkZobOzk46ODrnjklarJTQ0lKioKJKTkwkNDcVoNOLv739RYdDFegyWKhfe5+fm5piammJiYoK+vj7OnTuHyWTCYrGgUqnw9/cnOTmZpKQkEhMTMRgMaDQakWV1Ad4ZdT09PZw+fZrW1lbMZjM+Pj7ytZKUlERISAhGo/G8iZE4llcfb1v3eDxYLBbGx8cZGBigra2NgYEBuRNPTEwM6enpZGRkEBQUJIvr4rx8Mm63G4fDwfj4OGfOnKGpqYm+vj4AoqKiSEpKIi0tjbCwMPnYSojj+/fjbefSeRgZGeHMmTNyRyq1Wk1kZCTLli0jIyOD0NBQYeMCwcJECDUCwWJEuj6lVcD+/n66u7s5c+YMIyMjzM3N4evrS0hICElJSURHRxMeHi5ndHhPrMSN+XykvfjSZEsSZIaGhujs7GR4eJjp6WnUajXBwcGkpKSQnJxMXFyc3CUClu5x9bY9t9vN5OQkw8PDDAwM0NnZyejoKBaLRe4sExgYSHR0NNHR0QQHB2M0GjEajfKqtHdwuFSP2Xzhfa6k82WxWJicnGRiYoKRkRF6e3sZGxvDbDbjcDjkTlsxMTEkJiYSERFBeHj4eS3vxXm6MryP/9jYGP39/XR2dtLV1cXk5CROpxM/Pz+5dXVUVJTckUu6TsSx/2QutHcpI3VsbIyRkRG6u7sZHBxkamoKl8sld55LT08nNjaWyMjI87JlxLH++5DOh+R3+vr66Orqor29XW5hLXVjSk5OJioqShZwpO3d4lxczIV2Lgkyo6Oj8n14ZGTkvIWU9PR0EhMT5axHcVwFgkWBEGoEgqWGx+ORU/DfeecdDh8+TFJSElFRUczMzKBUKtHr9YSGhhIZGUlwcDDBwcGEhobKaflqtVpuVQlL52YuTZbcbjdOp1Ne9R4bG2N8fJzx8XFMJhNDQ0PMzs7icDjQ6/UEBQURERFBcnIy4eHhBAUF4e/vv2Rrb/y9uFwuZmZmMJvNmM1mBgYGGBwcZHx8nKmpKWZnZ+X2qJIthoSEEBgYKLc/DgoKQqfToVKpZFuUHkvFHv8WvCf9brcbl8slP8xmM5OTk5jNZqamppicnGRkZISpqSnsdjsOhwONRiO32Q0NDSUuLo6QkBD8/f3F9oR5wFs8Gx0dpbu7m6GhIUwmk5zlkZKSQlhYmOyvg4KC5J8+Pj4XXSNL8fx5271k79J207GxMSYnJ2X/bTKZmJubk4VHo9FIWFiY3CbcaDTKLdaX4rFaDLjdbmZmZmTBuLOzk6GhIcbHx5mdnZXvDREREURERMg2Hxoail6vl2MUye6X0nm8lJ3Pzs4yOjoqxynDw8MMDw8zNzdHb28vra2tctHfZcuWyYKXwWBYUsdGILjBEEKNQLBUcLlcjI+P09jYSH19PRaLhezsbEpKSoiJiUGj0WCz2Zibm8NqtWIymRgcHGRiYoLx8XEmJiaYm5vD4/GgUCjQ6/X4+fnh7+8vPwICAuQ6CxqNRhZ1vIOmaz1h8J6kXhjMSOKLw+HAarUyOTnJ1NQUZrMZi8Ui/5yZmcHtdgPg4+OD0WiUA8Hw8HCioqIwGAzodDo5k0AEO38/0mTLbrdjs9mw2+3Mzs4yMjLC6OioLOxID4fDIb9PpVLh6+srPwwGg/xTslODwXDexPXCh3Qeve3zep1Xb7u9MBC/1GNubo7p6Wmmp6eZmZlhZmaG2dlZZmdn5X/bbDZ5dVWpVOLr6ysLXf7+/hiNRllY1Gq1+Pj4oNVqxQR1gSJlqDU1NVFRUcHU1BTFxcWsXr0ai8Uii51SRtT09LTc3l6lUsnXgnQ9+Pn5yfbg5+d3nq/+OJ99rfy29B0l2/cWzL1/SrVipqammJ6exmKxYLFY5H/b7XbgowUEX19fOUsvKCiI8PBwIiIi8PX1RafTodVqRU2gRYTL5ZLjlJmZGYaGhhgeHmZiYkJ+2Gw24L/Pf0BAgGzrfn5+BAYGYjQa5cYLl7L7ay38XypO8Y5RnE4nDoeDmZkZOU7xjlGmp6eZnZ2V4zGtVktQUJAcp0gClq+vLx6Ph5aWFqqrq5mcnCQ3N5e1a9cSFRUlfL1AsLgRQo1AsJjxeDyMjo7S2NhIXV0dFouFnJwciouLiY2N/dRFD6WgwjuImJmZkUUOacIordhbLBb5td4PKQCSWtRqNBrUarX8U6FQyHVLvDN2lEqlLJxIKesXZg1IgY3D4bjoc5VKpTzhkAIxvV6P0WgkICCAgIAAWWySJrHSmKT3wdLJHFrMSPce6dxK59/hcMgTNkmokH5Kge3MzIxsH95CiCSOSDbnHah7B/KSjUrPARcF82q1Wv7bCoVCLqYpIdnupQJz7wmq9Drv8XhfPzqdDoPBIAtQ0sPX11eejOv1+usmkAquDR6Ph9nZWU6fPs3Ro0cZHBwkMzOTsrIy4uLiLunDvbf2SA+73S77aEnQkP5vNpvPuza8H5JP9n54+23vCS2c77elcXmPR7p+nU6nfG04HI7zrgXv69Lbd0v+W6fTXeS3JfEpMDAQnU63pDMqBJfmQpt3OBxMT0/Lgp70mJqakjM3vW1O8sEX2rtk69Lik7e9ewsdkr+XxiL5fe8sXW97v1R8dOHnGgwGAgMD5bjkwjjlQkFVGselkLZq19XVUVNTA8CqVatYtWoV4eHhIgNYIFh8CKFGIFhMSAHxxMQEx48fp7a2lpmZGbKzsykuLiYhIeGarqB4BymX+iltr7jUQwqSJEFGCnI8Hg+HDx/Gz8+P1atXA8groN6TCCmQUqvV+Pj4yP+/XM0d72Mggvily4VFQi/3b6nIpRRMX+5x4YTT22a9/+29Sn+pf1+YbSZNAiQbliYHcGlbFfa7NJHsyuFw0NHRwdGjRzl79iyxsbGsW7eOZcuWXdWuQpfz1dK/vQXwC/21w+E4T1SE//bbLpeL5557ju3btxMcHAxwyQmut1Dv7bOlx6XqkAjbF/ytXOoecGFNl4+LUyQBURL3pYw1m83G7373Ox566CH0ej2AXABcsnvvOEV6eMcq18vWpfvd4OAglZWVNDQ04OfnR2lpKQUFBQQEBFzVzxMIBNcMIdQIBAsdKViYmpqSxZnZ2VkyMzMpKSm55uLMtcbj8bB//360Wi233XbbfA9HIBAIrjqSKDI0NMTRo0dpamoiKCiIsrIyli9fjr+//6Ly4S6Xi6eeeopHH32UsLCw+R6OQHBNsdlsPPHEEzz55JP4+vrO93A+NZLf6erqoqKigjNnzhAVFUVpaSlZWVno9fpF5XcEghuMy16c6ss9IRAIrg9SzZmmpiZ5W1NWVhaf/exniYmJWTJFP6XsA2nlSiAQCJYKLpeL0dFR6uvrqaurQ6VSUVxczGOPPUZISIjYjiAQCK4ZUtH+9PR00tLSmJ2dpb29nYqKCnbv3k1qaiqlpaUkJiai0+mWREwpENwICKFGIJgHpPatkjgzPT1NdnY2O3fuXFLizIWoVCq5cKxAIBAsZjweD1NTUzQ3N1NRUcH09DQFBQU88sgjRERELOoMSIFAsDhRKBQYDAby8/PJzc3FYrHQ1NTE/v37mZqaYvny5axZs4bY2Fh5W5dAIFiYiCtUILgOeNecaWhooKamhpmZGbKysrj33nsX/bamT4tKpWJubm6+hyEQCARXjLRVfG5ujlOnTnHkyBGGhobIyMjgnnvuITEx8Ybw4wKBYHGgVCoJCAigtLSUtWvXMj4+Tn19Pc899xwej4dVq1ZRVFREWFjYde2OKBAIPh1CqBEIrhFSoTfvmjMWi4XMzEzuu+8+WZyBG6fYm9j6JBAIFhtSYdLu7m6OHj1Ke3s70dHRrF+/nmXLlqHT6YAbx48LBILFhSTChIaGcuutt3LLLbfQ399PdXU1v/jFL/D19WXt2rVyEWIh2ggECwMh1AgEVxmpdaL3tqasrCx27txJbGzskt3W9GkQQo1AIFgMSMU5BwcHqaqqoqmpicDAQNasWcOOHTsIDAy8Yf24QCBYvEgiTFxcHHFxcWzZsoWenh4qKip4++235SLEy5Ytw2AwCD8nEMwjQqgRCK4CUs2Z5uZm6urqMJvNZGdnc88999zw4ow3KpVKbnssEAgECw23283o6CgffvghdXV1AKxatYpvfetbBAcHo1Kp5nmEAoFAcPXQarWkpaWRmpqK1Wq9ZBHi5ORkfHx8RBwrEFxnhFAjEPwNeNecaWxspKamRs6c2bFjh6hVcBlERo1AIFhISHVnLBYLjY2NfPDBB5jNZgoKCvhf/+t/ER0djUqlEr5cIBAsaRQKBb6+vuTl5bF8+XKsViuNjY3s27ePqakpcnJyKC0tJS4uDqVSKXyiQHAdEEKNQPAp8a45IxUElmrO3HvvvbI4A6JWweVQqVQ4nc75HoZAILiB8S4K3NraygcffMDAwABpaWns2LGD5ORkIbQLBIIbFqVSicFgYM2aNaxZs4axsTGOHTvGH/7wBxwOB6tXr6akpETOMhS+UiC4NgihRiD4BFwuF5OTkzQ1NVFbW4vZbJbFmbi4OLGt6QoQW58EAsF84fF4sNls9Pb28sEHH3DmzBliYmIoLS0lIyND1GMQCAQCLyR/GBoayi233MLGjRsZGhqipqaGX/ziF+j1ekpKSigsLCQwMBClUjnPIxYIlhZCqBEILoFUc+bEiRPU1dUxMTFBf38/jzzyCKtXrxbizN+ItPWpra2NoaEhysrKLnscnU4nu3fv5q677sLPz+86j1QgECwVpKLANTU1NDY2yv7k7rvvZvXq1WJy8Qm4XC7efPNNtmzZQkhIyGVfd/bsWc6ePcvmzZuv4+gEgquLx+Ph4MGDFBUVER0dPd/DWVCo1WpiY2PZvn07d911F319fVRUVPDee+8RHh5OaWkpmZmZV0X09ng8HDp0iPT0dOLj4y/7OpPJRGVlJVu2bBG+XLDkEEKNQMB/p8J715wxm81kZWVx9913k5CQQEdHh1wY2Ps9QrD59EhCjffKi3QcL/Xanp4eRkdHOXnyJEVFReJYCwSCT0TyKWNjY9TX11NdXY3b7aa4uJhvfOMbhISE0N3dLU8mvH2Q8DEXo1AoSExMRKfTXdZfA1itVsbHx6moqCAnJ0d0xhIsWuLi4jAYDBfZu7Dnj1AoFGi1WlJSUkhOTsZms9He3s4HH3zAnj17SExMpLy8nLS0NDQajfyeKyU2NhY/P7+P9Tsul4uhoSEaGhqIiIggJiZGnCfBkkEINYIbFqnmjNlslmvOSNuaduzYQVJSklxzxmKx8Oqrr7J9+3Y6Oztpb28nJSWFbdu2zfO3WFyoVCocDgcffPABU1NTuFwu9u/fj0ajobCwkDfeeIOcnByGhob49re/zRe+8AVefvll6urqCAkJIS0tbb6/gkAgWIBIBd5nZmZoamri6NGjmM1m8vLyeOihh4iLi5Pb0trtdvbv309ubi5tbW3U1NQQEhLC5z//edHV6RLY7XZeeeUVEhISqKuro7e3l8zMTJqbm4mIiKC/v59169ZRVFSEXq/nG9/4BrfddhuPPPKIPEkTCBYLVquVV199lQceeICzZ8/S1tZGWloaW7dune+hLUgUCgU6nY6cnBxycnKYmZnhxIkTvPnmm0xMTMhFiGNjY6+o9pfVamX37t3cdtttmEwmWlpaiImJwWKxMDs7i81mIyUlhS1btnDnnXfyve99j/T0dL797W+LLGzBkkEINYIbDqnmTHNzM7W1tUxNTbFs2TJ27NhBfHz8Jbc1+fn5ERAQwNTUFEeOHGHTpk2sXLlynr7B4kWlUuHxeEhJSeHPf/4zJ06cwOFwsH37dlQqFVarlR07dvBP//RPmM1mwsPDSU1NxWAwCJFGIBBchMfjkVvKHj16lJ6eHlJTU9m6dStJSUmX9OcajYbo6GhGRkY4d+4c0dHRbNiwQaTNXwYfHx/UajXT09McOXKEe+65h7y8PDo7O4mNjSUrK4v333+f8vJyEhMTSUhIYPv27UKkESxK9Ho9/v7+TE5O8v7773PzzTezatWq+R7Wgkfys35+fpSUlFBUVMTY2BgNDQ288MIL2O12Vq5cSXFxMaGhoZ9YhFiv1xMcHMzExARHjhyhpKSE4uJiqqqqmJiY4I477uA//uM/2Lp1K3FxcSQkJLBjxw4h0giWFEKoEdwQuN1uxsfHaW5upq6ujqmpKTIzM9m+fTuxsbHodLpPpfKrVCq+9KUvsW/fPqqrq3niiSfkrVCCT+bC9twbN27EarXy4x//WF7NVqvVaDQa0cZbIBBcEo/Hg91u59y5c1RWVtLa2kpkZCTl5eV8/vOf/9T1ERQKBf/jf/wP9u/fz7/+67/y9NNPExAQcB2+weJErVbzla98hX379vHhhx+i0WhQq9VotVpRJF6w5FCpVHz5y19m37591NTU8IMf/EDEe1eAUqkkLCyMzZs3s2HDBkwmEzU1NTz77LNotVqKi4spLCwkODj4Y/21Uqnki1/8Iq+//jpVVVXk5+fLfkfKpBQIlipCqBEsSbxrzjQ1NVFTUyOLM1LNGR8fnytKwRwaGqK7u5vOzk4KCgo4cuSICE6vEGnr08DAACaTifr6eoxGI1FRUQwNDTE2Nsbo6ChjY2NMTk4SHh6OwWCgs7MTk8lEWFiY2HssENyASAH54OAg1dXVNDQ0oNPpKC0t5e6778bf3/9TZ8RIxYVdLhe1tbVkZGTQ3d2N0+m8xt9iceJyuRgdHaWrq4uJiQlWrFhBXV0dbreb4eFhnE4nY2NjOBwOeQLV3NxMSEiIyKoRLDpsNhvDw8OcO3dOxHtXCbVaTXR0NNu2beOuu+6it7eXo0eP8t577xEWFkZZWRk5OTn4+voCH4noNpuNoaEh9Ho9w8PD5Ofn8+677zIyMoLdbmd0dJTx8XHm5ubQaDT4+fnR1NREcnIyer1+nr+xQHB1UHyCEilkSsGi4cKaM5WVlVgsFjIzMykpKSE5OVmuOXOlk/3Z2VlaWlrQ6/X4+PgwPj5OcnKyEA6ukO7ubl5++WW2bt2K2WwmOjqa/v5+goOD8ff359y5cyQmJso/w8PDsdlstLS0kJCQQEhIiDjeAsENgiTOTE5OUldXR3V1NS6Xi1WrVlFcXCz73yv1CXa7nZaWFgCMRiNDQ0PExsYSGxsr/MsFuFwu/vmf/5nS0lKSkpLweDyYzWaSk5MZHBxEo9Hg4+PD5OQky5cvR6PRMDAwwOjoKNnZ2fI9VyBYDNhsNr7zne9wzz33EBQUJOK9a4Q097Tb7bS1tfHBBx/Q09NDQkKCXITY7XbT0tKCj48Pvr6+jI6OkpCQwNTUFDabjeDgYAYGBli+fLn8/Llz58jOzkan083zNxQIrojLOhYh1AgWNZI4MzU1RVNTE9XV1YyPj5Oenk5paSnJycmilfYCore3lxdeeIHvfe97oh6EQCC4JG63G4vFwsmTJ6msrGR8fJzs7GzWrl1LbGysyNK4jrhcLp566ikeffRRwsLC5ns4AsE1xWaz8cQTT/Dkk0/K2R2Ca4/H42F6elr2+WNjYxf5fBHHC5YwlzVusdQhWJR415ypqalhbGyMzMxMtm7dSmJiInq9Xjj1BYharcbtdos9xQKB4DwuVxT4zjvvlFtDC58uEAgESw+FQkFAQABr1qyhqKiIyclJjh8/zosvvojdbqewsJCioiIiIiJEZz7BDYUQagSLAmliPzk5SVNTk7zKumzZMrZs2UJKSorInFkESMWEhVAjENzYSD7A5XLR3d1NRUUFra2tcr2CL3zhC+j1epF5JxAIBDcQKpWKkJAQNm3axPr16xkZGZGLEOt0OkpKSuQixHDlpQwEgsWEEGoECxapPoHFYqG5uZmqqipGRkbk1qtpaWlyCrxw1IsDlUolMmoEghsYabvq8PAwVVVVHDt2DL1ez9q1a9m6dSuBgYGA8OkCgUBwI6NQKFCr1URFRbF161Y+85nPcO7cOSoqKnjmmWcIDQ2lrKxMrlEjvUcgWEoIoUawoJCCeKk+QXV1tSzO3HHHHSQnJ4sU+EWMWq0WbbcFghsM71pix44do7q6GqfTSUFBAf/wD/9AZGSkyJwRCAQCwSVRKBSoVCqSk5NJTk5mbm6Ojo4OKioq2LdvHwkJCZSVlZGSkiLmCIIlhRBqBAsCKYg/deqULM6kp6dz++23i5ozSwCXy8Xk5CTT09PMzMwwODiIXq8nNDRUnFeBYIkiFYg8deoUR48eZWxsjJycHB544AFiYmJEgcgFjNRty2q1YrVaGRkZwePxEBQUJIo5C5YcbrebsbEx5ubmmJubw2Qy4efnR3BwsBCRFyA6nY6cnByys7OxWCycOnWKgwcPYjKZ5CLEcXFx4h4jWPSIrk+CeUGyO7PZfFHmzJo1a0TmzBJjfHycz33uc7S1tTE5OYlWq2XTpk38/ve/F4XhBIIlguTXbTabXBS4q6uL5ORkysrKSE1NFbXEFgkul4vvfe977Nmzh5mZGfR6PbGxsezevZvo6Oj5Hp5AcFWZnp7mgQceoLGxkZmZGQwGAyUlJTz//PP4+PjM9/AEnwKXy4XZbObYsWNUVVVhs9koLCykuLiYqKgoFAqFuPcIFiqi65Ng/vGuOXPixAmqqqoYHR0lJSWFW2+9lbS0NPmGKJzp0sJoNBIfH8+BAwcAUCqVFBcXi5UqgWAJ4PF4cLlc9PT0UFlZyYkTJwgNDaW8vJwHH3wQX19f4dMXGUqlkpKSEn7+85/L21WLiooIDw+f55EJBFcfg8FAZmYmb7zxBh6Ph/Hxcb761a+iVotp0mJBpVIRFBTExo0b2bBhAyaTiZqaGn7961+jVqtZu3YtK1euxGg0CtFGsGgQGTWCa4okzkxPT9PS0kJ1dTUmk4mUlBTWrFkj9pPeQBw+fJg77rgDq9VKREQE77//PsuWLZvvYQkEgo9BihEu9NGSOCN15Pjwww/R6XQUFxfLHTmEX1/cDA4OUl5eTkdHBxqNhueff5777rtPnFfBkqS6uppNmzYxOztLcHAwhw8fJjc3d76HJfg7cTqd9PT0UFVVxcmTJwkNDWXt2rVyEWKxYChYAIiMGsG1wel0Mjs7S0BAwHm/l2rOnD59WhZn0tLSuPXWW0lMTBQrrDcgK1asICcnh/r6egoKCkhKSprvIQkEgo/BbrdTV1dHQUEBBoMB+Mi3T0xM0NjYSE1NDVarlRUrVvC1r32N8PBwsQK9hIiIiGD9+vV0dHQQExNDeXm5uG8LlizZ2dnk5+dTVVXFypUrSU1Nne8hCa4CarWa5ORkkpKSmJubo7Ozk8rKSrkIcWlpKSkpKefNS2ZmZujp6SEjI0MIOYJ5RURUgr8Zi8XCL3/5S8bGxvjJT36CSqViamqKlpYWampqMJlMpKamcsstt4jMGQH+/v7cfffdNDQ0cPvtt4t93wLBAsZsNvOzn/2M//zP/2Tv3v/P3nvHR11l///PyUwyk2Qy6Y30BCK9iXS+Igii4AdZEVxZV4VFXV2x7QqCul2xbbGgILuC4NJtiLKKItIUpCYQSCO9TpLpfeb9+4Pf++5MCkXp5PV4zCMz77zrfb/uueece+45G+jbty/5+fls374dvV5Pz549ufPOOzsTNl7BUCgU3HbbbaxYsYKu0jd+AAAgAElEQVSxY8eSmJh4sW+pE504b4iIiOCWW25hz549TJo0idDQ0It9S504h1AoFISGhtKrVy969OiBzWbj6NGjfPnll7z//vt0796dUaNGkZGRwQ8//MDMmTP5wx/+wPTp0zv11U5cNHQ6as4CrZeJne63vM3n8+Hz+cRveb23/N0/tFypVAqF1/97UFAQQUFB7SrDrbed7vdPhSRJVFZW8vTTT7Nu3TpSU1NFeHRjYyNZWVncdNNNIudMpwJ/ecKfz2fCdXm7P6dbc3/kyJGkp6czaNAgTCZTQCLh9rh/KfC9E1cXTsX7U23zl+1Am37g/7+goKAA7vtHoZyNrG9v20/lvyRJ1NTUMG/ePFavXo3H4+FPf/oT/fr1IzMzkwkTJpCbm9sp2y8DdMTl9jjsz1FZbkuSRPfu3enatSvXX389NptNzC7LnO2Ir/6/O2V0J843zobrrfUUj8cjto8YMYKUlBSGDh2KxWIRXJX53lpPl9Gpl1xeCAoKQqvVct111wl9dN++faxYsQKPx8OhQ4c4ceIEv/71rykoKGDu3LnodLpz8h5/rC3Znv0of/fXtVtzVJbR7f3PH6fb1snhi4OrMkeNP6n9P16vF6vVisViwWq1YrPZRKk+p9OJw+HAZrOJj9vtDlBoOvoOBITO+XcYf2NUPk5Ge98VCoVQiuTO1/p3cHAwoaGhhIWFERYWhkajQaPRoFar0Wg0hIWFER4ejlarRavVolQqA87VulO3brddu3bxxBNPsG/fPiRJQqlU8uCDD/LQQw+Rk5PTmRD4EoLMP6/X24afbrcbq9WK2WwWnG6P71arFYfDgcfjEcpNR5yXJKmNg8Wf+5IkUVBQQI8ePQgKCgoYkE7F99Ycl78rlUqCg4MFp0NDQwO4rtFoCA8PF3wPCwsTg1Rrvnfy9cqBrNS0J+ddLpeQ8VarFbvdLvgu/7Xb7eJ/Ho/nlHLeX277c92/H8h8k+E/Nsi/W3/353lHcl+pVLaR9TL31Wq1+J/Mf41G025/kn/7fD4OHjzII488wu7du8U9du3alS1btpCeni6epxMXHv7Kur/eYrfbMRqNmM1mLBYLNptNlNWWtzkcDrxeb8Cx/h9JktroJvL348ePk5GRgUajCXA++t9Ta3kq6xWyjJb1DVlOh4aGEh4eTkREBJGRkWi1WlQqVZvjO7l29aE9vcXn8+HxeDCbzZhMJqG3yDyXdRV/rrfH8/b0FH/ee71ejh07Ro8ePQK415rv7ekQreWyv94hcz4sLIyIiAh0Ol0bzvs78Dt5f+Ehv9ujR48yYcIEqqurgZNOuilTpvDyyy+Tnp7exmZrzS2v14vNZsNisQTIY6fT2aEtKfP1bHSM1vqG/PtUtqQ/f09lRwYFBaFSqc7YlgwPDyc4OLjNeTr5fMa4OnLUyB3E5XLhdDpxuVxYrVaam5sxGAxCuBuNRkwmEw6HQwwIMnH9FVt/xTcsLIyYmBhB2LCwMIKDgwOUifYMP/+/MlorQf6d3v9+WivyHRkf/tu8Xq/IGyMPXLJwaG5ubiMg7HY7Pp8v4H7UajU6nY7IyEh0Oh0RERFERUURHR3N119/zR//+EdqamrEvXm9XtxuN7m5uZ35CS4gJEnC4/EIrrtcLsxmc4d8d7lc4jggwMiT+S4LYJ1OR0JCQoADpLVCcSqHYWsvvD+/XC6XcOa1x3f5e0fOz/YMb5nrdrtdGNxms1kocP59Qe7r8v2Eh4ej0+kE13U6HdHR0cTExBAaGkpISAhqtZqQkJBOw+Eiw1/Gyx+73U5LSwstLS0BSrzJZMJisQQo2EAbR7bMe1nuJSQkCCVb5n1Hcr6187Aj2e7/u7Vs70jWt8f71r89Hk8Ax/25r9fr28h6WQb4GyiysRwREYFer2fRokWcOHFCzLwplUqampooKioKUFI7ce4hc0FW5h0OB83NzTQ2NtLS0kJzczMtLS0YjcYAZ19ISAgRERHCGSIbhjExMWi1WiIiItBoNO06QtqboIFA49XlchEcHNzmPuXvrSe8Wv+WnaNms1nI6YaGBjFRIBsz/rI5PDyc6OhoIYtjY2OJj48PMBg6mhnuxKUNmTP+hqvBYBA8NxgM4uOvt8jjdWueh4eHEx8fL7a35npHn/ZktUKhwOl0olarO9RPOtJFZL7LzlOZ37IzyWAwCGdSe5xXq9VERUUJfTsqKor4+HiioqICHPAdRRt34qdBbtOysjLMZjMqlUq80/Xr11NWVsZjjz1GWFgYJpNJfOT3CP/jqVqtFnq1zFP5HUZFRQVwV9YtO+Jo6+/+93s6WxLad9Scyo701y/8dQh5TDIYDAETWrJ+4fV6A3gp61T+unVUVBQxMTFEREQQEhIi9GuVStXJ6Q5w2UXU+Hw+bDYbZrMZs9lMQ0MDtbW1QnkxmUw4nU6Cg4PFTLss8GTCyIZYeHh4G2VF/g1XtgewtXPH/7vNZsNgMIj2NJlMGAwGamtrKSkpobq6Gr1ej8Viwev1EhISQnp6OnPnzqVLly5C6ZejdTrx4yG/D9nhUltbS319vVBiLBYLHo9H8F2r1RITEyOcbLLDLSoqitDQ0A6V9Cud6+0NQrJCZTabhWNL/siGv8PhEJFzYWFhoj2jo6NJSkoiLi5O8D00NPSKbscLBTmyUVZyZc7LMl6eMVWpVAQHBwvFJzo6WigDsnNZp9OJWZ7Wcv5qUXZb87+1vJflu9FopKamBovFgtFopKmpKcDh26VLF7KysoiKiiIyMpK4uDgSEhJEe8uzw1dDm54L+Mueuro6KioqaGxspKmpCavVikqlEoZbfHx8gMMiOjpaKPeyM83fWXg5Qeamf7SPHDmh1+uFg6qpqQm9Xo/dbsftdqNWq4mOjiYuLo6kpCRSU1OFkSsbs524uJAkCZfLJcbU6upqqquraWpqoqWlBbvdjlKpFDyXuS1/YmNjhd7iH6F1OessrZfDtnbuyG0jf/R6PUajEZfLhc/nEw7M+Ph4kpKSSE5OJjIyksjIyE7enwUkScLhcAhbsrGxkdraWpqamti7dy/79+9HrVYLB3FcXBzx8fHiu6xjR0VFERERESCHr6ZoklM56mXnZGtbsqWlBYvFgtvtFrJcq9UK+R0fH09iYmKAbnE59/kzRIcPd0k5alp7rA0GA/X19VRVVVFWVoZer8dkMuH1esUsYEJCAomJiQFeaJ1OJxTG1h7HTpw92ot0kCQJi8UiBhPZkVNTU4PRaMRut6NWq4VRm5aWRlJSEomJicTHxwco9Ffre/FvV4/HQ2NjIw0NDVRWVlJZWSkcMZIkiaiPxMRE4uLiBN/lGdOOZvQ7cfZoj++yUSVzvampiZqaGhobGzGZTHg8HsH3mJgY0tPTSUxMJCEhgZiYmDazd1cjWkeROJ1O9Ho99fX1VFZWUl1dLTjv9XqF00V2CMgOR9lR0Cnjzw86imyzWq3CcdPc3Ex9fX2AvFcqlWi1WiIjI0lMTCQlJUUoXFqtFuCqek/+7ej1etHr9VRWVlJYWEhVVRUGgwFAtFdqaipxcXHi0zqC72pos47QmpM+n084cvR6PVVVVVRUVNDc3IzX60Wn0xEfH09WVhapqamkpKSg1Wo72/I8wN/xIE8mlZWVUVpaSlNTEzabTTjTk5KSSEpKIjY2lri4OGJjY4UcvxomSM8W/m0r64my86alpYXKykohS7xer9A/MjIySEtLIyUlhcjIyKuubVvLC6PRSH19veCmbEvKTgKdTkdsbCxJSUlCz5An+OVE+Z16xk9HR7akf1S0PHFRW1sboFvIk6QpKSl06dKF+Ph4EhISApyTl/l7ufQcNfILkkO3y8vLqaiooLy8nMbGRux2O6GhoWK2JC0tTSjo8oxJwINc3i/oskdrHnk8ngDvaXl5OVVVVej1epxOp3i3mZmZpKWlkZGRIZaTXY6zgqeDrFzK7VJWVkZlZSVlZWW0tLTgcrnQarUkJCTQpUsXUlJSiI6OFl771pFJV1r7XG5ozXeXy4XRaMRoNKLX6zlx4oSYnZEdy0lJSWRkZJCRkUFKSgpqtVosJ7gS36cs391uN42NjZSXlwsnpNlsxufzCUdMSkoKycnJAZGPrZdRXoltdLmiNf8lSRKOHDkyRx7LTSYTQUEnEzempKQIeZ+UlERISEhAks7LHTLnLRYLRUVFFBUVUVpaisViEc+fnZ0tjNXo6OgA2X4ltMGFRmsuylEJDQ0NFBYWijFWrVaTnJxMbm4u11xzDfHx8SIhdme7nzlkjttsNk6cOEFxcTElJSUYDAaCgoJITEwkPT2djIwMYmNjiYmJITw8POAcne3909Ga9zabjZaWFhobGyktLRUOCYVCQXR0NF27dqVr165kZGSIJb1XwnvwtyWdTqdw2paVlVFfX4/VaiUkJEQ4YtLT04mNje20JS9RtOa17JQ3Go20tLRQUVFBZWUlDQ0NIiBAdk7KckfWHy+zKJyL76jxV2AqKyspKiqirKyMpqYmFAqF6ECZmZkkJCQEJP7sxJUDeWbWarXS2NhIWVkZFRUV1NbW4vP5iImJISsri5ycHNLS0oiIiLgsS7/6h/yWlpZSUlJCeXk5JpOJkJAQUlJSBN+jo6PRarUixLcTVwbkmXR5TXptba1wSDc0NBAUFERSUhJZWVl07dqVpKQksVzwcuS7vCy1oaGBoqIiysvLqa6uxu12o9PpSEtLEx+dTkd4eDgajeaye9ZOnDlkOSgvY6uqqhLOuubmZpRKJcnJyWRmZpKTkyOiby4XI0Lu40ajkePHj3PkyBHKy8uRJImMjAxyc3PJzs4mKipKRHRcDs91JUFe4mAymaiurqawsJDi4mIsFgsJCQl0796dXr16kZCQ0CmP2oGsuxsMBo4dO8axY8eoqKjA6/WSkpJC165dycnJEXknLpe+e6VDkiTcbjdms5mmpibhUKutrUWlUpGVlSUqukVFRV02703WNSwWCzU1NcKWrK+vR5Ik4uPjhW4t61RymovL4fk6cWaQl1ZZLBaam5uFbl1VVYXb7SYyMpKsrCyysrLIzMxEp9Nd6ksDL46jxuPx0NTURHl5Ofn5+ZSXl+PxeIRxkp2dTUpKChqNprPU51UMeUBxOBzo9XrKyso4fvw45eXlKJVKsrKy6NWrF1lZWcTFxV2SAld+hoaGBkpKSjhy5Ai1tbUApKWlCedTfHy8SHR3qT1DJy4M5ATIdrtdRB4UFBRQV1eHVqslJyeHPn36kJqaSnR09CXrvPP5fDQ3N1NdXc2RI0coKyvDarUSGRlJZmamGCDDw8NRq9WX2+xGJ84T/BPm2u12qqurqaio4NixY9TW1hIWFkZ2dja9e/cmLS1NLI+4lOByuaipqeHAgQMcOXIEh8MhDJ/c3Fx0Ol2nTnOJQnau2e12amtrOXLkCIcPH8Zms9GtWzcGDRpETk5OwFKpqw1yhFxFRQX79++npKQEn89HVlYW11xzDV27du3k+GUIWe5aLBahpxYVFREcHEyPHj0YOHAgaWlpl1y+PZ/PJ5Z65efnU1ZWhs1mIzY2luzsbLKzs0lPTxfFHy5VnakT5xeyU1lOwl9eXk5hYSGlpaX4fD4yMjLo2bMnOTk5JCQkXGpBAOffUSOfx+12U1payp49ezh+/DiSJJGenk6fPn3o1q0b0dHRl62RKiuYp5sVa539239f//Zu7xz+1+honzO9xx9z/KUAuY28Xi8Gg4GioiLh6AsKCiI7O5sBAwbQrVs3QkNDgQv/nPI92mw2jh07xvfff095eTkajYacnBx69eollL3LNaHY2fD9x3D9bK5xJtc+1XUuZfiH7srRKHl5edTW1hIaGkqPHj3o378/mZmZF22ZiPwuXS4XZWVl7N27l4KCAjGr2qtXLxEV5J875nLE2XDqYnP/dNe4XOCfC0qv11NcXMyRI0eorKxsI/PDwsKAC/vM8v01NjayY8cOfvjhBzQaDQMHDqR///6C9z/lvlrrDTJ+DP8uJ7T3DO21xfnsR3J4/dGjR9m7dy81NTWkp6dz/fXXk5ube6kp9OcF8mRTYWEh27Zt48SJEyQmJnLttdfSq1cvYmNjf/Ik2dly/FS69OUGmadw+lxdZ/vcP6YP+OvZjY2N5Ofns3//fpqbm+natSvDhg27KNz3v6+Kigp++OEH8vPzcbvddOnShd69e5Obm0tCQsJlWxTgUrcl/a8rH3u5tTEEcsnfOVlSUoLL5SIzM5OBAwfSvXv3Nnn0LgLOn6NGDm0uKipix44dnDhxgvj4ePHwiYmJV0wiq4aGBl599VXuueceevbs2eF+DoeDlStX4vF4uOeee4QzAaC0tJRvv/2Wu+++u91lXW63m3feeYeYmBimT59+1p3rwIEDfPnllygUCu677z7i4uIu+3b3FxpNTU0UFBTw3Xff0djYSGZmJqNGjSI3N/eChLVJkoTNZiMvL48dO3ag1+tJTU1l0KBBdOvWjZiYGLHv5dzukiSxe/duNm7cyHPPPRfA4dY4ceIEa9as4brrrmPMmDEByvaGDRvo2rUr/fv3b/fY2tpaXnrpJR577DEyMjLO6h6dTifvvfceBoOBqKgo7rnnHlH2+3KG/+BSW1vL4cOH2bdvHzabjZ49ezJixAjS09MviMNbXjZQWFjIjh07qKioIDY2lv79+3PNNdeQkpJy2ToiO4LH42HVqlVYrVYeeOCBUyrTO3fu5Ntvv2Xq1Knk5uaK/7lcLt555x3uvPNOYmNj2z3+hx9+YPXq1fz5z38+Zf9qDz6fjwMHDrB161aeeOKJK24G0V/m6/V6jh8/zp49e6ivryc5OZkRI0bQq1ev875cVJ6BPnToEF988QVut5uBAwcyZMgQkpKSgHPHe0mS2L59O1988QW/+93vWL58Ob/85S+JiIhoNxrtVPxrvZ/P57tkl5GvXbuWTZs2MW/ePLp37055eTnp6en88MMPrFmzhj59+pCRkcENN9zQ7vEGg4EXXniBu+66i379+v2ke/GfgMnLy2Pbtm20tLRw3XXXMXr06Es6wvHHQOZGXV0dW7duJS8vj/j4eIYPH07v3r2JiIgAzi3Hv/76a3bs2MFjjz3GsmXLmDlzJmFhYR1y/JtvvuG7775j+vTpZGdnn/I5LlWO+3w+vvrqK/bv309wcDCSJHHHHXeQlpbW5pnlxMFff/01u3fvZt68eafVa3bv3s1nn33Gs88++6N1IJn7ZrOZw4cP8+2332I0Ghk4cCCjR48mPj7+vMtaj8dDeXk5O3fupKCgAJ1OR//+/enZsyepqalXjK7R0NDAK6+8wn333UePHj063M/hcLBixQq8Xm+7tuT27du5++67230vbrebJUuWEBcXx7Rp087alvz73//OJ598QnBwMC+99BIDBgw4u4e8BOHv7zAYDBQWFvLdd99RXV1NYmIiI0eOpHfv3oSFhV0MjnV4wR8dT+zz+WhqauLbb7/lwIEDREZGMmzYMO68804iIyMv+47UHuSSbFarVUR7hISEEB4ejtlsFrkYTpw4waFDh5g/f35AoipJkoiOjuaGG27A6XTidDoBUKlUaLVaUSo1OTmZ6upq4KTQdLlc6HQ6zGazKPnm8/nQarVYrVZR9cfn87Fs2TIee+wxDhw4wIYNG3jggQcuSludS/h7hOXyeKNGjcJkMlFcXMzWrVt5//33GTBgAKNHjyYhIeGcDijyTG9VVRXffPMNx48fJzU1lfHjx5OTk3OxOvV5hUKhoHfv3qxYsUKsBbVaraIMocFgELzdvHkz6enpXHvttQHnkCSJQYMGERkZKRINut1ukXfI4XAIPjscDpHrQS5LazKZCA8Px+FwiDb2er1iRr2yspIffviBV155hXnz5jF27NgOFbnLCTKXVCqVyOly880309zcTH5+PmvWrMFmszFy5EiGDh16XuSt7CT65ptvyM/PJykpiWHDhjFjxowrVr7LUKlU9O7dm48//hhJkkQZSZ1OJ8oGh4aGEhwczNq1a5kxYwZpaWkB51AoFIwbN46wsDCam5tRqVR4PB5RgcNisRATE4PRaBRL4UwmE2FhYUjSySoI4eHh2Gw2IiMjcTqdBAcHCyVcTuSZl5d3MZrovMNf5ickJJCQkMDIkSOxWCyUlZWxc+dO1q5dS25uLmPGjCEjI+OcOi5lB83u3bv58ssv6dKlC9OnTyczM/MnOYNlR7/NZkOr1WK328Vz6nQ6+vTpw+rVq5EkiQkTJhASEsLKlSsZNWoUWVlZAc/ncrlYt24dd911F6mpqaKKlE6nw2azYbfb0Wq11NTUsHv3biZOnIhCoSA8PByTyURkZCQmkwmNRoMkSSiVSpH0WKPRBNy3nOtCqVQiSRI6nS5AXoeFhWEymcT+sj4iV2ezWCy4XC4iIyNxu90AwugYM2YMn3zyCUlJSZhMJl555RX+9Kc/kZWVRf/+/Rk9ejQajQaTySSMOXk5pcvlwu12o9VqRQloQOhkOp1OJGw/E8jtGx4eztChQxk8eDB6vZ4dO3bw0ksv0aNHD2655Rbi4+Mvexno9XopLS3l008/pbm5mZEjRzJ37lyio6N/ctSMP8dtNpuYkY+MjKRv37589NFHKBQKbr75ZoKCgli+fDk33nhjG8eF0+lk3bp1zJo1i+TkZFpaWkRfkTkeERFBeXk5Bw4c4KabbiIoKIjQ0FDMZnMbjgcFBQk9pnUSWZfLhcViaZfjwcHBaDQazGaz2F+hUCBJkuC42WzG4/Gg0+lwu90oFApx3cLCQlauXMnLL79MTEwM27dv56WXXuKvf/0rPp+PiIgIjEYjOp2OHTt2IEkSPXv2ZOPGjaI9rVYr4eHhhIaGigpbcLJqXE5ODgaDQYxPERERZy2n5HbX6XSMHDmS4cOH09TUxPfff88///lPkpOTmTRpEunp6efUISZJJ6syfffdd3z33XcEBwczdOhQJk6cKCpmXmk4U1uytLSUvLw8nn766XZtydGjR+NwOE5pS9bU1ABnZ0tK0slcP6+//jrh4eEkJydflHY61/CXLdHR0QwZMoTBgwdjtVopLy9n+/btbNiwgR49ejBmzJgA5+DFxFk7aiRJoq6ujo0bN1JaWsrQoUN59NFHiYmJuSQe6EJh/fr1IiHdzJkz2bFjB9XV1aI6VUlJCSUlJW0Inp+fz6effsrPfvYzFi1axMSJE9m+fTsvvvgiGzZsoLKyEqvVSmJiIhUVFSxatEjkfKitraWhoYHevXuj0+kYNWoUb775Jo8//jharRaLxYLRaCQmJoZrrrmGr7766iK1zvmHPOgPHDiQAQMG0NLSwu7du/nnP/9JamoqkydPJiUl5ScrU16vl5KSEj7++GPMZjM33HADP/vZz4iIiLjsFbUzhcViYdGiRURFReHz+bjllluEAf/ggw9SUFBAWFgYo0ePJioqShzn8Xh47733GDhwIEePHkWpVOLxeIiLi2Pq1Km8+OKLZGRkUFFRgSRJfPLJJxw5cgSLxcKkSZNYtGgR06dPJz8/nzvvvJOqqiqqqqqYMWMGgKiGdeDAAfH7SkVQUBBxcXGMHj2akSNHUl9fz9atW3n++ecZMGAAEyZMICoq6pzx/ZNPPsFgMDBq1Cjmzp0rnDNXC+dllJeX8/bbbxMZGUmPHj2Ijo7m6NGjFBUV8Zvf/Ibi4mL2799Pnz59Ao4zmUy8+uqrzJkzh7/97W+MGzeOPXv2MG3aNGJjY1myZAk5OTkYjUY8Hg+LFy9GkiRMJhM33HAD//znP/nNb37Dnj17+PWvf82GDRvo168fgwYNAkCr1TJ8+HD++9//XoxmuShQKBRERETQp08fevfujdls5tChQ7z//vuoVComTpxIr169zsowbw8+n4+jR4+yevVqUlJSePjhh0lOTj4nBoPD4eDvf/87MTEx9OrVi5UrV3LddddRWlrK2LFjxfs1Go289NJLPProo2zYsIGQkBARRSejqamJoqIi9u/fj8Vi4ZtvvkGSJG699VaRtNlisZCSksK2bdsICQlh27ZtPPfcc8ydO5c//elPvPbaayQnJxMTE0NFRQU6nQ6n08njjz8eYBg0Njby5JNPcuedd/LZZ58xf/587HY7W7duJT8/n3vvvZcXX3yRCRMmsGXLFvr164fD4aBXr14MGzaMN954g6ioKLp27UpoaCher5fJkycDEBMTQ1paGoWFhURHR5OUlER+fj5qtZpevXrx6aefotFoCAoKori4mKSkJCwWC3PmzOHVV18lOjqaoqIiRo0axaJFi7Db7Tz00EO88cYbPPbYYyQkJPzo9xUUFERCQgJTpkxh/Pjx7Nixg1deeYURI0Zw0003tXFoXQ6QJIn6+nrWrl1LU1MTkyZNok+fPucsItlqtfK3v/2NuLg4+vbty7/+9S9GjBhBYWEhEydOFNHozc3NLFy4kMcff5wNGzag0+kCIjThJO9KSko4cOAAer2enTt34vF4uP322ykpKRGJQ3U6Hfv370eSJL7//nt+97vfMX/+fF544QVeeOEFsrOziY2Npbi4mOjoaLxeL3PmzAlwZtTV1fHUU09x1113sWnTJn7/+9/T3NzM9u3bycvLY+bMmTz//PNMmjSJzz//nMGDB2MwGBg8eDB9+/Zl8eLFRERE0Lt3bzGhNGHCBAAOHz4sSgorFAquvfZaFi5cyIEDB1ixYgUvvfQSc+fO5be//S2ffPKJKEMMJ2f+33jjDZKTk6moqODJJ59k+/btHD9+HJfLxcCBAxk8eLB4hlWrVjFr1iwR8fdjERQURHx8PBMnTmTs2LEcOHCApUuXkpaWxtSpU4mNjf3JDr2WlhY2b97MwYMH6devH7NnzyYxMfGSy0l2PrFu3TpKS0sxm83MnDmT7du3U1NTI6oXdWRL5uXl8dlnn3Hbbbfx1ltvMXHiRHbs2MHChQtZv349VVVVWCwWkpKSAmzJrKwsqquraWxspFevXkBTFwMAACAASURBVERGRgrZ+dhjj4klQC6Xi23btqHVarnllluu2ETrCoUCrVZLr1696NmzJyaTiX379rF06VIiIyOZPHkyOTk5F9VheMZXlmf51q9fzyuvvEJWVha///3vmTx5MvHx8VeVk8blcrFx40a6d++Ox+MhLy+PoUOHkpGRwfHjx+nZsyc9evRgxIgRAccpFAoyMzPxeDyiusWtt94KnByQvvrqK+677z7GjBlDUFAQu3fvxuPx0L17d7Zs2cKvfvUrTCYTTqeTiRMnEh0dzbx580TpQzlkEk4KWa/Xe2Eb5iJAoVAQFBREbGwsEydO5Pe//z19+/blzTff5P3338dqtQaEu50pJEmiubmZxYsXs2LFCm688UaeffZZRo8ejU6nuyIFVkcoLy/n2LFj9O/fn127dqHRaIRiYDKZ6NGjB2PHjqVLly4BxwUHB5OWlobX6yUxMZHMzEz+7//+j5KSEgoKCvB4PMycOZNu3brh8XjYuHEjubm5BAUFUV1dzRNPPMHKlSu59dZbyc7OZuTIkUyfPl2c3+PxoNFoKCsrQ61Wi5naKx0qlYqUlBRmzJjBM888Q2hoKH/5y1/E0owfy/fGxkbeeustli9fzpgxY3juuecYP368CPe/mjgvY+fOnUiSRG5uLps2beKaa66hb9++1NbWipnMiRMniigvGVFRUURGRqJWq4mKimLQoEEMGTKEkpIStm7dysCBA/n5z39ObGwsBoOB7du3069fPwoLC0lMTGTq1KmsXbuWX/3qV0RGRnLPPfcwcODAi9QKlx7kWfWRI0eyYMECpk6dyhdffMFLL70kHL9nC3kZ95o1a1i9ejX33HMPDzzwACkpKedMSfN6vZSVlYnomaioKG644QZ+8YtfiNlzQFQCjIiIIDU1lbFjx7YxXpKTk8nJyeHmm29m27ZtJCUlkZCQwJYtW7j22mvp3r07hYWFpKSkMHDgQIYPH44kScTExBAZGUlwcDDx8fEMHjyYkSNHkpeXx4ABA9i7dy9NTU0B15KrVo0ZM4bMzExqampITk7muuuuE4UAYmNjmTp1KkOGDGHIkCFMnjyZY8eOsXv3blwuF927d+ezzz5j/PjxTJo0SZxboVAwduxYdu7cSUNDA1OmTGHXrl00NDSQm5tLZmYmLpeL5ORkkpOT+dnPfkZlZaWo8DF79mz69u2LSqXijjvuoL6+XkQcxsfHn5P3JivzN910EwsWLECv1/Piiy9SV1f3o7h2MSAvD9q+fTuvvPIKffv2ZcGCBQwaNOicGmEyx6Ojo4UhOG7cOKZPnx7A8bi4OMLCwoiMjCQtLY2xY8e2sSNSU1PJzs5mwoQJfPXVV6SlpRETE8O2bdsYNGgQ11xzjYhwvu666xg2bBg+n4+4uDgRVRIXF8eIESMYOHAgx44dY8CAAezatUtEpMiQ+9yNN95IcnIydXV1Ylm7w+EQk0zTpk1j0KBBDB8+nIkTJ1JQUMD27dtRKBTk5uby6aefcuuttzJu3Dhxbo/HE/BsciSDHP2t0+mIi4sjKCiI9PT0gEmvI0eOoFAoeOCBB4iNjWXHjh1s3ryZGTNmcN999/Hhhx+KyJ/ly5dzzz33kJiYeE7eJZzkfmhoKMOGDeO5554jOzubhQsX8v333+Pz+c76fHIupC1btvDXv/6VsLAwnn32We666y5SUlKuKieNy+Xi008/FbZkfn4+w4YNIyMjg2PHjp3WlnS73SQmJhIREcGkSZPw+Xzo9Xq+/vprYUsqlUp2796Nz+eje/fufPnll8yePRuTyYTb7Ra25Ny5c4UtqVAomDZtmlhu9fbbb1+M5rngkAMAbrjhBp577jnGjh3LihUrWLRoES0tLRdN1p+R9iFJEg0NDbz44ovYbDb++Mc/cuONN15ymcEvFBQKBUqlkqCgIH72s5+h1WpZunQpHo/nrF6kPEMtL+mQQzP9nS1KpZKkpCSefPJJfD4fffr04dChQ1gsljbnk0OW7XY7VVVV5OTknLNnvhwgh5qOHDmSZ599FpVKxfPPP09NTc1ZvRdJkjh27BgvvPACOTk5PPvss/Tv3/+qSCjYHmS+BwcHM3fuXNavX8/BgwcDkuOd6Xlkg8flcokB2Z/vQUFB3HrrrYwePRqVSkV2djZ79uxpVyE4evQo8fHx/OIXvyAhIYH8/Pxz8LSXD2RjdfLkycyfP59jx47x+uuvYzabz5rv+/fv58UXX6RHjx784Q9/YMCAAZ0VPfifDO7SpQsPP/wwCxcupL6+/qzPI3NfXuakUqkCkv3JSREfffRRUlNTRch3YWHheXiqKwcKhQKVSkVubi5PPvkk48eP5/XXX+frr78+KyNCdtIsXrwYi8XCggUL6Nq16zmfRdNoNMyfPx+DwcCSJUtEP/V4PKc1UDrq0zK3lEolgwYNYty4cbz88ssBkxQyz9qT2UqlEpVKJXSaefPmdZhXSb6WHAV25MiRgP/J55N/yzqNUqkkMTGR3/72t23aVI4wOHr0KADdunWjsbERp9PZxgHqfx232y3OJT9TVlYWERERfPjhh/Tt2/ecyy9Zkb/vvvu46aab+Nvf/vajHYMXGj6fj3Xr1rF9+3bmzp3L9ddff15kfFhYGPPnz6e2tpZ3331XbG/trGiN0+kTsn4wfPhwhg8fzquvvordbm+T+PR0HFepVMyfP5/IyMh2r+PP8TfeeIPjx48HnF++F/mvzPGgoCBSUlJ44okn2rRp9+7dqa2tFfdbWlpKly5diIiIOCs9SpZ3/pB/u1wuNBoNn3/++Xnho0KhQK1WM27cOJ588kk+++wzNm3adFYTwpIkYTabee211ygoKGDBggVMnjz5qopQ94fMNdmWDA8PF7YkdCzzT3Uuj8eDz+fr0Jb87W9/i9frpU+fPhw8eLBdWxJORnVqtVqysrJobm7+6Q97GUGhUBAcHEz//v155plnyMnJ4fnnnxcFki40TquFyAlc//GPfzB+/Hjuvvvuq7ZsocFgoLKykpKSEsaPH09eXh6NjY1ERETg8XjweDwYDAZRmrmioiLgeJ/PR0FBAbW1teLYEydOoNfrMRqNDBkyhKVLl7Jjxw5qamoYMGAAHo+HkpIS7HY7b7/9NsOHDyciIoK3336b+vp6XnzxRaxWK3BSYE+ePJmVK1eyfft2pkyZcjGa6aJDoVAQFhbGtGnTuP322/n73/9OXV3dGR0rSRLHjx/nX//6Fw8++CDjx4+/Kg1WSZIoKiqioaEBhUJBVlYWR44cEfkB5HwaRUVFlJeXk5+fj81mCziHzWYT0TNVVVWUlZVRVlZGfX09GRkZWK1W3n33XSoqKqivr2fChAnk5+dTX19PYWEhmzZt4v7772fjxo3s2rWLHTt2sGbNGnH+Hj16YDQa+de//kVTUxO9evW60M10SUChUBAbG8vDDz9M165dee2110Tui9NBkiS+++471q9fz+OPP86YMWOuWoekDK/Xy7Fjx6iqqqJHjx64XC5KSkpQqVRoNBqMRiNqtZrDhw/T0NBAXl6eUKxk1NfXU1tby4EDB2hubqa4uJjKykpqamoYNWoU27dvZ+3atTQ2NuJ2uxkyZAh5eXlYrVa2bNlCbW0t06ZN49VXX6W8vJz33nuP/fv3i/NbLBa2bdtGQ0ODWA5wNUN2dg0aNIh58+bx3Xff8dlnn52Vs2bdunWEh4dz7733nrdJKLPZzAcffIDX6yUjIwOv18vHH3/MunXrmDp1qpC5P/zwA3V1dRQVFZGYmMgXX3yBy+UKOFdTUxMNDQ3k5+dzww03UFZWRlVVlUjMarFYRILVkpIS6urqUCqVfPjhh6ICRn19PQUFBcTGxtK9e3eOHDmC0+lsY1AbDAYMBgPFxcXU1dVRXV0t+kJYWBiHDx8Wif4rKyspLi6msLBQLNX2+XwUFxcTFBTE5s2b2bRpU8D55aSh6enpBAcHc+2119K1a1fcbjfHjx8X4f/yOCLrXZGRkSxdupTCwkIqKytRKpXcfPPNNDU1nde8CkFBQQwZMoQZM2awePHigNwllyIkSeKLL76gvLycxx9//Lzm2DEajSIHTXp6Om63mw8//JCPPvqIqVOnUlhYSH19PXv37qW+vp6SkhLi4uL48ssv20TFNjY20tjYyNGjR7nxxhspLi6mpqZGcNxqteJ2uwkJCeHYsWM0Nzfj9XrZuHEjVquVkpISGhsbKSgoIDk5mezsbI4cOYLL5WrjMGxpaaGlpYXi4mLq6+upqqoK4PihQ4cEx6urqykqKqKoqIj6+noGDBiAw+GgtLQUpVLJxo0b2bJlC3BSNvXp04cRI0bw1ltvsWzZMtavX88jjzxCbGwsUVFRrF+/HpPJRGVlJampqWzbto2dO3dSX19PbGwskiSxePFimpubGTFiBBMmTGDlypW8++67TJkyBb1ej0KhYMqUKXz++efs3bv3vBmUCoWC5ORknnrqKQ4ePCiiTk8HOdfOa6+9xjXXXMNDDz1ETEzMValr+NuSN910k7AHtVqtsCVbWlo4evQoNTU1VFZWBhzv8/k4duwYdXV15OXlodfr27Uld+7cKWxJt9tNSUkJNptN2JJarbZdW1KSJNasWcO7777Lxo0b+fnPf34xmumiQ6FQEBISwoQJE3jggQdYunTpRXHWnLbqk8/n47XXXqNPnz4BFV2uRng8HpE8TzZW5XXzcqI9eWbM5XLh8/kCEuypVCqioqLweDyEhITgcrkIDw8XScJUKhUmk0kkM5OTTckzS3JyQLvdjs/nEwnV5ARQgEgiJSeVuprfFyDWLH/zzTc8+eSTp81fYLPZ+POf/8ysWbPIycm5attPXuooJ96TJAmr1UpoaKhIDilXL5BnVGSuytBqtWINuMxPlUqF0+kkKioKh8OBw+EQ0TqyUiR7s91ut+hnGo2G4ODggGTC8sBvt9vRaDSEh4dfte9Lhs/nY9WqVSiVyjOqGieH8D/55JM/eU37lQI5V4zb7SYqKgq73Y7T6RQOeafTKWao5O9Go1H0A4VCQVRUFJIkERwcjMfjQa1WC2eKnMxPXqoqrwk3mUyEhISIGVq5n8lJjP2TCctyXj731Toj2RFMJhMvvPACs2fPPqME41VVVSxatIhnnnmm3SiOcwU5Cag8fj/99NNMnz6dbt26odPpsNvt2Gw2kShXzs1iNpvbJDKNj4/H5XIFJKH0er1CL5Bn6sPCwrBarYSFheF2u/F4PELGyvzV6XQimWpwcDAtLS0Bzj95GUZYWBgOh4Pg4GCCg4OxWq1CLnu9XjQaDS6XS0SIyQm0bTYbTqcTrVYrlFz/CiZyRJPsJHa73eIccnupVCpxDTnJtsfjwWaziX2VSiX5+fmEhIRckKWCknSysqEkSUydOvWS7YNNTU0sXLiQBQsWBOSROx9ozfHf/va33HfffSLayWaz4XA4CAkJwel0Cqeo2WwOmN2X87K5XC7UarVIWC1JkjhPexx3uVxCFqvVaqFj6HQ6HA6H4Kzs1IH/RUrB/zgeEhKCSqXCarWiUqnw+Xx4vV5CQ0MDnJlerzeA4xEREfh8PhHhLcPr9YqEw6GhoULOWK1W4TiS+5W/PSGfT34+/2TCclSt2+0WY4XZbEaj0Zz3IhfyculXX32VZ555RlQIO9X+q1atIigoiDvuuOOqSpfRGufTltRqtSiVSmEHykndz8aWlPV9u93eqV/8/5AkieLiYpYtW8aCBQvOh57w46s+tbS00NDQwKhRo676F6VSqQJCgv1LMUdHRwfsK0kS5eXlAaHBYWFhjBo1qk0COn+FpXXIcUREhBCAMjH8k/y1TqCqVCrb3MvVDIVCwXXXXcemTZtoamo6rTFaUlJCQkLCVe2kgf9FJfkLI3+etq6Y4PV6KSgoQK/Xi22ZmZnthp/Lhml4eLhYEyvDv0/JiIuL6/Ae2zvH1Qx56dhf//pXbrvtttMmu9y7dy+DBw8+p2vaL3f4K+0QKIPVanUA3yIiIrDb7ezduxeHwwGcfAf9+vUjPT29w2u0F3bfHs/lPte6v3XK+VMjIiJCJD0/E0fN/v37GTp06Hl10sBJbsjvzWQy4XK5KC8vZ8CAAQQFBXUoz9xuN3v37hWGpWx4+udg8TfAW/NL5k9reeB/LY1Gg0ajwel0UlRUJCIkFYqTFQDldvRvozNNpuvfh9qDvKxChn+S1/Z4Lt9DcHCw6CMOh4Nly5YRHR19waKJFQoFI0eO5PXXX2fq1KkX5Jo/Bvn5+fTp06fD5T7nEv4cb2lpwe12U1FRQZ8+fQgKCkKr1QodwB8ul4u9e/eKKDiVSsXQoUMD5KI/F86U4611mNDQUBwOB4WFhUJmKxQK+vXrR0ZGRptj2uN4e330dBxXKpXtOsnaa4v2lh76619KpTJAV1IqleI+O1q2eK4hO4uzsrIoLCxsU/WzNTweDwcPHuTpp5++qp00cOnbknJOrva4ebVCoVCQk5NDfHw8JSUlbQpInE+c1lFjs9kICwv7yZUUfgzkUozyGr6ODGfZqy57IM9mXbnP5xOzlT81g7n8kWd3MjMzyczMFEnc5DWEchSCJElifal/ElB5m9frDViTKnv05bXk8iwWIGZg5f38127Lbeg/AyDPUMn5cfyvJa8t9z9O3iZf3/9+TrWPPCt3Lkunni1kg14elE8Fo9HYoWPgfMM/OkVuz1Pt63a7Lxrf5XuQQ+uVSqVIeOa/5tqfEz6fT8gRf77LfJZnes+E7/KMsLyfLB/kdmnvPGfC99bb/Ll8Jn3iUuC7rEQ6nc7TGlJGo/GiRtLIfDwdj1vLzDNt27PpU2d6rzIf5XNpNBrGjh0rZKp/Lhp/uSvnMJP7nswn+ZnksUHuU/J3/z4gH9d6PPGX+e3xu3UfkO9HjkKQ7wUCueu/xr11P27vXuRj/MfCi4XY2NiA5WKngtFoJCsr6zzfUSAiIiL4xz/+cUacjIyMDEjAe6aQ3+nZcD8kJITRo0efUu/yl5fyuVvrFe0d4y+rzzXUajUPPPDAOT/v6aDRaC75pYdGo/GCGfD+iIqK4o033jij9x0dHS2Ka/xYnI6DMjQaDTfddBMQOEbIuZxao71x5HS61Pnm+6WCqKioM1r6J4+H59sZ3hHOlBs/Vmdoz9b6sWjNHdmW9P+fvx3mr5e0pyf76xn+Ob5kHcFfFwECxndoO/53ZCf6n0fOB9WRnuOvI/jv439tfzuiPV3oQkOhUBAXF4fBYLig1z2toyYuLg6z2Yxerz9nGfTPFB6Ph+XLl+PxeE47AG/evJnDhw8zbdq0M5pBk1FeXs7LL7/MM88806ZqzdnA6XSybt06FAoFv/jFL8R2STqZiHnRokX8/Oc/p3v37lRVVbFy5Up8Ph9jxowhJyeHWbNm0dTUhM/n4/HHHyckJISSkhLMZjO33nor3bp1480330SlUhEREcGsWbNYuHAhmzdvRpIkevbsydtvv01hYSErVqwQIdwbNmygsLBQVNgJCwtjyZIlIrnfXXfdxR/+8Ae2bt2KRqNh0aJFZGZmiopJISEhzJo1i+DgYIxGIy+88AK/+93vUKvVrFixQoQ2z5gxA5VKRWNjI3//+9+ZP38+hw8f5ssvv0ShUDBp0iQGDhx4UTqX1Wqlubn5jGagMzIy2Lx5sxiALyR8Ph8bNmwQJRhP1VbyGubbbrvtrHKz1NXVsXDhQubMmUPXrl1/9L3K2eobGhp48MEHxXZJOllycfHixYwbN45BgwbR2NjIsmXL8Hg8DB48mMGDB/OrX/2KqqoqfD6fCIs+ePAgdrudG264gWuvvZa33npLCPcHH3yQd955h9WrVwOQnp7Ou+++S01NDUuWLOGpp54iJiaGzz77jP379+Pz+bjrrrtISkrirbfeEjN5s2bN4m9/+xsfffQRarWaV155hX79+rFmzRqamppQKE5WV1Cr1VitVl544QUefvhhoqOjef/998WyhJkzZwb0iaeeeoqKigo++ugjlEolo0ePvmhRiCdOnCA0NPSMZkO6devGtm3bGDVq1EUxquvr61m4cCG/+c1v6NatW4f7Wa1Wli5dilar5e67724TYdIRJEli48aNFBQUMHfu3B/9jB6PhzVr1lBTU4NarWb27Nli9qqhoYGlS5eiUqlIT0/njjvu4IMPPqC8vBybzcaDDz5IY2Mj69atQ6VSMWzYMMaOHcvu3bvZtWsXHo+H6dOnk5KSwubNmykrK2POnDlYrVYWLVoE/M8I1Wg0+Hw+Fi9eTN++fRkxYgTFxcUsXbqUefPmERUVxZIlS1ixYgXBwcH86U9/YuDAgaIPREZGMnPmTL788ksOHTqE0+nk7rvvxmKxsGnTJiRJIicnh2nTptHS0sKSJUsYM2YMgwcPRq/X8+6774p+PHbs2DZjm4zVq1cTHh4uSjBfDOzfv/+UnPJHTk4OeXl5jBgx4oL1WYXi/JW7l5XaLVu28P333/P73//+rI7997//jUaj4Ze//GW7+3i9XiEz58yZA0BBQQGLFi3ixRdfbBNZ4PV62bNnD5s3b+a55547L7LmYhnDdXV153050U9FdnY2n3/+OTfffPMFbafzyXF/yMbqnj17+Oijj3jppZfO6rpr165Fr9fzyCOPtPt/SZLYtGkT+fn5zJs3D6VSSVlZGS+//DJ//vOf20zu+Xw+Dh8+zNq1a3nuuecuyzLuZwKPx0NxcbGoAnoqhIaGolarqaysvCgR60VFRfzzn//kL3/5yymdlg0NDSxbtozs7Gxuv/32M75Pt9vNO++8I/Kc/VhIkkRBQQHvvfceCxYsCJClLpeLL774gpKSEh599FE8Hg+rVq2irq4OjUbD7Nmzeeutt1i3bh1w0p55/vnn+c9//oNarUaj0XD//ffzxRdfcODAAXw+HzNmzMButzNnzhyRuuD1118nNzeXZcuWcc011zBu3DgqKyv597//TUhICLm5uUyZMoWPP/6Y4uJi7HY7s2fPprKykqeeegq3283//d//MXfuXPbu3cu3336Lx+Ph9ttvF2PymjVr0Gg03HbbbWzbto19+/bh8XiYMWMGqampSJLE0qVL6dq1K4MGDeKNN94QKT1+9atfBURdXijIOdNGjx59Qa972ql4jUbDmDFjeO+99844QeW5QnBwML1796alpQU4mVisuroat9uN2WymvLyclpYWrFYrH3zwAddffz0pKSkB5zAYDJSVleFwOERiMpvNRnl5OY2NjaSkpKBWq7HZbFRVVeFyuaiurhYVExoaGqiurhZrCuVkS60hJyJrXRFETsZcXV0tMr5/9NFHDBw4kLvuuov//Oc/KJVKnn/+eTZs2MCjjz7K4MGD6dmzJ3PmzGHw4MH897//FQr9I488IpLrTZ48mQ0bNvDGG29w6623IkkSBoOBEydOiDWNGzduZPbs2fTp04cPPvhAVOuZNWsW27Ztw2g00qNHD1atWsWqVavIyclh586d2O12HnroIe655x6xNvzbb7/l2LFjeL1evvjiC7RaLQ8//DDTpk1DqVTidrvZunUrhYWF2O12Vq1axdSpU5kyZQr/+c9/Lsqsk8vlYvny5QwbNqzNMrH20KVLF1JTU/noo48ueHlzpVJJ7969hbe2qalJcNJqtVJeXk5TUxM2m40NGzYwbNiwNk5Js9lMWVkZNpuNpqYm6uvrsdvtImFvQkKCWI9aVVWF0+mktrZWVArS6/VUVVWJPtbRTInJZMJgMFBdXd3mf3q9nrq6OiwWC5Ik8d///pfMzExmzpzJunXrcLvdLFiwgA8++ICnnnqKkSNHkp2dzZw5c7jxxhvZuHEjBw8epL6+nkceeYTGxkby8vIYM2YM69ev55133uG2225DqVTS3NxMWVmZyFWwbt067r33XkaOHMmaNWs4fvw4er2e+++/n3379qHX60lLS2PlypWsWbNGZL6vrKzkoYceYvbs2YSEhODz+di1axf5+fm43W527tyJ0+nkoYce4pe//GWbPuF0Olm9ejUTJkzgF7/4BatXrz6jCK5zDYPBwPLly5kyZcoZGUO9e/fG4XDwzTff/KhSmz8VCQkJREZG4nQ6cblcVFVV0dTUhNfrpa6ujoqKChwOB0eOHKGkpISbb745YID2+XxUV1dTW1srjrfZbOj1esrLy3E4HPTp00ckQ5Xlf3V1tYh6qaysFPkKGhsb2+33dXV17Nq1i9/85jcYDAYOHDgg/rd3716hnH355Zc0Nzfz6aefMmvWLHQ6Hbt27WLt2rWMHj2ae++9l7Vr11JfX8+qVau47777ePzxx0lLS8NisWA0GkXiwIMHD9Lc3MycOXOora3lyJEjSJLE0aNH2bNnj8hJ0tLSIvoAnJxcWb58OevWrWPo0KEcO3YMo9HI7Nmz2bNnD3q9nvXr13PnnXfSrVs3vvrqK2JjY3nwwQe56667+OSTT3C5XO324/T0dGbNmsXatWsxm80BY5uMEydOsHPnzotWKUKSJA4ePMiRI0cYNWrUGR3Tr18/SktLKSsruyhVHc4l5ATha9euJTU1FYPBQE1NDc3NzbjdbqqqqjAajZhMJoxGIxUVFdjtdhwOBxUVFbS0tJCdnS3GAnlssNlsVFRUoNfrxVgllyxtamoSOZt8Ph8GgyEgubzT6cRoNFJaWnrZt68/HA4HH3zwgYjOuFTRrVs3rFYrhw4duqLaH07y/fDhw7z33nskJiZiNpupq6tDr9fj8Xiorq7GaDRiMBgwm81UVFSIvDCVlZU0NjbSrVs3mpubxb6SJIn+0NDQACDGEXkySo4u8Hg8mEymgPw6brdb6OJXWnvLkCSJvXv3olKpSEtLO+3+crLj9957T+QaupDIzs4WkX+yLJPzvFVXV1NdXY3L5WL79u14vV6uv/76ACeNx+MRdqNcWdfpdFJXV0dlZaWYMG9paaGxsRGDwYDRaKS2tjZAfprNZtxuN3q9vt02kIvSnDhxoo0u0lpH5w3mjQAAIABJREFUqK6uZs+ePTzyyCPo9XoOHz7MjTfeyIYNG1iyZAm33XYbOp2Oe+65h/vvv59du3ZRU1Mj9OQRI0awZs0a4uLiWLx4MevWreO+++6ja9eu6PV69Hq9sEc+/vhjunfvzv33389nn31GQ0MDH3/8Mffeey/x8fHs2LEDhULBn//8Zz744AN+/etfY7PZWLlyJb/85S954oknRFRQWVmZ0BFaWlrYsGEDs2fP5rHHHiMpKQlJOll59/vvv8dkMvHdd9/hcrl45JFHRBLvCw2v18uHH35IWlraeU1U3x5OG1GjUCgYO3YsjY2NvP7668yePZuoqKgL7g09evQoy5cvJzo6mm7duhETE8OJEyc4ePAgTzzxBLW1tZw4caJNArnjx4/z7LPPsnz5cr766iu0Wi1paWnk5eWxe/du/vCHPwBgt9v54x//yF//+leWLVvGwIEDSUtL4z//+Q86nY7evXtjt9tJSUlpV/lLTk6me/fubUoEBwUF0bNnTxGN5PV6KSsr4//9v/9Hly5daGhoICgoiD59+mA0GgFIS0tDqVRSUFDAvn37GDJkCCqVSoSYRUZGUllZyfjx44VBOWrUKJFATw4tlEOXvV4vqampbNmyhVtuuYWNGzfy9ddfExsbS3h4OBaLhQ8++AC1Ws3tt9/O4cOHKSsr44033iAzM5NJkyZRUFCATqcjLi4On8/HgQMH8Hq9vPbaa/To0YNx48Zx4MABUlJSiIyMFCHRHo+H5ORk4XC4UFEqckLQf//730RERDBx4sQz4qxSqeTuu+/mzTff5P333+eOO+5Ao9FccL6XlZXx5ptv0qVLF2JjY8nNzeX48ePs2bOH3/3ud1RXV1NaWsqQIUMCjquoqOCxxx5jyZIlovTegAED+OGHH9i9ezdPP/00cNKB9dxzzzF37lw2b95MTEwMw4YN4+233yYxMZEuXbqg0+lQqVTcfPPNbe4vLi6O3r17U1ZWFrBdoVCQm5srotN8Ph8lJSWMGTOG+Ph4zGYzTqeTfv36YbPZsFqtdOvWjZCQEIqLi9m5cyeDBw8WzhKA+Ph4Tpw4wfTp05EkiX379jF8+HBCQkIYMGCAiByRl194PB5SUlIoLi4mMTERm83G119/TVhYmEia+fHHHxMcHMyUKVM4cuQIpaWlvPXWWyQkJHD77bdTXl6OJEmkpKQgSRKHDh2isrKywz4hh5C63W5iY2PFYO6/Zvh8QpIkqqurWbx4MSNGjDjj9bNqtZpf//rX/OMf/6ClpYVbb731olU5+/e//43VaqWuro7777+f3bt3U1lZSXp6OuHh4dTW1tLQ0BDgjPf5fCxZsgSAxx9/nEWLFjFr1iz27dtHXV0dDodD5Ks4ePAgn3/+OXPmzGH+/PksWrRIRGzW1NTwwAMPsHbtWh5++OE2+Q9qamoIDw9HrVaTmppKUVERw4cPB07mYtq6dStbt24lOTmZiIgI0tPT+frrrykrK2PMmDHk5eXhdruJ/v/Ye/PoOMsrz/9TVZJKJdWq0r6WJFuWLVu2bEne8BJDwNgEDCQsgcBMlpNJQieZyZxM90z3NEMacnp6ks7pbExymsCE0EkgpMEOsUHGLF60W5atzZZk7XtpKakk1f77w7/nSUmWwYAXyX4+57xHZdf21vt+3/vee5/73MdmY2xsjK6uLs6dO8dvf/tbAoEADz30EImJiRQWFsp7iEgGinLb8+fPk5ubS3NzM4WFhfI14deAOCb79+8nMjKSvXv3kpKSgsvl4siRIxiNRsxmM/n5+bz77ru0tLSwe/duUlNTGRgY4PDhw6xfv16OmqWlpcky5Pb2dnbs2EFCQgJTU1NMTk7OubfBhanS1dXVlJaWXvPEnyjNfuedd3jzzTf51re+ddlz7GNiYvjCF77Az3/+c7797W+TkpKyZKcrBINBjh07xsjICDk5OUxMTFBRUcHhw4f53ve+x//8n/+ToqIi7HY71dXV5ObmMj09jdlslo2FrVYrQ0NDlJWVcf78ef7bf/tv/NM//RMZGRmcOXOGr3zlK/L72tvb+clPfsL69etlUufAgQMsW7aMTZs2AReO7/r16+VI742Ax+Ph17/+NcnJyaxdu3ZR60Wv1/PlL3+ZH/7wh0RFRbFq1apFvb8flaqqKurq6li3bh1TU1PU1tayf/9+vve97/H973+fnJwcEhISqK+vJycnh+HhYRwOB1NTU5hMJpYvX47T6eS9996jtraWf/iHf+BHP/oRcXFxnD17lgcffFBWYfT39/NP//RPlJSUyEHkw4cPYzKZuO2224ALx3vDhg2XXfm51AiFQtTW1vLHP/6R73znOxctGb4QGo2GwsJC+vv7+T//5//wta997brYWZ/Pxw9+8APi4uLo6uriq1/9Ku+++y6NjY3ceeeddHd309PTg8vlIjExUb7P6/Xy5JNPsmPHDm699VZ+8Ytf8J/+03+irKxMVgmJXn9vvPEGU1NTbNmyhZ/+9Kf8y7/8C//7f/9vEhMT6e7u5otf/CKvvfYa3/zmNy+qDImMjJwTx4UTFxdHYWEhp06dAi4kakwmE3q9Xvq8n//85wmFQlRXV7N582bsdjsTExMcPnyYzMxMLBYLOp2OQCBAamoqra2tJCYmkpycTEdHB2lpaZjNZiwWCw6HQ54fMYUqNjaWQCAgpwsfOXKE1tZWaeuPHTtGbW0t69atIzc3l5aWFl5++WVZOWw2m2VvRJ/Px+DgIK2trbz44ouEQiEefvhhIiIiOHPmDEVFRfK7RZsNq9VKV1fXNVvlVSRtf/e73zE4OMgTTzxxzavPL6u5hU6n4+GHH6aoqIinn36ao0eP4vV6r2lGVGTrkpKSOHz4MDk5OaSkpDAwMIDNZiMzM5Ndu3ZdZBjXr19PQUGBzGx/6lOfIi0tjYyMDCYmJmRyRDiwIjscCAR4//33iYiIIDExkbfffpsHH3yQW265Bbhw8tra2jh48KCcanE5XOqYhUIhjh07RmFhoRSB1+slPj6ezs5OSktLSU1N5dlnn6W5uVnO6+vv78fv9y/YV8VkMvHII4/w4osv8qc//QmdTsfExMQc59Xn8/H444/z9a9/HZPJxPPPP08wGGTjxo188Ytf5JVXXqG5uZn6+nrWrVs3p1fE9u3beeyxx3jhhRc4e/YsbW1tFBQUyPmTX/rSlzh06BAvvfSSnA95tRE9SioqKnj66adZvnw5/+E//IePVCYXExMjy7mffvppGhsb5W++VlRVVTEzM0NKSgpHjhwhLS2NtLQ0RkZGMBgMZGVlsWvXroua2uXn57N161bOnTvH7Owsd9xxB8nJyTgcDmZmZmSz35iYGLk0osPhIBgMUl5ejs/nIyUlhbfffps9e/awe/du+dldXV0cPHiQysrKj11tJI5hKBSisrKS/Px8mbzzeDzYbDZ6e3vJz8+noKCAZ599lrq6OjnXVWTgF5qmaDAYZNXOq6++ikajweVyERsbK+fVzszM8OCDD/KNb3wDh8PBz372MwKBAOvWreOLX/wiZWVlnD59mvLycoqLi+fMy920aZP8/PBrQlz7jz/+OOXl5bzwwgtMT09fM73PzMxw4MABfvSjH7Fv3z5uv/32j9S3yGq18t3vfpexsTGeeeYZWTV3LfU+OztLWVkZqampuN1uzp07x6pVqzCbzXR0dJCXl8eKFStYt27dnPfpdDoee+wxenp6GBoaoqSkhKysLFauXIndbqetrU2eH+EUxsfHy1VDjhw5QmpqqqwO++u//mtZeScqqd58800GBgbmnM9wHU9MTMhVEQKBgKxACYUurE4yMTHBo48+yqlTp/jVr34ll01OTEzksccew2Qy8eqrr150TNatW8eKFSv4+c9/Tn19PRqNhsOHD1NcXCyTOAudo3379vHEE0+wZs0a/uVf/kXafGGXp6am5Mo8JpNJVr74fD5iYmIuWTn6YXoIBoO89957FBQUEBUVdU01FAwG6erq4gc/+AGnTp3ir//6r0lNTb3sa1Cj0VBQUMCDDz4oP+N6VJhdCXQ6Henp6RQXF5OQkIDdbmf37t1ynr/dbueOO+7A4XDQ19dHeno65eXljI6O0t7ezqpVq9BqtWRkZHDXXXcxOjoqR5Ife+wxtm/fzltvvSW/7/jx46xZs4b7779fJq0feeSRiwYRbhREBdGPfvQjtFotjz766Eeyt9eLtLQ0vvnNb/L//t//44033rjmPvzVQqPRkJ6eztq1a+UA06c//WmMRiMej4eEhAS2b99OUVERbW1tZGRkcPLkSYaHh2lra2PlypVotVqSk5PZs2cPbrebvr4+2traePTRR9m7dy8HDhyQ31dTU0NWVhYPPPCA9EP27dvHrbfeer0OwTVDBK2vvvoqr7zyCv/5P/9nkpKSLtvOarVa7rjjDu655x5+8IMf8Kc//UnONLhWjI2NUVVVRXp6OufPn2diYoIVK1YQGRlJX18feXl5FBUVXdQawGAw8KUvfYmGhgaGhob49Kc/TUJCgvRT2tvbgQt6FAN8aWlpREZGMjIyQm1trRzk0Wg0/Jf/8l+k7yt8kcOHD8uY9HKYf9yE3+F0OhkbG5ODWmLAR6PRMDMzw3/8j/+R3//+9/z7v/+7PHciwV9SUrLgd917771ymvfQ0JDslQMXYs2xsTHWrFnDt7/9bR544AGeffZZhoeHiY+P59FHHyUhIYGXXnqJ999/n1WrVqHX6wkEAvj9flJTU3n88ccBeP311zly5Ajr16+Xfk5paSlpaWk8++yzNDU1XVZi8JMifP+GhgaefvppdDod3/rWt67L4iWX9WtFdcRtt93G6tWrefnllykrK+P2229n/fr116TiQK/XY7FYKC0tZfPmzfzjP/4j+/bt+9AAPDIyks9+9rP85Cc/4eGHH8ZgMPDMM8+wbdu2BTOWYslU8Z02m43Nmzeze/duuSSfuLjMZjMZGRkLLl0WDAaZnZ2VSw8KIiIiSE9PZ2hoCJvNRnx8vFyWuLOzk09/+tPAhVGqNWvWkJCQwHe/+10eeeQRvvSlL+Hz+ejs7GTZsmWEQiGOHDnC1q1bL+kobN++nVtvvZWXX36Z2dlZ6urqWL16Nffeey9vv/02fX19REVF4XA4SEhIoL29ncLCQvr6+jAYDERFRTEwMEBvby8vvPACzc3NHD9+HIfDwdjYGDExMeh0Ovr6+uju7uZXv/qVLFnbtWsXq1evpra2Vi49e7UIhUJ4PB4aGhrYv38/RqORv/qrvyI9Pf0ja1OjubCs4qOPPkpLSwuvvPIKUVFR7NmzRxr0a6X3NWvWsH79en7wgx+wZ8+eD53rrNPpuO+++3j66af57Gc/i81m4/vf/z4FBQXExsZeZNiDwaA0tuI7165dyy233CKbRYrzJqrRFlr28VJ612q1OBwOhoaGcDqdGI1GTCYTbrebhoYGvvzlLwMXpkvk5eWRkpLC1772NR5//HG+8IUvEAqF+Pu//3vZ/+Kdd95hw4YNCxpqjebCCl9btmzh0KFDmEwmGhsbycrK4u6776a8vJyOjg6sVis5OTnEx8czOztLdnY25eXlcg7v8PAwfX19vPjiizQ0NPDuu++SlZWF0+m85DVx9OhRPvOZz/Bf/+t/5ezZs/T09FxVgx4KXVg+saKigkOHDrFy5Uq59OrH0XtsbCxf+tKXaGpqknrfvXs3+fn516TCRqvVYjAYSExM5Bvf+AYdHR388Y9/JD8/X5aiX2rfs7KySElJ4bnnnuPrX/86HR0d/PKXv7woYSUC1fAmdgaDgaSkJL797W+TkZHB7OysvJ9ptVqSkpLk8piHDh3C5/PR29vLzp075Xzu2tpaioqK2LNnD4cOHaK1tZWRkRH27t2LyWTi+PHjfOc73+E73/kOHR0dtLe3k5WVJZPtZrOZvr6+i35bVFQUjz/+OMFgkL//+7/H4XBw7Ngx+vr6OHr0KF1dXezcufOiwYmenh45guzxeOQo8j333MPx48dpbm6mtbWVr3/965w/f54XX3yRkpIS7HY79913H1/5ylcYGhqak9AXCd3BwUGcTiexsbEX9SHx+/10d3dz9uxZOcX23nvvvWpNTEUDwq6uLt544w36+/u55557LmkfPgyNRsP69euJj4/nV7/6FeXl5XOSD0uNcF8G5vZw0WgurLJkNBrJycnhmWeewWQy0dHRwT//8z/PSdADc5Ynnp2dvci2iQaW4l4y31e6ERBB6rFjxzh48CB79+5l+/btS2YFG2Er/+Zv/oZ/+7d/45lnnuGBBx4gLy/vmgQ+V5vw5qNwsd7FMvYZGRl8//vfx2w209vby09/+lPuu+++Oe/R6XRS7zMzM3P0LgK48KatXq93jq90I+Lz+WhsbOSVV14hOzub//E//gexsbEf2TZqtVqKi4vJzc3lj3/8I0899RQ7duxg69atGI3Gq25rIyIiiImJISUlhSeffJITJ07gdrs/dEU0jUZDcXExzz//PIcOHeIb3/gGx48fp6ysjLy8PDlAI14bPlAR/p1PPfUUaWlp0l8Wz6emphIMBi/SkLA7kZGRF12naWlpTE5O4vV66evrk1Mwjxw5QnFxsWwPEAgEuOuuu6iurqalpYUtW7awdetWDh48iMViQavVcv78ebRaLXa7/aJzoNFosNvtfOtb32JkZISmpib0ej39/f1897vfJSEhgffee4+8vDzi4+PlZxqNRqKioqSf09HRQXd3Ny0tLZw6dQq/38/WrVtlE2+xPLlo9XDixAkSExO59dZbL4p/rxZisL+9vZ1///d/x+fz8eijj5KXl3fdEvI6MfXnEsx5UhzI4uJiHA4H77//PgcOHGB8fByr1YrBYLiiP8Tn81FWVkZLSwt79+6lurqa2dlZUlNTaWtrAy40oxTz45KTk8nJybloHxITE6mtreXee+/FaDRy5swZOdcvEAhw+vRpUlNT0ev1nDx5EpfLxezsLLt27ZK9KdLT03nrrbdwuVxkZWXJ4Eb0WOjp6eHAgQN0dnaSmZlJKBTi5z//OcXFxZw8eZKysjK8Xi85OTnk5ubyxhtvcPr0aXbv3s2yZcuoqKggMzNTLun6yiuvUFVVRWVlJdu2bcNms/Hcc89RU1NDQUEBGzduZHx8nObmZrZt24ZWq8Xj8fDWW29x/PhxjEYjDoeDF154gcrKSpxOJw899BB2u50jR45w+vRpoqOjue222/j1r39NfX09TU1NPPLII6xevZrjx49TU1NDSkoKn/3sZ9m+fTvFxcWcO3dOivbw4cOcPHmSFStWsG/fPrZt20ZRURGtra184Qtf4M9//rMsKXz44YcXNACfFNFXoqysjJdeeonR0VHuu+8+7rzzTmw22yf6Po3mwvKDor/Nn/70J9555x38fj9Wq/WKJygDgQDvvvsudXV17Nmzh/r6etxuN4mJiXLqmNPpxGq1UlFRgcViYfny5Rc5ijabjYaGBu68807i4uJobm6WvW08Hg9nz57FYrGQkJBAeXk509PTjI6Ocuedd1JRUcH09DQpKSlUVlbKagZABtI2m43BwUEOHDjAuXPnSE9PJzo6mh/96Eds2LCB5uZmDh06xOTkJDk5OSxfvpy33nqLuro6tm/fzurVq6mvr8disZCbmwtcKBU9evQoFRUV0r784he/oKamhszMTHbu3Mn09DRVVVXceuutsifSO++8w3vvvUd0dDTZ2dn89re/pby8nJ6eHj7/+c+TkpIiNejz+di7dy8vv/wyJ0+e5NSpUzz00EMUFhZSV1dHZWUlRqORBx54gJ07d1JSUkJHRwcPPvgg+fn5HD16lJqaGtLS0rj//vvlNdHa2sqjjz7K0aNH5e984IEHSE5OvuJ6Fz0mXnvtNf7whz8QFRXFI488wi233LJgAu2joNFoSExMZMuWLVgsFg4dOkRZWZl0Yq60fYcLfcdef/11rFYreXl5nDx5Ep1OR0JCAqdPnyY2Npbe3l6ioqKoq6tj/fr1FzlUYjnY3t5e7rzzTjweDydOnMBoNErno6Ghga1bt1JVVcXU1BStra2sXbsWk8lEfX09ERERJCUl8eMf/5h169YRHR2NVqslLi6OhIQEbDYbQ0NDvP/++2i1Wu655x4OHTpEZ2cnhYWFHDlyhPr6emw2G3v27KGzs5NTp05x5swZ9u3bR2Njo6y+vP/++8nNzSUUCvHmm2/S1dXF5z73Ofx+PwcOHKC5uVmOxP3yl7+ktrYWh8PBrbfeypYtWygtLWVmZoaSkhJycnI4cuQI77//PgaDgezsbP74xz9SVVVFTU0N999/PytXruS9996joaGBYDDIPffcw/j4ONXV1Zw8eZI9e/bgdDp5/fXXOXnyJMnJyezatYuGhgYOHTqEy+WS13FZWZm8jletWkV1dbW8ty1btozt27dTWlpKZGQkDoeD4uLiK6oXuJCAGBsb4/jx47z00kuyF82DDz6Iw+H4RBrVaDRYrVY2bdrExMQEv/nNbxgeHiYxMfETX1/XksnJSd555x05eLFy5UrKysrIzc3lxIkTWCwWioqKaGhowOl0YjabqayspKurC6vVSiAQoKenh+TkZI4cOcIdd9zB6OgoFRUVdHR08MADD1BTU8Pp06e56667OHz4ML29vbS0tFBcXMybb76Jx+ORvSumpqb485//TE1NDfHx8WRmZi6ZBIeoWqyoqJDNtL/85S9TUFCwJCppwhGDUOvXrycpKYlXX32VyspKTCYTVqv1E6+Md72YmZnh8OHDcgnowsJCysrKSE9P5+TJk0RERFBSUkJbWxsDAwMyDmhpacFsNhMZGcnZs2dxOByUlZXxqU99Cr/fz7Fjx2hpaeHhhx+mqamJqqoq7rzzTo4dO0ZnZydnz55l9erVVFZW0t/fL/2Z2dlZDh06RHl5uZxCshSTYSJJUF9fz/PPP09bWxsPPfSQnL3wSbRiMBhYu3Ytq1atoq6ujldffZXBwUEsFgsxMTFXfGWf9vZ2/vSnP5Gfn4/BYKC1tZXo6Gj5OCIiApfLhcvloqura8HpR2KF08jISEpLS3G5XNTU1GA0Guns7GRmZobu7m62bNnC22+/jd/vp7m5mU996lO4XC46OjowGAxER0fz3HPPUVpaKj8zPj6ehIQEQqEQZWVlHDt2DKPRSHZ2Ni+88IJM1IjFEdLS0li+fDn9/f0cO3aMyMhIPvOZzzA7O0ttbS27du1Cp9PR3d3N888/T1NTEx6Ph7vvvpuXX35Z+skPP/wwRqORsrIyNm/ejM1mIxQKcfbsWd544w2Gh4fJzs7m/Pnz/OEPf6C6uprdu3dTUFBAZ2cnJ0+e5PTp09xzzz309fVx4MABKisrWbt2Ldu3bycyMpKDBw9y/vx5HnroIW6//XbpI2RmZrJjxw48Hg9lZWX09/fz8MMP8+lPf1pOjSosLMRqtfKv//qv1NbWsmrVKjZt2nTFba/wK44ePcqLL75IW1sbu3fv5t577/1IVWOfgP91qSc0H1Jy9oFPih927NgxTpw4gcFgoLi4mHXr1pGUlCQP5Mf9gcJIBINBDAYDXq+XQCAgS6ZEOZfoSxEIBOY0YBKjgFarFZ/PJyshfD4fPp9vztKmERERshmecCD0ej0ej0d+p1gubSGD6/f75QiruKC8Xi96vR6v1yun/ogAQDTdE//2+Xxy2TFAvker1coR05mZmTmfITL+YtRKHC+xhJn47kAgQFRUlNxv8ZvE/3k8HrnKkcjmimOk1+vl+0TVihhh/6DX6PV6fD6f7EtzJUblw6cbDA0NUV9fL6cIrVu3jltuuYWEhISrsnSbGEXp7+/nnXfe4fTp08THx7N582ZWrVpFXFycfO0n0bs4N2LJT7/fLzPSosJF6D0UCnH+/Hl8Pp/83vT0dBISEuboSZwHMUokqgnEuRcj+9HR0Xi9Xvx+P3q9Xh7vhUZFxchqKBSSOhJ6F98nRm11Op3UodBu+NKuwEXv0Wg08vOjo6PnjHCF611oV6vVEh0djc/nk1oWrxO/SfyfuLYiIiLkteX3+/F6vfK3iL4cQr+Xo3dhA8TnXim9i6CppqZGJhU2btzIxo0bMZlMVyVYEHZxaGiIY8eOUVVVhdFopLi4mMLCQpKTkz+xfYe/6Eg4Px6PR45MejweaedEr62JiQkGBgbk+6Ojo+VIh1h+GpCNnMVIjbC1Yll4oVuhM2FnhYYX+k3i/AqNhC9nPd+mBgIBuf/iWhbXoPh8UYUmvlv8OxQKSa2K3yGuI/Gbwq+f+deA3++X9w5RgTf/Gljou8X9S3zXh13HGo3monubsCViNPGTVlSE2/zh4WEaGhqoqqqS5erbtm0jIyPjIy3Z/lG+e2pqinfffZd3332XjIwMdu3aJXtqLeaAVpxfobOoqChpy8S9Qdhq4RsB8rGotBR9t6Kjo6Wti4iIkNen0ILP55PVNKK/WLivJPZHfOaVsI9XE2Ej+vv7OX78OLW1tWRkZLBnzx7ZQ3CpI+zI2bNn5YDr5s2bKSkpkYHJYj5H4XyQ3oVtFPdor9cr7YXQu7BZwj8I90HCfWm/3y9trIgzwm3cpXzx69Hn8OMSrv3y8nKZXN2zZw/Lli27Ksm8YDDI1NQUlZWVHD16FL/fT1FRERs2bCA9PV1eb5/ke/1+P7Ozs0RFRckYLNzvEPGg2J+hoSHZgwguzKDIycmRx0dUGYr7qLB/4b600KPwp8V3RkVF4fP5FryPLBTHCX8VmOMjiCnG4X5JuJbFb1nIV5/vJ4fHyMI2eL1eALmf4T4NcJGfI/4d7jPM9zXE54f7CMIPFPeW8H0QMc/8+PeTIs610+mksbGRiooKxsfHWbFihVyY6Bonri/5RZ8oUSNf9P8f9O7ubhlMBAIBli9fzoYNG8jOzpajUVfzR8/MzFBdXT0ncF21apVs8KRYegijODMzQ0dHB6dPn6alpQWPx8PKlSvZvHkzDofjml6tGIDhAAAgAElEQVRQIoBvbW2loqKCc+fOodPpKCoqorCwkIyMDGlsruY+eb1eqqqqZICl0WhkA1DF0kQkSCYnJ2lra+PkyZO0t7cTERHB+vXrKS0tlZU611LvgUCAzs5OampqOHPmDH6/n9zcXIqKisjJyZEJo6u5T6FQiO7ublpbW+X/iSalN3LJ+c2GuAaEzT9z5gxNTU14vV7y8vLYtGkTubm512QKqtgfMYXs/fffZ2hoiIKCArZt20Z6erp0oJdKIKZYGKG7iYkJ6urqOH78ONPT05SUlLBt2zY5he9GO8/CxxocHOT48ePU1dWh0WjYunWrbDp9tW274voSPjBTV1dHRUUFXq+XDRs2sG3bNjn982prQOzH4OAg1dXVckGMnJwcSkpKWLZsGUaj8YoMEn3YfrS0tMyZkiwa+S61KjrFBYSdE6u9nT59mjNnzuB2u8nNzWXr1q0sX778mvkVC3B1EzXzCQQCDA0N0dTURH19PcPDw0RFRZGTk0NeXh7Lly8nNjaWyMhIJXrFHMT8wJmZGTo7O2lqaqKpqQm3201ycjKFhYUy+bZYSknF/NDTp0/T2Ngom3eKBqgOh4Po6OirMuqrWLqE3zimp6c5d+4cDQ0NtLa2EgwGyczMZN26dSxfvlw6y4uBQCDAyMgIZ8+epb6+noGBAbRaLenp6eTl5ZGXl4fVapUjV0rziksRfg243W7a29tpbGyktbVVrrJYWFhIfn7+orD5olFjdXU11dXVzMzM4HA4KCoqYsWKFRgMhiU7feRmJBgM4vV6GR4epra2ltraWrxeL/n5+dxyyy1kZWVdd81da0TfpxMnTtDc3AxAXl4ea9euJScnR1bOKY0vXcTgy+zsLB0dHdTW1tLQ0EBUVBRr1qxh48aNpKWlXXftB4NBnE4n586d4+TJk/T396PT6cjMzGTFihWsWLECk8l0TQZGFUsLEUt6PB56e3tpbGykoaEBl8uF3W6noKCAwsJCUlJSFstA37VN1Mz5gP+/jGtsbIyWlhaampro6ekBICkpCYfDwbJly0hKSsJoNC76kljFlUM46FNTUwwNDdHa2kpHRwcDAwOEQiHS09OlMbbZbEuiR4Bo8joyMkJDQwMtLS0MDw8TERFBWlqa1LtYGv06Zm8V1xhRGTY1NUVfX5/Uu9PplD09Vq5cSXZ2NlardUnYQnEznJiYoL29naamJs6fP4/X68VsNpOZmUl2djZZWVmYzearMvdcsXQIBoNMT08zNTVFd3c37e3tdHV1MTo6SkREBFlZWeTn55Obmyv7Ii1WrYipeM3NzdTU1NDb20tsbCx5eXkUFBSQlpaGyWS67sGO4i8I/Y2OjtLY2EhzczMDAwOYzWbWrFnD2rVrSUxMXBK292ojbPvY2BgNDQ3U1dXJJuO5ubmsWrWK9PR0TCaT8mMWOeJciv4rTU1NtLW1MTMzQ2pqKmvXrmXFihXExcUt2gFFES9MTEzQ2tpKY2MjHR0dBAIB7Ha79K1TU1MxGo2L+t6huLIIfbvdbpxOJ62trZw/f56+vj78fj9JSUmsWLFCrgYaGxu7aAY+w7h+iZqLPjCsYqK3t5f29nba29sZGxvD4/FgMpmw2+3k5OSQmZmJzWbDarUuWuOh+HBE9n58fJyxsTGGhobo6Oigv78fp9NJMBgkISGBvLw8srOzycjIkKvrLPVzLkbsRBfz8+fP09HRIRtZWywWUlJScDgcpKWlYbPZMJvNN8Qc+JsV4VCMj48zOjpKb28vPT09DAwMMDo6ik6nk43gcnJySE5OvmEqrsLnNo+MjNDR0UFHRwddXV1yvr/VaiU1NZXc3FwSExOxWq1XrdeO4voQDAZxu93yGuju7qavr4/BwUFcLhd6vZ7MzExyc3NxOBwkJSXJ3k9L8RoQdl6siNHS0iJHfxMSEli2bBnLli0jISEBs9mstH4NEL6m0+mkv7+fs2fP0tnZycjICLGxsRQUFLBq1SqysrJUlchlIPpYOZ1OWlpaOHfuHL29vcCFBQyysrJYsWKFbL6ukjfXD7/fz+joKMPDw7S3t8ugVVTqrl69mry8POLi4mTl61JD+Bqzs7MMDAzIWHJkZITp6WliYmJITEwkMzMTh8NBXFycrPRVuly6BAIBXC4XY2NjDA4O0tXVRX9/P8PDw/h8Pmw2G3l5eeTk5JCVlUVMTMxSqbhaPImai74grHnTzMwMg4ODDAwM0NPTQ19fH06nE5/Ph8FgwGKxkJqaSnp6OvHx8djtdrkMWPhJWOQn44YhvNEj/OUCcjqdOJ1OhoaG6O/vZ2JigvHxcYLBIGazmfT0dBmgJicnz+lfdKOfO1HqHwqFcLlcUu+dnZ0MDAzIBE5MTAxWq5XMzEzS0tKw2+3Y7Xa5fKHS+7UnXO/CSRgfH2dkZASn00lPTw+Dg4NMTEwwMzNDREQEJpMJh8NBdnY2ycnJJCQkoNfrr/oc68WEOF6BQIDR0VGp+e7uboaGhuTy2zExMdhsNjIyMkhNTSUuLg673T6n/43S/PVjvr0XDSBHR0dxOp0MDw/T09MjV1ScmZlBr9djt9vJzc0lPT2dpKQk7HY7ERERN+w1EH4/dDqddHd3c+7cOdra2hgbG0Ov12Oz2cjOzsbhcEi7IJJUSuOXR7geg8Eg4+Pjc+6nwrbo9XqSkpLIz88nMzNTrvB5M/gbV4twGzA5OUlPTw9dXV20trYyMDCA3+/HZDLJJGVKSgpJSUlYrdaLrnt1Dj4a4boPBAIyYB0cHJSJyImJCbRaLVarVU6/T01NxWKxXJHGvIuRcE16PB6GhoYYHBykp6eH7u5uxsbGmJmZISoqCovFImd0iFjSZrPJZG34sbnRjtNi5FK+hfCtBwYG6O3tZXx8nMnJSekvJicns3z5clJSUkhOTpb9i5aobV+8iZoFvzRsn8TKCxMTEzKwFQkcseSwWEXAYrGQnJxMfHw8cXFxMoMqAiOtVitXwlmiJ/KaIZyfQCAgV4DweDyMjY0xOjrK6Ogo4+PjDA8P43K58Hg8ckUIo9GI3W4nJSWFzMxMLBYLFosFk8mkDOAChOs9GAzicrmYmJiQK9wMDg5KgyW6v0dFRWG320lOTpY3GaF3sYJZuOZBHe9LIRIJQudC9zMzM1LrY2NjjI2NMTw8zNTUFB6PR3bJNxqNxMfHk56eTnp6OlarVS4xGY46/n8hXPMiwSts/MDAAAMDA4yMjDA2NiYrcaKjozGbzSQlJclRW7GJEXGhebGpY/7hXEr/4fZe6F889nq9clULo9EolzFPT0+XAyhi2VvBzXwuhN59Pp8c6W5sbGT//v10dnayevVqTCYTMTExcjAqMTFRVpwJmy62m+FYhid4xeZ2u2UANjQ0RF9fH5OTk3LFEJvNRkpKCsuXLycxMVGWuQtuhuN2vQgPtsSIt6iqHBgYYHh4mNnZWSIjI9Hr9aSnp5OamkpCQgIJCQkX6fxm9dOF5oUd9nq9shJd+IEiAdHa2ioHPjMyMsjJySEpKYm4uDhMJpP8zJvtGIYT7mtMT08zPj6Oy+ViZGRExpLDw8O43W60Wi2RkZGYzWaZQBe+9UJ+xkKJHcVcwn0LoWufzyerbYVP4XQ6pb8nfAsxeJecnCyrooRvEV4FdoMc/6WVqLkcwleG8Hg8zM7OMjU1xcDAgBTAyMgILpdLLtsnLkKLxSKnmMTGxhITE4PJZJL/joyMlMsXh980wm8eS4H5yZZwh0cs/ex2u3G5XExOTuJ2u5mensblcsn/93g8colbgNjYWMxmMzabDbvdLp2h6Ohouak5+Vee8DJPsU1MTDA0NCSN3ejoKJOTk3JpXrFEntVqldNLYmNjiY2NlXqPiYmRWg9P8IQHBEtJ7/O1HgwGpdZ9Ph9TU1O4XC6mpqZwu91S5+KvOHbBYBCdTofJZJKJxvj4eJKTk7FYLFLrIgm8VI7RUkKcT2HjPR4PbrebwcHBOQnjiYmJOXZKp9MRGxuLzWbDYrFIzcfExGA2mzGbzXKZykvZ+KV2TsOD2vm2Xth7r9fL5OSk1Pv09DRut5vJyUmmpqaYmpqas3y5TqfDaDRiMpmwWq3Y7XYSEhKkvdfr9RgMhiU7XelaI6YjnDp1ipqaGsbHx+XKhUlJSfh8Pqanp2UVjkjQi4ozAIPBIAeixOCH1WrFZrOh1+uJjIyUmg6354vh/MxPvAi7HK7NsbExJiYmmJyclAGVy+WasxSy2WwmLi5OTmuw2+0YDAZ5L1sMv1Uxl3BbLvq0DQ8PMzQ0JDeXyyWXOI6MjMRmsxEfH4/FYsFoNGKxWLBarRiNRqnz+QnMxaJ1QbgPPl/3Pp9vjs4nJyflYIXwRYLBIKFQSCbDrVYrycnJpKeno9Vqqa6u5tSpUwAUFRXJpdXVtLPLR5yj2dlZGUu63W6Gh4cZGRmZ42fMzs4CFxIDomI6Li5Oxo6xsbEYjUbMZvOCOg33MZaSrZqfbJnvWwcCARk7ilgy3LcQcbj4LJGEMZvNWCwW4uLi5ADc/FhyqRyjK8CNl6j5MBYajQkfJRQGUTisk5OTUmDz3wMQEREh15yPiooiKioKvV4/x+EXW/hzwnkC5qxyJRJH4rHYZ0AaZ/FYLDfu8/nwer0yaPH5fPh8PgKBAD6fTxp/8bzX65Wv8fv9ANJICMMhLhaj0TgniBfJLJFBnj+adxNdPEuCS+ld9IkQzm+4AZ2cnGRmZmbO64PBoLwJhetdr9fPCQTCt3Cti+sBmDMXONyB0mg0F1URiX8Lox/esV1oXug4fAvXutC7eE5cY0K3otor/KYqEo+iCkav11+kd7HPisVF+BST8E00TRQO8PykxOTkJF6v97J0H27D5+tejAyL1wm9iwEBQXi/qcvRPiD1HK7t+cHtpa4PkWwRSSuxRUVFycRLuP6FvRfz95dysnaxEQgEGB4epq6ujurqaqanp1m9ejWlpaWkp6d/YNPa8CAvfGBFBA/CKR4fH2d8fByv1ys1JBLNQpcGg0Em14S+hX7nJ53D/ZRw+zffPxE2WvTkCh9EEH6H8FnCnxN2OlyfkZGRMukkkqlWq5X4+HisVutFg2dKjzcO85MZYkBKjLBPTEzIYG98fBy32z0nQAyFQnNsdriehd0O92PCg8BwWz3fJ1/IH/d6vXN8k4X0Hu6Dz8zMzPFLhObFPcRoNMpBtHC/2263y55d4vUL2eHwPkzV1dXU1NQQCAQoKChg48aNpKenz7kXKT46l/IzPB6PbOsg/Ay3283U1JRMUAiNCpscCoXm+A/hfrPwncOT7QvFksJPCbfN86tL5k8LFY+9Xi+ATI6H+w/zfesPiiVDodAcH0Gn02EwGC4ZS1qtVmJjY1Us+cHcfImaj8L8+XHCCRGPxUW50Caco/mjQ/MvAGFQxUUjLlqNRiP/L1y04sITNxJxcc6/cBdKEoUH1uLGJALQ8GB5/hxhdcHcHFxK7+EBo3BC5mt9Ib2Ha10ElOKmEO7siO8SQbFWq5UGX2hPOO3AnED4w5Ki8/U+f2nohXogKb3fXFzKvs/XvdD+/KT4pRwZcV2EO0EiMR6ud/FY6PLDtL+QEycctIWSqOFbePDxQTZfcWURehoaGqK6upqqqiq8Xi9r1qxh06ZNZGZmXtFEw0I2XGwiUBSbsOfhTrfX653znBgYCk/GBINB3n33XTZu3CincwrdiqROZGQk0dHRGAyGObZaaFc8Fx0dTUxMzJzmvcoXUXwYH+SziGoIoWOxhQ9mhg9yiufDE/vwFx/F7/dz5MgRdu7cKe2o0Ht4BU/4yH948jM8ASo0L7bw6VxXWvPiWIil5quqqvB4PKxZs4aSkhIcDodKcl4DLuVfiPPzSWLJ8EF7MagjdCsez/crwpOPYjApPAn0Qb51+GtEHCniTvGZyn5fMVSi5lrxIcfziqMuCMX1ROldcbNyLbWvdL94EdNSh4aGqKys5OTJk4RCIQoLC2XlzGJtnHw5Gg4Ggzz11FM88cQTxMfHX9bnLrbfqVBcrr32er383d/9HU8++SQGg+Gy3rPY9C4SBOFJm+npaQoKCmTCWK1+tPhRPsZNxSVPgGomcoVRYlfcTCi9K25WlPZvXkTFoKicqaurAy70ifjKV76yZPpEfNT9W+y/R6G4FB9Hu0tV76LKISkpiTvvvJM77rgDp9NJfX09r7zyiuyPJZI2BoNhyf7WGxl1ThSgEjUKhUKhUCgUH4iYvtzb20tlZSWnT58mIiKC9evX89WvfnXOEtsKhUKxWNBqtSQkJLBr1y527tyJy+Xi9OnT7N+/H6fTSW5uLps2bSInJ4eYmBhlwxSKRYRK1CgUCoVCoVCEEd5EsrOzkxMnTtDY2EhUVBSlpaV885vfxG63L7qVZhQKhWIhRH8Sm83Gtm3buOWWW5icnKS+vp5Dhw4xPDxMdnY2W7ZsYfny5URHR8v3KRSK64NK1CgUCoVCoVDwl2lNHR0dlJeX09TUhNFoZOPGjdxxxx2yT4sKXhQKxVJFTI+yWCzccsstMmnT0NDAW2+9xW9+8xsyMjLYsmUL+fn5shmyQqG4tqhEjUKhUCgUipuWUCjEzMwM3d3dlJeX09zcjNVqpbi4mDvvvJP4+HgVpCgUihsSkXQ2m81s3ryZTZs2MTk5SUtLC8eOHeN3v/sdaWlpbNq0ifz8fIxGo7KHCsU1QiVqFAqFQqFQ3FSI5Ex7ezsnTpygra2NuLg4SktLufvuuzGbzeh0uuu9mwqFQnFN0Wg0mM1mSkpK2LBhA263m3PnzlFRUcEf/vAHUlNT2bRpE3l5eVitVlVdqFBcRVSiRqFQKBQKxQ2N6DkzOzvL2bNnOXr0KJ2dnSQkJLBlyxYefPBBjEajnBKgUCgUNztarRaTyURRURHr1q3D4/HQ0tLCiRMn+MMf/iDt5+rVqzGbzYCaFqpQXElUokahUCgUCsUNRygUkpUzTU1NVFZW0tHRQUpKClu2bOELX/gCJpMJUMGFQqFQXAqRwDYYDKxdu5a1a9fi9Xo5d+4cx48fZ//+/ZjNZrZs2cK6deswmUyq0bpCcQVQiRqFQqFQKBQ3DMFgkKmpKc6ePcuJEyfo6ekhPT2djRs38sgjj2A2m1UAoVAoFB8DYTv1ej2rV6+moKCA2dlZuTreoUOHsFgsFBcXs27dOqxWKzqdTtlcheJjoBI1CoVCoVAoljShUIiJiQmamppkQ+C8vDx27drFsmXL5LQmxcfD6XRitVqJjIy85Gump6eZnZ0lLi7uGu6ZQnHlGRsbQ6fTodfrr/euLHpEpU1+fj4rVqxgdnaW7u5uKisr+eEPf4jRaKS4uJjCwkISEhKuaO+vUCjE6OgoJpOJqKioS75udnYWt9tNXFycug8olhS6J5988oOe/8AnFQqFQqFQKK41oufM+Pg4VVVV/P73v+fNN9/E4/GwY8cOYmNjKSoqYsOGDej1euWcf0xCoRCHDx+mtbWVFStWyKliC3Hq1CkOHTrE8uXL0el0qhmzYskRCAQ4fPgww8PDJCYmYrPZrvcuLSk0Gg2RkZHY7XbWrFnD9u3bycjI4OzZs+zfv58TJ07gdrsxmUzExMTI93xcgsEgv/3tb0lKSsJisVzydc3Nzbz66qsUFBSg1WqVbVIsNv7XpZ5QFTUKhUKhUCgWPaFQiGAwyMTEBKdOnaKyspLR0VGWL1/O3r17yc3NRa/X4/f7mZycJDU1FafTSV1dHXa7nbVr16qEzccgFAqRn59PbGwsXV1dNDc3k52dzdDQEEajEafTSWFhISkpKaxZs4annnqKnTt3smfPHiIilJupWFr4/X5ycnKwWq309fXR0NBAZmYmeXl5yn58BDQaDVFRUeTm5pKTk0MgEKCnp4eKigp+/OMfo9PpKC4upri4mMTExI81Pcrj8ZCbm4vZbKanp4empiYyMjIYHx8nOjqa0dFRVq9eTWJiIhs2bODpp5+mtLSUe+655wOrAxWKxYK6gyoUCoVCoVi0+P1+mZypqKjA5XKRl5fH3XffjcPhIDo6eo6Dr9Vqqa2tpa+vj+7ubnJycoiIiCAUCqlA62Og0Wh47bXXyM3N5Te/+Q27d+9mYmKCI0eOkJmZSTAYpKGhgSeeeAKbzcYvf/lLkpOT0Wq113vXFYqPjFarpaysjNjYWA4ePMj27dsZHx+/3ru1pNFoNEREROBwOHA4HNx333309fVRU1PDs88+i0ajYd26dZSUlJCYmEhkZORl2WqdTsfBgweJiYnh1Vdf5bbbbmNiYoLjx49jNBoxGo3U1NTwne98h02bNvH8888r26RYUqhEjUKhUCgUikVFIBBgeHiYuro6qqurcbvdFBQU8LnPfY60tLSLkjPhaLVabDYbGo2G3Nxc3njjDW6//XaCwaBy0D8GWq0Wo9GITqcjPT2d119/nQcffBCTyURqaio2m42DBw8SCoXQ6/WYTCYyMzPVsVYsSXQ6HWazGa1Wi8Ph4PXXX2ffvn0q0XsFiYyMJCsri8zMTO666y6Gh4eprq7mX//1X/H7/axdu5aSkhKSk5M/sE9QZGSkPFdZWVm89tpr3H///RiNRlJSUsjOzqapqYlQKCRfm5WVpaY+KZYMKlGjUCgUCoXiuiH6zYRCIQYGBqipqaG2thaPx0NhYSGPPvooqamplz3KKj4rFAqxevVq1q9fz1NPPcW2bdtITEy8mj/lhiYQCLBnzx7cbjfPPfccmZmZ8jlxDgV+v18FtoolTSAQYMeOHWzfvp0f/vCHbN68mdjY2Ou9WzcUYnpUWloaqamp7N27F6fTSVVVFc899xx+v59Vq1axefNmMjMzZYJlvl0JBALcdttt7Nixg5/85CesWrVqwdeFQiF8Pp+yTYolg0rUKBQKhUKhuOaEQiECgQBDQ0NUV1dTXV1NMBhk7dq1PPbYY2RkZFzSMf8g3G43/f396HQ6/H4/vb297Nq1SzUG/Zh4PB78fj8NDQ1MTk4yNTXF7t27aWxspLe3F7fbjd/vZ2ZmBoPBwLZt2zh48CCPPPIIBoPheu++QvGRcLlcaDQazp49y9mzZxkbG2Pv3r1ER0df7127oRHTo5KSkti7dy979uzB6XRSW1vLiy++yMzMDCtXrmTTpk1kZ2cTERHByMgIU1NTNDc34/V6mZiYYPfu3XR0dDAwMIBOpyMYDDI9PY3RaGTHjh0cOnSIxx57TCXdFEsCzfxRkHl84JMKhUKhUCgUl4sY0RwaGqKqqora2lq0Wi1r1qyhpKSEtLQ01YB2EREIBHjqqad44oknSEhIuN67o1BcVTweD3/3d3/Hk08+KVclUlx/gsEgw8PDnDlzhsrKSlwuF6tWraK0tJTMzMwPnAqrUCwBLile5Q0pFAqFQqG4aoRCIbxeL/39/VRUVHDmzBl0Oh3r16/na1/7GgkJCWoFDoVCoVAsiFarJSkpiaSkJHbu3MnY2Bj19fW89tprjI6OkpeXJ6dHxcTEqKSN4oZBJWoUCoVCoVBcUUTlTFdXF1VVVZw5c4aoqChKS0v5q7/6K2w2GxEREcqhVigUCsVlo9PpiI+P51Of+hQ7duxgcnKS06dPs3//fpxOJw6Hg61bt7J8+XI5XU3dZxRLFZWoUSgUCoVC8YkJhUL4/X46Ozs5ceIEjY2NxMTEUFJSwre+9S3i4+PRaDTKaVYoFArFJ0Kj0aDT6bBardxyyy1s3boVt9vNmTNneOutt3jxxRfJzs5m48aNrFy5UiVtFEsSlahRKBQKhULxsQiFQng8HpmcaW5uxmKxUFxczB133EFCQoJyjBUKhUJx1RADACaTic2bN7Np0yZcLhfNzc28//77/O53vyMjI4PNmzezYsUKOT1K3ZsUix2VqFEoFAqFQnHZhEIhpqenOX/+POXl5bS1tWGz2SgtLeUzn/kMVqtVrtakWJqI5c2DweCcxyq4UdyICI2Hb0rvSxeNRoPFYmHjxo2UlpYyOTnJuXPnKC8v5/e//z2pqals2rSJ/Px8TCYTWq32eu+yQrEgatUnhUKhUCgUH4hIzrS1tXHixAnOnz9PQkICpaWlrFmzRjq7Kqi5MQgEAvzsZz+jsrKS+vp6VqxYQWJiIn/7t39LcnLy9d49heKK4na7+Yd/+Ae6urqoq6tj7dq1LFu2jL/9278lKirqeu+e4gogEnCzs7O0trZy/Phx2tvbSUxMZNOmTRQUFGC1WtU9THE9UKs+KRQKhUKhuDzEIM7s7CzNzc0cO3aMrq4ukpKS2Lx5M5///OeJjY1VI843KCLp9pvf/IZQKER9fT3bt2/HYrFc711TKK44UVFRDA4O8tJLLwHQ2NjIt7/9bVUZeAMh7lUxMTEUFhayZs0aPB6PTNrs378fu93Oxo0bKSwsxGKxqPub4rqjEjUKhUKhUCgACAaDTE9P09TUxPHjx+np6SEtLY0tW7bw+OOPYzQaAdWQ8UZHo9Fw2223kZCQwNDQEBqNhn379smGnArFjURERAT33HMPL774Ij6fj9jYWO666y41JeYGRqPREB0dTUFBAQUFBXg8Htra2igvL+fPf/4zFouFzZs3s3btWkwmEzqdTt33FNccNfVJoVAoFIobHNFzYaER4mAwiMvloqmpicrKSvr6+nA4HGzatInc3FxMJpNyUG9CfD4f999/P/v37ycxMZG3336bVatWKS0obkgGBgbYuXMnLS0tFBUVcfjwYWw22/XeLcU1RkyP6urqory8nObmZkwmE8XFxRQVFREXF3fJ+yigknuKj4Oa+qRQKBQKxc2I3+/nzTffxOfzcffdd6PRaAiFQoyNjdHU1ER5eTnDw8Pk5OSwY8cOli9fjsFgUA7nTU5ERAT33Xcfb7zxBiUlJSxbtkwlaRQ3LImJidx6662cO3eO3bt3q2l+NykajQaDwcCKFSvIy8vD4/HQ19dHeXk5//zP/0xMTAxFRUWsX7+epKQkeZ88evQoXV1dfO5zn0Ov11/nXxuWFEsAACAASURBVKG4UVCJGoVCoVDcFHxIBelHes/HCVivdZArGgD/9Kc/5R//8R8pLS1ly5YtNDY2cuLECZxOJ7m5udx+++3k5eXJppkqGL/x+Lja37FjBxkZGdx1111ERkZ+rM9RelJcaz6OTgH27dvHb3/7Wz7zmc987M9Rer9xENOjcnJyyM7Oxu/3093dTXV1NT/+8Y+JioqipKSEwsJCfv3rX/Nv//Zv1NbW8t//+3/Hbrdfl3v+lXrPUvBxbgbU1CeFQqFQLFrEPWr+MsHir9/vx+PxMDs7y+zsrHzs8XjweDx4vV58Ph9+vx+fz4fP55P/Lza/3z/HWRElzOL7598n5zcYDK880Wg0REZGEhUVddEWERFBZGQkERER6PX6OVt0dLTc9Ho9Op1ONnSd//dynKFQKER3dzd/8zd/w8svv4zP58NkMvHYY49RUlLCxo0byc7OVsmZRc58/c//G679S+nf7/fj9/vxer0Xad/n811S+8FgkGAwyOuvv86WLVtITEy8SOvhuomIiJBa1+v1REZGyr8RERFzdC90Hq776Oho2QciXOsfRfeKpc385bHn//V4PMzMzMzRu9hmZ2elnRf3hXCtC73P/y5BMBhkamqKgwcPsnfv3ouqCsM1qNFoLtK7eCz0LvS/kI0X+v8gG6/0vjgJt8nd3d1UVFRQXl7OSy+9xNDQEFqtll27dvHDH/6QgoKCy6pMvZTmQ6EQgUDgIjvv9Xrn6F9oXtj5hbaP4uPAXL9m/nWg0+ku0v18H0c8fykfJzIyckHti++6yfR/yR+rEjUKhUKhuKYIRyTcufD5fMzMzDA2NsbExAQul4vJyUmmp6dxu9243W5mZmbmODChUAitVnuRszw/CSKCxA9KoHyQUzLfYQh3bBZy9n0+34KOUvhvDQ8wRAAdHliEQqE5TotOp8NgMGA0GomJiSE2NhaTyYTJZMJms2G1WomOjiYiIgKdTkd1dTXf/e53qampkfun0Wj42c9+xle/+tWbzQladAidhCcRhf7Hx8el/oX2p6am8Hg8c7QfCoXmaHq+UxyeJFlI++La+DDtu1wuYmNj0el0FyVyBCKgEPoN13L4bwzX/fxrYL7uRTAcGxsrN6PRiNlsxmw2ExcXh8lkIjIycs7vVE0/Fxfh9j5cC263m9HRUWnvp6ampNbdbrdMIobbWKHj+TZebAvpPTyYjIiIuGSSXTweGxvDZrMtaNvDf9NCyU+Px3NRsmih5Gl40ig8MRMVFSV1HhMTI2281WolLi6O2NjYOcFwZGSkvFYU14dQKMSBAwf43Oc+h8fjAS6c05ycHJ555hl2795NKBSSfsH4+PhFNn56epqpqSmmp6cJBALSzxGIpN+l/Jz5yfCF7P0H+TXzk0mXq/2FEqHCz/kwHycQCMzRvqheCte/0WiU+rfZbMTExMzR/g2kf5WoUSgUCsW1QTglwvGYnp5mfHyc4eFhnE4nExMTjI+P43a7ZYNbMUITFxdHXFwcZrMZk8k052YdExMjX6vVaueMvi/1UffwEbXwTThtfr8ft9s9J3ifnJxkcnISl8uFy+VidnaWYDDI+Pg4lZWV9Pb2MjMzw/T0NDMzM4RCIfbs2cP//b//F6PRKJ3+pXrMFjPBYFCeJ+GEDwwMMDo6ytjYGKOjo0xOThIIBKSmo6Ojsdls2O12qf/w5ISoOBH6n7/B0huFXEj3QvOBQACfzyd1LoJ4EdSPjY0xPT0tg4NAICADXYvFgs1mIyUlhcTExDnJHjGSq7gyiCRduL0fGxtjaGiI8fFxqXe32y2T6yIBFxcXh81mm2PvRXIivLJwfoXhUg3OwrUujlu45mdnZ5mcnJTJKmHfxV+3243f75f3BJG8t1qtWK1WUlNTiY+PJzY2Vib0VeLy6uD3+5menmZycpLvfe97/OIXvyAUCmEwGIiJicFgMBAfH09paSk2m00OKlkslotsvBh4MRqNMvkQrvsbodpkoaqh+fZ+ZmZmjv6npqbkv8fHx5mZmZEJXJ/Ph16vx2g0YrFYsFgsJCYmkpycPEf/er1+KRwvlahRKBQKxScn/J7h8/kYHR1ldHQUp9NJd3c3w8PDjI+PMz09LZvyxcTEYLfbSUpKktUfNpsNi8UiEwXh0xxgaToi14P5lT3CARIjdyJIcjqd9Pf3MzY2ht/vZ2ZmBo1GQ2xsLFarleTkZNLS0oiLi8Nut2O1WomI+EsbO3U+/kL4MZ+enmZkZITR0VEGBgbo6elhbGwMl8uF3+9Hr9cTExODxWIhJSVFHltxHURFRV00xQfU8f4w5lcWiaDX5XIxNjYmt8HBQUZGRmQCASA2NhabzUZSUhLp6enY7XZ5XuaPOt/szB9Vn5yclHrv6+ujr6+PsbExpqamCAaDGAwGeXxTUlKw2WxyM5vN0t7PT6yrY31pFrLxIlAVNn58fByn08nAwIC8/87MzKDT6TAajdhsNtLS0khNTZU2fv5qfuocXGC+5sfHx2WCvbe3l/7+fsbHx2WiPTo6mqioKEwmk0yU2Ww24uLiZKXr/MqRpTyodD1YyN77/X5p70WV0nx7HwwGiY6Oxmq1YrfbycjIID4+Xl4D4v4ruI7nRCVqFAqFQvHREMGPz+djZGREOuYdHR2MjY0xOzsrqwDi4uLIyMggISFBju7FxsZedONTzsn1Yf69XvRjCHdwenp6GB0dZXx8HJ/PJ0cEHQ4HKSkppKamYrPZ5PSqm+FchvcIGBgYoK+vj+7ubrq7u5mYmCAYDMrpZ8nJyaSnp8+pEghPdoHS/7VkvuZDodAczYvEmtPpZHx8HL/fT2xsLMnJyTgcDlJTU0lNTZWVZ0u1iuNyCU94TU9PS3vf1dVFX18fk5OTaLVaWa0kAn+RdDSZTBdNobiRj9diYyEbHx7I9vb20tfXx+joKC6XCwCTyUR6ejqZmZmkpqaSnJwsp9DeDMmE8F53LpdLJmI6OzsZGhrC7XYTERGBxWIhLi6OlJQUmYAUmp+/VPeNfswWKwvlM6anp2UF98jICN3d3YyMjMiKbr1ej8ViISsri7S0NFmBGRUVJauZrhEqUaNQKBSKSyNG6KanpxkeHqatrU06Kx6PB4PBQEJCAikpKeTk5GC32+U8YrWM842DcFxF2fHY2BhdXV309PQwMDDAxMQEBoMBh8OBw+EgJyeHuLg4YmJilvw0qvBrYHBwkLa2Nrq6uhgcHMTr9WIymUhKSiIjIwOHwyEd9fmjcoqlhUhOuN1uXC4XQ0NDMjnR39/P7OwsNpvt/2PvvOOjqtP9/56SSS+T3nsjjRRKQgggRUA6Cujd1b2ud9dddX3t3dX7u7v2Ve+6XtuuiIoroIIoVUKvoQYSUiAJ6b33zGQmM0mmnN8fvuZcIqCiiIvM+/XixSvJOTNnvvM853y/z/d5Pg9RUVGEh4cTHByMk5MT9vb2t/T3bhGjHhoaorW1lfr6epqbm+nv78dkMuHh4YGXlxchISEEBwfj4uKCk5PTLe/ntzuXlyYPDg7S0dFBc3MzHR0ddHV1YTKZ8Pb2JioqioiICDFYeavf5yzPNkspdkNDA42NjbS0tDA8PIxMJsPd3R1/f39CQkLw8/MTS5Nul42J2wVBENDr9Wi1WtRqNS0tLWJgure3F6lUir+/v9jty8/P74f2AWugxooVK1as/B+CIDA6Okp/fz91dXVcunSJlpYWzGYzHh4ehIWFERoaSnBwMA4ODmM6VFi5/bBMcoeHh9FqtTQ2NtLY2EhDQwNqtRo7OztCQ0OJjY0lODgYDw+Pf3ktEEv6dH9/P/X19ZSVldHU1ITZbMbT05Pw8HDCw8MJDAzEwcHhCkFGKz9tLAva4eFhent7qa+vp76+ntbWVgwGAz4+PkRHRxMbGyvq4Pyr2/vIyAg9PT1UV1dTXl5Oe3s7MplMDMCHh4fj4+ODvb39Lb8wt3J9WOYEer2ejo4O0d47OzsBCAoKIjY2loiICDw9PcWSnn9VLM+sgYEBWltbKSsro6GhgaGhIZydnQkJCSE0NJSwsDBcXV3HdJ2zcvthySi0dJZra2sTg3ldXV0ABAQEEBMTQ2RkJJ6enjg4ONwoe7EGaqxYsWLlduXyGvfu7m4qKyu5cOEC3d3d2NraEhISIj583NzcxMmKdcJi5eu4vFZcpVJRV1dHSUkJjY2NCIJAUFAQqampREVF4e7u/qPblMUPVCoVNTU1FBYW0tLSglwuJzg4mHHjxhEVFYVSqbT6gJWrcnl3q6amJsrLy7l06RIajQZPT0+SkpJISEjA39//R9Xb+uo9v6KiguLiYrq7u3FwcCAiIoLY2FgiIyPFjIEf61qt/Gvy1VI4SzC7urqa4eFhAgICSEtLIzY2Fg8Pjx/9fmmx+cHBQWpraykuLqapqQlBEPD19RXv7z4+PrdFKaOV78/lPqDX62loaKC8vJyqqiqGhobw8vIiKSmJ+Ph4/Pz8vs991BqosWLFipXbDcsiurW1lby8PMrKygCIiooiJSWF0NBQnJ2dxeN/CpOW3t5eLly4QHp6Ok5OTtc8zmw2U1ZWhsFgICUlZUymhEUQOTIy8qpjMjo6Sm5urlgGcT2YzWYuXrxIV1cXgYGBjBs37ooa91uVyxeHKpWK+vp6ioqKaGhowNbWlpSUFCZOnIi3t/dN27m07Kqq1WoKCwvJz89ncHCQkJAQJkyYIAYnfwqipnV1dbS3t5ORkXGFNs7ljIyMUFBQgK+vL+Hh4eJnFgSBuro6XFxc8Pb2vuq5arWa/Px8Jk+ejIuLy3e6ztHRUSorK4mPj7/lbd9i88PDwzQ3N1NcXExFRQUGg4H4+HgyMjIICgq6aeVClkVFV1cXeXl5XLhwAbPZTFRUFKmpqYSGho65L96q9m4wGMjPzxdLdK6FJVBVXl7OhAkTxjzvDAYDly5dIi4uDoVCcdXzq6qq6OvrY/Lkyddtq+3t7ZSVlWFra0taWtrXPo9uBS5fL2o0Gurr6yksLKSurg4bGxvS0tKYNGkSXl5eNy0IYrm/9/f3U1RUREFBAYODgwQFBZGSkkJUVBQeHh4/qSYFGo2G/Px8xo8fj6en5zWPEwSBmpoaenp6mDRpEjY2NmNeo7m5mXHjxl01S9RkMnH+/HlcXV2JjY297nEzmUyUlpbS2dlJfHw8gYGBP4mxv9wH9Ho9TU1NXLx4kcrKSkZGRoiJiSE9PZ2wsLDrzUi85oHXfpJbsWLFipVbDsvEpbe3l9zcXAoKCrC3tyctLY1HHnnkpi6SfwxsbW3ZuXMnoaGhREZGXvO45uZmtm/fzvz58zGbzWMmK2q1mra2tmueL5PJqKiooKOj47oDNQaDgaKiIlJSUnj77bd59NFHSUpKuq7X+Ffl8mCHpc16WloaBoOBjo4OioqKeP/99wGYMmUKEydOvKLTzo3CUoNeVlZGTk4OarWa+Ph4Vq1aRWBg4K3SsvO6sLW1Zf369SQnJ49ZkH6V48ePU19ff9VAS3t7O2az+ZqBGhsbG/bu3UtAQABxcXHXfY2CIHD8+HE++ugjPvzww1s+UGOxIXt7e2JiYoiJicFkMtHT00NpaSmbN29Gp9MxadIkpkyZMmbReCMRBIHBwUHy8/M5c+YMgiCQlpbGr3/9azGD4Kdk71KplLa2NgoLC78xULN27VomTJjAyMjIGL8wmUzU1tYSExNzzfMlEgkff/wxaWlp122rRUVFuLm5UVlZyblz53jyySdv6dLJy+3HxcWF5ORkkpOTGR0dpaOjg8LCQtasWYONjQ1ZWVmkpqZe0VnqRmERBi8uLubUqVPodDoSEhJYtWoVAQEB//JlWd8HhULBiRMnsLW1ZerUqdc8bmBggA0bNjB//nxMJtOYQI1er6exsZHY2NirniuRSOjp6eH06dPXPOZaCILAvn376OjoICUlBZVKRWBg4HW9xr8ql9uUg4MD48aNY9y4cZhMJvr6+igvLyc7O5v+/n4SExOZNm0afn5+X7tx8k1YAzVWrFix8hPBMvHct2+fuIvy+OOP4+Hh8b0eFLcS9vb24s5lRUUFp06dwt/fn+nTp7Nnzx7UajWzZ8/m2LFjXLhwgblz544ZG7PZTFNTEyMjI5w7d47KykpsbW1xc3Nj3rx5VFRUcObMGUpLS8nKykKr1bJ79250Oh3Tp0/n8OHDYtmDk5MTSUlJ5OTksGTJEmxsbFAoFPz85z9HKpWiVCoZGhr6sYbqpiCRSFAoFKIg6cKFC+ns7OTUqVP87W9/IyIigjvvvJPg4OAbsogRBAGNRsPx48fJzc0lMDCQxYsXExYW9pMMzlyOq6srCoUCs9nMiRMnqKioIDU1laCgIPbu3YtUKmXevHns3r0biUTC/Pnzx4yHwWCgqakJZ2dntm7dilwup7e3l4kTJ5KYmMjJkyeprKykra0NgNbWVg4cOICzszPJycns3LmTO+64g8rKStLT0zEajXR1dXHHHXeI79PW1kZ3dzd2dnY/yhjdDGQyGb6+vvj6+nLHHXfQ29vLmTNneP311wkJCWH+/Pk31N67u7s5cOAAZWVlJCUl8ctf/lJcHPxU7d0i/NrZ2YlGo2H37t0MDw+zYMEC6urquHjxIhEREQQFBXH06FG8vb2ZNWvWmNfo6+ujp6eH7u5usrOziYyMpKWlhZUrVyKRSNizZw/d3d0MDw9jNps5ffo0ZWVlJCYmMjIywoULF5g5cyaFhYWsWLGC48ePk5aWRkBAAABz5sxBoVCgUCjYtm3bjzFMN4Wv3t9bW1s5fvw4+/fvJzk5mdmzZ+Pp6XlDbNFi74cOHaKkpISYmBhWrVpFUFDQbaOppFAocHV1BaClpYWDBw/i4uLCvHnzyMnJoa2tjczMTJqbm8nLy2Pq1Klj7reCINDS0sLg4KA4R/L19WV0dJTly5fT3t7OoUOHaGhowNXVldHRUfbv309PTw+ZmZkUFRVhNpsJCgpicHCQWbNmsXv3bpYsWYKDgwNms5ns7GweeughfHx88PX1/bGG6qYhk8nw9vbG29ubrKws1Go1BQUFfPDBBzg4OHDnnXeSkJDwnXT7bt3QrhUrVqxYAb588DY3N/PGG2/w+eefM3XqVJ599lmWLFki7qbebhgMBv7+978zadIkTpw4QU1NDRkZGXh7e3Po0CESExNJTk5m4sSJY86zZIPk5eXh4OBAWVkZU6dOZdu2bajVal5//XUyMzOZNGkSAPv376e3txd/f3+2bt1KXFwcJ06cYGhoiJSUFFED6PLUa5PJxD/+8Q90Ot3XZv381JBIJNjY2BAUFMR9993Hc889R2xsLO+99x7vvfce3d3dV22x+W2wCGEePnyYl156Ca1Wyx//+EceeeQRYmNjf9I7rF+ls7OTjRs3MmfOHD744ANGR0eZP3++KAAdFxfH7NmzCQoKGnOejY0NRqORhoYG9Ho9KpWKxMREtm7dSkNDAxs3bmT58uWEh4djNpt57733CAsLo7W1ldraWlxdXcnJycHDw0PsinX5JH1kZITc3FymTJlyS2cWXA9yuRxfX1+WL1/Oc889R2JiIu+//z5r166lr6/ve9m7Vqtl8+bNvPXWW3h5efHss8/y85//nODg4H95Ie8byZ49e1CpVHh7e/Pxxx8TFBTEvHnz+OyzzwgICCAiIoJFixaNySgA8PT0pKSkBKlUSmlpKTExMXR1dXHx4kW2b9+OVqtl8eLFODg40Nvby4YNG5gzZw4bNmwgKCiI+vp6Lly4wMSJE3F0dCQoKAgHBwfx9W1tbTGZTBQWFrJs2bKf/PdhCcqHh4fz4IMP8tRTT+Hs7Mwrr7zC9u3b0ev138veBwcH2bhxI2+88QZKpZKnn36ahx56iMjIyJ98EP5qmM1m1qxZQ0REBI2NjZw9e5akpCSSk5PZsmULcXFxxMbGMnPmzCvO9fPz4+zZszg5OXH+/HkyMzM5duwYPT09vPnmm8TGxnLHHXcgk8koKCggPz+flJQU1q1bx/jx48nNzaWjo0MsqQoLCxMzzkZHR+nq6qK8vJzPP/+cDRs23OSR+XGxBJHnzJnDM888w9KlSzly5Agvv/wylZWVmM3m6/KD2+NJacWKFSs/UQwGA7t372b16tVMnz6dP//5z0yYMOG2WphejeHhYZqamti7dy+CINDR0cGmTZs4evQoOp0OuVyOjY3NFZN3iUQiBrZkMhl2dna4ubmhUCgYGBhAIpEQEhKCvb09gFijXFhYiIuLC5MnT8ZoNDI4OCi2s504caI4iREEATs7Ox577DEmTJjA559/fnMH5l8EiUSCg4MDWVlZvPDCC0RHR/Pqq69y6tQpzGbzdb2WIAj09vby2muvUV1dzZNPPsl99913U7US/pXo7++nsbGRnTt34uLiQmlpKR999BEXL15kdHQUuVyOQqG4ainH5bbv6OiIh4cHJpOJ1tZWAgMD8fT0xNbWFqPRSGNjIzk5OfT29mJra8vSpUvJzc3Fw8MDhUIhlkdZxv/MmTM0NTVx/vx5WlpaqK+v/84Lt1sNiUSCvb09mZmZPP/884SGhvLXv/6VgoKC72TvtbW1vPjii8hkMp566inmz5//g5WZ/CsjCAJNTU1cuHCB4uJibG1t2b9/P5s2baK7uxuJRCLex786NlKpVLw/yGQyXFxccHd3R6fT0djYSFJSEs7OzshkMlQqFY2NjezYsQNnZ2fc3Ny48847OXDggLhITUlJQalUitc1OjrKoUOHSEhIYPLkybfVdyORSHBxcWHBggU899xz6HQ6XnzxRVFo/noQBIGSkhJefPFFHBwcePbZZ1mwYAGurq631Zh+FYPBQGNjI8eOHWNgYIDBwUG2bNlCdnY2Go0GmUwm3usvx2LvEokEqVQqZug4OjoyODiIWq0mJiZG7GjU1tZGdXU1R48exc3NjZiYGIKDg6mqqsLLywtbW1vS09OxtbUFvgz4u7m5MXfuXFauXMnp06dvm/v85Vg2pmJiYnjiiSdYtWoVmzZt4uOPP2Z4ePhbj8ntt81qxYoVKz8RRkZG2LBhAyMjIzz99NPfWdzzp4TRaGRkZASpVEpERASzZs0iKiqKY8eO4eDgwIwZM6ivr2d4eJjh4WEMBsOYYI1F22RkZITh4WFGR0fFf3Z2dphMJqqqqmhtbcXT05OIiAgAHnjgAWxtbTlx4gQrV67kk08+YfLkyYSEhHDhwgWmTp2KXC5Hr9ezd+9epk2bhkqlGrMDezsikUiwtbVl1qxZJCUl8cEHH9DV1cXy5cu/tSZER0cHb731FosWLWLKlCm3vO7Jd2VkZITR0VFcXV0JCQnhnnvuwdnZmdWrV5ORkYFerxftXq/XX6HNJAgCw8PDSKVSRkZGMJvNou17e3vT3NxMU1OTmPkUGRlJWloaU6ZMQSKRcOTIEZ588knWrVtHREQEQ0ND9Pf3k5qaikQiYfz48fj5+aFSqXB2dhbT928nLPY+d+5ckpKSePfdd1Gr1cycOfNbZRkJgkBxcTGbN2/mV7/6FVFRUbflYtViqyMjI4SHh6NQKLj//vsZHh7mhRde4He/+x2VlZXo9Xqx5bQgCGPGanR0VGzFazAYRP8xGAwEBgZSXFyMjY0NGo0GV1dXQkNDWb58OUqlkv7+fkwmE9OnT2fdunX85je/4fz588TGxuLp6YkgCGzcuJGCggLi4uLo7e1l4cKFV2wM/NSxBGzuv/9+ysrKWLNmDQ8++CDjxo37VnZrKeM8fPgwjz76KCEhIbelvV+OyWQS78+RkZFkZGQwefJkKioqKCoq4q677mLHjh1j5i9fLX2y+I7FPyz/ZDIZzs7OlJaW0tPTw/DwMMHBwYSHh/OLX/wCOzs7CgoKSE9PZ//+/Rw/fpwpU6aQm5srlljJZDLS0tI4e/Yszs7OYwTrb0cs3dDGjRvHU089xY4dO/j73//OY489hqOj4zeOjez555//ur9/7R+tWLFixcqPgyAI7N69G41Gw8MPP3zbL/gt9PT0MDAwgIuLC3PnzuXixYsMDg6SlpZGT08PSqUSe3t7Ub8hMDBwjMCk2WymqqoKqVSKvb09NjY2uLu7YzQaiYiIYMKECZSWluLt7Y2TkxMzZ85Eo9FQXV2NQqGgvr6etLQ0pFIpWq2WwMBAmpubCQ8PF3dvGxoaqKioIDAwkIULF16z48jthEQiwdHRkbS0NPbu3YtMJvtWQs16vZ4333yTu+++m/T09NumpOZq1NXVYTQaCQ8PJyEhgaKiImQyGcnJydTX1xMRESHupkqlUkJDQ8fYnkVg0jJZt7W1FTPHJk2ahJ+fH5WVlYSGhqJUKpk1axZVVVV0dnZiMBhQqVSkpqai0WiwtbXFzs5O9AFLBpWXlxeenp4olUqSkpJu26CaRCLBycmJlJQUNm7cSHBw8Nd2cLHQ1dXF2rVr+f3vf09oaOhtuwAyGAzU1NQgl8uZM2cOQ0ND1NTUEBQUhJeXF319fYSGhgKIemH+/v5jxqutrU3MrnRycsLV1RWj0YiLiwuzZ8+ms7OToaEhsbVzdHS0qM/R0tKCr68vwcHBdHR0EBoaSmdnJ15eXjg5OSEIAj09PeJC2NHRkbi4uNva3r29vYmIiGDdunVMmDBBvLdcC0vXoh07dvDHP/4RPz+/29beL2dwcJD29nbs7OxYtGgRlZWVdHV1kZCQwMjICHK5HKVSiUwmQyaT4eXlhbu7+5jXqK6uFrN7bW1t8fT0xGAw4Ofnx4wZM7h06RKOjo64uLgwdepUbGxsKCsrQ6FQUF1dTVJSEo6OjvT19REeHk5DQwNhYWFiIDIhIYGmpia0Wi2rVq0Ss3NuZywZNvHx8fT09HD27FnS0tIs4/LCNc+ztue2YsWKlVsPjUbDSy+9JNaB3+4Pwe+CIAjk5eVx7tw58XdeXl7cc889YhqvlZtPe3s7a9as4ZlnnvnG7yE/P5+8vDweffTR2zpIc72YTCZ27dpFc3Oz4PjWMwAAIABJREFU+Lu4uDjmzJljvZfcRCxlHYcOHeKJJ5742rEXBIFPP/0UDw8P5s6da/2eroO+vj4+++wzDAYD8GWJ38KFC8VgjpWbgyAI7Ny5E6lUypIlS77Whs1mM2+++SazZ88mKSnJau/fAUEQqKys5ODBg+LvnJycWLlypTUD+0dkZGSEF198kd/+9rcW8XFre24rVqxY+Smh1WpxcnL6VqmTVq7NpEmTxggKW9JUrfx4+Pj4IAgCOp3uGwM1DQ0NxMfHW7+z60QqlbJ06dIxdfLWMbz5WDSv+vr6MJvNX5txYcnkyMrKsn5X14m7uzu/+c1vxvzOGti9+UgkEhITE8nOzv7GY81mMwMDAwQHB1vt/TsikUiIjY0lOjp6zO+ttv/jolAoCAwMpKurS+wSdy2spU9WrFixcgtiMpk4duwY06ZN+1G6OpnNZs6dO0dTU5NY2nAt+vr6OHLkCE5OTte1i9Pf38/+/fvx8vL6XqVdIyMjnD59mkuXLuHr6ztm8d/V1cXx48fp6ekhICAAQRA4e/YsxcXFODs74+zsTE1NDcePH0er1YpdbMrLyzl58iSDg4P4+/tjMpm4ePEi9vb22NnZoVarOXLkCLW1tWPec3h4mPz8fDGNu6Ojg9bWVry8vNBqtWzdupWzZ8/S0tJCdHQ0KpWKo0eP0tzcjJ+fH1KplMLCQs6fPy+KAObl5XH27FlUKpV4zNDQEBcuXMDf31/8nEePHmVwcBBfX18kEglqtZqysjL8/PwYGRlh27ZtnDlzhsLCQgDx3JuNWq3m1KlTzJo16xs1HXp7e2lubmb8+PE3fTI/MDDA/v378fT0xNHR8ZrHmc1miouLKS8vJygo6FuXPwiCQEFBATU1Nd97sWIymSgpKRHLmSwBSYlEwvDwMBcvXhRt0uKvfX19+Pv7U19fz/bt2ykoKODixYuEhISQn59PQUEBOp0OHx8fdDodR48epbKyEm9vbwC2bt1Kbm4uhYWFYsvq3t5e6urq8PHxwWw2U1RURG5uLhKJBE9PTzQaDceOHaO+vh4fHx9GR0ev8Amj0cjZs2fJy8vDzs4OpVKJRCJhYGCAS5cuifZ86tQpCgoKcHV1Fe87XV1d4vvX1tZy/PhxNBqN6Dc/Bm1tbVRVVTF9+vSv/Y4lEgmVlZXY2dl94z33h0Cj0bB//36cnZ2/9j4uCAKXLl2iqKiI4ODg67L30tJS0ca+z/dhMpmoqKgAEDczLKWno6OjXLhwAR8fH6RSKWq1mqNHj9LR0UFAQADt7e1s3bqV8+fPU1xcTGBgICUlJZw7d47BwUHRvo4fP05paSkeHh7Y2NiwY8cOTp8+TWFhoahzo1KpqKiowM/PD7PZTGlpKadOncJoNOLl5YVOp+P48eOiMKsgCOzYsYMzZ85QV1dHdHQ0giBw/vx5zp49i1QqFVtda7VaiouL8ff3x2AwkJubS35+Pvb29ri5uV3hE01NTRw7dkz06x/D3i0aS5aAzTdlkOXl5RERESH6+M1EEATKysq4cOECoaGhXzteWq2WI0eOIAgCHh4e3/o9hoaG2L9/v/idfVeMRiP5+fkUFhbi4eEhPo8EQUClUpGTkyPOIeRyOcXFxeTl5WFjY4NSqaS1tZVjx47R3d1NQEAAUqmUhoYGcnJy6OjoEMuQKysrMRqNODs7o9frycnJoaysDE9PT3GOZjKZOHfuHJ6ensjlcgYGBqisrBT9Jjs7m5MnT1JVVUVUVBQjIyPk5ORQXV0tihNfunSJM2fOYDKZ8PT0pKysjJMnT9LV1YWvry9yuZyRkRGKiorEpgFf9WOpVIper6e4uBhfX18EQWDPnj0cP36cwsJC1Go14eHh33nMvw9ms5mDBw8yadIkiwD5NUufrCE1K1asWLkFcXFxISoqiv37919315AbgUQiwc7Ojl27dn2jev2nn376nSaFjo6OnD9/ntbW1u96mQiCwIEDB6iqqsJgMPDhhx9iMpmALycUr732GkqlkmPHjnHq1CnOnDnDsWPHUCqVvPbaa7S3t/P6668THh7O559/TllZGWVlZezcuZOUlBTkcjkmk4kzZ87w1FNP0dXVhclkYs2aNeh0OtRqNevXrxdbMubk5PD3v/8dg8FAa2srr7/+Ovv37we+DEzl5+fT09ODSqVCEAT+8Y9/AF8Ghnbt2kV5eTmfffYZ/v7+/O///i9arZb6+nrGjx/P1q1bycvLY2hoiE2bNvHmm2+KgrBvv/02Pj4+ZGdnc/HiRdRqNevWreP9998HvgwgWQQEi4uL6ejo+M5j/n0wGo18/vnnTJky5Rs1DABSUlK4dOkSTU1NN72zhIODA0VFRTQ1NX3tcd3d3Wzbtg0PD4/rvkYnJye++OIL0Wa/C2azmdzcXJ5++mna2trG/G14eJgtW7bw6quvYjKZMJlMrF69GqVSyZEjRzh37hwNDQ20trbS3d3N6dOnGRoaoqOjg6SkJNasWUNVVRUfffQRnZ2dmM1m3nnnHQYHBykrK6Onp4eioiK6u7vp7u7mH//4B1u2bAGgoqKC9evXExUVxdtvv01XVxcffPCBqMGwefNm+vv7OX/+/Bif2LlzJw0NDSQmJorjYjQa2bx5M59++ilms5mNGzcyMDDAuHHjMBqNwJfisR9++CHZ2dn09/fz2muvERYWxrZt2ygpKfnO4/t9sIz/tyk5k0gkzJw5kz179qDRaG66vdvZ2VFVVSUGQK6FWq3mk08+wdvb+7qfTW5ubuzYsUMsUfouWAKAzz77LLW1tWP+ZjAYyM7O5sUXXxQ7r7z//vvY2NhQUFDA0aNHaW1tpbGxkZ6eHk6fPi1qN40fP56NGzdSVFTE9u3bqaiowNHRkTfeeIOhoSFKSkro6emhpKSE1tZWBgYGWLt2LevWrQOgubmZNWvWEB0dzdq1a2lqauKzzz6jpaWFoaEh/vnPf6JWqzl37pyotSYIAgcPHqS4uJjx48eL42kymcjOzuaf//wnZrOZHTt20NTUdIVPfPrpp2zevBmtVsvf/vY3/P39OXToEGfOnPnO4/tdEQSBvr4+Dh8+zIwZM77xeKlUypw5c/j8888ZHh7+4S/wKiiVSnbs2MHo6OjXHnfgwAH6+/uvW4PI1taW+vp6ysrKvvM1CoJAYWEhx48fx9XVlbfeemuM/6xZswaj0UhtbS07duygpqaGTz75hKCgIF577TV6e3t55ZVX8PLy4vTp0+Tk5NDW1sa6deuIi4vD0dERs9nMxYsXef7556msrEQQBLZs2UJNTQ12dna8+eabGAwGMRD30ksvodVq6e/v57333uOjjz4CQKfTkZubS09PD/39/ZjNZjZt2kRHRweDg4OsX7+etrY23n77bcLDw1m9ejXd3d1UVFSQmJhIXl4eu3btErudWvwYYO3atcjlcgoLCzly5MhVn20XL16kp6eHioqKK+4NNwvLZuC31eGzlj5ZsWLFyi2IVCpl1apVvPnmm0gkEhYsWHBTO0pYxDAtHWJ27txJT08Ps2fPxmw2c+zYMVxcXJg5cyYHDhxgwoQJZGZmjnmNS5cusX79eh5++GFaWlpob28XuwkA3H///djb26PVann11VdZtWoVp06dws/PjylTprBt2zb6+/uZM2cOAwMDjI6Ocscdd4x5D7PZzNmzZ7n33nsJDQ1lx44d6PV6UfBxcHCQ0NBQvL29UalUKBQK3N3dCQ0NRa/X09HRga2tLQkJCUyYMIGzZ8+i0+kICAhAp9MRERGBXC4nIyODyMhI4P/aZt577704ODjw3//934yMjNDT04NarcbJyQn4MmNl9uzZVFZWimOamppKamoqfn5+YlvYkJAQhoeH6e3tZWhoCEdHR0JDQzEajZjNZlauXIkgCKIYpoODA3fddRf5+fkAqFQquru7SU5Opru7mxMnTpCcnMyCBQuoq6sDwNXVlRdeeAGJRML27dvJysr64YznKlg6UXz66aeMjo5+aw0OFxcXHnjgAVavXs1vf/vbm9phwsbGRtxFrKqq4sCBA7i5ubF48WL27dtHZ2cn06dPp7a2lry8PKZNm0ZycrJ4vslk4v3330cQBO677z4+/vhj5s6dS01NDTU1NSQnJxMdHY1UKiUvL4+Kigrmzp3Lpk2b+N3vfkdzczOHDh3Cw8ODhQsXsn37dlasWHFFNyWJREJ6evoV6e/w5eL7rrvu4sSJE8CXWRPNzc2kpKRgMBg4duwYf/rTn5g9ezbt7e0UFRURGBjIihUrMJlMODo6Mjo6Sm1tLb/85S8JDg5m27Zt2Nra8pe//AWJRMK2bdvIyMjA0dGRefPmiXoJDQ0NBAcHk5iYiL+/PxUVFQwMDDBlyhQxWwAY4xMSiYQDBw7w7//+7wiCIE50y8rKsLe3RyqVMjo6yvHjx3nsscewsbHB399fXMy4u7vT09NDT08PNjY2JCQkMGnSJHJzc0lJSbnxRnINBEFgaGiI9evX4+/vP6b88usICwtj6tSpvPXWWzz66KO4u7vfNHuXy+WivTc2NpKdnY2DgwPLly8Xs/7S09PFYMPkyZPHjKkgCHz00Uf09PTw0EMPsXnzZjIzM+nu7qasrIzo6GgyMjKQyWSUlJSQn5/PihUrWLduHQ8//DAqlYo9e/bg7OzM0qVL2bVrFwsXLsTLy2vMdUokEpKTk0lKSrriM9jY2DB37lz27dsHfBkoq6io4Fe/+hUeHh589tlnvPLKK0yePJn+/n5OnDhBcHAwgYGBmM1mnJ2dMRgMVFVVMW/ePFJTU/n4448ZHR3lueeeQyqVsmPHDmbMmIGbmxt33XUXH374IQAtLS14enqSkJBATEwMxcXFDAwMkJCQgFKppKSkBLPZLNqkt7c3crmcffv2sXjxYoxGI2FhYQDU1NRgNptRKBSYTCYOHDjAgw8+iCAIBAUFAVBaWir6hEqlwmAwkJSUhEaj4eTJk0yfPv3GG8k1EASB7u5uVq9ezfz5868Qdr4aludhbW0t77zzDr/+9a9vqh6fReBeJpOJml719fVMmjQJf39/9uzZg0QiYcmSJezduxdnZ+crxrStrY0333yTFStWYGNjw7lz51i8eDG7d+9meHiY++67T7yHvvvuu2RmZtLS0oJWq2XZsmXs3buXxsZGMjIycHFxobKykmXLll0xBnl5eSQmJjJlyhTWr19Pb28vfn5+AGPKxxobGxkaGsLe3p7Q0FDMZjM9PT3odDrGjx8PwP79+1Gr1Tg6OmIwGAgLC0Mul5OQkCD6s6XpwuLFi0lKSmLjxo2o1WokEgn19fWiT1p8YOPGjeK1xsTEMHnyZDFTemBggLS0NBwdHamsrBQ7D1rEifV6PcuXLwcQRY8tfrx3717gSz8uLy/noYcewsvLi02bNjF37twxzzYbGxv+9Kc/IZPJ2LNnD6mpqTfaZL4Rs9nM6dOn2bdvH3/4wx++VWDPmlFjxYoVK7coTk5O/OEPf6C5uZnXX3+dtrY2BEG46TutBQUFlJWVMWvWLNauXYuPjw8/+9nPyM/PRy6XExcXx7333ntFam94eDgajUbciUlISMDLy4t7772X9vZ2qqurxc/Z39+PVqvFw8ODuro6zp49S01NDdOmTeODDz4gODhYDJTAlw9Eo9HI6OgoWq1W7EKj1+vFHUeZTEZqaipvvPEGZ8+eZfz48cTHx1NUVMRrr71GXFwckZGRSCQS9uzZQ2VlJWq1mq6uLnp7e6moqOCFF15Ap9OhUCjErCGFQkFqairZ2dmcPn2avr4+dDodZ8+eFVsZW97/8ge1UqnEz8+Puro6nnnmGTo7O5kyZQrvvfceu3fvZsKECYSHh9PZ2cnf/vY3AgICcHJywmQy8e6779Lf309kZCRSqXRMNx+tVotMJhODawMDA0il0jElcxKJBLlcTl1dHY6Ojje1dbLZbKa2tpa//vWvKBQKHn744W8t5iyRSIiPj+c//uM/eP/999m7dy8jIyM31QdMJhPvvPMOGRkZtLa2UlRUxIwZM5g0aRJ79+4lNTWV5ORk5syZM+Y8qVRKYmIijY2NKBQKnJycCA0NZcKECSxYsIBt27aJturt7S12G2ttbUWn07F69WqysrKoq6ujrKyM9PR0sQ2rIAiYTCaMRiOCIGBjY3PVBY6lE4WFoaEhsUzEYityuRxBEMjNzSU1NRWpVIpOp+ONN94Qa+0nTZrEvn37OHnyJL29vZhMJmQyGdXV1bi6uuLs7IxMJhvzXgkJCTQ3N3PgwAGxQ0hmZiYbNmxg8+bNTJw4EQ8PD/z8/KitreWZZ56hqamJvr4+Ojo6OHz4MGvXrmVgYICamhpSUlKQSCTo9Xr6+/tpb29n586dfPrpp3R1ddHV1UVcXBwAAQEByOVy9uzZQ0VFBWq1+obbxdUQBEFc5PzP//wP/v7+/Nu//du3zjiUSqXMmzePrKws/vrXv3L+/HlMJtNNtXdBEPjggw9ITExEr9eLLXpnzpzJjh07SEpKIj4+noULF15hcykpKTQ0NGBjY4OtrS3R0dEkJiaybNkydu7cKWY/Wezdw8OD3t5eNBoN7733HqmpqQwMDJCbm8ukSZPEoPfV7P1qY/pVe7e0qZfJZDg6OqJSqcQSqXPnzpGcnIxEIsFgMLB69Wp0Oh3h4eGkp6dz5MgRcnJy6OnpwWg0IpfLaW5uRiaT4eHhgVQqHeN3UVFRqFQq9u3bR11dHRqNhvT0dLZu3co///lPJk6ciFKpJCQkhMbGRp5//nlqamro6emhs7OTM2fO8NZbb6HRaLh48SKTJk1CIpEwMjIi+sShQ4f44IMPGBgYoK6uTrx+T09P3N3dyc7OpqSk5Kbau9Fo5MyZM7z66qvcddddTJs27VsHW+RyOStXrmTcuHG89NJLFBcX33R7hy+Dyvv27WP58uWsX78ehULBvffeS2trKz09PSQlJbF06VICAwPHnGcp09FqtUilUmJiYnB3d2fZsmUoFAqxkYGNjQ0mk4mOjg4CAwOprKyktraWw4cPs3TpUj788EOcnZ3HBB8tY2s0GlGpVGKXSkv7bguZmZmsXbuWHTt2MGnSJEJDQ+nv7+eVV17B29ub0NBQfH19yc7O5sKFC6hUKrq6uujv76e+vp5nn32Wvr6+MT4llUqZPHkyhw8fFkvH9Xo9p06dYsqUKeK85qv3fAcHByIjI2ltbeXFF1+kvLycjIwMPv/8c9atW8fEiRPx9fVFoVDw8ssvi93CpFKpmM1m2ey43Leu5sdf9XXLHKe7uxutVntTS7sFQaC/v593332XU6dO8cc//hEvL69v5QfWjBorVqxYuUWxLLwfeeQRzp8/z9tvv01kZCQLFizA29v7prUC7e7upqamhsOHDxMSEsLevXvp6uqisbERk8k0Rh/gcuzs7Fi4cCFbtmwhJCSEzMxMjh07RnFxMZcuXWLRokVjPuvl/3d3d1NVVcWxY8cICwvD399/zOetrKzkyJEjeHl5YWdnx/DwMMPDw9jb24vHqdVqLly4IC54tm7diqurK0uWLGHWrFk8/vjjrFy5kueee46Ojg7q6upEjZqkpCRmzZrFF198gUajGaNRIpVKefDBB2lsbKSjowM/Pz/y8/OpqalBp9PR0NBAZWXlmOwKAHt7e2bNmoVEIiEvL4+ysjKOHj3KM888Q1dXF5999hkTJ05k4sSJ/OxnP+O//uu/xLaYjzzyCPv372fDhg08/fTTY17XyclJXMBoNJorWnVaMJlMnD59+qoLrBuNZWHV0tJCdnY2fX19rFy5kri4uOsuk5NIJMTExPCnP/2Jbdu28Ze//IVFixaRnJyMra3tD/5ZDAYD7e3tHDx4ELPZjFarZe3atWJpheUav/q5JBIJaWlpbNq0ib179zJu3DgMBgMbNmzAaDQyODg45nzL/xadjfb2dg4cOCDeBxISEsZc086dO+nu7mbWrFligOKbcHR0FIMJg4ODoq20t7czPDwsZqc4ODjwxBNP8NFHH/HFF1/wi1/8Qgyi+Pj4YGtri9ls5syZMyxduvSq30FISAhPP/00vb29ODs74+bmxpYtW3jyyScxmUy8/fbbZGZmij5x7tw5mpubUSqVZGVlYTQaeeaZZ3BxcWFwcJCWlhaqqqro7e1FqVQyY8YMoqKieOedd9DpdEilUsrKyigvL0en04l+3djYeF26Et8Fy5i2tLSwa9cu+vv7eeCBB8SMqetBKpUyY8YMIiMj+fTTTzly5AjLli0jMjISuVz+g9u70Wikra2NnJwcbGxs8PPz48MPP8RoNKLVakVb/+rzRyKREBcXh0KhYM+ePYSEhCCRSNi4cSMjIyNiKYTl2Mtt3mg00t7eztGjR5HJZLi5uREfHy++tslkYs+ePTQ3NzN16tRvvVtuyTgxGo1oNBqLXgR9fX10d3czf/584MtF4eOPP87OnTvZuHEj//mf/8m4ceNQqVR4enpib2+PyWTi5MmTzJ49+6rfgY+PD8899xzd3d04OTnh5eXF7t27efzxx/Hw8OAvf/kLs2fP5o477kAqlXLp0iWqqqpQKpVkZGSgVCp5/PHH2b9/P83NzfT391NbW0traytKpZJp06YxMjLC888/j7OzM4ODgzQ1NYk+8fTTT9Pa2opGo/nB5waCIIiZRzt37sTJyYknn3wSHx+f67ZPuVzOXXfdRVxcHJ999hkHDx5kyZIlREVF3RR7hy+zUpqamti5cychISGcPn2a2tpaLl26hNFoRCKRiJshlyOVSlmxYgUfffQRKSkpLF++nNLSUg4cOEBtba04x/mqvcOXpdCNjY3s2rWLsLAwPDw8xmj1tba2smvXLhwdHbGxsUGn02EwGJDJZOJGx+joKIcOHeK///u/UavVbNiwgaysLJKSkvjlL3/Jn//8Z5qamvjzn/9Ma2srBQUFBAYG4u7ujq2tLYsXL+bgwYN0dnbi6ekpvrclizshIQGVSoWXlxdNTU2iblljYyMXLlxg1qxZY8bDxsaGrKwsZDIZTU1NnD9/nrKyMn7/+9/j4uLCyy+/jIODA+7u7vy///f/ePXVVykqKiI9PZ3777+f+Ph41q5dy1tvvTXmdS1+bDKZ0Gq115zjWDJaJkyY8F3M4LqxCGIfOXKEgoICZs+ezYwZM66rq6g1UGPFihUrtzhyuZz09HSSkpI4e/Ysa9aswc3NjTvuuIPY2FhRPPRGYln0Dw0NERgYSGBgIIsXL8be3p6XX36Z+++/n5qaGvEYtVqN2WwesyiRSCRkZWWxbt06pk2bJqYGZ2ZmMjAwgEqlQqvVotFocHJyEgMdQ0NDJCcnExgYyNKlS3Fzc6OsrAyDwSA+gOPi4oiLi0MQBBwdHTl37hz19fWkpaUBsHPnTqZOnSqmAzc0NKBUKnF0dKS9vZ3a2lpxzKqqqnBxcaGrq4tVq1bR0tLCoUOHcHd3F8+xXGtvby9hYWGUlZXh4uJCeXk5c+fOZerUqSQmJqJWqzl06BABAQEMDw8zMDDA4OAgGo2GtrY26urqCAkJEYXuFAoFjY2NtLe34+bmJoob19fXMzIyglQq5ZNPPmHatGk0Njbi5eWF0Wikt7cXnU5Hf38/rq6u+Pr6UlhYSF5eHitWrMBgMNDX18fQ0BAqlQo3Nzdqa2tRKBRXlBLcaLvRarWUl5dz7Ngx9Ho98+fPJzU19Xu1RJdIJCiVSh566CGam5vZvXs32dnZTJkyhfT0dNzd3W+4eKbBYECr1TIyMkJsbCxJSUmkpaVRVVWFyWRi+vTpHD16VPQBvV5/hSi2vb098+bNY9u2bbz33nt0dXXR3t7OfffdR11dHf39/QwNDYmlMk1NTeLP48aNIzk5mfHjx+Pi4sIXX3zB7NmzcXJyQqFQsGrVKuD/BCWHhobo6+tjdHSUiooKRkdHSU1NFW2lr68PDw8PQkJCKCgo4PTp08ydOxez2czx48dJT08Hvgxw7tmzhylTptDe3k5iYiKVlZXY2tpSUVHBjBkzcHBwoKKiAicnJzEIYtn512q1qNVqMVBnKeFITExk9+7dNDY2MjIygouLCzU1NTQ1NREUFIRGoyE8PJwpU6aQk5ODg4MD8fHxLF68GJ1OR3V1NdXV1fj5+ZGSksLJkydRq9UkJSWxatUq9Ho9BQUFqNVqHBwcRB/t7Ozk7rvvvqG2YUEQBPR6PZWVlRw9ehStVsvcuXNJS0v73vYeFBTEH/7wB8rLy8VslJkzZ5KUlPSDlIhYAjF6vZ64uDgCAwPJysqivb2d3NxcFi9ezObNmxkcHGRoaIihoaErrkMul7N06VJWr17N2rVrUavV1NbW8uCDD1JeXk5/fz86nU7MhmxsbESr1WIwGIiLiyMqKoqMjAzc3NzIzs4mKysLpVIpvi4glrRqNBr6+/sZGRkRNWcyMjLEDMe+vj4CAwOJi4sjPz+fsrIyZs+eDcDJkydJS0tDKpWKWhdZWVk0Nzfj5eVFXV0dgiBQV1dHeno6bm5uNDU1YTabxWD+6Ogovb29aLVaVCoVMpmM+vp6bG1tGRkZYcKECRQUFNDU1MTAwAAODg60t7dTUlLCuHHjaG9vZ+nSpQwNDYkbEpGRkcyZMwedTkd7ezt5eXkEBASQkZFBTk4OdnZ2xMXFiT5RVVVFbW0tbm5ulJaW4u7uTkNDAw888MANtQ0LgiCgVqspLi7m+PHjODg4cM899xAdHf29mh5IJBJCQ0N54oknKC8vZ/fu3ej1ejIzM5kwYQKurq43/P5ueVbpdDqUSiXBwcHMnTsXV1dX3nnnHaZPn05fX9+YOY4lk/Dy605ISGB4eBgbGxucnZ0pLCwkPDwcJycnNBoNGo2GwcFBnJycaGhoQC6XMzQ0hK+vL0FBQcyfPx+lUklnZydNTU3MmDFD9P/HHntMFJs+cuQI9vb2BAUFif6RkZGBra0tjY2N9PX1idmNPT091NfXo9PpsLOzE7PXampquOdXhw7oAAAS7klEQVSee3BwcOD999/nwoULmM1mvLy8xOvs7+9neHiYxsZGJBIJNTU1ZGRkkJqaSmhoqKgDFRYWJs5FLD5gEeFOSUmhtraWe++9l4aGBpqamrCzs8PR0REnJyfUajWNjY2iX2zatImpU6dSX1+Pp6enqHdk8eOgoCDi4+PJy8ujvLycWbNmYTabxzzbfHx86O3tFedoP1SATxAERkdHaWpqIicnh7q6OjIyMnj66adxcXG57ve1dn2yYsWKlZ8AEokEhUJBWFgYmZmZODk5kZOTw/79+2lpacHe3h5HR8cbtgMlCAItLS24urqSmpoqdofx8PAgMTGR7u5uxo8fL+5+2tnZERAQcMVOnkX/JT4+HltbW0JCQujo6CAhIUHsoODi4sL06dPF7BQ3NzcmT56MUqmkoaEBd3d3ZDIZcrn8qnoFoaGhDA8Pi/XkNjY29PX1ERERQWJiIjU1NXh5ebFw4ULi4uLQ6/V0dnayfPly/Pz8qKyspKuri0WLFhEcHExAQAAuLi60trZyzz334OHhQVVVldhdxNfXVwxGREdHM336dBwcHHBxccHV1RV/f3+io6PF8gylUomTk5PYmaOnp4fFixcTGRlJUlKSOLlfvny5OOFtaWlh/vz5REdHo9FoaGhoIDQ0lAULFmA0Grl06ZKY2hsUFCR+zokTJ5KcnMzQ0BBVVVWiFo6vry9DQ0PExsbe8JbvliBDRUUFO3fuZNeuXQwPDzN//nyWLFlCSEjIDetcJpFIcHV1ZcKECSQkJFBTU8P27dspKSkRy3luVJaNZdHq4uLCwoULaWlpQaPRiLX+Li4uYscvV1dXvL29r+iWI5FICAwMJCEhgYCAAJydnbGzs0MQBMLDwzGZTPj4+BAVFUVgYCADAwNi+vz06dNpbm4W07hVKhWBgYFXaFUJgkBVVZUozuzn54fBYEAul+Ps7Cx2/jKbzQQFBZGUlERdXR3jx49n0qRJCIIgpu1bMiV6enpobm4mKSmJGTNmiOLBQUFBzJs3DxsbG7RaLePGjROzzVQqlRhMtLe3R6lUUlpail6v5+6778bDw4Px48eLwtD33HMP7u7uNDU10dvby+LFiwkLCyM+Ph6VSgXAsmXLcHNzw9XVFTc3N4KDgwkLCyMpKYnOzk4cHR1ZtGgRbm5uuLi4oFQqCQ0NJSAggMrKSjo7O1m4cCGhoaE3zOYtE/X6+nr27NnD9u3b6evr484772TZsmU31N4tvpuRkUFQUBB5eXlkZ2dTV1eHra0tzs7ON+yer9fr0Wg0ODs7c9ddd4mCoHFxcdjb26NQKAgODkahUKBUKsVA9uXvbbk/xsfHExISIt4Xh4eHiY2NZXR0FB8fH0JDQ4mKiqKrq4vY2Fjc3NyYMWMGHR0dYnc7S8e9rwa8LAEUi616e3sjkUgwm814eHhQWlqKr68vZrOZwMBAkpOTaW5uJiIiQmx9bjQaGTduHHK5HKlUysDAAI2NjURFRTF37lz0ej0lJSV4eHiwaNEiFAoFWq2WmJgYnJyckEgkaDQaMXBoY2Mjdq9RqVQsX74cX19fEhMT6ejoQKfTsXLlSjw9PWlra6Ozs5O5c+cSGxtLTEwMIyMj6HQ60SdcXV1xdXUlMDCQiIgIEhISxJLW5cuXj/GJkJAQQkJCqKuro6WlhTlz5hAdHX1D7X1oaIjy8nK2b9/Onj17kMvlLF++nHnz5t2w7F5L1oqvry/p6elERERw8eJFdu7cyaVLl8SylxuZRdna2oqTkxNRUVEkJSVRXV2Ng4MDaWlpdHZ2kpiYKAZg7OzsCAoKusK3ZTIZcXFxJCQk4ODgQFBQEL29vURERKBQKHBxccHFxYWsrCwGBgbEZ0VSUhJhYWHie1o0YyxaXZePi4+PD3Z2dqhUKlasWIGjoyO9vb0EBgYyceJEMQB09913Ex0dja2tLU1NTdx5551ER0fT0NBAc3MzM2bMIC4uDqVSSWBgIHV1dSxcuJDg4GAaGxsxm83Y2Njg5eUldhL09vZm4cKFoi9bnnvjxo1Dp9NRU1MjljP5+/vT3d1Na2srM2bMELWk2traGB4eZuXKlURERODh4UFtbS1TpkwhJSUFo9FITU0Nbm5u3H333cjlckpLS8XugQEBASQnJ9PS0kJ4eDjTpk3DaDSOebYFBASg1+sJCwsTu6LdSCzBmYMHD7JlyxZqa2uZPHkyq1atIjExETs7u697z2t2fZJ8Q53fzS0CtGLFihUrNwSLVs3g4CAXLlwQ2zcHBgaSlpZGTEwMbm5u4i7UD50+rNfr2bRpk1gbL5FImD59upjhYuWngWVOYREprKiooLCwkO7ubnx8fMjMzCQhIUFcvN+MEqvR0VGqq6s5ffo0jY2NKJVK0tLSiI+PF1v0/tDXIggCJSUlHDlyRPydq6sr9957r6izYeXWxHKv1Wq1VFdXU1BQQENDAy4uLuJOs2Un9Ye2MfhSZ6i0tJTTp0/T09NDQEAAEydOFAMeXy2x+KGorq5m9+7d4s8ODg7cd99936sNsZUfF4uNWTIaKioqKCgooLOzEz8/P6ZOnUp8fLwYFL4Z9j48PExVVRVnzpyhubkZNzc3UlL+f3vnt5221UTx7YAxEhAQDqQ2EJvgeDX1VdL2Afq8fRev3tRJmxhq/hsjkAQSQiD4LvrN6bEMdtNgB+P5raWFQhJboK3RzNY5c97h5OREPIi472MB/h5x9uuvv6LZbIr33r17h19++eXBlxZn7heK+YPBAJ8+fcLp6SnK5TJUVcXPP/+MH3/8ES9evPiSmL/0H7FRwzAMs+FQnLcsC+fn5zg9PUWlUsHW1hZevXqFt2/f4s2bN9A0TTRou4+pUovgBOZxQ703JpMJut0u/vzzT5ydnaHZbCIcDuPo6Ajv379HsVgU036+xTkn/dE0iNPTU/zxxx/wPA+5XA4nJydipEokEuFrgFkKNfG0bRvlchkfPnzAp0+f4LouCoXCNSMc+LZ6Hw6H+Pz5szCPQqEQisWimEaUSCSWNppe1TEEYb0/LsjstixLxHea9vL69Wv89NNPKJVKaxPfq9UqfvvtN3z8+BGO4yCbzeKHH37A8fGxGOG4qGfeqo4hCOv98UM9l8iY+fjxIyqVCjzPQz6fx/v373F8fCx64/yHc85GDcMwDPM3dNMxDAMXFxf48OEDLi4u4LouNE1DoVDA0dERCoUC4vE4otHoyud/M4+T2WwGx3EwHA5Rr9dxcXGBz58/4+rqColEAsfHx6J/RTKZXNhgcR2gZsaGYaBer+P3339HpVLBaDQS18CbN2+uXQPr+DmY+0U2ZUzTRLlcxvn5OSqVCqbTKQ4ODvD27VuRpK+rTqjY7vf7KJfLODs7Q7VaxXw+RzabRaFQEIVsPB6/N/OGWW9IJ7Zto9vt4vz8HOfn56jX6wiFQiiVSjg5ORHTR9ZZJ77vw7IstFotnJ2doVwuwzAMxGIx5PN5FItFlEolPH/+HKqq3ot5wzw+5vM5XNeFbdtiUYxKpYJarYZQKITXr1/j+++/R6lUgqZpq5pqx0YNwzAMsxgqWkejEZrNJsrlMiqVCtrtNjzPQyqVwv7+vujPQv0eaOQBs3lQsmKaJgzDQK1WQ61WQ6vVgmVZYj5+sVhEsVgUfUfW1Zi5C/kaaDQaoiC/vLyE7/vY3d1FLpfD4f+XMqVr4KFWVmPun/l8DsdxYBgGer0eKpUK6vU62u02JpMJkskkSqUSisUiDg8PV9rv6KEhs962bVSrVaH3Xq8nel7kcjlxbadSKcRiMTbsN4jZbIbBYADTNEVz2UajgU6nAwDIZDI4OjpCsVhEPp+HqqprbczcBS1breu6yHGq1Socx4GiKNjb20M+n8fBwQE0TUMymYSqqo/28zK3Qya8ZVkwTROtVgsXFxdoNpvQdR2hUAiZTAalUgmHh4f3fQ2wUcMwDMP8O+Q+I57nodVqodlsolqtol6vwzRNTKdTsaJLLpfD3t6eaCBJKzAEG94x3x75nk+Jimma6PV6orlxo9FAr9fDaDRCJBIRTVrz+TxyuRxevnwpmmwCm3lu5WuAzJtGo4G//voL7XYblmVha2sLqVQK2WwWr169QiaTQTqdFiMrgJvLyjMPT1DzNJKq1+tB13XU63U0m02xmgktD0sFKq2iQjFtE88l9Vyg5eVrtRoajQYqlQqurq5g2za2t7ehaRr29vZQKBSwu7srGgYHi5dN/I4eC0G900iqXq+HbreLWq2GdruNfr8v7uPZbBbFYhG5XA75fF6Ycpuqd+Cfabu+76Pb7V6L8f1+H6PRCNFoFOl0Gt99951Ytpo1v94E9U+9ZCjHuby8RL1eR7fbxWAwEAsQ7O3t4eDgAPv7+9jf30c0Gn2w/kZgo4ZhGIb5GoL3Ctu2RaHTaDTQarXEUtOz2QzRaBSKoiCdTmN3dxeapiGVSgkzhwp92jY5IXwo5GKLNnpiRIl6v99Hu91Gt9uFbdtwXRc7OzsiAaUkhf5MTSGJp3yO5GtgNpvBNE3ouo5Op4NqtYputysS/FAoBEVRhJGTSqXEd5pOp0WfBNb/1yEbarT5vi/ik5ycX15eYjAYYDweIxqNQtM0YTTv7+8L4yESiYif/5TPiaz36XSKfr8PXdfRbrdRq9Wg6zpM04TneYhEImIlr0wmI+I9xf5wOIxQKCT0Djzt7/a/Isd4Mh1pio+u6zAMA7quo9VqodPpwLZtzOdzqKoq9F4oFPDy5UtxboLLST9lZM17nifiR6vVEg8waJnpcDh8TfOU31COoygKx/gV82/ivWEY6HQ6aLfbIt6Hw2E8f/4c6XRaTPWkeJ9IJNbBcGOjhmEYhrlfKHGkpUSHwyE6nQ46nQ4Mw0C/34dpmnAcB1tbW3j27Bm2t7fFko60JRIJMfR4e3sbz549E4m+/ApsdmIpJ+K+72M6nYpXekpK5phlWWIY+3A4hO/7mM1mAIBEIoFUKiUSyUwmg93dXaiqCkVRoCjKypYLfurM53Ohf8dxhEmg6zr6/T4Mw8BgMBBLXofDYbHUrnwNkP4X6T4UCj3aKWZ3QYUo6ZwScdL+cDgUybhpmkL7pmliPB6LAlZV1WuFUyaTwYsXLxCLxaAoClRV3djv8CGZzWZC77Zt4+rqCpeXl8IYNgzjWryPRCJC67Lm0+k04vE4wuHwQq1vcv8Q0ngwxpPJ3uv1YJrmDb1PJhNRuMbjcWEGa5qGbDYLTdOgqir3mVsxs9kMrutiNBoJg4A0TzF+OBxiPp+LJcXj8fiNGE+6j0QiS+P8pps78qgmWfv0Kut/MBjc0L8c72k5ejIkM5kM4vG4yHPWfNoeGzUMwzDMt4VuypSETqdTTCaTa0noYDAQ0xIsy4LnefA8T/zfra0t7OzsQFVVqKqKnZ0d7OzsIBKJiH1FURCLxRCLxUSCKj/NpX35PbqBy/tUXCzC932xT8mC/BnJKKHX4Huj0UgUN6PRCOPxGOPxGJ7nYTwew3VduK4rviP5eOjYKfGLxWKi2E8mk9A0TSR/tG1yofNYIBOCzqlcjJGJORwORZFLoxXkp4ehUGih/mmLRqNC+2RGBPUefP0S7ZO+CVn7i3QffKUmjbZtw3EcoXvaSPeu62IymSAcDguzloxdTdOQSCRE8UMFaiwWu6Z5NmK+LXK8n0wm8H0fruvCsixhWJqmKfROBjNpZT6fIxwOi5iuKMoNvcuxnnpk3Rbr5akMchG8LD4uiu3EXVr3fV/EeBq9uEjvk8kE4/EYk8nkxnFTAZpIJIS5Raa7qqoc49cMOcbLOQ41I5cNBxrh6jjOtRgPAJFIRIxKjkajN/IcOc7Tg5agdoK6XxbbV6H9RfvUlDoY6ynHCerf9/1r+dj29jZisRhSqRTi8bh4iJdKpZBMJjct3rNRwzAMwzwO5OQguE2nUzFaYVGhNx6PxZMuSo5vSyZon36nbMAAy0fsyPdOOfEJminLXoOJ1qKCm4oQuUlvcLvtGJnHSVDz8nvy6AXZ4JMT3+FwKJJjeVTKIsPwS7UfzBmDRs8yzS/S/SKjiUa7yCbrIt2z5jeHRXEewDVjz3EcYeAti/Wj0eia0bPMPKHfKReewL/TuzxN6C6t0/THeDy+NM7LeqfpkHQsHOc3l9tyHOqLRjlOUPOe593Q/W1xnvbl6yqYv9x2jIQc6xeZQcFrIRKJ3BrrZaNVbtIr6/0JxXs2ahiGYZjN54572hf/rGDxCvxjxqwyeXgCiQhzz6xS+/LTXRkayr8qWPfM17AqzS/Tu9xTZxWw3pmvZZVxHsA1E0eGtf+g/GejZrT6Y2EYhmEYhmEYhmEYhnnSKMv+4i6jhmEYhmEYhmEYhmEYhnkguAU4wzAMwzAMwzAMwzDMmsBGDcMwDMMwDMMwDMMwzJrARg3DMAzDMAzDMAzDMMyawEYNwzAMwzAMwzAMwzDMmsBGDcMwDMMwDMMwDMMwzJrARg3DMAzDMAzDMAzDMMya8D90thyCg2WNwwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAfp7IfNVsqk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}