central_striker_value_predict.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/BattalBAKINDI/MarketValueOfCentralForwardsInFootball/blob/main/central_striker_value_predict.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# **1) Loading The Dataset And Necessary Libraries.**"
      ],
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        "id": "OLVkFC5Sauq3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import sklearn.metrics as mt\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, KFold\n",
        "from sklearn.linear_model import LinearRegression,Ridge,Lasso,ElasticNet\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn import preprocessing\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "psLN4eaYmgV6"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "X60iKOGwlyR0"
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      "outputs": [],
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/the_end_data.csv\")\n",
        "datacp = data.copy()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datacp"
      ],
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          "height": 424
        },
        "id": "4mifTj82meD0",
        "outputId": "312fa2d5-ee4a-4109-fb9f-ce23e9e1416b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     Unnamed: 0  skill  pot_skill                 name   age  value  height  \\\n",
              "0             0   92.2      100.0        Kylian Mbappé  24.0  146.8     178   \n",
              "1             1   93.2      100.0       Erling Haaland  22.0  139.0     195   \n",
              "2             2   93.9      100.0           Phil Foden  22.0  124.5     171   \n",
              "3             3   88.3       89.2           Harry Kane  29.0   89.8     188   \n",
              "4             4   91.7       93.9      Raheem Sterling  28.0   89.6     170   \n",
              "..          ...    ...        ...                  ...   ...    ...     ...   \n",
              "711         698   55.3       55.3           Jaime Mata  34.0    1.5     186   \n",
              "712         702   55.7       67.0           Max Fenger  21.0    1.5     182   \n",
              "713         706   53.9       54.2  Ernesto Torregrossa  30.0    1.4     184   \n",
              "714         707   52.8       62.2               Mikael  23.0    1.4     184   \n",
              "715         710   53.2       66.4         John Kennedy  20.0    1.4     181   \n",
              "\n",
              "     weight   foot  minute  goal  asisst  \n",
              "0        75  Right  6293.0    57      31  \n",
              "1        88   Left  4326.0    54      11  \n",
              "2        70   Left  4600.0    20      11  \n",
              "3        86  Right  6296.0    35      13  \n",
              "4        71  Right  4745.0    22      10  \n",
              "..      ...    ...     ...   ...     ...  \n",
              "711      79  Right   842.0     1       0  \n",
              "712      75  Right  1855.0     6       0  \n",
              "713      80   Left  1889.0    12       3  \n",
              "714      85   Both  1679.0     4       0  \n",
              "715      71  Right   524.0     2       0  \n",
              "\n",
              "[716 rows x 12 columns]"
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>skill</th>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>92.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Kylian Mbappé</td>\n",
              "      <td>24.0</td>\n",
              "      <td>146.8</td>\n",
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              "      <td>93.2</td>\n",
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              "      <td>93.9</td>\n",
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              "      <td>Phil Foden</td>\n",
              "      <td>22.0</td>\n",
              "      <td>124.5</td>\n",
              "      <td>171</td>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>88.3</td>\n",
              "      <td>89.2</td>\n",
              "      <td>Harry Kane</td>\n",
              "      <td>29.0</td>\n",
              "      <td>89.8</td>\n",
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              "      <td>Raheem Sterling</td>\n",
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              "      <td>89.6</td>\n",
              "      <td>170</td>\n",
              "      <td>71</td>\n",
              "      <td>Right</td>\n",
              "      <td>4745.0</td>\n",
              "      <td>22</td>\n",
              "      <td>10</td>\n",
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              "      <th>711</th>\n",
              "      <td>698</td>\n",
              "      <td>55.3</td>\n",
              "      <td>55.3</td>\n",
              "      <td>Jaime Mata</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>186</td>\n",
              "      <td>79</td>\n",
              "      <td>Right</td>\n",
              "      <td>842.0</td>\n",
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              "      <th>712</th>\n",
              "      <td>702</td>\n",
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              "      <td>67.0</td>\n",
              "      <td>Max Fenger</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>182</td>\n",
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              "      <td>1855.0</td>\n",
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              "      <th>713</th>\n",
              "      <td>706</td>\n",
              "      <td>53.9</td>\n",
              "      <td>54.2</td>\n",
              "      <td>Ernesto Torregrossa</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>184</td>\n",
              "      <td>80</td>\n",
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              "      <td>1889.0</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
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              "      <th>714</th>\n",
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              "      <td>52.8</td>\n",
              "      <td>62.2</td>\n",
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              "      <td>23.0</td>\n",
              "      <td>1.4</td>\n",
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          "metadata": {},
          "execution_count": 45
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**skill:** is the current ability of the player.\n",
        "\n",
        "**pot-skill:** the potential skill of the football player.\n",
        "\n",
        "**name:** is the name of the player.\n",
        "\n",
        "**age:** is the age of the player.\n",
        "\n",
        "**value:** is the market value of the player.\n",
        "\n",
        "**height:** is the length of the player in cm.\n",
        "\n",
        "**weight:** is the weight of the player in kg.\n",
        "\n",
        "**foot:** It is the foot that the player uses strongly.\n",
        "\n",
        "**minute:** The time the player played in 1.5 seasons.\n",
        "\n",
        "**assist:** The number of assists the player has made in 1.5 seasons.\n",
        "\n",
        "**goals:** The number of goals the player has made in 1.5 seasons.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ojdf5si8x3fC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ND4In7tVmqG6",
        "outputId": "11f1702c-c6ca-4f49-aa27-2f70cc423451"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 716 entries, 0 to 715\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Unnamed: 0  716 non-null    int64  \n",
            " 1   skill       716 non-null    float64\n",
            " 2   pot_skill   716 non-null    float64\n",
            " 3   name        716 non-null    object \n",
            " 4   age         716 non-null    float64\n",
            " 5   value       716 non-null    float64\n",
            " 6   height      716 non-null    int64  \n",
            " 7   weight      716 non-null    int64  \n",
            " 8   foot        716 non-null    object \n",
            " 9   minute      716 non-null    float64\n",
            " 10  goal        716 non-null    int64  \n",
            " 11  asisst      716 non-null    int64  \n",
            "dtypes: float64(5), int64(5), object(2)\n",
            "memory usage: 67.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The only column in the data that is in the object structure is foot"
      ],
      "metadata": {
        "id": "pwcGpHDbwPy5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8vKBUBW7BJF",
        "outputId": "a556345f-f899-4aa5-8bbc-2fc2f8282cf2"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0    0\n",
              "skill         0\n",
              "pot_skill     0\n",
              "name          0\n",
              "age           0\n",
              "value         0\n",
              "height        0\n",
              "weight        0\n",
              "foot          0\n",
              "minute        0\n",
              "goal          0\n",
              "asisst        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* There are no missing values in the data."
      ],
      "metadata": {
        "id": "VuqCNgSnxK3v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "1dg9v6FqOvDI"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datacp.drop(columns=[\"Unnamed: 0\"],inplace = True)"
      ],
      "metadata": {
        "id": "Gwzh9wgG62nO"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2) EDA & Feature Engineering"
      ],
      "metadata": {
        "id": "h_iFK9i97xvQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp[\"score_contribution\"] = datacp.goal + datacp.asisst\n",
        "datacp[\"score_efficiency\"] = datacp.score_contribution / datacp.minute\n",
        "datacp[\"assists_efficiency\"] = datacp.asisst / datacp.minute\n",
        "datacp[\"goal_efficiency\"] = datacp.goal / datacp.minute\n",
        "datacp[\"development\"] = datacp.pot_skill - datacp.skill\n",
        "datacp[\"body_mass_index\"] = datacp.weight / ((datacp.height/100)**2)\n"
      ],
      "metadata": {
        "id": "GFhZkygrmzP9"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Body mass index (BMI) or height weight index; It is a calculation method that shows the estimated fat percentage in a person's body. Body mass index has a direct effect on fitness and physical quality in athletes.\n",
        "* some players are limited to their current abilities, while others are more open to development, which is reflected in their market value. We have created a new column called development by subtracting current abilities from potential abilities.\n",
        "* The effect of a central striker on the score is very important in the unit time he plays. We have created extra columns for score, goal and assist efficiency.\n"
      ],
      "metadata": {
        "id": "uhXkXS1mggEk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp"
      ],
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          "height": 574
        },
        "id": "V-TehVUfSiTL",
        "outputId": "31fd17c5-b0bb-4321-9b46-12f0baa4caab"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     skill  pot_skill                 name   age  value  height  weight  \\\n",
              "0     92.2      100.0        Kylian Mbappé  24.0  146.8     178      75   \n",
              "1     93.2      100.0       Erling Haaland  22.0  139.0     195      88   \n",
              "2     93.9      100.0           Phil Foden  22.0  124.5     171      70   \n",
              "3     88.3       89.2           Harry Kane  29.0   89.8     188      86   \n",
              "4     91.7       93.9      Raheem Sterling  28.0   89.6     170      71   \n",
              "..     ...        ...                  ...   ...    ...     ...     ...   \n",
              "711   55.3       55.3           Jaime Mata  34.0    1.5     186      79   \n",
              "712   55.7       67.0           Max Fenger  21.0    1.5     182      75   \n",
              "713   53.9       54.2  Ernesto Torregrossa  30.0    1.4     184      80   \n",
              "714   52.8       62.2               Mikael  23.0    1.4     184      85   \n",
              "715   53.2       66.4         John Kennedy  20.0    1.4     181      71   \n",
              "\n",
              "      foot  minute  goal  asisst  score_contribution  score_efficiency  \\\n",
              "0    Right  6293.0    57      31                  88          0.013984   \n",
              "1     Left  4326.0    54      11                  65          0.015025   \n",
              "2     Left  4600.0    20      11                  31          0.006739   \n",
              "3    Right  6296.0    35      13                  48          0.007624   \n",
              "4    Right  4745.0    22      10                  32          0.006744   \n",
              "..     ...     ...   ...     ...                 ...               ...   \n",
              "711  Right   842.0     1       0                   1          0.001188   \n",
              "712  Right  1855.0     6       0                   6          0.003235   \n",
              "713   Left  1889.0    12       3                  15          0.007941   \n",
              "714   Both  1679.0     4       0                   4          0.002382   \n",
              "715  Right   524.0     2       0                   2          0.003817   \n",
              "\n",
              "     assists_efficiency  goal_efficiency  development  body_mass_index  \n",
              "0              0.004926         0.009058          7.8        23.671254  \n",
              "1              0.002543         0.012483          6.8        23.142669  \n",
              "2              0.002391         0.004348          6.1        23.938990  \n",
              "3              0.002065         0.005559          0.9        24.332277  \n",
              "4              0.002107         0.004636          2.2        24.567474  \n",
              "..                  ...              ...          ...              ...  \n",
              "711            0.000000         0.001188          0.0        22.835010  \n",
              "712            0.000000         0.003235         11.3        22.642193  \n",
              "713            0.001588         0.006353          0.3        23.629490  \n",
              "714            0.000000         0.002382          9.4        25.106333  \n",
              "715            0.000000         0.003817         13.2        21.672110  \n",
              "\n",
              "[716 rows x 17 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>skill</th>\n",
              "      <th>pot_skill</th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>value</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>foot</th>\n",
              "      <th>minute</th>\n",
              "      <th>goal</th>\n",
              "      <th>asisst</th>\n",
              "      <th>score_contribution</th>\n",
              "      <th>score_efficiency</th>\n",
              "      <th>assists_efficiency</th>\n",
              "      <th>goal_efficiency</th>\n",
              "      <th>development</th>\n",
              "      <th>body_mass_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>92.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Kylian Mbappé</td>\n",
              "      <td>24.0</td>\n",
              "      <td>146.8</td>\n",
              "      <td>178</td>\n",
              "      <td>75</td>\n",
              "      <td>Right</td>\n",
              "      <td>6293.0</td>\n",
              "      <td>57</td>\n",
              "      <td>31</td>\n",
              "      <td>88</td>\n",
              "      <td>0.013984</td>\n",
              "      <td>0.004926</td>\n",
              "      <td>0.009058</td>\n",
              "      <td>7.8</td>\n",
              "      <td>23.671254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>93.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Erling Haaland</td>\n",
              "      <td>22.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>195</td>\n",
              "      <td>88</td>\n",
              "      <td>Left</td>\n",
              "      <td>4326.0</td>\n",
              "      <td>54</td>\n",
              "      <td>11</td>\n",
              "      <td>65</td>\n",
              "      <td>0.015025</td>\n",
              "      <td>0.002543</td>\n",
              "      <td>0.012483</td>\n",
              "      <td>6.8</td>\n",
              "      <td>23.142669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>93.9</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Phil Foden</td>\n",
              "      <td>22.0</td>\n",
              "      <td>124.5</td>\n",
              "      <td>171</td>\n",
              "      <td>70</td>\n",
              "      <td>Left</td>\n",
              "      <td>4600.0</td>\n",
              "      <td>20</td>\n",
              "      <td>11</td>\n",
              "      <td>31</td>\n",
              "      <td>0.006739</td>\n",
              "      <td>0.002391</td>\n",
              "      <td>0.004348</td>\n",
              "      <td>6.1</td>\n",
              "      <td>23.938990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>88.3</td>\n",
              "      <td>89.2</td>\n",
              "      <td>Harry Kane</td>\n",
              "      <td>29.0</td>\n",
              "      <td>89.8</td>\n",
              "      <td>188</td>\n",
              "      <td>86</td>\n",
              "      <td>Right</td>\n",
              "      <td>6296.0</td>\n",
              "      <td>35</td>\n",
              "      <td>13</td>\n",
              "      <td>48</td>\n",
              "      <td>0.007624</td>\n",
              "      <td>0.002065</td>\n",
              "      <td>0.005559</td>\n",
              "      <td>0.9</td>\n",
              "      <td>24.332277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>91.7</td>\n",
              "      <td>93.9</td>\n",
              "      <td>Raheem Sterling</td>\n",
              "      <td>28.0</td>\n",
              "      <td>89.6</td>\n",
              "      <td>170</td>\n",
              "      <td>71</td>\n",
              "      <td>Right</td>\n",
              "      <td>4745.0</td>\n",
              "      <td>22</td>\n",
              "      <td>10</td>\n",
              "      <td>32</td>\n",
              "      <td>0.006744</td>\n",
              "      <td>0.002107</td>\n",
              "      <td>0.004636</td>\n",
              "      <td>2.2</td>\n",
              "      <td>24.567474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>55.3</td>\n",
              "      <td>55.3</td>\n",
              "      <td>Jaime Mata</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>186</td>\n",
              "      <td>79</td>\n",
              "      <td>Right</td>\n",
              "      <td>842.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.001188</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001188</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.835010</td>\n",
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              "    <tr>\n",
              "      <th>712</th>\n",
              "      <td>55.7</td>\n",
              "      <td>67.0</td>\n",
              "      <td>Max Fenger</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>182</td>\n",
              "      <td>75</td>\n",
              "      <td>Right</td>\n",
              "      <td>1855.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.003235</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003235</td>\n",
              "      <td>11.3</td>\n",
              "      <td>22.642193</td>\n",
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              "    <tr>\n",
              "      <th>713</th>\n",
              "      <td>53.9</td>\n",
              "      <td>54.2</td>\n",
              "      <td>Ernesto Torregrossa</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>184</td>\n",
              "      <td>80</td>\n",
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              "      <td>1889.0</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>15</td>\n",
              "      <td>0.007941</td>\n",
              "      <td>0.001588</td>\n",
              "      <td>0.006353</td>\n",
              "      <td>0.3</td>\n",
              "      <td>23.629490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>714</th>\n",
              "      <td>52.8</td>\n",
              "      <td>62.2</td>\n",
              "      <td>Mikael</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>184</td>\n",
              "      <td>85</td>\n",
              "      <td>Both</td>\n",
              "      <td>1679.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.002382</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.002382</td>\n",
              "      <td>9.4</td>\n",
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              "    <tr>\n",
              "      <th>715</th>\n",
              "      <td>53.2</td>\n",
              "      <td>66.4</td>\n",
              "      <td>John Kennedy</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>181</td>\n",
              "      <td>71</td>\n",
              "      <td>Right</td>\n",
              "      <td>524.0</td>\n",
              "      <td>2</td>\n",
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              "      <td>0.003817</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003817</td>\n",
              "      <td>13.2</td>\n",
              "      <td>21.672110</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>716 rows × 17 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
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        "FOOT"
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      "metadata": {
        "id": "cP2osNMtJXJc"
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    {
      "cell_type": "code",
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        "datacp.foot.value_counts()"
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        },
        "id": "hlYBAPT-UVb_",
        "outputId": "80f86770-5567-4f29-adf0-702e2a5fb428"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Right    560\n",
              "Left     118\n",
              "Both      38\n",
              "Name: foot, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1, 2,figsize=(10,5))\n",
        "sns.set()\n",
        "sns.barplot(data=datacp, x='foot', y='score_efficiency', ax=axes[0], ci=0)\n",
        "sns.barplot(data=datacp, x='foot', y='value', ax=axes[1], ci=0)"
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        "outputId": "214f9efa-1b96-4935-8408-b0c2877cc469"
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      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f688d55d460>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Although their contribution to the score is the same, left-footed forwards are more expensive because they are rarer.\n",
        "* For strikers who use both feet, the strongest foot is mostly the right foot."
      ],
      "metadata": {
        "id": "9wwvizjFeYHi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "AGE"
      ],
      "metadata": {
        "id": "14UHKsTceGZZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(3, 1,figsize=(12,12))\n",
        "sns.set()\n",
        "sns.barplot(data=datacp, x='age', y='value', ax=axes[0], ci=0)\n",
        "sns.barplot(data=datacp, x='age', y='score_efficiency', ax=axes[1], ci=0)\n",
        "sns.barplot(data=datacp, x='age', y='development', ax=axes[2], ci=0, palette=\"mako\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "id": "aYQum0_GdKmX",
        "outputId": "f04a77ea-a9f4-49e9-eefd-f622ba1cb286"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f688d5f9490>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "COMMENTS FROM THE GRAPHICS:\n",
        "* The market value of strikers in the 20-29 age range is higher.\n",
        "* The reason why football players aged 17, 18 contribute to the score is due to the fact that they play in the A2 teams of their clubs.\n",
        "* As players get older, their development decreases."
      ],
      "metadata": {
        "id": "2FOgjrFkg58r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp.age.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "Mnbi4VeeWemN",
        "outputId": "2c168144-23ab-499e-f6e1-76a738007b40"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f688cfb5850>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The distribution of the age attribute is similar to the normal distribution."
      ],
      "metadata": {
        "id": "F5crYl6lRqDU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "POT_SKİLL"
      ],
      "metadata": {
        "id": "O5dDP82ghmZ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp.pot_skill.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "t_ZaJ_pSOy2S",
        "outputId": "281d1909-acbe-4b1b-e7f0-8d3ab2788b9a"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f689104c2e0>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD7CAYAAACL+TRnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWK0lEQVR4nO3db2xT1/3H8Y8d6kBKwNgN4ATUaKggr5HGiDUesWmBNWwLf55sRBFsKqWbisroaNhYgbgCIuYkRVvVsLC2qjQJEa3SljSBETrRaRrSGFHHtIiKog4YXVwITrImQELje38PUP0bG0nsxH8an/frEb7nHp/ztW8+HF1fXzts27YFAMhqzkxPAACQeoQ9ABiAsAcAAxD2AGAAwh4ADEDYA4ABpsWz09atW/Xhhx/K6XQqLy9Pe/fuld/vV1lZmVwul3JzcyVJ1dXVWrFihSTp/Pnzqqmp0fDwsIqKilRfXy+v15u6SgAAo3LEc539wMCA8vPzJUm///3v1djYqN/+9rcqKytTU1OTFi9efN/+lmWpvLxcBw8eVCAQ0OHDh3Xt2jUdPHgwocn19d2SZSX+NQCvd6YikcGE+01l1GwG02o2rV5pcjU7nQ7NmfPwA9viWtl/GvSSNDg4KIfDMeb+XV1dys3NVSAQkCRVVlZq5cqVCYe9ZdkTCvtP+5qGms1gWs2m1Sulpua4wl6Sdu/erTNnzsi2bb322mux7dXV1bJtW6WlpdqxY4dmzZqlcDiswsLC2D4ej0eWZam/v19utzvuyXm9M+Pe978VFOSPv1OWoWYzmFazafVKqak57rCvra2VJLW0tKiurk6vvvqqjh49Kp/Pp7t376q2tlb79u1TQ0ND0iYXiQxO6H+4goJ89fQMJG0eUwE1m8G0mk2rV5pczU6nY9RFcsJX46xfv15nz55VX1+ffD6fJMnlcqmqqkrvvvuuJMnn86m7uzvWp7e3V06nM6FVPQAgecYN+1u3bikcDscenz59WrNnz1Zubq4GBu7972Pbtk6cOCG/3y9JKikp0dDQkDo7OyVJzc3NWr16dSrmDwCIw7ince7cuaPt27frzp07cjqdmj17tpqamhSJRLRt2zZFo1FZlqVFixYpGAxKkpxOp+rq6hQMBu+79BIAkBlxXXqZKZyzjx81m8G0mk2rV/oMnbMHAEw9cV+NAyDz7n4SzciliEPDIxr4+E7ax0XyEPbAFOJ6KEdrnm9N+7htL62TWSdTsg+ncQDAAIQ9ABiAsAcAAxD2AGAAwh4ADEDYA4ABCHsAMABhDwAGIOwBwACEPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAQh7ADAAYQ8ABiDsAcAAhD0AGICwBwADxPUbtFu3btWHH34op9OpvLw87d27V36/X5cvX9auXbvU398vt9utUCik4uJiSRqzDQCQXnGt7EOhkN566y21tLRo8+bNeuGFFyRJwWBQVVVV6ujoUFVVlWpqamJ9xmoDAKRXXGGfn58f+/fg4KAcDocikYguXLigiooKSVJFRYUuXLig3t7eMdsAAOkX12kcSdq9e7fOnDkj27b12muvKRwOa968ecrJyZEk5eTkaO7cuQqHw7Jte9Q2j8cT9+S83pkJlvP/Cgryx98py1AzUilTr7WJ73Eqao477GtrayVJLS0tqqur0/bt25M+mf8WiQzKsuyE+xUU5KunZyAFM/rsomYzZDL4MvFam/oeT7Rmp9Mx6iI54atx1q9fr7Nnz2r+/Pm6fv26otGoJCkajerGjRvy+Xzy+XyjtgEA0m/csL9165bC4XDs8enTpzV79mx5vV75/X61t7dLktrb2+X3++XxeMZsAwCk37ince7cuaPt27frzp07cjqdmj17tpqamuRwOPTiiy9q165dOnz4sGbNmqVQKBTrN1YbACC9xg37Rx55RL/+9a8f2LZo0SK9+eabCbcBANKLb9ACgAEIewAwAGEPAAYg7AHAAIQ9ABiAsAcAAxD2AGAAwh4ADEDYA4ABCHsAMABhDwAGIOwBwACEPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAQh7ADAAYQ8ABiDsAcAAhD0AGGDaeDv09fXpRz/6kf75z3/K5XLp0Ucf1b59++TxeLRkyRItXrxYTue9/zPq6uq0ZMkSSdLp06dVV1enaDSqxx9/XAcPHtSMGTNSWw0A4IHGXdk7HA5t2bJFHR0damtr08KFC9XQ0BBrb25uVmtrq1pbW2NBf+vWLe3du1dNTU16++239fDDD+v1119PXRUAgDGNG/Zut1vLly+PPV66dKm6u7vH7PPHP/5RJSUlKi4uliRVVlbqd7/73eRmCgCYsHFP4/wny7J07NgxlZWVxbZt2rRJ0WhUX/7yl7Vt2za5XC6Fw2EVFhbG9iksLFQ4HE7erAEACUko7Pfv36+8vDxt3LhRkvSHP/xBPp9Pg4OD2rlzpxobG/XDH/4waZPzemdOuG9BQX7S5jFVUDNSKVOvtYnvcSpqjjvsQ6GQrl69qqamptgHsj6fT5I0c+ZMfetb39Ibb7wR23727NlY3+7u7ti+iYhEBmVZdsL9Cgry1dMzkHC/qYyazZDJ4MvEa23qezzRmp1Ox6iL5LguvTx06JC6urrU2Ngol8slSfr3v/+toaEhSdLIyIg6Ojrk9/slSStWrNDf//53XblyRdK9D3G//vWvT2jyAIDJG3dlf+nSJR05ckTFxcWqrKyUJC1YsEBbtmxRTU2NHA6HRkZG9MUvflHbt2+XdG+lv2/fPn3/+9+XZVny+/3avXt3aisBAIxq3LB/7LHHdPHixQe2tbW1jdpv1apVWrVq1cRnBgBIGr5BCwAGIOwBwACEPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAQh7ADAAYQ8ABiDsAcAAhD0AGICwBwADEPYAYADCHgAMQNgDgAEIewAwAGEPAAYg7AHAAIQ9ABiAsAcAAxD2AGAAwh4ADDBu2Pf19enpp59WeXm51qxZo2effVa9vb2SpPPnz2vt2rUqLy/X5s2bFYlEYv3GagMApNe4Ye9wOLRlyxZ1dHSora1NCxcuVENDgyzL0s6dO1VTU6OOjg4FAgE1NDRI0phtAID0Gzfs3W63li9fHnu8dOlSdXd3q6urS7m5uQoEApKkyspKnTx5UpLGbAMApN+0RHa2LEvHjh1TWVmZwuGwCgsLY20ej0eWZam/v3/MNrfbHfd4Xu/MRKZ3n4KC/An3naqoGamUqdfaxPc4FTUnFPb79+9XXl6eNm7cqLfffjvpk/lvkcigLMtOuF9BQb56egZSMKPPLmo2QyaDLxOvtanv8URrdjodoy6S4w77UCikq1evqqmpSU6nUz6fT93d3bH23t5eOZ1Oud3uMdsAAOkX16WXhw4dUldXlxobG+VyuSRJJSUlGhoaUmdnpySpublZq1evHrcNAJB+467sL126pCNHjqi4uFiVlZWSpAULFqixsVF1dXUKBoMaHh5WUVGR6uvrJUlOp3PUNgBA+o0b9o899pguXrz4wLZly5apra0t4TYAQHrxDVoAMABhDwAGIOwBwACEPQAYgLAHAAMQ9gBggIRulwBAyp81Q9Nz+dPB1MIRCyRoeu40rXm+NSNjt720LiPjYurjNA4AGICwBwADEPYAYADCHgAMQNgDgAEIewAwAGEPAAYg7AHAAIQ9ABiAsAcAAxD2AGAAwh4ADEDYA4AB4rrrZSgUUkdHh/71r3+pra1NixcvliSVlZXJ5XIpNzdXklRdXa0VK1ZIks6fP6+amhoNDw+rqKhI9fX18nq9KSoDADCWuFb2K1eu1NGjR1VUVPQ/bS+//LJaW1vV2toaC3rLsrRz507V1NSoo6NDgUBADQ0NyZ05ACBucYV9IBCQz+eL+0m7urqUm5urQCAgSaqsrNTJkycnNkMAwKRN+sdLqqurZdu2SktLtWPHDs2aNUvhcFiFhYWxfTwejyzLUn9/v9xu92SHBAAkaFJhf/ToUfl8Pt29e1e1tbXat29fUk/XeL0zJ9y3oCA/afOYKqgZqZSp19rE9zgVNU8q7D89teNyuVRVVaVnnnkmtr27uzu2X29vr5xOZ8Kr+khkUJZlJzyvgoJ89fQMJNxvKqPm9I5roky91hzX8XM6HaMukid86eXt27c1MHBvQrZt68SJE/L7/ZKkkpISDQ0NqbOzU5LU3Nys1atXT3QoAMAkxbWyP3DggE6dOqWbN2/qySeflNvtVlNTk7Zt26ZoNCrLsrRo0SIFg0FJktPpVF1dnYLB4H2XXgIAMiOusN+zZ4/27NnzP9tbWlpG7bNs2TK1tbVNfGYAgKSZ9NU4ALLf3U+iGfms4u4n0bSPma0IewDjcj2UozXPt6Z93LaX1qV9zGzFvXEAwACEPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAQh7ADAAYQ8ABiDsAcAAhD0AGICwBwADEPYAYADCHgAMQNgDgAEIewAwAGEPAAYg7AHAAIQ9ABiAsAcAAxD2AGCAccM+FAqprKxMS5Ys0fvvvx/bfvnyZW3YsEHl5eXasGGDrly5ElcbACD9xg37lStX6ujRoyoqKrpvezAYVFVVlTo6OlRVVaWampq42gAA6Tdu2AcCAfl8vvu2RSIRXbhwQRUVFZKkiooKXbhwQb29vWO2AQAyY9pEOoXDYc2bN085OTmSpJycHM2dO1fhcFi2bY/a5vF4EhrH6505kelJkgoK8ifcd6qiZmQjE9/jVNQ8obBPl0hkUJZlJ9yvoCBfPT0DKZjRZxc1p3dcpA/HdfycTseoi+QJhb3P59P169cVjUaVk5OjaDSqGzduyOfzybbtUdsAAJkxoUsvvV6v/H6/2tvbJUnt7e3y+/3yeDxjtgEAMmPclf2BAwd06tQp3bx5U08++aTcbreOHz+uF198Ubt27dLhw4c1a9YshUKhWJ+x2gAA6Tdu2O/Zs0d79uz5n+2LFi3Sm2+++cA+Y7UBANKPb9ACgAEIewAwAGEPAAYg7AHAAIQ9ABiAsAcAAxD2AGAAwh4ADEDYA4ABCHsAMABhDwAGIOwBwACEPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAQh7ADAAYQ8ABiDsAcAAhD0AGGDaZJ+grKxMLpdLubm5kqTq6mqtWLFC58+fV01NjYaHh1VUVKT6+np5vd5JTxgAkLhJh70kvfzyy1q8eHHssWVZ2rlzpw4ePKhAIKDDhw+roaFBBw8eTMZwAIAEpeQ0TldXl3JzcxUIBCRJlZWVOnnyZCqGAgDEISkr++rqatm2rdLSUu3YsUPhcFiFhYWxdo/HI8uy1N/fL7fbnYwhAQAJmHTYHz16VD6fT3fv3lVtba327dunr33ta8mYm7zemRPuW1CQn5Q5TCXUjGxk4nuciponHfY+n0+S5HK5VFVVpWeeeUbf+c531N3dHdunt7dXTqcz4VV9JDIoy7ITnlNBQb56egYS7jeVUXN6x0X6cFzHz+l0jLpIntQ5+9u3b2tg4N6kbNvWiRMn5Pf7VVJSoqGhIXV2dkqSmpubtXr16skMBQCYhEmt7CORiLZt26ZoNCrLsrRo0SIFg0E5nU7V1dUpGAzed+klACAzJhX2CxcuVEtLywPbli1bpra2tsk8PQAgSfgGLQAYgLAHAAMQ9gBggKR8qQoAUuHuJ9GMXeo6NDyigY/vZGTsVCDsAXxmuR7K0ZrnWzMydttL65RNV/hzGgcADEDYA4ABCHsAMABhDwAGIOwBwACEPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAQh7ADAAYQ8ABuBGaADwAJm64+bdT6IpeV7CHgAeIFN33Gx7aV1KnpfTOABgAMIeAAxA2AOAAVIa9pcvX9aGDRtUXl6uDRs26MqVK6kcDgAwipR+QBsMBlVVVaV169aptbVVNTU1+tWvfpXKIWGQTP4+KTDVpCzsI5GILly4oDfeeEOSVFFRof3796u3t1cejyeu53A6HRMaO1MhMDw8osHBobSP+6mJvl5TleuhHD114FTax319zxOaO2dG2sf9VKbGNm3cTI490b/lsfo5bNu2JzqhsXR1denHP/6xjh8/Htv2jW98Q/X19Xr88cdTMSQAYBR8QAsABkhZ2Pt8Pl2/fl3R6L1vg0WjUd24cUM+ny9VQwIARpGysPd6vfL7/Wpvb5cktbe3y+/3x32+HgCQPCk7Zy9JH3zwgXbt2qWPP/5Ys2bNUigU0uc+97lUDQcAGEVKwx4A8NnAB7QAYADCHgAMQNgDgAEIewAwAGEPAAbIqrA34S6bfX19evrpp1VeXq41a9bo2WefVW9vryTp/PnzWrt2rcrLy7V582ZFIpEMzza5XnnlFS1ZskTvv/++pOyud3h4WMFgUE888YTWrFmjvXv3SsruY/ydd97R+vXrtW7dOq1du1anTt2771E21RwKhVRWVnbfcSyNXWPS6rezyKZNm+yWlhbbtm27paXF3rRpU4ZnlHx9fX32n//859jjn/70p/ZPfvITOxqN2qtWrbLPnTtn27ZtNzY22rt27crUNJOuq6vLfuqpp+yvfvWr9sWLF7O+3v3799u1tbW2ZVm2bdt2T0+PbdvZe4xblmUHAgH74sWLtm3b9nvvvWcvXbrUjkajWVXzuXPn7O7u7thx/KmxakxW/VkT9jdv3rRLS0vtkZER27Zte2RkxC4tLbUjkUiGZ5ZaJ0+etL/73e/af/vb3+xvfvObse2RSMReunRpBmeWPMPDw/a3v/1t+9q1a7E/kmyud3Bw0C4tLbUHBwfv257Nx7hlWfaXvvQlu7Oz07Zt2/7LX/5iP/HEE1lb83+G/Vg1JrP+rPnB8XA4rHnz5iknJ0eSlJOTo7lz5yocDmftLRosy9KxY8dUVlamcDiswsLCWJvH45FlWerv75fb7c7gLCfv5z//udauXasFCxbEtmVzvdeuXZPb7dYrr7yis2fP6uGHH9b27ds1ffr0rD3GHQ6Hfvazn2nr1q3Ky8vTrVu39Mtf/tKIv+uxarRtO2n1Z9U5e9Ps379feXl52rhxY6ankjJ//etf1dXVpaqqqkxPJW2i0aiuXbumz3/+8/rNb36j6upqbdu2Tbdv38701FJmZGRER44c0eHDh/XOO+/oF7/4hZ577rmsrjndsmZl/5932czJycn6u2yGQiFdvXpVTU1Ncjqd8vl86u7ujrX39vbK6XRO+VXuuXPn9MEHH2jlypWSpI8++khPPfWUNm3alJX1SveO5WnTpqmiokKS9IUvfEFz5szR9OnTs/YYf++993Tjxg2VlpZKkkpLSzVjxgzl5uZmbc2fGiu7bNtOWv1Zs7I36S6bhw4dUldXlxobG+VyuSRJJSUlGhoaUmdnpySpublZq1evzuQ0k+J73/ue/vSnP+n06dM6ffq05s+fr9dff11btmzJynqle6ekli9frjNnzki6dzVGJBJRcXFx1h7j8+fP10cffaR//OMfku7dRDESiejRRx/N2po/NVZ2JTPXsupGaCbcZfPSpUuqqKhQcXGxpk+fLklasGCBGhsb9e677yoYDGp4eFhFRUWqr6/XI488kuEZJ1dZWZmampq0ePHirK732rVreuGFF9Tf369p06bpueee01e+8pWsPsbfeustvfrqq3I47v203g9+8AOtWrUqq2o+cOCATp06pZs3b2rOnDlyu906fvz4mDUmq/6sCnsAwINlzWkcAMDoCHsAMABhDwAGIOwBwACEPQAYgLAHAAMQ9gBggP8DIiTyuj9uFuUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[10,5])\n",
        "sns.boxplot(x=datacp['pot_skill'], data=datacp);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "nVkV78IJZ4OE",
        "outputId": "ca580475-00cd-4a44-9b33-ef1324125087"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* the potential skill of any striker cannot be zero, we can remove the outlier from our data."
      ],
      "metadata": {
        "id": "1fbRbVDbhvbN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp = datacp.loc[datacp['pot_skill'] >= 40]"
      ],
      "metadata": {
        "id": "p3Ju3Zfbbo40"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "MİNUTE"
      ],
      "metadata": {
        "id": "2S6jQI8zY9x5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp.minute.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "M1G9EWqSlhPn",
        "outputId": "e703d964-d08e-4b22-d6e8-e2ffe5af3ce9"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f689120d130>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=\"minute\", y=\"score_efficiency\", data=datacp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "MFkZLog0Xsed",
        "outputId": "351ae762-f0d5-48e8-bf87-1ee427da7290"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f68911790d0>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* As the time played by the strikers increases, the score efficiency increases."
      ],
      "metadata": {
        "id": "ipY2zUGGY7QS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3)Preparing the data for the model**"
      ],
      "metadata": {
        "id": "sIdb14LdbV3i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp = pd.get_dummies(datacp, columns=['foot'], drop_first=True)"
      ],
      "metadata": {
        "id": "nSZHOfL8jMoF"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datacp.value.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "Dvx7MaG8mFS4",
        "outputId": "8fd2e6d1-eb70-4e3d-a2fc-208acc7d0031"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6893a7d6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datacp['log_value'] = np.log(datacp.value)\n",
        "datacp.log_value.hist();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "WO-2qjKMZ1M4",
        "outputId": "7ed09a44-9bbe-4f5d-bb63-6cc9b8b47426"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The value attribute has a right-skewed distribution, we correct the distribution with logarithms."
      ],
      "metadata": {
        "id": "CWYequGo-HkQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "_E0anMXFmZ6i",
        "outputId": "808aada4-8a49-4b6d-a1c7-d926261d0316"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     skill  pot_skill                 name   age  value  height  weight  \\\n",
              "0     92.2      100.0        Kylian Mbappé  24.0  146.8     178      75   \n",
              "1     93.2      100.0       Erling Haaland  22.0  139.0     195      88   \n",
              "2     93.9      100.0           Phil Foden  22.0  124.5     171      70   \n",
              "3     88.3       89.2           Harry Kane  29.0   89.8     188      86   \n",
              "4     91.7       93.9      Raheem Sterling  28.0   89.6     170      71   \n",
              "..     ...        ...                  ...   ...    ...     ...     ...   \n",
              "711   55.3       55.3           Jaime Mata  34.0    1.5     186      79   \n",
              "712   55.7       67.0           Max Fenger  21.0    1.5     182      75   \n",
              "713   53.9       54.2  Ernesto Torregrossa  30.0    1.4     184      80   \n",
              "714   52.8       62.2               Mikael  23.0    1.4     184      85   \n",
              "715   53.2       66.4         John Kennedy  20.0    1.4     181      71   \n",
              "\n",
              "     minute  goal  asisst  score_contribution  score_efficiency  \\\n",
              "0    6293.0    57      31                  88          0.013984   \n",
              "1    4326.0    54      11                  65          0.015025   \n",
              "2    4600.0    20      11                  31          0.006739   \n",
              "3    6296.0    35      13                  48          0.007624   \n",
              "4    4745.0    22      10                  32          0.006744   \n",
              "..      ...   ...     ...                 ...               ...   \n",
              "711   842.0     1       0                   1          0.001188   \n",
              "712  1855.0     6       0                   6          0.003235   \n",
              "713  1889.0    12       3                  15          0.007941   \n",
              "714  1679.0     4       0                   4          0.002382   \n",
              "715   524.0     2       0                   2          0.003817   \n",
              "\n",
              "     assists_efficiency  goal_efficiency  development  body_mass_index  \\\n",
              "0              0.004926         0.009058          7.8        23.671254   \n",
              "1              0.002543         0.012483          6.8        23.142669   \n",
              "2              0.002391         0.004348          6.1        23.938990   \n",
              "3              0.002065         0.005559          0.9        24.332277   \n",
              "4              0.002107         0.004636          2.2        24.567474   \n",
              "..                  ...              ...          ...              ...   \n",
              "711            0.000000         0.001188          0.0        22.835010   \n",
              "712            0.000000         0.003235         11.3        22.642193   \n",
              "713            0.001588         0.006353          0.3        23.629490   \n",
              "714            0.000000         0.002382          9.4        25.106333   \n",
              "715            0.000000         0.003817         13.2        21.672110   \n",
              "\n",
              "     foot_Left  foot_Right  log_value  \n",
              "0            0           1   4.989071  \n",
              "1            1           0   4.934474  \n",
              "2            1           0   4.824306  \n",
              "3            0           1   4.497585  \n",
              "4            0           1   4.495355  \n",
              "..         ...         ...        ...  \n",
              "711          0           1   0.405465  \n",
              "712          0           1   0.405465  \n",
              "713          1           0   0.336472  \n",
              "714          0           0   0.336472  \n",
              "715          0           1   0.336472  \n",
              "\n",
              "[714 rows x 19 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4336057-3722-427a-bae5-2be82a148039\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>skill</th>\n",
              "      <th>pot_skill</th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>value</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>minute</th>\n",
              "      <th>goal</th>\n",
              "      <th>asisst</th>\n",
              "      <th>score_contribution</th>\n",
              "      <th>score_efficiency</th>\n",
              "      <th>assists_efficiency</th>\n",
              "      <th>goal_efficiency</th>\n",
              "      <th>development</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>foot_Left</th>\n",
              "      <th>foot_Right</th>\n",
              "      <th>log_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>92.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Kylian Mbappé</td>\n",
              "      <td>24.0</td>\n",
              "      <td>146.8</td>\n",
              "      <td>178</td>\n",
              "      <td>75</td>\n",
              "      <td>6293.0</td>\n",
              "      <td>57</td>\n",
              "      <td>31</td>\n",
              "      <td>88</td>\n",
              "      <td>0.013984</td>\n",
              "      <td>0.004926</td>\n",
              "      <td>0.009058</td>\n",
              "      <td>7.8</td>\n",
              "      <td>23.671254</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4.989071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>93.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Erling Haaland</td>\n",
              "      <td>22.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>195</td>\n",
              "      <td>88</td>\n",
              "      <td>4326.0</td>\n",
              "      <td>54</td>\n",
              "      <td>11</td>\n",
              "      <td>65</td>\n",
              "      <td>0.015025</td>\n",
              "      <td>0.002543</td>\n",
              "      <td>0.012483</td>\n",
              "      <td>6.8</td>\n",
              "      <td>23.142669</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4.934474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>93.9</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Phil Foden</td>\n",
              "      <td>22.0</td>\n",
              "      <td>124.5</td>\n",
              "      <td>171</td>\n",
              "      <td>70</td>\n",
              "      <td>4600.0</td>\n",
              "      <td>20</td>\n",
              "      <td>11</td>\n",
              "      <td>31</td>\n",
              "      <td>0.006739</td>\n",
              "      <td>0.002391</td>\n",
              "      <td>0.004348</td>\n",
              "      <td>6.1</td>\n",
              "      <td>23.938990</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4.824306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>88.3</td>\n",
              "      <td>89.2</td>\n",
              "      <td>Harry Kane</td>\n",
              "      <td>29.0</td>\n",
              "      <td>89.8</td>\n",
              "      <td>188</td>\n",
              "      <td>86</td>\n",
              "      <td>6296.0</td>\n",
              "      <td>35</td>\n",
              "      <td>13</td>\n",
              "      <td>48</td>\n",
              "      <td>0.007624</td>\n",
              "      <td>0.002065</td>\n",
              "      <td>0.005559</td>\n",
              "      <td>0.9</td>\n",
              "      <td>24.332277</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4.497585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>91.7</td>\n",
              "      <td>93.9</td>\n",
              "      <td>Raheem Sterling</td>\n",
              "      <td>28.0</td>\n",
              "      <td>89.6</td>\n",
              "      <td>170</td>\n",
              "      <td>71</td>\n",
              "      <td>4745.0</td>\n",
              "      <td>22</td>\n",
              "      <td>10</td>\n",
              "      <td>32</td>\n",
              "      <td>0.006744</td>\n",
              "      <td>0.002107</td>\n",
              "      <td>0.004636</td>\n",
              "      <td>2.2</td>\n",
              "      <td>24.567474</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4.495355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>55.3</td>\n",
              "      <td>55.3</td>\n",
              "      <td>Jaime Mata</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>186</td>\n",
              "      <td>79</td>\n",
              "      <td>842.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.001188</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001188</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.835010</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.405465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>712</th>\n",
              "      <td>55.7</td>\n",
              "      <td>67.0</td>\n",
              "      <td>Max Fenger</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>182</td>\n",
              "      <td>75</td>\n",
              "      <td>1855.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.003235</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003235</td>\n",
              "      <td>11.3</td>\n",
              "      <td>22.642193</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.405465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>713</th>\n",
              "      <td>53.9</td>\n",
              "      <td>54.2</td>\n",
              "      <td>Ernesto Torregrossa</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>184</td>\n",
              "      <td>80</td>\n",
              "      <td>1889.0</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>15</td>\n",
              "      <td>0.007941</td>\n",
              "      <td>0.001588</td>\n",
              "      <td>0.006353</td>\n",
              "      <td>0.3</td>\n",
              "      <td>23.629490</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.336472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>714</th>\n",
              "      <td>52.8</td>\n",
              "      <td>62.2</td>\n",
              "      <td>Mikael</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>184</td>\n",
              "      <td>85</td>\n",
              "      <td>1679.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.002382</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.002382</td>\n",
              "      <td>9.4</td>\n",
              "      <td>25.106333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.336472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>715</th>\n",
              "      <td>53.2</td>\n",
              "      <td>66.4</td>\n",
              "      <td>John Kennedy</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>181</td>\n",
              "      <td>71</td>\n",
              "      <td>524.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.003817</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003817</td>\n",
              "      <td>13.2</td>\n",
              "      <td>21.672110</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.336472</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>714 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4336057-3722-427a-bae5-2be82a148039')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c4336057-3722-427a-bae5-2be82a148039');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datacp.drop(columns=[\"name\",\"value\"],inplace=True)"
      ],
      "metadata": {
        "id": "QEavfY8fmqJz"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[16,12])\n",
        "sns.heatmap(datacp.corr(), cmap=\"seismic\", annot=True, vmin=-1, vmax=1);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "id": "6k217Fvwj5MD",
        "outputId": "4e4c65ac-9445-4259-c0d2-9f022d2ba909"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 2 Axes>"
            ],
            "image/png": 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cAwcHNzQae3x9B3PkiOHP12LFzGnVagDVqnlhaqCSXrasExpN5giFDExMTLlz5/oTxadNSGDH/v2MePttrK2s8KpdG5+mTdm4c6de28AdO3i7Z08qlC1L+bJleatnTzYYOIlsDNqEBHb8+ScjBgxQcdWqhU+TJmw0UKUO3LmTt3v0UHGVKcNbPXqwYceOAr3Onr/+YmCvXlhZWuJYoQI9fH1Zt337Y8e7dWsgfn49qFrVDTs7e956azBbthjez/v27cTFxY3WrdX36MCBwwgODuLaNfU9WqVKVaysso9DExNTbt7MvZ/nz/+S117ri719yceOFVS/rlL37pyfPJm0+HiiDx4kfNMmquTTr7u1fTtJun7dzVWrsMvRr3tw/jzpuqp3RkYGGRkZWFerVqi4xItDkuBn4M8//yQ+Pp6tW7eyadMmpk2blrUsLCyMAQMG8MYbbzBgwICiC/I5Z1e9OhmpqTzIMdTx/unTesktwJUlSyjj7Y2VgwNmVlZU6dOHiN9/z1pezNaW2tOmcXLUKKPHee32bcxMTXEpXz7ruRqOjoTkrVoU0Hvz5lF72DB6fv45DatXp9YTdkavXw+mWrXsBKBaNXfu3YsiNlY/ub52LZiqVd2zfrey0lCxohPXr2cPH968eSXdujVk8OBuHDiQ90s7g7yuXTN8cickJBh39+y4atRwJyoqinsGkn7VNjsujUZD5cpOhISEEBMTw507d/Ksq0auIc8ADRt64enpwcyZ03n33fdyR52R8+cMgnMM8c6Xk5OqVt24kf1ccDDkqWJkadtWDXfetg1cXSEwMHuZiUnutiYmULXqo2MoiMw4cyT+BAfnv/727VXitHOnqm5vMNyhymKsToebG6Smqkp6prNnVSJnyFdfwZQp2UlQptu3VWW2Xz81NLZhQ6hcWQ2PNoZq1VScl3OcGPrnn/yT7M8/V0N688ZZs6baL507q+T36FEw5nC+0qWzk9pMt24VLEmsUkVtx5x69ID//U9t1xyfdUZXrpyKO+frh4WBgctbADVU+ssvVXW+UiU4cOCphHXtzh31OV+2bNZzNRwcCImMfKz1RMbEEBkTw6XISFrMmIHPrFkEbN9Oenq6UeONiAimUqXsY7JSJXcePIgiPr5wJ1WPH9/MRx/VY9y4xoSHB+Ht3fuJ4rt28yZmZma4VK6c9VyNatUIuXZNr23wtWvUyPE5U8PVleA87fqMGIF3t24M/fhjbj7mPskVV1iYiivz5F9mXNf1k/7ga9eokeNztEbVqgTnaddn1Ci8X3uNoVOm6MeV44snIyND738qiKtXg3Fzy97Pbm7u3L0bRUyM/n5WbXN/vzs6OnH1avZn7k8/LcTHpy7+/q+QmKilXbvsqvY//5whKOgcXbvmc2KyAGyqVyc9NZW4HP26mNOncyW3WfEuWUJpb28sdf26yn36EJmjXwfgOW8e/vHxtL94kcSICCK3bi10bOLFIMOhn4EaNWpw+fJlpk6dSsOGDWnZsiUAd+7coV+/fnz++ed45XedkwCgmI0NKbGxuZ5LiYmhWM6hjzoPgoPR3rhBl/Bw0lNTiTl7lj1Dh2Yt95g+nStLlpCQ4zpSY9EmJmKTZ7i4rZUV8YmJhVrfD0OGkJKWxqELF7gSGWnwzPzjSEjQYm2dPQzT2lptP602Hju73GdzExO12Nvnrl5oNDZotfEAdO3al/ff/x/W1rYcP36QmTM/pGTJMtSqVZ8GDZozc+ZIOnXqTaVKzvz88zxMTExIzGc7aLVabG2z47KxyY6rZMncccXHaymVp6pia2tDfHw8Wq1W93v2cWFjY0t8fHyu9kePHker1RIYuIGKFbOHpzVv3pxFixby2WefERUVzfr160jI0E/m9VhZqUpg7kBVpdCQnTvVw9FRVWHv3lXPnzsHZcqoJHnPHjXMuFIlVcE1Bo1GP864uPzj3L5dPSpXhg4dsuM8exbKllXx7d6tKt6OjmBhYZw4ra31h1XHxuoPIQaVOJqZwaZN0Ly5/vI1a2DePJUYAYwYoRKpZx1nx44qzi1b9K+zrVhRVYNdXVXls2pVdWLk8mV1bfCTsrDQryonJqpK68O0aqVOwuS83nXdOggPV883bqwS4blz1fqMzcJC/4RBQkL+x9nx4+pRtiw0amS8ofl5aJOSsMkTg62VFfGPWbmPjIkB4OClS2wePZrYhATeWbSICiVK8FqjRkaLNzlZi5VV9jFpZaU+HxMT47G2fvwqnpeXH15efty+fY2jRwOxsyv9RPFpExKwyfMZZGttTbzu81yvbY73l621NdqEBDIyMjAxMeHnb76hTs2aJCYl8c2SJbw/fjyBixdnjap6anElJmJjbZ1/XF9+SZ2XXlJxLV3K+5MnE7hgAcXMzGjeoAELf/2Vz8aOJfrePdZt305CIUaB5P1+z/k9mrdaq9VqKVky9/eotXX29ztAv37v0rfvIC5dusD+/buy1p2WlsYXX0xh9OiPn6hPUszGhtQC9uvigoNJuHGDjrp+XezZs+zP0a8D+HvIEP4eNozSTZpQtmVL0o15OclzRKqXxiPb8hmoXLkyv/32G97e3hw+fBh/f3+SkpKwt7fHxcWF/fv3F3WIz73UuDiK55kkpridHakGOjle8+ZhamHBulKlWGNtzY3162mhO2NYok4dKrRpw8Wvv34qcWosLYnL02mLS0zE+gmSmOJmZrSoVYs/L1xg92NOjrV796asSasmTBiIlZUGrTYua3nmzxqNtd7fWlrmbqvax2e1dXN7GTu7kpiZFaNRoxb4+Pjx559q+Fq9ek3p1284U6cO5803fShfvhJWVtaULVshK6769etSv35d3n13IBqNhri47NfK/NlQXNbWuduq9vFYW1uj0XVYci6Pj4/D2lp/PRqNht69X2fcuP8RrauQTZgwCUtLC3x92zNkyGA6dOhIhYJ8ySckqIQod6BgoLOUy82b6prhMbprtmJjVYWtd2+VLDVurDr1eStxhaXVFi7OGzfUNaBjx6rfY2JUzG+8oarZjRur6qWx4oyPz31tL6jf8+x3NBqYMQNGjza8nurVYflydX2ovb26JnjkSMPD1J92nFOmqOtaDclMIGfPVj+fP//wydUeV1KSfuJoYaGGRuenYUM1XPrnn1WVOlNoqKp+p6SoSmtioqoWPw1JSeoEU06Wlo8eJn7njhrS3fvJKpT50VhYEJcnhrjERKwf8ySQZfHiAAxs2RI7KyscS5WiV+PG/JHfde8FdOzYJkaPrsvo0XX5/vuBmJtrSEzMPiYzf7a01P9cfBzlyjnj4ODGqlVTn2g9Gisr4vJ8BsVptVgbODmnsbLKdVIzLj4ejZUVJroRNA3q1MG8eHHsbGyYOHQoNyMjuWygclvouOLjDcdlaZkrOY7TanPH5eGRHdfgwSou3YicSYMHY2FhQfsBAxj8ySd0bNWKCvlcJpXT9u2b8PGpi49PXUaOVN/v8fG5v/vA8PeoRpO7rWofr9fWxMQEd/eaWFhYsnixmihy/fr/o1o1d2rVMnA5xWNIjYujWAH7dXV1/bpNpUqx0dqasPXraZanEgxAejrRBw9i5ehI1Q8+eKL4xH+fJMHPQGRkJGZmZrRp04bx48dz9+5dYmJiMDc35/vvvyckJIQZM2bkmklQ5BZ76RImxYph4+qa9VyJOnWI+ecfvbYlPD25umwZyffukZ6czKW5cynTqBHmpUtTvmVLrJ2d6RwaSpeICGqMGYNj9+60P3HCKHE6lytHWno613IkA0E3b+Lq4PDE605LS+NGVNRj/U3r1p3ZvPkUmzefYtasxVSp4sblyxezll++HETJkmX0qsAAzs5uXL6cPblPQoKWiIhQqlRx1WsL6L7ss49hf/8+LF++gzVrDtG8eTvS09NwdnbLiuvEiVOcOHGKhQsX4+rqxsWL2XFdvBhEmTJl9KrAgK5tdlxarZYbN0JxdXXF3t6esmXLEpRjUqKgoCBcXQ3HnJ6eTmJiArduqZmrS5QowZw5X3LgwEF++20LGRkZeBQrwICZ0FBV5csxbA5XV/1JsQwxM1PV3kx//62Gwvr6qmtinZzynwzqcWXGmWPYIdWrF2ySo7z/36lTMGCAStSmTFETWOWZzKTQgoPVpFY5h1fXrq2/fldXlYDt2qW29cqVatKnq1fVdqtZUw2p3rVLDTcMDlZJe7t2GMXlyyrOnMPJX35Zf1KsqlVVPFu2qH35009qGPGFC2pfZH6O5R6Lb5wYQQ2DNjVV10hnqlAh/5MWdeuqqvqyZerETFG5fVvFnWPYMY6OqhL9KGZmalTFU+Bctqz6nM8xqVhQeDiueSbFehSXsmUpbmaWlSgBmDykfUE1aNCZL788xZdfnmLw4MU4OLgRFpb9+XrzZhC2tmUKVQXOKz09lagow3M9FJSzoyNpaWlcy7weHggKCcE1z+RTAG7OzgTluPwg6PJlvUmqcjKBQvetnCtV0o/ryhVcDZz0cXN2JijH52jQlSu4PeTkUM64StjZ8eX48RxcvZotixeTkZ6OR45LfvLTvn1n9uw5xZ49p/j668W4uLgREpK9n4ODgyhVqozBa3ZdXNwIDs79/R4WFoqLi+HvyrS0VMLC1H4+fvww+/fvomNHbzp29Obs2VMEBHzGF19MM/i3+Ym7dAnTPP06+zp1iDXQr7P39OT6smWk6Pp1l+fOpZSuX2eISbFi2PxHrwk2fQ4e/xX/pf/luXXx4kV69epF586d6dmzJ++++y7ldNdimZubExAQQHR0NJMnTzb6tUDGYmJmhpmFBaZmZlk/mxRieFFhpWm13Fy/Ho9p0zDTaCjTtCmV/P25auD2RnePHcOlXz+K29lhUqwYboMHow0LIzk6mpCFC9lcrRrbPD3Z5ulJyIIFhG/Zwr78bhPzmDQWFrT19CRg82a0SUmcuHyZ3adP429gaFt6ejpJKSmkpKWRASSlpJCcqm41cTkykj/OnSMxOZmUtDQ2HjnC8ZAQGrgVfhZGgLZt/dm2bS3Xr4cQFxfL//3ffNq162qwrbd3W65dC+bAge0kJyfx88/zcHFxx8lJfbHs37+NhIR40tPTOX78T3bv3kSTJj4AJCcncfXqJTIyMrh9O5yvv/6YLl36YWtreGZrf39/1q1bS0hICLGxsSxYMJ8uXQzH1aZNW4KDg9mxYztJSUl8//08qld3p2rVarp1dWHBgvnExMRw5cpl1qxZQ9eual0HDx7k/PnzpKWlERcXx+eff4adnR3VdF+WoaGh3Lt3j7S0NPbv/4PVq1fxQUFmQ09MVLM5DxqkKlW1a6skwtCELX5+atZdUIlj375qwqZM1aurDrxGA0OHqkTAwMRqhZKYqCbdevddFaeHB7zyiprMKS9//+w4XVxUwptzNvXMOK2t1W2ebt1St/4xBq1W3b5p8mS1HRo3VrNur1yZu90//6g4GjdWjyFD1PZq3FhV2U+fVol05sRiLi5q+Hne2xM9SZy//Qbjx6s4GzVSw8ZXr87d7sIFdUy0aKEeI0aoOFu0UEOzr11T1ymPHq2GKFevDt26qaHoxpCSomLw8VGTZDk5qeuW//5bv62HB7Rpoyroea/Jt7dXf2tmppJ/b2/1f+e8xtyYkpNVjJ06qe1StaqKz9Bt55o2zR6GXqGCOtGR48SaMWnMzWlbqxYBO3agTU7mxNWr7D5/Hv969fTaZn3Op6eTkZGR63PeytycDnXqsHjfPuISE4m8f59VR47QMr9r3wupYUN/Dh9eS0RECFptLNu3z6dRI8OfrwApKcmkpKhKd2pqCikpSVkJ26FDa3jwQI2ciYgIYceOhbi7N3mi+DRWVrRt3pyApUvRJiRw4uxZdh86hL+BkRD+7dqxdM0abt25w62oKJauXk1X3ciO4KtXuRASQlpaGvEJCXw2fz7lypalWiFHKmisrGjbrBkBy5eruM6dU3G1aaMfV5s2LF27lltRUSqutWvpqjvZFnztWu64fviBcmXKUE03x0doeDj3YmNJS0vjj6NHWbV1Kx/06fPY8b76qj+bN6/l6tUQHjyIZdmy+XTsaHg/t2jRlitXgtm7V32P/vjjPFxd3XF2rkZ6ejobNvxKbGwMGRkZ/PPPGdat+z+8vNR+njTpM2SIxQ0AACAASURBVFau3MpPPwXy00+B1KhRi3feGcr77+vfKeJh0rRawtavp6auX1e6aVMq+vtz3UC/7t6xY1Tp149iun5d1cGDSdD16yzKlsWxVy/MrK3B1JTy7dpR+fXXuW3kiU/Ff49JhpQf/7Om5p1g5wm0+OQTWk6Zkuu5fVOm8MfUJxsGBVC9gO3MS5ak0Y8/UqFtW5Kiozk9bhzXV66kbLNmtPj9d9bqhiWalypF/YAAKrRti6m5OTHnznFy1CjuGrgdUq1PPsHW1ZXDBmYjzOv1An6g3o+PZ8KKFRy6cIES1taM7tIFv4YNOR4czKB58zilmxn2yKVL9MszLLuhmxsrRo3ickQE4376iZCICMxMTalSrhzv+/rS1tBsrnmEuvo8dPnatUtZtWoRycmJNGvWnhEjpmKuuzZw4MCOvP76e7RurW43cfLkIb77bhq3boVTo0YdPvroUypUUNXAkSPf4MqVi0AGFSo40rv3e7Rq1RGAuLhYRo3qQ0TEDaysrGnfvhsDBnyIWY4TJzmLigDLli1l8eJFJCYm0q5de6ZMyY6rU6eOvPfee/j5qbgOHTrEjBnTCA8Px8OjDp9++imVKqkVJicnM3XqJ2zfvh1LS0sGDhzEgAHq9h3btv1OQMC33Lp1CwsLC2rX9mDUqFFZE2n9/vtWPv10Fg8ePMDZ2ZnRo8fwyrj/PXKbA2oY7MSJ0KCBGi48f7667rdOHTVZT2bHaeJEaNJEDfO8f19d+7toUfbQ1KlT1XJQSeXXX+snI4akFvBenXZ2Krls2FDFOW+eSrY8PdWsxbo5C5g8WSUWGo16/d271azSmXFOn559bevhw2pm5oLEWdAEtGRJWLBAJW5376p4Vq9WMQUGGp7UqXlz+PFHNbFWpm7dVJLq5KSqmr/+Ch9//OhKa0EvYShRQl0T27Kl+v+nTlXXzTZurOI1NJmdt7falrVqZT/n4AABASqRjoqCb79Vieij5LkuLl9WVmrG7WrVVPK+c6e6trtKFXW7pJkzVbuRI9UxkvN4OnNGzdBctqy6XVepUmp5ZCTs2FGwyuxjjmLJotGoE0U1aqjh54GB6hKBatXUSY/MCQ779lVVeAsLNRz95EkVc0HfF5kKOFT+vlbLhNWrOXTpkvqc79ABv7p1OX7lCoOWLOGUbnseuXyZfgty32O1YdWqrNAN1YxLTGTy2rXsCwrCztKSno0aMaRNm1zVYUN2WHZ+6PK89uxZyq5di0hJSaROnfb06jWV4sXV5+vMmR1p1+49GjRQ6/zkEx/u3s193fyUKbspXdqRn38ez/nzf5CUpMXGphR167anY8cPKV7c8FDwdrUKcGyg7sc7YfZsDp04QQk7O0YPGoRfmzYcP3OGQf/7H6d0J+oy7xO8VjfpUY8OHfjovfcwMTHh8MmTTPnmG27duYOVpSV1X36Zse+/j3PeLxtD8jlO7sfGMuHLLzl08iQlbG0ZPXAgfj4+HD97lkETJnBq8+bsuBYvZq0uzh6vvspHAwequE6dYkpAALeiolRcNWsydtCgrLi2/vEHs77/ngfx8ThXqsSYgQNp3qCBwXju2jx8csyVK5eyYsUikpISadWqPWPHZn+PvvFGR/r3f4/27dV+Pnr0EF9+OY3IyHBefrkOkyd/ioODI+np6YwaNYjz58+SmppCmTLl6NChK/37v2fwuBw8uC++vp3p3Lln1nN7Sxes/1m8ZEm8fvyRcm3bkhwdzblx47ixciWlmzWj2e+/szFHv65OQADldP262HPnODNqFPeOHcO8TBkar12LfZ06mJiaor1+nZCAAK4tXvzI1+/+L0yBvjVi376wRvwLt5shkgT/hxkzCX6aCpoEF7WCJsFF7VFJ8POiIP2S54Fps6ZFHULBPG5nv6gYqwr7tBlrMrKnraBJcFErbBL8rBnrevGn7HGT4KJS0CS4yP1LPj8flQQ/LwqaBBe1f2MSPPc56NsP+xduN0NkOLQQQgghhBBCiBeG3CJJCCGEEEIIIZ5zRV8H/u+QSrAQQgghhBBCiBeGJMFCCCGEEEIIIV4YMhxaCCGEEEIIIZ5zz+7mpP99UgkWQgghhBBCCPHCkCRYCCGEEEIIIcQLQ4ZDCyGEEEIIIcRzTqqXxiPbUgghhBBCCCHEC0MqwUIIIYQQQgjxnJPqpfHIthRCCCGEEEII8cKQJFgIIYQQQgghxAtDhkMLIYQQQgghxHNOqpfGI0nwf1j1og6ggC4VdQAF9MZin6IOoUA+/LCoIyiYoKCijqBg2vn6FnUI/y0lShR1BAVToUJRR1AwNWoUdQQFc+1aUUdQMM7ORR1BgbTrXbOoQyiQS4HnizqEAqn+L+mJRCUWdQQFk1DUAQhRAJIECyGEEEIIIcRzTirBxiPbUgghhBBCCCHEC0OSYCGEEEIIIYQQLwwZDi2EEEIIIYQQzzmpXhqPbEshhBBCCCGEEC8MqQQLIYQQQgghxHNOqpfGI9tSCCGEEEIIIcQLQ5JgIYQQQgghhBAvDBkOLYQQQgghhBDPOZOiDuA/RCrBQgghhBBCCCFeGFIJFkIIIYQQQojnnFlRB/AfIpVgIYQQQgghhBAvDEmChRBCCCGEEEK8MGQ4tBBCCCGEEEI856R6aTySBAvMS5ak4ZIlOLRrR1JUFKfHj+f6ypV67UzNzan37bdU7toVk+LFiTp4kGPvv09CeHiudjaurnQ4e5Yba9dyuG/fZ/VvANBgyBA8BwygXO3anFu5ko1vvfVMXz/Tq6+Cnx+Ym8PRo/Djj5Caqt+uUiX44AMoX179fvUqLF8OYWHZ62nfHmxsICkJDh+G//s/SE9/OnH//vsytmxZRFJSAg0btmfAgKkUL26u1y41NZnvvx/D1avniIoKY8KEn3jppUZPJyhgz55l7Nq1iJSUBDw92/Paa/nHtXz5GEJDz3H3bhjDh/+Em1ujXOvZv38F8fH3MDfXUK9eB7p0GYuZmRE+Ci0toXNnqFoVtFrYswfOndNv16QJ1KkD9vaq3fHjasfmVaUK9O8PBw7A3r1PHp+x4xw+HKytISND/X7jBvzyi/HitLWFUaOgfn2IiVFvIkPboVs38PcHOztITIQ//oCFC3O/Sbp0Ue1KlIDbt+GTT7LfZE/K2hreeQdq14YHD2DNGsP7s317aNtW/V+JiXDkCPz6a3ac3btDvXpQsSJs2gQbNhgnPp37CQlM3LSJg5cvU1KjYVTr1vjVrq3X7q+rV5m3fz/nIyKwt7Rkz4cfZi0Lj4mh47x5udprU1L4X9u2vN20qXECtbSEjh3BxQUSEtQ+P39ev12jRuDhofZ7QgKcOKG2aV5OTvDmm3DwoDo2Cun+gwdMnD+fg2fOUNLWllFvvIFf8+Z67TIyMvjil19Yu3s3AD1at2ZMnz6YmKg5XQ+fPcvsFSu4HhlJSVtb3u3ShV5t22b97YL161m1cyexWi0t6tZl+nvvYaPRFDpu7O1h+nRo2hTu34evv4YtW/JvX7w4rF+vjmsfH/VclSrw0Ufg6QlmZnD2LMyaBdeuFT4uncDAZaxbp75zvL3bM3iw4c92gNOnDzN//lTu3InA3d2DDz/8jHLlKgEweHBH7tzJ7oskJydRv/4rfPzxAsLCrrJ06WwuXDhFeno6bm61effdiTg6Vn1kfPcfPGBiQAAHT52ipJ0do/r1w69lS712GRkZfLF8OWt37ACgR7t2jOnfHxMTE47/8w+DpkzJ1V6bmEjAuHG09/bO9Xz/iRP568wZ/gkMpJjZ41/9+Sy2Z+bf/vjjbCIirmNnV5IePd7F17fXY8drXrIkjZcsoWK7diRGRfH3+PFcy6f/6aXrf5oWL86dgwc5kqf/WaVXLzw++QRrJycSIiM5NGAAd/7887FjEi8OOaHwBG7evMmqVasK/ffjxo3j559/1nv+7NmzjB49Ous1GjXK7sS7u7sTHx9f6Nc0xGvePNKTk9lQvjyH+vTBa/587GrW1GvnPmIEZZo04XcPDwIrViT53j3qz51rcH3Rx44ZNcaCehAezv4ZM/j7xx+L5PVB9cs6d4aZM2HECChXDnr0MNz23j345hsYNAjefVf144YNy15+4gRMmAADB8LYsaov4uv7dOI+c+YAv/22kHHjlvHNN3u5ffsm69cH5Nu+evV6vP/+bOztyz6dgHQuXDjArl0LGTZsGVOn7iUq6iZbt+YfV9Wq9ejXbzZ2dvpx1a7tw9ixG5gz5yQTJvxGWFgQf/yxwjiBdugAaWnw5ZcqgenQAcoa2DYmJhAYCLNnqzMaDRrAyy/nbmNqqpKmmzeNE9vTivPXX+Gzz9TDmAkwwNChkJICr72m1j98uHoD5HX4MAweDF27qjdR1arq50y+vuoxaZJ6Y06eDLGxxouzXz+1PYcOhQUL1ImLSpX02506BR9/DO+9p97UTk7Qrl328lu3YNUqOH3aeLHlMG3rVoqbmXFwzBjmdOvGlC1bCL59W6+dxtyc7p6ejNUlZjlVtLfn1IQJWY9NH3yAqYkJ7Qx8XxRa+/Zqe377LWzcqPZdmTL67UxM1MmCr75Sx6GXF+SNw9RUnXgwwgmPaUuWULxYMQ4uWsSc4cOZsmgRwTdu6LVbtWsXu44eZeMXX7Dpiy/Ye/w4v+7cCUBKaipD58yhV9u2nFi+nK9HjuSz5csJ0iWTgX/8wcb9+1k5YwYHfviBxORkpj/pd9mkSep99Mor6kvk44/B1TX/9m+/rb6YcrKzUyfLOnaE5s1VEvzdd08WF3Dy5AHWrVvIjBnL+PHHvURG3uSXXwx/tsfE3GXWrKG8+eYIVq48iqtrLWbPHpm1/Pvvt7BmzSnWrDnF6tUnKVOmAs2aqS/L+PgHNGzow4IF21ix4iDVq9dmxozBBYpx2oIFar+vWMGc0aOZMn8+wdev67VbtW0bu/76i40BAWyaO5e9R4/y67ZtAHi9/DKn1qzJeiz4+GM0VlY0r18/1zo27dtHalpageIy5Fltz9TUFGbNGoqvby9WrTrB2LFfs2TJZ1y9GvTYMTfU9T/Xli/PwT59aDh/PvYGPk9qjBhB2SZN2OLhwTpd/7NBjv5nhTZtqPv55xx+6y1+tbVlxyuvEHflymPH829g+hw8/iv+S//LMxcWFvZESXB+ateuzZdffmn09RpiptHg2L07ZydPJjU+nqiDBwnbtAkXAxVcaxcXIrdvJ/H2bdKTkghdtQr7PJ1ip169SLl/n1u6s+DPWtCGDVzcuBFtdHSRvD6oPsK+farfFR+v8oxXXjHcVquFqCj1s4mJKgplVoVBFa202uzlGRm5lxvTn38G0qJFDxwd3bC2tqdLl8EcOGC4GlWsmDm+vgNwd/fC1PTpfowcORJI48Y9cHBwQ6Oxx9d3MEeO5B9Xq1YDqFbNcFxlyzqh0djpfsvAxMSUO3f0OzSPrXhxeOklVbVKSVFV0UuX1BmRvA4dgshItTOjo+HiRahcOXebJk3gypXsg8NYjB3n02JpCc2aqWERiYnwzz8q2W3dWr9tRIR6o2VKT1fVVFBvmr59VXIaGprd/sED48Rpbq5ODqxbp4ZqXLqkkt081R3g0W/mP/+EM2dUVdPItMnJ7Dh/nhGtWmFtbo6XkxM+7u5sPHNGr61HpUp0qVOHyiVLPnK9G0+fxqtKFRxLlDBOoMWLQ40asH+/Oj5v3oTgYKhVS7/tX3+pEwcZGXD3rtr2jo652zRqpN5HT/h9oE1MZMdffzGid2+srazweuklfLy82Gigshy4bx9v+/lRoXRpypcuzVt+fmzYtw+AmLg44hIS8H/lFUxMTPBwdaWqoyMhupNde48fp4ePDw5lymBtZcWgLl3YeugQCUlJhQvcykqdaAkIUMfeyZPqve/nZ7h9pUrQqRMsWpT7+bNnVXU4JkYNafrpJ3Wyyd6+cHHp7N4dSNu2PahSxQ0bG3t69x7M7t2GP9sPH96Jk5MbzZq9irm5BW+8MYyrV4O4ceOyXttz547x4ME9mjZVJ5mqV/egXbue2NqWoFix4vj7DyAs7Cqxsff0/jYnbWIiOw4dYsSbb6r9/vLL+DRsyEYDI1IC9+zh7S5dqFCmjNrvXbqwIZ9+UODu3fg2bYrG0jLruQfx8cxbuZKPBgx4aEwP86y254MHMWi1cbRq5Y+JiQnVq3vg6FiV0NCQx4rXTKOhcvfunNb1P+8cPMjNfPqfNi4uhOfof15ftYoSOfqfdaZO5ey0aUQdOQIZGSSEh+uNUhQiL0mCddzd3QkICMDf35/27duzffv2rGX79++nS5cu+Pn50b9/f67rzgJOmzaNy5cv4+/vz/Dhw/Nd98mTJ+natSv+/v507NiR3377Ta/NX3/9hZ+fH5cuXeLIkSN069bN+P+kAXbVq5ORmsqD4OCs5+6fPq2X3AJcWbKEMt7eWDk4YGZlRZU+fYj4/fes5cVsbak9bRonR416JrE/rxwdIeeJ4tBQNQrTxib/v1m0SPX3+/dXxY+cmjaFxYvVCE8nJ3ha5xdu3gzGyalG1u9OTu7ExETx4MHDOwpPW0REMJUqZcdVqZI7Dx5EER9fuLiOH9/MRx/VY9y4xoSHB+Ht3fvJgyxdWiVfd+9mP3frluEKa15OTnDnTvbv9vZq2OETDN18JnGCqriOHg19+hj37EylSqoamLOCd+UKODsbbt+qlTrbtG6d6pxnDvcsU0b9b87OqlL9008qKdYNTX1iDg4qzsjI7OdCQw1XgkGd3PjhB/j+e3VCYc8e48TxCNeiozEzNcWldOms52qUL09I3v35GDIyMgg8c4audeoYI0SlVCn94/P27YIdn5Ur5z4+7ezUyR0jDIe8FhGBmZkZLpknV4Aazs5ZyWtOwTduUCPHcVrD2TmrYlymRAk6eXuzfu9e0tLSOHXxIuF37lC/RvbnW0aOdWVkZJCcksL1iIjCBe7srJLWnF9IFy/mXwmeOFENTUpMfPh6vbzUto6JKVxcOqGhwbi4ZP/vzs7u3L8fZTA5VW3ds363tNRQoYKTwcRrz54NNGnSHktLw8PIz507TsmSZbGze/iJnmthYep9k+P9XMPFhZDME2o5BIeGUsPFJVe7YAPttImJbDt0iC55Tuh99dNPvP7qq5QpwMmn/Dyr7VmyZBleeaUTu3evJy0tjaCgU9y5E07NmvX1/vZhDPU/750+nSu5zRSyZAllc/Q/nfv0IUzX/zQxNaWUlxeWZcviHxxM1xs3aDB3LmY5TjIIYYhcE5yDqakpGzdu5MqVK7z++ut4eXkBMHbsWH7++WdcXV1Zs2YNY8aMYc2aNXz88cd8/vnnrF+//qHrXbRoEe+88w6dOnUiIyODB3mqEJs2bWL58uUsXryY8uXLc8TQdU1PSTEbG1LyDA1MiYmhmK2tXtsHwcFob9ygS3g46ampxJw9y56hQ7OWe0yfzpUlS0gw1rV2/1KWltkFH8j+2dIS4uIM/82gQWBhoarIeYt/hw6pR4UKavkT9jvylZSkRaPJztStrNQxkJgYj61t4b+Yn1RyshYrK8NxWVs/flxeXn54eflx+/Y1jh4NxM6u9KP/6FHMzVUlMKfERPX8w7RooRKyv//Ofs7XN7tSa2zGjHPDBlVVBVVx69MH5s3TX39hWFnlfhOBqvZaWRluv3evelSsqIa/Zg7nzEye6tdXQ6VtbODTT9WbLMcJvEKzsNCv3CYkqDe7IYcPq0f58qrSbcxh2Q+hTU7GxsIi13O2FhbEP8G+OhEaSnRcHO2NORTa0PGZlPTo47N5c3V85qxst2uXXVF+QtrERGzyHHu2Gg3xBqr22sTEXNfw2mo0aBMTycjIwMTEhI7NmjFpwQJmLl0KwJRBg3DQDfduXrcuizdu5NUmTbC3tmZRYCBA4SvBGk3uURKgRkFYW+u3bd1aXe+7e7ca3ZCf8uXVEOvPPy9cTDkkJub+zrG2Vp/tCQnxeglqYqIWO7tSuZ6ztrYhISE+T7sEDh7czuTJ8w2+ZlRUJAsWTOWdd8Y9Mr68+xLA1tq6YPvd2hptQkLWfs+049AhStra0jDH6IazwcGcvHCBie++S+QTjP55ltuzRYuOzJ07iYULZwIwePAUypZ1eKx48+t/Fn9I/7O7rv95/+xZjun6n5bly2Nmbo5Tjx7saN6c9JQUWmzcSK1Jkzg9adJjxfRvINVL45EkOIeePXsCULVqVWrWrMnff/+NiYkJNWrUwFV35rR79+5MnTqVuPyyGQMaNWrE/PnzCQ0Nxdvbmzo5zpyvX78eCwsLli9fjs3DSoVPSWpcHMXt7HI9V9zOjlQDwwW95s3D1MKCdaVKkRofz0tjx9Li99/Z2bgxJerUoUKbNmyrW/dZhf7c8PZW8+IABAWpnCJnfynz50edXE9KUv2PBQvUHCR5+8eRkWp04FtvqZP1T+rgwU0sXfoJAO7u9bGw0JCQkH1cZ/5saWmgw/QUHTu2iV9/VXFVq1Yfc3MNiYnZcWX+/KRxlSvnjIODG6tWTWXQoCe8vi05WSVEOVlYqOfz06CBqlQtW6aqiQDVq6sOv6GJgIzBWHGCGkqd6eBBNYlWlSpqWOqTSkhQHficNJpHDxUOD1dVr2HDYNq07IRq9WqVDMTHqypxw4bGSYKTkvQTc0vLR7/Zb91Sb+b+/dVQ1adMY25OXJ5EKi4pCeu8x8Jj2HD6NO1q1sT6UQnq4zB0fJqbP/z4rF9fTUq2YkX28enqqv7uwgWjhKWxtCQuz7EXl5CAtYGTMhpLS+JznMCJ02rRWFpiYmLC5bAwRn3zDXPHjMHbw4NrERG8/9lnlCtZkpb169O9VSsioqLoN2UKqWlpvO3nx94TJ6hQupAn6rRa/YTXxkY/MbaygjFj4P33H76+kiXVsKRff4WtWx87nH37NjFvnvpsr1mzPpaWGrTa7M/2zJ+trPQ/2/O2Ve3j9doePrwDW9sS1KrVUG8dMTF3+fjjt+nQ4Q1atOj0yHg1lpbE5TkZF6fV5r/fcxwjcVotGiurXAkwqGHTXXx8sp5PT09n6vz5TBw06LEnwiqq7XnjxmVmzx7FhAlz8fT0Jjz8GtOmvU+pUuVo0KBlgePPr/+ZYqD/2VDX/1yt63++PHYsPr//zrbGjUnTbfeLc+eSoBuVc+Grr6j9H02ChfFIEvwMDBgwAB8fHw4dOsT06dPx9vZm5Eg1AYG7uzvHjx/n8uXLuZLjZyX20iVMihXDxtWVuBA1DKZEnTrE/POPXtsSnp6cmTiRZF2V5dLcuXhMn4556dKUb9kSa2dnOuuG/xSzscHEzIz2NWuyvf7jDZH5tzl4UD0yDRmicoHMgr6Tk5qUsyDnTUxMVB+wZEnDRSJTU+ONOvX27oy3d+es37//fjShoRdp1KgDAKGhQdjbl3nmVeAGDTrToEF2XMuWjSYs7CL16qm4bt4Mwta2TKGqwHmlp6cSFaU/ZO2xRUernVOqVPZQzvLl9YcPZ/L0VGdPli3LfX2qs7OqZmZeUmBhoa55LFdOTZr0vMRpSEbGw5c/jrAwVZWqWFEltqCGORdkNlozMzVMGVSi+bAE6klFRKjXK19eJbag3vAFGQ1jZqb26zPgXLo0aenpXIuOxlmXUAXduoVrQYYZG5CYksK28+f5rtfjzwb7UHfvquOzZMnsav7Djk8PDzXE/Oef9d9HDg5qMjXIfh+VLQtr1z52WM4ODqSlpXEtIgJn3bEVdO0arnmvQQbcKlcm6Pp1PNzcVLvr13HTXUsfHBqKs4MDzT09AahaqRIt6tVj/6lTtKxfH1NTU4b36sVw3Xb98/RpypcqRflSpfRep0CuXYNixdQXUuaQaHd3CMkz5LVKFfVeW6GbJLB4cZUs798PvXur96CdnUqA9+xRQ/oLoWXLzrRsmf3ZPmfOaK5evUjz5uqz/erVIEqUKGNwmLKTkxt79mRf35qYqCUiIhQnp9xDu/fsCcy6VjWnuLgYPv74bRo29KFXrw8KFK9zpUrqfRMejrNuKHzQ1au4OjnptXVzciLo6lU8qlfPaueWp13EnTscPXuWaUOGZMel1XIuJISRs2cDkKabLb7FgAF8O24cXgaGBmcqqu0ZGhpMpUrO1KunZkd3dKxKgwYtOHFi/2MlwZn9T1tXVx7ojsmSdepw30D/s6SnJ3/n6H8GzZ1LnenTsShdmqToaOJv3CAj53eQMb+PxH+WVNVzWLduHQDXrl3j/PnzeHp64unpSVBQEJcvq8kCNmzYQM2aNbGxscHGxqZAFeGrV6/i5ORE79696devH2fPns1a9vLLLzN37lzGjBnD0aNHn84/9hBpWi0316/HY9o0zDQayjRtSiV/f66u0J8x9+6xY7j060dxOztMihXDbfBgtGFhJEdHE7JwIZurVWObpyfbPD0JWbCA8C1b2Ne+/TP9f0zMzDCzsMDUzCzrZ5NC3GbgSRw4AC1bqssCNRp12eT+/Ybb1qql+h8mJupk/JtvqpP0mf3+li1V3wPU+vz91RxBT0OzZv788cdawsJCiI+PZePG+TRv3jXf9ikpySQnq+pSamoKyclJub+EjKRhQ38OH15LREQIWm0s27fPp1Gjh8eVkpIdV0pKdlyHDq3hwQM1SU5ERAg7dizE3b3JkweZkqKqTi1bqg5k5cqqs2lg4iFq1VK3Hvn5Z3V2JKd9+9Ssqz/8oB6XLqnJbPJeKF7UcdrZqb81NVXJXJMm6mA3MFtuoSQmqjNL/furymrNmurieEMXxPv6qovuQSWgvXplD9tOSlLXVvfsqd5gZcqo2bCNdclJcrK6dVS3bqry6OambnOU86xYphYt1O2RQCUcnTrlrvibmal9YmqqHsWLG+3aZY25OW1feomAffvQJidzIjSU3Rcv4m9gQrT0jAySUlNJSUsjA0hKTSU5z4y1O4OCsLe0pHF+12gXVkqKumb1lVfU/+/oqLapoVt4vfyyOo5XrtQ/PvfvV0NqlixRj+BgdUwYmI+jqD0SLgAAIABJREFUIDSWlrRt1IiAVavQJiZyIiiI3ceO4d+ihV5b/xYtWPrbb9yKjubW3bss3byZrrpb6tR0ceF6RASHz54lIyOD0MhI9p04gbtu1vP7Dx4QGhlJRkYGITdu8Nny5Qzp0aPwkw8mJMDOnWrmcisrqFtXvac3b87dLjhYDYfu1k09Jk9WJ8y6dVNDkKyt1aQUJ0+qWywZiY+PPzt3riU0NIS4uFhWr55P69aGP9ubNGnL9evBHDy4neTkJH79dR4uLu5Urlwtq01UVCRnzhzRW4dWG8fHH7/DSy/VY8CAMQWOT2NpSdsmTQj45Re138+fZ/eRI/i3aqXX1t/Hh6WBgWq/R0ezdMMGuua57nfj3r3UfeklnByyhw3bWltzYPlyAgMCCAwIYOEnqrK7/ptvshLqgnpW27Nq1ZqEh1/n9OnDZGRkEBERyrFj+3B2ds/7Mg+VptVyY/166uj6n2WbNsUxn/5n9LFjVM3R/6yu638m6Sa9u7x0KTWGDcOibFnMS5TgpZEjCSvk+/15V9QzQ/+XEkepBOeQlpZGly5dSEhIYNq0aZTWnTGfPXs2Y8aMITU1lVKlSjFnzhxAVXFdXFzo1KkTVatWJSCfYW0rVqzgyJEjFC9eHHNzcyblGZ5Ro0YNFixYwAcffMDkyZMxN+bwsgI4PngwjX78kW63b5MUHc3xDz4g9vx5yjZrRovff2etruN2aswY6gcE0Ck4GFNzc2LOneOA7lYkaQkJWUNSQA1zSU9MJMnYs9s+wiuTJtEyx/346vTty74pU/hj6tRnFsOZM6qvNWmS6scdO5a7+DB7tsppDh5UfYsBA1RhLjkZLl9Wd4PJvIzN3V316S0sVKHjyBF1G9KnwcPjFTp2HMisWf1ITk6kQYP2dOuWPeHbuHEd8fN7L6t6PHasL1FRYbr/SY0H/+qr3ZQtq18deRI1a75CmzYDmTu3HykpidSp054OHbLjmjmzI+3avZdVPZ4xw5e7d1Vc33+v4poyZTelSzty5cpJfvvta5KStNjYlKJu3fZ07Pih/osWxtat6hY8o0erzufWraqC5eQEb7yhdiyoSZysrNR9rzKdOaPaJyfnrlympKjHo4bXPus4LSxUMlmypJp459YtdRslY85sPHeuinH1ajUsIiBAVbNq1VL3H/P3V+1eflldI2BlpZKhAwdU5TrTvHnw4YcqWYqPV/Hrbl1iFMuXq200b54a7pF5o+/q1dUQ03ffVe3c3NS90iwt1f9z7JiayCvT22+ra1sz+furxMNI97n8pGNHJmzcSNMvvqCElRVTOnbErVw5jl+/zqBffuHUhAkAHLt+nX7Ll2f9ncfMmTSsUoUVOWatDTx9ms4eHnrVNqPYtk3dimfECHU8bdumruGuXFl9GH7xhWrXooXa5znvBX/unGqf932UmvrE76NPBg5kwvz5NB04kBI2NkwZNAi3ypU5fuECg2bO5JTudoe927blxq1b+Oluc9ijdWt662435VShAjMHD2bm0qWE3bmDrUaDX/Pm9NQlS/cePOD9zz4jMjqaUnZ29OvQIesewoU2fTrMmKHeFzEx6jKBkBA1jPyHH9QkV2lpuSejiIlRE5RlPtemjaq6u7rmvv2Yn1/2vACFUL/+K3TvPpCJE/uRlJRI06bt6dMn+7N98OCOvPbae7Rs2Rl7+1KMHz+XBQum8dVXH1G9eh0++uirXOvbu3cjNWp44uCQuwJ7+PBOgoPPEhoakmu25HnztlCuXEUe5pMPPmDCt9/S9M03KWFry5QPPsCtSpWse/+e0n0h9/b15UZkJH6661R7tGtH7zz3Mwzcs4d38kx6amJiQtkck2El6Y7b0iVKPPbw6Ge1PR0cnBg+fCYLF87kzp0wNBpbWrb0o127no8VL8DRwYNp8uOP9NT1P49+8AExuv6nz++/s0rX/zwxZgwNAgLw1/U/7587xx85jsWz06djUaYM/pcukZaYyPXVqzk7c+ZjxyNeLCYZT6N08y/k7u7OyZMnsTY0YcS/1Mqn0UF5CoxwBeEzcfH1f8db5UMj5XRPW97izfOq3V/TijqE/xYjJXVPXYUKRR1BwTytG4cbW0GGsj8POj36WtHnQm8jzGr/DFwKfErzGxhZ9X9JT+QSj1cdLipH3f8d/c83/4Up0MbnoG/v/y/cbob8l6raQgghhBBCCCHEQ8lwaJ2LFy8+8Tq6detGWp5rp+rUqcO0aVJJEkIIIYQQQojngSTBRvSo+wULIYQQQgghRGHIEF7jkW0phBBCCCGEEOKFIZVgIYQQQgghhHjOFf20WP8dUgkWQgghhBBCCPHCkCRYCCGEEEIIIcQL4//Zu++wKI7/gePvo3MUFRUwAoKCYMNeEoMaLGgUsabZkxh7STTGaIoa4zeaYjRqLPzsEY2FYi/YsQXFLgqGIlUFqUfnfn8s7bhDQVGMzut5eDzv5nY/O3szu7MzOyuGQwuCIAiCIAiCILzktKs6gFeI6AkWBEEQBEEQBEEQXhuiJ1gQBEEQBEEQBOElJ3ovK4/IS0EQBEEQBEEQBOG1IRrBgiAIgiAIgiAIwmtDDIcWBEEQBEEQBEF4yYney8oj8lIQBEEQBEEQBEF4bYie4FfYh/7+VR1CuXzk6VrVIZSLo5esqkMol3bLEqo6hPLZsaOqI3i1GBhUdQTlM3JkVUdQPuHhVR1B+VhaVnUE5dOmTVVHUD4PH1Z1BOUiuzWqqkMoF+WBpVUdQrnEDJpc1SGUS8Okm1UdQrkE/qWs6hBeWaL3svKIvBQEQRAEQRAEQRBeG6InWBAEQRAEQRAEQahUYWFhzJw5k6SkJKpXr87ChQuxtbVVSTNjxgxu375d9P/bt2+zfPlyunbtyh9//MGWLVswNzcHoFWrVnz//feVEptoBAuCIAiCIAiCILzk/mtDeL///ns++ugjPDw88PX15bvvvmPjxo0qaRYtWlT0Ojg4mBEjRuDi4lL0Xr9+/fjqq68qPbb/Wl4KgiAIgiAIgiAIVSAlJYWoqCi1v5SUFJV0CQkJ3Lx5kz59+gDQp08fbt68SWJiYpnL3rFjB+7u7ujp6T3XbQDREywIgiAIgiAIgiCUw4YNG1i2bJna+xMnTmTSpElF/4+NjcXCwgJtbW0AtLW1MTc3JzY2FjMzM7XvZ2dns3v3btavX6/y/t69ezl9+jS1a9dm0qRJtGzZslK2QzSCBUEQBEEQBEEQXnIvwxDeESNG0L9/f7X3TU1Nn2m5R44c4Y033qBRo0ZF733wwQeMHTsWXV1dAgICGD9+PPv27aNGjRrPtC4QjWBBEARBEARBEAShHExNTcvV4K1Tpw7x8fHk5eWhra1NXl4e9+/fp06dOhrT79y5k4EDB6q8V7t27aLXHTt2pE6dOoSEhNCuXbtn2whejgsKgiAIgiAIgiAIwmNovQR/5VWzZk0aNWrEnj17ANizZw+NGjXSOBQ6Li6Oixcv4u7urvJ+fHx80etbt24RHR2NnZ1dBaIom+gJFgRBEARBEARBECrVnDlzmDlzJitWrMDU1JSFCxcCMHr0aCZPnkyzZs0A8Pb25p133qFatWoq3//tt9+4ceMGWlpa6OrqsmjRIpXe4WchGsGCIAiCIAiCIAhCpWrQoAHbt29Xe3/NmjUq/x83bpzG7xc2mp8H0QgWBEEQBEEQBEF4ycmqOoBXiLgnWBAEQRAEQRAEQXhtiJ5gQRAEQRAEQRCEl5x2VQfwChE9wYIgCIIgCIIgCMJrQ/QECySlpzN70yYCbt2ihrExX3h44K7h+Vvnbt9m+b593IyMpJpcztEff1T5fNjixYTExJCdm4tVzZpMdnenW/PmlRprr17g7g56enDhAqxdC7m56unq1oVx48DCQvp/WBhs2ADR0cXLcXMDY2PIyoKzZ2HLFsjPr9Rwy9R2wgRajByJebNmXPfywnfUqOe+zqTkZGYvWEDAhQvUqF6dL8aOxd3NTS2dUqnklxUr2OHnB8Cgvn2ZPn48MpmMxKQkxs+YQVhEBHn5+TSwtWXGxIm0LtjPSqWS31evZteePSgyMmjcsCHfTZ+OQ/36lbcdGRnMPnSIgPBwahga8oWLC+4lHqxeyPOff/C5cYPolBRqGBryUYsWfNq2baXFocbAAPr2hfr1QaGAo0fh+nX1dG++Cc2bQ7VqUrrAQOkHWFq9ejBiBJw6BceOVVqYSVlZzD51ioDoaGro6/NF27a4N2igls7z6lV8QkKITk+nhr4+HzVqxKfOzkWfD9u3j5BHj8jOy8PKxITJrVrRrV69yoszI4PZfn4E3L1LDbmcL7p2xb1gFsmSzoWFsfzkSW7GxlLNwICjU6eqfH4rLo4f9u/ndnw8Rnp6vN+6NRM6d660ODEwgN69wc4OMjKkfXXzpnq69u3B2RlMTaV0Fy/C+fPq6WxsYOhQCAiAEycqLcyXtZ5PSk1l9tKlBAQFUcPUlC+GD8e9Sxe1dEqlkl82bGDHoUMADOrRg+kjRiCTyQi8cYPRc+aopFdkZrJ05kzcOnbku+XL2X38eNFnObm56OrqEvT3308fd1oas9etI+D6dWqYmPDFwIG4v/mmWrpzt26x3M+PmxERUn7+8ovK57/v2oX/pUvcjY1lnLs7k/r1e+qYHmfq1NZ89VV75HIdduy4w7hxh8nOztOYdvBgR+bO7YiVlQn37qUwa9YpfH1DARg+vAmTJ7fCwaEGKSnZbNlyi1mzTpKXp3ym+JIyM5l99CgB9+5Rw8CAL958E/eGDdXSeV66hM/t20SnplLDwICPmjbl01atAEhQKPjx9GkuREeTkZuLg5kZX3fsSHNLy2eKDWD79vV4ea0hKyuDTp3c+Pzzuejp6WlMe/HiWZYsmcv9+7E0auTMV1/9hKVlXZU0KSlJDB/eE2trO/74w6vo/b17t7Nly2oSEx/SrFkrZsxYQK1aFk+MLyk1ldnLlxNw+bJUjoYOxb1TJ7V0SqWSXzZtYsfhwwAM6t6d6cOGIZNJd5qevXqVRRs2EBEbSw1TUz4bMID3e/QA4Pz164z47jsM9fWLlvfd6NH0d3V9YnxPcvbsegIC1pCTk0Hjxm707j0XHR31/M3Ly2bnzunExFwnOTmaESM2Ymvbvujz3NxsDhyYT3DwEfLycrGxaUXv3nMxNX1yHgqvH9EIFpi3dSu6OjoELFzIragoxixfjpOVFQ5vvKGSTq6vz8C33qJPmzasOnBAbTmzBw/Gvk4ddLS1uRIWxsglSzg4dy7mpaY7f1rOzlIbY/58SEqCzz+HQYNg61b1tI8ewe+/w8OHIJNBjx4waRLMnCl9fvGidH6pUICREUydCj17wr59lRLqE6XGxHBy/nzs3dzQMTR8Ieuc9+uv6OrqErB3L7dCQhgzbRpODg5qDdRtPj4cOXkS302bkAGjpkzBqk4dPhwwACNDQxbMno2ttTUymQz/kycZ9+WXnNm3Dx0dHfb7+7Nzzx68Vq7kDUtLfl+1ihlz5+K9YUPlbYe/P7paWgSMG8et+/cZ4+2NU+3aONSqpZJOqVSysFcvHGvXJjIpiU927KCOiQm9nZwqLRYV774LeXnw669gaQkffgjx8fDggWo6mQx8fKTPzMxgyBBISYEbN4rTaGlJV2mioio9zHlnzkj599FH3EpIYMyhQziZmeFQo4ZKOiWwsHNnHM3MiExJ4ZMDB6hjZETvggbz7A4dsK9eHR0tLa7cv8/IAwc4OGgQ5nJ55cS5bx+62toETJ/Orbg4xmzZgpOFBQ7m5irp5Hp6DGzRgj5Nm7Lq1Cm15UzbuZPuTk5sGjGC6KQkPlq3DidLS7o6OlZKnLi5Sft9yRLpqtt778H9+1LlU5JMBn5+0mc1aki/j9RU1QazlhZ07158ta4Svaz1/LyVK6W4Nm3i1r//MmbePJzs7HAodUFl24EDHDl3Dt+lS5HJZIz69lusLCz4sFcv2jRpQlCJ2UfPX7vG2B9+wKV1a2kdEyYwb8KEos9nLl6MTOvZBsLN27xZ+n0uWcKtyEjG/P47TjY2ONRVbezI9fUZ6OJCn/btWVXwrMyS6pmbM/2999haiRe6SuvRw5aZM9vj6rqNmJg0vL37MXduR77++qRa2jfeMGbz5t54eHhz4EAY775bn+3b+2Jru5oHDxTI5bpMnXqU8+djqV1bjp9ff6ZPb8vChReeKcZ5J09K+TlqFLcePmTM3r041ayJQ82aKumUwMJu3XCsWZPI5GQ+8fOT6nUHBxQ5OTQzN2dmx47UNDRkx61bfLZ3L0eHDcOojAZreVy4cAovr9X8+usGatUy59tvJ7J+/VI++2y6Wtrk5ES+/34i06fP5623XFm79nfmzfucFStUL7isXv0L9eo1IL/ElffLl8/j6fkbv/22ESureixb9iM//DCNJUs2PzHGeatXS+Vo3TpuhYUx5scfcbK1xcHGRiXdtkOHOHL+PL6LF0vlaM4crMzN+bBnT3Jyc5m4cCFfDh/O+z16cC00lBHffUdzBwecCp7Lam5mxklPz6fJxjKFhp4iIGA1w4dvwMTEnG3bJnL8+FK6dVPPXwAbm1Z06DCc7dunqn12/vwGoqIuM3asHwYGJuze/S379//A++8vq9SYq5IYwlt5RF6+QNOmTWPAgAG4u7szYcIEkpOTAVi8eDHdu3dn8ODB/PzzzwwYMKDoO97e3gwePJgBAwYwfPhw/v3330qNSZGVxaGgIKa4u2NkYEAbe3tcnZ3x1dBD4WxrS7/27bEu1dgo5GRlhY62dLeCTCYjNy+PuEePKi1WFxc4flw6P0xPB29v0HChE5Aat4XnoDKZ1MNrUeJC4P37UprCz5VK1c+ft2Bvb277+qJISHgh61NkZHDo2DGmfPYZRnI5bZo3x9XFBV8NJ7k++/bx8YcfYmlujoW5OaM+/BDvgqsD+vr61K9XDy0tLZRKJVpaWiSnppKckgJAVGwsrZ2dsa5bF21tbfr27EloeHjlbUdODodCQpjSsSNGenq0sbLCtUEDfDX0vI1u144mFhboaGlR38yMrvb2XHoOjQsAdHWhUSOpFzAnB+7dgzt3pCs3pZ05A3Fx0o8uIQFu3wZra9U0b74J//6r3pB6RoqcHA6FhzOldWuMdHVpY2mJq40NvqGhamlHOzvTpFYtKf+qV6drvXpcun+/6HMnMzN0ChoSMpmM3Px84tLTKyfO7GwO3bzJlHfekfazjQ2ujo74Xr2qlta5bl36NW+OdalGfKHopCTcnZ3R1tLCxsyMVjY2hJbYjmeiqwtOTnDypLTfo6IgJASaNlVPe+6cdOFDqYTEROn3YWWlmqZ9e2m/V3K98LLW84rMTA6dOcOUoUMxMjSkTZMmuLZrh6+GBqHP0aN83K8flrVqYVGzJqP69cPb31/jcn38/en51lvIDQw0rvPg2bPP1HulyMriUGAgUwYMkPKzYUNcW7TA98wZtbTO9evT7623sC7juZb9336bzs7OGGmItbKMGNGE//u/a9y8mUBSUhY//HCWkSM1/EYBKysTkpIyOXAgDIB9+/4lPT2HBg2qA7By5WVOn44mJyefmJg0/vrrFh071tW4rPJS5ORw6O5dprRvL5X3N97A1dYW3zt31NKObtWKJrVrS/VSjRp0tbPjUmwsANbVqjGqRQvMjYzQ1tLi/SZNyMnLIywp6ZniO3jQh169BmFn54CJSTWGDRvPgQPeGtOePHkYW1sHunTphZ6ePiNGTOLu3WAiI+8Wpbl+/RJhYSH07DlA5btnzx6nc+ee2Nk5oKurx7Bh47l69R+ioyMfG58iM5ND584x5cMPpXLUuDGubdviW2L0QyGfY8f42MOjuBx5eOBdUN6S09JIUyjw6NIFmUyGs4MD9a2sCH0OF2NLunLFh5YtB2Fu7oChYTU6dRrP5cua81dbW48OHUZiY9MGmUy9CfPoURQNGryNsXEtdHT0adLkXR48CHmu8Qv/XaIR/ALNnj2bXbt2sXv3buzt7VmzZg1Hjx7l2LFj+Pr6sm3bNiIiIorSBwYGsn//fv766y927drFJ598wqxZsyo1pvD799HW0sKuRAvQycqK0IKDSkWNWb6cZpMmMXjhQto1bEjTUlchn4WVFZTIHiIjoXp1aUhzWdaskYZBjxgBvr6qn731Fnh6wurV0gjEMs6nXgnhkZFoa2tjV2J/ONnbE6rhokpIWBhODg7F6RwcCAkLU0njPnQozp07M27GDAb37UtNMzMAenfrxr3oaMIiI8nJzcV73z5cOnSovO1ITJR+rwXrA3CqXZvQJzQalEolgVFR2JdxYv/MataUrrQkJha/Fx8P5Xmgu42Nam9xtWrQokWlDoUtFJ6cjLZMhl2JXjunmjUJfUIjRqlUEhgXh3316irvjzl0iGbr1zPYz492lpY0raT8DU9IkPZziV4gJwsLQkv3qpfDiA4d8LlyhZy8PP59+JDL9+7xZmUNzzczU9/v9++Xb79bW6vud1NT6aLJ6dOVE1sJL2s9Hx4dLcVVovfUyc6O0Ej1k/6QyMii3qjCdCEa0ikyMzlw5gz9unbVuM5DZ85gZmpKW00XKsobd1ycVJ+WGGbrZG1N6PO6yPaMmjSpxZUrxRd+rlx5gKWlEWZm6g3vwMA4bt1KxN29AVpaMjw87MnKyuPqVc1lr1MnK27ceLaLNuFJSdLvoET94lSrFqEly5UGSqWSwNhY7EscD0q69eABOfn51HvG0Wjh4SE0aFA8gsje3pFHjx6SnKxeb0ppi0eZGBrKeeMNG8LCpAuNeXl5LF36A5Mnf1s0BLn0NhW/lv4NC1O/GKCyzpgY9XJka0vovXtqaUPu3cPJ1lYlXWE5qlW9On1cXNjl709eXh5BwcHE3L9P6xK3GyUmJ/PWyJG4jhnDgrVrUWRmPja28njwIAQLi+L8tbR0JD39IQpFxS+utWo1iHv3LpGaGk9OTgbXru3G3r6M3pL/KK2X4O9VIYZDv0C+vr7s3r2bnJwcFAoFtra25OTk0KtXL+QFQwj79evHihUrADh69CjBwcEMHjwYkCrHlIIet8qiyMzEuNRwXBNDQ9KfsmJbNWECOXl5nLl1i3/j4tB6xiFnJRkYFPfeQvFrAwNIS9P8ndGjQV9f6kUu3al25oz0Z2kpfV7QMf9KUmRkYGxkpPKeibEx6SUztIy0JsbGKBQKlEpl0UF79+bNZGVlcfjECXJycorS1q5Vi1bNm9Pz/ffR1tbG0tycDcsqbxiSIicH41LD2kz09UnPzn7s9/44c4Z8YGCTJpUWiwo9Penm8pIyM6X3H6dzZ2kowuXLxe/17Fnco1zJFLm56vmnq0v6E9b1R1CQlH+l7tFb1aMHOfn5nImO5t+kJLQ0nNQ9VZzZ2RiXuO8MCvZz6Twuhy4NG/KVtzdrz5whT6lkQqdOONd9tp6rIpr2e1bWk/e7i4u030v2bPfoUdyjXMle1npekZmJcanh8yZGRqRnZDwxrYmREYqMDJV6CaRGbg0TE9qV0cj19venn6urxgZIuePOysK4VM+tiVz+1Pn5vBkb65GcXPw7LXxtYqJHYqJqzPn5SjZuvMGWLX0wMNAhOzuPwYP9UCjUf5ejRjWlTRtLPv304DPFp8jJwVhXV+U9Ez29J9frFy6Qr1QyUMOcEGnZ2cw4coSJbdtiUqouqajMTAXGJa62GxmZAJCRkU61aqojUDIyFFSvrtooNzIyJiNDGiWza9cmGjVyxtGxqVrjtl07F+bN+5y+fT/AysqWjRuXI5PJyMp6/O9KYzmSy8tXjuRyFJmZReWot4sL3yxfzo//938AzBkzhjoFFzfr162Lz2+/Ub9uXaIfPGDm0qX8tG4d88aNe2x8T5KdrcDAoDh/9fVNCt5PRy7XPMKnLGZmtpia1uG33zohk2ljYdGQd9/99pniE15dohH8ggQGBuLl5cXWrVsxMzNj9+7d/P2ESTmUSiUDBw5kypQpzy0uuYEBaaUqyrTMzGcamqWrrU3npk3ZeOwYNrVr0/UpJ03p2BE++UR6HRwstSlKnscVvn7SeUdWltTLu3IlfPmldPtlSXFx0ijGUaOk+4hfRXJDQ9JKDVVNS0/HSMP9m3JDQ9JLpE1LT0cul6udNOrr69OnRw96ffABjRo2xMnBgeVr13L95k1O+PpSy8wMv4MHGTFxInu3bMGwEob7yXV1SSt1YpSWnf3Y+702BwXhc/MmWz74AD2d51TlZWdLV1tK0teX3i9L27ZSz9/69dI9pQANG0oNKE0TK1UCuY6Oev7l5GBU6gS0pM03b+ITEsKWPn3Q01Z/OIOulhadra3ZeOMGNqamdK2EybHkenqklWpcpmVlYVTBk9mkjAw+3byZ7959lz7NmvEwLY3Jf/9NTWNjhlTGJGma9rue3uP3e+vW0KwZbNpUvN/t7aXv3br17DFp8LLW83IDA9JKXYhLUygw0jBPgtzAQOWkPk2hQG5oqFYv+Rw9WmYjN+b+fS5cv878SZMqHKtKLPr6pJU68KRlZDzXIc0V8dFHjVi1SprM6NSpKNLSsjE1Lf6dmppK9WVqqvrvtGvXeixa1JkuXbZy6VI8rVtb4ufXn169dqr0Jnt42PO//3WiW7e/SUhQb2xVhFxXl7RSF3+eWK9fvYrP7dtsGTBArV7KzM1l7N69NLe0ZEzBfeEVcfiwH7/99j0Azs6tMTCQk55efKW98LWhoZHadw0NVdMCKBTpGBoa8fBhPLt2bWTVql0a19u69VuMHDmZ77+fjEKRxsCBI5DLjahd+/ETe2ksRxkZZZejEmnTMjKQGxggk8m4GxXFF7/+yh9ffUXH5s0Jj41l7I8/Ym5mRpc2bahdowa1C247sbaw4Mvhwxnz448VbgRfverHnj1S/tar1xo9PTlZWcV5VvhaT089f59k37655OVlM2PGeXR15QQErOGvv0bz6afbn/xl4bXzKvVqv9RSUlIwNjamevXqZGcherL6AAAgAElEQVRns3PnTgDatWvHwYMHycjIID8/H7+CGXkBXF1d8fX1JS4uDpCG0VzXNNvsM7A1NycvP5/wEvfIBUdFYV+nzjMvOy8vj3vPcE9jQAB8/LH0t2iR1FAteX5tYyNNkFVWL3BJMpl0rlrGbYNoab3Ye4JfNFsbG/Ly8ggvMTwqOCQEew3DQh3s7AgucY9ocEgIDiWGIZaWm5vLvYJhgMF37tCrWzcszc3R0dFhQO/epKSmElpqOPVTb4eZmfR7LTF8N/jBA+xLTZ5SaMe1a6y+cIENgwdjaWJSKTFolJAg/YhKDsuzsFCfFKtQixbSVZ5Nm6TJkQrZ2sIbb8AXX0h/TZpI94m+/36lhGlbrRp5SiXhJYY9BCcmYl9Gwdhx5w6rr1xhQ69eWBo9/oQkT6nkXslteZY4a9aU9nOJYe7B8fHYl2eYcQn3Hj1CW0uLfs2bo6OlhaWpKe82bcrJkEq6RywxUdrvJfPvcfvd2Vm633vLFvX9XqcOTJ4s/TVqJF0kGTSoUsJ8Wet527p1pbhiYorjCgvDXsPwagcbG4JL1CPBYWFqk/7EPnjAhWvX6FfG/b6+x47RqlEjrJ9xtmBbS0upPi04NgME37uHfWWNMHhGW7bcwsRkCSYmS3j33Z3cuPGQ5s2Ly07z5ubExaWr9QIDtGhhzsmTUVy8GI9SKQ2PPn8+lm7dig++bm62rFnjhrv7Lq5ff/Z5C2yrV5d+ByXu3Q1OSChzmPOOmzdZfekSGzw8sCx1P1R2Xh4T9u3DwsiIeRpmGS+P7t37sn9/EPv3B7FwoSe2tg7cvXu76PO7d4OpUaOWWi8wUJA2uOj/GRkKYmIisbOzJzj4GgkJDxg5sjcDBnRk2bIfCQ6+xoABHckruCDWv/8QNm8+xK5dZ+jUqQd5eXnY2TmorUdlnW+8oV6OwsOxLz3XBOBgbU1wiXk6SpajkMhIbN94A5eWLdHS0qJ+3bp0bt2ak5cuaVyvTCZTGb5dXs7OfZk1K4hZs4IYMsST2rUdiIsrzt/4+GCMjGpVuBcYIC4umObN+2NoWB0dHT3atx9GdPRVFIrHD63/L6nqodCvUsPxVdqWl5qLiws2Nja4ubkxdOhQGjduDEDXrl15++236du3L++99x7m5uaYFJyst23blqlTpzJu3Dj69u1Lnz598K/kG1fl+vp0b9GCpbt3o8jK4uLdu/hfuYJH+/ZqafPz88nKySEnLw8lkJWTQ3bB84nuxsVx4vp1MrOzycnLw/f8eQJDQ2nr8PjKuyJOnYIuXaTHH8nl0L+/NHpQk6ZNpQazTCb1GA8dKk2mVXiM6NJFugUPpOV5eKhOzvu8ybS10dbXR0tbu+i1TEMvW2WRGxrSvUsXlq5ZgyIjg4tXruB/6hQePXuqpfXo1Yt1Xl7E379P/IMHrPPyov+77wJw+fp1Aq9cITsnh8zMTFZv2sTDR49wLhhm3KxxYw4cPcrDxETy8/Px2b+f3Nxc6pWeAOhpt0NXl+4ODiwNCECRk8PF6Gj8Q0PxKChPJfndusXi06dZN2gQ1qXuZa10OTlSL16XLtJkSdbW4OioOty1UNOm4OoKmzdLV3FKOn4cli2DVaukvzt34NIl9Rvan5JcV5fu9eqx9NIlKf/i4/GPiMDD3l4trV9oKIsDA1nXqxfWhYWlwN2kJE7cu0dmbi45+fn4hoYSGBdH20p4FAlIPcHdGzVi6fHjKLKzuRgZif/t23homGgsX6kkKze3uF7KzSW74ITSrmZNlEolu69dI1+p5EFaGvtv3MCxsq545eRIE5t16iTtdysrcHDQ/GisJk2k34eXl/p+P3lSGqryf/8n/YWESEPkNcwm/DRe1npebmBA9zffZOlff6HIzOTizZv4nz+PxzvvqKX1cHVlnY8P8QkJxCcksM7bm/6l7vv1PXaMlo0aYVNG497n2DG17zxV3Pr6dG/dmqU+PlJ+hoTgHxSEx1tvqaV9XH6C9LimrJwclEoluXl5ZOXkkFfJz+rbuPEGn3ziTKNGNalWTZ9vvunA+vWaL6j/808sLi51ad5cmoW9RQtzXFysiu4JfucdG/76qw8DB/ryzz9xGpdRUXJdXbrXr8/SCxekeik2Fv+wMDw0PCLJ7/ZtFp87x7q+fbEuda9vTl4ekw8cQF9Hh4XdulXa7Rlubh7s27eD8PBQ0tJS2Lz5T3r27K8xrYtLd8LDQzhx4iDZ2Vls3Lic+vUdsbFpQLt2ndi69Sienj54evowcuRk7O0b4enpg7a2NtnZWYSF3UGpVBIfH8Ovv37HgAHDMTF5/D3NcgMDunfowFIvL6kc3bqF/4ULeGi4CODRpQvr/PykcpSYyDo/P/oXlLfGdnZExMZy9upVlEolkbGxHA8MxLHgHuJz164Rff8+SqWS2IcP+WXTJrpqeMxaRTVv7kFQ0A4ePAglMzOFkyf/pEULzfkL0mOQcnOlkUJ5eTnk5mYVNcbr1m3G1au+ZGamkpeXwz//bMHExBy5XPMFFeH1JlM+zWUcoVKlpaVhbGxMfn4+s2fPxtzcnM8///zZF3z0aLmSJaWnM2vTJs7cukV1IyOm9euHe7t2BIaEMHr5coIKxgifv3OH4YsXq3y3nYMDm774gruxsczcuJHQ2Fi0tbSoZ27O2J496d6ixRPX/5Fn+WfpfPdd6TnBurrwzz/S+WLh+cSiRVJbISBA6jwbPFjqmMvOhrt3pUcpFXaEjhkjdcbp60sdMufPw/btj78dz9Grcg6oAJ2//54upZ5reXzOHE7MnfvMy/6+jEmikpKTmbVgAWcuXKB6tWpMGzcOdzc3Ai9fZvQXXxBU8HtRKpX8vHy5ynOCv5wwAZlMxoVLl5i/eDH3YmLQ1damYYMGTPnsM9q2bAlAVlYWPy1dyuETJ1BkZFDPyorPx46lk4bnZ7Jjx1NtX1JGBrMOHuRMRATVDQ2ZVvCc4MCoKEbv2kXQ5MkAuK5ZQ3xamspQOfdGjZjXvXvFVhhXzhO9ks8JzsiQxuBfvy4NWfjoI/jpJyndpEnSFZi8Es/ovHpV8/O5+vaVfqDleXxKOYdiJmVlMevkSc7ExFBdX59pBc8JDoyLY/TBgwSNGAGA67ZtxKenq+afvT3zOnbkblISM0+eJDQpCW2ZjHqmpoxt3pzuJSZcKVM5L4gkZWQwy9eXM//+K+3nbt1wb9aMwIgIRv/1F0EFkwSeDw9neKlHcLWrV49NI0cCcDYsjF+OHCE8IQEDHR3eadiQ2b16YfiYIeAAlHdW87KeE2xtLfXgFz4Xdvx4MDFR3e/Xr4OGGdrp00fa7+WZHK2cE89VdT1f1n5PSk1l1pIlnLl8meomJkwbMQL3Ll2Knv1b+OgjpVLJz+vXqzwn+MuRI1WGPfccO5ZPBgxgcMFzTUsKCg5m1DffcHrjRrX7J1WUs1c7KS2NWWvXcubGDaobGzNt0CDc33yTwDt3GP3bbwStXAnA+eBghi9cqPLddo6ObCp4Xt9MT0+8AwJUPv/fJ58w4O23H7t+WceAx35e2ueft+Grr9phaKjDzp13GDu2+DnB16+PYsGCc2zZIg3HnzChJVOntsbCwogHDxQsXx7Eb78FAnD06Pu4uFiRmVnckD91Kop3392pcb3KJeW7hSEpM5NZR49y5t49qhsYMK3gOcGBMTGM3r2boDFjAHDduFGql0rch+7u6Mi8Ll24EB3NMB8fDHR0VHp41ri706bUo8BKixk0+bGf//33OrZuXUNWVqbac4JHjuzNkCFj6N69LwAXL55hyZJ5xMfH0KhRc2bO/B+Wluq//wMHdrF37/ai5wSnpaUwZcoQYmLuYWhoRK9eA/j446lol6iD30jSfKtMUmoqs5Yt48yVK1I5GjYM906dCLx5k9E//ECQl7QOpVLJzxs3suPIEQAGdevGl8OHF5WjfQEBrNi2jegHDzAxMsK9UyemDR2KlpYW63x9WevnR0paGtVNTOjeoQNThwxRm28AYMtl9QvTj3P27LqC5wRnqj0neMWK3rz99hicnaX8/f13V5KTVSehmzLFn+rVrVAoHnHgwHzu3j1DXl4O5uYOuLl9Td26Gp7UgHRo/q8JqqSLO8+i5SvSdBSN4JfAhAkTiI6OJjMzkyZNmjB37lyVSRieWjkbwVWtIo3gqlSZjeDnqaxG8EvnKRvBL1x5G8FV7SW5H/GJKmlUwHNXiY/2eq4qcfb15+q/st8r+bFkz0tFG8FVpbyN4Kr2pEbwy6KsRvDLpqKN4KoiGsFP51VpBIuJsV4Cy5cvr+oQBEEQBEEQBEEQXguiESwIgiAIgiAIgvCSE5M5VR6Rl4IgCIIgCIIgCMJrQ/QEC4IgCIIgCIIgvORE72XlEXkpCIIgCIIgCIIgvDZEI1gQBEEQBEEQBEF4bYjh0IIgCIIgCIIgCC+5qn9A0qtD9AQLgiAIgiAIgiAIrw3RCBYEQRAEQRAEQRBeG2I4tCAIgiAIgiAIwktOu6oDeIWInmBBEARBEARBEAThtSF6ggVBEARBEARBEF5yovey8oi8FARBEARBEARBEF4bMqVSqazqIITnIzKyqiMon7i4qo6gfNrZJ1Z1COUyt2bNqg6hXD6q6gDKySEhoapDKJ//SEG6SeOqDqFcGlv+N8o7Ov+NAV2XQk2rOoRyaWV8p6pDKJermQ2rOoRyCQ2t6gjKZ0BPRVWHUC6JmfKqDuGVYmZW1RFU3G1Z1T8kyfEVaTr+N46egiAIgiAIgiAIrzExhLfyiLwUBEEQBEEQBEEQXhuiJ1gQBEEQBEEQBOElJ3ovK4/IS0EQBEEQBEEQBOG1IRrBgiAIgiAIgiAIwmtDDIcWBEEQBEEQBEF4yYney8oj8lIQBEEQBEEQBEF4bYieYEEQBEEQBEEQhJec6L2sPCIvBUEQBEEQBEEQhNeGaAQLgiAIgiAIgiAIrw0xHFoQBEEQBEEQBOElJ3ovK4/IS0EQBEEQBEEQBOG1IXqCBUEQBEEQBEEQXnKyqg7gFSJ6ggVBEARBEARBEITXhugJfo3t3LmebdvWkJWVgYuLG5Mnz0VPT09j2kuXzrJs2Vzu34/FycmZL7/8CQuLugAsWjSTY8f2oKOjW5TexycQbW1tAPbt2862batJTHxI06atmDZtAbVqWVTKNuzfv569e6VtaNfOjZEj56Krq74NubnZrFgxnbCw6zx8GM2sWRtp1Kh9pcSQlJzM7AULCLhwgRrVq/PF2LG4u7mppVMqlfyyYgU7/PwAGNS3L9PHj0cmk5GYlMT4GTMIi4ggLz+fBra2zJg4kdbNmxd99/fVq9m1Zw+KjAwaN2zId9On41C/fqVsQ1naTphAi5EjMW/WjOteXviOGvVc16eJVo0aWPzf/yHv0YO8hw9J+PprUr28NKbVb9mS2r//jn6rVuSnp/NowQKSli5VSWPYqRNWJ06QOH8+Cd9++9RxvYj9vvfwYZZ6evIwIQE9PT06dejAt9OmYWxkVP44U1OZvXw5AZcvU8PUlC+GDsW9UyfNcW7axI7Dh6U4u3dn+rBhyGQyAm/eZPQPP6ikV2RmsnTGDNzefJPsnBx+2bSJfadPk5WdTW8XF2Z/8gm6OhU/xPj5rcfbWyrTb77pxtixmss0wNWrZ1m9ei4PHsTSsKEzkyb9hLm5VC+lpiaxatUcrlw5i0wGLVu+zZgxc5HLjYu+v3v3Bvbs2UByciK1atXh669XULeu3WPjexH73XvvXjZt3074vXsYGxnRp0cPvhg7Fp0K5GdScjKz588n4Nw5Kc4JE3Dv2VNznMuWscPXV4rTw4PpEycik8kIi4hg0dKlBF29Sn5+Ps0aN2b2tGnUt7VVW86IceM4FxjIjbNnKxRnoX371uPnt4bsbKku/+STsvf79etnWbduLg8fxmJv78zYsT9Ru7a03xMT41m7dg7BwRfR1zegX79xdO/+odoyTp704c8/v2L06Pm4ug5+YnxJqanMXrqUgKAgqRwNH457ly5q6ZRKJb9s2MCOQ4cAGNSjB9NHjJDK0Y0bjJ4zRyW9IjOTpTNn4taxI98tX87u48eLPsvJzUVXV5egv/9+Ynyl7dmzHh8fKT87dHBj9Oiy8/PatbN4ekr56eDgzIQJxfmZkBCPp+ccbt2S8nPgwHH06CHlZ0xMGJs2LeL27SDy8/Oxt2/GqFGzqVv36Y9Lp0+v58SJNeTkZNC0qRv9+s1FR0fzMX3btulERV0nKSma0aM3Ur++6jE9OvoGe/YsICbmJnp6hnTpMoaOHUdUKJ6k5GRmz51LwNmzUjmaPBn3Xr3U0imVSn5ZupQd3t4ADOrfn+mTJyOTSf123/7wAxcuXiQiMpIFc+YwoG9fle/+vmIFu3x9peO7oyPfff01Dg0aVChWAC+v9WzevIbMzAzeeceNGTPKPq/755+z/PLLXOLjY2nSxJlvvvmJOnWk/b5s2SIOH95LWloqpqbV8PB4n5Ejx0p5kpTIjBnjiYgIIz8/D1vbBkycOIPmzVu/cnEKrzbRE1wBjo6OpKenV+g78fHxDBs27JmX/8cff5CdnV2hdT/OP/+cYuvW1SxatJ7Nm48RGxvFxo1LNaZNTk5k7tyJjBgxhV27LtCwYVPmz/9cJc17733C7t1BRX+FDeArV86zbt1vzJ27gl27zmNpacWCBdMqZRuuXj3Fnj2rmTlzPb//foz796PYtUvzNgA0bNiKsWMXUa1a7UpZf6F5v/6Krq4uAXv38vOcOcz5+WdC/v1XLd02Hx+OnDyJ76ZN+G3axLHTp9lacMA0MjRkwezZnN2/n38OHWL00KGM+/JLcnNzAdjv78/OPXvYsnIlFw4epEXTpsyYO7dSt0OT1JgYTs6fz+W1a5/7uspivnw5yuxs/rWwIG7IEGr/+Sd6jRurpdOqWZM3DhwgedUq/q1Zk3B7e9ILTkSL6OhQe8kSMs6de+a4XsR+b+XsjNeqVVw8coQjO3aQm5fH76tWVSzO1avR1dEhYN06fp46lTmrVhESGake56FDHDl/Ht/Fi/H7/XeO/fMPWw8eBKBN48YEeXkV/a2cPRu5gQEuLVsCsHrXLq6HhrJnyRIOLl/OzX//5c/t2ysUJ0BQ0Cl27VrN3LnrWb36GPHxUXh5aS7TKSmJLFw4kQ8/nMKmTRdo0KApv/5aXC9t2fI7aWkprFrlz59/HiEpKYGtW/8o+vzw4e34++/gm29W4+UVxOzZqzA1rfHEGF/Efs/IymLW1KmcO3CA7Z6enAsMZO2WLRXKy3mLFkn7/eBBfv7hB+b89BMhd++qx+ntzZHjx/H96y/8tmzh2KlTbN21C4DUtDRcO3XiwI4dBBw8SLPGjRk/fbraMvz27yc3L69C8ZV05copfH1XM3v2epYuleryHTvK3u+//TaRwYOnsGbNBerXb8rSpcX7ffny6dSubcXKlQHMmLGabdsWc+OGanlPS0vG13clVlYO5Y5x3sqVUn5u2sTP06Yx588/CYmIUEu37cABjpw7h+/Spfj98QfHLlxg64EDALRp0oSg7duL/lZ+9x1yQ0NcWksn5fMmTFD5vE+nTvTs2LHcMRa6fPkUPj6r+f779axYIZWjbdvKzs+ff57IBx9MYd06KT8XLy7Ozz/+mI65uRWengF8/fVqtmxZzPXrUn6mp6fSpo0rS5YcwNMzAHv7ZixaNL7C8Ra6c+cUJ06s5tNP1/PVV8dITIziyJGyj+n16rXi/fcXYWKifkxPT09k3bpPadfufb799jzTpx/CweHtCsc073//k8q7vz8/L1jAnAULNJejnTs5cuwYvtu24ff33xw7cYKtO3YUfe7UsCFzvv6axk5Oat/df/gwO3182LJ2LReOH6eFszMzvvmmwrGeO3eKTZtW88cf6/H2PkZMTBSenprzLykpka+/nshnn03h4MELODk15dtvi/e7u/sgtm7dj7//JVat2sqhQ7s5flw6nhoaGjF79gL27z/LoUP/MHToaL78clxR/fWqxPmy0n4J/l4VohH8nFlYWLBp06ZnXs6yZcvIycmphIgkhw/70LPnIGxtHTAxqcaQIeM5dMhbY9rTpw9ja+tA58690NPTZ9iwSfz7bzCRkeoHgtLOnTuOi0tPbG0d0NXVY8iQ8Vy79g8xMeon4RV1+rQPnTsPwsrKASOjavTrN55TpzRvg46OHj17jsTRsQ1aWpX3s1dkZHDo2DGmfPYZRnI5bZo3x9XFBd+Ck56SfPbt4+MPP8TS3BwLc3NGffgh3vv2AaCvr0/9evXQ0tJCqVSipaVFcmoqySkpAETFxtLa2RnrunXR1tamb8+ehIaHV9p2lCXY25vbvr4oEhKe+7o0kcnlGA8cSMK336JMTyczIIB0Pz9MNFxYqvHFFygOHiR1yxaU2dko09LICQ5WTTNtGumHDqm9X1Evar/XsbDArHr1omVpa2kRERVV/jgzMzl07hxTPvwQI0ND2jRujGvbtviW6G0qivPYMT728MCyVi0satZklIcH3seOaVyuz7Fj9HzrLeQGBgAc/ecfhvXuTXUTE8yqVWNY797s9Pcvd5yFjh3zoVu3QdjYOGBsXI333hvPsWOay/S5c4extnagY0epXvrgg0mEhwcTFSXVS/HxUbRv3xW53BgjIxPat+/OvXuhAOTn57Nt2zI+/ngW1tb2yGQy6tSxwcSkusZ1FXpR+/2jAQNo06IFerq6WJib4+7mxqWrV8udj4qMDA4dPcqUsWOlOFu0wLVTJ3wL1q8S5549fDxkCJYWFlKcQ4bgvWcPAM5NmjDYw4Pq1aqhq6PDyI8+IiwigkdJSUXfT01LY7mnJ19OmlTu+Eo7edKHd94ZhLW1tN8HDBjPiROa9/s//xzGysqBDh2k/T5w4CQiIoKJjr5LZmY6N29eoH//cejo6FKvnhPt27tx/PhOlWVs3forbm7DMDF58kUPKChHZ84wZehQqRw1aYJru3b4aigfPkeP8nG/fsXlqF8/vMsoCz7+/irlqPQ6D549S39X13LFWNLx4z64uhbn56BB4zl+XHN+nj8vlaM335Ty8733pHIUHX2XjIx0bty4wIABUn7a2jrRoYMbR49K+eng4EzXroMxMamOjo4uvXuPJCYmjNTURxWOGeDSJR/atBmEhYUDhobVcHUdz8WLZR/T3357JLa2bZDJ1I/pp0+vx8HhbVq27IuOjh76+saYm1esZ1WRkcEhf3+mjB8vlaOWLXHt3BnfgvJRks/u3Xw8bFhxORo2DO/du4s+H/L++7zZvj36+vpq342KjqZ1y5ZYW1lJx/fevQnVcGHtSfbt88HdfRD16ztgalqNUaPGs3ev5vw7fvwwdnYOdO3aC319fT79dBIhIcGEh0v1Z7169TE0lBell8m0iIqSLvro6+tTr159lforNTWZlJTkVypO4dUnGsEVtGnTJgYOHEjXrl05WNBLAnDlyhWGDRvGgAEDGDBgAMcLTjKjoqJo3754iM7Bgwfp2bMn/fr1Y+XKlWq9v5qWP7egx++DDz7Aw8ODlIITpWcRERFCgwbFVyQbNHDk0aOHpKSoH7zCw0OoX9+x6P+GhnLeeMOGiIjQovd27/ZiwIB2jB8/gFOnDpZaglLDMu888zZERYVgY1O8DTY2jiQnP3zqA/DTCI+MRFtbGzsbm6L3nOztNR7AQsLCcHIo7nlwcnAgJCxMJY370KE4d+7MuBkzGNy3LzXNzADo3a0b96KjCYuMJCc3F+99+3Dp0OE5bdXLQ69hQ5S5ueSEhBS9l3XlCnpNmqilNejQgbzERKwCArCLj6eOnx861tZFn+vY2GD68cckzpv3zHG9qP0OEHjlCq27daNV164cOn6cEe+/X/44Y2LQ1tLCrm7d4vXb2hJ67556nPfu4VRimKuTra3GHmNFZiYHzpyh3zvvqLxfspQrlUriEhJIreDImXv3QrC1LS7TtraOJCVprpciI0OwtS2ulwwM5FhY2BQ1dHv1GkJg4HHS0pJJS0vm7NmDtGrlAkBCQhwJCXFERNzh0087M2aMK15eS8nPz39sfC9yv5f0z+XL2Ffg1oeiOOvVU1m/xjj//Renhg1V4yzjBDwwKIjaNWtSo8SFmd+WL+fDgQOpVbNmueMrLSoqhHr1yleXS2nV93tUVChKpfQrLPy38PW9e8X1R2joVf799zrduqkPkS5LeHS0ejmysyNUQ/kIiYzEyc5OJd1jy1HXrhrXeejMGcxMTWnbtGm54yxUOj/r1Ss7P+/dU89PS8vCclSYjyqlWyU/S7p1K5Dq1WuX++JCafHxIdSpUxx3nTqOpKU9JD294sf0yMjLyOXV+PPPD5g//002bBhLUlJMhZYRHhGBto6Oajlq2LB85ahhQ409xpr0dnPjXlQUYRER5OTk4L17Ny5vvVWhWAHCwkJwcCjOPwcHRxITH5KcrJ5/UlrV8zorKxvCworP6zZuXI2ra0s8PDqRmamgRw93lWUMHepO587OzJgxjr59B2NmVr464L8S58tK6yX4e1WIe4IryNjYmJ07d3Lx4kWmTp2Km5sbKSkpfP/996xevRpzc3Pu37/PoEGD2FPqauHDhw/57rvv2LZtG7a2tqxfv75cy//+++/ZsmULW7duxagC9wI+TkaGAiOj4nvjjIxMAFAo0tWGBGZmKqhWTfXkTC43RqGQTnD79x/G2LFfYWRkQmBgAD/+OJUaNWrRtGlr2rZ14ccfP6dPnw+oW9eWzZuXI5PJyMzMfOZtyMpSqNzfZ2hoUhBv+lMfhCtKkZGhdn+mibEx6QrFE9OaGBujUChQKpVF9w3t3ryZrKwsDp84odLzX7tWLVo1b07P999HW1sbS3NzNixb9py26uUhMzYmv9RFn/zkZLRMTNTS6lhZod+qFdHdu5N97Rq1Fi3C0suLqLelIXC1ly4t6lF+Vi9qvwO0ad6ci0eOEH//Pn/7+VG3Tp3yx5mZibFcrvKeiVxOesAWOacAACAASURBVEbGE9OayOUoMjNV4gQ4dO4cNUxNaVfiQoRLy5Zs3LOHDk2bkpefz6a9ewFpWK9JBeqsjAzVMi2XF5dpTfWSqalqvWRkZExGhrR/GzRoTG5uDsOHSxchnZ3fpGfPjwCpEQxw5UoAS5bsJj09hTlzPqFmTUt69HivzPhe5H4vtGP3bq7fusX8r78uMy61dSsUFYvT2FglXek4AeLi45m7aBEzPy8einjt5k0uXbnC7GnTiLt/v9zxlZaZqcDQUPN+L12Xa9rvcrkxmZnpGBoa4+jYil27VjBkyAyio0O5cOFQ0W8nPz+PtWvnMGrUdxUaEaSxHBkZla8cGRmhyMhQL0dnzlDDxIR2ZTRyvf396efqqvKd8srM1FyOMjLKn58ZGcX5uWPHCoYNm0FUVCjnzh3SeNtAQkIcnp5zGTFiZoXjLZSdrUBfvzhuAwOTgvfTMTKq2DE9OTmemJibfPzxWiwtHdm//2e2bv2CsWO3lnsZZZYjDccQhUJRrnKkSe3atWnVogU9+/WTju8WFmxYvbrccRYqfV5nbFx8Xletmmr+KRQKatRQrz8Lz+sAhg//jGHDRnPnzi1OnjyismyAzZt3k5WVxYkThys0UvG/Eqfw6hON4Ap69913AWjRogX3798nKyuLoKAgoqKiGD16dFE6mUxGREQENWoUF+grV67QuHFjbAt6WwYOHMj//ve/Jy5f0/CZivL39+P3378HoFmz1hgaylEo0oo+L3wtl6ufsBoYqKaV0qcXpXVwKD4Zbt++M66u7pw+fZimTVvTqtVbDB8+mblzJ6NQpDFgwAgMDY2oXduywtsQEODHunXSNjg6tkZfX05GRnFcha8NDCrnQkF5yA0NSSt1QExLT8eo1AlTYdqSB8+09HTkcrnaAVJfX58+PXrQ64MPaNSwIU4ODixfu5brN29ywteXWmZm+B08yIiJE9m7ZQuGGobSvSqUaWlomZqqvKdlakp+aqp62owM0r29yQoMBCBh7lwaJCSgZWqKYadOaJmYkPYUE8xo8qL2e0kW5ua4dOjAF99+i/eGDeWL08CAtFINn7SMDIwMDTWmLdlISsvIQG5goBanz7Fj9OvSReX9cYMGkZqejscXX6Cnq8t73btzKyyMWtUfP7z4xAk/Vq6UynSjRlK9VLJMF9Y7msq0gYFqWil9OoaGUtqff56Kra0jX3+9AqVSyfr1C/n99y/58ssl6OlJZaZfv08xMjLFyMgUN7f3uXTpxGMbwS96vx85cYLf/vyTdUuXqgyLfxK5XF6pcSY+esTHkybx0aBB9CmYBCw/P5+5Cxcye9q0Ck+Edfq0H56e0n53cmqtti8fV5drOh5lZKQXpZ0w4RfWrZvHxImdMTe35u23+xIVJfVcHjq0BRsbRxwcWlQoXo3lSKEouxyVaBynKRTIDQ3Vy9HRo2U2cmPu3+fC9evML+cQ81On/Fi1qrgclZWfhWWjpCeVoylTfsHTcx5jx3bGwsKaTp36qvUEJycn8sMPH+Pm9hFvv92nXDEDBAX54eMjxW1r2xo9PTlZWcWxZGZKr/X0Kn5M19XVp0mT7lhbOwPQtesE5s/vQGZmalHj+kk0lqO0NI2dEXK5vFzlXZPlq1dz/cYNThw4QK2aNfHbt48Rn33G3h07MNTwGyt08KAfCxdK+de8uVR/pqcX51/ha03ndVK8qvs9PT1dLa1MJsPRsTHnz5/G0/MPpkxRvRinr69Pjx59+OCDXjRs2Eilh/e/Fqfw+nmVerVfiMIGaeHET7m5uSiVShwdHfH19S36O3HiBM2aNauU5VeGrl37Fk1atWCBJ/XqOXD37u2iz+/eDaZGjVoar/Da2jpw927xPZQZGQpiYyOpV89e47qkSr94+JSHxxA2bDjE9u1ncHHpUTBLX/knJCnUsWNfPD2D8PQM4ssvPbGyciAysngbIiODqVat1gvrBQawtbEhLy+P8BLDS4NDQjQOXXSwsyM4NFQlnUOJYXOl5ebmci86Wkp75w69unXD0twcHR0dBvTuTUpqKqGlhle+arLv3EGmo4OuffFvTb95c7Jv3FBLm3X1qsowSEq8NuzaFf02bbCLjcUuNhbj99+n+tSp1PHxeaq4XtR+V/ssL4/IMj7TGOcbb5CXn094TPEwwODwcOxLDBMvitPamuAS95kHh4XhUGLYL0Dsw4dcuH6dfqVmxTXQ1+e7zz7j1P/9H/4rV1LdxIQm9es/sbetc+e+eHkF4eUVxHffeWJt7UBYWHGZDg8Ppnp1zfWSjY0D4eHF9VJmpoK4uEisre2LvtujxwcYGMgxNDSiZ88PuXTpJAB169qho6Nb6gT1ySerL3K/nzx7lm9++omVP/+Mo73muvaJcZYYhltmnPXrE3znjkq6krPOJ6ek8PHEibi6uDDu44+L3k9LT+f6rVt8PmsWHd3cGDRCmnW3c+/eBAYFPTa+t9/uy/r1QaxfH8TMmep1eURE2XW5lFZ1v8fHR2JlJeVR7dp1mTFjFatXn2P+/O2kpj6iQQOpIXTjxlkCA48wdmxHxo7tyJ07QWze/BPr1j3+FgnbunXVy1FYGPalygeAg40NwSXqZY3l6MEDLly7Rr8y7vf1PXaMVo0aYW1ZvovFLi592bw5iM2bg5g9W8rP8HDVclRWflpbOxARoZ6fheWodu26fP31KtauPcf//redlJRH2Ns7F6VPS0tm/vyPadPGlYEDx5Ur3kItW/Zl7twg5s4NYtQoTywsHIiNLY47Li4YY+NaFe4FBrC0dFT5/9P0qNvWq0debi7hJSZAC75zp3zl6M6dcs/uHHz7Nr3c3LC0sJCO7337kpKS8sT7gt3c+nL0aBBHjwaxeLEndnYOhIYW519ISDBmZrXUelcB7OwcCAlRPa+Ljo7Ezk5zXZOXl0t0dNlzueTm5hIdrX6bzX8pzv+Kqh4K/So1HF+lbakyLVu2JCIignMlZpy9WvqEHGjevDk3b94ksuDExNtb80QAmhgZGZGWlvbkhOXUvbsHBw7sICIilLS0FLZs+ZMePfprTNuxY3fCw0M4deog2dlZbN68HDs7R2xspAr+5MkDZGSkk5+fT2Dgafz9/XjzTengnp2dRVjYHZRKJffvx7B48Xf06zccE5Nqz7wNb7/twYkTO4iODiU9PQVf3z9xcdG8DQA5OdlkZ2cBkJubQ3Z2lto+qii5oSHdu3Rh6Zo1KDIyuHjlCv6nTuGh4VEkHr16sc7Li/j794l/8IB1Xl70L+j5v3z9OoFXrpCdk0NmZiarN23i4aNHOBcMOW3WuDEHjh7lYWIi+fn5+OzfT25uLvWsrJ4p/ieRaWujra+PlrZ20WuZ9oubG1CpUJC2axc1581DJpdj8NZbGHl4kKphsrmUdesw7t8fvebNQUcHs2+/JePUKfJTUkj49lsiGjYkskULIlu0IN3Pj+Q1a4h/ykc+vaj97nfwIDFx0tDd6NhYfl+5kjfbtCl/nAYGdO/QgaVeXigyM7l46xb+Fy7goeHRLh5durDOz4/4hATiExNZ5+dH/1L3/foeP05LJydsSg3JLvyOUqnk8u3/Z+/O42rM/gCOfyrtKcskS4hKjH0d+9IgJBkxZsZgzNgZxjr2bTCL8WMsw5C1wVhSITvZyr4PoiIkQijt2/398eS23BtlmqkZ3/fr1etV956e+73n3nPOc55znnNu8uuWLXz9ae7vuXyldWtXDh3axv37SpneunUZbdpoL9MffNCOe/eCOHlSqZe2bFmKjY0D1tZKvWRnV4ODB7eSmJhAYmIC+/dvVt/7aGhoTPPmnfDycic+PoanTx9x4MBmGjTQzJfM/qnP/eS5c4ybMYPFc+eqH8sLE2Nj2rVpw6LffsuI8+hRXNNfP0uczs6s2bgxI87ff+ejzspoXkxMDF99/TX1atdmbLZRyaJmZhzfvRvvDRvw3rCBFQsXArDdw4NaebyPtUULV/z8thEWpnzuXl7LaNVK++fesGE77t8P4vRp5XPfvn0pFSo4UK6c8rkrCzrFkJKSxPHjPly5cgJnZ6WcDx78Az//vJsffvDmhx+8qVy5Bm5uw+nZc5TW11Lnp5ER7Zo0YdGGDUo5un6dQ6dP45qtfAC4OjqyxttbKRORkazx8uKjbPf9+vj5UbdaNY1y9Iq3n5/G/+RFq1auHD6cUY48PZfRurX2/GzUSClHp04p+blt21IqVszIz7AwJT+Tk5M4dkzJz86dlfyMi4th9uyvcHCox+efa64anld167py7tw2IiKCiY+P5vDhZdSvn3ObnpKSRHJyRpuenJzRpjdo0I1r1w4SHn6D1NRkDh/+FRub+rkeBYb0cuToyKJly5RydOmSUo46a452u3buzJrff1fK0ePHrPHw4COXjHtTk5KTSUxU4ktJSSExMVG9BkHN6tXZe+AATyMjlfZ91y6lfddykeV1OnZ0ZefObdy5E8zLl9GsXbsMZ2ft+deqVTtu3w7Cz28fiYmJrF69FDs7B2xsbElLS8PL6w+io6NQqVRcu3YFT8+NNGjQBIA//7zE5cvnSE5OIiEhAQ+PFTx//pTq1Wtpfa1/a5ziv09H9Vd7Ae8QBwcHLly4oJ4Kk/nvK1euMG/ePKKiokhOTqZ8+fIsX76c8PBw3NzcOH36NAB79uxh4cKFGBsb07p1a9zd3bly5Qq6urqvPf6SJUvYuXMnRkZGeHh4YJ5tiqg2WtbiyGLbtjVs3rySpKQEmjd3YuTIjH3a+vd35tNPB/Hhh8pedhcuBLBkySwiIsKpWrU248Z9T+nSSgds1KjPuH37JqCidGlrPvlkEG3aOAMQExPN6NG9ePjwPsbGpjg5deOLL75Rj3QDpJ/jv5U9e9awa5fyHho2dKJfv4y9ECdMcMbFZRDNmnVJj9ORp0+zjqL973+HsLTMXUeykd0zrY+/iIpi0ty5BJw5QzELC8YMGYKLkxPnLl1iwOjRXDx8GFAWaJm3dGmWfUPHDRuGjo4OZy5cYPaCBdwPD0dfT48qtraMHDiQhulb0CQmJvLDokUcOHqUuPh4KlpbM2rwYFo2aaIRz8y/sEBNdq2mT6d1tn0tj8yYwdF82J7ps1ym0y1eHKvVqzFp147UyEgiJ0zg5aZNGDVvTrk9ewjJdH+wxeDBlJgyBR0TE+JPnODJ0KGkaFlN2WrNGlLCwnK1T7B9Ditj/xOf+4Lly/HavZvoly8xL1qUVk2bMnrIEIpbaLmIlENBevHyJZOWLCHg8mWKFS3KmN69cWnZUr3378X0PZdVKhXz1q9n28GDSpxt2zKuT58sIygdhg/nq65d6dG2bZbXOHvtGt/+8guRUVGUfu89hn38MV1atdIaz3U0t7fKzMdnDV5eSpnOvk/wiBHOuLkNolUrpUxfvhzAypWzePIkHHv72owY8T2lSinlOSLiPu7ucwgMvAiosLevSf/+Uylb1gZQTuCXLZvKuXNHMDU1p127Hnz88TD1+32/dMGV997DhnH+8mUMM+2bWb92bdwXLNAMKIepyC+iopj03XcEnD6txDl8OC4dOnDu4kUGjBzJxWPHMuJcvDjLPsHjvv4aHR0dvHbtYsLMmRhnmxbvu2ULZbONUoaFh/Ohq2uO+wRfCH59m+Xru4YdO1aSnJygsU/w2LHOdO06iObNlc/96tUA1q5VPnc7u9oMGfK9uh7fvXst3t7LSUpKwMamGr17T8LWVvusrFmzetO8eZcs+wTXM9O+aOOLly+Z9MsvBFy6pJSjvn1xad1avffvxfQtwVQqFfPWrs2yT/C4L77IWo4GD+arbt3o0b69xutcDAyk35QpnFi/XuM+5MyuJFTJ8TmAnTvX4OOjlKMPPnBi4MCM/Bw1yplu3QbRooWSn1euBLBqVUY5GjYsoxz5+q7F0zMjP/v1y8jPI0e8WLp0AoaGxmSeSbFggS+WlmUByDQZIleOH1/DsWPK9yD7PsELFjjTuvUg6tZV4v7xR0devMjapo8ff4jixZXYT53aiJ/fsvTY6+PqOp1ixbRfeOjWQfN+eUgvRzNmEHDqFMWKFWNM+j7B5y5cYMDw4VwMCADSP/dffsmyT/C4kSPVn3vv/v05c/58lmOvX7mSDxo0UNr3//2PA4cPK+17+fKMGj6cllq2x3qWkPN3AmDTpjV4eKwkMTFBY//dzz5zpm/fQTg5Kfl35kwA8+fP4tGjcKpXr83Uqd9Tpow1aWlpjB49gOvXr5KSksx775WiU6eP6Nt3EDo6Oly4cIYFC2YTHn4fPT19bG2rMHDgSOrWbfja2ApjnDmsRVioPXmLWQ35zfI/0nWUTvA/LCYmRr14gqenJ9u2bWNT+klofntTJ7iw+Cud4H9STp3gwiY/O8F/p9x2ggtaTp3gQudfUpDe1AkuLHLqBBc6ebwft6C8qRNcWOTUCS5s3tQJLizy2gkuKDl1ggubN3WCRd78GzvBkYWgE1zyP9J1/He0nv8hHh4e7N27l9TUVCwsLJg9e3ZBhySEEEIIIYQQ7wzpBP/DhgwZwpAheVs8QgghhBBCCPFuk8Wc8o/kpRBCCCGEEEKId4Z0goUQQgghhBBCvDNkOrQQQgghhBBCFHIyepl/JC+FEEIIIYQQQrwzZCRYCCGEEEIIIQo5Gb3MP5KXQgghhBBCCCHeGdIJFkIIIYQQQgjxzpDp0EIIIYQQQghRyMnoZf6RvBRCCCGEEEII8c6QkWAhhBBCCCGEKORk9DL/SF4KIYQQQgghhHhnyEjwf5i1dUFHkDuBgQUdQS5t21bQEeTKZwUdQC5tLOgAcml6QkJBh5A7oaEFHUGu2LV9v6BDyJ1LwQUdQe78S76f9Zo3L+gQcmfXv6NB2vVnlYIOIVfcJusUdAi5Eh2lKugQcqWEWVJBh5Arz2IMCjoEId5IOsFCCCGEEEIIUcjJFN78I3kphBBCCCGEEOKdISPBQgghhBBCCFHIyehl/pG8FEIIIYQQQgjxzpBOsBBCCCGEEEKId4ZMhxZCCCGEEEKIQk5GL/OP5KUQQgghhBBCiHx1584devbsiZOTEz179iRUy3aOixcvpkmTJri6uuLq6srMmTPVz8XHx/PNN9/Qrl07OnTogJ+fX77FJiPBQgghhBBCCCHy1fTp0/nss89wdXXFx8eHadOmsX79eo10Xbt25dtvv9V4fNWqVZiZmXHgwAFCQ0Pp1asX+/fvx9TU9C/HJiPBQgghhBBCCFHI6RaCn9yKjIzk+vXrdO7cGYDOnTtz/fp1nj17lutj7Nmzh549ewJgY2NDjRo1OHbsWB6iyJmMBAshhBBCCCGEeKPo6Giio6M1Hjc3N8fc3Fz998OHD7GyskJPTw8APT09SpUqxcOHDylRokSW//X19eXEiRNYWlry9ddfU7duXQDCw8MpV66cOl2ZMmV49OhRvrwP6QQLIYQQQgghRCGno6NT0CGwbt06lixZovH48OHD+frrr/N8vE8++YTBgwejr6+Pv78/Q4cOZffu3RQvXjw/ws2RdIKFEEIIIYQQQrxR3759+eijjzQezzwKDMqobUREBKmpqejp6ZGamsrjx48pU6ZMlnSWlpbq35s1a0aZMmUICgqiUaNGlC1blgcPHqhHjh8+fMgHH3yQL+9D7gkWQgghhBBCCPFG5ubmWFtba/xk7wSXLFmSatWqsWvXLgB27dpFtWrVNKZCR0REqH+/ceMGDx48oFKlSgB06NCBzZs3AxAaGsrVq1dp0aJFvrwPGQkWQgghhBBCiMKuyL+r6zZjxgwmTJjAr7/+irm5OT/++CMAAwYMYMSIEdSsWZP//e9/XLt2DV1dXfT19fnpp5/Uo8NfffUVEyZMoF27dujq6jJr1izMzMzyJbZ/V06Kv2Tt2rWsWrWS+Ph4nJycmD59JgYGBlrTnjx5ku++m8nDhw+pVasWc+f+oL4xPSkpiZkzp7Nv3z6MjY356qv+fPFFPwCCg4OZMGE89+/fB+D996szefIU7OzsAOVm+rlz53D8uLKy26effkbVqrm/f+Dw4bUcPLiS5OR46tRx4uOPZ6Kvr/keUlKSWLduLPfu/cmzZw8YMWI99vYfZDnOsWMexMY+x8DAhHr1OtG163j09PK/SLyIj2fy/v34h4ZS3NiY0S1a4FKtmkY697Nn8b52jQfR0RQ3NuazOnXo37Bhvsfzim7x4litWoVJ+/akPn1K5MSJvNy0SWtaw7p1sVy4EMN69UiLjeX53Lm8WLQoSxrjli2xPnqUZ7NnEzl16t8WtzYNhw2jzhdfUKpmTf7ctAmffv3+9td8ER3N5Hnz8D93juIWFozu3x+Xtm010qlUKn5esYJtu3cD0L1TJ8YOHKi+r8ehTRuMjYx4dZdPJ0dH5owbB8DarVvx8PLieVQUJsbGdGrThvGDB1MkfZGJt4o7NpbJmzfjf/MmxU1NGe3sjEv9+hrpTgUFsXT/fq6HhWFhbMzhadO0Hu9McDC9ly5lcLt2jOrUKc/xrF+/ltWrV5KQEE+7dk5MnZpzvXTq1EnmzJnJo0cPqVmzFrNn/0DZshn10nffTefAgX0YGRnTr19/+vbN+B54em5l1aoVPH36lHr16jFr1lxKlbJSx7BxowcvXjzHxMQEJ6dOTGzbJlf5/CImhsnu7vhfvUrxokUZ/fHHuDRtqhn79ess9fbmemgoFqamHF6wIMvzC7dt49D584SEhzPE1ZWvu3XLdR7mxovYWCavXYv/9esUNzNjdLduuGiZUnYqMJClu3Zx/d49LExMOPzDD+rnIqOjmfPHH5y5dYv4pCTsy5Zl4scfU7ty5dzH8eIFk6dMwT8ggOLFijF69Ghc0lcOzUylUvHz/Pls27YNgO7duzN2zBh1ublx4waTp0wh5PZtbCtXZs7s2VRLr1cXL1nC8t9+y/I92uHtTfny5QGYOm0aZ86e5e7du8ydM4duWqb1vfF9xMUxecsW/G/dUspRp064pC/kktmp4GCWHjzI9QcPlHI0aVKW5x3nzuXpy5fo6SoT8+pWrMjqgQPzHM+bNGwITZqAvj4EBsLevZCaqpmubFlo1QpKlwaVCu7ehf37ITY2azpdXejfHwwMQMutgXmmW7w4pVetwjS9PXryhvao1MKFGKW3R5GZ2qPKd+6gZ2WlfnPxAQGEOTn95fg2blzLunUrSUyMx9HRiQkTcq6nzpw5yU8/KfVUjRq1mD79B8qUUeqphQt/5NixQ0RGPsHS0op+/Qbj7NxV4xi+vt7MmPEtkyfPpmvXHhrPv4iKYvK0afifPKmUo5EjcXF21kinUqn4ecECtm3fDkD3bt0YO2pURjkKDGTytGmE3LmDbaVKzJk1i2pVqwLK+dqcH3/k2IkTAHzWsydfDx2qPvaNwEC++/57bt66hamJCT179GDY4MG5ys9Nm9by++9Kvd+mjRPjx+ecn2fPnuTnn2cSEfGQ6tVrMWVKRn4uWfITBw74EhPzEnNzC1xde/LFF0oML148Y/z4ody9e4e0tFRsbGwZPnw8tWtrtnfi72Nra8vWrVs1Hl+5cqX691cdY21MTExYlO18M7/IdOi/WUREBL17985VWgcHB2KztzTpFi9eTFJS0lvHceLEcdzdV7B69VoOHfLj/v0wFi/W/qV6/vwZI0YMZ8SIkZw6dYYaNWowevQo9fNLlizm7t27HDrkx9q161m1yl3dqS1VqhS//LKIU6fOEBBwCkdHR8aMyfjfH374noSEeA4ePMyWLVvZscOHU6c8c/Uebtw4zsGDK/j667XMnOnH06dh7N6dc8GoXLkeffr8hLm5pcZzNWs6Mn68F/PmXWDSpF08eBDI0aMeuYojr2YdOoS+ri7+Q4Ywr1MnZhw8SNDTpxrpVCoVP3bsyNnhw3F3c2PDxYv4Bgb+LTEBlFq6FFVSEretrHjUqxeWy5Zh8P77Gul0S5ak7N69RP32G7dLliTUzo7Y/fuzJipSBMtffiH+1Km/Ld7XeRkezrHZs7m0evU/9pqzfvkF/SJF8N++nXmTJzNj4UKC7tzRSLd5504O+vvj4+7ODnd3/E6e5I+dO7Ok8XF35+KePVzcs0fdAQZwbNoUrxUruODry67VqwkMCcHDM3flJce4PT3R19PDf9Ys5n3+OTO2bSPo4UONdCYGBrg1asR4F5ccj5WcmsocLy9qV6z4VrH4+x9n1aoVuLuvZd8+P8LCwli6NOd6adSo4QwfPpITJ85QvXoNxo3LqFt+/XUx9+7dZd8+P1avXs+aNe6cOKHUS2fPnmbRov+xaNGv+Pufplw5a8aPH6P+3zZtHNmyxYtTpy7g5bWLW7cC8cj+Hc/BrHXrlO/B0qXMGzKEGWvXEhQWppHOxNAQt5YtGf/JJ1qPU9HKirGffEKrOnVy9bp5NWvDBiXO+fOZ178/MzZsIOjBA+1xNmvG+O7dNZ6LS0ykpo0N26dM4czChXzUtCkDFy8mNiEh93F8952y+Mnx48ybN48ZM2cSFBSkkW7zli0cPHQIH29vdvj44HfkCH+kT4lLSkpi6LBhdHFx4ezp03Tt2pWhw4ZlaSM7duzIxfPn1T+vOsAAVR0cmDFtGu9rqe9y/T68vJT8nD6deZ99xozt2wnSsmKpiYEBbg0bMl5LB+WV5f36cXHOHC7OmfO3dIArVYKmTWHjRli6FIoVg5xmExoZwcWLSrolSyApCbRco6BxY4iLy78YrdLbo2ArKx726oVVDu2RXsmSWO/dy4vffiO4ZElu29kRl62sPnBxIahoUYKKFs2XDvDJk8dZt24Fv/66lh07/HjwIIzfftNeTykdr+EMHjySQ4fOUK1aDSZNyqinjI2N+d//luHnd54ZM35k/vw5XL58IcsxoqOjWLNmOZUr2+cY06w5c5RydOQI8374gRmzZxMUHKyRbvPWrRz088Nn2zZ2eHrid/Qof6R3SJKSkxk6YgRdOnfmrL8/XV1dGTpiBEnJyQB8/9NPxMfHaj5z+QAAIABJREFUc3jvXrZu3IjPzp14enmpjz3m229pWL8+Z06c4Pe1a9m0eTOH/PzemJ+nTh3Hw2MFixevxcvLj/DwMNzdc87PiROHM3DgSPbtO0PVqjWYOjUjP11cuvPHH3s4dOgCv/32B/v37+TIkf3peW3K5Mlz2bPnJPv3n+XzzwcwbtwQUlJS3hhjoVakSMH//EdIJ/hvZmVlhYfHX+9cLVmyhOT0iulteHt74+bWHXt7eywsLBgyZCje3l5a0x44cAA7O3s6dOiIoaEhw4Z9zc2bgdy+HZJ+LC+GDBmKhYUFtra2dO/eA6/0itHc3Jxy5azR0dFBpVKhq6vHvXv31Mf28zvMV1/1x9jYmHLlrHFz687Jk7k7qT992pvGjbtTpow9JiYWdOgwlNOntb+HIkUMaNPmC2xtG6Crq/k1t7SsgInJq3sXVOjo6PLkyd1cxZEXccnJ7A8KYmSzZpgaGNDA2hpHW1t8rl/XSDugUSOqW1lRRFeXyiVK8KGdHRe0nKTmBx0TE8zc3IicOhVVbCwJ/v7E7thBUS0XbIqPHk3cvn283LgRVVISqpgYkrN1zouPGUPs/v0aj/9TAr28uOnjQ1xk5D/yenHx8ew/doyRX36JqbExDWrWxLFpU3wOHNBI671/P1/26EFpS0usLC3p16MHXnv35up1KpQrh3n6tB+VSoWujg53/8J3Ii4xkf1XrjCyY0dMDQ1pULkyjtWr43PunEbaWhUr0rVhQ8qXLJnj8db4+dHMwYHKpUq9VTw+Pt5069YdOzulXho0aCg+PtrL9MGDB7C1tcfJSamXhgzJWi/t2OHFoEFKvVS5si1ubj3Uxzp69Ajt2nXAzs4efX0DBg0ayvnzZ7l/X6mbypevoL6XSaVS6oO7me5RyklcQgL7z55lpJsbpkZGNHBwwLFePXz8/TXS1rK1pWvz5pTPIa8+atGCVrVrY2pk9OaMy6O4xET2X7jASFdXJU57exxr18ZHy0WrWpUq0bVJE8q/957Gc+UtLenXvj2lihVDT1eXni1bkpySwp1c5BVAXFwc+w8cYOSIEZiamtKgfn0c27TBZ8cOjbTe3t582a8fpUuXxsrKin5ffKFuZ86cPUtKaip9+/bFwMCAPr17owJOnT6dqzh69epFkyZNMDQ0zFV6jfeRlMT+q1cZ6eSklKNKlXB8/318LlzQSFurQgW61q9P+Wz3wP2TatWCS5fg6VNISAB/f+UxbW7fVkaKk5IgJQXOnQNr66xpLCygRg0ICMif+HRMTCjq5sbT9PYo3t+fmB07MM+hPYrN1h4l/c3tjq+vN126dMfW1h5zcwu++moou3Zpr6cOHz5A5cr2tG2r1FMDB35NUFAgoaFKPTVo0AhsbGzR1dWlRo3a1KlTn6tXL2U5xtKl8+nZszfFimlfGVddjoYPx9TEhAb16uHYujU+2S6uAnjv2MGXffpklKO+ffHy8QEylaPevZVy1KsXKpVKXY4OHz1K/y+/xNjYGOty5ejerRue3t7qYz8ID8fF2Rk9PT0qlC9Pvbp1CQ4JeWN+7t7tjYtLdypXVvKzX7+h+Ppqz88jRw5QqZI9H36o5Gf//lnzs2LFyhgbm6jT6+joEhamnMsZGhpSsWJldHV1089HdXn5Moro6Kg3xijeDdIJzoU//viDmTNnAnDlyhUcHBy4cuUKoMx137x5M5cvX6Z3795069aNbt26ceTIEQDCwsKyrGK2b98+OnToQNeuXVm+fLnG6K+Hhwdubm58+OGH7Nu3D0D92p988gmurq5a9+Z6k+DgIBwcqqr/rlrVgadPn/L8+fMc0jqo/zYxMaF8+QoEBwcTFRXFkydPsh2rKsHZrkA2atSAOnVqMWfOdwwcOCjLcypV5t9VPHyoOQqgzcOHQZQrl/G65co58PLlU2JjNd9Dbpw7t5Nx4+oxYUJjwsMDadZM+wjNXxH67Bl6urpUynQCVNXSkuA3dNZUKhXnwsKw03Iimh8MqlRBlZJCcqYRmMTLlzGoXl0jrVHjxqQ+e4a1vz+VIiIos2MHRTKNqhSpUAHzL7/k2axZf0ushVFoWBh6enpUyjy6ZGtLcGioRtqg0FCq2tpmpLOzIyhbul4jR9KsWzeGT5tGWLbRpJ0HD1LP2ZnGXbsSGBLCJ68ZmX1j3E+eKN/HTB2xquXKEfwWe+49ePYMzzNnGPYXRlpCQrLWSw4ODkRGPuXFC80yraTVrJdCQjLqpSpVMh8re72UUfG8qoOCgm6pH/P13UnjxvVo0aIxt24F8kmbNm+MP/TRI+V7kGmly6rlyxOsZSS4IIVGRChxli6tfqyqtTXB4eF/6bg37t0jOSWFipaas220xhEaqsSRvuAJaG8/AIKCg6ma6fOuWrWqeqQrOEj5LmTeKsShSpUsx/Hz86NR48Y4d+7Mxhym1b4tdTnK9L6rlinzVuUIYOymTTSeMYMvV6wg8C9+Jtq89x48fpzxd0QEmJmBsfGb/7dCBaXznFn79nDkiNJJzg85tUeGObRHac+eUcHfH9uICMpla48AymzYgO3jx1jv24dhTr39PLh9Owh7+4y6pUoVB549015P3b4dRJUqGd9bY2MTypWrwO3bmt/xhIQErl//k8qV7dSPXbt2hRs3/sTN7dMc4wm9exe9IkWoZGOjfqyqg4PWDmhQSEjWcuTgkFGOgoNxsLfXLEeZj6PKXG+qssza6Pv553jv2EFycjK379zh0uXLNGncOMe4X7lzJ2t+2tsr+RkVpZmfStqs+WltXYE7dzLyc/36FTg61sXVtSUJCXG0b5+1jfz8cxdatarF+PFD6NKlByVK5HxhV7xbpBOcC02aNOHkyZOAcq9s3bp1OZV+Bf3kyZNUr16d6dOnM3/+fLZv387y5cuZNm2aRmf16dOnTJs2jeXLl+Pt7Y2Rliv+ZmZmeHp68tNPPzF79mwApk+fDiidcR8fH43V13IjLi6OokUzbiQ3Myua/rjm9OvY2DiKFi2a5bGiRc2IjY0lLn3+U+bnzcyKakzjPnPmHGfOnGPKlKlUq5YxpalFixasXLmC2NgY7t69y/btniQnx+fqPSQlxWFsnPEejI2VGBIStE8hf5MGDVyYN+8CU6fuo1mzTzA3z/+KMS45GbNs97kUNTQk9g1T2xcHBJAGuGk5CcgPOmZmpGX7fqZFRaGb7XMHKGJtjXnfvjwZOZLQChVIuXOH0plOKi0XLVKPKL8r4uLjMTMxyfJYUVNTYrXMD4yLj8+yiENRU1Pi4uNRpZ9c/L5wIYc3bWLP+vWUKlmSwRMnkpLpZj2Xtm254OvLPg8PPunShZJ/YUQpLjERs2z1TlEjI2ITE/N8rNleXuoR5beOJy4uS968qpe03RaipM36/TQzU+ql+Hjt9dKr+q1Zsxbs27eHmzcDSUhIYPnypejo6JCQaRqvs7MLp05dYNeuffTo8QklLSzeHH9iImbZehJFTUzyND34nxCXkKD5uRsb/6U4Y+LjGb96NcNdXCiarSzkGEe2zxugaPpnqDVtps+zqJkZcXFxqFQqYuPiKJrtOGZFM9qhjh06sNvXl5P+/nz33Xf8+uuv7PL1zetbzPl9JCZilu17X9TY+K3K0bxPP+XwpEn4TZrEB3Z2fLVyJdHxuWsTc8vAADKH9ur3HG7BVLO0hObN4fDhjMeqVFHuB751K+f/yyvdPLRH+unt0eORI7ldoQLJd+5QNlN79LBXL27b2HC7YkXi/Pyw3rcP3VyU5dfJqZ7Sdv4UHx+Hqan2eiq7H36Yjr29A02aKHPTU1NT+fHHGYwbN03rDLYs8ZiaZnnsteUoc/uTvRwVzTnWFs2asWLVKmJiY7l77x6eXl7EZ6ozWrdqxb4DB6jdsCEdu3She7du1KpRI8e4X1HyKHf5qa3eNzU1y5K2T5+BHDp0gbVrvejQwTXLsQF+/30nBw+eZ+bM+dSq9R+4H7igp0LLdOh3S8WKFUlMTOTRo0ecPHmSUaNGcfLkSR4+fEhycjKRkZGEhYUxYMAAXF1dGTBgADo6Oty9m3V67eXLl3n//fexSb965+bmpvFandIXlqlTpw6PHz8m8S0aVYAdO3ZQv35d6tevy8CB/TExMSEmJkb9/KvfTUxMNf7X1DRrWiV9LKamppikn+xkfj42NgZTU83jmJiY8MknnzJhwrdEpo98Tpo0BSMjQzp0cGLYsKF06uRMsWKlNf4X4OzZHYwZU5cxY+ry66/9MTAwISEh43Vf/W5kpPnaeVGqlA1lytizefPMv3QcbUz09YnJ1uGNSUrC9DVnH79fvIj39eus+OgjDP6mykYVE4NutospuubmpL18qZk2Pp5YLy8Sz51DlZhI5MyZGDdrhq65OaadO6NbtCgxW7b8LXEWVibGxsRk6/DGxMVhqqUzYGJsnOXkJCY2FhNjY/XV94a1a2Ogr4+5mRmThw8n7NEjQu5qTs23sbbG3saGmQsXvn3chobEZOv4xCQk5Lkje/jPP4lNSKCTloWAXmfH+fM0alSXRo3qMniwZr0UG6v8nlN98up5dezp9dKr6XDZj/WqfmvSpClDh45g9OgRODk5Uq5cOUxNTbGy0qx7Kla0wc7Onpnr1r3x/ZgYGhKTrcMSEx//t0xp/itMjIy0f+5vGWdCUhKDlyyhduXKDMrDYmjZP29QykOOn3fmNis2FhMTE3R0dDDVcpzYmIx2yM7ODqtSpdDT06Ne3br06dNHPbMqP5gYGhKTrW1+m3IEUL9SJYz09TE2MGCQoyNFjY05p2VtgbyoXh3GjlV+evZUpjZnDu3V76+7Flu8OHzyCRw4AOnrXKKvD46OykJZ+SktD+1RWnw8MV5eJKS3R08ztUegLISlSkhAFR/Psx9+IPXFC4zzuJ3Knj07aNmyLi1b1mXEiP4adc/rzp+MjTXrqVgt3/FffvmRkJAgvv/+F3VbsG3bRuzsHKhZ8/XrApiYmBCTrcP72nKUuf2JiXl9Ocp0nCkTJ2JoaIiTszNDR4zAuWNHSlspiwm+iIqi/+DBDBs8mCvnznH0wAFOBASw4Y8/NGLYt28Hjo51cXSsy6hR/TXy6NXv2vJTW70fGxurkVZHRwcHh/cxNDTC3X2xxnEMDQ1p374zHh4rCAoqmNu2ROEjneBcaty4MX5+fkRGRvLBBx/w5MkTjhw5wgcffIBKpcLBwQEfHx/1z9GjR6lZs2aeX+fVPUp66auSvu0N/F26dOH8+YucP3+RFSvcsbOz5+bNm+rnb94M5L333qN4cc17TpS0GZVEXFwc9+/fw87ODgsLCywtLQnMdA9OYGCgevXn7NLS0khIiFfvAVasWDHmzZvP8eP+7Nrli0qlokIF7dOVGjbswvz5F5k//yJDh7pTpow9Dx5kvIewsECKFn0PU1Pt983kRVpaCk+f3ntzwjyyKVGC1LQ0QjNNOw988gS7HO6z3Hb1KivOnGFdjx6U1nIVPL8k3bqFTpEi6Gf63Axr1ybp2jWNtIlXrqhHLYEs06OMP/wQwwYNqPTwIZUePsSsZ0+KffMNZTLdN/RfZGNtTWpqKqGZpr0GBgdjl2l62iv2NjYEZppeFhgSgr2WdK/oQNb8ziQlNZV7f+GeYBtLS+X7+ORJRjzh4diV1n4hKicng4L48/59mk2bRrNp09h96RLrjx5lyKpVr/2/LvXrc+bMRc6cucjy5e7Y2tpz61bWeqlkyfe03gtna6tZL4WF3cPWNqNeyvz8zZtZ66VPP+2Fr+9+jh4NoG3b9qSmpmJnp33hmZSUFO7l4j5Xm9Klle9BpmmwgffuYZf9JsoCZmNlpcSZ6T0F3r+PXdmyeT5WUnIyw5YuxapYMWZ9/nne4rCxUeLIdDtATu2HvZ2dRjtjn57Ozt6em7duZSknN2/ezLEdgpzL1NvIr3Kkzav1NP6Ka9fg55+Vn82blenMmW9FL1UKYmIgpwFnc3P49FM4cQL+/DPj8eLFlfuBe/eGESPAzU2ZVj1ihPL428qpPUrMoT0ih/ZIK5UKMk33zY2OHbtw7NhFjh27yKJF7lSubE9QUEY9FRQUSIkS2uspJW3G9zY+XqmnMk95/u23RQQEHGfx4lVZRmnPnj3JkSMHcXJqhpNTM65cucjChT/w009ZbzWyqViR1JQUQjNdLA28eRO7TLfdvGJva0tgpnO/wFu3MsqRnZ1mObp1S32cYhYWzP/xR/yPHMHX2xuVSkWt9PPa+2Fh6Onq0rVLF4oUKULp0qXp1KEDx44f14jByakLhw9f5PDhiyxY4E6lSvYEB2vmp4WFZn5WqqSZnw8e3KNSJe1lPTU1hQcPcj6XS0lJ4cGD+zk+/69Q0KPAMhL87mncuDErV66kbvrIR7169Vi5ciVNmjShbt263L17Vz1FGpR7h7M3ZLVr1+b69evqhaK8vLQvBKCNqampxhW7vHB1dcXTcxvBwcFER0ezfPkyunbVvi1E27btCAoKYv/+fSQmJvLrr0upUsWBypVt04/VleXLlxEVFcXt2yFs3bqVj9K3mPD39+f69eukpqYSExPDjz/+gLm5Obbpleq9e/d4/vw5qampHDt2lC1bNtOhw5BcvYdGjVw5eXIbDx8GExcXzb59y/jgg5y3tkhOTiI5Wblan5KSTHJyovozCQjYysuXyuj0w4fB7N+/AgeHJrmKIy9M9PVpZ2/PIn9/4pKTOf/gAYeCg3HVsurljhs3WHDiBGu6d6d8sWL5Hktmqrg4YrZvp+SsWeiYmGDUtCmmrq681LKIW/SaNZh99BEGtWtDkSKUmDqV+OPHSYuOJnLqVO5WqcK9OnW4V6cOsTt2ELVyJRH/wBZFmeno6aFnaIiunp76d52/sI3Qm5gYG9OuRQsWrVlDXHw8569e5VBAAK7t2mmkdW3fnjVbtxLx5AkRT5+yZssWPurQAYCgO3e4ERxMamoqsfHx/LBsGaUsLbFNX215q68vkekXUIJDQ1mxYQNN6tV7+7gNDWlXqxaL9uwhLjGR87dvc+jPP3Ft0EAjbVpaGonJySSnpaECEpOTSUq/KDeyY0f2TZqE99ixeI8di2P16vRo3Jjvc1j5OCdduriyffs2QkKUemnFimW4umov0x9+2I7g4CAOHFDqpeXLs9ZLLi5dWbEio17y9NyqPlZiYiJBQbfS1yAIZ+bMafTq1QeL9DN3T8+t6tkqISHBrFq1gia5uBXBxMiIdg0asMjTk7iEBM7fusWhCxdwbdZMe34mJZGcmopKpSIxKUmdnwDJKSkkJiWhUqlISU0lMSmJ1LS0POVnjnEaGtKuXj0W+fgon3twMIcuX8ZVy/176s/9VZyZPvfklBRGLF+OoYEBP3755WunbGqNw8SEdm3bsmjxYuLi4jh/4QKHDh/GtUsXjbSurq6sWbeOiIgIIh4/Zs2aNep2plHDhujp6rLew4OkpCR+37ABgMbp628cPHSIqKgoVCoVV65cweP33/nwww/Vx05KSiIxUWkPUpKTSUxMJC0PeW1iYEC7GjVYtH8/cUlJnL9zh0PXr+OqpWxmKUfZ8jP8+XPO37lDUkoKicnJuB85wvPYWOq95iLZ27h6FWrXVu4NNjSEZs0gfVkTDWZm0KsXnD+vrBKd2ZMnyorRq1YpP7t3K1snrVoFb7FUiZoqLo6X27fzXnp7ZNy0KWaurkS/pj0yTG+PSk6dSlx6e1SkfHmMmzYFfX10DA0pPnYseu+9R7yWheryolMnV3x8tnH7djAvX0azevUyOnfWXk+1adOOkJAgDh9W6il396XY2ztgY6PUU2vW/Ma+fbtYunSNRid6+vQf2LJlNxs2eLNhgzfVqtVgwIDhDB06Kks6dTlaulQpRxcvcsjPD1ct60W4dunCmvXrM8rRunV85OoKpJcjPT3Wb9iglKONG4GMcnTv/n2ev3hBamoqR48fZ/O2bQxJX728UsWKqICdvr6kpaXx5OlT9uzdi0OVKm/Mz44dXdm5cxt37ij5uXbtMpydtednq1btuH07CD8/JT9Xr16KnZ2Sn2lpaXh5/UF0tFLWr127gqfnRho0UM7l/vzzEpcvnyM5OYmEhAQ8PFbw/PlTqlf/6/eJi/8GHVV+Xh79D4uIiKBly5YsXLiQjh07snv3bkaNGsXx48cpVaoUV65cYd68eURFRZGcnEz58uVZvnw54eHhuLm5cTp9tb09e/awcOFCjI2Nad26Ne7u7ly5cgVdXV0cHBy4cOGCeipK5r+XLFnCzp07MTIywsPDI1f3BWdv09euXYO7+0oSEhJo396JGTMy9mXr3NmZQYMG4eKinIwEBAQwe/YswsPDqVWrNt9//z3lyimjG5n3CTYyMqJ//wHqfYL37t3DokW/EBERgaGhITVr1mL06NHqxW/27NnN99/P5eXLl9jY2DBmzFgSEnI/Venw4TXp+wQnULu2Ez17ZuwTPGeOM+3bD6JhQ+U9TJ/uyLNnWUfNZsw4RMmS1vz++0SuXz9KYmIcZmYlqFvXCWfnb9DXz3k6W/vQFbmOM7MX8fFM2rePgLt3KWZszJj0fYLPhYUxYPt2Lo4YAYDjypVExMRgkKnz5lKtGrO0dKxeJ2jQoDcnIn2f4NWrMWnXjtTISCInTODlpk0YNW9OuT17CMk0Em0xeDAlpkxBx8SE+BMneDJ0KClaFv+xWrOGlLCwXO0TvDH3b+mNWk2fTusZM7I8dmTGDI7O/OtT3KfnMPL6IjqaST/9RMD58xQzN2fMgAG4tG3LuStXGPDtt1zcswdQRqDm/fZbln2Cxw0ahI6ODicvXGDGwoVEPHmCsZERdatXZ/zgwdikjyRO/PFHjp4+TVx8PCUsLHBq3ZpvvvwSQ23T6S9d0nxMW9yxsUz64w8Cbt2imIkJYzp3xqV+fc6FhDBgxQoupu/Xdzo4mD5Ll2b530a2tngMH65xzAkbN2JVrFiu9glOaps1zbp1a1i9eiWJiQm0bevEtGkZ9VLXrs707z+Izp2VMn3yZABz587i4cNwataszezZWeulV/sEGxoa8eWXA9T7BEdHR/PFF70IC7uPiYkpXbt24+uvv1HPuJkyZSLHjx8lPj6O4sVL0L69E6NbNNOez9nzMyaGSStXEvDnnxQrWpQx6fsEn7t5kwHz5nHR3V3Jzxs36DN3btb8rFoVj8mTlTz87Te80vfjfOX7AQPo1rLl6wPI5X29L2JjmbR2LQHXr1PMzIwx6fsEn7t1iwGLFnExfbPX0zdv0ufnn7PGWaUKHuPGcebmTXr//DNGBgboZhpdWzliBA3edPLbvLkSx4sXTJoyhYCAAIoVK8aY9H2Cz507x4BBg7h4/jyQXm5+/plt6VuCdXdzY9zYseqpo9evX2fK1KkEh4So9wl+teXR6DFj8Pf3Jyk5GSsrKz779FP6ZFppuHefPpw5ezZLeOvXreODRo1g167c5WdcHJO2bFHKkakpY9L3CT53+zYDVq3i4pw5Sn6GhNBn+fKs+Vm5Mh5DhhD06BGjN2zgfmQkhvr6VC1blrGdOlEz20JP2sz9U/PCwes0aqTsE1ykiOY+wQMGKCs9X7umfEwtW2pOlc72lQCURbO6dHn9PsFuk3M3CqtbvDilV6/GNL09epLeHhk3b471nj0EZWqPiqW3R7rp7VFEentk8P77lN20CX1bW1QJCSRcusSTb78lMf079Tplol5/KrxhwxrWr1fqqTZtnJg4MaOe+vhjZ/r1G0THjspncvp0APPmzeLRo3CqV6/N9OnfU7asUk81bOiAvr4+RYroq4/dr98g+vXT3F930KDedOzYJcs+weZGygfzIiqKSVOnEnDqFMUsLBjzzTe4ODtz7vx5BgwZwsUzZ4D0crRgQdZylGmf4Os3bjBl+nSCX+23PXMm76fvt717717m/vSTcr5WsSJjR42iRaYLfCdPn+bnBQsIvXsXI0ND2rRuzeRvv8XY2JhnMa+vOzdtWoOHR0Z+Zt4n+LPPnOnbdxBOTkp+njkTwPz5Gfk5der3lCljTVpaGqNHD+D69aukpCTz3nul6NTpI/r2VdrXCxfOsGDBbMLD76Onp4+tbRUGDhxJ3boN1XEU4KLtb68wBP3sWUFHkC+kE/wPi4mJUU9/8fT0ZNu2bWzK55UrX8mnQYS/3cGDBR1B7rxtJ/iflttOcEHLz07w3ymnTnChk8tOcEHL3gkurAwunSnoEHKnkC3ClaP0TnChl8tOcEHLaye4oOS2E1zQ3tQJLixedYILuzd1gguLwtCfzLO33JIwX2Vebv5f7L8zsftfwsPDg71795KamoqFhYV6BWghhBBCCCGEEH8/6QT/w4YMGcKQIbm7B1YIIYQQQgghgP/UwlQFTRbGEkIIIYQQQgjxzpBOsBBCCCGEEEKId4aMqQshhBBCCCFEYSfTofONjAQLIYQQQgghhHhnyOUEIYQQQgghhCjsZCQ438hIsBBCCCGEEEKId4Z0goUQQgghhBBCvDNkTF0IIYQQQgghCjuZDp1vZCRYCCGEEEIIIcQ7QzrBQgghhBBCCCHeGTKmLoQQQgghhBCFnUyHzjcyEiyEEEIIIYQQ4p0hlxP+w3SbNy3oEHKlfYcOBR3Cf4p9ZGRBh5Ar0xMSCjqEXJlZrlxBh5ArzQs6gFyyK+gAculKQQeQSy6jRhV0CLkTGlrQEeTOvyTOYu91KegQcsXhX9IeYZRU0BHkyrMYg4IOIVdKDP64oEPInS1bCjqCvJOR4HwjI8FCCCGEEEIIId4Z0gkWQgghhBBCCPHOkDF1IYQQQgghhCjsZDp0vpGRYCGEEEIIIYQQ7wy5nCCEEEIIIYQQhZ2MBOcbGQkWQgghhBBCCPHOkE6wEEIIIYQQQoh3hoypCyGEEEIIIURhJ9Oh842MBAshhBBCCCGEeGfI5QQhhBBCCCGEKOxkJDjfyEiwEEIIIYQQQoh3hnSChRBCCCGEEEK8M2RMXQghhBBCCCEKO5kOnW9sLp1qAAAgAElEQVRkJFgIIYQQQgghxDtDLifkk6tXr7J27Vrmz5//1scICwvD39+fnj175mNkuVC0KEyaBI0aQVQULFsGBw5opuvZE7p3h2LFIC4ODh2CpUshNVV5vkYN+OYbqFgRHj6En3+GK1fyL04jI+jSBSpXVl7/8GH480/NdE2aQO3aYGGhpDt3Dk6e1ExXsSL07QvHj4Of338+zhdRUUyeOxf/M2coXqwYowcPxsXJSSOdSqXi519/ZduOHQB079KFsUOHoqOjw7MXLxg6fjx37t4lNS0NWxsbxg8fTv3atQHwPXCARe7uPI2MxMDAgJaNGzN1zBjMTE1zH2d0NJPnzcP/3DmKW1gwun9/XNq21R7nihVs271bibNTJ8YOHIiOjg4ADm3aYGxkhE56+k6OjswZNw6AtVu34uHlxfOoKEyMjenUpg3jBw+miJ5eruN8Gw2HDaPOF19QqmZN/ty0CZ9+/f7W19OmSPHiVFu1ipLt25P09CkhEycSsWmTRrrau3dTrEUL9d+6BgbE3bzJ6Vq11I+VHzGC8t98g0GpUiTcu8dlV1fig4LyJU7d4sUpuWoVRu3bk/b0Kc8nTiROS5wABnXrUnzhQgzq1UMVG0vU3Lm8XLQIXUtLSvzyC4atWqFrakrSn3/yfPRoks6cyZcYtdEvXpzaq1ZhmZ6/gRMn8kBL3LoGBlT/5RfKfPQROvr6PPP35+rgwSSEh/89gZmYKHV4lSoQGwu7d8OFC5rp2rSBBg2geHElXUBA1nqnbFno1g3KlIHERKXO0tZevKUX8fFM3rUL/zt3KG5szOg2bXCpUUMj3anQUJaeOMH1R4+wMDLi8PDhWZ6/8egR3+3fz83HjzE1MKBn3boMy/R9/ssKaT2fk1q1oF49ZRApJASOHoW0NM10VlbKqYClJahU8OABnDihhA7Kx9+wIbz3nvLx//7728XzT7RHXr6+eGzdSuj9+5iZmtK5fXtGDx5MkdeMpL2IimLytGn4nzypxDVyJC7OztrjWrCAbdu3K3F168bYUaPU7c+NwEAmT5tGyJ072FaqxJxZs6hWtSoA0dHRzPnxR46dOAHAZz178vXQoepjL1y8mEOHDxNy5w5DBg7M8tybbNq0lt9/X0lCQjxt2jgxfvxMDAwMtKY9e/YkP/88k4iIh1SvXospU36gTJlyACxZ8hMHDvgSE/MSc3MLXF178sUXgwG4dOkco0cPyHKs+Pg45s5dRJs2mp/ha5mawpAhyhf05UvYuBH8/TXTOTtDhw7K+WpCglKGPDwyvsTTpkGFCsoX/PFj2LJFKWv/RTISnG8kJ/NJzZo1/1IHGODBgwds3rz5n+8Ejx0LycnQuTPY2yud1+BguHMna7oTJ8DXF2JilIpo7lzo0QP++EP5e948+OknpXVt1075vUcPpWLLD506KR3u+fOhdGn49FOIiIAnT7Km09EBb2/luRIloFcviI6Ga9cy0ujqgpMThIXlT2z/gjhnzZ+Pvr4+/r6+3AgKYtCYMVS1t8e+cuUs6TZ7e3Pw2DF8PDzQAfqNHIl1mTJ82q0bpsbGzJ08GZvy5dHR0eHQsWMMGTeOgN27KVKkCPVq1WLTb79RolgxYuPimPbjjyz87TemjB6d+zh/+QX9IkXw376dG8HBDJo4kaq2tthXqpQ1zp07Oejvj4+7uxLnuHFKnF26qNP4uLtTsVw5jddwbNqUbh07Ym5mxovoaEbMmIGHpyf9Pv44T3maVy/Dwzk2ezZ2Tk4UMTb+W18rJw5Ll6JKSuK4lRVmdepQx9eXmMuXib1+PUu6y506Zfm7np8fzw8fVv9d9quvKPPVV1x2dib2xg2MK1cm+fnzfIuzRHqcYVZWGNSpQylfX5IvXyY5W5y6JUtSau9eno8aRey2begYGKBnba08Z2ZG4tmzPB89mtTHjzH76itK+frywMYGVWxsvsWaWc2lS0lLSmK/lRUWderQyNeXqMuXickWd6WRIynepAlHatUiJSqKWitWUGPxYs65uf0tcdGtG6SkwPTpUK4c9O+v9HAiIjTTbtyoXMgsWRIGDYLnz+HSJeW5zz+Hq1eVC6AlSsDXX0N4eNZ66y+YtXcv+np6+H/zDTciIhi0eTNVraywt7TMks5EXx+32rXp/P77/BYQoHGcMT4+tKtSBY/PP+dBVBSfrVtHVSsrPqxSJV/iLKz1vDblyysdYB8fpTPboYPS0T11SjOtoSFcvw737imd4BYtwNERdu1Snk9JgRs3lPPwevXePqZ/oj2KT0xk0jffUKt6dZ4/f86Q8eNZvXEjA/v0yTmuOXOUuI4c4UZgIIOGDaOqgwP2dnZZ49q6lYN+fvhs24aOjg79Bg7E2tqaTz/+mKTkZIaOGEHfzz/ns08+4Y+tWxk6YgT7fH0x0Nfn+59+Ij4+nsN79xL57Blf9O9P2TJlcPvoIwAqVqjA2NGj+WPLljzl6alTx/HwWMGSJet4771STJgwHHf3RQwdOlYj7YsXz5g4cTgTJ86meXNHVqxYyNSpo3B3V17TxaU7X301HGNjEx4/juCbb77ExqYyrVu3p06dBhw+fFF9rAsXTjNu3GAaN36Li0z9+ytfqgEDwMYGJk6Eu3c1y8K5c8rFobg4peM8Zgx07KickwKsXav8T1oa2NnB1KkwciS8eJH3mMQ7Q6ZDv4GDgwPLli3Dzc2NDz/8kJMnTzJ//ny6du1K586dCQkJAeD06dN069YNUEZ0P/jgAxYsWEDXrl1xcnLiXPoVqczpsv89a9YsQkJCcHV1ZcSIEQDcvn2b/v374+bmRpcuXfD09MzfN2hkBK1bw8qVEB+vjNyeOKG0ktk9eKB0gEFp2NPSIP1kk5o1ITJSqaTS0mDfPqXyadUqf+LU14dq1ZTjJyfD/ftw65Zy9TC7gAB49EhpwSMj4eZN5SwgsyZN4PZtePo0f+Ir5HHGxcez38+PkQMHYmpiQoPatXFs0QKfvXs10nrv3s2Xn35K6VKlsCpVin6ffopX+miroaEhlStWRFdXF5VKha6uLlEvXxIVHQ1AGSsrShQrpj6Wnq4ud/NwYhcXH8/+Y8cY+eWXmBob06BmTRybNsVHy0iT9/79fNmjB6UtLbGytKRfjx54aXk/2lQoVw5zMzNAuaKvq6PD3QcPch3n2wr08uKmjw9xkZF/+2tpo2tiQik3N0KmTiU1NpYof3+e7NhB6d69X/t/RhUrUqxFCx6uX688oKNDpenTCRo1itgbNwCIv32blHzqBOuYmGDi5saLqVNRxcaS6O9P3I4dmGqJ03z0aOL37SN240ZISkIVE0NKYCAAKXfu8HLBAlIfPYK0NGJWrkTHwAB9B4d8iTM7PRMTyri5cTM9f5/5+xOxYwfWWuI2qVSJJ/v2kfT4MWmJiYRv3kzR6tX/lrgwMFDqoL17ISlJucB57Zoy4pudn59S16elKR26a9cg8wWoEiWUEeRX9dbt20onMB/EJSWxPzCQka1aYWpgQIPy5XG0t8fn6lWNtLXKlaNrzZqUL15c67EevHiBS40a6OnqUqF4ceqVL09w9g7q2yqk9XxOHByUjuvz58ro7blzkD4oqeHePWWkODlZ6ZtcvZr14338WHmr6VX+W/mn2qPPunWjQZ06GOjrY1WqFC5OTlx4zey0uLg49h84wMjhw5W46tXDsXVrfHbu1Ixrxw6+7NOH0qVLY2VlRb++ffHy8QHgzNmzpKSm0rd3bwwMDOjTqxcqlYpTp08DcPjoUfp/+SXGxsZYlytH927d8PT2Vh/7I1dXWrVogWkeZlAB7N7tjYtLdypXtsfc3IJ+/Ybi6+ulNe2RIweoVMmeDz/siKGhIf37f01QUCChoco5bcWKlTE2NlGn19HRJSzsbo6v26ZNhyzpc8XQED74ADZvVr6YN28qX86WLTXTRkRkTEd4df6Z+Yt5717GqLBKBXp6ykU8IV5DOsG5YG5ujqenJ2PHjmXo0KHUq1cPb29vXF1dWbZsmdb/efHiBXXq1MHb25thw4bx888/v/F1pk2bhq2tLT4+PixatIiUlBTGjh3LxIkT8fT0ZOPGjaxYsULd8c4XFSooV7Pv3894LCgo60lPZu3aKVPf9u5VrrZlqrjR0cmaVkdHmSqWH0qWVCq4Z88yHouIUOZsvUmFClmvzltYQJ06yoh1fiukcYbeu4eenh6VKlRQP1bVzo7g27c10gbduUNVe/uMdPb2BGWbFeDy+efUatWKIePH06NLF0qWKKF+7tzly9Rv25Z6H37I/iNH6JuHmQ2hYWFKnJlOEqva2hIcGqoZZ2goVW1ts7yfoGzpeo0cSbNu3Rg+bRphjx5leW7nwYPUc3amcdeuBIaE8ImLS67j/LcyqVIFVUpKlinLMZcvY/qGzleZPn14cfw4CXeVkyBDa2uMypfHrEYNmt27R9Pbt6k0Y4ZmHfCWiqTHmZIpzuTLl9HXEqdh48akPXuGlb8/1hERWO7YgV72TkY6/dq10TEwIDk4OF/izM40Pe7YTHFHXb6stXN7b9UqSjRrhmGZMugZG1OuVy8e79nzt8SFpWVGp/aV8PDcdV4rVVI6ca8cO6Z0nnV1lePa2Ci9onwQ+uwZerq6VMp08lrVyuqtOq99GzXC++pVklNTuR0ZyaUHD2iSU7uWV4W0ns9JiRJK//uVyEhldryh4Zv/t2zZrG8zP/yT7VFmZy9dwu415yShd++iV6QIlWxsMl7PwYFgLedcQSEhVM10Ma2qgwNB6fVKcHAwDvb26qnRAA5VqmQ9jkqV6VcVQflwG8mdO0HY22dc3bC3d+DZs6dERWlenFTSZsRvbGyCtXUF7tzJqBvXr1+Bo2NdXF1bkpAQR/v2mm1kfHwcfn576dSpa94DLlNGOf98+DDjsbt3NS8SvdKsmTLiu3q1Uu8cPJj1+W+/Vebnf/+9Mp3h/+zdeXxMVxvA8d9M9skeS+yCROyxU0uLirVEy6uqRVFrLaWtWqqWbujbUqr2vZWqPWqtpZag1lIlEUssQRCyTtaZef+4k2QmmRCMxsvz/XzmI+6cufPMnbnLOec551r4PT0XbG0L/vGceH4+yVPUtm1bAKoaL2SaN28OQLVq1fg9j7FQGo0mq1zNmjWZOnXqI79vZGQkFy9eZKRJKml6ejqXLl2igsnF/xNxclLGfZlKSlLOkJb8/rvyKFVKSUXJPDueOaMMEgoMVMZGtWqlpNw5OlonTnt7paXQVEqKsvxBXnlFuTDPTOUDpZc7swXf2p7ROLXJybnG5bq6uJCU2bL6gLKuLi5otVoMBkPWSX3TTz+RmprK73v3kp4jvroBARzfuZPo27f5NSSEksWLP1qcOX57rs7Oecdp7M3NLKdNTs6K86cZMwioUoWU1FRmLFrEwDFj2LBwYda43w4tW9KhZUsir19nw44deV44PU9sXVzIyNGFkxEXh62r6wNfV6xnTyK/+CLr/47GDBCvVq34s3p1bD08qLVjB6nXr3Nj4cInjlPt4oIhR5z6uDjUFuK0KVUKl9q1iQ4MJO3vv/GcNo3CwcFEN2liVk7l6krhFSuInTQp17qtxdbFhfR8bt+kiAiSr12j1Y0b6DMySPj7bw7lGNdqNfb2ynHIVErKw2tBrVsrlV3TMdRnz0L37koGkY2NkvVj2oj6BLRpabjkiMnVwYGktLRHXlczPz8+CQlh8eHD6AwG3m/ShBolSlglzmf1OJ8XOzvzcDM3p6WPYapQIaW9w9ptM//m+SjTmk2bOHPuHF+MGZN3XFqt5bgsDJ3QarXm5x+TuJK0Wlxz7PMuJutp2rgx8xctYsqXXxITE8Pa9etJzrl/PobkZC3Oztkxubi4GmNNwt3dPGNCq9Xi6Wl+znN2dkGrzf6sPXv2p0ePfpw/f459+3aarTvTH3/swN3dk1q16j96wI6OSgaieWB5XzeGhiqPYsWUfSlnqvPUqcoxqXp15frTpKFBCEukJzgfHIwnZbVabTbBgFqtJiMjw+Jr8ipnY2ODwWTHTH3AGchgMODp6cnGjRuzHrt37yYwMPCJPo+Z5GRlfIUpZ+fstJO8XL+upNR9ZBxrEh+vtMJ166aM0WjYUElruX3bOnGmpeW+YHNwyD6bW1KvnpKeFhycPXlXxYrKmT/H+DyreUbj1Dg5kZjjRJ6YlISzhcYOjZOT2Uk/MSkJjUZj1qoNyn7xWqtWzF+xgjALrdjeRYvStGFDRo4f/2hx5vjtJWq1+Y/TySkrznoBAdjb2eHm4sK4IUO4fusWF6/kTufyKVUKPx8fJs2Yke84/19lJCZi6+ZmtszWzY2MB4zbd2/cGPtixbi9Zk3WMp3xwuXKtGlkxMWRcuUKUfPmUSjHOOLHpU9MRJUjTpWbG3oLcRqSk9GuX0/asWOQmkrcpEk4Nm5s9nqVoyNFN20i9fBh4qdMsUqMlmQkJmKXz+1bffZs1A4ObPPyYquzMzfXraPB0+oJTkvLfWHp4PDgGlCTJkoNaMGC7OOSRgP9+8OOHcrxftIkJa+2USOrhKmxtycxR0yJqak4P6xymUNscjLvBQfzfpMmnB49mr1Dh3Lg0iV+ttZEOc/ocT6Tn58yxLJfP2U+ofR08/q5nV32x8iLm5vy2gMHzDvqrOHfPh/t3LuX7+bMYcF335kN18n1XhqN5bgspCVrNBrzuBITs+Jy1mhIzBw6ZpRksp5Px4zBwcGB1u3bM3jYMNq3bUsxb+8848rL9u0htGhRixYtajFixHs4OWlISsp+38y/NZq84s8dY86yKpUKf/8qODg4snDhrFzr2bJlA23bdsr1feRLSorSEWPKySl3g11Ot24pDW/vvZf7OZ1OaWQKCIA6dR49pv8HBd0L/Bz1BEsl+F9WunRprl27RlxcHAaDgc2Zg/pRWgpND5zlypXD0dGRDSYpxxcvXsx1cH0iV68qLWeZY3tBSXPOOSmWJTY2Smtbpr/+gr59lZbtyZOVtC/jeMEnFhOj9EiY9tZ5e+eehCRTzZpK6syKFeYTc/n4KPldI0cqj6pVlTEp1pqM7BmN06dMGXQ6HZEmPTZhEREWU8P8ypUjzCRdNCwiItekVKYyMjK4lsd42gydjquPMNbWp1QpJU6TccRhFy7ga5KelhWnjw9hJullYRcv4mehXCYVmDVAPUmc/6+058+jsrXFyWSSF5eAAJIeMKlR8V69uLNuHTqTCz5teDj61NRcKX3WkmGM09YkTvuAANItxJl2+rR5i3/OOOztKbJhA7rr17k3YIDVYrQkyRi3s0ncbgEBJFiI261mTa4tXUr6/fvo09K4PGsWng0aYP80xrHduaMclwoXzl5WooR5mrOp+vWV2ZDmzFHuGJDJy0tJAz52TPk3Lg5OnlTGx1qBj5cXOr2eSJP827Dbt/HNT5qxiWv372OjVtOpRg1s1WqKubnRrmpV9llrKNEzepzPFBGhtF0sWKC0Sd+7Z/7VFy6stHPn1Qbi4qJMfH38uNUy3c38m+ejfYcO8emUKcz95hv8c0xulSuusmXRZWQQadJYGhYejq+FzDu/ChUICw/PLnf+fNbkWb6+voSfP292TAw/fz5rPR7u7nw7dSqhf/zB5g0bMBgM1Khe/YGxWdK6dUd27z7J7t0nmT59IeXK+XHhQnZMERFheHkVztULDFCunB8REWFZ/09O1hIVdZVy5SxvI50ug6ioq2bLoqNvcvLkEdq2fYxUaFBaV2xszIdllC2bv8wSGxtln8uLWm21uQrE80sqwf8yb29vevfuzRtvvEG3bt0oYnJy9/f3p1y5crz22msMGzYMW1tb5s6dy5YtW+jQoQPt27dn0qRJpD1GalieUlKUsUj9+ik9BdWrK9NBWppgqEMH5bYZoJy8e/RQzpKZKlZUDkwaDQwZovQCGyeCeGLp6UqFulkzpRm7dGlltg9Lk1xUq6ZcwP30U+50mT/+gB9+gHnzlMf588okL8YJLZ7XODVOTgQ2a8bMBQvQJidz/NQpdu3fT5CFCdCC2rZlSXAw0bdvE33nDkuCg3nd2MP315kzHDt1irT0dFJSUpi/YgV379+nhnGoQMj27dwwXlhH3bzJjLlzecnS5DsPirNpU2YuWaLE+fff7Dp4kCAL2Q9BrVqxZPVqou/cIfruXZb8+iuvGz9PxOXLnLtwAZ1OR1JyMlPmzKFokSJUKFsWgNWbNxNjnMTpQmQk83/+mZeeZKrTfFLZ2GDj4IDaxibrb9VTvi2TKb1Wy5116yg/eTJqjQb3Ro0oEhTErRUrLJZXOzri3bUrN5cuNV9PcjLRq1ZRdtQobFxccChZkpL9+3M3cwrZJ2TQatGuW4fH5MmoNBocGjVCExREkoU4k5Yswen117ELCABbW9zHjydl/34l5dnWliJr1mBITuZur15PPT1Op9Vyc906/CdPxkajwbNRI4oFBXHdQtyxR49SumdPbN3cUNna4jN4MMlRUaQ9jUnT0tKUGY7atFG6BH18lOOPpZ7R2rWVmY/nzs09GPTOHSWdt3Zt5V9XV6WCZ6WuQo29PYGVKjFz7160aWkcv3aNXefPE2ShgqA3GEjNyCBdp8Ng/DvN2MNarlAhDAYDm86cQW8wcCcxka1nz+JftKhV4nxWj/N5CQ9X2ik8PZWvv04dCAuzXNbZGYKClBFOebWN2dgodQyVKvvvR/FvnY8OHTvGxxMnMuurr7KWPTAujYbAli2ZOXs2Wq2W4ydPsmvPHoIszBcR1LEjS5YvJzo6mujbt1mybBmvBwUBUL9ePWxsbFj+88+kpaXx08qVADRs0ACAq9eucT82Fp1Ox979+1m1Zg2D+vfPWnd6ejqpqakY9HoyMjJITU1Fl5k98ABt2waxadMaLl++QEJCPEuXzqF9+9ctln3llUAuXYpgz57tpKamsnjxbHx9/fHxqYBer2f9+l+Ij1c6a/755zRr166kbt2XzNaxdetGqlevRalSZSy+x0OlpirXiG++qWRS+PsrGRP79uUu26KFkp4ASudLp07ZtyQrUUI5DtnZKT/Ipk2hSpWnl/EnnhsqgzWb78WzJb8paq6uMG6ccvAxvU9wQIBy+4fMe7SOG6fMYunkpJzMd+9WmpozK+WTJinPg3LvhenTlekoH8bSTNSWmN6XMTlZuU/xmTNKj3P37pCZ5jh0qHKwND1pnD6t3Bczp44dlZb5p3Wf4IKIM49xhbFxcYz96isOHjmCh7s7Hw4aRIfWrTn211/0GzmSk8bb3xgMBr6ZPdvsvowfv/8+KpWKIydO8MX06Vy7cQM7GxsqVqjA8P79qVerFgDT585l/ZYtxCck4ObqyiuNGjFy0CA83d1zB5RHylNsfDxjp03j4PHjeLi58WG/fnRo2ZJjp0/T75NPOGlMGTUYDHwzb57ZfYI/HjAAlUrFoRMnmDhjBtF37uDk6EitqlUZNXAgPsaMhzFTp7L3zz/RJifj5e5O62bN+KBPHxwspF1OsnCLpcf1yoQJNJs40WzZHxMnsnfSpCded5OHFwGU+wRXWbwYr8BA0mNiuDB6NNHBwXg0aULA1q3sNRnL5t2tGxWmTOGghR52G1dXKs+fT6H27cmIjeXGggVc/vzzh77/g/tisqk9PSm0eDGOgYHoY2K4P3o02uBgHJo0oejWrVwzidNl4EDcP/0UlUZD6oED3Bs8GN316zi8/DLF9u5Fr9Wa3RT1dtu2pBrv0ZmXx73DuZ2nJzUXL6awcfueGz2aqOBgvJo0ocHWrWw1xm3n5UW1mTMpEhiI2t6ehDNn+GfkSGKPHn2k9+swYkT+CpreJ1irVboIT5xQJr7q31+5NQkox3kPD2Vq4EzHj0NmOryvr3I7vSJFlMrg2bOwfv3Dx7TWrJmvMGOTkxn7228cvHwZDycnPjTeJ/jY1av0++UXTo4aBcCfV67QM8dNauuXKcMK40zchyIj+e/u3UTeu4ejrS3N/fwY16oVTpm5wHmxMAmfRQV8nP+x8Gf5i9MoIABq1bJ8n+Bu3ZSvOCJCyYCvXz/317lggfJviRJK/cNUVFTe9fbB3SzPqvVvnI96vP8+x0+dMjuu1wkIYOH06bkDMo7vjY2LY+z48Rw8fFiJ64MP6NC+PceOH6ffoEGcNI6PNxgMfDN9OmuMd+zo0rkzH5vcJ/jsuXN8OmECFy5dokL58nw5aRJVjBkTW7Zt46tp00hISMCnbFk+GjGCpo0bZ4Uyetw41hs/b6avP/+cNzp14l7ig4cGBAcvYcWKBaSmpuS6T3D37u3p1WsArVsrtxE8cuQg3347mVu3blC1agDjx39N8eKl0Ov1jBzZj7Nn/yYjI53ChYvSrt3r9Oo1wCzt+c032/D2233p2PE/ueLwGpjP2w06O8PgwUoHTGIi/PyzMu63UiUYOxYyb2c1aJDyA3Z0VIbfHT6szCqdnq5UigcPVjIa9XqlUW79esjPsfQRb0P1TDC5w0yBMd4f+/+dVIKfZ1Yap/XU5bcSLPLnaU2uY21WmAjk32DNSvDTlN9KcEHLbyW4oD1uJfjflu9KcEHLZyW4wOW3ElzAHrUSXFDyqgQ/c1xyT/r0LHpYJfhZke9KcEGTSvDjeU4qwZIOLYQQQgghhBDihfH8TPElhBBCCCGEEM+r52h25oImPcFCCCGEEEIIIV4Y0pwghBBCCCGEEM866Qm2GukJFkIIIYQQQgjxwpBKsBBCCCGEEEKIF4b0qQshhBBCCCHEs07Soa1GeoKFEEIIIYQQQrwwpDlBCCGEEEIIIZ510hNsNdITLIQQQgghhBDihSGVYCGEEEIIIYQQLwzpUxdCCCGEEEKIZ52kQ1uN9AQLIYQQQgghhHhhSHPC8ywjo6AjeL44OhZ0BPlz61ZBR5A/kZEFHUG+NCnoAPLpQEEHkE91CzqAfAor6ADyqcz06QUdQr4EfP99QYeQL39OmFDQIeTP7M8KOoL8uXChoCPIF33d+gUdQr5IJ6CQHwxHRLUAACAASURBVIH1SE+wEEIIIYQQQogXhlSChRBCCCGEEEK8MKRPXQghhBBCCCGedZIObTXSEyyEEEIIIYQQ4oUhzQlCCCGEEEII8ayTnmCrkZ5gIYQQQgghhBAvDKkECyGEEEIIIYR4YUifuhBCCCGEEEI86yQd2mqkJ1gIIYQQQgghxAtDKsFCCCGEEEIIIV4Y0qcuhBBCCCGEEM86SYe2GtmSQgghhBBCCCGs6vLly4wePZrY2Fg8PDyYOnUqPj4+ZmVmz57Nli1bUKvV2NnZMWLECJo2bQrA6NGjOXjwIJ6engC0adOGQYMGWSU2qQQLIYQQQgghxLPu/6wneMKECXTv3p2goCA2btzIZ599xvLly83K1KhRgz59+uDk5ERYWBjvvPMOBw4cwNHREYD+/fvzzjvvWD02GRMshBBCCCGEEOKh4uPjuX79eq5HfHy8WbmYmBjOnj3La6+9BsBrr73G2bNnuXfvnlm5pk2b4uTkBIC/vz8Gg4HY2Nin/jn+v5oTxNPh5gaffgoNGkBsLPz4I2zfnrvcW29B167g7g7JybBzJ8ycCTqd8nz16jByJPj4wI0bMG0anDplvTgdHaFjRyhfHrRa2L0bzpzJXe6llyAgQIlTq4Vjx+DQoeznhw0DZ2cwGJT/X7sGP/9stTBjU1MZt38/oVFReDo4MLJePTpUqJCr3MLTp9kQEUFUUhKeDg50r1yZ92rUyHq+x5YtRNy/T5pORylXV4bVrk3LsmUfP66EBMbNnk3oX3/h6ebGyHfeocPLL+cqZzAY+O+KFaz5/XcAugQG8lGPHqhUKo6dPUu/zz83K69NSWHmqFG0fukl0tLT+e+KFWw5cIDUtDTaN23KuL59sXuClsvYpCTGrVpFaHg4ns7OjGzfng516uQqdzgigtk7dnD2+nXcnZzY/dlnFtd35MIFesyezcDAQEa0a/fYceVk6+lJ5UWLKNSqFWl373JxzBiig4NzlQvYsgUPY5oPgNreHm14OH+afPelhw2j9AcfYF+0KClXr3IqKIjkiAirxfow9d5/n5rvvkvR6tU5ExzMxt69/7X3zqTy9MRp0SJsW7XCcPcuKWPGkG5hezpMmIDDuHGQmpq1LKFGDQyXLyv/UatxmDQJ+z59ULm6or9wgcTmzSEuzipxOnp60mbRIsq2akXy3bvsHzOGc5bidHenxfffU65tWwD++vFHDk6aZFam9rBh1PngAzRFi5Jw9Srrg4K4b6Xv3cbTk9KLFuHSqhW6u3e5OWYMsRbiBHCqVYsSM2bgVLs2+qQkbn/1FXdnzlQ+b0AAJWfNwqlGDXQJCcTMm8ftL76wSowAsSkpjNu9m9Br1/B0dGTkSy/RoWLFXOUWnjjBhvBwohIS8HR0pHu1arxXuzYAMVotXx44wJGoKJIzMvDz8mJM48YEFCtmtThtPD0pv2gR7q1akXH3LtfGjCHGwvb037IFV5P9XWVvT0p4OH/XqIFtkSKU/f573F55BbWzM8lnznBl5EiSjhyxWpyZatSA2rWVTqSLF2HvXtDrc5fz9ob69aFIEeUUGRUFBw4op1KAEiWgXj0oXFjZ5X76yeqhAhCbmMi4hQsJ/ftvPF1dGdm1Kx0aNcpV7vDZs8zesIGzkZG4Ozuze/p0q7z/0qVLWbRoAcnJybRu3ZoJEyZhb29vseyhQ4f4/PNJ3Lx5kxo1avDVV1MoWbIkAGlpaUyaNIHt27fj5ORE377v8e67yvH0r7/+YubM7zl79h/UajX169dn7NhPKVq0KKBUNL766kv2798HwFtvdefdd4c+MO6VK5eybNkCUlOTadGiNaNH5x33kSOHmDZtErdu3aRatRpMmDCF4sWVuGfOnMb27ZtJSkrA1dWdN954k969B2a99ssvx3PixBGuXbvC+PFf0aHDG4+wdU04O8OgQcoPNCEBVq6E0NDc5dq3hzZtwNUVUlKUa7oVK7J/xJ99BmXKKD/w27fh11+Vaz/xVCxbtowffvgh1/IhQ4YwdGj2b/TmzZt4e3tjY2MDgI2NDUWLFuXmzZt4eXlZXPeGDRsoU6YMxUyO10uWLGHVqlWULl2aDz/8kAoWrqkfh1SC/4/MmjULrVbLJ598Yt0Vf/wxpKcrB5iKFWH6dIiIgEuXzMvt2webNkFiolJxnjIF3nxTOWi5ucF33ynL9uyBVq3g22/h9deVA5s1tGunVLi//RaKFVMq5dHRcOeOeTmVCjZsUJ7z8oK334b4ePjnn+wyv/wCmRfJVjb54EHs1GpCu3fnXEwMA3bsoJKXF37G8QyZDMDUV17B38uLq/Hx9N22jeLOzrQ37tzjGjbE18MDW7WaU7dv8+62bWzv0oWiGs3jxTV/Pna2toQuWcK5y5cZ8OWXVPLxwa9MGbNyq3bsYOeff7Jx+nRUKhW9J06kVNGivNWmDXWrVOGkyYXen2fOMPDLL2laqxYA89et48yFC/z2/ffo9XoGfvUVc1avZthbbz1WzACT167FzsaG0MmTORcVxYAFC6hUogR+xYubldPY29O5fn1eq1WLeTt3WlxXuk7Hl+vXE/AEjQl58Z89G0NaGvu9vXGpWZOamzeTeOoUSWfPmpU7laPiXXvPHu7v3p31/xJ9+1K8b19OtW9P0rlzOJUvT/r9+1aP90ESbtxg3xdf4Nu6NbbG1tl/m6Nxe8Z7e2NTsybOmzejO3UKfY7tCZC+ahXJPXpYXI/DpEnYNmpE4ksvYbh6FXXVqspFlJW0nD0bXVoaP3p7U7RmTTpv3sztU6eIyRFn8+nTsdNomO/jg6ZoUd7ctYv4K1c4s3QpANX79qVG376sa9+emHPn8ChfnhQrfu8ljdvzrLc3TjVrUm7zZpJPnSI1R5w2hQpRbts2bowYQdyaNajs7bErVSrr+bIrVxK3fj0XmzXD3scH3wMHSDl1ivhNm6wS5+R9+5T9vXdvzt29y4DNm6lUqBB+hQqZlTMAU1u2xL9QIa7GxdE3JITirq609/NDm55O9aJFGd24MYWcnFhz7hz9N29md48eOOdRIXhUPsbtecLbG03Nmvhv3oz21CmSc2zP8Bz7e+U9e4g37u82Li4kHT3K1ZEjSb99myJ9++K/eTN/+figT0qySpwApUsrFeCNG5XKbJs2SkX38OHcZR0c4OxZuHpVqQQ3bQotWsBvvynPZ2TAuXNKXcPY5vBUTF62TDlfzZ7NuStXGPDtt1QqUwY/k98igMbBgc4vv8xrDRsyz0q/wQMH9rNw4XyWLFlG0aJFGTp0CLNmzeTDDz/KVfb+/XsMGzaEzz//gubNWzBz5gxGjhzBqlW/AvDDD7O4cuUKu3bt4e7du7z7bk8qVKhA06YvEx8fR9euXWnSpCk2NjZ88cVkxo0bw4IFiwCYMuVrUlKS2blzN/fuxdC797t4eZWgY8fOFuM+dGg/y5bN58cfl1GkSFE+/ngI8+bNZOjQ3HHHxt5j1KghfPrpFzRt2oK5c2cwduwIlixR4u7YsQv9+g3ByUnD7dvRDBnSh7Jly9OiRSsA/PwqERjYjh9++ObJNvZ77yk/qn79lA6UMWPgyhW4ft283LFjyrWlVqtUnD/8ENq2hc2bleeXLlVeo9eDry+MHw/DhysdO8+bZyAdulevXrz++uu5lru5uT3Reo8cOcL333/P4sWLs5aNGDGCIkWKoFar2bBhA++99x47d+7Mqlg/CUmHftE5OipnuHnzlN7dU6eUyq6xt8JMVJRSAQaloqnXQ+YJqUYNiImBXbuU5du2KQef5s2tE6edHVSurBwE09OV3tvz55X3zengQbh1SzmDx8RAeLhyFfAv0KansyMykuF16uBsZ0fdYsVoUaYMGy9cyFW2X40aVC1cGFu1mvIeHrxatiwnbt/Oer6Slxe2amUXValUZOj13HrMCyNtSgo7Dh9m+Ftv4ezkRN0qVWhRrx4b//gjV9kNe/bQJyiIYoUL412oEL2Dgli/Z4/F9W7Ys4c2jRqhMY7b2H30KD3at8fD1RUvd3d6tG/P2l27HitmAG1qKjtOn2Z427Y4OzhQt3x5WlStykYLLbw1ypalU716lM5xsWxqyZ49NPb3p7yxpd1a1BoNRTt35uL48eiSkogLDeVOSAjF8qiYZXIsWxaPpk25mTk+RqWi3IQJRIwYQdK5cwAkX7pExr9cCQ5bv57wjRvRxsT8q++bRaPBrnNnUsePh6QkdKGhpIeEYPeQ7ZmLhwcOH3xAcr9+GK5eBUD/zz9mvcZPwk6joWLnzhwYP570pCSiQkO5EBJCVQtxVujQgSPTppGRnEz8lSucXrSI6n36KE+qVDSaMIHdI0YQY/zeYy9dslolWK3R4N65MzfHj0eflERSaChxISF4WYizyMiRJGzfTuzKlRjS0tAnJpIaFpb1vL2PD/d//hn0etIuXSLpwAEcq1a1Spza9HR2XLzI8AYNcLa3p26JErTw8WHj+fO5yvarXZuqRYoox09PT14tV44TN28CUNrdnd41a1LU2RkbtZo3q1YlXafjspUuiNUaDV6dO3PduD0TQ0OJDQmh8EN+n/Zly+LatCl3jPt76uXL3Jo+nfRbt0Cv586CBajt7XH097dKnJn8/ZWK6/37yk//2DGoVMly2atXlZ7i9HSlbvL330qbc6bbt5VTb46sR6vSpqSw4+hRhnfujLOjI3X9/WlRuzYbLfQQ1qhQgU5NmlDaisf0DRs20LlzF/z8/HB3d2fQoMFs2LDeYtnff/8dX18/2rRpi4ODA++/P5Tw8DAuXbpoXNd6Bg0ajLu7OxUqVKBLl/+wfr2yrpdffoU2bdri4uKCk5MT3bu/w4kTJ7LWvWfPbvr2fQ8nJydKlixF585d2LRpbZ5xb968gY4du1Chgh9ubu707TuY336zHPfu3b9TvrwfLVsqcffvP5SIiDAiI5W4fXzK4+SU3eCuVqu5fv1K1v+7dn2b+vVfwt7eIZ9b1QIHByUDcdUq5YcZHq78OC1kqBEdnZ2OkHn9afrDvHo1u1fYYAAbG3jAtYB4Mm5ubpQqVSrXI2cluHjx4kRHR6MzZozqdDpu375N8RydGAAnT57k448/Zvbs2ZQvXz5rube3N2rjtXCnTp3QarXcunXLKp9DKsFP2fbt22nTpg2dOnVi7ty5+Pv7k5SUxL59++jUqRMdOnSgV69eXLmiHFzu3LlDjx49eOONN2jfvj3Tpk17ugGWKaP0rhovEAGlF9jkB2imdWslDfn338HPD9ZbPsBmsVLKAoUKKQc403EE0dFKztbDlCmTu7f49deVlsS331byv6wkMi4OG5WKcu7uWcsqFSrEhYdczBoMBo7duoWvh4fZ8gE7dlB96VL+ExJC/WLFqFa48OPFdeMGNmo15YwpWgCVfHy4cO1arrIR165RyWTmvko+PkSY/j6MtCkpbDt4kE45GjoMOT7XrZgYEh6z8h55544St8kFTqWSJbnwGAfAqHv3WHvkCO+3bv1YsTyIpmJFDBkZZinLiadO4fyQykHxnj2J3b+fFOP+71CqFI6lS+NSrRqNr16l0aVLlJs4UTnpv0DUFStCRgZ6k+2pO3UKmzy2p12HDrjGxOBy5gz2A7NT9myqV4eMDGy7dMH15k1cwsOxHzzYanF6VqyIPiPDLGX5zqlTFMrrezf5HlUqFYWrVQPAtVQp3EqXpnC1agy4epV+ly7R2Irfu4Nxe6aZxJly6hQOFuLUNGyI7t49fENDqRIdjU9ICHYmjYh3ZszAq2dPsLXFoWJFNC+9REIemRePKjI2VtnfTY6DlQoX5kKO8WM5GQwGjt28iW8e6XXn7twhXa+nrMlx+Uk4Gvf3FJPtmXTqFE4P2d+L9OxJwv79pF25YvF5TUAAKnt7Ui00mj4JLy+lPThTTAxoNEod5GFKlDA/7f4bIm/dwsbGhnImF8qVSpfmQs7ewafkwoUI/P2zWwkqVfLn7t273LdwHlfKZjdaaDQaSpcuw4ULF4iLi+POnTs51lWJC3l8v8eOHcXX189smcFg+reBixfzHh5x6VIEfn7Z71Wxoj/37t0lNjZ33JcuRVCxYnbcTk4aSpYsw6VL2bEtXTqfl1+uRfv2L5OcrKV16w55vvdjKV5cuf40Nl4BSi9wXp0WjRsrPb6LFyu9xjmPO598ouTnf/21ks6QM5vxeWFrW/CPfCpUqBCVK1fmN2MqyW+//UblypVzpUKfPn2aESNGMHPmTKrmOI5GR0dn/b1//37UajXeVrpuL/g+9efY3bt3+eyzz1i1ahU+Pj4sNaa9xcXFMWrUKH766Sd8fX1ZvXo1H330EatXr8bNzY25c+fi7OxMeno6ffv2Zd++fbxsqWXMGjQayFlBSUxUlluyfbvyKF1aSU/OPDv+/bdSIW3VSukNbtNG6SXOz1k2P+ztc/fepKQoyx/klVeUC8m//spetn599kG3QQOlIjx7tlV6h7QZGbjkiMnVzo6k9PQHvm7WyZPogc45xr7Na9WKdL2eg1FRXIqNRf2YF8XalBRccnynrhoNScnJDy3rqtGgTUnBYDCgMnn/HYcP4+nmRn2TA1bTWrVY/ttvNKxWDZ1ezwpjqlJyaiquzs6PHndqKi7GXuaseBwdSXqM7+qL9euzepStzdbFhYwcXSMZcXHYuro+8HXFevYk0mQ8paMxs8KrVSv+rF4dWw8Pau3YQer169xYuNDqcT+rVC4uGHJsT0NcHCoL2zP9119Jmz8fQ3Q0Ng0aoFm7FkNsLOm//IK6VClUHh7YVKxIQrlyqP38cN61C/3582RYoeJm5+JCWo44U+PisLcQZ+S2bTQYPZotvXrh7O1N9T59sDXuZ67G792nVSuWVq+Og4cH/9mxg4Tr1zlthe9d7eKCLkecurg4bCzEaV+qFJratbkYGEjK339TfNo0ygYHc6FJEwDif/uNMsuXU+Sjj1DZ2nJr0iSSrTT2TpuejoudndkyV3t7ktLSHvi6WUeOoDcY6Fy5cq7nEtPSGLVzJ0Pq1cPVSvu+zSNsT1OFe/YkKo/x0zaurlRYsYKoSZNyrftJ2dmZn94yN6el06qpQoWgbl3YutWq4TyUNjUVlxzDMFw1GpKsOIzhge+v1eLq6pL1fxcXV+PypKzbtGRKStLmuqB3dXUhKSkJrbHn0tXkd+Hi4kqShUbh8PAw5sz5kR9++DFrWdOmTVmwYD5Tpkzh7t0Y1q1bS0pK7nO2adwuLpbj9vAwjzs5WYuHh3ncLi4uZrG9+25/evXqx/nz5/jjj51m67YKR0clA9H8QyjLLQkNVR7FiinXdjkzO6ZOVXqAq1eHkiXNWxBEgZk4cSKjR4/mxx9/xM3NjalTpwLQr18/hg0bRvXq1Zk0aRIpKSl8ZjKXy7Rp0/D39+eTTz4hJiYGlUqFi4sLc+bMwdZKKeFSCX6KTp06RZUqVbLuh9W5c2e+/vprwsLCqFSpEr6+vlnLJ02aRGJiImq1mmnTpnHy5EkMBgN3794lLCzs6VWCM8dXmHJ2zk47ycu1a0or26hRSutbXBx89JEyBuPjj5XBRkeOKLlT1pCWlrtC7eCQfTa3pF49JV166dLsybsyY88UGqpMolW2rJLj9YQ0trYk5ogpMT0d5xwXdqZ+OnuWDRERrHztNewtjHGwU6t5pXRplv/zD2Xc3Hj1McazahwdSczxnSYmJ+NsYbynxtGRJJOyicnJaBwdzSrAoKRCd2rWzGz5oC5dSEhKImjkSOzt7OgaGMi5y5cpnKOHO99xOziQmOPCJzEl5ZErsrvPnCEpJYV2xrHL1paRmIhtjjQgWzc3Mh4wHt69cWPsixXj9po1Wct0xguCK9OmkREXR0ZcHFHz5lGoXbsXqhJsSExElWN7qtzcMFjYnnpj+jCA7tAh0r7/HrsuXUj/5RcMxu2ZMnkypKSg//tv0n/5Bdt27axSCU5PTMQ+R5z2bm6kWYhz17BhvDprFv0iIkiOieFccDCVjWPlM4xxHpk2jdS4OFLj4jg1bx7l27WzSiVYn5iITY44bdzc0FnansnJxK1fn1WxjZ40iWoxMajd3FDZ2FB+2zaihgzh/sqV2BUrRtk1a8iIjiZmzpwnjlNjZ0dijgbDxLS0B47j/en0aTaEh7PyjTdyHT9TMjIYuHkzAcWKMcDCZHqPS/cI2zOTS+PG2BUrxj2T/T2TytGRips2kXj4MDemTHni+Pz8oFkz5e8bN5TUZtNNmHk6etDp081NmYvowAHzjrp/g8bBgcQclaPE5GSc86ocPaGQ0FAmDBgAQJ06ddBoNCRmDv2CrL81mtwNuc7O5mWV8kk4OzujMTZyJSYm4mA8ZyUlJeKc45rrypUr9O/fjzFjxlK3bt2s5WPHfsqXX35OmzatcXf3oF279vz22+as57duDeHrrycAULOmEndSUv7idnIyL6vElpQrNpVKhb9/FQ4dOsD8+bMYMWJMrnU9tpQUyHkN4uT08Dkbbt1SruPee0+ZI8aUTqd0erRrp5Q7ftx68YrHUqFCBVavXp1r+YIFC7L+Xrs27zT/zA7Ep0HSoZ8xS5YsIT4+ntWrV7Np0yZatmxJqpXGr1l09arScmaaflKxYv7SSGxssscEA5w8Ce++C4GBMHGikq5iYRKbxxITA2q1kteVyds7d5pzppo1ldSZFSsePjGXFVsLfdzd0RkMRJrMPBt27x6+OVqPM605f575p06xrG1bij2kp1RnMHDtMScZ8ylRAp1eT+SNG9lxRUbiayHtyK90acIiI7PLXb6ca/Ksm3fvcuTMGTplXmkZOTo48Fn//uxftIhdc+fi4epK1fLls8ZzPHLcRYoocZt8z2E3buD7iLO8HoqI4My1azT+7DMaf/YZW/76i+V79zJo0aLHiisn7fnzqGxtcTI2bAG4BASQZDoZWw7Fe/Xizrp16Exa3rXh4ehTU81+k4YXsDVbf/482NqiNtmeNgEB6B6wPTMZDIasNGL96dOZC00LWC3O++fPo7a1xcMkzqIBAcRYiDPl/n02v/MOPxYvzpJq1VCp1dw0zgJ8LzycjBzfuzXjTDVuT3uTOB0DAki1EGfy6dN5xmFfvjwGnY77K1aATkd6VBSxv/yCm5VmWffx8FD2d5MenrCYmDzTnNecPcv8EydYFhREsRy9VGk6He9v2YK3szOTcxynnlSKcX93MNmemoAAkh/w+yzSqxf31q3LNeGVyt6eihs2kHb9OpeNFbEnFREBCxYoj82blYQt05E0hQsr7dx5XVq4uCg3Yjh+3Cptw4/Mp1gxdDodkSbDXsKuXsU3x6RY1tKxcWOOHz/J8eMnmT9/Ib6+foSHh2c9Hx4eRuHChXP1AgPGstlj5rVaLdeuXcXX1xd3d3eKFClCmMmY+rCwsKwOEICoqCj69OnNoEGDCQrqZLZuDw8PvvnmW/bvD+W33zZjMBioWjV7HpS2bTuyb99J9u07ycyZCylf3o+IiOy4IyLC8PIqnKsXGDCWzY4rOVnL9etXKV/eN1dZAJ0ug+vXcw+LeiI3byrXkabn87JlzTsq8mJj8+ChbGq1+XqfJwWdCv0MTMxlLVIJfooCAgI4e/YsV43jKTMnQ6hcuTJhYWFcvHgxa3mVKlVwcXEhISGBIkWK4ODgQHR0NLueYFKhfElJUSab6t9fSUGpUUOZlMBS/lNQEGSeBMqVUyq8R49mP1+xonJgcnZWbkMUHW15+snHkZ6uzOzRrJnSjF26tDLbR+ZFrqlq1ZTJvn76KXe6jJub8lq1Won1pZeU1O/8HHTzQWNnR2DZssw8cQJtejrHo6PZdeUKQSYnvUwhFy4w/dgxlrRtS+kcvQoXY2PZe+0aKRkZpOv1bLxwgWO3blHvMQ/qGkdHAhs2ZGZwMNqUFI6fO8euI0cIsnBxGNSsGUtCQoiOiSH63j2WhITweo5xvxv/+INalSpRJsfkBpmvMRgM/BUezo+//srQJ5gZWuPgQGCNGszcuhVtairHL11i15kzBJm0lmfS6/WkpqeTrtdjAFLT00nLyABgeNu2bB87lg0ffcSGjz6iRdWq/KdhQ77u1u2xYzN7b62WO+vWUX7yZGUSokaNKBIUxK0VKyyWVzs64t21KzdztHDqk5OJXrWKsqNGYePigkPJkpTs35+7mVOz/ktUNjbYODigtrHJ+ltlhZkY802rJX3dOhwmTwaNBptGjbALCiLdwva07dgRjJkGNvXq4TBsGOkbNwKgv3SJjH37lFso2dujrlQJu27dSLfS9kzXajm/bh1NJk/GTqOhZKNG+AYF8Y+FOD3Kl8fRywuVWk25Nm2o0b8/h4ypsRnJyYSvWkX9UaOwc3HBpWRJavTvz0UrxanXaolbt45ixt+nplEj3IOCuGchzvtLluD2+us4BgSArS3e48eTuH8/+vh4Us+fR6VS4fHWW6BSYevtjcebbyoVZyvQ2NkRWL48M48cUY6fN2+y6/JlgizcIikkPJzphw+zpGNHSucY65uu0zFs2zYcbG2Z2rLlYw8jyYteq+X+unWUMm5Pl0aN8AwK4m4e+7vK0RGvrl25m2N/V9na4rdmDfrkZC726vXU0jfDw5V5JT09lR7hOnXApF5mxtlZOdWfOWN+QwVTNjbKKVSlyv7bmjSOjgTWrcvMtWuV89X58+w6cYKgxo1zldXr9aSmpZGu02EwGEhNS8s67j+uoKAg1q5dw4ULF4iPj2fu3Dl06pR7JlyAli0DiYiIYMeO7aSmpvLjj7OpWNGf8uUrGNfViblz5xAXF8elSxdZvXp11qy60dHR9O7di7fffptu3XKfJ69evcr9+/fR6XTs27eXX39dRZ8+g/KMu127IDZuXMOlSxdISIhn8eI5vPaa5bibNw/k4sUIdu9W4l64cDZ+fv74+FRAr9ezbt0vxMfHYTAY+Oef06xevZJ69V7Ken16ehqpqakYDAYyMjJITU1Fb+meWw+Smgp//qncZcTBQbmmRHRjLQAAIABJREFUq1dPmZw1pxYtlOs3UFKdO3XKvkVmiRJKx4ednfKDbNoUqlSxXieMeG6pDC9iN8O/aOvWrcyYMQMnJyeaNWvGwoULOX36NAcOHGD69OlkZGTg5eXF5MmTKVu2LFFRUQwfPpyUlBS8vb3x8PDAx8eHoUOHPvotkurXz185NzdlOvn69ZW05tmzlXG/NWvCjBnZeVXjx0OjRkql8f59ZezvvHnZOVWff670voJyD7f//lcp9zDGm2g/lOl9gpOTlfc/c0aZ+Kp7d+X2TABDhyqfyTQF+vRp2LJFGbf8xhvK1UBGhlJR37kzf/le+UzFik1NZey+fRy8cQMPBwc+NN4n+NitW/Tbvp2TvXoB0GLVKqKTksxS+Dr4+jK5cWMuxsYyet8+LsTGYqNSUdbNjYEBAQSaTFiVpzy2Z2xCAmN/+IGDp07h4erKhz160OHll7Pu/Zt56yODwcA3y5ezxpgu2qVlSz7u2dMs7bnNkCH07dSJ/7RsafYeR//5h0++/56YuDiKFS7M+1270vGVVyzHadLb/CCxSUmM/eUXDp4/j4dGw4evvUaHOnU4dvEi/ebP56RxfMmfFy7Qc/Zss9fWr1CBFUOG5Frn6JUr8fbwyNd9gne1b5+vOG09PamyeDFegYGkx8RwYfRoooOD8WjShICtW9lrMi7Mu1s3KkyZwkEL36eNqyuV58+nUPv2ZMTGcmPBAi7nuDezJQfyFWX+vDJhAs0mTjRb9sfEiezNcV/bx/FBPsupPD1xWrwY28BADDExpIweTXpwMDZNmuC8dSvxxu3ptHIltq1aoXJwQH/9Omk//kjarFnZ6ylRQrnfcJMmGG7fJnXqVNLmz3/o+z+8hMLR05M2ixdTNjCQlJgY9o0ezbngYEo2aUKXrVv53hin/3/+Q4sZM3Dw8OD++fPs/eQTInfsyFqPvasrrebPp0L79qTExnJ6wQIO5eN7b5XPOG08PSm9eDEugYHoYmK4OXo0scHBODdpQrmtWzlj8vssNHAg3p9+ikqjIenAAaIGDybdOCmRS/PmFJ86FYeKFdEnJxO/aRNRw4dnpZ7nJeD77/MVZ2xKCmN37+bgtWt4ODryofE+wcdu3KDfpk2cNPaWtli+XDl+mtTAOvj7M7lZM45ERdFjwwYcbW3NWvoXdOhA3RIlHvj+fw4fnq84bTw9Kb94Me6BgWTExHBt9GhigoNxbdIE/61bOWa6Pbt1o/SUKfyVY393ffllquzdi06rNbtpb3jbtiQcePAefXz2o126BQRArVqW7xPcrZvS6xsRoYwBrl9faXc2lZm5WKKEUv8wFRWl3H7JksF1H++ex7GJiYxdsICDZ84o5yvjfYKPhYfT75tvOGkcJvDnuXP0/Oors9fWr1SJFePGPdL76euaXy8tXbqEhQsXkJKSQqtWrZk4Mft+u6+91p4BAwbQoUNHAA4ePMgXX0zmxo0b1KgRwNdff03Jkkqvtel9gh0dHXnvvX5Z9wmePfsHfvhhVlbadKbjx08CsHXrFr7++isSEhLw8fHhww8/ombNpjzIzz8vYfnyBaSmptC8eWvGjMmOu2vX9vTuPYC2bZW4//zzIN98M5lbt25QtWoAEyZ8TYkSpdDr9Qwf3o+zZ/8mPT2dIkWK8tprr/PuuwOyrgMGDOjBiRPm3+3cucupU6cBAG7vdc3fhnd2hsGDlXG8iYnw88/KMLVKlWDsWOjZUyk3aJDyA3Z0VKYmP3xYmVU6PV2pFA8erGQm6vXK9dz69eadNHn59df8xfks+e67go4ARo4s6AisQirBT1liYmLWZAJr165lzZo1BJvcZ/Wpym8luKDltxJc0J7SeCSr+3/ZnvmsBBe0/FaCC5o1K8FPU34rwQUtv5XggpbfSnBBy28luKDltxJc0B61ElxQHrcS/G/LWQl+VuUYfvzMyncluKBJJfjxPCeV4OcnsfsZtWLFCrZt24ZOp8Pd3Z0v8pgZUgghhBBCCCHE0yeV4Kds0KBBDBqU9xgOIYQQQgghhHio52hiqoImE2MJIYQQQgghhHhhSHOCEEIIIYQQQjzrpCfYaqQnWAghhBBCCCHEC0MqwUIIIYQQQgghXhjSpy6EEEIIIYQQzzpJh7Ya6QkWQgghhBBCCPHCkOYEIYQQQgghhHjWSU+w1UhPsBBCCCGEEEKIF4ZUgoUQQgghhBBCvDCkT10IIYQQQgghnnWSDm010hMshBBCCCGEEOKFIZVgIYQQQgghhBAvDOlTf56dOVPQEeSPh0dBR5A/775b0BHky1mqFHQI+eLb8v8kzoIOIJ/qFnQA+TSjoAPIp0YFHUA+VSroAPJp+/DhBR1CvrT+8suCDiFf4v5fDkz/J+d39d3bBR1C/jgWLegI8ic2tqAjeH5JOrTVSE+wEEIIIYQQQogXhjQnCCGEEEIIIcSzTnqCrUZ6goUQQgghhBBCvDCkEiyEEEIIIYQQ4oUhfepCCCGEEEII8ayTdGirkZ5gIYQQQgghhBAvDGlOEEIIIYQQQohnnfQEW430BAshhBBCCCGEeGFIJVgIIYQQQgghxAtD+tSFEEIIIYQQ4lkn6dBWIz3BQgghhBBCCCFeGNKcIIQQQgghhBDPOukJthrpCRZCCCGEEEII8cKQSrAAT0/45Re4cwfCwqBr1weXt7ODEycgIsJ8+SuvwMGDcOsW/PMP9Olj3ThdXWHCBAgJgRUroHlzy+XeeAOWLYP16yE4GAYOBHWOn3qnTrB8ubKuhQuhZEmrhRmbnMz7q1ZR86uvaD5jBpv+/ttiucOXL9Nj2TLqTJlCixkzcj1/7tYtui9ZQp0pU3j5u++YvXevVeILCVlK796N6d69NrNmjSE9PS3PsqdPH2LIkDa8+WYA48f34PbtqKznEhJi+e9/P6BHjwb07NmA6dM/RKtNNHv9pk3LGDCgBd261WTIkLZERV3O872WL19Ks2aNadiwNuPHjyEtLe+4Dh8+RIcObahXL4A+fXpw40Z2XGlpaYwfP4aGDWvTrFljli1bYvbatWtX065dIPXr12LgwL7cvh1tFkObNq/SsGFtWrRowtSpX6HLMwpzak9PiqxbR+nEREpGRqJ56608y9rXqoX33r2UTkig1K1buA4bpqyjSBEKr1xJyagoSsfG4n3gAPb16+czgvxReXqiWbcOt8REXCMjscsjTocJE3BLS8MtISHroSpXzuQDq3H4/HNco6Jwi4/H5cQJcHe3aqwPU+/99+l39CjjUlIIWrLk4S94Cmw9PQlYt44WiYk0iYykWB7bs9aWLTRPSMh6vJqaSsPTpwFwLF3a7LnmCQkEGgyUHTnSeoF6emK7bh32iYnYR0aiziNOmwkTsE9Lwz4hIeuB8XtX+flhu2ED9rdvYx8Tg922bagqVrRejICdpyc1163j1cREXo6MpHgecdbesoVXExKyHoGpqTQy2Z6mz72akEBra29PR0fo3Bk++gjefx+qVLFcrkED6NcPPvwQBg9W/m9JmTIwdqxyHn0Kdu9eytixjfn449r8/HPex/2MjDQWLRrGhAktGDrUn4iIP/Ms98UXbRk//uXHiic2IYH3v/ySml260LxPHzb98YfFcgaDgW+WLqVB9+406N6db5YuxWAwAHDsn3+o9Z//mD38O3Rge2goAJv37aP1wIHUefNNXnrnHT6ZPp1ErfbR4oyP5/2xY6kZGEjzLl3Y9Pvvecc5Zw4N2renQfv2fDNnTlacpjZs24Z/06as3rQpa9nClSt5rWdParVqRYuuXVm4cuUjxWhq5cqltG7dmGbNajN58oPPo0eOHKJLlzY0aRLAwIE9uHkz+zw6Y8ZU3nijFa+8UosuXdqwefMGs9fqdDrmzJlO27ZNeOWVWrz9dicSEuIfPWBrXNcVKQIbN5o/duxQ9k8hHkAqwf+i4OBgli5d+livXbp0KTExMdYNKNP06ZCWBj4+0Ls3fP89VK6cd/kRI+DuXfNltrZKRXrRIihWDHr2hClToHp168U5ZAikpyuV9ClTYNgwKFs2d7lDh5SLjddfh/79oXx55e9Mbdooj08/hY4dYfx4iH+Mg3ceJm/Zgp2NDaEffcQ3b7zBxM2bibh9O1c5jb09nWvWZFRgoMX1fLh2LfXKlOHIqFH89O67BB87xq7w8CeK7eTJ/axbN59Jk5Yyf/4eoqOvExw802LZ+Ph7TJ06hLfeGs6KFUeoUKEa3347Iuv5lStnkJgYz7x5u5gzZyexsTH88susrOd//301u3at4dNP5xMcfJJx4+bh5uZp8b1CQ/ezaNF8Fi5cyvbte7h+/TqzZ1uO6/79e4wYMYQhQ4Zz4MARqlatxscfZ8f144+zuHr1Ctu372Hx4uUsWbKQAwf2AXD06J/MnPkdM2f+SGjon5QsWYpRoz7Mem3z5i349df1HD58gvXrf+P8+TDWe3jka9t6zZ6NIS2N697e3H37bQrNmYOdhQtjdaFCFN22jcR587hWqBBRvr4k79ihPOfiQurRo9yqU4drXl4kLVtG0c2bUTk75yuG/HA0xhnv7Y327bdxmjMHdR4X8OmrVhHv6pr1MFzObsRwmDQJ20aNSHzpJeLd3ND26AEpKVaLMz8Sbtxg3xdf8Nfixf/q+5qqPHs2+rQ09np7c+btt6k0Zw7OFrbnyXbt2OPqmvWIPXiQ26tXA5By7ZrZc4eqV8eg0xG9dq3V4rSdPRvS0kjz9ib97bexnTMHVR7fu37VKtJcXbMeZH7vHh7oQ0JI8/cnzdsb/ZEj2G7caLUYIXt7/uHtzem336ZyHtvzRLt27HJ1zXrEHjxItMn2NH3u4FPYnrRuDTqdcr7cuFE5pxQunLucSqVc4H/3nXKOrFs3d4VZrYbAQIiKyv16Kzh3bj87d85n6NClTJq0h7t3r7Nli+XjK0D58rXp2XMabm5F8iyza9ciXFy8HjumyXPnYmdrS+iKFXzz4YdMnDOHiCtXcpVbtW0bOw8fZuPMmYTMmsWeI0f4Zds2AOpWrcrJ1auzHnM/+wyNkxNN69QBoHblygRPncrxVavYuWABGTodM1aseLQ4v/sOOzs7Qjdu5JvPPmPit98ScTl3Y+6qkBB27t/PxiVLCFm6lD2hofySY9+IS0hg7ooV+Jk2JqJUoKeOG8fRLVtY+N//8vO6dWzeufOR4gQ4dGg/y5bN58cflxISsoeoqOvMm2f5e46NvceoUUMYOHA4u3YdoXLlaowdm30edXJy4rvv5rBnz3EmTpzKt99+yalTJ7Kenz9/JqdPn2Tx4lX88ccJJk2ahr29wyPHbJXrujt3ICgo+zFggLJvHjjw6PH8P7C1LfjHc0Iqwf+it956i3ffffexXrt8+fKnUwnWaJRe0cmTISlJOdBs3gx59WKVLQvdusF//2u+3MtL6QXKbME8fhzCw6FSJevE6egITZooLYEpKUpP86FD8OqrucvevKl8lkx6PZQoofytUkGPHjB3Lly9ml0+IcEqYWrT0thx9izDmzfH2d6eumXK0MLfn43GHgpTNUqWpFNAAKU9LVcMo2Jj6VCjBjZqNWW8vKhdpgwXLFSmH8WePRto2bILZcr44eLiTteug9mzZ73FsocP/07p0n40btwWe3sHunUbSmRkGNevXwQgOvo6DRq8ikbjgrOzKw0aBHLt2gUA9Ho9q1b9QJ8+Yyld2heVSkXx4mVwdbVcody4cQNvvNEFX18/3N3dGTBgMBs3Wo5r587fqVDBj9at2+Lg4MCgQUMJDw/j0iUlrpCQ9QwYMBh3d3fKl69A587/yVrX3r1/EBjYBl9fP+zs7BkwYDDHjx/l2jXlt1C6dBnc3NwA5cJEpVJzw97+odtVpdGg6dyZ2PHjMSQlkRoaijYkBOcePXKVdRs5kuTt20lauRLS0jAkJpIRFgZAxuXLJEyfju7WLdDrSVywAJW9PXb+/g+NIV80Guw6dyZ1/HhISkIXGkp6SAh2FuJ8IA8PHD74gOR+/TAY9yP9P/9Aaqp14synsPXrCd+4Ee3TaiB8CLVGQ9HOnbk4fjy6pCRiQ0O5ExJC8YdsT8eyZfFs2pQby5dbfL5Ez57c37ePFAuVgsei0aDu3Bmd8Xs3hIaiDwlB/Yjfu+HoUfSLF8P9+5CRgW76dNSVKinHfyuw0Wjw7tyZCzm2Z4l8bs+of2t72tkp57Z9+5QL+OvXlcyoatVylz18GKKjwWCAe/fg/HkoVcq8TIMGcOkSPKXf8Z9/bqBhwy4UL+6HRuNOmzaD+fNPy8dXW1t7mjd/lwoV6qLOmUFldPfuNY4eDSEwsP9jxaNNSWHHwYMMf+cdnJ2cqFu1Ki3q12fjnj25ym7YvZs+nTpRrHBhvAsVonenTqzftcviejfs2kWbRo3QODoCULxIEbxMslNs1Gqu3LyZ/ziTk9mxdy/D+/bFWaOhbo0atGjcmI3bt+d+723b6NOtG8WKFsW7SBF6d+vG+q1bzcp8O28ePbp0wTNHxky/t9+mqr8/tra2lC9ThlebNOFEHhlkD7J58wY6duxChQp+uLm507fvYH77zfL3vHv375Qv70fLlsp5tH//oUREhBEZqZxHBwwYho9PBdRqNdWqBVCzZh3+x959x9d8/Q8cf2UnN0OCii1IrFZi71ErMRKx6RBqtegX3WZrVTfKL7RmW7UJomapvVotsQliFQlBxr3Zub8/zk1yk9wQcjVa7+fjcR+S+zn3c9851/2cfT4nTx4HIDY2huXLf2LcuKmUKlUGCwsLPD2rYGf3mI1gc9XrcmrbFk6eVN87IR5CGsEF9N5779GtWzcCAgIYPnw4MTExXL58md69e9O5c2f8/f1ZuHAhALNnz+aLL74A4K+//qJr164EBgbSqVMnfvnlFwBWrlxJhw4dCAwMJCAggEuXLjF37lyioqIYMWIEgYGBXLx40Xx/gJcXpKaC8TlPnsx7atf06TBxIiQkZH8+KgpWrlQjwJaW0KABlCunpkebQ5kyqmfPuKf88mU1em1Kq1Zq2szatarHcNMm9Xzx4mrqjIcHLF2qpkT37asax2ZwJToaK0tLKhYrlvlcNXd3Lt6589jn6teoEevDwkhJS+Py3bscv36dxpUqFSi+69fD8fDI6pjw8KjKgwd3iY29nyvttWvheHhkNb7s7TW4u5fPbOh26PAaR4/uJj4+hvj4GA4d2kadOs0BiI6+TXT0ba5evcCgQS15883WLF8+i/T0dJNxXboUTtWqWXFVrVqV6Oi7PHiQOy6VNisujUZDuXLluXTpIjExMdy5c4cqVYzPVS3HdyZrilrGbLXw8AuZz23atJFGjerQvHkjLlw4R6cHD0zGbMy6ShX0qamkGi0RSAkLw+bFF3OltWvUiPR793A/cICykZG8EBqKVblyJs9r4+ODha0tKWb6zltWqQKpqaQbxZkWFoaViTgBbAICcI6OxunUKWzfeivzeauaNSE1FesePXC+dQun8+exHTbMLDH+mzgaPnedUX7Gh4XhlEd+ZigdFMT9ffvybJSVCgri5o8/mi1OC8PnrjeKUx8WhkUecVoGBKjpzqdOYWn0uedK16IF+lu3VOPODDQm8jMuH/lZ5hH5WTooiL/NmJ8ULaoq4cZ/d1SUKlsepVw5NXKVwcUFvL2f6qjVrVvhlCmTdU0sU6YqcXF30WpzX1/zY82aqQQEvIutrf0Tvf7K33+rctJoGVK1ihW5mNExbST82jWqGY2cVqtYkXAT6XSJiWw9eJAuORpQR0+fpm7v3tTp1YvtBw/Sr3Pn/Md5/TpWVlZULF8+6/09PbloYiQ4PCKCapUrZ0tnPGJ84swZTp07xyuBgQ99T71ez9ETJ/DMMVqcH5cvh+PllfU5V6lSlXv3TJejly+HU6VKVjnq4KChTJnyXL6cu6xJTEzkzJlTVKrkCcDFixewsrJi586t+Pk1pXt3P1atWvrY8ZqtXpdT27aQx7T1/4TCHgX+D40E/3f+kkIybtw4ihp6wWfMmMH8+fNJTEykdevWvPnmmwDExMTket38+fMZOHAg/v7+6PV64gwjkV9++SVbtmyhRIkSJCcnk5aWxtChQ1m9ejWzZs2iipnXX+HomHsUNDYWnJxyp+3cGays1NSu5s1zH1+9GoKD4auv1O8jR5pvepeDA+Rcy6PVqudN2bVLPUqXVtPM7hsKgYxKSt26akqNkxN89pma3p2j1/ZJ6JKTccrRG+psZ4f2CUbIXq5ShY/WrWPRwYOk6fUMb9EC7wKuXU5I0KHRZH22Go0zAImJ2lxTlRMTdbi4ZB/hcXR0IiFB9cZWrlyD1NQUgoLUGjdv78a0b/8qoBrBAGFhB/j2241otbFMnDiQYsVK4uube825TqfDyej/nJOTikur1eLq6pYrrZtb9ricnJzQarUkJKj/I87OztnOpdOpmJs2bc6HH75Dz559qFDBg+++C8bCwoJEo2m8nToF0KlTAFevXiE0dD1uR4+ayMnsLJ2c0OeYUp8eE4OlURwZrMqWxalOHSLbtSP55EncvvyS4suXE9msWbZ0Fs7OFF+yhAeTJuU695OyMBGnPiYGCxNxpqxaRfK8eegjI7Fq2BDN2rXoHzwgZcUKLMuWxcLVFasqVYirWBFLLy8cd+4k/cIFUp9gGt+/lZWTE6k58jM1JgYrE/lprFRQEJenTjV5zLVZM2zd3Ylas8ZsceLklGvJhz6P/5/pq1aRNm8eREZi0bAhNmvXkvrgAekrVmRPWKYM1sHBpJpxnW1e+Wn9iPwsHRTEpUfkZ6Q589PWNvesh6Qk9fzDNG+uOlyNZwb5+maNKD8lyck6HByyrq8ODlnXfUdH0zOR8hIW9ivp6Wn4+LTLc73wo+gSE3HSaLI95+zoiDZn57qJtM6OjugSEgwzdbI6r7cfPIibszMNcozG13vxRf5cuZLI6GhWbdtGGXf3/MeZkIBTjqUozo6OaE2sK9YlJGQrw4zjTE9PZ+L06Xz8zjt5jq5nmL1oEenp6XTv2DHfcWbGkEc5qtPlLkcTEnS4upouR3P6/PNP8PKqSuPGqt4XFXWb+Pg4rl27woYNO7l+/QrDhvWnQgUPGjZsmv+AzVWvM/bSS2qfm3378h+HeG7JSHABbdiwIXMk+JdffuHs2bPUr1+f1atXM3PmTA4dOpQ5vdJYw4YNmTt3LnPmzOHEiROZaRo1asTo0aNZsmQJkZGROOR1MTAXrVZtTGDM2Rnis29whEYDU6eqzT1MqVJFTWkZPFhNi65bV60dbt/ePHEmJKgYcsZkotDM5uZNuHoV/vc/9XtGxWXVKvW3R0aq3kQzbT6ksbUlPkflKD4pCcfHnCb0ICGBQT//zPCWLTkxfjx73nmH/ZcusfSPPx7rPHv2hPLKK7V55ZXaTJ48CAcHDQkJWZ9txkZW9va515za22dPq9JrcXBQab/6ahSlS3uwbNlfLF36J+7u5Zg58wOAzBGCLl0G4ejoQokSZfHz681ff+3JjKtBg9qGzakGodFoiDf6P6fVqp8dTayF1Wg0mcczxMdrcXR0xMFBY/g9+7k0GnWexo2bMGzYCN59dwR+fq0pU6YMjo6OuLuXzPU+FSp44OnpxewSJXJnbA7p8fFY5PieW7i4kG5imr0+IQHdunUkHz0KSUnETJqEfdOm2V5vYW9PiY0bSTp8mNjPP3/k++eXPo849SbiTD97Vo3ypaeTdugQyd9+i02PHpl/A0Di5MmQmEj6yZOkrFiB9RNU3P7N0uLjsc6Rn9YuLqQ9ZHmFa9Om2JYsmWcjt3S/fkStXUuaicroE4uPVyOORvL63PVnz6qph+np6A8dIu3bb7E0fO6ZihfHZvt20ubMyd04LgBT+Wnl4kJqPvIzr0ZumX79iDR3fiYnQ85ruq2tej4vdeuqPTJWrVKjXwCenup1Z8+aLzbgjz9Cee+92rz3Xm3mzBmEra2GxMSsa2LGz6au+w+TlKRjw4av6NFjfIHi09jb59qgKl6nw9FEfUdjb5+tcRyv06FxcMjWAAY1bbpL69a5ns/gXqwYzevU4d0vv8x/nA4OxOf4fxOv0+GYsy5iSGvcgDSOc9m6dVStXJlaj5jR8PPatazfto15X36JbT6W4WzZEkqLFrVp0aI2I0YMylU2ZpSDGeWfMQeH3OWoVqvNVeZ+++0XXLoUzmeffZuZt3Z2qnwfNGg49vb2eHlVw9e3EwcOPObmneaq1xlr107NqviH96cQ/07SCC6Ao0ePsnz5chYsWMDGjRsZNWoUycnJ+Pn5sXTpUsqXL8/8+fP54IMPcr22f//+zJ07l6JFizJlyhRmzJgBwP/93/8xatQoEhISCAoKYo+ZdgTOU3i4mtpgNI2HmjXhzJns6Tw91XrgHTvUJinLl6sNsCIi1K6WNWqoKdU7dqg5puHhsHWr6uU2h7//VqPQxmtAKlWCK1ce/VorKyhVSv1848bDKyoF5FGsGGnp6VwxWtt1LjISz/xMkzNy/f59rCwt6eLjg7WlJSVdXOj40kvszbkj9yO0bNmZ5cuPsXz5MT7+eAHlynkREZG1udaVK+dwdS1ucsOq8uW9uHLlXObviYk6bt++Rrlynpmv9fXtg729BgcHR9q3f4W//lIbUJUpUxFra5scFZKsn1u27Mzvvx/j99+P8d13C6hc2YsLF7LiOn/+HMWKFc/Vew1QubIX589nxaXT6bhx4xqVK3tSpEgRXnjhhWzHz58/h6enZ+bvr7zyGps2bWfPnoO0betLWloanp5eJvMvNTWVW/mojKReuICFtTXWRu9j6+NDyunTudImnziRNQ8bsv8MYGvLC+vXk3bjBvcMs0nMJf3CBbC2xtIoTisfH9JMxJmTXq/PXDaQnjGS9bC/4zmgNXzuGqP8dPLxIf4h+VmqXz+iQkJMNsos7e0p0bOnWadCA+gNn7uFUZwWPj7o8/G5Y/S5A+Dqis327aSHhpI2bZpZ49SZyE/nR+RpoXI8AAAgAElEQVRnmX79iHxIfro/hfzk3j217Md4Pwd39+zTnI15e0PjxmrPDOMGvYeHKptGjFCP6tWhfn3I2enwmOrX78w33xzjm2+OMWzYAkqV8uLvv7OurzdunMPZufhjjwLfuXOV6Oi/mTnzNcaObcqCBf8jJuYOY8c2JTr6Rr7P41GmjConb97MfO5cRASeRtOOM3iVL885o2nF5yIi8MqR7tadO/x+8iRdWrd+6PumpqVx7fbt/MdZrhxpaWlcuX496/0vXjQ5VdmrYkXOGS1bOXfxYuYGWIf+/JMde/fSNDCQpoGBHDt1is+Dg5lsqPcBrNm0iXlLl/LjzJmUzEfHK0CHDp3Zu/cYe/ceY9asBVSq5EV4eNbnHB5+jqJFTZejKm1WOZmQoMrRjCnPAN9/P4uDB/cxe/bCbCPMXl5qGrVx+Z5X58NDmatel8HWFlq0+G9PhYbCnwr9H5oOLY3gAoiNjcXJyQlXV1eSk5NZa9h58urVq7zwwgt069aN4cOHc9LEBgcRERGUL1+ePn36EBQUxMmTJ0lNTeX69et4e3szZMgQmjZtyllDD7Gjo2PmlGmz0unUzpYTJqgeuEaNwN9fNXKNnT6tRnsbNVKP4cPVGqhGjVTDMixMNaQzbu9QsSJ06ACnTpknzsREOHAA+vVTmynUqAFNmoCpDTLat4eMHX3Ll4feveG42tCBpCTYswd69lRTbooXh44d4ciTTevKSWNrS7vq1Zm1eze65GT+vHaNnefPE+jtnSttul5PUmoqKWlp6IGk1FSSDSMEFYsVQ6/Xs/HkSdL1eu7Ex7Pl9GmqPsZULlNefjmQnTvXcP36RbTaWFavnkurVl1Npm3YsB3XroVz6NA2kpOTWLUqGA+PqpQtqzpMPD1fYseO1SQlJZKUlMj27SupUEEVjnZ2DjRr1pF16xaQkBDP3bu3+fXXldSr97LJ9+rcOZCQkDVcunSR2NhY5s2bS2Cg6bjatGnHxYvh/PrrNpKSkvjuu2CqVKlKpUoqroCALsybN9ewPv8Sa9euzjxXUlIS4eEX0Ov13Lp1k0mTPua114IoYtioZO3a1Zkb0F26dJGFC+dRKx+31NDrdOhCQnCdPBkLjQa7Jk3QBAaiNbETqXbxYhy6dsXGxwesrSkyYQKJ+/apacrW1rywZg36hATu9utn/oalTkdKSAh2kyeDRoNVkybYBAaSYiJO686dM79HVvXrYzdiBCmG3U7TL18mde9e7MaNA1tbLKtVw6ZPH1IMexv8UyysrLCys8PSyirzZwsrq3/s/dN1OqJCQqg8eTKWGg1FmjThhcBAbuWxA62lvT3uvXpxM4+7BJTo2pXU+/e5b2KDoALR6UgPCcHK8LlbNGmCZWAg6SbitDT63C3q18dqxAjSM3a5dXbGZts29AcOkDZmjHljBNJ0OiJDQvCcPBkrjQbXJk0oERjIzSfMT3dDft4zd36mpKiNH1u0UJtklS2r9tcwVd69+CK8/LIqU3PuL7B3r9qkceFC9QgPV2WVmb9HDRoEcujQGm7duohOF8u2bXNp2ND09VX9ecmkpKgZTampKaSkJKHX6ylVyospU3YzevR6Ro9ezyuvTMXZuRijR6/Hza1UnufLSWNvT7vGjZm1dCm6xET+PHOGnUeOEGji9jiBrVuzeP16IqOjiYyOZvG6dXTNse53w65d1K5enfI5GkWhu3dz07CZ5N9RUcxcsoTGPj75j9PBgXYtWjBr4UJ0CQn8eeIEO/fvJ9DPL3ecfn4sXrWKyDt3iLx7l8UrVtC1QwcAPh87ls0//8z6RYtYv2gRL1Wrxtv9+/PO4MEqzu3bmTFvHounT6dcXps95UPHjoFs2LCGy5cvEhcXy6JFc/H3N/05t2rVjkuXwvntN1WOLlgQjJdXVTw8VDm6ePH3bNv2C8HBi3M1osuWLU/t2vVYtOg7kpOTiYi4xPbtm2jWLI/bG+XFXPW6DE2bqk6mnM8LkQcLvakbmYl8SUlJ4YMPPuD06dO4ublRr149Tp48SdOmTdm4cSM2NmokbNSoUbRs2ZLZs2ej0+n46KOPmDx5MkeOHMHGxgZbW1vGjx9PtWrV6N+/P3FxcYbddEvx+eef4+bmxurVq1mwYAH29vZ888032Ua28mRiyo5Jbm6qIG7dWvVwT5igpmw1aQLr14OpXsnmzWHRIlXwZ+jWDcaMUReo2Fh1O4iPP350RT7HWsg8OTur6dh16qjzL1yo1oe89BJ8+qnaGh9UmgYNVCP3wQO1NuSHH7LWXGk0MGqUSqPVwubNapOsR8nnzt4PEhIYu2EDBy9fxtXBgffatiWgZk2OXr3K4KVLOTZ2LABHrlwhKMcIRYMKFVhieJ9DERF8vWMHV6Kjsbe2plWVKozr0AEHG5uHvv+ZWq8+9PiGDYtZt24+ycmJNG7sx1tvTcLGRo12jhjRie7d36RlS7V5SFjYQebPn8ydOzfx8vJhxIjPKFFC7WwaGXmdBQs+5dy5Y4AeL6+aDBo0gdKlPQA11Xru3AkcPbobR0cX2rXrSa9ewzN7jHP+F/7xx8UsWjSfpKRE2rb14+OPJ2VOCevSpRODBr2Jv7+K69Chg0ybNplbt25Ss6YPU6d+RpkyKq7k5GSmTPmEX3/dhp2dPQMGDKZfvzcA1XHVv/9r3LhxHY3GkS5duvG//43CytBwGj9+DPv27SEhQa079vX1o8e4Mdjm4zJp6eZGsUWLsG/XjvToaO6PHo1u+XLsmjWjxJYtXDdep/zWWxQZPx4LjYak/fu5N2wYaTduYNeiBSX37CFdp1Mb7xhEdehA0iM2zsnfjZzUfYIdFi3Cul079NHRJI4eTcry5Vg1a4bjli3EGuJ0WLYMa19fLOzsSL9xg+Q5c0ienXULLIvSpXFYuBDrZs3QR0WR9MUXJM+b98j3z31H7CfX8pNPeHnixGzP7Z44kT2TJhX43E3ymc7azY0XFy2iWLt2JEdHc3H0aG4vX45rs2bU3rKFXUafe8k+ffD8/HP257HxS+2tW4n9/XcuffxxvuPM911a3dywXrQIy3btIDqa1NGjSV++HItmzbDZskXdCgmwXrYMS19fsLNDf+MG6XPmkGb43C2DgrD58Uf0Wm2263pyjRpgNFpmyu58hmljlJ8p0dGEjx7NLUN+1t2yhZ058rPK55+zN4/8rLt1KzG//87Fx8hPv08/zV9Ce3vo1El1+CYkqLLozBm18VXv3ll3UBg2TJVdGVOgQTWWDbf5ycbfX1Xi8zEDbHu9sfmL0+C33xazY8d8UlIS8fHxo3fvrOv+p592wtf3TerXV9fXTz5pzb172ffzmDhxJ8WKZd/VOjz8CD/99AFTpuzN8319PS6YfP5BXBxjv/2Wg8eP4+rszHv9+hHw8sscPX2awRMncsxwu6uM+wSvMdxGroevLx/0759t5LH9W28xsFs3euaYeTbjp59Y99tvxMbH4+LkRMt69Xg3KAg3E0vUyONWeA9iYxn72WccPHoUVxcX3nvrLQLateNoWBiDP/iAY4a4Mu4TvMbQgdHD358Phg41OULa93//o7OvLz0DAgBo3asXkVFR2aZAB/j6Mvn993O9Ntb+4aPES5cu5qefVDnaqpUfY8ZklaO9enXijTfepEMH9TkfOXKQr76azO3bN3nxRR8++eQzSpdWn3H9+lWxsbHB2jqrvvHGG2/yxhtqo7yoqEimTBlLWNhfuLkVpV+/wXTr1iczrUuPfM4CNFe9DmDaNNU59TgzPwyf37/K778XdgRmW0JY2KQR/F+W30ZwYctvI7iwPeHtrf5pj2oEPyvy04/zLLhlZ56dw5+2/DaCC5s5G8FPU34bwYUt343gQra7sAPIp3w3ggvZ4zaCC0tejeBnTj7vB1/YHtUIflbkuxFc2KQR/GT+I41gmQ4thBBCCCGEEOK58d9Z3SyEEEIIIYQQ/1X/oY2pCpuMBAshhBBCCCGEeG5II1gIIYQQQgghxHNDxtSFEEIIIYQQ4lkn06HNRkaChRBCCCGEEEI8N6Q7QQghhBBCCCGedTISbDYyEiyEEEIIIYQQ4rkhjWAhhBBCCCGEEM8NGVMXQgghhBBCiGedTIc2GxkJFkIIIYQQQgjx3JDuBCGEEEIIIYR41slIsNnISLAQQgghhBBCiOeGNIKFEEIIIYQQQjw3ZEz9v8zevrAjyJ+SJQs7gvy5cqWwI8iXGu3vFXYI+XP8YmFHkC8nCjuAfDpX2AHkU5PCDiCfDhZ2APnkWtgB5JNfxYqFHUK+XBo3rrBDyJeLwWMLO4R88XV9UNgh5I+HR2FHkC+p8YUdQT4lJhZ2BP9dMh3abGQkWAghhBBCCCHEc0O6E4QQQgghhBDiWScjwWYjI8FCCCGEEEIIIZ4b0ggWQgghhBBCCPHckDF1IYQQQgghhHjWyXRos5GRYCGEEEIIIYQQzw3pThBCCCGEEEKIZ52MBJuNjAQLIYQQQgghhHhuSCNYCCGEEEIIIcRzQ8bUhRBCCCGEEOJZJ9OhzUZGgoUQQgghhBBCPDekO0EIIYQQQgghnnUyEmw2MhIshBBCCCGEEOK5IY1gIYQQQgghhBDPDWkEC3B1hZ9+guvXISwMund/eHobGzh8GE6dyv68pSWMHQunT8PVq7B7N7i4mC9OR0cYMQLmz4fp06FxY9Pp/Pzg66/h++/h22/h1VdVbBm6d4dPP4XFi6FrV/PFl8HeXr3H++/D8OFQo4bpdA0bwuDB8N57MGyY+t2U8uVVvrZsWaCwHsTEMPyjj6jVqhWtunZl47ZtJtPp9Xq+Cg6moZ8fDf38+Co4GL1eD8C9Bw/oM2QIDf38qNeuHb0HD+bPsLDM167btIlu/ftTp00bWnTuzJf/93+kpqYWLO74eIbPnEmtgQNpNWoUGw8eNJnu8Jkz9J02jbpDhtD6nXdyHZ+5Zg0BY8ZQo18/ZoeEFCim/LBxc6NeSAgd4uNpc+UKZV55xWQ6S1tbas6di+/t2/hFR1M/NBT70qWfWlz2bm50CQlhZHw8Q65coXoecdkVKUKHH35gWGQkwyIjafLJJ7nS1BkxgsGXLzMyPp4BZ87g5uVltjit3dzwCQmhdXw8za5coWQecdbevJlWcXGZjzZJSTQ6cUL9reXKZTvWKi6Odno9Fd5912xx5kf94cMZ/McfjEtMJHDx4n/0vfNi5eaGZ0gIdeLj8b5yhaJ55K/X5s3UiYvLfNRNSuJFQ/4+FUWKwNy5qnzZtw86d354ehsb2L4dDhzIes7NDVatgj//hOPHYc0aqFvXrGFaurnhHhJCxfh4yl+5glMe+QdgW7s2pffsoWJcHBVu36bIiBG50ti3aEFlvZ6iU6aYNc4M3t7Qvz8MGgStWmUvEo25u0NAAAwYAG+8Ab6+oNFkHS9dGgIDYeBAeP31pxIqYL7rfoHjiIlh+MiR1GrQgFa+vmzctMlkOr1ez1fTp9OwWTMaNmvGV9OnZ5abABMmTsQvIIBq3t6ErF+f7bXrNmygW69e1GnUiBZt2vDl9OlPXG4uX/4DnTo1pU2bOkydOobk5OQ80/7xxyF6927Pyy/7MHx4X27d+jvz2P/935cEBrakTZs6dO3aih9++C7z2LVrEXz44VA6dGiEr28DRo0ayNWrl58oXpydYepU2LZNfWfbtjWdrmdPWLECtmyBkBB4+22wsso67ukJs2fD5s3q+x4U9GTx/BtYWxf+4z/iuW4Eh4SEEBER8dA048aN4+jRowCMHj2an3/+ucDvs3PnTr744ovHPs9T89VXkJIC1arBkCHwzTfq57z8739w927u50ePhgYNVCO0QgUYOhSSkswXZ1AQpKWpi99330G/flCmTO50x47Bxx/Dm2+qxmP58qokzxAZCStXqgb/0+Dnp+L89lvYsAHat4fixXOns7CA0FDVoF+xAurVy91gtrSEdu3g779zv/4xTf7mG2xsbDiwaRNfTZzIxK++Ivxy7oJr5fr17Ni7lw1LlhC6ZAm79u9nxbp1ADg6ODBt3DgObdnCH9u3M/j11xn6wQeZBXZCUhJjR43i8NatrF6wgMNHj7Jo2bKCxf3jj9hYW3MgOJivhg5l4g8/EH7jRq50Gjs7urdowYd9+pg8TwV3d97v04eWtWoVKJ78qhkcTHpyMtvd3Tn22mvUnDsXJxMdIhVHjsStcWN2e3vza+nSpNy/z0uzZz+1uNoGB5OWnMwcd3c2vfYa7ebOpZiJuFrNmIGNRsM8Dw9+btCAF/v25aX+/bP+voED8R44kJBOnfjWyYkQf38STF0XnlB1Q/7tcXfn1GuvUW3uXBxNxHmsY0d2OTtnPh4cPEjU6tUAJF6/nu3YoZo10aelEbl2rdnizI+4mzfZO3Uqxxct+kff92EqBAejT07muLs7l197jQpz52JvIn/DO3bkL2fnzEf8wYPcN+TvUzF5siqPGjSAd96BKVPgYZ0rgwfDvXvZn9Nq4aOP1DW1Vi3VITp/fvZKcwEVDw6G5GSuuLsT9dprFJ87FxsT+WdZrBilt24l9vvviShWjGuenui2b8+eyNqa4t9+S+Lhw2aLz1i5clCnjiqOlixRfdMNGphOa2cHZ86odEuWqI+ideus46mpcPYsHDr0VELNZK7rfoHj+PRTVW7u3s1Xn3/OxKlTCb94MVe6latXs2PXLjasWUPo2rXs2rOHFUbfk2pVqzJx3DhqVK+e67UJiYmM/egjDu/bx+plyzh8+DCLfvjhsWM9fHgfS5bMY/bsH1i3bhc3b95gwYJZJtM+eHCPMWPeZsiQkWzb9jvVqr3EhAlZnQgBAT1YsWILO3f+xfffr2D79o3s3q3+38bFxdGsWWtWrNjK5s0HqF69Jh9+OOyx4wXUdzw1Fbp0Ud/1d98FD4/c6Q4cUD04HTqoup+nZ/YBm48/VnU6f381WNK1KzRt+mQxCbOKiIigd+/e+Pn50bt3b65cuZIrTVpaGpMmTaJt27a0a9eO1UbfnYcdK6hCawSnpaUV1ltnWrdunckPI0NaWhqffvop9erVM+v7tGnTho8++qhA5zQbjUZ1+06bpioOR46onrZevUynL19eHZs5M/vzRYrAW2/BqFGQUVCdPWu+RrCtLdSvD2vXqnNeuKAau6YuclFRoNOpny0sQK9X3dsZ9u+HEycgIcE8sRmzsVEdCHv3qtrDjRsQHg4vvZQ77eHDqkGu16tK3IULULZs9jQNG8LlyxAdXaCwdAkJbN+1i5FDhuCo0VDPx4fWzZuzYevWXGnXb97MgFdeoWSJEriXKMEbr7zCus2bAbCzs6NShQpYWlqi1+uxtLQkJi6OmNhYAF7t1o16tWpha2ODe4kSBPj58VcBRo10iYls/+MPRnbvjqO9PfWqVqV1nTpsMB71MfCuXJkuzZpRrkQJk+fq2rw5LX18cLS3f+J48stKo6FU9+6cnzCBNK2WewcOEBkaStm+fXOl1VSsyJ1t20iOiiI9KYmbK1fi/OKLTyUuG42GKt27s3/CBFK0Wv4+cICLoaG8aCKuygEB/P7ll6QmJBB79SonFi6k5oAB6qCFBU0++YTf3nmH6LNnAXhw+TKJ9++bJU5LjYYS3btzyZB/Dw4c4E5oKKVMxGnMvkIF3Jo35+ZPP5k8XjooiPt795J49apZ4syvc+vWcX7DBnQF/B6bi6VGg1v37tyYMIF0rZb4Awd4EBpK8Ufkr22FCjg3b87dPPK3wBwcVCfijBnqGn70KOzYkfeMnbJlVeV57tzszycnQ0SEurZaWKhOSVdX9TADC40Gp+7duTdhAnqtlsQDB9CFhuJsIv9c330X3bZtxC9bBsnJ6OPjSTl3Lnua995Dt307yTmeN5eqVVVxfP++Kj6PHs27n/vaNbh0SRVfqalw8iSULJl1PCpKFVWGS/5TYc7rfoHi0OnY/uuvjHz7bVVu1qlD65dfZsPGjbnSrg8NZUBQECVLlsTd3Z03+vVj3YYNmcdfe+UVGjdqhJ2dXa7Xvtq7N/Xq1lXlprs7AZ068dfx448d7+bN6wkI6EGlSl64uBThjTeGsWnTOpNpd+/+lYoVvWjTpgN2dnYMGvQ/wsPPceXKJQAqVKiEg0PWFAALC0tu3FDXzRdf9KZz554UKeKKtbUNffr059q1CGJiHvP6b2+vZrgtWKDqYydPqsaun1/utDdvQnx8RjCQnp59EKRkSfj1V/X8zZuqjmeqMS3+cZ988gmvvvoq27Zt49VXX+Xjjz/OlWbjxo1cu3aN7du3s3LlSmbPns0NQ1viYccK6pGN4ISEBEaMGEHHjh3p3LkzI0eOBGDNmjV07tyZzp070717d+4aRgDWr19PQEAAAQEBDB8+nGhDoR8SEkL//v0ZPnw4/v7+XLhwgbCwMPr27Uu3bt3o1q0bu3fvfmgscXFxjBkzhoCAADp37szkyZMB0Gq1jBkzBn9/f/z9/Zk/f37ma/r27csXX3zBK6+8Qps2bfj6668BWLt2LadOnWLq1KkEBgZy8OBBkzH27duXXbt2ZZ7v3Llz9OnTBz8/P8aPH5851SRnuozf83qfEUbToebNm5cZ+5gxY9BqtQDMnj2bd999l8GDB9O+fXuGDBlCgrkbbpUrq5Lu0qWs506fzruE/OIL1VuXM44aNVRFo3NnVdr+/ruaL2UupUqp89++nfXctWumR4JBTZX+/nuYM0d1g//2m/lieZiiRdVF2HhkIioKXnjh0a8tVw7u3Mn63cVFzWHbv7/AYV25dg0rKysqli+f+Vw1T08umhgJDo+IoJrRyEs1Ly/Cc8yYCHj9dbxbtmTohx/Ss3NnihUtavJ9/zh+HM9KlZ487tu3VdylSmXFU64cF810AXxaHKtUQZ+aijY8PPO5mLAwk43bawsXUrRpU+xKlcLKwYEyr71G1JYtTyUutypVSE9N5b5RXHfCwiiWV6PbwsLoRwuKGzpznMuWxaVcOYq/9BJvXrvG4MuXaTpxYrb0BZGRfzqjOOPDwnB6ROdA6aAg7u/bl2cjt1RQEDd//NEsMf6b2RvyN8kofxPCwnB4RP4WDwoibt8+kp9WJ0LFiuo6b3y9OXs275HgiRPV0pfERNPHN29Wr1+wQM22MVMnhI0h/1KM8i8pLAxbE/ln36gRaffuUebAATwiIykZGop1uXKZx63Ll8d5wADuG+ozT0PRotn/9Oho1f9toj2WS+nSuQfan7Zn5bp/5epVrKytqWjUmKpWtSoXjetLBuGXLlGtatVs6UyNGOfHH3/+iWflyo/9uoiIcLy8supuXl5VuXfvrsnGqUqbFa+Dg4ayZcsTEZEV808/zaN169oEBrYgMVGHr2+Ayfc9fvwoxYq9QJEibo8XcLly6vtu/LleupR347VtWzVI88svaiQ4NDTr2OrVatadlZU674svquUQ/0WFPRX6MaZDR0dHc+bMGfz9/QHw9/fnzJkz3MtxUdm8eTM9e/bE0tKSokWL0rZtW7YaBmkedqzAWfmoBPv370er1bLZMBIUExPDkSNH+P7771m2bBkvvPACWq0Wa2trLly4wNdff01ISAglSpRg5syZTJkyhZmGUcOwsDA2bNhA+fLliY2NJSgoiHnz5lGiRAmioqLo0aMHv/zyCy55rCOdNm0aGo2GDRs2YGlpmZmJc+bMIT09nY0bN6LVaunduzdVqlShpWEN5a1bt1i6dClarZa2bdvSo0cPunfvzvr16xkwYACtWrUCVEPdOEZTwsLCWLFiBXZ2dgwZMoRVq1bx+kMWxuT1Phn27NlDaGgoK1aswNHRkY8++og5c+bwwQcfAHDq1CnWrFmDs7MzAwcOZOPGjfTKa5T2STg6Qlxc9udiY8HJKXfaTp3UBWbTptwjsKVLq9FgT0+oXRsqVYL169UF7RGdG/liZ5e74Z2QoHoSTTl0SD3c3aFZs6fbbW3M1jb36HdSknr+YZo3Vw0I41FTX9+sEeUC0iUk4OTomO05ZycntBkj5g9J6+zkhE6nQ6/XY2Fo5Gz8+WeSkpL4dc8eUvKIb83GjZw6e5apY8Y8edxJSTg5OGSPW6NBm1el9xlh7eRESo7/c6kxMVg7O+dKqw0PJ+H6dXxv3iQ9NZW4kyc59PbbTyUuGycnknPElRQTg62JuK5s3UrD0aPZ3K8fju7u1BwwAGvD4kBnw4wFD19ffqhZEztXV3pu307cjRucWLCgwHFaOTmRaiL/rEzEaaxUUBCXp041ecy1WTNs3d2JWrOmwPH921k6OZFuIn8tH5G/xYKCuJlH/pqFo2PWaE+GuDj1fE6+vmq5yPbtee+n0LGjuvb6+alZOmZiKv/S88g/q7Jlca5Th1vt2pF88iRFv/ySEsuXc7NZMwCKz5qVOaL8tNjYZC+WMpaJmiqujBUrpmaUP6U+uTw9K9d9nU5nutw08VnpdDqcjOpNpsrN/Fizbh2nTp9m6qRJjx1vQoIOR8esGJycnA2xaXM1UHU6HW5u2TuvHR2d0Omy/ragoCH07TuYCxfOsnfvjmznzhAVdZuvv57EiBGjHzteHBzUDERj8fHZF6Eb27FDPcqWVd9p45lHhw6p5W+9e6tG2uLF8JRmVgiIjY0l1kS92sXFJVsb7tatW7i7u2NlWIpiZWVFiRIluHXrFkWNBk9u3bpFaaO9UEqVKsVtw6DXw44V1CNHgqtVq8alS5eYNGkSW7ZswdbWlt27dxMYGMgLhtEtR0dH7OzsOHLkCC1btqSEYVpKnz59OGS0cKROnTqZjctjx45x48YNBg8eTGBgIIMHD8bCwoKrD+lh3rVrFwMHDsTSsKNDRgYeOnSInj17YmFhgZOTE506dcr2vu3bt8fS0hJnZ2cqV67MtWvX8nwP4xhN6dixI46OjlhbW9OlSxcOF3ANz6FDh+jYsSNOTk5YWFjQq1evbLE3a9YMFxcXLCws8Pb2fmjsT0SrVRsTGHN2zl0R0WhUr/voPC50GYXTl3wA310AACAASURBVF+qn8+cUZsXtGtnnjiTktQF05i9fd4jABkiI1UvY79+5onjUZKTc3ev29pm1TpMqVsXatZUm0JkLBPw9FSvM0w1LSiNgwPxOQqbeK0WRxOFjcbBIVshH6/VotFochXkdnZ2+Pv6Mm/JEs4ZjYgA7Nizh+lz5zJ/+nSKFmAKosbOjvgcnR/xCQn/yJTmgkiNj8cmR2eetYsLqTk7nFBrhy3t7NhatChbHB25FRJCw6dU60yJj8c2R1y2Li4km4hr54gRpCYkMDg8nK4bNnB2+XLiDT32qYbP5PcvvyQpJobYq1cJ+/57KnXsaJY40+LjsTaRf2km4szg2rQptiVL5tnILd2vH1Fr15L2FBsb/xbp8fFY5shfKxcX0h+Sv05Nm2JTsiT3n2YnglabuwPWySl3RdnBQZVF+Rk9TU6GjRvVcp2H7XXxGEzln2Ue+adPSEC7bh1JR4+iT0ri/qRJODRtiqWLCxp/fyydndGuWmWWuDJ4eaml0oMHq77rlJTs/bAZ/QEPK5ZcXNRr9++HW7fMGt4jPSvXfY1GY7rcNNEpo9Fospeb8fEmy82H2bFzJ9NnzmT+3LkUdXv0qOq2baG0bl2b1q1r8847g3Bw0KDVZtXdMn7WaPKKN3s9T6vV5kprYWFB1ao1sLOzZ8GC7HtV3L9/j5EjB9C9+6v4+vrn++/MlJCQu4PL0TFrOVtebtxQs0UyNjh0dlZ72/z4o6pzdu+uFr136fL4Mf0LpGNZ6I8ff/yRNm3a5Hr8+C+bafXIkeBy5crxyy+/cPjwYfbu3cuMGTNo06bNE72Z8YVDr9dTtWpVli5d+kTnehzGazCsrKweuh7Z1MUtP6ysrEhPT8/8PclMa2Fzxm6u82a6dEn1mlWqpNaegppGkrMHrVIltR44Y2dEW1tVSp49q3rkT59Wzxvthpjt54K6dUuNQru7q4YtqHjys2GUlRU8hfVCJt27p0Yn3Nyyeind3bNPczbm7a2mbv/8c/YReQ8PNQU8Y9q8nZ3KzxdeUDsfPiaP8uVJS0vjyvXreBim4p0LDzc5VdmrYkXOXbyIt2Fq37nwcLwqVszz3KmpqVz/++/MKdR7Dx1i/OefM+/rr6nq6fnYsWaLu2RJFfft23gYFqadu3YNz5xrp58x2gsXsLC2xtHTE61hSpyLjw9xGd8TIy61anFu3DhSDP9fImbPptqUKdgWK0aymdeQ3r9wAUtra1w9PXlgiKuEjw/RJuJKvH+fTUazXJp/+im3fv8dgHvnz5OalPTUvu8Z+afx9ERniNPJx4d4E3FmKNWvH1EhISYbuZb29pTo2ZOwp7Eb/L9QoiF/7Tw9STLkr8bHh4SH5G/xfv24HxJC+tPsRIiIUNdrDw/I2EejenW1r4IxDw+1FGblSvW7jY2qCB85At26mS4XrK1VmWGG0aEUQ/7ZeHqSYsg/Ox8fkk3kX/KJE3l+TxzatMGuXj0qGFqZlkWKQFoatjVrcrsAFfjw8OxZ1rat2psxYxZv8eKqnZFXdcLJSa1s+vNPtf73n/asXPc9KlQgLTWVK1ev4lGhgorj/HmTU5W9Klfm3PnzeNesqdJduIDXY5R/e/fvZ/ykScwLDqZqlSr5eo2fX2f8/LJ2T//44/e4ePE8bduqzsjw8HMULVrc5DTlihW92Lw5a71wQoKOv/++RsWKpmNOS0vl77+zBmFiY2MYOXIAzZu3pn//ofmKN5fr19X3vWzZrCnRlStnffcfxspKzUAE9W96utphGlR967ffoFEjNSNRmF2/fv3oaqI8zTmTt1SpUkRGRpKWlpbZ/oqKiqKU0VKHjHQ3b97E29sbyD76+7BjBfXIkeDbhrUZbdu2ZcyYMdy7d48aNWqwYcOGzHXAWq2WpKQkGjZsyJ49e7hjqPCvWrWKJk2amDxv7dq1uXr1araR1BMnTmTbUj6nVq1asXDhwqzbtRimQzdu3Ji1a9ei1+uJj49n8+bNeb6vMUdHR+Ie0vNtytatW9HpdKSmprJhwwYaNWoEQPny5Tl58iQAFy9e5KzRCN7D3qdx48Zs2bKF+Ph49Ho9a9asyVfsZqPTqfUVY8ao0d6GDdUUspw902fPqtHKli3VY+RItda1ZUtV4bhyBQ4eVLf7sbWFKlVUZSSP2/A8tuRktZtHt27q/F5eartLExtl0LJl1uh26dJqt8AzZ7KOW1mpSpOlpXrY2JhtLSMpKXD+PLRooc5btqyKNeftpEB1Nrz8MixfDg8eZD+2d6/aAXvhQvUID1e3+vjllycKS+PgQLuXX2bW/PnoEhL4MyyMnfv2Edi+fa60gR06sHj5ciKjooi8c4fFy5fT1TDCd/zUKY6GhZGckkJiYiLzlizh7v37mQ3mQ0eP8sHEicyeNi3zuYLQ2NvTrl49Zq1diy4xkT8vXGDnX38RaGJDtPT0dJKSk0lJS0Ov15OUnEyy0W0mUlJTSUpORq/Xk5qWRlJyMmlGHVfmlKbTcSskhKqTJ2Ol0eDWpAklAwO5sWRJrrQP/viDckFBWLu4YGFtjcewYST8/bfZG8AAKTodF0JCaDZ5MjYaDWWaNMEzMJDTJuJyrVQJ+6JFsbC0pGL79ngPGcIhw1TY1IQEzq9cSYMPP8TGyQmnMmXwHjKES0/4/zOndJ2OqJAQKk+ejKVGQ5EmTXghMJBbJuIE1ch179WLm3nsqFqia1dS79/nvtG+Df8kCysrrOzssLSyyvzZwow7FT+udJ2O+yEhlDHkr1OTJrgGBnI3j/y1sLfHrVcv7j7BjrWPJSFBlRnvvKNGe+vWVSM763Js7nPhglqS06mTeowZo+5Y0KmT6jCtVUvN47WxUR2Ib76pWn5PsNmQKXqdjviQENwmT8ZCo8G+SRM0gYHEmci/uMWLcezaFVsfH7C2xm3CBBL27SM9NpZ7EyZwrUoVbtSqxY1atdCFhhI7fz5Rb7xhljgznD+v+hLc3FTxWbdu3n0Bjo7qFkinTmX1bedkZaWKTguLrJ/NyZzX/QLFodHQrm1bZgUHo9Pp+PPYMXbu2kVgQO61sYGdO7P4p5+IjIwkMiqKxT/+SNfAwMzjySkpJCUlqfInNZWkpKTMgZNDR47wwejRzJ4+PbMR/SQ6dAhk48Y1RERcJC4ulh9+mEunTqY7/lq2bMfly+Hs2rWNpKQkFi0KxtOzKh4elUlPT2fduhXExsag1+s5ffoEa9cuo149dVtKrTaeUaMG4u1dh2HD3n/ieElMVHWdAQPUzL6XXlLL10zVGzt1ytrYrkIFdX+ujDW/16+rf9u2Vf8pixZV9wEzsXZbmIeLiwtly5bN9cjZCC5WrBjVq1fnF0Pd4JdffqF69erZpkKDmrG7evVq0tPTuXfvHjt27MDPsEHaw44V1CNHgs+fP88333wDqIvNkCFDCAgIIDExkTfeeAMLCwtsbW357rvvqFKlCu+//z4DDDuIlitXLnPzqpyKFCnCnDlz+Oqrr5g2bRopKSmUK1eO7777Ls/pI2PGjGHatGn4+/tjZWVFgwYNGD9+PMOGDWPKlCkEGC5MnTt3pkWLFo/843v37s3nn3/OwoUL871bc82aNRkwYAD37t2jQYMGmetzBw8ezMiRI9m5cyc1atSghtGtEh72Pi1btuT8+fP0MWzv/9JLLzF06BP2qj2p999X91c7f16NXr73niohGzVSjeHy5dU03aiorNfcv696tI2fGzwYZs2CixdVhWTaNHWBM5cff1Rb5AcHq+naP/6oGuBVqqi/YcgQlc7LC3r0UBfV2Fj44w+1q3SGAQPUGtwMgYEwb55ZNqACYOtWdcEeOVJV6rZuVflRrpxar2LYnI2WLVVFz7jCc+qUSp+cnH2uWmqqamAXYE3UJ++/z9hp02jSsSOuRYow8YMP8KpUiaPHjzP43Xc5Ztg8rE/Xrly/eZMAwyhgj86d6WPo8UtOTmbqjBlcv3kTGysrqlSuzLyvv8bdsDRizuLFxGm1DHnvvcz3revjw4IZM5487v79GTt/Pk2GD8fV2ZmJ/fvjVbYsR8+fZ/BXX3HMsAb1j/PnCZo2LfN13gMH0qBaNZaMGwfAhIULWWf0GX8XGspngwfTLR/Xiidxctgwai1ahG9UFCnR0ZwcOpT4M2co2qwZDbdsYYuho+bM++/z0qxZtA4Px9LWlrhTpzj6FEcsdwwbRvtFixgWFUVidDS/Dh1K9JkzlGnWjB5btvCtIS73unVpPXMmdq6u3L9wgU2vvUa0UWfSjrffxnfePIbdvEnigwecmD+fk2a8BdDZYcN4cdEiXo6KIjk6mnNDh6I9cwbXZs2ovWULu4yWcZTo0oXUBw/ybOSW6tcvzwb0P6HF+PG8PHFi5u8+ffuye+JE9jzBuj9zuTpsGBUXLaJWVBSp0dFcHTqUxDNncGrWjCpbtvCXUf66delC2oMHxP0TnQgff6w2YPzjD9U5OGGC6gSsXx8WLVKdsWlp2W/T9+CBGgnKeM7WFj75RF1zU1NV2TZwYPbyqoDuDhtGiUWL8IiKIi06mrtDh5Jy5gz2zZpRassWIgz5l7BrF/fGjqXUpk1YaDQk7t9P5KuvAqCPjyfNaOlRekICeq2WdDPtsp7h+nV1M4XAQDUgfumS2rsyQ58+qj0RHq4ay0WKqOyuXz8rTcZ+o6VLZ59l+uabqhg22gjZLMx13S9wHOPHM3bCBJq8/LIqN8ePx8vTk6N//sngoUM5ZsjIPj17cv3GDQK6dQOgR/fu9OnZM/M8A4cM4XfD7TaPHT/OhEmT+GnRIhrWr8+c778nLj6eIcOybjNUt04dFnz3HY+jceMWvP76IIYPDyIpKZFWrfwYNChrE9ZXX+1Ev35v4ufXGTe3okybNptvvpnMxIkf8OKLPkyZMj0z7Z49vzJ37nRSU1MoXrwEPXq8Ts+eavfz3bt/5ezZk0REXMw2mrxs2SZKlnzMEbrp09XShg0bVH1t+nQ1qOLtrZbXZXTU16yp6pgODur7vnu3GiAANZgzYYL6z/juu2qKw8GD8LR2sS9kZurjKZBHbXNjbOLEiYwePZo5c+bg4uKSeYvYwYMHM2LECGrWrElgYCBhYWH4Gm5nOnz4cMoZZi0+7FhBWegfNvQq/t3y2LH3meP/BGtJCoOZ1pM9dW+9VdgR5M8T7pz5T9uY16Y7z5h/yxYg/8xdmgvuYGEHkE/mWYX99NV/yJKKZ8mlHDvhP6u2Bf87qm7D6v3+6ETPgn/o/vEFdS/+MVofhahol6fTuWx25hyo+Yc8bC3/P+VxGsHPsvzvcy2EEEIIIYQQolD820aCn2XPXCP47NmzjDaxA/Hrr79OT6OpJUIIIYQQQgghxON65hrB1atXZ4O5F5cIIYQQQgghhBA8g41gIYQQQgghhBDZPQvTof8rzLyxvRBCCCGEEEII8eySkWAhhBBCCCGEeMbJSLD5yEiwEEIIIYQQQojnhjSChRBCCCGEEEI8N2Q6tBBCCCGEEEI842Q6tPnISLAQQgghhBBCiOeGjAQLIYQQQgghxDNORoLNR0aChRBCCCGEEEI8N6QRLIQQQgghhBDiuSHTof/L3n67sCPIn2rVCjuC/ClZsrAjyB/rf8nXOjGxsCPIl4B33insEPKl/IwZhR1CvvxLvu24FnYA+bS5sAPIp7IREYUdQr5sL+wA8qlevcKOIJ8ePCjsCPIlGdvCDiFfirqmF3YI+dOlS2FH8J8l06HNR0aChRBCCCGEEEI8N6QRLIQQQgghhBDiufEvmTcphBBCCCGEEM8vmQ5tPjISLIQQQgghhBDiuSEjwUIIIYQQQgjxjJORYPORkWAhhBBCCCGEEM8NaQQLIYQQQgghhHhuyHRoIYQQQgghhHjGyXRo85GRYCGEEEIIIYQQzw0ZCRZCCCGEEEKIZ5yMBJuPjAQLIYQQQgghhHhuSCNYCCGEEEIIIcRzQ6ZDCyGEEEIIIcQzTqZDm4+MBAshhBBCCCGEeG7ISLAABwcIDARPT9Dp4Ndf4eTJ3OmaNoVatcDVVaX7/Xc4cCDr+DvvgJMTpKer369fh59+MluYDxISGBcayoFLl3DTaHi3TRsCatbMle5wRATBe/dy5tYtitjb89uoUZnHbsbE0Ck4OFt6XUoKH7Vrx4AmTcwTp1bLuCVLOHD2LG5OTrwbGEhAgwa54zx/nuDNmzlz7RpFNBp++/TTbMf7zphB+M2bJKemUrZYMUYEBNDWx+fJ44qJYdzUqRw4fBg3V1feHT6cgPbtc6XT6/V8/X//x5oNGwDoERjI+2+/jYWFBRFXr/LlrFkcO3GC9PR0ataowbj33qOSh0eu8/QbOpTDR49y+tAhrK2f/FLzQKtl3A8/cODMGZWf3boR0LBhrnSHz50j+JdfsvLz888zj0XHxvLpihX8fuECCcnJeJUuzZhevfCpVOmJ48pFo4HevaFKFdBqYfNm+Ouv3OlatYJ69cDNTaU7eBB27co6Xro0dOsGpUpBUhIcOqS+k2Zi5eZGuYULcfL1Je3uXW6NGcOD5ctNpnWoXZvSM2fiUKcO6VotUdOmcXfWLADsfXwoM3s2Dt7epMXFEf3990RNnWq2OHFzw3rhQix9feHuXVLHjCHdRJxWn3yC1bhxKq8Mkr29ISICCy8vrL76CssmTcDKCv0ff5A6YgT6CxfMF2fOeNzcqLhwIS6+vqTevcuNMWO4ZyJur82bcW7ePPN3C1tbEs+f57S391OL7VHqDx9Orf79KVGzJqeWL2fDG2/84zFYuLnhunAhtr6+pN+9S9yYMSSayD+nTz7Badw49Eaf+11vb9IiIgCw8/fH+bPPsPLwIPXECWIGDSL17FmzxWnn5sbLCxdSzteXxLt3OTJmDOEm4rQtUoRm335L+Q4dADg1Zw5HJ03KPF5/8mQqdumCW/Xq/Dl1arZj5rRlyw9s2jSfpKQEGjTwo3//SdjY2OZKl5qazJw57xMRcYq7d/9m7NifqF4963p75sxh1q8P5sqVMzg6FmHGjN/MEt8DrZZxy5Zx4Nw53BwdebdzZwLq1cuV7vCFCwRv3cqZ69fVdT6P/Po9PJy+s2bxlp8f7/j7Fzi+n376gUWL5pOYmEC7dn5MmDAJW9vc+Qdw+PAhPv10Erdv36JmTW+mTv2c0qXLALB162Z+/vknzp8/y0svebN48RKT5wgNXc+4cR8xceJUunfv+cj4Hjx4wLjx4zlw8KAq3999lwATf7der+frb75hzZo1APTo0YP333sPCwsLAM6ePcu48eO5dPkylStV4tOpU6levToAg4YM4c8//8w8V0pKChU9PNgYGvrI+PKMOzGRcXv2cODGDdzs7Xm3QQMCvLxypVtw/DjrL1zg7/h43OztebVGDQbVqgVAdEICnx44wO+3bpGQmoqXmxtjGjfGx939ieN6lslIsPk8143gK1euMMrQQBowYADe3t7Zfj9y5Ahdu3alnokLcYbly5eTlJRE//79/4mQn45OnSAtDb78EkqWhNdfh9u34c6d7OksLCAkBCIjVQW+Xz+IiYFTp7LSLF0Kly8/lTAnb96MjZUVB95/n7O3b/PmsmVUc3fHq0SJbOk0trZ0r1UL/5de4vt9+7IdK12kCMfGjs38/fr9+/jOno1vjRrmi3PFCmysrTnwxRecvXGDN4ODqVa2LF6lS2eP086O7k2a4F+vHt9v3ZrrPON69sSzVCmsrawIi4ig/7ffsm3SJEoUKfJkcX35pYpr2zbOXrjAm6NGUc3LC6/KlbOlW7luHTt272bD0qVYWFjwxttvU7Z0aV7p3p24+Hhat2jBZx9/jKOjI8Hz5zPs/ffZaihQM4Ru2UJqWtoTxZkr7qVLVdzffMPZ69d5c/ZslZ9lymRLp7Gzo3vTpvg3aMD3mzdnO6ZLSqKmhweje/WimIsLa/bvZ8js2fz22Wc42tubJU66dVOl0yefQJkyMGgQ/P23+r7ktGwZ3LoFxYrBm2/C/ftw/Lg69vrrqhMqOBiKFoX//Q9u3oTTp80SZpngYPTJyZxxd8ehVi0qbtpEQlgYSWfOZEtnVawYFbdu5eY77xCzZg0WtrbYlC2bebzCsmXErFvHpZdfxtbDA8/9+0kMCyN240azxGkdHAzJySS7u2NRqxY2mzaREhaGPkecAOkrV5Lat2/uk7i6kh4aSuobb0BcHFYff4z1hg2kGCp1T0MFQ/4ed3dHU6sWXps2oQsLIzFH3OEdO2b7vequXcT9Zp4GxZOKu3mTvVOn4unnh7WDQ6HEUMSQf1Hu7ljXqkXRTZtIDQsj1cTnnrhyJQ9MfO5Wnp64Ll3KvY4dSTl8GMcPPsAtNJQ71aqpss4MmgcHk56czA/u7hSvVYuOmzZxNyyM+znibDpjBtYaDT97eOBQogQBO3cSd/Uq53/4AYDYixc5/OGH1HjrLbPEZcqJE/v45Zd5jBnzI25uJZg5821CQmbRu/f7JtNXqVIHP78gZs8eleuYnZ2GFi2606iRPxs3fm+2GCevWqXK92nTVLn53XdUK1MGr1KlsqXT2NrSvVEj/OvW5fvt202eKyUtjU/XrsXHROfskzhwYB//z96Zh0VVvQ/8wzbAsCoCmqKoIJSF5r7gRimmbCK5JmrmXvp1KffcyrQ0zSXNcstcUwS0XHLJrdxNNATFBZcUAgWFYRhmmN8fdxgGGBRl/Gl1Ps/jI3Pve89959y555z3vO95z4oVy1mxYg2urm7873/vs2TJQkaNKll/9+/fY9So95k27RPatg1g8eIFfPjhKNat2wyAk5Mz77wTybVrVzlx4rjR+2VmZvLtt8vw8ippDJbGjJkzsbKy4ujhw1xMSGDwkCH4+vjgXcyg3LR5M3v37SMmOlrq3wcMoFq1avTs0QOVSsWw4cPpGxlJr1692LhpE8OGD2f3rl3IZDK+W768SFl9IiNpamRC+kmYceQIVubmHI2M5GJaGoN37cLXxQXvihWLyGmBOe3a4ePiwo0HDxjw009Usbens5cXirw8XnNzY3zz5rjY2rIlIYFBu3axv1cv7KysyqWf4N/NPyocWmOizquAPXv28PrrrxMdHU1ISEiJz59++ukjDWCAnj17/rMNYCsreOUV2L8fVCq4cQMSEiSPb3GOHJEG7vn5kJ4uyVWv/v+ipkKlYk98PCPbtcNOJqNR9eoE+PgQExdXQtavalXC6tXDo0KFx5Ybc+4cjWrUoJqzs2n0zM1lz9mzjAwOxs7GhkZeXgT4+RFzvGRn5+fpSVjTpnhUqmS0LN9q1bC0sADAzMwMtUbD3fv3n06vnBz27N/PyCFDsJPLaVS/PgGtWxNTzFgEiN6xg3d796ayuzvubm70792bbTt2SDrXrcvboaE4OzlhZWlJv169uJaczP2MDP31D7OyWPLdd3z4wQdPpWsRvXNz2XPmDCNDQ6X69PYmoF49Yo4dKyHrV7MmYc2bG61PD1dX+nfogJuzMxbm5nRv3Zo8tZprxgzUp0EmAz8/2LVLeo+uXZOMVmPtx4EDknGcny9NNP35J9SsWXi+YkXJg6zVSu/Z1avS5JQJMJfLceralTtTppCfnU320aNkxsZS0Ygh4Tp6NA937yZj/Xq0KhX5WVnkJiQUfmVPT+6vWwf5+aiuXiX7yBFs6tY1iZ7I5Zh37YpmyhTIzkZ79Cj5sbGYGzN0H4H25EnyV66UJhnUajTz52Pu6yvV8TPAXC6nQteu3NLVb9bRo2TExlLpMXrLatTAoVUr0kwYOfM0JGzbRmJMDIr09OdyfzO5HJuuXXk4ZQra7Gzyjh5FGRuL7RM+d+vAQFSHD5N39ChoNGTPmYNF1arI2rQxiZ6Wcjm1unblxJQpqLOzuXv0KNdjY6ljRM8awcGc/fxz1Dk5PExOJmHFCl5+9139+cTvv+fGrl3kPXxoEt2MceRING3aRFCtmjd2dk6EhQ3j8OFtxr+bpYyOHfvh49MIc/OSQ8Tatf3w9w/Dzc3DZPopcnPZc+4cI4OCsLO2plHt2gS89hoxJ06UkPXz9CSsSRM8XFxKLW/Vvn209PWlVrEJ8qclJiaa8PAIvLy8cXJyYvDgYcTEGK+/vXt/oXZtbwID38La2pqhQz8gMTGBq1evANC8eQs6duyEm1vpXsqvvppH7959cHZ+/BgGQKFQsOeXXxg5YgR2dnY0atiQgHbtiDHioY2Ojubd/v2pXLky7u7u9O/Xj23bpO9y4uRJ1BoNffv2RSaTEdmnD1rgmJHxy63btzl1+jRhYWFl0tGo3nl57Ll2jZGNG2NnZUWjKlUIqFGDmMuXS8gOrF+fuq6uWJqbU8vZmTc8PTlz9y4AHo6O9Pfzw83OTurfX3mFPI2GawbjEoHAGM/ECM7JyWHEiBF06tSJkJAQRo4cCcCWLVsICQkhJCSErl27kpaWBkgvZXBwMMHBwQwfPpx0XQccFRVFv379GD58OEFBQVy6dIlz587Rp08fwsPDCQ8P59dff32kLllZWUyaNImIiAiCg4P55JNP0Gg0xMbGsmbNGnbt2kVoaChLly4t8vnGjRv06dOHA7owxYcPHzJhwgSCg4MJCQlhxowZACxatIg5c+bo77d8+XIiIiLo0qULQ4YM4W+dN3XRokWMHj2agQMH0rFjRwYNGkROTg4AKpWKOXPmEBQUREhICMOHDwcgKCiIOAMjb9WqVUyZMqW8j6coLi6FRm0BKSlQls6jRg1ITS16LCICxo2DyEgwYSjK9fR0LMzNqWnQ8fm6u5NU3Fv9BGi1WqLj4uhSjhDj4lxPTZX0NPjuvtWqkXTnzlOVN3jJEl774APenjOHJnXq8OpTTjpcv3EDCwsLataoUaiXtzdJRrz2l69exbdOnSJyl0vx7p86exZXFxcqGEwifLlkCT27dqXSIwYpZdY7JUXS28AI9K1WjaS//ipXuRdv3CBPraaGq2t5VZRwdS00agv466+yGa81a0qRFwUcOiQZz+bmsbYi5AAAIABJREFUUrmenmCi8F3rOnVArUZlMMhQnjuHtRHjVd6sGZp79/A6epRXUlLwjI3FyqNw4Pv3ggVUjIwES0us69RB3rw5D/fuNYmeZjo9tQZ6as+dw6wUI9s8OBhZejpWFy5g/giPmnnr1mjv3IF790yiZ3Fs6tRBq1aTa6B3zrlz2D5mcqBSZCQPDx9GlZz8TPT6p2Che+4ag/pTnzuHZSn1Zx0cjHt6OpUuXEBe/Lnrwjv1f5uZYfnqqybR07lOHfLVajIN9Ew/d46KpehpVkyXiibSo6zcunWZ6tV99Z+rV/chMzONhw+fblLV1Oj7TYNxh2/VqiQZtotl5Pa9e2w9dozhuvBzU3DlymV8fArrz8fHh/T0NDIyStafJOuj/yyXy/HwqM6VK0llutf583H8+ecFunXrWWb9rl+/LvWTBpOpvr6+JCWVvOflpCR8DfTz9fXlsk4u6bKku+Hv1adOHaPlREdH06hhQ6oVi8h6Eq5nZkrP3WD84OviQtJj2metVsupO3fwKmUy82JaGnn5+dRwdHxq3V5k1Orn/+/fwjMJhz5y5AjZ2dn8rPMyZWZmcvz4cb755hvWr1+Pq6sr2dnZWFpacunSJebOnUtUVBRubm4sWLCAmTNnsmDBAgDOnTtHTEwM1atX58GDB0RGRrJ8+XLc3NxITU0lIiKCHTt24FjKj/2zzz6jcePGfPrpp+Tn5zN27Fi2bt1Kt27dSE5ORqFQMG7cOADUanWRz4bMmjULuVxOTEwM5ubm3DPyksbExHDz5k02b96Mubk569evZ/bs2cybNw+ACxcusGXLFhwcHBgwYADbt2+nW7duLF++nJs3bxIVFYVMJtOX3bt3bzZs2ICfnx9arZYNGzawULcez2RYWxdZSweAUil5th5Fu3bSwOLs2cJjW7dKA38zM2jWTDKEFy2SyisnCpUKe2vrIsccrK3JLq77E3D6xg3Ss7IINGEotEKpxL5YGKGDrS3ZT1kH3wwfTp5Gw28XL3L17l2jM/Nl0kuhwN7Orqhe9vZkKxQlZXNysLe3LyKnUCjQarVFOse7KSlM//xzxo8apT92Pj6eM+fOMWnMGO4WnyB5Gr2VSuyLhSuXpz4BsnJy+GjlSt4PDsZBLi+vihIyWcnfuVIpvV+PIjBQMnYNPR7x8dCrF7RtCxYWsHu3tL7eBJjb26N58KDIMU1mJhYODiVkZdWqIW/QgCvt26M8f54qn39OjQ0bSPL3B+DBjh1U//57XMeOxczSkrvTp5Nz6pRJ9MTeHorpqc3MxNyInvmbN6NZvhxSUjBr2hSrrVtRZ2SQv3FjUcGqVbFcsgT16NGm0dEI5vb25BfTW12K3oa4REbylynXU/9DMTNSf/mZmZgZqT/l5s0oli8nPyUFq6ZNqbB1K/kZGSg3biR3714c5sxB1qYNqt9+w37cOJDJMDPR+25lb09eMT1VmZlYGdHz5q5dvD5+PPv79sXW3R3fd9/F0lTtThnJzVUglxe26ba2kp5KZTYODmXzNj5LFLm5Jdt5G5unauc/2bKFkZ07Y/e4tvcJUCgURfpEe3up/rKzs0t4axUKBRUqFDXO7O3tyc7Ofux9NBoNn3wyjYkTP36ivr64fqDr343cU6FQYG/wOzXs37MVChyKlWPv4GC0nJjYWIaWM4RfkZeHfbFwZQeZjOy8vEdet+jUKfK1WroaGPMFZKlUfHTgAO83bIiDCX8Dgn8nz8QI9vX15cqVK0yfPp0mTZrQtm1bfv31V0JDQ3HVeV7sdAPy48eP06ZNG9x0M4A9evQgNDRUX1aDBg2orvN+nT17llu3bjFw4ED9eTMzM5KTk3nNSIIkgP379xMXF8eqVasAUCqVuD+Fh/LAgQNERUXpG6aKRmag9u/fz4ULF+jSpQsgNWiGDZO/v7/eWPfz8+PGjRv6ssePH69PslBQdmhoKEuWLCEjI4O4uDhcXFzw9fXFpOTmlhyoW1tLIZ2l0aSJFC69YkXR9VW67wPA4cOSTI0akJhYbjXlMhlZxQzerNzccnV0286do8Mrr2D3OIP/CZDb2JCl8/AXkKVUlmvdqZWFBW1efZXvDxyguqsrbzyF51oul5NVrCPLys7GzshgTG5rW6TTy8rORi6XFzGA792/z7sffECviAiCAgMByM/PZ/qcOUwaM6ZcibCK6GJjQ1axgVB56lOpUjFk8WLq1arF4GJrMsuFSgXFdTI2wWSIv7/k8V28uPA9ksth0CBp7f2ZM+DgAP36wcOHUgKtcpKflYVFsQlDC0dHNEZCMfNzcsjctk1v2KZMn86r6emYOzpiZmFBrV27uP3++9xfvx6rypWpsWUL6pQU0pcuLbeeZGVBMT3NHB3RGtFTa5DsSPv772i++grziIiiRnClSljt2YPm669LGscmJD8rC3Mj9Zv/iFBX+5YtsapcmfvF1tX/F9EaqT/zUp67YZKrvN9/J/urr7CJiEC5cSOaxEQy+/bFcfFiLKpUIeeHH1DHx5N/65ZJ9MzLysKqmJ4yR0ejIc1HRozAf9Eiel2+jDI9naQNG/DqWXYv39Nw9Ggsq1ZNBcDHpyHW1nJycrL05wv+trGxM3r9/zdya2uTtPP7z58nW6mkU8OG5dJnx45YZsyQ6q9Bg4ZS/5lVWH/Z2dLfdnYl608ul+vPF5CVlW1UtjgbN66nTh0f6tUzshztERTXD3T9e2n6Gcga9u92RsrJzsoqUc6p06dJS0sjsEOHJ9KzhC5WVmQVM3izVKpHruP94cIFoi9fZn1ICDLdcrEClGo1Q3btop6bG4Nff71cur3I/Js8sc+bZ2IEe3h4sGPHDo4dO8ahQ4eYP38+b7zxxlOVZfjyabVafHx8WLduXZmv12q1fP3113h4mG79yqPuNXToUCIiIoyetzYw2CwsLMh9jBdTLpcTHBxMVFQUJ06coHfv3ibVF5DCoM3NpTVyBd7typVLhjkX8Prr0KoVrFxZwlPzLPF0cUGTn8/19HQ8dWG2CSkpeD1lOKsyL49d8fEs7t7dlGri6eYm6ZmaiqduYifh1i28iiX3eBo0Gg03dUsInliv6tXRaDRcv3EDT92kUsLly3gZyY7sXasWCZcu4acL7Uu4fBlvA7nMBw949/33CWjViqEGa9uysrO5cPEio3SJxzS6LOFtOnfmq9mzafQUnZKnu7ukd0oKnrrJq4SbN/EqlmSsLKjy8hi+ZAnuzs7MeOedJ77+kfz9t/QeVaoEBc/opZeKhjkb0qQJBARIBnBmZuHxihWlsOoCj2pmphRt8fLLJjGCcy9dAktLZF5eqHQhbjb16pFrJOlWTlyctC65AIO/ZbVqodVouL9Wymyad/s2GRs34tipk0mMYK1OTzMvL7Q6Pc3q1UNbluRgWm3RUFhnZ6z27CE/NhbNrFnl1u1RKC9dwszSEmsvL3J1esvr1SPnEXpX6tuX+1FR5JfBU/RvR6N77hZeXmh09WdZrx7qMj53w4k65datKLduBcDMyQm3AQNQnTxpEj0zLl3C3NISJy8vMnV6utSrxz0jeubev88+g/am6aefkmpkraspadkyhJYtQ/Sfv/56DDduJNK0qTTxd+NGAk5OlV4ILzCU0m/evo3XE+ZC+P3SJS7cvElLXR/0UKnEwsyMS3/9xdJBg8pcTlBQCEFBhfX30UdjuHQpkY4dpfpLTEzAxaWS0TW7tWt7ExtbuF5YoVBw69YNatf2eux9jx//nVOnTnL48CFAiqJMSIgnIeEikyZ9XOp1np6eUj95/TqeumRgCQkJeHmVvKe3lxcJCQn46bLQJyQk4K2T8/L2ZuXq1UWivhITE+nVq1eRMqKjo2n/5ptlMuwfhaeTk/TcMzPx1CX8TEhPLzXMeUtCAsv/+IN1ISFULuaxVmk0DN+9G3c7O2a0bl0uvQT/HZ7JmuC7d+9iYWHBm2++yYQJE7h37x6vvPIKMTEx+nXA2dnZ5Obm0rRpUw4ePKhfO7t582ZalLJVzeuvv05ycjLHDJLixMXFoTUcqBUjICCA5cuX65Nq3bt3j5tPEVrYrl07VqxYob+XsXDogIAA1q9fT6ZuUKtSqUgwSCTzqLLXrFmDSud9NSy7V69erFmzhgsXLtChnLNuRsnLg4sXpQG5lZWU6MrXtzBTrSF+fvDmm7BmjZRoxhAnJ+laCwuwtJS2U5LLi3qHy4FcJqP9yy+z8NdfUahUnL5xg32JiYQa2U4kX6slV60mT6NBC+Sq1aiKJVX7JSEBJxsbmpkoe6ReT2tr2tevz8Lt21Hk5nL6yhX2nTtHqJEMivn5+eTm5RXqmZeHSjfFd+XuXQ5euIBSpSJPoyHm+HFOJSXR2MjWAWXSy9aW9u3asfCbb1Dk5HD63Dn2HTxIqBFvaGjnzqxav56U1FRS/v6bVT/8QBfdVgtZWVkM+OADGtSrx9hiia8c7O05/PPPRK9bR/S6dSzXLWmIWrsWv6dcAye3tqZ9gwYsjImR6jMpSarPZs1KyBapT622SH3mqdWMWLYMa5mMOe+++9Rh5aWiUkkZnTt2lEKjPT3h1VcLjVlDGjSATp1g2bKSa1P//lsy4Bo0kP53cJAiKp5yTXlx8hUKMqOiqDxjBuZyOfIWLXAKDeXe2pLbdNxftQrHLl2wqVcPLC1xnzKFrMOHyX/wgNxLlzAzM8O5Z09praW7O87du0uGsylQKMiPisJixgyQyzFr0QLz0FDyjehpHhIibdsGmDVujMWIEeTrtvfCwQGr3bvRHj2KZsIE0+j2CPIVCu5HRVFVV7/2LVrgHBpKmhG9AcxsbKjQrRtpukzBzxszCwssrK0xt7DQ/21WzNvyLNEqFCijonCYMQMzuRyrFi2wCQ0lx0j9WYeEYKZ77laNG2M3YgTKgucOWDZoAObmmFeqhNPy5eTGxqIxQVQSgFqh4GpUFI1nzMBSLqdyixZ4hoZyyYiejrVqYV2xImbm5lTv2JGXBw3itEHou7mlpVTP5uZF/jYl/v6hHDy4hdu3k8jOfkBMzFJatepSqnxengqVSpqkV6vzUKly9WOf/Px8VKpcNJo8tFotKlUuavUjIsfKgNzamvb16rHwp5+kdv7qVfadP0+oka0FH9XOj+zcmd1TphA9fjzR48cT8OqrvN2iBZ+V04EQEhJKVNQWrlxJ4sGDByxfvpTQUOP198Yb7UlKuswvv+wmNzeXZcuWUKeOD7VqSbswaDQacnNz0WjU0nfJzSVP5w395JPZxMb+zJYt0WzZEk3duq8yZMj7jBgxyui9CpDL5bR/800WLlqEQqHg9Jkz7Nu/n9CQkBKyoaGhrFqzhpSUFFJSU1m1apU+erFJ48ZYmJvz/dq1qFQqftA5nJoZjF+USiU7d+3SX1Me5FZWtK9Zk4UnT6LIy+P03bvsS04m1Mg4J/byZeafOMGqzp3xKBaFkafRMGLPHqwtLZnTrh3mhpOgAsEjeCae4MTERP062Pz8fAYNGkRwcDBKpZL+/ftjZmaGTCZj2bJl1KlTh7Fjx/KuzqPk4eGhTzpVHCcnJ77++mu++OILZs2aRV5eHh4eHixbtqxo4gkDJk6cyBdffEFoaChmZmZYWVkxceLEJ/YMT5gwgVmzZhEUFISFhQVNmjRh8uTJRWTCwsLIyMjgHd2sr1arpWfPno8NYR40aBDz5s0jLCwMKysratSooV/76+HhQa1atfDz8yt1T7pys2MHhIVJCa0UCti+XRqM16ghbddSsH/tG29Ihu3gwYXXxsVJ8jIZBAVJniy1WvKArV0LxUKDy8PUzp2ZGBNDi7lzcba1ZVrnzni7uXEqOZmB69bptz46mZxM5Jo1+uv8Pv2UJjVqsNYgi3f0uXOE+PmV+rspl549ezJx7VpafPQRznZ2TOvZE++XXuLU5csMXLKEszrj8GRSEpHz5xfqOWIETby9WTt6NGi1LP7pJ/733XdYmJtTw82N+e+9R91yZOOeOm4cE2fOpEWHDjg7OTFt/Hi8a9fm1NmzDBw5krOHpNnnHuHh3Lx9m2BdyF5EaCg9wsMB+OXXXzkfH0/S1av6jNEAP23ezEuVK+NqkJk5Vzep41KxYrnCo6f27s3E1atpMXo0zvb2TOvdG++qVTl16RIDFy7k7OLFAJy8fJnIuXP11/kNG0aTOnVY++GHnL1yhQNxcdjIZDTWJeoD+HbECBoZJAErF1u3SvsET58uvUdbt0pJ5mrWlEKcC4ywt94COztpX+0CTp+GLVuk8OlVq6R3qWtXaZIqPt6k+wTfHjYMj5UreSU1FU16OreGDiU3Ph47f39q7tzJBd16sawDB7g7cSK1fvoJM7mc7CNHuKHzCOQ/fMj18HCqzJlDtaVLyc/J4cH27aSYcF2retgwLFeuRJaaCunpqIcORRsfj5m/P1Y7d6LS6WneoweWK1eCtTXaW7fQzJlDvi7LsnmXLpg3aYK2bl1kBu+/6pVXTLbOujjJw4ZRc+VK6qemok5PJ3noUJTx8dj7+1Nn507OGKzHqxAWhiYjg4eG+0Q/R1pPnkzbadP0n+v16cOv06Zx8BntXWuMzGHDcF65ErfUVLTp6WQOHYo6Ph4rf38q7txJiq7+bHv0wFn33PNv3SJrzhxyDLJrO331FZb16kFeHjk//shDE68FPzxsGO1WrqRfairK9HQODx3K/fh4qvj703nnTr7T6enasCEtFyxA5uxM5qVL7Ovdu8g2Sm2+/RZfg99mw8mT2d+vH4kGfVh58fNrTefO7zFrViQqlZLGjQMJDx+hPz9+fGeCgwfrvccffdSRtLTbAHz++QAAvvxyH66u1UhMPMmsWZH6awcM8MPXtwmTJhmf6CkrU7t1Y+K6dbSYOFHqN7t3x7tKFU4lJTFw6VLO6saUJ69cIdIgN4rf6NE08fJi7ciR2NvYFFlbbGNlha21Nc7l9Fj6+7emf//3ePfdSHJzlbz5ZiDDhxfWX1hYZ957bzBBQSFUrFiRL79cxKxZM5gw4UNee60en3/+pV52+/YYpkwpnJBr1MiPkJAufPrp7BK5baysrLC3t8fhMTkFAKZ+/DETJ0+mhb8/zs7OTJs6FW9vb06dOsXAwYM5q9vft0f37ty8eZNg3bLDiK5d6aGLiJPJZCxZvJjJU6Yw78svqV2rFksWLy4y9ty7bx+ODg5FDOPyMNXfn4kHD9Li++9xtrFhmr8/3hUrcurOHQb+/DNnB0i/vwUnT5KRm0tEVJT+2mBvb2a0bs3ZlBQO3LiBjaUljXVLHwG+7dSJRiaIwnvREOHQpsNM+yg3quC5k5WVRceOHdm6deuTr2X+uPTwmRcKU69zflaYaJuaZ85jtvV6YTAWbfAiEh39vDUoE+cMJlReZP4hbzsm8mk/c0pucvZiUvZg1OfLP+Nth4bH/xlDtyYZxvfyfdFQtX0GkXbPAJll/vNWoWzoJvtfeJ5hosRnxXPeTh6Qgkf/DTwTT7DANGzYsIGlS5fy7rvvPlUyL4FAIBAIBAKBQPDvQHiCTce/wgi+ePEi48ePL3H8nXfe4e23334OGpmGnj170vMZZ5EUCAQCgUAgEAgEgv8S/woj+OWXXybGICGGQCAQCAQCgUAgEAgExvhXGMECgUAgEAgEAoFA8G9GhEObjmeyRZJAIBAIBAKBQCAQCAQvIsIIFggEAoFAIBAIBALBfwYRDi0QCAQCgUAgEAgELzgiHNp0CE+wQCAQCAQCgUAgEAj+MwhPsEAgEAgEAoFAIBC84AhPsOkQnmCBQCAQCAQCgUAgEPxnEEawQCAQCAQCgUAgEAj+M4hwaIFAIBAIBAKBQCB4wRHh0KZDeIIFAoFAIBAIBAKBQPCfQXiCBQKBQCAQCAQCgeAFR3iCTYcwgv/NpKU9bw3KxvXrz1uDstGo0fPWoEycSXJ83iqUiQb+/s9bhbLxD/l91vvqq+etQpnYPXLk81ahTATWrPm8VSgT1a5de94qlInlz1uBMjL1H/Ie3aj8vDUoK87PW4EyIctIfd4qlIn8Sm7PW4UyYW4pzAvBi48IhxYIBAKBQCAQCAQCwX8GMVUjEAgEAoFAIBAIBC84IhzadAhPsEAgEAgEAoFAIBAI/jMIT7BAIBAIBAKBQCAQvOAIT7DpEJ5ggUAgEAgEAoFAIBD8ZxBGsEAgEAgEAoFAIBAI/jOIcGiBQCAQCAQCgUAgeMER4dCmQ3iCBQKBQCAQCAQCgUDwn0F4ggUCgUAgEAgEAoHgBUd4gk2H8AQLBAKBQCAQCAQCgeA/gzCCBQKBQCAQCAQCgUDwn0GEQwsEAoFAIBAIBALBC44IhzYdwhMsEAgEAoFAIBAIBIL/DMITLBAIBAKBQCAQCAQvOMITbDqEESwoiVwO77wDL78MWVkQEwOnTpWUCwiAtm3Bzg5yc+H0adi2DfLzn41eNjbQuTPUrAk5OXDgAMTHl5Rr2hT8/MDRUZI7fRqOHy8pV7269D2PHoWDB59arYyHD5m0cCFHz56lgqMjoyMjCW7btoScVqtl7po1bNmzB4CIDh0Y27cvZmZmnPrzTwZOm1ZEXqFUsnD8eAJbtuTjJUvY/uuv+nN5ajVWVlac3bz5ifX9+efVxMZ+i0qVQ5MmgQwYMB0rK5lR2QsXfmfVqumkpd3By8uPIUNm4+paFYB791JYuXIaCQmnsba2ISxsKO3b9yxRxqFD0SxdOo6BAz8hIODtEuczMjKYNHkyR3/7jQrOzowePZrgoKASclqtlrnz5rFlyxYAIiIiGDtmDGZmZgBcvHiRSZMnc+XqVWrXqsWnn3zCyy+/DMCixYtZ9s03yGSF3zM2OhoPDw8Apnz8MSdOniQ5OZlZn35KeJcuT1Kl0vfIyWHSjh0cvXaNCra2jG7XjuBXXy0hd+z6dZYcOUL83bs42diw//33i5y/ePcuM/fsITE1FTuZjO6vv87wVq2eWJ9S9VQqmbR/P0dv3qSCjQ2jmzcnuE6dEnLfnTlDdGIitx8+pIKNDb1efZX3GjQAIF2h4NMjRzhx+zY5ajXeFSsyoWVL6lWubDI9rSpUoO6KFbh06EBeWhqXJ0zgzoYNJeQa/PwzFQzqx1wmIzsxkd/8/LDx8KBlsTbC0t6ehDFjSP7yS9Mo6uQEs2dDq1Zw/z588QXExj7ii1nBTz9J7WbLltKxChXgm2+gdm2wsICkJPjsM6ntMhFmFSrgvGIFsg4dyE9L4+GECSiN1Kf91KnYT5qENjdXfyzNzw/NtWsAWAcF4fDZZ1h4eqKOiyPzvfdQX7xoMj3LQuPhw6nfrx9ur73GhQ0biOnf///1/mCa9whgwfHj7Lt6lSv37zO0USM+aNLEJPpt3bqaTZu+JTc3h1atAhkxYnqR9s+QM2d+Z/Hi6aSm3sHX148PP5yNu3tVg/O/8e23X3Dr1jXs7R0ZMmQ8bdp0AmD+/CnExZ3g9u1kxoyZRWBguEn0z8jKYtJ333H0/HkqODgwuls3glu0KCF3LD6eJdHRxF+/jpOdHfvnzzfJ/fV6PHjApNmzOXryJBWcnBg9eDDB7duXkNNqtcxdtowtO3YAEBEUxNghQ/T9UwHRu3Yx7tNP+eSjj3g7OBiA1Zs2sXbrVu5nZiK3taVTQAAfDRuGpeXjh+irV69mxYpvycnJITAwkKlTS3/Ov//+OzNnTufOnTv4+fkxa9ZsqlaVnrNKpWL69Kns3r0bW1tbBgx4j379Ct+rnJwcPv98Drt27UStVuPj48sPP6zT67Bu3Vru37+PXC7nrbc6Mc5OjqX544NNX/T3SPDv5rkbwZMmTaJLly40atSoVJnVq1cTHByMi4uLye8/YcIE4uLi8Pb2ZsGCBUU+BwcHc+rUKcaNG1fq9SkpKYwdO5a1a9eaXLfnRvfuoNHA+PFQrRoMGwa3b8OdO0Xl4uLg998lQ1Muh4EDJaN4//5no1dgoKTXV1+Buzt06wapqZCWVlTOzEwahKamSoPLnj3h4cOiBrO5ObRvL32vcjJj2TKsLC05unYtF69eZfCMGfjWrIl3jRpF5Dbt2sXeY8eIWbgQMzMz+k+ZQjV3d3q+9RaN6tbl7I8/6mWPnz/PkJkzadWwoXSP4cOZMXy4/vz4+fMxK0MHU5xz5w4TE7OcyZPXUKGCG19++T5btiykZ8+xJWQfPLjHl1++z6BBn9CgQQA//riAhQtHMXOmZHgvWTKW6tV9+d//FnL79hVmzozkpZdqUrduM30ZWVmZxMQso1o179Lrb+ZMrKysOHr4MBcTEhg8ZAi+Pj54exe9ZtPmzezdt4+Y6Gip/gYMoFq1avTs0QOVSsWw4cPpGxlJr1692LhpE8OGD2f3rl36AcFbb73F3M8/N6qDr48Pnd56iy/mzXviOtV/j127sLKw4Oj//sfFlBQGb9qEr7s73q6uReTkVlZ0rVePoFde4ZvffitRzpiYGNrXqcPad97hdmYmvdaswdfdnTeMDAyeSs9DhyQ9+/fnYloag3/6CV8XF7yLta9aYM6bb+Lj4sKNzEwGxMZSxcGBzt7eKPLyeM3NjfEtW+Jia8uWixcZ9NNP7O/TB7tSBmBPystLlpCvUvGruzsO9evT4KefeHDuHNnFjNoznToV+dz4wAHu6dog5c2b7HNw0J+z9fSkVVISKVu3mkRHAGbMgLw8aNIEXnkFVqyAixfh8mXj8gMHwr17khFcQHY2jBsH16+DViu1Td9+C40bS22eCXBasgStSkWquzuW9etT8aefUJ87h9rIRKJy0yYy+vQpcdzCywvndeu416kTeceOYffhh1SIjeVvX1+T6VkWHv71F4c++QSvwEAsbW3/3+5riCneI4AaTk6MbdGCjRcumEy3kycPs3Hjcr74Yg0uLm5Mm/Y+33+/kPfeK9nOZ2beY/r09xk9+hOaNw9g9eoFfPLJKBYtktr55OQkPvtsDB9+OJuGDVuSnf2QrKyH+utr1fKlTZtOfPfdFybTH2DGmjVSv7pkCRd/1ecRAAAgAElEQVSTkxk8bx6+1avjXa1aETm5tTVdW7cmqFkzvtm+3aQ6AMz48kupf4qJ4WJSEoM/+ghfLy+8a9YsIrcpNpa9hw8Ts2qV1D+NGkW1KlXoGRaml8l8+JBla9eWuDbA35/wTp1wdHAg48EDRkyZwtotW+jfo8cjdTty5DDffbecVavW4ObmxgcfvM+iRQsZM6bkc75//x4jRrzPzJmf0K5dAAsXLmD06FFs2iQ958WLF5GcnMy+fQdIS0ujX79IateuTatWrQGYOnUKarWGn37aiZOTEwkJhRNfAQEBhIeH4+joSEZGBv/73wjWWlrSv379x9fvC/weCf79PPc1wZ9++ukjDWCA77//nvT0dJPfOy0tjd27d7N9+3YWLFhQ4vMbb7zxSAMYwN3d/d9lAMtk8PrrsH275N29ckUydo3NqqWlSQYwSIanVgtubs9GLysr8PWFQ4ekAeetW9Ig04injWPHICVF0ufePbh0STLmDWnaFK5ehXL+rhRKJXt++42R77yDna0tjerWJaBJE2IOHCghG71/P++GhVG5UiXcXVzoHxbGtn37jJYbvW8fHVu0QG5jY/Seu3//nS4BAU+s76FD0bRrF4GHhzf29k6Ehw/j4MFtRmVPnvyFatW8adbsLWQya7p2/YDk5ARu376CUplNfPwJunQZiqWlFTVq+NK0aSC//lrUuNi4cR6BgX1wcKhg9B4KhYI9v/zCyBEjsLOzo1HDhgS0a0eMEU9adHQ07/bvT+XKlXF3d6d/v35s2ybpfuLkSdQaDX379kUmkxHZpw9a4JixCAAj9O7dm+bNm2NtbV0m+RLfQ6ViT0ICI9u0wU4mo5GHBwHe3sScP19C1q9qVcJeew2PCsbr5HZGBsGvvoqFuTnVK1SggYcHSX///VR6ldAzL489V64wsmlTSc+XXiLA05OYS5dKyA5s0IC6rq5YmptTq0IF3qhZkzO6iTAPJyf616+Pm50dFubmdK9blzyNhmsZGSbR00Iux71rV5KmTEGTnU3G0aP8HRvLS0YMM0NsatSgQqtW3P7+e6PnX4qM5P6hQyiTk02iJ7a20uTc/PmgUEgRM3v3QmmRBNWqQVgYLF1a9LhKBdeuSW2WmZlkUDo7S/9MgJlcjk3XrjycMgVtdjZ5R4+ijI3F9jH1WRzrwEBUhw+Td/QoaDRkz5mDRdWqyNq0MYmeZSVh2zYSY2JQPINxQVkw1XsE0MXXlzY1aphs8gjgl1+i6dgxAk9PbxwcnOjdexh79hhv548c+QVPT2/atJHa+T59PuDq1QRu3LgCwLp1S+ncuTtNmrTBwsISR8cKvPRSdf31oaG9adCgOTLZ07WdxlAolew5eZKRXbtiZ2NDIx8fAho0IObo0RKyfrVrE+bvj8czGHsocnLYc/AgIwcMwE4up5GfHwEtWxKze3cJ2ehdu3i3Rw8qu7nh7upK/x492LZzZxGZed98Q5+ICCo4ORU5Xr1qVRx1k3VarRZzMzOSyzBBHx0dTdeuEXh7e+Pk5MTQocOIjjb+nH/55Re8vLzp2PEtrK2tGT78AxITE7h69YqurG0MHToMJycnateuTUTE2/r+9erVK+zfv58ZM2ZSsWJFLCwsqFu3cOxVvXp1HB0d9fqbmZmTnJn5WP1f9PfoRUWtfv7//i2UywgeM2YM4eHhBAcHM3z4cDIzM7l69Srdu3cnJCSEoKAgVqxYAcDevXsJDg4mNDSUoKAgjusGp3369OGAzmDYtGkTb731FqGhoQQHB3PlyhWWLl1KamoqI0aMIDQ0lKSkpFLLKo1t27bx9ttvEx4eTmRkJFevXiUrK4vIyEiUSiVdunRhyZIlRT6vXr2aqKgoRowYoS9ny5YthISEEBISQteuXUlLS+PWrVs0bdpUL3Pu3Dn69OlDeHg44eHh/KoLYS2Qmz9/PmFhYQQGBnLKIMT4wIEDhIeHExISQlhYGAkJCXz33XdMnz5dL5OWlkaLFi3IKTA8nwVublI4c2pq4bHbt+Gll4zLN2oE8+ZJIYBVq8Lhw89Gr4oVJb3u3Ss8lpoKxbxsRvHwAEMjwtFRCpc+cqTcal2/fRsLc3NqVi0MHfOtWZOkGzdKyF6+cQNfgxlg35o1uWxETqFUsuu33wh74w2j99zz229UdHSksbEJgMdw69ZlatTw1X+uXt2HzMw0Hj68X4qsj/6zjY0cd/fq3LqVhFarBdD/X/D3zZuF3q+kpDiuXr3Am2+WDJEu4Pr161hYWFDTsF58fUlKSiohezkpCV8fnyJyl3VySZcv4+PjUyT0zKdOnSLlHDhwgCbNmtE5KIj1RsJAy8P1e/ek34HB7LWvu/tTGa99mzQh+vx58jQarqan88ft2zQv5jl4aj0zMiQ9DYwr30qVSDJ8r4yg1Wo5decOXhUrGj1/8e+/ycvPp0axwd3TIq9TB61ajcLAm/rw3Dns69Z95HVVIyO5f/hwqUbuS5GR3F6zxiQ6AtLSDI1GMmALuHgRvEuJfJg2DebOBaXS+Pmff5au/+472Lix3JN0BVjUqQNqNRqD+lSfO4dlKfVpHRyMe3o6lS5cQD5kSNGThuGdZmZgZoblU7RF/2Se1XtkKpKTL1O7dmE7X7u2D/fvp/HgQcl2/vr1y9SqVdiu2trKeeml6iQnS23nxYt/ADBwYDDdu/sze/ZYHjwwzWRXaVy/e1fqF6pU0R/z9fAg6datZ3rfEnrcvCnpUb3Q6Pf18iLJ8H3XcfnaNXxr1y4id9lALi4+ngsJCfQMDTV6r+2//EKDwECaBQWRkJREj1LkDElKuoyPT+Fz9vX1IS0tjfv3Sz5nSbbwOcvlcjw8qpOUlERmZiZ///13sbIK++G4uPO89FJVFi1aSPPmTQkJCWbPnqITATt2bKdRowa0aNGMxMQEejymrYYX/z0S/PspVzj0pEmTqKj7Ec6fP59vv/0WpVJJQEAAgwcPBiBTNxu0cOFCZsyYweuvv45GozFqyH3++efs3LkTNzc3VCoVGo2GoUOH8uOPP7Jw4ULq6MIBR48e/diyCjh16hQ7d+5k3bp1yGQyDh48yMSJE9m4cSPLly+na9euxMTEABAaGlrkc1RUlL6c48eP880337B+/XpcXV3Jzs7G0tISpcFg5sGDB0ydOpXly5fj5uZGamoqERER7NCtEcnIyKB+/fqMGjWK2NhY5s6dy8aNG7l27RqTJ09m3bp1eHp6olKpUKlURERE0LlzZ8aOHYudnR2bNm0iKCgI22cZ/mVtXejdLSAnRzpujFOnpH+urpJ39eFD43LlRSaTPNOG5OZKxx9Fq1bSQC0urvBYhw6FHuVyolAqsZfLixxzsLMj28hvsrisg50dipwc3cxp4cByz2+/UcHBgSalDCy37dtHWEBAibVGZUGpVGBra6//LJc76I5nl/DWKpUKHB2LdjJyuT1KZTa2tvb4+DQgKuprevf+iNu3kzhxYg+OjlIZ+fkaVq6cRv/+H2P+iLBthUKBvb19kWMO9vZkZ2cblzUIbXWwt0ehUKDVaslWKHAoVo69g4O+nLc6dqRbt25UcnHhXFwcI0aMwNHRkaDOnUvV7UlQqFTYF3tHHKytyVapnristt7ejIuNZeWxY2i0Wob7++NX2iTUk+qZl4e9lVVRPWWyx+q56MQJ8rVauurWWBuSpVLx0d69vN+4MQ5P6UkvjoW9PeoHD4ocU2dmYmnw/I3xUmQkVz75xOg5Z39/ZO7upOjWlJsEOzspb4IhDx8WDXUuoEMHaRnGnj1SW2mMTp2kNi0wUIp+MRFm9vbkF6vP/MxMzIzUp3LzZhTLl5OfkoJV06ZU2LqV/IwMlBs3krt3Lw5z5iBr0wbVb79hP24cyGSYFWsD/+08i/fIlOTkKLCzK2wP7eyk56xQZOvb6AKUSgVOTiXbeYVCajvT0lLYuzeW2bNX4OLixuefj2fJkplMmPD0S0cehyI3F/ti4xwHuZzs0iaPnpUeOTnYF3uXHezsyFYojMsa9EGG/Xt+fj7TvvySj0eNKrU/DG7fnuD27bl+8ybRu3fjUkqkUJF7KhQ4OBTe096+8DlXKHZ9drZCP17X6+gg9bUK3fdxMGgP7O0L+8+UlLtcvnyJDh06cPDgYf744w+GDh1M7dpe1NYZ/kFBwQQFBXP9+nViYqJxUT7eWfOiv0eCfz/lMoJjYmLYvn07eXl5KBQKPD096datG1988QU5OTk0bdqUZs2k9YHNmjXjs88+o0OHDrRu3Vpv0BrSrFkzxo8fT7t27Wjbtq0+aY0xuceVVcD+/ftJSEjg7belhDxarZYHxQYDZeHXX38lNDQUV53n0c7IIOfs2bPcunWLgQMH6o+ZmZmRnJxMhQoVkMvltGvXDoD69eszZ84cAH777Tdat26Np6cnADKZTL+OMSAggJiYGLp168aPP/7I6tWrn1j3JyI3VwrxM8TGpqQBWpy//5bWDPfoAcuXm14vlaqkIS6TScdLo2FDeO01WLu2cL2al5d0nYkSuchtbMgq1iFmKRTYGZmokNvYFDGOsxQK5La2JRNn7N9fqpH7V2oqJy5c4JMPPiiTfkeOxPLdd1MB8PVtiI2NnJycwkF7wd82NiV/zzY2chSKogP8nJxsvezw4XNZtWoG77/fBjc3D/z9Q7h1S/I07dmznurVffD2fvSaILlcTlYxIyIrO9vo+yWXy8k2kM3KzkYul2NmZoadkXKys7L05Xh5eemPN3j9dSIjI9m9e7fJjGC5TEZWsXckKzf3iUOzMnJyeG/DBj4ODCTo1VdJy8pixNatuNjZ0fsxy0bKpKeVFVnFJn+yVKpH6vlDXBzRiYmsDw9HZmFR5JxSrWbITz9Rr3JlBuvWr5sCTVYWlrrwugIsHB1RP2KSzbllS2SVK5dq5Fbt25eUrVvRGJlgeWqys6HY5Av29tJxQ2xtpRwL7777+DJVKmk5yp49Uh6DhIRyq6nNysK8WH2aOzqiNVKfhkmu8n7/neyvvsImIgLlxo1oEhPJ7NsXx8WLsahShZwffkAdH0/+/7OH7nlj6veovOzbF8uCBVI7/9prDbG1Ldp2F/wtl5etnVcosvWyMpk1gYHhVKsmRaP07DmYceOebSIyubU1WcUmkrNycrAzsjzomepha0tWsXc5S6HAzsikj9zWtsjkrWH/vn7bNnxq16Z+Gbyjnh4eeHt6Mv3LL1n86adFzsXu2cNUXd6Khg0blug/C/429pzt7Iz0tVlSXyvXfZ+srCz9kqDs7ML+09raBisrK4YMGYqlpSVNmjShSZOmHD16RG8E6/X39MTLy5vpXy9m8VtvPfK7vmjv0T+Ff1M48vPmqY3gU6dOsWHDBjZu3EjFihXZvn07mzdvJjAwkPr163P06FG+/fZbtm7dyty5c5k4cSKJiYkcO3aMkSNH0r9/f7p161akzMWLF3P+/HmOHTtGZGQk06ZNo42RtUZlKasArVZL165dGTly5NN+1TKj1Wrx8fFh3bp1Jc7dunWrSMY+c3Nz1GX4Jb/zzjuMHTsWFxcXateurTeUnxmpqZK3wtW1MIS4WjX466/HX2thAZUqPRu97t2T9KpQQcrAClJyrNLCTf38oHlz+OGHot5pT0+oUgUKwtytraV1eK6u8BQeIs+qVdHk53P9r7/w1HnrEq5dw8sgfKoA7+rVSbh2DT/dpE3CtWt4F5O78/ffnDh/vkgSLENiDhygwcsv41HGLLz+/iH4+4foPy9aNIYbNxJp3lxKJpScnICTUyWja3arVfPm0KHC9UVKpYKUlBtUqyYZlK6uVfnoo2+KlF27th8Af/75OxcvnmTIkEOAlCDr+vV4kpMv0r//x/prPD090Wg0XL9+Xf/bTkhIKGK0FuDt5UVCQgJ+fn56OW+dnJe3NytXry7iVU9MTKRXr16l1o1hKHd58axYUfod3LuHp262PSE1Fa+yhOsbcPP+fSzMzQnTfcfKjo50qluXQ1eumMQI9nR2lvTMyMBTF4KWkJ5ealjZlvh4lp85w7ouXahczNhTaTQM//ln3O3smGEkG3p5UFy6hJmlJXIvLxS6kDyHevXI+vPPUq+p2rcvKVFRRo1ccxsb3N9+mz+eIuv3I7l2TWr3PD2lpFYgZdUvnhTL01NaLrJpk/TZygocHKSs9eHhxhP0WVpKGexNYARrLl0CS0ssvLzQ6OrTsl491I+oTz3FIlWUW7ei1CUWM3Nywm3AAFQnT5Zbx38SpnyPTMEbb4TwxhuF7fysWWO4ciVRn8H5ypUEKlSoVMILDODp6V1kvXBOjoI7d25Qo4bUthqGSgNPFYH0pHhWriz1C3fv4qnr6xJu3MCreG6PZ62Hh4ekx82beOqcMglJSXgZWZ7iXbMmCUlJ+L3yil6uIAHW76dPc/KPPzh07BgAmQ8eEH/5MheTkvh41KgSZak1Gm4YaRNCOnQgqNc7+s9jx44hMTGRt96SnnNiYgKVKlUq4QUG8PLyLrJeWKFQcPPmDby8vHBycsLV1ZWEhARa6jLWG/bDhmHUBTzqZ6DRqLlRhjXBL9p7JPj/JycnhwkTJvDnn39iYWHBuHHj9M5CQ/bu3cvXX3+NSqXS23Xv6iaVo6KimDVrlj7TebVq1ViyZEmZ7v/Ua4IfPHiAvb09zs7OqFQqtuo6xeTkZFxdXQkPD2f48OGc1yWGuXr1Kj4+PvTt25eQkBD98QLUajU3b97Ez8+PQYMG0bJlSy7qZqTt7Ox4aGDIPK4sQwo8qXfv3gVAo9Fw4Smyx7Vt25aYmBjSdJmIs7OzyS3m+Xn99ddJTk7mmK6hA4iLi3vsYLtly5YcOnSI67pBlEql0s/Y+fj44OzszKxZsx45oDcZKhX88QcEBUke01q1JIPyxImSsi1aFHpBKleWwv0SE5+NXnl5UtmtW0sDyGrVpHV3xp5l3bpSluoNG6B4op5Dh2DZMimD64oV0mD1jz9AF7L+pMhtbGjfvDkL161DoVRyOj6efcePE2rkJQ4NCGBVdDQp6emkpKezats2uhRb9xtz4ACvv/wy1Q3WQhkSfeBAiWuehFatQjlwYAu3biWRnf2AbduW0qaNccOgceP23Lx5mePHd6NS5RIVtYTq1X2oWlWa+b19+wo5OVmo1SoOH44hLu4InTtLHoIhQ2Yzd+7PzJ4dzezZ0dSq9Spdu75P9+5FO3y5XE77N99k4aJFKBQKTp85w779+wkNCSmhT2hoKKvWrCElJYWU1FRWrVpFF51R06RxYyzMzfl+7VpUKhU/6CaimunCTvfu20dmZiZarZa4uDjW/vADbxjUo0qlIjc3F61Wizovj9zcXPKfYKsvuUxGe19fFh48iEKl4vTNm+y7dInQ114rIZuv1ZKrVpOn0aDV/a3SRSrUdHFBq9Wy/cIF8rVa/s7KYmd8PD4mSvoit7Kifa1aLDxxAkVeHqfv3GHftWuEGommiU1MZP6xY6wKCcGj2FrfPI2GEbt2YW1pyZw338TcxANjjUJBSlQUXjNmYCGX49yiBW6hofxVShJCcxsb3Lt1469SImXcu3RBff8+94wkrCsXOTmwezeMGiV5exs2lDI7byuWnObSJWk7pM6dpX8TJkiJBTt3liJo6teX8itYWUkTc4MHSxOKf/xhEjW1CgXKqCgcZszATC7HqkULbEJDyTFSn9YhIZjpBqRWjRtjN2IESt0SIQDLBg3A3BzzSpVwWr6c3NhYNM+q3S8FMwsLLKytMbew0P9t9v/oFTLVewTSu5SrVkttT34+uWo1mnJuM9i+fSi7dm0hOTmJrKwHrF+/lA4djLfzLVu25/r1yxw+LLXzP/ywhJo1faheXWrnAwPD2bMnijt3bqJU5rBx43KaNm1bqH+eCpVKajs1GjUq1ZO1ncaQ29jQvlEjFm7dKvWrly6x78wZQgu2FDMgPz+fXJWqsD1VqVCZyFUmt7WlfevWLFyxAkVODqfj4th35AihgYElZEMDA1m1eTMpf/9NSloaqzZupIvOEzp74kR+/uEHoleuJHrlSl719eX9fv0YpYsa/HH7dtJ1k/tJ166x/IcfaF6GyJrQ0FC2bt1CUlISDx48YNmypYSFGX/Ob77ZnsuXL7Nnz25yc3P5+usl1KnjQ61atXVlhbFs2VJdbp8r/Pjjj/r+tVGjRlSpUoXly79BrVZz5sxpjh8/jr+/v6T/jz/qk9cmJSWxfPlympdhwuJFf49eVJ53UixTeqJXrFiBvb09v/zyC8uWLWPy5MlGl8O5urqydOlSduzYwcaNG9mwYUORvEotWrQgJiaGmJiYMhvAUA5PcKtWrYiNjSUwMJAKFSrQqFEjzp8/z86dO9m+fTtWVlaYmZkxceJEAObNm0dycjIWFhY4OjryabEwj/z8fMaPH8/Dhw8xMzOjSpUqjBkzBoDIyEgmTpyIjY0N8+bNY/78+Y8sy5DGjRvzv//9j6FDh6LRaMjLy6Njx468+oSJPJo2bcqgQYPo378/ZmZmyGQyli1bVkTGycmJr7/+mi+++IJZs2aRl5eHh4dHCbnieHp6MnPmTEaNGoVGo8HCwoLZs2frZ9/efvtt5s+fb3R25JmwcSP06QNz5khhfRs2SAO12rVh+HAYPVqSq10bQkKkQVtWFpw5I4XxPSt27ZIGjSNHSoPPXbukgaSHh7St09y5klybNtKA1HDvyAsXJHmVqmgItVotGdjlWGs0dehQJn71FS3eeQdnBwemDR2Kd40a+r1/C7Y+6tGxIzfv3iVYty9sRIcO9OjYsUhZ0fv3MyDc+D6LZxMSSElLo6ORgUBZqV+/NcHB7zFzZiR5eUqaNAkkIqIw+dvYsZ0JCxuMv38Ijo4VGTVqEatXz2DJkg/x8qrHiBGFe6ueO3eY6OhlqFRKPD1fZvz47/RriO3sioZeWlpaYWtrr1+DbMjUjz9m4uTJtPD3x9nZmWlTp+Lt7c2pU6cYOHgwZ3V7pfbo3p2bN28SrEsYEtG1Kz26dwekJQRLFi9m8pQpzPvyS2rXqsWSxYv10Rc///wzkyZNQpWXh7u7OwPfe48uBltXDHjvPU7ovFlnz55lytSpfL9mDU2fYK/BqR07MnHHDlosWICzrS3TOnbE29WVUzduMHDjRs5+9BEAJ2/cIPKHH/TX+c2ZQ5Pq1Vnbpw/21tYsiohg7v79TNu1CxtLS9p5ezNUN9gwBVPbtGHi/v20WLkSZxsbprVpg7eLC6f++ouB27dzVpfPYcHx42Tk5hJhsHVXsI8PM9q25ezduxy4fh0bS0saf/ut/vy3wcE0MtH65YvDhlF35UrapqaSl57OxaFDyY6Px9nfn4Y7dxbZ+sgtLAx1RkapRu5LffuWakCXm48/ltrKkyelSbcpU6TJtcaNYeVKaUmGRlN0K7eMDCnRX8ExmQymTpXaMrVamvAbMKBogsJykjlsGM4rV+KWmoo2PZ3MoUNRx8dj5e9PxZ07SdHVp22PHjivXAnW1uTfukXWnDnkGGTbdvrqKyzr1YO8PHJ+/JGHBf3B/yOtJ0+mrcG+6vX69OHXadM4aJBI8lljivcIYMqvv7LNwNu/7PRpPgsIILwc6x0bN25Nt27vMXZsJCqVEn//QCIjC9v5997rTM+eg3njjRCcnSsydeoiFi+ewezZH+LrW49Jkwrb+Y4dI0hJ+YsPPpCWlDVq1Irhwyfrz48fP4C4OGmSPD7+LPPnT2Hu3O+pV6+Ude9lZGq/fkz89ltaDB8u9av9+uFdrRqnEhMZ+MUXnP3uOwBOJiYSOWuW/jq/AQNo4uvL2kmTynV/vR5jxjDxs89oERKCs6Mj08aMwbtmTU6dO8fADz/k7J49APQIDeXmX3/xf+zdeXwM5x/A8U/uZHOQkMSZQxKJK4hoKEqjxBWJo0XVWXcdLapKtaiqoq6Wn5s6q0ISSlstcd9H3SEitwhJiCSbO/v7Y5LNsRuSWEI979crL7s7z8x+PTvzzDzzHOM9aBAgPSc4f3Irs2Jj7/V0dTExNlbOY3Hx6lUWr1mDPC0Ni8qV8WrXjk+HDXtmbG3avJP3PF9pUteOHb0YN67gd+7WrSsjR47E27s7FhYWLF36E3PmzGbKlM9xdW3MokLPSh83bjyzZn1D+/bvYmhoyLBhw5WPR9LT0+Pnn1cwY8ZXrF27hho1ajBv3nxlBfrSpYssXboYuVyOubkFnTp58Wkp5zR4lY8joWRPnjxRO7TUzMxMOVN4afzxxx/MmzcPkOpCDRs25OjRo3Qu1pW+cePGytempqY4ODgQExPzzKcLPYuWQpN9AoUXYvr06djb2zOsFIViEWPGvJiANO0ld3Eqt969KzqCUrmYopnnyb5obk1ek7u0hSqsrzQNPZ7oRfvrJQxN0QQvDc3I/aLFqpmp9lX0AmaKeCG+Wbq0okMolUjf8c9O9Aqwua+mF9mr6EUPNdOQ3Kov6DGUGqb987KKDqF0xr8ex1Fhs2dXdARgbv4TP//8s8rnY8eOZVwp56wBqQftwYMHlZO2zZw5E1tbW4YMKXnegdDQUPr3709gYCDW1tbs3r2b+fPnY21tjYmJCcOHD6ddKYdoPdfEWMKLFRcXx8CBA7G0tOSrr7569gqCIAiCIAiCIPwnvQoTYw0aNEjZXb6w4q3APXr04F4JcwqdPHmyzN/74MEDxowZwzfffIO1tTUgDVft0qULhoaG3Lhxg+HDh7Np0yaVSdvU+c9UgkeNGkVsoQdnA1SvXv2ZXZFfZdbW1vyl5qHsgiAIgiAIgiAIL1tpuz37F58fo5gaNWoQExOjbAmOjY3Fo4RHCCYkJDBkyBCGDRtWpLt04Ud/1a9fHzc3N65cufJmVYJf58quIAiCIAiCIAjC07wKLcGa0qlTJ3bs2EGjRo0IDw/n6tWr/Pij6jPIHz16xJAhQ+jfv7/ykbf54uLilK3CMTExec+xHl2q7//PVIIFQRAEQRAEQRCEV9/HH3/M1KlT6SkEjiMAACAASURBVNChA9ra2syePRuTvAnjli5dipWVFf369WP16tWEh4ezY8cOduQ9anDgwIH06tWLrVu3cvDgQXTynhAwceJE6uc9quxZRCVYEARBEARBEARBeGlkMhnLlqmfRG1CoUk0v/jiC7744gu16SZOnMjEcj6lQFSCBUEQBEEQBEEQXnH/pe7QFU27ogMQBEEQBEEQBEEQhJdFtAQLgiAIgiAIgiC84kRLsOaIlmBBEARBEARBEAThjSEqwYIgCIIgCIIgCMIbQ3SHFgRBEARBEARBeMWJ7tCaI1qCBUEQBEEQBEEQhDeGaAkWBEEQBEEQBEF4xYmWYM0RleD/sk6dKjqC0rGzq+gISic+vqIjKBW3qhUdQSn9HlzREZROeHhFR1AqZ775pqJDKBWv776r6BBKJXT69IoOoVQOVHQApfTN0qUVHUKpzJowoaJDKBXv1uMrOoRSsXlNrtgTda0qOoRSscjOrOgQSqdWrYqOQBCeSXSHFgRBEARBEARBEN4YoiVYEARBEARBEAThFfeadK54LYiWYEEQBEEQBEEQBOGNISrBgiAIgiAIgiAIwhtDdIcWBEEQBEEQBEF4xYnu0JojWoIFQRAEQRAEQRCEN4ZoCRYEQRAEQRAEQXjFiZZgzREtwYIgCIIgCIIgCMIbQ1SCBUEQBEEQBEEQhDeG6A4tCIIgCIIgCILwihPdoTVHtAQLgiAIgiAIgiAIbwzREiwIgiAIgiAIgvCKEy3BmiNaggVBEARBEARBEIQ3hqgEC4IgCIIgCIIgCG8M0R1a4LFczvTffuPE7duYGxszsUsXvJs2VUl3+s4dlv/zDzdiYqhkZMShadNU0vxy7Bi/HDtGYkoK1c3NWTF4MPaWluWLKzmZ6f/7HyeuXMHc1JSJH36Id5s2KukUCgULt27F7+BBAHq3b8/k/v3R0tIC4NTVq8zfvJmI+/cxNzVlhK8vfTp0UK67cvdudvz9N0/kcto2bcq3I0diIpOVK2aAxykpTN+wgRPXrklx9+qFd8uWKulO37zJ8j17uBERQSWZjEMLFxZZvmT3bg5evEhobCyjvb0Z5+tb7pggLz+XLePEpUuYm5kxceBAvNu1U0mnUChY+Msv+B04AEDvjh2ZPGgQWlpanL9+neEzZxZJL09PZ9nUqXi1asXXy5ez9/Bh5bKs7Gz09PS49Ntv5Y9bQ/un59y5xCcno6Mt3ftramvL+hEjyh2XCkND6N4d6tQBuRwOHYJr11TTtWwJjRtDpUpSuvPn4dQp1XS2tjBoEBw7BkFBGgtTx9ycOuvWUaljR7Lj44n68ksStm9XSee8fz+mhY43LX190m/d4qqrK7qWltguXYpZ27ZoGxuTdu0aERMnknr2rMbixNAQunYFe3tIS5Py4MYN1XQeHuDqCmZmUroLF+DMGdV0Njbw0Udw4gQcOaKxMLXNzbFctw5Zx47kxMeT+OWXpKjJTwD9pk2pumQJBm5u5Kam8njuXJKWLSuSxvCdd6h55AiP5swhccYMjcVpYG5Ou3XrqN2xI+nx8Zz58ktC1MSpX6kSrZcuxaZzZwCurVjB+VmzlMubz56Nva8v5vXqcWHOnCLLNOFxejrTDx3iRFQU5oaGTGzZEu+6dVXSrb14kYBbt4hJTsbc0JAPGzZkmJubcvmSM2c4ePcuoY8eMdrdnXFvvaXROEuj+Sef0GTwYKwaNeLa9u0EDhny0r57//6N7NmzhszMNN56y4uPP56Fnp6+2rTXrp1iw4ZZxMfH4ujoyqhR87C0rAlAYmIc69fPJDj4AgYGhvj6jqZDh37KdS9cOMSvvy7i4cMYbGycGTFiDrVqOZY5Xk2cNxOePOG7bds4e+sWaRkZONWsyZd9+9LYwaHM8RS3fftGtmxZQ3p6Gu++68WUKbPQ11efn+fOnWLhwlnExcXSoIErX301j+rVpfz8+ef5/P33PlJSkjEzq4SPTx8GDx6lXPfYsUOsXLmI2NgYHBycmTZtDvb2z87Px0lJTP/6a06cOoV55cpMnDAB765dVdIpFAoWLl6M3+7dAPTu2ZPJn32mvF6aMXMmZy9cICIigrmzZ9Oz0HWHf2Agm7duJTwyEhNjY7p17crE8ePR1S1/VeKxXM70Xbs4ERIind+9vPBu0kQl3enQUJYfOlRwfv/iC5U0v5w4wS8nTkjXn5Urs2LAgHJff77KRHdozXmtK8E//fQTcrmcL9QcDKXxzz//8OOPP2JgYMCiRYu4e/dukfefffYZO3bswNDQsMRtTJ8+nR49euDu7l7e/0aFm+3vj56uLie++Yab9+4xcv16XKpXx6latSLpZPr69GrenG5NmrDq0CGV7ew8cwa/s2dZ/fHHOFhZEZWQgNlzVCZnr1snxbVmDTfDwxn5/fe42NnhVLt2kXQ7/vmHf86eJXDhQrSAId9+Sy0rK/p17EhWdjZjFyzg8wED6PPee1wNDWXQzJk0dnLCxc6OgCNHCDx6lO1z5mBmbMzkZcv4dv16fhg7tvxxb9mCno4OJ5Yu5WZkJCOXLMHFxganmjWLpJMZGNCrTRu6eXiw6vffVbZja2XF5A8+4FcNVYBmr1wp5efmzdy8e5eRs2fjYm+Pk61tkXQ7/vyTf06fJnDZMrS0tBgyYwa1rK3p17kz7g0acGnnTmXaM1evMurbb2nTrJn0HZ98wuxPPlEun7p4MVraz9fhRFP7J8DKIUN4W80FtUZ06QI5OfDjj1CtGvTrB3Fx8PBh0XRaWhAQIC2zsID+/eHJE7h+vSCNtjZ4eUF0tMbDtFu+HEVmJhetrZE1aYLzvn3IL18mrVgF81aXLkXe1wsK4klevuqYmJB67hyREyeS9eABlh9/jPO+ffxrZ0duaqpmAvXykvJz6VKwtoYPPoAHDyA+vmg6LS3Ys0daZm4u5XtyctEKs7Y2dOgAMTGaia2QqsuXQ2Ym4dbWGDRpQrV9+8i4fJmsYvmpXaUKNf78k/jPPiPFzw8tfX10a9UqujFdXaouXUr66dMaj7PN8uXkZmay0dqaqk2a0GXfPuIvX+ZRsThbLV6MrkzGFjs7jKys8D54kOSICG5t3AjAkzt3OD1lCvVHjVLzLc9v9tGjUvk5ZAg34+MZuW8fLlWq4FSlSpF0CuCH997DuUoVIpOS+HjPHqqbmtLVyQkA20qVmPz22/yq7kbUS5J87x5H58zB0csLXSOjl/a9ly8fIzBwNV999Qvm5lYsWjQWP79l9Os3WSXtkyeJLFo0lhEj5uDm5snOnUtYtuwzvv1WunG5fPlkbGxc+PTTZcTEhPLttwOpUcOeBg1aEBsbzvLlk5kyZTVOTk3Yu3cdCxeO5scf/0BHp2yXl5o4b8rT02lkb8/Uvn2pYmaG39GjjFiyhEMLFmD8lOu4Zzl9+hibN6/m559/oWpVK6ZOHcvatcsYM0Y1Px8/TuTLL8fy5ZdzaN3ak9WrlzBjxmesXSvlp7d3bz7+eCxGRjIePIjj00+HYmdXh3btOhIVFc7MmZNZtGg1DRo0YevWdXz++Wh+/fWPZ1Y0Z3/3HXp6epw4fJibwcGM/OQTXJydcXIsWoHesXMn/wQFEejnJ53fR4ygVq1a9PvgAwBcnJ3p0qkTCxYvVvmOtPR0pn3xBa6urjxKTGT0uHGsNzNjxLBh5c1aZgcGSr/79OncjI1l5MaN0vnd2rpIOpm+Pr2aNaNb48asUnNNtPPcOfzOn2f1oEHS9WdiImYv8ZgTXk9vdHfoX3/9lfHjxxMQEECdOnVU3gcGBj61Agzw3XffvdYVYHlmJgeuXmWClxfGBga429vjWb8+gRcvqqR1tbHBt1kzaltYqCzLzc3l57//Zlr37jhaW6OlpYVN1apULmclWJ6ezoHTp5nQty/GRka416uHp7s7gWpacAIOH2aotzfVqlTBukoVhnh745/XGpmUkkJKWho+77yDlpYWro6O1KlVizt5lYug8+fp7elJ9apVMTYyYrivL/tPniQtI6N8cWdkcOD8eSb07ImxoSHudevi2aQJgSdPqqR1rVMH37ffpnYJdyp7tG5NW1fX5zp5K+NKT+fAyZNM+OgjKT8bNMDzrbcIVHMyCTh0iKG+vlSrWlXKT19f/PNa2VXSHjxIp7ffRqYmRnl6On+dOkUPT8/yx62h/fOF09ODevWk1sqsLIiKgtu3pRbK4k6ehPv3QaGAhAS4dQuK3dihZUu4e1e1wvectGUyLHr1InrGDHJTU0k5cYLHe/ZQdcCAp66nb2uLaZs2PNy0CYCMsDDuL15M1v37kJvLwzVr0NbXx9DZWTOB6umBiwscPSrlZ3Q0hIRAw4aqaU+flm4oKBSQmCjle/HKpYeHlJ8JCZqJL4+WTIZJr14kzpiBIjWV9BMnkO/Zg6ma/Kw8cSLyv/4iZds2yMxEkZJCVnBw0TSTJiE/cIDMYp8/L12ZjDq9enF2xgyyU1O5f+IE4Xv2UFdNnLbe3lyaP5/stDSSIyIIXreOekOHKpff2rSJyD//JCs5WaMxAsizsjgQGsoEDw+M9fVxr1EDTzs7Am/fVkk73M2NBpaW6GprU8fcnPb29lyMjVUu7+HiQltbW4xLaK17GYL9/bkVGIhcw/vdsxw9GsC77/amdm0nTEwq0bPnGI4c8Veb9ty5v6lVy4kWLTqjr29Ar17jiIgIJiYmlPT0VG7cOEuPHqPR1dXD1tYFDw8vDh/eBcCVK8dxdnbHxcUdHR1duncfTmJiHDdvnitTvJo6b9a2smKIlxdWlSujo61Nn3btyMrOJuz+/TLFU9z+/QF4e/emTh0nzMwqMWTIGPbtU5+fhw//jb29E+3bd8bAwIBhw8YREhJMeHgoALa2dTAyKrgu0tLSJjo6AoDTp4/TpIk7jRu7o6ury4ABw3n4MI5Ll56en3K5nAN//82EsWMxlslwd3PDs107AvfuVUkbsGcPQwcOpFq1alhbWzNk0CD8AwOVy/v360fLFi0wMDBQWffDPn1wb9YMfT09rK2t8e7alYv//vvsDCwp7sxMDly/zoQOHaTzu50dnvXqEXjpkkpa19q18XVzK/n68+BBpnXtWnD9WaVKua8/X3XZ2RX/91/x0ivBf/31F506dcLX15eVK1fi7OxMamoqR48exdfXF29vbwYNGkREhFQoPHz4kAEDBtCzZ0+6du3K/Pnzy/R9d+/eZdiwYfTq1Yvu3buza5dUeM+dO5cLFy6wcOFCBgwYoPIeUMYGEBoaytChQ/H29sbb2xt/f6kAHDBgAEF5FYmUlBSmT59O79698fb2Zs6cOeTk5CjT/fDDD/Tr14/27duzsFAXnri4OMaNG6fc9qpVq4iLi6N169ZkFKqMjRo1ir1qCrXnEf7wITra2kW6jLhUr86dMp407iclcT8pidv379N2zhw8585l2V9/kZubW764YmPR0dHBvkaNgrjs7JSV18JCoqJwsbMrki4kKgqAqpUr061VK3YHBZGTk8OlW7e49/AhzVxclOkVhbalUCjIzMoiotDFVJnivn9firtQK6VL7drceQEtUGURHhMj/c6F7qq72NtzJzJSJW1IZCQu9vZF0oWoSSdPT+fPkyfxbd9e7XceOHkSCzMzmquruJQ2bg3tn/kmb99Oi5kzGbp6NcH37pU7LhVVqkBurlQJyxcXB6XpimVjU7S1uFIlaNJEo1128xnWrYsiO5v0kBDlZ6mXL2PUoMFT17McOJDkY8fIzCuXi5M1boyWvj4Zd+5oJlALC9X8fPCgdPlZu3bR/DQzk25GHD+umdgK0cvLz6xC+Zlx+TL6avLTsEULchITqXniBHZxcVTbswfdQjc/dG1sMB06lEezZ2s8zsp165KbnU1SoTgTLl/GooTfPb9rZN4bLJ7jGC6L8MePpeO9cmXlZy5Vq3Kn8H6ghkKh4HxsLI4VcQPsFRQdHYKtbcE5zsbGmaSkeJKTH5WQtuDmlaGhDGtrG6Kj76BQSGfH/H/zX0dFhRTagqLYawVRUao3LZ7mRZ03b0ZGkpWdja2V1XNtJywsBCengvx0cnImMTGepCTV/JTSFuSnkZGMWrVsCAsrKBs3bVqNp2dTfHzeIT1dTseO3splxfMaFNy9+/T8DI+IQEdXF/vC10HOztwJDVVJGxIaikuhm5Uuzs6ElLPcPnfhAo7P0dU8PD5e/fk9Lq5M27n/5Il0/RkXR9t58/CcP59lf/9d7utP4c3xUivB8fHxfP3116xcuZKAgABlK2tSUhJTpkxh4cKF7N27l27dujF5stTNxMzMjJUrV7J7924CAgK4du0aR48eLdX3ZWdnM3nyZL788kt27drFtm3bWL16NaGhoUybNo2GDRvy1VdfsXnzZpX3xbczZswY3n//ffbu3cvevXtpp2Ys5ffff0/z5s3x8/MjMDCQxMREZaUbIDY2lq1btxIQEMDOnTsJDw8HYPLkyTRu3Fi57ffffx9ra2uaN2/O/v37AYiOjubatWt4eXmVNdufSp6RgUmxO36mRkaklrEl9H5SEgAnbt9m76RJbBo1in3//ovfubLdEVbGlZ6OSbGuLKYyGalpaerTFrrjZyqTIU9PV55MurZuzXI/Pxp9+CH9v/6az/r1o3rVqgC0adoUv4MHiX7wgOTUVNYEBAA8V0uwSbFWUVOZjNT09HJtT1OK5xGAqbFx6fLT2Bh5WlqRkzNIlVxzU1PeKuEC2f/gQXw9PYteUJc1bg3tnwAL+vXj0LRpBE2bhoejIx+vWcMTNf//ctHXh+IxpadLnz9N27ZSd97Cd9M7dSpoUdYwHRMTcp48KfJZTlISOqamT12v6sCBPMzrDquyTVNTHDZvJmbWLJVtl5u6/MzIeHZ+tmkj5eeVKwWfdexY0KKsYdomJuQW+z/nJiWhrSY/dWrVwnTQIOInTCDCxoassDCsCo3JrbpsmbJFWdP0TEzIKhZnZlISemrijPrzT5pOnYqeiQlmDg64DB2K7ktqUZFnZWGip1fkM1N9fVIzM5+63k9nz5KrUNCrXr0XGd5rIz1djpGRifK9TGaa97nqvpWeLlcuL0hvQnp6KkZGJjg7u7F79woyMzMIC7vO2bMHyMyUys2GDVty8+Y5btw4Q3Z2JgEBK8nOziIjo2znuxdx3kxJS2PK6tWM9fHB9Dn337Q0OcbGBflpYiLll1yump9yuVy5PJ+xsUmRtAMHjuDgwYts3OhPp04+ym03b96SS5fOcfHiGbKyMvnll5VkZWWR/ox8kMvlmBgbF/nM1MRE2YijGp9JkXRyuVzl/P4sfv7+XLt+naGDB5dpvSKxqDu/GxqW//ozJIS9Eyawafhw9l2+jN/58+WOTXgzvNRK8OXLl6lfvz52eXerevXqBUBwcDAuLi445o1d6NWrFzdv3iQlJYWcnBzmz59P9+7d6dmzJyEhIQSXsqtYeHg4oaGhTJw4ER8fH/r3709WVhZ3794tU9xhYWFkZ2fTOW+iEABzc3OVdIcOHWLdunX4+PjQo0cPrl+/TlhYmHJ5p06d0NbWxtTUFAcHByIjI0lNTeXSpUsMLlSQWOTdzR4wYADbtm0DpK7bvXr1KnEihvKSGRiQUqzASUlPx1hNV5inMcy7cBnWrh1mRkbUsrCgT4sWHLl5s3xxGRqSUqyCkpKWhrGaMR4yQ0NS5fKCdHI5MkNDtLS0CI2JYeKSJfwwdizXtm/n90WLWBsYyOELFwDo9e67dG3VioEzZ9J14kRa5FXoqhUbf1bquA0MSCl2wkpJS9NIl+bnITM0JKVQHoGUTyXmZ6G8T5HLkRkZqVRmAw4dKrGSe+/BA85eu4bvc3SFBs3tnwDN7O0x1NPDSF+fkZ6emBoZcb7Q8flcMjOheEwGBtLnJWneXGqh3L5dGvsKULeuVNFTNwGUBuSkpKBjZlbkMx0zM3Ke0rXVpFUr9KpVI9HPT2WZlqEhdffuJeX0ae7Nm6e5QNXlp77+0/OzWTNo1Ah++60gPx0dpfXKWQ49S25KCtrF8lPbzIxcNfmpSEsj1d+fjPPnUWRk8GjWLIxatULbzAxZt25om5qS+hwTyD1NVkoKesXi1DczU9ul+fj48WSnpfFhSAidAwO5s307KS9gbLo6Mj09UordrEjJzHxql+YtV64QcOsWq7t1Q19H50WH+Eo6fnwPgwc3ZfDgpsybNwxDQxlpaSnK5fmvDQ2NVdY1NJQhl6cU+SwtLVWZ9pNPFvLwYTRjx7Zl3bqZtG7dHQsLqcW2Zk0HRo+ex4YN3zJ6dBuSkx9Rs6YjVapYq3zP02j6vJmemcmopUtp7ODAyG7dyrz+X3/twdOzKZ6eTfnss2EYGclITS3Io/zXMplqfspkRdNK6VNV0mppaeHsXB8DA0PWrv0JADs7B2bMmMePP35Lt25tSEp6hL29I1ZWT89PmUxGSrEKb0pqKsbGJcVXkDYlJQWZTFamm9X/HDzIoiVLWPO//2Gh5lq4tNSe3zMyyn/9+c470vWnuTl9PDw4cutWuWN7lVV0V+j/UnfoV35irA0bNvDkyRN27tyJgYEBM2bMKNJF+GkUCgXm5uYEFhrv8CIpFApWrFhB7eLj+/IUHmOho6Oj7CpdEjc3N3Jycrhw4QL+/v74qbkIfV52lpbk5OYS/vAhdnldUoLv3cOx2KRDz2JvaYmejk6RgrT87X9gV706OTk5hMfGYle9uhRXeDiOxcf6AU61axMcEYFr3oQowRERysmzQiIjsatenTZ5sw3WqVmTtm5uHL10iXbNmqGtrc34Pn0Y36cPAMcvX8bawgLrcnars6tWTYr7/n3s8vIwOCoKx2KTe7xsdjVrSr/zvXvY5XUxDw4Lw9HGRiWtk40NwWFhuOZNIBUcFoZTsXSxDx9y9urVIpNgFRYYFIRbvXrULuN+pBK3hvZPdbS0tMp897tECQnS5EsWFgVdeK2tVSfFytekCbRqBRs3SpM45bOzgxo1YOJE6b2BgTTW1coKdux47jDTb99GS1cXA0dHZddlWePGpBWelKsYy0GDSNy9W2XCKy19feoGBJAZHU3YyJHPHVsRiYlSfpqbw6O8LodPy09XV2kc9ZYtqvlZvTqMHy+9z89PS0vQQHmalZefeo6OZOXlp0HjxmSqyc/MK1ek785X6LVR+/YYuLtjmzcMQ7tSJcjJQb9RI+4/56zwAI9v30ZbV5dKjo4k5cVZpXFjEtXEmfHoEQc/+kj53uO773igyVm/n8KucmXpeH/8GLu8LtHBCQkldnP2u3GD1RcvsrVHD6oVat1607Ru3Z3Wrbsr3//00yQiI2/RsqU0uV1ERDCVKlXF1FS1wlKrlhNHjxaMb01PlxMXF6mc4dnSsiZTpqwqsm0Hh4K5Djw8OuHh0QmA1NQnBAXtok6dRmWKX5PnzcysLD5Ztgxrc3NmDxpU5vUBvLy64+VVkJ9ffz2JO3du8d57Un6GhARjYVGVSpVU89Pe3on9+wvyMy1NTkxMZIkzPOfkZBMTUzDcyNOzE56eUn4mJz9h795d1Kv39Py0s7UlJzub8IgI7PImugy+dUttV2UnBweCb93CtZG0zeDbt1Umz3qao8eP89WsWaxevhzn55xk0q5qVel4j4/HLq93XnBsLI7WZbuJYl+1qkavP4U3x0ttCW7cuDE3btwgMm98Yf642nr16hEcHExo3vgFf39/6tevj4mJCcnJyVhaWmJgYEBcXBwHS5igRx17e3sMDQ0JyOviCtLY3pSUlKespX47urq6/PHHH8rPHj1SHQvi6enJ6tWrlZXbxMREovLGppbE2NiYpk2bsrFQV8PEQuOfBgwYwMSJE2natCnV8yqDmiTT16dDw4YsO3AAeWYmF8LCOHjjBj6FHjWRLzc3l4ysLLJyc1EoFGRkZZGZd0vISF+fLo0bs/bwYVLS07n/+DE7zpyhXf365YvL0JAOHh4s27EDeXo6F4KDOXjuHD5t26qk9Wnblg2//05cQgJxiYls2LuXHnnd1evb2xMRG8upq1dRKBRE3r/P4QsXcM47UTxOTiby/n0UCgV3oqKY98svfNK7N9rlnNFYZmBAh2bNWBYQgDwjgwshIRy8dAmft99WSavMz5wcFFAkP0F6vFBGVhYKhYLsnBwysrLIKecYF5mhIR1atmTZ1q1Sft64wcEzZ/B5912VtD6enmwICJDyMyGBDf7+9Cg27jcwKIim9ephU8I+GRAUpLJOueLW0P5579EjLoSFkZmXp2sPH+ZRaipuhcZQPZesLKm1sV07aVKn2rXB2blot9x8DRuCp6dUYXv8uOiyw4fh559h1Srp7/ZtuHgRNHQjL1cu59Hu3dSaPRttmQyTt9/G3MeH+GJDQPJpGRpi8cEHxBfrCq2lq4uTnx+5aWmEDhpUtHKnCVlZ0oRh77wj5WetWuDkpP6RUw0aSPm+fbtqfh49CitXwrp10l9IiNT1XM1s7OWhkMtJ2b0b89mz0ZLJMHz7bWQ+PiSryc/kDRsw7tED/caNQVcX8xkzSDt2jNwnT0icMYPIunWJbtKE6CZNkO/Zw5M1a3igoUfqZMvl3N29m+azZ6Mrk1Ht7bex8/Hhtpo4zerUwcDCAi1tbWw6daLeiBFcmDNHuVxbVxcdAwO0tLWLvNYEmZ4eHerUYdnZs8izsrgQG8vBsDB81Fxs77l1i8WnT7Ohe3dqV6qksjwrJ4eM7Gyp/MzNJSM7u9zlZ3lp6eigY2CAto6O8rXWS2itbtPGh6AgP6Kj75Ca+gR////Rtm0PtWmbN+9AVFQIZ878RWZmBrt3L8fGxpmaNaUKVExMKGlpKWRnZ3LsWCBXrhyna9eC/fLu3Wvk5ubw5Ekia9bMoFkzT+W6paWp82ZWdjbjly/HQF+fH4YNK/d5vLjOnX3Yu9ePsLA7JCc/YePG/9G1q/r8bNu2A3fvhhAU9BcZGRmsX78cR0dn7OwcyM3Nxd//V548SUKhUHD9+hV27dqGu3vBw29RVQAAIABJREFUo6CCg6+Rk5PDo0eJzJs3g9atPbGze3p+ymQyOrz3HsuWL0cul3Ph0iUOBgXh4+2tktane3c2bNpEXFwccQ8esOGXX+jh46NcnpmVRUZGhnTcZGeTkZGhHFt76swZPp86lZ8WLVJWop+HTF+fDg0asOzvv6Xze3i4dH5X8wjEp/3uRvr6dHF1Ze2RI6RkZHA/KYkd587RrtDcL/8lFd0K/F9qCdZSaKwppHT++OMPlixZgpGREe3atWPt2rVcuXKF48ePs3jxYrKzs7GwsGD27NnY2toSExPDhAkTSE9Px9ramsqVK2NnZ8e4ceNK9Yik8PBw5s6dS2xsLLm5uVSpUoUlS5ZgYWHBgAEDGDp0KO/mVQSKv3d2dubixYsYGxsTGhrK7NmzSUxMREtLi6FDh+Lr61tknZSUFBYsWMCFCxfQ0tJCT0+PadOm4e7u/tTviouLY9asWURGRqKtrU23bt0Ykff80szMTDw8PFi+fDlvqzkhPNWePaVK9lguZ9pvv3Hy9m0qGxszKe85rOfv3mX4unVc+u47AM6EhjJw5coi675Vpw6bR48GpG6qM/z8OBwcjJmhIe97ePDJe+89u5tNCRWRx8nJTPvf/zh55QqVTUyY1L8/3m3acP7mTYZ/9x2XtmwBpBb4BVu2FHlO8OcffaT83v0nT7LCz4+Yhw8xlcnwbtOGSR9+iLa2NmH37jFq3jzuJyRgYWbGwC5dGKLmxCH9B0t38+RxSgrT1q/n5PXrUty9e+PdsiXnb99m+KJFXMrLwzPBwQz84Yci677l7MzmqVMBmLp2Lf4nThRZ/v3HH9OzdeunB5B3R1UlruRkpi1dysl//6WyqSmTBg3Cu1075bN/8x99pFAoWLBxY5HnBH8+eHCR37HTqFF83LMn73fsqPI9l4KDGfLVVxzftOnpz1su5bAGTeyfIffvM3HrVqISEjDQ08OlRg0md+lCoxJ6bRRR2tkvCz8nOC0NDh6UKm02NvDhh5DfXXjcOGmypsI9Qa5cgbzx/0V07y61bJbiMVlnvvmmVGHqmJtTZ/16KnXoQHZCAlFTp5KwfTumrVvj/McfnC80TrRK377UnjePf4sdo6bvvEP9I0fIkculCazy3OrcmeRnTEDlkfd7PVNJzwmuXRv69IH8yQXHjAFT06L5ee0a/Pmn6ja7dZPysxSTjoVOn16qMLXNzbFavx6jDh3ISUggcepUUrZvx7B1a6r/8QdhhfLTbNQozL/6Ci2ZjPTjx3k4Zgw5aroaW27YQE50dKmeE3ygVFFKzwl+d/16anXoQHpCAmemTiVk+3aqt25N1z/+YG1enA7vv0+rJUvQr1yZpNu3Of3FF0QdKPiWdzdswKXYOMBDgwdz65dfnvr9o5cuLVWcj9PTmXboECejoqhsaMikvOcEn793j+F793Ipr9eB56ZNxKWmol+oouPt7MzsvBugUw8exL9YGfO9pyc9nzFueNaECaWKszTafvMN7Yo9V/3wzJkc0cCzlb0vPP3Sbd++DezZs4asrHSV5wRPntwVX9+Rytbjq1dPsnHjbB4+vIejY2NGj/4eS0up19X+/RsJCFhJZmY6dnb1GDBgGg4OBRWgmTP7ERERjI6OHi1adOKjj6ZiaFhQ7rulq87wrI4mzptng4MZ8MMPGOrro13ofLVm4kTcn9Fqmejy9Our7ds3sHnzGjIy0lWeE/zhh10ZNGiksvX47NmT/PjjbO7fv0eDBo2ZMeN7qlevRW5uLhMnDufGjatkZ2dRtaoVXbr0YNCgkcrz68iR/bhzR8pPT89OTJgwtchs0hYm6oeEPE5KYtqMGZw8fZrKlSox6dNP8e7alfMXLjB89Ggu5fXmUCgULFi8GL+8+Wp69+rF54WeEzxgyBDOFhtLu2n9ejyaN2fA0KFcuHgRg0LDE5q5ubG22HkXKPWNxsdyOdN27eJkSAiVZTImdeqEd5MmnA8LY/jGjVzKO1bO3L3LwDVriqz7lr09m/OulVPS05nh7y9dfxoZ8X7z5nxSmjlJevYsVZyvEjWXXS/dgdKeeF5xL70SnJKSohyUv2vXLvz8/NheaHIQoajz588zc+ZM9u7dW/YJhkpZCa5wmmqNe9HK2IOgwpRQCX7laPgxMC/MczwC4mUqbSW4opW6ElzBSlsJrmivy7VIaSvBFU2TleAX6VmV4FdFaSvBFe1ZleBXRUmV4FeOhnrbvHCiElwu/5VK8EsfE7x582b+/PNPcnJyqFSpEnMKdbMSipo2bRonT57khx9+eK4ZdgVBEARBEARBeL39l7ojV7SXXgkePXo0o/O6z2rKzp072ZLXNbawefPmUe81fmTC3LlzKzoEQRAEQRAEQRCE/5RXfnbo0nj//fd5//33KzoMQRAEQRAEQRCEF0K0BGvOS50dWhAEQRAEQRAEQRAqkqgEC4IgCIIgCIIgCG+M/0R3aEEQBEEQBEEQhP8y0R1ac0RLsCAIgiAIgiAIgvDGEJVgQRAEQRAEQRAE4Y0hukMLgiAIgiAIgiC84kR3aM0RLcGCIAiCIAiCIAjCG0O0BAuCIAiCIAiCILziREuw5oiWYEEQBEEQBEEQBOGNISrBgiAIgiAIgiAIwhtDS6FQKCo6COHFOHCgoiMonY6f1q/oEEpF6+aQig6hVC5f/ryiQyiV33+v6AhKp3Llio7gv8XRsaIjKJ07dyo6gtJxd6/oCEqnWrWKjqB04uMrOoLS2dtMq6JDKBXzpa/HJeb4jxIrOoRSuXHfoqJDKJX6/26r6BBK58MPKzqCMnN1regI4MqVio5AM0RLsCAIgiAIgiAIgvDGEBNjCYIgCIIgCIIgvOLExFiaI1qCBUEQBEEQBEEQhDeGqAQLgiAIgiAIgiAIbwzRHVoQBEEQBEEQBOEVJ7pDa45oCRYEQRAEQRAEQRDeGKIlWBAEQRAEQRAE4RUnWoI1R7QEC4IgCIIgCIIgCG8MUQkWBEEQBEEQBEEQ3hiiO7QgCIIgCIIgCMIrTnSH1hzREiwIgiAIgiAIgiC8MURLsCAIgiAIgiAIwitOtARrjmgJFgRBEARBEARBEN4YohIsCIIgCIIgCIIgvDFEd2gBgEOHNvLPP2vIykqjSRMvPvhgFnp6+irpsrMz+eWXyURGXiMxMYbx4zfh5ORRZDtHj24mNfUR+voy3Ny64Os7BR0dDexqlSrBt9/C22/D48eweDHs21dyej092L0bjI3B01P6zNYWPv8cmjQBHR24ehXmzoXw8OePr5BPP23GF194IJPp4ud3m9Gj/yYzM0dt2vffd2bWrFbUqmVKVNQTpk07RmDgHQAGDmzA+PFuODmZ8+RJJtu23WTatKPk5CjKHdvvv28kIGANmZlptGjhxfDh6n9rgKtXT7F27Szi42NxcnLlk0/mYWlZE4CEhDjWrp3JzZsXMDAwpFev0XTs2A+Ae/fC2Lx5PrduXSI3NxdHx0YMGTKdmjXrlDvu5s2hZUvpZw0Ohj//hBw1WVqjBrRtC9WqgUIBERFw4ACkphZNp60Nw4aBvj78/HO5wyrC1RXc3EBXF0JD4cgRyM1VTWdtDW+9BZaWUowxMXD8OMjlBf+H5s2halXIyIAtWzQT3+sWJ2iubCqcbt48HzIyUvn226Mai/N1ydM//tjIvn1ryMhI4623vBg8uOT8XLFiMmFh14iPj2HatE3Uq1eQnzdunCYgYDnh4TcwNq7E4sWHniuuXbs2smOHFFebNl6MHz8LfX315dLFi6f4+edZPHgQi4uLK59/Pg9r65qFlp9kzZoFREeHYWJixqhRU2nbtgsAixfP4MqVs8TERDBp0ly8vHqWKc79+zeyZ49Ufr71lhcff1xy+Xnt2ik2bJDKT0dHV0aNKig/ExPjWL9+JsHBUvnp6zuaDh36Kde9cOEQv/66iIcPY7CxcWbEiDnUquVYplhLq/knn9Bk8GCsGjXi2vbtBA4Z8kK+pzSaNIFmzaRyPiQEDh9WX85XqwYtWoCVlXQcRUdLx1z+ceTmBvXqgakppKVJp/qLF8sez+OkJKbPncuJs2cxr1yZiaNG4e3lpZJOoVCwcMUK/PbsAaB39+5MHjMGLS0tEh8/ZsyUKYRFRJCTm4uDnR1Txo6lWePGAHz9ww/s/esv5baysrPR09Pj0sGDZY53z56N+PtLx1HLll6MGlXy/nnlyilWr57Fw4ex1K3ryrhx87CykvbP5OTHrFo1k8uXT6GlBU2btmbkyFnIZCYAjBjhSVJSPNraOgA4Ozdl5sz1ZY73cVoa0/fs4URoKOYyGRPbt8e7USOVdKfDwlh+9Cg3YmOpZGjIoU8/LbL85v37fPvHH9yKi8NYX58+zZrxSdu2ZY7ndSC6Q2vOa9MS7OzsTGrxq1gNGDBgAEFBQRrfriZFR0ezY8eOF7b9mzeP8c8/qxk3biOzZgURHx/N/v3LSkxfp44bAwfOx8zMUmVZo0aeTJniz4IFF5k27XdiYoI5cmSzZgL96ivIyoJ33oEpU+Drr8HxKRcFQ4fCo0dFPzMzg0OHoGtXaNNGOjNqqgaUp2NHO6ZO9aB9+x3Y2q6iTp1KzJrVSm3aGjVM2LKlKxMnBmFmtpTPPz/Ctm3dsLSUASCT6fHpp4eoWvVnPDy20L69DZMnNy93bP/+e4yAgNV8881GVqwIIi4umh071P/WT54ksmDBWPr2ncCGDWepU6chixd/plz+00+TsbKqxdq1J/jyy9Vs27aYa9dOA5Camoy7uydLl/7J2rUncHRsxPz5Y8odt729dO9j2zZYvhwqV5Z+PnUMDeHSJSndzz9DZiZ066aarkWLggsmTahdW7rwCgyEzZulXe2tt9SnNTCAGzekdJs3S7t1/n0akE5yN2/CqVOai+91ixM0WzblO3hwHSYmFhqN83XJ0ytXjvH776uZOnUjS5YE8eBBNLt3l5yfdeu6MWrUfCpVUs1PAwMZ77zTi759pzx3XOfOHePXX1czf/5GtmwJIjY2mk2b1MeVlJTIrFljGTRoArt3n6Vu3YbMmVNQLkVE3OH77ycxZMinBAScZ9WqQJycGiqX16njwrhxM3F0rF/mOC9fPkZg4GqmT9/IsmVS/vn5lVx+Llo0lvffn8CaNVL5uWxZQZzLl0/G0rIWK1eeYMqU1ezYsZjr16XyMzY2nOXLJ/PxxzNZt+4cbm7vsnDhaHJyXszVb/K9exydM4d/15e9EqNJNjbg7g7+/rBhg3Tf20P1PhYgHUfXrsHGjVLazEzo0KFomgMHYNUq6bh0dQUnp7LHNPvHH9HT0+PEvn0smDmTmQsWEHL3rkq6HQEB/HP0KIGbN7Nn82aCjh/nV39/AIyNjJg7fTqn/viDcwcOMPyjjxj9+edk59VmZn/xBZcOHVL+devQgU7vvlvmWC9dOsbu3auZNWsjq1dL5/ft20veP3/4YSz9+k1g8+azODg05McfC/bPbduWkJLyhFWrDvK///3D48cJ/PrrT0W2MW3aSrZvv8T27ZfKVQEGmL1/P3o6OpyYPJkFPXsyc98+Qh48UEkn09enV5MmTCn+I+eZtGsXzW1sODtlClsGD2b7+fMcvHWrXDEJb47XphL8JouJiXmhleAzZwJo0aI31as7IZNVolOnMZw54682ra6uPu++OxgHB3e0tVV3H0tLG2Qys7x3CrS0tHn4MOL5gzQygo4dYdkyqeZy8SIEBYG3t/r0NWtKNZ81a4p+fvWq1DqclCRdbW7aBHXqSGdbDRk0qAHr1l3lxo0EHj/O4NtvTzF4cEO1aWvVMuXx43T+/DMMgP3775KamoWDQ2UAVq78l+PHY8jKyuXevRS2br1Jq1Y11W6rNA4fDsDTsze1azthYlKJ3r3HcPiw+t/6zJm/qV3biZYtO6Ovb8AHH4wjPDyYmJhQ0tJSuX79LD17jkZXVw87OxdatPDi0KFdADg5udK+/fuYmlZGV1ePrl0Hc+9eGMnJj9R+17O4usK//0J8PKSnw4kT0mfq3L0rtRRnZko/8fnzUKtW0TSVKkHDhnDyZLnCUcvZWaq8PHokteCdPw8uLurTRkZKrYVZWVKMV69KLRv5HjyA27fhyRPNxfe6xQmaLZsA4uOjOHduDx06jNBonK9Lnh4/HkDbtr2pVcsJY+NK+PqO4dixkvOzU6fBODurz08HB1dat/bFyqr2c8f1998BdOrUGzs7J0xNK9G//xgOHFAf1/Hjf2Nn50TbtlK5NGDAOO7eDSYyMhSArVv/R9eufXjrrbbo6OhiZmZOjRo2yvV9fPrj5tYSfX2DMsd59GgA775bUH727DmGI0fUx3nu3N/UquVEixZSnL16jSMiQio/09NTuXHjLD16SOWnra0LHh5eHD4slZ9XrhzH2dkdFxd3dHR06d59OImJcdy8ea7MMZdGsL8/twIDkSckvJDtl1a9enD9OiQmSsfR2bPSZ+pERMCdOwXl/JUrUL16wfKLF+HhQ6mV+PFj6bxQo0bZ4pGnpXEgKIgJI0ZgLJPh3rgxnm3aEPjnnyppA/bvZ2i/flSzssLayooh/frhv38/AAYGBtSxtUVbWxuFQoG2tjZJyckkqTnI5Wlp/HX4MD26dClbsEBQUADvvdcbGxtp//zggzEEBanfP0+fls7vrVpJ+2ffvtL5PTpaOo7i4qLx8GiPTGaCsbEpHh4diIq6U+aYnkaemcmBGzeY8O67GOvr425jg6ezM4FXrqikda1ZE9/Gjaltbq52WzGPH+Pt6oqOtjY2Fha42dhwR01l+r8gO7vi//4rXtlK8IEDB+jUqRM+Pj4sX75c+fnly5cZMGAAPXv2pGfPnhw+fBiA6dOn88svvyjT3b59m/bt26NQKEhJSWH69On07t0bb29v5syZQ46a/jXx8fF88skneHt74+3tTUBAgHKZp6cnCxcupGfPnnTo0IEthfqoeXp6snjxYvr06UO7du3Yu3cvGzdupHfv3nTo0IFz5wpOXEeOHKFv37707NmTPn368O+//wJw5swZfHx8+Prrr/H29qZ79+6EhkqF0ezZswkNDcXHx4fx48drJoMLiY0NoWbNgiu2mjWdSU6OJzW1fBWW8+f38vnnbkyd2oJ794Jp1arv8wdpZycdeRGFKtS3bpXcEjx9OixZItWYnsbdXTpTJiU9f4x5GjSoyuXLBYXv5csPqVbNGAsLQ5W058/f5+bNRLy9HdDW1sLHx5GMjByuXHmodtvvvFOL69fLf6ESHR2CrW3Bb21r60xSUrzaymlUVAi2ts7K94aGMqpVs8k7EeZ3xy7cLVtBVFSI2u+9efM8lStbYmqq/gT2LFWrSpWDfHFxYGIi3Rt5FhsbqfJcWMeOUjc7TRbmFhZQ+BoyIQFkMqnF4llq1JAu/F6G1yVO0HzZ5Oc3B2/viejrqx6Lz+N1ydPo6BBsbAry08am5OP/ZYqICMHBoSAuBwdnHj2K58kT1bjCw0OoU6egXDIyklGjhg0REdIF+s2b0jl1+HBv+vRpzbx5k3ny5LFG4ixefj4t/6S0RctPa2sboqPvoFBI5Wb+v/mvi5afimKvFURF3dbI/+NVZWFRtKyOj5dGMxmW4nCtWfPpx1HNmkWP0dIIj4xER0cHe5uCmygujo7cUdMSHBIWhkuhpmYXJydCwsKKpPH+6CNc27Zl9JQpvN+9O1UsVHukHAgKwqJyZZo3bVq2YJHO2XZ2BfunnZ0zjx+rP44iI0Ows1PdP/Mrup079+f8+cOkpCSRkpLEqVN/4eZWtPvV4sWTGTSoBTNnDiUsLLjM8YYnJKCjrY19lSrKz1ysrbnzUP31z9MMatGCgMuXycrJ4W58PP9GRdGyTvmHXwlvhleyEhwfH8+MGTNYsWIFgYGBynFBycnJfPPNN/z444/s3r2blStX8vXXX/PkyRN69OhRpNK6e/duevTogZaWFt9//z3NmzfHz8+PwMBAEhMT2bVrl8r3zpkzBycnJ/bu3cu6detYuHAht28XnHQSEhLYvXs327dvZ+XKlQQHFxz0mZmZ7Nixg2XLljFjxgz09PTw8/Pjs88+Y9GiRQBERkayYsUK1q5dy+7du5kzZw6fFhrXcOfOHfr27cvevXvp3LkzK1asAODrr7/GwcGBwMBAli0ruetaeWVmyjEyMlG+NzIyBSA9vXzdz93dvVmw4CIzZvxFq1Z9MTOr8uyVnkUmUx3UmZwsnSGLa99eGu/7rPE01tZSF+sffnj++AoxMdEnKSlD+T7/tamp6ric3FwFmzZdZ9u2bmRkTGTbtm6MHHkAuTxLJe2QIQ1xd6/GwoXlbw1IT5crx/QAyGTSb52WpvpbS2lNi3wmk5mQlpaKkZEJzs5u+PmtIDMzg7t3r3P69AEyMtJUtpOQcJ+1a2cxaNDUcsetry+1DOTLf13CkEElS0to3VrqAZ+vbl1pPPBtDV9P6ukVjTEzs3QxVqki3Yt5UV2Ki3td4gTNlk2XL/9Nbm4OjRur7073PF6XPM3IKHr8P29ZrylpaXKMjQviMjaW4pLL1ZdL+cvzyWQmyrTx8XH8888evvlmGRs3/kVGRgbLl3+rkTjT04vuj/nlo7r8K6n8TE8vKD9375bKz7Cw65w9e4DMTKn8bNiwJTdvnuPGjTNkZ2cSELCS7OwsMjKecVP3NaevX3DsQMFrPb2nr1elijT84Phx9cvzu1TfvFm2eORpaZgUu8YwNTEhVc04muJpTU1MkMvlRW507N2yhQv//MOPs2bRrISuTP779+PbuTNaWlplCxbpOFJ3fi/t/mlsbKK8FnBwqE92dhYDB3owcKAHOjo6dOr0oTLtZ58tYNWqQ6xeHUSjRh7Mnv0xqall674iz8zEpNidQlMDA1ILF6al1K5uXf66cYPG331H5+XL6d20Ka41y99rTngzvJITY12+fJn69etTJ+8uTp8+fVi4cCHXr18nOjqa4cOHK9NqaWkRERGBu7s7qamp3Lp1CwcHB37//XdlF+JDhw5x5coVNmzYAEB6ejrW1tYq33vq1CmmTpUu1K2srGjbti1nzpyhbt26APTu3RuAqlWr0q5dO86ePYtLXp+3LnldVxo0aEBaWhqdO3cGoGHDhkRGRgJw7NgxIiMj6d+/v/I7s7Ozic+79Wlvb0/9+tI4pSZNmrywscrnzu3h11+/AcDBoRn6+jLS01OUy/NfGxqqqWCWgZWVHdWrO7FjxyyGD3/OcbdyuWqF18REtWJsZASTJ8OoUU/fnrk5rF0Lv/4KeV2WyuvDD+uxalVHAI4diyYlJRMzs4KC3cws/yZOpsq67dvbMn9+W9q1+5WLF+No1qwae/b0oHPnXUVak318HPn++3d4773fSEhQrWiW5NixPaxaJf3W9eo1w9BQRlpawW+d/9rISPW3Lp4WpIvS/LQTJixk7drZjBrVFmvr2rzzTneVluCkpES+/XYoXl4f0rq1moG5JWjQAPIOIaKipIuhwufK/NeZqlmqZG4OffvC339L2wDpYsrTEzQxusDJCdq1k17fuyd1by1c6cm/cHtajGZm0vD048chNvb5Y3qd44QXVzZlZMgJDFzAqFGrNRLn65KnJ07sYcMGKT+dnZthYKD++H/esr6sDh7cw5IlUlyNGjXDyEiGXF4QV/5rmUx9uVQ4rZQ+VZlWX98AL6+e1KplD0C/fiP54ovyTfR0/Pge1q6V4nRxKbn8VJd/6uJMS0tVpv3kk4Vs2DCbsWPbYmVVm9atuxMdLZWfNWs6MHr0PDZs+JbHjx/SurU3NWs6UqWK6nXL68zZGfKHvt67Jx0vhY+j/NdZqveElSpVAh8fOHpU2kZxrq7S0IRdu9RPsPU0MiMjUopdY6SkpmIsk6lNW3jempTUVGQymUpl1sDAgG4dO9K5b1/q1a1bpPX43v37nL10iTlfflmq+I4c2cPKlQXndyOjovtn/v5X0v75tPP7ggWfYmfnzJdfrkChULBx4w8sWfI5n3++VPl9+Xr1GklQkD83bpyneXNPSkumr09KsQpvSkYGxqXpQlPI47Q0hm3ZwtddutCtUSPiU1IY/9tvVDExoX/z8s+h8qr6L3VHrmivZCW4JAqFAmdnZ7Zu3ap2ua+vL/7+/rz11ls4ODhQM+8ukEKhYMWKFdSu/fxjl0pikHfQ6ujoFHmvra2tnPwAoE2bNsyfP19l/dDQ0CIzYRZfT5OaN+9O8+bdle83bpxETMwt3Nykinx0dDCmplUxNi5f19XCcnOziY+PfO7tEB4uTbtqa1vQJdrZWRoUVJitrdS/cHPeZFx6elJl+ehRqUZ075509bl2rdQ8uGrVc4e2bdtNtm0ruMW8dWtXGje2ZOdOaVKGxo2tuH8/lcRE1bv4TZpYcfRoNBcuxAFS9+gzZ2J57z1bZSXYy8uONWu86Np1F9euxats42natOlOmzYFv/WSJZMID7/F229Lv3V4eDCVKlVV2025dm2nIuPd0tPlxMVFUru21AXd0rImX35ZkH9LlkzC0bHg7nZKShJz5gzF3d2TXr1Glynu69elv3w+PtIsoPl38q2sICVFmvVTHTMz6NdPqmBcu1bwubm5dNE0YID0XkdHqlCPHw+//FK2XvEhIdJfvvfek7pt541ioGpV6d5NSTe1TUyge3e4cEHzrdKvY5zw4sqmhw8jSEiIYckS6QZkTk4WaWnJTJvWikmTdlClSq1nbKGo1yVPW7XqTqtWBfm5YsUkIiNv4eEh5WdkZMnH/4vUvn132rcviGvu3EmEht5SzuAcGhqMuXlVzMxU47KzcyoyXjgtTU5sbCS2tlK5VLirNFCuFrV8rVt3p3Xrgjh/+knKv5YtpTgjIkrOv1q1nDh6VLX8zJ/h2dKyJlOmFJSfP/00CQeHgvLTw6MTHh6dAEhNfUJQ0C7q1FGdNfd1duuW9JfPy0s6dvKPrapVpfvcJY1qMjWFHj2kscPBanrj1q8v9bTw85POF2VntB06AAAgAElEQVRlZ2NDTk4O4VFR2OVdPwaHhOCopputk709wXfu4NqggTKdk719idvOzs4mKiamSCU48M8/cXN1pXYpWzDbtu1O27YF++eiRZMIC7tFq1YF5/fKldUfRzY2TkXGC6eny7l/v+D8Hh4ezIgR32BoKFX4O3Xqx7RpH6psp4BWkVbv0rCrUoWc3FzCExKwy+sSHRwXh6NlyRMbqhP16BE62tr45s22Xc3MjC4NG3I0JOQ/WQkWNOeV7A7dpEkTbty4QXjeY2t27twJSK2sERERnD59Wpn2ypUrygPP19eX33//nZ07d9KzZ8FjDzw9PVm9erVyHHBiYiJR+U1DhbRs2ZLffvsNgIcPH3LkyBFatGihXO6fN9NfYmIiR44cwaOkaQtL0KpVK44dO0bI/9m777AojjeA4196t8beQECwYsWKHbEhiDWx91iisRd+GjRqYlATWzSKQuydZkFUsKHYsBsUEBWxF0CEo3m/PxaPcgeCkkDifJ6HR7yd3Xvv5XZ3Zmd2NlPt6bqKCQCyMzQ0JP5TjuB5ZG3twLlze3nyJJyEhDiOHFlL06Y9cyyfkpJMSopUu0tNTSElJUnxNzh7dg9v30o33jx5Eo6//3osLJp/fpCJiVKX3oQJUm9vgwZSl56vb9ZyYWHScGgnJ+ln7lzpRiAnJ3j6VOpNXr9emjXj118/Py4VNm++xYgR9ahZszTFi+vwv/81w8PjpsqyFy8+wcamElZWZQGpUWxjU1lxT3C7dlXZtq07vXp5c/Hi08+OrU0bBwIC9hIVFc67d3Hs27eWtm1V/62trW15+DCM4OAjJCcnsXfvGqpVs6BSJVMAHj2KIDExnpSUZE6d8ub69TN07y71uCQkxLNw4QgsLBoycOC0z477xg2wspIqRTo60LKlNBGKKoaGMGCA1MC4ciXrshcvpBmjN26Ufg4dkipZGzd+/kREd+5Ik7iULCn1YDRqpLpiBtLX0MFBaqBnbuxnpqEhDdtWU8v4vSD8W+KEgjs2Vahgzo8/nmDWLC9mzfLi668XYmRUmlmzvChZskKO28urf0tOW7Vy4OTJvURHS/u/t/dabGxyz2dyckY+k5MzjvXv378nOTmJtLQU5HI5yclJpKbm0vWdC1tbB/z89vLgQTjx8XFs376WTp1Ux9WypS3374dx+rR0XNq6dQ0mJhZUrSodl+zsnPD338+TJ1HIZIns3Lmepk3bKn0muVxOWloqyclJvFf1LCsVbGwcCAzcy6NHUv48PdfSpo3qOJs0sSUqKozz56U49+9fQ9WqGcdPaYLBeFJTkzl9Wjp+duuW0WN9795N3r9PIy7uNRs2zKVRo/aKdQuamoYGGjo6qGtoKH5XS7+g/08KDZUarqVKSftRkyY5D2E2MJBO69euZb3Q+YGFhfRIPU/PTz+26+vpYdu2LSs3bCAhMZHL165x/PRpHDp3Virr0KUL7jt28Oz5c569eIH7jh2Kya2u3rzJpWvXSE5JQSaTsX7LFl6+eaNoMH/gdfjwJ02I9UHbtg4cP55xft+zZy3t2qn+fjZtKp3fz52Tvp+7d6/B2NiCypWl75iZWR2OHdtDUpKMpCQZ/v67FPe4v3jxmL/+uqzYlzw93Xj79g2Wlg3zFa++tja2NWuy8sQJEpKTufzwIcfv3MFBxVDx93I5SamppKSlIQeSUlNJTq/Tm5QujVwux/fGDd7L5byIj+fwrVtYqBjxKQiZqcnze+nmH+Lv78/y5cvR1dWlU6dOrFixgpCQECIiInB1dSU2NpaUlBSqVKnCunXrFLNXDh06lBs3bnDmzBn00mfNiY+Px9XVlcuXL6OmpoaWlhZz5syhcePGDBo0iOHDh9OuXTtevnzJvHnzFA3kESNG4OjoCEgN6W7duhEUFMTbt28ZMmQIAwcOVCxbt26dYti0hYUFISEhGBgY8OjRI3r16sX58+cBOHPmDCtXrkQmk5GSkkLDhg1ZtGgR58+fZ8mSJezfvx8gy/9TU1MZP3480dHRVK9ePc/3Bfv75z3fAQHu6c/ilGFlZUe/fhnPllu0qBudOo1R9ND88EN7Xr+OzrK+i8txSpeuzNats7l9+yRJSQkYGpaiQQM7unX7Hi2tnIe3dPo+j4+qKF4cFi6UzmyxsbB8ufSc4EaNpB7dxo2V12nSRLrn98MzSBwc4KeflJ+NY2//0TGJan/lfUjd5MmNmTnTGj09Tfbtu8u332Y8J/jmzWEsXhys6D0eP74B33/fiHLlDHjxIoE1a66wfPklAAIC+mFjUxmZLGNUwOnTj+jaVfme9g+uXZuea2y+vu54e28gOVlG06Z2jB6d8beePLkbTk5jFL3H16+fZePGBbx48RhzcyvGj/+JsmWlXrODBz3Yt28dyckyjI1rMmzYHExNpZ6KEyc8WbNmFjo6ekBGT8yvvx6kTBlpis4DB/KcTkC656t5c2lAQPbnBI8aJc30fOuWdA9w69bKQ1GXLlXeZtWqUo9cbk/JKlEi7zFaWUnXZ1Q9K7Z/f6lhHhYmfVWtrZWH+X2YzLxiRUg/9ChER0uP+igIhRlnbk81U6Wgjk2ZhYWdZ/Pm6bk+Jzj7IJOPKaycqjrs5ebwYXcOHJD2/yZN7Bg2LCOfs2Z1w95+jKL3ePLk9rx8mTWfy5cfp0yZyvz113kWLx6cZZmlpTXOzqofiZd5BmxV9u51Z9cuKa5WreyYNCnjOcEjR3bj66/HKHqPQ0LOsnr1Ap49e4ylpRXTp/9E+fIZf+M//1yJr+92ABo3tmH8+P9hZCQ9AWDq1EFcv34hy3svXboZKyvponb2SfSyO3jQHR8f6fuY/TnB06Z1w9FxjKL3+MaNs3h4SMdPMzMrxo79iTJlpDgPHfLAyyvj+DloUMbxE8DF5WsePAhFQ0OLZs06M3DgLEWvHIBvo0/v4c6uzQ8/0NbFJctrJ1xcODl//mdvu+SK/FUxGzSQTumamtI+GBiYcZwfMECaef3OHWkfatZM+Ti/bp3075Ah0gXRzEOg79yRtqfKxIGqZ9WKiY1lzuLFnL1wgRLFizN17Fjs7ey4dPUqo6ZM4Ur6hBNyuRzXNWuyPCd4+vjxqKmpcSEkhIW//krU48doaWhQw9SUSaNHZ5n86sqNGwybOJEzBw4o3Yec2e2nuT/ezdvbHU9PaT/K/pzgiRO70avXGEXv8bVrZ9mwIeP8PnFixvn92bMo3NwWERp6BZBjbl6XkSPnUrGiMQ8fhrF8+RSePo1CS0sHExNLBg+ehplZxve31tXtucapyG9iInO8vTl77x4l9PSY2rEj9nXrcunBA0Zt28aVOXMAOH//PoMzTX4LYF2tGluGDgXgXGQkS48d4/6rV+hqatKuRg2cu3RB72M3lH+TW+920aRiNP4/riAfL1mYimwjuKjJ3tD9N8hPI7gw5bkRXMjy0wguTB9rBBcV+W0EF5b8NIKFj8tvI7iw5LcRXFjy2wguLB9rBBcVH2sEFxUF2Qj+O+W3EVxYcmoEFzUfawQXFXltBBc60Qj+JP+VRvC/6p5gQRAEQRAEQRCEL5GYGKvgiEZwHgVkfsaKIAiCIAiCIAiC8EkSExOZPXs2t27dQkNDg5kzZ9Luw5TxmZw/f57Ro0djbGwMgLa2tmK+KIA1a9Yo5m3q2bMn48ePz9P7i0awIAiCIAiCIAiC8I/ZuHEjhoaGHD16lPv37zNgwAD8/f0xUHFfvKmpqWLepMwuXryIn58fB9LvsevTpw/W1tY0ycPM4EVydmhBEARBEARBEAQhQ2pq4f8UlMOHD9OvXz8AjI2NqVOnDqdO5TxhpSqHDh3C0dERXV1ddHV1cXR05NChQ3laV/QEC4IgCIIgCIIgCB8VFxdHnIpnjxUrVoxixYrleTuPHz+mUqbnYleoUIGnT1U/DvT+/fv07NkTTU1NvvnmG3r2lB7/9eTJE6ytrbNs4+LFi3l6f9EIFgRBEARBEARBKOLk8rw91/zv9Oeff7JaxbMlJ0yYwHfffaf4f8+ePXn8+LHKbZw9ezbP71e7dm1OnjyJkZERUVFRDBs2jHLlytGiRYv8B5+JaAQLgiAIgiAIgiAIHzVkyBBFT2xm2XuBP0xWlZOKFSsSHR1NqVLSo7+ePHlC06ZNlcoZGhoqfq9SpQodO3YkJCSEFi1aUKFChSwN7SdPnlChQoU8fQ5xT7AgCIIgCIIgCILwUcWKFaNy5cpKP/kZCg3QuXNndu3aBUjDnW/cuIGNjY1SuefPnyOXS88cj4mJISgoCEtLS8U2vLy8kMlkyGQyvLy86NKlS57eX/QEC4IgCIIgCIIgFHlphR0ABdWHOmLECGbNmoWtrS3q6uosWLBA0eu7YsUKypYty9dff42/vz87duxAU1OTtLQ0HB0d6dixIwBNmzalU6dOdOvWDQBHR8cs9wjnRjSCBUEQBEEQBEEQhH+Mvr4+K1euVLls0qRJit8HDhzIwIEDc9zOd999l+Ve5LwSjWBBEARBEARBEIQiryj0BGsVdgAFQtwTLAiCIAiCIAiCIHwxRCNYEARBEARBEARB+GKI4dCCIAiCIAiCIAhFXlEYDv3foCb/MOe08N+TwwOqi5q78RULO4Q8qeGn+ub9omZ/5YmFHUKe1O6lVtgh5InFq1eFHULehIcXdgR5U6JEYUeQNzExhR1B3vxb4vy3/N1TUws7gjxZealFYYeQJ28m/TuO86Oi/x1V4Yqazws7hDxJLlG2sEPIE23two4g/9TU3hV2CMjlBoUdQoEQPcGCIAiCIAiCIAhF3vvCDuA/Q9wTLAiCIAiCIAiCIHwxRCNYEARBEARBEARB+GKI4dCCIAiCIAiCIAhFnpgYq6CInmBBEARBEARBEAThiyEawYIgCIIgCIIgCMIXQwyHFgRBEARBEARBKPLEcOiCInqCBUEQBEEQBEEQhC+G6AkWBEEQBEEQBEEo8kRPcEERPcGCIAiCIAiCIAjCF0M0ggVBEARBEARBEIQvhhgOLQiCIAiCIAiCUOSJ4dAFRfQEC4IgCIIgCIIgCF8M0RMsCIIgCIIgCIJQ5L0v7AD+M0QjWCAmLg5nV1eCLl2iZPHiTBk5EvuOHZXKyeVylq5fz95DhwDo3bUr00aPRk1NDQCLdu3Q09VFLb181/btWTR9+mfH5+Xlwb59G0hKSqRlSzvGjZuPlpa2yrLXrp1j7dr5vHjxBAuLenz//c+ULVsJgHHjuvHixWNF2eTkJBo1as28eeuIjo7E3f0X/vrrCu/fv8fcvC6jRztTuXL1fMcbI5PhHBBAUFQUJXV1mdK8OfY1aiiVcwsJwevOHaLfvqWkri7f1KnDyIYNAXiVkMCiM2e4EB1NYmoq5qVKMbtlS6zKl893PLk5c8aDkyc3kJKSSJ06djg6zkdTUzm3qanJ7No1jUePbhITE82oUZupXr1pljLR0bc4cGAxjx/fRltbj7Ztx9Cy5ZDPjlG9ZEnKb9yIQadOpL18yYvZs3m7Y4fKsjoNGlD2t9/QbdiQ9+/e8WrxYmJWrgSgemQkGuXKQZo0lCjx7Fke2dl9clwxsbE4L15M0IULlCxRginffou9iu3J5XKW/v47e318AOjdowfTxo1DTU2N1zExjJsxg8gHD0h7/x5TY2NmTJhAIysrADwPHmTLnj3cj4rC0MCA7p06MeXbb9HULLhDd0x8PM5ubgTduEFJIyOm9O2LfYsWSuWCb99mjZcXt+/fp7iBAQG//lpgMQDEvH2L88qVBF25QslixZgyeDD2bdsqlZPL5Sz980/2+vsD0LtTJ6YNGYKamhqXbt1ilItLlvIJMhkrZ83CrmVLDp46xcrt23n55g3aWlq0btSIuWPGYKivX3Cfo4jkUymud+9w3r6doNBQShoYMKVHD+wbN1aO6+5d1vj5cTsqiuL6+gTMn69yexfCwhi0ciXf2tkxuXv3vy/uoprP+Hic3d0JunlTiqtXL+ybN1eO66+/WOPjw+0HD6R8Ll2qWPYqLo5F27dz4c4dEpOSMK9Uidn9+2Nlalrg8davD40agZYWhIXBiROKQ2EW5ctDs2ZQtizI5fDoEZw8CQkJ0vKGDaFmTTAygsREuHEDQkIKPFyVmowfT/2hQylbty43d+zAe9iwf+aNgT17PNixQ6qHtG5tx+TJ89HWVl0PuXz5HCtWzOf58yfUrFmPmTN/pnz5SlnKxMXFMHhwZ6pUMWHVqozz2cGDe9i+fT2vX7+kbt2GzJixmK++KvfR+GLi4nD++WeCLl6U6nFjxmBva6tUTi6Xs3TdOvYeOABA7+7dmfbtt4p63Adefn7MXLSIhTNm0MfeHgC37dvx8vMj+ulTSpYowTeOjoz85puPxqbK5s0ebNq0AZksEVtbO+bOzTmfwcHnWLRoPk+fPqFu3XosXPgzFStK+fTzO8TWrZu5c+cv6tSph7v7FsV6b968ZuLEcURGRvL+fRomJqZMmzaDBg0afVLMwn/PJw2HtrCw4N27d5/1xufPn8fJyemztlEUODs7c+nSpXyvN2vWLLZu3fo3RJR/C1asQEtTk6D9+3F1dsblt98Ii4xUKrfL15djQUF4u7nh4+ZG4Llz7PT1zVLG282NK4cPc+Xw4QJpAIeEnGbfvvUsXOjBpk2BPH36iG3bVqosGxv7msWLJzBw4CR27LiAmVkdfvllsmL5778fZM+eK+zZc4Xdu0P46qvytGrVGYB3795ibd2edev82LIliBo16rJw4bhPinnBqVNoaWgQNGwYrra2uJw8SdirV0rl5MCSjh25OHIkbvb2bLtxg4NhYQAkpKRQt2xZ9vfty4URI+hpacnogwd5l5z8STGpcvfuaU6eXM/IkR7MnBnI69ePOHZMdW4BqlVrSL9+v2BkVEZp2bt3r3F3H4m1dT/mzj3PtGn+mJu3KpA4y61Zgzw5mfBy5XgyYADl1q5Fu1YtpXIapUtT2c+PmD/+ILx0ae6ZmZGQ3lD6INrenjAjI8KMjD6rAQywYNkytLS0CDp4EFcXF1xcXQm7d0+p3C4vL46dOoX3li34bNlC4Jkz7PT0BMBAT4/Fzs6cO3yYi/7+jBo4kLHTp5OamgpAYlISc77/nmA/P/a4uRF86RKbtm//rLiVPseff0r7/5o1uI4di4uHB2GPHimV09fRoVfr1szo379A318Rx7p1UhxbtuA6dSoua9cS9uCBUrldfn4cCw7Ge+VKfFatIvDCBXb6+QHQuHZtruzZo/hZN28e+np62DSSKj0Na9Zkx5IlXN61i2MbNpCalsZvW7YovcdnfY4ikk+luHbvlo5LixfjOmQILrt2EfbkiXJc2tr0ataMGY6OOW4rJS2NRfv2YWVs/DdGLCmy+dy6VcrnihW4jh6Ny5YthEVHq47LxoYZffsqLUuQyahrYsL+H37gwurV9GzZktG//cY7maxAY61aFRo3Bk9PcHeH4sWhaVPVZXV04OZN8PCQyiYnQ/a2lL8//PEHeHtDvXpgbl6g4ebo7ePHnFq4kKubNv0zb5juwoXT7NixnmXLPNi5M5AnTx7h4ZFzPeSHHyYwfPgkfHwuYGFRhwULJiuVW79+KdWqZb3YcfXqedzclrNw4e/4+JynQoXK/Pjj1DzFuGD5cul85O2N67x5uCxbproe5+PDsdOn8XZ3x8fDg8CgIHZ6e2f9DG/fsm7LFsxNTLK8LpfLWeLszMVDh3BbupRt+/dz8NixPMWXWVDQaTZuXI+bmwdHjgTy6NEj1qxRnc83b14zefIEJkyYxJkzF6hduw7Tp2fks3jxEgwcOJjhw0cprauvb8CCBYs5deocQUEXGT58FBMmjFWcXwVB3BP8mRYtWkRjFVfT/y0SEhPxP3WKScOHY6CnR+O6dWnfogXeR48qlfXy92d4nz6UL1OGcmXKMKxPHzzTK59/l+PHvbC17U21auYYGhanf/9xHD/uqbLsuXNHqVrVnFatuqCtrcM333xHZGQoUVERSmVv3rzI27dvaNGiEwA1atSjU6c+GBmVQFNTCweHoURHRxIX9yZf8SakpOAfEcGkpk0x0NamccWKtDc2xvvuXaWyoxo2pHaZMmiqq1O9ZEk6mJgQkl4prVK8OMPq16esgQEa6ur0q12blLQ0ImNi8hVPbkJCvGjcuDflypmjp1ec9u3Hcfmy6txqamrTqtVQjI0bo6amfNg4c8YDc/NWNGjQA01NbXR0DClb9vN7M9T09THq1YuXc+cif/eOxKAg4n18KDZokFLZklOm8O7IEd5u3448ORl5fDzJoaGfHYMqCYmJ+AcGMmn0aAz09WlsZUV7Gxu8VewPXocOMfzrrylftizlypZl2Ndf45k+mkJHR4fq1aqhrq6OXC5HXV2d2LdviY2LA+AbJyca16+PtpYW5cqWxd7OjpDr1wvuc8hk+F+8yKRevTDQ1aWxhQXtGzbEOyhIqWw9U1McW7WiStmyBfb+WeI4e5ZJAwdKx6HatWlvbY13YKBSWa+AAIY7OlL+q68oV7o0wxwd8Tx+XOV2vY4fp3OLFujr6gJQoUwZShUvrliuoa7OAxUNwc/6HEUgn0pxJSXhf+0ak7p3x0BHh8amprSvWxfvCxeU4zI2xtHamiqlS+e4Pffjx2lpaUn1vzn2Ip3PS5eY5OQkxVWjBu3r18f77FnluKpXx7FFC6qUUb54WKVsWYbZ2VG2RAnpON+2LSmpqUQ+fVqg8dasCbduwevXkJQEFy5Ir6ny4AGEh0uN39RUuH4dKlTIWB4SAi9eSL3EMTFw7x5UrFig4eYo1NOTO97eJKi4qPx3OnLEiy5demNiYo6RUXEGDRqHn5/qc+WpU0cxNjanbVupHjJkyHdERITy8GFGPeTmzRAiI8Po3DlrR9C5cydo06YzJibmaGlpM2jQOK5fv0h09MNc40tITMT/5EkmjRghnY/q1aN9y5Z4HzmiVNbLz4/h/ftL56MyZRjWvz+ehw9nKbPsjz8Y1Ls3JTMdKwFGDRhAbQsLNDU1qV61Kh1atSLkxo1cY1PF29sLJ6femJmZU7x4ccaMGYe3t+p8Hjt2FFNTc+zsuqCjo8PYsd9x504o9+5J+WzevAWdO3elbFnl3nIdHR1MTKorzq8aGurExcUSGxub75iLlrQi8PPf8MmN4I0bN+Lg4ICdnR1HMu1op06dwtHREXt7e4YMGcKDTFfyf/31V2xtbenVqxcnTpxQvD569GgOZ9oJ/f39GT58eI7vff78eXr06MH//vc/7O3t6dmzJ2FhYUyaNImuXbsyYsQIEtLH7pw7d45+/fopYjp48KBiO6tXr6Zz5844ODjg6OhIXFwciYmJTJw4ka5du9KjRw8mTZqUax4GDRpEYHpFbdasWcybN4/BgwfTqVMnZsyYgVwuB+DZs2cMGTKErl27MmrUKN68yWhcxcfH4+zsTO/evbG3t2fhwoWkpaURERFBmzZtiE6/urx69WomT1a+ovg57j96hIaGBiZVqiheszQ1Jfz+faWyYffvY5lpmJalmRlh2coNmDSJlk5OTJg3j0cFcCJ/+DAMExNLxf+NjS2IiXmpsnEqlbVQ/F9XV5/y5avy8GG4UtmAAE+aN7dDV1f1MMibNy9RsmQZihUrma9478fEoKGujkmJEorXLL/6ivDXr3NdTy6Xc+nJE8xKlVK5/K8XL0h5/55q2U5Kn+PZszAqVMjIbYUKFsTHv+Tdu/w1/AEePryKvn5x1q7tz8KFzfnzz2+JiXn88RU/QrtGDeSpqaSk95ADJF27hk7t2kpldZs14/3r11QNCsL02TMq+figmel7DVBh2zZMnz+n8pEj6NSr98lx3X/4UNpvqlZVvGZpZka4ip7gsMhILDN1lViamytdobcfOJB6bdowdsYM+vToQekcvgcXr17FrHr+h+jn5P7Tp9LnyFTLtaxShXAVPW1/p/vR0dJ+UyljyKCliQnhD5Urf2EPH2KZqYfC0sSEMBXlEmQy/M6exbFDhyyvX7p1i0b9+tGwb1/8z55lSI8eBfc5ikg+s7v//LmU30wNRMtKlQj/hGN09OvX7AsOZnyXLgUZokpFNp8f4sp0e4pllSqEq+gJzo+/Hj4kJTWVagXckC9VCl6+zPj/y5dgYADp14ZyVamS1HjObfk/3Cb9x92/H4apaca50szMgjdvXhIbq3yulMpm1EP09PSpWLEqkZFSPSQtLY2VK39k4sS5SkOQAUWdUfpd+jcyUvkiepb3jIpSfT5S0RMcFhmpXI/LVO767dvcDA3laweHXN9TLpdz6fp1zLL1FudFREQYFhYZ+bSwsODVq5fExCjnUyqbkU99fX2qVKlKRIRyvS4nTk72NGpUj+++G0uvXn0oncsFPuHL8smNYHV1dby9vVm7di3z5s3j1atXvHr1ihkzZrB06VJ8fX3p3r0706ZNAyAgIICAgAC8vLzYvXs3kZl2uoEDB7I90xC/bdu28c1H7jOIiIhgwIAB+Pr6Ur9+fUaMGMHs2bM5dOgQ6urqisZurVq12L59O15eXri7u7NkyRJiY2OJiYnBw8MDLy8vvL292bp1K/r6+pw5c4Z3795x6NAhfHx8WLBgQb7yEhYWxoYNGzhw4AC3bt3ibPqV4YULF9KkSRMOHTrEvHnzuJDpCvxPP/1EkyZN2Lt3L97e3rx+/Zp9+/ZhamrK5MmTmTx5MmfOnMHX15cff/wxX/F8TEJiotL9cEYGBrz7cANQ9rKGhlnKJSQmKg7aW3/7jYAdOzi8eTNlS5fm29mzSVV101E+yGQJ6OtnvKeBgREAiYnKw/GlskZZXjMwMFQqK5MlEhR0hI4de6p8z5cvn7Ju3XxGjJiV73gTUlIw1NLK8pqRtvZHhzGvunCB93I5vVRcno9PTmbGsWNMaNIEIx2dfMeUk+TkBHR0MnKrq2uU/nr+b3WIjX1GSIgX3bvPYebME5QsWZmdO6d8dozqhoa8T+8V/eB9bMOUeqsAACAASURBVCzqRkZKZbUqV6bYkCE8nzSJe1WrkhIZScVM9w4/GTCAe8bG3KtWjYTAQCofOYL6J15USEhMxNDAIMtrRoaGOe83mcoaGRqSkJCQpbLju3Url48dY9n8+TTKoXG+19eXm3/9xfBPvAdLlYSkJAz19LK8ZqSvX+DDMT8ah0ym+jiUmPjRstmPQx/4nz1LSSMjrOvUyfJ649q1ubxrF6c8PBjh5ESlch+/3y7Pn6OI5DO7hKQkDLO1eIx0dT8proV79zKpWzcMCvBYlJN/VT4/M674xERmrF/PBAcHjArwHnUAbW2pZ/eDD79nO1UpKV0arK3hzBnVyz8Mqf7rr8+PsSiTyRKy1H1yq4ckJiYolmeUz6iH7N+/hZo162FhUUdpXWtrG06cOExERChJSTI2b16DmpoaSUm5f69Uno8+oR6XlpaGy/LlzJs8GXX13JsHqzZt4v379/Tq2jXXcirjTciaT0NDKV+qbrOUymbNp6GhYb5uydy/35fg4MssWbLsP3I/cGH3Av93eoI/eXaVPn36AFC9enVq1arF1atXUVNTw9LSEjMzMwB69erF/PnziY+P5/z583Tt2hWD9B21d+/e/P777wDY2NiwePFiIiKk4Q1RUVG0a9cu1/c3MTGhZnqDoVatWjx+/Jjy6Vdla9eureiBfv36NXPmzOHBgwdoaGgQGxtLZGQkdevWpWrVqsyYMYNWrVrRtm1bDA0NsbS0JCIigvnz52NtbU1bFROz5KZjx47opFcOatWqxcOHD2nZsiXnz5/nf//7HwBVqlSheaYJNAICArh+/Tru7u4AyGQyyqVXzBwdHQkODmb8+PFs27Yty4GjIOjr6RGf7UAZn5CAgYqTsL6eXpYDT/y7d+jr6SmuZjZJn8xHW0sL5wkTaNS9OxEPHmCRj56rEyd8WLPmBwBq1WqErq4+CQnxiuUfftfTM1BaN3tZqfw7pbLnzvljZFSCOnWslbYRG/uaefOG07XrN7Rpk//JXvS1tIhPScnyWnxyMgY5TPgAsPX6dbzu3GG7kxPaGhpZlslSU/n24EGsypdnTKPPO3hfueKDl5eUW2PjRmhr65OUlJEvmUz6XVtbObcfo6WlQ+3atlSpIjXgOnQYz8KFzZDJ3ioa15/ifXw86sWKZXlNvVgx3r99q1w2MZFET09k6ffov5w/H/NXr6TycXEkZhqq+Prnnyk2ZAh6Nja8S58gJD/09fSIz3YSjn/3Lu/7jb6+Ui+Ajo4O3Tt1okv//tSsUSNL7/GxkydZvnYt7itXUirTKIPPpa+jQ3y2hmZ8YiIGeekiKkD6urqqj0PZGkAfymZuHMcnJGQ5Dn3gFRCAY/v2KntbAMqVLo1Nw4ZM+eUXPFesKIBPUXTymZ2+jg7x2Rpo8TJZvuMKuHGDdzIZXT/zWJRX/6p8fkZcsuRkvl2xAitTU8YUwCRjFhbwoQr1+LHU6M18Cvrwe7ZTVRbFi4ODA5w6JW0ju3r1wNIS9u1TPcHWv9nRoz4sXy6dK+vVk+oh795lnCs//K6qHqKnl7UsZNRDXr58xv79m/njj/0q37dRoxYMHTqRH36YSEJCPL16DUFf34AyZXKfEFPl+Siv9bhMx8/tnp5YmJpSX8VIq8y27tuH15EjbF+9OsfJrDI7cMCHBQukfDZs2Ah9fX3i45XzaWCgnE99feV8xse/U1k2Nzo6OnTt2p0ePbpgaVkzS0+08OUqErNDq6mpZekN7tevHxrZGgPZZd7xNDQ0FA3PD/9PSkoCwMXFhfbt27N69WrU1NSws7MjKSkJDQ0Ndu/eTUhICMHBwTg5OeHm5oalpSUHDhwgODiYU6dO8euvv+Lr65tl+7nJHkdaHs4Ocrmc33//nSrZhm4CJCcnExYWhpGREa/+hjFHxpUrk5aWxv1HjzCuXBmA0PBwzFRMeGJubExoRAT10i8+hEZEYJ7LxChqoNQ78zFt2/agbduM4YmurlOJjLyDjY10tTEyMpQSJb5SOUy5alVzAgIy7iuRyRJ48uQhVauaZSkXEOBFu3YOSpXj+PhY5s0bjrV1e/r1G5uvuD8wLlGCtPfvuR8Tg3F6YyX01aschznvvX2b9SEhbOvZk/LZLnAkp6Ux/tAhyhkYsCCfF2NUadCgBw0aZOR2586pPHlyh3r1pNw+fRqKoeFXGBjkbwg4QPnyFln+n1PDI7+S795FTVMTLTMzUsKl4U86VlYk3bqlVDbp+vWM8WOQ9XdV5HL4xDiNq1aV9puoKIzT99vQsDCVQ5XNTUwIDQ+nXnrFIjQsTGnCkcxSU1OJio5WNIJPnTvH/37+mfVLl2JhZpbjep/0OcqXlz7H06cYp19EDH34ELP0Y8E/xbhSJWm/efwY4/QbDEMjIzHLNLzvA/OqVQmNjKRe+ozroZGRmGcr9+TFCy7cuMGC8eNzfd/UtDQeFuD9l0Uln0pxlS0r5ff5c4zTh9qGRkdjls/Z5s/dvcvNqChazpkDwFuZDA01Ne4+fsza0aMLPu6imk9VcUVFYVap0kfWVJacksL4lSspV7IkC4Z8/mz6AHfuSD8f2NnBV19Js0KD9Pu7d5BTx7WREfTsKd07rGpahVq1pIm29u6F+Hjl5f92trY9sLXNOFf++ONUIiLu0K6ddK6MiAilZMmvKF5c+VxpbGzOkSMZ9ZDExAQeP36IiYkZoaE3ePXqBUOHdgMgOVlGUlISTk4t2bPnFBoaGvTsOYCePQcAEBUVydatazExyX3mMeMqVZTPR+HhKocqK85H6ZNLhoaHK85H5y5f5uLVq5wKDgYgNi6O22Fh/BUezrz0W/H2HjzI+m3b2LZ6NeXzOGy/e/cedO+ekc8ZM6Zy9+4dOneW8nnnTiilS39FiRLK+TQ1NcfHJyOfCQkJPHr0EFPTTzsXpqam8uhRlGgEC8BnDIfet28fAPfv3+f27dvUr1+f+vXrExoaqujR9fT0pFatWhgaGtKsWTMOHz5MQkICaWlpivU/cHR05NixYxw6dEjRy1wQ3r59S6VKlVBTUyMoKEjRQxwfH8/r16+xtrZm4sSJ1KhRg7CwMJ6m3+vTsWNHZs+ezevXr4kpgMmImjVrpvjMUVFRnDt3TrGsffv2rF+/XtFgfv36NVFRUQD88ssv1K5dG3d3d3744QeeFvCEGfp6etja2LDS3Z2ExEQu37jB8bNncVAxtb5Dp06479nDsxcvePbyJe67d9OzszS7clhkJH+Fh5OWlsa7xER+XruWsmXKYFqt2mfF1769A0eP7uXhw3Di4+PYvXstHTqoHsbcvLktDx6EERR0hOTkJHbuXIOJiQVVqmTc//Ly5VOuXz+vtI2EhHjmzRtBzZoNGTp02ifHq6+lhW316qy8cIGElBQuP3nC8chIHFQ8Isnnzh1+DQ7GvUcPqmQblpuSlsZEPz90NDVZ0rEj6gXUqMysQQMHLl3ay7Nn4SQmxhEQsJZGjVTnFqTHJKWkJKX/nkJKSpLiIkfjxk7cunWMx4//Ii0thYCA3zE2bvRZvcAA8oQE3u7fz1cLFqCmr49eixYYOjgQp2JG3zh3dwx79kTHygo0NSk9dy4Jp0/zPi4OzSpV0GvRArS0UNPRoeS0aWh89RWJKibYyQt9PT1s27Zl5YYN0n5z7RrHT5/GIX1/yMyhSxfcd+zg2fPnPHvxAvcdO+iZPoTs6s2bXLp2jeSUFGQyGeu3bOHlmzeKBvO5S5eY7uLCqsWLFa8VJH1dXWwbN2blvn0kyGRcvnuX4yEhOLRsqVT2/fv3JCUnk5KWhlwuJyk5meQCmmVTX1cX2+bNWbltmxTH7dscP38eBxUjghzat8fdy4tnr17x7NUr3D096Zntvl/vwEAa1KxJ1cwz+gA+J07w+PlzAKKfP+e3LVtonj6CpcA+RxHIp1JcOjrYWlmx8uBBEpKSuHzvHsdv3MDBWnk0zPv370lKScmIKyVFEdekbt04MncuXrNm4TVrFu3r1KFPixb8NGDA3xN3Uc5no0as9PKS8hkWxvErV3BQ8eimLPmELPlMSU1l4po16Ghrs2TkyI8OQf1UoaFSw7VUKakXuEmTnIcwGxiAkxNcuybNEp2dhQU0by7NNJ3tTpW/nZqGBho6OqhraCh+V/tIh0lBsLNz4NChvdy/L9VDtm5dS+fOqs+VNja23L8fxsmTUj1k8+Y1VK9uQdWqplhbt2bnzgDc3Lxwc/Ni6NCJmJnVxM3NCw0NDZKTk4iMvItcLufZs8csWzYPJ6fBGBnlftuOvp4etq1bs3LjRul8dP06x8+cwUHFExAc7Oxw3707ox63cyc90+/v/3nOHA5t3YrXpk14bdpEHUtLJgwdyuRR0szLPv7+/Lp+Pe7Ll1PlM2ZD69HDgf379xIREU5cXBzr16/FwUF1Pjt0sCU8PIyjR4+QlJTEunVrqFHDgurVpXpdWloaSUlJpKWlSvtaUhIp6UMcrl27SkjIJVJSkpHJZGzcuJ5Xr15St+6nzwdSNBT2UOj/ztCPT+4JTktLw9HRkcTERBYsWKC40fyXX35h2rRppKamUqpUKVxdXQFo164dV69excHBgWLFimFtbc2zZ88U2zM0NMTGxgaZTEapHHrNPsXUqVOZP38+q1atom7duoob7OPj4/nuu++QyWTI5XJq1apFp06dCA4OZtmyZYB08ho9erRiaPLncHZ2ZsaMGRw4cIDKlSvTNNPzCebMmYOrqysODlLvpJaWFnPmzOHOnTtcuHCBPXv2oKOjw/jx45kyZQqbN28u0OeE/vD998z55RdaODlRolgxXL7/HnMTEy5dv86omTO5kj5pWX97e6IeP8Z+xAhAek5w//Tnx7188waX337j2YsX6Onq0qB2bf5YvBitz4yzUaPW9Oo1EmfnwSQlyWjRwo4BAyYqlo8b142+fcfQtm0PihcvxezZq1i3bgHLl0+nRg0rpk9fnmV7gYHeWFrWp0KFrD1H584dJSzsBg8fhmeZfXrNmoOULZu/g/0PbdowJyCAFps2UUJXF5c2bTAvXZpLjx8zyteXK2PGAPDb+fPEJCXRe88exbr2FhYsaNuWK0+fEnj/PrqamjTZsEGxfIO9PY0LaCpOC4vWtG49Eje3waSkyKhTx46OHTNy++uv3Wjbdoyi93jZss7ExEgTv7i7S9+BGTOOU7JkZUxNm2NnN5k//xxNcrIMY+NG9Ou3rEDifDZuHOU3bcLs+XPSXr3i2dixJN++jV6rVlQ+fJiw9PuDEwIDeTlnDpUOHkRdX5/EM2d4kn7/rLqREeXWrkXL1BS5TIbs6lUedenC+49MWJabH6ZNY87ixbTo2pUSxYvjMn065tWrc+nqVUZNmcKVgAAA+vfsKe03AwcC0nOC+/eUTvjJycks/PVXoh4/RktDgxqmpqxfupRy6TPJ/u7uztt37xg9NeMxGY2srHArwGeg/jB0KHM2bKDF+PGUMDLCZehQzCtX5tKdO4xydeWKmxsAF+/cYfDixYr16o0YgbWlJVucnQsmjrFjmbNiBS0GDpTiGDsW82rVFM/+vZK+n/Tv3Jmop0+xnzABkJ4T3D/bxQevgABGqHgEX8TDhyz18CAuPp5ihoa0adyYKYMHF0j8is9RRPKpFFffvszZto0Wc+ZQwsAAl379MK9QgUvh4Yxau5Yr6ee9ixERDF6Z8biSelOmYG1mxpZJkzDU1c1yL6yulhZ6OjqUyOfQxHzFXVTzOWgQczZtosXEiZQwNMRl0CDMK1Xi0t27jFq+nCvr1klx3b3L4CVLMuIaPRprCwu2zJrFlfBwAq9dQ1dbmyaZRi1smDKFxiounH6qBw+kWZ2dnEBTU5r9+fz5jOUDBsClS1Lvce3aGY9QyvwYpfSPQ7Nm0oRa/fplLLtzB1RM5F7gWv/vf7TN9Bxwq0GDOOHiwskcnmVdUKytW9O//0imTJHqIa1b2zF0aMa5cujQbgwYMAZb2x6UKFGK+fNXsWLFAhYvnk7NmlbMmyfVQ7S1tSlVKmOWcENDIzQ1NRWvJScnsXDhVB4/jkJPz4AuXZwYPjz3yVk/+GHqVOb89BMtevSQ6nFTp0r1uGvXGDV9OlfSHxfY38FBOh+ljzro3b07/dMnwSqWba4NLU1NDA0MMEofpfabmxsxsbH0zjTqw75TJxZMy1/HQatWrRk2bCTDh0v57NjRjvHjM/Lp6NiNkSPH0L17D0qVKsXy5atYvHgBs2dPp25dK375JaNe5+vrzdy5sxX/b9y4Hj169GTRop9JTk7m558X8uhRFJqaWpib12DNmvUqZ5IWvkxq8vyOV/2bpKam0qNHD37++WfqfcasrUImqm7kKYLuxv9Dz1f4TDX8cn6GblGyv/LEjxcqAmr3Kvje7b+Dxb9l6tPwvM+WWagK8J7mv1UBPo7sb/VvifPf8nf/lzxDdOUl5V7noujNpH/HcX5UdJGoCn9URc3nhR1CniSX+PsfVVYQ8nBLc5Gjpnbn44X+ZnK5xccL/QsUiecEHz9+HFtbW1q2bCkawIIgCIIgCIIgCMLfpkhMjNWhQwc6ZLunC+Dbb7/lyZMnWV6rUKEC6z6My/mHnDx5kuXLlyu9PmXKFNq0afOPxiIIgiAIgiAIgiB8uiLRCM7JP93YzUmbNm1EY1cQBEEQBEEQhEL0vrAD+M8oEsOhBUEQBEEQBEEQBOGfUKR7ggVBEARBEARBEAT4Lz2iqLCJnmBBEARBEARBEAThiyEawYIgCIIgCIIgCMIXQwyHFgRBEARBEARBKPLEcOiCInqCBUEQBEEQBEEQhC+GaAQLgiAIgiAIgiAIXwwxHFoQBEEQBEEQBKHIE8OhC4roCRYEQRAEQRAEQRC+GKInWBAEQRAEQRAEocgTPcEFRU0ul8sLOwjhb/LwYWFHkDcyWWFHkCePDWsUdgh5UrFEQmGHkCdxqfqFHUKeFNNNLuwQ8uS9pnZhh5An6i+fF3YIeVOiRGFHkCfJ/Dv+7tox/46/+2vNsoUdQp6U4nVhh5Anj2WlCjuEPNlQSa2wQ8gTFxYUdgh5In8wpLBDyJuqVQs7gnxTU7tY2CEglzcp7BAKhBgOLQiCIAiCIAiCIHwxxHBoQRAEQRAEQRCEIu99YQfwnyF6ggVBEARBEARBEIQvhugJFgRBEARBEARBKPLExFgFRfQEC4IgCIIgCIIgCF8M0QgWBEEQBEEQBEEQvhhiOLQgCIIgCIIgCEKRJ4ZDFxTREywIgiAIgiAIgiB8MURPsCAIgiAIgiAIQpEneoILiugJFgRBEARBEARBEL4YohEsCIIgCIIgCIIgfDHEcGhBEARBEARBEIQiTwyHLiiiJ1gQBEEQBEEQBEH4YoieYEEQBEEQBEEQhCLvfWEH8J8heoK/QDFxcYx3caG+vT3tBgzANyBAZTm5XI7rhg00dXKiqZMTrhs2IJfLFcstbG2pb29Pg/Qf52XLFMvi4uOZ+csvNO/Th+Z9+rBq8+b8x/n2LeMXLaJ+7960Gz4c3xMnco7Tw4Om33xD02++wdXDQxHnpVu3aNCnT5YfC3t7jgQFKW1niLMzFvb2pKZ92lCTPXs8cHJqSbduDVmyZDbJyck5lr18+RyDB3emc2crJk8exNOn0Upl4uJicHRsxnfffZ3l9YMH9zBggC1dujRgxowRvHz5LE/xxcTGMn7KFOo3b067Ll3wPXxYZTm5XI7rihU0bduWpm3b4rpiRZa/+9wff8TO0RHLhg3Z7+OjtO6va9Zg06kTjWxsGDRyJGEREXmKL7vt2z2ws2tJ27YNWbAg93xeuHCO3r0706qVFd9+O4gnTzLy+dtvS3By6kSbNg3o3bszBw96qdzGwYNeNGligZfXHpXLY2JjGT9pEvWtrWnXqRO+Bw+qLCeXy3FdvpymrVrRtFUrXJcvz5K/v0JDcerbF6smTXDq25e/QkMVy+Li4pjp7EzzNm1o3qYNq37/Pcu2f1u1CvuePalVv77Ssuw8PDywsWlJ48YNcXbOPX/nzp2ja9fONGhgxZAhg4iOzshfcnIyzs6zady4ITY2LfHwcFcsu3r1KsOHD6NZM2tatGjG999P5Pnz51k+z6xZM2nZsjktWzZn9epVOcYQExfH+DlzqG9rS7vevfE9elRlOblcjuvatTTt1o2m3brhunZtlvx+4OXnh4WNDXt8fRWvuW3fTvfBg2nQqRPt+/bFbfv2HOPJMc4C+h7MdXHBzt4ey3r12O+V9Tvp6e2NU9++NGzWjNYdOvDL8uWkpqbmO1aAzZs9aNu2Jc2aNWTu3Ny/B8HB57C370yTJlYMHz6Ix48zvgd+focYOLA/TZpYMWzYoBy34ePjRd26Fuzbp3o/yu6f+Lt77NpFh759aWhnRytHRxavXPnJ+dyxw4Nu3VrSoUNDFi7MPZ8XL56jX7/OtG1rxfjxWY9Lq1f/goNDGzp0aEjPnu3w8FiXZd3TpwMYMKA77ds3YNSo/kRGhucpvpjYWMbPnEn9du1o17MnvkeOqCwnl8txXbOGpnZ2NLWzw3XNGkU+X8fE0H/0aJra2dHY1pZ+o0Zx+do1xbrzliyhQfv2ip86rVvToEOHPMWXXVE/b36KJuPHM+riRZxlMhzc3T++wt/o+++b8uTJZGJjZ7Bxoz3a2ho5lu3Tpxa3b48lLm4Gt259i4ODhWLZkCH1SE115u3bmYqfNm2q5Tuef6L+CXArLIwBU6bQwN6eFn368Of+/fmOVfjv+6IbwceOHaNLly44Ojpy7969fK9//vx5zpw589Fyq1atYsmSJfnadkhICN27d8fR0ZGjR4+yYcOGfMeXkwWrVqGlqUnQ7t24zp6Ny4oVhN2/r1Ru18GDHDt7Fu8//sBn/XoCg4PZeeBAljLe69ZxxdeXK76+LJo6VfH6T2vXkiiTEbBlC3tWrcL72DH2+fnlL85166Q4t2zBdepUXNauJezBA+U4/fw4FhyM98qV+KxaReCFC+xMf6/GtWtzZc8exc+6efPQ19PDplGjLNvwOXHikxu/ABcunGbHjvUsW+bBzp2BPHnyCA+PlSrLxsa+5ocfJjB8+CR8fC5gYVGHBQsmK5Vbv34p1aqZZnnt6tXzuLktZ+HC3/HxOU+FCpX58cepSuuqsuCnn9DS0iLo+HFcFy/GZfFilQ3UXfv2cSwwEO9du/DZvZvAkyfZuXevYrlljRq4zJ5NLUtLpXUPHz3KPi8vtm/axIUTJ6hfrx4z/ve/PMWX2blzp/nzz/X8/rsHPj6BREc/4o8/VOczJuY1M2ZM4NtvJ3H8+AVq1qzDnDkZ+dTT02P58rUEBl7GxWUJy5Yt4tq1kCzbiIuLxd19HdWrm+cY04JFi6T8nTiB688/47JwIWHhyhXTXXv2SPnbuxefffuk/O2RGgTJKSmMmziRHt27czEoCEcHB8ZNnEhySgoAP/3yC4mJiQT4+bFn+3a8fX3Z5+mp2Ha1qlWZNmUKbWxscs3fmTOncXNbz6ZNHhw/HkhU1CNWrVKdvzdvXjNx4gQmTpxEcPAF6tSpw5QpGflbvXoVDx484PjxQDw8NrNxoxunT59S5K1v374cOxbA8eOBGBgY4Ow8W7Huzz//hEyWyLFjAezevQcfH2/25dBoXLB8uZRfb29c583DZdkywiIjlfPr48Ox06fxdnfHx8ODwKAgdnp7ZykT+/Yt67ZswdzEJMvrcrmcJc7OXDx0CLelS9m2fz8Hjx3LNZdKcRbA9wDA0sICF2dnatWsqbRuokzGnJkzCT59mj3btxMcHMwmD498xQkQFHSajRvX4+bmwZEjgTx69Ig1a3L+HkyePIEJEyZx5swFateuw/TpGd+D4sVLMHDgYIYPH5Xj+8XGxrJhwzrMzHLej7L7J/7u7Vu1wnPjRkKOHOHA5s2ERkSwJdMxLa+Cg0+zZct6Vq3ywNMzkMePH+HmlvNxafbsCYwePYkjRy5gaVmHuXMz8mlv35udOw9z/HgIf/yxE39/X06c8AcgKuo+Li7TmDHDBX//i7Rq1Y7p08fmqeG+YNkyKZ8HD+Lq4oKLqythKuo3u7y8OHbqFN5btuCzZQuBZ86wM/1YY6Cnx2JnZ84dPsxFf39GDRzI2OnTFe+/YOZMrgQEKH6629rSuV27fOfz33De/BRvHz/m1MKFXN206W97j7zo1Kk6s2a1oEOHrVSrtpLq1Uswf34blWUrVjRi61ZHpkzxp1ixX5g+/Rjbt/ekTBl9RZlz5x5hZLRE8XPypHJ97GP+ifrn69hYRs6ZQ79u3Ti/bx/+f/5Jq2x1PkGAL7wRvHPnTiZOnIiXlxfVq1fP9/oXLlwgSEWPYkHw9vbG0dERLy8vatasiZubW4FsNyExEf8zZ5g0dCgGeno0rlOH9s2b462iIuh19CjDe/emfJkylPvqK4b17o2nv3+e3icgOJiR/fqhp6tL5fLl6d25M/tyuCKtMk6ZDP+zZ5k0cKAUZ+3atLe2xjswUDnOgACGOzpS/quvKFe6NMMcHfE8flzldr2OH6dzixbo6+oqXnv77h1rduxg+tCheY4vuyNHvOjSpTcmJuYYGRVn0KBx+Pl5qix76tRRjI3Nadu2C9raOgwZ8h0REaE8fJjRIL15M4TIyDA6d3bKsu65cydo06YzJibmaGlpM2jQOK5fv0h09MNc40tITMT/+HEmjRuHgb4+jRs0oH2bNnhnO6kAePn6MnzQIMqXK0e5smUZNmgQnpl6VQb060fzpk3R0dFRWvdRdDSNGjSgSuXKaGho0KNbN8I/4QLTwYNe9OjRG1NTc4oVK86IEeM4cEB1PgMCjlK9ujkdO3ZBR0eH0aO/IywslPv3pXyOGTMRY2NT1NXVqVPHivr1G3HjxtUs21izZhn9+g2iRImSKt8jISEB/6NHmTRhgpS/hg1p37Yt3pny8oGXjw/DBw+mfPnylCtXjmFDhuCZXlm/cPEiqWlpDBk0CG1tbQYPGIBcV9P89QAAIABJREFULif4/Hnps5w8ycjhw9HT06NypUr0dnJiX6Zewp4ODrSxscHAwCDX/Hl5edGrV2/Mzc0pXrw4Y8eOw8tLdf6OHj2KmZk5nTtL+Rs//jvu3Anl3r2I9G15MnbsOIoXL46pqSm9e/fBM72y3Lp1Gzp37oKhoSF6enp8881AQkIyLjAEBgYwYsRI9PT0qFSpMr169WbfoUPK+U1MxP/kSSaNGCHlt1492rdsibeKY4aXnx/D+/enfNmylCtThmH9++OZbVTDsj/+YFDv3pQsXjzL66MGDKC2hQWamppUr1qVDq1aEXLjRq65zBJnAX0PAAZ8/TXNmzVTuR99068fjRs1QltLi3LlymHfrRshV68qlfsYb28vnJx6Y2YmfQ/GjBmHt7fq78GxY0cxNTXHzk76Howdm/V70Lx5Czp37krZsuVyfL8VK5YxYEDO+1F2/9TfvWqlShQzMgKkCyHqamo8iFbuRfyYQ4e8sLfvTfXq0nFp2LBxHDyoOp8nThzFxMScDh2kfI4cmfW4VK1adfT0MhoYamrqPHokNSqCg89Qv35jrKwao6mpyaBBo3jx4hlXrlzMNb6ExET8AwOZNHq0lE8rK9rb2OCt4gK016FDDP/6aymfZcsy7Ouv8UzfN3V0dKherRrq6upSvtTViX37lti4OJXveeTECXp27Zq3JGZS1M+bnyrU05M73t4kvHr1t2w/r4YMsWLjxqvcvv2CmBgZP/54mqFDrVSWrVzZiJgYGX5+Uj4PHQrn3btkTE3zti/nxT9V//TYu5dWjRrRo0MHtLW1MdTXx7Ra/nuti660IvDz3/DFNoIXL17M5cuXWbp0KYMGDeLUqVM4Ojpib2/PkCFDeJCpx3H9+vV0796d7t27M3v2bN69e8edO3fYuXMnXl5eODg4sH79+nzH8Pz5cyZOnEjv3r2xt7dn3TppOJSbmxuHDx9m8+bNODg44OzszNu3b3FwcKB///6f9bnvR0ejoaGBSeXKitcsTU0JV9HDGnb/PpaZLg5YVq+u1BM7YMoUWvbtywQXFx49fZp1A5mGrsjlcpVX+3KNU10dk0qVMt7fxITwh8onrbCHD7HMdOXf0sSEMBXlEmQy/M6exTHbsK3lmzfzdZcufFXy0w/29++HYWqa0TNqZmbBmzcviY19k0PZjGFGenr6VKxYVTHcLS0tjZUrf2TixLmoqakprS/Pklfp38jIu7nH9+ABGpqamGQ6EVjWqKGygRp27x6WNWpkKZfXIc3d7OyIevSIyAcPSElJwdPXF5sWLfK0bmb37oVhbp6Rzxo1LHj9+iUxMcr5vHcvjBo1suazUqWq3Lun3Dsnk8m4ffsm1aubKV67des6f/11k169vlYq/4Eif8bGitcsLSwIV5GXsIgILC0sspT70FMYHh6Ohbl5lr+rRY0aWbeTfb8JC8sxrpyEh4dhYZGRP0tLC16+fMmbN8r5k8pmxKuvr0+VKlUJDw8nNjaWFy9eZNuWJeEqej4BLl26qNQLmHnEqlwuV9krdT8qSjouVa2a8T5mZoSr6BEMi4zE0tQ0S7nMPYfXb9/mZmgoXzs4qIwxcyyXrl/HLFuvYW4K6nuQXxcvX8bM1PTjBbOJiMj6PbCwsODVK9X7kVRW+XsQEZG3mG/cuM6tWzfp2zfn/Si7f/Lv7nv0KA3t7GjWvTuh4eH0/8j3Q5XIyKzHJXNz6bik6jgvlc16XKpcuWqWYc2bN6+nffsGODi0RiZLoFMne8UyebbjAMi5d+8jx/mHD1XnU9VxPjISS/OMfdXS3FypB95+4EDq/Z+9+45q8noDOP4FlI2rWq0TBcRBVZy1bpRVtqtai6sOHHXUurWOuupstValKIgLC8iwbnFvAWcVBRVBUdRaWWEI5PdHIBATIPZnBfV+zsk5kNy875Mn77rvHenShVFTptDH2ZmPqlRRWs7Bo0epUqkSbSwti41NZbxl/Lz5rmvatBpXrhR0+75yJZEaNQypUkVPqWx4+CNu3nyGk1NDNDU1cHExJzMzh6tXC4a3WFrW4OnTSdy6NZpZszqhpaWc5+K8revPy1FRVDQyot/48bTv0weP2bNJKDRMRxDyfbCV4BkzZmBhYcGsWbP4+eefmTJlCsuXL2f37t04Ojry/fffA3D8+HFCQ0Px8/Nj9+7d5OTk8Ntvv2Fubk6/fv1wdXUlJCSEESNGvHYMU6dOxd3dnYCAAAIDAzlx4gSnT59m2LBhWFlZMWLECEJCQli4cCFGRkaEhITg5+f3f31uSXo6hvr6Cs8ZGRiQJpEol83IwLBQi5ORgQGS9HT5yWTrihUc2bqVfZs28fFHH+Exe7a8S3GnNm3w9PMjVSLh/sOHBB44QHpmpvpxZmSojjM9vcSyr8aZ7+CZM1Q2MqKthYX8uWvR0UTevMnXTk78PzIyJBgaGsr/NzCQtTqkp6cplU1Pl8hfLyhvKC+7a9cWGjduhrm5hdJ727btxLFj+7hzJ4rMzAx8fdeioaFBZmZGsfFJJBKF7xLAyNCQtDTl+CQSxc9iZGiIRCJROf7uVdWqVaNlixbYubrSvH179h86xPS8fel1vBqDoaFR3vPq5dOwiM+2ZMkczMzMad9e1p04JyeHn36ay+TJP6CpWfTh8E3lL00iwcio6Fg7deiA58aNpKalcT8ujsCgINIziv9ui4rXyEi9/KWlKcdkZCSLSZJ3XCj8uqGhkcrPfetWFOvW/cbkyVPkz3Xq1Inff/ckLS2V+/fvs2tXoMrjgCQ9XTm/RR2X0tMV81tof8/JyWHuypX8MHFisd8nwJpNm8jNzaXXa7Rgva39qLCAoCCu//UXQ/9FT5Wi9qOi41VvP3pVTk4OCxbMZcaM4vcjpXW+xe/dydqayAMHOLB9O/1cXfnoX9z0lB1r1NuvVOXTwMBQoezAgSMIC4vExycIOzsX+bLbtGnPpUsXiYw8z8uXWWzevJ6XL1+SUcKxQGU+DQ2Lzmfh87uK7XP31q1EHD7MinnzaNWsmcp1Bu3di6u9vcqKZ0nK+nnzXWdoWJ6kpILjbf7fRkbaSmVzc6X4+l5l+3Y3MjNnsH27GyNH7kEikQ3VOXEiDguLDXz88Qp69Qqgf/+mTJ78eje439b1Z+LTpwQfOsSM0aM5tm0btWvU4LuFC18r1rKttFuBRUvwe+XKlSs0atQIU1NZ61CvXr24efMmqampeRPGfIGhoSEaGhr07duXs2fP/t/rlEgkXLhwgQULFuDi4kKfPn148uQJd/7lJELq0tfTI/WVA05qWhoGrxyYAPR1dRUOTqkSCfp6evKTXZtmzdAuX54KhobMHD2aB48fcyevBXbW6NHo6OhgO3gwo+fMwaFbN2pUrap+nLq6ynFKJBjoKd/B1NfVVagcvxpnvuAjR3C1spI/n5uby7x165g5fDjltIqeLEKVQ4dCsbe3xN7ekqlTh6Grq09aWqr89fy/9fSUu63q6SmWBdlFlJ6eAc+eJbJrly/ffKM81gmgVavPGTx4HHPmjKN/fytq1KiFvr4B1arVKDZefX19Ul+5mE1NTVXZrVZfX1/hwjc1LQ19fX21LnLWenpy/a+/OL5/P1fPnWPsyJEMGjGCdBU3Lwrbty+Uzp0t6dzZknHjhuXFUJCj1NTUvNjUy2daWprSZ/vll5+4cyeaxYt/kX+WgIDtmJqa8+mnLYqNT2X+VKwjv6xC/lJT5fkz0NeXfxZVsc6aPl223zg4MHrcOBzs7alRvejup/lC//yTVq0sadXKkhEjZPkrvJ7i8mdgoBxTaqosJv2840Lh19PSlLeb+/fvM2LEcKZPn0Hr1q3lz8+YMQtdXR3s7GwZM2Y0X3zhQI1q1ZRi0NfTU86vRKL6uKSnp5jfQvv79qAgzE1MaNG0qXKSCtkaGEjwgQN4Ll2KtrbyBWFR3tR2oK7DYWGs/Plnfl+3jipqVNr+/DOUtm0tadvWEg8P5e0gfz8pOl7V20FJ/Py207ChOc2bF78fKa3zLX/vAMZ16mBmbMy8lStLLHvgQChWVpZYWVkyceIwpWNN/t+q9itV+UxLS1Mqq6Ghgbl5E3R0dPHykk0cZ2xswuzZS1ix4kccHTuRlPQP9eubFtsVHYrIZ1Hn91fzWcRxXkdHB0cbGzy3bCHqlV4pCY8fc+HSJVzt7YuNK9+7dt5813z1lYV80qq9e/uTmvqSChUKhlvk/52Sojz5WPfu9Vm6tDtdu/qirb2QLl024+XlSPPmsm3u3r0XxMa+QCqF69efMH/+SXr3Vp7PoDhv6/pTR0cH6w4daGZujo62NmPc3bl04wYpatzQEz4s4ieSSklubi4aGhoEBARQvnz5t7Ze41q1yMnJIfbBA4zzuqRE3b2LqYrxEmbGxkTdvUuzvAmQou7exayYcRUaFHQ5qlShAiumF0yQs3LjRpoV6mqnVpy5ucQmJGBcs6Zs/ffuYVqom5c8zrp1ibp3j2Z5XXij7t3D7JVyj54+5cK1a8wfM0b+XKpEwvWYGCYuXQpATq5s2vkugwfzy7RptC7mgsra2hlra2f5/z/+OIk7d27RrZusVenOnSgqV65KxYrKF67GxmYcOFAw7ik9XUJCQhz165sSFXWNv/9+yuDBDgBkZWWQmZlJz54d8Pc/gZaWFm5uA3BzGwBAfPw9tm5dR/36xU9EY1yvHjnZ2cTev49x3ncYdfs2pirGwps1aEDU7ds0y2sxj7p9GzM1u2JG3bqFva2tvOLW09mZRcuWEXP3Lp8Wk097e2fs7QvyOWvWJKKjb2FtLctndHQUVapUVTnWsEEDM4VxeenpEh48iFPo8rxhw2rOnDnJhg1bFFoeLl48S2TkRU6fLpjo6datG9y+fZMpU36Ql1OZv1u3VHZRNTMxIerWLZp9+qms3O3bmOXdYDM1NWXT5s1IpVL5yfzW7dt8lTfMoVLFiqwoNIneyl9+kS+nOM6Ojji6FoyD+/77Sdy6dQt7e1n+bt2KomrVqlRWUZEyNTVTGC8skUiIj4/D1NSUihUrUq1aNaKioujQoYPs80RFyW8YAjx8+JChQ4cwatRoXFxcFZZdqVIlli0rmLVz1aqVNFMxEZRxnTqy41J8PMZ16sjWExOjsquyWf36RMXE0KxJE3m5/ImQzkZEcPHyZU6cOwdAUnIyN6KjuRkTww8TZRfIAXv24LltG9t+/ZUaH39cZE5VeVPbgTpOnDrFrHnz8Fy7FvNCwxOK4+jojKNjwX40Zcokbt++hZ1dwXbw0Ueq9yMTEzNCQxW3gwcP4jAxKTnm8+fPEh5+UT5hWlJSElFRN4iKusnMmT8U+b63+b0Xlp2TQ5waY4JtbZ2xtS3I5w8/TCIm5hY9eigel1Qd5+vXN2PvXsXj0sOHsuO8Kjk52QpjVK2s7LCysgMgJSWZ3bsDady4+GOBcd26yvmMjlZ9nM/PZ95xOSo6WmlCscKys7OJf/hQoQt1yP79tGzWjDqFhi0V5107b75rtm+/zvbt1+X/b9vmRvPm1fH3vwFA8+bVefw4lefPlW9Kt2hRnRMn4oiIeATIukefP59Ajx71FbpU55Odw14vvrd1/Wlevz6Fg3v9PgrCh0K0BAMtWrQgKipK3gobFBREkyZNMDQ0pH379uzbt4/U1FSkUikBAQF8njfG0dDQkJSUlH+1TkNDQ1q1aqUwlvjRo0c8ffpUZdmMjIx//ZMOhenr6WHdsSOrN29Gkp5OxPXrhJ05g0uPHkplXXr0wDsggMRnz0h89gzvgADcbGwA2XiNmzEx5OTkkJaezpING/i4alVM8iqfcQkJ/JOcTE5ODscvXGDn3r2MGjBA/Th1dbFu357V27Yhycgg4sYNws6fx0XFDJQuVlZ4BweT+PffJP79N95BQbi9Mu435OhRLBs3pu4nn8ifMzIw4OTmzQSvXk3w6tV4zpkDwK6ff5ZXqNVla+vC3r0BxMbGkJqazNat67Czc1NZtlMna2Jjozl+/ABZWZn4+q6lQQNz6tY1oW3bzvj5HcHLKxgvr2AGDx6HqWljvLyC0dLSIisrk3v3biOVSklMTGDFih/o2XMgRkYVVa5Lnk89PaytrFi9bp3se798mbDjx3FxdFTOp6Mj3lu3kvjkCYlPnuC9ZQtuhbqLZ718SWZmJlKplOzsbDIzM8nNu4HwadOm7D90iGd//01ubi7Bf/5JdnY29VTcvCjOF1+4EBISwN27MaSkJLNp0zocHVXns1s3a+7ciebIkQNkZmbi5bUWMzNzjI1lFRNv7w0cOPAna9d6K138z5mzhD/+2Mu2bcFs2xZM48YWDB8+ltGjFS+c9fX1se7Rg9Vr1yKRSIi4dImwo0dxUdGN3sXZGW9fXxITE2X527wZt7zxh23btEFLSwvfbdvIyspia95P9HzWrh0AcfHx/PPihWy/OXmSnQEBjCo01OJlfu5zc+W5z1Exq7mLiwuBgQHExMSQnJzM+vXrcHVVnb8ePayJjo7m4EFZ/n77bS0NG5rToIFJ3rJcWb9+HUlJSdy9ewd/f3/c3GTLSkxMZMiQQQwYMIB+/ZTHgsbFxfHPP/+Qk5PDiRPH+eOPnYwaNEipnL6eHtadO7N640bZ9nn1KmGnTuFia6v82Wxt8f7jDxKfPpUdl/z8cMtriVoyYwZ7t24leNMmgjdtwqJRI8YOHszE4bIZjUMPHmSVpyfeK1dSJ+/m2ut4U9sBFL8fnT1/nsnTprFm5Uq1boIUxdnZhV27ArhzR7YdeHquw8VF9XbQvbs1MTHRHDok2w7Wr1fcDnJycvK2t2xyc3PJzMzkZd6s5gsWLCE0dC8BAcEEBATTtKkFHh5jGTdOdctcvrf1vfvv3s3feePhY+7dw3PrVtr/i9li7e1d2L07gHv3ZMclH591ODiozmeXLtbcvRvN0aOyfG7atBZTU9lxKTc3l6AgP5KTk5BKpfz111UCA7fTunV7+fujoq6Tk5PDP/88Z8mS2XTsaCU/phWbz65dWf3777J8XrlC2MmTuNjZKefT3h7vHTtkx/mnT/HesUM+udXl69cJv3KFrLwu2J5btvDsn3/kFeZ8wfv2/asJsfKV9fPmv6WhpYWWjg6aWlryvzVes7fZm+Dre5VvvmlB48ZVqVhRh1mzOuLjc0Vl2YsXE+jUqa685bdFixp06lRHPibYzs6Ejz+WtdCbm3/E7NmdCAl5vTHVb+v6s6etLYdPn+ZmTAwvs7P5bds2WllYYKRGr5Z3Q2l3hX5/ukNrSF93gNJ7xN3dnaFDh9KtWzdOnDjBqlWryM7OpkqVKsyfP596eXedPD09Cc37PVQLCwtmz56NgYEB8fHxjB07FgAHB4cixwWvWbOGTZs2KYyrGz16NN27d2fx4sXyiW8MDAxYuHAhJiYmTJs2DQsLC77++msAZs2aRUREBBUrVlR/XLCKyaFA9jttM1as4ExkJJWMjJg0bBhOVlaEX7vG8BkzuJQ306lUKmWZlxcBeTNw9ra3Z/KwYWhoaHD20iXmrl5N4rNn6OnqYtmkCVOGD5ff3dt7/DiLfvuNlLQ0jGvV4vthw+jUpo3qOIsY5/QiJYUZv/zCmcuXZXEOGoRT166E//UXw+fO5VLez43k/05wQN7Mgb1tbJg8eLBCty47Dw++6dmTPnkHUVUeJCbSfdgw/goOVtk9OsGw+IrxH3944+f3O5mZGXTubMvEifPkXS0HD3ZgwICR8rvgERFn+OWX+SQmJtC4cXOmTVtMjRq1lZa5f/8u9uzxZ82aHQCkpiYzfvwAEhLi0dMzwN6+J0OHTkCrULw1KymPrwHZ70fOmDuXM+fOUalSJSaNG4eTvT3hkZEMHzuWS2fOFOTzl18IyJsBuLebG5PHj5fn033YMC5ERCgs2/f332nXujWZmZksWbmSQ0eOIElPp16dOkwcO5bOea2IhSVnK3eBKmzbNm98fWX57NbNlunTC/LZt68DQ4aMlLcenz9/hmXL5vP4cQJNmzZnzpzF1Kwpy2ebNuaUL1+ecuUKelwMGTKSIUM8lNY5cqQ79vbOuLr2kT9XQTerIH+zZ8vyV7EikyZMwMnBgfCICIaPGsWlCxcK8rdqFQGBgbL89erF5IkT5fm7cfMms+bMIebuXUwaNGDhvHnyn8nZu38/i5YuJSUlBeN69fh+4kQ6FcrdtJkzCXrlt5kX//gjPV1dyS2n2K3Xx8cbL6/fycjIwMbGlrlzC/Ln6OjAyJEjcXKS5e/MmTMsWDCfhIQEmjVrzuLFi6lVS5a/rKws5s2bw4EDB9DV1WXYsOEMHjwEgLVrf+XXX9fIu03ni4i4BMC+fXtZvHiR7PMYGzNp0vd0bqS6R8iL5GRmLF7MmfBwKlWowCQPD5ysrQm/coXhkydzKW//zv+92IC8mc17OzoyedQold2M3b/9FmcbG/rkVVKt+vYl8ckThS7QTjY2zFc1br1SJdVxvqHtwH3IEC6Ehyss23fTJtq1aYP70KFEREaiUyjOVi1b4rVe8bdkAbIovjv35s3ebNok24969LDlhx8KtgNXVweGDRspbz0+e/YMixbN59GjBD79tDkLFhRsB8HBu5g9e7rCsp2d3Vi4cInSOocMccfR0ZlevQr2I+0XqiemeRvf+/RFizh+7hyS9HSqVKqEbdeuTBg2TOXM3M/LFd87YMcOb7ZsKTguTZlSkM+vvnJg0KCR8tbjCxfOsGJFwXFp9uzFfPJJbXJzc/nuu+HcuHGN7OyXVK36MV984cagQSPln2fkyP7ExEShpVUeKys7xo+fpjCbdBWeq85nUhIzFi3izIULsu1z1CicbG0Jv3yZ4d99x6W832XN/53ggLzjSW9nZyaPGYOGhgYXIiNZsGoV8QkJlNfSoqGJCeNHjFCY/OrStWsMGTeOU3/+qTQOubCEDOXJtAorK+fN32u9ufbCLnPm0HXuXIXnjs2dy/F58/7vZc9l/muVnzixHVOnfo6eXnkCA2/i4bGXrCxZJeb6dQ8WLTolbz0eM6Y1Eya0o3p1A54+lbB2bTgrV8p6Vyxb1gN3908xNNQmMTGNrVuv8eOPJ8nOzlW5Xul95Zud8HauPwG2797Num3byMjMpJWFBXO+/ZZPVPX8ec0b9GWBhkZoyYX+Y1Kpc8mF3gEfdCX4vVdEJbjM+RcT/5SGkirBZUVRleCypqRKcFmRXwku616tBJdVms/ekVk6i6gElzUlVYLLiqIqwWVNSZXgsqKoSnBZU1IluKx4k5Xg/9LrVoJLS1GV4DJHVIL/lfelEizGBAuCIAiCIAiCIJR5qlvfhdcnKsFvyM2bN5k2bZrS819//TV9+vRR8Q5BEARBEARBEAThbROV4DekcePGhISElHYYgiAIgiAIgiC8l96fialKm5gdWhAEQRAEQRAEQfhgiEqwIAiCIAiCIAiC8MEQ3aEFQRAEQRAEQRDKPNEd+k0RlWBBEARBEARBEAThrUlPT2f69On89ddfaGlpMXXqVLp166ZUztfXl8DAQPn/8fHx9OnTh+nTp3P+/HlGjBiBsbExANra2vj7+6u1flEJFgRBEARBEARBKPPen5bgjRs3YmhoyKFDh4iNjWXAgAEcPHgQAwMDhXIDBw5k4MCBALx8+ZLOnTvj6Ogof93ExIRdu3a99vpFJVgQBEEQBEEQBEEoUXJyMsnJyUrPV6hQgQoVKqi9nH379rFkyRIAjI2NsbCw4MSJE9jb2xf5nqNHj1KtWjU+/fTT1w/8FaISLAiCIAiCIAiCIJRo8+bN/Prrr0rPjx07lm+//Vbt5SQkJFCrVi35/5988gmPHz8u9j2BgYH07NlT4bnY2Fjc3NwoV64cX331FW5ubmqtX1SCBUEQBEEQBEEQyrzS7w49aNAglRXNV1uB3dzcSEhIULmMM2fOvPZ6nzx5wrlz51i8eLH8uaZNm3L8+HGMjIyIj49nyJAhVK9enc8//7zE5YlKsCAIgiAIgiAIglAidbs9BwUFFft6zZo1efjwIVWqVAHg0aNHtGvXrsjywcHBdOnSRV4ewNDQUP53nTp16NGjB5GRkWpVgsXvBAuCIAiCIAiCIJR5uWXg8WbY2dmxc+dOQNal+dq1a3Tq1KnI8oGBgfTq1UvhuSdPniCVSgF48eIFp0+fplGjRmqtX7QEC4IgCIIgCIIgCG/NN998w7Rp07C2tkZTU5P58+fLW3Z/+eUXPv74Y/r37w9AREQEEomEjh07Kizj4MGD7Nixg3LlypGTk4Orqys9evRQa/0a0vzqs/Deef68tCNQz7NnpR2Behpm3yjtENTyvEaT0g5BLVUMs0o7BLU8T9Uu7RDUUk7c0nyjsrNLOwL1VKn05u7K/5dy35GOZ5rZ78Zx6UbMu3FcalL1SWmHoBaN6htKOwS1zOWH0g5BLUPuvxtVi7p1SzuC16eh4VvaISCVDiztEN4IcdkkCIIgCIIgCIJQ5pX+xFjvi3fj1qwgCIIgCIIgCIIgvAGiJVgQBEEQBEEQBKHMEy3Bb4poCRYEQRAEQRAEQRA+GKISLAiCIAiCIAiCIHwwRHdoQRAEQRAEQRCEMk90h35TREuwIAiCIAiCIAiC8MEQLcGCIAiCIAiCIAhlnmgJflNES7AgCIIgCIIgCILwwRCVYEEQBEEQBEEQBOGDIbpDC4IgCIIgCIIglHm5pR3Ae0O0BAuCIAiCIAiCIAgfDFEJFgRBEARBEARBED4YohL8AduxwwcHhw50796SBQumk5WVVWTZixfP8uWXdnTt2pwxY9x59Oih/LVff12Ki0sXundviZtbN3x81qtcxt69wbRvb05oqP9rxRkc7IO7ewf69m3JL79M5+XLouO8cuUsHh529OrVnBkz3HnypCDO0aMd6NPHUv5wcWnC/PkeCu8dP96Nvn1bMmxYd/bv36lWfC9SUhizZAkt+vWj24gR7D5xQmU5qVTKMl9f2rm7087dnWW+vkilUvnrZ69exW2MrywhAAAgAElEQVTSJFp+9RXdPTzYefCg/LXz16/TqGdPLPv3lz+CjhxRK75XvY3v/cWL54wY0Q9b23ZYW7dm+PAvuXIlQuU6XiQlMWb8eFq0bUs3Gxt279mjspxUKmXZypW069iRdh07smzlSoX83YyKomffvjRv04aefftyMypK/lpycjJTZ86kfZcutO/ShTW//aaw7JtRUXw1aBCt2renc/furF2vehtW5W3k8/LlcKysLBUe7dubc/ToAbXj3L7dB1vbDnTt2pL584uP88KFs/TubUfHjs3x8FCMc/XqpTg4dKFr15Y4OXXD21sxVwsXzqZXL1vatm3E7t271I7vTcf5888/0bOnDV26WNK7tx179gQrvDcnJ4d161Zhb9+RLl0sGTDAlZSUZLXjfBvfe1zcPaZMGYW9/WfY2LRlwoRvuH//rlrxvXjxgjFjx9KiZUu6WVmx+88/VZaTSqUsW76cdp99RrvPPmPZ8uWK+9XNm/Ts1Yvmlpb07NWLmzdvyl8bNmIElq1ayR8WzZrh5OysVnw+Pj506tSB1q1bMnNm8fk7e/YsX3xhh6VlcwYNcufhw4L8ZWVlMXPmdFq3bkmnTh3w8fFWeG96ejrz5s2lfft2tGnTiq+/HqAQg7V1d1q3bknnzh1ZvHgR2dnZKmN4U8ep2XPnYuvkRKNmzdgVrLhNBoWE0LNvX1p+9hmdu3dn6cqVRcZTktBQH4YM6cBXX7VkzZriz5tXr55l7Fg7vvyyObNnK543U1JesHz5BNzd2zFwYDtWrZqERJIqf33ECCu+/LIZ/ftb0r+/JXPnDlUrvhfJyYyZMYMW1tZ0692b3YcOqSwnlUpZtm4d7RwcaOfgwLJ16xTymS94/37MO3XCf/du+XNe27fjOHAgljY2WPXti9f27WrFpo4JE9rx6NFEkpKmsHGjE9raWkWW7dOnCTdujCI5eQp//eWBi4u5/LVBg5qRnT2TlJSp8keXLvXeWJzFaTNmDMMvXmRmRgYu3t4lv+ENCgz0oW/fDri4tGT58uL3/8jIswwdaoejY3O+/96dxMSC7XPp0mnY21vg5GQpf+TkFMyevHevP4MGWePkZMn06d/w7Fnif/q53o6cMvB4P7zXleDDhw9jb2+Pq6srd++qd+FQ2Pnz5zl16lSJ5dasWUP79u1xcXHBzs6OSZMmIZFI5K+7uLiQkZFR4nKsrKy4ffu2ytd8fHz4+++/1Q++BOfOnWTLFk/WrPEhKOgoCQkP8PJarbLsixfPmT59LCNGjOfAgQs0amTB7NkT5a87OfXGz28fYWGRbNjgx8GDuzl27KDCMpKTk9i8eT0NGpi9VpyRkScJDPRkwQIfNm06yuPHD9i2TXWcSUnPWbRoLF9/PZ4dOy5gamrB0qUFcf722x78/S/h73+JP/6IpGrVGnTsaAdAdvZLFi0ai53dl+zcGcGUKavYuHEJ9+5FqVxXYfM9PSlfrhynvb1ZNmECczdsIDouTqnczoMHOXz+PCGrVhH6888cvXgRvwOySszL7GzG/vQTX9rYELFtG6smTWKJtzdR9+7J3/9xlSpc2rFD/nCzsnqtXMLb+9719AyYOXMR+/ad5eDBi3z99XAmTx6l8oJu/sKFlC9fntPHjrFsyRLmLlhAdEyMcv78/Tl89CghAQGEBgZy9Phx/PxlN1SyXr5k9LhxODs6cvH0aVxdXBg9bhxZL18CsHjpUtLT0zmyfz/+27cTsns3gUFB8mVPmjqVNq1aceHUKbb6+LBj507Cjh4tM/ls0aI1R45ckj+WL1+Pvr4+n33WqcQYAc6ePcnmzZ789psPoaFHefjwARs2FB3nlClj8fAYT1jYBRo3tmDGjII4nZ17ExCwj2PHItm40Y99+3Zz5EjB/m5m1oipU+fSqFETtWL7r+LU09Nj5cp1HD0awdy5P7FixUKuXImUv+7puZqrVy+xadNOjh2LZN68pWhr66gV59v63lNSUujY0Qo/v/3s3Xuaxo0/ZcqU0WrFOP/HH2X71cmTLFu2jLnz5hEdHa1Ubucff3A4LIyQ4GBCQ0I4euwYfjtlNwCzsrIYPWYMzk5OXDx/HldXV0aPGSO/YPXy9ORSRIT8YdmiBba2tiXGdurUSby8PNm0yYewsKPExz9gzRrV+fvnn+eMGzeWcePGc+7cBSwsLPjuu4L8/frrGu7fv09Y2FF8fHzZuNGLkycLbkTOmTObpKQk9uzZx7lzF5g+fbr8NSsrKwIDgwgPjyQ09E9u3Ypiy7ZtqvP5Bo5TAI3MzZk7cyZNGjdWem96RgYzpk7l3MmT+G/fzrlz59jk41NiPl916dJJdu3yZN48Hzw9j5KY+IAdO1TnNzn5OT/9NJb+/cezZcsFTEwsWLGiIL/bt/9MamoyGzaEsW7dYV68+Bs/vzUKy5gxYz07dlxix45LzJ27Sa0Y569cKctnSAjLfviBuStWEF3ofJdvZ2goh0+eJMTbm1AfH46ePo1fSIhCmaSUFNZv2YJZ/foKz0ulUn6aOZOLe/fitXw523btYs/hw2rFVxwbmwZMm/Y53btvpV691TRoUIl587qoLFuzphFbt7ry3XcHqVBhKZMnH2b7djeqVdOXlzl79gFGRj/JH8eP3/+/Y1RHSkICJxYs4PIm9b6zN+XixZP4+XmydKkPW7ce5dGjB/j6Fn1dN2/eWAYNGs+uXRdo2NCCBQsmKpTp2/cbdu++JH9oacluSFy5ch5v75XMm/cbu3adp0aN2ixaNOk//3zCu+O9rgT7+fkxbtw4goODadCgwWu//8KFC5w+fVqtsq6uroSEhBAaGsqjR4/YunWr/LWQkBB0dXVfe/2F+fr6vtFK8N69wTg59aZBAzMqVKjIkCGj2bMnSGXZY8cOUb++Gd2726Ojo8OwYd8SHR1FbOwdAOrVa4CeXsEBXUNDkwcPFA/i69atoG9fdypWrPxacYaFBWNt3Zt69cwwNKxIv36jCQtTHefZs4eoW9eMjh3t0dbW4auvvuXevSji4+8olb1+/SIpKf/w+ec2AKSkJCGRpNKtmwsaGho0bNiM2rUbEBenfJFTmCQjg4PnzjG+f38M9PRo3aQJVm3aEHLsmFLZ4KNHGeriQo2qVan+0UcMcXEhKK+ilZSaSqpEgkvXrmhoaNDMzIwGtWsT8+DBa+WrJG/re9fR0aFevQZoamoilUrR1NQkJSWJ5OQkhXVIJBIOHjrE+LFjMdDXp3XLllh17UpIobv5+YJDQxk6cCA1atSgevXqDBk0iKC8i6ELFy+SnZPDIHd3tLW1GThgAFKplHPnzwNw5Phxhg0dip6eHrVr1aJ3z54EFmqFeZiQgJODA1paWtStU4eWlpbE3FHebkorn6rW262bnUL54uzZE4yzc29MTGRxfvPNaP78U3WcR44cokEDM3r0kMU5YoRinMbGinFqairG2bfvANq2ba92hfK/inPkyHEYG5ugqamJhUVzWrRoxbVrlwHZTbkdO3yZOXMBn3xSCw0NDUxNG6Kjo17Mb+t7b9q0Gc7OfahYsRLlypWnX7/BxMXdIynpn2Ljk+9X48ZhYGBA61atsOrWjZDQUKWywcHBDB0ypGC/GjyYoLwbRPL9atAg2X7l7o4U5PtVYQ8ePiQ8IgJXV9cS8xccHEyvXr0xMzOjYsWKjBo1muBg1fk7dOgQpqZm2NnJ8jdmzLfcuhXF3bt38pYVxKhRo6lYsSImJib07t1HHv/du3c4cuQI8+f/SJUqVdDS0qJpUwv5suvWrUuFChUAWYVJQ0OT+/HxRefz/zxOAQzo35/2n32mclv76ssvad2qFdrly1O9enWcHByIvHy5xHy+6ujRYHr06E3durLzZt++ozl6VHV+z507RJ06ZnToIDtv9uv3LbGxUTx4IMtvYuID2rXrjr6+IQYGRrRrZ018fPHnxZJI0tM5ePw447/5RpbPZs2w6tCBkAPKPVuC9+9naL9+1Pj4Y6pXq8aQfv0I2rdPocyKDRtw792byhUrKjw/fMAAmpqbU65cORrUrUv3jh2JvHbt/4odYNCg5mzceJkbN57y4kUGP/54ksGDm6ssW7u2ES9eZLB/vyyfe/fGkJaWhYnJ610L/ReigoK4FRKC5A1eW6rj0KFg7Ox6Y2xshpFRRQYMGM3Bg6q3z1OnDmFsbEaXLrLt0939W+7ejSIuruTz87lzx+jUyQ5jYzPKl9dmwIDRXLt2kYQE5UaKd0tptwKLluAyb9GiRURERLB8+XLc3d05ceIErq6uODk5MWjQIO7fL7ho8/T0xNHREUdHR6ZPn05aWhq3bt3Cz8+P4OBgXFxc8PT0VGu92traWFpa8ujRI/lz5ubmpKWlARAeHo6TkxNOTk4sWLCAbt26KbT+7tu3jy+//BIrKyt5RXrdunU8efKEcePG4eLiQoyKu8+v6969aMzMGsn/NzMz5/nzZyovrmRlC7rv6OnpU7t2Xe7dK4jD19cTKytLXFw6k5EhwcbGSf7aX39dJSrqOm5u/V87zri4aOrXL4jT2NicFy+ekZysHKesbEGcurr61KhRV2VF9siRINq3t0VXV3bxWblyVTp3diQsbBc5OTlERV3i6dMEmjRpVWx8sQkJaGlqUr9WLflzjYyNiVFxIRUdH08jY2OFcvktxlUrVcKxUyd2hYWRk5PDpagoEp48oVWh1oLnSUl8PngwViNHsmjTJiRq9C541dv83gG+/tqJLl2aMWXKKJyd+1ClykcKr8fev49WuXLUL5wXc3OVFdDoO3doZG6uUC6/JSYmJgZzMzM0NDTkr5s3bKi4nEJd6KRSqUKr2KCvvyY4NJSXL19y9949Ll+5QvvPPlOK4VVvO58A6ekSjh7dzxdflFzZyHf3rmKcDRvK4nzxQjnOu3ejadhQMc5atepy925BnD4+nnTubImDQ2fS0yXY2irH+W+86TjzZWRkcOPGdRo0MAUgJuY2WlpahIXtx9a2A7162fLHH6pbAFUpje8dZN3iP/qoWok3E2NjY9HS0qJ+oZaxRo0aqTx3RMfEKO5XjRoV7FfR0ZibmyvvVyqWExwcTOtWrahd6FhYlJiYaMzNC/LXqJE5z549459/lPMnK1sQn76+PnXq1CUmJoakpCSePn36yrIKPufVq9eoWbMWa9aspn37djg7O3HwoGJF688/d9O6dUs+//wzbt2Kol+fPkoxvKnj1Ou6GBGBqYnJa78vPj4aY2P1z5vGxornzerV68oruvb2AwgPP0ZqahKpqUmcPXuAli0Ve6CsWvU9gwZ9xty5Q9XqPRUbHy/bPuvWlT/XyNSUGBUtwdH37tGoUA4amZoqtBhfvXGD61FR9HdxKXadUqmU8KtXMX2ltfjfaNq0GleuFHSrvXIlkRo1DKlSRU+pbHj4I27efIaTU0M0NTVwcTEnMzOHq1efyMtYWtbg6dNJ3Lo1mlmzOqGlpaG0nPfJ/fvRmJgUbJ8mJub884/q7TM2NpoGDRSPnzVr1uX+/YJ9avfuHfTs2ZbRo3ty8uSrN1KUu87HxqrucSl8eN7bn0iaMWMGN2/eZOjQoTRr1gwHBwe2bt2Kqakp/v7+fP/99/j7+3P8+HFCQ0Px8/PDwMCAqVOn8ttvvzF58mT69euHRCJh6tSpaq83NTWVixcvMnHiRKXXsrKy+O6771i5ciWtW7fm0KFDbNmyRaFMRkYGO3fu5MGDBzg5OeHm5saoUaPw9/dn9erVNGzY8P/ODcgupA0MDOX/GxoaASCRpCldYEkkEipXrqLwnIGBIRJJmvz/gQNH4O4+nNu3b3LixGH5snNycli+fC6TJv2Apubr33PJyJCgr18Qp4GBUV78aVSoUFmpbIUKynGmp6e9Ui6d06cPMHv2OoXnu3RxYM2aWXh6LgRg9Oi5VKv2SbHxSTIyMNRXbI0z0tcnLT29xLJG+vpIMjLyWiA0cOjUiVlr17Jw40YA5o4cySdVqwLQoFYtgleupEGtWjx8+pRpq1ezxNub+aNGFRvfq97W955v69bdZGZmcvz4IV7mdU1+dR2GBgYKzxkZGspvGimVNTRUKCeRSJBKpaRJJBgZGSmUNyy0nE4dOuC5cSNLFi7k77//JjAoiPRCNxG6dunC1Bkz2LR5Mzk5OYzx8KCZhQUledv5BDh27CAVK1bG0rJtifEVXnfh3BWOs1IlxTjT0yVUqqQYp+Er38ngwSMYNEgW57FjhxWW/f9403HmW7JkDmZm5rRvL7t4f/LkMampKcTFxRISEkZ8fCyjRw+mXj1j2rXrUGKcpfG9P3nymOXL5zFu3LQS43s1j1DCflVo31Har15ZjqGRkcrlhISGMsrDQ+n5ouIzMlKdv8qVFfOXliahShXF/BkZyT5L/rCjwvu+oWFBfImJj4mOvo2NjQ3Hj5/k8uXLjBo1EhMTU0zyKlaOjk44OjoRGxtLSEgwH32keKMuP943cZwqfDOhJAFBQVz/6y8WzJun9nvypacrnjf19WX5ych4/fOmiUkTsrNfMnBgOwCaNWuPnd1X8rITJy6jQYOmgJQ///Rl/vxv+PXXfRgYVCgyPkl6unI+DQxIKzSMTKFs4XwaGCBJT0cqlZKbm8vclSv5YeLEEq8v1mzaRG5uLr2++KLYcuowNCxPUlKm/P/8v42MtHn+XPHcn5srxdf3Ktu3u6GrW46srBz69AlAIpGdD0+ciMPCYgP377+gadOP2bmzJ9nZuSxZol4vxHfRq8fP/Os6iUT19lmxouL2qa9fcPx0c3PHw2MqBgZGhIefZuHCCVSuXBULi1a0adOJhQsn4ujYj1q1jNm6dS0aGhpqDU8UPgzvbUtwYVeuXKFRo0aYmspaAXrlTe6RmpqaN+HGFxgaGqKhoUHfvn05e/bsa68jODgYZ2dnOnToQLVq1fhMRSvS3bt30dXVpXXr1gBYW1vLu2Ll+yLvAF27dm0qVKjA48ePXzsWVQ4cCJVPqDNx4jD09PRJSyuY3CL/b319A6X36usrlpWVT1Mqq6Ghgbl5E3R0dPHyko0Z2rVrOyYm5lhYtFArzmPHQuUTV82ZMwxdXX2FSTjy/9bTU47z1bKy8mlKZc+ePYiRUSUsLAoqEfHxd1i69DsmTvyJoKDrrF37J4GBXly8eKzYePV1dUl95cSdmp6OgZ7yHWF9XV2Fk3xqejr6urpoaGhw58EDvluxgp/Gj+e6vz9/rl6NV3Awx8LDAahWuTKmdeqgqalJnerVmTxwIAfU2E5L63svTEdHBxsbR7Zs8SQ6WrGVQF9fn9RXLiRT09IwMCgqnoKyqamp6Ovro6GhgYG+PqmpyrHmL2fW9Ono6Ohg6+DA6HHjcLC3p0b16oBswpthHh6M8fDgang4xw8d4tSZM2zz81OKoSzkc+/eYOztXYu9oN63L5TOnS3p3NmSceOGKa07P1eq4nz1M+XH+ep3UjhOT0/lONXxNuL85ZefuHMnmsWLf5HnTEdHNjxl2LAx6OrqYmbWCBsbB06fPq4yztL+3v/55znjxw+lV6+vsLFxVBnjq+t8dX8odr8qVDY1La34/So1VWk54RERPHv2DFsbG5XxhO7eTatWlrRqZcmIEcOU4ivuezYwUPFZUmWfRT/vpmLh19PSCuLT0dGlfPnyeHiMQltbm7Zt29K2bTtOn1ae68PY2BhTUzPmLVigMkdv4jilrsNhYaz8+Wd+X7eOKpVL7jZ7/HiofGKq+fNl22d6uvJ5U1dX9XmzcFlZ+YLz5rJlE6hZ05jt2yPZti2C6tXr8PPPk+VlGzduhY6OLjo6evTqNRIDAyNu3AgvNl59PT3lfEokGOgrD+/Q19NTzKdEgr6eHhoaGmwPCsLcxIQWTZsWu76tgYEEHziA59KlaGtrF1tWla++spBPWrV3b39SU19SoUJBd/b8v1NSlCd36t69PkuXdqdrV1+0tRfSpctmvLwcad5cdv65d+8FsbEvkErh+vUnzJ9/kt69lceLv8vCwkLlk1bNmCHbPlVd16na/4u6rssva2bWlAoVKqOlVY527bpgZeXEqVOySdZatvycgQPHMW/eOL7+2orq1Wuhp2dAtWo1/quP+paUdldo0R1aeIWrqyuhoaEcPnyY27dvs/1fzkJYeJyQlpaWwix3/w9bW2f5pDqrVnlRv74ZMTG35K9HR0dRpUpVld3s6tc3U6i8pKdLePgwjvr1TVWuKycnm4cPZd18w8PPcuLEYRwcOuDg0IFr1y6xevUSli+fr/K9Xbs6yyevmjfPi7p1zbh3ryDOe/eiqFSpqtLdQoC6dc2IjS2IMyNDwqNHcdStqxjnkSPB8rG/+eLioqlVy5iWLTuhqalJ7doNaNOmCxERqmd6zmdcsyY5ubnEJiTIn4uKjcW0Th2lsmZ16hAVG1tQ7t49zPK6g0XHxWFcsyadLC3R1NSkQa1adGnVihORkUrLAdkFs6oZMl9VWt+7KtnZ2Tx8qNhN3LhePXKys4ktNDwh6tYtlV0AzUxMiLpVEHvU7duY5d3YMjU15dbt2wo5uXX7tnw5lSpWZMVPP3H62DH2BAcjlUpp9umnAMQ/eICWpiauzs6UK1eOGjVq8IWdHSdOnlSKobTzmZj4iEuXLmBvX3xXaHt7Z06cuMSJE5dYvdqLBg3MiI5WjvPV1lUgr6xinA8exMm7EquK88GDfzfG6r+Oc8OG1Zw5c5I1azYqtCbld08ufAworpJSmt97cnIS48cPpVMnKwYPVq/nh7GxMTk5OcQWPt5ERclvBBdmZmpKVKGZ1KOiogr2KzMz5f3q1i2l5QQHB2Pdo4fKSiGAs5MTERGXiIi4hKenF6amZtwqtC/fuhVF1apVlVqBgbyyBfFJJBLi4+MwNTWlYsWKVKtWTSn+/PgKd6POV1xdNCcnmzgVQ1ne1HFKHSdOnWLWvHms//VXzNXs+dWli7N8YqoffvCiTh3F82Zs7OudNx8/jqNOHVP5e21s+qGrq4+engF2dv2JjCzuvFjyucm4Th3Z9lko11ExMSq7KpvVr09Uoe7kUTEx8gmwzkZEcPjECTq4uNDBxYVL16+zZO1a5q9aJS8fsGcPntu2sfnnn6nx8cfFxlWU7duvyyet+uKLHfz111N5JRagefPqPH6cqtQKDNCiRXVOnIgjIuIRUqmse/T58wn06KG6W7asx8C/CrPM6t7dWT5p1aJFXtSrZ8adOwXb5507UVSurHr7NDY2484dxePno0dx1Kunep+SHccLtj8XlwFs3nwQf/8zdOpkQ25uDsbGrzdBq/D++iAqwS1atCAqKoo7eeN3goKCaNKkCYaGhrRv3559+/aRmpqKVColICCAzz//HJB1rUtJSXmtdVWrVo2ZM2eybt06pS4XDRo0ID09nYgI2U/FHD58mORk9X6Sw8DA4LVjKY69vQu7dwdw714MKSnJ+Pisw8HBTWXZLl2suXs3mqNHD5CZmcmmTWsxNTXH2NiE3NxcgoL8SE5OQiqV8tdfVwkM3E7r1u0BmDVrCTt27MXXNxhf32AaNbLgm2/G4uGh3F1cFSsrFw4dCiAuLobU1GT++GMd3burjrN9e2vu34/m9OkDZGVl4ue3lvr1zalTp+BC5dmzx1y9el5pGQ0aNCEh4T5XrpxFKpXy6FEcFy8eUxgrpYq+ri7Wn33G6h07kGRkEHHzJmEXLuDStatSWZeuXfEODSXx779JfP4c79BQ3Lp1A6BJ/frcf/SIs1evIpVKiXv0iGPh4ZjnjUE7d+0aD588kcX27BnLt2yhe1v1u8Pme1vf+/Xrl7lyJZyXL7PIyMhgyxZP/vnnGU2bNlPMn74+1j16sHrtWiQSCRGXLhF29CguTspjIl2cnfH29SUxMZHEJ0/w3rwZt7xxYG3btEFLSwvfbdvIyspia95NqM/aybrwxcXH88+LF+Tk5HD85El2BgQwasQIAOrXq4cU2L1nD7m5uTx99ox9+/erdQH6tvKZb9++ED791JLateuqXEdRvvjChZCQAO7elcW5adM6HB1Vx9mtmzV37kRz5IgsTi+vtZiZFcS5a5dinP7+22nTpiDOly+zyMzMRCqVkp2dTWZmJrm5uW81TgBv7w0cOPAna9d6K1Wia9eui6VlazZtWk9WVhb37t3h4ME9dOzYTa0439b3npaWyoQJ39CsWUtGj/5erdig0H61Zo1sv4qMJOzIEVxU/HyRi4sL3ps3F+xX3t64uck+S9s2bdDS1MR3yxbZfpU3c3L+fgWyITz79u+Xv0cdLi4uBAYGEBMTQ3JyMuvXr8PVVfX7e/SwJjo6moMHZfn77be1NGxoToMGJnnLcmX9+nUkJSVx9+4d/P395bG0bt2aTz75BE/PDWRnZxMZGcH58+fp2LEjAP7+/vIJJ2NiYvD09KR9oc+mlM//8zgFspnsi9o/zp4/z+Rp01izcqX8Jt2/0bWrC2FhAcTHx5CWloy//zq6dVOd33btrImLi+bsWdl5848/1mJsbE7t2rL8mppacPiwP5mZGWRmZnDw4E7q1ZOdF58+TeDmzQhevswiKyuToCAvUlL+oVGjlsXGp6+nh3XnzqzeuBFJejoRV68SduoULipmFnextcX7jz9IfPqUxGfP8Pbzw83eHoAlM2awd+tWgjdtInjTJiwaNWLs4MFMHD4cgNCDB1nl6Yn3ypXUqVnzX+fzVb6+V/nmmxY0blyVihV1mDWrIz4+V1SWvXgxgU6d6sorzS1a1KBTpzryMcF2diZ8/LHs5pG5+UfMnt2JkJC3M2ZVQ0sLLR0dNLW05H9raBX9U09virW1C/v3B3D/vuy6bvv2ddjYqN4+O3SwJjY2mpMnZdvn1q2y67q6dWXb54kT+0lPTyM3N5fw8FOEhYXSvr3slzOysjK5d092E+/JkwRWrfoBV9eBGBlVVLmud0dptwK/Py3BGlJ1mpPeUe7u7gwdOpRu3bpx4sQJVq1aRXZ2NlWqVGH+/G1v9vIAACAASURBVPnUqyf7LTZPT09C82bNtLCwYPbs2RgYGBAfH8/YsWMBcHBwYETeRfOr1uRdaBQeOzxw4ECsrKwYPHgw5ubmREZGYmBgwIULF/jxxx8BaNu2Lfv27SMwMJBPPvkEKysr1q9fLx/3W/h/f39/vLy80NXVZcWKFSrv6L/q+fPiX9+xw5stW34nMzODbt1smTJlnryr0FdfOTBo0EhsbWUXTRcunGHFivk8fpxA06bNmT17MZ98Upvc3Fy++244N25cIzv7JVWrfswXX7gxaNBIlS0ro0e7Y2fnjLNzweQjz54VH2dwsDeBgbI4P//cljFj5lG+vHbe8hzo23ckXbvK4rx8+Qzr18/n6dMEGjZszoQJi6levbZ8Wf7+GwgPP85PPym31J88uRc/v994+vQh+vpGdO3qxMCBk+RjjRpm31AZ34uUFGb8+itnrlyhkpERk9zdcercmfAbNxj+449c2rEDKPid4IC8n2jo3aMHkwcOlOdp7+nT/LZzJw+fPsXIwACnzp2Z9PXXaGpq4h0SwqbQUJJTU6lkZIT1Z58xYcAADFV0u35eo/ifpnkb33tk5AVWrVpAQkI8WlrlMTFpyIgR47G0bCOPo4qhrOvYi6QkZsyezZlz56hUsSKTJkzAycGB8IgIho8axaULFwryt2oVAYGBsvz16sXkiRPl+btx8yaz5swh5u5dTBo0YOG8efKfIdm7fz+Lli4lJSUF43r1+H7iRDp1KBj7efb8eZavWkXs/fvo6ujQrWtXZk6dip6eHs9Ti+8+9zb3oy+/tGPAgG8U9p985UqY4WHbNm98fQvinD69IM6+fR0YMmQk9vayOM+fP8OyZQVxzpmzmJo1ZXGOHy+L8+XLl1Sr9jGOjm4MHlwQ58iR7kRGXlBY9/r1vrRqpVy5+K/iBGjTxpzy5ctTrlx5+bKHDBnJkCGycatPniTy448zuHIlksqVqzBo0HB69uwnL1vSz7O+je99z54gFiyYhq6unsJ2sH37HmrUkF3UV6mk+gbDixcvmDFrFmfOnKFSpUpM+u47nBwdCQ8PZ/jIkVzKuxmb/zvBCvvV998X7Fc3bjBr9mxi7tyR7VcLFtCkScEx5s89e1ixYgVHwsKKbU3PfeWeu4+PN15ev5ORkYGNjS1z5xbkz9HRgZEjR+LkJMvfmTNnWLBgPgkJCTRr1pzFixdTq5bse87KymLevDkcOHAAXV1dhg0bzuDBQ+TriY6OZvbsWdy+fYuaNWsyfvxErK2tAZgxYzonThyXj9u2s7NlwujRKmduflPHKfchQ7gQrthd2HfTJtq1aYP70KFEREaiU6jLbquWLfFS8bvlN2KKPy6FhHgTFPQ7WVkZtG9vi4dHwXlz3DgHevUaSZcusvxeuXKG33+XnTfNzJozbtxiPv5Ylt/ExHi8vBYSFXUJkGJm9inDhs2mZk1j4uKiWbnyOx4/jqd8eR3q12/EwIHfY2paUIFvUvWJUmwg+53gGYsXcyY8nEoVKjDJwwMna2vCr1xh+OTJXDp4sCCf69YRkPc7170dHZk8apTKbc39229xtrGhT97NCau+fUl88kShC7STjQ3zv1e+oaRRfUOx+XzVxIntmDr1c/T0yhMYeBMPj71kZckqB9eve7Bo0Sm2b78OwJgxrZkwoR3Vqxvw9KmEtWvDWbnyHADLlvXA3f1TDA21SUxMY+vWa/z440mys1Xv13P54bXiLE6XOXPoOneuwnPH5s7l+L8Yh/6qIfeLr1oEBHizc6ds++zY0Zbx4wv2/2HDHOjffyTdu8u2z8jIM/z663wSExNo1Kg5kycvpkYN2fY5ceJX3L17C5BSo0Zt+vUbSbduDgCkpibz3XcDePQoHj09A2xtezJ48AT5TygB1H29+8llgobGktIOAam05Lkp3gXvdSW4LEpNTZV3yzt37hzTp08nLCzsX00aVZKSKsFlRUmV4LKiqEpwWVNSJbisyK8El3UlVYLLipIqwcLrKakSXFYUVQkua16tBJdVmtnvxnGppEpwWVFUJbised1KcGl5k5Xg/1JJleCyQlSC/533pRIsLpvesoMHD+Lj44NUKkVbW5vly5f/JxVgQRAEQRAEQRDeJ+9Pd+TSJirBarp58ybTpinf+fj666/po+J3BYvSs2dPevbs+SZDEwRBEARBEARBENQkKsFqaty4MSEhIaUdhiAIgiAIgiAIH6R3YwjMu0D0wxUEQRAEQRAEQRA+GKISLAiCIAiCIAiCIHwwRHdoQRAEQRAEQRCEMk9MjPWmiJZgQRAEQRAEQRAE4YMhWoIFQRAEQRAEQRDKPNES/KaIlmBBEARBEARBEAThgyEqwYIgCIIgCIIgCMIHQ3SHFgRBEARBEARBKPNEd+g3RbQEC4IgCIIgCIIgCB8MUQkWBEEQBEEQBEEQPhiiO7QgCIIgCIIgCEKZl1vaAbw3NKRSqbS0gxD+G4EaGqUdglrSSzsANWluezd2FTu70o7g/VLFo29ph/B+efGitCNQT0ZGaUegHlfX0o5APeXekXvutWuXdgTqeUe2z6zeX5V2CGrRfhxX2iGoJY66pR2CWrzrvRvXn3PewSqQhsa00g4BqXRJaYfwRrwjZyVBEARBEARBEIQPmZgY600RY4IFQRAEQRAEQRCED4aoBAuCIAiCIAiCIAgfDNEdWhAEQRAEQRAEocwT3aHfFNESLAiCIAiCIAiCIHwwREuwIAiCIAiCIAhCmSdagt8U0RIsCIIgCIIgCIIgfDBEJVgQBEEQBEEQBEH4YIju0IIgCIIgCIIgCGWe6A79poiWYEEQBEEQBEEQBOGDIVqCBUEQBEEQBEEQyrzc0g7gvSFaggVBEARBEARBEIQPhqgEC4IgCIIgCIIgCB8M0R1aEARBEARBEAShzBMTY70pohIsUL5yZVpt3Eh1Gxsynz3jr+nTid+xQ6mcprY2zX/5hZpubmiWL8/fp08T6eFBRkICAG22bKFa9+6UMzAg4/Fjbi9dSuzGjW8sTu3Klfls40Zq2tiQ8ewZl6dPJ7aIOFv/8gt18uJ8evo05z08SM+LE6Del1/SbM4cDOrWJf3xY84MHszTU6feWKxnz/pw+vTvvHyZTpMmtjg4zKNcOW2lcjk5WQQGfk9CwnWSkh4yaJAvxsbt5K9nZ2exf/8CoqIOk5OTTd26LXFwmEeFCtX/dWw7dviwdevvZGSk062bLVOmzENbWzk2gIsXz7J8+TwSEx/RtGkzZs1awief1ALg11+XcujQHlJTU6hQoSIuLl8yeLAHAC9ePGfKlNHcv3+P3NwcjI1NGDt2Cs2bt3rv4pQzMIBRo6BZM0hJge3b4fRp5XIODmBnB0ZGkJHxP/buO6zK+v/j+POwp4gDTUNF3Bs1R65EcSMquAdarkwzNUc4Q81V9kvLQebWXIDgNkeYuNPcAwQUBDfK3uf3x5EDh3MQNPre99HP47q4gvu+O7y4hfvc7/uz4PRp2LQJsl6N85k1CypUACMjePwYduyACxfePI++57S2hokToVEjePkS1q6F48e1j+vVC9zcoFgxVc6gIPDxUeUsXRrWrNE83twcVq8GX9+iyzl1Knz0kSqnjw8cOaJ9XO/e4O4ONjaQnAzHjsHKlZD56oamShUYPx4cHSEpCQIDYePGoskIvEhJYXpQEMFRUdiamTGxSRNcq1bVOm7NP/+w+84dHiQkYGtmxoBatRjeoAEAz5KTmR8czLmYGJIzMqhqa8s3zZtTv8zbX4905jx2jODISFXO5s1xrVZNO+fFi+y+fZsH8fGqnHXqMLxhQ/X+/zt7lqNhYdyNjeXzxo0Z16RJkWUEeJGUxHRfX4JDQrC1tGRix464vjpPuZ25e5dfjh3jxoMH2Jibc2zqVK1jNgQHsyE4mOcJCXxQvDgrBg/GoXTposmZnMz0wECC797F1sKCie3a4Vq3rnbO8HB+OXGCGzEx2JiZceyrrzT233z4kLkHDnD70SMsTUzo26gRX7Rp86/zbdy4nrVrVdd5F5eOzJyZ/3X+zJnTzJ//LQ8fxlC3bj3mzVtIuXKq6/zBg/vZvHkjt2/fpE6deqxbt0n9/8XGPufLL8cQHq66zjs4OPL111Nwcir4Ov8iLo7pS5cS/Pff2BYrxsTPPsPV2VnrOKVSyfdr1rDrwAEAPDp35uvhw1EoFABUd3HB3MwMxavju3zyCfMnTVL//9dDQvhu5UpuhIRgbmbGqP798ezVq1DnMDdf3/Vs3/4rqanJtGrVkS+/zP98Xrx4mp9//pbHj2OoUaMekycvpEwZ1flcvHgax4/vxcjIWH387t0XMDQ0BGD//p1s3+7D8+dPqVOnIZMmfUepUkV3Hcjtoy++oMHQodjVrcu1338nYNiw/+T7CO+f96Y7dPXq1UlMTJQ6BgB+fn58+eWXUsdQc/rlF7LS0thbpgznBw7EaeVKrGvV0jquyvjxlGjenCP16rGvXDnSYmNpsHy5ev+tBQs4WKkSgTY2nO7endrz5lE8103Jv9XkVc5dZcoQPHAgTVauxEZHzhrjx1O6eXP21auH76ucH+XKWbZ9e5wWLeL0sGFss7bmcOvWJISFFVnO0NC/CA72YciQ9Xz11XFiY6P4889l+R5foUJDevVajJWV9k3P2bMbiIr6h9GjA5k06S/MzIpx4MDct8525sxfbNrkw/Ll6/H3P050dBRr1ujO9uLFc775ZiwjR47n0KFz1KhRh5kzJ6j3u7p6sG3bAY4evcjq1ds4fHgPf/55GABzc0umT/+OAwdOc/jweQYNGsHkyZ+TkZHxTuXUMHw4ZGTAiBGwbJnqvx9+qH3chQuqomnoUJg0CSpWhM6dc/avXw8jR6r2+/jAuHFQvPib59H3nGPHQno69OkDCxfCl1+qMuR1+jSMGQM9e6ryVK6s+hzgyRNVgZz9MWqUqugswgdeTJigOp89esDcuarCvVIl7eOCg1XnvnNn8PRUFb3u7jn7Z82Cy5ehWzfVz9qzJ7RoUWQxvU+exNjAgOAhQ1ji7MyckycJef5c6zglsKhtW84PHcqaLl3Ycv06+0JDAUhKT6eunR1+vXpxztOTntWqMfLgQRLT04su54kTGBsaEjxsGEtcXJgTFETIs2e6c7Zvz/nhw1nj6sqWq1fZFxKi3l/RxoavP/6YNrp+Z4oiZ0CAKuf06Szp25c5u3cT8uiR1nEWJia4N2rElC5ddL7OzvPn2XXhAj6enlz69ltWe3pia2lZdDn371fl/PprlvTqxZx9+wh5/Fh3zgYNmOLiovN1Jvn68lGFCpybMoXNQ4fy+4ULHL19+19lCw7+i99+82HNmvUcOnScqKgofvlF93U+NvY5EyaMZezY8Zw8eY7atesweXLOdd7GpjiDBg3h009HaP9sFpZ4e3/HiROnCQ4+z6efjmDs2MJd572XL8fYyIjgHTtY8s03zPnpJ0IiIrSO275vH0dOnSJg9WoCfXw4fuYM2/bu1TgmYNUqLu3Zw6U9ezQK4OcvXzLcy4u+Xbty1teXwxs20LLRmz+IPX/+L7Zt82Hx4vVs3nycmJgoNm7UfT5fvnzOt9+OxdNzPH5+56hWrQ7z5k3QOKZPn8/Ys+eS+iO7AL58+Szr1i3l229X4Od3lrJlP+S77ybp+jZFIj46mhPz5vHP2rX/2ffQL5ky+Hg3vDdFsKCboYUF5d3duTFzJpmJiTwLDiY6MJCKgwdrHWvp4MCjQ4dIffyYrNRUorZvp1jt2ur98TdukJWWBqieiiqVSiwdHYssp727O5dnziQjMZEnwcFEBQbioCOnlYMD0YcOkfIq573t2ymeK2f9b7/lqrc3T8+eBaWS5OhojVbif+vy5d04OXlgZ1cVc3MbWrcewz//+Ov+uQxNaNZsKBUqNEah0P5zjI2NwtGxJVZWpTAyMqV27S48eRKi45UKZ//+3bi6elC5clWKFbNh2LAx7NunO9uff/6Bg0NV2rXrjKmpKcOHjyMk5BYREXcBqFixMubmFurjFQoDoqLuAWBqakrFipUxMDBAqVRiYGBAfPxL4uJevlM51UxNoWlT2L4dUlPh9m1VEdm6tfaxjx6pWvpUYVQtlmXL5uy/fz+ntVWpBENDKFnyzfLoe04zM2jZEjZsULXuXr+uKnbbtdM+NiYGcj/gzMqCcuV0v2779nD1qupnK6qcbdqoWpuTk1WvHRwMHTtqHxsdDQkJqs+zz2f58jn7y5aFP/5QbY+OhitXdBfTbyEpPZ3D4eGM/+gjLI2NafzBBzhXrEhAiPa1ZESDBtQuXRojAwMqFy9Ou0qVuPjwIQD2xYoxrF497CwtMTQwoG+tWqRnZhL+4kXR5bx7l/FNm2JpYkLjcuVwrlSJgDt3tHM2bJiT09aWdg4OXIyJUe/vWaMGbSpWxDKfVrB/lTMtjcPXrzPexQVLU1MaV6qEc82aBFy6pHVsPXt7ejRsiH2JElr7srKy+PnoUby6dqVKmTIoFAoqlCxJcQsLrWPfOueNG4xv21Z1PitUwLl6dQKuXNHOWb48PerXx97WVudrPXjxAtd69TA0MKBCiRI0rFCBUB3F9JsICNhNr14eVKlSFRsbG0aNGkNAgO7r/JEjf+DoWJWOHVXX+c8/H8ft27cIC1Nd55s3/5hOnbpgZ6fdGmlqaoqDQ8513tDQgLi4l7x8+frrfFJyModPnmT80KFYmpvTuE4dnJs3J0BHT4/df/zBpx4elC1dmjKlSjHMwwP/w4cLdR7W79pFy0aN6N6uHSYmJlhZWOD4Fg9v/vhjN506eVCpUlWsrW0YOHAMhw/rPp8nT/5BpUpVadOmMyYmpgwePI6wsFvcv3+3wO9z5syftGrViUqVqmJsbMLAgWO4evU80dH33zhzYdzy9+d2QABJOh6GCcK/8V4WwVeuXKFv3764urrSt29fruR6Q9i8eTMdOnTA3d2dZcuW0bRp03xfJzo6mhYtWpCe6yn4l19+ib+/PxkZGXz22Wf06tWLrl278s0335D2qkDMLW+rcN6vfXx88PDwoGfPnowePZonT5782x9fg1W1amRlZJCQ62bo5eXLGsVttvDffqNkixaYffABhubm2A8cyMNXXX+yNfjlF9wSE+l4+zYpMTE83L+/SHIWq1YNZUYG8blyxl6+rFHcZgv97TdKt2iB+auclQYO5MGrnAoDA0o0boxZ6dK4hYTQMzKSj5Yvx9DMrEhyAjx5EkKZMjXUX5ctW53ExKckJcW+8Ws1bOhBZORF4uMfkZ6ezNWre6hSRUfBUkjh4SFUrZqTrWrV6jx//pSXL7WzqY6trv7a3NyCDz+sQHh4qHrbxo0+ODs74ebWmpSUJDp0cNV4jUGDXGnTph5TpnxO9+69KVGicEWSvuRU++ADVQtjrptw7t0De3vdx7dooWpJXbtWVejkvamaOhU2b4YFC+DGDSiqngr6krN8eVXOBw9ytoWF5V8Utm0L/v6qLs6VK8O+fbqPa99eVWgWFXt7Vc6oqJxtd+/mn7N9ezhwAPbuVbUEBwbm7Nu5U9X93NBQ9bq1a8PffxdJzIiXLzE0MMAhV0t9jZIlCdXREpybUqnkQkwMVXQUcAA3nz4lPSuLisWKFU3OFy+0c5Yq9a9zFrWIp09VOXN1Wa7xwQeEvuHDlYdxcTx8+ZI7jx7RZuFCnBcvZtkff5CVVTRLoEQ8e6bKmevhVI0yZQh9i/sIz2bN2H35MumZmYQ9fco/kZE0r1z5X+W7ezeE6tVzrvPVq1fn2bOnvHihfZ1XHZtznbewsMDevgJ374ZqHZufXr1cadSoHuPGfY67e29KFvDQLuLBAwwNDXHI1VOmhqMjoffuaR0bEhFBjVzno0blyoTkOW7gxIm06NOHsXPmEPXqwRLAP7duYWNtTb/x42neuzejZ84k+i0eMNy7F4KjY875dHSsTmzsU+LitM9nREQIlStrvm+WK1eBe/dyzueePb/Tq1cTxozpxV9/HcrzCkodr6n9sEoQ5Oy9GxOclpbGl19+yYIFC2jevDmnTp3iyy+/5PDhw4SFhbF69WoCAgIoUaIE8+bNe+1rlStXjqpVq3LixAnatWtHbGwsZ8+eZeHChRgaGvL9999ja2uLUqlk6tSp+Pr60r9//0JnDQgIIDIykh07dmBgYMDWrVtZuHAhP/zww789DWpGVlZkxMVpbEt/+RIja2utYxNCQkiOjKRrdDRZGRnEXb3KibFjNY7554sv+GfcOEo2b07pTz4hKzW1yHKm68hprCNnfEgISZGRuL/K+eLqVc6/ymlWpgyGJiZU8PDgcKtWZKWn0yYggDozZnB5xowiyZqWloSZmZX6a1NT61fbE7Gw0P2UPT8lSlSiWLEPWLq0NQqFIWXKVKNLl5lvnS05OQlLy5xsVlaqbElJidjYaGZLSkrC1lbzptLS0oqkpJxWtyFDRjJ48Aju3LnJiRNHNF4bYPPmPaSmphIU9IfGw6J3JaeamZmqJVAzmGq7LsHBqo+yZVUtiXlb0hYtUhVDdeuqCkKl9g3HW9GXnObmOa3Q2RITVdt1OX5c9VGuHLi4QKyOB0516oCtLfz1V9FkzM6Zd5hNQgLk15J35Ijq48MPVa3FuXOePg1eXtC3r2qc9bp1cOtWkcRMSk/HythYY5u1iUmB3ZiXX7hAllKJe67iI1tCWhpTjh9nbKNGWJua/rc5dTxA1sh57pwqZ82aRZKjIEmpqVjl+ZmtzcxIfMP3u4evWiKDQ0LYM348cSkpfPbbb5S1saFPEYxhTkpL085pavrGOQE+qVaNqf7+rD11ikylki9at6Ze7p4Mb5MvKQkrK+3rfGJiIsWLF3ydt7KyeqNhbn5+quv80aOFu84nJSdjledv2drSksS81yYgKSUFq1zd2K0tLUlKTkapVKJQKNj8ww/Ur1mTlNRU/m/dOkbPnMnuVaswMjTk0ZMn3AgJYe2iRVR3cGDJr78ycf58tv30U6F/NtB+37S0zHnfLFZM83ympCRhY6N5Pi0sct43e/YczOjRU7G0tObChWDmz/8KW9tS1KnTiI8+asX8+RPo1q0f5ctXYvPmX1AoFKSkpLxRXuFtvTvdkaX23rUEh4eHY2xsTPPmzQH4+OOPMTY2Jjw8nHPnztGmTRtKvHqa7OHhUeDr9ezZE39/VXeTvXv34uzsjIWFBVlZWaxduxY3Nze6d+/OmTNnuHnz5htlPXbsGKdOnaJnz564ubmxdetWHuRuGSkCGQkJGOV5im9crBgZ8fFaxzr98gsGpqYElihBgKUlD/z8aJmnJRiArCyeBQdj/uGHVP788yLLaawjZ7qOnE1e5dxRogTbLC2J9PPD+VXOzFcFwO3ly0l++JDUZ8+4uXQp5fMZr1UYV64E8t13Tnz3nRNbtgzHxMSC1NQE9f7sz01M3nyc1/7935KZmcaUKWfx8vqHGjVc2LJFe8xTfg4dCsTZ2QlnZycmTBiOubkFiYk52bI/t7DQzmZhoXms6vhErWMVCgXVq9fC1NSMNWuWk5epqSkdOnRj0yYfQkJ039TrS858paRoF2jm5qrtr/PwIURGqsaK5pWZCf/8A/XrqyaHKgr6kjM5WbuQtLDQLuDzio5WtWyPG6e9z8VFNRa4KG/UkpNVE43lZmmpXcDnFRUF4eGq8cOgmlxryRJV928XF9VY4SZNVOOMi4CFsTEJeW76E9LSsMxTcOa2+do1doeE4NO5MyavxgJmS8nIYPTBg9S3s2OUk1ORZHxtztd0ad585Qq7b9/Gp1s3rZz/FQtTUxLyFJIJqalYvuHDALNX539469YUMzfnQ1tb+jZtStC/HGurzmliUiQ5XyQnM3zzZr5o04YrM2YQNGECJ+/eZcv582/0Onv3BtKkiRNNmjgxevRwLCwsSEjQvs5b6hgTres6n5CQqPPY1zE1NaVLl2789psPt2+//jpvYW5OQp6/5YTERCx1POSyMDPTKI4TkpKwMDdXT4z1Ub16mBgbU8zKiuljxhD18CF3799XZ3Jp0YJ61atjamLCF4MHc+nGDeILKPCPHg3E1dUJV1cnvLxU75tJSTnnKPtzXe+bZmaax6qOz3nfrFq1NsWK2WJoaETTpm1wdnbl5ElVL5qGDT9myJAv+fbbLxk0yJkyZcpjbm5J6dJltb6PIMjZe1cEF7UOHTpw4cIFYmNj8ff3x/3VRCd79uzh77//ZsuWLezZs4cBAwbo7A5taGio0fUpNdcbllKp5PPPPycgIICAgAD27t3Ltm3bijR/wp07GBgZYVWlinqbTf36xF2/rnWsTYMG3Fu/nvTYWLLS0ri7fDklmjbFJJ8uRQojI6yKaExw3J07KIyMsM6V07Z+fV7oyGnboAFh69eT9irnreXLKdW0KaYlS5L24gWJkZEoc7dY/cvWq3r1uuPldQkvr0sMHLiG0qWr8vBhzk3Mo0e3sLQs9catwAAPH96ifv2emJsXx8jIhKZNB/PgwRWSkl7fPTBbx47dOXbsEseOXeLHH9fg4FCV0NCcbCEhtyhRopRW6yqAg0NVjWIwOTmJBw/u4+BQRetYgMzMDB48yH9MUEZGBg8eROp1znzFxKhaRHOPma1YUVU4FsTQEF43u66Bgebr/hv6kvPBA9X3yz22t3Jl0DEhjRZDQ1W379xMTFTjnouyKzSozpuhoebEYo6Ohc+Z/fOVK6caC3zokOqhwpMnqtmjmzUrkpiVbGzIzMoiItcYyFvPnuXbfXjXrVv4/PMPG7p1o6yVZq+JtMxMvjh0iDKWlnjrGkv+b3IWL67KmavHwWtz3riBz8WLbHBz08r5X6pUqpQq59On6m23YmKo8oazZDuUKoWxoaG6UAJQvOb4N1WpZElVzlxjKW89ekSVN5x5OjI2FkMDA3rUr4+RgQFlixWjS506nNAxpvx1unXrzrlzlzh37hKrVq3B0bEqd+7kXOdv375FyZKltFqBARwduMmDAAAAIABJREFUq2oUrUlJSURF3cfRUfd1viAZGRlERb3+ulepfHkyMzOJyDXc4VZYGFV0jNetWqkSt3INB7kVFkbV14zrVYD6PqS6g4NqnoBc+wqjXbvu6kmrvvtuDRUrVuXu3ZzzeffuLWxtS2m1AgNUqlSVu3c13zdjYu5TsaLu86n6Hc25V3JzG8iGDYfZufMUrVp1eLW6gvZs88J/QepJsd6dluj3rgh2cHAgPT2dM2fOAHD69GkyMjJwcHCgSZMmnDhxguevxh9lt/C+jrm5Oe3atWPp0qUkJCTQuHFjAOLj47G1tcXKyor4+Hj25pklMFvFihW5ffs2aWlppKWlcehQzrgLZ2dntm7dqp68IS0tjVtF1D0uW2ZSEg/8/Kjl7Y2hhQUlP/6Ycm5u3Nu0SevY2PPnqThkCEbFiqEwMqLymDEkP3hA2rNnmJYuzYd9+2JoaQkGBpTp0AH7/v15fPRokeWM9POj/qucpT/+mA/d3AjXkfPZ+fNUHjIE41c5q40ZQ9KDB6S+uhG4u24dNcaNw7R0aUyKF6fmhAk8yOff523Ur+/GpUu7ePIklJSUOE6cWEmDBj3zPT4jI42MDNXDj8zMdDIyUtVvjuXL1+XKlQBSUuLJzEzn/PmtWFvbYWHxdmPfOnd2Y8+eXYSHhxIfH8f69Svp2lV3tjZtXAgLC+H48UOkpqaydu0vVKlSnUqVHMnKysLffxtxcS9RKpVcv34FX9+tNG6s6mFx7do/XL58gfT0NFJSUti0yYfY2KfUrl3vncqplpoKZ8+qurKamkL16qolc06c0D7W2Vm1nA+ouhD36AHXrqm+LlcOGjQAY2NVkdSqFdSqpRpvWxT0JWdKiqobtqenqqt2rVrw8ceg63rSqVPOrNQVKqh+tn/+0TymRQvVclB5txdFzhMn4NNPVTnr1FFN6HUo7/g5VEtOZeesWBEGDcoZ85v9EKJ9e9XNcIkSqnHOdwuepKYwLIyNcXFwYNn58ySlp/P3w4ccvXcPNx1LJAWGhPDjuXOs69oV+zy9b9IzM/ny8GFMjYxY1LYtBoqiLNle5axcmWXnzqlyxsRwNDwcNx1LJAXevs2PZ86wrnt37G1stPanZ2aSmpGBUqkkIyuL1IwMMotorK2FiQkutWuz7I8/SEpL4++ICI7euIGbjlbxrKwsUtPTSc/MRAmkpqeT9mpWYnMTE7rUq8eaoCASUlN5+PIl28+f55MaNbRe561z1qzJsj//VOW8f5+jt2/jVk/7+palVJKakZGTMyODtFfLdzmULIlSqWTP1atkKZU8SUjgwPXrVP+XS2N17+6Gn98u7t4NJS4uDh+flbi56b7Ot2vnQmhoCH/8obrOr1r1C9WqVadyZdWD9szMTFJTU8nMzFCd89RUdZfny5f/4eLFnOv8b7/58OzZU+rWff113sLcHJeWLVm2YQNJycn8fe0aR0+dwq19e61j3dq3Z92uXTx6+pRHT5+ybtcuenboAKjGC98MDSUzM5PE5GQWrl6NXalSOFaoAECvjh05EhzMzdBQ0jMyWLFlC43q1MH6DVu5XVzcOHhwF/fuhZKQEMfWrSvp0EH3+WzRwoWIiBD++usQaWmpbN78Cw4O1alQQXU+T5w4SHJyIllZWVy4cJKjRwNp3ly1NFRaWirh4XdQKpU8fhzNjz/OokePIVhba/8dFgWFoSGGpqYYGBqqP1f8j3p9CO82hVJZVIO45K169epcvHgRS0tLrly5wvz580lKSsLCwoLp06dT79WbwsaNG9m8eTNWVlY0a9aMw4cPc0TXmo+5XLhwgYEDBzJ+/HjGjBkDqIrgcePG8fDhQ0qWLEmVKlVITU1l4cKF+Pn58eeff7JsmWrq+lmzZnH69Gns7OyoUaMGT548Ue9bv349vq/Ws1QqlfTv35+BAwcW6mf2LeQNirGtLY3XrsXOxYW0Z8+4Nm0akb//TsmWLWl54AABr8bdmpQoQf1ly7BzccHAxIS4a9e4MnEisefPY1KqFM127cKmfn0UBgYk3btH6LJlRORdo1OHAjo4qpnY2tJ87Vo+cHEh9dkzLk2bRsTvv1O6ZUucDxxge66cHy1bxgevcr64do2/J07k2auuWwojIxr/9BMOAwaQmZLCvR07uDhlSoHjlw22FP5P5fTpda/WCU7RWid4xYqutGw5inr1ugPwf//nzMuXmt3cx48/SvHiH5KUFMvBg/O4e/cUmZnp2NlVpWPHbyhfPv83706dXp/t99/XsWnTr6SmpmitvztgQFc8PUfRsaMq27lzp/jhB28ePoymdu36zJy5gA8++JCsrCwmThzBjRtXychIp1QpO7p06Ymn5ygUCgUXL57jxx/nER0diaGhMY6O1Rg5cjxOTh8V+hzKJWeJ0X0KF9jSUrVUT926qnGhW7aoCrkaNVRjPYcMUR33+efg5KQqmuLi4MwZ1WzN6emqYnPMGFXLYlaWquXW3x/esNuhrHMWdiZha2vV0kwNG6q+/2+/qcb91qkD8+erljwC1TFNmqi6db94oRrzu369Kme2775TzYS9YUPhz1Nhu01bW8O0adC4sSrn6tWqcb/16sHixTl/kNOmqVp2s3P++afqZ8ruIdSwoWoJJ3t71cOKU6dUS1gVNH6zkF2mX6Sk4BUUxKmoKIqbmTHp1TrBF2JiGLF/P5c++wwA561beZSYiIlBzjNy16pV8W7dmnPR0QzeswczIyONJ+i/dulC47yt73kZFW4KkhcpKXgdO8apyEhVzlfrBF+IjmbEnj1cGjVKlXPjRu2c1avj/cknAEw7ehT/PA+NFzg706ugccO6lgvTlTMpCS9fX06FhFDcwoJJnTrh2qABF8LDGbF+PZe+/RaAs2FhDPn1V43/t4mDA5tGjgQgISWFmf7+/HnrFsXMzen90Ud84eys0TqsUyF/P18kJ+MVEMCpsDCKm5szqX17XOvW5cK9e4zYsoVLXl6qnBERDMnz99GkYkU2DR0KwOnwcL4/coSIZ88wMzKibbVqTO/cGfPXdKkHSPMY8Nr9GzasY+1a1XW+ffuOzJqVc53v0aMrw4ePols31XX+9OlTfPedNzEx0dStW5958xZQvrzq32v3bj9mzvxG47W7d+/J/PkLOX/+HAsXziMqKhIjI2OqVq3G2LHjadw45zpv8lB376AXcXF4/fADpy5epLi1NZOGD8fV2ZkLV68ywsuLS3v2AKr7syV51gme/Gqd4NOXLjFn2TIePX2KuZkZTrVqMWXECCrl+l3bumcPK7dsISU1lUZ16jB73Dg+sLPTynOfCq89n7t2rWP79l9JS0uhZcuOjB+fcz6HD+9K//6jaNdOdT4vXjzFzz978+hRNDVq1Gfy5AWULavKNGHCAMLCbgNKypb9kH79RtG2bVcAEhLimDhxIDExkZibW9KxYy+GDv1KvYQSwLqKRfeArM3s2XwyZ47Gtj/nzCHo1d/YvzFbD0sghWKI1BFQKotuDXspvTdFcGElJCSoJ2pYvnw59+7d4/vvv5c41dspbBEstcIWwVJ7kyJYSgUVwcKbKXQRLBROES2n85/Tl0leimjc8H+ukEWw5ApZBEtOT34/CyqC5SK/IlhuCiqC5aIoi+D/kn4WwYVrCPsvKZVbpI5QJPTkXel/54cffuDixYukp6djb2+Pt7e31JEEQRAEQRAEQRCEIiKK4Dxmz56tte3mzZtMmzZNa/ugQYPo3bv3/yKWIAiCIAiCIAiCUAREEVwINWvWJCAgQOoYgiAIgiAIgiC8t4pmgj/hPZwdWhAEQRAEQRAEQXh/iZZgQRAEQRAEQRAE2Xt31umVmmgJFgRBEARBEARBEN4boggWBEEQBEEQBEEQ3huiO7QgCIIgCIIgCILsie7QRUW0BAuCIAiCIAiCIAjvDdESLAiCIAiCIAiCIHuiJbioiJZgQRAEQRAEQRAE4b0himBBEARBEARBEAThfyYgIABXV1dq1arF5s2bX3vsjh07cHFxoX379nh7e5OVlVWofa8jimBBEARBEARBEATZy5LBR9GoWbMmP/74I926dXvtcZGRkfz8889s376dw4cPc+/ePQIDAwvcVxBRBAuCIAiCIAiCIAgFiouLIyoqSusjLi7ujV6nWrVqVKlSBQOD15ejhw4don379pQoUQIDAwN69+7N/v37C9xXEDEx1jvMXamUOoIg6L8dO6ROIAiCoJdMpA5QWBUqSJ2gUPQjJcwW95//GaWycK2c/6Xly5fz888/a20fO3Ys48aNK/LvFxMTQ7ly5dRflytXjpiYmAL3FUQUwYIgCIIgCIIgCEKBPD096dmzp9b2YsWKaXzds2dPoqOjdb7GqVOnMDQ0/E/yFZYoggVBEARBEARBEIQCFStWTKvg1cXf379Ivt8HH3ygUUxHR0fzwQcfFLivIGJMsCAIgiAIgiAIgiA7HTt25MiRIzx//pysrCx27txJ586dC9xXEIVSKTruC4IgCIIgCIIgCP8be/fuZfHixcTFxWFsbIy5uTlr166lSpUq/PTTT9jZ2dG/f38Atm3bxpo1awBo0aIFs2bNUnenft2+1xFFsCAIgiAIgiAIgvDeEN2hBUEQBEEQBEEQhPeGKIIFQRAEQRAEQRCE94YoggVBEARBEARBEIT3hiiCBUEQBEEQBEEQhPeGKIIFQRAEQRAEQRCE94YoggVB0CkhIaFQ26SmLzkFQRAEQRAEeRBFsPBOSE5Ofu2H8OYGDx5cqG1S05ecAOHh4Rw5cgSAxMREXrx4IXEi/XX37t1CbRMKZ9asWdy5c0fqGAVKTU2VOkKh7Nu3j4yMDKljFGj8+PGF2iZXWVlZUkfQkL2maUHbpCaun4IARlIHEORry5Ytr90/cODA/1GSgjk5OaFQKMi97HX21wqFgps3b0qYTlNERATffPMNjx494tixY1y/fp1jx44xbtw4qaMBkJGRQXp6OllZWaSkpKjPaXx8vKweKOhLzmz+/v6sXr2a9PR02rdvz6NHj/D29mb9+vVSRwNg8eLFr90/ZcqU/1GSwvn666/x9/cvcJvUUlNTCQwMJDIyUqMoktv5dHBwYNy4cZQqVYqBAwfSoUMHjIzkd4vg7OyMq6srAwYMoEKFClLHydfevXtZtGgR7u7u9OvXjzJlykgdSaf79+9rbQsLC5MgScEmTZrE3LlzsbCwAODRo0dMmjSJzZs3S5wsR0pKisbXWVlZvHz5UqI0+dOX6yfAs2fPWLBgATExMWzZsoVbt25x6dIlWT5cEPSL/N7hBNm4du2a1BEK7datW1JHKLQ5c+bw+eef88MPPwBQs2ZNpkyZIpsieNWqVfz8888oFAoaNGig3m5lZcWwYcMkTKZJX3Jm27BhA76+vuqHR5UrV+bp06cSp8qRfWMpd8+fP+f58+ekpqZy9+5djYcfSUlJEqfTNn78eNLT06lXrx4mJiZSx8nXsGHDGDZsGCdOnGDr1q0sXLgQDw8P+vXrh52dndTx1AIDA9m+fTuenp44OjoycOBA2rZtK3UsLStXriQqKort27fj7u5Ow4YNGTBgAM2aNZM6GgA7duxg+/btRERE4OHhod4eHx+Pg4ODhMny5+DggLu7O0uXLuXx48fMmTOHsWPHSh0LgDVr1rBmzRoSEhJo3ry5entKSgqurq4SJtOkb9dPgBkzZtC6dWu2bt0KqN47J0+eLIpg4V9TKHM3nQmC8J9zd3fH19eXHj16sHv3bgCNz+XC29ubWbNmSR2jQPqSs3fv3uzcuVPj39rNzY2AgACJk+mXDRs2sGHDBh4/fqxRnFlbWzNo0CB69+4tYTptnTt35sCBA1LHKLS4uDh8fX3ZsGEDlStXJjQ0lE8//ZShQ4dKHU1DZmYmR48e5bvvvsPAwIBBgwYxcOBATE1NpY6m5eLFi0ycOJGXL1/y4YcfMnv2bBo3bixppgcPHhAVFcXcuXM1rp9WVlZUr14dQ0NDCdPlLzg4mC+++IJixYqxbt06HB0dpY4EwJEjR6hRowYzZsxg/vz56u1WVlbY2NhImEyTvl0/AXr16oWfn5947xSKnGgJFvIVFBT02v1t2rT5HyUpWLNmzVAoFFrbs7tDnz59WoJUuhkaGpKenq7O++jRIwwM5Dc8P/vGKC0tjczMTPV2c3NzqSLppC85ixcvTnh4uPrfPSAggLJly0qcSreTJ09y8+ZNjfGXcmlx8fT0xNPTk1WrVjF69Gip4xTI3t6ehIQErKyspI7yWteuXWPLli0EBwfTrVs3Nm/ezIcffkhCQgLdunWTVRGcnJxMQEAAW7dupUKFCvTu3ZuzZ88yYsQINm7cKHU8QHU92r9/P7///juZmZl89dVXdOnShStXrjBlyhSOHTsmab7y5ctTvnx59u7dK2mONxETE8OyZcvo3LkzISEhbNq0CS8vL1n0sFixYgV+fn68fPmS8uXLSx0nX/p2/QS0hmXExcUh2u+EoiCKYCFfa9asyXefQqGQVRHs6+srdYRCGzBgAGPHjiU2Npbly5eze/duJkyYIHUsLX/88Qdz587l8ePHsh1fDfqT85tvvmHSpEmEh4fj7OyMmZkZq1atkjqWlu+//56rV68SGhpKu3btOHr0qEb3Prno2LEjqampmJqa8tdff3Hz5k369u0rq1YXULWwuLu706pVK42bdbmNCf7mm28YNGgQs2bN0niAZGVlJaubZW9vbw4fPoyzszPff/891apVA8DV1ZVOnTpJnC6Hs7MzTZs2Zdq0aTg5Oam3N27cWFZ/T2FhYaxcuVJrzPquXbskTKVb//79mThxIt27dyc9PZ1FixbRp08fWfSiSk9PZ+3atcTGxuqcT0VOc6gAjB49muTkZB4+fKjx8LhKlSoSptLNxcWFWbNmkZiYiJ+fH1u3bsXd3V3qWMI7QHSHFgQJXLhwgePHj6NUKnF2dpa8a5wuLi4uLFq0iAYNGsiypTqbvuQEVRfOiIgIlEolDg4Osuxy6Orqir+/P7169SIwMJBHjx4xY8YMfv31V6mjaXBzc2PXrl08fPgQT09PWrRowZMnT2T3YOHnn3/WuV0uLev65rfffsPDw0Pnw468XTylJKcsr9OjRw86depE/fr1Na5HTZo0kTCVbmFhYVSuXFlj25EjR2jfvr1EiXJcunSJgIAADhw4gLOzs9b+BQsWSJAqf1u2bOH777/HxsZG/b6pUCg4evSoxMl0CwwM5NixY+p7Jjc3N6kjCe8A0RIs5CsyMhJ7e3tCQ0N17pfTE8PJkyezZMkS3N3ddXaLlttT7caNG8uy8M3NxsaGhg0bSh2jQPqSc/z48fz0008aY9iyt8mJiYkJRkZGKBQK0tPTKVOmDA8fPpQ6lhYDAwOMjY0JCgqif//+jBgxQpY3RvpS7I4bN465c+dSvHhxAGJjY5kzZ47sfj9btWqFsbGx+uvExESio6OpWrWqrIrO3bt306dPH43z6evry/DhwyVOpikrK0tWLf2v4+DgwM6dO4mIiGDy5MlERUVRokQJqWMBqhUqnJycsLe357PPPpM6ToHWrl3L3r17Zd11O7fu3bvTvXt3qWMI7xhRBAv5mjdvHqtXr2bkyJFa++T2xNDT0xOAqVOnSpykYLoKdWtraxo0aMDw4cOxtLSUKJlK9vJCLi4ubN26lS5dumhMNiOXsbb6kjObvixFYmlpSXJyMk5OTkybNo3SpUtjZmYmdSwtqampPH36lOPHj/PVV18ByHKcWHJyMitWrODUqVMAtGzZktGjR8vu9zMyMlJdsAHY2trq/J2V2rRp09i+fbv6a2NjY6ZOnYqfn5+EqbTt27dP473T1taWvXv3yq4IbtCgAbdu3aJGjRpSRynQggULePbsGdevX2fy5MlYWlry3Xffyeoh92effcbp06e5e/cugwYN4tmzZ8TFxcluxu3SpUvrTQH85Zdf6mzckNsDOkH/iCJYyNfq1asBdE7gIbebzTp16gBQokQJrRbq4OBgKSLlq3nz5ty7d48ePXoAqgmS7OzsePToEXPmzGHJkiWS5su75rK3t7csx9rqS059W4pk6dKlGBoaMnXqVNatW0d8fLwsbzY8PT3p1KkTzZs3p27dukRGRmJtbS11LC1z584lMzMTLy8vQNUrxdvbW3bdIzMzM8nMzFR3iU1PTyctLU3iVNoyMzM1WoJNTEw0xjTKha73SDnmvHLlCn5+fjg4OGg8RJRTYZnt7Nmz7N69m549ewKqBwu5J++TAx8fH4KCgnjy5AmDBg0iPT0dLy8vfv/9d6mjAah79n388ccsXryYrl27avy7y6mHX7bcS6ClpqZy6NAh2cwKLug3UQQLBdqwYYO6pTXb7Nmz8fb2lihR/iZNmsSaNWsoXbo0AOfPn8fb25tDhw5JnCzH+fPnNVoy2rZtS79+/di+fTtdunSRMJmKvqy5rC85W7RoQcWKFZk7d67GZEjZS5HITalSpQBISkpi6NChsl0/uG/fvvTt21f9dfny5Vm3bp2EiXS7evUqe/bsUX/dsGFDWXbra9myJRMmTGDIkCEAbNy4kVatWkmcSpuRkZF6qA6oeljIcWx9pUqVWLduHUOHDkWpVLJ+/XoqVKggdSwt2Q9n9IGpqalGi2BWVpaEaXTbu3cvvr6+6qWGypYtS0JCgsSpcuTt2Xfw4EH153Lr4Zct+6FHtl69eulFl3NB/kQRLBTo2LFjlCtXDhcXFwDmz58vq4t6bt988w1jxoxhw4YNhIaGMn36dNlNlBMbG6ue1RZUS2m8fPkShUIhq26n2d2Nc5NbF06Qf059W4rk/v37fP3119y8eROFQkGtWrVYsmSJuuiQi/3799O6dWusrKz4v//7P65evcrEiROpXbu21NG0JCUlqR8m6Pp9lYOJEyeyevVqFi5cCMAnn3yicyiM1MaOHUv//v3VqxMEBQUxb948iVNpmz59OpMnT2bp0qUoFAqcnJxYvHix1LG0ZE+A9fz5c9mMr81PtWrVCAwMRKlUEhUVhY+PD40aNZI6lgYzMzONngqAzq68UpF6aa6ioFAoePTokdQxhHeAKIKFAi1fvpyhQ4dSsmRJgoKCiI6OZtmyZVLH0qlZs2YMGTKEESNG8PTpU37++Wet2SSl1rlzZ/r27Uvnzp0B1ZPYjh07kpiYKKsxOtndjXMzMjKiXr16zJ07VzbnVV9y6sukbbNnz6ZPnz7qJSj8/PyYNWuW7FpZV65cqV53NTg4mCFDhjB37ly2bdsmdTQNrq6u9O3bl65duwKq4l2OE3gZGxszduxY2U/k1bZtWzZt2qQeYz1y5EgqVqwocSptZcqUYePGjSQlJQHItkfF5cuX+eqrr8jKyiIoKIirV6+yY8cO5s6dK3U0LdOmTWPhwoU8efKEPn364OzsLLt5QMqWLcuFCxdQKBRkZWWxatUqqlatKnUsLbomPLW2tqZMmTISpHm93GOClUolt2/f5uOPP5Y4lfAuEEskCYUSHR2Np6cnDg4OrFixQmvxcqnlXZfP19eXmjVrUqtWLUB+a/QdP36cs2fPolAoaNKkicaYF7lYvXo1pqameHh4oFQq8ff3JzY2Fnt7e/z9/dm0aZPUEQH9yXnu3Dn156mpqezbtw87OzsmTpwoYSptbm5uBAQEFLhNaj179sTf35+VK1dSsmRJ+vTpo94mN0FBQZw5cwZQzQnQunVriRNpS01NJTAwUGu9WLmtZ6xP7t+/z/379zXGAme3YMtFv379mDdvHl9//bV6vd2uXbuyb98+iZPppydPnjB16lTOnTuHQqGgcePGfP/995QsWVLqaBqcnZ2JiYlRz6MQHx9PyZIlMTExYenSpTRo0EDihDlyX9MNDQ2pWLEi9evXlzCR8K6QVyUjyErelqvU1FRiYmLo168fIK8WrGvXrml8Xb16dbKysrS2y0F8fDwXL17k7t27pKSkcP36ddatW8fGjRuljqbh0KFDGjOuDhkyhF69euHn5yerVkF9yZl33c2WLVvSv39/idLkz8DAQGM9zvDwcFmOuVQoFOzfv5/9+/ezYsUKQDWZkxy1adNGdsVPXuPHjyc9PZ169ephYmIidZx8Xbx4kSVLlhAZGUlmZqZ6IrzTp09LHU3DDz/8wM6dO3F0dNRYh1Vuvwfp6elakyHl7c4rtbwPufOS00Pu0qVLs3btWpKTk8nKysLS0pIdO3bQp08fqaNpaNeuHU2bNlWvsXzkyBHOnDmDi4sL8+fPZ+fOnRInzJF3TLAgFBVRBAv50tXNKDU1lbi4OPXEU3Iht5lWX8fLywtHR0ciIiIYP348vr6+shzHmJycrDEBTWRkpLprn5yKIn3JmVdCQgJPnz6VOoaWCRMmMHDgQGrWrKnuerZo0SKpY2mZMWMGa9aswcPDA3t7eyIiImjatKnUsdSWLFnC5MmT9WZ5j3v37nHgwAGpYxRo+vTpjBkzhgYNGqiLSzk6ePAgR44cwcrKSuoor2ViYkJiYqL6dzQ0NFRjtmA5yH6YHRsby7lz52jevDkAp0+fpmnTprIqgrPlnpdixYoVsiuCz507x/Tp09Vft2/fnuXLlzNjxgxSUlIkTJYjv2tnNrldQwX9I4pgIV/ZLVcTJkzA29sbY2Nj3NzciI2NZdSoUVotW3KQe7Kcn376iStXrshuspx79+6xfPlyjh49Srdu3ejQoYN6RlY5+eqrr/Dw8FAvP3X9+nW+/fZbEhMT6dSpk8TpcuhLztw9K7KysoiKimLYsGESp9L20Ucf4efnx+3btwGoWbOmLMeJNWzYUN0CDKrZeGfOnClhIk3ZE/bIcaiDLvb29iQkJMi+aDMzM8PV1VXqGAUqXbq07M8lwOjRo/nss894/Pgx06ZN46+//pJ8mb68sh9yjxw5koCAAI0HnvPnz5cyWqHIcdRhVlYWFy9epGHDhgBcunRJPdu2XB4u6cu1U9BfoggWChQeHo61tTUHDx7nHZQ5AAAgAElEQVSkadOmeHl50bt3b1lOUZ97spyTJ0/KcrKc7K6GxsbGvHjxAhsbG54/fy5xKm0dO3akUaNGXLlyBYD69eurxzWNHj1aymga9CVn7p4VhoaG2NvbY2dnJ2Ei3fKuv6xQKGQ10Vj2km35zbQrlzGszs7OgGqinOyWq2xy67oLqklx3N3dadWqlUZ3aLmcz2ytW7cmKChIdt2K82rQoAETJ06kU6dOGi2rcsvdpk0bKleuzF9//YVSqeTzzz+X5URjoJqbJPcs9fb29kRFRUmYqHDkNDt0ttmzZzNhwgT1ihQpKSn88MMPJCYmMnToUGnDvSK6QQv/NVEECwXKniTl/PnztGnTBjMzM9k8Kcwre8Ku4OBgevfujaurK2vXrpU4laZKlSrx4sUL9ayx1tbWsmqpzq1UqVLqm3k504eccuw5ocuECRPynWhs9uzZkk80ll1QyHW23bwWL16sNVmXrm1Sc3BwwMHBQeoYBdq+fTurV6/G0tISExMT2Y4Jvnr1KoDG34scxwSDqpgcMGCA1DEKVKpUKX755Rf1Gry+vr7qdc2llt9DOaVSSXx8/P84TcEaN27MH3/8QXh4OKD6+89++CW34jMjIwNfX19u3rxJamqqers+DYMT5EkUwUKBHB0dGT58OGFhYUyaNEk240V00YfJcr7//nsAhg0bRt26dYmPj6dVq1YSp8rh6enJhg0baNasmcYTbLndbOpLzmz6MqGP3Ccay56YT+5L+dy7d4+IiAgSEhIICgpSb4+Pj5flWsFyP5/ZfH19pY5QKFI/LCqsCxcusHTpUvUs1nK9LgEsWrSI+fPnq7vDN2vWTDbzFbzuoZychr2kpaVhYmKivgZVqFABgMzMTJKTkzXGMsvFrFmzyMzM5OzZs/Tv35+9e/fSuHFjqWMJ7wCxRJJQoJSUFE6ePEn16tWxt7fn0aNH3L59W5bLfFy6dIlff/2Vpk2b4unpSUREBJs2bZLVWEG5e/z4MXZ2djx48EDnfrmsZawvObN17txZ54Q+cszp4+OjMe5uxIgRHDx4kB49eqiXUZHaunXr8PDwwNramsmTJ3P16lVmzJhBy5YtpY4GqJb18PPz49q1a+rx6gBWVlb07duXTz75RLpwOjx79owFCxYQExPDli1buHXrFpcuXZLlDOYJCQncu3dPtj1oQPUwbteuXdy7d4+vv/6aqKgoHj9+rB6DKRcdO3bkq6++ok6dOrK+Lr0rdu3ahYeHh2TfP3sZuRo1auh8eHzz5k3JsuXH1dWVPXv2qP8bHx/PmDFj9OZBkyBjSkF4j8yePVvqCHolPj5eee3aNaljFEgfcvbo0UPqCIVy8OBBZZMmTZSffvqp8tNPP1U2bdpUefDgQWVCQoJy5cqVUsdT69atm1KpVCpPnz6t/PTTT5V///23smfPnhKn0ubr6yt1hEIZPXq0cuvWrerzmpqaqv5cTv78809l69atlW3btlUqlUrllStXlKNGjZI4lbb58+crJ06cqOzYsaNSqVQqnz9/rnR3d5c4lTYPDw+pI7yRU6dOKbdt26bcvHmz+kOf6Mv7gJxk/924ubkpk5KSlEqlUtm5c2cpIwnvCNEdWnivXL58WeoIeiMoKIhZs2ZhaGjIsWPHuHr1Kr/88gurVq2SOpoGfcmpLxP6dOzYkcaNG6v/VuQ60Vj28ldnz57F1dWVhg0bynIW1pIlS2p0h84mt9+DR48e0b9/f7Zv3w6oJvCT49wPy5YtY9euXYwYMQKAunXrcv/+fYlTaTt79iy7d+9Wj6+0tbXVGM8oF926deP333+nc+fOGhN4ybFb7LRp07h27Rq1atWS9fJ3ryPHaxSouklv27ZNlitV2NjY8PLlS1q1asWIESOwtbWV5YoFgv4RRbAgCDrpy82mvuTUlwl9QFW4yX2iMTMzM3x8fNi7dy9bt25FqVTKbvw/wJo1a9Sfp6WlcfPmTWrVqiW7Ijh7UsFscXFxsr1hz7tOfe7ZrOXC1NRUo7tp9vIzclOyZElmzpyJt7c3IO9usZcuXWLv3r0YGxtLHeWtST1TdEpKCps3byYmJoYOHTrQtGlTfv/9d3755ReqVKkiyyLYx8cHQ0NDJkyYoO4O3aNHD6ljCe8AUQQLgpAvfbjZBP3IqS8T+uiLBQsWsHXrVqZMmULp0qWJjIyke/fuUsfSknfcWmhoKL/99ptEafLn4uLCrFmzSExMxM/Pj61bt+Lu7i51LC2WlpY8ffpUXUycPXsWa2triVNpq1atGoGBgSiVSqKiovDx8VGvHS0nS5cuZePGjdSuXVuWLf+5lS1bVuoIem/69Ok8fPgQJycnli5dip2dHbdv32b+/PmyezCXbc+ePXTq1AkzMzPc3NykjiO8Q0QRLAiCTvpys6kvOcVEM0WrVKlSmJmZsWPHDo1Cc+TIkRKmKliVKlW4fv261DG0jBgxgsDAQOLi4ggKCmLw4MGyvOH8+uuvGTFiBFFRUQwePJiIiAhWrlwpdSwt06ZNY+HChTx58oQ+ffrg7OyssVa4XNjZ2VG3bl2pYxRKpUqVGDp0KO3bt9d40Dlw4EAJU70ZqXtX3Lhxgz179mBkZERCQgItW7bk6NGj6iEvcnTs2DEWLVqEs7MzvXr1kuXDJEE/iSJYeK/I/Um3nOjLzabcc06ePJklS5bg7u6usyvcrl27JEil/7y8vHB0dCQiIoLx48fj6+sry9mCc48HzsrK4urVq1pdj+Wie/fusmxNz61evXps3LiRixcvAuDk5ESxYsUkTqXNysqKefPmSR2jQM2aNWPJkiV06dJFY0xwlSpVJEylW1paGhUqVODOnTtSR8nXr7/+qh6ao2vbwoULpYilZmZmpr7+WFlZUalSJVkXwKAa8vTixQv27t3L/PnzSUxMpFevXowaNUrqaIKeE0skCe+U8ePH89NPPxW4TSic+Ph42d9s7ty5k9q1a/PkyRNAfjmzl8g5d+6czv1NmjT5Hyd6N3Tv3p3AwED1shlpaWkMGTKEbdu2SR1Nw+DBg9WfGxkZUaFCBYYPH65egkpqGzZswNPTk0WLFul8SDNlyhQJUumvv//+m0aNGumcDA3kNyGarrH/CoWCo0ePSpBG/2UvQVTQNql8/PHHGuNpd+/erfG13P/eX7x4wdKlS9m5c6csx60L+kWej6MF4S3pmhApLCxMgiTvBmtra5o3b05mZiYAycnJsps19MaNG/z222+kp6fTrFkz4uPjadasGaVKlZI6GoB6jVhR7Bat7O6QxsbGvHjxAhsbG54/fy5xKm1yX8syu/XP0tJS4iSv5+npyYYNG2jWrJnO9U3lMsGcv78/jRo10pgQLZtCoZBdEXzs2DGpIxRIHx4sBAcHc/LkSR4/fszixYvV2xMSEiTvAp3bgAEDXvu1HGVmZnLixAn8/Pz4+++/adeuHZs3b5Y6lvAOEC3Bwjthx44dbN++nbCwMBwdHdXb4+PjcXBwkN1yOfrg8OHDzJs3T93CKudZQwEePnzIn3/+iY+PDzExMbLLGRYWxqpVq7h//z4ZGRnq7aI79Nv5+uuvmTFjBv7+/mzbtg1ra2vs7e358ccfpY6mISMjg+3bt3P27FlA1f20T58+su0SLVePHz/Gzs6OBw8e6Nwvxty/vTt37qh7qjRr1kx2XaFnzJjBvHnzNHpVZFMoFGzcuFGCVJrOnTvHuXPn2LZtG/369VNvt7Kyon379nz44YcSpntzu3btwsPDQ+oYALRs2ZJq1arRo0cPOnTogJmZmdSRhHeEKIKFd8KDBw+Iiopi7ty5zJo1S73dysqK6tWr6+2aglJycXFh0aJFNGjQQNZjqa9du8bp06c5ffo0T548wcnJiebNm9O5c2epo2lwdXXFzc2N2rVra/w+ihbif+/ChQvEx8fTqlUr2RWXM2fOJDo6Wt3lMCAggHLlyqmXpJGLefPmMXbsWIoXLw5AbGwsK1asYPr06RIn0/T8+XOsrKzUPQHS0tJISEigRIkSEifTtHv3btq2bYuNjQ2g6sZ54sQJ2Y253rJlC6tWreKTTz4BVGPYR48erRcthHmdOHGC1q1bS5rhzp07VKtWTdIMRUFOXbhjYmL44IMP8t0vp4Jd0C/yulsQhLdUvnx5ypcvz969ewFISkoCwMLCQspYes3GxoaGDRtKHaNAHh4eODk5MXHiRD766COp4+TLyMiI4cOHSx3jndS4cWOpI+Tr/Pnz7N+/X/0gqXPnznTt2lXiVNouXLigLoABbG1tOX/+vISJdBs1apRGy19GRgajR49mx44dEqbStnbtWo2xlsWLF2ft2rWyK4I3btzI7t271ZMjPX/+nP79++tlEfzjjz9KXgTb29vzww8/cOrUKUDVijl69GjZDSMqiJzax15XAIPqQY4ogoW3Id/mHUF4C5GRkfTp04emTZvSrFkz+vXrR2RkpNSx9EpycjLJycm4uLiwdetWXrx4od6WnJwsdTwtO3bsoG3btqxYsYLu3bszZ84cDh8+LHUsLa1atcp3TJvw7ipevDhpaWnqrzMyMmTXagmox/3nlrvbvlykpaVpFBQWFhakpqZKmKjwdJ1jqVlaWmrMDlyiRAnZjw/PjxwKt7lz5/L48WO8vLzw8vLi8ePHsuv1URi6JsmTKzn8uwv6SbQEC++UWbNm0adPH9zd3QHw8/Nj1qxZrFu3TuJk+sPJyQmFQqF+Y/H29lZ/LccxwfXq1aNevXq4ublx/PhxfHx82L59u+xyNm/enDFjxmBgYICJiYnsJvQRitaWLVsAqFq1Kn379qVLly4AHDx4UJbrstatW5d58+YxYsQIlEola9askWVOULVWZj9IePbsGVlZWRIn0la6dGkOHz5Mhw4dADh06JAsl6Jp0aIF06dPV7ek+fv706pVK0JDQwF5LpWUHzkUblevXmXPnj3qrxs2bCi71v93jRz+3QX9JIpg4Z3y/PlzjW4x7u7uspg0Q5/cunVL6ghvxNvbmzNnzpCSkkKzZs346quvaNasmdSxtMyaNYsFCxZQu3ZtWY+xForGtWvX1J/XqlWLiIgIAGrUqEF6erpEqfLn5eXF/Pnz6dGjBwqFgk8++QQvLy+pY2kZPHgw/fv3x83NDVCNsR45cqTEqbR5eXkxZswYlixZAoChoSErVqyQOJW2ffv2AWg9jNuzZ49YKuktJSUlqYdiybH3VGGI1lXhfSCKYOGdYmBgQFhYGJUrVwYgPDxcTIr1jqtevTpDhw6lQoUKUkd5LRsbGzp16iR1DOF/ZMGCBVJHeCNWVlZ6kdnDwwN7e3v10IK5c+fKcnI5R0dH9u/fT3h4OAAODg6yfC/ShyWSCksOhZurqyt9+/ZVj/vfv3+/+oGNnPz666+MGDEi320LFy6UItZbkcO/u6CfxOzQwjvlxIkTTJ06lZo1a6JUKrl9+zaLFy+mZcuWUkcT3nOrVq3CxsaGzp07q9dmBfRuwhShcPRhbVPQn5z6Ii0tDRMTk3xbAOX49x4aGqqxhFfuZQblKi0tjZcvX1K6dGn1tqCgIFn8vgYFBXHmzBlANQxG6sm6dNE1+7OcZoR+E7du3aJGjRpSxxD0kCiChXfO8+fPuXz5MgD169eX5SQ0wv+3d+dRUZ1nGMCfGXYQY7QurSIqURGNS0hdExdKjRHZEfQgIRZxq0sQMIoGEjCKe9XEoLExatoawHHEfakLaoxLcw6iRaQCAk1EYVBZBBmY/sFh6mRQY5Lxuxef3zk5J/fOP8/xMgfe+33f+754Hv0lLeUz1vTrkMNsU6BhhFNCQoLkc65cuRLR0dGYM2dOk2cA161bJyCVscZCwtnZ2SCnVL/varUaq1ev1heP6enpiIqKkuQ51oiICMTHx8PCwgLe3t4oKyvDtGnTEBYWJjqaEalOqDh79izOnDmDtLQ0gxXqiooKXL58GWq1WmC6pg0ePNjoO29vb4/+/fsjOjra4EUI0bPgdmgioudAbmet6ZdZsmQJ6uvrsWjRIkmvUgwaNAgAsHTpUjg4OAhO83iNY7BGjRolOMmTrVmzBoB8vu9ffPEFVCqVvpC4c+cOwsLCJFkE5+Xlwd7eHocOHcKgQYOwcOFCBAYGSqoILigoQFRUlP75u7i4YOXKlZL5bllYWMDOzg4KhcKgQG/Xrp0kz9YDQHBwMO7fv69veKpWq2FmZgYbGxt88MEHSEpKEpyQ5IpFMDUrR44cwQcffIA+ffpAp9MhJiYGCQkJcHd3Fx2NiF4wSqUS0dHRBt1ipeaLL77AuHHjMGfOHElvhdy7dy9GjRqF+/fvIzQ0VHScx4qMjIRKpUJoaCi2bdsmOs5P8uhKmpRX1RpHdl28eBEjRoyAjY2N5JoMxsXFSXpCxcCBAzFw4ECMHj0aPXr0EB3nJ0lPT0dKSor+esGCBfD398euXbskOXOd5INFMDUra9euxc6dO9G1a1cAQH5+PmbMmMEimIiEcHR0RFFRETp16iQ6SpN0Oh0SEhJQXFyMFStWGH0+f/58AamM5eTkAGhYBZJyEVxdXY3Dhw/jv//9b5PnrKVwZvVRnTt3xvr16xEUFAQASElJkcyq5Y85OTlhypQpyM3NRWRkJKqrq0VHMiKXCRUODg5YvXo1vvnmGwDAG2+8genTp0vyzPr9+/dx9+5dtGrVCgBQVlaGiooKAA0r20Q/F4tgalasrKz0BTAAdOnSBdbW1gITEdGLrLKyEl5eXnB1dTXYfiiVM6x/+ctfcOTIESiVSsmdX3xUnz594OrqipqaGgwZMkR/X2rztufNm4evv/4apaWl2LJli8FnCoVCckXwRx99hCVLlsDLywsKhQJDhw5FfHy86FhNWr58Oc6cOYOePXvC1tYWxcXFiIyMFB3LgFwmVCQkJKCurk4/Bi01NRXx8fGS7BAfEhICb29vg3PrU6ZMQWVlJV577TXB6UjO2BiLmpUNGzbA3NwcAQEB0Ol0UKlU0Gq1mDJlCnQ6nSTfchJR8/W4Lca+vr7POUnT9u3bh3HjxiEpKQnTp08XHeeJSkpKEBoais2bNxt91rFjRwGJjOXn56NLly5YtmwZFi5cKDpOs1JRUQEbGxuYmZnh+vXryMnJwR//+EdYWlqKjqZ/7qdPn8b8+fMlP6HC09PT4JiGTqeDl5eXZI9uXLt2DRcvXgQA/P73v5d0nwWSD64EU7Py6aefAjBeZfnkk08k2ZmTiJq3Dh06GKxcApDMqiXw/zPBhw8flnQRPG/ePKxZswYeHh6SKXibMm/ePKhUKsk3xnrcSKxGUluxBoB33nkHX331FSorKxEWFoYePXrg9OnTkphp2/jct2zZgv3798tiQkVVVZV+98fjRnpJxSuvvKJfUX90tx/RL8EimJoVqf/hQUQvlhUrVhitBjd1T5TGM8G3b9+WxZngo0ePYubMmYLTPF7jmeDvv/9e0meCf7xV+1FS3LYNNPys2traYv/+/QgMDMTs2bPh6ekpOhYAw+eemZmpb9iVmZkJQDrPvZGnpyeCgoL0jaUOHDhgMDJJSjIzMzFnzhxYWlpCp9NBq9Viw4YN6N27t+hoJHMsgomIiH5lN2/eRH5+PioqKgyKofLyckmtujSeCf7xyBSpkduZ4JKSEkmfCd6xY4foCM+spqYGDx8+xNmzZzFp0iQAkEx3aLk890ZTp05Fz5498e233wIAoqKiMHz4cMGpmvbxxx9j6dKl+u/9uXPnkJCQgJ07dwpORnLHM8FERES/st27d0OlUuHKlSvo06eP/n6LFi0QFBSEkSNHigvXhL1790pmVe1x5HAmuJFczgTrdDqkpqbi5s2biIqKQlFREW7fvi3JhkOffPIJtm3bBkdHRyQnJ6O0tBR//vOfkZycLDqanlyee6OqqioAkPQLMG9vb+zZs+ep94ieFYtgIiIiE1GpVPDz8xMd46nq6+uxa9cu5OfnIzo6WrLFUGVlJezs7ETH+Eny8vJw48YNuLu7o7KyErW1tfoxL1KxdOlSlJaW4urVqzh06BDKysoQHh6O1NRU0dGadO/ePdjb20OpVKKyshIVFRVo37696FiyU1BQgKioKP0RMhcXF6xcuVKS47EmTJiAiIgIDBo0CABw4cIFrFmzhivB9ItxOzQREZGJ+Pn5oaCgAAUFBairq9Pfl9r2yMTERH0xFB0dDTs7OyxdulRyxdCdO3cwZcoUFBcX4/jx47h69SqOHz+O2bNni45mYPfu3di0aRNqa2vh7u6O4uJixMfH48svvxQdzcD58+ehVqv13cpffvll1NTUCE71eEqlEleuXDHIyCL42cXFxSEwMBD+/v4AGl7WxcbGYuvWrYKTGYuJicHcuXP1XcBra2uxfv16wamoOWARTEREZCJr1qxBcnIynJyc9OcXpXhGUC7F0IcffogZM2Zg9erVAIBevXph/vz5kiuCt23bhl27diE4OBgA0K1bN5SUlAhOZczKygoKhUJ/XV9fLzDNkx04cADLly/H/fv30a5dOxQUFMDZ2VkyTebkRKPRICAgQH/t7++P7du3C0z0eH379sWRI0eQl5cHoKE7tIWFheBU1BywCCYiIjKRgwcP4tixY2jRooXoKE8kl2KovLwcw4cPx5o1awA0rAxK8Q9iCwsLo23bjSNepKRHjx5IS0uDTqdDUVERNm/eDFdXV9GxmpSUlASVSoWwsDCo1WqcPXsWhw8fFh1LlpRKJXJzc9GtWzcADVv3pfbz+eMGgo1btbVaLbRaLWxsbETEomaERTAREZGJtG3bVvIFMCCfYsjMzAy1tbX6gr24uFgyHYIf1apVK+Tl5elz7tmzBx06dBCcytiCBQuQmJiIO3fuIDAwEG5ubliwYIHoWE0yNzdHmzZt9McKhg0bhlWrVglOJS/5+fno0qUL5s2bh+DgYPTq1Qs6nQ7Z2dlNjkgTacCAAVAoFGhsXdT4XWrsCJ+VlSUyHjUDbIxFRERkIitWrMCtW7cwZswYWFlZ6e9LbTt0RUUFEhMTcfz4cQCAm5sbYmJiJNc1Vq1W4+DBg8jOzoa/vz/UajUiIiIwbtw40dEM5OXlITIyErm5uWjdujWsra2RlJSEzp07i44mWxMmTMA//vEPzJ49G4MGDULHjh2xfPlyrgY/Az8/P6hUKoSGhmLt2rXIyMgAAPTr1w+tW7cWnI7o+WIRTEREZCIhISFG9xQKhWTP3z1OamqqwRlCkS5duoQTJ05Ap9PBzc0Nr7/+uuhITaqrq0N+fj50Oh26du1qsN302rVrcHZ2Fpiugbu7O/z9/eHr6yvJlepHnTt3Dn369EFpaSk+/PBDlJeXIzIyEkOHDhUdTTbGjh2LuXPnYtWqVVi8eLHR51J7OUdkSiyCiYiI6Il8fX1l0YBo5syZ2Lhxo+gYTyWVf8+cnByoVCrs27cPr7zyCvz8/DB69GiDXQvUfBw7dgxff/01Ll26ZDC/HJDnyzmiX4JFMBERkQmdPn0a33zzDQDgjTfewLBhwwQnenY+Pj5Qq9WiYzwVc/48dXV1SE9PR0pKCi5duoQLFy6IjmSkuroa+/btQ0FBAbRarf7+/PnzBaaSp2XLlmHhwoWiYxAJJb1uEkRERM3Eli1bsHz5crRs2RItW7ZEYmIi/vrXv4qO9cwe7RwtZcz58+Tm5uLChQvIzMxE7969Rcdp0qxZs3DkyBGYmZnB1tZW/x89OxbAROwOTUREZDJ79uzBzp079R2iQ0JCMHHiRISFhQlORgRs374darUalZWV8PX1RXJyMn7729+KjtWkH374Afv37xcdg4iaCRbBREREJvToiCQ5jEtqCk9O/bqk8u95/fp1LFq0SJLjsH6se/fuuH37Ntq1ayc6ChE1AyyCiYiITKRPnz5YuHAhxo8fD6Chy/KPG9JIRUVFBW7evNnkdtjExEQBiZ6d1DscNwoODhYdAQCwZMkSVFRU4OrVq5LdBt1o1qxZCAwMhLOzs0HjrnXr1glMRURyxcZYREREJlJVVYWNGzfqG2MNHToUM2fOlNxZxlOnTiE2NhZmZmY4fvw4MjMz8emnnyIpKUl0NAMXL16Ei4sL7OzskJKSgszMTISHh8PBwUF0NAANc6GfRGpNnBqfu1KpxIkTJyT73IGGGbf9+/eHi4uLwbgpX19fgamISK64EkxERGQitra2iIqKEh3jqdavX4/U1FSEh4cDAF599VUUFBQITmUsPj4eaWlpyMnJwdatW+Hl5YVFixZJZrSL1F5uPI1cnjsA1NbWIjY2VnQMImomWAQTERGZyJIlSzBr1iy0atUKAFBWVoaNGzdi0aJFgpMZa9u2rcG1paWloCSPZ25uDoVCgfT0dEycOBEhISE4dOiQ6Fh6s2bNEh3hmcnhuQNA//79kZ2djZ49e4qOQkTNAItgIiIiE7l06ZK+AAaAl19+GRcvXhSYqGl2dnYoKSnRj+45f/487O3tBacyptVqkZGRgaNHjyIhIQFAw4xbKTpz5gyysrJQU1Ojvye1Ilkuzx0ALl++DH9/f3Tt2tXgTHBqaqrAVEQkVyyCiYiITKSpAk2r1QpI8mRRUVEIDw9HUVERQkJCkJ+fj88++0x0LCNz585FbGwshgwZgu7duyMvLw+Ojo6iYxlZtWoVMjMz8Z///Ad/+MMf8M9//hNDhgwRHctIZGSkLJ47AEnuniAi+WJjLCIiIhOJiYmBra0twsPDodPpsGXLFlRWVmLZsmWio+nV19fj+vXr6NixI7777jsAwIABA9CyZUvByYzduHEDTk5OT70nmqenJ3bv3g0/Pz+kpaWhuLgYixcvxueffy46GgDgwYMH+v+/f/8+rl69CgDo3bs3WrZsCRsbG1HRfraZM2di48aNomMQkUxwJZiIiMhEYmJi8PHHH8PHxwcKhQIjR45ETEyM6FgGlEoloqOjsXfvXowYMUJ0nCeKiorC7t27n3pPNEtLS/355draWrRv3x63bt0SHdxA4ckAAAwTSURBVEtvwIAB+i3QQMPc4kevs7KyRMT6Rb7//nvREYhIRlgEExERmUiLFi2euOqbmpqKgICA55ioaY6OjigqKkKnTp1ER2mSRqOBRqNBTU0Nbty4gcZNbOXl5aiqqhKczpidnR0ePHiAAQMGYMGCBWjbti2sra1Fx9K7du0aAGDjxo2wtLREUFAQdDodUlJSUFtbKzjdz/NoEU9E9DTcDk1ERCSIr6+vJFYxJ0+ejIyMDLi6uhqM+Vm3bp3AVP+3bds2bNu2Dbdv30a7du309+3t7TFp0iSMHz9eYDpjJSUlaNmyJerq6rB161aUl5cjJCQEv/vd70RHM9DUz5+fnx9UKpWgRD+fVL5LRCQPXAkmIiISRCrvob28vODl5SU6xmOFhoYiNDQUSUlJmD59uug4T/Wb3/wGAFBVVYV3331XsvODq6urcfPmTX1zsYKCAoPzwkREzRWLYCIiIkGksoXT19dXdISf5K233kJNTQ2srKxw+vRpZGVlISgoCC+99JLoaAYKCgoQFRWFrKwsKBQKuLi4YOXKlXBwcBAdzUBERAQCAwPRp08fAMC///1v/egpuenQoYPoCEQkI9wOTUREJIhUtnBqNBokJCTg3LlzAIBhw4Zh0aJFaN26teBkhry9vZGamopbt24hNDQUw4YNw507d5CUlCQ6moHJkyfDw8MD/v7+AACVSoV9+/Zh69atgpMZKy0tRUZGBgCgf//+knvmjcLCwjBp0iSMHDlSMi+PiEi+lKIDEBERvaik8h46Li4OXbp0wZ49e6BWq+Ho6IjY2FjRsYwolUpYWFjg1KlTmDhxIhISEvDDDz+IjmVEo9EgICAACoUCCoUC/v7+0Gg0omM1qU2bNnBzc4Obm5tkC2AACAoKwrZt2+Du7o7NmzejrKxMdCQikjEWwURERCZUUVGhn8P6Y4mJic85TdMKCgowd+5ctG/fHh06dMCcOXNQWFgoOpaRmpoalJSU4MSJExg8eDAA6bxIeJRSqURubq7+Oi8vD2ZmZgITyd/o0aPx5ZdfYvPmzbh9+zbGjRuH+fPn48qVK6KjEZEMsQgmIiIykVOnTsHDwwOzZ88GAGRmZho0dnJ2dhYVzUB9fT1KS0v116WlpaivrxeYqGmhoaEYM2YMbG1t8eqrr6KwsBD29vaiYxmJiIhAcHAw/vSnP2Hy5MkIDg5GRESE6FjNQuNWaAsLC1hZWeH999+XzMskIpIPngkmIiIyEX9/fyQlJSE8PBxqtRoAMHbsWBw4cEBwMkNqtRqrV6/GyJEjATQU75GRkfD29hYb7Cnq6upQV1cHS0tL0VEMPHjwAHfv3kV2djYAoFevXmjfvr3gVPJ2+PBh/O1vf0NJSQmCg4Ph4+MDOzs7aLVajB49GsePHxcdkYhkhN2hiYiITKht27YG11Ir2ADAx8cHvXv3xvnz5wEA77zzDrp37y441f/961//gqurK06dOtXk5yNGjHjOiZ5swIABUCgU+q3aCoUC5ubm6Nu3LxISEtCtWzfBCeVHpVIhPDwcb775psF9c3NzLF68WFAqIpIrFsFEREQmYmdnh5KSEv0WzvPnz0ty+65Go4Gjo6O+8H348CE0Go1kGiXt3r0brq6u2LJli9FnCoVCckVwREQErKysEBAQAJ1Oh927d6OsrAwODg6Ii4vDjh07REeUnU2bNj32Mzc3t+eYhIiaA26HJiIiMpHLly8jLi4ORUVFcHZ2Rn5+Pj777DP9XFapGD9+PLZv3w4bGxsAQFVVFd59910kJycLTiZPfn5+UKlUTd7z9PTE3r17BSWTH39//yeOREpNTX2OaYioueBKMBERkQnU19fD0tIS27dvx3fffQegYZtsy5YtBScz9vDhQ30BDAC2traoqakRmKhpFy9ehIuLC+zs7JCSkoLMzEyEh4fDwcFBdDQDDx48QGFhoT5XYWEhqqqqAIBdop/R+++/DwA4efIkcnNzERAQAKBhe3TXrl1FRiMiGWMRTEREZAJKpRLR0dHYu3ev5LbrNuXR7c9S7Q4dHx+PtLQ05OTkYOvWrfDy8sKiRYuwfft20dEMvPfeewgICNCv+F+9ehUfffQRKisrMWbMGMHp5GXgwIEAgJUrVyI5OVm/Kjxq1ChMmDBBZDQikjEWwURERCbi6OiIoqIidOrUSXSUJwoJCcHEiRPh7e0NnU6HtLQ0TJ06VXQsI+bm5lAoFEhPT8fEiRMREhKCQ4cOiY5l5K233sLrr7+OjIwMAEC/fv3Qpk0bADAYkUU/3b1791BTUwNra2sADbsX7t27JzgVEckVi2AiIiITqayshJeXF1xdXWFra6u/v27dOoGpjAUEBKBz5844efIkFAoF5syZAw8PD9GxjGi1WmRkZODo0aNISEgA0DAmSYratGnDhk2/orfffhtBQUEYO3YsAODgwYP6/ycielYsgomIiEzEy8sLXl5eomM8VUREBOLj49G3b194e3sjJSUFt27dQlhYmOhoBubOnYvY2FgMGTIE3bt3R15eHhwdHUXHoucgIiIC/fr1w4ULFwA0bDlvnGtNRPSs2B2aiIjoBefj4wO1Wo1Dhw7h3LlzWLhwIcaPH88uxiQ5jQ3GHt1ZQUT0rJSiAxARETVXGo0GERERGDx4MAYPHozIyEhoNBrRsYxotVoADd2Xhw8fDmtrayiV0vsTYevWrSgvLwcAREdHY8yYMThz5ozgVPQ8FBQUIDAwUP9dmjBhAgoLC0XHIiKZkt5vOCIiomYiLi4OXbp0wZ49e6BWq+Ho6IjY2FjRsYw4OTlhypQpOHHiBIYMGYLq6mrRkZqkUqlgb2+Pb7/9FhqNBkuXLsWaNWtEx6LnIC4uDoGBgcjIyEBGRgbGjx8vye8SEckDzwQTERGZSEFBATZs2KC/njNnDry9vQUmatry5ctx5swZ9OzZE7a2tiguLkZkZKToWEYaZ+yeP38enp6eeO2118BTXS8GjUajnxEMAP7+/pIbjUVE8sGVYCIiIhOpr69HaWmp/lqq83etra3h7u4OBwcHAED79u0xfPhwwamMWVtbY/Pmzdi3bx+GDRsGnU6H2tpa0bHoOVAqlcjNzdVf5+Xl6V+KEBE9K64EExERmUhYWBh8fHz0XWxPnTolyRVWuVi2bBn+/ve/Y/78+Wjbti0KCwtl0X2bfrmIiAgEBwejV69e0Ol0yM7OxooVK0THIiKZYndoIiIiE8rJycH58+cBAIMGDUL37t0FJ5Kv8vJybN68GdeuXUNNTY3+PrfFvhg0Gg0yMjIAAP369UPr1q0FJyIiueJKMBERkYloNBo4OjrqC9+HDx9Co9Hwj/efKSYmBk5OTsjPz8fcuXOxa9cu9O7dW3QsIiKSGZ4JJiIiMpFp06ahrq5Of63VajF9+nSBieTt5s2beO+992BtbY1x48Zh06ZNuHTpkuhY9BwcOXIEb7/9Nr766ivs2LEDHh4eOHbsmOhYRCRTXAkmIiIykYcPH8LGxkZ/bWtra7CNl56NpaUlAMDCwgJ3797FSy+9JMm5y/TrW7t2LXbu3ImuXbsCAPLz8zFjxgy4u7sLTkZEcsQimIiIyIQe3f4s1e7QctGlSxfcvXsXnp6eCAoKgr29PbdDvyCsrKz0BTDQ8LNgbW0tMBERyRkbYxEREZlIamoqPv/8c3h7e0On0yEtLQ1Tp06Fv7+/6Giyd+nSJZSXl+PNN9+EuTnf6TdXDx48AABs2bIF5ubmCAgIgE6ng0qlglarxaxZswQnJCI5YhFMRERkQhcuXMDJkyehUCjg4uICDw8P0ZGIZMPZ2RkKhQJN/bmqUCiQlZUlIBURyR2LYCIiIhOJiIhAfHw8LCws4O3tjbKyMkybNg1hYWGioxEREb2w2B2aiIjIRPLy8mBvb4+TJ09i8ODBSE9Ph1qtFh2LiIjohcYimIiIyES0Wi0A4OLFixg+fDisra2hVPJXLxERkUj8TUxERGQiTk5OmDJlCk6cOIEhQ4agurpadCQiIqIXHs8EExERmUh1dTXOnDmDnj17wsHBAcXFxcjOzsbw4cNFRyMiInphsQgmIiIiIiKiFwa3QxMREREREdELg0UwERERERERvTBYBBMREREREdELg0UwERERERERvTD+B0tsgWkd1OWAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We are removing the goal and goal efficiency attributes from our data due to the multicollinearity problem."
      ],
      "metadata": {
        "id": "XVaAQBnbdLnx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datacp.drop(columns=[\"goal\",\"goal_efficiency\"], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "g5n1zl17dK80"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = datacp.drop(columns='log_value')\n",
        "y = datacp.log_value\n",
        "X, X_test, y, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
        "\n",
        "X, y = np.array(X), np.array(y)"
      ],
      "metadata": {
        "id": "3i8Cs4S6j_V7"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "sm_X_train = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "UFsmnov865Hd"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X_sm = sm.add_constant(sm_X_train)\n",
        "lsm = sm.OLS(y,X_sm)\n",
        "fit4 = lsm.fit()\n",
        "fit4.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "id": "t9K9dXPT6j0e",
        "outputId": "c111c9a5-dc60-4ba1-fde5-59274b91932b"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   R-squared:                       0.793\n",
              "Model:                            OLS   Adj. R-squared:                  0.788\n",
              "Method:                 Least Squares   F-statistic:                     164.2\n",
              "Date:                Thu, 02 Feb 2023   Prob (F-statistic):          7.96e-181\n",
              "Time:                        11:59:39   Log-Likelihood:                -427.69\n",
              "No. Observations:                 571   AIC:                             883.4\n",
              "Df Residuals:                     557   BIC:                             944.2\n",
              "Df Model:                          13                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "const          1.5113      0.022     69.700      0.000       1.469       1.554\n",
              "x1             0.5392      0.018     30.407      0.000       0.504       0.574\n",
              "x2             0.4694      0.015     31.743      0.000       0.440       0.498\n",
              "x3            -0.1776      0.039     -4.613      0.000      -0.253      -0.102\n",
              "x4            -0.1140      0.329     -0.347      0.729      -0.760       0.532\n",
              "x5             0.1944      0.425      0.458      0.647      -0.640       1.029\n",
              "x6             0.2014      0.049      4.131      0.000       0.106       0.297\n",
              "x7             0.0707      0.075      0.946      0.344      -0.076       0.218\n",
              "x8            -0.2386      0.092     -2.596      0.010      -0.419      -0.058\n",
              "x9             0.1826      0.056      3.262      0.001       0.073       0.293\n",
              "x10           -0.1167      0.058     -1.998      0.046      -0.231      -0.002\n",
              "x11           -0.0367      0.034     -1.093      0.275      -0.103       0.029\n",
              "x12           -0.1314      0.327     -0.401      0.688      -0.775       0.512\n",
              "x13           -0.0515      0.042     -1.230      0.219      -0.134       0.031\n",
              "x14           -0.0437      0.042     -1.035      0.301      -0.127       0.039\n",
              "==============================================================================\n",
              "Omnibus:                        7.265   Durbin-Watson:                   1.972\n",
              "Prob(Omnibus):                  0.026   Jarque-Bera (JB):                7.446\n",
              "Skew:                           0.221   Prob(JB):                       0.0242\n",
              "Kurtosis:                       3.344   Cond. No.                     1.06e+16\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The smallest eigenvalue is 1.99e-29. This might indicate that there are\n",
              "strong multicollinearity problems or that the design matrix is singular.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.793</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.788</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   164.2</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Thu, 02 Feb 2023</td> <th>  Prob (F-statistic):</th> <td>7.96e-181</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>11:59:39</td>     <th>  Log-Likelihood:    </th> <td> -427.69</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   571</td>      <th>  AIC:               </th> <td>   883.4</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   557</td>      <th>  BIC:               </th> <td>   944.2</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    13</td>      <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th> <td>    1.5113</td> <td>    0.022</td> <td>   69.700</td> <td> 0.000</td> <td>    1.469</td> <td>    1.554</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x1</th>    <td>    0.5392</td> <td>    0.018</td> <td>   30.407</td> <td> 0.000</td> <td>    0.504</td> <td>    0.574</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x2</th>    <td>    0.4694</td> <td>    0.015</td> <td>   31.743</td> <td> 0.000</td> <td>    0.440</td> <td>    0.498</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x3</th>    <td>   -0.1776</td> <td>    0.039</td> <td>   -4.613</td> <td> 0.000</td> <td>   -0.253</td> <td>   -0.102</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x4</th>    <td>   -0.1140</td> <td>    0.329</td> <td>   -0.347</td> <td> 0.729</td> <td>   -0.760</td> <td>    0.532</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x5</th>    <td>    0.1944</td> <td>    0.425</td> <td>    0.458</td> <td> 0.647</td> <td>   -0.640</td> <td>    1.029</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x6</th>    <td>    0.2014</td> <td>    0.049</td> <td>    4.131</td> <td> 0.000</td> <td>    0.106</td> <td>    0.297</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x7</th>    <td>    0.0707</td> <td>    0.075</td> <td>    0.946</td> <td> 0.344</td> <td>   -0.076</td> <td>    0.218</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x8</th>    <td>   -0.2386</td> <td>    0.092</td> <td>   -2.596</td> <td> 0.010</td> <td>   -0.419</td> <td>   -0.058</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x9</th>    <td>    0.1826</td> <td>    0.056</td> <td>    3.262</td> <td> 0.001</td> <td>    0.073</td> <td>    0.293</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x10</th>   <td>   -0.1167</td> <td>    0.058</td> <td>   -1.998</td> <td> 0.046</td> <td>   -0.231</td> <td>   -0.002</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x11</th>   <td>   -0.0367</td> <td>    0.034</td> <td>   -1.093</td> <td> 0.275</td> <td>   -0.103</td> <td>    0.029</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x12</th>   <td>   -0.1314</td> <td>    0.327</td> <td>   -0.401</td> <td> 0.688</td> <td>   -0.775</td> <td>    0.512</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x13</th>   <td>   -0.0515</td> <td>    0.042</td> <td>   -1.230</td> <td> 0.219</td> <td>   -0.134</td> <td>    0.031</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x14</th>   <td>   -0.0437</td> <td>    0.042</td> <td>   -1.035</td> <td> 0.301</td> <td>   -0.127</td> <td>    0.039</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 7.265</td> <th>  Durbin-Watson:     </th> <td>   1.972</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.026</td> <th>  Jarque-Bera (JB):  </th> <td>   7.446</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.221</td> <th>  Prob(JB):          </th> <td>  0.0242</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 3.344</td> <th>  Cond. No.          </th> <td>1.06e+16</td>\n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 1.99e-29. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4) Train models with our data**"
      ],
      "metadata": {
        "id": "rDD0ulgYbiF6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "VALIDATION RESULTS AND MODEL SELECTION"
      ],
      "metadata": {
        "id": "l0yfThWQ1LCA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "linear_model_r2 = []\n",
        "ridge_model_r2 = []\n",
        "lasso_model_r2 = []\n",
        "dtr_model_r2 = []\n",
        "svr_model_r2 = []\n",
        "rfr_model_r2 = []\n",
        "xgboost_model_r2 =[]\n",
        "\n",
        "for train_ind, val_ind in kf.split(X,y):\n",
        "    \n",
        "    X_train, y_train = X[train_ind], y[train_ind]\n",
        "    X_val, y_val = X[val_ind], y[val_ind] \n",
        "    \n",
        "    linear_model = LinearRegression()\n",
        "    ridge_model = Ridge(alpha=7.31)\n",
        "    lasso_model = Lasso()\n",
        "    dtr_model = DecisionTreeRegressor()\n",
        "    svr_model = SVR()\n",
        "    rfr_model = RandomForestRegressor()\n",
        "    xgboost_model = GradientBoostingRegressor()\n",
        "\n",
        "    scaler = StandardScaler()\n",
        "    X_train_scaled = scaler.fit_transform(X_train)\n",
        "    X_val_scaled = scaler.transform(X_val)\n",
        "\n",
        "    linear_model.fit(X_train_scaled, y_train)\n",
        "    linear_model_r2.append(round(linear_model.score(X_val_scaled, y_val),3))\n",
        "    \n",
        "    ridge_model.fit(X_train_scaled, y_train)\n",
        "    ridge_model_r2.append(round(ridge_model.score(X_val_scaled, y_val),3))\n",
        "\n",
        "    lasso_model.fit(X_train_scaled, y_train)\n",
        "    lasso_model_r2.append(round(lasso_model.score(X_val_scaled, y_val),3))\n",
        "\n",
        "    dtr_model.fit(X_train_scaled, y_train)\n",
        "    dtr_model_r2.append(round(dtr_model.score(X_val_scaled, y_val),3))\n",
        "\n",
        "    svr_model.fit(X_train_scaled, y_train)\n",
        "    svr_model_r2.append(round(svr_model.score(X_val_scaled, y_val),3))\n",
        "\n",
        "    rfr_model.fit(X_train_scaled, y_train)\n",
        "    rfr_model_r2.append(round(rfr_model.score(X_val_scaled, y_val),3))\n",
        "\n",
        "    xgboost_model.fit(X_train_scaled, y_train)\n",
        "    xgboost_model_r2.append(round(xgboost_model.score(X_val_scaled, y_val),3))\n",
        "\n",
        "print('Linear Regression R2:', linear_model_r2)\n",
        "print('Ridge Regression R2:', ridge_model_r2, '\\n')\n",
        "print(\"lasso Regression R2:\", lasso_model_r2, \"\\n\")\n",
        "print(\"decision tree Regression:\", dtr_model_r2, \"\\n\")\n",
        "print(\"support vector Regression:\", svr_model_r2, \"\\n\")\n",
        "print(\"random forest Regression:\", svr_model_r2,\"\\n\")\n",
        "print(\"xgboost Regression:\", xgboost_model_r2, \"\\n\")\n",
        "\n",
        "print(f'Linear Regression R2: {np.mean(linear_model_r2):.3f} +- {np.std(linear_model_r2):.3f}')\n",
        "print(f'Ridge Regression R2: {np.mean(ridge_model_r2):.3f} +- {np.std(ridge_model_r2):.3f}')\n",
        "print(f'lasso Regression R2: {np.mean(lasso_model_r2):.3f} +- {np.std(lasso_model_r2):.3f}')\n",
        "print(f'decision tree Regression: {np.mean(dtr_model_r2):.3f} +- {np.std(dtr_model_r2):.3f}')\n",
        "print(f'support vector Regression: {np.mean(svr_model_r2):.3f} +- {np.std(svr_model_r2):.3f}')\n",
        "print(f'random forest Regression: {np.mean(rfr_model_r2):.3f} +- {np.std(rfr_model_r2):.3f}')\n",
        "print(f'xgboost Regression: {np.mean(xgboost_model_r2):.3f} +- {np.std(xgboost_model_r2):.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEZv_Zr9tRHf",
        "outputId": "a0a37761-87ec-4407-8117-01b1c0343f52"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression R2: [0.75, 0.736, 0.778, 0.798, 0.803]\n",
            "Ridge Regression R2: [0.759, 0.74, 0.776, 0.796, 0.804] \n",
            "\n",
            "lasso Regression R2: [-0.007, -0.0, -0.003, -0.017, -0.001] \n",
            "\n",
            "decision tree Regression: [0.592, 0.583, 0.629, 0.701, 0.638] \n",
            "\n",
            "support vector Regression: [0.767, 0.752, 0.694, 0.76, 0.796] \n",
            "\n",
            "random forest Regression: [0.767, 0.752, 0.694, 0.76, 0.796] \n",
            "\n",
            "xgboost Regression: [0.757, 0.798, 0.819, 0.819, 0.835] \n",
            "\n",
            "Linear Regression R2: 0.773 +- 0.026\n",
            "Ridge Regression R2: 0.775 +- 0.024\n",
            "lasso Regression R2: -0.006 +- 0.006\n",
            "decision tree Regression: 0.629 +- 0.042\n",
            "support vector Regression: 0.754 +- 0.033\n",
            "random forest Regression: 0.798 +- 0.024\n",
            "xgboost Regression: 0.806 +- 0.027\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* After applying the cross validation process to our data and training 7 different models with our data, the xgboost model gave the most successful result.\n",
        "\n",
        "* Lasso model structure, not suitable for our data."
      ],
      "metadata": {
        "id": "uKYMwsZWCiuv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**HYPERPARAMETER OPTIMIZATION AND TEST RESULTS**"
      ],
      "metadata": {
        "id": "curP_wfP1aRN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Before giving test data to our most successful models, hyper-parameter optimization was performed with GridSearchCv."
      ],
      "metadata": {
        "id": "SBg4gZw7Dm4K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X)\n",
        "X_test_scaled = scaler.transform(X_test.values)"
      ],
      "metadata": {
        "id": "M3NrafUSD5jX"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rdg_model = Ridge(alpha=7.43)\n",
        "xgb_model = GradientBoostingRegressor(max_depth= 3, n_estimators= 53)"
      ],
      "metadata": {
        "id": "xH-_gFFY_4jy"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = {\"alpha\":10**(np.linspace(-2,8,600))}\n",
        "parameters_xgb = {\"n_estimators\": range(10,55), \"max_depth\": range(1,15)}"
      ],
      "metadata": {
        "id": "KaWwfqEJ_TvO"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid = GridSearchCV(estimator=rdg_model,param_grid=parameters,cv=10)\n",
        "grid.fit(X_train_scaled,y)\n",
        "print(grid.best_params_)\n",
        "\n",
        "grid_xgb = GridSearchCV(estimator=xgb_model,param_grid=parameters_xgb,cv=8)\n",
        "grid_xgb.fit(X_train_scaled,y)\n",
        "print(grid_xgb.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XbboXBE_MvM",
        "outputId": "33263612-d6e0-4df6-fa05-e02301c5d321"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'alpha': 7.437935718532088}\n",
            "{'max_depth': 3, 'n_estimators': 53}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_model.fit(X_train_scaled,y)\n",
        "predict_xgb = xgb_model.predict(X_test_scaled)\n",
        "\n",
        "rdg_model.fit(X_train_scaled,y)\n",
        "predict = rdg_model.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "xSQFgBPBi9hI"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"ridge_score:\", r2_score(y_test,predict))\n",
        "print(\"xgb_score:\", r2_score(y_test,predict_xgb))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qo1t-Bjv7DZY",
        "outputId": "5726d2e8-e5bd-4af8-a9da-2ee38da6dabf"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ridge_score: 0.7621797668950052\n",
            "xgb_score: 0.8121340246427251\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Success rate of ridge regression and xgboost models on test data after hyper parameter optimization."
      ],
      "metadata": {
        "id": "XFHCLR4oEQoy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "ACTUAL VALUES AND OUR ESTIMATED VALUES"
      ],
      "metadata": {
        "id": "7TOI55-Em_Du"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': (predict_xgb)})\n",
        "df['Actual'] = np.expm1(df['Actual'])\n",
        "df['Predicted'] = np.expm1(df['Predicted'])\n",
        "df_ = df[83:89]"
      ],
      "metadata": {
        "id": "lu3z_CLX4xBm"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_[[\"Actual\",\"Predicted\"]].plot(kind='bar',figsize=(10,5))\n",
        "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
        "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "plt.title(\"comparison of actual and predicted values.\")\n",
        "plt.xlabel(\"Player's index\")\n",
        "plt.ylabel('Value')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "JpslFzCPH2qY",
        "outputId": "10828d36-a189-403b-dab1-ef0903c387c4"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}