level_2_project_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **MEHWISH ZAMAN (DATAT ANALYST)**\n",
        "  AS INTERN AT OASIS INFOBYTE\n",
        "\n",
        "\n",
        "  **PROJECT_2 PROPOSAL** ( LEVEL_2)\n",
        "  "
      ],
      "metadata": {
        "id": "OO4fNOzQEIzZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Idea: Wine Quality Prediction**\n",
        " **Description:**\n",
        "\n",
        " The focus is on predicting the quality of wine based on its chemical characteristics, offering a\n",
        "real-world application of machine learning in the context of viticulture. The dataset\n",
        "encompasses diverse chemical attributes, including density and acidity, which serve as the\n",
        "features for three distinct classifier models.\n",
        "\n",
        "**Key Concepts and Challenges:**\n",
        "\n",
        "1. Classifier Models: Utilizing Random Forest, Stochastic Gradient Descent, and Support Vector Classifier (SVC) for wine quality prediction.\n",
        "2. Chemical Qualities: Analyzing features like density and acidity as predictors for wine quality.\n",
        "3. Data Analysis Libraries: Employing Pandas for data manipulation and Numpy for array operations.\n",
        "4. Data Visualization: Using Seaborn and Matplotlib for visualizing patterns and insights in the dataset."
      ],
      "metadata": {
        "id": "TYbKNkysFDRU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b68od0w67EyR"
      },
      "outputs": [],
      "source": [
        "# IMPORTING LIBRARIES.\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/LEVEL_2_PROJECT_2.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sG_a89u08HI4",
        "outputId": "1dc61e51-3bfd-4cce-fd95-e6bf228356f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/LEVEL_2_PROJECT_2.zip\n",
            "replace WineQT.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LOADING DATA.\n",
        "df = pd.read_csv('/content/WineQT.csv')"
      ],
      "metadata": {
        "id": "9hnl2Phj8QKE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# DATA FIRST LOOK.\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "4eG2AgNY8qJG",
        "outputId": "f974ba97-5bb0-4d00-cb09-199ea6d44a57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  Id  \n",
              "0      9.4        5   0  \n",
              "1      9.8        5   1  \n",
              "2      9.8        5   2  \n",
              "3      9.8        6   3  \n",
              "4      9.4        5   4  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40178e02-b148-488f-80fe-7ed2cf3b8743\" 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>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "      <th>Id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40178e02-b148-488f-80fe-7ed2cf3b8743')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-40178e02-b148-488f-80fe-7ed2cf3b8743 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-40178e02-b148-488f-80fe-7ed2cf3b8743');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3a6facb9-8366-4138-a26c-5e90e2e1c74f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3a6facb9-8366-4138-a26c-5e90e2e1c74f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3a6facb9-8366-4138-a26c-5e90e2e1c74f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlARY5hd8v7O",
        "outputId": "158efc38-b9ee-410b-ca87-96edadd95914"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1143, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DATA INFORMATION.\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2woHBdJC844f",
        "outputId": "5dbf292a-6f94-4cd1-82dd-2a45c53c87b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1143 entries, 0 to 1142\n",
            "Data columns (total 13 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   fixed acidity         1143 non-null   float64\n",
            " 1   volatile acidity      1143 non-null   float64\n",
            " 2   citric acid           1143 non-null   float64\n",
            " 3   residual sugar        1143 non-null   float64\n",
            " 4   chlorides             1143 non-null   float64\n",
            " 5   free sulfur dioxide   1143 non-null   float64\n",
            " 6   total sulfur dioxide  1143 non-null   float64\n",
            " 7   density               1143 non-null   float64\n",
            " 8   pH                    1143 non-null   float64\n",
            " 9   sulphates             1143 non-null   float64\n",
            " 10  alcohol               1143 non-null   float64\n",
            " 11  quality               1143 non-null   int64  \n",
            " 12  Id                    1143 non-null   int64  \n",
            "dtypes: float64(11), int64(2)\n",
            "memory usage: 116.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DATA STATISTICS.\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "giudDCEW89H6",
        "outputId": "c7b27a08-f6c8-4e44-b67f-b49119b775b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count    1143.000000       1143.000000  1143.000000     1143.000000   \n",
              "mean        8.311111          0.531339     0.268364        2.532152   \n",
              "std         1.747595          0.179633     0.196686        1.355917   \n",
              "min         4.600000          0.120000     0.000000        0.900000   \n",
              "25%         7.100000          0.392500     0.090000        1.900000   \n",
              "50%         7.900000          0.520000     0.250000        2.200000   \n",
              "75%         9.100000          0.640000     0.420000        2.600000   \n",
              "max        15.900000          1.580000     1.000000       15.500000   \n",
              "\n",
              "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
              "count  1143.000000          1143.000000           1143.000000  1143.000000   \n",
              "mean      0.086933            15.615486             45.914698     0.996730   \n",
              "std       0.047267            10.250486             32.782130     0.001925   \n",
              "min       0.012000             1.000000              6.000000     0.990070   \n",
              "25%       0.070000             7.000000             21.000000     0.995570   \n",
              "50%       0.079000            13.000000             37.000000     0.996680   \n",
              "75%       0.090000            21.000000             61.000000     0.997845   \n",
              "max       0.611000            68.000000            289.000000     1.003690   \n",
              "\n",
              "                pH    sulphates      alcohol      quality           Id  \n",
              "count  1143.000000  1143.000000  1143.000000  1143.000000  1143.000000  \n",
              "mean      3.311015     0.657708    10.442111     5.657043   804.969379  \n",
              "std       0.156664     0.170399     1.082196     0.805824   463.997116  \n",
              "min       2.740000     0.330000     8.400000     3.000000     0.000000  \n",
              "25%       3.205000     0.550000     9.500000     5.000000   411.000000  \n",
              "50%       3.310000     0.620000    10.200000     6.000000   794.000000  \n",
              "75%       3.400000     0.730000    11.100000     6.000000  1209.500000  \n",
              "max       4.010000     2.000000    14.900000     8.000000  1597.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb1d1c91-1e51-4786-8d22-f99246999f99\" 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>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "      <th>Id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "      <td>1143.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.311111</td>\n",
              "      <td>0.531339</td>\n",
              "      <td>0.268364</td>\n",
              "      <td>2.532152</td>\n",
              "      <td>0.086933</td>\n",
              "      <td>15.615486</td>\n",
              "      <td>45.914698</td>\n",
              "      <td>0.996730</td>\n",
              "      <td>3.311015</td>\n",
              "      <td>0.657708</td>\n",
              "      <td>10.442111</td>\n",
              "      <td>5.657043</td>\n",
              "      <td>804.969379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.747595</td>\n",
              "      <td>0.179633</td>\n",
              "      <td>0.196686</td>\n",
              "      <td>1.355917</td>\n",
              "      <td>0.047267</td>\n",
              "      <td>10.250486</td>\n",
              "      <td>32.782130</td>\n",
              "      <td>0.001925</td>\n",
              "      <td>0.156664</td>\n",
              "      <td>0.170399</td>\n",
              "      <td>1.082196</td>\n",
              "      <td>0.805824</td>\n",
              "      <td>463.997116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.392500</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.995570</td>\n",
              "      <td>3.205000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>411.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>0.996680</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>794.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.100000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.997845</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1209.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1597.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb1d1c91-1e51-4786-8d22-f99246999f99')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-bb1d1c91-1e51-4786-8d22-f99246999f99 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-bb1d1c91-1e51-4786-8d22-f99246999f99');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-38992761-e292-441f-ad50-d8c4f546c876\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-38992761-e292-441f-ad50-d8c4f546c876')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-38992761-e292-441f-ad50-d8c4f546c876 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CHECKING NULL VALUES.\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3moEL9F19AwI",
        "outputId": "c5d0569f-a999-428c-c6a9-ab5c2bd80842"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           0\n",
              "volatile acidity        0\n",
              "citric acid             0\n",
              "residual sugar          0\n",
              "chlorides               0\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "density                 0\n",
              "pH                      0\n",
              "sulphates               0\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "Id                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CHECKING FOR OUTLIERS.\n",
        "input_columns = df.columns[:-1].tolist()  # Exclude the last column\n",
        "\n",
        "# Set up subplots\n",
        "num_columns = len(input_columns)\n",
        "num_rows = (num_columns + 1) // 2\n",
        "\n",
        "fig, axes = plt.subplots(nrows=num_rows, ncols=2, figsize=(10, 5 * num_rows))\n",
        "axes = axes.flatten()\n",
        "\n",
        "# Plot box plots for each selected variable\n",
        "for i, column in enumerate(input_columns):\n",
        "    sns.boxplot(x=df[column], ax=axes[i])\n",
        "    axes[i].set_title(column)\n",
        "    axes[i].set_xticks([])  # Remove existing x-axis ticks\n",
        "    axes[i].set_xticklabels([])  # Remove existing x-axis tick labels\n",
        "    axes[i].tick_params(axis='x', rotation=90)  # Set the rotation angle\n",
        "\n",
        "# Remove empty subplots, if any\n",
        "for j in range(num_columns, num_rows * 2):\n",
        "    fig.delaxes(axes[j])\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uRsQqZLrHQOm",
        "outputId": "be99dd35-34fa-4866-8278-74e6dabeca60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x3000 with 12 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAuuCAYAAADCoIGNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5hU5d0//s/s0pa2VOkiKIpiwVgiNjCJGgWDJRiViIi9RWI0xq8FiSVPNEaTPCqaKMYWDfYaW+QxIrbHQlBQg4AFpBdR+p7fH/52HmZ3gaXcLODrdV1c7Jy5z30+58zMuec9c+acXJZlWQAAAABJFNV0AQAAALA5E7wBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8oZreeOON2HvvvaNBgwaRy+XinXfeicsvvzxyudwGr+WOO+6IXC4XkyZN2mDLHDlyZORyuRg5cuRq2/bq1St69eqVvz1p0qTI5XJxxx13JKsPAKorxTha1VhXU+8TqmNNxuaBAwfGVlttVTAtl8vF5ZdfnqQ22BwJ3lANS5cujX79+sXs2bPj+uuvj7vuuis6duxY02Vt0p566ikDNgCbnHvvvTduuOGGmi5jo/PKK6/E5ZdfHnPnzq3pUmCjVKumC4BNwYQJE2Ly5Mnx5z//OU4++eT89EsuuSR+9atf1WBlG87+++8fCxcujDp16qzxvB07doyFCxdG7dq189OeeuqpuPHGG4VvADYp9957b4wdOzYGDx5cML2qsW5jtq71Lly4MGrV+r8o8corr8TQoUNj4MCB0aRJk/VUJWw+BG+ohunTp0dEVBpIatWqVTDobM6KioqiXr16azVvLpdb63kBYFOwqY1161rvprSusDFwqDmsxsCBA6Nnz54REdGvX7/I5XL53y9X/O3W8OHDI5fLxe23317Qx9VXXx25XC6eeuqp/LTx48fHj3/842jWrFnUq1cvdt9993jssccqLf+9996L733ve1FSUhLt27ePK6+8MsrKyqpV+5gxY2LgwIHRuXPnqFevXrRu3ToGDRoUs2bNqtT2888/j5NOOinatm0bdevWjU6dOsUZZ5wRS5YsiYiV/8b71ltvja233jpKSkpizz33jH/961+V+q74O7KBAwfGjTfeGBHfDPzl/7Isi6222ir69u1bqY9FixZFaWlpnHbaadVadwA2Dw888EDkcrn4n//5n0r33XLLLZHL5WLs2LH5af/85z9jv/32iwYNGkSTJk2ib9++MW7cuNUu59FHH43evXvnx8Gtt946rrjiili+fHm+Ta9eveLJJ5+MyZMn58eu8t8+r8lvpu++++7YbbfdoqSkJJo1axbHHHNMfPrpp6udb/LkyXHmmWfGdtttFyUlJdG8efPo169flb9Vnzt3bvz85z+PrbbaKurWrRvt27ePAQMGxMyZM1dZ7yOPPBI77rhj1KtXL3bcccd4+OGHq6xlxd94X3755XHBBRdERESnTp3y22bSpEnRs2fP2GWXXarsY7vttouDDz54tesNm4Nvx1d1sA5OO+20aNeuXVx99dXxs5/9LPbYY49o1apVlW1PPPHEeOihh+K8886LAw88MDp06BD//ve/Y+jQoXHSSSfFoYceGhHfhOl99tkn2rVrF7/61a+iQYMG8fe//z0OP/zwePDBB+OII46IiIgvvvgiDjjggFi2bFm+3a233holJSXVqv25556Ljz/+OE488cRo3bp1vPfee3HrrbfGe++9F6+++mr+Q4MpU6bEnnvuGXPnzo1TTz01unbtGp9//nk88MAD8fXXX6/08PLbbrstTjvttNh7771j8ODB8fHHH8ePfvSjaNasWXTo0GGV23TKlCnx3HPPxV133ZWfnsvl4qc//Wlcc801MXv27GjWrFn+vscffzzmz58fP/3pT6u17gBsHnr37h0NGzaMv//97/kPwsvdf//90a1bt9hxxx0jIuL555+PQw45JDp37hyXX355LFy4MP70pz/FPvvsE2+99ValE4St6I477oiGDRvGeeedFw0bNox//vOfcdlll8X8+fPj2muvjYiIiy++OObNmxefffZZXH/99RER0bBhwzVan6uuuiouvfTSOProo+Pkk0+OGTNmxJ/+9KfYf//94+23317lYdpvvPFGvPLKK3HMMcdE+/btY9KkSXHzzTdHr1694v3334/69etHRMSCBQtiv/32i3HjxsWgQYPiO9/5TsycOTMee+yx+Oyzz6JFixZV9v/ss8/GUUcdFTvssEP85je/iVmzZsWJJ54Y7du3X+U6HXnkkfHhhx/G3/72t7j++uvz/bds2TKOP/74OOWUU2Ls2LH5x6l8XT788MO45JJL1mj7wSYrA1brxRdfzCIiGzFiRMH0IUOGZBVfRlOnTs2aNWuWHXjggdnixYuzXXfdNdtyyy2zefPm5dt8//vfz3baaads0aJF+WllZWXZ3nvvnXXp0iU/bfDgwVlEZK+99lp+2vTp07PS0tIsIrKJEyeusu6vv/660rS//e1vWURkL730Un7agAEDsqKiouyNN96o1L6srKxgG7z44otZlmXZkiVLsi222CLr3r17tnjx4nz7W2+9NYuIrGfPnvlpEydOzCIiGz58eH7aWWedVWnbZVmWffDBB1lEZDfffHPB9B/96EfZVlttla8HgG+PY489Nttiiy2yZcuW5adNnTo1Kyoqyn7961/np3Xv3j3bYostslmzZuWnvfvuu1lRUVE2YMCA/LThw4dXGkerGjNPO+20rH79+gXjde/evbOOHTtWalvVWFfxfcKkSZOy4uLi7KqrriqY99///ndWq1atStMrqqrG0aNHZxGR3Xnnnflpl112WRYR2UMPPVSpffk4WlW93bt3z9q0aZPNnTs3P+3ZZ5/NIqLSOkdENmTIkPzta6+9tsr3JnPnzs3q1auXXXjhhQXTf/azn2UNGjTIFixYsMp1hs2FQ81hPWvdunXceOON8dxzz8V+++0X77zzTtx+++3RuHHjiIiYPXt2/POf/4yjjz46vvzyy5g5c2bMnDkzZs2aFQcffHB89NFH8fnnn0fENycg22uvvWLPPffM99+yZcvo379/tWpZ8ZvxRYsWxcyZM2OvvfaKiIi33norIiLKysrikUceicMOOyx23333Sn2s7DIob775ZkyfPj1OP/30gm/EBw4cGKWlpdWqryrbbrttfPe734177rknP2327Nnx9NNPR//+/Tfay7IAkM5PfvKTmD59esHPnR544IEoKyuLn/zkJxERMXXq1HjnnXdi4MCBBUdM7bzzznHggQcW/NyrKiuOmeXj83777Rdff/11jB8/fr2sx0MPPRRlZWVx9NFH58f/mTNnRuvWraNLly7x4osvVrvGpUuXxqxZs2KbbbaJJk2a5Mf1iIgHH3wwdtlll/wRdCta2Thavv1OOOGEgnH8wAMPjB122GFNVzWvtLQ0+vbtG3/7298iy7KIiFi+fHncf//9cfjhh0eDBg3Wum/YlAjekMAxxxwTvXv3jtdffz1OOeWU+P73v5+/7z//+U9kWRaXXnpptGzZsuDfkCFDIuL/TuY2efLk6NKlS6X+t9tuu2rVMXv27Dj33HOjVatWUVJSEi1btoxOnTpFRMS8efMiImLGjBkxf/78gsO/qmPy5MkREZXqq127dnTu3HmN+qpowIABMWrUqPwyRowYEUuXLo3jjz9+nfoFYNP0wx/+MEpLS+P+++/PT7v//vuje/fuse2220bE/41LVY2R22+/fcycOTO++uqrlS7jvffeiyOOOCJKS0ujcePG0bJly/zPm8rHzHX10UcfRZZl0aVLl0rvAcaNG5cf/1dm4cKFcdlll0WHDh2ibt260aJFi2jZsmXMnTu3oMYJEyast3E9ovrvO1ZmwIAB8cknn+TPA/P888/HtGnTjOt8q/iNNyQwa9asePPNNyMi4v3334+ysrIoKvrmc67yE6Odf/75Kz2hyDbbbLNe6jj66KPjlVdeiQsuuCC6d+8eDRs2jLKysvjhD39Y7RO01YRjjjkmfv7zn8c999wT/+///b+4++67Y/fdd1/ngR+ATVPdunXj8MMPj4cffjhuuummmDZtWowaNSquvvrq9dL/3Llzo2fPntG4ceP49a9/HVtvvXXUq1cv3nrrrbjwwgvX25hZVlYWuVwunn766SguLq50/+p+L37OOefE8OHDY/DgwdGjR48oLS2NXC4XxxxzzEY9rh988MHRqlWruPvuu2P//fePu+++O1q3bh0/+MEParo02GAEb0jgrLPOii+//DJ+85vfxEUXXRQ33HBDnHfeeRER+W+Da9euvdoBp2PHjvHRRx9Vmv7BBx+stoY5c+bECy+8EEOHDo3LLrssP71ify1btozGjRsXnBG2Ojp27Jjv73vf+15++tKlS2PixIkrPYNpuVUdMt6sWbPo3bt33HPPPdG/f/8YNWpU3HDDDWtUHwCbl5/85Cfx17/+NV544YUYN25cZFmWP8w84v/GparGyPHjx0eLFi1WeljzyJEjY9asWfHQQw/F/vvvn58+ceLESm3X5SdPW2+9dWRZFp06dcp/U78mHnjggTjhhBPiuuuuy09btGhRzJ07t9Jy1mVcr6g67ztWtV2Ki4vjuOOOizvuuCN++9vfxiOPPBKnnHJKlR8+wObKoeawnj3wwANx//33x3/913/Fr371qzjmmGPikksuiQ8//DAiIrbYYovo1atX3HLLLTF16tRK88+YMSP/96GHHhqvvvpqvP766wX3r/j755UpH8zKf09VrmKALSoqisMPPzwef/zx/Lf0K6o4f7ndd989WrZsGcOGDctfcizim7PCVnwDUJXyNz8ra3v88cfH+++/HxdccEEUFxfHMcccs9o+Adh8/eAHP4hmzZrF/fffH/fff3/sueee+Z9PRUS0adMmunfvHn/9618LxpaxY8fGs88+m7+ySFWqGjOXLFkSN910U6W2DRo0WOtDz4888sgoLi6OoUOHVhpfsyyr8nKfFeusON+f/vSngkueRUQcddRR8e6771Z5KbCVjesrbr8V1++5556L999/f5V1RVRvXJ8zZ06cdtppsWDBAlcp4VvHN96wHk2fPj3OOOOMOOCAA+Lss8+OiIj//u//jhdffDEGDhwYL7/8chQVFcWNN94Y++67b+y0005xyimnROfOnWPatGkxevTo+Oyzz+Ldd9+NiIhf/vKXcdddd8UPf/jDOPfcc/OXE+vYsWOMGTNmlbU0btw49t9//7jmmmti6dKl0a5du3j22Wer/PT+6quvjmeffTZ69uwZp556amy//fYxderUGDFiRLz88stVXtqkdu3aceWVV8Zpp50W3/ve9+InP/lJTJw4MYYPH16t33jvtttuERHxs5/9LA4++OBK4bp3797RvHnzGDFiRBxyyCGxxRZbrLZPADZftWvXjiOPPDLuu++++Oqrr+J3v/tdpTbXXnttHHLIIdGjR4846aST8pcTKy0tzV9zuip77713NG3aNE444YT42c9+FrlcLu66664qQ+puu+0W999/f5x33nmxxx57RMOGDeOwww6r1jpsvfXWceWVV8ZFF10UkyZNisMPPzwaNWoUEydOjIcffjhOPfXUOP/881c6f58+feKuu+6K0tLS2GGHHWL06NHx/PPPR/PmzQvaXXDBBfHAAw9Ev379YtCgQbHbbrvF7Nmz47HHHothw4at9Ki03/zmN9G7d+/Yd999Y9CgQTF79uz405/+FN26dYsFCxasct3Kx/WLL744jjnmmKhdu3Ycdthh+UC+6667xo477hgjRoyI7bffPr7zne9Ua5vBZqNGzqUOm5jqXk7syCOPzBo1apRNmjSpoN2jjz6aRUT229/+Nj9twoQJ2YABA7LWrVtntWvXztq1a5f16dMne+CBBwrmHTNmTNazZ8+sXr16Wbt27bIrrrgiu+2226p1ObHPPvssO+KII7ImTZpkpaWlWb9+/bIpU6ZUugRIlmXZ5MmTswEDBmQtW7bM6tatm3Xu3Dk766yz8pcKq3g5sXI33XRT1qlTp6xu3brZ7rvvnr300ktZz549V3s5sWXLlmXnnHNO1rJlyyyXy1V5abEzzzwzi4js3nvvXeV6AvDt8Nxzz2URkeVyuezTTz+tss3zzz+f7bPPPllJSUnWuHHj7LDDDsvef//9gjZVXU5s1KhR2V577ZWVlJRkbdu2zX75y19mzzzzTKWxb8GCBdlxxx2XNWnSpOAyW9W5nFi5Bx98MNt3332zBg0aZA0aNMi6du2anXXWWdkHH3ywyvWfM2dOduKJJ2YtWrTIGjZsmB188MHZ+PHjs44dO2YnnHBCQdtZs2ZlZ599dtauXbusTp06Wfv27bMTTjghmzlz5krrLa9t++23z+rWrZvtsMMO2UMPPZSdcMIJq72cWJZl2RVXXJG1a9cuKyoqqvJ9yjXXXJNFRHb11Vevcj1hc5TLspUcbwJQw37+85/HbbfdFl988UXUr1+/pssBANbBH/7wh/j5z38ekyZNii233LKmy4ENSvAGNkqLFi2KDh06RJ8+fWL48OE1XQ4AsA6yLItddtklmjdvvtrrlcPmyG+8gY3K9OnT4/nnn48HHnggZs2aFeeee25NlwQArKWvvvoqHnvssXjxxRfj3//+dzz66KM1XRLUCMEb2Ki8//770b9//9hiiy3ij3/8Y3Tv3r2mSwIA1tKMGTPiuOOOiyZNmsT/+3//L370ox/VdElQIxxqDgAAAAm5jjcAAAAkJHgDAABAQmv9G++ysrKYMmVKNGrUKHK53PqsCQC+1bIsiy+//DLatm0bRUXr9hm58RoA0liT8Xqtg/eUKVOiQ4cOazs7ALAan376abRv336d+jBeA0Ba1Rmv1zp4N2rUKL+Qxo0br203AEAF8+fPjw4dOuTH2nVhvAaANNZkvF7r4F1+uFrjxo0N5ACQwPo4NNx4DQBpVWe8dnI1AAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEatV0AWw8pk2bFvPmzavpMjYZpaWl0apVq5ouA4BNlHF39Yy1wOZC8CYivhn8f3r8gFi6ZHFNl7LJqF2nbtx9153eEACwxoy71WOsBTYXgjcRETFv3rxYumRxLOzcM8rqlW7QZRctnBslE1+KhZ32j7KSJht02WuraNG8iI//J+bNm+fNAABrrCbH3VXZmMZkYy2wORG8KVBWrzTKGrSomWWXNKmxZQNATajJcXdVjMkA65eTqwEAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCG1XwXrRoUXz44YexaNGimi4F2EzZz8D64bUElLM/gNXbqIL3J598Eqeeemp88sknNV0KsJmyn4H1w2sJKGd/AKu3UQVvAAAA2NwI3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQUK2aLgBgY7B8+fIYM2ZMzJ49O5o1axadO3eOa665JqZMmRJt27aNX/ziF/HCCy/kb++zzz7x85//PObNmxelpaVx7bXXxi233BJTpkyJ1q1bR7du3WLWrFnRtm3b6NWrVwwdOjSmTZsWrVq1iiFDhsTIkSPzfR100EExfPjw+Oyzz6J9+/Zx2mmnRUlJSZV1de3aNZ544on8vH369Inx48fn7+/WrVu89957+ds777xzFBcXV9nXqtqu6/Zbl75WZ8mSJfHoo4/mt0Hfvn2jTp06a9XX6rbvqvrekOsMsClZuHBh3HzzzfHBBx9Ew4YN48c//nHUqVMnpk+fHi+//HIsXry40ngXsfL96obc325q+/b1OSZuzjaGx1XwBr71Xnrppbjpppviiy++qPL+iRMnxqhRowqm3Xjjjfm/Fy1aFAMGDChoP3r06CrbzpgxI/r167fSvt5888145JFHYp999omDDz54lXVVnDci8m9QyrVu3TrOPPPMiIhKfa2s7f7777/S5VWlqu23tn2tzrBhw2LEiBEFdQ8bNiz69esXp59++hr1tbrHfVV9b8h1BtiUXHzxxZXGzP/93/+t1G7F8e6qq65a6X61V69eMXLkyA2yv93U9u3rc0zcnG0sj6tDzYFvtZdeeimGDBkSnTt3jhtvvDG23Xbbgvu7d+++yvkbNmxYaVrt2rWrbLvlllsW3K74iXTv3r3juOOOi9q1a8eoUaPisssuy9d11FFHFfTdt2/fgtt77bVX5HK5aNy4cUR888bnxhtvjM6dO8dll11WsI4XX3xxREQ0btw4crlcQdshQ4bESy+9tMp1XlHF7ffUU0+tdV+rM2zYsLjvvvuicePGcf7558eDDz4Y559/fjRu3Djuu+++GDZs2FrXXXH7XnDBBSvte0OuM8Cm5MYbb8yH7nbt2sXRRx9dqc0uu+wSu+66a0R88wHwqFGj4vTTT69yv1paWhr33XdflJaWJt/fbmr79vU5Jm7ONqbHVfAGvrWWL18eN910U/To0SOuvPLK6NixY3z44YeRy+Xiqaeeir322iveeeedaNq0aTzxxBMF8z700ENRWloaCxYsiFwuF3fffXf+vjvvvDPuv//+/O2//e1v0aRJk/jkk0+iadOm8dhjj0XEN4eH1apVK5566qlo2rRp/OMf/4iBAwfm74/4JkB36dIlHnnkkWjatGk8+eST0aNHj3jiiSeiR48e8eSTT0bTpk3j1Vdfje9+97sxYsSI2HvvveP222+Prl27xtChQ6Nu3bpRp06dGDp0aHTt2jVuu+222HvvvWPEiBHRo0ePfNsrr7wyevToETfffHPBp+fV3X7dunWL+vXrR7du3da4r9VZsmRJjBgxIpo2bRojRoyIPn36RPPmzaNPnz4F05csWbLGdVfcvnvvvXfcfffdccghh1Tqe0OuM8Cm5t13342Ibz4MvuOOO+Kll16K7373uwVtvvjii7jmmmti7733jhYtWkStWrVi/PjxseeeexbsV7t27Rrz5s2Lpk2bxrx586Jr167J9reb2r59fY6Jm7ON7XGt9qHmixcvjsWLF+dvz58/P0lBERGTJ09O1jdVs83Xju226VnxMRszZkx88cUXcemll0ZRUVH85je/iYiIAw88MOrXrx/t2rXL327YsGHUrVs3Fi9eHPXq1YtmzZpFixYtYt68edGlS5eCQ8sHDx4cLVu2zN/+7//+7+jevXuMHDkydtlll2jcuHG0bNkyZsyYEU2aNIn69evHoEGD4rrrrotHH300ttlmm/y8f/7zn6Ndu3axfPnyOOmkk6JOnTqx5557xujRo2PPPfeMOnXqxIEHHhh///vfo127dlGrVq3o379/nHXWWTFmzJiIiPy+e+zYsRER+XWu2HbXXXetdHtVKm6/FRUVFa1RX6vz6KOP5rdBrVqFQ1etWrUKtl/FQ/lXV/eKfdepU6dS3RUfmw21zmtrQ47XEfaDa8t2qz7bauNX8TE6/vjjY+zYsfHFF1/kjyTbfvvtY9y4cTFt2rQYO3Zsfn+56667xttvvx1169Yt2K+W76vPP//8+N3vflewX13f+9sNOZ6tD+tzTNycbWyPa7WD929+85sYOnRoylryrrrqqg2yHFhXnqubttmzZ0dERKdOnSIiYsqUKRER+UPjysNLeQAvKyuLiMgfzl3+SXLbtm3z80ZEzJs3L9+2vN/tttsuIiLq1asXERHNmjWLGTNm5Nv16NEj37ZZs2b5eT/77LPI5XIFberWrVvwf5s2bQrqKV+f8vWruL4rtqnYdmXzVqXiPBWtSV+rU759y7dBRStuv9VZ2eNe3kfFuqt6bDbEOq+tDTleR9gPkp7n2KanU6dO+Q+kFyxYEBERJ510Upx//vkR8c0+snzf2rx584iI+PLLLwv6qLgPrrhfXZ/72w05nq0P63NM3JxtbI9rtYP3RRddFOedd17+9vz586NDhw5Jirr44oujY8eOSfqmapMnTzawrQXP1U3Pis/18hA1ceLE6NatW7Rt2zYmTpwYf//73+P//b//lw+2n3/+eURE/tPS8m8Qy3+jPWXKlOjWrVt+GaWlpflvtCO+CeaLFi2KiMj/X76TL++z/A1K27ZtC4J3+/bto23btvk2ffr0yX8gUP7/1KlTC+qZOHFiwfqVW/F2+TpXbLuyeatScftVtCZ9rU7FbVDRittvdap63Ffsu2LdVT02G2Kd19aGHK8j7AfXlnG3+jzHNn4Vn88TJ07M7wfLz4Vy22235e9v1qxZfn85a9asiIho1KhRQZ8V98EV96vrc3+7KezbV7Q+x8TN2cb2uFY7eNetWzf/JjS1jh07VjrBEWyMPFc3bTvvvHO0bt067rnnnrjyyivjoosuij59+sRzzz0XgwcPzgfu5557LgYMGJAPuosWLYrZs2fHzJkzIyLio48+issuuyx/hvEbbrghioqK4ic/+UlERJx99tlxxhlnRMQ3v3+bP39+PpTPnTs3vv7667j99tujuLg4+vbtW/Bbo1NOOSVq164dw4YNi9tuuy0OOuigeP3116O4uDhef/316N27dzz33HMR8c0HBMuWLYt77rkn2rRpEzvvvHOUlZXl99077rhjFBUV5df517/+daW2K95e0+234mFca9rX6vTt2ze/DX74wx8WHFq3bNmygu23pnWv2PdBBx1UUHfFvouLizfYOq+tDTleR9gPkp7n2KbnrrvuiiuuuCJat26dHzvHjRsXERGtWrWKHXfcMYYMGRKtWrWKf//73xHxzYfJZWVl+f1q+b76tttui9atWxfsV9f3/nZDjmfrw/ocEzdnG9vj6uRqwLdWcXFxnHnmmTF69Oi45JJLYvLkybHttttGlmVx6KGHxquvvhq77LJLzJkzp9InykceeWTMmzcvGjRoEFmWxU9/+tP8fQMGDMiH7oiIY489NubOnRtbbrllzJkzJ370ox9FxDffUC9btiwOPfTQmDNnThx88MExfPjw/P0R3xxi+dFHH8Xhhx8ec+bMid69e8fo0aML/p8zZ07stdde8dprr0W/fv3ilVdeiUGDBsX48eNjyJAhsXjx4liyZEkMGTIkxo8fHyeddFK88sor0a9fvxg9enS+7SWXXBKjR4+OM844o1rXtqy4/d577734+uuv47333lvjvlanTp060a9fv5gzZ07069cvHn/88Zg5c2Y8/vjjBdOrc+3SinVX3L6vvPJK9O/fP5566qlKfW/IdQbY1Oyyyy4REfHqq6/GCSecEPvtt1+89tprBW1atWoVv/zlL+OVV16JmTNnxrJly6Jr167x+uuvF+xXx48fH6WlpTFnzpwoLS2N8ePHJ9vfbmr79vU5Jm7ONrbHNZdlWbY2M86fPz9KS0tj3rx5+d87rqsPP/wwTj311Lj11lt9srmBlW/7r3b4UZQ1aLFBl1301cxo8P5jNbLstVVes+fqpqeq/Ux1rue8oVX3Ot4VVbw2d5s2bfLftq/uOt7lbdfHdbzXtq/VqeqapcXFxcmu472yvlOv8/ocY1OM1xHG7HVVk+PuqmxMY7KxdtOx4v7gr3/9a6XreK/Kqq7j3aZNm+jZs2el63inGmM25Hi2PqzPMXFzlvJxXZMxttqHmgNsrvbff//YZ599YsyYMTF79uxo1qxZdO7cOa655pqYMmVKtG3bNn7xi1/ECy+8kL+9zz77xM9//vOYN29elJaWxrXXXhu33HJLTJkyJVq3bh3dunWLWbNmRdu2baNXr14xdOjQmDZtWrRq1SqGDBkSI0eOzPd10EEHxfDhw+Ozzz6L9u3bx2mnnRYlJSUREZXq6tq1azzxxBP5efv06RPjx4/P39+tW7d477338rd33nnn/Ce5FftaVdt13X5r29fqnH766TFo0KB49NFH89ugb9++a/WpflV1V9y+K+t7Q64zwKbkqquuioULF8bNN98cH3zwQTRs2DB+/OMfR506dWL69Onx8ssvx+LFiyuNd6var55yyikbZH+7qe3b1+eYuDnbWB5XwRsgvvmEuOKlJCqe+KjiJTlWvFZ3Ve1X9N///d+r7Gvw4MHVrqvivBXvX9klMarqa31dPqOqvlMpP8RufajO9l2TeQGIKCkpKTjJ44oOOeSQlc63sv3qhtzfbmr79vU5Jm7ONobH1W+8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAAS2qiC95Zbbhm33nprbLnlljVdCrCZsp+B9cNrCShnfwCrV6umC1hRvXr1Ytttt63pMoDNmP0MrB9eS0A5+wNYvY3qG28AAADY3AjeAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQUK2aLoCNS9GieRt+mQvnFvy/KaiJ7QTA5mdjG082pjF5Y9s2AOtC8CYiIkpLS6N2nboRH/9PjdVQMvGlGlv22qhdp26UlpbWdBkAbII2hnF3VTaWMdlYC2wuBG8iIqJVq1Zx9113xrx5Pl2urtLS0mjVqlVNlwHAJsi4Wz3GWmBzIXiT16pVK4MbAGwgxl2Abw8nVwMAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASKjW2s6YZVlERMyfP3+9FQMA/N/YWj7WrgvjNQCksSbj9VoH7y+//DIiIjp06LC2XQAAq/Dll19GaWnpOvcRYbwGgFSqM17nsrX8OL2srCymTJkSjRo1ilwut1YFAgCVZVkWX375ZbRt2zaKitbtV2HGawBIY03G67UO3gAAAMDqObkaAAAAJCR4AwAAQEKCN6wHWZbFqaeeGs2aNYtcLhfvvPNO9OrVKwYPHpx0uZdffnl079496TJyuVw88sgjK71/0qRJ+XWOiBg5cmTkcrmYO3du0roAIIXVjXtr00/FsbImVKeGimP4HXfcEU2aNNkg9cHmTvCG9eAf//hH3HHHHfHEE0/E1KlTY8cdd4yHHnoorrjiipoubZ1NnTo1DjnkkGq333vvvWPq1Kn5MzsatAHYnK3sQ/A1HT9T69ChQ/49SnX95Cc/iQ8//DB/e0N84A+bq7W+nBjwfyZMmBBt2rSJvffeOz+tWbNmNVjR+tO6des1al+nTp01ngcANjcb21hYXFy8xjWVlJRESUlJoorg28U33rCOBg4cGOecc0588sknkcvlYquttoqIKDjUfPz48VG/fv2499578/P9/e9/j5KSknj//fcjImLu3Llx8sknR8uWLaNx48bxve99L959992CZf3Xf/1XtGrVKho1ahQnnXRSLFq0aJW1LV++PE466aTo1KlTlJSUxHbbbRd/+MMfKrW7/fbbo1u3blG3bt1o06ZNnH322fn7Kh5y9/rrr8euu+4a9erVi9133z3efvvtgr5WPExt5MiRceKJJ8a8efMil8tFLpeLyy+/PH79619X+Yl79+7d49JLL13lOgFAVW699dZo27ZtlJWVFUzv27dvDBo0KH/75ptvjq233jrq1KkT2223Xdx1112r7PfCCy+MbbfdNurXrx+dO3eOSy+9NJYuXRoR3xzVNXTo0Hj33Xfz49wdd9wREas/ZH3s2LFxyCGHRMOGDaNVq1Zx/PHHx8yZM1faftasWXHsscdGu3bton79+rHTTjvF3/72t4I2ZWVlcc0118Q222wTdevWjS233DKuuuqqiKj6UPOnnnoqtt122ygpKYkDDjggJk2aVNDfiketrWxdBw0aFH369CmYb+nSpbHFFlvEbbfdtootC98yGbBO5s6dm/3617/O2rdvn02dOjWbPn16lmVZ1rNnz+zcc8/Nt7vxxhuz0tLSbPLkydmnn36aNW3aNPvDH/6Qv/8HP/hBdthhh2VvvPFG9uGHH2a/+MUvsubNm2ezZs3KsizL7r///qxu3brZX/7yl2z8+PHZxRdfnDVq1CjbZZddVlrbkiVLsssuuyx74403so8//ji7++67s/r162f3339/vs1NN92U1atXL7vhhhuyDz74IHv99dez66+/Pn9/RGQPP/xwlmVZ9uWXX2YtW7bMjjvuuGzs2LHZ448/nnXu3DmLiOztt9/OsizLXnzxxSwisjlz5mSLFy/Obrjhhqxx48bZ1KlTs6lTp2Zffvll9umnn2ZFRUXZ66+/nl/OW2+9leVyuWzChAlr+UgA8G02e/bsrE6dOtnzzz+fnzZr1qyCaQ899FBWu3bt7MYbb8w++OCD7LrrrsuKi4uzf/7zn/l5Vhz3sizLrrjiimzUqFHZxIkTs8ceeyxr1apV9tvf/jbLsiz7+uuvs1/84hdZt27d8uPc119/XamfiRMnFoyVc+bMyVq2bJlddNFF2bhx47K33norO/DAA7MDDjhgpev32WefZddee2329ttvZxMmTMj++Mc/ZsXFxdlrr72Wb/PLX/4ya9q0aXbHHXdk//nPf7J//etf2Z///Ocqa/jkk0+yunXrZuedd142fvz47O67785atWqVH8OzLMuGDx+elZaWrnJdR40alRUXF2dTpkzJ1/HQQw9lDRo0yL788stqPnqw+RO8YT24/vrrs44dOxZMqxi8syzLevfune23337Z97///eyggw7KysrKsizLsn/9619Z48aNs0WLFhW033rrrbNbbrkly7Is69GjR3bmmWcW3P/d7353lcG7KmeddVZ21FFH5W+3bds2u/jii1fafsU3DrfcckvWvHnzbOHChfn7b7755pUG7ywrHLRXdMghh2RnnHFG/vY555yT9erVa43WBQBW1Ldv32zQoEH527fcckvWtm3bbPny5VmWZdnee++dnXLKKQXz9OvXLzv00EPztysG74quvfbabLfddsvfHjJkSJVj8aqC9xVXXJEddNBBBe0//fTTLCKyDz74oDqrmmXZN+8rfvGLX2RZlmXz58/P6tatmw/aFVWs4aKLLsp22GGHgjYXXnjhKsfwla3rDjvskP8wIsuy7LDDDssGDhxY7fWAbwOHmsMGdPvtt8eYMWPirbfeijvuuCNyuVxERLz77ruxYMGCaN68eTRs2DD/b+LEiTFhwoSIiBg3blx897vfLeivR48eq13mjTfeGLvttlu0bNkyGjZsGLfeemt88sknERExffr0mDJlSnz/+9+vVv3jxo2LnXfeOerVq7dGNVTllFNOib/97W+xaNGiWLJkSdx7770FhwICwJrq379/PPjgg7F48eKIiLjnnnvimGOOiaKib97yjhs3LvbZZ5+CefbZZ58YN27cSvu8//77Y5999onWrVtHw4YN45JLLsmPo2vr3XffjRdffLFgzO/atWtERH7cr2j58uVxxRVXxE477RTNmjWLhg0bxjPPPJOvZdy4cbF48eI1GtPX5n1FVU4++eQYPnx4RERMmzYtnn76aWM6VODkarABvfvuu/HVV19FUVFRTJ06Ndq0aRMREQsWLIg2bdrEyJEjK82zLmcEv+++++L888+P6667Lnr06BGNGjWKa6+9Nl577bWIiBo9Ycphhx0WdevWjYcffjjq1KkTS5cujR//+Mc1Vg8Am77DDjsssiyLJ598MvbYY4/417/+Fddff/1a9zd69Ojo379/DB06NA4++OAoLS2N++67L6677rp1qnPBggVx2GGHxW9/+9tK95W/N6jo2muvjT/84Q9xww03xE477RQNGjSIwYMHx5IlSyKiZsf0AQMGxK9+9asYPXp0vPLKK9GpU6fYb7/9aqwe2BgJ3rCBzJ49OwYOHBgXX3xxTJ06Nfr37x9vvfVWlJSUxHe+85344osvolatWvmTs1W0/fbbx2uvvRYDBgzIT3v11VdXucxRo0bF3nvvHWeeeWZ+2oqfpDdq1Ci22mqreOGFF+KAAw5Y7Tpsv/32cdddd8WiRYvy33qvroY6derE8uXLK02vVatWnHDCCTF8+PCoU6dOHHPMMc6cCsA6qVevXhx55JFxzz33xH/+85/Ybrvt4jvf+U7+/u233z5GjRoVJ5xwQn7aqFGjYocddqiyv1deeSU6duwYF198cX7a5MmTC9qsbJxble985zvx4IMPxlZbbRW1alXv7fioUaOib9++8dOf/jQivjmR2ocffpivvUuXLlFSUhIvvPBCnHzyyavtb/vtt4/HHnusYNrajunNmzePww8/PIYPHx6jR4+OE088sVrrBN8mDjWHDeT000+PDh06xCWXXBK///3vY/ny5XH++edHRMQPfvCD6NGjRxx++OHx7LPPxqRJk+KVV16Jiy++ON58882IiDj33HPj9ttvj+HDh8eHH34YQ4YMiffee2+Vy+zSpUu8+eab8cwzz8SHH34Yl156abzxxhsFbS6//PK47rrr4o9//GN89NFH8dZbb8Wf/vSnKvs77rjjIpfLxSmnnBLvv/9+PPXUU/G73/1ulTVstdVWsWDBgnjhhRdi5syZ8fXXX+fvO/nkk+Of//xn/OMf/3BIGgDrRf/+/ePJJ5+M22+/Pfr3719w3wUXXBB33HFH3HzzzfHRRx/F73//+3jooYfy43FFXbp0iU8++STuu+++mDBhQvzxj3+Mhx9+uKDNVlttFRMnTox33nknZs6cmT/MfVXOOuusmD17dhx77LHxxhtvxIQJE+KZZ56JE088caUhvkuXLvHcc8/FK6+8EuPGjYvTTjstpk2blr+/Xr16ceGFF8Yvf/nLuPPOO2PChAnx6quvrvTM4qeffnp89NFHccEFF8QHH3wQ9957b/6M7CuzqnU9+eST469//WuMGzeu4IMN4P9X0z8yh83B6k6u9te//jVr0KBB9uGHH+bvf+2117LatWtnTz31VJZl35wU5Zxzzsnatm2b1a5dO+vQoUPWv3//7JNPPsnPc9VVV2UtWrTIGjZsmJ1wwgnZL3/5y1WeXG3RokXZwIEDs9LS0qxJkybZGWeckf3qV7+qNM+wYcOy7bbbLqtdu3bWpk2b7JxzzsnfFxVOMjN69Ohsl112yerUqZN17949e/DBB1d5crUsy7LTTz89a968eRYR2ZAhQwqWvd9++2XdunVb6ToAwJpYvnx51qZNmywiqrxSxk033ZR17tw5q127drbttttmd955Z8H9Fce9Cy64IGvevHnWsGHD7Cc/+Ul2/fXXF5xwbNGiRdlRRx2VNWnSJIuIbPjw4ZX6qXhisyzLsg8//DA74ogjsiZNmmQlJSVZ165ds8GDB+dPvFrRrFmzsr59+2YNGzbMtthii+ySSy7JBgwYkPXt27dg3a+88sqsY8eOWe3atbMtt9wyu/rqq1daw+OPP55ts802Wd26dbP99tsvu/3221d5crWVrWuWZVlZWVnWsWPHghPVAf8nl2VZVjORH/i2y7IsunTpEmeeeWacd955NV0OALCWFixYEO3atYvhw4fHkUceWdPlwEbHb7yBGjFjxoy477774osvvvBbMADYRJWVlcXMmTPjuuuuiyZNmsSPfvSjmi4JNkqCN1Ajtthii2jRokXceuut0bRp05ouBwBYC5988kl06tQp2rdvH3fccUe1TxYH3zYONQcAAICEnNUcAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvGEDGzlyZORyuRg5cuRG0U8KuVwuLr/88tW2u/zyyyOXy6UvCADWwFZbbRUDBw5cbbs77rgjcrlcTJo0KVktkyZNilwuF3fccUeyZQDpCd6wEbj33nvjhhtuqOkyAACABGrVdAHwbbP//vvHwoULo06dOvlp9957b4wdOzYGDx68Tv1sLBYuXBi1atm9ALBp+uCDD6KoyPdTwPpjjwIbWFFRUdSrV2+tB/RFixZFWVnZOveTUr169QRvADaIr776ar33Wbdu3ahdu/Z675dCKR472FhtfO/YYRP3+eefx0knnRRt27aNunXrRqdOneKMM86IJUuWRETl32b36tUrnnzyyZg8eXLkcrnI5XKx1VZbFbS977774pJLLol27dpF/fr1Y/78+Sv9jfdrr70Whx56aDRt2jQaNGgQO++8c/zhD39YZc2zZ8+O888/P3baaado2LBhNG7cOA455JB49913K7VdtGhRXH755bHttttGvXr1ok2bNnHkkUfGhAkT8m2q+o33yy+/HHvssUfUq1cvtt5667jlllvWbMMC8K1Xfm6Q999/P4477rho2rRp7Lvvvvn777777thtt92ipKQkmjVrFsccc0x8+umnBX189NFHcdRRR0Xr1q2jXr160b59+zjmmGNi3rx5+TZV/cb7vffei+9973tRUlIS7du3jyuvvDLKysoq1biy85xU7HNNxt7qWLp0aQwdOjS6dOkS9erVi+bNm8e+++4bzz33XL5Nr169olevXpXmHThwYP69R7lZs2bF8ccfH40bN44mTZrECSecEO+++26l35uPGTMmBg4cGJ07d4569epF69atY9CgQTFr1qyC/lb32MHmzldSsB5NmTIl9txzz5g7d26ceuqp0bVr1/j888/jgQceiK+//rrKw8IvvvjimDdvXnz22Wdx/fXXR0REw4YNC9pcccUVUadOnTj//PNj8eLFKz28/Lnnnos+ffpEmzZt4txzz43WrVvHuHHj4oknnohzzz13pXV//PHH8cgjj0S/fv2iU6dOMW3atLjllluiZ8+e8f7770fbtm0jImL58uXRp0+feOGFF+KYY46Jc889N7788st47rnnYuzYsbH11ltX2f+///3vOOigg6Jly5Zx+eWXx7Jly2LIkCHRqlWram1XAFhRv379okuXLnH11VdHlmUREXHVVVfFpZdeGkcffXScfPLJMWPGjPjTn/4U+++/f7z99tvRpEmTWLJkSRx88MGxePHiOOecc6J169bx+eefxxNPPBFz586N0tLSKpf3xRdfxAEHHBDLli2LX/3qV9GgQYO49dZbo6SkZK3Xobpjb3Vdfvnl8Zvf/CZOPvnk2HPPPWP+/Pnx5ptvxltvvRUHHnjgGvVVVlYWhx12WLz++utxxhlnRNeuXePRRx+NE044oVLb5557Lj7++OM48cQTo3Xr1vHee+/FrbfeGu+99168+uqrlU6iWtVjB98KGbDeDBgwICsqKsreeOONSveVlZVlWZZlL774YhYR2Ysvvpi/r3fv3lnHjh0rzVPetnPnztnXX39d5X3l/Sxbtizr1KlT1rFjx2zOnDlVLntlFi1alC1fvrxg2sSJE7O6detmv/71r/PTbr/99iwist///vcrXb8sy7KIyIYMGZK/ffjhh2f16tXLJk+enJ/2/vvvZ8XFxZndEADVNWTIkCwismOPPbZg+qRJk7Li4uLsqquuKpj+73//O6tVq1Z++ttvv51FRDZixIhVLqdjx47ZCSeckL89ePDgLCKy1157LT9t+vTpWWlpaRYR2cSJE/PTK46BK+uzumPvxIkTs4jIhg8fvsqad9lll6x3796rbNOzZ8+sZ8+elaafcMIJBe9DHnzwwSwishtuuCE/bfny5dn3vve9SrVUfH+SZVn2t7/9LYuI7KWXXspPW9ljB98WDjWH9aSsrCweeeSROOyww2L33XevdP+6XDbrhBNOWO2n6m+//XZMnDgxBg8eHE2aNFmjZdetWzf/W/Hly5fHrFmzomHDhrHddtvFW2+9lW/34IMPRosWLeKcc86p1MfKlrF8+fJ45pln4vDDD48tt9wyP3377bePgw8+eJV1AUBVTj/99ILbDz30UJSVlcXRRx8dM2fOzP9r3bp1dOnSJV588cWIiPw32s8880x8/fXX1V7eU089FXvttVfsueee+WktW7aM/v37r/U6VHfsra4mTZrEe++9Fx999NFa11TuH//4R9SuXTtOOeWU/LSioqI466yzKrVd8f3JokWLYubMmbHXXntFRFS5HhUfO/i2ELxhPZkxY0bMnz8/dtxxx/Xed6dOnVbbpvw31muz/LKysrj++uujS5cuUbdu3WjRokW0bNkyxowZU/CbtwkTJsR22223RidOmzFjRixcuDC6dOlS6b7ttttujWsFgIrj4kcffRRZlkWXLl2iZcuWBf/GjRsX06dPz8933nnnxV/+8pdo0aJFHHzwwXHjjTcWjHVVmTx58nofx6o79lbXr3/965g7d25su+22sdNOO8UFF1wQY8aMWavaJk+eHG3atIn69esXTN9mm20qtZ09e3ace+650apVqygpKYmWLVvmH5+q1qM672lgc+Q33rAJWJffkFXH1VdfHZdeemkMGjQorrjiimjWrFkUFRXF4MGDqzxxDADUpIrjYllZWeRyuXj66aejuLi4UvsVz51y3XXXxcCBA+PRRx+NZ599Nn72s5/Fb37zm3j11Vejffv2yWpevnx5we31Pfbuv//+MWHChPx6/eUvf4nrr78+hg0bFieffHJEfHN0WlbF76or1rYmjj766HjllVfiggsuiO7du0fDhg2jrKwsfvjDH1a5Hqnf08DGSvCG9aRly5bRuHHjGDt27BrPuy6HoZcrP7HZ2LFj4wc/+MEazfvAAw/EAQccELfddlvB9Llz50aLFi0KlvHaa6/F0qVLq32ZlZYtW0ZJSUmVh7598MEHa1QnAFRl6623jizLolOnTrHtttuutv1OO+0UO+20U1xyySXxyiuvxD777BPDhg2LK6+8ssr2HTt2rPY41rRp05g7d27BtCVLlsTUqVMLplV37F0TzZo1ixNPPDFOPPHEWLBgQey///5x+eWX54N306ZN4+OPP6403+TJkwtud+zYMV588cX4+uuvC771/s9//lPQbs6cOfHCCy/E0KFD47LLLstPXx+Hu8PmxqHmsJ4UFRXF4YcfHo8//ni8+eable6v6hPmcg0aNFirw8pW9J3vfCc6deoUN9xwQ6UBf1XLjogoLi6u1GbEiBHx+eefF0w76qijYubMmfHf//3flfpY2TKKi4vj4IMPjkceeSQ++eST/PRx48bFM888s8q6AKA6jjzyyCguLo6hQ4dWGo+yLMtf2mr+/PmxbNmygvt32mmnKCoqisWLF6+0/0MPPTReffXVeP311/PTZsyYEffcc0+ltltvvXW89NJLBdNuvfXWSt8qV3fsra6Kl+9q2LBhbLPNNgXrtfXWW8f48eNjxowZ+WnvvvtujBo1qmDegw8+OJYuXRp//vOf89PKysrixhtvrLQOEZXfA9xwww1rtQ6wOfONN6xHV199dTz77LPRs2fPOPXUU2P77bePqVOnxogRI+Lll1+udNKzcrvttlvcf//9cd5558Uee+wRDRs2jMMOO2yNll1UVBQ333xzHHbYYdG9e/c48cQTo02bNjF+/Ph47733Vhly+/TpE7/+9a/jxBNPjL333jv+/e9/xz333BOdO3cuaDdgwIC4884747zzzovXX3899ttvv/jqq6/i+eefjzPPPDP69u1bZf9Dhw6Nf/zjH7HffvvFmWeeGcuWLYs//elP0a1bt7X+/RkAlNt6663jyiuvjIsuuigmTZoUhx9+eDRq1CgmTpwYDz/8cJx66qlx/vnnxz//+c84++yzo1+/frHtttvGsmXL4q677ori4uI46qijVtr/L3/5y7jrrrvihz/8YZx77rn5y4l17Nix0jh28sknx+mnnx5HHXVUHHjggfHuu+/GM888U+lb7OqOvdW1ww47RK9evWK33XaLZs2axZtvvhkPPPBAnH322fk2gwYNit///vdx8MEHx0knnRTTp0+PYcOGRbdu3WL+/Pn5docffnjsueee8Ytf/CL+85//RNeuXeOxxx6L2bNnR8T/HanXuHHj2H///eOaa66JpUuXRrt27eLZZ5+NiRMnrtU6wGaths6mDputyZMnZwMGDMhatmyZ1a1bN+vcuXN21llnZYsXL86yrOrLiS1YsCA77rjjsiZNmmQRkb+kR3nbqi57UlU/WZZlL7/8cnbggQdmjRo1yho0aJDtvPPO2Z/+9KdV1rxo0aLsF7/4RdamTZuspKQk22effbLRo0dXedmRr7/+Orv44ouzTp06ZbVr185at26d/fjHP84mTJiQbxNVXErlf/7nf7Lddtstq1OnTta5c+ds2LBh+UuLAEB1lI8bM2bMqPL+Bx98MNt3332zBg0aZA0aNMi6du2anXXWWdkHH3yQZVmWffzxx9mgQYOyrbfeOqtXr17WrFmz7IADDsief/75gn4qXvory7JszJgxWc+ePbN69epl7dq1y6644orstttuq3Q5seXLl2cXXnhh1qJFi6x+/frZwQcfnP3nP/+p8nJi1Rl7q3s5sSuvvDLbc889syZNmmQlJSVZ165ds6uuuipbsmRJQbu7774769y5c1anTp2se/fu2TPPPFPpcmJZlmUzZszIjjvuuKxRo0ZZaWlpNnDgwGzUqFFZRGT33Xdfvt1nn32WHXHEEVmTJk2y0tLSrF+/ftmUKVMqvRdY3WMHm7tclrlyPQAAsGqPPPJIHHHEEfHyyy/HPvvsU9PlwCZF8AYAAAosXLiw4Azky5cvj4MOOijefPPN+OKLL5ydHNaQ33gDAAAFzjnnnFi4cGH06NEjFi9eHA899FC88sorcfXVVwvdsBZ84w0AABS4995747rrrov//Oc/sWjRothmm23ijDPOKDhZG1B9gjcAAAAk5DreAAAAkJDgDQAAAAmt9cnVysrKYsqUKdGoUaPI5XLrsyYA+FbLsiy+/PLLaNu2bRQVrdtn5MZrAEhjTcbrtQ7eU6ZMiQ4dOqzt7ADAanz66afRvn37derDeA0AaVVnvF7r4N2oUaP8Qho3bry23QAAFcyfPz86dOiQH2vXhfEaANJYk/F6rYN3+eFqjRs3NpADQALr49Bw4zUApFWd8drJ1QAAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEqpV0wVsSqZNmxbz5s2r6TLYxJSWlkarVq1qugyAzUKKsdh+GoDUBO9qmjZtWvz0+AGxdMnimi6FTUztOnXj7rvu9KYOYB2lGovtpwFITfCupnnz5sXSJYtjYeeeUVavtKbL2SgULZwbJRNfioWd9o+ykiY1Xc5GqWjRvIiP/yfmzZvnDR3AOqrOWLymY5P9NAAbguC9hsrqlUZZgxY1XcZGpaykiW0CwAZTnbHY2ATAxsTJ1QAAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABLaqIL3okWL4sMPP4xFixbVdCkAmzT7U1LzHFt3tiHAt8dGFbw/+eSTOPXUU+OTTz6p6VIANmn2p6TmObbubEOAb4+NKngDAADA5kbwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEatV0AQCwKVq+fHmMGTMmZs+eHc2aNYudd945iouLa7osNiFLliyJiIjrr78+SktLY/HixfHVV19Fly5dIpfLxRdffBHNmzePt99+O+bPnx8NGzaMZs2axZw5c6JFixbRqFGjmDFjRjRv3jw++eSTmD9/fjRu3DjOPPPMWL58eZSUlMQTTzwRU6dOjdatW8d2220Xb775ZkREdOrUKXbcccdo0qRJjBo1KqZMmRLt27eP0047LUpKSvLP7ylTpsTTTz8dCxYsiPr168cWW2wRy5Yti5122imOOOKIqFOnTsFrobi4OG666aaYP39+NGrUKJo2bRpz5syJLbbYIo499thYvHhxpbq6desWs2bNilatWkWWZTF9+vTYYostIpfLxbRp06Jly5YxderUmDp1arRp0ybatGkTM2bMKGjftm3bOOSQQ+Lpp5+Ozz77LGbOnBnNmzePNm3a5Ptp27Zt9O3bN+rUqbPax2bFdWrSpElERMydO7fgtb6m+4CFCxfGLbfcEp999lmV27piPytrX1WN1anr27TPWrJkSTz66KMxZcqUNXrcKbSpb8fqvI43JMEbANbQSy+9FDfddFN88cUX+WmtW7eOM888M/bff/8arIxNxcUXXxyjRo2KiIhx48YV3Pfhhx9WOc+iRYti5syZERExY8aM/PSJEycWtLn88ssrzTtx4sQYPXp0/vbYsWPj8ccfL2jz5ptvxiOPPBJdu3aNuXPnFjy/y73//vsREfHyyy/HLbfcEj169IgJEyZU2XbRokX5OmfMmBGXXHLJautaFzfeeONq2wwbNiz69esXp59++krbVPX6XlHr1q2jV69eMXLkyGrvA1Z8vCNWva1bt24dTZo0ifHjx1dqv88++8RVV1210n3Qyupa03o3ZcOGDYsRI0bE8uXLC6at7nGn0Ka+HavzOt7Qz3+HmgPAGnjppZdiyJAh0blz57jxxhvjqaeeihtvvDE6d+4cQ4YMiZdeeqmmS2QjVx7Cior+721YLpdbL31X7GfFb0jL1alTp2DZDRo0iIiIfffdN4qLi2P8+PExffr0VS6nTp06UVZWll+PqpazKuXta9X6v++Acrlc/natWrUKvo0q/7vitPr161dZW8eOHfO3y/vcY489onHjxnHffffFsGHDqqxrxdf3KaecEhERO+20U+y0006Ry+XilFNOidLS0rjvvvuitLS0WvuA8se7du3acdxxx8Xdd98dxx13XH5bL168uKCfxYsXx/jx46O4uLigfe3atWPUqFFx+umnV7kPWllda1rvpmzYsGFx3333RePGjeP888+PBx98MM4///zVPu4U2tS3Y3VexzXx/Be8AaCali9fHjfddFP06NEjrrzyyujWrVvUr18/unXrFldeeWX06NEjbr755oJvCGBFCxcujFGjRkWtWrWitLQ0IiKKiorikUceWad+W7ZsGRERWZZVWl7EN6G2RYsWEfHN4aNlZWX5Nl999VXstddeMWHChGjWrFlERP7+5s2bR0RE06ZNY6+99sofAr5kyZJ8CP7iiy/yy7n77rvz/bZq1Sq22GKLKutduHBh1K5dO19Tee1lZWWRy+Vi2bJlBa+j8ukrrt/y5cvjrrvuKug3l8vF0qVL49NPP42mTZtGkyZNoqysLJo2bRpvvfVW3HPPPdG0adMYMWJE/lD/Ffsrf30PHTo0Hn/88dh7773jD3/4Q/zhD3+IHj16xOOPPx7z5s2Lpk2bxrx586Jr166r3AeUP961a9eOJ598Mk499dRo3759nHTSSdGiRYvI5XIxZ86c6Ny5c9SvXz86d+4cc+bMyT9eJ510UrRv3z5OPfXUePLJJ6N27doxfvz42HPPPQv2QV27dq2yrvJv1Js2bRrz589fbb2bsiVLlsSIESPyj2+fPn2iefPm0adPn4LpFR93Cm3q27E6r+Mnnngihg4dusGf/9U+1Hzx4sWxePHi/O358+cnKSgiYvLkycn6XlsbY01sOjx/2NA859IYM2ZMfPHFF3HppZcWfGMY8U146t+/f5x11lkxZsyY2HXXXWukxg05Xkds2OdaymVtqPW49957IyJi1113jTfeeCMiIg466KAYPnx4RERsv/32lQ49j/jmsMjyQybr168fX3/9dURE1K1bNxYvXhwzZsyIPfbYI9/nim0ivgm1M2fOLGiz4t/t2rWLV199NSIiWrRoETNnzoyWLVvmDxU/6aSTolOnTnHWWWfFbrvtFv/7v/+bf7NaHtK32mqr+K//+q9K67Fi7c2aNYvZs2dHRES/fv3y26O8fVlZWRx00EHx7LPPFtSYZVkceOCB8dxzzxVMHzJkSMF2+sEPfhDPPfdcZFkWJ510UmRZFtddd13ssssuMXLkyHjyySdj0KBBcd1118Wjjz4a/fr1y8+74ut77NixlV7r5a/viIjzzz8/fve73xW81qvaB9xyyy35dV3xt7FjxoyJadOm5dfplltuicGDB+fbl6/Hiv3XqVMn9ttvv/jnP/8ZdevWLdgHlddesa7y5fziF7+I6667brX1bsoeffTRWL58eZx00kkFR1JEfHPUw8oedwpt6tuxuq/jsWPHbvDnf7WD929+85sYOnRoylryrrrqqg2yHNhQPKdh81AeGDp16lTl/eXTy9vVhA05XkdsPvu3Db0e5YE34ptDvMu/8R40aFBccMEFldqfcsopccUVV0RExAknnBA333xzREQMGDAg/vznP0dExPHHH5/vd8U2K1qxzYABA/J/r/hhTXlArF27dn5ajx498oeHd+/ePf73f/+3Ut+nnnpqXH/99RHxTVA///zz83+Xb99TTjklfvvb30ZExKGHHpoP3iuu99FHH50P3ivWePTRR+eDd/n0adOmFdSwYpsePXrkvyGvV69eRERMmTIl+vfvn/97RSu+vst/d77ia33Fv3v06FEwT8U25dM/++yz/LpWtazyesvblf9fPr1i/9ttt13885//jAULFlTZX8W6VjZ9ZfVuysofz/J1rah8esXHnUKb+nas7ut49uzZK31dpFLt4H3RRRfFeeedl789f/786NChQ5KiLr744oLf5mwMJk+evNm8uWDD2xif02ze7LPSKD8Md+LEidGtW7dK95ef5Kq8XU3YkON1xIbdv6V8Xm+o9bj33ntj5MiRBd82v/zyy9G+fft488034/bbb69yvvJwHRHx17/+Nf/3nXfemf97xcOuV2yzohXbrDhv3bp183+XH0K6dOnS/LTRo0fn37C+8847VfZ96623RqtWrWLGjBlx22235aev+PeK6/HUU0/l/15xvf/+979XWWNV08uXV1Wb0aNH54P3okWLIiKibdu2+Tfjbdu2Lah/xdd3Va/1FU9iV95Hxdd6xX1A+eP61FNPxamnnlppWeX1tm/fvqB9+fSK/X/wwQcREdGwYcMqa69Y18qmr6zeTVn54zl69Ojo06dPpftX9rhTaFPfjtV9HTdr1myDP/+rHbzr1q1bsFNOqWPHjrHttttukGXBhuA5DZuHnXfeOVq3bh333HNPXHnllQWHepaVlcU999wTbdq0iZ133rnGatyQ43XE5rN/21DrceGFF8bIkSPj7bffzl9q69lnn42HH344HnnkkSoPM4+IgjPzrngIefk31S1btiz4Fn3FNhHf/Pa5efPmBW1W/Pvzzz+PNm3axLJlywrORN68efOYNWtW3HbbbbHddtvFFltsEW+99VZERP4SVUVFRVFWVhaTJk2Ku+++O37605/GuHHj8pf7WrH2Fb9ZGjFiRP4w9PL1Lioqyn9jvWKNuVwunn/++fyyyqcPHTo0jjrqqHz7559/PnK5XORyubjtttsiy7IoKiqKd999N4qLi6N3797Rv3//KC4ujr59+xZsoxVf30OHDi14rUdE3HPPPdG6deuI+ObDhNatWxe81qvaB5x22mnxyCOPxIgRI2LgwIH5owl23nnnaNWqVTz//PP5diu2f/7556NVq1YF/S9ZsiT+9a9/5R/3srKy/D6ovPaKdZUv5/bbb6+0b9pY9lnrS9++fWPYsGFx2223xQ9/+MOCw6SXLVsWt99+e5WPO4U29e1YnddxmzZtYscdd4whQ4Zs0Oe/k6sBQDUVFxfHmWeeGaNHj45LLrkk3nvvvfj666/jvffei0suuSRGjx4dZ5xxxmZ7bVzWXUlJSeyzzz6xbNmymDdvXkR8E4AOP/zwdeq3PCyv7Kzm5b/xjvjmEPKKZzV/9dVXY+utt84H4/L7Z82aFRERc+bMiVdffTWmT58eWZblr98d8c1vuMuX89Of/jTf77Rp01Z6dvSSkpJYunRpwbfVuVwuioqKIsuySmc1L5++4voVFxfH8ccfX9BvlmVRu3btaN++fcyZMyfmzp0bRUVFMWfOnNh1112jf//+MWfOnEq/uS7vr/z1PWTIkOjTp0+88sorce6558a5554bo0ePjsMOOyxKS0tjzpw5UVpaGuPHj1/lPqD88V66dGn07t07brnllvj000/jL3/5S8ycOTOyLIumTZvGxx9/HF9//XV8/PHH0bRp0/zj9Ze//CU+/fTTuOWWW6J3796xdOnS6Nq1a7z++usF+6Dx48dXWdf48eOjSZMmMWfOnGjcuPFq692U1alTJ/r165d/fB9//PGYOXNmPP744wXTN6XrUNeETX07Vud13KdPnxgyZMgGf/7nsoqnv6ym+fPnR2lpacybNy8aN268Xor58MMP49RTT41bb711o/v0vLy2r3b4UZQ1aLH6Gb4Fir6aGQ3ef8w2WYXybbQxPqfZvG3M+9PNQVXXB23Tpk2cccYZ6+WaoOtzjE0xXkfUzHOsOmPxmo5NNbWfrnhd543Jqq7jvaKioqJVXsd7Y1RcXLzO1/Fu06ZN9OzZs9J1sVe1D1jZ413Vtm7Tpk0+PFe0qut4r6quNa13U1bV9aer87hTaFPfjtV5Ha+P5/+ajLHVPtQcAPjG/vvvH/vss0+MGTMmZs+eHc2aNYudd955s/jWiA3jqquuirFjx8bZZ58d22+/fZSWlsbixYvjq6++ii5dukQul4svvvgimjdvHm+//XbMnz8/GjZsGM2aNYs5c+ZEixYtolGjRvnDwT/55JOYP39+NG7cOM4888xYvnx5lJSUxBNPPBFTp06N1q1bx3bbbRdvvvlmRHxzgqEdd9wxmjRpEqNGjYopU6ZE+/bt47TTTouSkpJYvnx5jBkzJqZMmRJPP/10LFiwIOrXrx9bbLFFLFu2LHbaaac44ogj8t98l78WiouL46abbor58+dHo0aN8ofTb7HFFnHsscfG4sWLK9XVrVu3mDVrVv7Q9OnTp+cvWzZt2rRo2bJlTJ06NaZOnRpt2rSJNm3axIwZMwrat23bNg455JB4+umn47PPPouZM2dG8+bNo02bNvl+2rZtG3379l3tN3UVX99NmjSJiIi5c+cWvNZPOeWUau8Drrrqqli4cGHccsst8dlnn1W5rSv2s7L2VdVYnbrWpN5N2emnnx6DBg2KRx99NKZMmVLtx51Cm/p2rO7reEMSvAFgLRQXF2/yl9+hZpW/gf35z3+e7Nv2vffeu+D2wIEDK7X57ne/W2la+fN71113jd69e69yGRVfC7169VrjutaX9XV5o+q8vtd0H1BSUhKDBw+udj8ra7+6+dZ0+uao/HBp1s2mvh03tue833gDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACS0UQXvLbfcMm699dbYcssta7oUgE2a/SmpeY6tO9sQ4NujVk0XsKJ69erFtttuW9NlAGzy7E9JzXNs3dmGAN8eG9U33gAAALC5EbwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACChWjVdwKamaNG8mi5ho1G0cG7B/1Tm+QKw/q1q37qmY5P9NAAbguBdTaWlpVG7Tt2Ij/+npkvZ6JRMfKmmS9io1a5TN0pLS2u6DIBN3pqMxWsyNtlPA5Ca4F1NrVq1irvvujPmzfPJOGumtLQ0WrVqVdNlAGzyUo3F9tMApCZ4r4FWrVoZmAGgBhmLAdgUObkaAAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCtdZ2xizLIiJi/vz5660YAOD/xtbysXZdGK8BII01Ga/XOnh/+eWXERHRoUOHte0CAFiFL7/8MkpLS9e5jwjjNQCkUp3xOpet5cfpZWVlMWXKlGjUqFHkcrm1KhAAqCzLsvjyyy+jbdu2UVS0br8KM14DQBprMl6vdfAGAAAAVs/J1QAAACAhwRsAAAASErwBAAAgIcEbatikSZMil8vFO++8s1bz53K5eOSRR9ZrTWtiq622ihtuuGGVbWq6RgBYFwMHDozDDz98lW169eoVgwcPXq/Lvfzyy6N79+7rtU+gZqz15cSA9aNDhw4xderUaNGiRUREjBw5Mg444ICYM2dONGnSZLXzT506NZo2bZq4ypV74403okGDBjW2fABI7Q9/+EO1rtMLsDKCN9Sw4uLiaN269RrPt2TJkqhTp85azbs+tWzZskaXDwCrUj5erovVXZ+XQutjm8PmxqHmsAGUlZXFNddcE9tss03UrVs3ttxyy7jqqqsiovBQ80mTJsUBBxwQERFNmzaNXC4XAwcOjIhvDmE7++yzY/DgwdGiRYs4+OCDI6LyYdyfffZZHHvssdGsWbNo0KBB7L777vHaa6+ttLYLL7wwtt1226hfv3507tw5Lr300li6dGlBm8cffzz22GOPqFevXrRo0SKOOOKI/H0VDzX/6KOPYv/994969erFDjvsEM8999y6bDoAWCMrGy/Hjh0bhxxySDRs2DBatWoVxx9/fMycOTM/3wMPPBA77bRTlJSURPPmzeMHP/hBfPXVVxFR+VDzr776KgYMGBANGzaMNm3axHXXXVepjqp+ZtWkSZO444478rerMwavypw5c6J///7RsmXLKCkpiS5dusTw4cMj4psj6HK5XMydOzff/p133olcLheTJk3KT/vzn/8cHTp0iPr168cRRxwRv//97wuOuJswYUL07ds3WrVqFQ0bNow99tgjnn/++YI6ttpqq7jiiitiwIAB0bhx4zj11FOrvQ7wbeEbb9gALrroovjzn/8c119/fey7774xderUGD9+fKV2HTp0iAcffDCOOuqo+OCDD6Jx48ZRUlKSv/+vf/1rnHHGGTFq1Kgql7NgwYLo2bNntGvXLh577LFo3bp1vPXWW1FWVrbS2ho1ahR33HFHtG3bNv7973/HKaecEo0aNYpf/vKXERHx5JNPxhFHHBEXX3xx3HnnnbFkyZJ46qmnquyrrKwsjjzyyGjVqlW89tprMW/evPX+ezcAWJ2K4+XcuXPje9/7Xpx88slx/fXX/3/s3XecFdX9P/737sLu0mEFQRRQEEUQxK5ghBh7NxGNGnsvMdb48WukGNSfiS2JInYSNRrRqLGXiNEgGBsWBFTEElEQaRak7fz+8LE3e9ld2F32CMrz+Xj4cO/cM2fOnLnMmde9U2LBggVx3nnnxUEHHRRPP/10fPLJJ3HIIYfE7373uzjggAPiiy++iOeee67G08vPPffc+Ne//hUPPPBArL322vH//t//i1deeaXO12OvaAxekQsvvDDeeuutePTRR6Nt27bx7rvvxoIFC2q9/LFjx8ZJJ50Ul112Wey7777x1FNPxYUXXphX5ssvv4w999wzLr744igpKYm//OUvsc8++8SUKVOic+fOuXKXX355DB48OIYMGVLr5cMaJQOSmj9/flZSUpLdeOON1b4/bdq0LCKyV199NcuyLBszZkwWEdmcOXPyyg0YMCDbfPPNq8wfEdl9992XZVmWXX/99VmLFi2yzz//vN7t/f3vf59tueWWudfbb799dthhh9VYvkuXLtlVV12VZVmWPf7441mjRo2yjz/+OPf+o48+mtdGAEipuvHyt7/9bbbrrrvmTfvoo4+yiMimTJmSvfzyy1lEZO+//361dR555JHZfvvtl2VZln3xxRdZcXFxdvfdd+fe//zzz7MmTZpkv/rVr3LTqhv7WrVqld166601tn3ZMXjIkCHZZpttVmP5ffbZJzv66KOrfa+644lXX301i4hs2rRpWZZl2cEHH5zttddeefMddthhWatWrWpcZpZlWa9evbI//elPudddunTJ9t9//+XOA2s6p5pDYpMmTYqFCxfGT37yk5Wua8stt1zu+xMmTIjNN988ysrKal3n3/72t+jfv3906NAhmjdvHr/5zW/iww8/zKuztm2fNGlSdOrUKTp27Jibtv3229e6LQDQEJYdL1977bUYM2ZMNG/ePPdfjx49IuLbU6k322yz+MlPfhK9e/eOQYMGxY033hhz5syptu6pU6fGokWLYtttt81NKysri4033rjO7VzRGLwiJ598ctx1113Rt2/f+PWvfx3PP/98nZY/ZcqU2GabbfKmLfv6yy+/jHPOOSc22WSTaN26dTRv3jwmTZpUpZ1bbbVVnZYNaxrBGxKrfKr4ylrR3cPruqxx48bFYYcdFnvuuWc89NBD8eqrr8YFF1wQixYtqnedALCqLTtefvnll7HPPvvEhAkT8v6ruC9JUVFRPPnkk/Hoo49Gz549409/+lNsvPHGMW3atHq3oaCgoMqp6pWv367NGLwie+yxR3zwwQdx5plnxvTp0+MnP/lJnHPOORERUVj47WF+5TbU5frxCuecc07cd999cckll8Rzzz0XEyZMiN69e1dppyecwPIJ3pBY9+7do0mTJvHPf/6zVuUr7gK6dOnSOi+rT58+MWHChJg9e3atyj///PPRpUuXuOCCC2KrrbaK7t27xwcffFClztq2fZNNNomPPvooPvnkk9y08ePH134FACCBLbbYIiZOnBjrr79+bLjhhnn/VQTGgoKC6N+/fwwbNixeffXVKC4ujvvuu69KXd26dYvGjRvn3bh0zpw58fbbb+eVa9euXd54+M4778TXX3+de12bMbg22rVrF0ceeWTcfvvtcfXVV8cNN9yQmx4ReW2YMGFC3rwbb7xxvPjii3nTln09duzYOOqoo+KAAw6I3r17R4cOHfJuzgbUjuANiZWWlsZ5550Xv/71r+Mvf/lLTJ06NcaPHx8333xzteW7dOkSBQUF8dBDD8Vnn30WX375Za2Xdcghh0SHDh1i//33j7Fjx8Z7770X9957b4wbN67a8t27d48PP/ww7rrrrpg6dWr88Y9/rHKQMWTIkLjzzjtjyJAhMWnSpHjjjTfisssuq7a+nXfeOTbaaKM48sgj47XXXovnnnsuLrjgglq3HwBSOPXUU2P27NlxyCGHxIsvvhhTp06Nxx9/PI4++uhYunRpvPDCC3HJJZfESy+9FB9++GH8/e9/j88++yw22WSTKnU1b948jj322Dj33HPj6aefjjfffDOOOuqo3C/MFXbaaae45ppr4tVXX42XXnopTjrppGjcuHHu/dqMwSsyePDgeOCBB+Ldd9+NiRMnxkMPPZRr84YbbhidOnWKoUOHxjvvvBMPP/xwlbuv//KXv4xHHnkkrrzyynjnnXfi+uuvj0cffTQKCgry2vn3v/89JkyYEK+99loceuihy71pK1A9wRu+AxdeeGGcffbZMXjw4Nhkk03i4IMPjpkzZ1Zbdt11141hw4bF//3f/0X79u3jtNNOq/VyiouL44knnoi111479txzz+jdu3f8f//f/xdFRUXVlt93333jzDPPjNNOOy369u0bzz//fJW7mQ4cODBGjx4d//jHP6Jv376x0047xX/+859q6yssLIz77rsvFixYENtss00cd9xxucemAcCq0rFjxxg7dmwsXbo0dt111+jdu3ecccYZ0bp16ygsLIyWLVvGs88+G3vuuWdstNFG8Zvf/CauuOKK2GOPPaqt7/e//3386Ec/in322Sd23nnn2GGHHapcV37FFVdEp06d4kc/+lEceuihcc4550TTpk1z79dmDF6R4uLiOP/886NPnz65U+bvuuuuiIho3Lhx3HnnnTF58uTo06dPXHbZZTF8+PC8+fv37x8jR46MK6+8MjbbbLN47LHH4swzz4zS0tJcmSuvvDLatGkT/fr1i3322Sd222232GKLLerUTiCiIFv24hMAAGCNdPzxx8fkyZPjueeeW9VNgR8Uz/EGAIA11OWXXx677LJLNGvWLB599NH485//HCNGjFjVzYIfHL94AwDAGuqggw6KZ555Jr744ovo2rVr/PKXv4yTTjppVTcLfnAEbwAAAEjIzdUAAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8YRUZNWpUFBQUxEsvvbTCsgMHDoyBAwc26PLff//9KCgoiFGjRq2w7FFHHRXrr79+gy4fgDXPiy++GP369YtmzZpFQUFBTJgwYVU3qcEVFBTE0KFD86atLuu9bNsqjkXef//9JMt75plnoqCgIJ555pkVlk1xrAOrk0arugEAAPzwLV68OAYNGhSlpaVx1VVXRdOmTaNLly6rulnJranrDeQTvGEN1aVLl1iwYEE0btx4VTcFgDXA1KlT44MPPogbb7wxjjvuuFXdnO/M6rzehx9+ePz85z+PkpKSJPXvuOOOsWDBgiguLk5SP3yfONUc1jBLliyJRYsWRUFBQZSWlkZRUdGqbhIAa4CZM2dGRETr1q1XWParr75K3JrvTl3Wu7YqxvKVVVRUFKWlpVFQUNAAraqqsLAwSktLo7BQ5AD/CiChjz/+OI499tjo2LFjlJSUxAYbbBAnn3xy3mC5cOHCOOuss6Jdu3bRrFmzOOCAA+Kzzz5bYd0zZ86MY489Ntq3bx+lpaWx2WabxZ///Oe8MhXXcV9++eVx9dVXR7du3aKkpCTeeuutGq/xvv/++2PTTTeN0tLS2HTTTeO+++6rdvnl5eVx9dVXR69evaK0tDTat28fJ554YsyZMyev3EsvvRS77bZbtG3bNpo0aRIbbLBBHHPMMbXsQQB+CI466qgYMGBAREQMGjQoCgoKctfzHnXUUdG8efOYOnVq7LnnntGiRYs47LDDIqL2Y01ExKOPPho/+tGPolmzZtGiRYvYa6+9YuLEiSts2+LFi2PYsGHRvXv3KC0tjbXWWit22GGHePLJJ3Nlarr+eEX3QFneete2zuWN5TVZuHBhnHnmmdGuXbto0aJF7LvvvvHf//63SrmarvEeMWJE9OrVK0pKSqJjx45x6qmnxty5c3PvH3nkkVFaWhqTJk3Km2+33XaLNm3axPTp0yOi5mu8b7jhhujWrVs0adIkttlmm3juuedqXI8hQ4bEhhtuGCUlJdGpU6f49a9/HQsXLqxx3WF15VRzSGT69OmxzTbbxNy5c+OEE06IHj16xMcffxz33HNPfP3117lyv/zlL6NNmzYxZMiQeP/99+Pqq6+O0047Lf72t7/VWPeCBQti4MCB8e6778Zpp50WG2ywQYwePTqOOuqomDt3bvzqV7/KK3/rrbfGN998EyeccEKUlJREWVlZlJeXV6n3iSeeiJ/97GfRs2fPuPTSS+Pzzz+Po48+OtZbb70qZU888cQYNWpUHH300XH66afHtGnT4pprrolXX301xo4dG40bN46ZM2fGrrvuGu3atYv/+7//i9atW8f7778ff//731eiZwH4vjnxxBNj3XXXjUsuuSROP/302HrrraN9+/a595csWRK77bZb7LDDDnH55ZdH06ZNc/OtaKyJiLjtttviyCOPjN122y0uu+yy+Prrr+O6666LHXbYIV599dXlhuOhQ4fGpZdeGscdd1xss802MX/+/HjppZfilVdeiV122SXpetdFdWN5TY477ri4/fbb49BDD41+/frF008/HXvttVetljN06NAYNmxY7LzzznHyySfHlClT4rrrrosXX3wx1+d/+MMf4umnn44jjzwyxo0bF0VFRXH99dfHE088Ebfddlt07NixxvpvvvnmOPHEE6Nfv35xxhlnxHvvvRf77rtvlJWVRadOnXLlysvLY999941///vfccIJJ8Qmm2wSb7zxRlx11VXx9ttvx/3331/rvoPVQgYkccQRR2SFhYXZiy++WOW98vLy7NZbb80iItt5552z8vLy3HtnnnlmVlRUlM2dOzc3bcCAAdmAAQNyr6+++uosIrLbb789N23RokXZ9ttvnzVv3jybP39+lmVZNm3atCwispYtW2YzZ87Ma0PFe7feemtuWt++fbN11lknb9lPPPFEFhFZly5dctOee+65LCKyO+64I6/Oxx57LG/6fffdl0VEtX0AwJplzJgxWURko0ePzpt+5JFHZhGR/d///V/e9NqONV988UXWunXr7Pjjj88r9+mnn2atWrWqMn1Zm222WbbXXnstt8yy43DltlceH7MsyyIiGzJkSO51Tetd2zqXN5ZXZ8KECVlEZKecckre9EMPPbRK2yqORaZNm5ZlWZbNnDkzKy4uznbdddds6dKluXLXXHNNFhHZLbfckpv2+OOPZxGRDR8+PHvvvfey5s2bZ/vvv3/eMivWfcyYMVmWfXussvbaa2d9+/bNFi5cmCt3ww03ZBGR1x+33XZbVlhYmD333HN5dY4cOTKLiGzs2LEr7AtYnTjVHBIoLy+P+++/P/bZZ5/Yaqutqrxf+VqqE044Ie/1j370o1i6dGl88MEHNdb/yCOPRIcOHeKQQw7JTWvcuHGcfvrp8eWXX8a//vWvvPI/+9nPol27dstt8yeffBITJkyII488Mlq1apWbvssuu0TPnj3zyo4ePTpatWoVu+yyS8yaNSv335ZbbhnNmzePMWPGRMT/rmd76KGHYvHixctdPgBrtpNPPjnvdW3HmieffDLmzp0bhxxySF65oqKi2HbbbXPlatK6deuYOHFivPPOO8nWrSHUZiyP+PYYISLi9NNPz5t+xhlnrHDep556KhYtWhRnnHFG3nXZxx9/fLRs2TIefvjh3LRdd901TjzxxLjooovipz/9aZSWlsb111+/3PpfeumlmDlzZpx00kl5N1w76qij8o49Ir7d/ptsskn06NEjb7vutNNOEREr3K6wunGqOSTw2Wefxfz582PTTTddYdnOnTvnvW7Tpk1ERLXXr1X44IMPonv37lVuVrLJJpvk3q9sgw02WGE7Kubp3r17lfc23njjeOWVV3Kv33nnnZg3b16svfba1dZVcSOZAQMGxM9+9rMYNmxYXHXVVTFw4MDYf//949BDD012B1UAvn8aNWpU5bKm2o41FYG5IpAtq2XLlstd9kUXXRT77bdfbLTRRrHpppvG7rvvHocffnj06dOnrquRVG3G8ohvx/PCwsLo1q1b3vSNN964VvNWV7a4uDi6du1a5fji8ssvjwceeCAmTJgQf/3rX2vcVsvWv+yxRuPGjaNr16550955552YNGlSjV82VGx/+L4QvGEVq+mu4lmWNdgymjRp0mB1RXz7i/7aa68dd9xxR7XvVwySBQUFcc8998T48ePjwQcfjMcffzyOOeaYuOKKK2L8+PHRvHnzBm0XAN9PJSUlVb5Mru1YU3HPkttuuy06dOhQpVyjRss/3N1xxx1j6tSp8cADD8QTTzwRN910U1x11VUxcuTI3OO/CgoKqh2Xly5duuKVq0Fd62zosbwhvPrqq7kA/MYbb+SdibeyysvLo3fv3nHllVdW+37l68Hh+0DwhgTatWsXLVu2jDfffDNJ/V26dInXX389ysvL8w5UJk+enHu/PnVGRLWn2k2ZMiXvdbdu3eKpp56K/v371+pAYLvttovtttsuLr744vjrX/8ahx12WNx1112r3fNMAVh91Hasqfhld+21146dd965XssqKyuLo48+Oo4++uj48ssvY8cdd4yhQ4fmxqk2bdrEe++9V2W+5V0WtiIp6oz4djwvLy+PqVOn5v1yvexYXtO8FWUr/wK9aNGimDZtWl7/fvXVV3H00UdHz549o1+/fvG73/0uDjjggNh6661XWP8777yTd4bC4sWLY9q0abHZZpvlpnXr1i1ee+21+MlPfpLscWfwXXKNNyRQWFgY+++/fzz44IPx0ksvVXl/ZX/N3nPPPePTTz/Nu/P5kiVL4k9/+lM0b9489+iSulhnnXWib9++8ec//znmzZuXm/7kk09WeWTJQQcdFEuXLo3f/va3VepZsmRJ7pEjc+bMqbKuffv2jYjwKBAAlqu2Y81uu+0WLVu2jEsuuaTa+4ms6BGdn3/+ed7r5s2bx4Ybbpg3TnXr1i0mT56cV9drr70WY8eOrcsq5UlRZ0TEHnvsERERf/zjH/OmX3311Sucd+edd47i4uL44x//mDd+33zzzTFv3ry8O6Ofd9558eGHH8af//znuPLKK2P99dePI488crnj+1ZbbRXt2rWLkSNH5j1addSoUXmPK4v4dvt//PHHceONN1apZ8GCBT+oZ72zZvCLNyRyySWXxBNPPBEDBgzIPQbjk08+idGjR8e///3vlar7hBNOiOuvvz6OOuqoePnll2P99dePe+65J8aOHRtXX311tGjRol71XnrppbHXXnvFDjvsEMccc0zMnj07/vSnP0WvXr3iyy+/zJUbMGBAnHjiiXHppZfGhAkTYtddd43GjRvHO++8E6NHj44//OEPceCBB8af//znGDFiRBxwwAHRrVu3+OKLL+LGG2+Mli1bxp577rlSfQDAD1ttx5qWLVvGddddF4cffnhsscUW8fOf/zzatWsXH374YTz88MPRv3//uOaaa2pcTs+ePWPgwIGx5ZZbRllZWbz00ktxzz33xGmnnZYrc8wxx8SVV14Zu+22Wxx77LExc+bMGDlyZPTq1Svmz59fr/VLUWfEt19wH3LIITFixIiYN29e9OvXL/75z3/Gu+++u8J527VrF+eff34MGzYsdt9999h3331jypQpMWLEiNh6663jF7/4RUREPP300zFixIgYMmRIbLHFFhHx7ePOBg4cGBdeeGH87ne/q7b+xo0bx/Dhw+PEE0+MnXbaKQ4++OCYNm1a3HrrrVWu8T788MPj7rvvjpNOOinGjBkT/fv3j6VLl8bkyZPj7rvvjscff7zaG9jCamtV3lIdfug++OCD7IgjjsjatWuXlZSUZF27ds1OPfXUbOHChblHeCz7qK1lH72RZdU/cmTGjBnZ0UcfnbVt2zYrLi7OevfunfdosCz73yNIfv/731dpW3WPE8uyLLv33nuzTTbZJCspKcl69uyZ/f3vf6/2cSlZ9u3jP7bccsusSZMmWYsWLbLevXtnv/71r7Pp06dnWZZlr7zySnbIIYdknTt3zkpKSrK1114723vvvbOXXnqp1n0IwA/D8h4n1qxZsxrnW9FYU7n+3XbbLWvVqlVWWlqadevWLTvqqKNWOOYMHz4822abbbLWrVtnTZo0yXr06JFdfPHF2aJFi/LK3X777VnXrl2z4uLirG/fvtnjjz++Uo8Tq22dyxvLa7JgwYLs9NNPz9Zaa62sWbNm2T777JN99NFHK3ycWIVrrrkm69GjR9a4ceOsffv22cknn5zNmTMny7Ismz9/ftalS5dsiy22yBYvXpw335lnnpkVFhZm48aNy1v3ysc0WZZlI0aMyDbYYIOspKQk22qrrbJnn3222mOdRYsWZZdddlnWq1evrKSkJGvTpk225ZZbZsOGDcvmzZtX6/6A1UFBljXgHZwAAACAPK7xBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASKhRfWcsLy+P6dOnR4sWLaKgoKAh2wQAa7Qsy+KLL76Ijh07RmHhyn1HbrwGgDTqMl7XO3hPnz49OnXqVN/ZAYAV+Oijj2K99dZbqTqM1wCQVm3G63oH7xYtWuQW0rJly/pWAwAsY/78+dGpU6fcWLsyjNcAkEZdxut6B++K09VatmxpIAeABBri1HDjNQCkVZvx2s3VAAAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASarSqG/B9NWPGjJg3b16d52vVqlW0b98+QYsAgIj6j9HUjWMagNoTvOthxowZ8YvDj4jFixbWed7GxSVx+21/MVABQAIrM0ZTN45pAGpP8K6HefPmxeJFC2NB1wFRXtoqN71wwdxoMu3ZWLDBjlHepHWV+Qq/mRfx3r9i3rx5BikASKCmMXp1sKLjhO8TxzQAdSN4r4Ty0lZR3qxt1elNWlc7HQD4btQ0Rq8OHCcArHncXA0AAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAAS+sEH72+++Sbefvvt+Oabb1Z1U5L4oa8fAKsn4w9QHfsGqN4PPnh/+OGHccIJJ8SHH364qpuSxA99/QBYPRl/gOrYN0D1fvDBGwAAAFYlwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEmq0qhtQYdGiRfHAAw/E9OnTo2PHjrHffvtFcXFxredfunRpvP766zF79uxo2bJlvP322/HUU0/F119/nav/h6hivU455ZRYsmRJFBQURNOmTaNr167Rrl27KCsri0mTJsXXX38d66yzTuy1114xefLkiIjo27dv9O3bN4qKiiIivw/LysqiR48e8dBDD8X06dOjffv2kWVZzJw5Mzp06BBdu3aN+fPnR+vWrWPJkiXx5JNPxieffBIlJSXRpk2bWGeddWLzzTePvn37xtKlS1e4bSsvu3Xr1hERMXfu3CgrK4tevXrFxIkTq21X5foWLFgQ119/ffz3v/+N9dZbL0488cRo0qRJjXXX9HdZWVn06dMnIiKvP/r06RNFRUVV+qliek3rs6L6Vme16bvarktt+q2+batNfSsqP2/evLjgggtixowZ0b59+7j44oujefPmq3ybNXS/0TCWLl0aEyZMiAkTJkRE1f0pAP9Tn+P8muapz7j45ZdfxqWXXpqr6/zzz4/mzZs3+Lo0ZJuN/zWrT9+sbNZsCKtF8B45cmSMHj06li5dmjdt0KBBcdJJJ61w/meffTZGjBgRn376aY1lTjvttOjfv39cfPHFDdLm1cEFF1wQY8eOjYiIJUuWRERElmXx1VdfxRtvvFGl/LRp0+L555/Pvb7tttuidevWcdZZZ0VErLAP6+r222+P4uLiWLJkSZSXl+emL7ttV7T9KsJuTUaOHBkdOnSIjz/+ODftpZdeivvvvz969OgRc+fOrfN6VQ6WFTp06BADBw6MZ555Jq++Dh06xCmnnBI77rhjjetTU32V51vd1ObfVYUVrUt1da3M+te1vhWVP+yww/I+P5999lnst99+UVhYmPfZ/a63WUP3Gw3j2WefjSuvvDLv33Pl/altA/A/9TnOr2me7bbbLqZOnVqncfGkk07K/egU8e3x8N577x09evSIkSNHNti6RESDtdn4X7P69M3KZs2GsspPNR85cmTcdddd0bJlyzjnnHPi3nvvjXPOOSdatmwZd9111wr/QTz77LMxZMiQ6Nq1a+y8885V3t96661zf48dOzYuuOCCBl+HVaFy6K6PTp06Re/evWPu3LkxePDgGDx4cHTt2jWuvfba+NnPfhYREY0bN86Vb9q0ae5boVatWkVpaWm19ZaUlOS9XrRoUZSXl8e+++5b7batvP2OP/74KCgoiN69e0fv3r1zdbRs2TIKCgpiu+22y2vXueeeG+ecc04UFhbGxx9/HIWFhXHooYfG7bffHoceemgUFRXF5MmTY+HChXH88cdHRETv3r2jc+fOERHRuXPnvL979+4dBQUFsfPOO8fcuXNj7ty5cfzxx8cjjzwS1157bbRq1SruuuuuaNWqVVx77bW56V27do0hQ4bEs88+m7c+FWWOP/74auurPN/qpqbtUl1/HX/88ctdl+r6ZGXWv671raj8T3/601zo3mabbeKaa66Jbt26RUREeXl5tGvXbpVss4buNxrGs88+G4MHD465c+dG796948orr4wrrrgitz+1bQD+pz7H+TXNU1paGmPHjo3CwsJaj4sVobugoCB23XXXuOmmm2LXXXeNgoKCmDx5cp1C14rWpaHabPyvWX36ZmWzZkMqyLIsq8+M8+fPj1atWsW8efOiZcuW9Vr4okWLYo899oiWLVvG6NGjo1Gj//0Av2TJkhg0aFDMnz8/Hn300WpPBVi6dGkcdthh0bVr1xg8eHDsueeeUV5eHkVFRfHwww/HRRddFG+//XbMmjUrWrRoEV988UVERDz66KPRpEmTerU5IuLtt9+OE044Ib7quW+UN2ubm1741axo9tY/qkxf9v0bbrghNtpoo3ovf8GCBbHHHntUmV5xKktNvw4XFBREWVlZzJ49O7Isi3/84x9xySWXxPjx46OkpCQeeuihyLIst01uu+222HvvvaOwsDAeeuihOProo2P27NlRXl4ebdq0iVmzZuWWu3jx4mjcuHE8+OCDMWTIkHjhhRei4qNVcfr5HXfcEUVFRXnbtm3bttGtW7cYNmxYHH744dG1a9cYPnx4LF26NPbee++IiPjHP/4RQ4YMifHjx0ebNm3ib3/7WwwdOjSmTZsWN954Y65cUVFR7rOydOnSOPTQQ2PmzJmRZVmsvfbaseGGG+aWs3DhwiguLo6CgoJYuHBhlJSUxG233RaDBw+O//znP7H11ltHQUFBvP/++3H77bdHRMShhx4aixYtitLS0rj99ttzp7SUl5fHb37zm5g2bVqUl5dHt27dYvjw4VFYWJj7jG6wwQYREbn6ioqK8uarXN+qVvnfVeXtMmzYsPjFL36R64O//OUvMWTIkJg2bVre35XXpXJdFX1SoT7rX9f6VlT+vPPOixdffDEiIh555JFo2rRpbp5OnTrl3nvggQeiVatW39k2a+h+o2FU7Ffmzp0bW2yxRVx88cW5bVNeXh4XXHBBvPLKK1FWVrbS26YhxtgUdVVWMRau7JjW0Goao1cHKzpO+D5pqGMafngq/g2OGDEifvnLX9bpOL+mbLB06dI45JBDYs6cOVFeXp43T03j4pdffhl77713FBQUxKOPPpr3o9E333wTe+yxR2RZFg899NAKTztfXmb5+uuvY88994yI/x1L1LfNxv+a1advVjZr1kZdxthan2q+cOHCWLhwYd5CVtYDDzwQS5cujWOPPTavIyIiGjVqFMccc0xcccUV8cADD+RO4ajs9ddfj08//TQuvPDCePDBB3OnhB588MFRWloahx12WJx66qkREbHDDjvEo48+GhERl112WRx66KH1bvcHH3xQ73kbYv6//vWv1U6vuN67bdu2uVBcWZZl8fnnn8eWW24ZL7/8clx22WWx7bbbxvjx42PhwoXx5ptvxrvvvpvbJhX9VV5eHg8//HDMmDEjDj300PjrX/+aV3/FchctWhRvvfVWHH744TF+/Pjc+wsXLoxPP/00Xn/99dh8883ztu2MGTNi8ODB8eabb+a2ZWFhYbz22mu5z9tbb70V6667bkRE7LLLLlFcXJzbtpdeemlEfHtmw4svvpj7rLz++usxY8aM2GWXXeLJJ5+MmTNnxpAhQ3LLOeecc+Lyyy+PiMj9/eabb8Y222wT48aNi2233TY22mijOPXUU+P111+PiIgZM2bE2WefHVdccUVuXSIiCgsL8z5rgwcPzu0MKn9GIyJX3+abb543X+X6VrXKba68Xd588828PnjzzTdz7a/8d+V1qVxX5R1kRNRr/eta34rKf/755xER0aNHj9xAuew2e/HFF+OCCy6Ia6655jvbZg3dbzSMiv1KRMQvfvGLvG1TWFgYv/jFL2LcuHHxySefrNJtk2K8Xp6VHdMa2urWnh86/c2yKj4TzzzzTJ2P82vKBq+//nrMnDkzDjnkkLjzzjvz5qlpXKw4Rtxll12qnKlZWloaO++8czz55JNx6aWXrvBS1OVllocffjjv74p21afNxv+a1advVjZrNrRaB+9LL700hg0b1qALnz59ekREbL/99tW+XzG9otyyZs+eHRERG2ywQTz11FO56RXfOlX8yhgRuRAZ8e2O4Jlnnql/w1dS6uvMV/SNTd++fePll1+O6dOn550aPnv27LxtUvFLb8T/tsGee+5ZY/CvqKOm7VmxvSrqr7DBBhvEuHHjcn8vW3b27Nm5g8iKAF5RrqJdRxxxRLz44ou51xXzDxo0KJ588skqy6m8/Iq/Z8+eneuPkpKSattSuWxllT9rlf+u/Bmtrh+qW8aqVrnNlbfLsn1XeVsv+3d1dVWnrutf1/pWVL4ikFRcxrDsPIcffni8+OKLubBVnzbXR0P3Gw2jun+7ldX07/y7lmK8Xp4f0r1TqDvbn5p89tlnEVG34/yaskHFPnXPPfeMO++8s0o2qG5crChz0EEHVbv8imPEmnJGZcvLLNW1v75tNv7XrD59s7JZs6HVOniff/75uZtwRXx7wNqpU6eVWnjHjh0jImLcuHG504UrqzjQryi3rLKysoj49iYJlcs88sgjccIJJ8S0adNy0/bYY49c+B44cOBK/+K9MgPNBRdcEF26dKn3/H/961+X+8XBiu7gXnEX3o4dO+b9KlJWVpa3TSr3acXfjzzyyHLrLisry+v3Zd+rULFtI77dfpW3Za9evfLKlpWV5QJxxbW4Fcvo2LFj7lTnyu2smH/06NHVLqfy8iv+Lisri/fffz8ivv3FqGIZ1bW78rTK7am8DpXLVX6/8rzVLWNVq9zm6v6u3AeV21/duiy7XZdV1/Wva30rKt+yZcuYNWtWjB8/Po466qgq89x2220REdG+fft6t7k+GrrfaBjL/ttddtvU9O/8u5ZivF6elR3TGtrKjtHUzeq2/Vn1Kv4NtmvXLiLqdpxfUzao2KdWHIcumw2qGxcrjhHvvvvu+H//7/9VWX7FMWJNOaOy5WWW6tpf3zYb/2tWn75Z2azZ0GodvEtKSqrcOGtl7bfffjFy5Mi4+eabY/fdd69y3v0tt9wSRUVFsd9++1U7f58+faJDhw5xxx13xODBg+O6666L8vLy+Nvf/hZHHHFE3HHHHbnTrv/973/n5jvvvPNW6hrvldWlS5eVuh7qvPPOqzZ4V1zbXN1p5hH/u8b7lVdeydVzySWXRMS323fTTTeNXr165bbJbbfdFtdee20UFhbGXnvtFffcc0+MHj06ioqKarzGu2fPnjFkyJAoKCioco13xSO1Km/btm3bxh133BHDhg3Lbcvhw4fHpptumvu89ezZM/72t79FRMSTTz4Zxx13XNxxxx2xzjrrxPnnnx977713vPjii3mflT59+kT79u1zZ0Ksvfbaecu5+eabo3379lFQUBA333xzdOjQITbddNO4++67o6ioKF544YX4z3/+E+uss06u3e3bt49bbrklb1rEt6fiV7Sn4u+Ka08qPqMVZw9UnrfyfJXrW9Uq/7uqvF2GDRuW1webbrppDBkypMrfldelcl3VXY9T1/Wva30rKr/WWmvFe++9F5MnT46vv/46mjZtmptn1KhRuWu8Kw7iv6tt1tD9RsOo2K/MnTs3br/99irXeN9+++1RUlKS9wjBVSHFeL08Kzum8f1m+1OTgQMHxr333lun4/yaskGfPn1i7bXXjnvuuafKPDWNixXHiE8++WScddZZVa7xrjhGPP/881e4LsvLLHvttVdce+21ub8r1KfNxv+a1advVjZrNrRVelfz4uLiGDRoUMyZMycGDRoUDz74YMyaNSsefPDBvOk1nTpdVFQUp5xySowbNy4uuuii2GmnnSLi24vvd99993j++edj/fXXj4jI3Vitf//+qzR0N4QmTZpE//79q0xftGjRch+7VXGNd5Zl0alTp7jgggty12IvXLgwhgwZEu+8807sv//+MWfOnDjggAMi4tsP84EHHhhz5syJxYsXR/PmzePLL7/MW26WZbFo0aLYZ599Yvz48VH5nn0LFy6MbbbZJubMmVNl25566qkxbty4GDJkSOyzzz4xbty4+NWvfhVnnXVW7jrFn//85/HCCy/EdtttF3PmzIm99tornn/++TjssMNizJgxubucZ1kWt956a3z00Udx0003xaxZsyLLsmjTpk3su+++8fzzz8dZZ50VxcXFMWfOnCgpKcn9XVxcHGeddVaMHz8+Bg4cGOPHj899O7Zw4cKYPHlytG7dOubMmRMtW7bMBbWJEyfGb37zmxg3blycfPLJufX5zW9+ExMnToyFCxfG3nvvHePGjcurb9n5VqcbZFT+d1V5u5x11llRUlISc+bMicaNG8dZZ52VW6chQ4ZUuy6V66rok+r6rbbrX9f6VlT+pZdeyn0zuueee8a5554bEydOjKZNm+ZCd7t27aJx48bf6TZr6H6jYRQVFcWpp54aCxcuzO2rXn755Xj55ZfjV7/6VYwbNy4WLVpk2wDEt9fQ1vU4v6Zs8Mgjj8RXX30Vixcvjvbt28c777yzwnGxefPm0aNHj9yNgy+++OJ4++234+KLL87dWK1Hjx61ep738jLLYYcdlit32GGHrVSbjf81q0/frGzWbGir9K7mFap7tlpRUVGDPsc7IhrsOd6r+q7mFVb2kWIREW3atIkzzzwzIhr+Od4R3/7ysnjx4rxnIS+7bVf2Od5FRUVVnuNdob7P8W7Tpk1kWZb3nN511lknBgwYUOU53uuss06cfPLJy32Od031VZ5vdVOX53ivaF2qq2tl1r+u9a2o/LLP8a6w7HO8v+tt1tD9RsOo7jneEf/bnzbEtnFX8/pzV/PvhruaU5Nl9w31Oc6vaZ7qnom9onFx2ed4V2io53hXrEtE1ed417fNxv+a1advVjZrLk9dxtjVInhHfPur6QMPPBDTp0+Pjh07xn777Venbx+WLl0ar7/+esyePTtatmwZb7/9djz11FPx9ddfx4wZM+Kaa66JTTfddKXbGbH6BO+IiDfffDNOO+20aNSoUSxZsiQKCgqiadOm0bVr12jXrl2UlZXFpEmT4uuvv4511lkn9tprr9zOp2/fvtG3b9+8RxhU9GFZWVn06NEjHnrooZg+fXq0b98+siyLmTNnRocOHaJr164xf/78aN26dSxZsiSefPLJ+OSTT3Knla+zzjqx+eabR9++fWPp0qUr3LaVl926deuIiJg7d26UlZVFr169YuLEidW2q3J9CxYsiOuvvz7++9//xnrrrRcnnnhiNGnSpMa6a/q78mmilfujT58+uS8Bqpte0/qsqL7VWW36rrbrUpt+q2/balPfisrPmzcvLrjggpgxY0a0b98+Lr744mjevPkq32YN3W80jKVLl8aECRNy98xYdn+6sgTv+hO8vxuCNzWpbt9Qn+P8muapz7j45ZdfxqWXXpqr6/zzz6/VL911aVdDt9n4X7P69M3KZs2afC+DdyopDgxWp+C9uh74AFB/gnf9Cd7fDcGbmqyu+wZIoS5j7Cq9xhsAAAB+6ARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEjoBx+8O3fuHDfccEN07tx5VTcliR/6+gGwejL+ANWxb4DqNVrVDUittLQ0Ntpoo1XdjGR+6OsHwOrJ+ANUx74BqveD/8UbAAAAViXBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEmq0qhvwfVb4zbz81wvm5v1/ReUBgDRWxzF3RccJ3yerY/8CrM4E73po1apVNC4uiXjvX9W+32TaszXO27i4JFq1apWqaQCwRlvRGL06WN5xwveJYxqA2hO866F9+/Zx+21/iXnz6v5tb6tWraJ9+/YJWgUArMwYTd04pgGoPcG7ntq3b2+wAYDVkDEagNWNm6sBAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACTUqL4zZlkWERHz589vsMYAAP8bWyvG2pVhvAaANOoyXtc7eH/xxRcREdGpU6f6VgEALMcXX3wRrVq1Wuk6IozXAJBKbcbrgqyeX6eXl5fH9OnTo0WLFlFQUFCvBgIAVWVZFl988UV07NgxCgtX7qow4zUApFGX8brewRsAAABYMTdXAwAAgIQEbwAAAEhI8IbVxPvvvx8FBQUxYcKEGsuMGjUqWrduvdLLeuaZZ6KgoCDmzp2bfFkAUF9ZlsUJJ5wQZWVlKxwjV0cFBQVx//33515Pnjw5tttuuygtLY2+ffsmX/6y431Dj+21OXapzTEHrAnqfVdz4PurX79+8cknn6z03ZIBIKXHHnssRo0aFc8880x07do12rZtu6qbtFKGDBkSzZo1iylTpkTz5s2/8+UffPDBseeeezZYfZ06dYpPPvnke79d4LsgeMMaZvHixVFcXBwdOnRY1U0BgOWaOnVqrLPOOtGvX78ayyxatCiKi4u/w1bV39SpU2OvvfaKLl261LuOpUuXRkFBQb2eeNCkSZNo0qRJvZe9rKKiIscTUEtONYfvWHl5efzud7+LDTfcMEpKSqJz585x8cUX595/77334sc//nE0bdo0Nttssxg3btxy67vuuuuiW7duUVxcHBtvvHHcdtttee8XFBTEddddF/vuu280a9YsLr744mpP+xo1alR07tw5mjZtGgcccEB8/vnnVZb1wAMPxBZbbBGlpaXRtWvXGDZsWCxZsiQivj0dcOjQodG5c+coKSmJjh07xumnn74SPQXAmuyoo46KX/7yl/Hhhx9GQUFBrL/++hERMXDgwDjttNPijDPOiLZt28Zuu+0WERFvvvlm7LHHHtG8efNo3759HH744TFr1qxcfeXl5XHppZfGBhtsEE2aNInNNtss7rnnnuW2YcSIEdG9e/coLS2N9u3bx4EHHph7b/3114+rr746r3zfvn1j6NCh1dZVUFAQL7/8clx00UVRUFAQQ4cOrXY8njBhQhQUFMT7778fEf87Pfwf//hH9OzZM0pKSuLDDz+sdhmPPPJIbLTRRtGkSZP48Y9/nKujQnWnmi/vOOKYY46JPn36xMKFCyPi2y85Nt988zjiiCMiovpTzVfUhoiIf//73/GjH/0omjRpEp06dYrTTz89vvrqq2rXCX4wMuA79etf/zpr06ZNNmrUqOzdd9/NnnvuuezGG2/Mpk2blkVE1qNHj+yhhx7KpkyZkh144IFZly5dssWLF2dZlmW33npr1qpVq1xdf//737PGjRtn1157bTZlypTsiiuuyIqKirKnn346VyYisrXXXju75ZZbsqlTp2YffPBBNmbMmCwisjlz5mRZlmXjx4/PCgsLs8suuyybMmVK9oc//CFr3bp13rKeffbZrGXLltmoUaOyqVOnZk888US2/vrrZ0OHDs2yLMtGjx6dtWzZMnvkkUeyDz74IHvhhReyG264IXl/AvDDNHfu3Oyiiy7K1ltvveyTTz7JZs6cmWVZlg0YMCBr3rx5du6552aTJ0/OJk+enM2ZMydr165ddv7552eTJk3KXnnllWyXXXbJfvzjH+fqGz58eNajR4/ssccey6ZOnZrdeuutWUlJSfbMM89Uu/wXX3wxKyoqyv76179m77//fvbKK69kf/jDH3Lvd+nSJbvqqqvy5tlss82yIUOG5F5HRHbfffdlWZZln3zySdarV6/s7LPPzj755JPsiy++qDIeZ1mWvfrqq1lEZNOmTcuy7Nuxv3Hjxlm/fv2ysWPHZpMnT86++uqrKu398MMPs5KSkuyss87KJk+enN1+++1Z+/bt8+qv63HEF198kXXt2jU744wzsizLsnPOOSdbf/31s3nz5mVZluWOXV599dVat+Hdd9/NmjVrll111VXZ22+/nY0dOzbbfPPNs6OOOqra7QA/FII3fIfmz5+flZSUZDfeeGOV9yoGr5tuuik3beLEiVlEZJMmTcqyrOqA2a9fv+z444/Pq2fQoEHZnnvumXsdEbkBs8KyA/0hhxySN0+WZdnBBx+ct6yf/OQn2SWXXJJX5rbbbsvWWWedLMuy7Iorrsg22mijbNGiRSvoBQConauuuirr0qVL3rQBAwZkm2++ed603/72t9muu+6aN+2jjz7KIiKbMmVK9s0332RNmzbNnn/++bwyxx57bHbIIYdUu+x77703a9myZTZ//vxq369r8K7u/doG74jIJkyYUG07Kpx//vlZz54986add955yw3etTmOeP7557PGjRtnF154YdaoUaPsueeey723bPCuTRuOPfbY7IQTTsgr89xzz2WFhYXZggULlruO8H3mVHP4Dk2aNCkWLlwYP/nJT2os06dPn9zf66yzTkREzJw5s8b6+vfvnzetf//+MWnSpLxpW2211Qrbte222+ZN23777fNev/baa3HRRRdF8+bNc/8df/zx8cknn8TXX38dgwYNigULFkTXrl3j+OOPj/vuuy93GjoANKQtt9wy7/Vrr70WY8aMyRujevToERHfXlf97rvvxtdffx277LJLXpm//OUvMXXq1GqXscsuu0SXLl2ia9eucfjhh8cdd9wRX3/9dfJ1q05xcXHe8UF1ajOWVzfPio4jtt9++zjnnHPit7/9bZx99tmxww47rFQbXnvttRg1alTedthtt92ivLw8pk2bttz2wveZm6vBd6g2NzRp3Lhx7u+CgoKI+Pa6tJXRrFmzlZo/IuLLL7+MYcOGxU9/+tMq75WWlkanTp1iypQp8dRTT8WTTz4Zp5xySvz+97+Pf/3rX3nrBAAra9lx7csvv4x99tknLrvssipl11lnnXjzzTcjIuLhhx+OddddN+/9kpKSapfRokWLeOWVV+KZZ56JJ554IgYPHhxDhw6NF198MVq3bh2FhYWRZVnePIsXL67TelTcIK1yPdXV0aRJk9wxwXetvLw8xo4dG0VFRfHuu++udH1ffvllnHjiidXeB6Zz584rXT+srvziDd+h7t27R5MmTeKf//xng9S3ySabxNixY/OmjR07Nnr27Fnnel544YW8aePHj897vcUWW8SUKVNiww03rPJfxYFDkyZNYp999ok//vGP8cwzz8S4cePijTfeqMeaAUDtbbHFFjFx4sRYf/31q4xRzZo1y7sp2bLvd+rUqcZ6GzVqFDvvvHP87ne/i9dffz3ef//9ePrppyMiol27dvHJJ5/kys6fP7/Ov9i2a9cuIiKvnvo+q3yTTTaJ//znP3nTlh3Lq5tnRccRv//972Py5Mnxr3/9Kx577LG49dZbV6oNW2yxRbz11lvVHk98X+5OD/XhF2/4DpWWlsZ5550Xv/71r6O4uDj69+8fn332WUycOHG5p5/X5Nxzz42DDjooNt9889h5553jwQcfjL///e/x1FNP1ame008/Pfr37x+XX3557LfffvH444/HY489lldm8ODBsffee0fnzp3jwAMPjMLCwnjttdfizTffjOHDh8eoUaNi6dKlse2220bTpk3j9ttvjyZNmqzUI1MAoDZOPfXUuPHGG+OQQw6JX//611FWVhbvvvtu3HXXXXHTTTdFixYt4pxzzokzzzwzysvLY4cddoh58+bF2LFjo2XLlnHkkUdWqfOhhx6K9957L3bcccdo06ZNPPLII1FeXh4bb7xxRETstNNOMWrUqNhnn32idevWMXjw4CgqKqpTuyuC/9ChQ+Piiy+Ot99+O6644op69cFJJ50UV1xxRZx77rlx3HHHxcsvvxyjRo1a7jwrOo549dVXY/DgwXHPPfdE//7948orr4xf/epXMWDAgOjatWu92nDeeefFdtttF6eddlocd9xx0axZs3jrrbfiySefjGuuuaZe6w7fC6v6InNY0yxdujQbPnx41qVLl6xx48ZZ586ds0suuaTKDUqyLMvmzJmTRUQ2ZsyYLMuq3hQly7JsxIgRWdeuXbPGjRtnG220UfaXv/wl7/1Y5sYuWVb9zVxuvvnmbL311suaNGmS7bPPPtnll19eZVmPPfZY1q9fv6xJkyZZy5Yts2222SZ35/L77rsv23bbbbOWLVtmzZo1y7bbbrvsqaeeWpmuAmANV9PN1X71q19VKfv2229nBxxwQNa6deusSZMmWY8ePbIzzjgjKy8vz7Isy8rLy7Orr74623jjjbPGjRtn7dq1y3bbbbfsX//6V7XLfu6557IBAwZkbdq0yZo0aZL16dMn+9vf/pZ7f968ednBBx+ctWzZMuvUqVM2atSoOt9cLcuy7N///nfWu3fvrLS0NPvRj36UjR49usrN1ZYdj2vy4IMPZhtuuGFWUlKS/ehHP8puueWW5d5cLctqPo5YsGBB1rNnzyo3Qtt3332zfv36ZUuWLKn22GVFbciyLPvPf/6T7bLLLlnz5s2zZs2aZX369MkuvvjiWq0jfF8VZNkyF6cAAAAADcY13gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3lBP77//fhQUFMSoUaO+0+UWFBTE0KFD86a9+OKL0a9fv2jWrFkUFBTEhAkTvtM21dS2UaNGRUFBQbz//vtJlvfMM89EQUFBPPPMMyssO3DgwBg4cGCSdgCw5hk6dGgUFBSs0jakHmeBhtNoVTcA6uP555+PJ554Is4444xo3bp1veoYMWJENG3aNI466qgGbdt3bfHixTFo0KAoLS2Nq666Kpo2bRpdunRZ1c0CAFaBH8rxDfzQ+MWb76Xnn38+hg0bFnPnzq13HSNGjPjOf61OYerUqfHBBx/EOeecEyeccEL84he/iDZt2qzqZkVExOGHHx4LFixI9kXAjjvuGAsWLIgdd9wxSf0AsDqrbpz9oRzfwA+NX7zhe27mzJkREfX+5b86S5YsifLy8iguLl6peoqKiqKoqKiBWlVVYWFhlJaWJqsfAFZnqcdZoOH4xZvvnaFDh8a5554bEREbbLBBFBQU5F3ftGTJkvjtb38b3bp1i5KSklh//fXj//2//xcLFy7M1bH++uvHxIkT41//+ldu/orrf2fPnh3nnHNO9O7dO5o3bx4tW7aMPfbYI1577bV6tXfx4sUxbNiw6N69e5SWlsZaa60VO+ywQzz55JO5MjVdf3zUUUfF+uuvX2PdRx11VAwYMCAiIgYNGpS3HrWts+Ja9csvvzyuvvrqXL+99dZbNS534cKFceaZZ0a7du2iRYsWse+++8Z///vfKuVquvZsxIgR0atXrygpKYmOHTvGqaeemnf2wpFHHhmlpaUxadKkvPl22223aNOmTUyfPj0iar7G+4Ybbohu3bpFkyZNYptttonnnnuuxvUYMmRIbLjhhlFSUhKdOnWKX//613mfFQD497//HVtvvXWUlpZGt27d4vrrr6+23O233x5bbrllNGnSJMrKyuLnP/95fPTRR3llBg4cGJtuumm89dZb8eMf/ziaNm0a6667bvzud7+rUt+f/vSn6NWrVzRt2jTatGkTW221Vfz1r3/Nvb/sOFvT8c17770XBQUFcdVVV1VZxvPPPx8FBQVx5513rkQPASviF2++d37605/G22+/HXfeeWdcddVV0bZt24iIaNeuXUREHHfccfHnP/85DjzwwDj77LPjhRdeiEsvvTQmTZoU9913X0REXH311fHLX/4ymjdvHhdccEFERLRv3z4iIt577724//77Y9CgQbHBBhvEjBkz4vrrr48BAwbEW2+9FR07dqxTe4cOHRqXXnppHHfccbHNNtvE/Pnz46WXXopXXnkldtlll5XqixNPPDHWXXfduOSSS+L000+PrbfeOrcedXXrrbfGN998EyeccEKUlJREWVlZjWWPO+64uP322+PQQw+Nfv36xdNPPx177bVXrZYzdOjQGDZsWOy8885x8sknx5QpU+K6666LF198McaOHRuNGzeOP/zhD/H000/HkUceGePGjYuioqK4/vrr44knnojbbrttudvg5ptvjhNPPDH69esXZ5xxRrz33nux7777RllZWXTq1ClXrry8PPbdd9/497//HSeccEJssskm8cYbb8RVV10Vb7/9dtx///217jsAfrjeeOON2HXXXaNdu3YxdOjQWLJkSQwZMqTKeHvxxRfHhRdeGAcddFAcd9xx8dlnn8Wf/vSn2HHHHePVV1/NOzNtzpw5sfvuu8dPf/rTOOigg+Kee+6J8847L3r37h177LFHRETceOONcfrpp8eBBx4Yv/rVr+Kbb76J119/PV544YU49NBDq21rTcc3Xbt2jf79+8cdd9wRZ555Zt48d9xxR7Ro0SL222+/Buw1oIoMvod+//vfZxGRTZs2LW/6hAkTsojIjjvuuLzp55xzThYR2dNPP52b1qtXr2zAgAFV6v7mm2+ypUuX5k2bNm1aVlJSkl100UV50yIiu/XWW5fb1s022yzba6+9lltmwIAB1bblyCOPzLp06ZI3LSKyIUOG5F6PGTMmi4hs9OjR9aqzYj1atmyZzZw5c7ntzLL/9fEpp5ySN/3QQw+t0rZbb701bzvNnDkzKy4uznbddde8Pr7mmmuyiMhuueWW3LTHH388i4hs+PDh2XvvvZc1b94823///fOWWbHuY8aMybIsyxYtWpStvfbaWd++fbOFCxfmyt1www1ZROT1x2233ZYVFhZmzz33XF6dI0eOzCIiGzt27Ar7AoAfvv333z8rLS3NPvjgg9y0t956KysqKsoqDqXff//9rKioKLv44ovz5n3jjTeyRo0a5U0fMGBAFhHZX/7yl9y0hQsXZh06dMh+9rOf5abtt99+Wa9evZbbtmXH2Syr+fjm+uuvzyIimzRpUm7aokWLsrZt22ZHHnnkcpcDrDynmvOD8sgjj0RExFlnnZU3/eyzz46IiIcffniFdZSUlERh4bf/NJYuXRqff/55NG/ePDbeeON45ZVX6tym1q1bx8SJE+Odd96p87zfpZ/97Ge5swaWp6KPTz/99LzpZ5xxxgrnfeqpp2LRokVxxhln5Po4IuL444+Pli1b5m2fXXfdNU488cS46KKL4qc//WmUlpbWeGpfhZdeeilmzpwZJ510Ut716UcddVS0atUqr+zo0aNjk002iR49esSsWbNy/+20004RETFmzJgVrg8AP2xLly6Nxx9/PPbff//o3Llzbvomm2wSu+22W+713//+9ygvL4+DDjoob0zp0KFDdO/evcqY0rx58/jFL36Re11cXBzbbLNNvPfee7lprVu3jv/+97/x4osvNsi6HHTQQVFaWhp33HFHbtrjjz8es2bNymsLkIbgzQ/KBx98EIWFhbHhhhvmTe/QoUO0bt06PvjggxXWUV5eHldddVV07949SkpKom3bttGuXbt4/fXXY968eXVu00UXXRRz586NjTbaKHr37h3nnntuvP7663WuJ7UNNtigVuUq+rhbt2550zfeeONazVtd2eLi4ujatWuV7XP55ZdHWVlZTJgwIf74xz/G2muvXav6u3fvnje9cePG0bVr17xp77zzTkycODHatWuX999GG20UEf+7aR0Aa67PPvssFixYUGVcicgfy955553Isiy6d+9eZVyZNGlSlTFlvfXWq/IM8DZt2sScOXNyr88777xo3rx5bLPNNtG9e/c49dRTY+zYsfVel9atW8c+++yTd434HXfcEeuuu27uS2cgHdd484O07GBWF5dccklceOGFccwxx8Rvf/vbKCsri8LCwjjjjDOivLy8zvXtuOOOMXXq1HjggQfiiSeeiJtuuimuuuqqGDlyZBx33HG59mZZVmXepUuX1ns96lpnkyZN6r2sVF599dXcwcobb7wRhxxySIPVXV5eHr17944rr7yy2vcrXw8OAMtTXl4eBQUF8eijj1Z7l/HmzZvnva7pTuSVx+1NNtkkpkyZEg899FA89thjce+998aIESNi8ODBMWzYsHq184gjjojRo0fH888/H717945//OMfccopp+SdhQakIXjzvVRTsO7SpUuUl5fHO++8E5tssklu+owZM2Lu3Ll5z7msqY577rknfvzjH8fNN9+cN33u3Lm5G7nVVVlZWRx99NFx9NFHx5dffhk77rhjDB06NBe827Rpk3d6WYXa/EJfkxR1Rvyvj6dOnZr3bf+UKVNqNW9F2cq/QC9atCimTZsWO++8c27aV199FUcffXT07Nkz+vXrF7/73e/igAMOiK233nqF9b/zzjt5394vXrw4pk2bFptttlluWrdu3eK1116Ln/zkJyv1RQ0AP1zt2rWLJk2aVHu5WOVxr1u3bpFlWWywwQa5M6caQrNmzeLggw+Ogw8+OBYtWhQ//elP4+KLL47zzz+/xsdpLm9M23333aNdu3Zxxx13xLbbbhtff/11HH744Q3WXqBmvt7ie6lZs2YREXmPoIqI2HPPPSPi27t6Vlbxq2blO283a9asyvwR334LvewvxaNHj46PP/64Xm39/PPP8143b948Ntxww7xHVnXr1i0mT54cn332WW7aa6+9tlKnlKWoMyJyd1v94x//mDd92T6vzs477xzFxcXxxz/+Ma+Pb7755pg3b17e9jnvvPPiww8/jD//+c9x5ZVXxvrrrx9HHnnkch/1tdVWW0W7du1i5MiRsWjRotz0UaNGVdnWBx10UHz88cdx4403VqlnwYIF8dVXX61wfQD4YSsqKorddtst7r///vjwww9z0ydNmhSPP/547vVPf/rTKCoqimHDhlU5hsiyrMqxQG0sO09xcXH07NkzsiyLxYsX1zhfTcc3ERGNGjWKQw45JO6+++4YNWpU9O7dO/r06VPntgF15xdvvpe23HLLiIi44IIL4uc//3k0btw49tlnn9hss83iyCOPjBtuuCHmzp0bAwYMiP/85z/x5z//Ofbff//48Y9/nFfHddddF8OHD48NN9ww1l577dhpp51i7733josuuiiOPvro6NevX7zxxhtxxx13VLlGuLZ69uwZAwcOjC233DLKysripZdeinvuuSdOO+20XJljjjkmrrzyythtt93i2GOPjZkzZ8bIkSOjV69eMX/+/HotN0WdERF9+/aNQw45JEaMGBHz5s2Lfv36xT//+c949913Vzhvu3bt4vzzz49hw4bF7rvvHvvuu29MmTIlRowYEVtvvXXu5i5PP/10jBgxIoYMGRJbbLFFRHz7uLOBAwfGhRdeWO2zTiO+vZZ7+PDhceKJJ8ZOO+0UBx98cEybNi1uvfXWKtvv8MMPj7vvvjtOOumkGDNmTPTv3z+WLl0akydPjrvvvjsef/zx2GqrrerdTwD8MAwbNiwee+yx+NGPfhSnnHJKLFmyJPd87Yp7tnTr1i2GDx8e559/frz//vux//77R4sWLWLatGlx3333xQknnBDnnHNOnZa76667RocOHaJ///7Rvn37mDRpUlxzzTWx1157RYsWLWqcr6bjmwpHHHFE/PGPf4wxY8bEZZddVr9OAepuVd1OHVbWb3/722zdddfNCgsL8x6lsXjx4mzYsGHZBhtskDVu3Djr1KlTdv7552fffPNN3vyffvppttdee2UtWrTIe9TUN998k5199tnZOuuskzVp0iTr379/Nm7cuCqP56rt48SGDx+ebbPNNlnr1q2zJk2aZD169MguvvjibNGiRXnlbr/99qxr165ZcXFx1rdv3+zxxx9fqceJ1bbOivX4/e9/v9z1qGzBggXZ6aefnq211lpZs2bNsn322Sf76KOPVvg4sQrXXHNN1qNHj6xx48ZZ+/bts5NPPjmbM2dOlmVZNn/+/KxLly7ZFltskS1evDhvvjPPPDMrLCzMxo0bl7fuFY8TqzBixIhsgw02yEpKSrKtttoqe/bZZ6t9vNqiRYuyyy67LOvVq1dWUlKStWnTJttyyy2zYcOGZfPmzat1fwDww/avf/0r23LLLbPi4uKsa9eu2ciRI7MhQ4Zkyx5K33vvvdkOO+yQNWvWLGvWrFnWo0eP7NRTT82mTJmSKzNgwIBqHxO27Ph8/fXXZzvuuGO21lprZSUlJVm3bt2yc889N298qm6cren4prJevXplhYWF2X//+9/6dwpQJwVZVs3dlwAAgB+kzTffPMrKyuKf//znqm4KrDFc4w0AAGuIl156KSZMmBBHHHHEqm4KrFH84g0AAD9wb775Zrz88stxxRVXxKxZs+K9996r8c7oQMPzizcAAPzA3XPPPXH00UfH4sWL48477xS64TvmF28AAABIyC/eAAAAkJDgDQAAAAk1qu+M5eXlMX369GjRokUUFBQ0ZJsAYI2WZVl88cUX0bFjxygsXLnvyI3XAJBGXcbregfv6dOnR6dOneo7OwCwAh999FGst956K1WH8RoA0qrNeF3v4N2iRYvcQlq2bFnfagCAZcyfPz86deqUG2tXhvEaANKoy3hd7+Bdcbpay5YtDeQAkEBDnBpuvAaAtGozXru5GgAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQoI3AAAAJCR4AwAAQEKCNwAAACQkeAMAAEBCgjcAAAAkJHgDAABAQo1WdQO+SzNmzIh58+at6mZ8p1q1ahXt27df1c0AAFjl1sRjwbpw3AjprDHBe8aMGfGLw4+IxYsWruqmfKcaF5fE7bf9xU4UAFijranHgnXhuBHSWWOC97x582LxooWxoOuAKC9t1eD1Fy6YG02mPRsLNtgxypu0bvD666Pwm3kR7/0r5s2bZwcKAKzRUh8L1oXjRljzrDHBu0J5aasob9Y2Xf1NWietHwCA+kt9LFgXjhthzeHmagAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAkJ3gAAAJCQ4A0AAAAJCd4AAACQkOANAAAACQneAAAAkJDgDQAAAAmtVsH7m2++ibfffju++eabVd0UqBOfXYA1lzEAaGj2Kz88q1Xw/vDDD+OEE06IDz/8cFU3BerEZxdgzWUMABqa/coPz2oVvAEAAOCHRvAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQareoGwA/BkiVLIiLizjvvjJ49e8auu+4aN910U0yYMCEaNWoUO+20UzRu3DimT58es2bNioiIpk2bRufOnWPChAnx1VdfxUYbbRQ77LBDzJ07NyZOnBiff/55lJaWRtOmTaOoqCjWXXfd2HvvvWPy5Mkxa9asmDVrVkydOjUWLFgQbdq0iQULFsQ333wTZWVl0aJFiygqKopNN9003n///fjkk0+ivLw8mjVrFo0aNYo+ffpEYWFhzJ49O+bOnRutW7eOtm3bRp8+faKoqKje/bB06dJ4/fXXY/bs2VFWVrbS9UEqDfFZXZM/78uue69evWLixIl17otFixbFAw88ENOnT4+OHTvGfvvtF8XFxXXq27qWnTBhQrz88ssxZcqUWLhwYbRv3z66d+8ea621VrRt2zZ69OgRDz30UEyfPj06dOgQXbt2jfnz5+fVvewymzRpslL9CbAyvvzyy7j00ktz+62ePXvG7Nmz8/ary6rNvnPZffQee+wRjz76aJV9dk378pqm16UdFXV8/PHHERGxySabxNprr13nMXd1GLMFb1hJI0eOjLvvvjsiIsaMGRNjxoyJa6+9Nq/MzTffvMJ6pkyZEg8++OByyyxbb0Pr0KFDnHLKKbHjjjvWed5nn302RowYEZ9++mmD1AepNMRndU3+vFe37hVhtEJt+mLkyJExevTovPlGjhwZ2223XUydOrVWfVuX7fDss8/GlVdeGXPnzs2b/uabb8Y///nPWq17hw4dYuDAgfHMM8/kLXOttdaq1fwADe2kk06KyZMn515PmzYtxo0bl3s9cuTIGDRoUJx00km5abXZd1a3j172OHTkyJHRvXv3eOedd6rsy2uaXrktK2pHdW24//77q23v8qwuY7ZTzWEljBw5Mu66665o1qxZRHz7LVxlbdu2rTJP48aNq62roKAg93dh4f/+aVYuX3l6dd9eRkSN3961adMmSktLIyKiUaP/fee27777xrbbbhsREa1atYohQ4bEs88+W20dNXn22WdjyJAh0bVr17j22mvjkUceiWuvvTa6du1ar/oglYb4rK7Jn/dl1/2CCy6IgoKCaNmyZUREXHDBBbXqi4p9Z8uWLeOcc86Je++9N84555woLS2NsWPHRmFh4Qr7ti7b4dlnn43Bgwfnhe42bdpU27bK+9Bu3bpFixYtIiJi5513jlatWsVdd90VrVq1ylvmuuuuGxERr7zySv06FqAeKkJ3QUFBrL/++hGRf9zYvn37aNmyZdx1110xcuTIiKjdvnPZffR+++0XEf87Vt1vv/3inHPOiUaNGsXkyZOjUaNGefvymqZXbsuK2nHBBRfEXXfdlTt23WqrreLQQw+N1q1bR8S3x8S1GXNXpzFb8IZ6WrRoUYwePTratGkTl112WURETJo0KSIiWrduHa1atYpZs2bldhAVFi9enPe6YieWZVluWnl5ebRo0SK23377WGuttXJlysvLc2VatWoVEd+G6MqBvHKZivoLCwujuLg4WrRoEY0aNYolS5ZE69ato7i4OP7zn//E8OHDo1+/fjFv3rzYbrvt4rrrrsv7dnF5li5dGiNGjIjtt98+hg8fHr169YqmTZtGr169Yvjw4bH99tvXqT5IpSE+q2vy533Zde/Ro0fcfPPNsf3228fo0aOjX79+ccstt0SPHj2W2xeV952jR4+OvffeO9Zaa63YY489okWLFtG4ceP49NNPo3v37jX2bV22w9KlS+Paa6+NkpKSiPh2n7jddttFcXFxbL/99rHddtvlHaiWlZVFSUlJlJSUxMiRI+O+++6LNm3axJgxY2L27NnRpk2bmD9/fvTo0SO3zFNOOSUiIu65554f5LYHVj9ffvllLnQ/8MAD8dFHH0WbNm3i0UcfjcceeywKCgpixowZceutt+b2twsWLFjhvvPaa6/N20fvuuuu8dBDD+XqbtOmTTz00EMxcODAWLJkSRQUFMSiRYti1113jbXWWit23XXXWLRoURQUFMSSJUty0/fee+9cvXfffXdce+21NbZj2223jbFjx0br1q2jWbNm0a9fv/jd734XJ5xwQtxzzz3Rpk2bmDFjRmy77bbLHXNXtzG71qeaL1y4MBYuXJh7PX/+/CQNioj44IMPvhd1fl+syeue0lNPPRVLly6NvffeO3fdSYXjjjsuXnrppXjmmWdirbXWqnJqY0TE1ltvHS+++GJe4K6sa9eu8Ytf/CJOPfXUat//7LPPIiJixx13jKeffjo3fdn6siyLLMtixowZERGx0047xdNPPx19+/bNnS755ptvxmGHHRannnpqHHzwwTFu3Lh4/fXXY/PNN19hP7z++uvx6aefxoUXXpj3BUDEt99GVtRb2/oglYb4rH4fPu+pxutl1/3VV1/NvW7UqFGVda+pLx544IFYunRpHHvssXln31TUf8ghh8Sdd94ZDzzwQAwaNCgiqvZtRNR6O0REbv8X8e0+cdttt43x48fH4MGDIyJi/Pjxufcr9q0R356Gvvnmm8cxxxwTV1xxRXz22Wdx9tlnxxVXXJG3XhVtmDVrVjzyyCOx8cYbr3yH0+AcD9WOflo9rGg7XHrppRERscsuu8Tjjz+et19t1KhR7LzzzvHkk0/G73//+9w+7Prrr6/VvjMicnVVnOp97LHHRmlpaa6uSy+9NJYuXRq77LJLPPnkk7l99gMPPBBZllWZHvHtj0UV88+YMSMGDx5cbTsqziKqOFYdMmRIrlzlOtZdd90YP358jWPu6jZm1zp4X3rppTFs2LCUbcm5+OKLv5PlrCn0Z1q33XZblWnbb799vPnmmxFR9RfuCkcccUS8+OKLNdabZVlssMEGK1x+jx498oJ3bctXnLoTETF79uzYfvvtIyJyvwrNnj27VvVVlKuprRXTa1sfpNIQn9Xvw+c91Xi97Lov+7qm6cv2xfTp0yMicvucZevfc889484778yVq1BdffXdDhX7vxXtYyvmr9zWir9rqvuKK65Ybp2wunPc+P1QsY886KCD4uGHH46I/H3VoEGD4sknn4zp06fnpv/3v/+NiBXvOyvXtew+e9npBx10UG45y5u+bL3La0fFl8cVN65ctlxFHRXlatofr25jdq2D9/nnnx9nnXVW7vX8+fOjU6dOSRp1wQUXRJcuXRq0zg8++GCN3ZGk6E++/cX77rvvjsMPPzw6d+6c9/kaN25cfPPNNxFR8zXdf/nLX5Zbf0FBQUybNm2F7ah8Q43aqChf0b6Ib0+trFhWxU6srKysVvVVlJs2bVr06tWryvsV9da2PkilIT6r34fPe6rxetl1X/b1suteU1907NgxIr7dT+69995V6n/kkUfyylWorr76boeK/d+K9rEV81e+UVHF3zXVffbZZ/vFezW1Jh8L1oXjxtXDij6vHTt2jGnTpsXdd98d3bt3j4j8/ero0aNz5Sr2W+utt1689NJLK9x3Vq5r2X12RV2Vl1/xuvL/l51eud7Ky6uuHRU/Ai1YsKDachV1VJSraX+8uo3ZtQ7eFdc7fRe6dOkSG2200XeyrDWB/kxj/fXXj3vvvTceeuihKjvGm266KXfK9+eff17t/BW/dhcUFFR7uvl7770Xt99+e3To0CFmzJhRpUy7du3is88+i2effTYKCwtz13YvW19BQUEUFBREu3btory8PHcTiQkTJkRxcXGUlZXFpptuGkOGDIkOHTrEf/7zn1hnnXWiT58+teqHPn36RIcOHeKOO+6I4cOHV7ne/I477qhTfZBKQ3xWvw+f91Tj9bLrXvn1RRddlLfuy+uL/fbbL0aOHBk333xz7L777rnTzSvqu+eee6KwsDB3M5+I6vu2Ltuhffv2MXfu3Fi4cGEUFBTECy+8EO3bt4/bb789siyLxo0b585OateuXe70/E033TSWLFkSt9xySxQVFUVZWVnccsstVdarYv/btm3b2HPPPdeYx8rxw+S48fvh/PPPj7333juefPLJOO200/L2q0uWLImnnnoqIiLOPffcOProo6OoqChOPPHEGD9+/HL3ne3bt49Zs2bl6qq8z955551z+8Pzzz8/9ttvv3jqqaeioKAgt8/eb7/9YsSIEfHUU09FUVFR3r68Yn9aWFgY7dq1q7EdFZdwTpgwIdZee+28cpX3yR9//PFyx9zVbcx2czWop+Li4hg0aFDMmTMnzjvvvIj4313N586dG/Pmzav2+u5lfwGvCMnL3tX8iy++iHHjxsWsWbNyZSrvMObNmxcR3+7EKt9QrXI9FfWXl5fHokWL4osvvoglS5ZEo0aNYu7cubFo0aLYZptt4je/+U08//zz0apVqxg/fnycfPLJtT5wLCoqilNOOSXGjRsXv/nNb2LixInx9ddfx8SJE+M3v/lNjBs3rk71QSoN8Vldkz/vy6775MmT49hjj41x48bFoEGD4vnnn49jjjkmJk+evNy+qLzvHDRoUDz44IO5a6O/+OKLWLx4cXTo0CHeeeedGvu2LtuhqKgoTj311NzZPFmWxfjx42PRokUxbty4GD9+fN4lQbNnz85dJ3/iiSfGAQccEHPmzIkf//jHUVZWFnPmzImWLVvG5MmTc8scMWJEREQceOCBP8htD6x+mjdvHj169Igsy2K//faL9dZbL+bMmRN77LFH7L777pFlWbRv3z6OPvro3P62SZMmK9x3nnrqqXn76Mcffzz22muvXN1z5syJvfbaK8aMGRONGjWKLMuiuLg4Hn/88Zg1a1Y8/vjjUVxcHFmWRaNGjXLTH3zwwVy9Bx10UJx66qk1tuOFF16I/v37x9y5c+Orr76K559/Pn7961/H9ddfHwceeGDMmTMn2rdvHy+88MJyx9zVbcwuyGq6s9MKzJ8/P1q1ahXz5s3LPUZkZb399ttxwgknxA033NDg37RV1P1Vz32jvFnVRzytrMKvZkWzt/6RrP76qGhTiv7kfyqe473s3cS/j9ZZZ504+eSTG+w53itTH6TSEJ/V1J/3hhxjG3q8rs1zvGvTF9U9n7WoqKja53jXVF9dtkNNz/Gui3XWWScGDBhQ5Tnebdu2jVmzZhlvV3OpjwXrwnEjK1LbXLTsc7yXVVRUVKvneC+776xuH11d3dU9r3t501f0HO/K7VheG+oy5qYcs+syxgreDcQOdM321ltvxSmnnBI//vGPo2fPnrHrrrvGTTfdFBMmTIhGjRrFTjvtFI0bN47p06fHrFmzIiKiadOm0blz55gwYUJ89dVXsdFGG8UOO+wQc+fOjYkTJ8bnn38epaWl0bRp0ygqKop111039t5775g8eXLMmjUrZs2aFVOnTo0FCxZEmzZtYsGCBfHNN99EWVlZtGjRIoqKimLTTTeN999/Pz755JMoLy+PZs2aRaNGjaJPnz5RWFgYs2fPjrlz50br1q2jbdu20adPn5X61m/p0qXx+uuvx+zZs6OsrGyl64NUGuKzmvLzvjoH74iq696rV6+YOHFinfti0aJF8cADD8T06dOjY8eOsd9++0VxcXGd+rauZSdMmBAvv/xyTJkyJRYuXBjt27eP7t27x1prrRVt27aNHj16xEMPPRTTp0+PDh06RNeuXWP+/Pl5dS+7zCZNmsRJJ51kvF3NCd7L57hx9VKXXPTll1/GpZdemttv9ezZM2bPnp23X11Wbfady+6j99hjj3j00Uer7LNr2pfXNL0u7aioo+L080022STWXnvtOo+5qcbsuoyxtb7GG6hZxTWKhxxySG7nePbZZ9dq3iOOOKLKtN12263G8nV93MG2225bp/Iro6ioyCPD+F5oiM/qmvx5r27d69MXFaed16b+urRleWW33HLL2HLLLZdbrro2LW+Zb7/9dq2WD5BC8+bN63zjwNrsO6vbR1e3f6xpX17T9Lq0ozZ11MbqMGa7xhsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAICHBGwAAABISvAEAACAhwRsAAAASErwBAAAgIcEbAAAAEhK8AQAAIKHVKnh37tw5brjhhujcufOqbgrUic8uwJrLGAA0NPuVH55Gq7oBlZWWlsZGG220qpsBdeazC7DmMgYADc1+5YdntfrFGwAAAH5oBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEbwAAAEhI8AYAAICEBG8AAABISPAGAACAhARvAAAASEjwBgAAgIQEb4D/v707j5eyLvj//z7nyL4cBFFASRbFLVAzFzSXynJf0jTNUlwz1DLFu9tcANO6yzD9msudJVZupWlm7uV234poLpEKagKiQm6sKgJyzu8Pf8zNEUQ48PEc4Pl8PHx4Zuaaaz4z53B9rtfMNTMAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUtEZTD+CTVv3ejDLrnT29wf+bg1L3FQBgZdUc9o/sN8LqZ7UJ79ra2rRo2SoZ/0DR22kz4cGi619WLVq2Sm1tbVMPAwCgSX1S+4LLwn4jrD5Wm/BeZ511cvXvfpsZM1avZ/Nqa2uzzjrrNPUwAACa1Oq6L7gs7DdCOatNeCcfbHBtTAAAVk/2BYGm4sPVAAAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFLRGY69YX1+fJJk5c+YKGwwA8H9z64K5dnmYrwGgjGWZrxsd3rNmzUqS9OzZs7GrAACWYNasWamtrV3udSTmawAoZWnm66r6Rj6dXldXl8mTJ6dDhw6pqqpq1AABgEXV19dn1qxZ6dGjR6qrl+9dYeZrAChjWebrRoc3AAAA8PF8uBoAAAAUJLwBAACgIOENAAAABQlvKGTQoEHZf//9i61/2LBh2WKLLRY5b5111klVVVX+9Kc/FbvtBXbZZZecfPLJldO9evXKhRdeuMLWvzSP4YfHAABN4ZOcjxa3DwA0b43+OjFY2eyyyy7ZYostljkMG3u9T9rYsWMzfPjw3Hzzzdluu+2y5pprfuJjeOyxx9KuXbsVtr6LLrpohXyPMQCsSoYMGZKTTjqpcnrQoEGZPn36J/KkO9A4whtWES+++GKSZL/99luurwyaO3duWrZs2ajrdu3atdG3uzjL+/3FALAqat++fdq3b9/UwwCWgUPNWS0MGjQoDzzwQC666KJUVVWlqqoqEydOTJI88MAD2WabbdKqVat07949//mf/5n3339/idebP39+jj766PTu3Ttt2rTJRhttlIsuumiZxvTSSy9ln332yZprrpl27dpls802y+23354kueqqq9KpU6cGy//pT3/6yKAeNmxY9tlnnyRJdXV1ZbnFHfa2//77Z9CgQZXTvXr1yg9/+MMcfvjh6dixY4477rjF3sY777yTww8/PO3bt0/37t0zYsSIRZb58KHmkyZNyn777Zf27dunY8eOOfjgg/Paa68lScaNG5e2bdvm2muvrSz/hz/8IW3atMmzzz6bZNFDzZdmDHPmzMmQIUOy7rrrpl27dtl2221z//33L/Y+AUBjfNx89HFz0YJ5/q677somm2yS9u3bZ/fdd8+UKVMqy9x///3ZZptt0q5du3Tq1Ck77LBDXnrppSQNDzUfNmxYfvOb3+SWW26p7Kvcf//9+cIXvpATTzyxwbjeeOONtGzZMn/729/KPDDARxLerBYuuuiiDBw4MMcee2ymTJmSKVOmpGfPnnn11Vez5557Zuutt84//vGPXHbZZfn1r3+dc889d4nXq6ury3rrrZcbbrghzz77bM4+++z84Ac/yB/+8IelHtMJJ5yQOXPm5MEHH8w///nP/OQnP2n0s9dDhgzJyJEjk6QyzmXxs5/9LJtvvnmefPLJnHXWWYtd5rTTTssDDzyQW265JXfffXfuv//+PPHEEx+5zrq6uuy3336ZOnVqHnjggdxzzz0ZP358vva1ryVJNt544/zsZz/L4MGDM2nSpLzyyis5/vjj85Of/CSbbrppo8dw4oknZtSoUbn++uszZsyYHHTQQdl9993zwgsvLNNjAgAf5ePmo6WZi95999387Gc/y+9+97s8+OCDmTRpUoYMGZIkef/997P//vtn5513zpgxYzJq1Kgcd9xxi30CfsiQITn44IMr4T5lypRsv/32OeaYY3Lttddmzpw5lWWvvvrqrLvuuvnCF75Q8NEBFseh5qwWamtr07Jly7Rt2zbdunWrnH/ppZemZ8+e+cUvfpGqqqpsvPHGmTx5cr7//e/n7LPP/sjr1dTUZPjw4ZXTvXv3zqhRo/KHP/whBx988FKNadKkSTnwwAPTv3//JEmfPn0aff/at29feYV84XEurS984Qs59dRTP/Lyt99+O7/+9a9z9dVX54tf/GKS5De/+U3WW2+9j7zO3/72t/zzn//MhAkT0rNnzyTJb3/722y22WZ57LHHsvXWW2fw4MG5/fbb841vfCMtW7bM1ltv3eA9a8s6hkmTJmXkyJGZNGlSevTokeSDHZI777wzI0eOzI9+9KNle2AA4EM+bj5a2rlo3rx5ufzyy9O3b98kH8T6OeeckySZOXNmZsyYkb333rty+SabbLLY8bRv3z5t2rTJnDlzGuwDHHDAATnxxBNzyy23VPZNrrrqqgwaNGi53pIGNI7wZrU2duzYDBw4sMEEtMMOO+Ttt9/OK6+8kk996lMfed1LLrkkV155ZSZNmpTZs2dn7ty5y/QJo9/5znfy7W9/O3fffXd23XXXHHjggRkwYMDy3J1G++xnP7vEy1988cXMnTs32267beW8zp07Z6ONNvrI64wdOzY9e/asRHeSbLrppunUqVPGjh2brbfeOkly5ZVXpl+/fqmurs4zzzzzkTsDSzOGf/7zn5k/f3769evX4Lpz5sxJly5dlngfAWBpfNx8tLRzUdu2bStRnSTdu3fP66+/XlnfoEGDsttuu+VLX/pSdt111xx88MHp3r37Uo+zdevW+eY3v5krr7wyBx98cJ544ok8/fTT+fOf/9yo+w0sH+ENjXD99ddnyJAhGTFiRAYOHJgOHTrk/PPPz+jRo5d6Hcccc0x222233Hbbbbn77rvz4x//OCNGjMhJJ52U6urqRT7Ne968ecs8zqVdz4r8JPJl9Y9//CPvvPNOqqurM2XKlGXaqfiwt99+OzU1NXn88cdTU1PT4DIfQgPAJ2Fp56IWLVo0uKyqqqrBnD1y5Mh85zvfyZ133pnf//73OfPMM3PPPfdku+22W+qxHHPMMdliiy3yyiuvZOTIkfnCF76Q9ddfv5H3DFge3uPNaqNly5aZP39+g/M22WSTjBo1qsFE99BDD6VDhw6VQ8YWd72HHnoo22+/fQYPHpwtt9wyG2ywQeVTxZdFz549c/zxx+emm27KqaeemiuuuCLJB58OPmvWrLzzzjuVZZ966qllXn/Xrl0bvN97/vz5efrpp5d5PX379k2LFi0aPLEwbdq0PP/88x95nU022SQvv/xyXn755cp5zz77bKZPn155D/fUqVMzaNCgnHHGGRk0aFAOO+ywzJ49u9Fj2HLLLTN//vy8/vrr2WCDDRr815hD8AHgwz5uPlqRc9GWW26Z008/PQ8//HA+/elPN/hA0oUtbl8lSfr375/PfvazueKKK3LttdfmqKOOWqbbB1Yc4c1qo1evXhk9enQmTpyYN998M3V1dRk8eHBefvnlnHTSSRk3blxuueWWDB06NKecckqqq6s/8nobbrhh/v73v+euu+7K888/n7POOiuPPfbYMo3n5JNPzl133ZUJEybkiSeeyH333Vd5/9a2226btm3b5gc/+EFefPHFXHvttbnqqquW+T5/4QtfyG233Zbbbrst48aNy7e//e1Mnz59mdfTvn37HH300TnttNNy77335umnn86gQYMqj9Hi7Lrrrunfv38OO+ywPPHEE3n00Udz+OGHZ+edd64c2n788cenZ8+eOfPMM3PBBRdk/vz5lQ+WacwY+vXrl8MOOyyHH354brrppkyYMCGPPvpofvzjH+e2225b5vsNAB/2cfPRipiLJkyYkNNPPz2jRo3KSy+9lLvvvjsvvPDCR77Pu1evXhkzZkyee+65vPnmmw2ObjvmmGPyX//1X6mvr89XvvKV5X8AgEYR3qw2hgwZkpqammy66abp2rVrJk2alHXXXTe33357Hn300Wy++eY5/vjjc/TRR+fMM89c4vW+9a1v5YADDsjXvva1bLvttnnrrbcyePDgZRrP/Pnzc8IJJ2STTTbJ7rvvnn79+uXSSy9N8sF7u66++urcfvvt6d+/f6677roMGzZsme/zUUcdlSOOOKISvH369MnnP//5ZV5Pkpx//vnZcccds88++2TXXXfN5z73uWy11VYfuXxVVVVuueWWrLnmmtlpp52y6667pk+fPvn973+f5IMPWrv99tvzu9/9LmussUbatWuXq6++OldccUXuuOOORo9h5MiROfzww3Pqqadmo402yv7775/HHntsie/XB4Bl8XHz0fLORW3bts24ceNy4IEHpl+/fjnuuONywgkn5Fvf+tZilz/22GOz0UYb5bOf/Wy6du2ahx56qHLZoYcemjXWWCOHHnpoWrduvXx3HGi0qvoPvwEUAABYJUycODF9+/bNY489ls985jNNPRxYbQlvAABYxcybNy9vvfVWhgwZkgkTJjR4FRz45DnUHAAAVjEPPfRQunfvnsceeyyXX355Uw8HVnte8QYAAICCvOINAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAADTSxIkTU1VVlauuuqrR1/3Zz3624gcGNCvCG1YTvXr1yt57773Yy+6///5UVVXlxhtv/IRHBQCsSA8//HCGDRuW6dOnN/VQgIUIbwAAWEU8/PDDGT58uPCGZkZ4AwAAQEHCG1Zyw4YNS1VVVcaNG5eDDz44HTt2TJcuXfLd73437733XlMPDwCapVmzZuXkk09Or1690qpVq6y99tr50pe+lCeeeCLJB2/RGjRo0CLX22WXXbLLLrsscd2DBg1K+/btM378+Oy2225p165devTokXPOOSf19fWLvc4vf/nL9O3bN61atcrWW2+dxx57rMHlY8aMyaBBg9KnT5+0bt063bp1y1FHHZW33nqrssywYcNy2mmnJUl69+6dqqqqVFVVZeLEiZVlrr766my11VZp06ZNOnfunEMOOSQvv/xyg9t64YUXcuCBB6Zbt25p3bp11ltvvRxyyCGZMWPGEu838NHWaOoBACvGwQcfnF69euXHP/5xHnnkkfy///f/Mm3atPz2t7+tLDNv3ry8+eabi1zXRArA6ub444/PjTfemBNPPDGbbrpp3nrrrfzv//5vxo4dm8985jPLvf758+dn9913z3bbbZef/vSnufPOOzN06NC8//77Oeeccxose+2112bWrFn51re+laqqqvz0pz/NAQcckPHjx6dFixZJknvuuSfjx4/PkUcemW7duuWZZ57JL3/5yzzzzDN55JFHUlVVlQMOOCDPP/98rrvuuvz85z/PWmutlSTp2rVrkuS8887LWWedlYMPPjjHHHNM3njjjVx88cXZaaed8uSTT6ZTp06ZO3dudtttt8yZMycnnXRSunXrlldffTV/+ctfMn369NTW1i73YwOrpXpgpTZ06ND6JPX77rtvg/MHDx5cn6T+H//4R319fX39+uuvX59kif/dcMMNTXEXAOATV1tbW3/CCSd85OXrr79+/RFHHLHI+TvvvHP9zjvvXDk9YcKE+iT1I0eOrJx3xBFH1CepP+mkkyrn1dXV1e+11171LVu2rH/jjTcaXLdLly71U6dOrSx7yy231Cepv/XWWyvnvfvuu4uM5brrrqtPUv/ggw9Wzjv//PPrk9RPmDChwbITJ06sr6mpqT/vvPManP/Pf/6zfo011qic/+STT9ongAIcag6riBNOOKHB6ZNOOilJcvvtt1fO23bbbXPPPfcs8p+vMQFgddOpU6eMHj06kydPLnYbJ554YuXnqqqqnHjiiZk7d27++te/Nljua1/7WtZcc83K6R133DFJMn78+Mp5bdq0qfz83nvv5c0338x2222XJJXD45fkpptuSl1dXQ4++OC8+eablf+6deuWDTfcMPfdd1+SVF7Rvuuuu/Luu+8u610GPoJDzWEVseGGGzY43bdv31RXVzd4X9daa62VXXfddZHrrrGGTQEAq5ef/vSnOeKII9KzZ89stdVW2XPPPXP44YenT58+K2T91dXVi6yrX79+SdJgbk6ST33qUw1OL4jwadOmVc6bOnVqhg8fnuuvvz6vv/56g+WX5i1jL7zwQurr6xfZX1hgwSHtvXv3zimnnJILLrgg11xzTXbcccfsu++++cY3vuEwc1gO9rZhFVVVVdXUQwCAZuvggw/OjjvumJtvvjl33313zj///PzkJz/JTTfdlD322OMj59H58+enpqZmhY7lo9ZXv9AHsR188MF5+OGHc9ppp2WLLbZI+/btU1dXl9133z11dXUfext1dXWpqqrKHXfcsdjba9++feXnESNGZNCgQbnlllty99135zvf+U7lM2TWW2+9RtxDQHjDKuKFF15I7969K6f/9a9/pa6uLr169Wq6QQFAM9a9e/cMHjw4gwcPzuuvv57PfOYzOe+887LHHntkzTXXXOx3Yb/00ktL9ap4XV1dxo8fX3mVO0mef/75JFnmuXnatGn529/+luHDh+fss8+unP/CCy8ssuxHPWHQt2/f1NfXp3fv3g3G9FH69++f/v3758wzz8zDDz+cHXbYIZdffnnOPffcZRo78AHv8YZVxCWXXNLg9MUXX5wk2WOPPZpiOADQbM2fP3+Rw7PXXnvt9OjRI3PmzEnyQag+8sgjmTt3bmWZv/zlL4t89daS/OIXv6j8XF9fn1/84hdp0aJFvvjFLy7TeBe8Ql3/oa8iu/DCCxdZtl27dkmyyJMGBxxwQGpqajJ8+PBF1lNfX1/5WrKZM2fm/fffb3B5//79U11dXXlsgGXnFW9YRUyYMCH77rtvdt9994waNSpXX311vv71r2fzzTdv6qEBQLMya9asrLfeevnqV7+azTffPO3bt89f//rXPPbYYxkxYkSS5JhjjsmNN96Y3XffPQcffHBefPHFXH311enbt+9S3Ubr1q1z55135ogjjsi2226bO+64I7fddlt+8IMfVL7ea2l17NgxO+20U376059m3rx5WXfddXP33XdnwoQJiyy71VZbJUnOOOOMHHLIIWnRokX22Wef9O3bN+eee25OP/30TJw4Mfvvv386dOiQCRMm5Oabb85xxx2XIUOG5N57782JJ56Ygw46KP369cv777+f3/3ud6mpqcmBBx64TOMG/o/whlXE73//+5x99tn5z//8z6yxxho58cQTc/755zf1sACg2Wnbtm0GDx6cu+++u/Jp3xtssEEuvfTSfPvb306S7LbbbhkxYkQuuOCCnHzyyfnsZz+bv/zlLzn11FOX6jZqampy55135tvf/nZOO+20dOjQIUOHDm1wqPiyuPbaa3PSSSflkksuSX19fb785S/njjvuSI8ePRost/XWW+eHP/xhLr/88tx5552pq6vLhAkT0q5du/znf/5n+vXrl5///OcZPnx4kqRnz5758pe/nH333TdJsvnmm2e33XbLrbfemldffTVt27bN5ptvnjvuuKPyKerAsquq//CxJsBKZdiwYRk+fHjeeOONrLXWWk09HABY7Q0aNCg33nhj3n777aYeCtBMeI83AAAAFCS8AQAAoCDhDQAAAAV5jzcAAAAU5BVvAAAAKEh4AwAAQEGN/h7vurq6TJ48OR06dEhVVdWKHBMArNbq6+sza9as9OjRI9XVy/ccufkaAMpYlvm60eE9efLk9OzZs7FXBwA+xssvv5z11ltvudZhvgaAspZmvm50eHfo0KFyIx07dmzsagCAD5k5c2Z69uxZmWuXh/kaAMpYlvm60eG94HC1jh07msgBoIAVcWi4+RoAylqa+dqHqwEAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChojaYeADQHr732WmbMmNHUw2hytbW1WWeddZp6GADNwso8N9ieAzQvwpvV3muvvZZvfPPwzJs7p6mH0uRatGyVq3/3WztrwGpvZZ8bbM8BmhfhzWpvxowZmTd3Tmb32Tl1rWubdCzVs6enzYQHM7v3Tqlr0+mTve33ZiTjH8iMGTPsqAGrvRU1NzTFdt32HKD5Ed7w/6trXZu6dms19TCSJHVtOjWbsQCszlbU3GC7DrB68+FqAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCe8m9t577+X555/Pe++919RDAZoB2wRWFv5WWRn4OwWaC+HdxCZNmpTjjjsukyZNauqhAM2AbQIrC3+rrAz8nQLNhfAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFLRGUw9ggfnz52fMmDGZOnVqOnfunAEDBqSmpqaph9XAksY4d+7c3HLLLZk8eXK6du2aKVOmZMqUKVl33XWz/fbb5+2332629wtofurq6vLkk09WtjebbbZZnnnmmUydOjUdO3bM+PHj8+9//zs9evTIfvvtl5YtWyZpE9HV8wAAQjxJREFUuJ3q1KlTkmT69OlL3P4svP368Pqai+Y4xuY4JmDJFmwjX3/99YwdOzZJ0r179/Tp0yczZ86sbDfffPPNyuVrr712Zb+uVatW+dznPpfOnTvn4Ycfzquvvlo5b+21107ywTZ3wXqmTp1aOd2pU6eMHz8+U6ZMSZJssskmWWuttSrXWbCdfvvtt3PGGWfktddeS9euXbPddttl2rRple1MTU3NIvujSZZqP7ox+9uLu87Ct7e4uWb+/PmrzPZxeR+zpZ2LWXqNmX+bQ2s2i/B+8MEHc+mll+bf//535bxu3bpl8ODB2WmnnZpwZP9nSWN89tlnc8MNN2T+/PmLXO/vf/97brnllkWu01zuF9A8nXHGGXnrrbcqp2tqaha7jUmSyy+/PAcddFA23XTTRbZTC1vc9ufyyy9fZPu1YH3HH3/8Cro3y6c5jrE5jglYssXtyzXGQw89tFTnfZw//elPi5xXXV2durq6yuk33ngjzz77bOX0ZZddltatW+fdd9+tnLdw2C2wuO19Y/a3F3edxd3ewtq1a5fZs2c3uB8r6/ZxRT1mC9MCy6cx829zac0mP9T8wQcfzNChQ9OnT59ccskluf3223PJJZekT58+GTp0aB588MGmHuISx3j22Wfn+uuvT8eOHdO3b98kH2w0W7RokSRp37595dmUAQMGNKv7BTQ/TzzxRJJk3XXXzSWXXJIzzjgjSdKxY8fKMh06dKhsb/bdd9907Ngx119/fc4+++z06dMnxx57bJKkf//+6d+/f6qqqnLssccusv25/PLLK9uvIUOG5I9//GOGDBlSWd/ll1/+Sd71xWqOY2yOYwKW7IknnsjQoUNTXf3Brm+nTp3yuc99rnL5gv221q1bV87r3LnzIutp06bNIuetv/76lZ8XXH/BK9lJKtvrhW288caVnxcs27Fjx0qsLojbNdb4v9fI2rZtm7q6urz77rvZddddc/vtt+fYY4/N9OnTM3369Bx77LEfuR/dmP3txV1n4dvbddddkzScawYMGJB33nkndXV12XfffVfq7ePyPmbHHntsqqqqKo9PksXOxSy9xsy/zak1mzS858+fn0svvTQDBw7Mueeem8022yxt27bNZpttlnPPPTcDBw7MZZdd9pGv8jT1GM8+++wkH4T2yJEj8+KLL6ZFixa57bbb0rlz57Ro0SKzZ8/OLbfckhYtWmTMmDE544wzmsX9Apqf+fPn54YbbkiSDB48OBtvvHF+/etfZ/vtt88111yT5IPtzY033pgrrrgi22+/fR577LFcc801lZ3JM844I7feemu23377XHTRRbnooosycODA/OUvf8nw4cMr25/Zs2fnhhtuyJprrpkbbrghe++9d7p06ZK99967wflz585tssdj7ty5zW6MzXFMwMe74YYbsu222+bf//531lxzzfz+97/Pv/71rwwcODDbbbdd6urqUl1dnffeey+dOnXKNttsk6lTp2aNNdbIGmuskRYtWqRTp06ZPXt2g/VuvfXWeeWVV7Ltttumpqamcv2pU6dmu+22y7bbbpsXX3wxtbW1adWqVVq1apVtt902L7zwQrbbbrvKsltttVVmzpyZJNlqq60yffr0rLnmmrnzzjtz++23J0nefffdbLXVVllzzTVz3333paqqKrfeemsGDhxY2c63atVqkf3ouXPnLvP+9uL2f1u1alW5ve222y733XdfBg4cWJlrtt1224wZMya1tbUZOHBgHnvssXTq1Gml3D42plEWvs7w4cMrj9WCx2f77bdfZC7WAkuvMfNvc2vNpT7UfM6cOZkzZ07l9IKNw/IYM2ZM/v3vf+ess86q7DQuUF1dncMOOywnnHBCxowZky233HK5b29Fj/HWW29N8sF7Mc8///wkyUEHHZRx48bltddey9e//vVce+21ueOOO/LVr3411113Xa644ooG96tdu3ZJkpdeeumTvWNUeOwb8ng0neeee65yeHl1dXWD7c9tt92W5IPtzbPPPpstt9yysi257bbbKq+SXHHFFYtssxYs9/TTT1d+/u///u/Mnz8/Rx99dINXVJIPXmE56qijMmLEiNxyyy056KCDPsFH4f/ccsstzW6MzXFMH1Zivl6SVXWbsSrcr1XhPiyvBY/BW2+9lV122SV1dXU5+uij8+yzz1a2lc8//3weeeSRbLDBBvnXv/6VLbbYorJNHTBgQOVIpM6dO2f69OnZcMMN88ILLyT54N/b/Pnzs91226Vdu3a5995706VLl0yfPj3bbrttXnnllYwePTpbbrll7r///iTJeuutl9GjR2fbbbfNDjvskBEjRmTChAlJPnglvFevXnn88cfzpS99qRL+G220UZ577rlMmzatsp357//+78p9SNJgn3nh/ehbbrllmfe3F7f/u/B5Cx6zbbbZpnL5uuuumyTZbbfdsvPOOzdYZ3PZPi6txjTKwtd5+umnl2oubsrGWdk0Zv5tbq251OH94x//OMOHD1+hNz516tQkSe/evRd7+YLzFyzXFJY0xsmTJy/y85577pnnnnuu8vO1116byZMn54ADDsh1112XV155pcH9WhDe5513XtH7AUvL32LzsfD2569//etiz08aboteeeWVBpct/PPUqVMzcODABsstOP1hC85feN2ftAW33ZzG2BzH9GEl5uslsc1ovvxuGlrwStjAgQPz1FNPJflg+7ggzhccJt6mTZvKe0G7dOlSuf68efOSJK1ataqcN2PGjMp1N95449x7772V22nVqlXlSbCFD2Ff+Lytt946SSrv2d5uu+3y5ptvJkl69OhRuc4222yT5557LjNnzlxkO77w9n7hfeYPzxHLsr+9uP3fhc/78GO28P3q0aPHYtfZHLaPS6sxjbLwdUaNGrXI9Rc3Fzdl46xsGjP/NrfWXOrwPv3003PKKadUTs+cOTM9e/Zcrhtf8N6ZCRMmZLPNNlvk8gXP/i3uPTaflCWNceENYo8ePTJhwoTcfvvtlY3ogkODevToUfl5vfXWW+z9OuOMMxq8R4hPzksvvWTnZCH+FpvOc889lxEjRlROL7z9WXh7s/D5ScNt0XrrrZe///3vDbZZC29zFvy8YLlRo0Zl7733XmQsC3YaFl73J23BbTenMTbHMX1Yifl6SVbVbcaqMDesqr+bZbHw73HBpx6PGjWq8urshAkTKsH43nvvJUlmz56dDh06JEmDD7lc8D7whY8oqa2trVx33LhxDW5nzpw5lUhfsO4kDc5bsM1o27Zt3nvvvTzyyCOVbffCAfHoo48m+eB94Auus/D2foGF9y0/PEcsy/724vZ/Fz7vw4/Zwvdr8uTJi11nc9g+Lq3GNMrC11nc9Rc3Fzdl46xsGjP/NrfWXOrwXvC+lBVpwIAB6datW6655pqce+65DQ4BqKuryzXXXJPu3btXvragKSxpjPvss08uueSSVFdX57TTTstDDz2UG264Id/85jezzjrr5IYbbkhNTU322GOPfOUrX0nywYcqnHfeeZX79eKLLyb54IM5+vXr1yT3ERbmb7Hp9O3bN1dddVXeeuut1NXVNdj+nHnmmZXtzaabbtpgG7nXXnvlsssuS11dXY499tg88sgjlW1Wkspyn/70pzN06NB079493/rWt3Lrrbfm17/+dXbfffcGh229//77ufLKK1NTU5P99tuvqR6O7Lfffrn88sub1Rib45g+rMR8vSS2Gc2X383/6dKlS1599dVUV1fn17/+da6//vp069YtV199derr61NTU5Px48cnSZ566qnK4zZmzJisscYaqaqqqrwqtuAw8+SDf281NTV55JFH8ve//z3JB7FeXV2d0aNHp76+Pkny5JNPVv5dvvLKK6mpqcno0aMzbty4VFdXp3fv3pk6dWrGjRtXORrynnvuyXHHHZe5c+dWjqZcc801K9uZb33rW3nkkUdy9dVXJ0mDfeaF54j99tsvf/zjH5dpf3tx+78Lzlv4MXv00Ucr27tXX301SXLXXXfl5ZdfbrDO5rJ9XFqNaZSFrzN8+PAG108WPxc3ZeOsbBoz/za31mzSD1erqanJ4MGDM2rUqJx55pl55pln8u677+aZZ57JmWeemVGjRuXb3/52k37X3ZLGeM455yT54Bd35JFHpm/fvpk3b1722muvTJ06NfPmzUvr1q2z3377Zd68eenfv3/OO++8ZnG/gOanpqam8r6kSy+9NOPGjcvRRx+dhx9+OIcddliSD7Y3X/3qV3PMMcfk4YcfztZbb53DDjus8n7E8847L3vvvXcefvjhfPe73813v/vdyrPDQ4cOrWx/2rRpk4MOOijTpk3LQQcdlFtvvTVvvvlmbr311gbnN+X3rrZs2bLZjbE5jgn4eAcddFBGjx6dbt26Zdq0afna176WDTbYIKNGjcojjzxS+Rqv1q1bZ/r06Xn00UfTuXPnvP/++3n//fczb968TJ8+vcGh1Uny2GOPVd6zPX/+/Mr1O3funEceeSSjR49O3759M2PGjMrnL4wePTobbrhhHnnkkcqyjz/+eOXbKx5//PHU1tZm2rRp2W233bLnnnsm+eBV8ccffzzTpk3L5z//+dTX12efffbJqFGjMmrUqOy1116ZM2fOIvvRLVu2XOb97cXt/86ZM6dye4888kg+//nPZ9SoUZW5ZvTo0RkwYEBmzJiRUaNGZeutt860adNWyu1jYxpl4esMHTq08lgteHwefvjh7LXXXg3mYi2w9Boz/za31qyqX/BU3DKaOXNmamtrM2PGjAZfc9MYi/tute7du+fb3/52s/mOuyWNcUnf4/1hH75fzz//fI477rj88pe/9Kx0E1nwO3hn031T126tj79CQdXvvJl2z/65Scay4Lb9LTatBX+PXbp0Werv8V4Q7B/3Pd6L264u7vswF6yvuXzfanMcY+kxrcg5dkWua2Gr+vy1ouaGptiu257/n4X/Tv/973+vkO/xLunD3+O9uMuX5nu8F7e9b8z+9or6Hu+m3mY31op6zBbW3BpnZdOY+bdkay7LHLvUh5qXtNNOO2WHHXbImDFjMnXq1HTu3DkDBgxoVs8CLWmMO+20U4466qjccsstmTx5crp27ZopU6ZkypQpWXfddbP99tvn7bffbpb3C2iezjvvvMyePbuyvdlss83yzDPPZOrUqenYsWPGjx+ff//73+nRo0f222+/yjO8C2+nFt45+qjtz/HHH99g+/Xh9TUHzXGMzXFMwJItvC/3+uuvZ+zYsUk+2AHv06dPZs6cWdluvvnmm5XL11577cp+XatWrfK5z30unTt3zsMPP5xXX321ct7aa6+d5INt7oL1TJ06tXK6U6dOGT9+fKZMmZIk2WSTTSrf4b3wdvrtt9/OGWeckddeey1du3bNdtttl2nTplW2MzU1NYvsjyb52P3oxuxvf9R1Fr69xc018+fPXyW2jyviMVuauZil15j5t7m0ZrMI7+SDZyqa+8fpL2mMCw5/AFgRqqurF9neLHx6wYc4flhjtqUrw/arOY6xOY4JWLKFt5G77bbbEpf9uMu33XbbZb79j9p2L6y2tja/+MUvlrjM4rbzS7Ptb8wc8VHXWdJ6Fn7r1MpuRT5mrBiNmX+bw++kSd/jDQAAAKs64Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCe8m9qlPfSq//OUv86lPfaqphwI0A7YJrCz8rbIy8HcKNBdrNPUAVnetW7dOv379mnoYQDNhm8DKwt8qKwN/p0Bz4RVvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFDQGk09AGguqt+b0dRDSPXs6Q3+/4nedjO4/wDNzfJuG5tiu257DtD8CG9We7W1tWnRslUy/oGmHkpFmwkPNsnttmjZKrW1tU1y2wDNyYqeGz7p7brtOUDzIrxZ7a2zzjq5+ne/zYwZXiGora3NOuus09TDAGhyK/vcYHsO0LwIb8gHO1h2UABYmLkBgBXFh6sBAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoaI3GXrG+vj5JMnPmzBU2GADg/+bWBXPt8jBfA0AZyzJfNzq8Z82alSTp2bNnY1cBACzBrFmzUltbu9zrSMzXAFDK0szXVfWNfDq9rq4ukydPTocOHVJVVdWoAQIAi6qvr8+sWbPSo0ePVFcv37vCzNcAUMayzNeNDm8AAADg4/lwNQAAAChIeAMAAEBBwhsAAFaQXr165cILL1zq5SdOnJiqqqo89dRTxcYEND3hDauJXXbZJSeffPIi51911VXp1KnTJz4eAGDF+ah5HmgehDcAAAAUJLxhFbHLLrvkxBNPzIknnpja2tqstdZaOeuss+KLCwBg2dx4443p379/2rRpky5dumTXXXfNO++8s9hXlffff/8MGjToI9dVVVWVyy67LHvssUfatGmTPn365MYbb1xkufHjx+fzn/982rZtm8033zyjRo2qXPbWW2/l0EMPzbrrrpu2bdumf//+ue666yqXDxo0KA888EAuuuiiVFVVpaqqKhMnTkySPP3009ljjz3Svn37rLPOOvnmN7+ZN99882PvK7BiCW9YhfzmN7/JGmuskUcffTQXXXRRLrjggvzqV79q6mEBwEpjypQpOfTQQ3PUUUdl7Nixuf/++3PAAQcs1xPZZ511Vg488MD84x//yGGHHZZDDjkkY8eObbDMGWeckSFDhuSpp55Kv379cuihh+b9999Pkrz33nvZaqutctttt+Xpp5/Occcdl29+85t59NFHkyQXXXRRBg4cmGOPPTZTpkzJlClT0rNnz0yfPj1f+MIXsuWWW+bvf/977rzzzrz22ms5+OCDi91XYPHWaOoBACtOz5498/Of/zxVVVXZaKON8s9//jM///nPc+yxxyZJLr300kVC/P3330/r1q2bYrgA0OxMmTIl77//fg444ICsv/76SZL+/fsv1zoPOuigHHPMMUmSH/7wh7nnnnty8cUX59JLL60sM2TIkOy1115JkuHDh2ezzTbLv/71r2y88cZZd911M2TIkMqyJ510Uu6666784Q9/yDbbbJPa2tq0bNkybdu2Tbdu3SrL/eIXv8iWW26ZH/3oR5XzrrzyyvTs2TPPP/983n777RV+X4HF84o3rEK22267VFVVVU4PHDgwL7zwQubPn58kOeyww/LUU081+O+cc85pquECQLOz+eab54tf/GL69++fgw46KFdccUWmTZu2XOscOHDgIqc//Ir3gAEDKj937949SfL6668nSebPn58f/vCH6d+/fzp37pz27dvnrrvuyqRJk5Z4u//4xz9y3333pX379pX/Nt544yTJiy++WOS+AovnFW9YjdTW1maDDTZocN7aa6/dRKMBgOanpqYm99xzTx5++OHcfffdufjii3PGGWdk9OjRqa6uXuQw7Hnz5q2Q223RokXl5wVPotfV1SVJzj///Fx00UW58MIL079//7Rr1y4nn3xy5s6du8R1vv3229lnn33yk5/8ZJHLunfvvsT72rt37xVyv4APeMUbViGjR49ucPqRRx7JhhtumJqamiYaEQCsfKqqqrLDDjtk+PDhefLJJ9OyZcvcfPPN6dq1a6ZMmVJZbv78+Xn66ac/dn2PPPLIIqc32WSTpR7PQw89lP322y/f+MY3svnmm6dPnz55/vnnGyzTsmXLyhFuC3zmM5/JM888k169emWDDTZo8F+7du2WeF+BFUt4wypk0qRJOeWUU/Lcc8/luuuuy8UXX5zvfve7TT0sAFhpjB49Oj/60Y/y97//PZMmTcpNN92UN954I5tsskm+8IUv5Lbbbsttt92WcePG5dvf/namT5/+seu84YYbcuWVV+b555/P0KFD8+ijj+bEE09c6jFtuOGGlVemx44dm29961t57bXXGizTq1evjB49OhMnTsybb76Zurq6nHDCCZk6dWoOPfTQPPbYY3nxxRdz11135cgjj8z8+fOXeF+BFcuh5rAKOfzwwzN79uxss802qampyXe/+90cd9xxTT0sAFhpdOzYMQ8++GAuvPDCzJw5M+uvv35GjBiRPfbYI/Pmzcs//vGPHH744VljjTXyve99L5///Oc/dp3Dhw/P9ddfn8GDB6d79+657rrrsummmy71mM4888yMHz8+u+22W9q2bZvjjjsu+++/f2bMmFFZZsiQITniiCOy6aabZvbs2ZkwYUJ69eqVhx56KN///vfz5S9/OXPmzMn666+f3XffPdXV1Uu8r8CKVVXv+wJglbDLLrtkiy22yIUXXtjUQwEA/n9VVVW5+eabs//++zf1UIAm5FBzAAAAKEh4AwAAQEEONQcAAICCvOINAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLyhmbv//vtTVVWV+++/v8j6hw0blqqqqrz55psrbJ277LJLdtlllxW2PgBYXS1uP2DQoEHp1atXk40JWHbCGwAAVmLvvvtuhg0bVuxJemD5rdHUAwAAAJbeFVdckbq6usrpd999N8OHD08SR5xBMyW8AQBgJdKiRYumHgKwjBxqDk3kpZdeyuDBg7PRRhulTZs26dKlSw466KBMnDhxqa4/evTo7LnnnllzzTXTrl27DBgwIBdddFGDZe69997suOOOadeuXTp16pT99tsvY8eOXez6pk+fnkGDBqVTp06pra3NkUcemXfffbfBMu+//35++MMfpm/fvmnVqlV69eqVH/zgB5kzZ06jHgMAWJn87//+b7beeuu0bt06ffv2zX//939XPislSSZOnJiqqqpcddVVi1y3qqoqw4YNq5xenv2Ahd/jPXHixHTt2jVJMnz48FRVVVVua+TIkamqqsqTTz65yDp+9KMfpaamJq+++uoyPw7AsvOKNzSRxx57LA8//HAOOeSQrLfeepk4cWIuu+yy7LLLLnn22WfTtm3bj7zuPffck7333jvdu3fPd7/73XTr1i1jx47NX/7yl3z3u99Nkvz1r3/NHnvskT59+mTYsGGZPXt2Lr744uywww554oknFvlQloMPPji9e/fOj3/84zzxxBP51a9+lbXXXjs/+clPKsscc8wx+c1vfpOvfvWrOfXUUzN69Oj8+Mc/ztixY3PzzTcXeZwAoDn45z//mS9/+cvp2rVrhg0blvfffz9Dhw7NOuus06j1Lc9+wMK6du2ayy67LN/+9rfzla98JQcccECSZMCAAendu3dOOOGEXHPNNdlyyy0bXO+aa67JLrvsknXXXbdR4weWUT3QJN59991Fzhs1alR9kvrf/va3lfPuu++++iT19913X319fX39+++/X9+7d+/69ddfv37atGkNrl9XV1f5eYsttqhfe+216996663Kef/4xz/qq6ur6w8//PDKeUOHDq1PUn/UUUc1WNdXvvKV+i5dulROP/XUU/VJ6o855pgGyw0ZMqQ+Sf29995bOW/nnXeu33nnnT/+QQCAlcT+++9f37p16/qXXnqpct6zzz5bX1NTU79gl3rChAn1SepHjhy5yPWT1A8dOrRyurH7AfX19fVHHHFE/frrr185/cYbbyyy/gUOPfTQ+h49etTPnz+/ct4TTzzxkeMEynCoOTSRNm3aVH6eN29e3nrrrWywwQbp1KlTnnjiiY+83pNPPpkJEybk5JNPTqdOnRpctuBQtylTpuSpp57KoEGD0rlz58rlAwYMyJe+9KXcfvvti6z3+OOPb3B6xx13zFtvvZWZM2cmSeU6p5xySoPlTj311CTJbbfd9nF3GQBWSvPnz89dd92V/fffP5/61Kcq52+yySbZbbfdGrXOxu4HLKvDDz88kydPzn333Vc575prrkmbNm1y4IEHrrDbAZZMeEMTmT17ds4+++z07NkzrVq1ylprrZWuXbtm+vTpmTFjxkde78UXX0ySfPrTn/7IZV566aUkyUYbbbTIZZtssknefPPNvPPOOw3OX3hHIknWXHPNJMm0adMq66yurs4GG2zQYLlu3bqlU6dOldsEgFXNG2+8kdmzZ2fDDTdc5LLFzbVLo7H7AcvqS1/6Urp3755rrrkmSVJXV5frrrsu++23Xzp06LDCbgdYMu/xhiZy0kknZeTIkTn55JMzcODA1NbWpqqqKoccckiDrwj5pNTU1Cz2/Pr6+ganF7yqDgA09FFz5Pz58xc575PaD6ipqcnXv/71XHHFFbn00kvz0EMPZfLkyfnGN76xwm4D+HjCG5rIjTfemCOOOCIjRoyonPfee+9l+vTpS7xe3759kyRPP/10dt1118Uus/766ydJnnvuuUUuGzduXNZaa620a9dumca7/vrrp66uLi+88EI22WSTyvmvvfZapk+fXrlNAFjVdO3aNW3atMkLL7ywyGULz7ULjhb78Fy+uKPCGrsfsDgf96T44YcfnhEjRuTWW2/NHXfcka5duzb6EHmgcRxqDk2kpqZmkVeTL7744sU+K76wz3zmM+ndu3cuvPDCRSbnBevr3r17tthii/zmN79psMzTTz+du+++O3vuuecyj3fBdS688MIG519wwQVJkr322muZ1wkAK4Oamprstttu+dOf/pRJkyZVzh87dmzuuuuuyumOHTtmrbXWyoMPPtjg+pdeeuli19mY/YDFWfAJ6B8V7QMGDMiAAQPyq1/9Kn/84x9zyCGHZI01vP4GnyT/4qCJ7L333vnd736X2trabLrpphk1alT++te/pkuXLku8XnV1dS677LLss88+2WKLLXLkkUeme/fuGTduXJ555pnKDsD555+fPfbYIwMHDszRRx9d+Tqx2traBt8jurQ233zzHHHEEfnlL3+Z6dOnZ+edd86jjz6a3/zmN9l///3z+c9/vjEPAwCsFIYPH54777wzO+64YwYPHpz3338/F198cTbbbLOMGTOmstwxxxyT//qv/8oxxxyTz372s3nwwQfz/PPPL7K+xu4HLE6bNm2y6aab5ve//3369euXzp0759Of/nSDz4M5/PDDM2TIkCRxmDk0AeENTeSiiy5KTU1Nrrnmmrz33nvZYYcd8te//nWpDv3abbfdct9992X48OEZMWJE6urq0rdv3xx77LGVZXbdddfceeedGTp0aM4+++y0aNEiO++8c37yk5+kd+/ejRrzr371q/Tp0ydXXXVVbr755nTr1i2nn356hg4d2qj1AcDKYsCAAbnrrrtyyimn5Oyzz856662X4cOHZ8qUKQ3C++yzz84bb7yRG2+8MX/4wx+yxx575I477sjaa6/dYH3Lsx+wOL/61a9y0kkn5Xvf+17mzp2boUOHNgjvww47LN///vfTt2/fbLPNNo17EIBGq6r/8DEuAADAUhk2bFiGDx++yGHjzc2bb76Z7t275+yzz85ZZ53V1MOB1Y73eAMAwCruqquuyvz58/PNb36zqYcCqyWHmgMAwCrq3nvvzbPPPpvzzjsv+++/f3r16tXUQ4LVkvAGAIBV1DnnnJOHH344O+ywQy6++OKmHg6strzHGwAAAAryHm8AAAAoSHgDAABAQY1+j3ddXV0mT56cDh06pKqqakWOCQBWa/X19Zk1a1Z69OiR6urle47cfA0AZSzLfN3o8J48eXJ69uzZ2KsDAB/j5Zdfznrrrbdc6zBfA0BZSzNfNzq8O3ToULmRjh07NnY1AMCHzJw5Mz179qzMtcvDfA0AZSzLfN3o8F5wuFrHjh1N5ABQwIo4NNx8DQBlLc187cPVAAAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFLRGUw+AZffaa69lxowZTT0MPkZtbW3WWWedph4GABD7T6XY34GlI7xXMq+99lq+8c3DM2/unKYeCh+jRctWufp3vzUZAUATs/9UTnV1TS699JJsvPHGTT0UaNaE90pmxowZmTd3Tmb32Tl1rWubejhLrXr29LSZ8GBm994pdW06NfVwiqt+b0Yy/oHMmDFDeANAE1tZ95+au5oZr6T1q0/k5ZdfFt7wMYT3SqqudW3q2q3V1MNYZnVtOq2U4wYAVn4r6/5Tc1U9e3pTDwFWGj5cDQAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAU1KzC+7333svzzz+f9957r6mHAtBs2VbCqsu/b8B2YNXUrMJ70qRJOe644zJp0qSmHgpAs2VbCasu/74B24FVU7MKbwAAAFjVCG8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoaI2mHgAAZcyfPz9jxozJ5MmTc8cdd+Sdd95Jjx49cvrpp6d9+/ZFbnPq1Kk55ZRT8tZbb6VLly654IIL0rlz5yK31dQWPL5Tp05N586dM2DAgNTU1DT1sD4Rq9PvGYCV37hx43L88cdXTl9++eXZeOONP9ExCG+AVdCDDz6YSy+9NP/+978bnD9hwoTsvffe2XjjjXP55Zev0Ns84IADMnXq1MrpWbNm5YADDkjnzp1z0003rdDbamqLe3y7deuWwYMHZ6eddmrCkZW3Ov2eAVj57bLLLouctyDC77///k9sHA41B1jFPPjggxk6dGhmzZqVJKmqqso222yTAQMGVJb58DO/y2vhGNt0000zYsSIbLrppkk+eHX0gAMOWGG31dQWPL59+vTJJZdckttvvz2XXHJJ+vTpk6FDh+bBBx9s6iEWszr9ngFY+S0c3VVVVTnwwANTVVW12MtL84o3wCpk/vz5ufTSS7P11lvn0UcfTVVVVe644460bt06dXV1OfPMM/Piiy/m9ddfz7hx4/L2228v92HnU6dOrcTYX/7yl8r6ttpqq7z99tvZe++9K8us7IcjL3h8Bw4cmHPPPTfV1R88f73ZZpvl3HPPzZlnnpnLLrssO+ywwyp32Pnq9HsGYOU3bty4ys9XXXVVevXqlSQ56aSTMnHixAwaNKiy3Cdx2PlSh/ecOXMyZ86cyumZM2cWGVCSvPTSS8XWvbLz2Kxc/L4oYUl/V2PGjMm///3vrL322kmSL33pS2ndunWSpLq6OocddlhOOOGEbLXVVnn88cfz4x//OOedd95yjeeUU05J8sEroB+O+Pbt22eTTTbJ2LFjc8opp+Sqq65arttqagse37POOqsS3Qss/PiOGTMmW265ZZOMsdR8vTr9npsD88eK5fEsa8qUKXn++eebehirDH+vK8aCI/uqqqoq0b1Ar169UlVVlfr6+hx//PGfyCHnSx3eP/7xjzN8+PCSY6lY3p1AaC78LfNJW/CK5IwZM5IkBx98cIPLe/funSTZcsst8/jjj2fy5MnLfZtvvfVWkuToo49e7OVHHnlk/uM//qOy3MpsweO74HH8sAXnL/we6E9aqfl6dfo9NwfmD1YmV155Za688sqmHgYs1ke9DWqfffbJn//8509sHEsd3qeffnrl2e7kg2fQe/bsWWRQZ5xxRtZff/0i617ZvfTSSybjlYi/ZUpY0nZgwSG+tbW1SZI//OEP+cEPflC5fMKECUmSJ598MknSo0eP5R5Ply5dMmvWrPz617/OVltttcjlI0eOrCy3slvw+E6YMCGbbbbZIpcveHyb8lDrUvP16vR7bg7MHyuW/aeyjjrqqGy33XZNPYxVhr/XFeumm27KSSedtMj5t9566yc6jqUO71atWqVVq1Ylx1Kx/vrrp1+/fp/IbUFJ/pb5pA0YMCDdunWrHF5+zz335JRTTqm8x/uaa67JOuuskyeeeCLJB5G2vC644IIccMABefbZZxd5z/jbb7+dsWPHVpZb2S14fK+55poG7/FOUnl8u3fv3uCD7D5ppebr1en33ByYP1iZdO/e3d8rzc7ll1+e448/PvX19Zk4cWKDw80nTpyY+vr6ynKfBB+uBrAKqampyeDBgzN06NC0a9cu77zzTvbYY4989rOfzZw5czJmzJjKshtvvPEK+T7vzp07p3Pnzpk6dWr23nvvbLLJJjnyyCMzcuTISowtWGZlt/Dje+aZZ+awww5L7969M2HChFxzzTUZNWpUhg8fvsp9sFqyev2eAVj5LfyBaYMGDUpVVVX22Wef3HrrrZXo/vByJfk6MYBVzE477ZThw4enQ4cOSZL6+vo89thji0T3inyG96abbqoE19ixY/Mf//EfDWJsVfp+5wWP7/jx43PCCSdkzz33zAknnJAJEyZk+PDhq/T3eK9Ov2cAVn4Lf2hafX19/vznPzeI7k/ye7y94g2wCtppp52yww47ZMyYMZk8eXLuuOOOvPPOO+nRo0dOP/30FfJK94fddNNNmTp1ak455ZS89dZb6dKlSy644IJV8hXQhR/fBV+fNWDAgFXyle4PW51+zwCs/O6///6MGzeu8innyQeHl39Sr3QvILwBVlE1NTXZcssts+WWW2avvfb6RG6zc+fOq81XSS14fFdHq9PvGYCV38Ybb/yJvrq9OA41BwAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABTUrML7U5/6VH75y1/mU5/6VFMPBaDZsq2EVZd/34DtwKppjaYewMJat26dfv36NfUwAJo120pYdfn3DdgOrJqa1SveAAAAsKoR3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUJLwBAACgIOENAAAABQlvAAAAKEh4AwAAQEHCGwAAAAoS3gAAAFCQ8AYAAICChDcAAAAUtEZTD4DGqX5vRlMPYZlUz57e4P+rupXt9wMAqwPz84pVNfftph4CrDSE90qmtrY2LVq2SsY/0NRDaZQ2Ex5s6iF8Ylq0bJXa2tqmHgYArPZW9v2n5qy6uiY9e/Zs6mFAsye8VzLrrLNOrv7dbzNjhmdsm7va2tqss846TT0MAFjt2X8qx/4OLB3hvRJaZ511bOAAAJaB/SegKflwNQAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAGAACAgoQ3AAAAFCS8AQAAoCDhDQAAAAWt0dgr1tfXJ0lmzpy5wgYDAPzf3Lpgrl0e5msAKGNZ5utGh/esWbOSJD179mzsKgCAJZg1a1Zqa2uXex2J+RoASlma+bqqvpFPp9fV1WXy5Mnp0KFDqqqqGjVAAGBR9fX1mTVrVnr06JHq6uV7V5j5GgDKWJb5utHhDQAAAHw8H64GAAAABQlvAAAAKEh4AwAAQEHCG1YSEydOTFVVVZ566qlmtb5evXrlwgsvXCFjAgAaGjZsWLbYYovK6UGDBmX//fdvsvEAjdPorxMDAAA+WRdddFGD7wzeZZddssUWW3gSHJo54Q0AACuJj/uuYKB5cqg5NCN33nlnPve5z6VTp07p0qVL9t5777z44osfufwzzzyTvffeOx07dkyHDh2y4447Vpavq6vLOeeck/XWWy+tWrXKFltskTvvvHORdYwfPz6f//zn07Zt22y++eYZNWpUg8v/+Mc/ZrPNNkurVq3Sq1evjBgxYsXeaQBYSb3zzjs5/PDD0759+3Tv3j0jRozILrvskpNPPjlJUlVVlT/96U8NrtOpU6dcddVVldPf//73069fv7Rt2zZ9+vTJWWedlXnz5n3kbS58qPmgQYPywAMP5KKLLkpVVVWqqqoyYcKEbLDBBvnZz37W4HpPPfVUqqqq8q9//WtF3HVgGQlvaEbeeeednHLKKfn73/+ev/3tb6murs5XvvKV1NXVLbLsq6++mp122imtWrXKvffem8cffzxHHXVU3n///SQfHIo2YsSI/OxnP8uYMWOy2267Zd99980LL7zQYD1nnHFGhgwZkqeeeir9+vXLoYceWlnH448/noMPPjiHHHJI/vnPf2bYsGE566yzGuwwAMDq6rTTTssDDzyQW265JXfffXfuv//+PPHEE8u0jg4dOuSqq67Ks88+m4suuihXXHFFfv7zny/VdS+66KIMHDgwxx57bKZMmZIpU6bkU5/6VI466qiMHDmywbIjR47MTjvtlA022GCZxgesGA41h2bkwAMPbHD6yiuvTNeuXfPss8+mffv2DS675JJLUltbm+uvvz4tWrRIkvTr169y+c9+9rN8//vfzyGHHJIk+clPfpL77rsvF154YS655JLKckOGDMlee+2VJBk+fHg222yz/Otf/8rGG2+cCy64IF/84hdz1llnVdb/7LPP5vzzz8+gQYNW+P0HgJXF22+/nV//+te5+uqr88UvfjFJ8pvf/CbrrbfeMq3nzDPPrPzcq1evDBkyJNdff33+4z/+42OvW1tbm5YtW6Zt27bp1q1b5fxBgwbl7LPPzqOPPpptttkm8+bNy7XXXrvIq+DAJ8cr3tCMvPDCCzn00EPTp0+fdOzYMb169UqSTJo0aZFln3rqqey4446V6F7YzJkzM3ny5Oywww4Nzt9hhx0yduzYBucNGDCg8nP37t2TJK+//nqSZOzYsYtdxwsvvJD58+cv+x0EgFXEiy++mLlz52bbbbetnNe5c+dstNFGy7Se3//+99lhhx3SrVu3tG/fPmeeeeZi5/1l0aNHj+y111658sorkyS33npr5syZk4MOOmi51gs0nvCGZmSfffbJ1KlTc8UVV2T06NEZPXp0kmTu3LmLLNumTZsVcpsLh3tVVVWSLPbQdgBg2VRVVTX4BPIkDd6/PWrUqBx22GHZc88985e//CVPPvlkzjjjjMXO+8vqmGOOyfXXX5/Zs2dn5MiR+drXvpa2bdsu93qBxhHe0Ey89dZbee6553LmmWfmi1/8YjbZZJNMmzbtI5cfMGBA/ud//mexH8DSsWPH9OjRIw899FCD8x966KFsuummSz2mTTbZZLHr6NevX2pqapZ6PQCwqunbt29atGhReZI8SaZNm5bnn3++crpr166ZMmVK5fQLL7yQd999t3L64Ycfzvrrr58zzjgjn/3sZ7PhhhvmpZdeWqZxtGzZcrFHoe25555p165dLrvsstx555056qijlmm9wIrlPd7QTKy55prp0qVLfvnLX6Z79+6ZNGlS/vM///Mjlz/xxBNz8cUX55BDDsnpp5+e2traPPLII9lmm22y0UYb5bTTTsvQoUPTt2/fbLHFFhk5cmSeeuqpXHPNNUs9plNPPTVbb711fvjDH+ZrX/taRo0alV/84he59NJLV8RdBoCVVvv27XP00UfntNNOS5cuXbL22mvnjDPOSHX1/72u9YUvfCG/+MUvMnDgwMyfPz/f//73GxxptuGGG2bSpEm5/vrrs/XWW+e2227LzTffvEzj6NWrV0aPHp2JEyemffv26dy5c6qrq1NTU5NBgwbl9NNPz4YbbpiBAweusPsOLDuveEMzUV1dneuvvz6PP/54Pv3pT+d73/tezj///I9cvkuXLrn33nvz9ttvZ+edd85WW22VK664ojKhf+c738kpp5ySU089Nf3798+dd96ZP//5z9lwww2Xekyf+cxn8oc//CHXX399Pv3pT+fss8/OOeec44PVACDJ+eefnx133DH77LNPdt1113zuc5/LVlttVbl8xIgR6dmzZ3bcccd8/etfz5AhQxoc7r3vvvvme9/7Xk488cRsscUWefjhhysfaLq0hgwZkpqammy66abp2rVrg/eHH3300Zk7d26OPPLI5b+zwHKpqv/wG08AAIBG2WWXXbLFFlvkwgsvbOqh5H/+53/yxS9+MS+//HLWWWedph4OrNYcag4AAKuQOXPm5I033siwYcNy0EEHiW5oBhxqDgAAq5Drrrsu66+/fqZPn56f/vSnTT0cIA41BwAAgKK84g0AAAAFCW8AAAAoSHgDAABAQcIbAAAAChLeAAAAUJDwBgAAgIKENwAAABQkvAEAAKAg4Q0AAAAF/X9ir6YS55MuhwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Tukey's Method for identifying outliers and replace them with the mean\n",
        "# Assuming df is your DataFrame\n",
        "input_columns = df.columns[:-1].tolist()  # Exclude the last column\n",
        "\n",
        "# Function to replace outliers with mean using Tukey's Method\n",
        "def replace_outliers_with_mean_tukey(data, column):\n",
        "    Q1 = data[column].quantile(0.25)\n",
        "    Q3 = data[column].quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "    lower_bound = Q1 - 1.5 * IQR\n",
        "    upper_bound = Q3 + 1.5 * IQR\n",
        "    data[column] = np.where((data[column] < lower_bound) | (data[column] > upper_bound), data[column].mean(), data[column])\n",
        "\n",
        "# Replace outliers with mean using Tukey's Method for each numerical column\n",
        "for column in input_columns:\n",
        "    if np.issubdtype(df[column].dtype, np.number):  # Check if the column is numeric\n",
        "        replace_outliers_with_mean_tukey(df, column)"
      ],
      "metadata": {
        "id": "-_WPWcQzHxPF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Histograms\n",
        "df.hist(bins=10,figsize=(12, 12))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kDs7zlvv-DO1",
        "outputId": "65d3da4a-3e25-431c-cbec-0b1c747865e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 16 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create pivot_table\n",
        "colum_names = ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol']\n",
        "df_pivot_table = df.pivot_table(colum_names,\n",
        "               ['quality'], aggfunc='median')\n",
        "print(df_pivot_table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UOc6839C-Wbo",
        "outputId": "48a4a085-5a5e-4c34-a71e-3f5cb136576a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          alcohol  chlorides  citric acid   density  fixed acidity  \\\n",
            "quality                                                              \n",
            "4.000000    10.00     0.0780        0.070  0.996640       7.600000   \n",
            "5.000000     9.70     0.0820        0.230  0.996890       7.800000   \n",
            "5.657043    11.15     0.0735        0.420  0.996565       8.105556   \n",
            "6.000000    10.50     0.0790        0.245  0.996595       7.900000   \n",
            "7.000000    11.50     0.0740        0.400  0.996040       8.311111   \n",
            "\n",
            "          free sulfur dioxide     pH  residual sugar  sulphates  \\\n",
            "quality                                                           \n",
            "4.000000                 12.0  3.360             2.1       0.56   \n",
            "5.000000                 14.0  3.300             2.2       0.58   \n",
            "5.657043                  6.0  3.235             2.1       0.69   \n",
            "6.000000                 14.0  3.320             2.2       0.64   \n",
            "7.000000                 12.0  3.270             2.3       0.73   \n",
            "\n",
            "          total sulfur dioxide  volatile acidity  \n",
            "quality                                           \n",
            "4.000000             32.000000              0.62  \n",
            "5.000000             45.914698              0.58  \n",
            "5.657043             17.000000              0.41  \n",
            "6.000000             35.000000              0.50  \n",
            "7.000000             28.000000              0.36  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check for correlation between variables in the dataset\n",
        "corr_matrix = df.corr()\n",
        "print(corr_matrix[\"quality\"].sort_values(ascending=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FaSgSyOwAk69",
        "outputId": "6d4ecd73-59a0-4f16-f637-0a704c4c9b97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "quality                 1.000000\n",
            "alcohol                 0.456520\n",
            "sulphates               0.387878\n",
            "citric acid             0.231061\n",
            "fixed acidity           0.111535\n",
            "Id                      0.090490\n",
            "residual sugar          0.022308\n",
            "pH                     -0.045200\n",
            "free sulfur dioxide    -0.053477\n",
            "density                -0.148231\n",
            "chlorides              -0.158145\n",
            "total sulfur dioxide   -0.192150\n",
            "volatile acidity       -0.370029\n",
            "Name: quality, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation heatmap\n",
        "plt.figure(figsize=(15,8))\n",
        "sns.heatmap(df.corr(),annot = True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "g8n2qq72AzRO",
        "outputId": "5b5adb2a-7bc9-4a91-8e05-bb102161c7c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABMYAAAMNCAYAAABzhXg1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddXgUxxvA8e/FLu6GJnjR4hSoAMXdpRR3aYsV97ZQwUtbfrRIcSgUKMW1aIEAwZIQ4iEuxD253x9JLzlIUgpReD/Ps097e7NzM8tmd3Z25l2FSqVSIYQQQgghhBBCCCHEG0aruAsghBBCCCGEEEIIIURxkI4xIYQQQgghhBBCCPFGko4xIYQQQgghhBBCCPFGko4xIYQQQgghhBBCCPFGko4xIYQQQgghhBBCCPFGko4xIYQQQgghhBBCCPFGko4xIYQQQgghhBBCCPFGko4xIYQQQgghhBBCCPFGko4xIYQQQgghhBBCCPFGko4xIYQQQgghhBBCCPFGko4xIYQQQgghhBBCCFGgLl68SLdu3ShbtiwKhYJDhw796zYXLlygYcOGKJVKqlatytatWwu9nNIxJoQQQgghhBBCCCEKVHx8PG+//TY//PDDC6X39vamS5cutG7dGmdnZ6ZMmcLo0aM5efJkoZZToVKpVIX6C0IIIYQQQgghhBDijaVQKDh48CA9e/bMM82sWbM4evQoDx48UK8bOHAgUVFRnDhxotDKJiPGhBBCCCGEEEIIIcS/Sk5OJiYmRmNJTk4ukLyvXbtG27ZtNdZ16NCBa9euFUj+edEp1NzFGys13Ku4i1CiDW00rbiLUGJtmmxZ3EUo0Xw2hBR3EUosbd2M4i5CibYhyay4i1BiGcpzwnzNHZJW3EUoscy+uVLcRSjRLlq9U9xFKLHO6+kXdxFKtAZJ6cVdhBJtu35ScRehxNrte6i4i1AkivN+e/n6bSxZskRj3aJFi1i8ePEr5x0cHIydnZ3GOjs7O2JiYkhMTMTAwOCVfyM30jEmhBBCCCGEEEIIIf7VnDlzmDZNc6CHUqksptIUDOkYE0IIIYQQQgghhCgtMopvVKVSqSy0jjB7e3tCQjRnyISEhGBqalpoo8VAYowJIYQQQgghhBBCiGLWvHlzzp49q7Hu9OnTNG/evFB/VzrGhBBCCCGEEEIIIUSBiouLw9nZGWdnZwC8vb1xdnbGz88PyJyWOXToUHX68ePH4+XlxcyZM3Fzc+PHH39k3759TJ06tVDLKVMphRBCCCGEEEIIIUoLVel46ZSTkxOtW7dWf/4nNtmwYcPYunUrQUFB6k4ygEqVKnH06FGmTp3K2rVrKV++PL/88gsdOnQo1HJKx5gQQgghhBBCCCGEKFCtWrVCpVLl+f3WrVtz3ebOnTuFWKrnSceYEEIIIYQQQgghRGmRUTpGjJUWEmNMCCGEEEIIIYQQQryRpGNMCCGEEEIIIYQQQryRZCqlEEIIIYQQQgghRCmhKiXB90sLGTEmhBBCCCGEEEIIId5IMmJMCCGEEEIIIYQQorSQ4PsFSkaMCSGEEEIIIYQQQog3kowYE0IIIYQQQgghhCgtJMZYgZIRY0IIIYQQQgghhBDijSQdY0IIIYQQQgghhBDijfTad4ypVCrGjh2LpaUlCoUCZ2dnWrVqxZQpUwr1dxcvXkz9+vUL9TcUCgWHDh3K83sfHx91nQEuXLiAQqEgKiqqUMslhBBCCCGEEEKIQpKRXnzLa+i1jzF24sQJtm7dyoULF6hcuTLW1tb8/vvv6OrqFnfRXllQUBAWFhYvnL5FixYEBQVhZmYGwNatW5kyZcpr21Hm5HyfLbv24+LmQVhEJGuXL+DD91sUd7GKRN9pg2gzqB1GpkY8cnJj87wNBPsE5Zm+x8Q+NOn4DmWrlCclKRn3W4/Y/fWvBHkF5pp+1q8LqN+qESvHLMfp1PXCqkaB02nQBp0mHVEYmZER6k/q2Z1kBHvnvYHSAN33+qBTrSHoG6GKiSDl3G4yvO8DoFW+OrpNOqKwd0TL2Jzkg9+T7nGniGpT8Cw+7oLVmD7o2FiQ7OpN0JINJN1zzzWt+YAOmPdqg7K6IwCJDzwIXfFrnuntv5iE5UedCf5iI5FbDxdWFQqV+UddsRzVF21rC5LdvAj98ieS7udeX7N+HTHt8SHKag4AJD30IHz11ufS61WugM2MkRg0qYtCW5tkTz8CP/2StKCwQq9PQes4tR/NB7VB39QIH6dH/DZ/E+E+wXmmr9z0LdqM7Ub5upUws7Nk09gVPDjlpJFm0IoJNO37gcY617+c2Tjs60KpQ2H6cGpfmgxqjb6pEb5O7vwxfzMR+ewfx6Zv8d7YrpStWwlTOwt2jF2F6zP7p1aHJjQd/CHl6lbC0MKE9Z3nEOTiW9hVKVA6TTug+243FMbmZAT7knJ0MxkBnrmnbfAByt6TNNapUlNIWPqxxjqFTTn02g9G27EWaGmREfqE5D0rUUVHFFo9CtPiRTMYNfIjzM1NuXrViUmfzMHDI59rF1C2rD3Ll82lY4c2GBrq4+Hpw+jR07h1+x4APXt2YtyYITRsWA8rKwsaNWnP3bsPi6I6BcZ+eEfKTuyBno058S4+eM/bRJyzR65pDapXoOLMgRjVq4x+BVu8F24m6Oejmom0tKgwoz82fd5H18ac1JCnhO47z5PV+4ugNgXj/Wl9aDCoNUpTI544uXN83mae+oTku02joe14Z2wXjG3MCHH149SiXwm86wWAWXlrJl9Zm+t2Byasxe3YDWxrVqTFhG5UaFIDA0sTop+EcXvHWW5uOVng9SsoDiPaU2liN5S2ZsS6+PFw7hai7+R+3jGuUZ7qM/thWq8yhhVtcFnwKz4bj2uksXjnLSpP6oZZvUro21tya/gKQo475ZpfaSH3EuJN89qPGPP09KRMmTK0aNECe3t7dHR0sLS0xMTEpLiL9srs7e1RKpUvnF5PTw97e3sUCkUhlqrkSExMokbVysybPrG4i1Kkuo3vRcfhXdk0dwMLeswkOSGJ2dsXoavMuzO4ZrPanNp2nIU9Z7Ls48Xo6GozZ/tilAbPH1+dRnVDpSrMGhQO7RpN0G01gNSrf5C0bQmqMH+U/aaBYR7nAi1tlP1moGVqRfIfP5K0aS4pJ7eiiovKTqOrJCPMn9QzO4qkDoXJtMt72M0dQ9i6XXh1/5QkN28ctn6BtpVZrumNmtUl+shFfAbPwbvvdNKCwnD49Qt07KyeS2vSvjmG9d8iNTi8sKtRaEw6vY/N7LGE/7AT396fkPzIm/K/fIm2Ze77x7BpPWKPXsB/2Gz8Bk4jLTiM8pu+Qsc2e//oVihDxV0rSPHyx3/oLHx6TCTix12oklOKqloFps347rw/oiO/zfuFNT3nk5yYzPhtc9DJ57yjZ6hPgKsvBxZuyTdv1wvOLGwyTr1s/+T7gi5+oXtvfDeaj+jA4Xmb+annAlITkxi+bfa/7B8lQa6+HMln/+gZKvF1esTJr3cXRrELnXad5uh1Gkrq+f0k/jSLjGBf9IfNAyPTPLdRJSWQ8M2Y7GWlZkeZwsIOg9FLyQgLIGnzYhLXf07qXwdQpaUWdnUKxeczJjJ50kgmTp5Ni3e7EZ+QwLE/d+bb/jM3N+PihUOkpqbRtdvH1H27NTNnLuVpVLQ6jZGRIVeu3mDO3K+KohoFzqp7CxwXD+fJyn3c7fA58S6+1Nq9AF2r3I8dbQM9knxD8P1qBykhT3NNU25yT+yHdcB77i84v/8Zvl9up9zEntiP6lyYVSkwzcd3pcnwDhyfu4WtPRaSmpDMoO2z0c6v/df1HdrOH8yltb+zqet8Ql39GLh9NoZZ+zEmMII1jSdqLH+t3E9yXCKeF+4CUKZuJeIjYjg85Uc2tp3JlfWHaT1rAI2HtSuSev9XZXo0560lQ/BYuZ8r7eYQ89CXpnvmoGed97GT4BvKo692kZTHsaNjqE/sQ18ezs7/elZayL1EKaHKKL7lNfRad4wNHz6cTz75BD8/PxQKBY6OjgAaUynd3NwwNDRk165d6u327duHgYEBLi4uAERFRTF69GhsbGwwNTWlTZs23L17V+O3vv76a+zs7DAxMWHUqFEkJSXlW7b09HRGjRpFpUqVMDAwoEaNGqxd+/wTmc2bN1O7dm2USiVlypRh8uTJ6u+enUp548YNGjRogL6+Po0bN+bOHc2RKzmnUl64cIERI0YQHR2NQqFAoVCwePFili5dSp06dZ4rR/369VmwYEG+dSpp3mvehE/HDqPtBy2LuyhFqtOobhxcv49bp2/g5+bLj9PWYmFrSeP2zfLc5uthS7m4/xxPHvvj5+rDT9PXYVPelkp1q2ikc6hViS5jevC/z0vfjalO4w6k3btI+oPLqCICSTm1DVVqCjp13ss9fd33UBgYkXxoPRkBHqhiIsh44o4qzF+dJsP7PqmXD5L++HZRVaPQWI3sRdTeE0QfOEOKhz9B89eTkZiEed/2uaYPmLaCpzuPkuzqRYrXEwLnrAOFFkYt3tZIp2Nnhf3C8QRM+w5VWukdem0xvBfRvx0n5vfTpHj6EbLoezKSkjHrk/v+Cfr8W6J2HyXZzYsU7ycEz18LWloYNq+vTmM9ZRhxf90kbMVmkl09SfUPIv78ddIjo3PNsyT7YGQnTn1/kAenbxHk5seuaT9gamdB3faN89zG7YIzx1fu4/7Jm/nmnZaSSmxYtHpJjIkv6OIXupYjO3Lh+0O4nr5FiJs/v037CRM7c2rms3/cL9zlzMrfcDmZ96gD54OXOb/uIB5XHhRGsQudbouupDmdJe3OBVRhAaQc+RlVagq6DVvnvZFKhSouWr0Qr/n3otduIOnud0g9tZOMIB9UT0NId7sF8TGFXJvC8ekno1m2fC1Hjpzi/n1Xho/4jLJl7ejRo0Oe28z8fCJPngQyesw0bjo54+Pjz+kzF/Hyyh5NuHPnAb78ag1nz10qimoUuLLjuhGy8wyhe8+T6P4Er5n/Iz0xGdtBH+aaPu6uJ75fbCPi8BUyUnLvJDVpXIPIEzd5evY2yU/CiDj6N1F/3cWkQdXCrEqBaTqqI5fXH8L99C1C3fz5Y9pPmNiaU6N9ozy3aTa6E857znPvt4uEPw7g2NzNpCUm83b/zJG6qgwV8WHRGkuNjo1xPXqd1IRkAO7u+4vTS7bjd92NKP8wHhy8wt3fLlKjY5Miqfd/VWl8F/x3nOPJnr+Icw/gwee/kJ6YQvlBrXJNH+3shdvSnQQdukZGclquacLOOeP+9T5Cjud/PSst5F5CvIle646xtWvXsnTpUsqXL09QUBA3bz5/snrrrbdYsWIFEydOxM/PjydPnjB+/Hi++eYbatWqBUC/fv0IDQ3l+PHj3Lp1i4YNG/Lhhx8SGRkJZHakLV68mGXLluHk5ESZMmX48ccf8y1bRkYG5cuX57fffsPFxYWFCxcyd+5c9u3bp07z008/MWnSJMaOHcv9+/f5448/qFo194tzXFwcXbt2pVatWty6dYvFixczY8aMPH+/RYsWrFmzBlNTU4KCgggKCmLGjBmMHDkSV1dXjX11584d7t27x4gRI/Ktkyh+thXssLC15MHle+p1ibEJeDq7U61hjRfOx9DEEIC4qDj1Oj19PSavm8aWBRuJDosqsDIXCS1ttOwdyPB1ybFSRYavC1plq+S6iXbV+mQEeqLX9mMMJq5Gf/hSdJp1gddxxKWuDvp1qhJ/1Tl7nUpF/FVnDBu89UJZaBkoUehqkx4Vm71SoaDcyulE/HKA5Md+BVvmoqSrg37taiQ8s38SrjmjX7/mC2WhMFCi0NEmPTpr/ygUGLdqQqpPAOV/+ZIqV3ZTce9qjD9sXvDlL2RWFWwxtbXA/cp99bqk2ER8nT1wbFj9lfOv+k4tljr9jzlnV9H3y1EYmhu/cp5FyaKCLSa2Fnjm6LxKjk3kibMnFRtWK8aSFTNtbbTKVibdK/u4QaUi3fM+WhXyOW709DGY/gMGM35E+dHnKGzLZ3+nUKBdvSEZEUEoh87FcNbP6I/9Cu2aJfMG/d9UqlSRMmXsOHvusnpdTEwsN27c4Z1meXd2dO3anlu37rFn9/8IfHKXmzdOMmrkR0VR5CKh0NXBuF4Voi9lt3VQqYi+dA+TRi9/zol1eoTZe3XRr1wGAMNaDpg0fYun50p+iATzCjYY21rgczl7OmxybCIBzp6Uy+M8o6WrTZm6lfC+nKNjXaXC+/IDyuexjX0dR+xrO+K890K+5VGaGJAYVfIeYih0tTGtV4mIS5rnnfCL97Fo/OrXq9eB3EuIN9VrHWPMzMwMExMTtLW1sbe3zzPdxIkTOXbsGB9//DF6eno0adKETz75BIDLly9z48YNQkND1cPWV6xYwaFDh9i/fz9jx45lzZo1jBo1ilGjRgHw5ZdfcubMmXxHjenq6rJkyRL150qVKnHt2jX27dtH//791flMnz6dzz77TJ2uSZPcG3e7du0iIyODTZs2oa+vT+3atXny5AkTJkzINb2enh5mZmYoFAqNfWNsbEyHDh3YsmWL+re2bNnCBx98QOXKlfOsjygZzGzNAYgOj9JYHx0ejbnNi8WjUygUDF00CrebLjxxz+7MGLJwFO633Lh1+kZBFbfIKAxMUGhpo0rQHDGgSohBy7JM7tuY2aBVsSbpLn+TdGANWua26LUbAtrapF39oyiKXWR0LExR6GiT9sxxkxYehbJyhRfKw3bmCNJCIom/4qxeZzWuL6q0dCK3lu79pf3P/onQnEKRHv4UvUrl89hKk830kaSFRpJwNfMGS9vKHC0jQyzH9Cd87a+ErdiM0XuNKPv9fPyHzSbx5v1/ybHkMLExByAuTHPkTlxYtPq7l+X2lzP3Ttwg0j8UKwc7unw+kLFbZ7O29wJUGaVjHoaJTeZ029z2j7FN7lNx3wQKQ1MU2tqa09MBVVwUWtZlc90mIzyQlEM/kRHsC/qG6LbsjsGYL0n8fhqqmEgURqYolAbovteDlDN7ST21E+1q9VEOnE7SliVk+LgWQc0Kjr2dLQAhIZoxB0NCw7G3t81zu8qVKjJu3BDWrP2Zr79ZR+NG9Vmzeikpqals3/5boZa5KOhYmqDQ0SblmRvr1LBoDKqWe+l8A74/iLaxIQ0urUOVnoFCWwu/r3cR/nvJH1VnlNX+iw/XPM/Eh0djnMd52NDCBC0d7Vy2icGqSu5/g/UHtiLscQABtx7nWZZyjapRq+s77B2x4sUrUET0LE3R0tEm+ZnzcXJYNMbVXv7YeZ3IvUQpkvF6TmksLq91x9h/sXnzZqpXr46WlhYPHz5Ux+G6e/cucXFxWFlpxs1JTEzE0zMzSKOrqyvjx4/X+L558+acP38+39/84Ycf2Lx5M35+fiQmJpKSkqJ+k2VoaCiBgYF8+GHuQ8Kf5erqSr169dDX19cow8sYM2YMI0eOZNWqVWhpabFr1y5Wr16dZ/rk5GSSk5M11mklJ/+n+Gfi5bTs+T6jl2V3fn474stXznPEF2OpUN2BxX3nqNc1atuE2i3qMqfztFfOv9RQKFAlxJByamvmKIYQX1JNLNBt0vG16xh7VVbj+mHW9X18PpqNKmuKin6dqlgN74FX90+LuXTFz3JMP0w7f4D/0Jnq/YNW5jUm7tw1nv56CIBkNy8MGtTCfGDnEt0x1rBHS/ovG6P+/PPIbwrtt+4cuab+/6BH/gS5+jH/0jqqvlObx1dL5vTBt3u0pMeyUerP20Z+W4yleb1k+D8mwz/7hjzZzx2DT1ej06QdqWf3giJzIkS6mxNp1zIDq2cE+6JVsQa6TdqTXMI7xgYN6sVPP2T/PXXvMfSl8tHS0uLWrXvMX5D5kgpn54fUrl2DcWOGvBYdY4XFqnsLbHq/h/vENSQ+8seoTiUcl4wgJfgpYb9dKO7iaajdswWdc5xn9o74rtB/U0epS+3uLbj8/aE809hUL0+/n6dxae1BvC+V3OuYyCb3EkJkko6xLHfv3iU+Ph4tLS2CgoIoUyZzFElcXBxlypThwoULz21jbm7+0r+3Z88eZsyYwcqVK2nevDkmJiZ89913XL+e+VYOAwODl877VXXr1g2lUsnBgwfR09MjNTWVvn375pl++fLlGqPfAOZ//ikLZ36WxxaioNw6fQOPO9lvudPVywyKaWZtTlRo9ugWM2szfFzyf4MVwPClY2j4YROW9J9LZHD227tqt6iHnYM9m+7v1Eg/dcNM3G648sXA+a9alUKlSoxFlZGOwlAzsKrC0BRVfO7xnFTx0ZmvI84RHTQjIgiFsTloab9WrypOexqDKi0dHWtzjfU61uakheUeaPYfVqN7Yz2+L75D55H8yEe93rBxbbStzKh2aat6nUJHG7u5o7Ac0QOPD0YWYA0KV/o/+8dK80mptrUFaeH57x+LkX2wHNMf/5FzSXb30cwzNY1kD80ppsme/hg2qlVgZS8MD8/cYkWON7/pZJ13jG3MiMkxgsPYxozAAn5DYoR/KHERMVg72pXYjjHXM7fw19g/mU0tYxszYp/ZP6XtDZIFSZUQgyo9PfOcmoPC2Py5UWR5ykgnI8gbLUv7HHmmkRH6RDNZWADaFV98ClBxOXLkFDduZE/bUyr1ALCzsyE4OFS93s7WGud83iAZFBSKi6vmG3Dd3Dzo3at0BJH/N2mRsajS0tF7ZiSUro0ZqaFRL52v44KhBKw/SMThKwAkuPmhLG9NuU97l7iOscenb/NLjrcoamedZ4yszYjLsQ+MrM0IyeM8k/A0loy0dIysNUeuGlmbEh/2fNvorc7N0DVQcv9A7iPorKuV46Ndc3HefY4r+XSeFaeUyBgy0tJRPjNaV2ljRvIrHDulmdxLlF6q1zQIfnGRjjEgMjKS4cOHM2/ePIKCghg8eDC3b9/GwMCAhg0bEhwcjI6Ojjp4/7Nq1qzJ9evXGTo0+8ne33//ne9vXrlyhRYtWjBxYvYbE/8ZgQZgYmKCo6MjZ8+epXXrfILQ5ijD9u3bSUpKUo8a+7cy6OnpkZ7+/M29jo4Ow4YNY8uWLejp6TFw4MB8O+rmzJnDtGmavf9asQH/Wmbx6pLik0iKD9ZY9zQ0kjot6+GbdfEyMDagSv3qnN5xIt+8hi8dQ5MO7/DFgPmE+YdqfHf4pwOc23NaY913p9exbelmbp8tBYFGM9IzRw041CTd45+bDgVaDjVJu30u900CHqNd8x1AAWR2jmlZ2JERF/VadYoBkJpG0gMPjFrUJ/Z01nlDocCoeX0it/+Z52ZWY/tgPXEAfsMXkHTfQ+O76EPnNGOWARW3LCX60Hmi9mseSyVeahpJDx9j2Lw+cWezRjApFBi+U5+onXmPHrQc1RfL8QN5Mno+yQ+emXaSmkbSA/fnpmLqOZYjNVDz76+kSY5PIjleM1RATOhTqreoo+4IUxob4FC/Kld3FOy/tZm9JYYWxsSU4BuYlPgkIp/ZP7GhT6ncora6I0xpbED5+lW4vuNMcRSxZEhPJyPQC+3KdUh3zbqOKBRoV65D2vX8r1dqCgVadhVJd7+TnWeA53NTMbWsyqCKLvlvxY2LiycuTjMuU1BQCG1av8vdrI4wExNjmjZtwIaN2/LM5+q1m9Sorhk/s3q1yvj5vR5tM1VqGnH3PDF7ty6RJ7KmZCkUmL1bj+Atx186Xy0D5XNTtFXpGSXybe4p8UmkPHOeiQt9imPL2uqOMD1jA8rVr8LtPM4zGanpBN33xrFlbdxP3cpcqVDg2LIOTr+eei59/QEf4H7mNgmRsc99Z12tHIN3z+P+gUtc+K7kjkpUpaYTc88bq/fqEHI868UmCgVW79XBd/PJ4i1cMZF7CSEySccYMH78eCpUqMD8+fNJTk6mQYMGzJgxgx9++IG2bdvSvHlzevbsybfffkv16tUJDAzk6NGj9OrVi8aNG/PZZ58xfPhwGjduTMuWLdm5cycPHz7MNyZXtWrV2LZtGydPnqRSpUps376dmzdvUqlSJXWaxYsXM378eGxtbenUqROxsbFcuXJFHf8sp48++oh58+YxZswY5syZg4+PDytW5D+339HRkbi4OM6ePcvbb7+NoaEhhoaZgRJHjx5NzZqZQaWvXLmSbz5KpfK5aZOpKcXfAE1ISMTvSaD6c0BgCG7unpiZmlAmn9gcpd3xTUfo+Uk/gr0DCfUPpd/0j3gaGonTqevqNPN2LeXmyb859esxAEZ+OY4W3d9n5ZhlJMYnYpb1FDYhJoHU5BSiw6JyDZIZERj+3IWvpEpzOole59FkBPuQEeSNTuN2KHSVpD3IDGqs13k0qtinpF46kJne+Tw6DT5E98NBpN0+i8LCDt13upB6+2x2prpKFBbZx5LCzBqFbQVIjEcVG1mk9XtVEZsPUva7aSTef0ziXXesRvRAy1Bf3YlVdsU00oIjCF3xKwBWY/tiM+VjAqZ+S8qTULStM0dTZSQkokpIIj0qVjMQP6BKSyct7Ckp3qXv5uzp1oPYfz2dpAePSbr3CIthPdEyUBL9e+b+sf96OmmhEYSv2gqA5eh+WH06hKAZ35AaEPLc/gGI3HSAsqtmk+j0gITrdzF6rzHGrZvhP3RWsdTxVfy1+TjtPulFmE8wkf6hdJren5iQp9w/lf1GxQk753P/5E0ub8u8+dAzVGLtmB3j0qqCLWVrOZAQFUdUYAR6hko6fNaXeyeuExMWjXVFO7rN+YhwnxDcLt59rgwl2ZXNJ2j9SS8ifIJ56h9G2+n9iA2JwjXH/hm5cy4uJ534e1vmDameoRKrHPvHooINZbL2T3Rg5lN4AzMjzMtZY2KbeXxZZwUNjw2Lei6mWUmUevVPlL0nkRHgRXqAB7rNO6PQU5J6+wIAen0moYqJJPX0bgB0W/XJnE4ZGYxC3wjdd7ujMLch9Vb2eTn18h8o+09Fx8eVdO8HaFerj3aNRiRtXlwMNXx1677/hblzPuWxhxc+Pv4sWfw5gYEhHD6cfRN/6sReDh0+zo8/bQVg7dqfuXTxMLNnfcJv+4/QpEl9Ro8ezPiJM9XbWFiYU7FiOcqWsQOgelZHWnBw6HMxzUqiwP8dodraT4i760mc82PKjOmKtqGS0D2ZD7uqrvuElOBI/JZljk5R6OpgUD3zQYSWrg569lYY1nYkIz6JJJ/MToGnp50o/1kfUgLCSHjkj1HdSpQd143Q3bk/QCtpbmw6QctPehLpHUyUfxgfTO9LbGgUj/7p9AI+2jUH95NOOP2aee26/stxuq8cR9A9bwLvetJ0ZEd0DZXc++0vjbwtHOyo2Owt9gx/fsqmTfXyDN49F6+L97n+yzGMskZjqdIzcu1EK27eG45Sb90Eop29iLrjQaWxndExVPJkT2ad630/keTgSB59tQfIDNhv/M+xo6eNvr0lJrUdSI9PIsEnBABtQyWGlbLP1wYVbTGp7UBqVBxJARGUNnIvUUpIjLEC9cZ3jG3bto1jx45x584ddHR00NHRYceOHbz77rt07dqVTp06cezYMebNm8eIESMICwvD3t6e999/Hzu7zMbEgAED8PT0ZObMmSQlJdGnTx8mTJjAyZN5P3kYN24cd+7cYcCAASgUCgYNGsTEiRM5fjz7SdewYcNISkpi9erVzJgxA2tr6zynNBobG3PkyBHGjx9PgwYNqFWrFt988w19+vTJswwtWrRg/PjxDBgwgIiICBYtWsTixYuBzI67Fi1aEBkZSbNmeb+atyR74PaYkZ9k32B++/1GAHp0astX86cXV7EK3ZENB1Ea6jN6+UQMTY145OTK10OXkpqc/Xpyu4r2mFhkTytsN6QTAAv3faWR10/T13Fxf+loEP6b9Ec3STU0QbdlTxRGZmSE+pO8fzVkBeRXmFhCjiHJqtinJO9fhW7rgegPX4oq7impt86QduOYOo2WvSP6A7OPMb02gwBIe3CZlOObi6hmBSPm6CW0Lc2wmfIxOtYWJLt64TdiIekRUQDolrGBHE/SLQZ3RkupS4Uf52nkE7Z2J2HrdhVl0YtE7PGLaFuaYf3Jx2jbWJLs6smTMQuy909ZW41pt+aDuqClp0u5dZpTA8LX7yBifeaNWtyZqwQvXo/V2P7YzhtPivcTAj/9ksTbeU+RKqnObfgDPQMl/ZePwcDUEO+bj/jfsK9Jy3HesXaww8jSRP25Qr0qTN6zUP2554LMUdc39v/F7hk/oUrPoGzNijTp8z4GpkbEhD7l0cV7HFu1j/SUtKKrXAG4tOEIegZKei4fjb6pIb433dn6zP6xdLDDMMf+KVevMqP3LFB/7rJgCAC39//FgRn/A+Ctdo3ouyI7xunA9Zkx/c6uOcC5NQcKtU4FIf3BNVKMTNH9sD96xuZkBPmQtG0ZZE1x1zKzJiPHeUdhYIxez3GZ0y0T48kI9CLp5/mowrI729Ndb5Jy5Gd03++JXpcRZIQHkrxnJRl+j4q8fgXhuxU/YmRkyIYfv8Xc3JQrV27SpdvHGrFdK1d2wNraUv3Z6dZd+vYbzZdfzmb+vCl4+/gzbfoidu8+qE7TrWt7Nm/Kjh+7e+dPACz9YiVLv1hVBDV7NRF/XEXXyoyKMweia2NO/ENvXD76ktSsQPLKctYa1yw9Owvqn1mp/lxuYg/KTexB9NUHPOyzCACveb9QcdYgKn89Fh0rU1JDnhK8/TRPVpXcEVA5XdvwJ7qGSjovH4W+qSH+Tu7sGfoN6TnOMxYV7TCwyD7PuP75N0ZWJnwwrS9GNpnTLvcM/Yb4cM2XFb3d/wNigiLxuvh83LC3OjfFyNqMur3fpW7vd9Xro/zD+OHdKQVf0VcUdPgaelamVJ/ZDz1bc2If+nJj0NekZD1MMHjm2NG3t+S9c9mx/ypP6kblSd2IuOLC9d5LATCrX4V3DmZfz2otzbyePdnzF/c++6koqlWg5F5CvIkUKpWqdLzWSRQplUpFtWrVmDhx4nPTJF9EarhXIZTq9TG0kQSezMumyZb/nugN5rMhpLiLUGJp68qTs/xsSHpz34D4bwzRKu4ilGhzh5SujsiiZPZN/qPq33QXrd4p7iKUWOf19P890RusQdJrFraigG3XT/r3RG+o3b6HirsIRSL58dVi+21ltRbF9tuF5Y0fMSaeFxYWxp49ewgODmbEiBHFXRwhhBBCCCGEEEL8Q4LvFyjpGBPPsbW1xdramo0bN2JhYfHvGwghhBBCCCGEEEKUQtIxJp4js2uFEEIIIYQQQogSKkOmGxckCaohhBBCCCGEEEIIId5I0jEmhBBCCCGEEEIIId5IMpVSCCGEEEIIIYQQorSQ4PsFSkaMCSGEEEIIIYQQQog3kowYE0IIIYQQQgghhCgtMmTEWEGSEWNCCCGEEEIIIYQQ4o0kI8aEEEIIIYQQQgghSguJMVagZMSYEEIIIYQQQgghhHgjSceYEEIIIYQQQgghhHgjyVRKIYQQQgghhBBCiNJCgu8XKBkxJoQQQgghhBBCCCHeSDJiTAghhBBCCCGEEKKUUKnSi7sIrxUZMSaEEEIIIYQQQggh3kjSMSaEEEIIIYQQQggh3kgylVIIIYQQQgghhBCitFBJ8P2CJB1jolAMbTStuItQom27taq4i1BiWTm0Le4ilGirLVoUdxFKLMe41OIuQolWQ08u+XkplypxOvKzfatecRehxLpp37i4i1CiTc2ILu4ilFgOiuIuQcn2p05kcRehRBuZXq64iyDEa0VayUIIIYQQQgghhBClRYaMGCtIEmNMCCGEEEIIIYQQQryRZMSYEEIIIYQQQgghRGkhMcYKlIwYE0IIIYQQQgghhBBvJOkYE0IIIYQQQgghhBBvJJlKKYQQQgghhBBCCFFaZMgbtQuSjBgTQgghhBBCCCGEEG8kGTEmhBBCCCGEEEIIUVpI8P0CJSPGhBBCCCGEEEIIIcQbSTrGhBBCCCGEEEIIIcQbSaZSCiGEEEIIIYQQQpQWGTKVsiDJiDEhhBBCCCGEEEII8UaSEWNCCCGEEEIIIYQQpYUE3y9QMmJMCCGEEEIIIYQQQryRZMSYEEIIIYQQQgghRGkhMcYKlIwYE0IIIYQQQgghhBBvJOkYE0IIIYQQQgghhBBvpNe+Y0yhUHDo0KECzcfHxweFQoGzs/Mr5/uyXqQMFy5cQKFQEBUVBcDWrVsxNzcvkvIJIYQQQgghhBCiEGRkFN/yGpIYY89YvHgxhw4deq7DKSgoCAsLi+IpVC4qVKhAUFAQ1tbWL7zNgAED6Ny5s/pzXnUtLfpOG0SbQe0wMjXikZMbm+dtINgnKM/0PSb2oUnHdyhbpTwpScm433rE7q9/JcgrMNf0s35dQP1WjVg5ZjlOp64XVjWKjZPzfbbs2o+LmwdhEZGsXb6AD99vUdzFKhLz5k9h2IiBmJmZ8vfft5j22QI8PX3yTD9n7mfMmfeZxjr3R540bthOY13Tpg1YsHg6jRvXJz09nfv3XOnVYxhJScmFUY1XVnNYW+qO74KBjRmRrn5cW7CNcGevPNM7dmlKo8/7YlzemhifEG4u28OTc3fV3zt0akzNjz/Eqp4j+hYmHGw/l0gXP/X3xuWtGfD3mlzzPjtuHT5HbxRY3QpD+RHtqTixG3q25sS5+OI+dwsxdzxzTWtUozyVZ/bHpF4lDCra4r7gV/w3Hsszb4dPelB1/kf4bTzG4wW/FlYVXknj6X2oOag1SjNDgm+6c2nuFqJ9QvLdpvawttQfl3mMRbj6cWXhNkJzHGPaSl2aL/iIqt3fQVtPF/+/7nFp3lYSw2Oey0tpbky/U8swLmPJ5tpjSYlJAKDsOzXp/tu859L/2nASiWHRr1jrwuE4oh1VJnZDaWNGjIsfD+ZtJSqPY8m4Rnne+rwvZm9XxrCCDQ8WbMP75+NFXOKCVVKPpZLIemhnbMf1RNfGgkRXH54s3EjC3ce5prUa1A7LPq3Rr+EAQOJ9TwK/2a6R3qzjO1h/3BHDulXQsTDFreMUEl28i6QuhWXEjGF0HdQZYzNjHtx8yKq5awnwDsgzffch3egxtBv25e0A8HH35dc127lx/mau6b/ZvoxmrZsyf9RCLp+8Wih1KCy9pg6k1aC2GJoa8tjpEb/O30hIPm3lrhN70ajDO5SpUo7UpBQe337Evq+3E5yjrdxqUDve6fEujrUrY2BiyIR6Q0gowX9D+RkzYwTdP+qCiakx95we8O2c1TzJ59jpNbQ7vYd0p0wFewC83H3YvHobf5/PbL/Yl7fj4PU9uW47b9xizv35V8FX4j+qNawt9XK0/a4u2EZYPm2/Sl2a0jhH2+/Gsj3452j7ATSa0Ye3BrVGz8yQkJvuXJ67hRhvzXN6hTb1aTi1J5Y1K5KelErQ366cHr1G/b3125VpOmcA1nUdQQVhzp5c/2oPka5+CFEUXvsRYwXF3t4epVJZ3MVQ09bWxt7eHh2dF+/bNDAwwNbWthBLVXS6je9Fx+Fd2TR3Awt6zCQ5IYnZ2xehq9TNc5uazWpzattxFvacybKPF6Ojq82c7YtRGjz/79ppVDdUqsKsQfFLTEyiRtXKzJs+sbiLUqSmTBvHuAnDmfLpfNq06k1CfAK/H96KUqmX73YuLo+oWrmpemnfrr/G902bNuDAoa2cO3uZ1h/0otX7Pdn4v21kZJTMA6lSt2Y0WziYO6sPcrjTfCJd/Oi4Yxb6Vqa5prdtVI3WP0zCfc9fHOo4H98Tt2j7y1QsapRXp9E1VBJ88xE3l+3NNY/4wAh2NZiksdxasZ+UuESenL+b6zYlhW2P5lRbMhTvlQe42W42cQ99qb9nLrrWue8vLQMlib4heH61m+SQp/nmbVK/CuWGtiX2oW9hFL1A1J/Qlboj2nNp7mZ+77aI1MRkuuyYhXY+59wq3ZrRYsFgnNYc5EDn+US4+NFlu+Yx1mLRYBzaNuDU+O853O9LDO0s6LBxSq75tVoxOt8G8u73Z/Brw0nqJbcOkZKgbI93qLV4CO4rD3Cx/VxiHvrSbPds9PI4lrQN9Ij3C8X1y90k/cuxVBqUhmOppDDv9i7lFowkeM1eHnWZRqKrN1V2LEbHyizX9Mbv1OXp4Ut4DJiPe8+ZpASGU2XHYnTtLNVptAz1ib/pSuDybUVVjUI1aOIA+ozoxao5a5nQbTKJCUl8t+Nr9PI5nsKCwti4/BfGdp7IuM4TuX3lDl9tWopjdYfn0vYd3QdVKW0Qdh7fk3YjOrN13v9Y2nMOyYlJzNi2IN+2co1mtTm7/QRf9JrDt0OWoK2jzefbFqKXo62sZ6DH/b+cOfLj70VRjULz8cSB9BvZm29nr2ZUt4kkJiSxZue3/3rs/Lj8Z4Z3GseIzuO5deUO327+kkrVHQEIDQyjS/3eGsvP320hPi6Ba+eK/yF75W7NeGfhYG6vPsjBTpnn0k7/0vZr88MkHu35i4Md5+Nz4hbtnmn7vT2xK7VHtOfynM0c7raI1IRkOj1zTnfs3IRW68bjvvciv7ebyx+9luB56Jr6ex1DJZ12fE5cQASHuy3mSO+lpMQn0WnnTBQ62oW3Q0o5lSq92JbXUYntGNu4cSNly5Yl45mhej169GDkyJHqzz/99BNVqlRBT0+PGjVqsH379nzznTVrFtWrV8fQ0JDKlSuzYMECUlNTgcyphkuWLOHu3bsoFAoUCgVbt24F/n1K5oMHD+jUqRPGxsbY2dkxZMgQwsPD80wfERHBoEGDKFeuHIaGhtStW5fdu3drpMnIyODbb7+latWqKJVKKlasyFdffQXkPpXy2LFjVK9eHQMDA1q3bo2Pj49GfjmnUuZV15EjR9K1a1eN7VJTU7G1tWXTpk357Nmi1WlUNw6u38et0zfwc/Plx2lrsbC1pHH7Znlu8/WwpVzcf44nj/3xc/Xhp+nrsClvS6W6VTTSOdSqRJcxPfjf598XdjWK1XvNm/Dp2GG0/aBlcRelSE2cNILvvl3PsaNnePjAjXFjZlCmjB1du7XPd7u0tHRCQ8LVS2SE5g3q8m/m87+ftrJ65QbcXB/j8dibg78fIyUlpTCr89LqjO3Eo93nebzvIlGPA7kyewtpSclUH/hBrulrj+rAkwv3uL/hKNEegdxesZ+IBz7UHJ49as7jwBWc1xwi8NKDXPNQZahIDIvWWBw7Nsb7z+ukJZTMUXX/qDi+CwE7zhK05wLx7gG4ff4L6YkplB3UOtf0sc6eeCzdScihq2Qkp+aZr7ahkjo/TsZ1+kbSouIKq/ivrO6ojtz+/jA+p24T6ebP+SkbMLQzx7FDozy3qTemE667z/No30WePg7k4pzMY+ytAZnHmJ6JAW8NaMW1pTsJvOpC+H0fLkzfiH2T6tg20Dwv1xryIUpTI5z/l/eou8SIGI1jq6Q+3ag8rgt+O8/hv+cv4twDuDdzE+mJKVQc2CrX9NHOXrgu3UXg4WtkpKQVbWELQWk4lkoK29E9iNh9isjfzpL02B//OT+RkZiM1YC2uab3/WwV4duPk+jiTbJnAH4z16PQ0sLk3bfVaZ7+foHgtXuJvVyyH0a8qL6jerN93U6unLqKl6s3y6d8g7WdFe92yLttc+3M31w/d4MA7wCeeAew6dstJCYkUqthTY10VWtVYcC4vnw7fUVhV6NQdBjZlSPf7+fO6Zv4u/mycdr3mNtZ0LB90zy3WTnsSy7vP0/AY3/8XX35ZcZ6rMvbaLSVT20+ytGfDuJ5x70oqlFoBozuy9a127l06gqerl4s/Ww51nbWvN/h3Ty3uXz6GtfOXeeJdwD+Xk/43zebSIxPpE7DWkDmvVtk2FON5YNO73LuyAUSE5KKqGZ5qzu2E267z+Oe1fa7nNX2q5FH269OVtvv3oajRHkEcmvFfsIf+FA7R9uvzqiO3Fl3GN9Tt4l09edC1jndIeucrtDWovmSIdz4cjeuO84R7R1M1ONAvP7M7ig0r1oWfQsTbq3YT7RXEE/dA7i9+ncMbc0xKf/is6OEeBUltmOsX79+REREcP78efW6yMhITpw4weDBgwE4ePAgn332GdOnT+fBgweMGzeOESNGaGzzLBMTE7Zu3YqLiwtr167l559/ZvXq1UDmVMPp06dTu3ZtgoKCCAoKYsCAAf9a1qioKNq0aUODBg1wcnLixIkThISE0L9//zy3SUpKolGjRhw9epQHDx4wduxYhgwZwo0b2VOJ5syZw9dff82CBQtwcXFh165d2NnZ5Zqfv78/vXv3plu3bjg7OzN69Ghmz56d5+/nVdfRo0dz4sQJgoKyh1n/+eefJCQkvNC+KAq2FeywsLXkweV76nWJsQl4OrtTrWGNF87H0MQQgLgcN6N6+npMXjeNLQs2Eh0WVWBlFiWDo2MF7O1tuXD+inpdTEwsTjedadqsQb7bVqniyCOPa9x9cIFfNq+mfPmy6u+sbaxo0rQBYWERnD77Gx7eNzh2YjfvNG9caHV5FVq62ljXrUTgpYfZK1UqAi89xLZh1Vy3sW1U9bkOryd/3cO2Ue7pX4RVXUes6jjivrv4pxbkR6GrjUm9ykReup+9UqXi6cX7mDWu9kp51/h6FOFn7vD04v1/T1xMTCraYGRnzpMc//4psYmEOnti3zD3+mvpamNTtxJPLmseY08uPcQu65ixrlsJbT0djTRRnkHEPgnHvlF2vhbVytLos16cm7IB8hmB2e/EVwxxWk/XnbOwf8V/l8Ki0NXGrF4lwi/m+FtSqQi/9ACLElrmglRajqWSQKGrg2HdKpodWCoVsZfvYviCbR0tAyUKXW3SomILqZTFq0zFMljZWXHr0m31uvjYeFycXanVqNYL5aGlpUWb7q3QN9Dn4S0X9XqlvpL56+eyZt73RIaVvpGaNhXsMLe14OEVzbayl/Njqv6HtrKBuq38eh1DZSuWwdrOipuXb6nXxcfG43LHlTqNar9QHlpaWrTt3hp9Q33u33qYa5oadatTvU41juwp/o74f9p+Ac+0/QLyafvZNapKQD5tP5OKNhjamWukSY1NJMzZE7usc691XUeMy1iiylDR68SXDL61no7bP9cYdRbtGURSZCw1BrVCS1cbbX1dagxsxVP3AGL9wwpqFwiRrxIbY8zCwoJOnTqxa9cuPvzwQwD279+PtbU1rVtnPqFfsWIFw4cPZ+LEzKlg06ZN4++//2bFihXqNM+aP3+++v8dHR2ZMWMGe/bsYebMmRgYGGBsbIyOjg729vYvXNb169fToEEDli1bpl63efNmKlSogLu7O9WrV39um3LlyjFjxgz1508++YSTJ0+yb98+mjZtSmxsLGvXrmX9+vUMGzYMgCpVqvDuu7k/xfhn5NzKlSsBqFGjBvfv3+ebb77JNX1edW3RooV65N3MmTMB2LJlC/369cPY2DjXvJKTk0lO1hztka5KR1tROENfzWzNAYgOj9JYHx0ejbnNi8WBUygUDF00CrebLjxxz55OMWThKNxvuXHrdMmOdSRejq2dDQChoZqjOUNDw7G1tclzOycnZyaM+5zHj72xt7dh9pxPOXF6L+806UhcXDyVHCsAmbHI5s1bzv17Lgz6qDdHjm7nnSad8o1fVhz0LU3Q0tF+Lv5SYng0ZlXL5LqNgY35c1PTksJiMLQxf+ly/NPoCb2Ve7yckkLX0hQtHW1SntlfKWHRGFYrm8dW/86uZwtM6lXiZoe5r1rEQvXPv/Gz//6JYTEY2OY+pSu/Y8w86xgztDUjPTn1ufhOieHRGNhk5qulp8OH6yfx91e7iQuMwLTi8+EA4kOj+Gv2ZsLueaGtp0vNQa3otm8eB7svJvyBz8tUudDoZR1Lyc/sl+SwaIyrvvyxVFqU9GOpJNG2NEWho03qM22dtPAo9KuUz32jZ5SdM5TUkMjXZnTYsyyz2nyR4ZodV0/DorC0scxtE7VKb1Xix8Pr0FPqkRifyIIxi/F9nN0enLR4Ag9vPeTKqdIVU+wfZll/a88+5I0Ji1Z/928UCgWDF47A/aYrAe7+BVvAYmZlm3l8PNvpGRn+VP1dXqq8VYmNf/ygPnZmj16Iz+PcQyF0G9QZb3cf7jvl3nFWlF7kXPqs3Np+iWExGGQdQwb5ndOzzr0mWefahtN68/fSncT5h1F3XGe6/jaPfe/PIDkqntT4JP7s9xXtNk2lwWc9AYjxDub44G9Qpb+egd4LxGsaBL+4lNgRYwCDBw/mwIED6k6XnTt3MnDgQLS0Movt6upKy5aaQ6VbtmyJq6trnnnu3buXli1bYm9vj7GxMfPnz8fP79XiTNy9e5fz589jbGysXt566y0APD1zD6abnp7OF198Qd26dbG0tMTY2JiTJ0+qy+Lq6kpycrK6U/DfuLq60qyZ5jTC5s2bv1R9Ro8ezZYtWwAICQnh+PHjGtNXn7V8+XLMzMw0FpfogrvRbdnzfba47FYv/yWuWl5GfDGWCtUd+H7ySvW6Rm2bULtFXbYtKTlTRsWr6T+gB4Eh99WLru7LHTunT/3FoYPHefjAjbNnLtG390jMzEzp1bsLAIqsc9LmzbvZuX0/9+66MGfWlzx+7M3HQ/sVWH1eJ9r6ulTu2Rz3PReKuyjFQlnWiupfDuPhxO/znWpZHKr1bMEot1/Ui5Zu8cX3aDZ7AFEegTw+eCXPNNFeQbjuPEf4fR9Cbj3mwoyfCbn1mHqjOxZhSUVuStux9Dqxm9gHi+7v4TVmOaoSdo55WW17teH4oyPqReclr+kA/p7+jO4wjgndJnN4+xHmrJ6JQ7WKALRo15yGLeuzftGPBVX0Qte8x3v87+EO9aJdAH9rQ78YQ7kaFfnxk1UFUMLi1b5XW866H1Mvr3Iv4evpz7D2oxnddSIHtx1mwZrZOFZ7Pj6dUl+P9j0/LBGjxYqTQksBgPP3h/E5dpPw+z78NW0jKpWKSl0y71219XV5f8VoQm66c7j7Yo70WkLkoyd0+HUG2vp5x3wToiCV2BFjAN26dUOlUnH06FGaNGnCpUuX1NMeX8a1a9cYPHgwS5YsoUOHDpiZmbFnzx71KKuXFRcXR7du3XIdnVWmTO498N999x1r165lzZo11K1bFyMjI6ZMmaKOR2RgYPBKZXoVQ4cOZfbs2Vy7do2rV69SqVIl3nvvvTzTz5kzh2nTpmmsG11ncIGV59bpG3jkiGOgq5d5gjSzNicqNPtJj5m1GT4v8Gal4UvH0PDDJizpP5fI4Aj1+tot6mHnYM+m+zs10k/dMBO3G658MXD+s1mJEu7Y0TM43XRWf9bLCrBva2tNSHD20GxbW2vu33d5dvM8RUfH4unhTeUqmQ2hkOBQAB65aXYIP3LzoEKFkjcKJCkyloy0dPXTvH8YWJuRGJr7W/wSw6IweCY4uL6NKQkvOeW4Upem6Bgo8dh/+aW2L0qpkTFkpKWj98z+0rMxIyU06qXyNHm7Eno25jQ5/bV6nZaONubNa1J+ZAfOVxhcbFO9fE7fJsQ5+6GOtl5mU8HA2pSEHPU1sDEl4mHuD5byO8YSsp5WJ4RGo63URc/UUGOkj4G1mfqJdrkWtbB8qwJjO2fFxFFkNrCH3/2J298fxmlV7sGfQ529sG/y/Gjt4paSdSwpn9kvShszkl/yWCrJXodjqbikR8agSktH19pcY72OtTmp/zK1z3ZsT2wn9MZj8CKS3EruSz3+qyunruF6x039+Z/2oKW1BZGhker1FjbmeDzM/cH0P9JS0wjwyXzTovv9x7z1dg36jOrNqtlraNiyPmUdyvKny2GNbZZsXMT9Gw+Y0m96QVWpwNw5cxNP5+w2iLqtbGOuMWrM1MYMPxeff81vyJLRvN2mEcv6L+BpcOS/pi/pLp+6gsud7Haerl5me9DSxoKIHMeOpbUF7g898s0rLTWNJ1nHzqP77tSs/xYDRvfhm1maHYitu3yAvoGS47+dKqhqvJJ8z6X/oe1nYGNKYtYx9c9/DaxNSczjnP7Puf6pe/bbPjNS0oj1C8W4nBUAVXu2wLi8DYe7L1HHBz0/+QeGPvwfDu0b4fXH3y9V59eeqnSNGPvhhx/47rvvCA4O5u233+b777+nadO8Yx6uWbOGn376CT8/P6ytrenbty/Lly9HX1+/UMpXojvG9PX16d27Nzt37sTDw4MaNWrQsGFD9fc1a9bkypUr6qmGAFeuXKFWrdzjCly9ehUHBwfmzct+rbuvr2aDQU9Pj/T0//amhYYNG3LgwAEcHR1f+AnElStX6NGjBx9//DGQGazR3d1dXfZq1aphYGDA2bNnGT169L/mV7NmTf744w+NdX//nf9JJK+6WllZ0bNnT7Zs2cK1a9cYMWJEvvkolcrn3thZkNMok+KTSIoP1lj3NDSSOi3r4ZvVEWZgbECV+tU5veNEvnkNXzqGJh3e4YsB8wnzD9X47vBPBzi357TGuu9Or2Pb0s3cPpv7K7xFyRYXF09cXLzGuuDgUD5o1YL79zJHlpqYGNO4SX02/bIztyxyZWRkSKVKFdmz+yAAvr5PCAwMplq1yhrpqlarxOlTJS9+VkZqOuH3vSnzbm18T2bF11AoKPtubVy2ns51m9BbHpR9tzYPN51Uryv3Xh1Cb+XfgMxL9YGt8Dt9m6TIkh+3RJWaTuw9Lyzfq0v4cafMlQoFFu/V4cnmk/lvnIenFx/w9wczNNbVWjOBeI8AfNf/Uazxj1Ljk0iN1wwSHB8SRbl3axPhktnQ1TU2wLZ+FR5uP5trHhmp6YTd96Zcy9r45DjGyr1bmwdZx1j4fW/SU9Io17I23sczz7FmlctgUt6a4KzptafGrUVbP/uNsbZvV6b1yrEc7vMF0b6a5/CcrGtV1Oh4KSlUqelE3/PG+r06BJ/IPpas362Nz+aScfNUkF6HY6m4qFLTSLjviUnLekSfygpSrVBg0rIe4b/mPQLFdnwv7Cf3w2PIYhLvvdz5uaRKjE8kID5RY11ESAQN322Ah0tmR5ihsSG16tfkj21H/lPeCi0FelmdSbt+2MPR3cc1vt9y9hd+WPITV0+XzBv03NrKUaFPqdWirrojTN/YgMr1q3FuR/7XrSFLRtOoQ1OWD1xE+JOS97fxMhLiE0l45tgJD4mg8bsNefwwx7HToCa/bzucWxZ5Umgp1B2ROXUb2JlLp68SFZl7p1NR+6ftV+4/tP1Cstp+D3K0/crnaPvF+oWRkHVOj8xxTrepXwWXbZnn9PB7PqQlpWBepQwhNzMHOih0tDEub0NcQGZoEx0Dvcx2T46X5qgyVKDKHnEmSre9e/cybdo0NmzYQLNmzVizZg0dOnTg0aNH2No+H9pg165dzJ49m82bN9OiRQvc3d0ZPnw4CoWCVasKZxRrie4Yg8zplF27duXhw4fqTqR/fP755/Tv358GDRrQtm1bjhw5wu+//86ZM2dyzatatWr4+fmxZ88emjRpwtGjRzl48KBGGkdHR7y9vXF2dqZ8+fKYmJg81+nzrEmTJvHzzz8zaNAgZs6ciaWlJR4eHuzZs4dffvkFbe3nO4mqVavG/v37uXr1KhYWFqxatYqQkBB1x5i+vj6zZs1i5syZ6Onp0bJlS8LCwnj48CGjRo16Lr/x48ezcuVKPv/8c0aPHs2tW7fUb9TMS351HT16NF27diU9PV2j47GkOL7pCD0/6UewdyCh/qH0m/4RT0MjcTqV/YaTebuWcvPk35zKakCO/HIcLbq/z8oxy0iMT1THWEiISSA1OYXosKhcA+5HBIY/14n2OkhISMTvSaD6c0BgCG7unpiZmlDGvmTHXnkVP/6whc9nTsbTwwdf3yfMXzCVoKAQ/jySfVP6x9Ed/PnHSTb+L/Mtt18um8PxY2fx9wvAvowdc+dPIT09nd9+y254r1vzM3PmTeH+fTfu33Pho8G9qV69CkMHTyryOr6IBxuP8/7qcYTf9SbM2ZM6ozuiY6DEfW9mR977a8aREPwUp6/3AfBw00m67J9HnbGd8D/rTOUezbGuV5krszar89QzN8K4rBWG9plxX8yqZI6YVb8lMIuJox32zWpwcmjpedOX34aj1Fo3kRhnT2LueFJxbGe0DZUEZU0FrfX9JJKDI/H8KvPtwgpdbYyqZ8YB0tLTQWlvgXFtB9Ljk0j0CSE9Pol4N82YLekJSaQ+jXtufUlwf9MJGn3Sk2jvEGL9Q2kyoy8JIVHZHRVA191z8D7hxMNfMxvY934+TutV4wi7502osyf1RnVE10DJo32Zx1hKbCJuey/QYuFgkqPiSIlL5N2lQwl2cif0TuaNSswzHRYGFiYAPPUIVI8MqjuqA7H+YUS6B6Ct1KXmwFaUbVmbo4Nzj7FZ3Lz+d5T6aycQddeLqDseVB7TCW1DJX57MvdL/e8nkBT0FLdle4Cslz/8cyzp6qBfxgLT2g6kxSeR4BNSbPV4WSX5WCppQn85jMPKz0i470G882NsR3VDy1CfiH2Z7VyH1VNICY4g6JvMa5XthN6UmfYRPp+uJOVJKDpZ7ZyM+CQyst6Ip21mjF45G3TtMuMoKauUAyA17ClppfClQ/s3/c6QTwfzxDuAIP9gRs0YTnhIBJdPZk+ZXbnnWy6fuMLBrZkdHmNmj+L6+RuEBoRiYGxI255tqN/8bT4fnPnSqn/eJvis0IBQgv2Dn1tfUp3c/CfdP+lLiE8QYf6h9J4+iKiQp9w+lR1Hd+bORdw+eYMz2zI7Aod+MYZ3erzH2jFfk5RLWxkyR6GZ2Zhj55AZo7h8DQeS4hOJCAgnPrrkvl35WXt/2c/wT4fg7xVAkH8QYz4fSXhIOBdPZo9k/37vSv46fon9Ww8BMGH2aK6dv0FwQAhGxoa07/khDZvXZ8pHMzXyLu9Ylvrv1GP6kLxfhFYc7m88zgerxxGWo+2nm6Pt12rNOOKDn3Izq+33YNNJuu2fR92xnfA760yVrLbfpRxtvwebTtDg0+xzeuOsc/o/nW+pcYm47jhHw+l9iAuMIO5JBPUmZIYi+efNlE8uPqDpvEG0/Go4D7ecQqGl4O1J3chISyfwat4hkt54pSjG2KpVqxgzZox6wM2GDRs4evQomzdvzvWFgVevXqVly5Z89NFHQGa/xaBBg7h+/fpzaQtKie8Ya9OmDZaWljx69Ei9Y/7Rs2dP1q5dy4oVK/jss8+oVKkSW7ZsoVWrVrnm1b17d6ZOncrkyZNJTk6mS5cuLFiwgMWLF6vT9OnTh99//53WrVsTFRXFli1bGD58eL5lLFu2LFeuXGHWrFm0b9+e5ORkHBwc6Nixozoe2rPmz5+Pl5cXHTp0wNDQkLFjx9KzZ0+io7NvHhcsWICOjg4LFy4kMDCQMmXKMH78+Fzzq1ixIgcOHGDq1KnqYYnLli3LNzZYfnVt27YtZcqUoXbt2pQtW/Kmgh3ZcBCloT6jl0/E0NSIR06ufD10Kak54mjYVbTHxCJ7+G+7IZ0AWLjvK428fpq+jov7zxVNwUuQB26PGfnJLPXnb7/fCECPTm35an7JmyZQUNas+h9GhgasW78MMzNTrl1zok/PESRnNfgAKlWqiJVVdvDVcmXt2bx1LZaW5oSHR/L3VSc+bN2HiPDs4fc//rAFpb6S5d/Mw8LCnAf3XenRbSje3q8Ww7CweB+5jr6VKY1m9MHAxowIF19ODvmWpKwAqsblrDOf1mUJvfWY85N/pNHMfjSe1Z8Y72DOjF7N00dP1Gkc2jXk/dXj1J/b/PQJALdX/c6dHNOUqg/4gPigSAL+KrlvYnxW6OFr6FmZUnlmf5S25sQ+9MF50HJ1QH79claocjRQlPaWNDv3rfqzw6TuOEzqztMrD7nde2mRl/9VOf/0JzqGSj74eiR6poYE33Tn6JBvSc9xzjVzsMXA0kT92fPIdfQtTWkyvQ+GNmaEu/hydMi3GkF6ry7ZiSpDRfuNn6Gtp4P/X/e5NG/rfyqbtq4OzRcMxsjegrTEZCJc/flz0HICr5XMxnTg4b/RszKlxsy+KG3MiXnoy/VBX5MSnnksGZSz1hgxqG9vwQdns6fcVp3YjaoTuxF+1YVrvb8o8vK/qpJ8LJU0UUcuo2NpSplpH6FjY0GiizeeQ5aQlnWs6Ja11jjvWH/cES2lLpX/p3mTEbR6N8GrMztazdo1xWHVZ+rvKv3w+XNpSpPdP+5F31CfGd9MxdjUmPs3HzDz49mk5DieyjmUxcwye/qYubU5c9fMwtLWkvjYeLxcvfl88GyNt1u+Do5tOITSQJ/hy8djaGrE45turBj2hUZb2dbBHuMcf2sfDsmMzTh3r+a55ecZ67m8/zwArQe3p9eU7DfVz/vty+fSlAY7ftyDgaEBs7+djrGpMfdu3mfqx7PyPXYsrC1YuHYOVraWxMXG4+nqxZSPZnLz0i2NvLsO7ExoUBjX/3Iqsvq8CK8cbT/DrLbf8RznUqNc2n7nJv9I45n9aDKrP9HewZx+pu1398fMc/p732Se00NuunPiY81z+vUvd6NKS6fV2gno6OsReseDYwOWkRKd+VAi2jOIUyNW0XBqL7ofXoRKpSLigS8nhnyrMUVTlBy5vXwvt1lkACkpKdy6dYs5c+ao12lpadG2bVuuXbuWa/4tWrRgx44d3Lhxg6ZNm+Ll5cWxY8cYMmRIwVYkB4VKpSq++RqixIqLi6NcuXJs2bKF3r17/+ftBzn0LPhCvUa23Sr9gUwLi5VD2+IuQom22qJFcRehxHJMfT0CTBeWx7lM9RCZyqX+txAKb5qAYgyaX9I1U5T86eDFaWpG/L8nekM56OT+JlaRyT219Mc2K0wjFeWKuwgl1pgnO4q7CEUi8ezGYvvtby4FsmTJEo11ixYt0hhw9I/AwEDKlSvH1atXNV4OOHPmTP766688R4GtW7eOGTNmoFKpSEtLY/z48fz0008FWo+cSvRbKUXRy8jIIDQ0lC+++AJzc3O6d+9e3EUSQgghhBBCCCHEP1QZxbbMmTOH6OhojSXniLBXdeHCBZYtW8aPP/7I7du3+f333zl69ChffFF4I+VL/FRKUbT8/PyoVKkS5cuXZ+vWra/0OmMhhBBCCCGEEEK8PvKaNpkba2trtLW1CQnRjIsaEhKCvb19rtssWLCAIUOGqF9CWLduXeLj4xk7dizz5s3LM1zVq5BeD6HB0dERmV0rhBBCCCGEEEKUUKUk+L6enh6NGjXi7Nmz9OzZE8icpXb27FkmT56c6zYJCQnPdX7980LDwuqrkI4xIYQQQgghhBBCCFHgpk2bxrBhw2jcuDFNmzZlzZo1xMfHq99SOXToUMqVK8fy5csB6NatG6tWraJBgwY0a9YMDw8PFixYQLdu3dQdZAVNOsaEEEIIIYQQQgghRIEbMGAAYWFhLFy4kODgYOrXr8+JEyews7MDMsM55RwhNn/+fBQKBfPnzycgIAAbGxu6devGV199VWhllI4xIYQQQgghhBBCiNJCVTqmUv5j8uTJeU6dvHDhgsZnHR0dFi1axKJFi4qgZJnkrZRCCCGEEEIIIYQQ4o0kI8aEEEIIIYQQQgghSotSEny/tJARY0IIIYQQQgghhBDijSQdY0IIIYQQQgghhBDijSRTKYUQQgghhBBCCCFKC5lKWaBkxJgQQgghhBBCCCGEeCPJiDEhhBBCCCGEEEKI0kIlI8YKkowYE0IIIYQQQgghhBBvJBkxJoQQQgghhBBCCFFaSIyxAiUjxoQQQgghhBBCCCHEG0k6xoQQQgghhBBCCCHEG0mmUgohhBBCCCGEEEKUFhJ8v0BJx5goFJsmWxZ3EUo0K4e2xV2EEivC90xxF6FEu99ganEXocR6in5xF6FEi5Ix4nlK09Uu7iKUaH0cAoq7CCVW3Xuyb/LjVNmhuItQYj0Oktuw/NgbGhd3EUq0X1XpxV0EIV4rckYWQgghhBBCCCGEKC0k+H6BkufHQgghhBBCCCGEEOKNJB1jQgghhBBCCCGEEOKNJFMphRBCCCGEEEIIIUoLCb5foGTEmBBCCCGEEEIIIYR4I8mIMSGEEEIIIYQQQojSQoLvFygZMSaEEEIIIYQQQggh3kgyYkwIIYQQQgghhBCitJARYwVKRowJIYQQQgghhBBCiDeSdIwJIYQQQgghhBBCiDeSTKUUQgghhBBCCCGEKC1UquIuwWtFRowJIYQQQgghhBBCiDeSjBgTQgghhBBCCCGEKC0k+H6BkhFjQgghhBBCCCGEEOKNJB1jQgghhBBCCCGEEOKNJFMphRBCCCGEEEIIIUoLmUpZoGTEWAHw8fFBoVDg7Oz8UtsrFAoOHTpUoGX6LxwdHVmzZk2+aYq7jEIIIYQQQgghhBAFTUaMFYAKFSoQFBSEtbU1ABcuXKB169Y8ffoUc3Pzf90+KCgICwuLQi5l3m7evImRkVGx/f5/pdOgDTpNOqIwMiMj1J/UszvJCPbOewOlAbrv9UGnWkPQN0IVE0HKud1keN8HQKt8dXSbdERh74iWsTnJB78n3eNOEdWmcMybP4VhIwZiZmbK33/fYtpnC/D09Mkz/Zy5nzFn3mca69wfedK4YTuNdU2bNmDB4uk0blyf9PR07t9zpVePYSQlJRdGNYqNk/N9tuzaj4ubB2ERkaxdvoAP329R3MUqctbDOmM3rie6NhYkuvrgv3AjCc6Pc01rNagdVn1bo1/dAYCE+54Efrs9z/SlUfkR7ak4sRt6tubEufjiPncLMXc8c01rVKM8lWf2x6ReJQwq2uK+4Ff8Nx7TSFNuWDvKDW+HQQUbAOIfPcF75QEizjkXdlUKxHvT+vD2oNYoTQ0JcHLn5LwtPPUJyXebhkPb0mxsF4xszAh19eP0om0E3fVSf//RnnlUbF5TY5s7O85yct4W9Wf7epVpNXsA9nUcAQh09uTC8j2EuvoVXOVeUdPpfag9qDVKM0OCbrpzYe4Wov9l39Qd1pYG47pgaGNGuKsfFxduI9Q5e9/U/qg11Xu2wKaOI3omBmysPZaUmITn8nFoU58mU3piXbMiaUmpBF535djoNQVdxQJj2LsnRoMGoG1pSaqnJzGr15Hq6pZrWv3338No6GB0ypUDHW3SnwQQv2cfiSdP55redMZUjHp2J3rtehJ+O1CY1ShUs+d9ypBh/TE1M+XG37f5fNoivDx980w/c84nzJzzica6x+5eNG/cUf155ZqlvN+6Bfb2tsTHJ3Dz+m2WLFyBx2OvZ7MrVUwGdMdsWD+0rSxJcfck4psfSHnwKNe0hm3exWzUIHQrlgUdbdL8Aonetp/4o2eKuNSFo/yI9jjmuGa5/cs1q8rM/phmXbMeLfgVv2euWeWHtaN8jmtW3KMneJWia1ZOlkM6YzO2Nzo2FiS5ehO4+H8k3s29vWIxsD0Wvduo2zeJ9z0IXrFNI73tZ4Mw6/Y+emWsUaWmZaZZuZ1EZ/ciqU9h6DC1L80GtcHA1Ahvp0f8Pn8z4T7Beaav3PQtWo3tSrm6lTGzs2DL2JU8POWUZ/o+X42i+eC2HF66jUubjxdGFV5/KhkxVpBkxFgB0NbWxt7eHh2d/9bPmJKSAoC9vT1KpbIwivZCbGxsMDQ0LLbf/y+0azRBt9UAUq/+QdK2JajC/FH2mwaGJrlvoKWNst8MtEytSP7jR5I2zSXl5FZUcVHZaXSVZIT5k3pmR5HUobBNmTaOcROGM+XT+bRp1ZuE+AR+P7wVpVIv3+1cXB5RtXJT9dK+XX+N75s2bcCBQ1s5d/YyrT/oRav3e7Lxf9vIyFAVZnWKRWJiEjWqVmbe9InFXZRiY9HtXcovGEnQmr24dZ5Goos3VbcvRsfKLNf0Js3rEnn4Eo8HzOdRz5mkBoVTdcdidO0ti7jkhcO2R3OqLRmK98oD3Gw3m7iHvtTfMxdda9Nc02sZKEn0DcHzq90khzzNNU1yUASeX+7iRrs53Gg/l8jLD6j36+cY1ShfmFUpEM3Gd6XR8PacnLuZbT0WkZqQzIDts9BW6ua5zVtdm9Fm/mAurz3Ilq7zCXX1Y8D2WRhaae5D513n+L7xJPVyfvke9Xe6hkoGbPucmIAItvVczI4+S0mJT6L/tplo6WgXWn3/i4YTuvL2iPZcmLuZ37otIjUxme478t83Vbs1490Fg7m55iB7O88nwsWP7ttnYZBj3+gY6OF74R5O6//IM58qnZrQbu14XPddZE/7uRzovQT3Q9cKtH4FSb9Na0wnTyBuy6+EjxpLmocnlqu+RSuPh4oZsTHEbdtBxPhJhA8bTcKxE5jNmYVe0ybPpVW+/y56tWuRHhZWyLUoXJ9MGcOYcUOZMWURHdr0IyEhgX2/b/7Xa7qrizu1qrZQL13aD9L4/q7zQz6dMJsWTTrRv9dIFAoF+w9tRkur9N4aGLb/AMvp44j63w4CB00gxd0Lux+Xo2Vhnmv6jJgYon/ZRdDQzwjsN47YwyexXjID/eaNi7bghcCuR3NqLBmK18oDXG83m9iHvjTM55qlnXXNepzPNSspKAKPL3dxvd0crmdds+qXkmtWTmZd3qXMvNGErt2NR9cpJLl6U+nXpWjn0b4xblaXqD8u4jVoLp69PyclKJxK25aiY5fdvkn2DiRw0QbcO07Gs98sUgJCM/O0zH1/l3Stx3fj3REdOTBvE+t6LiAlMZkx22ajk891TM9QSaCrHwcXbv7X/Ot0aEzFBlWJDo4syGIL8UpK79WviGVkZPDtt99StWpVlEolFStW5KuvvgI0p1L6+PjQunVrACwsLFAoFAwfPhyAVq1aMXnyZKZMmYK1tTUdOnQAnp+m+OTJEwYNGoSlpSVGRkY0btyY69ev51m2WbNmUb16dQwNDalcuTILFiwgNTVVI82RI0do0qQJ+vr6WFtb06tXL/V3z06lfPz4Me+//z76+vrUqlWL06dzfxJbHHQadyDt3kXSH1xGFRFIyqltqFJT0KnzXu7p676HwsCI5EPryQjwQBUTQcYTd1Rh/uo0Gd73Sb18kPTHt4uqGoVq4qQRfPfteo4dPcPDB26MGzODMmXs6Nqtfb7bpaWlExoSrl4iIzQbRsu/mc//ftrK6pUbcHN9jMdjbw7+fkzdwfs6ea95Ez4dO4y2H7Qs7qIUG9sxPQjffYrIfWdJeuyP35yfyEhKxmpA21zT+3y6ivBtx0l08SbZMwDfz9ej0NLCpOXbRVzywlFxfBcCdpwlaM8F4t0DcPv8F9ITUyg7qHWu6WOdPfFYupOQQ1fJSE7NNU34qdtEnHUm0TuYRK8gvJbvJT0+CdNG1QqzKgWiyaiOXF1/mMenbxPm5s+f0zZgbGtO9faN8tym6ehO3N1znvu/XSTicSAn5m4hNTGZev0/0EiXmphCfFi0ekmJS1R/Z1WlLAYWJlxatZ9IryDCHwdwZc3vGNuaY1rOutDq+1+8PaojTt8fxvvUbSLc/DkzZQNGduZU7pD3vqk/phMPd5/Hdd9Fnj4O5PycLaQlJVNzQPa+ubvpJLd/PELIbY9c81Boa/HekiFc+XI3D3ecI8o7mKePA/H4M+/2Q3EzGtiPhCNHSTx2gjQfX6K/W4UqKQmDrp1yTZ9y5y7JFy+T5utHemAgCb8dIM3TE716dTTSaVlbYzblU6KWfoUqLb0oqlJoxk8cxqrvfuT4sbO4PHzExHEzsS9jS+eu7fLdLi0tndDQcPUSGal5Td+2dS/Xrjrh7xfAvbsuLPtiDeUrlKWiQ7nCrE6hMhvSh9jfjxN3+CSpXn5EfLkWVVIyJj075Jo+yekeCeevkOrtR9qTIGJ3HSTlsRf6DWoXcckLnsP4LjzZcZbArGuWa9Y1q1we16wYZ08ev8A1K/ysMwnewSR4BeGZdc0yKwXXrJysR/fk6d6TPN1/lmQPfwLm/UhGYjKW/XL/m/KfupLIHcdIcvUm2esJAbO/B4UWxjnaN9F//EX8lbuk+oeQ/NiPoC9/QdvUCP23HIuoVgXrvZGdOPP9QR6evkWQmx97pv2IqZ0Fddrn3WnsduEuJ1bu48HJvEeJAZjaWdBz8XB2ffYD6aX8/FzsMjKKb3kNScfYC5ozZw5ff/01CxYswMXFhV27dmFnZ/dcugoVKnDgQOZw/UePHhEUFMTatWvV3//666/o6elx5coVNmzY8Nz2cXFxfPDBBwQEBPDHH39w9+5dZs6cSUY+B6CJiQlbt27FxcWFtWvX8vPPP7N69Wr190ePHqVXr1507tyZO3fucPbsWZo2bZprXhkZGfTu3Rs9PT2uX7/Ohg0bmDVr1gvvp0KlpY2WvQMZvi45VqrI8HVBq2yVXDfRrlqfjEBP9Np+jMHE1egPX4pOsy6gUBRNmYuYo2MF7O1tuXD+inpdTEwsTjedadqsQb7bVqniyCOPa9x9cIFfNq+mfPmy6u+sbaxo0rQBYWERnD77Gx7eNzh2YjfvvAZPVcXzFLo6GNatQuzlu9krVSpiL93FqFGNF8pDy0CJQlebtKjYQipl0VHoamNSrzKRl+5nr1SpeHrxPmaNC+iGQEuBXc8WaBsqiXEq2VMvzCrYYGxrjs/lB+p1ybGJBDp7Uq5h7vtDS1cb+7qV8Ln8MHulSoXP5YeUa1hVI23tni349M5PjDq1nA9m9kdHP3tkTKRXEAmRsbw9oBVautroKHWpN6AV4Y8DiH5S/CODTCvaYGRnjv+l7H2TEptIiLMn9vnsG9u6lfB/Zt88ufQQ+0ZVc90mNzZ1HTEuYwkqFQOOf8kIp/V02/Y5liV1NIeODrrVq5PsdCt7nUpFstNt9Gq/WMeEXqOGaFesQIrzveyVCgXmC+YQt3svad4+BVvmIubgWAE7e1v+upA96i82Jo7bTndp3LR+vttWruLAg0eXcLp7lg2/rKBc+TJ5pjU0NOCjj3vj4+1PwJO8p0qVaDo66NWsTtL1HA85VSqSrt9GWa/WC2Wh37QBuo7lSbp9/98Tl2B5XbMiC+maFV3Cr1k5KXR1MKhTlbhn2jdxV5wxbPjf2jfpUXF5/obloI6kx8SR5OpTAKUuWpYVbDG1teDxlezrWFJsIn7OnjjkcR17UQqFgo9WT+LCxj8JefzkVYsqRIGSGGMvIDY2lrVr17J+/XqGDRsGQJUqVXj33XefS6utrY2lZebQWltb2+dijFWrVo1vv/02z9/atWsXYWFh3Lx5U51P1ar5N4znz5+v/n9HR0dmzJjBnj17mDlzJgBfffUVAwcOZMmSJep0b7+d+yiOM2fO4ObmxsmTJylbNrNjZNmyZXTqlPvTW4Dk5GSSkzVjTKWnpaMs4GktCgMTFFraqBJiNNarEmLQssy9wacws0GrYk3SXf4m6cAatMxt0Ws3BLS1Sbua93SU0srWLjPuQ2houMb60NBwbG1t8tzOycmZCeM+5/Fjb+ztbZg951NOnN7LO006EhcXTyXHCkBmLLJ585Zz/54Lgz7qzZGj23mnSad845eJ0kfH0hSFjjZpYVEa69PCo9Cv+mI32eXmDiU1JFKzc62U0rU0RUtHm5SwaI31KWHRGFYrm8dWL8aoZgUaH/0SLaUu6fFJ3Buxgnj3gFfKs7AZ25oDEB+ueS6OD4/ByCb3qSiGFiZo6WgTHx79zDbRWFXJPn8/PHyVmIBw4kKeYlOzIq1mD8SyShkOjst8wJQSn8SuAV/R5+eptPi0JwBPvYPZO/QbVOnF/wTT0MYcgIRn9k1CWAyGtrnvGwPLzH2T+MzxlRAejXnVvDsznmVW0RaAJlN7c2XpTmKehNFgbGd67ZvHjg9mkBwV/x9qUvi0zMxQ6GiT8cxIpozIp+g4VMxzO4WREbYHf0OhpwvpGUSvWkNKjs41o8GDID29VMcU+4etbeYoyLBcrul2+VzTbznd5ZMJs/F47I2dvQ2fz57Mnyd28d47XYmLyz4ORoz+iEVLP8fY2IjH7l707Tn8uRkHpYW2RebxlP7MaPf0iKfoZrVhcqMwNqTCqT0odHUhI4OIZetI+rt0zyDQy+eaZfSK1yzjmhVokuOadbcUXLNy0rbIat+Eax4naeFRKKu8WPvGftZwUkMiibvsrLHepE0TKqz7HC0DJWmhT/EespD0pzG5Z1KCmWRdx2OfOX7iwqIxybrGvazWE7qTnpbO5S0nXikfIQqDdIy9AFdXV5KTk/nwww9fOa9GjfKeSgHg7OxMgwYN1J1iL2Lv3r2sW7cOT09P4uLiSEtLw9Q0e067s7MzY8aMeaG8XF1dqVChgrpTDKB58+b5brN8+XKNTjeAuW3rM699/iOUioRCgSohhpRTW0GlIj3El1QTC3SbdHwtOsb6D+jBmnVfqj/36zPqpfI5feov9f8/fOCG001nHrheplfvLmzftg9FVsyRzZt3s3P7fgDu3XXhg1Yt+HhoP5Ys+u4VaiFeN3YT+2DR/T0e95uHKo8pGSJTgkcgN9rMRMfUENtu71Br3SRu91pcom40avVsQcdlI9WffxuxotB+6+7u8+r/D3v0hPjQKAbtnot5RVui/ELRUerS+dvRPHFy5/AnP6ClraDp2C702zKDX7stJK2Ij7fqPVvQ6uvsffPn8MLbN/9GoZU5EvrW94fxPH4TgDPTNzLixjqqdmnGw53niq1sBUmVkED4iNEoDAxQNm6I6eSJpAcGknLnLjo1qmPUrw/hI8cWdzFfSt/+3VixZqn680f9Xq4eZ09fVP+/y8NH3HK6i/ODC/To1Ul9DQfYv+8P/jp/BTs7GyZ9OopNW9fSuf1AkpNfvxAJeVHFJxI4YDxahgboN22A5YzxpAUEkeR07983fgPFewTyd9Y1y67bO9ReNwmnEnbNKkw24/ti1u09vAfNRZWieb2Ju3YPjy6foW1hiuXA9lRcPwuPXtNJj4jOI7eSoUGPlvRdNlr9edPIvAdwvIpydSrx7oiOrOkyt1DyfyOpXr84z8VJOsZegIGBQYHl9W9vf/yvv3Xt2jUGDx7MkiVL6NChA2ZmZuzZs4eVK1e+dJ7/1Zw5c5g2bZrGuvQfPskj9ctTJcaiykhHYagZyFJhaIoqPveLjio+GjLSNU4cGRFBKIzNQUs787tS7NjRMzjddFZ/1ssKxmtra01IcPa0Iltba+7fd3l28zxFR8fi6eFN5SqZb+AJCQ4F4JGb5ht7Hrl5UKHCqz19FCVPWmQMqrR0dJ55MqhjbU5qWO5Bef9hO64ndhN74/HRIhLd8n5rWmmSGhlDRlo6es+MhtKzMSMlNOqV8lalppOY9bbC2HvemNavQoUxnXH7/OdXyrcgeZy+zeYcbzLT0ctsOhhZmxKfo/5G1qaEuuT+ZsiEp7FkpKVjZK25D42szYgPy/umITDrdy0c7YjyC6VWzxaYlbdhW68l6vP6H5/+wJR7/6Na+0a4Hvn7per4srxP3ybEOXvfaGftG0NrUxJy7BtDG1PCH+a+bxIjM/eNwTPHl6G1GQn57JtnxYdk/l7k4+wb1IyUNKL9QjEpZ/XC+RSVjOhoVGnpaFlqvpVby9KCjIh8AjKrVKQHBAKQ5uGJjoMDxh8PJvLOXfTq1UXLwhzbA3vVyRU62phOnoBR/76E9RuUV64lwolj57jllD3KVk8v85puY2tNSMiz13TXF843JjoWT08fKlV20FgfGxNHbEwcXp6+ON28i4ffTbp0a8fv+4++Yk2KXvrTzONJ20rzeNK2siA9PJ/rlkpFmn/m8ZTyyBPdShUxGzmoVHeMpeRzzUouhGtWxTGdcS1B16z8pD/Nat9Yax4nOtbmpP1L+8Z6TC9sJvTB++MFJLn5PPe9KjGZFN8g8A0iwPkR1c/9D8v+7Qj7af/zmZUgLmdusco5O3aljl5mgH0TGzNic8wcMLYxI9DF56V/p3LTtzC2MmXe1e/V67R1tOk272PeG9mJZe9++tJ5C1EQJMbYC6hWrRoGBgacPXv2hdL/05BJT//vnS716tXD2dmZyMgXe0vH1atXcXBwYN68eTRu3Jhq1arh66t5M1qvXr0XLnvNmjXx9/cnKChIve7vv/O/0VAqlZiammosBT2NEoCMdDKCfdFyqJljpQIth5pkBOb++umMgMcozG2B7JhiWhZ2ZMRFlfpOMYC4uHi8vHzVi5vrY4KDQ/mgVQt1GhMTYxo3qc+N63deOF8jI0MqVaqo7hDz9X1CYGAw1apV1khXtVol/PzejKeEbxJVahoJ9z0xaVkve6VCgcm79Yi/lftr7wHsxveizKf98RiyhIR7uQcIL41UqenE3vPC8r262SsVCizeq0O0U+6vd39pWgoUeiXrmVVKfBJRviHqJfxxAHGhUTi2zI4DpWdsQNn6VQi4nfv+yEhNJ/i+t8Y2KBQ4tKxNQB7B5AFsa2dOqYvLupnTNdBDpVJpPOxQZahAlT1iqiilxicR7ROiXiLdA4gPiaL8u9n11DU2wK5+FYLz2Teh972p8My+Kf9ubYJvvfjfUeh9H9KSUjCvnD39UktHG9PyNsQ+Cc9ny2KSlkaquzvKRg2z1ykUKBs1JOXhw7y3e5aWFmTdyCWePE34sFGEjxitXtLDwojfvZfIaTMLuAIFLy4uHm8vP/XyyM2DkOBQ3v8ge+S+sYkRDRu/jdMN5xfO18jIEMdKFQgJCc0zjUKRGf/nnzZsqZOWRoqrO/pNc8xWUCjQb9qA5Hsv/mAw8xyc95v3SoO8rlmWhXDNUmgp0Cph16z8qFLTSHzggdEz7RvjFm+TcDvv9o31uN7YTh6A97DFJN5/wfNyKTmWkuOTiPANUS8hj58QE/qUai2yX2qiNDagYv0q+OZxHXsRt36/xKqOs1jdebZ6iQ6O5MLGI/w8dHlBVOXNI8H3C1TpOZMVI319fWbNmsXMmTPR09OjZcuWhIWF8fDhQ0aNen7qmoODAwqFgj///JPOnTtjYGCAsbHxC/3WoEGDWLZsGT179mT58uWUKVOGO3fuULZs2VynNFarVg0/Pz/27NlDkyZNOHr0KAcPHtRIs2jRIj788EOqVKnCwIEDSUtL49ixY7kG1W/bti3Vq1dn2LBhfPfdd8TExDBv3rwX3FOFL83pJHqdR5MR7ENGkDc6jduh0FWS9uAyAHqdR6OKfUrqpczYImnO59Fp8CG6Hw4i7fZZFBZ26L7ThdTbOToKdZUoLGzVHxVm1ihsK0BiPKrY0vca4R9/2MLnMyfj6eGDr+8T5i+YSlBQCH8eOaVO88fRHfz5x0k2/m87AF8um8PxY2fx9wvAvowdc+dPIT09nd9+O6LeZt2an5kzbwr377tx/54LHw3uTfXqVRg6eFKR17GwJSQk4vckUP05IDAEN3dPzExNKGNvm8+Wr4/Qnw/jsOozEu55kOD8GJtR3dAy0Cdi3xkAHFZPITU4gsBvMo8huwm9KTP9I3w+WUnKk1D1aLOM+CQyEpKKqxoFxm/DUWqtm0iMsycxdzypOLYz2oZKgvZcAKDW95NIDo7E86vdQGbwY6PqmfFKtPR0UNpbYFzbgfT4JPXT9irzBhFx1pmkgHC0jfWx7/0uFi1q4TxgWbHU8b+4uekELT7pSaR3CNH+obw3vS9xoVG4n8qO9TRw1xzcTzpx+9fMNxvf+OU4XVeOI+ieN0F3PWk8siN6hkru/ZY5ldu8oi21erbA85wzSVFx2LxVkQ8XDsbvb1fC3DLfJOx96QGt5wyi/ZfDubX1FAqFgncmdiMjLR3fay8+gqYw3d10gsaf9CTKO4RY/1CazehLfEgUXiez902P3XPwOuHE/ax94/zzcdquGkfoPW9CnD15e1RHdAyUuO7LnuZuaGOGoY0ZZo6ZL/6xeqsCqXGJxAZGkBwVT2pcIg92nKPZ9D7EBUUQ+ySCBuO7AOBxtGS+mTJ+z2+Yz5tNqps7qa6uGPbvi8JAn8SjmfFnzObPISMsjNj//QKA0ccfker2iPTAQBS6uiibN8OgQzuiV2S+cEgVE0NazDNxSNPSSY+IJN3fn9Jow4+/Mu3zCXh5Zl7T58yfQnBQKMf+zH5j+O9//MrRP0+zaeMOAJZ8OYuTx8/h7x+Ivb0ts+Z+Snp6Br//9ieQGdS/Z+/OXDh3mfDwSMqWteezaWNJSkriTI7QCqVN9PYD2Hwxk2QXd1IePMJ0cC8UBvrEHj4JgPUXM0kLDSfq+80AmI0cSLKLO2n+gSj09DB4tynGXdoSsWxdcVajQPhuOErtXK5ZgVnXrNpZ1yyP/3DNqjpvEOFZ1yydHNes26XgmpVT+C+HKL9yKon3PEi8647VyB5oGerzdH9m+6b8yqmkBkcQ8t02AKzH9cFu6mD8p6wg9UkIOtbmAGQkZLZvFAZKbCf1J+bMDdLCItG2MMVqSBd07a2IPnYlr2KUaJc2H+fDT3oS5hNMpH8oHaf3IybkKQ9OZb9xctzOeTw4eZMr2zLvL/QMlVg72qu/t6xgQ9laDiRExREVGEFCVBwJz7ywID0tndiwaMK8ghCiuEnH2AtasGABOjo6LFy4kMDAQMqUKcP48eNzTVuuXDmWLFnC7NmzGTFiBEOHDmXr1q0v9Dt6enqcOnWK6dOn07lzZ9LS0qhVqxY//PBDrum7d+/O1KlTmTx5MsnJyXTp0oUFCxawePFidZpWrVrx22+/8cUXX/D1119jamrK+++/n2t+WlpaHDx4kFGjRtG0aVMcHR1Zt24dHTt2fKHyF7b0RzdJNTRBt2VPFEZmZIT6k7x/NWQF5FeYWIIquxdbFfuU5P2r0G09EP3hS1HFPSX11hnSbhxTp9Gyd0R/YHYnoV6bzKkWaQ8uk3J8cxHVrOCsWfU/jAwNWLd+GWZmply75kSfniM0YoZUqlQRK6vsOHblytqzeetaLC3NCQ+P5O+rTnzYug8R4dkdgz/+sAWlvpLl38zDwsKcB/dd6dFtKN7euU8PKs0euD1m5CfZx8S3328EoEentnw1f3pxFatIPT1yGR1LU8pM/whdGwsSXbzxGLKEtKzg6XrlrDX+1qyHdERLqUvljbM18glatZug1XuKtOyFIfTwNfSsTKk8sz9KW3NiH/rgPGi5OrixfjkrVDmeoCntLWl2LjtOh8Ok7jhM6s7TKw+53TszhpCetSm1vp+I0s6CtNgE4lz8cB6wjMiLJf+NaNc3/ImeoZKOy0eib2rIEyd39g79lvQcMb4sKtpiaGGi/uz253UMrUx5b1ofjGzMCHXxZe/Qb9WB6tNT03BsWZsmIzuga6AkJiiSR8dvcvX7w+o8Ij2D2D9qFS2n9GLI74tQqVSEPPRl37BvNaZ1FqfbP/2JjqGS1l+PRGlqSNBNd44M0dw3Zg62GFhm7xuPI9cxsDSl6fTMfRPm4suRId+SmCOIf52PP6TptN7qz30OLADgzLT/4fbbJQCufrUbVXo67dZMQEdfj+A7HhwauIzk6ITCrvZLSTp3nhhzM4xHD0fb0pJUD08ip88i42nmlCZtO1uNJ9MKA33Mpk9B29YGVXIyab5+RC1dRtK583n9RKn3/ZqfMTIyYOW6LzAzM+X6tVsM6DNK45ruWKkCVjmmEJYtZ8/GzauwsLQgIjyS63/fouOH/YjICkyfnJTMOy0aM27iMMzNTQkLjeDa1Zt0bjuQ8PDS90DwHwmn/iLSwhyLCcPQtrYg5ZEnIRPnkhEZBYBOGVuN0aYKA32s5n6Ktq01quRkUn38CZv3NQmluHPwHyFZ16wqOa5Zt5+5ZvHMNat5jmuW46TuOE7qTuSVh9zKcc2qk+OaFevix+1Scs3KKfroZXSszLCbNhgdawuSXL3wHr6ItPAoAHTL2kBG9nFi9XEntJS6OPw0RyOfkDW7CF27G9IzUFYpj0OfD9G2MCU9KobEe4/x6j+b5Mels418fsMR9AyU9F0+GgNTQ7xvPuLnYV9rxPG0crDDKMd1rEK9ykzYs1D9uceCoQDc3P8Xe2dsKLrCC/GSFCqVRG0TBS/hu5H/nugNZr/4QnEXocSK8D1T3EUo0e43mFrcRSixnqboF3cRSrSb+iV/SkdxMX49ZwUUmD4OMmU+L3Xvyb7Jj9Mzcc1EtsdBL/6irTeRvWHJ7NAvKX5VvdhspDfRCp/dxV2EIpG4aUax/bbBqOJ72VBhkRhjQgghhBBCCCGEEOKNJFMphRBCCCGEEEIIIUoLlQx3L0gyYkwIIYQQQgghhBBCvJFkxJgQQgghhBBCCCFEKaHKkFDxBUlGjAkhhBBCCCGEEEKIN5J0jAkhhBBCCCGEEEKIN5JMpRRCCCGEEEIIIYQoLTIk+H5BkhFjQgghhBBCCCGEEOKNJCPGhBBCCCGEEEIIIUoLlYwYK0gyYkwIIYQQQgghhBBCvJGkY0wIIYQQQgghhBBCvJFkKqUQQgghhBBCCCFEaZGhKu4SvFZkxJgQQgghhBBCCCGEeCPJiDEhhBBCCCGEEEKI0iJDgu8XJBkxJoQQQgghhBBCCCHeSDJiTAghhBBCCCGEEKK0kBFjBUpGjAkhhBBCCCGEEEKIN5J0jAkhhBBCCCGEEEKIN5JMpRSFwmdDSHEXoURbbdGiuItQYt1vMLW4i1Ci1b2zuriLUGI9bDSluItQotml6RZ3EUqsMmlpxV2EEk3PTKZr5OVTiybFXYQS7bo0B/NkqJC/q/w4pZgVdxFKtHRduW698VSq4i7Ba0VGjAkhhBBCCCGEEEKIN5KMGBNCCCGEEEIIIYQoLST4foGSEWNCCCGEEEIIIYQQ4o0kHWNCCCGEEEIIIYQQ4o0kUymFEEIIIYQQQgghSosMCb5fkGTEmBBCCCGEEEIIIYR4I0nHmBBCCCGEEEIIIURpocoovuUl/PDDDzg6OqKvr0+zZs24ceNGvumjoqKYNGkSZcqUQalUUr16dY4dO/ZSv/0iZCqlEEIIIYQQQgghhChwe/fuZdq0aWzYsIFmzZqxZs0aOnTowKNHj7C1tX0ufUpKCu3atcPW1pb9+/dTrlw5fH19MTc3L7QySseYEEIIIYQQQgghRGlRimKMrVq1ijFjxjBixAgANmzYwNGjR9m8eTOzZ89+Lv3mzZuJjIzk6tWr6OrqAuDo6FioZZSplEIIIYQQQgghhBDiXyUnJxMTE6OxJCcn55o2JSWFW7du0bZtW/U6LS0t2rZty7Vr13Ld5o8//qB58+ZMmjQJOzs76tSpw7Jly0hPTy+U+oB0jAkhhBBCCCGEEEKIF7B8+XLMzMw0luXLl+eaNjw8nPT0dOzs7DTW29nZERwcnOs2Xl5e7N+/n/T0dI4dO8aCBQtYuXIlX375ZYHX5R8ylVIIIYQQQgghhBCilFBlvFwQ/IIwZ84cpk2bprFOqVQWWP4ZGRnY2tqyceNGtLW1adSoEQEBAXz33XcsWrSowH4nJ+kYE0IIIYQQQgghhBD/SqlUvnBHmLW1Ndra2oSEhGisDwkJwd7ePtdtypQpg66uLtra2up1NWvWJDg4mJSUFPT09F6+8HmQqZRCCCGEEEIIIYQQpUWGqviW/0BPT49GjRpx9uzZ7KJnZHD27FmaN2+e6zYtW7bEw8ODjByj4tzd3SlTpkyhdIqBdIwJIYQQQgghhBBCiEIwbdo0fv75Z3799VdcXV2ZMGEC8fHx6rdUDh06lDlz5qjTT5gwgcjISD777DPc3d05evQoy5YtY9KkSYVWRplKKYQQQgghhBBCCCEK3IABAwgLC2PhwoUEBwdTv359Tpw4oQ7I7+fnh5ZW9pitChUqcPLkSaZOnUq9evUoV64cn332GbNmzSq0Mr7xHWPDhw8nKiqKQ4cO5ZmmVatW1K9fnzVr1hTY7y5evJhDhw7h7OxcYHkKIYQQQgghhBDiNacqvuD7L2Py5MlMnjw51+8uXLjw3LrmzZvz999/F3Kpsr3xHWNr165Fpfpv82RF8bL4uAtWY/qgY2NBsqs3QUs2kHTPPde05gM6YN6rDcrqjgAkPvAgdMWveaa3/2ISlh91JviLjURuPVxYVShQNYe1pe74LhjYmBHp6se1BdsId/bKM71jl6Y0+rwvxuWtifEJ4eayPTw5d1f9vUOnxtT8+EOs6jmib2HCwfZziXTxU39vXN6aAX+vyTXvs+PW4XP0RoHVrShYD+uM3bie6NpYkOjqg//CjSQ4P841rdWgdlj1bY1+dQcAEu57Evjt9jzTv66cnO+zZdd+XNw8CIuIZO3yBXz4foviLlahsx7aGduxvdCxsSDR1ZuARRtJuJv7v73lwPZY9mmNfo3MYyXxvgdB327PTq+jTZkZH2PauhF6Fe3JiI0n9vJdAr/eRlpoZFFV6aW9NawtdSZknneeuvjx97+dd7o2pUHWeSfWOwSnHOcdhY42jWb2pXyb+hg72JAak0jg5Qc4LdtLYkiUOo96n3anwof1saztQHpKGrtqjSvsahaYiiPaU2liN/RszYh18cN17hai73jmmta4RnmqzuyHWb3KGFS0wXXBr/huPK6RpvKnPbDr3BSjamVJT0oh6qY77l/sIt4zqCiqU6CUXXpi0GcgWhaWpHl7krBhLWnubrmm1WvxHgb9P0arTDkUOjqkBz4h8fd9pJw/pU6jMLfAcMQ49Bo0QWFkTOrDu8RvWEtGYEBRVemVtZrWhwaDWqNvaoS/kzvH5m0m0ick320aD21Hi7FdMLYxI8TVj+OLfiXwbvbfpJGNGe3mfkTld+ugZ6xPhFcQl9Yfxu34TY18qrWpz/uf9sK2ZkXSklPx/duVfWNXF0o9/6uqw9vx1sQu6NuYEeXix+15vxKZz3mnfNem1J3VD6Os8869L3cTlKO903TNOCoNeF9jm6Dzd7n40bcA2DSvSZvf5+ea9+mOC4i8m/dvFzXHEe2oMrEbShszYlz8eDBvK1F5nGMAynRrxlsz+2FQwYZ472Bcv9xN6Fln9fd61mbUWjAImw/qoWtqSMTfbjyYt5V472B1GkMHW2ot+hjLZjXQ0tMh7Pw97s/dSkp4dGFW9aUU9TXLuLw1b0/pSZmWtTCwMSch5Cmev1/h3rrDZKSmF0WVX1nHqf1oPqgN+qZG+Dg94rf5mwj3Cc4zfeWmb9FmbDfK162EmZ0lm8au4MEpJ400g1ZMoGnfDzTWuf7lzMZhXxdKHYT4L0p1jLGUlJRXzsPMzAxzc/NXL8wboiD2+asw7fIednPHELZuF17dPyXJzRuHrV+gbWWWa3qjZnWJPnIRn8Fz8O47nbSgMBx+/QIdO6vn0pq0b45h/bdIDQ4v7GoUmErdmtFs4WDurD7I4U7ziXTxo+OOWehbmeaa3rZRNVr/MAn3PX9xqON8fE/cou0vU7GoUV6dRtdQSfDNR9xctjfXPOIDI9jVYJLGcmvFflLiEnly/m6u25RUFt3epfyCkQSt2Ytb52kkunhTdftidPI4nkya1yXy8CUeD5jPo54zSQ0Kp+qOxejaWxZxyYtXYmISNapWZt70icVdlCJj3vVdys4fRfDaPTzqOpVEVx8qb1+S57Fi3LwOT/+4iOfAeTzu9TkpgeFU2b4EXbvMY0XLQIlhnSqErNuLe5epeI/7GmXlclTeNK8oq/VSKnVvRtNFg3FedZA/Omaed9rvzOe807gaH/wwice7/+KPDvPxPXmLNpumYp513tEx0MOyriPOaw/xR8cFnBuzBrPKZWi7RfM14Fq6Onj/eQO3bWdz+5kSy75Hc95aMgSPlfu52m4OsQ99abxnDnrWue8vLQM9En1DefTVLpJCnuaaxqJ5Tfy2nOLvzgtw6vcVCh1tGu+di7Zhwb0qvSjovdcaozGTSNz1K9GfjiHd2xOTL1agMDPPNX1GbCyJe3cQM2MSUZNGknz6OMZTZ6HbsIk6jcn8r9C2L0vMF/OI+nQ0GaEhmH61CpT6RVSrV9NifFeaDu/A0blb2NRjIakJyQzePhttpW6e29Tq+g7t5w/mr7W/s7HrfIJd/Ri8fTaGOf4me66agFXlMuwZvZIN7WfjdsKJvj98in1tB3Watzo1oefqCTj/dpH/dZzDlj5LeHD4aqHW90VV6P4O9RcP5uHK3znVYT5RLn58sHs2yjzOO1aNq9H8p8l47brAyfbzCDjhRMst0zDL0d4BCDp3l8P1JqqXaxPWq7+LcHLX+O5wvYl47jxPnG9oieoUK9vjHWotHoL7ygNcbD+XmIe+NNs9O89zjEXjajT86RP8dl/gYrs5BB93osmW6Zi8lb1vmmydhmFFW24MX8Ff7eaQ+CSMd37LPsdoGyp5Z+9cUKm41udLrnRbjJauNk23zwCFokjq/aKK45plVrUsCi0trs7azME2s7ixeCdvDfmQRrP7F0mdX1Wb8d15f0RHfpv3C2t6zic5MZnx2+agk895SM9QnwBXXw4s3JJv3q4XnFnYZJx62f7J9wVd/DdHKQm+X1qUqo6xVq1aMXnyZKZMmYK1tTUdOnQA4MGDB3Tq1AljY2Ps7OwYMmQI4eHZnRv79++nbt26GBgYYGVlRdu2bYmPjwcyp1L27NlTnTY+Pp6hQ4dibGxMmTJlWLly5XPlUCgUz029NDc3Z+vWrerPs2bNonr16hgaGlK5cmUWLFhAamrqC9f16dOnDB48GBsbGwwMDKhWrRpbtmSeaC5cuIBCoSAqKkqd3tnZGYVCgY+Pj3rdzz//TIUKFTA0NKRXr16sWrVKoxPQ09OTHj16YGdnh7GxMU2aNOHMmTMa5XB0dOSLL75g6NChmJqaMnbs2BeuQ2GwGtmLqL0niD5whhQPf4LmrycjMQnzvu1zTR8wbQVPdx4l2dWLFK8nBM5ZBwotjFq8rZFOx84K+4XjCZj2Haq00vEkB6DO2E482n2ex/suEvU4kCuzt5CWlEz1gR/kmr72qA48uXCP+xuOEu0RyO0V+4l44EPN4e3UaTwOXMF5zSECLz3INQ9VhorEsGiNxbFjY7z/vE5aQnKh1LOw2I7pQfjuU0TuO0vSY3/85vxERlIyVgPa5pre59NVhG87TqKLN8meAfh+vh6FlhYmLd/ONf3r6r3mTfh07DDaftCyuItSZGxG9yBizykifztL8mN/nsz9kYzEZCz7536s+H22iojt2ceK/6z1oKWFcdaxkhGbgOfHC4k6eoVkrwAS7jziycL/YVivGrplrYuyav9Z7TGdcN91Ho99F4l+HMjV2VtIS0ymWh7nnVqjOhBw4R4Pss47d77LOu+MyDzvpMYmcmrQN/gcuU6MZxBhtz35e/42rN+ujFHZ7IcYzit/x+XnEzx18y+SehYUx/Fd8N9xjoA9fxHvHsDDz38hPTGFcoNa5Zo+xtmLR0t3EnzoGqrktFzT3Br0NQF7/yLu0RNiXfy4/9lPGFSwwbRepUKsScHT79Wf5BN/knzmOOn+vsSvXwlJSSjbd841fdp9Z1KuXSLd35eM4ECS/jhAurcXOrXqAqBVtjy6NWsT/8Mq0h+7kRHgT/wPq1DoKVF+8GFRVu2lNfs/e/cdHkXxBnD8e+m9N1oCofcO0kG6gKA0EZVeFQtVqiBVfogiKihSRJBmARXp0kUgQCCEhPTee+/3+yPhkgs5pKTK+3mee/Q2s3Mzw867e3Ozs5MGcPHLw3idukGUZzCHZ2/B1M6CRv3aatyn0+SB3Nx/ltuHLhDjHcrRRTvITs+k9ajCPlmrbX2u7TpJ2G0/EoKjubj5MBlJqVRrnn/MKLS1GPDRW5xa8yM39p4hzj+CGO9Q7h29WuZ1fhwNpw3Eb+9Z/A9cIMkrFJf5O8hJz6TOmJLjToPJA4g4e4f7W46S7B3G3fU/keAWQL2J6teLuVnZZEQnql7ZiWmqv+Vl56r9LTM+hRr92+B/4HyZ1vVJOU8bRNDevwjef54Ur1DuzN9ObnoWjq/1LDF9nSkDiT57G9+v/yDFO4z76w+R6OZP7Qn536uMnR2wateAOx/uINHVj1TfcO4s2IG2gR41huXPDrdq3wCjWra4vreVZM9gkj2DufXuFixaOmPTtWl5Vf2xVMQ5K/TcHS7N/pawC3dJCYom+NRN7m79E6eB7Uv8zMqmx8SBnNz8K3dP3SDcM4gfZ3+Fmb0lzfu107iP5zlXjn16ELcT1zWmAcjJyiY5OlH1Sk9KLe3iC/FUqtTAGMD333+Pnp4ely9fZuvWrSQkJPDiiy/SunVrXFxcOH78OJGRkYwalT8iHx4ezpgxY5g4cSIeHh6cO3eOV199VePtk/PmzeP8+fMcOXKEkydPcu7cOW7evPnE5TQ1NWXXrl3cu3ePTZs2sW3bNj777PGnoi9dupR79+5x7NgxPDw82LJlCzY2j/9l6fLly0yfPp333nsPV1dX+vbty+rVq9XSpKSk8NJLL3HmzBlu3brFgAEDGDJkCEFBQWrpNmzYQMuWLbl16xZLly597DKUOl0dDJrVI/Vv18JtSiWpf7ti1LrRY2WhZaiPQleb3ITkwo0KBTU+nUPsdz+T6R2keedKRktXG5vmdQi76F64Uakk7KI7dm3qlbiPXdt6Dw14hZy/g13bktM/DuvmtbFuVhuvfZXrQvHfKHR1MGpel+RLRWa5KZUkX7yNcduGj5XHg+Mpp+jxJP5z8o+VeqRcci3cqFSScuk2xm2eIfYUo21qjDIvj9xKfJGopauNdYuH4074JXeNccS2hLgTeu7RcUfXzBBlXh5ZSWka01QFCl1tzFrUIfaiW+FGpZLYC25YtGtQap+ja2oEQHZCSqnlWeZ0dNCp14As1xuF25RKslxvoNvo8b5Y67Rsg3bNWuTcvQOAQjf/Ee7KorPblUqU2dnoNG1eakUvKxa1bDG1s8TvUmH/ykxOJ9TVl5pt6pe4j5auNtWa18H/UpE+plTif+mu2j7BN7xpOuQFDMyNQaGg6ZAX0NHXJeCKBwDVmtXGrJoVyjwlU/5czQfXv+T17+dj26Bm8Y8sd1q62li2qEPkRfU6Rl68i03bktvFul099fRA+Lk72BSLO3adGjPU7WsGXvwfbddNQM/SRGM5avRvg56lKf77Lzx9ZUqZQlcb8xZ1iLmg3jYxF+9i2a7ktrFqW5/oC+ptE3Xujiq9ll7+rKC8DPV+lJeZg1XHhqo0SqWSvKzCH/3zMrNR5ilVaSqDynTO0jMzJLMKxGjrWnaY2VnidbnwvJWRnE6gqw+12zz7eaveC0342OUbFp7ZyIhVkzCy0NznhChPVW6Nsfr167N+/XrV+1WrVtG6dWvWrFmj2rZjxw5q1aqFl5cXKSkp5OTk8Oqrr+LklD9dvHnzki+OUlJS2L59O3v27KF37/xfFr///ntq1nzyi4IlSwrXJKhduzZz585l//79zJ8//7H2DwoKonXr1rRr106Vx5PYvHkzAwcOZO7cuQA0aNCAv//+mz/++EOVpmXLlrRsWTjTZeXKlfz666/89ttvagvjvfjii8yZM+eJPr8s6FiaodDRJicmQW17TkwC+s61HisPu/kTyImMI/Wyq2qb9bQRKHNyidv1WymWtuwZWJmipaNNerT6Wg7pMYmY16tW4j6GthakxySpbcuITsLI1uKpy9HwtZ7Ee4USdaNqrbOlY1VwPEUnqG3PiUnAoN7j9fkai94iOzJOfXBN/OdoF8Se7GKxJzsmAf26NR4rj+oLx+UfK5dLPlYU+rpUXziO+N8ukJeS/qxFLjP6D+JOsTVk0qMTMa/7iLgTXSzuxCRhqCHuaOvr0m7Ra/gdvkJ2JW6Lx6FnZYaWjjZZxeJ0ZnQixvUf79j5VwoFjVaNI/6qJymeIaWTZzlQmJmj0NZBmaB+u6gyIR5FLUfN+xkZY7n7J9DVg7xcUr/+nGzX/HVsckMCyY2KwGj8VFK/3IAyIwODYSPRtrUj1/LhJRQqGxM7CwBSi/WvlJhETDT0FyPL/D5ZfJ/UmCRs6lZXvf/p7S8Y8eUs5t/5ltzsHLLTszg49XPiA/PXLrN0tAOgx/vDOblqD4nB0bwwdRDjDizhy55zyEisuAF7vYK4k1GsH2VEJ2FWr3qJ+xjYWjyUPjM6EYOCNob89cRC/rxOalA0JrXtaL5wNN33zufM4I9QlnCrUJ0xPYk4d4f08MqzDuSDGJNZQl1NNLSNvp1FiekftE2KTxhpIdE0XjyGO/O+IyctA+dpL2FYwxr9gjTxN73JTcuk8ZLX8Vy7HxQKGi8eg5aOtlobV7TKcs4yrW1P4wn9uL7yxyevRDkzLahnSrFjJCU6UfW3p+V53pU7x68RFxyFtZM9g+a9xtRdH7Lp1aUl9jnxL/Kq1uL7lV2VGxhr21Z9Kvnt27c5e/YsJiYPjzb7+vrSr18/evfuTfPmzenfvz/9+vVjxIgRWFpalpg+KyuLjh07qrZZWVnRsOGT//Jx4MABvvjiC3x9fVWDc2ZmJd/LXpIZM2YwfPhwbt68Sb9+/Rg2bBidOz/+4tb379/nlVdeUdvWoUMHtYGxlJQUli9fztGjRwkPDycnJ4f09PSHZow9GJzTJDMzk8xM9VvospS56Cm0H7u85cF62kjMB3cn4PUPURb8wmXQrB7W44fi9/K7FVy6qknbQBfnYZ1w3XS4ootS7uxnDsfy5W54j1yMMvPxb5MWzx+7GcOxGNINn9EajhUdbWp/NR8UCkIWbyn/AlYiCh1tem6dhUKh4MrCXRVdnCqhybqJmDasxT8vf1TRRSkXyvQ0EmZNRmFoiG7LNhhNnkluRBg5bq6Qm0vy6qWYvDcfqwNHUebmkO16g6zr/1S6dY8Amg3rzOA1k1Tv9034X5l9Vq85IzAwM+KH19eQFpdMw37tGPHVLHaNXEnU/WAUWvk3kVz68rBqQf7f5n7D+/9spsmgjtz88a8yK1tFCT5S+LSzRM9gEu4FMfjq59h2bkJUkVl7AIbVrHDo2YIr074o72KWO2VOLi4TP6PlxqkMuP8deTm5xFy4S+SZWygK+lFWbDI3pnxO808mUWdyf5R5SsJ+/ZuE237P1UPNHuecZeRgSb898wn44xpeP54r1/I9jjZDuzBqzRTV+20TPymzz7r1+xXV/4ffDybcI4glF7+g3gtN8f675CVchCgvVW5gzNjYWO19SkoKQ4YM4ZNPHu7E1apVQ1tbm1OnTvH3339z8uRJNm/ezOLFi7l69Sp16jzdWhwKheKhoF90/bArV64wduxYVqxYQf/+/TE3N2f//v0lrlemycCBAwkMDOTPP//k1KlT9O7dm7fffpsNGzagVXDxUrQMT7J+2QNz587l1KlTbNiwgXr16mFoaMiIESMeWmC/eJsXt3btWlasWKG2baZFPd62Kr3bRABy4pNQ5uSiY2Ohtl3HxoKc6JIXKH7AevKr2EwfQeBbi8m8H6DabtSuKdrW5tS/uEu1TaGjjf2iSVhNGIpPj4mlWIPSlRGXTF5OLoa26ot/G9qYkx5V8hOB0qMTMCy2GKuBrRlpxWZNPa46gzqgY6iPz0+Xnmr/ipQTV3A8Ffv1S8fGgux/OZ7spg3Dfuar+Lz+EemegWVYSlEZ5BbEHt1isUfXxuKhGYfF2U4dhv2M4fiMXUaGZ8DDCQoGxfRq2OEzZkmlni0GkPkg7tgUizu25g/NXn0gPToBQ9ticcfGjPRibafQ0abX1lmY1LTm+Ki1VX62GEBWXBJ5ObnoFYvT+rbmZEYlPHP+jddMwLZvG64NW05mJZrF8jiUSYkoc3NQWKj/UKmwsEQZ/4i6KJXkhec/YTLXzwftWk4YjhxLsptr/jYfLxJnTUZhZAw6OiiTEjHbuIVc7/tlVZWn5nXqJt8UeXKgjl7+ZbmxjTkpRY4PExtzIu6VfK5Ji8/vk8bF+qSxjZlqxoelox0dxvdnS5/5RHvnt12kRxCOHRrS7q2+/Ll4h+rzHvwdIDcrh4SgKMxrVOxsu6yCuGNQrB8Z2JqRoeF6JyM64aH0+rbmZDyi36UGRZMRm4RpHfuHBsbqvNadrPhkQk88+fIqZelBjNF/ghiTGZVQYvqibZN4x58LfRaiY2qIlp4OWbHJdP1zJQlFHjoQfd6Nv154Hz0rU/JycslJSqPvnS2kHblCZVHR5yxDewsGHFpE1A0vLs/f/myVKSPup2+wwdVH9V6n4FZaE1tzkorU2cTWnDANcehpxQZHkRKbhE1texkYexoyy65UVbk1xopr06YN7u7u1K5dm3r16qm9HgzoKBQKunTpwooVK7h16xZ6enr8+uuvD+VVt25ddHV1uXq1cKHR+Ph4vLy81NLZ2toSHl74SHRvb2/S0grvKf/7779xcnJi8eLFtGvXjvr16xMY+OSBxNbWlnHjxrFnzx4+//xzvv32W9V2QK0Mrq6uavs2bNiQ69fVFz8s/v7y5cuMHz+eV155hebNm+Pg4KC2eP/jWrhwIYmJiWqvKZZ1nziff5WdQ8ZdH4w7tyrcplBg3KkVabdKfrQ7gPXU4di88xpBE5aR4eaj9rfEw3/hN+gd/IbMUr2yI2KI3fYLQeMrcD21x5CXnUuMmz/Vii5yqlBQvWtTom76lLhP1A0fqhdbFLVGt2ZE3Sg5/b9p8FpPgk7dJCOu6q2xpczOIc3NF9MuLQo3KhSYdm1B6g3NX6Dsp79CtXdH4fPmCtLuPF27iaol/1jxUS2cD4BCgUmXFqTe1Bx77Ka9isOs0fiOW0G6WwnHSsGgmH6d6viMXfrI9ccqi7zsXGLvPBx3qnVtqjGORN/wUU8P1OiuHncefMEwq2PP8dHryIyv/OuwPA5ldi5Jd/yx7tascKNCgXW3ZiS4eGne8TE0XjMB+5fac334StKDop+xpBUgJ4ccHy90WxW5E0ChQLdVG7I93TXvV5xCC4Xuw09KU6alokxKRKt6DXTqNSTrn8r3A05WagbxgZGqV7R3KMlR8dTpUthf9EwMqdGqLiE3S16uIC87l3A3f7V9UCio06WZah9dw/wnCRb/UVeZm4dCK38GUJibPzkZWVgXub1MS0cb85q2JIZU7NO687Jzib/jj32xuGPftRkxGpZxiHXxwa5Y3HHo3oyYR1zvGFazQt/ShPTIhIf+Vmd0DwIOXap0D2hSZueSeMcfm2IxxqZrU+JdSm6buBve2HRTbxvb7s1LTJ+TnE5WbDLGdRywaOlM5HGXh9JkxSWTk5SGdZem6NuYEXHixkNpKkpFnrOMHCwZ+NNiYu8EcOmDb6GSzqTLTM0gJjBS9YrwDiEpKp4GnQuPKX0TQ5xa1SPg5rOdt4ozd7DCyNKEpFL4oUiIZ1XlZowV9/bbb7Nt2zbGjBnD/PnzsbKywsfHh/379/Pdd9/h4uLCmTNn6NevH3Z2dly9epXo6GgaN278UF4mJiZMmjSJefPmYW1tjZ2dHYsXL1bN0HrgxRdf5Msvv6RTp07k5uayYMECdItclNWvX5+goCD2799P+/btOXr0aIkDcY+ybNky2rZtS9OmTcnMzOSPP/5QlblevXrUqlWL5cuXs3r1ary8vB6ajTZr1iy6d+/Oxo0bGTJkCH/99RfHjh1TTYF+UM5ffvmFIUOGoFAoWLp0KXlPca+yvr4++vrqj4gvq9soY3f8SvX/zSbdzZv0215YTxiKlpEBCT+dAqD6htnkRMQSteF7AKynjsD2/TcI/WA9WSFRaNvk/zKdl5aOMi2D3ITkh76MKnNyyYmOJ8s/lMru7rfH6P7ZNGJu+xPt6kuzyQPQMdTHq+CJSd0/n0ZaRDwu6w4C4L79BIN+WkyzqQMJPuOK89BO2LRw5vKCHao89SyMMalujZFDfls9WIPhwRMoHzCtbY9Dx4aceGtDeVW31EVtO4LTxvdIu+NDmqs3tpOGoGVoQOzB/KezOn32PtkRsYR98gMA9jNepdqc1wmY9SlZIVGq2WZ5qRnkpWVUVDXKXVpaOkEhYar3oWGReHr5Ym5mSjUHuwosWdmJ/u4Ijp++n3+s3PbCduLLaBkZEHfoDACOG98nOyKO8PW7AbCb/ioOs8cS+N4GskIiHz5WdLSps+VDDJs54zdxJQptLVWa3IQUlNklP42wMnDfdoyun00j9o4/0bd8aTolP+54F8SdbpumkRYez42CuHNv+wkG/rSYptMGEnLalTpDO2HdwpnL8/PjjkJHmxe/fRfr5rU5Ne5TtLS1VDNhMxNSyMvO/yJqXN0afUtjjKtbo6WthVXT/HWokvwjK/UTcQO2HqX5FzNIdPUj8ZYPtae+hLaRPqH789ur+eaZZEbE4bV6P5C/mLZJwYLnCj1tDBysMG3qRG5qBmkB+etBNVk3kWqvduHmuA3kpKSrZqTlJKeRl1F1bu3O+PUgJrMXkuvtSY6XJwZDR6AwMCTz1DEATGYvIi82mrTvtwFgMHIsud73yY0IRaGrh267jui/2I/Urzaq8tTr2pO8xATyoiPRru2M8dRZZP1ziexbD3+hr4yubj9Ot1nDiPOPICE4mp5zRpAclYDnycLBhjd/XIjnCReuf59/7XPlu2MM+3QaYXf8CbvtS8eJA9A10sf1UP4xFuMbRqx/BIPWTOLU6r2kx6fQsH87nLs1Y9/E/HN4Vko6LnvP0PODESSFxZEYGkOnaYMAKsWTKe9/c4yOm6YRd9ufWFdfGk4ZgI6RPv4F/ajjF9NJi4jHbc0BALy+O86Lvyyh4bSXCDtzC8ehnbBs6YzLvPxZOzpG+jSd8yohR6+THpWASW17Wi4dQ4p/JBHn7qh9tl3Xppg42eH349nyrfRj8vvmKK02zSDhth8Jt3xwnjIQbSN9ggraptXmGWSEx+O5Jj/G+G87Rudfl+E8fRBRp29RfVgnLFo6c2feNlWe1YZ0JCs2ifSQWEwb16LZqnFEHLtO9PnCBdlrvdaDZK9QsmKTsGzXgGYr38Lv22Ok+oZTmVTEOevBoFhKSAzXV/6IgXXhDDRNM9Uqk/M7jtF31itEB0QQFxzFwDmjSIqMx+1kYRydsXcJbieuc2n3CQD0jPSxqe2g+rt1LTuqN3EiLSGFhLBY9Iz06f/eCO4cv0pSdCI2jvYMWfg6MQGReF6QtXqfilLWGCtNVX5grHr16ly+fJkFCxbQr18/MjMzcXJyYsCAAWhpaWFmZsaFCxf4/PPPSUpKwsnJiU8//ZSBAweWmN///vc/1e2ZpqamzJkzh8RE9QD26aefMmHCBLp160b16tXZtGkTN24UXrC8/PLLfPDBB7zzzjtkZmYyaNAgli5dyvLlyx+7Xnp6eixcuJCAgAAMDQ3p1q0b+/fnn9B0dXXZt28fM2bMoEWLFrRv355Vq1YxcuRI1f5dunRh69atrFixgiVLltC/f38++OADvvzyS1WajRs3MnHiRDp37oyNjQ0LFiwgKSnpobJUJklHL6JtZY7t+2+gY2NJpocfQROWkRubAIBuNVu1aaWWY19CS1+XWl8vVssnetNeor+o/Atg/hv/369iYG1G27nDMbQ1J/ZeICfeXE9GwQL7JjVs1BazjLrhzdl3vqbt/JG0WzCKJP8ITk/+jPj7hQs2O/VtQ/fPpqnev7hlFgA3N/7CrY2/qLY3GN2D1PA4QotcJFU18b9fQsfKjGpzXkfX1pL0e/74vLmCnIJFWvVq2KiddGzeHICWvi7O336olk/4xn2Ef7a/XMteke56ejNx1gLV+/Wb82ezDh3Yh9VLKv5BHWUh4Y9L6FibU2326+jYWpJ+zw+/t5arHgaiV1099ti8MRAtfV3qbF2olk/EZ/uI+Hwfeg7WmPfLX8+y0XH1NWt8Ri8i5Z/Ke0uB/29XMbAyo3VB3IlzD+TkG4Vxx7h6sbjj4s35d76mzfyRtC2IO39N+oyEgrhj7GCJY//8WUPDTq1R+6xjI1YTUfDUvNbzhlN/VHfV34aeXPNQmsoo4sgV9KzNqD9/JPp2FiS5B+IyZp1qQX7DGjZqx46BgxVd/ipcHqLO20Oo8/YQ4i7f49qrHwPgOKEfAB0Pq68r5vbuFkIPVJ0nBGddPEuauQWGb0xEy9KKHD8fkpfNUy3Ir2Vrh7JIDFYYGGA88wO0bGxRZmWSGxJEyoZVZF0sHLDQsrTGaPLbaFlYkhcfS+aZE6Tv313udXtaf2/9Az0jfQavnYSBmRFBLl7sfesTcousT2jpaI+Rpanq/b0//sHY2pSes0dgYmtO5L1AfnzrE1IL+mReTi77xq+n94ev8dr2uegZ6xMXEMnh2d/gc7bwC+npNftQ5uYx7LMZ6BroEerqww9jVpNRCZ4OG/zbP+hbm9Js/ggMbM1JcA/k/OufkFlQR6Ma1mpxJ9bFmyszv6L5gpE0XziKFP8ILk/YSGJB3FHm5WHexJHao7qha2ZMRmQ8EefdcPvkEHlZ6j9MOI/pScw1L5J9KteAzwNhR/5Bz9qMhvNHoG+bH2OujllHVkzJMSbexZubM7+k0YJRNFo4mlT/CK5P+JTkIg/vMLCzoOnyNwtusYwn5OBFvD77Re1zjetWo9Gi19CzMCEtOBrvTYfx++bP8qn0E6iIc1b17s0wq+OAWR0HRt/YrJZmZ403yrK6peKvrb+hZ6jPqLVTMDQzwv/6fb4Zt46cInHIxskeY6vCOFSrRV3e2b9M9X7Y0rcAuPbTefbN3YIyN4/qjR1pP7w7hmbGJEXFc//CHf7ceJDcrMr7Y6B4fiiUz9MKic+5KVOm4OnpycWLF8v8s+7VHVTmn1GVXcl8+OEPIl9rReW/nawiNb/1WUUXodJyb/t+RRehUruV8/gPgHneVMuRi/JHad+ucg4IVAZf3X3yJ5c/Txpm/Xua55WRzPZ4pBjtyvUQr8rmjq6ctzT5LOD5+LE6demoCvts45UHK+yzy0qVnzEmNNuwYQN9+/bF2NiYY8eO8f333/P1119XdLGEEEIIIYQQQgjxtGTx/VIlA2P/YdeuXWP9+vUkJyfj7OzMF198weTJkyu6WEIIIYQQQgghhBCVggyM/YcdPPjfm+IohBBCCCGEEEI8z5RP8dA8oZnWvycRQgghhBBCCCGEEOK/RwbGhBBCCCGEEEIIIcRzSW6lFEIIIYQQQgghhKgqZPH9UiUzxoQQQgghhBBCCCHEc0lmjAkhhBBCCCGEEEJUFTJjrFTJjDEhhBBCCCGEEEII8VySGWNCCCGEEEIIIYQQVYUyr6JL8J8iM8aEEEIIIYQQQgghxHNJBsaEEEIIIYQQQgghxHNJbqUUQgghhBBCCCGEqCpk8f1SJTPGhBBCCCGEEEIIIcRzSWaMCSGEEEIIIYQQQlQRSpkxVqpkxpgQQgghhBBCCCGEeC7JwJgQQgghhBBCCCGEeC7JrZRCCCGEEEIIIYQQVYXcSlmqZGBMlAlt3byKLkKlVjslu6KLUGnFY1DRRajU3Nu+X9FFqLSa3vi8ootQqV1qvayii1Bp5aKo6CJUako5pWvkr8io6CJUanp6hhVdhErrkjKpootQqTlrmVR0ESq1dCQwC1GaZGBMCCGEEEIIIYQQoqrIk8HR0iRrjAkhhBBCCCGEEEKI55LMGBNCCCGEEEIIIYSoKmSNsVIlM8aEEEIIIYQQQgghxHNJBsaEEEIIIYQQQgghxHNJbqUUQgghhBBCCCGEqCrkVspSJTPGhBBCCCGEEEIIIcRzSWaMCSGEEEIIIYQQQlQRSqXMGCtNMmNMCCGEEEIIIYQQQjyXZGBMCCGEEEIIIYQQQjyX5FZKIYQQQgghhBBCiKpCFt8vVTJjTAghhBBCCCGEEEI8l2TGmBBCCCGEEEIIIURVITPGSpXMGBNCCCGEEEIIIYQQzyWZMSaEEEIIIYQQQghRRShlxlipkhljQgghhBBCCCGEEOK5JANjQgghhBBCCCGEEOK5JANjZSAgIACFQoGrq6vGNLt27cLCwuKZP+vcuXMoFAoSEhLK/LOEEEIIIYQQQghRwfKUFff6D5I1xqq4zp07Ex4ejrm5eUUXpdxYvD4Yq0kj0LaxJNPTj6hVW8hw8yoxrfnIAZgN7Y1+fScAMtx9iPls10Pp9ZxrYTt3Iobtm6PQ1ibTN4iwd1eREx5d5vUpbTUn9MNx5hD07CxIuReI16KdJN3yLTGtccOaOM8fhWmLOhg62uG19HuCv/1TY95Os4ZSb8nrBH37J95Lvy+rKpSZ0m6bGuP6UmN8Xwxr2QKQej8E/09/JvYv17KuSpmweesl7Ka+go6tJeke/oR+9C1pt71LTGv1Wj+shvfCoGF+30p38yF8/Q+F6XW0qTb3Dcx6tUXP0YG85FSSL90mbN1ucqLiyqtK5c7F1Y2dP/7EPU8fomPj2LR2Kb27d67oYpWJDnOG03RML/TNjQi/7sW5RTtJDIh85D7Nx/Wh9bRBGNmaE+MRxIVlu4ly9VP9venrvWgwrDO2zWqjZ2rIt02nkpWUppaHRR0HOi8ZQ7V2DdDW1SHGI4irG34i9IpHmdSzLDhN6IvzzCHo25mTdC8I90W7SNQQi0wa1qTB/BGYt3DGyNEW96W7Cfj2WDmXuOwYDB6G4YjX0LK0IsfPl9Qtm8jx8iwxrV7nbhiOfgPt6jVQ6OiQGxpC+i8HyfzrpCqNwsIS44nT0G3THi1jE7Lv3iZlyybywkLLq0qlbtgHr9FjTB+MzIzwdrnPD0u+JTIgXGP6QTNfoW3/F3CoW4PsjCx8bt7n0LofiPALU6XpMaYvLwztilNTZwxNjZjZ4k3Si/W1yqjb7OG0HNMLfTMjQl28OLF4J/H/EnfavNWHjlMHYWxrTpRHEKc+2k34bT+1NNXb1KPHvJFUa1UXZa6SqHuBHHjzE3IyszGvaUPnd4fh1LkJxrYWpETG4/7rZf7+8gh52bllWd1nNnb2G/R/vT/GZsZ4uHjw9aKvCAsI05h+5Nsj6TSgMzXr1iQrIwuPGx7sWruTUL/C/uPg5MCkxZNo0r4punq63Dh/g2+WbSUhJqEcalS6XvpgJJ3G9MbQzBh/l/scXPId0QERGtPX7dCY3lOHUKt5Hcztrdg29X+4nXR5KJ193Rq8/OHr1OvYBC0dLSK8Q9kx41Piw2LLsjpl7uUPRtNtTG+MzIzxcfFk75JtRD2ivQbOHEab/h1xqFuDrIwsfG/e5+d1e4n003wMClFRZMZYFZadnY2enh4ODg4oFIqKLk65MB3YHdsPpxLz1V4CX51F5n1/an63Cm2rkgcGjTq0IPnoOYLHfUjQa7PJiYim5vbV6NhZq9Lo1qqG448byPILJvitBQQMnUns1z+izMwqr2qVGruhnai/4i38P/2Z630/JMU9kFb7F6FrY1Ziei1DfdIDI/FdvY/MyPhH5m3aqi413upDsntgWRS9zJVF22SGx+K76keu9V3ItX6LiLt0lxbfz8O4Yc2yrEqZsBjclepLJhGxaT/3B39AukcAzj+sQMe65L5l0qkZ8b9dwPe1xXi/Mo+ssBjq/rACXXsrIL/9jJrVJfKLA3gN+gD/aevQd66B8/bF5VmtcpeenkHDes4snjOzootSptrMGEzLCf04t2gHh4Z8RHZ6Ji/vWYC2vq7GfeoN6UjXpWO5/vmvHHhpCbH3gnj5hwUYWhf2QR1DPQLP3cHly9805jN41xy0tLU5PHoNB15aQoxHEIN3zcHItmr8QFRt6As0XvEm3p/+zKW+i0h2D6Tj/g/R0xCLtA31SAuMwnP1PjL+JU5XNXrde2E89W3S9n5Pwqwp5Pr7YrZqAwpzixLTK5OTST+wh8TZbxM/cyIZp45hMnsBum3aq9KYLVuNtkN1kj9eTMI7k8mNisR8zUbQNyinWpWul6YPo++El9i9+BtWDltIVnoGs3cvRecRfa1hx6ac+eE4q15ZyIY3V6Cto82c3cvQM9RXpdEz1MPtvCt/fP1LeVSjVHScPpi24/txYtEOdg/9iOy0TEb/8Oi402hwR15cMpZLm35l5+AlRHkEMfqHBRgViTvV29Rj1Pfz8b9wl90vf8T3Ly/jxvenUCrzZ0VY1a2OQqHF8YU7+K7PAs58vJfWY3vTY/6oMq/zsxg+YwRDJgzhq4VfMefl2WSkZfDxnpXoPqK9mnVsztHvjzJ32ByWjl2Cjo4OK/esQr/g2NE31GflnlUolbDotYXMe3UuOro6LNuxrMp9F+kz/WW6TxjIwcXfsXHYYrLSM5ixe9Ej+5aekT6hHoEcWrZDYxobR3ve/2kFkb5hbB6zgk8GzOfE5p/Jzswui2qUmwHTh9J7wkD2LP6WNcMWkpWeyfu7lzyyvRp0bMrZH06w9pVFfPbmSrR1dPhg9xK1WCSeQV4Fvv6DZGDsGeTl5bF+/Xrq1auHvr4+jo6OrF69WvV3Pz8/evXqhZGRES1btuTKlSuPzG/Lli3UrVsXPT09GjZsyA8//KD2d4VCwZYtW3j55ZcxNjZm9erVJd5KuWvXLhwdHTEyMuKVV14hNvbhXyeOHDlCmzZtMDAwwNnZmRUrVpCTkwOAUqlk+fLlODo6oq+vT/Xq1Xn33XefoaVKj+X4V0g8dIykX06R5RtE5EebycvIxHx4vxLTh89bT8K+o2R6+pHlH0LEkk2gpYVRp1aqNDbvjyPl/HWiN+wg08OX7OBwUs9eJTcusZxqVXocpw8idM8ZwvefI9UrFM9535GbnkX1Mb1KTJ/s6ovPx3uJPPw3eY84YWsb6dPs63fwmPMtOQkpZVX8MlUWbRNz8iaxZ1xJ948g3S8cv7UHyE3NwKxt/bKsSpmwnTyU2P0niTt0hkzvYEIWfU1eeiZWo/qUmD7ovY3E/nCM9Hv+ZPqGErzgS9DSwqRLSwDyktPwfWMZCUcvk+kXStqt+4Qs+wajFvXRrW5TnlUrV906tefdqePo06NLRRelTLWcNACXzUfwP3mTWM9gTr+/FWN7C5z7t9W4T6spA3HfdxaPgxeI9w7j7MKd5GRk0nh0D1Wa29tPcPPr34m86VNiHgaWJlg4V+PG178T6xlMYkAkV9YeQNfIAKsqMiBdZ/oggvf8Rcj+86R4heI2bzu56VnUGtOzxPSJrn54fvwj4YevkJeZU76FLWOGr4wi49gfZJ46Rm5QICmbP0WZmYFBv5dKTJ/t5krW3xfJDQ4kLzyMjCM/k+vvh27T5gBo1aiJbuOmpHy5kRwvT3JDg0n9ciMKfX30e/Yuz6qVmr4TB/P75p+4deo6IZ6BbJu9GUt7S9r066Bxn43jVnH5p7OEeQcT7BHI9rlfYlPTltrN66rSnNpxlD+3/IrvrZJn3FdG7ScN4O8vj+B96ibRnsH8MXsrJnYWNOinOe50mDyQ2/vP4nboArHeYRxftJPs9ExajCqMO72XvsGNXSf5Z8vvxHiHEucXjufRq+Rm5fc3//N3+HPetwRcvEticDQ+p29yddufNBzQXtPHVgpDJw3lwOYDXD31DwGeAWz84FOs7Kzo1K+Txn0+emsZZ346TZBXEP4e/nw2ZyN2Ne2o17weAE3aNcGuph2fzdlI4P1AAu8H8tnsjdRrUZ8WBef/qqLHxJc4ufkX3E65EOYZxA+zv8Lc3pIW/TT/u3qcc+Xopwe4c+K6xjSD5r3GvbO3+G3dXkLcA4gJiuTu6RukxCaVRTXKTe+Jgzi6+Wdun3Ih1DOIHbO/xMLektaPaK9N41bz90/nCPMOIcQjkJ1zv8K6pi1OzZ3LseRCPB4ZGHsGCxcuZN26dSxdupR79+7x448/Ym9vr/r74sWLmTt3Lq6urjRo0IAxY8aoBp+K+/XXX3nvvfeYM2cOd+/eZdq0aUyYMIGzZ8+qpVu+fDmvvPIKbm5uTJw48aF8rl69yqRJk3jnnXdwdXWlV69erFq1Si3NxYsXeeutt3jvvfe4d+8e33zzDbt27VIN6v3888989tlnfPPNN3h7e3P48GGaN2/+rM317HR1MGhan7S/XQu3KZWkXXHFoFXjx8pCYaiPQkeb3MTkgg0KTHq2JzsglJrfraLu5X04HvgMk96aLxoqK4WuNqYtnIm76Fa4Uakk/oIb5u2ebaCm4bpJxJy+RfwFt39PXAmVZduoaCmwH9YZbSN9klyqzhcNAIWuDkbN65FyybVwo1JJyqXbGLdp9Fh5aBnqo9DVJjchWWMabVNjlHl55CalPmOJRUUyc7TF2N6C4It3VduyktOJdPXFoU3J/UlLVxu75nUIvuReuFGpJOSiOw5t6z32Z2fEpxDvE0aj4V3RMdRHoa1FszdeJC06kWg3/6euU3lR6Gpj3qIOMUXaDqWSmAt3sSitWFRV6OigU78B2a43CrcplWS73kCncdPHykK3VRu0a9Yi++4dABS6evnZZBeZ8a1UoszOVg2eVSW2teyxsLPE/fId1bb05DR8Xb2p16bhY+djaGoEQOoj4nNlZ17LFhM7CwIuFfadzOR0wlx9qfGIuOPQvA4BxeJOwCV3arTJjztG1mbUaFOPtNhE3vhlGbNcvuL1A4up2a7BI8ujb2pIeiX+odDe0QErOytci5zX05LTuO96n0ZtH++8DmBsagxASkFddfV1QQnZWYU/GGZlZqHMU9K0fZPSKXw5sK5lh7mdJfcvF14XZiSnE+jqQ20Nx9PjUCgUNO3Vmij/cGbsXsRql2+ZfXgVzfu1K41iVxibWnZY2FniUaS90pPT8HP1wfmpYlHl7Tvi+SUDY08pOTmZTZs2sX79esaNG0fdunXp2rUrkydPVqWZO3cugwYNokGDBqxYsYLAwEB8fEr+FXzDhg2MHz+emTNn0qBBA2bPns2rr77Khg0b1NK9/vrrTJgwAWdnZxwdHR/KZ9OmTQwYMID58+fToEED3n33Xfr376+WZsWKFXz44YeMGzcOZ2dn+vbty8qVK/nmm28ACAoKwsHBgT59+uDo6EiHDh2YMmWKxrbIzMwkKSlJ7ZWVV/pzLLUtzVDoaJMTq34rSW5MPDo2lo+Vh+2cieRExZH29638PK0t0DI2wmrKKFIvuhAyaTEpp/+m+uYlGLavWhfRulZmaOlokxWtPtMtKzoRPTuLp87XflhnTFvUwXf1vmcsYcUpq7YBMG5cix5+39MreC8N10/mzoQNpHpVrbVsHvSt7GLrg2THJKBja/FYeVRfOI7syDiSL98u8e8KfV2qLxxH/G8XyEtJf8YSi4pkVHBMpMWo//qdFp2EkV3JtzMaWpmipaNNerE+mBaT+MS3QB4esw7bZk5M89zGDJ+dtJoykN/eXE9mYuVfH0mvIBZlFmuHzOhE9J8xFlU1WmbmKLR1yItXP6fnxcejZWmlcT+FkTHWvxzD+vczmK1YR8qWL8i+lb/GT25wILmRERiPn4rCxAR0dDAcOQZtWzu0rKw15llZmRf0taToBLXtSdGJqr/9G4VCwZhlE/C67kGoV3DpFrAcmRT0j9RicSc1JgljDTHEyDI/7qTGJBbbJ1G1j4Vj/hqhXd9/ldv7znFw3Hoi7wbw2o8Lsaxt/1CeABZO9rQd1w/XvX89S5XKlKVt/nVxQox6/0qIScDC9vGumRUKBVOWT8X9ujuBXvnLaHje9CQjLYMJCyegb6CPvqE+kxZPRltHG0s7zf22sjEr6D/JxWJxcnSi6m9Pw8TGDAMTQ/rMGIrHeVe+fms1d05cZ9LWOdTr+Hg/4ldGmmJRcnTCE8Wi15aNx/u6J2FVOBZVJso8ZYW9/otkYOwpeXh4kJmZSe/emqfmt2jRQvX/1apVAyAqKkpjfl26qN9606VLFzw81BcTbtfu0b84eHh40LFjR7VtnTqpz366ffs2H3/8MSYmJqrXlClTCA8PJy0tjZEjR5Keno6zszNTpkzh119/1TjTDWDt2rWYm5urvb6JK3kR4YpkNWUkZi/1IOydj1E++KVLK389hJS/rhD//WEyPf2I23aI1HPXsHit5Fs5nif61a1psGoc7jM3P/JWy+dZmk8Y116cj8vAxYR+f4omX7yNcYMaFV2scmU3YzgWQ7rhP3UtypKOEx1tan81HxQKQhZvKf8CimfSYFhnpnp+p3pp6WpXaHl6rB5HWkwSPw9fyaEhH+F34gaDd87B6DkbWHpeKdPTiH97MgnvTSPt++8wnjIT3eat8v+Ym0vSqqVo16iJ9aGjWB8+gW6L1mRd/6dKPEXrhaHd2OK+R/XSLoW+9sbKKdRs6MjWWRtLoYTlp8mwzsy+953qpaVTNnFHoZX/VejW3vzbLSPdAzmzci9xfuFqt1s+YGJvyejd87n/5zVu7z9XJmV6Gj2H9eSQx0+ql04ptNeMVTNwauDE+rc/UW1Likti3Yy1dOjTkUOeP3HQ/RDG5sb4uPmgLIMfxUtLu6Fd+Z/796pXafStkigU+ceT2ykXzm3/k9B7gZzecgT3MzfpMrZvmXxmWeg4tCub3X9QvbR1n/15fa+vnEz1hrXYNuuzUiihEKVPnkr5lAwNDf81ja5u4WKEDxakzHvGk4axsfEz7Q+QkpLCihUrePXVVx/6m4GBAbVq1eL+/fucPn2aU6dOMXPmTP73v/9x/vx5tTo9sHDhQmbPnq22LbDdyGcuZ3G58Ukoc3LRsVb/pUvbxpKcmEcvSGw5cThWU0YRPHERmV4B6nlm55DpE6SWPtM3GKO2VWdKOEB2XBJ5ObnoFfvlVM/WnKyohKfK07RlHfRsLWh/ap1qm5aONhadGlNzYn/O1hpbJb5slEXbPKDMziW94IlYyXf8MWtVl1pTXsJz3rZnyrc8PehbujYWatt1bSzIKfbrYHG2U4dhP2M4PmOXkeEZ8HCCgkExvRp2+IxZIrPFqiD/UzeJdC38sUNbL//SwcjGjLQi/cfI1owY96DiuwOQHpdMXk4uhsX6oJGNOWnRj7+eY80uTanduzXbmk0ju+BYOr94F7W6NaPRiG7c/Pr3x86rImQVxCL9Yu2gb2tO5jPGoqomLykRZW4OWpbq53QtS0vy4h/x5Fqlkrzw/Fm56X4+aNdywnD0WLLdXAHI9fEi4Z3JKIyMQVcHZWIi5p9tIcf7fllVpdS4nr6On2vhk4B19PKvucxsLUgsEovNbM0Jvhfwr/m9sWIyrV5sy9pRS4mPqFpPA/Y5dZMdRZ7UqlMQd4xtzEgt0leMbcyIuldy3EmLz487xjbq/c3YxpzUgriTUpBXrI/6TO9YnzDMaqjPMjSxs+D1/YsIveHFsQ+3P1W9ysrVU1e5f6vwGH+wwL6FjSXxUYXXyBY2Fvjf83to/+Kmfzyd9r078OHIBcRGqK9VfOviLaZ0m4yZpRm5ubmkJqXyg8seIoI0P52wormddiGghL5lamuuNgvK1NackMfoW5qkxieRm51DhLf68RTpG4pzu8e/hbWiuZ52wc+18C4n3YL+VzwWmdpaPFYsGrNiEi1ebMP/Rn1U5WJRpVYFvoNVJTJj7CnVr18fQ0NDzpw5Uyr5NW7cmMuXL6ttu3z5Mk2aPNngTOPGjbl69aratn/++UftfZs2bbh//z716tV76KVV8MuZoaEhQ4YM4YsvvuDcuXNcuXIFN7eS15fS19fHzMxM7aWnVQaHVnYOGe7eagvno1Bg9EIrMlw9NO5mNWkE1jPGEDJlKZl3vdX/mJ1Dxl0v9OqoL9qsV7sG2WElz+6rrJTZuSTf8cOqW5FbQBUKLLs1I9HFW/OOjxB/4S7/9JjLtd4LVK+kW75E/HyJa70XVJmAXBZto5GWAoVe1frNQZmdQ5qbj2rhfCB//b0uLUi96alxP7tpr+IwazS+41aQ7lbCbeIFg2L6darjM3bpI9cfE5VXdmoGiQGRqlecVyipkQnU7Fq4DpSuiSH2reoScbPk/pSXnUuUmz+1uhRZO0qhoGbXpkTcKHmJgZLoGOoVZKj+I5MyT4lCq/I/EU2ZnUviHX9sujUr3KhQYN2tKQmlHYsqu5wccry90G1VZOF0hQLdVm3I8XDXvF9xWlooSvjRTpmWijIxEa3qNdCp35Csfy6VQqHLVkZqBlGBEapXmHcwCVHxNOlceO4yMDGkbqv6+Nx89EDfGysm06Z/B9a/vpyYkKp1PQOQlZpBQmCk6hXjHUpKVAK1i8QQPRNDqreqS+gj4k6Em7/aPigUOHVpSmjBAz4Sg6NJjojDyrma2r5Wzg4khhQOCJnYW/L6gcVEuAVwdO63oKxc1z/pqemEB4arXkFeQcRFxdGqyHnd0MSQhq0a4nlD83kd8gfFOg3oxOLXFhEZHKkxXVJ8EqlJqbTo3AJzG3OunrqqMW1Fy0zNICYwUvWK8A4hMSqeBsX6llOregRoOJ4eR252LkF3fLEvdjzZ1qlGXGj0U+db3jJTM4gOjFC9wrxDSIiKp1HnwnOXgYkhzq3q4fcvsWjMikm07t+BT19fUSVjkXh+VK1vb5WIgYEBCxYsYP78+ejp6dGlSxeio6Nxd3d/5O2VmsybN49Ro0bRunVr+vTpw++//84vv/zC6dOnnyifd999ly5durBhwwaGDh3KiRMnOH78uFqaZcuWMXjwYBwdHRkxYgRaWlrcvn2bu3fvsmrVKnbt2kVubi4dO3bEyMiIPXv2YGhoiJOT0xPXq7TF7/oVh3VzyLjrTcad+1iOG4aWoT6Jv5wCwGHdHHKiYonZuAsAq8kjsX73TcLnfkJ2aCTaBWuR5aWlo0zLACBu+89U3/gh6S53Sbt6G+Nu7TDp1ZHgtxZUSB2fRdDWozT5YiZJrr4k3fLFcepLaBvpE14w3b/J5rfJjIhTrRem0NXGuEH+oKCWng76DpaYNHUiNzWD9IBIclMzSPVUXwcgNy2D7PiUh7ZXdqXdNgB1F48h9owrGaExaJsY4PBqVyw7N8F19JoKqeOziP7uCI6fvk/aHR/SbnthO/FltIwMiDuUP/jvuPF9siPiCF+/GwC76a/iMHssge9tICskUrUWWV5qBnlpGaCjTZ0tH2LYzBm/iStRaGup0uQmpKDM/m89Xe+BtLR0gkLCVO9DwyLx9PLF3MyUag52FViy0nV7+3HazRpGgn8kycFRdJw7gtTIBPxOFC6kPnTfQvyOu+D2fX58dt12jD4bpxF1x59IV19aThqAjqE+HgfPq/YxsjXHyNYc84K1fawb1SI7JZ3ksFgyE1KJuOFNZmIqfT6bxrXPD5ObkUWT13thVsuWgDOu5doGT8t/61FafjGDBFc/Em/5UHvqQHSM9Anen98OLTfPICMinvur9wMFDw8pEosMHCwxa+pETmoGaQGav7RWBem/HsR0zkJyvD3Jue+JwbARKPQNyTh1DACTOYvIi40mbVf+DFzDUWPJ8b5PbngoCl099Np3RP/FfqR8WXiboF7XnigTE8iNjkSntjPG02eRdeUS2TddKqSOz+rUjj8YMmsEkQHhxARH8cqcMcRHxnPz5DVVmnl7P+LmiWuc2Z3fbm+unMILQ7vxxZR1pKemq9ZMSk9KIzsz/8EEZrYWmNtaYO/kAEDNhk5kpKYTFxpDamLlXBj7+vbjdJ41jDj/SBKDo+g2ZwQpUQl4nSyMO6/9uBCvEy7cLIg71747xuBPpxF+x5/w2760mzgAPSN97hwqjDtXvzlK1w+GE+URSKR7EM1HdMOqbnV+nf4FUDgolhQaw1+rf8TI2ky1b+oTzHgtb0e2H2H0u68RGhBGZFAEb8x9k7ioOK6cvKJKs3rfaq4cv8If3/8BwIxVM+kxtAerJq8kLTVdtR5ZWlIqWQXHTp+RfQj2CSYxLpFGbRozdflUjnx3mFC/qrW+6vkdf9J/1itEB4QTGxzFoDmjSYyM587JwidOvr13CXdOXOfi7hMA6BnpY1vbQfV361p21GjiRFpCCvFh+QOpZ779nfGb38fnmgfeV9xp3KMVzXq3ZfNrK8q3gqXszI6jDJo1nKiACGKCoxg6ZzQJkfHcKtJes/cu49aJa5zdnf/d8/WVk+k4tCtfTVlPRmpGibFIPIPKe/dylSQDY89g6dKl6OjosGzZMsLCwqhWrRrTp09/qryGDRvGpk2b2LBhA++99x516tRh586d9OzZ84nyeeGFF9i2bRsfffQRy5Yto0+fPixZsoSVK1eq0vTv358//viDjz/+mE8++QRdXV0aNWqkenCAhYUF69atY/bs2eTm5tK8eXN+//13rK0rfuHa5GMX0LYyx2bWG2jbWpHp4UvIlKXkxiYAoFvdTu1XPIsxg9DS06XGF0vU8on5cg+xX+4FIOX030Qs/xLrqaOwWzydLP8Qwt5dRfrNJ/jFupKIOnIFPWsznOePQt/OgmT3AFzHrFUtOm9Qw1ptDQh9Bys6/rVe9d7p7Zdxevtl4i+7c/PVj8u9/GWpLNpGz8aMJptnom9vSU5yGin3gnAdvYa4Kvj0zoQ/LqFjbU612a+jY2tJ+j0//N5aTk7Bgvx61W3VZgjavDEQLX1d6mxdqJZPxGf7iPh8H3oO1pj3y1/vsNHxL9TS+IxeRMo/d/kvuuvpzcRZhYPq6zd/C8DQgX1YvWRORRWr1N3c8gc6Rvr0WjcRfTMjwq978fub68ktssacuZMdhlamqvc+v1/F0MqMDnOGY2xrTvS9QH5/cz3pRRbTbvZGbzrMLrzNf/jPSwE4PfsbPA9dJCM+hd/fXM8L80fyyoGFaOnoEOcVwtFJG4n1KPl2qsom/Mg/6Fmb0WD+CPTtLEhyD+TamHWqWGRYw0ZtYVsDB0u6/VV4O3vdt4dQ9+0hxF6+xz+vrnwo/6ok68JZUs0tMHpjIlpWVuT4+pC0dB7KhPxbv7Tt7EBZGJcVBgaYvP0BWja2KLMyyQ0OIvl/q8i6UPgEby0rawynvo2WhSV5cbFknjlB2r7d5V630vLn1sPoGRowfu10jMyM8bruycZxK8kp0tfsnBwwKdLXXnxzAAAfHlA/Pr6b+yWXf8pvq15j+zHs/dGqvy06tOqhNJXN1a1/oGekz4C1EzEwMyLExYsDb6nHHUtHO4wsC9vC84+rGFmb0W12ftyJuhfIgbfWqz08xGXHCXT09ei99A0MLIyJ8gjiwNh1JATlz26p060ZVnUcsKrjwDvXNquVaZ3TG2Vc66f385afMDA0YNbaWRibGXPP5R7L3lxKdpH2cnCshplV4UDfoLcGAbDu0CdqeX02+zPO/JT/Y32NujUZt2A8JhYmRIVEcXDzAQ5/d7jsK1TKTm/9DT1DfV5bOxVDMyP8rt9ny7i1an3LxslerW85tqjLu/s/Ur1/dek4AK7+dI69c/PXUL1z4joHF2+jz8xhDF8+gSi/MHbM2IifS+W/nftRjm89gp6hAW+unYaRmRHe1z3ZNG61WnvZFmuvXm/mPwBu3gH1QcGdc7/i75/OlUu5hXhcCqWyks0FFv8J9xsNrOgiVGoh8Wb/nkiIElgbyBpdmjS98XlFF6FS+6b1soouQqVVJyu3ootQqXVsE/bviZ5T8zxsKroIlVpD5b+vyfu8uqR89Pq4zztnLZOKLkKlli7ThTTaFnCoootQLhJG96qwz7Y4UDl/QHkWMmNMCCGEEEIIIYQQoopQVpG1nqsKWXxfCCGEEEIIIYQQQjyXZMaYEEIIIYQQQgghRFUhd9OWKpkxJoQQQgghhBBCCCGeSzIwJoQQQgghhBBCCCGeS3IrpRBCCCGEEEIIIUQVIYvvly6ZMSaEEEIIIYQQQgghysRXX31F7dq1MTAwoGPHjly7du2x9tu/fz8KhYJhw4aVaflkYEwIIYQQQgghhBCiqsirwNcTOnDgALNnz+ajjz7i5s2btGzZkv79+xMVFfXI/QICApg7dy7dunV78g99QjIwJoQQQgghhBBCCCFK3caNG5kyZQoTJkygSZMmbN26FSMjI3bs2KFxn9zcXMaOHcuKFStwdnYu8zLKwJgQQgghhBBCCCFEFaHMq7hXZmYmSUlJaq/MzMwSy5mVlcWNGzfo06ePapuWlhZ9+vThypUrGuv38ccfY2dnx6RJk0q97UoiA2NCCCGEEEIIIYQQ4l+tXbsWc3NztdfatWtLTBsTE0Nubi729vZq2+3t7YmIiChxn0uXLrF9+3a2bdtW6mXXRJ5KKYQQQgghhBBCCCH+1cKFC5k9e7baNn19/VLJOzk5mTfffJNt27ZhY2NTKnk+DhkYE0IIIYQQQgghhKgqnmIR/NKir6//2ANhNjY2aGtrExkZqbY9MjISBweHh9L7+voSEBDAkCFDVNvy8vIrq6Ojw/3796lbt+4zlL5kciulEEIIIYQQQgghhChVenp6tG3bljNnzqi25eXlcebMGTp16vRQ+kaNGuHm5oarq6vq9fLLL9OrVy9cXV2pVatWmZRTZowJIYQQQgghhBBCVBHKCpwx9qRmz57NuHHjaNeuHR06dODzzz8nNTWVCRMmAPDWW29Ro0YN1q5di4GBAc2aNVPb38LCAuCh7aVJBsaEEEIIIYQQQgghRKkbPXo00dHRLFu2jIiICFq1asXx48dVC/IHBQWhpVWxNzPKwJgQQgghhBBCCCGEKBPvvPMO77zzTol/O3fu3CP33bVrV+kXqBgZGBNCCCGEEEIIIYSoKqrQrZRVgQyMiTKxNcO8ootQqTXUk66nSYI8EuSR7HN0K7oIldal1ssqugiV2rRbH1d0ESqt7F1rKroIldqXm8tmodv/gpeylBVdhEotQbuiS1B5dde2rOgiVG7StR6pXqaMighRmuTbuRBCCCGEEEIIIUQVUZUW368KZG6GEEIIIYQQQgghhHguycCYEEIIIYQQQgghhHguya2UQgghhBBCCCGEEFWE3EpZumTGmBBCCCGEEEIIIYR4LsmMMSGEEEIIIYQQQogqQmaMlS6ZMSaEEEIIIYQQQgghnksyY0wIIYQQQgghhBCiqlAqKroE/ykyY0wIIYQQQgghhBBCPJdkYEwIIYQQQgghhBBCPJfkVkohhBBCCCGEEEKIKkIW3y9dMmNMCCGEEEIIIYQQQjyXZMaYEEIIIYQQQgghRBWhzJPF90uTzBgTQgghhBBCCCGEEM8lGRgTQgghhBBCCCGEEM8luZVSCCGEEEIIIYQQooqQxfdL1zPNGFMqlUydOhUrKysUCgWurq6lVKzyoVAoOHz4sOq9p6cnL7zwAgYGBrRq1arMP//cuXMoFAoSEhIA2LVrFxYWFqWWf0BAwL/+uxQvgxBCCCGEEEIIIcTz4plmjB0/fpxdu3Zx7tw5nJ2dsbGxKa1yVYiPPvoIY2Nj7t+/j4mJSbl//ujRo3nppZdKLb9atWoRHh5e5f9dNBnwwUg6jXkRAzNjAlzuc2jJdmICIjSmd+7QiBenDqFm8zqY21uxfeoG7p50UUszZsMMOozoobbN47wr345bVyZ1eFrt5gyn8Zhe6JsbEXHdi4uLdpIYEPnIfZqO60OraYMwtDUn1iOIy8t2E+Xqp/q7tr4unZa+Tr2XX0BbT5fg83e4uHgX6TFJD+Wlb2HCyJNrMKlmxY6mU8lKSgOg+guNefnQ4ofSf9/mbdKjE5+x1k+n2+zhtBzTC30zI0JdvDixeCfx/9JWbd7qQ8epgzC2NSfKI4hTH+0m/HZhW72+fzGOnRqr7XNrzxlOLN6peu/QwpmeH47GoVltAMJcfTm3dj9RHkGlV7ln1GhcH5rNyD8m4u8F8c/S3cQUOSaKqz24A63njcCkpg3J/pG4rNlPyF+3AVDoaNN2/ghqvtgKEydbspPSCbt0F5c1B0iPTFDl0eLdl6nVuxVWTZ3IzcrhxybTyrqaT63DnOE0Lehn4de9OPcY/az5uD60njYII1tzYjyCuFCsnzV9vRcNhnXGtllt9EwN+bZI/3nAoo4DnZeMoVq7Bmjr6hDjEcTVDT8ResWjTOpZnlxc3dj540/c8/QhOjaOTWuX0rt754ouVpnTadULnfb9URibkxcdTPaZfeRF+GveQd8Q3a6voFO/DRgYo0yKJevsAfL83QDQqlkf3fYDUNg7oWViQebhL8n1cS2fypSCrrOH06JIXD71GHG59Vt96FAkLp/+aDcRReLyayXEZdc9ZzhZEJcNLEwYvGkmdo1rYWBhQlpsEj6nbnBh/SGyUtJLv5JPoe74vjScOQgDW3MS7gVxa/H3xD8iJtcc3IGmC0ZiXNOGFP9I7qzaR0RBTH7AtH51Wix+DdtOjVHoaJHkFcrfkzeRHhoLQI+fF2PXuYnaPr67z3BzwY7Sr+ATaDKuDy2m55+f4jyC+HvpbqIf0RZ1BnWgXcH5KSkgkmtr9hNcrC3azh1OozG90DM3IvK6F5cW7STJv/C4M6/jQIclY3Bo3wAtXR3iPIJw2fAT4X/nx976I7vR87OSz1k/tJxJRuzD10vlrSL61gPNRnSj3eSBWNVxIDMlnft/XuP00u9Lr3LPSOKOZs4T+lJ/5mAMbM1JvBfE7cXfE3/LV2P6GkM60mT+SIxq2ZDiH8HdVfuJPOOq+vurET+WuJ/bxz/i/fUf2HRuTPdflpaY5uyAJY+Me6KQUimL75emZxoY8/X1pVq1anTurPmiNisrCz09vWf5mHLj6+vLoEGDcHJyeuo8cnNzUSgUaGk9+WQ8Q0NDDA0Nn/qzi9PW1sbBwaHU8qtMXpz+Mt0nDODHOV8TGxzNwDmjmL57Iev6ziUnM7vEffSMDAj1COTqoXNM/GaOxrw9zrmyb94W1fuczJxSL/+zaDVjMM0n9OPs7G9ICoqm/bwRDNqzgAO9F5Croe51h3Sk89KxXFi0k6hbPjSfNIBBPyxgX895qgu5zh+NxfHFVpycvpms5DS6rhxH/2/f5/CrHz+UX88Nk4nzCMKkmlWJn7ev+1y1E35Jg2vloeP0wbQd34+jc74hITia7nNGMPqHBWzro7mtGg3uyItLxnJi8U7CXH1oP3EAo39YwLe95pFW5KLX9ce/uLjxZ9X77PQs1f/rGukzevc8vE/d4uSSXWhpa9F19nBG7Z7P153eIy8nt+wq/ZjqvNyRDh+N5e8PdxJ9y4emkwfQb+8Cfuk+r8SLe7t29enx1dvcWHuQ4NO3cH6lMy9u/4DfBiwh4X4IOoZ6WDWvjeumw8TdC0Lf3IiOK96kz87Z/P7SMlU+Wro6+P9xjagbPtR/rcdDn1NZtJkxmJYT+nG6oJ91nDeCl/cs4MdH9LN6QzrSdelYzi3aScQtH1pNGsDLPyxgb895pBe0qY6hHoHn7hB47g6dF44uMZ/Bu+aQ4B/J4dFryMnIouXkAQzeNYcfus4hrYIGmEtLenoGDes588qgfry/aFVFF6dcaDdsj27PUWSd3kNeuB+6bfqgP+J90ncsgbTkh3fQ0kZ/5GxISybzt60oU+JRmFmjzCwygKqrT15UMHlul9Af9nb5VaYUdJg+mDbj+/HnnG9IDI6m65wRjPxhAdv/JS73WjKWk4t3Eu7qQ7uJAxj1wwK+KxaXb//4F5c0xGVlXh4+p25wacMh0uKSsKjtQN+Px9FvjQl/vPt12VX4MdV8+QVaLh/LzQU7iL3lS4MpA+i+70OOd51LZgkx2bpdfTpueQe3NQcIP30Lx1c602XnbE71W0zS/RAAjJ3s6HV4Gf77zuO+4Weyk9Mxa1iTvAz1dvbb8xd31/+kep9bpN0qgvOQjrywbCyXFuZfszSbPICBexZwsIeG81Pb+rz41dtcX3eQoNO3qDusM32/+4BfBy4hvqAtWs4cTNMJ/Tj/wTckB0fTdu4IBu5ZwE8vFh53/b+fQ6J/JEcLYm+zSQPov2sOB7rMIT06Eb/f/yHk3B21z+7x2TS09XUrxaBYRfUtgHaTB9J+ykDOrdlH+C1fdI30Ma9pWzYVfQoSdzSrMfQFmi9/A9cFO4i76UO9KQPpsu9DTnWdQ2YJ1+9W7erTfss7uK85QMSpm9R6pQudds7mr36LSPLM729Hm89Q28ehdyvabJxC6B/XAIi97vVQmiYLRmLXrZkMiokK89S3Uo4fP55Zs2YRFBSEQqGgdu3aAPTs2ZN33nmH999/HxsbG/r37w/A3bt3GThwICYmJtjb2/Pmm28SExOjyi8vL4+1a9dSp04dDA0NadmyJT/99FNJH63y9ddfU79+fQwMDLC3t2fEiBGqv9WuXZvPP/9cLX2rVq1Yvnx5iXkpFApu3LjBxx9/jEKhYPny5SXeZujq6opCoSAgIAAovP3xt99+o0mTJujr6xMUVPKMkD///JMGDRpgaGhIr169VHk8UNKtlFu2bKFu3bro6enRsGFDfvjhB9XfJk6cSIsWLcjMzATyByFbt27NW2+9BZR8K+W/lQHg0qVLdOvWDUNDQ2rVqsW7775LampqiXWqKD0mDuTk5l+5e+oG4Z5B/Dj7K8zsLWner53GfTzPuXLs04O4nbj+yLxzsrJJjk5UvdKTKlfdm08awM3NRwg4eZM4z2DOvr8VI3sLavdvq3GfFlMG4rHvLPcPXiDeO4wLC3eSk5FJo9H5AxN6poY0Gt2TKx/vJezve8S4BXBuzrc4tG+AXeu6ank1ebM3+mbGuH7zp8bPS49NIj06UfVCqSydyj+h9pMG8PeXR/A+dZNoz2D+mL0VEzsLGvTT3FYdJg/k9v6zuB26QKx3GMcX7SQ7PZMWo9QHcbLTs0iNTlS9ig4EWtetjqGlKRc3/kScXzgx3qFc/vwXTOwsMKtROWZwNp0yEK8fz+Jz8AKJ3mH8/eFOctIzNQ5WNZnUn9Bzd7i79SiJPmHc+t9PxN4NoPGEvgBkJ6dzcswnBPx+lSTfcKJv+vLPkt3YtHTGuLq1Kh/XT3/h3rbjxHsGl0s9n1bLSQNw2XwE/5M3ifUM5vT7WzG2t8D5Ef2s1ZSBuO87i0dBPztb0M8ajy5s09vbT3Dz69+JvOlTYh4GliZYOFfjxte/E+sZTGJAJFfWHkDXyACrhjVLvZ7lrVun9rw7dRx9enSp6KKUG512fclxu0ju3csoY8PJOrUHZXYWOs26lpy+eVcUBsZkHv6KvDAflEmx5IV4oYwOUaXJ879L9uXD5PrcKq9qlJp2kwZw5csj+BTE5aMFcbn+I+Jyu8kDubP/LHcL4vKJgrjc/AnicmZSGq57zhDh5k9SaCxBl9259cNparZvWGZ1fRINpg3Ef+9ZAg5cINkrlBvzd5CbnkntMSXH5PqTBxBx9g5eW46S7B2G+/qfiHcLoN7Efqo0zT4cRcRft3FbtY+Eu4GkBkYRfvLmQwNtOemZZEYnql45FTyTpfnUgXjuO4vXwQskeIdx6cP8WNpQw/mp2aT+hJy7w52tR0nwCePGhp+IuRtA0/F9i6QZwK0vjhB48iZxHsGcK7h2ciqI6fqWJpg7V+P2V78T5xFMkn8k1wtir2VB7M3NyFa7tlHm5lG9cxPu7z9X5m3yOCqqb+mbGdFt7giOzv4GjyNXSAiKItozGJ/TN8usrk9K4o5m9ae9RMDeswTuP0+yVyi35m8nNz0TJw39rd6UAUSevY3313+Q7B3GvfWHSHDzx3lCYewpGk8yoxOp1r8t0ZfvkRYUBYAyO1ft71nxKVQb0JbA/efLpc7/Fcq8inv9Fz31wNimTZv4+OOPqVmzJuHh4Vy/XjjY8P3336Onp8fly5fZunUrCQkJvPjii7Ru3RoXFxeOHz9OZGQko0aNUu2zdu1adu/ezdatW3F3d+eDDz7gjTfe4Pz5kjuIi4sL7777Lh9//DH379/n+PHjdO/e/WmrQ3h4OE2bNmXOnDmEh4czd+7cx943LS2NTz75hO+++w53d3fs7OweShMcHMyrr77KkCFDcHV1ZfLkyXz44YePzPfXX3/lvffeY86cOdy9e5dp06YxYcIEzp49C8AXX3xBamqqKp/FixeTkJDAl19+WWJ+j1MGX19fBgwYwPDhw7lz5w4HDhzg0qVLvPPOO4/dHmXNupYdZnaWeF12U23LSE4n0NWH2m0aPHP+9V5owscu37DwzEZGrJqEkUX531ariamjLcb2FoRcvKvalpWcTpSrLw5t6pe4j5auNrbN6xByyb1wo1JJyEV37NvWA8CmeR209XTU0iT4hpMcEoND28J8LetXp+17r/DX+1shT/Ng18jjq3nT5UsG712AQ7uSy1XWzGvZYmJnQcClwrbKTE4nzNWXGo9oK4fmdQgo1lYBl9yp0aaeWtqmwzrz7q0tTDq5lh7zR6FjUDgzNs4vnLS4ZFqO7omWrjY6+rq0GN2TGO9QEkOiS7eiT0FLVxvrFnUIu6hez/BL7ti1rVfiPrZt6xFW5LgDCD13R2N6AF0zQ5R5eQ/dKljZmRX0s+Bi/SzyX/qZXfM6BJfQzxwe0UbFZcSnEO8TRqPhXdEx1EehrUWzN14kLTqRaLdH3HonKictbbTsncgLvFdko5K8IA+0qjuXuIt23Vbkhfmh1/t1DGdsxGD8CnQ6vgSKqn/LxIO4HHhJvW+Fu/pS/QnjcuAld6oXi8tNhnXmnVtbmHByLd2LxeXiTOwsaDCgPcFXPZ+tUqVAoauNZYs6RBaNsUolkRfvYt225HaxblePqGIxOeLcHawfxBuFgmp9WpHsF063fQsY4vY1Lx5dQfUBDw8EOL3ahZfdt9Lv7DqaLRqNtmHF3emhpauNTfM6hBY7P4VedMeuTcmx1L5tPUKLtUXI+cLzk6mjLUb2FmppspPTiXb1xb6gfTPjU0jwCaP+iMLY26gg9sZoiL31R3QlJz0T/6PXnqXKpaIi+1btbs1RKBSY2lsy6cwnzPjnC17+ahamGu4qKG8SdzRT6Gpj0aIOURfUY0/UxbtYabh+t2pbXz09EHnujsb0+jZmOPRpRcCP5zSWo1r/NuhbmsrAmKhQT30rpbm5OaampiXerle/fn3Wr1+ver9q1Spat27NmjVrVNt27NhBrVq18PLywsnJiTVr1nD69Gk6deoEgLOzM5cuXeKbb76hR4+HR6yDgoIwNjZm8ODBmJqa4uTkROvWrZ+2Ojg4OKCjo4OJickT336YnZ3N119/TcuWLTWmeTDz69NPPwWgYcOGuLm58cknn2jcZ8OGDYwfP56ZM2cCMHv2bP755x82bNhAr169MDExYc+ePfTo0QNTU1M+//xzzp49i5mZ2VOXYe3atYwdO5b3338fyP+3/OKLL+jRowdbtmzBwMDgidqmLJjaWgCQUuyWopToRNXfnpbneVfuHL9GXHAU1k72DJr3GlN3fcimV5eifMRAUHkxKqhf8VsT06OTMLQzL3EfAytTtHS0H1rjKz0mEYt61fLztTMnNzP7oQGM9JhEDG3z89XS06H3l2/zz+p9pITFYub48ABwalQC5z/cQfQdP7T1dGk8pidDDi7m15eXE3M34Gmq/NRM7Czyy1SsrVJjkjC2LbmtjCzz2yo1JrHYPolY162meu9+5G+SQmNIiYzHtrEjPT98Dau61fh12iYAslIz+HH0aoZv+4DO7w4DIN4/ggNvfYIyt+J/ZtF/cEwUq2d6dCLmRepZlKGtBenR6m2ZEZOEoYY+p62vS7tFr+F3+ArZlWgdjcfxoJ+lFTt20qKTMNLQzww19LO0Iv3scR0es45B299nmuc2lHlK0mOT+O3N9WQmVq0BRgEKQxMUWtooU9WPJWVqElpWJV9rKMxt0HJsRK7HP2T8sgktCzv0+owFLW1yrvxeHsUuM8aPiMsm/xKX00qIy1ZF4pXHkb9JLIjLdo0d6VEQlw8XxOUHhnzxNvX6tUHXUB+fUzc5vuC7UqjZs3kQkzOKxY+M6CRM61UvcR8DW4uH0mdGJ2JQ0Mb6NmbomhjS6J0h3P3kEHdW7cehVws6b3+fcyNWE3Ml/4t50K9/kxYSQ3pEAhZNatF88RhM61bjyqTPS72ej+NxrlmKM7S1KPm6qCCWGz7q2qnIcffnmHX0/e59xt8viL0xSRx/Yz1ZGmJvw9d64nv4CrkZJd+KV54qsm9ZONqh0NLihbdf5syKH8hMTqPb3JGM2vMhOwcsJC+7YpePkLij2YPYk1lCLNEYe+wsSkz/IPYU5zi6OzkpGYT9qfmOndqv9yLy3B3Sw+OerAJClKJnWmNMk7Zt1X+Nun37NmfPni1xQXtfX1+ys7NJS0ujb9++an97cGtgSfr27YuTkxPOzs4MGDCAAQMG8Morr2BkZFR6FXlMenp6tGjR4pFpPDw86Nixo9q2B4OAj9pn6tSpatu6dOnCpk2FwbZTp07MnTuXlStXsmDBArp2LfnWjMctw+3bt7lz5w579+5VbVMqleTl5eHv70/jxuoLTAJkZmaqbud8IEeZi45C+5H1e1xthnZh1JopqvfbJmoeTHxWt36/ovr/8PvBhHsEseTiF9R7oSnef999xJ5lo/6wznRfN1H1/s/xG8q9DA90/HA0CT5heP96WWOaRL9wEv3CVe8jb3hj5mRHi8kD8meZlaEmwzozYE1hWx2aUHZtdXvfWdX/R98PITUqgTH7FmHhaEdCUBQ6+rq8tH4yIS5eHJn1FVraCjpMHcTInXP5fsgyjevg/VcodLTpuXUWCoWCKwt3VXRx/lWDYZ3pWaSf/VGB/Qygx+pxpMUk8fPwleRmZNNkTE8G75zDwcHLSItKqNCyiXKgUKBMSyLr5G5QKsmNDCTbxALd9v2r3MBYk2Gd6VckLv9cTnE55n4IKVEJvFYkLj/w18o9XN70C1Z1qtF9wSheXDqWU0t2lVm5KopCK3+GYdjxm3h/exyARPdAbNrVp+6bvVUDY/57CtstyTOY9MgEev60GGMnO1IDox7O+D+s86pxpMcm8furK8nJyKbRmJ702zWHw4OWkV4s9tq1qYdlgxqce29LyZmVscrUtxRaCrT1dDizfDcBBbPyfp/1FW+7fIVjpyYEXHDTlHWZqExt88DzEndKUvu1ngT/cpk8Dde+htWssO/ZgqtTN5X4d6GZMq/qzySvTMpkYMzY2FjtfUpKCkOGDClxdlS1atW4ezc/iB49epQaNWqo/V1fX7/EzzA1NeXmzZucO3eOkydPsmzZMpYvX87169exsLBAS0sLZbF1jbKzn+zL6IMF9IvmU1IehoaGKCroFoe8vDwuX76MtrY2Pj4lr1nzJFJSUpg2bRrvvvvuQ39zdHQscZ+1a9eyYsUKtW0dzZvSyaLZM5cHwP30DTa4FtZNR08XABNbc5KiE1TbTWzNCbsXWCqf+UBscBQpsUnY1LavkIGxgFM3iXQtfCqMtl5+lzW0MVP7gmxoa0ase8lr22XEJZOXk6v2q2h+HuaqhbzTohLR1tdFz8xIbdaYoY256lfbGp2bYNWoFlNf6pD/x4JjfvztLdzcfASXjb+U+PlRrn44tH/2W1z/jc+pm+wo8gQdnYK2MrYxI7VIWxnbmBF1r+S2SovPbytjG/W2MrYxJ/URi56HFXyuZW17EoKiaDKsM+Y1bdn9ygrV+mq/vfsV79/5hvr92uLx+z9PVcfSkvngmChWT0Nbc41PD02PTsDQVn02qoGNGelF+iDkD4r12joLk5rWHB+1tkrMFvPX0M+MivUzI1szYjT0s3QN/cyoSD97HDW7NKV279ZsazZN1XbnF++iVrdmNBrRjZtfV62BkeedMj0FZV4uCmP1vqMwNkOZWvJxoUxNhLxctbUZ8+LCUZhYgJZ2/t+qCJ9TN1XxEQr7VklxOfJf4rLRE8bl8ILPtSiIyw88WAcozjec9IQUxv68jL+/OKxWnvL2ICYbFIsfBrZmZESVXMeM6ISH0uvbmpNRUI/MuGTysnNI8g5VS5PkHYZNB83rG8XdzG83kzr2FTIw9shrFg1tkR6dgKGNeh8ztC08Pz34r6GNmdoAV9Frp+pdmuLYpzW7mxbG3suLd1GjezMajOzG7a/UY2+j13sSczeAGLeAp6zps6lMfSul4PNivMNUadLjkkmPS8asyBqj5aUytc0DlTHuQGHs0X9ELCkuIyrhsdNbd2yIaf3qXJv2hcYyOL3Wg8z4ZMJPVJ416cTz6anXGHsSbdq0wd3dndq1a1OvXj21l7Gxsdqi9cX/XqtWLY356ujo0KdPH9avX8+dO3cICAjgr7/+AsDW1pbw8MKZK0lJSfj7P9n6LLa2+U9TKZpP0YXsn0Tjxo25dk19DYJ//nn0l+PGjRtz+bL67JzLly/TpEnhY7X/97//4enpyfnz5zl+/Dg7d+4sns0TlaFNmzbcu3fvoX+HevXqaXy66MKFC0lMTFR7tTd/eGbZ08pMzSAmMFL1ivAOISkqngadCwfe9E0McWpVj4CbXqX2uQDmDlYYWZqQVEEnruzUDJICIlWveK9QUiMTqNG1qSqNrokhdq3qEnHTu8Q88rJziXbzp0aXwn1QKKjRtSmRN/IHHGPc/MnNylFLY+5cDdOaNkTcyM/35LRNHOq/iEMDFnNowGLOz8+fBn5k+Erufn9aYx1smjiWyyyXrNQMEgIjVa8Y71BSohKoXaROeiaGVG9Vl9BHtFWEm7/aPigUOHVpSqiGxdIB7JrmDxo/uDjUNdTLH1Av8sVWmacEZeGv+BUpLzuX2Dv+VOuqXs9qXZsSdaPkekbf8FFPD9To3kwt/YNBMbM69hwfvY7M+JQyKX9py07NIDEgUvWKK+hnNYv1M/t/6WdRbv7UKnbs1OzalAgNbVoSnQdr++Sp33KrzFNWimNHPKG8XPIiA9FyLHpOVKDl2Ii8sJKfvJUX6oPCwg4o/PfWsrQnLyWhSg2KwcNxObYgLjsVi8vVWtUl7F/islMJcTnsMeLyo754PuhTD744VxRldi7xd/yxKxaT7bo2I/ZGye0S6+Kjnh6w796M2IJ4o8zOJc7VD9Nit8eb1nUgLSQGTSya5T+ZPSMy4Slq8uzysnOJcfNXu85BoaB616ZEafj3jrzhQ/VibVGzW+H5KTkomrQSrp1sW9UlsqB9H8ReZUmxt9iP3zpG+tQZ3JH7FbgeUmXqW6Eu+dfeRW8xNDA3xtDKlKRQzcdaWalMbVOSyhJ3ID9OJNzxx65b8djTlDiXktsm7oY3dt3UJz/YdW9eYvrar/ck/rYfiRoGICF/YCzo0EWUleCJ7VXNg68aFfH6LyqXHvn222+zbds2xowZw/z587GyssLHx4f9+/fz3XffYWpqyty5c/nggw/Iy8uja9euJCYmcvnyZczMzBg3btxDef7xxx/4+fnRvXt3LC0t+fPPP8nLy6Nhw/xfwV588UV27drFkCFDsLCwYNmyZWhrP9mtfQ8G5pYvX87q1avx8vJSrc/1pKZPn86nn37KvHnzmDx5Mjdu3GDXrl2P3GfevHmMGjWK1q1b06dPH37//Xd++eUXTp/OH4S4desWy5Yt46effqJLly5s3LiR9957jx49euDs/PCivo9ThgULFvDCCy/wzjvvMHnyZIyNjbl37x6nTp3SuKi/vr7+QzP7Sus2Sk3O7zhG31mvEB0QQVxwFAPnjCIpMh63ky6qNDP2LsHtxHUu7T4BgJ6RPja1C9d0sa5lR/UmTqQlpJAQFouekT793xvBneNXSYpOxMbRniELXycmIBLPC7fLtD5Pwm37cdrOGkaifyTJwVG0nzuCtMgEAk7cUKUZvG8h/sddcP/+FAB3th2j18ZpRN/xJ8rVlxaTBqBrqM/9g/kXdVnJ6XgeOEfnZWPJTEghKyWdrh+/RYSLF1EFv34lFfvl2NDSFIB4nzDVLLPmk/qTHBxNnFco2vq6NH6tJ9W7NOXo2LK7/fVRrm8/TudZw4jzjyQxOIpuc0aQEpWA18nCtnrtx4V4nXDhZkFbXfvuGIM/nUb4HX/Cb/vSbuIA9Iz0uXMov60sHO1oMqwzvn+5kpGQgm0jR3ovG0vQPx5EFzxp0f/iXXotHEO/VeO5seskCoWCF2YOIS8nl8ArHuXfECVw33aMrp9NI/aOP9G3fGk6ZQA6hvp4H8ivZ7dN00gLj+fGuoMA3Nt+goE/LabptIGEnHalztBOWLdw5vL8HUD+oNiL376LdfPanBr3KVraWqpf/DMTUlRrjBhXt0bf0hjj6tZoaWthVXARmeQfSU5aZvFiVpjb24/TbtYwEgr6Wce5I0iNTMCvSD8bum8hfsddcCs4dly3HaPPxmlE3fEn0tWXlpPy29TjYOGXJyNbc4xszTGvbQ+AdaNaZKekkxwWS2ZCKhE3vMlMTKXPZ9O49vlhcjOyaPJ6L8xq2RJwxrVc26AspKWlExRSOKsgNCwSTy9fzM1Mqebw8LqF/wU5LqfQGziRvMhA8sL90WnbB4WuPjl383/40hs4EWVKAtkX82fd5tw+h07rF9F98TVybv2FwtIO3Y6DyL55pjBTXf2CwbN8CnNbFLa1ICMVZXLlXqPFZftxOs0aRrx/JAlF4rJ3kbg8uiAu3yroWy7fHeOlT6cRUSQu6xrp41YkLjce1hm/v1xJT0jBrpEjvZaNJbhIXHbu1RIjG3MibvuRlZaBTYOa9Fw0hpDr90l6xEBRefH65hgdNk0j/rY/ca6+1J8yAB0jfQIKBl/afzGd9Ih47q45AID3d8fp+csSGkx7ifAzt6g1tBNWLZ25MW+7Ks/7W47Saessov/xJOryPRx6taBa3zacG74KAGMnOxxf7Uz4GVey4lIwb+JIqxVvEH3Fg0SPintysNu3x+jx2TSib/sT7epLs8n51yxeBeennp9PIzUinusF56e7208w5KfFNJ86kKAzrtQd2gmbFs5cXLBDlefd7cdp/W7htVO7gmunwIKYHnnDm6zEVHp+Po2bn+XH3oZje2Fay5agYrG37ssvoKWjjc8vmpeWqAgV1bfi/SPwPuFC74/e4MTCHWQlp9N9wSjifMMIqiTXPBJ3NPP+5k/abZpO/G0/4m/5Um/KQLSNDFQL4bfdPIOM8DjcC2KPz7bjdP91KfWmv0TEaVdqDeuEZUtnbs1TXzdNx8SQGkM64rZ870Of+YBt16YYO9kRsPdcmdVPiMdVLgNj1atX5/LlyyxYsIB+/fqRmZmJk5MTAwYMUN2uuHLlSmxtbVm7di1+fn5YWFjQpk0bFi1aVGKeFhYW/PLLLyxfvpyMjAzq16/Pvn37aNo0f8R74cKF+Pv7M3jwYMzNzVm5cuUTzxjT1dVl3759zJgxgxYtWtC+fXtWrVrFyJEjn7gNHB0d+fnnn/nggw/YvHkzHTp0YM2aNUycOFHjPsOGDWPTpk1s2LCB9957jzp16rBz50569uxJRkYGb7zxBuPHj2fIkCEATJ06laNHj/Lmm29y4cKFpypDixYtOH/+PIsXL6Zbt24olUrq1q3L6NGjn7jOZemvrb+hZ6jPqLVTMDQzwv/6fb4Zt05t7SYbJ3uMrUxV72u1qMs7+5ep3g9b+hYA1346z765W/Ifu93YkfbDu2NoZkxSVDz3L9zhz40Hyc3KKb/K/QvXLX+gY6RPj3UT0TMzIuK6F0ffXE9ukbqbO9lhWKTuvr9fxcDKjPZzhmNka07MvUCOvrlebSHav1fsRZmnpN+376Gtp0PweTcuLt71RGXT1tWh09KxGDtYkpOeSaxHMH+MWUtYBV0YXd36B3pG+gxYOxEDMyNCXLw48JZ6W1k62mFkWdhWnn9cxcjajG6zh2Nsa07UvUAOvLVetRB7bnYOtbs0pf3E/uga6pMUHsf9Y9f5e/MRVR5xvuH8NGkjXd5/hTd/+QilUkmkeyAHx62v8GnzD/j/ln9MtJ47HENbc+LcAzn5xnoyCuppXN1G7YETUS7enH/na9rMH0nbBaNI8o/gr0mfkXA/JD+9gyWOBY+9H3ZqjdpnHRuxmoiCY6D1vOHUH1X4BOGhJ9c8lKYyuFnQz3qtm4i+mRHh1734/V/6mc/vVzG0MqPDnPxjJ/peIL8X62fN3uhNh9mvqt4P/3kpAKdnf4PnoYtkxKfw+5vreWH+SF45sBAtHR3ivEI4OmkjsR6af3GtKu56ejNx1gLV+/WbvwVg6MA+rF4yp6KKVaZy718n28gE3S5DURiZkRcdTOZPn0Na/nGhMLNWn12aHE/mT5+h22s0BuOWo0yJJ/vmaXKuHVOl0XKojcHoear3er3yz9E5dy+TdVzzzPHK4FpBXO5XJC4fKhaXLUqIy4bWZnQtEpcPlRCX2xXE5eTwOLyOXedKkbick5FFyzE9eXHpWLT1dUkOi8XruAtXt1SO25NDfvsHfWtTms4fgYGtOQnugVx8/RMyC+poVMNa7WnQsS7eXJ35Fc0WjKTZwlGk+EdwecJGkgpiMkDYMRduLNhBo1kv03rlWyT7hnNl8iZir+XP8MnLzsG+WzPqT84fhEsLiyPk6HU8Pj9crnUvzu/3qxhYm9F2bv41S+y9QI4ViaXGNYqdn25489c7X9Nu/kjaLxhFon8EpyZ/RnyRtrj9dX5M7/ZJ/rVT5HUvjr9ReNxlxqdw7I31tJ8/kkEH82NvvFcIJydtJK5Y7G34Wg8Cjl2vdE9crqi+BXB09je8uGwsI3bORZmXR/BVTw69tZ68SjILSOKOZqFH/kHf2owm80egb2tBonsgl8esKxZ7CmdSxrl4c33mVzRZMJKmC0eT4h/BlQkbSfIMUcu35rBOgILgX//W+Nm1X+9J7LX7pPiEaUwjRHlRKIsvxCVEKfig9msVXYRKrWFuxU+frqwSyuUG76rLvvKM0VY6qXLsPNK0Wx9XdBEqrexda/490XPsy80ZFV2ESqtOllxGP0qCttwCrkl82d5cIf7j6mVW/FPOK6tXI36s6CKUi8A2fSrss51ual5Gp6qSrxFCCCGEEEIIIYQQ4rkk01aEEEIIIYQQQgghqghlnszILU0yY0wIIYQQQgghhBBCPJdkxpgQQgghhBBCCCFEFSErxZcumTEmhBBCCCGEEEIIIZ5LMjAmhBBCCCGEEEIIIZ5LciulEEIIIYQQQgghRBUhi++XLpkxJoQQQgghhBBCCCGeSzJjTAghhBBCCCGEEKKKUCplxlhpkhljQgghhBBCCCGEEOK5JANjQgghhBBCCCGEEOK5JLdSCiGEEEIIIYQQQlQRyryKLsF/i8wYE0IIIYQQQgghhBDPJZkxJoQQQgghhBBCCFFF5Mni+6VKZowJIYQQQgghhBBCiOeSzBgTQgghhBBCCCGEqCKUMmOsVMmMMSGEEEIIIYQQQgjxXJKBMSGEEEIIIYQQQgjxXJJbKUWZMJIx10eqkZ1b0UWotHJ0tSu6CJVatZycii5CpZWLTCl/lOxdayq6CJWW7vhFFV2ESq3xhsUVXYRKK1TOWY+UKmFZI1dSK7oIlZqxQr6mPkqavl5FF6HSerWiC1BOlHkSYEuTjF4IIYQQQgghhBBCiOeSDMULIYQQQgghhBBCVBFKZUWX4L9FZowJIYQQQgghhBBCiOeSDIwJIYQQQgghhBBCiOeS3EophBBCCCGEEEIIUUXI4vulS2aMCSGEEEIIIYQQQojnkswYE0IIIYQQQgghhKgi8pQyY6w0yYwxIYQQQgghhBBCCPFckhljQgghhBBCCCGEEFWEUmaMlSqZMSaEEEIIIYQQQgghnksyMCaEEEIIIYQQQgghnktyK6UQQgghhBBCCCFEFaFUVnQJ/ltkxpgQQgghhBBCCCGEeC7JjDEhhBBCCCGEEEKIKiJPFt8vVTJjTAghhBBCCCGEEEI8l2RgTAghhBBCCCGEEEI8l+RWSiGEEEIIIYQQQogqQim3UpaqSj1jbPz48QwbNqzM8l++fDmtWrV6aJu9vT0KhYLDhw+X2Wc/0LNnT95//33V+9q1a/P555+XWv6P04bFyyCEEEIIIYQQQghRGr766itq166NgYEBHTt25Nq1axrTbtu2jW7dumFpaYmlpSV9+vR5ZPrS8EQzxnr27EmrVq2eeODmafcrbx4eHqxYsYJff/2VF154AUtLy3Ivw/Xr1zE2Ni61/DZt2oTyP/os194fjKD9mF4YmBkT6OLFb0t2EBsQoTF97Q6N6DZ1MNWb18HM3pI9UzficdJFLU2T/u3pMLY3NZrXwcjSlC9fWkj4vcCyrkqZqj2hL3VnDkHf1pyke0HcXbyLhFu+JaY1aViTRvNGYN7SGaNattxduhv/bcfKucSlq8Oc4TQd0wt9cyPCr3txbtFOEgMiH7lP83F9aD1tEEa25sR4BHFh2W6iXP1Uf2/6ei8aDOuMbbPa6Jka8m3TqWQlpT2Uj9OLrWj//jBsGjuSk5FN2FUP/pz8eWlXsdQ4TuhHnZlD0LMzJ/leEB6LdpL4iGOl3vyRmLdwxtDRFo+l3xP4rfqx4vzuUOxf6oBx/erkZmSRcN0Lr5U/kuobXh7VKXNOE/riPHMI+nb5fct90a5HtleD+SMwb+GMkaMt7kt3E/Bt1e5bRem06oVO+/4ojM3Jiw4m+8w+8iL8Ne+gb4hu11fQqd8GDIxRJsWSdfYAef5uAGjVrI9u+wEo7J3QMrEg8/CX5Pq4lk9lKoiLqxs7f/yJe54+RMfGsWntUnp371zRxaoQz9N5q92c4TQuOEdFXPfi4mOco5qO60OraYMwtDUn1iOIy8XOUdr6unRa+jr1Xn4BbT1dgs/f4eLiXaTHJAFg3diRVm8PoVr7BhhYmZIcHM29PX/htuNEiZ/n0K4+Lx9aQtz9EH4asLj0Kv8UOs8eTvPXe6FvZkSYixenF+0k4V/aq9VbfWg3bRDGtuZEewTx17LdRNz2KzHtq9/Po06vlhyZ/Bk+J2+otvda8SY12jXAukFN4nzC+GFgxbbD4xoxewy9xvTB2MwYLxdPdiz+hogAzefgl2e+SvsBL1C9bk2yMrLwvuHJvnW7CfcLKzH9/O+X0qpnGzZOWYvLybL90loWXv5gNN3G9MbIzBgfF0/2LtlG1CO+S9Tv0Jj+U1/GqbkzFvZWfDV1Pa4nr6ulMbUxZ8SHb9CkWwsMzYzxvubBvo+2PzLfyqDX7OG0KfheFezixR+LdxD3L32r/Vt96TJ1ECa25kR4BHHso+8JLdK3LB3t6Lf4dRzbN0RHTxef87f586PvSS2IRQBjvpuNQxMnjK3NSE9Kxe+SO6fX7iM5KqGsqvqfUZW+4h84cIDZs2ezdetWOnbsyOeff07//v25f/8+dnZ2D6U/d+4cY8aMoXPnzhgYGPDJJ5/Qr18/3N3dqVGjRpmUsVLPGCtvvr75F11Dhw7FwcEBfX39p8onKyvrqctga2uLkZHRU+9fnLm5ORYWFqWWX2XRbfoQOk3oz5HFO9gybCnZ6RmM3/0hOvq6GvfRM9In3COQ35ftfGSaQJf7nFi3ryyKXe6qD32BJsvfxOvTn7nQbxFJ7oF03PchejZmJabXNtQjNSgKj1X7yIiML+fSlr42MwbTckI/zi3awaEhH5GdnsnLexag/YjjpN6QjnRdOpbrn//KgZeWEHsviJd/WIChdWGb6RjqEXjuDi5f/qYxn7oD29N303Q8Dl5gf79F/PzqCrwOXynV+pUmh6GdaLTiTXw+/Ym/+y4k2T2QdvsXajxWtAz1SA+M4v7qHzUeK5adGhO08yT/vLQUl5GrUeho0+7AIrSNni62VibVhr5A4xVv4v3pz1zqu4hk90A67n9030oLjMJz9X+jbxWl3bA9uj1HkX3ldzJ++BhlVDD6I94HI9OSd9DSRn/kbLTMbcj8bSsZO5aQdXI3ypQi7aKrT15UMNmn95ZLHSqD9PQMGtZzZvGcmRVdlAr1PJ23Ws0YTPMJ/bi4aAe/FJyjBv3LOarukI50XjoWl89/5eeCc9SgHxZgUOQc1fmjsTj1ac3J6Zs5MnIVRvaW9P/2fdXfbVrUJiMmiTPvbeFA7wXc3PwbHT4cRdNxfR/6PD0zI3p9Pp3Qy+6lWven0X7GYFpP6MfphTv48eWPyE7LZPi/tFfDIR3psXQsVz7/lR8GLSHaI4jhe9TP6Q+0mTTgkT8m3z1wnvt//FMqdSkPQ6a/Qv/xg9ix6BuWDl1ARlomH/6wDN1HtFfjjk05tfsYy4YtYO0by9HW1ebDHz5C3/Dh8/bASUOq1jfzYgZMH0rvCQPZs/hb1gxbSFZ6Ju/vXvLI7xL6RvqEeATy47LtGtO8/e18bGrZ8dWU9awcNJ/Y0Ghm71mGXgltWFl0mT6YjuP788einXw3dBlZaZm8+cOjv1c1HfwC/ZeM5dymX/hm8BIiPYJ444cPMS7oW7qG+ry550MAvh+zhu3DV6Ctq8Pr2+eiUBTeAuh/5R6H3t7M5hfncXD6Jqyc7Bi19b2yrbAodxs3bmTKlClMmDCBJk2asHXrVoyMjNixY0eJ6ffu3cvMmTNp1aoVjRo14rvvviMvL48zZ86UWRkfe2Bs/PjxnD9/nk2bNqFQKFAoFAQEBABw/vx5OnTogL6+PtWqVePDDz8kJyfnkfvl5uYyadIk6tSpg6GhIQ0bNmTTpk1PVPjAwECGDBmCpaUlxsbGNG3alD///BOAXbt2PTQgdPjwYbWOWNTy5csZMmRIfqNoaanSlXSb4bBhwxg/frzqfe3atVm5ciVvvfUWZmZmTJ06tcTPSE1N5a233sLExIRq1arx6aefPpSm+K2UQUFBDB06FBMTE8zMzBg1ahSRkfmj956enhgZGfHjjz+q0h88eBBDQ0Pu3bsHPHwr5eOUITMzk7lz51KjRg2MjY3p2LEj586dK7FOFaXLxAGc23wYj1M3iPQM5tDsLZjaW9C4XzuN+3idu83pTw9x74SLxjSuv17i7Be/4nP5blkUu9w5TxtE0N6/CN5/nhSvUO7M305uehaOr/UsMX2iqx8eH/9I2JEr5GXllG9hy0DLSQNw2XwE/5M3ifUM5vT7WzG2t8C5f1uN+7SaMhD3fWfxOHiBeO8wzi7cSU5GJo1H91Club39BDe//p3Imz4l5qHQ1qLbije5vGof7nv+IsE/gnjvMHz+uFrqdSwttacPInjPX4TuP0+qVyju874jNz2LGmN6lpg+ydWP+x/vJeLwFZSZJR8rN8asI/TAeVLuh5B8Lwi397ZgWMsWsxZ1yrAm5aNOQXuFFPQtt3n5fauWhvZKdPXD8+MfCT98hTwN7VVV6bTrS47bRXLvXkYZG07WqT0os7PQada15PTNu6IwMCbz8FfkhfmgTIolL8QLZXSIKk2e/12yLx8m1+dWeVWjwnXr1J53p46jT48uFV2UCvU8nbeaTxrAzc1HCDh5kzjPYM6+vxUjewtqP+Ic1WLKQDz2neV+wTnqQsE5qlHBOUrP1JBGo3ty5eO9hP19jxi3AM7N+RaH9g2wa10XgPsHLnB5+Q+E/+NJclA03r9e5v7BCzgPfPgaqvvaCfgcvkLkjZLPd+WpzaQBXN18BN9TN4nxDObYB1sxsbOgXj/N7dV28kDc9p3F/dAF4rzDOLVwJ9npmTQvck4HsG3iSLupL3Fi3rYS8zn70Q+47j5NYlB0qdapLA2YNJjDXx7ixqlrBHsGsmX2JizsrGjXr6PGfT4Zt5ILP50l1DuYII8Ats7ZjG1NO+o0r6uWzqlJbV6a8jLfzPuyrKtRZnpPHMTRzT9z+5QLoZ5B7Jj9JRb2lrTu117jPnfPuXL40/3cOlHy7Dj7OtWo26YBe5dsI+COL5F+YexdvA1dAz06vFx5Y/sLkwZw4cvD3C/4XvXr7C2Y2lnQ6BF9q9PkgdzcfxbXQxeI9g7lj0U7yE7PpPWo/L7l2K4BFjVtOTznG6LuBxN1P5hf52yleos61OncRJXPP9uPE3LLh8TQGIJveHPp69+p2boeWjraZV7vqi5PqaiwV2ZmJklJSWqvzMzMEsuZlZXFjRs36NOnj2qblpYWffr04cqVx5s0kJaWRnZ2NlZWVqXSdiV57IGxTZs20alTJ6ZMmUJ4eDjh4eHUqlWL0NBQXnrpJdq3b8/t27fZsmUL27dvZ9WqVY/cLy8vj5o1a3Lo0CHu3bvHsmXLWLRoEQcPHnzswr/99ttkZmZy4cIF3Nzc+OSTTzAxMXnyVgDmzp3Lzp35M4kelPNJbNiwgZYtW3Lr1i2WLl1aYpp58+Zx/vx5jhw5wsmTJzl37hw3b97UmGdeXh5Dhw4lLi6O8+fPc+rUKfz8/Bg9ejQAjRo1YsOGDcycOZOgoCBCQkKYPn06n3zyCU2aNCkxz8cpwzvvvMOVK1fYv38/d+7cYeTIkQwYMABvb+8napOyYlnLDlM7S3yLDF5lJqcT4uqLY5v6FViyykWhq415izrEXCgyyKdUEnPxLpbt/vvtZOZoi7G9BcEXC+uflZxOpKsvDhqOEy1dbeya1yH4UpFfxpVKQi66PhI6hwABAABJREFU49C23mN/tm3z2phUswKlktHHVjHB5UuG7J6HVcOaT12fsqTQ1casRR1iL7oVblQqib3ghkW7BqX2Obqm+bNhsxNSSi3PiqDqWxeL9a0Ld7F4DvqWGi1ttOydyAu8V2SjkrwgD7SqO5e4i3bdVuSF+aHX+3UMZ2zEYPwKdDq+BBp+uBLPj+fpvGVacI4KKXaOivqXc5Rt8zqElHCOsi84R9k0r4O2no5amgTfcJJDYnBoq7kN9UyNyEhIVdvWcFR3zBztcPnsl6epYqkyd7TFxM6CwEvq7RXu6kt1DfXS0tXGvnkdgoq1V9Ald6q1KTyn6xjoMWjz25xZsou06MQyq0N5sqtlj6WdFXcv3VZtS09Ow9fVm/ptGj52PkYF5+2UIudtPQM93v5iNruWbiMxOqHUylyebGrZYWFnicflwuue9OQ0/Fx9cH6C9ilORy9/hlV2ZrZqm1KpJCcrm/rtGz99gcuQZS1bTO0s8SvSTx58r6qpIRZp62pTvXkd/Ir0R6VSid+lu6p9tPV0oKDuD+RkZqPMU+LYvuQ2NjQ3pvmwLgTf8CYvJ7c0qifKyNq1azE3N1d7rV27tsS0MTEx5ObmYm9vr7bd3t6eiIjHu8V4wYIFVK9eXW1wrbQ99hpj5ubm6OnpYWRkhIODg2r7119/Ta1atfjyyy9RKBQ0atSIsLAwFixYwLJlyzTup62tzYoVK1Tv69Spw5UrVzh48CCjRo16rDIFBQUxfPhwmjdvDoCzc8kX4Y/DxMRENcOsaDkf14svvsicOXM0/j0lJYXt27ezZ88eevfuDcD3339PzZqavyifOXMGNzc3/P39qVWrFgC7d++madOmXL9+nfbt2zNz5kz+/PNP3njjDfT09Gjfvj2zZs166jIEBQWxc+dOgoKCqF69OpA/aHj8+HF27tzJmjVrHso3MzPzoRHiHGUuOoqyGek3tTXPr0+xi5eU6ERMCv4mQM/KDC0dbTKLtVNmdCIm9apXUKnKj5GtBQBpRdYxAEiLTsLIruTjxNDKFC0dbdKLtVlaTCIW9ao99mebO+bfK9/+g1e5/PFekkKiaT31JV45uJg9PeaSWezLR0V7cKxklXCsGNcvpfv4FQoarRpH/FVPUjxD/j19JfaovmVc/7/ft4pSGJqg0NJGmarez5SpSWhZlXwuVZjboOXYiFyPf8j4ZRNaFnbo9RkLWtrkXPm9PIotKqnn6bz14ByVXuwclR6dhKGGc5SBhnNUepFzlJGdObmZ2Q+te5kek4ihhmsk+7b1qTukI8fGb1BtM69tT8cPR3Nk+EqUuXlPVLeyYKzpnB6ThLGGej04p6fGPHxOt6pbeE7v+dEbhLl443tK84/VVY25nQUAicXqnhiTgHlBW/4bhULBmx9N4v51D0K8glTb31w2Ee8bntw4VfXWFHvgQRskFRvYS45+/PYpSYRvKLEh0bw6/3V+WPQtmemZ9J00CKvqNqp/k8rGpKBcKcWOldSYREw0tIWRZX7fenifJGzq5sfqkFs+ZKVl0vfD1ziz/iAoFPT5cDRaOtqqz3ygz4ev0WFcX/SMDAi+6c2PEzYgKreFCxcye/ZstW1PuwzVv1m3bh379+/n3LlzGBgYlMlnwBMuvl8SDw8POnXqpHaLYpcuXUhJSSEkJARHR0eN+3711Vfs2LGDoKAg0tPTycrKeugpkY/y7rvvMmPGDE6ePEmfPn0YPnw4LVq0eJbqPLV27TTfwgf565dlZWXRsWPh9GUrKysaNtT8q4SHhwe1atVSDYoBNGnSBAsLCzw8PGjfPn+q744dO2jQoAFaWlq4u7trvF30ccrg5uZGbm4uDRqozxLJzMzE2tq6xHzXrl2rNsgJ0NW8Gd0tmmus25NoObQLQ9dMUr3fPXF9qeQr/lsaDOtMz3UTVe//GF9xJ1WFVn4fvLH5CL7H8hdlPT3nWyZc+4J6gzrivvevCitbRWmybiKmDWvxz8sfVXRRREVTKFCmJZF1cjcoleRGBpJtYoFu+/4yMCb+s+oP60z3IueoPyvwHFWUZcOaDNj+ATc+/5WQgpl6Ci0Fvb98G5eNP5PoXzELhjca1pm+awvb69cyaq+6fdvg2LlJlVlMX5Muw7ozac101fv1E1Y/c54TVk6lVgNHVoxYpNrWpk97mnZuzsKXNE8GqIw6Du3KG2umqd5vnljyzJZnlZuTy9fTNzB+/Qw23dlFbk4uHpfdcDt7s9LMim4+rDNDinyv2jvhf2XyOWlxyRya+QWDVk+g44T+KPOUuP12hTA3f5R56mvT/f3NH9w6cA7zGjb0fP9VXvlsugyOPQalsuKOKX19/cceCLOxsUFbW1u1HNQDkZGR/zohacOGDaxbt47Tp0+X+TjPMw+MPa39+/czd+5cPv30Uzp16oSpqSn/+9//uHr18dfgmTx5Mv379+fo0aOcPHmStWvX8umnnzJr1iy0tLQeWkAzOztbQ06aPW4+pfkkySd1+/ZtUlNT0dLSIjw8nGrVHn9mS3EpKSloa2tz48YNtLXVZ3xpuk21pBHj1c2nPHUZivM4fYNg18K1LXT08g9bE1tzkov80mNia17lnyBZmrLiksjLyUW/2C+p+rbmZP4Hn/Tif+omka6FTy3TLjhOjGzMSCtSXyNbM2Lcg4rvDkB6XDJ5ObkP/apuZGP+RLdXpEbmf16cd6hqW15WDolBUZjWKHmAuSI9OFb0yuhYabxmArZ923Bt2HIyw+OeOb+K9rz1rUdRpqegzMtFYay+kLXC2Axlasl9RpmaCHm5aos258WFozCxAC3t/L+J59J/uW8FaDhHGRY7RxnamhGr4RyVoeEcZVjkHJUWlYi2vi56ZkZqs8YMbcwfmmlmWb86Q/YtxOPHs9z84ohqu66JIXYtnbFp6kTXleOA/MEyhZYWU/2/54+xnxD29z3Kku+pm0QUeRKptn7hOT216Dndxozoe48+pxvbPHxOTy1oi1qdm2DhZMc7d79VSzPkm/cIvXafg6OffYCpPNw4dQ2fW16q9w9u6TO3MSchqvChFOY2FgTee8QTgwuM/3gKrXu34+NRi4mLiFVtb9q5OXZODnzntkct/ftb5+N5zYNVr5W8nExFcz3tgl+R7xK6Bf3PzNZC7XZQU1sLgu8FPNNnBd314+OX5mFoaoT2/9m77/AoijeA499Lz6X3UENvofdelF40UkV6V4oiTemCNP0JgqBgAUR6saAiTbqAQAiBAAkhIY303uslvz8uXjhIAkiqvJ/nuUdvM7s3M8zs3s3OvKuvR1JMAvN/WUXArfyfrFvS7p10I/jGk+ciU1sLkh7pWya2FoQV8LsqJVbdt0wf61smtuZaK3p8L3jwRedZKK1MyVZlk5aQwpxrX3I7KOKx4yWREptEtF8YUT4hzLqykcrNa/GwgFi+onwxMDCgRYsWnDp1ShP7/J9A+tOnTy9wv08//ZSVK1dy/Pjxp05CKgrPNTBmYGCASqX9hbV+/fr8+OOP5OTkaGYqXbx4ETMzM80Svfz2u3jxIu3bt2fq1LynL/3zVMjnUaVKFd5++23efvtt5s+fz7fffsuMGTOws7MjMTGR5ORkzaCVu7v7cx/fzs5OK96YSqXi9u3bdOvW7bmOU7NmTfT19bly5YpmFl1sbCze3t506dIl333q169PUFAQQUFBmlljd+/eJS4uThNDLCYmhrFjx7Jw4UJCQ0MZMWIEbm5uGBsb/6s8NGvWDJVKRUREBJ06dXqmsuU3YlyUyygzktOISU7T2pYYEUuN9s6agTBDU2MqN63JlV1/Ftnnlnc5mSrib/lh26khYcdyHzigUGDb0Rn/bSdKN3PFIDM5jfjH2klyeByVOzoTlfulWd/UGIemNbm9M/8nmmRnqojw8KNKB2f8juc+pl2hoHJHZ259f/KZ8xLh4U9WWgaWNSoQek39RVVHTxfzynYkPoz6F6UrXjmZKhJu+WHTqSERR/Paik2nhgRsO/5Cx66/ahwOfVtx9Y3lpJajoMWFebRvhWvVlzMB/8G+VahsFdnhAehUrY/Kxz13owKdqvXIunEm/12CfdCt3wZQAOrBMR0rB7KT4mRQ7CX3X75uZSankZnPNapSR2eiH7lG2TetyZ1CrlGRHn5U6uCM/yPXqEodnbmde42K8vBDlZFFpQ7O+OXOWLaoUQGzyraEXc+LFWtVpxID9i3A+9AFrn56UOtzMhJT2d/9Q61tDUd3p2L7Bpx4+wsSS+BcnpmcRtxj9ZUUEUfVDs6agTADU2MqNK3JzULqK9zDj6odnPE5kVdfVTs4475DXV9Xv/oNj71ntfYb++cazi7fhe+f5efhH2nJaaQla8/ui42IwblDYwJyB3qMTY2p2bQ2f+46Vuixxi6fRMtebVgxbDGRjw1g/Lr5J87s0/6u/enJDexcvh23U9devCDFJD05jcjH6icuIpZ67RtqBsKMTI2p0bQW53a92Peef6Qmqgem7as5Uq1RTQ6v3Vckx31RBf2uqt7BWTMQ9s/vKtcCflepMlWEePhRvYMzXrl9S6FQUKNDQ67uePJcnRKrjlFXvX0DTGzNuVfIsuV/Vl38M7grCpZdijPGntesWbMYM2YMLVu2pHXr1qxfv57k5GTGjRsHwOjRo6lUqZImTtknn3zCkiVL2LNnD9WqVdPEIjM1Nf3XMeWf5rkGxqpVq8aVK1fw9/fH1NQUa2trpk6dyvr165kxYwbTp0/n3r17LF26lFmzZqGjo1PgfrVr1+aHH37g+PHjVK9enZ07d3Lt2jWqV3/2p5XNnDmTPn36UKdOHWJjYzlz5gz166sDG7Zp0walUsmCBQt49913uXLlCt9///3zFBdQxw6bNWsWR44coWbNmqxbt464uLjnPo6pqSkTJkxg7ty52NjYYG9vz8KFCzV1lJ/u3bvTqFEjRowYwfr168nKymLq1Kl06dJFM2r69ttvU6VKFRYtWkR6ejrNmjVjzpw5fPnll/8qD3Xq1GHEiBGMHj2atWvX0qxZMyIjIzl16hSNGzemX79+z1324nBx2zG6zXiDaP8wYoMi6T57CInhcXieyHvi5PjdC7h73JW/f1CfoA2UhthUy5uuaVXFjgoNnEiJSyI+RH03zNjCBMtKtpjZWwFgW0M9+y4xMu6JmGblwYOvj9B0wzvE3XxA3A0fakzqg67SkMB95wBouvEd0kJj8Vqlvlgr9HUxq6Me0NbR18OoghXmzk5kJaeR4h9e4OeUVTe3HqPlDBfi/MJJDIqgzZzBJIfH8eCfHxTA63vn8+CYKx65X5Ldvz1K93VTiLjlR7i7L00m9EbP2BDPA+c0+yjtLFDaWWBRTR1E0qZeFTKTUkkMiSY9LpnMpFRu7zpNm9mDSAqNJvFhNM3eVvcdnyNl88mU/luO0OiLd4h3f0D8DR+qTe6LrtKQ4Ny20mjjVNLDYvBemddWTHPbisJAFyNHa8ycnVA90lYarBlPhYEdcBvzGVlJqZoZaVmJKWSnPf8M3rLEb8sRmnzxDnGa+uqDntKQoNz6arLxHdLCYrm3Mp++ZaCHkWP57luPynI9iUGf8WSHB5Ad6odei+4o9A3Jun0RAIM+48lJiiPzgjqAd9bNs+g1ewX9V94k68ZpFFb26LfpR6bbIz9u9Q1RWNpr3ios7FDYVYG0ZHISy/+sw/ykpKQS+DBE8z44JBwvb18szM2o4GhfyJ7/LS/Tdctj6zFazHAhPvca1WrOYFLC4/IGvYD+e+fjd8yVO7nXqFvfHqXbuilE3vIjwt2XxhN6o29syL3ca1RGYipe+8/SfskI0uOSyEhKpePy0YS5ehORO0vEqm5lXts3n6BzHtz89qhmBlqOKpu0mETIySH2nnYsyNSoBFTpmU9sL0luW4/R9l0X4vzDiQ+MoMOcwSRFxOUNegGD987H55irZuDr+ndH6b12CmEefoS5+9J8Qm/0lYbczq2vlMj4fGeEJwZHkxCUNwBo6eSAvokhJnYW6BkZYNdAfYM5+n4w2Zllc0D/2NbfeWPGEML8QokMCmfI7LeIi4jB9UTe95AFe5bhevxvTuw4CsC4FZNp/1pn1k5aTWpyqibeVkpCCpnpGcRHxuUbcD86JPKJQbSy7tS2I/SbMYgI/zCigiJ4ffYw4sJjuXEib4Bv1u4l3Dh+lTM/qAcTDZVG2D/yW8K2ij1VGlQjOS6JmBD1jc8WfduSGJNATHAUlepV5c2l47hx4ip3L9wq2QI+h7+3HqPzDBdi/NS/q16ZPZjEiDjNoBfA6D3z8TruytXcvnX5u6O8sXYKIbf8CL7pS9vx6r5142De9+WmQzoT5RNCcnQCVVrUpvfSUVzeeozoB+pJJ5Wa1qRSkxoEXvMmNT4Zayd7us0eQox/GEFuZeOhb6JoDBs2jMjISJYsWUJYWBhNmzbl2LFjmoD8gYGBWmMSmzdvJiMjg8GDB2sdZ+nSpXz00UfFksfnGhibM2cOY8aMoUGDBqSmpuLn50e1atX4448/mDt3Lk2aNMHa2poJEyawaNGiQvebMmUKN27cYNiwYSgUCoYPH87UqVM5evToM+dHpVIxbdo0Hj58iLm5Ob179+bzzz8H1LGzdu3axdy5c/n222959dVX+eijj5g8efLzFJnx48dz8+ZNRo8ejZ6eHu+///5zzxb7x//+9z+SkpIYMGAAZmZmzJ49m/j4ggdbFAoFhw8fZsaMGXTu3BkdHR169+7Nxo0bAXUg/j/++IMbN26gp6eHnp4eu3btomPHjvTv358+ffr8qzxs376dFStWMHv2bIKDg7G1taVt27b079//X5W7OFzY8hsGxoa4rJ6IkbmSgGvefD9mDVmPPAXG2skBpbWZ5n2lxjWYuC9vine/xaMAcDt0jh/nfA1AvR4tGPxZXoyGNze9C8Cp9T9yev2PxVqm4hBy+G8MbMypO28whnaWJNwJ4MrwNWTkBss0rmQLj6zzN3K0osupNZr3taYOoNbUAURdusvlgR+XeP5flNvm39FTGtJtzXgMzZWEXvPmt1GfonqknVg42WP8SDvx+e0KxtbmtJ49CBM7CyLvBvDbqE+1AiQ3HPkqrWcN1Lwf9KO6Xf0562u8Dl4A4NLKveSoVPRY/w56RgaE3fDhlzdXkR6vHRC5rAg7fBkDG3NqzxuCob26rbgOX6MJyP9kW7Gmw+lPNO+rTxtA9WkDiLl4l6sDlwNQdVxPANr8oh1XzOPdzQTvP0d5Fprbt+rMG6ypr6uP1VfOY32r0+m8vlVz2gBqThtA9MW7/F0O+9ajVPeukak0Rb/D6yiU5mRHBpF+aD2kqPuMwtxGa9lkTmIs6Yc+R7/bMIzGfEROUiyZbn+SdTXv+q/jWA2jYXM17w26qZ/GnHX7IhnHtpdMwUrYba/7jJ/xgeb9pxvVS7te79OdlYvKVzyfF/EyXbfcc69RXdaMx8BcSdg1b4485Rrl+9sVjKzNaTV7EEo7C6LuBnDksWvUpWW7ycnOoec376FroEfQOQ8uLPxe8/eafVtjbGtBnUEdqTOoo2Z7YlAku9u/X7yFfgHXNv+OvrEhPVarr+nBrt789Fh9WVbVrq97udf0DrPU9RV5N4AfR336RBD/p+n56USqtMt7quDoY+qHUX3bfiYJZXAmOMBvW37GUGnExNXvoDQ3wdvVkzWjP9Z6YqJDVUfMrPKWwvcYpf7tsOTACq1jbZn9BecP5T8LuLw6tuUwBsZGjFo9BaW5kvvXvNgwZqXWbwk7JwdMH2lPTo1rMHdfXlzlYYvHAnDp0Fm2z1FPSrCwt2LoojGY21oSHxHL5Z/O8fvGsv0b4uKW3zFQGjJg9QSMzJUEunqza/Qn2r+rqjqgtMqrizu//42JjRndZg3G1E697HLX6E9IfqRv2daoQPd5wzC2NCXuYSQXNh3m8nd51/rM1Azq925F1/cHYWBsSGJkHD5nb3Fw4y+oMrJKpvCixEyfPr3ApZNnz57Veu/v71/8GXqMIufxAFpCFIGF1d4q7SyUaW3TpNsVJEC/eJ5m+l9RS74oFEhF+ZlSXhq6zS29WJhlnf7YBU9P9BI71rB8ByYvTsFyzSpUspyWC3RdUbaeUF3WmChKLRR2uVApx6C0s1BmfRSwu7SzUCL+rjjw6YmKSduQn0rts4tLwev4hBBCCCGEEEIIIYT4D5OheCGEEEIIIYQQQohyojwF3y8PZMaYEEIIIYQQQgghhHgpycCYEEIIIYQQQgghhHgpyVJKIYQQQgghhBBCiHIiR5ZSFimZMSaEEEIIIYQQQgghXkoyY0wIIYQQQgghhBCinMgu7Qz8x8iMMSGEEEIIIYQQQgjxUpIZY0IIIYQQQgghhBDlRA4SY6woyYwxIYQQQgghhBBCCPFSkoExIYQQQgghhBBCCPFSkqWUQgghhBBCCCGEEOVEdk5p5+C/RWaMCSGEEEIIIYQQQoiXkswYE0IIIYQQQgghhCgnsiX4fpGSGWNCCCGEEEIIIYQQ4qUkA2NCCCGEEEIIIYQQ4qUkSymFEEIIIYQQQgghyokcWUpZpGTGmBBCCCGEEEIIIYR4KcmMMVEsFozKKu0slGk7vzco7SyUWYOcgks7C2WagUV2aWehzMqRqinUpo1VSjsLZVb9zxaWdhbKtN63V5Z2FsqskS1mlXYWyrQJaUalnYUya1PWg9LOQpnmYla/tLNQpv2eEVjaWSizPirtDJQQ+dpbtGTGmBBCCCGEEEIIIYR4KcmMMSGEEEIIIYQQQohyQmKMFS2ZMSaEEEIIIYQQQgghXkoyMCaEEEIIIYQQQgghXkqylFIIIYQQQgghhBCinJDg+0VLZowJIYQQQgghhBBCiJeSzBgTQgghhBBCCCGEKCdkxljRkhljQgghhBBCCCGEEOKlJANjQgghhBBCCCGEEOKlJEsphRBCCCGEEEIIIcqJHBSlnYX/FJkxJoQQQgghhBBCCCFeSjJjTAghhBBCCCGEEKKcyJYJY0VKZowJIYQQQgghhBBCiJeSzBgTQgghhBBCCCGEKCeyJcZYkZIZY0IIIYQQQgghhBDipSQDY0IIIYQQQgghhBDipSQDY6Wsa9euzJw5s0Q+66OPPqJp06Yl8llCCCGEEEIIIYQoejml+PovkhhjL5E5c+YwY8YMzfuxY8cSFxfHL7/8UnqZ+hf0WvdCv+MAFKaWZIcFkHFkG9nBvvmnbdYFw4HTtLblZGaQsnyk1jaFXSUMeo5At1oD0NEhO+Ih6fvWkhMfXWzlKEotZw+i/vBuGFooCbvmzYUF24n3Dy90H+cx3Wk6pR/GdhZEewZycckPRLg/0Pxd11CfdovfotZrbdE10Cfo3C0uLPye1KiEJ45laGnKkBOrMK1gzTbnyWQkpBR5GYuKcqALJsOHoWttTaavLwmff0Gmp1e+aY06d8Jk9Aj0KlUCPV1UD4NJ3neA1OMn801vPud9TFxeI37DJlIO/licxSgWhv1cMB70JjpW1mT5+ZKyZQNZ3vnXjUH7ThgPHYlOhUoo9PRQhTwk9acDZJw5oUmjsLRCOW4KBs1aoTAxJfPOTZK3bCA7JLikilSkjPq7YDw4t34e+JK8+Sn1M2wkuhVz6ydYXT/pp7Xrx2T8FPSbt0LHxJTM2zdJ2lx+6qfjrEE0Ht4NQ3Mlwa7enFy4ndinnHeaje5O68n9MLGzIMIzkD+X/kDYzbzzzpv7FlK1XX2tfdx3neLEwu0AGFma0n/DVOzrV8HI0pSU6AR8Tl7n/KcHyUhKLfpCFpNq43pQc+oADO0sSLgbyO2F3xN3I//rmGndytSbOxiLJjVQVrHj9uIf8Pv2aAnnuPS5unuwfc8h7nr5EBkdw4bVi3m1c/vSzlaJGDJrOK8O74GJuQn3XL34buEWwvxDC0zvMnUQrXu3pWLNymSkpeN9/R671+wg9EGIJs2SfStwbtdQa7+Tu47x3cItxVaOolZlXE+qTR2Agb0FSXcD8VywnYQC+pFJ3crUmjcE88Y1MK5qh9fiHQR+o92Pqr/7OvZ9W2NSuyLZaRnEXfPG++M9pPgWXNdl3fsfTuXNUQMxtzDD9ao7i+esxP9B4DPt+/Z74/lgyXts27KLjxf+T7Pd1t6GBctm0bFLW0xMTXjg48+Xn3/Lsd9OFVcxik3v94fQbvgrGJmb4O96j4OLthLlH1Zg+hqt6/HK5AFUblQdCwdrtk7+jNsnXLXSDP/sHVoP7qK1zfOcO9+MWVMsZSguU+ZO4I0RAzA1N+XmNQ/WfLiWIL+HBaYfNNqFwWNcqFDFEYAH9/z47vPvuXT6iiZNJaeKzFw6jaatG6NvoM/lM1f438L1xETFFnt5hHgamTH2EjE1NcXGxqa0s/FCdBu2w6DPaDLPHCJ18wdkhwVgNGYhmJgXuE9OWgopn0zKe63VHihTWDlgPHE52ZHBpG37iNRNc8k89yM5WZnFXZwi0fSd/jQa15MLC7bx04ClZKam02/XB+ga6he4T80BbWi/eASu63/mx76LiL4bSL+dH2Bkk1eP7ZeOwKl7M068vZHDQ1agdLCi1zcz8z1e188mEuP5bF+0SpPRK90wn/4OSdt3EDVhMlk+vliv+xQdS8t802cnJpD0wy6i355G1JiJpPxxDIv5H2DQutUTaQ07d8TAuQGqyMhiLkXxMOjUDZNJ00jds4P4dyeh8vPF7OPPUFhY5ps+OzGR1P27SJgzjbhp40k/eRTT9z9Av3le3ZgtWomuY0USPl5I3LsTyY4Ix3zlOjA0KqFSFR2Dzt0wmTyNlN07iJuhrh/zFQXXT05u/cTPmkbs1PGknTyK6Szt+jFfoq6fxOULiZs+EVVEOBarykf9tH67P83H9uTEgm3sen0pmSnpDNlZ+HmnXv82dFs0gosbfmZH/0VEegYydOcHKG20z98395zmy5bTNK+zq/dp/paTnY3Pyev8NGEd33Wbwx9zvsGpQ0N6rhpXbGUtahVfb0uDj0bhvfZHzvdcQMKdANrs/RAD2/yvY7rGBiQHRuC5Yi9p4S/vj4fU1DTq1qrBwtlTSzsrJeq1t9+gz9j+fLdgCwtfn0daShoLdi5Fv5C+Vr+NM8d/OMoil3msHPkRuvq6LNz5EYbGhlrp/txzgsktx2peu1fvKO7iFBmH19tRd9kofNce4u8e80m8E0CLffML7UepARHcX7mH9AL6kVW7+gRtP8GVvotxHbIShZ4uLfYvQFdpmG/6sm7Ku+MYO3k4i+as4I2eI0lNSWXHwc0YGBo8dd/GzZx5a8xgPG/fe+Jv675aSY1a1Zg08j16dxrE8SOn2LT1fzRoVK84ilFsXnn7NTqP683Bhd+x3mUR6anpvP3DfPQK6VsGSiOCPQP4ccn2Qo/tedadJa2maF47Z2ws6uwXqzHT3uLNCYNY/cFnjO03hbSUVDbuXVto24kIjWDTyi2M6jWR0b0n4XrRjbXbV1OjTjUAjIyN+HLfOnJycnh78HtMeG0q+gb6fP7DGhQKCSL/b2SX4uu/SAbGSlBycjKjR4/G1NSUChUqsHbtWq2/p6enM2fOHCpVqoSJiQlt2rTh7Nmzmr9///33WFpacvz4cerXr4+pqSm9e/cmNDTvTtbZs2dp3bo1JiYmWFpa0qFDBwICAgDtpZQfffQRO3bs4PDhwygUChQKBWfPnuWVV15h+vTpWvmKjIzEwMCAU6dK/06Qfvv+ZLmeIuvGWXIig8n47VtyMjPQb96t4J1ycshJite8SI7X+rNBjzdRed8g88RuskP9yYkNR+V1HZKfnBlVFjWa0Bu3jYfxP+FGjFcQZ2ZuQelgSbVeLQrcp/GkPnjuPcO9A+eJvR/C+fnbyUpLp94w9R0uAzNj6g3ryuXluwm5dJcoD3/Ozv4Gx1Z1sG9WU+tYDUa9iqG5Ce5f/1Gs5SwKJm8OIeW3I6T+cYws/wDi/7eOnLQ0jPv3yTd9xo2bpJ//i6yAQFQhIaQc/JEsX18MGmvfZdextcVi5rvELV9JTpaqJIpS5IzeGEr6sd9J//MoqqAAkjethbQ0DHv2zTd9loc7GZcvoAoKIDsshLRff0Tl9wC9Bo0A0KlYGf36ziR/uQ7VfS+yg4NI/nIdCgNDDLu8WpJFKxLGbwwl7ejvpJ88iiowgKSNa8lJT8OogPrJ9HAn41Ju/YSGkHZYXT/6zrn1U0ldP0mb1pHl7YUqOIjkTetQGBpi2LXs10/LCb25vOkwPifdiPQK4sisLZjaW1K7Z8HnnZYT+3Br3xluHzxP9P0Qji/YTmZqOo2Gat9Zz0zNIDkyXvN6dCZYekIK7rtOEebhR0JwNIEX73Bj559UblW32Mpa1GpM6Ufg7tME7TtHkncwt+ZtRZWaQdU3u+abPt79AZ7L9xBy+DLZGVklm9kypFO7Vrw7eQzdu3Qo7ayUqL4TBvDTpgO4nrxKoFcAX87agJW9Na16tilwn9VjlnPu0Gke3g8iwNOfr2Z/gV1le2o00r5+Z6SmEx8Zp3mllqdZl2/34+Gu04TsO0eydzB3536HKjWDisO75ps+wf0B3st3E/bLZbLT8+9HbsPXELL/HMn3HpJ0N5Db723GuIod5o2rF2NJis/4KSPYtPZbTh49i9fd+8x+ZxEOjnb07PtKofspTYxZv2U1899fRnzck9+Fm7dqwo5v93LT7TZBAcFsWvstCfGJNGpSP5+jlV1dxvfhxMafuX3yOqFegeyZ9SXmDlY06tmywH28zrpzdO0BPI5fK/TYWRmZJEbGa16pCclFnf1iNXzSULau/4Fzx//Cx9OXJe+uxM7Bhq69OxW4z4WTl7h4+m+C/B4S+CCIr9Z8S0pyKo1aOAPQpHUjKlRxZNl7q/D1eoCv1wOWvruS+k3q0apj85IqmhAFkoGxEjR37lzOnTvH4cOHOXHiBGfPnsXNzU3z9+nTp3P58mX27dvHrVu3GDJkCL179+b+/fuaNCkpKXz22Wfs3LmT8+fPExgYyJw5cwDIysrCxcWFLl26cOvWLS5fvszkyZPzHYWfM2cOQ4cO1QyshYaG0r59eyZOnMiePXtIT0/XpN21axeVKlXilVcKv5AWO11ddCrWQPXAI29bTg4qXw90qtQpeD8DI4xnf4nxnK8wfGsuCvvKeX9TKNCt05zs6FAMRy9A+cG3GE1eiW79J2cElUVmVe0wcbDk4YXbmm0ZialEuPvi2Lx2vvvo6Oti16g6D/+6k7cxJ4eHF+7g0KIWALaNqqNroKeVJs43lMSHUTi2yDuuVe2KtHjvDU7P3ALZZXzFuZ4e+nXqkO56PW9bTg7prm4YODs/0yEMWjRHt2oVMtxv5W1UKLBcPJ+kvfvJ8vMv2jyXFD099GrVIcNdu24y3K+jX+/Z6kavSXN0K1ch67a6bhT66ruKORkZWsfMycxEL3dwqNzQ00Ovdh0yH6ufTPfr6NV/tvrRb6qun8zH6yfzyfrRL+P1Y1HFDlN7SwL+0j7vhLr7UrGQ845jo+r4P3beCfjrDhWb19JK28ClPdNvbGbcidV0njcUPaOC71Cb2ltSp3crgq7kv6S1rFHo62LRuDpR5/Pqjpwcoi7cxqpl/nUnXl72VRywsrfG46+8a05qYgo+7t7Ubv7sg8FKMyUASXFJWts7unTm2xs/8NmJDQyfNxKDQvpaWaLQ18WscXWiL2h/H4w574Fly0K+Dz4nvdx6y3ys3sqDKk6VsHe0469zecvYEhOTcL/uQfNWjQvdd/mnCzh98jwXH9n3UW7XbtLPpRcWluYoFAr6v9EbQ0ND/r7omm/6ssimij3m9lZ4X8xrQ2mJqQS4+1Ct+Yu3oVptG7Dc9Wvmn1rH4BUTUFqavvAxS0qlqhWwdbDh6oW8f8/kxGRu3/CkUctn+86jo6NDz9dfxVhpxK3r6uu+gYE+OTk5ZGTkrcjJSM8gOzubpq0Lb5NClASJMVZCkpKS2Lp1K7t27eLVV9WzAXbs2EHlyupBmsDAQLZv305gYCAVK1YE1INXx44dY/v27axatQqAzMxMtmzZQs2a6rt+06dPZ/ny5QAkJCQQHx9P//79NX+vXz//uzempqYYGxuTnp6Oo6OjZvvAgQOZPn06hw8fZujQoYB6ptrYsWMLnOaanp6uNZAGkJWlwlBP9/krqhAKpTkKXV1ykuK0tuckxaFjWzHffbKjQsj4ZTPZYQFgpES/w2sYT1pB6sZZ5CTEoDAxR2FojH6n18n4cz+ZJ3ajW7sphm/OJm37MrL9PYu0DEVNaWcJ8ETcr9TIBIztLfLdx8jaDB09XVIjtWfOpUbFY1mrgvq49hao0jOfiBWWGhWPsZ36uDoGery6aRp/r9xLUkg05lXti6JIxUbHwgKFni7ZMdpLKLJjYtFzqlrgfgoTE+x/PojCQB9U2cSvW0/GI4NrJiOGg0pVLmOK/UNhboFCV4+cOO26yYmLRVGlkLpRmmD1wyHQN4BsFclfrSfTXf1FSvUwAFVEGMqxk0ne9Bk5aWkYuQxB184elVX5WtKtk1s/2bGPtZ3YWPQrF14/1rvy6ifpy/Vk3sitn6AAVOFhmIydTNJGdf0Yv5FbP9Zlu35M7C0BSH7svJMclYCpXf7nHaWV+ryTEhX/2D7xWNesoHnvefgS8cFRJIXHYl+/Kl0+fBPrmhX4ZcoGrf0GfDGNWj2bo29siM9JN4598F0RlKz4GVibo6OnS/pj59/0yHhMa+V/HRMvL8vcvhYfFae1PT4qHks7q2c6hkKhYMzSCXhdu0uQd17Ig4uHzxMVHEFMeCxO9Z1468PRVKxZibVTPimq7Bebf/pRRj79yKR2paL5EIWCeivGEHvFiySvguMqlVV29rYAREVqx8qNiozW/C0//d/ojXPj+rze/a0C00wbP5dNWz/F3fcCmZmZpKam8fbo9wnwCyqazJcAs9zvz0mPtaGkyHjN3/4tr3Pu3Dp2lZigCGycHOg3900mf/8hGwYuJqes30QGbOzV30GiI7W/88RExmBjZ13ovjXr1WD77+rluqnJqcwdvxA/b38APNzukpaSxoxFb/Pl6m9QoGDGwrfR09PD1qFsf+8pq7JlCWqRkoGxEuLr60tGRgZt2uRNfbe2tqZuXfUdPw8PD1QqFXXqaN+lSE9P14oLplQqNYNeABUqVCAiIkJzvLFjx9KrVy969OhB9+7dGTp0KBUqVOBZGRkZMWrUKLZt28bQoUNxc3Pj9u3b/PrrrwXus3r1apYtW6a1bX6nBizs8mx3FYpTdtB9soPyZtylB3pj/O7n6LXqQeap/aBQT5pUebmSdfmIep+wAHSq1kW/VU/Sy9jAWG2X9nReM17z/o+xn5VaXtp8OIw4nxDu/3yx1PJQEnJSUogaNxGFsTGGLZtjPn0qqpAQMm7cRK9uHUyGDCJq/OTSzmapyElNIW6Gum70mzRHOXEqqrAQsjzcQaUiceViTN+bh/X+I+Sossh0v07Gtb/hJbmQ56SmEDtNXT8GTZtjMmkq2aEhZObWT8KKxZjNnIfNwdz6uZFbP5St+mng0p6eq/LOOz+OK77zzs29ZzT/H3XvIUkRcby5dwGWVe2JC4zQ/O30x7u4uOEnrKtXoPMHQ3ll8QhOLvq+2PIlREno6NKZSave0bxfM27FCx9z/MeTqVLHiaWD52ttP7U370EgQfcCiI2IZcnej3Go6kh4YMHBx18W9deMx7RuFa6+trS0s/JMXh/cl5VrF2veTxg+vZDU+atQ0YGlq+YxatAUMtIzCkw3e8E0zC3MGPHGJGKj4+jRtxubtn3K0H7juOfp86/yX9yav96Boasmad5/O774BoBv/HZZ8/+h94II9Qxk0YUvqNXWmfuXbheyZ+noPbAHCz6do3k/c9QH//pYAb6BvNV9PKbmJrzavxsffbGQyQNn4OftT1x0HB9MXsL8NbN5c8JgsrOzOfHLKTxv3SO7HAwYiv8+GRgrI5KSktDV1eX69evo6mrPtDI1zZt+q6+vHRBSoVCQk5N3Mtm+fTvvvvsux44dY//+/SxatIiTJ0/Stm3bZ87LxIkTadq0KQ8fPmT79u288sorODk5FZh+/vz5zJo1S2tb1uqiD4Sck5JAjkqFwtRSa7vC1PKJWWQFylaRHeqHjrXjI8fMIjtC+25gdmQwulXLXswa/5NuhLvnPXFJ10DdhY1tzUmJiNNsN7YzJ/pO/sHw02ISyc5SaWZ+afaxtSAl985ZSkQ8uob6GJgrtWaNGdtaaGaaVWrfAOt6VZjct7X6j7mDHWNvbsZt42Fc1/30YoUtYtnx8eRkqdCx1r7LrmNtRXZ0TME75uSgClY/ySvLxxc9JydMR44g5sZNDBo3QsfKEvsf92uSK/R0MZ/+DiZDBxM5ZHixlKWo5STEk6PKQmGpXTcKSytyYguvm+xQ9RMUVQ980K3ihPGQESR6uKu3+XgTP2MiCqUJ6OmRkxCP+brNqO4/Gcy3LMvOrR8dq8fajpUV2c9YP6n/1M+wEeqBMdT1Ezc9t3709ciJj8fi881klbH68TnpRsiNJ887JrbmJD9y3jGxNSf8bv7nnZRY9XlHaat93jGxtSD5sTv2jwrN/VzLag5aA2P/xB+L8Q0lNS6JET8u4dIXv2jlpyzKiEkgO0uF4WPnX0M7C9LLeN5F8XM9eZX7N7w17/UN1N/5LGwtiYvIm71hYWuB/12/px5v3PJJNH+1FR8NXUBMWOFP2fbJ/VzHamV/YOyffmRQTP2o3qpx2PVozjWXj0gPLeQcX4b8eews7tfzlgUaGKiXxdra2RAZHqXZbmtnw918AuoDNGzaAFt7G347k/fAEz09PVq3b8HoiW9St0IrKletyJhJw+nZfiD376nPz553vGnVrjmjJrzJojkvPphbHO78eZ3P3PMG7fRy+5apnQUJkXGa7aZ2FoTcDSjSz44OiiApOgHbag5lcmDs/PG/uO12V/PeILdubOysiI7IO29Y21njfef+E/s/Kiszi4f+6u89Xre8adCkHsMnDmbVPPUNtSvnruHS7k0srC1QZalISkji2M1fCA4IKeywogAynFi0ZGCshNSsWRN9fX2uXLlC1arqpTexsbF4e3vTpUsXmjVrhkqlIiIigk6dCg5s+CyaNWtGs2bNmD9/Pu3atWPPnj35DowZGBigUj0ZKLxRo0a0bNmSb7/9lj179rBp06ZCP8/Q0BBDQ+0n9iQX8TJKAFQqskMeoFujISrP3KCXCgW6NRqSdeXYsx1DoUDHoSoq7xt5xwz2fWIppo5NBXLio/I5QOnKTE4jMzlNa1tyeByVOjoTnfuDVN/UGPumNbmzM/+HJWRnqoj08KNSB2f8j+cuCVQoqNTRmdvfnwQgysMPVUYWlTo443dUXdcWNSpgVtmWsOvqi+KJKRvQfSQeiX2TGnRbO5nDgz4mPiCCMicri0xvbwxbNCf9Qu4sN4UCwxbNSf7p52c/jo4O5H5pSD1+UmtZJYD1uk9JPX6SlCPP2CbLgqwssny80W/agsy//1JvUyjQb9qctN+fo24UOij0n3yaU06KOuisTsVK6NWqS+rOrUWR65KTlUXWfXX9ZFx+rH5+fb6289T6qV2XlDJWPxnJaWQ8dt5JiojDqYMzEbnnHQNTYyo0rcmNXQWfd8I8/HDq4IzPibzzjlMHZ9x2nCzws+2d1dfLwga8FDrqQfl/BuzKspxMFfG3/LDt1JCwY7nxWxQKbDs647/tROE7i/+8tOQ00pK1B6ViI2Jo1KExAbkDYcamxtRqWoeTuwq/xoxbPonWvdqybNgiIoOefk2u5lw99/PK/pNPczJVJN7yw6ZTQyKP5vUj604NCdx2/IWOXW/VOOz7tsL1jeWkBpafp0wnJ6WQnKQd/iIiLJIOndtonixpamZC0xaN2LX9YL7HuHT+Cr06DNLa9ummZTy478+WDdvJzs7G2Fj91OTsHO3n0mWrstHRKVuznR+VnpxG+mPXsYSIWOq0b6gZCDM0NcapaS0u7Sr4mvRvWDhao7QyJaGM3vxISU4lJTlYa1tUeDStOrbA+456MNHEVEnDZvX5cccvz3VsHR0F+gZPxi6Mj1HfEGvZoTnWtlacP/HXv8u8EEWo7H+L/I8wNTVlwoQJzJ07FxsbG+zt7Vm4cCE6OuqlfHXq1GHEiBGMHj2atWvX0qxZMyIjIzl16hSNGzemX79+T/0MPz8/vvnmG1577TUqVqzIvXv3uH//PqNHj843fbVq1Th+/Dj37t3DxsYGCwsLzYy0iRMnMn36dExMTHjjjTeKriJeUOal3zEcOI3s4Aeogn3Qb9cXhYEhmW5nATAYNI2chBgyT+4FQL/rIPVyypgwFEYm6Hd8DYWlHZnX8368Zf71K4ZD30fP3xOV3210azdFt24L0rZ9VAolfH4eW4/RYoYL8X7hJAZF0GrOYFLC4/IGvYD+e+fjd8yVO7k/QG99e5Ru66YQecuPCHdfGk/ojb6xIfcOnAPUgbS99p+l/ZIRpMclkZGUSsflowlz9SYidwZHwmODX8ZWZgDE+oQ8EZusrEjedxDLhR+S6eVNpqcnyqGDURgbkZo7iGWxaD7ZkZEkfq2OV2Qy8i0yve6hCglBoa+PYbs2GPfqQfxnnwOQk5BAVoJ2nKWcLBWq6BhUQeUn1gZA2s8HMJ01H9V9L7K8vTB6fTAKI2PSTx4FwHTWArKjI0nZ8S0ARkNGoLp/D1VYMAp9A/RbtsHwlZ4kf7lOc0yDjl3Jjo8jOzIc3Wo1MJk8g4y//9LE2SpPUn8+gNns+WTd9yLrnhdGLoNRGBqT9k/9zM6tn+/V9WM8dARZ9++hClXXj0Erdf0kbdKun5z4OFSR4ehVq4HJ2zPIuPwXmW5lv35ctx6j3QwXYv3CiQuKoNPswSRFxHH/RN55Z9ie+Xgfd+VG7nnH9buj9F07hbBbfoTe9KXl+N7oKw3xOKg+71hWtae+S3senHYnNS4J+3pV6bZkBEF/exLppe5PNbo1QWlrQdjNB2SkpGFbpzJdFwzn4bV7JDwsezcz8vPg6yM03fAOcTcfEHfDhxqT+qCrNCRwn7oemm58h7TQWLxWqWdtKPR1Maujjkeqo6+HUQUrzJ2dyEpOI8U/vNTKUdJSUlIJfJg3qyA4JBwvb18szM2o4Fi2Y1y+iD+2/sYbM4YQ6hdCRFAEw2a/RWxEDNdO5AVGX7RnOdeO/83xHeqnQ09YMYUOr3Xmf5NWkZqcikVuzKSUhBQy0zNwqOpIB5fO3Dh9naS4RKrWc2L0kgnc/fs2gV5FO1umuPhvOULDL94hwf0B8Td8qDq5L7pKQ0Jy+1HDjVNJC4vBZ2VePzLN7UcKA12MHK0xy+1Hqbn9qP6a8TgO7ID7mM/ISkrVzEjLSkwhOy0zn1yUbdu+3s302ZPwfxBAUEAwsxZMIzwskhN/nNak2fXzN5w4cpofvttHclIK3l7aSyFTk1OJjYnTbPe974+fbwCr1i5m1dJ1xMbE0bPvK3Ts2pYJw2eUaPle1LltR+kx4w0i/cOICYqgz+yhJITH4nEi7xr8zu5FeBy/xl8/qAdcDZSG2FbLi81sU8Weig2cSIlLIi4kGgOlIb3eG8ytY1dIiIzHtqoDA+a/RZR/OF7nb5Z4Gf+tvd8eYMLMMQT5PSQ4MJR3PphIZHg0Z49d0KT56sB6zh49z4Ht6hUi0xZM4dLpvwl7GI7SVEnvgT1o0b4ZM4bP1uwzYFhf/O77ExsdR+OWDZm9/F32fHOAAN/y9Z25rMh+ehLxHGRgrAT973//IykpiQEDBmBmZsbs2bOJj89bQrJ9+3ZWrFjB7NmzCQ4OxtbWlrZt29K/f/9nOr5SqcTLy4sdO3YQHR1NhQoVmDZtGlOmTMk3/aRJkzh79iwtW7YkKSmJM2fO0LVrVwCGDx/OzJkzGT58OEZGRi9c9qKiun2ZDBNz9F8dioGpJdmh/qT9sAqS1fWoY2GrtU5dYWyKgcsU9XLL1GSyQx6Q9u0iciLz7oyoPK+R8du36Hd2waDfOLKjQkjft5bswLK1nKkg7pt/R09pSJc14zEwVxJ2zZsjoz5FlZ73Jc7CyR5jazPNe9/frmBkbU6r2YNQ2lkQdTeAI6M+1Qrif2nZbnKyc+j5zXvoGugRdM6DCwu/L8miFbm002dIsLTAdOJYdK2tyfTxJWb2B5qg6roO9pCdd5lRGBthMXsmuvZ25KSnkxUQSNzyVaSdPlPQR5RbGRfOkGJhifHI8ehYWZP1wIfEJXM1Afl17OzJeeQOscLICJOp76Nja0dORjqqh4EkfbaCjAt5daNjZYNy4jR0LK3Ijo0m/dRxUvf9UOJlKwoZ58+QbGGJcuR4dKytyfL1IWFxXv3o2tvDY/VjOu2R+gkKJPF/K8g4/0j9WNtgPDm3fmLU9ZOyt3zUz9Utv2OgNKTn6vEYmSt56OrNwdHa5x3LqvYorfLOO16/X8HYxpyOswZhYmdBxN0ADo7+lJTc844qM4tqHZxpOb4X+saGJIbG4H30Gpc3HtYcIystgybDu/LK4hHoGuqTGBKN9zFXrmz+reQK/4JCDv+NgY05decNxtDOkoQ7AVwZvoaM3AcTGFey1XrKr5GjFV1OrdG8rzV1ALWmDiDq0l0uD/y4xPNfWm573Wf8jLzYN59u/AaA1/t0Z+Wi2QXtVu79uuVnDJVGTF49FaW5CfdcPVk9ejmZj/Q1h6qOmFmZa973HNUHgI8OrNQ61lezv+DcodNkZWbRqENj+o7vj6GxEdGhUVw9epmfNh4omUIVgfDDlzGwMafmvCEY2luSeCcAt+FrNAH5jSrZagU6N3S0pt3pvLhS1aYNoNq0AcRcvIvrQPVDrKqM6wlAq1+044rdfnczIfvPFXeRitzXX2xHqTRm1bolmFuYce3KDcYOnaoVP8ypWmWsrC2f+ZhZWVmMf3M685a8x3e7v0BpoiTAL5A50xZz9s/yNevn9JZfMTA2ZOjqSRibK/G7do+vx6wh65G+ZevkgMkj35+rNK7J9H1LNO9dFqsnH1w9dI69czaTo8qmYv2qtBrUGWNzExIiYrl3/hZ/rDuAKiOr5Ar3gnZ8uQcjpTEL/jcXM3NT3K968O5bc7TaTuVqFbG0zlvObG1jybIvFmJrb0NSYjL37/oyY/hsrpzPG2h0qlmFaQsmY2FpTkhQGNu/2Mnur/cjRFmgyHk0QJUQufz9/alZsybXrl2jefPmz71/8uKhxZCr/46d35ePR6KXhtedgp+e6CVmYCH3hwqSI1VTqG13q5R2Fsqs+ulPhhUQeXrfXvn0RC+pkS1mPT3RS2xCWtm5uVrWTMkqWw94KmtczOqXdhbKtAvp8n25IK6hF56e6D9gf4URpfbZw0J3l9pnFxeZMSa0ZGZmEh0dzaJFi2jbtu2/GhQTQgghhBBCCCFE8cguu2H9yiWd0s6AKFsuXrxIhQoVuHbtGlu2bCnt7AghhBBCCCGEEEIUG5kxJrR07doVWV0rhBBCCCGEEEKUTdnIlLGiJDPGhBBCCCGEEEIIIcRLSQbGhBBCCCGEEEIIIcRLSZZSCiGEEEIIIYQQQpQTEvyoaMmMMSGEEEIIIYQQQgjxUpIZY0IIIYQQQgghhBDlRLbE3i9SMmNMCCGEEEIIIYQQQryUZMaYEEIIIYQQQgghRDmRXdoZ+I+RGWNCCCGEEEIIIYQQ4qUkA2NCCCGEEEIIIYQQ4qUkSymFEEIIIYQQQgghyomc0s7Af4zMGBNCCCGEEEIIIYQQLyWZMSaEEEIIIYQQQghRTmQrSjsH/y0yY0wIIYQQQgghhBBCvJRkYEwIIYQQQgghhBBCvJRkYEwIIYQQQgghhBCinMguxde/8eWXX1KtWjWMjIxo06YNV69eLTT9wYMHqVevHkZGRjRq1Ig//vjjX37ys5EYY6JYWHxysbSzUKZdc2xZ2lkosxrdCi7tLJRp71q1Ku0slFl+irTSzkKZ1jdDnl9UkGB93dLOQpk2ssWs0s5CmbXr+rrSzkKZ9nWzJaWdhTJrK3VLOwtlmlu2/EwtzCfZFUs7C0I8s/379zNr1iy2bNlCmzZtWL9+Pb169eLevXvY29s/kf7SpUsMHz6c1atX079/f/bs2YOLiwtubm40bNiwWPIoM8aEEEIIIYQQQgghyonyNGNs3bp1TJo0iXHjxtGgQQO2bNmCUqlk27Zt+abfsGEDvXv3Zu7cudSvX5+PP/6Y5s2bs2nTpn/x6c9GBsaEEEIIIYQQQgghxFOlp6eTkJCg9UpPT883bUZGBtevX6d79+6abTo6OnTv3p3Lly/nu8/ly5e10gP06tWrwPRFQQbGhBBCCCGEEEIIIcqJHEXpvVavXo2FhYXWa/Xq1fnmMyoqCpVKhYODg9Z2BwcHwsLC8t0nLCzsudIXBVm8LYQQQgghhBBCCCGeav78+cyapR1/1NDQsJRyUzRkYEwIIYQQQgghhBBCPJWhoeEzD4TZ2tqiq6tLeHi41vbw8HAcHR3z3cfR0fG50hcFWUophBBCCCGEEEIIUU6Ul+D7BgYGtGjRglOnTuXlPTubU6dO0a5du3z3adeunVZ6gJMnTxaYvijIjDEhhBBCCCGEEEIIUeRmzZrFmDFjaNmyJa1bt2b9+vUkJyczbtw4AEaPHk2lSpU0ccree+89unTpwtq1a+nXrx/79u3D1dWVb775ptjyKANjQgghhBBCCCGEEOXE887cKk3Dhg0jMjKSJUuWEBYWRtOmTTl27JgmwH5gYCA6OnmLGdu3b8+ePXtYtGgRCxYsoHbt2vzyyy80bNiw2PIoA2NCCCGEEEIIIYQQolhMnz6d6dOn5/u3s2fPPrFtyJAhDBkypJhzlUdijAkhhBBCCCGEEEKIl5LMGBNCCCGEEEIIIYQoJ3JKOwP/MTJjTAghhBBCCCGEEEK8lGTGmBBCCCGEEEIIIUQ5ka0o7Rz8t8iMMSGEEEIIIYQQQgjxUpKBMSGEEEIIIYQQQgjxUpKllEIIIYQQQgghhBDlRHZpZ+A/RmaMCSGEEEIIIYQQQoiXkswYEwB07dqVpk2bsn79eq3t33//PTNnziQuLq5U8lWYj5bOYcL4t7C0NOfSJVemzZiPj49foftUrOjI6lUL6N3rFZRKI3x8/Zk4cRbX3W4B4OLShymTRtG8eWNsbKxo0aonN2/eKYniFBnb0X2xn+KCvp0VqZ7+PFzyDSk37+eb1mZ4D6wHdcOorhMAqR6+hHyyUyu9Re+22I7sjbJRTfSszPHqPZPUu4XXc1n34cJ3GTVmKOYW5lz92425s5bywDegwPTz5s9g3vwZWtvuez+gXcvemvdr1y+nc7f2ODrak5ycwrUrbixb8hk+9x8UWzmKQtdZg2g2vBtG5iYEuXrzx8JtxPiHF7pPy9E9aD+5H6Z2FoR7BnJ06Q5CbuaV08TOgh4L3qJGx4YYmBoR/SCUC5sO43X0mtZxar/SlM7vvoF9/apkpWcS8LcnByZ/XizlLCou779Jl+HdUZorue96j52LviHcP7TA9P2mvkGLXm1xrFmJzLQMfNzucXDNTsIehGjSdBneg7avd8TJuQbGZkqmNh5FakJKSRTnX6s5tgd1p/bDyM6CuLuB3Fi4g1j3gtt65f6tcf5gCCaVbUnyC+fWir2Enb6plcasdkUaL3wTu3b1UejpkOAdzKWJG0gNjgagy48LsW/fQGsf3x9O4fbBtqIv4HNqOXsQ9Yd3w9BCSdg1by4s2E78U/qR85juNJ3SD2M7C6I9A7m45AciHqlDXUN92i1+i1qvtUXXQJ+gc7e4sPB7UqMSALCpX5Wm0wZQoVUdjKzNSAyK5O6u03hsO57v5zm2rM1rBxcRc+8hh3ovLLrCF5Ehs4bz6vAemJibcM/Vi+8WbiGskL7lMnUQrXu3pWLNymSkpeN9/R671+wg9JG+tWTfCpzbNdTa7+SuY3y3cEuxlaM0uLp7sH3PIe56+RAZHcOG1Yt5tXP70s5WsWg9exDOuX0t9Jo3Z5+hrzUa051mU/qhtLMgyjOQ84/1Nee3ulHHpT12DathYGbMN86TyXjkHFypbX3eOJh/nznQfwkRN8vmdb7SuF5UnToAA3tLku4G4L1gG4k3fPNNa1K3MtXnDcOscXWMq9rjvfh7Hn7zR4HHdprxOjUXjSDomyPcX7yjuIpQpDrMGkSjt7phaK4kxNWbkwu2E/eUttN0dHdaTemHiZ0FkZ6BnFryA2EF/HsP2jGX6t2a8MvEz/E5cV2zfU7grifS/jZtE/d++/vFClSMKo/r+Vjb2U5CIW2nxryhj7SdHQQ9pe3UWvQWgd/8UW7aTlklM8aKlswYE+XS3DlTmT5tPFOnf0j7jgNITknhj993Y2hoWOA+lpYWnD/7C5mZWfQfMJJGTboxb95yYuPiNWlMTJRcvHSV+QtWlkQxipzlgI5UWjyesPX7uddvFqmeftTc9RF6Nhb5pjdt24jYwxfwGbYIb5d5ZIREUXPXR+g7WGvS6CiNSL7mScjqH0qqGMVqxsxJTJoymjkzl9LrlSGkpKRw4KdtGBoaFLqf511vGtRqr3n16zlc6+833e/w7jsf0r5VH4a+MR6FQsGhX7aho1N2T7Pt3+5P67G9OLJgO1tfX0JmSjojdn6IrqF+gfs06N+WnotGcG7DT3zTfxFhnoGM2PkhShtzTRqXde9gU6MC+yauZUvPD/E65srgL9/F0dlJk6Zen1a4fP4O7gfP83Xv+WwftIzbhy8Va3lfVN+3Xegxri8/LPyaj13mk5GaxqwfFqNXSH3VbePMqZ3HWPHGfD4btQxdPV1m/7AEA+O8c5WBsQEe59z5/aufSqIYL6zya21p8tEI7q79iZO9FhF/N5DOez/E8JE28CiblrVps3k6fnvOcrLnQoKPudJh+yzM61bWpDFxsqfbL0tI8Anl7KAVnHhlPnc//4XstEytYz3YdZpfG0/VvG59vLdYy/osmr7Tn0bjenJhwTZ+GrCUzNR0+u36oNB+VHNAG9ovHoHr+p/5se8iou8G0m/nBxg9Uoftl47AqXszTry9kcNDVqB0sKLXNzM1f7dtXI20qAROvbeZ/a9+gNvGX2n94VCcx/R44vMMzJV0W/82wRfL5o2e195+gz5j+/Pdgi0sfH0eaSlpLNi5FP1C6rB+G2eO/3CURS7zWDnyI3T1dVm48yMMjbW/B/y55wSTW47VvHav/u/9EEtNTaNurRosnD21tLNSrJq/058m43pydsE2Dub2tdee0tdqDWhDx8UjuLb+Z/bn9rXXdn6A8SN9Tc/YgICzt3Dd9Gu+xwi97s225tO0Xnf2nCE+IKLMDorZv96O2stG47/2ENd6fEDSnQCa7luIvm3+52kdY0NSA8LxXbmH9PDYQo9t1rQmFUf3IPGOfzHkvHi0fqc/zcb15OT8bex+bSmZKekMfkrbqTugDV0Xj+Dy+p/Z2W8REZ6BDN71gdb3nX+0mNCbnJycAo91dNbXfNVimub16MBZWfNP2/Fb+yPXenyY23YWPEPb2ftMbafS6O4k3in4ZrQQpaXs/mITRapr165Mnz6d6dOnY2Fhga2tLYsXLy70JF6WvTtjIqtWb+C3307g4eHJ2HHvUbGiA6+/3qvAfebNncrDhyFMnDSLa67u+PsHcfLP8zx4kHdy3r37R1asXM+p0xdKohhFzn7i60TvPUHMwVOk3Q8iaP5mslPTsRnWPd/0Ae+tI2rnUVLv+pHuG0zgvE0odHQw69hEkyb2p7OEbdhP4l838z1GefP21DGs+99XHP3jFHfv3GPqlHk4VrCnb/8nf0w+KitLRURElOYVE6N98f/h+/1cvuRKUGAwt27eZdXH66lcpSJVnSoVZ3FeSJsJvbmw6Re8T14nwiuIX2Ztxszekno9WxS4T7uJfXDbd4abB88TdT+YIwu2kZmaTrOhXTRpqrSozdXvTxBy8wFxQZFc2PgLaQnJVGhUHQCFrg69l47m5Ko9XN99ihi/MKLuB3P3yJViL/OL6DG+P79tPMSNk9d46BXAt7M2YuVgRfOerQvcZ92YFVw8dIaQ+0EEeQawdc4mbCvbUa1RTU2ak9uO8Mfmn/G94V0SxXhhdab0wW/3Gfz3nyfRO5jr87ahSk2n2vAu+aavPbE3YWdu4b35CIn3Q7jz6SFiPfypNb6nJk3DD4cSdvomHiv2Enc7gOSACEJPuJEenaB1rKzUdNIj4zWvrKTUYi3rs2g0oTduGw/jf8KNGK8gzszcgtLBkmq9Cu5HjSf1wXPvGe4dOE/s/RDOz99OVlo69Yap69DAzJh6w7pyefluQi7dJcrDn7Ozv8GxVR3sm6nbzr3957n40U5C//YiMTCS+z9f5N6B89To0/KJz+u8ehw+v1wm/LpP8VTCC+o7YQA/bTqA68mrBHoF8OWsDVjZW9OqZ5sC91k9ZjnnDp3m4f0gAjz9+Wr2F9hVtqfGI30LICM1nfjIOM0rtQy0maLWqV0r3p08hu5dOpR2VopVkwm9cd14GL8TbkR7BfHnzC2YOFhSo5C+1nRSH+7sPYNnbl87k9vX6g/LO1/d3Hoct69+I9wt//6RnakiJTJe80qLTaJ6z+Z4Hjxf5GUsKlXe7k/IrlOE7jtLincw9+Z+S3ZqBhWHd8s3faK7L77LdxHxyyWy0zPzTQOgqzTE+asZeM3+mqy45OLKfpFrPqE3f288jO9JN6K8gvjj/S2Y2ltSq5DvOy0n9sFj7xluHzxP9P0QTs7fTmZqOg2HaV/r7BpUpeXkvhyb+22Bx0pPSNFqQ6pC6ri0VX27H8G5bSfZOxivud+hekrb8Vm+m/BnaDsNv5qO5+xvyIpLKq7sv1RySvH1XyQDYy+RHTt2oKenx9WrV9mwYQPr1q3ju+++K+1sPbfq1atSoYIDp07/pdmWkJDI1as3aNum4Atc//49uX79Fvv2fk3Iw5tcu3qcCePfKokslwiFvh7KRjW1B7Byckj86ybK5nWf6Rg6xoYo9HXJikssplyWLqdqVXBwtOfc2cuabYkJSbi53qRl66aF7lujphO3713A9eYptnz3GZUqVygwrVJpzFsjB+LvF0Tww7Ciyn6Rsqxih5m9FQ/+yptBkp6YSrC7L5Wb1853Hx19XSo0qo7fX7fzNubk4PfXba19gq7fx3lAW4wsTEChwHlAW/QM9fG/7AlAhYbVMK9gTU52DpP+WMn71zbx1o552NWp/PhHlhl2VRywtLfizsVbmm2piSn4ut+n1jP2LwBjMyUAyeW0jyn0dbFqXJ3wC9ptIPzCbWxa5N9ubFrWIuLR9EDY2VvYtKiVe1AFFbo3JfFBKJ32fsAAj6945cgyKvZ+8nzuNLADr93ZQs8za2i4YBi6xoXP9CxuZlXtMHGw5OEj5ctITCXC3RfHQvqRXaPqPHyk75GTw8MLd3DIrRPbRtXRNdDTShPnG0riwygcC6hnAAMzJWmP/VitO7Qz5lXtcf28bM5ItK/igJW9NR5/afctH3dvaj9H31Lm9q2kx35wdXTpzLc3fuCzExsYPm8kBkal22bEv2Oe29eCHutr4U/pa/aNqhOUT19z/Of88y9U79EcIyszPPeXzYExhb4uZo1rEHPBI29jTg4x5z0wb1nnhY5dZ81Eov68Qex5j6cnLiMsqtpham9JwF/abSfU3ZeKBZxPdfR1cWhUnYDH2k7gX3eo2Dyv7egZGdB/4zT+XPQ9KZHx+RxJ7dUVY5jqvpkRvy6j4dDOL16oYlJQ24k974FFy4KvPc+i7poJ5a7tiJeLxBh7iVSpUoXPP/8chUJB3bp18fDw4PPPP2fSpEkAfPXVV08MlGVlZWFkZFQa2S2Qo4M9AOHhkVrbwyOicHS0L3C/GtWrMmXKKNZv+JY1n3xByxZNWf/5cjIyM9m582Cx5rkk6Fqbo9DTJTMqTmt7VlQcRjWfbcCh4vzRZIbH/Gdmhz3O3t4WgMiIKK3tERFRONjbFbjfddebzHjnQ3zu++HgaMfcD6fz+7E9dGrbn6SkvB+h4ya+xdLlczE1NeG+9wMGu4wlM7Ns3hU0tbcEIDlK+4tcUlQ8pnaW+e6jtDJDR0/3iX2SoxKwrVlR8/7QtC8YvGkG8259gyozi8zUDA5MXk9sgDqWh1VVdT/tMnMQJ1bsIj4okraT+zFm/yI2dZ1NWnzZuwttkVsnCZFxWtsTIuM1f3sahULB8CXj8L7mSbB3UNFmsIQYWqvbQNpjPwDSIhMwq1Ux332M7CyfSJ8eGY9Rbhs0tDVH39SYetMHcPuTg9xasQ/Hbo1pv3UmZwevJOqyFwCBP18i5WEUqWFxWDaoQqOFwzGrWYHLE9YXeTmflTL33/6fuF//SI1MwNg+/yXsRrl1mPpYnaRGxWNZSz3grrS3QJWeqRXn6J80xnb5H9ehRW1qDmjD0bGfabZZVHOgzYfDODzoY3JUZTMaiWVuO4h/7NoVHxWPpZ3VMx1DoVAwZukEvK7dJcg7ULP94uHzRAVHEBMei1N9J976cDQVa1Zi7ZRPiir7ooT809dSHutrKZEJKAvoa8YF9LWUR/rav1H/zS4EnrtFcljMvz5GcdK3NkdHT5eMx65XGZFxKGvnf55+FvYu7TFrXB3XXvNfMIcly6SgthOVgEkB59N/2s6T33fisa6Z13a6LR1JsOt9fE+6Ffj5f312iMBLd8hKzaBa50Z0XzEWfRMjbmw/8S9LVHzy2o52uTMi41+o7Tjktp1rvRa8aBaFKDYyMPYSadu2LQqFQvO+Xbt2rF27FpVKBcCIESNYuFA7uOhPP/3EqlWrCj1ueno66enpWttycnK0PutFDB/+Bpu/zPsS+9rro//VcXR0dLh+/RaLFq8BwN39Ds7OdZkyadR/YmDsRTlMHYTVa524P3QhOWV4ivfzGDx0AJ+tX655/9aQyf/qOKdO5t0VvnvnHtddb+J++yyvv9GH3TsPaf526MCvnDtzEQcHO6a9O4Gt32+gb883SU/P+PeFKCINXdrTf9UEzfu94/5XbJ/VbfZgjMyV7HxrFSkxidTt2ZLBX87g+yEfE3EvCEVu3LW/Nv2iCcj/65yvmfn3Rhr0a4PbntPFlrdn1fb1ToxZNUXzfv34ws+Dz2Lkx5OoXLcqqwaXvcDnpUmho75WhBxz4/43xwCIvxOAbcva1Bz1qmZgzG/XGc0+CV5BpIbH0fXQQkyc7EkOiCiRvNZ2aU/nNeM17/94ZBCqNFnVrUzvre9zff3PPDyvnhWh0FHw6qZpuK77kXi/sjNztaNLZyatekfzfs24FS98zPEfT6ZKHSeWDtb+wX5qb94Pz6B7AcRGxLJk78c4VHUkPLDs1Il4Uh2X9nR9pK/9Xkb6momjNVW7NOb4OxtLOyslyrCiDXVWjOXG0BWFLpcrC+q7tKfH6ry281MxtZ2aPZpTtX0DfuhT+DX97y9+0fx/xJ0A9I0NaTWlX5kcGCsO6rYzhhtDV5b5tlPeZBfNT22RSwbGhIaFhQW1amlPLbe3L3gG1j9Wr17NsmXLtLYpdExR6OYfpPF5/fbbCa5evaF5/0+QdAcHO8LC8n4MOdjb4l7IEyRDQyO466kdw8fLy4eBb/QtknyWNlVMAjlZKvRtLbW269lakhlZeDBM+8ku2L8zEJ8RS0nz+u8ExDz2x2muu+bNfjMwULcdO3tbrRmH9va2eHh4PvNxE+IT8fX1p3oNJ63tiQlJJCYk8cA3ANdrN/EJvEa/AT346dCRFyzJi/M+6cbXjzxRSM9Affo3sbUgKSJOs93U1oKwu/m3gZTYRLKzVJjYat9hNbE1Jyn37qJVVXtaj+3F5u7ziLwfDEC4ZyBVW9el5ege/LFwm+bz/vk7gCoji7jACCwq2bxwWYuC+5/XeOCe93RWPQN1gF5zO0viH7kLb25nQdBd/6ceb+SyiTR9pQWrhy4mtozOMngW6THqNmD02F12Iztz0iLyX0aSFhn3RHpDOwvScttBekwi2ZlZJDzSHgAS7odg27rgpXQxbur2bFrdocQGxvxPuhHuntePdHP7kbGtOSmP9CNjO3Oi7wQ+vjsAabl1+PjML2NbC81SnJSIeHQN9TEwV2rNGjO2tXhi9otV7YoM2Dsfzz1ncPvisGa7vqkx9k1qYOvsRMePxwDqwTKFjg6T/Xbw+4hPCLl091/UwotxPXmV+4/E09PP7VsWtpbEReRdqyxsLfB/hicgj1s+ieavtuKjoQuICYsuNK1P7uc6VpOBsbLOr4C+pnysryntzIkqoK+lFtDXlI/0tedVf1hn0mIT8StkhlBpy4xJIDtLhcFjs5kN7CzJeKTunodZkxoY2FnS6mTejWodPV0s29Wn0vjenK3yFmSXjchDPifdCH3k+46uYV7bSX607diaE3G38Lbz5PcdC5Jz207V9g2wdLJnxu1vtNK89vV7BF+9x/5h+T/IK9Tdl3Yz30DXQA9VRtZzl6845bUd7XIb2Fm8QNupntt21mi2/dN2Ko/vxZkqI8pM2xEvNxkYe4lcuaId2Prvv/+mdu3a6OrqvtBx58+fz6xZs7S2WdnUe6FjPiopKVlruRpAaGg4r3TryM3cgTAzM1Nat27Glm8KfnLipcvXqFtHOyhvndo1CAwMLmCP8iUnM4sUD1/MOjQm/kTuv7VCgVmHxkTtKPixyfZvv4Hj9CH4jPqI1FtlMzDzv5Vf2wkPi6Bzl3bczh0IMzUzoXnLJmzf+uxPtzMxUVKtehUO7Cv4x7hCoV7e889gXGnLSE4jIzlNa1tiRCzVOzgTnjsQZmBqTKWmNXHd9We+x8jOVBHq4Uf1Ds7c++eJSgoF1Ts05NoO9Z1P/dwnwj3+YI8cVXbezCAPP7LSMrCpWYEgV/UPVR09XSwq2xH/UHuZa2lJS04jLVn7h3NcRCwN2jfSDIQZmRpTs2ltzuw6XuixRi6bSPNerfnkzaVEPSyZAZzikpOpIvaWH/YdnQk5ltcG7Ds2xKeAu9/Rrj7Yd3Tm/rfHNNscOjckOjcQfE6mihj3B5jV1F7aZFbTkZRC2oNlQ/XAdFp43AuU6PlkJqeR+Vg/Sg6Po1JHZ6Jzf2Dpmxpj37Qmd3aeyvcY2ZkqIj38qNTBGf/jeXVYqaMzt78/CUCUhx+qjCwqdXDGL3dWpUWNCphVtiXset6ArVWdSgzYtwDvQxe4+qn2zOeMxFT2d/9Qa1vD0d2p2L4BJ97+gsRA7XAEJSW/vhUbEUOjDo0JyB0IMzY1plbTOpzcdSy/Q2iMWz6J1r3asmzYIiKDnt63qjlXz/28wm8WidKXmZxGfD59rXJHZ6Ie6WsOTWtyu5C+FuHhR5UOzvg90tcqd3TmVm5fe171h3Tm3o9/kZ2l+lf7l4ScTBWJtx5g1akhUbnnDxQKrDo1JHhb4X2qILHnPbjSZbbWtvrr3yHFJ4SATYfL1MBGZnIacY+1naSIOJw6OBOZ23YMTI2p0LQm7oW0nXAPP6p2cM57gqRCQdUOztzYoW47V776DY+9Z7X2G/vnGs4s38WDP29QELsGVUmNSypzg2KQ13asOzUi6qiremNu23m4rfDvOgWJPX+bv7vM0drWYP07JPsEE7Dp1zLVdsqbshkgofySgbGXSGBgILNmzWLKlCm4ubmxceNG1q5d+8LHNTQ0xNBQ+/HoRbWMsiBfbPyOBfPf5b7PA/z9g1j20VxCQsI5fDjvpH3i2H5+OXyUrzZ/D8CGDd9y4fxhPvxgBgcP/UarVk2ZOHEEb0+dp9nHysqSqlUrUbGCAwB1cgfSwsIinohpVhZFfHcYp7XvkeLhQ7L7fewnDEBHaUT0AfVAh9PnM8kIiyb0k50A2L8zkAqz3sL/3bVkPIxAL/fuYnZyGtkp6i8VuhamGFSyQ9/BGgDDmuqnLGZGxpL1WPyK8mDLVzuYNfcdHvj6ExDwkPmLZhIWGsEfv+d9Sf7p1x0c+f0kW7/ZBcCyFR9w/OhpgoJCcHS054MF76JSZfPTwd8BdVB/l4F9OXv6L6KiYqhY0ZH3Zk0mLS2NP0+cK5VyPosrW4/RaYYLMX5hxAVF0nX2YBIj4vB65DHio/bMx+u4K9dyvwhe/u4oLmunEHLLj5CbvrQZ3xt9pSHuB9XljPINIdovjH6rJnBy5W5SY5Oo26slNTo1ZO949XKGjKRUXHefouv7g0kIiSE+OIp2U/oBlOknU57c9jsDZgwm3D+UqKAI3pg9nNjwWNxOXNWkmbt7KW7Hr3Lqh6MAjPp4Em1f78QXk9aQmpyK+T8xqRJSyMxdYmtuZ4mFnSUOTo4AVK7rRFpyKjHBUSTHl70nN3l/fZTWG6YQe9OPGHdfak/qjZ7SEP996jbQ6ou3SQ2L5faq/QDc/+4YXX9aRJ0pfQk9dYMqr7fDukkNrs/dqjnmvc1HaLdlBpF/exFx8S6O3RpToUdzzg5SL7MzcbKn6sD2hJ5yJyMmCYsGVWm6bCSRlz2J9yzdeG0eW4/RYoYL8X7hJAZF0GrOYFLC4/IGvYD+e+fjd8yVO7n96Na3R+m2bgqRt/yIcPel8YTe6Bsbcu+Aug4zElPx2n+W9ktGkB6XREZSKh2XjybM1ZuI3JkQVnUr89q++QSd8+Dmt0c1s2JyVNmkxSSqAybfe6iV19SoBFTpmU9sL21/bP2NN2YMIdQvhIigCIbNfovYiBiuncg7Hyzas5xrx//meO6NngkrptDhtc78b9IqUpNTNbH+UnL7lkNVRzq4dObG6eskxSVStZ4To5dM4O7ftwn8D82MBkhJSSXwYYjmfXBIOF7evliYm1GhkNir5c3NrcdoOcOFuNy+1mbOYJLD43jwSF97fe98HhxzxSO3r7l/e5Tu66YQccuPcHdfmkzojZ6xIZ4H8q7NSjsLlHYWWFRTf/ezqVeFzKRUEkOiSX/kYRaVOzhj4WTPnccGQ8qioC2/U/+LaSS6PyDhhg9VJvdFV2lIyL6zANTfOI30sBgerFTfFFTo62KS+wAcHQM9DB2tMXV2QpWcRqp/OKrkNJK9tM+1qpR0MmMTn9heFrltPUbbd12I9Q8nPjCCDnMGkxQRlzfoBQzZOx+fY66agS/X747SZ+0Uwj38CHX3pcUE9fed27lt558nTD4uMTia+CD1b4Ya3ZthYmtBqJsPWemZOHVqSNvpr3Htm4JvWJe2wC1HaPDFVBLcfUm44UvV3LYTmtt2GuS2Hd8C247VM7SdNDJjk8pF2xEvDxkYe4mMHj2a1NRUWrduja6uLu+99x6TJ/+7mEul7X+ffYWJiZItX32KpaU5Fy9eo9+AkVqxzmrUcMLW1lrz3vX6TQYPmciKFR+yaOFM/PyDmDV7KXv3/qxJM6B/T7Zt/Vzzfu/uzQAs/3gtyz9eVwIlezFxv/2FnrU5FWa9hZ6dFal3/fAdtYys3OCh+hVtycnOu79gO7I3Oob61Phae1ZB6Od7Cft8HwAWPVrjtO49zd+qfzn3iTTlycb132JiYszaLz7GwsKcK5evM2zQBK04YNWqV8HGJi/oc8VKjnyzbR1W1lZER8Vw5e/r9H51CNHR6lkH6WnptG3fkilTx2BpaU5kRDSXL12jb/c3iYoqu8vmLm35HQOlIf1XT8DIXEmgqze7R3+i9Rhxq6oOKK3MNO/v/v43JjZmdJ01GFM7C8LvBrBn9Cck5wa1zc5SsXfsp7z64Zu8uXUOBiaGxPiH88usr/E5k7es9c9Ve8lRZePy+TvoGxkQ7O7DzuErSXss2HhZ8seWXzAwNmLs6rdRmpvgfc2LdWM+JuuR+rJ3csTUOq++XhnVG4AP93+sdazv5mzi4iF1zKxuI3riMnOY5m8LDq54Ik1Z8vDXvzG0McN53mCM7CyIuxPAhbc+IT23DSgr2WjdAY52vc+VqV/S8IMhNJw/lCS/MC6OW0fCI4MzIUdduf7BNurNeI1mH48m0TeUyxM3EH1VPaMwOzMLh04NqT1RPQiXEhLDwyPX8Fz/S4mWPT/um39HT2lIlzXjMTBXEnbNmyOjPtXqRxZO9hg/0i58f7uCkbU5rWYPQmlnQdTdAI6M+lQriP+lZbvJyc6h5zfvoWugR9A5Dy4s/F7z95p9W2Nsa0GdQR2pM6ijZntiUCS7279fvIUuYr9u+RlDpRGTV09FaW7CPVdPVo9eTuYjdehQ1REzq7wQDT1H9QHgowPay5W+mv0F5w6dJiszi0YdGtN3fH8MjY2IDo3i6tHL/LTxQMkUqgTd9rrP+BkfaN5/ulG9tOv1Pt1ZuWh2QbuVO265fa3bmvEYmisJvebNb0/paz6/XcHY2pzWswdhYmdB5N0AfnusrzUc+SqtZw3UvB/042IA/pz1NV4HL2i2N3izC6HXvInzDS3OYhaJiMOX0bcxp8a8oRjYW5J4x5+bw1eRmTuQY1TJVus8behoTevTebFHnaa9htO014i9eIcbA5c9cfzy5urm39E3NqTnanXbCXb15sfH2o5lVe22c++3KyitzekwS32ejrwbwKFRnz4RxL8w2Zkqmo7uTrclI0ChIM4/nDMf7+HWnrJ3bf9HxOHLGOS2HcPctuM+fLUmIL9RJRut3xKGjta0Of2p5v2jbcdt4PInji9EWaXIeXy9i/hP6tq1K02bNmX9+vUl8nl6BpVK5HPKq2uOLUs7C2VWj7hnj/X1MnrXqlVpZ6HM8lOkPT3RS6xvetl6wnBZEq0nEWwLc0an7M1cLCt2XS/7N81K09fNlpR2FsqsBhml/2CesszNsGyEoiirmkkg+wK9Gr6/tLNQIlY7jSy1z54fsKvUPru46JR2BoQQQgghhBBCCCGEKA2ylFIIIYQQQgghhBCinMhGFv4VJRkYe0mcPXu2tLMghBBCCCGEEEIIUabIwJgQQgghhBBCCCFEOZH99CTiOUiMMSGEEEIIIYQQQgjxUpKBMSGEEEIIIYQQQgjxUpKllEIIIYQQQgghhBDlhITeL1oyY0wIIYQQQgghhBBCvJRkxpgQQgghhBBCCCFEOSHB94uWzBgTQgghhBBCCCGEEC8lGRgTQgghhBBCCCGEEC8lWUophBBCCCGEEEIIUU5kK0o7B/8tMmNMCCGEEEIIIYQQQryUZMaYEEIIIYQQQgghRDmRTU5pZ+E/RWaMCSGEEEIIIYQQQoiXkswYE0IIIYQQQgghhCgnZL5Y0ZIZY0IIIYQQQgghhBDipSQDY0IIIYQQQgghhBDipSRLKUWxOG/TtrSzUKa9nx1f2lkos1xrOJV2Fsq0K+GlnYOyy8DAuLSzUKbF6ZZ2DsquZHnkeaEmpBmVdhbKrK+bLSntLJRpU24sL+0slFm3m79f2lko08Y6yxeewuy4U6W0s1BmvVraGSgh2aWdgf8YmTEmhBBCCCGEEEIIIV5KMmNMCCGEEEIIIYQQopzIlvD7RUpmjAkhhBBCCCGEEEKIl5IMjAkhhBBCCCGEEEKIl5IspRRCCCGEEEIIIYQoJ2QhZdGSGWNCCCGEEEIIIYQQ4qUkM8aEEEIIIYQQQgghyons0s7Af4zMGBNCCCGEEEIIIYQQLyWZMSaEEEIIIYQQQghRTmRLlLEiJTPGhBBCCCGEEEIIIcRLSQbGhBBCCCGEEEIIIcRLSZZSCiGEEEIIIYQQQpQTspCyaMmMMSGEEEIIIYQQQgjxUpIZY0IIIYQQQgghhBDlRHZpZ+A/RmaMCSGEEEIIIYQQQoiXkgyMCSGEEEIIIYQQQoiXkiylFEIIIYQQQgghhCgnciT8fpGSGWMlpFq1aqxfv/6Z0/v7+6NQKHB3dy+2PAkhhBBCCCGEEEK8zGTG2H9c165dadq06XMNypV1jmN7U3Hq6xjYWZJ81x+/hVtJcvfJN61xnSpUnfcmJo1rYFTFHr8l2wj99oh2Ih0dqswZit2gzujbWZIZHkvEgTM8/PxQCZSmeIybM4b+w/tiamHK7Wt3WLdgA8F+wQWmf23UAF4fPQDHyg4A+HsHsGP9Tq6euZZv+k92rqJNt9YsmrCEv45fKpYylBSzYa9hMWYIujbWZHj7Ev3Jl2TcvpdvWuUrHbGYMBz9qhVBT5eswBDifzhE8pE/SzjXRaPW2B7Um9oPIzsL4u4G4rZwBzHuDwpMX7l/axp9MASTyrYk+oVza8VeQk/f1Py99fopVB/WWWuf0DM3Of/WpwDYtavPKz8tyvfYJ3svJuZmwZ9dGjrNGkST4d0wNFcS7OrN8YXbifUPL3Sf5qO702ZyP0zsLIjwDOTk0h8IfaxcFZvXosvcIVRoWpMcVQ4RdwPYP+oTstIzsahsS/t3XXBq3wATO0uSwmO58/NFLm06THamqjiLW6AGY7rT+O1+GNtZEOMZyKXFPxBZSDup3q81LecOxrSyLQn+4VxdtY+gR9oJQIs5g6g3vBsGFkrCr3nz14LtJPjl1a1FdUdaLxqOY6s66OjrEeMZiOtnhwi95AlA7SGd6Pr5lHw/f2eTqaRFJxRByV9M+1mDaPSWuv2EuHrz54LtxD2l/TQd3Z2WU9TtJ9IzkNNLfiCsgH4xcMdcqndrwuGJn+Nz4rpme7dlo6jUsg42dSoT4xPCzj4Li7RcRa3KuJ5UmzoAA3sLku4G4rlgOwk3fPNNa1K3MrXmDcG8cQ2Mq9rhtXgHgd8c1UpT/d3Xse/bGpPaFclOyyDumjfeH+8hxTe0JIpTJFrPHoTz8G4YWigJvebN2QXbiX9K22k0pjvNpvRDaWdBlGcg55f8QMQj/dT5rW7UcWmPXcNqGJgZ843zZDISUjR/r9S2Pm8czL+tHOi/hIgydn5+Hq7uHmzfc4i7Xj5ERsewYfViXu3cvrSzVexsR/fFfooL+nZWpHr683DJN6TcvJ9vWpvhPbAe1A2juk4ApHr4EvLJTq30Fr3bYjuyN8pGNdGzMser90xS7/qVSFmKmlF/F4wHv4mOlTVZD3xJ3ryBLG+vfNMatO+E8bCR6FashEJPD1XwQ1J/OkD66ROaNApLK0zGT0G/eSt0TEzJvH2TpM0byA4p+Lt3WVNa16x/GFmaMvr4KswqWLOp4WTSHzk/ifxJ8P2iJTPGRLli81p7qn00lodrD3Cz11yS7wbQYO9i9G3M802va2xAWkA4ASt3kREem2+aStNdcBzTC78F3+He+T0CVuyk0lQXHCf0Lc6iFJvhU4cxaNwbrJu/gXcGTCc1JY3/7VqDgaF+gftEhkbyzervmNx3KlP6TsXt4g1Wbl1OtTpOT6QdPHEQOTn/jam7yp5dsJ49hbivdxEy/B0yvB/g8NVqdKws802fnZBA/Hd7CB39HiFDppB4+Di2y+Zg1K5lyWa8CFR5rS1NPxrBnbU/caLXIuLuBtJl74cYFtCXbFrWpt3m6TzYc5bjPRcSfMyVDttnYVG3sla60NM3Odx4quZ1+Z1Nmr9Fu3pr/e1w46n47j5DUkBEmRsUa/N2f1qM7cnxBdv44fWlZKakM2znB+gW0o/q9W/DK4tG8NeGn9nefxERnoEM2/kBykfqtGLzWgzdMQ+/87f54bWl7HhtCdd3nNT0KeuaFVEodDg2fxvfdf+AU8t302zEq3SZN7TYy5yfGgPa0HbJCNw+/5mf+ywi+m4gfXZ9gFEB7cS+RW1e+XIa9/ad4+fei/A/dp0e372P1SPtpMnU/jiP68lf87dxeIC6bvvs0q7bXjtmo6Ony5Fhq/i5r/pze30/G2M7CwAe/PY3u5pN03oFnb1FyGXPMjEo1uqd/jQb15M/529jz2vqMg7aVXj7qTugDV0Wj+Dy+p/Z2W8RkZ6BDNr1Acb51HXzCb0LPQ/f3n+Oe7//XSRlKU4Or7ej7rJR+K49xN895pN4J4AW++ZjYFvwNT01IIL7K/eQXsA13apdfYK2n+BK38W4DlmJQk+XFvsXoKs0LM6iFJnm7/SnybienF2wjYMDlpKZms5rT2k7tQa0oePiEVxb/zP7c/vLazu1246esQEBZ2/huunXfI8Ret2bbc2nab3u7DlDfEBEuR4UA0hNTaNurRosnD21tLNSYiwHdKTS4vGErd/PvX6zSPX0o+auj9Czscg3vWnbRsQevoDPsEV4u8wjIySKmrs+Qt/BWpNGR2lE8jVPQlb/UFLFKBYGnbthMnkaKbt3EDdjEio/X8xXfIbCwjLf9DmJiaTu30X8rGnETh1P2smjmM76AP3mrTRpzJesRNexIonLFxI3fSKqiHAsVq0DQ6MSKtWLKe1rFkCv/00k0jPwhcsixL8lA2PP4dChQzRq1AhjY2NsbGzo3r07ycnJdO3alZkzZ2qldXFxYezYsQUeS6FQsHnzZvr06YOxsTE1atTg0KEnZyg9ePCAbt26oVQqadKkCZcvX9b8LTo6muHDh1OpUiWUSiWNGjVi7969mr+PHTuWc+fOsWHDBhQKBQqFAn9/fwBu375Nnz59MDU1xcHBgVGjRhEVFfXUspa2ilMGEL77TyL2nyHV+yEP5n2NKjUd++Gv5ps+6aYvAR//QPThi2RnZOabxqxlXWKOXSP2lBvpDyOJPvI3ceduYtasVnEWpdgMnjCQnV/s5uKJSzzw9GP1zE+wdbChY68OBe5z+c+/uXL6KsF+wTz0C2brp9tJTUmlQfP6WulqNajJsCmD+XT2Z8VdjBJhMWoQiT8dJenwcTIfBBK9YgM5aemYufTKN32a6y1Szlwk0y+QrIehJO75mYz7DzBq5lzCOX9xdaf04cHuM/jtP0+CdzCu87aRlZpO9eFd8k1fZ2Jvws7c4t7mIyTeD+H2p4eI8/Cn1vieWulUGZmkRcZrXpnxeXf8sjNVWn9Lj02iUq/m+O0/V6xl/TdaTejNpU2HuX/SjUivIH6ftQVTe0vq9GxR4D6tJ/bh5r4zeBw8T/T9EI4t2E5majqNh+bV6auLR3L9+xP8vfk3ou4HE/MgFK8jV1BlZAHgd+4Wf8z9Bv8Lt4kPisTnTzeufPsHdXu3Kuhji1WjyX3w2nsG7wPnibsfwl8fbicrLZ26b+bfThpO6MXDs7e4teUIcT4hXP/sEFG3/XEe2+ORNL258cVhAk64EeMZxNmZW1A6WOLUS123hlamWNSowM0vfyPGM4gEv3Curd6PvtJIM8CmSsskNTJe88pRZVOxfQPu7Ttb7HXyLJpP6M2VjYfxPelGlFcQR99Xt59ahbSfFhP74LH3DHcOnifmfggn56vbT6Nh2nVt16AqLSf35fjcb/M9zpmlO3H/4U/iAyOLtEzFodrb/Xi46zQh+86R7B3M3bnfoUrNoOLwrvmmT3B/gPfy3YT9cpns9Kx807gNX0PI/nMk33tI0t1Abr+3GeMqdpg3rl6MJSk6TSb0xnXjYfxOuBHtFcSfM7dg4mBJjV4Ft52mk/pwZ+8ZPA+cJ/Z+CGfmq/tp/Ufazs2tx3H76jfC3fKfYZ+dqSIlMl7zSotNonrP5ngePF/kZSxpndq14t3JY+jepeDvQf819hNfJ3rvCWIOniLtfhBB8zeTnZqOzbDu+aYPeG8dUTuPknrXj3TfYALnbUKho4NZxyaaNLE/nSVsw34S/7qZ7zHKC+M3hpJ29HfSTx5FFRhA0sa15KSnYdQz/xvimR7uZFy6gCoogOzQENIO/4jK7wH6zo0A0KlUGf36ziRtWkeWtxeq4CCSN61DYWiIYdf8f5+UNaV5zQJoMvJVDM1NcP3mjyIr08sgm5xSe/0XycDYMwoNDWX48OGMHz8eT09Pzp49y8CBA19o5szixYsZNGgQN2/eZMSIEbz55pt4enpqpVm4cCFz5szB3d2dOnXqMHz4cLKy1F8G09LSaNGiBUeOHOH27dtMnjyZUaNGcfXqVQA2bNhAu3btmDRpEqGhoYSGhlKlShXi4uJ45ZVXaNasGa6urhw7dozw8HCGDh1abGUtCgp9PUwb1yT+wq28jTk5xF+4hVmLOv/6uImu97Do1AijGhUAUDZwwqx1PWJP33jRLJe4ClUrYONgw/ULbpptyYnJ3HX3pEGLBs90DB0dHV55rStGxkbcuX5Xs93QyJBFmxawfuFGYiLzv1NfrujpYVC/DmlX8uqKnBzSrrhh2PjZ6sqodTP0q1Umzc2jmDJZPHT0dbFqXJ3wC7fzNubkEH7hNrYtaue7j03LWtrpgdCzt7BtoT2AbN+uPq97fEWfC/+jxZpxGFiZFpiPSr2aY2Blht++svXDy6KKHab2lvj/lVfe9MRUQtx9qdQ8//rR0dfFsVF1/P+6k7cxJwf/v+5Qqbm6jpQ25lRqXouU6HhG/rSEGa5f8tb+hVRuWfj5y9DMmNS4pBcv2HPS0dfFtlF1gi9olyn4wh3sm+d/48ChRS2CH2snD8/dwj63nZhVtUPpYKmVJjMxlUh3Xxxy2156bBJxPiHUHtwRPWNDFLo61Bv5CimR8UR55L9sp/bgjmSlpuN35OqLFLlIWFRVt5+AR9pPRmIqoe6+VCygf+no6+LQqDqBj7WfwL/uUOGRutYzMqDfxmmcWvQ9KZHxxVaGkqDQ18WscXWiLzxy/szJIea8B5ZP6RPPQ89MCUBmKfSh52Ve1Q4TB0uCLmi3nXB3XxwLOffYN6pO0GNt5+GFOzi2+Pc3+Kr3aI6RlRme+8vW+Vk8nUJfD2WjmtoDWDk5JP51E2Xzus90DB1jQxT6umTFJRZTLkuJnh56teuQ6f7IUr6cHDLdr6NX/9lucuo3bY5u5Spk3lb/HlHoG6gPk5mhdcyczEzN4FlZVtrXLOvaFWk78w2Ovr+FnOz/5oCLKB8kxtgzCg0NJSsri4EDB+LkpF5e1qjRi53shgwZwsSJEwH4+OOPOXnyJBs3buSrr77SpJkzZw79+vUDYNmyZTg7O+Pj40O9evWoVKkSc+bM0aSdMWMGx48f58CBA7Ru3RoLCwsMDAxQKpU4Ojpq0m3atIlmzZqxatUqzbZt27ZRpUoVvL29SUpKeq6ypqenk56errUtI0eFgUL3BWrnSXrWZij0dMmIjNPanhkZj3GtSv/6uMEbf0bXVEmzC1+Qo8pGoatD4Jo9RP104QVzXPKs7awAiInSHriKjYzD2s46v100qterzleHv8DA0IDU5FQWT/qIgPt5U5qnffQOd67f4eKJ8h1T7B+6VhYo9HRRRWvXlSo6Fv1qVQrcT2GqpMqJfSj09SE7m+hVX5D2t1uB6csiA2szdPR0SXvsS0paZALmtSrmu4+RneUT6dMj4zGyt9S8Dz1zk4d/XCM5MBLTavY0mj+Mzrvncar/0ny/7FQf3pWws7dIDY158UIVIdPcMiVHaS/JS45KwMQu/2UoSit1nSZHxT+2Tzw2NdWD7pZV7QDoOHMgp1fuJeJuAA0HduTNPfPZ2vPDfOOXWTo50GJMT86s3POixXpuRrntJPWxf/fUqHgsa1XIdx9jO0tSH6u31MgEjO0sNX9XHyO/NHl1+8fwNfT4biZj731LTnYOqVEJHBv5KRnx+cccqftmV3x/uYwqLf+ZwSXJJLeMKY+VMaWQ9mNsnX/7SYmKx7pmXl13XTqSENf7+J4sX+ec/BhYm6Ojp0tGPucVk9r//pquRaGg3ooxxF7xIsnrYdEcsxgpC2o7kQko7QtvO4/305RC+umzqP9mFwLP3SI5rGydn8XT6Vqbo9DTJTMqTmt7VlQcRjUr57/TYyrOH01meEy5nx32OB1zCxS6emTHan/3y46NRb9y1QL3UyhNsN51CPQNIFtF0pfrybzhCoAqKABVeBgmYyeTtPEzctLSMH5jCLp29qisbYq1PEWhNK9ZugZ69Ns4jfMr95IYEo1FVfsXKIkQL0YGxp5RkyZNePXVV2nUqBG9evWiZ8+eDB48GCsrq399zHbt2j3x/vGnUDZu3Fjz/xUqqE80ERER1KtXD5VKxapVqzhw4ADBwcFkZGSQnp6OUqks9HNv3rzJmTNnMDV9ciaHr68vPXv2fK6yrl69mmXLlmltG29SjwlmzzbrprTZvNYeu4Gd8J66ntR7QZg0rE61ZePICIsl8uDZ0s5eobq/8Qqz17yvef/hmH8fZDnIN4iJvaZgYmZCl36dmf/5PN4bPIuA+4G079GO5h2aMqnX20WR7XItJzmVkGFvo6M0xqh1M6znvE1WcChprreevvN/XNDhvJhG8V5BxN0NpP+V9di1b0DEo3cVAeMK1jh2bczlKV+UdDaf0MClPb1Xjde8PziueJYKK3TUk7Rv7FYvtwQIvxOAUwdnGg/twrlPD2ilN3WwYtgP87j3x1VulpElgiWl/YoxpEYn8NvAj8lKy6Te8K70/H42v/RbQmpEnFZa++a1sKpTibPvbS6VvNZzaU+P1Xnt5+exxdN+avZoTtX2Dcp8MP2ypP6a8ZjWrcLV15aWdlbyVcelPV3X5LWd34up7TwvE0drqnZpzPF3NpZ2VkQpcJg6CKvXOnF/6EJy0kv/ZkNZkJOaQuy0iSiMjTFo2hyTSVPJDg0h08MdVCoSVizGbOY8bA4eIUeVReaN62Rc+xtQlHbWn1CWrlkdPxhGjE8Inj9fLJY8/NfJ/LqiJQNjz0hXV5eTJ09y6dIlTpw4wcaNG1m4cCFXrlxBR0fniWWGmZlFcyHR188LeqhQqE+u2dnqZ1D873//Y8OGDaxfv55GjRphYmLCzJkzycjIyPdY/0hKSmLAgAF88sknT/ytQoUKhZa1evUnY3TMnz+fWbNmaW1zqzP6ucv6NFkxieRkqTDIvbPxD307CzIf+6H0PKotHk3wpp+JPqw+Kad4BWJY2ZZK7w4s8wNjF09cxvNG3lN09A3U7cXa1oqYiLy7vFZ2lvjcyf8pX//Iyswi2D8EAG+P+9RrUpdBEway7sP1NO/QlIpOFfn97mGtfZZ9sxSPq7eZOWR2URWpxKhi48nJUqFroz3gq2tjhSqqkKWiOTlkBanrKeOeL/rVq2Ixfni5GhjLiEkkO0uF0WN3Ao3szEmLyH+qe1pk3BPpDe0sSCuk7yUHRpIWnYBZdYcnBsaqv9mZjNhEgo+X/swXn5NubHvkKXh6BupLo4mtOcmPlM/E1pyIu/kHhk2JVdepia12HZnYWpCcO5MjKfdY0T7aT6mK9gnBvJL2XWVTe0ve2reA4OveHP1w678q14tKy20nxo/9uxvbWpBSQDtJjYzD+LHA6cZ25qTmzvT957/GtuZaA1zGduZE31HXbcUOzlTt3owfnKeQmZQKwMWF31Opc0PqDOnEzS9/0zp+vbe6EnXbnygP/39Z0hfje9KNsEfaj66huv0oH2s/SltzIgtoP6kx+bcf5SPtp0r7Blg62TP99jdaaQZ8/R7BV+9xYNjKoihOicmISSA7S4VBPueV9Be4pv+j3qpx2PVozjWXj0gvY7NS/+F30o1w90fajkFe20l5tO3YmRN1p/C283g/Vdpa/OvltvWHdSYtNhG//8DMxJeRKiaBnCwV+raWWtv1bC3JfEooDPvJLti/MxCfEUtJ8wooxlyWjuyEeHJUWeg8drNfx8qK7NhCzhM5OWSHqq/dqQ980K3ihPGwEeqBMUDl403c9IkolCagr0dOfDwWn28m637+TzkvTWXpmlW1fQNs61WhTt/W6j/m/tad6r6ZK5sOc2ndTy9UViGeh8QYew4KhYIOHTqwbNkybty4gYGBAT///DN2dnaEhuY9BlylUnH79u1CjqT2999/P/G+fv36BaR+0sWLF3n99dcZOXIkTZo0oUaNGnh7e2ulMTAwQKVSaW1r3rw5d+7coVq1atSqVUvrZWJiUmhZ82NoaIi5ubnWq6iXUQLkZGaRdMsXi46PLOtUKLDo2JjE694F7/gUOsaGTyzzylFlawYiy7LU5FSC/UM0L3/vAKLDo2nesZkmjdJUSYOm9bn7SLywZ6HQUWCQO9C258t9TOgxmYm9pmheAF8u28yaWf8rugKVpKwsMjy9MWqdV1coFBi1bkb6reeoKx0FCoOCn9pTFmVnqoi95YdDx0fiaSgUOHRsSNT1/B/lHu3qg31H7fgbjp0bEnU9/0DOoJ4VZmhlSmp43BN/qz6sC/4H/yInS/XkjiUsIzmNuIBwzSvqfjBJEXFU65BXXgNTYyo2rUmwW/71k52pIszDT2sfFAqcOjgTnBvsOj4oksSwGKxraC9vsq7hSPzDaM17Uwcr3tq/kDAPf47M+QZKKb5jdqaKKA8/Kj3WTip2dCaigADe4dd9qPhYO6ncqSERue0kMTCSlPA4rWPqmxpj17Qm4bltT884N15LtvaDyHOyc544L+spDanevw339pXeAxwyH2s/0d7q9lP1sfZToWlNQgroX9mZKsI9/LT2QaGgagdnQnPr+upXv7Gj5wJ+6L1Q8wI4u3wXx+Z8k99hy7ScTBWJt/yw6dQwb6NCgXWnhsS5/vtrOqgHxez7tsJ10MekluGHEGQmpxHvH655xXgHkxweR+XH+odD05qEFXLuifDwo8pjbadyR2fCCjk/F6b+kM7c+/EvssvA+Vk8v5zMLFI8fDHrkLfqBIUCsw6NSXEreKDG/u03cHx3KL6jl5F669+1nTIvK4us+97oN30kqLxCgX7T5mR53il4v8fp6KhDajwmJyWZnPh4dCpWQq92XTL+/qsIMl20ytI169e3N/BDr7w0J+Z9B8C+wR9zY8efxVH8/xQJvl+0ZMbYM7py5QqnTp2iZ8+e2Nvbc+XKFSIjI6lfvz4mJibMmjWLI0eOULNmTdatW0dcXNxTj3nw4EFatmxJx44d2b17N1evXmXr1mefGVC7dm0OHTrEpUuXsLKyYt26dYSHh9OgQd4SxmrVqnHlyhX8/f0xNTXF2tqaadOm8e233zJ8+HDmzZuHtbU1Pj4+7Nu3j++++w5XV9cCy1raQr7+jdobZpB005ck9/tUmNQfXaUhEftOA1DrixlkhMUQuGo3oA5AalxHHU9BR18PA0cblM7VyE5OI80/DIDYk65Ufm8QGcGRpNwLwqRRdSpOGUDE3tOlU8gXdGjrT4x6dwQP/YIJDQpjwpyxRIVH89fxvGnKa/d9yl/HLvLz9+oZYJM+nMCVM1eJCI7A2FRJd5dXaNquCXNHfAhATGRsvgH3I4IjCAsKK5mCFYP4nT9i9/E80u96k3H7HuYj3kBhbETi4eMA2H48j6yIKOI2bgPAYvybpN/1JisoBIWBAcYdW2ParzvRq0p/OeDzuvf1UdpsmELMTT+i3X2pO6k3ekpD/HIHGNp88TYpYbF4rNoPgPd3x3jlp0XUndKXkFM3qPp6O6ya1MB1rvqcpac0xHn2QB4euUZqRBym1Rxosng4SX7hhJ3Vnk1n39EZUyd7Huw5U7KFfg7Xth6j/QwXYvzCiQ+KoNPswSRFxOF9Ii9g75t75uN93BW3HScBuPrdUfqvnULoLT9Cb/rScnxvDJSG3DqYN2hz5esjdHx/EBGeAYTfCaTR4E5Y16zIz2+r29A/g2IJwVGcXrkH5SOPPU8uhWDrHt8cpcvnU4i86Uekuy8NJ/ZG39gQ79wniXZdP4XksFiurVEvA7299TgDDi2k0eQ+BJ5yp+br7bBtXIMLH2zTHPP21mM0e9eFeL9wEoMiaDlnMCnhcQQcV9dt+PX7ZMQn03X9FNw+/wVVWgZ1R3TDrIodgafctfJX87W26Ojp4vNT2VqG4bb1GG3fdSHOP5z4wAg6zFG3H59H2s/gvfPxOeaKe277uf7dUXqvnUKYhx9h7r40n9AbfaUhtw+o6/qfpwU+LjE4moSgvMEfSycH9E0MMbGzQM/IALsG6rg50feDyc4sWwMd/luO0PCLd0hwf0D8DR+qTu6LrtKQkNzzUMONU0kLi8Fn5T5AHbDfNPearjDQxcjRGjNnJ7KS00jNjdFXf814HAd2wH3MZ2QlpWpmpGUlppBdBmLQPc3NrcdoOcOFuNz+0WbOYJLD43hwPK/tvL53Pg+OueKR23bcvz1K93VTiLjlR7i7L00m9EbP2BDPA3nnHqWdBUo7CyyqOQBgU68KmUmpJIZEkx6X99Txyh2csXCy587esyVT4BKQkpJK4MMQzfvgkHC8vH2xMDejguN/M6ZRxHeHcVr7HikePiS738d+wgB0lEZEH1APNjh9PpOMsGhCP9kJgP07A6kw6y38311LxsMI9HJXZ2Qnp5GdkgaAroUpBpXs0HdQx6w1rKmOBZgZGUvWY/F/y7LUnw9gNns+Wfe9yLrnhZHLYBSGxqSdPAqA6ewFZEdHkvK9+imKxkNHkHX/HqrQYBT6Bhi0aoPhKz1J2rROc0yDjl3JiY9DFRmOXrUamLw9g4zLf5Hp5loqZXxepXXNig+I0PqbsbUZADE+IaQn5B9TVIjiIgNjz8jc3Jzz58+zfv16EhIScHJyYu3atfTp04fMzExu3rzJ6NGj0dPT4/3336dbt25PPeayZcvYt28fU6dOpUKFCuzdu1drUOtpFi1axIMHD+jVqxdKpZLJkyfj4uJCfHzeSWjOnDmMGTOGBg0akJqaip+fH9WqVePixYt88MEH9OzZk/T0dJycnOjduzc6OjqFlrW0Rf96CX0bC6rOexN9O0uS7/hx960VZOYGfzSsZAuPzP4ycLCi6Z9rNe8rTX2dSlNfJ/7Sbe4MUsccebDwO6p+MJwaayajZ2NOZngsYTtP8nDdwZItXBHZ+9V+jJRGzPnkfUzNTfG4dpt5Iz8k45E4EZWcKmJhnTf92dLWkgXrP8Da3prkxGQeePoxd8SHWk+3/C9KOXGOGCtLrN4Zg66tFRn3fAmfuoDsmDgA9CrYa83WURgbYbPgXXTtbclJTyfTP4jIhWtIOVF6s1X+raBf/8bQxoyG8wZjZGdB3J0Azr31Cem5wVeVlWy0ZlJGu97n8tQvafTBEBrNH0qSXxgXx60j/p46oHVOdjYWDapSbWgn9M1NSAuPJeycBx6fHCQ7I0vrs2sM70rUVW8SfUIpq65s+R0DpSG9V4/HyFzJQ1dv9o/+FNUj/ciqqj1KKzPNe6/fr6C0MafTrEGY2FkQcTeA/aM/1Qpo67rtOHqGBry6eCRGliZEeAayf8Qa4gLVXw6rd2qIdXVHrKs7Mv2qdnyfNU4ji7nUT3rw2xWMbMxpMWcQSjsLou8GcHTUp5rg+SaVbLXaScT1+5ye/hUt5w2h1QdDifcL4+TEz4m9lxf4/OZXv6OnNKTTJ+MxMFcSfs2bYyPz6jY9NomjIz+l1bwh9DswHx09PWK9H3JiwjpiPLWXddR9swv+R6+RUca+QF/b/Dv6xob0WD0eQ3Mlwa7e/DRKu/1YVrXX/AgAuPfbFYytzekwS13XkXcD+HHUp08ERH6anp9OpEq7vBtZo4+pH7TzbfuZJDyMesGSFa3ww5cxsDGn5rwhGNpbkngnALfhazQB+Y0ea1+Gjta0O50XBqLatAFUmzaAmIt3cR24HIAq43oC0OoX7bhit9/dTMj+sn+udtus7h/d1qjbTug1b357rO1YOGm3HZ/cttN6tvrcE3k3gN8e6acADUe+SutZAzXvB/24GIA/Z32N18G8hw01eLMLode8ifMtu+fn53Xb6z7jZ3ygef/pRvVsldf7dGflovIXCuJZxP32F3rW5lSY9RZ6dlak3vXDd9QysnK/L+tXtNWalWs7sjc6hvrU+PpDreOEfr6XsM/VA9MWPVrjtO49zd+qfzn3iTTlQcb5MyRbWKIcOR4da2uyfH1IWDyXnDj1DWBde3vIyasbhZERptPeR8fWjpyMdFRBgST+bwUZ5/Nu7ulY22A8eRo6llZkx0STfuo4KXt/KPGy/Vulec0SoqxQ5DweHEuUCIVCwc8//4yLi0tpZ6VYXKowqLSzUKYt0C35mR/lxfe2ZX8Ja2m6Ev7fvLtdFPwMpO0UxqZsTRYqUxIksEShGqVnPT3RS8rbQO4xF2bKjeWlnYUy63bz95+e6CVWxTmutLNQpu24U/AT1F92swN3lXYWSsSkakNK7bO/9S+eCSQxMTHMmDGD3377DR0dHQYNGsSGDRvyfWjgP+mXLl3KiRMnCAwMxM7ODhcXFz7++GMsLPJ/qmpB5KugEEIIIYQQQgghhCg1I0aM4M6dO5w8eZLff/+d8+fPM3ny5ALTh4SEEBISwmeffcbt27f5/vvvOXbsGBMmTHjuz5bbXEIIIYQQQgghhBDlRM5/LAi+p6cnx44d49q1a7Rs2RKAjRs30rdvXz777DMqVqz4xD4NGzbkxx9/1LyvWbMmK1euZOTIkWRlZaGn9+zDXTIwVkpkBasQQgghhBBCCCHKk/T0dNLT07W2GRoaYmho+K+PefnyZSwtLTWDYgDdu3dHR0eHK1eu8MYbbzzTceLj4zE3N3+uQTGQpZRCCCGEEEIIIYQQ5UZ2Kb5Wr16NhYWF1mv16tUvVJ6wsDDs7bVjKevp6WFtbU1YWNgzHSMqKoqPP/640OWXBZGBMSGEEEIIIYQQQgjxVPPnzyc+Pl7rNX/+/HzTfvjhhygUikJfXl5eL5ynhIQE+vXrR4MGDfjoo4+ee39ZSimEEEIIIYQQQgghnup5lk3Onj2bsWPHFpqmRo0aODo6EhERobU9KyuLmJgYHB0dC90/MTGR3r17Y2Zmxs8//4y+vv4z5e1RMjAmhBBCCCGEEEIIUU6Ul+D7dnZ22NnZPTVdu3btiIuL4/r167Ro0QKA06dPk52dTZs2bQrcLyEhgV69emFoaMivv/6KkZHRv8qnLKUUQgghhBBCCCGEEKWifv369O7dm0mTJnH1/+zdd3wURRvA8d+lXXpP6AFC70iVjtJBitJFehew0HuzoCjdgiIgiICir6IoTemIQIBQE9IL6b337PtH4iUHSUBIuIs83/ezn5fsze49M+7s7s3OzF66xPnz55k5cyYjRozQvJEyJCSE+vXrc+nSJSCvUaxnz56kpKSwfft2EhMTCQ8PJzw8nJycnH/1/dJjTAghhBBCCCGEEKKcyNV1AGXg22+/ZebMmXTr1g0DAwMGDx7M5s2bNZ9nZWVx9+5dUlNTAbh69SoXL14EoHbt2lr78vf3p0aNGo/83dIwJoQQQgghhBBCCCF0xt7enr179xb7eY0aNVCUgiGkXbt21fr7SchQSiGEEEIIIYQQQgjxTJIeY0IIIYQQQgghhBDlRG4p9ZQSeaTHmBBCCCGEEEIIIYR4JkmPMSGEEEIIIYQQQohyQvqLlS7pMSaEEEIIIYQQQgghnknSMCaEEEIIIYQQQgghnkkylFIIIYQQQgghhBCinMiVwZSlSnqMCSGEEEIIIYQQQohnkvQYE2XipImprkPQa9VVuo5Af3mHyWmpJOaqXF2HoLfOKYm6DkGvdTa003UIesudFF2HoNc+yfbTdQh6azv1dB2CXrvV4m1dh6C3Gl/doOsQ9FrqnMm6DkGv9fWJ1XUIQscU6TFWqqTHmBBCCCGEEEIIIYR4JknXDCGEEEIIIYQQQohyQsaQlC7pMSaEEEIIIYQQQgghnknSMCaEEEIIIYQQQgghnkkylFIIIYQQQgghhBCinMiVyfdLlfQYE0IIIYQQQgghhBDPJOkxJoQQQgghhBBCCFFOKNJjrFRJjzEhhBBCCCGEEEII8UyShjEhhBBCCCGEEEII8UySoZRCCCGEEEIIIYQQ5USurgP4j5EeY0IIIYQQQgghhBDimSQ9xoQQQgghhBBCCCHKCUWRyfdLk/QYE0IIIYQQQgghhBDPJOkxJoQQQgghhBBCCFFO5CI9xkqT9BgTQgghhBBCCCGEEM8kaRgTQgghhBBCCCGEEM8kaRjTMwEBAahUKtzd3fVqfzVq1GDjxo2lEpMQQgghhBBCCCEeT64Ol/8imWNMlAudZw/muZEvoLa24J6bF4eX7CAuIKLEbVqO6cHzU/ph6WRDhEcQx1bsIvS6HwA2VR2ZeX5Tkdv9OH0Tnr9fwrmBC+2n96da63qY2VuRcC+Kq3v+5PLOo6Wev9L28tsj6DqyO+bW5ni73WXX0i+JCAgrNv1Lr79My17PU6lWFbLSM/G+epfvP/iGcL9QTZquI3vw/MCO1GjkipmVOdObjiY1MfVpZKfUVB3fkxqv98fE2ZbkO4F4Lt5J4jXfItNa1KtKrfnDsG5aEzMXZ+4u20XQl79r729sD6qO64FZNScAku/ew2/dj8SccC/rrJSKGuN7UOv1/qidbEi8E8StJV8TX0x5AFTq35b684diVs2JFP9wPN7dR+Sf7prPTRxtaLhsJE5dmmJsbU7M357cWvI1Kf7hmjTm1Z1puOI17NvWw8DEiKiTN7i5+GsyoxPKMqulZtTs1+j1ai8srC3wcPPgs8WfEhoQWmz6oTOG0q53e6rWqkpmeiYeVzz4es1OQvxCNGkqVq/IxCUTadi6EcYmxlw5fYUvlm8lPjr+KeTo8XWcPZimI19AbW1OiJsXx5fsfOh5+bkx3WkzpR8WTjZEegTxx4rdhOeflwFG7F+CS7sGWtu47/mTY0t2aq1rPKQTrSb1wb5mRTKS07j7+yX+WLar9DJXBobMHskLI7tjYW2Bl5snO5Z8QXgJ5+UBr79C697PUzn/2PG+4sm+D3YT5lf08TZ/1zKad23B+slrcDt2qayyUSbeXvg6I0a/grWNFW6X3Fk29z0C/IIeadtpb05gwfI32bF1D+8s+Uiz3tHZgcWrZtOxy/NYWFrg5xPApxu2ceTXP8sqG2WiyvheuBS6bnkt3kFSCdetmvOHY5V/3fJa9jX37rtuFVZ91kBqLR1F8Je/4a3n9acojmP64jx1EMZOdqR5BHBv+ZekXvcuMq3DyB7YD34B03rVAUi76Uvoh99opbfp/TyOr/XGvEktjOys8ez9Fml3/J9KXnTFzf0mO/f+wB1PH6JiYtm0ZhndOrfXdVhlzqTbQNR9hqGysScn2Jf0PVvI8bv70O2M276A+etLybpyntTNy7U+M6jkgunwyRjVawqGhuSEBJK6ZRVKbGRZZaPM2I16CftJgzFysiPD05/w1Z+TfsOryLS2w3phM6gb6rp5dSv9lg+R63dppa/04dvYvtJDa7vkM24ET9QuQyF0SXqMCb3XbtpLtB7Xi8OLd/L1wOVkpWYw8puFGKqNi92mwUvP033pKM5u+h/bX1pKpEcQI75ZiLmDNQCJoTFsbPW61nJ63Q9kJKfhe+o6AJWa1CQlJpGDb33Gl93nc/6Tg7ywYDitxvYo9nv1Qd9pg+gxvi9fL/mC1YMWkZGWztzdyzAuobzqtW3En98c4Z2XF7F29CoMjQyZt3s5JmZqTRoTMxNunnbn18/+9zSyUeoqDGxHvVVj8Fv3Ixd7LCTpdiAt9i/G2NG6yPSGZmrSAiPwfm8fGRFxRaZJD4vB5929XOyxiIs9FxN77hbNd83Dol7VssxKqag88HkarhyN17ofOdNzMYm3A2m7byEmxZSHXas6tPh8FkH7TnGmxyLCD7vReuccrOoX5LX117Mxd3Hm0riPOd1jEWn3onj+wGIMzfOOI0NzNc9/txgUhQuD3+V8/5UYGBvS5pu5oFI9lXw/icHTh9B/fH8+XfQpcwbMJj01ndV73imxbjVu24Tfdv3G3EFzWDZqKUZGRryz513U+XVLbabmnT3voiiweMQi5r0yFyNjI5bvWI5Kj8ukzbSXaDGuJ8cW72DPwBVkpWYw9JsFJZ6X67/UlheWjuL8pp/Y9dJSojyCGPbNAs15+R/X957g01YzNMupNfu1Pm81qQ+d5g3l4ue/sqPHQr4f9QEBp2+WST5LS/9pL9NrXD92LP6CZQMXkJ6awcJvlpd47DRo24jjuw+zfNAC1ry2EkNjQxZ+s0Jz7BTWZ2J/KKevbZ/6xnjGTRnJ0rnv8nLP10hLTWPXgc8xUZs8dNumzzXi1bFD8Lj14A/a9Z+9h2vtGkx+7U16dxrM0d/+5JPtH9GwSf2yyEaZcB7YjjqrxhCw7gcu91hA8u1Amu9fUux1yyD/uuX73t5ir1v/sGpei8pjepB0O6AMIi97tv07UmXZBMI3fsfdfrNJ8/Cn1p6VGDnYFJne8vkmxB08i8/wpXgNmk9maDS19qzEuIK9Jo2BuSkplz0IXbP7aWVD59LS0qlX25Ulc17XdShPjXGbrpiOnEb6wd0kr5hGbrAvFnM/RGVlW+J2KscKmI6YSvbdGw98ZuBcCYulm8gNDSZ5zRySl04m45c9kJVZRrkoO1Z9O+O8eDLRn+zFf9As0j38cNnxDob2Rdct87ZNSTx0msDRiwgYNoes8Ghcdr6LUQUHrXTJp93wajdKs4S8vfZpZOc/TdHh//6LpGFMB44cOULHjh2xtbXFwcGBl156CV/f4ntp3L59m5deeglra2usrKzo1KmTJn1ubi6rV6+matWqqNVqmjdvzpEjRx7Yh5+fHy+88ALm5uY0a9aMCxcuaH3+448/0qhRI9RqNTVq1GDdunWlm+kn0GZib8598jNex68Q6RnML7M/x8rZlno9Wxa7TdtJfXDff5IbB84Q7R3C74t3kJ2WQbNhXQBQchVSohK0lnq9W+Hx20WyUjMAuP79aY6v+oagi57EB0dx66fzXD9whnq9Wz+VfD+uXhNe4tctP3Dt+GWCPQP5cvYWbCvY0aJnm2K3WTf2Xc79cJIQ72CCPQL5au4nOFZ1omaTWpo0x3b8xm+f/4TvtaKfGOm76tP6cW/Pn4TuP0WKVwge874iJy2TKiNfKDJ9orsv3qu/JeLnv8jNyCoyTfSxq0T/6U6qfzipfmH4rvmOnJR0bFrWKcuslArXqf0I+vYEwftPk+wVwo3528lJy8RlRNci09ec3Ieok9fx/ewQyd6h3F17gISb/tQY3wsAC9eK2Leqy42FO0hw9yPFN4wbC3ZgaGpClUF5T5/tW9fFvJoT7m9uJckzmCTPYK698Tm2zVxx7NjoaWX9sQ2cOJDvtnzHxeN/E+AZwPq312HvbE+7nu2K3WbFmOX8+cMfBHkF4e/hz4Y563Gu6kztJrUBaNiqIc5VndkwZz2BdwMJvBvIhtnrqd20Dk07NHtaWfvXWk3szYVPDuJz/CpRnsH8Nnsrls621CnhvNxqUh9u7D/JrQNniPEO5ejinWSlZdAk/7z8j6y0TK1zc2ZymuYztbU5neYO4bfZX+Bx8ALxQZFEeQbj88fVMstraeg98SV+/uQAV45fItgzkM9nb8LW2Z5WPdsWu82HY9/hTP55OcgjgK1ztuBU1VnrvAxQvWEN+k4ewBfzPinrbJSJCVNH8cm6bRw/fArPO97Mmb6UChWd6Nn3xRK3M7cwY+PWNSx6exUJ8YkPfN6idTN2bdvH9au3CA4M4ZN120hMSKJJswZF7E0/VZv2EqF7/iRs/ylSvUK4O28buWmZVC7mupXk7ovv6j1ElnDdgryHFI0+m4XnnC/Ijk8pq/DLlPOkgcTsO0bsgT9J9w4meNHn5KZl4DC8e5HpA99cT/Q3h0m740+GbwhB8z9BZWCAVceC82zc/04Rvuk7ks5df1rZ0LlO7VrzxpSxdO/SQdehPDUmvYeQefp3ss4eJTc0kLSvN6JkZmDSuXfxG6kMMJ+2mPSfdpEb+WBPX/XgiWRfv0j691+SG+RDbmQY2dcuoCTFl11GyojDhJeJ/+4ICT8eJ9MnmPDln5CbloHtkJ5Fpg+d8xFxe38jw8OPTL97hC3eBAYGWLTTvodRMrPIiY7TLLmJyU8jO0I8MmkY04GUlBRmz56Nm5sbf/75JwYGBrz88svk5j44YjckJITOnTujVqs5ceIEV65cYcKECWRnZwOwadMm1q1bx8cff8yNGzfo1asXAwYMwNtbuyv5kiVLmDt3Lu7u7tStW5eRI0dq9nHlyhWGDRvGiBEjuHnzJitXrmTZsmV8/fXXZV4WD2NbzQlLZzsCzt3WrMtISiPE3ZcqLYpufDAwNqRSk5r4n7tVsFJR8D93i6rFbFOxcQ0qNqqB+3enSoxHbWVGmh7fRDpVq4Ctsx23zxc8zUpLSsXP3ZvaLeo98n7MrMwBSI5PKvUYdUFlbIhVU1dizxbqVaIoxJ65iU2rUmrEMlBRYVB7DM3VJLjpd+OhytgQm6Y1iT6jXUeiz97CrpjysG9Zh6jC6YHIUzc06Q1M8nq+5KYXejqqKORmZGPftp4mjaIo5GYW/GDLzchCyVU0afRVBZeK2Dvb437OXbMuNSmVu+53qd/y0XugWFhZAJAcn3dDaKw2BgWyCpVJZkYmSq5Co9YNSyf4UmZTzQlLZ1sCC51jM5PSCHP3pXIJ5+WKTWpqnctRFALP3aZyi9paaRsOas/Ma58z/tgaOs8fhpFpQc+hGp2aoFKpsKpgx8Q/P2T635sZ8OksrCrZo6+cq1XAztmeW4V+bKclpeLr7k2df3FeNteclwt+TJiYmjBj82y+XraNhKj4Uov5aalWvQrOFZ04d/qiZl1SUjLuV27SonXTErddvXYxJ46f4XyhbQu7evk6/Qb1wsbWGpVKxUsv90atVvP3ebdSzUNZKem6Zd2q7hPtu+4Hk4j+4xpxZ/S7p2VxVMZGmDeppd2ApSgknbuO+SPWKQMzNSpjQ7L/I/c54hEZGmFYoy7Ztws9TFEUsm9fxbB28ddc9aDRKInxZJ05/OCHKhXGzdqSG34P87kfYLXlByyWf4JRi3LY2GhshGmj2qT85V6wTlFI+csds+ce7V7HwEyNysiQnATthi/ztk2o8/deXI9+ScVVMzC0tSrFwIV4cjLHmA4MHjxY6+8dO3bg5OTEnTt3sLS01Prs008/xcbGhv3792NsnPfDs27dghuijz/+mAULFjBixAgAPvzwQ06ePMnGjRv59NNPNenmzp1Lv379AFi1ahWNGjXCx8eH+vXrs379erp168ayZcs0+79z5w4fffQR48aNe2h+MjIyyMjI0FqXreRgpDJ8xBIpnoWzLQAp980/lBKdgKWTbZHbmNtZYWBkWMQ2iTjUqlzkNs1HdCXKO4SQK0XPTQFQpWUdGr70PN+N//jRM/CU2eSXyf0/kBKjEjSfPYxKpWLU8vF4XfYgxCu4dAPUERN7awyMDMmM0j4mMqMSsKhT9DHxqCwbVKP1b+9ioDYmJyWd6+M/JsUr5OEb6tA/5ZFxX3lkRCVgWbvo8lA72xaZ3jS/jib7hJJ6L4oGS0ZyY95XZKem4zq1L2ZVHFDnp4m76k1OagYNlr6K55r9oFLRYMlIDIwMNfvRV3ZOdgDER2sPT4qPjsc2/7OHUalUTF45hduXbxPoFQiA51VP0lPTGb9oPLs/3A0qGLdwPIZGhtg562djT8F5WbuXTkp0IpZOxQy1yD8vpxZxLrevVUnzt8fBv0gIiSY5Ig7nBi50WTgC+1qV+Hlq3pyQti7OqAwMeH7GAP5c9Q0ZSal0mjuUYXsWsrP3InKzckoxp6XDJr+8Eu7Le0J0/L86L49eMZG7lz2451Uw99bo5RPwvuLJlePla06xfzg5OwIQHRWjtT46KkbzWVFeerk3jZo2YGD3V4tNM2PCPD7ZvhZ337NkZWWRlpbOtDFvE+hfPq5rxprrVrzW+syoeMyf4LrlPKg9Vk1r4tZr0RNGqDuG9taojAzJum8exuzoeExrPdpUBpUXjSErIvaZ6h0mQGVlg8rQECVB+1quJMRhUKlakdsY1mmMSec+JC+bUvQ+rW1RmZmjfmkE6T/uJP37bRg3aY35rJWkfDCHnCKGXuorI7u8upVz371OTkw86lpFl8/9nOeNJzsylpTz1zTrUs5cIenoX2Tdi8DYpRLOc8ZS7avVBAybA0V0DBGPJvc/OqRRV6THmA54e3szcuRIXF1dsba2pkaNGgAEBT040ay7uzudOnXSNIoVlpiYSGhoKB06aD+R6NChAx4eHlrrmjYtePJaqVLej5DIyLzJID08PIrch7e3Nzk5D/+RsWbNGmxsbLSW0wm3H7pdURoNas+8O9s1i6HRkzeuPYyR2phGA9pzvYTeYk51qzJ022zObvoJ/7P684S13cBOfHF7j2YxNH7y8hrzzmSq1HPhs1nrSyHC/74Un1D+fnE+l/os4d6u4zTaPAOLulV0HdZTp2Tn4DZhAxauFel99yv6+u/CsX0jIv68ppn7KDMmiSuTN1KhZwv6+O6kt9d2jK3Nib/uh6Jn8yN1HdSVAx4/aBajUjgXTX93OtXrVmftjA816xJjE/lg+hradG/LAc8f+P72ASxsLPC56YOiJzeLDQe15607X2mWsjwvX993koAzN4m+e487P//Fb7O3Urd3a2xdnAFQGagwNDHiz5W7CThzk7Brvvw661PsalbEpZ1+9LDrMKgzO+7s1SyGRk/+DHL8O1OoVteFLTMLpjlo0b01jdo3YfeqHU+8/6dl4JC+3Aq8oFmMjf992VSqXIEV78/n7amLyMwofv6eOYtnYG1jxaiXJzOw26ts/+wbPtmxlnoNahe7zX+durIDdd8dx+3XN5c41PK/rsLrg7Eb0Am/yWtQnuFyEI/A1AzzqQtJ27keJfnBIdsAqPJ+Tmdd/YvMoz+SG+RLxm/7yb7+NyYv9n+Kweqew5ShWPfrwr3X30Ep1BM+8bczJJ+4SIZXAMl/XCB4ykrMmtXDvG0THUYrhDbpMaYD/fv3p3r16mzbto3KlSuTm5tL48aNycx88AbPzMysVL6zcMPaPxM6FzV083EsWrSI2bNna63b0LjopyoP4338Kl8VetuSoUneIWrhaENyZLxmvYWjDRF3AovcR2pcErnZOVg4avdcsHC0JiXqwTff1e/bFmMzNTd/PFvk/hzrVOHVvYtx33eC81t+/pc5KlvX/riMr3tBLzfj/OFsNk62Wr3GrJ1sCLoT8ND9jV41iWYvtuT9YcuIC48t7XB1JjM2kdzsHEzu681i4mRDRqHj6nEoWTmk5b+JL+mGP9bNa+EyuS8e87Y90X7L0j/lob6vPNQllEdGZHyR6dMLpU+44c+Z7oswsjLDwMSIzJgkOv7+DvGF3joYdfomJ55/CxN7K3Kzc8hOTKXHjc9JPag976GuXTx+kbvXCib0/meSdFtHO+IiC56k2jra4n/H74Ht7zdt9TRad2vDwqELiAnX7h1z7ew1JneahLWdNTk5OaQkpvCN2x7Cg8KL2dvT5XP8KqFFnpetSdE6L1sTcafoNwn+c142f+C8bFPkefkfYfnfa1ujAvFBkZrrQLR3wZsZ02KTSItNwrqyQ1G7eOquHL+ET6G5GI3+OS872hBf6NixcbQl8BHeeDdu9WSe69aK1cOWEFvo2GnUvgnO1Svy1c09Wunf2jofz0sevDti2ZNmpdT9ceQU7lcKHi6ZmOQNk3V0ciAqIlqz3tHJgTtFTKgP0Lh5QxydHfj1ZMFLGYyMjGjTviVjJo2gXqXWVHWpzNjJI+nZ/hW87+YdQx63vWjdrgWjJ45g6dx3yyJ7pSpLc92y1Vpv4mRL5mNet6yauWLiZEvr4wWN8wZGhti2a0CVCb05Ve1VyNWvhxRFyYlNRMnOwdjRVmu9kaMtWVElv3TAecognKe/gs+oFaR7Fn0fKf67lKQElJwcVDbaPb1VNnYoCQ/e9xo4V8bAqRLmbxU6Z+T/jrLecYzkhWPJjYlCyc4mN1T7eMoNDcKwbuPSz0QZyo7Lq1uGjtrlY+hgS3ZUyb8L7Ce+gsPUoQSNXULG3YAS02YFh5Mdm4BJ9cqkXpBem49L3x4ql3fSMPaUxcTEcPfuXbZt20anTp0AOHfuXLHpmzZtyq5du8jKynqg15i1tTWVK1fm/PnzdOlSMHnx+fPnadOm+InW79egQQPOnz+vte78+fPUrVsXQ8OH9wxQq9Wo1dpvyXrcYZSZKelkpqRrrUuOjKNGh0aahjATSzOqNK/F1T1/FLmP3Kwcwm76U6NDI7yOXclbqVJRo0Nj3HYdeyB98+Fd8PrjKqmxD84z4VinCqP2LeHmj2c59dGBx8pTWUpPSSc9RfvHc3xkHA3bN9E0hJlamuHavA4n9hwtcV+jV02iZa82rBmxguh75e/V0iVRsnJIuuGHfacmRB3On19GpcK+U2OCd5RcLv+WykCFgYl+n1qVrBwSbvjj2Kkx4UcKysOxYyMCdjxYRwBir3jj2KkR/tsK5tdw6tyEOLcHhx9nJ+VNlm5RsyK2zVy5++H3D6TJzK9vDh0aoXa0JvzolSfNVqlKS0kjLSVNa11sZCzNOzTTNISZWZpRr3k9Dn/ze4n7mrZ6Gu16t2PRsEVEBEcUmy4xLu9pdNP2TbFxtOHi8aLnTnraij4vx1O9QyMi8xvCTCzNqNS8Ftf2/FnkPnKzcgi/6U/1Do3wKXRert6hEVd3HS/2u50buQBoGuBC8ufvs69VieT8xntTGwvM7K1IDIkuch9PW1Hn5bjIWBp1aEpg/nnZzNKMWs3r8MeeB1+WU9i41ZNp1ast7w5fRlSw9nn5l8//x8n92tfBtcc38c3qnVz98/KTZ6QMpCSnkpKcqrUuMjyKDp3bat4saWllQfOWTdizs+hr7l9nLtKrg/aUFGs/WYWfdwBbN+0kNzcXMzNTAHIV7QeAuTm5GBjo79teC/vnumXXqTHRh/P/e6pU2HVqTMiOko+b4sSducnFLnO01jXYOJ1Un1ACPzlYLhrFAJSsbFJv+mLVoSkJx/LPkyoVVh2aEr2r+POx87SXqThzKD6jV5J2w+cpRSv0Sk42OQFeGDV8juyr+b99VCqMGj5H5h8/P5A8NyyIpMUTtdaZDp6AytSMtG8/JTcmKm+f/ncxqKg91NCgYlVyo4u/5uulrGzSb/tg0a4ZyX/kP7BUqbBo35y4b34tdjP7yUNwnD6coAlLSb9V/LQ0/zCq6IChrRXZkf+dh/Ci/NPvX2//QXZ2djg4OPDll19SqVIlgoKCWLhwYbHpZ86cyZYtWxgxYgSLFi3CxsaGv//+mzZt2lCvXj3mzZvHihUrqFWrFs2bN2fnzp24u7vz7bffPnJMc+bMoXXr1rzzzjsMHz6cCxcu8Mknn/DZZ5+VRpaf2KXtR+gwaxCx/uHEB0fRZc4QkiLjuXus4If0q3sX4XXUDbf8H1gXvzrMgHVTCbvhT+h1X9pM6I2xuZobB05r7duuegVc2tZn/7iPHvhep7pVGbVvMX5nbnLxq9+xyO8to+TkFtmIpi+O7jjEgFlDiAgIIyo4klfmjCQ+Io6rxwrmoJn/7QquHr3EH7vzGjnGvDOZ5wd2YtPkD0hPSdPMe5OamEpW/lAVGydbbJxsqVC9IgBV61UnPSWNmJBoUhL0/80ygVt/o9Hm10l09yXxmi8uU/piaK4mdP8pABptmUFGeCw+7+0D8iY+tqibN1eJgYkR6op2WDaqTk5KuqaHWO0lI4n+0530kGiMLE2p+EpH7No35Orw93WSx3/D74vfaL5pOvHX/Yi/5oPr5D4YmqsJ2p9XR5pvmU56WBye7+f1yvDfdpj2Py3HdVo/Iv+4RuVB7bBt5sqNQj3jKvVvS2ZMImn3YrBqUI3G744l/PBlok4X9BCpNqILSV4hZMYkYteqLo3fGYPfl4dJ8X3wLU/65uD2gwx/YwQhAaFEBIXz2tzRxEbGcuFYQW+39/a9x4UjFzi06xAA0999nS4Du/DupHdITUnTzEeWmpiiGQbWfWh3gn2CSYhNoH6LBkxZOYWDX/1MiJ/+zlXntv0I7WYNIs4/gvjgSDrNGUJyZDzehc7Lw/PPy9fyz8tuXx2m77qphN/wJ+y6L63yz8s388/Lti7ONBjUHr8T7qTFJ+Nc34UXlo8i+G8Pojzz5oWK8w/H+6gb3Va8xtFFO8hMSqPzgmHE+oYSdMHjwUD1xJHth3h51lDC/cOICo5g6JxXiY+Mxe1YQePn4r2rcDv6N8d25Z2Xx787hfYDOrNu8hrSijgvJ0TFFznhfkxo1AONaPpsxxffMnPOZAL8AgkODGH24hlEhEdx7PcTmjR7fvqSY7+dYPdX+0lJTsXLU7tRIy0ljbjYeM16X+8A/H0DeX/dMt5fsZ642Hh69n2Rjl2fZ+LIWU81f08ieOshGmyeQZK7H4nXfKh233WrQf51y6/Y65a91nUrJyWdFE/tOdZyUjPIikt6YL2+i/zqINXXvUnqTR9S3L1xntgfA3NTYr7PayyuvuEtMsNjCPvwGwCcp79CpdmvEvDGOjLvRWKUX59yU9LJTc1r+De0scSkihPGFfLmd1TXypsWISsqjuxy+HKLR5GamkbQvYIeuCGhEXh6+WJjbUWlis46jKzsZB75AbPJC8jx9yLHzxOTXoNRqU3JPJv3oNRsygJy46LJOLAdsrLIDQnQ2l5JzbvnLbw+4/B3mL++jOy7N8jxcMeoaWuMmrcjZY32iJryIGbHT1ReO5v0W96k3fDCftxADMzUxP+Ydy2vtHYO2RExRK37GgCHKUNwfHM0obPXknUvUtPbLDc1DSU1HZW5KU6zXiXx6HlyouLy5hibP4HMwDBSzunXQ9HyRj8m3PjvkIaxp8zAwID9+/fzxhtv0LhxY+rVq8fmzZvp2rVrkekdHBw4ceIE8+bNo0uXLhgaGtK8eXPNnGBvvPEGCQkJzJkzh8jISBo2bMgvv/xCnTqP/qa9Fi1a8P3337N8+XLeeecdKlWqxOrVqx9p4v2n4cLWQxibq+m7ZiKm1uYEu3mxf8yH5BSaF8LOpQJmdgVvN/E49DcWDlZ0mT0EC6e8YZf7x3z4wGTRzYZ1ITEsFr8i3sxUv28bLBxtaPJKR5q80lGzPj44ik87vlX6GS0lv2/9GbWZKePWTMPc2gLvy558PPYdsgqVl3P1iljaF5RXt9F5r6he/N07WvvaNvcTzv1wEoAXRvXk5beGaz5bcuDdB9Los4iDFzBxsKbW/GGonW1Juh3A1ZFrNBPym1Zx0JoAVF3RnnYn1mr+rjFjADVmDCD2/G2uvLIaABNHaxpveR11BTuyk1JJuhPE1eHvE1sO3vQVevBvTBysqTd/CGonWxJvB3Jx5Adk5k8QblbFUav3QJybN1df/4T6C4ZRf9FwUvzDuTx+HUme9zRpTJ1tabRydP4QyzjufX8Wrw3/0/pei1qVqL94BCa2lqQGR+G96Wf8vii5x5W++PHzHzA1M2XWmllYWFtwx+0Oy0cv06pbFV0qYW1vrfm735i8l558cOBDrX1tmL2BP3/I+wFXpVZVxi4Yh6WtJZH3Ivl+y3f8/NXPZZ+hJ3Bp6yFMzNX0XDMBU2tz7rl5cWDMWq3zsq2LM+aFzsuehy5i5mBNx9mDsXCyIfJOIAfGrCU1/7yck5VNjQ6NaDWhF8ZmapLCYvE6fJkLWw5qffdvs7/gxeWjGLJzLkpuLsEXPTkwZi252fo38f4/ft36E2pzUyatmY65tQVebh58MEb7vFzBpSJWdgXHTo/RfQBY/r32sL+tczZzphyccx/VF5t3Ym5uxvvrl2NtY8Xli9cYN+x1rfnDqteoip297SPvMzs7mwkjZjJ/+Zt89e1mzC3MCfQPYu6MZZz6o/he+vom8uAFjB2scZ0/DJP869b1ke+TpbluaZ+n1RXtaXOi4EFf9RkDqD5jAHHnb3PtlVVPPf6yFP/rOYzsrak0+1WMnOxIu+OP7+hVZOdfw4wrO2rN0+j4Wm8M1Ma4fqH9MDpswz7CN+Q9ALLp0Ybq69/UfFbz03kPpPmvueXpzYRZCzR/r93yJQAD+3TnvaVzitusXMu6dAqVtQ2mr4xDZWNHTpAvKR8vREnMG4ZrYO/8r3tPZl85T9rXG1G/NBKD12aSGxZM6paV5HjfevjGeibp9zNE2lvj9OZoDJ3syPDwI2jicnJi4gEwruwEhXrj2o7sh4GJMVU/WaK1n6jN3xK95VvIyUVdrybVXu6OoZUFWZGxpJy7StTGb1Ays59m1oQokUqRwamiDLxXfZSuQ9BrXqr0hyd6Rr2WJu31JUlXyTtTivOFSTET4woAOqse7e2ZzyJ3UnQdgl67kPzwefSeVduN6uk6BL1mp854eKJnVOOrG3Qdgl5LnTNZ1yHotdC/1A9P9Ixq4F0+Hrg+qV7V+ujsu48GH354onJGfoEKIYQQQgghhBBClBMK0r+pNEnXAyGEEEIIIYQQQgjxTJIeY0IIIYQQQgghhBDlRK70GCtV0mNMCCGEEEIIIYQQQjyTpGFMCCGEEEIIIYQQQjyTZCilEEIIIYQQQgghRDmhKDKUsjRJjzEhhBBCCCGEEEII8UySHmNCCCGEEEIIIYQQ5YRMvl+6pMeYEEIIIYQQQgghhHgmSY8xIYQQQgghhBBCiHJCkR5jpUp6jAkhhBBCCCGEEEKIZ5I0jAkhhBBCCCGEEEKIZ5IMpRRCCCGEEEIIIYQoJ3IVGUpZmqTHmBBCCCGEEEIIIYR4JkmPMSGEEEIIIYQQQohyQvqLlS7pMSaEEEIIIYQQQgghnknSMCaEEEIIIYQQQgghnkkylFIIIYQQQgghhBCinMiVwZSlShrGRJl4Lj1H1yHotUNGsboOQW9VNLfUdQh6zS3TRtch6C1XAzl2SiT3T8WyUMntUEkGWTXQdQh662quHDslGdcoQtch6K3UOZN1HYJeM1+3Tdch6DWvxkt1HYLekiuWeBxyNRdCCCGEEEIIIYQoJ6THWOmSOcaEEEIIIYQQQgghxDNJeowJIYQQQgghhBBClBOKIj3GSpP0GBNCCCGEEEIIIYQQzyRpGBNCCCGEEEIIIYQQzyQZSimEEEIIIYQQQghRTsjk+6VLeowJIYQQQgghhBBCiGeS9BgTQgghhBBCCCGEKCcU6TFWqqTHmBBCCCGEEEIIIYR4JknDmBBCCCGEEEIIIYTQmdjYWEaNGoW1tTW2trZMnDiR5OTkR9pWURT69OmDSqXi559//tffLQ1jQgghhBBCCCGEEOWEoig6W8rKqFGjuH37NsePH+fQoUOcOXOGKVOmPNK2GzduRKVSPfZ3yxxjQgghhBBCCCGEEEInPDw8OHLkCJcvX6ZVq1YAbNmyhb59+/Lxxx9TuXLlYrd1d3dn3bp1uLm5UalSpcf6fukxJoQQQgghhBBCCFFO5KLobMnIyCAxMVFrycjIeKL8XLhwAVtbW02jGED37t0xMDDg4sWLxW6XmprKq6++yqeffkrFihUf+/ulYUwIIYQQQgghhBBCPNSaNWuwsbHRWtasWfNE+wwPD8fZ2VlrnZGREfb29oSHhxe73dtvv0379u0ZOHDgE32/DKUUQgghhBBCCCGEKCfKcq6vh1m0aBGzZ8/WWqdWq4tMu3DhQj788MMS9+fh4fFYcfzyyy+cOHGCa9euPdb2hUnDmBBCCCGEEEIIIYR4KLVaXWxD2P3mzJnDuHHjSkzj6upKxYoViYyM1FqfnZ1NbGxssUMkT5w4ga+vL7a2tlrrBw8eTKdOnTh16tQjxQjSMPaftnLlSn7++Wfc3d0BGDduHPHx8Y/1+lIhhBBCCCGEEEKIR+Xk5ISTk9ND07Vr1474+HiuXLlCy5YtgbyGr9zcXNq2bVvkNgsXLmTSpEla65o0acKGDRvo37//v4pTGsaeIZs2bdLqctm1a1eaN2/Oxo0bdRfUY6g+vic1X++P2tmGpDtB3F68k4RrvkWmtaxXlbrzh2Ld1BVzFyfuLNtFwJeHtdLYPV8f1xn9sWlaE9OK9lwZ9zERh92eRlbKzOS54xnwaj+srC254XaLtYs2cM8/pNj0L48ZwCujB1CpWl5rvJ9XADs27Obvk5cAqFi1Aj9d3F/ktkumruTEodOln4kyYj+6L05TXsHIyY50D39CV35B2nXvItPajeiJ3SsvYlq3OgBpN30I/3i3VnrnN0di078zJpUcUbKy89Ks+4Y0d6+nkp8nUX9sdxpP74eZkw1xd4L4e9luot39ik1f46U2PDdvCJZVHUnyj8Dt/f3cO3EdAJWRIS3nD6Hqi82xrO5EVmIaoedu4fb+d6RFxANgWdWRZm8NolKHhpg52ZIaEYfv/85zY/NBcrNynkaWn1jft4fSbmQ3zKwt8He7y/dLvyIqoPh5D2q1aUC3Kf2p1qQmNhXs2TblI24ee/D8UqFWFQYsfJXabRtiYGRAuHcIO6avIy40piyz80Q6zh5M05EvoLY2J8TNi+NLdhIXEFHiNs+N6U6bKf2wcLIh0iOIP1bsJvx6wTE3Yv8SXNo10NrGfc+fHFuyEwBTW0te2vQ6zg2qYWprSWpMIj7Hr3Bm7QEyk9NKP5OlaMDbw+k0shvm1hb4uHny7dJtRJZw7NRp04BeUwZQvYkrthXs+XTKWtyPXdZKY+Vow5CFr9GwU1PMrC3wvuTBvhXbS9yvPur99lDajXwRU2sLAtzucmDpdqJLyINrm/q8OKU/VfPr1fYpH3Prvno18uPptBnSRWudx2l3vhz7QZnkoTR1mD2YJq/m1a1QNy+OL95J/EPqVvMx3Wk9Na9uRXkE8edy7bpV2OBd86j5QjN+nrQBn2NXNOvnBu15IO2vMz7h7q9/P1mGyojpS4MwGzICAzt7sv18Sfl8E9lenkWmNWnfCbPhr2FYuQoqIyNyQu6R9r/vyThxTJNGZWuHxYSpGLdojYGFJVm3rpP8+SZyQ4u/f9JnJt0Gou4zDJWNPTnBvqTv2UKO392Hbmfc9gXMX19K1pXzpG5ervWZQSUXTIdPxqheUzA0JCckkNQtq1BiI4vZW/nm5n6TnXt/4I6nD1ExsWxas4xundvrOqwyV3N8D2q//hJqJxsS7wRxY8ku4ov5rQVQuX9b6s8fink1R1L8w7n97n4i/3TXfK52tKbhspE4d2mKkbU5MX97cnPJLlL8y9e1St/koruhlGWhQYMG9O7dm8mTJ7N161aysrKYOXMmI0aM0LyRMiQkhG7durF7927atGlDxYoVi+xN5uLiQs2aNf/V98vk+88QGxubB7oZljeVBraj/qrR+Kz7gfM9FpF4O5A2+xdh4mhdZHpDMxNSAyO5+95e0iPiikxjZG5K0u1Abi/cWZahPzWvvT6CoRNeYe3CDUzs/zppqels/HYtJmrjYreJCoviszXbGNdnKuP7TuPK+Wus3fEuNevWACAyNIp+zV/RWrZ9tJOU5FQunCj+LSH6xqZfRyotmUTkpn34vPQW6R7+1Ny1GkMHmyLTW7ZtQvwvZ/AbuRjfV+aRGRZNzd2rMapgr0mT4R9K6IqtePWeie/QBWSGRObt077oY1Jf1BzQljYrRuG+/id+6b2U2DtB9Px2AaYORcft3KoOXT6dgfe+0/zSaymBR6/w4va3sa1XFQAjMxPsm9TAfdPP/NJ7GScmb8TGtRLddxbMP2BTuzIqAwP+WrCDn15cwKWV31J/dDdaLhz2VPL8pLpPG0Dn8X34fslXrB+0hMy0dKbvXoxRCXXLxFxNiEcgB5bvKDaNo0sF3vphFRG+oWwZuYoPe8/n6JYfycrIKotslIo2016ixbieHFu8gz0DV5CVmsHQbxZgWEJZ1H+pLS8sHcX5TT+x66WlRHkEMeybBZjfd8xd33uCT1vN0Cyn1hQ0yiu5ufgcv8L/Jq7nqxfm8vvcL6neoTE93x9fZnktDb2nDaTb+D7sWfIl7w9aRGZaBm/tXlrisaM2V3PPI5C9y7cXm2bGl/NxrObMp5PX8k6/+cSERDF7z3JMzB5teIM+eHHaADqP782BJV+xcdBSMtIymLZ70UPqlSkhHoH8uLzk67bHKXeWt56qWb6ZtaW0wy91baa/xHPje3J80Q6+HZBXt4bsKblu1evflq7LRnFh4098028pkR5BDNnzYN0CaDmxd4nz0hye/QWftZyhWQo3nOkTk84vYDFlBqnf7iJ+1mRy/H2xfvdjVDa2RaZXkpJI+24PCbNnEPf6BNKPH8Zy9gKMW7TWpLFe/h6GFSuTtHoJ8TMnkRMZgc3760Ft+pRyVXqM23TFdOQ00g/uJnnFNHKDfbGY+yEqK9sSt1M5VsB0xFSy79544DMD50pYLN1EbmgwyWvmkLx0Mhm/7IGszDLKhe6lpaVTr7YrS+a8rutQnprKA5+n0crXuLvuf5zuuYSE20G027ew2N9adq3q0PLzmQTtO8WpHosJO3yFtjtnY1W/qiZNm6/nYO7izMVx6zjdYzFp96Jpf2ARhubl51olno5vv/2W+vXr061bN/r27UvHjh358ssvNZ9nZWVx9+5dUlNTS/27pWFMR1JSUhgzZgyWlpZUqlSJdevW0bVrV9566y0AVCrVA0MebW1t+frrrzV/L1iwgLp162Jubo6rqyvLli0jK6v4H1Ljxo1j0KBBmn+fPn2aTZs2oVKpUKlU+Pv7U7t2bT7++GOt7dzd3VGpVPj4+JRG1p9IzWn9CN5zgnv7T5PsFcKteV+Rk5ZJ1ZFdi0yf4O6H5+pvCfv5ArkZ2UWmiTrhjtcH3xNx+HKRn5c3wycN4etN33D22Hl8PfxY/eYaHCs40rlXx2K3OXf8AhdOXOSefwjBfvf44sPtpKWk0bhFQwByc3OJjYrTWrr06ciJX0+Rlpr+lHL25BwnDSLuu6PE/fAnGT7BhCz5jNy0DOyH9igyffDb64jd8zvpHv5k+N0jZOEWUBlg2aGZJk3CL6dJOX+drOAIMryDCHv3KwytLTCtX+Mp5erxNJrcB6+9J/H5/gwJ3qH8tXAn2WkZ1BnRpcj0DSf2IuTUDW5t/Y0En1CuffQDMbcCaDA+r+yyktI4NvJDAn69SKJvGFFXffl76W4cm7liUdkBgJBTNzg3+0tCz9wiOSiK4ONXubX1d6r3aV3kd+qbLhP6cmzL/7h53I1QzyC+mf0pNhXsaNqz+Pg9Trnz27rvuHG0+PNLv3kjuHPyGr988C33bgcQHRTBrT+ukByTWBbZKBWtJvbmwicH8Tl+lSjPYH6bvRVLZ1vq9GxZ/DaT+nBj/0luHThDjHcoRxfvJCstgybDtI+5rLRMUqISNEvhnmAZiam47/mT8Jv+JIbEEHT+Nte++YOqreuVWV5LQ7cJ/fhty49cP+5GiGcQO2Z/gm0FO54r4di5dcqdn9ft59rRS0V+XqFmJWq1qMu3S7cRcMOXCL9Qvl2yDWNTE9oM6FBWWSl1XSb04diWn7h1/AphnkHsnf0p1hXsaNKzVbHbeJ5y5/C677lZQr0CyM7MIikqQbOkJaaUdvilrsXE3vy95SC+x68S7RnM72/n1a3aD6lbN/cV1K3ji/LqVuPh2nXLqaELrab05ci8bcXuKyMxldSoBM2So6cN9GYvDyP98CEyjh8mJyiQ5C3rUDLSMe3Zt8j0WTfdyfzrLDnBgeSGhZJ+8Edy/P0wbtQEAIMqVTFu0IjkT9aT7eVJTkgwKZ+sR6VWo+7a7WlmrVSY9B5C5unfyTp7lNzQQNK+3oiSmYFJ597Fb6QywHzaYtJ/2kVuZNgDH6sHTyT7+kXSv/+S3CAfciPDyL52ASUpvuwyomOd2rXmjSlj6d6l/JxTn1TtqX0J/PYkQftPk+QVwvX528lJy6B6MfeHtSb3JvLkdXw+O0Sydyieaw8Qf9OfmuN7AmDhWhH7VnW4sXAH8e5+JPuGcX3BDgxNTagyqN3TzNp/jqLD/5UVe3t79u7dS1JSEgkJCezYsQNLS0vN5zVq1EBRFLp27Vp8uSiKps3j35CGMR2ZN28ep0+f5uDBgxw7doxTp05x9erVf7UPKysrvv76a+7cucOmTZvYtm0bGzZseKRtN23aRLt27Zg8eTJhYWGEhYXh4uLChAkT2LlT+wnszp076dy5M7Vr1/5X8ZU2lbEh1k1rEnP2ZsFKRSH6zE3sWtXVXWB6pLJLJRwrOHD5XMET3pSkFO5c86Bxy0aPtA8DAwO6D3gBU3NTbl65XWSaek3qUrdxHX7d/3upxP00qIyNMGtcm+Rz1wtWKgrJ590xb/FoP6oNzNSojA3JiU8u9jvsR/YmJzGZdI+AUoi6bBgYG+LQtCahZwv991UUws7dxrll0fXcqWVtQs/e0loXcupGsekBjK3NUHJzyUws/qmOibUZGcWUpz5xqOaMjbMdd88XnH/Sk9IIdPehRos6j71flUpFoxeeI9I/jOm7F/Oe25fM/vndEhsFdM2mmhOWzrYEnis4HjKT0ghz96VyMWVhYGxIxSY1CTinfcwFnrtN5Rbax1DDQe2Zee1zxh9bQ+f5wzAyNSk2FktnW+r2bk3wxaKHT+kDx2rO2Drb4VHo2ElLSsXP3QfXRzz3FMXIJK8HUeGehYqikJ2ZRZ3WDYrbTK84VHPG2tkOryLr1ZNf12s/35DVbl+w6M/1DHl3Iua2lg/fSIdsXEqoWy2Lr1sVmtQk8L66FXRf3TIyNeGlLTP4Y+nXpEYlFBtDt3fH8rr754z6ZRWNh3V+8kyVBSMjjOrUJcu9UG82RSHL/QpGDR7tXse4eQsMq1Yj61ZezyiVcd55Rinc+0lRULKyNI1n5YahEYY16pJ9u9DvCkUh+/ZVDGs3LHYz9aDRKInxZJ05/OCHKhXGzdqSG34P87kfYLXlByyWf4JRi2enwehZoDI2xKZpTaLOFLrfUxSizt7CrlXR5yC7lnW00wORp25gn5/eIP9alZNeqJFdUcjNyMahrX4/1BLPFpljTAeSk5PZvn07e/bsoVu3vKdQu3btomrVqg/ZUtvSpUs1/65RowZz585l//79zJ8//6Hb2tjYYGJigrm5uda43HHjxrF8+XIuXbpEmzZtyMrKYu/evQ/0ItMFE3trDIwMybjvhi4jKgHLOlV0FJV+cXDOG+IXG6U9bDQ2Ok7zWXFq1a/Jl798ionahLSUNBZOWk6Ad2CRafuP7Iu/VwA33YpuONNHhnbWqIwMyY7WLpvs6HjUtR6t7lVcMI6siFiSz7lrrbd6sTXVNs/DwExNdmQc/qOXkxOnv7191PZWGBgZkhatXZfSohKwqVWpyG3MnGxJi9LOU3p0ImZOtkWmN1Qb02rxCPx+vkBWMXM/WdWoQIPxPbn8zt5/n4mnzDo/n0n3nX+SohI0nz0OS0drTC3N6D59IL+t+45fPviWBl2aM3HrHD4ZuRqfi4/3+uqyZOFsC0BKtPbxkBKdiKVT0cOSze3yjrnU+465lOgE7Asdcx4H/yIhJJrkiDicG7jQZeEI7GtV4uepm7S26795BrV7tsDYTI3P8ascWfBVKeSsbNjkHx+JUfFa65Oi4jWfPY5w3xBi7kXxyvxX+Wbxl2SkZdBjYj/sKzti4/z4+32arPLzn3xfvUqOStB89rg8T7tz48glYoMjcahegX7zRjDl64VsemUZSq5+zstikZ/n1PvqVmp0IhbF1C2z/PN5ykPq1gsrXiPEzRvf48U/hD338Q8E/XWb7LRManRuQvd3x2FsYcq1nceK3UYXDKxtUBkakRunfT3PjYvDuKpLsdupzC2w3/MDGJtAbg7Jn24k61re3HQ5wYHkRIRjMW4KyVs+RklPx+zloRg6OZNj71Cm+SltKisbVIaGKAna5aMkxGFQqVqR2xjWaYxJ5z4kL5tS9D6tbVGZmaN+aQTpP+4k/fttGDdpjfmslaR8MIecIoZeivLnn/vDon5rWdWuXOQ2ps62RaZX51+Hkn1CSb0XRcMlI7g+bzvZqenUmtoXsyoOmDrblUk+hHgc0jCmA76+vmRmZmq9XcHe3p569f5dq/l3333H5s2b8fX1JTk5mezsbKytn2xeo8qVK9OvXz927NhBmzZt+PXXX8nIyGDo0KHFbpORkUFGRobWuiwlB2OV4RPFIh6u58vdWfBhwRxOc8cseux9BfoGM7bnJCysLHmxX2eWbVzI64PfeqBxTG1qQs9B3di5afdjf1d55DRtCDb9O+E/cjFKpvbQkuQLN/Dp9yaGdtbYj+iJyycL8Hl5DjkxxT+V/y9TGRnSdessVCoVFxZ9XWQa84p29Nwzn4BDl/Dae+qpxvcoWg3syPD3J2v+/mJC2UzYrVLlddy+edyNU9vzemCG3AmkZou6dBjVQy8axhoOak/P9ydo/v5xfNk9KLm+76Tm39F375EcGc+IfYuxdXEmPqhgcucT7+zh/Kb/YV+zEp0XDOPFZaM4vvTrMovr32g7sCOvvT9V8/eWCWvK5HtysnP4bNrHjFs7nU03viYnOweP8ze5efIqqFRl8p1PqsXADgwrVK+2TfiwzL7r2q8XNP8OuxtMmEcQS89upvbzjfD+61YJWz49DQa1p8eagrr1v3FlU7dq9WiBS/uG7O6zpMR0f2/+WfPvyNuBGJupaT21n941jD0uJS2VuBmTUJmZYdK8BRaTXyc3LJSsm+6Qk0Piu8uwems+Dgd+Q8nJJuvaFTIv/w3oZ30qNaZmmE9dSNrO9SjJxTzUy79WZV39i8yjPwKQEeSLYZ1GmLzYnzRpGBPFULJzuDRhI8+tn0zfu9vIzc4h6swtIv50/89XrbKWW8J8keLfk4YxPaVSqR6YHLXw/GEXLlxg1KhRrFq1il69emFjY8P+/ftZt27dE3/3pEmTGD16NBs2bGDnzp0MHz4cc3PzYtOvWbOGVatWaa171bwRoywbP3EshWXGJpKbnYP6vqemaicbMiLjS/W7yotzx85z59odzd/GJnlDAeyd7IiJjNWst3e0w+t2yXPEZWdlcy8gFIC7N71o0Lw+wycN5sMF67XSvdCvC6Zmag4fKF83yjlxiSjZORg5aj+dMnK0JTuq6Bcz/MNx8ss4TR+M/2vLSPcMeOBzJS2DzMAwCAwjxP0udU98gf2wHkR9/kNpZqHUZMQmkZudg5mjdl0yc7IhrZghNmlR8Zg5aTe8mzpak3ZfLxiVkSEvbJ2FZVUHjgxbU2RvMbMKtvQ+sJjIK16cn1/8xOK6dPMPNwLcC94++s+wNSsnG62eP1ZONty7E/DY35MSl0hOVjbh3tpvPYvwDcG1Vf3H3m9p8jl+ldBCb6MyNMm7dbBwtCal0LnXwtGaiDtBRe4jNS7vmDO/75izcLQhpYRhXWH532tbo4JWw9g/84/F+oaRFp/MqB+X89fmn7Xi0RX3P9zwcy843xrnl5e1ky0JWseOLcFPcOwABN3yY3XfeZhZmWNobERybCKLfn6fwBvFvz1Ml27/cYWPC5XNP/XK8r56ZelkQ+idonssP66Y4EiSYxJxrFFBbxrGfI5f1RzjAIbqvGPF/L66Ze5oTWQxdSst/3xuUULdcmnfENvqzsy69aVWmgFfvEnIpbt8N/y9Ivcd5u5Lu7dextDEiJzMoudp1YXcxASUnGwM7LSv5wZ2duTGxRazFXnDt8LyzrVpfj4YVquO2fBReQ1jQI6PF/EzJ6EytwBjI5SEBGw2fE6298Pf5KhPlKQElJwcVDba5aOysUNJeLB8DJwrY+BUCfO33i2UOK/FwnrHMZIXjiU3JgolO5vcUO16mRsahGHd0r3fF7rzz/1hUb+10ou5vqZHxj/0t1nCDX9OdV+MkZUZBiZGZMYk0fn31cQX8+ZcIXRB5hjTgVq1amFsbMzFiwVv84uLi8PLy0vzt5OTE2FhBRNfent7a7194a+//qJ69eosWbKEVq1aUadOHQID/91NpImJCTk5OQ+s79u3LxYWFnz++eccOXKECRMmFLF1gUWLFpGQkKC1DLMo/flNlKwcEm/449Cp0AVYpcKhU2Pi3LyK3/A/LDUljXsBoZrF3yuA6IgYWnVsoUljbmlOw+cacKuY+cKKozJQYWzy4Fuw+o/oy9njfxEfW756QylZ2aTd8sGiQ9OClSoVlu2bkXq1+Jtex6mv4DxzOP5jV5J28xFfQGGgQlVE2emL3KwcYm74U6ljoblYVCoqdWxE5JWi8xh1xUc7PVClc2Ot9P80ilnXrMCR4R+QEffg3GHmFe3o88MSYm4EcO7tL0FPn3ZlpKQTHRihWcK975EQGUfd9gVzzZhamlG9eW0CrnqXsKeS5WTlEHTDlwqu2kNYnWpWIjYk6rH3W5oyU9KJD4zQLDHeISRHxlO9Q8HxYGJpRqXmtQgtpixys3IIv+mvtQ0qFdU7NCL0avH1yrlR3rCokhq8VAZ5P+D+abDTtYyUdKICwzVLqPc94iPjqN++4NplammGa/Pa+JVw7vk30pJSSY5NxLlGRWo0qYX7cf18mUxR9SoxMo66hcpGralXpXtdt6loj7mdJYl60Hj6j6z765ZXCXXrSvF1K+KmPy731S2XQnXr4me/sqvnYnb3XqJZAE6u3sORuV8WtVsgb7L+tPhkvWoUAyA7m2xvL4ybF3ohgUqFcfMWZHv8i3sdAwNUxg9eq5XUFJSEBAwqV8GoTj0y/z5XCkE/RTnZ5AR4YdTwuYJ1KhVGDZ8jx+fOA8lzw4JIWjyR5GVTNEv2tQvkeLiTvGwKuTFRefv0v4tBRe2hmAYVq5IbHVHWORJPiZKVQ8INf5w6aZ9PnDo2Is6t6HNQ3BVvnDppN446d25CbBHps5PSyIxJwqJmRWybuRJ2RD/felte/Bcn39cl/biLfMZYWloyceJE5s2bh4ODA87OzixZsgQDg4J2yhdffJFPPvmEdu3akZOTw4IFCzAudPGuU6cOQUFB7N+/n9atW/Pbb7/x008//as4atSowcWLFwkICMDS0hJ7e3sMDAwwNDRk3LhxLFq0iDp16tCuXclvDFGr1ajV2q/bLathlP5bf6Pp5ukkuPsRf82HmlP6YmSu5t7+0wA03fI6GeGx3H1vP5A3iaRl3bz5owxMDDGtaI9Vo+rkpKSTGpB3ITc0V2Nes2CeNTMXZ6waVScrPpn0kJgyyUdZ+u6rHxj3xmiC/UIICw5j8rwJREdEc+ZowY3dlu/WcfrwWX74+mcApi+cxIWTlwgPicDC0pyeg7rRol1z3npVe766qjUq0/z5pswZvfBpZqnURH/1M1XXvU3aDR/SrnvhMGEgBuamxP3wBwBV171NVngMER/lDRN1nDqYCm+PIvitj8m6F4GRoy0Auanp5KamozJT4zxjGIl/XCI7KhZDO2scRvfDuKIDCb+f11U2H8ntbYfpuGEqMTf8ibrmS6PJvTEyU+P9XV5d6rRpKqlhcVz54HsA7mw/Sp8fltBoah/u/eFOzYHtcGjqyvn5O4C8RrEXv3wDhyY1OD52HQaGBpjlP0HMiE8mNytH0yiWfC+ay+/sxdShoAdacT3V9MnpHb/Ta9bLRAWEERMcSb85w0mIiOPGsYJGiBnfLuXG0cuc3X0UABNzNU41Cs4vDtWcqdKwOqnxycSF5p1f/vzyV8ZteQufSx54X7hNgy7NadytJVtGaPfE1Sdu24/QbtYg4vwjiA+OpNOcISRHxuN9rOAmd/jeRXgddeParuN523x1mL7rphJ+w5+w6760mtAbY3M1Nw/kHXO2Ls40GNQevxPupMUn41zfhReWjyL4bw+iPIMBcH2hGeaONoRf9yMzNR3HulXpungk9y7fJfFe9NMviEf0547f6DdrMJEB4UQHRzJwznDiI+K4VujYmf3tcq4dvcTJ3UcAUJub4lzo2HGs5ky1hjVIiU8mNjQvry37Pk9SbCKxIdFUqe/CiBXjuXbsEnfOlp9hTad3HKbHrJeJCggnNjiSPnOGkRgRx81jbpo0079dys2jlzlXqF453levKufXq/jQGEzM1fR6cwg3jlwkMSoBR5cK9F/0KtEBEXieuf5ADPrk6vYjPP/GIOICIkgIiqTD3Ly65VOobg3dtwifI9p1q8+6qUTc9CfM3ZeWE/Pq1q3v8+rWP2+YvF9SSAwJwXkN8K7dn8PC0Yawqz5kZ2RRvVNjnp85gMtf6udLdtJ++h6rOYvI9vYk+64npoOGoFKbkX48b+J4yzmLyY2JIvXrvDdwmg0bRbb3XXLCQlAZm2DSui3qF3uS/ElBr3iTjl1REuLJiYrAqIYrFtNmkXnhHFlX3YqMQZ9lHvkBs8kLyPH3IsfPE5Neg1GpTck8m1eHzKYsIDcumowD2yEri9yQAK3tldS8B1uF12cc/g7z15eRffcGOR7uGDVtjVHzdqSsmc1/VWpqGkH3QjV/h4RG4Onli421FZUqOuswsrLj88XvtNg0jfjrfsRd86XW5D4YmpsSlP9bq8WW6aSFxeLx/ncA+G47QsefllFrWl8i/nCnyqB22DZzxX1ewdyflfu3JSMmkbR7MVg3qEaTd8cQdtiNqNM3i4xBCF2QhjEd+eijj0hOTqZ///5YWVkxZ84cEhIKblrWrVvH+PHj6dSpE5UrV2bTpk1cuVJwUzRgwADefvttZs6cSUZGBv369WPZsmWsXLnykWOYO3cuY8eOpWHDhqSlpeHv70+NGjUAmDhxIu+//z7jx48vrSyXirCDFzBxsKbu/KGYONuSdDuQSyM/IDP/hs+siiMUmlTXtKI9nU4UzGHiOqM/rjP6E3P+DhdfWQ2ATfNaPP/Tck2ahqvHAHBv/2luvPn508hWqdrz2X7MzM1YuHYOltaW3Lh8k7dfW0BmoTeXValeGRv7gm7Pdo52LN+0CAdne5KTUvD18OOtV+dz+az2k5yXRvQlMiyKi6fL300iQMJv5zBysKHC7FEYOdqR7uGH/7gVZEfHA2Bc2Unr+HF4rQ8GamOqf649d1vExr1EbtoHObmoa1Wl+uBuGNpZkxOfSNoNb/yGLSTDu+hhL/rC/5eLmNpb89zcwZg52RB7O5Bjr60lPX/SZ4vKjloTVEe6eXN65me0mD+UlguGkegfzomJG4i/ey8vfUU7XHrlPb0fdPx9re86POQ9wi94ULlzY6xrVsS6ZkWGX9milWZnldfKMrul4o+tv2BipmbEmimYWZvjd/kun49dQ3ahuuVYvQKW9laav12a1uKN/Ss0f7+ybCwAF384xbdz884vN45e5vsl2+j++iAGrxxPpF8oO6avx89Nf4fvXNp6CBNzNT3XTMDU2px7bl4cGLOWnEJlYevijLldQVl4HrqImYM1HWcPxsLJhsg7gRwYs1Yz0XhOVjY1OjSi1YReGJupSQqLxevwZS5sOajZR3Z6Js1GduXFZaMwVBuTFBqD1xE3Ln7+69PL/GM4svUgJmamjF4zFXNrc7wve7Jp7Htax47TfcdO9aauzNtf0Dg6fNk4AP764RQ7534KgI2zHcOWjsXa0ZaEyDgu/O80h7b8+HQyVUpO5NerYWsmY2Ztjv/lu3wx9oMH6pVFobKp1rQWM/cXXLcHLcu7bl/64TT75n6OkpNL5QYutB7cGTNrCxIj47h75ga/r/9e/3o/3efS54cwNsurW2prc0LcvPhx9IN1y6xQedz99SLm9tZ0mD0Ycycbou4E8sPotQ9M4l+S3Kwcmo/pzgvLR4FKRXxABCff2cuNvScfvrEOZJ45SYqNLeavTcDA3p5sXx8Sl81Dic+bGsHQ2RmUXE16lakpljPexsDRCSUzg5zgIJI+epfMMwX5M7B3wGzKDAxs7ciNjSHjz6Ok7iuf86lmXTqFytoG01fGobKxIyfIl5SPF6Ik5pWPgb2z1v3Oo8i+cp60rzeifmkkBq/NJDcsmNQtK8nx1o+hyWXhlqc3E2Yt0Py9dkteD8uBfbrz3tI5ugqrTIUe/Bu1gzX15w9B7WRL4u1A/h75ARn55xOzKg4ouQV1K87Nmyuvf0qDBUNpsGg4Kf7hXBy/niTPe5o0ps62NF75Wv6QzDiCvz/H3Q3/e+p5E6IkKuX+iayEznTt2pXmzZuzceNGXYfC2bNn6datG8HBwVSoUOFfb/97hRFlENV/xztG0u28OF+aWOo6BL3mlln0m8kEXDPOeniiZ1hVRX+H9+qatyrj4YmeYZbIy3SKUyVXnjGXZFyjYF2HoLeMneXYKYn5um26DkGv/d54qa5D0FsDw/X/beeloYFzG519t0fkJZ19d1mRM7LQkpGRQVRUFCtXrmTo0KGP1SgmhBBCCCGEEEIIUR7I5PtCy759+6hevTrx8fGsXbtW1+EIIYQQQgghhBCiEJl8v3RJjzE9curUKV2HwLhx4xg3bpyuwxBCCCGEEEIIIYQoc9IwJoQQQgghhBBCCFFO5MpU8aVKhlIKIYQQQgghhBBCiGeSNIwJIYQQQgghhBBCiGeSDKUUQgghhBBCCCGEKCf+q5Pg64r0GBNCCCGEEEIIIYQQzyTpMSaEEEIIIYQQQghRTsjk+6VLeowJIYQQQgghhBBCiGeSNIwJIYQQQgghhBBCiGeSDKUUQgghhBBCCCGEKCdk8v3SJT3GhBBCCCGEEEIIIcQzSXqMCSGEEEIIIYQQQpQTipKr6xD+U6THmBBCCCGEEEIIIYR4JkmPMSGEEEIIIYQQQohyIlfmGCtV0mNMCCGEEEIIIYQQQjyTpGFMCCGEEEIIIYQQQjyTZCilEEIIIYQQQgghRDmhKDKUsjRJw5goE9+Ypus6BL02IaeKrkPQW7uUHF2HoNdyjLN1HYLeSkPezlOS2hlSPsVJVZvoOgS9digzSNch6K0PcyvrOgS9tut2NV2HoLf6+sTqOgS95tV4qa5D0Gt9b72r6xCE+E+RhjEhhBBCCCGEEEKIckIm3y9dMseYEEIIIYQQQgghhHgmScOYEEIIIYQQQgghhHgmyVBKIYQQQgghhBBCiHJCJt8vXdJjTAghhBBCCCGEEEI8k6THmBBCCCGEEEIIIUQ5kSs9xkqV9BgTQgghhBBCCCGEEM8k6TEmhBBCCCGEEEIIUU4oSI+x0iQ9xoQQQgghhBBCCCHEM0kaxoQQQgghhBBCCCHEM0mGUgohhBBCCCGEEEKUE4pMvl+qpMeYEEIIIYQQQgghhHgmSY8xIYQQQgghhBBCiHIiVybfL1XSY0wIIYQQQgghhBBCPJOkYUwIIYQQQgghhBBCPJNkKKUQQgghhBBCCCFEOSGT75cu6TEmHqpr16689dZbug5DCCGEEEIIIYQQolRJjzHBuHHjiI+P5+eff9Z1KP/KkNkjeXFkDyysLbjr5smOJVsJDwgrNv3A1wfTuvfzVK5Vlcz0DLyu3GXfB7sI8wstMv2CXcto3rUl6yavwe3YxbLKxr/ScGx3mk7rh5mTDbEeQfy1bDdR7n7Fpq/Zrw2t5g3BsqojiQERXHp/P8EnrmulaTl3MPVHvoCJjTkRl704t3gnif4RWmmqvdicFm8Pwr6BCznpWYT97cHxSRs1nzs2c6XNouE4NqkBCkS5+3Lxvf3EegSVZvZLTa+3h9B25IuYWVvg73aX/y3dQXRAeLHpXdvUp+uUl6jSxBWbCnbsnLKO28fcik0/+L2JtBvVnYOrd3N2x+GyyEKZ6f32UNqNfBFTawsC3O5yYOn2h5bNi1P6U7VJTWwq2LN9ysfcuq9sRn48nTZDumit8zjtzpdjPyiTPDxNA94eTqeR3TC3tsDHzZNvl24jsoTy6vP6IFr0akvFWlXITM/E9+pdfvzgWyKKOQ/pK9fxPajz+kuYOtmQcCeI60t2EXfNt9j0Vfq3peH8oZhXcyTZP5xb7+4n4k93zeevhO8tcrubq/fi/dkhHNs3oPP/lhWZ5mTvpcSVcB7UlRdmD6bFyBcwtbYg2M2LQ0t2EBsQUeI2rcf0oMOUflg62RDuEcThFbsIuV6QNzsXZ3oueRWX1vUwMjHG5/R1fl+xi5ToRE2akV/NpmLD6lg4WJOWmILfudv8sWYfSZHxZZXVJzZ13kReHtUfS2tLrl++yQcL1xHsf6/Y9IPHDGLI2EFUqlYRAL+7/ny14Wv+OlFwra5SvTJvrZhB8zZNMTYx5sLJi3y0ZCOx0XFlnp/SVHV8T1xe74+Jsy3JdwLxWryTxGLqmkW9qrjOH4ZV05qYuTjjtWwXwV/+Xuy+q88aSO2lrxL05e94L9tVVlkoVe1nD6bJqy+gtjYn1M2LPxbvJP4h9ar5mO60mtoPCycbojyCOLF8N+HXiz5nvLJrHjVfaMbBSRvwOXblgc9NbS0Zc/R9rCrZ80njKWQkppZKvsqC3aiXsJ80GCMnOzI8/Qlf/TnpN7yKTGs7rBc2g7qhrlsdgPRbPkSu36WVvtKHb2P7Sg+t7ZLPuBE8cXnZZaKM1Bzfg9qvv4TayYbEO0HcWLKL+BKuYZX7t6V+/jUsxT+c2+/uJ7LQNUztaE3DZSNx7tIUI2tzYv725OaSXaT4F38/UN65ud9k594fuOPpQ1RMLJvWLKNb5/a6DuuZkis9xkqV9BgT5VL/aS/Te9xLbF+8lWUD55ORms7Cb1ZgrDYudpsGbRtxbPdhlg+az/uvrcTI2JBF36xEbaZ+IG2fif3Rt3ONa/+2PL98FFc3/MRPfZYScyeIPnsWYOpgXWR655Z1ePHTGdzdf5qfei8l4MgVenz1Nnb1qmrSNHv9JRqN78m5RTs42H8FWakZ9NmzAMNC5Vijb2u6bp6G13dn+F+Pxfzy8ip8f76g+dzIXE2fPfNIDonhYP+V/PrKajJT0unz7XxURoZlVyCP6YVp/ek4vjc/LtnO5kHLyEzLYPLuhRiVcOyYmKsJ9Qjip+U7Hrr/xr1a4fJcbRLCY0sz7KfixWkD6Dy+NweWfMXGQUvJSMtg2u5FDykbU0I8Avlx+c4S9+1xyp3lradqlm9mbSnt8J+63tMG0m18H/Ys+ZL3By0iMy2Dt3YvLbG86rZtxMlvjrLm5cVsGP0OhkZGvL17KSZFnIf0VZWBz9Nk5Wt4rvsfJ3ouIeF2EB32LUTtWPS5yL5VHVp/PpOAfac40WMxYYev0G7nbKzrF5yLfmsyXWu58tYXKLm5hBy6BEDMZa8H0vjvOUFKYKReNop1mPYSbcf14tDinXw1cDmZqRmM/qbk80yjl56n19JRnNr0P754aSkRHkG89s1CLPLP8cZmakbvWQjArpHvs33wKgyNjXh1+1xUKpVmP/4X7nBgxha2vDiP76dtwr66M8O2vlm2GX4CY2e8yoiJg1mz4GPG9ZtKemoaW/atw0RtUuw2kWGRfPLeVkb3msSY3pNxO3+VdTvX4Fq3BgCmZqZ8un89iqIwbcibTBzwOsYmxmzY/YFWWek754HtqLNqDP7rfuRyj4Uk3w6k+f7FGBdT1wzM1KQFRuD73j4yIkpuALRqXosqY7qTdDuwLEIvE62nv8Rz43vyx6Id7B2Qd88y+L57lvvV69+WLstGcWHjT3zTbylRHkEM3rMAsyLunVpM7P3QoUm9PppElJ4+9CvMqm9nnBdPJvqTvfgPmkW6hx8uO97B0N6myPTmbZuSeOg0gaMXETBsDlnh0bjsfBejCg5a6ZJPu+HVbpRmCXl77dPITqmqPPB5Gq18jbvr/sfp/GtYu30LMSmmXtm1qkPLz2cStO8Up/KvYW13zsaq0DWszddzMHdx5uK4dZzusZi0e9G0P7AIQ/Pyc23/t9LS0qlX25Ulc17XdShClAppGBNaUlJSGDNmDJaWllSqVIl169bpOqQi9ZnYn58++Z4rxy8R5BnIZ7M3YedsT6uebYvd5oOxqznzwwnueQcT5BHA53M241TVmZpNammlq96wJv0mD+SLefr1w73JlD547juJ1/dniPcO5dzCnWSnZ1BvRJci0zee2It7p25wY+tvxPuEcuXjH4i+FUCjcT0KpenNtc0HCTx2lViPYE69tRXzCrZU79USAJWhAe1WjebSu/vw2HOCBP9w4r1D8TtU8FTetnZlTO2suPLxDyT4hRHnFcLVDf/D3NkWq6qOZVsoj6HThD78seUnbh+/QphnEPtnf4Z1BTsa92xV7Daep65zZN333DpafC8xAOsKdgxaOY69b35KTnZOaYde5rpM6MOxLT9xK79s9s7+FOsKdjQpsWzcObzue24evVzivrMzs0iKStAsaYkppR3+U9dtQj9+2/Ij14+7EeIZxI7Zn2BbwY7nerYudptNY9/jrx9OEep9j3segeyc+ykOVZ2o3sT1KUb+ZOpM7UvAtycJ3H+aJK8Qrs3fTk5aBtWLORfVntybiJPX8f7sEEneodxZe4D4m/64ju+pSZMRlaC1VOrVkqjzd0gNigRAycrR+jwzLplKvVsSuP/0U8nzv/X8xN6c+eRn7h6/QoRnMD/N/hwrZ1vq92xZ7DbtJvXh6v6TuB84Q5R3CIcW7yArLYPnhuWVq0uruthWdeLnOV8QeTeYyLvB/DRnK5Wb1qRm+4aa/fy9/Qj3rvmQEBJN8BVvzn32K1Wfq42BHj6oABg5eRjbN+7m9NFz+Hj4svyN93Cq4EDX3p2K3ebs8b84f+Jvgv3vEeQXzGcfbCM1JY0mLRsB0KxNEypVq8iqN9/H19MPX08/VrzxHg2a1ad1xxZPK2tPzGVaP0L2/EnY/lOkeIXgOe8rctIyqTzyhSLTJ7n74rP6WyJ+/ovcjKxi92torqbxZzPxmPMl2fHJZRV+qWsxsTcXtxzE9/hVoj2DOfz2ViydbaldQr1qOakPN/ed5PaBM8R6h3J80U6y0jJoMlz7fOXU0IVWU/pydN62YvfV7LVuqK0tcCuhF56+cJjwMvHfHSHhx+Nk+gQTvvwTctMysB3Ss8j0oXM+Im7vb2R4+JHpd4+wxZvAwACLds200imZWeREx2mW3MTyc/z8o/bUvgR+e5Kg/GvY9Ydcw2pN7k3kyev4fHaIZO9QPPOvYTXzr2EWrhWxb1WHGwt3EO/uR7JvGNcX7MDQ1IQqg9o9zaw9VZ3ateaNKWPp3qWDrkN5ZimKorPlv0gaxoSWefPmcfr0aQ4ePMixY8c4deoUV69e1XVYWpyrVcDO2Z5b525o1qUlpeLr7kWdFvUeeT/mVuYAJBe6KTQxNWHm5tnsXPYlCVHxpRbzkzIwNsSxSU1Czt4uWKkohJy9jXOL2kVuU6FlbULO3tJad+/0DZxb5qW3cnHCvIKtVpqspDSi3H2p0LIOAI5NamBZyR4lV+HlI+8y6son9P5mnlavswTfMNJjk6g3sisGxoYYmhpTb0RX4rxCSAqOKq0iKBX21ZyxdrbD+3xBntOT0ghy96V6izpPtG+VSsWrG2Zw6stDRHgXPwRIXznkl43X+ZuadelJaQS6+1CjRd0n3n/t5xuy2u0LFv25niHvTsTc1vKJ96lLjtWcsXW2w6NQeaUlpeLn7oPrvzgPmeWfh1LKyY9TlbEhtk1rEnmm0LlFUYg8ewv7VkXXIfuWdbTTAxGnbhSbXu1oTcXuzQnYe6rYOCr1aoHazkovG8bsqjlh5WyH37mC83VGUhr33H2pWsx5xtDYkMpNauJ3rqCcFEXB79wtzTaGJkagKGRnFjR4ZGdkoeQquLQu+pgzs7GgyaAOBF/xJlcPG+uruFTCsYIDl84WPHRISUrh1jUPmrRq9Ej7MDAwoOfAbpiZm3LjSl6Zm5gYoygKmYXKKjMjk9zcXJq3aVq6mSgjKmNDrJq6Enu24ByDohB35iY2xdSdR1Xvg4lE/3GNuDM3H55YT9i4OGHpbEtgoTqSmZRGmLsvlVsWXR4GxoZUaFKToHPa905B525TqdC9k5GpCf22zODPpV+TGpVQ5L7s61Tm+bde5vDbW1Fy9fxHobERpo1qk/KXe8E6RSHlL3fMnqv/SLswMFOjMjIkJ0H72mTetgl1/t6L69EvqbhqBoa2VqUYeNlTGRti07QmUfddw6LO3sKumHpl17KOdnogstA1zMAkr8diTnqhxmhFITcjG4e2j34/IITQLZljTGgkJyezfft29uzZQ7du3QDYtWsXVatWLXG7jIwMMjIytNblKDkYqsrm6bSNsy0ACdHxWusTohOwdbJ7pH2oVCrGrJiI5+U73PMq6BI/evlEvK54cuX4pdIKt1SY2lthYGRI2n03bGnRCdjWrlTkNmZOtqQVmncGIC0qETMnW83nefsoKk1eV3srF2cAWsx+hb9Xf0tycBRNpvblpQNL+L7zXDLiU8hKSefQ0Pfosf1tnntzEACJ/uEcHvUhSk7uk2S71Fnl5yvpvnJMjkrAKr88HtcL0weQk53DuZ1Hnmg/uvJP/pPLoGw8T7tz48glYoMjcahegX7zRjDl64VsemWZ/v/AKIZNfpkk3teAnhQVr/nsYVQqFSOWj8P7siehXsGlG2AZUeefizLuO04yohKwql25yG1MnW2LTG+afy6/n8vwzmQnpxP6e/G9EGu8+gIRp26QFqZ/Q5Yt8/OVHK2d55ToBCyLOTbM7fLK9cFtEnGslVeu9675kJmaQY+FI/hz7fegUtF94XAMjAw13/mP7gtH0GZsD0zMTQm+6s3e8R+XSt5Km4Nz3jCtmCjtYX+xUbE4ONmXuG2t+q7sPPQ5JmoT0lLSmDdhCf5eAQDcvHqH9NR0Zi2dxqdrvkSFillLpmFkZITjfUPD9JWxvTUGRoZk3ld3MqMSMK9TdF17FBUGtceqaU0u91r8pCE+VRb5dSf1vnuW1OhELJyKHh5oln++SrmvXqVGJ2Bfq+DeqeuK1wh188b3eNEPgg1NjOi3ZQZn3ttHUmgMNvn3RvrKyM46r1Hrvvn0cmLiUdeq9kj7cJ43nuzIWFLOX9OsSzlzhaSjf5F1LwJjl0o4zxlLta9WEzBsDuTq1/1ecUrzGqb+51zvE0rqvSgaLhnB9XnbyU5Np9bUvphVccDU+dF+lwghdE96jAkNX19fMjMzadu2YDiivb099eqV/LRjzZo12NjYaC13ErxLLa4Ogzqz884+zWJk9OTtuePfmUK1utXZMrNgqGjL7q1p1L4Ju1dtf+L9/1eoDPLmYnHfcpCA3y8TfTOA07O/RFEUavbLO04MTY3p/PEkIi57cXDASn59eRWxd+/Ra9dcDE2Ln/fjaXhuYAfeu71Tsxgal82zgCqNa9JxfG++m7u1TPZfFloM7MAHt7/WLIbGZTfM6tqvF7j9xxXC7gZz65gbX01YS/Xmtan9/KP1CNEHbQd2ZMvtbzRLaRxLr74zicr1qrFt1oZSiPC/o8aIrgT/73yxQ8HMKtlToWtTAvaefMqRFa3JoPYsvrNds5TVkMXU2CQOvL6Zut1bsNhjO4tubcPU2oLQm/4PNDD/9cUhvui7hN2j1qDk5PLyhmllEtO/1fuVHpzxOapZjJ6gHgX6BvFq9wmM6zeVH3YfZOXmJdTMn2MsPiaeBVOW07lHB876HOOU12GsbCzxuHGX3HLaGF8a1JUdqPvuWG6/vqXEoZb6oP6g9szy+EqzlFW9qtWjBS7tG3Jy1TfFpum4YDixPqF4/HS+TGLQNw5ThmLdrwv3Xn8HpVCvy8TfzpB84iIZXgEk/3GB4CkrMWtWD/O2TXQYre4p2TlcmrARS9eK9L27jZf8v8axfUMi/nRHUcpHg6Eon3JRdLb8F0mPMfHEFi1axOzZs7XWTWo8qtT2f+X4JXyuFbwVxzi/y7KNoy3xkQVPw2wcbQi44//Q/Y1bPZkW3VqzathiYsNjNOsbtW9KheoV2X7zW630b2+dj+clD94ZsfRJs/LY0mOTyM3O0fTk+oeZow2pkUV3+0+LisfsvolEzZysScvv4fLP/5s5WpNW6G1lZk7WxNzO60WXmr8+zitE83luZjZJQZFYVsl76l57UHssqzpxcMAq/nljwcmZnzLm9hdU79kSv1/+fqw8l4Y7f1xhvbuP5m+j/GPHysmGpEI9fSydbAi9E/DY3+Papj6WDtYs+atgXjpDI0P6L3mNThP68H7HNx5732Xl9h9X+LiIsrF0stHqBZVXNqU7OXNMcCTJMYk41qiA91+3Hr6BHnD/ww2/QuVlbJJ3+bR2stUadm3lZEvwIxxLI1dNpOmLLfho2AriytGLGjLyz0Xq+85Faicb0ot562F6ZPwjp3doWw+rOpW5NHVzsTFUH9GFjLgkwo7qxzD/u8evElLobWaG+ceGpaMNyYXyaOFoQ3gxdSk1Lq9cLR21y8nC0VqrF6fv2Zts7jwbcztLcnNySU9MZe7lT7kVHHnf/pJJjUsmxj+caJ9QZl/cQtUWtbl31QddOnP0HLeu3tH8bZJ/3nFwsiMmsuB6bO9kj9ftkh+wZWdlcy8g79rkecOLhs3qM3LSEN6fn9c77uLpywxqNwIbextysnNITkzmyPWfCQksH2+AzYpNJDc7B5P76o6Jkw2Zj/mGUatmNTFxsqX18YI3AhsYGWLbrgFVJ/TiZLVRoCcNh77HrxJeuF6p8+qVuaM1KYXyb+5oTdSdoifDT8s/X1ncV6/MHW1Iya9X1do3xLa6MzNvfamVpv8XbxJy6S7fD38Pl/YNcaxfjbp92+R9mP8Ch9fdP+fiJwf5a/3/niivpS07LhElOwdDR+3eSoYOtmRHlXy9sZ/4Cg5ThxI0dgkZdwNKTJsVHE52bAIm1SuTeuF6iWn1RWlewzIKpU+44c+p7osxsjLDwMSIzJgkOv++mvhi3n4qhNA/0jAmNGrVqoWxsTEXL17ExcUFgLi4OLy8vOjSpegJKQHUajVqtfZbV0pzGGV6SjrpKdqvO46LjKVxh6YE5jeEmVmaUat5XY7vKXkY27jVk2nd63neGb6UqPt+SBz8/EdO7D+ute6j45vZvXoHV/8seWLxspablUP0TX+qdGxE4NH814erVFTu2Ig7Xx8vcpuIKz5U7tiIW9uPatZV7dSYyCt5P4ySgqJIjYinSsdGxObfVBpbmuHUvBZ3dv8JQPSNALLTM7GtVYmIy3mNkyojQyyrOpEcEg2AkZlJ3o10oYkYlVwFlIIeZ7qSkZJORkq61rrEyDjqtG+saexRW5rh0rwWF/YUXY6P4sr/zuJ9Tnuulsm7F3Hlp7NcPqB/cyBB8WVT976yqd68Nn89QdkUxaaiPeZ2liQ+5o87XchISSfqvvNQfGQc9ds31jSEmVqa4dq8Nqf3HC1iDwVGrprIc73a8PGIFUTfiywxrb5RsnKIv+GPc6dGhB3JnxdKpcK5YyN8dxwrcpvYK944d2qM77aC87Nz5ybEuj3Y8FHj1a7EXfcjoZgfupDXMBZ04CyKnsyZlZmSTux9dSkpMo6aHRppGsLUlmZUbV4Ltz1/FLmPnKwcQm/6U7NDIzyP5Z3jVSoVrh0ac2nXg+WaGpc370/N9g2xcLTmbjFDwKDgPPxP47cupaakkZoSorUuOiKG1h1b4nU779pkYWlO4+ca8OOun//Vvg0MVBibPPgmy4TYvAaQVh1aYO9ox5lj5x4v+KdMycoh6YYf9p2aEH24oK7ZdWrMvR0ln2OKE3fmFn93mau1ruHG6aT4hBD4yS960ygGkJWSTvx99So5Mh6XDo00DWEmlmZUal6L69/8WeQ+crNyiLjpj0uHRvgcK7h3cunQCPddede1S5/9ys19p7S2G/fHB5xavQffP/KGEf4ybRNGhd6SWrGZK73XTWH/kHeID9TDc3hWNum3fbBo14zkP/LfIq5SYdG+OXHf/FrsZvaTh+A4fThBE5aSfuvhIz+MKjpgaGtFdmT5ebijZOWQcMMfp06NCC90DXPq2Aj/Yq5hcVe8cerUGL9HuIZlJ6UBYFGzIrbNXPH48EDpZ0KIfP/VSfB1RRrGhIalpSUTJ05k3rx5ODg44OzszJIlSzAw0L8Rt4e3/8qgWUMJ9w8lMjiSoXNeJS4yFrdjBW9LXLJ3NZeP/s2xXXlvD5rw7lTaD+jMusnvk5aSppkHKDUxlayMTBKi4ouccD8mNPqBRjRduPnlYbpsmErUdX+i3H1pPKk3xmZqvL7La3jpunEqKeFxXP7gewBubT9K/x+W0GRKH4L+dKfWwHY4NnXl7IIdmn3e2n6E594YRIJ/BEnBkbSaO4TUiHhN41tWchoee07QYs5gkkNjSL4XQ9Pp/QA0b6a8d+YWbZaMpMN747i98xgqAxXNZvQnNzuH0L88nmYRPZKzOw7TbdYgogLCiQ2OpPecoSRGxHHrWMHkz1O/XcKto5c5vzvvJsnEXI1jjYqaz+2rOVG5YXVS45OJD40hNT6Z1PsmT8/JziEpKoEov7Cnk7FScHrHYXrMellTNn3mDCMxIo6bhcpm+rdLuXn0Mud25/0wu79sHKo5a5WNibmaXm8O4caRiyRGJeDoUoH+i14lOiACzzPl4wlzcf7c8Rv9Zg0mMiCc6OBIBs4ZTnxEHNeOFTSkz/52OdeOXuLk7rwb6lffmUTbgR35dPJa0lPSsf5nrr/881B54P3F77TaNI24637EXfOl9uQ+GJqbaibCb7llOulhsdx+/zsAfLYdofNPy6g9rS/hf7hTbVA77Jq5cm3eV1r7NbI0o0r/ttxc+e0D3/kPp46NsKjuTMC3p8osf6Xh7+1H6DxrELH+4cQFR/HinCEkRcZrGr0AxuxdhOdRNy7l/0C/8NVhXl43ldAb/oRc9+X5Cb0xNldzrVDjevOhnYn2CSUlJpFqLevQe8VoLmw/Qkz+eaZK81pUaeZK0GUv0hJSsK/uzAtzhhIbEE7w1dKb4qA07dv2PRPfGkuw/z1CgsKYvmASURExnDpyVpPms+83curwGb7fmdczZ8biqfx14m/C70VgbmlO71d60LL9c8waOUezTf/hffH3DiAuJp6mrRozZ/Ub7P3yewJ9y8d8fgBBW3+j4ebXSXT3JfGaLy5T+mJoriZs/ykAGm6ZQUZ4LL7v7QPyJha3qJs3L6yBiRHqinZYNqpOTko6aQER5KSkk+Kpnf+c1HSy4pIfWK+Prm4/wvNvDCI+IIKEoEg6zB1CcmR8QaMXMGTfInyOuGkavq58dZje66YSftOfcHdfWkzMq1e3vs+rV6lRCUVOuJ8UEkNi/guEEu5r/DKzz5twPtYnlIzE1DLJ65OK2fETldfOJv2WN2k3vLAfNxADMzXxP+aVS6W1c8iOiCFq3dcAOEwZguObowmdvZase5Ga3ma5qWkoqemozE1xmvUqiUfPkxMVlzfH2PwJZAaGkXLuSnFh6CWfL36nxaZpxOdfw2rlX8OC8q9hLbZMJy0sFo/8a5jvtiN0/GkZtab1JeIPd6oMaodtM1fcC13DKvdvS0ZMImn3YrBuUI0m744h7LAbUafLzwsu/q3U1DSC7hX0wA0JjcDTyxcbaysqVdTvefiEKIo0jAktH330EcnJyfTv3x8rKyvmzJlDQkLRQ/V06detP6E2N2XSmtcxt7bgrpsHH4xZTVahOTMquFTEyq5gKGGP0X0AWP79e1r7+nzOZs78cOLpBP4E/H69iKmDNS3nDsbcyYaYO4EcHr1WM3m+RRVHrXlmIq94c2LmZ7SaP5TWC4aR4B/O8UkbiLtb8MbE658dwshcTacPJ2BibU7EZS+OvLaWnELlePHdfSjZOXTdNB0jUxMir/nw+/D3yUzIuxlM8A3j2Pj1tHj7ZQYcXIGiKMTcCuTI6LVaQzT1xcmtv2JipmbImkmYWZvjf/ku28Z+QHahPDtUr4CFfcGblqo1dWX6/uWavwcuGwPA5R9Ol6t5xR7mxNZfMDFTM2zNZE3ZfHFf2Tg+UDa1mFmobAbll82lH06zb+7nKDm5VG7gQuvBnTGztiAxMo67Z27w+/rvycnMfnqZKwNHth7ExMyU0WumYm5tjvdlTzaNfU+rvJyqV8CyUHm9MLoXAPO+W6W1r51zP+WvH049lbifVMjBv1E7WNNw/hDUTrYk3A7k/MgPyMg/F5lXcdCaiDnWzZvLr39KwwVDabRoOMn+4VwYv55ET+23t1Yd1A5QEfzTX8V+d41XuxJz6S7JPvo9HO781kOYmKvpv2YiptbmBLl5sWfMh1rHhr1LBcztCo6N24f+xsLBihdmD8HSKW/Y5Z4xH5JSaLJxR9dKdJ8/HDNbS+LvRXH2k4Nc+Oqw5vOstEwa9G5N17cHY2KmJikqHp9TNziw5WCZu2QAAFCRSURBVGe9rW+7Pt2LqbkZiz+ah5W1Je6XbvLGq3PJLNRQXLVGZWztC4Yy2TvYsmrzEhydHUhOSsH7ji+zRs7h4pmCRvzqtaoxY/EUbGytCQ0OZ+fmb/j2i++eat6eVOTBC5g4WOM6fxhqZ1uSbgfgPnKNZkJ+0yoOKIXqmrqiPW1PrNX8XX3GAKrPGEDc+dtcfWX1U4+/tF3+/BDGZmp6rJmA2tqcEDcv/jda+57F1sVZ03AFcPfXi5jZW9Nhdt69U9SdQH4cvfaBSfz/a5J+P0OkvTVOb47G0MmODA8/giYuJycmHgDjyk5QaP4r25H9MDAxpuonS7T2E7X5W6K3fAs5uajr1aTay90xtLIgKzKWlHNXidr4DYqenluKE5p/Dauffw1LvB3I34WuYWb31as4N2+uvP4pDRYMpcGi4aT4h3Nx/HqSCl3DTJ1tabzytfwhmXEEf3+Ouxv0a4htabvl6c2EWQs0f6/dkjcceWCf7ry3dE5xmwmht1SK9METZWBk9UG6DkGvvZhjqesQ9NZdI/0YHqWvcv6jE16WhmTk2ClJn3TdD6XTVzfUuh32re8OZRY/tPVZ92Hu478h8lngrpbzTnH6qsvPEERd8Eqy1XUIeq3vrXd1HYLeMnZ01XUIT4WleU2dfXdy6sPn9S5v9G+MnBBCCCGEEEIIIYQQT4EMpRRCCCGEEEIIIYQoJxQZRVKqpMeYEEIIIYQQQgghhHgmSY8xIYQQQgghhBBCiHIiV6aKL1XSY0wIIYQQQgghhBBCPJOkYUwIIYQQQgghhBBCPJNkKKUQQgghhBBCCCFEOaHIUMpSJT3GhBBCCCGEEEIIIcQzSXqMCSGEEEIIIYQQQpQTCtJjrDRJjzEhhBBCCCGEEEII8UyShjEhhBBCCCGEEEIIoTOxsbGMGjUKa2trbG1tmThxIsnJyQ/d7sKFC7z44otYWFhgbW1N586dSUtL+1ffLQ1jQgghhBBCCCGEEOWEoig6W8rKqFGjuH37NsePH+fQoUOcOXOGKVOmlLjNhQsX6N27Nz179uTSpUtcvnyZmTNnYmDw75q6ZI4xIYQQQgghhBBCCKETHh4eHDlyhMuXL9OqVSsAtmzZQt++ffn444+pXLlykdu9/fbbvPHGGyxcuFCzrl69ev/6+6XHmBBCCCGEEEIIIUQ5ocseYxkZGSQmJmotGRkZT5SfCxcuYGtrq2kUA+jevTsGBgZcvHixyG0iIyO5ePEizs7OtG/fngoVKtClSxfOnTv3r79fGsaEEEIIIYQQQgghxEOtWbMGGxsbrWXNmjVPtM/w8HCcnZ211hkZGWFvb094eHiR2/j5+QGwcuVKJk+ezJEjR2jRogXdunXD29v7X32/NIwJIYQQQgghhBBClBOKDpdFixaRkJCgtSxatKjIOBcuXIhKpSpx8fT0fKwyyM3NBWDq1KmMHz+e5557jg0bNlCvXj127Njxr/Ylc4wJIYQQQgghhBBCiIdSq9Wo1epHSjtnzhzGjRtXYhpXV1cqVqxIZGSk1vrs7GxiY2OpWLFikdtVqlQJgIYNG2qtb9CgAUFBQY8U3z+kYUwIIYQQQgghhBBClConJyecnJwemq5du3bEx8dz5coVWrZsCcCJEyfIzc2lbdu2RW5To0YNKleuzN27d7XWe3l50adPn38XqCLEf1x6erqyYsUKJT09Xdeh6B0pm5JJ+RRPyqZkUj4lk/IpnpRNyaR8iidlUzIpn+JJ2ZRMyqd4UjaiNPXu3Vt57rnnlIsXLyrnzp1T6tSpo4wcOVLz+b1795R69eopFy9e1KzbsGGDYm1trRw4cEDx9vZWli5dqpiamio+Pj7/6rtViqIo/64pTYjyJTExERsbGxISErC2ttZ1OHpFyqZkUj7Fk7IpmZRPyaR8iidlUzIpn+JJ2ZRMyqd4UjYlk/IpnpSNKE2xsbHMnDmTX3/9FQMDAwYPHszmzZuxtLQEICAggJo1a3Ly5Em6du2q2e6DDz7g008/JTY2lmbNmrF27Vo6duz4r75bhlIKIYQQQgghhBBCCJ2xt7dn7969xX5eo0YNiurXtXDhQhYuXPhE3y1vpRRCCCGEEEIIIYQQzyRpGBNCCCGEEEIIIYQQzyRpGBP/eWq1mhUrVjzyK2WfJVI2JZPyKZ6UTcmkfEom5VM8KZuSSfkUT8qmZFI+xZOyKZmUT/GkbMR/hUy+L4QQQgghhBBCCCGeSdJjTAghhBBCCCGEEEI8k6RhTAghhBBCCCGEEEI8k6RhTAghhBBCCCGEEEI8k6RhTAghhBBCCCGEEEI8k6RhTPznrFixgsDAQF2HoZd27txJamqqrsMQ5UxWVhYTJkzA399f16GIci4zM5O7d++SnZ2t61CEEEIIIYQA5K2U4j+oefPm3Lp1iy5dujBx4kQGDx4srxDOV6FCBdLS0hg6dCgTJ06kffv2ug5J53755ZdHTjtgwIAyjES/2djY4O7uTs2aNXUdSrmRk5PDzZs3qV69OnZ2droOR6dSU1OZNWsWu3btAsDLywtXV1dmzZpFlSpVWLhwoY4j1C1fX1927tyJr68vmzZtwtnZmcOHD+Pi4kKjRo10HZ5OnTx5khdeeEHXYYhyKC0tDUVRMDc3ByAwMJCffvqJhg0b0rNnTx1HJ/Sdn58frq6uug5DCPGUSMOY+E+6du0aO3fuZN++fWRnZzNixAgmTJhA69atdR2aTmVnZ/Prr7/y9ddfc/jwYVxdXRk/fjxjx46lYsWKug5PJwwMtDvOqlQqCp8WVSqV5t85OTlPLS59M3bsWJo3b87bb7+t61D01ltvvUWTJk2YOHEiOTk5dOnShb/++gtzc3MOHTpE165ddR2izrz55pucP3+ejRs30rt3b27cuIGrqysHDx5k5cqVXLt2Tdch6szp06fp06cPHTp04MyZM3h4eODq6soHH3yAm5sbP/zwg65D1Cm1Wk3VqlU116pq1arpOiRRTvTs2ZNXXnmFadOmER8fT/369TE2NiY6Opr169czffp0XYf41G3evPmR077xxhtlGIn+MzAw0DxkHzJkCKamproOSQhRhqRhTPynZWVl8euvv7Jz506OHj1K/fr1mThxIuPGjcPGxkbX4elUREQEe/bsYdeuXXh6etK7d28mTpxI//79H2gselb88ccfLFiwgPfff5927doBcOHCBZYuXcr7779Pjx49dByh7rz77rusW7eObt260bJlSywsLLQ+f9ZvoAGqVq3Kzz//TKtWrfj555+ZMWMGJ0+e5JtvvuHEiROcP39e1yHqTPXq1fnuu+94/vnnsbKy4vr167i6uuLj40OLFi1ITEzUdYg6065dO4YOHcrs2bO1yubSpUu88sor3Lt3T9ch6lR0dDTffPMNu3bt4vbt27z44otMnDiRQYMGYWJiouvwdMbOzk7rwU1xYmNjn0I0+snR0ZHTp0/TqFEjvvrqK7Zs2cK1a9f48ccfWb58OR4eHroO8al71F7fKpUKPz+/Mo5Gv7m7u2sesmdmZjJ8+HAmTpxImzZtdB2azsyePfuR065fv74MIxGi9EnDmPhPy8zM5KeffmLHjh2cOHGC9u3bExoaSkREBNu2bWP48OG6DlGnLl68yI4dO9i1axeVKlUiLi4OOzs7du7c+Uz2bmncuDFbt26lY8eOWuvPnj3LlClTnsmb6H+UdDMtN9B5TE1N8fHxoWrVqkyZMgVzc3M2btyIv78/zZo1e6Ybf8zNzbl16xaurq5ajT/Xr1+nc+fOJCQk6DpEnbG0tOTmzZvUrFlTq2wCAgKoX78+6enpug5Rb1y9elXzQxXg1VdfZeLEiTRr1kzHkT19/wxLBlAUhenTp7N69WqcnZ210o0dO/Zph6Y3zM3N8fT0xMXFhWHDhtGoUSNWrFhBcHAw9erVkzlXxSPJzs7ml19+4euvv+bIkSPUrVuXCRMmMHr0aJycnHQd3lN1/7D2q1evkp2dTb169YC8aRIMDQ1p2bIlJ06c0EWIQjw+RYj/IDc3N2XGjBmKvb29UqlSJWXBggWKt7e35vPNmzcrzs7OOoxQd8LDw5WPPvpIadiwoWJqaqqMGDFCOX78uKIoipKcnKzMnz9fcXFx0XGUumFqaqrcvHnzgfXXr19XTE1NdRCRKE9cXFyUo0ePKtnZ2Uq1atWUQ4cOKYqiKLdu3VJsbW11HJ1uderUSdm8ebOiKIpiaWmp+Pn5KYqiKDNnzlR69eqly9B0rkqVKsr58+cVRckrG19fX0VRFOV///uf4urqqsvQ9FJISIiyYsUKRa1WKxYWFoqhoaHSsWNH5datW7oOTacKHzsiT5MmTZRNmzYpQUFBirW1tfLXX38pipJ3j1ihQgUdR6dfcnNzldzcXF2HodfS09OV9evXK2q1WlGpVIparVZGjx6thIaG6jo0nVi3bp3Sv39/JTY2VrMuNjZWGThwoPLxxx/rMDIhHo80jIn/nMaNGytGRkZK3759lZ9++knJzs5+IE1UVJSiUql0EJ1uvfTSS4qxsbHSqFEjZcOGDUpMTMwDaSIiIp7JslGUvB/vPXr0UMLDwzXrwsPDlZ49eyqdO3fWYWSiPFixYoViY2Oj1K9fX3FxcVHS09MVRVGU7du3K88//7yOo9Ots2fPKpaWlsq0adMUU1NT5c0331R69OihWFhYKG5ubroOT6fmzJmjdOzYUQkLC1OsrKwUb29v5dy5c4qrq6uycuVKXYenFzIzM5UDBw4offr0UYyMjJTnn39e2bZtm5KcnKz4+/sro0aNUho0aKDrMHVKGsYedODAAcXY2FgxMDBQunfvrln//vvvK71799ZhZPpj165dSuPGjRW1Wq2o1WqlSZMmyu7du3Udll65fPmyMn36dMXOzk6pWrWqsmTJEsXPz085c+aM0q1bN6V169a6DlEnKleuXOQDiZs3byqVKlXSQURCPBkjXfdYE6K0DRs2jAkTJlClSpVi0zg6OpKbm/sUo9IPzs7OnD59WjN/VlGcnJzw9/d/ilHpjx07dvDyyy/j4uKimeA5ODiYOnXq8PPPP+s2OD1w7949fvnlF4KCgsjMzNT6TOaSgJUrV9K4cWOCg4MZOnSo5m24hoaGz/xbFzt27Ii7uzsffPABTZo04dixY7Ro0YILFy7QpEkTXYenU++//z4zZsygWrVq5OTk0LBhQ3Jycnj11VdZunSprsPTuVmzZrFv3z4URWH06NGsXbuWxo0baz63sLDg448/pnLlyjqMUuijIUOG0LFjR8LCwrSG23br1o2XX35Zh5Hph/Xr17Ns2TJmzpxJhw4dADh37hzTpk0jOjr6mX/Zzvr169m5cyd3796lb9++7N69m759+2rm4a1ZsyZff/01NWrU0G2gOpKYmEhUVNQD66OiokhKStJBREI8GZljTPznrF69mrlz52pez/2PtLQ0PvroI5YvX66jyHRv9+7dDB8+XPOD/R+ZmZns37+fMWPG6Cgy/aEoCsePH8fT0xOABg0a0L1790ea5Pi/7M8//2TAgAG4urri6elJ48aNCQgIQFEUWrRoIXNJ3Cc9PV3eYCX+laCgIG7dukVycjLPPfccderU0XVIeqFbt25MmjSJV1555YFr1z+ys7M5f/48Xbp0ecrR6Y/C89MJbT4+Pvj6+tK5c2fMzMxQFOWZv6ZDXsPOqlWrHrj327VrFytXrnxmH5L+o06dOkyYMIFx48ZRqVKlItNkZmayb9++Z3IuvzFjxnD27FnWrVuneSHBxYsXmTdvHp06ddKaB1GI8kAaxsR/jqGhIWFhYQ9MQBsTE4OzszM5OTk6ikz3pGzE42rTpg19+vRh1apVmh9gzs7OjBo1it69ez+Tr72/X05ODu+//z5bt24lIiICLy8vXF1dWbZsGTVq1GDixIm6DvGp+jcvG7C2ti7DSER5dubMGdq3b4+RkfYgh+zsbP766y86d+6so8h06/63w3366ae89tprD7xx+1nuzRsTE8OwYcM4efIkKpUKb29vXF1dmTBhAnZ2dqxbt07XIeqUqakpt27donbt2lrrvb29adKkyTP/4o+AgABcXFweeFO7oigEBwfj4uKio8j0Q2pqKnPnzmXHjh1kZWWhKArGxsZMnDiRjz766IG3lwuh72QopfjPKe5J4PXr17G3t9dBRPqjuLK5d+/eAzfTz4rNmzczZcoUTE1N2bx5c4lp33jjjacUlf7x8PDQvAnOyMiItLQ0LC0tWb16NQMHDpSGMeC9995j165drF27lsmTJ2vWN27cmI0bNz5zDWO2traP3CvjWWuUl1feP7oXXnihyAc6CQkJvPDCC8/csfOPa9euaf3dvn37B94O/Kz3inr77bcxNjYmKCiIBg0aaNYPHz6c2bNnP/MNY7Vr1+b7779n8eLFWuu/+3979x6X8/3/D/xxhaQDYkUoHTSKjnwcx6Y2bD6MfDbEqObUTNYJG0oYPlmZljFyNmJOsznlPGuEdWShoxwKK6Sw0nX9/ujT9XO5EttXvS7X+3H/q+v1fv/xuF23urie79fz+dq6lTtWAdjY2FT72VNUVAQrKyvJfvZU0dfXx7fffovFixcjKysLQOV7xoIYvapYGCOtYWxsDJlMBplMhtdff13lP4QVFRUoKSnBpEmTBCYUx8XFRfneuLu7qzx5r6ioQE5ODgYMGCAwoThLlizBqFGjoKenhyVLljzzPplMJunCmIGBgXKumJmZGbKystCxY0cAwJ9//ikymsbYsGEDVq5cCXd3d5XPGicnJ2VrrpQcO3ZM+XNubi5mzJgBLy8v5YzDU6dOYf369Vi4cKGoiMI8XdSo6ch7qXvWA53CwkJJfwF78u+rSlUTiNQLYlXi4uJw8OBBtGnTRmXd1tYWV65cEZRKc4SFhWH48OH45ZdflDPG4uPjceTIEWzbtk1wOvGe1VRVUlIi6VEJHh4eL3Tfzp07azkJ0cvFwhhpja+//hoKhQI+Pj4ICwtT2QGlq6sLS0vLGofOa7MhQ4YAAJKTk9G/f38YGhoqr1W9N8OGDROUTqwnZ2hIfZ5GTbp3745ff/0VdnZ2eO+99xAYGIi0tDTs3LkT3bt3Fx1PI1y/fl2tJQUA5HI5ysvLBSQS68l5T3PnzkVkZCRGjhypXBs8eDAcHBywcuVKyc1nebKoERkZCSMjI6xfvx7GxsYAgDt37sDb2xu9e/cWFVG4qi9fMpkMXl5eKvPFKioqkJqaip49e4qKp1FWr16NJUuWICMjA0Bl4eezzz7DuHHjBCcTq7S0VG3eLFC54+dZ8+qkZNiwYUhISMCSJUuUBwzZ2dnhzJkzcHFxERtOoKodvTKZDCEhISq/QxUVFUhISICzs7OgdOJJtcOEtB8LY6Q1qr5YWVlZoWfPnmjQoIHgRJojNDQUAGBpaYnhw4dL+kkX/TORkZEoKSkBUPmUuaSkRNluIfVWryr29vY4efIk2rZtq7K+fft2SX/JACp3h61YsUJtvUuXLpL/8h4REYG4uDhlUQyo3AE9f/589OvXD4GBgQLTiVP15UuhUMDIyAiNGjVSXtPV1UX37t1VWpalKiQkBJGRkZgyZYrKbkx/f3/k5eVh7ty5ghOK07t3b2zYsAHz5s0DUFnokMvlCA8PR9++fQWn0wydO3fGpk2bRMfQKFU7ehUKBdLS0qCrq6u8pqurCycnJwQFBYmKJ9zatWtFRyCqFSyMkVYoLi5WDm92cXHBw4cP8fDhw2rvlfKQZ6ntyvi7hg0bhq5du2L69Okq6+Hh4Th79ix++OEHQcnEe/KkMwMDg2qLHFIXEhKCsWPH4vr165DL5di5cycuXbqEDRs24OeffxYdTyhzc3OsWrUK4eHhKusxMTEwNzcXlEoz8Mj76lV9+bK0tERQUJCk2yZrsnz5cqxatUptN6ajoyOmTJki6cJYeHg43N3dce7cOZSVlWHatGm4cOECioqKEB8fLzqeRpDL5cjMzMStW7cgl8tVrkn1YIuqHb3e3t5YunSppL83EEkJT6UkrfDkaYs6OjrVzteomlMitWGZzZo1w+XLl/Haa68p57A9S1FRUR0m0zwmJiY4evQoHBwcVNbT0tLw9ttv4+bNm4KS0avi5MmTmDt3LlJSUlBSUgJXV1eEhISgX79+oqMJtW/fPgwbNgzt2rVDt27dAABnzpxBRkYGduzYgffee09wQnF45D39XzRt2hRnz55VG5Z++fJldO3aFXfv3hUTTEPcu3cP0dHRKp/JkydPhpmZmehowp0+fRqenp64cuWK2jwtKf5/mYikjYUx0gonTpxAr169UL9+fZw4caLGe5+ceyMF69evx4gRI9CwYUOsW7euxsKY1HeUNWrUCMnJycoB2FUuXryo3IkoVc8qqspkMujp6aFdu3bw8vKCt7e3gHT0Krh69SqWL1+uPIjAzs4OkyZNkvyOsaePvAcqT36V8pH3rq6uOHLkCIyNjZWHxzxLYmJiHSbTPFOmTEGDBg3UWtqDgoLw8OFDLFu2TFAy8fLy8mBubl7t709eXh4sLCwEpNIczs7OeP311xEWFgYzMzO190mKs6Q8PDywbt06NG7c+LlD5jlcnki7sJWStMKTxS6pFb6e58lil5eXl7ggrwAHBwds3boVISEhKuuxsbGwt7cXlEozhISE4Msvv8S7776r3NVy5swZHDhwAJMnT0ZOTg58fX3x+PFjzv2hapmbm2PBggWiY2gcHnmv7v3331cOR686PIaebfXq1YiLi1MehJKQkIC8vDyMGTNGOUgcgOTmQVpZWSm7CZ5UWFgIKysrye+IysjIwPbt26s9NEaqmjRpoiwQSrEwSCRl3DFGWiE1NfWF73V0dKzFJJqnuLj4he+V+hyFn376CR4eHvD09ISbmxsA4MiRI9iyZQt++OEHSX9BGzZsGN555x1MmjRJZf27775DXFwcduzYgW+++QYrV65EWlqaoJR173ntyU+SWqtyamoqOnXqBB0dned+Rkvtc5noZXnRIfIymQxHjx6t5TSaRUdHBzdv3oSJiYnK+pUrV2Bvb4/S0lJByTSDm5sbpk2bhgEDBoiOQkQkHAtjpBWq5opVzRGridSeED5r5lp1pPbeVGfv3r1YsGABkpOT0ahRIzg6OiI0NFTyOxENDQ2RnJys9mQ5MzMTzs7OKCkpQVZWFhwdHSX1ZePJ+U+FhYWYP38++vfvr3I63MGDBzF79mz4+/uLiimEjo4OCgoKVGY/VvdfDinOsmG7zou7evUqZDIZ2rRpA6Byp+rmzZthb2+PCRMmCE5Hmqhql9zSpUsxfvx46OvrK69VVFQgISEB9erVk+QA/icfUmRlZWHWrFkIDg6Gg4OD2mnufGBBRFLCVkrSCjk5Ocqfk5KSEBQUhODgYJUvpxEREWonoklB1ek6AJCbm4sZM2bAy8tL5b1Zv349Fi5cKCqiRhk4cCAGDhwoOobGadasGX766Se14s5PP/2EZs2aAQBKS0thZGQkIp4wT7YqDxs2DHPnzsWnn36qXPPz80N0dDQOHz4sucJYTk6OcqfGk5/RxHadv8PT0xMTJkzARx99hIKCArz99tvo1KkTvv/+exQUFKi1vhMlJSUBqDx0KS0tDbq6usprurq6cHJyQlBQkKh4Qjk7O6s9pPDx8VH+/ORDZqk9sADw3JmGT5L6fEMibcMdY6R1unbtijlz5qidcrZv3z7Mnj0bv//+u6Bk4rm7u2PcuHEqx7oDwObNm7Fy5UocP35cTDDSeKtWrYKvry/ee+895Yyxs2fPYt++fVixYgU+/vhjRERE4MyZM9i6davgtGK8yK46UvciO31JuoyNjXH69Gm0b98eUVFR2Lp1K+Lj4xEXF4dJkyYhOztbdETSUN7e3li6dKnkx0Q86cqVKy98b9u2bWsxiWYKCwt74XtDQ0NrMQkR1TUWxkjrNGrUCImJibCzs1NZT09Ph6urq6RPFtTX10dKSkq1x7o7OzvjwYMHgpJphoqKCixZsgTbtm1DXl4eysrKVK5LbUbU0+Lj4xEdHY1Lly4BANq3b48pU6agZ8+egpNphrZt28LPzw+BgYEq6xEREYiKivpbX0i0jZeXF5YtW6Y2UD43NxcfffQRTp48KSiZeBcvXkSHDh2qvXbw4EH079+/jhNpFkNDQ5w/fx6WlpYYPHgwevXqhenTpyMvLw/t27eX9L/pRERE9HKwlZK0jp2dHRYuXIiYmBjl9vmysjIsXLhQrVgmNebm5li1apVaS2lMTAzMzc0FpdIcYWFhiImJQWBgIGbNmoWZM2ciNzcXu3fvZrsOgF69eqFXr16iY2issLAwjBs3DsePH0e3bt0AVJ4Od+DAAaxatUpwOrFSUlLg6OiITZs2Kdu4169fDz8/P+VBF1Ll6uqKxYsXY/Lkycq1v/76C4GBgYiJicGjR48EphOvY8eOWLFiBQYOHIhDhw5h3rx5AIAbN26gefPmgtORpjt37twzH3ZJfX4fUDln7Ouvv0Z6ejoAwN7eHlOnToWNjY3gZEREdYs7xkjrnDlzBoMGDYJCoVAODk1NTYVMJsNPP/2kbAOTon379mHYsGFo166d8ov7mTNnkJGRgR07dqi1n0qNjY0NoqKiMHDgQBgZGSE5OVm5dvr0aWzevFl0RGHy8vJqvG5hYVFHSTRbQkICoqKilF8y7Ozs4Ofnp/x7k6ry8nJ88cUXiIqKQmBgIDIzM7F//35ERkZi/PjxouMJtW3bNvj6+qJbt25Yu3Yt8vPz4enpCblcjo0bN+Jf//qX6IhCHT9+HEOHDkVxcTHGjh2LNWvWAAC++OILXLx4kcUNeqbY2FiMGTMG/fv3R1xcHPr164fLly/j5s2bGDp0KNauXSs6olAHDx7E4MGD4ezsrHzoFR8fj5SUFPz000945513BCcUi10ERNLCwhhppdLSUnz//fe4ePEigMovp56enmptPFJ09epVLF++XOW9mTRpEneMATAwMEB6ejosLCxgZmaGvXv3wtXVFdnZ2XBxccG9e/dERxTmeaebSnFIL/19oaGhmDdvHurXr48TJ04od49J3bVr1+Dt7Y2kpCSUlpbCy8sLERERKqfpSVlFRQWKi4thbGysXMvNzYW+vj5MTU0FJiNN5ujoiIkTJ2Ly5MkwMjJCSkoKrKysMHHiRJiZmf2teVLayMXFBf3798eiRYtU1mfMmIG4uDjJD5cPCQmpsYvAz89PdEQieonYSklaycDAgMe4P4O5uTkWLFggOoZGatOmDfLz82FhYQEbGxvExcXB1dUVZ8+eRcOGDUXHE6rqlK8q5eXlSEpKQmRkJL788ktBqcQrLi5WDnYuLi6u8V4pD4AuLy/HjBkzsGzZMnz++ef49ddf4eHhgdWrV0t+p2qVsrIyVFRUoKKiAmZmZtDT0xMdSWPUq1dPpSgGAJaWlmLC0CsjKytLecq0rq4uSktLIZPJ4O/vDzc3N8kXxtLT07Ft2za1dR8fH3z99dd1H0jDfP/991i1ahUGDhyIOXPmYOTIkbCxsYGjoyNOnz7NwhiRlmFhjLTCnj178O6776JBgwbYs2dPjfcOHjy4jlJphtTUVHTq1Ak6OjpITU2t8d6q1lOpGjp0KI4cOYJu3bphypQpGD16NFavXo28vDz4+/uLjieUk5OT2lqXLl3QqlUrLF68GB4eHgJSiWdsbIz8/HyYmpqiadOm1e6qqzp1Ucq76rp06YIHDx7g+PHj6N69OxQKBcLDw+Hh4QEfHx98++23oiMKExsbC19fX/Tu3RuXL19GcnIyvL29cfDgQWzcuBHW1taiIwp18+ZNBAUF4ciRI7h16xaebnSQ8t8V1czY2Bj3798HALRu3Rrnz5+Hg4MD7t69K/nDhgDAxMQEycnJagcyJScncycmgIKCAjg4OACoPASkqmvg3//+N2bPni0yGhHVAhbGSCsMGTIEBQUFMDU1xZAhQ555nxS/nDo7OyvfG2dnZ8hkMrUvFoA035unPdlOMHz4cLRt2xa//fYbbG1tMWjQIIHJNFf79u1x9uxZ0TGEOXr0KJo1awYAOHbsmOA0mqtLly6IiopStrPLZDJMnz4d/fr1w0cffSQ4nVgff/wxvvrqK/j6+gIA3nnnHaSmpmLSpElwdnZ+7k5Ebefl5YW8vDzMnj0bZmZmNbZ0Ez2pT58+OHToEBwcHPDBBx9g6tSpOHr0KA4dOgR3d3fR8YQbP348JkyYgOzsbOXp0vHx8fjvf/+LgIAAwenEYxcBkbRwxhiRlrty5QosLCwgk8lw5cqVGu9t27ZtHaWiV83TX84VCgXy8/MxZ84cXLx4EcnJyWKCaYjHjx9jwYIF8PHxQZs2bUTHeaX89ddfkv6ScenSJbRv377aaxs3bpR84dDIyAgnT56Es7Oz6Cj0iikqKsKjR4/QqlUryOVyhIeHKx92zZo1S609V2oUCgW+/vprRERE4MaNGwCAVq1aITg4GH5+fpIvQs+YMQONGzfGF198ga1bt2L06NGwtLRUdhE8PZuNiF5tLIwREdFzVTd8X6FQwNzcHLGxsRyijsov8GlpaZx99AwnTpzAV199pTyx097eHsHBwejdu7fgZJrh999/V3lvXF1dBSfSDPb29vj+++/h4uIiOgqR1qpqOTUyMhKcRHOdOnUKp06dYhcBkZZiYYy0jp+fH9q1a6c2FDM6OhqZmZmSHii6cOFCtGjRAj4+Pirra9aswe3btzF9+nRByUjTnThxQuW1jo4OTExM0K5dO9Svz658AHj//ffh4eGBsWPHio6icTZt2gRvb294eHigV69eACpbdnbt2oV169bB09NTcEJxbt26hREjRuD48eNo2rQpAODu3bvo27cvYmNjYWJiIjagYHFxcYiIiMB3333HojP9bXK5HJmZmbh16xbkcrnKtT59+ghKpRlycnLw+PFjtRljGRkZaNCgAf/eiEhSWBgjrdO6dWvs2bMHnTt3VllPTEzE4MGDce3aNUHJxLO0tMTmzZuVsySqJCQkYMSIEcjJyRGUjOjVt2LFCoSFhWHUqFHo3Lmzcp5WFakd/PEkOzs7TJgwQe0Qi8jISKxatUq5U0qKhg8fjuzsbGzYsAF2dnYAgD/++ANjx45Fu3btsGXLFsEJxTI2NsaDBw/w+PFj6Ovro0GDBirXi4qKBCUjTXf69Gl4enriypUrarNVOVcVePPNN+Hj46P2MGfTpk2IiYnB8ePHxQTTEBs2bKjx+pgxY+ooCRHVBRbGSOvo6enh/PnzaNeuncp6ZmYmOnXqhEePHglKJp6enh7S09NhZWWlsp6dnQ17e3tJvzdUs/Xr1+O1117DwIEDAQDTpk3DypUrYW9vjy1btnA+HSp30T2L1L+ENWzYEBcuXODncjWaNGmCw4cP41//+pfK+pkzZ9CvXz/cvXtXTDANsX79+hqvc4cmPYuzszNef/11hIWFVXtwQ5MmTQQl0wyNGzdGYmJitZ/LXbp0kfxnz9Mz6MrLy/HgwQPo6upCX1+fRXkiLcP+F9I67dq1w4EDB/Dpp5+qrO/fv1/yx96bm5sjPj5erTAWHx+PVq1aCUqlOc6ePQu5XI5u3bqprCckJKBevXro0qWLoGTiLViwAMuXLwdQOWcjOjoaX3/9NX7++Wf4+/tj586dghOK93SbDv1/5ubmOHLkiNoXsMOHD8Pc3FxQKs0gl8vVdkEBQIMGDfg7BRa+6J/LyMjA9u3b1T53qJJMJlPOFnvSvXv3JP0gp8qdO3fU1jIyMuDr64vg4GABiYioNrEwRlonICAAn376KW7fvg03NzcAwJEjRxARESHp+WJA5dHcn332GcrLy1Xem2nTpiEwMFBwOvEmT56MadOmqRXGrl+/jv/+979ISEgQlEy8q1evKr9c7N69G//5z38wYcIE9OrVC2+99ZbYcKTxAgMD4efnh+TkZGUrd3x8PNatW4elS5cKTieWm5sbpk6dii1btigfUFy/fh3+/v5wd3cXnE4zZGVlYe3atcjKysLSpUthamqK/fv3w8LCAh07dhQdjzRUt27dkJmZycLYM/Tp0wcLFy7Eli1bUK9ePQBARUUFFi5ciDfeeENwOs1ka2uLRYsWYfTo0bh48aLoOET0ErEwRlrHx8cHf/31F7788kvMmzcPQOVsreXLl0t+HkBwcDAKCwvxySefoKysDEBle+X06dPx+eefC04n3h9//FHtSXAuLi74448/BCTSHIaGhigsLISFhQXi4uIQEBAAoPL35+HDh4LTaQ6evFg9X19ftGzZEhEREdi2bRuAyrljW7duxfvvvy84nVjR0dEYPHgwLC0tlbvnrl69ik6dOmHTpk2C04l34sQJvPvuu+jVqxd++eUXfPnllzA1NUVKSgpWr16N7du3i45IGiQ1NVX585QpUxAYGIiCggI4ODio7cx0dHSs63ga5b///S/69OmD9u3bK/+NOnnyJIqLi3H06FHB6TRX/fr1cePGDdExiOgl44wx0mq3b99Go0aNYGhoKDqKRikpKUF6ejoaNWoEW1tbNGzYUHQkjdC8eXP8/PPP6NGjh8r6b7/9hoEDB1a7rV4qRo0ahYsXL8LFxQVbtmxBXl4emjdvjj179uCLL77A+fPnRUcUjicv0j+lUChw+PBh5Q4EOzs7vP3224JTaYYePXrggw8+QEBAAIyMjJCSkgJra2ucOXMGHh4ekj5Qh9Tp6OhAJpOpDduvUnVN6nMfq9y4cQPR0dFISUlBo0aN4OjoiE8//RTNmjUTHU24PXv2qLxWKBTIz89HdHQ0zM3NsX//fkHJiKg2sDBGRPQ/I0eORH5+Pn788UflUN67d+9iyJAhMDU1Ve50kaK7d+9i1qxZuHr1Knx9fTFgwAAAQGhoKHR1dTFz5kzBCcXjyYtEL5+hoSHS0tJgZWWlUhjLzc1Fhw4dJH1wA6m7cuXKC9/LQ2OoJk8fqCOTyWBiYgI3NzdERETAzMxMUDIiqg0sjJFW2r59O7Zt24a8vDxly2CVxMREQak0w7lz55753kh9gPr169fRp08fFBYWwsXFBQCQnJyMFi1a4NChQ5IfEk4148mLqpo1a4bLly/jtddeg7GxsdqJcE+S2uleUVFRmDBhAvT09BAVFVXjvX5+fnWUSjO1adMG27ZtQ8+ePVUKY7t27UJQUBCysrJERyR6ZTzZavo8Um81JSJp4Ywx0jpRUVGYOXMmvLy88OOPP8Lb2xtZWVk4e/YsJk+eLDqeULGxsRgzZgz69++PuLg49OvXD5cvX8bNmzcxdOhQ0fGEa926NVJTU/H9998r2wq8vb0xcuTIak+NI3oST15UtWTJEhgZGQGA5A8+edqSJUswatQo6OnpYcmSJc+8TyaTSb4wNmLECEyfPh0//PADZDIZ5HI54uPjERQUJPm5ofR8ly5dwjfffKPcsWtnZ4cpU6agffv2gpOJ4ezsXGOraRW2mkI5S/VFREZG1mISIqoL3DFGWqdDhw4IDQ3FyJEjVZ4uh4SEoKioCNHR0aIjCuPo6IiJEydi8uTJyvfGysoKEydOhJmZGcLCwkRHJHplLV++HJ999hl8fHyqPXlx4sSJghMSvXrKysowefJkrFu3DhUVFahfvz4eP36MUaNGYd26dcrT9IietmPHDowYMQJdunRRzg49ffo0zp49i9jYWAwbNkxwwrrHVtMX17dvXyQmJuLx48fKQurly5dRr149lYOaZDIZDysg0gIsjJHW0dfXR3p6Otq2bQtTU1McOnQITk5OyMjIQPfu3VFYWCg6ojAGBga4cOECLC0t0bx5cxw/fhwODg5IT0+Hm5sb8vPzRUesc3v27MG7776LBg0aqA1afdrgwYPrKBW9qnbt2oWIiAiV3QnBwcGSPHmxuLj4he9t3LhxLSYhbXD16lWkpaWhpKQELi4usLW1FR2JNJyNjQ1GjRqFuXPnqqyHhoZi06ZNbMP9nz/++ENtvIZMJsOgQYMEphIvMjISx48fx/r162FsbAwAuHPnDry9vdG7d28EBgYKTkhELxMLY6R1rK2tsWPHDri4uKBLly4YP348Jk6ciLi4OIwYMUJys2ye1KZNG+zfvx8ODg5wdHTE559/jpEjR+LUqVMYMGAA7t27JzpindPR0UFBQQFMTU3VBq0+iW0FRH9P1elwL0Jqf1ts0akZ3x96GfT19ZGamqrW3p6RkQEnJyc8ePBAUDLNkJ2djaFDhyItLU2lvbLqc1tqn8tPa926NeLi4tCxY0eV9fPnz6Nfv364ceOGoGREVBs4Y4y0jpubG/bs2QMXFxd4e3vD398f27dvx7lz5+Dh4SE6nlB9+vTBoUOH4ODggA8++ABTp07F0aNHcejQIbi7u4uOJ4RcLq/2ZyL6vzl27Jjy59zcXMyYMQNeXl7KlqZTp05h/fr1WLhwoaiIwiQlJam8fla7TufOnUXEE47vD70Mb731Fk6ePKlWGPv111/Ru3dvQak0x9SpU2FlZYUjR47AysoKCQkJKCoqQmBgIL766ivR8YQrLi7G7du31dZv376N+/fvC0hERLWJO8ZI68jlcsjlctSvX1n3jY2NxW+//QZbW1tMnDgRurq6ghOKU1RUhEePHqFVq1aQy+UIDw9XvjezZs1SbhWXovLycgwYMAArVqxgi87/uLi4vPCOH6me9vq80xafJOXdqu7u7hg3bhxGjhypsr5582asXLkSx48fFxNMA7Bdp2Z8f+ifWrFiBUJCQvDhhx+ie/fuACpnjP3www8ICwtDq1atlPdKcVTCa6+9hqNHj8LR0RFNmjTBmTNn0L59exw9ehSBgYFqBWqpGTNmDE6ePImIiAh07doVAJCQkIDg4GD07t0b69evF5yQiF4mFsaIiP7HxMREWSgk/K3DGEJDQ2sxieb6O/8xHjt2bC0m0Wz6+vpISUlR+9u6fPkynJ2dJd3SxHadmvH9oX+qpvEIT5LqqARjY2MkJibCysoKNjY2iImJQd++fZGVlQUHBwdJfy4DwIMHDxAUFIQ1a9agvLwcAFC/fn18/PHHWLx4MQwMDAQnJKKXia2URET/M3r0aKxevRqLFi0SHUUjSLXY9XdIudj1d5ibm2PVqlUIDw9XWY+JiYG5ubmgVJqB7To14/tD/xTHI9SsU6dOytPJu3XrhvDwcOjq6mLlypWwtrYWHU84fX19fPvtt1i8eLHyoAYbGxsWxIi0FAtjRET/8/jxY6xZswaHDx9G586d1f7zwyHP9DxyuRyZmZm4deuW2peyPn36CEol3pIlSzBs2DDs378f3bp1AwCcOXMGGRkZ2LFjh+B0Yg0dOhTe3t7VtutIfS4mwPeHqLbMmjULpaWlAIC5c+fi3//+N3r37o3mzZtj69atgtNpDgMDAzg6OoqOQUS1jK2URET/07dv3xqvPzlMXGoqKiqwZMkSbNu2Te1Yd0Da87OqnD59Gp6enrhy5Qqe/qdVqq06T7p27RqWL1+O9PR0AICdnR0mTZok+R1jbNepGd8f+juioqJe+F4/P79aTPJqKioq+luzM4mItAULY0RE9FwhISGIiYlBYGAgZs2ahZkzZyI3Nxe7d+9GSEgIv2AAcHZ2xuuvv46wsDCYmZmpfbFo0qSJoGT0KigtLWW7Tg34/tCLsLKyeqH7ZDIZsrOzazkNERG9KlgYI5KgzMxMZGVloU+fPmjUqBEUCgWfDgLw8fHB0qVLYWRkpLJeWlqKKVOmYM2aNYKSiWdjY4OoqCgMHDgQRkZGSE5OVq6dPn0amzdvFh1ROAMDA6SkpKBdu3aioxARERER0QtiYYy0gouLywsXdhITE2s5jeYqLCzE8OHDcfToUchkMmRkZMDa2ho+Pj4wNjZGRESE6IhC1atXD/n5+TA1NVVZ//PPP9GyZUs8fvxYUDLxDAwMkJ6eDgsLC5iZmWHv3r1wdXVFdnY2XFxccO/ePdERhXNzc8O0adMwYMAA0VGIiIiIiOgFcfg+aYUhQ4Yof3706BG+/fZb2Nvbo0ePHgAqZ/9cuHABn3zyiaCEmsHf3x/169dHXl4e7OzslOvDhw9HQECAZAtjxcXFUCgUUCgUuH//PvT09JTXKioqsG/fPrVimdS0adMG+fn5sLCwgI2NDeLi4uDq6oqzZ8+iYcOGouMJk5qaqvx5ypQpCAwMREFBARwcHNCgQQOVezm8l4io7vj4+NR4Xcq7wImISBULY6QVQkNDlT+PGzcOfn5+mDdvnto9V69eretoGiUuLg4HDx5EmzZtVNZtbW1x5coVQanEa9q0KWQyGWQyGV5//XW16zKZDGFhYQKSaY6hQ4fiyJEj6NatG6ZMmYLRo0dj9erVyMvLg7+/v+h4wjg7O0Mmk6kM23/yy1jVNSkP31coFLh69SpMTU1Vis5ERLXpzp07Kq/Ly8tx/vx53L17F25uboJSERGRJmIrJWmdJk2a4Ny5c7C1tVVZz8jIQJcuXSTd8mVkZITExETY2trCyMgIKSkpsLa2xrlz59C/f38UFhaKjijEiRMnoFAo4Obmhh07dqBZs2bKa7q6umjbti1atWolMKHmOXXqFE6dOgVbW1sMGjRIdBxh/k5BuW3btrWYRHPJ5XLo6enhwoULap/LUldeXo6JEydi9uzZLzw0nIj+OblcDl9fX9jY2GDatGmi4xARkYZgYYy0TsuWLbFo0SJ4eXmprK9btw7Tp0/HzZs3xQTTAO+99x46d+6MefPmwcjICKmpqWjbti1GjBgBuVyO7du3i44o1JUrV2BhYcGDCOgfWbhwIVq0aKHWvrNmzRrcvn0b06dPF5RMvI4dO2L16tXo3r276Cgap0mTJkhOTmZhjKiOXLp0CW+99Rby8/NFRyEiIg3BVkrSOp999hl8fX2RmJiIrl27AgASEhKwZs0azJ49W3A6scLDw+Hu7o5z586hrKwM06ZNw4ULF1BUVIT4+HjR8YRITU1Fp06doKOjg3v37iEtLe2Z90p5RtSGDRtqvD5mzJg6SqK5vvvuu2pP5+zYsSNGjBgh6cLYokWLEBwcjOXLl6NTp06i42iUIUOGYPfu3ZJuSSaqS1lZWZI+TIeIiNRxxxhppW3btmHp0qVIT08HANjZ2WHq1Kn48MMPBScT7969e4iOjkZKSgpKSkrg6uqKyZMnw8zMTHQ0IXR0dFBQUABTU1Po6OiozYuqIuUZUQBgbGys8rq8vBwPHjyArq4u9PX1UVRUJCiZ5tDT00N6errazp/s7GzY29vj0aNHgpKJZ2xsjAcPHuDx48fQ1dVFo0aNVK5L+fdn/vz5iIiIgLu7Ozp37gwDAwOV635+foKSEb3aAgICVF4rFArk5+dj7969GDt2LKKjowUlIyIiTcPCGBFJ2pPtk8+bFyXVGVHPkpGRAV9fXwQHB6N///6i4whna2uL0NBQjB49WmV948aNCA0NRXZ2tqBk4q1fv77G62PHjq2jJJqnphZKmUwm6d8bov+Lvn37qrzW0dGBiYkJ3Nzc4OPjg/r12ThDRESVWBgjrXT37l1s374d2dnZCAoKQrNmzZCYmIgWLVqgdevWouPVqdTU1Be+V8qtgvTPnDt3DqNHj8bFixdFRxEuPDwc4eHhWLx4sfLEsyNHjmDatGkIDAzE559/LjghEZF0PHjwAAqFQrkLMzc3F7t374adnR0f5hARkQo+KiGtk5qairfffhtNmjRBbm4uxo0bh2bNmmHnzp3Iy8t77qwkbePs7PzM9sAnSb1VEODw9H+ifv36uHHjhugYGiE4OBiFhYX45JNPUFZWBqCyvXL69OmSL4rl5eXVeN3CwqKOkhCRVAwZMgQeHh6YNGkS7t69i+7du6NBgwb4888/ERkZCV9fX9ERiYhIQ3DHGGmdt99+G66urggPD4eRkRFSUlJgbW2N3377DZ6ensjNzRUdsU49rz3wSVJvFbS0tMTmzZvRs2dPlfWEhASMGDECOTk5gpKJt2fPHpXXVbNaoqOjYW5ujv379wtKpnlKSkqQnp6ORo0awdbWFg0bNhQdSbiq+X3PIuWi/NOF+KetWbOmjpIQaZfXXnsNJ06cQMeOHRETE4NvvvkGSUlJ2LFjB0JCQpRzaImIiLhjjLTO2bNn8d1336mtt27dGgUFBQISiSX1YtffUVBQUO0hBCYmJpI/1n3IkCEqr2UymXJWS0REhJhQGsrQ0BD/+te/RMfQKElJSSqvy8vLkZSUhMjISHz55ZeCUmmGO3fuqLwuLy/H+fPncffuXWVLLhH9fQ8ePICRkREAIC4uDh4eHtDR0UH37t3/1kNDIiLSfiyMkdZp2LAhiouL1dYvX74MExMTAYnE2rNnD9599100aNBAbdfP0wYPHlxHqTSTubk54uPj1YZhx8fHo1WrVoJSaQa5XC46Ar3CnJyc1Na6dOmCVq1aYfHixfDw8BCQSjPs2rVLbU0ul8PX1xc2NjYCEhFph3bt2mH37t0YOnQoDh48CH9/fwDArVu30LhxY8HpiIhIk7CVkrTOuHHjUFhYiG3btqFZs2ZITU1FvXr1MGTIEPTp0wdff/216Ih1SkdHBwUFBTA1NYWOjs4z7+OMMQ5PJ6prmZmZcHJyQmlpqegoGufSpUt46623JL9bleif2r59Ozw9PVFRUQF3d3fExcUBqJwn+ssvv3AEABERKbEwRlrn3r17+M9//oNz587h/v37aNWqFQoKCtCjRw/s27dPeToR0dMUCgVmzJiBqKgoteHpISEhgtPVvYCAgBe+NzIyshaT0Kvu6V28VTPq5syZg4sXLyI5OVlMMA22b98+jB07Frdv3xYdheiVVVBQgPz8fDg5OSkfDp45cwaNGzdGhw4dBKcjIiJNwcIYaa34+HikpKSgpKQErq6uePvtt0VHEm7Dhg0YPny42jDwsrIyxMbGYsyYMYKSaRYOT6/Ut29fldeJiYl4/Pgx2rdvD6CyPblevXro3Lkzjh49KiIivSKqG76vUChgbm6O2NhY9OjRQ1Ay8Z4uQFcVDffu3YuxY8ciOjpaUDIiIiIiaWBhjLTOxYsXn/kU8ODBg+jfv38dJ9Ic9erVQ35+PkxNTVXWCwsLYWpqKvlWSnq2yMhIHD9+HOvXr4exsTGAyqHh3t7e6N27NwIDAwUnJE124sQJldc6OjowMTFBu3btUL++tMedvvXWWypFw6r3xs3NDT4+PpJ/f4iIiIhqGwtjpHX09fWxePFiTJ48Wbn2119/ITAwEDExMXj06JHAdGLp6Ojg5s2baocQpKSkoG/fvigqKhKUjDRd69atERcXh44dO6qsnz9/Hv369cONGzcEJSNN5erqiiNHjsDY2Bhz585FUFAQ9PX1RcfSCE8eikJEREREYvExJGmddevWwdfXF3v37sXatWuRn58PT09PyOVynDx5UnQ8IVxcXCCTySCTyeDu7q6yA6GiogI5OTkYMGCAwISk6YqLi6uddXT79m3cv39fQCLSdOnp6SgtLYWxsTHCwsLg6+vLwtj/DB06FAUFBTAxMXnmTl4iIiIiqhssjJHW+fDDD9GzZ094e3ujY8eOKC0thZeXFyIiIiT7pWzIkCEAgOTkZPTv3x+GhobKa7q6urC0tMSwYcMEpaNXwdChQ+Ht7Y2IiAh07doVAJCQkIDg4GB4eHgITkeayNnZGd7e3njjjTegUCiwePFilc+eJ0ntcAsTExOcPn0agwYNgkKhUJu/RkRERER1h4Ux0lplZWWoqKhARUUFzMzMoKenJzqSMKGhoQAAS0tLDB8+XNLvBf0zK1asQFBQEDw9PVFeXg4AqF+/Pj7++GMsXrxYcDrSROvWrUNoaCh+/vlnyGQy7N+/v9p5WTKZTHKFsUmTJuH9999X7uRt2bLlM+/l7EciIiKi2sUZY6R1YmNj4evri969e2P16tVITk6Gt7c32rZti40bN8La2lp0RKJXVmlpKbKysgAANjY2MDAwEJyIXgU6OjooKChgu+ATLl68iMzMTAwePBhr165F06ZNq73v/fffr9tgRERERBLDwhhpHQMDA3z11Vfw9fVVrt25cwcTJ07EgQMHUFxcLDCdWBUVFViyZAm2bduGvLw8lJWVqVzn8H0ioroVFhaG4OBgybb6ExEREYnGwhhpnUuXLqF9+/bVXtu4cSM++uijOk6kOUJCQhATE4PAwEDMmjULM2fORG5uLnbv3o2QkBD4+fmJjkgaxMPDA+vWrUPjxo2fO0ds586ddZSKXhV79ux54XsHDx5ci0mIiIiIiJ6NM8ZI6zyrKAZA0kUxAPj++++xatUqDBw4EHPmzMHIkSNhY2MDR0dHnD59moUxUtGkSRPlUPAmTZoITkOvmqpDP55HJpNJbo5W1UnBLyIxMbGW0xARERFJGwtjpBUCAgIwb948GBgYICAgoMZ7IyMj6yiV5ikoKICDgwMAwNDQEPfu3QMA/Pvf/8bs2bNFRiMNtHbt2mp/JnoRcrlcdASN9aJFQyIiIiKqfSyMkVZISkpSnpSXmJj4zCfxL/qEXlu1adMG+fn5sLCwgI2NDeLi4uDq6oqzZ8+iYcOGouORBnv48CEUCoVyDtKVK1ewa9cu2Nvbo1+/foLTEb1aqk4KJiIiIiLxOGOMtEJqaio6deoEHR0d0VE02owZM9C4cWN88cUX2Lp1K0aPHg1LS0vk5eXB398fixYtEh2RNFS/fv3g4eGBSZMm4e7du2jfvj10dXXx559/IjIyUuWwC6KnzZ07t8brISEhdZSEiIiIiEgVC2OkFerVq4f8/HyYmprC2toaZ8+eRfPmzUXH0ninTp3CqVOnYGtri0GDBomOQxrstddew4kTJ9CxY0fExMTgm2++QVJSEnbs2IGQkBCkp6eLjkgazMXFReV1eXk5cnJyUL9+fdjY2Eh6jpaOjk6Nu5mlNn+NiIiIqK6xlZK0QtOmTZGTkwNTU1Pk5uZyts0L6tGjB3r06CE6Br0CHjx4ACMjIwBAXFwcPDw8oKOjg+7du+PKlSuC05GmS0pKUlsrLi6Gl5cXhg4dKiCR5ti1a5fK6/LyciQlJWH9+vUICwsTlIqIiIhIOrhjjLTChAkTsGHDBpiZmSEvLw9t2rRBvXr1qr03Ozu7jtOJtWfPnhe+d/DgwbWYhF5ljo6OGDduHIYOHYpOnTrhwIED6NGjB37//XcMHDgQBQUFoiPSKygtLQ2DBg1Cbm6u6CgaZ/Pmzdi6dSt+/PFH0VGIiIiItBp3jJFWWLlyJTw8PJCZmQk/Pz+MHz9eubtF6l709DOZTMaWHXqmkJAQeHp6wt/fH25ubsqdhnFxcWptckQv6t69e8rTcUlV9+7dMWHCBNExiIiIiLQed4yR1vH29kZUVBQLY0QvWUFBAfLz8+Hk5KQ86OLMmTNo3LgxOnToIDgdabKoqCiV1wqFAvn5+di4cSPefPNNbN68WVAyzfTw4UN8/vnn2L9/Py5duiQ6DhEREZFWY2GMiIheWGZmJrKystCnTx80atQICoWixsHhRABgZWWl8lpHRwcmJiZwc3PD559/LukHGcbGxip/QwqFAvfv34e+vj42bdrEFnciIiKiWsbCGJHEnDhxAl999ZXyFEF7e3sEBwejd+/egpORJissLMSHH36IY8eOQSaTISMjA9bW1vDx8YGxsTEiIiJERyR6Ja1bt06lMFZVNOzWrRuMjY0FJiMiIiKSBhbGiCRk06ZN8Pb2hoeHB3r16gUAiI+Px65du7Bu3Tp4enoKTkiaasyYMbh16xZiYmJgZ2eHlJQUWFtb4+DBgwgICMCFCxdER6RXSHFxMY4ePYr27dvDzs5OdBwiIiIikjAWxogkxM7ODhMmTIC/v7/KemRkJFatWqXcRUb0tJYtW+LgwYNwcnKCkZGRsjCWnZ0NR0dHlJSUiI5IGuzDDz9Enz598Omnn+Lhw4dwcnJCbm4uFAoFYmNjMWzYMNERhTlw4AAMDQ3xxhtvAACWLVuGVatWwd7eHsuWLeOuMSIiIqJapiM6ABHVnezsbAwaNEhtffDgwcjJyRGQiF4VpaWl0NfXV1svKipCw4YNBSSiV8kvv/yibNfetWsXFAoF7t69i6ioKMyfP19wOrGCg4NRXFwMAEhLS0NAQADee+895OTkICAgQHA6IiIiIu3HwhiRhJibm+PIkSNq64cPH4a5ubmARPSq6N27NzZs2KB8LZPJIJfLER4ejr59+wpMRq+Ce/fuoVmzZgAqd0gNGzYM+vr6GDhwIDIyMgSnEysnJwf29vYAgB07dmDQoEFYsGABli1bhv379wtOR0RERKT96osOQER1JzAwEH5+fkhOTkbPnj0BVM4YW7duHZYuXSo4HWmy8PBwuLu749y5cygrK8O0adNw4cIFFBUVIT4+XnQ80nDm5uY4deoUmjVrhgMHDiA2NhYAcOfOHejp6QlOJ5auri4ePHgAoPIhxZgxYwAAzZo1U+4kIyIiIqLaw8IYkYT4+vqiZcuWiIiIwLZt2wBUzh3bunUr3n//fcHpSJN16tQJly9fRnR0NIyMjFBSUgIPDw9MnjwZZmZmouORhvvss88watQoGBoaom3btnjrrbcAVLZYOjg4iA0n2BtvvIGAgAD06tULZ86cwdatWwEAly9fRps2bQSnIyIiItJ+HL5PREQ1Ki8vx4ABA7BixQrY2tqKjkOvqN9//x15eXl45513YGhoCADYu3cvmjZtqjwlV4ry8vLwySef4OrVq/Dz88PHH38MAPD390dFRQWioqIEJyQiIiLSbiyMEUnIuHHjMHr0aOVuDaIXZWJigt9++42FMSIiIiIi0iocvk8kIbdv38aAAQNgbm6O4OBgJCcni45Er4jRo0dj9erVomMQERERERG9VNwxRiQxd+7cwQ8//IDNmzfj5MmT6NChA0aNGgVPT09YWlqKjkcaasqUKdiwYQNsbW3RuXNnGBgYqFyPjIwUlIyIiIiIiOifY2GMSMKuXbuGLVu2YM2aNcjIyMDjx49FRyIN1bdv32dek8lkOHr0aB2mISIiIiIiejl4KiWRRJWXl+PcuXNISEhAbm4uWrRoIToSabBjx46JjkBERERERPTSccYYkcQcO3YM48ePR4sWLeDl5YXGjRvj559/xrVr10RHIyItdvLkSYwePRo9evTA9evXAQAbN27Er7/+KjgZEREREUkZd4wRSUjr1q1RVFSEAQMGYOXKlRg0aBAaNmwoOhYRabkdO3bgo48+wqhRo5CUlIS//voLAHDv3j0sWLAA+/btE5ywbnl4eLzwvTt37qzFJERERETEwhiRhMyZMwcffPABmjZtKjoKEUnI/PnzsWLFCowZMwaxsbHK9V69emH+/PkCk4nRpEkT0RGIiIiI6H84fJ+IiIhqlb6+Pv744w9YWlrCyMgIKSkpsLa2RnZ2Nuzt7fHo0SPREYmIiIhIojhjjIiIiGpVy5YtkZmZqbb+66+/wtraWkAiIiIiIqJKbKUkIiKiWjV+/HhMnToVa9asgUwmw40bN3Dq1CkEBQVh9uzZouMJt337dmzbtg15eXkoKytTuZaYmCgoFREREZE0cMcYERER1aoZM2bA09MT7u7uKCkpQZ8+fTBu3DhMnDgRU6ZMER1PqKioKHh7e6NFixZISkpC165d0bx5c2RnZ+Pdd98VHY+IiIhI63HGGBEREdWJsrIyZGZmoqSkBPb29jA0NBQdSbgOHTogNDQUI0eOVJm/FhISgqKiIkRHR4uOSERERKTVuGOMiIiI6kReXh6uXr0KBwcHGBoags/mKt+Tnj17AgAaNWqE+/fvAwA++ugjbNmyRWQ0IiIiIklgYYyIiIhqVWFhIdzd3fH666/jvffeQ35+PgDg448/RmBgoOB0YrVs2RJFRUUAAAsLC5w+fRoAkJOTw8IhERERUR1gYYyIiIhqlb+/Pxo0aIC8vDzo6+sr14cPH44DBw4ITCaem5sb9uzZAwDw9vaGv78/3nnnHQwfPhxDhw4VnI6IiIhI+3HGGBEREdWqli1b4uDBg3ByclKZo5WdnQ1HR0eUlJSIjiiMXC6HXC5H/fqVB4XHxsbit99+g62tLSZOnAhdXV3BCYmIiIi0W33RAYiIiEi7lZaWquwUq1JUVISGDRsKSKQ5rl27BnNzc+XrESNGYMSIEVAoFLh69SosLCwEpiMiIiLSfmylJCIiolrVu3dvbNiwQflaJpNBLpcjPDwcffv2FZhMPCsrK9y+fVttvaioCFZWVgISEREREUkLd4wRERFRrQoPD4e7uzvOnTuHsrIyTJs2DRcuXEBRURHi4+NFxxNKoVBAJpOprZeUlEBPT09AIiIiIiJpYWGMiIiIalWnTp1w+fJlREdHw8jICCUlJfDw8MDkyZNhZmYmOp4QAQEBACp3z82ePVul1bSiogIJCQlwdnYWlI6IiIhIOlgYIyIiolpTXl6OAQMGYMWKFZg5c6boOBojKSkJQOWOsbS0NJUh+7q6unByckJQUJCoeERERESSwcIYERER1ZoGDRogNTVVdAyNc+zYMQCAt7c3li5disaNGwtORERERCRNMoVCoRAdgoiIiLSXv78/GjZsiEWLFomOotGuXbsGAGjTpo3gJERERETSwR1jREREVKseP36MNWvW4PDhw+jcuTMMDAxUrkdGRgpKJp5cLsf8+fMRERGBkpISAICRkRECAwMxc+ZM6OjwAHEiIiKi2sTCGBEREdWq8+fPw9XVFQBw+fJllWvVncgoJTNnzsTq1auxaNEi9OrVCwDw66+/Ys6cOXj06BG+/PJLwQmJiIiItBtbKYmIiOilS01NRadOnbjj6TlatWqFFStWYPDgwSrrP/74Iz755BNcv35dUDIiIiIiaeD/VomIiOilc3FxwZ9//gkAsLa2RmFhoeBEmqmoqAgdOnRQW+/QoQOKiooEJCIiIiKSFhbGiIiI6KVr2rQpcnJyAAC5ubmQy+WCE2kmJycnREdHq61HR0fDyclJQCIiIiIiaeGMMSIiInrphg0bhjfffBNmZmaQyWTo0qUL6tWrV+292dnZdZxOc4SHh2PgwIE4fPgwevToAQA4deoUrl69in379glOR0RERKT9OGOMiIiIasWBAweQmZkJPz8/zJ07F0ZGRtXeN3Xq1DpOpllu3LiBZcuW4eLFiwAAOzs7fPLJJ2jVqpXgZERERETaj4UxIiIiqlXe3t6Iiop6ZmFMyvLy8mBubl7t6Zx5eXmwsLAQkIqIiIhIOlgYIyIiIhKkXr16yM/Ph6mpqcp6YWEhTE1NUVFRISgZERERkTRw+D4RERGRIAqFotrdYiUlJdDT0xOQiIiIiEhaOHyfiIiIqI4FBAQAAGQyGWbPng19fX3ltYqKCiQkJMDZ2VlQOiIiIiLpYGGMiIiIqI4lJSUBqNwxlpaWBl1dXeU1XV1dODk5ISgoSFQ8IiIiIsngjDEiIiIiQby9vbF06VI0btxYdBQiIiIiSWJhjIiIiIiIiIiIJInD94mIiIiIiIiISJJYGCMiIiIiIiIiIkliYYyIiIiIiIiIiCSJhTEiIiIiIiIiIpIkFsaIiIiIiIiIiEiSWBgjIiIiIiIiIiJJYmGMiIiIiIiIiIgk6f8BXhKQvfXwvhgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here we see that fixed acidity does not give any specification to classify the quality.\n",
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'fixed acidity', data = df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "mfe2MeWnMxp2",
        "outputId": "9743edf4-3fc9-4125-87eb-13bb7ebd14ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='quality', ylabel='fixed acidity'>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "!pip install scikit-learn"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWqz-lUvLTsF",
        "outputId": "5aa225bb-7d23-44d7-9c18-b96983dbaebe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing libraries.\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "y_train = le.fit_transform(y_train)\n",
        "\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "print(rf_model)"
      ],
      "metadata": {
        "id": "bARi55ZHOwM5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b45e02ce-026f-4539-aa81-2ae38e93c64c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForestClassifier(random_state=42)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features and target variable\n",
        "X = df.drop(\"quality\", axis=1)\n",
        "y = df[\"quality\"]\n",
        "\n",
        "# Split 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)"
      ],
      "metadata": {
        "id": "Aw3ahaaG_axc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Build and Evaluate Random Forest Model:**"
      ],
      "metadata": {
        "id": "dfYZu4cWNrKa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define and train the Random Forest model\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "rf_predictions = rf_model.predict(X_test)\n",
        "\n",
        "# Evaluate model performance\n",
        "rf_accuracy = accuracy_score(y_test, rf_predictions)\n",
        "rf_report = classification_report(y_test, rf_predictions)\n",
        "\n",
        "print(\"Random Forest Accuracy:\", rf_accuracy)\n",
        "print(\"Random Forest Classification Report:\\n\", rf_report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JE4_9TbqKy5J",
        "outputId": "0ed62e3e-5146-4232-eed4-f929b8ba53a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy: 0.6812227074235808\n",
            "Random Forest Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           4       0.00      0.00      0.00         6\n",
            "           5       0.73      0.75      0.74        96\n",
            "           6       0.63      0.70      0.66        99\n",
            "           7       0.75      0.58      0.65        26\n",
            "           8       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.68       229\n",
            "   macro avg       0.42      0.40      0.41       229\n",
            "weighted avg       0.66      0.68      0.67       229\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Build and Evaluate SGD Model:**"
      ],
      "metadata": {
        "id": "nyM5ASlCN7WY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define and train the SGD model\n",
        "sgd_model = SGDClassifier(max_iter=1000, random_state=42)\n",
        "sgd_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "sgd_predictions = sgd_model.predict(X_test)\n",
        "\n",
        "# Evaluate model performance\n",
        "sgd_accuracy = accuracy_score(y_test, sgd_predictions)\n",
        "sgd_report = classification_report(y_test, sgd_predictions)\n",
        "\n",
        "print(\"SGD Accuracy:\", sgd_accuracy)\n",
        "print(\"SGD Classification Report:\\n\", sgd_report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBd78OtmK3bg",
        "outputId": "4df33f38-39b2-4c90-9db5-833d6b6575c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SGD Accuracy: 0.5327510917030568\n",
            "SGD Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           4       0.00      0.00      0.00         6\n",
            "           5       0.74      0.33      0.46        96\n",
            "           6       0.48      0.91      0.63        99\n",
            "           7       0.00      0.00      0.00        26\n",
            "           8       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.53       229\n",
            "   macro avg       0.25      0.25      0.22       229\n",
            "weighted avg       0.52      0.53      0.47       229\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Build and Evaluate SVC Model:**"
      ],
      "metadata": {
        "id": "X92267nOOEXc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define and train the SVC model\n",
        "svc_model = SVC(kernel=\"linear\", random_state=42)\n",
        "svc_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "svc_predictions = svc_model.predict(X_test)\n",
        "\n",
        "# Evaluate model performance\n",
        "svc_accuracy = accuracy_score(y_test, svc_predictions)\n",
        "svc_report = classification_report(y_test, svc_predictions)\n",
        "\n",
        "print(\"SVC Accuracy:\", svc_accuracy)\n",
        "print(\"SVC Classification Report:\\n\", svc_report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWFQ1dGnMFey",
        "outputId": "6b6f9134-eeb0-4b3a-acd5-985f82e190e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC Accuracy: 0.6375545851528385\n",
            "SVC Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           4       0.00      0.00      0.00         6\n",
            "           5       0.69      0.76      0.72        96\n",
            "           6       0.61      0.67      0.64        99\n",
            "           7       0.47      0.27      0.34        26\n",
            "           8       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.64       229\n",
            "   macro avg       0.35      0.34      0.34       229\n",
            "weighted avg       0.61      0.64      0.62       229\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6sJiKqhxMPRX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}