Copy_of_Untitled2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rYKJI58QPzM8"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "file_path = '/content/gender_submission.csv'\n",
        "df = pd.read_csv(file_path)\n",
        "print(df.head(10))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fv-xfjwjQOxZ",
        "outputId": "8c7b1fe7-45f4-4111-d478-d7cbae0a54d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   PassengerId  Survived\n",
            "0          892         0\n",
            "1          893         1\n",
            "2          894         0\n",
            "3          895         0\n",
            "4          896         1\n",
            "5          897         0\n",
            "6          898         1\n",
            "7          899         0\n",
            "8          900         1\n",
            "9          901         0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = '/content/gender_submission.csv'\n",
        "df = pd.read_csv(file_path)\n",
        "summary_stats = df.describe()\n",
        "print(\"Summary Statistics for Numerical Columns:\")\n",
        "print(summary_stats)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "588N2OsRRpN1",
        "outputId": "c593683d-abff-4d4d-f7e5-748dbfd99fb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary Statistics for Numerical Columns:\n",
            "       PassengerId    Survived\n",
            "count   418.000000  418.000000\n",
            "mean   1100.500000    0.363636\n",
            "std     120.810458    0.481622\n",
            "min     892.000000    0.000000\n",
            "25%     996.250000    0.000000\n",
            "50%    1100.500000    0.000000\n",
            "75%    1204.750000    1.000000\n",
            "max    1309.000000    1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "file_path = '/content/gender_submission.csv'\n",
        "df = pd.read_csv(file_path)\n",
        "missing_values = df.isnull().sum()\n",
        "print(\"Columns with missing values:\")\n",
        "for cost, count in missing_values.items():\n",
        "    if count > 0:\n",
        "        print(f\"{cost}: {count}\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "0FLRnYyRSK5t",
        "outputId": "6a06c084-6b9d-4546-dbf0-5ad2bc9302b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns with missing values:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],\n",
        "    'Age': [25, None, 30, None, 45],\n",
        "    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']\n",
        "}\n",
        "df = pd.DataFrame(data)\n",
        "print(\"Original DataFrame:\")\n",
        "print(df)\n",
        "\n",
        "median_age = df['Age'].median()\n",
        "\n",
        "df['Age'].fillna(median_age, inplace=True)\n",
        "\n",
        "print(\"\\nDataFrame after filling missing values:\")\n",
        "print(df)\n",
        "\n",
        "missing_values = df['Age'].isnull().sum()\n",
        "print(f\"\\nNumber of missing values in 'Age' column after filling: {missing_values}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5UWnSSfUdHO",
        "outputId": "c63f2afb-d19d-4283-97b2-037ea9b598c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original DataFrame:\n",
            "      Name   Age         City\n",
            "0    Alice  25.0     New York\n",
            "1      Bob   NaN  Los Angeles\n",
            "2  Charlie  30.0      Chicago\n",
            "3    David   NaN      Houston\n",
            "4      Eva  45.0      Phoenix\n",
            "\n",
            "DataFrame after filling missing values:\n",
            "      Name   Age         City\n",
            "0    Alice  25.0     New York\n",
            "1      Bob  30.0  Los Angeles\n",
            "2  Charlie  30.0      Chicago\n",
            "3    David  30.0      Houston\n",
            "4      Eva  45.0      Phoenix\n",
            "\n",
            "Number of missing values in 'Age' column after filling: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],\n",
        "    'Age': [25, 35, 30, 40, 45],\n",
        "    'Fare': [100.5, 200.75, 150.0, 80.0, 120.25]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(df['Fare'], bins=20, edgecolor='black', alpha=0.7)\n",
        "plt.title('Distribution of Fare')\n",
        "plt.xlabel('Fare')\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "9S2PYF8jVRfF",
        "outputId": "c0a1f6cd-7ce9-4cc2-fcb2-60e9e9ecf0c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def plot_histogram(df, column_name, bins=20):\n",
        "    \"\"\"\n",
        "\n",
        "    \"\"\"\n",
        "    if column_name not in df.columns:\n",
        "        print(f\"Column '{column_name}' does not exist in the DataFrame.\")\n",
        "        return\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.hist(df[column_name].dropna(), bins=bins, edgecolor='black', alpha=0.7)\n",
        "    plt.title(f'Distribution of {column_name}')\n",
        "    plt.xlabel(column_name)\n",
        "    plt.ylabel('Frequency')\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n",
        "data = {\n",
        "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],\n",
        "    'Age': [25, 35, 30, 40, 45],\n",
        "    'Fare': [100.5, 200.75, 150.0, 80.0, 120.25]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "plot_histogram(df, 'Fare')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "TlzYRhloVZtI",
        "outputId": "0257b6c4-d458-4cc3-aeb5-221b9871d138"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "    'PassengerId': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
        "    'Pclass': [1, 2, 3, 1, 2, 3, 1, 3, 2, 1],\n",
        "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva', 'Frank', 'Grace', 'Hannah', 'Ian', 'Jack'],\n",
        "    'Age': [25, 35, 30, 40, 45, 22, 28, 34, 19, 50],\n",
        "    'Fare': [100.5, 200.75, 150.0, 80.0, 120.25, 60.0, 70.5, 90.0, 110.0, 95.75]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "pclass_counts = df['Pclass'].value_counts().sort_index()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "pclass_counts.plot(kind='bar', color='skyblue', edgecolor='black')\n",
        "plt.title('Count of Passengers by Pclass')\n",
        "plt.xlabel('Passenger Class')\n",
        "plt.ylabel('Count of Passengers')\n",
        "plt.xticks(rotation=0)\n",
        "plt.grid(axis='y')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "acZZ15pFVt4I",
        "outputId": "c30086ea-b064-4281-b943-eec8d60dd42f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_bar_chart(df, column_name):\n",
        "    \"\"\"\n",
        "    Generates a bar chart for the specified column in the DataFrame.\n",
        "\n",
        "    Parameters:\n",
        "    df (pandas.DataFrame): The DataFrame containing the data.\n",
        "    column_name (str): The name of the column to plot the bar chart for.\n",
        "    \"\"\"\n",
        "    if column_name not in df.columns:\n",
        "        print(f\"Column '{column_name}' does not exist in the DataFrame.\")\n",
        "        return\n",
        "\n",
        "    value_counts = df[column_name].value_counts().sort_index()\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    value_counts.plot(kind='bar', color='skyblue', edgecolor='black')\n",
        "    plt.title(f'Count of Values in {column_name}')\n",
        "    plt.xlabel(column_name)\n",
        "    plt.ylabel('Count')\n",
        "    plt.xticks(rotation=0)\n",
        "    plt.grid(axis='y')\n",
        "    plt.show()\n",
        "\n",
        "data = {\n",
        "    'PassengerId': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
        "    'Pclass': [1, 2, 3, 1, 2, 3, 1, 3, 2, 1],\n",
        "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva', 'Frank', 'Grace', 'Hannah', 'Ian', 'Jack'],\n",
        "    'Age': [25, 35, 30, 40, 45, 22, 28, 34, 19, 50],\n",
        "    'Fare': [100.5, 200.75, 150.0, 80.0, 120.25, 60.0, 70.5, 90.0, 110.0, 95.75]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "plot_bar_chart(df, 'Pclass')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "yDy50uGnV7mF",
        "outputId": "a856a6aa-c995-4c94-a13c-97fe7ef75b7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "    'PassengerId': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
        "    'Pclass': [1, 2, 3, 1, 2, 3, 1, 3, 2, 1],\n",
        "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva', 'Frank', 'Grace', 'Hannah', 'Ian', 'Jack'],\n",
        "    'Age': [25, 35, 30, 40, 45, 22, 28, 34, 19, 50],\n",
        "    'Fare': [100.5, 200.75, 150.0, 80.0, 120.25, 60.0, 70.5, 90.0, 110.0, 95.75],\n",
        "    'Parch': [0, 1, 0, 2, 0, 1, 1, 2, 0, 1]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "df.boxplot(column='Fare', by='Pclass', grid=False, patch_artist=True)\n",
        "plt.title('Distribution of Fare by Pclass')\n",
        "plt.suptitle('')\n",
        "plt.xlabel('Pclass')\n",
        "plt.ylabel('Fare')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "WVjbsCQMWcSL",
        "outputId": "b02b49e5-5f77-4330-e42a-d6e5bcb3545e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "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": [
        "data = {\n",
        "    'PassengerId': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
        "    'Pclass': [1, 2, 3, 1, 2, 3, 1, 3, 2, 1],\n",
        "    'Age': [25, 35, 30, 40, 45, 22, 28, 34, 19, 50],\n",
        "    'Fare': [100.5, 200.75, 150.0, 80.0, 120.25, 60.0, 70.5, 90.0, 110.0, 95.75],\n",
        "    'Parch': [0, 1, 0, 2, 0, 1, 1, 2, 0, 1]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "numerical_columns = ['PassengerId', 'Pclass', 'Age', 'Fare', 'Parch']\n",
        "\n",
        "sns.pairplot(df[numerical_columns])\n",
        "plt.suptitle('Pair Plot of Numerical Columns in Titanic Dataset', y=1.02)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "id": "pPosl7gtWulj",
        "outputId": "4be3f9b9-479c-4b99-e408-e64d2917983b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1250x1250 with 30 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "    'PassengerId': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
        "    'Pclass': [1, 2, 3, 1, 2, 3, 1, 3, 2, 1],\n",
        "    'Age': [25, 35, 30, 40, 45, 22, 28, 34, 19, 50],\n",
        "    'Fare': [100.5, 200.75, 150.0, 80.0, 120.25, 60.0, 70.5, 90.0, 110.0, 95.75],\n",
        "    'Parch': [0, 1, 0, 2, 0, 1, 1, 2, 0, 1]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "corr_matrix = df.corr()\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1, fmt='.2f', annot_kws={\"size\": 10})\n",
        "plt.title('Correlation Matrix of Numerical Columns in Titanic Dataset')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "sBimyM9_XC1V",
        "outputId": "7fd74f15-5bed-4da1-d693-c820e0645d03"
      },
      "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": "code",
      "source": [
        "def plot_correlation_heatmap(df):\n",
        "    \"\"\"\n",
        "    \"\"\"\n",
        "    corr_matrix = df.corr()\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1, fmt='.2f', annot_kws={\"size\": 10})\n",
        "    plt.title('Correlation Matrix Heatmap')\n",
        "    plt.show()\n",
        "\n",
        "data = {\n",
        "    'PassengerId': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
        "    'Pclass': [1, 2, 3, 1, 2, 3, 1, 3, 2, 1],\n",
        "    'Age': [25, 35, 30, 40, 45, 22, 28, 34, 19, 50],\n",
        "    'Fare': [100.5, 200.75, 150.0, 80.0, 120.25, 60.0, 70.5, 90.0, 110.0, 95.75],\n",
        "    'Parch': [0, 1, 0, 2, 0, 1, 1, 2, 0, 1]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "plot_correlation_heatmap(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "laAYSjlMXT4q",
        "outputId": "eaf4fa96-bb94-46e6-a9bd-dda3b0efded5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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2djZatWqFHj164O7du1i2bBmaNGmCzp07f3AsRFTOldm6Z6JiunfvnjBw4EDB0dFR0NbWFgwNDYXGjRsLixcvltsSJCcnR5g6darg5OQkaGlpCfb29sLEiRPl6ghC/pY0HTt2LPA6r7ak2bFjR6FxXL16Vfjyyy8Fc3NzQSKRCA4ODkKPHj2EoKAgWZ3CtqQ5c+aM0KBBA0FXV1ewtbUVxo0bJ9s65MSJE7J6qampQu/evQUTExMBgGx7msK2pBEEQTh+/LjQuHFjQVdXVzAyMhI+//xz4fbt23J1Xm1JExMTI1deWJyFeXNLmqIUtSXNmDFjBBsbG0FXV1do3LixEBISUuhWMvv27RM8PT0FTU1Nuets1qyZULVq1UJf8812kpOTBQcHB6F27dpCTk6OXL3Ro0cLYrFYCAkJeec1ABCGDx9e6HOv3qs3t6QRBEF4+PCh0LdvX8Ha2lrQ0tIS7OzshE6dOgk7d+4slfehqN/jt68lISFB8PPzEywsLAQDAwOhbdu2QlhYmODg4CD069evwHX+888/wqBBgwRTU1PBwMBA6NOnj9zWR0T08RIJQglmmhMR0Udp/fr18PPzw8WLF1G3bt2yDoeIygDnFBIRERERO4VERERExE4hEREREYGdQiIiAuDr6wtBEDifkEgBp06dwueffw5bW1uIRCLs3bv3veecPHkStWvXhkQigaurK9avX1+gztKlS+Ho6AgdHR14e3vjwoULyg/+DewUEhEREX2AtLQ01KxZE0uXLi1W/fDwcHTs2BEtWrRAaGgoRo0ahQEDBuDIkSOyOq+2KAsICMCVK1dQs2ZNtG3bVnbXJlXg6mMiIiIiJRGJRNizZw+6dOlSZJ3x48fj0KFDcrdy7dmzJxITE3H48GEAgLe3N+rVq4clS5YAyL/Fpr29Pb777jtMmDBBJbEzU0hERET0lqysLCQnJ8sdyrpDUkhISIHbZLZt2xYhISEA8jeRv3z5slwdsVgMHx8fWR1VKDd3NDmk5V7WIdB/qt4p/O4cVPqMMlQ3TEAls/lp47IOgd7gZ87PqfLCoEHZ3e1GlX2Hiz/1wtSpU+XKAgIClHLXqKioKFhZWcmVWVlZITk5GRkZGUhISEBeXl6hdcLCwj749YtSbjqFREREROXFxIkT4e/vL1cmkUjKKJrSwU4hERERqSWRlkhlbUskEpV1Aq2trREdHS1XFh0dDSMjI+jq6kJDQwMaGhqF1rG2tlZJTADnFBIRERGVqoYNGyIoKEiu7NixY2jYsCEAQFtbG3Xq1JGrI5VKERQUJKujCswUEhERkVoSa6ouU1gSqampePDggexxeHg4QkNDYWZmhkqVKmHixIl4/vw5Nm7cCAAYMmQIlixZgnHjxuHbb79FcHAwtm/fjkOHDsna8Pf3R79+/VC3bl3Ur18fCxcuRFpaGvz8/FR2HewUEhEREX2AS5cuoUWLFrLHr+Yi9uvXD+vXr0dkZCSePn0qe97JyQmHDh3C6NGj8dtvv6FixYpYvXo12rZtK6vz9ddfIyYmBpMnT0ZUVBS8vLxw+PDhAotPlKnc7FPI1cflB1cflx9cfVx+cPVx+cLVx+VHWa4+PmJeVWVtt427pbK2yytmComIiEgtlZfh449FsTuFby/LfpcFCxYoFAwRERERlY1idwqvXr0q9/jKlSvIzc2Fu3v+sO+9e/egoaGBOnXqKDdCIiIiokKockuaT1GxO4UnTpyQ/XvBggUwNDTEhg0bYGpqCgBISEiAn58fmjZtqvwoiYiIiEilFJpT+Msvv+Do0aOyDiEAmJqaYsaMGWjTpg3GjBmjtACJiIiICsM5hcql0ObVycnJiImJKVAeExODlJSUDw6KiIiIiEqXQpnCrl27ws/PD7/88gvq168PADh//jzGjh2LL7/8UqkBEhERERWGcwqVS6FO4YoVK/DDDz+gd+/eyMnJyW9IUxP9+/fHvHnzlBogEREREameQp1CPT09LFu2DPPmzcPDhw8BAC4uLtDX11dqcERERERF4ZxC5fqgzav19fVRo0YNZcVCRERERGWkRJ3C4s4X3L17t0LBEBERERWXSIOZQmUqUafQ2NhYVXEQERERlYiYnUKlKlGncN26daqKg4iIiIjKUIn3KczJyYGmpiZu3rypiniIiIiIikUkFqns+BSVuFOopaWFSpUqIS8vTxXxEBEREVEZUOiOJj/99BN+/PFHxMfHKzseIiIiomIRaYhVdnyKFNqSZsmSJXjw4AFsbW3h4OBQYH/CK1euKCU4IiIiIiodCnUKu3TpouQwiIiIiEqGq4+VS6FOYUBAgLLjICIiIqIypPCgeWJiIlavXo2JEyfK5hZeuXIFz58/V1pwREREREXh6mPlUihTeP36dfj4+MDY2BiPHz/GwIEDYWZmht27d+Pp06fYuHGjsuMkIiIiksPhY+VSKFPo7+8PX19f3L9/Hzo6OrLyDh064NSpU0oLjoiIiIhKh0KZwosXL2LlypUFyu3s7BAVFfXBQRERERG9D+99rFwKZQolEgmSk5MLlN+7dw8VKlT44KCIiIiIqHQp1Cns3Lkzpk2bhpycHACASCTC06dPMX78eHTr1k2pARIREREVRiQWq+z4FCl01b/88gtSU1NhaWmJjIwMNGvWDK6urjA0NMTMmTOVHSMRERERqZhCcwqNjY1x7NgxnD59GtevX0dqaipq164NHx8fZcdHREREVKhPdesYVVGoU/hKkyZN0KRJE2XFQkRERERlRKFO4aJFiwotF4lE0NHRgaurKz777DNoaGh8UHBEREREReE+hcqlUKfw119/RUxMDNLT02FqagoASEhIgJ6eHgwMDPDy5Us4OzvjxIkTsLe3V2rARERERACHj5VNoYUms2bNQr169XD//n3ExcUhLi4O9+7dg7e3N3777Tc8ffoU1tbWGD16tLLjJSIiIiIVUChTOGnSJOzatQsuLi6yMldXV8yfPx/dunXDo0ePMHfuXG5PQ0RERCrzqW4doyoKvZuRkZHIzc0tUJ6bmyu7o4mtrS1SUlI+LDoiIiIiKhUKdQpbtGiBwYMH4+rVq7Kyq1evYujQoWjZsiUA4MaNG3ByclJOlERERERvEYlFKjs+RQoNH69Zswb/+9//UKdOHWhpaQHIzxK2atUKa9asAQAYGBjgl19+UV6kpcysSV04j+kP49rVoGNriUvdhiF6f9C7z/msPjznT4CBpxsyn0XiQeByRGzcI1fHYWhvOPv3h8S6ApKvh+HWqOlIunhDlZfyUdh/8BB27tqN+IQEODs5YdiQwajiXrnI+qf+PY0NmzYhOvol7Gxt0d/PF/Xr1QWQ/7u6fuMmXLx0CZFRUdDX10ctr5ro79sP5ubmpXVJamvn38HYvP8w4hOT4OpgD//+vVHVzbnQuo+ePcfvW/ci7NETRMXE4XvfnujZqXWRbW/c8xeWb96FHh19MNqvl6ou4aMiCAIuHF6MW+d2ICsjGTZOtdH8qwCYVHAs1vmXg1Yh5NAC1GzaF027/ggASI6PwMYZhe87267vQrh6tVNW+B+V7cfPYOPf/yAuKQVu9jYY900XVHOpVGjd3SfP49CZy3gYkT+65uFoh+FftZerv3LPURw5H4rouERoaWrCw9EOw75qj+pFtEn0oRTKFFpbW+PYsWO4ffs2duzYgR07duD27ds4evQorKysAORnE9u0aaPUYEuThr4ekq/fxc2RU4tVX9exIurtX4m4k+dxuu4XCF+8AdVXzoBF69f7ONp0bw+PeRNxf8ZSnK7fFSnXw+B9aA20K5ip6jI+CidP/YtVv69Gn969sHTRQjg7OeGnnycjMTGx0Pq3bt9B4Nx5aNemDZYt+g2NGjbA1Bkz8fjxEwBAVlYWHjx8iN69vsbSRQsx+aeJiIh4joBpM0rxqtTT8TMXsGjDNvTv3hnr5wbAzdEeo2f8ivikgvdCB4DMrGzYWlXAsD7dYG5i/M62bz8Ix95j/8DVoaIqQv9oXQlejWv//oHm3aeg+6jt0NLWxf6VA5Cbk/Xec6Of3sDNkG0wt3GXKzcwsYHflH/ljvptv4OWRA+VPJqq6lLU2tHzoVjw5wEM+qI1Nk8dhcr2thgxfzXik1MLrX857CHaNvDCygmDse7nEbAyM8Hw+b/jZXySrE4l6woY/78u2DZzDNb8NAw2FmYYPu93JBTR5qdIrCFS2fEp+qAZmlWqVEHnzp3RuXNnuLu7v/8ENRJz5BTuBSxE9L7jxarvMKgnMsIjcGfcHKSGPcKTZZsRtesInL73ldVxGuWHZ2u2I2LDbqTeeYgbwwKQl54Je18uyHmX3Xv2ol27tmjb2gcOlSph5IhhkOhIcOTosULr792/H3Xr1Eb3bl+iUiV79PvfN3B1ccG+gwcBAPr6+pg9czqaNW0K+4oV4VGlCoYPHYz7Dx7g5cuXpXlpaufPA0fR2eczdGrZBE72thg36H+QSLRxMPh0ofU9XZ3wXd8eaN3EG1paRQ9MpGdkYspvv2PCkH4w1NdXVfgfHUEQcO3URtRtPQTO1VrBwtYdPr3nIC35JR7dfPdnV3ZWGo5u/gEte0yHRM9I7jmxWAP6RhXkjkc3j8O1ZntoS/jzKcymw6fQtZk3On9WD852VvjR90voaGth36kLhdafOaQ3erRqBHcHOzjZWuLn/t0hSAVcuH1fVqd9w1rwrloZFS3N4VLRGv69P0daRibuP4ssrcuiT4xCncK8vDysWbMGvXv3ho+PD1q2bCl3fIpMGnghNjhErizm2GmYNvACAIi0tGBcuypig86+riAIiA0+C5MGtUoxUvWSk5OD+w8eoLZXTVmZWCxGLS8v3A67W+g5d8LCUMvLS66sTu1auBMWVuTrpKWlQyQSQd/AQClxf4xycnJx99ET1KvhISsTi8WoV90TN+8+/KC256/ejEa1a6B+Dc8PDfOTkhwfgfSUGNhXbiQrk+gawqpSDUQ9Dn3nuf/smgZHj+Zy5xbl5bObiH1+B57e/AO2MDm5uQh7/Bz1q7rJysRiMepXdcONB0+K1UZmVjZy8/JgZKBX5GvsPnEOBno6cKtkq5S4PwacU6hcCs0p/P7777F+/Xp07NgR1apVg0hUsjcvKysLWVnyQxs5ghRaIvVdWi6xskBWdKxcWVZ0LLSMDSHWkUDL1BhiTU1kvYx7q04c9N0Ln49FQHJyMqRSKUxMTOXKTU1M8OxZRKHnJCQkwtTEpED9hITEQutnZ2djzbr1aN7sM+jrFf6BTEBiSgrypFKYGctnlcxMjPDkueKZi2Onz+Nu+BOsnf3zh4b4yUlPjgEA6BnKz4XVM7RAekpsYacAAO5dPYSYiNvoMXpnsV7n9vldMLVygY1TbcWD/YglpqQhTyqFubH8H5XmxgZ4HFm80YdF2/+ChYkRvD3d5MpPhd7Gj8s2IzM7BxbGhlg2dhBMDZmtfYVb0iiXQp3CrVu3Yvv27ejQoYNCLxoYGIipU+Xn6vUSmaGPhoVC7REpKjc3FzMD5wAQ8N3wYWUdzicnOjYev67bikU/+0OirVXW4ZR7dy8fwMkdAbLHnQasKHEbKQmR+HfPLHwxZC00tSTvrZ+bnYl7Vw6iXpuhJX4tKp51B4Nx9HwoVk0YUuD/g3oervhz+mgkpqRhzz/nMWHpH9gQMBJmRhzVIOVTqFOora0NV1dXhV904sSJ8Pf3lysLNqujcHvlQVZ0LCRW8p1aiZUFcpJSIM3MQnZsAqS5uZBYmr9VxxxZUUX/Rf+pMzIyglgsRmJiglx5QmKi7BaLbzM1NUHCW4tQ8uubyJXl5uZi5uw5iI55ibmzZjJL+B4mhobQEIsLLCqJT0x+7yKSooQ9eoyEpGT4jpsmK8uTShF65x52/R2Mf/5cCQ0NZgJecaraAlaVasge5+VlAwDSU+Kgb2QpK09PiYWFnUeB8wEgJuIWMlLjsG3Bl7IyQZqHF48u4fqZzRg69zrE4tf3rX9w/QhyczJRpW4XJV/Nx8PEUB8aYjHikuQXgMQlpcLC2PCd52786yTWHzqB5eMGFTosrCvRhr2VBeytLFDd1QFdxs3B3n8u4NvPP82pWm/7VId5VUWhTuGYMWPw22+/YcmSJSUeOgYAiUQCiUT+L1R1HjoGgMRzoajQ/jO5MotWjZBwLhQAIOTkIOnKLVi0bPh6axuRCOYtGuLJsk2lHK360NLSgpurK66GXkejhg0BAFKpFKGh19C5U8dCz/GoUgWh167hyy5fyMquXA2FR5UqssevOoTPX7zA3MBZMDIyKqwpeoOWlibcnR1w6cYdNKufP4wolUpx6cYdfNVesS+outU9sGmB/KjBzKXr4GBnjW+6tGeH8C3aOgbQ1nmdIRIEAXqGFRBxPwQV/usEZmemIvrpdVRrXPiWPhXdGqDX2P1yZUFbf4SppTNqtxwg1yEEgNvnd8KpagvoGnCXhKJoaWqiiqMdLt5+gBZ1qgHI/3/j4u0H6OFT9JzNDYdOYM2BYCz9YQA8neyL9VpSqRQ5hdw8gkgZFOoUnj59GidOnMDff/+NqlWryvYqfGX37t1KCa4saejrQd/19V5Qek4VYVSzCrLjk5D5LBLuM/yhY2eFa37jAQBPVm2Fw7A+qBI4Fs/W74JFiwaw6d4eFzsPlrURvnAdaq6dg8TLN5F08TocR/aDpr4unm1Q//dLlb7s2gXzF/yKym6ucK9cGXv27UNmZibatM7fR23uLwtgYW6Ob337AQC6dO6MsRMmYufuPahfry7+OfUv7j94gFHfjQCQ3yGcPms2Hjx8iGkBkyHNkyI+Pj8TaWhoUOD3mV7r9XkbTF+yBlVcHFHV1QlbDx1HZlYWOrVoDACYumg1KpibYlif/AUJOTm5CI94ASD/fY+JT8C98KfQ1ZHA3sYK+rq6cKkkvwWNjkQCI0ODAuVUkEgkQs3P+uLSsRUwsXCEoZkdzh9eBH0jSzhXe73P4N7lvnCu5oMaTb+Bto4BzG3k9/jU1NaFjp5JgfLEmCd48egSPh+wqlSuR5190+4zBPy+DR5OFVHN2R5bjvyLjKxsdG5aDwAweeWfqGBqjO965E+7Wn/oBFbsPoKZQ3rDxsIUsYn5GXg9HQn0dCTIyMrGmv1BaFbLExYmRkhMScP2oLOISUyGT70aRcbxqWGmULkU6hSamJiga9euyo6lXDGuUw0Ng/6QPfacn7+p67ONu3G9/0RIbCpA195G9nzG4whc7DwYnr9MhON3fZEZEYUbgych9tjrrToid/wN7QpmqBwwMn/z6mt3cKHTAGS/tfiE5DX/rCmSkpKwcdNmJCQkwNnZGTOnTZUNH8fExED8Rsa6qqcHJoz9ARv+2IT1GzbC1s4WAZN+gqOjAwAgNi4O586fBwAM+26k3GvNDZyFmjWql9KVqR+fxvWRkJyC1Vv3Ii4xGW6O9vj1p9Ew+2/4ODo2HuI3PqRjExLRb+zrTOCW/UewZf8R1PJ0x7Jp40o9/o9R7ZYDkJudgRM7Jv+3eXUdfD7od7n5gkmxT5GRlvCOVgp358IuGBhbo5J7Y2WG/FFq4+2FhOQ0rNh9BHFJKahcyRaLfxgA8/+Gj6PiE+U6MDuDQ5CTm4dxS/6Qa2dQl9YY3LUNxCIRHke+xMHTl5CYmgZjA31UdaqI1T8Og0tF61K9Nvp0iARBEMo6CAA4pPVx7XOozqreOVDWIdB/jDK4b2J5sfkpO0bliZ85P6fKC4MGncvste/1Ut3ddSr/eVhlbZdXCk/Yyc3NxfHjx7Fy5UqkpKQAAF68eIHUVO60TkRERKRuFBo+fvLkCdq1a4enT58iKysLrVu3hqGhIebMmYOsrCysWFHybRKIiIiISoL7FCqXQu/m999/j7p16yIhIQG6urqy8q5duyIoKEhpwREREREVpTzd+3jp0qVwdHSEjo4OvL29ceFC4bc4BIDmzZtDJBIVODp2fL2rhq+vb4Hn27VT3XA5oGCm8N9//8XZs2ehra0tV+7o6Ijnz58rJTAiIiIidbBt2zb4+/tjxYoV8Pb2xsKFC9G2bVvcvXsXlpaWBerv3r0b2dnZssdxcXGoWbMmunfvLlevXbt2WLdunezx29v5KZtCnUKpVIq8vLwC5RERETA0fPdGnURERETKUF62pFmwYAEGDhwIPz8/AMCKFStw6NAhrF27FhMmTChQ38xMft/PrVu3Qk9Pr0CnUCKRwNq69FabKzR83KZNGyxcuFD2WCQSITU1FQEBAQrf+o6IiIiovMjKykJycrLckZWVVaBednY2Ll++DB+f13uDisVi+Pj4ICQkpFivtWbNGvTs2RP6+vL3tT558iQsLS3h7u6OoUOHIi5OtVvYKdQp/OWXX3DmzBl4enoiMzMTvXv3lg0dz5kzR9kxEhERERUgEotVdgQGBsLY2FjuCAwMLBBDbGws8vLyYGVlJVduZWWFqKio917DhQsXcPPmTQwYMECuvF27dti4cSOCgoIwZ84c/PPPP2jfvn2hI7XKotDwccWKFXHt2jVs27YN165dQ2pqKvr3748+ffrILTwhIiIiUkcTJ06Ev7+/XJkq5vStWbMG1atXR/369eXKe/bsKft39erVUaNGDbi4uODkyZNo1aqV0uMAFOwUAoCmpib69OmDPn36KDMeIiIiomJR5ZxCiURSrE6ghYUFNDQ0EB0dLVceHR393vmAaWlp2Lp1K6ZNm/be13F2doaFhQUePHigsk6hQsPHGzZswKFDh2SPx40bBxMTEzRq1AhPnjxRWnBERERE5Zm2tjbq1KkjtyWfVCpFUFAQGjZs+M5zd+zYgaysLHzzzTfvfZ2IiAjExcXBxsbmvXUVpVCncNasWbJh4pCQECxZsgRz586FhYUFRo8erdQAiYiIiAojEotUdpSEv78/fv/9d2zYsAF37tzB0KFDkZaWJluN3LdvX0ycOLHAeWvWrEGXLl1gbm4uV56amoqxY8fi3LlzePz4MYKCgvDFF1/A1dUVbdu2VfwNew+Fho+fPXsGV1dXAMDevXvx1VdfYdCgQWjcuDGaN2+uzPiIiIiIClVe7mjy9ddfIyYmBpMnT0ZUVBS8vLxw+PBh2eKTp0+fQvxWrHfv3sXp06dx9OjRAu1paGjg+vXr2LBhAxITE2Fra4s2bdpg+vTpKt2rUKFOoYGBAeLi4lCpUiUcPXpUNhFTR0cHGRkZSg2QiIiIqLwbMWIERowYUehzJ0+eLFDm7u4OQRAKra+rq4sjR44oM7xiUahT2Lp1awwYMAC1atXCvXv3ZHsT3rp1C46OjsqMj4iIiKhQ5WXz6o+FQnnXpUuXomHDhoiJicGuXbtkY+GXL19Gr169lBogEREREameQplCExMTLFmypED51KlTPzggIiIiouIoL3MKPxYKvZuHDx/G6dOnZY+XLl0KLy8v9O7dGwkJCUoLjoiIiIhKh0KdwrFjxyI5ORkAcOPGDYwZMwYdOnRAeHh4gd2/iYiIiFRCJFLd8QlSaPg4PDwcnp6eAIBdu3ahU6dOmDVrFq5cuSJbdEJERERE6kOhTqG2tjbS09MBAMePH0ffvn0BAGZmZrIMIhEREZEqcfWxcinUKWzSpAn8/f3RuHFjXLhwAdu2bQMA3Lt3DxUrVlRqgERERESF4UIT5VLo3VyyZAk0NTWxc+dOLF++HHZ2dgCAv//+G+3atVNqgERERESkegplCitVqoSDBw8WKP/1118/OCAiIiKi4uDwsXIp1Cl8U2ZmJrKzs+XKjIyMPrRZIiIiIipFCnUK09LSMH78eGzfvh1xcXEFns/Ly/vgwIiIiIjehXMKlUuhd3PcuHEIDg7G8uXLIZFIsHr1akydOhW2trbYuHGjsmMkIiIiIhVTKFN44MABbNy4Ec2bN4efnx+aNm0KV1dXODg4YPPmzejTp4+y4yQiIiKSwzmFyqVQpjA+Ph7Ozs4A8ucPxsfHA8jfqubUqVPKi46IiIiISoVCnUJnZ2eEh4cDAKpUqYLt27cDyM8gmpiYKC04IiIioqKIxCKVHZ8ihYaP/fz8cO3aNTRr1gwTJkzA559/jiVLliAnJwcLFixQdoxEREREBXGhiVKVqFMolUoxb9487N+/H9nZ2Xjx4gUCAgIQFhaGy5cvw9XVFTVq1FBVrERERESkIiXqFM6cORNTpkyBj48PdHV18dtvv+Hly5dYu3YtHBwcVBUjERERUQEi0ac5zKsqJcq7bty4EcuWLcORI0ewd+9eHDhwAJs3b4ZUKlVVfERERERUCkqUKXz69Ck6dOgge+zj4wORSIQXL16gYsWKSg+OiIiIqCjcvFq5SvRu5ubmQkdHR65MS0sLOTk5Sg2KiIiIiEpXiTKFgiDA19cXEolEVpaZmYkhQ4ZAX19fVrZ7927lRUhERERUiE916xhVKVGnsF+/fgXKvvnmG6UFQ0RERERlo0SdwnXr1qkqDiIiIqKS4ZxCpeK7SURERESK3dGEiIiIqKxxTqFysVNIREREakkk4oCnMpWbTmHVOwfKOgT6zy2Pz8s6BPpP9pnbZR0C/aefzfGyDoHe8Ny4VlmHQP9xL+sASGnKTaeQiIiIqEQ4fKxUzLsSERERETOFREREpJ54mzvl4rtJRERERMwUEhERkXriljTKxUwhERERETFTSERERGqK+xQqFTuFREREpJY4fKxc7GITERERETOFREREpKa4JY1S8d0kIiIiImYKiYiISD2JRJxTqEzMFBIRERERM4VERESkpjinUKn4bhIRERERM4VERESknrhPoXKxU0hERETqiXc0USq+m0REREQfaOnSpXB0dISOjg68vb1x4cKFIuuuX78eIpFI7tDR0ZGrIwgCJk+eDBsbG+jq6sLHxwf3799X6TWwU0hERETqSSxS3VEC27Ztg7+/PwICAnDlyhXUrFkTbdu2xcuXL4s8x8jICJGRkbLjyZMncs/PnTsXixYtwooVK3D+/Hno6+ujbdu2yMzMVOitKg52ComIiIg+wIIFCzBw4ED4+fnB09MTK1asgJ6eHtauXVvkOSKRCNbW1rLDyspK9pwgCFi4cCEmTZqEL774AjVq1MDGjRvx4sUL7N27V2XXwU4hERERqSWRSKyyo7iys7Nx+fJl+Pj4yMrEYjF8fHwQEhJS5HmpqalwcHCAvb09vvjiC9y6dUv2XHh4OKKiouTaNDY2hre39zvb/FDsFBIRERG9JSsrC8nJyXJHVlZWgXqxsbHIy8uTy/QBgJWVFaKiogpt293dHWvXrsW+ffuwadMmSKVSNGrUCBEREQAgO68kbSoDO4VERESknlQ4pzAwMBDGxsZyR2BgoFLCbtiwIfr27QsvLy80a9YMu3fvRoUKFbBy5UqltK8obklDRERE9JaJEyfC399frkwikRSoZ2FhAQ0NDURHR8uVR0dHw9raulivpaWlhVq1auHBgwcAIDsvOjoaNjY2cm16eXmV5DJKhJlCIiIiUksisVhlh0QigZGRkdxRWKdQW1sbderUQVBQkKxMKpUiKCgIDRs2LNZ15OXl4caNG7IOoJOTE6ytreXaTE5Oxvnz54vdpiKYKSQiIiL1JCofdzTx9/dHv379ULduXdSvXx8LFy5EWloa/Pz8AAB9+/aFnZ2dbPh52rRpaNCgAVxdXZGYmIh58+bhyZMnGDBgAID8lcmjRo3CjBkz4ObmBicnJ/z888+wtbVFly5dVHYdSusUJiYmwsTERFnNEREREamFr7/+GjExMZg8eTKioqLg5eWFw4cPyxaKPH36FGLx68HZhIQEDBw4EFFRUTA1NUWdOnVw9uxZeHp6yuqMGzcOaWlpGDRoEBITE9GkSRMcPny4wCbXyiQSBEEo6Ulz5syBo6Mjvv76awBAjx49sGvXLlhbW+Ovv/5CzZo1SxzI4wf3SnwOqcYtj8/LOgT6T/aZ22UdAv2nlWZwWYdAb4g0rlLWIdB/3F3sy+y109dPVVnber4BKmu7vFJoTuGKFStgb5//S3Ds2DEcO3YMf//9N9q3b4+xY8cqNUAiIiIiUj2Fho+joqJkncKDBw+iR48eaNOmDRwdHeHt7a3UAImIiIgKVU7mFH4sFMoUmpqa4tmzZwCAw4cPy3bcFgQBeXl5youOiIiIiEqFQpnCL7/8Er1794abmxvi4uLQvn17AMDVq1fh6uqq1ACJiIiICiMSc2c9ZVKoU/jrr7/C0dERz549w9y5c2FgYAAAiIyMxLBhw5QaIBERERGpnkKdQi0tLfzwww8FykePHv3BAREREREVi4iZQmVS6N3csGEDDh06JHs8btw4mJiYoFGjRnjy5InSgiMiIiIqkgrvffwpUqhTOGvWLOjq6gIAQkJCsHTpUsydOxcWFhbMFhIRERGpIYWGj589eyZbULJ3715069YNgwYNQuPGjdG8eXNlxkdERERUKBGHj5VKoXfTwMAAcXFxAICjR4+idevWAAAdHR1kZGQoLzoiIiIiKhUKZQpbt26NAQMGoFatWrh37x46dOgAALh16xYcHR2VGR8RERFR4T7RuX+qolCncOnSpZg0aRKePXuGXbt2wdzcHABw+fJl9OrVS6kBlqX9Bw9h567diE9IgLOTE4YNGYwq7pWLrH/q39PYsGkToqNfws7WFv39fFG/Xl0AQG5uLtZv3ISLly4hMioK+vr6qOVVE/19+8nePyqcWZO6cB7TH8a1q0HH1hKXug1D9P6gd5/zWX14zp8AA083ZD6LxIPA5YjYuEeujsPQ3nD27w+JdQUkXw/DrVHTkXTxhiov5aMhCAKO7V6Ciyd2ICM9BY6Va6GL72RYWDsWec6541txLngrEmKeAwCsKrqiVZehcK/5mazO+eDtCA05hBePbyMrMw0BK85BV99I1ZejtrYf/QebDgQhLikZbpXsMNa3O6q6OhZa9+GzSKzceRBhj54hMjYeo//XDb07tJCrk5aRiRXbD+LkpWtISEpFZceKGNPvK1R1cSiFq1F/giBgy6YNOHr4L6SlpcLDsyqGDv8etnYV33neoQP7sGfXdiQkxMPJyQWDho5AZffX93ZeuvhXXLt6BfHxcdDR0UUVT0/4+g1ERftKqr4k+sQoNHxsYmKCJUuWYN++fWjXrp2sfOrUqfjpp5+UFlxZOnnqX6z6fTX69O6FpYsWwtnJCT/9PBmJiYmF1r91+w4C585DuzZtsGzRb2jUsAGmzpiJx4/zV2NnZWXhwcOH6N3rayxdtBCTf5qIiIjnCJg2oxSvSj1p6Osh+fpd3BxZvBuf6zpWRL39KxF38jxO1/0C4Ys3oPrKGbBo3URWx6Z7e3jMm4j7M5bidP2uSLkeBu9Da6BdwUxVl/FR+efQGpw9ugld/AIwfMpWaEl0sXbuIORkZxV5jpGZFdr1GI3vpu/AiGk74OLpjY2/jkB0xH1ZnZzsTLjXaIIWnQeVxmWotaMhl7Hwjz0Y0K09/pg1Hm4Odvhu9lLEJ6UUWj8zOxt2lhYY0aszzE0K72jPWLUF52+EYeqwfvhz7o9oUKMKhs9cjJfxiSq8ko/H7p3bcHD/Hgwd8T3m/boEEh0dBPw8AdnZ2UWe8+8/J7Dm9xXo2ft/+HXxCjg6OyPg5wlITEyQ1XFxdcPI0WOxdOVaTJ0xGxCAyZPG8w5iQP6WNKo6PkEfdNXp6ekICwvD9evX5Y6Pwe49e9GuXVu0be0Dh0qVMHLEMEh0JDhy9Fih9ffu34+6dWqje7cvUamSPfr97xu4urhg38GDAAB9fX3MnjkdzZo2hX3FivCoUgXDhw7G/QcP8PLly9K8NLUTc+QU7gUsRPS+48Wq7zCoJzLCI3Bn3Bykhj3Ck2WbEbXrCJy+95XVcRrlh2drtiNiw26k3nmIG8MCkJeeCXvfbiq6io+HIAg4c3gjWnYejKp1WsGmkju+HjwbyYkvcfty0Rlcz9otUMWrGSysHVHBxhFtu4+Cto4enj54/ZnRpF1fNP98IOxda5bGpai1LYeC0aVlI3Ru3hDOFW0wsX9P6GhrY//JkELrV3VxwPd9uqJNo7rQ1iw4SJSZnY0TF0IxsncX1PZwhb11BQz6qiPsrStg17F/VX05ak8QBOzfuxs9evZBg4aN4eTkjNFjxiM+Lg7nQs4Ued6+PbvQpl0H+LRph0qVHDBsxChIJBIcP3pYVqdd+06oVr0GrKys4eLqhj59/RAbE4OXL6NL49LoE6JQpzAmJgYdO3aEoaEhqlatilq1askd6i4nJwf3HzxAba/XX0xisRi1vLxwO+xuoefcCQtDLS8vubI6tWvhTlhYka+TlpYOkUgE/f/uCEPKYdLAC7HB8l+MMcdOw7SBFwBApKUF49pVERt09nUFQUBs8FmYNFD/319Vi4+JQEpSLFyrNZSV6egZwt65Bp48CC1WG1JpHq6F/IXsrAxUcmMHsKRycnMRFv4M9au5y8rEYjHqV3PHjfvhCrWZlydFnlQKbW0tuXKJthZC7z78oHg/BdFRkUhIiEdNr9qyMn19A1R298DdO7cLPScnJwcPHtyD1xvniMVi1PSqjbCwws/JzMxA0LHDsLK2hoVFBeVehDoSiVR3fIIUmlM4atQoJCUl4fz582jevDn27NmD6OhozJgxA7/88ouyYyx1ycnJkEqlMDExlSs3NTHBs2cRhZ6TkJAIUxOTAvUTEhILrZ+dnY0169ajebPPoK+np4yw6T8SKwtkRcfKlWVFx0LL2BBiHQm0TI0h1tRE1su4t+rEQd/duTRDVUupifnvrYGxhVy5gbE5UpNiCztFJurZPSyb2gu5OdnQ1tHD/75fBCs73i+9pBKTU5EnlcLM2FCu3MzYCI9fKJY90tfVQXU3J6zZ/TecbK1gZmKEI2cu4ca9cFS0ZufjfRIS8od7TUzlvzdMTEyQkBBf6DnJyUn53zUFzjHF82fP5Mr+OrgP69f+jszMTNhVtMe0mXOhpSXfgf8k8d7HSqVQpzA4OBj79u1D3bp1IRaL4eDggNatW8PIyAiBgYHo2LHjO8/PyspCVlbWW2XZkEi0FQlH7eTm5mJm4BwAAr4bzntFU/l29cwB7Fk3RfbYd8wKhduysHHEyJm7kZmeipsXjmDHqh8x6KcN7BiWE9OG98W0FZvRYfgkaIjFcHeyR5tGdREW/rSsQyt3Tp4IwrLFv8oeT546U6Wv16xFK3jVqoP4+Hjs3b0DcwOnY87836Ct/Wl8b1LpUKhTmJaWBktLSwCAqakpYmJiULlyZVSvXh1Xrlx57/mBgYGYOlV+0cD3343AqJHfKRKO0hkZGUEsFstN9AWAhMREmL71F90rpqYmSHhrEUp+fRO5stzcXMycPQfRMS8xd9ZMZglVICs6FhIr+SyWxMoCOUkpkGZmITs2AdLcXEgszd+qY46sqHdnuj5FnrVbwt61huxxXk7+pPnUpFgYmbzOIKUmxcHGoUqB89+kqakNC6v8lawVnaoiIvwmzhz5A19+W7xFRJTPxMgAGmJxgUUl8UnJRS4iKY6KVhWwKmAUMjKzkJaRCQtTY0z8bS3sLC3ef/Inpr53Q7kVwrk5OQCAxIQEmJm9/mxJTEyEs7NLoW0YGRnnf9ckyH/XJCYmwMRM/rtGX98A+voGsLWrCPcqHujdoytCzp5Gs+YtlXVJ6ukTXRCiKgq9m+7u7rh7N39uXc2aNbFy5Uo8f/4cK1asgI2NzXvPnzhxIpKSkuSOoYMHKxKKSmhpacHN1RVXQ19PgJdKpQgNvQbPKu6FnuNRpQpCr12TK7tyNRQeVd740PivQ/j8xQvMnjkDRkbcakMVEs+FwrxlA7kyi1aNkHAuFAAg5OQg6cotWLR8PScOIhHMWzRE4rmrpRipepDo6sPCykF2WNq5wtDYAg9unZPVycxIxbNH1+Hg6lWitqVSQfZlSsWnpamJKk72uHjz9RxnqVSKi7fuobqb0we3r6sjgYWpMZJT03Hu+h18Vrf6B7f5sdHT04OtrZ3ssK/kAFNTM1y79vozJD09Dffu3oG7h2ehbWhpacHVtTKuXXudTJFKpbgeehVVqhR+Tj4BAvj/DimfQpnC77//HpGRkQCAgIAAtGvXDps3b4a2tjbWr1//3vMlEgkkEolcWXw5Gzr+smsXzF/wKyq7ucK9cmXs2bcPmZmZaNPaBwAw95cFsDA3x7e+/QAAXTp3xtgJE7Fz9x7Ur1cX/5z6F/cfPMCo70YAyO8QTp81Gw8ePsS0gMmQ5kkRH5//16GhoQHnhryDhr4e9F1f78el51QRRjWrIDs+CZnPIuE+wx86dla45jceAPBk1VY4DOuDKoFj8Wz9Lli0aACb7u1xsfPrPzzCF65DzbVzkHj5JpIuXofjyH7Q1NfFsw27S/361I1IJELjdn0RvG8lLKwdYFahIo7uXAQjE0t41mklq/d7oB+q1vVBo9Z9AACHty1A5ZqfwcTcBtmZaQg9exDhYRfw7djfZeekJMYgJSkWcdH5w5VREfcg0dGHibkN9AxMSvU6y7veHVti6vI/4OFcCVVdHfHn3yeQkZWFz5vl/0EUsGwjKpgaY0SvLwDkL055FBEl+3dMQiLuPo6Ano4E9v/NGQy5dhuCADjYWiIiKga/bdkLR1srdG7WsPAgSEYkEqFzly+xfetm2NrawcrKGpv/WA8zc3M0aNhYVm/SxLFo0KgxOn3eBQDwRdduWLhgLlzd3FG5sjv279uNzKxMtGqdv91bVOQL/HvqJGrVrgtjY2PExsZi146tkGhro069+mVxqeULN69WKoU6hd98843s33Xq1MGTJ08QFhaGSpUqwcLi4xhmaP5ZUyQlJWHjps1ISEiAs7MzZk6bKhs+jomJgfiN1UlVPT0wYewP2PDHJqzfsBG2drYImPQTHB3zh8pi4+Jw7vx5AMCw70bKvdbcwFmoWYN/iRfFuE41NAz6Q/bYc/6PAIBnG3fjev+JkNhUgK796wx1xuMIXOw8GJ6/TITjd32RGRGFG4MnIfbYaVmdyB1/Q7uCGSoHjMzfvPraHVzoNADZby0+ocI169gf2VkZ2L02AJnpKXCsXBt+Y1dBS/v1H3txL58hLeX1sFhqcjy2r5yAlMQY6OgawqZSZXw79ne4VW8kq3MueBuC9iyTPV45oy8A4KuBM1H3s66lcGXqo03DOkhMTsXKnYcQl5iCyg52WDRhuGz4OCo2HqI3PqNiEpLwzcTZssebDgZh08Eg1PZwxcrJowAAqemZWLp1P17GJ8LIQA8t63th2NefQ1NTo1SvTV19+dXXyMzMxNLFvyItNRWeVathyrTZcvP+oiJfIDkpSfa4abMWSEpOwpY/1v/3XeOCKdMCZd81WtrauH3rJvbv24201FSYmJiiarXqmPPLogKLIYk+lEgQBKGsgwCAxw/ulXUI9J9bHp+XdQj0n+wzhW9LQaWvlWZwWYdAb4g0fvf8VSo97i72ZfbamfuWqKxtnS9GqKzt8qrYmUJ/f/9iN7pgwQKFgiEiIiKislHsTuHVq8WbgC/6RDd8JCIiolLGPodSFbtTeOLECVXGQURERFQy3LxaqRR6N5OSkhAfX3CH9vj4eCQnJ39wUERERERUuhTqFPbs2RNbt24tUL59+3b07Nnzg4MiIiIiei/e+1ipFOoUnj9/Hi1atChQ3rx5c5z/b9sVIiIiIlIfCu1TmJWVhdzc3ALlOTk5yMjI+OCgiIiIiN6Lt7lTKoXezfr162PVqlUFylesWIE6dep8cFBEREREVLoUyhTOmDEDPj4+uHbtGlq1yr+tVVBQEC5evIijR48qNUAiIiKiQnH1sVIp9G42btwY586dg729PbZv344DBw7A1dUV169fR9OmTZUdIxERERGpWIkyhVKpFPPmzcP+/fuRnZ2Nli1bYvXq1dDV1VVVfERERESF+0RXCatKiTKFM2fOxI8//ggDAwPY2dlh0aJFGD58uKpiIyIiIiqaSKy64xNUoqveuHEjli1bhiNHjmDv3r04cOAANm/eDKlUqqr4iIiIiKgUlKhT+PTpU3To0EH22MfHByKRCC9evFB6YERERETvxM2rlapEncLc3Fzo6OjIlWlpaSEnJ0epQRERERFR6SrRQhNBEODr6wuJRCIry8zMxJAhQ6Cvry8r2717t/IiJCIiIioMt6RRqhJ1Cvv161eg7JtvvlFaMERERERUNkrUKVy3bp2q4iAiIiIqEeETnfunKsy7EhEREZFit7kjIiIiKnOf6H6CqsJ3k4iIiIiYKSQiIiI1xUyhUrFTSERERGqJC02Ui11sIiIiImKmkIiIiNQUh4+Viu8mERER0QdaunQpHB0doaOjA29vb1y4cKHIur///juaNm0KU1NTmJqawsfHp0B9X19fiEQiuaNdu3YqvQZ2ComIiEg9iUSqO0pg27Zt8Pf3R0BAAK5cuYKaNWuibdu2ePnyZaH1T548iV69euHEiRMICQmBvb092rRpg+fPn8vVa9euHSIjI2XHn3/+qfBbVRzsFBIRERF9gAULFmDgwIHw8/ODp6cnVqxYAT09Paxdu7bQ+ps3b8awYcPg5eWFKlWqYPXq1ZBKpQgKCpKrJ5FIYG1tLTtMTU1Veh3sFBIREZF6EotVdxRTdnY2Ll++DB8fnzfCEsPHxwchISHFaiM9PR05OTkwMzOTKz958iQsLS3h7u6OoUOHIi4urthxKYILTYiIiIjekpWVhaysLLkyiUQCiUQiVxYbG4u8vDxYWVnJlVtZWSEsLKxYrzV+/HjY2trKdSzbtWuHL7/8Ek5OTnj48CF+/PFHtG/fHiEhIdDQ0FDwqt6NmUIiIiJSS4JIpLIjMDAQxsbGckdgYKDSr2H27NnYunUr9uzZAx0dHVl5z5490blzZ1SvXh1dunTBwYMHcfHiRZw8eVLpMbzCTCERERGpJxVuSTNx4kT4+/vLlb2dJQQACwsLaGhoIDo6Wq48Ojoa1tbW73yN+fPnY/bs2Th+/Dhq1KjxzrrOzs6wsLDAgwcP0KpVq2JeRckwU0hERET0FolEAiMjI7mjsE6htrY26tSpI7dI5NWikYYNGxbZ/ty5czF9+nQcPnwYdevWfW88ERERiIuLg42NjWIXVAzMFBIREZFaEsrJ5tX+/v7o168f6tati/r162PhwoVIS0uDn58fAKBv376ws7OTDT/PmTMHkydPxpYtW+Do6IioqCgAgIGBAQwMDJCamoqpU6eiW7dusLa2xsOHDzFu3Di4urqibdu2KrsOdgqJiIiIPsDXX3+NmJgYTJ48GVFRUfDy8sLhw4dli0+ePn0K8RsrmpcvX47s7Gx89dVXcu0EBARgypQp0NDQwPXr17FhwwYkJibC1tYWbdq0wfTp0wvNViqLSBAEQWWtl8DjB/fKOgT6zy2Pz8s6BPpP9pnbZR0C/aeVZnBZh0BviDSuUtYh0H/cXezL7LVTzx9QWdsG3p/ed2H5yLsSERERUZni8DERERGppfIyp/BjwXeTiIiIiJgpJCIiIjUlEpV1BB8VdgqJiIhIPXH4WKnKTafQKONlWYdA/+GK1/JDu7FnWYdA/8m9vLqsQ6A3VHp6qqxDoFdc+pR1BKQk5aZTSERERFQSAoePlYp5VyIiIiJippCIiIjUFOcUKhXfTSIiIiJippCIiIjUkwDOKVQmZgqJiIiIiJlCIiIiUk+8zZ1ysVNIRERE6omdQqXiu0lEREREzBQSERGReuLm1crFTCERERERMVNIRERE6okLTZSL7yYRERERMVNIREREaopzCpWKmUIiIiIiYqaQiIiI1BPnFCoXO4VERESklnjvY+ViF5uIiIiImCkkIiIi9cThY+Xiu0lEREREzBQSERGRmuKWNErFTCERERERMVNIRERE6klgbkup+G4SERERETOFREREpJ4EzilUKnYKiYiISC1xSxrl4rtJRERERMwUEhERkXribe6Ui5lCIiIiImKmkIiIiNQT5xQqF99NIiIiImKmkIiIiNQTt6RRLmYKiYiIiIiZQiIiIlJPXH2sXAp3CrOzsxEeHg4XFxdoarJvSURERKWLC02Uq8TvZnp6Ovr37w89PT1UrVoVT58+BQB89913mD17ttIDJCIiIiLVK3GncOLEibh27RpOnjwJHR0dWbmPjw+2bdum1OCIiIiIiiJApLLjU1Ticd+9e/di27ZtaNCgAURvrPqpWrUqHj58qNTgiIiIiKh0lLhTGBMTA0tLywLlaWlpcp1EIiIiIlXinELlKnGnsG7dujh06BC+++47AJB1BFevXo2GDRsqN7oytvPvYGzefxjxiUlwdbCHf//eqOrmXGjdR8+e4/etexH26AmiYuLwvW9P9OzUusi2N+75C8s370KPjj4Y7ddLVZfw0RAEAcd2L8HFEzuQkZ4Cx8q10MV3MiysHYs859zxrTgXvBUJMc8BAFYVXdGqy1C41/xMVud88HaEhhzCi8e3kZWZhoAV56Crb6Tqy1FbZk3qwnlMfxjXrgYdW0tc6jYM0fuD3n3OZ/XhOX8CDDzdkPksEg8ClyNi4x65Og5De8PZvz8k1hWQfD0Mt0ZNR9LFG6q8lI/CzsPB2Lz/yOvPqG97vfszatu+Nz6jvkbPju/5jNqyGz06+GC0X09VXcJHZevJi9hw9CziklNRuaIVxn/dHtWd7Aqtu+vfKzh4/hoevIgBAHhWssGIL1rK1Y9LTsXC3UE4d+chUtIzUdvNAeO/bgcHK/NSuR769JS4iz1r1iz8+OOPGDp0KHJzc/Hbb7+hTZs2WLduHWbOnKmKGMvE8TMXsGjDNvTv3hnr5wbAzdEeo2f8ivik5ELrZ2Zlw9aqAob16QZzE+N3tn37QTj2HvsHrg4VVRH6R+mfQ2tw9ugmdPELwPApW6El0cXauYOQk51V5DlGZlZo12M0vpu+AyOm7YCLpzc2/joC0RH3ZXVysjPhXqMJWnQeVBqXofY09PWQfP0ubo6cWqz6uo4VUW//SsSdPI/Tdb9A+OINqL5yBixaN5HVseneHh7zJuL+jKU4Xb8rUq6HwfvQGmhXMFPVZXwU8j+jtqN/98+xfs5kuDnYY/TMhe/+jLIsyWfUKX5GlcCRS7fwy86jGNypGf78cRAqV7TGsMWbEZ+cVmj9S/ceo13davh9dF9sHPctrEyNMHTRJkQn5P/8BEHA6OXb8Dw2Ab8O/RpbfxoEG3NjDPltEzKyskvz0so1zilUrhJ3Cps0aYLQ0FDk5uaievXqOHr0KCwtLRESEoI6deqoIsYy8eeBo+js8xk6tWwCJ3tbjBv0P0gk2jgYfLrQ+p6uTviubw+0buINLa2iE7DpGZmY8tvvmDCkHwz19VUV/kdFEAScObwRLTsPRtU6rWBTyR1fD56N5MSXuH256CyVZ+0WqOLVDBbWjqhg44i23UdBW0cPTx9cl9Vp0q4vmn8+EPauNUvjUtRezJFTuBewENH7jhervsOgnsgIj8CdcXOQGvYIT5ZtRtSuI3D63ldWx2mUH56t2Y6IDbuReuchbgwLQF56Jux9u6noKj4Ofx48hs6tmqJTi1efUd9Aov2+z6juaN24/vs/oxatxoQhfWGor6eq8D86fxwPwZeNa6NLIy+42FbApN4doaOlhb1nrxZaP7D/l/i6eT1UsbeGk7UFAv73OQRBwIW74QCApy/jcT38OX7s3QHVHO3gaG2Bn3p1RGZODv6+eLM0L42KaenSpXB0dISOjg68vb1x4cKFd9bfsWMHqlSpAh0dHVSvXh1//fWX3POCIGDy5MmwsbGBrq4ufHx8cP/+/SJaUw6FBuNdXFzw+++/48KFC7h9+zY2bdqE6tWrKzu2MpOTk4u7j56gXg0PWZlYLEa96p64effDFtPMX70ZjWrXQP0anh8a5icjPiYCKUmxcK32enqCjp4h7J1r4MmD0GK1IZXm4VrIX8jOykAlN3YAS4tJAy/EBofIlcUcOw3TBl4AAJGWFoxrV0Vs0NnXFQQBscFnYdKgVilGql5ef0a9/hwRi8WoV8MDN+89+qC256/ZjEa1q/MzqgRycvNw52kkvD2cZGVisQjeHk64/iiiWG1kZucgN08KYz1dAEB2bi4AQPJGB14sFkFbUxNXHzxTYvTqTRCJVXaUxLZt2+Dv74+AgABcuXIFNWvWRNu2bfHy5ctC6589exa9evVC//79cfXqVXTp0gVdunTBzZuvO/xz587FokWLsGLFCpw/fx76+vpo27YtMjMzP+g9e5cSdwqTk5MLPVJSUpCd/XGktBNTUpAnlcLMWH5umZmJEeISkxRu99jp87gb/gRD+zADUhKpibEAAANjC7lyA2NzpCbFvvPcqGf3MHlAHUzy88Ke9VPxv+8XwcrOVWWxkjyJlQWyouV/RlnRsdAyNoRYRwJtC1OINTWR9TLurTpxkFjL/7zptcSU1MI/o4w/8DPqzAXcffQUQ3vzM6okElLTkScVYG4kP/pjbqiP2OTUYrWxcHcQKhgbwtsjf06oo7UFbMyMsWhPMJLTMpCTm4d1R84gOiEZsckpSr8GdVVeho8XLFiAgQMHws/PD56enlixYgX09PSwdu3aQuv/9ttvaNeuHcaOHQsPDw9Mnz4dtWvXxpIlS/KvSxCwcOFCTJo0CV988QVq1KiBjRs34sWLF9i7d++Hvm1FKnGn0MTEBKampgUOExMT6OrqwsHBAQEBAZBKpUW2kZWVVaBTmfWRdCiLEh0bj1/XbcXUkQMh0dYq63DKtatnDmDygDqyIy8vV+G2LGwcMXLmbgybshUNWn6NHat+RPTzB0qMlujjkP8Z9Semfj+An1GlbO3h0zhy6SYWDOkhywxqaWjgl8Hd8eRlHD4bMw8NRs7CxbuP0biqK8Tc6aNUFNpXySo4jz07OxuXL1+Gj4+PrEwsFsPHxwchISEF6gNASEiIXH0AaNu2rax+eHg4oqKi5OoYGxvD29u7yDaVocSrj9evX4+ffvoJvr6+qF+/PgDgwoUL2LBhAyZNmoSYmBjMnz8fEokEP/74Y6FtBAYGYupU+Ynq44b4YfywbxW4BOUzMTSEhlhcYMJ2fGLyeydoFyXs0WMkJCXDd9w0WVmeVIrQO/ew6+9g/PPnSmhocGk9AHjWbgl71xqyx3k5+X8wpCbFwsikgqw8NSkONg5V3tmWpqY2LKwcAAAVnaoiIvwmzhz5A19+W7yFEvRhsqJjIbGSz/hJrCyQk5QCaWYWsmMTIM3NhcTS/K065siKencW+FNmYmhQ+GdU0od8Rj1BQlIKfMdNl5Xlf0bdx67Dwfhnywp+RhXB1EAPGmIR4t5aVBKXkgYLI4N3nrvh6FmsPXIGK0f9D5UrWsk95+lgi+2TBiMlIxM5uXkwM9THN7NXw9PBVunXoK4EFXaQC+urBAQEYMqUKXJlsbGxyMvLg5WV/M/PysoKYWFhhbYdFRVVaP2oqCjZ86/KiqqjCiXuFG7YsAG//PILevToISv7/PPPUb16daxcuRJBQUGoVKkSZs6cWWSncOLEifD395crS7t/qaShqIyWlibcnR1w6cYdNKtfGwAglUpx6cYdfNW+pUJt1q3ugU0L5H+5Zi5dBwc7a3zTpT0/bN8g0dWHRPf1MIwgCDA0tsCDW+dg65A/zzMzIxXPHl1Hg1Yl2ypDKhWQm5Oj1HipaInnQlGh/WdyZRatGiHhXCgAQMjJQdKVW7Bo2fD11jYiEcxbNMSTZZtKOVr1If8ZlT/3Mv8zKgxftWuhUJt1q3tg0y9vfUYtWwcHW35GvY+WpgY8KtngQlg4Wnrl/6EqlQq4EBaOns3rFXneuiNnsObv01g2sg+qvqOjZ6ibf/ewJ9FxuP0kEsM6K/YzppIprK8ikUjKKJrSUeJO4dmzZ7FixYoC5bVq1ZKlNJs0aSK7J3JhJBJJgTc2V1u7pKGoVK/P22D6kjWo4uKIqq5O2HroODKzstCpRWMAwNRFq1HB3BTD/psfmJOTi/CIFwCA3NxcxMQn4F74U+jqSGBvYwV9XV24VJLf3kFHIoGRoUGBcpInEonQuF1fBO9bCQtrB5hVqIijOxfByMQSnnVayer9HuiHqnV90Kh1HwDA4W0LULnmZzAxt0F2ZhpCzx5EeNgFfDv2d9k5KYkxSEmKRVx0/u9rVMQ9SHT0YWJuAz0Dk1K9TnWgoa8HfddKssd6ThVhVLMKsuOTkPksEu4z/KFjZ4VrfuMBAE9WbYXDsD6oEjgWz9bvgkWLBrDp3h4XOw+WtRG+cB1qrp2DxMs3kXTxOhxH9oOmvi6ebdhd6tenTnp1ao3pS9eiiotD4Z9Ri9eggplJ0Z9RcYlvfUbpwKWS/J56OhLt/z6jCt9rj177n09D/Lx+LzwdbFHN0Rabg88jIzsHXzTyAgBMWrcXliaGGNk1/zNr3ZEzWHbgJAK//RK25iaITcqfe6gn0YaeTv734dHLt2FqoAcbM2Pcf/4Sc7cfRgsvdzTydCmLSyyXBEF1mcLC+iqFsbCwgIaGBqKjo+XKo6OjYW1tXeg51tbW76z/6r/R0dGwsbGRq+Pl5VWSyyiREncK7e3tsWbNGsyePVuufM2aNbC3twcAxMXFwdTUVDkRlhGfxvWRkJyC1Vv3Ii4xGW6O9vj1p9Ew+29oJjo2HmLx61/G2IRE9Bv7+q/sLfuPYMv+I6jl6Y5l08aVevwfm2Yd+yM7KwO71wYgMz0FjpVrw2/sKmhpv/4fNu7lM6SlJMgepybHY/vKCUhJjIGOriFsKlXGt2N/h1v1RrI654K3IWjPMtnjlTP6AgC+GjgTdT/rWgpXpl6M61RDw6A/ZI895+ePBjzbuBvX+0+ExKYCdO1ff4BlPI7Axc6D4fnLRDh+1xeZEVG4MXgSYo+93jYlcsff0K5ghsoBI/M3r752Bxc6DUD2W4tPSF7+Z1QqVm/b98Zn1Kg3PqPi5OaexSYkot8b01e2HDiCLQeOoJZnZSybys+oD9W2blUkpKRh+YGTiE1OhXtFKyz7rjfM/xs+joxPkrvr1/Z/LiEnNw8/rNoh187gjp9h6OfNAQCxSSn4ZedRxCWnooKxITo1qIFBHeQz71T2tLW1UadOHQQFBaFLly4A8jP3QUFBGDFiRKHnNGzYEEFBQRg1apSs7NixY7KbgDg5OcHa2hpBQUGyTmBycjLOnz+PoUOHquxaRIIgCCU5Yf/+/ejevTuqVKmCevXy0+KXLl3CnTt3sGvXLnTq1AnLly/H/fv3sWDBgmK3G3+j8L21qPT9k/Fx3ZlGnWk35rYg5UXDy6vLOgR6g25c0aNRVLp0W/Qps9e+//CJytp2c3Eodt1t27ahX79+WLlyJerXr4+FCxdi+/btCAsLg5WVFfr27Qs7OzsEBgYCyB91bdasGWbPno2OHTti69atmDVrFq5cuYJq1aoBAObMmYPZs2djw4YNcHJyws8//4zr16/j9u3b0NHRUck1lzhT2LlzZ9y9excrVqzAvXv3AADt27fH3r17kZqan/5WZS+WiIiIqDz5+uuvERMTg8mTJyMqKgpeXl44fPiwbKHI06dPIRa/npfbqFEjbNmyBZMmTcKPP/4INzc37N27V9YhBIBx48YhLS0NgwYNQmJiIpo0aYLDhw+rrEMIKJApfFtycjL+/PNPrF27FpcuXUJeXp5C7TBTWH4wU1h+MFNYfjBTWL4wU1h+lGWm8N5D1f0eVHap9P5KHxmFl5OdOnUK/fr1g62tLX755Re0aNEC586dU2ZsREREREUqL5tXfyxKNHwcFRWF9evXY82aNUhOTkaPHj2QlZWFvXv3wtOTGQ0iIiIidVXsTOHnn38Od3d3XL9+HQsXLsSLFy+wePFiVcZGREREVCRmCpWr2JnCv//+GyNHjsTQoUPh5uamypiIiIiIqJQVO1N4+vRppKSkoE6dOvD29saSJUsQG8vbUBEREVHZYKZQuYrdKWzQoAF+//13REZGYvDgwdi6dStsbW0hlUpx7NgxpKSkqDJOIiIiIlKhEq8+1tfXx7fffovTp0/jxo0bGDNmDGbPng1LS0t07txZFTESERERFSAIIpUdn6IPusO5u7s75s6di4iICPz555/KiomIiIiISlmJ72hSGA0NDXTp0kV2zz8iIiIiVftU5/6pygdlComIiIjo46CUTCERERFRaWOmULnYKSQiIiK1xE6hcnH4mIiIiIiYKSQiIiL19KluHaMqzBQSERERETOFREREpJ6knFOoVMwUEhEREREzhURERKSeuPpYuZgpJCIiIiJmComIiEg9cfWxcrFTSERERGqJw8fKxeFjIiIiImKmkIiIiNQTh4+Vi5lCIiIiImKmkIiIiNQT5xQqFzOFRERERMRMIREREaknzilULmYKiYiIiIiZQiIiIlJP0rIO4CPDTiERERGpJQ4fKxeHj4mIiIiImUIiIiJST9ySRrmYKSQiIiIiZgqJiIhIPXFOoXIxU0hEREREzBQSERGReuKcQuVippCIiIiImCkkIiIi9SQVyjqCjws7hURERKSWOHysXOWmU7j5aeOyDoH+08/meFmHQP/Jvby6rEOg/4TUGVDWIdAbMk7fKesQ6D9flXUApDTlplNIREREVBLckka5uNCEiIiIiJgpJCIiIvUkcKGJUjFTSERERETMFBIREZF6knL1sVIxU0hEREREzBQSERGReuLqY+VippCIiIjUkiCo7lCV+Ph49OnTB0ZGRjAxMUH//v2Rmpr6zvrfffcd3N3doauri0qVKmHkyJFISkqSqycSiQocW7duLVFszBQSERERlZI+ffogMjISx44dQ05ODvz8/DBo0CBs2bKl0PovXrzAixcvMH/+fHh6euLJkycYMmQIXrx4gZ07d8rVXbduHdq1ayd7bGJiUqLY2CkkIiIitaRut7m7c+cODh8+jIsXL6Ju3boAgMWLF6NDhw6YP38+bG1tC5xTrVo17Nq1S/bYxcUFM2fOxDfffIPc3Fxoar7uypmYmMDa2lrh+Dh8TERERPSWrKwsJCcnyx1ZWVkf1GZISAhMTExkHUIA8PHxgVgsxvnz54vdTlJSEoyMjOQ6hAAwfPhwWFhYoH79+li7di2EEo6Ds1NIREREakkqqO4IDAyEsbGx3BEYGPhB8UZFRcHS0lKuTFNTE2ZmZoiKiipWG7GxsZg+fToGDRokVz5t2jRs374dx44dQ7du3TBs2DAsXry4RPFx+JiIiIjoLRMnToS/v79cmUQiKbTuhAkTMGfOnHe2d+fOnQ+OKTk5GR07doSnpyemTJki99zPP/8s+3etWrWQlpaGefPmYeTIkcVun51CIiIiUkuq3JJGItEushP4tjFjxsDX1/eddZydnWFtbY2XL1/Klefm5iI+Pv69cwFTUlLQrl07GBoaYs+ePdDS0npnfW9vb0yfPh1ZWVnFvg52ComIiIg+QIUKFVChQoX31mvYsCESExNx+fJl1KlTBwAQHBwMqVQKb2/vIs9LTk5G27ZtIZFIsH//fujo6Lz3tUJDQ2FqalrsDiHATiERERGpKVXuJ6gKHh4eaNeuHQYOHIgVK1YgJycHI0aMQM+ePWUrj58/f45WrVph48aNqF+/PpKTk9GmTRukp6dj06ZNskUvQH5nVENDAwcOHEB0dDQaNGgAHR0dHDt2DLNmzcIPP/xQovjYKSQiIiK1pI73Pt68eTNGjBiBVq1aQSwWo1u3bli0aJHs+ZycHNy9exfp6ekAgCtXrshWJru6usq1FR4eDkdHR2hpaWHp0qUYPXo0BEGAq6srFixYgIEDB5YoNnYKiYiIiEqJmZlZkRtVA4Cjo6PcVjLNmzd/79Yy7dq1k9u0WlHsFBIREZFaUrfh4/KO+xQSERERETOFREREpJ5UuSXNp4iZQiIiIiJippCIiIjUk5RzCpWKmUIiIiIiYqaQiIiI1BNXHysXO4VERESklgQ13Ly6POPwMRERERExU0hERETqiQtNlIuZQiIiIiJippCIiIjUExeaKBczhURERETETCERERGpJ2YKlYuZQiIiIiJippCIiIjUk1TgPoXKpHCm8OHDh5g0aRJ69eqFly9fAgD+/vtv3Lp1S2nBERERERVFEFR3fIoU6hT+888/qF69Os6fP4/du3cjNTUVAHDt2jUEBAQoNUAiIiIiUj2FOoUTJkzAjBkzcOzYMWhra8vKW7ZsiXPnziktOCIiIqKiMFOoXAp1Cm/cuIGuXbsWKLe0tERsbOwHB0VEREREpUuhTqGJiQkiIyMLlF+9ehV2dnYfHBQRERHR+0gF1R2fIoU6hT179sT48eMRFRUFkUgEqVSKM2fO4IcffkDfvn2VHSMRERERqZhCW9LMmjULw4cPh729PfLy8uDp6Ym8vDz07t0bkyZNUnaMZUYQBFw4vBi3zu1AVkYybJxqo/lXATCp4Fis8y8HrULIoQWo2bQvmnb9EQCQHB+BjTN8Cq3fru9CuHq1U1b4H5XtR//BpgNBiEtKhlslO4z17Y6qro6F1n34LBIrdx5E2KNniIyNx+j/dUPvDi3k6qRlZGLF9oM4eekaEpJSUdmxIsb0+wpVXRxK4WrU287Dwdi8/wjiE5Pg6mAP/297oaqbc6F1Hz17jt+37UPYoyeIionD975fo2fH1kW2vXHPX1i+ZTd6dPDBaL+eqrqEj4JZk7pwHtMfxrWrQcfWEpe6DUP0/qB3n/NZfXjOnwADTzdkPovEg8DliNi4R66Ow9DecPbvD4l1BSRfD8OtUdORdPGGKi/loyEIAoJ2L8bFkzuQmZ4CB7da6OwbAAtrxyLPOR/0J84Hb0VizHMAgKWdK1p0GQb3mp8BANJTExG0ewke3DyDxLhI6BuawbNOK/h0GwkdPcPSuKxyTeCWNEpV4kyhIAiIiorCokWL8OjRIxw8eBCbNm1CWFgY/vjjD2hoaKgizjJxJXg1rv37B5p3n4Luo7ZDS1sX+1cOQG5O1nvPjX56AzdDtsHcxl2u3MDEBn5T/pU76rf9DloSPVTyaKqqS1FrR0MuY+EfezCgW3v8MWs83Bzs8N3spYhPSim0fmZ2NuwsLTCiV2eYmxgVWmfGqi04fyMMU4f1w59zf0SDGlUwfOZivIxPVOGVqL/jZy5g0Ybt6N/9c6yfMxluDvYYPXMh4pOSC62fmZUNW8sKGNanG8xNjN/Z9u0H4dh77BRcHSqqIvSPjoa+HpKv38XNkVOLVV/XsSLq7V+JuJPncbruFwhfvAHVV86AResmsjo23dvDY95E3J+xFKfrd0XK9TB4H1oD7QpmqrqMj8q/h1Yj5NgmfOE7BUMDtkFLoof18wYiJ7vo7wwjM2u07eGPYdN2YtjUHXD2bIDNC0cgOuI+ACAl8SVSEl+iXa9xGDlrP7oNmoV71//F7jUfTwKGyg+FOoWurq6IiIiAvb09OnTogB49esDNzU0V8ZUZQRBw7dRG1G09BM7VWsHC1h0+vecgLfklHt08/s5zs7PScHTzD2jZYzokevKdErFYA/pGFeSORzePw7Vme2hL9FV5SWpry6FgdGnZCJ2bN4RzRRtM7N8TOtra2H8ypND6VV0c8H2frmjTqC60NQsmwzOzs3HiQihG9u6C2h6usLeugEFfdYS9dQXsOvavqi9Hrf158Bg6t2qKTi2awMneFuMGfQOJtjYOBp8utL6nqxO+69sdrRvXh5ZW0QMT6RmZmLJoNSYM6QtDfT1Vhf9RiTlyCvcCFiJ637s/j15xGNQTGeERuDNuDlLDHuHJss2I2nUETt/7yuo4jfLDszXbEbFhN1LvPMSNYQHIS8+EvW83FV3Fx0MQBJw5shHNOw+BZ51WsK7kju6DZyMl8SXuXCn6Z+RRqwXcazaDhbUjLGyc0Kb7KGjr6OHZw2sAAKuKldF75CJ41GoBc6tKcPFsgNbdRyHs6gnk5eWW1uWVW1x9rFwl7hSKxWK4ubkhLi5OFfGUG8nxEUhPiYF95UayMomuIawq1UDU49B3nvvPrmlw9Ggud25RXj67idjnd+DpzQ/dwuTk5iIs/BnqV3udcRWLxahfzR037ocr1GZenhR5Uim0tbXkyiXaWgi9+/CD4v2Y5eTk4u6jJ6hXw1NWJhaLUa+GB27ee/RBbc9fsxmNaldH/TfaJuUyaeCF2GD5P6Rijp2GaQMvAIBISwvGtasiNujs6wqCgNjgszBpUKsUI1VPCTERSE2KhUvVhrIyHT1DVHSugacPrhWrDak0D9fPHUJ2VjoquXoVWS8zPQUSXQNoaPCmZKRcCi00mT17NsaOHYubN28qO55yIz05BgCgZ2guV65naIH0lKK33bl39RBiIm6jYUf/Yr3O7fO7YGrlAhun2ooH+xFLTE5FnlQKM2P5uTNmxkaISyx8yPJ99HV1UN3NCWt2/42Y+ETkSaX4698LuHEvHLEKtvkpSEx59bOQz37n/yySFG732JkLuPvoKYb25h9GqiSxskBWtPxnV1Z0LLSMDSHWkUDbwhRiTU1kvYx7q04cJNYWpRmqWkpJyn9vDYzlvzMMjC2QmhjzznOjnt3D1IF1EPBtTexbPxV9vl8MSzvXQuumpSTg5L7lqNe8h3ICV3NcfaxcCv2Z0bdvX6Snp6NmzZrQ1taGrq6u3PPx8fHvPD8rKwtZWfJzLHJytKGlJVEkHKW4e/kATu54fTeWTgNWlLiNlIRI/LtnFr4YshaaxbiW3OxM3LtyEPXaDC3xa9GHmTa8L6at2IwOwydBQyyGu5M92jSqi7Dwp2Ud2iclOjYev677E4t+9ofkrcwtUXkWevYA9q2bInvcd8xyhduysHHEiBm7kZmeipsXj2DnqokY+OPGAh3DzIxUbPxlCCrYuaJV1+EKv97H5FMd5lUVhTqFCxcu/KAXDQwMxNSp8pOj2/WajA59pnxQux/CqWoLWFWqIXucl5cNAEhPiYO+kaWsPD0lFhZ2HoW2ERNxCxmpcdi24EtZmSDNw4tHl3D9zGYMnXsdYvHrhTgPrh9Bbk4mqtTtouSr+XiYGBlAQywusKgkPim5yEUkxVHRqgJWBYxCRmYW0jIyYWFqjIm/rYWdJTMiRTExfPWzkM+m5v8s3r2IpChhj54gISkFvuOmy8rypFKE3rmPXYeD8c+WFdDQUPgW7fSGrOhYSKzkf78lVhbISUqBNDML2bEJkObmQmJp/lYdc2RF8aYEb/Oo1RL2Lq+/M3Jz8r8zUpPiYGTy+jsjNSkWNg6Ff2e8oqmpDXOr/J0P7Jyq4vmjGzh79A908Xv9PZmVkYYN8wZCoqOHPiMXQ0OTf0SR8inUKezXr98HvejEiRPh7y8/vLo6WLuI2qVDW8cA2joGsseCIEDPsAIi7oegwn+dwOzMVEQ/vY5qjXsV2kZFtwboNXa/XFnQ1h9haumM2i0HyHUIAeD2+Z1wqtoCugZc2VcULU1NVHGyx8Wbd9G8Xk0AgFQqxcVb99C9zWcf3L6ujgS6OhIkp6bj3PU7+K73Fx/c5sdKS0sT7s4OuHTjDprVz59jJpVKcelGGL5q1+I9ZxeubnUPbPpF/g/EmcvWwcHWGt90ac8OoRIlngtFhfby/89YtGqEhHOhAAAhJwdJV27BomXD11vbiEQwb9EQT5ZtKuVoyz+Jrj4kuq8XBwqCAANjCzy6fQ62/3UCMzNSEfHoOrxblWx7JUEQZJ3MV+2snzsAmlra+Gb0Mmhpl92oWnnDTKFyffAs1czMTGRnZ8uVGRm9O4MjkUggkcj/Umtpla+frEgkQs3P+uLSsRUwsXCEoZkdzh9eBH0jSzhXe73P4N7lvnCu5oMaTb+Bto4BzG0qy7Wjqa0LHT2TAuWJMU/w4tElfD5gValcjzrr3bElpi7/Ax7OlVDV1RF//n0CGVlZ+LxZAwBAwLKNqGBqjBG98jt0Obm5eBQRJft3TEIi7j6OgJ6OBPbWFQAAIdduQxAAB1tLRETF4Lcte+Foa4XOzRoWHgQBAHp1ao3pS9eiiosDqro6Yeuh48jMykKnFo0BAFMXr0EFMxMM65M/PzAnJxfhES8AALm5uYiJS8S98KfQ1ZHA3sYK+ro6cKkkfxckHYk2jAwNCpSTPA19Pei7VpI91nOqCKOaVZAdn4TMZ5Fwn+EPHTsrXPMbDwB4smorHIb1QZXAsXi2fhcsWjSATff2uNh5sKyN8IXrUHPtHCRevomki9fhOLIfNPV18WzD7lK/PnUjEonQuG1fnNi3AuZWDjCtUBHHdy2CoYklPGq//s5YM9sPnnV80LB1HwDAke0LULlGU5iY2yIrMw3XQg4iPOwCfMf+DuBVh7A/srMz0X3IXGRlpCIrIxUAoG9kViDZQPQhFOoUpqWlYfz48di+fXuhq5Dz8vI+OLDyoHbLAcjNzsCJHZP/27y6Dj4f9LvcfMGk2KfISEsocdt3LuyCgbE1Krk3VmbIH6U2DesgMTkVK3ceQlxiCio72GHRhOGy4eOo2HiIRK83MI1JSMI3E2fLHm86GIRNB4NQ28MVKyePAgCkpmdi6db9eBmfCCMDPbSs74VhX38OTU1+wL6LT+P6SEhOxept+xCXmAw3R3v8+tMomP03fBwdGwfxGz+L2IRE9Bs3TfZ4y4Ej2HLgCGp5VsayqeNKPf6PiXGdamgY9Ifssef8/A3yn23cjev9J0JiUwG69jay5zMeR+Bi58Hw/GUiHL/ri8yIKNwYPAmxx15vJxS5429oVzBD5YCR+ZtXX7uDC50GIPvlx73bhLI07TgA2VkZ2LsuAJnpyXBwqw3fH1bJZfbiXz5Fesrr74y05DjsXDUBKYkx0NE1hLV9ZfiO/R2u1fK/G148vo1nD68DABaMbSv3ej/8chymFT7tP54+1QUhqiIShJInX4cPH44TJ05g+vTp+N///oelS5fi+fPnWLlyJWbPno0+ffqUOJDFh/iTLS/62RRv3zNSvVxNnbIOgf4TUmdAWYdAb8g4faesQ6D/fOVddtM8Vr/7Jj4fZEAr1bVdXimUKTxw4AA2btyI5s2bw8/PD02bNoWrqyscHBywefNmhTqFRERERCXBOYXKpVD3Pj4+Hs7O+fc6NTIykm1B06RJE5w6dUp50RERERFRqVCoU+js7Izw8Py7SVSpUgXbt28HkJ9BNDExUVpwREREREWRSlV3fIpK1Cl89OgRpFIp/Pz8cO1a/m17JkyYgKVLl0JHRwejR4/G2LFjVRIoERER0Zt472PlKtGcQjc3N0RGRmL06NEAgK+//hqLFi1CWFgYLl++DFdXV9SoUeM9rRARERFReVOiTOHbC5X/+usvpKWlwcHBAV9++SU7hERERFRqmClULt4ugIiIiIhKNnwsEonkNgl+VUZERERU2rh5tXKVqFMoCAJ8fX1lt6jLzMzEkCFDoK+vL1dv927eEomIiIhInZSoU9ivXz+5x998841SgyEiIiIqLgVuylYCn95IaIk6hevWrVNVHERERERUhhS6zR0RERFRWftUVwmrCjuFREREpJY+1TuPqAq3pCEiIiIqJfHx8ejTpw+MjIxgYmKC/v37IzU19Z3nNG/eXLYDzKtjyJAhcnWePn2Kjh07Qk9PD5aWlhg7dixyc3NLFBszhURERKSW1HH4uE+fPoiMjMSxY8eQk5MDPz8/DBo0CFu2bHnneQMHDsS0adNkj/X09GT/zsvLQ8eOHWFtbY2zZ88iMjISffv2hZaWFmbNmlXs2NgpJCIiIioFd+7cweHDh3Hx4kXUrVsXALB48WJ06NAB8+fPh62tbZHn6unpwdrautDnjh49itu3b+P48eOwsrKCl5cXpk+fjvHjx2PKlCnQ1tYuVnwcPiYiIiK1JBVUd6hCSEgITExMZB1CAPDx8YFYLMb58+ffee7mzZthYWGBatWqYeLEiUhPT5drt3r16rCyspKVtW3bFsnJybh161ax42OmkIiIiOgtWVlZyMrKkiuTSCSyG3goIioqCpaWlnJlmpqaMDMzQ1RUVJHn9e7dGw4ODrC1tcX169cxfvx43L17V3azkKioKLkOIQDZ43e1+zZmComIiEgtCYLqjsDAQBgbG8sdgYGBhcYxYcKEAgtB3j7CwsIUvs5Bgwahbdu2qF69Ovr06YONGzdiz549ePjwocJtFoaZQiIiIqK3TJw4Ef7+/nJlRWUJx4wZA19f33e25+zsDGtra7x8+VKuPDc3F/Hx8UXOFyyMt7c3AODBgwdwcXGBtbU1Lly4IFcnOjoaAErULjuFREREpJYEVU3+Q8mGiitUqIAKFSq8t17Dhg2RmJiIy5cvo06dOgCA4OBgSKVSWUevOEJDQwEANjY2snZnzpyJly9fyoanjx07BiMjI3h6eha7XQ4fExERkVpSt4UmHh4eaNeuHQYOHIgLFy7gzJkzGDFiBHr27Clbefz8+XNUqVJFlvl7+PAhpk+fjsuXL+Px48fYv38/+vbti88++ww1atQAALRp0waenp743//+h2vXruHIkSOYNGkShg8fXqI5kOwUEhEREZWSzZs3o0qVKmjVqhU6dOiAJk2aYNWqVbLnc3JycPfuXdnqYm1tbRw/fhxt2rRBlSpVMGbMGHTr1g0HDhyQnaOhoYGDBw9CQ0MDDRs2xDfffIO+ffvK7WtYHBw+JiIiIrWkjptXm5mZvXOjakdHRwhvXJi9vT3++eef97br4OCAv/7664NiY6aQiIiIiJgpJCIiIvUkVeFCk08RM4VERERExEwhERERqSd1nFNYnjFTSERERETMFBIREZF6YqZQudgpJCIiIrUkZa9QqTh8TERERETMFBIREZF6EqRlHcHHhZlCIiIiImKmkIiIiNSTwDmFSsVMIRERERExU0hERETqSco5hUrFTCERERERMVNIRERE6olzCpWLnUIiIiJSS1L2CZWKw8dEREREVH4yhX7mB8o6BPrPc+NaZR0C/afS01NlHQL9J+P0nbIOgd6g28SjrEOgV3LultlLC0wVKhUzhURERERUfjKFRERERCXBdSbKxUwhERERETFTSEREROpJyjmFSsVMIRERERExU0hERETqiZtXKxc7hURERKSWBN77WKk4fExEREREzBQSERGRepJy+FipmCkkIiIiImYKiYiISD1xoYlyMVNIRERERMwUEhERkXri5tXKxUwhERERETFTSEREROqJUwqVi51CIiIiUksCh4+VisPHRERERMRMIREREaknbl6tXMwUEhEREREzhURERKSeOKdQuZgpJCIiIiJmComIiEg9MVOoXMwUEhEREREzhURERKSemChULmYKiYiIiIiZQiIiIlJPnFOoXOwUEhERkVoSuHm1UnH4mIiIiIiYKSQiIiL1JOXwsVJ9UKYwOzsbERERePr0qdxBRERERAXFx8ejT58+MDIygomJCfr374/U1NQi6z9+/BgikajQY8eOHbJ6hT2/devWEsWmUKbw/v37+Pbbb3H27Fm5ckEQIBKJkJeXp0izRERERMWmjnMK+/Tpg8jISBw7dgw5OTnw8/PDoEGDsGXLlkLr29vbIzIyUq5s1apVmDdvHtq3by9Xvm7dOrRr10722MTEpESxKdQp9PX1haamJg4ePAgbGxuIRCJFmiEiIiL6ZNy5cweHDx/GxYsXUbduXQDA4sWL0aFDB8yfPx+2trYFztHQ0IC1tbVc2Z49e9CjRw8YGBjIlZuYmBSoWxIKdQpDQ0Nx+fJlVKlSReEXJiIiIvoQ6rYlTUhICExMTGQdQgDw8fGBWCzG+fPn0bVr1/e2cfnyZYSGhmLp0qUFnhs+fDgGDBgAZ2dnDBkyBH5+fiVK3CnUKfT09ERsbKwipxIRERGVe1lZWcjKypIrk0gkkEgkCrcZFRUFS0tLuTJNTU2YmZkhKiqqWG2sWbMGHh4eaNSokVz5tGnT0LJlS+jp6eHo0aMYNmwYUlNTMXLkyGLHV+yFJsnJybJjzpw5GDduHE6ePIm4uDi555KTk4v94kRERESKEqSCyo7AwEAYGxvLHYGBgYXGMWHChCIXg7w6wsLCPvh6MzIysGXLFvTv37/Acz///DMaN26MWrVqYfz48Rg3bhzmzZtXovaLnSk0MTGRS0EKgoBWrVrJ1eFCEyIiIiotUhUuNJk4cSL8/f3lyorKEo4ZMwa+vr7vbM/Z2RnW1tZ4+fKlXHlubi7i4+OLNRdw586dSE9PR9++fd9b19vbG9OnT0dWVlaxs5vF7hSeOHGiuFWJiIiI1FpJhoorVKiAChUqvLdew4YNkZiYiMuXL6NOnToAgODgYEilUnh7e7/3/DVr1qBz587Feq3Q0FCYmpqWaLi72J3CZs2aFbtRIiIiIlVTt4UmHh4eaNeuHQYOHIgVK1YgJycHI0aMQM+ePWUrj58/f45WrVph48aNqF+/vuzcBw8e4NSpU/jrr78KtHvgwAFER0ejQYMG0NHRwbFjxzBr1iz88MMPJYpPoYUm69atg4GBAbp37y5XvmPHDqSnp6Nfv36KNEtERET0Udu8eTNGjBiBVq1aQSwWo1u3bli0aJHs+ZycHNy9exfp6ely561duxYVK1ZEmzZtCrSppaWFpUuXYvTo0RAEAa6urliwYAEGDhxYothEggI7P1auXBkrV65EixYt5Mr/+ecfDBo0CHfv3i1pk0g9t7/E56ja9uNnsPHvfxCXlAI3exuM+6YLqrlUKrTu7pPncejMZTyMyF895OFoh+FftZerv3LPURw5H4rouERoaWrCw9EOw75qj+pFtFlWnleoVdYhFCAIArZs2oCjh/9CWloqPDyrYujw72FrV/Gd5x06sA97dm1HQkI8nJxcMGjoCFR2f72V0tLFv+La1SuIj4+Djo4uqnh6wtdvICral4+fSaWnp8o6hAK2nryIDUfPIi45FZUrWmH81+1R3cmu0Lq7/r2Cg+ev4cGLGACAZyUbjPiipVz9uORULNwdhHN3HiIlPRO13Rww/ut2cLAyL5XrKa5Der3KOoRCCYKAoN2LcfHkDmSmp8DBrRY6+wbAwtqxyHPOB/2J88FbkRjzHABgaeeKFl2Gwb3mZwCA9NREBO1eggc3zyAxLhL6hmbwrNMKPt1GQkfPsDQu6710m3iUdQgyZk3qwnlMfxjXrgYdW0tc6jYM0fuD3n3OZ/XhOX8CDDzdkPksEg8ClyNi4x65Og5De8PZvz8k1hWQfD0Mt0ZNR9LFG6q8FIV0zCn5d76y9P058v2VFLRxuo3K2i6vFLrN3dOnT+Hk5FSg3MHB4aO5zd3R86FY8OcBDPqiNTZPHYXK9rYYMX814pMLvxXN5bCHaNvACysnDMa6n0fAyswEw+f/jpfxSbI6lawrYPz/umDbzDFY89Mw2FiYYfi835FQRJv02u6d23Bw/x4MHfE95v26BBIdHQT8PAHZ2dlFnvPvPyew5vcV6Nn7f/h18Qo4Ojsj4OcJSExMkNVxcXXDyNFjsXTlWkydMRsQgMmTxnOxVBGOXLqFX3YexeBOzfDnj4NQuaI1hi3ejPjktELrX7r3GO3qVsPvo/ti47hvYWVqhKGLNiE6IX+XAkEQMHr5NjyPTcCvQ7/G1p8GwcbcGEN+24SMrKJ/tvTav4dWI+TYJnzhOwVDA7ZBS6KH9fMGIic7q8hzjMys0baHP4ZN24lhU3fA2bMBNi8cgeiI+wCAlMSXSEl8iXa9xmHkrP3oNmgW7l3/F7vXTCqty1IrGvp6SL5+FzdHTi1WfV3Hiqi3fyXiTp7H6bpfIHzxBlRfOQMWrZvI6th0bw+PeRNxf8ZSnK7fFSnXw+B9aA20K5ip6jKIFOsUWlpa4vr16wXKr127BnPz8vXXvaI2HT6Frs280fmzenC2s8KPvl9CR1sL+05dKLT+zCG90aNVI7g72MHJ1hI/9+8OQSrgwu37sjrtG9aCd9XKqGhpDpeK1vDv/TnSMjJx/5nq/tL5GAiCgP17d6NHzz5o0LAxnJycMXrMeMTHxeFcyJkiz9u3ZxfatOsAnzbtUKmSA4aNGAWJRILjRw/L6rRr3wnVqteAlZU1XFzd0KevH2JjYvDyZXRpXJra+eN4CL5sXBtdGnnBxbYCJvXuCB0tLew9e7XQ+oH9v8TXzeuhir01nKwtEPC/zyEIAi7cDQcAPH0Zj+vhz/Fj7w6o5mgHR2sL/NSrIzJzcvD3xZuleWlqSRAEnDmyEc07D4FnnVawruSO7oNnIyXxJe5cOV7keR61WsC9ZjNYWDvCwsYJbbqPgraOHp49vAYAsKpYGb1HLoJHrRYwt6oEF88GaN19FMKunkBeXm5pXZ7aiDlyCvcCFiJ6X9Hv+ZscBvVERngE7oybg9SwR3iybDOidh2B0/e+sjpOo/zwbM12RGzYjdQ7D3FjWADy0jNh79tNRVehnqRSQWXHp0ihTmGvXr0wcuRInDhxAnl5ecjLy0NwcDC+//579OzZU9kxlrqc3FyEPX6O+lXdZGVisRj1q7rhxoMnxWojMysbuXl5MDLQK/I1dp84BwM9HbhVKnhbG3otOioSCQnxqOlVW1amr2+Ayu4euHvndqHn5OTk4MGDe/B64xyxWIyaXrURFlb4OZmZGQg6dhhW1tawsHj/yq5PTU5uHu48jYS3x+tRArFYBG8PJ1x/FFGsNjKzc5CbJ4Wxni4AIDs3v4Mh0Xo9vVksFkFbUxNXHzxTYvQfp4SYCKQmxcKlakNZmY6eISo618DTB9eK1YZUmofr5w4hOysdlVy9iqyXmZ4Cia4BNDQUmopObzBp4IXY4BC5sphjp2HawAsAINLSgnHtqogNOvu6giAgNvgsTBqUv+k99PFQ6P/u6dOn4/Hjx2jVqhU0NfObkEql6Nu3L2bNmqXUAMtCYkoa8qRSmBvL31PQ3NgAjyNfFnGWvEXb/4KFiRG8Pd3kyk+F3saPyzYjMzsHFsaGWDZ2EEwN9ZUW+8coISF/uNfE1FSu3MTEBAkJ8YWek5ycBKlUWsg5pnj+TL6z8dfBfVi/9ndkZmbCrqI9ps2cCy0tLSVewcchITUdeVIB5kbyv6/mhvp4HFW8Oxwt3B2ECsaG8PZwBgA4WlvAxswYi/YE4+c+HaEr0camoHOITkhGbHKK0q/hY5OSlP++GxjLj9AYGFsgNTHmnedGPbuHldN6ITcnC9o6eujz/WJY2rkWWjctJQEn9y1HveY9lBP4J05iZYGsaPn/Z7KiY6FlbAixjgRapsYQa2oi62XcW3XioO/uXJqhlnvqtvq4vCtxp1AQBERFRWH9+vWYMWMGQkNDoauri+rVq8PBwaFYbRR265ic7BxItD+OL+J1B4Nx9HwoVk0YUuCa6nm44s/po5GYkoY9/5zHhKV/YEPASJgZGRTR2qfn5IkgLFv8q+zx5KkzVfp6zVq0gletOoiPj8fe3TswN3A65sz/Ddra2ip93U/N2sOnceTSTaz27yfLDGppaOCXwd0x5Y8D+GzMPGiIRfCu4ozGVV0B8MP+baFnD2Dfuimyx33HLFe4LQsbR4yYsRuZ6am4efEIdq6aiIE/bizQMczMSMXGX4aggp0rWnUdrvDrEamCAmtl6R0U6hS6urri1q1bcHNzg5ub2/tPektgYCCmTpWfkDuxf0/8OKB8rO4zMdSHhliMuCT5BSBxSamwMH73yruNf53E+kMnsHzcoEKHhXUl2rC3soC9lQWquzqgy7g52PvPBXz7eUulXoM6q+/dUG6FcG5ODgAgMSEBZmavMyKJiYlwdnYptA0jI2OIxWIkJiTIlScmJsDETD57qK9vAH19A9jaVYR7FQ/07tEVIWdPo1lz/kzeZGqgBw2xCHFvLSqJS0mDxXv+qNlw9CzWHjmDlaP+h8oVreSe83SwxfZJg5GSkYmc3DyYGerjm9mr4enAaRVv86jVEvYuNWSPc3PyF+OkJsXByOT1/VRTk2Jh4/Du1bmamtowt8r/Q97OqSqeP7qBs0f/QBe/15/NWRlp2DBvICQ6eugzcjE0ND+OP9zLWlZ0LCRWFnJlEisL5CSlQJqZhezYBEhzcyGxNH+rjjmyipmVJ1JEiecUisViuLm5IS4u7v2VizBx4kQkJSXJHWP6fqVwe8qmpamJKo52uHj7gaxMKpXi4u0HqO5adDZ0w6ETWL0/CEvGDICnk32xXksqlSInlxO336SnpwdbWzvZYV/JAaamZrh27fVihvT0NNy7ewfuHp6FtqGlpQVX18q4du2KrEwqleJ66FVUqVL4OfkECBBkHVF6TUtTAx6VbHAhLFxWJpUKuBAWjhrORW8NtO7IGfz+179Y9l0fVH1HR89QVwdmhvp4Eh2H208i0bymu1Lj/xhIdPVhbuUgOyztXGFgbIFHt8/J6mRmpCLi0XVUcq1ZorYFQZB1Ml+1s25uf2hoauGb0cugpV38uyLQuyWeC4V5ywZyZRatGiHhXCgAQMjJQdKVW7Bo+XquKEQimLdoiMRzhS/q+lQJUqnKjk+RQnMKZ8+ejbFjx2L58uWoVq1aic8v7NYxqeVs6Pibdp8h4Pdt8HCqiGrO9thy5F9kZGWjc9N6AIDJK/9EBVNjfNejAwBg/aETWLH7CGYO6Q0bC1PEJuZvuaGnI4GejgQZWdlYsz8IzWp5wsLECIkpadgedBYxicnwqVejyDgIEIlE6NzlS2zfuhm2tnawsrLG5j/+3969R1VR9f8Df3ORI1cRRC6FeEFAvKNmkijGSSjlkULSJ/oChqIo+ri+XogsxcqFN8ywr1hJXFykovIo3hIN8BZLEQNNBdFALCV+CoigXM/8/iAnTyBxOVwOvF9rzVrOzJ6Zz8z2zGz2ntk7CgaGhnh1wmtiuo+DVuBV+9cw3dUNADDjbXds3bIRloOtYWVljYRD8aiorIDTGy4AgIL793D2TApG241Fr1698ODBAxzYtwcSDQ2MGfdKQ6F0e/8jnYBPog7C1sIMw/qbITbpAp5WVWOG/SgAwMeRB9FXXxdL3q4bFz3yxHlsP5yCkA/egZmhPh78WfuuJdGAVs+65vnE9OvoraMFU4NeyPm9EBvjfsCUUdawt224Fpj+oqKigtecvZB8aAcMjS3Q2+hlnDoQBl39vhhiJxXTRayfA9sxUkx4wxMAcCJuC6xGOEDf0AyVFeXITD2C3KyL8FnxLYC6AmHURl9UVVXAY8FGVD4tQ+XTurzT1jOAqqpa+59sJ6amrQVty7/6NtUa8DL0RtqgqugRKu7eh/Xn/4ueLxkjc04gAODON3tgsdATNiErcDfqAPpMeRWmHm8i7V/zxX3kbo3EyO82oCT9FzxKu4L+S7yhrq2Ju9Hx7X5+1H20qFDo5eWFJ0+eYOTIkdDQ0ICmpqbc+qKihl/+VyZTx49CcWk5dsSfwMNHj2HVzwzbls+F4Z/NxwVFJVBRVRHT709KRXVNLVZ+tUtuP35ub2D+21OhqqKCvPuFOHLuEkrKytFLRxtDB7yMnR8txKCX/3kQ7O7unZmzUFFRgf/b9gXKy8pgO3QYgj9dL/feX8H9eyh99Fe/kA6Tp+BR6SN8vysKxcXFGDhwEII/DUHvPz8+6aGhgevXfkHCoXiUl5VBX783hg4bjg2hYdDX710vBgKcxw5F8eNyhB9OwYPSMli/bIzti9+D4Z/Nx/eLHkFF5a/fRdzpS6iuqcXyb/bJ7Wf+tEnwd3UEADx49Bih+xPxsLQMRr10Mf3VEfB7a1J7nZLSc5g2F1WVT3Ewcg0qnpTCYrAdfJZ/I1ezV1SYjyeP/3qVorz0IfZ/8yEel/w/9NTUhYm5FXxWfAvLYXV/ZN3Lu467t+u6HduywlnueMtDT6G3UcOdlXdXvcYMw4Qf/7r3227+CABwNyYeV3yDIDE1gqb5Xx0hP837DWn/mg/b0CD0X+yFit8KcHX+x3hw8pyY5v6+49AwMoDVmiV1nVdn3sDF6XNRVdjyVrquqLt2HdNWWjSiSXR0dKPrWzLMXWcc0aS76owjmnRXnXFEk+6qs45o0l11phFNuruOHNFk1vKmdRPXEns3N+3j2a6kRTWFHNuYiIiIOhq/PlasVvdCWlFRUW+oMT09vdbuloiIiIjaUYsKheXl5QgMDERcXFyDXyFz3FgiIiJqa+y8WrFaNMzdypUrkZSUhPDwcEgkEuzcuRNr166FmZkZYmJiFB0jERERUT2CTGizqTtqUU3h4cOHERMTA0dHR8yZMwcODg6wtLSEhYUFYmNj4enpqeg4iYiIiKgNtaimsKioCAMH1o2/qKenJ3ZBM3HiRJw5w68liYiIqO3JBFmbTd1RiwqFAwcORG5u3agGNjY2iIuLA1BXg6ivr6+w4IiIiIiofbSo+XjOnDnIzMzE5MmT8eGHH8LV1RVfffUVqqursWXLFkXHSERERFRPd333r600q1Aok8mwadMmJCQkoKqqCvfu3cOaNWuQlZWF9PR0WFpaYsQIDtlGREREpGyaVShct24dgoODIZVKoampiS+//BKFhYX47rvvYGHR/Xr+JiIioo7DmkLFatY7hTExMdi+fTtOnDiBgwcP4vDhw4iNjYVM1j1fyCQiIiLqKppVKMzPz8dbb70lzkulUqioqODevXsKD4yIiIioMYIgtNnUHTWr+bimpgY9e/aUW9ajRw9UV1crNCgiIiKif8KWSsVqVqFQEAT4+PhAIpGIyyoqKrBgwQJoa2uLy+Lj4xUXIRERERG1uWYVCr29veste//99xUWDBEREVFT8UMTxWpWoTAyMrKt4iAiIiKiDtSizquJiIiIOprQTYejaystGuaOiIiIiLoW1hQSERGRUuI7hYrFmkIiIiIiYk0hERERKSfWFCoWC4VERESklGT80ESh2HxMRERERKwpJCIiIuXE5mPFYk0hEREREbGmkIiIiJSTIOM7hYrEmkIiIiIiYk0hERERKSe+U6hYrCkkIiIiItYUEhERkXIS2E+hQrFQSEREREpJxuZjhWLzMRERERGxppCIiIiUE7ukUSzWFBIRERERawqJiIhIObFLGsViTSERERERsaaQiIiIlBO7pFEs1hQSERERtZN169bB3t4eWlpa0NfXb9I2giBg9erVMDU1haamJqRSKXJycuTSFBUVwdPTE3p6etDX14evry/KysqaFRsLhURERKSUBJnQZlNbqaqqgoeHB/z9/Zu8zcaNGxEWFoYdO3bgwoUL0NbWhrOzMyoqKsQ0np6euHbtGk6ePIkjR47gzJkz8PPza1ZsbD4mIiIipaSMXdKsXbsWABAVFdWk9IIgYOvWrfj4448xY8YMAEBMTAyMjY1x8OBBzJ49Gzdu3MAPP/yAtLQ0jB07FgCwbds2vPXWW9i8eTPMzMyadCzWFBIRERH9TWVlJUpLS+WmysrKdo8jNzcXBQUFkEql4rJevXph/PjxSE1NBQCkpqZCX19fLBACgFQqhaqqKi5cuNDkY3WamkKdV//V0SG0SmVlJUJCQhAUFASJRNLR4bSKdUcHoABdJj8GeXZ0BK3WVfJiZkcHoABdJS8AANXZHR1Bq3SpvOhA5w5PbrN9BwcHi7V6z6xZswbBwcFtdsyGFBQUAACMjY3llhsbG4vrCgoK0LdvX7n16urqMDAwENM0BWsKFaSyshJr167tkL8iqD7mR+fBvOg8mBedB/Oi8wsKCsKjR4/kpqCgoAbTfvjhh1BRUWl0ysrKauczaL5OU1NIRERE1FlIJJIm1+IuW7YMPj4+jaYZOHBgi+IwMTEBAPzxxx8wNTUVl//xxx8YNWqUmKawsFBuu5qaGhQVFYnbNwULhUREREStYGRkBCMjozbZ94ABA2BiYoIff/xRLASWlpbiwoUL4hfMEyZMQElJCdLT0zFmzBgAQFJSEmQyGcaPH9/kY7H5mIiIiKid5OfnIyMjA/n5+aitrUVGRgYyMjLk+hS0sbHBf//7XwCAiooKli5dis8//xwJCQm4evUqvLy8YGZmBjc3NwDAkCFD4OLignnz5uHixYs4f/48AgICMHv27CZ/eQywplBhJBIJ1qxZwxeGOwnmR+fBvOg8mBedB/Oi+1q9ejWio6PF+dGjRwMAkpOT4ejoCADIzs7Go0ePxDQrV65EeXk5/Pz8UFJSgokTJ+KHH35Az549xTSxsbEICAiAk5MTVFVV4e7ujrCwsGbFpiIIAkeTJiIiIurm2HxMRERERCwUEhERERELhUREREQEFgq7DUdHRyxdurSjw2gzXf38iKj7SklJgYqKCkpKSjo6FOrilKpQ6OPjI/YMrqGhAUtLS3z66aeoqanp6NA6lI+Pj/hZujJj/iqX1NRUqKmpYdq0aR0dSrf1/G/m+enWrVsdHVqXxnsVdVVKVSgEABcXF9y/fx85OTlYtmwZgoODsWnTpo4Oq0NUVVV1dAgKx/xVHhEREVi8eDHOnDmDe/fudXQ43daz38zz04ABA5q1j9raWshksjaKsGtS1L2K1546E6UrFEokEpiYmMDCwgL+/v6QSqVISEjAli1bMHz4cGhra8Pc3BwLFy6U6wjyzp07cHV1Re/evaGtrY2hQ4fi2LFjAIDi4mJ4enrCyMgImpqaGDx4MCIjI8Vt7969i3fffRf6+vowMDDAjBkzkJeXJ65/VlO3efNmmJqawtDQEIsWLUJ1dbWY5v79+5g2bRo0NTUxYMAAfP/99+jfvz+2bt0qpikpKcHcuXNhZGQEPT09vP7668jMzBTXBwcHY9SoUdi5cycGDBgg1z/R88rLy+Hl5QUdHR2YmpoiNDS0tZe93bwofwHg/PnzcHR0hJaWFnr37g1nZ2cUFxc3uJ9du3Zh7Nix0NXVhYmJCd577z25IYAay/OqqioEBATA1NQUPXv2hIWFBUJCQtr+5JVIWVkZ9u7dC39/f0ybNg1RUVFy6xMSEjB48GD07NkTU6ZMQXR0dL3mr3PnzsHBwQGampowNzfHkiVLUF5e3r4n0gU8+808P3355ZeN3g+joqKgr6+PhIQE2NraQiKRID8/H5WVlVi+fDleeuklaGtrY/z48UhJSem4k+vEWvosauzaBwYGwtzcHBKJBJaWloiIiJA7Znp6OsaOHQstLS3Y29sjOzu7vU+bujilKxT+naamJqqqqqCqqoqwsDBcu3YN0dHRSEpKwsqVK8V0ixYtQmVlJc6cOYOrV69iw4YN0NHRAQB88sknuH79Oo4fP44bN24gPDwcffr0AQBUV1fD2dkZurq6OHv2LM6fPw8dHR24uLjI1dQlJyfj9u3bSE5ORnR0NKKiouQelF5eXrh37x5SUlJw4MABfPPNN/XGKfTw8EBhYSGOHz+O9PR02NnZwcnJCUVFRWKaW7du4cCBA4iPj0dGRkaD12TFihU4ffo0Dh06hMTERKSkpODy5cutvdQd4ln+ZmRkwMnJCba2tkhNTcW5c+fg6uqK2traBrerrq7GZ599hszMTBw8eBB5eXly41I2ludhYWFISEhAXFwcsrOzERsbi/79+7fD2SqPuLg42NjYwNraGu+//z6+++47POvyNDc3FzNnzoSbmxsyMzMxf/58rFq1Sm7727dvw8XFBe7u7rhy5Qr27t2Lc+fOISAgoCNOp8v5p/shADx58gQbNmzAzp07ce3aNfTt2xcBAQFITU3Fnj17cOXKFXh4eMDFxQU5OTkddCbKo6nPIqDha+/l5YXdu3cjLCwMN27cwNdffy0+o55ZtWoVQkNDcenSJairq+ODDz5oz1Ok7kBQIt7e3sKMGTMEQRAEmUwmnDx5UpBIJMLy5cvrpd23b59gaGgozg8fPlwIDg5ucL+urq7CnDlzGly3a9cuwdraWpDJZOKyyspKQVNTUzhx4oQYl4WFhVBTUyOm8fDwEGbNmiUIgiDcuHFDACCkpaWJ63NycgQAwhdffCEIgiCcPXtW0NPTEyoqKuSOP2jQIOHrr78WBEEQ1qxZI/To0UMoLCx84XV5/PixoKGhIcTFxYnrHz58KGhqagr/+c9/GjzHzqKx/P33v/8tvPbaay/cdvLkyY2eX1pamgBAePz4sSAIjef54sWLhddff10uz0mevb29sHXrVkEQBKG6ulro06ePkJycLAiCIAQGBgrDhg2TS79q1SoBgFBcXCwIgiD4+voKfn5+cmnOnj0rqKqqCk+fPm3z+LsKb29vQU1NTdDW1hanmTNn1kv39/thZGSkAEDIyMgQl925c0dQU1MTfv/9d7ltnZychKCgoLY7CSXUmmdRQ9c+OztbACCcPHmyweMlJycLAIRTp06Jy44ePSoA4O+FFErphrk7cuQIdHR0UF1dDZlMhvfeew/BwcE4deoUQkJCkJWVhdLSUtTU1KCiogJPnjyBlpYWlixZAn9/fyQmJkIqlcLd3R0jRowAAPj7+8Pd3R2XL1/G1KlT4ebmBnt7ewBAZmYmbt26BV1dXbk4KioqcPv2bXF+6NChUFNTE+dNTU1x9epVAHXD1airq8POzk5cb2lpid69e4vzmZmZKCsrg6Ghodxxnj59KnccCwuLRgfdvn37NqqqquQGwDYwMIC1tfU/X9xO4EX5O27cOHh4eDR5P+np6QgODkZmZiaKi4vFd3by8/Nha2vbaJ77+PjgjTfegLW1NVxcXDB9+nRMnTq1Tc5XGWVnZ+PixYviuJzq6uqYNWsWIiIi4OjoiOzsbIwbN05um1deeUVuPjMzE1euXEFsbKy4TBAEyGQy5ObmYsiQIW1/Il3ElClTEB4eLs5ra2v/4/0QADQ0NMR7IABcvXoVtbW1sLKyktt/ZWVlvfsStfxZBNS/9hkZGVBTU8PkyZMbPebz25iamgIACgsL0a9fvzY4Q+qOlK5Q+OwGqKGhATMzM6irqyMvLw/Tp0+Hv78/1q1bBwMDA5w7dw6+vr6oqqqClpYW5s6dC2dnZxw9ehSJiYkICQlBaGgoFi9ejDfffBN37tzBsWPHcPLkSTg5OWHRokXYvHkzysrKMGbMGLmH1zPPF8569Oght05FRaVZLw+XlZXB1NS0wfd39PX1xX9ra2s3eZ/KqKH8BeqaZpqqvLwczs7OcHZ2RmxsLIyMjJCfnw9nZ2exyb+xPLezs0Nubi6OHz+OU6dO4d1334VUKsX+/fvb5JyVTUREBGpqauQGWRcEARKJBF999VWT9lFWVob58+djyZIl9dbxAdc82trasLS0FOebcj8E6n5TKioq4nZlZWVQU1NDenq63B+4AOo1Y1LLn0VA/Wvf1Pvb88+ZZ9vzIxVSJKUrFP79BgjU1QrJZDKEhoZCVbXuNcm4uLh625qbm2PBggVYsGABgoKC8O2332Lx4sUA6gp43t7e8Pb2hoODA1asWCEWEPbu3Yu+fftCT0+vRTFbW1ujpqYGP//8M8aMGQOg7t3A5z+SsLOzQ0FBAdTV1Vv1/tqgQYPQo0cPXLhwQXy4FhcX4+bNm//4V2hn0FD+AnV/If/4449Yu3btP+4jKysLDx8+xPr162Fubg4AuHTpUr10L8pzANDT08OsWbMwa9YszJw5Ey4uLigqKoKBgUErz1C51dTUICYmBqGhofVqT93c3LB7925YW1uLH3E9k5aWJjdvZ2eH69evN5jX1DpNvR/+3ejRo1FbW4vCwkI4ODi0dZhKrzXPor8bPnw4ZDIZTp8+DalU2ibxEjWF0n9oAtQ1xVZXV2Pbtm349ddfsWvXLuzYsUMuzdKlS3HixAnk5ubi8uXLSE5OFpuoVq9ejUOHDuHWrVu4du0ajhw5Iq7z9PREnz59MGPGDJw9exa5ublISUnBkiVL8NtvvzUpPhsbG0ilUvj5+eHixYv4+eef4efnJ/fXolQqxYQJE+Dm5obExETk5eXhp59+wqpVqxos0LyIjo4OfH19sWLFCiQlJeGXX36Bj4+PeINSVkFBQUhLS8PChQtx5coVZGVlITw8HA8ePKiXtl+/ftDQ0BD/PyQkJOCzzz6TS9NYnm/ZsgW7d+9GVlYWbt68iX379sHExESuxra7OnLkCIqLi+Hr64thw4bJTe7u7oiIiMD8+fORlZWFwMBA3Lx5E3FxceJHV8/+vwcGBuKnn35CQEAAMjIykJOTg0OHDvFDEwVoyv2wIVZWVvD09ISXlxfi4+ORm5uLixcvIiQkBEePHm2HyJVfS699//794e3tjQ8++AAHDx4UnzNNKVASKZJylxT+NHLkSGzZsgUbNmzAsGHDEBsbW68LkdraWixatAhDhgyBi4sLrKyssH37dgB173cEBQVhxIgRmDRpEtTU1LBnzx4AgJaWFs6cOYN+/frhnXfewZAhQ+Dr64uKiopm1RzGxMTA2NgYkyZNwttvv4158+ZBV1dX7FZGRUUFx44dw6RJkzBnzhxYWVlh9uzZuHPnDoyNjZt1PTZt2gQHBwe4urpCKpVi4sSJYg2lsrKyskJiYiIyMzPxyiuvYMKECTh06JDYvPw8IyMjREVFYd++fbC1tcX69evFGsBnGstzXV1dbNy4EWPHjsW4ceOQl5eHY8eOKX3BWhEiIiIglUrRq1eveuvc3d1x6dIlPH78GPv370d8fDxGjBiB8PBw8etjiUQCoK7m9/Tp07h58yYcHBwwevRorF69Wq5JmlqmKffDF4mMjISXlxeWLVsGa2truLm5IS0tjU36TdSaax8eHo6ZM2di4cKFsLGxwbx589hFE7U7FUH4sx8Jale//fYbzM3NcerUKTg5OXV0OERtat26ddixYwfu3r3b0aEQEdELKN07hcoqKSkJZWVlGD58OO7fv4+VK1eif//+mDRpUkeHRqRw27dvx7hx42BoaIjz589j06ZNbBomIurkWChsJ9XV1fjoo4/w66+/QldXF/b29oiNja331TJRV5CTk4PPP/8cRUVF6NevH5YtW4agoKCODouIiBrB5mMiIiIi6hofmhARERFR67BQSEREREQsFBIRERERC4VEREREBBYKiYiIiAgsFBIRERERWCgkIiIiIrBQSERERERgoZCIiIiIAPx/SlAn71TzRdQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}