Music and Mental Health.ipynb

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      "source": [
        "# **Music and Mental Health**\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "***Data Exploration and Pre-Processing***\n",
        "\n",
        "***Feature Engineering and Selection***\n",
        "\n",
        "***Model Selection and Training***\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "**Group Members**\n",
        "\n",
        "Hamna Naveed (346470)\n",
        "\n",
        "Syeda Sana Zehra Zaidi (339278)\n",
        "\n",
        "Tanzeel-ur-Rehman (345510)\n",
        "\n",
        "---\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Data Exploration and Pre-Processing**\n"
      ],
      "metadata": {
        "id": "Mz9ocVkHhu6J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "---\n",
        "**Load the dataset and examine the columns, their data types, missing values, duplicate values and class imbalance.**\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "D6vZfpf0iT-I"
      }
    },
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        "outputId": "666caf11-74a4-4525-c213-c724eef3b88a"
      },
      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: bayesian-optimization in /usr/local/lib/python3.10/dist-packages (1.4.3)\n",
            "Requirement already satisfied: scikit-learn>=0.18.0 in /usr/local/lib/python3.10/dist-packages (from bayesian-optimization) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from bayesian-optimization) (1.10.1)\n",
            "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from bayesian-optimization) (1.22.4)\n",
            "Requirement already satisfied: colorama>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from bayesian-optimization) (0.4.6)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18.0->bayesian-optimization) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18.0->bayesian-optimization) (3.1.0)\n"
          ]
        }
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      "source": [
        "# IMPORTING REQUIRED LIBRARIES\n",
        "\n",
        "import math\n",
        "import warnings\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# For encoding textual data to numeric\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# For scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# For splitting data into training and testing datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# For checking normal distribution of the dataset\n",
        "from scipy.stats import jarque_bera\n",
        "from numpy.random import randn\n",
        "\n",
        "# For checking linearity of the dataset\n",
        "from statsmodels.stats.stattools import durbin_watson\n",
        "\n",
        "# For handling class imbalance\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Machine Learning Model\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "# Metrics\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# For Hyperparameter Tuning\n",
        "!pip install bayesian-optimization\n",
        "from bayes_opt import BayesianOptimization\n",
        "\n",
        "# For model persistence\n",
        "import joblib\n",
        "from joblib import dump, load\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading the CSV file with column names given in the 0th row\n",
        "df = pd.read_csv('master_song_data.csv', header=0)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "6OlPO3C0ikzE",
        "outputId": "5a0b8bb0-30d4-4567-91ac-e002597b2967"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Participant_ID   Song_name             Artist              Spotify_ID  \\\n",
              "0               2  Here again  Elevation Worship  1wuqCUWHniGdEmOKZbQ1cg   \n",
              "1               2   The Stand    Hillsong United  0j2sui6BmkPy974W3VG3RZ   \n",
              "2               2  Here I Bow      Brian Johnson  3RMcL7vuHuRzJQfMEuoJJP   \n",
              "3               3   On my way        Alan Walker  5csliehPVWFrDaWJvdSvbB   \n",
              "4               3     Bad guy      Billie Eilish  2Fxmhks0bxGSBdJ92vM42m   \n",
              "\n",
              "   Loudness  Valence  Danceability  Acousticness  Instrumental Audio_class  \\\n",
              "0    -8.210    0.103         0.124        0.0128      0.000000         Sad   \n",
              "1    -8.128    0.133         0.449        0.1470      0.000000         Sad   \n",
              "2    -7.395    0.218         0.389        0.6780      0.000001         Sad   \n",
              "3    -5.952    0.295         0.616        0.0480      0.000119       Tense   \n",
              "4   -10.965    0.562         0.701        0.3280      0.130000       Relax   \n",
              "\n",
              "                                              Lyrics Sentiment_class  \\\n",
              "0  Can't go back to the beginning\\n Can't control...        Positive   \n",
              "1  You stood before creation\\n Eternity in your h...        Positive   \n",
              "2  [Adolf Hitler:]\\n Vader... This is your last c...        Positive   \n",
              "3  Today I'm kinda feelin' like a ghost\\n Call my...        Positive   \n",
              "4  White shirt now red, my bloody nose\\n Sleepin'...        Positive   \n",
              "\n",
              "  Audio + Lyrics analysis  Total_mental_health Mental_health_severity_class  \n",
              "0                     Sad                    8                          Low  \n",
              "1                     Sad                    8                          Low  \n",
              "2                     Sad                    8                          Low  \n",
              "3     Exciting,Disturbing                   15                          Med  \n",
              "4                   Relax                   15                          Med  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Participant_ID</th>\n",
              "      <th>Song_name</th>\n",
              "      <th>Artist</th>\n",
              "      <th>Spotify_ID</th>\n",
              "      <th>Loudness</th>\n",
              "      <th>Valence</th>\n",
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              "      <th>Acousticness</th>\n",
              "      <th>Instrumental</th>\n",
              "      <th>Audio_class</th>\n",
              "      <th>Lyrics</th>\n",
              "      <th>Sentiment_class</th>\n",
              "      <th>Audio + Lyrics analysis</th>\n",
              "      <th>Total_mental_health</th>\n",
              "      <th>Mental_health_severity_class</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>Here again</td>\n",
              "      <td>Elevation Worship</td>\n",
              "      <td>1wuqCUWHniGdEmOKZbQ1cg</td>\n",
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              "      <td>The Stand</td>\n",
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              "      <td>0.449</td>\n",
              "      <td>0.1470</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Sad</td>\n",
              "      <td>You stood before creation\\n Eternity in your h...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>Sad</td>\n",
              "      <td>8</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Here I Bow</td>\n",
              "      <td>Brian Johnson</td>\n",
              "      <td>3RMcL7vuHuRzJQfMEuoJJP</td>\n",
              "      <td>-7.395</td>\n",
              "      <td>0.218</td>\n",
              "      <td>0.389</td>\n",
              "      <td>0.6780</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>Sad</td>\n",
              "      <td>[Adolf Hitler:]\\n Vader... This is your last c...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>Sad</td>\n",
              "      <td>8</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>On my way</td>\n",
              "      <td>Alan Walker</td>\n",
              "      <td>5csliehPVWFrDaWJvdSvbB</td>\n",
              "      <td>-5.952</td>\n",
              "      <td>0.295</td>\n",
              "      <td>0.616</td>\n",
              "      <td>0.0480</td>\n",
              "      <td>0.000119</td>\n",
              "      <td>Tense</td>\n",
              "      <td>Today I'm kinda feelin' like a ghost\\n Call my...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>Exciting,Disturbing</td>\n",
              "      <td>15</td>\n",
              "      <td>Med</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Bad guy</td>\n",
              "      <td>Billie Eilish</td>\n",
              "      <td>2Fxmhks0bxGSBdJ92vM42m</td>\n",
              "      <td>-10.965</td>\n",
              "      <td>0.562</td>\n",
              "      <td>0.701</td>\n",
              "      <td>0.3280</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>Relax</td>\n",
              "      <td>White shirt now red, my bloody nose\\n Sleepin'...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>Relax</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "# Examining columns and their datatypes\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "426czg_WilDF",
        "outputId": "af1fd56d-ab64-4a75-a56d-3a8361611a31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 206 entries, 0 to 205\n",
            "Data columns (total 15 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   Participant_ID                206 non-null    int64  \n",
            " 1   Song_name                     206 non-null    object \n",
            " 2   Artist                        206 non-null    object \n",
            " 3   Spotify_ID                    206 non-null    object \n",
            " 4   Loudness                      206 non-null    float64\n",
            " 5   Valence                       206 non-null    float64\n",
            " 6   Danceability                  206 non-null    float64\n",
            " 7   Acousticness                  206 non-null    float64\n",
            " 8   Instrumental                  206 non-null    float64\n",
            " 9   Audio_class                   206 non-null    object \n",
            " 10  Lyrics                        206 non-null    object \n",
            " 11  Sentiment_class               206 non-null    object \n",
            " 12  Audio + Lyrics analysis       206 non-null    object \n",
            " 13  Total_mental_health           206 non-null    int64  \n",
            " 14  Mental_health_severity_class  206 non-null    object \n",
            "dtypes: float64(5), int64(2), object(8)\n",
            "memory usage: 24.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Renaming target variable as 'Target' for convenience\n",
        "df = df.rename(columns={\"Mental_health_severity_class\": \"Target\"})\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylXtaIYNq5v0",
        "outputId": "b13678b5-1f62-4cc6-c3ad-46ef335a4f97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Participant_ID', 'Song_name', 'Artist', 'Spotify_ID', 'Loudness',\n",
              "       'Valence', 'Danceability', 'Acousticness', 'Instrumental',\n",
              "       'Audio_class', 'Lyrics', 'Sentiment_class', 'Audio + Lyrics analysis',\n",
              "       'Total_mental_health', 'Target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "irrelevant_colms = ['Participant_ID','Spotify_ID', 'Song_name', 'Artist', 'Lyrics']\n",
        "\n",
        "for colm in irrelevant_colms:\n",
        "  df = df.drop(colm, axis=1)"
      ],
      "metadata": {
        "id": "uj9miX5My4Ls"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for duplicate records\n",
        "print(df.duplicated().sum())\n",
        "\n",
        "# Removing duplicates\n",
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qa1ClJ4AildG",
        "outputId": "c0a72c36-3bfc-47fd-8b37-40c9cf3574f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_colms = ['Loudness', 'Valence', 'Danceability', 'Acousticness', 'Instrumental', 'Total_mental_health']\n",
        "non_numeric_colms = ['Audio_class',\t'Sentiment_class',\t'Audio + Lyrics analysis', 'Target']"
      ],
      "metadata": {
        "id": "B6-JYF8F0gOw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min = 0\n",
        "max = 1\n",
        "\n",
        "# Checking if the values of features are in range\n",
        "\n",
        "for colm in ['Valence', 'Danceability', 'Acousticness']:\n",
        "  # Check if each value in the column falls within the specified range\n",
        "  is_in_range = (df[colm] >= min) & (df[colm] <= max)\n",
        "  print(f\"{colm}: {sum(is_in_range)} values within range\")\n",
        "\n",
        "is_in_range = (df['Loudness'] >= -60) & (df['Loudness'] <=0)\n",
        "print(f\"Loudness: {sum(is_in_range)} values within range\")\n",
        "\n",
        "# Checking if the values of target are in range\n",
        "is_in_range = (df['Total_mental_health'] >= 0) & (df['Total_mental_health'] <=27)\n",
        "print(f\"Total Mental Health: {sum(is_in_range)} values within range\")\n",
        "print('')\n",
        "\n",
        "for column in non_numeric_colms:\n",
        "    unique_values = df[column].unique() # get unique values in the column\n",
        "    print(f\"Column {column} has {len(unique_values)} unique values: {unique_values}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HIkUnHiJ0IuR",
        "outputId": "65b41f1e-3357-4dc7-84d8-b4b08ef13f4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valence: 202 values within range\n",
            "Danceability: 202 values within range\n",
            "Acousticness: 202 values within range\n",
            "Loudness: 202 values within range\n",
            "Total Mental Health: 199 values within range\n",
            "\n",
            "Column Audio_class has 4 unique values: ['Sad' 'Tense' 'Relax' 'Happy']\n",
            "Column Sentiment_class has 2 unique values: ['Positive' 'Negative']\n",
            "Column Audio + Lyrics analysis has 4 unique values: ['Sad' 'Exciting,Disturbing' 'Relax' 'Happy']\n",
            "Column Target has 3 unique values: ['Low' 'Med' 'High']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The possible range of Total Mental Health according PHQ-9 is 0 to 27. Therefore, the rows with out of range values are removed."
      ],
      "metadata": {
        "id": "CvynauS51byP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove records where the specified column has the specified value, modifying the original DataFrame\n",
        "df.drop(df[df['Total_mental_health'] == 28].index, inplace=True)\n",
        "\n",
        "# Filter the DataFrame to include only the records where the target value is less than 28\n",
        "df = df[df['Total_mental_health'] <= 27]\n",
        "\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "d_xcY0oY0fFv",
        "outputId": "621fec31-69b6-4811-fb5a-c93e0515fa81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Loudness     Valence  Danceability  Acousticness  Instrumental  \\\n",
              "count  199.000000  199.000000    199.000000    199.000000    199.000000   \n",
              "mean    -8.163688    0.357760      0.522327      0.303768      0.028793   \n",
              "std      3.298418    0.220756      0.169762      0.324850      0.139195   \n",
              "min    -25.268000    0.036400      0.124000      0.000059      0.000000   \n",
              "25%     -9.238000    0.181000      0.400000      0.021450      0.000000   \n",
              "50%     -7.831000    0.318000      0.489000      0.147000      0.000000   \n",
              "75%     -5.962000    0.499500      0.662000      0.545500      0.000016   \n",
              "max     -1.061000    0.930000      0.905000      0.995000      0.924000   \n",
              "\n",
              "       Total_mental_health  \n",
              "count           199.000000  \n",
              "mean             12.859296  \n",
              "std               3.840284  \n",
              "min               7.000000  \n",
              "25%              10.000000  \n",
              "50%              12.000000  \n",
              "75%              15.000000  \n",
              "max              23.000000  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loudness</th>\n",
              "      <th>Valence</th>\n",
              "      <th>Danceability</th>\n",
              "      <th>Acousticness</th>\n",
              "      <th>Instrumental</th>\n",
              "      <th>Total_mental_health</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>199.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>199.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-8.163688</td>\n",
              "      <td>0.357760</td>\n",
              "      <td>0.522327</td>\n",
              "      <td>0.303768</td>\n",
              "      <td>0.028793</td>\n",
              "      <td>12.859296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.298418</td>\n",
              "      <td>0.220756</td>\n",
              "      <td>0.169762</td>\n",
              "      <td>0.324850</td>\n",
              "      <td>0.139195</td>\n",
              "      <td>3.840284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-25.268000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <th>50%</th>\n",
              "      <td>-7.831000</td>\n",
              "      <td>0.318000</td>\n",
              "      <td>0.489000</td>\n",
              "      <td>0.147000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-5.962000</td>\n",
              "      <td>0.499500</td>\n",
              "      <td>0.662000</td>\n",
              "      <td>0.545500</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-1.061000</td>\n",
              "      <td>0.930000</td>\n",
              "      <td>0.905000</td>\n",
              "      <td>0.995000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-09ca5611-c104-499d-9283-46c54bc48669 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-09ca5611-c104-499d-9283-46c54bc48669');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "**Convert Categorical Features to Numerical Features using Label Encoding**\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "drYxIO3trKu1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "\n",
        "for colm in ['Audio_class',\t'Sentiment_class',\t'Audio + Lyrics analysis']:\n",
        "  df[colm] = le.fit_transform(df[colm])\n",
        "\n",
        "labels = {'Low': 0, 'Med': 1, 'High':2}\n",
        "\n",
        "encoder = LabelEncoder().fit(df['Target'])\n",
        "df['Target'] = df['Target'].map(labels)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "EmR6R0xHn4j2",
        "outputId": "6c55358f-08d7-4983-cdb8-e094f8e49bd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Loudness  Valence  Danceability  Acousticness  Instrumental  Audio_class  \\\n",
              "0    -8.210    0.103         0.124        0.0128      0.000000            2   \n",
              "1    -8.128    0.133         0.449        0.1470      0.000000            2   \n",
              "2    -7.395    0.218         0.389        0.6780      0.000001            2   \n",
              "3    -5.952    0.295         0.616        0.0480      0.000119            3   \n",
              "4   -10.965    0.562         0.701        0.3280      0.130000            1   \n",
              "\n",
              "   Sentiment_class  Audio + Lyrics analysis  Total_mental_health  Target  \n",
              "0                1                        3                    8       0  \n",
              "1                1                        3                    8       0  \n",
              "2                1                        3                    8       0  \n",
              "3                1                        0                   15       1  \n",
              "4                1                        2                   15       1  "
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              "      <th></th>\n",
              "      <th>Loudness</th>\n",
              "      <th>Valence</th>\n",
              "      <th>Danceability</th>\n",
              "      <th>Acousticness</th>\n",
              "      <th>Instrumental</th>\n",
              "      <th>Audio_class</th>\n",
              "      <th>Sentiment_class</th>\n",
              "      <th>Audio + Lyrics analysis</th>\n",
              "      <th>Total_mental_health</th>\n",
              "      <th>Target</th>\n",
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              "      <td>-8.210</td>\n",
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              "      <th>2</th>\n",
              "      <td>-7.395</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1e1c7936-fa58-4e9c-ad4f-f520cea170bf');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
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      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Checking for Outliers and Handling**\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "8y_w-3zSu_kb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "HTbvsczebNbJ",
        "outputId": "0ddc6df4-29ad-4b69-cb38-77afe99ba188"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(12, 8))\n",
        "\n",
        "# Creating boxplots for analyzing dataset for outliers\n",
        "for ax, col in zip(axs.flatten(), numeric_colms):\n",
        "    ax.boxplot(df[col])\n",
        "    ax.set_title(col)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "lyLc-qVYb0Jy",
        "outputId": "3c789e4a-6939-4c24-9253-14a6ebd1387d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.set_style(\"whitegrid\")\n",
        "fig, axs = plt.subplots(ncols=3, nrows=2, figsize=(12, 8))\n",
        "\n",
        "# Looping through columns and creating density plot for each\n",
        "for i, col in enumerate(numeric_colms):\n",
        "    row = i // 3\n",
        "    pos = i % 3\n",
        "    sns.kdeplot(data=df[col], ax=axs[row, pos])\n",
        "    axs[row, pos].set_title(col)\n",
        "\n",
        "# Adjusting spacing and displaying plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8sl0Wc61KFt7",
        "outputId": "4ae0bb70-1cb3-43f2-b4d7-5adaac54cd28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column Loudness has 3 outliers.\n",
            "Column Valence has 0 outliers.\n",
            "Column Danceability has 0 outliers.\n",
            "Column Acousticness has 0 outliers.\n",
            "Column Instrumental has 5 outliers.\n",
            "Column Total_mental_health has 0 outliers.\n"
          ]
        }
      ],
      "source": [
        "# Function to count outliers using z-score\n",
        "def count_outliers_zscore(data, threshold=3):\n",
        "    z_scores = np.abs((data - data.mean()) / data.std())\n",
        "    return z_scores[z_scores > threshold].count()\n",
        "\n",
        "# Counting outliers in each column\n",
        "for col in numeric_colms:\n",
        "    n_outliers = count_outliers_zscore(df[col])\n",
        "    print(f\"Column {col} has {n_outliers} outliers.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loop through non-numeric columns\n",
        "for col in numeric_colms:\n",
        "    # Replace outliers with the mean of non-outlier values\n",
        "    non_outliers = df[col][np.abs(df[col] - np.mean(df[col])) < 2 * np.std(df[col])]\n",
        "    mean_non_outliers = np.mean(non_outliers)\n",
        "    df.loc[np.abs(df[col] - np.mean(df[col])) >= 2 * np.std(df[col]), col] = mean_non_outliers"
      ],
      "metadata": {
        "id": "cLv6g5FDB-C_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting outliers in each column\n",
        "for col in numeric_colms:\n",
        "    n_outliers = count_outliers_zscore(df[col])\n",
        "    print(f\"Column {col} has {n_outliers} outliers.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ooIBBowJI46",
        "outputId": "71fb942d-e590-4faa-e7c4-3f21d4a6bf35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column Loudness has 0 outliers.\n",
            "Column Valence has 0 outliers.\n",
            "Column Danceability has 0 outliers.\n",
            "Column Acousticness has 0 outliers.\n",
            "Column Instrumental has 6 outliers.\n",
            "Column Total_mental_health has 0 outliers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check for zero or negative values\n",
        "zero_neg_values = (df['Instrumental'] <= 0).sum()\n",
        "if zero_neg_values > 0:\n",
        "    # replace zero or negative values with a small positive value\n",
        "    small_value = 1e-7\n",
        "    df.loc[df['Instrumental'] <= 0, 'Instrumental'] = small_value\n",
        "\n",
        "# apply the logarithm transformation\n",
        "df['Instrumental'] = np.log(df['Instrumental'])\n",
        "\n",
        "# count the outliers\n",
        "instrumental_outliers = count_outliers_zscore(df['Instrumental'])\n",
        "print(f\"Column Instrumental has {instrumental_outliers} outliers.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZa2aNC0c1xi",
        "outputId": "d27b2d5c-3624-41bd-b609-663ea2cbb396"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column Instrumental has 0 outliers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style(\"whitegrid\")\n",
        "fig, axs = plt.subplots(ncols=3, nrows=2, figsize=(12, 8))\n",
        "\n",
        "# Looping through columns and creating density plot for each\n",
        "for i, col in enumerate(numeric_colms):\n",
        "    row = i // 3\n",
        "    pos = i % 3\n",
        "    sns.kdeplot(data=df[col], ax=axs[row, pos])\n",
        "    axs[row, pos].set_title(col)\n",
        "\n",
        "# Adjusting spacing and displaying plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "bLxRBobAs67w",
        "outputId": "7bc8869a-4dd2-42da-b5c1-8b8d043e5b56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 6 Axes>"
            ],
            "image/png": 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Zog7Yu3dvs6WxVZ577jmcPXsWjz76KB599FFIJBJ89913qK+vx//7f/9Pvd3YsWPh4eGBf/zjH0hNTYVEIsHevXvh6OjYbLSUk5MTnnjiCWzZsgXPPPMMJkyYgBs3buDkyZN3vXp+NzY2NnjrrbewatUqzJkzB9OnT4eTkxNyc3Px+++/Y+jQofjnP/+J9PR0LF68GNOmTcPAgQMhkUgQHR2NwsJCzJgxAwCwf/9+fPPNN5g8eTK8vb1RVVWFPXv2wMbGptudExGRIfHy8kJYWBiOHTsGAM2SUvfccw8+/fRTvPrqqwgLC0NycjIOHjzY7Gp0W9588008+uijmDVrFubPnw8vLy8UFhYiLi4Ot2/fxoEDBzoVp42NDV599VW8/vrreOihhzBz5kzY2dkhMTERtbW12LBhA8RiMd5991089dRTmDlzJubMmQN3d3fk5+fj/PnzsLGxwX/+85/OvUBERHrk5MmTSE1NhUwmQ2FhIc6fP48zZ87Aw8MD//73v2Fubg5zc3OMGDECW7duRUNDA9zd3XHmzJlu1V/q27cvli1bhs8++wyPPvoopk6dCjMzM8THx8PNzQ0rV67s8Hd9GxubTseXnZ2NZcuWYdy4cYiLi8OBAwcwc+ZM+Pv7d7lNgLLg+sCBA3HkyBH4+PjAwcEBgwYNgq+vr3qbyMhIbNiwAQA4dc/AMSlF1AHffPNNq/fPmTMHu3fvxsaNG7FlyxYoFAoEBwfj//7v/9QrWQDKqRabN2/G2rVr8fHHH8PV1RWPP/447Ozs8Oqrrzbb54svvggzMzN8++23OH/+PIKDg7Ft2zY888wzXY5/1qxZcHNzw+eff44vvvgC9fX1cHd3x/DhwzFnzhwAQK9evTBjxgzExMTgwIEDkEgk6N+/Pz766CPcd999AJSFzuPj43H48GEUFhbC1tYWwcHB+OCDDzp0MkVEZExmzZqF2NhYBAcHN5sat2zZMtTU1ODgwYM4fPgwBg8ejC1btmDjxo3t7nPgwIHYu3cvNm/ejP3796O0tBROTk4YPHgwli9f3qU4582bB2dnZ3z++ef47LPPYGJigv79+2Px4sXqbUaNGoXvvvsOn332Gb766itUV1fD1dUVwcHBePjhh7t0XCIifbFp0yYAyu/0Dg4O8PX1xWuvvYY5c+Y0K769ceNGvPPOO/j666+hUCgwduxY/Pe//22zDmBHvPDCC/D09MRXX32Ff/3rX7C0tISfnx9mz56t3qYj3/W7Et9HH32Ejz/+GBs3boSJiQkWLlyIVatWdbktd3r33XfxzjvvICoqCg0NDVixYkWzpNSsWbPU5xiq6e9kmESKzlRRIyIiIiIiIiLqQcXFxRg3bhyeffbZLl90If3AmlJEREREREREpDP2798PmUzWbEQYGSZO3yMiIiIiIiIiwcXExODWrVv4z3/+g8mTJ3d7pT/SfUxKEREREREREZHgPvvsM8TGxiIsLAxvvPGG0OGQFrCmFBER6aUtW7bg119/RWpqKiwsLBAWFoZXXnkF/fv3v+vzjhw5go8//hg5OTnw8fHBK6+8ggkTJqgfVygU2LRpE/73v/+hvLwcQ4cOxVtvvQUfH58ebhEREWlDV/qPffv2tVicRrUKGhERdR1rShERkV66cOECFixYgD179mD79u1obGzE0qVLUV1d3eZzLl++jJUrV+Khhx7CDz/8gHvvvRfLly9HcnKyepv//ve/2LVrF9566y3s2bMHlpaWWLp0Kerq6rTRLCIi6mFd6T8AwMbGBqdPn1b/HD9+XEsRExEZLo6UIiIig1BcXIwxY8bgq6++wogRI1rd5sUXX0RNTQ22bNmivm/+/Pnw9/fH22+/DYVCgXHjxmHJkiVYunQpAKCiogLh4eFYv349ZsyYoZW2EBGR9nSk/9i3bx/WrVuHP/74Q8vREREZNtaU6iK5XI7GxkaIxWKIRCKhwyEi6lEKhQJyuRwmJiYQi3VzkG1FRQUAwN7evs1t4uLisHjx4mb3RUREIDo6GgCQnZ0NqVSK8PBw9eO2trYICQlBbGxsh5JS7B+IyJjoQ//Qno70HwBQXV2NiRMnQi6XY/DgwXj55ZcxaNCgDh+H/QMRGZOO9g9MSnVRY2Mj55ATkdEJCgqCmZmZ0GG0IJfLsW7dOgwdOhS+vr5tbldYWAgXF5dm9zk7O6OwsBAAIJVK1fe1tU172D8QkTHS1f6hPR3tP/r164d169bBz88PFRUV2LZtGx555BH89NNP6NWrV4eOxf6BiIxRe/0Dk1JdpMr0BQUFQSKRCBxN18lkMsTHx+t9O4TC16/7+Bp2nzZeQ9UxdPUq+Nq1a5GSkoKvv/5a6FB09jUiIupJ+vrZ19H+IywsDGFhYc1uT58+Hd9++y1efPHFDh1LX18jIqLuaO+zj0mpLlINuZVIJAZxIm0o7RAKX7/u42vYfdp4DXVxusHbb7+NEydO4Kuvvmr3arWLi0uLEU9FRUXq0VOurq7q+9zc3Jpt4+/v36F4VK+RKkloKIlXtkO3sB26xVDaAXS+LartdbF/aE9n+o+/MjU1RUBAADIzMzv8nL/2D39lSL9Hd8N2Gha203Bouo0d7R+YlCIiIr2kUCjwzjvv4OjRo9i1axe8vLzafU5oaCjOnTvXrK7U2bNnERoaCgDw9PSEq6srYmJiEBAQAACorKzElStX8Le//a1T8f01SWgoiVe2Q7ewHbrFUNoBGFZb/qor/cdfyWQyJCcnY8KECZ1+bnuvrSG/9ndiOw0L22k4tN1GJqWIiEgvrV27FocOHcJnn30Ga2trdT0oW1tbWFhYAABWrVoFd3d3rFy5EgDw2GOPYdGiRdi2bRsmTJiAw4cP49q1a3j77bcBKK9iP/bYY/j3v/+Nvn37wtPTEx9//DHc3NwwefJkYRpKREQa1ZX+Y/PmzQgNDUXfvn1RXl6OL774Arm5uZg3b55g7SAiMgRMShERkV765ptvAACLFi1qdn9UVBTmzJkDAMjLy2s2j33o0KH44IMP8NFHH+HDDz+Ej48PPv3002bFbZ966inU1NTgn//8J8rLyzFs2DBs3boV5ubmWmgVERH1tK70H+Xl5XjjjTcglUphb2+PIUOG4Ntvv8XAgQO1FzgRkQFiUoqIiPRSUlJSu9vs2rWrxX33338/7r///jafIxKJ8MILL+CFF17oVnxERKSbutJ/vPbaa3jttdd6KiQiIqPFJSCIiIiIiIiIiEjrmJQiIiIiIiIiIiKtY1KKiIiIiIiIiIi0jkkpIiIiIiIiIiLSOialiIiIiIiIiIhI6wRPSu3evRuTJk1CUFAQ5s2bh6tXr7a5bUpKCp577jlMmjQJfn5+2LFjR4ttVI/99Wft2rXqbRYtWtTi8X/+85890TwiIiIiIiIiImqFiZAHP3z4MKKiorB27VqEhIRg586dWLp0KX7++Wc4Ozu32L6mpgaenp6YNm0aoqKiWt3n999/D5lMpr6dkpKCJUuWYNq0ac22mz9/Pp5//nn1bUtLSw21ioiIiIiIiIiI2iNoUmr79u2YP38+5s6dCwBYu3YtTpw4gb179+Lpp59usX1wcDCCg4MBABs3bmx1n05OTs1uf/755/D29sbIkSOb3W9hYQFXV1dNNINIULekldgVk4FLGSVwtzPH1CG9MHeoJyRikdChERHpHZlcgeiEfBy9kY+EvHJU18vQx8ESw30c8fAIL/S250UsIiLSnAaZHHsvZeOn+DyUVNfDv5cdnhjbD4M97IQOjUgrBEtK1dfX4/r163jmmWfU94nFYoSHhyM2NlZjxzhw4ACWLFkCkaj5CfrBgwdx4MABuLq6YuLEiXj22We7NFrqzlFZ+kgVv763QyhCv37Hk6R48bs4VNYpjx+fA0QnFODw1Tz8a34w7CxNBYmrM4R+DQ2BNl5Dvj9kDM7eLMRbB68jOb+y2f1phVU4fbMQn/x2E4vDffDKVD9YmkkEipKIiAxFWXUD/r77Es7eKlLfdy2nHD/E5mDlVD/8/Z4BAkZHpB2CJaVKSkogk8laTNNzdnZGamqqRo4RHR2NiooKPPjgg83unzlzJjw8PODm5oakpCR88MEHSEtLw+bNmzt9jPj4eI3EKjRDaYdQhHj9kovq8Y/jxZArgAAXU0wbYIXcShn2J1TiRLIUD392Em/f4wRTiX6MmOLvYPfxNSTqGrlcgX9FJ2Pz8ZtQKAA7CxPMH+6F0f2dYW1ugtTCShyIy8X5tGJ8cToNx5MKsGPxSHg7WwkdOhER6am6RhkWbTuPq9llsDKTYPnEgRjgao19l3Pw6418bPg5EeYmYjwR0U/oUIl6lKDT93ra3r17MX78eLi7uze7/+GHH1b/38/PD66urli8eDEyMzPh7e3dqWMEBQVBItHfq6UymQzx8fF63w6hCPX6Vdc34uXNZyFXAPcNccdH80NgZqJct+DRnDIs2nYRycUN+CHLFO9FBmotrq7g72D3aeM1VB2DyNA0yuRYtfcq9l3OAQD8baQX1twfAPs7RpqOGeCMBaP64nhSAdbsvYpUaRXm/PsMvnxiFKdXEBFRl2w4koSr2WVwtDLF7idHq/uT+4b0wmcnbuH/fknCOz/dQH9Xa9zj5yZwtEQ9R7CklKOjIyQSCYqKiprdX1RUBBcXl27vPycnB2fPnsUnn3zS7rYhISEAgIyMjE4npSQSiUGcSBtKO4Si7ddv8/FkZBRVo7e9Bd5/KASW5n+ePIV4O+Hjv4XhiR0X8e3FbEwd0guT/N3vsjfdwN/B7uNrSNQ5crkC/+/7q9gfmwOJWIQNc4Px0DDPNref6OeGAysisHj7RSTkleOxbRew9+9j0NfZWotRExGRvjuXWoRtZ9IAAB/MC2l2gUMkEuHZewYgu6QG31zIxGv74nH05QmwNjfo8SRkxMRCHdjMzAxDhgxBTEyM+j65XI6YmBiEhYV1e//79u2Ds7Mz7rnnnna3TUhIAAAWPie9UFxVjy9jMgAA78wObHY1X2WinxuebBrq+9aBG6htYD0gIqK/2vBLIvbH5sBELMK/Fwy9a0JKxd3OAt8+PRoBve1QWFmHx7ddQFlNgxaiJSIiQ6BQKBB1JBEA8LeR3rg3oOXFY5FIhDdmBsDLyRK5ZbX44NckbYdJpDWCJaUAYMmSJdizZw/279+PW7du4a233kJNTQ3mzJkDAFi1alWzVfbq6+uRkJCAhIQE1NfXIz8/HwkJCcjIyGi2X7lcjn379iEyMhImJs0zypmZmfj0009x7do1ZGdn49ixY1i9ejVGjBgBf3//nm80UTdtO52GmgYZgvrY496AtofyvjDZF+525sgsrsYXp9O0GCERke47dDUPW35X1rB8/6FgTB3Sq8PPtbc0xc4nRqCPgyXSi6rx8ndxkMsVPRUqEREZkF+u5+NKVimszCR4eYpvm9tZmZng3cggAMCumAxkFVdrK0QirRI0KTV9+nSsXr0amzZtwuzZs5GQkICtW7eqp+/l5eVBKpWqty8oKEBkZCQiIyMhlUqxbds2REZG4vXXX2+237NnzyI3Nxdz585tcUxTU1PExMRg6dKluP/++7FhwwZMnToV//nPf3q2sUQaUF3fiJ0x6QCA5RMHtFhV8k425iZYc78y0fr5yVRU1PJKPhERAORWNOIfP1wDoPwsnTO0/RFSf+Vma4Eti4bBzESMY4kF2HpaM4u0EBGR4VIoFNh0LAUAsDSiH1xtze+6/QRfV4wb5IJGuQKf/JaijRCJtE7wiakLFy7EwoULW31s165dzW57enoiKan9oYsRERFtbte7d2989dVXnQ+USAf8ej0fFbWN8HaywtTB7V/VfyCkDz757SZSpVX4MiYDyycO1EKURES6q65Bhg/PlaKyToaR/Zzw0uS2r1K3J7CPPd6aNQSv7Y/HB78kY7yvK/x7sfA5ERG17lJGCW7klcPCVIylHVxV76UpvjiVUoi9l3OwfOJA1jEkgyPoSCki6py9l7MBAHOG9oFY3PYoKRWJWITnJikTUVtPpaKmnrWliMi4rTuShLTSRjhZm+GTv4XBRNK9r0J/G+mFyQFuqJfJ8fJ3V9Aok2soUiIiMjQ7m+rCzg7pAwcrsw49Z6i3I8b7ukImV2D7mfQejI5IGExKEemJ22W1OH2zEAAwJ6zjU01mBXvAy8kSJdUN+CEup6fCIyLSeWduFuKr85kQAfhwXjDc7Sy6vU+RSISoOcGwtzTFjbxy7DqX0f6TiIjI6BRU1OJIfB4AYNGYvp167lPjlKOqvr+Ujcq6Ro3HRiQkJqWI9MSBKzlQKICRPk7wdrbq8PNMJGI8NtoHALDzbDoUChbjJSLjU13fiDX7rgIApg20wrhBLhrbt6utOVZN8wMAfPhrMgrKazW2byIiMgwHr+ShUa5AmLcDAvvYd+q5EQNdMNDNBpV1jdh7KbuHIiQSBpNSRHoi+kYBAGBmSO9OP3f+cC9YmkqQeLsC59OKNR0aEZHO++CXZGQV18DDwQILAm00vv9HRngjxNMeFXWNeO9wgsb3T0RE+u3HphkLD4b16fRzRSIRHm8aXfVlDC8yk2FhUopID5RU1eOPDGUyaZK/W6efb29lisimDvDbC5kajY2ISNddzizB9rNpAIB3Zw+Bpanmv/5IxCK8GxkEkQj4MS4XZ5umWxMREaVKK3E1uwwSsQjTgzp/gRkAIsP6wMJUjFvSKsRmlWo2QCIBMSlFpAd+T5ZCrgD8e9nC07HjU/fuNH+4sg7Vz9dvo6K2QZPhERHprAaZHK/ujYdCoVwkYoKva48dK8jTHotGK69krz14AzI5r2QTERFw4EouAGDcIBe42Jh3aR+2FqaYHqhMaH3PKXxkQJiUItIDxxKVU/e6MkpKJdTLAf1drVHbIMeRa7c1FRoRkU77MiYDSfkVcLQyxRszBvf48VZO8YO9pSmS8ivUK6YSEZFx+7npu/esYI9u7eehpovMB6/koraBq2qTYWBSikjHyeQK/J6kTErdG9D1pJRIJMLcocqOjAUSicgYFFTU4qOjyQCAVdP84WjdseW3u8PeyhQrJg4EoCx6XlPPkwYiImOWVVyNxNsVkIhF3fouDwCj+zmjj4MlKmobcSyhQEMREgmLSSkiHZeQV47y2kbYmpsgxNOhW/uKDOsDkQg4n1aMrOJqzQRIRKSjNhxJQkVdI4I97TF/uJfWjrtoTF/0cbDE7fJabDuTprXjEhGR7olumvEwvK8jHKy6d3FELBapFz06HJ/X7diIdAGTUkQ6LuZWEQBgRD8nmEi69yfbx8ESY/o7AwD2x+Z0OzYiIl11KaNYPX3u7dmBkIhFWju2hakE/+8+PwDAv0/cQlFlndaOTUREukU1omnKYHeN7G9GU6H03xILOBqXDAKTUkQ67lyqMimlSiZ1l2oK377L2VxOlogMkkyuwD9/vA4AeHi4F0K9HLQewwMhHhjiYYfKukZsPn5T68cnIiLhVdXLcTG9BIDmklJBfezh5WSJmgYZjidxCh/pPyaliHSYTK7AhbRiAMBoDSWlpgX2gpWZBOlF1bicWaKRfRIR6ZJvLmTiem457CxMsGqanyAxiMUirJ7mDwDYfT4TeWU1gsRBRETCiS+oR6Ncgf6u1ujrbK2RfYpEIkxvGi31E6fwkQFgUopIh13PLUNFXSNsLUww2MNOI/u0NjfBtCG9AAAHr7AjI/118eJFLFu2DBEREfDz80N0dPRdt1+zZg38/Pxa/MyYMUO9zSeffNLi8WnTpvV0U0iDSqvr8cGvSQCAlVP94NzFpbc1YdwgF4z0cUJ9oxyfcrQUEZHRuZKvnL49fpCrRvernsKXwCl8pP+YlCLSYapRUiN9nDRaD0V1deXna7chl3MKH+mn6upq+Pn54c033+zQ9v/4xz9w+vRp9c/vv/8OBweHFkmnQYMGNdvu66+/7onwqYdsOnYTpdUN8O9liwWjvAWNRSQS4eWpvgCA7y5mIbuEC0wQERkLhUKBuNv1AIDxvi4a3XdQH3t4OnIKHxkGJqWIdFhsVikAYGhfR43uN2KQC2zMTXC7vFZ9DCJ9M2HCBLz00kuYMmVKh7a3tbWFq6ur+ufatWsoKyvDnDlzmm0nkUiabefk5NQT4VMPSC+swq5z6QCA12cM7vbiEJowur8zwgc4o0GmwCfHOFqKiMhYZBRXo6BaBlOJCKP6aaYMh4pIJFKPluIUPtJ3wn9bI6I2xWWWAgDCvB00ul8LUwnuDXADABxhR0ZG6vvvv0d4eDj69OnT7P6MjAxERETg3nvvxcqVK5GbmytQhNRZG35ORINMgXv8XBExSLNXpbtjZdNoqe8vZyOjqErgaIiISBtOpygXKxrq7QhrcxON7386p/CRgdD8XwcRaUR+eS1ySmsgFgHBng4a3//9gb3xY1wujly7jX/MCIBIpL3l0omElp+fj5MnT+KDDz5odn9wcDCioqLQr18/SKVSfPrpp1iwYAEOHjwIGxubTh1DJpO1+q++0vV2/JFRgiPXbkMsAlbd59tmnEK0I9TTHuMHueBkSiE+ik7GBw8Fd3ufuv5+dBTboXs62xZDaDNRT4hpWkF77ADNjpJSCfa0Rx8HS+SU1uDMzUJM1tDqfkTaxqQUkY6KbRol5etuC5seuLpyj58rrMwkyCmtwdXsMoQIsGQ6kVB++OEH2NraYvLkyc3unzBhgvr//v7+CAkJwcSJE3HkyBHMmzevU8eIj4+/6219pYvtUCgUeP03ZQ2+ST6WqMm7hbh2BoFqux0zvBU4mQL8GJuLe9zq4Wmnmc91XXw/uoLt0D2G1BYibVMoFLiYrlzlelS/nikDIBKJMDnADTtjMnAsMZ9JKdJbTEoR6ai4plpPYd6arSelYmEqwUR/N/x0NQ+Hr+UxKUVGQ6FQYO/evZg9ezbMzMzuuq2dnR18fHyQmZnZ6eMEBQVBIpFAJpMhPj5efVtf6XI7forPQ0pxPqzMJHhn/ii42Vm0ua1Q7QgFcDTnMqITC3A0zwQfjw/t1v50+f3oDLZD93S2LartiehPqYVVKKqqh6kYCPK077Hj3BvgrkxKJRRALldArMGFkYi0hUkpIh0Vm6m8uhLWg8mi6YG98dPVPByJv4010/w5hY+MwoULF5CRkYGHHnqo3W2rqqqQlZUFV9fOL+UskUiandD99ba+0rV21DXK8H+/JgMAnhk/AL0drTv0PCHa8dJUX0QnFuCn+Nt4blI1/HrZdnufuvZ+dBXboXsMqS1E2qZaQdvX2RTmJj1XxnlUfydYm0lQUFGHa7llPVLyg6insdA5kQ6SyxW4llMGAD06gukeP1dYmIqRWVyN67nlPXYcop5QVVWFhIQEJCQkAACys7ORkJCgLky+ceNGrFq1qsXzvv/+e4SEhMDX17fFYxs2bMCFCxeQnZ2Ny5cvY8WKFRCLxZg5c2bPNoa6bFdMBrKKa+Bma46nxvcTOpy7GuJhj/sDe0GhAP51NFnocIiIqIeoklIBLncfkd1d5iYSjPdVXjiLTijo0WMR9RQmpYh0UHpRFarqZTA3EWOAa8eu+neFtbkJxg9SdmRHb+T32HGIesK1a9cQGRmJyMhIAEBUVBQiIyOxadMmAIBUKkVeXvPCQhUVFfj111/bHCV1+/ZtvPzyy5g2bRpefPFFODg4YM+ePXBy6pl6ENQ91fWN+PeJWwCAl6b4wspM9weAvzTFFyIR8PP12+qLD0REZFhUSanBrj2blAKASf7KFbWPJfC7POkn3f/2RmSEVKOWAnrbwUTSs7njKYPd8euNfBy9kY+XprQcOUKkq0aNGoWkpKQ2H1+/fn2L+2xtbXHlypU2n/Ovf/1LI7GRdnwZk4Giqnp4O1nhoWGeQofTIb7utpgV7IEDV3Lx4dFkbFs8QuiQiIhIg7JLqpFTWgOJWARfZ9MeP95EfzeIRMrzh7yyGvS2t+zxYxJpEkdKEemga7nKq+eBfex6/FiT/N0gFgE38sqRU1rT48cjItKEyrpGbPldOUrq+XsHwbSHE/ia9OLkQZCIRfgtsQCXMkqEDoeIiDToYrpylFSghx0se7CelIqLjbm6Bu0xTuEjPaQ/3+CIjMj1HOVIqSEePbdah4qzjTmG9VWu8BfNKXxEpCd2nk1HSXUD+rlYIzLUQ+hwOqW/qw3mDu0DAPjwaNuj/YiISP+opu6N8OmZFbRbc2+AOwBO4SP9xKQUkY5RKBR/jpTSQlIKUE7hA4BodmREpAcqahvw+clUAMAL9w7q8WnOPeG5SYNgKhHhzM0inL1VKHQ4RESkIX8mpbRXj3JyU1LqzK0iVNc3au24RJqgf9/iiAxcblktSqsbYCIWwbeXjVaOqerIzqUWoby2QSvHJCLqql3nMlBW04ABrtaYFaJfo6RUvJys8LeR3gCAjb8mQ6FQCBwRERF1V2FlHW5JqwAAw/s6aO24vu426ONgifpGOc7eLNLacYk0gUkpIh1zvWk1poFuNjA3kWjlmP1dbTDA1RoNMgV+T5Jq5ZhERF1R3yjHzrPpAIBn7xkIiVgkbEDdsGLiQJibiHEpowQn+NlLRKT3LjaNkvLvZQsHq55feU9FJBKpV+H7LYl1pUi/MClFpGNSCioBKFfe06bJTVP4jrKuFBHpsENXc5FfXgc3W3O9HSWl4mZngcfDfQAAH/yaxNFSRER6TrV4xXAt1pNSUSWljicWsD8hvcKkFJGOSbpdAQAY5K6dqXsqU5uSUseTCtAgk2v12EREHaFQKLD1VBoA4PFwH5hpYVWjnrZswgBYm0lwPbccP1+7LXQ4RETUDbFZpQCAMC/tJ6XGDHCGhakYeWW1SGw6nyDSB/r/bY7IwCTnKzsRXzdbrR431MsRztZmqKhtVBdoJCLSJTG3inAjrxyWphIsGOUtdDga4WRthqUR/QAAHx5NhkzOq9tERPqoQSbHtaYyHGHeDlo/voWpBOEDXAAAvyVyCh/pD8GTUrt378akSZMQFBSEefPm4erVq21um5KSgueeew6TJk2Cn58fduzY0WKbTz75BH5+fs1+pk2b1myburo6rF27FqNGjUJYWBiee+45FBZy5RsSXqNMjtSm4oh+vbSblJKIRbg3QDnsl1P4iEgXbT2tHCX10DBPrdbq6GlLx/WHvaUpUgoqceBKjtDhEBFRFyTmVaCuUQ57S1P0c7EWJIaJd0zhI9IXgialDh8+jKioKCxfvhz79++Hv78/li5diqKi1lcMqKmpgaenJ1auXAlXV9c29zto0CCcPn1a/fP11183e3zdunU4fvw4PvroI+zatQsFBQVYsWKFRttG1BXpRdWol8lhaSpBHwdLrR9ftQrf0Rv5nItORDrlZkElfkssgEgEPNE0sshQ2Fua4unx/QEA/zqawinURER6KDZLWU8qxMsBIpEwi3Co6kpdzixBSVW9IDEQdZagSant27dj/vz5mDt3LgYOHIi1a9fCwsICe/fubXX74OBgrF69GjNmzICZWdtXSCUSCVxdXdU/Tk5O6scqKiqwd+9erFmzBmPGjEFgYCDWrVuH2NhYxMXFabqJRJ2Skv9nPSmxACtKjRvkCnMTMXJKazgXnYh0yjcXMgEA9/q7CXYFuictGesDFxszZBZX49uLWUKHQ0REnRSXWQoACPNyECyGPg6W8O9lC7kCOJnCVV1JP5gIdeD6+npcv34dzzzzjPo+sViM8PBwxMbGdmvfGRkZiIiIgLm5OUJDQ7Fy5Up4eChX6Ll27RoaGhoQHh6u3n7AgAHw8PBAXFwcQkNDO3UsmUzWrViFpopf39shFE2/fkm3ywEAg9xsBHlPzCRAxEBnHEuU4pdrefB16/kTP/4Odp82XkO+PySkukYZ9l3OBgA8aiC1pP7KyswEz987CP/88To+jk7Gg2F9YGMu2Nc0IiLqpLimIuehAtSTutNEfzck3q7Ab4kFmB3aR9BYiDpCsG87JSUlkMlkcHZ2bna/s7MzUlNTu7zf4OBgREVFoV+/fpBKpfj000+xYMECHDx4EDY2NigsLISpqSns7OxaHFcq7Xw2OT4+vsux6hJDaYdQNPX6XUgqBQBYN5YLNnLP16YOxwAcupyBcU6VWjsufwe7j68hGapfruejpLoBve0tMMHXTehweszfRnpj+5l0pBVW4fOTqXh5iq/QIRERUQeUVtcjtVBZFzbU00HQWCb5u+HfJ27h92QpGmVymEgELyNNdFcGdwluwoQJ6v/7+/sjJCQEEydOxJEjRzBv3jyNHy8oKAgSiUTj+9UWmUyG+Ph4vW+HUDT9+kl/Pw0AmBDqi1C/tuum9aQ+A+rw7z+O42ZJA3r394e7nUWPHo+/g92njddQdQwiIexpms42b7gXJAJMbdYWU4kYq+7zw993X8Z/T6Zi4ShvuPXwZzCRMdqyZQt+/fVXpKamwsLCAmFhYXjllVfQv3//uz7vyJEj+Pjjj5GTkwMfHx+88sorzc49yHipRkn1c7GGo7WwC3GEeTnA3tIUpdUNiM0qxQgfp/afRCQgwZJSjo6OkEgkLYqaFxUVwcXFRWPHsbOzg4+PDzIzlbUoXFxc0NDQgPLy8majpYqKiu5aPL0tEonEIE6kDaUdQtHE61ffKEda0xWWAA97wd6PXg5WCPFywJWsUvyeUoS/jdTOVBn+DnYfX0MyRLfLanHmlnKF3HnDPAWOpudNC+yFMG8HxGaW4l/RKYiaEyR0SEQG58KFC1iwYAGCgoIgk8nw4YcfYunSpfjpp59gZWXV6nMuX76MlStX4uWXX8bEiRNx8OBBLF++HPv27YOvL0c1Gjv11D0B60mpmEjEmODrigNXcvFbYgGTUqTzBBvLZ2ZmhiFDhiAmJkZ9n1wuR0xMDMLCwjR2nKqqKmRlZakTToGBgTA1NW123NTUVOTm5na6nhSRJqUXVaFRroCtuQl62wt7ZXxKgHJ6TPSNfEHjICI6cCUHCgUwwscRXk6tnywaEpFIhNemBwAAvruYiZsFXHSCSNO++OILzJkzB4MGDYK/vz/Wr1+P3NxcXL9+vc3nfPnllxg3bhyefPJJDBgwAC+++CIGDx6Mr776SouRk66KVRU5F7ielIpqFb7jiQUCR0LUPkGn7y1ZsgSrV69GYGAggoODsXPnTtTU1GDOnDkAgFWrVsHd3R0rV64EoCyOfuvWLfX/8/PzkZCQACsrK/Tt2xcAsGHDBkycOBEeHh4oKCjAJ598ArFYjJkzZwIAbG1tMXfuXKxfvx729vawsbHBu+++i7CwMCalSFDJTSvvDXS3EWwZWZV7A9zxwa/JOH2zEDX1MliacfQNEQljf2wuACAyzHiKtY7wccLUwe749UY+1h9JwtbHhwsdEpFBq6hQfgezt7dvc5u4uDgsXry42X0RERGIjo7u9PHaWjzEWBZ/MbR2KhQKXGkaKRXkYdeifUK0M2KgE8QiIPF2BbKKKuHhYNljxzK097MtxtBOTbexo/sRNCk1ffp0FBcXY9OmTZBKpQgICMDWrVvV0/fy8vIgFv85mKugoACRkZHq29u2bcO2bdswcuRI7Nq1CwBw+/ZtvPzyyygtLYWTkxOGDRuGPXv2wMnpz2GLr732GsRiMZ5//nnU19cjIiICb775pnYaTdSG5NvKL0R+7rYCRwL497JFHwdL5JTW4MzNQkwe7C50SERkhJLzK5CQVw5TiQgzgnoLHY5WrZrmj2OJBYhOyMf51CKM6u/c/pOIqNPkcjnWrVuHoUOH3nUaXmFhYYsSI87OzigsLOz0Mdur0WgsNRwNpZ25FY0orWmAqRioK0hFXGHzi8tCtXOQkymSihqw61gs7hvQ8yONDeX9bI8xtFPbbRS80PnChQuxcOHCVh9TJZpUPD09kZSUdNf9/etf/2r3mObm5njzzTeZiCKdkpyvXOlukA4kpUQiESYHuGFnTAaiE/KZlCIiQRyOzwMAjB/kCgcrYQvHattANxs8MsILu89n4t2fEvDj8rEQG3CRdyKhrF27FikpKfj666+1dsy2FiYxlsVfDK2d6XG5AAoR6OmAEUP/LEMjdDtnlt5C0tEU3Kwyx+oenBEkdDu1xRjaqek2dnShJMGTUkSklNxUN8TX3UbgSJTuDXDHzpgMHEssgFyu4MkQEWndz9duA1AW/zZGL03xxYG4XMTnlOH7y9mYP9xL6JCIDMrbb7+NEydO4KuvvkKvXnf/nHFxcWkxKqqrCzS1tzCJsSxcYijtvJZbDkBZ5Ly19gjVznsDemHj0RScTS1CgxywMO3ZGAzl/WyPMbRT220UrNA5Ef2ptkGG9KaV93Rh+h4AjOrvBBtzE0gr6hCfUyZ0OERkZNIKq5B4uwImYhGmGOloTRcbczx/7yAAwPs/J6GitkHgiIgMg0KhwNtvv42jR49i586d8PJqP+EbGhqKc+fONbvv7NmzrElLiM9Wfk8O6tN2TTIhBPS2RW97C9Q2yBGTWtT+E4gEwqQUkQ5IK6yCXAHYW5rC1dZc6HAAAOYmEoz3VV79i07gKnxEpF1Hrimn7o0Z4Gx0U/fu9Hi4D/q5WKOwsg6fHr8ldDhEBmHt2rU4cOAANm7cCGtra0ilUkilUtTW1qq3WbVqFTZu3Ki+/dhjj+HUqVPYtm0bbt26hU8++QTXrl1rswwJGQeZXIHrTSOlgj11KyklEolwjx9X4SPdx6QUkQ5IlSpHSQ1wtRZ85b073euvHJ0QncCOjIi06+gNZTLcWKfuqZiZiPH6jAAAwLbTacgoqhI4IiL9980336CiogKLFi1CRESE+ufw4cPqbfLy8iCVStW3hw4dig8++ADfffcdZs+ejV9++QWffvrpXYujk+G7Ja1ETYMM1mYS9HPRjRIcd5rkr0xK/ZZYAIVCIXA0RK1jTSkiHZBWqCxyrmud2UR/N4hFQEJeOXJKa9CnB5eTJSJSKaqsQ1zT8tqq5Lgxm+TvhvG+rjiZLMV7PyXg88eGCx0SkV5rb+EkoOWCSwBw//334/777++JkEhPXW2aujekjz0kOlh/dexAZ5iZiJFdUoObBZU6saAS0V9xpBSRDkhtqifV39Va4Eiac7I2w7C+jgCAY5zCR0Ra8nuyFAoFMLi3HXrZWwgdjuBEIhHemBEAiViEX2/k43RK55egJyIizYvPLgWge/WkVKzMTDC6vzMA5WgpIl3EpBSRDkhrSkr1c9GtpBQATA7gFD4i0i7VF2fVtAMCBrnbYtHovgCAtw9dR4NMLnBERER0tWkxIF2rJ3Wne++Ywkeki5iUItIBupyUurcpKXXuVhEq6xoFjoaIDF2jTI6Tyco6LhOZlGrmpcm+cLQyRXJ+Jb6MyRA6HCIio9Yok+NGU5FzXR0pBfx5geePjBKUVXMVV9I9TEoRCaykqh6lTR2Ej7PuJaUGuFrDx9kK9TI5TiVL238CEVE3XMkuRXltIxysTBHq5SB0ODrF3soUa+73BwB8fOwmimtkAkdERGS8UgoqUdcoh625iU5+h1fxcrLCQDcbyOQKnEjmaCnSPUxKEQlMVU/Kw94ClmYSgaNpSSQScQof6aSLFy9i2bJliIiIgJ+fH6Kjo++6/fnz5+Hn59fi587VlQBg9+7dmDRpEoKCgjBv3jxcvXq1J5tBf3E6pQgAMHagi04WjRXavGFeCPN2QFW9DDuuVAgdDhGR0YpvKnIe2MceYh3vr6YOVn6X/+X6bYEjIWqJSSkigamn7ulYkfM7qabwHU8qgEzO5WRJN1RXV8PPzw9vvvlmp573888/4/Tp0+ofZ2dn9WOHDx9GVFQUli9fjv3798Pf3x9Lly5FUVGRpsOnNpy5qSziPXaAi8CR6CaxWIR3ZgdCLALOZNXi7C3+bhIRCeFqTikAIEiH60mp3B/YGwBwPFGKmnqOsiXdwqQUkcDSCisBAP1dbASOpG3DfRxhZ2GC4qp6xGaWCB0OEQBgwoQJeOmllzBlypROPc/Z2Rmurq7qH7H4z65w+/btmD9/PubOnYuBAwdi7dq1sLCwwN69ezUdPrWiqq4Rl5s+YyIGMinVlsA+9lgwyhsAsPbgDdQ3sug5EZG2qUZK6XI9KZXAPnbo42CJmgYZTqawHAfpFialiASmy0XOVUwlYnXBYU7hI30XGRmJiIgILFmyBJcuXVLfX19fj+vXryM8PFx9n1gsRnh4OGJjY4UI1ehcSCtGo1wBLydLeDtbCR2OTnt58iDYm4txU1qFbWfShA6HiMio1DfKkXBbOYVal1feUxGJRJgW2AsA8Ms1TuEj3WIidABExi5VqvvT9wDlFL4f43IRnZCvLrRLpE9cXV2xdu1aBAYGor6+Hv/73//w2GOPYc+ePRgyZAhKSkogk8maTecDlCOrUlNTO308mUzW6r/6ShvtON109XZMf+ceO46hvB/WZmI8FmyLTy6WYdOxFMwIdIeHg6XQYXWaobwfhtIOoPNtMYQ2E3VWcn4F6hvlsLMwgbeTflxEmRbYC1+cTsPRhHzUN8phZsLxKaQbmJQiEpBcrlCPlOqvwyOlAGCCrytMxCLcLKhERlEV+urwKiNErenfvz/69++vvj106FBkZWVhx44d+L//+z+NHy8+Pv6ut/VVT7bjxA1lPSkPSQXi4uJ67DiAYbwfE/paIDqtGgmFDVj1zXm8MsZB6JC6zBDeD8Bw2gEYVluINC0+p2nqnqc9RCLdLnKuMszbEa625pBW1CEmtQgTfF2FDokIAJNSRILKK69FXaMcphIR+uj4FW57S1OM7OeEs7eKEJ1QgKUR/YQOiajbgoKCcPnyZQCAo6MjJBJJi6LmRUVFcHHpfH2joKAgSCQSyGQyxMfHq2/rq55uR2VdI9K+V66gOHd8aI+N+jG092PD/GF48D/nEZNdiwprT4wbpF+1uAzt/dD3dgCdb4tqeyJjclVdT8pB2EA6QSwWYepgd+w+n4mfr91mUop0BpNSRAJKa5q65+1kBROJ7g+hvTfAXZmUupHPpBQZhMTERLi6Kr+UmZmZYciQIYiJicHkyZMBAHK5HDExMVi4cGGn9y2RSJqd0P31tr7qqXZcyS6GXAF4OlrCy7nnF34wlPdjSB8HPD7GB9vOpOHtQwk48uI4mJvoX7sM5f0wlHYAhtUWIk2Lb1p5Tx/qSd1pWmAv7D6fiaM3buPdyEBIxPoxyosMm+6fBRMZMNXKe/10eOW9O00OUBY7v5BejLLqBoGjIWNXVVWFhIQEJCQkAACys7ORkJCA3NxcAMDGjRuxatUq9fY7duxAdHQ0MjIykJycjPfeew/nzp3DggUL1NssWbIEe/bswf79+3Hr1i289dZbqKmpwZw5c7TbOCN0Ia0YADCyn5PAkeifF6cMgqutOVILq7D1FIueExH1pLpGGZKaipzrw8p7dxrd3xn2lqYorKzHudSi9p9ApAUcKUUkoFRVPSkdL3Ku0tfZGoPcbJBSUIkTyQWYHdpH6JDIiF27dg2PPfaY+nZUVBQA4MEHH8T69eshlUqRl5enfryhoQEbNmxAfn4+LC0t4evri+3bt2P06NHqbaZPn47i4mJs2rQJUqkUAQEB2Lp1a5em71HnqJNSPkxKdZadhSlenxGAF76Nwye/pWB2qAc8HfWj8C4Rkb5Jul2BBpkCjlam8HTU7fIbf2UqEWN6UG98cyETP8TmYOxAfr8h4TEpRSQgVZHzfjpe5PxO9wa4I6WgEscSmJQiYY0aNQpJSUltPr5+/fpmt5966ik89dRT7e534cKFXZquR11X1yhDXHYpAGAER0p1yQMhHvj6fCbOpxXjnUM3sGXRcKFDIiIySKp6UoF99KfI+Z0eDOuDby5k4si123gnMhAWppymS8Li9D0iAeljUmrKYOUUvhNJBWiQyQWOhogMwY3cctQ3yuFkbabzK5HqKpFIhHea6oP8cj0fx5MKhA6JiMggxTclpfStnpTK8L6O6ONgicq6RhxLYF9BwmNSikgg9Y1yZBVXA4BenYSFejnCydoM5bWNuJheLHQ4RGQAYjNLAQBhXg56edVZV/i62+KJsT4AgLcOXEdtg0zYgIiIDNDVHP1bee9OYrEIkWEeAID9sTkCR0PEpBSRYLJKqiFXAFZmErjamgsdTodJxCJM9FOOluLVFSLShMuZJQCAMG8HYQMxAC9M9oW7nTkyiqrx35OpQodDRGRQahtkSMlXFjnX15FSABDZVILjRFIBiqvqBY6GjB2TUkQCySxSjpLydrLSu5EBqil80Qn5UCgUAkdDRPpOPVLK21HYQAyAjbkJXp8xGACw+fhN9YhcIiLqvoS8cjTKFXC2NkNvewuhw+myQe62COxjh0a5Aj/F57X/BKIexKQUkUDSi5T1pHyc9Wfqnsq4Qa4wk4iRUVSNW9JKocMhIj1WUF6LnNIaiET6fdVZl8wM7o3wAc6oa5Rj7cEbQodDRGQw4lVT9zz1s8j5nVSjpX7gFD4SGJNSRALJaBop1ddZ/5bttjY3wZgBzgCAaE7hI6JuiM0qBQD4utnC1sJU2GAMhEgkwtuzh8BELEJ0Qj6OJeQLHRIRkUFQrbwX3Ef/L6I8EOIBsQi4lFGCmwW8yEzCYVKKSCCZTVMqvPUwKQUAkwNUdaV4skNEXXelKSkV6uUgaByGZqCbLZaO6wcAeOsgi54TEWmCauW9IE8HYQPRADc7C9wb4A4A+OpchsDRkDFjUopIIBlN0/f6Ounf9D0AmNTUiV3KKGGBRCLqsuu55QCAQE7d07jnJw1CLzsLZBXX4D+/3xI6HCIivVZTL0NKgbLIeZABjJQCgEWj+wIA9l7KRlVdo8DRkLFiUopIADK5AlnFNQD0c/oeAPRxsMTg3naQK4DjiZzCR0Sdp1AocK2pPkegh53A0Rgea3MTvDFTWfT8sxO31AtsEBFR513PLYNcAbjamsPdTn9Wzr6biIEu6OdijYq6RvwQx9pSJAwmpYgEcLu8FvUyOUzEIr1euUM1hS+aU/iIqAvyy+tQVFUPiViEgN5MSvWE6UG9EDHQBfWNcqw9eF3ocIiI9NaVpql7IQZQ5FxFLBZhwShvAMCumAyuqk2CYFKKSACqqXteTlYwkejvn6FqHvrJZCnqGlmvhIg653qu8gv+AFdrWJhKBI7GMIlEIqydPQSmEhGOJRbg6A1eRCAi6or47FIAQLAB1JO607xhXrAwFSPxdgUuppcIHQ4ZIcHPhnfv3o1JkyYhKCgI8+bNw9WrV9vcNiUlBc899xwmTZoEPz8/7Nixo8U2W7Zswdy5cxEWFoYxY8bg2WefRWpqarNtFi1aBD8/v2Y///znPzXdNKI2qaZQeDvp59Q9laA+9nCzNUdVvQznU4uFDoeI9My1nKZ6Uh6GUZtDVw1wtcFT4/oDAN46cB019byIQETUWVfVRc4Nq8+ytzLF7JA+AIBdLHhOAhA0KXX48GFERUVh+fLl2L9/P/z9/bF06VIUFRW1un1NTQ08PT2xcuVKuLq6trrNhQsXsGDBAuzZswfbt29HY2Mjli5diurq5nUU5s+fj9OnT6t/Vq1apfH2EbUlo2nlPX2tJ6UiFotwL6fwEVEXqUZKDTGQgrG6bMWkgfCwt0BOaQ3+feKm0OEQEemVspoGpBYqZzqEGNhIKQBYNEZZ8PxIfB6yS1h/kLRL0KTU9u3bMX/+fMydOxcDBw7E2rVrYWFhgb1797a6fXBwMFavXo0ZM2bAzMys1W2++OILzJkzB4MGDYK/vz/Wr1+P3NxcXL/evI6ChYUFXF1d1T82NjYabx9RW1TT9/R9pBQATG6awncsoYDz0ImoU1Qr7w1hkfMeZ2Vmgn/OUhY9/8/JVJ50EBF1wvWmRTk8HS3hZN36eag+C+xjj/ABzmiUK/Dfk6ntP4FIgwRLStXX1+P69esIDw//MxixGOHh4YiNjdXYcSoqlMt22ts3vwp78OBBjBo1CjNnzsTGjRtRU1OjsWMStSejSDVSylrgSLpv7EAXWJiKkVNag8TbFUKHQ0R6oqSqHjmlyr53MJNSWnHfkF4IH+CM+kY51h9JFDocIiK98WeRcwdhA+lByycOBAB8ezEL0oo6gaMhY2Ii1IFLSkogk8ng7Ozc7H5nZ+cWNaC6Si6XY926dRg6dCh8fX3V98+cORMeHh5wc3NDUlISPvjgA6SlpWHz5s2dPoZMpt91GVTx63s7hNKV10+hUKiTUl6OFnr/2puKgbEDnHEsUYpfr9+Gr1vnEm38Hew+bbyGfH9I01SjpHycrWBnYSpwNMZBJBLhjZmDMWPTKRy6mofF4cUY7uMkdFhERDrvalORc0OrJ3Wn8AHOCPVyQFxWKf7z+y28MXOw0CGRkRAsKaUNa9euRUpKCr7++utm9z/88MPq//v5+cHV1RWLFy9GZmYmvL29O3WM+Ph4jcQqNENph1A68/qV18lRWdcIACjOSkFcrv4vKetrXYdjAA5dTkeEY9dGS/F3sPv4GpI+UdeTYpFzrQrobYeHR3jjmwuZePvQDfzw7FiIxfrfDxER9SRVkfNgA05KiUQivDTFF49vu4Bd5zLwREQ/9HGwFDosMgKCJaUcHR0hkUhaFDUvKiqCi4tLt/f/9ttv48SJE/jqq6/Qq1evu24bEhICAMjIyOh0UiooKAgSif4uYy2TyRAfH6/37RBKV16/uKxSAAXoZWeOUcPCejQ+bfHoX4t/XzqBlOIGePT3h5udRYefy9/B7tPGa6g6BpGmXFPVk+rDqXvatnKqLw5eycXV7DLsj83B3GGeQodERKSziirrkFNaA5FIufK0IRs/yAWj+zvhXGoxPjqajP+bFyJ0SGQEBEtKmZmZYciQIYiJicHkyZMBKKfbxcTEYOHChV3er0KhwDvvvIOjR49i165d8PLyavc5CQkJANDmin53I5FIDOJE2lDaIZTOvH5ZJbUAAG9na4N5zXs7WiPEywFXskrxe0oRHhnZueQuwN9BTeBrSPpEVTSWI6W0z8XGHM9NGoioI4l4/5dETAvsBWtzgx48T0TUZapRUv1drGFr4NPNRSIRVk3zx5zPzuL7y9lYNKYvgg24jhbpBkFX31uyZAn27NmD/fv349atW3jrrbdQU1ODOXPmAABWrVqFjRs3qrevr69HQkICEhISUF9fj/z8fCQkJCAjI0O9zdq1a3HgwAFs3LgR1tbWkEqlkEqlqK1VJgIyMzPx6aef4tq1a8jOzsaxY8ewevVqjBgxAv7+/tp9AcgoqYucG8DKe3eaEuAGAIhOyBc4EiLSdZV1jUhrWoWUK+8JY/FYH3g7WSG/vA5bfr8ldDhERDrrSlM9KWNJzgz1dsSDYX2gUABv/HgdcjlX16aeJehlsenTp6O4uBibNm2CVCpFQEAAtm7dqp6+l5eXB7H4z7xZQUEBIiMj1be3bduGbdu2YeTIkdi1axcA4JtvvgEALFq0qNmxoqKiMGfOHJiamiImJgZffvklqqur0bt3b0ydOhXPPvtsD7eWSCmjWHki1tfZsJJS9wa444Nfk3H6ZiFq6mWwNOOIHSJqXUJeORQKoLe9BVxszIUOxyiZm0jw2vQALPvqEracTMXDI71ZO4SIqBXxRlBP6q9evd8fR2/k40pWKXady8Dj4T5Ch0QGTPCx2gsXLmxzup4q0aTi6emJpKSku+6vvcd79+6Nr776qnNBEmlQpmqklHPnVqnTdf69bNHHwRI5pTU4c7MQkwe7Cx0SEemohDxlPanBvTlKSkj3DXHHqH5OOJ9WjA1HErHpb4ZR55CISFMUCgWuqJNSDsIGo0VudhZYNc0P//zxOtYfScQEX1f4uBjWuQvpDkGn7xEZo4xiVVLKsEZKiUQiTOYUPiLqgOR85Sqdvr1sBY7EuIlEIrwxczBEIuDAlVxcyigWOiQiIp2SV1aLwso6mIhFRjfdfOGovhjT3xk1DTK88G0s6hplQodEBopJKSItqqprhLSiDgDQ18nwrjaoRkcdSyzg/HMialNyfiUAwNfdRuBIKLCPPeYPUy4K895PCVAo+NlNRKSiKnLu624LC1PjKk0hFovwf/OCYW9piivZZVh78IbQIZGBYlKKSIsym0ZJ2Vuawt7K8FbvGNXPGTbmJpBW1OFq08paRER3UigUf46UcudIKV2wcqovLE0luJxZiqM3ONKViEjlqrrIufHUk7qTp6MVPnokFCIR8PX5TPz3ZKrQIZEBYlKKSIvUK+8Z2NQ9FTMTMSb4ugIAjnEKHxG1QlpZh9LqBohFwABXjpTSBW52FngiwgcA8P4vSWiUyYUNiIhIR1w1wnpSfzXRzw2rpylXqX/vcAK+Pp8pcERkaJiUItKiTPXKe4Y3dU9l8mBlXSlebSei1qQ0Td3r62xtdFMhdNkzEwbAwcoUNwsqse9yjtDhEBEJTqFQGP1IKZVnxvfH0+P7AwBe2x+PTcdSON2bNIZJKSItUo+UcjLMkVIAcI+vG8QiIPF2BbJLqoUOh4h0jGrq3iA3jpLSJXYWplgxcSAA4MOjyahtYEFbIjJuGUXVKK9thJmJGH5GvjCHSCTCq/f7Y/nEAQCU/cTSnX+oa+USdQeTUkRapEpKeRvo9D0AcLQ2w3AfJwDAsYQCgaMhQ3bx4kUsW7YMERER8PPzQ3R09F23//XXX7FkyRKMHj0aQ4cOxcMPP4xTp0412+aTTz6Bn59fs59p06b1ZDOMDutJ6a6Fo/uij4MlbpfXYufZdKHDISIS1JWmUVKDe9vBVMLTZpFIhP93nz/WPRgEMxMxfksswKQPTmDLyVRUNXRv2rdCoUBxVT2yiquRVliFmwUVyCurQX0jp5MbAxOhAyAyJhmq6XsGPFIKAKYEuONCWjGiE/LxeLiP0OGQgaquroafnx/mzp2LFStWtLv9xYsXER4ejpdeegl2dnbYt28f/v73v2PPnj0YPHiwertBgwZh+/bt6tsSCaeYaZJ65T0jv+qsiyxMJXhpii9e+d8VfHr8Jh4Z4W2Qi3IQEXXElSxlPakQI5+691ePjvJGqJcDVu+9ivicMrz/SzIsTESYknoFE/zcENTHHp6OlrAyk0AkEkGhUKC8thGFlXWQVtQht7QGuaU1yCmtQXaJ8t/c0hrUtpLYEosAH2drDO3riIl+bpg82A3mJvxeZmiYlCLSkgaZHLmltQAMu6YUANwb4Ib3DifgXGoRKmobYGvBkxrSvAkTJmDChAkd3v4f//hHs9svv/wyjh07ht9++61ZUkoikcDV1VVjcdKfmq+8x+l7uujBsD7478lUJOVX4N+/38Ka+/2FDomISBCxWSUAgDBvR4Ej0T2DPezw4/Kx2Bebgy2/30JKQSUOXs3Dwat5zbYTiZT/drT8lIWpGKZiMcRiESrrGiGTK5BaWIXUwip8fykbDlameGJsPzwR0Q825kxlGAq+k0RaklNSA5lcAXMTMdxszYUOp0f1d7VBf1drpEqrcDK5EDOCewsdElELcrkcVVVVcHBwaHZ/RkYGIiIiYG5ujtDQUKxcuRIeHh6d3r9MJmv1X32liXbcLqtFRW0jJGIRvB0tBXlN+H6075Wpg/DUrsvYfiYNj4/2gpudhcaPocL3Q/d0ti2G0Gaiv6prlOF6TjkAIMzbQdhgdJRYLMJDwzwRGdIL//vtItIb7RGbWYaE2+WoqG0E0DwZZWNuAhcbM/S2t0QfR0v0cVD+6+lgCQ8HS/R2sGg2CkqhUKCgog4JeeU4e6sIB6/kIq+sFh8eTcbu8xl4/6EQ9arfpN+YlCLSkozipiLnzlYQi0UCR9PzJge443NpKqIT8pmUIp30xRdfoLq6Gvfff7/6vuDgYERFRaFfv36QSqX49NNPsWDBAhw8eBA2Np0b2RMfH3/X2/qqO+2Iva0siNrLWoyEa1c1FVKX8P1om6NCAT9nUyQVNeDdfRfwRKidxo/xV3w/dI8htYWos27klqNeJoeTtRm8DbzsRneJRCL4OpthfqifuuRBZV0jahtkkCsUUCgAe0vTTq+4KxKJ4G5nAXc7C9zj54bV0/xxOD4PH/yahIyiajy+7QKenzQQL03xhUhk+OdWhoxJKSItyShS1pPydjLsqXsqkwPc8fnJVBxPKkCjTA4TFogkHXLw4EF8+umn+Oyzz+Ds7Ky+/87pgP7+/ggJCcHEiRNx5MgRzJs3r1PHCAoKgkQigUwmQ3x8vPq2vtJEOy6dTgNQguC+LggNDdVofB3F96Nj/mFbiMe2/4GjabV47cGR6GXfM6Ol+H7ons62RbU9kSGJzSwFAIR5OTDh0QU25iYan14nEYswK8QDkwPcsf5IAnbGZGDTbzeRW1aL9+cGG8VFf0PFpBSRlqhW3utrwCvv3WmotwMcrUxRUt2ASxklGNXfuf0nEWnBTz/9hNdffx0ff/wxwsPD77qtnZ0dfHx8kJmZ2enjSCSSZid0f72tr7rTjptSZXLe191O8NeC78fdjfN1wwgfR1xML8Hnp9Kwdnagxo9xJ74fuseQ2kLUWbFZpQA4dU8XWZpJsHZ2IAZ72OG1/dfw/aVsWJpK8PbsIUwg6ikOXSDSEmNLSplIxJjo5wYAOJZYIHA0REqHDh3Cq6++io0bN+Kee+5pd/uqqipkZWWx8LmGJKlW3nPnynu6TiQS4aUpvgCAby5kIbe0RuCIiIi0JzaTRc513cMjvPHh/BCIRMCucxn44nSa0CFRFzEpRaQlmcXKEQKGvvLenSYPdgcARN/IFzgSMkRVVVVISEhAQkICACA7OxsJCQnIzc0FAGzcuBGrVq1Sb3/w4EGsXr0aq1evRkhICKRSKaRSKSoqKtTbbNiwARcuXEB2djYuX76MFStWQCwWY+bMmdptnAFSKBS4yZX39Er4ABeM6ueEepkcn524KXQ4RBp18eJFLFu2DBEREfDz80N0dPRdtz9//jz8/Pxa/EilUi1FTNpSUFGL7JIaiERAsKe90OHQXcwO7YN/zlSuoBx1JBHnU4sEjoi6gkkpIi2QyxXIVBU6N6JiieMGucBUIkJqYRVuSSuFDocMzLVr1xAZGYnIyEgAQFRUFCIjI7Fp0yYAgFQqRV7en0sT79mzB42NjXj77bcRERGh/nnvvffU29y+fRsvv/wypk2bhhdffBEODg7Ys2cPnJyctNo2Q5RTWoOqehlMJSL4uBhPcl7fqUZLfXcxC9kl1QJHQ6Q51dXV8PPzw5tvvtmp5/388884ffq0+ufOuoRkGOKa6kn5utnC1sJU2GCoXYvDfRAZ6gGZXIGXvotDRW2D0CFRJ7GmFJEWFFTUobZBDolYhD6OlkKHozW2FqYY3d8Zp1IKcSwhHwNcOTqCNGfUqFFISkpq8/H169c3u71r16529/mvf/2r23FR624WKBPT/VysYcqFD/TG6P7OCB/gjLO3ivDp8VuImhMkdEhEGjFhwoRmi1t0lLOzM+zsen5FShIO60npF5FIhHVzgnA5sxSZxdVYfyQR7z3IvkqfMClFpAWqlfc8HCyM7mRsymB3nEopRPSNAjw9foDQ4RCRQNIKlZ+D/ThKSu+8NMUXZ2/F4H9/ZOHZewbAy4hG/BL9VWRkJOrr6zFo0CCsWLECw4YN6/Q+ZDLZXe9v63FDoevtvJyhrCcV4mnfrRh1vZ2aogvtNJeIsO7BIVj4xUXsPp+JyNDeGKrhemC60M6epuk2dnQ/TEoRaUFG09Q9HyOqJ6Uyyd8N//zxOv7IKEZJVT0crc2EDomIBJDelJTi1D39M8LHCREDXXD6ZiE+P5mKdyJ7diU+Il3k6uqKtWvXIjAwEPX19fjf//6Hxx57DHv27MGQIUM6ta/4+PhuPW4odLGdMrkCV5qKnFtU5SEurrDb+9TFdvYEodtpCWCSjyV+S6/Ba/+7jKhJTj2yGp/Q7dQGbbeRSSkiLchsWnnP2wivLns6WiGgtx0S8spxPKkAc4Z6Ch0SEQkgrelzsJ8RJucNwbMTB+D0zULs+SMLz987CK625kKHRKRV/fv3R//+/dW3hw4diqysLOzYsQP/93//16l9BQUFQSKRtLhfJpMhPj6+zccNhS63MyGvHLWyfNiYm2DmuOEQi7ue1NDldmqSLrXzvf61uPdfp5BS3IAssTseCPHQ2L51qZ09RdNtVO2vPUxKEWmBaqRUX2fjS0oBwOQANyTklSM6IZ9JKSIjxZFS+m1Mf2eEejkgLqsU28+kYdU0f6FDIhJcUFAQLl++3OnnSSSSu57wtfe4odDFdsZllwMAQr0cYGqqmVNlXWxnT9CFdvZ2tMbfJwzAxqPJ2PTbLTwQ6glJNxKLrdGFdvY0bbfRuIrbEAlEVVPK28k4T8YmB7gDAE4mF6Ku0XDnYRNR6+ob5eqV21hTSj+JRCL8/R5lXcBdMRko5+pGREhMTISrq6vQYZAG/ZFeDAAY1lez9YhIe5ZE9IODlSnSCqtw6Gqu0OFQBzApRaQFGU3TVnxcjHOkVFAfe7jZmqOyrhHnU4uFDoeItCyrpBpyBWBlJoEbp33prSkB7hjoZoOKukbsPpcpdDhE3VJVVYWEhAQkJCQAALKzs5GQkIDcXOVJ7MaNG7Fq1Sr19jt27EB0dDQyMjKQnJyM9957D+fOncOCBQsEiZ96xsV0ZT2pET5OAkdCXWVjboInI/oBAD757SbkcoXAEVF7mJQi6mFl1Q0oq1FeUTbGmlIAIBaLcG+AGwDgWEK+wNEQkbappu71dbbukaKjpB1isQjLJihHS31xOg21DRz5Svrr2rVriIyMRGRkJAAgKioKkZGR2LRpEwBAKpUiLy9PvX1DQwM2bNiAWbNmYdGiRUhMTMT27dsxZswYIcKnHpBXVoOc0hpIxCKEejsIHQ51w2PhPrC1MMHNgkqcSC4QOhxqB2tKEfWwjGLlyZirrTmszIz3T25ygDu+uZCF6IQCvPWAgiemREYkrSkp1c9IR4saktmhHvjw1yTkltXi+0vZWDi6r9AhEXXJqFGjkJSU1Obj69evb3b7qaeewlNPPdXTYZGA/mgaJRXQ2xY25sb7nd0Q2FmY4pERXvjvqTRsP5OOSf7uQodEd8GRUkQ9TDV1r6+RjpJSGTvQBRamYuSU1iAhr0LocIhIi9Kb6ur5cOU9vWcqEeOp8coVyD4/mYpGmVzgiIiINENVT2p4X07dMwSPjfGBWAScSilESj7PPXRZl5JSWVlZmo6DyGCpi5wb6cp7KhamEkQMVBYD5RQ+48Y+xPikF6rq6jEpZQgeGeENJ2szZBZX46f4vPafQKRB7EOop7CelGHxcrJSL7a061yGwNHQ3XQpKTVlyhQsWrQIP/74I+rq6jQdE5FBURc55wgBTBmsrCsVzaSUUWMfYnz+nL7Hz0FDYGkmweJwHwDAf0+lQqFgEVnSHvYh1BMqahuQeLscADDchyvvGQrVFPMfYnNYB1GHdSkptX//fvj5+WH9+vUYO3Ys/vnPf+Lq1auajo3IIGQUN03fM/KRUgAw0U+ZlLqSXQZpBb9IGiv2IcalrlGG3LIaAEzOG5KFo/vC3ESMaznl+COjROhwyIiwD6GeEJtZCrkC8HKyhLudhdDhkIaMHeiCPg6WKK9txC/XbwsdDrWhS0mpgIAAvP766zh16hTWrVuHgoICPProo5g5cya2b9+O4mIu+U6kktk0UspYV967k5udBQL72AEATiZLBY6GhMI+xLhkFVdDoQCszSRwsTETOhzSECdrM8wZ2gcAsO10msDRkDFhH0I9QVVPagTrSRkUiViEucM8AQB7/uDUX13VrULnJiYmmDp1KjZt2oRXXnkFGRkZ2LBhAyZMmIBVq1ahoIDLL5Jxq22Q4XZ5LQDlUuj052ip35L4+WDs2IcYh7Q76klx1U3Dsji8HwDgl+u3kdU0KphIW9iHkCap6kkNZz0pgzOvKSl15mYR8ppGbpNu6VZSKj4+Hm+99RYiIiKwfft2PPHEEzh69Ci2b9+OgoICPPvss+3uY/fu3Zg0aRKCgoIwb968uw6/TUlJwXPPPYdJkybBz88PO3bs6NI+6+rqsHbtWowaNQphYWF47rnnUFhY2Km2E3VEZtOXdFsLEzhamQocjW6Y6K9MSp1MlnLVJiOniT6EdF96Uz0pFjk3PH69bBEx0AVyBfBlTLrQ4ZCRYR9CmtIgkyMuqxQAMIL1pAyOl5OV+n09dIWLc+iiLiWltm/fjlmzZuFvf/sbCgoKsGHDBhw/fhwvvfQSvLy8MHz4cKxfvx43bty4634OHz6MqKgoLF++HPv374e/vz+WLl2KoqKiVrevqamBp6cnVq5cCVdX1y7vc926dTh+/Dg++ugj7Nq1CwUFBVixYkVXXgqiu1IVOe/rbMURAk1CPB3gaGWKitpGXM4sFTocEoCm+hDSD2lNK5D242hRg/REhA8A4NuLWaiqaxQ2GDIK7ENI027klqOmQQZ7S1MMcLUROhzqAQ+EeAAADlzJFTgSak2XklLffPMNZs6cid9++w2fffYZJk6cCLG4+a6cnJzw3nvv3XU/27dvx/z58zF37lwMHDgQa9euhYWFBfbu3dvq9sHBwVi9ejVmzJgBM7PW61K0t8+Kigrs3bsXa9aswZgxYxAYGIh169YhNjYWcXFxnX8xiO4io+lkrK8TT8ZUJGIRJvgqk8q/JXJovTHSVB9C+oEjpQzbPb5u6OdijYraRuy9nC10OGQE2IeQpp1LVQ5eGOHjCLGYF5EN0fSg3pCIRYjPKVOvCEy6o0tJqW3btuGpp56Cm5tbs/sVCgVyc5XZRzMzMzz44INt7qO+vh7Xr19HeHj4n8GIxQgPD0dsbGxXwurQPq9du4aGhoZm2wwYMAAeHh5MSpHGqabveXPlvWZUU/hOsK6UUdJEH0L6Q5WU6ufCz0FDJBaLsDjcBwCw/Uw65HKFsAGRwWMfQpqmSkqN7u8scCTUU5xtzDF2oAsA4BBHS+kck648acqUKTh9+jScnZv/4ZaWluLee+9FQkJCu/soKSmBTCZrsQ9nZ2ekpqZ2JawO7bOwsBCmpqaws7NrsY1U2vnVwGQyWZdi1RWq+PW9HUJp7/VTnYx5O1ryNb7D2AFOEIuAxNsVyCqqBMDfwe7Qxt+xJvetiT6E9ENtgwy5ZcrFHnw4fc9gPTTMEx/8moS0wir8nixVX3gg6gnsQ0iTGmVydZFzJqUM24ygXjiZLMUvN27juXsHCR0O3aFLSSmFovWrYNXV1TA3N+9WQPomPj5e6BA0wlDaIZS2Xr+UPGUnV1+cg7g4FtO/0yAnUyQVNeCbE1cxdYAVfwc1QF9eQ/YhxkNVV8/WwgRO1q1Puyf9Z21ugkdGeOG/p9Lwxek0JqWoR7EPIU26nluOyrpG2FmYIKC3XftPIL01OcAdYlE8ruWUI7ukGp6OHMGtKzqVlIqKigIAiEQifPzxx7C0tFQ/JpPJcPXqVfj7+3doX46OjpBIJC2KmhcVFcHFxaUzYXVqny4uLmhoaEB5eXmz0VJFRUVtFk+/m6CgIEgkki7FqwtkMhni4+P1vh1Cudvr1yiTQ7r3KABg0shgeDhYtrYLozWj5BaSolNws9ocU6H/f0tC0sbfseoY3aHJPoT0Q5p66p41F3swcI+N8cEXp9Nw+mYhbhZUYqAbiwWTZrEPoZ6gmro3sp8zJKwnZdCcbcwx3McJF9KK8ev1fDwR0U/okKhJp5JSqlUsFAoFkpOTYWr65xL3ZmZm8Pf3xxNPPNGhfZmZmWHIkCGIiYnB5MmTAQByuRwxMTFYuHBhZ8Lq1D4DAwNhamqKmJgY3HfffQCA1NRU5ObmIjQ0tNPHlEgkBnEibSjtEEprr19uWR0a5QqYmYjRx9GahRP/4t7B7vgwOgVnbxXjycEu/B3UAF1/DTXZh5B+SG9a7IFT9wyfl5MVJvq54VhiAb65kIk3Zg4WOiQyMOxDqCf8WU/KSeBISBvuG9ILF9KK8fP120xK6ZBOJaV27doFAHj11Vfxj3/8AzY23bsKtmTJEqxevRqBgYEIDg7Gzp07UVNTgzlz5gAAVq1aBXd3d6xcuRKAspD5rVu31P/Pz89HQkICrKys0Ldv3w7t09bWFnPnzsX69ethb28PGxsbvPvuuwgLC+tSUoqoLappK95OVkxItWJwbzu42ZqjoKION6T1GCl0QNTjNN2HkO7jynvGZeHovjiWWIDvL2Xj/93nBwtT3U2Sk/5hH0Kadmc9qTEDWE/KGEwd7I53Dt3ApYwSlNU0wN7StP0nUY/rUk0p1fDZ7po+fTqKi4uxadMmSKVSBAQEYOvWreqpdnl5ec2WeC0oKEBkZKT69rZt27Bt2zaMHDlS3VG1t08AeO211yAWi/H888+jvr4eERERePPNNzXSJiKVjGLlyVhfJ85Xbo1IJMJEPzd890cWLt+uw2KhAyKt0VQfQrovjSvvGZXxvq7o42CJnNIa/HQ1D3OHeQodEhkg9iGkKdea6knZW5oioBfrSRkDLycrDHC1xi1pFc7cLMT0oN5Ch0ToRFJqxYoVWL9+PWxsbLBixYq7brt58+YOB7Bw4cI2p+upEk0qnp6eSEpK6tY+AcDc3BxvvvkmE1HUozJVI6WceTLWlon+rvjujyxcyqsTOhTqYT3Vh5Bu4/Q94yIRi/DoKG/83y9J2H0+g0kp0hj2IdQT/qwn5cRZDUbkHj833JKm4fckKZNSOqLDSSlbW9tW/09ErVOdjHGkVNsiBrnCVCLC7UoZ0gqrMNCdV6kMFfsQ41Nd34j8cmXCuR+n7xmNecM98a+jybicWYobueUY7MHPdeo+9iHUE/6sJ8Wpe8Zkgq8rvjidht+TpVAoFFyIRQd0OCl151BZDpslap+qplRfnoy1ycbcBEO9HXE+rRinbxYyKWXA2IcYn/RC5Wegg5UpHKzMBI6GtMXN1gL3DemFn+Lz8PWFDLwbGSR0SGQA2IeQpjXK5LiYVgyARc6Nzch+TrAwFeN2eS2S8ivgz6mbghO3v0lLtbW1qKmpUd/OycnBjh07cPr0aY0FRqTP5HKFupZKfyal7mrcIOXVqdMpRQJHQtrCPsQ4cOqe8VowyhsAsP9yDirrGgWOhgwN+xDShKs5Zaiql7GelBGyMJVgTNPouBNJUoGjIaCLSalnn30WP/zwAwCgvLwc8+bNw/bt2/Hss8/i66+/1mR8RHopr7wWdY1ymEpE6ONgKXQ4Oi1ioHIRgpjUIjTI5AJHQ9qgqT7k4sWLWLZsGSIiIuDn54fo6Oh2n3P+/Hk8+OCDCAwMxJQpU7Bv374W2+zevRuTJk1CUFAQ5s2bh6tXr3Y4JvrTn0XOmZQyNmMGOKO/izWq6mU4EJcrdDhkYHgeQppwOqUQADCmvzPrSRmhe/zcAAC/MymlE7qUlLp+/TqGDx8OAPjll1/g4uKC48ePY8OGDS2KkxMZozSp8mTM28kKJpIu/ZkZjSG97WBnJkJVvQyXM0qEDoe0QFN9SHV1Nfz8/Dq8aEVWVhaeeeYZjBo1Cj/++CMef/xxvP766zh16pR6m8OHDyMqKgrLly/H/v374e/vj6VLl6KoiCP5Oiu9kCOljJVIpCx4DgC7z2dAoVAIHBEZEp6HkCacvqlMSkUMcmlnSzJEE3xdAQB/ZBRzRK8O6PL0PWtr5ZfM06dPY+rUqRCLxQgNDUVuLq+IEaUVVgIA+rnYCByJ7hOLRQh2NwcAnGq6akWGTVN9yIQJE/DSSy9hypQpHdr+22+/haenJ9asWYMBAwZg4cKFuO+++7Bjxw71Ntu3b8f8+fMxd+5cDBw4EGvXroWFhQX27t3bqTbSHdP3XLjYgzF6aJgnzEzEuJ5bjrisUqHDIQPC8xDqrqq6RsRmKi+EjmNSyij5uFjDx9kKDTIFzt7k+YfQOlzo/E7e3t6Ijo7GlClTcPr0aSxevBgAUFRUBBsbnoQTparqSblyhEBHhPQyw+msWpxKkeKV+/yEDod6mFB9SFxcHMaMGdPsvoiICKxbtw4AUF9fj+vXr+OZZ55RPy4WixEeHo7Y2NhOH08mk7X6r77qbDtU0/e8HS11qu3G+n5om625BNMDe+GHuFx8eyETwX1ar9mi6+3oKENpB9D5tmi7zTwPoe46n1aEBpkCno6W8OYq2UZrgq8r0mMycCJZiqlDegkdjlHrUlJq+fLleOWVVxAVFYUxY8YgLCwMAHDmzBkEBARoNEAifcRaKp0T0jRS6mpOGUqq6uFozZW6DJlQfUhhYSFcXJpfEXVxcUFlZSVqa2tRVlYGmUwGZ+fmS0M7OzsjNTW108eLj4+/62191ZF2VDfIUVhZDwCoyLuFuELdm8ZsTO+HUIY61OMHAAficvCAVz0sTNr+PdDldnSGobQD0N228DyEuku1uM64QS4QiVhPylhN8HPFzpgMdX0xEk6XklLTpk3DsGHDIJVK4e/vr75/zJgxmDx5ssaCI9JXTEp1jrOlBL7uNkjOr8Tpm4WYFeIhdEjUg4ylDwkKCoJEIoFMJkN8fLz6tr7qTDuu5ZQBKICTtRnGjhiqnQA7yBjfD6GEKBTYevUUMourkSN2x9zQPi220Yd2dIShtAPofFtU22uLsfQh1HNO31QWt44Y6CpwJCSkET5OkIhFyCyuRm5pDTy4OJVgupSUAgBXV1e4ujb/Qw4ODu52QET6rr5RjqziagBAfyalOmzcQBck51fiVIqUSSkjIEQf4uLigsLC5lfDCgsLYWNjAwsLC4jFYkgkkhZFzYuKilqMsOoIiUTS7ITur7f1VUfakVlSC0CZmNfVNhvT+yGk+cM98cGvyfj+cg7mj/Bucztdb0dHGUo7AN1uC89DqKvyy2uRnF8JkQgIH+Dc/hPIYNlamCKwjz2uZJXiXGoR5gz1FDoko9WlpFR1dTU+//xznDt3DkVFRZDLmy/jfuzYMY0ER6SPMourIVcA1mYSuNqaCx2O3ogY5IwvzqTjVEohFAoFh1MbMKH6kNDQUJw8ebLZfWfPnkVoaCgAwMzMDEOGDEFMTIz6artcLkdMTAwWLlzYIzEZKq68Rypzh3niw6PJuJBWjLTCKo4gpm7jeQh1x5mmotaBHvYsF0EY3d+JSSkd0KWk1Ouvv44LFy5g9uzZcHV15ckj0R3UU/dcrfm30Qkj+jrBzESMvLJa3JJWYqCbrdAhUQ/RVB9SVVWFzMxM9e3s7GwkJCTA3t4eHh4e2LhxI/Lz8/H+++8DAB555BHs3r0b77//PubOnYtz587hyJEj2LJli3ofS5YswerVqxEYGIjg4GDs3LkTNTU1mDNnTvcabWTSVCvvObOArLHrbW+J8b6uOJEkxf/+yMKqaf7tP4noLngeQt2hqh8UwVX3CMDo/s7Y8nsqzqUWCx2KUetSUurkyZPYsmULhg0bpul4iPReWmElAKCfC1eA6QxLMwlG9XPCqZRC/J5cyKSUAdNUH3Lt2jU89thj6ttRUVEAgAcffBDr16+HVCpFXl6e+nEvLy9s2bIFUVFR+PLLL9GrVy+8++67GDdunHqb6dOno7i4GJs2bYJUKkVAQAC2bt3apel7xkw9UoqjYgjA/OFeOJEkxd7L2Xh5ii9MJLpX+J70B89DqKsUCgVON42UihjIfp2a15XKKa1BH9aVEkSXklJ2dnZwcHDQcChEhiFVqjwZYz2pzhs3yAWnUgpxKkWKpRH9hA6Heoim+pBRo0YhKSmpzcfXr1/f6nN++OGHu+534cKFnK7XTRlFyrp6nKpFADA5wB1O1mbIL6/DqZRCTPR3Ezok0mM8D6GuSsqvQEFFHSxMxRjW11HocEgH2JibIKiPPeKySnHuVhHmDuMUPiF06VLVCy+8gI8//hg1NTWajodI76U2jRDo78qTsc4aN0hZtPRcahHqGmUCR0M9hX2IYSuvbUBRVT0AjpQiJTMTMSKbVt777mKWwNGQvmMfQl11PFG56t6Y/s6wMNXNIv6kfaP7Kwven0stamdL6ildGim1fft2ZGZmIjw8HJ6enjAxab6b/fv3ayQ4In2krinFk7FO8+9lC1dbc0gr6nApvQThHFptkNiHGDbV1D0XG3PYmHd5kV8yMA+P8MK2M2mITshHUWUdnG24EAh1DfsQ6qoTSQUAwNGa1Mzo/k74z++3cC6NSSmhdOnbompVIiJqrqK2AdKKOgAcIdAVIpEI4wa5YN/lHJxMKWRSykCxDzFsfybmWeSc/uTXyxYhnva4kl2G/bE5eHJcf6FDIj3FPoS6ory2AX9klAAA7vFlUor+NLyprlRWcQ2yS6rh6cjvL9rWpaTUihUrNB0HkUFIL1TWUXGxMYedhanA0einsQOUSakYDqE1WOxDDJvqc9DHmYl5am7ecC9cyS7Dnj+ysDSiH1dNoy5hH0JdcSalEDK5Av1dreHNlWHpDs3qSqUW46Fh/P3Qti4vf1JeXo7//e9/2LhxI0pLSwEA169fR35+vqZiI9I7qU0r77HIedeNGaCc1x2fXYry2gaBo6Gewj7EcKUXceU9at0DoR4wNxEjOb8SV7LLhA6H9Bj7EOqs401T9zhKilrDulLC6lJSKjExEffddx/++9//Ytu2baioqAAA/Prrr9i4caNGAyTSJ6wn1X0eDpbwcbaCXAFcSC0WOhzqAexDDBs/B6ktdhammB7UGwCw5w8WPKeuYR9CnaVQKHAiSVnkfKK/q8DRkC4a1c8JAPBHOs89hNClpNT69evx4IMP4tdff4WZmZn6/gkTJuCPP/7QWHBE+iZVyhECmjBmgLKWFKfwGSb2IYZNPVKK0/eoFfOGK5fbPhiXi5p6rrJKncc+hDrrRl45CirqYGkqwcim5APRnYZ6OwIA0ouq1fWBSXu6lJSKj4/HI4880uJ+d3d3SKXSbgdFpK9SCpTT9wa62QgciX5TTeE7e4tJKUPEPsRwlVbXo7RaOe3Wh4XOqRWj+znD28kKFXWN+Pl6ntDhkB5iH0KdpRolNXagM8xNJAJHQ7rI3soUvu7K87dLTQXxSXu6lJQyMzNDZWVli/vT09Ph5MTsMxknmVyBW1Ll34XqQ426ZkzTvO6EvHIUV9ULHA1pGvsQw6WauuduZw4rsy6tpUIGTiwW4aFhytFSey5mCxwN6SP2IdRZJ5rqSU3wYz0patuwvsrPj0sZnMKnbV1KSk2aNAmffvopGhr+LEKcm5uLDz74AFOnTtVYcET6JLO4GvWNcliYiuHFpUS7xdXWXJ3YO88pfAaHfYjh4tQ96oi5wzwhEimnaGcVVwsdDukZ9iHUGWXVDbicWQoAuMeX9aSobSN8lFP4/uBIKa3rUlJqzZo1qK6uxpgxY1BXV4dFixZh6tSpsLa2xksvvaTpGIn0QnK+stDmQDcbiMVc5rq7VKOlOIXP8LAPMVxphcoEA4uc0930cbBExEBl7cC9l3MEjob0DfsQ6oxTN6WQyRUY6GYDLydeNKa2DW8aKXUtpwy1Dax5qE1dGltva2uL7du349KlS0hMTER1dTWGDBmC8PBwTcdHpDdSmpJSg9xsBY7EMIwZ4IKdMRk4e6tQ6FBIw9iHGK70Qi72QB3z0DBPnEopxL7YHIyfbC90OKRH2IdQZ6jqSXGUFLXHy8kSrrbmkFbU4Wp2GYvia1Gnk1JyuRz79u3D0aNHkZOTA5FIhD59+sDV1RUKhQIiEUeIkHFKzlfWNxjEelIaMbq/E0Qi4Ja0CgXltXCzsxA6JNIA9iGGjdP3qKPuG9ILthYmyCmtxbUCSwwVOiDSC+xDqDPkcoU6KTXRn/Wk6O5EIhGG93XEkWu38UdGMZNSWtSp6XsKhQJ///vf8frrryM/Px++vr4YOHAgcnNzsWbNGixfvryn4iTSeaqV93w5UkojHKzMMLi3HQBl3RHSf+xDDJtCoVAXOuf0PWqPhakEs0M9AAC/pdcIHA3pA/Yh1Fk38spRWFkHKzMJhjfVCyK6m2F9lb8nl9JZV0qbOjVSat++fbh48SJ27NiB0aNHN3ssJiYGy5cvxw8//IDIyEhNxkik85qvvMeklKaED3DG9dxynL1ZhNmhfYQOh7qJfYhhK66qR0VtIwCgrzPrdlD75g3zwlfnMnE+uxblNQ1wtOFS7dQ29iHUWb8lKlfdCx/gAnMTfr5Q+4b7NK3Al1kCuVzBOsFa0qmRUj/99BOWLVvWoiMAgDFjxuDpp5/GwYMHNRYckb64c+U9T0dLocMxGOEDlIVwOVLKMLAPMWyqqXse9hawMOWXf2pfsKc9BrnZoF4OHLqaJ3Q4pOPYh1BnRSfkAwCmDObUPeqYIR52sDAVo7S6AamFlUKHYzQ6lZRKSkrCuHHj2nx8/PjxSExM7HZQRPpGVU+KK+9p1oh+TpCIRcgsruay4QaAfYhhU628xyLn1FEikQjzhilHwX7PVfioHexDqDPyy2txNbsMAOtJUceZSsQI8XQAAPzBKXxa06mkVFlZGZydndt83NnZGWVlZd0OikjfsJ5Uz7AxN0Gwp3JVJo6W0n/sQwwbV96jrpgd6gGJCLiSXYbkplVsiVrDPoQ641iCcupeqJcD3Gy5WA51nKr+2B8ZTEppS6eSUjKZDCYmbZehkkgkkMlknQ5i9+7dmDRpEoKCgjBv3jxcvXr1rtsfOXIE06ZNQ1BQEGbNmoXff/+92eN+fn6t/mzdulW9zaRJk1o8/vnnn3c6diIAuFmgWnmPSSlNCx+g/AJ67haTUvqup/oQ0g1pTdP3+nHlPeoEFxtzDOttDgD43x9ZAkdDuox9CHXGn1P33AWOhPTNUG9lUiouq1TYQIxIpwqdKxQKrFmzBmZmZq0+Xl9f3+kADh8+jKioKKxduxYhISHYuXMnli5dip9//rnVqyGXL1/GypUr8fLLL2PixIk4ePAgli9fjn379sHX1xcAcPr06WbPOXnyJP7xj3/gvvvua3b/888/j/nz56tvW1vzizR1jXqklLuNwJEYnjH9XfDp8Vs4e6uIyz3ruZ7oQ0h3cKQUddWkfpa4kFuH/bE5WDXNH6aSTl0zJSPBPoQ6qrq+EadvFgIAJgcwKUWdE+rlAEA56KCspgH2lqbCBmQEOtXrP/jgg3B2doatrW2rP87Ozp1e8WL79u2YP38+5s6di4EDB2Lt2rWwsLDA3r17W93+yy+/xLhx4/Dkk09iwIABePHFFzF48GB89dVX6m1cXV2b/Rw7dgyjRo2Cl5dXs31ZW1s3287KiqsFUefJ5AqkNq28N4jT9zRuaF8HmIhFuF1ei+wSLhuuz3qiDyHdoFAo1Empfi7sS6lzwnqZw9naDIWV9TiRJBU6HNJRmu5DLl68iGXLliEiIgJ+fn6Ijo5u9znnz5/Hgw8+iMDAQEyZMgX79u3rRouop5xOKUR9oxyejpa8YEyd5mxjDm8n5XeZq9mlwgZjJDo1UioqKkqjB6+vr8f169fxzDPPqO8Ti8UIDw9HbGxsq8+Ji4vD4sWLm90XERHRZkdSWFiI33//HevXr2/x2H//+1/8+9//Ru/evTFz5kwsXrz4rsOCW6Pvw4RV8et7O4Qik8lwu0qGepkClqYS9LYz42vZSe39DppLRAjytEdsZilibhXCw76PNsPTC9r4O9bEvjXdh5DukFbWoapeBpEI8HJiUoo6x0QswoNhHth6Oh17/sjidBtqlab7kOrqavj5+WHu3LlYsWJFu9tnZWXhmWeewSOPPIIPPvgAMTExeP311+Hq6nrXAuykfaqpe5MD3DnCnrokzNsBmcXViM0sxbhBrkKHY/A6l4HRsJKSEshkshbT9JydnZGamtrqcwoLC+Hi4tJi+8LCwla3379/P6ytrTF16tRm9y9atAiDBw+Gvb09YmNj8eGHH0IqleLVV1/tVBvi4+M7tb2uMpR2CCG7vBEA4GEjwtWrVwSORn/d7Xewr2UDYgH8fOkmBoh5Fb0t/DsmoaQ3rbznYW8JcxOJwNGQPnpomCe2nk7H8cQCSCvq4GprLnRIZOAmTJiACRMmdHj7b7/9Fp6enlizZg0AYMCAAbh06RJ27NjBpJQOkckV6iLnTHBTV4V5OeDHuFzEZrLYuTYImpTShr1792LWrFkwN2/+5WbJkiXq//v7+8PU1BRvvvkmVq5c2eZc9dYEBQVBItHfL+AymQzx8fF63w6hyGQyfH8jBgAQ7OOG0NBggSPSPx35HSyxlOKHpEu4VSFCaGiodgPUA9r4O1Ydg6g1f07dYz0p6ppBbjYI8XLAlaxS/BCbg6fG9xc6JKJm4uLiMGbMmGb3RUREYN26dQJFRK2JyypFUVU9bM1NMMLHSehwSE+F3VHsnDVte56gSSlHR0dIJBIUFTVfVauoqKjFaCgVFxeXFqOi2tr+jz/+QFpaGj766KN2YwkJCUFjYyOys7PRv3/HvwhJJBKDSOYYSjuEkF6mHCnl38uOr2E33O13cGR/Z4hEQEZRNQqrGuBux6V9W8O/YxKKauU9H9aTom6YP9wTV7JK8b9LWXhyXD+eBJBOaW22houLCyorK1FbWwsLi45/N2lrSryxlNXoyXZG37gNABjv6wKJSCHoa8n3U3/5ulnDzESMkuoGpEor4ONsbZDt/CtNt7Gj+xE0KWVmZoYhQ4YgJiYGkydPBgDI5XLExMRg4cKFrT4nNDQU586da1ZX6uzZs62Onvj+++8xZMgQ+Pv7txtLQkICxGJxqyv+Ed1NemkDAGCwh53AkRguOwtTDO5th+u55biQVoxZIR5Ch0REd1CvvOfMkVLUdbNCPPD2wRtIzq/Elewy9QpIRIamvZHHxjIyuSfaeShWOXhhkFUN4uLiNL7/ruD7qZ/62UuQVCTHj6evYkJfS/X9htbO1mi7jYJP31uyZAlWr16NwMBABAcHY+fOnaipqcGcOXMAAKtWrYK7uztWrlwJAHjsscewaNEibNu2DRMmTMDhw4dx7do1vP322832W1lZiZ9//hmrV69ucczY2FhcuXIFo0ePhrW1NWJjYxEVFYUHHngA9vb2Pd9oMhiVdY24XanMAAf0ZlKqJ43s58SkFJGOSi9S1pTi9D3qDjsLU9wf2As/xOXiu4uZTEqRTmlttkZhYSFsbGw6NUoKaLv8h7GU1eipdmYUVSOr/DYkYhEemzIc9pamGtt3V/D91G9jcxOQdCYDJSJ7hIYONth23knTbexo+Q/Bk1LTp09HcXExNm3aBKlUioCAAGzdulU9PDYvLw9isVi9/dChQ/HBBx/go48+wocffggfHx98+umn8PX1bbbfn376CQqFAjNnzmxxTDMzMxw+fBibN29GfX09PD09sXjx4mZ1pog6Iul2BRQA3G3N4WLDoqw9aVQ/J2w/k44LacVCh0JEd1AoFMhQT99jUoq6528jvfFDXC5+jMvFa9MDYGsh7EklkUpoaChOnjzZ7L62Zmu0p73p9sYyHV/T7TyerEwajvBxhJON7pR64Pupn8K8nYAzGbiSU9asXYbWztZou42CJ6UAYOHChW1O19u1a1eL++6//37cf//9d93nww8/jIcffrjVx4YMGYI9e/Z0PlCiv0jIqwDAUVLaoCpWmZRfgZKqejhad3xBAjJcu3fvxhdffAGpVAp/f3+88cYbCA5ufcGBRYsW4cKFCy3unzBhAj7//HMAwJo1a7B///5mj0dEROCLL77QfPAGoqCiDtX1MohFgJcja0pR94zs54QBrta4Ja3Cj3G5WDi6r9AhkYGqqqpCZmam+nZ2djYSEhJgb28PDw8PbNy4Efn5+Xj//fcBAI888gh2796N999/H3PnzsW5c+dw5MgRbNmyRagm0F8cS8gHAEwO4Kp71H1h3g4AgBu55ahtkMFUfPftqet0IilFpK9u5JUDAAJ62wocieFztjHHQDcb3CyoxMX0Ykwd0kvokEhghw8fRlRUFNauXYuQkBDs3LkTS5cuxc8//9xqfcBPPvkEDQ0N6tulpaWYPXs2pk2b1my7cePGISoqSn27MyuyGqO0pnpSno5WMDPhNzbqHpFIhL+N9Ma7PyXg6/OZWDDKmwXPqUdcu3YNjz32mPq26nP/wQcfxPr16yGVSpGXl6d+3MvLC1u2bEFUVBS+/PJL9OrVC++++y7GjRun9dippbLqBpxvGk0/ZTCTUtR9fRws4WprDmlFHa7llCHMi2V+egqTUkTdoBopNZgjpbRiZD8n3CyoxIU0JqUI2L59O+bPn4+5c+cCANauXYsTJ05g7969ePrpp1ts7+Dg0Oz2Tz/9BAsLixZJKTMzM7i6uvZY3IZGXeScU/dIQ+YO9cT7vyThRl45rmaXIYS1pagHjBo1CklJSW0+vn79+laf88MPP/RgVNRVJ5ILIJMrMNDNBn256AZpgEgkQpiXA369kY+4rFImpXoQk1JEXdQokyMpXzV9jyOltGFUPyd8fT4TF9JZV8rY1dfX4/r163jmmWfU94nFYoSHhyM2NrZD+9i7dy9mzJgBK6vmU84uXLiAMWPGwM7ODqNHj8aLL74IR0fHTsf412V19X0J4bbakSqtBAD0dbLUizYa+vuhb1prh52FBPcPccePV/Kw+3wGAj10v481lPcD6HxbDKHNpP+OJRQA4NQ90qxQb2VSKjazFEvCOZ28pzApRdRFyfmVqGuUw9JEhL5OrKOiDaq6UtdyylBZ1wgbc36EGauSkhLIZLIW0/ScnZ2Rmpra7vOvXr2K5ORkvPfee83uHzduHKZMmQJPT09kZWXhww8/xFNPPYXvvvuu0wUf/7raiKEsIfzXdlxJLQEAmNaW6Mzy2x1hqO+HvvprO4Y51uNHAAdiczDLsx5WelLMw1DeD8Cw2kKGrUEmx/EkZVJqymA3gaMhQxLmpbwoGZtZInAkho1ndERdFJ9TCgAY4GgKsZj1LrTBw8ESXk6WyCquwaWMEkzw5RQr6prvv/8evr6+LYqiz5gxQ/1/Pz8/+Pn5YfLkyerRU52hWk7XUJYQbqsdJSdPA6jD2GBfhPrp/t+kob8f+qatdoQoFNh5/TRuSauQLnfBo6HeAkbZPkN5P4DOt6WjS34T9ZSLacWoqG2Es7UZQr06P7KZqC3BnvYQi4Dcslrkl9cKHY7BYlKKqIuuZJcBAAY4cblqbRrp44ys4mxcSCtiUsqIOTo6QiKRoKioqNn9RUVFcHFxuetzq6ur8dNPP+H5559v9zheXl5wdHRERkZGp5NSf11O11CWEL6zHXK5AhlF1QCAAW62etU+Q3w/9Flr7VAVPP/mYjYWjvHRi4LnhvJ+AIbVFjJsR5tW3Zvo7wYJLxSTBlmbm8DX3RaJtysQl1UGTg7tGfoxFppIB13NLgUADHRkUkqbRvVXTuG7kMa6UsbMzMwMQ4YMQUxMjPo+uVyOmJgYhIWF3fW5P//8M+rr6/HAAw+0e5zbt2+jtLSUhc/bcLu8FnWNcpiIRfB0tBQ6HDIwc4d6wsxErC54TkT0VwqFAtFNSSnWk6KeEOatHH0Xl1UqbCAGjEkpoi6obZAhsWnlvYFOHHCoTaP6KZNSV7LKUNvA4qrGbMmSJdizZw/279+PW7du4a233kJNTQ3mzJkDAFi1ahU2btzY4nnff/89Jk+e3KJ4eVVVFTZs2IC4uDhkZ2cjJiYGzz77LPr27cslv9ugWnnPy8kKJhJ+pSDNcrQ2w/RA5Uqru89nCBwNEemilIJKZBXXwMxEjHGD7j5SmqgrwrwdADAp1ZN4Nk3UBYm3K9AoV8DJyhSuVhzark3eTlZwtzNHfnkd4rJKMbq/c/tPIoM0ffp0FBcXY9OmTZBKpQgICMDWrVvV0/fy8vIgFjdPlKSmpuLSpUvYtm1bi/1JJBIkJyfjhx9+QEVFBdzc3DB27Fi88MILMDMz00qb9E1akTIp5ePMxR6oZywY3Rc/xOXix7hcvHp/AByt+bdIRH86ekM5Sip8gDOsuQAO9YChTUmp+JxyyIYx8dkT+JdL1AWqqXtBnvZ6UePCkIhEIozwccKhq3m4mFbMpJSRW7hwIRYuXNjqY7t27WpxX//+/ZGUlNTq9hYWFvjiiy80Gp+hU42U8nGxFjgSMlTD+zpiiIcdrueW49uLWfj7PQOEDomIdAin7lFP6+9iA1sLE1TUNiKzvBHDhA7IAHGsPVEXXMpQLgsa6ukgbCBGamTTFL4L6awrRSSktEJlkfN+TEpRDxGJRFgc7gMA2BWTjkaZXNiAiEhnFFTUqqdUMSlFPUUsFiHUywEAkFzUIGwwBopJKaIuUCWlhvV1EDYQIzXCR5mUupxRwhMUIgGlq6fvMSlFPWdWiAecrM2QW1arnqpDRPRbQgEUCiDY0x697C2EDocMWJg6KVUvbCAGikkpok7KL69FdkkNxCIgpOkDirTLz90WdhYmqKqX4UZeudDhEBklmVyBjKakFEdKUU+yMJXg0ZHeAIDtZ9OFDYaIdAan7pG2hDbVlUop5kipnsCkFFEn/ZGuHCXl38sONiyoKAixWKQeLXUhjVP4iISQU1KDBpkCZiZieDhYCh0OGbiFo/vCRCzChbRiXM8tEzocIhJYdX0jTqUUAgCmDGZSinpWqJdyxeacChnKapiY0jQmpYg6STV1b7iPYztbUk8a0VRX6iLrShEJIrWwEgDQz9kaEjEXfKCe1cveAvcH9QYA7DiTLmwwRCS40ymFqGuUo4+DJfx72QodDhk4J2sz9G1aaVhVx4w0h0kpok66lKFMggzry6SUkFQjpS6ml0ChUAgcDZHxSZVy6h5pl6rg+Y9XclFUWSdsMEQkKFV9uSmD3bkSNmmFqq7UlSyO1tU0JqWIOqGyrhHXcpU1jIY3JUVIGEF97GFhKkZxVT1uSSuFDofI6KQVNiWlXJmUIu0Y6u2AYE971DfK8e3FLKHDISKByOQK/JZYAIBT90h7Qr3sAQCXOVJK45iUIuqEC2lFkMkV6OtshT6soSIoMxOxennWC2klwgZDZIRUSan+HClFWiISidSjpXbFZKCBq68SGaW4rBIUVdXD1sIEI/vxIjFpR6h6pFQp5HLO0tAkJqWIOuHszSIAQPgAZ4EjIQAY2U/5PrCuFJH2pTaNUOzPkVKkRTOCe8PFxhy3y2txIC5X6HCISAC/Nk3dm+jnBlMJT2dJO/x72cJMApTXNiK16cIcaQb/iok64ewtZVJqzAAXgSMhABjJFfiIBFFTL0NuWS0AoJ+LjcDRkDExN5HgiQgfAMCWk7d4tZrICEU3JaUmc+oeaZGpRIwBjqYAgMuZnKWhSUxKEXXQ/2fvzuOiqt4Hjn9mhlVkk8UF3EAFRRDcRdEkNddyt0xNMzWzxbRMrUzbrH5Wpra4ZWqmmVuZpqm5Ju647yiIG7Jvss7c3x/EfCMXFoELw/N+vXiVd+7cec5luHfmOec8Jz41k7O3cupJtfGQkVJlQUAtB3RaDTcS0riRkKZ2OEJUGOGxOT2E9tbmOFYyVzkaUdE826o2lS3NuBiVYqwrI4SoGK5EpxAWnYqZVsNjXi5qhyMqGC8nCwBCryWoG4iJkaSUEAUUciVnlFSDqpVxsbVUORoBYGNpRuMadgAcltFSQpQaYz0pFxtZ9UiUOntrc55tXQuA73aHqRyNEKI0bT+XM0qqtYcTdlbSKSJKVwOnnPdcqIyUKlaSlBKigHb+0xsbVF96ZcqS3AKXh6SulBClJreeVF0pci5UMrJtXSx0Wo5ExEtdQSEqkG3/TN2TVfeEGhpUyUlKXYhKJiUjW+VoTIckpYQoAINBYeeFnKRUsLerytGIf2shdaWEKHVXZOU9oTJXOyv6NXMD4LtdMlpKiIogNiWDoxE5I1Qebyifx0Xpc7TW4eZghaLAycgEtcMxGZKUEqIATt1IJCYlk8qWZsYkiCgbcn8fl++kEJeaqXI0QlQM/5u+J0XOhXpGBXmg0cCO83e4cDtZ7XCEECVs54VoDAo0rG6Hu2MltcMRFZR/TQdAip0XJ0lKCVEAuYVU29VzxsJM/mzKEkcbC+q75nwxlikcQpQ8RVG4Ep2TlJLpe0JNHi6V6da4GgDzpbaUECZv29nbgEzdE+oKqOUASLHz4iTfroUogNyiisEyVLhMavFPXSkpdi5EyYu/m0ViWhYAdZwkKSXU9WIHTwB+PXGT6/F3VY5GCFFS0rP07LkYA0DnhpKUEuoJ+GekVGhkAoqiqBuMiZCklBD5CI9J5czNJHRaDY9LPakyqZUUOxei1ORO3XNzsMbaQqdyNKKi83N3oG09J/QGRVbiE8KE7Q+LIS1LTzU7Kxq72akdjqjAGlW3w8JMS1xqJhGx0hlSHCQpJUQ+fj95E4BATyecKluqHI24n9y6UmduJpEqK2EIUaKuxsrUPVG2vBJcH4CfD0dyIyFN5WiEECVh29mcUhqdGrmi0WhUjkZUZBZmWhrXyEmMSl2p4iFJKSHy8fvJWwD09KuuciTiQWo4WOPmYI3eoMjNQYgSdlXqSYkyprWHE208nMjSK3y987La4QghipnBoBhLaXRuVE3laISAprUcAakrVVwkKSXEQ1yKSub87WTMtBqe8JGbYFnWUupKCVEqrv4zVF2SUqIsGd8pZ7TUL0cipbaUECbm5I1EopMzsLHQ0dpDVsEW6gvITUpFSmd4cSgTSakVK1YQHByMr68vAwYM4OTJkw/d/48//qBr1674+vrSq1cvdu/enefxyZMn4+Xlledn5MiRefZJSEhg4sSJNG3alObNmzN16lRSU1OLvW2ifPv5cCQAj3m54FDJQuVoxMPkTuE7KEkpIUpU2J0UADxcJCklyo5WHk4EeuaOlpLaUkKYku1nc0ZJdfBywdJMahkK9eWuwHfuVjJ3M6V0yKNSPSm1efNmZs6cybhx41i/fj3e3t6MHDmS2NjY++5/7NgxJk6cSP/+/dmwYQOPP/4448aN4+LFi3n2CwoKYt++fcafL774Is/jb7zxBpcvX2bJkiV89913HDlyhGnTppVYO0X5k5GtZ+2x6wA807KWytGI/OSOlDoemUBGtl7laIQwTdkGhfB/Rko1qGqrcjRC5PV65wZAzmipyDgZLSWEqdh2Nnfqnqy6J8qGGg7WVLOzQm9QOHU9Ue1wyj3Vk1JLlixh4MCB9OvXj3r16jFjxgysrKxYu3btffdftmwZQUFBvPDCC3h6ejJ+/HgaNWrEjz/+mGc/CwsLXFxcjD/29vbGx8LCwti7dy8ffvghTZo0oXnz5rzzzjts2rSJqKioEm2vKD+2noki/m4W1eys6NDARe1wRD48XWxwsrEgI9vA6RtycxCiJNxO0ZNtULCx0FHd3krtcITIo0WdKrSr50y2QWpLCWEqrsXe5UJUMjqtho5esgq2KDtyR0sdk7pSj0zVpFRmZiZnzpwhMDDQuE2r1RIYGEhoaOh9n3P8+HHatGmTZ1u7du04fvx4nm2HDh2iTZs2PPHEE7z33nvEx/9vvmdoaCh2dnb4+voatwUGBqLVavOdOigqjuUh4QAMbFETM53q+VuRD41GQ/M6OfO7D12V+d1ClITIpJwh6vVcK8vqR6JMyq0ttebodRktJYQJ2PZPgfMWdRyllIYoU3KLncsiS4/OTM0Xj4+PR6/X4+TklGe7k5MTV65cue9zYmJicHZ2vmf/mJgY47+DgoLo3Lkz7u7uREZG8sUXXzBq1Ch+/vlndDodMTExVKmSt0iemZkZ9vb2REdHF6oNen35niaUG395b0dxOxIez+HweCx0Gp5u7vbA8yPn79EV5zlsXtuRrWeiOHglltFBdR75eOVFabwP5T0uAK4bk1IydU+UTc3rVCGovjN7L8UwZ8cl/m9AE7VDEkI8gtx6Up0aytQ9UbY0rf1PUioiHkVRpLPuEaialCopPXr0MP5/bqHzTp06GUdPFadTp04V6/HUYirtKC6f7cvJeLevZcWtK+e5lc/+cv4eXXGcQ/vMLAAOXY3haGgougp2c5D3oShpuUmp+lUrqxyJEA/2eucG7L0Uw9pj1xnTwZN6rvJ+FaI8SribyaHwnAVspJ6UKGsau9lhaaYlNjWTqzGpeLjIvaaoVE1KOTo6otPp7ilqHhsbe89oqFzOzs55RkXltz9AzZo1cXR0JCIigjZt2uDs7ExcXN4VurKzs0lMTMTFpXC1g3x9fdHpyu8qEHq9nlOnTpX7dhSnk9cTOXrrNhoNTO7d/KHLnsv5e3TFeQ4b6w3M2LOD1Ew9lap50rC6XTFFWbaVxvsw9zVExWZMSsmXfFGGNa3lSOdGVdl2NopZWy/w3dBmaockhCiCXRei0RsUGlStTG0nWfFVlC2WZjqa1HTg0NU4joTHS1LqEaialLKwsMDHx4eQkBA6deoEgMFgICQkhCFDhtz3Of7+/hw4cIDhw4cbt+3fvx9/f/8Hvs7t27dJSEgwJpwCAgJISkri9OnTNG7cGIADBw5gMBjw8/MrVBt0Op1JJCNMpR2PSlEUZv5xAYA+/m7Uq1qwpIacv0dXHOdQp9PRtLYjey/FcCQigcbujsUUXflQEd+HK1asYPHixURHR+Pt7c277777wOv4unXrmDJlSp5tFhYWeZJtiqIwZ84cfvnlF5KSkmjatCnTp0+nTp06JdmMckFvULiRnJuUkul7omx78wkvdpyLYsuZ2xyPTMC/poPaIQkhCim3npRM3RNlVfPajhy6Gsfh8DgGtqipdjjllurVm0eMGMHq1atZv349YWFhTJ8+nbS0NPr27QvApEmT+Pzzz437Dxs2jL179/L9998TFhbG3LlzOX36tDGJlZqayqeffsrx48e5fv06ISEhvPTSS9SuXZugoCAAPD09CQoK4t133+XkyZMcPXqUDz74gB49elC1qlz0KrItp29zKDwOK3MtbzzhpXY4oghae+TUqAu5EpvPnqK827x5MzNnzmTcuHGsX78eb29vRo4cec/o23+rXLky+/btM/7s3Lkzz+MLFy5k+fLlTJ8+ndWrV2Ntbc3IkSPJyMgo6eaUeZHxd8kygKWZFjdHa7XDEeKhGlS1pW9TdwA+/eM8iqKoHJEQojAysvXsvpBT61em7omyqkWdnDrVRyKk2PmjUL2mVPfu3YmLi2POnDlER0fTsGFDFi1aZJyOd+vWLbTa/+XOmjZtyqxZs5g9ezZffPEFderU4euvv6ZBgwZAzkiBixcvsmHDBpKTk3F1daVt27a89tprWFj8b8WGWbNm8cEHH/Dcc8+h1Wrp0qUL77zzTuk2XpQp8amZTPvtDACjgzyo4SBfusqj3KTUwatxGAwKWm3FqitVkSxZsoSBAwfSr18/AGbMmMGuXbtYu3Yto0ePvu9zNBrNA6dpK4rCsmXLGDt2rHH07meffUZgYCDbt2/PU6+wIrp8JxUATxcbdPJ3JcqB8Z3q89vxm4RciWXPpRg6NChciQYhhHoOXokjJSMbF1tLmrg7qB2OEPfVtJYjGg1cjUklOjkDF1tLtUMql1RPSgEMGTLkgdP1li9ffs+2bt260a1bt/vub2VlxeLFi/N9TQcHhzwjsETFpigK0347Q3RyBp4uNrzUsZ7aIYki8nO3x8ZCR8LdLM7dTsKnhr3aIYkSkJmZyZkzZxgzZoxxm1arJTAwkNDQ0Ac+7+7du3Ts2BGDwUCjRo2YMGEC9evnLCF//fp1oqOjCQwMNO5va2tLkyZNCA0NLXRS6r+rIpb3FQwvRSUBOUmp8twWU/l9SDvyV93OkiGta/L93xF89sd5Aus6llhHhan8PqDwbTGFNouyZ5tx1T1X6WAUZZZ9JXO8qtpy/nYyRyPi6dq4mtohlUtlIiklhNqW/B3OxhM30Wrg84H+WJlXrLo8psRcp6VF3SrsuhBNSFisJKVMVHx8PHq9HicnpzzbnZycuHLlyn2fU7duXT7++GO8vLxITk7m+++/5+mnn2bTpk1Uq1aN6Oho4zH+e8z/LrBREP8tDF/eC8UfuZgAQGVDCsePH1c1luJQ3n8fuaQdDxfkZGClmYYzt5L45vcDtKtVsqOgTeX3AabVFlG+KIrCdqknJcqJZrUdOX87mSPhcZKUKiJJSokKb8e5KD7cdBaAKd0aSjFUE9DGw4ldF6I5cCWWF4I81A5HlBEBAQEEBATk+Xf37t1ZtWoV48ePL/bXy10N0VRW6Yz5ez+QTtvGnvj7Vlc7nCIzld+HtKPgxqReZvaOy6y9lMWYHq0w1xV/SVVT+X1A4dsiq7OK4nbmZhK3EtOxNtfRtt6DV1gXoixoUacKKw5e47DUlSoySUqJCm3n+TuM/fEYBgX6N3PnhaC6aockikEbz//VldIbFKl/Y4IcHR3R6XT3FDWPjY011iTMj7m5OQ0bNuTatWsAxlpTsbGxuLq65jmmt7d3oWP872qI5Xl1RINB4UpMTk0pr2q25bYd/1aefx//Ju3I36j2nvx48BrX4u7yy7GbDG1du0ReB0zn9wGm1RZRvuRO3Quq7yyzF0SZ17xOzmrfZ24kkpapx9pC3rOFpfrqe0Ko5ccDEbyw7AiZegNdfaoxs68vGo0kL0yBTw17bK3MSE7P5szNRLXDESXAwsICHx8fQkJCjNsMBgMhISF5RkM9jF6v5+LFi8ZklLu7Oy4uLnmOmZKSwokTJwp8TFN1Kymdu5l6zDRQq0oltcMRolBsLM14JTindtycHZe4m5mtckRCiIcxTt2TVfdEOeDmYE11eyuyDQrHIxPUDqdckqSUqHAS07IYvyqUdzacRm9Q6BPgxtzBASUynF+oQ6fV0KpuzhKtIWGx+ewtyqsRI0awevVq1q9fT1hYGNOnTyctLY2+ffsCMGnSpDwLWsybN499+/YRGRnJmTNnePPNN7l58yYDBgwAclbmGzZsGN9++y07duzgwoULTJo0CVdXV+NqfBXVpahkAKrbmmEm10pRDj3Tsha1qlQiOjmD7/ddVTscIcQD3EhI48zNJDQaeNzbNf8nCKEyjUZD8zo53zsOh8epHE35JNP3RIWhNyj8ciSSWX9eJCYlA60G3njCi7EdPGWElAlq7eHE9nN32B8Wy5gOnmqHI0pA9+7diYuLY86cOURHR9OwYUMWLVpknL5369YttNr/JVCSkpJ49913iY6Oxt7eHh8fH1atWkW9ev9bbXPUqFGkpaUxbdo0kpKSaNasGYsWLcLSsmIv8Xv5TgoA7nbysUGUTxZmWiZ2acBrq47z3e4rPN2yFs6VK/bftRBl0Z9nbgPQrJYjTvI3KsqJFnUc2XjiJoeuSlKqKOTTpagQ9l2K4cNNZzl/O6e3v66zDbMGNKFZbUeVIxMlJbeu1OHwOLL0BhkJZ6KGDBnCkCFD7vvY8uXL8/x76tSpTJ069aHH02g0vPbaa7z22mvFFqMpuPjPSCl3O6mTIMqvXn41WLj3CqdvJDF3xyVmPNVY7ZCEEP+x9Z+klKxiJsqT1h453zuORMSRmW3Awky+dxSGnC1h0i7fSWHkD4cZsvgg528nY29tzrSejdg6vr0kpExcw2p2OFQy526mnpPXpa6UEI8iN6Ff295c5UiEKDqtVsPU7g0BWHHwGlf/Kd4vhCgb4lIzjSNNnvCRpJQoP+q7VsbJxoL0LAMnrieoHU65I0kpYZLiUzN579fTPDF7DzvO38FMq2FE2zrsfvMxnm9XV7LXFYBWq6HNP70Wf1+OUTkaIcqvbL2BC/8kpeo4yABrUb4FejrT0cuFbIPCZ1vOqx2OEOJftp+NwqBAo+p21JRFNUQ5otFojKOlDkg920KTb+bCpBgMCisPXeOxWbtYGhKB3qDQqWFV/ny9Pe/18sGhkoXaIYpSFFQ/Z1W1PRejVY5EiPLrakwqGdkGbCx0VLWR6Xui/JvcrSFaDfxx+jZHI+LVDkcI8Y/cqXsySkqUR63/KR0SckWSUoUlSSlhMi5GJTNwfghT1p0iMS2LhtXt+OmFVix6rjkeLpXVDk+oIKh+TsHr0MgEktKzVI5GiPLp7K0kABpUs0Uri0IIE+BVzZYBzWoCMHPzORRFUTkiIURKRjZ7/xnZ/kTjqipHI0ThtfHIWYHvaEQ8Gdl6laMpXyQpJco9RVFYuj+cnnP3cSQinkoWOt7t2YiNL7clsJ6z2uEJFdWsUgkPZxv0BoUQGUorRJHkJqUaVbNVORIhis/rnRtgZa7lSEQ8W89EqR2OUMmKFSsIDg7G19eXAQMGcPLkyQfuu27dOry8vPL8+Pr6lmK0pm3XhTtkZhuo41QJr6pyvxHlj6dLZZwrW5KRbeD4tQS1wylXJCklyrX41ExGLTvKe7+dITPbQLC3K9sndGBku7qYyWprgv+NlpIpfEIUzblbOfWkvKvLlwRhOqrZW/FCOw8APttyniy9QeWIRGnbvHkzM2fOZNy4caxfvx5vb29GjhxJbOyDO7EqV67Mvn37jD87d+4sxYhNW25y+AmfamhkVK4oh3LqSuWMljpwJU7laMoX+dYuyq0Lt5PpOXcf289FYaHT8l6vRix+rjk1HKzVDk2UIbl1pfZekmLnQhTFuX9GSjWsZqdyJEIUrzEdPHCyseBKTCqrDkeqHY4oZUuWLGHgwIH069ePevXqMWPGDKysrFi7du0Dn6PRaHBxcTH+ODvLiPzikJGtZ+f5OwA80VjqSYnyq42xrpR87ygMSUqJcmnnhTv0+3Y/NxLSqO1UiXUvBTKibV3pWRH3aOPphLlOw7W4u4TL8t9CFEp0cgbRyRloNOBVTWrzCdNia2XOa53qA/DV9oukZGSrHJEoLZmZmZw5c4bAwEDjNq1WS2BgIKGhoQ983t27d+nYsSMdOnRg7NixXLp0qTTCNXn7L8eSkpGNq60l/u4OaocjRJHlrsB37FoC6VlSV6qgZG1nUe6sOBjBuxtOY1CgVd0qfDekGY42sqqeuD8bSzOa1nLk4NU49l6Kpo6zjdohCVFu5I6SqutkQyUL+cggTM8zLWux5O9wrsaksmB3GBO6eKkdkigF8fHx6PV6nJyc8mx3cnLiypUr931O3bp1+fjjj/Hy8iI5OZnvv/+ep59+mk2bNlGtWuFG9+j19/+ymrv9QY+biv+284/TtwDo3NAVRTFgKs2vqL9PU1WQdtZ2tMLV1pI7yRkcDY81JqnKi+L+XRb0OPIJU5QrPx6I4J0NpwEY2NydD3v7YmEmA/7Ew7Vv4MLBq3HsuRTD0DZ11A5HiHIjt8h5w+oydU+YJnOdlre6evHij8dYuPcqz7auTVU7K7XDEmVQQEAAAQEBef7dvXt3Vq1axfjx4wt1rFOnTj3S46bi1KlT6BWFLady6n56WqVw/PhxdYMqARXp91kR5NfOBg4a7iTD+v1nsUoqn/U4S/t3KUkpUW6sPHTNmJAa3d6DKd28ZbqeKJD29V34v60XCAmLJUtvwFyK4AtRILkjpRrVkKSUMF1P+FSjaS0Hjl1L4MttF/mkn5/aIYkS5ujoiE6nu6eoeWxsbIHrRJmbm9OwYUOuXbtW6Nf39fVFp9Pds12v13Pq1KkHPm4q/t3Oo9cSScqIwt7anMGdWpjUZ7SK+PuUdkLP7OvsizzN5RQz/P39Sy/AYlDcv8vc4+VHklKiXFh9OJIp63Le0CPb1ZWElCgUnxp2VLGxIC41kyPh8cYihEKIhzMWOZeV94QJ02g0vN2jIf2+DWH1kUieb1eXBrIkvUmzsLDAx8eHkJAQOnXqBIDBYCAkJIQhQ4YU6Bh6vZ6LFy/SoUOHQr++Tqd76Be+/B43FTqdjj/P5RQ4f9zbFSsLc5UjKhkV6fcp7YQO3q4AnLyeSHKGHodK5a/MTGn/Lk0nFS1M1pqj13lr3UkARrStwzs9GkpCShSKVqvhMa+cVfh2nItSORohyof0LD1h0TmLA8j0PWHqmtWuQlefahgU+PSP82qHI0rBiBEjWL16NevXrycsLIzp06eTlpZG3759AZg0aRKff/65cf958+axb98+IiMjOXPmDG+++SY3b95kwIABajWh3DMYFP44dRuArrLqnjAR1e2taVC1MgYF9l2WVfgKQpJSokxbH3qdN9ecQFFgWJvaTOvZSBJSokg6N6wKwLZzUSiKonI0QpR9l6JS0BsUHCuZU01q7IgKYFJXL3RaDTvO3yEkLDb/J4hyrXv37rz11lvMmTOHp556inPnzrFo0SLj9L1bt24RHR1t3D8pKYl3332Xbt26MXr0aFJSUli1ahX16tVTqwnl3rHIBG4npWNraUb7Bi5qhyNEsWlfP+f9vOdidD57CpDpe6IM+/X4DSauzklIPduqFjOe9JGElCiyoAYuWOi0RMTeJSw6hXquMjVDiIc5968i53LtFRWBh0tlBresxfIDEcz84xwbXmqLVivvfVM2ZMiQB07XW758eZ5/T506lalTp5ZGWBXG5n9GSXVuVBUrc9Of9iUqjvYNXFi07yp7LsagKIp8jsqHjJQSZdLGEzd5/efjGBR4pmVNPniqsfwxi0dS2dLMWEtq29k7KkcjRNl3+mYiIFP3RMXyWqf62FjoOHk9kd9P3VI7HCFMlkFR2HI6JynVw6+6ytEIUbxa1q2CpZmW20npXLqTonY4ZZ4kpUSZs/nULcb/k5Aa2Nydj3r7Sk+lKBadGuVM4dsudaWEyNeJ6zlJKT93e5UjEaL0OFe25MUOngB8tuU8Gdl6lSMSwjRdiM0iKjkDWysz2tUv2IqHQpQXVuY6WnnkdIbLFL78SVJKlClbTt/m1ZWh6A0K/Zq680lfP0lIiWLTqWHOahjHrsUTk5KhcjRClF2Z2QbO3cyZvtfE3UHdYIQoZS8EeVDVzpLr8WksD4lQOxwhTNL+yHQAujSqhqWZTN0Tpqf9P8nW3ZKUypckpUSZ8eeZ27z80zGyDQp9A9z4rL8kpETxqm5vTWM3OxQF/jovU/iEeJALt5PJ1BuwtzantlMltcMRolRZW+iY0LkBAHP/ukzi3SyVIxLCtOgNCiHXc5JSPWXqnjBRHf4p3n/oahzpWTLq9mEkKSXKhB3nohj3T0LqKf8a/N+AJugkISVKQKd/VuHbflam8AnxICeuJwA5U/eknp+oiPo3q0mDqpVJTMvim12X1Q5HCJNyNCKe+HQDdlZmtK0nU/eEaarnWpnq9lZkZBs4cEVWdH0YSUoJ1e08f4exPx4jS6/Q0686n0tCSpSgzv/Uldp7KUZ6LYR4gJP/JKVk6p6oqHRaDVO6NQRgyf5wrsffVTkiIUzHxpM5iwh0aVQVCzP5OipMk0aj4TGvnNFSMkPj4eQqIFS1+2I0Y348SqbeQHffaswe5I+ZTt6WouQ0qm6Hm4M1aVl6uUEI8QAnpci5EDzm5UIbDycysw188edFtcMRwiRkZhvYdCpn1b0n/WuoHI0QJSu3M3zb2SgURVE5mrJLvv0L1fx1PopRS4+QmW3gCZ+qfPV0gCSkRInTaDT0bJJTv+C34zdVjkaIsiclI5uLUckANKnpoG4wQqhIo9EwtXvOaKn1x29w+kaiyhEJUf7tunCHxLQsqlhpaV23itrhCFGiAj2dqWSh41ZiOqdvJKkdTpklGQChij/P3GbM8pwRUk/4VGXuM00xl4SUKCVPNsnpmfvrwh2S0qWArRD/diIyAYMCbg7WVLWzUjscIVTl627PU/41UBSY+cc56ekW4hGtD70BQLtaVlKuQ5g8K3OdseD5n2dvqxxN2VUmsgArVqwgODgYX19fBgwYwMmTJx+6/x9//EHXrl3x9fWlV69e7N692/hYVlYW//d//0evXr3w9/enXbt2TJo0iaiovEWNg4OD8fLyyvOzYMGCEmmfyGvTyVu8tCKnhlQPv+rMG9xU5pOLUtWouh2eLjZkZhv484wUPBfi345GxAPQrLajypEIUTa80cULC52Wvy/HsudSjNrhCFFuJaZlseNcTumEDrWtVY5GiNLRxed/U/jE/ameCdi8eTMzZ85k3LhxrF+/Hm9vb0aOHEls7P0r1B87doyJEyfSv39/NmzYwOOPP864ceO4eDFnrn96ejpnz55l7NixrFu3jnnz5nH16lXGjh17z7FeffVV9u3bZ/wZMmRIibZVwK/Hb/DqqlCyDQq9/Wvw1SB/GSElSp1Go+HJJm4A/HZCpvAJ8W+SlBIir5pVKjGsTW0AZm4+h94go6WEKIo/Tt0iU2+gQdXK1LY3UzscIUpFRy9XdFoN528nExGbqnY4ZZLq2YAlS5YwcOBA+vXrR7169ZgxYwZWVlasXbv2vvsvW7aMoKAgXnjhBTw9PRk/fjyNGjXixx9/BMDW1pYlS5bQvXt3PDw88Pf359133+XMmTPcvJn3y6eNjQ0uLi7Gn0qVKpV4eyuypfvDGf/zcfQGhf7N3Pl8oBQ1F+rJLa759+UYYlMyVI5GiLLBYFA4dk2SUkL818vB9bCzMuP87WTWHbuudjhClEu5U/d6+9dAo5Gpe6JicKhkQat/6qfJaKn7UzUjkJmZyZkzZwgMDDRu02q1BAYGEhoaet/nHD9+nDZt2uTZ1q5dO44fP/7A10lJSUGj0WBnZ5dn+8KFC2nVqhW9e/dm0aJFZGdnF70x4oEMBoWZm8/x3m9nUBR4tlUtPuvnJ/PIharqOtvg62aP3qCw+bTM8RYC4NKdFJLTs6lkocO7mq3a4QhRZjhUsuDl4HoAfP7nRdKz9CpHJET5ciMhjYNX4wDo5Vdd5WiEKF1d/lmF709JSt2XquMm4+Pj0ev1ODk55dnu5OTElStX7vucmJgYnJ2d79k/Jub+c/wzMjKYNWsWPXr0oHLlysbtQ4cOpVGjRtjb2xMaGsoXX3xBdHQ0U6ZMKVQb9Pry/aEkN/6SakdGtoFJa0/y+8mcL/1vdKnPi+09UBQD5fzUASV//ioCNc9hT79qnLqRyG/HbzC4hXupv35xKY1zWFbf4ytWrGDx4sVER0fj7e3Nu+++i5+f3333Xb16NRs2bODSpUsA+Pj4MGHChDz7T548mfXr1+d5Xrt27Vi8eHHJNaIMORKR84WhibuDjGQV4j+GtanD0v0R3EhIY/G+q4zrWE/tkIQoN9YdzRlh2KpuFWo4WHNH5XiEKE2dGlVl+sazHAmPIzYlA6fKlmqHVKaY9GTerKwsXnvtNRRFYcaMGXkeGzFihPH/vb29MTc357333mPixIlYWFgU+DVOnTpVbPGqqSTakZpp4NP98ZyJzkKngZda2NPGPpkTJ04U+2upzVTeB2pS4xzW1erRAIfD4/lj3xGqVy7fl8SK9j7MrUk4Y8YMmjRpwtKlSxk5ciRbtmy5p7MD4ODBg/To0YOmTZtiYWHBokWLeP7559m0aRNVq1Y17hcUFMTMmTON/y7MPaG8O/RPL3ZLWaZbiHtYmet48wkvxv98nG93hfF0i5ryxUKIAjAYFH4+EgnAwOY1VY5GiNLn7lgJnxp2nLmZxNYzUQxuVUvtkMoUVb+BOTo6otPp7ilqHhsbe89oqFzOzs73jIq63/5ZWVmMHz+emzdvsnTp0jyjpO6nSZMmZGdnc/36dTw8PArcBl9fX3Q6XYH3L2v0ej2nTp0q9nbcTEjj+aVHuRSdRWVLHd8MDqBtvfv/Tsuzkjp/FYna5zDo4hH2XIrhZIot3dp5lfrrF4fSOIe5r1GW/LsmIcCMGTPYtWsXa9euZfTo0ffs//nnn+f594cffsjWrVsJCQmhd+/exu0WFha4uLiUaOxlkaIoHLiScz9u7XFvUk8IAU82qcHCvVc4czOJuX9dZvqTPmqHJESZ93dYDNfj07C1MqO7r0zdExVTryY1OHMziQ3Hb0hS6j9UTUpZWFjg4+NDSEgInTp1AsBgMBASEvLAlfD8/f05cOAAw4cPN27bv38//v7+xn/nJqQiIiJYtmwZjo75F2s9d+4cWq32vr3rD6PT6UwiGVGc7Th7M4kRPxwiKimDqnaWLBnekkY17PJ/YjlmKu8DNal1Dp9tXZs9l2JYe+wGbzzhjYVZ+Z2yVJHeh7k1CceMGWPcll9Nwv9KS0sjOzsbe3v7PNsPHTpEmzZtsLOzo3Xr1owfP75A95H/+u+0yrI6BTLX1ZhUopIysDDT0sTN9p54y0s78iPtKFvKYzve6urFsO8P8+OBCIa2rkkdJ5ty2Y4HKWxbTKHNomStOpwzSqq3vxvWFjp5z4gK6ckmNfh0y3kOXY3jRkIabg7WaodUZqg+V2XEiBG89dZbNG7cGD8/P5YuXUpaWhp9+/YFYNKkSVStWpWJEycCMGzYMIYOHcr3339Phw4d2Lx5M6dPn+b9998HchJSr776KmfPnmX+/Pno9Xqio6MBsLe3x8LCgtDQUE6cOEHr1q2xsbEhNDSUmTNn8uSTT97z5UQUzr5LMbz441FSMrJpULUyP4xoSQ35gxNl2OPerlS1syQqKYOtZ27Tq0kNtUMSBVCUmoT/NWvWLFxdXfMsthEUFETnzp1xd3cnMjKSL774glGjRvHzzz8XOuH335FlZW2k2X9tu3IXgPqOOs6deXCsZb0dBSXtKFvKUztsgIBqFoTezuSd1Yd5o42D8bHy1I78mFJbhHpiUzL480xObdmnW8rUPVFx1XCwplXdKhy4Esdvx28y9jFPtUMqM1RPSnXv3p24uDjmzJlDdHQ0DRs2ZNGiRcbpeLdu3UKr/d/IhaZNmzJr1ixmz57NF198QZ06dfj6669p0KABAFFRUfz1118APPXUU3lea9myZbRq1QoLCws2b97MvHnzyMzMxN3dneHDh+epMyUKb92x60xac5Jsg0JrjyrMH9oce2tztcMS4qHMdFoGNa/JnL8u89PBa5KUqiAWLFjA5s2bWbZsGZaW/6sJ06NHD+P/e3l54eXlRadOnYyjpwojdzql2lNUC2rpxRNAEo/71sLfv/49j5eXduRH2lG2lNd2fFQtmR7z/ibkejo41cG3hm25bMf9FPZ3Uhand4uyY33oDbL0Cr5u9vjUkM5/UbH19nfjwJU4NoTekKTUv6ielAIYMmTIA6frLV++/J5t3bp1o1u3bvfd393dnQsXLjz09Xx8fFi9enXhAxX3pSgK3+wK4/+25pz3Xk1qMGuAH5Zm5ftDmag4BrWsxbydlwm5EsuV6BQ8XB5eg06oryg1CXMtXryYBQsWsGTJEry9vR+6b82aNXF0dCQiIqLQSan/Tqcsy9MrFUUh5EpOkfPWns4PjbMst6MwpB1lS3lrRyM3B/o3deeXo9f5ZMsFVr7QEih/7XgYU2qLUIeiKKw8dA2QUVJCAHTzrc60X89wISqZc7eSaFjdtEvcFFT5LZ4iygS9QWHar2eMCakxHTz4apC/JKREueLmYM1jXq4Axg9Pomz7d03CXLk1CQMCAh74vIULF/LNN9+waNEifH19832d27dvk5CQYPKFz8/dSiY6OQNrcx3Nahe+fpYQFdGELg2wMtdyODye7edkgXsh/utweDxh0alYm+t4UkaiC4G9tTkdvXM+U244fkPlaMoOSUqJIkvP0vPKymMsPxCBRgPTezViSreGaLUatUMTotAGt8xZBWP1keukZGSrHI0oiBEjRrB69WrWr19PWFgY06dPv6cm4b9X3FuwYAFfffUVH3/8MW5ubkRHRxMdHU1qaioAqampfPrppxw/fpzr168TEhLCSy+9RO3atQkKClKljaVlz6Wc2outPapIp4IQBVTd3pqR7eoC8NnWi+gNisoRCVG2LPn7KgBP+dfA1kpKeggBOVP4AH47fhOD3DeAMjJ9T5Q/SelZjFp6hINX47DQafliUBN6+kkPiCi/Onq7UtfZhqsxqaw8eI1R7T3UDknko7A1CVetWmVcDOPfXn75ZV555RV0Oh0XL15kw4YNJCcn4+rqStu2bXnttdewsLAo1baVtj0Xc5JS7RuY9ogwIYrbmA6erDwUyZWYVLZf1dGsqdoRCVE2RMbdZes/Bc5HtK2rcjRClB0dvV2xtTLjVmI6f4fFEFRfPntJUkoU2p2kdJ5bcphzt5KobGnGgmHNCPR8eA0XIco6nVbDix08eGvtKRbuvcKwwNoyYqQcKExNwtxFMB7EysqKxYsXF1ts5cXdzGyOhMcDkpQSorDsrMx57fH6vPfbGX4+k8K4ntnYV5J7hxDLQsIxKNCunjNe1WzVDkeIMsPKXEffADeWhkTw44EISUoh0/dEId1OTGfQggOcu5WEc2VLVo1uLQkpYTL6BLhT3d6KO8kZrD0q87xFxRASFkum3oC7ozUezjZqhyNEufNMy1rUdqpEYoaBxfuuqh2OEKpLychm1eFIAOMUVyHE/wxpXRuAbWejuJWYpnI06pOklCiwmwlpDFoQwtWYVNwdrVk3NpDGbrK0qzAdFmZaXgjKmbb33e4wsvUGlSMSouRtOxsFQEcvVzQaqQkoRGFZmGl5s0sDABbuDedOUrrKEQmhrrVHr5Ocno2Hsw0dZASuEPeoX9WW1h5VMCiw8qAssiRJKVEg1+PvMmhBCBGxd6lZxZpVo1tTy6mS2mEJUeyeaVkTx0rmXIu7y6ZTt9QOR4gSpTcobD+Xk5Tq4lNV5WiEKL+6+lSlQRVz0rL0fLn9ktrhCKEag0ExFjgf0baOLIAkxAMMbV0HgJWHI8nMrtgd4ZKUEvmKjLvLoPkHiIxLo7ZTJX4e3QZ3R0lICdNUycKM5/8pyDlnxyUZLSVMWui1eGJSMrG1MqNVXSe1wxGi3NJoNAxrklM35+fD1zh3K0nliIRQx5YztwmPvYudlRl9m7qrHY4QZVYXn6q42FoSnZxhXBSgopKklHioW4lpPL3gADcS0qjrbMOq0a2p4WCtdlhClKhhgXVwrGROWHQqKw/JkFphunKn7gV7u2JhJh8JhHgUDZ0t6Na4KgYF3l5/Spb6FhWOwaDw1T8jBUe0rYuNpaypJcSDmOu0PNOyFgDLD0SoHI265BOoeKC41EyGLj6UJyFV3V4SUsL02Vub83rnnPogX26/RGJalsoRCVH8FEVh8+mcKaqdG8nUPSGKwzvdG2JjoePYtQR+PhKpdjhClKotZ25zISoZW8v/jToXQjzYMy1rotNqOHQ1jtM3EtUORzWSlBL3lZKRzYglh7h8J4VqdlYsH9mSqnZWaoclRKkZ3LIW9VwrE5eaydc7L6sdjhDFLjQygci4NCpZ6Aj2dlU7HCFMQjV7KyZ28QLgkz/OE5OSoXJEQpSOPKOk2tXFvpK5yhEJUfZVt7emp191AL7dHaZyNOqRpJS4R0a2njHLj3DieiIOlcxZPrKl1JASFY6ZTsvb3RsC8MPf4UTEpqockRDF67fjNwHo0qgqlSxkioUQxWVYm9r41LAjMS2LjzedUzscIUrFv0dJjZRRUkIU2NjHPAH449QtrsZUzO8bkpQSeWTrDby28jh/X47FxkLHDyNaUr+qrdphCaGKx7xcCKrvTKbewPsbz6IoUh9EmIZsvYHfT+YkpZ4KcFM5GiFMi5lOy0d9fNFoYF3oDXZeuKN2SEKUKL2MkhKiyLyr2RHs7YpBgW8q6OwMSUoJI0VReHv9abacuY2FTsuCYc3xr+mgdlhCqEaj0TCtZyMsdFp2nL/D2mM31A5JiGKx51I0MSmZVLGxoF09Z7XDEcLk+Nd0YERgzmiRyWtPknhXahMK0/XLkUgZJSXEI3gluB6Q05FxJTpF5WhKnySlhNEnW87z85FItBqY80wAbeWLihDUr2rL+M71AZix8Qy3EtNUjkiIR/fTwZwCzH0D3DDXyUcBIUrCm094UdfZhqikDN7//aza4QhRIhLTsvi/rRcAeK1TfRklJUQRBNRy5HFv15xRhzsuqR1OqZNPogKAhXuvMn/3FQA+6etH18bVVI5IiLJjdJAH/jUdSE7PZtKakzKNT5RrtxLT+Ot8FABP/7MUsRCi+Flb6Jg1wA+tBtYeu862s1FqhyREsZuz4xKxqZl4utgwrE0dtcMRotzKXfn7txM3K9xKfJKUEuwKT+OTLTk9HJO7eTOwRU2VIxKibDHTafl8YBMszbTsvRTD8gMRaockRJH9fDgSgwKt6lahnmtltcMRwqQ1q12FUUEeAExZd4o7yekqRyRE8bl8J5ml+8MBmNbLBwsz+WopRFE1drOnt38NFAXe/71i1bKVK0cFt/P8Hb4+kpOJHRVUlzHtPVSOSIiyydOlMm919Qbg/Y1nORwep3JEQhReepaeH/9Jqg5uJaOkhCgNr3dugFdVW2JSMhi/6jh6Q8X5oiFMl6IozNh4lmyDQqeGrnRo4KJ2SEKUe5O6emNppuXQ1Tj+OH1b7XBKjSSlKrCjEXG8vOo4BgV6+9dgSreGaDQatcMSoswa0bYOPf2qk21QGPvjUW4mSH0pUb6sPXadmJRM3Bys6e5bXe1whKgQrMx1fP1sUypZ6NgfFsvcvypevRBhetYdu8HeSzFY6LS806OR2uEIYRJqOFgzpoMnkFPLNjm9YiySIUmpCurC7WRGLDlMepaBptUs+aRvY7RaSUgJ8TAajYbP+vvRsLodMSmZjFl+lPQsvdphCVEgeoPCwj05tQNHtqsrBc6FKEX1XCvzUZ/GAHy14xJ/X45ROSIhii4qKZ0ZG88AML5zfeo426gckRCm46XHPKnjVImopAxm/bOIgKmTT6QV0PX4uwz7/iBJ6dk0reXAG20c5MuJEAVUycKMBUObUcXGglM3Ehm34hiZ2Qa1wxIiX+uOXSc89i4OlcwZJLUDhSh1fQLcebpFTRQFXv7pGOExqWqHJEShGQwKE1YfJyk9Gz93e0YHSekPIYqTlbmOD3v7ArA0JIL9YabfiSGZiArmZkIazyw8QFRSBg2qVmbh0KZYmskIKSEKo2aVSnz7bFMszbTsOH+H11aFkq2XxJQou9Kz9MzenjNlaGwHT2wszVSOSIiKafqTPvi52xN/N4sRPxwmPjVT7ZCEKJRvd4fx9+VYrM11fDHQHzPp2Bai2LWr78wzLXM6EN9YfYLENNOexidXkQrkVmJOQioyLo3aTpVY9nwrHCpZqB2WEOVSKw8nFgxrjoVOyx+nbzPxlxNSvFaUWctDIriRkEY1OyueC6yjdjhCVFhW5joWPdccNwdrrsakMmb5UTKyZRq4KB/2Xorm8z9zphPNeNJHVnAVogS906MRtapU4mZiOm/+csKkV+OTpFQFcTsxncELDxIRe5eaVaxZOao11eyt1A5LiHKtQwMXvnm2KWZaDb8ev8mY5Ue5m5mtdlhC5HEzIY0vt18EYELnBliZ61SOSIiKzdXWiiUjWmBracah8DjGrzpOloy2FWXc1ZhUXv4pFIMCA5u7M6C5u9ohCWHSbCzNmPtMABY6LX+ejeK73VfUDqnESFKqAgiLTqHft/u5GpOKm0NOQqqGg7XaYQlhEjo1qsrXzzbFwkzL9nNRDJp/gDvJ6WqHJQSQs2T3tF/PcDdTT/PajvRvJl8ihCgLGlS15dshzTDXafjj9G1e/knqE4qy605yOsO+P0hiWhb+NR14/6nGsmK3EKWgSU0HpvXKWd3ys63n+ePULZUjKhmSlDJxJyITGPBdCDcS0qjrbMOq0a1xd6ykdlhCmJQnfKqxclQrY/HzPl/vJ/RavNphCcGqw5FsPxeFmVbDx319ZZVVIcqQdvWdmT+0GRY6LVvPRPHSimMylU+UOXGpmQxbfMhY/mPhsOYy4laIUvRsq1oMa1MbRYHxPx8nJCxW7ZCKnSSlTNjWM7cZvPAAcamZ+LrZ88uLbahZRRJSQpSEZrWrsP6lQOo623AjIY0B34Xwza7LGKTOlFDJ+dtJxiW733zCiwZVbVWOSAjxX8HeVVkwrJlxtO1z3x8iToqfizLiTlI6gxce4PztZFxsLVk6oiUutpZqhyVEhaLRaJjWsxGdGrqSkW1g5NLDHLoap3ZYxUqSUiZIb1D4v63nGbP8KKmZetrVc2bl6NY4V5abiBAlqbaTDRvGtaWnX3WyDQqfbbnAs4sOclWW/RalLCopneeXHCY9y0D7Bi6MkiW7hSizHvNy5fvnWmBjoePAlTh6zd3HmZuJaoclKrjLd5Lp++1+Y0Jq5ajW1HG2UTssISokM52WeYObElTfmbuZeoYuPsj2s1Fqh1VsJCllYqKS0hm+5BBf7wwD4Pm2dVkyogWVZflvIUqFvbU5c58J4LN+flib6wi5EssTX+7h8z8vkJYp0zJEyYtNyeC57w9xMzEdDxcb5jztL9P2hCjj2tV3Zv24ttRxqsSNhDT6fbuflYeumfRqS6Ls2nL6Fr2/3s/1+DTqOFVizYttZKU9IVRmZa5jwdDmPO6dM2Jq9PIjfLsrzCTuE5KUMhGKovDz4Wt0+mI3ey/FYGWu5aun/ZnWqxHmOvk1C1GaNBoNA1vU5I/XgmjfwIVMvYG5f13m8c93seJghBSzFSXmRkIaTy/ImWrhXNmSH4a3xKGShdphCSEKoEFVW34d146g+s6kZxmYsu4Uzy46yLXYu2qHJiqIpPQs3lpzkhd/PEZKRjYt61ZhzdhAajvJCCkhygJrCx3fDW3GMy1rYlDg0y3nGfb9ISLjyvd9QrIVJuB4ZAKDFx7krbWnSE7Ppom7Pb+93I6n/N3UDk2ICq2Osw1LR7TguyFNqWFvxc3EdN5ef5rH/m8nP/x9laT0LLVDFCZk76Voes3dx6U7KVS3t2L1mNbUcpI6gkKUJ/aVzPlhREve6dEQK3Mt+8Ni6TJ7N59uOU+81JoSJSRbb2B5SDiP/d8ufj4SiUYDYzp4sOKFVlL+Q4gyxlyn5eM+vnzYuzEWZlr2Xorhidl7+H7fVbL15bPjW+Z0lWOnrify1Y6LbD93BwArcy0TO3vxfLu66GSqhhBlgkajoWvj6jzm5cqqQ9f4ZlcYNxPTmb7xLJ9tvcBT/jXo36wmATUdZIqVKJKYlAw+//MCKw9FAtDYzY75Q5vj5mCtcmRCiKLQaTW8EORB50ZVmbz2FCFXYvl2VxjL9oczvG0dBreqLX/folikZ+n57fhNFuy9wuU7KQB4utjwUR9fWns4qRydEOJBNBoNQ1rXJtDTicnrTnHoahzv/36WJfuv8mIHT/o1dS9Xq2SWiZFSK1asIDg4GF9fXwYMGMDJkycfuv8ff/xB165d8fX1pVevXuzevTvP44qi8NVXX9GuXTv8/PwYPnw44eHhefZJSEhg4sSJNG3alObNmzN16lRSU8t+MeKEu5ksPxBBz7l76TVvH9vP3UGrgX5N3dn2egdGtfeQhJQQZZCVuY7hbeuyZ1JH3n/Kh3qulbmbqWfloUj6fbufwE/+YvpvZ9hzMZq7mdlqh1tuqHH/KCuux99l5uZzdPhspzEhNbR1bda8GChfWIUwAbWdbPhpVCsWDmtOo+p2pGbq+XpnGO0+/Yuhiw+yIfSGjJ56RMV9DykP9AaFoxFxfPj7WVp9vINJa09y+U4KjpXMef8pH7aMby8JKSHKCQ+Xyqwa1ZqP+jTGycaCyLg03l5/mqDPdvLh72c5EZlQLmpOqT5SavPmzcycOZMZM2bQpEkTli5dysiRI9myZQtOTvdeEI8dO8bEiROZMGECHTt2ZOPGjYwbN45169bRoEEDABYuXMjy5cv55JNPcHd356uvvmLkyJFs3rwZS8ucIahvvPEG0dHRLFmyhKysLKZOncq0adP4/PPPS7X9+UlMy+LMzUQOX41n98U7HI9MIHeFeQudlu6+1Xjl8fp4ukjxQSHKAytzHcPa1GFo69ocuhrHykPX2HY2ittJ6fywP5wf9odjrtPgX9OBgFqO+NSww6eGHbWq2GBhVib6EcoMte4fasnSGzh1I5EDV2LZdT6aQ+H/Ww64ibs9b/doRMu6VVSMUAhR3DQaDZ0bVaVTQ1e2noli6f5wQq7EsvdSDHsvxaDRQBN3B9rWc8LXzQFfd3tq2Fuh0UgHZX5K4h5SFmVmG7hwO5njkfGEXktg98VoYv+VzHR3tGZI69o807IW9tbmKkYqhCgKrVbDs61q0zfAnVWHr7FgzxVuJaazaN9VFu27irujNW08nGhZtwrNajtS28mmzA1iUT0ptWTJEgYOHEi/fv0AmDFjBrt27WLt2rWMHj36nv2XLVtGUFAQL7zwAgDjx49n//79/Pjjj7z//vsoisKyZcsYO3YsnTp1AuCzzz4jMDCQ7du306NHD8LCwti7dy9r1qzB19cXgHfeeYfRo0czadIkqlatWqJtTs/Sk5KRTWpG9j//1ZOakU10Sga3EtK5mZDGzcQ0wmNTiYxLu+f53tVsGdi8Jn0C3HC0kQK2QpRHGo2GVh5OtPJwIj1Lz75LMWw5c5v9l2O4mZjO4fB4DofHG/fXaqC6vTU1q1hTq0olqtpZ4VjJAkcbc+ytzIiKzcT6djI2VuZYmev++dFiodOa7JcTNe4fJUlRFMKiU4lOziAuNZO41AyiUzIJj0nlSkwKl++kkJ6Vt1ZA23pOjAisy+MNXU329yyEyJ0KXo2ujatxLfYua45dZ+vp21yISuZ4ZALHIxOM+1a2NMPd0Rp3x0q4O1rj5mCNQyVz7K3/+alkjrW5DkszHZZmWizNtVia6crcl5SSVtz3kNKQpTeQkW0gI0uf899sA6kZ2SSlZZGQlkXC3SziUjO4Hp/Gtbi7XIu7y82ENGOHdi5bKzMe83Klt38NHvNyrXC/eyFMkbWFjhFt6/Jsq9rsunCH307cZPu5KK7Hp/HL0ev8cvQ6ABZmWjxdKlPHqRKutpa42lnhamuJs60llcy0XIvPwj4mFTtrC6wtdFib59wfSvJzpqpJqczMTM6cOcOYMWOM27RaLYGBgYSGht73OcePH2f48OF5trVr147t27cDcP36daKjowkMDDQ+bmtrS5MmTQgNDaVHjx6EhoZiZ2dnTEgBBAYGotVqOXnyJJ07d8439txhcJmZmeh0BZ+v+X9bL/DjwcgC729tpqGGgxU+Nexo41GFQE8nqtv/b1pGZuajDdvW6/XG4xSmHSKHnL9HJ+cwZx51+3qOtK/niKIoXI9P4+i1eM7dTOZsVDKXbqeQlqUnLiWduJR0TlyLv/+B9uy/72YzrQbdPz8uthZ8/UwANasUrgB27u+prAwBVuv+URD/vT8U9D3+5fZL/LA/4oGPa4BqthY0r+NI89qOdPRyNt4PsrJKvmi+qfytSjvKFmlH4VWzNePlDnV4uUMdbielcyAsluORiZy7ncTlO6lk6/VExKQQEZNSqOPaWZsze6Af/u52QMHbUtbuDwVREveQgsjv+8OD3kc7zt3h7V/PkJapL/Br5bLUabCzNqdxDTsau9nRvLYjTWs5GFfn1mdnUfijPhpT+bvPj7TTtJSndj5WvwqP1a9CaoY3x64lcPRaPEevJXDhVjIZ2QbCo5MJj05+8AF27cvzTytzLdN7NaJb42qFiqOg9wdVk1Lx8fHo9fp7hsg6OTlx5cqV+z4nJiYGZ2fne/aPiYkBIDo62rjtQfvExMRQpUreKQ5mZmbY29sbn58fgyGnt/rs2bMF2j9X1xrQtU9RR2LFE3MtnpgiPvthCtsOkZecv0cn5zAvTx141oSeNa0Aq2I9dsKNMBJuFO25udc+tal1/yiIB90f8nuPd6oKnQp8f0gg5lpCidwP8mMqf6vSjrJF2lF0nmbgWReoawPYPNrBkq6T24TCtqWs3B8KoiTuIQVR0O8P/33cFVjYw/n+OxfKXUi5y/mzRfwQUMxM5e8+P9JO01Le2ukAPO4Kj7s+4ncKJZpTpwqWK/mv/O4Pqk/fK6/MzMzw9fVFqzXdqTFCCJFLURQMBgNmZnLbyI/cH4QQFYncHwpO7g9CiIqkoPcHVe8ejo6O6HQ6YmNj82yPjY29pycil7Oz8z09Ev/e38XFxbjN1dU1zz7e3t7GY8TFxeU5RnZ2NomJicbn50er1WJhIfWchBBCDWrdPwpC7g9CCFG2lcQ9pCDk/iCEEPdSdSknCwsLfHx8CAkJMW4zGAyEhIQQEBBw3+f4+/tz4MCBPNv279+Pv78/AO7u7ri4uOQ5ZkpKCidOnDAeMyAggKSkJE6fPm3c58CBAxgMBvz8/IqreUIIIUqIWvcPIYQQ5V9J3EOEEEIUjerri48YMYLVq1ezfv16wsLCmD59OmlpafTt2xeASZMm8fnnnxv3HzZsGHv37uX7778nLCyMuXPncvr0aYYMGQLkrE4ybNgwvv32W3bs2MGFCxeYNGkSrq6uxtWUPD09CQoK4t133+XkyZMcPXqUDz74gB49epT4yntCCCGKhxr3DyGEEKahuO8hQgghikb1yd/du3cnLi6OOXPmEB0dTcOGDVm0aJFxKOytW7fQav+XO2vatCmzZs1i9uzZfPHFF9SpU4evv/6aBg0aGPcZNWoUaWlpTJs2jaSkJJo1a8aiRYuwtLQ07jNr1iw++OADnnvuObRaLV26dOGdd94pvYYLIYR4JGrdP4QQQpR/JXEPEUIIUXgapTyt3yqEEEIIIYQQQgghTILq0/eEEEIIIYQQQgghRMUjSSkhhBBCCCGEEEIIUeokKSWEEEIIIYQQQgghSp0kpYQQQgghhBBCCCFEqZOkVAX17bff8vTTT9OkSROaN29+3328vLzu+dm0aVMpR1p2FeQc3rx5k9GjR9OkSRPatGnDp59+SnZ2dilHWn4EBwff855bsGCB2mGVaStWrCA4OBhfX18GDBjAyZMn1Q5JAAkJCUycOJGmTZvSvHlzpk6dSmpq6kP3/+CDD3jiiSfw8/Pjscce48MPPyQ5ObkUoy78++mPP/6ga9eu+Pr60qtXL3bv3l1KkT5cYdqxevVqBg8eTIsWLWjRogXDhw8vM39HRf373rRpE15eXrz00kslHGHBFLYdSUlJzJgxg3bt2tG4cWOeeOKJMvHeKmw7fvjhB+PfdIcOHfj444/JyMgopWjv7/Dhw7z44ou0a9cOLy8vtm/fnu9zDh48SJ8+fWjcuDGdO3dm3bp1pRCp6TKV62x+TOU6nB9TuU7nx1Su4w9jCtf4/JTZe4AiKqSvvvpKWbJkiTJz5kylWbNm992nQYMGytq1a5U7d+4Yf9LT00s50rIrv3OYnZ2t9OzZUxk+fLhy9uxZZdeuXUqrVq2Uzz//XIVoy4eOHTsq8+bNy/OeS01NVTusMmvTpk2Kj4+PsmbNGuXSpUvKO++8ozRv3lyJiYlRO7QKb+TIkcqTTz6pHD9+XDl8+LDSuXNnZcKECQ/c/8KFC8rLL7+s7NixQ4mIiFD279+vdOnSRXnllVdKLebCvp+OHj2qNGzYUFm4cKFy+fJl5csvv1R8fHyUCxculFrM91PYdkyYMEH58ccflbNnzyqXL19WJk+erDRr1ky5fft2KUeeV1H/viMjI5WgoCBl8ODBytixY0sp2gcrbDsyMjKUvn37KqNGjVKOHDmiREZGKgcPHlTOnTtXypHnVdh2/Pbbb0rjxo2V3377TYmMjFT27t2rtG3bVvn4449LOfK8du3apXzxxRfKn3/+qTRo0EDZtm3bQ/e/du2a0qRJE2XmzJnK5cuXleXLlysNGzZU9uzZU0oRmxZTuc7mx1Suw/kxlet0fkzlOv4wpnKNz09ZvQdIUqqCW7t27UOTUvm9UcWDz+GuXbsUb29vJTo62rjtp59+Upo2bapkZGSUZojlRseOHZUlS5aoHUa50b9/f2XGjBnGf+v1eqVdu3bK/PnzVYxKXL58WWnQoIFy8uRJ47bdu3crXl5ehfqAvXnzZsXHx0fJysoqiTDvUdj302uvvaaMHj06z7YBAwYo7777bonGmZ9H/bvIzs5WAgIClPXr15dQhAVTlHZkZ2crgwYNUlavXq289dZbZeLLTmHb8dNPPymPP/64kpmZWVohFkhh2zFjxgxl2LBhebbNnDlTefrpp0s0zsIoyOe8zz77TOnRo0eebePHj1eef/75kgzNZJnKdTY/pnIdzo+pXKfzYyrX8YcxxWt8fsrSPUCm74mHmjFjBq1ataJ///6sWbMGRVHUDqncOH78OA0aNMDZ2dm4rV27dqSkpHD58mUVIyvbFi5cSKtWrejduzeLFi2S6Y4PkJmZyZkzZwgMDDRu02q1BAYGEhoaqmJkIjQ0FDs7O3x9fY3bAgMD0Wq1hZqOkJKSQuXKlTEzMyuJMPMoyvvp+PHjtGnTJs+2du3acfz48ZIM9aGK4+8iLS2N7Oxs7O3tSyrMfBW1HV9//TVOTk4MGDCgNMLMV1Ha8ddff+Hv78/7779PYGAgPXv25LvvvkOv15dW2PcoSjsCAgI4c+aM8W8+MjKS3bt306FDh1KJubiUxb/z8spUrrP5MZXrcH5M5TqdH1O5jj9MRb7G56e0rkEl/0lXlFuvvvoqrVu3xtramn379jFjxgzu3r3LsGHD1A6tXIiJicmTkAKM/46OjlYjpDJv6NChNGrUCHt7e0JDQ/niiy+Ijo5mypQpaodW5sTHx6PX63Fycsqz3cnJiStXrqgUlYCcv/0qVark2WZmZoa9vX2B//bj4uL45ptvGDRoUEmEeI+ivJ/ud41zcnIiJiamxOLMT3H8XcyaNQtXV9c8H05LW1HaceTIEdasWcOGDRtKIcKCKUo7IiMjOXDgAL169WLBggVcu3aNGTNmkJ2dzcsvv1waYd+jKO3o1asX8fHxDB48GEVRyM7O5umnn+bFF18sjZCLzYM+y6SkpJCeno6VlZVKkZU/pnKdzY+pXIfzYyrX6fyYynX8YSryNT4/pXUPkKSUCZk1axYLFy586D6bN2/G09OzQMcbN26c8f8bNWpEWloaixcvNumkVHGfQ1G4czpixAjjNm9vb8zNzXnvvfeYOHEiFhYWJR2qEA9V0Pfyo0pJSWHMmDF4enqWyQ9vpmzBggVs3ryZZcuWYWlpqXY4BZaSksKkSZP44IMP7kmIljeKouDk5MQHH3yATqejcePGREVFsXjx4nL193Dw4EHmz5/Pe++9h5+fH9euXeOjjz7i66+/zvP5SgiRV3m9DufHlK7T+TGV6/jDyDW+eElSyoQ8//zz9OnT56H71KxZs8jHb9KkCd988w2ZmZkmmyAoznPo7Ox8z1Sd3J4tFxeXogVYDj3KOW3SpAnZ2dlcv34dDw+Pkgiv3HJ0dESn0xEbG5tne2xs7D09GqJ4FPS97OzsTFxcXJ7t2dnZJCYm5vu3n5KSwgsvvICNjQ1ff/015ubmjxx3QRTl/eTs7HxPb73a779H+btYvHgxCxYsYMmSJXh7e5dkmPkqbDsiIyO5ceMGY8eONW4zGAxATqfSli1bqFWrVskGfR9F+X24uLhgZmaGTqczbvPw8CA6Olq1zx9FacdXX33Fk08+aZyi4+Xlxd27d5k2bRpjx45Fqy0fFTTu93ceExND5cqVZZRUIZnKdTY/pnIdzo+pXKfzYyrX8YepyNf4/JTWPcA0zpYAoEqVKnh6ej7051EuAufOncPe3r7MXUiKU3GeQ39/fy5evJjnArd//34qV65MvXr1SqoJZc6jnNNz586h1WrvGU4rwMLCAh8fH0JCQozbDAYDISEhBAQEqBiZ6SroezkgIICkpCROnz5tfO6BAwcwGAz4+fk98PgpKSmMHDkSc3Nzvv3221LtIS7K+8nf358DBw7k2bZ//378/f1LMtSHKurfxcKFC/nmm29YtGhRnlpgailsOzw8PNi4cSMbNmww/gQHB9OqVSs2bNhAtWrVSjN8o6L8Ppo2bcq1a9eMX9YAwsPDcXFxUe3zR1HakZ6efs+XktwvaOWpPmdZ/Dsvr0zlOpsfU7kO58dUrtP5MZXr+MNU5Gt8fkrtGlSsZdNFuXHjxg3l7Nmzyty5cxV/f3/l7NmzytmzZ5WUlBRFURRlx44dyurVq5ULFy4o4eHhyooVK5QmTZooX331lcqRlx35ncPs7GylZ8+eyvPPP6+cO3dO2bNnj9K6dWvl888/VznysunYsWPKkiVLlHPnzinXrl1Tfv31V6V169bKpEmT1A6tzNq0aZPSuHFjZd26dcrly5eVd999V2nevHmeFR+FOkaOHKn07t1bOXHihHLkyBGlS5cuyoQJE4yP3759W3niiSeUEydOKIqiKMnJycqAAQOUnj17KhEREcqdO3eMP9nZ2aUSc37vpzfffFOZNWuWcf+jR48qjRo1UhYvXqxcvnxZmTNnTplYqryw7Zg/f77i4+OjbNmyJc95z72Wq6Ww7fivsrKqU2HbcfPmTSUgIEB5//33lStXrig7d+5U2rRpo3zzzTdqNUFRlMK3Y86cOUpAQIDy+++/K9euXVP27dundOrUSXnttddUakGOlJQU4+eVBg0aKEuWLFHOnj2r3LhxQ1EURZk1a5by5ptvGvfPXQ78008/VS5fvqz8+OOPJbIceEVhKtfZ/JjKdTg/pnKdzo+pXMcfxlSu8fkpq/cAmb5XQc2ZM4f169cb/927d28Ali1bRqtWrTAzM2PFihV8/PHHANSqVYvJkyczcOBANcItk/I7hzqdju+++47p06czaNAgrK2t6dOnD6+++qpKEZdtFhYWbN68mXnz5pGZmYm7uzvDhw/PU2dK5NW9e3fi4uKYM2cO0dHRNGzYkEWLFpXpYf0VxaxZs/jggw947rnn0Gq1dOnShXfeecf4eFZWFlevXiUtLQ2AM2fOcOLECQA6d+6c51g7duzA3d29xGPO7/1069atPL2CTZs2ZdasWcyePZsvvviCOnXq8PXXX9OgQYMSj/VhCtuOVatWkZWVdc+1+eWXX+aVV14p1dj/rbDtKKsK247q1auzePFiZs6cyZNPPknVqlUZNmwYo0aNUqsJQOHbMXbsWDQaDbNnzyYqKooqVarQsWNHXn/9dbWaAMDp06fz1AadOXMmAH369OGTTz4hOjqaW7duGR+vWbMm8+fPZ+bMmSxbtoxq1arx4YcfEhQUVOqxmwJTuc7mx1Suw/kxlet0fkzlOv4wpnKNz09ZvQdoFMWExpcJIYQQQgghhBBCiHKh/KduhRBCCCGEEEIIIUS5I0kpIYQQQgghhBBCCFHqJCklhBBCCCGEEEIIIUqdJKWEEEIIIYQQQgghRKmTpJQQQgghhBBCCCGEKHWSlBJCCCGEEEIIIYQQpU6SUkIIIYQQQgghhBCi1ElSSohywsvLi+3bt6sdhhBCiFI2dOhQPvroI7XDEEIIYeKCg4P54YcfHrrPv7+TXL9+HS8vL86dOwfAwYMH8fLyIikpqaRDFSZEklJCFMHkyZN56aWX1A5DCCFEGffiiy8ycuTI+z525MgRvLy8OH/+fClHJYQQojhMnjwZLy8vvLy88PHxITAwkBEjRrBmzRoMBoPa4ZWIffv20b59+/s+FhAQwL59+7C1tQVg3bp1NG/evDTDE+WQJKWEEEIIIUpI//792b9/P7dv377nsbVr19K4cWO8vb1ViEwIIURxCAoKYt++ffz1118sXLiQVq1a8dFHHzFmzBiys7PVDq/Yubi4YGFhcd/HLCwscHFxQaPRlHJUojyTpJQQxezQoUP079+fxo0b065dO2bNmpXnhnS/YbFPPfUUc+fONf47PDycZ599Fl9fX7p3787ff/+dZ//cobJ//vknQ4cOpUmTJjz55JOEhobm2e/IkSMMHjwYPz8/OnTowIcffsjdu3eNj69YsYIuXbrg6+tLYGAgr776qvGxLVu20KtXL/z8/GjVqhXDhw/P81whhBD5e+yxx6hSpQrr1q3Lsz01NZUtW7bQqVMnJkyYQFBQEE2aNKFXr178/vvvDz1mZmYmn376KUFBQfj7+zNgwAAOHjxofDy3Z3rv3r1069aNgIAARo4cyZ07d/IcZ82aNfTo0cN4v3r//feNjyUlJfH222/TunVrmjZtyrBhw2RElxBC3EduIqZq1ar4+Pjw4osv8s0337Bnzx7Wr18PwJIlS+jVqxf+/v506NCB6dOnk5qaajxGaVy3r127xtixYwkMDCQgIIB+/fqxf//+e9qTmprKhAkT8Pf3JygoiBUrVuR5/GElRf49fe/gwYNMmTKF5ORk42iyuXPnMm/ePHr27HnPc5966ilmz56d/wkXJkeSUkIUo6ioKEaPHo2vry+//vor06dPZ82aNXz77bcFPobBYOCVV17B3NycX375hRkzZjBr1qz77vvll18ycuRINmzYQJ06dZg4caIxAXbt2jVGjRpFly5d+O233/jyyy85evQoH3zwAQCnTp3io48+4tVXX2XLli0sWrTIOLz2zp07TJw4kX79+rF582aWLVtG586dURTlEc+QEEJULGZmZjz11FOsX78+zzV0y5YtGAwGnnzySXx8fFiwYAG///47AwcOZNKkSZw8efKBx3z//fcJDQ3lyy+/5LfffqNr16688MILhIeHG/dJT0/n+++/57PPPuPHH3/k1q1bfPrpp8bHf/rpJ95//30GDhzIxo0b+eabb6hVq5bx8ddee43Y2FgWLlzIunXr8PHx4bnnniMhIaFYz48QQpiiNm3a4O3tzZ9//gmARqPh7bff5vfff+eTTz7hwIED/N///V+e55T0dfvu3bt06NCBH374gfXr1xMUFMSLL77IzZs388SxePFivL29Wb9+PaNHj+ajjz66p4O8IAICApg6dSqVK1dm37597Nu3j+eff57+/fsTFhaW5z539uxZLly4QL9+/Qr9OqL8M1M7ACFMyU8//US1atWYNm0aGo0GT09PoqKimDVrFuPGjUOrzT8PvH//fq5cucKiRYuoWrUqAK+//jqjRo26Z9/nn3+exx57DIBXX32VHj16EBERgaenJ/Pnz6dXr14MHz4cgDp16vD2228zdOhQpk+fzq1bt7C2tuaxxx6jcuXKuLm50ahRIwCio6PJzs6mc+fOuLm5ATm9IkIIIQqvX79+LF68mEOHDtGqVSsgp1e8S5cuuLm55ak5NXToUPbt28cff/yBn5/fPce6efMm69atY+fOncZ7xMiRI9m7dy/r1q1jwoQJAGRlZTFjxgzjF5Znn32Wb775xnicb7/9lhEjRvDcc88Zt+W+3pEjRzh58iQhISHGKRpvvfUW27dvZ+vWrQwaNKg4T48QQpgkDw8PLly4AGD8PA7g7u7O+PHjee+995g+fbpxe0lft729vfNMFx8/fjzbt2/nr7/+YsiQIcbtTZs2ZfTo0QDUrVuXY8eO8cMPP9C2bdtCtd/CwgJbW1s0Gg0uLi7G7TY2NrRr145169YZ41+3bh0tWrSgZs2ahXoNYRokKSVEMQoLCyMgICDPPOpmzZpx9+5dbt++TY0aNQp0jGrVqhm/bEBOT8P9/DtRlHuxj4uLw9PTk/Pnz3PhwgU2btxo3EdRFAwGA9evXycwMJAaNWrQqVMngoKCCAoKonPnzlhbW+Pt7U2bNm3o1asX7dq1o127djzxxBPY29sX+pwIIURF5+npSUBAAGvXrqVVq1ZERERw5MgRli1bhl6v57vvvmPLli1ERUWRlZVFZmYmVlZW9z3WxYsX0ev1dO3aNc/2zMxMHBwcjP+2trbO04Pu6upKbGwsALGxsdy5c4c2bdrc9zUuXLjA3bt3jQm0XOnp6Vy7dq0op0AIISocRVGM3wn279/P/PnzuXLlCikpKej1ejIyMkhLS8Pa2hoo+et2amoq8+bNY9euXURHR6PX60lPT79npJS/v/89/166dGnRT8R9DBw4kKlTpzJlyhQ0Gg0bN25kypQpxfoaovyQpJQQpex+hf+KWgTR3Nz8nuPmrvRx9+5dnn76aYYOHXrP86pXr46FhQXr16/n0KFD7Nu3jzlz5jBv3jzWrFmDnZ0dS5Ys4dixY/z9998sX76cL7/8ktWrV0sPhhBCFEH//v358MMPmTZtGuvWraNWrVq0bNmShQsXsmzZMqZOnYqXlxfW1tZ8/PHHZGVl3fc4d+/eRafTsXbtWnQ6XZ7HKlWqZPx/M7O8H/E0Go1x+qClpeVDY01NTcXFxYXly5ff81juikpCCCEeLiwsDHd3d65fv86YMWN45plneP3117G3t+fo0aO8/fbbZGVlGZNSJX3d/vTTT9m/fz9vvfUWtWrVwsrKildfffWB95uS1LFjRywsLNi2bRvm5uZkZ2ff09kiKg5JSglRjDw9Pdm6dWuenpGjR49iY2NDtWrVAKhSpUqeooUpKSlcv349zzFu377NnTt3cHV1BeD48eOFjqVRo0ZcvnyZ2rVrP3AfMzMzAgMDCQwM5OWXX6ZFixYcOHCALl26oNFoaNasGc2aNWPcuHF07NiR7du3M2LEiELHIoQQFV23bt346KOP+P3339mwYQPPPPMMGo2GY8eO8fjjj/PUU08BOR0L4eHheHp63vc4DRs2RK/XExcXV+RltnOnbIeEhNC6det7Hvfx8SEmJgadToe7u3uRXkMIISqykJAQLl68yPDhwzlz5gyKojB58mRjKY8//vijUMcrjut2aGgoffr0oXPnzkBOIuvGjRv37HfixIl7/v2ge1J+zM3N0ev192w3MzOjd+/erFu3DnNzc3r06PHAEcLC9ElSSogiSk5O5ty5c3m2DRw4kKVLl/LBBx/w7LPPcvXqVebOncuIESOMN6HWrVuzfv16goODsbW1Zc6cOXlqTQUGBlKnTh0mT57MpEmTSElJ4csvvyx0fKNGjWLQoEG8//77DBgwAGtray5fvsz+/fuZNm0aO3fuJDIykhYtWmBnZ8fu3bsxGAzUrVuXEydOEBISQtu2bXFycuLEiRPExcXh4eHxaCdNCCEqKBsbG7p3784XX3xBSkoKffr0AaB27dps3bqVY8eOYW9vz5IlS4iJiXngF4C6devSq1cvJk2axOTJk2nYsCHx8fGEhITg5eVlrDOYn1deeYX33nsPJycn2rdvT2pqKseOHWPo0KEEBgbi7+/PuHHjePPNN6lTpw537txh9+7ddOrUCV9f3+I6LUIIUe5lZmYSHR2NwWAgJiaGvXv3Mn/+fDp27Ejv3r25dOkSWVlZLF++nODgYI4ePcqqVasK/TqPet2uXbs227ZtIzg4GI1Gw+zZs40zLP7t2LFjLFy4kE6dOrF//362bNnC/Pnzi3Ru3NzcuHv3rvEeZW1tbRwZNmDAALp37w7AypUri3R8YRokKSVEER06dIjevXvn2da/f38WLFjAZ599xurVq3FwcKB///6MHTvWuM+YMWOMw3htbW157bXX8oyU0mq1zJs3j7fffpv+/fvj5ubGO++8wwsvvFCo+Ly9vVm+fDmzZ89m8ODBANSsWdN48be1tWXbtm3MmzePjIwMateuzeeff079+vUJCwvj8OHDLF26lJSUFGrUqMHkyZPp0KFDEc+WEEKI/v37s2bNGjp06GCsGzh27FgiIyMZOXIk1tbWDBw4kE6dOpGcnPzA48ycOZNvv/2WTz75hDt37uDg4IC/v3+BE1IAffr0ISMjgx9++IHPPvsMBwcH49QJjUbDggULmD17NlOmTCE+Ph5nZ2eaN2+Os7PzI50DIYQwNXv37qVdu3aYmZlhZ2eHt7c377zzDn369EGr1eLt7c2UKVNYuHAhX3zxBc2bN2fChAm89dZbhXqdR71uT548malTp/L000/j6OjIqFGjSE1Nved1RowYwenTp/n666+pXLkykydPJigoqEjnpmnTpjz99NOMHz+ehIQEXn75ZV555RUgZxGmgIAAEhMTadKkSZGOL0yDRpE13oUQQgghhBBCCFFKFEWhS5cuDB48WMqDVHAyUkoIIYQQQgghhBClIi4ujk2bNhETE0Pfvn3VDkeoTJJSQgghhBBCCCGEKBVt2rTB0dGR999/H3t7e7XDESqT6XtCCCGEEEIIIYQQotRp899FCCGEEEIIIYQQQojiJUkpIYQQQgghhBBCCFHqJCklhBBCCCGEEEIIIUqdJKWEEEIIIYQQQgghRKmTpJQQQgghhBBCCCGEKHWSlBJCCCGEEEIIIYQQpU6SUkIIIYQQQgghhBCi1ElSSgghhBBCCCGEEEKUOklKCSGEEEIIIYQQQohSJ0kpIYQQQgghhBBCCFHqJCklhBBCCCGEEEIIIUqdJKWEEEIIIYQQQgghRKmTpJQQQgghhBBCCCGEKHWSlBJCCCGEEEIIIYQQpU6SUkKUUwcPHsTLy4uDBw+qHYoQQghRbIYOHcrQoUPVDkMIIYQQpUCSUqLCWLFiBV5eXgwYMEDtUAplxYoVrFu3Tu0whBDCJK1btw4vLy9OnTpV7Me+fPkyc+fO5fr168V+7LLq2LFjzJ07l6SkJLVDEUKIYiOdwf8zefJkgoODC/WcoUOH0rNnzxKKKP/XLmhHh5pxVmSSlBIVxsaNG3Fzc+PkyZNERESoHU6BrVy5kvXr19+zvUWLFpw8eZIWLVqoEJUQQoj8XL58mXnz5nHjxg21Qyk1oaGhzJs3T5JSQohH5uXlVaCfgiSKvvvuO7Zv314KUZdtFbGz5L+ioqKYO3cu586dUzsU8Q8ztQMQojRERkYaPyhPmzaNjRs38vLLL6sd1iPRarVYWlqqHYYQQohioCgKGRkZWFlZqR2KEEKUCZ999lmef//666/8/fff92z39PTM91jz58/niSeeoFOnTsUaY3mT21nSsmVL3N3d1Q5HFXfu3GHevHm4ubnRsGFDtcMRyEgpUUFs3LgRe3t7OnTowBNPPMHGjRvv2ScpKYmPP/6Y4OBgGjduTPv27Zk0aRJxcXHGfWJjY5k6dSqBgYH4+vry5JNP3jOK6UHDe69fv46Xl1eeqXjR0dFMmTKF9u3b07hxY9q1a8fYsWONvRfBwcFcunSJQ4cOGXuDcoefPuh1Tpw4wahRo2jRogX+/v706tWLpUuXGh+fPHkyAQEBREVF8dJLLxEQEEDr1q359NNP0ev1eY5lMBj44Ycf6NGjB76+vgQGBjJt2jQSExPz7Hfq1ClGjhxJq1at8PPzIzg4mClTpuTZZ9OmTfTt25eAgACaNm16T1xCCFEWFOYa+bDr2rp163jttdcAGDZs2D09+sHBwYwZM4a9e/fSt29f/Pz8WLVq1X3vFbm8vLyYO3eu8d9z587Fy8uLq1ev8sYbb9CsWTNat27N7NmzURSFW7duMXbsWJo2bUrbtm35/vvv7zlmZmYmc+bMoXPnzjRu3JgOHTrw2WefkZmZec9rv//++2zfvp2ePXvSuHFjevTowZ49e/LEk/tl8fHHHze2OfeetnbtWoYNG0abNm1o3Lgx3bt356effir070gIUTE89dRTeX7q1Klz3+3Ozs7qBiqEeCQyUkpUCBs3bqRz585YWFjQs2dPVq5cycmTJ/Hz8wMgNTWVZ599lrCwMPr160ejRo2Ij4/nr7/+IioqiipVqpCens7QoUO5du0azz77LO7u7mzZsoXJkyeTlJTEc889V+i4XnnlFS5fvsyQIUNwc3MjLi6Ov//+m1u3buHu7s7UqVP54IMPqFSpEi+++CLAQ2+8f//9N2PGjMHV1ZVhw4bh7OxMWFgYu3btyhOfXq9n5MiR+Pn5MWnSJEJCQvj++++pWbMmgwcPNu43bdo01q9fT9++fRk6dCjXr19nxYoVnD17lpUrV2Jubk5sbCwjR47E0dGR0aNHY2dnx/Xr19m2bVueuCZMmECbNm144403ALhy5QrHjh0r0nkTQoiSVJBrZH7XtRYtWjB06FCWL1/Oiy++iIeHB5C3R//q1atMnDiRQYMGMXDgQOrWrVukeF9//XU8PT2ZOHEiu3fv5ttvv8XBwYFVq1bRunVr3njjDTZu3Minn36Kr6+vcdq3wWBg7NixHD16lIEDB+Lp6cnFixdZunQp4eHhfPPNN3le5+jRo/z5558MHjwYGxsbli9fzquvvsrOnTtxdHSkc+fOhIeH8/vvvzNlyhQcHR0BqFKlCpAzHb1+/foEBwdjZmbGzp07mTFjBoqi8Oyzzxap7UKIiu3u3bvMmTOHP/74g9jYWNzc3Bg4cCDPP/88Go0GyEmqA6xfv97YmdynTx8++eQTbty4wcKFCwkJCeHWrVtYW1vTqlUrJk2a9MgjidatW8eUKVNYsWIFW7Zs4ffffyc7O5vu3bvzzjvvkJ6ezocffsjOnTsBGDBgAG+++aYxbsi5Ti9btoxffvmFa9euYWtrS6dOnZg4cSL29vbG/YKDg6lfvz6jRo3ik08+4cKFC7i6uvLKK6/Qu3fvPPFATmdJrmXLltGqVSu2b9/O6tWrOXv2LAkJCVSrVo0+ffrw4osvotPpHulc5Lp8+TIzZszg5MmT2NnZMWzYMEaNGpVnn8zMTL777js2btzIrVu3cHJyokePHowfPx4LCwvjfmvXruXXX3/l0qVLJCcnU6tWLYYMGZLnu8x/HTx40Nj2KVOmGM/HzJkz6du3b6HiFMVHklLC5J0+fZorV67w7rvvAtCsWTOqVavGxo0bjUmpxYsXc/HiRebNm0fnzp2Nz33ppZdQFAWAn3/+mbCwMP7v//6PJ598EoCnn36aoUOHMnv2bPr160flypULHFdSUhKhoaFMmjSJkSNHGrePGTPG+P+dOnVi9uzZODo68tRTTz30eHq9nmnTpuHq6sqGDRuws7MzPpbbhlwZGRl069aNcePGAfDMM8/Qp08f1qxZY7yQHzlyhF9++YVZs2bRq1cv43NbtWrFCy+8wJYtW+jVqxehoaEkJiayePFifH19jfu9/vrrxv/ftWsXlStXZvHixcV2UxNCiJJSkGtkfte1mjVr0rx5c5YvX05gYCCtWrW6Z5+IiAgWLVpEUFCQcVtR6nz4+fnx/vvvAzBo0CCCg4P55JNPmDBhAqNHjwagZ8+eBAUFsXbtWmNSauPGjezfv5/ly5fTvHlz4/Hq16/Pe++9x7Fjx2jatKlxe1hYGJs3b6ZWrVpAzv3gqaeeYtOmTQwZMgRvb28aNWrE77//TqdOne75Qvfjjz/mmZ44ZMgQRo4cyZIlSyQpJYQoNEVRGDt2LAcPHqR///40bNiQvXv38tlnnxEVFcXUqVOBnGmA77zzDn5+fgwcOBDAeB07deoUoaGh9OjRg2rVqnHjxg1WrlzJsGHD2LRpE9bW1o8c54cffoizszOvvPIKJ06c4Oeff8bW1pbQ0FCqV6/O66+/zp49e1i8eDENGjQwJpGgYB3EuSIiInjttdfo378/ffr0Ye3atUyePBkfHx/q16+fb2fJ+vXrqVSpEiNGjKBSpUocOHCAOXPmkJKSwltvvfXI5yExMZEXXniBzp07061bN7Zu3cqsWbNo0KABHTp0AArXWVKUjg5PT09effVV5syZw6BBg2jWrBlAnntdQeIUxUuSUsLkbdy4EWdnZ+MXAo1GQ/fu3fntt9+YPHkyOp2OP//8E29v7zwJqVy5vRV79uzBxcUlz4oM5ubmDB06lAkTJnD48GE6duxY4LisrKwwNzfn0KFD9O/fP09vR1GcPXuW69evM2XKlDwJqX+34d+eeeaZPP9u1qwZv/32m/HfW7ZswdbWlrZt2+aZwujj40OlSpU4ePAgvXr1wtbWFsj5gubt7Z3n5pjLzs6OtLQ0/v77b9q3b/9I7RRCiNKQ3zWyOK5r7u7ueRJSRdW/f3/j/+t0Oho3bszt27fzbLezs6Nu3bpERkYat23ZsgVPT088PDzyXOdbt24N5PQo//uDemBgoPGLHIC3tzeVK1fOc8yH+XdCKjk5maysLFq2bMm+fftITk423k+EEKIgduzYwYEDBxg/fjxjx44F4Nlnn+XVV19l2bJlDBkyhFq1avHUU08xffp0ataseU8n72OPPUbXrl3zbOvYsSODBg1i69ateRJEReXk5MTChQvRaDQ8++yzXLt2jcWLFzNo0CBmzJgB/K9DYe3atcbXLGgHca6rV6+yYsUKYydDt27d6NChA+vWreOtt97Kt7Pk888/z3OdfuaZZ5g2bRorV67k9ddfzzNKqSju3LnDp59+amxf//79jW3OTfYUprOkKB0dzs7OtG/fnjlz5uDv73/fTv+CxCmKl9SUEiZNr9ezadMmWrVqxfXr14mIiCAiIgI/Pz9iYmIICQkB4Nq1a9SvX/+hx7px4wa1a9dGq837Z5Pbu3Dz5s1CxWZhYcEbb7zBnj17aNu2Lc8++ywLFy4kOjq6UMfJlfuloEGDBvnua2lpaZxOkcve3j5PraiIiAiSk5Np06bNPT93794lNjYWgJYtW/LEE08wb948WrduzdixY1m7dm2eeiSDBw+mTp06jBo1ivbt2zNlypQ8dUiEEKIsKcg1sjiua8VVZLZGjRp5/m1ra3vfNtja2uZZFS8iIoJLly7dc41/4oknAIzX+VzVq1e/57Xt7e0LvNLe0aNHGT58OP7+/jRv3pw2bdrwxRdfADlJKiGEKIw9e/ag0+mM9VZzPf/88yiKUqBr8r+TGllZWcTHx1OrVi3s7Ow4e/ZsscTZv3//PB3Efn5+KIpy3w6F/3Yc/LuDOPfn3x3E/1avXr08iZwqVarc0xnxMP8+FykpKcTFxdG8eXPS0tK4cuVKodv9X5UqVcqTBLKwsMDX1/ehnSW5P//uLLlfvMnJycTFxdGyZUsiIyMf6Z5SkDhF8ZKRUsKkHThwgOjoaDZt2sSmTZvueXzjxo20a9euWF/zfqOSIGc46n8NHz6c4OBgtm/fzr59+/jqq69YsGABS5cupVGjRsUa178VZAqdwWDAycmJWbNm3ffx3C87Go2GOXPmcPz4cXbu3MnevXuZOnUqS5Ys4eeff8bGxgYnJyc2bNjAvn372LNnD3v27GHdunX07t2bTz/9tFjbJoQQj6og18jiuK7db6W9B91D/ltk/d/+21kCD27Dv6dzGwwGGjRocM/CFLmqVatW6GM+yLVr1xg+fDgeHh5MnjyZ6tWrY25uzu7du/nhhx/ue48UQoiHuXHjBq6urveUz8jtML5x40a+x0hPT2f+/PmsW7eOqKioPNez4kqW36/jAO5N9Nva2j6wg/h+Ctpx8N8Fih7k0qVLzJ49mwMHDpCSkpLnseI4F9WqVbvnHmdvb8+FCxeM/46IiCAsLKxAbT569Chz587l+PHjpKWl3RNvUUffFiROUbwkKSVM2saNG3FycmLatGn3PLZt2za2bdvGjBkzqFWrFpcuXXrosdzc3Lhw4QIGgyHPF4DcnoPcG07u1Ln/XrwfdGOsVasWzz//PM8//zzh4eH07t2b77//3pgMetAXlP+qWbMmABcvXiQwMLBAz3mYWrVqERISQtOmTQu0RLm/vz/+/v68/vrrbNy4kTfeeIPNmzczYMAAIKeXITg4mODgYAwGA9OnT+fnn3/mpZdeonbt2o8crxBClLb8rmsFvX7/W+5U7v+OPirsaNyCqFWrFufPn6dNmzZFivV+HnScv/76i8zMTL799ts8X9D+29MvhBCl6YMPPmDdunU899xz+Pv7Y2tri0aj4fXXXy9Qwr0g7tdx8LDtuQraQZzrUeq2JiUlMWTIECpXrsyrr75KrVq1sLS05MyZM8yaNatYOg4K2ilekM6SkuzokPq3pU+SUsJkpaen8+eff9K1a9d75ooDuLq68vvvv/PXX3/RpUsXvv76a7Zt23ZPXSlFUdBoNLRv3559+/axefNmY12p7Oxsli9fTqVKlYyFY93c3NDpdBw+fJhOnToZj7Ny5co8x01LS0Or1WJpaWncVqtWLWxsbPJMfbO2ti7Q1AgfHx/c3d1ZtmwZffv2vafQeWG/cHTr1o2ffvqJb775hgkTJuR5LDs7m7t372JnZ0diYiJ2dnZ5jt+wYUMAYzvi4+ONqzBBzk04dyWU/y47LoQQ5UFBrmu5BXIL08NcuXJlHB0dOXLkCMOHDzdu/+mnn4oh6ry6devG7t27Wb16NYMGDcrzWHp6OgaDgUqVKhXqmA9qc+6H/P+OQli7dm1RQhdCCNzc3AgJCSElJSXPaKncDmM3N7d8j5FbN2ry5MnGbRkZGWViSnFhO4gL4kHfBw4dOkRCQgLz5s0zfqeBoi2+8SgK2lnyKB0dxdUJI4qPJKWEyfrrr79ITU0lODj4vo/7+/tTpUoVfvvtNz7//HO2bt3Ka6+9Rr9+/fDx8SExMZG//vqLGTNm4O3tzaBBg/j555+ZPHkyZ86cwc3Nja1bt3Ls2DGmTp1qvBna2trStWtXfvzxRzQaDTVr1mTXrl33DLENDw9n+PDhdO3alXr16qHT6di+fTsxMTH06NHDuJ+Pjw8rV67km2++oXbt2lSpUuW+Q1q1Wi3Tp09n7Nix9O7dm759++Li4sKVK1e4fPkyixcvLtT5a9myJYMGDWL+/PmcO3eOtm3bYm5uTnh4OFu2bOHtt9+ma9eurF+/npUrV9KpUydq1apFamoqq1evpnLlysbiv++88w6JiYm0bt2aqlWrcvPmTX788UcaNmyYZ3l0IYQoLwpyXWvYsCE6nY6FCxeSnJyMhYUFrVu3xsnJ6aHHHjBgAAsWLODtt9+mcePGHDlyhKtXrxZ7G5566in++OMP3nvvPWNRc71ez5UrV9iyZQuLFi3Ks6pqQfj4+ADw5Zdf0r17d8zNzenYsaPxHvLiiy/y9NNPk5qayi+//IKTk1ORaykKISq29u3b8/PPP7NixYo8q1f/8MMPxg7lXJUqVbpvJ+/9RsUsX778oVOmS0tBO4gL40EdB7mjtv7dcZCZmVkiHSIPU9DOkkfp6Mg9BwWthyhKniSlhMn67bffsLS0pG3btvd9XKvV8thjj7Fx40YyMzNZsWIFc+fOZdu2baxfvx4nJyfatGlD1apVgZzaH8uXL2fWrFmsX7+elJQU6taty8yZM+nbt2+eY7/zzjtkZ2ezatUqLCws6Nq1K5MmTcqzcl+1atXo0aMHISEh/Pbbb+h0Ojw8PJg9e7axyCzAuHHjuHnzJosWLSI1NZWWLVs+cJ51UFAQS5cu5euvv+b7779HURRq1qxpXP62sN5//30aN27MqlWr+PLLL9HpdLi5ufHkk08aV75o2bIlgeJKfwAA4lZJREFUp06dYvPmzcTExGBra4ufnx+zZs0yTil88sknWb16NT/99BNJSUm4uLjQrVs3XnnllXyHLgshRFlUkOuai4sLM2bMYP78+bz99tvo9XqWLVuWb1Jq3LhxxMXFsXXrVv744w/at2/PokWLHnjtLyqtVsvXX3/NDz/8wK+//sq2bduwtrbG3d2doUOHUrdu3UIf08/Pj9dee41Vq1axd+9eDAYDO3bswMPDgzlz5jB79mw+/fRTnJ2deeaZZ6hSpYpx2XYhhCiM4OBgWrVqxZdffsmNGzfw8vLi77//ZseOHTz33HN5Vgv18fEhJCSEJUuW4Orqiru7O02aNOGxxx7j119/pXLlytSrV4/jx4+zf/9+HBwc1GvYPwraQVwYD+osCQgIwN7ensmTJzN06FA0Gg2//vprsU1hLKiCdpY8SkdHbiH7VatWYWNjQ6VKlfDz8zN+bxGlT6OU9jtNCCGEEEIIIYQohPfff58VK1bkKTidmprKnDlz2Lx5M/Hx8bi5uTFw4ECef/75PNO0rly5wrRp0zh16hTp6en06dOHTz75hKSkJGbOnMnOnTvJyMigadOmvP3227zwwgu0bNmSTz75BMiZFjZs2DCWLVtGq1atChTvunXrmDJlCmvWrMkz6nTu3LnMmzePkJCQPHWhJk+ezNatWwkNDc1znNWrV7Nq1SrCwsKMHcTt27fnueeew9XVFchJ0NWvX5/58+fneW7uyoTLly83bvvll1+YP38+N2/eNHaWtGrVimPHjvHpp59y/vx57OzsePLJJ2nTpg0jR47M0+7Jkydz6NAh/vrrrwKdh9w44uPj+f333/Nsv9+xsrKyjJ0lERERxs6S4OBghg8fbpyd8tdffzF79mzCw8Pv6ejYsWOHcYXb+52DHTt28MUXXxAeHk52drZxkEFh4hTFR5JSQgghhBBCCCGEEKLUybwZIYQQQgghhBBCCFHqpKaUEEIIIYQQQghRAOnp6fmuzmdvb4+FhUUpRaSehIQEsrKyHvi4TqfLM0VRiPuR6XtCCCGEEEIIIUQB5NaKepjC1J4qz4YOHcqhQ4ce+Libm5vUYRL5kqSUEEIIIYQQQghRAHfu3OHy5csP3cfHxwd7e/tSikg9p0+fJikp6YGPW1pa0qxZs1KMSJRHkpQSQgghhBBCCCGEEKVOCp0LIYQQQgghhBBCiFInhc6LyGAwkJ2djVarRaPRqB2OEEKUKEVRMBgMmJmZodVKf8bDyP1BCFGRyP2h4OT+IISoSAp6f5CkVBFlZ2dz6tQptcMQQohS5evrWyFWk3kUcn8QQlREcn/In9wfhBAVUX73B0lKFVFups/X1xedTldir6PX6zl16lSJv05xk7hLl8Rduipi3LnPlV7w/JXU/aG8vu+KqqK1Fypem6W9pkHuDwVXWt8fCstU35vFQc7Ng8m5eTA5NzkKen+QpFQR5Q651el0pfJGK63XKW4Sd+mSuEtXRYxbphvkr6TvD+X1fVdUFa29UPHaLO01DXJ/yF9pf38orLIaV1kg5+bB5Nw8mJybHPndH6RLQwghhBBCCCGEEEKUOklKCSGEEEIIIYQQQohSJ0kpIYQQQgghhBBCCFHqJCklhBBCCCGEEEIIIUqdJKWEEEIIIYQQQgghRKmTpJQQQgghhBBCCCGEKHWSlBJCCCGEEEIIIYQQpU6SUkIIIYQQQgghhBCi1ElSSgghRLm1YsUKgoOD8fX1ZcCAAZw8efKB+166dIlXXnmF4OBgvLy8+OGHH+67X1RUFG+88QatWrXCz8+PXr16cerUqRJqgRBCCCGEEBWXJKWEEEKUS5s3b2bmzJmMGzeO9evX4+3tzciRI4mNjb3v/mlpabi7uzNx4kRcXFzuu09iYiLPPPMM5ubmLFy4kE2bNvHWW29hb29fkk0RQgghhBCiQjJTOwBRMWVk69lx7g5W5lpa1nWisqW8FYUQhbNkyRIGDhxIv379AJgxYwa7du1i7dq1jB49+p79/fz88PPzA+Dzzz+/7zEXLlxItWrVmDlzpnFbzZo1SyD6iuVmQhr7LsfQok4V6jrbqB2OEEIIUSYlpWdxICyW20np6LQa6jrZ0KyOI5ZmOrVDE6LESCZAlLr9l2N4c81JbiSkAWBracZ3Q5vRtp6zypEJIcqLzMxMzpw5w5gxY4zbtFotgYGBhIaGFvm4f/31F+3atePVV1/l8OHDVK1alcGDBzNw4MDiCLtCOnk9gRFLDhObmglA36ZufD6gCRqNRuXIhBBCiLLhVmIan225wKaTt8jUG/I8ZmtlRr+m7ozrWA8XW0uVIhSi5EhSSpSqyLi7jPnxKMnp2bjaWmKu03IjIY2RSw+z7PlWtKxbRe0QhRDlQHx8PHq9HicnpzzbnZycuHLlSpGPGxkZycqVKxkxYgQvvvgip06d4sMPP8Tc3Jw+ffoU6lh6vb7IcTzseMV93JJ0OzGdwQsPkJKhx9XWktjUTNYdu0HL2o4MaO7+0OeWx/Y+qorWZmmvaTC19ghR2jadvMWba05wNzPnb8nDxYYGrrZk6Q2cupHIneQMftgfzuojkbzToxHPtKwpHTvCpEhSSpSabL2Bl386RnJ6NgG1HPjphdZotfDi8qPsvBDN6z8fZ8fEDliZy/BUIYQ6FEWhcePGTJgwAYBGjRpx6dIlVq1aVeikVEkVRy9PRdeXHE8iJUOPh4MZMx6z58+wuyw/lcL0jWeokhmFU6X8r/flqb3FpaK1WdorhKioFu29woebzgHQtJYD03r50MTd3ph0MhgU9l2O4fNtFzkRmcDU9af4+3IMnw9sIt+ZhMmQpJQoNZtO3eLE9UTsrMyYN7gp1hY5F9Jvnm1Gpy92cyMhjQV7rvDq4/VVjlQIUdY5Ojqi0+nuKWoeGxuLs3PRpwK7uLjg6emZZ5uHhwdbt24t9LF8fX3R6YrvA6Ner+fUqVPFftySEpuayfYNuwB4t3cTAuu70KqZwqmEAxyPTOTUXTvGBz74el/e2lscKlqbpb2mIbddQojCWX040piQGtG2Du/0aIROm3cElFaroX0DF9rVc+b7v6/y6ZbzbDp1i+sJaSwd0QKHShZqhC5EsZKklCgViqLw3e6cKTWjgjxwc7A2PmZtoWNyN29eWRnKN7su83TLmrjaWqkVqhCiHLCwsMDHx4eQkBA6deoEgMFgICQkhCFDhhT5uE2bNuXq1at5toWHh+Pm5lboY+l0uhL54llSxy1uKw5Gkp5lwNfNnse8qqLRaNDp4Pl2Hry6MpQ1x24wvrPXPR/A/6u8tLc4VbQ2S3uFEBVNSFgsU9bnJHNfesyTSV29H7q/VqvhhSAP/NwdGL38CCciExj2/SF+fKEVdlbmpRGyECVGq3YAomLYfTGac7eSqGShY2ib2vc83tOvOk1qOpCeZWDVoUgVIhRClDcjRoxg9erVrF+/nrCwMKZPn05aWhp9+/YFYNKkSXlW2cvMzOTcuXOcO3eOzMxMoqKiOHfuHBEREcZ9nnvuOU6cOMF3331HREQEGzduZPXq1QwePLjU21febT51C4CR7ermqX3RpVFVHCqZcysxnT2XotUKTwghhFBFfGomr/98HL1Bobd/Dd58wqvAz21Ztwqrx7Shio0FJ68nMmLJYVIzskswWiFKniSlRKlYeegaAE+3qHXfYaYajYYRgXUA+OngNbL/s+qEEEL8V/fu3XnrrbeYM2cOTz31FOfOnWPRokXG6Xu3bt0iOvp/SY87d+7Qu3dvevfuTXR0NN9//z29e/fmnXfeMe7j5+fHvHnz2LRpEz179uSbb75h6tSpPPnkk6XevvIsPCaVS3dSMNNq6OjtmucxK3MdfQNyipyvOXJdjfCEEEII1by94RS3k9LxcLHh476+hS5a3qCqLctHtsTOyoyjEfGMXHqYjGxZcECUXzJ9T5S45PQsdl7I+WL4sNWWuvlW44PfLbidlM72c1F0bVy9tEIUQpRTQ4YMeeB0veXLl+f5t7u7OxcuXMj3mB07dqRjx47FEl9Fte1sFACtPKpgb33vtIIn/Wvw/d9X2XMxmiy9AXOd9JEJIYQwfTvP32HzqdvotBrmPB1AJYuifR33qWHP8pGteHbRQQ5ciePt9af5v/5+siqfKJfkU6AocTvO3SEz24CHiw3e1WwfuJ+lmY5BLWoCsFp6z4UQotzKTUp1blj1vo/7utnjWMmc5IxsjkcmlGJkQgghhDrSs/RM++00AM+3rUNjN/tHOl6Tmg5882xTtBpYc/Q6i/ddzf9JQpRBkpQSJe73kzl1RXr61cg3e98nIKeY8N5L0SSmZZV4bEIIIYpXYloWRyLiAOjU6P5JKZ1WQ7v6LgDsuSh1pYQQQpi+pfvDiYxLo7q9FeM7NSiWY7Zv4MK7PRsB8PHmc+y8cKdYjitEaZKklChRqRnZxi8cPf3yn45Xv6otDapWJkuvGHvahRBClB/HrsVjUKC2UyXcHSs9cL/29XNqf+2WpJQQQggTl5iWxTe7wgCY2MULG8viq6IzPLAOT7eoiUGBV38KJSI2tdiOLURpkKSUKFGHwuPJ1Btwd7SmQdUHT937tx6+NQDYdPJmSYYmhBCiBByLiAegWW3Hh+7XvkHOSKlTNxKJS80s8biEEEIItSzYE0ZiWhYNqlY2zgwpLhqNhvefakyz2o4kZ2Qz7qdjpGdJ4XNRfkhSSpSo/WExAAT90yNeED38qgGw73IMSekyhU8IIcqTI+E5Sanmtas8dL+qdlbUd62MosDRfxJZQgghhKlJTMti6f4IIGeUlE5b/MXILcy0zBscgGMlc07fSOLjzeeK/TWEKCmSlBIlan9YTl2RtvUKnpSq52qLh7MNWXqFvy/FlFRoQgghilmW3mAsXN68zsNHSgE0rZWzT+g1SUoJIYQwTctDwknJyMarqu0DFwApDtXtrflikD8Ay0Ii+F1mnYhyQpJSosQkpOs5fzsZgEDPgielADp45UzrkFojQghRfpy7lURalh47KzPquVTOd/+AWg5ATh0qIYQQwtSkZer5/u9wAF7q6Im2BEZJ/VtHL1deeswTgClrT3E9/m6Jvp4QxUGSUqLEnLqTUyPEp4YdVWwsCvXcx7xcAdh1IRpFUYo9NiGEEMXv6L/qSRXkg3fAPyOlTl5PJFtvKNHYhBBCiNK28cRN4lIzcXe0podv/os+FYcJnRvQtJYDyRnZvPHLCQwG+S4lyjZJSokSczY6JynVxsOp0M9tVbcKlmZabielczEqpbhDE0IIUQJO30gCoElNhwLtX9+1MraWZtzN1Mu1XghRrFasWEFwcDC+vr4MGDCAkydPPnDfS5cu8corrxAcHIyXlxc//PDDIx9TCEVRWHYgHIAhrWtjpiudr95mOi1fDPSnkoWOA1fi+P7vq6XyukIUVZlPSh0+fJgXX3yRdu3a4eXlxfbt2x+6/+TJk/Hy8rrnp0ePHsZ95s6de8/jXbt2LemmVDiX4nKKlOe3AtP9WJnraOOZk8zaffFOscYlhBCiZJy7lZOUalTdrkD7a7UaYwJLpvAJIYrL5s2bmTlzJuPGjWP9+vV4e3szcuRIYmNj77t/Wloa7u7uTJw4ERcXl2I5phChkQmcvpGEhZmWgc1rlupr13G24d2ejQD4bMsFLvxTUkWIsqjMJ6Xu3r2Ll5cX7733XoH2f/vtt9m3b5/xZ/fu3Tg4ONyTdKpfv36e/X766aeSCL/CupuZTURCNvC/6RmF1e6f4uj7w+RmL4QQZV1mtoFLd3I+9DYsYFIK/ldXKrdAuhBCPKolS5YwcOBA+vXrR7169ZgxYwZWVlasXbv2vvv7+fnx1ltv0aNHDyws7l9yorDHFGJ5SM6Ke738ahS6lElxeLpFTR73diVTb2D8z8fJyNaXegxCFISZ2gHkp0OHDnTo0KHA+9va2mJra2v89/bt20lMTKRv37559tPpdA/sCRGP7uT1RAxANXsrqtlbFekYuSOlDl+NI1tvKLUhr0IIIQovLDqFLL2CrZUZ7o7WBX6eTw174H+jrIQQ4lFkZmZy5swZxowZY9ym1WoJDAwkNDS0TBxTry9byYHceMpaXGVBUc9NbGomm/5Z/e7Zlu6qnduP+/jQ7at4zt1K4ss/L/DmE17Fdmx53zyYnJscBW1/mU9KPao1a9YQGBiIm5tbnu0RERG0a9cOS0tL/P39mThxIjVq1Cj08Uv6jVZe39DH/il2G+BuX+TYG7jYYG9tTmJaFicj4wtco+RRlNfzLXGXrooYd3lrqyh9uUmlhtXt0GgKvrqQT42cUVWXolLI0hswlw4IIcQjiI+PR6/X4+SUt6apk5MTV65cKRPHPHXqVJHiKGllNa6yoLDnZt35FDL1Cp6OZiix4RxXceLHC/42fLY/gfl7rlLLLBEvp+IdtSXvmweTc1MwJp2UioqKYs+ePcyaNSvPdj8/P2bOnEndunWJjo7m66+/5tlnn2Xjxo1Urpz/Etb/VlpvtPL2ht5zNicp5Wp2l+PHjxf5OA0ctRxOg/V/n0bxLtzv5lGUt/OdS+IuXRK3EP9T2HpSudwdrbG1NCM5I5uw6BS8qxXu+UIIUd74+vqi0+nUDsNIr9dz6tSpMhdXWVCUc2MwKLy6bTcAo4O98fd3L8kQ8+XvD5fTTrIu9CZLTmfy27jmWJg9egeQvG8eTM5NjtzzkB+TTkpt2LABW1tbOnXqlGf7v6cDent706RJEzp27Mgff/zBgAEDCvUaJf1GK49vaEVRuPrHTgC6tfDGv27hV9/L9cTdcA7fPM+1DCv8/f2LKcIHK4/nGyTu0lYR4y7oTUVUXGeNI6Vs89kzL41GQ8PqdhwKj+PszSRJSgkhHomjoyM6ne6eAuSxsbE4OzuXiWPqdLoy+fmhrMZVFhTm3By8GsONhHTsrMx4yv//2bvz+KjK6/Hjn5lJJiH7HggJhC0JS0LYJYIoohXUoiJYLaI0LrXYakXRflut2AWsohW1/aksClIRC2hV3HAFCTuBEMKWfd/3bZKZ+f0xmQFkyzKTO8t5v168NJM79547mcyTe+55zhNpF6/p0zeN5PuTFZwqa+CtnTn87tphVtu3vG8uTl6bznHapJTRaGTz5s3Mnj37og0Lzfz8/IiOjiYvL6/Lx+mtN5ojvaFL61qoaNChBkb1D+hR3ElDQoHj7M+pBpUajbrzU0J6wpFe77NJ3L1L4hbijOPFXW9ybja8ny97c6qkr5QQose0Wi0jR44kJSXFcmPaYDCQkpLC/Pnz7WafwnltPlgIwE2jI/B0t4+/twK9tTxz8wge2ZjKa9+cZlZ8P4aG9d4sFCEuxWkbN+zdu5fc3Fxuv/32y27b2NhIfn6+ND63kvSiWgAi/Nzoo+3ZB3FsX198PNxo1OllKVMhhLBTVY06Kht1AAwL61qlFMCIjr5SxyQpJYSwgoULF7Jp0ya2bt1KZmYmzz77LM3NzZaFj5YsWcKKFSss2+t0OjIyMsjIyECn01FaWkpGRga5ubmd3qcQAI2t7Xx2tBiAOWP7X2br3vXz0RFcHRuKTm/g/7akYTAYlQ5JCMABKqUaGxvPqWAqKCggIyMDf39/IiIiWLFiBaWlpfzjH/8453n//e9/GT16NDExMeft8/nnn+eaa64hIiKCsrIyXn31VdRqNTfddJPNz8cVHCsyXVQMDuj520ujVpEYFcDO0xUcyq+2XLgIIYSwH5nlDQD0D+jTrZsR5uqqjOJ6jEZjlxqlCyHET82aNYuqqipWrlxJeXk5w4cPZ9WqVZapdsXFxajVZ+7Nl5WVccstt1i+XrNmDWvWrGHixImsX7++U/sUAuCL9BKadHoGBnsxdkCg0uGcQ6VS8ddbRnH9yz+wN6eKD1MLuW2ssv2uhAAHSEodPXqUBQsWWL5etmwZALfeeivLly+nvLyc4uLic55TX1/Pl19+yR//+McL7rOkpITHHnuMmpoagoKCGDduHJs2bSIoKMh2J+JC0juSUtEB7lbZ35gBpqTUwdwafjlpoFX2KYQQwnoyy0xJqSHdnAoQE+6LWmWquCqrbyXcz9Oa4QkhXND8+fMvOrXOnGgyi4yM5MSJEz3apxAAWzqm7t02JtIub7BEBnrx2+nDeP7z4yz/7Dg/G9kXbw+7TwkIJ2f378BJkyZdcpBYvnz5eY/5+vpy+PDhiz7n5Zdftkps4sLMSalBVqiUAix3GQ7lV1tlf0IIIazLXCk1JNS7W8/3dNcwMNib7IpGTpc1SFJKCCGEwymubebHzAoAbrOzqXtn+9WUaDbuyyO3sol/fXeaJ34Wp3RIwsU5bU8poYy6ljbyqpoAGGSlSqnEqAAAssobqWnSWWWfQgghrCezvBGAIaHdb5pqbrh6qlT6BwohhHA8Hx4qwmiEiYOCiAryUjqci/Jw0/DHWcMBeGtHNnmVTQpHJFydJKWEVWV0VEn18/fE18M6b69Aby2DQ0x33w/l11hln0IIIazHXCnVk5V8hnU893THvoQQQghHYTQa2XKwALC/BucXct2IcKYOC0HXbuCvnx5TOhzh4iQpJazKvJz3iH5dX33pUhIHBABwKFem8AkhhD1padOT31Eha51KKUlKCSGEcCwnSxs4VdaAVqPmhlH9lA7nslQqFc/cNAKNWsWXx0o5kFuldEjChUlSSljVyY5mtzHh1k1KjbH0laqx6n6FEEL0TE5lIwYj+Hm6EeKj7fZ+hoWZxo3TZZKUEkII4Vg+PVIEwFUxofj3sU4LE1sbFu7L3HGm1fdWfHlS4WiEK5OklLAqcy+QYT2YwnEhYzsqpVLzajAYjFbdtxBCiO7LLOvoJxXm06OVhoaEmaZpVzbqqGxotUpsQgghhK0ZjUY+STOtBn9Tgv1XSZ3t4elDcdeo2JVZSUpmpdLhCBclSSlhNUajkZMd0y6snZSKDffFS6uhvrVd+o0IIYQdyak0JaUGBXdv5T0zL60bkYF9AKmWEkII4TgyiuvJKm9E66bm2uFhSofTJZGBXtw5cQAAL311AqNRbv6L3idJKWE15fWt1Da3oVZ1f1nwi3HTqEmI9AfgoPSVEkJ02LBhA9OnTyc+Pp65c+dy5MiRi2576tQpfvvb3zJ9+nRiY2N5++23L7nvN998k9jYWP72t79ZOWrnklNhSkoN7GFSCqTZuRBCCMfzaZpp6t41saH4ejrG1L2zLbpmKB5uavblVPPDqQqlwxEuSJJSwmrMVVLRwd54uGusvn9LX6m8GqvvWwjheLZt28ayZctYtGgRW7duJS4ujuTkZCorL1x+3tzcTGRkJIsXLyY0NPSS+z5y5AgbN24kNjbWFqE7ldyOJufRIT1f/trc7Nw8JVAIIYSwZ0ajkU+PmKbu3ZgQoXA03RPu58n8KwYC8NKXUi0lep8kpYTVnDT3kwq37tQ9s7GWZudSKSWEgLVr1zJv3jzmzJnD0KFDWbp0KZ6enmzevPmC2yckJPDkk09y4403otVevCF3Y2MjTzzxBH/961/x9/e3VfhOI6/SlJQaENTzpFR0iKnayjwlUAghhLBn6UV15FQ24eGm5to4x5q6d7aHrh6Cp7uawwW17MmWlfhE75KklLAac1LK2ivvmY3umL53uqyBJl27TY4hhHAMOp2O9PR0kpKSLI+p1WqSkpI4dOhQj/b93HPPMW3atHP2LS6spU1PSV0LYKqS7SnzPsxTAoUQQgh79klHldT0uDC8PdwUjqb7Qnw8mDPWtBLfqh1ZCkcjXI3j/uYIu3OmUso2SakwP0/CfD0oq2/lWFEd46ODbHIcIYT9q66uRq/XExwcfM7jwcHBZGV1/4+pTz/9lGPHjvHf//63pyGi1+t7vI8L7c/a++2J7HLT576vpxu+HuoexzYg0BOAvKomWnVtgH2dr63Z48/YluR8nYOznY8QnWU0Gi39pG5y0Kl7Z/vVlEFs2JPH9owyssobGBxqm9kvQvyUJKWE1WR13NkeasMPsPj+/nx9vIy0wlpJSgkhrKq4uJi//e1vrFmzBg8Pjx7vLy0tzQpR9d5+u2NfkalKKqwPHD58uMf7MxiNaNWgMxj5du9h+vq42dX59hZXO2c5XyGEI0orrCW/qpk+7hquibt0r0pHMCTUhxnDw9ieUcbqndn87dZ4pUMSLkKSUsIqqht11DSZ7mpbo9ntxYw6KyklhHBdgYGBaDSa85qaV1ZWEhIS0q19pqenU1lZyW233WZ5TK/Xs2/fPjZs2EBaWhoaTecXcYiPj+/S9pej1+tJS0uz+n574kBDNlBDbGQIiYmJVtnnwB92cqqsAc+QKGgptqvztTV7/BnbkpyvczCflxCuxtzgfPrwMLy0znFZnTxlMNszyth8sIDF18cS5H3xHpxCWItz/PYIxWV3NKXt6+eJl9bNZqXc8f1NfaWOSlJKCJem1WoZOXIkKSkpzJgxAwCDwUBKSgrz58/v1j6vuOIKPv7443Me+8Mf/sDgwYO5//77u3wRqdFobHLhaav9dkd+talSalCIt9ViGhTizamyBvKqWwjoY1/n21tc7ZzlfIUQjsZoNFr6Sd2c0E/haKznisFBjIzwI72ojg27c/nttcOUDkm4AGl0LqzC3JR2UEjPG91eSrw0OxdCdFi4cCGbNm1i69atZGZm8uyzz9Lc3GypdFqyZAkrVqywbK/T6cjIyCAjIwOdTkdpaSkZGRnk5uYC4OPjQ0xMzDn/vLy8CAgIICYmRpFztHe5VaaV9wYGWe+z/8wKfE1W26cQQghhTan5NRTWNOOl1XB1rOOuuvdTKpWK+6cOBmD97lza9QaFIxKuQCqlhFVkdySlom2clAr38yTU14Py+lYyiusYN1D6SgnhqmbNmkVVVRUrV66kvLyc4cOHs2rVKsv0veLiYtTqM/deysrKuOWWWyxfr1mzhjVr1jBx4kTWr1/f2+E7hbyOKtkBwdabtm1Zga+yESLlzxQhhBD2xzx1b8bwcDzdnavycVZ8P5775Bhl9a18d6KcGSPClQ5JODn5a09YRbalUsp2/aTM4vv7883xMtIKaiUpJYSLmz9//kWn6/000RQZGcmJEye6tH9JVl2cwWCkqMY0fS8ysI/V9mvuS5hb2QT4WW2/QgghhDUYDEa2pZmSUjc60dQ9M62bmlvH9Gf1zmze358vSSlhczJ9T1jFmaSU7ZcOHdXRV+qI9JUSQgjFVDS0otMbUKtMVazWYp4GXlDdjN5gtNp+hRBCCGs4lF9NUW0LPh5uTItx/FX3LuSOCVEAfHO8jLL6FoWjEc5OklKix4xG41k9pXqnUgqk2bkQQiipoKYZMC1w4a6x3p8T4b6eeLqraTcYKWuyzaIZQgghRHeZG5xfN8L5pu6ZxYT7MmZAAHqDkc0HCpUORzg5SUqJHiuvb6VRp0etgqig3ktKSbNzIYRQTlFHUqq/FafuAajVKktfqeJ6SUoJIYSwH+dM3Yt3vql7Z7tjvKla6oP9+RiNUrksbEeSUqLHzFP3+gf2wcPN9ncLwv08CPHxwGCEjOI6mx9PCCHE+QqrO5JSAdZNSgEM7GicXtwgNx6EEELYj305VZTWteLr6cbUmBClw7Gpm0ZH4KXVkFXRyL6caqXDEU5MklKix3qznxSYliqN729qfptWIFP4hBBCCYU2qpSCMyu5ljRIpZQQQgj7YZ6697ORfXvlZrySfDzcuKmjkfvGfXkKRyOcmSSlRI9ldywJPsiKS4JfjnkKX1qhVEoJIYQSzlRKWf+zf5B5+p5USgkhhLAT7XoDnx01JaVucsJV9y5kbscUvi/TS2lpkxtFwjYkKSV6LLvcXCnl3WvHNK/Al14klVJCCKEEc6VURID1Vt4zM1dKSU8pIYQQ9mJPdhUVDToCvNy5cqhzT90zGzcgkH7+njS0tvP9yXKlwxFOSpJSosdyOiqlohVISp0qa5CsvRBCKMBcKRVpg+l75pscZU162vQGq+9fCCGE6KpPjhQBcMPIvlZdddaeqdUqZnU0dDdPXRTC2lzjt0nYjMFgJKeyCYDBvdRTCqCfvydB3lr0BiMnSup77bhCCCGgtrmN+lbT1LoIGzQ6D/P1oI+7BoMRCjqSX0IIIYRS2vQGPjtaAsBNCREKR9O7zFMVv84opVknxQDC+iQpJXqkqLYZXbsBd43KJlM4LkalUjEywtTs/KhM4RNCiF5V1DF1L8hbi5fWzer7V6lUlhX4zDc+hBBCCKXsyqykpqmNYG8tVwwOUjqcXpUYFUBkYB+adHq+PVGmdDjCCUlSSvSIeeW9AUFeuPVyGevICHNfKWl2LoQQvelMk3PrV0mZRXckpXI7pogLIYQQStmWZqqSmhnft9eveZSmUqm4McE8ha9I4WiEM7L736h9+/bx61//milTphAbG8v27dsvuf2ePXuIjY097195+bmN2TZs2MD06dOJj49n7ty5HDlyxJan4bRyKnq/ybmZuVIqvVAqpYQQojfZssm5mblXlUzfE0IIoaQ2g5Evj5UCrjd1z+ymeNN5f3O8jMZWWRlXWJfdJ6WampqIjY3lz3/+c5ee9/nnn7Nz507Lv+DgYMv3tm3bxrJly1i0aBFbt24lLi6O5ORkKisrrR2+08vqSEpFB/d+Usrc7DyjpF4a4QohRC8yJ6X6B3jZ7BgDgkz7zq+SpJQQQgjlHC5ppa6lnTBfDyZEu9bUPbNR/f0YGOxFS5uBr4/LFD5hXXaflJo2bRq///3vue6667r0vODgYEJDQy3/1Oozp7p27VrmzZvHnDlzGDp0KEuXLsXT05PNmzdbO3ynZ66U6s2V98wGBnnh4+GGrt1AZnlDrx9fCCFclWX6ng1W3jMzV0rlV0tPKSGEEMr5Mb8FgFnx/dCoVQpHowyVSmVpeP5ZmqzCJ6zL7pNS3XXLLbcwZcoUFi5cyIEDByyP63Q60tPTSUpKsjymVqtJSkri0KFDSoTq0PKqTBcL5oa0vUmtVjGin3kKn/SVEkKI3lJQY/ueUlFnTd8zGo02O44QQghxMS1tevYVtQJw8+h+CkejrJ+N7AvADyfLaW2XVfiE9Vh/yRyFhYaGsnTpUkaNGoVOp+ODDz5gwYIFbNq0iZEjR1JdXY1erz9nOh+YKquysrK6fDy93ra/kOb92/o43WE0Gi29PiL8Pc6JsbfiHhHhy96cKtIKa7glsecDhT2/3pcicfcuV4zb0c5V2Ja5UirShpVS/QP6oAIadXqqGnUE+3jY7FhCCCHEhXyVUUZzu5H+AZ6MiQpUOhxFjYrwJ8zXg7L6VvZkVXHlENecyiisz+mSUoMHD2bw4MGWr8eOHUt+fj5vv/02L7zwgtWPl5aWZvV9Knmcrqhu1tPabkAFlOeepCb//HJWW8ft02a6MNp7sojU/q1W2689vt6dIXH3LolbuKKWNj0VDabPW1tWSnm4awjso6aq2UB+dbMkpYQQQvS6rYcKAbglsT9qF526Z6ZWq7h2eBjv7c3n64xSSUoJq3G6pNSFxMfHc/DgQQACAwPRaDTnNTWvrKwkJCSkW/vWaDRWifNC9Ho9aWlpNj9OdxzIrQbK6evvyYSxY875Xm/F3advPa/t+5G8egMJCaN7PFjY8+t9KRJ373LFuM3PFaK41tRbo4+7hgAvd5seK9xbY0pKVTWRGBVg02MJIYQQZyurb2HHqQoAbh3jmqvu/dS1ceG8tzef7RllPH1jnNLhCCfhEkmp48ePExoaCoBWq2XkyJGkpKQwY8YMAAwGAykpKcyfP7/L+9ZoNL1yUdpbx+mKolrTnfIBQV4Xjc3WcQ/r64fWTU1Dq57C2larNVy3x9e7MyTu3iVxC1d0dpNzlcq2d43DvDVkVLRZ+hcKIYQQveWjQ0UYjBAb7M4gBRZ1skdXDg3Bw01NYU0zJ0tloSlhHXaflGpsbCQvL8/ydUFBARkZGfj7+xMREcGKFSsoLS3lH//4BwBvv/02kZGRDBs2jNbWVj744AN2797NmjVrLPtYuHAhTz75JKNGjSIhIYF33nmH5uZmbrvttl4/P0eW33GREBXU+03Ozdw1aob39eVwQS1Hi2oVWQVQCCFcSWGN6bPfllP3zMK9TcnTAlmBTwghRC/bfLAAgGkDbT/eOYo+Wg1XDg3hm+NlfH28jKQApSMSzsDuk1JHjx5lwYIFlq+XLVsGwK233sry5cspLy+nuPjMspRtbW08//zzlJaW0qdPH2JiYli7di1XXHGFZZtZs2ZRVVXFypUrKS8vZ/jw4axatapb0/dcmXmZ7qhA5ZJSACMi/E1JqcI6bkqQ0lohhLClsyulbC3M2/RnSn5Vs82PJYQQQpgdK6rjeEk9Wo2KpChPpcOxK9cOD+Ob42V8c7yMpCskYSd6zu6TUpMmTeLEiRMX/f7y5cvP+fr+++/n/vvvv+x+58+f363peuIM80VCVJCyH0aj+vsBkF5Uq2gcQgjhCgpqOpJSvVApFdZRKSXT94QQQvSmLR1VUtPjwvDVunaD85+6Ni6cP3KU1IJaalu0SocjnIBa6QCE47JUSik4fQ9My5MCpBfVYTQaFY1FCCGcXVEvJqX6diSlimqa0Rvk810IIYTt6doNfJhaBMCtY/orHI396evvyaj+fhiNcKDYequfC9clSSnRLe16g2UFJqWn78X29UWjVlHVqKOkrkXRWIQQwtmV1pn+AO3rb/vpDIF91Gg1KtoNRoprZQqfEEII2/vyWAkVDa2E+nowLUbau1zItXHhABws0SkciXAGkpQS3VJc24LeYETrpibM10PRWDzdNQwL8wHgaGGdorEIIYQzMxqNlHTckOjrZ/uklFqlIqKjIkum8AkhhOgNG3abFtn6xYQo3DVyuXwhV3Uk69JKW6WSWfSY/JaJbjGvvBcZ0Ae1Wvl51iMipK+UEELYWn1rO81tegDCeyEpBRDV0VC9QJqdCyGEsLHTZQ2kZFWiVsEvJg5QOhy7NToyAB8PNxrajKQXSVGA6BlJSoluMfeTilS4n5SZua+UVEoJ4Vo2bNjA9OnTiY+PZ+7cuRw5cuSi2546dYrf/va3TJ8+ndjYWN5+++3ztnnjjTeYM2cOY8aMYfLkyfzmN78hKyvLhmfgWEo7qqT8PN3oo9X0yjHNfQvN444QQnRGV8YHgM8++4wbbriB+Ph4br75Zr7//vtzvt/Y2Mhzzz3HVVddRUJCArNmzeK9996z5SkIBWzYkwuYGpz3Ru9ER+WmUZM0JBiAHacqFI5GODpJSolusay81wtLgnfGyI5KqWNSKSWEy9i2bRvLli1j0aJFbN26lbi4OJKTk6msrLzg9s3NzURGRrJ48WJCQ0MvuM3evXv55S9/yaZNm1i7di3t7e0kJyfT1CQJEcDSt683+kmZRXaMM/kyfU8I0UldHR8OHjzI4sWLuf322/nwww+59tprWbRoESdPnrRss3z5cnbs2MELL7zAtm3buOeee/jLX/7C119/3VunJWysWadn8wHTqnu/vGKgwtHYv6lDTUmpnaclKSV6RpJSolvsZeU9M/P0vaLaFqoapeGeEK5g7dq1zJs3jzlz5jB06FCWLl2Kp6cnmzdvvuD2CQkJPPnkk9x4441otRdewnj16tXcdtttDBs2jLi4OJYvX05RURHp6em2PBWHYe4n1VtT9+DMYhrSU0oI0VldHR/WrVvH1KlTue+++xgyZAiPPvooI0aM4N1337Vsc+jQIW655RYmTZpEZGQkd9xxB3FxcZetwBKO4+MjRdS1tBMV1Idpwy5880qcceVQU1+pQ/k1NLS2KxyNcGSSlBLdYr5jrfTKe2a+nu5EB5tikb5SQjg/nU5Heno6SUlJlsfUajVJSUkcOnTIasepr68HwN/f32r7dGSldb3X5NwsKqijUqpaekoJIS6vO+NDamoqkydPPuexKVOmkJqaavl6zJgxfPPNN5SWlmI0Gtm9ezfZ2dlMmTLFJuchepfRaGRdSg4Ad00caBc9c+3dwGAvwr01tOmN7Mm6cBWiEJ3hpnQAwjGZLw7MFwv2YGR/f3IqmzhaWMdUubshhFOrrq5Gr9cTHBx8zuPBwcFW6wFlMBj4+9//ztixY4mJieny8/V6vVXi+On+rL3frjBXSoX6am0eh3n/EX6mFV7L61tpaNb1Wi8rJdjDz7g3yfk6B3s7n+6MDxUVFYSEhJy3fUXFmWlJTz/9NE8//TRXXXUVbm5uqFQq/vrXvzJhwoQux2hvr5mzvje74sfTFRwtrMPTXc2csRHnvSau/NpcjF6vZ3S4li+zmvn+ZBlXx4Rc/kkuQt43Jp09f0lKiS5radNTXt8K2E+lFJianX96pJijUiklhLCCpUuXcurUKf7zn/906/lpaWlWjsi2++2ME/nVALTVVZCa2jvT6fJOZ+DlpqKp3cj23YeI8nP+P12U/BkrQc5XOIL169eTmprKv//9byIiIti/fz9Lly4lLCzsnKqszrDX94C9xtUbXvi+CoDpAz3JP3WM/J9835Vfm0tJ7OvBl1nNbD9ayOxIaaHyU/K+6Rzn/8tOWF1BRz8pHw83ArzcFY7mjDPNzmUFPiGcXWBgIBqN5rymtZWVlefd7e6O5557ju+++453332Xvn37dmsf8fHxaDTWq+rR6/WkpaVZfb9d0bJrF9DK+BFDSRweZtNjmc83ISGB6B93c6y4Hp+wASTG2fa4SrKHn3FvkvN1DubzshfdGR9CQkLOqYr66fYtLS28/PLLvPbaa1x99dUAxMXFkZGRwerVq7uclLK394Czvjc763B+DWllJbipVfzh1glEnLXqnqu/Npei1+tp1B1GrYKiej1h0bHnvHauTN43Jp0dHyQpJbrMvPJeZGAfVCr7mW9tTkplVzRS39KGr6f9JMyEENal1WoZOXIkKSkpzJgxAzBNt0tJSWH+/Pnd3q/RaOQvf/kLX331FevXrycqKqrb+9JoNDb5Q8RW++2M0jpTlWxEgFevxaDRaIgK8uJYcT2Fta0u8cedkj9jJcj5CmvqzviQmJjI7t27uffeey2P7dq1i8TERADa29tpa2s77+9ejUaD0Wjscoz2+h6w17hs7Y0d2QDMTuxPVLDPBbdx1dfmcry1akZHBXAor4ZdWVXcMWGA0iHZFXnfdI40OhddZm8r75kF+3jQr2OZ8ozieoWjEULY2sKFC9m0aRNbt24lMzOTZ599lubmZm677TYAlixZwooVKyzb63Q6MjIyyMjIQKfTUVpaSkZGBrm5uZZtli5dyv/+9z9WrFiBt7c35eXllJeX09LS0uvnZ2/a9QYqGkxJqfCOPk+9RVbgE0J0RVfHhwULFrBjxw7WrFlDZmYmr776KkePHrUksXx8fJg4cSIvvPACe/bsIT8/ny1btvDhhx9aEl/CMZ0uq+eL9FJUKnjo6sFKh+OQrhxi6t+WkinNzkX3SKWU6LK8Svtaee9sIyP8Ka5t4WhhLRMHBSkdjhDChmbNmkVVVRUrV66kvLyc4cOHs2rVKst0i+LiYtTqM/deysrKuOWWWyxfr1mzhjVr1jBx4kTWr18PwHvvvQfA3Xfffc6xli1bZrmYcVUVDToMRtCoVQT79HJSquMmSL4kpYQQndDV8WHs2LG8+OKL/POf/+Sll14iOjqa119//ZxFLl566SVeeuklHn/8cWpra4mIiOD3v/89d955Z6+fn7Cef32bCcD1I8IZGuarcDSOadKgIF77NpOUrEqMRqNdzaQRjkGSUqLLCmvOTN+zNyMj/NieUUq69JUSwiXMnz//otMxzIkms8jISE6cOHHJ/V3u+66spM5ULRbm64Gml5fKHmBOSnWs/CqEEJfTlfEBYObMmcycOfOi+wsNDWXZsmVWi08o70RJPVtTCwFYdM1QhaNxXGMHBKDVqCmtayW7opHBoReeAinExcj0PdFlRR1Jqf52mJQa1d8fgHRZgU8IIayqpNaUlAr38+z1Y0cFmcab/KqmbvVvEUIIIX7qhS+OYzTCrPi+JEQGKB2Ow/J01zBmQAAAu7OqlA1GOCRJSokuM1dK9bfD1RVG9Tc1Oz9V1kBLm17haIQQwnmUdlRK9VUgKRXZMV28obWdmqa2Xj++EEII55KSWcn2jDI0ahWLr49VOhyHd8Xgjr5SWdJXSnSdJKVEl7S06alo0AH2mZTq6+dJkLcWvcHIiRJpdi6EENZinr7X203OwXQXNszXdFzzYhtCCCFEd7TrDSz9OB2AOydGMUSmm/XY5LOanUtFs+gqSUqJLinumL7Rx11DgJe7wtGcT6VSMTLCVC11VKbwCSGE1ZgrpcL9e79SCs40O5cV+IQQQvTEhj15HC+pJ8DLncXXSZWUNSRGBeDhpqaioZXM8kalwxEORpJSokvM/aQiAjztdmWFkRHmvlLS7FwIIaxFyel7AFGB5r5S0uxcCCFE9xTVNPPCF6ZFTRZfH0ugt1bhiJyDp7uGsQMCAZnCJ7pOklKiSwqrzU3OvRSO5OLMfaXSC6VSSgghrMXc6FyxpJRlBT6plBJCCNF1RqORZz46SkNrO2MHBHDXxAFKh+RUzFP4dmdKUkp0jSSlRJecaXKuzEVJZ5grpTJK6mnTGxSORgghnENpXSug/PS9fJm+J4QQohs27stne0YZ7hoVy+ckoFHb56wPR2VJSmVJXynRNZKUEl1imb7nb39Nzs0GBnnh6+mGrt0gzc6FEMIKGlrbaWhtByBcsel7kpQSQgjRPadK6y3NzR+/PpaYcF+FI3I+CZH+eLqrqWzUcaqsQelwhAORpJTokqJa8/Q9+01KqdUqRkcGAHCkQKbwCSFET5n7Sfl4uOHj4aZIDAOCTUmpwppm9Aa5AyuEEKJzWtr0/Pa9Q7S0GZg6LIT7pw5WOiSn5OGmYfzAIAD2SF8p0QWSlBJdYu4pFRFgv0kpMGXqAQ7n1ygbiBBCOIHSjn5S4X4eisXQ188Td42KNr2Rko4kmRBCCHE5f/30GMdL6gnx0bJi3mjUMm3PZiZEm5JSe3OqFY5EOBJJSolOMxiMFHVcmPS386TU6KgAAA4X1CgahxBCOANzEqivQv2kADRqFZEdU/jyKmUKnxBCiMt7b28e7+7OA2DFvETCfO23L64zmDDItALfvuwq6SslOk2SUqLTKht16NoNqFTK9RTpLPP0vZOl9TTp2pUNRgghHJw5KaX0Z780OxdCCNFZKZmVPP3hUQB+PyOGaTGhCkfk/MZEBeKuUVFS10JBxwwbIS5HklKi08xNzsN9PdG62fdbp6+/J+F+HhiMkF5Up3Q4Qgjh0M5M31M4KdXRzzBPklJCCCEuIbeykYc2HKDdYOTm0RH87tqhSofkEvpoNYzqb2qjsje7SuFohKOw78yCsCuF5pX3Auy7SsosoaNaSvpKCSFEz5TWtQKmvk5KGmCulKqWpJQQQogLq2tpI/md/dQ0tTE60p8Xbk9ApZI+Ur1lYkdfqX05kpQSnWP3Sal9+/bx61//milTphAbG8v27dsvuf2XX37JwoULueKKKxg7dix33HEHO3bsOGebV199ldjY2HP+3XDDDbY8DadQVOMYTc7NEi19pWQFPiGE6Al7mb5nTkpJpZQQQogLadcbePg/hzhd1kBfP0/eWjAeT3eN0mG5lDPNziUpJTpHmXWdu6CpqYnY2FjmzJnDww8/fNnt9+3bR1JSEr///e/x8/Njy5YtPPTQQ2zatIkRI0ZYths2bBhr1661fK3RyIfV5ZgrpfoHOkZSSlbgE0II6yi1g0bnID2lhBBCXNrftmXww8lyPN3VrLpnPGF23gfXGY2PNjU7zypvpKKhlRAf5VbuFY7B7pNS06ZNY9q0aZ3e/o9//OM5Xz/22GN8/fXXfPPNN+ckpTQaDaGh0uyuK8yVUva+8p5ZQv8AwHRHvbpRR6C3VtmAhBDCAekNRsrq7WP6njkpVdGgo7G1HW8Pu/8zRgghRC95b28ea3/MAeDleYmW3kaidwV4aYkN9+VEaT37squYGd9P6ZCEnXP6v+YMBgONjY0EBASc83hubi5TpkzBw8ODxMREFi9eTERERJf3r9frrRTppfdv6+N0hnkFhb6+HpeNxx7i9vFQEx3sRU5lE4fyqjq14oY9xN0dEnfvcsW4He1chfVUNrSiNxhRqyDER9nkvn8fd/z7uFPb3EZBdTOxfX0VjUcIIYR9OFJQw58/SgfgsetiJBGisAmDAjlRWs/eHElKictz+qTU6tWraWpqYubMmZbHEhISWLZsGYMGDaK8vJzXX3+dX/7yl3z88cf4+Ph0af9paWnWDlnR41xKfkU9APWluaS2FnXqOUrHPcDbQE4lfLn/BP5NhZ1+ntJxd5fE3bskbuEKzE3OQ3w8cNMo34pyQJAXaYW15FU1SVJKCCEEtU1t/GbDQXR6A9ePCOe302WlPaVNiA7i3d150uxcdIpTJ6U+/vhjXn/9df71r38RHBxsefzs6YBxcXGMHj2aa665hs8++4y5c+d26Rjx8fE27Uel1+tJS0uz+XEup1mnp+6DEgCmT0rEr4/7Jbe3l7ivasrhh7zjlLX3ITEx8bLb20vcXSVx9y5XjNv8XOF6Suykn5TZ2UkpIYQQrs1gMLL4g1QKqpsZEOTFC3NHy0p7dmDiIFOz82NFddS3tOHreelrR+HanDYp9emnn/KnP/2JV155haSkpEtu6+fnR3R0NHl5eV0+jkaj6ZWL0t46zsWUNpim7vl4uBHg7dHpD3ul4x4zwPSBeLigFrVa7TBxd5fE3bskbuEK7GXlPbPIIFNfQ2l2LoQQYvXObLZnlKF1U/OvX47F/zI3zkXv6Offh8jAPhRUN3Mwr6ZTbVSE61K+Dt8GPvnkE/7whz+wYsUKrr766stu39jYSH5+vjQ+v4TCjn5SEQGeDnX3YWSEH1qNmspGHbmVcgEjhBBdVVprTkrZx+o5A2QFPiGEEMDpsnpe+PIEAM/cNEIam9uZidGm4oB92TKFT1ya3SelGhsbycjIICMjA4CCggIyMjIoKjL1NFqxYgVLliyxbP/xxx/z5JNP8uSTTzJ69GjKy8spLy+nvr7ess3zzz/P3r17KSgo4ODBgzz88MOo1Wpuuumm3j05B+JoK++ZebpriI80DVAHcqsVjkYIIRyPZfqenVRKmZNSMn1PCCFcl95g5PEPjqBrNzAtJpRfThqgdEjiJyZ0TOHbK32lxGXY/fS9o0ePsmDBAsvXy5YtA+DWW29l+fLllJeXU1xcbPn+pk2baG9v57nnnuO5556zPG7eHqCkpITHHnuMmpoagoKCGDduHJs2bSIoKKiXzsrxmJNSEQ6WlAIYNzCQA7nV7M+tZs64SKXDEUIIh1JqZ9P3LJVS1U0YjUaHqt4VQghhHRv35ZGaX4OvhxvLbouXscAOmftKpebX0Nqux8NNWkeIC7P7pNSkSZM4ceLERb9vTjSZrV+//rL7fPnll3scl6spcPCkFMCBXMnSCyFEV5XaWaPziIA+qFXQ0magvKGVMF/7iEsIIUTvqG7U8cIXpuvDx66PccjrE1cwOMSbEB8tFQ060gpqGR8tBSDiwux++p6wD446fQ/OJKVOljZQ29ymcDRCCOFYSmrta/qeu0ZNP39pdi6EEK7qla9PUdPURlxfX+6+YqDS4YiLUKlUjB8oU/jE5UlSSnRKUY3poqR/oOMlpUJ8PIgONk33OJgnfaWEcCYbNmxg+vTpxMfHM3fuXI4cOXLRbU+dOsVvf/tbpk+fTmxsLG+//XaP9+nsmnV66lraAQizk6QUSF8pIYRwVflVTWzYkwvA0zeNwE0jl7P2zNxXSpqdi0uR32JxWQaDkeJax52+BzCuI0t/IEeSUkI4i23btrFs2TIWLVrE1q1biYuLIzk5mcrKygtu39zcTGRkJIsXL77oaqtd3aezMzc57+Ouwc/Tfmb8W5JSlc0KRyKEEKI3vbz9JG16I1OHhXDl0BClwxGXMSHa3EalGoPBqHA0wl5JUkpcVnlDK216Ixq1inBf+1gSvKvGn/WBKIRwDmvXrmXevHnMmTOHoUOHsnTpUjw9Pdm8efMFt09ISODJJ5/kxhtvRKvVWmWfzu7sflL21EQ2Kqhj+l61VEoJIYSryKlo5MNDhQA88bNYhaMRnTGinx9eWg11Le2cLKtXOhxhpyQpJS6rsKOfVF8/T4ctkTX3lUrNr6FNb1A4GiFET+l0OtLT00lKSrI8plarSUpK4tChQ3azT0d3ZuU9+7ohESXT94QQwuW88UMWBiNMjwsjITJA6XBEJ7hp1IwdYLoO2yczVsRF2E8tvrBbRZaV9+ynn0hXDQ31wc/TjbqWdjKK62QgE8LBVVdXo9frCQ4OPufx4OBgsrKy7GKfer2+W3Fcbn/W3u+lmD//w309evW4cOnzjewYj/Kqmno9LltS4mesJDlf5+Bs5yPsU1ldC5sPFADw0NVDFI5GdMX46EB2nq5gf06VNKYXFyRJKXFZZ5JSjtlPCkCtVjFuYCDfnijnQG61JKWEEDaXlpbmUPu9kKOZdab/aakjNTW11457tgudb22rqeK1tLaFfQcO4a6xn6mF1tCbP2N7IOcrhLicdSm56PQGxg0MZEJ0kNLhiC4w/7z2S6WUuAhJSonLKqx2/KQUYElK7c+tZuGVg5QORwjRA4GBgWg0mvMakFdWVhIS0r3Gp9beZ3x8PBqNpluxXIheryctLc3q+70UQ8YhoIn4oVEkJkb3yjHNLnW+RqMRr8+306TTEzIwhkEh3r0am60o8TNWkpyvczCflxC20tquZ+O+PACSp8jf8I4mMSoAjVpFYU0zhTXN9Hfwa0phfZKUEpdVWGPqKeLoHyDjO7L0e7OrMBqNdtW0VwjRNVqtlpEjR5KSksKMGTMAMBgMpKSkMH/+fLvYp0ajscmFp632eyFl9ToAIgK8FLuIvtj5Dgjy4nhJPQU1LQwN91MgMtvpzZ+xPZDzFUJcyudHS6ho0BHu58F1I8KVDkd0kbeHG6Mi/DhcUMv+nCr6J/ZXOiRhZxyza7XoVebpe46elBozIAAPNzXl9a1kljcoHY4QoocWLlzIpk2b2Lp1K5mZmTz77LM0Nzdz2223AbBkyRJWrFhh2V6n05GRkUFGRgY6nY7S0lIyMjLIzc3t9D5dTUmtudG5/fUUNDc7z5dm50II4dTWp5jG6bsmDsTdQRddcnXm4oB9OVUKRyLskVRKicsqqnWO6XsebhrGRwfy4+lKdmVWMjTMV+mQhBA9MGvWLKqqqli5ciXl5eUMHz6cVatWWabaFRcXo1af+eO1rKyMW265xfL1mjVrWLNmDRMnTmT9+vWd2qcrMRiMlNWbklJ9/e0vKTVAVuATQginl1newP7cajRqFXdOjFI6HNFNE6IDWb0zW/pKiQuyWVIqPz+fqCj54HB0ja3t1DS1AY69+p5Z0pAQU1LqdCULJkcrHY4QLsma48P8+fMvOrXOnGgyi4yM5MSJEz3apyupatLRpjcCEOrjoXA054sKNN0oya9qVjgSIYS1yPWD+CnzinvTYkIJs8OqXdE54waaKqVOlNZT29SGv5e7whEJe2Kz+sfrrruOu+++m48++ojW1lZbHUbYmHnqnp+nG76ejv/hMXmIaan33dmVGAxGhaMRwjXJ+OAYzFP3Qny0aN3sb7rEgGCplBLC2cj4IM6mNxjZeqgQgDljIxWORvREqK8Hg0K8MRrhYJ5US4lz2eyvzK1btxIbG8vy5cu58soreeaZZzhy5IitDidspLDGOabumSX098fHw42apjYySuqUDkcIlyTjg2MwT92zx35ScGb6Xn5VE0aj3GQQwhnI+CDOlpJZSXFtC36eblw7PEzpcEQPTYgOBGCv9JUSP2GzpNTw4cP505/+xI4dO/j73/9OWVkZd911FzfddBNr166lqkrejI6gyElW3jNz06gtH4gpmZWX2VoIYQsyPjiGklpTlUJfO01KRQaaklL1Z00zF0I4NhkfxNk+OVIEwI0JEXi6y4qVjs7c7Hy/JKXET9i8Ht/NzY3rr7+elStX8vjjj5Obm8vzzz/PtGnTWLJkCWVlZbYOQfRAYY1pWkT/QOdISoGprxTALklKCaEoGR/sW0ldR6WUHTY5B/B011gSZtmVjQpHI4SwJhkfRJvewOfpJQDcnNBP4WiENUzoSEodzq+lpU2vcDTCntg8KZWWlsazzz7LlClTWLt2Lb/61a/46quvWLt2LWVlZfzmN7+xdQiiB8yVUs4yfQ/O9JXam11Fu96gcDRCuC4ZH+xbaUdPqXBf+0xKAQwK8QYgp0KSUkI4ExkfxK7MSmqa2gjx0TJxUJDS4QgriA72IsRHi05v4GhhrdLhCDtis9X31q5dy5YtW8jOzuaqq66y3N0wL88dFRXF8uXLmT59uq1CEFbgbD2lAEb088O/jzu1zW2kFdYyZkCg0iEJ4VJkfHAM5kqpvv72t/KeWXSINylZlWRLUkoIpyDjgzD7tGPq3g2j+uKmsb/FNkTXqVQqxg8M4vP0EvblVFum8wlhs6TUe++9x5w5c7j11lsJC7twY7qgoCD+9re/2SoEYQWF1aaklLP0lAJQq1VMHhzM5+kl7MqslKSUEL1MxgfHUFpn343OAQZ3VEpJUkoI5yDjgwDTqntfHSsFYNYombrnTCYMMielqniIIUqHI+yEzZJSa9asISIiwnJnw8xoNFJcXExERARarZZbb73VViGIHtIbjJY75c6UlAJIGmpKSu04Vc6ia4YqHY4QLkXGB8dQaqmUst+kVLR5+p70lBLCKcj4IAAO5lVT3dSGfx93JsjUPadiXnBqf04VBoMRtVqlcETCHtisFvK6666jurr6vMdramq49tprbXVYYUVl9S3oDUbc1CpCfe13+kZ3TIsJBWB/TjV1LbJqkxC9ScYH+9fSpqe6Y0U7e119D870lMoub8RoNCocjRCip6w5PmzYsIHp06cTHx/P3LlzOXLkyCW3/+yzz7jhhhuIj4/n5ptv5vvvvz9vm8zMTH79618zbtw4EhMTmTNnDkVFRV2KS1yeuUrqmthQ3GXqnlMZ0c8PL62GupZ2TpU1KB2OsBM2+y2/2B+HTU1NeHg4V4LDWRV19JPqF+CJxsmy2AODvRkc6k27wcjOUxVKhyOES5Hxwf6V1bUCoHVT49/HXeFoLm5AkBdqFTTq9JQ3tCodjhCih6w1Pmzbto1ly5axaNEitm7dSlxcHMnJyVRWXnjl5YMHD7J48WJuv/12PvzwQ6699loWLVrEyZMnLdvk5eVx1113MXjwYNavX8///vc/fvOb38i4ZQPbO5JS143oq3AkwtrcNGrGDAgAYF9OlbLBCLth9el7y5YtA0yNzF555RX69Dkz7Uuv13PkyBHi4uKsfVhhAwUd/aQi/J1r6p7Z9Ngwssqz+eZ4GbPiZb66ELYm44PjsDQ59/NEpbLfmxJaNzX9A/uQX9VMdnkjYXa8UqAQ4uKsPT6sXbuWefPmMWfOHACWLl3Kd999x+bNm3nggQfO237dunVMnTqV++67D4BHH32UXbt28e677/Lcc88B8PLLL3PVVVexZMkSy/MGDBjQ9ZMVl5RZ3kBWRSPuGhVXxYQoHY6wgfEDg/jxdCX7c6qYf8VApcMRdsDqSaljx44BpjsdJ0+exN39zB1WrVZLXFwcv/rVr6x9WGEDRTXO2U/KbHpcGKt2ZvPdiTKZ0yxEL5DxwXGUnpWUsneDQnzIr2omp7KRSYODlQ5HCNEN1hwfdDod6enpPPjgg5bH1Go1SUlJHDp06ILPSU1N5d577z3nsSlTprB9+3YADAYD3333Hffddx/JyckcO3aMyMhIHnzwQWbMmNGVUwVMiTZ7Yo7HHuL6NsNUJTUxOggvd7XiMdnTa2NvuvvajBsYAMDenCqnfV3lfWPS2fO3elJq/fr1APzhD3/gj3/8Iz4+PtY+hOglhTVNAPQPdM6k1PjoIHw83Kho0HGksJbEqAClQxLCqcn44DgsK+/ZcZNzs0HBXvwAZMkKfEI4LGuOD9XV1ej1eoKDz01SBwcHk5WVdcHnVFRUEBISct72FRWmFg+VlZU0NTXx1ltv8eijj/L444+zY8cOHn74YdatW8fEiRO7FGNaWlqXtu8t9hDXJwdMU7qGeutITU1VNpiz2MNrY6+6+tqo2w2oVaYCiK92HSDUS2OjyJQn75vOsdnqe+YyXOG4zJVSEU5aKaV1UzN1WAifHS3hm+NlkpQSopfI+GD/Smo7klIOsMiFudl5jiSlhHB49jo+GAwGAK699lpLRdXw4cM5ePAgGzdu7HJSKj4+Ho3Gfi7E9Xo9aWlpisfV0qYnY+vXAPzi6gRiwn0Vi8XMXl4be9ST12bk3l2kFdbR7N2PxNERNopQOfK+MTG/Dpdj1aTUww8/zPLly/Hx8eHhhx++5LavvfaaNQ8tbKCwo6eUs07fA7gmLozPjpbw7fEyHrsuRulwhHBaMj44FktPKQeolIo2r8AnSSkhHJK1x4fAwEA0Gs15Tc0rKyvPq4YyCwkJsVRFXWj7wMBA3NzcGDJkyDnbDBkyhAMHDlw2pp/SaDR2eaGqdFz7M6tobTfQz9+TuH7+dtXTUOnXxp5157WZEB1MWmEdB/NquXVslI0iU568bzrHqqvv+fr6nvP/l/on7J959T1nrZQCuCY2DIC0wlrKOi7ChBDWJ+ODY7FM33OInlKmpFRuZRMGw4VX7hJC2C9rjw9arZaRI0eSkpJiecxgMJCSksKYMWMu+JzExER27959zmO7du0iMTHRss/4+Hiys7PP2SYnJ4f+/ft3Ki5xed+fKAdgWkyoXSWkhPVNiA4EZAU+YWLVSqmzS26tVX67b98+Vq9ezdGjRykvL+f111+/bEPBPXv2sHz5ck6dOkW/fv146KGHuO22287ZZsOGDaxevZry8nLi4uJ4+umnSUhIsErMzqC2uY361nbAuSulQn09GB3pz+GCWr47Uc6csc5XPiqEPbDF+CBsx5EqpfoH9MFdo6K13UBxXYtTj1lCOCNbjA8LFy7kySefZNSoUSQkJPDOO+/Q3NxsuR5YsmQJ4eHhLF68GIAFCxZw9913s2bNGqZNm8a2bds4evSoZeU9gOTkZH7/+98zYcIEJk2axI4dO/j2229Zt26dVWIWsCvTVK02ZZisuufsxkcHAXCitJ7a5jb8+7hf5hnCmVm1UupsLS0tNDc3W74uLCzk7bffZufOnV3aT1NTE7Gxsfz5z3/u1Pb5+fk8+OCDTJo0iY8++oh77rmHP/3pT+zYscOyzbZt21i2bBmLFi1i69atxMXFkZycfF6ZryszV0kFeWvpo3XuksPpceEAfHmsROFIhHAN1hofhG0YjUZK61oBx1h9z02jJirIC4DscpnCJ4Qjs9b4MGvWLJ588klWrlzJ7NmzycjIYNWqVZbpeMXFxZSXl1u2Hzt2LC+++CLvv/8+s2fP5osvvuD1118nJuZMa4frrruOZ599llWrVnHzzTfzwQcfsHLlSsaPH9/DsxYAVY06jpfUA3CFrKTq9EJ9PRgU4o3RCAdzq5UORyjMZo3Of/Ob33Dddddx5513UldXx9y5c3F3d6e6upqnnnqKu+66q1P7mTZtGtOmTev0cTdu3EhkZCRPPfUUcGau99tvv83UqVMBWLt2LfPmzWPOnDkALF26lO+++47NmzfzwAMPdPFMnZMr9JMymxXfl5e3n+T7k+XUNbcpHY4QTs9a44OwjZqmNnTtpqa+oQ7Q6BxgcIg3WeWNZFc2yh12IRyYNceH+fPnM3/+/At+z7za39lmzpzJzJkzL7nP22+/ndtvv73TMYjO25ttKg6ICfchxMcxxh7RM+MHBpJd0ci+nCquiQtTOhyhIJtVSqWnp1vuHHzxxReEhITw7bff8vzzz19wILCW1NRUJk+efM5jU6ZMsSwpqtPpSE9PJykpyfJ9tVpNUlIShw4dsllcjqao1txPyv7vkvfUsHBfYsN9adMb+TKjVOlwhHB6So0PonPMU/cCvdzxdHeMStno4I5m51IpJYRDk/HBde3OMvUWkiop1zGhYwrf/hyplHJ1NquUamlpwdvb9Efizp07uf7661Gr1SQmJlJUVGSrw1JRUXHeyhohISE0NDTQ0tJCbW0ter2e4OBzP/CCg4PJysrq8vH0en2P4u3s/m19nJ/Kr2oCIMLfs1vHViru7po5KpwTpfV8criYR8e4O0zcZo72eptJ3L2rJ3Fb81yVGh9E55Q4UJNzM/MKfDmVkpQSwpHJ+OC6dmeZKqUkKeU6xnc0O08tqKG1XY+Hm2PcCBPWZ7Ok1IABA9i+fTvXXXcdO3fu5N577wVMy6v6+PjY6rC9Li0tzamOY3YspwYAY2OVpcqsO3o77u4a4m5q6r4rs5LkEWEOE/dPSdy9S+LuHlcZHxxVaa3jNDk3G9yRlMqukKSUEI5MxgfXVNnQauknNXFQkMLRiN4yKMSbEB8tFQ06jhbWMm6g/Oxdlc2SUosWLeLxxx9n2bJlTJ482bIE648//sjw4cNtdVhCQkKoqKg457GKigp8fHzw9PRErVaj0WjOa2peWVl5XoVVZ8THx6PR2C6rq9frSUtLs/lxfqppdwrQwsSRQ0gc2bfLz1cq7u5KBP59+EeOFdezu7CFx2ZPcoi4zRzt9TaTuHtXT+I2P9calBofROc4UpNzs0GhpqRUflUTunYDWjebdScQQtiQjA+uaW+2aeqe9JNyLSqVivEDg/g8vYS92dWSlHJhNktK3XDDDYwbN47y8nLi4uIsj0+ePJkZM2bY6rAkJibyww8/nPPYrl27SExMBECr1TJy5EhSUlIscRgMBlJSUi7aDPFSNBpNr1yU9tZxzIo67pRHBnn36Li9HXdP3DQ6gmPFJ/gxv4UnHCjusznS6302ibt3KR23UuOD6Bzz9L0wB0pK9fXzxMfDjYbWdnIqG4kJ91U6JCFEN8j44JrMU/cmy9Q9lzM+OpDP00vYn1MFDFE6HKEQm95KDA0NZcSIEajVZw6TkJDAkCGdf8M1NjaSkZFBRkYGAAUFBWRkZFjmla9YsYIlS5ZYtv/FL35Bfn4+//jHP8jMzGTDhg189tlnlvJfgIULF7Jp0ya2bt1KZmYmzz77LM3Nzdx22209PGPnoGs3UFZvulPuCqvvmd0UHwFAepmOioZWhaMRwrlZY3wQtlHakZRypEoplUrF0DDT1J6TpfUKRyOE6AkZH1yPNDl3XZZm57nVGAxGhaMRSrFZpVRTUxNvvvkmu3fvprKyEoPBcM73v/76607t5+jRoyxYsMDy9bJlywC49dZbWb58OeXl5RQXF1u+HxUVxRtvvMGyZctYt24dffv25a9//StTp061bDNr1iyqqqpYuXIl5eXlDB8+nFWrVnVr+p4zKqltwWgET3c1Qd5apcPpNQOCvUjo78+Rwlq2pZWwcMpgpUMSwilZa3wQtlFi6SnlWFMoYsJ9SM2v4WRpg9KhCCG6ScYH11PZ0MqJUukn5apGRPjRx11DbXMbp8sbpNLZRdksKfWnP/2JvXv3Mnv2bEJDQ1GpVN3az6RJkzhx4sRFv798+fILPufDDz+85H7nz5/frel6rqCgpmPlvYA+3f65OaqfJ/bjSGEtHxwo4N4rB7nc+QvRG6w1PgBs2LCB1atXW6Z6PP300yQkJFx0+88++4xXXnmFwsJCoqOjefzxx5k2bZrl+42NjaxYsYLt27dTU1NDZGQkd999N3feeWe3Y3Q0pQ64+h5g+UP2lFRKCeGwrDk+CMdg7icVG+5LsPSTcjnuGjVjBwbw4+lK9mRXSVLKRdksKfXDDz/wxhtvMG7cOFsdQthIUY3pgsSVpu6Z3ZIYwfLPjnOsuJ60wloSIgOUDkkIp2Ot8WHbtm0sW7aMpUuXMnr0aN555x2Sk5P5/PPPCQ4+fwrAwYMHWbx4MY899hjXXHMNH3/8MYsWLWLLli3ExMQAphsdu3fv5oUXXqB///78+OOPLF26lLCwMK699toexesIdO0GKht1gGNN3wMY1vGHrEzfE8JxyfWD69mTbZ66J1VSrmrSoGB+PF3J7sxK7r5ioNLhCAXYrKeUn58fAQEBttq9sKHC6mbANZNSgV5aJkeaLsTe25uvcDRCOCdrjQ9r165l3rx5zJkzh6FDh7J06VI8PT3ZvHnzBbdft24dU6dO5b777mPIkCE8+uijjBgxgnfffdeyzaFDh7jllluYNGkSkZGR3HHHHcTFxXHkyJEex+sIyupNNyXcNSoCvRxr+nZMuKmnVE5lE63teoWjEUJ0h1w/uJ4DudUAjI+WpJSrmjzEdCNxd1YlRqP0lXJFNktKPfLII7zyyis0Nzfb6hDCRopqTD+zCBdMSgHMGGQ67/+lFtLY2q5wNEI4H2uMDzqdjvT0dJKSkiyPqdVqkpKSOHTo0AWfk5qayuTJk895bMqUKaSmplq+HjNmDN988w2lpaUYjUZ2795NdnY2U6ZM6XasjsQ8dS/M1xO12rGmzfT188TXww29wUh2RaPS4QghukGuH1xLs05PRnEdAGMHBiocjVBKQqQ/nu5qKht1nCqTvpCuyGbT99auXUteXh5JSUlERkbi5nbuobZu3WqrQ4seKqxx3UopgJGhWqKDvcipbOKTI0XcMWGA0iEJ4VSsMT5UV1ej1+vPm6YXHBxMVlbWBZ9TUVFx3oIWwcHBVFRUWL5++umnefrpp7nqqqtwc3NDpVLx17/+lQkTJnT29Cz0eutW65j3Z+39nq2oo1I23M/DpsfpjO6c79BwHw7l1XC8uI5hod62Cs1meuNnbE/kfJ2DNc9Hrh9cy5GCGtoNRsL9PIjwd6wp48J6PNw0jB8YxM7TFaRkVkpfKRdks6TUjBkzbLVrYWOuXimlUqmYNz6Sf3xxkvf25ktSSggrs+fxYf369aSmpvLvf/+biIgI9u/fb+kpdXZVVmekpaXZJEZb7Rfg4ElThZGHoeWcCjIldeV8gzWtAOw8cpooQ6mtQrI5W/6M7ZGcrzCz5/FBWN/BvBoAxg4IlKb2Lm7ykGBLUuqepGilwxG9zGZJqYcffthWuxY2ZDQaLZVSkYGumZQCmDO2Py99dYrU/BoyiusY3s9P6ZCEcBrWGB8CAwPRaDRUVlae83hlZeV51VBmISEh51RF/XT7lpYWXn75ZV577TWuvvpqAOLi4sjIyGD16tVdTkrFx8ej0Wi69JxL0ev1pKWlWX2/Z/us+DhQz/CB4SQmDrfJMTqrO+d7RWMO27OPU6fyITEx0bYB2kBv/IztiZyvczCflzXI9YNrOZhn6ic1doBM3XN15kb3e7IrMRiMDtdCQPSMzZJSAHV1dXzxxRfk5eWRnJxMQEAA6enphISEEB4ebstDi26qbNTR2m5ApXK85cCtKcTHg5+N7MunacWs/TGbf9w+WumQhHAqPR0ftFotI0eOJCUlxXJn3WAwkJKSwvz58y/4nMTERHbv3s29995reWzXrl2W5EV7ezttbW3n3a3VaDTdaryp0WhscuFpq/0ClNabVt6LCPCym4vmrpxvbMcNhNNlDXYTf3fY8mdsj+R8xdnk+sE1GI1GDpmTUgMDlA1GKC4hMgAvrYbqpjZOlNZLQYCLsVmj8+PHj/Ozn/2Mt956izVr1lBfb1qi+csvv2TFihW2OqzoIfPKe+G+nmjdbPb2cAjJUwcBsPVQoaX5rxCi56w1PixcuJBNmzaxdetWMjMzefbZZ2lubua2224DYMmSJefsb8GCBezYsYM1a9aQmZnJq6++ytGjRy1JLB8fHyZOnMgLL7zAnj17yM/PZ8uWLXz44YcuM6WkpNY0BvR10N4e5j4UOZWNtLQ5V98eIVyBXD+4jryqJioadGg1akZG+CsdjlCYu0ZtWYExJbPyMlsLZ2OzrMPy5cu59dZb+fLLL9FqzywrPW3aNPbv32+rw4oeOtNPyjEvSKxp7IBAJkQH0qY3svbHHKXDEcJpWGt8mDVrFk8++SQrV65k9uzZZGRksGrVKst0vOLiYsrLyy3bjx07lhdffJH333+f2bNn88UXX/D6668TExNj2eall14iPj6exx9/nBtvvJE333yT3//+99x5551WOHP7V1xrSsD3c9CkVJivB36ebhiMkFUuK/AJ4Wjk+sF1mKfujezvh6e7VA4KmDzYtHjN7ixJSrkam03fS0tL47nnnjvv8fDw8HMuEoR9say8F+ilcCT24cGrhrAvZz8bduey6Joh+Hq6Kx2SEA7PmuPD/PnzLzpdb/369ec9NnPmTGbOnHnR/YWGhrJs2bIuxeAsDAajpSrUUadvq1QqYsJ92Z9bzamyekZESPm/EI5Erh9cx8HcGkD6SYkzzvSVqpK+Ui7GZpVSWq2WhoaG8x7PyckhKCjIVocVPVRQLZVSZ5seF8aQUG/qW9vZuDdf6XCEcAoyPtinykYdbXojKhWE+TruGDCsYwrfydJ6hSMRQnSVjA+uQ5qci5+K7++Pj4cbtc1tHCuuUzoc0YtslpSaPn06r7/+Om1tbZbHioqKePHFF7n++uttdVjRQ+bpe5EBrrvy3tnUahUPXjUEgNU7s9G1GxSOSAjHJ+ODfTJXSYX4eDh0T8GYcB8ATpaef2ErhLBvMj64hiZdO8dLTDcOpMm5MHPTqJkQbUpSyhQ+12KzvzqfeuopmpqamDx5Mq2trdx9991cf/31eHt78/vf/95WhxU9VGjpKSVJKbPZYyII8/WgpK6F/x4oUDocIRyejA/2ydH7SZnFSKWUEA5LxgfXcDi/Fr3BSD9/T/r5yzWHOOMK6SvlkmzWU8rX15e1a9dy4MABjh8/TlNTEyNHjiQpKclWhxRWUGTpKSUDhJmHm4aHrh7C0o+P8crXJ7ltbH9pyChED8j4YJ8sK+85aD8ps7i+pqRUbmUTDa3t+HjY7E8dIYSVyfjgGg7ly9Q9cWGTh5iSUnuyq9AbjGikr5RLsMlfagaDgS1btvDVV19RWFiISqWif//+hIaGYjQaUankzWWPmnTtVDeZyqWlUupcd00awKod2RTWNPPOrhwenDZE6ZCEcEgyPtgvZ6mUCvbxoK+fJyV1LWQU1zEhWvrQCOEIZHxwHUcLawFIiPRXOBJhb0ZG+OPr6UZ9SzvpRbUkRAYoHZLoBVafvmc0GnnooYf405/+RGlpKTExMQwdOpSioiKeeuopFi1aZO1DCisxV0n5errhJ6vMncPDTcPvrzMtG/+v7zKpbW67zDOEED8l44N9K+lISvV1gqkUIztW3UvvuPARQtg3GR9cy5EC02dzfH9JSolzadQqJg0y3UxKyZQpfK7C6pVSW7ZsYd++fbz99ttcccUV53wvJSWFRYsW8eGHH3LLLbdY+9Cih8wr7/WXKqkLunVMf974PpNTZQ289UMWj/8sVumQhHAoMj7YN2eplAJTUurr42WkF8nqPUI4AhkfXEd1o85yzTFSklLiAq4YHMz2jDJ2ZVbK7BQXYfVKqU8//ZRf//rX5w0oAJMnT+aBBx7g448/tvZhhRUU1ZguSCQpdWEatYrF15sSUat3ZltWqhJCdI6MD/atpM5cKeX4SakREaYLHUlKCeEYZHxwHUeLTFVS0cFe+PeRmRnifFcODQFgT3Ylre16haMRvcHqSakTJ04wderUi37/qquu4vjx49Y+rLCCwpomQPpJXcrPRoYzZkAAzW16/r4tQ+lwhHAoMj7YL6PRSHFHo3NnqZQCOFVWj67doHA0QojLkfHBdaR1TKseJVVS4iLi+voS6utBS5uBAznVSocjeoHVk1K1tbUEBwdf9PvBwcHU1kqPB3tkqZSSlfcuSqVSsfTnI1Gp4KPUIlmuVIgukPHBftU1t9PSZkrehDv46nsAkYF98O/jTpveyMnSeqXDEUJchowPrsPc5Fz6SYmLUalUTB1mqpb64VSFwtGI3mD1pJRer8fN7eKtqjQaDXq9lOHZo8KO+d1SKXVpCZEB3DlxAAB//iidNr3chReiM2R8sF/FdabP/0AvdzzdNQpH03MqlYoR/UzVUkel2bkQdk/GB9eRJkkp0QlXDQsFYMepcoUjEb3B6o3OjUYjTz31FFqt9oLf1+l01j6ksJL8atP0vUiplLqsJ66PZVtaMSdK61mXkkvylEFKhySE3ZPxwX4VO9HKe2ajowJIyarkcEENv+i4kSCEsE8yPriGmiYd+VXS5FxcnrmvVHpRHRUNrYT4eCgckbAlqyelbr311stuIytn2J/Wdr2lyW1UoJfC0di/QG8tT94Qxx+2pPHyVyeZOaqvVJgJcRkyPtivEidaec8sMcp0wZOaL5VSQtg7GR9cw9FC0+ITA6XJubiMUF8PRvTz41hxHT+ermB2Yn+lQxI2ZPWk1LJly6y9S9ELimtaMBqhj7uGEJ8L36US57pjfBSb9udzKK+GP25NY829E1CpVEqHJYTdkvHBfp2plHKepNToqAAATpTU0aRrx0tr9T95hBBWIuODa5Am56IrpsaEcKy4jh9OSlLK2Vm9p5RwTGdP3ZPESueo1Sr+MScBrUbNtyfK2XKwUOmQhBCiW0rMK+85QZNzs37+fQj388BgPHN3XgghhHKkybnoirP7ShmNRoWjEbYkSSkBYJnfHRUkU/e6Yli4L4/MGAbA0o/TKeuYAimEEI7EGSulAEZHBgBwOL9G0TiEEEJIk3PRNeMGBuLprqasvpWTpQ1KhyNsSJJSAjhTKRUlTc677MGrBhPf35+6lnb+b+tRyeQLIRzOmZ5SzjUGmKfwpUpSSgghFFXTpCOvynS9MSpCklLi8jzdNUwaFAzADydlFT5nJkkpAUB+lXn6nlRKdZWbRs0LcxNw16jYnlHKxn35SockhBBdUuKklVJjBgQAcCC3Wm4YCCGEgszTqAcEeeHvJU3ORedcFWOawvfdyTKFIxG2JEkpAUBBtXn6nnPdJe8tcX39eOJnsYBpGt/pMikxFUI4hobWdupb2wEnTEpFBeKmVlFS12IZ54QQQvQ+mbonumN6XBgAe7KqqG9pUzgaYSsOkZTasGED06dPJz4+nrlz53LkyJGLbnv33XcTGxt73r8HHnjAss1TTz113veTk5N741TsVkG1VEr11H1TBjNlaAgtbQYe2XiI1na90iEJIcRlmaukfD3d8PFwrhXq+mg1llWe9uVUKRyNEEK4rqOy8p7ohkEh3gwO8abdYGTHqQqlwxE2YvdJqW3btrFs2TIWLVrE1q1biYuLIzk5mcrKygtu/+qrr7Jz507Lv08++QSNRsMNN9xwznZTp049Z7uXXnqpN07HLjXp2qlo0AHS6Lwn1GoVK+aNJtDLnfSiOlZ8eVLpkIQQ4rLO9JNyriops4mDggBJSgkhhJKkUkp0l7la6usMmcLnrOw+KbV27VrmzZvHnDlzGDp0KEuXLsXT05PNmzdfcPuAgABCQ0Mt/3788Uc8PT3PS0pptdpztvP3d90PSPOUBj9PN/z7yBzvngj38+Qft48G4M0fsthxSpryCSHsW1GNaQzo62RNzs3GDwwEYG+2JKWEEEIJtU1tlibnkpQSXXXt8HAAvjtRht4g/SGdkV3X6et0OtLT03nwwQctj6nVapKSkjh06FCn9rF582ZuvPFGvLzOrQDau3cvkydPxs/PjyuuuIJHH32UwMDALseo19t2ipZ5/7Y8Tm6Fqf9RZGAfqx2nN+K2BWvEPT02hF9OjGLD3nwWbzrMJ7+9kmBvrbVCvCBXfr2V4IpxO9q5is4r7EhK9Q9wzqTUhGhTpVRmeSOVDa0E+3goHJEQQriWo0WmKilpci66Y3x0IL6eblQ26jhcUMPYAV2/Zhf2za6TUtXV1ej1eoKDg895PDg4mKysrMs+/8iRI5w8eZK//e1v5zw+depUrrvuOiIjI8nPz+ell17i/vvv5/3330ej0XQpxrS0tC5t3122PM7u040A+Kp1pKamWnXfvfX6WFtP476xv5Hv/dwoqGvlwTU7+b8pgahVKitFd3Gu+norReIWzuBMUso5p+8FemuJCffhZGkDe7KrmBXfT+mQhBDCpcjUPdET7ho102JC+eRIMd9klElSygnZdVKqp/773/8SExNDQkLCOY/feOONlv83NzqfMWOGpXqqK+Lj47ucyOoKvV5PWlqaTY+zreg4UM+o6H4kJsZZZZ+9EbctWDPuNyLrue3fKRwq0XGgIYD7pw6yUpTnk9e7d7li3ObnCudjnr7XP9A5K6UArhwawsnSBnaerpCklBBC9LI0aXIueuja4WF8cqSYr4+X8XjHiufCedh1UiowMBCNRnNeU/PKykpCQkIu+dympiY+/fRTfve73132OFFRUQQGBpKbm9vlpJRGo+mVi1JbHqeg44JkQLC31Y/RW6+PtVkj7pH9A3jm5hH8cetRXvzyJJMGBzPGxpl9V369lSBxC2dgrpSKcNKeUgBThoaw9sccdsrKPUII0euOSqWU6KFpMWGoVZBRXEdRTTMRTtpywFXZdaNzrVbLyJEjSUlJsTxmMBhISUlhzJgxl3zu559/jk6n4+c///llj1NSUkJNTQ2hoaE9jtkR5VeZLkiiguSX29rumjiAG+P70W4w8tv3DlHb3KZ0SEI4lQ0bNjB9+nTi4+OZO3cuR44cueT2n332GTfccAPx8fHcfPPNfP/99+dtk5mZya9//WvGjRtHYmIic+bMoaioyFanoCiDwUhxjWn1PWeulJo0OBg3tYq8qibyKpuUDkcI0UtsMUaYPfPMM8TGxvL2229bOWrnUtvURm7H5+6o/n4KRyMcVZC31jJt7+vjsgqfs7HrpBTAwoUL2bRpE1u3biUzM5Nnn32W5uZmbrvtNgCWLFnCihUrznvef//7X2bMmHFe8/LGxkaef/55UlNTKSgoICUlhd/85jcMHDiQqVOn9so52Zv8atNAERXodZktRVepVCqWzYknKqgPBdXN/GHLEYxGWTVCCGvYtm0by5YtY9GiRWzdupW4uDiSk5PPq641O3jwIIsXL+b222/nww8/5Nprr2XRokWcPHnSsk1eXh533XUXgwcPZv369fzvf//jN7/5DR4eztkcu6KhFZ3egFoFff2cs6cUgI+HG2MGBACw87RUSwnhCmwxRph99dVXHD58mLCwMFufhsMzNzmPCupDgJdtF/4Rzm3GCNMqfF8cLVE4EmFtdp+UmjVrFk8++SQrV65k9uzZZGRksGrVKsv0veLiYsrLy895TlZWFgcOHOD2228/b38ajYaTJ0/y0EMPccMNN/DHP/6RkSNHsmHDBrRa1/ugrG1uo76lHXDuu+RK8vN059U7x+KmVrEtrYQNe/KUDkkIp7B27VrmzZvHnDlzGDp0KEuXLsXT05PNmzdfcPt169YxdepU7rvvPoYMGcKjjz7KiBEjePfddy3bvPzyy1x11VUsWbKEESNGMGDAAK699trzFtxwFubp2339PHHT2P2fBD1y5VDT3w07TpVfZkshhDOwxRgBUFpayl/+8hdefPFF3N1lJbnLkSbnwlpmjuoLQEpWJVWNOoWjEdZk1z2lzObPn8/8+fMv+L3169ef99jgwYM5ceLEBbf39PRk9erVVo3PkeVXmaqkQny0eGkd4u3gkBKjAnjyhjj+ti2D5z45xtgBgYyIkBJmIbpLp9ORnp7Ogw8+aHlMrVaTlJTEoUOHLvic1NRU7r333nMemzJlCtu3bwdM08O/++477rvvPpKTkzl27BiRkZE8+OCDzJgxw2bnoiRXaHJudk1sGP/cfoofTpbT2q7Hw036qgnhrGwxRoBpnHjiiSdITk5m2LBhNond2UiTc2EtA4O9GRnhR3pRHV+ml/CLiQOUDklYiWQhXFxBx9S9SJm6Z3PJUwaRklXJN8fLePi9g3z88BS8PeRXUIjuqK6uRq/Xn1fBFBwcTFZW1gWfU1FRcd4iGcHBwVRUmKZzVVZW0tTUxFtvvcWjjz7K448/zo4dO3j44YdZt24dEydO7FKMer2+S9t3dn/W3K/5xkQ/f0+rx9tT1j7fEX19CPP1oKy+lV2ny7lqmP31kbTFz9ieyfk6B3s8H1uMEQBvvfUWbm5uLFiwoEfx2dtrZsv3ZlqBKSk1sp+v3Z13Zzjr7601KPHa3DAynPSiOj5NK2buuP69dtyukveNSWfPX66IXdyZJueSlLI1tVrFi3NHM+uVHWSVN/LMR+msmDda6bCEEB0MBgMA1157reVu+fDhwzl48CAbN27sclIqLS3N2iFafb+HT9UB4NZaR2pqqtX2a03WPN/RoWq+qoeNO47h12i/d+1t9d6xV3K+whEcPXqUdevWsWXLFlQqVY/2Za/vAWvH1agzkNdx88NYmUdqQ4FV99+b7PVnZg9687WJ1pjazuw6XcGOvQfx1dp36wF533SOJKVc3Jkm584/dcMeBHlreeUXidz51m42HywgaUgwc8ZFKh2WEA4nMDAQjUZzXsPaysrK8+50m4WEhJxzx/un2wcGBuLm5saQIUPO2WbIkCEcOHCgyzHGx8ej0VhvipheryctLc2q+21LOwA0MSZ2IImJ9lUGb4vzvaNPOV9lHeBwuYHRo0f3+MLS2mxxzvZMztc5mM/LnthijNi/fz+VlZVcc801lu/r9Xqef/551q1bxzfffNPp+OztPWCr9+auzEqgjMjAPkydNNZq++1Nzvp7aw1KvDaJwOupP3K8pJ5STRhTE+3zOkreNyadHR8kKeXizFM3pFKq90waHMwj18bw8vaTPP3RURIHBDAk1EfpsIRwKFqtlpEjR5KSkmLp92QwGEhJSbloD8LExER27959Ts+QXbt2kZiYaNlnfHw82dnZ5zwvJyeH/v27XiKu0Whs8oeINfdbWNMCQGSQt93+0WTN850yLBQvrYaSulbSiuoZMyDw8k9SgK3eO/ZKzldYmy3GiNmzZ5OUlHTOc5KTk5k9e7ZlVfDOstf3gLXjOlZcD5ianNvj+XaFvf7M7EFvvzaz4vtxvKSez9NLuWPiwF47bnfI+6Zz7LveTdhcTqUpKTUwWJJSvenh6UOZPDiYJp2eRRsO0tLm2vONheiOhQsXsmnTJrZu3UpmZibPPvsszc3NlouDJUuWsGLFCsv2CxYsYMeOHaxZs4bMzExeffVVjh49es4FSnJyMp999hmbNm0iNzeXd999l2+//ZY777yz18+vN5gbnUcGuEa1rKe7hus6lpT+3+EihaMRQtiStceIwMBAYmJizvnn7u5OSEgIgwcPVuQc7Z00ORe2MCvetArfztMV1Da3KRyNsAZJSrmwdr3BUik1KMRb4Whci0at4p+/SCTYW8vxknr+9mmG0iEJ4XBmzZrFk08+ycqVK5k9ezYZGRmsWrXKMtWiuLiY8vJyy/Zjx47lxRdf5P3332f27Nl88cUXvP7668TExFi2ue6663j22WdZtWoVN998Mx988AErV65k/PjxvX5+tlbf0kZdi6k3Q4SLJKUAZidGAPDx4WL0BqPC0QghbMUWY4TomqMdSamESElKCesZGubLsDAf2vRGvkwvUTocYQUyfc+FFdW00G4w4uGmJtzXU+lwXE64nycr5o3m3rX7WL87l6QhwcyM76d0WEI4lPnz5190Ksb69evPe2zmzJnMnDnzkvu8/fbbuf32260Snz0r7KiSCvByd6mVQKcOCyXQy52KhlZSMiuZMuzC/WWEEI7PFmPE2brSR8rV1LW0WWZkjIqQpJSwrp+PjmDFVyfZcrCQueOjlA5H9JBUSrmwnMpGwDR1T622r2avruLq2DAenGYq+V6y+Yilck0IIWzNPHUvwt91qqQA3DVqyw2AzQcddyUoIYSwZ+YqqcjAPgR6axWORjibW8eaen2mZFXK9ZMTkKSUC8u1JKVk6p6SHr8+ljEDAqhvaee37x2iTW9QOiQhhAsorDYlpfq74Oqr8zruqn6aVkxNk07haIQQwvmYk1Lx0k9K2EBkoBdJQ4IB2HqoUOFoRE9JUsqFmUtqo6XJuaLcNWpW/mIMfp5upObX8OIXJ5QOSQjhAswr7/V3oX5SZqMj/RnRzw9du4H/HpBqKSGEsLYjBdLkXNjWnLGRgKnq2WiUHpGOTJJSLkwqpexHVJAX/7g9AYA3fsji2xNlCkckhHB25p5SrpiUUqlU/PKKAQD8Z08eBml4LoQQViWVUsLWZsb3xVurIbeyif251UqHI3pAklIu7EyllCSl7MENo/px9xUDAVi86TCldS0KRySEcGaF1aYxwBWn7wHMTuyPr4cbWRWNfH1cbgQIIYS1nN3kXJJSwla8tG7MMveIlKpnhyZJKRelNxjJ6xgsBsr0PbvxxxuHM7yfH1WNOh7ZeEiWKxdC2ExRx/S9CBeslALw8XDjlx03Av713Wkp/RdCCCsxV0n1D5Am58K25owzTeH75EgxzTq9wtGI7pKklIsqqWtBpzfgrlG57AWJPfJ01/D6XWPw0mrYnVXFq9+cUjokIYQT0rUbKK133Z5SZr+aEo3WTc2hvBp2Z1UpHY4QQjgFmbonesvE6CCigvrQ0NrOp2nFSocjukmSUi4qt8LUTyoqyAuNWqVwNOJsg0N9+OstowBY+fUpdmdVKhyREMLZlNa1YDSC1k1NiI/r3sUO8/Vk3njTXdYXvzwh1VJCCGEFaYV1AMRHSlJK2JZareIXE0w9Iten5CgbjOg2SUq5KOknZd9uGxvJnLGRGIzw6MZUqhplyXIhhPXkm/tJBfRBpXLtGxO/nT4MT3c1B3Kr+SK9VOlwhBDC4ZkrpWTlPdEbfjEhCq1GzeGCWg7n1ygdjugGSUq5qBzLynvST8pePTd7JINDvSmpa+GJDw7LHXwhhNWYewoOCJIxINzPk/umDAZg+WcZtLRJTwohhOiuupY2sjtmZMj0PdEbgn08uDHB1PD8HamWckiSlHJROR2DhVRK2S9vDzdevXMMWjc1Xx8vY+2POUqHJIRwErlVstDF2R6cNphQXw9yKpv417enlQ5HCCEcVnrH1L3+AX0IkibnopfckxQNwMeHiyiplRXMHY0kpVxUrnn6XogkpezZyAh//nTjcACWfZZBWkGtwhEJIZyBVEqdy9fTnaU/HwnAv7/PJKO4TuGIhBDCMZ2ZuuencCTClSRGBTAxOog2vZG1u7KVDkd0kSSlXJDBYCS3ylwpJRck9u7uKwZy/Yhw2vRGfvveQRpa25UOSQjh4MxjwECplrWYOaovM4abPmsf2XhIlpYWQohuSOtISiVEBigbiHA5D1xlmor/n9151LW0KRyN6ApJSrmgsvpWWtoMuKlVLr0UuKNQqVT84/YEIvw9yals4k9b06S/lBCi24xGo6VaVqbvnaFSqVg+J55QXw9Oljaw9ON0+awVQoguSpMm50Ih0+PCGBrmQ31rO+9I2xOHIkkpF2Ruch4Z2Ac3jbwFHEGAl5aVd45Bo1bxYWoRmw8WKh2SEMJB1Ta3Ud9iqriMCpSk1NlCfDx4eV4iKhVs3JfPupRcpUMSQgiHIU3OhZLUahW/nT4UgLd2ZFHbLNVSjkIyEi4ot1KmbTii8dFB/H7GMACe/vAop8saFI5ICOGIzFVSYb4e9NFqFI7G/kwZFsJTN8QB8Nwnx/j8aInCEQkhhGOQJudCaTclRDA0zIe6lnZW75TeUo5CklIuKLtCpm04qoeuHkrSkGCa2/Q8/J+DTrt0uUyZEcJ2ZOW9y3vgqsHMHReJ3mDq5fft8TKlQxJCCLsnTc6F0jRqFb+fEQPAWz9kUVonK/E5AjelAxC9L7PcVGEzWFbeczgatYp/3pHIzFd2cLyknmf/l87yOQlKh9VtLW169hS28FHBMY4V1ZNX1URNUxs6vYE+7hrC/DwYEurDuIGBXB0byoh+fqhUKqXDFsKh5XVUyw4IkjHgYlQqFctui6epTc+nR4p58N0DrLlnAlOGhSgdmhBC2C1zPymZuieUNCu+L2MHBHAwr4YXvjjBi3NHKx2SuAyplHJBmR3TvoaG+SocieiOMD9PXr7jTM+T/+zJUzqkLjEajXx7ooxF/znIhL9/wz921bAuJY/9udWU1bei0xsAaG7Tk1vZxDfHy3jhixPcuHIn1738A+tTcmRVLCF6QJqcd46bRs0/70jkuhHh6NoN3LduH19nlCodlhBC2K0jBTUAxMvKe0JBKpWKP900AoDNBws4kFulcETicqRSysXo2g2WqRtDw3wUjkZ011UxoTx+fSwvfHGCP//vKDFh3th7/VCb3sAnR4p44/ssjpfUWx4P9VIzc3QkYwcEMTjUmxAfD7Ruappa9RTWNJNRXEdKViXfnyzndFkDT3+Uzmvfnmbx9bHcPjYStdrez1wI+yLT9zrPXaPmtbvG8Jt3D/L18TIeWH+AZbfGM29ClNKhCSGEXaltaiOn46ZHglRKCYWNHRDI7eMi+e+BAp7anMYnv5uCh5v00bRXkpRyMTmVjegNRnw83Aj381A6HNEDv7l6CGkFtXyeXsKi/xzib9Psd/7+tyfK+Osnx8gsN00b8tZquGPCAG5O6Iu+PIsxY0ag0fxkoPCBAcFeTB4SzK+mDKK+pY0tBwt584csCmuaWfLfI2w5WMALt48mKkguroXorPyOpJT83nSOh5uG/3f3OP6wJY3/HihgyeYjlNa18PD0oTKdWAghOpin7g0I8iJQmpwLO/DHWcP57kQZp8oaWPn1KZ74WZzSIYmLcIjpexs2bGD69OnEx8czd+5cjhw5ctFtt2zZQmxs7Dn/4uPjz9nGaDTyyiuvMGXKFBISErj33nvJycmx8VnYB/OKbUPCfOSPaQenUql4cd5ohob5UFrfyt93VtPY2q50WOfIqWhk4dq9LFy7j8zyRoK8tTzxs1h2PXUtz9w8goRI/06/D3093bknKZpvHp/GH2bG0cddw+6sKmat3MEX6bI6lhCd0dKmp6Sj6edASUp1mrtGzQu3J7DomiEArPjqJE9/dBS9QRZlEEIIgMOWqXtSJSXsQ6C3ludmjwLgX99lsvNUhcIRiYux+6TUtm3bWLZsGYsWLWLr1q3ExcWRnJxMZWXlRZ/j4+PDzp07Lf++/fbbc77/1ltvsX79ep599lk2bdpEnz59SE5OprW11danozhzUmpoqEzdcwY+Hm6svmc8QV7uZNW088j7h2nv6MmkJIPByJqd2dzwyg98e6IcN7WK+6YM4tvHr2bRNUPx93Lv9r493DQ8OG0IXzx6FWMHBFDf0s6D6w/w+renZdU+IS6joLoJo9H02SHLdXeNSqXiiZ/FsfTnI1Gp4N3defxmwwGnXQVVCCG6Iq3AVCk1WpJSwo7Miu/HXZMGYDTCIxsPkdcxxVTYF7tPSq1du5Z58+YxZ84chg4dytKlS/H09GTz5s0XfY5KpSI0NNTyLyTkzGo5RqORdevW8dBDDzFjxgzi4uL4xz/+QVlZGdu3b++NU1KUeeU96SflPAYGe/Pm3ePQquHbE+U8+3G6osmZ7IpG7ngzhec+OUZLm4HJg4P58vdX8aebRuDfp/vJqJ8aEOzF+w9OZuGV0QC88MUJ/vJJhiSmhLgEc5PzAUFeUi3bTfckRfP6XWPRatR8kV7KgtV7qW1qUzosIYRQlKXJef8AReMQ4qeeuWkEo/r7Udmo4961e6lq1CkdkvgJu05K6XQ60tPTSUpKsjymVqtJSkri0KFDF31eU1MT11xzDdOmTeOhhx7i1KlTlu8VFBRQXl5+zj59fX0ZPXr0JffpLCyVUpKUcipjBgTw6BUBlrv3z39+oteTMwaDkdU7s5n5yg/sy6nGW6vhr7eMYsN9kxhso8o8d42aP988kqU/HwnAmh+zWf7ZcUlMCXERsvKedcyK78c7v5qIr4cbe3OqmPvGLopqmpUOSwghFFFe30pRbQsqlUzfE/bH013Dmnsm0D+gD1kVjdzxRgoltS1KhyXOYteNzqurq9Hr9QQHB5/zeHBwMFlZWRd8zqBBg/j73/9ObGws9fX1rFmzhl/84hd8+umn9O3bl/Lycss+frrPioquzzPV621btm/evzWOYzAYLZVSg4L72DR2a8bdmxw57kn9PXnmxjiWfnKc//d9JgaDgSU/i+mVaojsikae2nKU/bnVACQNCWbZrSOJDPTCaDRwsZfTWq/3/ElRqFVGnv7oGG/8kIVaBYuvG2azc3fk98nZ/+3Oc4Vjy+tocj5AklI9NnlIMJt+PZl71+7lZGkDc/69i3d+NZGYcF+lQxNCiF6VVlgDwJBQH3w87PryUrioMD9P1iVP5Jdv7eFUWQO3vP4jK+8cw8RBQUqHJrDzpFR3jBkzhjFjxpzz9axZs9i4cSOPPvqo1Y+XlpZm9X3a6jhlje20tBlwU0NV3klqC2yfrOit18faHDXuhD41JI/xZfWhet7ckU1JaSkLEnxtl5wxGvnkZBMbj9ajM4Cnm4p7Eny5brAbFbknqcjt3H6s8XqP0GI5939/n0VTTTk/j/Hu8X4vxVHfJ44a94Vs2LCB1atXU15eTlxcHE8//TQJCQkX3f6zzz7jlVdeobCwkOjoaB5//HGmTZt2wW2feeYZ3n//ff7whz9w77332ugMeldupWkFzAHS5NwqhvfzY/NDSdyzZi+Z5aa7r+uTJzFKlkMXQriQw/mmflIJ8tkn7NiQUB8+6LiZlFneyC/eTOEXEwfwyLXDCPfzVDo8l2bXSanAwEA0Gs15Tc0rKyvP6RN1Ke7u7gwfPpy8vDwAQkNDLfsICws7Z59xcV1fJjI+Pv78peytSK/Xk5aWZpXjfHeiHKhgUIgP48aOuez2PWHNuHuTM8SdmKghKjKXZz/O4H8nm2jX+vH8nFF4aa37636qrIGntqSRml8PwJShwfz9llH0D+zTrbit8XonJkJIeDbPf36C9UfquWZMLFOHde6zoiuc4X3S1bjNz7Un5oUwli5dyujRo3nnnXdITk7m888/P68aFuDgwYMsXryYxx57jGuuuYaPP/6YRYsWsWXLFmJiYs7Z9quvvuLw4cPnjBPOwFwpNTDItglbVxIZ6MV/f53EvW/v43B+DXe9tZv1yZMYHRWgdGhCCNErzP2kEmTqnrBzUUFe/O/hKTzzUTqbDxbwnz15bNqXz9WxYVwVE8Lwfn6E+nigUatw06ho1xtpbtPTrNPTpNPTpjfQR6sh3NeTiABP3DR23Q3JYdh1Ukqr1TJy5EhSUlKYMWMGAAaDgZSUFObPn9+pfej1ek6ePGm5Ex4ZGUloaCgpKSkMHz4cgIaGBg4fPsydd97Z5Rg1Gk2vXJRa4zjZHb1EhoX79NqFdG+9Ptbm6HHfe+VgvLTu/PHDNLYdLSGropG3FownygrVEc06PW/8kMm/vs1Epzfg6+HGn24azrzxUd2uyLLm6/3raUPIKm/kgwMF/G5jKh89PIVBIba5AHf094mjO3shDIClS5fy3XffsXnzZh544IHztl+3bh1Tp07lvvvuA+DRRx9l165dvPvuuzz33HOW7UpLS/nLX/7C6tWrefDBB3vnZHqBwWAkv9rU90h6SllXoLeW9ckTWbh2Hwdyq5m/ag9v/2oi4wYGKh2aEELYlNFoJK2wo1JKkvHCAXh7uLFi3mjumBDFC18cZ19ONdszStmeUdql/Xi6qxk/MIhZ8f2YnRiBt0xd7Ta7T+0tXLiQTZs2sXXrVjIzM3n22Wdpbm7mtttuA2DJkiWsWLHCsv1rr73Gzp07yc/PJz09nSeeeIKioiLmzp0LmFbmW7BgAf/+97/5+uuvOXHiBEuWLCEsLMyS+HJWlibnNmo6LezLvAlRvHf/FYT4aDleUs+NK3ew+UBBt5uAGwxGthws4JoXv+Of20+h0xu4JjaULx+7ijsmDLCblbxUKhV/vXUUYwcEUNfSzv3r9lPfIitjOZvuLISRmprK5MmTz3lsypQppKamWr42GAw88cQTJCcnM2zYMJvErpTiuhZ07Qbc1Cr6+UuZurX5ebrzzq8mMnFQEPWt7SxYvYe92VVKhyWEEDZVVNtCRYMON7WKEf38lA5HiE6bOCiID36dxBePXsUTP4vlqphQIgP74K3V4Omuxl2joo+7hiBvLf0D+jA0zIfh/fwYGOyF1k1NS5uBnacr+L+taUxe9jWrd2bTpjcofVoOye7TebNmzaKqqoqVK1dSXl7O8OHDWbVqlWX6XnFxMWr1mdxaXV0dTz/9NOXl5fj7+zNy5Eg2btzI0KFDLdvcf//9NDc388wzz1BXV8e4ceNYtWoVHh4evX5+vcmclBoiK++5jPHRQfzv4Sk89O4BDhfUsviDw2zcl8cTP4tjQnRgpxJJTbp2Nh8sZO2P2WSVm/rR9A/owx9mxXFjfD+7SUadzcNNw/+bP46fv/Yjp8saePZ/x1gxb7TSYQkr6s5CGBUVFedN/f7pIhdvvfUWbm5uLFiwoMcxWrs5fE8b7J8urQNM/aRUGO2+eb0jLijQx03F6gVjefDdQ+zKrOSeNXtZc8+4TjdSdcRz7gk5X+fgbOcjuiatY+peTLgvnu6OX4UtXE9sX19i+/qy6JrOP8dgMHK6vIFvj5fx/r58sioa+csnx/j4cBGv3TWGfn7OnVewNrtPSgHMnz//otP11q9ff87X//d//8f//d//XXJ/KpWKRx55hEceecRqMdo7o9H0iwMwVJJSLiUioA+bH0rijR+yePWbU+zLqWbeGymM6u/Hz0dHMGVoKDHhPpY50UajkfL6Vg7m1fDt8TI+Ty+httlUaeTr4cZD1wzhV1cOsvs/PML8PHn1rjHc8UYKmw8WMD0ujBsT+ikdlrBjR48eZd26dWzZssUqyVZb9eDq7n5/OG1KKgdr28+pDrN39tbLrDMeHu1GU4OW1FIdC9fu409XBTI8RNvp5zviOfeEnK8QjivV3ORc+kkJF6JWq4gJ9yUm3Jf7pg7mg/35LPvsOKn5Ndz86k7W3jte6RAdikMkpUTPVTbqqGlqQ6WCwSGSlHI1bho1i64Zym1j+7Py69NsPljA0cI6jhbWAcdxU6sI8taiVqmoa2mjSXfuXc+BwV4sTIrm9vFRDrXU74ToIB66egivf5vJ/21NY9zAQPrKtCWn0J2FMEJCQs6pivrp9vv376eyspJrrjlzq0yv1/P888+zbt06vvnmmy7FaO1G+D1tsP9h/jGgnjFDIkhMjLVaXLbiqAsKmG1I0PPguwfZebqSZT/W8vbC8YwdcOkeU45+zl0l5+sc7HEhDNF7DuVVAzBmQICygQihEI1axS8mDuDKoSH8ZsNB0gprmb96L3+80p9EpYNzEI5zdSl6JLNj6l7/gD700TrPH0Kia/r592HZbfE88bNYPj5cxPaMUg7l1dDQ2k5ZfatlO5UKYsJ8mTQ4iOtH9GXykGA0avubptcZj1wbww8nK0grrOXxDw6z7lcTUTvouYgzurMQRmJiIrt37+bee++1PLZr1y4SExMBmD179jk9qgCSk5OZPXu2pY9hV9iqoXx395vTsdjF0DBfh7ogdtTG/N4aDW8tmEDyO/vYlVnJwrcPsD55ImMuk5gCxz3n7pLzFcIxtesNHCkwVUp15rNNCGcWFeTFew9cQfLb+9iTXcXfd1YzYXQjQ8Ol19rlSFLKRcjUPXG2IG8t9yRFc09SNAaDkdL6FiobdIBpRYp+/p52Pz2vs7Rual6+I5GbXt3BztMVvL8/nzsnDlA6LGEFCxcu5Mknn2TUqFEkJCTwzjvvnLcQRnh4OIsXLwZgwYIF3H333axZs4Zp06axbds2jh49all5LzAwkMDAc/+odnd3JyQkhMGDB/fuydmA+ebE4FDbrEYpztdHq2H1PRNY+PZedmdVsWDNXjbcN4mEyAClQxNCiB47XlJPc5seX083WUhJCMDHw421CydwxxsppBXWcd+6A3z82yn4erorHZpds/vV94R1nCipB0xNCIU4m1qtop9/H0b192dUf38GhXg7TULKbGiYD49fb5qutGxbBuVnVYUJxzVr1iyefPJJVq5cyezZs8nIyDhvIYzy8nLL9mPHjuXFF1/k/fffZ/bs2XzxxRe8/vrrxMTEKHUKvaZJ105RbQsAQ+TCoVeZE1MTo4Oob2ln/qo9HO1YPl0IIRyZeepeYlSAVKEL0cFL68aqBeMI8VKTU9nEH7akdXv1c1chlVIu4nixKSkV11eSUsI13ZsUzYephRwtrOOvnx7jlV+MUTokYQVdWQgDYObMmcycObPT++9qHyl7lV1hanIe6OVOoHfnG24L6/D2cGPNwgncu2Yv+3Or+eWqPfzn/kmMjJDGwEIIx3UorwaQqXtC/FSIjwePXRHAM99V88mRYqbFhDJ3fJTSYdktqZRyAUajkeMlpqXA4/rKnFbhmtw0apbdmoBaBR+lFvHDyfLLP0kIJ5FZbkpKDZYqKcWYS/rHDAigtrmN+av2WMZmIYRwRAc7KqXGSpNzIc4TG6zl0RlDAfjLJ8coq2tROCL7JUkpF1Bc20JdSztuahVDwqSXiHBd8ZH+3JMUDcCfPjxKS5v+0k8QwklkdfQVHBwiY4CSfD3deedXExkd6U91Uxu/fGsPJ0vrlQ5LCCG6rKpRZ1lAY0yUVEoJcSH3TxlEfH9/6lraefqjo0qHY7ckKeUCzHdiB4d64+HmXL2ChOiqxdfH0tfPk7yqJlbvzFY6HCF6xakyWezCXvh5urMueRLx/f2pbNRx11u7OV0miSkhhGMx95MaEuqNv5c0cRbiQtw0al6Ym4BGreKL9FKZqXERkpRyARmWflIydU8IHw83npoZB8Dr356WUlrhEk6VymIX9sS/jzvrkycyop8fFQ067nxrD5kd1WxCCOEIpJ+UEJ0T19ePeyZHA/DcJ8do0xuUDcgOSVLKBZhX3ovrJxcjQgD8fHQEiVEBNOn0vPDFCaXDEcKm2vQGS6PzYeFSKWUvAry0bLhvEnF9fSmvb+XON3dbfk5CCGHvzvSTkqSUEJfzyIxhBHtrOV3WwPv78pUOx+5IUsoFnGlyLkkpIQDUahXP3DwCgP8eLCCtQJZnF84rp6KRNr0Rb62G/gF9lA5HnCXQ25SYig33pay+lTve3MPJSp3SYQkhxCXpDUYO59cAMEaanAtxWf593PndtcMAeO2b09LX9ickKeXkmnV6Tnf0EhneT6bvCWE2dkAgtyRGYDTCc5+kYzQalQ5JCJs4WdrRTyrcF5VKpXA04qeCfTzYcP8kRkb4Udmo48/fVfFFeonSYQkhxEWdLK2nUafHW6uRaeFCdNIvJkYR4e9JSV0L/9mTp3Q4dkWSUk7ueEkdBiMEe2vp6+epdDhC2JUlN8Th6a5mX041X2eUKR2OEDZhXt0tRpqc260QHw82PTiZa2JD0Rlg0XupvP7taQwGSZYLIeyPeere6KgANGq52SFEZ3i4afhtR7XUv77LpEnXrnBE9kOSUk4uvcg0dW9kf3+5Qy7ET0QE9OHepEEAvPDFCfRyASic0KkyaXLuCLw93Ph/vxzDDUO8MBpNn0n3rN1LRUOr0qEJ4bA2bNjA9OnTiY+PZ+7cuRw5cuSS23/22WfccMMNxMfHc/PNN/P9999bvtfW1sYLL7zAzTffTGJiIlOmTGHJkiWUlpba+jTszp6sKgDGRwcpHIkQjuX2cZEMCPKioqGVdSm5SodjNyQp5eTSi0y9ckZFyNQ9IS7koWlD8PN040RpPf87XKh0OEJYnXn6njQ5t39uGjX3jfFl+a2j8HRXs+NUBbNe2cG3x+27krNdb6CmSUezTi9ToYXd2LZtG8uWLWPRokVs3bqVuLg4kpOTqaysvOD2Bw8eZPHixdx+++18+OGHXHvttSxatIiTJ08C0NLSwrFjx3jooYfYsmULr732GtnZ2Tz00EO9eVqKMxqN7M02JaWuGCRJKSG6wl2j5pGOaqn/930mDa1SLQXgpnQAwraOFpoqpUb191c4EiHsk7+XO7++egj/+PwEL311khvjI9C6Sb5eOIfWdj05HSu6SaWUY1CpVMwdH8mYgUEs+s9BTpc1sPDtffxsZDh/vnkkEQo1qzcajeRWNnEgt5qTpfWcKmsgq7yBykYd9S1n/qhWqcDXw42YcF9GRvgRHxnAtJhQQn09FIlbuK61a9cyb9485syZA8DSpUv57rvv2Lx5Mw888MB5269bt46pU6dy3333AfDoo4+ya9cu3n33XZ577jl8fX1Zu3btOc95+umnmTt3LkVFRURERNj+pOxAXlUTJXUtuGtUjJGV94ToslvG9Of1b0+TVdHIxr153Dd1sNIhKU6SUk5M127gRIlp2saoCElKCXExC5MGsfbHHPKrmtm4L48Fk6OVDkkIqzhV2kC7wYifpxv9/KWvoCOJ7evL/x6+kpe/OsmaH3P4Ir2UHacquPuKgSRPGUSYjftE6g1GjpfUsS+7in051ezNqaK8/vJTCY1GqGtpZ39uNftzq4FcVCqYMjSEhVdGc3VMGGrpQSNsTKfTkZ6ezoMPPmh5TK1Wk5SUxKFDhy74nNTUVO69995zHpsyZQrbt2+/6HEaGhpQqVT4+XVtRoJeb18rb5nj6UxcKacrAEiI9Eersb9zsbauvDauRl6bi7vca3PflGj+78N0Vu/MZv6kKNw1znlDvLPvDUlKObFTZfXo9AZ8Pd2ICpJlwIW4mD5aDb+7dhhPf3iUlV+fZs7YSLw95ONROL6MYlO17IgIP+kr6IC8tG788cYRzBkXyZ+2HmV/bjVv/JDF2h9zuG1sf+aMi2TcgECrJHkaW9tJK6zlQG41e7OrOJhbTf1PphW4a1TE9/cnvr8/Q8N8GBLmQ5ivJ4Fe7vj1cUfXbqBR105lg46M4jqOFdWxJ7uKtMJadpyqYMepCqKDvfjt9GHcOqZ/j2MW4mKqq6vR6/UEBwef83hwcDBZWVkXfE5FRQUhISHnbV9RUXHB7VtbW3nxxRe58cYb8fHp2vTotLS0Lm3fWzoT1+cHawAY2KeN1NRU2wZkR+z1Z2YP5LW5uIu9NoPURgI81BTXtvCvT/YwbaBrX6vLVZcTO1po6ic1Ui5GhLisO8ZH8dYPWeRVNbH2x2wenj5M6ZCE6LFj5qRUP6mWdWRxff344NeT+fZEGf/6NpP9udVs3JfPxn35hPt5cP2IvowdGEBCZACDgr0vmaQyGo2UN7SSVd5IZnkDR/JrOVxQw8nSen661oOPhxtjBwYyMTqQ8dFBJEYF4Omuuei+3TVqvD3cCPP1ZHg/P24ba3o8v6qJdSk5bNyXT05lE4s/OMyGPbn8+abh1nh5hOh1bW1tPPLIIxiNRpYuXdrl58fHx6PRXPx3qbfp9XrS0tI6Fdfp7abm7zdfEUfisNDeCE9RXXltXI28NhfXmdcmuT6TFV+d4ss8A7/7+WinvF43vw6XI0kpJ5aab0pKjY4KUDYQIRyA1k3N4utjeGRjKm98n8UvJw0k0FurdFhC9Ii5Ump4P+kn5ehUKhXT48KZHhfO3uwqNu7L46v0UkrrWlm/O5f1u02r+Gjd1IT5ehDq64FPR8Wn0Qj1re1UNbZSUa+jue3C5fT9/D0ZMyCACdFBTIgOIq6vL25WmFIQFeTFH28cwaMzYliXksur35ziYF4Nt/w7hZ/HeDNilIE+ckEjrCgwMBCNRnNeU/PKysrzqqHMQkJCzquKutD2bW1tPProoxQVFfHOO+90uUoKQKPR2OVF/OXiKqxppqC6GY1axYRBIXZ5DrZirz8zeyCvzcVd6rVZMHkQ//4+i+Ml9ezKquaqGOdP8l6MJKWcWGp+DQBjJCklRKfcnBDBv7/L5HhJPW/8kMVTM+OUDkmIbjMajRwrOjN9TziPiYOCmDgoiNZ2PTtOVrDzdAVHCmpIL6qjtd1AQbXpwvFiVCqIDOzD4BAfRkb4MToqgMSoAMJt3KfK28ONh64ewq1j+vP3bRn873ARH51oJOetPbx211iigrxsenzhOrRaLSNHjiQlJYUZM2YAYDAYSElJYf78+Rd8TmJiIrt37z6nr9SuXbtITEy0fG1OSOXm5rJu3ToCA12r0feeLFOSb1R/f0vSWwjRPf5e7twxIYq1P+bw5g9ZkpQSzqdJ187JUlOTc6mUEqJz1GoVj18fy33r9vP2rmx+NSWaMF9pDi0cU2FNM3Ut7bhrVAwLk0opZ+ThpmHGiHBmjAgHoE1voKS2hbL6VsrrW2jS6VGpQIUKL62GYB8Pgr219PX3vOQ0PFvr6+/JyjvHcMPIMJ744DCHC2qZ9coOXrkzkelx4YrFJZzLwoULefLJJxk1ahQJCQm88847NDc3c9tttwGwZMkSwsPDWbx4MQALFizg7rvvZs2aNUybNo1t27Zx9OhRnnvuOcCUkPrd737HsWPHeOONN9Dr9ZSXlwPg7++PVuv81dV7s6sAmDQoSOFIhHAOyVMGsS4ll52nKzhWVOeyNxElKeWk0ovq0BuMhPt50M/ftRunCdEV1w4PY3RUAIfza/j3d5n8+eaRSockRLdkFJtuTAwJ9UHr5pyruohzuWvURAV5OUzF0c9G9kVVU8ibR9s4lFfDfe/s5+mbRrDwykFKhyacwKxZs6iqqmLlypWUl5czfPhwVq1aZZmOV1xcjFp95rNx7NixvPjii/zzn//kpZdeIjo6mtdff52YmBgASktL+eabbwCYPXv2Ocdat24dkyZN6qUzU84eSUoJYVWRgV7cMKovnx4pZu2P2bwwd7TSISlCklJOKjWvBoDRkQGKxiGEo1GpVDx+fQx3r97Lht153D91MBEBktgVjie9yNRX0FXvugnHEOat4b37xvDsxxls3JfP0o+PkV3RyDM3jbBKPyvh2ubPn3/R6Xrr168/77GZM2cyc+bMC24fGRnJiRMnrBqfIymrayG7ohGVCsZHS1JKCGv51ZWD+PRIMR+lFvHkzDhCfDyUDqnXyWjvpFILagBIHBCgaBxCOKIpQ0OYNCgInd7Aa9+eVjocIbrlSIEpKZXQX1beE/bNXaNm2W3x/KGjj9+6lFx++94hWtsv3JBdCNH7Ujr6SY3o54d/H3eFoxHCeYwdEMDoqAB0egP/2ZOndDiKkKSUkzqUWw1AovSTEqLLVCoVi6+PBWDTvnzyKpsUjkiIrjEajRzpuDmRIOOAcAAqlYoHpw3hX78ci1aj5rOjJdy/7gDNOklMCWEPvj9p6p81ZdiFVy8UQnSPSqXiV1dGA7B+d65L3pCRpJQTKqhuoqi2BTe1SpJSQnTTxEFBXBUTSrvByCtfn1I6HCG6pLCmmYoGHW5qFSP6yfQ94Thmxfdj9b3j6eOu4YeT5dyzZi91LW1KhyWESzMYjPxwsgKAaS68QpgQtjIrvh/hfh6U17fy6ZFipcPpdZKUckL7c0xVUiP7++OllbZhQnTX4utMzU23HirgdFmDwtEI0XnmqXuxfX0VXWVNiO6YOiyU9ckT8fV0Y29OFXe9tZuqRp3SYQnhso4V11HR0IqXVsP4gdJPSghrc9eoWTA5GoDVO7MxGo3KBtTLJCnlhPblmFbGmDAwUOFIhHBso6MCuG5EOAYj/HP7SaXDEaLTDpun7sliF8JBjY8O4r37ryDYW8vRwjrueCOFsroWpcMSwiX9cMo0dS9pSLCs5iqEjdw5cQAebmrSi+rY11Fk4ioc4lNlw4YNTJ8+nfj4eObOncuRI0cuuu2mTZu46667mDBhAhMmTODee+89b/unnnqK2NjYc/4lJyfb+jR6jblSSlbGEKLnHuuolvrkSDEZxXUKRyNE5xzJN1VKjY6UJufCcY3q78+mX0+mn78np8oamPdGCoU1zUqHJYTL+f6EKSl1lUzdE8Jmgry13Da2PwBrf8xWOJreZfdJqW3btrFs2TIWLVrE1q1biYuLIzk5mcrKygtuv2fPHm688UbWrVvHxo0b6devH7/61a8oLS09Z7upU6eyc+dOy7+XXnqpN07H5mqadJworQdgfLRUSgnRU8P7+XFTQj8AXvpKqqWE/dMbjKQVdqy8J5VSwsENCfVh04OTiQzsQ05lE/P+Xwq5lY1KhyWEy2hobedAxwJK0k9KCNtaeOUgAL5ILyG/ynUWWrL7pNTatWuZN28ec+bMYejQoSxduhRPT082b958we1XrFjBL3/5S4YPH86QIUP461//isFgICUl5ZzttFotoaGhln/+/s5xN9lc6jc4xJsQHw+FoxHCOTw6Iwa1Cr46Vsrh/BqlwxFn6UolLcBnn33GDTfcQHx8PDfffDPff/+95XttbW288MIL3HzzzSQmJjJlyhSWLFly3k0Ne3e8pI6G1nZ8PdyI7eurdDhC9FhUkBcf/Hoyg0O8KaxpZt4bKdLnT4hesut0Be0GIwODvRgY7K10OEI4tZhwX6YOC8FghHUpOUqH02vsugu2TqcjPT2dBx980PKYWq0mKSmJQ4cOdWofzc3NtLe3n5d02rt3L5MnT8bPz48rrriCRx99lMDArlcW6fW2XbLRvP/OHmdnx5zvKwYH2Ty2S+lq3PZC4u5djhL3oOA+3JIYwZZDRbz45QlW3z0GsP+4f6onr7c9nqu5knbp0qWMHj2ad955h+TkZD7//HOCg4PP2/7gwYMsXryYxx57jGuuuYaPP/6YRYsWsWXLFmJiYmhpaeHYsWM89NBDxMXFUVdXx9/+9jceeughtmzZosAZdo95CveYgYFo1CqFoxHCOvr592Hjg1dw96q9nCit5443UlifPIkREbK6pBC2ZO4nJVVSQvSOX105iB2nKti4L59HZsTg42HXKRursOszrK6uRq/Xn3dxERwcTFZWVqf28eKLLxIWFkZSUpLlsalTp3LdddcRGRlJfn4+L730Evfffz/vv/8+Gk3XVilKS0vr0vbd1dnjfJtuWq41QlNPamqqDSPqnN56faxN4u5djhD3tX3b+UgFO05V8P43BxgRqnWIuC/EUeP+qbMraQGWLl3Kd999x+bNm3nggQfO237dunVMnTqV++67D4BHH32UXbt28e677/Lcc8/h6+vL2rVrz3nO008/zdy5cykqKiIiIsL2J2UF+zumWYyXxS6Ekwnz9eS9B65gwZo9HC2s4863drN24QTGDpD3uhC2YDQa+c7cT2qYJKWE6A3TYkIZHOJNVkUjmw8UcE9StNIh2ZxdJ6V66s0332Tbtm2sW7cOD48zU9luvPFGy/+bG53PmDHDUj3VFfHx8V1OZHWFXq8nLS2tU8epaGgl74NvAbjjmrEEeWttFtfldCVueyJx9y5Hi3teRTrv7cvnf9kGhocYSUhIcIi4zXryepufay+6U0mbmprKvffee85jU6ZMYfv27Rc9TkNDAyqVCj8/x6nG2N+xAqv0FRTOKMhby4b7rmDh2r0czKvhrrd2s/IXY7h+ZF+lQxPC6WRVNFJQ3Yy7RsXkIedXIAshrE+tVnHvldE881E6b+/K4e4rBqJ28sp3u05KBQYGotFozmtqXllZSUhIyCWfu3r1at58803Wrl1LXFzcJbeNiooiMDCQ3NzcLielNBpNr1yUduY4u7NNd8dH9PMj1K+PzWPqjN56faxN4u5djhL372YMY/OhQvbl1nBkYCBjHCTun3KU1/tSulNJW1FRcd7YERwcTEVFxQW3b21t5cUXX+TGG2/Ex8enyzFae8pjZ6ZfFtU0U1zbgkatIj7C1y6nXXaWo0zvtSZXO+funq+PVs3b947ndxsP893Jch589wDP3DicBZMH2iJMq3HWn6+znY8446tjpp6KkwYF4+0CU4iEsBdzxkby/9u77/iazj+A45/cDIkkItOKPSIiE7WSklAjilo1o7aaVVqUUuvXqFViVKwU1aKKGqEtSo0YP2LWjp2IDLLJur8/0tyfNEESyb03yff9euXFPfe5536fc+89zznPnP/bde5EJnD4xhO86pbTdEiFSqvPLgYGBjg4OBAUFETr1q0BVJOW9+vX75WvW716NStXrmTt2rU4Ojq+8X0eP37Ms2fPsLYu2t1ST9zKqLxrJi0ZQhSKCmZG9G1chYDjd/npcjz92yg1HZIoJCkpKXzyyScolUpmzpyZr30UVs+y1+33r3tJAFQz0+XG35cL5f3VTZt66KlLSctzfvM70lGBXooRB+4kMXPPVYJv3MPHyRSFjna3KJe0z1cUXb9feQxAW4fifUMshLYxLqVH73eqsOqvENYduyuVUpo2cOBAJk2aRP369XFycmL9+vUkJSXRtWtXACZOnEi5cuWYMGECkDFkz8/Pj4ULF1KpUiUiIjLGQZcuXRpjY2MSEhJYtmwZbdu2xcrKigcPHjB//nyqVq2Kh4eHxvL5tpRKJUduZOTVvfbre5EJIfJvRMuabD79gJvRKRy6FkGb+hU0HVKJlJ+etFZWVtl6ReWUPiUlhXHjxhEaGsr69evz1UsKCn54d26GX/50+xIQg1f9yri42BXYe2tCURveWxBKWp4LIr8rXZV8dySEhX/cZNeNRJL1TfmmW32tnBi2uH6+2ja8WxSMJ7HPOXf/GQDv1ZPhsUKoW/+mVVlzNIRjtyK5/jiuWK+orH0l9r94e3sTHR2Nn58fERER2Nvbs2bNGtVNRFhYGAqFQpV+8+bNpKSkMHbs2Cz7GT16NGPGjEFXV5cbN26wc+dO4uLisLGxoXnz5nzyyScYGGhuDqa3dTUsjsexzzHS16VJDekpJURhsTE1pH/TKvj/dYdvD9ykdb3yxX6ctzbKT09aFxcXTp48mWVeqRMnTuDi4qJ6nFkhde/ePTZs2JCvVVkzFdYwyVftV6lUcuJ2RiWde23rYnPTWxyGm+ZVScvz2+Z3TKs62FqUZuK2i+y/Es6tiARW9mtALZv8VSgXtpL2+Yqi6Y+rGUP3nCuXpbyZoYajEaLksTUvTbv65Qm89JiA43eY281J0yEVGq2vlALo16/fK28yNm7cmOXxoUOHXrsvQ0ND1q5dW2CxaYs/rz8BoHktSwz15UJHiMI01KM6G07c5erjOPZeCqOjc9FYla24yWtP2v79++Pj48O6deto0aIFgYGBXL58mVmzZgGoGjT+/vtv/P39SUtLU/W2NTMz0/qGi7tRiYTGPMdAV0EjmeRclDBdXG2pYlGakZvOcetJPJ2XHWNuNyc5PwuRT/suydA9ITRtYPPqBF56zI7gR0xsV1ejC5kVJsWbk4ii4M9rGZVSLe1sNByJEMWfeWkDOtUxBmDeb9d4kSqTvGqCt7c3kyZNws/Pj86dO3P16tVsPWkzK5UA3NzcWLBgAVu2bKFz58789ttvLF++nDp16gAQHh7OoUOHePz4MZ07d8bd3V3196oV/bTJsVsZQxNdq5SltEGRaHMSokA1qGrBnjEeNK5uQUJyGmN+CubTLeeJfZ6i6dCEKFIi4l5w4nZGmdLBUaYpEEJTGlY1x7GSGS9S0/np9H1Nh1No5Kq1GHiakMy5+xkr73nWlUopIdSho11p/nyQwoPoJNafuMuwd2tqOqQSKS89aQHat29P+/btc0xva2vL9evXCzQ+dTrxT6WUey2ZV1CUXNampdg0pDFLDt5k+Z+32BH8iNN3opnbzRGP2kV7QRsh1GXf5TDSleBsa0ZVS2NNhyNEiaWjo8Mg92p8uuUCG4LuMtSjBgZ6xa9fUfHLUQn0x9/hpCvBvkIZKpU10nQ4QpQIRnoKJrxXG4ClB28RFf9CwxGJkiw5NZ1jNzMqpZrLYheihNPTVTChjR0/f9yUKhalefQsCZ+1pxn7UzBP4p5rOjwhtN7uC6EAMvxVCC3QwbEi1qalCI99wb7LYZoOp1BIpVQxEPjPl9O7vqyMIYQ6dXGtRL0KZYh7kcqSgzc1HY4owc7cjSbuRSpWJga42JbVdDhCaIUGVS0I/MSDj5pWRaEDuy6E0mrBETYE3SUtXanp8ITQSg+fJnLmbsYIjA5OMnRPCE0z0FPQv0lVANYdu4NSWfzKL6mUKuJiElM4/s+QjfYy5lsItdJV6PBlB3sANp26z60ncRqOSJRUB69mzCvoaWcjq0EK8RKTUnrM7FyfX0e542RrRtyLVKb/eoVOy46phrwKIf7vl7OPAGhW05IKZjICQwht0KdxFQz0FFx4GMPpO9GaDqfASaVUEXfgajgpaUrqlDPR2qWPhSjOmtWyorV9OdLSlUz/9UqxbL0Q2k2pVHLwWsbS3a3sZZUkIXLiaGvGjpHNmd3ZAVNDPa6ExtJnzSkGBJzm2uNYTYcnhFZIT1ey7dwDAHo0tNVwNEKITJYmpejRIOM3ufhA8RudIZVSRdyuf8Z8t68vvaSE0JTp79ejlJ6CE7ejVL9JIdTldkQ896ISMdBV4CHzSQnxSroKHXyaVuPwZy0Z0KwaegodDl+PoP2So3z28wXCYpI0HaIQGnXqbjQPopMwLaVHOwe5txBCm4z0rIW+rg5BIVGcDInSdDgFSiqlirDw2OccvZmx3PkHrpU0HI0QJVcVy9KM9qwFwOw9V2X5caFWey5mzCvYvJYlxqVkUV0h3sTSpBQzOjlwYHwLOjhVQKmEbWcf0nL+Yb7Zf03O4aLE2nLmIQDvO1fAyEBXw9EIIV5WqawRPRtVBmDxgRsajqZgSaVUEbYz+BHpSmhY1ZzqVrJcqxCaNKxFDWpYGRMZ/4JFvxevgkJoL6VSqeqd18lFVkkSIi+qWRmzvI8bO0Y2451qFrxITee7w7dpOf8w3x+/Q3JquqZDFEJtnj5PY/+VxwD0bVxVw9EIIXIysmUtDHQVnAyJJuh28ektJZVSRZRSqeSXcxmtGd0ayJhvITStlJ4usz+oD8CGoLucf/BMswGJEuHvsFhCIhIopaegtcwnJUS+uFYxZ8vwJqzu35Ca1sZEJyQzY/fftPn2CIGXwmSuQFEiHAhJIiVNiVuVstSvZKbpcIQQOahYTHtLSaVUEXX23lNuhMdjqK+Q5VqF0BLNa1nxgUtF0pXw2c8XeJ6SpumQRDGX2UvKq64Npob6Go5GiKJLR0eH9+qV47dx7/KfLvWxMinF3ahERm46R7fvTkhDgyjWklPT+T0kEYD+TatpNhghxGuN9KyJga6CU3ei+fPaE02HUyCkUqqIWh90D4APXCpRRm5EhNAaMzo5YG1ailtP4ln4+3VNhyOKsZS0dLafy1i6u7MM3ROiQOjpKujbuCqHP2/JJ61qY6Svy7n7z/hg+XEmbbtIVPwLTYcoRIHbdTGU6KR0rE1K0d6xvKbDEUK8RgUzIwY0rwbA7D1/F4uh5lIpVQSFxz5n36WMiW19msqYbyG0SdnSBszt6gjAmmN3OH0nWsMRieLq4NVwIuJeYGVSilYydE+IAmVSSo9P36vDkc9b0v2faRK2/PcBngsOsyHoLmnpMqRPFA/p6UpW/XUHgEHu1SilJxOcC6HtRnvVwsrEgJDIBDYE3dV0OG9NKqWKoA1Bd0lNV9KomjkOFWXMtxDappV9OT5saIvyn2F8cbKSkygEP55+AECPhrbo60pxLkRhsCljyIIezvwyoin1KpQh9nkq03+9Qqdlx7j8KEbT4Qnx1n7/+zG3IxIw1teh9z9z1QghtFsZQ30+a2MHwJKDN4t8L165ii1iYpJS2HAiY+jeYPcaGo5GCPEqX75fj0pljbgfncjEbRdlolxRoO5GJnD0ZgQAveQmQohC16CqBbvHuDO7swNlDPW4EhpL5+XHmbvvmswfKIqstHQlC/9ZMbh9rdKYGuppOCIhRG71aFiZehXKEPc8lYV/FO1Jz6VSqojZcOIucS9SqVPOhDb1ZLiGENqqjKE+S/u4oq+rw77Lj1l77I6mQxLFiP9fISiV4GlnTVVLY02HI0SJoKvQwadpNQ5OaEkHxwqkpStZeeQ27Zcc5WRI8VmaW5QcO4IfcfNJPGZG+nSqI2WJEEWJrkKHrzrWA+Cn0/c5c7foThkilVJFyLPEZNb8c2M7yrMWCoWOhiMSQryOWxVzpr2fUVj47rsm80uJAvEk9jm/nH0IwIiWtTQcjRAlj7VpKZb3dWOVTwPKlSnFncgEeq06yRfbLxErw7VFEZGUnMa3//SuGP5udYwN5LZQiKKmcQ1LejTImDJkwtYLJLxI1XRI+SJnnyJk6aFbxCSlYFfOlPedZKUlIYoCnyZV6exSkbR0JSM3neNBdKKmQxJF3Jrjd0lOS8etSlkaVTPXdDhClFhtHMrzx/gW9GlcBchoqX5v0REO/B2u4ciEeLPlf97i0bMkKpU1on8TWThJiKJqWsf/TxnydeBVTYeTL1IpVUTceWlm/akd7NGVXlJCFAk6Ojr4dnWkbnlTIuNf8NG600V+MkKhOU8S0th48j4AY7xqo6MjZYEQmlTGUJ+vuziyeVgTqlsZEx77giEb/suYn4LlXC+0VkhEPKv+CgFg2vv1MDKQFfeEKKrKGOozv7sTAJtO3efIjQgNR5R3UilVBCiVSqbuvEJKmpIWdax5t461pkMSQuRBaQM91g96h0pljQiJTGDQ92eKbPdaoVmbr8SRnJpO0xqWtLSTskAIbdGkhiX7PvFgeIsaKHRg94VQWi86ws7gR7LQhdAqqWnpTPj5Aslp6bSoY01bB5mjVoiirlktKwY0qwZkrPwdHvtcswHlkVRKFQF/3Eni1J1ojPR1md25vqbDEULkQ7kyhqwf9A7mpfW58DCG4RvPkpgsFVMi907fiebIvYyLjC+860ovKSG0jKG+Ll+0t2fnqObULW/K08QUxm05z+D1/yX0WZKmwxMCyFgoI/j+M0xL6eHb1VHKEiGKiUnt6lKnnAkRcS8Y8cNZXqQWnZVhpVJKy918Es/35+MA+KytHVUsS2s4IiFEftWyMWHdgEaUNtDl2K1IfNaeJiZRJsUVb/Y8JY0vdlwG4MOGtjjZltVsQEKIV3KyLcvuMe581qYOBroKDl17Qptv/2LjyXukSa8poUEnbkey8PfrAEzvWI+KZY00HJEQoqAYGeji79MQU0M9zt1/xqRtF0lPLxpljlRKabH4F6mM/jGYF2lKmte0VHXJE0IUXa5VzNk4uDFmRvqcvfeUD/2DilwXW6F+/9l7lbtRiVgYKpjS3k7T4Qgh3kBfV8For9oEfuKOW5WyxL9IZcbuq0z8I0pWYhUa8SA6kTE/BpOuhK5ulejewFbTIQkhClh1K2OW93FDT6HDzvOhfB14tUgMIZdKKS2VnJrOiB/OcisiAQtDBYs+dJLJzYUoJhpUNWfr8KbYmJbiengcnZcd57935SZF5GxH8EM2nrwHwIiGZpga6ms4IiFEbtWyMeXnj5sxq7MDZkb63I1Jpfea04z5KViG9Am1iYx/gc/aU0QlJFOvQhm+7iLD9oQort6tY83cbhkTn685doe5+65pfcWUVEppoeTUdD7dcp6jNyMx0tdlUnNzrExKaTosIUQBsitvyi8jmlHD2pjHsc/pueokK4/cLjLdbIV6nLgdyaRfLgEw2rMmbhWkLBCiqNFV6NC/aTUOfOpBmxpG6PwzEbrXwsP47rvK04RkTYcoirEnsc/ps/okd6MSsTU3ImBgIwz1ZbU9IYqz7g1smdXZAciYR27itoukpKVrOKpXk0opLRP/IpWPfzjL3kth6OvqsKy3C7UspFVciOKoskVpdo12p7NLRdLSlczdd42+a05x60mcpkMTWuBUSBRD1/+X5NR02tQrx1ivWpoOSQjxFiyMDRjewIxfRzajUTVznqek438kBI95f7LojxtES+WUKGA3wuPovjKIG+HxlCtTig2D3qFcGUNNhyWEUIP+TavxdRdHFDrw89mH9FtziidaOmWIVEppkVtP4uiy/DiHrj2hlJ6CVf0bypLfQhRzJqX0WNzTBd+ujpTSUxAUEkW7xUfxDbxK3HOZBL2k+vX8I3zWniYhOY1mNS3x6+0qQ7iFKCYcKpZh6/CmrP2oIfYVyhD/IhW/gzdpNvcgU3dc4nZEvKZDFEWcUqlkR/BDPlh+nPvRiVSxKM22j5tRw9pE06EJIdSoT+MqrO7fEGMDXU7dicbb7yj7LoVpOqxspFJKCzxPSWP5n7fw9jvGzScZLRk/Dm2Cp52NpkMTQqiBjo4Ovd+pwoHxLWhtX47UdCX+f4XQbO4hvtl/jSdx2tmqIQpe7PMUJv9ykU82nyc5LaOH1LoBMtRCiOJGR0eHVvbl2DvGnRV93XCsZMbzlHQ2nbpPq4VH+NA/iG1nH5KYnKrpUEURcy8qgaEbzvLplgsk/tOwsXNUcypbyAreQpRErezLsWuMO3XLmxIZn8yITefwWXuKiw+faTo0lSJRKbVp0ya8vLxwdHSkR48eXLx48bXp9+3bR7t27XB0dKRjx44cOXIky/NKpZIlS5bg7u6Ok5MTAwYM4O7du4WYg5wlJaex8eQ9Wi08wvzfrpOcmk5LO2t2j3GnQVVztccjhNCsyhalWfNRQwIGNKKWjQlxz1P57vBt3L/5k1GbzvH7lcckp2rveHBNKE7lw8mQKLwWHGbzmQfo6MAoz5p816+BVEgJUYwpFDp4O1Zg1+jmbB7WhNb2Nih04PSdaD77+QJus/9g6Ib/su3sQ6LiX2g63CKnOJURb3I7Ip4pOy7x3qK/OHA1HH1dHT5rU4cNg97BwthA0+EJITSoprUJv45uzmjPWugpdDh6M5JOy44zfON/OXE7UuNz2mp9pVRgYCC+vr6MGjWKHTt2ULduXQYPHkxUVFSO6c+dO8eECRPo3r07O3fupFWrVowaNYobN26o0qxevZqNGzcyY8YMtm7dipGREYMHD+bFi8Iv7JNT0/n9ymM+2RxMwzl/MG3nZR49S6KCmSHf9nQmYEAjbExlrLcQJZlnXRt+H/cuq/s3xK1KWZJT09l7KYxhG8/ScM4ffLzxLBuD7nLrSbzGCxFNKm7lw9YzD4iMT6amtTE/DmnC523rypA9IUoIHR0dmtSwZM1HjTg+2YvP29pRzbI0z1PS+ePvcD77+QIN5hyg7bd/MWPXFXYGP+JGeBypWjxxraYVtzLi35RKJQ+iE1l37A6dlx2j1cIj/HjqPslp6XjUtmLvWA9Ge9VGT1frb/eEEGpQSk+Xz9racWhCS7q6VkJHB367Ek6f1afwmPcn83+7xuk70RppANdT+zvmUUBAAB9++CHdunUDYObMmRw+fJhffvmFYcOGZUu/YcMGPDw8GDJkCADjxo3jxIkT/PDDD8yaNQulUsmGDRsYMWIErVu3BmDevHk0a9aMAwcO0KFDh0LNz4SfL7D7QqjqcRWL0gxqXo1e71SR1nAhhIpCocN79crR2t6GK6Gx/Hr+EbsuhBIe+4L9Vx6z/8pjAEob6GJfoQx25U2pbF4aW3MjKpkbYVvWCHNjA/SL8cVocSsfpr1fjw5OFfCobY2BXvH93IQQr1fBzIhRnrUY2bImV8Pi2H/lMb9fecy1x3FcD8/4y2Sgp8CunCm1y5lgWzbj/F+xbMafeWkDyhjqldhKieJWRtyJTOD64zhCIuO5GhbHmTvRPH5p0mJdhQ4t61gz7N0avFPdAh0dadQQQmRXxbI0i3q6MKJlTdYdv8OeC2E8epbE8j9vs/zP2xjqK3CtbP7P/YUJNa1NqGJZulA7zmh1pVRycjJXrlxh+PDhqm0KhYJmzZoRHByc42vOnz/PgAEDsmxzd3fnwIEDADx8+JCIiAiaNWumet7U1BRnZ2eCg4MLvUCpalGamtbGtLSz4X2nCrhULiuFhhDilXR0dKhfyYz6lcyY3N6eCw+fceJWJMduRRJ8/xmJyWmcvfeUs/ee5vh6k1J6mBnpY2akj6mhLkbpSSysk4ylqZGac1KwimP5YG5sQCv7coX6HkKIokNHR4d6FctQr2IZxr9Xh6j4F5wMiebUnSiuhMZyNSyWxOQ0Lj2K4dKjmFfuJ7McKGOkj6mhHqX0FBjoKiil/8+/eroY6CmwMDZgQLNqmBeDoV7FrYzYdOoeU3dczrZdT6GDc+WydHSqwPvOFbEyKVVoMQghipfa5Uzx7erEVx0d+P3vcH67/JiTIVFEJSQTFBJFUEjWXqW+XR3p/U6VQolFqyulnj59SlpaGpaWllm2W1paEhISkuNrIiMjsbKyypY+MjISgIiICNW2V6XJDaUyY8hMcnIyurq57+E01rM6Yz2rqx6npLx+da20tLR8vY+mSdzqJXGrlybjrl/emPrljRnmXpXUtHTuRiVyPTyOkMgEHsc859HT54TFJPEk7gXpyoxYo+PTiI7/f2vqxfvRNK+dt5U9M/Ocee7TtOJYPrxJUf295FdJyy+UvDxLfvPG1ECH9+pa8l7djHNUerqSh88Suf44nvvRiYQ+e87j2OeExTzncewLEl6kqt733+XAq9gY69GjoW2e4tK28gG0t4zIb/lgZaRLDUtDLIwNqGZZmuqWJjhXMcOxohlGBv/fT3Jycq73+bKS9lvMCzk2rybH5tWK0rFRAO3srWhnb4VSqeRWRAKXHj7jdkQCt54kcD86gZjnaZTSVeb5HJPb8kGrK6W0WXp6xljLv//+Wy3vp673KWgSt3pJ3OqlLXFXAapYAVYApf75e43nj7l06XG+3ivz3CderbDLB2353qlLScsvlLw8S37fTnmgvBlgBhmX9ib//OXHUy5dyrnn7ZtI+fBm+S0frID5XmVf2hIPcfHcuv6owGKDkvdbzAs5Nq8mx+bViuqxqa0PtSsCFfWBsv9sjeTSpdw30r7sTeWDVldKmZubo6urm21CwqioqGwtGZmsrKyytVa8nN7a2lq1zcbGJkuaunXr5jo2PT09HB0dUSgUMvxOCFHsKZVK0tPT0dPTjmJDygchhNAO2lY+gPaWEVI+CCFKktyWD9pTeuTAwMAABwcHgoKCVBMKpqenExQURL9+/XJ8jYuLCydPnswyJvzEiRO4uLgAYGtri7W1NUFBQdjb2wMQHx/PhQsX6N27d65jUygUGBgU/TH3QghRFEn5IIQQ4lW0tYyQ8kEIIbLT+uU4Bg4cyNatW9mxYwe3b99mxowZJCUl0bVrVwAmTpzIwoULVen79+/P0aNHWbduHbdv32bp0qVcvnxZVQDp6OjQv39/vvvuOw4ePMj169eZOHEiNjY2qkJLCCGE9pPyQQghxKtIGSGEEEWDVveUAvD29iY6Oho/Pz8iIiKwt7dnzZo1qq60YWFhKBT/r1tzc3NjwYIFLF68mEWLFlGtWjWWL19OnTp1VGmGDh1KUlIS06dPJzY2lgYNGrBmzRpKlZIVK4QQoqiQ8kEIIcSrSBkhhBBFg45Sm5bKEEIIIYQQQgghhBAlgtYP3xNCCCGEEEIIIYQQxY9USgkhhBBCCCGEEEIItZNKKSGEEEIIIYQQQgihdlIpJYQQQgghhBBCCCHUTiqlNOzZs2dMmDABNzc3GjZsyJQpU0hISHht+tmzZ9O2bVucnJxo2bIlc+bMIS4uLks6Ozu7bH979+59q1g3bdqEl5cXjo6O9OjRg4sXL742/b59+2jXrh2Ojo507NiRI0eOZHleqVSyZMkS3N3dcXJyYsCAAdy9e/etYnzbuLdu3UqfPn1o1KgRjRo1YsCAAdnST548OduxHTx4sEbj3r59e7aYHB0ds6RRx/HOS8w+Pj45fk+HDRumSqOOY33mzBk+/vhj3N3dsbOz48CBA298zalTp+jSpQv169fnvffeY/v27dnS5PX3Uthx//777wwcOJAmTZrg5uZGz549OXr0aJY0S5cuzXa827VrV6Bxi7x7+PAhU6ZMwcvLCycnJ1q3bo2fnx/JyclZ0l27do0+ffrg6OhIixYtWL16tYYifnvfffcdvXr1wtnZmYYNG+aYpjDKOU3KTZ5DQ0MZNmwYzs7ONG3alG+++YbU1FQ1R1o4vLy8sn2eq1at0nRYBaqwywUh8kLK/P970zWVuu5ZtNWbjo+67o20jb+/P926dcPV1ZWmTZsycuRIQkJCsqR58eIFM2fOpHHjxri6ujJmzBgiIyM1FLH20tN0ACXdZ599RkREBAEBAaSkpDBlyhSmT5/OwoULc0z/5MkTnjx5wqRJk6hVqxaPHj1ixowZPHnyBD8/vyxpfX198fDwUD0uU6ZMvuMMDAzE19eXmTNn4uzszPr16xk8eDD79+/H0tIyW/pz584xYcIExo8fj6enJ7t372bUqFFs375dtbTu6tWr2bhxI3PnzsXW1pYlS5YwePBgAgMDC2xp3bzGferUKTp06ICbmxsGBgasWbOGQYMGsXfvXsqVK6dK5+Hhga+vr+qxgYFBgcSb37gBTExM2L9/v+qxjo5OlucL+3jnNealS5eSkpKievzs2TM6d+6c7YKosI91YmIidnZ2dOvWjdGjR78x/YMHDxg+fDi9evViwYIFBAUF8eWXX2Jtba36veXn8yvsuM+cOUOzZs349NNPKVOmDNu3b2fEiBFs3bqVevXqqdLVrl2bgIAA1WNdXd0CiVfkX0hICEqlklmzZlG1alVu3LjBtGnTSEpKYtKkSQDEx8czePBgmjZtysyZM7lx4wZTpkyhTJky9OzZU8M5yLuUlBTatWuHi4sL27Zte2W6giznNO1NeU5LS2P48OFYWVmxefNm1bWAvr4+48eP10DEBW/s2LF8+OGHqsfGxsYajKZgqaNcECKvpMzP8KZrKnXcs2iz3FxzFvb1ujY6ffo0ffv2xdHRkbS0NBYtWsTgwYPZu3cvpUuXBuDrr7/myJEjLF68GFNTU2bPns3o0aPZvHmzhqPXMkqhMbdu3VLWqVNHefHiRdW2I0eOKO3s7JSPHz/O9X4CAwOVDg4OypSUFNW2OnXqKP/4448Ci7V79+7KmTNnqh6npaUp3d3dlf7+/jmm/+STT5TDhg3Lsq1Hjx7KadOmKZVKpTI9PV3ZvHlz5Zo1a1TPx8bGKuvXr6/cs2ePxuL+t9TUVKWrq6tyx44dqm2TJk1SjhgxosBizEle4/7ll1+UDRo0eOX+1HG83/ZYBwQEKF1dXZUJCQmqbeo41i/Lze9m3rx5yg4dOmTZNm7cOOWgQYNUj9/2WORVfn/v3t7eyqVLl6oe+/n5KTt16lSQoYlCsnr1aqWXl5fq8aZNm5SNGjVSvnjxQrVt/vz5yrZt22oivALzunNbQZdz2uJVeT58+LCybt26yoiICNW2H3/8Uenm5pblcy+qPD09lQEBAZoOo9Cou1wQ4k2kzM/Zv8sWdd2zFBU5lb3qvl7XVlFRUco6deooT58+rVQqM74nDg4Oyn379qnSZN7/BwcHayhK7STD9zQoODiYMmXKZBlm1axZMxQKRZ66dMfHx2NiYoKeXtaOb5ldBbt37862bdtQKpX5ijM5OZkrV67QrFkz1TaFQkGzZs0IDg7O8TXnz5+nadOmWba5u7tz/vx5IGM4SkRERJZ9mpqa4uzs/Mp9qiPuf0tKSiI1NRUzM7Ms20+fPk3Tpk1p27YtX331FU+fPi2QmN8m7sTERDw9PWnRogUjRozg5s2bqucK+3gXxLH+5Zdf6NChg6plIVNhHuv8eNN3uyCOhTqkp6eTkJBA2bJls2y/d+8e7u7utGrVigkTJhAaGqqZAMVrxcXFZTkvnT9/noYNG2ZpmXR3d+fOnTvExMRoIkS1KKhyrig4f/48derUwcrKSrXN3d2d+Ph4bt26pcHICs7q1atp3LgxH3zwAWvWrCk2QxOLSrkgSh4p899MHfcsxYG2Xa9rQuZ0OpnXZ5cvXyYlJSXLd6dmzZpUrFhRdd8gMsjwPQ2KjIzEwsIiyzY9PT3MzMyIiIjI1T6io6NZsWJFtuEZY8eOpUmTJhgZGXHs2DFmzpxJYmIi/fv3z3OcT58+JS0tLVv3cktLy2zjZjNFRkZmuXDOTJ85hjYzfznts6DG2eYn7n9bsGABNjY2WU4mHh4evPfee9ja2vLgwQMWLVrE0KFD2bJlS4F0e85P3NWrV+frr7/Gzs6OuLg41q1bR69evdi7dy/ly5cv9OP9tsf64sWL3Lhxg//85z9Zthf2sc6PnL7bVlZWxMfH8/z5c2JiYt76e6cOa9euJTExkfbt26u2OTk54evrS/Xq1YmIiGD58uX07duX3bt3Y2JiosFoxcvu3bvHDz/8oBq6BxnfS1tb2yzpMr+nkZGR2SrWi4OCLOeKglede4BcXzNoMx8fH+rVq4eZmRnBwcEsWrSIiIgIvvjiC02H9tYK4npEiIImZX7uqOOepajTxut1dUtPT+frr7/Gzc1NNVVNZGQk+vr62aYWsLS0LBbldkGSSqlCsGDBgjdOMBsYGPjW7xMfH8/w4cOpWbNmtvG9o0aNUv2/Xr16JCUlsXbt2mJ7sV4YVq1aRWBgIBs2bMgyXrxDhw6q/2dO5te6dWtVC4EmuLq64urqmuWxt7c3mzdvZty4cRqJKS+2bdtGnTp1cHJyyrJdG491cbB7926WL1/OihUrslxktWjRQvX/unXr4uzsjKenJ/v27aNHjx6aCLVYy21ZUbNmTdXj8PBwhgwZQrt27bLMvVMU5Ce/r1MUyrmCznNRk5f8Dxw4ULWtbt266Ovr89VXXzFhwoQSMTeJEOomZb4oKHK9ntFz++bNm/z444+aDqVIkkqpQjBo0CC6dOny2jSVK1fGysqK6OjoLNtTU1OJiYnB2tr6ta+Pj49nyJAhGBsbs3z5cvT19V+b3tnZmRUrVpCcnJzniztzc3N0dXWJiorKsj0qKipbq20mKyurbK0HL6fPzF9UVBQ2NjZZ0tStWzdP8RVk3JnWrl3LqlWrCAgIeGM8lStXxtzcnHv37hXIifdt4s6kr6+Pvb099+/fBwr/eL9NzImJiezdu5exY8e+8X0K+ljnR07f7cjISExMTDA0NEShULz151eY9u7dy5dffsmSJUuy9ADMSZkyZahWrZrqeyQKVm7Likzh4eH0798fV1dXZs+enSXdq76Xmc9pg7zmN6/eppwrLAWZZysrq2xD+zM/4zddM2jK2+Tf2dmZ1NRUHj58SI0aNQojPLUpiHJdiMImZX7O1HHPUtxow/W6Os2aNYvDhw/zww8/UL58edV2KysrUlJSiI2NzdJbKioqSmvLbU2RSqlCYGFhkW1YXk5cXV2JjY3l8uXL1K9fH4CTJ0+Snp6ercfIyzJXWTIwMOC7777L1aoPV69exczMLF8X6gYGBjg4OBAUFETr1q2BjC6KQUFB9OvXL8fXuLi4cPLkSQYMGKDaduLECVxcXACwtbXF2tqaoKAg7O3tVfm6cOECvXv3znOMBRU3ZMxpsXLlStauXZtlvq9Xefz4Mc+ePSuwk0t+435ZWloaN27cULWCFfbxfpuY9+/fT3JyMp06dXrj+xT0sc4PFxcX/vrrryzbXv5uF8TnV1j27NnDlClTWLRoES1btnxj+oSEBB48eCAFZyHJbVkB/6+QcnBwwNfXF4Ui65SQLi4uLF68mJSUFFUjxYkTJ6hevbrWDN3LS37z423KucJSkHl2cXFh5cqVREVFqXo4njhxAhMTE2rVqlUg71HQ3ib/V69eRaFQFIuV6bS5XBAik5T5OVPHPUtxow3X6+qgVCqZPXs2f/zxBxs3bszWyFK/fn309fUJCgqibdu2QMaKyqGhoar7BpFBKqU0qGbNmnh4eDBt2jRmzpxJSkoKs2fPpkOHDpQrVw7IuBH56KOPmDdvHk5OTsTHxzNo0CCSkpKYP38+8fHxxMfHAxkXf7q6uhw6dIioqCicnZ0pVaoUx48fx9/fn0GDBuU71oEDBzJp0iTq16+Pk5MT69evJykpia5duwIwceJEypUrx4QJEwDo378/Pj4+rFu3jhYtWhAYGMjly5eZNWsWADo6OvTv35/vvvuOqlWrqpZXtbGxUV2wFYS8xr1q1Sr8/PxYuHAhlSpVUo33LV26NMbGxiQkJLBs2TLatm2LlZUVDx48YP78+VStWjXLsuTqjnvZsmW4uLhQtWpVYmNjWbt2LaGhoaru1+o43nmNOdO2bdto3bo15ubmWbar61gnJCRkaRV8+PCh6ua2YsWKLFy4kPDwcObNmwdAr1692LRpE/PmzaNbt26cPHmSffv24e/vn+tjoYm4d+/ezeTJk5kyZQrOzs6q77ahoSGmpqYAfPPNN3h6elKxYkWePHnC0qVLUSgUvP/++wUWt8i78PBwfHx8qFixIpMmTcrSwzbzgq9jx44sX76cqVOnMnToUG7evMmGDRuK7Hw8oaGhxMTEEBoaSlpaGlevXgWgSpUqGBsbF0o5p2lvyrO7uzu1atVi4sSJfP7550RERLB48WL69u2rVRVx+REcHMyFCxdo0qQJxsbGBAcH4+vrS6dOnbSmUvVtqaNcECIvpMz/vzddU6njnkWbve74mJmZqeV6XRvNnDmTPXv2sGLFCoyNjVXX1qampqrr627dujF37lzMzMwwMTFhzpw5uLq6SqXUv0illIYtWLCA2bNn89FHH6FQKGjTpg1ffvml6vmUlBTu3LlDUlISAFeuXOHChQsAvPfee1n2dfDgQWxtbdHT02PTpk18/fXXQMYF7eTJk99q/hFvb2+io6Px8/MjIiICe3t71qxZo+p2HhYWlqXl3s3NjQULFrB48WIWLVpEtWrVWL58uWriN4ChQ4eSlJTE9OnTiY2NpUGDBqxZsyZXPb8KK+7NmzeTkpKSbSjZ6NGjGTNmDLq6uty4cYOdO3cSFxeHjY0NzZs355NPPinQm4K8xh0bG8u0adOIiIjAzMwMBwcHNm/enKX1vLCPd15jhozWgrNnz7Ju3bps+1PXsb58+XKWOWh8fX0B6NKlC3PnziUiIoKwsDDV85UrV8bf3x9fX182bNhA+fLlmTNnTpaC903HQhNxb926ldTUVGbNmqWqHH45PWS0bI0fP55nz55hYWFBgwYN2Lp1a6H2bhFvdvz4ce7du8e9e/d49913szx3/fp1IOMCaO3atcyaNYuuXbtibm7OyJEjsy2CUVT4+fmxY8cO1eMPPvgAgA0bNtC4ceNCKec07U151tXVZeXKlcyYMYOePXtiZGREly5dcjX0WdsZGBgQGBjIsmXLSE5OxtbWlgEDBmSZZ6qoU0e5IEReSJn/f2+6plLHPYs2e93xmTFjhlqu17XRTz/9BGQs1PEyX19fVYPDlClTUCgUjB07luTkZNzd3fnqq6/UHqu201EW5/WThRBCCCGEEEIIIYRWUrw5iRBCCCGEEEIIIYQQBUsqpYQQQgghhBBCCCGE2kmllBBCCCGEEEIIIYRQO6mUEkIIIYQQQgghhBBqJ5VSQgghhBBCCCGEEELtpFJKCCGEEEIIIYQQQqidVEoJIYQQQgghhBBCCLWTSikhhBBCCCGEEEIIoXZSKSVEEWVnZ8eBAwc0HYYQQgiRa1J2CSGKq+J8fjt16hR2dnbExsa+Me327dtp2LBhoce0dOlSOnfu/No0Dx8+xM7OjqtXrxZ6PCL/pFJKiH8EBwdjb2/PsGHDNB1KFq864R47dox3331XAxEJIUTJMXnyZEaOHFkg+/Ly8uL7778vkH1pklzkCyG0mZ2d3Wv/li5d+srXFvfzm7oqjDShIMtroV56mg5ACG2xbds2+vXrx7Zt2wgPD6dcuXKaDum1rK2tNR2CEEKIApaWloaOjg4KhbQbCiFEfhw7dkz1/8DAQPz8/Ni/f79qW+nSpTURlhDiFeSKRwggISGBwMBAevfuTcuWLdmxY0eW5w8dOkS3bt1wdHSkcePGjBo1SvVcTEwMEydOpFGjRjg7OzNkyBDu3r2rej6nnk7ff/89Xl5eqsenTp2ie/fuuLi40LBhQ3r16sWjR4/Yvn07y5Yt49q1a6rWne3btwPZuwg/fvyY8ePH88477+Di4kLXrl25cOFClhh27tyJl5cXDRo04NNPPyU+Pl71+vT0dPz9/fHy8sLJyYlOnTplKcBjYmKYMGECTZo0wcnJiTZt2vDLL78AkJyczKxZs3B3d8fR0RFPT0/8/f3z+3EIIYRW8vHxYc6cOcybN4933nmH5s2bZ2lxVyqVLF26lJYtW1K/fn3c3d2ZM2eO6rWPHj3C19dXdT6H/7daHzx4EG9vbxwdHQkNDcXHx4f//Oc/Wd5/5MiRTJ48WfXYy8uLFStWMHHiRFxdXfH09OTgwYNER0czYsQIXF1d6dixI5cuXcqyn//+97/06dMHJycnWrRowZw5c0hMTMyy35UrV/LFF1/g6upKy5Yt2bJli+r5Vq1aAfDBBx9gZ2eHj48PABcvXmTgwIE0btyYBg0a0K9fP65cuVIQh14IIXLN2tpa9WdqaoqOjo7qsaWlJQEBAbz77rvUr1+fzp0789dff6leq47zW2ZvrMDAQNW5uFu3bty5c4eLFy/StWtXXF1dGTJkCNHR0Vle+/PPP9O+fXscHR1p164dmzZtyrbf33//HR8fH5ydnenUqRPBwcFAxv3GF198QVxcXLZeYzt37lS9b/PmzZkwYQJRUVH5yl+mo0eP0r59e1xdXRk8eDBPnjzJdV4A5s+fT9u2bXF2dqZVq1YsXryYlJSUHN9r6dKl7Nixg4MHD6rydurUKdXzDx48yPGYCO0glVJCAPv27aNGjRrUqFGDTp068csvv6BUKgE4fPgwo0ePpkWLFuzcuZP169fj5OSkeu3kyZO5fPky3333HVu2bEGpVDJs2LBXnjT/LTU1lVGjRtGoUSN27drFli1b6NmzJzo6Onh7ezNo0CBq167NsWPHOHbsGN7e3tn2kZCQQL9+/QgPD2fFihX8+uuvDBkyhPT0dFWa+/fvc/DgQVauXIm/vz9nzpxh9erVquf9/f3ZuXMnM2fOZO/evQwYMIDPP/+c06dPA7BkyRJu377N6tWrCQwMZMaMGZibmwOwceNGDh06xOLFi9m/fz/z58+nUqVKef8ghBBCy+3YsYPSpUuzdetWPv/8c5YvX87x48cB+O233/j++++ZOXMmv//+OytWrKBOnTpAxgVz+fLlGTt2rOp8nun58+esXr2aOXPmsGfPHiwtLXMdz/r163Fzc2PHjh20aNGCiRMnMnHiRDp16sT27dupUqUKkyZNUpVp9+/fZ+jQobRp04Zdu3bx7bffcvbsWWbPnp1lvwEBAdSvX5+dO3fSp08fZsyYQUhICJBxIwEZDSzHjh1T3dQkJCTwwQcf8OOPP7J161aqVq3KsGHDsjSACCGEJm3YsIGAgAAmTZrErl27cHd3Z+TIkaoGZXWe35YuXcqIESPYsWMHenp6TJgwgfnz5zN16lQ2bdrE/fv3WbJkiSr9rl27WLJkCZ9++imBgYGMHz8ePz+/bI3p3377LYMHD2bnzp1Uq1aNCRMmkJqaiqurK1OmTMHExERVDg0aNAjIuB/55JNP2LVrF8uXL+fRo0dZGkHy6vnz56xbt4558+bxww8/EBYWxjfffJOnvBgbG+Pr68vevXuZOnUqP//88yuHwA8aNIj27dvj4eGhypurq+sbj4nQDjJ8Twgyhu516tQJAA8PD+Li4jh9+jSNGzdm5cqVeHt7M3bsWFX6unXrAnD37l0OHTrETz/9hJubGwALFiygZcuWHDhwgPbt27/xvePj44mLi8PT05MqVaoAULNmTdXzpUuXRldX97XD9fbs2UN0dDTbtm2jbNmyAFStWjVLGqVSia+vLyYmJgB06tSJoKAgPv30U5KTk/H39ycgIEB1Aq9cuTJnz55ly5YtvPPOO4SGhmJvb4+joyMAtra2qn2HhYVRtWpVGjRogI6OjlRICSGKLTs7O0aPHg1AtWrV+OGHHwgKCqJ58+aEhYVhZWVFs2bN0NfXp2LFiqpGjLJly6Krq4uxsXG283lKSgozZsxQlS158e6779KrVy8ARo0axU8//YSjo6Oq/Bk6dCg9e/YkMjISa2tr/P396dixIwMGDFDlYerUqfj4+DBjxgxKlSql2m/fvn1V+/j+++85deoUNWrUwMLCQpWnl/PStGnTLLHNnj2bhg0bcubMGTw9PfOcNyGEKGhr165l6NChdOjQAYDPP/+cU6dOsX79er766iu1nt8GDRqEh4cHAP3792f8+PF8//33NGjQAIDu3burRkhARiXW5MmTadOmDZBxrX7r1i22bNlCly5dsuy3ZcuWAIwdO5YOHTpw7949atasmaXn2Mu6d++u+n/lypWZOnUq3bt3JyEhAWNj4zznLSUlhZkzZ6rubfr27cuKFSvylJeX54eytbXlzp077N27l6FDh2Z7P2NjYwwNDUlOTs7xnul1x0RonlRKiRIvJCSES5cusXz5cgD09PTw9vZm27ZtNG7cmKtXr9KjR48cX3v79m309PRwdnZWbTM3N6d69ercvn07V+9ftmxZunbtyuDBg2nevDlNmzalffv22NjY5DoPV69epV69eqoKqZxUqlRJVSEFYGNjo+qWe+/ePZKSklStJZlSUlKwt7cHoHfv3owdO5a///6b5s2b07p1a1VFXJcuXRg0aBDt2rXDw8ODli1b4u7unuv4hRCiqMgcdpfJ2tpadS5t164d69evp3Xr1nh4eNCiRQs8PT3R03v95Za+vn62/eYnHisrKwBV7yxA1esqKioKa2trrl27xvXr19m9e7cqjVKpJD09nYcPH6ou0F/er46ODlZWVm8cyhEZGcnixYs5ffo0UVFRpKenk5SURGhoaL7yJoQQBSk+Pp4nT56orl8zubm5ce3atde+tjDOby+fZzPP1f/eljl8LzExkfv37zN16lSmTZumSpOamoqpqekr95tZQRMdHf3aCpjLly+rpgyJiYlR9a4NCwujVq1aec6bkZGRqkIKst535DYvgYGBbNiwgQcPHpCYmEhqamqWe5m8yM8xEeojlVKixNu2bRupqamqlgrIuEA3MDBg+vTpGBoavtX+dXR0VCf2TP/uLurr64uPjw9Hjx5l3759LF68mICAAFxcXHL1HrmJMaebosy4MucS8ff3zzbBu4GBAQAtWrTgzz//5MiRIxw/fpwBAwbQt29fJk2ahIODAwcPHuSvv/7ixIkTjBs3jmbNmuHn55er+IUQoqj497n05XN8hQoV2L9/PydOnODEiRPMnDmTtWvXsnHjRvT19V+5T0NDQ3R0dF6530w5DTV4OZ7Mfbz8XpnbXj7f9+rVSzVPyssqVKiQq3y+yqRJk3j27BlTp06lYsWKGBgY0LNnz1wPZxdCCG1VGOe3nM7V/z6nZ07FkXmtPnv27CyN4UC2hTFy2u/LU3r8W2JiIoMHD8bd3Z0FCxZgbm5OWFgYgwcPznf+XleG5CYvwcHBfPbZZ4wZMwZ3d3dMTU3Zu3cvAQEB+Yonr8dEqJdUSokSLTU1lV9//ZXJkyfTvHnzLM+NGjWKPXv2UKdOHYKCgujWrVu219esWZPU1FQuXLiganV5+vQpd+7cUbUqWFhYEBkZiVKpVJ0Ec1pmtl69etSrV4/hw4fTs2dP9uzZg4uLC/r6+m88adrZ2fHzzz/z7Nmz1/aWepWaNWtiYGBAaGgo77zzzivTWVhY0KVLF7p06cLmzZuZN28ekyZNAsDExARvb2+8vb1p27YtQ4YMyXc8QghRVBkaGuLl5YWXlxd9+vShffv23LhxAwcHh1ydzzNZWFgQERGhepyWlsbNmzdp3LjxW8VXr149bt26lW2Id15kXtynpaVl2X7u3Dm++uorWrRoAWS0sD99+jT/wQohRAEyMTHBxsaGc+fOZbnePXfunGqotbae36ysrLCxseHBgweqKUfyQ19fP1veQkJCePbsGZ999pmqceLy5ctvFe/r5CYvwcHBVKxYkREjRqi2valXWl7KWKFdpFJKlGiHDx8mJiaG7t27Z+v62qZNG7Zt28bEiRMZMGAAVapUoUOHDqSmpnLkyBGGDRtGtWrVaNWqFdOmTWPmzJmYmJiwYMECypUrp1q9o3HjxsyaNYvVq1fTrl07jh49ytGjR1XdTx88eMDWrVvx8vLCxsaGO3fucPfuXdWKfZUqVeLhw4dcvXqVcuXKYWJiouq9lKlDhw6sXLmSUaNGMX78eGxsbPj777+xsbHJMsnfq5iYmDBo0CB8fX1RKpU0aNCAuLg4zp07h4mJCV26dGHJkiU4ODhQu3ZtkpOTOXz4sKrLa0BAANbW1tjb26NQKNi/fz/W1taUKVPmrT8jIYQoKrZv305aWhrOzs4YGRmxa9cuDA0NqVixIpBxPj9z5gwdOnRAX19fNXdJTpo0acLcuXM5fPgwlStX5vvvvyc2NvatY8ycY2rWrFn06NEDIyMjbt26xYkTJ5g+fXqu9mFpaYmhoSFHjx6lfPnylCpVClNTU6pVq8auXbtwdHQkPj6eefPmvXVvYyGEKEiDBw9m6dKlVKlShbp167J9+3auXbvGggULAO0+v40dO5Y5c+ZgamqKh4cHycnJXL58mdjYWAYOHJirfVSqVInExESCgoKws7PDyMiIihUroq+vz8aNG+nduzc3btzIMv+TJvJStWpVwsLC2Lt3L46Ojhw+fDjLquOvytuxY8cICQmhbNmy2e7thPaS1fdEibZt2zaaNWuW40mrbdu2XL58GTMzM5YsWcKhQ4fo3LkzH330UZbltX19fXFwcODjjz+mZ8+eKJVKVq1apWppqVmzJl999RU//vgjnTt35uLFi1nmbjIyMiIkJIQxY8bQtm1bpk+fTt++fVUT17Zt2xYPDw/69+9P06ZN2bNnT7ZYDQwMWLduHZaWlgwbNoyOHTuyatUqdHV1c30sxo0bx8iRI/H398fb25shQ4Zw+PBh1YTm+vr6LFq0iE6dOtGvXz8UCgWLFi0CMiYXXLNmDd27d6d79+48evSIVatWZetOLIQQxVmZMmX4+eef6d27t2oxiZUrV6pWKh07diyPHj2idevW2SbN/bdu3brxwQcfMGnSJHx8fKhcufJb95KCjIU6Nm7cyN27d+nTpw9dunTBz88vT/MY6unp8eWXX7JlyxY8PDxUk9H+5z//ISYmhi5dujBx4kR8fHzytJKgEEIUtv79+zNw4EDmzp1Lp06dOHr0KCtWrKBatWqAdp/fevTowZw5c9i+fTsdO3bEx8eHHTt2ZFl86E3c3Nzo1asX48aNo2nTpqxZswYLCwvmzp3L/v378fb2ZvXq1aqREIXlTXlp1aoVH330EbNmzaJz584EBwdn6TWVkw8//JDq1avTrVs3mjZtyrlz5wo1D6Lg6CjfNEGAEEIIIYQQQgghhBAFTLoxCCGEEEIIIYQQQgi1kzmlhBBCCCGEEEKIArBy5Ur8/f1zfK5BgwasWbNGzREVrCFDhnD27Nkcnxs+fDgff/yxmiMSRZ0M3xNCCCGEEEIIIQrAs2fPiImJyfE5Q0NDypUrp+aIClZ4eDjPnz/P8TkzMzNZeVvkmVRKCSGEEEIIIYQQQgi1kzmlhBBCCCGEEEIIIYTaSaWUEEIIIYQQQgghhFA7qZQSQgghhBBCCCGEEGonlVJCCCGEEEIIIYQQQu2kUkoIIYQQQgghhBBCqJ1USgkhhBBCCCGEEEIItZNKKSGEEEIIIYQQQgihdlIpJYQQQgghhBBCCCHU7n8x163c90VU/wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "---\n",
        "**Checking for Class Imbalance**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "vIg2t8wlsVoE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting the number of samples in each class\n",
        "class_counts = df[\"Target\"].value_counts()\n",
        "print(class_counts)\n",
        "\n",
        "# Computing the class imbalance ratio\n",
        "imbalance_ratio = class_counts.min() / class_counts.max()\n",
        "\n",
        "print(\"Class imbalance ratio:\", imbalance_ratio)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wlF2xvILjC9t",
        "outputId": "5ee2c714-426d-4e6b-8b4c-98ba869204cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    140\n",
            "0     41\n",
            "2     18\n",
            "Name: Target, dtype: int64\n",
            "Class imbalance ratio: 0.12857142857142856\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Observations**\n",
        "1.   The dataset consists of 199 rows.\n",
        "2.   There are no missing values.\n",
        "3.   There are no duplicate records now.\n",
        "4.   Class is extremely imbalanced (class imbalance ratio < 1).\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "3nrl9wRvjY6u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Dividing the dataset again into training and testing sets**\n",
        "\n",
        "\n",
        "---\n"
      ],
      "metadata": {
        "id": "wKg3kzTcwE_O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividing the given columns into target and feature columns\n",
        "X = df.drop('Target', axis=1)\n",
        "y = df[['Target']]\n",
        "\n",
        "# Splitting data into training and testing sets\n",
        "X_train,X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2)\n",
        "print('X_train dimension = ', X_train.shape)\n",
        "print('X_test dimension = ', X_test.shape)\n",
        "print('y_train dimension = ', y_train.shape)\n",
        "print('y_test dimension = ', y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rI2o_a6Ivl6w",
        "outputId": "5c5c07c9-a82f-411e-a334-7572ff5fea34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train dimension =  (159, 9)\n",
            "X_test dimension =  (40, 9)\n",
            "y_train dimension =  (159, 1)\n",
            "y_test dimension =  (40, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "**Scaling**\n",
        "\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "9seGkmYdeZzp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling the input data\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "mpbi3KWMZYUc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "**Handling Class Imbalance**\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "PZIbV2UwtAPj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Synthetic Minority Over-sampling Technique (SMOTE)\n",
        "smote = SMOTE(random_state=42)\n",
        "\n",
        "X, y = smote.fit_resample(df.drop('Target', axis = 1), df['Target'])\n",
        "\n",
        "# Converting smotes to dataframes\n",
        "X_smote_df = pd.DataFrame(X, columns=X.columns)\n",
        "y_smote_df = pd.DataFrame(y, columns=['Target'])\n",
        "\n",
        "# Joining X_smote_df and y_smote_df back to df\n",
        "df = pd.concat([X_smote_df, y_smote_df], axis=1)"
      ],
      "metadata": {
        "id": "vEMeJt9irasJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Ensuring class imbalance has been removed**\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "MD_YDA5ytWGg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting the number of samples in each class\n",
        "class_counts = df[\"Target\"].value_counts()\n",
        "print(class_counts)\n",
        "\n",
        "# Computing the class imbalance ratio\n",
        "imbalance_ratio = class_counts.min() / class_counts.max()\n",
        "\n",
        "print(\"Class imbalance ratio:\", imbalance_ratio)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tXdXilLuoB26",
        "outputId": "342c3bdb-be87-4ac8-af85-f72762af82ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    140\n",
            "1    140\n",
            "2    140\n",
            "Name: Target, dtype: int64\n",
            "Class imbalance ratio: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Checking for Normal Distribution of the Data**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "Qbl03NpGGH-D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing Jarque-Bera test for normality\n",
        "stats, p = jarque_bera(df)\n",
        "\n",
        "if p > 0.05:\n",
        "    print('Normally distributed')\n",
        "else:\n",
        "    print('Not normally distributed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZXsaPN6F2VE",
        "outputId": "89144287-135d-4365-9ed3-0137dcb9bea7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Not normally distributed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the number of rows and columns needed for the plot\n",
        "n_cols = 3\n",
        "n_rows = math.ceil(len(numeric_colms) / n_cols)\n",
        "\n",
        "# Set the size of the plot\n",
        "fig, axs = plt.subplots(n_rows, n_cols, figsize=(15, n_rows*3))\n",
        "\n",
        "# Flatten the axs array to make it easier to iterate over\n",
        "axs = axs.ravel()\n",
        "\n",
        "# Create a histogram for each column in the dataframe\n",
        "for i, col in enumerate(numeric_colms):\n",
        "    axs[i].hist(df[col], bins=30, alpha=0.5)\n",
        "    axs[i].set_title(col)\n",
        "\n",
        "# Remove any unused axes\n",
        "for i in range(len(numeric_colms), len(axs)):\n",
        "    axs[i].set_visible(False)\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "x_ZeL0NhmshJ",
        "outputId": "61e25248-3e92-462c-d82d-67b8943561b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "**Using Durbin-Watson test to check if the dataset is Linear**\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "8v5J3Uks-ka6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Computing the Durbin-Watson test statistic\n",
        "dw_statistic = np.apply_along_axis(durbin_watson, 0, X)\n",
        "\n",
        "# Checking if the data is linear\n",
        "if np.all(dw_statistic > 1.5) and np.all(dw_statistic < 2.5):\n",
        "    print(\"The data is linear\")\n",
        "else:\n",
        "    print(\"The data is not linear\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3YtBRAU2lpu",
        "outputId": "5b1421e7-78a6-4a3f-f156-8afab7adeca1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The data is not linear\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Feature Engineering and Selection**"
      ],
      "metadata": {
        "id": "beggGFAmwq-Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "**Using Correlation Feature Selection with Heatmap to identify the most Important Features**\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "hKRa0Qs2yqFZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Computing correlation matrix using spearman method because the data is not normal"
      ],
      "metadata": {
        "id": "U_sRsN5mYw7d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Computing the correlation matrix\n",
        "corr_matrix = df.corr(method='spearman')\n",
        "\n",
        "# Heatmap\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(corr_matrix, cmap=\"coolwarm\", annot=True)\n",
        "plt.title(\"Feature Correlation Heatmap\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "rB8kvDTFZN5B",
        "outputId": "10b69b78-75f9-4285-df8c-8eaa89467adf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Observations**\n",
        "1.   Sentiment_class has no correlation with the target variable; therfore it can be eliminated\n"
      ],
      "metadata": {
        "id": "VkMCaOowe8T_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Dimensionality Reduction**\n",
        "\n",
        "\n",
        "---\n"
      ],
      "metadata": {
        "id": "V0I2dlgj1yxN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drop_colms = ['Sentiment_class']\n",
        "for colm in drop_colms:\n",
        "  df = df.drop(colm, axis=1)  \n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "-NduWwyu1yP7",
        "outputId": "e09678a8-47fa-4110-ac3e-4fc715caafe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Loudness  Valence  Danceability  Acousticness  Instrumental  Audio_class  \\\n",
              "0    -8.210    0.103      0.520371        0.0128    -16.118096            2   \n",
              "1    -8.128    0.133      0.449000        0.1470    -16.118096            2   \n",
              "2    -7.395    0.218      0.389000        0.6780    -13.530332            2   \n",
              "3    -5.952    0.295      0.616000        0.0480     -9.036387            3   \n",
              "4   -10.965    0.562      0.701000        0.3280     -2.040221            1   \n",
              "\n",
              "   Audio + Lyrics analysis  Total_mental_health  Target  \n",
              "0                        3                  8.0       0  \n",
              "1                        3                  8.0       0  \n",
              "2                        3                  8.0       0  \n",
              "3                        0                 15.0       1  \n",
              "4                        2                 15.0       1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e6dd44f6-9fea-4cdf-86d3-39114afe2b21\">\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>Loudness</th>\n",
              "      <th>Valence</th>\n",
              "      <th>Danceability</th>\n",
              "      <th>Acousticness</th>\n",
              "      <th>Instrumental</th>\n",
              "      <th>Audio_class</th>\n",
              "      <th>Audio + Lyrics analysis</th>\n",
              "      <th>Total_mental_health</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-8.210</td>\n",
              "      <td>0.103</td>\n",
              "      <td>0.520371</td>\n",
              "      <td>0.0128</td>\n",
              "      <td>-16.118096</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-8.128</td>\n",
              "      <td>0.133</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>0.1470</td>\n",
              "      <td>-16.118096</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-7.395</td>\n",
              "      <td>0.218</td>\n",
              "      <td>0.389000</td>\n",
              "      <td>0.6780</td>\n",
              "      <td>-13.530332</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-5.952</td>\n",
              "      <td>0.295</td>\n",
              "      <td>0.616000</td>\n",
              "      <td>0.0480</td>\n",
              "      <td>-9.036387</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-10.965</td>\n",
              "      <td>0.562</td>\n",
              "      <td>0.701000</td>\n",
              "      <td>0.3280</td>\n",
              "      <td>-2.040221</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6dd44f6-9fea-4cdf-86d3-39114afe2b21')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \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",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e6dd44f6-9fea-4cdf-86d3-39114afe2b21 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e6dd44f6-9fea-4cdf-86d3-39114afe2b21');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "\n",
        "**Dividing the dataset again into training and testing sets after handling Dimentionality Reduction**\n",
        "\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "9ZbaSi3segdQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividing the given columns into target and feature columns\n",
        "X = df.drop('Target', axis=1)\n",
        "y = df[['Target']]\n",
        "\n",
        "# Splitting data into training and testing sets\n",
        "X_train,X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state=42)\n",
        "print('X_train dimension = ', X_train.shape)\n",
        "print('X_test dimension = ', X_test.shape)\n",
        "print('y_train dimension = ', y_train.shape)\n",
        "print('y_test dimension = ', y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvoQ3Znqe7FZ",
        "outputId": "1e07e9a8-9eef-4816-8733-3576f9b5a787"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train dimension =  (336, 8)\n",
            "X_test dimension =  (84, 8)\n",
            "y_train dimension =  (336, 1)\n",
            "y_test dimension =  (84, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Observations**\n",
        "\n",
        "1.   The dataset is non-linear."
      ],
      "metadata": {
        "id": "eYxI8Eh3zrf2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Model Selection and Training**"
      ],
      "metadata": {
        "id": "_SBmo4Tjd8WP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "**Dataset Charachteristics**\n",
        "\n",
        "---\n",
        "\n",
        "1.   Small\n",
        "2.   Non-Linear\n",
        "3.   Not Normally Distributed\n",
        "4.   Multi-Class Target Variable\n",
        "\n",
        "\n",
        "---\n",
        "**Conclusion**\n",
        "\n",
        "---\n",
        "\n",
        "The following models are suitable for our dataset:\n",
        "\n",
        "1.   Decision Tree\n",
        "2.   K-Nearest Neighbours\n",
        "3.   Support Vector Machine"
      ],
      "metadata": {
        "id": "AVI3e-nnqOBd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Comparison of Different Machine Learning Algorithms for our Dataset**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "d0X5iZwCBBhS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Storing models for comparison\n",
        "comparison_models = []\n",
        "\n",
        "# Storing accuracies for comparison\n",
        "train_accuracies = []\n",
        "test_accuracies = []\n",
        "\n",
        "dtc = DecisionTreeClassifier(random_state=42)\n",
        "knn = KNeighborsClassifier()\n",
        "svm = SVC(random_state=42)\n",
        "\n",
        "models = [dtc, knn, svm]\n",
        "\n",
        "for model in models:\n",
        "    \n",
        "    model.fit(X_train, y_train)\n",
        "    y_train_pred = model.predict(X_train)\n",
        "    y_test_pred = model.predict(X_test)\n",
        "    \n",
        "    # Comparing training and testing accuracies\n",
        "    train_acc = accuracy_score(y_train, y_train_pred)\n",
        "    test_acc = accuracy_score(y_test, y_test_pred)\n",
        "    \n",
        "    print(model.__class__.__name__)\n",
        "    print(f\"Training Accuracy: {train_acc:.3f}\")\n",
        "    print(f\"Testing Accuracy: {test_acc:.3f}\")\n",
        "    print('-'*50)\n",
        "\n",
        "    comparison_models.append(model)\n",
        "    train_accuracies.append(train_acc)\n",
        "    test_accuracies.append(test_acc)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Elpd9uxpjsN",
        "outputId": "ff2da429-e14d-437f-c370-d1a45284f45f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTreeClassifier\n",
            "Training Accuracy: 1.000\n",
            "Testing Accuracy: 0.976\n",
            "--------------------------------------------------\n",
            "KNeighborsClassifier\n",
            "Training Accuracy: 0.952\n",
            "Testing Accuracy: 0.869\n",
            "--------------------------------------------------\n",
            "SVC\n",
            "Training Accuracy: 0.848\n",
            "Testing Accuracy: 0.762\n",
            "--------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Comparison of Various Models**\n"
      ],
      "metadata": {
        "id": "7FFkAhvE6zZP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a bar chart of training and testing accuracies\n",
        "labels = [model.__class__.__name__ for model in comparison_models]\n",
        "\n",
        "x = np.arange(len(labels))\n",
        "width = 0.35\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "rects1 = ax.bar(x - width/2, train_accuracies, width, label='Training Accuracy')\n",
        "rects2 = ax.bar(x + width/2, test_accuracies, width, label='Testing Accuracy')\n",
        "\n",
        "ax.set_ylabel('Accuracy')\n",
        "ax.set_title('Comparison of Training and Testing Accuracies')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.legend()\n",
        "\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "ZqNmHtLKpknZ",
        "outputId": "389c4cdd-9244-489b-926c-5809a2ea8af9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing models with overfitting\n",
        "comparison_models.pop()\n",
        "comparison_models.pop()\n",
        "\n",
        "train_accuracies.pop()\n",
        "train_accuracies.pop()\n",
        "\n",
        "test_accuracies.pop()\n",
        "test_accuracies.pop()\n",
        "\n",
        "print(comparison_models)\n",
        "print(train_accuracies)\n",
        "print(test_accuracies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7bNy0FyK75nT",
        "outputId": "9f4dc6e6-80a7-401c-92d6-1a9139b4cf94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[DecisionTreeClassifier(random_state=42)]\n",
            "[1.0]\n",
            "[0.9761904761904762]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Improved SVC** \n",
        "\n",
        "Overfitting in the initial SVC model is handled by early stopping."
      ],
      "metadata": {
        "id": "qOK6aOMF0yUN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with warnings.catch_warnings():\n",
        "    warnings.simplefilter(\"ignore\")\n",
        "\n",
        "    # Parameters for early stopping\n",
        "    max_iter = 50                         # maximum number of iterations\n",
        "    tol = 1e-4                            # tolerance for convergence\n",
        "    n_no_improvement = 5                  # maximum number of iterations with no improvement in validation score\n",
        "\n",
        "    best_val_score = 0\n",
        "    no_improvement_count = 0\n",
        "\n",
        "    # Training the SVM classifier with early stopping\n",
        "    for i in range(max_iter):\n",
        "        svm.fit(X_train, y_train)\n",
        "        train_score = svm.score(X_train, y_train)\n",
        "        val_score = svm.score(X_test, y_test)\n",
        "        \n",
        "        # Checking for early stopping\n",
        "        if val_score > best_val_score:\n",
        "            best_val_score = val_score\n",
        "            no_improvement_count = 0\n",
        "\n",
        "        else:\n",
        "            no_improvement_count += 1\n",
        "\n",
        "        if no_improvement_count >= n_no_improvement:\n",
        "            print(\"Validation score has not improved for {} iterations. Stopping training...\".format(n_no_improvement))\n",
        "            break\n",
        "\n",
        "    y_train_pred = svm.predict(X_train)\n",
        "    y_test_pred = svm.predict(X_test)\n",
        "    \n",
        "    # Comparing training and testing accuracies\n",
        "    train_acc = accuracy_score(y_train, y_train_pred)\n",
        "    test_acc = accuracy_score(y_test, y_test_pred)\n",
        "\n",
        "    print('Training accuracy:', train_acc)\n",
        "    print('Testing accuracy:', test_acc)\n",
        "    \n",
        "    comparison_models.append(svm)\n",
        "    train_accuracies.append(train_acc)\n",
        "    test_accuracies.append(test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEfBgjnkaWgq",
        "outputId": "fc2e97ca-a65b-47ca-89df-a94caac6f329"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation score has not improved for 5 iterations. Stopping training...\n",
            "Training accuracy: 0.8482142857142857\n",
            "Testing accuracy: 0.7619047619047619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Improved KNN** \n",
        "\n",
        "To select the best value of k, we have compared the training and testing accuracies for each value of k and selected the value of k that resulted in the minimum difference between the two accuracies."
      ],
      "metadata": {
        "id": "o6F_fOJkZhQm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with warnings.catch_warnings():\n",
        "    warnings.simplefilter(\"ignore\")\n",
        "\n",
        "    # Initialize empty lists to store the results\n",
        "    train_accs = []\n",
        "    test_accs = []\n",
        "        \n",
        "    # Define a list of k values to try\n",
        "    k_values = [3, 5, 7, 9]\n",
        "\n",
        "    # Initialize variables to store the best model and difference between test and train accuracy\n",
        "    best_model = None\n",
        "    min_diff = float('inf')\n",
        "\n",
        "    # Loop over the k values and evaluate the model on training and test data\n",
        "    for k in k_values:\n",
        "        # Train the model on the training set\n",
        "        model = KNeighborsClassifier(n_neighbors=k)\n",
        "        model.fit(X_train, y_train)\n",
        "        y_train_pred = model.predict(X_train)\n",
        "        y_test_pred = model.predict(X_test)\n",
        "\n",
        "        train_acc = accuracy_score(y_train, y_train_pred)\n",
        "        test_acc = accuracy_score(y_test, y_test_pred)\n",
        "        \n",
        "        # Store the results in the lists\n",
        "        train_accs.append(train_acc)\n",
        "        test_accs.append(test_acc)\n",
        "\n",
        "        # Compute the difference between test and train accuracy\n",
        "        diff = abs(test_acc - train_acc)\n",
        "        \n",
        "        # Check if the current model has a smaller difference and satisfies the condition\n",
        "        if diff < min_diff and diff < 0.4:\n",
        "            best_model = knn\n",
        "            min_diff = diff\n",
        "            best_k = k\n",
        "        \n",
        "        # Print the results for each model\n",
        "        print(\"k = %d, Training Accuracy: %0.2f, Test Accuracy: %0.2f, Difference: %0.2f\" % (k, train_acc, test_acc, diff))\n",
        "        \n",
        "    # Print the best model and k value\n",
        "    print(\"Best model has k = %d, with difference between test and train accuracy of %0.2f\" % (best_k, min_diff))\n",
        "    print(best_model)\n",
        "\n",
        "    knn = best_model\n",
        "    comparison_models.append(knn)\n",
        "    train_accuracies.append(train_acc)\n",
        "    test_accuracies.append(test_acc)\n",
        "\n",
        "    # Plot the results\n",
        "    plt.plot(k_values, train_accs, label=\"Training Accuracy\")\n",
        "    plt.plot(k_values, test_accs, label=\"Test Accuracy\")\n",
        "    plt.xlabel(\"k\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "AY5dDkUY5-ow",
        "outputId": "d23db29e-a015-4d55-d654-1a9913a43f0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "k = 3, Training Accuracy: 0.96, Test Accuracy: 0.86, Difference: 0.11\n",
            "k = 5, Training Accuracy: 0.95, Test Accuracy: 0.87, Difference: 0.08\n",
            "k = 7, Training Accuracy: 0.94, Test Accuracy: 0.88, Difference: 0.06\n",
            "k = 9, Training Accuracy: 0.90, Test Accuracy: 0.88, Difference: 0.02\n",
            "Best model has k = 9, with difference between test and train accuracy of 0.02\n",
            "KNeighborsClassifier()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a bar chart of training and testing accuracies\n",
        "labels = [model.__class__.__name__ for model in comparison_models]\n",
        "\n",
        "x = np.arange(len(labels))\n",
        "width = 0.35\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "rects1 = ax.bar(x - width/2, train_accuracies, width, label='Training Accuracy')\n",
        "rects2 = ax.bar(x + width/2, test_accuracies, width, label='Testing Accuracy')\n",
        "\n",
        "ax.set_ylabel('Accuracy')\n",
        "ax.set_title('Comparison of Training and Testing Accuracies')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.legend()\n",
        "\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "mormkGJHB5CE",
        "outputId": "0f75d9fa-bded-42ca-ebce-644479e7dcdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "---\n",
        "\n",
        "**Comparison of Decision Tree, KNN and SVC after Handling Overfitting**\n",
        "\n",
        "---\n"
      ],
      "metadata": {
        "id": "dJ_XM8DIYHJT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision Tree depicts Low Variance-Low Bias tradeoff. This means that the model is consistent and highly accurate on prediction. Taking under consideration the tradeoff and the results shown by the graph, it is concluded that Decision Tree best suits our problem. "
      ],
      "metadata": {
        "id": "AuBT0wWCDlxu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Model Improvement**"
      ],
      "metadata": {
        "id": "JiWbo7z1eP_y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Hyperparameter Tuning of Decision Tree** \n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "igHbtBmYVr6e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Define the objective function for optimization\n",
        "def dt_objective_function(max_depth, min_samples_split, min_samples_leaf):\n",
        "    dt = DecisionTreeClassifier(max_depth=int(max_depth), min_samples_split=int(min_samples_split), \n",
        "                                min_samples_leaf=int(min_samples_leaf), random_state=42)\n",
        "    \n",
        "    dt.fit(X_train, y_train)\n",
        "    y_pred = dt.predict(X_test)\n",
        "    score = accuracy_score(y_test, y_pred)\n",
        "    return score\n",
        "\n",
        "# Set up the parameter ranges for Bayesian optimization\n",
        "pbounds = {'max_depth': (1, 20), 'min_samples_split': (2, 20), 'min_samples_leaf': (1, 20)}\n",
        "\n",
        "# Create the Bayesian optimization object and maximize the objective function\n",
        "dt_bo = BayesianOptimization(dt_objective_function, pbounds)\n",
        "dt_bo.maximize(n_iter=10, init_points=5)\n",
        "\n",
        "# Print the best parameters and score\n",
        "print(\"Best parameters: \", dt_bo.max['params'])\n",
        "print(\"Best score: \", dt_bo.max['target'])\n",
        "\n",
        "# Visualize the hyperparameter search process\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(np.arange(1, len(dt_bo.res)+1), [x['target'] for x in dt_bo.res], color='blue', marker='o', linestyle='--')\n",
        "plt.title('Hyperparameter Search Process')\n",
        "plt.xlabel('Iteration')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "E7ofE7qN91I5",
        "outputId": "f8600bfc-3a7f-4e8e-bec2-ca1d05179c0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "|   iter    |  target   | max_depth | min_sa... | min_sa... |\n",
            "-------------------------------------------------------------\n",
            "| \u001b[0m1        \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m19.71    \u001b[0m | \u001b[0m5.766    \u001b[0m | \u001b[0m3.416    \u001b[0m |\n",
            "| \u001b[0m2        \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m11.01    \u001b[0m | \u001b[0m2.979    \u001b[0m | \u001b[0m16.68    \u001b[0m |\n",
            "| \u001b[0m3        \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m8.783    \u001b[0m | \u001b[0m2.952    \u001b[0m | \u001b[0m15.18    \u001b[0m |\n",
            "| \u001b[0m4        \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m6.608    \u001b[0m | \u001b[0m13.24    \u001b[0m | \u001b[0m5.539    \u001b[0m |\n",
            "| \u001b[0m5        \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m13.57    \u001b[0m | \u001b[0m16.54    \u001b[0m | \u001b[0m14.8     \u001b[0m |\n",
            "| \u001b[0m6        \u001b[0m | \u001b[0m0.619    \u001b[0m | \u001b[0m1.12     \u001b[0m | \u001b[0m19.38    \u001b[0m | \u001b[0m2.179    \u001b[0m |\n",
            "| \u001b[0m7        \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m12.55    \u001b[0m | \u001b[0m9.009    \u001b[0m | \u001b[0m8.883    \u001b[0m |\n",
            "| \u001b[0m8        \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m7.754    \u001b[0m | \u001b[0m3.312    \u001b[0m | \u001b[0m2.0      \u001b[0m |\n",
            "| \u001b[0m9        \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m4.881    \u001b[0m | \u001b[0m11.85    \u001b[0m | \u001b[0m20.0     \u001b[0m |\n",
            "| \u001b[0m10       \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m20.0     \u001b[0m | \u001b[0m17.13    \u001b[0m | \u001b[0m4.176    \u001b[0m |\n",
            "| \u001b[0m11       \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m20.0     \u001b[0m | \u001b[0m9.765    \u001b[0m | \u001b[0m20.0     \u001b[0m |\n",
            "| \u001b[0m12       \u001b[0m | \u001b[0m0.9643   \u001b[0m | \u001b[0m20.0     \u001b[0m | \u001b[0m1.0      \u001b[0m | \u001b[0m12.59    \u001b[0m |\n",
            "| \u001b[0m13       \u001b[0m | \u001b[0m0.619    \u001b[0m | \u001b[0m1.0      \u001b[0m | \u001b[0m5.391    \u001b[0m | \u001b[0m9.531    \u001b[0m |\n",
            "| \u001b[0m14       \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m12.79    \u001b[0m | \u001b[0m10.61    \u001b[0m | \u001b[0m2.0      \u001b[0m |\n",
            "| \u001b[0m15       \u001b[0m | \u001b[0m0.9881   \u001b[0m | \u001b[0m20.0     \u001b[0m | \u001b[0m11.72    \u001b[0m | \u001b[0m11.11    \u001b[0m |\n",
            "=============================================================\n",
            "Best parameters:  {'max_depth': 19.712074849340958, 'min_samples_leaf': 5.766138353452542, 'min_samples_split': 3.4164176036045046}\n",
            "Best score:  0.9880952380952381\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Best Model**\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "h5KtdWzMkbcU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve the best hyperparameters and create a new DecisionTreeClassifier object with them\n",
        "best_params = dt_bo.max['params']\n",
        "dt_best = DecisionTreeClassifier(max_depth=int(best_params['max_depth']), \n",
        "                                 min_samples_split=int(best_params['min_samples_split']), \n",
        "                                 min_samples_leaf=int(best_params['min_samples_leaf']), \n",
        "                                 random_state=42)\n",
        "\n",
        "#f Fit the best model to the training data and make predictions on new data\n",
        "dt_best.fit(X_train, y_train)\n",
        "y_pred = dt_best.predict(X_test)\n",
        "\n",
        "# Print evaluation metrics for the best model\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Precision:\", precision_score(y_test, y_pred, average='weighted'))\n",
        "print(\"Recall:\", recall_score(y_test, y_pred, average='weighted'))\n",
        "print(\"F1 score:\", f1_score(y_test, y_pred, average='weighted'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QXh8mEpxCNXq",
        "outputId": "be74c1ac-25e9-4188-d34d-9915cc159256"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9880952380952381\n",
            "Precision: 0.9885361552028219\n",
            "Recall: 0.9880952380952381\n",
            "F1 score: 0.988113064932472\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Model Deployment**"
      ],
      "metadata": {
        "id": "JO7Br4HOfX1w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "\n",
        "**Training Model on Entire Dataset**\n",
        "\n",
        "\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "N8hfGogJfa_W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dt_best.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "etDPkwD9f1Ir",
        "outputId": "5c1abe12-71bb-4080-d1aa-a6201a2331b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=19, min_samples_leaf=5, min_samples_split=3,\n",
              "                       random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=19, min_samples_leaf=5, min_samples_split=3,\n",
              "                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=19, min_samples_leaf=5, min_samples_split=3,\n",
              "                       random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "\n",
        "**Save the Model**\n",
        "\n",
        "\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "D7NPVJlafjOg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'dt.joblib'\n",
        "dump(dt_best, filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgr25vtAgSJX",
        "outputId": "05b36f4b-3bf8-4cda-ea0f-9a02ab7534f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['dt.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the model\n",
        "model = joblib.load('dt.joblib')\n",
        "\n",
        "# Use the model\n",
        "predictions = model.predict(X_test)\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Precision:\", precision_score(y_test, y_pred, average='weighted'))\n",
        "print(\"Recall:\", recall_score(y_test, y_pred, average='weighted'))\n",
        "print(\"F1 score:\", f1_score(y_test, y_pred, average='weighted'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYR_xxUyFlQp",
        "outputId": "168b7fa7-bd92-48a8-8bab-2ba0174577e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9880952380952381\n",
            "Precision: 0.9885361552028219\n",
            "Recall: 0.9880952380952381\n",
            "F1 score: 0.988113064932472\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define an example input\n",
        "example_input = [[-5.952,\t0.295,\t0.616000,\t0.0480,\t-9.036387,\t3,\t0,\t15.0]]\n",
        "\n",
        "# Use the model to make a prediction on the example input\n",
        "prediction = model.predict(example_input)\n",
        "\n",
        "# Print the prediction\n",
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMIpwxGjG4K4",
        "outputId": "e8be8eff-2999-47b5-b0ad-34765ad42c9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}